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diyjac/SDC-P5 | 818a2de532c37f16761e2913ca3ff18d2de9f828 | vehicleLab/chogtrainingRGB5.py | python | get_hog_features | (img, orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True) | [] | def get_hog_features(img, orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True):
# Call with two outputs if vis==True
if vis == True:
features, hog_image = hog(img, orientations=orient, pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block), transform_sqrt=True,
visualise=vis, feature_vector=feature_vec)
return features, hog_image
# Otherwise call with one output
else:
features = hog(img, orientations=orient, pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block), transform_sqrt=True,
visualise=vis, feature_vector=feature_vec)
return features | [
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||||
holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/bokeh-1.4.0-py3.7.egg/bokeh/document/document.py | python | Document.apply_json_patch_string | (self, patch) | Apply a JSON patch provided as a string.
Args:
patch (str) :
Returns:
None | Apply a JSON patch provided as a string. | [
"Apply",
"a",
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"patch",
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] | def apply_json_patch_string(self, patch):
''' Apply a JSON patch provided as a string.
Args:
patch (str) :
Returns:
None
'''
json_parsed = loads(patch)
self.apply_json_patch(json_parsed) | [
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||
scikit-learn/scikit-learn | 1d1aadd0711b87d2a11c80aad15df6f8cf156712 | sklearn/utils/__init__.py | python | gen_batches | (n, batch_size, *, min_batch_size=0) | Generator to create slices containing batch_size elements, from 0 to n.
The last slice may contain less than batch_size elements, when batch_size
does not divide n.
Parameters
----------
n : int
batch_size : int
Number of element in each batch.
min_batch_size : int, default=0
Minimum batch size to produce.
Yields
------
slice of batch_size elements
See Also
--------
gen_even_slices: Generator to create n_packs slices going up to n.
Examples
--------
>>> from sklearn.utils import gen_batches
>>> list(gen_batches(7, 3))
[slice(0, 3, None), slice(3, 6, None), slice(6, 7, None)]
>>> list(gen_batches(6, 3))
[slice(0, 3, None), slice(3, 6, None)]
>>> list(gen_batches(2, 3))
[slice(0, 2, None)]
>>> list(gen_batches(7, 3, min_batch_size=0))
[slice(0, 3, None), slice(3, 6, None), slice(6, 7, None)]
>>> list(gen_batches(7, 3, min_batch_size=2))
[slice(0, 3, None), slice(3, 7, None)] | Generator to create slices containing batch_size elements, from 0 to n. | [
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"""Generator to create slices containing batch_size elements, from 0 to n.
The last slice may contain less than batch_size elements, when batch_size
does not divide n.
Parameters
----------
n : int
batch_size : int
Number of element in each batch.
min_batch_size : int, default=0
Minimum batch size to produce.
Yields
------
slice of batch_size elements
See Also
--------
gen_even_slices: Generator to create n_packs slices going up to n.
Examples
--------
>>> from sklearn.utils import gen_batches
>>> list(gen_batches(7, 3))
[slice(0, 3, None), slice(3, 6, None), slice(6, 7, None)]
>>> list(gen_batches(6, 3))
[slice(0, 3, None), slice(3, 6, None)]
>>> list(gen_batches(2, 3))
[slice(0, 2, None)]
>>> list(gen_batches(7, 3, min_batch_size=0))
[slice(0, 3, None), slice(3, 6, None), slice(6, 7, None)]
>>> list(gen_batches(7, 3, min_batch_size=2))
[slice(0, 3, None), slice(3, 7, None)]
"""
if not isinstance(batch_size, numbers.Integral):
raise TypeError(
"gen_batches got batch_size=%s, must be an integer" % batch_size
)
if batch_size <= 0:
raise ValueError("gen_batches got batch_size=%s, must be positive" % batch_size)
start = 0
for _ in range(int(n // batch_size)):
end = start + batch_size
if end + min_batch_size > n:
continue
yield slice(start, end)
start = end
if start < n:
yield slice(start, n) | [
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CGCookie/retopoflow | 3d8b3a47d1d661f99ab0aeb21d31370bf15de35e | addon_common/common/drawing.py | python | CC_DRAW.border | (cls, *, width=None, color=None) | [] | def border(cls, *, width=None, color=None):
s = Drawing._instance.scale
if width is not None:
CC_DRAW._border_width = s(width)
if color is not None:
CC_DRAW._border_color = color
cls.update() | [
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||||
plastex/plastex | af1628719b50cf25fbe80f16a3e100d566e9bc32 | plasTeX/Renderers/PageTemplate/simpletal/simpleTAL.py | python | SubTemplate.getProgram | (self) | return (self.commandList, self.startRange, self.symbolTable[self.endRangeSymbol]+1, self.symbolTable) | Returns a tuple of (commandList, startPoint, endPoint, symbolTable) | Returns a tuple of (commandList, startPoint, endPoint, symbolTable) | [
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] | def getProgram (self):
""" Returns a tuple of (commandList, startPoint, endPoint, symbolTable) """
return (self.commandList, self.startRange, self.symbolTable[self.endRangeSymbol]+1, self.symbolTable) | [
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|
uvemas/ViTables | 2ce8ec26f85c7392677cf0c7c83ad1ddd7d071e0 | vitables/plugins/dbstreesort/dbs_tree_sort.py | python | customiseDBsTreeModel | () | Slot connected to the convenience dbtree_model_created signal. | Slot connected to the convenience dbtree_model_created signal. | [
"Slot",
"connected",
"to",
"the",
"convenience",
"dbtree_model_created",
"signal",
"."
] | def customiseDBsTreeModel():
"""Slot connected to the convenience dbtree_model_created signal.
"""
# The absolute path of the INI file
ini_filename = os.path.join(os.path.dirname(__file__),
'sorting_algorithm.ini')
config = configparser.ConfigParser()
try:
config.read(ini_filename)
initial_sorting = config.get('DBsTreeSorting', 'algorithm')
except (IOError, configparser.ParsingError):
log.error(
translate('DBsTreeSort', 'The configuration file of the '
'dbs_tree_sort plugin cannot be read.',
'DBsTreeSort error message'))
return
# The essence of the plugin is pretty simple, just monkeypatch
# the insertRows() method of the model to get the desired result.
# TODO how can the nodes be chronologically sorted?
if initial_sorting == 'human':
dbstreemodel.DBsTreeModel.insertRows = humanSort
elif initial_sorting == 'alphabetical':
dbstreemodel.DBsTreeModel.insertRows = alphabeticalSort
else:
log.warning(
translate('DBsTreeSort', 'Unknown sorting algorithm: {}.',
'DBsTreeSort error message').format(initial_sorting)) | [
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||
treigerm/WaterNet | 5f30e796b03519b1d79be2ac1f148b873bf9e877 | waterNet/model.py | python | get_matrix_form | (features, labels, tile_size) | return np.array(features), np.array(labels) | Transform a list of triples of features and labels. To a matrix which contains
only the tiles used for training the model. | Transform a list of triples of features and labels. To a matrix which contains
only the tiles used for training the model. | [
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] | def get_matrix_form(features, labels, tile_size):
"""Transform a list of triples of features and labels. To a matrix which contains
only the tiles used for training the model."""
features = [tile for tile, position, path in features]
labels = [tile for tile, position, path in labels]
# The model will have one output corresponding to each pixel in the feature tile.
# So we need to transform the labels which are given as a 2D bitmap into a vector.
labels = np.reshape(labels, (len(labels), tile_size * tile_size))
return np.array(features), np.array(labels) | [
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|
mtianyan/OnlineMooc | 51a910e27c8d2808a8a5198b4db31f463e646bf6 | tyadmin_api/utils.py | python | random_str | (random_length=8) | return str_base | [] | def random_str(random_length=8):
str_base = ''
chars = 'AaBbCcDdEeFfGgHhIiJjKkLlMmNnOoPpQqRrSsTtUuVvWwXxYyZz0123456789'
length = len(chars) - 1
random = Random()
for i in range(random_length):
str_base += chars[random.randint(0, length)]
return str_base | [
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|||
Source-Python-Dev-Team/Source.Python | d0ffd8ccbd1e9923c9bc44936f20613c1c76b7fb | addons/source-python/packages/site-packages/pytz/tzinfo.py | python | StaticTzInfo.fromutc | (self, dt) | return (dt + self._utcoffset).replace(tzinfo=self) | See datetime.tzinfo.fromutc | See datetime.tzinfo.fromutc | [
"See",
"datetime",
".",
"tzinfo",
".",
"fromutc"
] | def fromutc(self, dt):
'''See datetime.tzinfo.fromutc'''
if dt.tzinfo is not None and dt.tzinfo is not self:
raise ValueError('fromutc: dt.tzinfo is not self')
return (dt + self._utcoffset).replace(tzinfo=self) | [
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|
learningequality/ka-lite | 571918ea668013dcf022286ea85eff1c5333fb8b | kalite/packages/bundled/django/contrib/gis/geos/geometry.py | python | GEOSGeometry.__init__ | (self, geo_input, srid=None) | The base constructor for GEOS geometry objects, and may take the
following inputs:
* strings:
- WKT
- HEXEWKB (a PostGIS-specific canonical form)
- GeoJSON (requires GDAL)
* buffer:
- WKB
The `srid` keyword is used to specify the Source Reference Identifier
(SRID) number for this Geometry. If not set, the SRID will be None. | The base constructor for GEOS geometry objects, and may take the
following inputs: | [
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] | def __init__(self, geo_input, srid=None):
"""
The base constructor for GEOS geometry objects, and may take the
following inputs:
* strings:
- WKT
- HEXEWKB (a PostGIS-specific canonical form)
- GeoJSON (requires GDAL)
* buffer:
- WKB
The `srid` keyword is used to specify the Source Reference Identifier
(SRID) number for this Geometry. If not set, the SRID will be None.
"""
if isinstance(geo_input, bytes):
geo_input = force_text(geo_input)
if isinstance(geo_input, six.string_types):
wkt_m = wkt_regex.match(geo_input)
if wkt_m:
# Handling WKT input.
if wkt_m.group('srid'): srid = int(wkt_m.group('srid'))
g = wkt_r().read(force_bytes(wkt_m.group('wkt')))
elif hex_regex.match(geo_input):
# Handling HEXEWKB input.
g = wkb_r().read(force_bytes(geo_input))
elif gdal.HAS_GDAL and json_regex.match(geo_input):
# Handling GeoJSON input.
g = wkb_r().read(gdal.OGRGeometry(geo_input).wkb)
else:
raise ValueError('String or unicode input unrecognized as WKT EWKT, and HEXEWKB.')
elif isinstance(geo_input, GEOM_PTR):
# When the input is a pointer to a geomtry (GEOM_PTR).
g = geo_input
elif isinstance(geo_input, memoryview):
# When the input is a buffer (WKB).
g = wkb_r().read(geo_input)
elif isinstance(geo_input, GEOSGeometry):
g = capi.geom_clone(geo_input.ptr)
else:
# Invalid geometry type.
raise TypeError('Improper geometry input type: %s' % str(type(geo_input)))
if bool(g):
# Setting the pointer object with a valid pointer.
self.ptr = g
else:
raise GEOSException('Could not initialize GEOS Geometry with given input.')
# Post-initialization setup.
self._post_init(srid) | [
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||
CellProfiler/CellProfiler | a90e17e4d258c6f3900238be0f828e0b4bd1b293 | cellprofiler/modules/untangleworms.py | python | UntangleWorms.get_graph_from_branching_areas_and_segments | (
self, branch_areas_binary, segments_binary
) | return Result(
branch_areas_binary,
counts,
i,
j,
branch_ij,
branch_counts,
incidence_matrix,
incidence_directions,
) | Turn branches + segments into a graph
branch_areas_binary - binary mask of branch areas
segments_binary - binary mask of segments != branch_areas
Given two binary images, one containing "branch areas" one containing
"segments", returns a structure describing the incidence relations
between the branch areas and the segments.
Output is same format as get_graph_from_binary(), so for details, see
get_graph_from_binary | Turn branches + segments into a graph | [
"Turn",
"branches",
"+",
"segments",
"into",
"a",
"graph"
] | def get_graph_from_branching_areas_and_segments(
self, branch_areas_binary, segments_binary
):
"""Turn branches + segments into a graph
branch_areas_binary - binary mask of branch areas
segments_binary - binary mask of segments != branch_areas
Given two binary images, one containing "branch areas" one containing
"segments", returns a structure describing the incidence relations
between the branch areas and the segments.
Output is same format as get_graph_from_binary(), so for details, see
get_graph_from_binary
"""
branch_areas_labeled, num_branch_areas = scipy.ndimage.label(
branch_areas_binary, centrosome.cpmorphology.eight_connect
)
i, j, labels, order, distance, num_segments = self.trace_segments(
segments_binary
)
ooo = numpy.lexsort((order, labels))
i = i[ooo]
j = j[ooo]
labels = labels[ooo]
order = order[ooo]
distance = distance[ooo]
counts = (
numpy.zeros(0, int)
if len(labels) == 0
else numpy.bincount(labels.flatten())[1:]
)
branch_ij = numpy.argwhere(branch_areas_binary)
if len(branch_ij) > 0:
ooo = numpy.lexsort(
[
branch_ij[:, 0],
branch_ij[:, 1],
branch_areas_labeled[branch_ij[:, 0], branch_ij[:, 1]],
]
)
branch_ij = branch_ij[ooo]
branch_labels = branch_areas_labeled[branch_ij[:, 0], branch_ij[:, 1]]
branch_counts = numpy.bincount(branch_areas_labeled.flatten())[1:]
else:
branch_labels = numpy.zeros(0, int)
branch_counts = numpy.zeros(0, int)
#
# "find" the segment starts
#
starts = order == 0
start_labels = numpy.zeros(segments_binary.shape, int)
start_labels[i[starts], j[starts]] = labels[starts]
#
# incidence_directions = True for starts
#
incidence_directions = self.make_incidence_matrix(
branch_areas_labeled, num_branch_areas, start_labels, num_segments
)
#
# Get the incidence matrix for the ends
#
ends = numpy.cumsum(counts) - 1
end_labels = numpy.zeros(segments_binary.shape, int)
end_labels[i[ends], j[ends]] = labels[ends]
incidence_matrix = self.make_incidence_matrix(
branch_areas_labeled, num_branch_areas, end_labels, num_segments
)
incidence_matrix |= incidence_directions
class Result(object):
"""A result graph:
image_size: size of input image
segments: a list for each segment of a forward (index = 0) and
reverse N x 2 array of coordinates of pixels in a segment
segment_indexes: the index of label X into segments
segment_counts: # of points per segment
segment_order: for each pixel, its order when tracing
branch_areas: an N x 2 array of branch point coordinates
branch_area_indexes: index into the branch areas per branchpoint
branch_area_counts: # of points in each branch
incidence_matrix: matrix of areas x segments indicating connections
incidence_directions: direction of each connection
"""
def __init__(
self,
branch_areas_binary,
counts,
i,
j,
branch_ij,
branch_counts,
incidence_matrix,
incidence_directions,
):
self.image_size = tuple(branch_areas_binary.shape)
self.segment_coords = numpy.column_stack((i, j))
self.segment_indexes = numpy.cumsum(counts) - counts
self.segment_counts = counts
self.segment_order = order
self.segments = [
(
self.segment_coords[
self.segment_indexes[i] : (
self.segment_indexes[i] + self.segment_counts[i]
)
],
self.segment_coords[
self.segment_indexes[i] : (
self.segment_indexes[i] + self.segment_counts[i]
)
][::-1],
)
for i in range(len(counts))
]
self.branch_areas = branch_ij
self.branch_area_indexes = numpy.cumsum(branch_counts) - branch_counts
self.branch_area_counts = branch_counts
self.incidence_matrix = incidence_matrix
self.incidence_directions = incidence_directions
return Result(
branch_areas_binary,
counts,
i,
j,
branch_ij,
branch_counts,
incidence_matrix,
incidence_directions,
) | [
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|
kubernetes-client/python | 47b9da9de2d02b2b7a34fbe05afb44afd130d73a | kubernetes/client/models/v1_ingress_rule.py | python | V1IngressRule.http | (self, http) | Sets the http of this V1IngressRule.
:param http: The http of this V1IngressRule. # noqa: E501
:type: V1HTTPIngressRuleValue | Sets the http of this V1IngressRule. | [
"Sets",
"the",
"http",
"of",
"this",
"V1IngressRule",
"."
] | def http(self, http):
"""Sets the http of this V1IngressRule.
