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google-research/rigl
f18abc7d82ae3acc6736068408a0186c9efa575c
rigl/experimental/jax/pruning/masked.py
python
_PerNeuronShuffle.__init__
(self, init_rng, sparsity)
Creates the per-neuron shuffle class, with initial RNG state. Args: init_rng: The initial random number generator state to use. sparsity: The per-layer sparsity of the mask (i.e. % of zeroes), 1.0 will mask all weights, while 0 will mask none.
Creates the per-neuron shuffle class, with initial RNG state.
[ "Creates", "the", "per", "-", "neuron", "shuffle", "class", "with", "initial", "RNG", "state", "." ]
def __init__(self, init_rng, sparsity): """Creates the per-neuron shuffle class, with initial RNG state. Args: init_rng: The initial random number generator state to use. sparsity: The per-layer sparsity of the mask (i.e. % of zeroes), 1.0 will mask all weights, while 0 will mask none. """ self._rng = init_rng self._sparsity = sparsity
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https://github.com/google-research/rigl/blob/f18abc7d82ae3acc6736068408a0186c9efa575c/rigl/experimental/jax/pruning/masked.py#L380-L389
facebookresearch/pytorch_GAN_zoo
b75dee40918caabb4fe7ec561522717bf096a8cb
models/trainer/DCGAN_trainer.py
python
DCGANTrainer.initModel
(self)
[]
def initModel(self): self.model = DCGAN(useGPU=self.useGPU, **vars(self.modelConfig))
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https://github.com/facebookresearch/pytorch_GAN_zoo/blob/b75dee40918caabb4fe7ec561522717bf096a8cb/models/trainer/DCGAN_trainer.py#L33-L35
biopython/biopython
2dd97e71762af7b046d7f7f8a4f1e38db6b06c86
Bio/SearchIO/HmmerIO/hmmer3_tab.py
python
Hmmer3TabWriter.write_file
(self, qresults)
return qresult_counter, hit_counter, hsp_counter, frag_counter
Write to the handle. Returns a tuple of how many QueryResult, Hit, and HSP objects were written.
Write to the handle.
[ "Write", "to", "the", "handle", "." ]
def write_file(self, qresults): """Write to the handle. Returns a tuple of how many QueryResult, Hit, and HSP objects were written. """ handle = self.handle qresult_counter, hit_counter, hsp_counter, frag_counter = 0, 0, 0, 0 try: first_qresult = next(qresults) except StopIteration: handle.write(self._build_header()) else: # write header handle.write(self._build_header(first_qresult)) # and then the qresults for qresult in chain([first_qresult], qresults): if qresult: handle.write(self._build_row(qresult)) qresult_counter += 1 hit_counter += len(qresult) hsp_counter += sum(len(hit) for hit in qresult) frag_counter += sum(len(hit.fragments) for hit in qresult) return qresult_counter, hit_counter, hsp_counter, frag_counter
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https://github.com/biopython/biopython/blob/2dd97e71762af7b046d7f7f8a4f1e38db6b06c86/Bio/SearchIO/HmmerIO/hmmer3_tab.py#L222-L247
nansencenter/nansat
5700ec673fbf522c19b8dedcb01cc15f7cd29a6a
nansat/vrt.py
python
VRT.fix_global_metadata
(self, rm_metadata)
Remove unwanted global metadata and escape special characters
Remove unwanted global metadata and escape special characters
[ "Remove", "unwanted", "global", "metadata", "and", "escape", "special", "characters" ]
def fix_global_metadata(self, rm_metadata): """Remove unwanted global metadata and escape special characters""" metadata = remove_keys(self.dataset.GetMetadata(), rm_metadata) # Apply escaping to metadata strings to preserve special characters (in XML/HTML format) metadata_escaped = {} for key, val in list(metadata.items()): # Keys not escaped - this may be changed if needed... metadata_escaped[key] = gdal.EscapeString(val, gdal.CPLES_XML) self.dataset.SetMetadata(metadata_escaped) self.dataset.FlushCache()
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https://github.com/nansencenter/nansat/blob/5700ec673fbf522c19b8dedcb01cc15f7cd29a6a/nansat/vrt.py#L801-L810
zhl2008/awd-platform
0416b31abea29743387b10b3914581fbe8e7da5e
web_flaskbb/lib/python2.7/site-packages/pkg_resources/_vendor/packaging/_structures.py
python
Infinity.__eq__
(self, other)
return isinstance(other, self.__class__)
[]
def __eq__(self, other): return isinstance(other, self.__class__)
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https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_flaskbb/lib/python2.7/site-packages/pkg_resources/_vendor/packaging/_structures.py#L21-L22
TencentCloud/tencentcloud-sdk-python
3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2
tencentcloud/tci/v20190318/models.py
python
ModifyLibraryResponse.__init__
(self)
r""" :param LibraryId: 人员库唯一标识符 :type LibraryId: str :param LibraryName: 人员库名称 :type LibraryName: str :param RequestId: 唯一请求 ID,每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str
r""" :param LibraryId: 人员库唯一标识符 :type LibraryId: str :param LibraryName: 人员库名称 :type LibraryName: str :param RequestId: 唯一请求 ID,每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str
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def __init__(self): r""" :param LibraryId: 人员库唯一标识符 :type LibraryId: str :param LibraryName: 人员库名称 :type LibraryName: str :param RequestId: 唯一请求 ID,每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.LibraryId = None self.LibraryName = None self.RequestId = None
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https://github.com/TencentCloud/tencentcloud-sdk-python/blob/3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2/tencentcloud/tci/v20190318/models.py#L3152-L3163
phonopy/phonopy
816586d0ba8177482ecf40e52f20cbdee2260d51
phonopy/api_phonopy.py
python
Phonopy._shape_supercell_matrix
(self, smat)
return shape_supercell_matrix(smat)
[]
def _shape_supercell_matrix(self, smat): return shape_supercell_matrix(smat)
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https://github.com/phonopy/phonopy/blob/816586d0ba8177482ecf40e52f20cbdee2260d51/phonopy/api_phonopy.py#L3543-L3544
openshift/openshift-tools
1188778e728a6e4781acf728123e5b356380fe6f
openshift/installer/vendored/openshift-ansible-3.10.0-0.29.0/roles/lib_openshift/library/oc_adm_policy_group.py
python
SecurityContextConstraints.groups
(self)
return self._groups
groups property getter
groups property getter
[ "groups", "property", "getter" ]
def groups(self): ''' groups property getter ''' if self._groups is None: self._groups = self.get_groups() return self._groups
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https://github.com/openshift/openshift-tools/blob/1188778e728a6e4781acf728123e5b356380fe6f/openshift/installer/vendored/openshift-ansible-3.10.0-0.29.0/roles/lib_openshift/library/oc_adm_policy_group.py#L1872-L1876
cournape/Bento
37de23d784407a7c98a4a15770ffc570d5f32d70
bento/private/version.py
python
NormalizedVersion.__eq__
(self, other)
return self.parts == other.parts
[]
def __eq__(self, other): if not isinstance(other, NormalizedVersion): self._cannot_compare(other) return self.parts == other.parts
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https://github.com/cournape/Bento/blob/37de23d784407a7c98a4a15770ffc570d5f32d70/bento/private/version.py#L197-L200
david8862/keras-YOLOv3-model-set
e9f0f94109430973525219e66eeafe8a2f51363d
common/backbones/shufflenet.py
python
ShuffleNet
(include_top=True, input_tensor=None, scale_factor=1.0, pooling=None, input_shape=None, groups=1, weights='imagenet', num_shuffle_units=[3, 7, 3], bottleneck_ratio=0.25, classes=1000, **kwargs)
return model
ShuffleNet implementation for Keras 2 ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, Jian Sun https://arxiv.org/pdf/1707.01083.pdf Note that only TensorFlow is supported for now, therefore it only works with the data format `image_data_format='channels_last'` in your Keras config at `~/.keras/keras.json`. Parameters ---------- include_top: bool(True) whether to include the fully-connected layer at the top of the network. input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. scale_factor: scales the number of output channels input_shape: pooling: Optional pooling mode for feature extraction when `include_top` is `False`. - `None` means that the output of the model will be the 4D tensor output of the last convolutional layer. - `avg` means that global average pooling will be applied to the output of the last convolutional layer, and thus the output of the model will be a 2D tensor. - `max` means that global max pooling will be applied. groups: int number of groups per channel num_shuffle_units: list([3,7,3]) number of stages (list length) and the number of shufflenet units in a stage beginning with stage 2 because stage 1 is fixed e.g. idx 0 contains 3 + 1 (first shuffle unit in each stage differs) shufflenet units for stage 2 idx 1 contains 7 + 1 Shufflenet Units for stage 3 and idx 2 contains 3 + 1 Shufflenet Units bottleneck_ratio: bottleneck ratio implies the ratio of bottleneck channels to output channels. For example, bottleneck ratio = 1 : 4 means the output feature map is 4 times the width of the bottleneck feature map. classes: int(1000) number of classes to predict Returns ------- A Keras model instance References ---------- - [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices] (http://www.arxiv.org/pdf/1707.01083.pdf)
ShuffleNet implementation for Keras 2 ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, Jian Sun https://arxiv.org/pdf/1707.01083.pdf Note that only TensorFlow is supported for now, therefore it only works with the data format `image_data_format='channels_last'` in your Keras config at `~/.keras/keras.json`. Parameters ---------- include_top: bool(True) whether to include the fully-connected layer at the top of the network. input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. scale_factor: scales the number of output channels input_shape: pooling: Optional pooling mode for feature extraction when `include_top` is `False`. - `None` means that the output of the model will be the 4D tensor output of the last convolutional layer. - `avg` means that global average pooling will be applied to the output of the last convolutional layer, and thus the output of the model will be a 2D tensor. - `max` means that global max pooling will be applied. groups: int number of groups per channel num_shuffle_units: list([3,7,3]) number of stages (list length) and the number of shufflenet units in a stage beginning with stage 2 because stage 1 is fixed e.g. idx 0 contains 3 + 1 (first shuffle unit in each stage differs) shufflenet units for stage 2 idx 1 contains 7 + 1 Shufflenet Units for stage 3 and idx 2 contains 3 + 1 Shufflenet Units bottleneck_ratio: bottleneck ratio implies the ratio of bottleneck channels to output channels. For example, bottleneck ratio = 1 : 4 means the output feature map is 4 times the width of the bottleneck feature map. classes: int(1000) number of classes to predict Returns ------- A Keras model instance References ---------- - [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices] (http://www.arxiv.org/pdf/1707.01083.pdf)
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def ShuffleNet(include_top=True, input_tensor=None, scale_factor=1.0, pooling=None, input_shape=None, groups=1, weights='imagenet', num_shuffle_units=[3, 7, 3], bottleneck_ratio=0.25, classes=1000, **kwargs): """ ShuffleNet implementation for Keras 2 ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, Jian Sun https://arxiv.org/pdf/1707.01083.pdf Note that only TensorFlow is supported for now, therefore it only works with the data format `image_data_format='channels_last'` in your Keras config at `~/.keras/keras.json`. Parameters ---------- include_top: bool(True) whether to include the fully-connected layer at the top of the network. input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. scale_factor: scales the number of output channels input_shape: pooling: Optional pooling mode for feature extraction when `include_top` is `False`. - `None` means that the output of the model will be the 4D tensor output of the last convolutional layer. - `avg` means that global average pooling will be applied to the output of the last convolutional layer, and thus the output of the model will be a 2D tensor. - `max` means that global max pooling will be applied. groups: int number of groups per channel num_shuffle_units: list([3,7,3]) number of stages (list length) and the number of shufflenet units in a stage beginning with stage 2 because stage 1 is fixed e.g. idx 0 contains 3 + 1 (first shuffle unit in each stage differs) shufflenet units for stage 2 idx 1 contains 7 + 1 Shufflenet Units for stage 3 and idx 2 contains 3 + 1 Shufflenet Units bottleneck_ratio: bottleneck ratio implies the ratio of bottleneck channels to output channels. For example, bottleneck ratio = 1 : 4 means the output feature map is 4 times the width of the bottleneck feature map. classes: int(1000) number of classes to predict Returns ------- A Keras model instance References ---------- - [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices] (http://www.arxiv.org/pdf/1707.01083.pdf) """ if K.backend() != 'tensorflow': raise RuntimeError('Only TensorFlow backend is currently supported, ' 'as other backends do not support ') name = "ShuffleNet_%.2gX_g%d_br_%.2g_%s" % (scale_factor, groups, bottleneck_ratio, "".join([str(x) for x in num_shuffle_units])) input_shape = _obtain_input_shape(input_shape, default_size=224, min_size=28, require_flatten=include_top, data_format=K.image_data_format()) out_dim_stage_two = {1: 144, 2: 200, 3: 240, 4: 272, 8: 384} if groups not in out_dim_stage_two: raise ValueError("Invalid number of groups.") if pooling not in ['max','avg', None]: raise ValueError("Invalid value for pooling.") if not (float(scale_factor) * 4).is_integer(): raise ValueError("Invalid value for scale_factor. Should be x over 4.") exp = np.insert(np.arange(0, len(num_shuffle_units), dtype=np.float32), 0, 0) out_channels_in_stage = 2 ** exp out_channels_in_stage *= out_dim_stage_two[groups] # calculate output channels for each stage out_channels_in_stage[0] = 24 # first stage has always 24 output channels out_channels_in_stage *= scale_factor out_channels_in_stage = out_channels_in_stage.astype(int) if input_tensor is None: img_input = Input(shape=input_shape) else: #if not K.is_keras_tensor(input_tensor): #img_input = Input(tensor=input_tensor, shape=input_shape) #else: #img_input = input_tensor img_input = input_tensor # create shufflenet architecture x = YoloConv2D(filters=out_channels_in_stage[0], kernel_size=(3, 3), padding='same', use_bias=False, strides=(2, 2), activation="relu", name="conv1")(img_input) x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same', name="maxpool1")(x) # create stages containing shufflenet units beginning at stage 2 for stage in range(0, len(num_shuffle_units)): repeat = num_shuffle_units[stage] x = _block(x, out_channels_in_stage, repeat=repeat, bottleneck_ratio=bottleneck_ratio, groups=groups, stage=stage + 2) if include_top: #x = Dense(units=classes, name="fc")(x) #x = Activation('softmax', name='softmax')(x) x = GlobalAveragePooling2D(name='global_avg_pool')(x) x = Dense(units=classes, activation='softmax', use_bias=True, name='Logits')(x) else: if pooling == 'avg': x = GlobalAveragePooling2D(name='global_avg_pool')(x) elif pooling == 'max': x = GlobalMaxPooling2D(name='global_max_pool')(x) # Ensure that the model takes into account # any potential predecessors of `input_tensor`. if input_tensor is not None: inputs = get_source_inputs(input_tensor) else: inputs = img_input # Create model. model = Model(inputs=inputs, outputs=x, name=name) # Load weights. if weights == 'imagenet': if K.image_data_format() == 'channels_first': raise ValueError('Weights for "channels_first" format ' 'are not available.') if include_top: model_name = ('shufflenet_weights_tf_dim_ordering_tf_kernels_' + str(alpha) + '_' + str(rows) + '.h5') weigh_path = BASE_WEIGHT_PATH + model_name weights_path = get_file( model_name, weigh_path, cache_subdir='models') else: model_name = ('shufflenet_weights_tf_dim_ordering_tf_kernels_' + str(alpha) + '_' + str(rows) + '_no_top' + '.h5') weigh_path = BASE_WEIGHT_PATH + model_name weights_path = get_file( model_name, weigh_path, cache_subdir='models') model.load_weights(weights_path) elif weights is not None: model.load_weights(weights) return model
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https://github.com/david8862/keras-YOLOv3-model-set/blob/e9f0f94109430973525219e66eeafe8a2f51363d/common/backbones/shufflenet.py#L23-L181
tp4a/teleport
1fafd34f1f775d2cf80ea4af6e44468d8e0b24ad
server/www/packages/packages-darwin/x64/PIL/ImageDraw.py
python
_color_diff
(rgb1, rgb2)
return abs(rgb1[0]-rgb2[0]) + abs(rgb1[1]-rgb2[1]) + abs(rgb1[2]-rgb2[2])
Uses 1-norm distance to calculate difference between two rgb values.
