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home-assistant-libs/pytradfri | ef2614b0ccdf628abc3b9559dc00b95ec6e4bd72 | pytradfri/group.py | python | Group.remove_member | (self, memberid) | return self._gateway.remove_group_member(
{ATTR_GROUP_ID: self.id, ATTR_ID: [memberid]}
) | Remove a member from this group. | Remove a member from this group. | [
"Remove",
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"member",
"from",
"this",
"group",
"."
] | def remove_member(self, memberid):
"""Remove a member from this group."""
return self._gateway.remove_group_member(
{ATTR_GROUP_ID: self.id, ATTR_ID: [memberid]}
) | [
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openstack/swift | b8d7c3dcb817504dcc0959ba52cc4ed2cf66c100 | swift/proxy/controllers/obj.py | python | ObjectControllerRouter.register | (cls, policy_type) | return register_wrapper | Decorator for Storage Policy implementations to register
their ObjectController implementations.
This also fills in a policy_type attribute on the class. | Decorator for Storage Policy implementations to register
their ObjectController implementations. | [
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] | def register(cls, policy_type):
"""
Decorator for Storage Policy implementations to register
their ObjectController implementations.
This also fills in a policy_type attribute on the class.
"""
def register_wrapper(controller_cls):
if policy_type in cls.policy_type_to_controller_map:
raise PolicyError(
'%r is already registered for the policy_type %r' % (
cls.policy_type_to_controller_map[policy_type],
policy_type))
cls.policy_type_to_controller_map[policy_type] = controller_cls
controller_cls.policy_type = policy_type
return controller_cls
return register_wrapper | [
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weewx/weewx | cb594fce224560bd8696050fc5c7843c7839320e | bin/weecfg/database.py | python | CalcMissing._progress | (message, overprint=True) | Utility function to show our progress. | Utility function to show our progress. | [
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] | def _progress(message, overprint=True):
"""Utility function to show our progress."""
if overprint:
print(message + "\r", end='')
else:
print(message)
sys.stdout.flush() | [
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msracver/Deformable-ConvNets | 6aeda878a95bcb55eadffbe125804e730574de8d | lib/rpn/rpn.py | python | assign_anchor | (feat_shape, gt_boxes, im_info, cfg, feat_stride=16,
scales=(8, 16, 32), ratios=(0.5, 1, 2), allowed_border=0) | return label | assign ground truth boxes to anchor positions
:param feat_shape: infer output shape
:param gt_boxes: assign ground truth
:param im_info: filter out anchors overlapped with edges
:param feat_stride: anchor position step
:param scales: used to generate anchors, affects num_anchors (per location)
:param ratios: aspect ratios of generated anchors
:param allowed_border: filter out anchors with edge overlap > allowed_border
:return: dict of label
'label': of shape (batch_size, 1) <- (batch_size, num_anchors, feat_height, feat_width)
'bbox_target': of shape (batch_size, num_anchors * 4, feat_height, feat_width)
'bbox_inside_weight': *todo* mark the assigned anchors
'bbox_outside_weight': used to normalize the bbox_loss, all weights sums to RPN_POSITIVE_WEIGHT | assign ground truth boxes to anchor positions
:param feat_shape: infer output shape
:param gt_boxes: assign ground truth
:param im_info: filter out anchors overlapped with edges
:param feat_stride: anchor position step
:param scales: used to generate anchors, affects num_anchors (per location)
:param ratios: aspect ratios of generated anchors
:param allowed_border: filter out anchors with edge overlap > allowed_border
:return: dict of label
'label': of shape (batch_size, 1) <- (batch_size, num_anchors, feat_height, feat_width)
'bbox_target': of shape (batch_size, num_anchors * 4, feat_height, feat_width)
'bbox_inside_weight': *todo* mark the assigned anchors
'bbox_outside_weight': used to normalize the bbox_loss, all weights sums to RPN_POSITIVE_WEIGHT | [
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scales=(8, 16, 32), ratios=(0.5, 1, 2), allowed_border=0):
"""
assign ground truth boxes to anchor positions
:param feat_shape: infer output shape
:param gt_boxes: assign ground truth
:param im_info: filter out anchors overlapped with edges
:param feat_stride: anchor position step
:param scales: used to generate anchors, affects num_anchors (per location)
:param ratios: aspect ratios of generated anchors
:param allowed_border: filter out anchors with edge overlap > allowed_border
:return: dict of label
'label': of shape (batch_size, 1) <- (batch_size, num_anchors, feat_height, feat_width)
'bbox_target': of shape (batch_size, num_anchors * 4, feat_height, feat_width)
'bbox_inside_weight': *todo* mark the assigned anchors
'bbox_outside_weight': used to normalize the bbox_loss, all weights sums to RPN_POSITIVE_WEIGHT
"""
def _unmap(data, count, inds, fill=0):
"""" unmap a subset inds of data into original data of size count """
if len(data.shape) == 1:
ret = np.empty((count,), dtype=np.float32)
ret.fill(fill)
ret[inds] = data
else:
ret = np.empty((count,) + data.shape[1:], dtype=np.float32)
ret.fill(fill)
ret[inds, :] = data
return ret
DEBUG = False
im_info = im_info[0]
scales = np.array(scales, dtype=np.float32)
base_anchors = generate_anchors(base_size=feat_stride, ratios=list(ratios), scales=scales)
num_anchors = base_anchors.shape[0]
feat_height, feat_width = feat_shape[-2:]
if DEBUG:
print 'anchors:'
print base_anchors
print 'anchor shapes:'
print np.hstack((base_anchors[:, 2::4] - base_anchors[:, 0::4],
base_anchors[:, 3::4] - base_anchors[:, 1::4]))
print 'im_info', im_info
print 'height', feat_height, 'width', feat_width
print 'gt_boxes shape', gt_boxes.shape
print 'gt_boxes', gt_boxes
# 1. generate proposals from bbox deltas and shifted anchors
shift_x = np.arange(0, feat_width) * feat_stride
shift_y = np.arange(0, feat_height) * feat_stride
shift_x, shift_y = np.meshgrid(shift_x, shift_y)
shifts = np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(), shift_y.ravel())).transpose()
# add A anchors (1, A, 4) to
# cell K shifts (K, 1, 4) to get
# shift anchors (K, A, 4)
# reshape to (K*A, 4) shifted anchors
A = num_anchors
K = shifts.shape[0]
all_anchors = base_anchors.reshape((1, A, 4)) + shifts.reshape((1, K, 4)).transpose((1, 0, 2))
all_anchors = all_anchors.reshape((K * A, 4))
total_anchors = int(K * A)
# only keep anchors inside the image
inds_inside = np.where((all_anchors[:, 0] >= -allowed_border) &
(all_anchors[:, 1] >= -allowed_border) &
(all_anchors[:, 2] < im_info[1] + allowed_border) &
(all_anchors[:, 3] < im_info[0] + allowed_border))[0]
if DEBUG:
print 'total_anchors', total_anchors
print 'inds_inside', len(inds_inside)
# keep only inside anchors
anchors = all_anchors[inds_inside, :]
if DEBUG:
print 'anchors shape', anchors.shape
# label: 1 is positive, 0 is negative, -1 is dont care
labels = np.empty((len(inds_inside),), dtype=np.float32)
labels.fill(-1)
if gt_boxes.size > 0:
# overlap between the anchors and the gt boxes
# overlaps (ex, gt)
overlaps = bbox_overlaps(anchors.astype(np.float), gt_boxes.astype(np.float))
argmax_overlaps = overlaps.argmax(axis=1)
max_overlaps = overlaps[np.arange(len(inds_inside)), argmax_overlaps]
gt_argmax_overlaps = overlaps.argmax(axis=0)
gt_max_overlaps = overlaps[gt_argmax_overlaps, np.arange(overlaps.shape[1])]
gt_argmax_overlaps = np.where(overlaps == gt_max_overlaps)[0]
if not cfg.TRAIN.RPN_CLOBBER_POSITIVES:
# assign bg labels first so that positive labels can clobber them
labels[max_overlaps < cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0
# fg label: for each gt, anchor with highest overlap
labels[gt_argmax_overlaps] = 1
# fg label: above threshold IoU
labels[max_overlaps >= cfg.TRAIN.RPN_POSITIVE_OVERLAP] = 1
if cfg.TRAIN.RPN_CLOBBER_POSITIVES:
# assign bg labels last so that negative labels can clobber positives
labels[max_overlaps < cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0
else:
labels[:] = 0
# subsample positive labels if we have too many
num_fg = int(cfg.TRAIN.RPN_FG_FRACTION * cfg.TRAIN.RPN_BATCH_SIZE)
fg_inds = np.where(labels == 1)[0]
if len(fg_inds) > num_fg:
disable_inds = npr.choice(fg_inds, size=(len(fg_inds) - num_fg), replace=False)
if DEBUG:
disable_inds = fg_inds[:(len(fg_inds) - num_fg)]
labels[disable_inds] = -1
# subsample negative labels if we have too many
num_bg = cfg.TRAIN.RPN_BATCH_SIZE - np.sum(labels == 1)
bg_inds = np.where(labels == 0)[0]
if len(bg_inds) > num_bg:
disable_inds = npr.choice(bg_inds, size=(len(bg_inds) - num_bg), replace=False)
if DEBUG:
disable_inds = bg_inds[:(len(bg_inds) - num_bg)]
labels[disable_inds] = -1
bbox_targets = np.zeros((len(inds_inside), 4), dtype=np.float32)
if gt_boxes.size > 0:
bbox_targets[:] = bbox_transform(anchors, gt_boxes[argmax_overlaps, :4])
bbox_weights = np.zeros((len(inds_inside), 4), dtype=np.float32)
bbox_weights[labels == 1, :] = np.array(cfg.TRAIN.RPN_BBOX_WEIGHTS)
if DEBUG:
_sums = bbox_targets[labels == 1, :].sum(axis=0)
_squared_sums = (bbox_targets[labels == 1, :] ** 2).sum(axis=0)
_counts = np.sum(labels == 1)
means = _sums / (_counts + 1e-14)
stds = np.sqrt(_squared_sums / _counts - means ** 2)
print 'means', means
print 'stdevs', stds
# map up to original set of anchors
labels = _unmap(labels, total_anchors, inds_inside, fill=-1)
bbox_targets = _unmap(bbox_targets, total_anchors, inds_inside, fill=0)
bbox_weights = _unmap(bbox_weights, total_anchors, inds_inside, fill=0)
if DEBUG:
print 'rpn: max max_overlaps', np.max(max_overlaps)
print 'rpn: num_positives', np.sum(labels == 1)
print 'rpn: num_negatives', np.sum(labels == 0)
_fg_sum = np.sum(labels == 1)
_bg_sum = np.sum(labels == 0)
_count = 1
print 'rpn: num_positive avg', _fg_sum / _count
print 'rpn: num_negative avg', _bg_sum / _count
labels = labels.reshape((1, feat_height, feat_width, A)).transpose(0, 3, 1, 2)
labels = labels.reshape((1, A * feat_height * feat_width))
bbox_targets = bbox_targets.reshape((1, feat_height, feat_width, A * 4)).transpose(0, 3, 1, 2)
bbox_weights = bbox_weights.reshape((1, feat_height, feat_width, A * 4)).transpose((0, 3, 1, 2))
label = {'label': labels,
'bbox_target': bbox_targets,
'bbox_weight': bbox_weights}
return label | [
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SanPen/GridCal | d3f4566d2d72c11c7e910c9d162538ef0e60df31 | src/GridCal/Engine/Devices/upfc.py | python | UPFC.plot_profiles | (self, time_series=None, my_index=0, show_fig=True) | Plot the time series results of this object
:param time_series: TimeSeries Instance
:param my_index: index of this object in the simulation
:param show_fig: Show the figure? | Plot the time series results of this object
:param time_series: TimeSeries Instance
:param my_index: index of this object in the simulation
:param show_fig: Show the figure? | [
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... | def plot_profiles(self, time_series=None, my_index=0, show_fig=True):
"""
Plot the time series results of this object
:param time_series: TimeSeries Instance
:param my_index: index of this object in the simulation
:param show_fig: Show the figure?
"""
if time_series is not None:
fig = plt.figure(figsize=(12, 8))
ax_1 = fig.add_subplot(211)
ax_2 = fig.add_subplot(212, sharex=ax_1)
x = time_series.results.time
# loading
y = time_series.results.loading.real * 100.0
df = pd.DataFrame(data=y[:, my_index], index=x, columns=[self.name])
ax_1.set_title('Loading', fontsize=14)
ax_1.set_ylabel('Loading [%]', fontsize=11)
df.plot(ax=ax_1)
# losses
y = np.abs(time_series.results.losses)
df = pd.DataFrame(data=y[:, my_index], index=x, columns=[self.name])
ax_2.set_title('Losses', fontsize=14)
ax_2.set_ylabel('Losses [MVA]', fontsize=11)
df.plot(ax=ax_2)
plt.legend()
fig.suptitle(self.name, fontsize=20)
if show_fig:
plt.show() | [
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pyqt/examples | 843bb982917cecb2350b5f6d7f42c9b7fb142ec1 | src/pyqt-official/ipc/sharedmemory/sharedmemory.py | python | Dialog.loadFromMemory | (self) | This slot function is called in the second Dialog process, when the
user presses the "Load Image from Shared Memory" button. First, it
attaches the process to the shared memory segment created by the first
Dialog process. Then it locks the segment for exclusive access, copies
the image data from the segment into a QBuffer, and streams the QBuffer
into a QImage. Then it unlocks the shared memory segment, detaches
from it, and finally displays the QImage in the Dialog. | This slot function is called in the second Dialog process, when the
user presses the "Load Image from Shared Memory" button. First, it
attaches the process to the shared memory segment created by the first
Dialog process. Then it locks the segment for exclusive access, copies
the image data from the segment into a QBuffer, and streams the QBuffer
into a QImage. Then it unlocks the shared memory segment, detaches
from it, and finally displays the QImage in the Dialog. | [
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""" This slot function is called in the second Dialog process, when the
user presses the "Load Image from Shared Memory" button. First, it
attaches the process to the shared memory segment created by the first
Dialog process. Then it locks the segment for exclusive access, copies
the image data from the segment into a QBuffer, and streams the QBuffer
into a QImage. Then it unlocks the shared memory segment, detaches
from it, and finally displays the QImage in the Dialog.
"""
if not self.sharedMemory.attach():
self.ui.label.setText(
"Unable to attach to shared memory segment.\nLoad an "
"image first.")
return
buf = QBuffer()
ins = QDataStream(buf)
image = QImage()
self.sharedMemory.lock()
buf.setData(self.sharedMemory.constData())
buf.open(QBuffer.ReadOnly)
ins >> image
self.sharedMemory.unlock()
self.sharedMemory.detach()
self.ui.label.setPixmap(QPixmap.fromImage(image)) | [
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IJDykeman/wangTiles | 7c1ee2095ebdf7f72bce07d94c6484915d5cae8b | experimental_code/tiles_3d/venv_mac/lib/python2.7/site-packages/pip/_vendor/distlib/wheel.py | python | Wheel.build | (self, paths, tags=None, wheel_version=None) | return pathname | Build a wheel from files in specified paths, and use any specified tags
when determining the name of the wheel. | Build a wheel from files in specified paths, and use any specified tags
when determining the name of the wheel. | [
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] | def build(self, paths, tags=None, wheel_version=None):
"""
Build a wheel from files in specified paths, and use any specified tags
when determining the name of the wheel.
