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cobbler/cobbler | eed8cdca3e970c8aa1d199e80b8c8f19b3f940cc | cobbler/items/system.py | python | NetworkInterface.serialize | (self) | This method is a proxy for :meth:`~cobbler.items.item.Item.to_dict` and contains additional logic for
serialization to a persistent location.
:return: The dictionary with the information for serialization. | This method is a proxy for :meth:`~cobbler.items.item.Item.to_dict` and contains additional logic for
serialization to a persistent location. | [
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tp4a/teleport | 1fafd34f1f775d2cf80ea4af6e44468d8e0b24ad | server/www/packages/packages-darwin/x64/mako/runtime.py | python | Namespace.get_cached | (self, key, **kwargs) | return self.cache.get(key, **kwargs) | Return a value from the :class:`.Cache` referenced by this
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The advantage to this method versus direct access to the
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The advantage to this method versus direct access to the
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DeepGraphLearning/GMNN | e8a2232bca60b8b6a43e97a9c5d8121ed83780a1 | unsupervised/codes/loader.py | python | Vocab.__init__ | (self, file_name, cols, with_padding=False) | [] | def __init__(self, file_name, cols, with_padding=False):
self.itos = []
self.stoi = {}
self.vocab_size = 0
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self.stoi[string] = self.vocab_size
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self.vocab_size += 1
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items = line.strip().split('\t')
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item = items[col]
strings = item.strip().split(' ')
for string in strings:
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chrisconlan/algorithmic-trading-with-python | ebe01087c7d9172db72bc3c9adc1eee5e882ac49 | listings/chapter_3/3_6_calculate_chaikin_money_flow.py | python | calculate_money_flow_volume | (df: pd.DataFrame, n: int=20) | return calculate_money_flow_volume_series(df).rolling(n).sum() | Calculates money flow volume, or q_t in our formula | Calculates money flow volume, or q_t in our formula | [
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"""
Calculates money flow volume, or q_t in our formula
"""
return calculate_money_flow_volume_series(df).rolling(n).sum() | [
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chadmv/cmt | 1b1a9a4fb154d1d10e73373cf899e4d83c95a6a1 | scripts/pyparsing/core.py | python | Forward.leaveWhitespace | (self) | return self | [] | def leaveWhitespace(self):
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return self | [
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seppius-xbmc-repo/ru | d0879d56ec8243b2c7af44fda5cf3d1ff77fd2e2 | plugin.video.online.anidub.com/BeautifulSoup.py | python | PageElement.findAllNext | (self, name=None, attrs={}, text=None, limit=None,
**kwargs) | return self._findAll(name, attrs, text, limit, self.nextGenerator,
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log2timeline/plaso | fe2e316b8c76a0141760c0f2f181d84acb83abc2 | plaso/cli/helpers/hashers.py | python | HashersArgumentsHelper.AddArguments | (cls, argument_group) | Adds command line arguments to an argument group.
This function takes an argument parser or an argument group object and adds
to it all the command line arguments this helper supports.
Args:
argument_group (argparse._ArgumentGroup|argparse.ArgumentParser):
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"""Adds command line arguments to an argument group.
This function takes an argument parser or an argument group object and adds
to it all the command line arguments this helper supports.
Args:
argument_group (argparse._ArgumentGroup|argparse.ArgumentParser):
argparse group.
"""
argument_group.add_argument(
'--hasher_file_size_limit', '--hasher-file-size-limit',
dest='hasher_file_size_limit', type=int, action='store', default=0,
metavar='SIZE', help=(
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argument_group.add_argument(
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default=cls._DEFAULT_HASHER_STRING, metavar='HASHER_LIST', help=(
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'separated list where each entry is the name of a hasher, such as '
'"md5,sha256". "all" indicates that all hashers should be '
'enabled. "none" disables all hashers. Use "--hashers list" or '
'"--info" to list the available hashers.')) | [
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emmetio/livestyle-sublime-old | c42833c046e9b2f53ebce3df3aa926528f5a33b5 | tornado/httpserver.py | python | HTTPRequest.get_ssl_certificate | (self, binary_form=False) | Returns the client's SSL certificate, if any.
To use client certificates, the HTTPServer must have been constructed
with cert_reqs set in ssl_options, e.g.::
server = HTTPServer(app,
ssl_options=dict(
certfile="foo.crt",
keyfile="foo.key",
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ca_certs="cacert.crt"))
By default, the return value is a dictionary (or None, if no
client certificate is present). If ``binary_form`` is true, a
DER-encoded form of the certificate is returned instead. See
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"""Returns the client's SSL certificate, if any.
To use client certificates, the HTTPServer must have been constructed
with cert_reqs set in ssl_options, e.g.::
server = HTTPServer(app,
ssl_options=dict(
certfile="foo.crt",
keyfile="foo.key",
cert_reqs=ssl.CERT_REQUIRED,
ca_certs="cacert.crt"))
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client certificate is present). If ``binary_form`` is true, a
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details.
http://docs.python.org/library/ssl.html#sslsocket-objects
"""
try:
return self.connection.stream.socket.getpeercert(
binary_form=binary_form)
except ssl.SSLError:
return None | [
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r9y9/deepvoice3_pytorch | a5c24624bad314db5a5dcb0ea320fc3623a94f15 | synthesis.py | python | tts | (model, text, p=0, speaker_id=None, fast=False) | return waveform, alignment, spectrogram, mel | Convert text to speech waveform given a deepvoice3 model.
Args:
text (str) : Input text to be synthesized
p (float) : Replace word to pronounciation if p > 0. Default is 0. | Convert text to speech waveform given a deepvoice3 model. | [
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] | def tts(model, text, p=0, speaker_id=None, fast=False):
"""Convert text to speech waveform given a deepvoice3 model.
Args:
text (str) : Input text to be synthesized
p (float) : Replace word to pronounciation if p > 0. Default is 0.
"""
model = model.to(device)
model.eval()
if fast:
model.make_generation_fast_()
sequence = np.array(_frontend.text_to_sequence(text, p=p))
sequence = torch.from_numpy(sequence).unsqueeze(0).long().to(device)
text_positions = torch.arange(1, sequence.size(-1) + 1).unsqueeze(0).long().to(device)
speaker_ids = None if speaker_id is None else torch.LongTensor([speaker_id]).to(device)
# Greedy decoding
with torch.no_grad():
mel_outputs, linear_outputs, alignments, done = model(
sequence, text_positions=text_positions, speaker_ids=speaker_ids)
linear_output = linear_outputs[0].cpu().data.numpy()
spectrogram = audio._denormalize(linear_output)
alignment = alignments[0].cpu().data.numpy()
mel = mel_outputs[0].cpu().data.numpy()
mel = audio._denormalize(mel)
# Predicted audio signal
waveform = audio.inv_spectrogram(linear_output.T)
return waveform, alignment, spectrogram, mel | [
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jython/frozen-mirror | b8d7aa4cee50c0c0fe2f4b235dd62922dd0f3f99 | lib-python/2.7/ntpath.py | python | relpath | (path, start=curdir) | return join(*rel_list) | Return a relative version of a path | Return a relative version of a path | [
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raise ValueError("no path specified")
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rel_list = [pardir] * (len(start_list)-i) + path_list[i:]
if not rel_list:
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return join(*rel_list) | [
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mysql/mysql-connector-python | c5460bcbb0dff8e4e48bf4af7a971c89bf486d85 | lib/mysql/connector/utils.py | python | intread | (buf) | Unpacks the given buffer to an integer | Unpacks the given buffer to an integer | [
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"""Unpacks the given buffer to an integer"""
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return buf
length = len(buf)
if length == 1:
return buf[0]
elif length <= 4:
tmp = buf + b'\x00'*(4-length)
return struct.unpack('<I', tmp)[0]
tmp = buf + b'\x00'*(8-length)
return struct.unpack('<Q', tmp)[0]
except:
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appknox/AFE | bc9c909ea306506985b00f27ca17b1df5d1a92cc | internals/urlgrabber/grabber.py | python | URLGrabberOptions.derive | (self, **kwargs) | return URLGrabberOptions(delegate=self, **kwargs) | Create a derived URLGrabberOptions instance.
This method creates a new instance and overrides the
options specified in kwargs. | Create a derived URLGrabberOptions instance.
This method creates a new instance and overrides the
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"""Create a derived URLGrabberOptions instance.
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/sympy/solvers/diophantine.py | python | gaussian_reduce | (w, a, b) | return c[0]*w + b*c[1], c[0] | r"""
Returns a reduced solution `(x, z)` to the congruence
`X^2 - aZ^2 \equiv 0 \ (mod \ b)` so that `x^2 + |a|z^2` is minimal.
Details
=======
Here ``w`` is a solution of the congruence `x^2 \equiv a \ (mod \ b)`
References
==========
.. [1] Gaussian lattice Reduction [online]. Available:
http://home.ie.cuhk.edu.hk/~wkshum/wordpress/?p=404
.. [2] Efficient Solution of Rational Conices, J. E. Cremona and D. Rusin,
Mathematics of Computation, Volume 00, Number 0. | r"""
Returns a reduced solution `(x, z)` to the congruence
`X^2 - aZ^2 \equiv 0 \ (mod \ b)` so that `x^2 + |a|z^2` is minimal. | [
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r"""
Returns a reduced solution `(x, z)` to the congruence
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Details
=======
Here ``w`` is a solution of the congruence `x^2 \equiv a \ (mod \ b)`
References
==========
.. [1] Gaussian lattice Reduction [online]. Available:
http://home.ie.cuhk.edu.hk/~wkshum/wordpress/?p=404
.. [2] Efficient Solution of Rational Conices, J. E. Cremona and D. Rusin,
Mathematics of Computation, Volume 00, Number 0.
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u = (0, 1)
v = (1, 0)
if dot(u, v, w, a, b) < 0:
v = (-v[0], -v[1])
if norm(u, w, a, b) < norm(v, w, a, b):
u, v = v, u
while norm(u, w, a, b) > norm(v, w, a, b):
k = dot(u, v, w, a, b) // dot(v, v, w, a, b)
u, v = v, (u[0]- k*v[0], u[1]- k*v[1])
u, v = v, u
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c = v
else:
c = (u[0] - v[0], u[1] - v[1])
return c[0]*w + b*c[1], c[0] | [
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joxeankoret/nightmare | 11b22bb7c346611de90f479ee781c9228af453ea | lib/interfaces/vtrace/__init__.py | python | Trace.resumeThread | (self, threadid) | Resume a suspended thread. | Resume a suspended thread. | [
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"""
Resume a suspended thread.
"""
self.requireNotRunning()
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self.platformResumeThread(threadid)
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IronLanguages/ironpython2 | 51fdedeeda15727717fb8268a805f71b06c0b9f1 | Src/StdLib/Lib/decimal.py | python | _decimal_lshift_exact | (n, e) | Given integers n and e, return n * 10**e if it's an integer, else None.
The computation is designed to avoid computing large powers of 10
unnecessarily.
>>> _decimal_lshift_exact(3, 4)
30000
>>> _decimal_lshift_exact(300, -999999999) # returns None | Given integers n and e, return n * 10**e if it's an integer, else None. | [
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""" Given integers n and e, return n * 10**e if it's an integer, else None.
The computation is designed to avoid computing large powers of 10
unnecessarily.
>>> _decimal_lshift_exact(3, 4)
30000
>>> _decimal_lshift_exact(300, -999999999) # returns None
"""
if n == 0:
return 0
elif e >= 0:
return n * 10**e
else:
# val_n = largest power of 10 dividing n.
str_n = str(abs(n))
val_n = len(str_n) - len(str_n.rstrip('0'))
return None if val_n < -e else n // 10**-e | [
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AlexanderParkin/ChaLearn_liveness_challenge | 6dc26c1bd68beb6bd6e3d24107ee2d9557dcb9a3 | attributes_trainer/trainer.py | python | Model.calculate_metrics | (self, output, target) | return batch_result | Calculates test metrix for given batch and its input | Calculates test metrix for given batch and its input | [
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"""
Calculates test metrix for given batch and its input
"""
batch_result = None
t = target
o = output
if self.opt.loss_type == 'bce':
binary_accuracy = (t.byte()==(o>0.5)).float().mean(0).cpu().numpy()
batch_result = binary_accuracy
elif self.opt.loss_type =='mse':
mean_average_error = torch.abs(t-o.squeeze()).mean(0).cpu().numpy()
batch_result = mean_average_error
elif self.opt.loss_type == 'cce' or self.opt.loss_type == 'arc_margin':
top1_accuracy = (torch.argmax(o, 1)==t).float().mean().item()
batch_result = top1_accuracy
else:
raise Exception('This loss function is not implemented yet')
return batch_result | [
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Asana/python-asana | 9b54ab99423208bd6aa87dbfaa628c069430b127 | asana/resources/stories.py | python | Stories.create_on_task | (self, task, params={}, **options) | return self.client.post(path, params, **options) | Adds a comment to a task. The comment will be authored by the
currently authenticated user, and timestamped when the server receives
the request.
Returns the full record for the new story added to the task.
Parameters
----------
task : {Id} Globally unique identifier for the task.
[data] : {Object} Data for the request
- text : {String} The plain text of the comment to add. | Adds a comment to a task. The comment will be authored by the
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"""Adds a comment to a task. The comment will be authored by the
currently authenticated user, and timestamped when the server receives
the request.
Returns the full record for the new story added to the task.
Parameters
----------
task : {Id} Globally unique identifier for the task.
[data] : {Object} Data for the request
- text : {String} The plain text of the comment to add.
"""
path = "/tasks/%s/stories" % (task)
return self.client.post(path, params, **options) | [
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Ha0Tang/SelectionGAN | 80aa7ad9f79f643c28633c40c621f208f3fb0121 | semantic_synthesis/util/html.py | python | HTML.get_image_dir | (self) | return self.img_dir | [] | def get_image_dir(self):
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snowflakedb/snowflake-connector-python | 1659ec6b78930d1f947b4eff985c891af614d86c | src/snowflake/connector/auth_okta.py | python | _is_prefix_equal | (url1, url2) | return (
parsed_url1.hostname == parsed_url2.hostname
and port1 == port2
and parsed_url1.scheme == parsed_url2.scheme
) | Checks if URL prefixes are identical.
The scheme, hostname and port number are compared. If the port number is not specified and the scheme is https,
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"""Checks if URL prefixes are identical.
The scheme, hostname and port number are compared. If the port number is not specified and the scheme is https,
the port number is assumed to be 443.
