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spotify/luigi | c3b66f4a5fa7eaa52f9a72eb6704b1049035c789 | luigi/contrib/s3.py | python | S3Client.put_multipart | (self, local_path, destination_s3_path, part_size=DEFAULT_PART_SIZE, **kwargs) | Put an object stored locally to an S3 path
using S3 multi-part upload (for files > 8Mb).
:param local_path: Path to source local file
:param destination_s3_path: URL for target S3 location
:param part_size: Part size in bytes. Default: 8388608 (8MB)
:param kwargs: Keyword arguments are passed to the boto function `upload_fileobj` as ExtraArgs | Put an object stored locally to an S3 path
using S3 multi-part upload (for files > 8Mb).
:param local_path: Path to source local file
:param destination_s3_path: URL for target S3 location
:param part_size: Part size in bytes. Default: 8388608 (8MB)
:param kwargs: Keyword arguments are passed to the boto function `upload_fileobj` as ExtraArgs | [
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"""
Put an object stored locally to an S3 path
using S3 multi-part upload (for files > 8Mb).
:param local_path: Path to source local file
:param destination_s3_path: URL for target S3 location
:param part_size: Part size in bytes. Default: 8388608 (8MB)
:param kwargs: Keyword arguments are passed to the boto function `upload_fileobj` as ExtraArgs
"""
self._check_deprecated_argument(**kwargs)
from boto3.s3.transfer import TransferConfig
# default part size for boto3 is 8Mb, changing it to fit part_size
# provided as a parameter
transfer_config = TransferConfig(multipart_chunksize=part_size)
(bucket, key) = self._path_to_bucket_and_key(destination_s3_path)
self.s3.meta.client.upload_fileobj(
Fileobj=open(local_path, 'rb'), Bucket=bucket, Key=key, Config=transfer_config, ExtraArgs=kwargs) | [
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||
lad1337/XDM | 0c1b7009fe00f06f102a6f67c793478f515e7efe | site-packages/cherrypy/lib/cptools.py | python | SessionAuth.do_logout | (self, from_page='..', **kwargs) | Logout. May raise redirect, or return True if request handled. | Logout. May raise redirect, or return True if request handled. | [
"Logout",
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"or",
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"handled",
"."
] | def do_logout(self, from_page='..', **kwargs):
"""Logout. May raise redirect, or return True if request handled."""
sess = cherrypy.session
username = sess.get(self.session_key)
sess[self.session_key] = None
if username:
cherrypy.serving.request.login = None
self.on_logout(username)
raise cherrypy.HTTPRedirect(from_page) | [
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||
yt-project/unyt | ec5b3301c110787c9a67c600a66d8051ab8c831e | unyt/_version.py | python | plus_or_dot | (pieces) | return "+" | Return a + if we don't already have one, else return a . | Return a + if we don't already have one, else return a . | [
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] | def plus_or_dot(pieces):
"""Return a + if we don't already have one, else return a ."""
if "+" in pieces.get("closest-tag", ""):
return "."
return "+" | [
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|
mitmproxy/pdoc | 5d32ea9f1320b39e8de7e28398da1d18eec85834 | pdoc/search.py | python | precompile_index | (documents: list[dict], compile_js: Path) | This method tries to precompile the Elasticlunr.js search index by invoking `nodejs` or `node`.
If that fails, an unprocessed index will be returned (which will be compiled locally on the client side).
If this happens and the index is rather large (>3MB), a warning with precompile instructions is printed.
We currently require nodejs, but we'd welcome PRs that support other JaveScript runtimes or
– even better – a Python-based search index generation similar to
[elasticlunr-rs](https://github.com/mattico/elasticlunr-rs) that could be shipped as part of pdoc. | This method tries to precompile the Elasticlunr.js search index by invoking `nodejs` or `node`.
If that fails, an unprocessed index will be returned (which will be compiled locally on the client side).
If this happens and the index is rather large (>3MB), a warning with precompile instructions is printed. | [
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"""
This method tries to precompile the Elasticlunr.js search index by invoking `nodejs` or `node`.
If that fails, an unprocessed index will be returned (which will be compiled locally on the client side).
If this happens and the index is rather large (>3MB), a warning with precompile instructions is printed.
We currently require nodejs, but we'd welcome PRs that support other JaveScript runtimes or
– even better – a Python-based search index generation similar to
[elasticlunr-rs](https://github.com/mattico/elasticlunr-rs) that could be shipped as part of pdoc.
"""
raw = json.dumps(documents)
try:
if shutil.which("nodejs"):
executable = "nodejs"
else:
executable = "node"
out = subprocess.check_output(
[executable, compile_js],
input=raw.encode(),
cwd=Path(__file__).parent / "templates",
stderr=subprocess.STDOUT,
)
index = json.loads(out)
index["_isPrebuiltIndex"] = True
except Exception as e:
if len(raw) > 3 * 1024 * 1024:
print(
f"pdoc failed to precompile the search index: {e}\n"
f"Search will work, but may be slower. "
f"This error may only show up now because your index has reached a certain size. "
f"See https://pdoc.dev/docs/pdoc/search.html for details."
)
if isinstance(e, subprocess.CalledProcessError):
print(f"{' Node.js Output ':=^80}")
print(
textwrap.indent(e.output.decode("utf8", "replace"), " ").rstrip()
)
print("=" * 80)
return raw
else:
return json.dumps(index) | [
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||
naftaliharris/tauthon | 5587ceec329b75f7caf6d65a036db61ac1bae214 | Parser/asdl.py | python | ASDLParser.p_definition_1 | (self, (definitions, definition)) | return definitions + definition | definitions ::= definition definitions | definitions ::= definition definitions | [
"definitions",
"::",
"=",
"definition",
"definitions"
] | def p_definition_1(self, (definitions, definition)):
" definitions ::= definition definitions "
return definitions + definition | [
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|
mit-han-lab/data-efficient-gans | 6858275f08f43a33026844c8c2ac4e703e8a07ba | DiffAugment-stylegan2/training/networks_stylegan2.py | python | D_stylegan2 | (
images_in, # First input: Images [minibatch, channel, height, width].
num_channels=3, # Number of input color channels. Overridden based on dataset.
label_size=0, # Label dimensionality.
resolution=1024, # Input resolution. Overridden based on dataset.
fmap_base=16384, # Overall multiplier for the number of feature maps.
fmap_decay=1.0, # log2 feature map reduction when doubling the resolution.
fmap_min=1, # Minimum number of feature maps in any layer.
fmap_max=512, # Maximum number of feature maps in any layer.
architecture='resnet', # Architecture: 'orig', 'skip', 'resnet'.
nonlinearity='lrelu', # Activation function: 'relu', 'lrelu', etc.
mbstd_group_size=4, # Group size for the minibatch standard deviation layer, 0 = disable.
mbstd_num_features=1, # Number of features for the minibatch standard deviation layer.
dtype='float32', # Data type to use for activations and outputs.
resample_kernel=[1, 3, 3, 1], # Low-pass filter to apply when resampling activations. None = no filtering.
impl='cuda',
avg_pooling=False,
**_kwargs) | return scores_out | [] | def D_stylegan2(
images_in, # First input: Images [minibatch, channel, height, width].
num_channels=3, # Number of input color channels. Overridden based on dataset.
label_size=0, # Label dimensionality.
resolution=1024, # Input resolution. Overridden based on dataset.
fmap_base=16384, # Overall multiplier for the number of feature maps.
fmap_decay=1.0, # log2 feature map reduction when doubling the resolution.
fmap_min=1, # Minimum number of feature maps in any layer.
fmap_max=512, # Maximum number of feature maps in any layer.
architecture='resnet', # Architecture: 'orig', 'skip', 'resnet'.
nonlinearity='lrelu', # Activation function: 'relu', 'lrelu', etc.
mbstd_group_size=4, # Group size for the minibatch standard deviation layer, 0 = disable.
mbstd_num_features=1, # Number of features for the minibatch standard deviation layer.
dtype='float32', # Data type to use for activations and outputs.
resample_kernel=[1, 3, 3, 1], # Low-pass filter to apply when resampling activations. None = no filtering.
impl='cuda',
avg_pooling=False,
**_kwargs): # Ignore unrecognized keyword args.
resolution_log2 = int(np.ceil(np.log2(resolution)))
pad = (2 ** resolution_log2 - resolution) // 2
def nf(stage): return np.clip(int(fmap_base / (2.0 ** (stage * fmap_decay))), fmap_min, fmap_max)
assert architecture in ['orig', 'skip', 'resnet']
act = nonlinearity
images_in.set_shape([None, num_channels, resolution, resolution])
images_in = tf.cast(images_in, dtype)
# Building blocks for main layers.
def fromrgb(x, y, res): # res = 2..resolution_log2
with tf.variable_scope('FromRGB'):
t = apply_bias_act(conv2d_layer(y, fmaps=nf(res - 1), kernel=1, impl=impl), act=act, impl=impl)
return t if x is None else x + t
def block(x, res): # res = 2..resolution_log2
t = x
with tf.variable_scope('Conv0'):
x = apply_bias_act(conv2d_layer(x, fmaps=nf(res - 1), kernel=3, impl=impl), act=act, impl=impl)
with tf.variable_scope('Conv1_down'):
x = apply_bias_act(conv2d_layer(x, fmaps=nf(res - 2), kernel=3, down=True, resample_kernel=resample_kernel, impl=impl), act=act, impl=impl)
if architecture == 'resnet':
with tf.variable_scope('Skip'):
t = conv2d_layer(t, fmaps=nf(res - 2), kernel=1, down=True, resample_kernel=resample_kernel, impl=impl)
x = (x + t) * (1 / np.sqrt(2))
return x
def downsample(y):
with tf.variable_scope('Downsample'):
return downsample_2d(y, k=resample_kernel, impl=impl)
# Main layers.
x = None
y = tf.pad(images_in, [[0, 0], [0, 0], [pad, pad], [pad, pad]])
for res in range(resolution_log2, 2, -1):
with tf.variable_scope('%dx%d' % (2**res, 2**res)):
if architecture == 'skip' or res == resolution_log2:
x = fromrgb(x, y, res)
x = block(x, res)
if architecture == 'skip':
y = downsample(y)
# Final layers.
with tf.variable_scope('4x4'):
if architecture == 'skip':
x = fromrgb(x, y, 2)
if mbstd_group_size > 1:
with tf.variable_scope('MinibatchStddev'):
x = minibatch_stddev_layer(x, mbstd_group_size, mbstd_num_features)
with tf.variable_scope('Conv'):
x = apply_bias_act(conv2d_layer(x, fmaps=nf(1), kernel=3, impl=impl), act=act, impl=impl)
if avg_pooling:
x = tf.reduce_mean(x, axis=[2, 3])
with tf.variable_scope('Dense0'):
x = apply_bias_act(dense_layer(x, fmaps=nf(0)), act=act, impl=impl)
with tf.variable_scope('Output'):
if label_size > 0:
scores_out = apply_bias_act(dense_layer(x, fmaps=label_size), impl=impl)
else:
scores_out = tf.squeeze(apply_bias_act(dense_layer(x, fmaps=1), impl=impl), axis=1)
assert scores_out.dtype == tf.as_dtype(dtype)
scores_out = tf.identity(scores_out, name='scores_out')
return scores_out | [
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mjq11302010044/RRPN_pytorch | a966f6f238c03498514742cde5cd98e51efb440c | maskrcnn_benchmark/modeling/rrpn/rrpn.py | python | build_rpn | (cfg) | return RPNModule(cfg) | This gives the gist of it. Not super important because it doesn't change as much | This gives the gist of it. Not super important because it doesn't change as much | [
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"""
This gives the gist of it. Not super important because it doesn't change as much
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return RPNModule(cfg) | [
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SteveDoyle2/pyNastran | eda651ac2d4883d95a34951f8a002ff94f642a1a | pyNastran/utils/dict_to_h5py.py | python | load_obj_from_hdf5 | (hdf5_filename: str, custom_types_dict=None, log=None, debug=False) | return model | loads an hdf5 file into an object
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hdf5_filename : str
the h5 filename to load
custom_types_dict : dict[key] : function()
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"""
loads an hdf5 file into an object
Parameters
----------
hdf5_filename : str
the h5 filename to load
custom_types_dict : dict[key] : function()
the custom mapper
"""
check_path(hdf5_filename, 'hdf5_filename')
log = get_logger2(log=log, debug=debug, encoding='utf-8')
log.info('hdf5_filename = %r' % hdf5_filename)
model = {}
with h5py.File(hdf5_filename, 'r') as h5_file:
load_obj_from_hdf5_file(model, h5_file, custom_types_dict=custom_types_dict, log=log, debug=debug)
return model | [
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caiiiac/Machine-Learning-with-Python | 1a26c4467da41ca4ebc3d5bd789ea942ef79422f | MachineLearning/venv/lib/python3.5/site-packages/scipy/sparse/dok.py | python | dok_matrix.__getitem__ | (self, index) | return newdok | If key=(i,j) is a pair of integers, return the corresponding
element. If either i or j is a slice or sequence, return a new sparse
matrix with just these elements. | If key=(i,j) is a pair of integers, return the corresponding
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i_slice = slice(i, i+1) if i_intlike else i
j_slice = slice(j, j+1) if j_intlike else j
i_indices = i_slice.indices(self.shape[0])
j_indices = j_slice.indices(self.shape[1])
i_seq = xrange(*i_indices)
j_seq = xrange(*j_indices)
newshape = (len(i_seq), len(j_seq))
newsize = _prod(newshape)
if len(self) < 2*newsize and newsize != 0:
# Switch to the fast path only when advantageous
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#
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i, j = self._index_to_arrays(i, j)
if i.size == 0:
return dok_matrix(i.shape, dtype=self.dtype)
min_i = i.min()
if min_i < -self.shape[0] or i.max() >= self.shape[0]:
raise IndexError('index (%d) out of range -%d to %d)' %
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if min_i < 0:
i = i.copy()
i[i < 0] += self.shape[0]
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newdok = dok_matrix(i.shape, dtype=self.dtype)
for a in xrange(i.shape[0]):
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v = dict.get(self, (i[a,b], j[a,b]), zero)
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dict.__setitem__(newdok, (a, b), v)
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|
ahmetozlu/tensorflow_object_counting_api | 630a1471a1aa19e582d09898f569c5eea031a21d | mask_rcnn_counting_api/spaghetti_counter_training/training/mrcnn/model.py | python | build_fpn_mask_graph | (rois, feature_maps, image_meta,
pool_size, num_classes, train_bn=True) | return x | Builds the computation graph of the mask head of Feature Pyramid Network.
rois: [batch, num_rois, (y1, x1, y2, x2)] Proposal boxes in normalized
coordinates.
feature_maps: List of feature maps from different layers of the pyramid,
[P2, P3, P4, P5]. Each has a different resolution.
