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2 | is_guessed_to_be_created_on_project_creation | def is_guessed_to_be_created_on_project_creation(self) -> bool:
# TODO(mgaeta): Bug: Rule is optional.
delta = abs(self.rule.date_added - self.project.date_added)
guess: bool = delta.total_seconds() < 30 and self.rule.label == DEFAULT_RULE_LABEL
return guess
| 654c6627307359956c6d44f83791d6b177841363 | 11 | event_frequency.py | 75 | ref(types): Add types to conditions and filters (#32393) | 19,359 | 0 | 62 | 45 | 26 | 96,980 | 27 | sentry | 12 | src/sentry/rules/conditions/event_frequency.py | Python | 13 | {
"docstring": "\n Best effort approximation on whether a rule with this condition was\n created on project creation based on how closely the rule and project\n are created; and if the label matches the default name used on project\n creation.\n\n :return:\n bool: True if rule is approximated to be created on project creation, False otherwise.\n ",
"language": "en",
"n_whitespaces": 106,
"n_words": 52,
"vocab_size": 38
} | https://github.com/getsentry/sentry.git |
|
1 | test_ohe_drop_first_explicit_categories | def test_ohe_drop_first_explicit_categories(handle_unknown):
X = [["a", 0], ["b", 2], ["b", 1]]
ohe = OneHotEncoder(
drop="first",
sparse=False,
handle_unknown=handle_unknown,
categories=[["b", "a"], [1, 2]],
)
ohe.fit(X)
X_test = [["c", 1]]
X_expected = np.array([[0, 0]])
warn_msg = (
r"Found unknown categories in columns \[0\] during transform. "
r"These unknown categories will be encoded as all zeros"
)
with pytest.warns(UserWarning, match=warn_msg):
X_trans = ohe.transform(X_test)
assert_allclose(X_trans, X_expected)
| 7f0006c8aad1a09621ad19c3db19c3ff0555a183 | 12 | test_encoders.py | 198 | ENH Adds infrequent categories to OneHotEncoder (#16018)
* ENH Completely adds infrequent categories
* STY Linting
* STY Linting
* DOC Improves wording
* DOC Lint
* BUG Fixes
* CLN Address comments
* CLN Address comments
* DOC Uses math to description float min_frequency
* DOC Adds comment regarding drop
* BUG Fixes method name
* DOC Clearer docstring
* TST Adds more tests
* FIX Fixes mege
* CLN More pythonic
* CLN Address comments
* STY Flake8
* CLN Address comments
* DOC Fix
* MRG
* WIP
* ENH Address comments
* STY Fix
* ENH Use functiion call instead of property
* ENH Adds counts feature
* CLN Rename variables
* DOC More details
* CLN Remove unneeded line
* CLN Less lines is less complicated
* CLN Less diffs
* CLN Improves readiabilty
* BUG Fix
* CLN Address comments
* TST Fix
* CLN Address comments
* CLN Address comments
* CLN Move docstring to userguide
* DOC Better wrapping
* TST Adds test to handle_unknown='error'
* ENH Spelling error in docstring
* BUG Fixes counter with nan values
* BUG Removes unneeded test
* BUG Fixes issue
* ENH Sync with main
* DOC Correct settings
* DOC Adds docstring
* DOC Immprove user guide
* DOC Move to 1.0
* DOC Update docs
* TST Remove test
* DOC Update docstring
* STY Linting
* DOC Address comments
* ENH Neater code
* DOC Update explaination for auto
* Update sklearn/preprocessing/_encoders.py
Co-authored-by: Roman Yurchak <[email protected]>
* TST Uses docstring instead of comments
* TST Remove call to fit
* TST Spelling error
* ENH Adds support for drop + infrequent categories
* ENH Adds infrequent_if_exist option
* DOC Address comments for user guide
* DOC Address comments for whats_new
* DOC Update docstring based on comments
* CLN Update test with suggestions
* ENH Adds computed property infrequent_categories_
* DOC Adds where the infrequent column is located
* TST Adds more test for infrequent_categories_
* DOC Adds docstring for _compute_drop_idx
* CLN Moves _convert_to_infrequent_idx into its own method
* TST Increases test coverage
* TST Adds failing test
* CLN Careful consideration of dropped and inverse_transform
* STY Linting
* DOC Adds docstrinb about dropping infrequent
* DOC Uses only
* DOC Numpydoc
* TST Includes test for get_feature_names_out
* DOC Move whats new
* DOC Address docstring comments
* DOC Docstring changes
* TST Better comments
* TST Adds check for handle_unknown='ignore' for infrequent
* CLN Make _infrequent_indices private
* CLN Change min_frequency default to None
* DOC Adds comments
* ENH adds support for max_categories=1
* ENH Describe lexicon ordering for ties
* DOC Better docstring
* STY Fix
* CLN Error when explicity dropping an infrequent category
* STY Grammar
Co-authored-by: Joel Nothman <[email protected]>
Co-authored-by: Roman Yurchak <[email protected]>
Co-authored-by: Guillaume Lemaitre <[email protected]> | 75,666 | 0 | 142 | 123 | 50 | 259,234 | 60 | scikit-learn | 21 | sklearn/preprocessing/tests/test_encoders.py | Python | 18 | {
"docstring": "Check drop='first' and handle_unknown='ignore'/'infrequent_if_exist'\n during fit with categories passed in.",
"language": "en",
"n_whitespaces": 12,
"n_words": 10,
"vocab_size": 10
} | https://github.com/scikit-learn/scikit-learn.git |
|
8 | ip6_interfaces | def ip6_interfaces():
# Provides:
# ip_interfaces
if salt.utils.platform.is_proxy():
return {}
ret = {}
ifaces = _get_interfaces()
for face in ifaces:
iface_ips = []
for inet in ifaces[face].get("inet6", []):
if "address" in inet:
iface_ips.append(inet["address"])
for secondary in ifaces[face].get("secondary", []):
if "address" in secondary and secondary.get("type") == "inet6":
iface_ips.append(secondary["address"])
ret[face] = iface_ips
return {"ip6_interfaces": ret}
| 75c0cb7181d14f780b24ee5dd126f2836730053b | 15 | core.py | 207 | Filter secondary IP address by type (#61434)
* Add filter for secondary ip addresses
Should improve #61370
* Remove unnecessary space
* Add test case for secondary IP address
Test data for IPv6 secondary IP looks wrong but this is what _interfaces_ip() could return looking at the current code
* Change order of tests because of caching issues
Change order of test_network_grains_secondary_ip and test_network_grains_cache because of caching issues when running after test_network_grains_cache
* Unify style in _interfaces_ip
Unify coding style in _interfaces_ip for secondary ip addresses with the style for regular addresses. Also align the attributes for IPv6 secondary ip addresses with regular ipv6 addresses
* Align IPv6 secondary IP attributes with changes to _interfaces_ip
* Add changelog for fix of issue 61370
* Use salt.loader.grain_funcs for secondary ip test
To work around caching issues when changing order of test_network_grains_cache and test_network_grains_secondary_ip use use salt.loader.grain_funcs in both functions. Also we hope this solves the issue, that this test worked in my local dev environment but not on the saltstack jenkins instances.
* Use side_effect to simulate test data
I don't understand what is different when these tests are run on the Jenkins infrastructure. Hope copying this from test_network_grains_cache make the tests work on them.
* Changed checking for secondaryip address type
* Add filter for secondary ip addresses
Should improve #61370
* Remove unnecessary space
* Add test case for secondary IP address
Test data for IPv6 secondary IP looks wrong but this is what _interfaces_ip() could return looking at the current code
* Change order of tests because of caching issues
Change order of test_network_grains_secondary_ip and test_network_grains_cache because of caching issues when running after test_network_grains_cache
* Unify style in _interfaces_ip
Unify coding style in _interfaces_ip for secondary ip addresses with the style for regular addresses. Also align the attributes for IPv6 secondary ip addresses with regular ipv6 addresses
* Align IPv6 secondary IP attributes with changes to _interfaces_ip
* Add changelog for fix of issue 61370
* Use salt.loader.grain_funcs for secondary ip test
To work around caching issues when changing order of test_network_grains_cache and test_network_grains_secondary_ip use use salt.loader.grain_funcs in both functions. Also we hope this solves the issue, that this test worked in my local dev environment but not on the saltstack jenkins instances.
* Use side_effect to simulate test data
I don't understand what is different when these tests are run on the Jenkins infrastructure. Hope copying this from test_network_grains_cache make the tests work on them.
* Changed checking for secondaryip address type
* Satisfy black code formatting
Co-authored-by: Shane Lee <[email protected]>
Co-authored-by: mayrstefan <[email protected]> | 54,487 | 0 | 166 | 118 | 35 | 216,260 | 53 | salt | 14 | salt/grains/core.py | Python | 15 | {
"docstring": "\n Provide a dict of the connected interfaces and their ip6 addresses\n The addresses will be passed as a list for each interface\n ",
"language": "en",
"n_whitespaces": 32,
"n_words": 22,
"vocab_size": 20
} | https://github.com/saltstack/salt.git |
|
9 | match | 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
| 8198943edd73a363c266633e1aa5b2a9e9c9f526 | 11 | pytree.py | 145 | add python 3.10.4 for windows | 55,510 | 0 | 198 | 93 | 29 | 218,860 | 52 | XX-Net | 10 | python3.10.4/Lib/lib2to3/pytree.py | Python | 14 | {
"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
} | https://github.com/XX-net/XX-Net.git |
|
1 | reset | def reset(self) -> None:
self._recording_start = dt_util.utcnow()
self._current_run_info = None
| f073f170402bd02e6d6c7597ce5d842a016e97be | 8 | run_history.py | 39 | Refactor tracking of the recorder run history (#70456)
Co-authored-by: Erik Montnemery <[email protected]> | 98,089 | 0 | 31 | 22 | 9 | 299,152 | 10 | core | 6 | homeassistant/components/recorder/run_history.py | Python | 7 | {
"docstring": "Reset the run when the database is changed or fails.\n\n Must run in the recorder thread.\n ",
"language": "en",
"n_whitespaces": 30,
"n_words": 16,
"vocab_size": 13
} | https://github.com/home-assistant/core.git |
|
2 | position_cursor | def position_cursor(self) -> Control:
if self._shape is not None:
_, height = self._shape
return Control(
ControlType.CARRIAGE_RETURN,
(ControlType.ERASE_IN_LINE, 2),
*(
(
(ControlType.CURSOR_UP, 1),
(ControlType.ERASE_IN_LINE, 2),
)
* (height - 1)
)
)
return Control()
| f3166e673fe8d40277b804d35d77dcdb760fc3b3 | 15 | live_render.py | 105 | check point progress on only bringing in pip==22.0.4 (#4966)
* vendor in pip==22.0.4
* updating vendor packaging version
* update pipdeptree to fix pipenv graph with new version of pip.
* Vendoring of pip-shims 0.7.0
* Vendoring of requirementslib 1.6.3
* Update pip index safety restrictions patch for pip==22.0.4
* Update patches
* exclude pyptoject.toml from black to see if that helps.
* Move this part of the hash collection back to the top (like prior implementation) because it affects the outcome of this test now in pip 22.0.4 | 3,537 | 0 | 250 | 70 | 27 | 20,773 | 33 | pipenv | 10 | pipenv/patched/notpip/_vendor/rich/live_render.py | Python | 20 | {
"docstring": "Get control codes to move cursor to beginning of live render.\n\n Returns:\n Control: A control instance that may be printed.\n ",
"language": "en",
"n_whitespaces": 45,
"n_words": 20,
"vocab_size": 18
} | https://github.com/pypa/pipenv.git |
|
2 | is_fedora | def is_fedora():
(osname, osrelease, oscodename) = (
x.strip('"').strip("'") for x in linux_distribution()
)
return osname == "Fedora"
@real_memoize | f2a783643de61cac1ff3288b40241e5ce6e1ddc8 | @real_memoize | 12 | platform.py | 68 | Update to latest ``pyupgrade`` hook. Stop skipping it on CI.
Signed-off-by: Pedro Algarvio <[email protected]> | 54,302 | 1 | 36 | 36 | 18 | 215,982 | 18 | salt | 8 | salt/utils/platform.py | Python | 5 | {
"docstring": "\n Simple function to return if host is Fedora or not\n ",
"language": "en",
"n_whitespaces": 17,
"n_words": 10,
"vocab_size": 10
} | https://github.com/saltstack/salt.git |
1 | default_prng_impl | def default_prng_impl():
impl_name = config.jax_default_prng_impl
assert impl_name in PRNG_IMPLS, impl_name
return PRNG_IMPLS[impl_name]
### key operations
| 026b91b85db17bb60d49309da7698d33122f751f | 7 | random.py | 36 | add `random.default_prng_impl` to retrieve the default PRNG implementation | 26,542 | 0 | 18 | 21 | 13 | 119,074 | 15 | jax | 5 | jax/_src/random.py | Python | 4 | {
"docstring": "Get the default PRNG implementation.\n\n The default implementation is determined by ``config.jax_default_prng_impl``,\n which specifies it by name. This function returns the corresponding\n ``jax.prng.PRNGImpl`` instance.\n ",
"language": "en",
"n_whitespaces": 28,
"n_words": 24,
"vocab_size": 21
} | https://github.com/google/jax.git |
|
1 | test_all | def test_all(self):
assert validate(all(int, lambda n: 0 < n < 5), 3) == 3
assert validate(all(transform(int), lambda n: 0 < n < 5), 3.33) == 3
with self.assertRaises(ValueError) as cm:
validate(all(int, float), 123)
assert_validationerror(cm.exception, )
| 3d44da082b3ba202b9d0557bfd8ce747a1d7960c | 12 | test_api_validate.py | 123 | plugin.api.validate: implement ValidationError
- Implement `ValidationError`
- Inherit from `ValueError` to preserve backwards compatiblity
- Allow collecting multiple errors (AnySchema)
- Keep an error stack of parent `ValidationError`s or other exceptions
- Format error stack when converting error to string
- Raise `ValidationError` instead of `ValueError`
- Add error contexts where it makes sense
- Add schema names to error instances
- Add and update tests | 45,704 | 0 | 73 | 81 | 23 | 187,143 | 35 | streamlink | 13 | tests/test_api_validate.py | Python | 9 | {
"docstring": "\n ValidationError(type):\n Type of 123 should be 'float', but is 'int'\n ",
"language": "en",
"n_whitespaces": 42,
"n_words": 10,
"vocab_size": 10
} | https://github.com/streamlink/streamlink.git |
|
1 | temporal_padding | def temporal_padding(x, padding=(1, 1)):
assert len(padding) == 2
pattern = [[0, 0], [padding[0], padding[1]], [0, 0]]
return tf.compat.v1.pad(x, pattern)
@keras_export("keras.backend.spatial_2d_padding")
@tf.__internal__.dispatch.add_dispatch_support
@doc_controls.do_not_generate_docs | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | @keras_export("keras.backend.spatial_2d_padding")
@tf.__internal__.dispatch.add_dispatch_support
@doc_controls.do_not_generate_docs | 9 | backend.py | 117 | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 80,237 | 1 | 31 | 61 | 22 | 269,618 | 22 | keras | 15 | keras/backend.py | Python | 14 | {
"docstring": "Pads the middle dimension of a 3D tensor.\n\n Args:\n x: Tensor or variable.\n padding: Tuple of 2 integers, how many zeros to\n add at the start and end of dim 1.\n\n Returns:\n A padded 3D tensor.\n ",
"language": "en",
"n_whitespaces": 77,
"n_words": 36,
"vocab_size": 31
} | https://github.com/keras-team/keras.git |
1 | set_login_api_ready_event | def set_login_api_ready_event():
login_api.extra["ready-event"].set()
login_api = FastAPI(on_startup=[set_login_api_ready_event])
login_api.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_methods=["*"],
allow_headers=["*"],
)
| 1a6dee5e9eb71e6e6d1d3492002e9cd674ab9f9b | 9 | cloud.py | 87 | Add login with a browser to `prefect cloud login` (#7334) | 11,953 | 0 | 23 | 14 | 12 | 59,848 | 12 | prefect | 11 | src/prefect/cli/cloud.py | Python | 2 | {
"docstring": "\nThis small API server is used for data transmission for browser-based log in.\n",
"language": "en",
"n_whitespaces": 12,
"n_words": 13,
"vocab_size": 12
} | https://github.com/PrefectHQ/prefect.git |
|
2 | hide_splashscreen | def hide_splashscreen():
try:
import pyi_splash # type: ignore # pylint: disable=import-outside-toplevel
pyi_splash.update_text("Terminal Loaded!")