"""
parsed_url1 = urlsplit(url1)
parsed_url2 = urlsplit(url2)
port1 = parsed_url1.port
if not port1 and parsed_url1.scheme == "https":
port1 = "443"
port2 = parsed_url1.port
if not port2 and parsed_url2.scheme == "https":
port2 = "443"
return (
parsed_url1.hostname == parsed_url2.hostname
and port1 == port2
and parsed_url1.scheme == parsed_url2.scheme
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jgyates/genmon | 2cb2ed2945f55cd8c259b09ccfa9a51e23f1341e | genmonlib/generac_evolution.py | python | Evolution.WriteIndexedRegister | (self, register, value) | [] | def WriteIndexedRegister(self, register, value):
try:
with self.ModBus.CommAccessLock:
#
LowByte = value & 0x00FF
HighByte = value >> 8
Data= []
Data.append(HighByte) # Value for indexed register (High byte)
Data.append(LowByte) # Value for indexed register (Low byte)
self.ModBus.ProcessWriteTransaction("0004", len(Data) / 2, Data)
LowByte = register & 0x00FF
HighByte = register >> 8
Data= []
Data.append(HighByte) # indexed register to be written (High byte)
Data.append(LowByte) # indexed register to be written (Low byte)
self.ModBus.ProcessWriteTransaction("0003", len(Data) / 2, Data)
except Exception as e1:
self.LogErrorLine("Error in WriteIndexedRegister: " + str(e1)) | [
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exaile/exaile | a7b58996c5c15b3aa7b9975ac13ee8f784ef4689 | xlgui/cover.py | python | CoverWidget.do_expose_event | (self, event) | Paints alpha transparency | Paints alpha transparency | [
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"""
Paints alpha transparency
"""
opacity = 1 - settings.get_option('gui/transparency', 0.3)
context = self.props.window.cairo_create()
background = self.style.bg[Gtk.StateType.NORMAL]
context.set_source_rgba(
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float(background.blue) / 256 ** 2,
opacity,
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context.set_operator(cairo.OPERATOR_SOURCE)
context.paint()
Gtk.EventBox.do_expose_event(self, event) | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/sqlalchemy/sql/util.py | python | ColumnAdapter.wrap | (self, adapter) | return ac | [] | def wrap(self, adapter):
ac = self.__class__.__new__(self.__class__)
ac.__dict__.update(self.__dict__)
ac._wrap = adapter
ac.columns = util.populate_column_dict(ac._locate_col)
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conjure-up/conjure-up | d2bf8ab8e71ff01321d0e691a8d3e3833a047678 | conjureup/controllers/juju/credentials/tui.py | python | CredentialsController.render | (self) | [] | def render(self):
self.load_credentials()
if app.provider.cloud_type == cloud_types.LOCAL:
# no credentials required for localhost
self.finish()
elif not self.credentials:
utils.warning("You attempted to do an install against a cloud "
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events.Shutdown.set(1)
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events.Shutdown.set(1)
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self.finish() | [
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menpo/menpo | a61500656c4fc2eea82497684f13cc31a605550b | menpo/shape/labelled.py | python | LabelledPointUndirectedGraph._view_2d | (
self,
with_labels=None,
without_labels=None,
group="group",
figure_id=None,
new_figure=False,
image_view=True,
render_lines=True,
line_colour=None,
line_style="-",
line_width=1,
render_markers=True,
marker_style="o",
marker_size=5,
marker_face_colour=None,
marker_edge_colour=None,
marker_edge_width=1.0,
render_numbering=False,
numbers_horizontal_align="center",
numbers_vertical_align="bottom",
numbers_font_name="sans-serif",
numbers_font_size=10,
numbers_font_style="normal",
numbers_font_weight="normal",
numbers_font_colour="k",
render_legend=True,
legend_title="",
legend_font_name="sans-serif",
legend_font_style="normal",
legend_font_size=10,
legend_font_weight="normal",
legend_marker_scale=None,
legend_location=2,
legend_bbox_to_anchor=(1.05, 1.0),
legend_border_axes_pad=None,
legend_n_columns=1,
legend_horizontal_spacing=None,
legend_vertical_spacing=None,
legend_border=True,
legend_border_padding=None,
legend_shadow=False,
legend_rounded_corners=False,
render_axes=True,
axes_font_name="sans-serif",
axes_font_size=10,
axes_font_style="normal",
axes_font_weight="normal",
axes_x_limits=None,
axes_y_limits=None,
axes_x_ticks=None,
axes_y_ticks=None,
figure_size=(10, 8),
) | return landmark_viewer.render(
image_view=image_view,
render_lines=render_lines,
line_colour=line_colour,
line_style=line_style,
line_width=line_width,
render_markers=render_markers,
marker_style=marker_style,
marker_size=marker_size,
marker_face_colour=marker_face_colour,
marker_edge_colour=marker_edge_colour,
marker_edge_width=marker_edge_width,
render_numbering=render_numbering,
numbers_horizontal_align=numbers_horizontal_align,
numbers_vertical_align=numbers_vertical_align,
numbers_font_name=numbers_font_name,
numbers_font_size=numbers_font_size,
numbers_font_style=numbers_font_style,
numbers_font_weight=numbers_font_weight,
numbers_font_colour=numbers_font_colour,
render_legend=render_legend,
legend_title=legend_title,
legend_font_name=legend_font_name,
legend_font_style=legend_font_style,
legend_font_size=legend_font_size,
legend_font_weight=legend_font_weight,
legend_marker_scale=legend_marker_scale,
legend_location=legend_location,
legend_bbox_to_anchor=legend_bbox_to_anchor,
legend_border_axes_pad=legend_border_axes_pad,
legend_n_columns=legend_n_columns,
legend_horizontal_spacing=legend_horizontal_spacing,
legend_vertical_spacing=legend_vertical_spacing,
legend_border=legend_border,
legend_border_padding=legend_border_padding,
legend_shadow=legend_shadow,
legend_rounded_corners=legend_rounded_corners,
render_axes=render_axes,
axes_font_name=axes_font_name,
axes_font_size=axes_font_size,
axes_font_style=axes_font_style,
axes_font_weight=axes_font_weight,
axes_x_limits=axes_x_limits,
axes_y_limits=axes_y_limits,
axes_x_ticks=axes_x_ticks,
axes_y_ticks=axes_y_ticks,
figure_size=figure_size,
) | Visualize the labelled point undirected graph.
Parameters
----------
with_labels : ``None`` or `str` or `list` of `str`, optional
If not ``None``, only show the given label(s). Should **not** be
used with the ``without_labels`` kwarg.
without_labels : ``None`` or `str` or `list` of `str`, optional
If not ``None``, show all except the given label(s). Should **not**
be used with the ``with_labels`` kwarg.
group : `str` or `None`, optional
The name of the labelled point undirected graph. It is used in
the legend.
figure_id : `object`, optional
The id of the figure to be used.
new_figure : `bool`, optional
If ``True``, a new figure is created.
image_view : `bool`, optional
If ``True``, the x and y axes are flipped.
render_lines : `bool`, optional
If ``True``, the edges will be rendered.
line_colour : See Below, optional
The colour of the lines.
Example options::
{r, g, b, c, m, k, w}
or
(3, ) ndarray
It can either be one of the above or a `list` of those defining a
value per label.
line_style : ``{'-', '--', '-.', ':'}``, optional
The style of the lines.
line_width : `float`, optional
The width of the lines.
render_markers : `bool`, optional
If ``True``, the markers will be rendered.
marker_style : See Below, optional
The style of the markers. Example options ::
{., ,, o, v, ^, <, >, +, x, D, d, s, p, *, h, H, 1, 2, 3, 4, 8}
marker_size : `int`, optional
The size of the markers in points.
marker_face_colour : See Below, optional
The face (filling) colour of the markers.
Example options ::
{r, g, b, c, m, k, w}
or
(3, ) ndarray
It can either be one of the above or a `list` of those defining a
value per label.
marker_edge_colour : See Below, optional
The edge colour of the markers.
Example options ::
{r, g, b, c, m, k, w}
or
(3, ) ndarray
It can either be one of the above or a `list` of those defining a
value per label.
marker_edge_width : `float`, optional
The width of the markers' edge.
render_numbering : `bool`, optional
If ``True``, the landmarks will be numbered.
numbers_horizontal_align : ``{center, right, left}``, optional
The horizontal alignment of the numbers' texts.
numbers_vertical_align : ``{center, top, bottom, baseline}``, optional
The vertical alignment of the numbers' texts.
numbers_font_name : See Below, optional
The font of the numbers. Example options ::
{serif, sans-serif, cursive, fantasy, monospace}
numbers_font_size : `int`, optional
The font size of the numbers.
numbers_font_style : ``{normal, italic, oblique}``, optional
The font style of the numbers.
numbers_font_weight : See Below, optional
The font weight of the numbers.
Example options ::
{ultralight, light, normal, regular, book, medium, roman,
semibold, demibold, demi, bold, heavy, extra bold, black}
numbers_font_colour : See Below, optional
The font colour of the numbers.
Example options ::
{r, g, b, c, m, k, w}
or
(3, ) ndarray
render_legend : `bool`, optional
If ``True``, the legend will be rendered.
legend_title : `str`, optional
The title of the legend.
legend_font_name : See Below, optional
The font of the legend.
Possible options ::
{serif, sans-serif, cursive, fantasy, monospace}
legend_font_style : {``normal``, ``italic``, ``oblique``}, optional
The font style of the legend.
legend_font_size : `int`, optional
The font size of the legend.
legend_font_weight : See Below, optional
The font weight of the legend.
Possible options ::
{ultralight, light, normal, regular, book, medium, roman,
semibold, demibold, demi, bold, heavy, extra bold, black}
legend_marker_scale : `float`, optional
The relative size of the legend markers with respect to the original
legend_location : `int`, optional
The location of the legend. The predefined values are:
=============== ===
'best' 0
'upper right' 1
'upper left' 2
'lower left' 3
'lower right' 4
'right' 5
'center left' 6
'center right' 7
'lower center' 8
'upper center' 9
'center' 10
=============== ===
legend_bbox_to_anchor : (`float`, `float`), optional
The bbox that the legend will be anchored.
legend_border_axes_pad : `float`, optional
The pad between the axes and legend border.
legend_n_columns : `int`, optional
The number of the legend's columns.
legend_horizontal_spacing : `float`, optional
The spacing between the columns.
legend_vertical_spacing : `float`, optional
The vertical space between the legend entries.
legend_border : `bool`, optional
If ``True``, a frame will be drawn around the legend.
legend_border_padding : `float`, optional
The fractional whitespace inside the legend border.
legend_shadow : `bool`, optional
If ``True``, a shadow will be drawn behind legend.
legend_rounded_corners : `bool`, optional
If ``True``, the frame's corners will be rounded (fancybox).
render_axes : `bool`, optional
If ``True``, the axes will be rendered.
axes_font_name : See Below, optional
The font of the axes.
Example options ::
{serif, sans-serif, cursive, fantasy, monospace}
axes_font_size : `int`, optional
The font size of the axes.
axes_font_style : {``normal``, ``italic``, ``oblique``}, optional
The font style of the axes.
axes_font_weight : See Below, optional
The font weight of the axes.
Example options ::
{ultralight, light, normal, regular, book, medium, roman,
semibold, demibold, demi, bold, heavy, extra bold, black}
axes_x_limits : `float` or (`float`, `float`) or ``None``, optional
The limits of the x axis. If `float`, then it sets padding on the
right and left of the LabelledPointUndirectedGraph as a percentage
of the LabelledPointUndirectedGraph's width. If `tuple` or `list`,
then it defines the axis limits. If ``None``, then the limits are
set automatically.
axes_y_limits : (`float`, `float`) `tuple` or ``None``, optional
The limits of the y axis. If `float`, then it sets padding on the
top and bottom of the LabelledPointUndirectedGraph as a percentage
of the LabelledPointUndirectedGraph's height. If `tuple` or `list`,
then it defines the axis limits. If ``None``, then the limits are
set automatically.
axes_x_ticks : `list` or `tuple` or ``None``, optional
The ticks of the x axis.
axes_y_ticks : `list` or `tuple` or ``None``, optional
The ticks of the y axis.
figure_size : (`float`, `float`) or `None`, optional
The size of the figure in inches.
Raises
------
ValueError
If both ``with_labels`` and ``without_labels`` are passed. | Visualize the labelled point undirected graph. | [
"Visualize",
"the",
"labelled",
"point",
"undirected",
"graph",
"."
] | def _view_2d(
self,
with_labels=None,
without_labels=None,
group="group",
figure_id=None,
new_figure=False,
image_view=True,
render_lines=True,
line_colour=None,
line_style="-",
line_width=1,
render_markers=True,
marker_style="o",
marker_size=5,
marker_face_colour=None,
marker_edge_colour=None,
marker_edge_width=1.0,
render_numbering=False,
numbers_horizontal_align="center",
numbers_vertical_align="bottom",
numbers_font_name="sans-serif",
numbers_font_size=10,
numbers_font_style="normal",
numbers_font_weight="normal",
numbers_font_colour="k",
render_legend=True,
legend_title="",
legend_font_name="sans-serif",
legend_font_style="normal",
legend_font_size=10,
legend_font_weight="normal",
legend_marker_scale=None,
legend_location=2,
legend_bbox_to_anchor=(1.05, 1.0),
legend_border_axes_pad=None,
legend_n_columns=1,
legend_horizontal_spacing=None,
legend_vertical_spacing=None,
legend_border=True,
legend_border_padding=None,
legend_shadow=False,
legend_rounded_corners=False,
render_axes=True,
axes_font_name="sans-serif",
axes_font_size=10,
axes_font_style="normal",
axes_font_weight="normal",
axes_x_limits=None,
axes_y_limits=None,
axes_x_ticks=None,
axes_y_ticks=None,
figure_size=(10, 8),
):
"""
Visualize the labelled point undirected graph.
Parameters
----------
with_labels : ``None`` or `str` or `list` of `str`, optional
If not ``None``, only show the given label(s). Should **not** be
used with the ``without_labels`` kwarg.
without_labels : ``None`` or `str` or `list` of `str`, optional
If not ``None``, show all except the given label(s). Should **not**
be used with the ``with_labels`` kwarg.
group : `str` or `None`, optional
The name of the labelled point undirected graph. It is used in
the legend.
figure_id : `object`, optional
The id of the figure to be used.
new_figure : `bool`, optional
If ``True``, a new figure is created.
image_view : `bool`, optional
If ``True``, the x and y axes are flipped.
render_lines : `bool`, optional
If ``True``, the edges will be rendered.
line_colour : See Below, optional
The colour of the lines.
Example options::
{r, g, b, c, m, k, w}
or
(3, ) ndarray
It can either be one of the above or a `list` of those defining a
value per label.
line_style : ``{'-', '--', '-.', ':'}``, optional
The style of the lines.
line_width : `float`, optional
The width of the lines.
render_markers : `bool`, optional
If ``True``, the markers will be rendered.
marker_style : See Below, optional
The style of the markers. Example options ::
{., ,, o, v, ^, <, >, +, x, D, d, s, p, *, h, H, 1, 2, 3, 4, 8}
marker_size : `int`, optional
The size of the markers in points.
marker_face_colour : See Below, optional
The face (filling) colour of the markers.
Example options ::
{r, g, b, c, m, k, w}
or
(3, ) ndarray
It can either be one of the above or a `list` of those defining a
value per label.
marker_edge_colour : See Below, optional
The edge colour of the markers.
Example options ::
{r, g, b, c, m, k, w}
or
(3, ) ndarray
It can either be one of the above or a `list` of those defining a
value per label.
marker_edge_width : `float`, optional
The width of the markers' edge.
render_numbering : `bool`, optional
If ``True``, the landmarks will be numbered.
numbers_horizontal_align : ``{center, right, left}``, optional
The horizontal alignment of the numbers' texts.
numbers_vertical_align : ``{center, top, bottom, baseline}``, optional
The vertical alignment of the numbers' texts.
numbers_font_name : See Below, optional
The font of the numbers. Example options ::
{serif, sans-serif, cursive, fantasy, monospace}
numbers_font_size : `int`, optional
The font size of the numbers.
numbers_font_style : ``{normal, italic, oblique}``, optional
The font style of the numbers.
numbers_font_weight : See Below, optional
The font weight of the numbers.
