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google-research/sound-separation | 0b23ae22123b041b9538295f32a92151cb77bff9 | models/neurips2020_mixit/model.py | python | model_fn | (features, labels, mode, params) | return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions,
loss=loss,
eval_metric_ops=metrics,
train_op=train_op,
training_hooks=[logging_hook]) | Constructs a spectrogram_lstm model with summaries.
Args:
features: Dictionary {name: Tensor} of model inputs.
labels: Any training-only inputs.
mode: Build mode, one of tf.estimator.ModeKeys.
params: Dictionary of Model hyperparameters.
Returns:
EstimatorSpec describing the model. | Constructs a spectrogram_lstm model with summaries. | [
"Constructs",
"a",
"spectrogram_lstm",
"model",
"with",
"summaries",
"."
] | def model_fn(features, labels, mode, params):
"""Constructs a spectrogram_lstm model with summaries.
Args:
features: Dictionary {name: Tensor} of model inputs.
labels: Any training-only inputs.
mode: Build mode, one of tf.estimator.ModeKeys.
params: Dictionary of Model hyperparameters.
Returns:
EstimatorSpec describing the model.
"""
del labels
hparams = params['hparams']
mixture_waveforms = features['receiver_audio']
batch_size = signal_util.static_or_dynamic_dim_size(mixture_waveforms, 0)
# Create mixtures of mixtures (MoMs) on-the-fly by splitting batch in half.
if mode == tf.estimator.ModeKeys.TRAIN or mode == tf.estimator.ModeKeys.EVAL:
mixture_waveforms_1mix = mixture_waveforms
# Build MoMs by splitting batch in half.
with tf.control_dependencies([tf.compat.v1.assert_equal(
tf.mod(batch_size, 2), 0)]):
mixture_waveforms = tf.reshape(mixture_waveforms,
(batch_size // 2, 2, -1))
# Create the MoMs by summing up single mixtures.
mix_of_mix_waveforms = tf.reduce_sum(mixture_waveforms, axis=1,
keepdims=True)
else:
# Inference mode, mixture_waveforms is just an input placeholder.
mix_of_mix_waveforms = mixture_waveforms
# In eval mode, separate both MoMs and single mixtures.
if mode == tf.estimator.ModeKeys.EVAL:
input_waveforms = tf.concat([mix_of_mix_waveforms,
mixture_waveforms_1mix], axis=0)
else:
input_waveforms = mix_of_mix_waveforms
# Separate the input waveforms.
separated_waveforms = separate_waveforms(input_waveforms, hparams)
# In eval mode, split into separated from MoMs and from single mixtures.
if mode == tf.estimator.ModeKeys.EVAL:
# Separated sources from single mixtures.
separated_waveforms_1mix = separated_waveforms[batch_size // 2:, :, :]
# Separated sources from MoMs.
separated_waveforms = separated_waveforms[:batch_size // 2, :, :]
predictions = {'separated_waveforms': separated_waveforms}
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
# Get reference sources.
source_waveforms = features['source_images'][:, :, 0]
max_sources = signal_util.static_or_dynamic_dim_size(source_waveforms, 1)
source_waveforms_1mix = tf.concat([source_waveforms,
tf.zeros_like(source_waveforms)], axis=1)
if batch_size > 1:
source_waveforms = tf.reshape(source_waveforms,
(batch_size // 2, 2 * max_sources, -1))
else:
source_waveforms = tf.concat([source_waveforms,
tf.zeros_like(source_waveforms)], axis=1)
# MixIT loss.
loss, _ = mixit.apply(log_mse_loss, mixture_waveforms, separated_waveforms)
loss = tf.identity(tf.reduce_mean(loss), name='loss_mixit')
tf.losses.add_loss(loss)
# Build the optimizer.
loss = tf.losses.get_total_loss()
learning_rate = tf.train.exponential_decay(
hparams.lr,
tf.train.get_or_create_global_step(),
decay_steps=hparams.lr_decay_steps,
decay_rate=hparams.lr_decay_rate)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
if params.get('use_tpu', False):
optimizer = tf.tpu.CrossShardOptimizer(optimizer)
# Build the train_op.
train_op = optimizer.minimize(
loss,
global_step=tf.compat.v1.train.get_or_create_global_step())
# Permute separated to match references for summaries.
unique_signal_types = list(set(hparams.signal_types))
loss_fns = {signal_type: log_mse_loss for signal_type in unique_signal_types}
_, separated_waveforms = groupwise.apply(
loss_fns, hparams.signal_types, source_waveforms, separated_waveforms,
unique_signal_types)
if mode == tf.estimator.ModeKeys.EVAL:
# Also align sources separated from single mixtures.
_, separated_waveforms_1mix = groupwise.apply(
loss_fns, hparams.signal_types, source_waveforms_1mix,
separated_waveforms_1mix, unique_signal_types)
# In eval mode, evaluate separated from single mixtures, instead of from MoMs.
if mode == tf.estimator.ModeKeys.EVAL:
separated_waveforms = separated_waveforms_1mix
source_waveforms = source_waveforms_1mix
mix_of_mix_waveforms = mixture_waveforms_1mix
# Compute spectrograms to be used in summaries.
transformer = signal_transformer.SignalTransformer(
sample_rate=hparams.sr,
window_time_seconds=hparams.ws,
hop_time_seconds=hparams.hs)
source_spectrograms = transformer.forward(source_waveforms)
mixture_spectrograms = transformer.forward(mix_of_mix_waveforms)
separated_spectrograms = transformer.forward(separated_waveforms)
summary_dict = {}
# Audio summaries.
summary_dict['audio'] = summaries.compute_audio_summaries(
signal_names=hparams.signal_names,
separated_waveforms=separated_waveforms,
source_waveforms=source_waveforms,
mixture_waveforms=mix_of_mix_waveforms)
# Spectrogram image summaries.
summary_dict['images'] = summaries.compute_spectrogram_summaries(
signal_names=hparams.signal_names,
separated_spectrograms=separated_spectrograms,
source_spectrograms=source_spectrograms,
mixture_spectrograms=mixture_spectrograms)
scalars = {}
weights = {}
# Only compute scalar summaries for nonzero reference sources.
source_is_nonzero = _weights_for_nonzero_refs(source_waveforms)
# Metrics for single-source examples.
weights_1src = tf.logical_and(
source_is_nonzero,
_weights_for_num_sources(source_waveforms, 1))
scalars_1src, weights_1src = summaries.scalar_snr_metrics_weighted(
hparams.signal_names,
separated_waveforms,
source_waveforms,
mix_of_mix_waveforms,
weights_1src)
scalars.update({name + '_1src_ref_nonzero': value
for name, value in scalars_1src.items()})
weights.update({name + '_1src_ref_nonzero': value
for name, value in weights_1src.items()})
# Metrics for multi-source examples.
max_sources = len(hparams.signal_names)
if max_sources > 1:
weights_multisource = _weights_for_num_sources(source_waveforms, 2)
for num_sources in range(3, max_sources + 1):
weights_multisource = tf.logical_or(
weights_multisource,
_weights_for_num_sources(source_waveforms, num_sources))
weights_multisource = tf.logical_and(source_is_nonzero, weights_multisource)
scalars_msrc, weights_msrc = summaries.scalar_snr_metrics_weighted(
hparams.signal_names,
separated_waveforms,
source_waveforms,
mix_of_mix_waveforms,
weights_multisource)
scalars.update({name + '_min2src_ref_nonzero': value
for name, value in scalars_msrc.items()})
weights.update({name + '_min2src_ref_nonzero': value
for name, value in weights_msrc.items()})
summary_dict['scalars'] = scalars
summary_util.create_summaries(sample_rate=hparams.sr, **summary_dict)
metrics = {name: tf.metrics.mean(s, weights=weights.get(name, None))
for name, s in scalars.items()}
logging_hook = tf.train.LoggingTensorHook({'loss': loss}, every_n_secs=10)
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions,
loss=loss,
eval_metric_ops=metrics,
train_op=train_op,
training_hooks=[logging_hook]) | [
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AutodeskRoboticsLab/Mimic | 85447f0d346be66988303a6a054473d92f1ed6f4 | mimic/scripts/extern/pyqtgraph_0_11_0/pyqtgraph/parametertree/Parameter.py | python | Parameter.clearChildren | (self) | Remove all child parameters. | Remove all child parameters. | [
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"""Remove all child parameters."""
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mlrun/mlrun | 4c120719d64327a34b7ee1ab08fb5e01b258b00a | mlrun/frameworks/tf_keras/mlrun_interface.py | python | TFKerasMLRunInterface.note_rank_0_callback | (self, callback_name: str) | Note an additional custom callback to be applied only on rank 0 when using horovod.
:param callback_name: The name of the callback. | Note an additional custom callback to be applied only on rank 0 when using horovod. | [
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] | def note_rank_0_callback(self, callback_name: str):
"""
Note an additional custom callback to be applied only on rank 0 when using horovod.
:param callback_name: The name of the callback.
"""
self._RANK_0_ONLY_CALLBACKS.append(callback_name) | [
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tensorwerk/hangar-py | a6deb22854a6c9e9709011b91c1c0eeda7f47bb0 | src/hangar/records/commiting.py | python | _commit_ref | (stageenv: lmdb.Environment) | return res | Query and format all staged data records, and format it for ref storage.
Parameters
----------
stageenv : lmdb.Environment
lmdb environment where the staged record data is actually stored.
Returns
-------
DigestAndBytes
Serialized and compressed version of all staged record data along with
digest of commit refs. | Query and format all staged data records, and format it for ref storage. | [
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] | def _commit_ref(stageenv: lmdb.Environment) -> DigestAndBytes:
"""Query and format all staged data records, and format it for ref storage.
Parameters
----------
stageenv : lmdb.Environment
lmdb environment where the staged record data is actually stored.
Returns
-------
DigestAndBytes
Serialized and compressed version of all staged record data along with
digest of commit refs.
"""
from .queries import RecordQuery # needed to avoid cyclic import
querys = RecordQuery(dataenv=stageenv)
allRecords = tuple(querys._traverse_all_records())
res = commit_ref_db_val_from_raw_val(allRecords)
return res | [
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stephenmcd/mezzanine | e38ffc69f732000ce44b7ed5c9d0516d258b8af2 | mezzanine/utils/email.py | python | send_mail_template | (
subject,
template,
addr_from,
addr_to,
context=None,
attachments=None,
fail_silently=None,
addr_bcc=None,
headers=None,
) | Send email rendering text and html versions for the specified
template name using the context dictionary passed in. | Send email rendering text and html versions for the specified
template name using the context dictionary passed in. | [
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subject,
template,
addr_from,
addr_to,
context=None,
attachments=None,
fail_silently=None,
addr_bcc=None,
headers=None,
):
"""
Send email rendering text and html versions for the specified
template name using the context dictionary passed in.