image_meta: [batch, (meta data)] Image details. See compose_image_meta()
pool_size: The width of the square feature map generated from ROI Pooling.
num_classes: number of classes, which determines the depth of the results
train_bn: Boolean. Train or freeze Batch Norm layers
Returns: Masks [batch, roi_count, height, width, num_classes] | Builds the computation graph of the mask head of Feature Pyramid Network. | [
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pool_size, num_classes, train_bn=True):
"""Builds the computation graph of the mask head of Feature Pyramid Network.
rois: [batch, num_rois, (y1, x1, y2, x2)] Proposal boxes in normalized
coordinates.
feature_maps: List of feature maps from different layers of the pyramid,
[P2, P3, P4, P5]. Each has a different resolution.
image_meta: [batch, (meta data)] Image details. See compose_image_meta()
pool_size: The width of the square feature map generated from ROI Pooling.
num_classes: number of classes, which determines the depth of the results
train_bn: Boolean. Train or freeze Batch Norm layers
Returns: Masks [batch, roi_count, height, width, num_classes]
"""
# ROI Pooling
# Shape: [batch, boxes, pool_height, pool_width, channels]
x = PyramidROIAlign([pool_size, pool_size],
name="roi_align_mask")([rois, image_meta] + feature_maps)
# Conv layers
x = KL.TimeDistributed(KL.Conv2D(256, (3, 3), padding="same"),
name="mrcnn_mask_conv1")(x)
x = KL.TimeDistributed(BatchNorm(),
name='mrcnn_mask_bn1')(x, training=train_bn)
x = KL.Activation('relu')(x)
x = KL.TimeDistributed(KL.Conv2D(256, (3, 3), padding="same"),
name="mrcnn_mask_conv2")(x)
x = KL.TimeDistributed(BatchNorm(),
name='mrcnn_mask_bn2')(x, training=train_bn)
x = KL.Activation('relu')(x)
x = KL.TimeDistributed(KL.Conv2D(256, (3, 3), padding="same"),
name="mrcnn_mask_conv3")(x)
x = KL.TimeDistributed(BatchNorm(),
name='mrcnn_mask_bn3')(x, training=train_bn)
x = KL.Activation('relu')(x)
x = KL.TimeDistributed(KL.Conv2D(256, (3, 3), padding="same"),
name="mrcnn_mask_conv4")(x)
x = KL.TimeDistributed(BatchNorm(),
name='mrcnn_mask_bn4')(x, training=train_bn)
x = KL.Activation('relu')(x)
x = KL.TimeDistributed(KL.Conv2DTranspose(256, (2, 2), strides=2, activation="relu"),
name="mrcnn_mask_deconv")(x)
x = KL.TimeDistributed(KL.Conv2D(num_classes, (1, 1), strides=1, activation="sigmoid"),
name="mrcnn_mask")(x)
return x | [
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|
rowliny/DiffHelper | ab3a96f58f9579d0023aed9ebd785f4edf26f8af | Tool/SitePackages/PIL/_binary.py | python | si32le | (c, o=0) | return unpack_from("<i", c, o)[0] | Converts a 4-bytes (32 bits) string to a signed integer.
:param c: string containing bytes to convert
:param o: offset of bytes to convert in string | Converts a 4-bytes (32 bits) string to a signed integer. | [
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"""
Converts a 4-bytes (32 bits) string to a signed integer.
:param c: string containing bytes to convert
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"""
return unpack_from("<i", c, o)[0] | [
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|
galaxyproject/galaxy | 4c03520f05062e0f4a1b3655dc0b7452fda69943 | lib/galaxy/webapps/galaxy/api/job_files.py | python | JobFilesAPIController.create | (self, trans, job_id, payload, **kwargs) | return {"message": "ok"} | create( self, trans, job_id, payload, **kwargs )
* POST /api/jobs/{job_id}/files
Populate an output file (formal dataset, task split part, working
directory file (such as those related to metadata)). This should be
a multipart post with a 'file' parameter containing the contents of
the actual file to create.
:type job_id: str
:param job_id: encoded id string of the job
:type payload: dict
:param payload: dictionary structure containing::
'job_key' = Key authenticating
'path' = Path to file to create.
..note:
This API method is intended only for consumption by job runners,
not end users.
:rtype: dict
:returns: an okay message | create( self, trans, job_id, payload, **kwargs )
* POST /api/jobs/{job_id}/files
Populate an output file (formal dataset, task split part, working
directory file (such as those related to metadata)). This should be
a multipart post with a 'file' parameter containing the contents of
the actual file to create. | [
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"""
create( self, trans, job_id, payload, **kwargs )
* POST /api/jobs/{job_id}/files
Populate an output file (formal dataset, task split part, working
directory file (such as those related to metadata)). This should be
a multipart post with a 'file' parameter containing the contents of
the actual file to create.
:type job_id: str
:param job_id: encoded id string of the job
:type payload: dict
:param payload: dictionary structure containing::
'job_key' = Key authenticating
'path' = Path to file to create.
..note:
This API method is intended only for consumption by job runners,
not end users.
:rtype: dict
:returns: an okay message
"""
job = self.__authorize_job_access(trans, job_id, **payload)
path = payload.get("path")
self.__check_job_can_write_to_path(trans, job, path)
# Is this writing an unneeded file? Should this just copy in Python?
if '__file_path' in payload:
file_path = payload.get('__file_path')
upload_store = trans.app.config.nginx_upload_job_files_store
assert upload_store, ("Request appears to have been processed by"
" nginx_upload_module but Galaxy is not"
" configured to recognize it")
assert file_path.startswith(upload_store), \
("Filename provided by nginx (%s) is not in correct"
" directory (%s)" % (file_path, upload_store))
input_file = open(file_path)
else:
input_file = payload.get("file",
payload.get("__file", None)).file
target_dir = os.path.dirname(path)
util.safe_makedirs(target_dir)
try:
shutil.move(input_file.name, path)
finally:
try:
input_file.close()
except OSError:
# Fails to close file if not using nginx upload because the
# tempfile has moved and Python wants to delete it.
pass
return {"message": "ok"} | [
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|
OpenMDAO/OpenMDAO1 | 791a6fbbb7d266f3dcbc1f7bde3ae03a70dc1317 | openmdao/core/system.py | python | System._rec_set_param | (self, name, value) | [] | def _rec_set_param(self, name, value):
parts = name.split('.', 1)
if len(parts) == 1:
self.params[name] = value
else:
return self._subsystems[parts[0]]._rec_set_param(parts[1], value) | [
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||||
NVIDIA/DeepLearningExamples | 589604d49e016cd9ef4525f7abcc9c7b826cfc5e | PyTorch/Detection/Efficientdet/effdet/layers/nms_layer.py | python | soft_nms | (
boxes,
scores,
method_gaussian: bool = True,
sigma: float = 0.5,
iou_threshold: float = .5,
score_threshold: float = 0.005
) | return idxs_out[:count], scores_out[:count] | Soft non-max suppression algorithm.
Implementation of [Soft-NMS -- Improving Object Detection With One Line of Codec]
(https://arxiv.org/abs/1704.04503)
Args:
boxes_remain (Tensor[N, ?]):
boxes where NMS will be performed
if Boxes, in (x1, y1, x2, y2) format
if RotatedBoxes, in (x_ctr, y_ctr, width, height, angle_degrees) format
scores_remain (Tensor[N]):
scores for each one of the boxes
method_gaussian (bool): use gaussian method if True, otherwise linear
sigma (float):
parameter for Gaussian penalty function
iou_threshold (float):
iou threshold for applying linear decay. Nt from the paper
re-used as threshold for standard "hard" nms
score_threshold (float):
boxes with scores below this threshold are pruned at each iteration.
Dramatically reduces computation time. Authors use values in [10e-4, 10e-2]
Returns:
tuple(Tensor, Tensor):
[0]: int64 tensor with the indices of the elements that have been kept
by Soft NMS, sorted in decreasing order of scores
[1]: float tensor with the re-scored scores of the elements that were kept | Soft non-max suppression algorithm.
Implementation of [Soft-NMS -- Improving Object Detection With One Line of Codec]
(https://arxiv.org/abs/1704.04503)
Args:
boxes_remain (Tensor[N, ?]):
boxes where NMS will be performed
if Boxes, in (x1, y1, x2, y2) format
if RotatedBoxes, in (x_ctr, y_ctr, width, height, angle_degrees) format
scores_remain (Tensor[N]):
scores for each one of the boxes
method_gaussian (bool): use gaussian method if True, otherwise linear
sigma (float):
parameter for Gaussian penalty function
iou_threshold (float):
iou threshold for applying linear decay. Nt from the paper
re-used as threshold for standard "hard" nms
score_threshold (float):
boxes with scores below this threshold are pruned at each iteration.
Dramatically reduces computation time. Authors use values in [10e-4, 10e-2]
Returns:
tuple(Tensor, Tensor):
[0]: int64 tensor with the indices of the elements that have been kept
by Soft NMS, sorted in decreasing order of scores
[1]: float tensor with the re-scored scores of the elements that were kept | [
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] | def soft_nms(
boxes,
scores,
method_gaussian: bool = True,
sigma: float = 0.5,
iou_threshold: float = .5,
score_threshold: float = 0.005
):
"""
Soft non-max suppression algorithm.
Implementation of [Soft-NMS -- Improving Object Detection With One Line of Codec]
(https://arxiv.org/abs/1704.04503)
Args:
boxes_remain (Tensor[N, ?]):
boxes where NMS will be performed
if Boxes, in (x1, y1, x2, y2) format
if RotatedBoxes, in (x_ctr, y_ctr, width, height, angle_degrees) format
scores_remain (Tensor[N]):
scores for each one of the boxes
method_gaussian (bool): use gaussian method if True, otherwise linear
sigma (float):
parameter for Gaussian penalty function
iou_threshold (float):
iou threshold for applying linear decay. Nt from the paper
re-used as threshold for standard "hard" nms
score_threshold (float):
boxes with scores below this threshold are pruned at each iteration.
Dramatically reduces computation time. Authors use values in [10e-4, 10e-2]
Returns:
tuple(Tensor, Tensor):
[0]: int64 tensor with the indices of the elements that have been kept
by Soft NMS, sorted in decreasing order of scores
[1]: float tensor with the re-scored scores of the elements that were kept
"""
# st = time.perf_counter()
device = boxes.device
boxes_remain = boxes.clone()
scores_remain = scores.clone()
num_elem = scores_remain.size()[0]
idxs = torch.arange(num_elem)
idxs_out = torch.zeros(num_elem, dtype=torch.int64, device=device)
scores_out = torch.zeros(num_elem, dtype=torch.float32, device=device)
area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
boxes_remain = torch.cat((boxes_remain, area.unsqueeze(1)), dim=1) # [N, 5] BS, x1, y1, x2, y2, area
count: int = 0
# print("[SOFTMAX] before loop starts in softnms {}".format(time.perf_counter() - st))
while scores_remain.numel() > 0:
# st1 = time.perf_counter()
top_idx = 0 # torch.argmax(scores_remain)
idxs_out[count] = idxs[top_idx]
scores_out[count] = scores_remain[top_idx]
count += 1
top_box = boxes_remain[top_idx]
ious = pairwise_iou(top_box.unsqueeze(0), boxes_remain)[0]
# st2 = time.perf_counter()
# print("[SOFTMAX] Before gaussian in softnms {}".format(st2 - st1))
if method_gaussian:
decay = torch.exp(-torch.pow(ious, 2) / sigma)
else:
decay = torch.ones_like(ious)
decay_mask = ious > iou_threshold
decay[decay_mask] = 1 - ious[decay_mask]
# st3 = time.perf_counter()
# print("[SOFTMAX] Gaussian in softnms {}".format(st3 - st2))
scores_remain *= decay
keep = scores_remain > score_threshold
keep[top_idx] = torch.tensor(False, device=device)
boxes_remain = boxes_remain[keep]
scores_remain = scores_remain[keep]
idxs = idxs[keep]
# st4 = time.perf_counter()
# print("[SOFTMAX] Remaining in softnms {}".format(st4 - st3))
# print("[SOFTMAX] Entire loop takes in softnms {}".format(st4 - st1))
# st5 = time.perf_counter()
# print("[SOFTMAX] Remaining in softnms {}".format(st5 - st))
return idxs_out[:count], scores_out[:count] | [
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] | https://github.com/NVIDIA/DeepLearningExamples/blob/589604d49e016cd9ef4525f7abcc9c7b826cfc5e/PyTorch/Detection/Efficientdet/effdet/layers/nms_layer.py#L56-L134 |
|
barnumbirr/coinmarketcap | 2b951be7cedd7efc484f50f27600ee8a003f3c94 | coinmarketcap/core.py | python | Market.stats | (self, **kwargs) | return response | This endpoint displays the global data found at the top of coinmarketcap.com.
Optional parameters:
(string) convert - return pricing info in terms of another currency.
Valid fiat currency values are: "AUD", "BRL", "CAD", "CHF", "CLP", "CNY", "CZK",
"DKK", "EUR", "GBP", "HKD", "HUF", "IDR", "ILS", "INR", "JPY", "KRW", "MXN",
"MYR", "NOK", "NZD", "PHP", "PKR", "PLN", "RUB", "SEK", "SGD", "THB", "TRY",
"TWD", "ZAR"
Valid cryptocurrency values are: "BTC", "ETH" "XRP", "LTC", and "BCH" | This endpoint displays the global data found at the top of coinmarketcap.com. | [
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"found",
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"coinmarketcap",
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"com",
"."
] | def stats(self, **kwargs):
"""
This endpoint displays the global data found at the top of coinmarketcap.com.
Optional parameters:
(string) convert - return pricing info in terms of another currency.
Valid fiat currency values are: "AUD", "BRL", "CAD", "CHF", "CLP", "CNY", "CZK",
"DKK", "EUR", "GBP", "HKD", "HUF", "IDR", "ILS", "INR", "JPY", "KRW", "MXN",
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"TWD", "ZAR"
Valid cryptocurrency values are: "BTC", "ETH" "XRP", "LTC", and "BCH"
"""
params = {}
params.update(kwargs)
response = self.__request('global/', params)
return response | [
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|
collinsctk/PyQYT | 7af3673955f94ff1b2df2f94220cd2dab2e252af | ExtentionPackages/Crypto/PublicKey/DSA.py | python | _DSAobj.verify | (self, M, signature) | return pubkey.pubkey.verify(self, M, signature) | Verify the validity of a DSA signature.
:Parameter M: The expected message.
:Type M: byte string or long
:Parameter signature: The DSA signature to verify.
:Type signature: A tuple with 2 longs as return by `sign`
:Return: True if the signature is correct, False otherwise. | Verify the validity of a DSA signature. | [
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"signature",
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] | def verify(self, M, signature):
"""Verify the validity of a DSA signature.
:Parameter M: The expected message.
:Type M: byte string or long
:Parameter signature: The DSA signature to verify.