Example options ::
{ultralight, light, normal, regular, book, medium, roman,
semibold, demibold, demi, bold, heavy, extra bold, black}
numbers_font_colour : See Below, optional
The font colour of the numbers.
Example options ::
{r, g, b, c, m, k, w}
or
(3, ) ndarray
render_legend : `bool`, optional
If ``True``, the legend will be rendered.
legend_title : `str`, optional
The title of the legend.
legend_font_name : See Below, optional
The font of the legend.
Possible options ::
{serif, sans-serif, cursive, fantasy, monospace}
legend_font_style : {``normal``, ``italic``, ``oblique``}, optional
The font style of the legend.
legend_font_size : `int`, optional
The font size of the legend.
legend_font_weight : See Below, optional
The font weight of the legend.
Possible options ::
{ultralight, light, normal, regular, book, medium, roman,
semibold, demibold, demi, bold, heavy, extra bold, black}
legend_marker_scale : `float`, optional
The relative size of the legend markers with respect to the original
legend_location : `int`, optional
The location of the legend. The predefined values are:
=============== ===
'best' 0
'upper right' 1
'upper left' 2
'lower left' 3
'lower right' 4
'right' 5
'center left' 6
'center right' 7
'lower center' 8
'upper center' 9
'center' 10
=============== ===
legend_bbox_to_anchor : (`float`, `float`), optional
The bbox that the legend will be anchored.
legend_border_axes_pad : `float`, optional
The pad between the axes and legend border.
legend_n_columns : `int`, optional
The number of the legend's columns.
legend_horizontal_spacing : `float`, optional
The spacing between the columns.
legend_vertical_spacing : `float`, optional
The vertical space between the legend entries.
legend_border : `bool`, optional
If ``True``, a frame will be drawn around the legend.
legend_border_padding : `float`, optional
The fractional whitespace inside the legend border.
legend_shadow : `bool`, optional
If ``True``, a shadow will be drawn behind legend.
legend_rounded_corners : `bool`, optional
If ``True``, the frame's corners will be rounded (fancybox).
render_axes : `bool`, optional
If ``True``, the axes will be rendered.
axes_font_name : See Below, optional
The font of the axes.
Example options ::
{serif, sans-serif, cursive, fantasy, monospace}
axes_font_size : `int`, optional
The font size of the axes.
axes_font_style : {``normal``, ``italic``, ``oblique``}, optional
The font style of the axes.
axes_font_weight : See Below, optional
The font weight of the axes.
Example options ::
{ultralight, light, normal, regular, book, medium, roman,
semibold, demibold, demi, bold, heavy, extra bold, black}
axes_x_limits : `float` or (`float`, `float`) or ``None``, optional
The limits of the x axis. If `float`, then it sets padding on the
right and left of the LabelledPointUndirectedGraph as a percentage
of the LabelledPointUndirectedGraph's width. If `tuple` or `list`,
then it defines the axis limits. If ``None``, then the limits are
set automatically.
axes_y_limits : (`float`, `float`) `tuple` or ``None``, optional
The limits of the y axis. If `float`, then it sets padding on the
top and bottom of the LabelledPointUndirectedGraph as a percentage
of the LabelledPointUndirectedGraph's height. If `tuple` or `list`,
then it defines the axis limits. If ``None``, then the limits are
set automatically.
axes_x_ticks : `list` or `tuple` or ``None``, optional
The ticks of the x axis.
axes_y_ticks : `list` or `tuple` or ``None``, optional
The ticks of the y axis.
figure_size : (`float`, `float`) or `None`, optional
The size of the figure in inches.
Raises
------
ValueError
If both ``with_labels`` and ``without_labels`` are passed.
"""
from menpo.visualize import LandmarkViewer2d
if with_labels is not None and without_labels is not None:
raise ValueError(
"You may only pass one of `with_labels` or " "`without_labels`."
)
elif with_labels is not None:
lmark_group = self.with_labels(with_labels)
elif without_labels is not None:
lmark_group = self.without_labels(without_labels)
else:
lmark_group = self # Fall through
landmark_viewer = LandmarkViewer2d(figure_id, new_figure, group, lmark_group)
return landmark_viewer.render(
image_view=image_view,
render_lines=render_lines,
line_colour=line_colour,
line_style=line_style,
line_width=line_width,
render_markers=render_markers,
marker_style=marker_style,
marker_size=marker_size,
marker_face_colour=marker_face_colour,
marker_edge_colour=marker_edge_colour,
marker_edge_width=marker_edge_width,
render_numbering=render_numbering,
numbers_horizontal_align=numbers_horizontal_align,
numbers_vertical_align=numbers_vertical_align,
numbers_font_name=numbers_font_name,
numbers_font_size=numbers_font_size,
numbers_font_style=numbers_font_style,
numbers_font_weight=numbers_font_weight,
numbers_font_colour=numbers_font_colour,
render_legend=render_legend,
legend_title=legend_title,
legend_font_name=legend_font_name,
legend_font_style=legend_font_style,
legend_font_size=legend_font_size,
legend_font_weight=legend_font_weight,
legend_marker_scale=legend_marker_scale,
legend_location=legend_location,
legend_bbox_to_anchor=legend_bbox_to_anchor,
legend_border_axes_pad=legend_border_axes_pad,
legend_n_columns=legend_n_columns,
legend_horizontal_spacing=legend_horizontal_spacing,
legend_vertical_spacing=legend_vertical_spacing,
legend_border=legend_border,
legend_border_padding=legend_border_padding,
legend_shadow=legend_shadow,
legend_rounded_corners=legend_rounded_corners,
render_axes=render_axes,
axes_font_name=axes_font_name,
axes_font_size=axes_font_size,
axes_font_style=axes_font_style,
axes_font_weight=axes_font_weight,
axes_x_limits=axes_x_limits,
axes_y_limits=axes_y_limits,
axes_x_ticks=axes_x_ticks,
axes_y_ticks=axes_y_ticks,
figure_size=figure_size,
) | [
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... | https://github.com/menpo/menpo/blob/a61500656c4fc2eea82497684f13cc31a605550b/menpo/shape/labelled.py#L504-L816 | |
pytorch/benchmark | 5c93a5389563a5b6ccfa17d265d60f4fe7272f31 | torchbenchmark/models/maml/learner.py | python | Learner.forward | (self, x, vars=None, bn_training=True) | return x | This function can be called by finetunning, however, in finetunning, we dont wish to update
running_mean/running_var. Thought weights/bias of bn is updated, it has been separated by fast_weights.
Indeed, to not update running_mean/running_var, we need set update_bn_statistics=False
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:param x: [b, 1, 28, 28]
:param vars:
:param bn_training: set False to not update
:return: x, loss, likelihood, kld | This function can be called by finetunning, however, in finetunning, we dont wish to update
running_mean/running_var. Thought weights/bias of bn is updated, it has been separated by fast_weights.
Indeed, to not update running_mean/running_var, we need set update_bn_statistics=False
but weight/bias will be updated and not dirty initial theta parameters via fast_weiths.
:param x: [b, 1, 28, 28]
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:param bn_training: set False to not update
:return: x, loss, likelihood, kld | [
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"""
This function can be called by finetunning, however, in finetunning, we dont wish to update
running_mean/running_var. Thought weights/bias of bn is updated, it has been separated by fast_weights.
Indeed, to not update running_mean/running_var, we need set update_bn_statistics=False
but weight/bias will be updated and not dirty initial theta parameters via fast_weiths.
:param x: [b, 1, 28, 28]
:param vars:
:param bn_training: set False to not update
:return: x, loss, likelihood, kld
"""
if vars is None:
vars = self.vars
idx = 0
bn_idx = 0
for name, param in self.config:
if name is 'conv2d':
w, b = vars[idx], vars[idx + 1]
# remember to keep synchrozied of forward_encoder and forward_decoder!
x = F.conv2d(x, w, b, stride=param[4], padding=param[5])
idx += 2
# print(name, param, '\tout:', x.shape)
elif name is 'convt2d':
w, b = vars[idx], vars[idx + 1]
# remember to keep synchrozied of forward_encoder and forward_decoder!
x = F.conv_transpose2d(x, w, b, stride=param[4], padding=param[5])
idx += 2
# print(name, param, '\tout:', x.shape)
elif name is 'linear':
w, b = vars[idx], vars[idx + 1]
x = F.linear(x, w, b)
idx += 2
# print('forward:', idx, x.norm().item())
elif name is 'bn':
w, b = vars[idx], vars[idx + 1]
running_mean, running_var = self.vars_bn[bn_idx], self.vars_bn[bn_idx+1]
x = F.batch_norm(x, running_mean, running_var, weight=w, bias=b, training=bn_training)
idx += 2
bn_idx += 2
elif name is 'flatten':
# print(x.shape)
x = x.view(x.size(0), -1)
elif name is 'reshape':
# [b, 8] => [b, 2, 2, 2]
x = x.view(x.size(0), *param)
elif name is 'relu':
x = F.relu(x, inplace=param[0])
elif name is 'leakyrelu':
x = F.leaky_relu(x, negative_slope=param[0], inplace=param[1])
elif name is 'tanh':
x = F.tanh(x)
elif name is 'sigmoid':
x = torch.sigmoid(x)
elif name is 'upsample':
x = F.upsample_nearest(x, scale_factor=param[0])
elif name is 'max_pool2d':
x = F.max_pool2d(x, param[0], param[1], param[2])
elif name is 'avg_pool2d':
x = F.avg_pool2d(x, param[0], param[1], param[2])
else:
raise NotImplementedError
# make sure variable is used properly
assert idx == len(vars)
assert bn_idx == len(self.vars_bn)
return x | [
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mozillazg/pypy | 2ff5cd960c075c991389f842c6d59e71cf0cb7d0 | lib-python/2.7/mailbox.py | python | BabylMessage.set_visible | (self, visible) | Set the Message representation of visible headers. | Set the Message representation of visible headers. | [
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self._visible = Message(visible) | [
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prkumar/uplink | 3472806f68a60a93f7cb555d36365551a5411cc5 | uplink/models.py | python | dumps._wrap_delegate | (self, delegate) | return RequestBodyConverterFactory(delegate) | [] | def _wrap_delegate(self, delegate):
return RequestBodyConverterFactory(delegate) | [
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hatRiot/zarp | 2e772350a01c2aeed3f4da9685cd0cc5d6b3ecad | src/lib/libmproxy/flow.py | python | HTTPMsg.size | (self, **kwargs) | Size in bytes of a fully rendered message, including headers and
HTTP lead-in. | Size in bytes of a fully rendered message, including headers and
HTTP lead-in. | [
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"""
Size in bytes of a fully rendered message, including headers and
HTTP lead-in.
"""
hl = len(self._assemble_head(**kwargs))
if self.content:
return hl + len(self.content)
else:
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openstack/ironic | b392dc19bcd29cef5a69ec00d2f18a7a19a679e5 | ironic/drivers/modules/irmc/inspect.py | python | _get_mac_addresses | (node) | return [a for c, a in zip(node_classes, mac_addresses)
if c == NODE_CLASS_OID_VALUE['primary']] | Get mac addresses of the node.
:param node: node object.
:raises: SNMPFailure if SNMP operation failed.
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"""Get mac addresses of the node.
:param node: node object.
:raises: SNMPFailure if SNMP operation failed.
:returns: a list of mac addresses.
"""
d_info = irmc_common.parse_driver_info(node)
snmp_client = snmp.SNMPClient(d_info['irmc_address'],
d_info['irmc_snmp_port'],
d_info['irmc_snmp_version'],
d_info['irmc_snmp_community'],
d_info['irmc_snmp_security'])
node_classes = snmp_client.get_next(NODE_CLASS_OID)
mac_addresses = [':'.join(['%02x' % x for x in mac])
for mac in snmp_client.get_next(MAC_ADDRESS_OID)]
return [a for c, a in zip(node_classes, mac_addresses)
if c == NODE_CLASS_OID_VALUE['primary']] | [
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magenta/magenta | be6558f1a06984faff6d6949234f5fe9ad0ffdb5 | magenta/models/melody_rnn/melody_rnn_pipeline.py | python | EncoderPipeline.transform | (self, input_object) | return [encoded] | [] | def transform(self, input_object):
melody = input_object
melody.squash(
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self._transpose_to_key)
encoded = pipelines_common.make_sequence_example(
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translate/virtaal | 8b0a6fbf764ed92cc44a189499dec399c6423761 | virtaal/views/placeablesguiinfo.py | python | StringElemGUI.render | (self, offset=-1) | return offset | Render the string element string and its associated widgets. | Render the string element string and its associated widgets. | [
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] | def render(self, offset=-1):
"""Render the string element string and its associated widgets."""
buffer = self.textbox.buffer
if offset < 0:
offset = 0
buffer.set_text('')
if self.has_start_widget():
anchor = buffer.create_child_anchor(buffer.get_iter_at_offset(offset))
self.textbox.add_child_at_anchor(self.widgets[0], anchor)
self.widgets[0].show()
offset += 1
for child in self.elem.sub:
if isinstance(child, StringElem):
child.gui_info.render(offset)
offset += child.gui_info.length()
else:
buffer.insert(buffer.get_iter_at_offset(offset), child)
offset += len(child)
if self.has_end_widget():
anchor = buffer.create_child_anchor(buffer.get_iter_at_offset(offset))
self.textbox.add_child_at_anchor(self.widgets[1], anchor)
self.widgets[1].show()
offset += 1
return offset | [
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ZhixiuYe/NER-pytorch | bc6c7db2a522a4b21721481eb84546b2f448807d | utils.py | python | adjust_learning_rate | (optimizer, lr) | shrink learning rate for pytorch | shrink learning rate for pytorch | [
"shrink",
"learning",
"rate",
"for",
"pytorch"
] | def adjust_learning_rate(optimizer, lr):
"""
shrink learning rate for pytorch
"""
for param_group in optimizer.param_groups:
param_group['lr'] = lr | [
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hortonworks/ansible-hortonworks | d4c303e036d3b0d28ea5962b946644a8c4910a80 | inventory/gce/gce.py | python | GceInventory.parse_cli_args | (self) | Command line argument processing | Command line argument processing | [
"Command",
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"processing"
] | def parse_cli_args(self):
''' Command line argument processing '''
parser = argparse.ArgumentParser(
description='Produce an Ansible Inventory file based on GCE')
parser.add_argument('--list', action='store_true', default=True,
help='List instances (default: True)')
parser.add_argument('--host', action='store',
help='Get all information about an instance')
parser.add_argument('--instance-tags', action='store',
help='Only include instances with this tags, separated by comma')
parser.add_argument('--pretty', action='store_true', default=False,
help='Pretty format (default: False)')
parser.add_argument(
'--refresh-cache', action='store_true', default=False,
help='Force refresh of cache by making API requests (default: False - use cache files)')
self.args = parser.parse_args() | [
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aliutkus/speechmetrics | 6e1542913bd7c64dc66317ae6c753426c59cb2c0 | speechmetrics/relative/sisdr.py | python | load | (window, hop=None) | return SISDR(window, hop) | [] | def load(window, hop=None):
return SISDR(window, hop) | [
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hakril/PythonForWindows | 61e027a678d5b87aa64fcf8a37a6661a86236589 | windows/rpc/ndr.py | python | NdrHyper.get_alignment | (self) | return 8 | [] | def get_alignment(self):
return 8 | [
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.9.40/roles/lib_openshift/library/oc_volume.py | python | Yedit.separator | (self) | return self._separator | getter method for separator | getter method for separator | [
"getter",
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"separator"
] | def separator(self):
''' getter method for separator '''
return self._separator | [
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spyder-ide/spyder | 55da47c032dfcf519600f67f8b30eab467f965e7 | spyder/plugins/variableexplorer/widgets/dataframeeditor.py | python | DataFrameModel.sort | (self, column, order=Qt.AscendingOrder) | return True | Overriding sort method | Overriding sort method | [
"Overriding",
"sort",
"method"
] | def sort(self, column, order=Qt.AscendingOrder):
"""Overriding sort method"""
if self.complex_intran is not None:
if self.complex_intran.any(axis=0).iloc[column]:
QMessageBox.critical(self.dialog, "Error",
"TypeError error: no ordering "
"relation is defined for complex numbers")
return False
try:
ascending = order == Qt.AscendingOrder
if column >= 0:
try:
self.df.sort_values(by=self.df.columns[column],
ascending=ascending, inplace=True,
kind='mergesort')
except AttributeError:
# for pandas version < 0.17
self.df.sort(columns=self.df.columns[column],
ascending=ascending, inplace=True,
kind='mergesort')
except ValueError as e:
