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4 | 11 | def check_compressionlib(cls, value):
try:
compresser = from_qualified_name(value)
except (ImportError, AttributeError) as exc:
raise ValueError(
f"Failed to import requested compression library: {value!r}."
) from exc
if not callable(getattr(compresser, "compress", None)):
raise ValueError(
f"Compression library at {value!r} does not have a 'compress' method."
)
if not callable(getattr(compresser, "decompress", None)):
raise ValueError(
f"Compression library at {value!r} does not have a 'decompress' method."
)
return value
| src/prefect/serializers.py | 139 | prefect | {
"docstring": "\n Check that the given pickle library is importable and has compress/decompress\n methods.\n ",
"language": "en",
"n_whitespaces": 34,
"n_words": 12,
"vocab_size": 12
} | 62 | Python | 42 | 295fd5d4b65dc967d8ddc99817b52d8273301063 | serializers.py | 59,407 | 16 | 75 | check_compressionlib | https://github.com/PrefectHQ/prefect.git | Add `CompressedSerializer` for compression of other result serializers (#7164)
Co-authored-by: Terrence Dorsey <[email protected]> | 226 | 0 | 11,900 | 12 |
|
4 | 10 | def prefer_url(self, url1, url2):
result = url2
if url1:
s1 = self.score_url(url1)
s2 = self.score_url(url2)
if s1 > s2:
result = url1
if result != url2:
logger.debug('Not replacing %r with %r', url1, url2)
else:
logger.debug('Replacing %r with %r', url1, url2)
return result
| .venv/lib/python3.8/site-packages/pip/_vendor/distlib/locators.py | 113 | transferlearning | {
"docstring": "\n Choose one of two URLs where both are candidates for distribution\n archives for the same version of a distribution (for example,\n .tar.gz vs. zip).\n\n The current implementation favours https:// URLs over http://, archives\n from PyPI over those from other locations, wheel compatibility (if a\n wheel) and then the archive name.\n ",
"language": "en",
"n_whitespaces": 100,
"n_words": 50,
"vocab_size": 41
} | 42 | Python | 27 | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | locators.py | 62,018 | 12 | 69 | prefer_url | https://github.com/jindongwang/transferlearning.git | upd; format | 170 | 0 | 12,828 | 13 |
|
2 | 15 | def system_exec(command):
try:
res = subprocess.run(command.split(' '), stdout=subprocess.PIPE).stdout.decode('utf-8')
except Exception as e:
logger.debug('Can not evaluate command {} ({})'.format(command, e))
res = ''
return res.rstrip()
| glances/compat.py | 109 | glances | {
"docstring": "Execute a system command and return the result as a str",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 10
} | 24 | Python | 22 | b3c009b22ef6c47a54faa4c8bf4e10bb62caeef4 | compat.py | 69,944 | 7 | 61 | system_exec | https://github.com/nicolargo/glances.git | Correct unitary test failed | 57 | 0 | 15,190 | 16 |
|
1 | 4 | def getdata(im, offset=(0, 0), **params):
| src/PIL/GifImagePlugin.py | 28 | Pillow | {
"docstring": "\n Legacy Method\n\n Return a list of strings representing this image.\n The first string is a local image header, the rest contains\n encoded image data.\n\n To specify duration, add the time in milliseconds,\n e.g. ``getdata(im_frame, duration=1000)``\n\n :param im: Image object\n :param offset: Tuple of (x, y) pixels. Defaults to (0, 0)\n :param \\\\**params: e.g. duration or other encoder info parameters\n :returns: List of bytes containing GIF encoded frame data\n\n ",
"language": "en",
"n_whitespaces": 102,
"n_words": 68,
"vocab_size": 59
} | 5 | Python | 5 | 1997c814abcbc071fb9f289fda021e8d08cad4a7 | GifImagePlugin.py | 242,759 | 24 | 50 | getdata | https://github.com/python-pillow/Pillow.git | Move useful comment into docstring | 8 | 0 | 69,911 | 6 |
|
2 | 20 | def test_shared_deployment_handle(serve_instance):
ray_dag, _ = get_shared_deployment_handle_dag()
with DAGNodeNameGenerator() as node_name_generator:
serve_root_dag = ray_dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(node, node_name_generator)
)
print(f"Serve DAG: \n{serve_root_dag}")
deployments = extract_deployments_from_serve_dag(serve_root_dag)
assert len(deployments) == 2
for deployment in deployments:
deployment.deploy()
_validate_consistent_python_output(
deployments[1], ray_dag, "Combine", input=1, output=4
)
| python/ray/serve/pipeline/tests/test_generate.py | 143 | ray | {
"docstring": "\n Test we can re-use the same deployment handle multiple times or in\n multiple places, without incorrectly parsing duplicated deployments.\n ",
"language": "en",
"n_whitespaces": 29,
"n_words": 19,
"vocab_size": 18
} | 40 | Python | 36 | 5c06e3f14900e3812061416759c25ff2b88c8a23 | test_generate.py | 138,804 | 14 | 83 | test_shared_deployment_handle | https://github.com/ray-project/ray.git | [DAG] add basic plotting on Ray DAGs (#24223)
To add basic plotting feature for Ray DAGs.
`ray.experimental.dag.plot(dag: DAGNode, to_file=None)`
### Behavior
1. dump the dag plot (Dot) to file.
2. also render the image whenever possible. E.g. if running in Jupyter notebook, the image will not only be saved, but also rendered in the notebook.
3. when to_file is not set (i.e. None), it will be saved to a tempfile for rendering purpose only. This is common when users plot DAGs in notebook env to explore the DAG structure without wanting to save it to a file. | 106 | 0 | 31,529 | 13 |
|
1 | 4 | async def async_disable_motion_detection(self) -> None:
self._attr_motion_detection_enabled = False
self.async_write_ha_state()
| homeassistant/components/demo/camera.py | 34 | core | {
"docstring": "Disable the motion detection in base station (Disarm).",
"language": "en",
"n_whitespaces": 7,
"n_words": 8,
"vocab_size": 8
} | 9 | Python | 9 | 57fd84e20c9e98df52a6e81af1fa84ee86028aa8 | camera.py | 315,082 | 4 | 18 | async_disable_motion_detection | https://github.com/home-assistant/core.git | Improve type hints in demo (#74236) | 30 | 0 | 113,679 | 7 |
|
2 | 18 | def directed_modularity_matrix(G, nodelist=None, weight=None):
import numpy as np
if nodelist is None:
nodelist = list(G)
A = nx.to_scipy_sparse_array(G, nodelist=nodelist, weight=weight, format="csr")
k_in = A.sum(axis=0)
k_out = A.sum(axis=1)
m = k_in.sum()
# Expected adjacency matrix
X = np.outer(k_out, k_in) / m
return A - X
| networkx/linalg/modularitymatrix.py | 147 | networkx | {
"docstring": "Returns the directed modularity matrix of G.\n\n The modularity matrix is the matrix B = A - <A>, where A is the adjacency\n matrix and <A> is the expected adjacency matrix, assuming that the graph\n is described by the configuration model.\n\n More specifically, the element B_ij of B is defined as\n\n .. math::\n B_{ij} = A_{ij} - k_i^{out} k_j^{in} / m\n\n where :math:`k_i^{in}` is the in degree of node i, and :math:`k_j^{out}` is the out degree\n of node j, with m the number of edges in the graph. When weight is set\n to a name of an attribute edge, Aij, k_i, k_j and m are computed using\n its value.\n\n Parameters\n ----------\n G : DiGraph\n A NetworkX DiGraph\n\n nodelist : list, optional\n The rows and columns are ordered according to the nodes in nodelist.\n If nodelist is None, then the ordering is produced by G.nodes().\n\n weight : string or None, optional (default=None)\n The edge attribute that holds the numerical value used for\n the edge weight. If None then all edge weights are 1.\n\n Returns\n -------\n B : Numpy array\n The modularity matrix of G.\n\n Examples\n --------\n >>> G = nx.DiGraph()\n >>> G.add_edges_from(\n ... (\n ... (1, 2),\n ... (1, 3),\n ... (3, 1),\n ... (3, 2),\n ... (3, 5),\n ... (4, 5),\n ... (4, 6),\n ... (5, 4),\n ... (5, 6),\n ... (6, 4),\n ... )\n ... )\n >>> B = nx.directed_modularity_matrix(G)\n\n\n Notes\n -----\n NetworkX defines the element A_ij of the adjacency matrix as 1 if there\n is a link going from node i to node j. Leicht and Newman use the opposite\n definition. This explains the different expression for B_ij.\n\n See Also\n --------\n to_numpy_array\n modularity_spectrum\n adjacency_matrix\n modularity_matrix\n\n References\n ----------\n .. [1] E. A. Leicht, M. E. J. Newman,\n \"Community structure in directed networks\",\n Phys. Rev Lett., vol. 100, no. 11, p. 118703, 2008.\n ",
"language": "en",
"n_whitespaces": 598,
"n_words": 303,
"vocab_size": 177
} | 44 | Python | 35 | 8a325d26aa7fdd3a72580c4720fa97f971bbefcb | modularitymatrix.py | 177,335 | 10 | 92 | directed_modularity_matrix | https://github.com/networkx/networkx.git | Use scipy.sparse array datastructure (#6037)
* Use scipy.sparse array datastructure
* Add reminder to rm wrapper when scipy adds creation fns.
* Rm mention of np matrix from code comment.
* Update networkx/algorithms/bipartite/matrix.py
Co-authored-by: Stefan van der Walt <[email protected]>
Co-authored-by: Ross Barnowski <[email protected]>
Co-authored-by: Stefan van der Walt <[email protected]> | 81 | 0 | 42,354 | 10 |
|
2 | 5 | def get_nccl_reduce_op(reduce_op):
if reduce_op not in NCCL_REDUCE_OP_MAP:
raise RuntimeError("NCCL does not support reduce op: '{}'.".format(reduce_op))
return NCCL_REDUCE_OP_MAP[reduce_op]
| python/ray/util/collective/collective_group/nccl_util.py | 47 | ray | {
"docstring": "Map the reduce op to NCCL reduce op type.\n\n Args:\n reduce_op (ReduceOp): ReduceOp Enum (SUM/PRODUCT/MIN/MAX).\n Returns:\n (nccl.ncclRedOp_t): the mapped NCCL reduce op.\n ",
"language": "en",
"n_whitespaces": 45,
"n_words": 22,
"vocab_size": 17
} | 17 | Python | 16 | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | nccl_util.py | 133,019 | 4 | 27 | get_nccl_reduce_op | https://github.com/ray-project/ray.git | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | 33 | 0 | 29,926 | 12 |
|
2 | 6 | def audit_enum(self) -> AuditMode:
try:
return AuditMode(self.audit)
except ValueError:
raise ValueError(f'Docker completion entry "{self.name}" has an invalid value "{self.audit}" for the "audit" setting.') from None
| test/lib/ansible_test/_internal/completion.py | 62 | ansible | {
"docstring": "The audit requirements for the container. Raises an exception if the value is invalid.",
"language": "en",
"n_whitespaces": 13,
"n_words": 14,
"vocab_size": 13
} | 25 | Python | 25 | cda16cc5e9aa8703fb4e1ac0a0be6b631d9076cc | completion.py | 268,709 | 6 | 28 | audit_enum | https://github.com/ansible/ansible.git | ansible-test - Improve container management. (#78550)
See changelogs/fragments/ansible-test-container-management.yml for details. | 68 | 0 | 79,610 | 13 |
|
1 | 24 | def test_post_build_adapt_update_dataset(self):
input_dataset = tf.data.Dataset.from_tensor_slices(
np.array([[1], [2], [3], [4], [5], [0]])
)
input_data = keras.Input(shape=(1,))
layer = AddingPreprocessingLayer()
output = layer(input_data)
model = keras.Model(input_data, output)
model._run_eagerly = test_utils.should_run_eagerly()
layer.adapt(input_dataset)
self.assertAllEqual([[16], [17], [18]], model.predict([1.0, 2.0, 3.0]))
| keras/engine/base_preprocessing_layer_test.py | 193 | keras | {
"docstring": "Test that preproc layers can adapt() after build() is called.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | 35 | Python | 30 | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | base_preprocessing_layer_test.py | 271,003 | 11 | 133 | test_post_build_adapt_update_dataset | https://github.com/keras-team/keras.git | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 116 | 0 | 80,650 | 12 |
|
1 | 2 | def iconsize(self):
return self["iconsize"]
| packages/python/plotly/plotly/graph_objs/layout/mapbox/layer/_symbol.py | 22 | plotly.py | {
"docstring": "\n Sets the symbol icon size (mapbox.layer.layout.icon-size). Has\n an effect only when `type` is set to \"symbol\".\n\n The 'iconsize' property is a number and may be specified as:\n - An int or float\n\n Returns\n -------\n int|float\n ",
"language": "en",
"n_whitespaces": 94,
"n_words": 35,
"vocab_size": 34
} | 4 | Python | 4 | 43e3a4011080911901176aab919c0ecf5046ddd3 | _symbol.py | 232,063 | 2 | 11 | iconsize | https://github.com/plotly/plotly.py.git | switch to black .22 | 18 | 0 | 63,507 | 7 |
|
10 | 17 | def gnu_getopt(args, shortopts, longopts = []):
opts = []
prog_args = []
if isinstance(longopts, str):
longopts = [longopts]
else:
longopts = list(longopts)
# Allow options after non-option arguments?
if shortopts.startswith('+'):
shortopts = shortopts[1:]
all_options_first = True
elif os.environ.get("POSIXLY_CORRECT"):
all_options_first = True
else:
all_options_first = False
while args:
if args[0] == '--':
prog_args += args[1:]
break
if args[0][:2] == '--':
opts, args = do_longs(opts, args[0][2:], longopts, args[1:])
elif args[0][:1] == '-' and args[0] != '-':
opts, args = do_shorts(opts, args[0][1:], shortopts, args[1:])
else:
if all_options_first:
prog_args += args
break
else:
prog_args.append(args[0])
args = args[1:]
return opts, prog_args
| python3.10.4/Lib/getopt.py | 339 | XX-Net | {
"docstring": "getopt(args, options[, long_options]) -> opts, args\n\n This function works like getopt(), except that GNU style scanning\n mode is used by default. This means that option and non-option\n arguments may be intermixed. The getopt() function stops\n processing options as soon as a non-option argument is\n encountered.\n\n If the first character of the option string is `+', or if the\n environment variable POSIXLY_CORRECT is set, then option\n processing stops as soon as a non-option argument is encountered.\n\n ",
"language": "en",
"n_whitespaces": 102,
"n_words": 75,
"vocab_size": 53
} | 96 | Python | 54 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | getopt.py | 217,531 | 30 | 209 | gnu_getopt | https://github.com/XX-net/XX-Net.git | add python 3.10.4 for windows | 325 | 0 | 54,836 | 16 |
|
2 | 6 | def _dict_like(x):
try:
_ = dict(x)
except (TypeError, ValueError):
return False
return True
| ludwig/utils/numerical_test_utils.py | 43 | ludwig | {
"docstring": "Returns true if an object is a dict or convertible to one, false if not.",
"language": "en",
"n_whitespaces": 14,
"n_words": 15,
"vocab_size": 14
} | 13 | Python | 12 | caaab8ba561850c1b274088f278ff2d27a6f5227 | numerical_test_utils.py | 8,509 | 6 | 25 | _dict_like | https://github.com/ludwig-ai/ludwig.git | Check for nans before testing equality in test_training_determinism (#2687)
* Adds test_numerical_test_utils
* Check finite metrics before checking equality.
* Catch TypeError and ValueError in _dict_like and _enumerable.
* Edits comments. | 39 | 0 | 1,440 | 10 |
|
3 | 31 | def to_rotation_matrix(self, v=None, normal=False):
q = self
s = q.norm()**-2
# diagonal elements are different according to parameter normal
if normal:
m00 = s*(q.a**2 + q.b**2 - q.c**2 - q.d**2)
m11 = s*(q.a**2 - q.b**2 + q.c**2 - q.d**2)
m22 = s*(q.a**2 - q.b**2 - q.c**2 + q.d**2)
else:
m00 = 1 - 2*s*(q.c**2 + q.d**2)
m11 = 1 - 2*s*(q.b**2 + q.d**2)
m22 = 1 - 2*s*(q.b**2 + q.c**2)
m01 = 2*s*(q.b*q.c - q.d*q.a)
m02 = 2*s*(q.b*q.d + q.c*q.a)
m10 = 2*s*(q.b*q.c + q.d*q.a)
m12 = 2*s*(q.c*q.d - q.b*q.a)
m20 = 2*s*(q.b*q.d - q.c*q.a)
m21 = 2*s*(q.c*q.d + q.b*q.a)
if not v:
return Matrix([[m00, m01, m02], [m10, m11, m12], [m20, m21, m22]])
else:
(x, y, z) = v
m03 = x - x*m00 - y*m01 - z*m02
m13 = y - x*m10 - y*m11 - z*m12
m23 = z - x*m20 - y*m21 - z*m22
m30 = m31 = m32 = 0
m33 = 1
return Matrix([[m00, m01, m02, m03], [m10, m11, m12, m13],
[m20, m21, m22, m23], [m30, m31, m32, m33]])
| sympy/algebras/quaternion.py | 690 | sympy | {
"docstring": "Returns the equivalent rotation transformation matrix of the quaternion\n which represents rotation about the origin if v is not passed.\n\n Parameters\n ==========\n\n v : tuple or None\n Default value: None\n normal : bool\n When True, gives an expression that may be more efficient for\n symbolic calculations but less so for direct evaluation. Both\n formulas are mathematically equivalent.\n Default value: False\n\n Returns\n =======\n\n tuple\n Returns the equivalent rotation transformation matrix of the quaternion\n which represents rotation about the origin if v is not passed.\n\n Examples\n ========\n\n >>> from sympy import Quaternion\n >>> from sympy import symbols, trigsimp, cos, sin\n >>> x = symbols('x')\n >>> q = Quaternion(cos(x/2), 0, 0, sin(x/2))\n >>> trigsimp(q.to_rotation_matrix())\n Matrix([\n [cos(x), -sin(x), 0],\n [sin(x), cos(x), 0],\n [ 0, 0, 1]])\n\n Generates a 4x4 transformation matrix (used for rotation about a point\n other than the origin) if the point(v) is passed as an argument.\n\n Examples\n ========\n\n >>> from sympy import Quaternion\n >>> from sympy import symbols, trigsimp, cos, sin\n >>> x = symbols('x')\n >>> q = Quaternion(cos(x/2), 0, 0, sin(x/2))\n >>> trigsimp(q.to_rotation_matrix((1, 1, 1)))\n Matrix([\n [cos(x), -sin(x), 0, sin(x) - cos(x) + 1],\n [sin(x), cos(x), 0, -sin(x) - cos(x) + 1],\n [ 0, 0, 1, 0],\n [ 0, 0, 0, 1]])\n\n ",
"language": "en",
"n_whitespaces": 589,
"n_words": 202,
"vocab_size": 100
} | 173 | Python | 92 | 34555f1ebe2a2ed1fab2a0a2ae9a8457a75eaa26 | quaternion.py | 200,685 | 28 | 464 | to_rotation_matrix | https://github.com/sympy/sympy.git | changed homogeneous to normal | 450 | 0 | 49,764 | 15 |
|
1 | 24 | def test_overriding_has_module_permission(self):
articles = Article._meta.verbose_name_plural.title()
sections = Section._meta.verbose_name_plural.title()
index_url = reverse("admin7:index")
self.client.force_login(self.superuser)
response = self.client.get(index_url)
self.assertContains(response, sections)
self.assertNotContains(response, articles)
self.client.logout()
self.client.force_login(self.viewuser)
response = self.client.get(index_url)
self.assertNotContains(response, "admin_views")
self.assertNotContains(response, articles)
self.client.logout()
self.client.force_login(self.adduser)
response = self.client.get(index_url)
self.assertNotContains(response, "admin_views")
self.assertNotContains(response, articles)
self.client.logout()
self.client.force_login(self.changeuser)
response = self.client.get(index_url)
self.assertNotContains(response, "admin_views")
self.assertNotContains(response, articles)
self.client.logout()
self.client.force_login(self.deleteuser)
response = self.client.get(index_url)
self.assertNotContains(response, articles)