:Type signature: A tuple with 2 longs as return by `sign`
:Return: True if the signature is correct, False otherwise.
"""
return pubkey.pubkey.verify(self, M, signature) | [
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|
jython/jython3 | def4f8ec47cb7a9c799ea4c745f12badf92c5769 | lib-python/3.5.1/xmlrpc/server.py | python | SimpleXMLRPCDispatcher._dispatch | (self, method, params) | Dispatches the XML-RPC method.
XML-RPC calls are forwarded to a registered function that
matches the called XML-RPC method name. If no such function
exists then the call is forwarded to the registered instance,
if available.
If the registered instance has a _dispatch method then that
method will be called with the name of the XML-RPC method and
its parameters as a tuple
e.g. instance._dispatch('add',(2,3))
If the registered instance does not have a _dispatch method
then the instance will be searched to find a matching method
and, if found, will be called.
Methods beginning with an '_' are considered private and will
not be called. | Dispatches the XML-RPC method. | [
"Dispatches",
"the",
"XML",
"-",
"RPC",
"method",
"."
] | def _dispatch(self, method, params):
"""Dispatches the XML-RPC method.
XML-RPC calls are forwarded to a registered function that
matches the called XML-RPC method name. If no such function
exists then the call is forwarded to the registered instance,
if available.
If the registered instance has a _dispatch method then that
method will be called with the name of the XML-RPC method and
its parameters as a tuple
e.g. instance._dispatch('add',(2,3))
If the registered instance does not have a _dispatch method
then the instance will be searched to find a matching method
and, if found, will be called.
Methods beginning with an '_' are considered private and will
not be called.
"""
func = None
try:
# check to see if a matching function has been registered
func = self.funcs[method]
except KeyError:
if self.instance is not None:
# check for a _dispatch method
if hasattr(self.instance, '_dispatch'):
return self.instance._dispatch(method, params)
else:
# call instance method directly
try:
func = resolve_dotted_attribute(
self.instance,
method,
self.allow_dotted_names
)
except AttributeError:
pass
if func is not None:
return func(*params)
else:
raise Exception('method "%s" is not supported' % method) | [
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||
fooying/3102 | 0faee38c30b2e24154f41e68457cfd8f7a61c040 | thirdparty/dns/rdataset.py | python | Rdataset.to_text | (self, name=None, origin=None, relativize=True,
override_rdclass=None, **kw) | return s.getvalue()[:-1] | Convert the rdataset into DNS master file format.
@see: L{dns.name.Name.choose_relativity} for more information
on how I{origin} and I{relativize} determine the way names
are emitted.
Any additional keyword arguments are passed on to the rdata
to_text() method.
@param name: If name is not None, emit a RRs with I{name} as
the owner name.
@type name: dns.name.Name object
@param origin: The origin for relative names, or None.
@type origin: dns.name.Name object
@param relativize: True if names should names be relativized
@type relativize: bool | Convert the rdataset into DNS master file format. | [
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] | def to_text(self, name=None, origin=None, relativize=True,
override_rdclass=None, **kw):
"""Convert the rdataset into DNS master file format.
@see: L{dns.name.Name.choose_relativity} for more information
on how I{origin} and I{relativize} determine the way names
are emitted.
Any additional keyword arguments are passed on to the rdata
to_text() method.
@param name: If name is not None, emit a RRs with I{name} as
the owner name.
@type name: dns.name.Name object
@param origin: The origin for relative names, or None.
@type origin: dns.name.Name object
@param relativize: True if names should names be relativized
@type relativize: bool"""
if not name is None:
name = name.choose_relativity(origin, relativize)
ntext = str(name)
pad = ' '
else:
ntext = ''
pad = ''
s = StringIO.StringIO()
if not override_rdclass is None:
rdclass = override_rdclass
else:
rdclass = self.rdclass
if len(self) == 0:
#
# Empty rdatasets are used for the question section, and in
# some dynamic updates, so we don't need to print out the TTL
# (which is meaningless anyway).
#
print >> s, '%s%s%s %s' % (ntext, pad,
dns.rdataclass.to_text(rdclass),
dns.rdatatype.to_text(self.rdtype))
else:
for rd in self:
print >> s, '%s%s%d %s %s %s' % \
(ntext, pad, self.ttl, dns.rdataclass.to_text(rdclass),
dns.rdatatype.to_text(self.rdtype),
rd.to_text(origin=origin, relativize=relativize, **kw))
#
# We strip off the final \n for the caller's convenience in printing
#
return s.getvalue()[:-1] | [
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] | https://github.com/fooying/3102/blob/0faee38c30b2e24154f41e68457cfd8f7a61c040/thirdparty/dns/rdataset.py#L170-L218 |
|
twisted/twisted | dee676b040dd38b847ea6fb112a712cb5e119490 | src/twisted/words/protocols/jabber/error.py | python | exceptionFromStanza | (stanza) | return exception | Build an exception object from an error stanza.
@param stanza: the error stanza
@type stanza: L{domish.Element}
@return: the generated exception object
@rtype: L{StanzaError} | Build an exception object from an error stanza. | [
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] | def exceptionFromStanza(stanza):
"""
Build an exception object from an error stanza.
@param stanza: the error stanza
@type stanza: L{domish.Element}
@return: the generated exception object
@rtype: L{StanzaError}
"""
children = []
condition = text = textLang = appCondition = type = code = None
for element in stanza.elements():
if element.name == "error" and element.uri == stanza.uri:
code = element.getAttribute("code")
type = element.getAttribute("type")
error = _parseError(element, NS_XMPP_STANZAS)
condition = error["condition"]
text = error["text"]
textLang = error["textLang"]
appCondition = error["appCondition"]
if not condition and code:
condition, type = CODES_TO_CONDITIONS[code]
text = str(stanza.error)
else:
children.append(element)
if condition is None:
# TODO: raise exception instead?
return StanzaError(None)
exception = StanzaError(condition, type, text, textLang, appCondition)
exception.children = children
exception.stanza = stanza
return exception | [
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|
jliljebl/flowblade | 995313a509b80e99eb1ad550d945bdda5995093b | flowblade-trunk/Flowblade/tools/fluxity.py | python | FluxityContextPrivate.error_on_wrong_method | (self, method_name, required_method) | [] | def error_on_wrong_method(self, method_name, required_method):
if required_method == self.current_method:
return
error_str = "'FluxityContext." + method_name + "' has to called in script method '" + self.method_name[required_method] + "'."
_raise_contained_error(error_str) | [
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||||
etetoolkit/ete | 2b207357dc2a40ccad7bfd8f54964472c72e4726 | ete3/nexml/_nexml.py | python | TreeAndNetworkSet.build | (self, node) | [] | def build(self, node):
self.buildAttributes(node, node.attrib, [])
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_) | [
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||||
jameskermode/f90wrap | 6a6021d3d8c01125e13ecd0ef8faa52f19e5be3e | f90wrap/transform.py | python | set_intent | (attributes, intent) | return attributes | Remove any current "intent" from attributes and replace with intent given | Remove any current "intent" from attributes and replace with intent given | [
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] | def set_intent(attributes, intent):
"""Remove any current "intent" from attributes and replace with intent given"""
attributes = [attr for attr in attributes if not attr.startswith('intent')]
attributes.append(intent)
return attributes | [
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|
great-expectations/great_expectations | 45224cb890aeae725af25905923d0dbbab2d969d | great_expectations/datasource/batch_kwargs_generator/query_batch_kwargs_generator.py | python | QueryBatchKwargsGenerator._build_batch_kwargs | (self, batch_parameters) | return SqlAlchemyDatasourceQueryBatchKwargs(batch_kwargs) | Build batch kwargs from a partition id. | Build batch kwargs from a partition id. | [
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"id",
"."
] | def _build_batch_kwargs(self, batch_parameters):
"""Build batch kwargs from a partition id."""
data_asset_name = batch_parameters.pop("data_asset_name")
raw_query = self._get_raw_query(data_asset_name=data_asset_name)
partition_id = batch_parameters.pop("partition_id", None)
batch_kwargs = self._datasource.process_batch_parameters(**batch_parameters)
batch_kwargs["query"] = raw_query
if partition_id:
if not batch_kwargs["query_parameters"]:
batch_kwargs["query_parameters"] = {}
batch_kwargs["query_parameters"]["partition_id"] = partition_id
return SqlAlchemyDatasourceQueryBatchKwargs(batch_kwargs) | [
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|
seopbo/nlp_classification | 21ea6e3f5737e7074bdd8dd190e5f5172f86f6bf | A_Structured_Self-attentive_Sentence_Embedding_cls/model/utils.py | python | Vocab.padding_token | (self) | return self._padding_token | [] | def padding_token(self):
return self._padding_token | [
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|||
cuthbertLab/music21 | bd30d4663e52955ed922c10fdf541419d8c67671 | music21/bar.py | python | typeToMusicXMLBarStyle | (value) | Convert a music21 barline name into the musicxml name --
essentially just changes the names of 'double' and 'final'
to 'light-light' and 'light-heavy'
Does not do error checking to make sure it's a valid name,
since setting the style on a Barline object already does that.
>>> bar.typeToMusicXMLBarStyle('final')
'light-heavy'
>>> bar.typeToMusicXMLBarStyle('regular')
'regular' | Convert a music21 barline name into the musicxml name --
essentially just changes the names of 'double' and 'final'
to 'light-light' and 'light-heavy' | [
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"to",
"light",
"-",
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"and",
"light",
"-",
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] | def typeToMusicXMLBarStyle(value):
'''
Convert a music21 barline name into the musicxml name --
essentially just changes the names of 'double' and 'final'
to 'light-light' and 'light-heavy'
Does not do error checking to make sure it's a valid name,
since setting the style on a Barline object already does that.
>>> bar.typeToMusicXMLBarStyle('final')
'light-heavy'
>>> bar.typeToMusicXMLBarStyle('regular')
'regular'
'''
if value.lower() in reverseBarTypeDict:
return reverseBarTypeDict[value.lower()]
else:
return value | [
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||
home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/zwave_js/helpers.py | python | get_zwave_value_from_config | (node: ZwaveNode, config: ConfigType) | return node.values[value_id] | Get a Z-Wave JS Value from a config. | Get a Z-Wave JS Value from a config. | [
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"."
] | def get_zwave_value_from_config(node: ZwaveNode, config: ConfigType) -> ZwaveValue:
"""Get a Z-Wave JS Value from a config."""
endpoint = None
if config.get(ATTR_ENDPOINT):
endpoint = config[ATTR_ENDPOINT]
property_key = None
if config.get(ATTR_PROPERTY_KEY):
property_key = config[ATTR_PROPERTY_KEY]
value_id = get_value_id(
node,
config[ATTR_COMMAND_CLASS],
config[ATTR_PROPERTY],
endpoint,
property_key,
)
if value_id not in node.values:
raise vol.Invalid(f"Value {value_id} can't be found on node {node}")
return node.values[value_id] | [
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|
f-dangel/backpack | 1da7e53ebb2c490e2b7dd9f79116583641f3cca1 | backpack/core/derivatives/elementwise.py | python | ElementwiseDerivatives.df | (
self,
module: Module,
g_inp: Tuple[Tensor],
g_out: Tuple[Tensor],
subsampling: List[int] = None,
) | Elementwise first derivative.
Args:
module: PyTorch activation module.
g_inp: Gradients of the module w.r.t. its inputs.
g_out: Gradients of the module w.r.t. its outputs.
subsampling: Indices of active samples. ``None`` means all samples.
Returns:
Tensor containing the derivatives `f'(input[i]) ∀ i`. | Elementwise first derivative. | [
"Elementwise",
"first",
"derivative",
"."
] | def df(
self,
module: Module,
g_inp: Tuple[Tensor],
g_out: Tuple[Tensor],
subsampling: List[int] = None,
):
"""Elementwise first derivative.
Args:
module: PyTorch activation module.
g_inp: Gradients of the module w.r.t. its inputs.
g_out: Gradients of the module w.r.t. its outputs.
subsampling: Indices of active samples. ``None`` means all samples.
Returns:
Tensor containing the derivatives `f'(input[i]) ∀ i`.
"""
raise NotImplementedError("First derivatives not implemented") | [
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||
donnemartin/gitsome | d7c57abc7cb66e9c910a844f15d4536866da3310 | gitsome/lib/github3/users.py | python | User.impersonate | (self, scopes=None) | return self._instance_or_null(Authorization, json) | Obtain an impersonation token for the user.
The retrieved token will allow impersonation of the user.
This is only available for admins of a GitHub Enterprise instance.
:param list scopes: (optional), areas you want this token to apply to,
i.e., 'gist', 'user'
:returns: :class:`Authorization <Authorization>` | Obtain an impersonation token for the user. | [
"Obtain",
"an",
"impersonation",
"token",
"for",
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"."
] | def impersonate(self, scopes=None):
"""Obtain an impersonation token for the user.
The retrieved token will allow impersonation of the user.
This is only available for admins of a GitHub Enterprise instance.
:param list scopes: (optional), areas you want this token to apply to,
i.e., 'gist', 'user'
:returns: :class:`Authorization <Authorization>`
"""
url = self._build_url('admin', 'users', self.id, 'authorizations')
data = {}
if scopes:
data['scopes'] = scopes
json = self._json(self._post(url, data=data), 201)
return self._instance_or_null(Authorization, json) | [
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|
spulec/moto | a688c0032596a7dfef122b69a08f2bec3be2e481 | moto/dynamodb2/comparisons.py | python | ConditionExpressionParser._apply_between | (self, nodes) | return output | Apply condition := operand BETWEEN operand AND operand. | Apply condition := operand BETWEEN operand AND operand. | [
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] | def _apply_between(self, nodes):
"""Apply condition := operand BETWEEN operand AND operand."""
output = deque()
while nodes:
if self._matches(nodes, ["*", "BETWEEN"]):
self._assert(
self._matches(
nodes, ["OPERAND", "BETWEEN", "OPERAND", "AND", "OPERAND"]
),
"Bad BETWEEN expression",
list(nodes)[:5],
)
lhs = nodes.popleft()
between_node = nodes.popleft()
low = nodes.popleft()
and_node = nodes.popleft()
high = nodes.popleft()
all_children = [lhs, between_node, low, and_node, high]
nodes.appendleft(
self.Node(
nonterminal=self.Nonterminal.CONDITION,
kind=self.Kind.BETWEEN,
text=" ".join([t.text for t in all_children]),
value=None,
children=[lhs, low, high],
)
)
else:
output.append(nodes.popleft())
return output | [
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] | https://github.com/spulec/moto/blob/a688c0032596a7dfef122b69a08f2bec3be2e481/moto/dynamodb2/comparisons.py#L551-L580 |
|
ArduPilot/pymavlink | 9d6ea618e8d0622bee95fa902b6251882e225afb | quaternion.py | python | QuaternionBase.__eq__ | (self, other) | return NotImplemented | Equality test (same orientation, not necessarily same rotation)
:param other: a QuaternionBase
:returns: true if the quaternions are equal | Equality test (same orientation, not necessarily same rotation) | [
"Equality",
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"orientation",
"not",
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"rotation",
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] | def __eq__(self, other):
"""
Equality test (same orientation, not necessarily same rotation)
:param other: a QuaternionBase
:returns: true if the quaternions are equal
"""
if isinstance(other, QuaternionBase):
return abs(self.q.dot(other.q)) > 1 - np.finfo(float).eps
return NotImplemented | [
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|
kbandla/ImmunityDebugger | 2abc03fb15c8f3ed0914e1175c4d8933977c73e3 | 1.85/Libs/immlib.py | python | Debugger.setBreakpointOnName | (self,name) | return debugger.set_breakpoint_on_name(name) | Set a Breakpoint.