"""
if tags is None:
tags = {}
libkey = list(filter(lambda o: o in paths, ('purelib', 'platlib')))[0]
if libkey == 'platlib':
is_pure = 'false'
default_pyver = [IMPVER]
default_abi = [ABI]
default_arch = [ARCH]
else:
is_pure = 'true'
default_pyver = [PYVER]
default_abi = ['none']
default_arch = ['any']
self.pyver = tags.get('pyver', default_pyver)
self.abi = tags.get('abi', default_abi)
self.arch = tags.get('arch', default_arch)
libdir = paths[libkey]
name_ver = '%s-%s' % (self.name, self.version)
data_dir = '%s.data' % name_ver
info_dir = '%s.dist-info' % name_ver
archive_paths = []
# First, stuff which is not in site-packages
for key in ('data', 'headers', 'scripts'):
if key not in paths:
continue
path = paths[key]
if os.path.isdir(path):
for root, dirs, files in os.walk(path):
for fn in files:
p = fsdecode(os.path.join(root, fn))
rp = os.path.relpath(p, path)
ap = to_posix(os.path.join(data_dir, key, rp))
archive_paths.append((ap, p))
if key == 'scripts' and not p.endswith('.exe'):
with open(p, 'rb') as f:
data = f.read()
data = self.process_shebang(data)
with open(p, 'wb') as f:
f.write(data)
# Now, stuff which is in site-packages, other than the
# distinfo stuff.
path = libdir
distinfo = None
for root, dirs, files in os.walk(path):
if root == path:
# At the top level only, save distinfo for later
# and skip it for now
for i, dn in enumerate(dirs):
dn = fsdecode(dn)
if dn.endswith('.dist-info'):
distinfo = os.path.join(root, dn)
del dirs[i]
break
assert distinfo, '.dist-info directory expected, not found'
for fn in files:
# comment out next suite to leave .pyc files in
if fsdecode(fn).endswith(('.pyc', '.pyo')):
continue
p = os.path.join(root, fn)
rp = to_posix(os.path.relpath(p, path))
archive_paths.append((rp, p))
# Now distinfo. Assumed to be flat, i.e. os.listdir is enough.
files = os.listdir(distinfo)
for fn in files:
if fn not in ('RECORD', 'INSTALLER', 'SHARED', 'WHEEL'):
p = fsdecode(os.path.join(distinfo, fn))
ap = to_posix(os.path.join(info_dir, fn))
archive_paths.append((ap, p))
wheel_metadata = [
'Wheel-Version: %d.%d' % (wheel_version or self.wheel_version),
'Generator: distlib %s' % __version__,
'Root-Is-Purelib: %s' % is_pure,
]
for pyver, abi, arch in self.tags:
wheel_metadata.append('Tag: %s-%s-%s' % (pyver, abi, arch))
p = os.path.join(distinfo, 'WHEEL')
with open(p, 'w') as f:
f.write('\n'.join(wheel_metadata))
ap = to_posix(os.path.join(info_dir, 'WHEEL'))
archive_paths.append((ap, p))
# Now, at last, RECORD.
# Paths in here are archive paths - nothing else makes sense.
self.write_records((distinfo, info_dir), libdir, archive_paths)
# Now, ready to build the zip file
pathname = os.path.join(self.dirname, self.filename)
self.build_zip(pathname, archive_paths)
return pathname | [
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ifwe/digsby | f5fe00244744aa131e07f09348d10563f3d8fa99 | digsby/lib/pyxmpp/jabber/dataforms.py | python | Form.__init__ | (self, xmlnode_or_type = "form", title = None, instructions = None,
fields = None, reported_fields = None, items = None, strict=True) | Initialize a `Form` object.
:Parameters:
- `xmlnode_or_type`: XML element to parse or a form title.
- `title`: form title.
- `instructions`: instructions for the form.
- `fields`: form fields.
- `reported_fields`: fields reported in multi-item data.
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- `items`: `list` of `Item` | Initialize a `Form` object. | [
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] | def __init__(self, xmlnode_or_type = "form", title = None, instructions = None,
fields = None, reported_fields = None, items = None, strict=True):
"""Initialize a `Form` object.
:Parameters:
- `xmlnode_or_type`: XML element to parse or a form title.
- `title`: form title.
- `instructions`: instructions for the form.
- `fields`: form fields.
- `reported_fields`: fields reported in multi-item data.
- `items`: items of multi-item data.
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- `items`: `list` of `Item`
"""
if isinstance(xmlnode_or_type, libxml2.xmlNode):
self.__from_xml(xmlnode_or_type, strict)
elif xmlnode_or_type not in self.allowed_types:
raise ValueError, "Form type %r not allowed." % (xmlnode_or_type,)
else:
self.type = xmlnode_or_type
self.title = title
self.instructions = instructions
if fields:
self.fields = list(fields)
else:
self.fields = []
if reported_fields:
self.reported_fields = list(reported_fields)
else:
self.reported_fields = []
if items:
self.items = list(items)
else:
self.items = [] | [
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stevearc/dql | 9666cfba19773c20c7b4be29adc7d6179cf00d44 | dql/cli.py | python | DQLClient.completedefault | (self, text, line, *_) | Autocomplete table names in queries | Autocomplete table names in queries | [
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"""Autocomplete table names in queries"""
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moloch--/RootTheBox | 097272332b9f9b7e2df31ca0823ed10c7b66ac81 | handlers/BaseHandlers.py | python | BaseHandler.head | (self, *args, **kwargs) | Ignore it | Ignore it | [
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.9.14-1/roles/lib_utils/filter_plugins/oo_filters.py | python | lib_utils_oo_dict_to_list_of_dict | (data, key_title='key', value_title='value') | return rval | Take a dict and arrange them as a list of dicts
Input data:
{'region': 'infra', 'test_k': 'test_v'}
Return data:
[{'key': 'region', 'value': 'infra'}, {'key': 'test_k', 'value': 'test_v'}]
Written for use of the oc_label module | Take a dict and arrange them as a list of dicts | [
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"""Take a dict and arrange them as a list of dicts
Input data:
{'region': 'infra', 'test_k': 'test_v'}
Return data:
[{'key': 'region', 'value': 'infra'}, {'key': 'test_k', 'value': 'test_v'}]
Written for use of the oc_label module
"""
if not isinstance(data, dict):
# pylint: disable=line-too-long
raise errors.AnsibleFilterError("|failed expects first param is a dict. Got %s. Type: %s" % (str(data), str(type(data))))
rval = []
for label in data.items():
rval.append({key_title: label[0], value_title: label[1]})
return rval | [
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freqtrade/freqtrade-strategies | 484569e9c9b662c37563f5490162a0692675162d | user_data/strategies/Strategy002.py | python | Strategy002.populate_sell_trend | (self, dataframe: DataFrame, metadata: dict) | return dataframe | Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column | Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column | [
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"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
dataframe.loc[
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(dataframe['sar'] > dataframe['close']) &
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return dataframe | [
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yuanwenq/dailyfresh | d834f01a8ec15f880302009e8ffb1eaf0b99177b | apps/order/views.py | python | CheckPayView.post | (self, request) | 查询支付结果 | 查询支付结果 | [
"查询支付结果"
] | def post(self, request):
"""查询支付结果"""
# 用户是否登录
user = request.user
if not user.is_authenticated():
return JsonResponse({'res': 0, 'errmsg': '用户未邓丽'})
# 接收参数
order_id = request.POST.get('order_id')
# 校验参数
if not order_id:
return JsonResponse({'res': 1, 'errmsg': '无效的订单id'})
try:
order = OrderInfo.objects.get(order_id=order_id,
user=user,
pay_method=3,
order_status=1)
except OrderInfo.DoesNotExist:
return JsonResponse({'res': 2, 'errmsg': '订单错误'})
app_private_key_string = open(os.path.join(settings.BASE_DIR, 'apps/order/app_private_key.pem')).read()
alipay_public_key_string = open(os.path.join(settings.BASE_DIR, 'apps/order/alipay_public_key.pem')).read()
# 业务处理：使用Python sdk调用支付宝的支付接口
# 初始化
alipay = AliPay(
appid="2016092400581852", # 应用id
app_notify_url=None, # 默认回调url
app_private_key_string=app_private_key_string,
# 支付宝的公钥，验证支付宝回传消息使用，不是你自己的公钥,
alipay_public_key_string=alipay_public_key_string,
sign_type="RSA2", # RSA 或者 RSA2
debug=True # 默认False True就会访问沙箱地址
)
# 调用支付宝交易查询接口
while True:
response = alipay.api_alipay_trade_query(order_id)
"""
响应参数
response = {
"trade_no": "2017032121001004070200176844", # 支付宝交易号
"code": "10000", # 接口调用是否成功
"invoice_amount": "20.00",
"open_id": "20880072506750308812798160715407",
"fund_bill_list": [
{
"amount": "20.00",
"fund_channel": "ALIPAYACCOUNT"
}
],
"buyer_logon_id": "csq***@sandbox.com",
"send_pay_date": "2017-03-21 13:29:17",
"receipt_amount": "20.00",
"out_trade_no": "out_trade_no15",
"buyer_pay_amount": "20.00",
"buyer_user_id": "2088102169481075",
"msg": "Success",
"point_amount": "0.00",
"trade_status": "TRADE_SUCCESS", # 支付结果
"total_amount": "20.00"
}
"""
code = response.get('code')
if code == '10000' and response.get('trade_status') == 'TRADE_SUCCESS':
# 支付成功
# 获取支付宝交易号
trade_no = response.get('trade_no')
# 更新订单状态
order.trade_no = trade_no
order.order_status = 4 # 待评价状态
order.save()
# 返回结果
return JsonResponse({'res': 3, 'message': '支付成功'})
elif code == '40004' or (code == '10000' and response.get('trade_status') == 'WAIT_BUYER_PAY'):
# 等待卖家付款
# 业务处理失败，可能一会就会成功
import time
time.sleep(5)
continue
else:
# 支付出错
return JsonResponse({'res': 4, 'errmsg': '支付失败'}) | [
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stanford-mast/nn_dataflow | 198a5274b9529125c6aa2b8b72b365d60cf83778 | nn_dataflow/tools/nn_dataflow_search.py | python | argparser | () | return ap | Argument parser. | Argument parser. | [
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] | def argparser():
''' Argument parser. '''
ap = argparse.ArgumentParser()
ap.add_argument('net',
help='network name, should be a .py file under "nns". '
'Choices: {}.'.format(', '.join(all_networks())))
ap.add_argument('--batch', type=int, required=True,
help='batch size')
ap.add_argument('--word', type=int, default=16,
help='word size in bits')
ap.add_argument('--nodes', type=int, nargs=2, required=True,
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help='Parallel node partitioning dimensions')
ap.add_argument('--array', type=int, nargs=2, required=True,
metavar=('H', 'W'),
help='PE array dimensions')
ap.add_argument('--regf', type=int, required=True,
help='register file size in bytes per PE')
ap.add_argument('--gbuf', type=int, required=True,
help='global buffer size in bytes')
ap.add_argument('--bus-width', type=int, default=0,
help='array bus width in bits. set 0 to ignore')
ap.add_argument('--dram-bw', type=float, default='inf',
help='total DRAM bandwidth in bytes per cycle.')
ap.add_argument('--op-cost', type=float, default=1,
help='cost of arithmetic operation')
ap.add_argument('--hier-cost', type=float, nargs=4, default=[200, 6, 2, 1],
metavar=('DRAM_COST', 'GBUF_COST', 'ITCN_COST',
'REGF_COST'),
help='cost of access to memory hierarchy')
ap.add_argument('--hop-cost', type=float, default=10,
help='cost of access through one NoC hop')
ap.add_argument('--unit-idle-cost', type=float, default=0,
help='static cost over all nodes for unit execution time')
ap.add_argument('--mem-type', default='2D', choices=['2D', '3D'],
help='memory type. "2D" has memory only on edge nodes; '
'"3D" has memory vertially on top of all nodes.')
ap.add_argument('--disable-bypass', nargs='*', default=[],
choices=['i', 'o', 'f'],
help='whether disallowing gbuf bypass for i (input), o '
'(output), or f (filter)')
ap.add_argument('--solve-loopblocking', action='store_true',
help='Use analytical solver to choose loop blocking. '
'Otherwise use exhaustive search.')
ap.add_argument('--enable-access-forwarding', action='store_true',
help='Each node fetches a subset of data and forwards to '
'other nodes.')
ap.add_argument('--enable-gbuf-sharing', action='store_true',
help='Share gbuf capacity across nodes through NoC.')
ap.add_argument('--enable-save-writeback', action='store_true',
help='Allow to save the writeback to memory for the '
'intermediate data between layers if able to '
'store the entire data set in on-chip buffers.')
ap.add_argument('--disable-interlayer-opt',
'--basic-interlayer-partition',
action='store_true',
help='Disable optimizations and only allow basic '
'inter-layer pipeline.')
ap.add_argument('--hybrid-partition',
'--hybrid-partition2d', # deprecated old name
action='store_true',
help='Use hybrid partition for layer for node mapping. '
'Otherwise use naive method based on layer type.')
ap.add_argument('--batch-partition', action='store_true',
help='Allow partitioning batch, i.e., consider data '
'parallelism.')
ap.add_argument('--ifmaps-partition', '--ifmap-partition',
action='store_true',
help='Allow partitioning ifmap channel dimension, which '
'requires extra data synchronization.')
ap.add_argument('--interlayer-partition', '--inter-layer-partition',
action='store_true',
help='Allow partitioning resources across multiple layers '
'and process them simultaneously as an inter-layer '
'pipeline.')
ap.add_argument('--layer-pipeline-time-overhead',
type=float, default=float('inf'),
help='maximum allowed execution time overhead due to '
'layer pipelining.')
ap.add_argument('--layer-pipeline-max-degree',
type=float, default=float('inf'),
help='maximum allowed layer pipelining degree, i.e., '
'number of vertices in a pipeline segment.')
ap.add_argument('-g', '--goal', default='e',
choices=['e', 'd', 'ed', 'E', 'D', 'ED'],
help='Goal of optimization: E(nergy), D(elay), or ED.')
ap.add_argument('-t', '--top', type=int, default=1,
help='Number of top schedules to keep during search.')
ap.add_argument('-p', '--processes', type=int,
default=multiprocessing.cpu_count()//2,
help='Number of parallel processes to use for search.')
ap.add_argument('-v', '--verbose', action='store_true',
help='Show progress and details.')
return ap | [
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mu-editor/mu | 5a5d7723405db588f67718a63a0ec0ecabebae33 | mu/modes/pyboard.py | python | PyboardMode.workspace_dir | (self) | Return the default location on the filesystem for opening and closing
files. | Return the default location on the filesystem for opening and closing
files. | [
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"""
Return the default location on the filesystem for opening and closing
files.
"""
device_dir = None
# Attempts to find the path on the filesystem that represents the
# plugged in Pyboard board.
if os.name == "posix":
# We're on Linux or OSX
for mount_command in ["mount", "/sbin/mount"]:
try:
mount_output = check_output(mount_command).splitlines()
mounted_volumes = [x.split()[2] for x in mount_output]
for volume in mounted_volumes:
if volume.endswith(b"PYBFLASH"):
device_dir = volume.decode("utf-8")
except FileNotFoundError:
next
elif os.name == "nt":
# We're on Windows.
def get_volume_name(disk_name):
"""
Each disk or external device connected to windows has an
attribute called "volume name". This function returns the
volume name for the given disk/device.
Code from http://stackoverflow.com/a/12056414
"""
vol_name_buf = ctypes.create_unicode_buffer(1024)
ctypes.windll.kernel32.GetVolumeInformationW(
ctypes.c_wchar_p(disk_name),
vol_name_buf,
ctypes.sizeof(vol_name_buf),
None,
None,
None,
None,
0,
)
return vol_name_buf.value
#
# In certain circumstances, volumes are allocated to USB
# storage devices which cause a Windows popup to raise if their
# volume contains no media. Wrapping the check in SetErrorMode
# with SEM_FAILCRITICALERRORS (1) prevents this popup.
#
old_mode = ctypes.windll.kernel32.SetErrorMode(1)
try:
for disk in "ABCDEFGHIJKLMNOPQRSTUVWXYZ":
path = "{}:\\".format(disk)
if (
os.path.exists(path)
and get_volume_name(path) == "PYBFLASH"
):
return path
finally:
ctypes.windll.kernel32.SetErrorMode(old_mode)
else:
# No support for unknown operating systems.
raise NotImplementedError('OS "{}" not supported.'.format(os.name))
if device_dir:
# Found it!
self.connected = True
return device_dir
else:
# Not plugged in? Just return Mu's regular workspace directory
# after warning the user.
wd = super().workspace_dir()
if self.connected:
m = _("Could not find an attached PyBoard device.")
info = _(
"Python files for PyBoard MicroPython devices"
" are stored on the device. Therefore, to edit"
" these files you need to have the device plugged in."
