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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | ansible/roles/lib_oa_openshift/library/oc_adm_router.py | python | SecretConfig.create_dict | (self) | assign the correct properties for a secret dict | assign the correct properties for a secret dict | [
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"secret",
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] | def create_dict(self):
''' assign the correct properties for a secret dict '''
self.data['apiVersion'] = 'v1'
self.data['kind'] = 'Secret'
self.data['type'] = self.type
self.data['metadata'] = {}
self.data['metadata']['name'] = self.name
self.data['metadata']['namespace'] = self.namespace
self.data['data'] = {}
if self.secrets:
for key, value in self.secrets.items():
self.data['data'][key] = value
if self.annotations:
self.data['metadata']['annotations'] = self.annotations | [
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/idlelib/CallTipWindow.py | python | _calltip_window | (parent) | [] | def _calltip_window(parent): # htest #
from Tkinter import Toplevel, Text, LEFT, BOTH
top = Toplevel(parent)
top.title("Test calltips")
top.geometry("200x100+%d+%d" % (parent.winfo_rootx() + 200,
parent.winfo_rooty() + 150))
text = Text(top)
text.pack(side=LEFT, fill=BOTH, expand=1)
text.insert("insert", "string.split")
top.update()
calltip = CallTip(text)
def calltip_show(event):
calltip.showtip("(s=Hello world)", "insert", "end")
def calltip_hide(event):
calltip.hidetip()
text.event_add("<<calltip-show>>", "(")
text.event_add("<<calltip-hide>>", ")")
text.bind("<<calltip-show>>", calltip_show)
text.bind("<<calltip-hide>>", calltip_hide)
text.focus_set() | [
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FederatedAI/FATE | 32540492623568ecd1afcb367360133616e02fa3 | python/federatedml/evaluation/metrics/classification_metric.py | python | BiClassPrecision.compute_metric_from_confusion_mat | (self, confusion_mat, formatted=True, impute_val=1.0) | [] | def compute_metric_from_confusion_mat(self, confusion_mat, formatted=True, impute_val=1.0):
numerator = confusion_mat['tp']
denominator = (confusion_mat['tp'] + confusion_mat['fp'])
zero_indexes = (denominator == 0)
denominator[zero_indexes] = 1
precision_scores = numerator / denominator
precision_scores[zero_indexes] = impute_val # impute_val is for prettifying when drawing pr curves
if formatted:
score_formatted = [[0, i] for i in precision_scores]
return score_formatted
else:
return precision_scores | [
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zopefoundation/Zope | ea04dd670d1a48d4d5c879d3db38fc2e9b4330bb | src/Products/PageTemplates/ZopePageTemplate.py | python | ZopePageTemplate.ZScriptHTML_tryParams | (self) | return [] | Parameters to test the script with. | Parameters to test the script with. | [
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XiaoMi/minos | 164454a0804fb7eb7e14052327bdd4d5572c2697 | client/deploy_utils.py | python | get_local_package_path_general | (path, artifact, version) | return ("%s/%s-%s.tar.gz" % (path, artifact, version)) | Get the local tarball path of the package of specified artifact and version
@param path the base path of the tarball
@param artifact the artifact of the package
@param version the version of the package
@return string the full path of the tarball
Note: This method is for internal use, users shouldn't call it directly.
Users who want to obtain the local package path should call
get_local_package_path(). | Get the local tarball path of the package of specified artifact and version | [
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"tarball",
"path",
"of",
"the",
"package",
"of",
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] | def get_local_package_path_general(path, artifact, version):
'''
Get the local tarball path of the package of specified artifact and version
@param path the base path of the tarball
@param artifact the artifact of the package
@param version the version of the package
@return string the full path of the tarball
Note: This method is for internal use, users shouldn't call it directly.
Users who want to obtain the local package path should call
get_local_package_path().
'''
return ("%s/%s-%s.tar.gz" % (path, artifact, version)) | [
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google/grr | 8ad8a4d2c5a93c92729206b7771af19d92d4f915 | grr/server/grr_response_server/gui/api_call_router_without_checks.py | python | ApiCallRouterWithoutChecks.GrantClientApproval | (self, args, context=None) | return api_user.ApiGrantClientApprovalHandler() | [] | def GrantClientApproval(self, args, context=None):
return api_user.ApiGrantClientApprovalHandler() | [
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xiadingZ/video-caption.pytorch | 0597647c9f1f756202ba7ff9898ad0a26847480f | coco-caption/pycocotools/cocoeval.py | python | COCOeval.accumulate | (self, p = None) | Accumulate per image evaluation results and store the result in self.eval
:param p: input params for evaluation
:return: None | Accumulate per image evaluation results and store the result in self.eval
:param p: input params for evaluation
:return: None | [
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'''
Accumulate per image evaluation results and store the result in self.eval
:param p: input params for evaluation
:return: None
'''
print('Accumulating evaluation results...')
tic = time.time()
if not self.evalImgs:
print('Please run evaluate() first')
# allows input customized parameters
if p is None:
p = self.params
p.catIds = p.catIds if p.useCats == 1 else [-1]
T = len(p.iouThrs)
R = len(p.recThrs)
K = len(p.catIds) if p.useCats else 1
A = len(p.areaRng)
M = len(p.maxDets)
precision = -np.ones((T,R,K,A,M)) # -1 for the precision of absent categories
recall = -np.ones((T,K,A,M))
scores = -np.ones((T,R,K,A,M))
# create dictionary for future indexing
_pe = self._paramsEval
catIds = _pe.catIds if _pe.useCats else [-1]
setK = set(catIds)
setA = set(map(tuple, _pe.areaRng))
setM = set(_pe.maxDets)
setI = set(_pe.imgIds)
# get inds to evaluate
k_list = [n for n, k in enumerate(p.catIds) if k in setK]
m_list = [m for n, m in enumerate(p.maxDets) if m in setM]
a_list = [n for n, a in enumerate(map(lambda x: tuple(x), p.areaRng)) if a in setA]
i_list = [n for n, i in enumerate(p.imgIds) if i in setI]
I0 = len(_pe.imgIds)
A0 = len(_pe.areaRng)
# retrieve E at each category, area range, and max number of detections
for k, k0 in enumerate(k_list):
Nk = k0*A0*I0
for a, a0 in enumerate(a_list):
Na = a0*I0
for m, maxDet in enumerate(m_list):
E = [self.evalImgs[Nk + Na + i] for i in i_list]
E = [e for e in E if not e is None]
if len(E) == 0:
continue
dtScores = np.concatenate([e['dtScores'][0:maxDet] for e in E])
# different sorting method generates slightly different results.
# mergesort is used to be consistent as Matlab implementation.
inds = np.argsort(-dtScores, kind='mergesort')
dtScoresSorted = dtScores[inds]
dtm = np.concatenate([e['dtMatches'][:,0:maxDet] for e in E], axis=1)[:,inds]
dtIg = np.concatenate([e['dtIgnore'][:,0:maxDet] for e in E], axis=1)[:,inds]
gtIg = np.concatenate([e['gtIgnore'] for e in E])
npig = np.count_nonzero(gtIg==0 )
if npig == 0:
continue
tps = np.logical_and( dtm, np.logical_not(dtIg) )
fps = np.logical_and(np.logical_not(dtm), np.logical_not(dtIg) )
tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float)
fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float)
for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)):
tp = np.array(tp)
fp = np.array(fp)
nd = len(tp)
rc = tp / npig
pr = tp / (fp+tp+np.spacing(1))
q = np.zeros((R,))
ss = np.zeros((R,))
if nd:
recall[t,k,a,m] = rc[-1]
else:
recall[t,k,a,m] = 0
# numpy is slow without cython optimization for accessing elements
# use python array gets significant speed improvement
pr = pr.tolist(); q = q.tolist()
for i in range(nd-1, 0, -1):
if pr[i] > pr[i-1]:
pr[i-1] = pr[i]
inds = np.searchsorted(rc, p.recThrs, side='left')
try:
for ri, pi in enumerate(inds):
q[ri] = pr[pi]
ss[ri] = dtScoresSorted[pi]
except:
pass
precision[t,:,k,a,m] = np.array(q)
scores[t,:,k,a,m] = np.array(ss)
self.eval = {
'params': p,
'counts': [T, R, K, A, M],
'date': datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
'precision': precision,
'recall': recall,
'scores': scores,
}
toc = time.time()
print('DONE (t={:0.2f}s).'.format( toc-tic)) | [
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PyHDI/veriloggen | 2382d200deabf59cfcfd741f5eba371010aaf2bb | veriloggen/core/task.py | python | Task.Integer | (self, name, width=None, dims=None, signed=False, value=None) | return t | [] | def Integer(self, name, width=None, dims=None, signed=False, value=None):
t = vtypes.Integer(width, dims, signed, value, name=name)
self.variable[name] = t
return t | [
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.10.0-0.29.0/roles/lib_openshift/library/oc_serviceaccount.py | python | Yedit.exists | (self, path, value) | return entry == value | check if value exists at path | check if value exists at path | [
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''' check if value exists at path'''
try:
entry = Yedit.get_entry(self.yaml_dict, path, self.separator)
except KeyError:
entry = None
if isinstance(entry, list):
if value in entry:
return True
return False
elif isinstance(entry, dict):
if isinstance(value, dict):
rval = False
for key, val in value.items():
if entry[key] != val:
rval = False
break
else:
rval = True
return rval
return value in entry
return entry == value | [
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binaryage/drydrop | 2f27e15befd247255d89f9120eeee44851b82c4a | dryapp/jinja2/ext.py | python | Extension.preprocess | (self, source, name, filename=None) | return source | This method is called before the actual lexing and can be used to
preprocess the source. The `filename` is optional. The return value
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"""This method is called before the actual lexing and can be used to
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Borda/pyImSegm | 7584b40a8d5bba04d3bf46f540f22b5d923e4b03 | experiments_ovary_detect/run_ovary_egg-segmentation.py | python | segment_active_contour | (img, centers) | return segm, centers, None | segmentation using acive contours
:param ndarray img: input image / segmentation
:param list(tuple(int,int)) centers: position of centres / seeds
:return tuple(ndarray, list(tuple(int,int))): resulting segmentation, updated centres | segmentation using acive contours | [
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] | def segment_active_contour(img, centers):
""" segmentation using acive contours
:param ndarray img: input image / segmentation
:param list(tuple(int,int)) centers: position of centres / seeds
:return tuple(ndarray, list(tuple(int,int))): resulting segmentation, updated centres
"""
logging.debug('segment: active_contour...')
# http://scikit-image.org/docs/dev/auto_examples/edges/plot_active_contours.html
segm = np.zeros(img.shape[:2])
img_smooth = ndimage.filters.gaussian_filter(img, 5)
center_circles, _, _ = create_circle_center(img.shape[:2], centers)
for i, snake in enumerate(center_circles):
snake = segmentation.active_contour(
img_smooth,
snake.astype(float),
alpha=0.015,
beta=10,
gamma=0.001,
w_line=0.0,
w_edge=1.0,
max_px_move=1.0,
max_iterations=2500,
convergence=0.2
)
seg = np.zeros(segm.shape, dtype=bool)
x, y = np.array(snake).transpose().tolist()
# rr, cc = draw.polygon(x, y)
seg[map(int, x), map(int, y)] = True
seg = morphology.binary_dilation(seg, selem=morphology.disk(3))
bb_area = int((max(x) - min(x)) * (max(y) - min(y)))
logging.debug('bounding box area: %d', bb_area)
seg = morphology.remove_small_holes(seg, min_size=bb_area)
segm[seg] = i + 1
return segm, centers, None | [
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out0fmemory/GoAgent-Always-Available | c4254984fea633ce3d1893fe5901debd9f22c2a9 | server/lib/google/appengine/datastore/datastore_query.py | python | Batcher.next | (self) | return self.next_batch(self.AT_LEAST_ONE) | Get the next batch. See .next_batch(). | Get the next batch. See .next_batch(). | [
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zvtvz/zvt | 054bf8a3e7a049df7087c324fa87e8effbaf5bdc | src/zvt/factors/algorithm.py | python | intersect_ranges | (range_list) | return result | [] | def intersect_ranges(range_list):
if len(range_list) == 1:
return range_list[0]
result = intersect(range_list[0], range_list[1])
for range_i in range_list[2:]:
result = intersect(result, range_i)
return result | [
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edisonlz/fastor | 342078a18363ac41d3c6b1ab29dbdd44fdb0b7b3 | base/site-packages/django/views/debug.py | python | default_urlconf | (request) | return HttpResponse(t.render(c), content_type='text/html') | Create an empty URLconf 404 error response. | Create an empty URLconf 404 error response. | [
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"Create an empty URLconf 404 error response."