@type Name: STRING
@param Name: name of the function to bp
@rtype: DWORD
@return: Address of name | Set a Breakpoint. | [
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] | def setBreakpointOnName(self,name):
"""
Set a Breakpoint.
@type Name: STRING
@param Name: name of the function to bp
@rtype: DWORD
@return: Address of name
"""
return debugger.set_breakpoint_on_name(name) | [
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|
python-rope/rope | bcdfe6b70b1437d976e21c56b6ec1281b22823aa | rope/base/history.py | python | History.undo | (self, change=None, drop=False, task_handle=taskhandle.NullTaskHandle()) | return result | Redo done changes from the history
When `change` is `None`, the last done change will be undone.
If change is not `None` it should be an item from
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it will be undone. In both cases the list of undone changes
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] | def undo(self, change=None, drop=False, task_handle=taskhandle.NullTaskHandle()):
"""Redo done changes from the history
When `change` is `None`, the last done change will be undone.
If change is not `None` it should be an item from
`self.undo_list`; this change and all changes that depend on
it will be undone. In both cases the list of undone changes
will be returned.
If `drop` is `True`, the undone change will not be appended to
the redo list.
"""
if not self._undo_list:
raise exceptions.HistoryError("Undo list is empty")
if change is None:
change = self.undo_list[-1]
dependencies = self._find_dependencies(self.undo_list, change)
self._move_front(self.undo_list, dependencies)
self._perform_undos(len(dependencies), task_handle)
result = self.redo_list[-len(dependencies) :]
if drop:
del self.redo_list[-len(dependencies) :]
return result | [
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|
nosmokingbandit/watcher | dadacd21a5790ee609058a98a17fcc8954d24439 | lib/sqlalchemy/orm/scoping.py | python | scoped_session.__init__ | (self, session_factory, scopefunc=None) | Construct a new :class:`.scoped_session`.
:param session_factory: a factory to create new :class:`.Session`
instances. This is usually, but not necessarily, an instance
of :class:`.sessionmaker`.
:param scopefunc: optional function which defines
the current scope. If not passed, the :class:`.scoped_session`
object assumes "thread-local" scope, and will use
a Python ``threading.local()`` in order to maintain the current
:class:`.Session`. If passed, the function should return
a hashable token; this token will be used as the key in a
dictionary in order to store and retrieve the current
:class:`.Session`. | Construct a new :class:`.scoped_session`. | [
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] | def __init__(self, session_factory, scopefunc=None):
"""Construct a new :class:`.scoped_session`.
:param session_factory: a factory to create new :class:`.Session`
instances. This is usually, but not necessarily, an instance
of :class:`.sessionmaker`.
:param scopefunc: optional function which defines
the current scope. If not passed, the :class:`.scoped_session`
object assumes "thread-local" scope, and will use
a Python ``threading.local()`` in order to maintain the current
:class:`.Session`. If passed, the function should return
a hashable token; this token will be used as the key in a
dictionary in order to store and retrieve the current
:class:`.Session`.
"""
self.session_factory = session_factory
if scopefunc:
self.registry = ScopedRegistry(session_factory, scopefunc)
else:
self.registry = ThreadLocalRegistry(session_factory) | [
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Source-Python-Dev-Team/Source.Python | d0ffd8ccbd1e9923c9bc44936f20613c1c76b7fb | addons/source-python/packages/source-python/memory/manager.py | python | CustomType.on_dealloc | (self) | Call the destructor.
This method is automatically called, when the pointer gets
deallocated. It then calls the destructor if it was specified. | Call the destructor. | [
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] | def on_dealloc(self):
"""Call the destructor.
This method is automatically called, when the pointer gets
deallocated. It then calls the destructor if it was specified.
"""
# Call the destructor if it was specified
if self._destructor is not None:
self._destructor() | [
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||
CalebBell/thermo | 572a47d1b03d49fe609b8d5f826fa6a7cde00828 | thermo/chemical.py | python | Chemical.calc_S_excess | (self, T, P) | return S_dep | [] | def calc_S_excess(self, T, P):
S_dep = 0
if self.phase_ref == 'g' and self.phase == 'g':
S_dep += self.eos.to_TP(T, P).S_dep_g - self.S_dep_ref_g
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S_dep += 0
elif self.phase_ref == 'g' and self.phase == 'l':
S_dep += self.S_dep_Tb_Pb_g - self.S_dep_Tb_P_ref_g
S_dep += (self.eos.to_TP(T, P).S_dep_l - self._eos_T_101325.S_dep_l)
elif self.phase_ref == 'l' and self.phase == 'g':
S_dep += self.S_dep_T_ref_Pb - self.S_dep_ref_l
S_dep += (self.eos.to_TP(T, P).S_dep_g - self.S_dep_Tb_Pb_g)
return S_dep | [
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|||
onnx/sklearn-onnx | 8e19d19b8a9bcae7f17d5b7cc2514cf6b89f8199 | skl2onnx/operator_converters/label_binariser.py | python | convert_sklearn_label_binariser | (scope: Scope, operator: Operator,
container: ModelComponentContainer) | Converts Scikit Label Binariser model to onnx format. | Converts Scikit Label Binariser model to onnx format. | [
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] | def convert_sklearn_label_binariser(scope: Scope, operator: Operator,
container: ModelComponentContainer):
"""Converts Scikit Label Binariser model to onnx format."""
binariser_op = operator.raw_operator
classes = binariser_op.classes_
if (hasattr(binariser_op, 'sparse_input_') and
binariser_op.sparse_input_):
raise RuntimeError("sparse is not supported for LabelBinarizer.")
if (hasattr(binariser_op, 'y_type_') and
binariser_op.y_type_ == "multilabel-indicator"):
if binariser_op.pos_label != 1:
raise RuntimeError("pos_label != 1 is not supported "
"for LabelBinarizer.")
if list(classes) != list(range(len(classes))):
raise RuntimeError("classes != [0, 1, ..., n_classes] is not "
"supported for LabelBinarizer.")
container.add_node('Identity', operator.inputs[0].full_name,
operator.output_full_names,
name=scope.get_unique_operator_name('identity'))
else:
zeros_tensor = np.full((1, len(classes)),
binariser_op.neg_label, dtype=np.float)
unit_tensor = np.full((1, len(classes)),
binariser_op.pos_label, dtype=np.float)
classes_tensor_name = scope.get_unique_variable_name('classes_tensor')
equal_condition_tensor_name = scope.get_unique_variable_name(
'equal_condition_tensor')
zeros_tensor_name = scope.get_unique_variable_name('zero_tensor')
unit_tensor_name = scope.get_unique_variable_name('unit_tensor')
where_result_name = scope.get_unique_variable_name('where_result')
class_dtype = onnx_proto.TensorProto.STRING
if np.issubdtype(binariser_op.classes_.dtype, np.signedinteger):
class_dtype = onnx_proto.TensorProto.INT64
else:
classes = np.array([s.encode('utf-8') for s in classes])
container.add_initializer(classes_tensor_name, class_dtype,
[len(classes)], classes)
container.add_initializer(
zeros_tensor_name, onnx_proto.TensorProto.FLOAT,
zeros_tensor.shape, zeros_tensor.ravel())
container.add_initializer(
unit_tensor_name, onnx_proto.TensorProto.FLOAT,
unit_tensor.shape, unit_tensor.ravel())
reshaped_input_name = scope.get_unique_variable_name('reshaped_input')
apply_reshape(scope, operator.inputs[0].full_name, reshaped_input_name,
container, desired_shape=[-1, 1])
# Models with classes_/inputs of string type would fail in the
# following step as Equal op does not support string comparison.
container.add_node('Equal', [classes_tensor_name, reshaped_input_name],
equal_condition_tensor_name,
name=scope.get_unique_operator_name('equal'))
container.add_node(
'Where',
[equal_condition_tensor_name, unit_tensor_name, zeros_tensor_name],
where_result_name,
name=scope.get_unique_operator_name('where'))
where_res = where_result_name
if len(binariser_op.classes_) == 2:
array_f_name = scope.get_unique_variable_name(
'array_feature_extractor_result')
pos_class_index_name = scope.get_unique_variable_name(
'pos_class_index')
container.add_initializer(
pos_class_index_name, onnx_proto.TensorProto.INT64, [], [1])
container.add_node(
'ArrayFeatureExtractor',
[where_result_name, pos_class_index_name],
array_f_name, op_domain='ai.onnx.ml',
name=scope.get_unique_operator_name('ArrayFeatureExtractor'))
where_res = array_f_name
apply_cast(scope, where_res, operator.output_full_names, container,
to=onnx_proto.TensorProto.INT64) | [
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||
bcbio/bcbio-nextgen | c80f9b6b1be3267d1f981b7035e3b72441d258f2 | bcbio/variation/multi.py | python | _diff_dict | (orig, new) | return final | Diff a nested dictionary, returning only key/values that differ. | Diff a nested dictionary, returning only key/values that differ. | [
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"""
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for k, v in new.items():
if isinstance(v, dict):
v = _diff_dict(orig.get(k, {}), v)
if len(v) > 0:
final[k] = v
elif v != orig.get(k):
final[k] = v
for k, v in orig.items():
if k not in new:
final[k] = None
return final | [
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onejgordon/flow-dashboard | 993320e2eb0f86d89b9904a3d5415c7479c5918e | tools.py | python | dt_from_ts | (ms) | Convert timestamp in ms to datetime
>>> dt_from_ts(1494269497212)
datetime.datetime(2017, 5, 8, 18, 51, 37, 212000) | Convert timestamp in ms to datetime | [
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'''
Convert timestamp in ms to datetime
>>> dt_from_ts(1494269497212)
datetime.datetime(2017, 5, 8, 18, 51, 37, 212000)
'''
if ms == 0:
return None
else:
return datetime.utcfromtimestamp(float(ms) / 1000) | [
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||
Delgan/loguru | 3d5234541c81318e7f6f725eca7bab294fe09c23 | loguru/_logger.py | python | Logger.parse | (file, pattern, *, cast={}, chunk=2 ** 16) | Parse raw logs and extract each entry as a |dict|.
The logging format has to be specified as the regex ``pattern``, it will then be
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Parameters
----------
file : |str|, |Path| or |file-like object|_
The path of the log file to be parsed, or an already opened file object.
pattern : |str| or |re.Pattern|_
The regex to use for logs parsing, it should contain named groups which will be included
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A function that should convert in-place the regex groups parsed (a dict of string
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Yields
------
:class:`dict`
The dict mapping regex named groups to matched values, as returned by |match.groupdict|
and optionally converted according to ``cast`` argument.
Examples
--------
>>> reg = r"(?P<lvl>[0-9]+): (?P<msg>.*)" # If log format is "{level.no} - {message}"
>>> for e in logger.parse("file.log", reg): # A file line could be "10 - A debug message"
... print(e) # => {'lvl': '10', 'msg': 'A debug message'}
>>> caster = dict(lvl=int) # Parse 'lvl' key as an integer
>>> for e in logger.parse("file.log", reg, cast=caster):
... print(e) # => {'lvl': 10, 'msg': 'A debug message'}
>>> def cast(groups):
... if "date" in groups:
... groups["date"] = datetime.strptime(groups["date"], "%Y-%m-%d %H:%M:%S")
...
>>> with open("file.log") as file:
... for log in logger.parse(file, reg, cast=cast):
... print(log["date"], log["something_else"]) | Parse raw logs and extract each entry as a |dict|. | [
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"""Parse raw logs and extract each entry as a |dict|.
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Parameters
----------
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The path of the log file to be parsed, or an already opened file object.
pattern : |str| or |re.Pattern|_
The regex to use for logs parsing, it should contain named groups which will be included
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Examples
--------
>>> reg = r"(?P<lvl>[0-9]+): (?P<msg>.*)" # If log format is "{level.no} - {message}"
>>> for e in logger.parse("file.log", reg): # A file line could be "10 - A debug message"
... print(e) # => {'lvl': '10', 'msg': 'A debug message'}
>>> caster = dict(lvl=int) # Parse 'lvl' key as an integer
>>> for e in logger.parse("file.log", reg, cast=caster):
... print(e) # => {'lvl': 10, 'msg': 'A debug message'}
>>> def cast(groups):
... if "date" in groups:
... groups["date"] = datetime.strptime(groups["date"], "%Y-%m-%d %H:%M:%S")
...
>>> with open("file.log") as file:
... for log in logger.parse(file, reg, cast=cast):
... print(log["date"], log["something_else"])
"""
if isinstance(file, (str, PathLike)):
should_close = True
fileobj = open(str(file))
elif hasattr(file, "read") and callable(file.read):
should_close = False
fileobj = file
else:
raise TypeError(
"Invalid file, it should be a string path or a file object, not: '%s'"
% type(file).__name__
)
if isinstance(cast, dict):
def cast_function(groups):
for key, converter in cast.items():
if key in groups:
groups[key] = converter(groups[key])
elif callable(cast):
cast_function = cast
else:
raise TypeError(
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try:
regex = re.compile(pattern)
except TypeError:
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for match in matches:
groups = match.groupdict()
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yield groups
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fileobj.close() | [
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/iai/v20200303/iai_client.py | python | IaiClient.SearchFaces | (self, request) | 用于对一张待识别的人脸图片，在一个或多个人员库中识别出最相似的 TopK 人员，识别结果按照相似度从大到小排序。
支持一次性识别图片中的最多 10 张人脸，支持一次性跨 100 个人员库（Group）搜索。
单次搜索的人员库人脸总数量和人员库的算法模型版本（FaceModelVersion）相关。算法模型版本为2.0的人员库，单次搜索人员库人脸总数量不得超过 100 万张；算法模型版本为3.0的人员库，单次搜索人员库人脸总数量不得超过 300 万张。
与[人员搜索](https://cloud.tencent.com/document/product/867/44992)及[人员搜索按库返回](https://cloud.tencent.com/document/product/867/44991)接口不同的是，本接口将该人员（Person）下的每个人脸（Face）都作为单独个体进行验证，而人员搜索及人员搜索按库返回接口 会将该人员（Person）下的所有人脸（Face）进行融合特征处理，即若某个Person下有4张 Face，本接口会将4张 Face 的特征进行融合处理，生成对应这个 Person 的特征，使搜索更加准确。
本接口需与[人员库管理相关接口](https://cloud.tencent.com/document/product/867/45015)结合使用。
>
- 公共参数中的签名方式请使用V3版本，即配置SignatureMethod参数为TC3-HMAC-SHA256。
>
- 不可同时搜索不同算法模型版本（FaceModelVersion）的人员库。
:param request: Request instance for SearchFaces.
