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mahmoud/lithoxyl | b4bfa92c54df85b4bd5935fe270e2aa3fb25c412 | misc/tabulate.py | python | _format_table | (fmt, headers, rows, colwidths, colaligns) | return "\n".join(lines) | Produce a plain-text representation of the table. | Produce a plain-text representation of the table. | [
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"-",
"text",
"representation",
"of",
"the",
"table",
"."
] | def _format_table(fmt, headers, rows, colwidths, colaligns):
"""Produce a plain-text representation of the table."""
lines = []
hidden = fmt.with_header_hide if (headers and fmt.with_header_hide) else []
pad = fmt.padding
headerrow = fmt.headerrow
padded_widths = [(w + 2*pad) for w in colwidths]
padded_headers = _pad_row(headers, pad)
padded_rows = [_pad_row(row, pad) for row in rows]
if fmt.lineabove and "lineabove" not in hidden:
lines.append(_build_line(padded_widths, colaligns, fmt.lineabove))
if padded_headers:
lines.append(_build_row(padded_headers, padded_widths, colaligns, headerrow))
if fmt.linebelowheader and "linebelowheader" not in hidden:
lines.append(_build_line(padded_widths, colaligns, fmt.linebelowheader))
if padded_rows and fmt.linebetweenrows and "linebetweenrows" not in hidden:
# initial rows with a line below
for row in padded_rows[:-1]:
lines.append(_build_row(row, padded_widths, colaligns, fmt.datarow))
lines.append(_build_line(padded_widths, colaligns, fmt.linebetweenrows))
# the last row without a line below
lines.append(_build_row(padded_rows[-1], padded_widths, colaligns, fmt.datarow))
else:
for row in padded_rows:
lines.append(_build_row(row, padded_widths, colaligns, fmt.datarow))
if fmt.linebelow and "linebelow" not in hidden:
lines.append(_build_line(padded_widths, colaligns, fmt.linebelow))
return "\n".join(lines) | [
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PythonCharmers/python-future | 80523f383fbba1c6de0551e19d0277e73e69573c | src/future/standard_library/__init__.py | python | scrub_py2_sys_modules | () | return scrubbed | Removes any Python 2 standard library modules from ``sys.modules`` that
would interfere with Py3-style imports using import hooks. Examples are
modules with the same names (like urllib or email).
(Note that currently import hooks are disabled for modules like these
with ambiguous names anyway ...) | Removes any Python 2 standard library modules from ``sys.modules`` that
would interfere with Py3-style imports using import hooks. Examples are
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"""
Removes any Python 2 standard library modules from ``sys.modules`` that
would interfere with Py3-style imports using import hooks. Examples are
modules with the same names (like urllib or email).
(Note that currently import hooks are disabled for modules like these
with ambiguous names anyway ...)
"""
if PY3:
return {}
scrubbed = {}
for modulename in REPLACED_MODULES & set(RENAMES.keys()):
if not modulename in sys.modules:
continue
module = sys.modules[modulename]
if is_py2_stdlib_module(module):
flog.debug('Deleting (Py2) {} from sys.modules'.format(modulename))
scrubbed[modulename] = sys.modules[modulename]
del sys.modules[modulename]
return scrubbed | [
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spyder-ide/spyder | 55da47c032dfcf519600f67f8b30eab467f965e7 | spyder/app/mainwindow.py | python | MainWindow.set_splash | (self, message) | Set splash message | Set splash message | [
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] | def set_splash(self, message):
"""Set splash message"""
if self.splash is None:
return
if message:
logger.info(message)
self.splash.show()
self.splash.showMessage(message,
int(Qt.AlignBottom | Qt.AlignCenter |
Qt.AlignAbsolute),
QColor(Qt.white))
QApplication.processEvents() | [
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python-discord/bot | 26c5587ac13e5414361bb6e7ada42983b81014d2 | bot/exts/backend/sync/_cog.py | python | Sync.sync_roles_command | (self, ctx: Context) | Manually synchronise the guild's roles with the roles on the site. | Manually synchronise the guild's roles with the roles on the site. | [
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"""Manually synchronise the guild's roles with the roles on the site."""
await _syncers.RoleSyncer.sync(ctx.guild, ctx) | [
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hyperledger/aries-cloudagent-python | 2f36776e99f6053ae92eed8123b5b1b2e891c02a | aries_cloudagent/protocols/issue_credential/v1_0/handlers/credential_ack_handler.py | python | CredentialAckHandler.handle | (self, context: RequestContext, responder: BaseResponder) | Message handler logic for credential acks.
Args:
context: request context
responder: responder callback | Message handler logic for credential acks. | [
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"""
Message handler logic for credential acks.
Args:
context: request context
responder: responder callback
"""
r_time = get_timer()
self._logger.debug("CredentialAckHandler called with context %s", context)
assert isinstance(context.message, CredentialAck)
self._logger.info(
"Received credential ack message: %s",
context.message.serialize(as_string=True),
)
if not context.connection_ready:
raise HandlerException("No connection established for credential ack")
credential_manager = CredentialManager(context.profile)
await credential_manager.receive_credential_ack(
context.message, context.connection_record.connection_id
)
trace_event(
context.settings,
context.message,
outcome="CredentialAckHandler.handle.END",
perf_counter=r_time,
) | [
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Stiivi/bubbles | b3c9332b8a9534655bd77821586e45a5086b1ddc | bubbles/backends/sql/objects.py | python | SQLStatement.representations | (self) | return ["sql", "rows", "records"] | Return list of possible object representations | Return list of possible object representations | [
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"""Return list of possible object representations"""
return ["sql", "rows", "records"] | [
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SpamScope/mail-parser | 87ad78f1f1028b509bce7fe684117619d84d0d40 | mailparser/mailparser.py | python | MailParser._make_mail | (self, complete=True) | return mail | This method assigns the right values to all tokens of email.
Returns a parsed object
Keyword Arguments:
complete {bool} -- If True returns all mails parts
(default: {True})
Returns:
dict -- Parsed email object | This method assigns the right values to all tokens of email.
Returns a parsed object | [
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"""
This method assigns the right values to all tokens of email.
Returns a parsed object
Keyword Arguments:
complete {bool} -- If True returns all mails parts
(default: {True})
Returns:
dict -- Parsed email object
"""
mail = {}
keys = get_mail_keys(self.message, complete)
for i in keys:
log.debug("Getting header or part {!r}".format(i))
value = getattr(self, i)
if value:
mail[i] = value
# add defects
mail["has_defects"] = self.has_defects
if self.has_defects:
mail["defects"] = self.defects
mail["defects_categories"] = list(self.defects_categories)
return mail | [
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robotlearn/pyrobolearn | 9cd7c060723fda7d2779fa255ac998c2c82b8436 | pyrobolearn/simulators/bullet.py | python | Bullet.get_camera_image | (self, width, height, view_matrix=None, projection_matrix=None, light_direction=None,
light_color=None, light_distance=None, shadow=None, light_ambient_coeff=None,
light_diffuse_coeff=None, light_specular_coeff=None, renderer=None, flags=None) | return width, height, rgba, depth, segmentation | The `get_camera_image` API will return a RGB image, a depth buffer and a segmentation mask buffer with body
unique ids of visible objects for each pixel. Note that PyBullet can be compiled using the numpy option:
using numpy will improve the performance of copying the camera pixels from C to Python.
Note that copying pixels from C/C++ to Python can be really slow for large images, unless you compile PyBullet
using NumPy. You can check if NumPy is enabled using `PyBullet.isNumpyEnabled()`. `pip install pybullet` has
NumPy enabled, if available on the system.
Args:
width (int): horizontal image resolution in pixels
height (int): vertical image resolution in pixels
view_matrix (np.array[float[4,4]]): 4x4 view matrix, see `compute_view_matrix`
projection_matrix (np.array[float[4,4]]): 4x4 projection matrix, see `compute_projection`
light_direction (np.array[float[3]]): `light_direction` specifies the world position of the light source,
the direction is from the light source position to the origin of the world frame.
light_color (np.array[float[3]]): directional light color in [RED,GREEN,BLUE] in range 0..1
light_distance (float): distance of the light along the normalized `light_direction`
shadow (bool): True for shadows, False for no shadows
light_ambient_coeff (float): light ambient coefficient
light_diffuse_coeff (float): light diffuse coefficient
light_specular_coeff (float): light specular coefficient
renderer (int): ER_BULLET_HARDWARE_OPENGL (=131072) or ER_TINY_RENDERER (=65536). Note that DIRECT (=2)
mode has no OpenGL, so it requires ER_TINY_RENDERER (=65536).
flags (int): ER_SEGMENTATION_MASK_OBJECT_AND_LINKINDEX (=1), See below in description of
segmentationMaskBuffer and example code. Use ER_NO_SEGMENTATION_MASK (=4) to avoid calculating the
segmentation mask.
Returns:
int: width image resolution in pixels (horizontal)
int: height image resolution in pixels (vertical)
np.array[int[width, height, 4]]: RBGA pixels (each pixel is in the range [0..255] for each channel).
np.array[float[width, height]]: Depth buffer. Bullet uses OpenGL to render, and the convention is non-linear
z-buffer. See https://stackoverflow.com/questions/6652253/getting-the-true-z-value-from-the-depth-buffer
Using the projection matrix, the depth is computed as:
`depth = far * near / (far - (far - near) * depthImg)`, where `depthImg` is the depth from Bullet
`get_camera_image`, far=1000. and near=0.01.
np.array[int[width, height]]: Segmentation mask buffer. For each pixels the visible object unique id.
If ER_SEGMENTATION_MASK_OBJECT_AND_LINKINDEX (=1) is used, the segmentationMaskBuffer combines the
object unique id and link index as follows: value = objectUniqueId + (linkIndex+1)<<24.
So for a free floating body without joints/links, the segmentation mask is equal to its body unique id,
since its link index is -1. | The `get_camera_image` API will return a RGB image, a depth buffer and a segmentation mask buffer with body
unique ids of visible objects for each pixel. Note that PyBullet can be compiled using the numpy option:
using numpy will improve the performance of copying the camera pixels from C to Python. | [
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light_color=None, light_distance=None, shadow=None, light_ambient_coeff=None,
light_diffuse_coeff=None, light_specular_coeff=None, renderer=None, flags=None):
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width (int): horizontal image resolution in pixels
height (int): vertical image resolution in pixels
view_matrix (np.array[float[4,4]]): 4x4 view matrix, see `compute_view_matrix`
projection_matrix (np.array[float[4,4]]): 4x4 projection matrix, see `compute_projection`
light_direction (np.array[float[3]]): `light_direction` specifies the world position of the light source,
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light_color (np.array[float[3]]): directional light color in [RED,GREEN,BLUE] in range 0..1
light_distance (float): distance of the light along the normalized `light_direction`
shadow (bool): True for shadows, False for no shadows
light_ambient_coeff (float): light ambient coefficient
light_diffuse_coeff (float): light diffuse coefficient
light_specular_coeff (float): light specular coefficient
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Returns:
int: width image resolution in pixels (horizontal)
int: height image resolution in pixels (vertical)
np.array[int[width, height, 4]]: RBGA pixels (each pixel is in the range [0..255] for each channel).
np.array[float[width, height]]: Depth buffer. Bullet uses OpenGL to render, and the convention is non-linear
z-buffer. See https://stackoverflow.com/questions/6652253/getting-the-true-z-value-from-the-depth-buffer
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np.array[int[width, height]]: Segmentation mask buffer. For each pixels the visible object unique id.
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object unique id and link index as follows: value = objectUniqueId + (linkIndex+1)<<24.
So for a free floating body without joints/links, the segmentation mask is equal to its body unique id,
since its link index is -1.
"""
kwargs = {}
if view_matrix is not None:
if isinstance(view_matrix, np.ndarray):
kwargs['viewMatrix'] = view_matrix.T.ravel().tolist()
else:
kwargs['viewMatrix'] = view_matrix
if projection_matrix is not None:
if isinstance(projection_matrix, np.ndarray):
kwargs['projectionMatrix'] = projection_matrix.T.ravel().tolist()
else:
kwargs['projectionMatrix'] = projection_matrix
if light_direction is not None:
if isinstance(light_direction, np.ndarray):
kwargs['lightDirection'] = light_direction.ravel().tolist()
else:
kwargs['lightDirection'] = light_direction
if light_color is not None:
if isinstance(light_color, np.ndarray):
kwargs['lightColor'] = light_color
else:
kwargs['lightColor'] = light_color
if light_distance is not None:
kwargs['lightDistance'] = light_distance
if shadow is not None:
kwargs['shadow'] = int(shadow)
if light_ambient_coeff is not None:
kwargs['lightAmbientCoeff'] = light_ambient_coeff
if light_diffuse_coeff is not None:
kwargs['lightDiffuseCoeff'] = light_diffuse_coeff
if light_specular_coeff is not None:
kwargs['lightSpecularCoeff'] = light_specular_coeff
if renderer is not None:
kwargs['renderer'] = renderer
if flags is not None:
kwargs['flags'] = flags
width, height = int(width), int(height)
width, height, rgba, depth, segmentation = self.sim.getCameraImage(width, height, **kwargs)
rgba = np.asarray(rgba).reshape(width, height, 4)
depth = np.asarray(depth).reshape(width, height)
segmentation = np.asarray(segmentation).reshape(width, height)
return width, height, rgba, depth, segmentation | [
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evennia/evennia | fa79110ba6b219932f22297838e8ac72ebc0be0e | evennia/locks/lockfuncs.py | python | has_account | (accessing_obj, accessed_obj, *args, **kwargs) | return hasattr(accessing_obj, "has_account") and accessing_obj.has_account | Only returns true if accessing_obj has_account is true, that is,
this is an account-controlled object. It fails on actual accounts!
This is a useful lock for traverse-locking Exits to restrain NPC
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This is a useful lock for traverse-locking Exits to restrain NPC
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sympy/sympy | d822fcba181155b85ff2b29fe525adbafb22b448 | sympy/strategies/rl.py | python | sort | (key, new=new) | return sort_rl | Create a rule to sort by a key function.
Examples
========
>>> from sympy.strategies import sort
>>> from sympy import Basic, S
>>> sort_rl = sort(str)
>>> sort_rl(Basic(S(3), S(1), S(2)))
Basic(1, 2, 3) | Create a rule to sort by a key function. | [
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""" Create a rule to sort by a key function.
Examples
========
>>> from sympy.strategies import sort
>>> from sympy import Basic, S
>>> sort_rl = sort(str)
>>> sort_rl(Basic(S(3), S(1), S(2)))
Basic(1, 2, 3)
"""
def sort_rl(expr):
return new(expr.__class__, *sorted(expr.args, key=key))
return sort_rl | [
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polakowo/vectorbt | 6638735c131655760474d72b9f045d1dbdbd8fe9 | vectorbt/base/combine_fns.py | python | to_2d_one_nb | (a: tp.Array) | return np.expand_dims(a, axis=1) | Expand the dimensions of array `a` along axis 1.
!!! note
* `a` must be strictly homogeneous | Expand the dimensions of array `a` along axis 1. | [
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"""Expand the dimensions of array `a` along axis 1.