:param http: The http of this V1IngressRule. # noqa: E501
:type: V1HTTPIngressRuleValue
"""
self._http = http | [
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||
pyannote/pyannote-audio | a448164b4abe56a2c0da11e143648d4fed5967f8 | pyannote/audio/pipeline/overlap_detection.py | python | OverlapDetection.to_overlap | (reference: Annotation) | return overlap.support().to_annotation() | Get overlapped speech reference annotation
Parameters
----------
reference : Annotation
File yielded by pyannote.database protocols.
Returns
-------
overlap : `pyannote.core.Annotation`
Overlapped speech reference. | Get overlapped speech reference annotation | [
"Get",
"overlapped",
"speech",
"reference",
"annotation"
] | def to_overlap(reference: Annotation) -> Annotation:
"""Get overlapped speech reference annotation
Parameters
----------
reference : Annotation
File yielded by pyannote.database protocols.
Returns
-------
overlap : `pyannote.core.Annotation`
Overlapped speech reference.
"""
overlap = Timeline(uri=reference.uri)
for (s1, t1), (s2, t2) in reference.co_iter(reference):
l1 = reference[s1, t1]
l2 = reference[s2, t2]
if l1 == l2:
continue
overlap.add(s1 & s2)
return overlap.support().to_annotation() | [
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|
thunlp/OpenNE | d9cbf34aff87c9d09fa58a074907ed40a0e06146 | src/openne/gcn/utils.py | python | chebyshev_polynomials | (adj, k) | return sparse_to_tuple(t_k) | Calculate Chebyshev polynomials up to order k. Return a list of sparse matrices (tuple representation). | Calculate Chebyshev polynomials up to order k. Return a list of sparse matrices (tuple representation). | [
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"""Calculate Chebyshev polynomials up to order k. Return a list of sparse matrices (tuple representation)."""
print("Calculating Chebyshev polynomials up to order {}...".format(k))
adj_normalized = normalize_adj(adj)
laplacian = sp.eye(adj.shape[0]) - adj_normalized
largest_eigval, _ = eigsh(laplacian, 1, which='LM')
scaled_laplacian = (
2. / largest_eigval[0]) * laplacian - sp.eye(adj.shape[0])
t_k = list()
t_k.append(sp.eye(adj.shape[0]))
t_k.append(scaled_laplacian)
def chebyshev_recurrence(t_k_minus_one, t_k_minus_two, scaled_lap):
s_lap = sp.csr_matrix(scaled_lap, copy=True)
return 2 * s_lap.dot(t_k_minus_one) - t_k_minus_two
for i in range(2, k+1):
t_k.append(chebyshev_recurrence(t_k[-1], t_k[-2], scaled_laplacian))
return sparse_to_tuple(t_k) | [
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|
jamiecaesar/securecrt-tools | f3cbb49223a485fc9af86e9799b5c940f19e8027 | securecrt_tools/sessions.py | python | CRTSession.close | (self) | A method to close the SecureCRT tab associated with this CRTSession. | A method to close the SecureCRT tab associated with this CRTSession. | [
"A",
"method",
"to",
"close",
"the",
"SecureCRT",
"tab",
"associated",
"with",
"this",
"CRTSession",
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] | def close(self):
"""
A method to close the SecureCRT tab associated with this CRTSession.
"""
if self.tab.Index != self.script.crt.GetScriptTab().Index:
self.tab.Close() | [
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||
algorhythms/LeetCode | 3fb14aeea62a960442e47dfde9f964c7ffce32be | 961 N-Repeated Element in Size 2N Array.py | python | Solution.repeatedNTimes | (self, A: List[int]) | Counter. Straightforward. O(N) space
O(1) space
2N items, N + 1 unique, 1 repeat N times
N = 2
a t b t
t a b t
N = 3
a t b t c t
window 2, cannot find the target
window 3, can find the target? no [9,5,6,9]
window 4, can find
* There is a major element in a length 2 subarray, or;
* Every length 2 subarray has exactly 1 major element, which means that
a length 4 subarray that begins at a major element will have 2 major
elements. | Counter. Straightforward. O(N) space | [
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"space"
] | def repeatedNTimes(self, A: List[int]) -> int:
"""
Counter. Straightforward. O(N) space
O(1) space
2N items, N + 1 unique, 1 repeat N times
N = 2
a t b t
t a b t
N = 3
a t b t c t
window 2, cannot find the target
window 3, can find the target? no [9,5,6,9]
window 4, can find
* There is a major element in a length 2 subarray, or;
* Every length 2 subarray has exactly 1 major element, which means that
a length 4 subarray that begins at a major element will have 2 major
elements.
"""
n = len(A)
for i in range(n - 1):
for j in range(3):
if A[i] == A[min(n - 1, i + 1 + j)]:
return A[i]
raise | [
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||
JasonKessler/scattertext | ef33f06d4c31f9d64b551a7ab86bf157aca82644 | scattertext/TermDocMatrix.py | python | TermDocMatrix.get_scaled_f_scores | (self,
category,
scaler_algo=DEFAULT_SCALER_ALGO,
beta=DEFAULT_BETA) | return np.array(scores) | Computes scaled-fscores
Parameters
----------
category : str
category name to score
scaler_algo : str
Function that scales an array to a range \in [0 and 1]. Use 'percentile', 'normcdf'. Default.
beta : float
Beta in (1+B^2) * (Scale(P(w|c)) * Scale(P(c|w)))/(B^2*Scale(P(w|c)) + Scale(P(c|w))). Default.
Returns
-------
np.array of harmonic means of scaled P(word|category) and scaled P(category|word) | Computes scaled-fscores
Parameters
----------
category : str
category name to score
scaler_algo : str
Function that scales an array to a range \in [0 and 1]. Use 'percentile', 'normcdf'. Default.
beta : float
Beta in (1+B^2) * (Scale(P(w|c)) * Scale(P(c|w)))/(B^2*Scale(P(w|c)) + Scale(P(c|w))). Default.
Returns
-------
np.array of harmonic means of scaled P(word|category) and scaled P(category|word) | [
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category,
scaler_algo=DEFAULT_SCALER_ALGO,
beta=DEFAULT_BETA):
''' Computes scaled-fscores
Parameters
----------
category : str
category name to score
scaler_algo : str
Function that scales an array to a range \in [0 and 1]. Use 'percentile', 'normcdf'. Default.
beta : float
Beta in (1+B^2) * (Scale(P(w|c)) * Scale(P(c|w)))/(B^2*Scale(P(w|c)) + Scale(P(c|w))). Default.
Returns
-------
np.array of harmonic means of scaled P(word|category) and scaled P(category|word)
'''
assert beta > 0
cat_word_counts, not_cat_word_counts = self._get_catetgory_and_non_category_word_counts(category)
scores = self._get_scaled_f_score_from_counts(cat_word_counts, not_cat_word_counts, scaler_algo, beta)
return np.array(scores) | [
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|
GoogleCloudPlatform/PerfKitBenchmarker | 6e3412d7d5e414b8ca30ed5eaf970cef1d919a67 | perfkitbenchmarker/pkb.py | python | SetUpPKB | () | Set globals and environment variables for PKB.
After SetUpPKB() returns, it should be possible to call PKB
functions, like benchmark_spec.Prepare() or benchmark_spec.Run().
SetUpPKB() also modifies the local file system by creating a temp
directory and storing new SSH keys. | Set globals and environment variables for PKB. | [
"Set",
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"and",
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"."
] | def SetUpPKB():
"""Set globals and environment variables for PKB.
After SetUpPKB() returns, it should be possible to call PKB
functions, like benchmark_spec.Prepare() or benchmark_spec.Run().
SetUpPKB() also modifies the local file system by creating a temp
directory and storing new SSH keys.
"""
try:
_InitializeRunUri()
except errors.Error as e:
logging.error(e)
sys.exit(1)
# Initialize logging.
vm_util.GenTempDir()
if FLAGS.use_pkb_logging:
log_util.ConfigureLogging(
stderr_log_level=log_util.LOG_LEVELS[FLAGS.log_level],
log_path=vm_util.PrependTempDir(LOG_FILE_NAME),
run_uri=FLAGS.run_uri,
file_log_level=log_util.LOG_LEVELS[FLAGS.file_log_level])
logging.info('PerfKitBenchmarker version: %s', version.VERSION)
# Translate deprecated flags and log all provided flag values.
disk.WarnAndTranslateDiskFlags()
_LogCommandLineFlags()
# Register skip pending runs functionality.
RegisterSkipPendingRunsCheck(_SkipPendingRunsFile)
# Check environment.
if not FLAGS.ignore_package_requirements:
requirements.CheckBasicRequirements()
for executable in REQUIRED_EXECUTABLES:
if not vm_util.ExecutableOnPath(executable):
raise errors.Setup.MissingExecutableError(
'Could not find required executable "%s"' % executable)
# Check mutually exclusive flags
if FLAGS.run_stage_iterations > 1 and FLAGS.run_stage_time > 0:
raise errors.Setup.InvalidFlagConfigurationError(
'Flags run_stage_iterations and run_stage_time are mutually exclusive')
vm_util.SSHKeyGen()
if FLAGS.static_vm_file:
with open(FLAGS.static_vm_file) as fp:
static_virtual_machine.StaticVirtualMachine.ReadStaticVirtualMachineFile(
fp)
events.initialization_complete.send(parsed_flags=FLAGS)
benchmark_lookup.SetBenchmarkModuleFunction(benchmark_sets.BenchmarkModule)
package_lookup.SetPackageModuleFunction(benchmark_sets.PackageModule)
# Update max_concurrent_threads to use at least as many threads as VMs. This
# is important for the cluster_boot benchmark where we want to launch the VMs
# in parallel.
if not FLAGS.max_concurrent_threads:
FLAGS.max_concurrent_threads = max(
background_tasks.MAX_CONCURRENT_THREADS,
FLAGS.num_vms)
logging.info('Setting --max_concurrent_threads=%d.',
FLAGS.max_concurrent_threads) | [
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||
Komodo/KomodoEdit | 61edab75dce2bdb03943b387b0608ea36f548e8e | util/cmdln.py | python | RawCmdln.do_help | (self, argv) | ${cmd_name}: give detailed help on a specific sub-command
Usage:
${name} help [COMMAND] | ${cmd_name}: give detailed help on a specific sub-command | [
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"""${cmd_name}: give detailed help on a specific sub-command
Usage:
${name} help [COMMAND]
"""
if len(argv) > 1: # asking for help on a particular command
doc = None
cmdname = self._get_canonical_cmd_name(argv[1]) or argv[1]
if not cmdname:
return self.helpdefault(argv[1], False)
else:
helpfunc = getattr(self, "help_"+cmdname, None)
if helpfunc:
doc = helpfunc()
else:
handler = self._get_cmd_handler(cmdname)
if handler:
doc = handler.__doc__
if doc is None:
return self.helpdefault(argv[1], handler != None)
else: # bare "help" command
doc = self.__class__.__doc__ # try class docstring
if doc is None:
# Try to provide some reasonable useful default help.
if self.cmdlooping: prefix = ""
else: prefix = self.name+' '
doc = """Usage:
%sCOMMAND [ARGS...]
%shelp [COMMAND]
${option_list}
${command_list}
${help_list}
""" % (prefix, prefix)
cmdname = None
if doc: # *do* have help content, massage and print that
doc = self._help_reindent(doc)
doc = self._help_preprocess(doc, cmdname)
doc = doc.rstrip() + '\n' # trim down trailing space
self.stdout.write(self._str(doc))
self.stdout.flush() | [
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||
donnemartin/gitsome | d7c57abc7cb66e9c910a844f15d4536866da3310 | xonsh/tokenize.py | python | untokenize | (iterable) | return out | Transform tokens back into Python source code.
It returns a bytes object, encoded using the ENCODING
token, which is the first token sequence output by tokenize.
Each element returned by the iterable must be a token sequence
with at least two elements, a token number and token value. If
only two tokens are passed, the resulting output is poor.
Round-trip invariant for full input:
Untokenized source will match input source exactly
Round-trip invariant for limited intput:
# Output bytes will tokenize the back to the input
t1 = [tok[:2] for tok in tokenize(f.readline)]
newcode = untokenize(t1)
readline = BytesIO(newcode).readline
t2 = [tok[:2] for tok in tokenize(readline)]
assert t1 == t2 | Transform tokens back into Python source code.
It returns a bytes object, encoded using the ENCODING
token, which is the first token sequence output by tokenize. | [
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] | def untokenize(iterable):
"""Transform tokens back into Python source code.
It returns a bytes object, encoded using the ENCODING
token, which is the first token sequence output by tokenize.
Each element returned by the iterable must be a token sequence
with at least two elements, a token number and token value. If
only two tokens are passed, the resulting output is poor.
Round-trip invariant for full input:
Untokenized source will match input source exactly
Round-trip invariant for limited intput:
# Output bytes will tokenize the back to the input
t1 = [tok[:2] for tok in tokenize(f.readline)]
newcode = untokenize(t1)
readline = BytesIO(newcode).readline
t2 = [tok[:2] for tok in tokenize(readline)]
assert t1 == t2
"""
ut = Untokenizer()
out = ut.untokenize(iterable)
if ut.encoding is not None:
out = out.encode(ut.encoding)
return out | [
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|
1012598167/flask_mongodb_game | 60c7e0351586656ec38f851592886338e50b4110 | python_flask/venv/Lib/site-packages/pip-19.0.3-py3.6.egg/pip/_vendor/appdirs.py | python | user_cache_dir | (appname=None, appauthor=None, version=None, opinion=True) | return path | r"""Return full path to the user-specific cache dir for this application.
"appname" is the name of application.
If None, just the system directory is returned.
"appauthor" (only used on Windows) is the name of the
appauthor or distributing body for this application. Typically
it is the owning company name. This falls back to appname. You may
pass False to disable it.
"version" is an optional version path element to append to the
path. You might want to use this if you want multiple versions
of your app to be able to run independently. If used, this
would typically be "<major>.<minor>".
Only applied when appname is present.
"opinion" (boolean) can be False to disable the appending of
"Cache" to the base app data dir for Windows. See
discussion below.
Typical user cache directories are:
Mac OS X: ~/Library/Caches/<AppName>
Unix: ~/.cache/<AppName> (XDG default)
Win XP: C:\Documents and Settings\<username>\Local Settings\Application Data\<AppAuthor>\<AppName>\Cache
Vista: C:\Users\<username>\AppData\Local\<AppAuthor>\<AppName>\Cache
On Windows the only suggestion in the MSDN docs is that local settings go in
the `CSIDL_LOCAL_APPDATA` directory. This is identical to the non-roaming
app data dir (the default returned by `user_data_dir` above). Apps typically
put cache data somewhere *under* the given dir here. Some examples:
...\Mozilla\Firefox\Profiles\<ProfileName>\Cache
...\Acme\SuperApp\Cache\1.0
OPINION: This function appends "Cache" to the `CSIDL_LOCAL_APPDATA` value.
This can be disabled with the `opinion=False` option. | r"""Return full path to the user-specific cache dir for this application. | [
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"path",
"to",
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"cache",
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] | def user_cache_dir(appname=None, appauthor=None, version=None, opinion=True):
r"""Return full path to the user-specific cache dir for this application.
"appname" is the name of application.
If None, just the system directory is returned.
"appauthor" (only used on Windows) is the name of the
appauthor or distributing body for this application. Typically
it is the owning company name. This falls back to appname. You may
pass False to disable it.
"version" is an optional version path element to append to the
path. You might want to use this if you want multiple versions
of your app to be able to run independently. If used, this
would typically be "<major>.<minor>".
Only applied when appname is present.
"opinion" (boolean) can be False to disable the appending of
"Cache" to the base app data dir for Windows. See
discussion below.
Typical user cache directories are:
Mac OS X: ~/Library/Caches/<AppName>
Unix: ~/.cache/<AppName> (XDG default)
Win XP: C:\Documents and Settings\<username>\Local Settings\Application Data\<AppAuthor>\<AppName>\Cache
Vista: C:\Users\<username>\AppData\Local\<AppAuthor>\<AppName>\Cache
On Windows the only suggestion in the MSDN docs is that local settings go in
the `CSIDL_LOCAL_APPDATA` directory. This is identical to the non-roaming
app data dir (the default returned by `user_data_dir` above). Apps typically
put cache data somewhere *under* the given dir here. Some examples:
...\Mozilla\Firefox\Profiles\<ProfileName>\Cache
...\Acme\SuperApp\Cache\1.0
OPINION: This function appends "Cache" to the `CSIDL_LOCAL_APPDATA` value.