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c = Context({})
return HttpResponse(t.render(c), content_type='text/html') | [
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kovidgoyal/calibre | 2b41671370f2a9eb1109b9ae901ccf915f1bd0c8 | src/calibre/ebooks/conversion/utils.py | python | HeuristicProcessor.punctuation_unwrap | (self, length, content, format) | return content | Unwraps lines based on line length and punctuation
supports a range of html markup and text files
the lookahead regex below is meant look for any non-full stop characters - punctuation
characters which can be used as a full stop should *not* be added below - e.g. ?!“”. etc
the reason for this is to prevent false positive wrapping. False positives are more
difficult to detect than false negatives during a manual review of the doc
This function intentionally leaves hyphenated content alone as that is handled by the
dehyphenate routine in a separate step | Unwraps lines based on line length and punctuation
supports a range of html markup and text files | [
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"and",
"punctuation",
"supports",
"a",
"range",
"of",
"html",
"markup",
"and",
"text",
"files"
] | def punctuation_unwrap(self, length, content, format):
'''
Unwraps lines based on line length and punctuation
supports a range of html markup and text files
the lookahead regex below is meant look for any non-full stop characters - punctuation
characters which can be used as a full stop should *not* be added below - e.g. ?!“”. etc
the reason for this is to prevent false positive wrapping. False positives are more
difficult to detect than false negatives during a manual review of the doc
This function intentionally leaves hyphenated content alone as that is handled by the
dehyphenate routine in a separate step
'''
def style_unwrap(match):
style_close = match.group('style_close')
style_open = match.group('style_open')
if style_open and style_close:
return style_close+' '+style_open
elif style_open and not style_close:
return ' '+style_open
elif not style_open and style_close:
return style_close+' '
else:
return ' '
# define the pieces of the regex
# (?<!\&\w{4});) is a semicolon not part of an entity
lookahead = "(?<=.{"+str(length)+r"}([a-zა-ჰäëïöüàèìòùáćéíĺóŕńśúýâêîôûçąężıãõñæøþðßěľščťžňďřů,:)\\IAß]|(?<!\&\w{4});))"
em_en_lookahead = "(?<=.{"+str(length)+"}[\u2013\u2014])"
soft_hyphen = "\xad"
line_ending = "\\s*(?P<style_close></(span|[iub])>)?\\s*(</(p|div)>)?"
blanklines = "\\s*(?P<up2threeblanks><(p|span|div)[^>]*>\\s*(<(p|span|div)[^>]*>\\s*</(span|p|div)>\\s*)</(span|p|div)>\\s*){0,3}\\s*"
line_opening = "<(p|div)[^>]*>\\s*(?P<style_open><(span|[iub])[^>]*>)?\\s*"
txt_line_wrap = "((\u0020|\u0009)*\n){1,4}"
if format == 'txt':
unwrap_regex = lookahead+txt_line_wrap
em_en_unwrap_regex = em_en_lookahead+txt_line_wrap
shy_unwrap_regex = soft_hyphen+txt_line_wrap
else:
unwrap_regex = lookahead+line_ending+blanklines+line_opening
em_en_unwrap_regex = em_en_lookahead+line_ending+blanklines+line_opening
shy_unwrap_regex = soft_hyphen+line_ending+blanklines+line_opening
unwrap = re.compile("%s" % unwrap_regex, re.UNICODE)
em_en_unwrap = re.compile("%s" % em_en_unwrap_regex, re.UNICODE)
shy_unwrap = re.compile("%s" % shy_unwrap_regex, re.UNICODE)
if format == 'txt':
content = unwrap.sub(' ', content)
content = em_en_unwrap.sub('', content)
content = shy_unwrap.sub('', content)
else:
content = unwrap.sub(style_unwrap, content)
content = em_en_unwrap.sub(style_unwrap, content)
content = shy_unwrap.sub(style_unwrap, content)
return content | [
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.9.40/roles/lib_vendored_deps/library/oc_pvc.py | python | Yedit.read | (self) | return contents | read from file | read from file | [
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] | def read(self):
''' read from file '''
# check if it exists
if self.filename is None or not self.file_exists():
return None
contents = None
with open(self.filename) as yfd:
contents = yfd.read()
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ray-project/ray | 703c1610348615dcb8c2d141a0c46675084660f5 | python/ray/util/sgd/torch/worker_group.py | python | WorkerGroupInterface.load_state_dict | (self, state_dict, blocking=False) | See TorchTrainer.load_state_dict. | See TorchTrainer.load_state_dict. | [
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] | def load_state_dict(self, state_dict, blocking=False):
"""See TorchTrainer.load_state_dict."""
raise NotImplementedError | [
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huntfx/MouseTracks | 4dfab6386f9461be77cb19b54c9c498d74fb4ef6 | mousetracks/utils/os/placeholders.py | python | read_env_var | (text) | return None | Detect if text is an environment variable and read it.
Returns:
Value/None if successful or not. | Detect if text is an environment variable and read it.
Returns:
Value/None if successful or not. | [
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] | def read_env_var(text):
"""Detect if text is an environment variable and read it.
Returns:
Value/None if successful or not.
"""
return None | [
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zhanlaoban/Transformers_for_Text_Classification | 5e12b21616b29e445e11fe307948e5c55084bb0e | transformers/data/processors/glue.py | python | QqpProcessor.get_dev_examples | (self, data_dir) | return self._create_examples(
self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") | See base class. | See base class. | [
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] | def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") | [
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F5Networks/f5-ansible | d61253b0980eb0feab448fcb977e3b089d49b79a | ansible_collections/f5networks/f5_modules/plugins/module_utils/common.py | python | fq_name | (partition, value, sub_path='') | return value | Returns a 'Fully Qualified' name
A BIG-IP expects most names of resources to be in a fully-qualified
form. This means that both the simple name, and the partition need
to be combined.
The Ansible modules, however, can accept (as names for several
resources) their name in the FQ format. This becomes an issue when
the FQ name and the partition are both specified as separate values.
Consider the following examples.
# Name not FQ
name: foo
partition: Common
# Name FQ
name: /Common/foo
partition: Common
This method will rectify the above situation and will, in both cases,
return the following for name.
/Common/foo
Args:
partition (string): The partition that you would want attached to
the name if the name has no partition.
value (string): The name that you want to attach a partition to.
This value will be returned unchanged if it has a partition
attached to it already.
sub_path (string): The sub path element. If defined the sub_path
will be inserted between partition and value.
This will also work on FQ names.
Returns:
string: The fully qualified name, given the input parameters. | Returns a 'Fully Qualified' name | [
"Returns",
"a",
"Fully",
"Qualified",
"name"
] | def fq_name(partition, value, sub_path=''):
"""Returns a 'Fully Qualified' name
A BIG-IP expects most names of resources to be in a fully-qualified
form. This means that both the simple name, and the partition need
to be combined.
The Ansible modules, however, can accept (as names for several
resources) their name in the FQ format. This becomes an issue when
the FQ name and the partition are both specified as separate values.
Consider the following examples.
# Name not FQ
name: foo
partition: Common
# Name FQ
name: /Common/foo
partition: Common
This method will rectify the above situation and will, in both cases,
return the following for name.
/Common/foo
Args:
partition (string): The partition that you would want attached to
the name if the name has no partition.
value (string): The name that you want to attach a partition to.
This value will be returned unchanged if it has a partition
attached to it already.
sub_path (string): The sub path element. If defined the sub_path
will be inserted between partition and value.
This will also work on FQ names.
Returns:
string: The fully qualified name, given the input parameters.
"""
if value is not None and sub_path == '':
try:
int(value)
return '/{0}/{1}'.format(partition, value)
except (ValueError, TypeError):
if not value.startswith('/'):
return '/{0}/{1}'.format(partition, value)
if value is not None and sub_path != '':
try:
int(value)
return '/{0}/{1}/{2}'.format(partition, sub_path, value)
except (ValueError, TypeError):
if value.startswith('/'):
dummy, partition, name = value.split('/')
return '/{0}/{1}/{2}'.format(partition, sub_path, name)
if not value.startswith('/'):
return '/{0}/{1}/{2}'.format(partition, sub_path, value)
return value | [
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cuthbertLab/music21 | bd30d4663e52955ed922c10fdf541419d8c67671 | music21/converter/subConverters.py | python | ConverterNoteworthy.parseData | (self, nwcData) | r'''Open Noteworthy data from a string or list
>>> nwcData = ('!NoteWorthyComposer(2.0)\n|AddStaff\n|Clef|' +
... 'Type:Treble\n|Note|Dur:Whole|Pos:1^')
>>> c = converter.subConverters.ConverterNoteworthy()
>>> c.parseData(nwcData)
>>> c.stream.show('text')
{0.0} <music21.stream.Part ...>
{0.0} <music21.stream.Measure 0 offset=0.0>
{0.0} <music21.clef.TrebleClef>
{0.0} <music21.note.Note C> | r'''Open Noteworthy data from a string or list | [
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] | def parseData(self, nwcData):
# noinspection PyShadowingNames
r'''Open Noteworthy data from a string or list
>>> nwcData = ('!NoteWorthyComposer(2.0)\n|AddStaff\n|Clef|' +
... 'Type:Treble\n|Note|Dur:Whole|Pos:1^')
>>> c = converter.subConverters.ConverterNoteworthy()
>>> c.parseData(nwcData)
>>> c.stream.show('text')
{0.0} <music21.stream.Part ...>
{0.0} <music21.stream.Measure 0 offset=0.0>
{0.0} <music21.clef.TrebleClef>
{0.0} <music21.note.Note C>
'''
from music21.noteworthy import translate as noteworthyTranslate
self.stream = noteworthyTranslate.NoteworthyTranslator().parseString(nwcData) | [
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keiffster/program-y | 8c99b56f8c32f01a7b9887b5daae9465619d0385 | src/programy/parser/pattern/nodes/base.py | python | MultiValueDict.remove | (self, key, value) | [] | def remove(self, key, value):
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if value in self[key]:
self[key].remove(value)
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ajinabraham/OWASP-Xenotix-XSS-Exploit-Framework | cb692f527e4e819b6c228187c5702d990a180043 | external/Scripting Engine/Xenotix Python Scripting Engine/Lib/lib2to3/fixer_util.py | python | ArgList | (args, lparen=LParen(), rparen=RParen()) | return node | A parenthesised argument list, used by Call() | A parenthesised argument list, used by Call() | [
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"""A parenthesised argument list, used by Call()"""
node = Node(syms.trailer, [lparen.clone(), rparen.clone()])
if args:
node.insert_child(1, Node(syms.arglist, args))
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Blosc/bloscpack | 5efdadf5b6f61e995df1817943afb9629ce28c89 | bloscpack/append.py | python | append_fp | (original_fp, new_content_fp, new_size, blosc_args=None) | return nchunks | Append from a file pointer to a file pointer.
Parameters
----------
original : file_like
the original file_pointer
new_content : str
the file pointer for the data to be appended
new_size : int
the size of the new_content
blosc_args : dict
the blosc_args
Returns
-------
nchunks_written : int
the total number of new chunks written to the file
Notes
-----
The blosc_args argument can be supplied if different blosc arguments are
desired. | Append from a file pointer to a file pointer. | [
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] | def append_fp(original_fp, new_content_fp, new_size, blosc_args=None):
""" Append from a file pointer to a file pointer.
Parameters
----------
original : file_like
the original file_pointer
new_content : str
the file pointer for the data to be appended
new_size : int
the size of the new_content
blosc_args : dict
the blosc_args
Returns
-------
nchunks_written : int
the total number of new chunks written to the file
Notes
-----
The blosc_args argument can be supplied if different blosc arguments are
desired.