"""
if context is None:
context = {}
if attachments is None:
attachments = []
if fail_silently is None:
fail_silently = settings.EMAIL_FAIL_SILENTLY
# Add template accessible settings from Mezzanine to the context
# (normally added by a context processor for HTTP requests).
context.update(context_settings())
# Allow for a single address to be passed in.
# Python 3 strings have an __iter__ method, so the following hack
# doesn't work: if not hasattr(addr_to, "__iter__"):
if isinstance(addr_to, str) or isinstance(addr_to, bytes):
addr_to = [addr_to]
if addr_bcc is not None and (
isinstance(addr_bcc, str) or isinstance(addr_bcc, bytes)
):
addr_bcc = [addr_bcc]
# Loads a template passing in vars as context.
render = lambda type: loader.get_template(f"{template}.{type}").render(context)
# Create and send email.
msg = EmailMultiAlternatives(
subject, render("txt"), addr_from, addr_to, addr_bcc, headers=headers
)
msg.attach_alternative(render("html"), "text/html")
for attachment in attachments:
msg.attach(*attachment)
msg.send(fail_silently=fail_silently) | [
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Source-Python-Dev-Team/Source.Python | d0ffd8ccbd1e9923c9bc44936f20613c1c76b7fb | addons/source-python/packages/site-packages/mutagen/id3/_frames.py | python | Frame.pprint | (self) | return "%s=%s" % (type(self).__name__, self._pprint()) | Return a human-readable representation of the frame. | Return a human-readable representation of the frame. | [
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] | def pprint(self):
"""Return a human-readable representation of the frame."""
return "%s=%s" % (type(self).__name__, self._pprint()) | [
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wrye-bash/wrye-bash | d495c47cfdb44475befa523438a40c4419cb386f | Mopy/bash/gui/combos.py | python | DoubleListBox.left_items | (self) | return self._left_list.lb_get_str_items() | Returns a list of the items in the left list. | Returns a list of the items in the left list. | [
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"""Returns a list of the items in the left list."""
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zlai0/MAST | a57b043ca597b9b7ef6842b1fa965c9f1ee71526 | models/resnet.py | python | resnet101 | (pretrained=False, progress=True, **kwargs) | return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
**kwargs) | r"""ResNet-101 model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>'_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr | r"""ResNet-101 model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>'_ | [
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r"""ResNet-101 model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>'_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
**kwargs) | [
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pyscf/pyscf | 0adfb464333f5ceee07b664f291d4084801bae64 | pyscf/pbc/tdscf/krhf_slow_supercell.py | python | PhysERI.eri_mknj | (self, item, pairs_row=None, pairs_column=None) | return r / len(self.model.kpts) | Retrieves the merged ERI block using 'mknj' notation with all k-indexes.
Args:
item (str): a 4-character string of 'mknj' letters;
pairs_row (Iterable): iterator for pairs of row k-points (first index in the output matrix);
pairs_column (Iterable): iterator for pairs of column k-points (second index in the output matrix);
Returns:
The corresponding block of ERI (phys notation). | Retrieves the merged ERI block using 'mknj' notation with all k-indexes.
Args:
item (str): a 4-character string of 'mknj' letters;
pairs_row (Iterable): iterator for pairs of row k-points (first index in the output matrix);
pairs_column (Iterable): iterator for pairs of column k-points (second index in the output matrix); | [
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"""
Retrieves the merged ERI block using 'mknj' notation with all k-indexes.
Args:
item (str): a 4-character string of 'mknj' letters;
pairs_row (Iterable): iterator for pairs of row k-points (first index in the output matrix);
pairs_column (Iterable): iterator for pairs of column k-points (second index in the output matrix);
Returns:
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"""
if pairs_row is None:
pairs_row = product(range(len(self.model.kpts)), range(len(self.model.kpts)))
if pairs_column is None:
pairs_column = product(range(len(self.model.kpts)), range(len(self.model.kpts)))
# Second index has to support re-iterations
pairs_column = tuple(pairs_column)
result = []
for k1, k2 in pairs_row:
result.append([])
for k3, k4 in pairs_column:
result[-1].append(self.eri_mknj_k(item, (k1, k2, k3, k4)))
r = numpy.block(result)
return r / len(self.model.kpts) | [
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python-telegram-bot/python-telegram-bot | ade1529986f5b6d394a65372d6a27045a70725b2 | telegram/files/chatphoto.py | python | ChatPhoto.__init__ | (
self,
small_file_id: str,
small_file_unique_id: str,
big_file_id: str,
big_file_unique_id: str,
bot: 'Bot' = None,
**_kwargs: Any,
) | [] | def __init__(
self,
small_file_id: str,
small_file_unique_id: str,
big_file_id: str,
big_file_unique_id: str,
bot: 'Bot' = None,
**_kwargs: Any,
):
self.small_file_id = small_file_id
self.small_file_unique_id = small_file_unique_id
self.big_file_id = big_file_id
self.big_file_unique_id = big_file_unique_id
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/Python-2.7.9/Demo/tkinter/ttk/ttkcalendar.py | python | Calendar.__place_widgets | (self) | [] | def __place_widgets(self):
# header frame and its widgets
hframe = ttk.Frame(self)
lbtn = ttk.Button(hframe, style='L.TButton', command=self._prev_month)
rbtn = ttk.Button(hframe, style='R.TButton', command=self._next_month)
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self._calendar = ttk.Treeview(show='', selectmode='none', height=7)
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czhu95/ternarynet | 1a67251f7f5a1cdf854f87f90f841655c7c9f11c | tensorpack/train/base.py | python | Trainer._start_concurrency | (self) | Run all threads before starting training | Run all threads before starting training | [
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mme/vergeml | 3dc30ba4e0f3d038743b6d468860cbcf3681acc6 | vergeml/config.py | python | _parse_device_memory | (res, section) | Parse the memory option in the device section. | Parse the memory option in the device section. | [
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res['memory'] = value
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raise _invalid_option('device.memory', 'device')
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giantbranch/python-hacker-code | addbc8c73e7e6fb9e4fcadcec022fa1d3da4b96d | 我手敲的代码(中文注释)/chapter11/volatility-2.3/build/lib/volatility/plugins/gui/win32k_core.py | python | _MM_SESSION_SPACE.find_shared_info | (self) | return obj.NoneObject("Cannot find win32k!gSharedInfo") | Find this session's tagSHAREDINFO structure.
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posativ/acrylamid | 222e2eb7b33924138498ff8186dff9f0aeb78cea | acrylamid/views/sitemap.py | python | Sitemap.context | (self, conf, env, data) | return env | If resources are included in sitemap, create a map for each entry and its
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NiaOrg/NiaPy | 08f24ffc79fe324bc9c66ee7186ef98633026005 | niapy/problems/sphere.py | python | Sphere.__init__ | (self, dimension=4, lower=-5.12, upper=5.12, *args, **kwargs) | r"""Initialize Sphere problem..
Args:
dimension (Optional[int]): Dimension of the problem.
lower (Optional[Union[float, Iterable[float]]]): Lower bounds of the problem.
upper (Optional[Union[float, Iterable[float]]]): Upper bounds of the problem.
See Also:
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r"""Initialize Sphere problem..
Args:
dimension (Optional[int]): Dimension of the problem.
lower (Optional[Union[float, Iterable[float]]]): Lower bounds of the problem.
upper (Optional[Union[float, Iterable[float]]]): Upper bounds of the problem.
See Also:
:func:`niapy.problems.Problem.__init__`
"""
super().__init__(dimension, lower, upper, *args, **kwargs) | [
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PlasmaPy/PlasmaPy | 78d63e341216475ce3318e1409296480407c9019 | plasmapy/particles/particle_class.py | python | Particle.element | (self) | return self._attributes["element"] | The atomic symbol if the particle corresponds to an element, and
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>>> alpha = Particle('alpha')
>>> alpha.element
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"""
The atomic symbol if the particle corresponds to an element, and
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>>> alpha = Particle('alpha')
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tacnetsol/ida | faf13a13c4f5070fe8f0666c1508f928ffe40e23 | plugins/shims/ida_shims.py | python | get_canon_feature | (insn) | return idaapi.cmd.get_canon_feature() | Get operands for the provided instruction.
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'''
Get operands for the provided instruction.
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'''
if idaapi.IDA_SDK_VERSION >= 700:
return insn.get_canon_feature()
return idaapi.cmd.get_canon_feature() | [
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mozillazg/pypy | 2ff5cd960c075c991389f842c6d59e71cf0cb7d0 | rpython/jit/backend/arm/assembler.py | python | AssemblerARM._check_frame_depth_debug | (self, mc) | double check the depth size. It prints the error (and potentially
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""" double check the depth size. It prints the error (and potentially
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"""
if not self.DEBUG_FRAME_DEPTH:
return
descrs = self.cpu.gc_ll_descr.getframedescrs(self.cpu)
ofs = self.cpu.unpack_fielddescr(descrs.arraydescr.lendescr)
mc.LDR_ri(r.ip.value, r.fp.value, imm=ofs)
stack_check_cmp_ofs = mc.currpos()
for _ in range(mc.get_max_size_of_gen_load_int()):
mc.NOP()
mc.CMP_rr(r.ip.value, r.lr.value)
jg_location = mc.currpos()
mc.BKPT()
mc.MOV_rr(r.r0.value, r.fp.value)
mc.MOV_ri(r.r1.value, r.lr.value)
self.mc.BL(self.cpu.realloc_frame_crash)
# patch the JG above
currpos = self.mc.currpos()
pmc = OverwritingBuilder(mc, jg_location, WORD)
pmc.B_offs(currpos, c.GE)
self.frame_depth_to_patch.append(stack_check_cmp_ofs) | [
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HonglinChu/SiamTrackers | 8471660b14f970578a43f077b28207d44a27e867 | SiamRPN/SiamRPN/siamrpn/transforms.py | python | RandomStretch.__call__ | (self, sample) | return cv2.resize(sample, shape, cv2.INTER_LINEAR) | Args:
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sample(numpy array): 3 or 1 dim image | [
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scale_w = 1.0 + np.random.uniform(-self.max_stretch, self.max_stretch)
h, w = sample.shape[:2]
shape = int(w * scale_w), int(h * scale_h)
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cbfinn/maml | a7f45f1bcd7457fe97b227a21e89b8a82cc5fa49 | maml.py | python | MAML.construct_conv_weights | (self) | return weights | [] | def construct_conv_weights(self):
weights = {}
dtype = tf.float32
conv_initializer = tf.contrib.layers.xavier_initializer_conv2d(dtype=dtype)
fc_initializer = tf.contrib.layers.xavier_initializer(dtype=dtype)
k = 3
weights['conv1'] = tf.get_variable('conv1', [k, k, self.channels, self.dim_hidden], initializer=conv_initializer, dtype=dtype)
weights['b1'] = tf.Variable(tf.zeros([self.dim_hidden]))
weights['conv2'] = tf.get_variable('conv2', [k, k, self.dim_hidden, self.dim_hidden], initializer=conv_initializer, dtype=dtype)
weights['b2'] = tf.Variable(tf.zeros([self.dim_hidden]))
weights['conv3'] = tf.get_variable('conv3', [k, k, self.dim_hidden, self.dim_hidden], initializer=conv_initializer, dtype=dtype)
weights['b3'] = tf.Variable(tf.zeros([self.dim_hidden]))
weights['conv4'] = tf.get_variable('conv4', [k, k, self.dim_hidden, self.dim_hidden], initializer=conv_initializer, dtype=dtype)
weights['b4'] = tf.Variable(tf.zeros([self.dim_hidden]))
if FLAGS.datasource == 'miniimagenet':
# assumes max pooling
weights['w5'] = tf.get_variable('w5', [self.dim_hidden*5*5, self.dim_output], initializer=fc_initializer)
weights['b5'] = tf.Variable(tf.zeros([self.dim_output]), name='b5')
else:
weights['w5'] = tf.Variable(tf.random_normal([self.dim_hidden, self.dim_output]), name='w5')
weights['b5'] = tf.Variable(tf.zeros([self.dim_output]), name='b5')
return weights | [
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ajinabraham/OWASP-Xenotix-XSS-Exploit-Framework | cb692f527e4e819b6c228187c5702d990a180043 | external/Scripting Engine/packages/IronPython.StdLib.2.7.4/content/Lib/logging/handlers.py | python | MemoryHandler.close | (self) | Flush, set the target to None and lose the buffer. | Flush, set the target to None and lose the buffer. | [
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leancloud/satori | 701caccbd4fe45765001ca60435c0cb499477c03 | satori-rules/plugin/libs/pymongo/pool.py | python | Pool.__del__ | (self) | [] | def __del__(self):
# Avoid ResourceWarnings in Python 3
for sock_info in self.sockets:
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albertz/music-player | d23586f5bf657cbaea8147223be7814d117ae73d | mac/pyobjc-framework-Cocoa/Examples/AppKit/PackageManager/packman.py | python | setString | (field, value) | Set an NSTextField to the specified value. Clears the field if 'value'
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yinwenpeng/Attentive_Convolution | 92461591999cd4594e025d31a223b754cbe167f9 | src/word2embeddings/nn/networks.py | python | SimpleVLblNce._create_adagrad_matrices | (self) | Create AdaGrad gradient matrices for all updatable variables.