This can be disabled with the `opinion=False` option.
"""
if system == "win32":
if appauthor is None:
appauthor = appname
path = os.path.normpath(_get_win_folder("CSIDL_LOCAL_APPDATA"))
if appname:
if appauthor is not False:
path = os.path.join(path, appauthor, appname)
else:
path = os.path.join(path, appname)
if opinion:
path = os.path.join(path, "Cache")
elif system == 'darwin':
path = os.path.expanduser('~/Library/Caches')
if appname:
path = os.path.join(path, appname)
else:
path = os.getenv('XDG_CACHE_HOME', os.path.expanduser('~/.cache'))
if appname:
path = os.path.join(path, appname)
if appname and version:
path = os.path.join(path, version)
return path | [
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|
lsbardel/python-stdnet | 78db5320bdedc3f28c5e4f38cda13a4469e35db7 | stdnet/odm/related.py | python | Many2ManyThroughModel | (field) | Create a Many2Many through model with two foreign key fields and a
CompositeFieldId depending on the two foreign keys. | Create a Many2Many through model with two foreign key fields and a
CompositeFieldId depending on the two foreign keys. | [
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"keys",
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] | def Many2ManyThroughModel(field):
'''Create a Many2Many through model with two foreign key fields and a
CompositeFieldId depending on the two foreign keys.'''
from stdnet.odm import ModelType, StdModel, ForeignKey, CompositeIdField
name_model = field.model._meta.name
name_relmodel = field.relmodel._meta.name
# The two models are the same.
if name_model == name_relmodel:
name_relmodel += '2'
through = field.through
# Create the through model
if through is None:
name = '{0}_{1}'.format(name_model, name_relmodel)
class Meta:
app_label = field.model._meta.app_label
through = ModelType(name, (StdModel,), {'Meta': Meta})
field.through = through
# The first field
field1 = ForeignKey(field.model,
related_name=field.name,
related_manager_class=makeMany2ManyRelatedManager(
field.relmodel,
name_model,
name_relmodel)
)
field1.register_with_model(name_model, through)
# The second field
field2 = ForeignKey(field.relmodel,
related_name=field.related_name,
related_manager_class=makeMany2ManyRelatedManager(
field.model,
name_relmodel,
name_model)
)
field2.register_with_model(name_relmodel, through)
pk = CompositeIdField(name_model, name_relmodel)
pk.register_with_model('id', through) | [
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] | https://github.com/lsbardel/python-stdnet/blob/78db5320bdedc3f28c5e4f38cda13a4469e35db7/stdnet/odm/related.py#L77-L114 |
||
SiCKRAGE/SiCKRAGE | 45fb67c0c730fc22a34c695b5a62b11970621c53 | sickrage/libs/adba/aniDBresponses.py | python | UptimeResponse.__init__ | (self, cmd, restag, rescode, resstr, datalines) | attributes:
data:
uptime - udpserver uptime in milliseconds | attributes: | [
"attributes",
":"
] | def __init__(self, cmd, restag, rescode, resstr, datalines):
"""
attributes:
data:
uptime - udpserver uptime in milliseconds
"""
Response.__init__(self, cmd, restag, rescode, resstr, datalines)
self.codestr = 'UPTIME'
self.codehead = ()
self.codetail = ('uptime',)
self.coderep = () | [
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||
emesene/emesene | 4548a4098310e21b16437bb36223a7f632a4f7bc | emesene/e3/xmpp/SleekXMPP/sleekxmpp/plugins/base.py | python | PluginManager.__iter__ | (self) | return self._plugins.__iter__() | Return an iterator over the set of enabled plugins. | Return an iterator over the set of enabled plugins. | [
"Return",
"an",
"iterator",
"over",
"the",
"set",
"of",
"enabled",
"plugins",
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] | def __iter__(self):
"""Return an iterator over the set of enabled plugins."""
return self._plugins.__iter__() | [
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|
reviewboard/rbtools | b4838a640b458641ffd233093ae65971d0b4d529 | rbtools/clients/__init__.py | python | scan_usable_client | (config, options, client_name=None) | return repository_info, tool | Scan for a usable SCMClient.
Args:
config (dict):
The loaded user config.
options (argparse.Namespace):
The parsed command line arguments.
client_name (unicode, optional):
A specific client name, which can come from the configuration. This
can be used to disambiguate if there are nested repositories, or to
speed up detection.
Returns:
tuple:
A 2-tuple, containing the repository info structure and the tool
instance. | Scan for a usable SCMClient. | [
"Scan",
"for",
"a",
"usable",
"SCMClient",
"."
] | def scan_usable_client(config, options, client_name=None):
"""Scan for a usable SCMClient.
Args:
config (dict):
The loaded user config.
options (argparse.Namespace):
The parsed command line arguments.
client_name (unicode, optional):
A specific client name, which can come from the configuration. This
can be used to disambiguate if there are nested repositories, or to
speed up detection.
Returns:
tuple:
A 2-tuple, containing the repository info structure and the tool
instance.
"""
repository_info = None
tool = None
# TODO: We should only load all of the scm clients if the client_name
# isn't provided.
if SCMCLIENTS is None:
load_scmclients(config, options)
if client_name:
if client_name not in SCMCLIENTS:
logging.error('The provided repository type "%s" is invalid.',
client_name)
sys.exit(1)
else:
scmclients = {
client_name: SCMCLIENTS[client_name]
}
else:
scmclients = SCMCLIENTS
# First go through and see if any repositories are configured in
# remote-only mode. For example, SVN can post changes purely with a remote
# URL and no working directory.
for name, tool in six.iteritems(scmclients):
if tool.is_remote_only():
break
else:
tool = None
# Now scan through the repositories to find any local working directories.
# If there are multiple repositories which appear to be active in the CWD,
# choose the deepest and emit a warning.
if tool is None:
candidate_repos = []
for name, tool in six.iteritems(scmclients):
logging.debug('Checking for a %s repository...', tool.name)
local_path = tool.get_local_path()
if local_path:
candidate_repos.append((local_path, tool))
if len(candidate_repos) == 1:
tool = candidate_repos[0][1]
elif candidate_repos:
logging.debug('Finding deepest repository of multiple matching '
'repository types.')
deepest_repo_len = 0
deepest_repo_tool = None
deepest_local_path = None
found_multiple = False
for local_path, tool in candidate_repos:
if len(os.path.normpath(local_path)) > deepest_repo_len:
if deepest_repo_tool is not None:
found_multiple = True
deepest_repo_len = len(local_path)
deepest_repo_tool = tool
deepest_local_path = local_path
if found_multiple:
logging.warn('Multiple matching repositories were found. '
'Using %s repository at %s.',
tool.name, deepest_local_path)
logging.warn('Define REPOSITORY_TYPE in .reviewboardrc if '
'you wish to use a different repository.')
tool = deepest_repo_tool
repository_info = tool and tool.get_repository_info()
if repository_info is None:
if client_name:
logging.error('The provided repository type was not detected '
'in the current directory.')
elif getattr(options, 'repository_url', None):
logging.error('No supported repository could be accessed at '
'the supplied url.')
else:
logging.error('The current directory does not contain a checkout '
'from a supported source code repository.')
sys.exit(1)
# Verify that options specific to an SCM Client have not been mis-used.
if (getattr(options, 'change_only', False) and
not tool.supports_changesets):
logging.error('The --change-only option is not valid for the '
'current SCM client.\n')
sys.exit(1)
if (getattr(options, 'parent_branch', None) and
not tool.supports_parent_diffs):
logging.error('The --parent option is not valid for the '
'current SCM client.')
sys.exit(1)
from rbtools.clients.perforce import PerforceClient
if (not isinstance(tool, PerforceClient) and
(getattr(options, 'p4_client', None) or
getattr(options, 'p4_port', None))):
logging.error('The --p4-client and --p4-port options are not '
'valid for the current SCM client.\n')
sys.exit(1)
return repository_info, tool | [
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|
veusz/veusz | 5a1e2af5f24df0eb2a2842be51f2997c4999c7fb | veusz/setting/setting.py | python | Setting.path | (self) | return '/'.join(path) | Return full path of setting. | Return full path of setting. | [
"Return",
"full",
"path",
"of",
"setting",
"."
] | def path(self):
"""Return full path of setting."""
path = []
obj = self
while obj is not None:
# logic easier to understand here
# do not add settings name for settings of widget
if not obj.iswidget and obj.parent.iswidget:
pass
else:
if obj.name == '/':
path.insert(0, '')
else:
path.insert(0, obj.name)
obj = obj.parent
return '/'.join(path) | [
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|
Confusezius/Deep-Metric-Learning-Baselines | 60772745e28bc90077831bb4c9f07a233e602797 | losses.py | python | TripletLoss.__init__ | (self, margin=1, sampling_method='random') | Basic Triplet Loss as proposed in 'FaceNet: A Unified Embedding for Face Recognition and Clustering'
Args:
margin: float, Triplet Margin - Ensures that positives aren't placed arbitrarily close to the anchor.
Similarl, negatives should not be placed arbitrarily far away.
sampling_method: Method to use for sampling training triplets. Used for the TupleSampler-class. | Basic Triplet Loss as proposed in 'FaceNet: A Unified Embedding for Face Recognition and Clustering'
Args:
margin: float, Triplet Margin - Ensures that positives aren't placed arbitrarily close to the anchor.
Similarl, negatives should not be placed arbitrarily far away.
sampling_method: Method to use for sampling training triplets. Used for the TupleSampler-class. | [
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"""
Basic Triplet Loss as proposed in 'FaceNet: A Unified Embedding for Face Recognition and Clustering'
Args:
margin: float, Triplet Margin - Ensures that positives aren't placed arbitrarily close to the anchor.
Similarl, negatives should not be placed arbitrarily far away.
sampling_method: Method to use for sampling training triplets. Used for the TupleSampler-class.
"""
super(TripletLoss, self).__init__()
self.margin = margin
self.sampler = TupleSampler(method=sampling_method) | [
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IntelAI/models | 1d7a53ccfad3e6f0e7378c9e3c8840895d63df8c | models/language_modeling/tensorflow/bert_large/training/fp32/create_pretraining_data.py | python | write_instance_to_example_files | (instances, tokenizer, max_seq_length,
max_predictions_per_seq, output_files) | Create TF example files from `TrainingInstance`s. | Create TF example files from `TrainingInstance`s. | [
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] | def write_instance_to_example_files(instances, tokenizer, max_seq_length,
max_predictions_per_seq, output_files):
"""Create TF example files from `TrainingInstance`s."""
writers = []
for output_file in output_files:
writers.append(tf.io.TFRecordWriter(output_file))
writer_index = 0
total_written = 0
for (inst_index, instance) in enumerate(instances):
input_ids = tokenizer.convert_tokens_to_ids(instance.tokens)
input_mask = [1] * len(input_ids)
segment_ids = list(instance.segment_ids)
assert len(input_ids) <= max_seq_length
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
masked_lm_positions = list(instance.masked_lm_positions)
masked_lm_ids = tokenizer.convert_tokens_to_ids(instance.masked_lm_labels)
masked_lm_weights = [1.0] * len(masked_lm_ids)
while len(masked_lm_positions) < max_predictions_per_seq:
masked_lm_positions.append(0)
masked_lm_ids.append(0)
masked_lm_weights.append(0.0)
next_sentence_label = 1 if instance.is_random_next else 0
features = collections.OrderedDict()
features["input_ids"] = create_int_feature(input_ids)
features["input_mask"] = create_int_feature(input_mask)
features["segment_ids"] = create_int_feature(segment_ids)
features["masked_lm_positions"] = create_int_feature(masked_lm_positions)
features["masked_lm_ids"] = create_int_feature(masked_lm_ids)
features["masked_lm_weights"] = create_float_feature(masked_lm_weights)
features["next_sentence_labels"] = create_int_feature([next_sentence_label])
tf_example = tf.train.Example(features=tf.train.Features(feature=features))
writers[writer_index].write(tf_example.SerializeToString())
writer_index = (writer_index + 1) % len(writers)
total_written += 1
if inst_index < 20:
tf.compat.v1.logging.info("*** Example ***")
tf.compat.v1.logging.info("tokens: %s" % " ".join(
[tokenization.printable_text(x) for x in instance.tokens]))
for feature_name in features.keys():
feature = features[feature_name]
values = []
if feature.int64_list.value:
values = feature.int64_list.value
elif feature.float_list.value:
values = feature.float_list.value
tf.compat.v1.logging.info(
"%s: %s" % (feature_name, " ".join([str(x) for x in values])))
for writer in writers:
writer.close()
tf.compat.v1.logging.info("Wrote %d total instances", total_written) | [
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] | https://github.com/IntelAI/models/blob/1d7a53ccfad3e6f0e7378c9e3c8840895d63df8c/models/language_modeling/tensorflow/bert_large/training/fp32/create_pretraining_data.py#L99-L169 |
||
selfteaching/selfteaching-python-camp | 9982ee964b984595e7d664b07c389cddaf158f1e | exercises/1901100088/d10/mymodule/stats_word.py | python | stats_text | (text,count) | Calculate the occurrence number of the word both in english and chiness. | Calculate the occurrence number of the word both in english and chiness. | [
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"the",
"occurrence",
"number",
"of",
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"word",
"both",
"in",
"english",
"and",
"chiness",
"."
] | def stats_text(text,count):
"""Calculate the occurrence number of the word both in english and chiness.
"""
if type(text) == str and type(count) == int:
return(stats_text_en(text,count)+stats_text_cn(text,count))
else:
raise ValueError('参数类型错误，需传入字符串及整数参数') | [
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||
avocado-framework/avocado | 1f9b3192e8ba47d029c33fe21266bd113d17811f | avocado/utils/lv_utils.py | python | lv_revert | (vg_name, lv_name, lv_snapshot_name) | Revert the origin logical volume to a snapshot.
:param str vg_name: name of the volume group
:param str lv_name: name of the logical volume
:param str lv_snapshot_name: name of the snapshot to be reverted
:raises: :py:class:`process.CmdError` on failure to revert snapshot
:raises: :py:class:`LVException` if preconditions or execution fails | Revert the origin logical volume to a snapshot. | [
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"the",
"origin",
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"volume",
"to",
"a",
"snapshot",
"."
] | def lv_revert(vg_name, lv_name, lv_snapshot_name):
"""
Revert the origin logical volume to a snapshot.
:param str vg_name: name of the volume group
:param str lv_name: name of the logical volume
:param str lv_snapshot_name: name of the snapshot to be reverted
:raises: :py:class:`process.CmdError` on failure to revert snapshot
:raises: :py:class:`LVException` if preconditions or execution fails
"""
try:
if not vg_check(vg_name):
raise LVException("Volume group could not be found")
if not lv_check(vg_name, lv_snapshot_name):
raise LVException("Snapshot could not be found")
if (not lv_check(vg_name, lv_snapshot_name) and not lv_check(vg_name,
lv_name)):
raise LVException("Snapshot and its origin could not be found")
if (lv_check(vg_name, lv_snapshot_name) and not lv_check(vg_name,
lv_name)):
raise LVException("Snapshot origin could not be found")
cmd = ("lvconvert --merge --interval 1 /dev/%s/%s" % (vg_name, lv_snapshot_name))
result = process.run(cmd, sudo=True)
if (("Merging of snapshot %s will start next activation." %
lv_snapshot_name) in result.stdout_text):
raise LVException("The Logical volume %s is still active" %
lv_name)
except process.CmdError as ex:
# detect if merge of snapshot was postponed
# and attempt to reactivate the volume.
active_lv_pattern = re.escape("%s [active]" % lv_snapshot_name)
lvdisplay_output = process.run("lvdisplay", sudo=True).stdout_text
if ('Snapshot could not be found' in ex.result.stderr_text and
re.search(active_lv_pattern, lvdisplay_output) or
"The Logical volume %s is still active" % lv_name in ex.result.stderr_text):
log_msg = "Logical volume %s is still active! Attempting to deactivate..."
LOGGER.debug(log_msg, lv_name)
lv_reactivate(vg_name, lv_name)
LOGGER.error("Continuing after reactivation")
elif 'Snapshot could not be found' in ex.result.stderr_text:
LOGGER.error("Could not revert to snapshot:")
LOGGER.error(ex.result)
else:
raise ex | [
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||
fxsjy/jiebademo | ba3e5a34cd84b612e13f4dfb9f3ec037928c4339 | jiebademo/bottle.py | python | Bottle.__call__ | (self, environ, start_response) | return self.wsgi(environ, start_response) | Each instance of :class:'Bottle' is a WSGI application. | Each instance of :class:'Bottle' is a WSGI application. | [
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|
cylc/cylc-flow | 5ec221143476c7c616c156b74158edfbcd83794a | cylc/flow/pathutil.py | python | parse_rm_dirs | (rm_dirs: Iterable[str]) | return result | Parse a list of possibly colon-separated dirs (or files or globs).