# Not possible to sort on duplicate columns
# See spyder-ide/spyder#5225.
QMessageBox.critical(self.dialog, "Error",
"ValueError: %s" % to_text_string(e))
except SystemError as e:
# Not possible to sort on category dtypes
# See spyder-ide/spyder#5361.
QMessageBox.critical(self.dialog, "Error",
"SystemError: %s" % to_text_string(e))
else:
# Update index list
self.recalculate_index()
# To sort by index
self.df.sort_index(inplace=True, ascending=ascending)
except TypeError as e:
QMessageBox.critical(self.dialog, "Error",
"TypeError error: %s" % str(e))
return False
self.reset()
return True | [
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inducer/pycuda | 9f3b898ec0846e2a4dff5077d4403ea03b1fccf9 | pycuda/sparse/packeted.py | python | PacketedSpMV.find_local_row_costs_and_remaining_coo | (
self, csr_mat, dof_to_packet_nr, old2new_fetch_indices
) | return local_row_costs, remaining_coo | [] | def find_local_row_costs_and_remaining_coo(
self, csr_mat, dof_to_packet_nr, old2new_fetch_indices
):
h, w = self.shape
local_row_costs = [0] * h
rem_coo_values = []
rem_coo_i = []
rem_coo_j = []
iptr = csr_mat.indptr
indices = csr_mat.indices
data = csr_mat.data
for i in range(h):
for idx in range(iptr[i], iptr[i + 1]):
j = indices[idx]
if dof_to_packet_nr[i] == dof_to_packet_nr[j]:
local_row_costs[i] += 1
else:
rem_coo_values.append(data[idx])
rem_coo_i.append(old2new_fetch_indices[i])
rem_coo_j.append(old2new_fetch_indices[j])
from scipy.sparse import coo_matrix
remaining_coo = coo_matrix(
(rem_coo_values, (rem_coo_i, rem_coo_j)), self.shape, dtype=self.dtype
)
return local_row_costs, remaining_coo | [
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keiffster/program-y | 8c99b56f8c32f01a7b9887b5daae9465619d0385 | src/programy/storage/stores/sql/engine.py | python | SQLStorageEngine.user_store | (self) | return SQLUserStore(self) | [] | def user_store(self):
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pymedusa/Medusa | 1405fbb6eb8ef4d20fcca24c32ddca52b11f0f38 | ext/oauthlib/oauth2/rfc6749/endpoints/pre_configured.py | python | MobileApplicationServer.__init__ | (self, request_validator, token_generator=None,
token_expires_in=None, refresh_token_generator=None, **kwargs) | Construct a new implicit grant server.
:param request_validator: An implementation of
oauthlib.oauth2.RequestValidator.
:param token_expires_in: An int or a function to generate a token
expiration offset (in seconds) given a
oauthlib.common.Request object.
:param token_generator: A function to generate a token from a request.
:param refresh_token_generator: A function to generate a token from a
request for the refresh token.
:param kwargs: Extra parameters to pass to authorization-,
token-, resource-, and revocation-endpoint constructors. | Construct a new implicit grant server. | [
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"."
] | def __init__(self, request_validator, token_generator=None,
token_expires_in=None, refresh_token_generator=None, **kwargs):
"""Construct a new implicit grant server.
:param request_validator: An implementation of
oauthlib.oauth2.RequestValidator.
:param token_expires_in: An int or a function to generate a token
expiration offset (in seconds) given a
oauthlib.common.Request object.
:param token_generator: A function to generate a token from a request.
:param refresh_token_generator: A function to generate a token from a
request for the refresh token.
:param kwargs: Extra parameters to pass to authorization-,
token-, resource-, and revocation-endpoint constructors.
"""
implicit_grant = ImplicitGrant(request_validator)
bearer = BearerToken(request_validator, token_generator,
token_expires_in, refresh_token_generator)
AuthorizationEndpoint.__init__(self, default_response_type='token',
response_types={
'token': implicit_grant},
default_token_type=bearer)
ResourceEndpoint.__init__(self, default_token='Bearer',
token_types={'Bearer': bearer})
RevocationEndpoint.__init__(self, request_validator,
supported_token_types=['access_token'])
IntrospectEndpoint.__init__(self, request_validator,
supported_token_types=['access_token']) | [
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facebookresearch/SlowFast | 39ef35c9a086443209b458cceaec86a02e27b369 | slowfast/utils/misc.py | python | is_eval_epoch | (cfg, cur_epoch, multigrid_schedule) | return (cur_epoch + 1) % cfg.TRAIN.EVAL_PERIOD == 0 | Determine if the model should be evaluated at the current epoch.
Args:
cfg (CfgNode): configs. Details can be found in
slowfast/config/defaults.py
cur_epoch (int): current epoch.
multigrid_schedule (List): schedule for multigrid training. | Determine if the model should be evaluated at the current epoch.
Args:
cfg (CfgNode): configs. Details can be found in
slowfast/config/defaults.py
cur_epoch (int): current epoch.
multigrid_schedule (List): schedule for multigrid training. | [
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"""
Determine if the model should be evaluated at the current epoch.
Args:
cfg (CfgNode): configs. Details can be found in
slowfast/config/defaults.py
cur_epoch (int): current epoch.
multigrid_schedule (List): schedule for multigrid training.
"""
if cur_epoch + 1 == cfg.SOLVER.MAX_EPOCH:
return True
if multigrid_schedule is not None:
prev_epoch = 0
for s in multigrid_schedule:
if cur_epoch < s[-1]:
period = max(
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return (s[-1] - 1 - cur_epoch) % period == 0
prev_epoch = s[-1]
return (cur_epoch + 1) % cfg.TRAIN.EVAL_PERIOD == 0 | [
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danielfm/pybreaker | 1dbcbda76dda1d1d3a9df25cbe1912655dbbf6cc | src/pybreaker.py | python | CircuitBreaker.add_excluded_exception | (self, exception) | Adds an exception to the list of excluded exceptions. | Adds an exception to the list of excluded exceptions. | [
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"""
Adds an exception to the list of excluded exceptions.
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with self._lock:
self._excluded_exceptions.append(exception) | [
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ComplianceAsCode/content | 6760a14e7fe3fb34205bd1a165095a9334412e95 | ssg/build_renumber.py | python | FileLinker._get_input_fname | (self) | return fnames.pop() if fnames else None | Returns the input filename referenced from the related check.
Raises SSGError if there are more than one filenames related to
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Raises SSGError if there are more than one filenames related to
this check system.
"""
fnames = self._get_fnames_from_related_checks()
if len(fnames) > 1:
msg = ("referencing more than one file per check system "
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raise SSGError(msg)
return fnames.pop() if fnames else None | [
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zzzeek/sqlalchemy | fc5c54fcd4d868c2a4c7ac19668d72f506fe821e | lib/sqlalchemy/engine/interfaces.py | python | Dialect.is_disconnect | (self, e, connection, cursor) | Return True if the given DB-API error indicates an invalid
connection | Return True if the given DB-API error indicates an invalid
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SheffieldML/GPy | bb1bc5088671f9316bc92a46d356734e34c2d5c0 | GPy/kern/src/psi_comp/rbf_psi_gpucomp.py | python | PSICOMP_RBF_GPU._initGPUCache | (self, N, M, Q) | [] | def _initGPUCache(self, N, M, Q):
import pycuda.gpuarray as gpuarray
if self.gpuCache == None:
self.gpuCache = {
'l_gpu' :gpuarray.empty((Q,),np.float64,order='F'),
'Z_gpu' :gpuarray.empty((M,Q),np.float64,order='F'),
'mu_gpu' :gpuarray.empty((N,Q),np.float64,order='F'),
'S_gpu' :gpuarray.empty((N,Q),np.float64,order='F'),
'psi1_gpu' :gpuarray.empty((N,M),np.float64,order='F'),
'psi2_gpu' :gpuarray.empty((M,M),np.float64,order='F'),
'psi2n_gpu' :gpuarray.empty((N,M,M),np.float64,order='F'),
'dL_dpsi1_gpu' :gpuarray.empty((N,M),np.float64,order='F'),
'dL_dpsi2_gpu' :gpuarray.empty((M,M),np.float64,order='F'),
'log_denom1_gpu' :gpuarray.empty((N,Q),np.float64,order='F'),
'log_denom2_gpu' :gpuarray.empty((N,Q),np.float64,order='F'),
# derivatives
'dvar_gpu' :gpuarray.empty((self.blocknum,),np.float64, order='F'),
'dl_gpu' :gpuarray.empty((Q,self.blocknum),np.float64, order='F'),
'dZ_gpu' :gpuarray.empty((M,Q),np.float64, order='F'),
'dmu_gpu' :gpuarray.empty((N,Q,self.blocknum),np.float64, order='F'),
'dS_gpu' :gpuarray.empty((N,Q,self.blocknum),np.float64, order='F'),
# grad
'grad_l_gpu' :gpuarray.empty((Q,),np.float64, order='F'),
'grad_mu_gpu' :gpuarray.empty((N,Q,),np.float64, order='F'),
'grad_S_gpu' :gpuarray.empty((N,Q,),np.float64, order='F'),
}
else:
assert N==self.gpuCache['mu_gpu'].shape[0]
assert M==self.gpuCache['Z_gpu'].shape[0]
assert Q==self.gpuCache['l_gpu'].shape[0] | [
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marcosfede/algorithms | 1ee7c815f9d556c9cef4d4b0d21ee3a409d21629 | union-find/count_islands/count_islands.py | python | Union.__init__ | (self) | [] | def __init__(self):
self.parents = {}
self.sizes = {}
self.count = 0 | [
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] | https://github.com/marcosfede/algorithms/blob/1ee7c815f9d556c9cef4d4b0d21ee3a409d21629/union-find/count_islands/count_islands.py#L16-L19 | ||||
aws-samples/aws-kube-codesuite | ab4e5ce45416b83bffb947ab8d234df5437f4fca | src/kubernetes/client/models/v1_cinder_volume_source.py | python | V1CinderVolumeSource.__eq__ | (self, other) | return self.__dict__ == other.__dict__ | Returns true if both objects are equal | Returns true if both objects are equal | [
"Returns",
"true",
"if",
"both",
"objects",
"are",
"equal"
] | def __eq__(self, other):
"""
Returns true if both objects are equal
"""
if not isinstance(other, V1CinderVolumeSource):
return False
return self.__dict__ == other.__dict__ | [
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researchmm/tasn | 5dba8ccc096cedc63913730eeea14a9647911129 | tasn-mxnet/python/mxnet/rnn/rnn_cell.py | python | BaseRNNCell.unpack_weights | (self, args) | return args | Unpack fused weight matrices into separate
weight matrices.
For example, say you use a module object `mod` to run a network that has an lstm cell.
In `mod.get_params()[0]`, the lstm parameters are all represented as a single big vector.
`cell.unpack_weights(mod.get_params()[0])` will unpack this vector into a dictionary of
more readable lstm parameters - c, f, i, o gates for i2h (input to hidden) and
h2h (hidden to hidden) weights.
Parameters
----------
args : dict of str -> NDArray
Dictionary containing packed weights.
usually from `Module.get_params()[0]`.
Returns
-------
args : dict of str -> NDArray
Dictionary with unpacked weights associated with
this cell.
See Also
--------
pack_weights: Performs the reverse operation of this function. | Unpack fused weight matrices into separate
weight matrices. | [
"Unpack",
"fused",
"weight",
"matrices",
"into",
"separate",
"weight",
"matrices",
"."
] | def unpack_weights(self, args):
"""Unpack fused weight matrices into separate
weight matrices.
For example, say you use a module object `mod` to run a network that has an lstm cell.
In `mod.get_params()[0]`, the lstm parameters are all represented as a single big vector.
`cell.unpack_weights(mod.get_params()[0])` will unpack this vector into a dictionary of
more readable lstm parameters - c, f, i, o gates for i2h (input to hidden) and
h2h (hidden to hidden) weights.
Parameters
----------
args : dict of str -> NDArray
Dictionary containing packed weights.
usually from `Module.get_params()[0]`.
Returns
-------
args : dict of str -> NDArray
Dictionary with unpacked weights associated with
this cell.
See Also
--------
pack_weights: Performs the reverse operation of this function.
"""
args = args.copy()
if not self._gate_names:
return args
h = self._num_hidden
for group_name in ['i2h', 'h2h']:
weight = args.pop('%s%s_weight'%(self._prefix, group_name))
bias = args.pop('%s%s_bias' % (self._prefix, group_name))
for j, gate in enumerate(self._gate_names):
wname = '%s%s%s_weight' % (self._prefix, group_name, gate)
args[wname] = weight[j*h:(j+1)*h].copy()
bname = '%s%s%s_bias' % (self._prefix, group_name, gate)
args[bname] = bias[j*h:(j+1)*h].copy()
return args | [
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nortikin/sverchok | 7b460f01317c15f2681bfa3e337c5e7346f3711b | nodes/object_nodes/getsetprop_mk2.py | python | apply_alias | (eval_str, nodetree=None) | return eval_str | - apply standard aliases
- will raise error if it isn't an bpy path | - apply standard aliases
- will raise error if it isn't an bpy path | [
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"-",
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"raise",
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"if",
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"an",
"bpy",
"path"
] | def apply_alias(eval_str, nodetree=None):
'''
- apply standard aliases
- will raise error if it isn't an bpy path
'''
if not eval_str.startswith("bpy."):
# special case for the nodes alias, end early
if eval_str.startswith("nodes") and nodetree:
string_path_to_current_tree = f'bpy.data.node_groups["{nodetree.name}"].nodes'
eval_str = eval_str.replace("nodes", string_path_to_current_tree, 1)
return eval_str
# all other aliases
for alias, expanded in aliases.items():
if eval_str.startswith(alias):
eval_str = eval_str.replace(alias, expanded, 1)
break
if not eval_str.startswith("bpy."):
raise NameError
return eval_str | [
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joelibaceta/video-to-ascii | 906abad87083afbd2b13a44a31e614566446bbb9 | video_to_ascii/render_strategy/image_processor.py | python | rgb_to_ansi | (r, g, b) | return int(ansi) | Convert an rgb color to ansi color | Convert an rgb color to ansi color | [
"Convert",
"an",
"rgb",
"color",
"to",
"ansi",
"color"
] | def rgb_to_ansi(r, g, b):
"""
Convert an rgb color to ansi color
"""
(r, g, b) = int(r), int(g), int(b)
if r == g & g == b:
if r < 8:
return int(16)
if r > 248:
return int(230)
return int(round(((r - 8) / 247) * 24) + 232)
to_ansi_range = lambda a: int(round(a / 51.0))
r_in_range = to_ansi_range(r)
g_in_range = to_ansi_range(g)
b_in_range = to_ansi_range(b)
ansi = 16 + (36 * r_in_range) + (6 * g_in_range) + b_in_range
return int(ansi) | [
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/lib-python/3/tkinter/__init__.py | python | Menubutton.__init__ | (self, master=None, cnf={}, **kw) | [] | def __init__(self, master=None, cnf={}, **kw):
Widget.__init__(self, master, 'menubutton', cnf, kw) | [
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davidmalcolm/gcc-python-plugin | 4deefc840e69e3c2c42f8a50963b8fb69c17efee | libcpychecker/initializers.py | python | StructInitializer.function_ptr_field | (self, fieldname) | return tree.operand | Extract the initializer for the given field, as a gcc.FunctionDecl,
or None for NULL. | Extract the initializer for the given field, as a gcc.FunctionDecl,
or None for NULL. | [
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] | def function_ptr_field(self, fieldname):
"""
Extract the initializer for the given field, as a gcc.FunctionDecl,
or None for NULL.