"""
bloscpack_header, metadata, metadata_header, offsets = \
_read_beginning(original_fp)
checksum_impl = bloscpack_header.checksum_impl
if not offsets:
raise RuntimeError('Appending to a file without offsets '
'is not yet supported')
if blosc_args is None:
blosc_args = dict(zip(BLOSC_ARGS, [None] * len(BLOSC_ARGS)))
# handle blosc_args
if blosc_args['typesize'] is None:
if bloscpack_header.typesize == -1:
raise NonUniformTypesize('Non uniform type size, '
'can not append to file.')
else:
# use the typesize from the bloscpack header
blosc_args['typesize'] = bloscpack_header.typesize
if blosc_args['clevel'] is None:
# use the default
blosc_args['clevel'] = DEFAULT_CLEVEL
if blosc_args['shuffle'] is None:
blosc_args['shuffle'] = DEFAULT_SHUFFLE
if blosc_args['cname'] is None:
blosc_args['cname'] = DEFAULT_CNAME
_check_blosc_args(blosc_args)
offsets_pos = (BLOSCPACK_HEADER_LENGTH +
(METADATA_HEADER_LENGTH + metadata_header['max_meta_size'] +
CHECKSUMS_LOOKUP[metadata_header['meta_checksum']].size
if metadata is not None else 0))
# seek to the final offset
original_fp.seek(offsets[-1], 0)
# decompress the last chunk
compressed, blosc_header, digest = _read_compressed_chunk_fp(original_fp,
checksum_impl)
# TODO check digest
decompressed = blosc.decompress(compressed)
# figure out how many bytes we need to read to rebuild the last chunk
ultimo_length = len(decompressed)
bytes_to_read = bloscpack_header.chunk_size - ultimo_length
if new_size <= bytes_to_read:
# special case
# must squeeze data into last chunk
fill_up = new_content_fp.read(new_size)
# seek back to the position of the original last chunk
original_fp.seek(offsets[-1], 0)
# write the chunk that has been filled up
compressed = _compress_chunk_str(decompressed + fill_up, blosc_args)
digest = checksum_impl(compressed)
_write_compressed_chunk(original_fp, compressed, digest)
# return 0 to indicate that no new chunks have been written
# build the new header
bloscpack_header.last_chunk += new_size
# create the new header
raw_bloscpack_header = bloscpack_header.encode()
original_fp.seek(0)
original_fp.write(raw_bloscpack_header)
return 0
# figure out what is left over
new_new_size = new_size - bytes_to_read
# read those bytes
fill_up = new_content_fp.read(bytes_to_read)
# figure out how many chunks we will need
nchunks, chunk_size, last_chunk_size = \
calculate_nchunks(new_new_size,
chunk_size=bloscpack_header.chunk_size)
# make sure that we actually have that kind of space
if nchunks > bloscpack_header.max_app_chunks:
raise NotEnoughSpace('not enough space')
# seek back to the position of the original last chunk
original_fp.seek(offsets[-1], 0)
# write the chunk that has been filled up
compressed = _compress_chunk_str(decompressed + fill_up, blosc_args)
digest = checksum_impl(compressed)
_write_compressed_chunk(original_fp, compressed, digest)
# append to the original file, again original_fp should be adequately
# positioned
sink = CompressedFPSink(original_fp)
sink.configure(blosc_args, bloscpack_header)
# allocate new offsets
sink.offset_storage = list(itertools.repeat(-1, nchunks))
# read from the new input file, new_content_fp should be adequately
# positioned
source = PlainFPSource(new_content_fp)
source.configure(chunk_size, last_chunk_size, nchunks)
# read, compress, write loop
for i, chunk in enumerate(source):
log.debug("Handle chunk '%d' %s" % (i,'(last)' if i == nchunks -1
else ''))
compressed = _compress_chunk_str(chunk, blosc_args)
sink.put(i, compressed)
# build the new header
bloscpack_header.last_chunk = last_chunk_size
bloscpack_header.nchunks += nchunks
bloscpack_header.max_app_chunks -= nchunks
# create the new header
raw_bloscpack_header = bloscpack_header.encode()
original_fp.seek(0)
original_fp.write(raw_bloscpack_header)
# write the new offsets, but only those that changed
original_fp.seek(offsets_pos)
# FIXME: write only those that changed
_write_offsets(sink.output_fp, offsets + sink.offset_storage)
return nchunks | [
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CheckPointSW/Karta | b845928487b50a5b41acd532ae0399177a4356aa | src/thumbs_up/analyzers/analyzer.py | python | Analyzer.cleanPtr | (self, ptr_ea) | return ptr_ea | Clean a pointer from the code type metadata.
Args:
ptr_ea (int): code type annotated effective address
Return Value:
dest address, stripped from the code type annotations | Clean a pointer from the code type metadata. | [
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"""Clean a pointer from the code type metadata.
Args:
ptr_ea (int): code type annotated effective address
Return Value:
dest address, stripped from the code type annotations
"""
# By default, there is only the default code type
return ptr_ea | [
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yoyoyohamapi/mit-ml | bafefeec9a30b80bf0c29c246e517519d69b0f20 | neural_network/nn.py | python | gradientDescent | (Thetas, X, y, alpha, theLambda) | return J, Thetas | 梯度下降
Args:
X 样本
y 标签
alpha 学习率
theLambda 正规化参数
Returns:
J 预测代价
Thetas 更新后的各层权值矩阵 | 梯度下降 | [
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] | def gradientDescent(Thetas, X, y, alpha, theLambda):
"""梯度下降
Args:
X 样本
y 标签
alpha 学习率
theLambda 正规化参数
Returns:
J 预测代价
Thetas 更新后的各层权值矩阵
"""
# 样本数，特征数
m, n = X.shape
# 前向传播计算各个神经元的激活值
a = fp(Thetas, X)
# 反向传播计算梯度增量
D = bp(Thetas, a, y, theLambda)
# 计算预测代价
J = computeCost(Thetas,y,theLambda,a=a)
# 更新权值
Thetas = updateThetas(m, Thetas, D, alpha, theLambda)
if np.isnan(J):
J = np.inf
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ladybug-tools/honeybee-legacy | bd62af4862fe022801fb87dbc8794fdf1dff73a9 | src/Honeybee_Honeybee.py | python | EPZone.getCurrentLoads | (self, returnDictionary = False, component = None) | [] | def getCurrentLoads(self, returnDictionary = False, component = None):
# assign the default is there is no schedule assigned
if not self.isLoadsAssigned:
self.assignLoadsBasedOnProgram(component)
if not returnDictionary:
report = " Internal Loads [SI]:\n" + \
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"numOfPeoplePerArea: " + "%.6f"%self.numOfPeoplePerArea + "\n" + \
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return report
else:
loadsDict = {"EquipmentsLoadPerArea" : "%.4f"%self.equipmentLoadPerArea,
"infiltrationRatePerArea" : "%.4f"%self.infiltrationRatePerArea,
"lightingDensityPerArea" : "%.4f"%self.lightingDensityPerArea,
"numOfPeoplePerArea" : "%.4f"%self.numOfPeoplePerArea,
"ventilationPerArea" : "%.4f"%self.ventilationPerArea,
"ventilationPerPerson" : "%.4f"%self.ventilationPerPerson}
return loadsDict | [
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AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/lib/django-0.96/django/core/handlers/wsgi.py | python | safe_copyfileobj | (fsrc, fdst, length=16*1024, size=0) | A version of shutil.copyfileobj that will not read more than 'size' bytes.
This makes it safe from clients sending more than CONTENT_LENGTH bytes of
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tp4a/teleport | 1fafd34f1f775d2cf80ea4af6e44468d8e0b24ad | server/www/packages/packages-darwin/x64/ldap3/strategy/mockBase.py | python | MockBaseStrategy.evaluate_filter_node | (self, node, candidates) | After evaluation each 2 sets are added to each MATCH node, one for the matched object and one for unmatched object.
The unmatched object set is needed if a superior node is a NOT that reverts the evaluation. The BOOLEAN nodes mix the sets
returned by the MATCH nodes | After evaluation each 2 sets are added to each MATCH node, one for the matched object and one for unmatched object.
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node.matched = set()
node.unmatched = set()
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self.evaluate_filter_node(element, candidates)
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first_element = node.elements[0]
node.matched.update(first_element.matched)
node.unmatched.update(first_element.unmatched)
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node.matched.intersection_update(element.matched)
node.unmatched.intersection_update(element.unmatched)
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node.matched.update(element.matched)
node.unmatched.update(element.unmatched)
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node.matched = node.elements[0].unmatched
node.unmatched = node.elements[0].matched
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attr_name = node.assertion['attr']
attr_value = node.assertion['value']
for candidate in candidates:
if attr_name in self.connection.server.dit[candidate]:
for value in self.connection.server.dit[candidate][attr_name]:
if value.isdigit() and attr_value.isdigit(): # int comparison
if int(value) >= int(attr_value):
node.matched.add(candidate)
else:
node.unmatched.add(candidate)
else:
if to_unicode(value, SERVER_ENCODING).lower() >= to_unicode(attr_value, SERVER_ENCODING).lower(): # case insensitive string comparison
node.matched.add(candidate)
else:
node.unmatched.add(candidate)
elif node.tag == MATCH_LESS_OR_EQUAL:
attr_name = node.assertion['attr']
attr_value = node.assertion['value']
for candidate in candidates:
if attr_name in self.connection.server.dit[candidate]:
for value in self.connection.server.dit[candidate][attr_name]:
if value.isdigit() and attr_value.isdigit(): # int comparison
if int(value) <= int(attr_value):
node.matched.add(candidate)
else:
node.unmatched.add(candidate)
else:
if to_unicode(value, SERVER_ENCODING).lower() <= to_unicode(attr_value, SERVER_ENCODING).lower(): # case insentive string comparison
node.matched.add(candidate)
else:
node.unmatched.add(candidate)
elif node.tag == MATCH_EXTENSIBLE:
self.connection.last_error = 'Extensible match not allowed in Mock strategy'
if log_enabled(ERROR):
log(ERROR, '<%s> for <%s>', self.connection.last_error, self.connection)
raise LDAPDefinitionError(self.connection.last_error)
elif node.tag == MATCH_PRESENT:
attr_name = node.assertion['attr']
for candidate in candidates:
if attr_name in self.connection.server.dit[candidate]:
node.matched.add(candidate)
else:
node.unmatched.add(candidate)
elif node.tag == MATCH_SUBSTRING:
attr_name = node.assertion['attr']
# rebuild the original substring filter
if 'initial' in node.assertion and node.assertion['initial'] is not None:
substring_filter = re.escape(to_unicode(node.assertion['initial'], SERVER_ENCODING))
else:
substring_filter = ''
if 'any' in node.assertion and node.assertion['any'] is not None:
for middle in node.assertion['any']:
substring_filter += '.*' + re.escape(to_unicode(middle, SERVER_ENCODING))
if 'final' in node.assertion and node.assertion['final'] is not None:
substring_filter += '.*' + re.escape(to_unicode(node.assertion['final'], SERVER_ENCODING))
if substring_filter and not node.assertion.get('any', None) and not node.assertion.get('final', None): # only initial, adds .*
substring_filter += '.*'
regex_filter = re.compile(substring_filter, flags=re.UNICODE | re.IGNORECASE) # unicode AND ignorecase
for candidate in candidates:
if attr_name in self.connection.server.dit[candidate]:
for value in self.connection.server.dit[candidate][attr_name]:
if regex_filter.match(to_unicode(value, SERVER_ENCODING)):
node.matched.add(candidate)
else:
node.unmatched.add(candidate)
else:
node.unmatched.add(candidate)
elif node.tag == MATCH_EQUAL or node.tag == MATCH_APPROX:
attr_name = node.assertion['attr']
attr_value = node.assertion['value']
for candidate in candidates:
# if attr_name in self.connection.server.dit[candidate] and attr_value in self.connection.server.dit[candidate][attr_name]:
if attr_name in self.connection.server.dit[candidate] and self.equal(candidate, attr_name, attr_value):
node.matched.add(candidate)
else:
node.unmatched.add(candidate) | [
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allenai/allennlp | a3d71254fcc0f3615910e9c3d48874515edf53e0 | allennlp/confidence_checks/task_checklists/utils.py | python | toggle_punctuation | (data: str) | return ret | If `data` contains any punctuation, it is removed.
Otherwise, a `.` is added to the string.
Returns a list of strings.
Eg.
`data` = "This was great!"
Returns ["This was great", "This was great."]
`data` = "The movie was good"
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"""
If `data` contains any punctuation, it is removed.
Otherwise, a `.` is added to the string.
Returns a list of strings.
Eg.
`data` = "This was great!"
Returns ["This was great", "This was great."]
`data` = "The movie was good"
Returns ["The movie was good."]
"""
s = strip_punctuation(data)
ret = []
if s != data:
ret.append(s)
if s + "." != data:
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return ret | [
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google-research/language | 61fa7260ac7d690d11ef72ca863e45a37c0bdc80 | language/totto/totto_parent_eval.py | python | _table_reader | (table_file) | Yields tables from the table file.