Return the set of all the dirs.
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part += os.sep
result.add(part)
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|
triaquae/triaquae | bbabf736b3ba56a0c6498e7f04e16c13b8b8f2b9 | TriAquae/models/Centos_5.9/Crypto/Util/number.py | python | getRandomNumber | (N, randfunc=None) | return getRandomNBitInteger(N, randfunc) | Deprecated. Use getRandomInteger or getRandomNBitInteger instead. | Deprecated. Use getRandomInteger or getRandomNBitInteger instead. | [
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"""Deprecated. Use getRandomInteger or getRandomNBitInteger instead."""
warnings.warn("Crypto.Util.number.getRandomNumber has confusing semantics"+
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return getRandomNBitInteger(N, randfunc) | [
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|
home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/webostv/device_trigger.py | python | async_validate_trigger_config | (
hass: HomeAssistant, config: ConfigType
) | return config | Validate config. | Validate config. | [
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hass: HomeAssistant, config: ConfigType
) -> ConfigType:
"""Validate config."""
config = TRIGGER_SCHEMA(config)
try:
if async_is_device_config_entry_not_loaded(hass, config[CONF_DEVICE_ID]):
return config
except ValueError as err:
raise InvalidDeviceAutomationConfig(err) from err
if config[CONF_TYPE] == TURN_ON_PLATFORM_TYPE:
device_id = config[CONF_DEVICE_ID]
try:
device = async_get_device_entry_by_device_id(hass, device_id)
async_get_client_wrapper_by_device_entry(hass, device)
except ValueError as err:
raise InvalidDeviceAutomationConfig(err) from err
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|
angr/angr | 4b04d56ace135018083d36d9083805be8146688b | angr/analyses/disassembly.py | python | FuncComment._render | (self, formatting=None) | return ['##', '## Function ' + self.func.name, '##'] | [] | def _render(self, formatting=None):
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|||
phimpme/phimpme-generator | ba6d11190b9016238f27672e1ad55e6a875b74a0 | Phimpme/site-packages/requests/packages/urllib3/packages/ordered_dict.py | python | OrderedDict.iterkeys | (self) | return iter(self) | od.iterkeys() -> an iterator over the keys in od | od.iterkeys() -> an iterator over the keys in od | [
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|
plotly/plotly.py | cfad7862594b35965c0e000813bd7805e8494a5b | packages/python/plotly/plotly/graph_objs/choroplethmapbox/colorbar/_title.py | python | Title.font | (self) | return self["font"] | Sets this color bar's title font. Note that the title's font
used to be set by the now deprecated `titlefont` attribute.
The 'font' property is an instance of Font
that may be specified as:
- An instance of :class:`plotly.graph_objs.choroplethmapbox.colorbar.title.Font`
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color
family
HTML font family - the typeface that will be
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Provide multiple font families, separated by
commas, to indicate the preference in which to
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|
erikdubois/Aureola | 005fb14b3cab0ba1929ebf9ac3ac68d2c6e1c0ef | shailen/dropbox.py | python | lansync | (argv) | u"""enables or disables LAN sync
dropbox lansync [y/n]
options:
y dropbox will use LAN sync (default)
n dropbox will not use LAN sync | u"""enables or disables LAN sync
dropbox lansync [y/n] | [
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u"""enables or disables LAN sync
dropbox lansync [y/n]
options:
y dropbox will use LAN sync (default)
n dropbox will not use LAN sync
"""
if len(argv) != 1:
console_print(lansync.__doc__, linebreak=False)
return
s = argv[0].lower()
if s.startswith('y') or s.startswith('-y'):
should_lansync = True
elif s.startswith('n') or s.startswith('-n'):
should_lansync = False
else:
should_lansync = None
if should_lansync is None:
console_print(lansync.__doc__,linebreak=False)
else:
with closing(DropboxCommand()) as dc:
dc.set_lan_sync(lansync='enabled' if should_lansync else 'disabled') | [
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||
beeware/ouroboros | a29123c6fab6a807caffbb7587cf548e0c370296 | ouroboros/calendar.py | python | Calendar.itermonthdates | (self, year, month) | Return an iterator for one month. The iterator will yield datetime.date
values and will always iterate through complete weeks, so it will yield
dates outside the specified month. | Return an iterator for one month. The iterator will yield datetime.date
values and will always iterate through complete weeks, so it will yield
dates outside the specified month. | [
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Return an iterator for one month. The iterator will yield datetime.date
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dates outside the specified month.
"""
date = datetime.date(year, month, 1)
# Go back to the beginning of the week
days = (date.weekday() - self.firstweekday) % 7
date -= datetime.timedelta(days=days)
oneday = datetime.timedelta(days=1)
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||
LudovicRousseau/pyscard | c0a5e2f626be69a0fc7b530631471cf014e4b20e | smartcard/pcsc/PCSCPart10.py | python | parseFeatureRequest | (response) | return features | Get the list of Part10 features supported by the reader.
@param response: result of CM_IOCTL_GET_FEATURE_REQUEST commmand
@rtype: list
@return: a list of list [[tag1, value1], [tag2, value2]] | Get the list of Part10 features supported by the reader. | [
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] | def parseFeatureRequest(response):
""" Get the list of Part10 features supported by the reader.
@param response: result of CM_IOCTL_GET_FEATURE_REQUEST commmand
@rtype: list
@return: a list of list [[tag1, value1], [tag2, value2]]
"""
features = []
while (len(response) > 0):
tag = response[0]
control = ((((((response[2] << 8) +
response[3]) << 8) +
response[4]) << 8) +
response[5])
try:
features.append([Features[tag], control])
except KeyError:
pass
del response[:6]
return features | [
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|
quantumlib/OpenFermion | 6187085f2a7707012b68370b625acaeed547e62b | src/openfermion/circuits/low_rank.py | python | get_chemist_two_body_coefficients | (two_body_coefficients, spin_basis=True) | return one_body_correction, chemist_two_body_coefficients | r"""Convert two-body operator coefficients to low rank tensor.
The input is a two-body fermionic Hamiltonian expressed as
$\sum_{pqrs} h_{pqrs} a^\dagger_p a^\dagger_q a_r a_s$
We will convert this to the chemistry convention expressing it as
$\sum_{pqrs} g_{pqrs} a^\dagger_p a_q a^\dagger_r a_s$
but without the spin degree of freedom.
In the process of performing this conversion, constants and one-body
terms come out, which will be returned as well.
Args:
two_body_coefficients (ndarray): an N x N x N x N
numpy array giving the $h_{pqrs}$ tensor.
spin_basis (bool): True if the two-body terms are passed in spin
orbital basis. False if already in spatial orbital basis.
Returns:
one_body_correction (ndarray): an N x N array of floats giving
coefficients of the $a^\dagger_p a_q$ terms that come out.
chemist_two_body_coefficients (ndarray): an N x N x N x N numpy array
giving the $g_{pqrs}$ tensor in chemist notation.
Raises:
TypeError: Input must be two-body number conserving
FermionOperator or InteractionOperator. | r"""Convert two-body operator coefficients to low rank tensor. | [
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] | def get_chemist_two_body_coefficients(two_body_coefficients, spin_basis=True):
r"""Convert two-body operator coefficients to low rank tensor.
The input is a two-body fermionic Hamiltonian expressed as
$\sum_{pqrs} h_{pqrs} a^\dagger_p a^\dagger_q a_r a_s$
We will convert this to the chemistry convention expressing it as
$\sum_{pqrs} g_{pqrs} a^\dagger_p a_q a^\dagger_r a_s$
but without the spin degree of freedom.
In the process of performing this conversion, constants and one-body
terms come out, which will be returned as well.
Args:
two_body_coefficients (ndarray): an N x N x N x N
numpy array giving the $h_{pqrs}$ tensor.
spin_basis (bool): True if the two-body terms are passed in spin
orbital basis. False if already in spatial orbital basis.
Returns:
one_body_correction (ndarray): an N x N array of floats giving
coefficients of the $a^\dagger_p a_q$ terms that come out.
chemist_two_body_coefficients (ndarray): an N x N x N x N numpy array
giving the $g_{pqrs}$ tensor in chemist notation.
Raises:
TypeError: Input must be two-body number conserving
FermionOperator or InteractionOperator.
"""
# Initialize.
n_orbitals = two_body_coefficients.shape[0]
chemist_two_body_coefficients = numpy.transpose(two_body_coefficients,
[0, 3, 1, 2])
# If the specification was in spin-orbitals, chop down to spatial orbitals
# assuming a spin-symmetric interaction.
if spin_basis:
n_orbitals = n_orbitals // 2
alpha_indices = list(range(0, n_orbitals * 2, 2))
beta_indices = list(range(1, n_orbitals * 2, 2))
chemist_two_body_coefficients = chemist_two_body_coefficients[numpy.ix_(
alpha_indices, alpha_indices, beta_indices, beta_indices)]
# Determine a one body correction in the spin basis from spatial basis.
one_body_correction = numpy.zeros((2 * n_orbitals, 2 * n_orbitals), complex)
for p, q, r, s in itertools.product(range(n_orbitals), repeat=4):
for sigma, tau in itertools.product(range(2), repeat=2):
if (q == r) and (sigma == tau):
one_body_correction[2 * p + sigma, 2 * s + tau] -= (
chemist_two_body_coefficients[p, q, r, s])
# Return.
return one_body_correction, chemist_two_body_coefficients | [
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|
mrkipling/maraschino | c6be9286937783ae01df2d6d8cebfc8b2734a7d7 | lib/sqlalchemy/ext/sqlsoup.py | python | SqlSoup.delete | (self, instance) | Mark an instance as deleted. | Mark an instance as deleted. | [
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] | def delete(self, instance):
"""Mark an instance as deleted."""
self.session.delete(instance) | [
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||
Qiskit/qiskit-terra | b66030e3b9192efdd3eb95cf25c6545fe0a13da4 | qiskit/circuit/library/standard_gates/h.py | python | HGate.__init__ | (self, label: Optional[str] = None) | Create new H gate. | Create new H gate. | [
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||
m-rtijn/mpu6050 | 0626053a5e1182f4951b78b8326691a9223a5f7d | mpu6050/mpu6050.py | python | mpu6050.get_all_data | (self) | return [accel, gyro, temp] | Reads and returns all the available data. | Reads and returns all the available data. | [
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"""Reads and returns all the available data."""
temp = self.get_temp()
accel = self.get_accel_data()
gyro = self.get_gyro_data()
return [accel, gyro, temp] | [
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|
pypa/pipenv | b21baade71a86ab3ee1429f71fbc14d4f95fb75d | pipenv/vendor/more_itertools/more.py | python | seekable.elements | (self) | return SequenceView(self._cache) | [] | def elements(self):
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|||
spesmilo/electrum | bdbd59300fbd35b01605e66145458e5f396108e8 | electrum/util.py | python | base_unit_name_to_decimal_point | (unit_name: str) | [] | def base_unit_name_to_decimal_point(unit_name: str) -> int:
# e.g. "BTC" -> 8
try:
return base_units[unit_name]
except KeyError:
raise UnknownBaseUnit(unit_name) from None | [
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||||
paperswithcode/sota-extractor | 6a13c5091900432bea7ea7cae3a12944c8d5ab57 | sota_extractor/serialization.py | python | dumps | (tdb: TaskDB) | return json.dumps(tdb.export(), indent=2, sort_keys=True) | Render sota data to a json string. | Render sota data to a json string. | [
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] | def dumps(tdb: TaskDB) -> str:
"""Render sota data to a json string."""
return json.dumps(tdb.export(), indent=2, sort_keys=True) | [
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|
twilio/twilio-python | 6e1e811ea57a1edfadd5161ace87397c563f6915 | twilio/rest/messaging/v1/service/phone_number.py | python | PhoneNumberInstance.fetch | (self) | return self._proxy.fetch() | Fetch the PhoneNumberInstance
:returns: The fetched PhoneNumberInstance
:rtype: twilio.rest.messaging.v1.service.phone_number.PhoneNumberInstance | Fetch the PhoneNumberInstance | [
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"""
Fetch the PhoneNumberInstance
:returns: The fetched PhoneNumberInstance
:rtype: twilio.rest.messaging.v1.service.phone_number.PhoneNumberInstance
"""
return self._proxy.fetch() | [
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|
rembo10/headphones | b3199605be1ebc83a7a8feab6b1e99b64014187c | lib/mako/codegen.py | python | _GenerateRenderMethod.write_render_callable | (self, node, name, args, buffered, filtered,
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this could be the main render() method or that of a top-level def. | write a top-level render callable. | [
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"""write a top-level render callable.
this could be the main render() method or that of a top-level def."""
if self.in_def:
decorator = node.decorator
if decorator:
self.printer.writeline(
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self.printer.start_source(node.lineno)
self.printer.writelines(
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if buffered or filtered or cached:
self.printer.writeline("context._push_buffer()")
self.identifier_stack.append(
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"%s=%s" % (x, x) for x in
self.identifiers.argument_declared
]))
self.write_variable_declares(self.identifiers, toplevel=True)
for n in self.node.nodes:
n.accept_visitor(self)
self.write_def_finish(self.node, buffered, filtered, cached)
self.printer.writeline(None)
self.printer.write_blanks(2)
if cached:
self.write_cache_decorator(
node, name,
args, buffered,
self.identifiers, toplevel=True) | [
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||
googleads/google-ads-python | 2a1d6062221f6aad1992a6bcca0e7e4a93d2db86 | google/ads/googleads/v8/services/services/bidding_strategy_service/client.py | python | BiddingStrategyServiceClient.common_project_path | (project: str,) | return "projects/{project}".format(project=project,) | Return a fully-qualified project string. | Return a fully-qualified project string. | [
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] | def common_project_path(project: str,) -> str:
"""Return a fully-qualified project string."""
return "projects/{project}".format(project=project,) | [
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|
xingyizhou/CenterTrack | d3d52145b71cb9797da2bfb78f0f1e88b286c871 | src/lib/model/data_parallel.py | python | data_parallel | (module, inputs, device_ids=None, output_device=None, dim=0, module_kwargs=None) | return gather(outputs, output_device, dim) | r"""Evaluates module(input) in parallel across the GPUs given in device_ids.
This is the functional version of the DataParallel module.
Args:
module: the module to evaluate in parallel
inputs: inputs to the module
device_ids: GPU ids on which to replicate module
output_device: GPU location of the output Use -1 to indicate the CPU.
(default: device_ids[0])
Returns:
a Variable containing the result of module(input) located on
output_device | r"""Evaluates module(input) in parallel across the GPUs given in device_ids. | [
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] | def data_parallel(module, inputs, device_ids=None, output_device=None, dim=0, module_kwargs=None):
r"""Evaluates module(input) in parallel across the GPUs given in device_ids.
This is the functional version of the DataParallel module.
Args:
module: the module to evaluate in parallel
inputs: inputs to the module
device_ids: GPU ids on which to replicate module
output_device: GPU location of the output Use -1 to indicate the CPU.
(default: device_ids[0])
Returns:
a Variable containing the result of module(input) located on
output_device
"""
if not isinstance(inputs, tuple):
inputs = (inputs,)
if device_ids is None:
device_ids = list(range(torch.cuda.device_count()))
if output_device is None:
output_device = device_ids[0]
inputs, module_kwargs = scatter_kwargs(inputs, module_kwargs, device_ids, dim)
if len(device_ids) == 1:
return module(*inputs[0], **module_kwargs[0])
used_device_ids = device_ids[:len(inputs)]
replicas = replicate(module, used_device_ids)
outputs = parallel_apply(replicas, inputs, module_kwargs, used_device_ids)
return gather(outputs, output_device, dim) | [
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|
inkandswitch/livebook | 93c8d467734787366ad084fc3566bf5cbe249c51 | public/pypyjs/modules/numpy/core/fromnumeric.py | python | compress | (condition, a, axis=None, out=None) | return compress(condition, axis, out) | Return selected slices of an array along given axis.
When working along a given axis, a slice along that axis is returned in
`output` for each index where `condition` evaluates to True. When
working on a 1-D array, `compress` is equivalent to `extract`.