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the matrices contain the sum of squared gradients for all parameters in
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Caution: Do not put the matrices into a dict. In that case pickling does
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"""Create AdaGrad gradient matrices for all updatable variables.
New matrices are only created if they don't exist yet, i.e., if a model
was loaded, the already existing matrices are not overwritten.
the matrices contain the sum of squared gradients for all parameters in
the model.
Caution: Do not put the matrices into a dict. In that case pickling does
not work.
"""
for p in self.updatable_parameters:
name = 'adagrad_matrix_' + p
# Have we already created the gradient matrices? This might happen after
# we loaded a model.
if name in self.__dict__:
continue
self.__dict__[name] = \
theano.shared(np.zeros(self.__dict__[p].shape.eval(),
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hatRiot/zarp | 2e772350a01c2aeed3f4da9685cd0cc5d6b3ecad | src/lib/scapy/route.py | python | Route.add | (self, *args, **kargs) | Ex:
add(net="192.168.1.0/24",gw="1.2.3.4") | Ex:
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vslavik/bakefile | 0757295c3e4ac23cd1e0767c77c14c2256ed16e1 | 3rdparty/antlr3/python-runtime/antlr3/streams.py | python | TokenStream.toString | (self, start=None, stop=None) | Return the text of all tokens from start to stop, inclusive.
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kbandla/dpkt | 7a91ae53bb20563607f32e6781ef40d2efe6520d | dpkt/pcap.py | python | Reader.dispatch | (self, cnt, callback, *args) | return processed | Collect and process packets with a user callback.
Return the number of packets processed, or 0 for a savefile.
Arguments:
cnt -- number of packets to process;
or 0 to process all packets until EOF
callback -- function with (timestamp, pkt, *args) prototype
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"""Collect and process packets with a user callback.
Return the number of packets processed, or 0 for a savefile.
Arguments:
cnt -- number of packets to process;
or 0 to process all packets until EOF
callback -- function with (timestamp, pkt, *args) prototype
*args -- optional arguments passed to callback on execution
"""
processed = 0
if cnt > 0:
for _ in range(cnt):
try:
ts, pkt = next(iter(self))
except StopIteration:
break
callback(ts, pkt, *args)
processed += 1
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for ts, pkt in self:
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EricssonResearch/calvin-base | bc4645c2061c30ca305a660e48dc86e3317f5b6f | calvin/runtime/south/transports/base_transport.py | python | BaseTransport.get_coder | (self) | return self._coder | Return the current coder used or none if none set | Return the current coder used or none if none set | [
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/compiler/ast.py | python | UnaryAdd.getChildNodes | (self) | return self.expr, | [] | def getChildNodes(self):
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/django/db/backends/postgresql/client.py | python | DatabaseClient.runshell | (self) | [] | def runshell(self):
DatabaseClient.runshell_db(self.connection.get_connection_params()) | [
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caiiiac/Machine-Learning-with-Python | 1a26c4467da41ca4ebc3d5bd789ea942ef79422f | MachineLearning/venv/lib/python3.5/site-packages/pyparsing.py | python | line | ( loc, strg ) | Returns the line of text containing loc within a string, counting newlines as line separators. | Returns the line of text containing loc within a string, counting newlines as line separators. | [
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"""
lastCR = strg.rfind("\n", 0, loc)
nextCR = strg.find("\n", loc)
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jmcnamara/XlsxWriter | fd30f221bf4326ca7814cec0d3a87a89b9e3edd5 | xlsxwriter/chart_scatter.py | python | ChartScatter.combine | (self, chart=None) | Create a combination chart with a secondary chart.
Note: Override parent method to add a warning.
Args:
chart: The secondary chart to combine with the primary chart.
Returns:
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Create a combination chart with a secondary chart.
Note: Override parent method to add a warning.
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chart: The secondary chart to combine with the primary chart.
Returns:
Nothing.
"""
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warn('Combined chart not currently supported with scatter chart '
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Qirky/FoxDot | 76318f9630bede48ff3994146ed644affa27bfa4 | FoxDot/lib/Utils/__init__.py | python | sliceToRange | (s) | [] | def sliceToRange(s):
start = s.start if s.start is not None else 0
stop = s.stop
step = s.step if s.step is not None else 1
try:
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facebookresearch/detectron2 | cb92ae1763cd7d3777c243f07749574cdaec6cb8 | detectron2/data/common.py | python | AspectRatioGroupedDataset.__init__ | (self, dataset, batch_size) | Args:
dataset: an iterable. Each element must be a dict with keys
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batch_size (int): | Args:
dataset: an iterable. Each element must be a dict with keys
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Ha0Tang/SelectionGAN | 80aa7ad9f79f643c28633c40c621f208f3fb0121 | gaugan_pix2pixhd_guided/data/pix2pix_dataset.py | python | Pix2pixDataset.postprocess | (self, input_dict) | return input_dict | [] | def postprocess(self, input_dict):
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djblets/djblets | 0496e1ec49e43d43d776768c9fc5b6f8af56ec2c | djblets/util/decorators.py | python | blocktag | (*args, **kwargs) | Creates a block template tag with beginning/end tags.
This does all the hard work of creating a template tag that can
parse the arguments passed in and then parse all nodes between a
beginning and end tag (such as myblock/endmyblock).
By default, the end tag is prefixed with "end", but that can be
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resolve_vars (bool, optional):
Whether to automatically resolve all variables provided to the
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can turn this off if they want to handle variable parsing
manually.
Returns:
callable:
The resulting template tag function.
Example:
.. code-block:: python
@register.tag
@blocktag
def divify(context, nodelist, div_id=None):
s = "<div"
if div_id:
s += " id='%s'" % div_id
return s + ">" + nodelist.render(context) + "</div>" | Creates a block template tag with beginning/end tags. | [
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"""Creates a block template tag with beginning/end tags.
This does all the hard work of creating a template tag that can
parse the arguments passed in and then parse all nodes between a
beginning and end tag (such as myblock/endmyblock).
By default, the end tag is prefixed with "end", but that can be
changed by passing ``end_prefix="end_"`` or similar to @blocktag.
blocktag will call the wrapped function with `context` and `nodelist`
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.. code-block:: python
@register.tag
@blocktag
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s = "<div"
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class BlockTagNode(template.Node):
def __init__(self, tag_name, tag_func, nodelist, args):
self.tag_name = tag_name
self.tag_func = tag_func
self.nodelist = nodelist
self.args = args
def render(self, context):
if kwargs.get('resolve_vars', True):
args = [Variable(var).resolve(context) for var in self.args]
else:
args = self.args
return self.tag_func(context, self.nodelist, *args)
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def _setup_tag(parser, token):
bits = token.split_contents()
tag_name = bits[0]
del(bits[0])
params, varargs, xxx, defaults = getargspec(tag_func)
max_args = len(params) - 2 # Ignore context and nodelist
min_args = max_args - len(defaults or [])
if len(bits) < min_args or (not varargs and len(bits) > max_args):
if not varargs and min_args == max_args:
raise TemplateSyntaxError(
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else:
raise TemplateSyntaxError(
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nodelist = parser.parse((('%s%s' % (end_prefix, tag_name)),))
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update_wrapper(_setup_tag, tag_func)
return _setup_tag
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# This is being called in the @blocktag form.
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angr/angr | 4b04d56ace135018083d36d9083805be8146688b | angr/engines/pcode/behavior.py | python | OpBehaviorIntSborrow.evaluate_binary | (self, size_out: int, size_in: int, in1: BV, in2: BV) | return a | [] | def evaluate_binary(self, size_out: int, size_in: int, in1: BV, in2: BV) -> BV:
res = in1 - in2
a = (in1 >> (size_in * 8 - 1)) & 1 # Grab sign bit
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AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/lib/django-1.3/django/db/models/options.py | python | Options.get_all_related_objects_with_model | (self, local_only=False,
include_hidden=False) | return filter(lambda t: all([p(*t) for p in predicates]),
self._related_objects_cache.items()) | Returns a list of (related-object, model) pairs. Similar to
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"""
try:
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self._fill_related_objects_cache()
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... | https://github.com/AppScale/gts/blob/46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9/AppServer/lib/django-1.3/django/db/models/options.py#L362-L378 | |
dropbox/securitybot | 8cc4846602011db396df621d2e84e5808a6f3441 | scripts/query_db.py | python | main | (args) | [] | def main(args):
# type: (Any) -> None
if args.blacklist:
fields, matrix = blacklist(args)
elif args.ignored:
fields, matrix = ignored(args)
else:
fields, matrix = alerts(args)
pretty_print(fields, matrix) | [
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linxid/Machine_Learning_Study_Path | 558e82d13237114bbb8152483977806fc0c222af | Machine Learning In Action/Chapter5-LogisticRegression/venv/Lib/site-packages/pip/index.py | python | PackageFinder._get_pages | (self, locations, project_name) | Yields (page, page_url) from the given locations, skipping
locations that have errors. | Yields (page, page_url) from the given locations, skipping
locations that have errors. | [
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"""
Yields (page, page_url) from the given locations, skipping
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"""
seen = set()
for location in locations:
if location in seen:
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treeio/treeio | bae3115f4015aad2cbc5ab45572232ceec990495 | treeio/projects/views.py | python | _process_mass_form | (f) | return wrap | Pre-process request to handle mass action form for Tasks and Milestones | Pre-process request to handle mass action form for Tasks and Milestones | [
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] | def _process_mass_form(f):
"Pre-process request to handle mass action form for Tasks and Milestones"
def wrap(request, *args, **kwargs):
"Wrap"
if 'massform' in request.POST:
for key in request.POST:
if 'mass-milestone' in key:
try:
milestone = Milestone.objects.get(pk=request.POST[key])
form = MassActionForm(
request.user.profile, request.POST, instance=milestone)
if form.is_valid() and request.user.profile.has_permission(milestone, mode='w'):
form.save()
except Exception:
pass
for key in request.POST:
if 'mass-task' in key:
try:
task = Task.objects.get(pk=request.POST[key])
form = MassActionForm(
request.user.profile, request.POST, instance=task)
if form.is_valid() and request.user.profile.has_permission(task, mode='w'):
form.save()
except Exception:
pass
return f(request, *args, **kwargs)
wrap.__doc__ = f.__doc__
wrap.__name__ = f.__name__
return wrap | [
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yahoo/TensorFlowOnSpark | c2790b797b57acc540414c94909f4f6ec7e3895c | tensorflowonspark/pipeline.py | python | HasBatchSize.getBatchSize | (self) | return self.getOrDefault(self.batch_size) | [] | def getBatchSize(self):
return self.getOrDefault(self.batch_size) | [
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bruderstein/PythonScript | df9f7071ddf3a079e3a301b9b53a6dc78cf1208f | PythonLib/full/xml/sax/saxutils.py | python | prepare_input_source | (source, base="") | return source | This function takes an InputSource and an optional base URL and
returns a fully resolved InputSource object ready for reading. | This function takes an InputSource and an optional base URL and
returns a fully resolved InputSource object ready for reading. | [
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"""This function takes an InputSource and an optional base URL and
returns a fully resolved InputSource object ready for reading."""
if isinstance(source, os.PathLike):
source = os.fspath(source)
if isinstance(source, str):
source = xmlreader.InputSource(source)
elif hasattr(source, "read"):
f = source
source = xmlreader.InputSource()
if isinstance(f.read(0), str):
source.setCharacterStream(f)
else:
source.setByteStream(f)
if hasattr(f, "name") and isinstance(f.name, str):
source.setSystemId(f.name)
if source.getCharacterStream() is None and source.getByteStream() is None:
sysid = source.getSystemId()
basehead = os.path.dirname(os.path.normpath(base))
sysidfilename = os.path.join(basehead, sysid)
if os.path.isfile(sysidfilename):
source.setSystemId(sysidfilename)
f = open(sysidfilename, "rb")
else:
source.setSystemId(urllib.parse.urljoin(base, sysid))
f = urllib.request.urlopen(source.getSystemId())
source.setByteStream(f)
return source | [
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golismero/golismero | 7d605b937e241f51c1ca4f47b20f755eeefb9d76 | thirdparty_libs/nltk/inference/discourse.py | python | DiscourseTester.retract_sentence | (self, sentence, verbose=True) | Remove a sentence from the current discourse.