Tables are parsed into a list of tuples with tokenized entries.
Args:
table_file: String filename. | Yields tables from the table file. | [
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"""Yields tables from the table file.
Tables are parsed into a list of tuples with tokenized entries.
Args:
table_file: String filename.
"""
with io.open(table_file, encoding="utf-8") as f:
for line in f:
entries = line.lower().split("\t")
# pylint: disable=g-complex-comprehension
table = [[
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yield table | [
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aws/aws-cli | d697e0ed79fca0f853ce53efe1f83ee41a478134 | awscli/customizations/s3/transferconfig.py | python | RuntimeConfig.build_config | (self, **kwargs) | return runtime_config | Create and convert a runtime config dictionary.
This method will merge and convert S3 runtime configuration
data into a single dictionary that can then be passed to classes
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"""Create and convert a runtime config dictionary.
This method will merge and convert S3 runtime configuration
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that use this runtime config.
:param kwargs: Any key in the ``DEFAULTS`` dict.
:return: A dictionary of the merged and converted values.
"""
runtime_config = DEFAULTS.copy()
if kwargs:
runtime_config.update(kwargs)
self._convert_human_readable_sizes(runtime_config)
self._convert_human_readable_rates(runtime_config)
self._validate_config(runtime_config)
return runtime_config | [
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Chaffelson/nipyapi | d3b186fd701ce308c2812746d98af9120955e810 | nipyapi/nifi/models/versioned_remote_process_group.py | python | VersionedRemoteProcessGroup.yield_duration | (self) | return self._yield_duration | Gets the yield_duration of this VersionedRemoteProcessGroup.
When yielding, this amount of time must elapse before the remote process group is scheduled again.
:return: The yield_duration of this VersionedRemoteProcessGroup.
:rtype: str | Gets the yield_duration of this VersionedRemoteProcessGroup.
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"""
Gets the yield_duration of this VersionedRemoteProcessGroup.
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:return: The yield_duration of this VersionedRemoteProcessGroup.
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return self._yield_duration | [
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beeware/colosseum | cbf974be54fd7f6fddbe7285704cfaf7a866c5c5 | src/colosseum/dimensions.py | python | Box.absolute_border_box_bottom | (self) | return (
self.__origin_top + self._content_top
+ self.content_height
+ self.padding_bottom
+ self.border_bottom_width
) | [] | def absolute_border_box_bottom(self):
return (
self.__origin_top + self._content_top
+ self.content_height
+ self.padding_bottom
+ self.border_bottom_width
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kubernetes-client/python | 47b9da9de2d02b2b7a34fbe05afb44afd130d73a | kubernetes/client/models/v1_cron_job_status.py | python | V1CronJobStatus.__init__ | (self, active=None, last_schedule_time=None, last_successful_time=None, local_vars_configuration=None) | V1CronJobStatus - a model defined in OpenAPI | V1CronJobStatus - a model defined in OpenAPI | [
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"""V1CronJobStatus - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._active = None
self._last_schedule_time = None
self._last_successful_time = None
self.discriminator = None
if active is not None:
self.active = active
if last_schedule_time is not None:
self.last_schedule_time = last_schedule_time
if last_successful_time is not None:
self.last_successful_time = last_successful_time | [
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... | https://github.com/kubernetes-client/python/blob/47b9da9de2d02b2b7a34fbe05afb44afd130d73a/kubernetes/client/models/v1_cron_job_status.py#L47-L63 | ||
MongoEngine/django-mongoengine | e8a75e8e5860545ecfbadaf1b1285495022bd7cb | django_mongoengine/mongo_auth/managers.py | python | get_user_document | () | return getattr(module, name[dot + 1:]) | Get the user document class used for authentication.
This is the class defined in settings.MONGOENGINE_USER_DOCUMENT, which
defaults to `mongoengine.django.auth.User`. | Get the user document class used for authentication. | [
"Get",
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"user",
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"for",
"authentication",
"."
] | def get_user_document():
"""Get the user document class used for authentication.
This is the class defined in settings.MONGOENGINE_USER_DOCUMENT, which
defaults to `mongoengine.django.auth.User`.
"""
name = MONGOENGINE_USER_DOCUMENT
dot = name.rindex('.')
module = import_module(name[:dot])
return getattr(module, name[dot + 1:]) | [
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jkehler/awslambda-psycopg2 | c7b1b2f6382bbe5893d95c4e7f4b5ffdf05ab3b4 | psycopg2-3.8/extras.py | python | ReplicationCursor.drop_replication_slot | (self, slot_name) | Drop streaming replication slot. | Drop streaming replication slot. | [
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"replication",
"slot",
"."
] | def drop_replication_slot(self, slot_name):
"""Drop streaming replication slot."""
command = "DROP_REPLICATION_SLOT %s" % quote_ident(slot_name, self)
self.execute(command) | [
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bruderstein/PythonScript | df9f7071ddf3a079e3a301b9b53a6dc78cf1208f | PythonLib/full/bdb.py | python | user_call | (self, frame, argument_list) | Called if we might stop in a function. | Called if we might stop in a function. | [
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"""Called if we might stop in a function."""
pass | [
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ZhaoJ9014/High-Performance-Face-Recognition | aa8b240e34856d4a038198c8f73390d94ced2c6d | src/Pre-_and_post-processing/FaceAlign-Resize-w-Padding.PyTorch/box_utils.py | python | _preprocess | (img) | return img | Preprocessing step before feeding the network.
Arguments:
img: a float numpy array of shape [h, w, c].
Returns:
a float numpy array of shape [1, c, h, w]. | Preprocessing step before feeding the network. | [
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"."
] | def _preprocess(img):
"""Preprocessing step before feeding the network.
Arguments:
img: a float numpy array of shape [h, w, c].
Returns:
a float numpy array of shape [1, c, h, w].
"""
img = img.transpose((2, 0, 1))
img = np.expand_dims(img, 0)
img = (img - 127.5)*0.0078125
return img | [
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exaile/exaile | a7b58996c5c15b3aa7b9975ac13ee8f784ef4689 | xl/metadata/_base.py | python | BaseFormat.load | (self) | Loads the tags from the file. | Loads the tags from the file. | [
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"""
Loads the tags from the file.
"""
if self.MutagenType:
try:
self.mutagen = self.MutagenType(self.loc)
except Exception:
raise NotReadable | [
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lovelylain/pyctp | fd304de4b50c4ddc31a4190b1caaeb5dec66bc5d | option/ctp/ApiStruct.py | python | TransferQryDetailReq.__init__ | (self, FutureAccount='') | [] | def __init__(self, FutureAccount=''):
self.FutureAccount = 'AccountID' | [
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Komodo/KomodoEdit | 61edab75dce2bdb03943b387b0608ea36f548e8e | contrib/paramiko/paramiko/proxy.py | python | ProxyCommand.__init__ | (self, command_line) | Create a new CommandProxy instance. The instance created by this
class can be passed as an argument to the `.Transport` class.
:param str command_line:
the command that should be executed and used as the proxy. | Create a new CommandProxy instance. The instance created by this
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"""
Create a new CommandProxy instance. The instance created by this
class can be passed as an argument to the `.Transport` class.
:param str command_line:
the command that should be executed and used as the proxy.
"""
self.cmd = shlsplit(command_line)
self.process = Popen(self.cmd, stdin=PIPE, stdout=PIPE, stderr=PIPE,
bufsize=0)
self.timeout = None | [
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nlpinaction/learning-nlp | d59fd75bdbbc0f4d1ef0d741065ab86639608924 | chapter-10/seq2seq/main.py | python | Seq2seq.data_iter | (self, train_src, train_targets, batches, sample_num) | return {self.model.encoder_inputs: batch_inputs,
self.model.encoder_inputs_length: batch_inputs_length,
self.model.decoder_targets: batch_targets,
self.model.decoder_targets_length: batch_targets_length, } | 获取batch
最大长度为每个batch中句子的最大长度
并将数据作转换:
[batch_size, time_steps] -> [time_steps, batch_size] | 获取batch
最大长度为每个batch中句子的最大长度
并将数据作转换:
[batch_size, time_steps] -> [time_steps, batch_size] | [
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''' 获取batch
最大长度为每个batch中句子的最大长度
并将数据作转换:
[batch_size, time_steps] -> [time_steps, batch_size]
'''
batch_inputs = []
batch_targets = []
batch_inputs_length = []
batch_targets_length = []
# 随机样本
shuffle = np.random.randint(0, sample_num, batches)
en_max_seq_length = max([len(train_src[i]) for i in shuffle])
de_max_seq_length = max([len(train_targets[i]) for i in shuffle])
for index in shuffle:
_en = train_src[index]
inputs_batch_major = np.zeros(
shape=[en_max_seq_length], dtype=np.int32) # == PAD
for seq in range(len(_en)):
inputs_batch_major[seq] = _en[seq]
batch_inputs.append(inputs_batch_major)
batch_inputs_length.append(len(_en))
_de = train_targets[index]
inputs_batch_major = np.zeros(
shape=[de_max_seq_length], dtype=np.int32) # == PAD
for seq in range(len(_de)):
inputs_batch_major[seq] = _de[seq]
batch_targets.append(inputs_batch_major)
batch_targets_length.append(len(_de))
batch_inputs = np.array(batch_inputs).swapaxes(0, 1)
batch_targets = np.array(batch_targets).swapaxes(0, 1)
return {self.model.encoder_inputs: batch_inputs,
self.model.encoder_inputs_length: batch_inputs_length,
self.model.decoder_targets: batch_targets,
self.model.decoder_targets_length: batch_targets_length, } | [
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tensorflow/graphics | 86997957324bfbdd85848daae989b4c02588faa0 | tensorflow_graphics/rendering/opengl/rasterization_backend.py | python | rasterize | (vertices: type_alias.TensorLike,
triangles: type_alias.TensorLike,
view_projection_matrices: type_alias.TensorLike,
image_size: Tuple[int, int],
enable_cull_face: bool,
num_layers: int,
name: str = "rasterization_backend_rasterize") | Rasterizes the scene.
This rasterizer estimates which triangle is associated with each pixel using
OpenGL.
Note:
In the following, A1 to An are optional batch dimensions which must be
broadcast compatible for inputs `vertices` and `view_projection_matrices`.
Args:
vertices: A tensor of shape `[batch, num_vertices, 3]` containing batches
vertices, each defined by a 3D point.
triangles: A tensor of shape `[num_triangles, 3]` each associated with 3
vertices from `scene_vertices`
view_projection_matrices: A tensor of shape `[batch, 4, 4]` containing
batches of view projection matrices
image_size: An tuple of integers (width, height) containing the dimensions
in pixels of the rasterized image.
enable_cull_face: A boolean, which will enable BACK face culling when True
and no face culling when False. Default is True.
num_layers: Number of depth layers to render. Not supported by current
backend yet, but exists for interface compatibility.
name: A name for this op. Defaults to "rasterization_backend_rasterize".
Returns:
A Framebuffer containing the rasterized values: barycentrics, triangle_id,
foreground_mask, vertex_ids. Returned Tensors have shape
[batch, num_layers, height, width, channels]
Note: triangle_id contains the triangle id value for each pixel in the
output image. For pixels within the mesh, this is the integer value in the
range [0, num_vertices] from triangles. For vertices outside the mesh this
is 0; 0 can either indicate belonging to triangle 0, or being outside the
mesh. This ensures all returned triangle ids will validly index into the
vertex array, enabling the use of tf.gather with indices from this tensor.
The barycentric coordinates can be used to determine pixel validity instead.
See framebuffer.py for a description of the Framebuffer fields. | Rasterizes the scene. | [
"Rasterizes",
"the",
"scene",
"."
] | def rasterize(vertices: type_alias.TensorLike,
triangles: type_alias.TensorLike,
view_projection_matrices: type_alias.TensorLike,
image_size: Tuple[int, int],
enable_cull_face: bool,
num_layers: int,
name: str = "rasterization_backend_rasterize") -> fb.Framebuffer:
"""Rasterizes the scene.
This rasterizer estimates which triangle is associated with each pixel using
OpenGL.
Note:
In the following, A1 to An are optional batch dimensions which must be
broadcast compatible for inputs `vertices` and `view_projection_matrices`.