# The app list displays Sections but not Articles as the latter has
# ModelAdmin.has_module_permission() = False.
self.client.force_login(self.superuser)
response = self.client.get(reverse("admin7:app_list", args=("admin_views",)))
self.assertContains(response, sections)
self.assertNotContains(response, articles)
| tests/admin_views/tests.py | 459 | django | {
"docstring": "\n If has_module_permission() always returns False, the module shouldn't\n be displayed on the admin index page for any users.\n ",
"language": "en",
"n_whitespaces": 40,
"n_words": 18,
"vocab_size": 17
} | 79 | Python | 39 | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | tests.py | 207,841 | 31 | 280 | test_overriding_has_module_permission | https://github.com/django/django.git | Refs #33476 -- Reformatted code with Black. | 310 | 0 | 52,128 | 13 |
|
1 | 11 | def test_precedence(self):
with self.settings(
INSTALLED_APPS=[
"admin_scripts.complex_app",
"admin_scripts.simple_app",
"django.contrib.auth",
"django.contrib.contenttypes",
]
):
out = StringIO()
call_command("duplicate", stdout=out)
self.assertEqual(out.getvalue().strip(), "complex_app")
with self.settings(
INSTALLED_APPS=[
"admin_scripts.simple_app",
"admin_scripts.complex_app",
"django.contrib.auth",
"django.contrib.contenttypes",
]
):
out = StringIO()
call_command("duplicate", stdout=out)
self.assertEqual(out.getvalue().strip(), "simple_app")
| tests/admin_scripts/tests.py | 187 | django | {
"docstring": "\n Apps listed first in INSTALLED_APPS have precedence.\n ",
"language": "en",
"n_whitespaces": 22,
"n_words": 7,
"vocab_size": 7
} | 34 | Python | 19 | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | tests.py | 207,308 | 23 | 102 | test_precedence | https://github.com/django/django.git | Refs #33476 -- Reformatted code with Black. | 299 | 0 | 51,924 | 13 |
|
2 | 6 | def clear(self):
for key in self.conn.keys():
self.conn.delete(key)
| .venv/lib/python3.8/site-packages/pip/_vendor/cachecontrol/caches/redis_cache.py | 43 | transferlearning | {
"docstring": "Helper for clearing all the keys in a database. Use with\n caution!",
"language": "en",
"n_whitespaces": 18,
"n_words": 12,
"vocab_size": 12
} | 7 | Python | 7 | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | redis_cache.py | 61,481 | 3 | 25 | clear | https://github.com/jindongwang/transferlearning.git | upd; format | 32 | 0 | 12,588 | 10 |
|
1 | 15 | def upfirdn_2d(x, k, upx=1, upy=1, downx=1, downy=1, padx0=0, padx1=0, pady0=0, pady1=0, impl='cuda'):
r
impl_dict = {
'ref': _upfirdn_2d_ref,
'cuda': _upfirdn_2d_cuda,
}
return impl_dict[impl](x=x, k=k, upx=upx, upy=upy, downx=downx, downy=downy, padx0=padx0, padx1=padx1, pady0=pady0, pady1=pady1)
#----------------------------------------------------------------------------
| reconstruction/ostec/external/stylegan2/dnnlib/tflib/ops/upfirdn_2d.py | 144 | insightface | {
"docstring": "Pad, upsample, FIR filter, and downsample a batch of 2D images.\n\n Accepts a batch of 2D images of the shape `[majorDim, inH, inW, minorDim]`\n and performs the following operations for each image, batched across\n `majorDim` and `minorDim`:\n\n 1. Pad the image with zeros by the specified number of pixels on each side\n (`padx0`, `padx1`, `pady0`, `pady1`). Specifying a negative value\n corresponds to cropping the image.\n\n 2. Upsample the image by inserting the zeros after each pixel (`upx`, `upy`).\n\n 3. Convolve the image with the specified 2D FIR filter (`k`), shrinking the\n image so that the footprint of all output pixels lies within the input image.\n\n 4. Downsample the image by throwing away pixels (`downx`, `downy`).\n\n This sequence of operations bears close resemblance to scipy.signal.upfirdn().\n The fused op is considerably more efficient than performing the same calculation\n using standard TensorFlow ops. It supports gradients of arbitrary order.\n\n Args:\n x: Input tensor of the shape `[majorDim, inH, inW, minorDim]`.\n k: 2D FIR filter of the shape `[firH, firW]`.\n upx: Integer upsampling factor along the X-axis (default: 1).\n upy: Integer upsampling factor along the Y-axis (default: 1).\n downx: Integer downsampling factor along the X-axis (default: 1).\n downy: Integer downsampling factor along the Y-axis (default: 1).\n padx0: Number of pixels to pad on the left side (default: 0).\n padx1: Number of pixels to pad on the right side (default: 0).\n pady0: Number of pixels to pad on the top side (default: 0).\n pady1: Number of pixels to pad on the bottom side (default: 0).\n impl: Name of the implementation to use. Can be `\"ref\"` or `\"cuda\"` (default).\n\n Returns:\n Tensor of the shape `[majorDim, outH, outW, minorDim]`, and same datatype as `x`.\n ",
"language": "en",
"n_whitespaces": 442,
"n_words": 277,
"vocab_size": 153
} | 33 | Python | 33 | 7375ee364e0df2a417f92593e09557f1b2a3575a | upfirdn_2d.py | 9,408 | 43 | 103 | upfirdn_2d | https://github.com/deepinsight/insightface.git | initialize ostec | 58 | 0 | 1,608 | 9 |
|
1 | 2 | def start(self) -> 'BasePod':
...
| jina/peapods/pods/__init__.py | 20 | jina | {
"docstring": "Start to run all :class:`Pea` in this BasePod.\n\n .. note::\n If one of the :class:`Pea` fails to start, make sure that all of them\n are properly closed.\n ",
"language": "en",
"n_whitespaces": 63,
"n_words": 27,
"vocab_size": 23
} | 5 | Python | 5 | 933415bfa1f9eb89f935037014dfed816eb9815d | __init__.py | 9,880 | 8 | 9 | start | https://github.com/jina-ai/jina.git | feat: star routing (#3900)
* feat(proto): adjust proto for star routing (#3844)
* feat(proto): adjust proto for star routing
* feat(proto): generate proto files
* feat(grpc): refactor grpclet interface (#3846)
* feat: refactor connection pool for star routing (#3872)
* feat(k8s): add more labels to k8s deployments
* feat(network): refactor connection pool
* feat(network): refactor k8s pool
* feat: star routing graph gateway (#3877)
* feat: star routing - refactor grpc data runtime (#3887)
* feat(runtimes): refactor grpc dataruntime
* fix(tests): adapt worker runtime tests
* fix(import): fix import
* feat(proto): enable sending multiple lists (#3891)
* feat: star routing gateway (#3893)
* feat: star routing gateway all protocols (#3897)
* test: add streaming and prefetch tests (#3901)
* feat(head): new head runtime for star routing (#3899)
* feat(head): new head runtime
* feat(head): new head runtime
* style: fix overload and cli autocomplete
* feat(network): improve proto comments
Co-authored-by: Jina Dev Bot <[email protected]>
* feat(worker): merge docs in worker runtime (#3905)
* feat(worker): merge docs in worker runtime
* feat(tests): assert after clean up
* feat(tests): star routing runtime integration tests (#3908)
* fix(tests): fix integration tests
* test: test runtimes fast slow request (#3910)
* feat(zmq): purge zmq, zed, routing_table (#3915)
* feat(zmq): purge zmq, zed, routing_table
* style: fix overload and cli autocomplete
* feat(zmq): adapt comment in dependency list
* style: fix overload and cli autocomplete
* fix(tests): fix type tests
Co-authored-by: Jina Dev Bot <[email protected]>
* test: add test gateway to worker connection (#3921)
* feat(pea): adapt peas for star routing (#3918)
* feat(pea): adapt peas for star routing
* style: fix overload and cli autocomplete
* feat(pea): add tests
* feat(tests): add failing head pea test
Co-authored-by: Jina Dev Bot <[email protected]>
* feat(tests): integration tests for peas (#3923)
* feat(tests): integration tests for peas
* feat(pea): remove _inner_pea function
* feat: star routing container pea (#3922)
* test: rescue tests (#3942)
* fix: fix streaming tests (#3945)
* refactor: move docker run to run (#3948)
* feat: star routing pods (#3940)
* feat(pod): adapt pods for star routing
* feat(pods): adapt basepod to star routing
* feat(pod): merge pod and compound pod
* feat(tests): fix tests
* style: fix overload and cli autocomplete
* feat(test): add container pea int test
* feat(ci): remove more unnecessary tests
* fix(tests): remove jinad runtime
* feat(ci): remove latency tracking
* fix(ci): fix ci def
* fix(runtime): enable runtime to be exited
* fix(tests): wrap runtime test in process
* fix(runtimes): remove unused runtimes
* feat(runtimes): improve cancel wait
* fix(ci): build test pip again in ci
* fix(tests): fix a test
* fix(test): run async in its own process
* feat(pod): include shard in activate msg
* fix(pea): dont join
* feat(pod): more debug out
* feat(grpc): manage channels properly
* feat(pods): remove exitfifo
* feat(network): add simple send retry mechanism
* fix(network): await pool close
* fix(test): always close grpc server in worker
* fix(tests): remove container pea from tests
* fix(tests): reorder tests
* fix(ci): split tests
* fix(ci): allow alias setting
* fix(test): skip a test
* feat(pods): address comments
Co-authored-by: Jina Dev Bot <[email protected]>
* test: unblock skipped test (#3957)
* feat: jinad pea (#3949)
* feat: jinad pea
* feat: jinad pea
* test: remote peas
* test: toplogy tests with jinad
* ci: parallel jobs
* feat(tests): add pod integration tests (#3958)
* feat(tests): add pod integration tests
* fix(tests): make tests less flaky
* fix(test): fix test
* test(pea): remote pea topologies (#3961)
* test(pea): remote pea simple topology
* test: remote pea topologies
* refactor: refactor streamer result handling (#3960)
* feat(k8s): adapt K8s Pod for StarRouting (#3964)
* test: optimize k8s test
* test: increase timeout and use different namespace
* test: optimize k8s test
* test: build and load image when needed
* test: refactor k8s test
* test: fix image name error
* test: fix k8s image load
* test: fix typoe port expose
* test: update tests in connection pool and handling
* test: remove unused fixture
* test: parameterize docker images
* test: parameterize docker images
* test: parameterize docker images
* feat(k8s): adapt k8s pod for star routing
* fix(k8s): dont overwrite add/remove function in pool
* fix(k8s): some fixes
* fix(k8s): some more fixes
* fix(k8s): linting
* fix(tests): fix tests
* fix(tests): fix k8s unit tests
* feat(k8s): complete k8s integration test
* feat(k8s): finish k8s tests
* feat(k8s): fix test
* fix(tests): fix test with no name
* feat(k8s): unify create/replace interface
* feat(k8s): extract k8s port constants
* fix(tests): fix tests
* fix(tests): wait for runtime being ready in tests
* feat(k8s): address comments
Co-authored-by: bwanglzu <[email protected]>
* feat(flow): adapt Flow for StarRouting (#3986)
* feat(flow): add routes
* feat(flow): adapt flow to star routing
* style: fix overload and cli autocomplete
* feat(flow): handle empty topologies
* feat(k8s): allow k8s pool disabling
* style: fix overload and cli autocomplete
* fix(test): fix test with mock
* fix(tests): fix more tests
* feat(flow): clean up tests
* style: fix overload and cli autocomplete
* fix(tests): fix more tests
* feat: add plot function (#3994)
* fix(tests): avoid hanging tests
* feat(flow): add type hinting
* fix(test): fix duplicate exec name in test
* fix(tests): fix more tests
* fix(tests): enable jinad test again
* fix(tests): random port fixture
* fix(style): replace quotes
Co-authored-by: Jina Dev Bot <[email protected]>
Co-authored-by: Joan Fontanals <[email protected]>
* feat(ci): bring back ci (#3997)
* feat(ci): enable ci again
* style: fix overload and cli autocomplete
* feat(ci): add latency tracking
* feat(ci): bring back some tests
* fix(tests): remove invalid port test
* feat(ci): disable daemon and distributed tests
* fix(tests): fix entrypoint in hub test
* fix(tests): wait for gateway to be ready
* fix(test): fix more tests
* feat(flow): do rolling update and scale sequentially
* fix(tests): fix more tests
* style: fix overload and cli autocomplete
* feat: star routing hanging pods (#4011)
* fix: try to handle hanging pods better
* test: hanging pods test work
* fix: fix topology graph problem
* test: add unit test to graph
* fix(tests): fix k8s tests
* fix(test): fix k8s test
* fix(test): fix k8s pool test
* fix(test): fix k8s test
* fix(test): fix k8s connection pool setting
* fix(tests): make runtime test more reliable
* fix(test): fix routes test
* fix(tests): make rolling update test less flaky
* feat(network): gurantee unique ports
* feat(network): do round robin for shards
* fix(ci): increase pytest timeout to 10 min
Co-authored-by: Jina Dev Bot <[email protected]>
Co-authored-by: Joan Fontanals <[email protected]>
* fix(ci): fix ci file
* feat(daemon): jinad pod for star routing
* Revert "feat(daemon): jinad pod for star routing"
This reverts commit ed9b37ac862af2e2e8d52df1ee51c0c331d76f92.
* feat(daemon): remote jinad pod support (#4042)
* feat(daemon): add pod tests for star routing
* feat(daemon): add remote pod test
* test(daemon): add remote pod arguments test
* test(daemon): add async scale test
* test(daemon): add rolling update test
* test(daemon): fix host
* feat(proto): remove message proto (#4051)
* feat(proto): remove message proto
* fix(tests): fix tests
* fix(tests): fix some more tests
* fix(tests): fix more tests
* fix(tests): fix more tests
* fix(tests): fix more tests
* fix(tests): fix more tests
* feat(proto): put docs back in data
* fix(proto): clean up
* feat(proto): clean up
* fix(tests): skip latency tracking
* fix(test): fix hub test
* fix(tests): fix k8s test
* fix(test): some test clean up
* fix(style): clean up style issues
* feat(proto): adjust for rebase
* fix(tests): bring back latency tracking
* fix(tests): fix merge accident
* feat(proto): skip request serialization (#4074)
* feat: add reduce to star routing (#4070)
* feat: add reduce on shards to head runtime
* test: add reduce integration tests with fixed order
* feat: add reduce on needs
* chore: get_docs_matrix_from_request becomes public
* style: fix overload and cli autocomplete
* docs: remove undeterministic results warning
* fix: fix uses_after
* test: assert correct num docs after reducing in test_external_pod
* test: correct asserts after reduce in test_rolling_update
* fix: no reduce if uses_after_address is set
* fix: get_docs_from_request only if needed
* fix: fix tests after merge
* refactor: move reduce from data_request_handler to head
* style: fix overload and cli autocomplete
* chore: apply suggestions
* fix: fix asserts
* chore: minor test fix
* chore: apply suggestions
* test: remove flow tests with external executor (pea)
* fix: fix test_expected_messages_routing
* fix: fix test_func_joiner
* test: adapt k8s test
Co-authored-by: Jina Dev Bot <[email protected]>
* fix(k8s): fix static pool config
* fix: use custom protoc doc generator image (#4088)
* fix: use custom protoc doc generator image
* fix(docs): minor doc improvement
* fix(docs): use custom image
* fix(docs): copy docarray
* fix: doc building local only
* fix: timeout doc building
* fix: use updated args when building ContainerPea
* test: add container PeaFactory test
* fix: force pea close on windows (#4098)
* fix: dont reduce if uses exist (#4099)
* fix: dont use reduce if uses exist
* fix: adjust reduce tests
* fix: adjust more reduce tests
* fix: fix more tests
* fix: adjust more tests
* fix: ignore non jina resources (#4101)
* feat(executor): enable async executors (#4102)
* feat(daemon): daemon flow on star routing (#4096)
* test(daemon): add remote flow test
* feat(daemon): call scale in daemon
* feat(daemon): remove tail args and identity
* test(daemon): rename scalable executor
* test(daemon): add a small delay in async test
* feat(daemon): scale partial flow only
* feat(daemon): call scale directly in partial flow store
* test(daemon): use asyncio sleep
* feat(daemon): enable flow level distributed tests
* test(daemon): fix jinad env workspace config
* test(daemon): fix pod test use new port rolling update
* feat(daemon): enable distribuetd tests
* test(daemon): remove duplicate tests and zed runtime test
* test(daemon): fix stores unit test
* feat(daemon): enable part of distributed tests
* feat(daemon): enable part of distributed tests
* test: correct test paths
* test(daemon): add client test for remote flows
* test(daemon): send a request with jina client
* test(daemon): assert async generator
* test(daemon): small interval between tests
* test(daemon): add flow test for container runtime
* test(daemon): add flow test for container runtime
* test(daemon): fix executor name
* test(daemon): fix executor name
* test(daemon): use async client fetch result
* test(daemon): finish container flow test
* test(daemon): enable distributed in ci
* test(daemon): enable distributed in ci
* test(daemon): decare flows and pods
* test(daemon): debug ci if else
* test(daemon): debug ci if else
* test(daemon): decare flows and pods
* test(daemon): correct test paths
* test(daemon): add small delay for async tests
* fix: star routing fixes (#4100)
* docs: update docs
* fix: fix Request.__repr__
* docs: update flow remarks
* docs: fix typo
* test: add non_empty_fields test
* chore: remove non_empty_fields test
* feat: polling per endpoint (#4111)
* feat(polling): polling per endpoint configurable
* fix: adjust tests
* feat(polling): extend documentation
* style: fix overload and cli autocomplete
* fix: clean up
* fix: adjust more tests
* fix: remove repeat from flaky test
* fix: k8s test
* feat(polling): address pr feedback
* feat: improve docs
Co-authored-by: Jina Dev Bot <[email protected]>
* feat(grpc): support connect grpc server via ssl tunnel (#4092)
* feat(grpc): support ssl grpc connect if port is 443
* fix(grpc): use https option instead of detect port automatically
* chore: fix typo
* fix: update jina/peapods/networking.py
Co-authored-by: Joan Fontanals <[email protected]>
* fix: update jina/peapods/networking.py
Co-authored-by: Joan Fontanals <[email protected]>
* fix: update jina/peapods/networking.py
Co-authored-by: Joan Fontanals <[email protected]>
* test(networking): add test for peapods networking
* fix: address comments
Co-authored-by: Joan Fontanals <[email protected]>
* feat(polling): unify polling args (#4113)
* fix: several issues for jinad pods (#4119)
* fix: activate for jinad pods
* fix: dont expose worker pod in partial daemon
* fix: workspace setting
* fix: containerized flows
* fix: hub test
* feat(daemon): remote peas on star routing (#4112)
* test(daemon): fix request in peas
* test(daemon): fix request in peas
* test(daemon): fix sync async client test
* test(daemon): enable remote peas test
* test(daemon): replace send message to send request
* test(daemon): declare pea tests in ci
* test(daemon): use pea args fixture
* test(daemon): head pea use default host
* test(daemon): fix peas topologies
* test(daemon): fix pseudo naming
* test(daemon): use default host as host
* test(daemon): fix executor path
* test(daemon): add remote worker back
* test(daemon): skip local remote remote topology
* fix: jinad pea test setup
* fix: jinad pea tests
* fix: remove invalid assertion
Co-authored-by: jacobowitz <[email protected]>
* feat: enable daemon tests again (#4132)
* feat: enable daemon tests again
* fix: remove bogy empty script file
* fix: more jinad test fixes
* style: fix overload and cli autocomplete
* fix: scale and ru in jinad
* fix: fix more jinad tests
Co-authored-by: Jina Dev Bot <[email protected]>
* fix: fix flow test
* fix: improve pea tests reliability (#4136)
Co-authored-by: Joan Fontanals <[email protected]>
Co-authored-by: Jina Dev Bot <[email protected]>
Co-authored-by: Deepankar Mahapatro <[email protected]>
Co-authored-by: bwanglzu <[email protected]>
Co-authored-by: AlaeddineAbdessalem <[email protected]>
Co-authored-by: Zhaofeng Miao <[email protected]> | 19 | 0 | 1,750 | 6 |
|
6 | 19 | def get_date_list(self, queryset, date_type=None, ordering="ASC"):
date_field = self.get_date_field()
allow_empty = self.get_allow_empty()
if date_type is None:
date_type = self.get_date_list_period()
if self.uses_datetime_field:
date_list = queryset.datetimes(date_field, date_type, ordering)
else:
date_list = queryset.dates(date_field, date_type, ordering)
if date_list is not None and not date_list and not allow_empty:
raise Http404(
_("No %(verbose_name_plural)s available")
% {
"verbose_name_plural": queryset.model._meta.verbose_name_plural,
}
)
return date_list
| django/views/generic/dates.py | 175 | django | {
"docstring": "\n Get a date list by calling `queryset.dates/datetimes()`, checking\n along the way for empty lists that aren't allowed.\n ",
"language": "en",
"n_whitespaces": 39,
"n_words": 17,
"vocab_size": 17
} | 55 | Python | 38 | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | dates.py | 206,860 | 17 | 108 | get_date_list | https://github.com/django/django.git | Refs #33476 -- Reformatted code with Black. | 230 | 0 | 51,761 | 15 |
|
6 | 16 | def unquote_unreserved(uri):
parts = uri.split("%")
for i in range(1, len(parts)):
h = parts[i][0:2]
if len(h) == 2 and h.isalnum():
try:
c = chr(int(h, 16))
except ValueError:
raise InvalidURL(f"Invalid percent-escape sequence: '{h}'")
if c in UNRESERVED_SET:
parts[i] = c + parts[i][2:]
else:
parts[i] = f"%{parts[i]}"
else:
parts[i] = f"%{parts[i]}"
return "".join(parts)
| pipenv/patched/pip/_vendor/requests/utils.py | 215 | pipenv | {
"docstring": "Un-escape any percent-escape sequences in a URI that are unreserved\n characters. This leaves all reserved, illegal and non-ASCII bytes encoded.\n\n :rtype: str\n ",
"language": "en",
"n_whitespaces": 31,
"n_words": 22,
"vocab_size": 22
} | 50 | Python | 37 | cd5a9683be69c86c8f3adcd13385a9bc5db198ec | utils.py | 22,163 | 16 | 119 | unquote_unreserved | https://github.com/pypa/pipenv.git | Rename notpip to pip. Vendor in pip-22.2.1 and latest requirementslib and vistir. | 198 | 0 | 4,233 | 16 |
|
1 | 9 | def set_3d_properties(self, path, zs=0, zdir='z'):
Patch3D.set_3d_properties(self, path.vertices, zs=zs, zdir=zdir)
self._code3d = path.codes
| lib/mpl_toolkits/mplot3d/art3d.py | 63 | matplotlib | {
"docstring": "\n Set the *z* position and direction of the path patch.\n\n Parameters\n ----------\n path :\n zs : float\n The location along the *zdir* axis in 3D space to position the\n path patch.\n zdir : {'x', 'y', 'z', 3-tuple}\n Plane to plot path patch orthogonal to. Default: 'z'.\n See `.get_dir_vector` for a description of the values.\n ",
"language": "en",
"n_whitespaces": 148,
"n_words": 54,
"vocab_size": 41
} | 12 | Python | 12 | df6f95703b60348e01603f98a439b133da2938a0 | art3d.py | 109,925 | 3 | 41 | set_3d_properties | https://github.com/matplotlib/matplotlib.git | Improve mpl_toolkit documentation | 33 | 0 | 23,832 | 8 |
|
1 | 4 | def name(self) -> str:
return self._name
| airbyte-cdk/python/airbyte_cdk/sources/declarative/declarative_stream.py | 22 | airbyte | {
"docstring": "\n :return: Stream name. By default this is the implementing class name, but it can be overridden as needed.\n ",
"language": "en",
"n_whitespaces": 33,
"n_words": 18,
"vocab_size": 18
} | 6 | Python | 6 | 150ab593f8ca1f1aa960a0811aece26c46ba6c75 | declarative_stream.py | 5,311 | 5 | 12 | name | https://github.com/airbytehq/airbyte.git | Low code connectors: core structure (#12850)
* checkout from alex/cac
* doc
* doc
* remove broken test
* rename
* rename file
* delete unused file
* rename
* abstract property
* isort
* update state
* Update comment
* remove incremental mixin
* delete comment
* update comments
* update comments
* remove no_state
* rename package
* pass parameters through kwargs
* update interface to pass source in interface
* update interface to pass source in interface
* rename to stream_slicer
* Low code connectors: string interpolation with jinja (#12852)
* checkout from alex/cac
* Add missing tests
* Add missing files
* missing file
* rename
* jinja dependency
* Add comment
* comment
* comment
* Revert "delete unused file"
This reverts commit 758e939367775ddbefcd52c6e1e832976d3ba9fe.