Uses 1-norm distance to calculate difference between two rgb values.
[ "Uses", "1", "-", "norm", "distance", "to", "calculate", "difference", "between", "two", "rgb", "values", "." ]
def _color_diff(rgb1, rgb2): """ Uses 1-norm distance to calculate difference between two rgb values. """ return abs(rgb1[0]-rgb2[0]) + abs(rgb1[1]-rgb2[1]) + abs(rgb1[2]-rgb2[2])
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https://github.com/tp4a/teleport/blob/1fafd34f1f775d2cf80ea4af6e44468d8e0b24ad/server/www/packages/packages-darwin/x64/PIL/ImageDraw.py#L383-L387
intel/IntelSEAPI
7997a782fd3fa5621e275bd31060f9795564e6ca
runtool/exporters/DGML.py
python
DGML.get_targets
(self)
return [self.args.output + ".dgml"]
[]
def get_targets(self): return [self.args.output + ".dgml"]
[ "def", "get_targets", "(", "self", ")", ":", "return", "[", "self", ".", "args", ".", "output", "+", "\".dgml\"", "]" ]
https://github.com/intel/IntelSEAPI/blob/7997a782fd3fa5621e275bd31060f9795564e6ca/runtool/exporters/DGML.py#L15-L16
pwnieexpress/pwn_plug_sources
1a23324f5dc2c3de20f9c810269b6a29b2758cad
src/wifitap/scapy.py
python
PacketListField.do_copy
(self, x)
return map(lambda p:p.copy(), x)
[]
def do_copy(self, x): return map(lambda p:p.copy(), x)
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https://github.com/pwnieexpress/pwn_plug_sources/blob/1a23324f5dc2c3de20f9c810269b6a29b2758cad/src/wifitap/scapy.py#L4029-L4030
boto/boto
b2a6f08122b2f1b89888d2848e730893595cd001
boto/gs/bucket.py
python
Bucket.set_def_xml_acl
(self, acl_str, headers=None)
return self.set_xml_acl(acl_str, '', headers, query_args=DEF_OBJ_ACL)
Sets a bucket's default ACL to an XML string. :type acl_str: string :param acl_str: A string containing the ACL XML. :type headers: dict :param headers: Additional headers to set during the request.
Sets a bucket's default ACL to an XML string.
[ "Sets", "a", "bucket", "s", "default", "ACL", "to", "an", "XML", "string", "." ]
def set_def_xml_acl(self, acl_str, headers=None): """Sets a bucket's default ACL to an XML string. :type acl_str: string :param acl_str: A string containing the ACL XML. :type headers: dict :param headers: Additional headers to set during the request. """ return self.set_xml_acl(acl_str, '', headers, query_args=DEF_OBJ_ACL)
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https://github.com/boto/boto/blob/b2a6f08122b2f1b89888d2848e730893595cd001/boto/gs/bucket.py#L574-L584
twilio/twilio-python
6e1e811ea57a1edfadd5161ace87397c563f6915
twilio/rest/api/v2010/account/usage/record/__init__.py
python
RecordInstance.uri
(self)
return self._properties['uri']
:returns: The URI of the resource, relative to `https://api.twilio.com` :rtype: unicode
:returns: The URI of the resource, relative to `https://api.twilio.com` :rtype: unicode
[ ":", "returns", ":", "The", "URI", "of", "the", "resource", "relative", "to", "https", ":", "//", "api", ".", "twilio", ".", "com", ":", "rtype", ":", "unicode" ]
def uri(self): """ :returns: The URI of the resource, relative to `https://api.twilio.com` :rtype: unicode """ return self._properties['uri']
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https://github.com/twilio/twilio-python/blob/6e1e811ea57a1edfadd5161ace87397c563f6915/twilio/rest/api/v2010/account/usage/record/__init__.py#L687-L692
Bitmessage/PyBitmessage
97612b049e0453867d6d90aa628f8e7b007b4d85
src/network/tcp.py
python
TCPConnection.antiIntersectionDelay
(self, initial=False)
This is a defense against the so called intersection attacks. It is called when you notice peer is requesting non-existing objects, or right after the connection is established. It will estimate how long an object will take to propagate across the network, and skip processing "getdata" requests until then. This means an attacker only has one shot per IP to perform the attack.
This is a defense against the so called intersection attacks.
[ "This", "is", "a", "defense", "against", "the", "so", "called", "intersection", "attacks", "." ]
def antiIntersectionDelay(self, initial=False): """ This is a defense against the so called intersection attacks. It is called when you notice peer is requesting non-existing objects, or right after the connection is established. It will estimate how long an object will take to propagate across the network, and skip processing "getdata" requests until then. This means an attacker only has one shot per IP to perform the attack. """ # estimated time for a small object to propagate across the # whole network max_known_nodes = max( len(knownnodes.knownNodes[x]) for x in knownnodes.knownNodes) delay = math.ceil(math.log(max_known_nodes + 2, 20)) * ( 0.2 + invQueue.queueCount / 2.0) # take the stream with maximum amount of nodes # +2 is to avoid problems with log(0) and log(1) # 20 is avg connected nodes count # 0.2 is avg message transmission time if delay > 0: if initial: self.skipUntil = self.connectedAt + delay if self.skipUntil > time.time(): logger.debug( 'Initial skipping processing getdata for %.2fs', self.skipUntil - time.time()) else: logger.debug( 'Skipping processing getdata due to missing object' ' for %.2fs', delay) self.skipUntil = time.time() + delay
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https://github.com/Bitmessage/PyBitmessage/blob/97612b049e0453867d6d90aa628f8e7b007b4d85/src/network/tcp.py#L96-L127
Axelrod-Python/Axelrod
00e18323c1b1af74df873773e44f31e1b9a299c6
axelrod/strategy_transformers.py
python
mixed_reclassifier
(original_classifier, probability, m_player)
return original_classifier
Function to reclassify the strategy
Function to reclassify the strategy
[ "Function", "to", "reclassify", "the", "strategy" ]
def mixed_reclassifier(original_classifier, probability, m_player): """Function to reclassify the strategy""" # If a single probability, player is passed if isinstance(probability, float) or isinstance(probability, int): m_player = [m_player] probability = [probability] if min(probability) == max(probability) == 0: # No probability given return original_classifier if 1 in probability: # If all probability given to one player player = m_player[probability.index(1)] original_classifier["stochastic"] = player.classifier["stochastic"] return original_classifier # Otherwise: stochastic. original_classifier["stochastic"] = True return original_classifier
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https://github.com/Axelrod-Python/Axelrod/blob/00e18323c1b1af74df873773e44f31e1b9a299c6/axelrod/strategy_transformers.py#L621-L638
google/grr
8ad8a4d2c5a93c92729206b7771af19d92d4f915
grr/server/grr_response_server/gui/api_labels_restricted_call_router.py
python
ApiLabelsRestrictedCallRouter.ListClientApprovals
(self, args, context=None)
return self.delegate.ListClientApprovals(args, context=context)
[]
def ListClientApprovals(self, args, context=None): # Everybody can list their own user client approvals. return self.delegate.ListClientApprovals(args, context=context)
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https://github.com/google/grr/blob/8ad8a4d2c5a93c92729206b7771af19d92d4f915/grr/server/grr_response_server/gui/api_labels_restricted_call_router.py#L265-L268
makelove/OpenCV-Python-Tutorial
e428d648f7aa50d6a0fb4f4d0fb1bd1a600fef41
cv-Tkinter-GUI/kivy-GUI/kivy_cv1.py
python
KivyCamera.__init__
(self, capture, fps, **kwargs)
[]
def __init__(self, capture, fps, **kwargs): super(KivyCamera, self).__init__(**kwargs) self.capture = capture Clock.schedule_interval(self.update, 1.0 / fps)
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https://github.com/makelove/OpenCV-Python-Tutorial/blob/e428d648f7aa50d6a0fb4f4d0fb1bd1a600fef41/cv-Tkinter-GUI/kivy-GUI/kivy_cv1.py#L27-L30
gkrizek/bash-lambda-layer
703b0ade8174022d44779d823172ab7ac33a5505
bin/docutils/utils/math/math2html.py
python
Newline.process
(self)
Process contents
Process contents
[ "Process", "contents" ]
def process(self): "Process contents" self.html = ['<br/>\n']
[ "def", "process", "(", "self", ")", ":", "self", ".", "html", "=", "[", "'<br/>\\n'", "]" ]
https://github.com/gkrizek/bash-lambda-layer/blob/703b0ade8174022d44779d823172ab7ac33a5505/bin/docutils/utils/math/math2html.py#L3718-L3720
gpodder/mygpo
7a028ad621d05d4ca0d58fd22fb92656c8835e43
mygpo/search/index.py
python
search_podcasts
(query)
return results
Search for podcasts according to 'query
Search for podcasts according to 'query
[ "Search", "for", "podcasts", "according", "to", "query" ]
def search_podcasts(query): """Search for podcasts according to 'query'""" if is_query_too_short(query): logger.debug('Found no podcasts for "{query}". Query is too short', query=query) return Podcast.objects.none() logger.debug('Searching for "{query}" podcasts"', query=query) query = SearchQuery(query) results = ( Podcast.objects.annotate(rank=SearchRank(F("search_vector"), query)) .annotate( order=ExpressionWrapper( F("rank") * F("subscribers"), output_field=FloatField() ) ) .filter(rank__gte=SEARCH_CUTOFF) .order_by("-order")[:100] .prefetch_related("slugs") ) logger.debug( 'Found {count} podcasts for "{query}"', count=len(results), query=query ) return results
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https://github.com/gpodder/mygpo/blob/7a028ad621d05d4ca0d58fd22fb92656c8835e43/mygpo/search/index.py#L24-L50
n1nj4sec/pupy
a5d766ea81fdfe3bc2c38c9bdaf10e9b75af3b39
pupy/network/lib/rpc/core/protocol.py
python
Connection._handle_getattr
(self, oid, name)
return self._access_attr(oid, name, (), "_rpyc_getattr", "allow_getattr", getattr)
[]
def _handle_getattr(self, oid, name): return self._access_attr(oid, name, (), "_rpyc_getattr", "allow_getattr", getattr)
[ "def", "_handle_getattr", "(", "self", ",", "oid", ",", "name", ")", ":", "return", "self", ".", "_access_attr", "(", "oid", ",", "name", ",", "(", ")", ",", "\"_rpyc_getattr\"", ",", "\"allow_getattr\"", ",", "getattr", ")" ]
https://github.com/n1nj4sec/pupy/blob/a5d766ea81fdfe3bc2c38c9bdaf10e9b75af3b39/pupy/network/lib/rpc/core/protocol.py#L662-L663
zhanghe06/python
a678ce38a3770c91ad12e617810bf9f5ccf7898b
fuck/pconline.py
python
get_link
(url, token)
return down_link
组装下载链接
组装下载链接
[ "组装下载链接" ]
def get_link(url, token): """ 组装下载链接 """ file_name = url.split('/')[-1] print file_name print token down_link = url.rstrip(file_name)+token+'/'+file_name print down_link return down_link
[ "def", "get_link", "(", "url", ",", "token", ")", ":", "file_name", "=", "url", ".", "split", "(", "'/'", ")", "[", "-", "1", "]", "print", "file_name", "print", "token", "down_link", "=", "url", ".", "rstrip", "(", "file_name", ")", "+", "token", "+", "'/'", "+", "file_name", "print", "down_link", "return", "down_link" ]
https://github.com/zhanghe06/python/blob/a678ce38a3770c91ad12e617810bf9f5ccf7898b/fuck/pconline.py#L43-L52
sagemath/sage
f9b2db94f675ff16963ccdefba4f1a3393b3fe0d
src/sage/geometry/polyhedron/plot.py
python
Projection.tikz
(self, view=[0, 0, 1], angle=0, scale=1, edge_color='blue!95!black', facet_color='blue!95!black', opacity=0.8, vertex_color='green', axis=False)