!!! note
* `a` must be strictly homogeneous"""
if a.ndim > 1:
return a
return np.expand_dims(a, axis=1) | [
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kiryha/Houdini | a9a45325042755295096d66c06c6728089600eec | Eve/tools/houdini/asset_manager.py | python | AssetManager.__init__ | (self) | [] | def __init__(self):
super(AssetManager, self).__init__()
self.setupUi(self)
self.setParent(hou.ui.mainQtWindow(), QtCore.Qt.Window)
# Setup environment
self.eve_root = os.environ['EVE_ROOT']
self.project_name = os.environ['EVE_PROJECT_NAME']
self.SQL_FILE_PATH = settings.SQL_FILE_PATH.format(self.eve_root)
# Get Project Manager data
self.eve_data = None
self.project = None
self.model_assets = None
# Get asset data
self.asset_data = None
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self.btnAssetCrete.clicked.connect(self.create_asset_scene)
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/forked_daapd/media_player.py | python | ForkedDaapdMaster._update_callback | (self, available) | Call update method. | Call update method. | [
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mesonbuild/meson | a22d0f9a0a787df70ce79b05d0c45de90a970048 | mesonbuild/dependencies/dev.py | python | GTestDependencyPC.__init__ | (self, name: str, environment: 'Environment', kwargs: T.Dict[str, T.Any]) | [] | def __init__(self, name: str, environment: 'Environment', kwargs: T.Dict[str, T.Any]):
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glouppe/info8006-introduction-to-ai | 8360cc9a8c418559e402be1454ec885b6c1062d7 | projects/project1/pacman_module/graphicsDisplay.py | python | PacmanGraphics.swapImages | (self, agentIndex, newState) | Changes an image from a ghost to a pacman or vis versa (for capture) | Changes an image from a ghost to a pacman or vis versa (for capture) | [
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"""
Changes an image from a ghost to a pacman or vis versa (for capture)
"""
prevState, prevImage = self.agentImages[agentIndex]
for item in prevImage:
remove_from_screen(item)
if newState.isPacman:
image = self.drawPacman(newState, agentIndex)
self.agentImages[agentIndex] = (newState, image)
else:
image = self.drawGhost(newState, agentIndex)
self.agentImages[agentIndex] = (newState, image)
refresh() | [
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epfl-lts2/pygsp | a3412ce7696c02c8a55439e89d0c9ab8ae863269 | pygsp/filters/approximations.py | python | compute_jackson_cheby_coeff | (filter_bounds, delta_lambda, m) | return ch, jch | r"""
To compute the m+1 coefficients of the polynomial approximation of an ideal band-pass between a and b, between a range of values defined by lambda_min and lambda_max.
Parameters
----------
filter_bounds : list
[a, b]
delta_lambda : list
[lambda_min, lambda_max]
m : int
Returns
-------
ch : ndarray
jch : ndarray
References
----------
:cite:`tremblay2016compressive` | r"""
To compute the m+1 coefficients of the polynomial approximation of an ideal band-pass between a and b, between a range of values defined by lambda_min and lambda_max. | [
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r"""
To compute the m+1 coefficients of the polynomial approximation of an ideal band-pass between a and b, between a range of values defined by lambda_min and lambda_max.
Parameters
----------
filter_bounds : list
[a, b]
delta_lambda : list
[lambda_min, lambda_max]
m : int
Returns
-------
ch : ndarray
jch : ndarray
References
----------
:cite:`tremblay2016compressive`
"""
# Parameters check
if delta_lambda[0] > filter_bounds[0] or delta_lambda[1] < filter_bounds[1]:
_logger.error("Bounds of the filter are out of the lambda values")
raise()
elif delta_lambda[0] > delta_lambda[1]:
_logger.error("lambda_min is greater than lambda_max")
raise()
# Scaling and translating to standard cheby interval
a1 = (delta_lambda[1]-delta_lambda[0])/2
a2 = (delta_lambda[1]+delta_lambda[0])/2
# Scaling bounds of the band pass according to lrange
filter_bounds[0] = (filter_bounds[0]-a2)/a1
filter_bounds[1] = (filter_bounds[1]-a2)/a1
# First compute cheby coeffs
ch = np.empty(m+1, dtype=float)
ch[0] = (2/(np.pi))*(np.arccos(filter_bounds[0])-np.arccos(filter_bounds[1]))
for i in range(1, len(ch)):
ch[i] = (2/(np.pi * i)) * \
(np.sin(i * np.arccos(filter_bounds[0])) - np.sin(i * np.arccos(filter_bounds[1])))
# Then compute jackson coeffs
jch = np.empty(m+1, dtype=float)
alpha = (np.pi/(m+2))
for i in range(len(jch)):
jch[i] = (1/np.sin(alpha)) * \
((1 - i/(m+2)) * np.sin(alpha) * np.cos(i * alpha) +
(1/(m+2)) * np.cos(alpha) * np.sin(i * alpha))
# Combine jackson and cheby coeffs
jch = ch * jch
return ch, jch | [
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cloudera/hue | 23f02102d4547c17c32bd5ea0eb24e9eadd657a4 | desktop/core/ext-py/celery-4.2.1/celery/bin/celery.py | python | report.__init__ | (self, *args, **kwargs) | Custom initialization for report command.
We need this custom initialization to make sure that
everything is loaded when running a report.
There has been some issues when printing Django's
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"""Custom initialization for report command.
We need this custom initialization to make sure that
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There has been some issues when printing Django's
settings because Django is not properly setup when
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super(report, self).__init__(*args, **kwargs)
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IJDykeman/wangTiles | 7c1ee2095ebdf7f72bce07d94c6484915d5cae8b | experimental_code/tiles_3d/venv_mac/lib/python2.7/site-packages/pip/_vendor/pyparsing.py | python | ParseResults.asList | ( self ) | return [res.asList() if isinstance(res,ParseResults) else res for res in self.__toklist] | Returns the parse results as a nested list of matching tokens, all converted to strings.
Example::
patt = OneOrMore(Word(alphas))
result = patt.parseString("sldkj lsdkj sldkj")
# even though the result prints in string-like form, it is actually a pyparsing ParseResults
print(type(result), result) # -> <class 'pyparsing.ParseResults'> ['sldkj', 'lsdkj', 'sldkj']
# Use asList() to create an actual list
result_list = result.asList()
print(type(result_list), result_list) # -> <class 'list'> ['sldkj', 'lsdkj', 'sldkj'] | Returns the parse results as a nested list of matching tokens, all converted to strings. | [
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"""
Returns the parse results as a nested list of matching tokens, all converted to strings.
Example::
patt = OneOrMore(Word(alphas))
result = patt.parseString("sldkj lsdkj sldkj")
# even though the result prints in string-like form, it is actually a pyparsing ParseResults
print(type(result), result) # -> <class 'pyparsing.ParseResults'> ['sldkj', 'lsdkj', 'sldkj']
# Use asList() to create an actual list
result_list = result.asList()
print(type(result_list), result_list) # -> <class 'list'> ['sldkj', 'lsdkj', 'sldkj']
"""
return [res.asList() if isinstance(res,ParseResults) else res for res in self.__toklist] | [
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pinterest/pymemcache | 071191455363943e7d5919701465455e78bb64ae | pymemcache/client/base.py | python | Client.cache_memlimit | (self, memlimit) | return True | The memcached "cache_memlimit" command.
Args:
memlimit: int, the number of megabytes to set as the new cache memory
limit.
Returns:
If no exception is raised, always returns True. | The memcached "cache_memlimit" command. | [
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] | def cache_memlimit(self, memlimit):
"""
The memcached "cache_memlimit" command.
Args:
memlimit: int, the number of megabytes to set as the new cache memory
limit.
Returns:
If no exception is raised, always returns True.
"""
memlimit = self._check_integer(memlimit, "memlimit")
self._fetch_cmd(b"cache_memlimit", [memlimit], False)
return True | [
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librahfacebook/Detection | 84504d086634950224716f4de0e4c8a684493c34 | object_detection/utils/visualization_utils.py | python | save_image_array_as_png | (image, output_path) | Saves an image (represented as a numpy array) to PNG.
Args:
image: a numpy array with shape [height, width, 3].
output_path: path to which image should be written. | Saves an image (represented as a numpy array) to PNG. | [
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Args:
image: a numpy array with shape [height, width, 3].
output_path: path to which image should be written.
"""
image_pil = Image.fromarray(np.uint8(image)).convert('RGB')
with tf.gfile.Open(output_path, 'w') as fid:
image_pil.save(fid, 'PNG') | [
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manolomartinez/greg | b781656bd1a749c9dce6988a37e0345d31bed267 | greg/aux_functions.py | python | tag | (placeholders) | Tag the file at podpath with the information in podcast and entry | Tag the file at podpath with the information in podcast and entry | [
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"""
Tag the file at podpath with the information in podcast and entry
"""
# We first recover the name of the file to be tagged...
template = placeholders.feed.retrieve_config("file_to_tag", "{filename}")
filename = substitute_placeholders(template, placeholders)
podpath = os.path.join(placeholders.directory, filename)
# ... and this is it
# now we create a dictionary of tags and values
tagdict = placeholders.feed.defaulttagdict # these are the defaults
try: # We do as if there was a section with potential tag info
feedoptions = placeholders.feed.config.options(placeholders.name)
# this monstruous concatenation of classes... surely a bad idea.
tags = [[option.replace("tag_", ""), placeholders.feed.config[
placeholders.name][option]] for option in feedoptions if "tag_" in
option] # these are the tags to be filled
if tags:
for tag in tags:
tagdict[tag[0]] = tag[1]
except configparser.NoSectionError:
pass
for tag in tagdict:
metadata = substitute_placeholders(tagdict[tag], placeholders)
tagdict[tag] = metadata
file_to_tag = eyed3.load(podpath)
if file_to_tag.tag == None:
file_to_tag.initTag()
for mytag in tagdict:
try:
attribute = getattr(file_to_tag.tag, mytag)
if isinstance(attribute, eyed3.id3.tag.DltAccessor):
attribute.set(tagdict[mytag])
else:
setattr(file_to_tag.tag, mytag, tagdict[mytag])
except AttributeError:
setattr(file_to_tag.tag, mytag, tagdict[mytag])
file_to_tag.tag.save() | [
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ironport/shrapnel | 9496a64c46271b0c5cef0feb8f2cdf33cb752bb6 | coro/ssh/keys/openssh_authorized_keys.py | python | OpenSSH_Authorized_Keys.write | (self) | write() -> None
Writes the keyfile to disk, safely overwriting the keyfile that
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# Avoid concurrent races here?
tmp_filename = self.filename + '.tmp'
fd = os.open(tmp_filename, os.O_WRONLY | os.O_CREAT | os.O_TRUNC, 0644)
write = lambda x, y=fd: os.write(y, x)
map(write, map(self.keydict_to_string, self.keys))
os.close(fd)
os.rename(tmp_filename, self.filename) | [
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httpie/httpie | 4c56d894ba9e2bb1c097a3a6067006843ac2944d | httpie/output/lexers/http.py | python | request_method | (lexer, match, ctx) | [] | def request_method(lexer, match, ctx):
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lexer,
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frappe/frappe | b64cab6867dfd860f10ccaf41a4ec04bc890b583 | frappe/integrations/doctype/connected_app/connected_app.py | python | ConnectedApp.get_user_token | (self, user=None, success_uri=None) | return redirect | Return an existing user token or initiate a Web Application Flow. | Return an existing user token or initiate a Web Application Flow. | [
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"""Return an existing user token or initiate a Web Application Flow."""
user = user or frappe.session.user
token_cache = self.get_token_cache(user)
if token_cache:
return token_cache
redirect = self.initiate_web_application_flow(user, success_uri)
frappe.local.response['type'] = 'redirect'
frappe.local.response['location'] = redirect
return redirect | [
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zhaoolee/StarsAndClown | b2d4039cad2f9232b691e5976f787b49a0a2c113 | node_modules/npm/node_modules/node-gyp/gyp/pylib/gyp/mac_tool.py | python | MacTool._Relink | (self, dest, link) | Creates a symlink to |dest| named |link|. If |link| already exists,
it is overwritten. | Creates a symlink to |dest| named |link|. If |link| already exists,
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"""Creates a symlink to |dest| named |link|. If |link| already exists,
it is overwritten."""
if os.path.lexists(link):
os.remove(link)
os.symlink(dest, link) | [
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EmbarkStudios/blender-tools | 68e5c367bc040b6f327ad9507aab4c7b5b0a559b | utils/ui.py | python | get_icon | (name) | Get an internal Blender icon ID from an icon name. | Get an internal Blender icon ID from an icon name. | [
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] | def get_icon(name):
"""Get an internal Blender icon ID from an icon name."""
try:
return __icon_manager__.icons[name].icon_id
except KeyError:
print(f"Error: Failed to find icon named '{name}'!")
return None | [
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vshymanskyy/blynk-library-python | fee389f17e84b17e6655c571100f49688b453307 | BlynkTimer.py | python | BlynkTimer.set_timeout | (self, value, func) | return timer.id | Runs function once after timeout | Runs function once after timeout | [
"Runs",
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"after",
"timeout"
] | def set_timeout(self, value, func):
'''Runs function once after timeout'''
timer = self._add(func, post_run=self._delete)
timer.set_interval(value)
return timer.id | [
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csparpa/pyowm | 0474b61cc67fa3c95f9e572b96d3248031828fce | pyowm/agroapi10/agro_manager.py | python | AgroManager.soil_data | (self, polygon) | return Soil.from_dict(the_dict) | Retrieves the latest soil data on the specified polygon
:param polygon: the reference polygon you want soil data for
:type polygon: `pyowm.agro10.polygon.Polygon` instance
:returns: a `pyowm.agro10.soil.Soil` instance | Retrieves the latest soil data on the specified polygon | [
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"""
Retrieves the latest soil data on the specified polygon
:param polygon: the reference polygon you want soil data for
:type polygon: `pyowm.agro10.polygon.Polygon` instance
:returns: a `pyowm.agro10.soil.Soil` instance
"""
assert polygon is not None
assert isinstance(polygon, Polygon)
polyd = polygon.id
status, data = self.http_client.get_json(
SOIL_URI,
params={'appid': self.API_key,
'polyid': polyd},
headers={'Content-Type': 'application/json'})
the_dict = {
'reference_time': data['dt'],
'surface_temp': data['t0'],
'ten_cm_temp': data['t10'],
'moisture': data['moisture'],
'polygon_id': polyd,
}
return Soil.from_dict(the_dict) | [
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elyra-ai/elyra | 5bb2009a7c475bda63f1cc2767eb8c4dfba9a239 | elyra/pipeline/validation.py | python | ValidationResponse.response | (self) | return self._response | :return: The dict of validation errors and warnings found in the pipeline | :return: The dict of validation errors and warnings found in the pipeline | [
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"""
:return: The dict of validation errors and warnings found in the pipeline
"""
return self._response | [
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fox-it/dissect.cstruct | c28f864acbcf943fc1cb8a03ac792bf32d6b1581 | dissect/cstruct/types/instance.py | python | Instance.dumps | (self) | return s.getvalue() | Dump this structure to a byte string.
Returns:
The raw bytes of this structure. | Dump this structure to a byte string. | [
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] | def dumps(self):
"""Dump this structure to a byte string.
Returns:
The raw bytes of this structure.