"""
if fieldname not in self.fielddict:
return None # implicit NULL
tree = self.fielddict[fieldname]
# go past casts:
if isinstance(tree, gcc.NopExpr):
tree = tree.operand
if isinstance(tree, gcc.IntegerCst):
if tree.constant == 0:
return None # NULL
check_isinstance(tree, gcc.AddrExpr)
return tree.operand | [
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... | https://github.com/davidmalcolm/gcc-python-plugin/blob/4deefc840e69e3c2c42f8a50963b8fb69c17efee/libcpychecker/initializers.py#L73-L90 | |
freesunshine0316/neural-graph-to-seq-mp | 330b24acb5439797d858de30aeefedd2d907c91d | src_g2s/generator_utils.py | python | _mask_and_avg | (values, loss_weights) | return tf.reduce_mean(values_per_ex) | Applies mask to values then returns overall average (a scalar)
Args:
values: a list length max_dec_steps containing arrays shape (batch_size).
loss_weights: tensor shape (batch_size, max_dec_steps) containing 1s and 0s.
Returns:
a scalar | Applies mask to values then returns overall average (a scalar) | [
"Applies",
"mask",
"to",
"values",
"then",
"returns",
"overall",
"average",
"(",
"a",
"scalar",
")"
] | def _mask_and_avg(values, loss_weights):
"""Applies mask to values then returns overall average (a scalar)
Args:
values: a list length max_dec_steps containing arrays shape (batch_size).
loss_weights: tensor shape (batch_size, max_dec_steps) containing 1s and 0s.
Returns:
a scalar
"""
if loss_weights == None:
return tf.reduce_mean(tf.stack(values, axis=0))
dec_lens = tf.reduce_sum(loss_weights, axis=1) # shape batch_size. float32
values_per_step = [v * loss_weights[:,dec_step] for dec_step,v in enumerate(values)]
values_per_ex = sum(values_per_step)/dec_lens # shape (batch_size); normalized value for each batch member
return tf.reduce_mean(values_per_ex) | [
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Pyomo/pyomo | dbd4faee151084f343b893cc2b0c04cf2b76fd92 | pyomo/contrib/parmest/create_ef.py | python | get_objs | (scenario_instance) | return scenario_objs | return the list of objective functions for scenario_instance | return the list of objective functions for scenario_instance | [
"return",
"the",
"list",
"of",
"objective",
"functions",
"for",
"scenario_instance"
] | def get_objs(scenario_instance):
""" return the list of objective functions for scenario_instance"""
scenario_objs = scenario_instance.component_data_objects(pyo.Objective,
active=True, descend_into=True)
scenario_objs = list(scenario_objs)
if (len(scenario_objs) == 0):
raise RuntimeError("Scenario " + sname + " has no active "
"objective functions.")
if (len(scenario_objs) > 1):
print("WARNING: Scenario", sname, "has multiple active "
"objectives. Selecting the first objective for "
"inclusion in the extensive form.")
return scenario_objs | [
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... | https://github.com/Pyomo/pyomo/blob/dbd4faee151084f343b893cc2b0c04cf2b76fd92/pyomo/contrib/parmest/create_ef.py#L14-L26 | |
tobiasfshr/MOTSFusion | 32fa6d7d3282c5046e45842370d225fa5a06603e | eval/mot_eval/munkres.py | python | Munkres.__step3 | (self) | return step | Cover each column containing a starred zero. If K columns are
covered, the starred zeros describe a complete set of unique
assignments. In this case, Go to DONE, otherwise, Go to Step 4. | Cover each column containing a starred zero. If K columns are
covered, the starred zeros describe a complete set of unique
assignments. In this case, Go to DONE, otherwise, Go to Step 4. | [
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".",
"In",
"this",
"case",
"Go",
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"DONE",
"othe... | def __step3(self):
"""
Cover each column containing a starred zero. If K columns are
covered, the starred zeros describe a complete set of unique
assignments. In this case, Go to DONE, otherwise, Go to Step 4.
"""
n = self.n
count = 0
for i in range(n):
for j in range(n):
if self.marked[i][j] == 1:
self.col_covered[j] = True
count += 1
if count >= n:
step = 7 # done
else:
step = 4
return step | [
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dropbox/dropbox-sdk-python | 015437429be224732990041164a21a0501235db1 | dropbox/sharing.py | python | UpdateFolderMemberError.is_no_permission | (self) | return self._tag == 'no_permission' | Check if the union tag is ``no_permission``.
:rtype: bool | Check if the union tag is ``no_permission``. | [
"Check",
"if",
"the",
"union",
"tag",
"is",
"no_permission",
"."
] | def is_no_permission(self):
"""
Check if the union tag is ``no_permission``.
:rtype: bool
"""
return self._tag == 'no_permission' | [
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] | https://github.com/dropbox/dropbox-sdk-python/blob/015437429be224732990041164a21a0501235db1/dropbox/sharing.py#L10402-L10408 | |
oracle/oci-python-sdk | 3c1604e4e212008fb6718e2f68cdb5ef71fd5793 | src/oci/object_storage/object_storage_client.py | python | ObjectStorageClient.update_bucket | (self, namespace_name, bucket_name, update_bucket_details, **kwargs) | Performs a partial or full update of a bucket's user-defined metadata.
Use UpdateBucket to move a bucket from one compartment to another within the same tenancy. Supply the compartmentID
of the compartment that you want to move the bucket to. For more information about moving resources between compartments,
see `Moving Resources to a Different Compartment`__.
__ https://docs.cloud.oracle.com/iaas/Content/Identity/Tasks/managingcompartments.htm#moveRes
:param str namespace_name: (required)
The Object Storage namespace used for the request.
:param str bucket_name: (required)
The name of the bucket. Avoid entering confidential information.
Example: `my-new-bucket1`
:param oci.object_storage.models.UpdateBucketDetails update_bucket_details: (required)
Request object for updating a bucket.
:param str if_match: (optional)
The entity tag (ETag) to match with the ETag of an existing resource. If the specified ETag matches the ETag of
the existing resource, GET and HEAD requests will return the resource and PUT and POST requests will upload
the resource.
:param str opc_client_request_id: (optional)
The client request ID for tracing.
:param obj retry_strategy: (optional)
A retry strategy to apply to this specific operation/call. This will override any retry strategy set at the client-level.
This should be one of the strategies available in the :py:mod:`~oci.retry` module. This operation will not retry by default, users can also use the convenient :py:data:`~oci.retry.DEFAULT_RETRY_STRATEGY` provided by the SDK to enable retries for it.
The specifics of the default retry strategy are described `here <https://docs.oracle.com/en-us/iaas/tools/python/latest/sdk_behaviors/retries.html>`__.
To have this operation explicitly not perform any retries, pass an instance of :py:class:`~oci.retry.NoneRetryStrategy`.
:return: A :class:`~oci.response.Response` object with data of type :class:`~oci.object_storage.models.Bucket`
:rtype: :class:`~oci.response.Response`
:example:
Click `here <https://docs.cloud.oracle.com/en-us/iaas/tools/python-sdk-examples/latest/objectstorage/update_bucket.py.html>`__ to see an example of how to use update_bucket API. | Performs a partial or full update of a bucket's user-defined metadata. | [
"Performs",
"a",
"partial",
"or",
"full",
"update",
"of",
"a",
"bucket",
"s",
"user",
"-",
"defined",
"metadata",
"."
] | def update_bucket(self, namespace_name, bucket_name, update_bucket_details, **kwargs):
"""
Performs a partial or full update of a bucket's user-defined metadata.
Use UpdateBucket to move a bucket from one compartment to another within the same tenancy. Supply the compartmentID
of the compartment that you want to move the bucket to. For more information about moving resources between compartments,
see `Moving Resources to a Different Compartment`__.
__ https://docs.cloud.oracle.com/iaas/Content/Identity/Tasks/managingcompartments.htm#moveRes
:param str namespace_name: (required)
The Object Storage namespace used for the request.
:param str bucket_name: (required)
The name of the bucket. Avoid entering confidential information.
Example: `my-new-bucket1`
:param oci.object_storage.models.UpdateBucketDetails update_bucket_details: (required)
Request object for updating a bucket.
:param str if_match: (optional)
The entity tag (ETag) to match with the ETag of an existing resource. If the specified ETag matches the ETag of
the existing resource, GET and HEAD requests will return the resource and PUT and POST requests will upload
the resource.
:param str opc_client_request_id: (optional)
The client request ID for tracing.
:param obj retry_strategy: (optional)
A retry strategy to apply to this specific operation/call. This will override any retry strategy set at the client-level.
This should be one of the strategies available in the :py:mod:`~oci.retry` module. This operation will not retry by default, users can also use the convenient :py:data:`~oci.retry.DEFAULT_RETRY_STRATEGY` provided by the SDK to enable retries for it.
The specifics of the default retry strategy are described `here <https://docs.oracle.com/en-us/iaas/tools/python/latest/sdk_behaviors/retries.html>`__.
To have this operation explicitly not perform any retries, pass an instance of :py:class:`~oci.retry.NoneRetryStrategy`.
:return: A :class:`~oci.response.Response` object with data of type :class:`~oci.object_storage.models.Bucket`
:rtype: :class:`~oci.response.Response`
:example:
Click `here <https://docs.cloud.oracle.com/en-us/iaas/tools/python-sdk-examples/latest/objectstorage/update_bucket.py.html>`__ to see an example of how to use update_bucket API.
"""
resource_path = "/n/{namespaceName}/b/{bucketName}"
method = "POST"
# Don't accept unknown kwargs
expected_kwargs = [
"retry_strategy",
"if_match",
"opc_client_request_id"
]
extra_kwargs = [_key for _key in six.iterkeys(kwargs) if _key not in expected_kwargs]
if extra_kwargs:
raise ValueError(
"update_bucket got unknown kwargs: {!r}".format(extra_kwargs))
path_params = {
"namespaceName": namespace_name,
"bucketName": bucket_name
}
path_params = {k: v for (k, v) in six.iteritems(path_params) if v is not missing}
for (k, v) in six.iteritems(path_params):
if v is None or (isinstance(v, six.string_types) and len(v.strip()) == 0):
raise ValueError('Parameter {} cannot be None, whitespace or empty string'.format(k))
header_params = {
"accept": "application/json",
"content-type": "application/json",
"if-match": kwargs.get("if_match", missing),
"opc-client-request-id": kwargs.get("opc_client_request_id", missing)
}
header_params = {k: v for (k, v) in six.iteritems(header_params) if v is not missing and v is not None}
retry_strategy = self.base_client.get_preferred_retry_strategy(
operation_retry_strategy=kwargs.get('retry_strategy'),
client_retry_strategy=self.retry_strategy
)
if retry_strategy:
if not isinstance(retry_strategy, retry.NoneRetryStrategy):
self.base_client.add_opc_client_retries_header(header_params)
retry_strategy.add_circuit_breaker_callback(self.circuit_breaker_callback)
return retry_strategy.make_retrying_call(
self.base_client.call_api,
resource_path=resource_path,
method=method,
path_params=path_params,
header_params=header_params,
body=update_bucket_details,
response_type="Bucket")
else:
return self.base_client.call_api(
resource_path=resource_path,
method=method,
path_params=path_params,
header_params=header_params,
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/vpc/v20170312/models.py | python | DescribeVpcInstancesResponse.__init__ | (self) | r"""
:param InstanceSet: 云主机实例列表。
:type InstanceSet: list of CvmInstance
:param TotalCount: 满足条件的云主机实例个数。
:type TotalCount: int
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:type RequestId: str | r"""
:param InstanceSet: 云主机实例列表。
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pyparallel/pyparallel | 11e8c6072d48c8f13641925d17b147bf36ee0ba3 | Lib/site-packages/pandas-0.17.0-py3.3-win-amd64.egg/pandas/core/base.py | python | IndexOpsMixin.strides | (self) | return self.values.strides | return the strides of the underlying data | return the strides of the underlying data | [
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""" return the strides of the underlying data """
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/benchmarks/src/benchmarks/whoosh/benchmark/dictionary.py | python | VulgarTongue.whoosh_schema | (self) | return schema | [] | def whoosh_schema(self):
ana = analysis.StemmingAnalyzer()
#ana = analysis.StandardAnalyzer()
schema = fields.Schema(head=fields.ID(stored=True),
body=fields.TEXT(analyzer=ana, stored=True))
return schema | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/modular/modform_hecketriangle/subspace.py | python | SubSpaceForms.coordinate_vector | (self, v) | return self._module.coordinate_vector(self.ambient_coordinate_vector(v)) | r"""
Return the coordinate vector of ``v`` with respect to
the basis ``self.gens()``.
INPUT:
- ``v`` -- An element of ``self``.
OUTPUT:
The coordinate vector of ``v`` with respect
to the basis ``self.gens()``.
Note: The coordinate vector is not an element of ``self.module()``.
EXAMPLES::
sage: from sage.modular.modform_hecketriangle.space import ModularForms, QuasiCuspForms
sage: MF = ModularForms(n=6, k=20, ep=1)
sage: subspace = MF.subspace([(MF.Delta()*MF.E4()^2).as_ring_element(), MF.gen(0)])
sage: subspace.coordinate_vector(MF.gen(0) + MF.Delta()*MF.E4()^2).parent()
Vector space of dimension 2 over Fraction Field of Univariate Polynomial Ring in d over Integer Ring
sage: subspace.coordinate_vector(MF.gen(0) + MF.Delta()*MF.E4()^2)
(1, 1)
sage: MF = ModularForms(n=4, k=24, ep=-1)
sage: subspace = MF.subspace([MF.gen(0), MF.gen(2)])
sage: subspace.coordinate_vector(subspace.gen(0)).parent()
Vector space of dimension 2 over Fraction Field of Univariate Polynomial Ring in d over Integer Ring
sage: subspace.coordinate_vector(subspace.gen(0))
(1, 0)
sage: MF = QuasiCuspForms(n=infinity, k=12, ep=1)
sage: subspace = MF.subspace([MF.Delta(), MF.E4()*MF.f_inf()*MF.E2()*MF.f_i(), MF.E4()*MF.f_inf()*MF.E2()^2, MF.E4()*MF.f_inf()*(MF.E4()-MF.E2()^2)])
sage: el = MF.E4()*MF.f_inf()*(7*MF.E4() - 3*MF.E2()^2)
sage: subspace.coordinate_vector(el)
(7, 0, -3)
sage: subspace.ambient_coordinate_vector(el)
(7, 21/(8*d), 0, -3) | r"""
Return the coordinate vector of ``v`` with respect to
the basis ``self.gens()``. | [
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Return the coordinate vector of ``v`` with respect to
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OUTPUT:
The coordinate vector of ``v`` with respect
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Note: The coordinate vector is not an element of ``self.module()``.