" Until you plug in a device, Mu will use the"
" directory found here:\n\n"
" {}\n\n...to store your code."
)
self.view.show_message(m, info.format(wd))
self.connected = False
return wd | [
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gkrizek/bash-lambda-layer | 703b0ade8174022d44779d823172ab7ac33a5505 | bin/docutils/nodes.py | python | NodeVisitor.dispatch_visit | (self, node) | return method(node) | Call self."``visit_`` + node class name" with `node` as
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"""
Call self."``visit_`` + node class name" with `node` as
parameter. If the ``visit_...`` method does not exist, call
self.unknown_visit.
"""
node_name = node.__class__.__name__
method = getattr(self, 'visit_' + node_name, self.unknown_visit)
self.document.reporter.debug(
'docutils.nodes.NodeVisitor.dispatch_visit calling %s for %s'
% (method.__name__, node_name))
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.11.28-1/roles/lib_vendored_deps/library/oc_serviceaccount_secret.py | python | Yedit.update | (self, path, value, index=None, curr_value=None) | return (False, self.yaml_dict) | put path, value into a dict | put path, value into a dict | [
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''' put path, value into a dict '''
try:
entry = Yedit.get_entry(self.yaml_dict, path, self.separator)
except KeyError:
entry = None
if isinstance(entry, dict):
# AUDIT:maybe-no-member makes sense due to fuzzy types
# pylint: disable=maybe-no-member
if not isinstance(value, dict):
raise YeditException('Cannot replace key, value entry in dict with non-dict type. ' +
'value=[{}] type=[{}]'.format(value, type(value)))
entry.update(value)
return (True, self.yaml_dict)
elif isinstance(entry, list):
# AUDIT:maybe-no-member makes sense due to fuzzy types
# pylint: disable=maybe-no-member
ind = None
if curr_value:
try:
ind = entry.index(curr_value)
except ValueError:
return (False, self.yaml_dict)
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ind = index
if ind is not None and entry[ind] != value:
entry[ind] = value
return (True, self.yaml_dict)
# see if it exists in the list
try:
ind = entry.index(value)
except ValueError:
# doesn't exist, append it
entry.append(value)
return (True, self.yaml_dict)
# already exists, return
if ind is not None:
return (False, self.yaml_dict)
return (False, self.yaml_dict) | [
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appinho/SARosPerceptionKitti | 148683b23bef294762ced937e4386501e56c8d64 | pre_processing/kitti2bag.py | python | inv | (transform) | return transform_inv | Invert rigid body transformation matrix | Invert rigid body transformation matrix | [
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"Invert rigid body transformation matrix"
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t = transform[0:3, 3]
t_inv = -1 * R.T.dot(t)
transform_inv = np.eye(4)
transform_inv[0:3, 0:3] = R.T
transform_inv[0:3, 3] = t_inv
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ocelma/python-recsys | 6c6343c6f57271e745773ab6aefea0a5d29f0620 | recsys/datamodel/data.py | python | Data.get | (self) | return self._data | :returns: a list of tuples | :returns: a list of tuples | [
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/cpdp/v20190820/cpdp_client.py | python | CpdpClient.CheckAcct | (self, request) | 商户绑定提现银行卡的验证接口
:param request: Request instance for CheckAcct.
:type request: :class:`tencentcloud.cpdp.v20190820.models.CheckAcctRequest`
:rtype: :class:`tencentcloud.cpdp.v20190820.models.CheckAcctResponse` | 商户绑定提现银行卡的验证接口 | [
"商户绑定提现银行卡的验证接口"
] | def CheckAcct(self, request):
"""商户绑定提现银行卡的验证接口
:param request: Request instance for CheckAcct.
:type request: :class:`tencentcloud.cpdp.v20190820.models.CheckAcctRequest`
:rtype: :class:`tencentcloud.cpdp.v20190820.models.CheckAcctResponse`
"""
try:
params = request._serialize()
body = self.call("CheckAcct", params)
response = json.loads(body)
if "Error" not in response["Response"]:
model = models.CheckAcctResponse()
model._deserialize(response["Response"])
return model
else:
code = response["Response"]["Error"]["Code"]
message = response["Response"]["Error"]["Message"]
reqid = response["Response"]["RequestId"]
raise TencentCloudSDKException(code, message, reqid)
except Exception as e:
if isinstance(e, TencentCloudSDKException):
raise
else:
raise TencentCloudSDKException(e.message, e.message) | [
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.9.14-1/roles/lib_vendored_deps/library/oc_pvc.py | python | Utils.get_resource_file | (sfile, sfile_type='yaml') | return contents | return the service file | return the service file | [
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''' return the service file '''
contents = None
with open(sfile) as sfd:
contents = sfd.read()
if sfile_type == 'yaml':
# AUDIT:no-member makes sense here due to ruamel.YAML/PyYAML usage
# pylint: disable=no-member
if hasattr(yaml, 'RoundTripLoader'):
contents = yaml.load(contents, yaml.RoundTripLoader)
else:
contents = yaml.safe_load(contents)
elif sfile_type == 'json':
contents = json.loads(contents)
return contents | [
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reviewboard/reviewboard | 7395902e4c181bcd1d633f61105012ffb1d18e1b | reviewboard/admin/context_processors.py | python | read_only | (request) | return {
'is_read_only': is_site_read_only_for(request.user),
'read_only_message': siteconfig.get('read_only_message', ''),
} | Return a dictionary with the read-only state.
Args:
request (django.http.HttpRequest):
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dict:
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"""Return a dictionary with the read-only state.
Args:
request (django.http.HttpRequest):
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Returns:
dict:
State to add to the context.
"""
siteconfig = SiteConfiguration.objects.get_current()
return {
'is_read_only': is_site_read_only_for(request.user),
'read_only_message': siteconfig.get('read_only_message', ''),
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happinesslz/TANet | 2d4b2ab99b8e57c03671b0f1531eab7dca8f3c1f | second.pytorch_with_TANet/second/script.py | python | eval_multi_threshold | () | [] | def eval_multi_threshold():
config_path = "./configs/car.fhd.config"
ckpt_name = "/path/to/your/model_ckpt" # don't forget to change this.
assert "/path/to/your" not in ckpt_name
config = pipeline_pb2.TrainEvalPipelineConfig()
with open(config_path, "r") as f:
proto_str = f.read()
text_format.Merge(proto_str, config)
model_cfg = config.model.second
threshs = [0.3]
for thresh in threshs:
model_cfg.nms_score_threshold = thresh
# don't forget to change this.
result_path = Path.home() / f"second_test_eval_{thresh:.2f}"
evaluate(
config,
result_path=result_path,
ckpt_path=str(ckpt_name),
batch_size=1,
measure_time=True) | [
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nvaccess/nvda | 20d5a25dced4da34338197f0ef6546270ebca5d0 | source/NVDAObjects/__init__.py | python | NVDAObject._get_role | (self) | return controlTypes.Role.UNKNOWN | The role or type of control this object represents (example: button, list, dialog). | The role or type of control this object represents (example: button, list, dialog). | [
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DIYer22/boxx | d271bc375a33e01e616a0f74ce028e6d77d1820e | boxx/tool/toolStructObj.py | python | _SliceToInt.__getitem__ | (self, index) | return int(round(index)) | [] | def __getitem__(self, index):
def f(t):
if isinstance(t, tuple):
return tuple(f(i) for i in t)
else:
if isinstance(t, slice):
return self.__intSlice(t)
if isinstance(t, float):
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return int(round(index)) | [
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wistbean/learn_python3_spider | 73c873f4845f4385f097e5057407d03dd37a117b | stackoverflow/venv/lib/python3.6/site-packages/twisted/persisted/dirdbm.py | python | DirDBM.__delitem__ | (self, k) | C{del dirdbm[foo]}
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jgagneastro/coffeegrindsize | 22661ebd21831dba4cf32bfc6ba59fe3d49f879c | App/dist/coffeegrindsize.app/Contents/Resources/lib/python3.7/matplotlib/patches.py | python | FancyArrowPatch.get_mutation_scale | (self) | return self._mutation_scale | Return the mutation scale.
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scale : scalar
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ctxis/canape | 5f0e03424577296bcc60c2008a60a98ec5307e4b | CANAPE.Scripting/Lib/ssl.py | python | SSLSocket.do_handshake | (self) | Perform a TLS/SSL handshake. | Perform a TLS/SSL handshake. | [
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"""Perform a TLS/SSL handshake."""
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NoGameNoLife00/mybolg | afe17ea5bfe405e33766e5682c43a4262232ee12 | libs/sqlalchemy/connectors/zxJDBC.py | python | ZxJDBCConnector._driver_kwargs | (self) | return {} | Return kw arg dict to be sent to connect(). | Return kw arg dict to be sent to connect(). | [
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tendenci/tendenci | 0f2c348cc0e7d41bc56f50b00ce05544b083bf1d | tendenci/libs/model_report/report.py | python | ReportAdmin._get_grouper_text | (self, groupby_field, value) | return value | [] | def _get_grouper_text(self, groupby_field, value):
try:
model_field = [mfield for mfield, field in self.model_fields if field == groupby_field][0]
except:
model_field = None
value = self.get_grouper_text(value, groupby_field, model_field)
if value is None or str(value) == u'None':
if groupby_field is None or str(groupby_field) == u'None':
value = force_text(_('Results'))
else:
value = force_text(_('Nothing'))
return value | [
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EricZgw/PyramidBox | 03f67d8422f2f03e5ee580e2f92d4040c24bbdb4 | nets/np_methods.py | python | bboxes_nms_fast | (classes, scores, bboxes, nms_threshold=0.45) | return classes[keep], scores[keep], bboxes[keep] | Apply non-maximum selection to bounding boxes. | Apply non-maximum selection to bounding boxes. | [
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"""Apply non-maximum selection to bounding boxes.
"""
y1 = bboxes[:, 0]
x1 = bboxes[:, 1]
y2 = bboxes[:, 2]
x2 = bboxes[:, 3]
areas = (x2 - x1 ) * (y2 - y1 )
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
w = np.maximum(0.0, xx2 - xx1)
h = np.maximum(0.0, yy2 - yy1)
inter = w * h
ovr = inter / (areas[i] + areas[order[1:]] - inter)
inds = np.where(ovr <= nms_threshold)[0]
order = order[inds + 1]
keep = keep[0:200]
return classes[keep], scores[keep], bboxes[keep] | [
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jgagneastro/coffeegrindsize | 22661ebd21831dba4cf32bfc6ba59fe3d49f879c | App/dist/coffeegrindsize.app/Contents/Resources/lib/python3.7/pandas/io/pytables.py | python | IndexCol.is_indexed | (self) | return whether I am an indexed column | return whether I am an indexed column | [
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] | def is_indexed(self):
""" return whether I am an indexed column """
try:
return getattr(self.table.cols, self.cname).is_indexed
except AttributeError:
False | [
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cltk/cltk | 1a8c2f5ef72389e2579dfce1fa5af8e59ebc9ec1 | src/cltk/phonology/non/utils.py | python | Vowel.match | (self, abstract_vowel) | [] | def match(self, abstract_vowel):
if isinstance(abstract_vowel, AbstractVowel):
res = True
if abstract_vowel.height is not None:
res = res and abstract_vowel.height == self.height
if abstract_vowel.backness is not None:
res = res and abstract_vowel.backness == self.backness
if abstract_vowel.rounded is not None:
res = res and abstract_vowel.rounded == self.rounded
if abstract_vowel.length is not None:
res = res and abstract_vowel.length == self.length
return res
elif abstract_vowel is None:
return True
else:
return False | [
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eliben/pykaleidoscope | 977d234e7bffb01e3dc91f6dd862bb03ada1063b | chapter7and8.py | python | TestParser._flatten | (self, ast) | Test helper - flattens the AST into a sexpr-like nested list. | Test helper - flattens the AST into a sexpr-like nested list. | [
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] | def _flatten(self, ast):
"""Test helper - flattens the AST into a sexpr-like nested list."""
if isinstance(ast, NumberExprAST):
return ['Number', ast.val]
elif isinstance(ast, VariableExprAST):
return ['Variable', ast.name]
elif isinstance(ast, UnaryExprAST):
return ['Unary', ast.op, self._flatten(ast.operand)]
elif isinstance(ast, BinaryExprAST):
return ['Binop', ast.op,
self._flatten(ast.lhs), self._flatten(ast.rhs)]
elif isinstance(ast, VarExprAST):
vars = [[name, self._flatten(init)] for name, init in ast.vars]
return ['Var', vars, self._flatten(ast.body)]
elif isinstance(ast, CallExprAST):
args = [self._flatten(arg) for arg in ast.args]
return ['Call', ast.callee, args]
elif isinstance(ast, PrototypeAST):
return ['Proto', ast.name, ' '.join(ast.argnames)]
elif isinstance(ast, FunctionAST):
return ['Function',
self._flatten(ast.proto), self._flatten(ast.body)]
else:
raise TypeError('unknown type in _flatten: {0}'.format(type(ast))) | [
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scikit-learn/scikit-learn | 1d1aadd0711b87d2a11c80aad15df6f8cf156712 | sklearn/linear_model/_least_angle.py | python | lars_path | (
X,
y,
Xy=None,
*,
Gram=None,
max_iter=500,
alpha_min=0,
method="lar",
copy_X=True,
eps=np.finfo(float).eps,
copy_Gram=True,
verbose=0,
return_path=True,
return_n_iter=False,
positive=False,
) | return _lars_path_solver(
X=X,
y=y,
Xy=Xy,
Gram=Gram,
n_samples=None,
max_iter=max_iter,
alpha_min=alpha_min,
method=method,
copy_X=copy_X,
eps=eps,
copy_Gram=copy_Gram,
verbose=verbose,
return_path=return_path,
return_n_iter=return_n_iter,
positive=positive,
) | Compute Least Angle Regression or Lasso path using LARS algorithm [1]
The optimization objective for the case method='lasso' is::
(1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1
in the case of method='lars', the objective function is only known in
the form of an implicit equation (see discussion in [1])
Read more in the :ref:`User Guide <least_angle_regression>`.
Parameters
----------
X : None or array-like of shape (n_samples, n_features)
Input data. Note that if X is None then the Gram matrix must be
specified, i.e., cannot be None or False.
y : None or array-like of shape (n_samples,)
Input targets.
Xy : array-like of shape (n_samples,) or (n_samples, n_targets), \
default=None
Xy = np.dot(X.T, y) that can be precomputed. It is useful
only when the Gram matrix is precomputed.
Gram : None, 'auto', array-like of shape (n_features, n_features), \
default=None
Precomputed Gram matrix (X' * X), if ``'auto'``, the Gram
matrix is precomputed from the given X, if there are more samples
than features.
max_iter : int, default=500
Maximum number of iterations to perform, set to infinity for no limit.
alpha_min : float, default=0
Minimum correlation along the path. It corresponds to the
regularization parameter alpha parameter in the Lasso.
method : {'lar', 'lasso'}, default='lar'
Specifies the returned model. Select ``'lar'`` for Least Angle
Regression, ``'lasso'`` for the Lasso.
copy_X : bool, default=True
If ``False``, ``X`` is overwritten.
eps : float, default=np.finfo(float).eps
The machine-precision regularization in the computation of the
Cholesky diagonal factors. Increase this for very ill-conditioned
systems. Unlike the ``tol`` parameter in some iterative
optimization-based algorithms, this parameter does not control
the tolerance of the optimization.
copy_Gram : bool, default=True
If ``False``, ``Gram`` is overwritten.
verbose : int, default=0
Controls output verbosity.
return_path : bool, default=True
If ``return_path==True`` returns the entire path, else returns only the
last point of the path.
return_n_iter : bool, default=False
Whether to return the number of iterations.
positive : bool, default=False
Restrict coefficients to be >= 0.