:type request: :class:`tencentcloud.iai.v20200303.models.SearchFacesRequest`
:rtype: :class:`tencentcloud.iai.v20200303.models.SearchFacesResponse` | 用于对一张待识别的人脸图片，在一个或多个人员库中识别出最相似的 TopK 人员，识别结果按照相似度从大到小排序。 | [
"用于对一张待识别的人脸图片，在一个或多个人员库中识别出最相似的",
"TopK",
"人员，识别结果按照相似度从大到小排序。"
] | def SearchFaces(self, request):
"""用于对一张待识别的人脸图片，在一个或多个人员库中识别出最相似的 TopK 人员，识别结果按照相似度从大到小排序。
支持一次性识别图片中的最多 10 张人脸，支持一次性跨 100 个人员库（Group）搜索。
单次搜索的人员库人脸总数量和人员库的算法模型版本（FaceModelVersion）相关。算法模型版本为2.0的人员库，单次搜索人员库人脸总数量不得超过 100 万张；算法模型版本为3.0的人员库，单次搜索人员库人脸总数量不得超过 300 万张。
与[人员搜索](https://cloud.tencent.com/document/product/867/44992)及[人员搜索按库返回](https://cloud.tencent.com/document/product/867/44991)接口不同的是，本接口将该人员（Person）下的每个人脸（Face）都作为单独个体进行验证，而人员搜索及人员搜索按库返回接口 会将该人员（Person）下的所有人脸（Face）进行融合特征处理，即若某个Person下有4张 Face，本接口会将4张 Face 的特征进行融合处理，生成对应这个 Person 的特征，使搜索更加准确。
本接口需与[人员库管理相关接口](https://cloud.tencent.com/document/product/867/45015)结合使用。
>
- 公共参数中的签名方式请使用V3版本，即配置SignatureMethod参数为TC3-HMAC-SHA256。
>
- 不可同时搜索不同算法模型版本（FaceModelVersion）的人员库。
:param request: Request instance for SearchFaces.
:type request: :class:`tencentcloud.iai.v20200303.models.SearchFacesRequest`
:rtype: :class:`tencentcloud.iai.v20200303.models.SearchFacesResponse`
"""
try:
params = request._serialize()
body = self.call("SearchFaces", params)
response = json.loads(body)
if "Error" not in response["Response"]:
model = models.SearchFacesResponse()
model._deserialize(response["Response"])
return model
else:
code = response["Response"]["Error"]["Code"]
message = response["Response"]["Error"]["Message"]
reqid = response["Response"]["RequestId"]
raise TencentCloudSDKException(code, message, reqid)
except Exception as e:
if isinstance(e, TencentCloudSDKException):
raise
else:
raise TencentCloudSDKException(e.message, e.message) | [
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||
aquario-crypto/Book_on_Python_Algorithms_and_Data_Structure | 234b4b1fc84faf4a06843c1fba1d05ccc18f80e6 | book/ebook_src/searching_and_sorting/sorting/count_sort.py | python | count_sort_dict | (a) | return b | an example of counting sort using default dictionaries | an example of counting sort using default dictionaries | [
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"using",
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''' an example of counting sort using default dictionaries '''
b, c = [], defaultdict(list)
for x in a:
c[x].append(x) # we could have used key = lambda x:x
for k in range(min(c), max(c) + 1):
b.extend(c[k])
return b | [
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|
biopython/biopython | 2dd97e71762af7b046d7f7f8a4f1e38db6b06c86 | Bio/Nexus/Nexus.py | python | _seqmatrix2strmatrix | (matrix) | return {t: str(matrix[t]) for t in matrix} | Convert a Seq-object matrix to a plain sequence-string matrix (PRIVATE). | Convert a Seq-object matrix to a plain sequence-string matrix (PRIVATE). | [
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] | def _seqmatrix2strmatrix(matrix):
"""Convert a Seq-object matrix to a plain sequence-string matrix (PRIVATE)."""
return {t: str(matrix[t]) for t in matrix} | [
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|
idanr1986/cuckoo-droid | 1350274639473d3d2b0ac740cae133ca53ab7444 | analyzer/android/lib/api/androguard/dvm.py | python | ProtoIdItem.get_return_type_idx | (self) | return self.return_type_idx | Return the index into the type_ids list for the return type of this prototype
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"""
Return the index into the type_ids list for the return type of this prototype
:rtype: int
"""
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|
CouchPotato/CouchPotatoServer | 7260c12f72447ddb6f062367c6dfbda03ecd4e9c | libs/xmpp/protocol.py | python | JID.__init__ | (self, jid=None, node='', domain='', resource='') | Constructor. JID can be specified as string (jid argument) or as separate parts.
Examples:
JID('node@domain/resource')
JID(node='node',domain='domain.org') | Constructor. JID can be specified as string (jid argument) or as separate parts.
Examples:
JID('node | [
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""" Constructor. JID can be specified as string (jid argument) or as separate parts.
Examples:
JID('node@domain/resource')
JID(node='node',domain='domain.org')
"""
if not jid and not domain: raise ValueError('JID must contain at least domain name')
elif type(jid)==type(self): self.node,self.domain,self.resource=jid.node,jid.domain,jid.resource
elif domain: self.node,self.domain,self.resource=node,domain,resource
else:
if jid.find('@')+1: self.node,jid=jid.split('@',1)
else: self.node=''
if jid.find('/')+1: self.domain,self.resource=jid.split('/',1)
else: self.domain,self.resource=jid,'' | [
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||
gkrizek/bash-lambda-layer | 703b0ade8174022d44779d823172ab7ac33a5505 | bin/docutils/utils/math/math2html.py | python | ContainerSize.set | (self, width = None, height = None) | return self | Set the proper size with width and height. | Set the proper size with width and height. | [
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] | def set(self, width = None, height = None):
"Set the proper size with width and height."
self.setvalue('width', width)
self.setvalue('height', height)
return self | [
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|
openstack/swift | b8d7c3dcb817504dcc0959ba52cc4ed2cf66c100 | swift/common/http.py | python | is_redirection | (status) | return 300 <= status <= 399 | Check if HTTP status code is redirection.
:param status: http status code
:returns: True if status is redirection, else False | Check if HTTP status code is redirection. | [
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] | def is_redirection(status):
"""
Check if HTTP status code is redirection.
:param status: http status code
:returns: True if status is redirection, else False
"""
return 300 <= status <= 399 | [
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|
spyder-ide/spyder | 55da47c032dfcf519600f67f8b30eab467f965e7 | spyder/plugins/plots/widgets/figurebrowser.py | python | ThumbnailScrollBar.set_figureviewer | (self, figure_viewer) | Set the namespace for the FigureViewer. | Set the namespace for the FigureViewer. | [
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] | def set_figureviewer(self, figure_viewer):
"""Set the namespace for the FigureViewer."""
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||
google/apitools | 31cad2d904f356872d2965687e84b2d87ee2cdd3 | apitools/base/py/encoding_helper.py | python | PyValueToMessage | (message_type, value) | return JsonToMessage(message_type, json.dumps(value)) | Convert the given python value to a message of type message_type. | Convert the given python value to a message of type message_type. | [
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] | def PyValueToMessage(message_type, value):
"""Convert the given python value to a message of type message_type."""
return JsonToMessage(message_type, json.dumps(value)) | [
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|
garrickbrazil/M3D-RPN | bf204e3f95f647d73a132535385119b12c8d6c36 | lib/util.py | python | init_log_file | (folder_path, suffix=None, log_level=logging.INFO) | return file_path | This function inits a log file given a folder to write the log to.
it automatically adds a timestamp and optional suffix to the log.
Anything written to the log will automatically write to console too.
Example:
import logging
init_log_file('output/logs/')
logging.info('this will show up in both the log AND console!') | This function inits a log file given a folder to write the log to.
it automatically adds a timestamp and optional suffix to the log.
Anything written to the log will automatically write to console too. | [
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] | def init_log_file(folder_path, suffix=None, log_level=logging.INFO):
"""
This function inits a log file given a folder to write the log to.
it automatically adds a timestamp and optional suffix to the log.
Anything written to the log will automatically write to console too.
Example:
import logging
init_log_file('output/logs/')
logging.info('this will show up in both the log AND console!')
"""
timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
log_format = '[%(levelname)s]: %(asctime)s %(message)s'
if suffix is not None:
file_name = timestamp + '_' + suffix
else:
file_name = timestamp
file_path = os.path.join(folder_path, file_name)
logging.basicConfig(filename=file_path, level=log_level, format=log_format)
logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))
return file_path | [
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|
deanishe/alfred-convert | 97407f4ec8dbca5abbc6952b2b56cf3918624177 | src/workflow/workflow.py | python | Workflow.delete_password | (self, account, service=None) | Delete the password stored at ``service/account``.
Raise :class:`PasswordNotFound` if account is unknown.
:param account: name of the account the password is for, e.g.
"Pinboard"
:type account: ``unicode``
:param service: Name of the service. By default, this is the workflow's
bundle ID
:type service: ``unicode`` | Delete the password stored at ``service/account``. | [
"Delete",
"the",
"password",
"stored",
"at",
"service",
"/",
"account",
"."
] | def delete_password(self, account, service=None):
"""Delete the password stored at ``service/account``.
Raise :class:`PasswordNotFound` if account is unknown.
:param account: name of the account the password is for, e.g.
"Pinboard"
:type account: ``unicode``
:param service: Name of the service. By default, this is the workflow's
bundle ID
:type service: ``unicode``
"""
if not service:
service = self.bundleid
self._call_security('delete-generic-password', service, account)
self.logger.debug('deleted password : %s:%s', service, account) | [
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||
zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/django/db/backends/base/introspection.py | python | BaseDatabaseIntrospection.installed_models | (self, tables) | return {
m for m in all_models
if self.table_name_converter(m._meta.db_table) in tables
} | Returns a set of all models represented by the provided list of table names. | Returns a set of all models represented by the provided list of table names. | [
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] | def installed_models(self, tables):
"Returns a set of all models represented by the provided list of table names."
from django.apps import apps
from django.db import router
all_models = []
for app_config in apps.get_app_configs():
all_models.extend(router.get_migratable_models(app_config, self.connection.alias))
tables = list(map(self.table_name_converter, tables))
return {
m for m in all_models
if self.table_name_converter(m._meta.db_table) in tables
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|
adamewing/bamsurgeon | 826921a48cb4aa91e419a0e0946255e17223ea39 | bamsurgeon/common.py | python | minorbase | (basepile) | returns tuple: (minor base, count) | returns tuple: (minor base, count) | [
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":",
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"base",
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] | def minorbase(basepile):
"""returns tuple: (minor base, count)
"""
c = Counter(basepile)
if len(list(c.elements())) > 1:
return c.most_common(2)[-1]
else:
return c.most_common()[0] | [
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||
linuxscout/mishkal | 4f4ae0ebc2d6acbeb3de3f0303151ec7b54d2f76 | interfaces/web/lib/paste/urlmap.py | python | URLMap.sort_apps | (self) | Make sure applications are sorted with longest URLs first | Make sure applications are sorted with longest URLs first | [
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] | def sort_apps(self):
"""
Make sure applications are sorted with longest URLs first
"""
def key(app_desc):
(domain, url), app = app_desc
if not domain:
# Make sure empty domains sort last:
return '\xff', -len(url)
else:
return domain, -len(url)
apps = [(key(desc), desc) for desc in self.applications]
apps.sort()
self.applications = [desc for (sortable, desc) in apps] | [
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||
PyCQA/astroid | a815443f62faae05249621a396dcf0afd884a619 | astroid/rebuilder.py | python | TreeRebuilder.visit_lambda | (self, node: "ast.Lambda", parent: NodeNG) | return newnode | visit a Lambda node by returning a fresh instance of it | visit a Lambda node by returning a fresh instance of it | [
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"a",
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"returning",
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"it"
] | def visit_lambda(self, node: "ast.Lambda", parent: NodeNG) -> nodes.Lambda:
"""visit a Lambda node by returning a fresh instance of it"""
if sys.version_info >= (3, 8):
newnode = nodes.Lambda(
lineno=node.lineno,
col_offset=node.col_offset,
end_lineno=node.end_lineno,
end_col_offset=node.end_col_offset,
parent=parent,
)
else:
newnode = nodes.Lambda(node.lineno, node.col_offset, parent)
newnode.postinit(self.visit(node.args, newnode), self.visit(node.body, newnode))
return newnode | [
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|
BerkeleyAutomation/dex-net | cccf93319095374b0eefc24b8b6cd40bc23966d2 | src/dexnet/learning/discrete_selection_policies.py | python | DiscreteSelectionPolicy.choose_next | (self) | Choose the next index of the model to sample | Choose the next index of the model to sample | [
"Choose",
"the",
"next",
"index",
"of",
"the",
"model",
"to",
"sample"
] | def choose_next(self):
"""
Choose the next index of the model to sample
"""
pass | [
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"pass"
] | https://github.com/BerkeleyAutomation/dex-net/blob/cccf93319095374b0eefc24b8b6cd40bc23966d2/src/dexnet/learning/discrete_selection_policies.py#L44-L48 |
||
IntelAI/models | 1d7a53ccfad3e6f0e7378c9e3c8840895d63df8c | models/language_translation/tensorflow/transformer_mlperf/inference/int8/transformer/utils/tokenizer.py | python | _list_to_index_dict | (lst) | return {item: n for n, item in enumerate(lst)} | Create dictionary mapping list items to their indices in the list. | Create dictionary mapping list items to their indices in the list. | [
"Create",
"dictionary",
"mapping",
"list",
"items",
"to",
"their",
"indices",
"in",
"the",
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"."
] | def _list_to_index_dict(lst):
"""Create dictionary mapping list items to their indices in the list."""
return {item: n for n, item in enumerate(lst)} | [
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|
nltk/nltk | 3f74ac55681667d7ef78b664557487145f51eb02 | nltk/parse/projectivedependencyparser.py | python | projective_prob_parse_demo | () | A demo showing the training and use of a projective
dependency parser. | A demo showing the training and use of a projective
dependency parser. | [
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"training",
"and",
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"of",
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"parser",
"."
] | def projective_prob_parse_demo():
"""
A demo showing the training and use of a projective
dependency parser.