"""
s = BytesIO()
self.write(s)
return s.getvalue() | [
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Arelle/Arelle | 20f3d8a8afd41668e1520799acd333349ce0ba17 | arelle/webserver/bottle-no2to3.py | python | SimpleTemplate.re_pytokens | (cls) | return re.compile(r'''
(''(?!')|""(?!")|'{6}|"{6} # Empty strings (all 4 types)
|'(?:[^\\']|\\.)+?' # Single quotes (')
|"(?:[^\\"]|\\.)+?" # Double quotes (")
|'{3}(?:[^\\]|\\.|\n)+?'{3} # Triple-quoted strings (')
|"{3}(?:[^\\]|\\.|\n)+?"{3} # Triple-quoted strings (")
|\#.* # Comments
)''', re.VERBOSE) | This matches comments and all kinds of quoted strings but does
NOT match comments (#...) within quoted strings. (trust me) | This matches comments and all kinds of quoted strings but does
NOT match comments (#...) within quoted strings. (trust me) | [
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] | def re_pytokens(cls):
''' This matches comments and all kinds of quoted strings but does
NOT match comments (#...) within quoted strings. (trust me) '''
return re.compile(r'''
(''(?!')|""(?!")|'{6}|"{6} # Empty strings (all 4 types)
|'(?:[^\\']|\\.)+?' # Single quotes (')
|"(?:[^\\"]|\\.)+?" # Double quotes (")
|'{3}(?:[^\\]|\\.|\n)+?'{3} # Triple-quoted strings (')
|"{3}(?:[^\\]|\\.|\n)+?"{3} # Triple-quoted strings (")
|\#.* # Comments
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Azure/azure-devops-cli-extension | 11334cd55806bef0b99c3bee5a438eed71e44037 | azure-devops/azext_devops/devops_sdk/v5_1/dashboard/dashboard_client.py | python | DashboardClient.get_dashboards | (self, team_context) | return self._deserialize('DashboardGroup', response) | GetDashboards.
[Preview API] Get a list of dashboards.
:param :class:`<TeamContext> <azure.devops.v5_1.dashboard.models.TeamContext>` team_context: The team context for the operation
:rtype: :class:`<DashboardGroup> <azure.devops.v5_1.dashboard.models.DashboardGroup>` | GetDashboards.
[Preview API] Get a list of dashboards.
:param :class:`<TeamContext> <azure.devops.v5_1.dashboard.models.TeamContext>` team_context: The team context for the operation
:rtype: :class:`<DashboardGroup> <azure.devops.v5_1.dashboard.models.DashboardGroup>` | [
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"""GetDashboards.
[Preview API] Get a list of dashboards.
:param :class:`<TeamContext> <azure.devops.v5_1.dashboard.models.TeamContext>` team_context: The team context for the operation
:rtype: :class:`<DashboardGroup> <azure.devops.v5_1.dashboard.models.DashboardGroup>`
"""
project = None
team = None
if team_context is not None:
if team_context.project_id:
project = team_context.project_id
else:
project = team_context.project
if team_context.team_id:
team = team_context.team_id
else:
team = team_context.team
route_values = {}
if project is not None:
route_values['project'] = self._serialize.url('project', project, 'string')
if team is not None:
route_values['team'] = self._serialize.url('team', team, 'string')
response = self._send(http_method='GET',
location_id='454b3e51-2e6e-48d4-ad81-978154089351',
version='5.1-preview.2',
route_values=route_values)
return self._deserialize('DashboardGroup', response) | [
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allegro/ralph | 1e4a9e1800d5f664abaef2624b8bf7512df279ce | src/ralph/networks/forms.py | python | SimpleNetworkForm._validate_ip_uniquness | (self, address) | Validate if there is any IP with passed address (exluding current ip). | Validate if there is any IP with passed address (exluding current ip). | [
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] | def _validate_ip_uniquness(self, address):
"""
Validate if there is any IP with passed address (exluding current ip).
"""
qs = IPAddress.objects.filter(address=address)
if self.ip:
qs = qs.exclude(pk=self.ip.pk)
if qs.exists():
raise ValidationError(
_('Address %(ip)s already exist.'),
params={'ip': address},
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modflowpy/flopy | eecd1ad193c5972093c9712e5c4b7a83284f0688 | flopy/mf6/mfbase.py | python | MFFileMgmt.get_sim_path | (self, last_loaded_path=False) | Get the simulation path. | Get the simulation path. | [
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] | def get_sim_path(self, last_loaded_path=False):
"""Get the simulation path."""
if last_loaded_path:
return self._last_loaded_sim_path
else:
return self._sim_path | [
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jgagneastro/coffeegrindsize | 22661ebd21831dba4cf32bfc6ba59fe3d49f879c | App/dist/coffeegrindsize.app/Contents/Resources/lib/python3.7/PIL/ImageMorph.py | python | LutBuilder._string_permute | (self, pattern, permutation) | return ''.join(pattern[p] for p in permutation) | string_permute takes a pattern and a permutation and returns the
string permuted according to the permutation list. | string_permute takes a pattern and a permutation and returns the
string permuted according to the permutation list. | [
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] | def _string_permute(self, pattern, permutation):
"""string_permute takes a pattern and a permutation and returns the
string permuted according to the permutation list.
"""
assert(len(permutation) == 9)
return ''.join(pattern[p] for p in permutation) | [
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minimaxir/automl-gs | 62773ce361c72e890829b476ba4aa5490b000de4 | automl_gs/utils_automl.py | python | get_input_types | (df, col_types, target_field) | return field_types | Get the input types for each field in the DataFrame that corresponds
to an input type to be fed into the model.
Valid values are ['text', 'categorical', 'numeric', 'datetime', 'ignore']
# Arguments:
df: A pandas DataFrame.
col_types: A dict of explicitly defined {field_name: type} mappings.
target_field: string indicating the target field
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"""Get the input types for each field in the DataFrame that corresponds
to an input type to be fed into the model.
Valid values are ['text', 'categorical', 'numeric', 'datetime', 'ignore']
# Arguments:
df: A pandas DataFrame.
col_types: A dict of explicitly defined {field_name: type} mappings.
target_field: string indicating the target field
# Returns:
A dict of {field_name: type} mappings.
"""
fields = df.columns
nrows = df.shape[0]
avg_spaces = -1
field_types = OrderedDict()
for field in fields:
if field in col_types:
field_types[field] = col_types[field]
continue
field_type = df[field].dtype
num_unique_values = df[field].nunique()
if field_type == 'object':
avg_spaces = df[field].str.count(' ').mean()
# Automatically ignore `id`-related fields
if field.lower() in ['id', 'uuid', 'guid', 'pk', 'name']:
field_types[field] = 'ignore'
# Foreign key fields are always categorical
# else if "_id" in field or "_uuid" in field:
# field_types[field] = 'categorical'
# Datetime is a straightforward data type.
elif field_type == 'datetime64[ns]':
field_types[field] = 'datetime'
# Assume a float is always numeric.
elif field_type == 'float64':
field_types[field] = 'numeric'
# If it's an object where the contents has
# many spaces on average, it's text
elif field_type == 'object' and avg_spaces >= 2.0:
field_types[field] = 'text'
# If the field has very few distinct values, it's categorical
elif num_unique_values <= 10:
field_types[field] = 'categorical'
# If the field has many distinct integers, assume numeric.
elif field_type == 'int64':
field_types[field] = 'numeric'
# If the field has many distinct nonintegers, it's not helpful.
elif num_unique_values > 0.9 * nrows:
field_types[field] = 'ignore'
# The rest (e.g. bool) is categorical
else:
field_types[field] = 'categorical'
# Print to console for user-level debugging
print("Modeling with field specifications:")
print("\n".join(["{}: {}".format(k, v) for k, v in field_types.items() if k != target_field]))
field_types = {k: v for k, v in field_types.items() if v != 'ignore'}
return field_types | [
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HonglinChu/SiamTrackers | 8471660b14f970578a43f077b28207d44a27e867 | SiamMask/SiamMask-pysot/toolkit/datasets/lasot.py | python | LaSOTVideo.load_tracker | (self, path, tracker_names=None, store=True) | Args:
path(str): path to result
tracker_name(list): name of tracker | Args:
path(str): path to result
tracker_name(list): name of tracker | [
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"""
Args:
path(str): path to result
tracker_name(list): name of tracker
"""
if not tracker_names:
tracker_names = [x.split('/')[-1] for x in glob(path)
if os.path.isdir(x)]
if isinstance(tracker_names, str):
tracker_names = [tracker_names]
for name in tracker_names:
traj_file = os.path.join(path, name, self.name+'.txt')
if os.path.exists(traj_file):
with open(traj_file, 'r') as f :
pred_traj = [list(map(float, x.strip().split(',')))
for x in f.readlines()]
else:
print("File not exists: ", traj_file)
if self.name == 'monkey-17':
pred_traj = pred_traj[:len(self.gt_traj)]
if store:
self.pred_trajs[name] = pred_traj
else:
return pred_traj
self.tracker_names = list(self.pred_trajs.keys()) | [
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zsdonghao/text-to-image | c1fbfa4349f5aa1c74c9fe17743cc0cff34f1aab | tensorlayer/layers.py | python | Conv2d | (net, n_filter=32, filter_size=(3, 3), strides=(1, 1), act = None,
padding='SAME', W_init = tf.truncated_normal_initializer(stddev=0.02), b_init = tf.constant_initializer(value=0.0),
W_init_args = {}, b_init_args = {}, name ='conv2d',) | return net | Wrapper for :class:`Conv2dLayer`, if you don't understand how to use :class:`Conv2dLayer`, this function may be easier.
Parameters
----------
net : TensorLayer layer.
n_filter : number of filter.
filter_size : tuple (height, width) for filter size.
strides : tuple (height, width) for strides.
act : None or activation function.
others : see :class:`Conv2dLayer`.
Examples
--------
>>> w_init = tf.truncated_normal_initializer(stddev=0.01)
>>> b_init = tf.constant_initializer(value=0.0)
>>> inputs = InputLayer(x, name='inputs')
>>> conv1 = Conv2d(inputs, 64, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv1_1')
>>> conv1 = Conv2d(conv1, 64, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv1_2')
>>> pool1 = MaxPool2d(conv1, (2, 2), padding='SAME', name='pool1')
>>> conv2 = Conv2d(pool1, 128, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv2_1')
>>> conv2 = Conv2d(conv2, 128, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv2_2')
>>> pool2 = MaxPool2d(conv2, (2, 2), padding='SAME', name='pool2') | Wrapper for :class:`Conv2dLayer`, if you don't understand how to use :class:`Conv2dLayer`, this function may be easier. | [
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] | def Conv2d(net, n_filter=32, filter_size=(3, 3), strides=(1, 1), act = None,
padding='SAME', W_init = tf.truncated_normal_initializer(stddev=0.02), b_init = tf.constant_initializer(value=0.0),
W_init_args = {}, b_init_args = {}, name ='conv2d',):
"""Wrapper for :class:`Conv2dLayer`, if you don't understand how to use :class:`Conv2dLayer`, this function may be easier.
Parameters
----------
net : TensorLayer layer.
n_filter : number of filter.
filter_size : tuple (height, width) for filter size.
strides : tuple (height, width) for strides.
act : None or activation function.
others : see :class:`Conv2dLayer`.
Examples
--------
>>> w_init = tf.truncated_normal_initializer(stddev=0.01)
>>> b_init = tf.constant_initializer(value=0.0)
>>> inputs = InputLayer(x, name='inputs')
>>> conv1 = Conv2d(inputs, 64, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv1_1')
>>> conv1 = Conv2d(conv1, 64, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv1_2')
>>> pool1 = MaxPool2d(conv1, (2, 2), padding='SAME', name='pool1')
>>> conv2 = Conv2d(pool1, 128, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv2_1')
>>> conv2 = Conv2d(conv2, 128, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv2_2')
>>> pool2 = MaxPool2d(conv2, (2, 2), padding='SAME', name='pool2')
"""
assert len(strides) == 2, "len(strides) should be 2, Conv2d and Conv2dLayer are different."
if act is None:
act = tf.identity
net = Conv2dLayer(net,
act = act,
shape = [filter_size[0], filter_size[1], int(net.outputs.get_shape()[-1]), n_filter], # 32 features for each 5x5 patch
strides = [1, strides[0], strides[1], 1],
padding = padding,
W_init = W_init,
W_init_args = W_init_args,
b_init = b_init,
b_init_args = b_init_args,
name = name)
return net | [
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WeblateOrg/weblate | 8126f3dda9d24f2846b755955132a8b8410866c8 | weblate/trans/validators.py | python | validate_check_flags | (val) | Validate check influencing flags. | Validate check influencing flags. | [
"Validate",
"check",
"influencing",
"flags",
"."
] | def validate_check_flags(val):
"""Validate check influencing flags."""
try:
flags = Flags(val)
except (ParseException, re.error) as error:
raise ValidationError(_("Failed to parse flags: %s") % error)
flags.validate() | [
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dmlc/dgl | 8d14a739bc9e446d6c92ef83eafe5782398118de | examples/pytorch/ogb/line/model.py | python | async_update | (num_threads, model, queue) | Asynchronous embedding update for entity embeddings. | Asynchronous embedding update for entity embeddings. | [
"Asynchronous",
"embedding",
"update",
"for",
"entity",
"embeddings",
"."
] | def async_update(num_threads, model, queue):
""" Asynchronous embedding update for entity embeddings.
"""
torch.set_num_threads(num_threads)
print("async start")
while True:
(grad_u, grad_v, grad_v_neg, nodes, neg_nodes, first_flag) = queue.get()
if grad_u is None:
return
with torch.no_grad():
if first_flag:
model.fst_u_embeddings.weight.data.index_add_(0, nodes[:, 0], grad_u)
model.fst_u_embeddings.weight.data.index_add_(0, nodes[:, 1], grad_v)
if neg_nodes is not None:
model.fst_u_embeddings.weight.data.index_add_(0, neg_nodes, grad_v_neg)
else:
model.snd_u_embeddings.weight.data.index_add_(0, nodes[:, 0], grad_u)
model.snd_v_embeddings.weight.data.index_add_(0, nodes[:, 1], grad_v)
if neg_nodes is not None:
model.snd_v_embeddings.weight.data.index_add_(0, neg_nodes, grad_v_neg) | [
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huawei-noah/vega | d9f13deede7f2b584e4b1d32ffdb833856129989 | vega/security/load_pickle.py | python | restricted_loads | (file, fix_imports=True, encoding="ASCII", errors="strict", security=False) | return RestrictedUnpickler(file, fix_imports=fix_imports, encoding=encoding, errors=errors,
security=security).load() | Load obj. | Load obj. | [
"Load",
"obj",
"."
] | def restricted_loads(file, fix_imports=True, encoding="ASCII", errors="strict", security=False):
"""Load obj."""
return RestrictedUnpickler(file, fix_imports=fix_imports, encoding=encoding, errors=errors,
security=security).load() | [
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KhronosGroup/OpenXR-SDK-Source | 76756e2e7849b15466d29bee7d80cada92865550 | specification/scripts/spec_tools/consistency_tools.py | python | XMLChecker.check_command | (self, name, info) | Check a command's XML data for consistency.
Called from check.
May extend. | Check a command's XML data for consistency. | [
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"command",
"s",
"XML",
"data",
"for",
"consistency",
"."
] | def check_command(self, name, info):
"""Check a command's XML data for consistency.
Called from check.