Updates ``self._input``, ``self._sentences`` and ``self._readings``.
:param sentence: An input sentence
:type sentence: str
:param verbose: If ``True``, report on the updated list of sentences. | Remove a sentence from the current discourse. | [
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"""
Remove a sentence from the current discourse.
Updates ``self._input``, ``self._sentences`` and ``self._readings``.
:param sentence: An input sentence
:type sentence: str
:param verbose: If ``True``, report on the updated list of sentences.
"""
try:
self._input.remove(sentence)
except ValueError:
print "Retraction failed. The sentence '%s' is not part of the current discourse:" % sentence
self.sentences()
return None
self._sentences = dict([('s%s' % i, sent) for i, sent in enumerate(self._input)])
self.readings(verbose=False)
if verbose:
print "Current sentences are "
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aneisch/home-assistant-config | 86e381fde9609cb8871c439c433c12989e4e225d | custom_components/hacs/repositories/theme.py | python | HacsThemeRepository.validate_repository | (self) | return self.validate.success | Validate. | Validate. | [
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] | async def validate_repository(self):
"""Validate."""
# Run common validation steps.
await self.common_validate()
# Custom step 1: Validate content.
compliant = False
for treefile in self.treefiles:
if treefile.startswith("themes/") and treefile.endswith(".yaml"):
compliant = True
break
if not compliant:
raise HacsException(
f"Repository structure for {self.ref.replace('tags/','')} is not compliant"
)
if self.data.content_in_root:
self.content.path.remote = ""
# Handle potential errors
if self.validate.errors:
for error in self.validate.errors:
if not self.hacs.status.startup:
self.logger.error("%s %s", self, error)
return self.validate.success | [
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tanghaibao/jcvi | 5e720870c0928996f8b77a38208106ff0447ccb6 | jcvi/apps/fetch.py | python | bisect | (args) | %prog bisect acc accession.fasta
determine the version of the accession by querying entrez, based on a fasta file.
This proceeds by a sequential search from xxxx.1 to the latest record. | %prog bisect acc accession.fasta | [
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] | def bisect(args):
"""
%prog bisect acc accession.fasta
determine the version of the accession by querying entrez, based on a fasta file.
This proceeds by a sequential search from xxxx.1 to the latest record.
"""
p = OptionParser(bisect.__doc__)
p.set_email()
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
acc, fastafile = args
arec = get_first_rec(fastafile)
valid = None
for i in range(1, 100):
term = "%s.%d" % (acc, i)
try:
query = list(batch_entrez([term], email=opts.email))
except AssertionError as e:
logging.debug(f"no records found for {term}. terminating. {e}")
return
id, term, handle = query[0]
brec = next(SeqIO.parse(handle, "fasta"))
match = print_first_difference(
arec, brec, ignore_case=True, ignore_N=True, rc=True
)
if match:
valid = term
break
if valid:
printf()
printf("[green]{} matches the sequence in `{}`".format(valid, fastafile)) | [
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ales-tsurko/cells | 4cf7e395cd433762bea70cdc863a346f3a6fe1d0 | packaging/macos/python/lib/python3.7/distutils/command/bdist_rpm.py | python | bdist_rpm._format_changelog | (self, changelog) | return new_changelog | Format the changelog correctly and convert it to a list of strings | Format the changelog correctly and convert it to a list of strings | [
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] | def _format_changelog(self, changelog):
"""Format the changelog correctly and convert it to a list of strings
"""
if not changelog:
return changelog
new_changelog = []
for line in changelog.strip().split('\n'):
line = line.strip()
if line[0] == '*':
new_changelog.extend(['', line])
elif line[0] == '-':
new_changelog.append(line)
else:
new_changelog.append(' ' + line)
# strip trailing newline inserted by first changelog entry
if not new_changelog[0]:
del new_changelog[0]
return new_changelog | [
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dmlc/tensorboard | d36e8e921cdd5306c7e2535adbc0fe45be47ceed | python/tensorboard/event_file_writer.py | python | EventFileWriter.close | (self) | Flushes the event file to disk and close the file.
Call this method when you do not need the summary writer anymore. | Flushes the event file to disk and close the file.
Call this method when you do not need the summary writer anymore. | [
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"""Flushes the event file to disk and close the file.
Call this method when you do not need the summary writer anymore.
"""
self.flush()
self._ev_writer.close()
self._closed = True | [
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joblib/joblib | 7742f5882273889f7aaf1d483a8a1c72a97d57e3 | joblib/_parallel_backends.py | python | ParallelBackendBase.terminate | (self) | Shutdown the workers and free the shared memory. | Shutdown the workers and free the shared memory. | [
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"""Shutdown the workers and free the shared memory.""" | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/rings/qqbar.py | python | an_binop_expr | (a, b, op) | return ANBinaryExpr(a, b, op) | r"""
Add, subtract, multiply or divide algebraic numbers represented as
binary expressions.
INPUT:
- ``a``, ``b`` -- two elements
- ``op`` -- an operator
EXAMPLES::
sage: a = QQbar(sqrt(2)) + QQbar(sqrt(3))
sage: b = QQbar(sqrt(3)) + QQbar(sqrt(5))
sage: type(a._descr); type(b._descr)
<class 'sage.rings.qqbar.ANBinaryExpr'>
<class 'sage.rings.qqbar.ANBinaryExpr'>
sage: from sage.rings.qqbar import an_binop_expr
sage: x = an_binop_expr(a, b, operator.add); x
<sage.rings.qqbar.ANBinaryExpr object at ...>
sage: x.exactify()
-6/7*a^7 + 2/7*a^6 + 71/7*a^5 - 26/7*a^4 - 125/7*a^3 + 72/7*a^2 + 43/7*a - 47/7 where a^8 - 12*a^6 + 23*a^4 - 12*a^2 + 1 = 0 and a in 3.12580...?
sage: a = QQbar(sqrt(2)) + QQbar(sqrt(3))
sage: b = QQbar(sqrt(3)) + QQbar(sqrt(5))
sage: type(a._descr)
<class 'sage.rings.qqbar.ANBinaryExpr'>
sage: x = an_binop_expr(a, b, operator.mul); x
<sage.rings.qqbar.ANBinaryExpr object at ...>
sage: x.exactify()
2*a^7 - a^6 - 24*a^5 + 12*a^4 + 46*a^3 - 22*a^2 - 22*a + 9 where a^8 - 12*a^6 + 23*a^4 - 12*a^2 + 1 = 0 and a in 3.1258...? | r"""
Add, subtract, multiply or divide algebraic numbers represented as
binary expressions. | [
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r"""
Add, subtract, multiply or divide algebraic numbers represented as
binary expressions.
INPUT:
- ``a``, ``b`` -- two elements
- ``op`` -- an operator
EXAMPLES::
sage: a = QQbar(sqrt(2)) + QQbar(sqrt(3))
sage: b = QQbar(sqrt(3)) + QQbar(sqrt(5))
sage: type(a._descr); type(b._descr)
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<class 'sage.rings.qqbar.ANBinaryExpr'>
sage: from sage.rings.qqbar import an_binop_expr
sage: x = an_binop_expr(a, b, operator.add); x
<sage.rings.qqbar.ANBinaryExpr object at ...>
sage: x.exactify()
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sage: a = QQbar(sqrt(2)) + QQbar(sqrt(3))
sage: b = QQbar(sqrt(3)) + QQbar(sqrt(5))
sage: type(a._descr)
<class 'sage.rings.qqbar.ANBinaryExpr'>
sage: x = an_binop_expr(a, b, operator.mul); x
<sage.rings.qqbar.ANBinaryExpr object at ...>
sage: x.exactify()
2*a^7 - a^6 - 24*a^5 + 12*a^4 + 46*a^3 - 22*a^2 - 22*a + 9 where a^8 - 12*a^6 + 23*a^4 - 12*a^2 + 1 = 0 and a in 3.1258...?
"""
return ANBinaryExpr(a, b, op) | [
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demisto/content | 5c664a65b992ac8ca90ac3f11b1b2cdf11ee9b07 | Packs/TrendMicroDeepSecurity/Integrations/TrendMicroDeepSecurity/TrendMicroDeepSecurity.py | python | Client.reset_computer_setting | (self, computer_id: int, setting_name: str, overrides: bool) | return self._http_request(method="DELETE", url_suffix=f"/computers/{computer_id}/settings/{setting_name}",
params={"overrides": overrides}) | Reset the setting of an existing computer inside Trend Micro.
Args:
computer_id (int): The computer id to obtain.
setting_name (str): The name of the computer's setting.
overrides (bool): Whether to get the overridden properties or not.
Returns:
Dict[str, Any]: The information about computer's setting. | Reset the setting of an existing computer inside Trend Micro. | [
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] | def reset_computer_setting(self, computer_id: int, setting_name: str, overrides: bool) -> Dict[str, Any]:
"""
Reset the setting of an existing computer inside Trend Micro.
Args:
computer_id (int): The computer id to obtain.
setting_name (str): The name of the computer's setting.
overrides (bool): Whether to get the overridden properties or not.
Returns:
Dict[str, Any]: The information about computer's setting.
"""
return self._http_request(method="DELETE", url_suffix=f"/computers/{computer_id}/settings/{setting_name}",
params={"overrides": overrides}) | [
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abrignoni/iLEAPP | 5376dc82b7fd0a27896088167b5b0b250a39a8be | scripts/ktx/ios_ktx2png.py | python | KTX_reader.get_uncompressed_texture_data | (self, f) | return b'' | Just read the texture data which is lzfse compressed, uncompress and return it.
Exceptions raised are ValueError or liblzfse.error | Just read the texture data which is lzfse compressed, uncompress and return it.
Exceptions raised are ValueError or liblzfse.error | [
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'''Just read the texture data which is lzfse compressed, uncompress and return it.
Exceptions raised are ValueError or liblzfse.error
'''
if self.glInternalFormat == 0x93B0:
if self.is_aapl_file:
f.seek(self.aapl_data_pos)
data = f.read(self.aapl_data_size)
if self.aapl_is_compressed:
decompressed = liblzfse.decompress(data)
return decompressed
else:
return data
else:
f.seek(0x40)
k_v_data = f.read(self.bytesOfKeyValueData)
compressed = True if k_v_data.find(b'Compression_APPLE') >= 0 else False
f.seek(0x40 + self.bytesOfKeyValueData)
data = f.read()
if compressed:
if data[12:15] == b'bvx':
decompressed = liblzfse.decompress(data[12:])
return decompressed
else:
raise ValueError('Unsupported compression, not lzfse!')
else:
return data[4:] # first 4 bytes is size (which is practically rest of file)
else:
raise ValueError('Unsupported Format')
return b'' | [
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Nuitka/Nuitka | 39262276993757fa4e299f497654065600453fc9 | nuitka/build/inline_copy/lib/scons-2.3.2/SCons/Taskmaster.py | python | Task.exc_clear | (self) | Clears any recorded exception.