"""
from nltk.parse.dependencygraph import conll_data2
graphs = [DependencyGraph(entry) for entry in conll_data2.split("\n\n") if entry]
ppdp = ProbabilisticProjectiveDependencyParser()
print("Training Probabilistic Projective Dependency Parser...")
ppdp.train(graphs)
sent = ["Cathy", "zag", "hen", "wild", "zwaaien", "."]
print("Parsing '", " ".join(sent), "'...")
print("Parse:")
for tree in ppdp.parse(sent):
print(tree) | [
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||
holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/xml/etree/ElementTree.py | python | Element.findtext | (self, path, default=None, namespaces=None) | return ElementPath.findtext(self, path, default, namespaces) | Find text for first matching element by tag name or path.
*path* is a string having either an element tag or an XPath,
*default* is the value to return if the element was not found,
*namespaces* is an optional mapping from namespace prefix to full name.
Return text content of first matching element, or default value if
none was found. Note that if an element is found having no text
content, the empty string is returned. | Find text for first matching element by tag name or path. | [
"Find",
"text",
"for",
"first",
"matching",
"element",
"by",
"tag",
"name",
"or",
"path",
"."
] | def findtext(self, path, default=None, namespaces=None):
"""Find text for first matching element by tag name or path.
*path* is a string having either an element tag or an XPath,
*default* is the value to return if the element was not found,
*namespaces* is an optional mapping from namespace prefix to full name.
Return text content of first matching element, or default value if
none was found. Note that if an element is found having no text
content, the empty string is returned.
"""
return ElementPath.findtext(self, path, default, namespaces) | [
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|
phonopy/phonopy | 816586d0ba8177482ecf40e52f20cbdee2260d51 | phonopy/phonon/modulation.py | python | Modulation._get_phase_factor | (self, modulation, argument) | return phase_factor | [] | def _get_phase_factor(self, modulation, argument):
u = np.ravel(modulation)
index_max_elem = np.argmax(abs(u))
max_elem = u[index_max_elem]
phase_for_zero = max_elem / abs(max_elem)
phase_factor = np.exp(1j * np.pi * argument / 180) / phase_for_zero
return phase_factor | [
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|||
missionpinball/mpf | 8e6b74cff4ba06d2fec9445742559c1068b88582 | mpf/core/placeholder_manager.py | python | TextTemplate.__init__ | (self, machine: "MachineController", text: str) | Initialise placeholder. | Initialise placeholder. | [
"Initialise",
"placeholder",
"."
] | def __init__(self, machine: "MachineController", text: str) -> None:
"""Initialise placeholder."""
self.machine = machine
self.text = str(text)
self._change_callback = None | [
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||
mcneel/rhinoscriptsyntax | c49bd0bf24c2513bdcb84d1bf307144489600fd9 | Scripts/rhinoscript/toolbar.py | python | CloseToolbarCollection | (name, prompt=False) | return False | Closes a currently open toolbar collection
Parameters:
name (str): name of a currently open toolbar collection
prompt (bool, optional): if True, user will be prompted to save the collection file
if it has been modified prior to closing
Returns:
bool: True or False indicating success or failure
Example:
import rhinoscriptsyntax as rs
names = rs.ToolbarCollectionNames()
if names:
for name in names: rs.CloseToolbarCollection( name, True )
See Also:
IsToolbarCollection
OpenToolbarCollection
ToolbarCollectionCount
ToolbarCollectionNames
ToolbarCollectionPath | Closes a currently open toolbar collection
Parameters:
name (str): name of a currently open toolbar collection
prompt (bool, optional): if True, user will be prompted to save the collection file
if it has been modified prior to closing
Returns:
bool: True or False indicating success or failure
Example:
import rhinoscriptsyntax as rs
names = rs.ToolbarCollectionNames()
if names:
for name in names: rs.CloseToolbarCollection( name, True )
See Also:
IsToolbarCollection
OpenToolbarCollection
ToolbarCollectionCount
ToolbarCollectionNames
ToolbarCollectionPath | [
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"OpenToolbarCollection",
"ToolbarCollectionCount",
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] | def CloseToolbarCollection(name, prompt=False):
"""Closes a currently open toolbar collection
Parameters:
name (str): name of a currently open toolbar collection
prompt (bool, optional): if True, user will be prompted to save the collection file
if it has been modified prior to closing
Returns:
bool: True or False indicating success or failure
Example:
import rhinoscriptsyntax as rs
names = rs.ToolbarCollectionNames()
if names:
for name in names: rs.CloseToolbarCollection( name, True )
See Also:
IsToolbarCollection
OpenToolbarCollection
ToolbarCollectionCount
ToolbarCollectionNames
ToolbarCollectionPath
"""
tbfile = Rhino.RhinoApp.ToolbarFiles.FindByName(name, True)
if tbfile: return tbfile.Close(prompt)
return False | [
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|
bendmorris/static-python | 2e0f8c4d7ed5b359dc7d8a75b6fb37e6b6c5c473 | Mac/Tools/bundlebuilder.py | python | BundleBuilder.build | (self) | Build the bundle. | Build the bundle. | [
"Build",
"the",
"bundle",
"."
] | def build(self):
"""Build the bundle."""
builddir = self.builddir
if builddir and not os.path.exists(builddir):
os.mkdir(builddir)
self.message("Building %s" % repr(self.bundlepath), 1)
if os.path.exists(self.bundlepath):
shutil.rmtree(self.bundlepath)
if os.path.exists(self.bundlepath + '~'):
shutil.rmtree(self.bundlepath + '~')
bp = self.bundlepath
# Create the app bundle in a temporary location and then
# rename the completed bundle. This way the Finder will
# never see an incomplete bundle (where it might pick up
# and cache the wrong meta data)
self.bundlepath = bp + '~'
try:
os.mkdir(self.bundlepath)
self.preProcess()
self._copyFiles()
self._addMetaFiles()
self.postProcess()
os.rename(self.bundlepath, bp)
finally:
self.bundlepath = bp
self.message("Done.", 1) | [
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||
fluentpython/example-code | d5133ad6e4a48eac0980d2418ed39d7ff693edbe | 04-text-byte/sanitize.py | python | dewinize | (txt) | return txt.translate(multi_map) | Replace Win1252 symbols with ASCII chars or sequences | Replace Win1252 symbols with ASCII chars or sequences | [
"Replace",
"Win1252",
"symbols",
"with",
"ASCII",
"chars",
"or",
"sequences"
] | def dewinize(txt):
"""Replace Win1252 symbols with ASCII chars or sequences"""
return txt.translate(multi_map) | [
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|
tkipf/pygcn | 1600b5b748b3976413d1e307540ccc62605b4d6d | pygcn/models.py | python | GCN.forward | (self, x, adj) | return F.log_softmax(x, dim=1) | [] | def forward(self, x, adj):
x = F.relu(self.gc1(x, adj))
x = F.dropout(x, self.dropout, training=self.training)
x = self.gc2(x, adj)
return F.log_softmax(x, dim=1) | [
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|||
Chaffelson/nipyapi | d3b186fd701ce308c2812746d98af9120955e810 | nipyapi/nifi/models/remote_process_group_port_dto.py | python | RemoteProcessGroupPortDTO.target_id | (self) | return self._target_id | Gets the target_id of this RemoteProcessGroupPortDTO.
The id of the target port.
:return: The target_id of this RemoteProcessGroupPortDTO.
:rtype: str | Gets the target_id of this RemoteProcessGroupPortDTO.
The id of the target port. | [
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] | def target_id(self):
"""
Gets the target_id of this RemoteProcessGroupPortDTO.
The id of the target port.
:return: The target_id of this RemoteProcessGroupPortDTO.
:rtype: str
"""
return self._target_id | [
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|
rpm-software-management/dnf | a96abad2cde8c46f7f36d7774d9d86f2d94715db | dnf/exceptions.py | python | MarkingErrors.__init__ | (self, no_match_group_specs=(), error_group_specs=(), no_match_pkg_specs=(),
error_pkg_specs=(), module_depsolv_errors=()) | Initialize the marking error instance. | Initialize the marking error instance. | [
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"the",
"marking",
"error",
"instance",
"."
] | def __init__(self, no_match_group_specs=(), error_group_specs=(), no_match_pkg_specs=(),
error_pkg_specs=(), module_depsolv_errors=()):
"""Initialize the marking error instance."""
msg = _("Problems in request:")
if (no_match_pkg_specs):
msg += "\n" + _("missing packages: ") + ", ".join(no_match_pkg_specs)
if (error_pkg_specs):
msg += "\n" + _("broken packages: ") + ", ".join(error_pkg_specs)
if (no_match_group_specs):
msg += "\n" + _("missing groups or modules: ") + ", ".join(no_match_group_specs)
if (error_group_specs):
msg += "\n" + _("broken groups or modules: ") + ", ".join(error_group_specs)
if (module_depsolv_errors):
msg_mod = dnf.util._format_resolve_problems(module_depsolv_errors[0])
if module_depsolv_errors[1] == \
libdnf.module.ModulePackageContainer.ModuleErrorType_ERROR_IN_DEFAULTS:
msg += "\n" + "\n".join([P_('Modular dependency problem with Defaults:',
'Modular dependency problems with Defaults:',
len(module_depsolv_errors)),
msg_mod])
else:
msg += "\n" + "\n".join([P_('Modular dependency problem:',
'Modular dependency problems:',
len(module_depsolv_errors)),
msg_mod])
super(MarkingErrors, self).__init__(msg)
self.no_match_group_specs = no_match_group_specs
self.error_group_specs = error_group_specs
self.no_match_pkg_specs = no_match_pkg_specs
self.error_pkg_specs = error_pkg_specs
self.module_depsolv_errors = module_depsolv_errors | [
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dylanaraps/pywal | 236aa48e741ff8d65c4c3826db2813bf2ee6f352 | pywal/__main__.py | python | parse_args | (parser) | Process args. | Process args. | [
"Process",
"args",
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] | def parse_args(parser):
"""Process args."""
args = parser.parse_args()
if args.q:
logging.getLogger().disabled = True
sys.stdout = sys.stderr = open(os.devnull, "w")
if args.a:
util.Color.alpha_num = args.a
if args.i:
image_file = image.get(args.i, iterative=args.iterative,
recursive=args.recursive)
colors_plain = colors.get(image_file, args.l, args.backend,
sat=args.saturate)
if args.theme:
colors_plain = theme.file(args.theme, args.l)
if args.R:
colors_plain = theme.file(os.path.join(CACHE_DIR, "colors.json"))
if args.w:
cached_wallpaper = util.read_file(os.path.join(CACHE_DIR, "wal"))
colors_plain = colors.get(cached_wallpaper[0], args.l, args.backend,
sat=args.saturate)
if args.b:
args.b = "#%s" % (args.b.strip("#"))
colors_plain["special"]["background"] = args.b
colors_plain["colors"]["color0"] = args.b
if not args.n:
wallpaper.change(colors_plain["wallpaper"])
if args.p:
theme.save(colors_plain, args.p, args.l)
sequences.send(colors_plain, to_send=not args.s, vte_fix=args.vte)
if sys.stdout.isatty():
colors.palette()
export.every(colors_plain)
if not args.e:
reload.env(tty_reload=not args.t)
if args.o:
for cmd in args.o:
util.disown([cmd])
if not args.e:
reload.gtk() | [
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||
JiYou/openstack | 8607dd488bde0905044b303eb6e52bdea6806923 | packages/source/cinder/cinder/volume/drivers/netapp/iscsi.py | python | NetAppISCSIDriver.delete_volume | (self, volume) | Driver entry point for destroying existing volumes. | Driver entry point for destroying existing volumes. | [
"Driver",
"entry",
"point",
"for",
"destroying",
"existing",
"volumes",
"."
] | def delete_volume(self, volume):
"""Driver entry point for destroying existing volumes."""
name = volume['name']
project = volume['project_id']
self._remove_destroy(name, project) | [
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||
nlloyd/SubliminalCollaborator | 5c619e17ddbe8acb9eea8996ec038169ddcd50a1 | libs/twisted/words/protocols/irc.py | python | IRCClient._safeMaximumLineLength | (self, command) | return MAX_COMMAND_LENGTH - len(theoretical) - fudge | Estimate a safe maximum line length for the given command.
This is done by assuming the maximum values for nickname length,
realname and hostname combined with the command that needs to be sent
and some guessing. A theoretical maximum value is used because it is
possible that our nickname, username or hostname changes (on the server
side) while the length is still being calculated. | Estimate a safe maximum line length for the given command. | [
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] | def _safeMaximumLineLength(self, command):
"""
Estimate a safe maximum line length for the given command.
This is done by assuming the maximum values for nickname length,
realname and hostname combined with the command that needs to be sent
and some guessing. A theoretical maximum value is used because it is
possible that our nickname, username or hostname changes (on the server
side) while the length is still being calculated.
"""
# :nickname!realname@hostname COMMAND ...
theoretical = ':%s!%s@%s %s' % (
'a' * self.supported.getFeature('NICKLEN'),
# This value is based on observation.
'b' * 10,
# See <http://tools.ietf.org/html/rfc2812#section-2.3.1>.
'c' * 63,
command)
# Fingers crossed.
fudge = 10
return MAX_COMMAND_LENGTH - len(theoretical) - fudge | [
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|
zzzeek/sqlalchemy | fc5c54fcd4d868c2a4c7ac19668d72f506fe821e | lib/sqlalchemy/engine/base.py | python | Connection.invalidate | (self, exception=None) | Invalidate the underlying DBAPI connection associated with
this :class:`_engine.Connection`.
An attempt will be made to close the underlying DBAPI connection
immediately; however if this operation fails, the error is logged
but not raised. The connection is then discarded whether or not
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:meth:`_engine.Connection.execute` method or similar),
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the DBAPI connection is closed. The :class:`_engine.Connection`
will not allow a reconnection to proceed until the
:class:`.Transaction` object is ended, by calling the
:meth:`.Transaction.rollback` method; until that point, any attempt at
continuing to use the :class:`_engine.Connection` will raise an
:class:`~sqlalchemy.exc.InvalidRequestError`.
This is to prevent applications from accidentally
continuing an ongoing transactional operations despite the
fact that the transaction has been lost due to an
invalidation.
The :meth:`_engine.Connection.invalidate` method,
just like auto-invalidation,
will at the connection pool level invoke the
:meth:`_events.PoolEvents.invalidate` event.
:param exception: an optional ``Exception`` instance that's the
reason for the invalidation. is passed along to event handlers
and logging functions.