* delete unused field
* delete unused field
* rename
* pass kwargs directly
* isort
* Revert "isort"
This reverts commit 4a792239440bc9950813ccc6ed368641ce2a96e4.
* format
* decoder
* better error handling
* remove nostate
* isort
* delete dead code
* Update mapping type to [str, Any]
* add comment
* Add comment
* pass parameters through kwargs
* move test to right module
* Add missing test
* Use authbase instead of deprecated class
* leverage generator
* rename to declarative
* rename the classes too | 20 | 0 | 749 | 6 |
|
15 | 58 | def lattice_reference(G, niter=5, D=None, connectivity=True, seed=None):
import numpy as np
from networkx.utils import cumulative_distribution, discrete_sequence
local_conn = nx.connectivity.local_edge_connectivity
if len(G) < 4:
raise nx.NetworkXError("Graph has fewer than four nodes.")
if len(G.edges) < 2:
raise nx.NetworkXError("Graph has fewer that 2 edges")
# Instead of choosing uniformly at random from a generated edge list,
# this algorithm chooses nonuniformly from the set of nodes with
# probability weighted by degree.
G = G.copy()
keys, degrees = zip(*G.degree()) # keys, degree
cdf = cumulative_distribution(degrees) # cdf of degree
nnodes = len(G)
nedges = nx.number_of_edges(G)
if D is None:
D = np.zeros((nnodes, nnodes))
un = np.arange(1, nnodes)
um = np.arange(nnodes - 1, 0, -1)
u = np.append((0,), np.where(un < um, un, um))
for v in range(int(np.ceil(nnodes / 2))):
D[nnodes - v - 1, :] = np.append(u[v + 1 :], u[: v + 1])
D[v, :] = D[nnodes - v - 1, :][::-1]
niter = niter * nedges
# maximal number of rewiring attempts per 'niter'
max_attempts = int(nnodes * nedges / (nnodes * (nnodes - 1) / 2))
for _ in range(niter):
n = 0
while n < max_attempts:
# pick two random edges without creating edge list
# choose source node indices from discrete distribution
(ai, ci) = discrete_sequence(2, cdistribution=cdf, seed=seed)
if ai == ci:
continue # same source, skip
a = keys[ai] # convert index to label
c = keys[ci]
# choose target uniformly from neighbors
b = seed.choice(list(G.neighbors(a)))
d = seed.choice(list(G.neighbors(c)))
bi = keys.index(b)
di = keys.index(d)
if b in [a, c, d] or d in [a, b, c]:
continue # all vertices should be different
# don't create parallel edges
if (d not in G[a]) and (b not in G[c]):
if D[ai, bi] + D[ci, di] >= D[ai, ci] + D[bi, di]:
# only swap if we get closer to the diagonal
G.add_edge(a, d)
G.add_edge(c, b)
G.remove_edge(a, b)
G.remove_edge(c, d)
# Check if the graph is still connected
if connectivity and local_conn(G, a, b) == 0:
# Not connected, revert the swap
G.remove_edge(a, d)
G.remove_edge(c, b)
G.add_edge(a, b)
G.add_edge(c, d)
else:
break
n += 1
return G
@py_random_state(3)
@not_implemented_for("directed")
@not_implemented_for("multigraph") | networkx/algorithms/smallworld.py | 858 | @py_random_state(3)
@not_implemented_for("directed")
@not_implemented_for("multigraph") | networkx | {
"docstring": "Latticize the given graph by swapping edges.\n\n Parameters\n ----------\n G : graph\n An undirected graph.\n\n niter : integer (optional, default=1)\n An edge is rewired approximatively niter times.\n\n D : numpy.array (optional, default=None)\n Distance to the diagonal matrix.\n\n connectivity : boolean (optional, default=True)\n Ensure connectivity for the latticized graph when set to True.\n\n seed : integer, random_state, or None (default)\n Indicator of random number generation state.\n See :ref:`Randomness<randomness>`.\n\n Returns\n -------\n G : graph\n The latticized graph.\n\n Raises\n ------\n NetworkXError\n If there are fewer than 4 nodes or 2 edges in `G`\n\n Notes\n -----\n The implementation is adapted from the algorithm by Sporns et al. [1]_.\n which is inspired from the original work by Maslov and Sneppen(2002) [2]_.\n\n References\n ----------\n .. [1] Sporns, Olaf, and Jonathan D. Zwi.\n \"The small world of the cerebral cortex.\"\n Neuroinformatics 2.2 (2004): 145-162.\n .. [2] Maslov, Sergei, and Kim Sneppen.\n \"Specificity and stability in topology of protein networks.\"\n Science 296.5569 (2002): 910-913.\n ",
"language": "en",
"n_whitespaces": 302,
"n_words": 156,
"vocab_size": 119
} | 350 | Python | 221 | 9d5e11f27033049282e2d244132b0e946df6557d | smallworld.py | 177,545 | 52 | 533 | lattice_reference | https://github.com/networkx/networkx.git | bug fix in smallworld.py: random_reference and lattice_reference (#6151)
* raise exception if graph has less than 2 edges in random_reference and lattice_reference and tested
* Updated lattice_reference doc
* Update networkx/algorithms/smallworld.py
Co-authored-by: Ross Barnowski <[email protected]>
* Update networkx/algorithms/tests/test_smallworld.py
Co-authored-by: Ross Barnowski <[email protected]>
* Added some suggestions
* Added some final suggestions
Co-authored-by: Ross Barnowski <[email protected]> | 997 | 1 | 42,433 | 17 |
1 | 10 | def binary_matches(y_true, y_pred, threshold=0.5):
y_pred = tf.convert_to_tensor(y_pred)
threshold = tf.cast(threshold, y_pred.dtype)
y_pred = tf.cast(y_pred > threshold, y_pred.dtype)
return tf.cast(tf.equal(y_true, y_pred), tf.int8) | keras/utils/metrics_utils.py | 98 | keras | {
"docstring": "Creates int Tensor, 1 for label-prediction match, 0 for mismatch.\n\n Args:\n y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`.\n y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`.\n threshold: (Optional) Float representing the threshold for deciding whether\n prediction values are 1 or 0.\n\n Returns:\n Binary matches. shape = `[batch_size, d0, .. dN]`\n ",
"language": "en",
"n_whitespaces": 75,
"n_words": 55,
"vocab_size": 40
} | 21 | Python | 17 | 119cd4655d01570a70c70879dff4461ea46161bf | metrics_utils.py | 268,987 | 5 | 66 | binary_matches | https://github.com/keras-team/keras.git | Added util metric method for binary_matches. Decoupled from public metric binarry_acc | 26 | 0 | 79,806 | 9 |
|
2 | 5 | def _flatten_parameters(self):
[m.flatten_parameters() for m in self._to_flatten]
| synthesizer/models/sublayer/cbhg.py | 33 | MockingBird | {
"docstring": "Calls `flatten_parameters` on all the rnns used by the WaveRNN. Used\n to improve efficiency and avoid PyTorch yelling at us.",
"language": "en",
"n_whitespaces": 26,
"n_words": 20,
"vocab_size": 19
} | 7 | Python | 7 | 6abdd0ebf06ddede5cdf91329143b56167492a17 | cbhg.py | 161,297 | 2 | 19 | _flatten_parameters | https://github.com/babysor/MockingBird.git | Refactor (#649)
* Refactor model
* Refactor and fix bug to save plots | 21 | 0 | 38,959 | 8 |
|
2 | 7 | def compat_system(source_dir):
try:
system = load_system(source_dir)
except (FileNotFoundError, KeyError):
system = {}
system.setdefault(
'build-backend',
'setuptools.build_meta:__legacy__',
)
system.setdefault('requires', ['setuptools', 'wheel'])
return system
| .venv/lib/python3.8/site-packages/pip/_vendor/pep517/build.py | 87 | transferlearning | {
"docstring": "\n Given a source dir, attempt to get a build system backend\n and requirements from pyproject.toml. Fallback to\n setuptools but only if the file was not found or a build\n system was not indicated.\n ",
"language": "en",
"n_whitespaces": 49,
"n_words": 33,
"vocab_size": 26
} | 21 | Python | 18 | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | build.py | 62,961 | 11 | 48 | compat_system | https://github.com/jindongwang/transferlearning.git | upd; format | 70 | 0 | 13,077 | 10 |
|
2 | 7 | def table(self, data=None):
if _use_arrow():
return self.dg._arrow_table(data)
else:
return self.dg._legacy_table(data)
| lib/streamlit/elements/dataframe_selector.py | 59 | streamlit | {
"docstring": "Display a static table.\n\n This differs from `st.dataframe` in that the table in this case is\n static: its entire contents are laid out directly on the page.\n\n Parameters\n ----------\n data : pandas.DataFrame, pandas.Styler, pyarrow.Table, numpy.ndarray, Iterable, dict, or None\n The table data.\n Pyarrow tables are not supported by Streamlit's legacy DataFrame serialization\n (i.e. with `config.dataFrameSerialization = \"legacy\"`).\n To use pyarrow tables, please enable pyarrow by changing the config setting,\n `config.dataFrameSerialization = \"arrow\"`.\n\n Example\n -------\n >>> df = pd.DataFrame(\n ... np.random.randn(10, 5),\n ... columns=('col %d' % i for i in range(5)))\n ...\n >>> st.table(df)\n\n .. output::\n https://share.streamlit.io/streamlit/docs/main/python/api-examples-source/data.table.py\n height: 480px\n\n ",
"language": "en",
"n_whitespaces": 277,
"n_words": 98,
"vocab_size": 83
} | 10 | Python | 9 | 72703b38029f9358a0ec7ca5ed875a6b438ece19 | dataframe_selector.py | 118,729 | 5 | 35 | table | https://github.com/streamlit/streamlit.git | Replace static apps with live Cloud apps (#4317)
Co-authored-by: kajarenc <[email protected]> | 53 | 0 | 26,386 | 11 |
|
8 | 41 | def _galois_group_degree_4_simple(T, max_tries=30, randomize=False):
r
from sympy.combinatorics.permutations import Permutation
from sympy.combinatorics.named_groups import (
CyclicGroup, AbelianGroup, DihedralGroup, AlternatingGroup, SymmetricGroup
)
# Consider the resolvent for the form
# F = X0*X1^2 + X1*X2^2 + X2*X3^2 + X3*X0^2
# and the group G = S4. In this case, the stabilizer H is C4 = < (0123) >,
# and a set of representatives of G/H is {I, (01), (02), (03), (12), (23)}.
X = symbols('X0 X1 X2 X3')
F = X[0]*X[1]**2 + X[1]*X[2]**2 + X[2]*X[3]**2 + X[3]*X[0]**2
s = [
Permutation(3),
Permutation(3)(0, 1),
Permutation(3)(0, 2),
Permutation(3)(0, 3),
Permutation(3)(1, 2),
Permutation(3)(2, 3),
]
R = Resolvent(F, X, s)
history = set()
for i in range(max_tries):
R_dup, _, _ = R.eval_for_poly(T)
# If R is squarefree, we can proceed. Otherwise, apply a
# Tschirnhausen transformation on T and try again.
if dup_sqf_p(R_dup, ZZ):
break
_, T = tschirnhausen_transformation(T, max_tries=max_tries, history=history, fixed_order=not randomize)
else:
raise MaxTriesException
# Compute list L of degrees of irreducible factors of R, in increasing order:
fl = dup_factor_list(R_dup, ZZ)
L = sorted(sum([
[len(r) - 1] * e for r, e in fl[1]
], []))
if L == [6]:
return (AlternatingGroup(4), True) if has_square_disc(T) else (SymmetricGroup(4), False)
if L == [1, 1, 4]:
return (CyclicGroup(4), False)
if L == [2, 2, 2]:
return (AbelianGroup(2, 2), True)
assert L == [2, 4]
return (DihedralGroup(4), False)
| sympy/polys/numberfields/galoisgroups.py | 522 | sympy | {
"docstring": "\n Compute the Galois group of a polynomial of degree 4, using Alg 6.3.6\n of Cohen.\n\n References\n ==========\n\n .. [1] Cohen, H. *A Course in Computational Algebraic Number Theory*.\n\n ",
"language": "en",
"n_whitespaces": 47,
"n_words": 28,
"vocab_size": 26
} | 223 | Python | 154 | d3c0fc825c4a80904a1fb9a2092137c3d9e0c3fe | galoisgroups.py | 195,690 | 46 | 353 | _galois_group_degree_4_simple | https://github.com/sympy/sympy.git | Add a `galois_group()` function | 429 | 0 | 47,373 | 16 |
|
1 | 4 | def unsaved_files(self) -> List[str]:
| certbot-apache/certbot_apache/_internal/interfaces.py | 20 | certbot | {
"docstring": "\n Returns a list of file paths that have been changed since the last save\n (or the initial configuration parse). The intended use for this method\n is to tell the Reverter which files need to be included in a checkpoint.\n\n This is typically called for the root of the ParserNode tree.\n\n :returns: list of file paths of files that have been changed but not yet\n saved to disk.\n ",
"language": "en",
"n_whitespaces": 121,
"n_words": 67,
"vocab_size": 47
} | 4 | Python | 4 | 7d9e9a49005de7961e84d2a7c608db57dbab3046 | interfaces.py | 186,652 | 11 | 11 | unsaved_files | https://github.com/certbot/certbot.git | Add typing to certbot.apache (#9071)
* Add typing to certbot.apache
Co-authored-by: Adrien Ferrand <[email protected]> | 11 | 0 | 45,560 | 6 |
|
4 | 31 | def adjust_bbox(fig, bbox_inches, fixed_dpi=None):
origBbox = fig.bbox
origBboxInches = fig.bbox_inches
orig_layout = fig.get_layout_engine()
fig.set_layout_engine(None)
_boxout = fig.transFigure._boxout
old_aspect = []
locator_list = []
sentinel = object()
for ax in fig.axes:
locator_list.append(ax.get_axes_locator())
current_pos = ax.get_position(original=False).frozen()
ax.set_axes_locator(lambda a, r, _pos=current_pos: _pos)
# override the method that enforces the aspect ratio on the Axes
if 'apply_aspect' in ax.__dict__:
old_aspect.append(ax.apply_aspect)
else:
old_aspect.append(sentinel)
ax.apply_aspect = lambda pos=None: None
| lib/matplotlib/_tight_bbox.py | 221 | matplotlib | {
"docstring": "\n Temporarily adjust the figure so that only the specified area\n (bbox_inches) is saved.\n\n It modifies fig.bbox, fig.bbox_inches,\n fig.transFigure._boxout, and fig.patch. While the figure size\n changes, the scale of the original figure is conserved. A\n function which restores the original values are returned.\n ",
"language": "en",
"n_whitespaces": 66,
"n_words": 42,
"vocab_size": 33
} | 63 | Python | 51 | ec4dfbc3c83866f487ff0bc9c87b0d43a1c02b22 | _tight_bbox.py | 107,130 | 32 | 274 | adjust_bbox | https://github.com/matplotlib/matplotlib.git | ENH: implement and use base layout_engine for more flexible layout. | 164 | 0 | 22,596 | 13 |
|
2 | 7 | def get_currency(symbol) -> str:
ticker_info = yf.Ticker(symbol).info
if "financialCurrency" in ticker_info:
return ticker_info["financialCurrency"]
return "Not Specified"
| openbb_terminal/stocks/fundamental_analysis/yahoo_finance_model.py | 56 | OpenBBTerminal | {
"docstring": "Quick helper to get currency for financial statements",
"language": "en",
"n_whitespaces": 7,
"n_words": 8,
"vocab_size": 8
} | 16 | Python | 15 | 92991fc4e3097fdd9ac9f4f39bdd8e46289176cd | yahoo_finance_model.py | 285,622 | 6 | 30 | get_currency | https://github.com/OpenBB-finance/OpenBBTerminal.git | Get rid of option expirations in the past for Nasdaq + bugs (#2498)
* Get rid of option expirations in the past for Nasdaq + clean up bug
* Add in currency for yfinance financials
* Added fixes
Co-authored-by: Colin Delahunty <[email protected]>
Co-authored-by: colin99d <[email protected]> | 35 | 0 | 85,330 | 9 |
|
1 | 23 | def _solve_eigen(self, X, y, shrinkage, covariance_estimator):
| sklearn/discriminant_analysis.py | 46 | """Eigenvalue solver.