r""" Return a string ``tikz_pic`` consisting of a tikz picture of ``self`` according to a projection ``view`` and an angle ``angle`` obtained via Jmol through the current state property. INPUT: - ``view`` - list (default: [0,0,1]) representing the rotation axis (see note below). - ``angle`` - integer (default: 0) angle of rotation in degree from 0 to 360 (see note below). - ``scale`` - integer (default: 1) specifying the scaling of the tikz picture. - ``edge_color`` - string (default: 'blue!95!black') representing colors which tikz recognize. - ``facet_color`` - string (default: 'blue!95!black') representing colors which tikz recognize. - ``vertex_color`` - string (default: 'green') representing colors which tikz recognize. - ``opacity`` - real number (default: 0.8) between 0 and 1 giving the opacity of the front facets. - ``axis`` - Boolean (default: False) draw the axes at the origin or not. OUTPUT: - LatexExpr -- containing the TikZ picture. .. NOTE:: The inputs ``view`` and ``angle`` can be obtained by visualizing it using ``.show(aspect_ratio=1)``. This will open an interactive view in your default browser, where you can rotate the polytope. Once the desired view angle is found, click on the information icon in the lower right-hand corner and select *Get Viewpoint*. This will copy a string of the form '[x,y,z],angle' to your local clipboard. Go back to Sage and type ``Img = P.projection().tikz([x,y,z],angle)``. The inputs ``view`` and ``angle`` can also be obtained from the viewer Jmol:: 1) Right click on the image 2) Select ``Console`` 3) Select the tab ``State`` 4) Scroll to the line ``moveto`` It reads something like:: moveto 0.0 {x y z angle} Scale The ``view`` is then [x,y,z] and ``angle`` is angle. The following number is the scale. Jmol performs a rotation of ``angle`` degrees along the vector [x,y,z] and show the result from the z-axis. EXAMPLES:: sage: P1 = polytopes.small_rhombicuboctahedron() sage: Image1 = P1.projection().tikz([1,3,5], 175, scale=4) sage: type(Image1) <class 'sage.misc.latex.LatexExpr'> sage: print('\n'.join(Image1.splitlines()[:4])) \begin{tikzpicture}% [x={(-0.939161cm, 0.244762cm)}, y={(0.097442cm, -0.482887cm)}, z={(0.329367cm, 0.840780cm)}, sage: with open('polytope-tikz1.tex', 'w') as f: # not tested ....: _ = f.write(Image1) sage: P2 = Polyhedron(vertices=[[1, 1],[1, 2],[2, 1]]) sage: Image2 = P2.projection().tikz(scale=3, edge_color='blue!95!black', facet_color='orange!95!black', opacity=0.4, vertex_color='yellow', axis=True) sage: type(Image2) <class 'sage.misc.latex.LatexExpr'> sage: print('\n'.join(Image2.splitlines()[:4])) \begin{tikzpicture}% [scale=3.000000, back/.style={loosely dotted, thin}, edge/.style={color=blue!95!black, thick}, sage: with open('polytope-tikz2.tex', 'w') as f: # not tested ....: _ = f.write(Image2) sage: P3 = Polyhedron(vertices=[[-1, -1, 2],[-1, 2, -1],[2, -1, -1]]) sage: P3 A 2-dimensional polyhedron in ZZ^3 defined as the convex hull of 3 vertices sage: Image3 = P3.projection().tikz([0.5,-1,-0.1], 55, scale=3, edge_color='blue!95!black',facet_color='orange!95!black', opacity=0.7, vertex_color='yellow', axis=True) sage: print('\n'.join(Image3.splitlines()[:4])) \begin{tikzpicture}% [x={(0.658184cm, -0.242192cm)}, y={(-0.096240cm, 0.912008cm)}, z={(-0.746680cm, -0.331036cm)}, sage: with open('polytope-tikz3.tex', 'w') as f: # not tested ....: _ = f.write(Image3) sage: P = Polyhedron(vertices=[[1,1,0,0],[1,2,0,0],[2,1,0,0],[0,0,1,0],[0,0,0,1]]) sage: P A 4-dimensional polyhedron in ZZ^4 defined as the convex hull of 5 vertices sage: P.projection().tikz() Traceback (most recent call last): ... NotImplementedError: The polytope has to live in 2 or 3 dimensions. .. TODO:: Make it possible to draw Schlegel diagram for 4-polytopes. :: sage: P=Polyhedron(vertices=[[1,1,0,0],[1,2,0,0],[2,1,0,0],[0,0,1,0],[0,0,0,1]]) sage: P A 4-dimensional polyhedron in ZZ^4 defined as the convex hull of 5 vertices sage: P.projection().tikz() Traceback (most recent call last): ... NotImplementedError: The polytope has to live in 2 or 3 dimensions. Make it possible to draw 3-polytopes living in higher dimension.
r""" Return a string ``tikz_pic`` consisting of a tikz picture of ``self`` according to a projection ``view`` and an angle ``angle`` obtained via Jmol through the current state property.
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def tikz(self, view=[0, 0, 1], angle=0, scale=1, edge_color='blue!95!black', facet_color='blue!95!black', opacity=0.8, vertex_color='green', axis=False): r""" Return a string ``tikz_pic`` consisting of a tikz picture of ``self`` according to a projection ``view`` and an angle ``angle`` obtained via Jmol through the current state property. INPUT: - ``view`` - list (default: [0,0,1]) representing the rotation axis (see note below). - ``angle`` - integer (default: 0) angle of rotation in degree from 0 to 360 (see note below). - ``scale`` - integer (default: 1) specifying the scaling of the tikz picture. - ``edge_color`` - string (default: 'blue!95!black') representing colors which tikz recognize. - ``facet_color`` - string (default: 'blue!95!black') representing colors which tikz recognize. - ``vertex_color`` - string (default: 'green') representing colors which tikz recognize. - ``opacity`` - real number (default: 0.8) between 0 and 1 giving the opacity of the front facets. - ``axis`` - Boolean (default: False) draw the axes at the origin or not. OUTPUT: - LatexExpr -- containing the TikZ picture. .. NOTE:: The inputs ``view`` and ``angle`` can be obtained by visualizing it using ``.show(aspect_ratio=1)``. This will open an interactive view in your default browser, where you can rotate the polytope. Once the desired view angle is found, click on the information icon in the lower right-hand corner and select *Get Viewpoint*. This will copy a string of the form '[x,y,z],angle' to your local clipboard. Go back to Sage and type ``Img = P.projection().tikz([x,y,z],angle)``. The inputs ``view`` and ``angle`` can also be obtained from the viewer Jmol:: 1) Right click on the image 2) Select ``Console`` 3) Select the tab ``State`` 4) Scroll to the line ``moveto`` It reads something like:: moveto 0.0 {x y z angle} Scale The ``view`` is then [x,y,z] and ``angle`` is angle. The following number is the scale. Jmol performs a rotation of ``angle`` degrees along the vector [x,y,z] and show the result from the z-axis. EXAMPLES:: sage: P1 = polytopes.small_rhombicuboctahedron() sage: Image1 = P1.projection().tikz([1,3,5], 175, scale=4) sage: type(Image1) <class 'sage.misc.latex.LatexExpr'> sage: print('\n'.join(Image1.splitlines()[:4])) \begin{tikzpicture}% [x={(-0.939161cm, 0.244762cm)}, y={(0.097442cm, -0.482887cm)}, z={(0.329367cm, 0.840780cm)}, sage: with open('polytope-tikz1.tex', 'w') as f: # not tested ....: _ = f.write(Image1) sage: P2 = Polyhedron(vertices=[[1, 1],[1, 2],[2, 1]]) sage: Image2 = P2.projection().tikz(scale=3, edge_color='blue!95!black', facet_color='orange!95!black', opacity=0.4, vertex_color='yellow', axis=True) sage: type(Image2) <class 'sage.misc.latex.LatexExpr'> sage: print('\n'.join(Image2.splitlines()[:4])) \begin{tikzpicture}% [scale=3.000000, back/.style={loosely dotted, thin}, edge/.style={color=blue!95!black, thick}, sage: with open('polytope-tikz2.tex', 'w') as f: # not tested ....: _ = f.write(Image2) sage: P3 = Polyhedron(vertices=[[-1, -1, 2],[-1, 2, -1],[2, -1, -1]]) sage: P3 A 2-dimensional polyhedron in ZZ^3 defined as the convex hull of 3 vertices sage: Image3 = P3.projection().tikz([0.5,-1,-0.1], 55, scale=3, edge_color='blue!95!black',facet_color='orange!95!black', opacity=0.7, vertex_color='yellow', axis=True) sage: print('\n'.join(Image3.splitlines()[:4])) \begin{tikzpicture}% [x={(0.658184cm, -0.242192cm)}, y={(-0.096240cm, 0.912008cm)}, z={(-0.746680cm, -0.331036cm)}, sage: with open('polytope-tikz3.tex', 'w') as f: # not tested ....: _ = f.write(Image3) sage: P = Polyhedron(vertices=[[1,1,0,0],[1,2,0,0],[2,1,0,0],[0,0,1,0],[0,0,0,1]]) sage: P A 4-dimensional polyhedron in ZZ^4 defined as the convex hull of 5 vertices sage: P.projection().tikz() Traceback (most recent call last): ... NotImplementedError: The polytope has to live in 2 or 3 dimensions. .. TODO:: Make it possible to draw Schlegel diagram for 4-polytopes. :: sage: P=Polyhedron(vertices=[[1,1,0,0],[1,2,0,0],[2,1,0,0],[0,0,1,0],[0,0,0,1]]) sage: P A 4-dimensional polyhedron in ZZ^4 defined as the convex hull of 5 vertices sage: P.projection().tikz() Traceback (most recent call last): ... NotImplementedError: The polytope has to live in 2 or 3 dimensions. Make it possible to draw 3-polytopes living in higher dimension. """ if self.polyhedron_ambient_dim > 3 or self.polyhedron_ambient_dim < 2: raise NotImplementedError("The polytope has to live in 2 or 3 dimensions.") elif self.polyhedron_dim < 2 or self.polyhedron_dim > 3: raise NotImplementedError("The polytope has to be 2 or 3-dimensional.") elif self.polyhedron_ambient_dim == 2: # self is a polygon in 2-space return self._tikz_2d(scale, edge_color, facet_color, opacity, vertex_color, axis) elif self.polyhedron_dim == 2: # self is a polygon in 3-space return self._tikz_2d_in_3d(view, angle, scale, edge_color, facet_color, opacity, vertex_color, axis) else: # self is a 3-polytope in 3-space return self._tikz_3d_in_3d(view, angle, scale, edge_color, facet_color, opacity, vertex_color, axis)
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https://github.com/sagemath/sage/blob/f9b2db94f675ff16963ccdefba4f1a3393b3fe0d/src/sage/geometry/polyhedron/plot.py#L1181-L1309
SymbiFlow/prjxray
5349556bc2c230801d6df0cf11bccb9cfd171639
prjxray/tile_segbits.py
python
parsebit
(val)
return Bit( word_column=int(seg_word_column), word_bit=int(word_bit_n), isset=isset, )
Return "!012_23" => (12, 23, False)
Return "!012_23" => (12, 23, False)
[ "Return", "!012_23", "=", ">", "(", "12", "23", "False", ")" ]
def parsebit(val): '''Return "!012_23" => (12, 23, False)''' isset = True # Default is 0. Skip explicit call outs if val[0] == '!': isset = False val = val[1:] # 28_05 => 28, 05 parts = val.split('_') assert len(parts) == 2, val seg_word_column, word_bit_n = parts return Bit( word_column=int(seg_word_column), word_bit=int(word_bit_n), isset=isset, )
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https://github.com/SymbiFlow/prjxray/blob/5349556bc2c230801d6df0cf11bccb9cfd171639/prjxray/tile_segbits.py#L41-L57
researchmm/tasn
5dba8ccc096cedc63913730eeea14a9647911129
tasn-mxnet/python/mxnet/symbol/symbol.py
python
Symbol.broadcast_like
(self, *args, **kwargs)
return op.broadcast_like(self, *args, **kwargs)
Convenience fluent method for :py:func:`broadcast_like`. The arguments are the same as for :py:func:`broadcast_like`, with this array as data.
Convenience fluent method for :py:func:`broadcast_like`.
[ "Convenience", "fluent", "method", "for", ":", "py", ":", "func", ":", "broadcast_like", "." ]
def broadcast_like(self, *args, **kwargs): """Convenience fluent method for :py:func:`broadcast_like`. The arguments are the same as for :py:func:`broadcast_like`, with this array as data. """ return op.broadcast_like(self, *args, **kwargs)
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https://github.com/researchmm/tasn/blob/5dba8ccc096cedc63913730eeea14a9647911129/tasn-mxnet/python/mxnet/symbol/symbol.py#L2018-L2024
miyakogi/pyppeteer
f5313d0e7f973c57ed31fa443cea1834e223a96c
pyppeteer/dialog.py
python
Dialog.accept
(self, promptText: str = '')
Accept the dialog. * ``promptText`` (str): A text to enter in prompt. If the dialog's type is not prompt, this does not cause any effect.
Accept the dialog.