Parameters
----------
condition : 1-D array of bools
Array that selects which entries to return. If len(condition)
is less than the size of `a` along the given axis, then output is
truncated to the length of the condition array.
a : array_like
Array from which to extract a part.
axis : int, optional
Axis along which to take slices. If None (default), work on the
flattened array.
out : ndarray, optional
Output array. Its type is preserved and it must be of the right
shape to hold the output.
Returns
-------
compressed_array : ndarray
A copy of `a` without the slices along axis for which `condition`
is false.
See Also
--------
take, choose, diag, diagonal, select
ndarray.compress : Equivalent method in ndarray
np.extract: Equivalent method when working on 1-D arrays
numpy.doc.ufuncs : Section "Output arguments"
Examples
--------
>>> a = np.array([[1, 2], [3, 4], [5, 6]])
>>> a
array([[1, 2],
[3, 4],
[5, 6]])
>>> np.compress([0, 1], a, axis=0)
array([[3, 4]])
>>> np.compress([False, True, True], a, axis=0)
array([[3, 4],
[5, 6]])
>>> np.compress([False, True], a, axis=1)
array([[2],
[4],
[6]])
Working on the flattened array does not return slices along an axis but
selects elements.
>>> np.compress([False, True], a)
array([2]) | Return selected slices of an array along given axis. | [
"Return",
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"an",
"array",
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"given",
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] | def compress(condition, a, axis=None, out=None):
"""
Return selected slices of an array along given axis.
When working along a given axis, a slice along that axis is returned in
`output` for each index where `condition` evaluates to True. When
working on a 1-D array, `compress` is equivalent to `extract`.
Parameters
----------
condition : 1-D array of bools
Array that selects which entries to return. If len(condition)
is less than the size of `a` along the given axis, then output is
truncated to the length of the condition array.
a : array_like
Array from which to extract a part.
axis : int, optional
Axis along which to take slices. If None (default), work on the
flattened array.
out : ndarray, optional
Output array. Its type is preserved and it must be of the right
shape to hold the output.
Returns
-------
compressed_array : ndarray
A copy of `a` without the slices along axis for which `condition`
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See Also
--------
take, choose, diag, diagonal, select
ndarray.compress : Equivalent method in ndarray
np.extract: Equivalent method when working on 1-D arrays
numpy.doc.ufuncs : Section "Output arguments"
Examples
--------
>>> a = np.array([[1, 2], [3, 4], [5, 6]])
>>> a
array([[1, 2],
[3, 4],
[5, 6]])
>>> np.compress([0, 1], a, axis=0)
array([[3, 4]])
>>> np.compress([False, True, True], a, axis=0)
array([[3, 4],
[5, 6]])
>>> np.compress([False, True], a, axis=1)
array([[2],
[4],
[6]])
Working on the flattened array does not return slices along an axis but
selects elements.
>>> np.compress([False, True], a)
array([2])
"""
try:
compress = a.compress
except AttributeError:
return _wrapit(a, 'compress', condition, axis, out)
return compress(condition, axis, out) | [
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|
demisto/content | 5c664a65b992ac8ca90ac3f11b1b2cdf11ee9b07 | Packs/RiskIQDigitalFootprint/Integrations/RiskIQDigitalFootprint/RiskIQDigitalFootprint.py | python | get_asset_command | (client: Client, args: Dict[str, Any]) | return command_results | Retrieve the asset of the specified UUID from Global Inventory.
:param client: The Client object used for request
:param args: The command arguments
:return: CommandResults
:return: CommandResults | Retrieve the asset of the specified UUID from Global Inventory. | [
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] | def get_asset_command(client: Client, args: Dict[str, Any]) -> Union[str, List[CommandResults]]:
"""
Retrieve the asset of the specified UUID from Global Inventory.
:param client: The Client object used for request
:param args: The command arguments
:return: CommandResults
:return: CommandResults
"""
uuid, asset_type = validate_and_fetch_get_asset_arguments(args)
params = get_asset_params(args)
if uuid:
resp = client.http_request(method='GET', url_suffix=COMMAND_URL_SUFFIX['GET_ASSET_BY_UUID'].format(uuid),
params=params)
else:
resp = client.http_request(method='GET', url_suffix=COMMAND_URL_SUFFIX['GET_ASSET_BY_NAME_AND_TYPE']
.format(asset_type), params=params)
command_results = get_asset_outputs(resp)
return command_results | [
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|
ionelmc/python-redis-lock | 1842f8a6780613e8c89938218121e0ee464d2a50 | src/redis_lock/__init__.py | python | reset_all | (redis_client) | Forcibly deletes all locks if its remains (like a crash reason). Use this with care.
:param redis_client:
An instance of :class:`~StrictRedis`. | Forcibly deletes all locks if its remains (like a crash reason). Use this with care. | [
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"""
Forcibly deletes all locks if its remains (like a crash reason). Use this with care.
:param redis_client:
An instance of :class:`~StrictRedis`.
"""
Lock.register_scripts(redis_client)
reset_all_script(client=redis_client) | [
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||
rm-hull/luma.oled | 5cbac38eaa3d7b06cf97f24d76877693c72d3233 | luma/oled/device/__init__.py | python | sh1106.display | (self, image) | Takes a 1-bit :py:mod:`PIL.Image` and dumps it to the SH1106
OLED display.
:param image: Image to display.
:type image: :py:mod:`PIL.Image` | Takes a 1-bit :py:mod:`PIL.Image` and dumps it to the SH1106
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"""
Takes a 1-bit :py:mod:`PIL.Image` and dumps it to the SH1106
OLED display.
:param image: Image to display.
:type image: :py:mod:`PIL.Image`
"""
assert(image.mode == self.mode)
assert(image.size == self.size)
image = self.preprocess(image)
set_page_address = 0xB0
image_data = image.getdata()
pixels_per_page = self.width * 8
buf = bytearray(self.width)
for y in range(0, int(self._pages * pixels_per_page), pixels_per_page):
self.command(set_page_address, 0x02, 0x10)
set_page_address += 1
offsets = [y + self.width * i for i in range(8)]
for x in range(self.width):
buf[x] = \
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(image_data[x + offsets[1]] and 0x02) | \
(image_data[x + offsets[2]] and 0x04) | \
(image_data[x + offsets[3]] and 0x08) | \
(image_data[x + offsets[4]] and 0x10) | \
(image_data[x + offsets[5]] and 0x20) | \
(image_data[x + offsets[6]] and 0x40) | \
(image_data[x + offsets[7]] and 0x80)
self.data(list(buf)) | [
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||
kubernetes-sigs/kubespray | cd601c77c7df953ef4f098a5c728cdd8afe9fdbd | contrib/terraform/terraform.py | python | iter_host_ips | (hosts, ips) | Update hosts that have an entry in the floating IP list | Update hosts that have an entry in the floating IP list | [
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] | def iter_host_ips(hosts, ips):
'''Update hosts that have an entry in the floating IP list'''
for host in hosts:
host_id = host[1]['id']
if host_id in ips:
ip = ips[host_id]
host[1].update({
'access_ip_v4': ip,
'access_ip': ip,
'public_ipv4': ip,
'ansible_ssh_host': ip,
})
if 'use_access_ip' in host[1]['metadata'] and host[1]['metadata']['use_access_ip'] == "0":
host[1].pop('access_ip')
yield host | [
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||
ronf/asyncssh | ee1714c598d8c2ea6f5484e465443f38b68714aa | asyncssh/channel.py | python | SSHClientChannel.send_break | (self, msec: int) | Send a break to the remote process
This method requests that the server perform a break
operation on the remote process or service as described in
:rfc:`4335`.
:param msec:
The duration of the break in milliseconds
:type msec: `int`
:raises: :exc:`OSError` if the channel is not open | Send a break to the remote process | [
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] | def send_break(self, msec: int) -> None:
"""Send a break to the remote process
This method requests that the server perform a break
operation on the remote process or service as described in
:rfc:`4335`.
:param msec:
The duration of the break in milliseconds
:type msec: `int`
:raises: :exc:`OSError` if the channel is not open
"""
self.logger.info('Sending %d msec break', msec)
self._send_request(b'break', UInt32(msec)) | [
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||
holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/sympy/geometry/point.py | python | Point.is_collinear | (self, *args) | return Point.affine_rank(*points) <= 1 | Returns `True` if there exists a line
that contains `self` and `points`. Returns `False` otherwise.
A trivially True value is returned if no points are given.
Parameters
==========
args : sequence of Points
Returns
=======
is_collinear : boolean
See Also
========
sympy.geometry.line.Line
Examples
========
>>> from sympy import Point
>>> from sympy.abc import x
>>> p1, p2 = Point(0, 0), Point(1, 1)
>>> p3, p4, p5 = Point(2, 2), Point(x, x), Point(1, 2)
>>> Point.is_collinear(p1, p2, p3, p4)
True
>>> Point.is_collinear(p1, p2, p3, p5)
False | Returns `True` if there exists a line
that contains `self` and `points`. Returns `False` otherwise.
A trivially True value is returned if no points are given. | [
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"""Returns `True` if there exists a line
that contains `self` and `points`. Returns `False` otherwise.
A trivially True value is returned if no points are given.
Parameters
==========
args : sequence of Points
Returns
=======
is_collinear : boolean
See Also
========
sympy.geometry.line.Line
Examples
========
>>> from sympy import Point
>>> from sympy.abc import x
>>> p1, p2 = Point(0, 0), Point(1, 1)
>>> p3, p4, p5 = Point(2, 2), Point(x, x), Point(1, 2)
>>> Point.is_collinear(p1, p2, p3, p4)
True
>>> Point.is_collinear(p1, p2, p3, p5)
False
"""
points = (self,) + args
points = Point._normalize_dimension(*[Point(i) for i in points])
points = list(uniq(points))
return Point.affine_rank(*points) <= 1 | [
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|
linxid/Machine_Learning_Study_Path | 558e82d13237114bbb8152483977806fc0c222af | Machine Learning In Action/Chapter4-NaiveBayes/venv/Lib/site-packages/pip/_vendor/requests/packages/chardet/utf8prober.py | python | UTF8Prober.reset | (self) | [] | def reset(self):
CharSetProber.reset(self)
self._mCodingSM.reset()
self._mNumOfMBChar = 0 | [
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||||
NVIDIA/DeepLearningExamples | 589604d49e016cd9ef4525f7abcc9c7b826cfc5e | PyTorch/Detection/Efficientdet/effdet/bench.py | python | _post_process | (config, cls_outputs, box_outputs) | return cls_outputs_all_after_topk, box_outputs_all_after_topk, indices_all, classes_all | Selects top-k predictions.
Post-proc code adapted from Tensorflow version at: https://github.com/google/automl/tree/master/efficientdet
and optimized for PyTorch.
Args:
config: a parameter dictionary that includes `min_level`, `max_level`, `batch_size`, and `num_classes`.
cls_outputs: an OrderDict with keys representing levels and values
representing logits in [batch_size, height, width, num_anchors].
box_outputs: an OrderDict with keys representing levels and values
representing box regression targets in [batch_size, height, width, num_anchors * 4]. | Selects top-k predictions. | [
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] | def _post_process(config, cls_outputs, box_outputs):
"""Selects top-k predictions.
Post-proc code adapted from Tensorflow version at: https://github.com/google/automl/tree/master/efficientdet
and optimized for PyTorch.
Args:
config: a parameter dictionary that includes `min_level`, `max_level`, `batch_size`, and `num_classes`.
cls_outputs: an OrderDict with keys representing levels and values
representing logits in [batch_size, height, width, num_anchors].
box_outputs: an OrderDict with keys representing levels and values
representing box regression targets in [batch_size, height, width, num_anchors * 4].
"""
batch_size = cls_outputs[0].shape[0]
if config.fused_focal_loss:
batch_size, channels, _, _ = cls_outputs[0].shape
padded_classes = (config.num_classes + 7) // 8 * 8
anchors = channels // padded_classes
_cls_outputs_all = []
for level in range(config.num_levels):
_, _, height, width = cls_outputs[level].shape
_cls_output = cls_outputs[level].permute(0, 2, 3, 1)
_cls_output = _cls_output.view(batch_size, height, width, anchors, padded_classes)
_cls_output = _cls_output[..., :config.num_classes]
_cls_output = _cls_output.reshape([batch_size, -1, config.num_classes])
_cls_outputs_all.append(_cls_output)
cls_outputs_all = torch.cat(_cls_outputs_all, 1)
else:
cls_outputs_all = torch.cat([
cls_outputs[level].permute(0, 2, 3, 1).reshape([batch_size, -1, config.num_classes])
for level in range(config.num_levels)], 1)
box_outputs_all = torch.cat([
box_outputs[level].permute(0, 2, 3, 1).reshape([batch_size, -1, 4])
for level in range(config.num_levels)], 1)
_, cls_topk_indices_all = torch.topk(cls_outputs_all.reshape(batch_size, -1), dim=1, k=MAX_DETECTION_POINTS, sorted=False)
indices_all = cls_topk_indices_all // config.num_classes
classes_all = cls_topk_indices_all % config.num_classes
box_outputs_all_after_topk = torch.gather(
box_outputs_all, 1, indices_all.unsqueeze(2).expand(-1, -1, 4))
cls_outputs_all_after_topk = torch.gather(
cls_outputs_all, 1, indices_all.unsqueeze(2).expand(-1, -1, config.num_classes))
cls_outputs_all_after_topk = torch.gather(
cls_outputs_all_after_topk, 2, classes_all.unsqueeze(2))
return cls_outputs_all_after_topk, box_outputs_all_after_topk, indices_all, classes_all | [
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|
zulip/python-zulip-api | 70b86614bd15347e28ec2cab4c87c01122faae16 | zulip/zulip/__init__.py | python | Client.get_subscriptions | (self, request: Optional[Dict[str, Any]] = None) | return self.call_endpoint(
url="users/me/subscriptions",
method="GET",
request=request,
) | See examples/get-subscriptions for example usage. | See examples/get-subscriptions for example usage. | [
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"""
See examples/get-subscriptions for example usage.
"""
return self.call_endpoint(
url="users/me/subscriptions",
method="GET",
request=request,
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|
spesmilo/electrum | bdbd59300fbd35b01605e66145458e5f396108e8 | electrum/gui/qt/util.py | python | MySortModel.lessThan | (self, source_left: QModelIndex, source_right: QModelIndex) | [] | def lessThan(self, source_left: QModelIndex, source_right: QModelIndex):
item1 = self.sourceModel().itemFromIndex(source_left)
item2 = self.sourceModel().itemFromIndex(source_right)
data1 = item1.data(self._sort_role)
data2 = item2.data(self._sort_role)
if data1 is not None and data2 is not None:
return data1 < data2
v1 = item1.text()
v2 = item2.text()
try:
return Decimal(v1) < Decimal(v2)
except:
return v1 < v2 | [
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||||
espeed/bulbs | 628e5b14f0249f9ca4fa1ceea6f2af2dca45f75a | bulbs/neo4jserver/index.py | python | Index.count | (self, key=None, value=None, **pair) | return total_size | Return the number of items in the index for the key and value.
:param key: The index key.
:type key: str
:param value: The key's value.
:type value: str or int
:param pair: Optional key/value pair. Example: name="James"
:type pair: name/value pair
:rtype: int | Return the number of items in the index for the key and value. | [
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"""
Return the number of items in the index for the key and value.
:param key: The index key.
:type key: str
:param value: The key's value.
:type value: str or int
:param pair: Optional key/value pair. Example: name="James"
:type pair: name/value pair
:rtype: int
"""
key, value = self._get_key_value(key,value,pair)
script = self.client.scripts.get('index_count')
params = dict(index_name=self.index_name,key=key,value=value)
resp = self.client.gremlin(script,params)
total_size = int(resp.content)
return total_size | [
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|
JacksonWuxs/DaPy | b2bf72707ffcc92d05af1ac890e0786d5787816e | DaPy/core/base/Sheet.py | python | SeriesSet.map | (self, func, cols=None, inplace=False) | return self._map(func, cols) | apply(func, col=None, *args, **kwrds)
apply a function to columns or rows
Parameters
----------
func : callable object or dict-like object
col : str, str in list (default='all')
the columns that you expect to process
inplace : True or False (default=0)
update values in current dataset or return new values
Returns
-------
mapped_sheet : SeriesSet
Notes
-----
1. Function may be locked when `inplace` is True and
sheet.locked is False. When you operate the
column which is an Index, it will be locked. | apply(func, col=None, *args, **kwrds)
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apply a function to columns or rows
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return self._map(func, cols) | [
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|
CouchPotato/CouchPotatoServer | 7260c12f72447ddb6f062367c6dfbda03ecd4e9c | libs/pytwitter/__init__.py | python | List.AsDict | (self) | return data | A dict representation of this twitter.List instance.