Args:
vertices: A tensor of shape `[batch, num_vertices, 3]` containing batches
vertices, each defined by a 3D point.
triangles: A tensor of shape `[num_triangles, 3]` each associated with 3
vertices from `scene_vertices`
view_projection_matrices: A tensor of shape `[batch, 4, 4]` containing
batches of view projection matrices
image_size: An tuple of integers (width, height) containing the dimensions
in pixels of the rasterized image.
enable_cull_face: A boolean, which will enable BACK face culling when True
and no face culling when False. Default is True.
num_layers: Number of depth layers to render. Not supported by current
backend yet, but exists for interface compatibility.
name: A name for this op. Defaults to "rasterization_backend_rasterize".
Returns:
A Framebuffer containing the rasterized values: barycentrics, triangle_id,
foreground_mask, vertex_ids. Returned Tensors have shape
[batch, num_layers, height, width, channels]
Note: triangle_id contains the triangle id value for each pixel in the
output image. For pixels within the mesh, this is the integer value in the
range [0, num_vertices] from triangles. For vertices outside the mesh this
is 0; 0 can either indicate belonging to triangle 0, or being outside the
mesh. This ensures all returned triangle ids will validly index into the
vertex array, enabling the use of tf.gather with indices from this tensor.
The barycentric coordinates can be used to determine pixel validity instead.
See framebuffer.py for a description of the Framebuffer fields.
"""
with tf.name_scope(name):
if num_layers != 1:
raise ValueError("OpenGL rasterizer only supports single layer.")
vertices = tf.convert_to_tensor(value=vertices)
triangles = tf.convert_to_tensor(value=triangles)
view_projection_matrices = tf.convert_to_tensor(
value=view_projection_matrices)
shape.check_static(
tensor=vertices,
tensor_name="vertices",
has_rank=3,
has_dim_equals=((-1, 3)))
shape.check_static(
tensor=triangles,
tensor_name="triangles",
has_rank=2,
has_dim_equals=((-1, 3)))
shape.check_static(
tensor=view_projection_matrices,
tensor_name="view_projection_matrices",
has_rank=3,
has_dim_equals=((-1, 4), (-2, 4)))
shape.compare_batch_dimensions(
tensors=(vertices, view_projection_matrices),
tensor_names=("vertices", "view_projection_matrices"),
last_axes=(-3, -3),
broadcast_compatible=True)
geometry = tf.gather(vertices, triangles, axis=-2)
# Extract batch size in order to make sure it is preserved after `gather`
# operation.
batch_size = _dim_value(vertices.shape[0])
rasterized = render_ops.rasterize(
num_points=geometry.shape[-3],
alpha_clear=0.0,
enable_cull_face=enable_cull_face,
variable_names=("view_projection_matrix", "triangular_mesh"),
variable_kinds=("mat", "buffer"),
variable_values=(view_projection_matrices,
tf.reshape(geometry, shape=[batch_size, -1])),
output_resolution=image_size,
vertex_shader=vertex_shader,
geometry_shader=geometry_shader,
fragment_shader=fragment_shader)
triangle_index = tf.cast(rasterized[..., 0], tf.int32)
# Slicing of the tensor will result in all batch dimensions being
# `None` for tensorflow graph mode, therefore we have to fix it in order to
# have explicit shape.
width, height = image_size
triangle_index = tf.reshape(triangle_index,
[batch_size, num_layers, height, width, 1])
barycentric_coordinates = rasterized[..., 1:3]
barycentric_coordinates = tf.concat(
(barycentric_coordinates, 1.0 - barycentric_coordinates[..., 0:1] -
barycentric_coordinates[..., 1:2]),
axis=-1)
barycentric_coordinates = tf.reshape(
barycentric_coordinates, [batch_size, num_layers, height, width, 3])
mask = rasterized[..., 3]
mask = tf.reshape(mask, [batch_size, num_layers, height, width, 1])
barycentric_coordinates = mask * barycentric_coordinates
vertex_ids = tf.gather(triangles, triangle_index[..., 0])
# Stop gradient for tensors coming out of custom op in order to avoid
# confusing Tensorflow that they are differentiable.
barycentric_coordinates = tf.stop_gradient(barycentric_coordinates)
mask = tf.stop_gradient(mask)
return fb.Framebuffer(
foreground_mask=mask,
triangle_id=triangle_index,
vertex_ids=vertex_ids,
barycentrics=fb.RasterizedAttribute(
value=barycentric_coordinates, d_dx=None, d_dy=None)) | [
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omz/PythonistaAppTemplate | f560f93f8876d82a21d108977f90583df08d55af | PythonistaAppTemplate/PythonistaKit.framework/pylib/site-packages/xhtml2pdf/util.py | python | getSize | (value, relative=0, base=None, default=0.0) | Converts strings to standard sizes.
That is the function taking a string of CSS size ('12pt', '1cm' and so on)
and converts it into a float in a standard unit (in our case, points).
>>> getSize('12pt')
12.0
>>> getSize('1cm')
28.346456692913385 | Converts strings to standard sizes.
That is the function taking a string of CSS size ('12pt', '1cm' and so on)
and converts it into a float in a standard unit (in our case, points). | [
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Converts strings to standard sizes.
That is the function taking a string of CSS size ('12pt', '1cm' and so on)
and converts it into a float in a standard unit (in our case, points).
>>> getSize('12pt')
12.0
>>> getSize('1cm')
28.346456692913385
"""
try:
original = value
if value is None:
return relative
elif type(value) is types.FloatType:
return value
elif isinstance(value, int):
return float(value)
elif type(value) in (types.TupleType, types.ListType):
value = "".join(value)
value = str(value).strip().lower().replace(",", ".")
if value[-2:] == 'cm':
return float(value[:-2].strip()) * cm
elif value[-2:] == 'mm':
return float(value[:-2].strip()) * mm # 1mm = 0.1cm
elif value[-2:] == 'in':
return float(value[:-2].strip()) * inch # 1pt == 1/72inch
elif value[-2:] == 'inch':
return float(value[:-4].strip()) * inch # 1pt == 1/72inch
elif value[-2:] == 'pt':
return float(value[:-2].strip())
elif value[-2:] == 'pc':
return float(value[:-2].strip()) * 12.0 # 1pc == 12pt
elif value[-2:] == 'px':
# XXX W3C says, use 96pdi
# http://www.w3.org/TR/CSS21/syndata.html#length-units
return float(value[:-2].strip()) * dpi96
elif value[-1:] == 'i': # 1pt == 1/72inch
return float(value[:-1].strip()) * inch
elif value in ("none", "0", "auto"):
return 0.0
elif relative:
if value[-2:] == 'em': # XXX
# 1em = 1 * fontSize
return float(value[:-2].strip()) * relative
elif value[-2:] == 'ex': # XXX
# 1ex = 1/2 fontSize
return float(value[:-2].strip()) * (relative / 2.0)
elif value[-1:] == '%':
# 1% = (fontSize * 1) / 100
return (relative * float(value[:-1].strip())) / 100.0
elif value in ("normal", "inherit"):
return relative
elif value in _relativeSizeTable:
if base:
return max(MIN_FONT_SIZE, base * _relativeSizeTable[value])
return max(MIN_FONT_SIZE, relative * _relativeSizeTable[value])
elif value in _absoluteSizeTable:
if base:
return max(MIN_FONT_SIZE, base * _absoluteSizeTable[value])
return max(MIN_FONT_SIZE, relative * _absoluteSizeTable[value])
else:
return max(MIN_FONT_SIZE, relative * float(value))
try:
value = float(value)
except:
log.warn("getSize: Not a float %r", value)
return default # value = 0
return max(0, value)
except Exception:
log.warn("getSize %r %r", original, relative, exc_info=1)
return default | [
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"... | https://github.com/omz/PythonistaAppTemplate/blob/f560f93f8876d82a21d108977f90583df08d55af/PythonistaAppTemplate/PythonistaKit.framework/pylib/site-packages/xhtml2pdf/util.py#L211-L283 | ||
hakril/PythonForWindows | 61e027a678d5b87aa64fcf8a37a6661a86236589 | windows/winproxy/apis/kernel32.py | python | FindNextChangeNotification | (hChangeHandle) | return FindNextChangeNotification.ctypes_function(hChangeHandle) | [] | def FindNextChangeNotification(hChangeHandle):
return FindNextChangeNotification.ctypes_function(hChangeHandle) | [
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GoogleCloudPlatform/cloudml-samples | efddc4a9898127e55edc0946557aca4bfaf59705 | cloudml-template/examples/classification/census/trainer/model.py | python | _update_optimizer | (args) | return tf.train.AdamOptimizer(learning_rate=learning_rate) | Create an optimizer with an update learning rate.
Arg:
args: experiment parameters
Returns:
Optimizer | Create an optimizer with an update learning rate.
Arg:
args: experiment parameters
Returns:
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"""Create an optimizer with an update learning rate.
Arg:
args: experiment parameters
Returns:
Optimizer
"""
# decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps)
# See: https://www.tensorflow.org/api_docs/python/tf/train/exponential_decay
learning_rate = tf.train.exponential_decay(
learning_rate=args.learning_rate,
global_step=tf.train.get_global_step(),
decay_steps=args.train_steps,
decay_rate=args.learning_rate_decay_factor
)
tf.summary.scalar('learning_rate', learning_rate)
# By default, AdamOptimizer is used. You can change the type of the optimizer.
return tf.train.AdamOptimizer(learning_rate=learning_rate) | [
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JacquesLucke/animation_nodes | b1e3ace8dcb0a771fd882fc3ac4e490b009fa0d1 | animation_nodes/execution/code_generator.py | python | getGlobalizeStatement | (nodes, variables) | return "global " + ", ".join(socketNames) | [] | def getGlobalizeStatement(nodes, variables):
socketNames = [variables[socket] for socket in iterUnlinkedSockets(nodes) if socket.dataType != "Node Control"]
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openedx/edx-platform | 68dd185a0ab45862a2a61e0f803d7e03d2be71b5 | lms/djangoapps/verify_student/utils.py | python | submit_request_to_ss | (user_verification, copy_id_photo_from) | Submit our verification attempt to Software Secure for validation.
Submits the task to software secure and If the task creation fails,
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] | def submit_request_to_ss(user_verification, copy_id_photo_from):
"""
Submit our verification attempt to Software Secure for validation.
Submits the task to software secure and If the task creation fails,
set the verification status to "must_retry".
"""
try:
send_request_to_ss_for_user.delay(
user_verification_id=user_verification.id, copy_id_photo_from=copy_id_photo_from
)
except Exception as error: # pylint: disable=broad-except
log.error(
"Software Secure submit request %r failed, result: %s", user_verification.user.username, str(error)
)
user_verification.mark_must_retry() | [
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princewen/leetcode_python | 79e6e760e4d81824c96903e6c996630c24d01932 | sort_by_myself/easy/其他/58_length_of_last_word.py | python | Solution.lengthOfLastWord | (self, s) | return length | :type s: str
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return 0
length = 0
for i in range(len(s) - 1, -1, -1):
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length = length + 1
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openvinotoolkit/open_model_zoo | 5a4232f6c35b4916eb6e8750141f4e45761237ef | tools/accuracy_checker/openvino/tools/accuracy_checker/launcher/tf_lite_launcher.py | python | TFLiteLauncher.predict | (self, inputs, metadata=None, **kwargs) | return results | Args:
inputs: dictionary where keys are input layers names and values are data for them.
metadata: metadata of input representations
Returns:
raw data from network. | Args:
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metadata: metadata of input representations
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metadata: metadata of input representations
Returns:
raw data from network.
"""
results = []
for dataset_input in inputs:
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self._interpreter.invoke()
res = {output['name']: self._interpreter.get_tensor(output['index']) for output in self._output_details}
results.append(res)
if metadata is not None:
for meta_ in metadata:
meta_['input_shape'] = self.inputs_info_for_meta()
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kubernetes-client/python | 47b9da9de2d02b2b7a34fbe05afb44afd130d73a | kubernetes/client/models/v1_job_condition.py | python | V1JobCondition.to_dict | (self) | return result | Returns the model properties as a dict | Returns the model properties as a dict | [
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"""Returns the model properties as a dict"""
result = {}
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value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
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elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
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else:
result[attr] = value
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trakt/Plex-Trakt-Scrobbler | aeb0bfbe62fad4b06c164f1b95581da7f35dce0b | Trakttv.bundle/Contents/Libraries/FreeBSD/i386/ucs2/cryptography/x509/base.py | python | CertificateBuilder.not_valid_after | (self, time) | return CertificateBuilder(
self._issuer_name, self._subject_name,
self._public_key, self._serial_number, self._not_valid_before,
time, self._extensions
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"""
Sets the certificate expiration time.