pyi_splash.close()
except Exception as e:
logger.info(e)
| ab4de1dd70fba866930150e440a03e461a6ca6a8 | 10 | terminal_helper.py | 61 | 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]> | 84,482 | 0 | 58 | 31 | 18 | 283,232 | 19 | OpenBBTerminal | 8 | gamestonk_terminal/terminal_helper.py | Python | 7 | {
"docstring": "Hide the splashscreen on Windows bundles.\n\n `pyi_splash` is a PyInstaller \"fake-package\" that's used to communicate\n with the splashscreen on Windows.\n Sending the `close` signal to the splash screen is required.\n The splash screen remains open until this function is called or the Python\n program is terminated.\n ",
"language": "en",
"n_whitespaces": 64,
"n_words": 46,
"vocab_size": 34
} | https://github.com/OpenBB-finance/OpenBBTerminal.git |
|
3 | predict | def predict(self, data, tokenizer):
batchify_fn = lambda samples, fn=Tuple(
Pad(axis=0, pad_val=tokenizer.pad_token_id), # input
Pad(axis=0, pad_val=tokenizer.pad_token_id), # segment
): fn(samples)
all_embeddings = []
examples = []
for idx, text in enumerate(tqdm(data)):
input_ids, segment_ids = convert_example(
text,
tokenizer,
max_seq_length=self.max_seq_length,
pad_to_max_seq_len=True)
examples.append((input_ids, segment_ids))
if (len(examples) > 100):
input_ids, segment_ids = batchify_fn(examples)
self.input_handles[0].copy_from_cpu(input_ids)
self.input_handles[1].copy_from_cpu(segment_ids)
self.predictor.run()
logits = self.output_handle.copy_to_cpu()
all_embeddings.append(logits)
examples = []
all_embeddings = np.concatenate(all_embeddings, axis=0)
np.save('corpus_embedding', all_embeddings)
| 4a1b98b9390aa0ec9c16530b28ba8d311787867b | 15 | feature_extract.py | 295 | Update Readme and Update Mivus feature extraction module (#1699)
* add recall inference similarity
* update examples
* updatea readme
* update dir name
* update neural search readme
* update milvus readme
* update domain adaptive pretraining readme
* fix the mistakes
* update readme
* add recall Paddle Serving Support
* update readme
* update readme and format the code
* reformat the files
* move the files
* reformat the code
* remove redundant code
* update changes
* Add C++ Paddle Serving
* Update Readme and Update Mivus feature extraction module
* check formats
* update serving config
* deleted redundant code
Co-authored-by: Zeyu Chen <[email protected]>
Co-authored-by: tianxin <[email protected]> | 118,117 | 0 | 342 | 188 | 48 | 322,298 | 64 | PaddleNLP | 35 | applications/neural_search/recall/milvus/feature_extract.py | Python | 24 | {
"docstring": "\n Predicts the data labels.\n\n Args:\n data (obj:`List(str)`): The batch data whose each element is a raw text.\n tokenizer(obj:`PretrainedTokenizer`): This tokenizer inherits from :class:`~paddlenlp.transformers.PretrainedTokenizer` \n which contains most of the methods. Users should refer to the superclass for more information regarding methods.\n\n Returns:\n results(obj:`dict`): All the predictions labels.\n ",
"language": "en",
"n_whitespaces": 124,
"n_words": 46,
"vocab_size": 39
} | https://github.com/PaddlePaddle/PaddleNLP.git |
|
1 | get_tables | def get_tables(self) -> Response:
q = f
return self.native_query(q)
| b4b66f241b6b2905e1dba81c42c2edd095c257bc | 9 | vertica_handler.py | 41 | ALMOST Completed But Dialect not working | 25,565 | 0 | 39 | 19 | 9 | 115,816 | 9 | mindsdb | 6 | mindsdb/integrations/handlers/vertica_handler/vertica_handler.py | Python | 12 | {
"docstring": "\n Get a list with all of the tabels in VERTICA\n SELECT \n TABLE_NAME,\n TABLE_SCHEMA\n from v_catalog.tables \n WHERE table_schema='{self.schema_name}' \n order by\n table_name;",
"language": "en",
"n_whitespaces": 79,
"n_words": 20,
"vocab_size": 20
} | https://github.com/mindsdb/mindsdb.git |
|
3 | upsample_2d | def upsample_2d(x, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda'):
r
assert isinstance(factor, int) and factor >= 1
if k is None:
k = [1] * factor
k = _setup_kernel(k) * (gain * (factor ** 2))
p = k.shape[0] - factor
return _simple_upfirdn_2d(x, k, up=factor, pad0=(p+1)//2+factor-1, pad1=p//2, data_format=data_format, impl=impl)
#----------------------------------------------------------------------------
| 7375ee364e0df2a417f92593e09557f1b2a3575a | 13 | upfirdn_2d.py | 171 | initialize ostec | 1,605 | 0 | 70 | 95 | 39 | 9,405 | 47 | insightface | 16 | reconstruction/ostec/external/stylegan2/dnnlib/tflib/ops/upfirdn_2d.py | Python | 29 | {
"docstring": "Upsample a batch of 2D images with the given filter.\n\n Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]`\n and upsamples each image with the given filter. The filter is normalized so that\n if the input pixels are constant, they will be scaled by the specified `gain`.\n Pixels outside the image are assumed to be zero, and the filter is padded with\n zeros so that its shape is a multiple of the upsampling factor.\n\n Args:\n x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.\n k: FIR filter of the shape `[firH, firW]` or `[firN]` (separable).\n The default is `[1] * factor`, which corresponds to nearest-neighbor\n upsampling.\n factor: Integer upsampling factor (default: 2).\n gain: Scaling factor for signal magnitude (default: 1.0).\n data_format: `'NCHW'` or `'NHWC'` (default: `'NCHW'`).\n impl: Name of the implementation to use. Can be `\"ref\"` or `\"cuda\"` (default).\n\n Returns:\n Tensor of the shape `[N, C, H * factor, W * factor]` or\n `[N, H * factor, W * factor, C]`, and same datatype as `x`.\n ",
"language": "en",
"n_whitespaces": 348,
"n_words": 181,
"vocab_size": 105
} | https://github.com/deepinsight/insightface.git |
|
1 | copy | def copy(self, message_set, new_mailbox):
return self._simple_command('COPY', message_set, new_mailbox)
| 8198943edd73a363c266633e1aa5b2a9e9c9f526 | 8 | imaplib.py | 34 | add python 3.10.4 for windows | 55,013 | 0 | 22 | 21 | 7 | 217,918 | 8 | XX-Net | 5 | python3.10.4/Lib/imaplib.py | Python | 2 | {
"docstring": "Copy 'message_set' messages onto end of 'new_mailbox'.\n\n (typ, [data]) = <instance>.copy(message_set, new_mailbox)\n ",
"language": "en",
"n_whitespaces": 26,
"n_words": 12,
"vocab_size": 12
} | https://github.com/XX-net/XX-Net.git |
|
4 | extract_relative_time | def extract_relative_time(relative_time_text):
mobj = re.search(r'(?P<start>today|yesterday|now)|(?P<time>\d+)\s*(?P<unit>microsecond|second|minute|hour|day|week|month|year)s?\s*ago', relative_time_text)
if mobj:
start = mobj.group('start')
if start:
return datetime_from_str(start)
try:
return datetime_from_str('now-%s%s' % (mobj.group('time'), mobj.group('unit')))
except ValueError:
return None
| f0d785d3ed59e879a69f69f3c9334754f11747e0 | 16 | youtube.py | 113 | [youtube:tab] Extract more playlist metadata (#2069)
* Add fields modified_date, modified_timestamp
* Add field playlist_count
* [youtube:tab] Extract view_count, playlist_count, modified_date
Authored by: coletdjnz, pukkandan | 39,169 | 0 | 135 | 64 | 21 | 162,160 | 25 | yt-dlp | 9 | yt_dlp/extractor/youtube.py | Python | 10 | {
"docstring": "\n Extracts a relative time from string and converts to dt object\n e.g. 'streamed 6 days ago', '5 seconds ago (edited)', 'updated today'\n ",
"language": "en",
"n_whitespaces": 44,
"n_words": 22,
"vocab_size": 22
} | https://github.com/yt-dlp/yt-dlp.git |
|
8 | make_parser | def make_parser(self, parser, optname, metavar=None, short=None):
if optname not in self._options:
return
o = self._options[optname]
| fdde9ba3b3caaa2654048cec0af07bfcc3a6a3f8 | 8 | optmanager.py | 53 | use Python 3.9+ typing | 73,636 | 0 | 47 | 308 | 15 | 251,210 | 15 | mitmproxy | 8 | mitmproxy/optmanager.py | Python | 58 | {
"docstring": "\n Auto-Create a command-line parser entry for a named option. If the\n option does not exist, it is ignored.\n ",
"language": "en",
"n_whitespaces": 48,
"n_words": 18,
"vocab_size": 17
} | https://github.com/mitmproxy/mitmproxy.git |
|
9 | update | def update(self, value=None, background_color=None, text_color=None, font=None, visible=None):
if not self._widget_was_created(): # if widget hasn't been created yet, then don't allow
return
if value is not None:
self.DisplayText = str(value)
self.TKStringVar.set(str(value))
if background_color not in (None, COLOR_SYSTEM_DEFAULT):
self.TKText.configure(background=background_color)
if text_color not in (None, COLOR_SYSTEM_DEFAULT):
self.TKText.configure(fg=text_color)
if font is not None:
self.TKText.configure(font=font)
if visible is False:
element._pack_forget_save_settings()
# self.TKText.pack_forget()
elif visible is True:
self._pack_restore_settings()
# self.TKText.pack(padx=self.pad_used[0], pady=self.pad_used[1])
if visible is not None:
self._visible = visible
| e575a0b8dc72561ce6565edaf804dc8c6b5053e5 | 11 | PySimpleGUI.py | 233 | Fixed problem with making elements invisible causing the pack settings to be lost. Converted Text, Input, Multiline, StatusBar, Frame, Combo to see if this is the right approach | 53,445 | 0 | 258 | 147 | 46 | 212,837 | 73 | PySimpleGUI | 21 | PySimpleGUI.py | Python | 18 | {
"docstring": "\n Changes some of the settings for the Text Element. Must call `Window.Read` or `Window.Finalize` prior\n\n Changes will not be visible in your window until you call window.read or window.refresh.\n\n If you change visibility, your element may MOVE. If you want it to remain stationary, use the \"layout helper\"\n function \"pin\" to ensure your element is \"pinned\" to that location in your layout so that it returns there\n when made visible.\n\n :param value: new text to show\n :type value: (str)\n :param background_color: color of background\n :type background_color: (str)\n :param text_color: color of the text\n :type text_color: (str)\n :param font: specifies the font family, size, etc. Tuple or Single string format 'name size styles'. Styles: italic * roman bold normal underline overstrike\n :type font: (str or (str, int[, str]) or None)\n :param visible: set visibility state of the element\n :type visible: (bool)\n ",
"language": "en",
"n_whitespaces": 335,
"n_words": 140,
"vocab_size": 95
} | https://github.com/PySimpleGUI/PySimpleGUI.git |
|
2 | get_template_name | def get_template_name(self):
if self.template_name is not None:
return self.template_name
model_opts = self.queryset.model._meta
return f'{model_opts.app_label}/{model_opts.model_name}.html'
| 54834c47f8870e7faabcd847c3270da0bd3d2884 | 9 | object_views.py | 64 | Refactor generic views; add plugins dev documentation | 77,675 | 0 | 53 | 30 | 12 | 264,304 | 14 | netbox | 9 | netbox/netbox/views/generic/object_views.py | Python | 5 | {
"docstring": "\n Return self.template_name if defined. Otherwise, dynamically resolve the template name using the queryset\n model's `app_label` and `model_name`.\n ",
"language": "en",
"n_whitespaces": 39,
"n_words": 17,
"vocab_size": 16
} | https://github.com/netbox-community/netbox.git |
|
2 | persist | def persist(self, backfill=False, **kwargs) -> FrozenEvent:
event, context = self.build_event(**kwargs)
if backfill:
self.get_success(
self._storage_controllers.persistence.persist_events(
[(event, context)], backfilled=True
)
)
else:
self.get_success(
self._storage_controllers.persistence.persist_event(event, context)
)
return event
| d8cc86eff484b6f570f55a5badb337080c6e4dcd | 14 | test_events.py | 118 | Remove redundant types from comments. (#14412)
Remove type hints from comments which have been added
as Python type hints. This helps avoid drift between comments
and reality, as well as removing redundant information.
Also adds some missing type hints which were simple to fill in. | 73,153 | 0 | 169 | 75 | 23 | 249,821 | 26 | synapse | 14 | tests/replication/slave/storage/test_events.py | Python | 17 | {
"docstring": "\n Returns:\n The event that was persisted.\n ",
"language": "en",
"n_whitespaces": 32,
"n_words": 6,
"vocab_size": 6
} | https://github.com/matrix-org/synapse.git |
|
3 | from_env | def from_env() -> Settings:
# Since os.environ is a Dict[str, str] we can safely hash it by contents, but we
# must be careful to avoid hashing a generator instead of a tuple
cache_key = hash(tuple((key, value) for key, value in os.environ.items()))
if cache_key not in _FROM_ENV_CACHE:
_FROM_ENV_CACHE[cache_key] = Settings()
return _FROM_ENV_CACHE[cache_key]
| 1d4218a287ef343f32f1e32482592b471be5df1d | 14 | settings.py | 84 | Move `prefect.settings` to `prefect.settings.from_env()` | 10,794 | 0 | 81 | 51 | 44 | 53,410 | 52 | prefect | 11 | src/prefect/settings.py | Python | 11 | {
"docstring": "\n Returns a settings object populated with default values and overrides from\n environment variables.\n\n Calls with the same environment return a cached object instead of reconstructing.\n ",
"language": "en",
"n_whitespaces": 38,
"n_words": 25,
"vocab_size": 21
} | https://github.com/PrefectHQ/prefect.git |
|
5 | nr_of_successful_buys | def nr_of_successful_buys(self) -> int:
return len([o for o in self.orders if o.ft_order_side == 'buy' and
o.status in NON_OPEN_EXCHANGE_STATES and
o.filled > 0])
| 813a2cd23b0d9fa9f384c8c1f0b558ca5c4363e2 | 13 | models.py | 64 | Add useful helper methods for adjust_trade_position implementation | 34,265 | 0 | 74 | 39 | 20 | 148,484 | 22 | freqtrade | 10 | freqtrade/persistence/models.py | Python | 8 | {
"docstring": "\n Helper function to count the number of buy orders that have been filled.\n :return: int count of buy orders that have been filled for this trade.\n ",
"language": "en",
"n_whitespaces": 48,
"n_words": 26,
"vocab_size": 19
} | https://github.com/freqtrade/freqtrade.git |
|
1 | _get_cmap_norms | def _get_cmap_norms():
# Create a colormap and specify the levels it represents.
cmap = mpl.colormaps["RdBu"].resampled(5)
clevs = [-5., -2.5, -.5, .5, 1.5, 3.5]
# Define norms for the colormaps.
norms = dict()
norms['neither'] = BoundaryNorm(clevs, len(clevs) - 1)
norms['min'] = BoundaryNorm([-10] + clevs[1:], len(clevs) - 1)
norms['max'] = BoundaryNorm(clevs[:-1] + [10], len(clevs) - 1)
norms['both'] = BoundaryNorm([-10] + clevs[1:-1] + [10], len(clevs) - 1)
return cmap, norms
| a17f4f3bd63e3ca3754f96d7db4ce5197720589b | 13 | test_colorbar.py | 230 | MNT: convert tests and internal usage way from using mpl.cm.get_cmap | 23,565 | 0 | 100 | 151 | 43 | 109,392 | 67 | matplotlib | 10 | lib/matplotlib/tests/test_colorbar.py | Python | 9 | {
"docstring": "\n Define a colormap and appropriate norms for each of the four\n possible settings of the extend keyword.\n\n Helper function for _colorbar_extension_shape and\n colorbar_extension_length.\n ",
"language": "en",
"n_whitespaces": 39,
"n_words": 23,
"vocab_size": 19
} | https://github.com/matplotlib/matplotlib.git |
|
1 | test_get_unread_push_actions_for_user_in_range | def test_get_unread_push_actions_for_user_in_range(self) -> None:
user_id, token, _, other_token, room_id = self._create_users_and_room()
# Create two events, one of which is a highlight.
self.helper.send_event(
room_id,
type="m.room.message",
content={"msgtype": "m.text", "body": "msg"},
tok=other_token,
)
event_id = self.helper.send_event(
room_id,
type="m.room.message",
content={"msgtype": "m.text", "body": user_id},
tok=other_token,
)["event_id"]
# Fetch unread actions for HTTP pushers.
http_actions = self.get_success(
self.store.get_unread_push_actions_for_user_in_range_for_http(
user_id, 0, 1000, 20
)
)
self.assertEqual(2, len(http_actions))
# Fetch unread actions for email pushers.
email_actions = self.get_success(
self.store.get_unread_push_actions_for_user_in_range_for_email(
user_id, 0, 1000, 20
)
)
self.assertEqual(2, len(email_actions))
# Send a receipt, which should clear any actions.
self.get_success(
self.store.insert_receipt(
room_id,
"m.read",
user_id=user_id,
event_ids=[event_id],
thread_id=None,
data={},
)
)
http_actions = self.get_success(
self.store.get_unread_push_actions_for_user_in_range_for_http(
user_id, 0, 1000, 20
)
)
self.assertEqual([], http_actions)
email_actions = self.get_success(
self.store.get_unread_push_actions_for_user_in_range_for_email(
user_id, 0, 1000, 20
)
)
self.assertEqual([], email_actions)
| 2fae1a3f7862bf38cd0b52dfd3ea3ae76794d2b7 | 13 | test_event_push_actions.py | 381 | Improve tests for get_unread_push_actions_for_user_in_range_*. (#13893)
* Adds a docstring.
* Reduces a small amount of duplicated code.
* Improves tests. | 72,985 | 0 | 638 | 245 | 66 | 249,545 | 122 | synapse | 26 | tests/storage/test_event_push_actions.py | Python | 49 | {
"docstring": "Test getting unread push actions for HTTP and email pushers.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | https://github.com/matrix-org/synapse.git |
|
4 | screen | def screen(self) -> "Screen":
# Get the node by looking up a chain of parents
# Note that self.screen may not be the same as self.app.screen
from .screen import Screen
node = self
while node and not isinstance(node, Screen):
node = node._parent
if not isinstance(node, Screen):
raise NoScreen("{self} has no screen")
return node
| 6a22c96a9e1831a7ac5889738bec8d5386fd111f | 10 | dom.py | 87 | screen fix | 44,581 | 0 | 131 | 48 | 42 | 184,433 | 53 | textual | 7 | src/textual/dom.py | Python | 9 | {
"docstring": "Get the screen that this node is contained within. Note that this may not be the currently active screen within the app.",
"language": "en",
"n_whitespaces": 21,
"n_words": 22,
"vocab_size": 17
} | https://github.com/Textualize/textual.git |
|
1 | test_graphical_lasso_cv_alphas_iterable | def test_graphical_lasso_cv_alphas_iterable(alphas_container_type):
true_cov = np.array(
[
[0.8, 0.0, 0.2, 0.0],
[0.0, 0.4, 0.0, 0.0],
[0.2, 0.0, 0.3, 0.1],
[0.0, 0.0, 0.1, 0.7],
]
)
rng = np.random.RandomState(0)
X = rng.multivariate_normal(mean=[0, 0, 0, 0], cov=true_cov, size=200)
alphas = _convert_container([0.02, 0.03], alphas_container_type)
GraphicalLassoCV(alphas=alphas, tol=1e-1, n_jobs=1).fit(X)
# TODO: Remove `score` and `test_score` suffix in 1.2
@pytest.mark.parametrize("suffix", ["score", "test_score"])
@pytest.mark.filterwarnings("ignore:Key*:FutureWarning:sklearn") | abbee570f31a91243c22b1892e42056bb915c056 | @pytest.mark.parametrize("suffix", ["score", "test_score"])
@pytest.mark.filterwarnings("ignore:Key*:FutureWarning:sklearn") | 10 | test_graphical_lasso.py | 213 | FIX accept NumPy arrays for alphas in GraphicalLassoCV (#22493) | 75,504 | 1 | 132 | 160 | 47 | 258,976 | 56 | scikit-learn | 23 | sklearn/covariance/tests/test_graphical_lasso.py | Python | 13 | {
"docstring": "Check that we can pass an array-like to `alphas`.\n\n Non-regression test for:\n https://github.com/scikit-learn/scikit-learn/issues/22489\n ",
"language": "en",
"n_whitespaces": 22,
"n_words": 13,
"vocab_size": 13
} | https://github.com/scikit-learn/scikit-learn.git |
1 | test_change_view_with_show_delete_extra_context | def test_change_view_with_show_delete_extra_context(self):
instance = UndeletableObject.objects.create(name="foo")
response = self.client.get(
reverse("admin:admin_views_undeletableobject_change", args=(instance.pk,))
)
self.assertNotContains(response, "deletelink")
| 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | 13 | tests.py | 83 | Refs #33476 -- Reformatted code with Black. | 52,138 | 0 | 59 | 48 | 12 | 207,867 | 13 | django | 14 | tests/admin_views/tests.py | Python | 6 | {
"docstring": "\n The 'show_delete' context variable in the admin's change view controls\n the display of the delete button.\n ",
"language": "en",
"n_whitespaces": 38,
"n_words": 16,
"vocab_size": 14
} | https://github.com/django/django.git |
|
1 | test_meta_charset | def test_meta_charset(self) -> None:
encodings = _get_html_media_encodings(
b,
"text/html",
)
self.assertEqual(list(encodings), ["ascii", "utf-8", "cp1252"])
# A less well-formed version.
encodings = _get_html_media_encodings(
b,
"text/html",
)
self.assertEqual(list(encodings), ["ascii", "utf-8", "cp1252"])
| 7e91107be1a4287873266e588a3c5b415279f4c8 | 9 | test_html_preview.py | 111 | Add type hints to `tests/rest` (#12146)
* Add type hints to `tests/rest`
* newsfile
* change import from `SigningKey` | 71,652 | 0 | 129 | 62 | 19 | 247,396 | 29 | synapse | 6 | tests/rest/media/v1/test_html_preview.py | Python | 22 | {
"docstring": "A character encoding is found via the meta tag.\n <html>\n <head><meta charset=\"ascii\">\n </head>\n </html>\n \n <html>\n <head>< meta charset = ascii>\n </head>\n </html>\n ",
"language": "en",
"n_whitespaces": 93,
"n_words": 22,
"vocab_size": 18
} | https://github.com/matrix-org/synapse.git |
|
2 | get_current_settings | def get_current_settings() -> Settings:
from prefect.context import ProfileContext
profile = ProfileContext.get()
if profile is not None:
return profile.settings
return get_settings_from_env()
| 95b47e807fa5ccc626a06efc2cced0d8ff8eadfa | 8 | settings.py | 58 | Rewrite temporary settings to use copy_with_update | 11,185 | 0 | 42 | 34 | 18 | 55,038 | 20 | prefect | 9 | src/prefect/settings.py | Python | 10 | {
"docstring": "\n Returns a settings object populated with values from the current profile or, if no\n profile is active, the environment.\n ",
"language": "en",
"n_whitespaces": 29,
"n_words": 19,
"vocab_size": 17
} | https://github.com/PrefectHQ/prefect.git |
|
3 | unset_existing_data | def unset_existing_data(company):
linked = frappe.db.sql(
,
as_dict=True,
)
# remove accounts data from company
update_values = {d.fieldname: "" for d in linked}
frappe.db.set_value("Company", company, update_values, update_values)
# remove accounts data from various doctypes
for doctype in [
"Account",
"Party Account",
"Mode of Payment Account",
"Tax Withholding Account",
"Sales Taxes and Charges Template",
"Purchase Taxes and Charges Template",
]:
frappe.db.sql(
.format(doctype) % (company) # nosec
)
| 494bd9ef78313436f0424b918f200dab8fc7c20b | 13 | chart_of_accounts_importer.py | 140 | style: format code with black | 13,733 | 0 | 46 | 82 | 48 | 64,834 | 65 | erpnext | 13 | erpnext/accounts/doctype/chart_of_accounts_importer/chart_of_accounts_importer.py | Python | 19 | {
"docstring": "select fieldname from tabDocField\n\t\twhere fieldtype=\"Link\" and options=\"Account\" and parent=\"Company\"delete from `tab{0}` where `company`=\"%s\"",
"language": "en",
"n_whitespaces": 12,
"n_words": 14,
"vocab_size": 11
} | https://github.com/frappe/erpnext.git |
|
7 | __getitem__ | def __getitem__(self, key):
getitem = self._data.__getitem__
if is_integer(key) or is_float(key):
# GH#44051 exclude bool, which would return a 2d ndarray
key = com.cast_scalar_indexer(key, warn_float=True)
return getitem(key)
if isinstance(key, slice):
# This case is separated from the conditional above to avoid
# pessimization com.is_bool_indexer and ndim checks.
result = getitem(key)
# Going through simple_new for performance.
return type(self)._simple_new(result, name=self._name)
if com.is_bool_indexer(key):
# if we have list[bools, length=1e5] then doing this check+convert
# takes 166 µs + 2.1 ms and cuts the ndarray.__getitem__
# time below from 3.8 ms to 496 µs
# if we already have ndarray[bool], the overhead is 1.4 µs or .25%
key = np.asarray(key, dtype=bool)
result = getitem(key)
# Because we ruled out integer above, we always get an arraylike here
if result.ndim > 1:
deprecate_ndim_indexing(result)
if hasattr(result, "_ndarray"):
# error: Item "ndarray[Any, Any]" of "Union[ExtensionArray,
# ndarray[Any, Any]]" has no attribute "_ndarray" [union-attr]
# i.e. NDArrayBackedExtensionArray
# Unpack to ndarray for MPL compat
return result._ndarray # type: ignore[union-attr]
return result
# NB: Using _constructor._simple_new would break if MultiIndex
# didn't override __getitem__
return self._constructor._simple_new(result, name=self._name)
| d603d43df2057ecdf74010d9dadc735e37f8f7b5 | 11 | base.py | 236 | TYP: Ignore numpy related issues (#45244) | 39,411 | 0 | 511 | 139 | 123 | 163,265 | 178 | pandas | 27 | pandas/core/indexes/base.py | Python | 17 | {
"docstring": "\n Override numpy.ndarray's __getitem__ method to work as desired.\n\n This function adds lists and Series as valid boolean indexers\n (ndarrays only supports ndarray with dtype=bool).\n\n If resulting ndim != 1, plain ndarray is returned instead of\n corresponding `Index` subclass.\n\n ",
"language": "en",
"n_whitespaces": 81,
"n_words": 38,
"vocab_size": 36
} | https://github.com/pandas-dev/pandas.git |
|
6 | del_param | def del_param(self, param, header='content-type', requote=True):
if header not in self:
return
new_ctype = ''
for p, v in self.get_params(header=header, unquote=requote):
if p.lower() != param.lower():
if not new_ctype:
new_ctype = _formatparam(p, v, requote)
else:
new_ctype = SEMISPACE.join([new_ctype,
_formatparam(p, v, requote)])
if new_ctype != self.get(header):
del self[header]
self[header] = new_ctype
| 8198943edd73a363c266633e1aa5b2a9e9c9f526 | 18 | message.py | 177 | add python 3.10.4 for windows | 57,063 | 0 | 242 | 113 | 32 | 223,782 | 48 | XX-Net | 15 | python3.10.4/Lib/email/message.py | Python | 14 | {
"docstring": "Remove the given parameter completely from the Content-Type header.\n\n The header will be re-written in place without the parameter or its\n value. All values will be quoted as necessary unless requote is\n False. Optional header specifies an alternative to the Content-Type\n header.\n ",
"language": "en",
"n_whitespaces": 78,
"n_words": 42,
"vocab_size": 33
} | https://github.com/XX-net/XX-Net.git |
|
3 | set_positions | def set_positions(self, posA, posB):
if posA is not None:
self._posA_posB[0] = posA
if posB is not None:
self._posA_posB[1] = posB
self.stale = True
| 03a0b5ea238014ba87f74ef766928287726aa00a | 10 | patches.py | 67 | Doc: Fix grammar and spelling | 24,044 | 0 | 73 | 43 | 15 | 110,304 | 23 | matplotlib | 6 | lib/matplotlib/patches.py | Python | 6 | {
"docstring": "\n Set the start and end positions of the connecting path.\n\n Parameters\n ----------\n posA, posB : None, tuple\n (x, y) coordinates of arrow tail and arrow head respectively. If\n `None` use current value.\n ",
"language": "en",
"n_whitespaces": 90,
"n_words": 32,
"vocab_size": 28
} | https://github.com/matplotlib/matplotlib.git |
|
6 | send | def send(self, msg, timeout=None, callback=None, raw=False, future=None, tries=3):
message_id = self._message_id()
header = {"mid": message_id}
if future is None:
future = salt.ext.tornado.concurrent.Future()
future.tries = tries
future.attempts = 0
future.timeout = timeout
if callback is not None:
| 43277294a3454e5dcd9079e005f747bf880801f6 | 13 | tcp.py | 127 | Test fix | 54,046 | 0 | 115 | 191 | 27 | 215,599 | 36 | salt | 17 | salt/transport/tcp.py | Python | 25 | {
"docstring": "\n Send given message, and return a future\n ",
"language": "en",
"n_whitespaces": 22,
"n_words": 7,
"vocab_size": 7
} | https://github.com/saltstack/salt.git |
|
18 | histogramdd | def histogramdd(sample, bins=10, range=None, density=None, weights=None):
try:
# Sample is an ND-array.
N, D = sample.shape
except (AttributeError, ValueError):
# Sample is a sequence of 1D arrays.
sample = np.atleast_2d(sample).T
N, D = sample.shape
nbin = np.empty(D, int)
edges = D*[None]
dedges = D*[None]
if weights is not None:
weights = np.asarray(weights)
try:
M = len(bins)
if M != D:
raise ValueError(
'The dimension of bins must be equal to the dimension of the '
' sample x.')