May extend."""
elem = info.elem
self.check_params(elem.findall('param'))
# Some minimal return code checking
errorcodes = elem.get("errorcodes")
if errorcodes:
errorcodes = errorcodes.split(",")
else:
errorcodes = []
successcodes = elem.get("successcodes")
if successcodes:
successcodes = successcodes.split(",")
else:
successcodes = []
if not successcodes and not errorcodes:
# Early out if no return codes.
return
# Create a set for each group of codes, and check that
# they aren't duplicated within or between groups.
errorcodes_set = set(errorcodes)
if len(errorcodes) != len(errorcodes_set):
self.record_error("Contains a duplicate in errorcodes")
successcodes_set = set(successcodes)
if len(successcodes) != len(successcodes_set):
self.record_error("Contains a duplicate in successcodes")
if not successcodes_set.isdisjoint(errorcodes_set):
self.record_error("Has errorcodes and successcodes that overlap")
self.check_command_return_codes_basic(
name, info, successcodes_set, errorcodes_set)
# Continue to further return code checking if not "complicated"
if not self.should_skip_checking_codes(name):
codes_set = successcodes_set.union(errorcodes_set)
self.check_command_return_codes(
name, info, successcodes_set, errorcodes_set, codes_set) | [
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LabPy/lantz | 3e878e3f765a4295b0089d04e241d4beb7b8a65b | lantz/drivers/andor/ccd.py | python | CCD.EM_gain_range | (self) | return (mini.value, maxi.value) | Returns the minimum and maximum values of the current selected EM
Gain mode and temperature of the sensor. | Returns the minimum and maximum values of the current selected EM
Gain mode and temperature of the sensor. | [
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"and",
"temperature",
"of",
"the",
"sensor",
"."
] | def EM_gain_range(self):
"""Returns the minimum and maximum values of the current selected EM
Gain mode and temperature of the sensor.
"""
mini, maxi = ct.c_int(), ct.c_int()
self.lib.GetEMGainRange(ct.pointer(mini), ct.pointer(maxi))
return (mini.value, maxi.value) | [
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pyansys/pymapdl | c07291fc062b359abf0e92b95a92d753a95ef3d7 | ansys/mapdl/core/_commands/solution/analysis_options.py | python | AnalysisOptions.thopt | (
self,
refopt="",
reformtol="",
ntabpoints="",
tempmin="",
tempmax="",
algo="",
**kwargs,
) | return self.run(command, **kwargs) | Specifies nonlinear transient thermal solution options.
APDL Command: THOPT
Parameters
----------
refopt
Matrix reform option.
FULL - Use the full Newton-Raphson solution option (default). All subsequent input
values are ignored.
QUASI - Use a selective reform solution option based on REFORMTOL.
reformtol
Property change tolerance for Matrix Reformation (.05 default). The
thermal matrices are reformed if the maximum material property
change in an element (from the previous reform time) is greater
than the reform tolerance. Valid only when Refopt = QUASI.
ntabpoints
Number of points in Fast Material Table (64 default). Valid only
when Refopt = QUASI.
tempmin
Minimum temperature for Fast Material Table. Defaults to the
minimum temperature defined by the MPTEMP command for any material
property defined. Valid only when Refopt = QUASI.
tempmax
Maximum temperature for Fast Material Table. Defaults to the
maximum temperature defined by the MPTEMP command for any material
property defined. Valid only when Refopt = QUASI.
--
Reserved field.
algo
Specifies which solution algorithm to apply:
0 - Multipass (default).
1 - Iterative.
Notes
-----
The QUASI matrix reform option is supported by the ICCG, JCG, and
sparse solvers only (EQSLV).
For Refopt = QUASI:
Results from a restart may be different than results from a single run
because the stiffness matrices are always recreated in a restart run,
but may or may not be in a single run (depending on the behavior
resulting from the REFORMTOL setting). Additionally, results may differ
between two single runs as well, if the matrices are reformed as a
result of the REFORMTOL setting.
Midside node temperatures are not calculated if 20-node thermal solid
elements (SOLID90 or SOLID279) are used.
For more information, see Solution Algorithms Used in Transient Thermal
Analysis in the Thermal Analysis Guide. | Specifies nonlinear transient thermal solution options. | [
"Specifies",
"nonlinear",
"transient",
"thermal",
"solution",
"options",
"."
] | def thopt(
self,
refopt="",
reformtol="",
ntabpoints="",
tempmin="",
tempmax="",
algo="",
**kwargs,
):
"""Specifies nonlinear transient thermal solution options.
APDL Command: THOPT
Parameters
----------
refopt
Matrix reform option.
FULL - Use the full Newton-Raphson solution option (default). All subsequent input
values are ignored.
QUASI - Use a selective reform solution option based on REFORMTOL.
reformtol
Property change tolerance for Matrix Reformation (.05 default). The
thermal matrices are reformed if the maximum material property
change in an element (from the previous reform time) is greater
than the reform tolerance. Valid only when Refopt = QUASI.
ntabpoints
Number of points in Fast Material Table (64 default). Valid only
when Refopt = QUASI.
tempmin
Minimum temperature for Fast Material Table. Defaults to the
minimum temperature defined by the MPTEMP command for any material
property defined. Valid only when Refopt = QUASI.
tempmax
Maximum temperature for Fast Material Table. Defaults to the
maximum temperature defined by the MPTEMP command for any material
property defined. Valid only when Refopt = QUASI.
--
Reserved field.
algo
Specifies which solution algorithm to apply:
0 - Multipass (default).
1 - Iterative.
Notes
-----
The QUASI matrix reform option is supported by the ICCG, JCG, and
sparse solvers only (EQSLV).
For Refopt = QUASI:
Results from a restart may be different than results from a single run
because the stiffness matrices are always recreated in a restart run,
but may or may not be in a single run (depending on the behavior
resulting from the REFORMTOL setting). Additionally, results may differ
between two single runs as well, if the matrices are reformed as a
result of the REFORMTOL setting.
Midside node temperatures are not calculated if 20-node thermal solid
elements (SOLID90 or SOLID279) are used.
For more information, see Solution Algorithms Used in Transient Thermal
Analysis in the Thermal Analysis Guide.
"""
command = f"THOPT,{refopt},{reformtol},{ntabpoints},{tempmin},{tempmax},{algo}"
return self.run(command, **kwargs) | [
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dmlc/dgl | 8d14a739bc9e446d6c92ef83eafe5782398118de | python/dgl/nn/pytorch/conv/egatconv.py | python | EGATConv.reset_parameters | (self) | Reinitialize learnable parameters. | Reinitialize learnable parameters. | [
"Reinitialize",
"learnable",
"parameters",
"."
] | def reset_parameters(self):
"""
Reinitialize learnable parameters.
"""
gain = init.calculate_gain('relu')
init.xavier_normal_(self.fc_node.weight, gain=gain)
init.xavier_normal_(self.fc_ni.weight, gain=gain)
init.xavier_normal_(self.fc_fij.weight, gain=gain)
init.xavier_normal_(self.fc_nj.weight, gain=gain)
init.xavier_normal_(self.attn, gain=gain)
init.constant_(self.bias, 0) | [
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replit-archive/empythoned | 977ec10ced29a3541a4973dc2b59910805695752 | dist/lib/python2.7/email/message.py | python | Message.get_content_maintype | (self) | return ctype.split('/')[0] | Return the message's main content type.
This is the `maintype' part of the string returned by
get_content_type(). | Return the message's main content type. | [
"Return",
"the",
"message",
"s",
"main",
"content",
"type",
"."
] | def get_content_maintype(self):
"""Return the message's main content type.
This is the `maintype' part of the string returned by
get_content_type().
"""
ctype = self.get_content_type()
return ctype.split('/')[0] | [
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] | https://github.com/replit-archive/empythoned/blob/977ec10ced29a3541a4973dc2b59910805695752/dist/lib/python2.7/email/message.py#L456-L463 | |
QUANTAXIS/QUANTAXIS | d6eccb97c8385854aa596d6ba8d70ec0655519ff | QUANTAXIS/QAFetch/QATdx.py | python | QA_fetch_get_future_min | (code, start, end, frequence='1min', ip=None,
port=None) | 期货数据 分钟线 | 期货数据 分钟线 | [
"期货数据",
"分钟线"
] | def QA_fetch_get_future_min(code, start, end, frequence='1min', ip=None,
port=None):
'期货数据 分钟线'
ip, port = get_extensionmarket_ip(ip, port)
apix = TdxExHq_API(raise_exception=True)
type_ = ''
start_date = str(start)[0:10]
today_ = datetime.date.today()
lens = QA_util_get_trade_gap(start_date, today_)
global extension_market_list
extension_market_list = QA_fetch_get_extensionmarket_list(
) if extension_market_list is None else extension_market_list
if str(frequence) in ['5', '5m', '5min', 'five']:
frequence, type_ = 0, '5min'
lens = 48 * lens * 2.5
elif str(frequence) in ['1', '1m', '1min', 'one']:
frequence, type_ = 8, '1min'
lens = 240 * lens * 2.5
elif str(frequence) in ['15', '15m', '15min', 'fifteen']:
frequence, type_ = 1, '15min'
lens = 16 * lens * 2.5
elif str(frequence) in ['30', '30m', '30min', 'half']:
frequence, type_ = 2, '30min'
lens = 8 * lens * 2.5
elif str(frequence) in ['60', '60m', '60min', '1h']:
frequence, type_ = 3, '60min'
lens = 4 * lens * 2.5
if lens > 20800:
lens = 20800
# print(lens)
with apix.connect(ip, port):
code_market = extension_market_list.query(
'code=="{}"'.format(code)).iloc[0]
data = pd.concat([apix.to_df(
apix.get_instrument_bars(frequence, int(code_market.market), str(
code), (int(lens / 700) - i) * 700, 700)) for i in
range(int(lens / 700) + 1)], axis=0, sort=False)
# print(data)
# print(data.datetime)
data = data \
.assign(tradetime=data['datetime'].apply(str), code=str(code),
datetime=pd.to_datetime(
data['datetime'].apply(QA_util_future_to_realdatetime, 1), utc=False)) \
.drop(['year', 'month', 'day', 'hour', 'minute'], axis=1,
inplace=False) \
.assign(date=data['datetime'].apply(lambda x: str(x)[0:10]),
date_stamp=data['datetime'].apply(
lambda x: QA_util_date_stamp(x)),
time_stamp=data['datetime'].apply(
lambda x: QA_util_time_stamp(x)),
type=type_).set_index('datetime', drop=False,
inplace=False)
return data.assign(datetime=data['datetime'].apply(lambda x: str(x)))[
start:end].sort_index() | [
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spyder-ide/spyder | 55da47c032dfcf519600f67f8b30eab467f965e7 | spyder/plugins/ipythonconsole/plugin.py | python | IPythonConsole.set_spyder_breakpoints | (self) | Set Spyder breakpoints into all clients | Set Spyder breakpoints into all clients | [
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kubeflow/pipelines | bea751c9259ff0ae85290f873170aae89284ba8e | sdk/python/kfp/dsl/dsl_utils.py | python | resolve_cmd_lines | (cmds: Optional[List[_CommandlineArgumentType]],
is_output_parameter: Callable[[str], bool]) | Resolves a list of commands/args. | Resolves a list of commands/args. | [
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] | def resolve_cmd_lines(cmds: Optional[List[_CommandlineArgumentType]],
is_output_parameter: Callable[[str], bool]) -> None:
"""Resolves a list of commands/args."""
def _resolve_cmd(cmd: Optional[_CommandlineArgumentType]) -> Optional[str]:
"""Resolves a single command line cmd/arg."""
if cmd is None:
return None
elif isinstance(cmd, (str, float, int)):
return str(cmd)
elif isinstance(cmd, _structures.InputValuePlaceholder):
return _input_parameter_placeholder(cmd.input_name)
elif isinstance(cmd, _structures.InputPathPlaceholder):
return _input_artifact_path_placeholder(cmd.input_name)
elif isinstance(cmd, _structures.InputUriPlaceholder):
return _input_artifact_uri_placeholder(cmd.input_name)
elif isinstance(cmd, _structures.OutputPathPlaceholder):
if is_output_parameter(cmd.output_name):
return _output_parameter_path_placeholder(cmd.output_name)
else:
return _output_artifact_path_placeholder(cmd.output_name)
elif isinstance(cmd, _structures.OutputUriPlaceholder):
return _output_artifact_uri_placeholder(cmd.output_name)
elif isinstance(cmd, _structures.ExecutorInputPlaceholder):
return _executor_input_placeholder()
else:
raise TypeError('Got unexpected placeholder type for %s' % cmd)
if not cmds:
return
for idx, cmd in enumerate(cmds):
cmds[idx] = _resolve_cmd(cmd) | [
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allenai/allennlp | a3d71254fcc0f3615910e9c3d48874515edf53e0 | allennlp/commands/subcommand.py | python | Subcommand.name | (self) | return self._reverse_registry[self.__class__] | [] | def name(self) -> str:
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/wagtail_bak/wagtailcore/query.py | python | PageQuerySet.exact_type | (self, model) | return self.filter(self.exact_type_q(model)) | This filters the QuerySet to only contain pages that are an instance of the specified model
(matching the model exactly, not subclasses). | This filters the QuerySet to only contain pages that are an instance of the specified model
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"""
This filters the QuerySet to only contain pages that are an instance of the specified model
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"""
return self.filter(self.exact_type_q(model)) | [
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mozillazg/pypy | 2ff5cd960c075c991389f842c6d59e71cf0cb7d0 | rpython/tool/setuptools_msvc.py | python | SystemInfo._find_dot_net_versions | (self, bits) | Find Microsoft .NET Framework versions.
Parameters
----------
bits: int
Platform number of bits: 32 or 64.
Return
------
tuple of str
versions | Find Microsoft .NET Framework versions. | [
"Find",
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"versions",
"."
] | def _find_dot_net_versions(self, bits):
"""
Find Microsoft .NET Framework versions.
Parameters
----------
bits: int
Platform number of bits: 32 or 64.
Return
------
tuple of str
versions
"""
# Find actual .NET version in registry
reg_ver = self.ri.lookup(self.ri.vc, 'frameworkver%d' % bits)
dot_net_dir = getattr(self, 'FrameworkDir%d' % bits)
ver = reg_ver or self._use_last_dir_name(dot_net_dir, 'v') or ''
# Set .NET versions for specified MSVC++ version
if self.vs_ver >= 12.0:
return ver, 'v4.0'
elif self.vs_ver >= 10.0:
return 'v4.0.30319' if ver.lower()[:2] != 'v4' else ver, 'v3.5'
elif self.vs_ver == 9.0:
return 'v3.5', 'v2.0.50727'
elif self.vs_ver == 8.0:
return 'v3.0', 'v2.0.50727' | [
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tp4a/teleport | 1fafd34f1f775d2cf80ea4af6e44468d8e0b24ad | server/www/packages/packages-windows/x86/ldap3/utils/conv.py | python | json_hook | (obj) | return obj | [] | def json_hook(obj):
if hasattr(obj, 'keys') and len(list(obj.keys())) == 2 and 'encoding' in obj.keys() and 'encoded' in obj.keys():
return b64decode(obj['encoded'])
return obj | [
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lukalabs/cakechat | 844507281b30d81b3fe3674895fe27826dba8438 | cakechat/dialog_model/factory.py | python | get_trained_model | (is_reverse_model=False, reverse_model=None, fetch_from_s3=True) | return model | Get a trained model, direct or reverse.