The eigenvalue solver computes the optimal solution of thecoefficient (basically the ratio of between class scatter to within | scikit-learn | {
"docstring": "Eigenvalue solver.\n\n The eigenvalue solver computes the optimal solution of the Rayleigh\n coefficient (basically the ratio of between class scatter to within",
"language": "en",
"n_whitespaces": 35,
"n_words": 22,
"vocab_size": 19
} | 6 | Python | 6 | e1db2a8173ca37e561cdfa4384481501c4d50868 | discriminant_analysis.py | 258,768 | 18 | 187 | _solve_eigen | https://github.com/scikit-learn/scikit-learn.git | Use check_finite=False in discriminant analysis (#18909)
Co-authored-by: Guillaume Lemaitre <[email protected]>
Co-authored-by: Jérémie du Boisberranger <[email protected]> | 13 | 2 | 75,415 | 5 |
2 | 7 | def model_call_inputs(model, keep_original_batch_size=False):
input_specs = model.save_spec(dynamic_batch=not keep_original_batch_size)
if input_specs is None:
return None, None
input_specs = _enforce_names_consistency(input_specs)
return input_specs
| keras/saving/saving_utils.py | 63 | keras | {
"docstring": "Inspect model to get its input signature.\n\n The model's input signature is a list with a single (possibly-nested) object.\n This is due to the Keras-enforced restriction that tensor inputs must be\n passed in as the first argument.\n\n For example, a model with input {'feature1': <Tensor>, 'feature2': <Tensor>}\n will have input signature: [{'feature1': TensorSpec, 'feature2': TensorSpec}]\n\n Args:\n model: Keras Model object.\n keep_original_batch_size: A boolean indicating whether we want to keep using\n the original batch size or set it to None. Default is `False`, which means\n that the batch dim of the returned input signature will always be set to\n `None`.\n\n Returns:\n A tuple containing `(args, kwargs)` TensorSpecs of the model call function\n inputs.\n `kwargs` does not contain the `training` argument.\n ",
"language": "en",
"n_whitespaces": 189,
"n_words": 119,
"vocab_size": 87
} | 19 | Python | 14 | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | saving_utils.py | 276,243 | 6 | 38 | model_call_inputs | https://github.com/keras-team/keras.git | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 41 | 0 | 81,601 | 10 |
|
1 | 10 | def test_login_redirect_for_direct_get(self):
response = self.client.get(reverse("admin:login"))
self.assertEqual(response.status_code, 200)
self.assertEqual(response.context[REDIRECT_FIELD_NAME], reverse("admin:index"))
| tests/admin_views/tests.py | 77 | django | {
"docstring": "\n Login redirect should be to the admin index page when going directly to\n /admin/login/.\n ",
"language": "en",
"n_whitespaces": 36,
"n_words": 14,
"vocab_size": 13
} | 9 | Python | 9 | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | tests.py | 207,740 | 4 | 45 | test_login_redirect_for_direct_get | https://github.com/django/django.git | Refs #33476 -- Reformatted code with Black. | 37 | 0 | 52,079 | 11 |
|
3 | 19 | def tokenizer_from_json(json_string):
tokenizer_config = json.loads(json_string)
config = tokenizer_config.get("config")
word_counts = json.loads(config.pop("word_counts"))
word_docs = json.loads(config.pop("word_docs"))
index_docs = json.loads(config.pop("index_docs"))
# Integer indexing gets converted to strings with json.dumps()
index_docs = {int(k): v for k, v in index_docs.items()}
index_word = json.loads(config.pop("index_word"))
index_word = {int(k): v for k, v in index_word.items()}
word_index = json.loads(config.pop("word_index"))
tokenizer = Tokenizer(**config)
tokenizer.word_counts = word_counts
tokenizer.word_docs = word_docs
tokenizer.index_docs = index_docs
tokenizer.word_index = word_index
tokenizer.index_word = index_word
return tokenizer
| keras/preprocessing/text.py | 274 | keras | {
"docstring": "Parses a JSON tokenizer configuration and returns a tokenizer instance.\n\n Deprecated: `tf.keras.preprocessing.text.Tokenizer` does not operate on\n tensors and is not recommended for new code. Prefer\n `tf.keras.layers.TextVectorization` which provides equivalent functionality\n through a layer which accepts `tf.Tensor` input. See the\n [text loading tutorial](https://www.tensorflow.org/tutorials/load_data/text)\n for an overview of the layer and text handling in tensorflow.\n\n Args:\n json_string: JSON string encoding a tokenizer configuration.\n\n Returns:\n A Keras Tokenizer instance\n ",
"language": "en",
"n_whitespaces": 107,
"n_words": 66,
"vocab_size": 53
} | 70 | Python | 41 | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | text.py | 275,787 | 17 | 161 | tokenizer_from_json | https://github.com/keras-team/keras.git | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 124 | 0 | 81,466 | 11 |
|
1 | 4 | def shape(self):
return (self.rows, self.cols)
| sympy/matrices/common.py | 27 | sympy | {
"docstring": "The shape (dimensions) of the matrix as the 2-tuple (rows, cols).\n\n Examples\n ========\n\n >>> from sympy import zeros\n >>> M = zeros(2, 3)\n >>> M.shape\n (2, 3)\n >>> M.rows\n 2\n >>> M.cols\n 3\n ",
"language": "en",
"n_whitespaces": 110,
"n_words": 33,
"vocab_size": 27
} | 5 | Python | 5 | 59d22b6bb7287613d598611027f640d068ca5748 | common.py | 196,368 | 2 | 16 | shape | https://github.com/sympy/sympy.git | Moved imports to higher level | 19 | 0 | 47,868 | 7 |
|
3 | 20 | def test_sync_call_healthy_only(self):
actors = [Actor.remote(i) for i in range(4)]
manager = FaultTolerantActorManager(actors=actors)
results = []
for _ in range(10):
results.extend(
manager.foreach_actor(
lambda w: w.call(), healthy_only=True
).ignore_errors()
)
# Wait for actors to recover.
wait_for_restore()
# Notice that since we only fire calls against healthy actors,
# we wouldn't be aware that the actors have been recovered.
# So once an actor is taken out of the lineup (10% chance),
# it will not go back in, and we should have few results here.
# Basically takes us 10 calls to kill all the actors.
# Note that we can hardcode 10 here because we are using deterministic
# sequences of random numbers.
self.assertEqual(len(results), 10)
| rllib/utils/tests/test_actor_manager.py | 144 | ray | {
"docstring": "Test synchronous remote calls to only healthy actors.",
"language": "en",
"n_whitespaces": 7,
"n_words": 8,
"vocab_size": 8
} | 114 | Python | 86 | d329147ae28c57b290f6b932f9f3044523f67c4e | test_actor_manager.py | 135,567 | 12 | 83 | test_sync_call_healthy_only | https://github.com/ray-project/ray.git | [RLlib] Introduce FaultTolerantActorManager (#29703)
Signed-off-by: Jun Gong <[email protected]> | 298 | 0 | 30,658 | 16 |
|
5 | 23 | def _get_time_micros(self) -> npt.NDArray[np.int64]:
values = self._data._local_timestamps()
reso = self._data._reso
ppd = periods_per_day(reso)
frac = values % ppd
if reso == NpyDatetimeUnit.NPY_FR_ns.value:
micros = frac // 1000
elif reso == NpyDatetimeUnit.NPY_FR_us.value:
micros = frac
elif reso == NpyDatetimeUnit.NPY_FR_ms.value:
micros = frac * 1000
elif reso == NpyDatetimeUnit.NPY_FR_s.value:
micros = frac * 1_000_000
else: # pragma: no cover
raise NotImplementedError(reso)
micros[self._isnan] = -1
return micros
| pandas/core/indexes/datetimes.py | 185 | pandas | {
"docstring": "\n Return the number of microseconds since midnight.\n\n Returns\n -------\n ndarray[int64_t]\n ",
"language": "en",
"n_whitespaces": 46,
"n_words": 10,
"vocab_size": 10
} | 64 | Python | 35 | 80c005e67f96f431674a37ecd8a9e8a2808f7db4 | datetimes.py | 167,469 | 24 | 113 | _get_time_micros | https://github.com/pandas-dev/pandas.git | ENH: DatetimeIndex.indexer_between_time support non-nano (#47535) | 204 | 0 | 40,025 | 10 |
|
2 | 5 | def test_whether_worker_leaked_when_task_finished_with_errors(ray_start_regular):
driver_template = | python/ray/tests/test_advanced_2.py | 22 | driver_template = """
import ray
import os
import ray
import numpy as np
import time
ray.init(address="{address}", namespace="test")@ray.remote | ray | {
"docstring": "\nimport ray\nimport os\nimport ray\nimport numpy as np\nimport time\n\nray.init(address=\"{address}\", namespace=\"test\")\n\n# The util actor to store the pid cross jobs.\[email protected]",
"language": "en",
"n_whitespaces": 17,
"n_words": 25,
"vocab_size": 20
} | 4 | Python | 4 | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | test_advanced_2.py | 131,209 | 60 | 139 | test_whether_worker_leaked_when_task_finished_with_errors | https://github.com/ray-project/ray.git | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | 7 | 2 | 29,485 | 5 |
1 | 14 | async def endpoint_discovery(self, empty, context) -> jina_pb2.EndpointsProto:
endpointsProto = jina_pb2.EndpointsProto()
endpointsProto.endpoints.extend(
list(self._data_request_handler._executor.requests.keys())
)
return endpointsProto
| jina/serve/runtimes/worker/__init__.py | 73 | jina | {
"docstring": "\n Process the the call requested and return the list of Endpoints exposed by the Executor wrapped inside this Runtime\n\n :param empty: The service expects an empty protobuf message\n :param context: grpc context\n :returns: the response request\n ",
"language": "en",
"n_whitespaces": 72,
"n_words": 36,
"vocab_size": 31
} | 15 | Python | 14 | 65d6d6da50cb795499ea5e361bf14908f62a3168 | __init__.py | 12,303 | 13 | 44 | endpoint_discovery | https://github.com/jina-ai/jina.git | feat: gateway endpoint discovery (#4756) | 61 | 0 | 2,252 | 14 |
|
1 | 7 | def to_json(self) -> dict:
return {
"name": self.name,
"type": self.type.name,
"class": self.class_.name,
}
@dataclass | mitmproxy/dns.py | 61 | @dataclass | mitmproxy | {
"docstring": "\n Converts the question into json for mitmweb.\n Sync with web/src/flow.ts.\n ",
"language": "en",
"n_whitespaces": 32,
"n_words": 10,
"vocab_size": 10
} | 14 | Python | 14 | ea6f9727dab03b0811c180bab761d28b7e57ef50 | dns.py | 250,941 | 10 | 33 | to_json | https://github.com/mitmproxy/mitmproxy.git | [dns] use snake_case in web flows | 67 | 1 | 73,570 | 9 |
4 | 23 | def test_higher_rank_inputs_for_importance_weights(self):
for fw in framework_iterator(frameworks=("torch", "tf"), session=True):
vtrace = vtrace_tf if fw != "torch" else vtrace_torch
if fw == "tf":
inputs_ = {
"log_rhos": tf1.placeholder(
dtype=tf.float32, shape=[None, None, 1]
),
"discounts": tf1.placeholder(
dtype=tf.float32, shape=[None, None, 1]
),
"rewards": tf1.placeholder(
dtype=tf.float32, shape=[None, None, 42]
),
"values": tf1.placeholder(dtype=tf.float32, shape=[None, None, 42]),
"bootstrap_value": tf1.placeholder(
dtype=tf.float32, shape=[None, 42]
),
}
else:
inputs_ = {
"log_rhos": Box(-1.0, 1.0, (8, 10, 1)).sample(),
"discounts": Box(-1.0, 1.0, (8, 10, 1)).sample(),
"rewards": Box(-1.0, 1.0, (8, 10, 42)).sample(),
"values": Box(-1.0, 1.0, (8, 10, 42)).sample(),
"bootstrap_value": Box(-1.0, 1.0, (10, 42)).sample(),
}
output = vtrace.from_importance_weights(**inputs_)
check(int(output.vs.shape[-1]), 42)
| rllib/agents/impala/tests/test_vtrace.py | 447 | ray | {
"docstring": "Checks support for additional dimensions in inputs.",
"language": "en",
"n_whitespaces": 6,
"n_words": 7,
"vocab_size": 7
} | 96 | Python | 47 | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | test_vtrace.py | 133,740 | 29 | 315 | test_higher_rank_inputs_for_importance_weights | https://github.com/ray-project/ray.git | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | 583 | 0 | 30,091 | 18 |
|
11 | 36 | def CCompiler_spawn(self, cmd, display=None, env=None):
env = env if env is not None else dict(os.environ)
if display is None:
display = cmd
if is_sequence(display):
display = " ".join(list(display))
log.info(display)
try:
if self.verbose:
subprocess.check_output(cmd, env=env)
else:
subprocess.check_output(cmd, stderr=subprocess.STDOUT, env=env)
except subprocess.CalledProcessError as exc:
o = exc.output
s = exc.returncode
except OSError as e:
# OSError doesn't have the same hooks for the exception
# output, but exec_command() historically would use an
# empty string for EnvironmentError (base class for
# OSError)
# o = b''
# still that would make the end-user lost in translation!
o = f"\n\n{e}\n\n\n"
try:
o = o.encode(sys.stdout.encoding)
except AttributeError:
o = o.encode("utf8")
# status previously used by exec_command() for parent
# of OSError
s = 127
else:
# use a convenience return here so that any kind of
# caught exception will execute the default code after the
# try / except block, which handles various exceptions
return None
if is_sequence(cmd):
cmd = " ".join(list(cmd))
if self.verbose:
forward_bytes_to_stdout(o)
if re.search(b"Too many open files", o):
msg = "\nTry rerunning setup command until build succeeds."
else:
msg = ""
raise DistutilsExecError(
'Command "%s" failed with exit status %d%s' % (cmd, s, msg)
)
| sklearn/externals/_numpy_compiler_patch.py | 374 | scikit-learn | {
"docstring": "\n Execute a command in a sub-process.\n\n Parameters\n ----------\n cmd : str\n The command to execute.\n display : str or sequence of str, optional\n The text to add to the log file kept by `numpy.distutils`.\n If not given, `display` is equal to `cmd`.\n env: a dictionary for environment variables, optional\n\n Returns\n -------\n None\n\n Raises\n ------\n DistutilsExecError\n If the command failed, i.e. the exit status was not 0.\n\n ",
"language": "en",
"n_whitespaces": 134,
"n_words": 66,
"vocab_size": 51
} | 195 | Python | 126 | 8a6cf1a33e80d0e4caa16205ce199a9e1bea7657 | _numpy_compiler_patch.py | 259,371 | 35 | 213 | CCompiler_spawn | https://github.com/scikit-learn/scikit-learn.git | BLD Monkeypatch windows build to stablize build (#22693) | 481 | 0 | 75,736 | 15 |
|
1 | 4 | def not_public(self):
return self.filter(self.private_q())
| wagtail/query.py | 31 | wagtail | {
"docstring": "\n Filters the QuerySet to only contain pages that are in a private\n section and their descendants.\n ",
"language": "en",
"n_whitespaces": 38,
"n_words": 16,
"vocab_size": 16
} | 4 | Python | 4 | 180d43a200163f5b7c75280f7bbf7cb4e5de1b91 | query.py | 79,209 | 2 | 17 | not_public | https://github.com/wagtail/wagtail.git | Fix Page queryset.not_public returning all pages when no page restrictions exist. (#9067)
Fixes #8952 | 18 | 0 | 16,893 | 9 |
|
1 | 10 | def get_previous_release(self, project):
return (
ReleaseProject.objects.filter(project=project, release__date_added__lt=self.date_added)
.order_by("-release__date_added")
.first()
)
| src/sentry/models/release.py | 60 | sentry | {
"docstring": "Get the release prior to this one. None if none exists",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | 10 | Python | 10 | 272d35503a2d5174dfa8cad57f94a2354e453bf3 | release.py | 93,634 | 6 | 36 | get_previous_release | https://github.com/getsentry/sentry.git | feat(ingest): Automatically associate commits to checksum release (#36491)
Feature for Workflow 2.0. If the SDK is configured to file an event with the release version matching the release commit SHA, ingest will look to see if there have been commits between the release version and the previous release on Github. If there has been, it will register those GH commits as sentry commits and add them to the release.
This will allow sentry to only notify developers who worked on the current release and reduce notification spam. | 64 | 0 | 19,000 | 14 |
|
2 | 6 | def trace(log_dir, create_perfetto_link=False, create_perfetto_trace=False):
start_trace(log_dir, create_perfetto_link, create_perfetto_trace)
try:
yield
finally:
stop_trace()
| jax/_src/profiler.py | 51 | jax | {
"docstring": "Context manager to take a profiler trace.\n\n The trace will capture CPU, GPU, and/or TPU activity, including Python\n functions and JAX on-device operations.\n\n The resulting trace can be viewed with TensorBoard. Note that TensorBoard\n doesn't need to be running when collecting the trace.\n\n Only once trace may be collected a time. A RuntimeError will be raised if a\n trace is started while another trace is running.\n\n Args:\n log_dir: The directory to save the profiler trace to (usually the\n TensorBoard log directory).\n create_perfetto_link: A boolean which, if true, creates and prints link to\n the Perfetto trace viewer UI (https://ui.perfetto.dev). The program will\n block until the link is opened and Perfetto loads the trace.\n create_perfetto_trace: A boolean which, if true, additionally dumps a\n ``perfetto_trace.json.gz`` file that is compatible for upload with the\n Perfetto trace viewer UI (https://ui.perfetto.dev). The file will also be\n generated if ``create_perfetto_link`` is true. This could be useful if you\n want to generate a Perfetto-compatible trace without blocking the\n processs.\n ",
"language": "en",
"n_whitespaces": 218,
"n_words": 161,
"vocab_size": 97
} | 11 | Python | 11 | 260f1d8b843483df46cf397ae5a1afc0abc9c64f | profiler.py | 121,807 | 6 | 30 | trace | https://github.com/google/jax.git | Add option to generate perfetto trace without generating link | 21 | 0 | 27,075 | 10 |
|
4 | 13 | def repartition(self, axis=None):
if StorageFormat.get() == "Hdk":
# Hdk uses only one partition, it makes
# no sense for it to repartition the dataframe.
return self
axes = [0, 1] if axis is None else [axis]
new_query_compiler = self
for _ax in axes:
new_query_compiler = new_query_compiler.__constructor__(
new_query_compiler._modin_frame.apply_full_axis(
_ax, lambda df: df, keep_partitioning=False
)
)
return new_query_compiler
# End of DataFrame methods
| modin/core/storage_formats/base/query_compiler.py | 113 | modin | {
"docstring": "\n Repartitioning QueryCompiler objects to get ideal partitions inside.\n\n Allows to improve performance where the query compiler can't improve\n yet by doing implicit repartitioning.\n\n Parameters\n ----------\n axis : {0, 1, None}, optional\n The axis along which the repartitioning occurs.\n `None` is used for repartitioning along both axes.\n\n Returns\n -------\n BaseQueryCompiler\n The repartitioned BaseQueryCompiler.\n ",
"language": "en",
"n_whitespaces": 156,
"n_words": 52,
"vocab_size": 45
} | 61 | Python | 49 | 704ded959541bcf55acadfb49f3fda804267b767 | query_compiler.py | 155,395 | 12 | 70 | repartition | https://github.com/modin-project/modin.git | FEAT-#5367: Introduce new API for repartitioning Modin objects (#5366)
Co-authored-by: Iaroslav Igoshev <[email protected]>
Co-authored-by: Vasily Litvinov <[email protected]>
Signed-off-by: Anatoly Myachev <[email protected]> | 210 | 0 | 36,375 | 14 |
|
2 | 12 | def show_compilers():
# XXX this "knows" that the compiler option it's describing is
# "--compiler", which just happens to be the case for the three
# commands that use it.
from distutils.fancy_getopt import FancyGetopt
compilers = []
for compiler in compiler_class.keys():
compilers.append(("compiler="+compiler, None,
compiler_class[compiler][2]))
compilers.sort()
pretty_printer = FancyGetopt(compilers)
pretty_printer.print_help("List of available compilers:")
| python3.10.4/Lib/distutils/ccompiler.py | 106 | XX-Net | {
"docstring": "Print list of available compilers (used by the \"--help-compiler\"\n options to \"build\", \"build_ext\", \"build_clib\").\n ",
"language": "en",
"n_whitespaces": 20,
"n_words": 14,
"vocab_size": 14
} | 52 | Python | 44 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | ccompiler.py | 222,587 | 9 | 61 | show_compilers | https://github.com/XX-net/XX-Net.git | add python 3.10.4 for windows | 114 | 0 | 56,655 | 12 |
|
1 | 12 | def test_acquire_unavailable(ray_start_4_cpus):
manager = PlacementGroupResourceManager(update_interval_s=0)
assert not manager.acquire_resources(REQUEST_2_CPU)
manager.request_resources(REQUEST_2_CPU)
ray.wait(manager.get_resource_futures(), num_returns=1)
assert manager.acquire_resources(REQUEST_2_CPU)
| python/ray/air/tests/test_resource_manager_placement_group.py | 82 | ray | {
"docstring": "Test that acquiring resources that are not available returns None.\n\n - Try to acquire\n - Assert this does not work\n - Request resources\n - Wait until ready\n - Acquire\n - Assert this did work\n ",
"language": "en",
"n_whitespaces": 55,
"n_words": 34,
"vocab_size": 23
} | 13 | Python | 11 | edb17fd2069844f12237c85ba6607afae536401d | test_resource_manager_placement_group.py | 138,061 | 6 | 49 | test_acquire_unavailable | https://github.com/ray-project/ray.git | [air/tune] Internal resource management 1 - Ray AIR resource manager implementation (#30777)
Prerequisite to #30016
This PR adds a new Ray AIR resource manager to replace the PlacementGroupManager of Ray Tune. Details can be found in #30016.