except TypeError:
# bins is an integer
bins = D*[bins]
# normalize the range argument
if range is None:
range = (None,) * D
elif len(range) != D:
raise ValueError('range argument must have one entry per dimension')
# Create edge arrays
for i in _range(D):
if np.ndim(bins[i]) == 0:
if bins[i] < 1:
raise ValueError(
'`bins[{}]` must be positive, when an integer'.format(i))
smin, smax = _get_outer_edges(sample[:,i], range[i])
try:
n = operator.index(bins[i])
except TypeError as e:
raise TypeError(
"`bins[{}]` must be an integer, when a scalar".format(i)
) from e
edges[i] = np.linspace(smin, smax, n + 1)
elif np.ndim(bins[i]) == 1:
edges[i] = np.asarray(bins[i])
if np.any(edges[i][:-1] > edges[i][1:]):
raise ValueError(
'`bins[{}]` must be monotonically increasing, when an array'
.format(i))
else:
raise ValueError(
'`bins[{}]` must be a scalar or 1d array'.format(i))
nbin[i] = len(edges[i]) + 1 # includes an outlier on each end
dedges[i] = np.diff(edges[i])
# Compute the bin number each sample falls into.
Ncount = tuple(
# avoid np.digitize to work around gh-11022
np.searchsorted(edges[i], sample[:, i], side='right')
for i in _range(D)
)
# Using digitize, values that fall on an edge are put in the right bin.
# For the rightmost bin, we want values equal to the right edge to be
# counted in the last bin, and not as an outlier.
for i in _range(D):
# Find which points are on the rightmost edge.
on_edge = (sample[:, i] == edges[i][-1])
# Shift these points one bin to the left.
Ncount[i][on_edge] -= 1
# Compute the sample indices in the flattened histogram matrix.
# This raises an error if the array is too large.
xy = np.ravel_multi_index(Ncount, nbin)
# Compute the number of repetitions in xy and assign it to the
# flattened histmat.
hist = np.bincount(xy, weights, minlength=nbin.prod())
# Shape into a proper matrix
hist = hist.reshape(nbin)
# This preserves the (bad) behavior observed in gh-7845, for now.
hist = hist.astype(float, casting='safe')
# Remove outliers (indices 0 and -1 for each dimension).
core = D*(slice(1, -1),)
hist = hist[core]
if density:
# calculate the probability density function
s = hist.sum()
for i in _range(D):
shape = np.ones(D, int)
shape[i] = nbin[i] - 2
hist = hist / dedges[i].reshape(shape)
hist /= s
if (hist.shape != nbin - 2).any():
raise RuntimeError(
"Internal Shape Error")
return hist, edges
| 2215054472616df563faa4613734426c790d4217 | 17 | histograms.py | 914 | DEP: Remove `normed=` keyword argument from histogroms
The normed keyword argument has been deprecated for a long time.
This removes it, replacing its position with the new density
argument. | 38,643 | 0 | 1,104 | 570 | 244 | 160,494 | 454 | numpy | 58 | numpy/lib/histograms.py | Python | 71 | {
"docstring": "\n Compute the multidimensional histogram of some data.\n\n Parameters\n ----------\n sample : (N, D) array, or (D, N) array_like\n The data to be histogrammed.\n\n Note the unusual interpretation of sample when an array_like:\n\n * When an array, each row is a coordinate in a D-dimensional space -\n such as ``histogramdd(np.array([p1, p2, p3]))``.\n * When an array_like, each element is the list of values for single\n coordinate - such as ``histogramdd((X, Y, Z))``.\n\n The first form should be preferred.\n\n bins : sequence or int, optional\n The bin specification:\n\n * A sequence of arrays describing the monotonically increasing bin\n edges along each dimension.\n * The number of bins for each dimension (nx, ny, ... =bins)\n * The number of bins for all dimensions (nx=ny=...=bins).\n\n range : sequence, optional\n A sequence of length D, each an optional (lower, upper) tuple giving\n the outer bin edges to be used if the edges are not given explicitly in\n `bins`.\n An entry of None in the sequence results in the minimum and maximum\n values being used for the corresponding dimension.\n The default, None, is equivalent to passing a tuple of D None values.\n density : bool, optional\n If False, the default, returns the number of samples in each bin.\n If True, returns the probability *density* function at the bin,\n ``bin_count / sample_count / bin_volume``.\n weights : (N,) array_like, optional\n An array of values `w_i` weighing each sample `(x_i, y_i, z_i, ...)`.\n Weights are normalized to 1 if density is True. If density is False,\n the values of the returned histogram are equal to the sum of the\n weights belonging to the samples falling into each bin.\n\n Returns\n -------\n H : ndarray\n The multidimensional histogram of sample x. See density and weights\n for the different possible semantics.\n edges : list\n A list of D arrays describing the bin edges for each dimension.\n\n See Also\n --------\n histogram: 1-D histogram\n histogram2d: 2-D histogram\n\n Examples\n --------\n >>> r = np.random.randn(100,3)\n >>> H, edges = np.histogramdd(r, bins = (5, 8, 4))\n >>> H.shape, edges[0].size, edges[1].size, edges[2].size\n ((5, 8, 4), 6, 9, 5)\n\n ",
"language": "en",
"n_whitespaces": 612,
"n_words": 340,
"vocab_size": 182
} | https://github.com/numpy/numpy.git |
|
5 | _check_list_display_links | def _check_list_display_links(self, obj):
from django.contrib.admin.options import ModelAdmin
if obj.list_display_links is None:
return []
elif not isinstance(obj.list_display_links, (list, tuple)):
return must_be(
"a list, a tuple, or None",
option="list_display_links",
obj=obj,
id="admin.E110",
)
# Check only if ModelAdmin.get_list_display() isn't overridden.
elif obj.get_list_display.__func__ is ModelAdmin.get_list_display:
return list(
chain.from_iterable(
self._check_list_display_links_item(
obj, field_name, "list_display_links[%d]" % index
)
for index, field_name in enumerate(obj.list_display_links)
)
)
return []
| 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | 16 | checks.py | 168 | Refs #33476 -- Reformatted code with Black. | 50,325 | 0 | 334 | 107 | 50 | 203,351 | 60 | django | 23 | django/contrib/admin/checks.py | Python | 21 | {
"docstring": "Check that list_display_links is a unique subset of list_display.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | https://github.com/django/django.git |
|
3 | handle_trial_end | def handle_trial_end(self, data):
hyper_params = nni.load(data['hyper_params'])
if self.is_created_in_previous_exp(hyper_params['parameter_id']):
# The end of the recovered trial is ignored
return
logger.debug('Tuner handle trial end, result is %s', data)
self._handle_trial_end(hyper_params['parameter_id'])
if data['trial_job_id'] in self.job_id_para_id_map:
del self.job_id_para_id_map[data['trial_job_id']]
| bcc640c4e5e687a03fe21503692dad96e0b97fa7 | 10 | bohb_advisor.py | 119 | [nas] fix issue introduced by the trial recovery feature (#5109) | 24,917 | 0 | 108 | 68 | 30 | 113,472 | 33 | nni | 11 | nni/algorithms/hpo/bohb_advisor/bohb_advisor.py | Python | 8 | {
"docstring": "receive the information of trial end and generate next configuaration.\n\n Parameters\n ----------\n data: dict()\n it has three keys: trial_job_id, event, hyper_params\n trial_job_id: the id generated by training service\n event: the job's state\n hyper_params: the hyperparameters (a string) generated and returned by tuner\n ",
"language": "en",
"n_whitespaces": 114,
"n_words": 42,
"vocab_size": 36
} | https://github.com/microsoft/nni.git |
|
26 | handle_merge | def handle_merge(self, loader, conflicts):
if self.interactive:
questioner = InteractiveMigrationQuestioner(prompt_output=self.stdout)
else:
questioner = MigrationQuestioner(defaults={'ask_merge': True})
for app_label, migration_names in conflicts.items():
# Grab out the migrations in question, and work out their
# common ancestor.
merge_migrations = []
for migration_name in migration_names:
migration = loader.get_migration(app_label, migration_name)
migration.ancestry = [
mig for mig in loader.graph.forwards_plan((app_label, migration_name))
if mig[0] == migration.app_label
]
merge_migrations.append(migration)
| 0ab58c120939093fea90822f376e1866fc714d1f | 15 | makemigrations.py | 169 | Refs #29026 -- Allowed customizing InteractiveMigrationQuestioner's prompt destination.
Previously, the questioner did not obey the value of stdout provided
to the command. | 50,163 | 0 | 251 | 520 | 45 | 202,901 | 59 | django | 23 | django/core/management/commands/makemigrations.py | Python | 66 | {
"docstring": "\n Handles merging together conflicted migrations interactively,\n if it's safe; otherwise, advises on how to fix it.\n ",
"language": "en",
"n_whitespaces": 38,
"n_words": 16,
"vocab_size": 16
} | https://github.com/django/django.git |
|
11 | line_search | def line_search(self, X, y, sample_weight):
# line search parameters
beta, sigma = 0.5, 0.00048828125 # 1/2, 1/2**11
eps = 16 * np.finfo(self.loss_value.dtype).eps
t = 1 # step size
# gradient_times_newton = self.gradient @ self.coef_newton
# was computed in inner_solve.
armijo_term = sigma * self.gradient_times_newton
_, _, raw_prediction_newton = self.linear_loss.weight_intercept_raw(
self.coef_newton, X
)
self.coef_old = self.coef
self.loss_value_old = self.loss_value
self.gradient_old = self.gradient
# np.sum(np.abs(self.gradient_old))
sum_abs_grad_old = -1
is_verbose = self.verbose >= 2
if is_verbose:
print(" Backtracking Line Search")
print(f" eps=10 * finfo.eps={eps}")
for i in range(21): # until and including t = beta**20 ~ 1e-6
self.coef = self.coef_old + t * self.coef_newton
raw = self.raw_prediction + t * raw_prediction_newton
self.loss_value, self.gradient = self.linear_loss.loss_gradient(
coef=self.coef,
X=X,
y=y,
sample_weight=sample_weight,
l2_reg_strength=self.l2_reg_strength,
n_threads=self.n_threads,
raw_prediction=raw,
)
# Note: If coef_newton is too large, loss_gradient may produce inf values,
# potentially accompanied by a RuntimeWarning.
# This case will be captured by the Armijo condition.
# 1. Check Armijo / sufficient decrease condition.
# The smaller (more negative) the better.
loss_improvement = self.loss_value - self.loss_value_old
check = loss_improvement <= t * armijo_term
if is_verbose:
print(
f" line search iteration={i+1}, step size={t}\n"
f" check loss improvement <= armijo term: {loss_improvement} "
f"<= {t * armijo_term} {check}"
)
if check:
break
# 2. Deal with relative loss differences around machine precision.
tiny_loss = np.abs(self.loss_value_old * eps)
check = np.abs(loss_improvement) <= tiny_loss
if is_verbose:
print(
" check loss |improvement| <= eps * |loss_old|:"
f" {np.abs(loss_improvement)} <= {tiny_loss} {check}"
)
if check:
if sum_abs_grad_old < 0:
sum_abs_grad_old = scipy.linalg.norm(self.gradient_old, ord=1)
# 2.1 Check sum of absolute gradients as alternative condition.
sum_abs_grad = scipy.linalg.norm(self.gradient, ord=1)
check = sum_abs_grad < sum_abs_grad_old
if is_verbose:
print(
" check sum(|gradient|) < sum(|gradient_old|): "
f"{sum_abs_grad} < {sum_abs_grad_old} {check}"
)
if check:
break
t *= beta
else:
warnings.warn(
f"Line search of Newton solver {self.__class__.__name__} at iteration "
f"#{self.iteration} did no converge after 21 line search refinement "
"iterations. It will now resort to lbfgs instead.",
ConvergenceWarning,
)
if self.verbose:
print(" Line search did not converge and resorts to lbfgs instead.")
self.use_fallback_lbfgs_solve = True
return
self.raw_prediction = raw
| ff9344f3d8d11d38fa3a2497199113e5bac9537c | 16 | _newton_solver.py | 645 | FEA add (single) Cholesky Newton solver to GLMs (#24637)
* FEA add NewtonSolver, CholeskyNewtonSolver and QRCholeskyNewtonSolver
* ENH better singular hessian special solve
* CLN fix some typos found by reviewer
* TST assert ConvergenceWarning is raised
* MNT add BaseCholeskyNewtonSolver
* WIP colinear design in GLMs
* FIX _solve_singular
* FIX false unpacking in
* TST add tests for unpenalized GLMs
* TST fix solutions of glm_dataset
* ENH add SVDFallbackSolver
* CLN remove SVDFallbackSolver
* ENH use gradient step for singular hessians
* ENH print iteration number in warnings
* TST improve test_linalg_warning_with_newton_solver
* CLN LinAlgWarning fron scipy.linalg
* ENH more robust hessian
* ENH increase maxls for lbfgs to make it more robust
* ENH add hessian_warning for too many negative hessian values
* CLN some warning messages
* ENH add lbfgs_step
* ENH use lbfgs_step for hessian_warning
* TST make them pass
* TST tweek rtol for lbfgs
* TST add rigoros test for GLMs
* TST improve test_warm_start
* ENH improve lbfgs options for better convergence
* CLN fix test_warm_start
* TST fix assert singular values in datasets
* CLN address most review comments
* ENH enable more vebosity levels for lbfgs
* DOC add whatsnew
* CLN remove xfail and clean a bit
* CLN docstring about minimum norm
* More informative repr for the glm_dataset fixture cases
* Forgot to run black
* CLN remove unnecessary filterwarnings
* CLN address review comments
* Trigger [all random seeds] on the following tests:
test_glm_regression
test_glm_regression_hstacked_X
test_glm_regression_vstacked_X
test_glm_regression_unpenalized
test_glm_regression_unpenalized_hstacked_X
test_glm_regression_unpenalized_vstacked_X
test_warm_start
* CLN add comment for lbfgs ftol=64 * machine precision
* CLN XXX code comment
* Trigger [all random seeds] on the following tests:
test_glm_regression
test_glm_regression_hstacked_X
test_glm_regression_vstacked_X
test_glm_regression_unpenalized
test_glm_regression_unpenalized_hstacked_X
test_glm_regression_unpenalized_vstacked_X
test_warm_start
* CLN link issue and remove code snippet in comment
* Trigger [all random seeds] on the following tests:
test_glm_regression
test_glm_regression_hstacked_X
test_glm_regression_vstacked_X
test_glm_regression_unpenalized
test_glm_regression_unpenalized_hstacked_X
test_glm_regression_unpenalized_vstacked_X
test_warm_start
* CLN add catch_warnings
* Trigger [all random seeds] on the following tests:
test_glm_regression
test_glm_regression_hstacked_X
test_glm_regression_vstacked_X
test_glm_regression_unpenalized
test_glm_regression_unpenalized_hstacked_X
test_glm_regression_unpenalized_vstacked_X
test_warm_start
* Trigger [all random seeds] on the following tests:
test_glm_regression
test_glm_regression_hstacked_X
test_glm_regression_vstacked_X
test_glm_regression_unpenalized
test_glm_regression_unpenalized_hstacked_X
test_glm_regression_unpenalized_vstacked_X
test_warm_start
* [all random seeds]
test_glm_regression
test_glm_regression_hstacked_X
test_glm_regression_vstacked_X
test_glm_regression_unpenalized
test_glm_regression_unpenalized_hstacked_X
test_glm_regression_unpenalized_vstacked_X
test_warm_start
* Trigger with -Werror [all random seeds]
test_glm_regression
test_glm_regression_hstacked_X
test_glm_regression_vstacked_X
test_glm_regression_unpenalized
test_glm_regression_unpenalized_hstacked_X
test_glm_regression_unpenalized_vstacked_X
test_warm_start
* ENH increase maxls to 50
* [all random seeds]
test_glm_regression
test_glm_regression_hstacked_X
test_glm_regression_vstacked_X
test_glm_regression_unpenalized
test_glm_regression_unpenalized_hstacked_X
test_glm_regression_unpenalized_vstacked_X
test_warm_start
* Revert "Trigger with -Werror [all random seeds]"
This reverts commit 99f4cf99ca41b4ad2bdad537ad60f936970e3a88.
* TST add catch_warnings to filterwarnings
* TST adapt tests for newton solvers
* CLN cleaner gradient step with gradient_times_newton
* DOC add whatsnew
* ENH always use lbfgs as fallback
* TST adapt rtol
* TST fix test_linalg_warning_with_newton_solver
* CLN address some review comments
* Improve tests related to convergence warning on collinear data
* overfit -> fit
* Typo in comment
* Apply suggestions from code review
* ENH fallback_lbfgs_solve
- Do not use lbfgs steps, fall back complete to lbfgs
* ENH adapt rtol
* Improve test_linalg_warning_with_newton_solver
* Better comments
* Fixed Hessian casing and improved warning messages
* [all random seeds]
test_linalg_warning_with_newton_solver
* Ignore ConvergenceWarnings for now if convergence is good
* CLN remove counting of warnings
* ENH fall back to lbfgs if line search did not converge
* DOC better comment on performance bottleneck
* Update GLM related examples to use the new solver
* CLN address reviewer comments
* EXA improve some wordings
* CLN do not pop "solver in parameter constraints
* CLN fix typos
* DOC fix docstring
* CLN remove solver newton-qr-cholesky
* DOC update PR number in whatsnew
* CLN address review comments
* CLN remove unnecessary catch_warnings
* CLN address some review comments
* DOC more precise whatsnew
* CLN use init_zero_coef
* CLN use and test init_zero_coef
* CLN address some review comments
* CLN mark NewtonSolver as private by leading underscore
* CLN exact comments for inner_solve
* TST add test_newton_solver_verbosity
* TST extend test_newton_solver_verbosity
* TST logic in test_glm_regression_unpenalized
* TST use count_nonzero
* CLN remove super rare line search checks
* MNT move Newton solver to new file _newton_solver.py
Co-authored-by: Olivier Grisel <[email protected]>
Co-authored-by: Julien Jerphanion <[email protected]> | 76,792 | 0 | 1,364 | 350 | 201 | 261,382 | 339 | scikit-learn | 52 | sklearn/linear_model/_glm/_newton_solver.py | Python | 70 | {
"docstring": "Backtracking line search.\n\n Sets:\n - self.coef_old\n - self.coef\n - self.loss_value_old\n - self.loss_value\n - self.gradient_old\n - self.gradient\n - self.raw_prediction\n ",
"language": "en",
"n_whitespaces": 109,
"n_words": 18,
"vocab_size": 12
} | https://github.com/scikit-learn/scikit-learn.git |
|
3 | extrema_bounding | def extrema_bounding(G, compute="diameter"):
import warnings
msg = "extrema_bounding is deprecated and will be removed in networkx 3.0\n"
# NOTE: _extrema_bounding does input checking, so it is skipped here
if compute in {"diameter", "radius", "periphery", "center"}:
msg += f"Use nx.{compute}(G, usebounds=True) instead."
if compute == "eccentricities":
msg += f"Use nx.eccentricity(G) instead."
warnings.warn(msg, DeprecationWarning, stacklevel=2)
return _extrema_bounding(G, compute=compute)
| 2ef5c096fb870638fd565c62c84999364c21beaf | 10 | distance_measures.py | 116 | Deprecate extrema bounding (#5422)
* Add public wrapper and convert impl to private.
* Add deprecation warning to public fn.
* Add test for deprecation warning.
* Add deprecation note.
