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1 | 20 | def paths(self, request, pk):
obj = get_object_or_404(self.queryset, pk=pk)
cablepaths = CablePath.objects.filter(_nodes__contains=obj).prefetch_related('origin', 'destination')
serializer = serializers.CablePathSerializer(cablepaths, context={'request': request}, many=True)
return Response(serializer.data)
#
# Regions
#
| netbox/dcim/api/views.py | 113 | netbox | {
"docstring": "\n Return all CablePaths which traverse a given pass-through port.\n ",
"language": "en",
"n_whitespaces": 24,
"n_words": 9,
"vocab_size": 9
} | 24 | Python | 20 | 3a461d02793e6f9d41c2b1a92647e691de1abaac | views.py | 264,877 | 5 | 68 | paths | https://github.com/netbox-community/netbox.git | Update Cable instantiations to match new signature | 56 | 0 | 77,892 | 12 |
|
1 | 19 | def test_read_only_buffer():
rng = np.random.RandomState(0)
clf = ElasticNet(alpha=0.1, copy_X=True, random_state=rng)
X = np.asfortranarray(rng.uniform(size=(100, 10)))
X.setflags(write=False)
y = rng.rand(100)
clf.fit(X, y)
| sklearn/linear_model/tests/test_coordinate_descent.py | 118 | scikit-learn | {
"docstring": "Test that sparse coordinate descent works for read-only buffers",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | 20 | Python | 17 | 3bb4bad1425ee7add6001a32f0d83cb459ffa30c | test_coordinate_descent.py | 260,476 | 7 | 76 | test_read_only_buffer | https://github.com/scikit-learn/scikit-learn.git | MNT Replaced `np.ndarray` with memview where applicable in `linear_model/_cd_fast.pyx` (#23147)
Co-authored-by: Thomas J. Fan <[email protected]> | 41 | 0 | 76,274 | 12 |
|
3 | 20 | def test_cases(self) -> Dict[str, Type[unittest.TestCase]]:
test_cases = {}
for category, items_map in self._filtered_test_items.items():
test_case_name = str('OnnxBackend{}Test').format(category)
test_case = self._get_test_case(test_case_name)
for name, item in sorted(items_map.items()):
setattr(test_case, name, item.func)
test_cases[test_case_name] = test_case
return test_cases
| onnx/backend/test/runner/__init__.py | 137 | onnx | {
"docstring": "\n List of test cases to be applied on the parent scope\n Example usage:\n globals().update(BackendTest(backend).test_cases)\n ",
"language": "en",
"n_whitespaces": 47,
"n_words": 14,
"vocab_size": 14
} | 32 | Python | 24 | 83fa57c74edfd13ddac9548b8a12f9e3e2ed05bd | __init__.py | 255,147 | 14 | 86 | test_cases | https://github.com/onnx/onnx.git | Use Python type annotations rather than comments (#3962)
* These have been supported since Python 3.5.
ONNX doesn't support Python < 3.6, so we can use the annotations.
Diffs generated by https://pypi.org/project/com2ann/.
Signed-off-by: Gary Miguel <[email protected]>
* Remove MYPY conditional logic in gen_proto.py
It breaks the type annotations and shouldn't be needed.