Specifically, this PR
- Adds the main resource manager abstractions
- Renames (and moves) PlacementGroupFactory to ResourceRequest
- Adds implementations and tests for a placement group based manager and a budget based manager
Signed-off-by: Kai Fricke <[email protected]>
Signed-off-by: Kai Fricke <[email protected]>
Co-authored-by: matthewdeng <[email protected]> | 31 | 0 | 31,300 | 9 |
|
23 | 25 | def multiset_derangements(s):
ms = multiset(s)
mx = max(ms.values())
n = len(s)
# special cases
# 0) impossible case
if mx*2 > n:
return
# 1) singletons
if len(ms) == n:
for p in generate_derangements(s):
yield p
return
for M in ms:
if ms[M] == mx:
break
inonM = [i for i in range(n) if s[i] != M]
iM = [i for i in range(n) if s[i] == M]
rv = [None]*n
# 2) half are the same
if 2*mx == n:
for i in inonM:
rv[i] = M
for p in multiset_permutations([s[i] for i in inonM]):
for i, pi in zip(iM, p):
rv[i] = pi
yield rv
return
# 3) single repeat covers all but 1 of the non-repeats
if n - 2*mx == 1 and len(ms.values()) - 1 == n - mx:
for i in range(len(inonM)):
i1 = inonM[i]
ifill = inonM[:i] + inonM[i+1:]
for j in ifill:
rv[j] = M
for p in permutations([s[j] for j in ifill]):
rv[i1] = s[i1]
for j, pi in zip(iM, p):
rv[j] = pi
k = i1
for j in iM:
rv[j], rv[k] = rv[k], rv[j]
yield rv
k = j
return
| sympy/utilities/iterables.py | 486 | sympy | {
"docstring": "Generate derangements of the elements of s *in place*.\n\n Examples\n ========\n\n >>> from sympy.utilities.iterables import multiset_derangements, uniq\n\n Because the derangements of multisets (not sets) are generated\n in place, copies of the return value must be made if a collection\n of derangements is desired or else all values will be the same:\n\n >>> list(uniq([i for i in multiset_derangements('1233')]))\n [['3', '3', '2', '1']]\n >>> [i.copy() for i in multiset_derangements('1233')]\n [['3', '3', '1', '2'], ['3', '3', '2', '1']]\n ",
"language": "en",
"n_whitespaces": 108,
"n_words": 75,
"vocab_size": 54
} | 190 | Python | 90 | 25aaf2c3a6ac0d39da710d6e67f244930b56d669 | iterables.py | 195,921 | 45 | 358 | multiset_derangements | https://github.com/sympy/sympy.git | fix repeat covers all but 1 | 569 | 0 | 47,476 | 16 |
|
1 | 7 | def content_type(model):
return ContentType.objects.get_for_model(model)
@register.filter() | netbox/utilities/templatetags/builtins/filters.py | 38 | @register.filter() | netbox | {
"docstring": "\n Return the ContentType for the given object.\n ",
"language": "en",
"n_whitespaces": 14,
"n_words": 7,
"vocab_size": 6
} | 5 | Python | 5 | 7c105019d8ae9205051c302e7499b33a455f9176 | filters.py | 264,445 | 2 | 15 | content_type | https://github.com/netbox-community/netbox.git | Closes #8600: Document built-in template tags & filters | 10 | 1 | 77,731 | 8 |
4 | 18 | def set_weights(self, weights):
params = self.weights
if len(params) != len(weights):
raise ValueError(
"Length of the specified weight list ("
+ str(len(weights))
+ ") does not match the number of weights "
"of the optimizer (" + str(len(params)) + ")"
)
weight_value_tuples = []
param_values = backend.batch_get_value(params)
for pv, p, w in zip(param_values, params, weights):
if pv.shape != w.shape:
raise ValueError(
"Optimizer weight shape "
+ str(pv.shape)
+ " not compatible with "
"provided weight shape " + str(w.shape)
)
weight_value_tuples.append((p, w))
backend.batch_set_value(weight_value_tuples)
| keras/optimizers/optimizer_v1.py | 212 | keras | {
"docstring": "Sets the weights of the optimizer, from Numpy arrays.\n\n Should only be called after computing the gradients\n (otherwise the optimizer has no weights).\n\n Args:\n weights: a list of Numpy arrays. The number of arrays and their shape\n must match number of the dimensions of the weights of the optimizer\n (i.e. it should match the output of `get_weights`).\n\n Raises:\n ValueError: in case of incompatible weight shapes.\n ",
"language": "en",
"n_whitespaces": 148,
"n_words": 65,
"vocab_size": 45
} | 82 | Python | 56 | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | optimizer_v1.py | 275,342 | 21 | 125 | set_weights | https://github.com/keras-team/keras.git | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 341 | 0 | 81,383 | 17 |
|
1 | 11 | def test__render_filenames_undefined_template():
path = "/srv/salt/saltines"
dest = "/srv/salt/cheese"
saltenv = "base"
template = "biscuits"
ret = (path, dest)
pytest.raises(
CommandExecutionError, cp._render_filenames, path, dest, saltenv, template
)
| tests/pytests/unit/modules/test_cp.py | 73 | salt | {
"docstring": "\n Test if _render_filenames fails upon getting a template not in\n TEMPLATE_REGISTRY.\n ",
"language": "en",
"n_whitespaces": 21,
"n_words": 11,
"vocab_size": 11
} | 26 | Python | 21 | ba58c71c55f8d65e702525faf435c2de91aae85c | test_cp.py | 215,688 | 9 | 42 | test__render_filenames_undefined_template | https://github.com/saltstack/salt.git | move cp exec module tests to pytest | 57 | 0 | 54,099 | 8 |
|
9 | 10 | def match(self, node, results=None):
if self.type is not None and node.type != self.type:
return False
if self.content is not None:
r = None
if results is not None:
r = {}
if not self._submatch(node, r):
return False
if r:
results.update(r)
if results is not None and self.name:
results[self.name] = node
return True
| python3.10.4/Lib/lib2to3/pytree.py | 145 | XX-Net | {
"docstring": "\n Does this pattern exactly match a node?\n\n Returns True if it matches, False if not.\n\n If results is not None, it must be a dict which will be\n updated with the nodes matching named subpatterns.\n\n Default implementation for non-wildcard patterns.\n ",
"language": "en",
"n_whitespaces": 83,
"n_words": 40,
"vocab_size": 36
} | 52 | Python | 29 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | pytree.py | 218,860 | 14 | 93 | match | https://github.com/XX-net/XX-Net.git | add python 3.10.4 for windows | 198 | 0 | 55,510 | 11 |
|
2 | 5 | def endswith_cr(line):
return line.endswith("\r" if isinstance(line, str) else b"\r")
| django/core/files/base.py | 43 | django | {
"docstring": "Return True if line (a text or bytestring) ends with '\\r'.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | 9 | Python | 9 | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | base.py | 204,478 | 2 | 23 | endswith_cr | https://github.com/django/django.git | Refs #33476 -- Reformatted code with Black. | 15 | 0 | 50,741 | 10 |
|
3 | 10 | def _get_n_args(self, args, example, n):
# type: (List[str], str, int) -> Any
if len(args) != n:
msg = (
'Got unexpected number of arguments, expected {}. '
'(example: "{} config {}")'
).format(n, get_prog(), example)
raise PipError(msg)
if n == 1:
return args[0]
else:
return args
| .venv/lib/python3.8/site-packages/pip/_internal/commands/configuration.py | 93 | transferlearning | {
"docstring": "Helper to make sure the command got the right number of arguments\n ",
"language": "en",
"n_whitespaces": 19,
"n_words": 12,
"vocab_size": 11
} | 45 | Python | 43 | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | configuration.py | 60,598 | 11 | 56 | _get_n_args | https://github.com/jindongwang/transferlearning.git | upd; format | 165 | 0 | 12,217 | 13 |
|
5 | 19 | def tmpfile(extension="", dir=None):
extension = extension.lstrip(".")
if extension:
extension = "." + extension
handle, filename = tempfile.mkstemp(extension, dir=dir)
os.close(handle)
os.remove(filename)
try:
yield filename
finally:
if os.path.exists(filename):
with suppress(OSError): # sometimes we can't remove a generated temp file
if os.path.isdir(filename):
shutil.rmtree(filename)
else:
os.remove(filename)
@contextmanager | dask/utils.py | 179 | @contextmanager | dask | {
"docstring": "\n Function to create and return a unique temporary file with the given extension, if provided.\n\n Parameters\n ----------\n extension : str\n The extension of the temporary file to be created\n dir : str\n If ``dir`` is not None, the file will be created in that directory; otherwise,\n Python's default temporary directory is used.\n\n Returns\n -------\n out : str\n Path to the temporary file\n\n See Also\n --------\n NamedTemporaryFile : Built-in alternative for creating temporary files\n tmp_path : pytest fixture for creating a temporary directory unique to the test invocation\n\n Notes\n -----\n This context manager is particularly useful on Windows for opening temporary files multiple times.\n ",
"language": "en",
"n_whitespaces": 180,
"n_words": 103,
"vocab_size": 69
} | 43 | Python | 35 | bf66221722cce8f09a9b09895bdb4596f14a5430 | utils.py | 156,915 | 16 | 100 | tmpfile | https://github.com/dask/dask.git | `tmpfile` does not end files with period on empty extension (#9429) | 167 | 1 | 36,805 | 17 |
2 | 19 | def test_normalized_P5_directed(self):
G = nx.DiGraph()
nx.add_path(G, range(5))
b_answer = {0: 0, 1: 1.0 / 12.0, 2: 1.0 / 12.0, 3: 0, 4: 0, 5: 0}
b = nx.betweenness_centrality_subset(
G, sources=[0], targets=[3], normalized=True, weight=None
)
for n in sorted(G):
assert b[n] == pytest.approx(b_answer[n], abs=1e-7)
| networkx/algorithms/centrality/tests/test_betweenness_centrality_subset.py | 162 | networkx | {
"docstring": "Betweenness Centrality Subset: Normalized Directed P5",
"language": "en",
"n_whitespaces": 5,
"n_words": 6,
"vocab_size": 6
} | 43 | Python | 36 | 4376a6f751874dceff9dadc0a6a6bfc2dfa04000 | test_betweenness_centrality_subset.py | 177,474 | 9 | 120 | test_normalized_P5_directed | https://github.com/networkx/networkx.git | PR for issue #6033 Improve test coverage for algorithms in betweenness_subset.py #6033 (#6083)
* Updated test_betweenness_centrality_subset.py
* add test of normalized in test_betweenness_centrality_subset.py
* add test of normalized in test_betweenness_centrality_subset.py
* update test of normalized in test_betweenness_centrality_subset.py
* update weight of test_betweenness_centrality_subset.py
* add docstring
* add docstring in test_betweenness_centrality_subset.py
* add docstring in test_betweenness_centrality_subset.py | 114 | 0 | 42,386 | 11 |
|
2 | 10 | def window_frame_rows_start_end(self, start=None, end=None):
if not self.connection.features.supports_over_clause:
raise NotSupportedError("This backend does not support window expressions.")
return self.window_frame_start(start), self.window_frame_end(end)
| django/db/backends/base/operations.py | 71 | django | {
"docstring": "\n Return SQL for start and end points in an OVER clause window frame.\n ",
"language": "en",
"n_whitespaces": 28,
"n_words": 13,
"vocab_size": 13
} | 18 | Python | 17 | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | operations.py | 204,877 | 4 | 43 | window_frame_rows_start_end | https://github.com/django/django.git | Refs #33476 -- Reformatted code with Black. | 50 | 0 | 50,950 | 10 |
|
4 | 17 | def completion_item_yank(self, sel=False):
text = self._cmd.selectedText()
if not text:
index = self.currentIndex()
if not index.isValid():
raise cmdutils.CommandError("No item selected!")
text = self._model().data(index)
if not utils.supports_selection():
sel = False
utils.set_clipboard(text, selection=sel)
| qutebrowser/completion/completionwidget.py | 133 | qutebrowser | {
"docstring": "Yank the current completion item into the clipboard.\n\n Args:\n sel: Use the primary selection instead of the clipboard.\n ",
"language": "en",
"n_whitespaces": 43,
"n_words": 18,
"vocab_size": 14
} | 30 | Python | 22 | a20bb67a878b2e68abf8268c1b0a27f018d01352 | completionwidget.py | 320,775 | 10 | 78 | completion_item_yank | https://github.com/qutebrowser/qutebrowser.git | mypy: Upgrade to PyQt5-stubs 5.15.6.0
For some unknown reason, those new stubs cause a *lot* of things now to be
checked by mypy which formerly probably got skipped due to Any being implied
somewhere.
The stubs themselves mainly improved, with a couple of regressions too.
In total, there were some 337 (!) new mypy errors. This commit fixes almost all
of them, and the next commit improves a fix to get things down to 0 errors
again.
Overview of the changes:
==== qutebrowser/app.py
- Drop type ignore due to improved stubs.
==== qutebrowser/browser/browsertab.py
- Specify the type of _widget members more closely than just QWidget.
This is debatable: I suppose the abstract stuff shouldn't need to know
anything about the concrete backends at all. But it seems like we cut some
corners when initially implementing things, and put some code in browsertab.py
just because the APIs of both backends happened to be compatible. Perhaps
something to reconsider once we drop QtWebKit and hopefully implement a dummy
backend.
- Add an additional assertion in AbstractAction.run_string. This is already
covered by the isinstance(member, self.action_base) above it, but that's too
dynamic for mypy to understand.
- Fix the return type of AbstractScroller.pos_px, which is a QPoint (with x
and y components), not a single int.
- Fix the return type of AbstractScroller.pos_perc, which is a Tuple (with x
and y components), not a single int.
- Fix the argument types of AbstractScroller.to_perc, as it's possible to pass
fractional percentages too.
- Specify the type for AbstractHistoryPrivate._history. See above (_widget) re
this being debatable.
- Fix the return type of AbstractTabPrivate.event_target(), which can be None
(see #3888).
- Fix the return type of AbstractTabPrivate.run_js_sync, which is Any (the JS
return value), not None.
- Fix the argument type for AbstractTabPrivate.toggle_inspector: position can
be None to use the last used position.
- Declare the type of sub-objects of AbstractTab.
- Fix the return value of AbstractTab.icon(), which is the QIcon, not None.
==== qutebrowser/browser/commands.py
- Make sure the active window is a MainWindow (with a .win_id attribute).
==== qutebrowser/browser/downloadview.py
- Add _model() which makes sure that self.model() is a DownloadModel, not None
or any other model. This is needed because other methods access a variety of
custom attributes on it, e.g. last_index().
==== qutebrowser/browser/greasemonkey.py
- Add an ignore for AbstractDownload.requested_url which we patch onto the
downloads. Probably would be nicer to add it as a proper attribute which always
gets set by the DownloadManager.
==== qutebrowser/browser/hints.py
- Remove type ignores for QUrl.toString().
- Add a new type ignore for combining different URL flags (which works, but is
not exactly type safe... still probably a regression in the stubs).
- Make sure the things we get back from self._get_keyparser are what we actually
expect. Probably should introduce a TypedDict (and/or overloads for
_get_keyparser with typing.Literal) to teach mypy about the exact return value.
See #7098.
This is needed because we access Hint/NormalKeyParser-specific attributes such
as .set_inhibited_timout() or .update_bindings().
==== qutebrowser/browser/inspector.py
- Similar changes than in browsertab.py to make some types where we share API
(e.g. .setPage()) more concrete. Didn't work out unfortunately, see next
commit.
==== qutebrowser/browser/network/pac.py
- Remove now unneeded type ignore for signal.
==== qutebrowser/browser/qtnetworkdownloads.py
- Make sure that downloads is a qtnetworkdownloads.DownloadItem (rather than an
AbstractDownload), so that we can call ._uses_nam() on it.
==== qutebrowser/browser/qutescheme.py
- Remove now unneeded type ignore for QUrl flags.
==== qutebrowser/browser/urlmarks.py
- Specify the type of UrlMarkManager._lineparser, as those only get initialized
in _init_lineparser of subclasses, so mypy doesn't know it's supposed to exist.
==== qutebrowser/browser/webelem.py
- New casts to turn single KeyboardModifier (enum) entries into
KeyboardModifiers (flags). Might not be needed anymore with Qt 6.
- With that, casting the final value is now unneeded.
==== qutebrowser/browser/webengine/notification.py
- Remove now unneeded type ignore for signal.
- Make sure the self.sender() we get in HerbeNotificationAdapter._on_finished()
is a QProcess, not just any QObject.
==== qutebrowser/browser/webengine/webenginedownloads.py
- Remove now unneeded type ignores for signals.
==== qutebrowser/browser/webengine/webengineelem.py
- Specify the type of WebEngineElement._tab.
- Remove now unneeded type ignore for mixed flags.
==== qutebrowser/browser/webengine/webengineinspector.py
- See changes to inspector.py and next commit.
- Remove now unneeded type ignore for signal.
==== qutebrowser/browser/webengine/webenginequtescheme.py
- Remove now unneeded type ignore for mixed flags.
==== qutebrowser/browser/webengine/webenginesettings.py
- Ignore access of .setter attribute which we patch onto QWebEngineProfile.
Would be nice to have a subclass or wrapper-class instead.
==== qutebrowser/browser/webengine/webenginetab.py
- Specified the type of _widget members more closely than just QWidget.
See browsertab.py changes for details.
- Remove some now-unneeded type ignores for creating FindFlags.
- Specify more concrete types for WebEngineTab members where we actually need to
access WebEngine-specific attributes.
- Make sure the page we get is our custom WebEnginePage subclass, not just any
QWebEnginePage. This is needed because we access custom attributes on it.
==== qutebrowser/browser/webengine/webview.py
- Make sure the page we get is our custom WebEnginePage subclass, not just any
QWebEnginePage. This is needed because we access custom attributes on it.
==== qutebrowser/browser/webkit/network/networkreply.py
- Remove now unneeded type ignores for signals.
==== qutebrowser/browser/webkit/webkitinspector.py
- See changes to inspector.py and next commit.
==== qutebrowser/browser/webkit/webkittab.py
- Specify the type of _widget members more closely than just QWidget.
See browsertab.py changes for details.
- Add a type ignore for WebKitAction because our workaround needs to
treat them as ints (which is allowed by PyQt, even if not type-safe).
- Add new ignores for findText calls: The text is a QString and can be None; the
flags are valid despite mypy thinking they aren't (stubs regression?).
- Specify the type for WebKitHistoryPrivate._history, because we access
WebKit-specific attributes. See above (_widget) re this being debatable.
- Make mypy aware that .currentFrame() and .frameAt() can return None (stubs
regression?).
- Make sure the .page() and .page().networkAccessManager() are our subclasses
rather than the more generic QtWebKit objects, as we use custom attributes.
- Add new type ignores for signals (stubs regression!)