* Add release note. | 41,903 | 0 | 94 | 62 | 47 | 176,440 | 56 | networkx | 9 | networkx/algorithms/distance_measures.py | Python | 9 | {
"docstring": "Compute requested extreme distance metric of undirected graph G\n\n .. deprecated:: 2.8\n\n extrema_bounding is deprecated and will be removed in NetworkX 3.0.\n Use the corresponding distance measure with the `usebounds=True` option\n instead.\n\n Computation is based on smart lower and upper bounds, and in practice\n linear in the number of nodes, rather than quadratic (except for some\n border cases such as complete graphs or circle shaped graphs).\n\n Parameters\n ----------\n G : NetworkX graph\n An undirected graph\n\n compute : string denoting the requesting metric\n \"diameter\" for the maximal eccentricity value,\n \"radius\" for the minimal eccentricity value,\n \"periphery\" for the set of nodes with eccentricity equal to the diameter,\n \"center\" for the set of nodes with eccentricity equal to the radius,\n \"eccentricities\" for the maximum distance from each node to all other nodes in G\n\n Returns\n -------\n value : value of the requested metric\n int for \"diameter\" and \"radius\" or\n list of nodes for \"center\" and \"periphery\" or\n dictionary of eccentricity values keyed by node for \"eccentricities\"\n\n Raises\n ------\n NetworkXError\n If the graph consists of multiple components\n ValueError\n If `compute` is not one of \"diameter\", \"radius\", \"periphery\", \"center\", or \"eccentricities\".\n Notes\n -----\n This algorithm was proposed in the following papers:\n\n F.W. Takes and W.A. Kosters, Determining the Diameter of Small World\n Networks, in Proceedings of the 20th ACM International Conference on\n Information and Knowledge Management (CIKM 2011), pp. 1191-1196, 2011.\n doi: https://doi.org/10.1145/2063576.2063748\n\n F.W. Takes and W.A. Kosters, Computing the Eccentricity Distribution of\n Large Graphs, Algorithms 6(1): 100-118, 2013.\n doi: https://doi.org/10.3390/a6010100\n\n M. Borassi, P. Crescenzi, M. Habib, W.A. Kosters, A. Marino and F.W. Takes,\n Fast Graph Diameter and Radius BFS-Based Computation in (Weakly Connected)\n Real-World Graphs, Theoretical Computer Science 586: 59-80, 2015.\n doi: https://doi.org/10.1016/j.tcs.2015.02.033\n ",
"language": "en",
"n_whitespaces": 456,
"n_words": 280,
"vocab_size": 175
} | https://github.com/networkx/networkx.git |
|
8 | _GetTextInside | def _GetTextInside(text, start_pattern):
r
# TODO(sugawarayu): Audit cpplint.py to see what places could be profitably
# rewritten to use _GetTextInside (and use inferior regexp matching today).
# Give opening punctuations to get the matching close-punctuations.
matching_punctuation = {'(': ')', '{': '}', '[': ']'}
closing_punctuation = set(matching_punctuation.itervalues())
# Find the position to start extracting text.
match = re.search(start_pattern, text, re.M)
if not match: # start_pattern not found in text.
return None
start_position = match.end(0)
assert start_position > 0, (
'start_pattern must ends with an opening punctuation.')
assert text[start_position - 1] in matching_punctuation, (
'start_pattern must ends with an opening punctuation.')
# Stack of closing punctuations we expect to have in text after position.
punctuation_stack = [matching_punctuation[text[start_position - 1]]]
position = start_position
while punctuation_stack and position < len(text):
if text[position] == punctuation_stack[-1]:
punctuation_stack.pop()
elif text[position] in closing_punctuation:
# A closing punctuation without matching opening punctuations.
return None
elif text[position] in matching_punctuation:
punctuation_stack.append(matching_punctuation[text[position]])
position += 1
if punctuation_stack:
# Opening punctuations left without matching close-punctuations.
return None
# punctuations match.
return text[start_position:position - 1]
# Patterns for matching call-by-reference parameters.
#
# Supports nested templates up to 2 levels deep using this messy pattern:
# < (?: < (?: < [^<>]*
# >
# | [^<>] )*
# >
# | [^<>] )*
# >
_RE_PATTERN_IDENT = r'[_a-zA-Z]\w*' # =~ [[:alpha:]][[:alnum:]]*
_RE_PATTERN_TYPE = (
r'(?:const\s+)?(?:typename\s+|class\s+|struct\s+|union\s+|enum\s+)?'
r'(?:\w|'
r'\s*<(?:<(?:<[^<>]*>|[^<>])*>|[^<>])*>|'
r'::)+')
# A call-by-reference parameter ends with '& identifier'.
_RE_PATTERN_REF_PARAM = re.compile(
r'(' + _RE_PATTERN_TYPE + r'(?:\s*(?:\bconst\b|[*]))*\s*'
r'&\s*' + _RE_PATTERN_IDENT + r')\s*(?:=[^,()]+)?[,)]')
# A call-by-const-reference parameter either ends with 'const& identifier'
# or looks like 'const type& identifier' when 'type' is atomic.
_RE_PATTERN_CONST_REF_PARAM = (
r'(?:.*\s*\bconst\s*&\s*' + _RE_PATTERN_IDENT +
r'|const\s+' + _RE_PATTERN_TYPE + r'\s*&\s*' + _RE_PATTERN_IDENT + r')')
| cc4d0564756ca067516f71718a3d135996525909 | 14 | cpp_lint.py | 397 | Balanced joint maximum mean discrepancy for deep transfer learning | 12,101 | 0 | 405 | 173 | 164 | 60,372 | 282 | transferlearning | 23 | code/deep/BJMMD/caffe/scripts/cpp_lint.py | Python | 43 | {
"docstring": "Retrieves all the text between matching open and close parentheses.\n\n Given a string of lines and a regular expression string, retrieve all the text\n following the expression and between opening punctuation symbols like\n (, [, or {, and the matching close-punctuation symbol. This properly nested\n occurrences of the punctuations, so for the text like\n printf(a(), b(c()));\n a call to _GetTextInside(text, r'printf\\(') will return 'a(), b(c())'.\n start_pattern must match string having an open punctuation symbol at the end.\n\n Args:\n text: The lines to extract text. Its comments and strings must be elided.\n It can be single line and can span multiple lines.\n start_pattern: The regexp string indicating where to start extracting\n the text.\n Returns:\n The extracted text.\n None if either the opening string or ending punctuation could not be found.\n ",
"language": "en",
"n_whitespaces": 181,
"n_words": 129,
"vocab_size": 87
} | https://github.com/jindongwang/transferlearning.git |
|
2 | rsa_key_size | def rsa_key_size(self) -> Optional[int]:
key = self._private_key()
if isinstance(key, RSAPrivateKey):
return key.key_size
return None
| 212c2ba990758cb9acd2b200e55302534988089a | 8 | storage.py | 53 | error out when --reuse-key conflicts with other flags (#9262)
* error out when --reuse-key conflicts with other flags
* add unit test
* add integration tests
* lint | 45,648 | 0 | 53 | 32 | 13 | 186,897 | 14 | certbot | 9 | certbot/certbot/_internal/storage.py | Python | 9 | {
"docstring": "\n :returns: If the private key is an RSA key, its size.\n :rtype: int\n ",
"language": "en",
"n_whitespaces": 35,
"n_words": 13,
"vocab_size": 13
} | https://github.com/certbot/certbot.git |
|
1 | test_get_user_ip_and_agents_combined_data | def test_get_user_ip_and_agents_combined_data(self) -> None:
self.reactor.advance(12345678)
user_id = "@user:id"
user = UserID.from_string(user_id)
# Insert user IPs
self.get_success(
self.store.insert_client_ip(
user_id, "access_token", "ip_1", "user_agent_1", "MY_DEVICE_1"
)
)
self.get_success(
self.store.insert_client_ip(
user_id, "access_token", "ip_2", "user_agent_2", "MY_DEVICE_2"
)
)
# Trigger the storage loop and wait for the rate limiting period to be over
self.reactor.advance(10 + LAST_SEEN_GRANULARITY / 1000)
# Update the user agent for the second device, without running the storage loop
self.get_success(
self.store.insert_client_ip(
user_id, "access_token", "ip_2", "user_agent_3", "MY_DEVICE_2"
)
)
# Check that the new IP and user agent has not been stored yet
db_result = self.get_success(
self.store.db_pool.simple_select_list(
table="user_ips",
keyvalues={},
retcols=("access_token", "ip", "user_agent", "last_seen"),
),
)
self.assertEqual(
db_result,
[
{
"access_token": "access_token",
"ip": "ip_1",
"user_agent": "user_agent_1",
"last_seen": 12345678000,
},
{
"access_token": "access_token",
"ip": "ip_2",
"user_agent": "user_agent_2",
"last_seen": 12345678000,
},
],
)
# Check that data from the database and memory are combined together correctly
self.assertCountEqual(
self.get_success(self.store.get_user_ip_and_agents(user)),
[
{
"access_token": "access_token",
"ip": "ip_1",
"user_agent": "user_agent_1",
"last_seen": 12345678000,
},
{
"access_token": "access_token",
"ip": "ip_2",
"user_agent": "user_agent_3",
"last_seen": 12345688000 + LAST_SEEN_GRANULARITY,
},
],
)
| 3ac412b4e2f8c5ba11dc962b8a9d871c1efdce9b | 13 | test_client_ips.py | 451 | Require types in tests.storage. (#14646)
Adds missing type hints to `tests.storage` package
and does not allow untyped definitions. | 73,278 | 0 | 989 | 250 | 90 | 250,114 | 167 | synapse | 21 | tests/storage/test_client_ips.py | Python | 64 | {
"docstring": "Test that `get_user_ip_and_agents` combines persisted and unpersisted data\n together correctly\n ",
"language": "en",
"n_whitespaces": 24,
"n_words": 10,
"vocab_size": 10
} | https://github.com/matrix-org/synapse.git |
|
1 | _show_diff_helper | def _show_diff_helper(self, frame_data, expected_frame_data):
import matplotlib.gridspec as gridspec # type: ignore
import matplotlib.pyplot as plt
gs = gridspec.GridSpec(2, 2)
fig = plt.figure()
fig.suptitle(f"Test for {str(self.scene).replace('Test', '')}", fontsize=16)
ax = fig.add_subplot(gs[0, 0])
ax.imshow(frame_data)
ax.set_title("Generated :")
ax = fig.add_subplot(gs[0, 1])
ax.imshow(expected_frame_data)
ax.set_title("Expected :")
ax = fig.add_subplot(gs[1, :])
diff_im = expected_frame_data.copy()
diff_im = np.where(
frame_data != np.array([0, 0, 0, 255]),
np.array([0, 255, 0, 255], dtype="uint8"),
np.array([0, 0, 0, 255], dtype="uint8"),
) # Set any non-black pixels to green
np.putmask(
diff_im,
expected_frame_data != frame_data,
np.array([255, 0, 0, 255], dtype="uint8"),
) # Set any different pixels to red
ax.imshow(diff_im, interpolation="nearest")
ax.set_title("Differences summary : (green = same, red = different)")
plt.show()
plt.savefig(f"{self.scene}.png")
| c4217731e08470d5a56cf02cf76cae01c03fb78f | 14 | GraphicalUnitTester.py | 407 | Added MyPy Support (#1972)
* MyPy Support
* MyPy Hook
* Removing MyPy Hook
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Delete __init__.pyi
* Delete color.pyi
* Update .mypy.ini
Co-authored-by: Christopher Besch <[email protected]>
* changes
* quick fix
* MyPy Hook
* MyPy Hook
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Christopher Besch <[email protected]> | 46,030 | 0 | 329 | 240 | 69 | 189,389 | 106 | manim | 31 | tests/utils/GraphicalUnitTester.py | Python | 28 | {
"docstring": "Will visually display with matplotlib differences between frame generated and the one expected.",
"language": "en",
"n_whitespaces": 12,
"n_words": 13,
"vocab_size": 13
} | https://github.com/ManimCommunity/manim.git |
|
2 | test_multi_sync_same_node | def test_multi_sync_same_node(ray_start_2_cpus, temp_data_dirs, num_workers):
tmp_source, tmp_target = temp_data_dirs
assert_file(True, tmp_source, "level0.txt")
assert_file(True, tmp_source, "subdir/level1.txt")
node_ip = ray.util.get_node_ip_address()
futures = [
_sync_dir_on_same_node(
ip=node_ip,
source_path=tmp_source,
target_path=tmp_target,
return_futures=True,
)
for _ in range(num_workers)
]
ray.get(futures)
assert_file(True, tmp_target, "level0.txt")
assert_file(True, tmp_target, "subdir/level1.txt")
@pytest.mark.parametrize("num_workers", [1, 8]) | 6313ddc47cf9df4df8c8907997df559850a1b874 | @pytest.mark.parametrize("num_workers", [1, 8]) | 10 | test_util_file_transfer.py | 168 | [tune] Refactor Syncer / deprecate Sync client (#25655)
This PR includes / depends on #25709
The two concepts of Syncer and SyncClient are confusing, as is the current API for passing custom sync functions.
This PR refactors Tune's syncing behavior. The Sync client concept is hard deprecated. Instead, we offer a well defined Syncer API that can be extended to provide own syncing functionality. However, the default will be to use Ray AIRs file transfer utilities.
New API:
- Users can pass `syncer=CustomSyncer` which implements the `Syncer` API
- Otherwise our off-the-shelf syncing is used
- As before, syncing to cloud disables syncing to driver
Changes:
- Sync client is removed
- Syncer interface introduced
- _DefaultSyncer is a wrapper around the URI upload/download API from Ray AIR
- SyncerCallback only uses remote tasks to synchronize data
- Rsync syncing is fully depracated and removed
- Docker and kubernetes-specific syncing is fully deprecated and removed
- Testing is improved to use `file://` URIs instead of mock sync clients | 32,557 | 1 | 135 | 92 | 31 | 141,978 | 41 | ray | 23 | python/ray/tune/tests/test_util_file_transfer.py | Python | 17 | {
"docstring": "Check that multiple competing syncs to the same node+dir don't interfere",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | https://github.com/ray-project/ray.git |
5 | yarn_check | def yarn_check(file_list):
if file_list is None or os.environ.get("SKIP_YARN_CHECK"):
return False
if "package.json" in file_list and "yarn.lock" not in file_list:
sys.stdout.write(
"\033[33m"
+
+ "\033[0m"
+ "\n"
)
return True
return False
| 09e1a361f3590dcf345ba7b2a1e35b74cba97ccb | 13 | engine.py | 98 | fix(dx): Check for prettier verison in dependencies also (#33100) | 19,455 | 0 | 116 | 50 | 23 | 97,663 | 31 | sentry | 8 | src/sentry/lint/engine.py | Python | 16 | {
"docstring": "\n Checks if package.json was modified WITHOUT a corresponding change in the Yarn\n lockfile. This can happen if a user manually edited package.json without running Yarn.\n\n This is a user prompt right now because there ARE cases where you can touch package.json\n without a Yarn lockfile change, e.g. Jest config changes, license changes, etc.\n Warning: package.json modified without accompanying yarn.lock modifications.\n\nIf you updated a dependency/devDependencies in package.json, you must run `yarn install` to update the lockfile.\n\nTo skip this check, run `SKIP_YARN_CHECK=1 git commit [options]`",
"language": "en",
"n_whitespaces": 98,
"n_words": 85,
"vocab_size": 63
} | https://github.com/getsentry/sentry.git |
|
2 | _get_base_market_data_info | def _get_base_market_data_info(self) -> Union[Dict[str, Any], Any]:
market_dct = {}
market_data = self.coin.get("market_data", {})
for stat in [
"total_supply",
"max_supply",
"circulating_supply",
"price_change_percentage_24h",
"price_change_percentage_7d",
"price_change_percentage_30d",
]:
market_dct[stat] = market_data.get(stat)
prices = create_dictionary_with_prefixes(
["current_price"], market_data, DENOMINATION
)
market_dct.update(prices)
return market_dct
| 59d8b36bb0467a1a99513b10e8b8471afaa56fd6 | 10 | pycoingecko_model.py | 139 | [IMPROVE] Fix Docstring formatting/Fix missing, incomplete type hints (#3412)
* Fixes
* Update stocks_helper.py
* update git-actions set-output to new format
* Update stocks_helper.py
* Update terminal_helper.py
* removed LineAnnotateDrawer from qa_view
* lint
* few changes
* updates
* sdk auto gen modules done
* Update stocks_helper.py
* updates to changed imports, and remove first sdk_modules
* Update generate_sdk.py
* Update generate_sdk.py
* pylint
* revert stocks_helper
* Update generate_sdk.py
* Update sdk.py
* Update generate_sdk.py
* full auto generation, added sdk.py/controllers creation
* missed enable forecasting
* added running black in subprocess after sdk files generation completes
* removed deleted sdk_arg_logger
* comment out tests
* property doc fix
* clean up
* Update generate_sdk.py
* make trailmap classes useable for doc generation
* Update generate_sdk.py
* added lineon to trailmap class for linking to func in markdown
* changed lineon to dict
* added full_path to trailmap for linking in docs
* updated portfolio
* feat: initial files
* feat: added meta head
* feat: added funcdef
* added func_def to trailmap attributes for markdown in docs, added missing type hints to covid functions
* feat: added view and merged with jaun
* Update generate_sdk.py
* Update generate_sdk.py
* Update generate_sdk.py
* Update generate_sdk.py
* init
* fix returns
* fix: random stuff
* fix: random
* fixed encoding issue on windows
* fix: generate tabs
* update
* Update generate_sdk_markdown.py
* Create .pydocstyle.ini
* added type hint classes for views
* fixes
* alt, ba
* alt-economy
* Update finviz_compare_model.py
* fixs
* Update substack_model.py
* Update generate_sdk.py
* last of my section
* porfolio
* po
* Update optimizer_model.py
* fixing more things
* few more
* keys done
* update
* fixes
* Update generate_sdk_markdown.py
* Update generate_sdk_markdown.py
* mypy forecast fix
* Update generate_sdk_markdown.py
* Update generate_sdk_markdown.py
* Update generate_sdk_markdown.py
* fixes
* forecast fixes
* one more fix
* Update coinbase_model.py
* Update generate_sdk_markdown.py
Co-authored-by: Colin Delahunty <[email protected]>
Co-authored-by: James Maslek <[email protected]>
Co-authored-by: jose-donato <[email protected]>
Co-authored-by: andrewkenreich <[email protected]> | 85,888 | 0 | 188 | 84 | 33 | 286,572 | 37 | OpenBBTerminal | 15 | openbb_terminal/cryptocurrency/due_diligence/pycoingecko_model.py | Python | 24 | {
"docstring": "Helper method that fetches all the base market/price information about given coin. [Source: CoinGecko]\n\n Returns\n ----------\n Dict[str, Any]\n All market related information for given coin\n ",
"language": "en",
"n_whitespaces": 64,
"n_words": 25,
"vocab_size": 23
} | https://github.com/OpenBB-finance/OpenBBTerminal.git |
|
2 | test_dataset_shard_with_task_parallelization | def test_dataset_shard_with_task_parallelization(self):
config = {
"input": "dataset",
"input_config": {
"format": "json",
"paths": self.dset_path,
"parallelism": 10,
},
}
NUM_WORKERS = 4
_, shards = get_dataset_and_shards(config, num_workers=NUM_WORKERS)
assert len(shards) == NUM_WORKERS + 1
assert shards[0] is None
assert all(
isinstance(remote_shard, ray.data.Dataset) for remote_shard in shards[1:]
)
| 569fe0109629048d08e1d9e023f7769f10bd2244 | 11 | test_dataset_reader.py | 143 | [RLlib] improved unittests for dataset_reader and fixed bugs (#26458) | 27,737 | 0 | 196 | 86 | 38 | 124,997 | 44 | ray | 16 | rllib/offline/tests/test_dataset_reader.py | Python | 16 | {
"docstring": "Tests whether the dataset_shard function works correctly with parallelism\n for reading the dataset.",
"language": "en",
"n_whitespaces": 19,
"n_words": 13,
"vocab_size": 12
} | https://github.com/ray-project/ray.git |
|
1 | homogeneity_score | def homogeneity_score(labels_true, labels_pred):
return homogeneity_completeness_v_measure(labels_true, labels_pred)[0]
| 4253eace9893eb6aef36ca631e7978b6a8808fbc | 8 | _supervised.py | 29 | DOC Ensures that homogeneity_score passes numpydoc validation (#23006) | 75,819 | 0 | 12 | 18 | 6 | 259,555 | 6 | scikit-learn | 4 | sklearn/metrics/cluster/_supervised.py | Python | 2 | {
"docstring": "Homogeneity metric of a cluster labeling given a ground truth.\n\n A clustering result satisfies homogeneity if all of its clusters\n contain only data points which are members of a single class.\n\n This metric is independent of the absolute values of the labels:\n a permutation of the class or cluster label values won't change the\n score value in any way.\n\n This metric is not symmetric: switching ``label_true`` with ``label_pred``\n will return the :func:`completeness_score` which will be different in\n general.\n\n Read more in the :ref:`User Guide <homogeneity_completeness>`.\n\n Parameters\n ----------\n labels_true : int array, shape = [n_samples]\n Ground truth class labels to be used as a reference.\n\n labels_pred : array-like of shape (n_samples,)\n Cluster labels to evaluate.\n\n Returns\n -------\n homogeneity : float\n Score between 0.0 and 1.0. 1.0 stands for perfectly homogeneous labeling.\n\n See Also\n --------\n completeness_score : Completeness metric of cluster labeling.\n v_measure_score : V-Measure (NMI with arithmetic mean option).\n\n References\n ----------\n\n .. [1] `Andrew Rosenberg and Julia Hirschberg, 2007. V-Measure: A\n conditional entropy-based external cluster evaluation measure\n <https://aclweb.org/anthology/D/D07/D07-1043.pdf>`_\n\n Examples\n --------\n\n Perfect labelings are homogeneous::\n\n >>> from sklearn.metrics.cluster import homogeneity_score\n >>> homogeneity_score([0, 0, 1, 1], [1, 1, 0, 0])\n 1.0\n\n Non-perfect labelings that further split classes into more clusters can be\n perfectly homogeneous::\n\n >>> print(\"%.6f\" % homogeneity_score([0, 0, 1, 1], [0, 0, 1, 2]))\n 1.000000\n >>> print(\"%.6f\" % homogeneity_score([0, 0, 1, 1], [0, 1, 2, 3]))\n 1.000000\n\n Clusters that include samples from different classes do not make for an\n homogeneous labeling::\n\n >>> print(\"%.6f\" % homogeneity_score([0, 0, 1, 1], [0, 1, 0, 1]))\n 0.0...\n >>> print(\"%.6f\" % homogeneity_score([0, 0, 1, 1], [0, 0, 0, 0]))\n 0.0...\n ",
"language": "en",
"n_whitespaces": 443,
"n_words": 263,
"vocab_size": 162
} | https://github.com/scikit-learn/scikit-learn.git |
|
13 | config | def config(self, s):
from traitlets.config.loader import Config
# some IPython objects are Configurable, but do not yet have
# any configurable traits. Exclude them from the effects of
# this magic, as their presence is just noise:
configurables = sorted(set([ c for c in self.shell.configurables
if c.__class__.class_traits(config=True)
]), key=lambda x: x.__class__.__name__)
classnames = [ c.__class__.__name__ for c in configurables ]
line = s.strip()
if not line:
# print available configurable names
print("Available objects for config:")
for name in classnames:
print(" ", name)
return
elif line in classnames:
# `%config TerminalInteractiveShell` will print trait info for
# TerminalInteractiveShell
c = configurables[classnames.index(line)]
cls = c.__class__
help = cls.class_get_help(c)
# strip leading '--' from cl-args:
help = re.sub(re.compile(r'^--', re.MULTILINE), '', help)
print(help)
return
elif reg.match(line):
cls, attr = line.split('.')