Signed-off-by: Gary Miguel <[email protected]>
* Get rid of MYPY bool from more scripts
Signed-off-by: Gary Miguel <[email protected]>
* move Descriptors class above where its referenced in type annotation
Signed-off-by: Gary Miguel <[email protected]>
* fixes
Signed-off-by: Gary Miguel <[email protected]>
* remove extra blank line
Signed-off-by: Gary Miguel <[email protected]>
* fix type annotations
Signed-off-by: Gary Miguel <[email protected]>
* fix type annotation in gen_docs
Signed-off-by: Gary Miguel <[email protected]>
* fix Operators.md
Signed-off-by: Gary Miguel <[email protected]>
* fix TestCoverage.md
Signed-off-by: Gary Miguel <[email protected]>
* fix protoc-gen-mypy.py
Signed-off-by: Gary Miguel <[email protected]> | 119 | 0 | 74,735 | 13 |
|
1 | 5 | def _may_have_unstable_default(self) -> bool:
return callable(self._default)
| bokeh/core/property/bases.py | 27 | bokeh | {
"docstring": " False if we have a default that is immutable, and will be the\n same every time (some defaults are generated on demand by a function\n to be called).\n\n ",
"language": "en",
"n_whitespaces": 50,
"n_words": 28,
"vocab_size": 26
} | 6 | Python | 6 | 1b3e6acd6eebd352106cc5ecf5e12dbf90e0607c | bases.py | 212,147 | 7 | 15 | _may_have_unstable_default | https://github.com/bokeh/bokeh.git | Add Init signatures to Bokeh models (#12035)
* Add signatures to Bokeh Model initializers
* use explicit type for override default
* move InstanceDefault to bokeh.core.properties
* enable assertions | 20 | 0 | 53,177 | 8 |
|
6 | 17 | def __call__(self, bboxes1, bboxes2, mode='iou', is_aligned=False):
bboxes1 = get_box_tensor(bboxes1)
bboxes2 = get_box_tensor(bboxes2)
assert bboxes1.size(-1) in [0, 4, 5]
assert bboxes2.size(-1) in [0, 4, 5]
if bboxes2.size(-1) == 5:
bboxes2 = bboxes2[..., :4]
if bboxes1.size(-1) == 5:
bboxes1 = bboxes1[..., :4]
if self.dtype == 'fp16':
# change tensor type to save cpu and cuda memory and keep speed
bboxes1 = cast_tensor_type(bboxes1, self.scale, self.dtype)
bboxes2 = cast_tensor_type(bboxes2, self.scale, self.dtype)
overlaps = bbox_overlaps(bboxes1, bboxes2, mode, is_aligned)
if not overlaps.is_cuda and overlaps.dtype == torch.float16:
# resume cpu float32
overlaps = overlaps.float()
return overlaps
return bbox_overlaps(bboxes1, bboxes2, mode, is_aligned)
| mmdet/models/task_modules/assigners/iou2d_calculator.py | 279 | mmdetection | {
"docstring": "Calculate IoU between 2D bboxes.\n\n Args:\n bboxes1 (Tensor or :obj:`BaseBoxes`): bboxes have shape (m, 4)\n in <x1, y1, x2, y2> format, or shape (m, 5) in <x1, y1, x2,\n y2, score> format.\n bboxes2 (Tensor or :obj:`BaseBoxes`): bboxes have shape (m, 4)\n in <x1, y1, x2, y2> format, shape (m, 5) in <x1, y1, x2, y2,\n score> format, or be empty. If ``is_aligned `` is ``True``,\n then m and n must be equal.\n mode (str): \"iou\" (intersection over union), \"iof\" (intersection\n over foreground), or \"giou\" (generalized intersection over\n union).\n is_aligned (bool, optional): If True, then m and n must be equal.\n Default False.\n\n Returns:\n Tensor: shape (m, n) if ``is_aligned `` is False else shape (m,)\n ",
"language": "en",
"n_whitespaces": 311,
"n_words": 115,
"vocab_size": 64
} | 94 | Python | 54 | d915740fa8228cf57741b27d9e5d66e358456b8e | iou2d_calculator.py | 245,712 | 17 | 183 | __call__ | https://github.com/open-mmlab/mmdetection.git | [Refactor] Refactor anchor head and base head with boxlist (#8625)
* Refactor anchor head
* Update
* Update
* Update
* Add a series of boxes tools
* Fix box type to support n x box_dim boxes
* revert box type changes
* Add docstring
* refactor retina_head
* Update
* Update
* Fix comments
* modify docstring of coder and ioucalculator
* Replace with_boxlist with use_box_type | 275 | 0 | 70,858 | 12 |
|
4 | 11 | def to_key_val_list(value):
if value is None:
return None
if isinstance(value, (str, bytes, bool, int)):
raise ValueError("cannot encode objects that are not 2-tuples")
if isinstance(value, Mapping):
value = value.items()
return list(value)