This also changes the "exception_raise" attribute to point
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"""
Clears any recorded exception.
This also changes the "exception_raise" attribute to point
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"""
self.exception = (None, None, None)
self.exception_raise = self._no_exception_to_raise | [
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grow/grow | 97fc21730b6a674d5d33948d94968e79447ce433 | grow/translations/locales.py | python | Locales.to_message | (self) | return message | [] | def to_message(self):
message = messages.LocalesMessage()
message.groups = []
for group_name in self.list_groups():
group_message = messages.LocaleGroupMessage()
group_message.group_name = group_name
group_message.regions = self.get_regions(group_name)
group_message.languages = self.get_languages(group_name)
message.groups.append(group_message)
return message | [
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viewfinderco/viewfinder | 453845b5d64ab5b3b826c08b02546d1ca0a07c14 | marketing/tornado/web.py | python | RequestHandler.set_header | (self, name, value) | Sets the given response header name and value.
If a datetime is given, we automatically format it according to the
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"""Sets the given response header name and value.
If a datetime is given, we automatically format it according to the
HTTP specification. If the value is not a string, we convert it to
a string. All header values are then encoded as UTF-8.
"""
self._headers[name] = self._convert_header_value(value) | [
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Autodesk/molecular-design-toolkit | 5f45a47fea21d3603899a6366cb163024f0e2ec4 | moldesign/utils/utils.py | python | if_not_none | (item, default) | Equivalent to `item if item is not None else default` | Equivalent to `item if item is not None else default` | [
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""" Equivalent to `item if item is not None else default` """
if item is None:
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return item | [
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vcheckzen/FODI | 3bb23644938a33c3fdfb9611a622e35ed4ce6532 | back-end-py/main/3rd/PIL/ImageFile.py | python | PyDecoder.setimage | (self, im, extents=None) | Called from ImageFile to set the core output image for the decoder
:param im: A core image object
:param extents: a 4 tuple of (x0, y0, x1, y1) defining the rectangle
for this tile
:returns: None | Called from ImageFile to set the core output image for the decoder | [
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"""
Called from ImageFile to set the core output image for the decoder
:param im: A core image object
:param extents: a 4 tuple of (x0, y0, x1, y1) defining the rectangle
for this tile
:returns: None
"""
# following c code
self.im = im
if extents:
(x0, y0, x1, y1) = extents
else:
(x0, y0, x1, y1) = (0, 0, 0, 0)
if x0 == 0 and x1 == 0:
self.state.xsize, self.state.ysize = self.im.size
else:
self.state.xoff = x0
self.state.yoff = y0
self.state.xsize = x1 - x0
self.state.ysize = y1 - y0
if self.state.xsize <= 0 or self.state.ysize <= 0:
raise ValueError("Size cannot be negative")
if (
self.state.xsize + self.state.xoff > self.im.size[0]
or self.state.ysize + self.state.yoff > self.im.size[1]
):
raise ValueError("Tile cannot extend outside image") | [
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optuna/optuna | 2c44c1a405ba059efd53f4b9c8e849d20fb95c0a | optuna/trial/_frozen.py | python | FrozenTrial.datetime_start | (self) | return self._datetime_start | [] | def datetime_start(self) -> Optional[datetime.datetime]:
return self._datetime_start | [
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jbjorne/TEES | caf19a4a1352ac59f5dc13a8684cc42ce4342d9d | ExampleBuilders/EdgeExampleBuilder.py | python | EdgeExampleBuilder.buildExamplesFromGraph | (self, sentenceGraph, outfile, goldGraph = None, structureAnalyzer=None) | return exampleIndex | Build examples for a single sentence. Returns a list of examples.
See Core/ExampleUtils for example format. | Build examples for a single sentence. Returns a list of examples.
See Core/ExampleUtils for example format. | [
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] | def buildExamplesFromGraph(self, sentenceGraph, outfile, goldGraph = None, structureAnalyzer=None):
"""
Build examples for a single sentence. Returns a list of examples.
See Core/ExampleUtils for example format.
"""
#examples = []
exampleIndex = 0
# example directionality
if self.styles["directed"] == None and self.styles["undirected"] == None: # determine directedness from corpus
examplesAreDirected = structureAnalyzer.hasDirectedTargets() if structureAnalyzer != None else True
elif self.styles["directed"]:
assert self.styles["undirected"] in [None, False]
examplesAreDirected = True
elif self.styles["undirected"]:
assert self.styles["directed"] in [None, False]
examplesAreDirected = False
if not self.styles["no_trigger_features"]:
self.triggerFeatureBuilder.initSentence(sentenceGraph)
if self.styles["evex"]:
self.evexFeatureBuilder.initSentence(sentenceGraph)
# if self.styles["sdb_merge"]:
# self.determineNonOverlappingTypes(structureAnalyzer)
# Filter entities, if needed
sentenceGraph.mergeInteractionGraph(True)
entities = sentenceGraph.mergedEntities
entityToDuplicates = sentenceGraph.mergedEntityToDuplicates
self.exampleStats.addValue("Duplicate entities skipped", len(sentenceGraph.entities) - len(entities))
# Connect to optional gold graph
entityToGold = None
if goldGraph != None:
entityToGold = EvaluateInteractionXML.mapEntities(entities, goldGraph.entities)
paths = None
if not self.styles["no_path"]:
undirected = sentenceGraph.dependencyGraph.toUndirected()
paths = undirected
if self.styles["filter_shortest_path"] != None: # For DDI use filter_shortest_path=conj_and
paths.resetAnalyses() # just in case
paths.FloydWarshall(self.filterEdge, {"edgeTypes":self.styles["filter_shortest_path"]})
# Generate examples based on interactions between entities or interactions between tokens
if self.styles["token_nodes"]:
loopRange = len(sentenceGraph.tokens)
else:
loopRange = len(entities)
for i in range(loopRange-1):
for j in range(i+1,loopRange):
eI = None
eJ = None
if self.styles["token_nodes"]:
tI = sentenceGraph.tokens[i]
tJ = sentenceGraph.tokens[j]
else:
eI = entities[i]
eJ = entities[j]
tI = sentenceGraph.entityHeadTokenByEntity[eI]
tJ = sentenceGraph.entityHeadTokenByEntity[eJ]
if eI.get("type") == "neg" or eJ.get("type") == "neg":
continue
if self.styles["skip_extra_triggers"]:
if eI.get("source") != None or eJ.get("source") != None:
continue
# only consider paths between entities (NOTE! entities, not only named entities)
if self.styles["headsOnly"]:
if (len(sentenceGraph.tokenIsEntityHead[tI]) == 0) or (len(sentenceGraph.tokenIsEntityHead[tJ]) == 0):
continue
examples = self.buildExamplesForPair(tI, tJ, paths, sentenceGraph, goldGraph, entityToGold, eI, eJ, structureAnalyzer, examplesAreDirected)
for categoryName, features, extra in examples:
# make example
if self.styles["binary"]:
if categoryName != "neg":
category = 1
else:
category = -1
extra["categoryName"] = "i"
else:
category = self.classSet.getId(categoryName)
example = [sentenceGraph.getSentenceId()+".x"+str(exampleIndex), category, features, extra]
ExampleUtils.appendExamples([example], outfile)
exampleIndex += 1
return exampleIndex | [
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MozillaSecurity/dharma | 6b1e5119646064a80122ca18944a98238d5eadb1 | dharma/core/websocket.py | python | BaseWebSocketHandler.should_close | (self) | return False | When this returns true, the message loop will exit. | When this returns true, the message loop will exit. | [
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"""When this returns true, the message loop will exit."""
return False | [
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akanazawa/human_dynamics | 0887f37464c9a079ad7d69c8358cecd0f43c4f2a | src/ops.py | python | call_hmr_ief | (phi, omega_start, scope, num_output=85, num_stage=3,
is_training=True, predict_delta_keys=(),
use_delta_from_pred=False, use_optcam=False) | return theta_here, deltas_predictions | Wrapper for doing HMR-style IEF.
If predict_delta, then also makes num_delta_t predictions forward and
backward in time, with each step of delta_t.
Args:
phi (Bx2048): Image features.
omega_start (Bx85): Starting Omega as input to first IEF.
scope (str): Name of scope for reuse.
num_output (int): Size of output.
num_stage (int): Number of iterations for IEF.
is_training (bool): If False, don't apply dropout.
predict_delta_keys (iterable): List of keys for delta_t.
use_delta_from_pred (bool): If True, initializes delta prediction from
current frame prediction.
use_optcam (bool): If True, only outputs 82 and uses [1, 0, 0] as cam.
Returns:
Final theta (Bx{num_output})
Deltas predictions (List of outputs) | Wrapper for doing HMR-style IEF. | [
"Wrapper",
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"doing",
"HMR",
"-",
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"."
] | def call_hmr_ief(phi, omega_start, scope, num_output=85, num_stage=3,
is_training=True, predict_delta_keys=(),
use_delta_from_pred=False, use_optcam=False):
"""
Wrapper for doing HMR-style IEF.
If predict_delta, then also makes num_delta_t predictions forward and
backward in time, with each step of delta_t.
Args:
phi (Bx2048): Image features.
omega_start (Bx85): Starting Omega as input to first IEF.
scope (str): Name of scope for reuse.
num_output (int): Size of output.
num_stage (int): Number of iterations for IEF.
is_training (bool): If False, don't apply dropout.
predict_delta_keys (iterable): List of keys for delta_t.
use_delta_from_pred (bool): If True, initializes delta prediction from
current frame prediction.
use_optcam (bool): If True, only outputs 82 and uses [1, 0, 0] as cam.
Returns:
Final theta (Bx{num_output})
Deltas predictions (List of outputs)
"""
theta_here = hmr_ief(
phi=phi,
omega_start=omega_start,
scope=scope,
num_output=num_output,
num_stage=num_stage,
is_training=is_training,
)
# Delta only needs to do cam/pose, no shape!
if use_optcam:
num_output_delta = 72
else:
num_output_delta = 3 + 72
deltas_predictions = {}
for delta_t in predict_delta_keys:
if delta_t == 0:
# This should just be the normal IEF.
continue
elif delta_t > 0:
scope_delta = scope + '_future{}'.format(delta_t)
elif delta_t < 0:
scope_delta = scope + '_past{}'.format(abs(delta_t))
omega_start_delta = theta_here if use_delta_from_pred else omega_start
# append this later.
beta = omega_start_delta[:, -10:]
if use_optcam:
# trim the first 3D camera + last shpae
# DEBUG!
# just pose = 3:3+72
omega_start_delta = omega_start_delta[:, 3:3 + num_output_delta]
else:
# Uncomment this to be backwards compatible
# Drop the shape.
# just cam + pose = 3:3+72
omega_start_delta = omega_start_delta[:, :num_output_delta]
delta_pred = hmr_ief(
phi=phi,
omega_start=omega_start_delta,
scope=scope_delta,
num_output=num_output_delta,
num_stage=num_stage,
is_training=is_training
)
if use_optcam:
# Add camera + shape
scale = tf.ones([delta_pred.shape[0], 1])
trans = tf.zeros([delta_pred.shape[0], 2])
delta_pred = tf.concat([scale, trans, delta_pred, beta], 1)
else:
delta_pred = tf.concat([delta_pred[:, :75], beta], 1)
deltas_predictions[delta_t] = delta_pred
return theta_here, deltas_predictions | [
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pyg-team/pytorch_geometric | b920e9a3a64e22c8356be55301c88444ff051cae | torch_geometric/transforms/radius_graph.py | python | RadiusGraph.__repr__ | (self) | return f'{self.__class__.__name__}(r={self.r})' | [] | def __repr__(self) -> str:
return f'{self.__class__.__name__}(r={self.r})' | [
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/tuya/scene.py | python | TuyaSceneEntity.name | (self) | return self.scene.name | Return Tuya scene name. | Return Tuya scene name. | [
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] | def name(self) -> str | None:
"""Return Tuya scene name."""