"""
if not isinstance(time, datetime.datetime):
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if self._not_valid_after is not None:
raise ValueError('The not valid after may only be set once.')
time = _convert_to_naive_utc_time(time)
if time <= _UNIX_EPOCH:
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if (self._not_valid_before is not None and
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raise ValueError(
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return CertificateBuilder(
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gramps-project/gramps | 04d4651a43eb210192f40a9f8c2bad8ee8fa3753 | gramps/plugins/lib/libgedcom.py | python | GedcomParser.__repo_email | (self, line, state) | @param line: The current line in GedLine format
@type line: GedLine
@param state: The current state
@type state: CurrentState | [] | def __repo_email(self, line, state):
"""
@param line: The current line in GedLine format
@type line: GedLine
@param state: The current state
@type state: CurrentState
"""
url = Url()
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python/cpython | e13cdca0f5224ec4e23bdd04bb3120506964bc8b | Lib/mailbox.py | python | _ProxyFile.__init__ | (self, f, pos=None) | Initialize a _ProxyFile. | Initialize a _ProxyFile. | [
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AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/lib/django-1.3/django/template/defaultfilters.py | python | center | (value, arg) | return value.center(int(arg)) | Centers the value in a field of a given width. | Centers the value in a field of a given width. | [
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] | def center(value, arg):
"""Centers the value in a field of a given width."""
return value.center(int(arg)) | [
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CedricGuillemet/Imogen | ee417b42747ed5b46cb11b02ef0c3630000085b3 | bin/Lib/ftplib.py | python | ftpcp | (source, sourcename, target, targetname = '', type = 'I') | Copy file from one FTP-instance to another. | Copy file from one FTP-instance to another. | [
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] | def ftpcp(source, sourcename, target, targetname = '', type = 'I'):
'''Copy file from one FTP-instance to another.'''
if not targetname:
targetname = sourcename
type = 'TYPE ' + type
source.voidcmd(type)
target.voidcmd(type)
sourcehost, sourceport = parse227(source.sendcmd('PASV'))
target.sendport(sourcehost, sourceport)
# RFC 959: the user must "listen" [...] BEFORE sending the
# transfer request.
# So: STOR before RETR, because here the target is a "user".
treply = target.sendcmd('STOR ' + targetname)
if treply[:3] not in {'125', '150'}:
raise error_proto # RFC 959
sreply = source.sendcmd('RETR ' + sourcename)
if sreply[:3] not in {'125', '150'}:
raise error_proto # RFC 959
source.voidresp()
target.voidresp() | [
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twilio/twilio-python | 6e1e811ea57a1edfadd5161ace87397c563f6915 | twilio/rest/autopilot/v1/assistant/model_build.py | python | ModelBuildList.get | (self, sid) | return ModelBuildContext(self._version, assistant_sid=self._solution['assistant_sid'], sid=sid, ) | Constructs a ModelBuildContext
:param sid: The unique string that identifies the resource
:returns: twilio.rest.autopilot.v1.assistant.model_build.ModelBuildContext
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"""
Constructs a ModelBuildContext
:param sid: The unique string that identifies the resource
:returns: twilio.rest.autopilot.v1.assistant.model_build.ModelBuildContext
:rtype: twilio.rest.autopilot.v1.assistant.model_build.ModelBuildContext
"""
return ModelBuildContext(self._version, assistant_sid=self._solution['assistant_sid'], sid=sid, ) | [
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ruotianluo/Image_Captioning_AI_Challenger | ad230614efb8b964b4ced49a3622f4585d4de1cc | scripts/prepro_labels.py | python | encode_captions | (imgs, params, wtoi) | return L, label_start_ix, label_end_ix, label_length | encode all captions into one large array, which will be 1-indexed.
also produces label_start_ix and label_end_ix which store 1-indexed
and inclusive (Lua-style) pointers to the first and last caption for
each image in the dataset. | encode all captions into one large array, which will be 1-indexed.
also produces label_start_ix and label_end_ix which store 1-indexed
and inclusive (Lua-style) pointers to the first and last caption for
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"""
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also produces label_start_ix and label_end_ix which store 1-indexed
and inclusive (Lua-style) pointers to the first and last caption for
each image in the dataset.
"""
max_length = params['max_length']
N = len(imgs)
M = sum(len(img['final_captions']) for img in imgs) # total number of captions
label_arrays = []
label_start_ix = np.zeros(N, dtype='uint32') # note: these will be one-indexed
label_end_ix = np.zeros(N, dtype='uint32')
label_length = np.zeros(M, dtype='uint32')
caption_counter = 0
counter = 1
for i,img in enumerate(imgs):
n = len(img['final_captions'])
assert n > 0, 'error: some image has no captions'
Li = np.zeros((n, max_length), dtype='uint32')
for j,s in enumerate(img['final_captions']):
label_length[caption_counter] = min(max_length, len(s)) # record the length of this sequence
caption_counter += 1
for k,w in enumerate(s):
if k < max_length:
Li[j,k] = wtoi[w]
# note: word indices are 1-indexed, and captions are padded with zeros
label_arrays.append(Li)
label_start_ix[i] = counter
label_end_ix[i] = counter + n - 1
counter += n
L = np.concatenate(label_arrays, axis=0) # put all the labels together
assert L.shape[0] == M, 'lengths don\'t match? that\'s weird'
assert np.all(label_length > 0), 'error: some caption had no words?'
print('encoded captions to array of size ', L.shape)
return L, label_start_ix, label_end_ix, label_length | [
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tobegit3hub/deep_image_model | 8a53edecd9e00678b278bb10f6fb4bdb1e4ee25e | java_predict_client/src/main/proto/tensorflow/python/ops/math_ops.py | python | reduce_all | (input_tensor, reduction_indices=None, keep_dims=False,
name=None) | return gen_math_ops._all(input_tensor, _ReductionDims(input_tensor,
reduction_indices),
keep_dims, name=name) | Computes the "logical and" of elements across dimensions of a tensor.
Reduces `input_tensor` along the dimensions given in `reduction_indices`.
Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions
are retained with length 1.
If `reduction_indices` has no entries, all dimensions are reduced, and a
tensor with a single element is returned.
For example:
```python
# 'x' is [[True, True]
# [False, False]]
tf.reduce_all(x) ==> False
tf.reduce_all(x, 0) ==> [False, False]
tf.reduce_all(x, 1) ==> [True, False]
```
Args:
input_tensor: The boolean tensor to reduce.
reduction_indices: The dimensions to reduce. If `None` (the default),
reduces all dimensions.
keep_dims: If true, retains reduced dimensions with length 1.
name: A name for the operation (optional).
Returns:
The reduced tensor. | Computes the "logical and" of elements across dimensions of a tensor. | [
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Reduces `input_tensor` along the dimensions given in `reduction_indices`.
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name: A name for the operation (optional).
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icometrix/dicom2nifti | 60eca00a79c32a80e9c5909b56434db96c438b60 | dicom2nifti/settings.py | python | enable_validate_instance_number | () | Enable the slice increment validation again (DEFAULT ENABLED) | Enable the slice increment validation again (DEFAULT ENABLED) | [
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Enable the slice increment validation again (DEFAULT ENABLED)
"""
global validate_instance_number
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bigboNed3/bert_serving | 44d33920da6888cf91cb72e6c7b27c7b0c7d8815 | run_classifier.py | python | MrpcProcessor.get_dev_examples | (self, data_dir) | return self._create_examples(
self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") | See base class. | See base class. | [
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"""See base class."""
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LukeB42/Emissary | 31629a8baedc91a9b60c551a01b2b45372b9a8c7 | emissary/resources/feedgroups.py | python | FeedGroupArticles.get | (self, groupname) | return make_response(request.url, query) | Retrieve articles by feedgroup. | Retrieve articles by feedgroup. | [
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"by",
"feedgroup",
"."
] | def get(self, groupname):
"""
Retrieve articles by feedgroup.
"""
key = auth()
# Summon the group or 404.
fg = FeedGroup.query.filter(and_(FeedGroup.key == key, FeedGroup.name == groupname)).first()
if not fg: restful.abort(404)
parser = restful.reqparse.RequestParser()
parser.add_argument("page", type=int, default=1)
parser.add_argument("per_page", type=int, default=10)
parser.add_argument("content", type=bool, default=None)
args = parser.parse_args()
if args.content == True:
query = Article.query.filter(
and_(Article.feed.has(group=fg), Article.content != None))\
.order_by(desc(Article.created)).paginate(args.page, args.per_page)
response = make_response(request.url, query)
# for doc in response['data']:
# if not doc['content_available']:
# response['data'].remove(doc)
# return response
if args.content == False:
query = Article.query.filter(
and_(Article.feed.has(group=fg), Article.content == None))\
.order_by(desc(Article.created)).paginate(args.page, args.per_page)
return make_response(request.url, query)
query = Article.query.filter(
Article.feed.has(group=fg))\
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return make_response(request.url, query) | [
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tomplus/kubernetes_asyncio | f028cc793e3a2c519be6a52a49fb77ff0b014c9b | kubernetes_asyncio/client/api/networking_v1_api.py | python | NetworkingV1Api.delete_collection_namespaced_ingress | (self, namespace, **kwargs) | return self.delete_collection_namespaced_ingress_with_http_info(namespace, **kwargs) | delete_collection_namespaced_ingress # noqa: E501
delete collection of Ingress # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_collection_namespaced_ingress(namespace, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: object name and auth scope, such as for teams and projects (required)
:param str pretty: If 'true', then the output is pretty printed.
:param str _continue: The continue option should be set when retrieving more results from the server. Since this value is server defined, clients may only use the continue value from a previous query result with identical query parameters (except for the value of continue) and the server may reject a continue value it does not recognize. If the specified continue value is no longer valid whether due to expiration (generally five to fifteen minutes) or a configuration change on the server, the server will respond with a 410 ResourceExpired error together with a continue token. If the client needs a consistent list, it must restart their list without the continue field. Otherwise, the client may send another list request with the token received with the 410 error, the server will respond with a list starting from the next key, but from the latest snapshot, which is inconsistent from the previous list results - objects that are created, modified, or deleted after the first list request will be included in the response, as long as their keys are after the \"next key\". This field is not supported when watch is true. Clients may start a watch from the last resourceVersion value returned by the server and not miss any modifications.
:param str dry_run: When present, indicates that modifications should not be persisted. An invalid or unrecognized dryRun directive will result in an error response and no further processing of the request. Valid values are: - All: all dry run stages will be processed
:param str field_selector: A selector to restrict the list of returned objects by their fields. Defaults to everything.
:param int grace_period_seconds: The duration in seconds before the object should be deleted. Value must be non-negative integer. The value zero indicates delete immediately. If this value is nil, the default grace period for the specified type will be used. Defaults to a per object value if not specified. zero means delete immediately.
:param str label_selector: A selector to restrict the list of returned objects by their labels. Defaults to everything.
:param int limit: limit is a maximum number of responses to return for a list call. If more items exist, the server will set the `continue` field on the list metadata to a value that can be used with the same initial query to retrieve the next set of results. Setting a limit may return fewer than the requested amount of items (up to zero items) in the event all requested objects are filtered out and clients should only use the presence of the continue field to determine whether more results are available. Servers may choose not to support the limit argument and will return all of the available results. If limit is specified and the continue field is empty, clients may assume that no more results are available. This field is not supported if watch is true. The server guarantees that the objects returned when using continue will be identical to issuing a single list call without a limit - that is, no objects created, modified, or deleted after the first request is issued will be included in any subsequent continued requests. This is sometimes referred to as a consistent snapshot, and ensures that a client that is using limit to receive smaller chunks of a very large result can ensure they see all possible objects. If objects are updated during a chunked list the version of the object that was present at the time the first list result was calculated is returned.
:param bool orphan_dependents: Deprecated: please use the PropagationPolicy, this field will be deprecated in 1.7. Should the dependent objects be orphaned. If true/false, the \"orphan\" finalizer will be added to/removed from the object's finalizers list. Either this field or PropagationPolicy may be set, but not both.
:param str propagation_policy: Whether and how garbage collection will be performed. Either this field or OrphanDependents may be set, but not both. The default policy is decided by the existing finalizer set in the metadata.finalizers and the resource-specific default policy. Acceptable values are: 'Orphan' - orphan the dependents; 'Background' - allow the garbage collector to delete the dependents in the background; 'Foreground' - a cascading policy that deletes all dependents in the foreground.