==== qutebrowser/browser/webkit/webpage.py
- Make sure the .networkAccessManager() is our subclass rather than the more
generic QtWebKit object, as we use custom attributes.
- Replace a cast by a type ignore. The cast didn't work anymore.
==== qutebrowser/browser/webkit/webview.py
- Make sure the .page() is our subclass rather than the more generic QtWebKit
object, as we use custom attributes.
==== qutebrowser/commands/userscripts.py
- Remove now unneeded type ignore for signal.
==== qutebrowser/completion/completer.py
- Add a new _completion() getter (which ensures it actually gets the completion
view) rather than accessing the .parent() directly (which could be any QObject).
==== qutebrowser/completion/completiondelegate.py
- Make sure self.parent() is a CompletionView (no helper method as there is only
one instance).
- Remove a now-unneeded type ignore for adding QSizes.
==== qutebrowser/completion/completionwidget.py
- Add a ._model() getter which ensures that we get a CompletionModel (with
custom attributes) rather than Qt's .model() which can be any QAbstractItemModel
(or None).
- Removed a now-unneeded type ignore for OR-ing flags.
==== qutebrowser/completion/models/completionmodel.py
- Remove now unneeded type ignores for signals.
- Ignore a complaint about .set_pattern() not being defined. Completion
categories don't share any common parent class, so it would be good to introduce
a typing.Protocol for this. See #7098.
==== qutebrowser/components/misccommands.py
- Removed a now-unneeded type ignore for OR-ing flags.
==== qutebrowser/components/readlinecommands.py
- Make sure QApplication.instance() is a QApplication (and not just a
QCoreApplication). This includes the former "not None" check.
==== qutebrowser/components/scrollcommands.py
- Add basic annotation for "funcs" dict. Could have a callable protocol to
specify it needs a count kwarg, see #7098.
==== qutebrowser/config/stylesheet.py
- Correctly specify that stylesheet apply to QWidgets, not any QObject.
- Ignore an attr-defined for obj.STYLESHEET. Perhaps could somehow teach mypy
about this with overloads and protocols (stylesheet for set_register being None
=> STYLESHEET needs to be defined, otherwise anything goes), but perhaps not
worth the troble. See #7098.
==== qutebrowser/keyinput/keyutils.py
- Remove some now-unneeded type ignores and add a cast for using a single enum
value as flags. Might need to look at this again with Qt 6 support.
==== qutebrowser/keyinput/modeman.py
- Add a FIXME for using a TypedDict, see comments for hints.py above.
==== qutebrowser/mainwindow/mainwindow.py
- Remove now-unneeded type ignores for calling with OR-ed flags.
- Improve where we cast from WindowType to WindowFlags, no int needed
- Use new .tab_bar() getter, see below.
==== qutebrowser/mainwindow/prompt.py
- Remove now-unneeded type ignores for calling with OR-ed flags.
==== qutebrowser/mainwindow/statusbar/bar.py
- Adjust type ignores around @pyqtProperty. The fact one is still needed seems
like a stub regression.
==== qutebrowser/mainwindow/statusbar/command.py
- Fix type for setText() override (from QLineEdit): text can be None
(QString in C++).
==== qutebrowser/mainwindow/statusbar/url.py
- Adjust type ignores around @pyqtProperty. The fact one is still needed seems
like a stub regression.
==== qutebrowser/mainwindow/tabbedbrowser.py
- Specify that TabDeque manages browser tabs, not any QWidgets. It accesses
AbstractTab-specific attributes.
- Make sure that the .tabBar() we get is a tabwidget.TabBar, as we access
.maybe_hide.
- Fix the annotations for stored marks: Scroll positions are a QPoint, not int.
- Add _current_tab() and _tab_by_idx() wrappers for .currentWidget() and
.widget(), which ensures that the return values are valid AbstractTabs (or None
for _tab_by_idx). This is needed because we access AbstractTab-specific
attributes.
- For some places, where the tab can be None, continue using .currentTab() but
add asserts.
- Remove some now-unneeded [unreachable] ignores, as mypy knows about the None
possibility now.
==== qutebrowser/mainwindow/tabwidget.py
- Add new tab_bar() and _tab_by_idx() helpers which check that the .tabBar() and
.widget() are of type TabBar and AbstractTab, respectively.
- Add additional assertions where we expect ._tab_by_idx() to never be None.
- Remove dead code in get_tab_fields for handling a None y scroll position. I
was unable to find any place in the code where this could be set to None.
- Remove some now-unneeded type ignores and casts, as mypy now knows that
_type_by_idx() could be None.
- Work around a strange instance where mypy complains about not being able to
find the type of TabBar.drag_in_progress from TabWidget._toggle_visibility,
despite it clearly being shown as a bool *inside* that class without any
annotation.
- Add a ._tab_widget() getter in TabBar which ensures that the .parent() is in
fact a TabWidget.
==== qutebrowser/misc/crashsignal.py
- Remove now unneeded type ignores for signals.
==== qutebrowser/misc/editor.py
- Remove now unneeded type ignores for signals.
==== qutebrowser/misc/ipc.py
- Remove now unneeded type ignores for signals.
- Add new type ignores for .error() which is both a signal and a getter
(stub regression?). Won't be relevant for Qt 6 anymore, as the signal was
renamed to errorOccurred in 5.15.
==== qutebrowser/misc/objects.py
- Make sure mypy knows that objects.app is our custom Application (with custom
attributes) rather than any QApplication.
==== qutebrowser/utils/objreg.py
- Ignore attr-defined for .win_id attributes. Maybe could add a typing.Protocol,
but ideally, the whole objreg stuff should die one day anyways.
==== tests/unit/completion/test_completer.py
- Make CompletionWidgetStub inherit from CompletionView so that it passes the
new isinstance() asserts in completer.py (see above). | 124 | 0 | 117,342 | 12 |
|
1 | 3 | def show_panel_furniture(self):
return self.is_shown()
| wagtail/admin/panels.py | 23 | wagtail | {
"docstring": "\n Whether this panel shows the panel furniture instead of being rendered outside of it.\n ",
"language": "en",
"n_whitespaces": 37,
"n_words": 14,
"vocab_size": 12
} | 4 | Python | 4 | 9a1606c809b2daee005591d98e9e2058e4823c79 | panels.py | 79,326 | 2 | 12 | show_panel_furniture | https://github.com/wagtail/wagtail.git | Add show_panel_furniture() in BoundPanel
This allows TabbedInterface to hide a tab but still render its children | 26 | 0 | 16,917 | 7 |
|
2 | 11 | def header_encode(header_bytes, charset='iso-8859-1'):
# Return empty headers as an empty string.
if not header_bytes:
return ''
# Iterate over every byte, encoding if necessary.
encoded = header_bytes.decode('latin1').translate(_QUOPRI_HEADER_MAP)
# Now add the RFC chrome to each encoded chunk and glue the chunks
# together.
return '=?%s?q?%s?=' % (charset, encoded)
_QUOPRI_BODY_ENCODE_MAP = _QUOPRI_BODY_MAP[:]
for c in b'\r\n':
_QUOPRI_BODY_ENCODE_MAP[c] = chr(c)
| python3.10.4/Lib/email/quoprimime.py | 107 | XX-Net | {
"docstring": "Encode a single header line with quoted-printable (like) encoding.\n\n Defined in RFC 2045, this `Q' encoding is similar to quoted-printable, but\n used specifically for email header fields to allow charsets with mostly 7\n bit characters (and some 8 bit) to remain more or less readable in non-RFC\n 2045 aware mail clients.\n\n charset names the character set to use in the RFC 2046 header. It\n defaults to iso-8859-1.\n ",
"language": "en",
"n_whitespaces": 89,
"n_words": 67,
"vocab_size": 57
} | 58 | Python | 48 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | quoprimime.py | 223,870 | 5 | 37 | header_encode | https://github.com/XX-net/XX-Net.git | add python 3.10.4 for windows | 90 | 0 | 57,121 | 11 |
|
3 | 47 | def forward(self, src_word, trg_word):
r
src_max_len = paddle.shape(src_word)[-1]
trg_max_len = paddle.shape(trg_word)[-1]
src_slf_attn_bias = paddle.cast(
src_word == self.bos_id,
dtype=paddle.get_default_dtype()).unsqueeze([1, 2]) * -1e4
src_slf_attn_bias.stop_gradient = True
trg_slf_attn_bias = self.transformer.generate_square_subsequent_mask(
trg_max_len)
trg_slf_attn_bias.stop_gradient = True
trg_src_attn_bias = src_slf_attn_bias
src_pos = paddle.cast(
src_word != self.bos_id, dtype=src_word.dtype) * paddle.arange(
start=0, end=src_max_len, dtype=src_word.dtype)
trg_pos = paddle.cast(
trg_word != self.bos_id, dtype=src_word.dtype) * paddle.arange(
start=0, end=trg_max_len, dtype=trg_word.dtype)
with paddle.static.amp.fp16_guard():
src_emb = self.src_word_embedding(src_word)
src_pos_emb = self.src_pos_embedding(src_pos)
src_emb = src_emb + src_pos_emb
enc_input = F.dropout(
src_emb, p=self.dropout,
training=self.training) if self.dropout else src_emb
trg_emb = self.trg_word_embedding(trg_word)
trg_pos_emb = self.trg_pos_embedding(trg_pos)
trg_emb = trg_emb + trg_pos_emb
dec_input = F.dropout(
trg_emb, p=self.dropout,
training=self.training) if self.dropout else trg_emb
dec_output = self.transformer(
enc_input,
dec_input,
src_mask=src_slf_attn_bias,
tgt_mask=trg_slf_attn_bias,
memory_mask=trg_src_attn_bias)
predict = self.linear(dec_output)
return predict
| paddlenlp/transformers/transformer/modeling.py | 457 | PaddleNLP | {
"docstring": "\n The Transformer forward methods. The input are source/target sequences, and\n returns logits.\n\n Args:\n src_word (Tensor):\n The ids of source sequences words. It is a tensor with shape\n `[batch_size, source_sequence_length]` and its data type can be\n int or int64.\n trg_word (Tensor):\n The ids of target sequences words. It is a tensor with shape\n `[batch_size, target_sequence_length]` and its data type can be\n int or int64.\n\n Returns:\n Tensor:\n Output tensor of the final layer of the model whose data\n type can be float32 or float64 with shape\n `[batch_size, sequence_length, vocab_size]`.\n\n Example:\n .. code-block::\n\n import paddle\n from paddlenlp.transformers import TransformerModel\n\n transformer = TransformerModel(\n src_vocab_size=30000,\n trg_vocab_size=30000,\n max_length=257,\n num_encoder_layers=6,\n num_decoder_layers=6,\n n_head=8,\n d_model=512,\n d_inner_hid=2048,\n dropout=0.1,\n weight_sharing=True,\n bos_id=0,\n eos_id=1)\n\n batch_size = 5\n seq_len = 10\n predict = transformer(\n src_word=paddle.randint(low=3, high=30000, shape=[batch_size, seq_len]),\n trg_word=paddle.randint(low=3, high=30000, shape=[batch_size, seq_len]))\n ",
"language": "en",
"n_whitespaces": 706,
"n_words": 128,
"vocab_size": 85
} | 115 | Python | 67 | b0c35d5e1ff02a634fa26392b60d3885c2c78677 | modeling.py | 322,101 | 84 | 301 | forward | https://github.com/PaddlePaddle/PaddleNLP.git | Fix the attention mask for fp16 (#1585) | 528 | 0 | 118,058 | 14 |
|
1 | 5 | def is_nan(self, a):
a = _convert_other(a, raiseit=True)
return a.is_nan()
| python3.10.4/Lib/_pydecimal.py | 40 | XX-Net | {
"docstring": "Return True if the operand is a qNaN or sNaN;\n otherwise return False.\n\n >>> ExtendedContext.is_nan(Decimal('2.50'))\n False\n >>> ExtendedContext.is_nan(Decimal('NaN'))\n True\n >>> ExtendedContext.is_nan(Decimal('-sNaN'))\n True\n >>> ExtendedContext.is_nan(1)\n False\n ",
"language": "en",
"n_whitespaces": 95,
"n_words": 25,
"vocab_size": 19
} | 9 | Python | 9 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | _pydecimal.py | 219,742 | 3 | 24 | is_nan | https://github.com/XX-net/XX-Net.git | add python 3.10.4 for windows | 30 | 0 | 55,761 | 9 |
|
12 | 21 | def _set_hyperopt_defaults(self):
if not self.hyperopt:
return
scheduler = self.hyperopt.get("executor", {}).get("scheduler")
if not scheduler:
return
if EXECUTOR in self.hyperopt:
set_default_value(self.hyperopt[EXECUTOR], TYPE, RAY)
# Disable early stopping when using a scheduler. We achieve this by setting the parameter
# to -1, which ensures the condition to apply early stopping is never met.
early_stop = self.trainer.early_stop
if early_stop is not None and early_stop != -1:
warnings.warn("Can't utilize `early_stop` while using a hyperopt scheduler. Setting early stop to -1.")
self.trainer.early_stop = -1
max_t = scheduler.get("max_t")
time_attr = scheduler.get("time_attr")
epochs = self.trainer.to_dict().get("epochs", None)
if max_t is not None:
if time_attr == "time_total_s":
if epochs is None:
setattr(self.trainer, "epochs", sys.maxsize) # continue training until time limit hit
# else continue training until either time or trainer epochs limit hit
elif epochs is not None and epochs != max_t:
raise ValueError(
"Cannot set trainer `epochs` when using hyperopt scheduler w/different training_iteration `max_t`. "
"Unset one of these parameters in your config or make sure their values match."
)
else:
setattr(self.trainer, "epochs", max_t) # run trainer until scheduler epochs limit hit
elif epochs is not None:
scheduler["max_t"] = epochs # run scheduler until trainer epochs limit hit
| ludwig/schema/model_config.py | 326 | ludwig | {
"docstring": "This function was migrated from defaults.py with the intention of setting some hyperopt defaults while\n the hyperopt section of the config object is not fully complete.\n\n Returns:\n None -> modifies trainer and hyperopt sections\n ",
"language": "en",
"n_whitespaces": 66,
"n_words": 34,
"vocab_size": 29
} | 189 | Python | 106 | 4d2d81f9fdefc52eea6a9bf0826a6f2ffc8d681b | model_config.py | 8,418 | 28 | 188 | _set_hyperopt_defaults | https://github.com/ludwig-ai/ludwig.git | Config Object (#2426)
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Travis Addair <[email protected]>
Co-authored-by: w4nderlust <[email protected]> | 517 | 0 | 1,427 | 14 |
|
6 | 29 | def update(self, data):
data = np.atleast_1d(np.array(data, dtype=object))
# check if convertible to number:
convertible = True
for val in OrderedDict.fromkeys(data):
# OrderedDict just iterates over unique values in data.
_api.check_isinstance((str, bytes), value=val)
if convertible:
# this will only be called so long as convertible is True.
convertible = self._str_is_convertible(val)
if val not in self._mapping:
self._mapping[val] = next(self._counter)
if data.size and convertible:
_log.info('Using categorical units to plot a list of strings '
'that are all parsable as floats or dates. If these '
'strings should be plotted as numbers, cast to the '
'appropriate data type before plotting.')
# Register the converter with Matplotlib's unit framework
units.registry[str] = StrCategoryConverter()
units.registry[np.str_] = StrCategoryConverter()
units.registry[bytes] = StrCategoryConverter()
units.registry[np.bytes_] = StrCategoryConverter()
| lib/matplotlib/category.py | 238 | matplotlib | {
"docstring": "\n Map new values to integer identifiers.\n\n Parameters\n ----------\n data : iterable of str or bytes\n\n Raises\n ------\n TypeError\n If elements in *data* are neither str nor bytes.\n ",
"language": "en",
"n_whitespaces": 95,
"n_words": 27,
"vocab_size": 26
} | 117 | Python | 85 | c0a384e9f41673207eac75e276b293418bd32965 | category.py | 108,041 | 14 | 100 | update | https://github.com/matplotlib/matplotlib.git | Fix incorrect deprecation warning | 317 | 0 | 23,035 | 13 |
|
9 | 17 | def putpixel(self, xy, value):
if self.readonly:
self._copy()
self.load()
if self.pyaccess:
return self.pyaccess.putpixel(xy, value)
if (
self.mode in ("P", "PA")
and isinstance(value, (list, tuple))
and len(value) in [3, 4]
):
# RGB or RGBA value for a P or PA image
if self.mode == "PA":
alpha = value[3] if len(value) == 4 else 255
value = value[:3]
value = self.palette.getcolor(value, self)
if self.mode == "PA":
value = (value, alpha)
return self.im.putpixel(xy, value)
| src/PIL/Image.py | 225 | Pillow | {
"docstring": "\n Modifies the pixel at the given position. The color is given as\n a single numerical value for single-band images, and a tuple for\n multi-band images. In addition to this, RGB and RGBA tuples are\n accepted for P and PA images.\n\n Note that this method is relatively slow. For more extensive changes,\n use :py:meth:`~PIL.Image.Image.paste` or the :py:mod:`~PIL.ImageDraw`\n module instead.\n\n See:\n\n * :py:meth:`~PIL.Image.Image.paste`\n * :py:meth:`~PIL.Image.Image.putdata`\n * :py:mod:`~PIL.ImageDraw`\n\n :param xy: The pixel coordinate, given as (x, y). See\n :ref:`coordinate-system`.\n :param value: The pixel value.\n ",
"language": "en",
"n_whitespaces": 191,
"n_words": 81,
"vocab_size": 60
} | 71 | Python | 49 | a37593f004247ebf69d5582524da6dc5143cb023 | Image.py | 243,180 | 18 | 142 | putpixel | https://github.com/python-pillow/Pillow.git | Allow RGB and RGBA values for PA image putpixel | 264 | 0 | 70,002 | 14 |
|
11 | 54 | def batch(_func=None, max_batch_size=10, batch_wait_timeout_s=0.0):
| python/ray/serve/batching.py | 167 | """Converts a function to asynchronously handle batches.