return getattr(configurables[classnames.index(cls)],attr)
elif '=' not in line:
msg = "Invalid config statement: %r, "\
"should be `Class.trait = value`."
ll = line.lower()
for classname in classnames:
if ll == classname.lower():
msg = msg + '\nDid you mean %s (note the case)?' % classname
break
raise UsageError( msg % line)
# otherwise, assume we are setting configurables.
# leave quotes on args when splitting, because we want
# unquoted args to eval in user_ns
cfg = Config()
exec("cfg."+line, self.shell.user_ns, locals())
for configurable in configurables:
try:
configurable.update_config(cfg)
except Exception as e:
error(e)
| 93c8b4d5380d861bf77c590660b93e495bef893b | 16 | config.py | 463 | Update ipdoctest test | 52,525 | 0 | 775 | 276 | 148 | 208,792 | 216 | ipython | 48 | IPython/core/magics/config.py | Python | 38 | {
"docstring": "configure IPython\n\n %config Class[.trait=value]\n\n This magic exposes most of the IPython config system. Any\n Configurable class should be able to be configured with the simple\n line::\n\n %config Class.trait=value\n\n Where `value` will be resolved in the user's namespace, if it is an\n expression or variable name.\n\n Examples\n --------\n\n To see what classes are available for config, pass no arguments::\n\n In [1]: %config\n Available objects for config:\n AliasManager\n DisplayFormatter\n HistoryManager\n IPCompleter\n LoggingMagics\n MagicsManager\n OSMagics\n PrefilterManager\n ScriptMagics\n TerminalInteractiveShell\n\n To view what is configurable on a given class, just pass the class\n name::\n\n In [2]: %config IPCompleter\n IPCompleter(Completer) options\n ----------------------------\n IPCompleter.backslash_combining_completions=<Bool>\n Enable unicode completions, e.g. \\\\alpha<tab> . Includes completion of latex\n commands, unicode names, and expanding unicode characters back to latex\n commands.\n Current: True\n IPCompleter.debug=<Bool>\n Enable debug for the Completer. Mostly print extra information for\n experimental jedi integration.\n Current: False\n IPCompleter.disable_matchers=<list-item-1>...\n List of matchers to disable.\n Current: []\n IPCompleter.greedy=<Bool>\n Activate greedy completion\n PENDING DEPRECATION. this is now mostly taken care of with Jedi.\n This will enable completion on elements of lists, results of function calls, etc.,\n but can be unsafe because the code is actually evaluated on TAB.\n Current: False\n IPCompleter.jedi_compute_type_timeout=<Int>\n Experimental: restrict time (in milliseconds) during which Jedi can compute types.\n Set to 0 to stop computing types. Non-zero value lower than 100ms may hurt\n performance by preventing jedi to build its cache.\n Current: 400\n IPCompleter.limit_to__all__=<Bool>\n DEPRECATED as of version 5.0.\n Instruct the completer to use __all__ for the completion\n Specifically, when completing on ``object.<tab>``.\n When True: only those names in obj.__all__ will be included.\n When False [default]: the __all__ attribute is ignored\n Current: False\n IPCompleter.merge_completions=<Bool>\n Whether to merge completion results into a single list\n If False, only the completion results from the first non-empty\n completer will be returned.\n As of version 8.5.0, setting the value to ``False`` is an alias for:\n ``IPCompleter.suppress_competing_matchers = True.``.\n Current: True\n IPCompleter.omit__names=<Enum>\n Instruct the completer to omit private method names\n Specifically, when completing on ``object.<tab>``.\n When 2 [default]: all names that start with '_' will be excluded.\n When 1: all 'magic' names (``__foo__``) will be excluded.\n When 0: nothing will be excluded.\n Choices: any of [0, 1, 2]\n Current: 2\n IPCompleter.profile_completions=<Bool>\n If True, emit profiling data for completion subsystem using cProfile.\n Current: False\n IPCompleter.profiler_output_dir=<Unicode>\n Template for path at which to output profile data for completions.\n Current: '.completion_profiles'\n IPCompleter.suppress_competing_matchers=<Union>\n Whether to suppress completions from other `Matchers`_.\n When set to ``None`` (default) the matchers will attempt to auto-detect\n whether suppression of other matchers is desirable. For example, at the\n beginning of a line followed by `%` we expect a magic completion to be the\n only applicable option, and after ``my_dict['`` we usually expect a\n completion with an existing dictionary key.\n If you want to disable this heuristic and see completions from all matchers,\n set ``IPCompleter.suppress_competing_matchers = False``. To disable the\n heuristic for specific matchers provide a dictionary mapping:\n ``IPCompleter.suppress_competing_matchers = {'IPCompleter.dict_key_matcher':\n False}``.\n Set ``IPCompleter.suppress_competing_matchers = True`` to limit completions\n to the set of matchers with the highest priority; this is equivalent to\n ``IPCompleter.merge_completions`` and can be beneficial for\n performance, but will sometimes omit relevant candidates from matchers\n further down the priority list.\n Current: False\n IPCompleter.use_jedi=<Bool>\n Experimental: Use Jedi to generate autocompletions. Default to True if jedi\n is installed.\n Current: True\n\n but the real use is in setting values::\n\n In [3]: %config IPCompleter.greedy = True\n\n and these values are read from the user_ns if they are variables::\n\n In [4]: feeling_greedy=False\n\n In [5]: %config IPCompleter.greedy = feeling_greedy\n\n ",
"language": "en",
"n_whitespaces": 2076,
"n_words": 566,
"vocab_size": 324
} | https://github.com/ipython/ipython.git |
|
1 | test_ne_filters | def test_ne_filters(self, ds, documents):
ds.write_documents(documents)
result = ds.get_all_documents(filters={"year": {"$ne": "2020"}})
assert len(result) == 3
| 4dfddf0d1039e134b1ce5aac748de853e2516735 | 14 | test_weaviate.py | 72 | refactor: Refactor Weaviate tests (#3541)
* refactor tests
* fix job
* revert
* revert
* revert
* use latest weaviate
* fix abstract methods signatures
* pass class_name to all the CRUD methods
* finish moving all the tests
* bump weaviate version
* raise, don't pass | 75,197 | 0 | 42 | 41 | 14 | 258,198 | 14 | haystack | 9 | test/document_stores/test_weaviate.py | Python | 4 | {
"docstring": "\n Weaviate doesn't include documents if the field is missing,\n so we customize this test\n ",
"language": "en",
"n_whitespaces": 36,
"n_words": 14,
"vocab_size": 14
} | https://github.com/deepset-ai/haystack.git |
|
1 | previewoutput | def previewoutput(self) -> Tuple[Image.Image, ImageTk.PhotoImage]:
assert self._previewoutput is not None
return self._previewoutput
| dab823a3eb7a5257cb1e0818ee10ed234d3de97f | 7 | image.py | 44 | Typing - lib.gui.display_command | 21,319 | 0 | 33 | 28 | 11 | 101,941 | 12 | faceswap | 7 | lib/gui/utils/image.py | Python | 9 | {
"docstring": " Tuple: First item in the tuple is the extract or convert preview image\n (:class:`PIL.Image`), the second item is the image in a format that tkinter can display\n (:class:`PIL.ImageTK.PhotoImage`).\n\n The value of the property is ``None`` if no extract or convert task is running or there are\n no files available in the output folder. ",
"language": "en",
"n_whitespaces": 82,
"n_words": 53,
"vocab_size": 36
} | https://github.com/deepfakes/faceswap.git |
|
9 | fit | def fit(self, df):
# threshold - items below this number get set to zero in cooccurrence counts
df.createOrReplaceTempView(self.f("{prefix}df_train_input"))
if self.timedecay_formula:
# WARNING: previously we would take the last value in training dataframe and set it
# as a matrix U element
# for each user-item pair. Now with time decay, we compute a sum over ratings given
# by a user in the case
# when T=np.inf, so user gets a cumulative sum of ratings for a particular item and
# not the last rating.
# Time Decay
# does a group by on user item pairs and apply the formula for time decay there
# Time T parameter is in days and input time is in seconds,
# so we do dt/60/(T*24*60)=dt/(T*24*3600)
# the following is the query which we want to run
query = self.f(
)
# replace with timedecayed version
df = self.spark.sql(query)
else:
# since SQL is case-insensitive, this check needs to be performed similar
if self.header["col_timestamp"].lower() in [
s.name.lower() for s in df.schema
]:
# we need to de-duplicate items by using the latest item
query = self.f(
)
df = self.spark.sql(query)
df.createOrReplaceTempView(self.f("{prefix}df_train"))
log.info("sarplus.fit 1/2: compute item cooccurrences...")
# compute cooccurrence above minimum threshold
query = self.f(
)
item_cooccurrence = self.spark.sql(query)
item_cooccurrence.write.mode("overwrite").saveAsTable(
self.f("{prefix}item_cooccurrence")
)
# compute the diagonal used later for Jaccard and Lift
if self.similarity_type == SIM_LIFT or self.similarity_type == SIM_JACCARD:
item_marginal = self.spark.sql(
self.f(
"SELECT i1 i, value AS margin FROM {prefix}item_cooccurrence WHERE i1 = i2"
)
)
item_marginal.createOrReplaceTempView(self.f("{prefix}item_marginal"))
if self.similarity_type == SIM_COOCCUR:
self.item_similarity = item_cooccurrence
elif self.similarity_type == SIM_JACCARD:
query = self.f(
)
self.item_similarity = self.spark.sql(query)
elif self.similarity_type == SIM_LIFT:
query = self.f(
)
self.item_similarity = self.spark.sql(query)
else:
raise ValueError(
"Unknown similarity type: {0}".format(self.similarity_type)
)
# store upper triangular
log.info(
"sarplus.fit 2/2: compute similarity metric %s..." % self.similarity_type
)
self.item_similarity.write.mode("overwrite").saveAsTable(
self.f("{prefix}item_similarity_upper")
)
# expand upper triangular to full matrix
query = self.f(
)
self.item_similarity = self.spark.sql(query)
self.item_similarity.write.mode("overwrite").saveAsTable(
self.f("{prefix}item_similarity")
)
# free space
self.spark.sql(self.f("DROP TABLE {prefix}item_cooccurrence"))
self.spark.sql(self.f("DROP TABLE {prefix}item_similarity_upper"))
self.item_similarity = self.spark.table(self.f("{prefix}item_similarity"))
| 2b98f1045321475f6537986af134fb53f8320268 | 14 | SARPlus.py | 669 | Correct typos | 7,143 | 0 | 1,172 | 375 | 175 | 39,221 | 329 | recommenders | 29 | contrib/sarplus/python/pysarplus/SARPlus.py | Python | 109 | {
"docstring": "Main fit method for SAR.\n\n Expects the dataframes to have row_id, col_id columns which are indexes,\n i.e. contain the sequential integer index of the original alphanumeric user and item IDs.\n Dataframe also contains rating and timestamp as floats; timestamp is in seconds since Epoch by default.\n\n Arguments:\n df (pySpark.DataFrame): input dataframe which contains the index of users and items.\n \n SELECT\n {col_user}, {col_item}, \n SUM({col_rating} * EXP(-log(2) * (latest_timestamp - CAST({col_timestamp} AS long)) / ({time_decay_coefficient} * 3600 * 24))) as {col_rating}\n FROM {prefix}df_train_input,\n (SELECT CAST(MAX({col_timestamp}) AS long) latest_timestamp FROM {prefix}df_train_input)\n GROUP BY {col_user}, {col_item} \n CLUSTER BY {col_user} \n \n SELECT {col_user}, {col_item}, {col_rating}\n FROM\n (\n SELECT\n {col_user}, {col_item}, {col_rating}, \n ROW_NUMBER() OVER (PARTITION BY {col_user}, {col_item} ORDER BY {col_timestamp} DESC) latest\n FROM {prefix}df_train_input\n )\n WHERE latest = 1\n \n SELECT A.{col_item} i1, B.{col_item} i2, COUNT(*) value\n FROM {prefix}df_train A INNER JOIN {prefix}df_train B\n ON A.{col_user} = B.{col_user} AND A.{col_item} <= b.{col_item} \n GROUP BY A.{col_item}, B.{col_item}\n HAVING COUNT(*) >= {threshold}\n CLUSTER BY i1, i2\n \n SELECT i1, i2, value / (M1.margin + M2.margin - value) AS value\n FROM {prefix}item_cooccurrence A \n INNER JOIN {prefix}item_marginal M1 ON A.i1 = M1.i \n INNER JOIN {prefix}item_marginal M2 ON A.i2 = M2.i\n CLUSTER BY i1, i2\n \n SELECT i1, i2, value / (M1.margin * M2.margin) AS value\n FROM {prefix}item_cooccurrence A \n INNER JOIN {prefix}item_marginal M1 ON A.i1 = M1.i \n INNER JOIN {prefix}item_marginal M2 ON A.i2 = M2.i\n CLUSTER BY i1, i2\n \n SELECT i1, i2, value\n FROM\n (\n (SELECT i1, i2, value FROM {prefix}item_similarity_upper)\n UNION ALL\n (SELECT i2 i1, i1 i2, value FROM {prefix}item_similarity_upper WHERE i1 <> i2)\n )\n CLUSTER BY i1\n ",
"language": "en",
"n_whitespaces": 854,
"n_words": 255,
"vocab_size": 133
} | https://github.com/microsoft/recommenders.git |
|
4 | check_docker_permission | def check_docker_permission(verbose) -> bool:
permission_denied = False
docker_permission_command = ["docker", "info"]
try:
_ = run_command(
docker_permission_command,
verbose=verbose,
no_output_dump_on_exception=True,
capture_output=True,
text=True,
check=True,
)
except subprocess.CalledProcessError as ex:
permission_denied = True
if ex.stdout and 'Got permission denied while trying to connect' in ex.stdout:
console.print('ERROR: You have `permission denied` error when trying to communicate with docker.')
console.print(
'Most likely you need to add your user to `docker` group: \
https://docs.docker.com/ engine/install/linux-postinstall/ .'
)
return permission_denied
| 4ffd4f09532fceb67675fce4c1f5cd383eff992e | 13 | docker_command_utils.py | 139 | Prepare Breeze2 for prime time :) (#22713)
This is a review and clean-up for all the parameters and
commands for Breeze2 in order to prepare it for being
used by the contribugors.
There are various small fixes here and there, removal
of duplicated code, refactoring and moving code around
as well as cleanup and review all the parameters used
for all implemented commands.
The parameters, default values and their behaviours were
updated to match "new" life of Breeze rather than old
one.
Some improvements are made to the autocomplete and
click help messages printed. Full list of choices is
always displayed, parameters are groups according to
their target audience, and they were sorted according
to importance and frequency of use.
Various messages have been colourised according to their
meaning - warnings as yellow, errors as red and
informational messages as bright_blue.
The `dry-run` option has been added to just show what
would have been run without actually running some
potentially "write" commands (read commands are still
executed) so that you can easily verify and manually
copy and execute the commands with option to modify
them before. The `dry_run` and `verbose` options are
now used for all commands.
The "main" command now runs "shell" by default similarly
as the original Breeze.
All "shortcut" parameters have been standardized - i.e
common options (verbose/dry run/help) have one and all
common flags that are likely to be used often have an
assigned shortcute.
The "stop" and "cleanup" command have been added
as they are necessary for average user to complete the
regular usage cycle.
Documentation for all the important methods have been
updated. | 8,991 | 0 | 247 | 82 | 62 | 46,787 | 72 | airflow | 17 | dev/breeze/src/airflow_breeze/utils/docker_command_utils.py | Python | 29 | {
"docstring": "\n Checks if we have permission to write to docker socket. By default, on Linux you need to add your user\n to docker group and some new users do not realize that. We help those users if we have\n permission to run docker commands.\n\n :param verbose: print commands when running\n :return: True if permission is denied.\n ",
"language": "en",
"n_whitespaces": 74,
"n_words": 55,
"vocab_size": 42
} | https://github.com/apache/airflow.git |
|
1 | test_ppo_legacy_config | def test_ppo_legacy_config(self):
ppo_config = ppo.DEFAULT_CONFIG
# Expect warning.
print(f"Accessing learning-rate from legacy config dict: {ppo_config['lr']}")
# Build Algorithm.
ppo_trainer = ppo.PPO(config=ppo_config, env="CartPole-v1")
print(ppo_trainer.train())
| e50165492587556dac318418a0121e72bf93baa7 | 11 | test_ppo.py | 79 | Revert "[RLlib] @deprecate(error=True|False) escalation. (#28733)" (#28795)
Signed-off-by: Amog Kamsetty [email protected]
Reverts #28733
Breaks ray-air/examples:rl_offline_example and ray-air/examples:rl_online_example | 28,577 | 0 | 72 | 38 | 21 | 127,982 | 23 | ray | 11 | rllib/algorithms/ppo/tests/test_ppo.py | Python | 5 | {
"docstring": "Tests, whether the old PPO config dict is still functional.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | https://github.com/ray-project/ray.git |
|
3 | command_coverage_analyze_targets_missing | def command_coverage_analyze_targets_missing(args): # type: (CoverageAnalyzeTargetsMissingConfig) -> None
host_state = prepare_profiles(args) # coverage analyze targets missing
if args.delegate:
raise Delegate(host_state=host_state)
from_targets, from_path_arcs, from_path_lines = read_report(args.from_file)
to_targets, to_path_arcs, to_path_lines = read_report(args.to_file)
target_indexes = {} # type: TargetIndexes
if args.only_gaps:
arcs = find_gaps(from_path_arcs, from_targets, to_path_arcs, target_indexes, args.only_exists)
lines = find_gaps(from_path_lines, from_targets, to_path_lines, target_indexes, args.only_exists)
else:
arcs = find_missing(from_path_arcs, from_targets, to_path_arcs, to_targets, target_indexes, args.only_exists)
lines = find_missing(from_path_lines, from_targets, to_path_lines, to_targets, target_indexes, args.only_exists)
report = make_report(target_indexes, arcs, lines)
write_report(args, report, args.output_file)
| a06fa496d3f837cca3c437ab6e9858525633d147 | 12 | missing.py | 216 | ansible-test - Code cleanup and refactoring. (#77169)
* Remove unnecessary PyCharm ignores.
* Ignore intentional undefined attribute usage.
* Add missing type hints. Fix existing type hints.
* Fix docstrings and comments.
* Use function to register completion handler.
* Pass strings to display functions.
* Fix CompositeAction handling of dest argument.
* Use consistent types in expressions/assignments.
* Use custom function to keep linters happy.
* Add missing raise for custom exception.
* Clean up key/value type handling in cloud plugins.
* Use dataclass instead of dict for results.
* Add custom type_guard function to check lists.
* Ignore return type that can't be checked (yet).
* Avoid changing types on local variables. | 78,543 | 0 | 144 | 147 | 47 | 266,732 | 76 | ansible | 26 | test/lib/ansible_test/_internal/commands/coverage/analyze/targets/missing.py | Python | 15 | {
"docstring": "Identify aggregated coverage in one file missing from another.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | https://github.com/ansible/ansible.git |
|
7 | get | def get(self, url, html=None, video_id=None, note=None, note2='Executing JS on webpage', headers={}, jscode='saveAndExit();'):
if 'saveAndExit();' not in jscode:
raise ExtractorError('`saveAndExit();` not found in `jscode`')
if not html:
html = self.extractor._download_webpage(url, video_id, note=note, headers=headers)
with open(self._TMP_FILES['html'].name, 'wb') as f:
f.write(html.encode('utf-8'))
self._save_cookies(url)
replaces = self.options
replaces['url'] = url
user_agent = headers.get('User-Agent') or std_headers['User-Agent']
replaces['ua'] = user_agent.replace('"', '\\"')
replaces['jscode'] = jscode
for x in self._TMP_FILE_NAMES:
replaces[x] = self._TMP_FILES[x].name.replace('\\', '\\\\').replace('"', '\\"')
with open(self._TMP_FILES['script'].name, 'wb') as f:
f.write(self._TEMPLATE.format(**replaces).encode('utf-8'))
if video_id is None:
self.extractor.to_screen('%s' % (note2,))
else:
self.extractor.to_screen('%s: %s' % (video_id, note2))
p = subprocess.Popen([
self.exe, '--ssl-protocol=any',
self._TMP_FILES['script'].name
], stdout=subprocess.PIPE, stderr=subprocess.PIPE)
out, err = process_communicate_or_kill(p)
if p.returncode != 0:
raise ExtractorError(
'Executing JS failed\n:' + encodeArgument(err))
with open(self._TMP_FILES['html'].name, 'rb') as f:
html = f.read().decode('utf-8')
self._load_cookies()
return (html, encodeArgument(out))
| 0700fde6403aa9eec1ff02bff7323696a205900c | 15 | openload.py | 604 | [utils, etc] Kill child processes when yt-dl is killed
* derived from PR #26592, closes #26592
Authored by: Unrud | 22,352 | 0 | 404 | 352 | 92 | 106,355 | 121 | youtube-dl | 44 | youtube_dl/extractor/openload.py | Python | 33 | {
"docstring": "\n Downloads webpage (if needed) and executes JS\n\n Params:\n url: website url\n html: optional, html code of website\n video_id: video id\n note: optional, displayed when downloading webpage\n note2: optional, displayed when executing JS\n headers: custom http headers\n jscode: code to be executed when page is loaded\n\n Returns tuple with:\n * downloaded website (after JS execution)\n * anything you print with `console.log` (but not inside `page.execute`!)\n\n In most cases you don't need to add any `jscode`.\n It is executed in `page.onLoadFinished`.\n `saveAndExit();` is mandatory, use it instead of `phantom.exit()`\n It is possible to wait for some element on the webpage, for example:\n var check = function() {\n var elementFound = page.evaluate(function() {\n return document.querySelector('#b.done') !== null;\n });\n if(elementFound)\n saveAndExit();\n else\n window.setTimeout(check, 500);\n }\n\n page.evaluate(function(){\n document.querySelector('#a').click();\n });\n check();\n ",
"language": "en",
"n_whitespaces": 446,
"n_words": 125,
"vocab_size": 99
} | https://github.com/ytdl-org/youtube-dl.git |
|
1 | all | def all(x, axis=None, keepdims=False):
x = tf.cast(x, tf.bool)
return tf.reduce_all(x, axis, keepdims)
@keras_export("keras.backend.argmax")
@tf.__internal__.dispatch.add_dispatch_support
@doc_controls.do_not_generate_docs | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | @keras_export("keras.backend.argmax")
@tf.__internal__.dispatch.add_dispatch_support
@doc_controls.do_not_generate_docs | 9 | backend.py | 85 | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 80,171 | 1 | 21 | 37 | 15 | 269,545 | 15 | keras | 14 | keras/backend.py | Python | 3 | {
"docstring": "Bitwise reduction (logical AND).\n\n Args:\n x: Tensor or variable.\n axis: axis along which to perform the reduction.\n keepdims: whether the drop or broadcast the reduction axes.\n\n Returns:\n A uint8 tensor (0s and 1s).\n ",
"language": "en",
"n_whitespaces": 70,
"n_words": 33,
"vocab_size": 29
} | https://github.com/keras-team/keras.git |
1 | test_get_db_records | def test_get_db_records(self):
string = StringIndexer.objects.create(organization_id=123, string="oop")
collection = KeyCollection({123: {"oop"}})
key = "123:oop"
assert indexer_cache.get(key) is None
assert indexer_cache.get(string.id) is None
self.indexer._get_db_records(self.use_case_id, collection)
assert indexer_cache.get(string.id) is None
assert indexer_cache.get(key) is None
| 7f60db924ea37f34e0cfe6856777239e2a2ffe13 | 12 | test_postgres_indexer.py | 147 | feat(metrics): make indexer more configurable (#35604)
This makes the sentry_metrics indexer more configurable in the following ways, to enable indexing on the ingest-performance-metrics topic:
- configurable input Kafka topic
- configurable output Kafka topic
- configurable model from which to pull index results
- tags for internal metrics to distinguish between the two modes operationally | 18,794 | 0 | 94 | 89 | 18 | 91,722 | 31 | sentry | 16 | tests/sentry/sentry_metrics/test_postgres_indexer.py | Python | 9 | {
"docstring": "\n Make sure that calling `_get_db_records` doesn't populate the cache\n ",
"language": "en",
"n_whitespaces": 24,
"n_words": 9,
"vocab_size": 9
} | https://github.com/getsentry/sentry.git |
|
2 | weekday | def weekday(year, month, day):
if not datetime.MINYEAR <= year <= datetime.MAXYEAR:
year = 2000 + year % 400
return datetime.date(year, month, day).weekday()
| 8198943edd73a363c266633e1aa5b2a9e9c9f526 | 10 | calendar.py | 68 | add python 3.10.4 for windows | 56,278 | 0 | 38 | 44 | 18 | 221,226 | 22 | XX-Net | 8 | python3.10.4/Lib/calendar.py | Python | 4 | {
"docstring": "Return weekday (0-6 ~ Mon-Sun) for year, month (1-12), day (1-31).",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | https://github.com/XX-net/XX-Net.git |
|
5 | select_config | def select_config(Xraw, yraw, current, newpoint, bounds, num_f):
length = select_length(Xraw, yraw, bounds, num_f)
Xraw = Xraw[-length:, :]
yraw = yraw[-length:]
base_vals = np.array(list(bounds.values())).T
oldpoints = Xraw[:, :num_f]
old_lims = np.concatenate(
(np.max(oldpoints, axis=0), np.min(oldpoints, axis=0))
).reshape(2, oldpoints.shape[1])
limits = np.concatenate((old_lims, base_vals), axis=1)
X = normalize(Xraw, limits)
y = standardize(yraw).reshape(yraw.size, 1)
fixed = normalize(newpoint, oldpoints)
kernel = TV_SquaredExp(
input_dim=X.shape[1], variance=1.0, lengthscale=1.0, epsilon=0.1
)
try:
m = GPy.models.GPRegression(X, y, kernel)
except np.linalg.LinAlgError:
# add diagonal ** we would ideally make this something more robust...
X += np.eye(X.shape[0]) * 1e-3
m = GPy.models.GPRegression(X, y, kernel)
try:
m.optimize()
except np.linalg.LinAlgError:
# add diagonal ** we would ideally make this something more robust...
X += np.eye(X.shape[0]) * 1e-3
m = GPy.models.GPRegression(X, y, kernel)
m.optimize()
m.kern.lengthscale.fix(m.kern.lengthscale.clip(1e-5, 1))
if current is None:
m1 = deepcopy(m)
else:
# add the current trials to the dataset
padding = np.array([fixed for _ in range(current.shape[0])])
current = normalize(current, base_vals)
current = np.hstack((padding, current))
Xnew = np.vstack((X, current))
ypad = np.zeros(current.shape[0])
ypad = ypad.reshape(-1, 1)
ynew = np.vstack((y, ypad))
# kernel = GPy.kern.RBF(input_dim=X.shape[1], variance=1.,
# lengthscale=1.)
kernel = TV_SquaredExp(
input_dim=X.shape[1], variance=1.0, lengthscale=1.0, epsilon=0.1
)
m1 = GPy.models.GPRegression(Xnew, ynew, kernel)
m1.optimize()
xt = optimize_acq(UCB, m, m1, fixed, num_f)
# convert back...
xt = xt * (np.max(base_vals, axis=0) - np.min(base_vals, axis=0)) + np.min(
base_vals, axis=0
)
xt = xt.astype(np.float32)
return xt
| 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | 17 | pb2.py | 783 | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | 29,714 | 0 | 497 | 536 | 127 | 132,255 | 216 | ray | 63 | python/ray/tune/schedulers/pb2.py | Python | 49 | {
"docstring": "Selects the next hyperparameter config to try.\n\n This function takes the formatted data, fits the GP model and optimizes the\n UCB acquisition function to select the next point.\n\n Args:\n Xraw (np.array): The un-normalized array of hyperparams, Time and\n Reward\n yraw (np.array): The un-normalized vector of reward changes.\n current (list): The hyperparams of trials currently running. This is\n important so we do not select the same config twice. If there is\n data here then we fit a second GP including it\n (with fake y labels). The GP variance doesn't depend on the y\n labels so it is ok.\n newpoint (np.array): The Reward and Time for the new point.\n We cannot change these as they are based on the *new weights*.\n bounds (dict): Bounds for the hyperparameters. Used to normalize.\n num_f (int): The number of fixed params. Almost always 2 (reward+time)\n\n Return:\n xt (np.array): A vector of new hyperparameters.\n ",
"language": "en",
"n_whitespaces": 277,
"n_words": 147,
"vocab_size": 100
} | https://github.com/ray-project/ray.git |
|
1 | broadcast | def broadcast(self, tensors, broadcast_options=BroadcastOptions()):
root_rank = broadcast_options.root_rank
| 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | 7 | gloo_collective_group.py | 32 | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | 29,889 | 0 | 21 | 31 | 7 | 132,977 | 7 | ray | 6 | python/ray/util/collective/collective_group/gloo_collective_group.py | Python | 13 | {
"docstring": "Broadcast tensors to all other processes following options.\n\n Args:\n tensors (List): tensors to be broadcast or received.\n broadcast_options: broadcast options.\n\n Returns:\n None\n ",
"language": "en",
"n_whitespaces": 76,
"n_words": 22,
"vocab_size": 17
} | https://github.com/ray-project/ray.git |
|
8 | _handle_fk_field_node | def _handle_fk_field_node(self, node, field):
# Check if there is a child node named 'None', returning None if so.
if node.getElementsByTagName("None"):
return None
else:
model = field.remote_field.model
if hasattr(model._default_manager, "get_by_natural_key"):
keys = node.getElementsByTagName("natural")
if keys:
# If there are 'natural' subelements, it must be a natural key
field_value = [getInnerText(k).strip() for k in keys]
try:
obj = model._default_manager.db_manager(
self.db
).get_by_natural_key(*field_value)
except ObjectDoesNotExist:
if self.handle_forward_references:
return base.DEFER_FIELD
else:
raise
obj_pk = getattr(obj, field.remote_field.field_name)
# If this is a natural foreign key to an object that
# has a FK/O2O as the foreign key, use the FK value
if field.remote_field.model._meta.pk.remote_field:
obj_pk = obj_pk.pk
else:
# Otherwise, treat like a normal PK
field_value = getInnerText(node).strip()
obj_pk = model._meta.get_field(
field.remote_field.field_name
).to_python(field_value)
return obj_pk
else:
field_value = getInnerText(node).strip()
return model._meta.get_field(field.remote_field.field_name).to_python(
field_value
)
| 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | 20 | xml_serializer.py | 324 | Refs #33476 -- Reformatted code with Black. | 50,882 | 0 | 769 | 194 | 83 | 204,769 | 126 | django | 29 | django/core/serializers/xml_serializer.py | Python | 32 | {
"docstring": "\n Handle a <field> node for a ForeignKey\n ",
"language": "en",
"n_whitespaces": 22,
"n_words": 7,
"vocab_size": 6
} | https://github.com/django/django.git |
|
3 | _get_relevant_site_root_paths | def _get_relevant_site_root_paths(self, cache_object=None):
return tuple(
srp
for srp in self._get_site_root_paths(cache_object)
if self.url_path.startswith(srp.root_path)
)
| 29a7f701611d22ab9c7f12f1134aeac1d31b9438 | 11 | __init__.py | 56 | Add Page._get_relevant_site_root_paths() for use in signal handlers | 15,553 | 0 | 67 | 35 | 12 | 70,769 | 13 | wagtail | 9 | wagtail/core/models/__init__.py | Python | 6 | {
"docstring": "\n .. versionadded::2.16\n\n Returns a tuple of root paths for all sites this page belongs to.\n ",
"language": "en",
"n_whitespaces": 37,
"n_words": 15,
"vocab_size": 15
} | https://github.com/wagtail/wagtail.git |
|
14 | _get_data_iterator_from_dataset | def _get_data_iterator_from_dataset(dataset, dataset_type_spec):
if dataset_type_spec == list:
if len(dataset) == 0:
raise ValueError('Received an empty list dataset. '
'Please provide a non-empty list of arrays.')
if _get_type_spec(dataset[0]) is np.ndarray:
expected_shape = dataset[0].shape
for i, element in enumerate(dataset):
if np.array(element).shape[0] != expected_shape[0]:
raise ValueError('Received a list of NumPy arrays with different '
f'lengths. Mismatch found at index {i}, '
f'Expected shape={expected_shape} '
f'Received shape={np.array(element).shape}.'
f'Please provide a list of NumPy arrays with '
f'the same length.')
else:
raise ValueError('Expected a list of `numpy.ndarray` objects,'
f'Received: {type(dataset[0])}')
return iter(zip(*dataset))
elif dataset_type_spec == tuple:
if len(dataset) == 0:
raise ValueError('Received an empty list dataset.'
'Please provide a non-empty tuple of arrays.')
if _get_type_spec(dataset[0]) is np.ndarray:
expected_shape = dataset[0].shape
for i, element in enumerate(dataset):
if np.array(element).shape[0] != expected_shape[0]:
raise ValueError('Received a tuple of NumPy arrays with different '
f'lengths. Mismatch found at index {i}, '
f'Expected shape={expected_shape} '
f'Received shape={np.array(element).shape}.'
f'Please provide a tuple of NumPy arrays with '
'the same length.')
else:
raise ValueError('Expected a tuple of `numpy.ndarray` objects, '
f'Received: {type(dataset[0])}')
return iter(zip(*dataset))
elif dataset_type_spec == tf.data.Dataset:
if is_batched(dataset):
dataset = dataset.unbatch()
return iter(dataset)
elif dataset_type_spec == np.ndarray:
return iter(dataset)
| 06f5ef7989db314ee210455b04fe6f71e8dc57a7 | 22 | dataset_utils.py | 486 | Export split_dataset utility.
PiperOrigin-RevId: 447783753 | 79,995 | 0 | 651 | 248 | 73 | 269,271 | 188 | keras | 24 | keras/utils/dataset_utils.py | Python | 43 | {
"docstring": "Get the iterator from a dataset.\n\n Args:\n dataset : A `tf.data.Dataset` object or a list/tuple of arrays.\n dataset_type_spec : the type of the dataset\n\n Raises:\n ValueError:\n - If the dataset is empty.\n - If the dataset is not a `tf.data.Dataset` object\n or a list/tuple of arrays.\n - If the dataset is a list/tuple of arrays and the\n length of the list/tuple is not equal to the number\n\n Returns:\n iterator: An `iterator` object.\n ",
"language": "en",
"n_whitespaces": 176,
"n_words": 72,
"vocab_size": 36
} | https://github.com/keras-team/keras.git |
|
1 | get | def get(self):
return self.val
__test__ = {"_TestClass": _TestClass,
"string": r,
"bool-int equivalence": r,
"blank lines": r,
"ellipsis": r,
"whitespace normalization": r,
}
| 8198943edd73a363c266633e1aa5b2a9e9c9f526 | 7 | doctest.py | 78 | add python 3.10.4 for windows | 56,895 | 0 | 100 | 10 | 18 | 223,429 | 22 | XX-Net | 5 | python3.10.4/Lib/doctest.py | Python | 2 | {
"docstring": "get() -> return TestClass's associated value.\n\n >>> x = _TestClass(-42)\n >>> print(x.get())\n -42\n \n Example of a string object, searched as-is.\n >>> x = 1; y = 2\n >>> x + y, x * y\n (3, 2)\n \n In 2.2, boolean expressions displayed\n 0 or 1. By default, we still accept\n them. This can be disabled by passing\n DONT_ACCEPT_TRUE_FOR_1 to the new\n optionflags argument.\n >>> 4 == 4\n 1\n >>> 4 == 4\n True\n >>> 4 > 4\n 0\n >>> 4 > 4\n False\n \n Blank lines can be marked with <BLANKLINE>:\n >>> print('foo\\n\\nbar\\n')\n foo\n <BLANKLINE>\n bar\n <BLANKLINE>\n \n If the ellipsis flag is used, then '...' can be used to\n elide substrings in the desired output:\n >>> print(list(range(1000))) #doctest: +ELLIPSIS\n [0, 1, 2, ..., 999]\n \n If the whitespace normalization flag is used, then\n differences in whitespace are ignored.\n >>> print(list(range(30))) #doctest: +NORMALIZE_WHITESPACE\n [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,\n 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n 27, 28, 29]\n ",
"language": "en",
"n_whitespaces": 1118,
"n_words": 169,
"vocab_size": 123
} | https://github.com/XX-net/XX-Net.git |
|
6 | _where | def _where(condition, x=None, y=None):
if x is None or y is None:
raise ValueError("Either both or neither of the x and y arguments should "
"be provided to jax.numpy.where, got {} and {}."
.format(x, y))
if not issubdtype(_dtype(condition), bool_):
condition = lax.ne(condition, zeros_like(condition))
x, y = _promote_dtypes(x, y)
condition, x, y = broadcast_arrays(condition, x, y)
try: is_always_empty = core.is_empty_shape(np.shape(x))
except: is_always_empty = False # can fail with dynamic shapes
return lax.select(condition, x, y) if not is_always_empty else x
_WHERE_DOC =
@_wraps(np.where, update_doc=False, lax_description=_WHERE_DOC) | d9dcd1394aedf760272f14c3560cd5415495c28a | @_wraps(np.where, update_doc=False, lax_description=_WHERE_DOC) | 13 | lax_numpy.py | 217 | djax: let make_jaxpr build dyn shape jaxprs | 26,551 | 1 | 135 | 120 | 60 | 119,182 | 83 | jax | 25 | jax/_src/numpy/lax_numpy.py | Python | 12 | {
"docstring": "\\\nAt present, JAX does not support JIT-compilation of the single-argument form\nof :py:func:`jax.numpy.where` because its output shape is data-dependent. The\nthree-argument form does not have a data-dependent shape and can be JIT-compiled\nsuccessfully. Alternatively, you can specify the optional ``size`` keyword:\nif specified, the first ``size`` True elements will be returned; if there\nare fewer True elements than ``size`` indicates, the index arrays will be\npadded with ``fill_value`` (default is 0.)\n",
"language": "en",
"n_whitespaces": 64,
"n_words": 72,
"vocab_size": 54
} | https://github.com/google/jax.git |
1 | _get_variables_path | def _get_variables_path(export_dir):
return tf.io.gfile.join(
tf.compat.as_text(_get_variables_dir(export_dir)),
tf.compat.as_text(tf.saved_model.VARIABLES_FILENAME),
)
| 84afc5193d38057e2e2badf9c889ea87d80d8fbf | 11 | saved_model_experimental.py | 66 | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 81,598 | 0 | 30 | 41 | 7 | 276,217 | 7 | keras | 11 | keras/saving/saved_model_experimental.py | Python | 5 | {
"docstring": "Return the variables path, used as the prefix for checkpoint files.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 10
} | https://github.com/keras-team/keras.git |
|
5 | test_quantize_time_jitter | def test_quantize_time_jitter(self):
i = j = None
starting_key = quantize_time(self.now, 0, duration=10)
for i in range(11):
current_key = quantize_time(self.now + timedelta(seconds=i), 0, duration=10)
if current_key != starting_key:
break
other_key = quantize_time(self.now, 5, duration=10)
for j in range(11):
current_key = quantize_time(self.now + timedelta(seconds=j), 5, duration=10)
if current_key != other_key:
break
assert i != j
| 51403cc4c85c9c595a3b2d0ab5c2c1c4e33a3a1e | 14 | test_snuba.py | 173 | fix(sessions): Prevent query mutation behavior in `_prepare_query_params` (#31422)
* fix(sessions): Prevent query mutation behavior in `_prepare_query_params`
Fixes the behavior of `_prepare_query_params`
that mutates the conditions passed from the query.
* Add test that validates the change | 19,259 | 0 | 176 | 113 | 26 | 95,947 | 53 | sentry | 13 | tests/sentry/utils/test_snuba.py | Python | 13 | {
"docstring": "Different key hashes should change keys at different times\n\n While starting_key and other_key might begin as the same values they should change at different times\n ",
"language": "en",
"n_whitespaces": 39,
"n_words": 25,
"vocab_size": 20
} | https://github.com/getsentry/sentry.git |
|
3 | clip | def clip(a, min_a, max_a):
if a < min_a:
return min_a
elif a > max_a:
return max_a
return a
| 8841e39a20b501e38091df126a62bb7440931089 | 8 | simple_functions.py | 42 | Document and type `simple_functions.py` (#2674)
* 🏷️ Add types to simple_functions.py
* 💄 Neaten binary_search()
Add spacing between signature and code.
Remove and expressions and address IDE warnings
* 📝 Add docstrings for functions in simple_functions.py
* 🎨 Reorder functions alphabetically
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* 🐛 Reformat code examples
These were causing checks to fail due to missing spaces after `>>>`
I had wanted to change these to be more consistent with iterables.py anyway.
* 🎨 Change single tics to double
Change \` to \`` - this ensures that the variable names are actually
displayed as code (and not italics)
* improved docstrings, rewrote examples as doctests
* fix (???) unrelated failing doctest
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fixed typo
* Update manim/utils/simple_functions.py
Co-authored-by: Luca <[email protected]>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Benjamin Hackl <[email protected]> | 46,218 | 0 | 44 | 26 | 14 | 189,806 | 18 | manim | 4 | manim/utils/simple_functions.py | Python | 6 | {
"docstring": "Clips ``a`` to the interval [``min_a``, ``max_a``].\n\n Accepts any comparable objects (i.e. those that support <, >).\n Returns ``a`` if it is between ``min_a`` and ``max_a``.\n Otherwise, whichever of ``min_a`` and ``max_a`` is closest.\n\n Examples\n --------\n ::\n\n >>> clip(15, 11, 20)\n 15\n >>> clip('a', 'h', 'k')\n 'h'\n ",
"language": "en",
"n_whitespaces": 96,
"n_words": 47,
"vocab_size": 42
} | https://github.com/ManimCommunity/manim.git |
|
1 | fromiter | def fromiter(*args, **kwargs):
raise NotImplementedError(
"jnp.fromiter() is not implemented because it may be non-pure and thus unsafe for use "
"with JIT and other JAX transformations. Consider using jnp.asarray(np.fromiter(...)) "
"instead, although care should be taken if np.fromiter is used within a jax transformations "
"because of its potential side-effect of consuming the iterable object; for more information see "
"https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#pure-functions")
@_wraps(np.fromfunction) | fbfc3d8edfbf8dc5eb2f38c2bb315646a94ad399 | @_wraps(np.fromfunction) | 9 | lax_numpy.py | 54 | Better error messages for jnp.fromiter and jnp.fromfile | 26,720 | 1 | 78 | 19 | 54 | 119,949 | 62 | jax | 7 | jax/_src/numpy/lax_numpy.py | Python | 7 | {
"docstring": "Unimplemented JAX wrapper for jnp.fromiter.\n\n This function is left deliberately unimplemented because it may be non-pure and thus\n unsafe for use with JIT and other JAX transformations. Consider using\n ``jnp.asarray(np.fromiter(...))`` instead, although care should be taken if ``np.fromiter``\n is used within jax transformations because of its potential side-effect of consuming the\n iterable object; for more information see `Common Gotchas: Pure Functions\n <https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#pure-functions>`_.\n ",
"language": "en",
"n_whitespaces": 69,
"n_words": 62,
"vocab_size": 54
} | https://github.com/google/jax.git |
1 | bcoo_multiply_sparse | def bcoo_multiply_sparse(lhs, rhs):
out_data, out_indices, out_shape = _bcoo_multiply_sparse(
lhs.data, lhs.indices, rhs.data, rhs.indices, lhs_spinfo=lhs._info,
rhs_spinfo=rhs._info)
return BCOO((out_data, out_indices), shape=out_shape)
| 3184dd65a222354bffa2466d9a375162f5649132 | 10 | bcoo.py | 82 | [sparse] Update docstrings for bcoo primitives.