:param is_reverse_model: boolean, if True, a reverse trained model will be returned to be used during inference
in direct model in *_reranking modes; if False, a direct trained model is returned
:param reverse_model: object of a reverse model to be used in direct model in *_reranking inference modes
:param fetch_from_s3: boolean, if True, download trained model from Amazon S3 if the the model is not found locally;
if False, the model presence will be checked only locally
:return: | Get a trained model, direct or reverse.
:param is_reverse_model: boolean, if True, a reverse trained model will be returned to be used during inference
in direct model in *_reranking modes; if False, a direct trained model is returned
:param reverse_model: object of a reverse model to be used in direct model in *_reranking inference modes
:param fetch_from_s3: boolean, if True, download trained model from Amazon S3 if the the model is not found locally;
if False, the model presence will be checked only locally
:return: | [
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... | def get_trained_model(is_reverse_model=False, reverse_model=None, fetch_from_s3=True):
"""
Get a trained model, direct or reverse.
:param is_reverse_model: boolean, if True, a reverse trained model will be returned to be used during inference
in direct model in *_reranking modes; if False, a direct trained model is returned
:param reverse_model: object of a reverse model to be used in direct model in *_reranking inference modes
:param fetch_from_s3: boolean, if True, download trained model from Amazon S3 if the the model is not found locally;
if False, the model presence will be checked only locally
:return:
"""
if fetch_from_s3:
resolver_factory = get_s3_model_resolver(S3_MODELS_BUCKET_NAME, S3_NN_MODEL_REMOTE_DIR)
else:
resolver_factory = None
w2v_model_id = get_w2v_model_id() if USE_PRETRAINED_W2V_EMBEDDINGS_LAYER else None
model = InferenceCakeChatModel(
index_to_token=_get_index_to_token(fetch_from_s3),
index_to_condition=_get_index_to_condition(fetch_from_s3),
training_data_param=ModelParam(value=None, id=TRAIN_CORPUS_NAME),
validation_data_param=ModelParam(value=None, id=get_validation_data_id(get_validation_sets_names())),
w2v_model_param=ModelParam(value=None, id=w2v_model_id),
model_resolver=resolver_factory,
is_reverse_model=is_reverse_model,
reverse_model=reverse_model)
model.init_model()
model.resolve_model()
return model | [
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OpenNMT/OpenNMT-py | 4815f07fcd482af9a1fe1d3b620d144197178bc5 | onmt/bin/server.py | python | _get_parser | () | return parser | [] | def _get_parser():
parser = configargparse.ArgumentParser(
config_file_parser_class=configargparse.YAMLConfigFileParser,
description="OpenNMT-py REST Server")
parser.add_argument("--ip", type=str, default="0.0.0.0")
parser.add_argument("--port", type=int, default="5000")
parser.add_argument("--url_root", type=str, default="/translator")
parser.add_argument("--debug", "-d", action="store_true")
parser.add_argument("--config", "-c", type=str,
default="./available_models/conf.json")
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orestis/pysmell | 14382f377f7759a1b6505120990898dd51f175e6 | pysmell/codefinder.py | python | ModuleDict.enterClass | (self, klass, bases, docstring) | [] | def enterClass(self, klass, bases, docstring):
fullClass = "%s.%s" % (self.currentModule, klass)
self['CLASSES'][fullClass] = {}
self['CLASSES'][fullClass]['methods'] = []
self['CLASSES'][fullClass]['properties'] = []
self['CLASSES'][fullClass]['constructor'] = []
self['CLASSES'][fullClass]['bases'] = bases
self['CLASSES'][fullClass]['docstring'] = docstring | [
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openai/mujoco-worldgen | 39f52b1b47aed499925a6a214b58bdbdb4e2f75e | mujoco_worldgen/objs/obj.py | python | Obj.generate_xml_dict | (self) | Generate XML DOM nodes needed for MuJoCo model.
doc - XML Document, used to create elements/nodes
name_indexes - dictionary to keep track of names,
see obj_util.get_name_index() for internals
Returns a dictionary with keys as names of top-level nodes:
e.g. 'worldbody', 'materials', 'assets'
And the values are lists of XML DOM nodes | Generate XML DOM nodes needed for MuJoCo model.
doc - XML Document, used to create elements/nodes
name_indexes - dictionary to keep track of names,
see obj_util.get_name_index() for internals
Returns a dictionary with keys as names of top-level nodes:
e.g. 'worldbody', 'materials', 'assets'
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Generate XML DOM nodes needed for MuJoCo model.
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see obj_util.get_name_index() for internals
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raise NotImplementedError('Implement in subclasses!') | [
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/benchmarks/src/benchmarks/sympy/sympy/polys/partfrac.py | python | apart_full_decomposition | (P, Q) | return assemble_partfrac_list(apart_list(P/Q, P.gens[0])) | Bronstein's full partial fraction decomposition algorithm.
Given a univariate rational function ``f``, performing only GCD
operations over the algebraic closure of the initial ground domain
of definition, compute full partial fraction decomposition with
fractions having linear denominators.
Note that no factorization of the initial denominator of ``f`` is
performed. The final decomposition is formed in terms of a sum of
:class:`RootSum` instances.
References
==========
1. [Bronstein93]_ | Bronstein's full partial fraction decomposition algorithm. | [
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] | def apart_full_decomposition(P, Q):
"""
Bronstein's full partial fraction decomposition algorithm.
Given a univariate rational function ``f``, performing only GCD
operations over the algebraic closure of the initial ground domain
of definition, compute full partial fraction decomposition with
fractions having linear denominators.
Note that no factorization of the initial denominator of ``f`` is
performed. The final decomposition is formed in terms of a sum of
:class:`RootSum` instances.
References
==========
1. [Bronstein93]_
"""
return assemble_partfrac_list(apart_list(P/Q, P.gens[0])) | [
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line/line-bot-sdk-python | d97d488876d504ab3cb6ecfc1574ea42bd669565 | linebot/api.py | python | LineBotApi.delete_rich_menu | (self, rich_menu_id, timeout=None) | Call delete rich menu API.
https://developers.line.biz/en/reference/messaging-api/#delete-rich-menu
:param str rich_menu_id: ID of an uploaded rich menu
:param timeout: (optional) How long to wait for the server
to send data before giving up, as a float,
or a (connect timeout, read timeout) float tuple.
Default is self.http_client.timeout
:type timeout: float | tuple(float, float) | Call delete rich menu API. | [
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] | def delete_rich_menu(self, rich_menu_id, timeout=None):
"""Call delete rich menu API.
https://developers.line.biz/en/reference/messaging-api/#delete-rich-menu
:param str rich_menu_id: ID of an uploaded rich menu
:param timeout: (optional) How long to wait for the server
to send data before giving up, as a float,
or a (connect timeout, read timeout) float tuple.
Default is self.http_client.timeout
:type timeout: float | tuple(float, float)
"""
self._delete(
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timeout=timeout
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cloudera/hue | 23f02102d4547c17c32bd5ea0eb24e9eadd657a4 | desktop/core/ext-py/py4j-0.9/src/py4j/java_gateway.py | python | JavaGateway.__init__ | (
self, gateway_client=None, auto_field=False,
python_proxy_port=DEFAULT_PYTHON_PROXY_PORT,
start_callback_server=False, auto_convert=False, eager_load=False,
gateway_parameters=None, callback_server_parameters=None) | :param gateway_parameters: An instance of `GatewayParameters` used to
configure the various options of the gateway.
:param callback_server_parameters: An instance of
`CallbackServerParameters` used to configure various options of the
gateway server. Must be provided to start a gateway server.
Otherwise, callbacks won"t be available. | :param gateway_parameters: An instance of `GatewayParameters` used to
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self, gateway_client=None, auto_field=False,
python_proxy_port=DEFAULT_PYTHON_PROXY_PORT,
start_callback_server=False, auto_convert=False, eager_load=False,
gateway_parameters=None, callback_server_parameters=None):
"""
:param gateway_parameters: An instance of `GatewayParameters` used to
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:param callback_server_parameters: An instance of
`CallbackServerParameters` used to configure various options of the
gateway server. Must be provided to start a gateway server.
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"""
self.gateway_parameters = gateway_parameters
if not gateway_parameters:
self.gateway_parameters = GatewayParameters(
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eager_load=eager_load)
self.callback_server_parameters = callback_server_parameters
if not callback_server_parameters:
# No parameters were provided so do not autostart callback server.
self.callback_server_parameters = CallbackServerParameters(
port=python_proxy_port, eager_load=False)
# Check for deprecation warnings
if auto_field:
deprecated("JavaGateway.auto_field", "1.0", "GatewayParameters")
if auto_convert:
deprecated("JavaGateway.auto_convert", "1.0", "GatewayParameters")
if eager_load:
deprecated("JavaGateway.eager_load", "1.0", "GatewayParameters")
if start_callback_server:
deprecated(
"JavaGateway.start_callback_server and python_proxy_port",
"1.0", "CallbackServerParameters")
self.callback_server_parameters.eager_load = True
if gateway_client:
deprecated("JavaGateway.gateway_client", "1.0",
"GatewayParameters")
else:
gateway_client = GatewayClient(
address=self.gateway_parameters.address,
port=self.gateway_parameters.port,
auto_close=self.gateway_parameters.auto_close)
self.gateway_property = GatewayProperty(
self.gateway_parameters.auto_field, PythonProxyPool())
self._python_proxy_port = python_proxy_port
# Setup gateway client
self.set_gateway_client(gateway_client)
# Setup callback server property
self._callback_server = None
if self.gateway_parameters.eager_load:
self._eager_load()
if self.callback_server_parameters.eager_load:
self.start_callback_server(self.callback_server_parameters) | [
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lordmauve/pgzero | 46a889fb918ccd606d39ba63680386d7f8fcbc5a | examples/basic/demo3.py | python | set_alien_normal | () | Set the current alien sprite to the normal image. | Set the current alien sprite to the normal image. | [
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"""Set the current alien sprite to the normal image."""
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x0rz/EQGRP_Lost_in_Translation | 6692b1486f562f027567a49523b8c151a4050988 | windows/fuzzbunch/pyreadline/modes/notemacs.py | python | NotEmacsMode.prefix_meta | (self, e) | Metafy the next character typed. This is for keyboards without a
meta key. Typing ESC f is equivalent to typing M-f. | Metafy the next character typed. This is for keyboards without a
meta key. Typing ESC f is equivalent to typing M-f. | [
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] | def prefix_meta(self, e): # (ESC)
'''Metafy the next character typed. This is for keyboards without a
meta key. Typing ESC f is equivalent to typing M-f. '''
self.next_meta = True | [
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mozillazg/pypy | 2ff5cd960c075c991389f842c6d59e71cf0cb7d0 | pypy/module/zlib/interp_zlib.py | python | crc32 | (space, string, start = rzlib.CRC32_DEFAULT_START) | return space.newint(checksum) | crc32(string[, start]) -- Compute a CRC-32 checksum of string.
An optional starting value can be specified. The returned checksum is
an integer. | crc32(string[, start]) -- Compute a CRC-32 checksum of string. | [
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] | def crc32(space, string, start = rzlib.CRC32_DEFAULT_START):
"""
crc32(string[, start]) -- Compute a CRC-32 checksum of string.
An optional starting value can be specified. The returned checksum is
an integer.
"""
ustart = r_uint(start)
checksum = rzlib.crc32(string, ustart)
# This is, perhaps, a little stupid. zlib returns the checksum unsigned.
# CPython exposes it as a signed value, though. -exarkun
# Note that in CPython < 2.6 on 64-bit platforms the result is
# actually unsigned, but it was considered to be a bug so we stick to
# the 2.6 behavior and always return a number in range(-2**31, 2**31).
checksum = unsigned_to_signed_32bit(checksum)
return space.newint(checksum) | [
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pythonarcade/arcade | 1ee3eb1900683213e8e8df93943327c2ea784564 | arcade/gl/program.py | python | Program._setup_out_attributes | (self) | Set up transform feedback varyings | Set up transform feedback varyings | [
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"varyings"
] | def _setup_out_attributes(self):
"""Set up transform feedback varyings"""
if not self._out_attributes:
return
# Covert names to char**
c_array = (c_char_p * len(self._out_attributes))()
for i, name in enumerate(self._out_attributes):
c_array[i] = name.encode()
ptr = cast(c_array, POINTER(POINTER(c_char)))
# NOTE: We only support interleaved attributes for now
gl.glTransformFeedbackVaryings(
self._glo, # program
len(
self._out_attributes
), # number of varying variables used for transform feedback
ptr, # zero-terminated strings specifying the names of the varying variables
gl.GL_INTERLEAVED_ATTRIBS,
) | [
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beer-garden/beer-garden | 7b1b7dd64ab1f19f370451c9438362d11f3a06e4 | src/app/beer_garden/api/http/handlers/vbeta/file.py | python | RawFileAPI.delete | (self, file_id) | ---
summary: Delete a file
parameters:
- name: file_name
in: path
required: true
description: The file ID
type: string
responses:
204:
description: The file and all of its contents have been removed.
schema:
$ref: '#/definitions/FileStatus'
400:
$ref: '#/definitions/400Error'
50x:
$ref: '#/definitions/50xError'
tags:
- Files | ---
summary: Delete a file
parameters:
- name: file_name
in: path
required: true
description: The file ID
type: string
responses:
204:
description: The file and all of its contents have been removed.
schema:
$ref: '#/definitions/FileStatus'
400:
$ref: '#/definitions/400Error'
50x:
$ref: '#/definitions/50xError'
tags:
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"""
---
summary: Delete a file
parameters:
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required: true
description: The file ID
type: string
responses:
204:
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schema:
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"""
db_file = RawFile.objects.get(id=file_id)
db_file.file.delete()
db_file.save()
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languitar/pass-git-helper | 0ccb8e5bcc4252dcb131468a2453eb4d2e051b7f | passgithelper.py | python | parse_mapping | (mapping_file: Optional[IO]) | Parse the file containing the mappings from hosts to pass entries.
Args:
mapping_file:
Name of the file to parse. If ``None``, the default file from the
XDG location is used. | Parse the file containing the mappings from hosts to pass entries. | [
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] | def parse_mapping(mapping_file: Optional[IO]) -> configparser.ConfigParser:
"""
Parse the file containing the mappings from hosts to pass entries.
Args:
mapping_file:
Name of the file to parse. If ``None``, the default file from the
XDG location is used.
"""
LOGGER.debug("Parsing mapping file. Command line: %s", mapping_file)
def parse(mapping_file: IO) -> configparser.ConfigParser:
config = configparser.ConfigParser()
config.read_file(mapping_file)
return config
# give precedence to the user-specified file
if mapping_file is not None:
LOGGER.debug("Parsing command line mapping file")
return parse(mapping_file)
# fall back on XDG config location
xdg_config_dir = xdg.BaseDirectory.load_first_config("pass-git-helper")
if xdg_config_dir is None:
raise RuntimeError(
"No mapping configured so far at any XDG config location. "
"Please create {config_file}".format(config_file=DEFAULT_CONFIG_FILE)
)
default_file = os.path.join(xdg_config_dir, CONFIG_FILE_NAME)
LOGGER.debug("Parsing mapping file %s", mapping_file)
with open(default_file, "r") as file_handle:
return parse(file_handle) | [
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cournape/Bento | 37de23d784407a7c98a4a15770ffc570d5f32d70 | bento/core/pkg_objects.py | python | PathOption.__init__ | (self, name, default_value, description=None) | [] | def __init__(self, name, default_value, description=None):
self.name = name
self.default_value = default_value
self.description = description | [
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CouchPotato/CouchPotatoV1 | 135b3331d1b88ef645e29b76f2d4cc4a732c9232 | library/sqlalchemy/dialects/mysql/base.py | python | TINYINT.__init__ | (self, display_width=None, **kw) | Construct a TINYINT.