The function can be a standalonea class method. Inthe function must betake a list ofits solereturn a list of the sameainvokedthe caller passes a single object. These will beand executed asynchronously oncea batch ofor `batch_wait_timeout_s` hasoccurs first:
>>> | ray | {
"docstring": "Converts a function to asynchronously handle batches.\n\n The function can be a standalone function or a class method. In both\n cases, the function must be `async def` and take a list of objects as\n its sole argument and return a list of the same length as a result.\n\n When invoked, the caller passes a single object. These will be batched\n and executed asynchronously once there is a batch of `max_batch_size`\n or `batch_wait_timeout_s` has elapsed, whichever occurs first.\n\n Example:\n\n >>> @serve.batch(max_batch_size=50, batch_wait_timeout_s=0.5)",
"language": "en",
"n_whitespaces": 104,
"n_words": 81,
"vocab_size": 59
} | 4 | Python | 4 | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | batching.py | 130,855 | 21 | 137 | batch | https://github.com/ray-project/ray.git | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | 7 | 14 | 29,405 | 10 |
1 | 3 | def n(self):
return self.args[0]
| sympy/combinatorics/graycode.py | 23 | sympy | {
"docstring": "\n Returns the dimension of the Gray code.\n\n Examples\n ========\n\n >>> from sympy.combinatorics import GrayCode\n >>> a = GrayCode(5)\n >>> a.n\n 5\n ",
"language": "en",
"n_whitespaces": 78,
"n_words": 21,
"vocab_size": 18
} | 4 | Python | 4 | 498015021131af4dbb07eb110e5badaba8250c7b | graycode.py | 196,096 | 2 | 13 | n | https://github.com/sympy/sympy.git | Updated import locations | 18 | 0 | 47,596 | 7 |
|
1 | 6 | def test_session_not_accessed(self):
response = self.client.get("/auth_processor_no_attr_access/")
self.assertContains(response, "Session not accessed")
| tests/auth_tests/test_context_processors.py | 45 | django | {
"docstring": "\n The session is not accessed simply by including\n the auth context processor\n ",
"language": "en",
"n_whitespaces": 34,
"n_words": 12,
"vocab_size": 12
} | 9 | Python | 9 | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | test_context_processors.py | 201,203 | 3 | 24 | test_session_not_accessed | https://github.com/django/django.git | Refs #33476 -- Reformatted code with Black. | 30 | 0 | 49,899 | 9 |
|
4 | 10 | def iterencode(iterator, encoding, errors='strict', **kwargs):
encoder = getincrementalencoder(encoding)(errors, **kwargs)
for input in iterator:
output = encoder.encode(input)
if output:
yield output
output = encoder.encode("", True)
if output:
yield output
| python3.10.4/Lib/codecs.py | 100 | XX-Net | {
"docstring": "\n Encoding iterator.\n\n Encodes the input strings from the iterator using an IncrementalEncoder.\n\n errors and kwargs are passed through to the IncrementalEncoder\n constructor.\n ",
"language": "en",
"n_whitespaces": 38,
"n_words": 22,
"vocab_size": 20
} | 28 | Python | 20 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | codecs.py | 221,370 | 9 | 60 | iterencode | https://github.com/XX-net/XX-Net.git | add python 3.10.4 for windows | 75 | 0 | 56,383 | 10 |
|
1 | 9 | def indices(self) -> dict[Hashable, npt.NDArray[np.intp]]:
return self.grouper.indices
| pandas/core/groupby/groupby.py | 41 | pandas | {
"docstring": "\n Dict {group name -> group indices}.\n ",
"language": "en",
"n_whitespaces": 21,
"n_words": 6,
"vocab_size": 6
} | 7 | Python | 7 | f65417656ba8c59438d832b6e2a431f78d40c21c | groupby.py | 167,771 | 5 | 26 | indices | https://github.com/pandas-dev/pandas.git | TYP: more return annotations in core/ (#47618)
* TYP: more return annotations in core/
* from __future__ import annotations
* more __future__ | 21 | 0 | 40,114 | 7 |
|
1 | 15 | def register(model, field_name, mappings):
logger.debug(f'Registering denormalized field {model}.{field_name}')
field = model._meta.get_field(field_name)
rel_model = field.related_model
registry['denormalized_fields'][rel_model].append(
(model, field_name, mappings)
)
@receiver(post_save) | netbox/netbox/denormalized.py | 97 | @receiver(post_save) | netbox | {
"docstring": "\n Register a denormalized model field to ensure that it is kept up-to-date with the related object.\n\n Args:\n model: The class being updated\n field_name: The name of the field related to the triggering instance\n mappings: Dictionary mapping of local to remote fields\n ",
"language": "en",
"n_whitespaces": 72,
"n_words": 41,
"vocab_size": 33
} | 20 | Python | 17 | e96620260a6c1b5cf8cff2112d40d061984a7b2c | denormalized.py | 265,475 | 7 | 50 | register | https://github.com/netbox-community/netbox.git | Closes #9903: Implement a mechanism for automatically updating denormalized fields | 44 | 1 | 78,110 | 10 |
1 | 9 | def switch_platform_only():
with patch(
"homeassistant.components.zha.PLATFORMS",
(
Platform.DEVICE_TRACKER,
Platform.SENSOR,
Platform.SELECT,
Platform.SWITCH,
),
):
yield
@pytest.fixture | tests/components/zha/test_switch.py | 61 | @pytest.fixture | core | {
"docstring": "Only setup the switch and required base platforms to speed up tests.",
"language": "en",
"n_whitespaces": 11,
"n_words": 12,
"vocab_size": 12
} | 14 | Python | 14 | 4bc5d7bfed07c20d6f3438ab91c734a620505a33 | test_switch.py | 313,987 | 11 | 32 | switch_platform_only | https://github.com/home-assistant/core.git | Speed up zha tests (#73627) | 94 | 1 | 112,598 | 11 |
3 | 9 | def report_start(self, out, test, example):
if self._verbose:
if example.want:
out('Trying:\n' + _indent(example.source) +
'Expecting:\n' + _indent(example.want))
else:
out('Trying:\n' + _indent(example.source) +
'Expecting nothing\n')
| python3.10.4/Lib/doctest.py | 104 | XX-Net | {
"docstring": "\n Report that the test runner is about to process the given\n example. (Only displays a message if verbose=True)\n ",
"language": "en",
"n_whitespaces": 41,
"n_words": 18,
"vocab_size": 17
} | 23 | Python | 16 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | doctest.py | 223,420 | 8 | 57 | report_start | https://github.com/XX-net/XX-Net.git | add python 3.10.4 for windows | 127 | 0 | 56,893 | 17 |
|
1 | 9 | def test_cached_file_client(get_loader, minion_opts):
with patch("salt.channel.client.ReqChannel.factory", Mock()):
loader_a = SaltCacheLoader(minion_opts)
loader_b = SaltCacheLoader(minion_opts)
assert loader_a._file_client is loader_b._file_client
| tests/pytests/unit/utils/jinja/test_salt_cache_loader.py | 67 | salt | {
"docstring": "\n Multiple instantiations of SaltCacheLoader use the cached file client\n ",
"language": "en",
"n_whitespaces": 16,
"n_words": 9,
"vocab_size": 9
} | 16 | Python | 14 | 56045b0ee4c11b395895cb0a11279dfea8c2242f | test_salt_cache_loader.py | 215,577 | 5 | 38 | test_cached_file_client | https://github.com/saltstack/salt.git | Clean up salt.transport.(client,server) references | 39 | 0 | 54,037 | 11 |
|
1 | 8 | def get_css_variables(self) -> dict[str, str]:
variables = self.design.generate(self.dark)
return variables
| src/textual/app.py | 44 | textual | {
"docstring": "Get a mapping of variables used to pre-populate CSS.\n\n Returns:\n dict[str, str]: A mapping of variable name to value.\n ",
"language": "en",
"n_whitespaces": 44,
"n_words": 19,
"vocab_size": 16
} | 10 | Python | 9 | b115db9d8d4f1c9ab20a3d3bef5d5a729ea8b57a | app.py | 182,785 | 8 | 27 | get_css_variables | https://github.com/Textualize/textual.git | docstring | 31 | 0 | 43,965 | 9 |
|
1 | 26 | def test_multi_sso_redirect_to_cas(self) -> None:
channel = self.make_request(
"GET",
"/_synapse/client/pick_idp?redirectUrl="
+ urllib.parse.quote_plus(TEST_CLIENT_REDIRECT_URL)
+ "&idp=cas",
shorthand=False,
)
self.assertEqual(channel.code, 302, channel.result)
location_headers = channel.headers.getRawHeaders("Location")
assert location_headers
cas_uri = location_headers[0]
cas_uri_path, cas_uri_query = cas_uri.split("?", 1)
# it should redirect us to the login page of the cas server
self.assertEqual(cas_uri_path, CAS_SERVER + "/login")
# check that the redirectUrl is correctly encoded in the service param - ie, the
# place that CAS will redirect to
cas_uri_params = urllib.parse.parse_qs(cas_uri_query)
service_uri = cas_uri_params["service"][0]
_, service_uri_query = service_uri.split("?", 1)
service_uri_params = urllib.parse.parse_qs(service_uri_query)
self.assertEqual(service_uri_params["redirectUrl"][0], TEST_CLIENT_REDIRECT_URL)
| tests/rest/client/test_login.py | 239 | synapse | {
"docstring": "If CAS is chosen, should redirect to the CAS server",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 9
} | 86 | Python | 66 | 64c73c6ac88a740ee480a0ad1f9afc8596bccfa4 | test_login.py | 246,600 | 20 | 143 | test_multi_sso_redirect_to_cas | https://github.com/matrix-org/synapse.git | Add type hints to `tests/rest/client` (#12066) | 260 | 0 | 71,290 | 13 |
|
5 | 7 | def merge_hooks(request_hooks, session_hooks, dict_class=OrderedDict):
if session_hooks is None or session_hooks.get("response") == []:
return request_hooks
if request_hooks is None or request_hooks.get("response") == []:
return session_hooks
return merge_setting(request_hooks, session_hooks, dict_class)
| pipenv/patched/pip/_vendor/requests/sessions.py | 90 | pipenv | {
"docstring": "Properly merges both requests and session hooks.\n\n This is necessary because when request_hooks == {'response': []}, the\n merge breaks Session hooks entirely.\n ",
"language": "en",
"n_whitespaces": 31,
"n_words": 22,
"vocab_size": 22
} | 28 | Python | 17 | cd5a9683be69c86c8f3adcd13385a9bc5db198ec | sessions.py | 22,107 | 6 | 55 | merge_hooks | https://github.com/pypa/pipenv.git | Rename notpip to pip. Vendor in pip-22.2.1 and latest requirementslib and vistir. | 54 | 0 | 4,184 | 10 |
|
1 | 12 | def _decode(self, pre_chars, features, hidden):
| ppocr/modeling/heads/table_att_head.py | 28 | """
Predict tablecoordinates for each | PaddleOCR | {
"docstring": "\n Predict table label and coordinates for each step",
"language": "en",
"n_whitespaces": 15,
"n_words": 8,
"vocab_size": 8
} | 5 | Python | 5 | ddaa2c2552e19635cd6cdf38619f1f176c358f89 | table_att_head.py | 24,443 | 7 | 60 | _decode | https://github.com/PaddlePaddle/PaddleOCR.git | add SLANet | 12 | 2 | 4,732 | 7 |
3 | 9 | def _full_shape(self) -> Tuple[int]:
sampled_shape = tuple()
for d in self._expected_shape:
if isinstance(d, int):
sampled_shape += (d,)
else:
sampled_shape += (1,)
return sampled_shape
| rllib/models/specs/specs_base.py | 76 | ray | {
"docstring": "Converts the expected shape to a shape by replacing the unknown dimension\n sizes with a value of 1.",
"language": "en",
"n_whitespaces": 24,
"n_words": 18,
"vocab_size": 15
} | 23 | Python | 19 | 3e7c207f02e7368e1245e2cfafd27cb0bf179ff7 | specs_base.py | 128,381 | 10 | 47 | _full_shape | https://github.com/ray-project/ray.git | [RLlib] Introduce TensorSpec data structure for RLModule / Model definitions (#28946)
* added tensor specs
Signed-off-by: Kourosh Hakhamaneshi <[email protected]>
* lint
Signed-off-by: Kourosh Hakhamaneshi <[email protected]>
* 1. Added numpy specs
2. Added spec.sample()
Signed-off-by: Kourosh Hakhamaneshi <[email protected]>
* added unittests for sampling
Signed-off-by: Kourosh Hakhamaneshi <[email protected]>
* added tensorflow specs
Signed-off-by: Kourosh Hakhamaneshi <[email protected]>
* added jax
Signed-off-by: Kourosh Hakhamaneshi <[email protected]>
* lint
Signed-off-by: Kourosh Hakhamaneshi <[email protected]>
* removed jax test to be able to merge this part
Signed-off-by: Kourosh Hakhamaneshi <[email protected]>
* lint
Signed-off-by: Kourosh Hakhamaneshi <[email protected]>
* added docs
Signed-off-by: Kourosh Hakhamaneshi <[email protected]>
* fixed typo
Signed-off-by: Kourosh Hakhamaneshi <[email protected]>
* use full/fill instead of sample
Signed-off-by: Kourosh Hakhamaneshi <[email protected]>
* changed the input delimiter to be comma instead of whitespace. It also ignores whitespaces now.
Signed-off-by: Kourosh Hakhamaneshi <[email protected]>
* simplified parser code
Signed-off-by: Kourosh Hakhamaneshi <[email protected]>
* simplified parser code
Signed-off-by: Kourosh Hakhamaneshi <[email protected]>
Signed-off-by: Kourosh Hakhamaneshi <[email protected]> | 103 | 0 | 28,689 | 12 |
|
2 | 21 | def laplacian_matrix(G, nodelist=None, weight="weight"):
import scipy as sp
import scipy.sparse # call as sp.sparse
if nodelist is None:
nodelist = list(G)
A = nx.to_scipy_sparse_array(G, nodelist=nodelist, weight=weight, format="csr")
n, m = A.shape
# TODO: rm csr_array wrapper when spdiags can produce arrays
D = sp.sparse.csr_array(sp.sparse.spdiags(A.sum(axis=1), 0, m, n, format="csr"))
return D - A
@not_implemented_for("directed") | networkx/linalg/laplacianmatrix.py | 167 | @not_implemented_for("directed") | networkx | {
"docstring": "Returns the Laplacian matrix of G.\n\n The graph Laplacian is the matrix L = D - A, where\n A is the adjacency matrix and D is the diagonal matrix of node degrees.\n\n Parameters\n ----------\n G : graph\n A NetworkX graph\n\n nodelist : list, optional\n The rows and columns are ordered according to the nodes in nodelist.\n If nodelist is None, then the ordering is produced by G.nodes().\n\n weight : string or None, optional (default='weight')\n The edge data key used to compute each value in the matrix.\n If None, then each edge has weight 1.\n\n Returns\n -------\n L : SciPy sparse array\n The Laplacian matrix of G.\n\n Notes\n -----\n For MultiGraph, the edges weights are summed.\n\n See Also\n --------\n to_numpy_array\n normalized_laplacian_matrix\n laplacian_spectrum\n ",
"language": "en",
"n_whitespaces": 213,
"n_words": 121,
"vocab_size": 76
} | 53 | Python | 43 | 8a325d26aa7fdd3a72580c4720fa97f971bbefcb | laplacianmatrix.py | 177,333 | 9 | 98 | laplacian_matrix | https://github.com/networkx/networkx.git | Use scipy.sparse array datastructure (#6037)
* Use scipy.sparse array datastructure
* Add reminder to rm wrapper when scipy adds creation fns.
* Rm mention of np matrix from code comment.
* Update networkx/algorithms/bipartite/matrix.py
Co-authored-by: Stefan van der Walt <[email protected]>
Co-authored-by: Ross Barnowski <[email protected]>
Co-authored-by: Stefan van der Walt <[email protected]> | 87 | 1 | 42,352 | 13 |
5 | 15 | def global_efficiency(G):
n = len(G)
denom = n * (n - 1)
if denom != 0:
lengths = nx.all_pairs_shortest_path_length(G)
g_eff = 0
for source, targets in lengths:
for target, distance in targets.items():
if distance > 0:
g_eff += 1 / distance
g_eff /= denom
# g_eff = sum(1 / d for s, tgts in lengths
# for t, d in tgts.items() if d > 0) / denom
else:
g_eff = 0
# TODO This can be made more efficient by computing all pairs shortest
# path lengths in parallel.
return g_eff
@not_implemented_for("directed") | networkx/algorithms/efficiency_measures.py | 138 | @not_implemented_for("directed") | networkx | {
"docstring": "Returns the average global efficiency of the graph.\n\n The *efficiency* of a pair of nodes in a graph is the multiplicative\n inverse of the shortest path distance between the nodes. The *average\n global efficiency* of a graph is the average efficiency of all pairs of\n nodes [1]_.\n\n Parameters\n ----------\n G : :class:`networkx.Graph`\n An undirected graph for which to compute the average global efficiency.\n\n Returns\n -------\n float\n The average global efficiency of the graph.\n\n Examples\n --------\n >>> G = nx.Graph([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3)])\n >>> round(nx.global_efficiency(G), 12)\n 0.916666666667\n\n Notes\n -----\n Edge weights are ignored when computing the shortest path distances.\n\n See also\n --------\n local_efficiency\n\n References\n ----------\n .. [1] Latora, Vito, and Massimo Marchiori.\n \"Efficient behavior of small-world networks.\"\n *Physical Review Letters* 87.19 (2001): 198701.\n <https://doi.org/10.1103/PhysRevLett.87.198701>\n\n ",
"language": "en",
"n_whitespaces": 248,
"n_words": 129,
"vocab_size": 86
} | 92 | Python | 55 | 435b4622d106d14a3627e162ee163b113bac9854 | efficiency_measures.py | 176,967 | 14 | 76 | global_efficiency | https://github.com/networkx/networkx.git | added examples to efficiency_measures.py (#5643)
* added example on efficiency
* added example on global_efficiency
* added example on local_efficiency
* adjused round up | 227 | 1 | 42,195 | 15 |
1 | 5 | def image(self) -> ImageTk.PhotoImage:
assert self._preview_image_tk is not None
return self._preview_image_tk
| lib/gui/utils/image.py | 35 | faceswap | {
"docstring": ":class:`PIL.ImageTk.PhotoImage` The preview image for displaying in a tkinter canvas ",
"language": "en",
"n_whitespaces": 10,
"n_words": 10,
"vocab_size": 10
} | 11 | Python | 10 | 2e8ef5e3c8f2df0f1cca9b342baa8aaa6f620650 | image.py | 101,978 | 4 | 21 | image | https://github.com/deepfakes/faceswap.git | GUI - Preview updates
- Training preview. Embed preview pop-out window
- Bugfix - convert/extract previews | 32 | 0 | 21,352 | 7 |
|
1 | 20 | def test_ddp_sharded_strategy_fit_ckpt_path_downsize_gpus(tmpdir):
model = BoringModel()
trainer = Trainer(strategy="ddp_sharded_spawn", fast_dev_run=True, gpus=2)
trainer.fit(model)
checkpoint_path = os.path.join(tmpdir, "model.pt")
trainer.save_checkpoint(checkpoint_path)
model = BoringModel()
trainer = Trainer(strategy="ddp_sharded_spawn", fast_dev_run=True, gpus=1)
trainer.fit(model, ckpt_path=checkpoint_path)
@RunIf(min_gpus=1, skip_windows=True, fairscale=True) | tests/strategies/test_sharded_strategy.py | 158 | @RunIf(min_gpus=1, skip_windows=True, fairscale=True) | lightning | {
"docstring": "Test to ensure that resuming from checkpoint works when downsizing number of GPUS.",
"language": "en",
"n_whitespaces": 12,
"n_words": 13,
"vocab_size": 13
} | 29 | Python | 20 | 650c710efacd633fa283955145342bb64063c883 | test_sharded_strategy.py | 241,585 | 9 | 82 | test_ddp_sharded_strategy_fit_ckpt_path_downsize_gpus | https://github.com/Lightning-AI/lightning.git | Rename training plugin test files & names to strategy (#11303) | 55 | 1 | 69,610 | 10 |
1 | 4 | def test_cancel_logcontexts(self):
complete_lookup: "Deferred[None]" = Deferred()
| tests/util/caches/test_descriptors.py | 27 | synapse | {
"docstring": "Test that cancellation does not break logcontexts.\n\n * The `CancelledError` must be raised with the correct logcontext.\n * The inner lookup must not resume with a finished logcontext.\n * The inner lookup must not restore a finished logcontext when done.\n ",
"language": "en",
"n_whitespaces": 68,
"n_words": 40,
"vocab_size": 26
} | 6 | Python | 6 | 2fcf4b3f6cd2a0be6597622664636d2219957c2a | test_descriptors.py | 247,588 | 16 | 81 | test_cancel_logcontexts | https://github.com/matrix-org/synapse.git | Add cancellation support to `@cached` and `@cachedList` decorators (#12183)
These decorators mostly support cancellation already. Add cancellation
tests and fix use of finished logging contexts by delaying cancellation,
as suggested by @erikjohnston.