return self.scene.name | [
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Calysto/calysto_scheme | 15bf81987870bcae1264e5a0a06feb9a8ee12b8b | calysto_scheme/scheme.py | python | aparse_sexps | () | [] | def aparse_sexps():
if (False if ((token_type_q(first(tokens_reg), symbol_end_marker)) is False) else True):
GLOBALS['value2_reg'] = fail_reg
GLOBALS['value1_reg'] = symbol_emptylist
GLOBALS['pc'] = apply_cont2
else:
GLOBALS['k_reg'] = make_cont4(b_cont4_9_d, senv_reg, src_reg, handler_reg, k_reg)
GLOBALS['pc'] = read_sexp | [
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tensorflow/datasets | 2e496976d7d45550508395fb2f35cf958c8a3414 | tensorflow_datasets/audio/spoken_digit/spoken_digit.py | python | SpokenDigit._split_generators | (self, dl_manager) | return [
tfds.core.SplitGenerator(
name=tfds.Split.TRAIN, gen_kwargs={"path": path})
] | Returns Split Generators. | Returns Split Generators. | [
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] | def _split_generators(self, dl_manager):
"""Returns Split Generators."""
dl_path = dl_manager.download_and_extract(_DOWNLOAD_URL)
extracted_dir_path = os.path.join(dl_path,
"free-spoken-digit-dataset-1.0.9")
path = os.path.join(extracted_dir_path, "recordings")
# There is no predefined train/val/test split for this dataset.
return [
tfds.core.SplitGenerator(
name=tfds.Split.TRAIN, gen_kwargs={"path": path})
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pyppeteer/pyppeteer | d55b6f46a0d1315ca176a74a1d358bbbbec7e825 | pyppeteer/page.py | python | Page.title | (self) | return await frame.title() | Get page's title. | Get page's title. | [
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FutunnOpen/py-futu-api | b8f9fb1f26f35f99630ca47863f5595b6e635533 | futu/common/sys_config.py | python | SysConfig.get_init_rsa_obj | (cls) | return SysConfig.RSA_OBJ | :return: str , private key for init connect protocol | :return: str , private key for init connect protocol | [
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eirannejad/pyRevit | 49c0b7eb54eb343458ce1365425e6552d0c47d44 | site-packages/werkzeug/serving.py | python | WSGIRequestHandler.connection_dropped | (self, error, environ=None) | Called if the connection was closed by the client. By default
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xiaoxu193/PyTeaser | b8792b1efe1a8642d24db84816e5281253ab2a92 | goose/text.py | python | encodeValue | (value) | return value | [] | def encodeValue(value):
string_org = value
try:
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CouchPotato/CouchPotatoV1 | 135b3331d1b88ef645e29b76f2d4cc4a732c9232 | cherrypy/wsgiserver/ssl_pyopenssl.py | python | pyOpenSSLAdapter.get_context | (self) | return c | Return an SSL.Context from self attributes. | Return an SSL.Context from self attributes. | [
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IronLanguages/ironpython2 | 51fdedeeda15727717fb8268a805f71b06c0b9f1 | Src/StdLib/Lib/email/header.py | python | Header.__unicode__ | (self) | return UEMPTYSTRING.join(uchunks) | Helper for the built-in unicode function. | Helper for the built-in unicode function. | [
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spro/pytorch-seq2seq-intent-parsing | a6b89459ce83438d1105db123dfdded105d68797 | data.py | python | GloVeLang.__str__ | (self) | return "%s(size = %d)" % (self.__class__.__name__, self.size) | [] | def __str__(self):
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/tasmota/light.py | python | TasmotaLight.state_updated | (self, state: bool, **kwargs: Any) | Handle state updates. | Handle state updates. | [
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abhi2610/ohem | 1f07dd09b50c8c21716ae36aede92125fe437579 | lib/transform/torch_image_transform_layer.py | python | TorchImageTransformLayer.reshape | (self, bottom, top) | Reshaping happens during the call to forward. | Reshaping happens during the call to forward. | [
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alanhamlett/pip-update-requirements | ce875601ef278c8ce00ad586434a978731525561 | pur/packages/pip/_vendor/pyparsing.py | python | matchOnlyAtCol | (n) | return verifyCol | Helper method for defining parse actions that require matching at
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oracle/oci-python-sdk | 3c1604e4e212008fb6718e2f68cdb5ef71fd5793 | src/oci/regions.py | python | endpoint_for | (service, region=None, endpoint=None, service_endpoint_template=None, endpoint_service_name=None) | return _endpoint_for(service, region, service_endpoint_template, endpoint_service_name) | Returns the base URl for a service, either in the given region or at the specified endpoint.
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"regionIdentifier" : string
}
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/tiw/v20190919/models.py | python | DescribeOnlineRecordResponse.__init__ | (self) | r"""
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:param RecordStopTime: 实际停止录制时间，Unix 时间戳，单位秒
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:param ExceptionCnt: 录制过程中出现异常的次数
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:type RequestId: str | r"""
:param FinishReason: 录制结束原因，
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- STOPPED: 表示录制已停止，正在处理并上传视频
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:type Status: str
:param RoomId: 房间号
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:type GroupId: str
:param RecordUserId: 录制用户Id
:type RecordUserId: str
:param RecordStartTime: 实际开始录制时间，Unix 时间戳，单位秒
:type RecordStartTime: int
:param RecordStopTime: 实际停止录制时间，Unix 时间戳，单位秒
:type RecordStopTime: int
:param TotalTime: 回放视频总时长（单位：毫秒）
:type TotalTime: int
:param ExceptionCnt: 录制过程中出现异常的次数
:type ExceptionCnt: int
:param OmittedDurations: 拼接视频中被忽略的时间段，只有开启视频拼接功能的时候，这个参数才是有效的
:type OmittedDurations: list of OmittedDuration
:param VideoInfos: 录制视频列表
:type VideoInfos: list of VideoInfo
:param ReplayUrl: 回放URL，需配合信令播放器使用。此字段仅适用于`视频生成模式`
注意：此字段可能返回 null，表示取不到有效值。
:type ReplayUrl: str
:param Interrupts: 视频流在录制过程中断流次数
注意：此字段可能返回 null，表示取不到有效值。
:type Interrupts: list of Interrupt
:param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。
:type RequestId: str
"""
self.FinishReason = None
self.TaskId = None
self.Status = None
self.RoomId = None
self.GroupId = None
self.RecordUserId = None
self.RecordStartTime = None
self.RecordStopTime = None
self.TotalTime = None
self.ExceptionCnt = None
self.OmittedDurations = None
self.VideoInfos = None
self.ReplayUrl = None
self.Interrupts = None
self.RequestId = None | [
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andresriancho/w3af | cd22e5252243a87aaa6d0ddea47cf58dacfe00a9 | w3af/core/ui/gui/entries.py | python | RememberingWindow.quit | (self, widget, event) | return False | Windows quit, saves the position and size.
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:param event: the event that happened
"""
if self.onDestroy is not None:
if not self.onDestroy():
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self.winconfig[self.id_size] = self.get_size()
self.winconfig[self.id_position] = self.get_position()
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# https://github.com/andresriancho/w3af/issues/8890
pass
return False | [
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GoogleCloudPlatform/gsutil | 5be882803e76608e2fd29cf8c504ccd1fe0a7746 | gslib/sig_handling.py | python | InitializeSignalHandling | () | Initializes global signal handling.
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"""Initializes global signal handling.
Sets up global signal handler for each signal we handle.
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for signal_num in GetCaughtSignals():
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# Make main signal handler catch the signal.
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demisto/content | 5c664a65b992ac8ca90ac3f11b1b2cdf11ee9b07 | Packs/Slack/Integrations/SlackV3/SlackV3.py | python | send_slack_request_sync | (client: slack_sdk.WebClient, method: str, http_verb: str = 'POST', file_: str = '',
body: dict = None) | return response | Sends a request to slack API while handling rate limit errors.
Args:
client: The slack client.
method: The method to use.
http_verb: The HTTP method to use.
file_: A file path to send.
body: The request body.
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body: dict = None) -> SlackResponse:
"""
Sends a request to slack API while handling rate limit errors.
Args:
client: The slack client.
method: The method to use.
http_verb: The HTTP method to use.
file_: A file path to send.
body: The request body.
Returns:
The slack API response.
"""
if body is None:
body = {}
set_name_and_icon(body, method)
total_try_time = 0
while True:
try:
demisto.debug(f'Sending slack {method} (sync). Body is: {str(body)}')
if http_verb == 'POST':
if file_:
response = client.api_call(method, files={"file": file_}, data=body)
else:
response = client.api_call(method, json=body)
else:
response = client.api_call(method, http_verb='GET', params=body)
except SlackApiError as api_error:
demisto.debug(f'Got rate limit error (sync). Body is: {str(body)}\n{api_error}')
response = api_error.response
headers = response.headers # type: ignore
if 'Retry-After' in headers:
retry_after = int(headers['Retry-After'])
total_try_time += retry_after
if total_try_time < MAX_LIMIT_TIME:
time.sleep(retry_after)
continue
raise
break
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wbond/package_control | cfaaeb57612023e3679ecb7f8cd7ceac9f57990d | package_control/providers/bitbucket_repository_provider.py | python | BitBucketRepositoryProvider.get_renamed_packages | (self) | return {} | For API-compatibility with RepositoryProvider | For API-compatibility with RepositoryProvider | [
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nosmokingbandit/watcher | dadacd21a5790ee609058a98a17fcc8954d24439 | lib/infi/pkg_resources/__init__.py | python | ResourceManager.extraction_error | (self) | Give an error message for problems extracting file(s) | Give an error message for problems extracting file(s) | [
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"""Give an error message for problems extracting file(s)"""
old_exc = sys.exc_info()[1]
cache_path = self.extraction_path or get_default_cache()
tmpl = textwrap.dedent("""
Can't extract file(s) to egg cache
The following error occurred while trying to extract file(s) to the Python egg
cache:
{old_exc}
The Python egg cache directory is currently set to:
{cache_path}
Perhaps your account does not have write access to this directory? You can
change the cache directory by setting the PYTHON_EGG_CACHE environment
variable to point to an accessible directory.
""").lstrip()
err = ExtractionError(tmpl.format(**locals()))
err.manager = self
err.cache_path = cache_path
err.original_error = old_exc
raise err | [
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NeuralEnsemble/python-neo | 34d4db8fb0dc950dbbc6defd7fb75e99ea877286 | neo/core/baseneo.py | python | BaseNeo.merge_annotations | (self, *others) | Merge annotations from the other object into this one.
Merging follows these rules:
All keys that are in the either object, but not both, are kept.
For keys that are present in both objects:
For arrays or lists: concatenate the two arrays
For dicts: merge recursively
For strings: concatenate with ';'
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] | def merge_annotations(self, *others):
"""
Merge annotations from the other object into this one.
Merging follows these rules:
All keys that are in the either object, but not both, are kept.