EXAMPLES::
sage: from sage.modular.modform_hecketriangle.space import ModularForms, QuasiCuspForms
sage: MF = ModularForms(n=6, k=20, ep=1)
sage: subspace = MF.subspace([(MF.Delta()*MF.E4()^2).as_ring_element(), MF.gen(0)])
sage: subspace.coordinate_vector(MF.gen(0) + MF.Delta()*MF.E4()^2).parent()
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sage: subspace.coordinate_vector(MF.gen(0) + MF.Delta()*MF.E4()^2)
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sage: MF = ModularForms(n=4, k=24, ep=-1)
sage: subspace = MF.subspace([MF.gen(0), MF.gen(2)])
sage: subspace.coordinate_vector(subspace.gen(0)).parent()
Vector space of dimension 2 over Fraction Field of Univariate Polynomial Ring in d over Integer Ring
sage: subspace.coordinate_vector(subspace.gen(0))
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sage: MF = QuasiCuspForms(n=infinity, k=12, ep=1)
sage: subspace = MF.subspace([MF.Delta(), MF.E4()*MF.f_inf()*MF.E2()*MF.f_i(), MF.E4()*MF.f_inf()*MF.E2()^2, MF.E4()*MF.f_inf()*(MF.E4()-MF.E2()^2)])
sage: el = MF.E4()*MF.f_inf()*(7*MF.E4() - 3*MF.E2()^2)
sage: subspace.coordinate_vector(el)
(7, 0, -3)
sage: subspace.ambient_coordinate_vector(el)
(7, 21/(8*d), 0, -3)
"""
return self._module.coordinate_vector(self.ambient_coordinate_vector(v)) | [
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yongheng1991/3D-point-capsule-networks | f2d55d845f4bf74e931ee1eaeaf60f481e4c59ad | dataloaders/modelnet40_loader.py | python | load_h5 | (h5_filename) | return (data, label) | [] | def load_h5(h5_filename):
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data = f['data'][:]
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shiweibsw/Translation-Tools | 2fbbf902364e557fa7017f9a74a8797b7440c077 | venv/Lib/site-packages/pip-9.0.3-py3.6.egg/pip/utils/__init__.py | python | dist_is_local | (dist) | return is_local(dist_location(dist)) | Return True if given Distribution object is installed locally
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Return True if given Distribution object is installed locally
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wxWidgets/Phoenix | b2199e299a6ca6d866aa6f3d0888499136ead9d6 | wx/lib/agw/shortcuteditor.py | python | HTMLHelpWindow.OnUpdateUI | (self, event) | Handles all the ``wx.EVT_UPDATE_UI`` events for :class:`HTMLHelpWindow`.
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Handles all the ``wx.EVT_UPDATE_UI`` events for :class:`HTMLHelpWindow`.
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"""
evId = event.GetId()
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learningequality/ka-lite | 571918ea668013dcf022286ea85eff1c5333fb8b | kalite/packages/bundled/django/utils/timezone.py | python | get_current_timezone | () | return getattr(_active, "value", get_default_timezone()) | Returns the currently active time zone as a tzinfo instance. | Returns the currently active time zone as a tzinfo instance. | [
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pythonanywhere/dirigible-spreadsheet | c771e9a391708f3b219248bf9974e05b1582fdd0 | documentation/BeautifulSoup.py | python | UnicodeDammit._subMSChar | (self, match) | return sub | Changes a MS smart quote character to an XML or HTML
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kennethreitz/records | 0e2d186857f16686139c20be10a1f70a689c9ea0 | records.py | python | Database.close | (self) | Closes the Database. | Closes the Database. | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/django/db/models/expressions.py | python | OrderBy.get_source_expressions | (self) | return [self.expression] | [] | def get_source_expressions(self):
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xtiankisutsa/MARA_Framework | ac4ac88bfd38f33ae8780a606ed09ab97177c562 | tools/androwarn/androguard/core/bytecode.py | python | method2format | ( output, _format="png", mx = None, raw = False ) | Export method to a specific file format
@param output : output filename
@param _format : format type (png, jpg ...) (default : png)
@param mx : specify the MethodAnalysis object
@param raw : use directly a dot raw buffer | Export method to a specific file format | [
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"""
Export method to a specific file format
@param output : output filename
@param _format : format type (png, jpg ...) (default : png)
@param mx : specify the MethodAnalysis object
@param raw : use directly a dot raw buffer
"""
try :
import pydot
except ImportError :
error("module pydot not found")
buff = "digraph code {\n"
buff += "graph [bgcolor=white];\n"
buff += "node [color=lightgray, style=filled shape=box fontname=\"Courier\" fontsize=\"8\"];\n"
if raw == False :
buff += method2dot( mx )
else :
buff += raw
buff += "}"
d = pydot.graph_from_dot_data( buff )
if d :
getattr(d, "write_" + _format)( output ) | [
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talebolano/yolov3-network-slimming | 335227f60477f7f078d2f9595a69faf7fc9541b2 | parse_config.py | python | ImageFolder.__getitem__ | (self, index) | return img_path, input_img | [] | def __getitem__(self, index):
img_path = self.files[index % len(self.files)]
# Extract image
img = np.array(Image.open(img_path))
h, w, _ = img.shape
dim_diff = np.abs(h - w)
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pad1, pad2 = dim_diff // 2, dim_diff - dim_diff // 2
# Determine padding
pad = ((pad1, pad2), (0, 0), (0, 0)) if h <= w else ((0, 0), (pad1, pad2), (0, 0))
# Add padding
input_img = np.pad(img, pad, 'constant', constant_values=127.5) / 255.
# Resize and normalize
input_img = resize(input_img, (*self.img_shape, 3), mode='reflect')
# Channels-first
input_img = np.transpose(input_img, (2, 0, 1))
# As pytorch tensor
input_img = torch.from_numpy(input_img).float()
return img_path, input_img | [
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AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/lib/django-1.5/django/db/models/query.py | python | QuerySet.create | (self, **kwargs) | return obj | Creates a new object with the given kwargs, saving it to the database
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kubernetes-client/python | 47b9da9de2d02b2b7a34fbe05afb44afd130d73a | kubernetes/client/models/v2beta1_horizontal_pod_autoscaler_spec.py | python | V2beta1HorizontalPodAutoscalerSpec.__init__ | (self, max_replicas=None, metrics=None, min_replicas=None, scale_target_ref=None, local_vars_configuration=None) | V2beta1HorizontalPodAutoscalerSpec - a model defined in OpenAPI | V2beta1HorizontalPodAutoscalerSpec - a model defined in OpenAPI | [
"V2beta1HorizontalPodAutoscalerSpec",
"-",
"a",
"model",
"defined",
"in",
"OpenAPI"
] | def __init__(self, max_replicas=None, metrics=None, min_replicas=None, scale_target_ref=None, local_vars_configuration=None): # noqa: E501
"""V2beta1HorizontalPodAutoscalerSpec - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._max_replicas = None
self._metrics = None
self._min_replicas = None
self._scale_target_ref = None
self.discriminator = None
self.max_replicas = max_replicas
if metrics is not None:
self.metrics = metrics
if min_replicas is not None:
self.min_replicas = min_replicas
self.scale_target_ref = scale_target_ref | [
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davidoren/CuckooSploit | 3fce8183bee8f7917e08f765ce2a01c921f86354 | lib/cuckoo/core/scheduler.py | python | AnalysisManager.init_storage | (self) | return True | Initialize analysis storage folder. | Initialize analysis storage folder. | [
"Initialize",
"analysis",
"storage",
"folder",
"."
] | def init_storage(self):
"""Initialize analysis storage folder."""
self.storage = os.path.join(CUCKOO_ROOT,
"storage",
"analyses",
str(self.task.id))
# If the analysis storage folder already exists, we need to abort the
# analysis or previous results will be overwritten and lost.
if os.path.exists(self.storage):
log.error("Analysis results folder already exists at path \"%s\","
" analysis aborted", self.storage)
return False
# If we're not able to create the analysis storage folder, we have to
# abort the analysis.
try:
create_folder(folder=self.storage)
except CuckooOperationalError:
log.error("Unable to create analysis folder %s", self.storage)
return False
return True | [
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hsoft/pdfmasher | 08943dd1ad9d8fa91c547d5e91d2f90114d76094 | ebooks/metadata/book.py | python | Metadata.get_all_user_metadata | (self, make_copy) | return res | return a dict containing all the custom field metadata associated with
the book. | return a dict containing all the custom field metadata associated with
the book. | [
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] | def get_all_user_metadata(self, make_copy):
'''
return a dict containing all the custom field metadata associated with
the book.
'''
_data = object.__getattribute__(self, '_data')
user_metadata = _data['user_metadata']
if not make_copy:
return user_metadata
res = {}
for k in user_metadata:
res[k] = copy.deepcopy(user_metadata[k])
return res | [
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robotlearn/pyrobolearn | 9cd7c060723fda7d2779fa255ac998c2c82b8436 | pyrobolearn/terminal_conditions/body_conditions.py | python | PositionCondition.__init__ | (self, body, bounds=(None, None), dim=None, out=False, stay=False, all=False) | Initialize the world position terminal condition.
Args:
body (Body): body instance.
bounds (tuple of 2 float / np.array[3]): bounds on the body position.
dim (None, int, int[3]): dimensions that we should consider when looking at the bounds. If None, it will
consider all 3 dimensions. If one dimension is provided it will only check along that dimension. If
a np.array of 0 and 1 is provided, it will consider the dimensions that are equal to 1. Thus, [1,0,1]
means to consider the bounds along the x and z axes.
out (bool): if True, we are outside the provided bounds. If False, we are inside the provided bounds.
stay (bool): if True, it must stay in the bounds defined by in_bounds or out_bounds; if the position
leaves the bounds it results in a failure. if :attr:`stay` is False, it must get outside these bounds;
if the position leaves the bounds, it results in a success.
all (bool): this is only used if they are multiple dimensions. if True, all the dimensions of the state
are checked if they are inside or outside the bounds depending on the other parameters. if False, any
dimensions will be checked. | Initialize the world position terminal condition. | [
"Initialize",
"the",
"world",
"position",
"terminal",
"condition",
"."
] | def __init__(self, body, bounds=(None, None), dim=None, out=False, stay=False, all=False):
"""
Initialize the world position terminal condition.
Args:
body (Body): body instance.
bounds (tuple of 2 float / np.array[3]): bounds on the body position.
dim (None, int, int[3]): dimensions that we should consider when looking at the bounds. If None, it will
consider all 3 dimensions. If one dimension is provided it will only check along that dimension. If
a np.array of 0 and 1 is provided, it will consider the dimensions that are equal to 1. Thus, [1,0,1]
means to consider the bounds along the x and z axes.
out (bool): if True, we are outside the provided bounds. If False, we are inside the provided bounds.
stay (bool): if True, it must stay in the bounds defined by in_bounds or out_bounds; if the position
leaves the bounds it results in a failure. if :attr:`stay` is False, it must get outside these bounds;
if the position leaves the bounds, it results in a success.
all (bool): this is only used if they are multiple dimensions. if True, all the dimensions of the state
are checked if they are inside or outside the bounds depending on the other parameters. if False, any
dimensions will be checked.
"""
super(PositionCondition, self).__init__(body, bounds=bounds, dim=dim, out=out, stay=stay, all=all)
# check the bounds
self.bounds = self._check_bounds(bounds=bounds) | [
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ergoithz/browsepy | 274028eaa460652a9d5a8c2062db1d410d09f3b7 | browsepy/appconfig.py | python | Config.gendict | (cls, *args, **kwargs) | return dict((gk(k), v) for k, v in dict(*args, **kwargs).items()) | Pre-translated key dictionary constructor.
See :type:`dict` for more info.
:returns: dictionary with uppercase keys
:rtype: dict | Pre-translated key dictionary constructor. | [
"Pre",
"-",
"translated",
"key",
"dictionary",
"constructor",
"."
] | def gendict(cls, *args, **kwargs):
'''
Pre-translated key dictionary constructor.
See :type:`dict` for more info.
:returns: dictionary with uppercase keys
:rtype: dict
'''
gk = cls.genkey
return dict((gk(k), v) for k, v in dict(*args, **kwargs).items()) | [
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svenevs/exhale | d6fa84fa32ee079c6b70898a1b0863a38e703591 | exhale/utils.py | python | verbose_log | (msg, ansi_fmt=None) | [] | def verbose_log(msg, ansi_fmt=None):
if configs.verboseBuild:
if ansi_fmt:
log = _use_color(msg, ansi_fmt, sys.stderr)
else:
log = msg
sys.stderr.write("{log}\n".format(log=log)) | [
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catap/namebench | 9913a7a1a7955a3759eb18cbe73b421441a7a00f | nb_third_party/dns/rcode.py | python | from_flags | (flags, ednsflags) | return value | Return the rcode value encoded by flags and ednsflags.
@param flags: the DNS flags
@type flags: int
@param ednsflags: the EDNS flags
@type ednsflags: int
@raises ValueError: rcode is < 0 or > 4095
@rtype: int | Return the rcode value encoded by flags and ednsflags. | [
"Return",
"the",
"rcode",
"value",
"encoded",
"by",
"flags",
"and",
"ednsflags",
"."
] | def from_flags(flags, ednsflags):
"""Return the rcode value encoded by flags and ednsflags.