[ "Accept", "the", "dialog", "." ]
async def accept(self, promptText: str = '') -> None: """Accept the dialog. * ``promptText`` (str): A text to enter in prompt. If the dialog's type is not prompt, this does not cause any effect. """ self._handled = True await self._client.send('Page.handleJavaScriptDialog', { 'accept': True, 'promptText': promptText, })
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https://github.com/miyakogi/pyppeteer/blob/f5313d0e7f973c57ed31fa443cea1834e223a96c/pyppeteer/dialog.py#L71-L81
AI-ON/Multitask-and-Transfer-Learning
31e0798d436e314ddbc64c4a6b935df1b2160e50
architectures/chainer/models/predictive_autoencoder.py
python
normalize_2d
(x)
return exp / denominator
[]
def normalize_2d(x): exp = F.exp(x[0]) sums = F.sum(F.sum(exp, axis=-1), axis=-1) expanded = F.expand_dims(F.expand_dims(sums, axis=-1), axis=-1) denominator = F.tile(expanded, (1, 160, 210)) return exp / denominator
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https://github.com/AI-ON/Multitask-and-Transfer-Learning/blob/31e0798d436e314ddbc64c4a6b935df1b2160e50/architectures/chainer/models/predictive_autoencoder.py#L247-L252
Tencent/bk-bcs-saas
2b437bf2f5fd5ce2078f7787c3a12df609f7679d
bcs-app/backend/container_service/clusters/views/node.py
python
NodeLabelQueryCreateViewSet.create_node_labels
(self, request, project_id)
return Response({"code": 0, "message": _("创建成功!")})
添加节点标签
添加节点标签
[ "添加节点标签" ]
def create_node_labels(self, request, project_id): """添加节点标签""" # 解析参数 node_id_list, node_label_info = self.get_create_label_params(request) # 校验label中key和value self.label_regex(node_label_info) # 获取数据库中节点的label # NOTE: 节点为正常状态时,才允许设置标签 project_node_info = self.get_node_list(request, project_id, None).get('results') or [] if not project_node_info: raise error_codes.APIError(_("当前项目下节点为空,请确认")) all_node_id_list = [] all_node_id_ip_map = {} for info in project_node_info: all_node_id_list.append(info["id"]) all_node_id_ip_map[info["id"]] = {"inner_ip": info["inner_ip"], "cluster_id": info["cluster_id"]} if info['id'] in node_id_list and info['status'] != CommonStatus.Normal: raise error_codes.CheckFailed(_("节点不是正常状态时,不允许设置标签")) diff_node_id_list = set(node_id_list) - set(all_node_id_list) if diff_node_id_list: raise error_codes.CheckFailed(_("节点ID [{}] 不属于当前项目,请确认").format(",".join(diff_node_id_list))) # 校验权限 self.check_perm(request, project_id, all_node_id_ip_map, node_id_list) # 匹配数据 pre_node_labels = self.get_labels_by_node(request, project_id, node_id_list) label_operation_map = self.get_label_operation( pre_node_labels, node_label_info, node_id_list, all_node_id_ip_map ) # k8s 是以节点为维度 self.create_node_label_via_k8s(request, project_id, label_operation_map) # 写入数据库 self.create_or_update(request, project_id, label_operation_map) client.ContextActivityLogClient( project_id=project_id, user=request.user.username, resource_type="node", resource=str(node_id_list), resource_id=str(node_id_list), extra=json.dumps(node_label_info), description=_("节点打标签"), ).log_add(activity_status="succeed") return Response({"code": 0, "message": _("创建成功!")})
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https://github.com/Tencent/bk-bcs-saas/blob/2b437bf2f5fd5ce2078f7787c3a12df609f7679d/bcs-app/backend/container_service/clusters/views/node.py#L725-L767
guildai/guildai
1665985a3d4d788efc1a3180ca51cc417f71ca78
guild/external/setuptools/command/sdist.py
python
sdist.make_distribution
(self)
Workaround for #516
Workaround for #516
[ "Workaround", "for", "#516" ]
def make_distribution(self): """ Workaround for #516 """ with self._remove_os_link(): orig.sdist.make_distribution(self)
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https://github.com/guildai/guildai/blob/1665985a3d4d788efc1a3180ca51cc417f71ca78/guild/external/setuptools/command/sdist.py#L73-L78
jython/frozen-mirror
b8d7aa4cee50c0c0fe2f4b235dd62922dd0f3f99
lib-python/2.7/hotshot/__init__.py
python
Profile.stop
(self)
Stop the profiler.
Stop the profiler.
[ "Stop", "the", "profiler", "." ]
def stop(self): """Stop the profiler.""" self._prof.stop()
[ "def", "stop", "(", "self", ")", ":", "self", ".", "_prof", ".", "stop", "(", ")" ]
https://github.com/jython/frozen-mirror/blob/b8d7aa4cee50c0c0fe2f4b235dd62922dd0f3f99/lib-python/2.7/hotshot/__init__.py#L38-L40
fossasia/x-mario-center
fe67afe28d995dcf4e2498e305825a4859566172
build/lib.linux-i686-2.7/softwarecenter/ui/gtk3/app.py
python
SoftwareCenterAppGtk3.show_available_packages
(self, packages)
Show packages given as arguments in the available_pane If the list of packages is only one element long show that, otherwise turn it into a comma seperated search
Show packages given as arguments in the available_pane If the list of packages is only one element long show that, otherwise turn it into a comma seperated search
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def show_available_packages(self, packages): """ Show packages given as arguments in the available_pane If the list of packages is only one element long show that, otherwise turn it into a comma seperated search """ try: search_text, app = parse_packages_args(packages) except DebFileOpenError as e: LOG.exception("show_available_packages: can not open %r, error:", packages) dialogs.error(None, _("Error"), _("The file “%s” could not be opened.") % e.path) search_text = app = None LOG.info('show_available_packages: search_text is %r, app is %r.', search_text, app) if search_text: self.available_pane.init_view() self.available_pane.searchentry.set_text(search_text) elif app is not None: self.show_app(app) else: # normal startup, show the lobby (it will have a spinner when # its not ready yet) - it will also initialize the view self.view_manager.set_active_view(ViewPages.AVAILABLE)
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https://github.com/fossasia/x-mario-center/blob/fe67afe28d995dcf4e2498e305825a4859566172/build/lib.linux-i686-2.7/softwarecenter/ui/gtk3/app.py#L1324-L1350
NervanaSystems/ngraph-python
ac032c83c7152b615a9ad129d54d350f9d6a2986
ngraph/transformers/exop.py
python
TensorViewDecl.key
(self)
return self.tensor_description.parameter_key
Returns: A tuple unique to this view of the tensor.
Returns: A tuple unique to this view of the tensor.
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def key(self): """ Returns: A tuple unique to this view of the tensor. """ return self.tensor_description.parameter_key
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https://github.com/NervanaSystems/ngraph-python/blob/ac032c83c7152b615a9ad129d54d350f9d6a2986/ngraph/transformers/exop.py#L1175-L1180
Tautulli/Tautulli
2410eb33805aaac4bd1c5dad0f71e4f15afaf742
lib/html5lib/treebuilders/base.py
python
TreeBuilder.elementInActiveFormattingElements
(self, name)
return False
Check if an element exists between the end of the active formatting elements and the last marker. If it does, return it, else return false
Check if an element exists between the end of the active formatting elements and the last marker. If it does, return it, else return false
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def elementInActiveFormattingElements(self, name): """Check if an element exists between the end of the active formatting elements and the last marker. If it does, return it, else return false""" for item in self.activeFormattingElements[::-1]: # Check for Marker first because if it's a Marker it doesn't have a # name attribute. if item == Marker: break elif item.name == name: return item return False
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https://github.com/Tautulli/Tautulli/blob/2410eb33805aaac4bd1c5dad0f71e4f15afaf742/lib/html5lib/treebuilders/base.py#L269-L281
ducksboard/libsaas
615981a3336f65be9d51ae95a48aed9ad3bd1c3c
libsaas/services/bitbucket/issues.py
python
RepoIssues.filter
(self, filters)
return http.Request('GET', url), parsers.parse_json
Search through the issues applying filters. Look at https://confluence.atlassian.com/display/BITBUCKET/Issues to get a complete list of possible filters. :var filters: A dictionary of filters. Keys are strings corresponding to the filter names and values are ether string filter values or tuples, in which case their conditions are implicitly ORed. For example, {"title": ("~one", "~two")} would mean issues with the title containing either "one" or "two" :vartype filters: dict of str to str or tuple of str
Search through the issues applying filters.
[ "Search", "through", "the", "issues", "applying", "filters", "." ]
def filter(self, filters): """ Search through the issues applying filters. Look at https://confluence.atlassian.com/display/BITBUCKET/Issues to get a complete list of possible filters. :var filters: A dictionary of filters. Keys are strings corresponding to the filter names and values are ether string filter values or tuples, in which case their conditions are implicitly ORed. For example, {"title": ("~one", "~two")} would mean issues with the title containing either "one" or "two" :vartype filters: dict of str to str or tuple of str """ # because http.Request needs params to be a dict of strings to strings # (roughly) and since BitBucket wants repeated parameters to express # OR, we'll do the quoting by hand ourselves def flatten_conditions(filters): for key, val in filters.items(): if isinstance(val, (list, tuple)): for v in val: yield (port.to_b(key), port.to_b(v)) else: yield (port.to_b(key), port.to_b(val)) to_encode = tuple(flatten_conditions(filters)) qs = port.urlencode(to_encode) url = '{0}/?{1}'.format(self.get_url(), qs) return http.Request('GET', url), parsers.parse_json
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https://github.com/ducksboard/libsaas/blob/615981a3336f65be9d51ae95a48aed9ad3bd1c3c/libsaas/services/bitbucket/issues.py#L128-L157
openstack/cinder
23494a6d6c51451688191e1847a458f1d3cdcaa5
cinder/zonemanager/utils.py
python
get_formatted_wwn
(wwn_str)
Utility API that formats WWN to insert ':'.
Utility API that formats WWN to insert ':'.
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def get_formatted_wwn(wwn_str): """Utility API that formats WWN to insert ':'.""" if (len(wwn_str) != 16): return wwn_str.lower() else: return (':'.join([wwn_str[i:i + 2] for i in range(0, len(wwn_str), 2)])).lower()
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https://github.com/openstack/cinder/blob/23494a6d6c51451688191e1847a458f1d3cdcaa5/cinder/zonemanager/utils.py#L67-L73
ailabx/ailabx
4a8c701a3604bbc34157167224588041944ac1a2
codes/qlib-main/qlib/workflow/online/utils.py
python
OnlineToolR.get_online_tag
(self, recorder: Recorder)
return tags.get(self.ONLINE_KEY, self.OFFLINE_TAG)
Given a model recorder and return its online tag. Args: recorder (Recorder): an instance of recorder Returns: str: the online tag
Given a model recorder and return its online tag.
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def get_online_tag(self, recorder: Recorder) -> str: """ Given a model recorder and return its online tag. Args: recorder (Recorder): an instance of recorder Returns: str: the online tag """ tags = recorder.list_tags() return tags.get(self.ONLINE_KEY, self.OFFLINE_TAG)
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https://github.com/ailabx/ailabx/blob/4a8c701a3604bbc34157167224588041944ac1a2/codes/qlib-main/qlib/workflow/online/utils.py#L118-L129
bruceyang2012/Face-detection-with-mobilenet-ssd
58fafb6e93d28531797aac1e9a4436730c8cee7c
keras_ssd_loss.py
python
SSDLoss.compute_loss
(self, y_true, y_pred)
return total_loss
Compute the loss of the SSD model prediction against the ground truth. Arguments: y_true (array): A Numpy array of shape `(batch_size, #boxes, #classes + 8)`, where `#boxes` is the total number of boxes that the model predicts per image. Be careful to make sure that the index of each given box in `y_true` is the same as the index for the corresponding box in `y_pred`. The last axis must have length `#classes + 8` and contain `[classes one-hot encoded, 4 ground truth box coordinates, 4 arbitrary entries]` in this order, including the background class. The last four entries of the last axis are not used by this function and therefore their contents are irrelevant, they only exist so that `y_true` has the same shape as `y_pred`, where the last four entries of the last axis contain the anchor box coordinates, which are needed during inference. Important: Boxes that you want the cost function to ignore need to have a one-hot class vector of all zeros. y_pred (Keras tensor): The model prediction. The shape is identical to that of `y_true`. Returns: A scalar, the total multitask loss for classification and localization.
Compute the loss of the SSD model prediction against the ground truth.