:param str resource_version: resourceVersion sets a constraint on what resource versions a request may be served from. See https://kubernetes.io/docs/reference/using-api/api-concepts/#resource-versions for details. Defaults to unset
:param str resource_version_match: resourceVersionMatch determines how resourceVersion is applied to list calls. It is highly recommended that resourceVersionMatch be set for list calls where resourceVersion is set See https://kubernetes.io/docs/reference/using-api/api-concepts/#resource-versions for details. Defaults to unset
:param int timeout_seconds: Timeout for the list/watch call. This limits the duration of the call, regardless of any activity or inactivity.
:param V1DeleteOptions body:
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: V1Status
If the method is called asynchronously,
returns the request thread. | delete_collection_namespaced_ingress # noqa: E501 | [
"delete_collection_namespaced_ingress",
"#",
"noqa",
":",
"E501"
] | def delete_collection_namespaced_ingress(self, namespace, **kwargs): # noqa: E501
"""delete_collection_namespaced_ingress # noqa: E501
delete collection of Ingress # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_collection_namespaced_ingress(namespace, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: object name and auth scope, such as for teams and projects (required)
:param str pretty: If 'true', then the output is pretty printed.
:param str _continue: The continue option should be set when retrieving more results from the server. Since this value is server defined, clients may only use the continue value from a previous query result with identical query parameters (except for the value of continue) and the server may reject a continue value it does not recognize. If the specified continue value is no longer valid whether due to expiration (generally five to fifteen minutes) or a configuration change on the server, the server will respond with a 410 ResourceExpired error together with a continue token. If the client needs a consistent list, it must restart their list without the continue field. Otherwise, the client may send another list request with the token received with the 410 error, the server will respond with a list starting from the next key, but from the latest snapshot, which is inconsistent from the previous list results - objects that are created, modified, or deleted after the first list request will be included in the response, as long as their keys are after the \"next key\". This field is not supported when watch is true. Clients may start a watch from the last resourceVersion value returned by the server and not miss any modifications.
:param str dry_run: When present, indicates that modifications should not be persisted. An invalid or unrecognized dryRun directive will result in an error response and no further processing of the request. Valid values are: - All: all dry run stages will be processed
:param str field_selector: A selector to restrict the list of returned objects by their fields. Defaults to everything.
:param int grace_period_seconds: The duration in seconds before the object should be deleted. Value must be non-negative integer. The value zero indicates delete immediately. If this value is nil, the default grace period for the specified type will be used. Defaults to a per object value if not specified. zero means delete immediately.
:param str label_selector: A selector to restrict the list of returned objects by their labels. Defaults to everything.
:param int limit: limit is a maximum number of responses to return for a list call. If more items exist, the server will set the `continue` field on the list metadata to a value that can be used with the same initial query to retrieve the next set of results. Setting a limit may return fewer than the requested amount of items (up to zero items) in the event all requested objects are filtered out and clients should only use the presence of the continue field to determine whether more results are available. Servers may choose not to support the limit argument and will return all of the available results. If limit is specified and the continue field is empty, clients may assume that no more results are available. This field is not supported if watch is true. The server guarantees that the objects returned when using continue will be identical to issuing a single list call without a limit - that is, no objects created, modified, or deleted after the first request is issued will be included in any subsequent continued requests. This is sometimes referred to as a consistent snapshot, and ensures that a client that is using limit to receive smaller chunks of a very large result can ensure they see all possible objects. If objects are updated during a chunked list the version of the object that was present at the time the first list result was calculated is returned.
:param bool orphan_dependents: Deprecated: please use the PropagationPolicy, this field will be deprecated in 1.7. Should the dependent objects be orphaned. If true/false, the \"orphan\" finalizer will be added to/removed from the object's finalizers list. Either this field or PropagationPolicy may be set, but not both.
:param str propagation_policy: Whether and how garbage collection will be performed. Either this field or OrphanDependents may be set, but not both. The default policy is decided by the existing finalizer set in the metadata.finalizers and the resource-specific default policy. Acceptable values are: 'Orphan' - orphan the dependents; 'Background' - allow the garbage collector to delete the dependents in the background; 'Foreground' - a cascading policy that deletes all dependents in the foreground.
:param str resource_version: resourceVersion sets a constraint on what resource versions a request may be served from. See https://kubernetes.io/docs/reference/using-api/api-concepts/#resource-versions for details. Defaults to unset
:param str resource_version_match: resourceVersionMatch determines how resourceVersion is applied to list calls. It is highly recommended that resourceVersionMatch be set for list calls where resourceVersion is set See https://kubernetes.io/docs/reference/using-api/api-concepts/#resource-versions for details. Defaults to unset
:param int timeout_seconds: Timeout for the list/watch call. This limits the duration of the call, regardless of any activity or inactivity.
:param V1DeleteOptions body:
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: V1Status
If the method is called asynchronously,
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kwargs['_return_http_data_only'] = True
return self.delete_collection_namespaced_ingress_with_http_info(namespace, **kwargs) | [
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hyperledger/fabric-sdk-py | 8ee33a8981887e37950dc0f36a7ec63b3a5ba5c3 | hfc/fabric_ca/caservice.py | python | ca_service | (target=DEFAULT_CA_ENDPOINT,
ca_certs_path=None, crypto=ecies(), ca_name="") | return CAService(target, ca_certs_path, crypto, ca_name) | Create ca service
:param target: url (Default value = DEFAULT_CA_ENDPOINT)
:param ca_certs_path: certs path (Default value = None)
:param crypto: crypto (Default value = ecies())
:param ca_name: CA name
:return: ca service instance (Default value = "") | Create ca service | [
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"service"
] | def ca_service(target=DEFAULT_CA_ENDPOINT,
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:param target: url (Default value = DEFAULT_CA_ENDPOINT)
:param ca_certs_path: certs path (Default value = None)
:param crypto: crypto (Default value = ecies())
:param ca_name: CA name
:return: ca service instance (Default value = "")
"""
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metabrainz/picard | 535bf8c7d9363ffc7abb3f69418ec11823c38118 | picard/file.py | python | File.can_edit_tags | (self) | return True | Return if this object supports tag editing. | Return if this object supports tag editing. | [
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seanbell/opensurfaces | 7f3e987560faa62cd37f821760683ccd1e053c7c | server/common/geom.py | python | triangle_segment_intersects | (t0, t1, t2, p0, p1) | return True | Return whether line p0,p1 intersects triangle t0,t1,t2. Containment
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# if all 3 vertices are on the same side of the line, there is no
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rossant/galry | 6201fa32fb5c9ef3cea700cc22caf52fb69ebe31 | galry/scene.py | python | SceneCreator.__init__ | (self, constrain_ratio=False,) | Initialize the scene. | Initialize the scene. | [
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benedekrozemberczki/littleballoffur | dbfec92c0ead8c5da134c9aead7987c7a36234b4 | littleballoffur/backend.py | python | NetworkXBackEnd.get_neighbors | (self, graph: NXGraph, node: int) | return [node for node in graph.neighbors(node)] | Given a graph and node return the neighbors. | Given a graph and node return the neighbors. | [
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minerllabs/minerl | 0123527c334c96ebb3f0cf313df1552fa4302691 | minerl/herobraine/hero/handlers/agent/observations/pov.py | python | POVObservation.to_string | (self) | return 'pov' | [] | def to_string(self):
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] | https://github.com/minerllabs/minerl/blob/0123527c334c96ebb3f0cf313df1552fa4302691/minerl/herobraine/hero/handlers/agent/observations/pov.py#L16-L17 | |||
pymedusa/Medusa | 1405fbb6eb8ef4d20fcca24c32ddca52b11f0f38 | ext/tvdbapiv2/models/user_ratings_data.py | python | UserRatingsData.to_dict | (self) | return result | Returns the model properties as a dict | Returns the model properties as a dict | [
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] | def to_dict(self):
"""
Returns the model properties as a dict
"""
result = {}
for attr, _ in iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
else:
result[attr] = value
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pyqt/examples | 843bb982917cecb2350b5f6d7f42c9b7fb142ec1 | src/pyqt-official/tools/customcompleter/customcompleter.py | python | TextEdit.setCompleter | (self, c) | [] | def setCompleter(self, c):
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OpenNMT/OpenNMT-py | 4815f07fcd482af9a1fe1d3b620d144197178bc5 | onmt/inputters/corpus.py | python | build_sub_vocab | (corpora, transforms, opts, n_sample, stride, offset) | return sub_counter_src, sub_counter_tgt, sub_counter_src_feats | Build vocab on (strided) subpart of the data. | Build vocab on (strided) subpart of the data. | [
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"""Build vocab on (strided) subpart of the data."""
sub_counter_src = Counter()
sub_counter_tgt = Counter()
sub_counter_src_feats = defaultdict(Counter)
datasets_iterables = build_corpora_iters(
corpora, transforms, opts.data,
skip_empty_level=opts.skip_empty_level,
stride=stride, offset=offset)
for c_name, c_iter in datasets_iterables.items():
for i, item in enumerate(c_iter):
maybe_example = DatasetAdapter._process(item, is_train=True)
if maybe_example is None:
if opts.dump_samples:
build_sub_vocab.queues[c_name][offset].put("blank")
continue
src_line, tgt_line = (maybe_example['src']['src'],
maybe_example['tgt']['tgt'])
src_line_pretty = src_line
for feat_name, feat_line in maybe_example["src"].items():
if feat_name not in ["src", "src_original"]:
sub_counter_src_feats[feat_name].update(
feat_line.split(' '))
if opts.dump_samples:
src_line_pretty = append_features_to_example(
src_line_pretty, feat_line)
sub_counter_src.update(src_line.split(' '))
sub_counter_tgt.update(tgt_line.split(' '))
if opts.dump_samples:
build_sub_vocab.queues[c_name][offset].put(
(i, src_line_pretty, tgt_line))
if n_sample > 0 and ((i+1) * stride + offset) >= n_sample:
if opts.dump_samples:
build_sub_vocab.queues[c_name][offset].put("break")
break
if opts.dump_samples:
build_sub_vocab.queues[c_name][offset].put("break")
return sub_counter_src, sub_counter_tgt, sub_counter_src_feats | [
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rytilahti/python-miio | b6e53dd16fac77915426e7592e2528b78ef65190 | miio/integrations/vacuum/roborock/vacuum_cli.py | python | find | (vac: RoborockVacuum) | Find the robot. | Find the robot. | [
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"""Find the robot."""
click.echo("Sending find the robot calls.")
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Tautulli/Tautulli | 2410eb33805aaac4bd1c5dad0f71e4f15afaf742 | lib/jaraco/classes/properties.py | python | NonDataProperty.__get__ | (self, obj, objtype=None) | return self.fget(obj) | [] | def __get__(self, obj, objtype=None):
if obj is None:
return self
return self.fget(obj) | [
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softlayer/softlayer-python | cdef7d63c66413197a9a97b0414de9f95887a82a | SoftLayer/CLI/image/detail.py | python | cli | (env, identifier) | Get details for an image. | Get details for an image. | [
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"""Get details for an image."""
image_mgr = SoftLayer.ImageManager(env.client)
image_id = helpers.resolve_id(image_mgr.resolve_ids, identifier, 'image')
image = image_mgr.get_image(image_id, mask=image_mod.DETAIL_MASK)
children_images = image.get('children')
total_size = utils.lookup(image, 'firstChild', 'blockDevicesDiskSpaceTotal') or 0
table = formatting.KeyValueTable(['name', 'value'])
table.align['name'] = 'r'
table.align['value'] = 'l'
table.add_row(['id', image['id']])
table.add_row(['global_identifier',
image.get('globalIdentifier', formatting.blank())])
table.add_row(['name', image['name'].strip()])
table.add_row(['status', formatting.FormattedItem(
utils.lookup(image, 'status', 'keyname'),
utils.lookup(image, 'status', 'name'),
)])
table.add_row([
'active_transaction',
formatting.listing(_get_transaction_groups(children_images), separator=','),
])
table.add_row(['account', image.get('accountId', formatting.blank())])
table.add_row(['visibility',
image_mod.PUBLIC_TYPE if image['publicFlag']
else image_mod.PRIVATE_TYPE])
table.add_row(['type',
formatting.FormattedItem(
utils.lookup(image, 'imageType', 'keyName'),
utils.lookup(image, 'imageType', 'name'),
)])
table.add_row(['flex', image.get('flexImageFlag')])
table.add_row(['note', image.get('note')])
table.add_row(['created', image.get('createDate')])
table.add_row(['total_size', formatting.b_to_gb(total_size)])
table.add_row(['datacenters', _get_datacenter_table(children_images)])
env.fout(table) | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/prometheus_client/metrics.py | python | MetricWrapperBase.labels | (self, *labelvalues, **labelkwargs) | Return the child for the given labelset.