@param flags: the DNS flags
@type flags: int
@param ednsflags: the EDNS flags
@type ednsflags: int
@raises ValueError: rcode is < 0 or > 4095
@rtype: int
"""
value = (flags & 0x000f) | ((ednsflags >> 20) & 0xff0)
if value < 0 or value > 4095:
raise ValueError('rcode must be >= 0 and <= 4095')
return value | [
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liyi14/mx-DeepIM | f1c850e5f8f75f1051a89c40daff9185870020f5 | lib/dataset/LM6D_REFINE_SYN.py | python | LM6D_REFINE_SYN.__init__ | (
self,
cfg,
image_set,
root_path,
devkit_path,
class_name,
result_path=None,
mask_size=-1,
binary_thresh=None,
mask_syn_name="",
) | fill basic information to initialize imdb
:param image_set: 2007_trainval, 2007_test, etc
:param root_path: 'selective_search_data' and 'cache'
:param devkit_path: data and results
:return: imdb object | fill basic information to initialize imdb
:param image_set: 2007_trainval, 2007_test, etc
:param root_path: 'selective_search_data' and 'cache'
:param devkit_path: data and results
:return: imdb object | [
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":",
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"and",
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":",
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"devkit_path",
":",
"data",
"and",
"results",
... | def __init__(
self,
cfg,
image_set,
root_path,
devkit_path,
class_name,
result_path=None,
mask_size=-1,
binary_thresh=None,
mask_syn_name="",
):
"""
fill basic information to initialize imdb
:param image_set: 2007_trainval, 2007_test, etc
:param root_path: 'selective_search_data' and 'cache'
:param devkit_path: data and results
:return: imdb object
"""
# image_set = image_set[len(year) + 1 : len(image_set)]
if len(mask_syn_name) > 0:
super(LM6D_REFINE_SYN, self).__init__(
"LM6D_data_syn_light" + mask_syn_name, image_set, root_path, devkit_path, result_path
) # set self.name
else:
super(LM6D_REFINE_SYN, self).__init__(
"LM6D_data_syn_light", image_set, root_path, devkit_path, result_path
) # set self.name
self.root_path = root_path
self.devkit_path = os.path.join(devkit_path, "../LM6d_refine_syn")
print("*******************lm6d data syn devkit path: {}".format(self.devkit_path))
self.gt_observed_data_path = os.path.join(self.devkit_path, "data", "gt_observed")
self.observed_data_path = os.path.join(self.devkit_path, "data", "observed")
self._linemod_path = self._get_default_path()
if image_set.startswith("train") or image_set.startswith("my_val") or image_set.startswith("my_minival"):
self.rendered_data_path = os.path.join(self.devkit_path, "data", "rendered")
else:
raise Exception("unknown prefix of " + image_set)
print("LM6d data syn v1 rendered path: {}".format(self.rendered_data_path))
if image_set.startswith("train"):
self.phase = "train"
elif image_set.startswith("my_val") or image_set.startswith("my_minival"):
self.phase = "val"
else:
raise Exception("unknown prefix of " + image_set)
self.classes = [
"ape",
"benchvise",
"cam",
"can",
"cat",
"driller",
"duck",
"eggbox",
"glue",
"holepuncher",
"iron",
"lamp",
"phone",
]
self.cur_class = class_name
self.num_classes = len(self.classes)
self.image_set_index = self.load_image_set_index()
self.num_pairs = len(self.image_set_index)
print("num_pairs", self.num_pairs)
self.mask_size = mask_size
self.binary_thresh = binary_thresh | [
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robotframework/RIDE | 6e8a50774ff33dead3a2757a11b0b4418ab205c0 | src/robotide/preferences/configobj.py | python | InterpolationEngine._parse_match | (self, match) | Implementation-dependent helper function.
Will be passed a match object corresponding to the interpolation
key we just found (e.g., "%(foo)s" or "$foo"). Should look up that
key in the appropriate config file section (using the ``_fetch()``
helper function) and return a 3-tuple: (key, value, section)
``key`` is the name of the key we're looking for
``value`` is the value found for that key
``section`` is a reference to the section where it was found
``key`` and ``section`` should be None if no further
interpolation should be performed on the resulting value
(e.g., if we interpolated "$$" and returned "$"). | Implementation-dependent helper function. | [
"Implementation",
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"dependent",
"helper",
"function",
"."
] | def _parse_match(self, match):
"""Implementation-dependent helper function.
Will be passed a match object corresponding to the interpolation
key we just found (e.g., "%(foo)s" or "$foo"). Should look up that
key in the appropriate config file section (using the ``_fetch()``
helper function) and return a 3-tuple: (key, value, section)
``key`` is the name of the key we're looking for
``value`` is the value found for that key
``section`` is a reference to the section where it was found
``key`` and ``section`` should be None if no further
interpolation should be performed on the resulting value
(e.g., if we interpolated "$$" and returned "$").
"""
raise NotImplementedError() | [
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] | https://github.com/robotframework/RIDE/blob/6e8a50774ff33dead3a2757a11b0b4418ab205c0/src/robotide/preferences/configobj.py#L388-L404 | ||
XuShaohua/bcloud | 4b54e0fdccf2b3013285fef05c97354cfa31697b | bcloud/RequestCookie.py | python | RequestCookie.load_list | (self, raw_items) | 读取多个以字符串形式存放的cookie. | 读取多个以字符串形式存放的cookie. | [
"读取多个以字符串形式存放的cookie",
"."
] | def load_list(self, raw_items):
'''读取多个以字符串形式存放的cookie.'''
if not raw_items:
return
for item in raw_items:
self.load(item) | [
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jankrepl/pychubby | 04d8f66273da6f9bd2f85b740681b56ec5c25ddb | pychubby/data.py | python | get_pretrained_68 | (folder=None, verbose=True) | Get pretrained landmarks model for dlib.
Parameters
----------
folder : str or pathlib.Path or None
Folder where to save the .dat file.
verbose : bool
Print some output.
References
----------
[1] C. Sagonas, E. Antonakos, G, Tzimiropoulos, S. Zafeiriou, M. Pantic.
300 faces In-the-wild challenge: Database and results. Image and Vision Computing (IMAVIS),
Special Issue on Facial Landmark Localisation "In-The-Wild". 2016.
[2] C. Sagonas, G. Tzimiropoulos, S. Zafeiriou, M. Pantic.
A semi-automatic methodology for facial landmark annotation. Proceedings of IEEE Int’l Conf.
Computer Vision and Pattern Recognition (CVPR-W), 5th Workshop on Analysis and Modeling of
Faces and Gestures (AMFG 2013). Oregon, USA, June 2013.
[3] C. Sagonas, G. Tzimiropoulos, S. Zafeiriou, M. Pantic.
300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge.
Proceedings of IEEE Int’l Conf. on Computer Vision (ICCV-W), 300 Faces in-the-Wild | Get pretrained landmarks model for dlib. | [
"Get",
"pretrained",
"landmarks",
"model",
"for",
"dlib",
"."
] | def get_pretrained_68(folder=None, verbose=True):
"""Get pretrained landmarks model for dlib.
Parameters
----------
folder : str or pathlib.Path or None
Folder where to save the .dat file.
verbose : bool
Print some output.
References
----------
[1] C. Sagonas, E. Antonakos, G, Tzimiropoulos, S. Zafeiriou, M. Pantic.
300 faces In-the-wild challenge: Database and results. Image and Vision Computing (IMAVIS),
Special Issue on Facial Landmark Localisation "In-The-Wild". 2016.
[2] C. Sagonas, G. Tzimiropoulos, S. Zafeiriou, M. Pantic.
A semi-automatic methodology for facial landmark annotation. Proceedings of IEEE Int’l Conf.
Computer Vision and Pattern Recognition (CVPR-W), 5th Workshop on Analysis and Modeling of
Faces and Gestures (AMFG 2013). Oregon, USA, June 2013.
[3] C. Sagonas, G. Tzimiropoulos, S. Zafeiriou, M. Pantic.
300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge.
Proceedings of IEEE Int’l Conf. on Computer Vision (ICCV-W), 300 Faces in-the-Wild
"""
url = "https://raw.githubusercontent.com/"
url += "davisking/dlib-models/master/shape_predictor_68_face_landmarks.dat.bz2"
folder = pathlib.Path(CACHE_FOLDER) if folder is None else pathlib.Path(folder)
filepath = folder / "shape_predictor_68_face_landmarks.dat"
if filepath.is_file():
return
if verbose:
print("Downloading and decompressing {} to {}.".format(url, filepath))
req = urllib.request.urlopen(url)
CHUNK = 16 * 1024
decompressor = bz2.BZ2Decompressor()
with open(str(filepath), 'wb') as fp:
while True:
chunk = req.read(CHUNK)
if not chunk:
break
fp.write(decompressor.decompress(chunk))
req.close() | [
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malwaredllc/byob | 3924dd6aea6d0421397cdf35f692933b340bfccf | web-gui/buildyourownbotnet/modules/miner.py | python | Job.merkle_root_bin | (self, extranounce2_bin) | return merkle_root | Builds a merkle root from the merkle tree | Builds a merkle root from the merkle tree | [
"Builds",
"a",
"merkle",
"root",
"from",
"the",
"merkle",
"tree"
] | def merkle_root_bin(self, extranounce2_bin):
'''Builds a merkle root from the merkle tree'''
coinbase_bin = unhexlify(self._coinb1) + unhexlify(self._extranounce1) + extranounce2_bin + unhexlify(self._coinb2)
coinbase_hash_bin = sha256d(coinbase_bin)
merkle_root = coinbase_hash_bin
for branch in self._merkle_branches:
merkle_root = sha256d(merkle_root + unhexlify(branch))
return merkle_root | [
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pyside/pyside2-setup | d526f801ced4687d5413907a93dedcd782ef72fa | examples/widgets/itemviews/stardelegate/starrating.py | python | StarRating.sizeHint | (self) | return PAINTING_SCALE_FACTOR * QSize(self.maxStarCount, 1) | Tell the caller how big we are. | Tell the caller how big we are. | [
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] | def sizeHint(self):
""" Tell the caller how big we are. """
return PAINTING_SCALE_FACTOR * QSize(self.maxStarCount, 1) | [
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mesonbuild/meson | a22d0f9a0a787df70ce79b05d0c45de90a970048 | docs/refman/loaderbase.py | python | _Resolver.__init__ | (self) | [] | def __init__(self) -> None:
self.type_map: T.Dict[str, Object] = {}
self.func_map: T.Dict[str, T.Union[Function, Method]] = {}
self.processed_funcs: T.Set[str] = set() | [
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dhlab-epfl/dhSegment | cca94e94aec52baa9350eaaa60c006d7fde103b7 | dh_segment/io/input_utils.py | python | extract_patches_fn | (image: tf.Tensor, patch_shape: Tuple[int, int], offsets: Tuple[int, int]) | Will cut a given image into patches.
:param image: tf.Tensor
:param patch_shape: shape of the extracted patches [h, w]
:param offsets: offset to add to the origin of first patch top-right coordinate, useful during data augmentation \
to have slighlty different patches each time. This value will be multiplied by [h/2, w/2] (range values [0,1])
:return: patches [batch_patches, h, w, c] | Will cut a given image into patches. | [
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"."
] | def extract_patches_fn(image: tf.Tensor, patch_shape: Tuple[int, int], offsets: Tuple[int, int]) -> tf.Tensor:
"""Will cut a given image into patches.
:param image: tf.Tensor
:param patch_shape: shape of the extracted patches [h, w]
:param offsets: offset to add to the origin of first patch top-right coordinate, useful during data augmentation \
to have slighlty different patches each time. This value will be multiplied by [h/2, w/2] (range values [0,1])
:return: patches [batch_patches, h, w, c]
"""
with tf.name_scope('patch_extraction'):
h, w = patch_shape
c = image.get_shape()[-1]
offset_h = tf.cast(tf.round(offsets[0] * h // 2), dtype=tf.int32)
offset_w = tf.cast(tf.round(offsets[1] * w // 2), dtype=tf.int32)
offset_img = image[offset_h:, offset_w:, :]
offset_img = offset_img[None, :, :, :]
patches = tf.extract_image_patches(offset_img, ksizes=[1, h, w, 1], strides=[1, h // 2, w // 2, 1],
rates=[1, 1, 1, 1], padding='VALID')
patches_shape = tf.shape(patches)
return tf.reshape(patches, [tf.reduce_prod(patches_shape[:3]), h, w, int(c)]) | [
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huawei-noah/vega | d9f13deede7f2b584e4b1d32ffdb833856129989 | vega/core/pipeline/conf.py | python | SearchSpaceConfig.from_dict | (cls, data, skip_check=True) | return cls | Restore config from a dictionary or a file. | Restore config from a dictionary or a file. | [
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"""Restore config from a dictionary or a file."""
cls = super(SearchSpaceConfig, cls).from_dict(data, skip_check)
if "type" in data and not data["type"]:
if hasattr(cls, "hyperparameters"):
del cls.hyperparameters
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blurstudio/cross3d | 277968d1227de740fc87ef61005c75034420eadf | cross3d/softimage/softimagescene.py | python | SoftimageScene.currentFileName | (cls) | return xsi.ActiveProject.ActiveScene.FileName.Value | \remarks implements AbstractScene.currentFileName method to return the current filename for the scene that is active in the application
\return <str> | \remarks implements AbstractScene.currentFileName method to return the current filename for the scene that is active in the application
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"""
\remarks implements AbstractScene.currentFileName method to return the current filename for the scene that is active in the application
\return <str>
"""
return xsi.ActiveProject.ActiveScene.FileName.Value | [
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keiffster/program-y | 8c99b56f8c32f01a7b9887b5daae9465619d0385 | src/programy/oob/callmom/map.py | python | MapOutOfBandProcessor.parse_oob_xml | (self, oob: ET.Element) | [] | def parse_oob_xml(self, oob: ET.Element):
if oob is not None and oob.text is not None:
self._location = oob.text
return True
else:
YLogger.error(self, "Unvalid geomap oob command - missing location text!")
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pytoolz/toolz | 294e981edad035a7ac6f0e2b48f1738368fa4b34 | versioneer.py | python | render_pep440_pre | (pieces) | return rendered | TAG[.post.devDISTANCE] -- No -dirty.
Exceptions:
1: no tags. 0.post.devDISTANCE | TAG[.post.devDISTANCE] -- No -dirty. | [
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] | def render_pep440_pre(pieces):
"""TAG[.post.devDISTANCE] -- No -dirty.
Exceptions:
1: no tags. 0.post.devDISTANCE
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"]:
rendered += ".post.dev%d" % pieces["distance"]
else:
# exception #1
rendered = "0.post.dev%d" % pieces["distance"]
return rendered | [
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twilio/twilio-python | 6e1e811ea57a1edfadd5161ace87397c563f6915 | examples/client_validation.py | python | example | () | Example of using the ValidationClient for signed requests to Twilio.
This is only available to enterprise customers.
This will walkthrough creating an API Key, generating an RSA keypair, setting up a
ValidationClient with these values and making requests with the client. | Example of using the ValidationClient for signed requests to Twilio.
This is only available to enterprise customers. | [
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] | def example():
"""
Example of using the ValidationClient for signed requests to Twilio.
This is only available to enterprise customers.
This will walkthrough creating an API Key, generating an RSA keypair, setting up a
ValidationClient with these values and making requests with the client.
"""
client = Client(ACCOUNT_SID, AUTH_TOKEN)
# Using Client Validation requires using API Keys for auth
# First create an API key using the standard account sid, auth token client
print('Creating new api key...')
api_key = client.new_keys.create(friendly_name='ClientValidationApiKey')
# Generate a new RSA Keypair
print('Generating RSA key pair...')
key_pair = rsa.generate_private_key(
public_exponent=65537,
key_size=2048,
backend=default_backend()
)
public_key = key_pair.public_key().public_bytes(Encoding.PEM, PublicFormat.SubjectPublicKeyInfo)
private_key = key_pair.private_bytes(Encoding.PEM, PrivateFormat.PKCS8, NoEncryption())
# Register the public key with Twilio
print('Registering public key with Twilio...')
credential = client.accounts.credentials.public_key.create(
public_key,
friendly_name='ClientValidationPublicKey'
)
# Create a new ValidationClient with the keys we created
validation_client = ValidationClient(
ACCOUNT_SID,
api_key.sid,
credential.sid,
private_key
)
# Create a REST Client using the validation_client
client = Client(api_key.sid, api_key.secret, ACCOUNT_SID, http_client=validation_client)
# Use the library as usual
print('Trying out client validation...')
messages = client.messages.list(limit=10)
for m in messages:
print('Message {}'.format(m.sid))
print('Client validation works!') | [
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uber/doubles | 15e68dcf98f709b19a581915fa6af5ef49ebdd8a | doubles/allowance.py | python | Allowance.__call__ | (self, *args, **kwargs) | return self.with_args(*args, **kwargs) | A short hand syntax for with_args
Allows callers to do:
allow(module).foo.with_args(1, 2)
With:
allow(module).foo(1, 2)
:param args: Any positional arguments required for invocation.