[ "Compute", "the", "loss", "of", "the", "SSD", "model", "prediction", "against", "the", "ground", "truth", "." ]
def compute_loss(self, y_true, y_pred): ''' Compute the loss of the SSD model prediction against the ground truth. Arguments: y_true (array): A Numpy array of shape `(batch_size, #boxes, #classes + 8)`, where `#boxes` is the total number of boxes that the model predicts per image. Be careful to make sure that the index of each given box in `y_true` is the same as the index for the corresponding box in `y_pred`. The last axis must have length `#classes + 8` and contain `[classes one-hot encoded, 4 ground truth box coordinates, 4 arbitrary entries]` in this order, including the background class. The last four entries of the last axis are not used by this function and therefore their contents are irrelevant, they only exist so that `y_true` has the same shape as `y_pred`, where the last four entries of the last axis contain the anchor box coordinates, which are needed during inference. Important: Boxes that you want the cost function to ignore need to have a one-hot class vector of all zeros. y_pred (Keras tensor): The model prediction. The shape is identical to that of `y_true`. Returns: A scalar, the total multitask loss for classification and localization. ''' batch_size = tf.shape(y_pred)[0] # Output dtype: tf.int32 n_boxes = tf.shape(y_pred)[ 1] # Output dtype: tf.int32, note that `n_boxes` in this context denotes the total number of boxes per image, not the number of boxes per cell # 1: Compute the losses for class and box predictions for every box classification_loss = tf.cast( self.log_loss(y_true[:, :, :-12], y_pred[:, :, :-12]), dtype=tf.float32) # Output shape: (batch_size, n_boxes) localization_loss = tf.cast( self.smooth_L1_loss(y_true[:, :, -12:-8], y_pred[:, :, -12:-8]), dtype=tf.float32) # Output shape: (batch_size, n_boxes) # 2: Compute the classification losses for the positive and negative targets # Create masks for the positive and negative ground truth classes negatives = y_true[:, :, 0] # Tensor of shape (batch_size, n_boxes) positives = tf.cast(tf.reduce_max(y_true[:, :, 1:-12], axis=-1), dtype=tf.float32) # Tensor of shape (batch_size, n_boxes) # Count the number of positive boxes (classes 1 to n) in y_true across the whole batch n_positive = tf.reduce_sum(positives) # Now mask all negative boxes and sum up the losses for the positive boxes PER batch item # (Keras loss functions must output one scalar loss value PER batch item, rather than just # one scalar for the entire batch, that's why we're not summing across all axes) pos_class_loss = tf.reduce_sum(classification_loss * positives, axis=-1) # Tensor of shape (batch_size,) # Compute the classification loss for the negative default boxes (if there are any) # First, compute the classification loss for all negative boxes neg_class_loss_all = classification_loss * negatives # Tensor of shape (batch_size, n_boxes) n_neg_losses = tf.math.count_nonzero(neg_class_loss_all, dtype=tf.int32) # The number of non-zero loss entries in `neg_class_loss_all` # What's the point of `n_neg_losses`? For the next step, which will be to compute which negative boxes enter the classification # loss, we don't just want to know how many negative ground truth boxes there are, but for how many of those there actually is # a positive (i.e. non-zero) loss. This is necessary because `tf.nn.top-k()` in the function below will pick the top k boxes with # the highest losses no matter what, even if it receives a vector where all losses are zero. In the unlikely event that all negative # classification losses ARE actually zero though, this behavior might lead to `tf.nn.top-k()` returning the indices of positive # boxes, leading to an incorrect negative classification loss computation, and hence an incorrect overall loss computation. # We therefore need to make sure that `n_negative_keep`, which assumes the role of the `k` argument in `tf.nn.top-k()`, # is at most the number of negative boxes for which there is a positive classification loss. # Compute the number of negative examples we want to account for in the loss # We'll keep at most `self.neg_pos_ratio` times the number of positives in `y_true`, but at least `self.n_neg_min` (unless `n_neg_loses` is smaller) n_negative_keep = tf.minimum( tf.maximum(self.neg_pos_ratio * tf.cast(n_positive, dtype=tf.int32), self.n_neg_min), n_neg_losses) # In the unlikely case when either (1) there are no negative ground truth boxes at all # or (2) the classification loss for all negative boxes is zero, return zero as the `neg_class_loss` def f1(): return tf.zeros([batch_size]) # Otherwise compute the negative loss def f2(): # Now we'll identify the top-k (where k == `n_negative_keep`) boxes with the highest confidence loss that # belong to the background class in the ground truth data. Note that this doesn't necessarily mean that the model # predicted the wrong class for those boxes, it just means that the loss for those boxes is the highest. # To do this, we reshape `neg_class_loss_all` to 1D... neg_class_loss_all_1D = tf.reshape(neg_class_loss_all, [-1]) # Tensor of shape (batch_size * n_boxes,) # ...and then we get the indices for the `n_negative_keep` boxes with the highest loss out of those... values, indices = tf.nn.top_k(neg_class_loss_all_1D, n_negative_keep, False) # We don't need sorting # ...and with these indices we'll create a mask... negatives_keep = tf.scatter_nd(tf.expand_dims(indices, axis=1), updates=tf.ones_like(indices, dtype=tf.int32), shape=tf.shape( neg_class_loss_all_1D)) # Tensor of shape (batch_size * n_boxes,) negatives_keep = tf.cast( tf.reshape(negatives_keep, [batch_size, n_boxes]), dtype=tf.float32) # Tensor of shape (batch_size, n_boxes) # ...and use it to keep only those boxes and mask all other classification losses neg_class_loss = tf.reduce_sum(classification_loss * negatives_keep, axis=-1) # Tensor of shape (batch_size,) return neg_class_loss neg_class_loss = tf.cond(tf.equal(n_neg_losses, tf.constant(0)), f1, f2) class_loss = pos_class_loss + neg_class_loss # Tensor of shape (batch_size,) # 3: Compute the localization loss for the positive targets # We don't penalize localization loss for negative predicted boxes (obviously: there are no ground truth boxes they would correspond to) loc_loss = tf.reduce_sum(localization_loss * positives, axis=-1) # Tensor of shape (batch_size,) # 4: Compute the total loss total_loss = (self.beta * class_loss + self.alpha * loc_loss) / tf.maximum(1.0, n_positive) # In case `n_positive == 0` return total_loss
[ "def", "compute_loss", "(", "self", ",", "y_true", ",", "y_pred", ")", ":", "batch_size", "=", "tf", ".", "shape", "(", "y_pred", ")", "[", "0", "]", "# Output dtype: tf.int32", "n_boxes", "=", "tf", ".", "shape", "(", "y_pred", ")", "[", "1", "]", "# Output dtype: tf.int32, note that `n_boxes` in this context denotes the total number of boxes per image, not the number of boxes per cell", "# 1: Compute the losses for class and box predictions for every box", "classification_loss", "=", "tf", ".", "cast", "(", "self", ".", "log_loss", "(", "y_true", "[", ":", ",", ":", ",", ":", "-", "12", "]", ",", "y_pred", "[", ":", ",", ":", ",", ":", "-", "12", "]", ")", ",", "dtype", "=", "tf", ".", "float32", ")", "# Output shape: (batch_size, n_boxes)", "localization_loss", "=", "tf", ".", "cast", "(", "self", ".", "smooth_L1_loss", "(", "y_true", "[", ":", ",", ":", ",", "-", "12", ":", "-", "8", "]", ",", "y_pred", "[", ":", ",", ":", ",", "-", "12", ":", "-", "8", "]", ")", ",", "dtype", "=", "tf", ".", "float32", ")", "# Output shape: (batch_size, n_boxes)", "# 2: Compute the classification losses for the positive and negative targets", "# Create masks for the positive and negative ground truth classes", "negatives", "=", "y_true", "[", ":", ",", ":", ",", "0", "]", "# Tensor of shape (batch_size, n_boxes)", "positives", "=", "tf", ".", "cast", "(", "tf", ".", "reduce_max", "(", "y_true", "[", ":", ",", ":", ",", "1", ":", "-", "12", "]", ",", "axis", "=", "-", "1", ")", ",", "dtype", "=", "tf", ".", "float32", ")", "# Tensor of shape (batch_size, n_boxes)", "# Count the number of positive boxes (classes 1 to n) in y_true across the whole batch", "n_positive", "=", "tf", ".", "reduce_sum", "(", "positives", ")", "# Now mask all negative boxes and sum up the losses for the positive boxes PER batch item", "# (Keras loss functions must output one scalar loss value PER batch item, rather than just", "# one scalar for the entire batch, that's why we're not summing across all axes)", "pos_class_loss", "=", "tf", ".", "reduce_sum", "(", "classification_loss", "*", "positives", ",", "axis", "=", "-", "1", ")", "# Tensor of shape (batch_size,)", "# Compute the classification loss for the negative default boxes (if there are any)", "# First, compute the classification loss for all negative boxes", "neg_class_loss_all", "=", "classification_loss", "*", "negatives", "# Tensor of shape (batch_size, n_boxes)", "n_neg_losses", "=", "tf", ".", "math", ".", "count_nonzero", "(", "neg_class_loss_all", ",", "dtype", "=", "tf", ".", "int32", ")", "# The number of non-zero loss entries in `neg_class_loss_all`", "# What's the point of `n_neg_losses`? For the next step, which will be to compute which negative boxes enter the classification", "# loss, we don't just want to know how many negative ground truth boxes there are, but for how many of those there actually is", "# a positive (i.e. non-zero) loss. This is necessary because `tf.nn.top-k()` in the function below will pick the top k boxes with", "# the highest losses no matter what, even if it receives a vector where all losses are zero. In the unlikely event that all negative", "# classification losses ARE actually zero though, this behavior might lead to `tf.nn.top-k()` returning the indices of positive", "# boxes, leading to an incorrect negative classification loss computation, and hence an incorrect overall loss computation.", "# We therefore need to make sure that `n_negative_keep`, which assumes the role of the `k` argument in `tf.nn.top-k()`,", "# is at most the number of negative boxes for which there is a positive classification loss.", "# Compute the number of negative examples we want to account for in the loss", "# We'll keep at most `self.neg_pos_ratio` times the number of positives in `y_true`, but at least `self.n_neg_min` (unless `n_neg_loses` is smaller)", "n_negative_keep", "=", "tf", ".", "minimum", "(", "tf", ".", "maximum", "(", "self", ".", "neg_pos_ratio", "*", "tf", ".", "cast", "(", "n_positive", ",", "dtype", "=", "tf", ".", "int32", ")", ",", "self", ".", "n_neg_min", ")", ",", "n_neg_losses", ")", "# In the unlikely case when either (1) there are no negative ground truth boxes at all", "# or (2) the classification loss for all negative boxes is zero, return zero as the `neg_class_loss`", "def", "f1", "(", ")", ":", "return", "tf", ".", "zeros", "(", "[", "batch_size", "]", ")", "# Otherwise compute the negative loss", "def", "f2", "(", ")", ":", "# Now we'll identify the top-k (where k == `n_negative_keep`) boxes with the highest confidence loss that", "# belong to the background class in the ground truth data. Note that this doesn't necessarily mean that the model", "# predicted the wrong class for those boxes, it just means that the loss for those boxes is the highest.", "# To do this, we reshape `neg_class_loss_all` to 1D...", "neg_class_loss_all_1D", "=", "tf", ".", "reshape", "(", "neg_class_loss_all", ",", "[", "-", "1", "]", ")", "# Tensor of shape (batch_size * n_boxes,)", "# ...and then we get the indices for the `n_negative_keep` boxes with the highest loss out of those...", "values", ",", "indices", "=", "tf", ".", "nn", ".", "top_k", "(", "neg_class_loss_all_1D", ",", "n_negative_keep", ",", "False", ")", "# We don't need sorting", "# ...and with these indices we'll create a mask...", "negatives_keep", "=", "tf", ".", "scatter_nd", "(", "tf", ".", "expand_dims", "(", "indices", ",", "axis", "=", "1", ")", ",", "updates", "=", "tf", ".", "ones_like", "(", "indices", ",", "dtype", "=", "tf", ".", "int32", ")", ",", "shape", "=", "tf", ".", "shape", "(", "neg_class_loss_all_1D", ")", ")", "# Tensor of shape (batch_size * n_boxes,)", "negatives_keep", "=", "tf", ".", "cast", "(", "tf", ".", "reshape", "(", "negatives_keep", ",", "[", "batch_size", ",", "n_boxes", "]", ")", ",", "dtype", "=", "tf", ".", "float32", ")", "# Tensor of shape (batch_size, n_boxes)", "# ...and use it to keep only those boxes and mask all other classification losses", "neg_class_loss", "=", "tf", ".", "reduce_sum", "(", "classification_loss", "*", "negatives_keep", ",", "axis", "=", "-", "1", ")", "# Tensor of shape (batch_size,)", "return", "neg_class_loss", "neg_class_loss", "=", "tf", ".", "cond", "(", "tf", ".", "equal", "(", "n_neg_losses", ",", "tf", ".", "constant", "(", "0", ")", ")", ",", "f1", ",", "f2", ")", "class_loss", "=", "pos_class_loss", "+", "neg_class_loss", "# Tensor of shape (batch_size,)", "# 3: Compute the localization loss for the positive targets", "# We don't penalize localization loss for negative predicted boxes (obviously: there are no ground truth boxes they would correspond to)", "loc_loss", "=", "tf", ".", "reduce_sum", "(", "localization_loss", "*", "positives", ",", "axis", "=", "-", "1", ")", "# Tensor of shape (batch_size,)", "# 4: Compute the total loss", "total_loss", "=", "(", "self", ".", "beta", "*", "class_loss", "+", "self", ".", "alpha", "*", "loc_loss", ")", "/", "tf", ".", "maximum", "(", "1.0", ",", "n_positive", ")", "# In case `n_positive == 0`", "return", "total_loss" ]
https://github.com/bruceyang2012/Face-detection-with-mobilenet-ssd/blob/58fafb6e93d28531797aac1e9a4436730c8cee7c/keras_ssd_loss.py#L101-L215
getting-things-gnome/gtg
4b02c43744b32a00facb98174f04ec5953bd055d
GTG/core/datastore.py
python
TaskSource.get_task_filter_for_backend
(self)
return lambda task: backend_filter(self.requester, task, {"tags": set(attached_tags)})
Filter that checks if the task should be stored in this backend. @returns function: a function that accepts a task and returns True/False whether the task should be stored or not
Filter that checks if the task should be stored in this backend.
[ "Filter", "that", "checks", "if", "the", "task", "should", "be", "stored", "in", "this", "backend", "." ]
def get_task_filter_for_backend(self): """ Filter that checks if the task should be stored in this backend. @returns function: a function that accepts a task and returns True/False whether the task should be stored or not """ def backend_filter(req, task, parameters): """ Filter that checks if two tags sets intersect. It is used to check if a task should be stored inside a backend @param task: a task object @param tags_to_match_set: a *set* of tag names """ try: tags_to_match_set = parameters['tags'] except KeyError: return [] all_tasks_tag = req.get_alltag_tag().get_name() if all_tasks_tag in tags_to_match_set: return True task_tags = set(task.get_tags_name()) return task_tags.intersection(tags_to_match_set) attached_tags = self.backend.get_attached_tags() return lambda task: backend_filter(self.requester, task, {"tags": set(attached_tags)})
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https://github.com/getting-things-gnome/gtg/blob/4b02c43744b32a00facb98174f04ec5953bd055d/GTG/core/datastore.py#L699-L726
kobiso/CBAM-keras
796ae9ea31253d87f46ac4908e94ad5d799fbdd5
models/.ipynb_checkpoints/mobilenets-checkpoint.py
python
_depthwise_conv_block
(inputs, pointwise_conv_filters, alpha, depth_multiplier=1, strides=(1, 1), block_id=1, attention_module=None)
return x
Adds a depthwise convolution block. A depthwise convolution block consists of a depthwise conv, batch normalization, relu6, pointwise convolution, batch normalization and relu6 activation. # Arguments inputs: Input tensor of shape `(rows, cols, channels)` (with `channels_last` data format) or (channels, rows, cols) (with `channels_first` data format). pointwise_conv_filters: Integer, the dimensionality of the output space (i.e. the number output of filters in the pointwise convolution). alpha: controls the width of the network. - If `alpha` < 1.0, proportionally decreases the number of filters in each layer. - If `alpha` > 1.0, proportionally increases the number of filters in each layer. - If `alpha` = 1, default number of filters from the paper are used at each layer. depth_multiplier: The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to `filters_in * depth_multiplier`. strides: An integer or tuple/list of 2 integers, specifying the strides of the convolution along the width and height. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1. block_id: Integer, a unique identification designating the block number. # Input shape 4D tensor with shape: `(batch, channels, rows, cols)` if data_format='channels_first' or 4D tensor with shape: `(batch, rows, cols, channels)` if data_format='channels_last'. # Output shape 4D tensor with shape: `(batch, filters, new_rows, new_cols)` if data_format='channels_first' or 4D tensor with shape: `(batch, new_rows, new_cols, filters)` if data_format='channels_last'. `rows` and `cols` values might have changed due to stride. # Returns Output tensor of block.