PiperOrigin-RevId: 438685829 | 26,730 | 0 | 31 | 57 | 18 | 119,978 | 18 | jax | 14 | jax/experimental/sparse/bcoo.py | Python | 5 | {
"docstring": "An element-wise multiplication of two sparse arrays.\n\n Args:\n lhs: A BCOO-format array.\n rhs: A BCOO-format array.\n\n Returns:\n An BCOO-format array containing the result.\n ",
"language": "en",
"n_whitespaces": 35,
"n_words": 23,
"vocab_size": 18
} | https://github.com/google/jax.git |
|
6 | sparse_top_k_categorical_matches | def sparse_top_k_categorical_matches(y_true, y_pred, k=5):
reshape_matches = False
y_true = tf.convert_to_tensor(y_true)
y_pred = tf.convert_to_tensor(y_pred)
y_true_rank = y_true.shape.ndims
y_pred_rank = y_pred.shape.ndims
y_true_org_shape = tf.shape(y_true)
# Flatten y_pred to (batch_size, num_samples) and y_true to (num_samples,)
if (y_true_rank is not None) and (y_pred_rank is not None):
if y_pred_rank > 2:
y_pred = tf.reshape(y_pred, [-1, y_pred.shape[-1]])
if y_true_rank > 1:
reshape_matches = True
y_true = tf.reshape(y_true, [-1])
matches = tf.cast(
tf.math.in_top_k(
predictions=y_pred, targets=tf.cast(y_true, "int32"), k=k
),
dtype=backend.floatx(),
)
# returned matches is expected to have same shape as y_true input
if reshape_matches:
return tf.reshape(matches, shape=y_true_org_shape)
return matches
| 84afc5193d38057e2e2badf9c889ea87d80d8fbf | 15 | metrics_utils.py | 268 | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 81,815 | 0 | 220 | 172 | 61 | 276,983 | 92 | keras | 22 | keras/utils/metrics_utils.py | Python | 22 | {
"docstring": "Creates float Tensor, 1.0 for label-TopK_prediction match, 0.0 for mismatch.\n\n Args:\n y_true: tensor of true targets.\n y_pred: tensor of predicted targets.\n k: (Optional) Number of top elements to look at for computing accuracy.\n Defaults to 5.\n\n Returns:\n Match tensor: 1.0 for label-prediction match, 0.0 for mismatch.\n ",
"language": "en",
"n_whitespaces": 82,
"n_words": 46,
"vocab_size": 33
} | https://github.com/keras-team/keras.git |
|
2 | validate_duplication | def validate_duplication(self):
term = frappe.db.sql(
,
(self.academic_year, self.term_name, self.name),
)
if term:
frappe.throw(
_(
"An academic term with this 'Academic Year' {0} and 'Term Name' {1} already exists. Please modify these entries and try again."
).format(self.academic_year, self.term_name)
)
| 494bd9ef78313436f0424b918f200dab8fc7c20b | 14 | academic_term.py | 84 | style: format code with black | 14,043 | 0 | 27 | 53 | 35 | 65,856 | 38 | erpnext | 12 | erpnext/education/doctype/academic_term/academic_term.py | Python | 12 | {
"docstring": "select name from `tabAcademic Term` where academic_year= %s and term_name= %s\n and docstatus<2 and name != %s",
"language": "en",
"n_whitespaces": 19,
"n_words": 17,
"vocab_size": 12
} | https://github.com/frappe/erpnext.git |
|
3 | get_repositories | def get_repositories(self) -> Sequence[JSONData]:
# Explicitly typing to satisfy mypy.
repos: JSONData = self.get_with_pagination(
"/installation/repositories", response_key="repositories"
)
return [repo for repo in repos if not repo.get("archived")]
# XXX: Find alternative approach | d6bcead1be02914e9734ab23f5e476b3d6f3f2cb | 11 | client.py | 73 | fix(github): Add pagination when fetching repositories (#39750)
We are not using pagination for Github's repositories endpoint. This means we were getting up to a maximum of 100 repositories.
I do not know how no one hit any issues in the past.
This is work to support WOR-2234 and creating automatic code mappings. | 18,160 | 0 | 80 | 41 | 30 | 86,729 | 31 | sentry | 9 | src/sentry/integrations/github/client.py | Python | 9 | {
"docstring": "\n This fetches all repositories accessible to the Github App\n https://docs.github.com/en/rest/apps/installations#list-repositories-accessible-to-the-app-installation\n ",
"language": "en",
"n_whitespaces": 32,
"n_words": 10,
"vocab_size": 10
} | https://github.com/getsentry/sentry.git |
|
8 | handle_display_options | def handle_display_options(self, option_order):
from distutils.core import gen_usage
# User just wants a list of commands -- we'll print it out and stop
# processing now (ie. if they ran "setup --help-commands foo bar",
# we ignore "foo bar").
if self.help_commands:
self.print_commands()
print('')
print(gen_usage(self.script_name))
return 1
# If user supplied any of the "display metadata" options, then
# display that metadata in the order in which the user supplied the
# metadata options.
any_display_options = 0
is_display_option = {}
for option in self.display_options:
is_display_option[option[0]] = 1
for (opt, val) in option_order:
if val and is_display_option.get(opt):
opt = translate_longopt(opt)
value = getattr(self.metadata, "get_"+opt)()
if opt in ['keywords', 'platforms']:
print(','.join(value))
elif opt in ('classifiers', 'provides', 'requires',
'obsoletes'):
print('\n'.join(value))
else:
print(value)
any_display_options = 1
return any_display_options
| 8198943edd73a363c266633e1aa5b2a9e9c9f526 | 17 | dist.py | 267 | add python 3.10.4 for windows | 56,798 | 0 | 461 | 152 | 88 | 222,884 | 122 | XX-Net | 22 | python3.10.4/Lib/distutils/dist.py | Python | 24 | {
"docstring": "If there were any non-global \"display-only\" options\n (--help-commands or the metadata display options) on the command\n line, display the requested info and return true; else return\n false.\n ",
"language": "en",
"n_whitespaces": 55,
"n_words": 27,
"vocab_size": 23
} | https://github.com/XX-net/XX-Net.git |
|
1 | test_push_unread_count_group_by_room | def test_push_unread_count_group_by_room(self):
# Carry out common push count tests and setup
self._test_push_unread_count()
# Carry out our option-value specific test
#
# This push should still only contain an unread count of 1 (for 1 unread room)
self._check_push_attempt(6, 1)
| 40771773909cb03d9296e3f0505e4e32372f10aa | 7 | test_http.py | 38 | Prevent duplicate push notifications for room reads (#11835) | 71,168 | 0 | 87 | 19 | 29 | 246,345 | 38 | synapse | 4 | tests/push/test_http.py | Python | 3 | {
"docstring": "\n The HTTP pusher will group unread count by number of unread rooms.\n ",
"language": "en",
"n_whitespaces": 27,
"n_words": 12,
"vocab_size": 11
} | https://github.com/matrix-org/synapse.git |
|
1 | test_https_good_referer | def test_https_good_referer(self):
req = self._get_POST_request_with_token()
req._is_secure_override = True
req.META["HTTP_HOST"] = "www.example.com"
req.META["HTTP_REFERER"] = "https://www.example.com/somepage"
mw = CsrfViewMiddleware(post_form_view)
mw.process_request(req)
resp = mw.process_view(req, post_form_view, (), {})
self.assertIsNone(resp)
| 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | 9 | tests.py | 118 | Refs #33476 -- Reformatted code with Black. | 50,121 | 0 | 88 | 68 | 20 | 202,419 | 25 | django | 13 | tests/csrf_tests/tests.py | Python | 9 | {
"docstring": "\n A POST HTTPS request with a good referer is accepted.\n ",
"language": "en",
"n_whitespaces": 25,
"n_words": 10,
"vocab_size": 10
} | https://github.com/django/django.git |
|
7 | as_Boolean | def as_Boolean(e):
from sympy.core.symbol import Symbol
if e == True:
return true
if e == False:
return false
if isinstance(e, Symbol):
z = e.is_zero
if z is None:
return e
return false if z else true
if isinstance(e, Boolean):
return e
raise TypeError('expecting bool or Boolean, not `%s`.' % e)
@sympify_method_args | 0bed141b2d875829e5caf6923431185ba16c625a | @sympify_method_args | 9 | boolalg.py | 117 | Cache replacement tuples, do not lookup true/false, new replacements | 49,274 | 1 | 124 | 71 | 33 | 199,450 | 51 | sympy | 14 | sympy/logic/boolalg.py | Python | 14 | {
"docstring": "Like ``bool``, return the Boolean value of an expression, e,\n which can be any instance of :py:class:`~.Boolean` or ``bool``.\n\n Examples\n ========\n\n >>> from sympy import true, false, nan\n >>> from sympy.logic.boolalg import as_Boolean\n >>> from sympy.abc import x\n >>> as_Boolean(0) is false\n True\n >>> as_Boolean(1) is true\n True\n >>> as_Boolean(x)\n x\n >>> as_Boolean(2)\n Traceback (most recent call last):\n ...\n TypeError: expecting bool or Boolean, not `2`.\n >>> as_Boolean(nan)\n Traceback (most recent call last):\n ...\n TypeError: expecting bool or Boolean, not `nan`.\n\n ",
"language": "en",
"n_whitespaces": 144,
"n_words": 81,
"vocab_size": 53
} | https://github.com/sympy/sympy.git |
4 | call_exchanges | def call_exchanges(self, other_args):
filters = (
pycoingecko_model.EXCHANGES_FILTERS + coinpaprika_model.EXCHANGES_FILTERS
)
parser = argparse.ArgumentParser(
prog="exchanges",
add_help=False,
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description=,
)
parser.add_argument(
"-l",
"--limit",
dest="limit",
type=check_positive,
help="display N number records",
default=15,
)
parser.add_argument(
"-s",
"--sortby",
dest="sortby",
type=str,
help="Sort by given column. Default: Rank",
default="Rank",
choices=filters,
)
parser.add_argument(
"-r",
"--reverse",
action="store_true",
dest="reverse",
default=False,
help=(
"Data is sorted in descending order by default. "
"Reverse flag will sort it in an ascending way. "
"Only works when raw data is displayed."
),
)
parser.add_argument(
"-u",
"--urls",
dest="urls",
action="store_true",
help="Flag to add a url column. Works only with CoinGecko source",
default=False,
)
parser.add_argument(
"--vs",
help="Quoted currency. Default: USD. Works only with CoinPaprika source",
dest="vs",
default="USD",
type=str,
choices=CURRENCIES,
)
ns_parser = self.parse_known_args_and_warn(
parser, other_args, EXPORT_ONLY_RAW_DATA_ALLOWED
)
if ns_parser:
if ns_parser.source == "CoinGecko":
pycoingecko_view.display_exchanges(
limit=ns_parser.limit,
export=ns_parser.export,
sortby=ns_parser.sortby,
ascend=ns_parser.reverse,
links=ns_parser.urls,
)
elif ns_parser.source == "CoinPaprika":
coinpaprika_view.display_all_exchanges(
symbol=ns_parser.vs,
limit=ns_parser.limit,
ascend=ns_parser.reverse,
sortby=ns_parser.sortby,
export=ns_parser.export,
)
| 0ae89d6cc20be84bf49c31e437fda38a845ebc68 | 14 | overview_controller.py | 446 | Style fixing: removing --ascend/--descend (#3395)
* stocks candle to use reverse
* qa raw to use reverse
* etf candle to use reverse
* oss rossix to use reverse
* crypto/defi to use reverse
* crypto/disc to use reverse
* added test
* crypto/dd to use reverse
* crypto/onchain to use reverse
* crypto/ov to use revert
* forex candle to use revert
* conibase controller to use revert
* tests to use reverse
* covid to use reverse
* removing ascend
* removing ascend from econ
* more removing ascend
* more removing ascend
* more removing ascend
* fixing stuff on .md files
* fixed economy controller tests
* fixed screener tests
* fa controller to use comma separated when multiple inputs | 85,855 | 0 | 1,004 | 278 | 108 | 286,532 | 143 | OpenBBTerminal | 42 | openbb_terminal/cryptocurrency/overview/overview_controller.py | Python | 83 | {
"docstring": "Process exchanges commandShows Top Crypto Exchanges\n You can display only N number exchanges with --limit parameter.\n You can sort data by Trust_Score, Id, Name, Country, Year_Established, Trade_Volume_24h_BTC with --sortby\n Or you can sort data by 'name', 'currencies', 'markets', 'fiats', 'confidence',\n 'volume_24h', 'volume_7d', 'volume_30d', 'sessions_per_month'\n if you are using the alternative source CoinPaprika\n and also with --reverse flag to sort ascending.\n Flag --urls will display urls.\n Displays: Trust_Score, Id, Name, Country, Year_Established, Trade_Volume_24h_BTC",
"language": "en",
"n_whitespaces": 191,
"n_words": 72,
"vocab_size": 54
} | https://github.com/OpenBB-finance/OpenBBTerminal.git |
|
4 | autocomplete | def autocomplete(self):
texts = []
for field in self.search_fields:
for current_field, value in self.prepare_field(self.obj, field):
if isinstance(current_field, AutocompleteField):
texts.append((value))
return " ".join(texts)
| d10f15e55806c6944827d801cd9c2d53f5da4186 | 14 | mysql.py | 91 | Reformat with black | 16,409 | 0 | 95 | 56 | 20 | 75,478 | 22 | wagtail | 13 | wagtail/search/backends/database/mysql/mysql.py | Python | 7 | {
"docstring": "\n Returns all values to index as \"autocomplete\". This is the value of all AutocompleteFields\n ",
"language": "en",
"n_whitespaces": 29,
"n_words": 14,
"vocab_size": 13
} | https://github.com/wagtail/wagtail.git |
|
1 | test_issue_alert_team | def test_issue_alert_team(self, mock_func):
# add a second organization
org = self.create_organization(owner=self.user)
OrganizationIntegration.objects.create(organization=org, integration=self.integration)
# add a second user to the team so we can be sure it's only
# sent once (to the team, and not to each individual user)
user2 = self.create_user(is_superuser=False)
self.create_member(teams=[self.team], user=user2, organization=self.organization)
self.idp = IdentityProvider.objects.create(type="slack", external_id="TXXXXXXX2", config={})
self.identity = Identity.objects.create(
external_id="UXXXXXXX2",
idp=self.idp,
user=user2,
status=IdentityStatus.VALID,
scopes=[],
)
NotificationSetting.objects.update_settings(
ExternalProviders.SLACK,
NotificationSettingTypes.ISSUE_ALERTS,
NotificationSettingOptionValues.ALWAYS,
user=user2,
)
# update the team's notification settings
ExternalActor.objects.create(
actor=self.team.actor,
organization=self.organization,
integration=self.integration,
provider=ExternalProviders.SLACK.value,
external_name="goma",
external_id="CXXXXXXX2",
)
NotificationSetting.objects.update_settings(
ExternalProviders.SLACK,
NotificationSettingTypes.ISSUE_ALERTS,
NotificationSettingOptionValues.ALWAYS,
team=self.team,
)
event = self.store_event(
data={"message": "Hello world", "level": "error"}, project_id=self.project.id
)
action_data = {
"id": "sentry.mail.actions.NotifyEmailAction",
"targetType": "Team",
"targetIdentifier": str(self.team.id),
}
rule = Rule.objects.create(
project=self.project,
label="ja rule",
data={
"match": "all",
"actions": [action_data],
},
)
notification = Notification(event=event, rule=rule)
with self.options({"system.url-prefix": "http://example.com"}), self.tasks():
self.adapter.notify(notification, ActionTargetType.TEAM, self.team.id)
# check that only one was sent out - more would mean each user is being notified
# rather than the team
assert len(responses.calls) == 1
# check that the team got a notification
data = parse_qs(responses.calls[0].request.body)
assert data["channel"] == ["CXXXXXXX2"]
assert "attachments" in data
attachments = json.loads(data["attachments"][0])
assert len(attachments) == 1
assert attachments[0]["title"] == "Hello world"
assert (
attachments[0]["footer"]
== f"{self.project.slug} | <http://example.com/settings/{self.organization.slug}/teams/{self.team.slug}/notifications/?referrer=issue_alert-slack-team|Notification Settings>"
)
| 1730c481f1a8a71446326fa1ff72e10663016385 | 12 | test_issue_alert.py | 724 | fix(notifications): Use `metrics_key` (#34572) | 19,653 | 0 | 822 | 430 | 138 | 99,575 | 196 | sentry | 71 | tests/sentry/integrations/slack/notifications/test_issue_alert.py | Python | 63 | {
"docstring": "Test that issue alerts are sent to a team in Slack.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | https://github.com/getsentry/sentry.git |
|
1 | check_pt_to_tf_equivalence | def check_pt_to_tf_equivalence(self, config, decoder_config, tf_inputs_dict):
encoder_decoder_config = EncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config)
# Output all for aggressive testing
encoder_decoder_config.output_hidden_states = True
# All models tested in this file have attentions
encoder_decoder_config.output_attentions = True
pt_model = EncoderDecoderModel(encoder_decoder_config)
with tempfile.TemporaryDirectory() as encoder_tmp_dirname, tempfile.TemporaryDirectory() as decoder_tmp_dirname:
pt_model.encoder.save_pretrained(encoder_tmp_dirname)
pt_model.decoder.save_pretrained(decoder_tmp_dirname)
tf_model = TFEncoderDecoderModel.from_encoder_decoder_pretrained(
encoder_tmp_dirname, decoder_tmp_dirname, encoder_from_pt=True, decoder_from_pt=True
)
# This is only for copying some specific attributes of this particular model.
tf_model.config = pt_model.config
self.check_pt_tf_equivalence(tf_model, pt_model, tf_inputs_dict)
| 6561fbcc6e6d6e1a29fb848dc34710aa25feae78 | 11 | test_modeling_tf_encoder_decoder.py | 171 | Update TF(Vision)EncoderDecoderModel PT/TF equivalence tests (#18073)
Co-authored-by: Joao Gante <[email protected]>
Co-authored-by: ydshieh <[email protected]> | 5,885 | 0 | 213 | 106 | 56 | 32,231 | 69 | transformers | 25 | tests/models/encoder_decoder/test_modeling_tf_encoder_decoder.py | Python | 13 | {
"docstring": "EncoderDecoderModel requires special way to cross load (PT -> TF)",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | https://github.com/huggingface/transformers.git |
|
1 | test_get_error_lines_error_empty_lines_around_error | def test_get_error_lines_error_empty_lines_around_error(self):
m = mock_open()
m.return_value.readlines.return_value = ['this is line 1\n', 'this is line 2\n', 'this is line 3\n', ' \n', ' \n', ' ']
with patch('builtins.open', m):
self.obj.ansible_pos = ('foo.yml', 5, 1)
e = AnsibleError(self.message, self.obj)
self.assertEqual(
e.message,
("This is the error message\n\nThe error appears to be in 'foo.yml': line 5, column 1, but may\nbe elsewhere in the file depending on "
"the exact syntax problem.\n\nThe offending line appears to be:\n\nthis is line 2\nthis is line 3\n^ here\n")
)
| b61380827758f8357b6a2721e4a8f290f05c6eaa | 12 | test_errors.py | 154 | Remove obsolete unit test builtins compat. | 78,515 | 0 | 201 | 78 | 56 | 266,685 | 80 | ansible | 13 | test/units/errors/test_errors.py | Python | 11 | {
"docstring": "Test that trailing whitespace after the error is removed",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | https://github.com/ansible/ansible.git |
|
6 | handler | def handler(request, operation, current_url):
if operation != QNetworkAccessManager.Operation.GetOperation:
return networkreply.ErrorNetworkReply(
request, "Unsupported request type",
QNetworkReply.NetworkError.ContentOperationNotPermittedError)
url = request.url()
if ((url.scheme(), url.host(), url.path()) ==
('qute', 'settings', '/set')):
if current_url != QUrl('qute://settings/'):
log.network.warning("Blocking malicious request from {} to {}"
.format(current_url.toDisplayString(),
url.toDisplayString()))
return networkreply.ErrorNetworkReply(
request, "Invalid qute://settings request",
QNetworkReply.NetworkError.ContentAccessDenied)
try:
mimetype, data = qutescheme.data_for_url(url)
except qutescheme.Error as e:
errors = {
qutescheme.NotFoundError:
QNetworkReply.NetworkError.ContentNotFoundError,
qutescheme.UrlInvalidError:
QNetworkReply.NetworkError.ContentOperationNotPermittedError,
qutescheme.RequestDeniedError:
QNetworkReply.NetworkError.ContentAccessDenied,
qutescheme.SchemeOSError:
QNetworkReply.NetworkError.ContentNotFoundError,
qutescheme.Error:
QNetworkReply.NetworkError.InternalServerError,
}
exctype = type(e)
log.misc.error("{} while handling qute://* URL".format(
exctype.__name__))
return networkreply.ErrorNetworkReply(request, str(e), errors[exctype])
except qutescheme.Redirect as e:
qtutils.ensure_valid(e.url)
return networkreply.RedirectNetworkReply(e.url)
return networkreply.FixedDataNetworkReply(request, data, mimetype)
| 0877fb0d78635692e481c8bde224fac5ad0dd430 | 15 | webkitqutescheme.py | 418 | Run scripts/dev/rewrite_enums.py | 117,576 | 0 | 483 | 264 | 76 | 321,172 | 93 | qutebrowser | 47 | qutebrowser/browser/webkit/network/webkitqutescheme.py | Python | 38 | {
"docstring": "Scheme handler for qute:// URLs.\n\n Args:\n request: QNetworkRequest to answer to.\n operation: The HTTP operation being done.\n current_url: The page we're on currently.\n\n Return:\n A QNetworkReply.\n ",
"language": "en",
"n_whitespaces": 63,
"n_words": 26,
"vocab_size": 25
} | https://github.com/qutebrowser/qutebrowser.git |
|
2 | parsed_pipfile | def parsed_pipfile(self) -> Union[tomlkit.toml_document.TOMLDocument, TPipfile]:
contents = self.read_pipfile()
# use full contents to get around str/bytes 2/3 issues
cache_key = (self.pipfile_location, contents)
if cache_key not in _pipfile_cache:
parsed = self._parse_pipfile(contents)
_pipfile_cache[cache_key] = parsed
return _pipfile_cache[cache_key]
| 4b996c0fa85824b323ad9eff3364dbe2213ebb4c | 10 | project.py | 92 | Convert type comments to type annotations | 3,731 | 0 | 99 | 58 | 28 | 21,231 | 35 | pipenv | 14 | pipenv/project.py | Python | 10 | {
"docstring": "Parse Pipfile into a TOMLFile and cache it\n\n (call clear_pipfile_cache() afterwards if mutating)",
"language": "en",
"n_whitespaces": 19,
"n_words": 13,
"vocab_size": 13
} | https://github.com/pypa/pipenv.git |
|
1 | _get_free_vram | def _get_free_vram(self) -> List[float]:
vram = self._all_vram
self._log("debug", f"GPU VRAM free: {vram}")
return vram
| bdbbad4d310fb606b6f412aa81e9f57ccd994e97 | 9 | amd.py | 51 | Refactor lib.gpu_stats (#1218)
* inital gpu_stats refactor
* Add dummy CPU Backend
* Update Sphinx documentation | 20,027 | 0 | 42 | 27 | 13 | 100,563 | 14 | faceswap | 7 | lib/gpu_stats/amd.py | Python | 19 | {
"docstring": " Obtain the amount of VRAM that is available, in Megabytes, for each connected AMD\n GPU.\n\n Notes\n -----\n There is no useful way to get free VRAM on PlaidML. OpenCL loads and unloads VRAM as\n required, so this returns the total memory available per card for AMD GPUs, which is\n not particularly useful.\n\n Returns\n -------\n list\n List of `float`s containing the amount of VRAM available, in Megabytes, for each\n connected GPU as corresponding to the values in :attr:`_handles\n ",
"language": "en",
"n_whitespaces": 172,
"n_words": 77,
"vocab_size": 55
} | https://github.com/deepfakes/faceswap.git |
|
3 | _mark_tests | def _mark_tests(items):
if os.environ.get("NETWORKX_GRAPH_CONVERT"):
plugin_name = os.environ["NETWORKX_GRAPH_CONVERT"]
backend = plugins[plugin_name].load()
if hasattr(backend, "on_start_tests"):
getattr(backend, "on_start_tests")(items)
| 0f91550007fd3a95261d858b1a6a623ef8bda38a | 13 | backends.py | 91 | plugin based backend infrastructure to use multiple computation backends (#6000)
* Wrappers classes to dispatch to a backend
* Rework the backend dispatching
- Use __networkx_plugin__=name to find graph-like objects instead of
subclassing
- Add PluginInfo to smooth over differences in importlib.metadata across
python versions
- Add dispatch behavior override via environment variable to aid in
testing plugins
* Dispatch more algorithms and improve auto-test capabilities
* Allow dispatcher decorator without a name
- Name is taken from the decorated function
- Raise error if backend doesn't implement a decorated function which is called
- Check for duplicate names for dispatching algorithms
* Make sphinx pick up backend docs
* make black happy
* Rename decorator to _dispatch as it's experimental
* A few more dispatched functions
* Make convert to and from methods for auto-testing
- Rename `convert` to `convert_from_nx`
- Add `convert_to_nx` function
These will allow backends to return native objects when dispatching,
but provide a mechanism to convert the result to the type expected
by NetworkX tests for the auto-test plugin mechanism.