Signed-off-by: Sean Quah <[email protected]> | 20 | 0 | 71,762 | 8 |
|
1 | 9 | def test_supports_transactions(self):
with mock.patch(
"django.db.connection.features._mysql_storage_engine", "InnoDB"
):
self.assertTrue(connection.features.supports_transactions)
del connection.features.supports_transactions
with mock.patch(
"django.db.connection.features._mysql_storage_engine", "MyISAM"
):
self.assertFalse(connection.features.supports_transactions)
del connection.features.supports_transactions
| tests/backends/mysql/test_features.py | 105 | django | {
"docstring": "\n All storage engines except MyISAM support transactions.\n ",
"language": "en",
"n_whitespaces": 22,
"n_words": 7,
"vocab_size": 7
} | 18 | Python | 12 | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | test_features.py | 201,697 | 11 | 58 | test_supports_transactions | https://github.com/django/django.git | Refs #33476 -- Reformatted code with Black. | 111 | 0 | 49,980 | 11 |
|
2 | 16 | def _apply_mask(cls, y_true, y_pred, mask_channel, mask_prop=1.0):
if mask_channel == -1:
logger.debug("No mask to apply")
return y_true[..., :3], y_pred[..., :3]
logger.debug("Applying mask from channel %s", mask_channel)
mask = K.tile(K.expand_dims(y_true[..., mask_channel], axis=-1), (1, 1, 1, 3))
mask_as_k_inv_prop = 1 - mask_prop
mask = (mask * mask_prop) + mask_as_k_inv_prop
m_true = y_true[..., :3] * mask
m_pred = y_pred[..., :3] * mask
return m_true, m_pred
| lib/model/losses_plaid.py | 187 | faceswap | {
"docstring": " Apply the mask to the input y_true and y_pred. If a mask is not required then\n return the unmasked inputs.\n\n Parameters\n ----------\n y_true: tensor or variable\n The ground truth value\n y_pred: tensor or variable\n The predicted value\n mask_channel: int\n The channel within y_true that the required mask resides in\n mask_prop: float, optional\n The amount of mask propagation. Default: `1.0`\n\n Returns\n -------\n tuple\n (n_true, n_pred): The ground truth and predicted value tensors with the mask applied\n ",
"language": "en",
"n_whitespaces": 208,
"n_words": 75,
"vocab_size": 52
} | 61 | Python | 42 | 94c3dcff7ebd02a5a5758f33a3eb2bfc66282117 | losses_plaid.py | 100,868 | 11 | 127 | _apply_mask | https://github.com/deepfakes/faceswap.git | Training updates
- Add multiple selected loss functions
- Unlock loss as a model configuration
- Phaze-A remove encoder scaling max xap | 146 | 0 | 20,319 | 12 |
|
11 | 39 | def wasLastResponseDelayed():
# 99.9999999997440% of all non time-based SQL injection affected
# response times should be inside +-7*stdev([normal response times])
# Math reference: http://www.answers.com/topic/standard-deviation
deviation = stdev(kb.responseTimes.get(kb.responseTimeMode, []))
threadData = getCurrentThreadData()
if deviation and not conf.direct and not conf.disableStats:
if len(kb.responseTimes[kb.responseTimeMode]) < MIN_TIME_RESPONSES:
warnMsg = "time-based standard deviation method used on a model "
warnMsg += "with less than %d response times" % MIN_TIME_RESPONSES
logger.warning(warnMsg)
lowerStdLimit = average(kb.responseTimes[kb.responseTimeMode]) + TIME_STDEV_COEFF * deviation
retVal = (threadData.lastQueryDuration >= max(MIN_VALID_DELAYED_RESPONSE, lowerStdLimit))
if not kb.testMode and retVal:
if kb.adjustTimeDelay is None:
msg = "do you want sqlmap to try to optimize value(s) "
msg += "for DBMS delay responses (option '--time-sec')? [Y/n] "
kb.adjustTimeDelay = ADJUST_TIME_DELAY.DISABLE if not readInput(msg, default='Y', boolean=True) else ADJUST_TIME_DELAY.YES
if kb.adjustTimeDelay is ADJUST_TIME_DELAY.YES:
adjustTimeDelay(threadData.lastQueryDuration, lowerStdLimit)
return retVal
else:
delta = threadData.lastQueryDuration - conf.timeSec
if Backend.getIdentifiedDbms() in (DBMS.MYSQL,): # MySQL's SLEEP(X) lasts 0.05 seconds shorter on average
delta += 0.05
return delta >= 0
| lib/core/common.py | 327 | sqlmap | {
"docstring": "\n Returns True if the last web request resulted in a time-delay\n ",
"language": "en",
"n_whitespaces": 18,
"n_words": 11,
"vocab_size": 11
} | 153 | Python | 110 | df4293473d2fb6e887e31522cab5aff95e201581 | common.py | 123,465 | 23 | 200 | wasLastResponseDelayed | https://github.com/sqlmapproject/sqlmap.git | Fixing DeprecationWarning (logger.warn) | 360 | 0 | 27,379 | 18 |
|
2 | 20 | def generate_navigator(os=None, navigator=None, platform=None, device_type=None):
if platform is not None:
os = platform
warn(
"The `platform` option is deprecated." " Use `os` option instead.",
stacklevel=3,
)
device_type, os_id, navigator_id = pick_config_ids(device_type, os, navigator)
system = build_system_components(device_type, os_id, navigator_id)
app = build_app_components(os_id, navigator_id)
ua_template = choose_ua_template(device_type, navigator_id, app)
user_agent = ua_template.format(system=system, app=app)
app_version = build_navigator_app_version(
os_id, navigator_id, system["platform_version"], user_agent
)
return {
# ids
"os_id": os_id,
"navigator_id": navigator_id,
# system components
"platform": system["platform"],
"oscpu": system["oscpu"],
# app components
"build_version": app["build_version"],
"build_id": app["build_id"],
"app_version": app_version,
"app_name": app["name"],
"app_code_name": "Mozilla",
"product": "Gecko",
"product_sub": app["product_sub"],
"vendor": app["vendor"],
"vendor_sub": "",
# compiled user agent
"user_agent": user_agent,
}
| build/pyinstaller/user_agent/base.py | 321 | OpenBBTerminal | {
"docstring": "\n Generates web navigator's config\n\n :param os: limit list of oses for generation\n :type os: string or list/tuple or None\n :param navigator: limit list of browser engines for generation\n :type navigator: string or list/tuple or None\n :param device_type: limit possible oses by device type\n :type device_type: list/tuple or None, possible values:\n \"desktop\", \"smartphone\", \"tablet\", \"all\"\n\n :return: User-Agent config\n :rtype: dict with keys (os, name, platform, oscpu, build_version,\n build_id, app_version, app_name, app_code_name,\n product, product_sub, vendor, vendor_sub,\n user_agent)\n :raises InvalidOption: if could not generate user-agent for\n any combination of allowed platforms and navigators\n :raise InvalidOption: if any of passed options is invalid\n ",
"language": "en",
"n_whitespaces": 231,
"n_words": 99,
"vocab_size": 69
} | 102 | Python | 79 | ab4de1dd70fba866930150e440a03e461a6ca6a8 | base.py | 283,199 | 31 | 190 | generate_navigator | https://github.com/OpenBB-finance/OpenBBTerminal.git | Create a packaged app bundle with Pyinstaller (#1525)
* Add dashboard widget assets
* Add ipywidgets and ipyflex to project
* Add currencies dashboard notebook
* Update docs and docstrings
* Add pyinstaller to project deps
* Add pyinstaller artifacts to gitignore
* Fix linter errors in terminal.py
* Update cspell hook and action with a pyinstaller specific word
* Add pyinstaller specfile and artifacts
* Add splashscreen image
* Add app icon
* adding splash screen support to terminal.spec and terminal.py
* Restore the conda env build files
* Sync deps
* Add border to the splashscreen image
* Clean up terminal launcher
* Add support for default feature flags in packages apps
* Fix types and linting
* Add splashscreen management to app bootup
* Check prediction feature flag when entering crypto/pred
* Update pyinstaller spec file
* fix .spec file to work for splash and icon - removed the ".."
* Allows to export when using installer (#1568)
* fix export for packaged apps
* fix filename
* Git : replace commit_hash when it is set in config_terminal
* Add update of the git commit hash in gtff default during build
* Add packaged app name and feature flag to logs
* Add platform specific icon assignment
* Add macOS build assets
* Add tensorflow to hidden imports
* Move LOGGING_COMMIT_HASH to gtff
* Adding files/folders needed to .spec and pyinstaller folder. This will make certain commands work again.
* Linting
* Workflow : ignore ./build/pyinstaller from codespell
* Workflow : exclude ./build/pyinstaller from flake8
* Poetry + Workflow : add types-six
* Pyinstaller : remove property_cached, user_agent and vaderSentiment
* Revert "Pyinstaller : remove property_cached, user_agent and vaderSentiment"
This reverts commit dbb3e2b81086f97819ebd21457148c7160a4d703.
* Clean up local paths in specfile
* Validate deps have correct Jinja version (they do)
* Fix logging commit hash to be set correctly for the logger to see it
Co-authored-by: Andrew <[email protected]>
Co-authored-by: didierlopes.eth <[email protected]>
Co-authored-by: Chavithra PARANA <[email protected]> | 311 | 0 | 84,465 | 11 |
|
1 | 3 | def isTechnical(self):
return self.technical
| nuitka/nodes/ModuleNodes.py | 19 | Nuitka | {
"docstring": "Must be present as it's used in CPython library initialization.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | 4 | Python | 4 | 3b1c76ce9d79543de81353f358b3108df91078fc | ModuleNodes.py | 178,747 | 2 | 10 | isTechnical | https://github.com/Nuitka/Nuitka.git | Standalone: Exclude more of standard library modules
* This removes tkinter and many modules expected to never
be implicit dependencies.
* The real reduction will be achieved using Python PGO once
it covers bytecode too.
* Don't keep required extension modules as root modules,
instead make them proper early init modules. | 18 | 0 | 42,811 | 6 |
|
8 | 17 | def fes(self, name=None, frame=None):
# what frames are we searching in?
if frame is not None:
if isinstance(frame, int):
frames = [self.frame(frame)]
elif isinstance(frame, str):
frames = self.frames(frame)
else:
frames = [frame]
else:
frames = self.frames()
return PrettyList(
fe
for f in frames
for fename, fe in f.FE.items()
if name is None or re.search(name, fename, re.I)
)
| nltk/corpus/reader/framenet.py | 169 | nltk | {
"docstring": "\n Lists frame element objects. If 'name' is provided, this is treated as\n a case-insensitive regular expression to filter by frame name.\n (Case-insensitivity is because casing of frame element names is not always\n consistent across frames.) Specify 'frame' to filter by a frame name pattern,\n ID, or object.\n\n >>> from nltk.corpus import framenet as fn\n >>> fn.fes('Noise_maker')\n [<fe ID=6043 name=Noise_maker>]\n >>> sorted([(fe.frame.name,fe.name) for fe in fn.fes('sound')]) # doctest: +NORMALIZE_WHITESPACE\n [('Cause_to_make_noise', 'Sound_maker'), ('Make_noise', 'Sound'),\n ('Make_noise', 'Sound_source'), ('Sound_movement', 'Location_of_sound_source'),\n ('Sound_movement', 'Sound'), ('Sound_movement', 'Sound_source'),\n ('Sounds', 'Component_sound'), ('Sounds', 'Location_of_sound_source'),\n ('Sounds', 'Sound_source'), ('Vocalizations', 'Location_of_sound_source'),\n ('Vocalizations', 'Sound_source')]\n >>> sorted([(fe.frame.name,fe.name) for fe in fn.fes('sound',r'(?i)make_noise')]) # doctest: +NORMALIZE_WHITESPACE\n [('Cause_to_make_noise', 'Sound_maker'),\n ('Make_noise', 'Sound'),\n ('Make_noise', 'Sound_source')]\n >>> sorted(set(fe.name for fe in fn.fes('^sound')))\n ['Sound', 'Sound_maker', 'Sound_source']\n >>> len(fn.fes('^sound$'))\n 2\n\n :param name: A regular expression pattern used to match against\n frame element names. If 'name' is None, then a list of all\n frame elements will be returned.\n :type name: str\n :return: A list of matching frame elements\n :rtype: list(AttrDict)\n ",
"language": "en",
"n_whitespaces": 382,
"n_words": 156,
"vocab_size": 95
} | 57 | Python | 40 | 8a4cf5d94eb94b6427c5d1d7907ba07b119932c5 | framenet.py | 42,538 | 16 | 108 | fes | https://github.com/nltk/nltk.git | Docstring tests (#3050)
* fixed pytests
* fixed more pytests
* fixed more pytest and changed multiline pytest issues fixes for snowball.py and causal.py
* fixed pytests (mainly multiline or rounding issues)
* fixed treebank pytests, removed test for return_string=True (deprecated)
* fixed destructive.py pytests, removed test for return_string=True (deprecated)
* fixed pytest (rounding issues)
* fixed pytest (initialised missing object)
* fixed pytest (formatting issues)
* fixed pytest (formatting issues)
* fixed pytest (formatting issues)
* added pytest +SKIP for deprecated module stanford
* updated AUTHORS.md
* changed docstring corrections by usage of ELLIPSIS and different roundings
* fixed AUTHORS.md to be consistent
* Fix framenet doctest formatting with pprint
* Change docstring on MultiListBox.__init__
I believe the original typo was misinterpreted and changed to something that was not originally intended.
Co-authored-by: Jan Lennartz <[email protected]>
Co-authored-by: Tom Aarsen <[email protected]>
Co-authored-by: Tom Aarsen <[email protected]> | 232 | 0 | 7,600 | 13 |
|
1 | 41 | def test_mark_task_instance_state(test_app):
from airflow.models import DAG, DagBag, TaskInstance
from airflow.operators.dummy import DummyOperator
from airflow.utils.session import create_session
from airflow.utils.state import State
from airflow.utils.timezone import datetime
from airflow.utils.types import DagRunType
from airflow.www.views import Airflow
from tests.test_utils.db import clear_db_runs
clear_db_runs()
start_date = datetime(2020, 1, 1)
with DAG("test_mark_task_instance_state", start_date=start_date) as dag:
task_1 = DummyOperator(task_id="task_1")
task_2 = DummyOperator(task_id="task_2")
task_3 = DummyOperator(task_id="task_3")
task_4 = DummyOperator(task_id="task_4")
task_5 = DummyOperator(task_id="task_5")
task_1 >> [task_2, task_3, task_4, task_5]
dagrun = dag.create_dagrun(
start_date=start_date,
execution_date=start_date,
data_interval=(start_date, start_date),
state=State.FAILED,
run_type=DagRunType.SCHEDULED,
)
| tests/www/views/test_views.py | 285 | airflow | {
"docstring": "\n Test that _mark_task_instance_state() does all three things:\n - Marks the given TaskInstance as SUCCESS;\n - Clears downstream TaskInstances in FAILED/UPSTREAM_FAILED state;\n - Set DagRun to QUEUED.\n ",
"language": "en",
"n_whitespaces": 42,
"n_words": 26,
"vocab_size": 24
} | 78 | Python | 57 | 2b4bf7fe67fc656ceb7bdaad36453b7a5b83ef04 | test_views.py | 44,001 | 56 | 437 | test_mark_task_instance_state | https://github.com/apache/airflow.git | Use `DagRun.run_id` instead of `execution_date` when updating state of TIs(UI & REST API) (#18724)
We can now use run_id as well as execution_date to update states
of task instances
Co-authored-by: Tzu-ping Chung <[email protected]>
Co-authored-by: Ash Berlin-Taylor <[email protected]> | 197 | 0 | 8,118 | 12 |
|
1 | 16 | def test_delete_view_uses_get_deleted_objects(self):
book = Book.objects.create(name="Test Book")
response = self.client.get(
reverse("admin2:admin_views_book_delete", args=(book.pk,))
)
# BookAdmin.get_deleted_objects() returns custom text.
self.assertContains(response, "a deletable object")
@override_settings(ROOT_URLCONF="admin_views.urls") | tests/admin_views/tests.py | 98 | @override_settings(ROOT_URLCONF="admin_views.urls") | django | {
"docstring": "The delete view uses ModelAdmin.get_deleted_objects().",
"language": "en",
"n_whitespaces": 4,
"n_words": 5,
"vocab_size": 5
} | 22 | Python | 21 | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | tests.py | 207,727 | 6 | 48 | test_delete_view_uses_get_deleted_objects | https://github.com/django/django.git | Refs #33476 -- Reformatted code with Black. | 74 | 1 | 52,072 | 13 |
1 | 5 | def _apply_from_right_to(self, op, **options):
return dispatch_method(self, '_apply_from_right_to', op, **options)
| sympy/physics/quantum/state.py | 37 | sympy | {
"docstring": "Apply an Operator to this Ket as Operator*Ket\n\n This method will dispatch to methods having the format::\n\n ``def _apply_from_right_to_OperatorName(op, **options):``\n\n Subclasses should define these methods (one for each OperatorName) to\n teach the Ket how to implement OperatorName*Ket\n\n Parameters\n ==========\n\n op : Operator\n The Operator that is acting on the Ket as op*Ket\n options : dict\n A dict of key/value pairs that control how the operator is applied\n to the Ket.\n ",
"language": "en",
"n_whitespaces": 170,
"n_words": 70,
"vocab_size": 51
} | 9 | Python | 8 | 00ed353dda66aa068dd43d44018f6a394d1fb0a1 | state.py | 200,169 | 2 | 23 | _apply_from_right_to | https://github.com/sympy/sympy.git | Fix the Ket*Op->Op*Ket bug | 23 | 0 | 49,559 | 8 |
|
1 | 10 | def unrank_gray(self, rank, superset):
graycode_bitlist = GrayCode.unrank(len(superset), rank)
return Subset.subset_from_bitlist(superset, graycode_bitlist)
| sympy/combinatorics/subsets.py | 50 | sympy | {
"docstring": "\n Gets the Gray code ordered subset of the specified rank.\n\n Examples\n ========\n\n >>> from sympy.combinatorics import Subset\n >>> Subset.unrank_gray(4, ['a', 'b', 'c']).subset\n ['a', 'b']\n >>> Subset.unrank_gray(0, ['a', 'b', 'c']).subset\n []\n\n See Also\n ========\n\n iterate_graycode, rank_gray\n ",
"language": "en",
"n_whitespaces": 120,
"n_words": 35,
"vocab_size": 27
} | 11 | Python | 11 | 498015021131af4dbb07eb110e5badaba8250c7b | subsets.py | 196,203 | 3 | 32 | unrank_gray | https://github.com/sympy/sympy.git | Updated import locations | 32 | 0 | 47,703 | 10 |
|
6 | 12 | def convert_ids_to_tokens(self, ids, skip_special_tokens=False):
if not isinstance(ids, (list, tuple)):
return self._convert_id_to_token(ids)
tokens = [self._convert_id_to_token(_id) for _id in ids]
if skip_special_tokens:
return [
token for token in tokens
if token not in self.all_special_tokens
]
return tokens
| paddlenlp/transformers/prophetnet/tokenizer.py | 100 | PaddleNLP | {
"docstring": "\r\n Converts a single index or a sequence of indices to a token or\r\n a sequence of tokens, using the vocabulary and added tokens.\r\n\r\n Args:\r\n ids (int or List[int]):\r\n The token id (or token ids) to be converted to token(s).\r\n skip_special_tokens (bool, optional):\r\n Whether or not to remove special tokens in the decoding.\r\n Defaults to `False` and we do not remove special tokens.\r\n\r\n Returns:\r\n str or List[str]: The decoded token(s).\r\n ",
"language": "en",
"n_whitespaces": 183,
"n_words": 69,
"vocab_size": 46
} | 35 | Python | 23 | 487162262196bead8d9b4c2306f313b8f64edf9b | tokenizer.py | 322,456 | 10 | 66 | convert_ids_to_tokens | https://github.com/PaddlePaddle/PaddleNLP.git | Add model Prohetnet (#1698)
* add Prohetnet model
* update prohetnet
* update format
* pre commit
* add prophetnet example
* update tokenizer.py,run_train.sh,train_prophetnet.py
* remove evaluate/gigaword/__init__.py
Co-authored-by: smallv0221 <[email protected]> | 133 | 0 | 118,173 | 11 |
|
2 | 9 | def vocabulary_size(self):
if tf.executing_eagerly():
return (
int(self.lookup_table.size().numpy())
+ self._token_start_index()
)
else:
return self.lookup_table.size() + self._token_start_index()
| keras/layers/preprocessing/index_lookup.py | 90 | keras | {
"docstring": "Gets the current size of the layer's vocabulary.\n\n Returns:\n The integer size of the vocabulary, including optional mask and oov indices.\n ",
"language": "en",
"n_whitespaces": 44,
"n_words": 21,
"vocab_size": 17
} | 15 | Python | 12 | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | index_lookup.py | 273,174 | 8 | 52 | vocabulary_size | https://github.com/keras-team/keras.git | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 99 | 0 | 81,097 | 16 |
|
1 | 7 | def get_host_target_type_map() -> t.Dict[t.Type[HostConfig], t.Type[TargetFilter]]:
return get_type_map(TargetFilter, HostConfig)
| test/lib/ansible_test/_internal/commands/integration/filters.py | 48 | ansible | {
"docstring": "Create and return a mapping of HostConfig types to TargetFilter types.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | 8 | Python | 8 | 3eb0485dd92c88cc92152d3656d94492db44b183 | filters.py | 267,900 | 3 | 31 | get_host_target_type_map | https://github.com/ansible/ansible.git | ansible-test - Use more native type hints. (#78435)
* ansible-test - Use more native type hints.
Simple search and replace to switch from comments to native type hints for return types of functions with no arguments.
* ansible-test - Use more native type hints.
Conversion of simple single-line function annotation type comments to native type hints.
* ansible-test - Use more native type hints.
Conversion of single-line function annotation type comments with default values to native type hints.
* ansible-test - Use more native type hints.
Manual conversion of type annotation comments for functions which have pylint directives. | 14 | 0 | 79,176 | 7 |