This option is only allowed with method 'lasso'. Note that the model
coefficients will not converge to the ordinary-least-squares solution
for small values of alpha. Only coefficients up to the smallest alpha
value (``alphas_[alphas_ > 0.].min()`` when fit_path=True) reached by
the stepwise Lars-Lasso algorithm are typically in congruence with the
solution of the coordinate descent lasso_path function.
Returns
-------
alphas : array-like of shape (n_alphas + 1,)
Maximum of covariances (in absolute value) at each iteration.
``n_alphas`` is either ``max_iter``, ``n_features`` or the
number of nodes in the path with ``alpha >= alpha_min``, whichever
is smaller.
active : array-like of shape (n_alphas,)
Indices of active variables at the end of the path.
coefs : array-like of shape (n_features, n_alphas + 1)
Coefficients along the path
n_iter : int
Number of iterations run. Returned only if return_n_iter is set
to True.
See Also
--------
lars_path_gram
lasso_path
lasso_path_gram
LassoLars
Lars
LassoLarsCV
LarsCV
sklearn.decomposition.sparse_encode
References
----------
.. [1] "Least Angle Regression", Efron et al.
http://statweb.stanford.edu/~tibs/ftp/lars.pdf
.. [2] `Wikipedia entry on the Least-angle regression
<https://en.wikipedia.org/wiki/Least-angle_regression>`_
.. [3] `Wikipedia entry on the Lasso
<https://en.wikipedia.org/wiki/Lasso_(statistics)>`_ | Compute Least Angle Regression or Lasso path using LARS algorithm [1] | [
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"Least",
"Angle",
"Regression",
"or",
"Lasso",
"path",
"using",
"LARS",
"algorithm",
"[",
"1",
"]"
] | def lars_path(
X,
y,
Xy=None,
*,
Gram=None,
max_iter=500,
alpha_min=0,
method="lar",
copy_X=True,
eps=np.finfo(float).eps,
copy_Gram=True,
verbose=0,
return_path=True,
return_n_iter=False,
positive=False,
):
"""Compute Least Angle Regression or Lasso path using LARS algorithm [1]
The optimization objective for the case method='lasso' is::
(1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1
in the case of method='lars', the objective function is only known in
the form of an implicit equation (see discussion in [1])
Read more in the :ref:`User Guide <least_angle_regression>`.
Parameters
----------
X : None or array-like of shape (n_samples, n_features)
Input data. Note that if X is None then the Gram matrix must be
specified, i.e., cannot be None or False.
y : None or array-like of shape (n_samples,)
Input targets.
Xy : array-like of shape (n_samples,) or (n_samples, n_targets), \
default=None
Xy = np.dot(X.T, y) that can be precomputed. It is useful
only when the Gram matrix is precomputed.
Gram : None, 'auto', array-like of shape (n_features, n_features), \
default=None
Precomputed Gram matrix (X' * X), if ``'auto'``, the Gram
matrix is precomputed from the given X, if there are more samples
than features.
max_iter : int, default=500
Maximum number of iterations to perform, set to infinity for no limit.
alpha_min : float, default=0
Minimum correlation along the path. It corresponds to the
regularization parameter alpha parameter in the Lasso.
method : {'lar', 'lasso'}, default='lar'
Specifies the returned model. Select ``'lar'`` for Least Angle
Regression, ``'lasso'`` for the Lasso.
copy_X : bool, default=True
If ``False``, ``X`` is overwritten.
eps : float, default=np.finfo(float).eps
The machine-precision regularization in the computation of the
Cholesky diagonal factors. Increase this for very ill-conditioned
systems. Unlike the ``tol`` parameter in some iterative
optimization-based algorithms, this parameter does not control
the tolerance of the optimization.
copy_Gram : bool, default=True
If ``False``, ``Gram`` is overwritten.
verbose : int, default=0
Controls output verbosity.
return_path : bool, default=True
If ``return_path==True`` returns the entire path, else returns only the
last point of the path.
return_n_iter : bool, default=False
Whether to return the number of iterations.
positive : bool, default=False
Restrict coefficients to be >= 0.
This option is only allowed with method 'lasso'. Note that the model
coefficients will not converge to the ordinary-least-squares solution
for small values of alpha. Only coefficients up to the smallest alpha
value (``alphas_[alphas_ > 0.].min()`` when fit_path=True) reached by
the stepwise Lars-Lasso algorithm are typically in congruence with the
solution of the coordinate descent lasso_path function.
Returns
-------
alphas : array-like of shape (n_alphas + 1,)
Maximum of covariances (in absolute value) at each iteration.
``n_alphas`` is either ``max_iter``, ``n_features`` or the
number of nodes in the path with ``alpha >= alpha_min``, whichever
is smaller.
active : array-like of shape (n_alphas,)
Indices of active variables at the end of the path.
coefs : array-like of shape (n_features, n_alphas + 1)
Coefficients along the path
n_iter : int
Number of iterations run. Returned only if return_n_iter is set
to True.
See Also
--------
lars_path_gram
lasso_path
lasso_path_gram
LassoLars
Lars
LassoLarsCV
LarsCV
sklearn.decomposition.sparse_encode
References
----------
.. [1] "Least Angle Regression", Efron et al.
http://statweb.stanford.edu/~tibs/ftp/lars.pdf
.. [2] `Wikipedia entry on the Least-angle regression
<https://en.wikipedia.org/wiki/Least-angle_regression>`_
.. [3] `Wikipedia entry on the Lasso
<https://en.wikipedia.org/wiki/Lasso_(statistics)>`_
"""
if X is None and Gram is not None:
raise ValueError(
"X cannot be None if Gram is not None"
"Use lars_path_gram to avoid passing X and y."
)
return _lars_path_solver(
X=X,
y=y,
Xy=Xy,
Gram=Gram,
n_samples=None,
max_iter=max_iter,
alpha_min=alpha_min,
method=method,
copy_X=copy_X,
eps=eps,
copy_Gram=copy_Gram,
verbose=verbose,
return_path=return_path,
return_n_iter=return_n_iter,
positive=positive,
) | [
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Tinkoff/Nginx-builder | 1c8bbd1e51bf6e856b3756f59aa911a3687b3138 | src/builder.py | python | repair_keys | (block) | return repaired_keys_dict | Преобразованиие формата названий параметров
:param block:
:return repaired_keys_dict: | Преобразованиие формата названий параметров
:param block:
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] | def repair_keys(block):
"""
Преобразованиие формата названий параметров
:param block:
:return repaired_keys_dict:
"""
repaired_keys_dict = OrderedDict()
for block_part in block:
repaired_keys = OrderedDict()
for key in block_part:
z = ''.join(x for x in key.title() if not x.isspace()).replace('_', '-')
repaired_keys[z] = block_part[key]
index = list(repaired_keys)[0]
index_q = '{}_{}'.format(index, repaired_keys[index])
repaired_keys_dict[index_q] = repaired_keys
return repaired_keys_dict | [
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... | https://github.com/Tinkoff/Nginx-builder/blob/1c8bbd1e51bf6e856b3756f59aa911a3687b3138/src/builder.py#L233-L248 | |
oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/lib-tk/Tix.py | python | HList.item_cget | (self, entry, col, opt) | return self.tk.call(self._w, 'item', 'cget', entry, col, opt) | [] | def item_cget(self, entry, col, opt):
return self.tk.call(self._w, 'item', 'cget', entry, col, opt) | [
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gothinkster/flask-realworld-example-app | 4b95fb2227dfeb5dd1a45d89b2bf48630b93fd28 | conduit/extensions.py | python | CRUDMixin.delete | (self, commit=True) | return commit and db.session.commit() | Remove the record from the database. | Remove the record from the database. | [
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"the",
"record",
"from",
"the",
"database",
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] | def delete(self, commit=True):
"""Remove the record from the database."""
db.session.delete(self)
return commit and db.session.commit() | [
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cbfinn/gps | 82fa6cc930c4392d55d2525f6b792089f1d2ccfe | python/gps/gui/image_visualizer.py | python | ImageVisualizer.draw | (self) | [] | def draw(self):
self._ax_image.draw_artist(self._ax_image.patch)
self._ax_image.draw_artist(self._plot)
self._ax_image.draw_artist(self._overlay_plot_initial)
self._ax_image.draw_artist(self._overlay_plot_target)
self._fig.canvas.update()
self._fig.canvas.flush_events() | [
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yougov/mongo-connector | 557cafd4b54c848cd54ef28a258391a154650cb4 | mongo_connector/connector.py | python | Connector.oplog_thread_join | (self) | Stops all the OplogThreads | Stops all the OplogThreads | [
"Stops",
"all",
"the",
"OplogThreads"
] | def oplog_thread_join(self):
"""Stops all the OplogThreads
"""
LOG.info("MongoConnector: Stopping all OplogThreads")
for thread in self.shard_set.values():
thread.join() | [
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univ-of-utah-marriott-library-apple/firmware_password_manager | 198694a650acd3fa61b85d25dd61e83782bb25d5 | firmware_password_manager.py | python | FWPM_Object.secure_delete | (self) | return | attempts to securely delete the keyfile with medium overwrite and zeroing settings | attempts to securely delete the keyfile with medium overwrite and zeroing settings | [
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"to",
"securely",
"delete",
"the",
"keyfile",
"with",
"medium",
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"and",
"zeroing",
"settings"
] | def secure_delete(self):
"""
attempts to securely delete the keyfile with medium overwrite and zeroing settings
"""
if self.logger:
self.logger.info("%s: activated" % inspect.stack()[0][3])
if self.logger:
self.logger.info("Deleting keyfile")
use_srm = bool(os.path.exists("/usr/bin/srm"))
if self.args.testmode:
if self.logger:
self.logger.info("Test mode, keyfile not deleted.")
return
if use_srm:
try:
subprocess.call(["/usr/bin/srm", "-mz", self.config_options["keyfile"]["path"]])
if self.logger:
self.logger.info("keyfile deleted successfuly.")
except Exception as exception_message:
if self.logger:
self.logger.critical("Issue with attempt to remove keyfile. %s" % exception_message)
else:
try:
deleted_keyfile = subprocess.call(["/bin/rm", "-Pf", self.config_options["keyfile"]["path"]])
print("return: %r" % deleted_keyfile)
if self.logger:
self.logger.info("keyfile deleted successfuly.")
except Exception as exception_message:
if self.logger:
self.logger.critical("Issue with attempt to remove keyfile. %s" % exception_message)
# is this really needed?
if os.path.exists(self.config_options["keyfile"]["path"]):
if self.logger:
self.logger.critical("Failure to remove keyfile.")
else:
if self.logger:
self.logger.info("Keyfile removed.")
return | [
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kubernetes-client/python | 47b9da9de2d02b2b7a34fbe05afb44afd130d73a | kubernetes/client/configuration.py | python | Configuration.logger_format | (self) | return self.__logger_format | The logger format.
The logger_formatter will be updated when sets logger_format.
:param value: The format string.
:type: str | The logger format. | [
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"."
] | def logger_format(self):
"""The logger format.
The logger_formatter will be updated when sets logger_format.
:param value: The format string.
:type: str
"""
return self.__logger_format | [
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] | https://github.com/kubernetes-client/python/blob/47b9da9de2d02b2b7a34fbe05afb44afd130d73a/kubernetes/client/configuration.py#L276-L284 | |
thinkle/gourmet | 8af29c8ded24528030e5ae2ea3461f61c1e5a575 | gourmet/plugins/nutritional_information/nutritionDisplay.py | python | NutritionModel.connect_treeview_signals | (self,tv) | [] | def connect_treeview_signals (self,tv):
tv.connect('row-expanded',self.row_expanded_cb) | [
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oracle/oci-python-sdk | 3c1604e4e212008fb6718e2f68cdb5ef71fd5793 | src/oci/opsi/operations_insights_client.py | python | OperationsInsightsClient.summarize_exadata_insight_resource_utilization_insight | (self, compartment_id, resource_type, resource_metric, **kwargs) | Gets current utilization, projected utilization and days to reach projectedUtilization for an exadata system over specified time period. Valid values for ResourceType DATABASE are CPU,MEMORY,IO and STORAGE. Valid values for ResourceType HOST are CPU and MEMORY. Valid values for ResourceType STORAGE_SERVER are STORAGE, IOPS and THROUGHPUT.
:param str compartment_id: (required)
The `OCID`__ of the compartment.
__ https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm
:param str resource_type: (required)
Filter by resource.
Supported values are HOST , STORAGE_SERVER and DATABASE
:param str resource_metric: (required)
Filter by resource metric.
Supported values are CPU , STORAGE, MEMORY, IO, IOPS, THROUGHPUT
:param str analysis_time_interval: (optional)
Specify time period in ISO 8601 format with respect to current time.
Default is last 30 days represented by P30D.
If timeInterval is specified, then timeIntervalStart and timeIntervalEnd will be ignored.
Examples P90D (last 90 days), P4W (last 4 weeks), P2M (last 2 months), P1Y (last 12 months), . Maximum value allowed is 25 months prior to current time (P25M).
:param datetime time_interval_start: (optional)
Analysis start time in UTC in ISO 8601 format(inclusive).
Example 2019-10-30T00:00:00Z (yyyy-MM-ddThh:mm:ssZ).
The minimum allowed value is 2 years prior to the current day.
timeIntervalStart and timeIntervalEnd parameters are used together.
If analysisTimeInterval is specified, this parameter is ignored.
:param datetime time_interval_end: (optional)
Analysis end time in UTC in ISO 8601 format(exclusive).
Example 2019-10-30T00:00:00Z (yyyy-MM-ddThh:mm:ssZ).
timeIntervalStart and timeIntervalEnd are used together.
If timeIntervalEnd is not specified, current time is used as timeIntervalEnd.
:param list[str] exadata_insight_id: (optional)
Optional list of exadata insight resource `OCIDs`__.
__ https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm
:param list[str] exadata_type: (optional)
Filter by one or more Exadata types.
Possible value are DBMACHINE, EXACS, and EXACC.
:param int forecast_start_day: (optional)
Number of days used for utilization forecast analysis.
:param int forecast_days: (optional)
Number of days used for utilization forecast analysis.
:param list[str] cdb_name: (optional)
Filter by one or more cdb name.
:param list[str] host_name: (optional)
Filter by hostname.
:param int limit: (optional)
For list pagination. The maximum number of results per page, or items to
return in a paginated \"List\" call.
For important details about how pagination works, see
`List Pagination`__.
Example: `50`
__ https://docs.cloud.oracle.com/Content/API/Concepts/usingapi.htm#nine
:param str page: (optional)
For list pagination. The value of the `opc-next-page` response header from
the previous \"List\" call. For important details about how pagination works,
see `List Pagination`__.
__ https://docs.cloud.oracle.com/Content/API/Concepts/usingapi.htm#nine
:param list[str] defined_tag_equals: (optional)
A list of tag filters to apply. Only resources with a defined tag matching the value will be returned.
Each item in the list has the format \"{namespace}.{tagName}.{value}\". All inputs are case-insensitive.
Multiple values for the same key (i.e. same namespace and tag name) are interpreted as \"OR\".
Values for different keys (i.e. different namespaces, different tag names, or both) are interpreted as \"AND\".
:param list[str] freeform_tag_equals: (optional)
A list of tag filters to apply. Only resources with a freeform tag matching the value will be returned.
The key for each tag is \"{tagName}.{value}\". All inputs are case-insensitive.
Multiple values for the same tag name are interpreted as \"OR\". Values for different tag names are interpreted as \"AND\".
:param list[str] defined_tag_exists: (optional)
A list of tag existence filters to apply. Only resources for which the specified defined tags exist will be returned.