For keys that are present in both objects:
For arrays or lists: concatenate the two arrays
For dicts: merge recursively
For strings: concatenate with ';'
Otherwise: fail if the annotations are not equal
"""
other_annotations = [other.annotations for other in others]
merged_annotations = merge_annotations(self.annotations,
*other_annotations)
self.annotations.update(merged_annotations) | [
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pantsbuild/pex | 473c6ac732ed4bc338b4b20a9ec930d1d722c9b4 | pex/vendor/_vendored/setuptools/pkg_resources/__init__.py | python | NullProvider.metadata_isdir | (self, name) | return self.egg_info and self._isdir(self._fn(self.egg_info, name)) | [] | def metadata_isdir(self, name):
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beetbox/beets | 2fea53c34dd505ba391cb345424e0613901c8025 | beets/library.py | python | DefaultTemplateFunctions.tmpl_lower | (s) | return s.lower() | Convert a string to lower case. | Convert a string to lower case. | [
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tp4a/teleport | 1fafd34f1f775d2cf80ea4af6e44468d8e0b24ad | server/www/packages/packages-linux/x64/cryptography/hazmat/primitives/padding.py | python | ANSIX923.padder | (self) | return _ANSIX923PaddingContext(self.block_size) | [] | def padder(self):
return _ANSIX923PaddingContext(self.block_size) | [
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PixarAnimationStudios/OpenTimelineIO | 990a54ccbe6488180a93753370fc87902b982962 | contrib/opentimelineio_contrib/adapters/hls_playlist.py | python | Byterange.from_string | (cls, byterange_string) | return cls.from_match_dict(m.groupdict()) | Construct a :class:`Byterange` given a string in HLS format.
:param byterange_string: (:class:`str`) a byterange string.
:return: (:class:`Byterange`) The instance for the provided string. | Construct a :class:`Byterange` given a string in HLS format. | [
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"""Construct a :class:`Byterange` given a string in HLS format.
:param byterange_string: (:class:`str`) a byterange string.
:return: (:class:`Byterange`) The instance for the provided string.
"""
m = BYTERANGE_RE.match(byterange_string)
return cls.from_match_dict(m.groupdict()) | [
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boto/boto | b2a6f08122b2f1b89888d2848e730893595cd001 | boto/auth.py | python | S3HmacAuthV4Handler.presign | (self, req, expires, iso_date=None) | return '%s://%s%s?%s' % (req.protocol, req.host, req.path,
urllib.parse.urlencode(req.params)) | Presign a request using SigV4 query params. Takes in an HTTP request
and an expiration time in seconds and returns a URL.
http://docs.aws.amazon.com/AmazonS3/latest/API/sigv4-query-string-auth.html | Presign a request using SigV4 query params. Takes in an HTTP request
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"""
Presign a request using SigV4 query params. Takes in an HTTP request
and an expiration time in seconds and returns a URL.
http://docs.aws.amazon.com/AmazonS3/latest/API/sigv4-query-string-auth.html
"""
if iso_date is None:
iso_date = datetime.datetime.utcnow().strftime('%Y%m%dT%H%M%SZ')
region = self.determine_region_name(req.host)
service = self.determine_service_name(req.host)
params = {
'X-Amz-Algorithm': 'AWS4-HMAC-SHA256',
'X-Amz-Credential': '%s/%s/%s/%s/aws4_request' % (
self._provider.access_key,
iso_date[:8],
region,
service
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'X-Amz-Date': iso_date,
'X-Amz-Expires': expires,
'X-Amz-SignedHeaders': 'host'
}
if self._provider.security_token:
params['X-Amz-Security-Token'] = self._provider.security_token
headers_to_sign = self.headers_to_sign(req)
l = sorted(['%s' % n.lower().strip() for n in headers_to_sign])
params['X-Amz-SignedHeaders'] = ';'.join(l)
req.params.update(params)
cr = self.canonical_request(req)
# We need to replace the payload SHA with a constant
cr = '\n'.join(cr.split('\n')[:-1]) + '\nUNSIGNED-PAYLOAD'
# Date header is expected for string_to_sign, but unused otherwise
req.headers['X-Amz-Date'] = iso_date
sts = self.string_to_sign(req, cr)
signature = self.signature(req, sts)
# Add signature to params now that we have it
req.params['X-Amz-Signature'] = signature
return '%s://%s%s?%s' % (req.protocol, req.host, req.path,
urllib.parse.urlencode(req.params)) | [
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naver/sqlova | fc68af6008fd2fd5839210e4b06a352007f609b6 | sqlova/utils/utils_wikisql.py | python | pred_wvi_se | (wn, s_wv) | return pr_wvi | s_wv: [B, 4, mL, 2]
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"""
s_wv_st, s_wv_ed = s_wv.split(1, dim=3) # [B, 4, mL, 2] -> [B, 4, mL, 1], [B, 4, mL, 1]
s_wv_st = s_wv_st.squeeze(3) # [B, 4, mL, 1] -> [B, 4, mL]
s_wv_ed = s_wv_ed.squeeze(3)
pr_wvi_st_idx = s_wv_st.argmax(dim=2) # [B, 4, mL] -> [B, 4, 1]
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pr_wvi = []
for b, wn1 in enumerate(wn):
pr_wvi1 = []
for i_wn in range(wn1):
pr_wvi_st_idx11 = pr_wvi_st_idx[b][i_wn]
pr_wvi_ed_idx11 = pr_wvi_ed_idx[b][i_wn]
pr_wvi1.append([pr_wvi_st_idx11.item(), pr_wvi_ed_idx11.item()])
pr_wvi.append(pr_wvi1)
return pr_wvi | [
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plotly/plotly.py | cfad7862594b35965c0e000813bd7805e8494a5b | packages/python/plotly/plotly/graph_objs/heatmapgl/_hoverlabel.py | python | Hoverlabel.__init__ | (
self,
arg=None,
align=None,
alignsrc=None,
bgcolor=None,
bgcolorsrc=None,
bordercolor=None,
bordercolorsrc=None,
font=None,
namelength=None,
namelengthsrc=None,
**kwargs
) | Construct a new Hoverlabel object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of
:class:`plotly.graph_objs.heatmapgl.Hoverlabel`
align
Sets the horizontal alignment of the text content
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alignsrc
Sets the source reference on Chart Studio Cloud for
`align`.
bgcolor
Sets the background color of the hover labels for this
trace
bgcolorsrc
Sets the source reference on Chart Studio Cloud for
`bgcolor`.
bordercolor
Sets the border color of the hover labels for this
trace.
bordercolorsrc
Sets the source reference on Chart Studio Cloud for
`bordercolor`.
font
Sets the font used in hover labels.
namelength
Sets the default length (in number of characters) of
the trace name in the hover labels for all traces. -1
shows the whole name regardless of length. 0-3 shows
the first 0-3 characters, and an integer >3 will show
the whole name if it is less than that many characters,
but if it is longer, will truncate to `namelength - 3`
characters and add an ellipsis.
namelengthsrc
Sets the source reference on Chart Studio Cloud for
`namelength`.
Returns
-------
Hoverlabel | Construct a new Hoverlabel object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of
:class:`plotly.graph_objs.heatmapgl.Hoverlabel`
align
Sets the horizontal alignment of the text content
within hover label box. Has an effect only if the hover
label text spans more two or more lines
alignsrc
Sets the source reference on Chart Studio Cloud for
`align`.
bgcolor
Sets the background color of the hover labels for this
trace
bgcolorsrc
Sets the source reference on Chart Studio Cloud for
`bgcolor`.
bordercolor
Sets the border color of the hover labels for this
trace.
bordercolorsrc
Sets the source reference on Chart Studio Cloud for
`bordercolor`.
font
Sets the font used in hover labels.
namelength
Sets the default length (in number of characters) of
the trace name in the hover labels for all traces. -1
shows the whole name regardless of length. 0-3 shows
the first 0-3 characters, and an integer >3 will show
the whole name if it is less than that many characters,
but if it is longer, will truncate to `namelength - 3`
characters and add an ellipsis.
namelengthsrc
Sets the source reference on Chart Studio Cloud for
`namelength`. | [
"Construct",
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"H... | def __init__(
self,
arg=None,
align=None,
alignsrc=None,
bgcolor=None,
bgcolorsrc=None,
bordercolor=None,
bordercolorsrc=None,
font=None,
namelength=None,
namelengthsrc=None,
**kwargs
):
"""
Construct a new Hoverlabel object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of
:class:`plotly.graph_objs.heatmapgl.Hoverlabel`
align
Sets the horizontal alignment of the text content
within hover label box. Has an effect only if the hover
label text spans more two or more lines
alignsrc
Sets the source reference on Chart Studio Cloud for
`align`.
bgcolor
Sets the background color of the hover labels for this
trace
bgcolorsrc
Sets the source reference on Chart Studio Cloud for
`bgcolor`.
bordercolor
Sets the border color of the hover labels for this
trace.
bordercolorsrc
Sets the source reference on Chart Studio Cloud for
`bordercolor`.
font
Sets the font used in hover labels.
namelength
Sets the default length (in number of characters) of
the trace name in the hover labels for all traces. -1
shows the whole name regardless of length. 0-3 shows
the first 0-3 characters, and an integer >3 will show
the whole name if it is less than that many characters,
but if it is longer, will truncate to `namelength - 3`
characters and add an ellipsis.
namelengthsrc
Sets the source reference on Chart Studio Cloud for
`namelength`.
Returns
-------
Hoverlabel
"""
super(Hoverlabel, self).__init__("hoverlabel")
if "_parent" in kwargs:
self._parent = kwargs["_parent"]
return
# Validate arg
# ------------
if arg is None:
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError(
"""\
The first argument to the plotly.graph_objs.heatmapgl.Hoverlabel
constructor must be a dict or
an instance of :class:`plotly.graph_objs.heatmapgl.Hoverlabel`"""
)
# Handle skip_invalid
# -------------------
self._skip_invalid = kwargs.pop("skip_invalid", False)
self._validate = kwargs.pop("_validate", True)
# Populate data dict with properties
# ----------------------------------
_v = arg.pop("align", None)
_v = align if align is not None else _v
if _v is not None:
self["align"] = _v
_v = arg.pop("alignsrc", None)
_v = alignsrc if alignsrc is not None else _v
if _v is not None:
self["alignsrc"] = _v
_v = arg.pop("bgcolor", None)
_v = bgcolor if bgcolor is not None else _v
if _v is not None:
self["bgcolor"] = _v
_v = arg.pop("bgcolorsrc", None)
_v = bgcolorsrc if bgcolorsrc is not None else _v
if _v is not None:
self["bgcolorsrc"] = _v
_v = arg.pop("bordercolor", None)
_v = bordercolor if bordercolor is not None else _v
if _v is not None:
self["bordercolor"] = _v
_v = arg.pop("bordercolorsrc", None)
_v = bordercolorsrc if bordercolorsrc is not None else _v
if _v is not None:
self["bordercolorsrc"] = _v
_v = arg.pop("font", None)
_v = font if font is not None else _v
if _v is not None:
self["font"] = _v
_v = arg.pop("namelength", None)
_v = namelength if namelength is not None else _v
if _v is not None:
self["namelength"] = _v
_v = arg.pop("namelengthsrc", None)
_v = namelengthsrc if namelengthsrc is not None else _v
if _v is not None:
self["namelengthsrc"] = _v
# Process unknown kwargs
# ----------------------
self._process_kwargs(**dict(arg, **kwargs))
# Reset skip_invalid
# ------------------
self._skip_invalid = False | [
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"... | https://github.com/plotly/plotly.py/blob/cfad7862594b35965c0e000813bd7805e8494a5b/packages/python/plotly/plotly/graph_objs/heatmapgl/_hoverlabel.py#L371-L503 | ||
google/capirca | 679e3885e3a5e5e129dc2dfab204ec44d63b26a4 | capirca/lib/junipermsmpc.py | python | JuniperMSMPC._BuildTokens | (self) | return supported_tokens, supported_sub_tokens | Build supported tokens for platform.
Returns:
tuple containing both supported tokens and sub tokens | Build supported tokens for platform. | [
"Build",
"supported",
"tokens",
"for",
"platform",
"."
] | def _BuildTokens(self):
"""Build supported tokens for platform.