# From mitsuhiko/werkzeug (used with permission). | pipenv/patched/pip/_vendor/requests/utils.py | 88 | pipenv | {
"docstring": "Take an object and test to see if it can be represented as a\n dictionary. If it can be, return a list of tuples, e.g.,\n\n ::\n\n >>> to_key_val_list([('key', 'val')])\n [('key', 'val')]\n >>> to_key_val_list({'key': 'val'})\n [('key', 'val')]\n >>> to_key_val_list('string')\n Traceback (most recent call last):\n ...\n ValueError: cannot encode objects that are not 2-tuples\n\n :rtype: list\n ",
"language": "en",
"n_whitespaces": 122,
"n_words": 54,
"vocab_size": 46
} | 36 | Python | 31 | cd5a9683be69c86c8f3adcd13385a9bc5db198ec | utils.py | 22,153 | 8 | 54 | to_key_val_list | https://github.com/pypa/pipenv.git | Rename notpip to pip. Vendor in pip-22.2.1 and latest requirementslib and vistir. | 71 | 0 | 4,224 | 10 |
|
2 | 11 | def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, ViltEncoder):
module.gradient_checkpointing = value
VILT_START_DOCSTRING = r
VILT_INPUTS_DOCSTRING = r
VILT_IMAGES_AND_TEXT_CLASSIFICATION_INPUTS_DOCSTRING = r
@add_start_docstrings(
"The bare ViLT Model transformer outputting raw hidden-states without any specific head on top.",
VILT_START_DOCSTRING,
) | src/transformers/models/vilt/modeling_vilt.py | 71 | @add_start_docstrings(
"The bare ViLT Model transformer outputting raw hidden-states without any specific head on top.",
VILT_START_DOCSTRING,
) | transformers | {
"docstring": "\n This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ subclass. Use\n it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`ViltConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n\n Args:\n input_ids (`torch.LongTensor` of shape `({0})`):\n Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`BertTokenizer`]. See\n [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input\n IDs?](../glossary#input-ids)\n\n attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):\n Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:\n - 1 for tokens that are **not masked**,\n - 0 for tokens that are **masked**.\n [What are attention masks?](../glossary#attention-mask)\n\n token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):\n Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,\n 1]`:\n - 0 corresponds to a *sentence A* token,\n - 1 corresponds to a *sentence B* token.\n [What are token type IDs?](../glossary#token-type-ids)\n\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`ViltFeatureExtractor`]. See\n [`ViltFeatureExtractor.__call__`] for details.\n\n pixel_mask (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):\n Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`:\n\n - 1 for pixels that are real (i.e. **not masked**),\n - 0 for pixels that are padding (i.e. **masked**).\n `What are attention masks? <../glossary.html#attention-mask>`__\n\n head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):\n Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:\n - 1 indicates the head is **not masked**,\n - 0 indicates the head is **masked**.\n\n inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):\n Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This\n is useful if you want more control over how to convert `input_ids` indices into associated vectors than the\n model's internal embedding lookup matrix.\n\n image_embeds (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_size)`, *optional*):\n Optionally, instead of passing `pixel_values`, you can choose to directly pass an embedded representation.\n This is useful if you want more control over how to convert `pixel_values` into patch embeddings.\n\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned\n tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n\n Args:\n input_ids (`torch.LongTensor` of shape `({0})`):\n Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`BertTokenizer`]. See\n [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input\n IDs?](../glossary#input-ids)\n\n attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):\n Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:\n - 1 for tokens that are **not masked**,\n - 0 for tokens that are **masked**.