Each item in the list has the format \"{namespace}.{tagName}.true\" (for checking existence of a defined tag)
or \"{namespace}.true\". All inputs are case-insensitive.
Currently, only existence (\"true\" at the end) is supported. Absence (\"false\" at the end) is not supported.
Multiple values for the same key (i.e. same namespace and tag name) are interpreted as \"OR\".
Values for different keys (i.e. different namespaces, different tag names, or both) are interpreted as \"AND\".
:param list[str] freeform_tag_exists: (optional)
A list of tag existence filters to apply. Only resources for which the specified freeform tags exist the value will be returned.
The key for each tag is \"{tagName}.true\". All inputs are case-insensitive.
Currently, only existence (\"true\" at the end) is supported. Absence (\"false\" at the end) is not supported.
Multiple values for different tag names are interpreted as \"AND\".
:param str opc_request_id: (optional)
Unique Oracle-assigned identifier for the request. If you need to contact
Oracle about a particular request, please provide the request ID.
:param obj retry_strategy: (optional)
A retry strategy to apply to this specific operation/call. This will override any retry strategy set at the client-level.
This should be one of the strategies available in the :py:mod:`~oci.retry` module. This operation will not retry by default, users can also use the convenient :py:data:`~oci.retry.DEFAULT_RETRY_STRATEGY` provided by the SDK to enable retries for it.
The specifics of the default retry strategy are described `here <https://docs.oracle.com/en-us/iaas/tools/python/latest/sdk_behaviors/retries.html>`__.
To have this operation explicitly not perform any retries, pass an instance of :py:class:`~oci.retry.NoneRetryStrategy`.
:return: A :class:`~oci.response.Response` object with data of type :class:`~oci.opsi.models.SummarizeExadataInsightResourceUtilizationInsightAggregation`
:rtype: :class:`~oci.response.Response`
:example:
Click `here <https://docs.cloud.oracle.com/en-us/iaas/tools/python-sdk-examples/latest/opsi/summarize_exadata_insight_resource_utilization_insight.py.html>`__ to see an example of how to use summarize_exadata_insight_resource_utilization_insight API. | Gets current utilization, projected utilization and days to reach projectedUtilization for an exadata system over specified time period. Valid values for ResourceType DATABASE are CPU,MEMORY,IO and STORAGE. Valid values for ResourceType HOST are CPU and MEMORY. Valid values for ResourceType STORAGE_SERVER are STORAGE, IOPS and THROUGHPUT. | [
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"M... | def summarize_exadata_insight_resource_utilization_insight(self, compartment_id, resource_type, resource_metric, **kwargs):
"""
Gets current utilization, projected utilization and days to reach projectedUtilization for an exadata system over specified time period. Valid values for ResourceType DATABASE are CPU,MEMORY,IO and STORAGE. Valid values for ResourceType HOST are CPU and MEMORY. Valid values for ResourceType STORAGE_SERVER are STORAGE, IOPS and THROUGHPUT.
:param str compartment_id: (required)
The `OCID`__ of the compartment.
__ https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm
:param str resource_type: (required)
Filter by resource.
Supported values are HOST , STORAGE_SERVER and DATABASE
:param str resource_metric: (required)
Filter by resource metric.
Supported values are CPU , STORAGE, MEMORY, IO, IOPS, THROUGHPUT
:param str analysis_time_interval: (optional)
Specify time period in ISO 8601 format with respect to current time.
Default is last 30 days represented by P30D.
If timeInterval is specified, then timeIntervalStart and timeIntervalEnd will be ignored.
Examples P90D (last 90 days), P4W (last 4 weeks), P2M (last 2 months), P1Y (last 12 months), . Maximum value allowed is 25 months prior to current time (P25M).
:param datetime time_interval_start: (optional)
Analysis start time in UTC in ISO 8601 format(inclusive).
Example 2019-10-30T00:00:00Z (yyyy-MM-ddThh:mm:ssZ).
The minimum allowed value is 2 years prior to the current day.
timeIntervalStart and timeIntervalEnd parameters are used together.
If analysisTimeInterval is specified, this parameter is ignored.
:param datetime time_interval_end: (optional)
Analysis end time in UTC in ISO 8601 format(exclusive).
Example 2019-10-30T00:00:00Z (yyyy-MM-ddThh:mm:ssZ).
timeIntervalStart and timeIntervalEnd are used together.
If timeIntervalEnd is not specified, current time is used as timeIntervalEnd.
:param list[str] exadata_insight_id: (optional)
Optional list of exadata insight resource `OCIDs`__.
__ https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm
:param list[str] exadata_type: (optional)
Filter by one or more Exadata types.
Possible value are DBMACHINE, EXACS, and EXACC.
:param int forecast_start_day: (optional)
Number of days used for utilization forecast analysis.
:param int forecast_days: (optional)
Number of days used for utilization forecast analysis.
:param list[str] cdb_name: (optional)
Filter by one or more cdb name.
:param list[str] host_name: (optional)
Filter by hostname.
:param int limit: (optional)
For list pagination. The maximum number of results per page, or items to
return in a paginated \"List\" call.
For important details about how pagination works, see
`List Pagination`__.
Example: `50`
__ https://docs.cloud.oracle.com/Content/API/Concepts/usingapi.htm#nine
:param str page: (optional)
For list pagination. The value of the `opc-next-page` response header from
the previous \"List\" call. For important details about how pagination works,
see `List Pagination`__.
__ https://docs.cloud.oracle.com/Content/API/Concepts/usingapi.htm#nine
:param list[str] defined_tag_equals: (optional)
A list of tag filters to apply. Only resources with a defined tag matching the value will be returned.
Each item in the list has the format \"{namespace}.{tagName}.{value}\". All inputs are case-insensitive.
Multiple values for the same key (i.e. same namespace and tag name) are interpreted as \"OR\".
Values for different keys (i.e. different namespaces, different tag names, or both) are interpreted as \"AND\".
:param list[str] freeform_tag_equals: (optional)
A list of tag filters to apply. Only resources with a freeform tag matching the value will be returned.
The key for each tag is \"{tagName}.{value}\". All inputs are case-insensitive.
Multiple values for the same tag name are interpreted as \"OR\". Values for different tag names are interpreted as \"AND\".
:param list[str] defined_tag_exists: (optional)
A list of tag existence filters to apply. Only resources for which the specified defined tags exist will be returned.
Each item in the list has the format \"{namespace}.{tagName}.true\" (for checking existence of a defined tag)
or \"{namespace}.true\". All inputs are case-insensitive.
Currently, only existence (\"true\" at the end) is supported. Absence (\"false\" at the end) is not supported.
Multiple values for the same key (i.e. same namespace and tag name) are interpreted as \"OR\".
Values for different keys (i.e. different namespaces, different tag names, or both) are interpreted as \"AND\".
:param list[str] freeform_tag_exists: (optional)
A list of tag existence filters to apply. Only resources for which the specified freeform tags exist the value will be returned.
The key for each tag is \"{tagName}.true\". All inputs are case-insensitive.
Currently, only existence (\"true\" at the end) is supported. Absence (\"false\" at the end) is not supported.
Multiple values for different tag names are interpreted as \"AND\".
:param str opc_request_id: (optional)
Unique Oracle-assigned identifier for the request. If you need to contact
Oracle about a particular request, please provide the request ID.
:param obj retry_strategy: (optional)
A retry strategy to apply to this specific operation/call. This will override any retry strategy set at the client-level.
This should be one of the strategies available in the :py:mod:`~oci.retry` module. This operation will not retry by default, users can also use the convenient :py:data:`~oci.retry.DEFAULT_RETRY_STRATEGY` provided by the SDK to enable retries for it.
The specifics of the default retry strategy are described `here <https://docs.oracle.com/en-us/iaas/tools/python/latest/sdk_behaviors/retries.html>`__.
To have this operation explicitly not perform any retries, pass an instance of :py:class:`~oci.retry.NoneRetryStrategy`.
:return: A :class:`~oci.response.Response` object with data of type :class:`~oci.opsi.models.SummarizeExadataInsightResourceUtilizationInsightAggregation`
:rtype: :class:`~oci.response.Response`
:example:
Click `here <https://docs.cloud.oracle.com/en-us/iaas/tools/python-sdk-examples/latest/opsi/summarize_exadata_insight_resource_utilization_insight.py.html>`__ to see an example of how to use summarize_exadata_insight_resource_utilization_insight API.
"""
resource_path = "/exadataInsights/resourceUtilizationInsight"
method = "GET"
# Don't accept unknown kwargs
expected_kwargs = [
"retry_strategy",
"analysis_time_interval",
"time_interval_start",
"time_interval_end",
"exadata_insight_id",
"exadata_type",
"forecast_start_day",
"forecast_days",
"cdb_name",
"host_name",
"limit",
"page",
"defined_tag_equals",
"freeform_tag_equals",
"defined_tag_exists",
"freeform_tag_exists",
"opc_request_id"
]
extra_kwargs = [_key for _key in six.iterkeys(kwargs) if _key not in expected_kwargs]
if extra_kwargs:
raise ValueError(
"summarize_exadata_insight_resource_utilization_insight got unknown kwargs: {!r}".format(extra_kwargs))
query_params = {
"compartmentId": compartment_id,
"resourceType": resource_type,
"resourceMetric": resource_metric,
"analysisTimeInterval": kwargs.get("analysis_time_interval", missing),
"timeIntervalStart": kwargs.get("time_interval_start", missing),
"timeIntervalEnd": kwargs.get("time_interval_end", missing),
"exadataInsightId": self.base_client.generate_collection_format_param(kwargs.get("exadata_insight_id", missing), 'multi'),
"exadataType": self.base_client.generate_collection_format_param(kwargs.get("exadata_type", missing), 'multi'),
"forecastStartDay": kwargs.get("forecast_start_day", missing),
"forecastDays": kwargs.get("forecast_days", missing),
"cdbName": self.base_client.generate_collection_format_param(kwargs.get("cdb_name", missing), 'multi'),
"hostName": self.base_client.generate_collection_format_param(kwargs.get("host_name", missing), 'multi'),
"limit": kwargs.get("limit", missing),
"page": kwargs.get("page", missing),
"definedTagEquals": self.base_client.generate_collection_format_param(kwargs.get("defined_tag_equals", missing), 'multi'),
"freeformTagEquals": self.base_client.generate_collection_format_param(kwargs.get("freeform_tag_equals", missing), 'multi'),
"definedTagExists": self.base_client.generate_collection_format_param(kwargs.get("defined_tag_exists", missing), 'multi'),
"freeformTagExists": self.base_client.generate_collection_format_param(kwargs.get("freeform_tag_exists", missing), 'multi')
}
query_params = {k: v for (k, v) in six.iteritems(query_params) if v is not missing and v is not None}
header_params = {
"accept": "application/json",
"content-type": "application/json",
"opc-request-id": kwargs.get("opc_request_id", missing)
}
header_params = {k: v for (k, v) in six.iteritems(header_params) if v is not missing and v is not None}
retry_strategy = self.base_client.get_preferred_retry_strategy(
operation_retry_strategy=kwargs.get('retry_strategy'),
client_retry_strategy=self.retry_strategy
)
if retry_strategy:
if not isinstance(retry_strategy, retry.NoneRetryStrategy):
self.base_client.add_opc_client_retries_header(header_params)
retry_strategy.add_circuit_breaker_callback(self.circuit_breaker_callback)
return retry_strategy.make_retrying_call(
self.base_client.call_api,
resource_path=resource_path,
method=method,
query_params=query_params,
header_params=header_params,
response_type="SummarizeExadataInsightResourceUtilizationInsightAggregation")
else:
return self.base_client.call_api(
resource_path=resource_path,
method=method,
query_params=query_params,
header_params=header_params,
response_type="SummarizeExadataInsightResourceUtilizationInsightAggregation") | [
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jython/frozen-mirror | b8d7aa4cee50c0c0fe2f4b235dd62922dd0f3f99 | lib-python/2.7/decimal.py | python | Context.logical_or | (self, a, b) | return a.logical_or(b, context=self) | Applies the logical operation 'or' between each operand's digits.
The operands must be both logical numbers.
>>> ExtendedContext.logical_or(Decimal('0'), Decimal('0'))
Decimal('0')
>>> ExtendedContext.logical_or(Decimal('0'), Decimal('1'))
Decimal('1')
>>> ExtendedContext.logical_or(Decimal('1'), Decimal('0'))
Decimal('1')
>>> ExtendedContext.logical_or(Decimal('1'), Decimal('1'))
Decimal('1')
>>> ExtendedContext.logical_or(Decimal('1100'), Decimal('1010'))
Decimal('1110')
>>> ExtendedContext.logical_or(Decimal('1110'), Decimal('10'))
Decimal('1110')
>>> ExtendedContext.logical_or(110, 1101)
Decimal('1111')
>>> ExtendedContext.logical_or(Decimal(110), 1101)
Decimal('1111')
>>> ExtendedContext.logical_or(110, Decimal(1101))
Decimal('1111') | Applies the logical operation 'or' between each operand's digits. | [
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"""Applies the logical operation 'or' between each operand's digits.
The operands must be both logical numbers.
>>> ExtendedContext.logical_or(Decimal('0'), Decimal('0'))
Decimal('0')
>>> ExtendedContext.logical_or(Decimal('0'), Decimal('1'))
Decimal('1')
>>> ExtendedContext.logical_or(Decimal('1'), Decimal('0'))
Decimal('1')
>>> ExtendedContext.logical_or(Decimal('1'), Decimal('1'))
Decimal('1')
>>> ExtendedContext.logical_or(Decimal('1100'), Decimal('1010'))
Decimal('1110')
>>> ExtendedContext.logical_or(Decimal('1110'), Decimal('10'))
Decimal('1110')
>>> ExtendedContext.logical_or(110, 1101)
Decimal('1111')
>>> ExtendedContext.logical_or(Decimal(110), 1101)
Decimal('1111')
>>> ExtendedContext.logical_or(110, Decimal(1101))
Decimal('1111')
"""
a = _convert_other(a, raiseit=True)
return a.logical_or(b, context=self) | [
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LxMLS/lxmls-toolkit | d95c623dd13e31135f4887c3788959eb9ad3d269 | lxmls/deep_learning/numpy_models/mlp.py | python | NumpyMLP.cross_entropy_loss | (self, input, output) | return -log_probability[range(num_examples), output].mean() | Cross entropy loss | Cross entropy loss | [
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"loss"
] | def cross_entropy_loss(self, input, output):
"""Cross entropy loss"""
num_examples = input.shape[0]
log_probability, _ = self.log_forward(input)
return -log_probability[range(num_examples), output].mean() | [
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compas-dev/compas | 0b33f8786481f710115fb1ae5fe79abc2a9a5175 | src/compas/geometry/intersections/intersections.py | python | intersection_polyline_plane | (polyline, plane, expected_number_of_intersections=None, tol=1e-6) | return intersections | Calculate the intersection point of a plane with a polyline. Reduce expected_number_of_intersections to speed up.
Parameters
----------
polyline : sequence[point] or :class:`compas.geometry.Polyline`
Polyline to test intersection.
plane : [point, vector] or :class:`compas.geometry.Plane`
Plane to compute intersection.
expected_number_of_intersections : int, optional
Number of useful or expected intersections.
Default is the number of line segments of the polyline.
tol : float, optional
A tolerance for membership verification.
Returns
-------
list[[float, float, float]]
The intersection points between the polyline segments and the plane. | Calculate the intersection point of a plane with a polyline. Reduce expected_number_of_intersections to speed up. | [
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"""Calculate the intersection point of a plane with a polyline. Reduce expected_number_of_intersections to speed up.
Parameters
----------
polyline : sequence[point] or :class:`compas.geometry.Polyline`
Polyline to test intersection.
plane : [point, vector] or :class:`compas.geometry.Plane`
Plane to compute intersection.
expected_number_of_intersections : int, optional
Number of useful or expected intersections.
Default is the number of line segments of the polyline.
tol : float, optional
A tolerance for membership verification.