The return value uses the same key names as the JSON representation.
Return:
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'''A dict representation of this twitter.List instance.
The return value uses the same key names as the JSON representation.
Return:
A dict representing this twitter.List instance
'''
data = {}
if self.id:
data['id'] = self.id
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data['name'] = self.name
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data['slug'] = self.slug
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data['description'] = self.description
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data['full_name'] = self.full_name
if self.mode:
data['mode'] = self.mode
if self.uri:
data['uri'] = self.uri
if self.member_count is not None:
data['member_count'] = self.member_count
if self.subscriber_count is not None:
data['subscriber_count'] = self.subscriber_count
if self.following is not None:
data['following'] = self.following
if self.user is not None:
data['user'] = self.user.AsDict()
return data | [
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|
tribe29/checkmk | 6260f2512e159e311f426e16b84b19d0b8e9ad0c | cmk/special_agents/agent_aws.py | python | GlacierLimits.get_live_data | (self, *args) | return self._get_response_content(response, "VaultList") | There's no API method for getting account limits thus we have to
fetch all vaults. | There's no API method for getting account limits thus we have to
fetch all vaults. | [
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"""
There's no API method for getting account limits thus we have to
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"""
response = self._client.list_vaults()
return self._get_response_content(response, "VaultList") | [
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|
smart-mobile-software/gitstack | d9fee8f414f202143eb6e620529e8e5539a2af56 | python/Lib/site-packages/django/dispatch/dispatcher.py | python | Signal.send | (self, sender, **named) | return responses | Send signal from sender to all connected receivers.
If any receiver raises an error, the error propagates back through send,
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receivers called if a raises an error.
Arguments:
sender
The sender of the signal Either a specific object or None.
named
Named arguments which will be passed to receivers.
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Send signal from sender to all connected receivers.
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receivers called if a raises an error.
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The sender of the signal Either a specific object or None.
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Named arguments which will be passed to receivers.
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responses = []
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return responses
for receiver in self._live_receivers(_make_id(sender)):
response = receiver(signal=self, sender=sender, **named)
responses.append((receiver, response))
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|
oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/distutils/cmd.py | python | Command.finalize_options | (self) | Set final values for all the options that this command supports.
This is always called as late as possible, ie. after any option
assignments from the command-line or from other commands have been
done. Thus, this is the place to code option dependencies: if
'foo' depends on 'bar', then it is safe to set 'foo' from 'bar' as
long as 'foo' still has the same value it was assigned in
'initialize_options()'.
This method must be implemented by all command classes. | Set final values for all the options that this command supports.
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done. Thus, this is the place to code option dependencies: if
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This is always called as late as possible, ie. after any option
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done. Thus, this is the place to code option dependencies: if
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||
holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/sympy/stats/error_prop.py | python | variance_prop | (expr, consts=(), include_covar=False) | return var_expr | r"""Symbolically propagates variance (`\sigma^2`) for expressions.
This is computed as as seen in [1]_.
Parameters
==========
expr : Expr
A sympy expression to compute the variance for.
consts : sequence of Symbols, optional
Represents symbols that are known constants in the expr,
and thus have zero variance. All symbols not in consts are
assumed to be variant.
include_covar : bool, optional
Flag for whether or not to include covariances, default=False.
Returns
=======
var_expr : Expr
An expression for the total variance of the expr.
The variance for the original symbols (e.g. x) are represented
via instance of the Variance symbol (e.g. Variance(x)).
Examples
========
>>> from sympy import symbols, exp
>>> from sympy.stats.error_prop import variance_prop
>>> x, y = symbols('x y')
>>> variance_prop(x + y)
Variance(x) + Variance(y)
>>> variance_prop(x * y)
x**2*Variance(y) + y**2*Variance(x)
>>> variance_prop(exp(2*x))
4*exp(4*x)*Variance(x)
References
==========
.. [1] https://en.wikipedia.org/wiki/Propagation_of_uncertainty | r"""Symbolically propagates variance (`\sigma^2`) for expressions.
This is computed as as seen in [1]_. | [
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r"""Symbolically propagates variance (`\sigma^2`) for expressions.
This is computed as as seen in [1]_.
Parameters
==========
expr : Expr
A sympy expression to compute the variance for.
consts : sequence of Symbols, optional
Represents symbols that are known constants in the expr,
and thus have zero variance. All symbols not in consts are
assumed to be variant.
include_covar : bool, optional
Flag for whether or not to include covariances, default=False.
Returns
=======
var_expr : Expr
An expression for the total variance of the expr.
The variance for the original symbols (e.g. x) are represented
via instance of the Variance symbol (e.g. Variance(x)).
Examples
========
>>> from sympy import symbols, exp
>>> from sympy.stats.error_prop import variance_prop
>>> x, y = symbols('x y')
>>> variance_prop(x + y)
Variance(x) + Variance(y)
>>> variance_prop(x * y)
x**2*Variance(y) + y**2*Variance(x)
>>> variance_prop(exp(2*x))
4*exp(4*x)*Variance(x)
References
==========
.. [1] https://en.wikipedia.org/wiki/Propagation_of_uncertainty
"""
args = expr.args
if len(args) == 0:
if expr in consts:
return S.Zero
elif isinstance(expr, RandomSymbol):
return Variance(expr).doit()
elif isinstance(expr, Symbol):
return Variance(RandomSymbol(expr)).doit()
else:
return S.Zero
nargs = len(args)
var_args = list(map(variance_prop, args, repeat(consts, nargs),
repeat(include_covar, nargs)))
if isinstance(expr, Add):
var_expr = Add(*var_args)
if include_covar:
terms = [2 * Covariance(_arg0_or_var(x), _arg0_or_var(y)).doit() \
for x, y in combinations(var_args, 2)]
var_expr += Add(*terms)
elif isinstance(expr, Mul):
terms = [v/a**2 for a, v in zip(args, var_args)]
var_expr = simplify(expr**2 * Add(*terms))
if include_covar:
terms = [2*Covariance(_arg0_or_var(x), _arg0_or_var(y)).doit()/(a*b) \
for (a, b), (x, y) in zip(combinations(args, 2),
combinations(var_args, 2))]
var_expr += Add(*terms)
elif isinstance(expr, Pow):
b = args[1]
v = var_args[0] * (expr * b / args[0])**2
var_expr = simplify(v)
elif isinstance(expr, exp):
var_expr = simplify(var_args[0] * expr**2)
else:
# unknown how to proceed, return variance of whole expr.
var_expr = Variance(expr)
return var_expr | [
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] | https://github.com/holzschu/Carnets/blob/44effb10ddfc6aa5c8b0687582a724ba82c6b547/Library/lib/python3.7/site-packages/sympy/stats/error_prop.py#L12-L94 |
|
GoogleCloudPlatform/professional-services | 0c707aa97437f3d154035ef8548109b7882f71da | examples/cloudml-sklearn-pipeline/trainer/util/preprocess_utils.py | python | get_preprocess_pipeline | (feature_columns, categorical_names,
numerical_names) | return preprocessor | Helper function that construct the preprocessing pipeline based on the
type of the feature, i.e., numerical or categorical.
Args:
feature_columns: (List[string]), name of all the columns for the data
goes into preprocessing pipeline
categorical_names: (List[string]), name of all categorical features
numerical_names: (List[string]), name of all numerical features
Returns:
sklearn.compose.ColumnTransformer | Helper function that construct the preprocessing pipeline based on the
type of the feature, i.e., numerical or categorical. | [
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numerical_names):
"""Helper function that construct the preprocessing pipeline based on the
type of the feature, i.e., numerical or categorical.
Args:
feature_columns: (List[string]), name of all the columns for the data
goes into preprocessing pipeline
categorical_names: (List[string]), name of all categorical features
numerical_names: (List[string]), name of all numerical features
Returns:
sklearn.compose.ColumnTransformer
"""
# Currently, this reply on the settings (numerical columns and
# categorical columns) in metadata.py. May consider move it to a dedicated
# config or setting file together with get_transform_pipeline
numeric_transformer = pipeline.Pipeline([
('imputer', impute.SimpleImputer(strategy='median')),
('scaler', preprocessing.StandardScaler()),
])
# Apply scale transformation to numerical attributes.
# Log transformation is used here.
numeric_log_transformer = pipeline.Pipeline([
('imputer', impute.SimpleImputer(strategy='median')),
('log', preprocessing.FunctionTransformer(
func=np.log1p, inverse_func=np.expm1, validate=True)),
('scaler', preprocessing.StandardScaler()),
])
# Bucketing numerical attributes
numeric_bin_transformer = pipeline.Pipeline([
('imputer', impute.SimpleImputer(strategy='median')),
('bin', preprocessing.KBinsDiscretizer(n_bins=3, encode='onehot-dense')),
])
categorical_transformer = pipeline.Pipeline([
('imputer', impute.SimpleImputer(
strategy='constant', fill_value=None)),
('onehot', preprocessing.OneHotEncoder(
handle_unknown='ignore', sparse=False)),
])
boolean_mask = functools.partial(utils.boolean_mask, feature_columns)
numerical_boolean = boolean_mask(numerical_names)
categorical_boolean = boolean_mask(categorical_names)
transform_list = []
# If there exist numerical columns
if any(numerical_boolean):
transform_list.extend([
('numeric', numeric_transformer, numerical_boolean),
('numeric_log', numeric_log_transformer, numerical_boolean),
('numeric_bin', numeric_bin_transformer, numerical_boolean),
])
# If there exist categorical columns
if any(categorical_boolean):
transform_list.extend([
('categorical', categorical_transformer, categorical_boolean),
])
preprocessor = compose.ColumnTransformer(transform_list)
return preprocessor | [
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|
google/grr | 8ad8a4d2c5a93c92729206b7771af19d92d4f915 | grr/server/grr_response_server/artifact.py | python | ParseResults.Responses | (self) | return iter(self._responses) | [] | def Responses(self) -> Iterator[rdfvalue.RDFValue]:
return iter(self._responses) | [
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|||
zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/pip/wheel.py | python | _cache_for_link | (cache_dir, link) | return os.path.join(cache_dir, "wheels", *parts) | Return a directory to store cached wheels in for link.
Because there are M wheels for any one sdist, we provide a directory
to cache them in, and then consult that directory when looking up
cache hits.
We only insert things into the cache if they have plausible version
numbers, so that we don't contaminate the cache with things that were not
unique. E.g. ./package might have dozens of installs done for it and build
a version of 0.0...and if we built and cached a wheel, we'd end up using
the same wheel even if the source has been edited.
:param cache_dir: The cache_dir being used by pip.
:param link: The link of the sdist for which this will cache wheels. | Return a directory to store cached wheels in for link. | [
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"link",
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] | def _cache_for_link(cache_dir, link):
"""
Return a directory to store cached wheels in for link.
Because there are M wheels for any one sdist, we provide a directory
to cache them in, and then consult that directory when looking up
cache hits.
We only insert things into the cache if they have plausible version
numbers, so that we don't contaminate the cache with things that were not
unique. E.g. ./package might have dozens of installs done for it and build
a version of 0.0...and if we built and cached a wheel, we'd end up using
the same wheel even if the source has been edited.
:param cache_dir: The cache_dir being used by pip.
:param link: The link of the sdist for which this will cache wheels.
"""
# We want to generate an url to use as our cache key, we don't want to just
# re-use the URL because it might have other items in the fragment and we
# don't care about those.
key_parts = [link.url_without_fragment]
if link.hash_name is not None and link.hash is not None:
key_parts.append("=".join([link.hash_name, link.hash]))
key_url = "#".join(key_parts)
# Encode our key url with sha224, we'll use this because it has similar
# security properties to sha256, but with a shorter total output (and thus
# less secure). However the differences don't make a lot of difference for
# our use case here.
hashed = hashlib.sha224(key_url.encode()).hexdigest()
# We want to nest the directories some to prevent having a ton of top level
# directories where we might run out of sub directories on some FS.
parts = [hashed[:2], hashed[2:4], hashed[4:6], hashed[6:]]
# Inside of the base location for cached wheels, expand our parts and join
# them all together.
return os.path.join(cache_dir, "wheels", *parts) | [
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|
MDudek-ICS/TRISIS-TRITON-HATMAN | 15a00af7fd1040f0430729d024427601f84886a1 | decompiled_code/library/logging/__init__.py | python | Logger.removeHandler | (self, hdlr) | Remove the specified handler from this logger. | Remove the specified handler from this logger. | [
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] | def removeHandler(self, hdlr):
"""
Remove the specified handler from this logger.
"""
_acquireLock()
try:
if hdlr in self.handlers:
self.handlers.remove(hdlr)
finally:
_releaseLock() | [
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||
tensorflow/lattice | 784eca50cbdfedf39f183cc7d298c9fe376b69c0 | tensorflow_lattice/python/linear_layer.py | python | Linear.assert_constraints | (self, eps=1e-4) | return linear_lib.assert_constraints(
weights=self.kernel,
monotonicities=utils.canonicalize_monotonicities(self.monotonicities),
monotonic_dominances=self.monotonic_dominances,
range_dominances=self.range_dominances,
input_min=utils.canonicalize_input_bounds(self.input_min),
input_max=utils.canonicalize_input_bounds(self.input_max),
normalization_order=self.normalization_order,
eps=eps) | Asserts that weights satisfy all constraints.
In graph mode builds and returns list of assertion ops.
In eager mode directly executes assertions.
Args:
eps: Allowed constraints violation.
Returns:
List of assertion ops in graph mode or immediately asserts in eager mode. | Asserts that weights satisfy all constraints. | [
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] | def assert_constraints(self, eps=1e-4):
"""Asserts that weights satisfy all constraints.
In graph mode builds and returns list of assertion ops.
In eager mode directly executes assertions.
Args:
eps: Allowed constraints violation.
Returns:
List of assertion ops in graph mode or immediately asserts in eager mode.
"""
return linear_lib.assert_constraints(
weights=self.kernel,
monotonicities=utils.canonicalize_monotonicities(self.monotonicities),
monotonic_dominances=self.monotonic_dominances,
range_dominances=self.range_dominances,
input_min=utils.canonicalize_input_bounds(self.input_min),
input_max=utils.canonicalize_input_bounds(self.input_max),
normalization_order=self.normalization_order,
eps=eps) | [
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|
mesalock-linux/mesapy | ed546d59a21b36feb93e2309d5c6b75aa0ad95c9 | pypy/module/__builtin__/compiling.py | python | eval | (space, w_code, w_globals=None, w_locals=None) | return w_code.exec_code(space, w_globals, w_locals) | Evaluate the source in the context of globals and locals.
The source may be a string representing a Python expression
or a code object as returned by compile(). The globals and locals
are dictionaries, defaulting to the current current globals and locals.
If only globals is given, locals defaults to it. | Evaluate the source in the context of globals and locals.
The source may be a string representing a Python expression
or a code object as returned by compile(). The globals and locals
are dictionaries, defaulting to the current current globals and locals.
If only globals is given, locals defaults to it. | [
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"""Evaluate the source in the context of globals and locals.
The source may be a string representing a Python expression
or a code object as returned by compile(). The globals and locals
are dictionaries, defaulting to the current current globals and locals.
If only globals is given, locals defaults to it.
"""
if (space.isinstance_w(w_code, space.w_bytes) or
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w_code = compile(space,
space.call_method(w_code, 'lstrip',
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if not isinstance(w_code, PyCode):
raise oefmt(space.w_TypeError,
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if space.is_none(w_globals):
caller = space.getexecutioncontext().gettopframe_nohidden()
if caller is None:
w_globals = space.newdict()
if space.is_none(w_locals):
w_locals = w_globals
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if space.is_none(w_locals):
w_locals = caller.getdictscope()
elif space.is_none(w_locals):
w_locals = w_globals
# xxx removed: adding '__builtins__' to the w_globals dict, if there
# is none. This logic was removed as costly (it requires to get at
# the gettopframe_nohidden()). I bet no test fails, and it's a really
# obscure case.
return w_code.exec_code(space, w_globals, w_locals) | [
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] | https://github.com/mesalock-linux/mesapy/blob/ed546d59a21b36feb93e2309d5c6b75aa0ad95c9/pypy/module/__builtin__/compiling.py#L67-L103 |
|
cupy/cupy | a47ad3105f0fe817a4957de87d98ddccb8c7491f | cupy/_manipulation/join.py | python | column_stack | (tup) | return concatenate(lst, axis=1) | Stacks 1-D and 2-D arrays as columns into a 2-D array.