\n [What are attention masks?](../glossary#attention-mask)\n\n token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):\n Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,\n 1]`:\n - 0 corresponds to a *sentence A* token,\n - 1 corresponds to a *sentence B* token.\n [What are token type IDs?](../glossary#token-type-ids)\n\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_images, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`ViltFeatureExtractor`]. See\n [`ViltFeatureExtractor.__call__`] for details.\n\n pixel_mask (`torch.LongTensor` of shape `(batch_size, num_images, height, width)`, *optional*):\n Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`:\n\n - 1 for pixels that are real (i.e. **not masked**),\n - 0 for pixels that are padding (i.e. **masked**).\n `What are attention masks? <../glossary.html#attention-mask>`__\n\n head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):\n Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:\n - 1 indicates the head is **not masked**,\n - 0 indicates the head is **masked**.\n\n inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):\n Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This\n is useful if you want more control over how to convert `input_ids` indices into associated vectors than the\n model's internal embedding lookup matrix.\n\n image_embeds (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_size)`, *optional*):\n Optionally, instead of passing `pixel_values`, you can choose to directly pass an embedded representation.\n This is useful if you want more control over how to convert `pixel_values` into patch embeddings.\n\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned\n tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n",
"language": "en",
"n_whitespaces": 1685,
"n_words": 802,
"vocab_size": 200
} | 36 | Python | 31 | ac227093e41cecb07c7e0f2fc9a504850907bd06 | modeling_vilt.py | 34,307 | 3 | 24 | _set_gradient_checkpointing | https://github.com/huggingface/transformers.git | Add ViLT (#14895)
* First commit
* Add conversion script
* Make conversion script work for base model
* More improvements
* Update conversion script, works for vqa
* Add indexing argument to meshgrid
* Make conversion script work for ViltForPreTraining
* Add ViltForPreTraining to docs
* Fix device issue
* Add processor
* Add MinMaxResize to feature extractor
* Implement call method of ViltProcessor
* Fix tests
* Add integration test
* Add loss calculation for VQA
* Improve tests
* Improve some more tests
* Debug tests
* Small improvements
* Add support for attention_mask
* Remove mask_it
* Add pixel_mask
* Add tests for ViltFeatureExtractor
* Improve tests
* Add ViltForNaturalLanguageVisualReasoning
* Add ViltForNaturalLanguageVisualReasoning to conversion script
* Minor fixes
* Add support for image_embeds, update docstrings to markdown
* Update docs to markdown
* Improve conversion script
* Rename ViltForPreTraining to ViltForMaskedLM
* Improve conversion script
* Convert docstrings to markdown
* Fix code example of retrieval model
* Properly convert masked language model
* Add integration test for nlvr
* Fix code quality
* Apply suggestions from code review
* Add copied from statements
* Fix pretrained_config_archive_map
* Fix docs
* Add model to README
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <[email protected]>
* Apply more suggestions from code review
* Make code more readable
* Add ViltForNaturalLanguageVisualReasoning to the tests
* Rename ViltForVisualQuestionAnswering to ViltForQuestionAnswering
* Replace pixel_values_2 by single tensor
* Add hidden_states and attentions
* Fix one more test
* Fix all tests
* Update year
* Fix rebase issues
* Fix another rebase issue
* Remove ViltForPreTraining from auto mapping
* Rename ViltForImageRetrievalTextRetrieval to ViltForImageAndTextRetrieval
* Make it possible to use BertTokenizerFast in the processor
* Use BertTokenizerFast by default
* Rename ViltForNaturalLanguageVisualReasoning, define custom model output