Returns
-------
list[[float, float, float]]
The intersection points between the polyline segments and the plane.
"""
if not expected_number_of_intersections:
expected_number_of_intersections = len(polyline)
intersections = []
for segment in pairwise(polyline):
if len(intersections) == expected_number_of_intersections:
break
point = intersection_segment_plane(segment, plane, tol)
if point:
intersections.append(point)
return intersections | [
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cisco/mindmeld | 809c36112e9ea8019fe29d54d136ca14eb4fd8db | mindmeld/text_preparation/spacy_model_factory.py | python | SpacyModelFactory.validate_spacy_language | (language) | Check if the language is valid.
Args:
language (str, optional): Language as specified using a 639-1/2 code. | Check if the language is valid. | [
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"""Check if the language is valid.
Args:
language (str, optional): Language as specified using a 639-1/2 code.
"""
if language not in SPACY_SUPPORTED_LANGUAGES:
raise ValueError("Spacy does not currently support: {!r}.".format(language)) | [
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flosell/trailscraper | 2509b8da81b49edf375a44fbc22a58fd9e2ea928 | trailscraper/iam.py | python | BaseElement.json_repr | (self) | JSON representation of the class | JSON representation of the class | [
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saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/utils/vault.py | python | write_cache | (connection) | Write the vault token to cache | Write the vault token to cache | [
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] | def write_cache(connection):
"""
Write the vault token to cache
"""
# If uses is 1 and unlimited_use_token is not true, then this is a single use token and should not be cached
# In that case, we still want to cache the vault metadata lookup information for paths, so continue on
if (
connection.get("uses", None) == 1
and "unlimited_use_token" not in connection
and "vault_secret_path_metadata" not in connection
):
log.debug("Not caching vault single use token")
__context__["vault_token"] = connection
return True
elif (
"vault_secret_path_metadata" in __context__
and "vault_secret_path_metadata" not in connection
):
# If session storage is being used, and info passed is not the already saved metadata
log.debug("Storing token only for this session")
__context__["vault_token"] = connection
return True
elif "vault_secret_path_metadata" in __context__:
# Must have been passed metadata. This is already handled by _get_secret_path_metadata
# and does not need to be resaved
return True
cache_file = os.path.join(__opts__["cachedir"], "salt_vault_token")
try:
log.debug("Writing vault cache file")
# Detect if token was issued without use limit
if connection.get("uses") == 0:
connection["unlimited_use_token"] = True
else:
connection["unlimited_use_token"] = False
with salt.utils.files.fpopen(cache_file, "w", mode=0o600) as fp_:
fp_.write(salt.utils.json.dumps(connection))
return True
except OSError:
log.error(
"Failed to cache vault information", exc_info_on_loglevel=logging.DEBUG
)
return False | [
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Source-Python-Dev-Team/Source.Python | d0ffd8ccbd1e9923c9bc44936f20613c1c76b7fb | addons/source-python/Python3/collections/__init__.py | python | Counter.elements | (self) | return _chain.from_iterable(_starmap(_repeat, self.items())) | Iterator over elements repeating each as many times as its count.
>>> c = Counter('ABCABC')
>>> sorted(c.elements())
['A', 'A', 'B', 'B', 'C', 'C']
# Knuth's example for prime factors of 1836: 2**2 * 3**3 * 17**1
>>> prime_factors = Counter({2: 2, 3: 3, 17: 1})
>>> product = 1
>>> for factor in prime_factors.elements(): # loop over factors
... product *= factor # and multiply them
>>> product
1836
Note, if an element's count has been set to zero or is a negative
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'''Iterator over elements repeating each as many times as its count.
>>> c = Counter('ABCABC')
>>> sorted(c.elements())
['A', 'A', 'B', 'B', 'C', 'C']
# Knuth's example for prime factors of 1836: 2**2 * 3**3 * 17**1
>>> prime_factors = Counter({2: 2, 3: 3, 17: 1})
>>> product = 1
>>> for factor in prime_factors.elements(): # loop over factors
... product *= factor # and multiply them
>>> product
1836
Note, if an element's count has been set to zero or is a negative
number, elements() will ignore it.
'''
# Emulate Bag.do from Smalltalk and Multiset.begin from C++.
return _chain.from_iterable(_starmap(_repeat, self.items())) | [
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Cue/ocstyle | aec49228f7a4aee9d20f9de51ae50bb969075aee | src/ocstyle/rules.py | python | keyword | (_) | return None | Matches keywords. | Matches keywords. | [
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google-research/language | 61fa7260ac7d690d11ef72ca863e45a37c0bdc80 | language/compir/dataset_parsers/scan_parser.py | python | ScanParser._get_atom_map | (self) | return atom_map | Gets (utterance, program) pairs, used for adding brackets to programs. | Gets (utterance, program) pairs, used for adding brackets to programs. | [
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"(",
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] | def _get_atom_map(self):
"""Gets (utterance, program) pairs, used for adding brackets to programs."""
atoms = [
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("turn right", "I_TURN_RIGHT"),
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("run opposite right", "I_TURN_RIGHT I_TURN_RIGHT I_RUN"),
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("jump opposite left", "I_TURN_LEFT I_TURN_LEFT I_JUMP"),
("jump opposite right", "I_TURN_RIGHT I_TURN_RIGHT I_JUMP"),
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),
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""
" I_JUMP"),
("jump around right",
"I_TURN_RIGHT I_JUMP I_TURN_RIGHT I_JUMP I_TURN_RIGHT I_JUMP "
"I_TURN_RIGHT I_JUMP"),
]
atom_map = {atom[0]: atom[1] for atom in atoms}
# Adds "X twice" actions.
atom_map.update({
"{} twice".format(atom[0]): "{} {}".format(atom[1], atom[1])
for atom in atoms
})
# Adds "X thrice" actions.
atom_map.update({
"{} thrice".format(atom[0]):
"{} {} {}".format(atom[1], atom[1], atom[1]) for atom in atoms
})
return atom_map | [
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guildai/guildai | 1665985a3d4d788efc1a3180ca51cc417f71ca78 | guild/op_util.py | python | _default_run_label | (flag_vals) | return " ".join(
flag_util.flag_assigns(non_null, truncate_floats=True, shorten_paths=True)
) | Returns a default run label for a map of flag values.
The default label is a string containing flag assign as NAME=VALUE. | Returns a default run label for a map of flag values. | [
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] | def _default_run_label(flag_vals):
"""Returns a default run label for a map of flag values.
The default label is a string containing flag assign as NAME=VALUE.
"""
non_null = {name: val for name, val in flag_vals.items() if val is not None}
return " ".join(
flag_util.flag_assigns(non_null, truncate_floats=True, shorten_paths=True)
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alicevision/meshroom | 92004286fd0bc8ab36bd3575b4c0166090e665e1 | meshroom/ui/reconstruction.py | python | ViewpointWrapper.pose | (self) | return QMatrix4x4(
self._R[0], -self._R[1], -self._R[2], self._T[0],
self._R[3], -self._R[4], -self._R[5], self._T[1],
self._R[6], -self._R[7], -self._R[8], self._T[2],
0, 0, 0, 1
) | Get the camera pose of 'viewpoint' as a 4x4 matrix. | Get the camera pose of 'viewpoint' as a 4x4 matrix. | [
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] | def pose(self):
""" Get the camera pose of 'viewpoint' as a 4x4 matrix. """
if self._R is None or self._T is None:
return None
# convert transform matrix for Qt
return QMatrix4x4(
self._R[0], -self._R[1], -self._R[2], self._T[0],
self._R[3], -self._R[4], -self._R[5], self._T[1],
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PyTorchLightning/pytorch-lightning | 5914fb748fb53d826ab337fc2484feab9883a104 | pytorch_lightning/utilities/memory.py | python | get_memory_profile | (mode: str) | return memory_map | r"""
.. deprecated:: v1.5
This function was deprecated in v1.5 in favor of
`pytorch_lightning.accelerators.gpu._get_nvidia_gpu_stats` and will be removed in v1.7.
Get a profile of the current memory usage.
Args:
mode: There are two modes:
- 'all' means return memory for all gpus
- 'min_max' means return memory for max and min
Return:
A dictionary in which the keys are device ids as integers and
values are memory usage as integers in MB.
If mode is 'min_max', the dictionary will also contain two additional keys:
- 'min_gpu_mem': the minimum memory usage in MB
- 'max_gpu_mem': the maximum memory usage in MB | r"""
.. deprecated:: v1.5
This function was deprecated in v1.5 in favor of
`pytorch_lightning.accelerators.gpu._get_nvidia_gpu_stats` and will be removed in v1.7. | [
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r"""
.. deprecated:: v1.5
This function was deprecated in v1.5 in favor of
`pytorch_lightning.accelerators.gpu._get_nvidia_gpu_stats` and will be removed in v1.7.
Get a profile of the current memory usage.
Args:
mode: There are two modes:
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Return:
A dictionary in which the keys are device ids as integers and
values are memory usage as integers in MB.
If mode is 'min_max', the dictionary will also contain two additional keys:
- 'min_gpu_mem': the minimum memory usage in MB
- 'max_gpu_mem': the maximum memory usage in MB
"""
memory_map = get_gpu_memory_map()
if mode == "min_max":
min_index, min_memory = min(memory_map.items(), key=lambda item: item[1])
max_index, max_memory = max(memory_map.items(), key=lambda item: item[1])
memory_map = {"min_gpu_mem": min_memory, "max_gpu_mem": max_memory}
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RTIInternational/gobbli | d9ec8132f74ce49dc4bead2fad25b661bcef6e76 | gobbli/io.py | python | validate_X | (X: List[str]) | Confirm a given array matches the expected type for input.
Args:
X: Something that should be valid model input. | Confirm a given array matches the expected type for input. | [
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Confirm a given array matches the expected type for input.
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X: Something that should be valid model input.
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vilemduha/blendercam | 3bd98ace230464d68d9ff86eae595481ec98ffc0 | scripts/addons/cam/nc/nc.py | python | Creator.set_plane | (self, plane) | Set plane | Set plane | [
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pass | [
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pyproj4/pyproj | 24eade78c52f8bf6717e56fb7c878f7da9892368 | pyproj/crs/coordinate_operation.py | python | MercatorAConversion.__new__ | (
cls,
latitude_natural_origin: float = 0.0,
longitude_natural_origin: float = 0.0,
false_easting: float = 0.0,
false_northing: float = 0.0,
scale_factor_natural_origin: float = 1.0,
) | return cls.from_json_dict(merc_json) | Parameters
----------
longitude_natural_origin: float, default=0.0
Latitude of natural origin (lat_0).
longitude_natural_origin: float, default=0.0
Longitude of natural origin (lon_0).
false_easting: float, default=0.0
False easting (x_0).
false_northing: float, default=0.0
False northing (y_0).
scale_factor_natural_origin: float, default=1.0
Scale factor at natural origin (k or k_0). | Parameters
----------
longitude_natural_origin: float, default=0.0
Latitude of natural origin (lat_0).
longitude_natural_origin: float, default=0.0
Longitude of natural origin (lon_0).
false_easting: float, default=0.0
False easting (x_0).
false_northing: float, default=0.0
False northing (y_0).
scale_factor_natural_origin: float, default=1.0
Scale factor at natural origin (k or k_0). | [
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longitude_natural_origin: float = 0.0,
false_easting: float = 0.0,
false_northing: float = 0.0,
scale_factor_natural_origin: float = 1.0,
):
"""
Parameters
----------
longitude_natural_origin: float, default=0.0
Latitude of natural origin (lat_0).
longitude_natural_origin: float, default=0.0
Longitude of natural origin (lon_0).
false_easting: float, default=0.0
False easting (x_0).
false_northing: float, default=0.0
False northing (y_0).
scale_factor_natural_origin: float, default=1.0
Scale factor at natural origin (k or k_0).
"""
merc_json = {
"$schema": "https://proj.org/schemas/v0.2/projjson.schema.json",
"type": "Conversion",
"name": "unknown",
"method": {
"name": "Mercator (variant A)",
"id": {"authority": "EPSG", "code": 9804},
},
"parameters": [
{
"name": "Latitude of natural origin",
"value": latitude_natural_origin,
"unit": "degree",
"id": {"authority": "EPSG", "code": 8801},
},
{
"name": "Longitude of natural origin",
"value": longitude_natural_origin,
"unit": "degree",
"id": {"authority": "EPSG", "code": 8802},
},
{
"name": "Scale factor at natural origin",
"value": scale_factor_natural_origin,
"unit": "unity",
"id": {"authority": "EPSG", "code": 8805},
},
{
"name": "False easting",
"value": false_easting,
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},
{
"name": "False northing",
"value": false_northing,
"unit": "metre",
"id": {"authority": "EPSG", "code": 8807},
},
],
}
return cls.from_json_dict(merc_json) | [
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PacificBiosciences/FALCON | ab80e88e1278879f28fe83b9fa0382249213444e | falcon_kit/run_support.py | python | update_job_sections | (config) | More for backwards compatibility with stuff from 'General' section. | More for backwards compatibility with stuff from 'General' section. | [
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"from",
"General",
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"."
] | def update_job_sections(config):
"""More for backwards compatibility with stuff from 'General' section.
"""
update_job_defaults_section(config)
General = config['General']
# Update a few where the names change and the section is non-default.
def update_step_job_opts(name):
if General.get('sge_option_'+name) and 'JOB_OPTS' not in config['job.step.'+name]:
config['job.step.'+name]['JOB_OPTS'] = General['sge_option_'+name]
def update_step_njobs(name):
if General.get(name+'_concurrent_jobs') and 'njobs' not in config['job.step.'+name]:
config['job.step.'+name]['njobs'] = int(General[name+'_concurrent_jobs'])
for name in ['da', 'la', 'pda', 'pla', 'cns', 'fc', 'asm']:
update_step_job_opts(name)
update_step_njobs(name)
# Prefer 'asm' to 'fc'.
asm = dict(config['job.step.asm'])
config['job.step.asm'] = config['job.step.fc']
del config['job.step.fc']
config['job.step.asm'].update(asm) | [
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omz/PythonistaAppTemplate | f560f93f8876d82a21d108977f90583df08d55af | PythonistaAppTemplate/PythonistaKit.framework/pylib/httplib.py | python | HTTPConnection._send_output | (self, message_body=None) | Send the currently buffered request and clear the buffer.