Note: following the usual MySQL conventions, TINYINT(1) columns
reflected during Table(..., autoload=True) are treated as
Boolean columns.
:param display_width: Optional, maximum display width for this number.
:param unsigned: a boolean, optional.
:param zerofill: Optional. If true, values will be stored as strings
left-padded with zeros. Note that this does not effect the values
returned by the underlying database API, which continue to be
numeric. | Construct a TINYINT. | [
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] | def __init__(self, display_width=None, **kw):
"""Construct a TINYINT.
Note: following the usual MySQL conventions, TINYINT(1) columns
reflected during Table(..., autoload=True) are treated as
Boolean columns.
:param display_width: Optional, maximum display width for this number.
:param unsigned: a boolean, optional.
:param zerofill: Optional. If true, values will be stored as strings
left-padded with zeros. Note that this does not effect the values
returned by the underlying database API, which continue to be
numeric.
"""
super(TINYINT, self).__init__(display_width=display_width, **kw) | [
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utiasSTARS/liegroups | fe1d376b7d33809dec78724b456f01833507c305 | liegroups/torch/so3.py | python | SO3Matrix.rotx | (cls, angle_in_radians) | return cls(mat.squeeze_()) | Form a rotation matrix given an angle in rad about the x-axis. | Form a rotation matrix given an angle in rad about the x-axis. | [
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] | def rotx(cls, angle_in_radians):
"""Form a rotation matrix given an angle in rad about the x-axis."""
s = angle_in_radians.sin()
c = angle_in_radians.cos()
mat = angle_in_radians.new_empty(
angle_in_radians.shape[0], cls.dim, cls.dim).zero_()
mat[:, 0, 0] = 1.
mat[:, 1, 1] = c
mat[:, 1, 2] = -s
mat[:, 2, 1] = s
mat[:, 2, 2] = c
return cls(mat.squeeze_()) | [
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tensorflow/tfx | b4a6b83269815ed12ba9df9e9154c7376fef2ea0 | tfx/utils/kube_utils.py | python | is_inside_kfp | () | return (
is_inside_cluster()
and KFP_POD_NAME in os.environ
and KFP_NAMESPACE in os.environ
) | Whether current running environment is inside the KFP runtime. | Whether current running environment is inside the KFP runtime. | [
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] | def is_inside_kfp() -> bool:
"""Whether current running environment is inside the KFP runtime."""
return (
is_inside_cluster()
and KFP_POD_NAME in os.environ
and KFP_NAMESPACE in os.environ
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Thinklab-SJTU/R3Det_Tensorflow | 3e092fa65dee2b9f7722b0985b3791811a1de5ae | libs/networks/mobilenet/conv_blocks.py | python | _split_divisible | (num, num_ways, divisible_by=8) | return result | Evenly splits num, num_ways so each piece is a multiple of divisible_by. | Evenly splits num, num_ways so each piece is a multiple of divisible_by. | [
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] | def _split_divisible(num, num_ways, divisible_by=8):
"""Evenly splits num, num_ways so each piece is a multiple of divisible_by."""
assert num % divisible_by == 0
assert num / num_ways >= divisible_by
# Note: want to round down, we adjust each split to match the total.
base = num // num_ways // divisible_by * divisible_by
result = []
accumulated = 0
for i in range(num_ways):
r = base
while accumulated + r < num * (i + 1) / num_ways:
r += divisible_by
result.append(r)
accumulated += r
assert accumulated == num
return result | [
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ssjssh/algorithm | 2db90930340ba4a9bf5a37b3386e9dbb48673e28 | src/ssj/graph/weighted_graph.py | python | WeightedGraph.lws | (self, from_node, to_node) | return __lws[Edge(from_node, to_node)] | 在有向无环带权图里面查找最长路径
令list(s,t)是从s到t的最长带权路径。
那么可以使用递归式表示这个问题
list(s,t) = max(list(s,t-1))+list(t-1,t)(唯一)
用动态规划自下而上解决，因为自上而下解决首先遇到的问题就是
查找指向一个节点的节点在邻接表中比较困难
:param from_node:
:param to_node:
:return: | 在有向无环带权图里面查找最长路径
令list(s,t)是从s到t的最长带权路径。
那么可以使用递归式表示这个问题
list(s,t) = max(list(s,t-1))+list(t-1,t)(唯一)
用动态规划自下而上解决，因为自上而下解决首先遇到的问题就是
查找指向一个节点的节点在邻接表中比较困难
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"""
在有向无环带权图里面查找最长路径
令list(s,t)是从s到t的最长带权路径。
那么可以使用递归式表示这个问题
list(s,t) = max(list(s,t-1))+list(t-1,t)(唯一)
用动态规划自下而上解决，因为自上而下解决首先遇到的问题就是
查找指向一个节点的节点在邻接表中比较困难
:param from_node:
:param to_node:
:return:
"""
__lws = {}
# 为了计算方便，这里把开始节点到开始节点插入字典中，
zero_edge = Edge(from_node, from_node)
__lws[zero_edge] = 0
graph_stack = Queue()
graph_stack.enter(from_node)
while not graph_stack.empty():
cur_node = graph_stack.exit()
cur_edge_list = self.__adj_list.get(cur_node)
if cur_edge_list is None:
print ",".join(map(lambda edge: str(edge), __lws.iteritems()))
print ",".join(map(lambda edge: str(edge), __lws))
return __lws[Edge(from_node, to_node)]
for edge_end_node in cur_edge_list:
graph_stack.enter(edge_end_node.key)
last_weighted_length = __lws[Edge(from_node, cur_node)]
cur_edge = Edge(from_node, edge_end_node.key)
cur_weight_length = last_weighted_length + edge_end_node.weight
# 如果不存在这个边，那么就插入
if cur_edge not in __lws:
__lws[cur_edge] = cur_weight_length
# 如果存在，那么就把最大值插入
elif cur_weight_length > __lws[cur_edge]:
__lws[cur_edge] = cur_weight_length
print ",".join(map(lambda edge: str(edge), __lws.iteritems()))
print ",".join(map(lambda edge: str(edge), __lws))
return __lws[Edge(from_node, to_node)] | [
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... | https://github.com/ssjssh/algorithm/blob/2db90930340ba4a9bf5a37b3386e9dbb48673e28/src/ssj/graph/weighted_graph.py#L83-L121 | |
hyperledger/aries-cloudagent-python | 2f36776e99f6053ae92eed8123b5b1b2e891c02a | aries_cloudagent/protocols/issue_credential/v2_0/handlers/cred_issue_handler.py | python | V20CredIssueHandler.handle | (self, context: RequestContext, responder: BaseResponder) | Message handler logic for credential offers.
Args:
context: request context
responder: responder callback | Message handler logic for credential offers. | [
"Message",
"handler",
"logic",
"for",
"credential",
"offers",
"."
] | async def handle(self, context: RequestContext, responder: BaseResponder):
"""
Message handler logic for credential offers.
Args:
context: request context
responder: responder callback
"""
r_time = get_timer()
self._logger.debug("V20CredIssueHandler called with context %s", context)
assert isinstance(context.message, V20CredIssue)
self._logger.info(
"Received v2.0 credential issue message: %s",
context.message.serialize(as_string=True),
)
if not context.connection_ready:
raise HandlerException("No connection established for credential issue")
cred_manager = V20CredManager(context.profile)
cred_ex_record = await cred_manager.receive_credential(
context.message, context.connection_record.connection_id
) # mgr only finds, saves record: on exception, saving null state is hopeless
r_time = trace_event(
context.settings,
context.message,
outcome="V20CredIssueHandler.handle.END",
perf_counter=r_time,
)
# Automatically move to next state if flag is set
if context.settings.get("debug.auto_store_credential"):
try:
cred_ex_record = await cred_manager.store_credential(cred_ex_record)
except (
BaseModelError,
IndyHolderError,
StorageError,
V20CredManagerError,
) as err:
# treat failure to store as mangled on receipt hence protocol error
self._logger.exception(err)
if cred_ex_record:
async with context.profile.session() as session:
await cred_ex_record.save_error_state(
session,
reason=err.roll_up, # us: be specific
)
await responder.send_reply(
problem_report_for_record(
cred_ex_record,
ProblemReportReason.ISSUANCE_ABANDONED.value, # them: vague
)
)
cred_ack_message = await cred_manager.send_cred_ack(cred_ex_record)
trace_event(
context.settings,
cred_ack_message,
outcome="V20CredIssueHandler.handle.STORE",
perf_counter=r_time,
) | [
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cyverse/atmosphere | 4a3e522f1f7b58abd9fa944c10b7455dc5cddac1 | core/plugins.py | python | AllocationSourcePluginManager.ensure_user_allocation_sources | (cls, user, provider=None) | return _has_valid_allocation_sources | Load each Allocation Source Plugin and call `plugin.ensure_user_allocation_source(user)`
Depending on the plugin this may create allocation sources if they don't already exist.
:param user: The user to check
:type user: core.models.AtmosphereUser
:param provider: The provider (optional, not used by all plugins)
:type provider: core.models.Provider
:return: Whether the user has valid allocation sources
:rtype: bool | Load each Allocation Source Plugin and call `plugin.ensure_user_allocation_source(user)` | [
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"Allocation",
"Source",
"Plugin",
"and",
"call",
"plugin",
".",
"ensure_user_allocation_source",
"(",
"user",
")"
] | def ensure_user_allocation_sources(cls, user, provider=None):
"""Load each Allocation Source Plugin and call `plugin.ensure_user_allocation_source(user)`
Depending on the plugin this may create allocation sources if they don't already exist.
:param user: The user to check
:type user: core.models.AtmosphereUser
:param provider: The provider (optional, not used by all plugins)
:type provider: core.models.Provider
:return: Whether the user has valid allocation sources
:rtype: bool
"""
_has_valid_allocation_sources = False
for AllocationSourcePlugin in cls.load_plugins(cls.list_of_classes):
plugin = AllocationSourcePlugin()
try:
inspect.getcallargs(
getattr(plugin, 'ensure_user_allocation_source'),
user=user,
provider=provider
)
except AttributeError:
logger.info(
"Allocation Source plugin %s missing method 'ensure_user_allocation_source'",
AllocationSourcePlugin
)
except TypeError:
logger.info(
"Allocation Source plugin %s does not accept kwargs `user` & `provider`",
AllocationSourcePlugin
)
_has_valid_allocation_sources = plugin.ensure_user_allocation_source(
user=user, provider=provider
)
if _has_valid_allocation_sources:
return _has_valid_allocation_sources
return _has_valid_allocation_sources | [
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":"... | https://github.com/cyverse/atmosphere/blob/4a3e522f1f7b58abd9fa944c10b7455dc5cddac1/core/plugins.py#L139-L174 | |
PacktPublishing/Mastering-OpenCV-4-with-Python | ea5372c6d8758ebc56ef5c775f9785d4427f81e6 | Chapter08/01-chapter-content/contours_shape_recognition.py | python | draw_contour_outline | (img, cnts, color, thickness=1) | Draws contours outlines of each contour | Draws contours outlines of each contour | [
"Draws",
"contours",
"outlines",
"of",
"each",
"contour"
] | def draw_contour_outline(img, cnts, color, thickness=1):
"""Draws contours outlines of each contour"""
for cnt in cnts:
cv2.drawContours(img, [cnt], 0, color, thickness) | [
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TheAlgorithms/Python | 9af2eef9b3761bf51580dedfb6fa7136ca0c5c2c | bit_manipulation/single_bit_manipulation_operations.py | python | flip_bit | (number: int, position: int) | return number ^ (1 << position) | Flip the bit at position.
Details: perform bitwise xor for given number and X.
Where X is a number with all the bits – zeroes and bit on given
position – one.
>>> flip_bit(0b101, 1) # 0b111
7
>>> flip_bit(0b101, 0) # 0b100
4 | Flip the bit at position. | [
"Flip",
"the",
"bit",
"at",
"position",
"."
] | def flip_bit(number: int, position: int) -> int:
"""
Flip the bit at position.
Details: perform bitwise xor for given number and X.
Where X is a number with all the bits – zeroes and bit on given
position – one.
>>> flip_bit(0b101, 1) # 0b111
7
>>> flip_bit(0b101, 0) # 0b100
4
"""
return number ^ (1 << position) | [
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Source-Python-Dev-Team/Source.Python | d0ffd8ccbd1e9923c9bc44936f20613c1c76b7fb | addons/source-python/packages/site-packages/sqlalchemy/engine/interfaces.py | python | Dialect.get_foreign_keys | (self, connection, table_name, schema=None, **kw) | Return information about foreign_keys in `table_name`.
Given a :class:`.Connection`, a string
`table_name`, and an optional string `schema`, return foreign
key information as a list of dicts with these keys:
name
the constraint's name
constrained_columns
a list of column names that make up the foreign key
referred_schema
the name of the referred schema
referred_table
the name of the referred table
referred_columns
a list of column names in the referred table that correspond to
constrained_columns | Return information about foreign_keys in `table_name`. | [
"Return",
"information",
"about",
"foreign_keys",
"in",
"table_name",
"."
] | def get_foreign_keys(self, connection, table_name, schema=None, **kw):
"""Return information about foreign_keys in `table_name`.