* More dispatching
* Include name with `convert_**_nx` methods
* Remove known plugin names
This check is not needed, as any plugin can register itself in the entry points section.
The dispatching and auto-testing explicitly specify the plugin to use, so there is no
need to hardcode the options.
These were originally included for security, but any malicious actor would simply use one
of the valid names, so having a hardcoded list does not actually provide any meaningful
security.
* Add `dispatchname` to dispatchable functions
Co-authored-by: Jim Kitchen <[email protected]>
Co-authored-by: Erik Welch <[email protected]> | 42,378 | 0 | 53 | 51 | 13 | 177,442 | 15 | networkx | 11 | networkx/classes/backends.py | Python | 6 | {
"docstring": "Allow backend to mark tests (skip or xfail) if they aren't able to correctly handle them",
"language": "en",
"n_whitespaces": 15,
"n_words": 16,
"vocab_size": 15
} | https://github.com/networkx/networkx.git |
|
4 | _get_formatter | def _get_formatter(self, **kwargs):
config = {
attr: getattr(self, attr)
for attr in [
"include_sign",
"group_with_commas",
"num_decimal_places",
]
}
config.update(kwargs)
return "".join(
[
"{",
config.get("field_name", ""),
":",
"+" if config["include_sign"] else "",
"," if config["group_with_commas"] else "",
".",
str(config["num_decimal_places"]),
"f",
"}",
],
)
| 902e7eb4f0147b5882a613b67467e38a1d47f01e | 12 | numbers.py | 163 | Hide more private methods from the docs. (#2468)
* hide privs from text_mobject.py
* hide privs from tex_mobject.py
* hide privs from code_mobject.py
* hide privs from svg_mobject.py
* remove SVGPath and utils from __init__.py
* don't import string_to_numbers
* hide privs from geometry.py
* hide privs from matrix.py
* hide privs from numbers.py
* hide privs from three_dimensions.py
* forgot underscore under set_stroke_width_from_length
* there were more i missed
* unhidea method that was used in docs
* forgot other text2hash
* remove svg_path from docs | 46,054 | 0 | 319 | 92 | 38 | 189,440 | 42 | manim | 10 | manim/mobject/numbers.py | Python | 23 | {
"docstring": "\n Configuration is based first off instance attributes,\n but overwritten by any kew word argument. Relevant\n key words:\n - include_sign\n - group_with_commas\n - num_decimal_places\n - field_name (e.g. 0 or 0.real)\n ",
"language": "en",
"n_whitespaces": 87,
"n_words": 29,
"vocab_size": 26
} | https://github.com/ManimCommunity/manim.git |
|
1 | parametrize_backend | def parametrize_backend(cls):
assert not hasattr(cls, "backend")
cls.backend = SessionsReleaseHealthBackend()
| cd803d173c72b64d06c0687170bf9a945d0b503c | 9 | test_sessions.py | 39 | fix(snuba): Add appropriate `UseCaseKey` for indexer [TET-146] (#36308)
* fix(snuba): Add appropriate `UseCaseKey` for indexer
Update indexer invocation call to have the appropriate
`UseCaseKey` depending on use case.
In `src/sentry/sentry_metrics/indexer/base.py::StringIndexer`
when using `resolve` and `reverse_resolve` callers should not
rely on the default use_case_id.
Important changes:
- Add required parameter `use_case_id: UseCaseKey` to `get_series` from `src/sentry/snuba/metrics/datasource.py#L612`;
- Add required parameter to `get_metrics` in `src/sentry/snuba/metrics/datasource.py`
- Add required parameter to `get_tags` in `src/sentry/snuba/metrics/datasource.py`
- Add required parameter to `get_tag_values` in `src/sentry/snuba/metrics/datasource.py` | 18,965 | 0 | 18 | 104 | 9 | 93,002 | 9 | sentry | 5 | tests/snuba/sessions/test_sessions.py | Python | 16 | {
"docstring": "\n hack to parametrize test-classes by backend. Ideally we'd move\n over to pytest-style tests so we can use `pytest.mark.parametrize`, but\n hopefully we won't have more than one backend in the future.\n ",
"language": "en",
"n_whitespaces": 43,
"n_words": 30,
"vocab_size": 28
} | https://github.com/getsentry/sentry.git |
|
3 | handle_error_code | def handle_error_code(requests_obj, error_code_map):
for error_code, error_msg in error_code_map.items():
if requests_obj.status_code == error_code:
console.print(error_msg)
| 401e4c739a6f9d18944e0ab49c782e97b56fda94 | 11 | helper_funcs.py | 53 | Output Missing API Key Message to Console (#1357)
* Decorator to output error msg to console of missing API Key
* Refactor FMP & alpha advantage
* Refactor FRED & QUANDL
* Refactor Polygon
* Refactor FRED
* Refactor FRED
* Refactor Finnhub & coinmarketcap & Newsapi
* Allow disabling of check api
* Updating tests : disable check api for tests
* Refactor Finnhub & SI & Binance
* Fix linting
* Fix test & add black formatting
* Fix test failing
* Fix test failing
* Refactor CryptoPanic & Whales alert & Glassnode & Coinglass
* Refactor ETHexplorer & Smartstake & Alpha Advanage & Coinbase
* Add decorators to controllers
* Fix test & Refactor Coinbase, RH, Reddit
* Add contributing guideline
* Update CONTRIBUTING.md
* Update CONTRIBUTING.md
* fix tests
* add decorator to snews cmd
Co-authored-by: Chavithra PARANA <[email protected]>
Co-authored-by: didierlopes.eth <[email protected]> | 84,294 | 0 | 37 | 32 | 13 | 282,770 | 13 | OpenBBTerminal | 9 | gamestonk_terminal/helper_funcs.py | Python | 4 | {
"docstring": "\n Helper function to handle error code of HTTP requests.\n\n Parameters\n ----------\n requests_obj: Object\n Request object\n error_code_map: Dict\n Dictionary mapping of HTTP error code and output message\n\n ",
"language": "en",
"n_whitespaces": 59,
"n_words": 26,
"vocab_size": 22
} | https://github.com/OpenBB-finance/OpenBBTerminal.git |
|
1 | render_landing_page | def render_landing_page(self, request, form_submission=None, *args, **kwargs):
context = self.get_context(request)
context["form_submission"] = form_submission
return TemplateResponse(
request, self.get_landing_page_template(request), context
)
| d10f15e55806c6944827d801cd9c2d53f5da4186 | 9 | models.py | 72 | Reformat with black | 15,921 | 0 | 64 | 46 | 15 | 72,991 | 18 | wagtail | 10 | wagtail/contrib/forms/models.py | Python | 6 | {
"docstring": "\n Renders the landing page.\n\n You can override this method to return a different HttpResponse as\n landing page. E.g. you could return a redirect to a separate page.\n ",
"language": "en",
"n_whitespaces": 56,
"n_words": 27,
"vocab_size": 20
} | https://github.com/wagtail/wagtail.git |
|
2 | get_combiner_conds | def get_combiner_conds():
combiner_types = sorted(list(combiner_registry.keys()))
conds = []
for combiner_type in combiner_types:
combiner_cls = combiner_registry[combiner_type]
schema_cls = combiner_cls.get_schema_cls()
combiner_schema = schema.get_custom_schema_from_marshmallow_class(schema_cls)
combiner_props = combiner_schema["properties"]
combiner_cond = schema.create_cond({"type": combiner_type}, combiner_props)
conds.append(combiner_cond)
return conds
# super class to house common properties | 23a33eef3bc7ea3ba33ec56dc9b56ba38462648a | 13 | combiners.py | 130 | feat: Modify Trainer to use marshmallow_dataclass syntax for handling hyperparameters. Add basic scripting for docstring extraction to marshmallow schema. Fix some existing marshmallow issues. (#1606) | 1,011 | 0 | 95 | 76 | 32 | 6,534 | 39 | ludwig | 18 | ludwig/combiners/combiners.py | Python | 11 | {
"docstring": "Returns a list of if-then JSON clauses for each combiner type in `combiner_registry` and its properties'\n constraints.",
"language": "en",
"n_whitespaces": 19,
"n_words": 17,
"vocab_size": 17
} | https://github.com/ludwig-ai/ludwig.git |
|
15 | ravel_multi_index | def ravel_multi_index(multi_index, dims, mode='raise', order='C'):
assert len(multi_index) == len(dims), f"len(multi_index)={len(multi_index)} != len(dims)={len(dims)}"
dims = tuple(core.concrete_or_error(operator.index, d, "in `dims` argument of ravel_multi_index().") for d in dims)
_check_arraylike("ravel_multi_index", *multi_index)
for index in multi_index:
if mode == 'raise':
core.concrete_or_error(array, index,
"The error occurred because ravel_multi_index was jit-compiled"
" with mode='raise'. Use mode='wrap' or mode='clip' instead.")
if not issubdtype(_dtype(index), integer):
raise TypeError("only int indices permitted")
if mode == "raise":
if _any(any((i < 0) | (i >= d)) for i, d in zip(multi_index, dims)):
raise ValueError("invalid entry in coordinates array")
elif mode == "clip":
multi_index = [clip(i, 0, d - 1) for i, d in zip(multi_index, dims)]
elif mode == "wrap":
multi_index = [i % d for i, d in zip(multi_index, dims)]
else:
raise ValueError(f"invalid mode={mode!r}. Expected 'raise', 'wrap', or 'clip'")
if order == "F":
strides = np.cumprod((1,) + dims[:-1])
elif order == "C":
strides = np.cumprod((1,) + dims[1:][::-1])[::-1]
else:
raise ValueError(f"invalid order={order!r}. Expected 'C' or 'F'")
result = array(0, dtype=multi_index[0].dtype)
for i, s in zip(multi_index, strides):
result = result + i * int(s)
return result
_UNRAVEL_INDEX_DOC =
@_wraps(np.unravel_index, lax_description=_UNRAVEL_INDEX_DOC) | 3ad08543a9d766d8e6b9d7272cebfe4f2c431980 | @_wraps(np.unravel_index, lax_description=_UNRAVEL_INDEX_DOC) | 16 | lax_numpy.py | 533 | [x64] make jnp.histogram and related functions work with strict promotion | 26,928 | 1 | 246 | 297 | 113 | 120,708 | 175 | jax | 35 | jax/_src/numpy/lax_numpy.py | Python | 30 | {
"docstring": "\\\nUnlike numpy's implementation of unravel_index, negative indices are accepted\nand out-of-bounds indices are clipped into the valid range.\n",
"language": "en",
"n_whitespaces": 16,
"n_words": 19,
"vocab_size": 17
} | https://github.com/google/jax.git |
1 | _build_ui | def _build_ui(self) -> None:
container = ttk.PanedWindow(self,
orient=tk.VERTICAL)
container.pack(fill=tk.BOTH, expand=True)
setattr(container, "preview_display", self._display) # TODO subclass not setattr
self._image_canvas = ImagesCanvas(container, self._tk_vars)
container.add(self._image_canvas, weight=3)
options_frame = ttk.Frame(container)
self._cli_frame = ActionFrame(
options_frame,
self._available_masks,
self._samples.predictor.has_predicted_mask,
self._patch.converter.cli_arguments.color_adjustment.replace("-", "_"),
self._patch.converter.cli_arguments.mask_type.replace("-", "_"),
self._config_tools,
self._refresh,
self._samples.generate,
self._tk_vars)
self._opts_book = OptionsBook(options_frame,
self._config_tools,
self._refresh)
container.add(options_frame, weight=1)
self.update_idletasks()
container.sashpos(0, int(400 * get_config().scaling_factor))
| 1022651eb8a7741014f5d2ec7cbfe882120dfa5f | 14 | preview.py | 306 | Bugfix: convert - Gif Writer
- Fix non-launch error on Gif Writer
- convert plugins - linting
- convert/fs_media/preview/queue_manager - typing
- Change convert items from dict to Dataclass | 20,851 | 0 | 346 | 198 | 46 | 101,438 | 53 | faceswap | 43 | tools/preview/preview.py | Python | 25 | {
"docstring": " Build the elements for displaying preview images and options panels. ",
"language": "en",
"n_whitespaces": 11,
"n_words": 10,
"vocab_size": 10
} | https://github.com/deepfakes/faceswap.git |
|
5 | update_config | def update_config(self) -> None:
for section, items in self.tk_vars.items():
for item, value in items.items():
try:
new_value = str(value.get())
except tk.TclError as err:
# When manually filling in text fields, blank values will
# raise an error on numeric data types so return 0
logger.debug("Error getting value. Defaulting to 0. Error: %s", str(err))
new_value = str(0)
old_value = self._config.config[section][item]
if new_value != old_value:
logger.trace("Updating config: %s, %s from %s to %s", # type: ignore
section, item, old_value, new_value)
self._config.config[section][item] = new_value
| 1022651eb8a7741014f5d2ec7cbfe882120dfa5f | 16 | preview.py | 184 | Bugfix: convert - Gif Writer
- Fix non-launch error on Gif Writer
- convert plugins - linting
- convert/fs_media/preview/queue_manager - typing
- Change convert items from dict to Dataclass | 20,834 | 0 | 331 | 113 | 63 | 101,420 | 80 | faceswap | 19 | tools/preview/preview.py | Python | 14 | {
"docstring": " Update :attr:`config` with the currently selected values from the GUI. ",
"language": "en",
"n_whitespaces": 11,
"n_words": 10,
"vocab_size": 9
} | https://github.com/deepfakes/faceswap.git |
|
8 | readline | def readline(self, size=-1):
r
# For backwards compatibility, a (slowish) readline().
if hasattr(self, "peek"): | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | 8 | _pyio.py | 35 | add python 3.10.4 for windows | 55,865 | 0 | 34 | 110 | 14 | 219,860 | 14 | XX-Net | 4 | python3.10.4/Lib/_pyio.py | Python | 32 | {
"docstring": "Read and return a line of bytes from the stream.\n\n If size is specified, at most size bytes will be read.\n Size should be an int.\n\n The line terminator is always b'\\n' for binary files; for text\n files, the newlines argument to open can be used to select the line\n terminator(s) recognized.\n ",
"language": "en",
"n_whitespaces": 94,
"n_words": 52,
"vocab_size": 41
} | https://github.com/XX-net/XX-Net.git |
|
17 | _text2settings | def _text2settings(self):
t2xs = [
(self.t2f, "font"),
(self.t2s, "slant"),
(self.t2w, "weight"),
(self.t2c, "color"),
]
setting_args = {arg: getattr(self, arg) for _, arg in t2xs}
settings = self._get_settings_from_t2xs(t2xs)
settings.extend(self._get_settings_from_gradient(setting_args))
# Handle overlaps
settings.sort(key=lambda setting: setting.start)
for index, setting in enumerate(settings):
if index + 1 == len(settings):
break
next_setting = settings[index + 1]
if setting.end > next_setting.start:
new_setting = self._merge_settings(setting, next_setting, setting_args)
new_index = index + 1
while (
new_index < len(settings)
and settings[new_index].start < new_setting.start
):
new_index += 1
settings.insert(new_index, new_setting)
# Set all text settings (default font, slant, weight)
temp_settings = settings.copy()
start = 0
for setting in settings:
if setting.start != start:
temp_settings.append(TextSetting(start, setting.start, **setting_args))
start = setting.end
if start != len(self.text):
temp_settings.append(TextSetting(start, len(self.text), **setting_args))
settings = sorted(temp_settings, key=lambda setting: setting.start)
if re.search(r"\n", self.text):
line_num = 0
for start, end in self._find_indexes("\n", self.text):
for setting in settings:
if setting.line_num == -1:
setting.line_num = line_num
if start < setting.end:
line_num += 1
new_setting = copy.copy(setting)
setting.end = end
new_setting.start = end
new_setting.line_num = line_num
settings.append(new_setting)
settings.sort(key=lambda setting: setting.start)
break
for setting in settings:
if setting.line_num == -1:
setting.line_num = 0
return settings
| 902e7eb4f0147b5882a613b67467e38a1d47f01e | 18 | text_mobject.py | 612 | Hide more private methods from the docs. (#2468)
* hide privs from text_mobject.py
* hide privs from tex_mobject.py
* hide privs from code_mobject.py
* hide privs from svg_mobject.py
* remove SVGPath and utils from __init__.py
* don't import string_to_numbers
* hide privs from geometry.py
* hide privs from matrix.py
* hide privs from numbers.py
* hide privs from three_dimensions.py
* forgot underscore under set_stroke_width_from_length
* there were more i missed
* unhidea method that was used in docs
* forgot other text2hash
* remove svg_path from docs | 46,094 | 0 | 888 | 389 | 99 | 189,494 | 182 | manim | 38 | manim/mobject/svg/text_mobject.py | Python | 52 | {
"docstring": "Converts the texts and styles to a setting for parsing.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | https://github.com/ManimCommunity/manim.git |
|
7 | karate_club_graph | def karate_club_graph():
# Create the set of all members, and the members of each club.
all_members = set(range(34))
club1 = {0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 16, 17, 19, 21}
# club2 = all_members - club1
G = nx.Graph()
G.add_nodes_from(all_members)
G.name = "Zachary's Karate Club"
zacharydat =
for row, line in enumerate(zacharydat.split("\n")):
thisrow = [int(b) for b in line.split()]
for col, entry in enumerate(thisrow):
if entry >= 1:
G.add_edge(row, col, weight=entry)
# Add the name of each member's club as a node attribute.
for v in G:
G.nodes[v]["club"] = "Mr. Hi" if v in club1 else "Officer"
return G
| 290ebce534b84f9db20ec58b98cbb170e65a0ba1 | 14 | social.py | 241 | Add weights to karate club graph (#5285)
Add weights to the karate_club_graph.
Modifies `non_randomness` and `naive_greedy_modularity_communities` to
accept a `weight` parameter and modifies tests that use the kcg accordingly
Co-authored-by: Kevin Berry <[email protected]>
Co-authored-by: Dan Schult <[email protected]> | 41,796 | 0 | 193 | 154 | 77 | 176,261 | 106 | networkx | 24 | networkx/generators/social.py | Python | 49 | {
"docstring": "Returns Zachary's Karate Club graph.\n\n Each node in the returned graph has a node attribute 'club' that\n indicates the name of the club to which the member represented by that node\n belongs, either 'Mr. Hi' or 'Officer'. Each edge has a weight based on the\n number of contexts in which that edge's incident node members interacted.\n\n Examples\n --------\n To get the name of the club to which a node belongs::\n\n >>> G = nx.karate_club_graph()\n >>> G.nodes[5][\"club\"]\n 'Mr. Hi'\n >>> G.nodes[9][\"club\"]\n 'Officer'\n\n References\n ----------\n .. [1] Zachary, Wayne W.\n \"An Information Flow Model for Conflict and Fission in Small Groups.\"\n *Journal of Anthropological Research*, 33, 452--473, (1977).\n \\\n0 4 5 3 3 3 3 2 2 0 2 3 2 3 0 0 0 2 0 2 0 2 0 0 0 0 0 0 0 0 0 2 0 0\n4 0 6 3 0 0 0 4 0 0 0 0 0 5 0 0 0 1 0 2 0 2 0 0 0 0 0 0 0 0 2 0 0 0\n5 6 0 3 0 0 0 4 5 1 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 0 0 3 0\n3 3 3 0 0 0 0 3 0 0 0 0 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n3 0 0 0 0 0 2 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n3 0 0 0 0 0 5 0 0 0 3 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n3 0 0 0 2 5 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n2 4 4 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n2 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 4 3\n0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2\n2 0 0 0 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n1 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n3 5 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3\n0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 2\n0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 4\n0 0 0 0 0 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2\n2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1\n0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 1\n2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0\n0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 4 0 2 0 0 5 4\n0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 3 0 0 0 2 0 0\n0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 2 0 0 0 0 0 0 7 0 0\n0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 2\n0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 3 0 0 0 0 0 0 0 0 4\n0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2\n0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 4 0 0 0 0 0 3 2\n0 2 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3\n2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 7 0 0 2 0 0 0 4 4\n0 0 2 0 0 0 0 0 3 0 0 0 0 0 3 3 0 0 1 0 3 0 2 5 0 0 0 0 0 4 3 4 0 5\n0 0 0 0 0 0 0 0 4 2 0 0 0 3 2 4 0 0 2 1 1 0 3 4 0 0 2 4 2 2 3 4 5 0",
"language": "en",
"n_whitespaces": 1308,
"n_words": 1263,
"vocab_size": 85
} | https://github.com/networkx/networkx.git |
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