Adds a depthwise convolution block. A depthwise convolution block consists of a depthwise conv, batch normalization, relu6, pointwise convolution, batch normalization and relu6 activation. # Arguments inputs: Input tensor of shape `(rows, cols, channels)` (with `channels_last` data format) or (channels, rows, cols) (with `channels_first` data format). pointwise_conv_filters: Integer, the dimensionality of the output space (i.e. the number output of filters in the pointwise convolution). alpha: controls the width of the network. - If `alpha` < 1.0, proportionally decreases the number of filters in each layer. - If `alpha` > 1.0, proportionally increases the number of filters in each layer. - If `alpha` = 1, default number of filters from the paper are used at each layer. depth_multiplier: The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to `filters_in * depth_multiplier`. strides: An integer or tuple/list of 2 integers, specifying the strides of the convolution along the width and height. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1. block_id: Integer, a unique identification designating the block number. # Input shape 4D tensor with shape: `(batch, channels, rows, cols)` if data_format='channels_first' or 4D tensor with shape: `(batch, rows, cols, channels)` if data_format='channels_last'. # Output shape 4D tensor with shape: `(batch, filters, new_rows, new_cols)` if data_format='channels_first' or 4D tensor with shape: `(batch, new_rows, new_cols, filters)` if data_format='channels_last'. `rows` and `cols` values might have changed due to stride. # Returns Output tensor of block.
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def _depthwise_conv_block(inputs, pointwise_conv_filters, alpha, depth_multiplier=1, strides=(1, 1), block_id=1, attention_module=None): """Adds a depthwise convolution block. A depthwise convolution block consists of a depthwise conv, batch normalization, relu6, pointwise convolution, batch normalization and relu6 activation. # Arguments inputs: Input tensor of shape `(rows, cols, channels)` (with `channels_last` data format) or (channels, rows, cols) (with `channels_first` data format). pointwise_conv_filters: Integer, the dimensionality of the output space (i.e. the number output of filters in the pointwise convolution). alpha: controls the width of the network. - If `alpha` < 1.0, proportionally decreases the number of filters in each layer. - If `alpha` > 1.0, proportionally increases the number of filters in each layer. - If `alpha` = 1, default number of filters from the paper are used at each layer. depth_multiplier: The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to `filters_in * depth_multiplier`. strides: An integer or tuple/list of 2 integers, specifying the strides of the convolution along the width and height. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1. block_id: Integer, a unique identification designating the block number. # Input shape 4D tensor with shape: `(batch, channels, rows, cols)` if data_format='channels_first' or 4D tensor with shape: `(batch, rows, cols, channels)` if data_format='channels_last'. # Output shape 4D tensor with shape: `(batch, filters, new_rows, new_cols)` if data_format='channels_first' or 4D tensor with shape: `(batch, new_rows, new_cols, filters)` if data_format='channels_last'. `rows` and `cols` values might have changed due to stride. # Returns Output tensor of block. """ channel_axis = 1 if K.image_data_format() == 'channels_first' else -1 pointwise_conv_filters = int(pointwise_conv_filters * alpha) x = DepthwiseConv2D((3, 3), padding='same', depth_multiplier=depth_multiplier, strides=strides, use_bias=False, name='conv_dw_%d' % block_id)(inputs) x = BatchNormalization(axis=channel_axis, name='conv_dw_%d_bn' % block_id)(x) x = Activation(relu6, name='conv_dw_%d_relu' % block_id)(x) x = Conv2D(pointwise_conv_filters, (1, 1), padding='same', use_bias=False, strides=(1, 1), name='conv_pw_%d' % block_id)(x) x = BatchNormalization(axis=channel_axis, name='conv_pw_%d_bn' % block_id)(x) x = Activation(relu6, name='conv_pw_%d_relu' % block_id)(x) if attention_module == 'se_block': x = se_block(x) if attention_module == 'cbam_block': x = cbam_block(x) return x
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https://github.com/kobiso/CBAM-keras/blob/796ae9ea31253d87f46ac4908e94ad5d799fbdd5/models/.ipynb_checkpoints/mobilenets-checkpoint.py#L477-L546
microsoft/unilm
65f15af2a307ebb64cfb25adf54375b002e6fe8d
infoxlm/fairseq/fairseq/progress_bar.py
python
simple_progress_bar.print
(self, stats, tag='', step=None)
Print end-of-epoch stats.
Print end-of-epoch stats.
[ "Print", "end", "-", "of", "-", "epoch", "stats", "." ]
def print(self, stats, tag='', step=None): """Print end-of-epoch stats.""" postfix = self._str_pipes(self._format_stats(stats)) print('{} | {}'.format(self.prefix, postfix), flush=True)
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https://github.com/microsoft/unilm/blob/65f15af2a307ebb64cfb25adf54375b002e6fe8d/infoxlm/fairseq/fairseq/progress_bar.py#L194-L197
Yelp/mrjob
091572e87bc24cc64be40278dd0f5c3617c98d4b
mrjob/emr.py
python
EMRJobRunner.get_cluster_id
(self)
return self._cluster_id
Get the ID of the cluster our job is running on, or ``None``.
Get the ID of the cluster our job is running on, or ``None``.
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def get_cluster_id(self): """Get the ID of the cluster our job is running on, or ``None``.""" return self._cluster_id
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https://github.com/Yelp/mrjob/blob/091572e87bc24cc64be40278dd0f5c3617c98d4b/mrjob/emr.py#L2388-L2390
Komodo/KomodoEdit
61edab75dce2bdb03943b387b0608ea36f548e8e
contrib/paramiko/paramiko/transport.py
python
Transport.open_channel
(self, kind, dest_addr=None, src_addr=None, window_size=None, max_packet_size=None, timeout=None)
Request a new channel to the server. `Channels <.Channel>` are socket-like objects used for the actual transfer of data across the session. You may only request a channel after negotiating encryption (using `connect` or `start_client`) and authenticating. .. note:: Modifying the the window and packet sizes might have adverse effects on the channel created. The default values are the same as in the OpenSSH code base and have been battle tested. :param str kind: the kind of channel requested (usually ``"session"``, ``"forwarded-tcpip"``, ``"direct-tcpip"``, or ``"x11"``) :param tuple dest_addr: the destination address (address + port tuple) of this port forwarding, if ``kind`` is ``"forwarded-tcpip"`` or ``"direct-tcpip"`` (ignored for other channel types) :param src_addr: the source address of this port forwarding, if ``kind`` is ``"forwarded-tcpip"``, ``"direct-tcpip"``, or ``"x11"`` :param int window_size: optional window size for this session. :param int max_packet_size: optional max packet size for this session. :param float timeout: optional timeout opening a channel, default 3600s (1h) :return: a new `.Channel` on success :raises SSHException: if the request is rejected, the session ends prematurely or there is a timeout openning a channel .. versionchanged:: 1.15 Added the ``window_size`` and ``max_packet_size`` arguments.
Request a new channel to the server. `Channels <.Channel>` are socket-like objects used for the actual transfer of data across the session. You may only request a channel after negotiating encryption (using `connect` or `start_client`) and authenticating.
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def open_channel(self, kind, dest_addr=None, src_addr=None, window_size=None, max_packet_size=None, timeout=None): """ Request a new channel to the server. `Channels <.Channel>` are socket-like objects used for the actual transfer of data across the session. You may only request a channel after negotiating encryption (using `connect` or `start_client`) and authenticating. .. note:: Modifying the the window and packet sizes might have adverse effects on the channel created. The default values are the same as in the OpenSSH code base and have been battle tested. :param str kind: the kind of channel requested (usually ``"session"``, ``"forwarded-tcpip"``, ``"direct-tcpip"``, or ``"x11"``) :param tuple dest_addr: the destination address (address + port tuple) of this port forwarding, if ``kind`` is ``"forwarded-tcpip"`` or ``"direct-tcpip"`` (ignored for other channel types) :param src_addr: the source address of this port forwarding, if ``kind`` is ``"forwarded-tcpip"``, ``"direct-tcpip"``, or ``"x11"`` :param int window_size: optional window size for this session. :param int max_packet_size: optional max packet size for this session. :param float timeout: optional timeout opening a channel, default 3600s (1h) :return: a new `.Channel` on success :raises SSHException: if the request is rejected, the session ends prematurely or there is a timeout openning a channel .. versionchanged:: 1.15 Added the ``window_size`` and ``max_packet_size`` arguments. """ if not self.active: raise SSHException('SSH session not active') timeout = 3600 if timeout is None else timeout self.lock.acquire() try: window_size = self._sanitize_window_size(window_size) max_packet_size = self._sanitize_packet_size(max_packet_size) chanid = self._next_channel() m = Message() m.add_byte(cMSG_CHANNEL_OPEN) m.add_string(kind) m.add_int(chanid) m.add_int(window_size) m.add_int(max_packet_size) if (kind == 'forwarded-tcpip') or (kind == 'direct-tcpip'): m.add_string(dest_addr[0]) m.add_int(dest_addr[1]) m.add_string(src_addr[0]) m.add_int(src_addr[1]) elif kind == 'x11': m.add_string(src_addr[0]) m.add_int(src_addr[1]) chan = Channel(chanid) self._channels.put(chanid, chan) self.channel_events[chanid] = event = threading.Event() self.channels_seen[chanid] = True chan._set_transport(self) chan._set_window(window_size, max_packet_size) finally: self.lock.release() self._send_user_message(m) start_ts = time.time() while True: event.wait(0.1) if not self.active: e = self.get_exception() if e is None: e = SSHException('Unable to open channel.') raise e if event.is_set(): break elif start_ts + timeout < time.time(): raise SSHException('Timeout openning channel.') chan = self._channels.get(chanid) if chan is not None: return chan e = self.get_exception() if e is None: e = SSHException('Unable to open channel.') raise e
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https://github.com/Komodo/KomodoEdit/blob/61edab75dce2bdb03943b387b0608ea36f548e8e/contrib/paramiko/paramiko/transport.py#L746-L836
AppScale/gts
46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9
AppTaskQueue/appscale/taskqueue/queue.py
python
PostgresPullQueue.get_task
(self, task, omit_payload=False)
return self._task_from_row(columns, row, id=task.id)
Gets a task from the queue. Args: task: A Task object. omit_payload: A boolean indicating that the payload should not be fetched. Returns: A task object or None.
Gets a task from the queue.
[ "Gets", "a", "task", "from", "the", "queue", "." ]
def get_task(self, task, omit_payload=False): """ Gets a task from the queue. Args: task: A Task object. omit_payload: A boolean indicating that the payload should not be fetched. Returns: A task object or None. """ if omit_payload: columns = ['task_name', 'time_enqueued', 'lease_expires', 'lease_count', 'tag'] else: columns = ['payload', 'task_name', 'time_enqueued', 'lease_expires', 'lease_count', 'tag'] pg_connection = pg_wrapper.get_connection() with pg_connection: with pg_connection.cursor() as pg_cursor: pg_cursor.execute( 'SELECT {columns} FROM "{tasks_table}" ' 'WHERE task_name = %(task_name)s AND time_deleted IS NULL' .format(columns=', '.join(columns), tasks_table=self.tasks_table_name), vars={ 'task_name': task.id, } ) row = pg_cursor.fetchone() if not row: return None return self._task_from_row(columns, row, id=task.id)
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https://github.com/AppScale/gts/blob/46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9/AppTaskQueue/appscale/taskqueue/queue.py#L313-L345
andresriancho/w3af
cd22e5252243a87aaa6d0ddea47cf58dacfe00a9
w3af/core/ui/gui/tools/proxywin.py
python
ProxiedRequests.reload_options
(self)
Reload options. 1. Stop proxy 2. Try to start proxy with new params 3. If can't => alert 4. If everything is ok then start proxy 5. Set Trap options 6. Save options
Reload options. 1. Stop proxy 2. Try to start proxy with new params 3. If can't => alert 4. If everything is ok then start proxy 5. Set Trap options 6. Save options
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def reload_options(self): """Reload options. 1. Stop proxy 2. Try to start proxy with new params 3. If can't => alert 4. If everything is ok then start proxy 5. Set Trap options 6. Save options """ new_port = self.pref.get_value('proxy', 'ipport') if new_port != self._prev_ip_port: self.w3af.mainwin.sb(_("Stopping local proxy")) if self.proxy: self.proxy.stop() try: self._start_proxy() except ProxyException: # Ups, port looks already used..:( # Let's show alert and focus Options tab self.w3af.mainwin.sb(_("Failed to start local proxy")) self.fuzzable = None self.waiting_requests = False self.keep_checking = False # Focus Options tab self.nb.set_current_page(2) return else: self.fuzzable = None self.waiting_requests = True self.keep_checking = True # Config test try: self.proxy.set_what_to_trap(self.pref.get_value('proxy', 'trap')) self.proxy.set_what_not_to_trap(self.pref.get_value('proxy', 'notrap')) self.proxy.set_methods_to_trap(self.pref.get_value('proxy', 'methodtrap')) except BaseFrameworkException, w3: self.show_alert(_("Invalid configuration!\n" + str(w3))) self._prev_ip_port = new_port httpeditor = self.reqresp.request.get_view_by_id('HttpRawView') httpeditor.set_show_line_numbers(self.pref.get_value('editor', 'display_line_num')) httpeditor.set_highlight_current_line(self.pref.get_value('editor', 'highlight_current_line')) httpeditor.set_highlight_syntax(self.pref.get_value('editor', 'highlight_syntax')) httpeditor.set_wrap(self.pref.get_value('editor', 'wrap')) self.pref.save() if self._layout != self.pref.get_value('proxy', 'trap_view'): self.show_alert(_('Some of options will take effect after you' ' restart proxy tool'))
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https://github.com/andresriancho/w3af/blob/cd22e5252243a87aaa6d0ddea47cf58dacfe00a9/w3af/core/ui/gui/tools/proxywin.py#L238-L291
mathics/Mathics
318e06dea8f1c70758a50cb2f95c9900150e3a68
mathics/builtin/structure.py
python
Apply.apply_invalidlevel
(self, f, expr, ls, evaluation, options={})
Apply[f_, expr_, ls_, OptionsPattern[Apply]]
Apply[f_, expr_, ls_, OptionsPattern[Apply]]
[ "Apply", "[", "f_", "expr_", "ls_", "OptionsPattern", "[", "Apply", "]]" ]
def apply_invalidlevel(self, f, expr, ls, evaluation, options={}): "Apply[f_, expr_, ls_, OptionsPattern[Apply]]" evaluation.message("Apply", "level", ls)
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https://github.com/mathics/Mathics/blob/318e06dea8f1c70758a50cb2f95c9900150e3a68/mathics/builtin/structure.py#L434-L437
Trusted-AI/adversarial-robustness-toolbox
9fabffdbb92947efa1ecc5d825d634d30dfbaf29
art/attacks/evasion/pe_malware_attack.py
python
MalwareGDTensorFlow.check_valid_size
( self, y: np.ndarray, sample_sizes: np.ndarray, append_perturbation_size: np.ndarray, )
return adv_label_vector
Checks that we can append the l0 perturbation to the malware sample and not exceed the maximum file size. A new label vector with just the valid files indicated is created. :param y: Labels. :param sample_sizes: The size of the original file, before it was padded to the input size required by MalConv. :param append_perturbation_size: Size of the perturbations in L0 terms to put at end of file. :return adv_label_vector: Labels which indicate which malware samples have enough free features to accommodate all the adversarial perturbation.