.. seealso::
:ref:`pool_connection_invalidation` | Invalidate the underlying DBAPI connection associated with
this :class:`_engine.Connection`. | [
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"""Invalidate the underlying DBAPI connection associated with
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An attempt will be made to close the underlying DBAPI connection
immediately; however if this operation fails, the error is logged
but not raised. The connection is then discarded whether or not
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.. seealso::
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if self.__branch_from:
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return
if self.closed:
raise exc.ResourceClosedError("This Connection is closed")
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self._dbapi_connection.invalidate(exception)
self._dbapi_connection = None | [
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SheffieldML/GPy | bb1bc5088671f9316bc92a46d356734e34c2d5c0 | GPy/util/input_warping_functions.py | python | KumarWarping.__init__ | (self, X, warping_indices=None, epsilon=None, Xmin=None, Xmax=None) | [] | def __init__(self, X, warping_indices=None, epsilon=None, Xmin=None, Xmax=None):
super(KumarWarping, self).__init__(name='input_warp_kumar')
if warping_indices is not None and np.max(warping_indices) > X.shape[1] -1:
raise ValueError("Kumar warping indices exceed feature dimension")
if warping_indices is not None and np.min(warping_indices) < 0:
raise ValueError("Kumar warping indices should be larger than 0")
if warping_indices is not None and np.any(list(map(lambda x: not isinstance(x, int), warping_indices))):
raise ValueError("Kumar warping indices should be integer")
if Xmin is None and Xmax is None:
Xmin = X.min(axis=0)
Xmax = X.max(axis=0)
else:
if Xmin is None or Xmax is None:
raise ValueError("Xmin and Xmax need to be provide at the same time!")
if len(Xmin) != X.shape[1] or len(Xmax) != X.shape[1]:
raise ValueError("Xmin and Xmax should have n_feature values!")
if epsilon is None:
epsilon = 1e-6
self.epsilon = epsilon
self.Xmin = Xmin - self.epsilon
self.Xmax = Xmax + self.epsilon
self.scaling = 1.0 / (self.Xmax - self.Xmin)
self.X_normalized = (X - self.Xmin) / (self.Xmax - self.Xmin)
if warping_indices is None:
warping_indices = range(X.shape[1])
self.warping_indices = warping_indices
self.warping_dim = len(self.warping_indices)
self.num_parameters = 2 * self.warping_dim
# create parameters
self.params = [[Param('a%d' % i, 1.0), Param('b%d' % i, 1.0)] for i in range(self.warping_dim)]
# add constraints
for i in range(self.warping_dim):
self.params[i][0].constrain_bounded(0.0, 10.0)
self.params[i][1].constrain_bounded(0.0, 10.0)
# set priors and add them into handler
for i in range(self.warping_dim):
self.params[i][0].set_prior(LogGaussian(0.0, 0.75))
self.params[i][1].set_prior(LogGaussian(0.0, 0.75))
self.link_parameter(self.params[i][0])
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||||
google/trax | d6cae2067dedd0490b78d831033607357e975015 | trax/rl/space_serializer.py | python | create | (space, vocab_size) | return {
gym.spaces.Box: BoxSpaceSerializer,
gym.spaces.Discrete: DiscreteSpaceSerializer,
gym.spaces.MultiDiscrete: MultiDiscreteSpaceSerializer,
}[type(space)](space, vocab_size) | Creates a SpaceSerializer for the given Gym space. | Creates a SpaceSerializer for the given Gym space. | [
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] | def create(space, vocab_size):
"""Creates a SpaceSerializer for the given Gym space."""
return {
gym.spaces.Box: BoxSpaceSerializer,
gym.spaces.Discrete: DiscreteSpaceSerializer,
gym.spaces.MultiDiscrete: MultiDiscreteSpaceSerializer,
}[type(space)](space, vocab_size) | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/kombu/transport/redis.py | python | _after_fork_cleanup_channel | (channel) | [] | def _after_fork_cleanup_channel(channel):
channel._after_fork() | [
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||||
apache/libcloud | 90971e17bfd7b6bb97b2489986472c531cc8e140 | libcloud/common/upcloud.py | python | UpcloudNodeOperations.destroy_node | (self, node_id) | Destroys the node.
:param node_id: Id of the Node
:type node_id: ``int`` | Destroys the node. | [
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] | def destroy_node(self, node_id):
"""
Destroys the node.
:param node_id: Id of the Node
:type node_id: ``int``
"""
self.connection.request("1.2/server/{0}".format(node_id), method="DELETE") | [
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demisto/content | 5c664a65b992ac8ca90ac3f11b1b2cdf11ee9b07 | Packs/ApiModules/Scripts/MicrosoftApiModule/MicrosoftApiModule.py | python | MicrosoftClient._get_self_deployed_token_auth_code | (
self, refresh_token: str = '', resource: str = '', scope: Optional[str] = None) | return access_token, expires_in, refresh_token | Gets a token by authorizing a self deployed Azure application.
Returns:
tuple: An access token, its expiry and refresh token. | Gets a token by authorizing a self deployed Azure application.
Returns:
tuple: An access token, its expiry and refresh token. | [
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self, refresh_token: str = '', resource: str = '', scope: Optional[str] = None) -> Tuple[str, int, str]:
"""
Gets a token by authorizing a self deployed Azure application.
Returns:
tuple: An access token, its expiry and refresh token.
"""
data = assign_params(
client_id=self.client_id,
client_secret=self.client_secret,
resource=self.resource if not resource else resource,
redirect_uri=self.redirect_uri
)
if scope:
data['scope'] = scope
refresh_token = refresh_token or self._get_refresh_token_from_auth_code_param()
if refresh_token:
data['grant_type'] = REFRESH_TOKEN
data['refresh_token'] = refresh_token
else:
if SESSION_STATE in self.auth_code:
raise ValueError('Malformed auth_code parameter: Please copy the auth code from the redirected uri '
'without any additional info and without the "session_state" query parameter.')
data['grant_type'] = AUTHORIZATION_CODE
data['code'] = self.auth_code
response_json: dict = {}
try:
response = requests.post(self.token_retrieval_url, data, verify=self.verify)
if response.status_code not in {200, 201}:
return_error(f'Error in Microsoft authorization. Status: {response.status_code},'
f' body: {self.error_parser(response)}')
response_json = response.json()
except Exception as e:
return_error(f'Error in Microsoft authorization: {str(e)}')
access_token = response_json.get('access_token', '')
expires_in = int(response_json.get('expires_in', 3595))
refresh_token = response_json.get('refresh_token', '')
return access_token, expires_in, refresh_token | [
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|
krintoxi/NoobSec-Toolkit | 38738541cbc03cedb9a3b3ed13b629f781ad64f6 | NoobSecToolkit /tools/sqli/waf/denyall.py | python | detect | (get_page) | return retval | [] | def detect(get_page):
retval = False
for vector in WAF_ATTACK_VECTORS:
page, headers, code = get_page(get=vector)
retval = re.search(r"\Asessioncookie=", headers.get(HTTP_HEADER.SET_COOKIE, ""), re.I) is not None
retval |= code == 200 and re.search(r"\ACondition Intercepted", page, re.I) is not None
if retval:
break
return retval | [
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|||
s-leger/archipack | 5a6243bf1edf08a6b429661ce291dacb551e5f8a | pygeos/op_buffer.py | python | BufferSubGraph.add | (self, node, nodeStack: list) | * Adds the argument node and all its out edges to the subgraph
* @param node the node to add
* @param nodeStack the current set of nodes being traversed | * Adds the argument node and all its out edges to the subgraph
* | [
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"""
* Adds the argument node and all its out edges to the subgraph
* @param node the node to add
* @param nodeStack the current set of nodes being traversed
"""
node.isVisited = True
self.nodes.append(node)
star = node.star
for de in star.edges:
self._edgeEnds.append(de)
sym = de.sym
symNode = sym.node
"""
* NOTE: this is a depth-first traversal of the graph.
* This will cause a large depth of recursion.
* It might be better to do a breadth-first traversal.
"""
if not symNode.isVisited:
nodeStack.append(symNode) | [
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||
digidotcom/xbee-python | 0757f4be0017530c205175fbee8f9f61be9614d1 | digi/xbee/devices.py | python | XBeeDevice._send_data_64_16 | (self, x64addr, x16addr, data,
transmit_options=TransmitOptions.NONE.value) | return self.send_packet_sync_and_get_response(packet) | Blocking method. This method sends data to the remote XBee with the
given 64-bit/16-bit address.
This method waits for the packet response. The default timeout is
:attr:`.XBeeDevice._DEFAULT_TIMEOUT_SYNC_OPERATIONS`.
Args:
x64addr (:class:`.XBee64BitAddress`): 64-bit address of the
destination XBee.
x16addr (:class:`.XBee16BitAddress`): 16-bit address of the
destination XBee, :attr:`.XBee16BitAddress.UNKNOWN_ADDRESS` if unknown.
data (String or Bytearray): Raw data to send.
transmit_options (Integer, optional): Transmit options, bitfield of
:class:`.TransmitOptions`. Default to `TransmitOptions.NONE.value`.
Returns:
:class:`.XBeePacket`: The response.
Raises:
ValueError: If `x64addr`, `x16addr` or `data` is `None`.
TimeoutException: If response is not received before the read
timeout expires.
InvalidOperatingModeException: If the XBee's operating mode is not
API or ESCAPED API. This method only checks the cached value of
the operating mode.
TransmitException: If the status of the response received is not OK.
XBeeException: If the XBee's communication interface is closed.
.. seealso::
| :class:`.XBee64BitAddress`
| :class:`.XBee16BitAddress`
| :class:`.XBeePacket` | Blocking method. This method sends data to the remote XBee with the
given 64-bit/16-bit address. | [
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transmit_options=TransmitOptions.NONE.value):
"""
Blocking method. This method sends data to the remote XBee with the
given 64-bit/16-bit address.
This method waits for the packet response. The default timeout is
:attr:`.XBeeDevice._DEFAULT_TIMEOUT_SYNC_OPERATIONS`.
Args:
x64addr (:class:`.XBee64BitAddress`): 64-bit address of the
destination XBee.
x16addr (:class:`.XBee16BitAddress`): 16-bit address of the
destination XBee, :attr:`.XBee16BitAddress.UNKNOWN_ADDRESS` if unknown.
data (String or Bytearray): Raw data to send.
transmit_options (Integer, optional): Transmit options, bitfield of
:class:`.TransmitOptions`. Default to `TransmitOptions.NONE.value`.
Returns:
:class:`.XBeePacket`: The response.
Raises:
ValueError: If `x64addr`, `x16addr` or `data` is `None`.
TimeoutException: If response is not received before the read
timeout expires.
InvalidOperatingModeException: If the XBee's operating mode is not
API or ESCAPED API. This method only checks the cached value of
the operating mode.
TransmitException: If the status of the response received is not OK.
XBeeException: If the XBee's communication interface is closed.
.. seealso::
| :class:`.XBee64BitAddress`
| :class:`.XBee16BitAddress`
| :class:`.XBeePacket`
"""
if x64addr is None:
raise ValueError("64-bit address cannot be None")
if x16addr is None:
raise ValueError("16-bit address cannot be None")
if not isinstance(data, (str, bytearray, bytes)):
raise ValueError("Data must be a string or bytearray")
if self.is_remote():
raise OperationNotSupportedException(
message="Cannot send data to a remote device from a remote device")
if isinstance(data, str):
data = data.encode(encoding="utf8", errors="ignore")
packet = TransmitPacket(self.get_next_frame_id(), x64addr, x16addr,
0, transmit_options, rf_data=data)
return self.send_packet_sync_and_get_response(packet) | [
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|
SpaceNetChallenge/SpaceNet_Off_Nadir_Solutions | 014c4ca27a70b5907a183e942228004c989dcbe4 | selim_sef/training/losses.py | python | lovasz_hinge_flat | (logits, labels) | return loss | Binary Lovasz hinge loss
logits: [P] Variable, logits at each prediction (between -\infty and +\infty)
labels: [P] Tensor, binary ground truth labels (0 or 1)
ignore: label to ignore | Binary Lovasz hinge loss
logits: [P] Variable, logits at each prediction (between -\infty and +\infty)
labels: [P] Tensor, binary ground truth labels (0 or 1)
ignore: label to ignore | [
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"""
Binary Lovasz hinge loss
logits: [P] Variable, logits at each prediction (between -\infty and +\infty)
labels: [P] Tensor, binary ground truth labels (0 or 1)
ignore: label to ignore
"""
if len(labels) == 0:
# only void pixels, the gradients should be 0
return logits.sum() * 0.
signs = 2. * labels.float() - 1.
errors = (1. - logits * Variable(signs))
errors_sorted, perm = torch.sort(errors, dim=0, descending=True)
perm = perm.data
gt_sorted = labels[perm]
grad = lovasz_grad(gt_sorted)
loss = torch.dot(F.relu(errors_sorted), Variable(grad))
return loss | [
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|
twisted/twisted | dee676b040dd38b847ea6fb112a712cb5e119490 | src/twisted/conch/insults/insults.py | python | ITerminalTransport.singleWidthLine | () | Make the current line a single-width, single-height line. | Make the current line a single-width, single-height line. | [
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"""
Make the current line a single-width, single-height line.
""" | [
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||
scipy-lectures/scipy-lecture-notes | 7c91eb4afc4b7f0c77bd022e3bd0c33a4c9a1f50 | intro/solutions/path_site.py | python | find_module | (module) | return result | [] | def find_module(module):
result = []
# Loop over the list of paths in sys.path
for subdir in sys.path:
# Join the subdir path with the module we're searching for
pth = os.path.join(subdir, module)
# Use glob to test if the pth is exists
res = glob.glob(pth)
# glob returns a list, if it is not empty, the pth exists
if len(res) > 0:
result.append(res)
return result | [
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|||
microsoft/nni | 31f11f51249660930824e888af0d4e022823285c | nni/algorithms/hpo/metis_tuner/metis_tuner.py | python | MetisTuner.update_search_space | (self, search_space) | Update the self.x_bounds and self.x_types by the search_space.json
Parameters
----------
search_space : dict | Update the self.x_bounds and self.x_types by the search_space.json | [
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"""
Update the self.x_bounds and self.x_types by the search_space.json
Parameters
----------
search_space : dict
"""
validate_search_space(search_space, ['choice', 'randint', 'uniform', 'quniform'])
self.x_bounds = [[] for i in range(len(search_space))]
self.x_types = [NONE_TYPE for i in range(len(search_space))]
for key in search_space:
self.key_order.append(key)
key_type = {}
if isinstance(search_space, dict):
for key in search_space:
key_type = search_space[key]['_type']
key_range = search_space[key]['_value']
idx = self.key_order.index(key)
if key_type == 'quniform':
if key_range[2] == 1 and key_range[0].is_integer(
) and key_range[1].is_integer():
self.x_bounds[idx] = [key_range[0], key_range[1] + 1]
self.x_types[idx] = 'range_int'
else:
low, high, q = key_range
bounds = np.clip(
np.arange(
np.round(
low / q),
np.round(
high / q) + 1) * q,
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high)
self.x_bounds[idx] = bounds
self.x_types[idx] = 'discrete_int'
elif key_type == 'randint':
self.x_bounds[idx] = [key_range[0], key_range[1]]
self.x_types[idx] = 'range_int'
elif key_type == 'uniform':
self.x_bounds[idx] = [key_range[0], key_range[1]]
self.x_types[idx] = 'range_continuous'
elif key_type == 'choice':
self.x_bounds[idx] = key_range
for key_value in key_range:
if not isinstance(key_value, (int, float)):
raise RuntimeError(
"Metis Tuner only support numerical choice.")
self.x_types[idx] = 'discrete_int'
else:
logger.info(
"Metis Tuner doesn't support this kind of variable: %s",
str(key_type))
raise RuntimeError(
"Metis Tuner doesn't support this kind of variable: %s" %
str(key_type))
else:
logger.info("The format of search space is not a dict.")
raise RuntimeError("The format of search space is not a dict.")
self.minimize_starting_points = _rand_init(
self.x_bounds, self.x_types, self.selection_num_starting_points) | [
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||
IronLanguages/ironpython3 | 7a7bb2a872eeab0d1009fc8a6e24dca43f65b693 | Src/StdLib/Lib/multiprocessing/resource_sharer.py | python | _ResourceSharer._start | (self) | [] | def _start(self):
from .connection import Listener
assert self._listener is None
util.debug('starting listener and thread for sending handles')
self._listener = Listener(authkey=process.current_process().authkey)
self._address = self._listener.address
t = threading.Thread(target=self._serve)
t.daemon = True
t.start()
self._thread = t | [
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||||
passiomatic/coldsweat | f48961e21d192b9b19415e9290307314df3820f3 | coldsweat/frontend.py | python | FrontendApp._redirect | (self, klass, location) | return response | Return a temporary or permament redirect response object.