A 1-D array is first converted to a 2-D column array. Then, the 2-D arrays
are concatenated along the second axis.
Args:
tup (sequence of arrays): 1-D or 2-D arrays to be stacked.
Returns:
cupy.ndarray: A new 2-D array of stacked columns.
.. seealso:: :func:`numpy.column_stack` | Stacks 1-D and 2-D arrays as columns into a 2-D array. | [
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"-",
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] | def column_stack(tup):
"""Stacks 1-D and 2-D arrays as columns into a 2-D array.
A 1-D array is first converted to a 2-D column array. Then, the 2-D arrays
are concatenated along the second axis.
Args:
tup (sequence of arrays): 1-D or 2-D arrays to be stacked.
Returns:
cupy.ndarray: A new 2-D array of stacked columns.
.. seealso:: :func:`numpy.column_stack`
"""
if any(not isinstance(a, cupy.ndarray) for a in tup):
raise TypeError('Only cupy arrays can be column stacked')
lst = list(tup)
for i, a in enumerate(lst):
if a.ndim == 1:
a = a[:, cupy.newaxis]
lst[i] = a
elif a.ndim != 2:
raise ValueError(
'Only 1 or 2 dimensional arrays can be column stacked')
return concatenate(lst, axis=1) | [
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|
microsoft/debugpy | be8dd607f6837244e0b565345e497aff7a0c08bf | src/debugpy/_vendored/pydevd/pydevd_attach_to_process/winappdbg/sql.py | python | MemoryDTO.toMBI | (self, getMemoryDump = False) | return mbi | Returns a L{win32.MemoryBasicInformation} object using the data
retrieved from the database.
@type getMemoryDump: bool
@param getMemoryDump: (Optional) If C{True} retrieve the memory dump.
Defaults to C{False} since this may be a costly operation.
@rtype: L{win32.MemoryBasicInformation}
@return: Memory block information. | Returns a L{win32.MemoryBasicInformation} object using the data
retrieved from the database. | [
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"}",
"object",
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"the",
"data",
"retrieved",
"from",
"the",
"database",
"."
] | def toMBI(self, getMemoryDump = False):
"""
Returns a L{win32.MemoryBasicInformation} object using the data
retrieved from the database.
@type getMemoryDump: bool
@param getMemoryDump: (Optional) If C{True} retrieve the memory dump.
Defaults to C{False} since this may be a costly operation.
@rtype: L{win32.MemoryBasicInformation}
@return: Memory block information.
"""
mbi = win32.MemoryBasicInformation()
mbi.BaseAddress = self.address
mbi.RegionSize = self.size
mbi.State = self._parse_state(self.state)
mbi.Protect = self._parse_access(self.access)
mbi.Type = self._parse_type(self.type)
if self.alloc_base is not None:
mbi.AllocationBase = self.alloc_base
else:
mbi.AllocationBase = mbi.BaseAddress
if self.alloc_access is not None:
mbi.AllocationProtect = self._parse_access(self.alloc_access)
else:
mbi.AllocationProtect = mbi.Protect
if self.filename is not None:
mbi.filename = self.filename
if getMemoryDump and self.content is not None:
mbi.content = self.content
return mbi | [
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|
mozillazg/pypy | 2ff5cd960c075c991389f842c6d59e71cf0cb7d0 | lib-python/2.7/string.py | python | split | (s, sep=None, maxsplit=-1) | return s.split(sep, maxsplit) | split(s [,sep [,maxsplit]]) -> list of strings
Return a list of the words in the string s, using sep as the
delimiter string. If maxsplit is given, splits at no more than
maxsplit places (resulting in at most maxsplit+1 words). If sep
is not specified or is None, any whitespace string is a separator.
(split and splitfields are synonymous) | split(s [,sep [,maxsplit]]) -> list of strings | [
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"(",
"s",
"[",
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"]]",
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] | def split(s, sep=None, maxsplit=-1):
"""split(s [,sep [,maxsplit]]) -> list of strings
Return a list of the words in the string s, using sep as the
delimiter string. If maxsplit is given, splits at no more than
maxsplit places (resulting in at most maxsplit+1 words). If sep
is not specified or is None, any whitespace string is a separator.
(split and splitfields are synonymous)
"""
return s.split(sep, maxsplit) | [
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|
Ttl/evolutionary-circuits | 6a6000ecbfbd64c9fd9df79574aad8682957c954 | evolutionary/chromosomes/common.py | python | multipliers | (x) | Convert values with si multipliers to numbers | Convert values with si multipliers to numbers | [
"Convert",
"values",
"with",
"si",
"multipliers",
"to",
"numbers"
] | def multipliers(x):
"""Convert values with si multipliers to numbers"""
try:
return float(x)
except:
pass
try:
a = x[-1]
y = float(x[:-1])
endings = {'G':9,'Meg':6,'k':3,'m':-3,'u':-6,'n':-9,'p':-12,'s':0}
return y*(10**endings[a])
except:
raise ValueError("I don't know what {} means".format(x)) | [
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] | https://github.com/Ttl/evolutionary-circuits/blob/6a6000ecbfbd64c9fd9df79574aad8682957c954/evolutionary/chromosomes/common.py#L38-L50 |
||
theotherp/nzbhydra | 4b03d7f769384b97dfc60dade4806c0fc987514e | libs/pycparser/_ast_gen.py | python | ASTCodeGenerator.generate | (self, file=None) | Generates the code into file, an open file buffer. | Generates the code into file, an open file buffer. | [
"Generates",
"the",
"code",
"into",
"file",
"an",
"open",
"file",
"buffer",
"."
] | def generate(self, file=None):
""" Generates the code into file, an open file buffer.
"""
src = Template(_PROLOGUE_COMMENT).substitute(
cfg_filename=self.cfg_filename)
src += _PROLOGUE_CODE
for node_cfg in self.node_cfg:
src += node_cfg.generate_source() + '\n\n'
file.write(src) | [
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||
moggers87/salmon | 1d89164836f88aa25e85932b08192e99ba8d21c3 | salmon/_version.py | python | get_versions | () | return {"version": "0+unknown", "full-revisionid": None,
"dirty": None,
"error": "unable to compute version", "date": None} | Get version information or return default if unable to do so. | Get version information or return default if unable to do so. | [
"Get",
"version",
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"or",
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"if",
"unable",
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"do",
"so",
"."
] | def get_versions(): # noqa: C901
"""Get version information or return default if unable to do so."""
# I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have
# __file__, we can work backwards from there to the root. Some
# py2exe/bbfreeze/non-CPython implementations don't do __file__, in which
# case we can only use expanded keywords.
cfg = get_config()
verbose = cfg.verbose
try:
return git_versions_from_keywords(get_keywords(), cfg.tag_prefix,
verbose)
except NotThisMethod:
pass
try:
root = os.path.realpath(__file__)
# versionfile_source is the relative path from the top of the source
# tree (where the .git directory might live) to this file. Invert
# this to find the root from __file__.
for i in cfg.versionfile_source.split('/'):
root = os.path.dirname(root)
except NameError:
return {"version": "0+unknown", "full-revisionid": None,
"dirty": None,
"error": "unable to find root of source tree",
"date": None}
try:
pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)
return render(pieces, cfg.style)
except NotThisMethod:
pass
try:
if cfg.parentdir_prefix:
return versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
except NotThisMethod:
pass
return {"version": "0+unknown", "full-revisionid": None,
"dirty": None,
"error": "unable to compute version", "date": None} | [
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|
wistbean/fxxkpython | 88e16d79d8dd37236ba6ecd0d0ff11d63143968c | vip/qyxuan/projects/venv/lib/python3.6/site-packages/pygame/sysfont.py | python | initsysfonts_win32 | () | return fonts | initialize fonts dictionary on Windows | initialize fonts dictionary on Windows | [
"initialize",
"fonts",
"dictionary",
"on",
"Windows"
] | def initsysfonts_win32():
"""initialize fonts dictionary on Windows"""
fontdir = join(os.environ.get('WINDIR', 'C:\\Windows'), 'Fonts')
TrueType_suffix = '(TrueType)'
mods = ('demibold', 'narrow', 'light', 'unicode', 'bt', 'mt')
fonts = {}
# add fonts entered in the registry
# find valid registry keys containing font information.
# http://docs.python.org/lib/module-sys.html
# 0 (VER_PLATFORM_WIN32s) Win32s on Windows 3.1
# 1 (VER_PLATFORM_WIN32_WINDOWS) Windows 95/98/ME
# 2 (VER_PLATFORM_WIN32_NT) Windows NT/2000/XP
# 3 (VER_PLATFORM_WIN32_CE) Windows CE
if sys.getwindowsversion()[0] == 1:
key_name = "SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Fonts"
else:
key_name = "SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\Fonts"
key = _winreg.OpenKey(_winreg.HKEY_LOCAL_MACHINE, key_name)
for i in xrange_(_winreg.QueryInfoKey(key)[1]):
try:
# name is the font's name e.g. Times New Roman (TrueType)
# font is the font's filename e.g. times.ttf
name, font = _winreg.EnumValue(key, i)[0:2]
except EnvironmentError:
break
# try to handle windows unicode strings for file names with
# international characters
if PY_MAJOR_VERSION < 3:
# here are two documents with some information about it:
# http://www.python.org/peps/pep-0277.html
# https://www.microsoft.com/technet/archive/interopmigration/linux/mvc/lintowin.mspx#ECAA
try:
font = str(font)
except UnicodeEncodeError:
# MBCS is the windows encoding for unicode file names.
try:
font = font.encode('MBCS')
except:
# no success with str or MBCS encoding... skip this font.
continue
if splitext(font)[1].lower() not in OpenType_extensions:
continue
if not dirname(font):
font = join(fontdir, font)
if name.endswith(TrueType_suffix):
name = name.rstrip(TrueType_suffix).rstrip()
name = name.lower().split()
bold = italic = 0
for m in mods:
if m in name:
name.remove(m)
if 'bold' in name:
name.remove('bold')
bold = 1
if 'italic' in name:
name.remove('italic')
italic = 1
name = ''.join(name)
name = _simplename(name)
_addfont(name, bold, italic, font, fonts)
return fonts | [
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|
google-research/language | 61fa7260ac7d690d11ef72ca863e45a37c0bdc80 | language/labs/drkit/wikidata/preprocessing/distantly_supervise.py | python | SlingExtractor.get_date_property | (self, prop, tail) | return None | Returns date if property accepts '/w/time' as target. | Returns date if property accepts '/w/time' as target. | [
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] | def get_date_property(self, prop, tail):
"""Returns date if property accepts '/w/time' as target."""
if "target" not in prop:
return None
if prop.target.id != "/w/time":
return None
prop_id = self.get_frame_id(prop)
if isinstance(tail, int):
return (prop_id, tail)
elif (isinstance(tail, sling.Frame) and "is" in tail and
isinstance(tail["is"], int)):
return (prop_id, tail["is"])
return None | [
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|
happinesslz/TANet | 2d4b2ab99b8e57c03671b0f1531eab7dca8f3c1f | second.pytorch_with_TANet/second/pytorch/models/loss_utils.py | python | prepare_loss_weights | (labels,
pos_cls_weight=1.0,
neg_cls_weight=1.0,
loss_norm_type=LossNormType.NormByNumPositives,
dtype=torch.float32) | return cls_weights, reg_weights, cared | get cls_weights and reg_weights from labels. | get cls_weights and reg_weights from labels. | [
"get",
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"and",
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"from",
"labels",
"."
] | def prepare_loss_weights(labels,
pos_cls_weight=1.0,
neg_cls_weight=1.0,
loss_norm_type=LossNormType.NormByNumPositives,
dtype=torch.float32):
"""get cls_weights and reg_weights from labels.
"""
cared = labels >= 0
# cared: [N, num_anchors]
positives = labels > 0
negatives = labels == 0
negative_cls_weights = negatives.type(dtype) * neg_cls_weight
cls_weights = negative_cls_weights + pos_cls_weight * positives.type(dtype)
reg_weights = positives.type(dtype)
if loss_norm_type == LossNormType.NormByNumExamples:
num_examples = cared.type(dtype).sum(1, keepdim=True)
num_examples = torch.clamp(num_examples, min=1.0)
cls_weights /= num_examples
bbox_normalizer = positives.sum(1, keepdim=True).type(dtype)
reg_weights /= torch.clamp(bbox_normalizer, min=1.0)
elif loss_norm_type == LossNormType.NormByNumPositives: # for focal loss
pos_normalizer = positives.sum(1, keepdim=True).type(dtype)
reg_weights /= torch.clamp(pos_normalizer, min=1.0)
cls_weights /= torch.clamp(pos_normalizer, min=1.0)
elif loss_norm_type == LossNormType.NormByNumPosNeg:
pos_neg = torch.stack([positives, negatives], dim=-1).type(dtype)
normalizer = pos_neg.sum(1, keepdim=True) # [N, 1, 2]
cls_normalizer = (pos_neg * normalizer).sum(-1) # [N, M]
cls_normalizer = torch.clamp(cls_normalizer, min=1.0)
# cls_normalizer will be pos_or_neg_weight/num_pos_or_neg
normalizer = torch.clamp(normalizer, min=1.0)
reg_weights /= normalizer[:, 0:1, 0]
cls_weights /= cls_normalizer
else:
raise ValueError(
f"unknown loss norm type. available: {list(LossNormType)}")
return cls_weights, reg_weights, cared | [
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|
profusion/sgqlc | 465a5e800f8b408ceafe25cde45ee0bde4912482 | sgqlc/operation/__init__.py | python | GraphQLErrors.__init__ | (self, errors) | [] | def __init__(self, errors):
assert len(errors) > 0
msg = str(errors[0].get('message'))
super(RuntimeError, self).__init__(msg)
self.errors = errors | [
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||||
xiepaup/dbatools | 8549f2571aaee6a39f5c6f32179ac9c5d301a9aa | mysqlTools/mysql_utilities/mysql/utilities/common/database.py | python | Database.__build_exclude_patterns | (self, exclude_param) | return str | Return a string to add to where clause to exclude objects.
This method will add the conditions to exclude objects based on
name if there is a dot notation or by a search pattern as specified
by the options.
exclude_param[in] Name of column to check.
Returns (string) String to add to where clause or "" | Return a string to add to where clause to exclude objects. | [
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] | def __build_exclude_patterns(self, exclude_param):
"""Return a string to add to where clause to exclude objects.
This method will add the conditions to exclude objects based on
name if there is a dot notation or by a search pattern as specified
by the options.
exclude_param[in] Name of column to check.
Returns (string) String to add to where clause or ""
"""
from mysql.utilities.common.options import obj2sql
oper = 'NOT REGEXP' if self.use_regexp else 'NOT LIKE'
str = ""
for pattern in self.exclude_patterns:
value = None
if pattern.find(".") > 0:
db, name = pattern.split(".")
if db == self.db_name:
value = name
else:
value = pattern
if value is not None:
str += " AND {0} {1} {2}".format(exclude_param, oper,
obj2sql(value))
return str | [
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|
eea/odfpy | 574f0fafad73a15a5b11b115d94821623274b4b0 | odf/element.py | python | Text.toXml | (self,level,f) | Write XML in UTF-8 | Write XML in UTF-8 | [
"Write",
"XML",
"in",
"UTF",
"-",
"8"
] | def toXml(self,level,f):
""" Write XML in UTF-8 """
if self.data:
f.write(_sanitize(unicode(self.data))) | [
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||
qiime2/qiime2 | 3906f67c70a1321e99e7fc59e79550c2432a8cee | qiime2/core/path.py | python | OutPath.__new__ | (cls, dir=False, **kwargs) | return obj | Create a tempfile, return pathlib.Path reference to it. | Create a tempfile, return pathlib.Path reference to it. | [
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] | def __new__(cls, dir=False, **kwargs):
"""
Create a tempfile, return pathlib.Path reference to it.
"""
if dir:
name = tempfile.mkdtemp(**kwargs)
else:
fd, name = tempfile.mkstemp(**kwargs)
# fd is now assigned to our process table, but we don't need to do
# anything with the file. We will call `open` on the `name` later
# producing a different file descriptor, so close this one to
# prevent a resource leak.
os.close(fd)
obj = super().__new__(cls, name)
obj._destructor = weakref.finalize(obj, cls._destruct, str(obj))
return obj | [
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|
deepfakes/faceswap | 09c7d8aca3c608d1afad941ea78e9fd9b64d9219 | scripts/train.py | python | Train._monitor | (self, thread) | return err | Monitor the background :func:`_training` thread for key presses and errors.