Given a :class:`.Connection`, a string
`table_name`, and an optional string `schema`, return foreign
key information as a list of dicts with these keys:
name
the constraint's name
constrained_columns
a list of column names that make up the foreign key
referred_schema
the name of the referred schema
referred_table
the name of the referred table
referred_columns
a list of column names in the referred table that correspond to
constrained_columns
"""
raise NotImplementedError() | [
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ym2011/POC-EXP | 206b22d3a6b2a172359678df33bbc5b2ad04b6c3 | K8/Web-Exp/sqlmap/tamper/symboliclogical.py | python | tamper | (payload, **kwargs) | return retVal | Replaces AND and OR logical operators with their symbolic counterparts (&& and ||)
>>> tamper("1 AND '1'='1")
"1 %26%26 '1'='1" | Replaces AND and OR logical operators with their symbolic counterparts (&& and ||) | [
"Replaces",
"AND",
"and",
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"logical",
"operators",
"with",
"their",
"symbolic",
"counterparts",
"(",
"&&",
"and",
"||",
")"
] | def tamper(payload, **kwargs):
"""
Replaces AND and OR logical operators with their symbolic counterparts (&& and ||)
>>> tamper("1 AND '1'='1")
"1 %26%26 '1'='1"
"""
retVal = payload
if payload:
retVal = re.sub(r"(?i)\bAND\b", "%26%26", re.sub(r"(?i)\bOR\b", "%7C%7C", payload))
return retVal | [
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/encodings/mac_iceland.py | python | getregentry | () | return codecs.CodecInfo(
name='mac-iceland',
encode=Codec().encode,
decode=Codec().decode,
incrementalencoder=IncrementalEncoder,
incrementaldecoder=IncrementalDecoder,
streamreader=StreamReader,
streamwriter=StreamWriter,
) | [] | def getregentry():
return codecs.CodecInfo(
name='mac-iceland',
encode=Codec().encode,
decode=Codec().decode,
incrementalencoder=IncrementalEncoder,
incrementaldecoder=IncrementalDecoder,
streamreader=StreamReader,
streamwriter=StreamWriter,
) | [
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krintoxi/NoobSec-Toolkit | 38738541cbc03cedb9a3b3ed13b629f781ad64f6 | NoobSecToolkit - MAC OSX/tools/sqli/plugins/dbms/mysql/syntax.py | python | Syntax.__init__ | (self) | [] | def __init__(self):
GenericSyntax.__init__(self) | [
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"__init__",
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":",
"GenericSyntax",
".",
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")"
] | https://github.com/krintoxi/NoobSec-Toolkit/blob/38738541cbc03cedb9a3b3ed13b629f781ad64f6/NoobSecToolkit - MAC OSX/tools/sqli/plugins/dbms/mysql/syntax.py#L14-L15 | ||||
exaile/exaile | a7b58996c5c15b3aa7b9975ac13ee8f784ef4689 | xl/radio.py | python | RadioList.__str__ | (self) | return self.name | Returns a string representation of the list | Returns a string representation of the list | [
"Returns",
"a",
"string",
"representation",
"of",
"the",
"list"
] | def __str__(self):
"""
Returns a string representation of the list
"""
return self.name | [
"def",
"__str__",
"(",
"self",
")",
":",
"return",
"self",
".",
"name"
] | https://github.com/exaile/exaile/blob/a7b58996c5c15b3aa7b9975ac13ee8f784ef4689/xl/radio.py#L127-L131 | |
fuzzbunch/fuzzbunch | 4b60a6c7cf9f84cf389d3fcdb9281de84ffb5802 | fuzzbunch/pyreadline/modes/notemacs.py | python | NotEmacsMode.tab_insert | (self, e) | Insert a tab character. | Insert a tab character. | [
"Insert",
"a",
"tab",
"character",
"."
] | def tab_insert(self, e): # (M-TAB)
'''Insert a tab character. '''
ws = ' ' * (self.tabstop - (self.line_cursor%self.tabstop))
self.insert_text(ws) | [
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Kkevsterrr/backdoorme | f9755ca6cec600335e681752e7a1c5c617bb5a39 | backdoors/shell/__pupy/pupy/packages/windows/x86/psutil/_psosx.py | python | per_cpu_times | () | return ret | Return system CPU times as a named tuple | Return system CPU times as a named tuple | [
"Return",
"system",
"CPU",
"times",
"as",
"a",
"named",
"tuple"
] | def per_cpu_times():
"""Return system CPU times as a named tuple"""
ret = []
for cpu_t in cext.per_cpu_times():
user, nice, system, idle = cpu_t
item = scputimes(user, nice, system, idle)
ret.append(item)
return ret | [
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dmlc/dgl | 8d14a739bc9e446d6c92ef83eafe5782398118de | python/dgl/_deprecate/graph.py | python | DGLGraph.recv | (self,
v=ALL,
reduce_func="default",
apply_node_func="default",
inplace=False) | Receive and reduce incoming messages and update the features of node(s) :math:`v`.
Optionally, apply a function to update the node features after receive.
* `reduce_func` will be skipped for nodes with no incoming message.
* If all ``v`` have no incoming message, this will downgrade to an :func:`apply_nodes`.
* If some ``v`` have no incoming message, their new feature value will be calculated
by the column initializer (see :func:`set_n_initializer`). The feature shapes and
dtypes will be inferred.
The node features will be updated by the result of the ``reduce_func``.
Messages are consumed once received.
The provided UDF maybe called multiple times so it is recommended to provide
function with no side effect.
Parameters
----------
v : node, container or tensor, optional
The node to be updated. Default is receiving all the nodes.
reduce_func : callable, optional
Reduce function on the node. The function should be
a :mod:`Node UDF <dgl.udf>`.
apply_node_func : callable
Apply function on the nodes. The function should be
a :mod:`Node UDF <dgl.udf>`.
inplace: bool, optional
If True, update will be done in place, but autograd will break.
Examples
--------
Create a graph object for demo.
.. note:: Here we use pytorch syntax for demo. The general idea applies
to other frameworks with minor syntax change (e.g. replace
``torch.tensor`` with ``mxnet.ndarray``).
>>> import torch as th
>>> g = dgl.DGLGraph()
>>> g.add_nodes(3)
>>> g.ndata['x'] = th.tensor([[1.], [2.], [3.]])
>>> g.add_edges([0, 1], [1, 2])
>>> # Define the function for sending node features as messages.
>>> def send_source(edges): return {'m': edges.src['x']}
>>> # Set the function defined to be the default message function.
>>> g.register_message_func(send_source)
>>> # Sum the messages received and use this to replace the original node feature.
>>> def simple_reduce(nodes): return {'x': nodes.mailbox['m'].sum(1)}
>>> # Set the function defined to be the default message reduce function.
>>> g.register_reduce_func(simple_reduce)
Send and receive messages. Note that although node :math:`0` has no incoming edges,
its feature gets changed from :math:`1` to :math:`0` as it is also included in
``g.nodes()``.
>>> g.send(g.edges())
>>> g.recv(g.nodes())
>>> g.ndata['x']
tensor([[0.],
[1.],
[2.]])
Once messages are received, one will need another call of :func:`send` again before
another call of :func:`recv`. Otherwise, nothing will happen.
>>> g.recv(g.nodes())
>>> g.ndata['x']
tensor([[0.],
[1.],
[2.]]) | Receive and reduce incoming messages and update the features of node(s) :math:`v`. | [
"Receive",
"and",
"reduce",
"incoming",
"messages",
"and",
"update",
"the",
"features",
"of",
"node",
"(",
"s",
")",
":",
"math",
":",
"v",
"."
] | def recv(self,
v=ALL,
reduce_func="default",
apply_node_func="default",
inplace=False):
"""Receive and reduce incoming messages and update the features of node(s) :math:`v`.
Optionally, apply a function to update the node features after receive.
* `reduce_func` will be skipped for nodes with no incoming message.
* If all ``v`` have no incoming message, this will downgrade to an :func:`apply_nodes`.
* If some ``v`` have no incoming message, their new feature value will be calculated
by the column initializer (see :func:`set_n_initializer`). The feature shapes and
dtypes will be inferred.
The node features will be updated by the result of the ``reduce_func``.
Messages are consumed once received.
The provided UDF maybe called multiple times so it is recommended to provide
function with no side effect.
Parameters
----------
v : node, container or tensor, optional
The node to be updated. Default is receiving all the nodes.
reduce_func : callable, optional
Reduce function on the node. The function should be
a :mod:`Node UDF <dgl.udf>`.
apply_node_func : callable
Apply function on the nodes. The function should be
a :mod:`Node UDF <dgl.udf>`.
inplace: bool, optional
If True, update will be done in place, but autograd will break.
Examples
--------
Create a graph object for demo.
.. note:: Here we use pytorch syntax for demo. The general idea applies
to other frameworks with minor syntax change (e.g. replace
``torch.tensor`` with ``mxnet.ndarray``).
>>> import torch as th
>>> g = dgl.DGLGraph()
>>> g.add_nodes(3)
>>> g.ndata['x'] = th.tensor([[1.], [2.], [3.]])
>>> g.add_edges([0, 1], [1, 2])
>>> # Define the function for sending node features as messages.
>>> def send_source(edges): return {'m': edges.src['x']}
>>> # Set the function defined to be the default message function.
>>> g.register_message_func(send_source)
>>> # Sum the messages received and use this to replace the original node feature.
>>> def simple_reduce(nodes): return {'x': nodes.mailbox['m'].sum(1)}
>>> # Set the function defined to be the default message reduce function.
>>> g.register_reduce_func(simple_reduce)
Send and receive messages. Note that although node :math:`0` has no incoming edges,
its feature gets changed from :math:`1` to :math:`0` as it is also included in
``g.nodes()``.
>>> g.send(g.edges())
>>> g.recv(g.nodes())
>>> g.ndata['x']
tensor([[0.],
[1.],
[2.]])
Once messages are received, one will need another call of :func:`send` again before
another call of :func:`recv`. Otherwise, nothing will happen.
>>> g.recv(g.nodes())
>>> g.ndata['x']
tensor([[0.],
[1.],
[2.]])
"""
if reduce_func == "default":
reduce_func = self._reduce_func
if apply_node_func == "default":
apply_node_func = self._apply_node_func
assert reduce_func is not None
if is_all(v):
v = F.arange(0, self.number_of_nodes())
elif isinstance(v, int):
v = [v]
v = utils.toindex(v)
if len(v) == 0:
# no vertex to be triggered.
return
with ir.prog() as prog:
scheduler.schedule_recv(graph=AdaptedDGLGraph(self),
recv_nodes=v,
reduce_func=reduce_func,
apply_func=apply_node_func,
inplace=inplace)
Runtime.run(prog) | [
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treeio/treeio | bae3115f4015aad2cbc5ab45572232ceec990495 | treeio/services/forms.py | python | MassActionForm.__init__ | (self, user, *args, **kwargs) | Sets allowed values | Sets allowed values | [
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] | def __init__(self, user, *args, **kwargs):
"Sets allowed values"
if 'instance' in kwargs:
self.instance = kwargs['instance']
del kwargs['instance']
super(MassActionForm, self).__init__(*args, **kwargs)
self.fields['status'].queryset = Object.filter_permitted(
user, TicketStatus.objects, mode='x')
self.fields['status'].label = _("Status")
self.fields['service'].queryset = Object.filter_permitted(
user, Service.objects, mode='x')
self.fields['service'].label = _("Service")
self.fields['queue'].queryset = Object.filter_permitted(
user, TicketQueue.objects, mode='x')
self.fields['queue'].label = _("Queue")
self.fields['delete'] = forms.ChoiceField(label=_("Delete"), choices=(('', '-----'),
('delete', _(
'Delete Completely')),
('trash', _('Move to Trash'))),
required=False) | [
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QUANTAXIS/QUANTAXIS | d6eccb97c8385854aa596d6ba8d70ec0655519ff | QUANTAXIS/QAFetch/QAhuobi_realtime.py | python | QA_Fetch_Huobi.run_subscription_batch_jobs | (self) | 请求 KLine 实时数据 | 请求 KLine 实时数据 | [
"请求",
"KLine",
"实时数据"
] | def run_subscription_batch_jobs(self):
"""
请求 KLine 实时数据
"""
websocket.enableTrace(False)
self.__ws = websocket.WebSocketApp(
self.HUOBIPRO_WEBSOCKET_URL,
on_message=self.on_message,
on_open=self.on_open,
on_error=self.on_error,
on_close=self.on_close
)
self.__locked = True
# 如果意外退出，等待10秒重新运行
while (True):
self.__ws.run_forever()
QA_util_log_expection("FTW! it quit! Retry 10 seconds later...")
time.sleep(10) | [
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errbotio/errbot | 66e1de8e1d7daa62be7f9ed1b2ac8832f09fa25d | errbot/core_plugins/plugins.py | python | Plugins.plugin_reload | (self, _, args) | reload a plugin: reload the code of the plugin leaving the activation status intact. | reload a plugin: reload the code of the plugin leaving the activation status intact. | [
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"leaving",
"the",
"activation",
"status",
"intact",
"."
] | def plugin_reload(self, _, args):
"""reload a plugin: reload the code of the plugin leaving the activation status intact."""
name = args.strip()
if not name:
yield (
f"Please tell me which of the following plugins to reload:\n"
f"{self.formatted_plugin_list(active_only=False)}"
)
return
if name not in self._bot.plugin_manager.get_all_plugin_names():
yield (
f"{name} isn't a valid plugin name. "
f"The current plugins are:\n{self.formatted_plugin_list(active_only=False)}"
)
return
if name not in self._bot.plugin_manager.get_all_active_plugin_names():
answer = f"Warning: plugin {name} is currently not activated. "
answer += f"Use `{self._bot.prefix}plugin activate {name}` to activate it."
yield answer
try:
self._bot.plugin_manager.reload_plugin_by_name(name)
yield f"Plugin {name} reloaded."
except PluginActivationException as pae:
yield f"Error activating plugin {name}: {pae}." | [
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kuri65536/python-for-android | 26402a08fc46b09ef94e8d7a6bbc3a54ff9d0891 | python3-alpha/python-libs/gdata/sites/client.py | python | SitesClient.get_revision_feed | (self, entry_or_uri_or_id, auth_token=None, **kwargs) | return self.get_feed(uri, desired_class=gdata.sites.data.RevisionFeed,
auth_token=auth_token, **kwargs) | Retrieves the revision feed containing the revision history for a node.
Args:
entry_or_uri_or_id: string or gdata.sites.data.ContentEntry A full URI,
content entry node ID, or a content entry object of the entry to
retrieve revision information for.
auth_token: (optional) gdata.gauth.ClientLoginToken, AuthSubToken, or
OAuthToken which authorizes this client to edit the user's data.
kwargs: Other parameters to pass to self.get_feed().
Returns:
gdata.sites.data.RevisionFeed | Retrieves the revision feed containing the revision history for a node. | [
"Retrieves",
"the",
"revision",
"feed",
"containing",
"the",
"revision",
"history",
"for",
"a",
"node",
"."
] | def get_revision_feed(self, entry_or_uri_or_id, auth_token=None, **kwargs):
"""Retrieves the revision feed containing the revision history for a node.
Args:
entry_or_uri_or_id: string or gdata.sites.data.ContentEntry A full URI,
content entry node ID, or a content entry object of the entry to
retrieve revision information for.
auth_token: (optional) gdata.gauth.ClientLoginToken, AuthSubToken, or
OAuthToken which authorizes this client to edit the user's data.
kwargs: Other parameters to pass to self.get_feed().
Returns:
gdata.sites.data.RevisionFeed
"""
uri = self.make_revision_feed_uri()
if isinstance(entry_or_uri_or_id, gdata.sites.data.ContentEntry):
uri = entry_or_uri_or_id.FindRevisionLink()
elif entry_or_uri_or_id.find('/') == -1:
uri += entry_or_uri_or_id
else:
uri = entry_or_uri_or_id
return self.get_feed(uri, desired_class=gdata.sites.data.RevisionFeed,
auth_token=auth_token, **kwargs) | [
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FSecureLABS/drozer | df11e6e63fbaefa9b58ed1e42533ddf76241d7e1 | src/mwr/common/console.py | python | _get_size_linux | () | return int(cr[1]), int(cr[0]) | Attempt to discover the dimensions of a terminal window, on Linux. | Attempt to discover the dimensions of a terminal window, on Linux. | [
"Attempt",
"to",
"discover",
"the",
"dimensions",
"of",
"a",
"terminal",
"window",
"on",
"Linux",
"."
] | def _get_size_linux():
"""
Attempt to discover the dimensions of a terminal window, on Linux.
"""
def ioctl_GWINSZ(fd):
"""
Attempt to discover the dimensions of a terminal window, using IOCTL.