All metrics can have labels, allowing grouping of related time series.
Taking a counter as an example:
from prometheus_client import Counter
c = Counter('my_requests_total', 'HTTP Failures', ['method', 'endpoint'])
c.labels('get', '/').inc()
c.labels('post', '/submit').inc()
Labels can also be provided as keyword arguments:
from prometheus_client import Counter
c = Counter('my_requests_total', 'HTTP Failures', ['method', 'endpoint'])
c.labels(method='get', endpoint='/').inc()
c.labels(method='post', endpoint='/submit').inc()
See the best practices on [naming](http://prometheus.io/docs/practices/naming/)
and [labels](http://prometheus.io/docs/practices/instrumentation/#use-labels). | Return the child for the given labelset. | [
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] | def labels(self, *labelvalues, **labelkwargs):
'''Return the child for the given labelset.
All metrics can have labels, allowing grouping of related time series.
Taking a counter as an example:
from prometheus_client import Counter
c = Counter('my_requests_total', 'HTTP Failures', ['method', 'endpoint'])
c.labels('get', '/').inc()
c.labels('post', '/submit').inc()
Labels can also be provided as keyword arguments:
from prometheus_client import Counter
c = Counter('my_requests_total', 'HTTP Failures', ['method', 'endpoint'])
c.labels(method='get', endpoint='/').inc()
c.labels(method='post', endpoint='/submit').inc()
See the best practices on [naming](http://prometheus.io/docs/practices/naming/)
and [labels](http://prometheus.io/docs/practices/instrumentation/#use-labels).
'''
if not self._labelnames:
raise ValueError('No label names were set when constructing %s' % self)
if self._labelvalues:
raise ValueError('%s already has labels set (%s); can not chain calls to .labels()' % (
self,
dict(zip(self._labelnames, self._labelvalues))
))
if labelvalues and labelkwargs:
raise ValueError("Can't pass both *args and **kwargs")
if labelkwargs:
if sorted(labelkwargs) != sorted(self._labelnames):
raise ValueError('Incorrect label names')
labelvalues = tuple(unicode(labelkwargs[l]) for l in self._labelnames)
else:
if len(labelvalues) != len(self._labelnames):
raise ValueError('Incorrect label count')
labelvalues = tuple(unicode(l) for l in labelvalues)
with self._lock:
if labelvalues not in self._metrics:
self._metrics[labelvalues] = self.__class__(
self._name,
documentation=self._documentation,
labelnames=self._labelnames,
unit=self._unit,
labelvalues=labelvalues,
**self._kwargs
)
return self._metrics[labelvalues] | [
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/benchmarks/src/benchmarks/sympy/doc/ext/docscrape.py | python | NumpyDocString._parse_see_also | (self, content) | return items | func_name : Descriptive text
continued text
another_func_name : Descriptive text
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name, role = parse_item_name(name)
items.append((name, list(rest), role))
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current_func = None
rest = []
for line in content:
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m = self._name_rgx.match(line)
if m and line[m.end():].strip().startswith(':'):
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current_func, line = line[:m.end()], line[m.end():]
rest = [line.split(':', 1)[1].strip()]
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rest = []
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current_func = None
if ',' in line:
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if func.strip():
push_item(func, [])
elif line.strip():
current_func = line
elif current_func is not None:
rest.append(line.strip())
push_item(current_func, rest)
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/sympy/physics/vector/dyadic.py | python | Dyadic.__xor__ | (self, other) | return ol | For a cross product in the form: Dyadic x Vector.
Parameters
==========
other : Vector
The Vector that we are crossing this Dyadic with
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, outer, cross
>>> N = ReferenceFrame('N')
>>> d = outer(N.x, N.x)
>>> cross(d, N.y)
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Parameters
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other : Vector
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Examples
========
>>> from sympy.physics.vector import ReferenceFrame, outer, cross
>>> N = ReferenceFrame('N')
>>> d = outer(N.x, N.x)
>>> cross(d, N.y)
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"""
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other = _check_vector(other)
ol = Dyadic(0)
for i, v in enumerate(self.args):
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return ol | [
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daoluan/decode-Django | d46a858b45b56de48b0355f50dd9e45402d04cfd | Django-1.5.1/django/contrib/localflavor/is_/forms.py | python | ISIdNumberField._format | (self, value) | return smart_text(value[:6]+'-'+value[6:]) | Takes in the value in canonical form and returns it in the common
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aws-samples/aws-kube-codesuite | ab4e5ce45416b83bffb947ab8d234df5437f4fca | src/dateutil/rrule.py | python | rruleset.rrule | (self, rrule) | Include the given :py:class:`rrule` instance in the recurrence set
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linxid/Machine_Learning_Study_Path | 558e82d13237114bbb8152483977806fc0c222af | Machine Learning In Action/Chapter8-Regression/venv/Lib/site-packages/pip-9.0.1-py3.6.egg/pip/_vendor/ipaddress.py | python | IPv4Address.__init__ | (self, address) | Args:
address: A string or integer representing the IP
Additionally, an integer can be passed, so
IPv4Address('192.0.2.1') == IPv4Address(3221225985).
or, more generally
IPv4Address(int(IPv4Address('192.0.2.1'))) ==
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Raises:
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address: A string or integer representing the IP
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or, more generally
IPv4Address(int(IPv4Address('192.0.2.1'))) ==
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Raises:
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"""
# Efficient constructor from integer.
if isinstance(address, _compat_int_types):
self._check_int_address(address)
self._ip = address
return
# Constructing from a packed address
if isinstance(address, bytes):
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AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/lib/django-1.3/django/contrib/gis/gdal/field.py | python | Field.as_double | (self) | return capi.get_field_as_double(self._feat, self._index) | Retrieves the Field's value as a double (float). | Retrieves the Field's value as a double (float). | [
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misterch0c/shadowbroker | e3a069bea47a2c1009697941ac214adc6f90aa8d | windows/Resources/Python/Core/Lib/compiler/transformer.py | python | Transformer.com_augassign | (self, node) | Return node suitable for lvalue of augmented assignment
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aio-libs/aiokafka | ccbcc25e9cd7dd5a980c175197f5315e0953e87c | aiokafka/producer/message_accumulator.py | python | MessageAccumulator.add_batch | (self, builder, tp, timeout) | Add BatchBuilder to queue by topic-partition.
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if self._closed:
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youtify/youtify | 82cbc4a4ca6283f14f7179d4aeba30ed1ee1fea8 | dropbox/oauth.py | python | OAuthDataStore.lookup_consumer | (self, key) | -> OAuthConsumer. | -> OAuthConsumer. | [
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python/mypy | 17850b3bd77ae9efb5d21f656c4e4e05ac48d894 | mypyc/build.py | python | group_name | (modules: List[str]) | return h.hexdigest()[:20] | Produce a probably unique name for a group from a list of module names. | Produce a probably unique name for a group from a list of module names. | [
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Parsl/parsl | af2535341152b2640fdd1a3b73b891992bf1b3ea | parsl/providers/googlecloud/googlecloud.py | python | GoogleCloudProvider.cancel | (self, job_ids) | return statuses | Cancels the resources identified by the job_ids provided by the user.
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statuses = []
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/whoosh/idsets.py | python | BitSet.intersection | (self, other) | return BitSet(source=(n for n in self if n in other)) | [] | def intersection(self, other):
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jacoxu/encoder_decoder | 6829c3bc42105aedfd1b8a81c81be19df4326c0a | keras/layers/core.py | python | Layer.count_params | (self) | return sum([K.count_params(p) for p in self.trainable_weights]) | Return the total number of floats (or ints)
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intel/virtual-storage-manager | 00706ab9701acbd0d5e04b19cc80c6b66a2973b8 | source/vsm/vsm/api/openstack/wsgi.py | python | ResponseObject.__setitem__ | (self, key, value) | Sets a header with the given name to the given value. | Sets a header with the given name to the given value. | [
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exodrifter/unity-python | bef6e4e9ddfbbf1eaf7acbbb973e9aa3dd64a20d | Lib/mailbox.py | python | MH.iterkeys | (self) | return iter(sorted(int(entry) for entry in os.listdir(self._path)
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bendmorris/static-python | 2e0f8c4d7ed5b359dc7d8a75b6fb37e6b6c5c473 | Lib/decimal.py | python | Decimal.__hash__ | (self) | return -2 if ans == -1 else ans | x.__hash__() <==> hash(x) | x.__hash__() <==> hash(x) | [
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axcore/tartube | 36dd493642923fe8b9190a41db596c30c043ae90 | tartube/config.py | python | SystemPrefWin.on_default_ffmpeg_button_clicked | (self, button, entry) | Called from callback in self.setup_downloader_ffmpeg_tab().
Sets the path to the ffmpeg binary to the default path.
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self.app_obj.set_ffmpeg_path(self.app_obj.default_ffmpeg_path)
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OneDrive/onedrive-sdk-python | e5642f8cad8eea37a4f653c1a23dfcfc06c37110 | src/onedrivesdk/model/audio.py | python | Audio.album_artist | (self) | Gets and sets the albumArtist
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sunnyxiaohu/R-C3D.pytorch | e8731af7b95f1dc934f6604f9c09e3c4ead74db5 | lib/tf_model_zoo/models/slim/preprocessing/cifarnet_preprocessing.py | python | preprocess_for_train | (image,
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image: A `Tensor` representing an image of arbitrary size.
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padding: The amound of padding before and after each dimension of the image.
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image: A `Tensor` representing an image of arbitrary size.
output_height: The height of the image after preprocessing.
output_width: The width of the image after preprocessing.
padding: The amound of padding before and after each dimension of the image.
Returns:
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tf.image_summary('image', tf.expand_dims(image, 0))
# Transform the image to floats.
image = tf.to_float(image)
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gramps-project/gramps | 04d4651a43eb210192f40a9f8c2bad8ee8fa3753 | gramps/gui/editors/editdate.py | python | EditDate.switch_dual_dated | (self, obj) | Changed whether this is a dual dated year, or not.
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JacquesLucke/animation_nodes | b1e3ace8dcb0a771fd882fc3ac4e490b009fa0d1 | animation_nodes/nodes/object/utility_nodes/transform_object.py | python | TransformObjectNode.execute | (self, object, matrix) | return object | [] | def execute(self, object, matrix):
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... | https://github.com/JacquesLucke/animation_nodes/blob/b1e3ace8dcb0a771fd882fc3ac4e490b009fa0d1/animation_nodes/nodes/object/utility_nodes/transform_object.py#L23-L32 | |||
pseewald/fprettify | cb254022c2034ad3b8f744f4dad1bf5b7438815d | fprettify/__init__.py | python | F90Aligner.get_lines_indent | (self) | return self._line_indents | after processing, retrieve the indents of all line parts. | after processing, retrieve the indents of all line parts. | [
"after",
"processing",
"retrieve",
"the",
"indents",
"of",
"all",
"line",
"parts",
"."
] | def get_lines_indent(self):
"""after processing, retrieve the indents of all line parts."""
return self._line_indents | [
"def",
"get_lines_indent",
"(",
"self",
")",
":",
"return",
"self",
".",
"_line_indents"
] | https://github.com/pseewald/fprettify/blob/cb254022c2034ad3b8f744f4dad1bf5b7438815d/fprettify/__init__.py#L739-L741 | |
chibisov/drf-extensions | ecdf3a95d7f18ccf9cffa55809635c3715179605 | rest_framework_extensions/bulk_operations/mixins.py | python | ListDestroyModelMixin.pre_delete_bulk | (self, queryset) | Placeholder method for calling before deleting an queryset. | Placeholder method for calling before deleting an queryset. | [
"Placeholder",
"method",
"for",
"calling",
"before",
"deleting",
"an",
"queryset",
"."
] | def pre_delete_bulk(self, queryset):
"""
Placeholder method for calling before deleting an queryset.
"""
pass | [
"def",
"pre_delete_bulk",
"(",
"self",
",",
"queryset",
")",
":",
"pass"
] | https://github.com/chibisov/drf-extensions/blob/ecdf3a95d7f18ccf9cffa55809635c3715179605/rest_framework_extensions/bulk_operations/mixins.py#L47-L51 |
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