Returns
-------
bool
``True`` if there has been an error in the background thread otherwise ``False`` | Monitor the background :func:`_training` thread for key presses and errors. | [
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] | def _monitor(self, thread):
""" Monitor the background :func:`_training` thread for key presses and errors.
Returns
-------
bool
``True`` if there has been an error in the background thread otherwise ``False``
"""
logger.debug("Launching Monitor")
logger.info("===================================================")
logger.info(" Starting")
if self._args.preview:
logger.info(" Using live preview")
logger.info(" Press '%s' to save and quit",
"Stop" if self._args.redirect_gui or self._args.colab else "ENTER")
if not self._args.redirect_gui and not self._args.colab:
logger.info(" Press 'S' to save model weights immediately")
logger.info("===================================================")
keypress = KBHit(is_gui=self._args.redirect_gui)
err = False
while True:
try:
if self._args.preview:
with self._lock:
for name, image in self._preview_buffer.items():
cv2.imshow(name, image) # pylint: disable=no-member
cv_key = cv2.waitKey(1000) # pylint: disable=no-member
else:
cv_key = None
if thread.has_error:
logger.debug("Thread error detected")
err = True
break
if self._stop:
logger.debug("Stop received")
break
# Preview Monitor
if not self._preview_monitor(cv_key):
break
# Console Monitor
if keypress.kbhit():
console_key = keypress.getch()
if console_key in ("\n", "\r"):
logger.debug("Exit requested")
break
if console_key in ("s", "S"):
logger.info("Save requested")
self._save_now = True
# GUI Preview trigger update monitor
self._process_gui_triggers()
sleep(1)
except KeyboardInterrupt:
logger.debug("Keyboard Interrupt received")
break
keypress.set_normal_term()
logger.debug("Closed Monitor")
return err | [
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] | https://github.com/deepfakes/faceswap/blob/09c7d8aca3c608d1afad941ea78e9fd9b64d9219/scripts/train.py#L350-L412 |
|
ganglia/gmond_python_modules | 2f7fcab3d27926ef4a2feb1b53c09af16a43e729 | gpu/nvidia/nvidia-ml-py-3.295.00/build/lib/pynvml.py | python | nvmlDeviceGetCurrPcieLinkWidth | (handle) | return width.value | [] | def nvmlDeviceGetCurrPcieLinkWidth(handle):
fn = _nvmlGetFunctionPointer("nvmlDeviceGetCurrPcieLinkWidth")
width = c_uint()
ret = fn(handle, byref(width))
_nvmlCheckReturn(ret)
return width.value | [
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|||
dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/blobs/migrate_metadata.py | python | get_shared_domain | (doc) | return SHARED_DOMAIN | [] | def get_shared_domain(doc):
return SHARED_DOMAIN | [
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|||
makerbot/ReplicatorG | d6f2b07785a5a5f1e172fb87cb4303b17c575d5d | skein_engines/skeinforge-35/skeinforge_application/skeinforge_plugins/craft_plugins/speed.py | python | getCraftedTextFromText | (gcodeText, repository=None) | return SpeedSkein().getCraftedGcode(gcodeText, repository) | Speed a gcode linear move text. | Speed a gcode linear move text. | [
"Speed",
"a",
"gcode",
"linear",
"move",
"text",
"."
] | def getCraftedTextFromText(gcodeText, repository=None):
"Speed a gcode linear move text."
if gcodec.isProcedureDoneOrFileIsEmpty( gcodeText, 'speed'):
return gcodeText
if repository == None:
repository = settings.getReadRepository( SpeedRepository() )
if not repository.activateSpeed.value:
return gcodeText
return SpeedSkein().getCraftedGcode(gcodeText, repository) | [
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|
home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/elmax/common.py | python | ElmaxCoordinator.panel_entry | (self) | return self._panel_entry | Return the panel entry. | Return the panel entry. | [
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] | def panel_entry(self) -> PanelEntry | None:
"""Return the panel entry."""
return self._panel_entry | [
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|
Netflix/dispatch | f734b7cb91cba0e3a95b4d0adaa7198bfc94552b | src/dispatch/report/scheduled.py | python | incident_report_reminders | (db_session: SessionLocal, project: Project) | Sends report reminders to incident commanders for active incidents. | Sends report reminders to incident commanders for active incidents. | [
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"."
] | def incident_report_reminders(db_session: SessionLocal, project: Project):
"""Sends report reminders to incident commanders for active incidents."""
incidents = incident_service.get_all_by_status(
db_session=db_session, project_id=project.id, status=IncidentStatus.active
)
for incident in incidents:
for report_type in ReportTypes:
try:
remind_after = incident.created_at
if report_type == ReportTypes.tactical_report:
notification_hour = incident.incident_priority.tactical_report_reminder
if incident.last_tactical_report:
remind_after = incident.last_tactical_report.created_at
elif report_type == ReportTypes.executive_report:
notification_hour = incident.incident_priority.executive_report_reminder
if incident.last_executive_report:
remind_after = incident.last_executive_report.created_at
now = datetime.utcnow() - remind_after
# we calculate the number of hours and seconds since last report was sent
hours, seconds = divmod((now.days * 86400) + now.seconds, 3600)
q, r = divmod(hours, notification_hour)
if q >= 1 and r == 0: # it's time to send the reminder
send_incident_report_reminder(incident, report_type, db_session)
except Exception as e:
# we shouldn't fail to send all reminders when one fails
log.exception(e) | [
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||
CvvT/dumpDex | 92ab3b7e996194a06bf1dd5538a4954e8a5ee9c1 | python/idaapi.py | python | channel_redir_t.__init__ | (self, *args) | __init__(self) -> channel_redir_t | __init__(self) -> channel_redir_t | [
"__init__",
"(",
"self",
")",
"-",
">",
"channel_redir_t"
] | def __init__(self, *args):
"""
__init__(self) -> channel_redir_t
"""
this = _idaapi.new_channel_redir_t(*args)
try: self.this.append(this)
except: self.this = this | [
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||
user-cont/conu | 0d8962560f6f7f17fe1be0d434a4809e2a0ea51d | conu/backend/buildah/image.py | python | BuildahImage.inspect | (self, refresh=True) | return self._inspect_data | provide metadata about the image; flip refresh=True if cached metadata are enough
:param refresh: bool, update the metadata with up to date content
:return: dict | provide metadata about the image; flip refresh=True if cached metadata are enough | [
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] | def inspect(self, refresh=True):
"""
provide metadata about the image; flip refresh=True if cached metadata are enough
:param refresh: bool, update the metadata with up to date content
:return: dict
"""
if refresh or not self._inspect_data:
identifier = self._id or self.get_full_name()
if not identifier:
raise ConuException("This image does not have a valid identifier.")
self._inspect_data = self._inspect(identifier)
return self._inspect_data | [
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|
AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/google/appengine/ext/mapreduce/handlers.py | python | MapperWorkerCallbackHandler._attempt_slice_recovery | (self, shard_state, tstate) | return self._TASK_DIRECTIVE.RECOVER_SLICE | Recover a slice.
This is run when a slice had been previously attempted and output
may have been written. If an output writer requires slice recovery,
we run those logic to remove output duplicates. Otherwise we just retry
the slice.
If recovery is needed, then the entire slice will be dedicated
to recovery logic. No data processing will take place. Thus we call
the slice "recovery slice". This is needed for correctness:
An output writer instance can be out of sync from its physical
medium only when the slice dies after acquring the shard lock but before
committing shard state to db. The worst failure case is when
shard state failed to commit after the NAMED task for the next slice was
added. Thus, recovery slice has a special logic to increment current
slice_id n to n+2. If the task for n+1 had been added, it will be dropped
because it is behind shard state.
Args:
shard_state: an instance of Model.ShardState.
tstate: an instance of Model.TransientShardState.
Returns:
_TASK_DIRECTIVE.PROCEED_TASK to continue with this retry.
_TASK_DIRECTIVE.RECOVER_SLICE to recover this slice.
The next slice will start at the same input as
this slice but output to a new instance of output writer.
Combining outputs from all writer instances is up to implementation. | Recover a slice. | [
"Recover",
"a",
"slice",
"."
] | def _attempt_slice_recovery(self, shard_state, tstate):
"""Recover a slice.
This is run when a slice had been previously attempted and output
may have been written. If an output writer requires slice recovery,
we run those logic to remove output duplicates. Otherwise we just retry
the slice.
If recovery is needed, then the entire slice will be dedicated
to recovery logic. No data processing will take place. Thus we call
the slice "recovery slice". This is needed for correctness:
An output writer instance can be out of sync from its physical
medium only when the slice dies after acquring the shard lock but before
committing shard state to db. The worst failure case is when
shard state failed to commit after the NAMED task for the next slice was
added. Thus, recovery slice has a special logic to increment current
slice_id n to n+2. If the task for n+1 had been added, it will be dropped
because it is behind shard state.
Args:
shard_state: an instance of Model.ShardState.
tstate: an instance of Model.TransientShardState.
Returns:
_TASK_DIRECTIVE.PROCEED_TASK to continue with this retry.
_TASK_DIRECTIVE.RECOVER_SLICE to recover this slice.
The next slice will start at the same input as
this slice but output to a new instance of output writer.
Combining outputs from all writer instances is up to implementation.
"""
mapper_spec = tstate.mapreduce_spec.mapper
if not (tstate.output_writer and
tstate.output_writer._supports_slice_recovery(mapper_spec)):
return self._TASK_DIRECTIVE.PROCEED_TASK
tstate.output_writer = tstate.output_writer._recover(
tstate.mapreduce_spec, shard_state.shard_number,
shard_state.retries + 1)
return self._TASK_DIRECTIVE.RECOVER_SLICE | [
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|
007gzs/dingtalk-sdk | 7979da2e259fdbc571728cae2425a04dbc65850a | dingtalk/client/api/taobao.py | python | TbWuDaoKou.alibaba_wdk_fulfill_bill_return_warehouse_on_task_status_changed | (
self,
return_warehouse_result=None
) | return self._top_request(
"alibaba.wdk.fulfill.bill.return.warehouse.on.task.status.changed",
{
"return_warehouse_result": return_warehouse_result
}
) | 退仓结果回传
退货入仓结果回传
文档地址：https://open-doc.dingtalk.com/docs/api.htm?apiId=44157
:param return_warehouse_result: 退仓结果 | 退仓结果回传
退货入仓结果回传
文档地址：https://open-doc.dingtalk.com/docs/api.htm?apiId=44157 | [
"退仓结果回传",
"退货入仓结果回传",
"文档地址：https",
":",
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".",
"com",
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"docs",
"/",
"api",
".",
"htm?apiId",
"=",
"44157"
] | def alibaba_wdk_fulfill_bill_return_warehouse_on_task_status_changed(
self,
return_warehouse_result=None
):
"""
退仓结果回传
退货入仓结果回传
文档地址：https://open-doc.dingtalk.com/docs/api.htm?apiId=44157
:param return_warehouse_result: 退仓结果
"""
return self._top_request(
"alibaba.wdk.fulfill.bill.return.warehouse.on.task.status.changed",
{
"return_warehouse_result": return_warehouse_result
}
) | [
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|
quodlibet/quodlibet | e3099c89f7aa6524380795d325cc14630031886c | quodlibet/packages/raven/versioning.py | python | fetch_git_sha | (path, head=None) | >>> fetch_git_sha(os.path.dirname(__file__)) | >>> fetch_git_sha(os.path.dirname(__file__)) | [
">>>",
"fetch_git_sha",
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"(",
"__file__",
"))"
] | def fetch_git_sha(path, head=None):
"""
>>> fetch_git_sha(os.path.dirname(__file__))
"""
if not head:
head_path = os.path.join(path, '.git', 'HEAD')
if not os.path.exists(head_path):
raise InvalidGitRepository(
'Cannot identify HEAD for git repository at %s' % (path,))
with open(head_path, 'r') as fp:
head = text_type(fp.read()).strip()
if head.startswith('ref: '):
head = head[5:]
revision_file = os.path.join(
path, '.git', *head.split('/')
)
else:
return head
else:
revision_file = os.path.join(path, '.git', 'refs', 'heads', head)
if not os.path.exists(revision_file):
if not os.path.exists(os.path.join(path, '.git')):
raise InvalidGitRepository(
'%s does not seem to be the root of a git repository' % (path,))
# Check for our .git/packed-refs' file since a `git gc` may have run
# https://git-scm.com/book/en/v2/Git-Internals-Maintenance-and-Data-Recovery
packed_file = os.path.join(path, '.git', 'packed-refs')
if os.path.exists(packed_file):
with open(packed_file) as fh:
for line in fh:
line = line.rstrip()
if line and line[:1] not in ('#', '^'):
try:
revision, ref = line.split(' ', 1)
except ValueError:
continue
if ref == head:
return text_type(revision)
raise InvalidGitRepository(
'Unable to find ref to head "%s" in repository' % (head,))
with open(revision_file) as fh:
return text_type(fh.read()).strip() | [
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||
SeldonIO/alibi | ce961caf995d22648a8338857822c90428af4765 | alibi/explainers/ale.py | python | ALE.explain | (self, X: np.ndarray, features: Optional[List[int]] = None, min_bin_points: int = 4) | return self.build_explanation(
ale_values=ale_values,
ale0=ale0,
constant_value=constant_value,
feature_values=feature_values,
feature_deciles=feature_deciles,
feature_names=feature_names
) | Calculate the ALE curves for each feature with respect to the dataset `X`.
Parameters
----------
X
An NxF tabular dataset used to calculate the ALE curves. This is typically the training dataset
or a representative sample.
features:
Features for which to calculate ALE.
min_bin_points
Minimum number of points each discretized interval should contain to ensure more precise
ALE estimation.
Returns
-------
An `Explanation` object containing the data and the metadata of the calculated ALE curves. | Calculate the ALE curves for each feature with respect to the dataset `X`. | [
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] | def explain(self, X: np.ndarray, features: Optional[List[int]] = None, min_bin_points: int = 4) -> Explanation:
"""
Calculate the ALE curves for each feature with respect to the dataset `X`.
Parameters
----------
X
An NxF tabular dataset used to calculate the ALE curves. This is typically the training dataset
or a representative sample.
features:
Features for which to calculate ALE.
min_bin_points
Minimum number of points each discretized interval should contain to ensure more precise
ALE estimation.
Returns
-------
An `Explanation` object containing the data and the metadata of the calculated ALE curves.
"""
self.meta['params'].update(min_bin_points=min_bin_points)
if X.ndim != 2:
raise ValueError('The array X must be 2-dimensional')
n_features = X.shape[1]
# set feature and target names, this is done here as we don't know n_features at init time
if self.feature_names is None:
self.feature_names = [f'f_{i}' for i in range(n_features)]
if self.target_names is None:
pred = np.atleast_2d(self.predictor(X[0].reshape(1, -1)))
n_targets = pred.shape[1]
self.target_names = [f'c_{i}' for i in range(n_targets)]
self.feature_names = np.array(self.feature_names) # type: ignore
self.target_names = np.array(self.target_names) # type: ignore
# only calculate ALE for the specified features and return the explanation for this subset
if features:
feature_names = self.feature_names[features] # type: ignore
else:
feature_names = self.feature_names
features = list(range(n_features))
feature_values = []
ale_values = []
ale0 = []
feature_deciles = []
# TODO: use joblib to paralelise?
for feature in features:
q, ale, a0 = ale_num(
self.predictor,
X=X,
feature=feature,
min_bin_points=min_bin_points,
check_feature_resolution=self.check_feature_resolution,
low_resolution_threshold=self.low_resolution_threshold,
extrapolate_constant=self.extrapolate_constant,
extrapolate_constant_perc=self.extrapolate_constant_perc,
extrapolate_constant_min=self.extrapolate_constant_min
)
deciles = get_quantiles(X[:, feature], num_quantiles=11)
feature_values.append(q)
ale_values.append(ale)
ale0.append(a0)
feature_deciles.append(deciles)
constant_value = self.predictor(X).mean()
# TODO: an ALE plot ideally requires a rugplot to gauge density of instances in the feature space.
# I've replaced this with feature deciles which is coarser but has constant space complexity
# as opposed to a rugplot. Alternatively, could consider subsampling to produce a rug with some
# maximum number of points.
return self.build_explanation(
ale_values=ale_values,
ale0=ale0,
constant_value=constant_value,
feature_values=feature_values,
feature_deciles=feature_deciles,
feature_names=feature_names
) | [
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Subsets and Splits