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"""
self._buffer.extend(("", ""))
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IlyaGusev/rupo | 83a5fac0dddd480a401181e5465f0e3c94ad2dcc | rupo/stress/word.py | python | Stress.__str__ | (self) | return str(self.position) + "\t" + str(self.type) | [] | def __str__(self):
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khanhnamle1994/natural-language-processing | 01d450d5ac002b0156ef4cf93a07cb508c1bcdc5 | assignment1/.env/lib/python2.7/site-packages/pkg_resources/_vendor/packaging/version.py | python | _parse_local_version | (local) | Takes a string like abc.1.twelve and turns it into ("abc", 1, "twelve"). | Takes a string like abc.1.twelve and turns it into ("abc", 1, "twelve"). | [
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"""
if local is not None:
return tuple(
part.lower() if not part.isdigit() else int(part)
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angr/angr | 4b04d56ace135018083d36d9083805be8146688b | angr/procedures/linux_kernel/fstat64.py | python | fstat64._store_amd64 | (self, stat_buf, stat) | [] | def _store_amd64(self, stat_buf, stat):
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store(0x88, self.state.solver.BVV(0, 64)) | [
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cournape/Bento | 37de23d784407a7c98a4a15770ffc570d5f32d70 | bento/parser/lexer.py | python | t_insidemstringnotcontinued_WS | (t) | return t | [] | def t_insidemstringnotcontinued_WS(t):
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NTMC-Community/MatchZoo-py | 0e5c04e1e948aa9277abd5c85ff99d9950d8527f | matchzoo/tasks/ranking.py | python | Ranking.list_available_metrics | (cls) | return ['map'] | :return: a list of available metrics. | :return: a list of available metrics. | [
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yt-project/yt | dc7b24f9b266703db4c843e329c6c8644d47b824 | yt/frontends/enzo_e/data_structures.py | python | EnzoEHierarchy._setup_derived_fields | (self) | [] | def _setup_derived_fields(self):
super()._setup_derived_fields()
for fname, field in self.ds.field_info.items():
if not field.particle_type:
continue
if isinstance(fname, tuple):
continue
if field._function is NullFunc:
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pypa/pipenv | b21baade71a86ab3ee1429f71fbc14d4f95fb75d | pipenv/patched/notpip/_internal/operations/install/wheel.py | python | install_wheel | (
name, # type: str
wheel_path, # type: str
scheme, # type: Scheme
req_description, # type: str
pycompile=True, # type: bool
warn_script_location=True, # type: bool
direct_url=None, # type: Optional[DirectUrl]
requested=False, # type: bool
) | [] | def install_wheel(
name, # type: str
wheel_path, # type: str
scheme, # type: Scheme
req_description, # type: str
pycompile=True, # type: bool
warn_script_location=True, # type: bool
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):
# type: (...) -> None
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_install_wheel(
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polakowo/vectorbt | 6638735c131655760474d72b9f045d1dbdbd8fe9 | vectorbt/utils/checks.py | python | is_index | (arg: tp.Any) | return isinstance(arg, pd.Index) | Check whether the argument is `pd.Index`. | Check whether the argument is `pd.Index`. | [
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vispy/vispy | 26256fdc2574259dd227022fbce0767cae4e244b | vispy/visuals/graphs/layouts/networkx_layout.py | python | NetworkxCoordinates.__call__ | (self, adjacency_mat, directed=False) | Parameters
----------
adjacency_mat : sparse adjacency matrix.
directed : bool, default False
Returns
---------
(node_vertices, line_vertices, arrow_vertices) : tuple
Yields the node and line vertices in a tuple. This layout only yields a
single time, and has no builtin animation | Parameters
----------
adjacency_mat : sparse adjacency matrix.
directed : bool, default False | [
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"""
Parameters
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adjacency_mat : sparse adjacency matrix.
directed : bool, default False
Returns
---------
(node_vertices, line_vertices, arrow_vertices) : tuple
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"""
if issparse(adjacency_mat):
adjacency_mat = adjacency_mat.tocoo()
line_vertices, arrows = _straight_line_vertices(
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yield self.positions, line_vertices, arrows | [
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kuri65536/python-for-android | 26402a08fc46b09ef94e8d7a6bbc3a54ff9d0891 | python3-alpha/python-libs/bs4/element.py | python | PageElement.find_next_sibling | (self, name=None, attrs={}, text=None, **kwargs) | return self._find_one(self.find_next_siblings, name, attrs, text,
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"""Returns the closest sibling to this Tag that matches the
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magmax/python-inquirer | ef7487247b46f33032f54a1547c9b8d9b8287c2b | noxfile.py | python | activate_virtualenv_in_precommit_hooks | (session: Session) | Activate virtualenv in hooks installed by pre-commit.
This function patches git hooks installed by pre-commit to activate the
session's virtual environment. This allows pre-commit to locate hooks in
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session: The Session object. | Activate virtualenv in hooks installed by pre-commit. | [
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"""Activate virtualenv in hooks installed by pre-commit.
This function patches git hooks installed by pre-commit to activate the
session's virtual environment. This allows pre-commit to locate hooks in
that environment when invoked from git.
Args:
session: The Session object.
"""
assert session.bin is not None # noqa: S101
# Only patch hooks containing a reference to this session's bindir. Support
# quoting rules for Python and bash, but strip the outermost quotes so we
# can detect paths within the bindir, like <bindir>/python.
bindirs = [
bindir[1:-1] if bindir[0] in "'\"" else bindir for bindir in (repr(session.bin), shlex.quote(session.bin))
]
virtualenv = session.env.get("VIRTUAL_ENV")
if virtualenv is None:
return
headers = {
# pre-commit < 2.16.0
"python": f"""\
import os
os.environ["VIRTUAL_ENV"] = {virtualenv!r}
os.environ["PATH"] = os.pathsep.join((
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))
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# pre-commit >= 2.16.0
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PATH={shlex.quote(session.bin)}"{os.pathsep}$PATH"
""",
}
hookdir = Path(".git") / "hooks"
if not hookdir.is_dir():
return
for hook in hookdir.iterdir():
if hook.name.endswith(".sample") or not hook.is_file():
continue
if not hook.read_bytes().startswith(b"#!"):
continue
text = hook.read_text()
if not any(Path("A") == Path("a") and bindir.lower() in text.lower() or bindir in text for bindir in bindirs):
continue
lines = text.splitlines()
for executable, header in headers.items():
if executable in lines[0].lower():
lines.insert(1, dedent(header))
hook.write_text("\n".join(lines))
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tsenghungchen/SA-tensorflow | 6aad7dbf609e64dd652e9204518eddc6674168a4 | pycocoevalcap/bleu/bleu_scorer.py | python | BleuScorer.score_ratio | (self, option=None) | return (self.fscore(option=option), self.ratio(option=option)) | return (bleu, len_ratio) pair | return (bleu, len_ratio) pair | [
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IdentityPython/pysaml2 | 6badb32d212257bd83ffcc816f9b625f68281b47 | src/saml2/ws/wspol.py | python | operator_content_type__from_string | (xml_string) | return saml2.create_class_from_xml_string(OperatorContentType_, xml_string) | [] | def operator_content_type__from_string(xml_string):
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bendichter/brokenaxes | 191a534e64054f0e155cd7398832e44c681ed539 | brokenaxes.py | python | BrokenAxes.set_title | (self, *args, **kwargs) | return self.big_ax.set_title(*args, **kwargs) | [] | def set_title(self, *args, **kwargs):
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MaurizioFD/RecSys2019_DeepLearning_Evaluation | 0fb6b7f5c396f8525316ed66cf9c9fdb03a5fa9b | CNN_on_embeddings/IJCAI/CFM_our_interface/Dataset_wrapper.py | python | dict_to_sparse_matrix | (d:dict, shape:tuple) | return sparse_matrix | Convert a dictionary to a sparse matrix | Convert a dictionary to a sparse matrix | [
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tot_items = 0
row_ind = []
col_ind = []
for k, v in d.items():
c = len(v)
row_ind.extend([k] * c)
col_ind.extend(v)
tot_items += c
data = np.ones(tot_items)
sparse_matrix = sps.csr_matrix((data, (row_ind, col_ind)), shape=shape)
assert sparse_matrix.nnz == len(data), "Duplicate entries found"
return sparse_matrix | [
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chapmanb/bcbb | dbfb52711f0bfcc1d26c5a5b53c9ff4f50dc0027 | nextgen/bcbio/broad/metrics.py | python | RNASeqPicardMetrics.report | (self, align_bam, ref_file, gtf_file, is_paired=False,
rrna_file="null") | return summary_info, graphs | Produce report metrics for a RNASeq experiment using Picard
with a sorted aligned BAM file. | Produce report metrics for a RNASeq experiment using Picard
with a sorted aligned BAM file. | [
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] | def report(self, align_bam, ref_file, gtf_file, is_paired=False,
rrna_file="null"):
"""Produce report metrics for a RNASeq experiment using Picard
with a sorted aligned BAM file.
"""
# collect duplication metrics
dup_bam, dup_metrics = self._get_current_dup_metrics(align_bam)
align_metrics = self._collect_align_metrics(align_bam, ref_file)
insert_graph, insert_metrics = (None, None)
if is_paired:
insert_graph, insert_metrics = self._insert_sizes(align_bam)
rnaseq_metrics = self._rnaseq_metrics(align_bam, gtf_file, rrna_file)
summary_info = self._parser.get_summary_metrics(align_metrics,
dup_metrics,
insert_metrics=insert_metrics,
rnaseq_metrics=rnaseq_metrics)
pprint.pprint(summary_info)
graphs = []
if insert_graph and file_exists(insert_graph):
graphs.append((insert_graph,
"Distribution of paired end insert sizes"))
return summary_info, graphs | [
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microsoft/unilm | 65f15af2a307ebb64cfb25adf54375b002e6fe8d | infoxlm/fairseq/fairseq/models/fairseq_model.py | python | BaseFairseqModel.upgrade_state_dict_named | (self, state_dict, name) | Upgrade old state dicts to work with newer code.
Args:
state_dict (dict): state dictionary to upgrade, in place
name (str): the state dict key corresponding to the current module | Upgrade old state dicts to work with newer code. | [
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] | def upgrade_state_dict_named(self, state_dict, name):
"""Upgrade old state dicts to work with newer code.
Args:
state_dict (dict): state dictionary to upgrade, in place
name (str): the state dict key corresponding to the current module
"""
assert state_dict is not None
def do_upgrade(m, prefix):
if len(prefix) > 0:
prefix += '.'
for n, c in m.named_children():
name = prefix + n
if hasattr(c, 'upgrade_state_dict_named'):
c.upgrade_state_dict_named(state_dict, name)
elif hasattr(c, 'upgrade_state_dict'):
c.upgrade_state_dict(state_dict)
do_upgrade(c, name)
do_upgrade(self, name) | [
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | ansible/roles/lib_oa_openshift/library/oc_adm_router.py | python | Router.__init__ | (self,
router_config,
verbose=False) | Constructor for OpenshiftOC
a router consists of 3 or more parts
- dc/router
- svc/router
- sa/router
- secret/router-certs
- clusterrolebinding/router-router-role | Constructor for OpenshiftOC | [
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] | def __init__(self,
router_config,
verbose=False):
''' Constructor for OpenshiftOC
a router consists of 3 or more parts
- dc/router
- svc/router
- sa/router
- secret/router-certs
- clusterrolebinding/router-router-role
'''
super(Router, self).__init__(router_config.namespace, router_config.kubeconfig, verbose)
self.config = router_config
self.verbose = verbose
self.router_parts = [{'kind': 'dc', 'name': self.config.name},
{'kind': 'svc', 'name': self.config.name},
{'kind': 'sa', 'name': self.config.config_options['service_account']['value']},
{'kind': 'secret', 'name': self.config.name + '-certs'},
{'kind': 'clusterrolebinding', 'name': 'router-' + self.config.name + '-role'},
]
self.__prepared_router = None
self.dconfig = None
self.svc = None
self._secret = None
self._serviceaccount = None
self._rolebinding = None | [
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blurstudio/cross3d | 277968d1227de740fc87ef61005c75034420eadf | cross3d/abstract/abstractcontainer.py | python | AbstractContainer.deselect | (self) | return self.setSelected(False) | Deselects the objects on this object group from the scene
:return: bool | Deselects the objects on this object group from the scene
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InsaneLife/dssm | 1d32e137654e03994f7ba6cfde52e1d47601027c | model/bert/tokenization.py | python | whitespace_tokenize | (text) | return tokens | Runs basic whitespace cleaning and splitting on a peice of text. | Runs basic whitespace cleaning and splitting on a peice of text. | [
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] | def whitespace_tokenize(text):
"""Runs basic whitespace cleaning and splitting on a peice of text."""
text = text.strip()
if not text:
return []
tokens = text.split()
return tokens | [
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krintoxi/NoobSec-Toolkit | 38738541cbc03cedb9a3b3ed13b629f781ad64f6 | NoobSecToolkit /tools/inject/thirdparty/bottle/bottle.py | python | BaseRequest.json | (self) | return None | If the ``Content-Type`` header is ``application/json``, this
property holds the parsed content of the request body. Only requests
smaller than :attr:`MEMFILE_MAX` are processed to avoid memory
exhaustion. | If the ``Content-Type`` header is ``application/json``, this
property holds the parsed content of the request body. Only requests
smaller than :attr:`MEMFILE_MAX` are processed to avoid memory
exhaustion. | [
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property holds the parsed content of the request body. Only requests
smaller than :attr:`MEMFILE_MAX` are processed to avoid memory
exhaustion. '''
if 'application/json' in self.environ.get('CONTENT_TYPE', '') \
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return json_loads(self.body.read(self.MEMFILE_MAX))
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misterch0c/shadowbroker | e3a069bea47a2c1009697941ac214adc6f90aa8d | windows/Resources/Python/Core/Lib/pipes.py | python | Template.clone | (self) | return t | t.clone() returns a new pipeline template with identical
initial state as the current one. | t.clone() returns a new pipeline template with identical
initial state as the current one. | [
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] | def clone(self):
"""t.clone() returns a new pipeline template with identical
initial state as the current one."""
t = Template()
t.steps = self.steps[:]
t.debugging = self.debugging
return t | [
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beeware/ouroboros | a29123c6fab6a807caffbb7587cf548e0c370296 | ouroboros/mailcap.py | python | parsefield | (line, i, n) | return line[start:i].strip(), i | Separate one key-value pair in a mailcap entry. | Separate one key-value pair in a mailcap entry. | [
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] | def parsefield(line, i, n):
"""Separate one key-value pair in a mailcap entry."""
start = i
while i < n:
c = line[i]
if c == ';':
break
elif c == '\\':
i = i+2
else:
i = i+1
return line[start:i].strip(), i | [
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erikdubois/Aureola | 005fb14b3cab0ba1929ebf9ac3ac68d2c6e1c0ef | shailen mc/dropbox.py | python | relpath | (path, start=curdir) | return join(*rel_list) | Return a relative version of a path | Return a relative version of a path | [
"Return",
"a",
"relative",
"version",
"of",
"a",
"path"
] | def relpath(path, start=curdir):
"""Return a relative version of a path"""
if not path:
raise ValueError("no path specified")
if type(start) is unicode:
start_list = unicode_abspath(start).split(sep)
else:
start_list = abspath(start).split(sep)
if type(path) is unicode:
path_list = unicode_abspath(path).split(sep)
else:
path_list = abspath(path).split(sep)
# Work out how much of the filepath is shared by start and path.
i = len(commonprefix([start_list, path_list]))
rel_list = [pardir] * (len(start_list)-i) + path_list[i:]
if not rel_list:
return curdir
return join(*rel_list) | [
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asciidoc-py/asciidoc-py | b40326325e5985c8b60366237eabe850b32d2176 | asciidoc/a2x.py | python | A2X.xsl_stylesheet | (self, file_name=None) | return self.asciidoc_conf_file(os.path.join('docbook-xsl', file_name)) | Return full path name of file in asciidoc docbook-xsl configuration
directory.
If an XSL file was specified with the --xsl-file option then it is
returned. | Return full path name of file in asciidoc docbook-xsl configuration
directory.
If an XSL file was specified with the --xsl-file option then it is
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'''
Return full path name of file in asciidoc docbook-xsl configuration
directory.
If an XSL file was specified with the --xsl-file option then it is
returned.
'''
if self.xsl_file is not None:
return self.xsl_file
if not file_name:
file_name = self.format + '.xsl'
return self.asciidoc_conf_file(os.path.join('docbook-xsl', file_name)) | [
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fossasia/pslab-python | bb53a334b729d0956ed9f4ce6899903f3e4868ef | pslab/bus/uart.py | python | UART.write_byte | (self, data: int) | Write a single byte to the UART bus.
Parameters
----------
data : int
Byte value to write to the UART bus. | Write a single byte to the UART bus. | [
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] | def write_byte(self, data: int):
"""Write a single byte to the UART bus.
Parameters
----------
data : int
Byte value to write to the UART bus.
"""
self._write_byte(data) | [
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biolab/orange3 | 41685e1c7b1d1babe680113685a2d44bcc9fec0b | Orange/widgets/unsupervised/owcorrespondence.py | python | ScatterPlotItem.paint | (self, painter, option, widget=None) | [] | def paint(self, painter, option, widget=None):
painter.setRenderHint(QPainter.SmoothPixmapTransform, True)
painter.setRenderHint(QPainter.Antialiasing, True)
super().paint(painter, option, widget) | [
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HSLCY/ABSA-BERT-pair | 7d238eb8c772946b9e572373c144b11151e4187f | evaluation.py | python | get_y_pred | (task_name, pred_data_dir) | return pred, score | Read file to obtain y_pred and scores. | Read file to obtain y_pred and scores. | [
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] | def get_y_pred(task_name, pred_data_dir):
"""
Read file to obtain y_pred and scores.