Caller may return it or raise it. | Return a temporary or permament redirect response object.
Caller may return it or raise it. | [
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] | def _redirect(self, klass, location):
'''
Return a temporary or permament redirect response object.
Caller may return it or raise it.
'''
response = klass(location=location)
if self.alert_message:
response.set_cookie('alert_message', self.alert_message)
return response | [
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|
JDAI-CV/fast-reid | 31d99b793fe0937461b9c9bc8a8a11f88bf5642c | fastreid/evaluation/roc.py | python | evaluate_roc | (
distmat,
q_pids,
g_pids,
q_camids,
g_camids,
use_cython=True
) | Evaluates CMC rank.
Args:
distmat (numpy.ndarray): distance matrix of shape (num_query, num_gallery).
q_pids (numpy.ndarray): 1-D array containing person identities
of each query instance.
g_pids (numpy.ndarray): 1-D array containing person identities
of each gallery instance.
q_camids (numpy.ndarray): 1-D array containing camera views under
which each query instance is captured.
g_camids (numpy.ndarray): 1-D array containing camera views under
which each gallery instance is captured.
use_cython (bool, optional): use cython code for evaluation. Default is True.
This is highly recommended as the cython code can speed up the cmc computation
by more than 10x. This requires Cython to be installed. | Evaluates CMC rank.
Args:
distmat (numpy.ndarray): distance matrix of shape (num_query, num_gallery).
q_pids (numpy.ndarray): 1-D array containing person identities
of each query instance.
g_pids (numpy.ndarray): 1-D array containing person identities
of each gallery instance.
q_camids (numpy.ndarray): 1-D array containing camera views under
which each query instance is captured.
g_camids (numpy.ndarray): 1-D array containing camera views under
which each gallery instance is captured.
use_cython (bool, optional): use cython code for evaluation. Default is True.
This is highly recommended as the cython code can speed up the cmc computation
by more than 10x. This requires Cython to be installed. | [
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distmat,
q_pids,
g_pids,
q_camids,
g_camids,
use_cython=True
):
"""Evaluates CMC rank.
Args:
distmat (numpy.ndarray): distance matrix of shape (num_query, num_gallery).
q_pids (numpy.ndarray): 1-D array containing person identities
of each query instance.
g_pids (numpy.ndarray): 1-D array containing person identities
of each gallery instance.
q_camids (numpy.ndarray): 1-D array containing camera views under
which each query instance is captured.
g_camids (numpy.ndarray): 1-D array containing camera views under
which each gallery instance is captured.
use_cython (bool, optional): use cython code for evaluation. Default is True.
This is highly recommended as the cython code can speed up the cmc computation
by more than 10x. This requires Cython to be installed.
"""
if use_cython and IS_CYTHON_AVAI:
return evaluate_roc_cy(distmat, q_pids, g_pids, q_camids, g_camids)
else:
return evaluate_roc_py(distmat, q_pids, g_pids, q_camids, g_camids) | [
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out0fmemory/GoAgent-Always-Available | c4254984fea633ce3d1893fe5901debd9f22c2a9 | server/lib/google/appengine/ext/bulkload/bulkloader_config.py | python | GenericImporter.__reserve_entity_key | (self, entity) | Collect entity key to be reserved if it has a numeric id in its path.
Keys to reserve are stored in self.keys_to_reserve.
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openhatch/oh-mainline | ce29352a034e1223141dcc2f317030bbc3359a51 | vendor/packages/twisted/twisted/conch/ui/ansi.py | python | AnsiParser.parseString | (self, str) | Turn a string input into a list of L{ColorText} elements. | Turn a string input into a list of L{ColorText} elements. | [
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self.writeString(self.formatText(parts[0]))
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/pip/_vendor/requests/packages/urllib3/request.py | python | RequestMethods.request_encode_body | (self, method, url, fields=None, headers=None,
encode_multipart=True, multipart_boundary=None,
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'typedfile': ('bazfile.bin', open('bazfile').read(),
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'nonamefile': 'contents of nonamefile field',
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else:
body, content_type = urlencode(fields), 'application/x-www-form-urlencoded'
extra_kw['body'] = body
extra_kw['headers'] = {'Content-Type': content_type}
extra_kw['headers'].update(headers)
extra_kw.update(urlopen_kw)
return self.urlopen(method, url, **extra_kw) | [
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hakril/PythonForWindows | 61e027a678d5b87aa64fcf8a37a6661a86236589 | windows/alpc.py | python | MessageAttribute._extract_alpc_attributes_values | (self, value) | return [KNOWN_ALPC_ATTRIBUTES_MAPPING[x] for x in attrs] | [] | def _extract_alpc_attributes_values(self, value):
attrs = []
for mask in (1 << i for i in range(64)):
if value & mask:
attrs.append(mask)
return [KNOWN_ALPC_ATTRIBUTES_MAPPING[x] for x in attrs] | [
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nltk/nltk_contrib | c9da2c29777ca9df650740145f1f4a375ccac961 | nltk_contrib/toolbox/text.py | python | Text.set_file | (self, file) | Change file path set upon initialization. | Change file path set upon initialization. | [
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devitocodes/devito | 6abd441e3f5f091775ad332be6b95e017b8cbd16 | devito/types/basic.py | python | AbstractFunction.__shape_setup__ | (cls, **kwargs) | return () | Extract the object shape from ``kwargs``. | Extract the object shape from ``kwargs``. | [
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agoragames/kairos | 0b062d543b0f4a46df460fa0eb6ec281232ab179 | kairos/redis_backend.py | python | RedisBackend.delete | (self, name) | return len(keys) | Delete all the data in a named timeseries. | Delete all the data in a named timeseries. | [
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ddbourgin/numpy-ml | b0359af5285fbf9699d64fd5ec059493228af03e | numpy_ml/neural_nets/modules/modules.py | python | MultiHeadedAttentionModule.gradients | (self) | return {
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"V": self.projections["V"].gradients,
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/jupyter_client/threaded.py | python | IOLoopThread.run | (self) | Run my loop, ignoring EINTR events in the poller | Run my loop, ignoring EINTR events in the poller | [
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] | def run(self):
"""Run my loop, ignoring EINTR events in the poller"""
if 'asyncio' in sys.modules:
# tornado may be using asyncio,
# ensure an eventloop exists for this thread
import asyncio
asyncio.set_event_loop(asyncio.new_event_loop())
self.ioloop = ioloop.IOLoop()
# signal that self.ioloop is defined
self._start_event.set()
while True:
try:
self.ioloop.start()
except ZMQError as e:
if e.errno == errno.EINTR:
continue
else:
raise
except Exception:
if self._exiting:
break
else:
raise
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/Python-2.7.9/Lib/hotshot/__init__.py | python | Profile.stop | (self) | Stop the profiler. | Stop the profiler. | [
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||
bruderstein/PythonScript | df9f7071ddf3a079e3a301b9b53a6dc78cf1208f | PythonLib/min/modulefinder.py | python | ModuleFinder.ensure_fromlist | (self, m, fromlist, recursive=0) | [] | def ensure_fromlist(self, m, fromlist, recursive=0):
self.msg(4, "ensure_fromlist", m, fromlist, recursive)
for sub in fromlist:
if sub == "*":
if not recursive:
all = self.find_all_submodules(m)
if all:
self.ensure_fromlist(m, all, 1)
elif not hasattr(m, sub):
subname = "%s.%s" % (m.__name__, sub)
submod = self.import_module(sub, subname, m)
if not submod:
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||||
OCA/l10n-spain | 99050907670a70307fcd8cdfb6f3400d9e120df4 | l10n_es_aeat_mod115/models/mod115.py | python | L10nEsAeatMod115Report._check_tipo_declaracion | (self) | [] | def _check_tipo_declaracion(self):
for rec in self:
if rec.casilla_05 <= 0.0 and rec.tipo_declaracion != "N":
raise ValidationError(
_(
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)
)
elif rec.casilla_05 > 0.0 and rec.tipo_declaracion == "N":
raise ValidationError(
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||||
sethmlarson/virtualbox-python | 984a6e2cb0e8996f4df40f4444c1528849f1c70d | virtualbox/library.py | python | IUnattended.product_key | (self, value) | return self._set_attr("productKey", value) | [] | def product_key(self, value):
if not isinstance(value, basestring):
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|||
mars-project/mars | 6afd7ed86db77f29cc9470485698ef192ecc6d33 | mars/dataframe/sort/sort_values.py | python | series_sort_values | (
series,
axis=0,
ascending=True,
inplace=False,
kind="quicksort",
na_position="last",
ignore_index=False,
parallel_kind="PSRS",
psrs_kinds=None,
) | Sort by the values.
Sort a Series in ascending or descending order by some
criterion.
Parameters
----------
series : input Series.
axis : {0 or 'index'}, default 0
Axis to direct sorting. The value 'index' is accepted for
compatibility with DataFrame.sort_values.
ascending : bool, default True
If True, sort values in ascending order, otherwise descending.
inplace : bool, default False
If True, perform operation in-place.
kind : {'quicksort', 'mergesort' or 'heapsort'}, default 'quicksort'
Choice of sorting algorithm. See also :func:`numpy.sort` for more
information. 'mergesort' is the only stable algorithm.
na_position : {'first' or 'last'}, default 'last'
Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at
the end.
ignore_index : bool, default False
If True, the resulting axis will be labeled 0, 1, …, n - 1.
Returns
-------
Series
Series ordered by values.
Examples
--------
>>> import mars.dataframe as md
>>> raw = pd.Series([np.nan, 1, 3, 10, 5])
>>> s = md.Series(raw)
>>> s.execute()
0 NaN
1 1.0
2 3.0
3 10.0
4 5.0
dtype: float64
Sort values ascending order (default behaviour)
>>> s.sort_values(ascending=True).execute()
1 1.0
2 3.0
4 5.0
3 10.0
0 NaN
dtype: float64
Sort values descending order
>>> s.sort_values(ascending=False).execute()
3 10.0
4 5.0
2 3.0
1 1.0
0 NaN
dtype: float64
Sort values inplace
>>> s.sort_values(ascending=False, inplace=True)
>>> s.execute()
3 10.0
4 5.0
2 3.0
1 1.0
0 NaN
dtype: float64
Sort values putting NAs first | Sort by the values. | [
"Sort",
"by",
"the",
"values",
"."
] | def series_sort_values(
series,
axis=0,
ascending=True,
inplace=False,
kind="quicksort",
na_position="last",
ignore_index=False,
parallel_kind="PSRS",
psrs_kinds=None,
):
"""
Sort by the values.
Sort a Series in ascending or descending order by some
criterion.
Parameters
----------
series : input Series.
axis : {0 or 'index'}, default 0
Axis to direct sorting. The value 'index' is accepted for
compatibility with DataFrame.sort_values.
ascending : bool, default True
If True, sort values in ascending order, otherwise descending.
inplace : bool, default False
If True, perform operation in-place.
kind : {'quicksort', 'mergesort' or 'heapsort'}, default 'quicksort'
Choice of sorting algorithm. See also :func:`numpy.sort` for more
information. 'mergesort' is the only stable algorithm.
na_position : {'first' or 'last'}, default 'last'
Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at
the end.
ignore_index : bool, default False
If True, the resulting axis will be labeled 0, 1, …, n - 1.
Returns
-------
Series
Series ordered by values.
Examples
--------
>>> import mars.dataframe as md
>>> raw = pd.Series([np.nan, 1, 3, 10, 5])
>>> s = md.Series(raw)
>>> s.execute()
0 NaN
1 1.0
2 3.0
3 10.0
4 5.0
dtype: float64
Sort values ascending order (default behaviour)
>>> s.sort_values(ascending=True).execute()
1 1.0
2 3.0
4 5.0
3 10.0
0 NaN
dtype: float64
Sort values descending order
>>> s.sort_values(ascending=False).execute()
3 10.0
4 5.0
2 3.0
1 1.0
0 NaN
dtype: float64
Sort values inplace
>>> s.sort_values(ascending=False, inplace=True)
>>> s.execute()
3 10.0
4 5.0
2 3.0
1 1.0
0 NaN
dtype: float64
Sort values putting NAs first
"""
if na_position not in ["last", "first"]: # pragma: no cover
raise TypeError(f"invalid na_position: {na_position}")
axis = validate_axis(axis, series)
if axis != 0:
raise NotImplementedError("Only support sort on axis 0")
psrs_kinds = _validate_sort_psrs_kinds(psrs_kinds)
op = DataFrameSortValues(
axis=axis,
ascending=ascending,
inplace=inplace,
kind=kind,
na_position=na_position,
ignore_index=ignore_index,
parallel_kind=parallel_kind,
psrs_kinds=psrs_kinds,
output_types=[OutputType.series],
gpu=series.op.is_gpu(),
)
sorted_series = op(series)
if inplace:
series.data = sorted_series.data
else:
return sorted_series | [
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||
ajinabraham/OWASP-Xenotix-XSS-Exploit-Framework | cb692f527e4e819b6c228187c5702d990a180043 | external/Scripting Engine/Xenotix Python Scripting Engine/bin/x86/Debug/Lib/logging/__init__.py | python | Logger.removeHandler | (self, hdlr) | Remove the specified handler from this logger. | Remove the specified handler from this logger. | [
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"""
Remove the specified handler from this logger.
"""
_acquireLock()
try:
if hdlr in self.handlers:
self.handlers.remove(hdlr)
finally:
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