Checks that we can append the l0 perturbation to the malware sample and not exceed the maximum file size. A new label vector with just the valid files indicated is created.
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def check_valid_size( self, y: np.ndarray, sample_sizes: np.ndarray, append_perturbation_size: np.ndarray, ) -> np.ndarray: """ Checks that we can append the l0 perturbation to the malware sample and not exceed the maximum file size. A new label vector with just the valid files indicated is created. :param y: Labels. :param sample_sizes: The size of the original file, before it was padded to the input size required by MalConv. :param append_perturbation_size: Size of the perturbations in L0 terms to put at end of file. :return adv_label_vector: Labels which indicate which malware samples have enough free features to accommodate all the adversarial perturbation. """ adv_label_vector = np.zeros_like(y) for i, label in enumerate(y): if label == 1: if sample_sizes[i] + append_perturbation_size[i] <= self.param_dic["maxlen"]: adv_label_vector[i] = 1 logger.info("size to append on sample %d is %d", i, append_perturbation_size[i]) return adv_label_vector
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https://github.com/Trusted-AI/adversarial-robustness-toolbox/blob/9fabffdbb92947efa1ecc5d825d634d30dfbaf29/art/attacks/evasion/pe_malware_attack.py#L170-L194
python/cpython
e13cdca0f5224ec4e23bdd04bb3120506964bc8b
Lib/importlib/metadata/__init__.py
python
distributions
(**kwargs)
return Distribution.discover(**kwargs)
Get all ``Distribution`` instances in the current environment. :return: An iterable of ``Distribution`` instances.
Get all ``Distribution`` instances in the current environment.
[ "Get", "all", "Distribution", "instances", "in", "the", "current", "environment", "." ]
def distributions(**kwargs): """Get all ``Distribution`` instances in the current environment. :return: An iterable of ``Distribution`` instances. """ return Distribution.discover(**kwargs)
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https://github.com/python/cpython/blob/e13cdca0f5224ec4e23bdd04bb3120506964bc8b/Lib/importlib/metadata/__init__.py#L956-L961
nitishsrivastava/deepnet
f4e4ff207923e01552c96038a1e2c29eb5d16160
eigenmat/eigenmat.py
python
EigenMatrix.overwrite
(self, array)
Overwrites self with array. 'array' should have a size smaller than that of the array used to initialize the EigenMatrix. The method will not throw an Exception just yet if this is not true. It will throw exceptions or behave in strange ways later on.
Overwrites self with array. 'array' should have a size smaller than that of the array used to initialize the EigenMatrix. The method will not throw an Exception just yet if this is not true. It will throw exceptions or behave in strange ways later on.
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def overwrite(self, array): """Overwrites self with array. 'array' should have a size smaller than that of the array used to initialize the EigenMatrix. The method will not throw an Exception just yet if this is not true. It will throw exceptions or behave in strange ways later on. """ assert type(array) == np.ndarray, 'array must be a np.ndarray.' array = reformat(array) self.numpy_array = array _eigenmat.init_from_array(self.p_mat, array.ctypes.data_as(ct.POINTER(ct.c_float)), ct.c_int(array.shape[0]), ct.c_int(array.shape[1]))
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https://github.com/nitishsrivastava/deepnet/blob/f4e4ff207923e01552c96038a1e2c29eb5d16160/eigenmat/eigenmat.py#L88-L99
XUSean0118/DVSNet
2b67d991ca13de0a1210fbfbab4ad68f8c2f193a
inference.py
python
get_arguments
()
return parser.parse_args()
Parse all the arguments provided from the CLI. Returns: A list of parsed arguments.
Parse all the arguments provided from the CLI. Returns: A list of parsed arguments.
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def get_arguments(): """Parse all the arguments provided from the CLI. Returns: A list of parsed arguments. """ parser = argparse.ArgumentParser(description="Dynamic Video Segmentation Network") parser.add_argument("--data_dir", type=str, default=DATA_DIRECTORY, help="Path to the directory containing the dataset.") parser.add_argument("--data_list", type=str, default=DATA_LIST_PATH, help="Path to the file listing the images in the dataset.") parser.add_argument("--restore_from", type=str, default=RESTORE_FROM, help="Where restore model parameters from.") parser.add_argument("--decision_from", type=str, default=RESTORE_FROM, help="Where restore decision model parameters from.") parser.add_argument("--save_dir", type=str, default=SAVE_DIR, help="Where to save segmented output.") parser.add_argument("--num_steps", type=int, default=NUM_STEPS, help="Number of images in the video.") parser.add_argument("--overlap", type=int, default=OVERLAP, help="Overlapping size.") parser.add_argument("--target", type=float, default=TARGET, help="Confidence score threshold.") parser.add_argument("--dynamic", action="store_true", help="Whether to dynamically adjust target") return parser.parse_args()
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https://github.com/XUSean0118/DVSNet/blob/2b67d991ca13de0a1210fbfbab4ad68f8c2f193a/inference.py#L29-L54
pallets/werkzeug
9efe8c00dcb2b6fc086961ba304729db01912652
src/werkzeug/datastructures.py
python
MultiDict.getlist
(self, key, type=None)
return result
Return the list of items for a given key. If that key is not in the `MultiDict`, the return value will be an empty list. Just like `get`, `getlist` accepts a `type` parameter. All items will be converted with the callable defined there. :param key: The key to be looked up. :param type: A callable that is used to cast the value in the :class:`MultiDict`. If a :exc:`ValueError` is raised by this callable the value will be removed from the list. :return: a :class:`list` of all the values for the key.
Return the list of items for a given key. If that key is not in the `MultiDict`, the return value will be an empty list. Just like `get`, `getlist` accepts a `type` parameter. All items will be converted with the callable defined there.
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def getlist(self, key, type=None): """Return the list of items for a given key. If that key is not in the `MultiDict`, the return value will be an empty list. Just like `get`, `getlist` accepts a `type` parameter. All items will be converted with the callable defined there. :param key: The key to be looked up. :param type: A callable that is used to cast the value in the :class:`MultiDict`. If a :exc:`ValueError` is raised by this callable the value will be removed from the list. :return: a :class:`list` of all the values for the key. """ try: rv = dict.__getitem__(self, key) except KeyError: return [] if type is None: return list(rv) result = [] for item in rv: try: result.append(type(item)) except ValueError: pass return result
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https://github.com/pallets/werkzeug/blob/9efe8c00dcb2b6fc086961ba304729db01912652/src/werkzeug/datastructures.py#L395-L419
TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials
5bb97d7e3ffd913abddb4cfa7d78a1b4c868890e
tensorflow_dl_models/research/lfads/synth_data/synthetic_data_utils.py
python
spikify_data
(data_e, rng, dt=1.0, max_firing_rate=100)
return spikes_e
Apply spikes to a continuous dataset whose values are between 0.0 and 1.0 Args: data_e: nexamples length list of NxT trials dt: how often the data are sampled max_firing_rate: the firing rate that is associated with a value of 1.0 Returns: spikified_e: a list of length b of the data represented as spikes, sampled from the underlying poisson process.
Apply spikes to a continuous dataset whose values are between 0.0 and 1.0 Args: data_e: nexamples length list of NxT trials dt: how often the data are sampled max_firing_rate: the firing rate that is associated with a value of 1.0 Returns: spikified_e: a list of length b of the data represented as spikes, sampled from the underlying poisson process.
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def spikify_data(data_e, rng, dt=1.0, max_firing_rate=100): """ Apply spikes to a continuous dataset whose values are between 0.0 and 1.0 Args: data_e: nexamples length list of NxT trials dt: how often the data are sampled max_firing_rate: the firing rate that is associated with a value of 1.0 Returns: spikified_e: a list of length b of the data represented as spikes, sampled from the underlying poisson process. """ E = len(data_e) spikes_e = [] for e in range(E): data = data_e[e] N,T = data.shape data_s = np.zeros([N,T]).astype(np.int) for n in range(N): f = data[n,:] s = rng.poisson(f*max_firing_rate*dt, size=T) data_s[n,:] = s spikes_e.append(data_s) return spikes_e
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https://github.com/TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials/blob/5bb97d7e3ffd913abddb4cfa7d78a1b4c868890e/tensorflow_dl_models/research/lfads/synth_data/synthetic_data_utils.py#L128-L151
nosmokingbandit/watcher
dadacd21a5790ee609058a98a17fcc8954d24439
lib/sqlalchemy/orm/strategy_options.py
python
subqueryload
(loadopt, attr)
return loadopt.set_relationship_strategy(attr, {"lazy": "subquery"})
Indicate that the given attribute should be loaded using subquery eager loading. This function is part of the :class:`.Load` interface and supports both method-chained and standalone operation. examples:: # subquery-load the "orders" collection on "User" query(User).options(subqueryload(User.orders)) # subquery-load Order.items and then Item.keywords query(Order).options(subqueryload(Order.items).subqueryload(Item.keywords)) # lazily load Order.items, but when Items are loaded, # subquery-load the keywords collection query(Order).options(lazyload(Order.items).subqueryload(Item.keywords)) .. seealso:: :ref:`loading_toplevel` :func:`.orm.joinedload` :func:`.orm.lazyload` :paramref:`.relationship.lazy`
Indicate that the given attribute should be loaded using subquery eager loading.
[ "Indicate", "that", "the", "given", "attribute", "should", "be", "loaded", "using", "subquery", "eager", "loading", "." ]
def subqueryload(loadopt, attr): """Indicate that the given attribute should be loaded using subquery eager loading. This function is part of the :class:`.Load` interface and supports both method-chained and standalone operation. examples:: # subquery-load the "orders" collection on "User" query(User).options(subqueryload(User.orders)) # subquery-load Order.items and then Item.keywords query(Order).options(subqueryload(Order.items).subqueryload(Item.keywords)) # lazily load Order.items, but when Items are loaded, # subquery-load the keywords collection query(Order).options(lazyload(Order.items).subqueryload(Item.keywords)) .. seealso:: :ref:`loading_toplevel` :func:`.orm.joinedload` :func:`.orm.lazyload` :paramref:`.relationship.lazy` """ return loadopt.set_relationship_strategy(attr, {"lazy": "subquery"})
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https://github.com/nosmokingbandit/watcher/blob/dadacd21a5790ee609058a98a17fcc8954d24439/lib/sqlalchemy/orm/strategy_options.py#L770-L801
gem/oq-engine
1bdb88f3914e390abcbd285600bfd39477aae47c
openquake/calculators/base.py
python
create_gmf_data
(dstore, prim_imts, sec_imts=(), data=None)
Create and possibly populate the datasets in the gmf_data group
Create and possibly populate the datasets in the gmf_data group
[ "Create", "and", "possibly", "populate", "the", "datasets", "in", "the", "gmf_data", "group" ]
def create_gmf_data(dstore, prim_imts, sec_imts=(), data=None): """ Create and possibly populate the datasets in the gmf_data group """ oq = dstore['oqparam'] R = dstore['full_lt'].get_num_rlzs() M = len(prim_imts) n = 0 if data is None else len(data['sid']) items = [('sid', U32 if n == 0 else data['sid']), ('eid', U32 if n == 0 else data['eid'])] for m in range(M): col = f'gmv_{m}' items.append((col, F32 if data is None else data[col])) for imt in sec_imts: items.append((str(imt), F32 if n == 0 else data[imt])) if oq.investigation_time: eff_time = oq.investigation_time * oq.ses_per_logic_tree_path * R else: eff_time = 0 dstore.create_df('gmf_data', items, 'gzip') dstore.set_attrs('gmf_data', num_events=len(dstore['events']), imts=' '.join(map(str, prim_imts)), effective_time=eff_time) if data is not None: df = pandas.DataFrame(dict(items)) avg_gmf = numpy.zeros((2, n, M + len(sec_imts)), F32) for sid, df in df.groupby(df.sid): df.pop('eid') df.pop('sid') avg_gmf[:, sid] = stats.avg_std(df.to_numpy()) dstore['avg_gmf'] = avg_gmf
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https://github.com/gem/oq-engine/blob/1bdb88f3914e390abcbd285600bfd39477aae47c/openquake/calculators/base.py#L1141-L1171
linxid/Machine_Learning_Study_Path
558e82d13237114bbb8152483977806fc0c222af
Machine Learning In Action/Chapter4-NaiveBayes/venv/Lib/site-packages/pip/_vendor/ipaddress.py
python
_BaseNetwork.hostmask
(self)
return x
[]
def hostmask(self): x = self._cache.get('hostmask') if x is None: x = self._address_class(int(self.netmask) ^ self._ALL_ONES) self._cache['hostmask'] = x return x
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https://github.com/linxid/Machine_Learning_Study_Path/blob/558e82d13237114bbb8152483977806fc0c222af/Machine Learning In Action/Chapter4-NaiveBayes/venv/Lib/site-packages/pip/_vendor/ipaddress.py#L826-L831
dropbox/dropbox-sdk-python
015437429be224732990041164a21a0501235db1
dropbox/base.py
python
DropboxBase.sharing_create_shared_link
(self, path, short_url=False, pending_upload=None)
return r
Create a shared link. If a shared link already exists for the given path, that link is returned. Previously, it was technically possible to break a shared link by moving or renaming the corresponding file or folder. In the future, this will no longer be the case, so your app shouldn't rely on this behavior. Instead, if your app needs to revoke a shared link, use :meth:`sharing_revoke_shared_link`. :param str path: The path to share. :type short_url: bool :param Nullable[:class:`dropbox.sharing.PendingUploadMode`] pending_upload: If it's okay to share a path that does not yet exist, set this to either ``PendingUploadMode.file`` or ``PendingUploadMode.folder`` to indicate whether to assume it's a file or folder. :rtype: :class:`dropbox.sharing.PathLinkMetadata` :raises: :class:`.exceptions.ApiError` If this raises, ApiError will contain: :class:`dropbox.sharing.CreateSharedLinkError`
Create a shared link. If a shared link already exists for the given path, that link is returned. Previously, it was technically possible to break a shared link by moving or renaming the corresponding file or folder. In the future, this will no longer be the case, so your app shouldn't rely on this behavior. Instead, if your app needs to revoke a shared link, use :meth:`sharing_revoke_shared_link`.