"""
try:
import fcntl, termios
cr = struct.unpack('hh', fcntl.ioctl(fd, termios.TIOCGWINSZ,'1234'))
except:
return None
return cr
cr = ioctl_GWINSZ(0) or ioctl_GWINSZ(1) or ioctl_GWINSZ(2)
if not cr:
try:
fd = os.open(os.ctermid(), os.O_RDONLY)
cr = ioctl_GWINSZ(fd)
os.close(fd)
except:
pass
if not cr:
try:
cr = (env['LINES'], env['COLUMNS'])
except:
return None
return int(cr[1]), int(cr[0]) | [
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google/macops | 8442745359c0c941cd4e4e7d243e43bd16b40dec | gmacpyutil/gmacpyutil/macdisk.py | python | MountedVolumes | () | return volumes | Returns a list of all Disk objects that are mounted volumes. | Returns a list of all Disk objects that are mounted volumes. | [
"Returns",
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"list",
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"all",
"Disk",
"objects",
"that",
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"mounted",
"volumes",
"."
] | def MountedVolumes():
"""Returns a list of all Disk objects that are mounted volumes."""
volumes = []
volumenames = MountedVolumeNames()
for partition in Partitions():
if partition.volumename in volumenames:
volumes.append(partition)
return volumes | [
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Blizzard/heroprotocol | 3d36eaf44fc4c8ff3331c2ae2f1dc08a94535f1c | heroprotocol/versions/protocol84249.py | python | decode_replay_tracker_events | (contents) | Decodes and yields each tracker event from the contents byte string. | Decodes and yields each tracker event from the contents byte string. | [
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"."
] | def decode_replay_tracker_events(contents):
"""Decodes and yields each tracker event from the contents byte string."""
decoder = VersionedDecoder(contents, typeinfos)
for event in _decode_event_stream(decoder,
tracker_eventid_typeid,
tracker_event_types,
decode_user_id=False):
yield event | [
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... | https://github.com/Blizzard/heroprotocol/blob/3d36eaf44fc4c8ff3331c2ae2f1dc08a94535f1c/heroprotocol/versions/protocol84249.py#L424-L431 | ||
Source-Python-Dev-Team/Source.Python | d0ffd8ccbd1e9923c9bc44936f20613c1c76b7fb | addons/source-python/packages/site-packages/sphinx/domains/cpp.py | python | ASTBase.prefix_nested_name | (self, prefix) | Prefix a name node (a node returned by :meth:`get_name`). | Prefix a name node (a node returned by :meth:`get_name`). | [
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":",
"get_name",
")",
"."
] | def prefix_nested_name(self, prefix):
"""Prefix a name node (a node returned by :meth:`get_name`)."""
raise NotImplementedError(repr(self)) | [
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] | https://github.com/Source-Python-Dev-Team/Source.Python/blob/d0ffd8ccbd1e9923c9bc44936f20613c1c76b7fb/addons/source-python/packages/site-packages/sphinx/domains/cpp.py#L527-L529 | ||
damnever/pigar | 2a4f66c4a8125ffa30d5e11d1a0945b511ed1f79 | pigar/core.py | python | search_packages_by_names | (names) | Search package information by names(`import XXX`). | Search package information by names(`import XXX`). | [
"Search",
"package",
"information",
"by",
"names",
"(",
"import",
"XXX",
")",
"."
] | def search_packages_by_names(names):
"""Search package information by names(`import XXX`).
"""
downloader = Downloader()
results = collections.defaultdict(list)
not_found = list()
installed_pkgs = parse_installed_packages()
for name in names:
logger.debug('Searching package name for "{0}" ...'.format(name))
# If exists in local environment, do not check on the PyPI.
if name in installed_pkgs:
results[name].append(list(installed_pkgs[name]) + ['local'])
# Check information on the PyPI.
else:
rows = None
with database() as db:
rows = db.query_all(name)
if rows:
for row in rows:
try:
version = downloader.download_package(row.package
).version()
results[name].append((row.package, version, 'PyPI'))
except HTTPError as e:
logger.error('checking %s failed: %e', row.package, e)
else:
not_found.append(name)
for name in results:
print('Found package(s) for "{0}":'.format(Color.GREEN(name)))
print_table(results[name], headers=['PACKAGE', 'VERSION', 'WHERE'])
if not_found:
msg = '"{0}" not found.\n'.format(Color.RED(', '.join(not_found)))
msg += 'Maybe you need to update the database.'
print(Color.YELLOW(msg)) | [
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Rayhane-mamah/Tacotron-2 | ab5cb08a931fc842d3892ebeb27c8b8734ddd4b8 | tacotron/models/tacotron.py | python | Tacotron.add_loss | (self) | Adds loss to the model. Sets "loss" field. initialize must have been called. | Adds loss to the model. Sets "loss" field. initialize must have been called. | [
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"initialize",
"must",
"have",
"been",
"called",
"."
] | def add_loss(self):
'''Adds loss to the model. Sets "loss" field. initialize must have been called.'''
hp = self._hparams
self.tower_before_loss = []
self.tower_after_loss= []
self.tower_stop_token_loss = []
self.tower_regularization_loss = []
self.tower_linear_loss = []
self.tower_loss = []
total_before_loss = 0
total_after_loss= 0
total_stop_token_loss = 0
total_regularization_loss = 0
total_linear_loss = 0
total_loss = 0
gpus = ["/gpu:{}".format(i) for i in range(hp.tacotron_num_gpus)]
for i in range(hp.tacotron_num_gpus):
with tf.device(tf.train.replica_device_setter(ps_tasks=1, ps_device="/cpu:0", worker_device=gpus[i])):
with tf.variable_scope('loss') as scope:
if hp.mask_decoder:
# Compute loss of predictions before postnet
before = MaskedMSE(self.tower_mel_targets[i], self.tower_decoder_output[i], self.tower_targets_lengths[i],
hparams=self._hparams)
# Compute loss after postnet
after = MaskedMSE(self.tower_mel_targets[i], self.tower_mel_outputs[i], self.tower_targets_lengths[i],
hparams=self._hparams)
#Compute <stop_token> loss (for learning dynamic generation stop)
stop_token_loss = MaskedSigmoidCrossEntropy(self.tower_stop_token_targets[i],
self.tower_stop_token_prediction[i], self.tower_targets_lengths[i], hparams=self._hparams)
#Compute masked linear loss
if hp.predict_linear:
#Compute Linear L1 mask loss (priority to low frequencies)
linear_loss = MaskedLinearLoss(self.tower_linear_targets[i], self.tower_linear_outputs[i],
self.targets_lengths, hparams=self._hparams)
else:
linear_loss=0.
else:
# Compute loss of predictions before postnet
before = tf.losses.mean_squared_error(self.tower_mel_targets[i], self.tower_decoder_output[i])
# Compute loss after postnet
after = tf.losses.mean_squared_error(self.tower_mel_targets[i], self.tower_mel_outputs[i])
#Compute <stop_token> loss (for learning dynamic generation stop)
stop_token_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
labels=self.tower_stop_token_targets[i],
logits=self.tower_stop_token_prediction[i]))
if hp.predict_linear:
#Compute linear loss
#From https://github.com/keithito/tacotron/blob/tacotron2-work-in-progress/models/tacotron.py
#Prioritize loss for frequencies under 2000 Hz.
l1 = tf.abs(self.tower_linear_targets[i] - self.tower_linear_outputs[i])
n_priority_freq = int(2000 / (hp.sample_rate * 0.5) * hp.num_freq)
linear_loss = 0.5 * tf.reduce_mean(l1) + 0.5 * tf.reduce_mean(l1[:,:,0:n_priority_freq])
else:
linear_loss = 0.
# Compute the regularization weight
if hp.tacotron_scale_regularization:
reg_weight_scaler = 1. / (2 * hp.max_abs_value) if hp.symmetric_mels else 1. / (hp.max_abs_value)
reg_weight = hp.tacotron_reg_weight * reg_weight_scaler
else:
reg_weight = hp.tacotron_reg_weight
# Regularize variables
# Exclude all types of bias, RNN (Bengio et al. On the difficulty of training recurrent neural networks), embeddings and prediction projection layers.
# Note that we consider attention mechanism v_a weights as a prediction projection layer and we don't regularize it. (This gave better stability)
regularization = tf.add_n([tf.nn.l2_loss(v) for v in self.all_vars
if not('bias' in v.name or 'Bias' in v.name or '_projection' in v.name or 'inputs_embedding' in v.name
or 'RNN' in v.name or 'LSTM' in v.name)]) * reg_weight
# Compute final loss term
self.tower_before_loss.append(before)
self.tower_after_loss.append(after)
self.tower_stop_token_loss.append(stop_token_loss)
self.tower_regularization_loss.append(regularization)
self.tower_linear_loss.append(linear_loss)
tower_loss = before + after + stop_token_loss + regularization + linear_loss
self.tower_loss.append(tower_loss)
total_before_loss += before
total_after_loss += after
total_stop_token_loss += stop_token_loss
total_regularization_loss += regularization
total_linear_loss += linear_loss
total_loss += tower_loss
self.before_loss = total_before_loss / hp.tacotron_num_gpus
self.after_loss = total_after_loss / hp.tacotron_num_gpus
self.stop_token_loss = total_stop_token_loss / hp.tacotron_num_gpus
self.regularization_loss = total_regularization_loss / hp.tacotron_num_gpus
self.linear_loss = total_linear_loss / hp.tacotron_num_gpus
self.loss = total_loss / hp.tacotron_num_gpus | [
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JulianEberius/SublimePythonIDE | d70e40abc0c9f347af3204c7b910e0d6bfd6e459 | server/lib/python2/rope/base/libutils.py | python | analyze_modules | (project, task_handle=taskhandle.NullTaskHandle()) | Perform static object analysis on all python files in the project
Note that this might be really time consuming. | Perform static object analysis on all python files in the project | [
"Perform",
"static",
"object",
"analysis",
"on",
"all",
"python",
"files",
"in",
"the",
"project"
] | def analyze_modules(project, task_handle=taskhandle.NullTaskHandle()):
"""Perform static object analysis on all python files in the project
Note that this might be really time consuming.
"""
resources = project.pycore.get_python_files()
job_set = task_handle.create_jobset('Analyzing Modules', len(resources))
for resource in resources:
job_set.started_job(resource.path)
project.pycore.analyze_module(resource)
job_set.finished_job() | [
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nvaccess/nvda | 20d5a25dced4da34338197f0ef6546270ebca5d0 | source/gui/guiHelper.py | python | BoxSizerHelper.addLabeledControl | (self, labelText, wxCtrlClass, **kwargs) | return labeledControl.control | Convenience method to create a labeled control
@param labelText: Text to use when constructing the wx.StaticText to label the control.
@type LabelText: String
@param wxCtrlClass: Control class to construct and associate with the label
@type wxCtrlClass: Some wx control type EG wx.TextCtrl
@param kwargs: keyword arguments used to construct the wxCtrlClass. As taken by guiHelper.LabeledControlHelper
Relies on guiHelper.LabeledControlHelper and thus guiHelper.associateElements, and therefore inherits any
limitations from there. | Convenience method to create a labeled control
@param labelText: Text to use when constructing the wx.StaticText to label the control.
@type LabelText: String
@param wxCtrlClass: Control class to construct and associate with the label
@type wxCtrlClass: Some wx control type EG wx.TextCtrl
@param kwargs: keyword arguments used to construct the wxCtrlClass. As taken by guiHelper.LabeledControlHelper | [
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""" Convenience method to create a labeled control
@param labelText: Text to use when constructing the wx.StaticText to label the control.
@type LabelText: String
@param wxCtrlClass: Control class to construct and associate with the label
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"""
parent = self._parent
if isinstance(self.sizer, wx.StaticBoxSizer):
parent = self.sizer.GetStaticBox()
labeledControl = LabeledControlHelper(parent, labelText, wxCtrlClass, **kwargs)
if(isinstance(labeledControl.control, (wx.ListCtrl,wx.ListBox,wx.TreeCtrl))):
self.addItem(labeledControl.sizer, flag=wx.EXPAND, proportion=1)
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huggingface/transformers | 623b4f7c63f60cce917677ee704d6c93ee960b4b | src/transformers/trainer_tf.py | python | TFTrainer.setup_comet | (self) | Setup the optional Comet.ml integration.
Environment:
COMET_MODE:
(Optional): str - "OFFLINE", "ONLINE", or "DISABLED"
COMET_PROJECT_NAME:
(Optional): str - Comet.ml project name for experiments
COMET_OFFLINE_DIRECTORY:
(Optional): str - folder to use for saving offline experiments when `COMET_MODE` is "OFFLINE"
For a number of configurable items in the environment, see `here
<https://www.comet.ml/docs/python-sdk/advanced/#comet-configuration-variables>`__ | Setup the optional Comet.ml integration. | [
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"""
Setup the optional Comet.ml integration.
Environment:
COMET_MODE:
(Optional): str - "OFFLINE", "ONLINE", or "DISABLED"
COMET_PROJECT_NAME:
(Optional): str - Comet.ml project name for experiments
COMET_OFFLINE_DIRECTORY:
(Optional): str - folder to use for saving offline experiments when `COMET_MODE` is "OFFLINE"
For a number of configurable items in the environment, see `here
<https://www.comet.ml/docs/python-sdk/advanced/#comet-configuration-variables>`__
"""
comet_mode = os.getenv("COMET_MODE", "ONLINE").upper()
args = {"project_name": os.getenv("COMET_PROJECT_NAME", "huggingface")}
experiment = None
if comet_mode == "ONLINE":
experiment = comet_ml.Experiment(**args)
logger.info("Automatic Comet.ml online logging enabled")
elif comet_mode == "OFFLINE":
args["offline_directory"] = os.getenv("COMET_OFFLINE_DIRECTORY", "./")
experiment = comet_ml.OfflineExperiment(**args)
logger.info("Automatic Comet.ml offline logging enabled; use `comet upload` when finished")
if experiment is not None:
experiment._set_model_graph(self.model, framework="transformers")
experiment._log_parameters(self.args, prefix="args/", framework="transformers")
experiment._log_parameters(self.model.config, prefix="config/", framework="transformers") | [
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O365/python-o365 | 7f77005c3cee8177d0141e79b8eda8a7b60c5124 | O365/address_book.py | python | Contact.business_address | (self) | return self.__business_address | Business Address
:getter: Get the address of contact
:setter: Update the address
:type: dict | Business Address | [
"Business",
"Address"
] | def business_address(self):
""" Business Address
:getter: Get the address of contact
:setter: Update the address
:type: dict
"""
return self.__business_address | [
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microsoft/unilm | 65f15af2a307ebb64cfb25adf54375b002e6fe8d | beit/dataset_folder.py | python | has_file_allowed_extension | (filename: str, extensions: Tuple[str, ...]) | return filename.lower().endswith(extensions) | Checks if a file is an allowed extension.
Args:
filename (string): path to a file
extensions (tuple of strings): extensions to consider (lowercase)
Returns:
bool: True if the filename ends with one of given extensions | Checks if a file is an allowed extension. | [
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"""Checks if a file is an allowed extension.
Args:
filename (string): path to a file
extensions (tuple of strings): extensions to consider (lowercase)
Returns:
bool: True if the filename ends with one of given extensions
"""
return filename.lower().endswith(extensions) | [
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jabbalaci/Bash-Utils | c6fb115834a221c4aaba8eaa37f650beea45ef29 | lib/jhash.py | python | str_to_base64 | (s) | return data.decode() | >>> str_to_base64("László")
'TMOhc3psw7M=' | >>> str_to_base64("László")
'TMOhc3psw7M=' | [
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] | def str_to_base64(s):
"""
>>> str_to_base64("László")
'TMOhc3psw7M='
"""
data = base64.b64encode(s.encode())
return data.decode() | [
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