Returns:
tuple containing both supported tokens and sub tokens
"""
supported_tokens, supported_sub_tokens = super()._BuildTokens()
supported_tokens |= {
'destination_prefix', 'destination_prefix_except', 'icmp_code',
'logging', 'owner', 'source_prefix', 'source_prefix_except'
}
supported_sub_tokens.update({
'option': {
'established',
# TODO(sneakywombat): add all options to lex.
'.*', # make ArbitraryOptions work, yolo.
'tcp-established',
'inactive'
}
})
return supported_tokens, supported_sub_tokens | [
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... | https://github.com/google/capirca/blob/679e3885e3a5e5e129dc2dfab204ec44d63b26a4/capirca/lib/junipermsmpc.py#L341-L362 | |
lad1337/XDM | 0c1b7009fe00f06f102a6f67c793478f515e7efe | site-packages/cherrypy/process/servers.py | python | check_port | (host, port, timeout=1.0) | Raise an error if the given port is not free on the given host. | Raise an error if the given port is not free on the given host. | [
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"if",
"the",
"given",
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"is",
"not",
"free",
"on",
"the",
"given",
"host",
"."
] | def check_port(host, port, timeout=1.0):
"""Raise an error if the given port is not free on the given host."""
if not host:
raise ValueError("Host values of '' or None are not allowed.")
host = client_host(host)
port = int(port)
import socket
# AF_INET or AF_INET6 socket
# Get the correct address family for our host (allows IPv6 addresses)
try:
info = socket.getaddrinfo(host, port, socket.AF_UNSPEC,
socket.SOCK_STREAM)
except socket.gaierror:
if ':' in host:
info = [(socket.AF_INET6, socket.SOCK_STREAM, 0, "", (host, port, 0, 0))]
else:
info = [(socket.AF_INET, socket.SOCK_STREAM, 0, "", (host, port))]
for res in info:
af, socktype, proto, canonname, sa = res
s = None
try:
s = socket.socket(af, socktype, proto)
# See http://groups.google.com/group/cherrypy-users/
# browse_frm/thread/bbfe5eb39c904fe0
s.settimeout(timeout)
s.connect((host, port))
s.close()
raise IOError("Port %s is in use on %s; perhaps the previous "
"httpserver did not shut down properly." %
(repr(port), repr(host)))
except socket.error:
if s:
s.close() | [
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oracle/graalpython | 577e02da9755d916056184ec441c26e00b70145c | graalpython/lib-python/3/sndhdr.py | python | whathdr | (filename) | Recognize sound headers. | Recognize sound headers. | [
"Recognize",
"sound",
"headers",
"."
] | def whathdr(filename):
"""Recognize sound headers."""
with open(filename, 'rb') as f:
h = f.read(512)
for tf in tests:
res = tf(h, f)
if res:
return SndHeaders(*res)
return None | [
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"... | https://github.com/oracle/graalpython/blob/577e02da9755d916056184ec441c26e00b70145c/graalpython/lib-python/3/sndhdr.py#L58-L66 | ||
taokong/FoveaBox | 50ce41e5af9cfba562877a318231e53c3b3ce767 | tools/detectron2pytorch.py | python | convert | (src, dst, depth) | Convert keys in detectron pretrained ResNet models to pytorch style. | Convert keys in detectron pretrained ResNet models to pytorch style. | [
"Convert",
"keys",
"in",
"detectron",
"pretrained",
"ResNet",
"models",
"to",
"pytorch",
"style",
"."
] | def convert(src, dst, depth):
"""Convert keys in detectron pretrained ResNet models to pytorch style."""
# load arch_settings
if depth not in arch_settings:
raise ValueError('Only support ResNet-50 and ResNet-101 currently')
block_nums = arch_settings[depth]
# load caffe model
caffe_model = mmcv.load(src, encoding='latin1')
blobs = caffe_model['blobs'] if 'blobs' in caffe_model else caffe_model
# convert to pytorch style
state_dict = OrderedDict()
converted_names = set()
convert_conv_fc(blobs, state_dict, 'conv1', 'conv1', converted_names)
convert_bn(blobs, state_dict, 'res_conv1_bn', 'bn1', converted_names)
for i in range(1, len(block_nums) + 1):
for j in range(block_nums[i - 1]):
if j == 0:
convert_conv_fc(blobs, state_dict,
'res{}_{}_branch1'.format(i + 1, j),
'layer{}.{}.downsample.0'.format(i, j),
converted_names)
convert_bn(blobs, state_dict,
'res{}_{}_branch1_bn'.format(i + 1, j),
'layer{}.{}.downsample.1'.format(i, j),
converted_names)
for k, letter in enumerate(['a', 'b', 'c']):
convert_conv_fc(blobs, state_dict,
'res{}_{}_branch2{}'.format(i + 1, j, letter),
'layer{}.{}.conv{}'.format(i, j, k + 1),
converted_names)
convert_bn(blobs, state_dict,
'res{}_{}_branch2{}_bn'.format(i + 1, j, letter),
'layer{}.{}.bn{}'.format(i, j,
k + 1), converted_names)
# check if all layers are converted
for key in blobs:
if key not in converted_names:
print('Not Convert: {}'.format(key))
# save checkpoint
checkpoint = dict()
checkpoint['state_dict'] = state_dict
torch.save(checkpoint, dst) | [
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"[",... | https://github.com/taokong/FoveaBox/blob/50ce41e5af9cfba562877a318231e53c3b3ce767/tools/detectron2pytorch.py#L34-L75 | ||
IJDykeman/wangTiles | 7c1ee2095ebdf7f72bce07d94c6484915d5cae8b | experimental_code/tiles_3d/venv_mac/lib/python2.7/site-packages/pip/_vendor/distlib/database.py | python | Distribution.matches_requirement | (self, req) | return result | Say if this instance matches (fulfills) a requirement.
:param req: The requirement to match.
:rtype req: str
:return: True if it matches, else False. | Say if this instance matches (fulfills) a requirement.
:param req: The requirement to match.
:rtype req: str
:return: True if it matches, else False. | [
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":",
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"matches",
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... | def matches_requirement(self, req):
"""
Say if this instance matches (fulfills) a requirement.
:param req: The requirement to match.
:rtype req: str
:return: True if it matches, else False.
"""
# Requirement may contain extras - parse to lose those
# from what's passed to the matcher
r = parse_requirement(req)
scheme = get_scheme(self.metadata.scheme)
try:
matcher = scheme.matcher(r.requirement)
except UnsupportedVersionError:
# XXX compat-mode if cannot read the version
logger.warning('could not read version %r - using name only',
req)
name = req.split()[0]
matcher = scheme.matcher(name)
name = matcher.key # case-insensitive
result = False
for p in self.provides:
p_name, p_ver = parse_name_and_version(p)
if p_name != name:
continue
try:
result = matcher.match(p_ver)
break
except UnsupportedVersionError:
pass
return result | [
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"... | https://github.com/IJDykeman/wangTiles/blob/7c1ee2095ebdf7f72bce07d94c6484915d5cae8b/experimental_code/tiles_3d/venv_mac/lib/python2.7/site-packages/pip/_vendor/distlib/database.py#L399-L431 | |
certtools/intelmq | 7c1ab88b77bcbb30c7cabca5bb3525ad4aaac138 | intelmq/lib/bot.py | python | Bot.check | (parameters: dict) | The bot's own check function can perform individual checks on it's
parameters.
`init()` is *not* called before, this is a staticmethod which does not
require class initialization.
Parameters:
parameters: Bot's parameters, defaults and runtime merged together
Returns:
output: None or a list of [log_level, log_message] pairs, both
strings. log_level must be a valid log level. | The bot's own check function can perform individual checks on it's
parameters.
`init()` is *not* called before, this is a staticmethod which does not
require class initialization. | [
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"""
The bot's own check function can perform individual checks on it's
parameters.
`init()` is *not* called before, this is a staticmethod which does not
require class initialization.
Parameters:
parameters: Bot's parameters, defaults and runtime merged together
Returns:
output: None or a list of [log_level, log_message] pairs, both
strings. log_level must be a valid log level.
"""
pass | [
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] | https://github.com/certtools/intelmq/blob/7c1ab88b77bcbb30c7cabca5bb3525ad4aaac138/intelmq/lib/bot.py#L887-L901 | ||
xonsh/xonsh | b76d6f994f22a4078f602f8b386f4ec280c8461f | xonsh/parsers/base.py | python | BaseParser.p_op_factor | (self, p) | op_factor : times_tok factor
| at_tok factor
| divide_tok factor
| mod_tok factor
| doublediv_tok factor | op_factor : times_tok factor
| at_tok factor
| divide_tok factor
| mod_tok factor
| doublediv_tok factor | [
"op_factor",
":",
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"|",
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"factor",
"|",
"divide_tok",
"factor",
"|",
"mod_tok",
"factor",
"|",
"doublediv_tok",
"factor"
] | def p_op_factor(self, p):
"""
op_factor : times_tok factor
| at_tok factor
| divide_tok factor
| mod_tok factor
| doublediv_tok factor
"""
p1 = p[1]
op = self._term_binops[p1.value]
if op is None:
self._set_error(
f"operation {p1!r} not supported",
self.currloc(lineno=p.lineno, column=p.lexpos),
)
p[0] = [op(lineno=p1.lineno, col_offset=p1.lexpos), p[2]] | [
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reahl/reahl | 86aac47c3a9b5b98e9f77dad4939034a02d54d46 | reahl-browsertools/reahl/browsertools/browsertools.py | python | XPath.link | (cls) | return cls.any('a') | Returns an XPath to find an HTML <a>.
.. versionadded:: 5.0 | Returns an XPath to find an HTML <a>. | [
"Returns",
"an",
"XPath",
"to",
"find",
"an",
"HTML",
"<a",
">",
"."
] | def link(cls):
"""Returns an XPath to find an HTML <a>.
.. versionadded:: 5.0
"""
return cls.any('a') | [
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"cls",
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"'a'",
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] | https://github.com/reahl/reahl/blob/86aac47c3a9b5b98e9f77dad4939034a02d54d46/reahl-browsertools/reahl/browsertools/browsertools.py#L658-L663 | |
gem/oq-engine | 1bdb88f3914e390abcbd285600bfd39477aae47c | openquake/hazardlib/__init__.py | python | _get_ebruptures | (fname, conv=None, ses_seed=None) | return ebrs | :param fname: path to a rupture file (XML or CSV)
:param conv: RuptureConverter instanc, used for XML ruptures
:param ses_seed: used for XML ruptures
:returns: a list of one or more EBRuptures | :param fname: path to a rupture file (XML or CSV)
:param conv: RuptureConverter instanc, used for XML ruptures
:param ses_seed: used for XML ruptures
:returns: a list of one or more EBRuptures | [
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"ruptures",
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"param",
"ses_seed",
":",
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"XML",
"ruptures",
... | def _get_ebruptures(fname, conv=None, ses_seed=None):
"""
:param fname: path to a rupture file (XML or CSV)
:param conv: RuptureConverter instanc, used for XML ruptures
:param ses_seed: used for XML ruptures
:returns: a list of one or more EBRuptures
"""
if fname.endswith('.xml'):
[rup_node] = nrml.read(fname)
rup = conv.convert_node(rup_node)
rup.tectonic_region_type = '*' # no TRT for scenario ruptures
rup.rup_id = ses_seed
ebrs = [EBRupture(rup, 'NA', 0, id=rup.rup_id, scenario=True)]
return ebrs
assert fname.endswith('.csv'), fname
aw = get_ruptures(fname)
ebrs = []
for i, rec in enumerate(aw.array):
rupture = _get_rupture(rec, aw.geoms[i], aw.trts[rec['trt_smr']])
ebr = EBRupture(rupture, rec['source_id'], rec['trt_smr'],
rec['n_occ'], rec['id'], rec['e0'])
ebrs.append(ebr)
return ebrs | [
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