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"""A short hand syntax for with_args
Allows callers to do:
allow(module).foo.with_args(1, 2)
With:
allow(module).foo(1, 2)
:param args: Any positional arguments required for invocation.
:param kwargs: Any keyword arguments required for invocation.
"""
return self.with_args(*args, **kwargs) | [
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uvemas/ViTables | 2ce8ec26f85c7392677cf0c7c83ad1ddd7d071e0 | vitables/vtwidgets/renamedlg.py | python | RenameDlg.__init__ | (self, name, pattern, info) | A customised `QInputDialog`. | A customised `QInputDialog`. | [
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] | def __init__(self, name, pattern, info):
"""A customised `QInputDialog`.
"""
vtapp = vitables.utils.getVTApp()
# Makes the dialog and gives it a layout
super(RenameDlg, self).__init__(vtapp.gui)
self.setupUi(self)
# Sets dialog caption
self.setWindowTitle(info[0])
self.generalInfo.setText(info[1])
self.troubled_name = name
self.cpattern = re.compile(pattern)
# The result returned by this dialog will be contained in the
# action dictionary. Its values can be:
# If Overwrite --> True, troubled_name
# If Rename --> False, new_name
# If Cancel --> False, ''
self.action = {'overwrite': False, 'new_name': ''}
# The dialog buttons: Rename, Overwrite and Cancel
self.overwrite_button = self.buttonsBox.addButton(
translate('RenameDlg', 'Overwrite', 'A button label'),
QtWidgets.QDialogButtonBox.AcceptRole)
self.rename_button = self.buttonsBox.addButton(
translate('RenameDlg', 'Rename', 'A button label'),
QtWidgets.QDialogButtonBox.AcceptRole)
self.rename_button.setDefault(1)
self.cancel_button = self.buttonsBox.button(
QtWidgets.QDialogButtonBox.Cancel)
# Setup a validator for checking the entered node name
validator = QtGui.QRegExpValidator(self)
qt_pattern = QtCore.QRegExp(pattern)
validator.setRegExp(qt_pattern)
self.valueLE.setValidator(validator)
self.valueLE.setText(self.troubled_name)
self.valueLE.selectAll()
# Make sure that buttons are in the proper activation state
self.valueLE.textChanged.emit(self.valueLE.text()) | [
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robotframework/SeleniumLibrary | 0d8caf35cd8325ff391c27fe814744060470018e | src/SeleniumLibrary/keywords/selectelement.py | python | SelectElementKeywords.select_all_from_list | (self, locator: Union[WebElement, str]) | Selects all options from multi-selection list ``locator``.
See the `Locating elements` section for details about the locator
syntax. | Selects all options from multi-selection list ``locator``. | [
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See the `Locating elements` section for details about the locator
syntax.
"""
self.info(f"Selecting all options from list '{locator}'.")
select = self._get_select_list(locator)
if not select.is_multiple:
raise RuntimeError(
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)
for index in range(len(select.options)):
select.select_by_index(index) | [
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kamalgill/flask-appengine-template | 11760f83faccbb0d0afe416fc58e67ecfb4643c2 | src/lib/gae_mini_profiler/util.py | python | short_method_fmt | (s) | return s[s.rfind("/") + 1:] | [] | def short_method_fmt(s):
return s[s.rfind("/") + 1:] | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/graphs/digraph.py | python | DiGraph.in_branchings | (self, source, spanning=True) | return _rec_in_branchings(depth) | r"""
Return an iterator over the in branchings rooted at given vertex in
``self``.
An in-branching is a directed tree rooted at ``source`` whose arcs are
directed to source from leaves. An in-branching is spanning if it
contains all vertices of the digraph.
If no spanning in branching rooted at ``source`` exist, raises
ValueError or return non spanning in branching rooted at ``source``,
depending on the value of ``spanning``.
INPUT:
- ``source`` -- vertex used as the source for all in branchings.
- ``spanning`` -- boolean (default: ``True``); if ``False`` return
maximum in branching to ``source``. Otherwise, return spanning in
branching if exists.
OUTPUT:
An iterator over the in branchings rooted in the given source.
.. SEEALSO::
- :meth:`~sage.graphs.digraph.DiGraph.out_branchings`
-- iterator over out-branchings rooted at given vertex.
- :meth:`~sage.graphs.graph.Graph.spanning_trees`
-- returns all spanning trees.
- :meth:`~sage.graphs.generic_graph.GenericGraph.spanning_trees_count`
-- counts the number of spanning trees.
ALGORITHM:
Recursively computes all in branchings.
At each step:
0. clean the graph (see below)
1. pick an edge e incoming to source
2. find all in branchings that do not contain e by first
removing it
3. find all in branchings that do contain e by first
merging the end vertices of e
Cleaning the graph implies to remove loops and replace multiedges by a
single one with an appropriate label since these lead to similar steps
of computation.
EXAMPLES:
A bidirectional 4-cycle::
sage: G = DiGraph({1:[2,3], 2:[1,4], 3:[1,4], 4:[2,3]}, format='dict_of_lists')
sage: list(G.in_branchings(1))
[Digraph on 4 vertices,
Digraph on 4 vertices,
Digraph on 4 vertices,
Digraph on 4 vertices]
With the Petersen graph turned into a symmetric directed graph::
sage: G = graphs.PetersenGraph().to_directed()
sage: len(list(G.in_branchings(0)))
2000
With a non connected ``DiGraph`` and ``spanning = True``::
sage: G = graphs.PetersenGraph().to_directed() + graphs.PetersenGraph().to_directed()
sage: G.in_branchings(0)
Traceback (most recent call last):
...
ValueError: no spanning in branching to vertex (0) exist
With a non connected ``DiGraph`` and ``spanning = False``::
sage: g=DiGraph([(1,0), (1,0), (2,1), (3,4)],multiedges=True)
sage: list(g.in_branchings(0,spanning=False))
[Digraph on 3 vertices, Digraph on 3 vertices]
With multiedges::
sage: G = DiGraph({0:[1,1,1], 1:[2,2]}, format='dict_of_lists', multiedges=True)
sage: len(list(G.in_branchings(2)))
6
With a DiGraph already being a spanning in branching::
sage: G = DiGraph({0:[], 1:[0], 2:[0], 3:[1], 4:[1], 5:[2]}, format='dict_of_lists')
sage: next(G.in_branchings(0)) == G
True
TESTS:
The empty ``DiGraph``::
sage: G = DiGraph()
sage: G.in_branchings(0)
Traceback (most recent call last):
...
ValueError: vertex (0) is not a vertex of the digraph
sage: edges = [(0,0,'x'), (0,0,'y')]
sage: G = DiGraph(edges, multiedges=True, loops=True, weighted=True)
sage: list(G.in_branchings(0))
[Digraph on 1 vertex]
sage: edges = [(0,1,'x'), (0,1,'y'), (1,2,'z'), (2,0,'w')]
sage: G = DiGraph(edges, multiedges=True, loops=True, weighted=True)
sage: len(list(G.in_branchings(0)))
1 | r"""
Return an iterator over the in branchings rooted at given vertex in
``self``. | [
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"rooted",
"at",
"given",
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] | def in_branchings(self, source, spanning=True):
r"""
Return an iterator over the in branchings rooted at given vertex in
``self``.
An in-branching is a directed tree rooted at ``source`` whose arcs are
directed to source from leaves. An in-branching is spanning if it
contains all vertices of the digraph.
If no spanning in branching rooted at ``source`` exist, raises
ValueError or return non spanning in branching rooted at ``source``,
depending on the value of ``spanning``.
INPUT:
- ``source`` -- vertex used as the source for all in branchings.
- ``spanning`` -- boolean (default: ``True``); if ``False`` return
maximum in branching to ``source``. Otherwise, return spanning in
branching if exists.
OUTPUT:
An iterator over the in branchings rooted in the given source.
.. SEEALSO::
- :meth:`~sage.graphs.digraph.DiGraph.out_branchings`
-- iterator over out-branchings rooted at given vertex.
- :meth:`~sage.graphs.graph.Graph.spanning_trees`
-- returns all spanning trees.
- :meth:`~sage.graphs.generic_graph.GenericGraph.spanning_trees_count`
-- counts the number of spanning trees.
ALGORITHM:
Recursively computes all in branchings.
At each step:
0. clean the graph (see below)
1. pick an edge e incoming to source
2. find all in branchings that do not contain e by first
removing it
3. find all in branchings that do contain e by first
merging the end vertices of e
Cleaning the graph implies to remove loops and replace multiedges by a
single one with an appropriate label since these lead to similar steps
of computation.
EXAMPLES:
A bidirectional 4-cycle::
sage: G = DiGraph({1:[2,3], 2:[1,4], 3:[1,4], 4:[2,3]}, format='dict_of_lists')
sage: list(G.in_branchings(1))
[Digraph on 4 vertices,
Digraph on 4 vertices,
Digraph on 4 vertices,
Digraph on 4 vertices]
With the Petersen graph turned into a symmetric directed graph::
sage: G = graphs.PetersenGraph().to_directed()
sage: len(list(G.in_branchings(0)))
2000
With a non connected ``DiGraph`` and ``spanning = True``::
sage: G = graphs.PetersenGraph().to_directed() + graphs.PetersenGraph().to_directed()
sage: G.in_branchings(0)
Traceback (most recent call last):
...
ValueError: no spanning in branching to vertex (0) exist
With a non connected ``DiGraph`` and ``spanning = False``::
sage: g=DiGraph([(1,0), (1,0), (2,1), (3,4)],multiedges=True)
sage: list(g.in_branchings(0,spanning=False))
[Digraph on 3 vertices, Digraph on 3 vertices]
With multiedges::
sage: G = DiGraph({0:[1,1,1], 1:[2,2]}, format='dict_of_lists', multiedges=True)
sage: len(list(G.in_branchings(2)))
6
With a DiGraph already being a spanning in branching::
sage: G = DiGraph({0:[], 1:[0], 2:[0], 3:[1], 4:[1], 5:[2]}, format='dict_of_lists')
sage: next(G.in_branchings(0)) == G
True
TESTS:
The empty ``DiGraph``::
sage: G = DiGraph()
sage: G.in_branchings(0)
Traceback (most recent call last):
...
ValueError: vertex (0) is not a vertex of the digraph
sage: edges = [(0,0,'x'), (0,0,'y')]
sage: G = DiGraph(edges, multiedges=True, loops=True, weighted=True)
sage: list(G.in_branchings(0))
[Digraph on 1 vertex]
sage: edges = [(0,1,'x'), (0,1,'y'), (1,2,'z'), (2,0,'w')]
sage: G = DiGraph(edges, multiedges=True, loops=True, weighted=True)
sage: len(list(G.in_branchings(0)))
1
"""
def _rec_in_branchings(depth):
r"""
The recursive function used to enumerate in branchings.
This function makes use of the following to keep track of partial in
branchings:
list_edges -- list of edges in self.
list_merged_edges -- list of edges that are currently merged
graph -- a copy of self where edges have an appropriate label
"""
if not depth:
# We have enough merged edges to form a in_branching
# We iterate over the lists of labels in list_merged_edges and
# yield the corresponding in_branchings
for indexes in product(*list_merged_edges):
yield DiGraph([list_edges[index] for index in indexes],
format='list_of_edges', pos=self.get_pos())
# 1) Clean the graph
# delete loops on source if any
D.delete_edges(D.outgoing_edge_iterator(source))
# merge multi-edges if any by concatenating their labels
if D.has_multiple_edges():
merged_multiple_edges = {}
for u, v, l in D.multiple_edges():
D.delete_edge(u, v, l)
if (u, v) not in merged_multiple_edges:
merged_multiple_edges[(u, v)] = l
else:
merged_multiple_edges[(u, v)] += l
D.add_edges([(u, v, l) for (u, v),l in merged_multiple_edges.items()])
# 2) Pick an edge e incoming to the source
try:
x, s, l = next(D.incoming_edge_iterator(source))
except:
return
# 3) Find all in_branchings that do not contain e
# by first removing it
D.delete_edge(x, s, l)
if len(list(D.depth_first_search(source, neighbors=D.neighbor_in_iterator))) == depth + 1:
for in_branch in _rec_in_branchings(depth):
yield in_branch
D.add_edge(x, s, l)
# 4) Find all in_branchings that do contain e by merging
# the end vertices of e
# store different edges to unmerged the end vertices of e
saved_edges = D.incoming_edges(source)
saved_edges.remove((x, s, l))
saved_edges += D.outgoing_edges(x)
saved_edges += D.incoming_edges(x)
D.merge_vertices((source, x))
list_merged_edges.add(l)
for in_branch in _rec_in_branchings(depth - 1):
yield in_branch
list_merged_edges.remove(l)
# unmerge the end vertices of e
D.delete_vertex(source)
D.add_edges(saved_edges)
def _singleton_in_branching():
r"""
Returns a DiGraph containing only ``source`` and no edges.
"""
D = DiGraph()
D.add_vertex(source)
yield D
if not self.has_vertex(source):
raise ValueError("vertex ({0}) is not a vertex of the digraph".format(source))
# check if self.order == 1
if self.order() == 1:
return _singleton_in_branching()
# check if the source can access to every other vertex
if spanning:
depth = self.order() - 1
if len(list(self.depth_first_search(source, neighbors=self.neighbor_in_iterator))) < self.order():
raise ValueError("no spanning in branching to vertex ({0}) exist".format(source))
else:
depth = len(list(self.depth_first_search(source, neighbors=self.neighbor_in_iterator))) - 1
# if vertex is isolated
if not depth:
return _singleton_in_branching()
# We build a copy of self in which each edge has a distinct label.
# On the way, we remove loops and edges incoming to source.
D = DiGraph(multiedges=True, loops=True)
list_edges = list(self.edges(sort=False))
for i, (u, v, _) in enumerate(list_edges):
if u != v and u != source:
D.add_edge(u, v, (i,))
list_merged_edges = set()
return _rec_in_branchings(depth) | [
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gdraheim/docker-systemctl-replacement | 9cbe1a00eb4bdac6ff05b96ca34ec9ed3d8fc06c | files/docker/systemctl.py | python | Systemctl.reload_modules | (self, *modules) | return self.reload_units(units) and found_all | [UNIT]... -- reload these units | [UNIT]... -- reload these units | [
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cvxgrp/cvxportfolio | 3985059af9341b58d3f6219280da64e2c85d2749 | cvxportfolio/result.py | python | SimulationResult.annual_growth_rate | (self) | return self.growth_rates.sum() * self.PPY / self.growth_rates.size | The annualized growth rate PPY/T sum_{t=1}^T log(v_{t+1}/v_t) | The annualized growth rate PPY/T sum_{t=1}^T log(v_{t+1}/v_t) | [
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bruderstein/PythonScript | df9f7071ddf3a079e3a301b9b53a6dc78cf1208f | PythonLib/full/logging/config.py | python | _resolve | (name) | return found | Resolve a dotted name to a global object. | Resolve a dotted name to a global object. | [
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lbryio/torba | 190304344c0ff68f8a24cf50272307a11bf7f62b | torba/server/daemon.py | python | Daemon.mempool_hashes | (self) | return await self._send_single('getrawmempool') | Update our record of the daemon's mempool hashes. | Update our record of the daemon's mempool hashes. | [
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