"""
pred=[]
score=[]
if task_name in ["sentihood_NLI_M", "sentihood_QA_M"]:
with open(pred_data_dir, "r", encoding="utf-8") as f:
s=f.readline().strip().split()
while s:
pred.append(int(s[0]))
score.append([float(s[1]),float(s[2]),float(s[3])])
s = f.readline().strip().split()
elif task_name in ["sentihood_NLI_B", "sentihood_QA_B"]:
count = 0
tmp = []
with open(pred_data_dir, "r", encoding="utf-8") as f:
s = f.readline().strip().split()
while s:
tmp.append([float(s[2])])
count += 1
if count % 3 == 0:
tmp_sum = np.sum(tmp)
t = []
for i in range(3):
t.append(tmp[i] / tmp_sum)
score.append(t)
if t[0] >= t[1] and t[0] >= t[2]:
pred.append(0)
elif t[1] >= t[0] and t[1] >= t[2]:
pred.append(1)
else:
pred.append(2)
tmp = []
s = f.readline().strip().split()
elif task_name == "sentihood_single":
count = 0
with open(pred_data_dir + "loc1_general.txt", "r", encoding="utf-8") as f1_general, \
open(pred_data_dir + "loc1_price.txt", "r", encoding="utf-8") as f1_price, \
open(pred_data_dir + "loc1_safety.txt", "r", encoding="utf-8") as f1_safety, \
open(pred_data_dir + "loc1_transit.txt", "r", encoding="utf-8") as f1_transit:
s = f1_general.readline().strip().split()
while s:
count += 1
pred.append(int(s[0]))
score.append([float(s[1]), float(s[2]), float(s[3])])
if count % 4 == 0:
s = f1_general.readline().strip().split()
if count % 4 == 1:
s = f1_price.readline().strip().split()
if count % 4 == 2:
s = f1_safety.readline().strip().split()
if count % 4 == 3:
s = f1_transit.readline().strip().split()
with open(pred_data_dir + "loc2_general.txt", "r", encoding="utf-8") as f2_general, \
open(pred_data_dir + "loc2_price.txt", "r", encoding="utf-8") as f2_price, \
open(pred_data_dir + "loc2_safety.txt", "r", encoding="utf-8") as f2_safety, \
open(pred_data_dir + "loc2_transit.txt", "r", encoding="utf-8") as f2_transit:
s = f2_general.readline().strip().split()
while s:
count += 1
pred.append(int(s[0]))
score.append([float(s[1]), float(s[2]), float(s[3])])
if count % 4 == 0:
s = f2_general.readline().strip().split()
if count % 4 == 1:
s = f2_price.readline().strip().split()
if count % 4 == 2:
s = f2_safety.readline().strip().split()
if count % 4 == 3:
s = f2_transit.readline().strip().split()
elif task_name in ["semeval_NLI_M", "semeval_QA_M"]:
with open(pred_data_dir,"r",encoding="utf-8") as f:
s=f.readline().strip().split()
while s:
pred.append(int(s[0]))
score.append([float(s[1]), float(s[2]), float(s[3]), float(s[4]), float(s[5])])
s = f.readline().strip().split()
elif task_name in ["semeval_NLI_B", "semeval_QA_B"]:
count = 0
tmp = []
with open(pred_data_dir, "r", encoding="utf-8") as f:
s = f.readline().strip().split()
while s:
tmp.append([float(s[2])])
count += 1
if count % 5 == 0:
tmp_sum = np.sum(tmp)
t = []
for i in range(5):
t.append(tmp[i] / tmp_sum)
score.append(t)
if t[0] >= t[1] and t[0] >= t[2] and t[0]>=t[3] and t[0]>=t[4]:
pred.append(0)
elif t[1] >= t[0] and t[1] >= t[2] and t[1]>=t[3] and t[1]>=t[4]:
pred.append(1)
elif t[2] >= t[0] and t[2] >= t[1] and t[2]>=t[3] and t[2]>=t[4]:
pred.append(2)
elif t[3] >= t[0] and t[3] >= t[1] and t[3]>=t[2] and t[3]>=t[4]:
pred.append(3)
else:
pred.append(4)
tmp = []
s = f.readline().strip().split()
else:
count = 0
with open(pred_data_dir+"price.txt","r",encoding="utf-8") as f_price, \
open(pred_data_dir+"anecdotes.txt", "r", encoding="utf-8") as f_anecdotes, \
open(pred_data_dir+"food.txt", "r", encoding="utf-8") as f_food, \
open(pred_data_dir+"ambience.txt", "r", encoding="utf-8") as f_ambience, \
open(pred_data_dir+"service.txt", "r", encoding="utf-8") as f_service:
s = f_price.readline().strip().split()
while s:
count += 1
pred.append(int(s[0]))
score.append([float(s[1]), float(s[2]), float(s[3]), float(s[4]), float(s[5])])
if count % 5 == 0:
s = f_price.readline().strip().split()
if count % 5 == 1:
s = f_anecdotes.readline().strip().split()
if count % 5 == 2:
s = f_food.readline().strip().split()
if count % 5 == 3:
s = f_ambience.readline().strip().split()
if count % 5 == 4:
s = f_service.readline().strip().split()
return pred, score | [
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wistbean/fxxkpython | 88e16d79d8dd37236ba6ecd0d0ff11d63143968c | vip/qyxuan/projects/Snake/venv/lib/python3.6/site-packages/pip-19.0.3-py3.6.egg/pip/_vendor/requests/cookies.py | python | RequestsCookieJar._find_no_duplicates | (self, name, domain=None, path=None) | Both ``__get_item__`` and ``get`` call this function: it's never
used elsewhere in Requests.
:param name: a string containing name of cookie
:param domain: (optional) string containing domain of cookie
:param path: (optional) string containing path of cookie
:raises KeyError: if cookie is not found
:raises CookieConflictError: if there are multiple cookies
that match name and optionally domain and path
:return: cookie.value | Both ``__get_item__`` and ``get`` call this function: it's never
used elsewhere in Requests. | [
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] | def _find_no_duplicates(self, name, domain=None, path=None):
"""Both ``__get_item__`` and ``get`` call this function: it's never
used elsewhere in Requests.
:param name: a string containing name of cookie
:param domain: (optional) string containing domain of cookie
:param path: (optional) string containing path of cookie
:raises KeyError: if cookie is not found
:raises CookieConflictError: if there are multiple cookies
that match name and optionally domain and path
:return: cookie.value
"""
toReturn = None
for cookie in iter(self):
if cookie.name == name:
if domain is None or cookie.domain == domain:
if path is None or cookie.path == path:
if toReturn is not None: # if there are multiple cookies that meet passed in criteria
raise CookieConflictError('There are multiple cookies with name, %r' % (name))
toReturn = cookie.value # we will eventually return this as long as no cookie conflict
if toReturn:
return toReturn
raise KeyError('name=%r, domain=%r, path=%r' % (name, domain, path)) | [
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zopefoundation/Zope | ea04dd670d1a48d4d5c879d3db38fc2e9b4330bb | src/OFS/PropertySheets.py | python | View.tpURL | (self) | return self.getId() | [] | def tpURL(self):
return self.getId() | [
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CouchPotato/CouchPotatoV1 | 135b3331d1b88ef645e29b76f2d4cc4a732c9232 | library/sqlalchemy/orm/session.py | python | Session.object_session | (cls, instance) | return object_session(instance) | Return the ``Session`` to which an object belongs. | Return the ``Session`` to which an object belongs. | [
"Return",
"the",
"Session",
"to",
"which",
"an",
"object",
"belongs",
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] | def object_session(cls, instance):
"""Return the ``Session`` to which an object belongs."""
return object_session(instance) | [
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facebookresearch/pytorch3d | fddd6a700fa9685c1ce2d4b266c111d7db424ecc | pytorch3d/structures/meshes.py | python | Meshes.faces_list | (self) | return self._faces_list | Get the list representation of the faces.
Returns:
list of tensors of faces of shape (F_n, 3). | Get the list representation of the faces. | [
"Get",
"the",
"list",
"representation",
"of",
"the",
"faces",
"."
] | def faces_list(self):
"""
Get the list representation of the faces.
Returns:
list of tensors of faces of shape (F_n, 3).
"""
if self._faces_list is None:
assert (
self._faces_padded is not None
), "faces_padded is required to compute faces_list."
self._faces_list = struct_utils.padded_to_list(
self._faces_padded, self.num_faces_per_mesh().tolist()
)
return self._faces_list | [
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tortoise/tortoise-orm | 5bf910a3dcd1e729106b7f0dee16aae362d35f46 | tortoise/queryset.py | python | QuerySet.select_related | (self, *fields: str) | return queryset | Return a new QuerySet instance that will select related objects.
If fields are specified, they must be ForeignKey fields and only those
related objects are included in the selection. | Return a new QuerySet instance that will select related objects. | [
"Return",
"a",
"new",
"QuerySet",
"instance",
"that",
"will",
"select",
"related",
"objects",
"."
] | def select_related(self, *fields: str) -> "QuerySet[MODEL]":
"""
Return a new QuerySet instance that will select related objects.
If fields are specified, they must be ForeignKey fields and only those
related objects are included in the selection.
"""
queryset = self._clone()
for field in fields:
queryset._select_related.add(field)
return queryset | [
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apple/ccs-calendarserver | 13c706b985fb728b9aab42dc0fef85aae21921c3 | contrib/performance/benchmarks/unbounded_recurrence_autoaccept.py | python | measure | (host, port, dtrace, attendeeCount, samples) | return _measure(
calendar, organizerSequence, events,
host, port, dtrace, samples) | [] | def measure(host, port, dtrace, attendeeCount, samples):
calendar = "unbounded-recurrence-autoaccept"
organizerSequence = 1
# An infinite stream of recurring VEVENTS to PUT to the server.
events = ((i, makeEvent(i, organizerSequence, attendeeCount)) for i in count(2))
return _measure(
calendar, organizerSequence, events,
host, port, dtrace, samples) | [
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"... | https://github.com/apple/ccs-calendarserver/blob/13c706b985fb728b9aab42dc0fef85aae21921c3/contrib/performance/benchmarks/unbounded_recurrence_autoaccept.py#L45-L54 | |||
nilearn/nilearn | 9edba4471747efacf21260bf470a346307f52706 | nilearn/plotting/img_plotting.py | python | _MNI152Template.__str__ | (self) | return "<MNI152Template>" | [] | def __str__(self):
return "<MNI152Template>" | [
"def",
"__str__",
"(",
"self",
")",
":",
"return",
"\"<MNI152Template>\""
] | https://github.com/nilearn/nilearn/blob/9edba4471747efacf21260bf470a346307f52706/nilearn/plotting/img_plotting.py#L384-L385 | |||
pypa/pipenv | b21baade71a86ab3ee1429f71fbc14d4f95fb75d | pipenv/vendor/pythonfinder/_vendor/pep514tools/_registry.py | python | get_value_from_tuple | (value, vtype) | return None | [] | def get_value_from_tuple(value, vtype):
if vtype == winreg.REG_SZ:
if '\0' in value:
return value[:value.index('\0')]
return value
return None | [
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... | https://github.com/pypa/pipenv/blob/b21baade71a86ab3ee1429f71fbc14d4f95fb75d/pipenv/vendor/pythonfinder/_vendor/pep514tools/_registry.py#L27-L32 | |||
karmab/kcli | fff2a2632841f54d9346b437821585df0ec659d7 | kvirt/providers/sampleprovider.py | python | Kbase.delete | (self, name, snapshots=False) | return {'result': 'success'} | :param name:
:param snapshots:
:return: | [] | def delete(self, name, snapshots=False):
"""
:param name:
:param snapshots:
:return:
"""
print("not implemented")
return {'result': 'success'} | [
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] | https://github.com/karmab/kcli/blob/fff2a2632841f54d9346b437821585df0ec659d7/kvirt/providers/sampleprovider.py#L262-L270 | ||
mrJean1/PyGeodesy | 7da5ca71aa3edb7bc49e219e0b8190686e1a7965 | pygeodesy/lcc.py | python | Conic.__init__ | (self, latlon0, par1, par2=None, E0=0, N0=0,
k0=1, opt3=0, name=NN, auth=NN) | New Lambert conformal conic projection.
@arg latlon0: Origin with (ellipsoidal) datum (C{LatLon}).
@arg par1: First standard parallel (C{degrees90}).
@kwarg par2: Optional, second standard parallel (C{degrees90}).
@kwarg E0: Optional, false easting (C{meter}).
@kwarg N0: Optional, false northing (C{meter}).
@kwarg k0: Optional scale factor (C{scalar}).
@kwarg opt3: Optional meridian (C{degrees180}).
@kwarg name: Optional name of the conic (C{str}).
@kwarg auth: Optional authentication authority (C{str}).
@return: A Lambert projection (L{Conic}).
@raise TypeError: Non-ellipsoidal B{C{latlon0}}.
@raise ValueError: Invalid B{C{par1}}, B{C{par2}},
B{C{E0}}, B{C{N0}}, B{C{k0}}
or B{C{opt3}}.
@example:
>>> from pygeodesy import Conic, Datums, ellipsoidalNvector
>>> ll0 = ellipsoidalNvector.LatLon(23, -96, datum=Datums.NAD27)
>>> Snyder = Conic(ll0, 33, 45, E0=0, N0=0, name='Snyder') | New Lambert conformal conic projection. | [
"New",
"Lambert",
"conformal",
"conic",
"projection",
"."
] | def __init__(self, latlon0, par1, par2=None, E0=0, N0=0,
k0=1, opt3=0, name=NN, auth=NN):
'''New Lambert conformal conic projection.
@arg latlon0: Origin with (ellipsoidal) datum (C{LatLon}).
@arg par1: First standard parallel (C{degrees90}).
@kwarg par2: Optional, second standard parallel (C{degrees90}).
@kwarg E0: Optional, false easting (C{meter}).
@kwarg N0: Optional, false northing (C{meter}).
@kwarg k0: Optional scale factor (C{scalar}).
@kwarg opt3: Optional meridian (C{degrees180}).
@kwarg name: Optional name of the conic (C{str}).
@kwarg auth: Optional authentication authority (C{str}).
@return: A Lambert projection (L{Conic}).
@raise TypeError: Non-ellipsoidal B{C{latlon0}}.
@raise ValueError: Invalid B{C{par1}}, B{C{par2}},
B{C{E0}}, B{C{N0}}, B{C{k0}}
or B{C{opt3}}.
@example:
>>> from pygeodesy import Conic, Datums, ellipsoidalNvector
>>> ll0 = ellipsoidalNvector.LatLon(23, -96, datum=Datums.NAD27)
>>> Snyder = Conic(ll0, 33, 45, E0=0, N0=0, name='Snyder')
'''
if latlon0 is not None:
_xinstanceof(_LLEB, latlon0=latlon0)
self._phi0, self._lam0 = latlon0.philam
self._par1 = Phi_(par1=par1)
self._par2 = self._par1 if par2 is None else Phi_(par2=par2)
if k0 != 1:
self._k0 = Scalar_(k0=k0)
if E0:
self._E0 = Northing(E0=E0, falsed=True)
if N0:
self._N0 = Easting(N0=N0, falsed=True)
if opt3:
self._opt3 = Lam_(opt3=opt3)
self.toDatum(latlon0.datum)._dup2(self)
self._register(Conics, name)
elif name:
self.name = name
if auth:
self._auth = str(auth) | [
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bruderstein/PythonScript | df9f7071ddf3a079e3a301b9b53a6dc78cf1208f | PythonLib/full/asynchat.py | python | async_chat._collect_incoming_data | (self, data) | [] | def _collect_incoming_data(self, data):
self.incoming.append(data) | [
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