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def sharing_create_shared_link(self, path, short_url=False, pending_upload=None): """ Create a shared link. If a shared link already exists for the given path, that link is returned. Previously, it was technically possible to break a shared link by moving or renaming the corresponding file or folder. In the future, this will no longer be the case, so your app shouldn't rely on this behavior. Instead, if your app needs to revoke a shared link, use :meth:`sharing_revoke_shared_link`. :param str path: The path to share. :type short_url: bool :param Nullable[:class:`dropbox.sharing.PendingUploadMode`] pending_upload: If it's okay to share a path that does not yet exist, set this to either ``PendingUploadMode.file`` or ``PendingUploadMode.folder`` to indicate whether to assume it's a file or folder. :rtype: :class:`dropbox.sharing.PathLinkMetadata` :raises: :class:`.exceptions.ApiError` If this raises, ApiError will contain: :class:`dropbox.sharing.CreateSharedLinkError` """ warnings.warn( 'create_shared_link is deprecated. Use create_shared_link_with_settings.', DeprecationWarning, ) arg = sharing.CreateSharedLinkArg(path, short_url, pending_upload) r = self.request( sharing.create_shared_link, 'sharing', arg, None, ) return r
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https://github.com/dropbox/dropbox-sdk-python/blob/015437429be224732990041164a21a0501235db1/dropbox/base.py#L4071-L4109
cisco/mindmeld
809c36112e9ea8019fe29d54d136ca14eb4fd8db
mindmeld/system_entity_recognizer.py
python
SystemEntityRecognizer.load_from_app_path
(app_path)
If the application configuration is empty, we do not use Duckling. Otherwise, we return the Duckling recognizer with the URL defined in the application's config, default to the DEFAULT_DUCKLING_URL. Args: app_path (str): Application path Returns: (SystemEntityRecognizer)
If the application configuration is empty, we do not use Duckling.
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def load_from_app_path(app_path): """If the application configuration is empty, we do not use Duckling. Otherwise, we return the Duckling recognizer with the URL defined in the application's config, default to the DEFAULT_DUCKLING_URL. Args: app_path (str): Application path Returns: (SystemEntityRecognizer) """ if not app_path: raise SystemEntityError( "App path must be valid to load entity recognizer config." ) if is_duckling_configured(app_path): url = get_system_entity_url_config(app_path=app_path) return DucklingRecognizer.get_instance(url) else: return NoOpSystemEntityRecognizer.get_instance()
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https://github.com/cisco/mindmeld/blob/809c36112e9ea8019fe29d54d136ca14eb4fd8db/mindmeld/system_entity_recognizer.py#L118-L139
Delta-ML/delta
31dfebc8f20b7cb282b62f291ff25a87e403cc86
delta/utils/solver/utils/callbacks.py
python
ParallelModelCheckpoint.__init__
(self, model, filepath, monitor='val_loss', verbose=0, save_best_only=False, save_weights_only=False, mode='auto', save_freq='epoch', load_weights_on_restart=False, period=1)
[]
def __init__(self, model, filepath, monitor='val_loss', verbose=0, save_best_only=False, save_weights_only=False, mode='auto', save_freq='epoch', load_weights_on_restart=False, period=1): self.model_to_save = model super().__init__( filepath=filepath, monitor=monitor, verbose=verbose, save_best_only=save_best_only, save_weights_only=save_weights_only, mode=mode, save_freq=save_freq, load_weights_on_restart=load_weights_on_restart, period=period)
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https://github.com/Delta-ML/delta/blob/31dfebc8f20b7cb282b62f291ff25a87e403cc86/delta/utils/solver/utils/callbacks.py#L160-L181
vmware/vsphere-automation-sdk-python
ba7d4e0742f58a641dfed9538ecbbb1db4f3891e
samples/vmc/draas/site_recovery_activation_ops.py
python
SiteRecoveryActivationOperations.deactivate_srm
(self)
[]
def deactivate_srm(self): if self.cleanup: try: srm_deactivation_task = self.vmc_client.draas.SiteRecovery.delete(self.org_id, self.sddc_id, force=True) except InvalidRequest as e: # Convert InvalidRequest to ErrorResponse to get error message error_response = e.data.convert_to(ErrorResponse) raise Exception(error_response.error_messages) wait_for_task(task_client=self.vmc_client.draas.Task, org_id=self.org_id, task_id=srm_deactivation_task.id, interval_sec=self.interval_sec)
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https://github.com/vmware/vsphere-automation-sdk-python/blob/ba7d4e0742f58a641dfed9538ecbbb1db4f3891e/samples/vmc/draas/site_recovery_activation_ops.py#L81-L95
chainer/chainer-chemistry
efe323aa21f63a815130d673781e7cca1ccb72d2
chainer_chemistry/dataset/networkx_preprocessors/reddit_coo.py
python
get_reddit_coo_data
(dirpath)
return PaddingGraphData( x=reddit_data['feature'].astype(numpy.float32), adj=adj, y=reddit_data['label'].astype(numpy.int32), label_num=41 )
Temporary function to obtain reddit coo data for GIN (because it takes to much time to convert it to networkx) Returns: PaddingGraphData: `PaddingGraphData` of reddit
Temporary function to obtain reddit coo data for GIN
[ "Temporary", "function", "to", "obtain", "reddit", "coo", "data", "for", "GIN" ]
def get_reddit_coo_data(dirpath): """Temporary function to obtain reddit coo data for GIN (because it takes to much time to convert it to networkx) Returns: PaddingGraphData: `PaddingGraphData` of reddit """ print("Loading node feature and label") reddit_data = numpy.load(os.path.join(dirpath, "reddit_data.npz")) print("Loading edge data") coo_adj = scipy.sparse.load_npz(os.path.join(dirpath, "reddit_graph.npz")) row = coo_adj.row.astype(numpy.int32) col = coo_adj.col.astype(numpy.int32) data = coo_adj.data.astype(numpy.float32) # ensure row is sorted if not numpy.all(row[:-1] <= row[1:]): order = numpy.argsort(row) row = row[order] col = col[order] assert numpy.all(row[:-1] <= row[1:]) adj = chainer.utils.CooMatrix( data=data, row=row, col=col, shape=coo_adj.shape, order='C') return PaddingGraphData( x=reddit_data['feature'].astype(numpy.float32), adj=adj, y=reddit_data['label'].astype(numpy.int32), label_num=41 )
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https://github.com/chainer/chainer-chemistry/blob/efe323aa21f63a815130d673781e7cca1ccb72d2/chainer_chemistry/dataset/networkx_preprocessors/reddit_coo.py#L11-L46
Symbo1/wsltools
0b6e536fc85c707a1c81f0296c4e91ca835396a1
wsltools/utils/faker/providers/address/fr_FR/__init__.py
python
Provider.street_prefix
(self)
return self.random_element(self.street_prefixes)
:example 'rue'
:example 'rue'
[ ":", "example", "rue" ]
def street_prefix(self): """ :example 'rue' """ return self.random_element(self.street_prefixes)
[ "def", "street_prefix", "(", "self", ")", ":", "return", "self", ".", "random_element", "(", "self", ".", "street_prefixes", ")" ]
https://github.com/Symbo1/wsltools/blob/0b6e536fc85c707a1c81f0296c4e91ca835396a1/wsltools/utils/faker/providers/address/fr_FR/__init__.py#L141-L145
chribsen/simple-machine-learning-examples
dc94e52a4cebdc8bb959ff88b81ff8cfeca25022
venv/lib/python2.7/site-packages/numpy/ma/core.py
python
asanyarray
(a, dtype=None)
return masked_array(a, dtype=dtype, copy=False, keep_mask=True, subok=True)
Convert the input to a masked array, conserving subclasses. If `a` is a subclass of `MaskedArray`, its class is conserved. No copy is performed if the input is already an `ndarray`. Parameters ---------- a : array_like Input data, in any form that can be converted to an array. dtype : dtype, optional By default, the data-type is inferred from the input data. order : {'C', 'F'}, optional Whether to use row-major ('C') or column-major ('FORTRAN') memory representation. Default is 'C'. Returns ------- out : MaskedArray MaskedArray interpretation of `a`. See Also -------- asarray : Similar to `asanyarray`, but does not conserve subclass. Examples -------- >>> x = np.arange(10.).reshape(2, 5) >>> x array([[ 0., 1., 2., 3., 4.], [ 5., 6., 7., 8., 9.]]) >>> np.ma.asanyarray(x) masked_array(data = [[ 0. 1. 2. 3. 4.] [ 5. 6. 7. 8. 9.]], mask = False, fill_value = 1e+20) >>> type(np.ma.asanyarray(x)) <class 'numpy.ma.core.MaskedArray'>
Convert the input to a masked array, conserving subclasses.
[ "Convert", "the", "input", "to", "a", "masked", "array", "conserving", "subclasses", "." ]
def asanyarray(a, dtype=None): """ Convert the input to a masked array, conserving subclasses. If `a` is a subclass of `MaskedArray`, its class is conserved. No copy is performed if the input is already an `ndarray`. Parameters ---------- a : array_like Input data, in any form that can be converted to an array. dtype : dtype, optional By default, the data-type is inferred from the input data. order : {'C', 'F'}, optional Whether to use row-major ('C') or column-major ('FORTRAN') memory representation. Default is 'C'. Returns ------- out : MaskedArray MaskedArray interpretation of `a`. See Also -------- asarray : Similar to `asanyarray`, but does not conserve subclass. Examples -------- >>> x = np.arange(10.).reshape(2, 5) >>> x array([[ 0., 1., 2., 3., 4.], [ 5., 6., 7., 8., 9.]]) >>> np.ma.asanyarray(x) masked_array(data = [[ 0. 1. 2. 3. 4.] [ 5. 6. 7. 8. 9.]], mask = False, fill_value = 1e+20) >>> type(np.ma.asanyarray(x)) <class 'numpy.ma.core.MaskedArray'> """ return masked_array(a, dtype=dtype, copy=False, keep_mask=True, subok=True)
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https://github.com/chribsen/simple-machine-learning-examples/blob/dc94e52a4cebdc8bb959ff88b81ff8cfeca25022/venv/lib/python2.7/site-packages/numpy/ma/core.py#L7566-L7609
mesalock-linux/mesapy
ed546d59a21b36feb93e2309d5c6b75aa0ad95c9
lib-python/2.7/mailbox.py
python
MH.get_message
(self, key)
return msg
Return a Message representation or raise a KeyError.
Return a Message representation or raise a KeyError.
[ "Return", "a", "Message", "representation", "or", "raise", "a", "KeyError", "." ]
def get_message(self, key): """Return a Message representation or raise a KeyError.""" try: if self._locked: f = open(os.path.join(self._path, str(key)), 'r+') else: f = open(os.path.join(self._path, str(key)), 'r') except IOError, e: if e.errno == errno.ENOENT: raise KeyError('No message with key: %s' % key) else: raise try: if self._locked: _lock_file(f) try: msg = MHMessage(f) finally: if self._locked: _unlock_file(f) finally: f.close() for name, key_list in self.get_sequences().iteritems(): if key in key_list: msg.add_sequence(name) return msg
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https://github.com/mesalock-linux/mesapy/blob/ed546d59a21b36feb93e2309d5c6b75aa0ad95c9/lib-python/2.7/mailbox.py#L1004-L1029
seppius-xbmc-repo/ru
d0879d56ec8243b2c7af44fda5cf3d1ff77fd2e2
plugin.video.torrent.gnu/resources/lib/torr2xbmc.py
python
stream
(params)
[]
def stream (params): torr_link='f4a94963c11a47f213b145697f494b5fc5485b02' TSplayer=tsengine() out=TSplayer.load_torrent(torr_link,'INFOHASH',port=aceport) if out=='Ok': TSplayer.play_url_ind(0,'stream',None) TSplayer.end()
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https://github.com/seppius-xbmc-repo/ru/blob/d0879d56ec8243b2c7af44fda5cf3d1ff77fd2e2/plugin.video.torrent.gnu/resources/lib/torr2xbmc.py#L112-L118
plaid/plaid-python
8c60fca608e426f3ff30da8857775946d29e122c
plaid/model/payment_initiation_optional_restriction_bacs.py
python
PaymentInitiationOptionalRestrictionBacs.openapi_types
()
return { 'account': (str,), # noqa: E501 'sort_code': (str,), # noqa: E501 }
This must be a method because a model may have properties that are of type self, this must run after the class is loaded Returns openapi_types (dict): The key is attribute name and the value is attribute type.
This must be a method because a model may have properties that are of type self, this must run after the class is loaded
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def openapi_types(): """ This must be a method because a model may have properties that are of type self, this must run after the class is loaded Returns openapi_types (dict): The key is attribute name and the value is attribute type. """ lazy_import() return { 'account': (str,), # noqa: E501 'sort_code': (str,), # noqa: E501 }
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https://github.com/plaid/plaid-python/blob/8c60fca608e426f3ff30da8857775946d29e122c/plaid/model/payment_initiation_optional_restriction_bacs.py#L82-L95
zhl2008/awd-platform
0416b31abea29743387b10b3914581fbe8e7da5e
web_hxb2/lib/python3.5/site-packages/django/utils/feedgenerator.py
python
SyndicationFeed.root_attributes
(self)
return {}
Return extra attributes to place on the root (i.e. feed/channel) element. Called from write().
Return extra attributes to place on the root (i.e. feed/channel) element. Called from write().
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def root_attributes(self): """ Return extra attributes to place on the root (i.e. feed/channel) element. Called from write(). """ return {}
[ "def", "root_attributes", "(", "self", ")", ":", "return", "{", "}" ]
https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_hxb2/lib/python3.5/site-packages/django/utils/feedgenerator.py#L170-L175

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