Co-authored-by: Sylvain Gugger <[email protected]> | 54 | 1 | 6,254 | 9 |
1 | 7 | def expunge(self):
name = 'EXPUNGE'
typ, dat = self._simple_command(name)
return self._untagged_response(typ, dat, name)
| python3.10.4/Lib/imaplib.py | 51 | XX-Net | {
"docstring": "Permanently remove deleted items from selected mailbox.\n\n Generates 'EXPUNGE' response for each deleted message.\n\n (typ, [data]) = <instance>.expunge()\n\n 'data' is list of 'EXPUNGE'd message numbers in order received.\n ",
"language": "en",
"n_whitespaces": 56,
"n_words": 28,
"vocab_size": 27
} | 13 | Python | 12 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | imaplib.py | 217,981 | 4 | 30 | expunge | https://github.com/XX-net/XX-Net.git | add python 3.10.4 for windows | 41 | 0 | 55,053 | 8 |
|
3 | 15 | def test_next_dagrun_after_auto_align(self):
dag = DAG(
dag_id='test_scheduler_auto_align_1',
start_date=timezone.datetime(2016, 1, 1, 10, 10, 0),
schedule_interval="4 5 * * *",
)
EmptyOperator(task_id='dummy', dag=dag, owner='airflow')
next_info = dag.next_dagrun_info(None)
assert next_info and next_info.logical_date == timezone.datetime(2016, 1, 2, 5, 4)
dag = DAG(
dag_id='test_scheduler_auto_align_2',
start_date=timezone.datetime(2016, 1, 1, 10, 10, 0),
schedule_interval="10 10 * * *",
)
EmptyOperator(task_id='dummy', dag=dag, owner='airflow')
next_info = dag.next_dagrun_info(None)
assert next_info and next_info.logical_date == timezone.datetime(2016, 1, 1, 10, 10)
| tests/models/test_dag.py | 235 | airflow | {
"docstring": "\n Test if the schedule_interval will be auto aligned with the start_date\n such that if the start_date coincides with the schedule the first\n execution_date will be start_date, otherwise it will be start_date +\n interval.\n ",
"language": "en",
"n_whitespaces": 69,
"n_words": 33,
"vocab_size": 21
} | 66 | Python | 32 | 49e336ae0302b386a2f47269a6d13988382d975f | test_dag.py | 47,565 | 17 | 156 | test_next_dagrun_after_auto_align | https://github.com/apache/airflow.git | Replace usage of `DummyOperator` with `EmptyOperator` (#22974)
* Replace usage of `DummyOperator` with `EmptyOperator` | 209 | 0 | 9,160 | 11 |
|
1 | 2 | def tickwidth(self):
return self["tickwidth"]
| packages/python/plotly/plotly/graph_objs/_ohlc.py | 22 | plotly.py | {
"docstring": "\n Sets the width of the open/close tick marks relative to the \"x\"\n minimal interval.\n\n The 'tickwidth' property is a number and may be specified as:\n - An int or float in the interval [0, 0.5]\n\n Returns\n -------\n int|float\n ",
"language": "en",
"n_whitespaces": 97,
"n_words": 38,
"vocab_size": 35
} | 4 | Python | 4 | 43e3a4011080911901176aab919c0ecf5046ddd3 | _ohlc.py | 227,484 | 2 | 11 | tickwidth | https://github.com/plotly/plotly.py.git | switch to black .22 | 18 | 0 | 59,157 | 7 |
|
1 | 5 | def median_approximate(self, method="default"):
return self.quantile(q=0.5, method=method)
| dask/dataframe/core.py | 39 | dask | {
"docstring": "Return the approximate median of the values over the requested axis.\n\n Parameters\n ----------\n method : {'default', 'tdigest', 'dask'}, optional\n What method to use. By default will use Dask's internal custom\n algorithm (``\"dask\"``). If set to ``\"tdigest\"`` will use tdigest\n for floats and ints and fallback to the ``\"dask\"`` otherwise.\n ",
"language": "en",
"n_whitespaces": 111,
"n_words": 49,
"vocab_size": 40
} | 6 | Python | 6 | 142de2608df2494bf11e08038aadddb544b4500c | core.py | 156,986 | 2 | 25 | median_approximate | https://github.com/dask/dask.git | Add `DataFrame` and `Series` `median` method (#9483) | 20 | 0 | 36,823 | 8 |
|
1 | 15 | def add_metadata_summerizer():
docs = [
Document(
content=,
meta={
"sub_content": "Pegasus Example",
"topic": "California's Electricity",
"context": "Dummy - PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires.",
},
),
Document(
content=,
meta={"sub_content": "Paris best tour best tour", "topic": "Eiffel tower"},
),
]
# Original input is overwrote after the "predict". So adding the same input as check_output to assess the output
check_output = deepcopy(docs)
summarizer = TransformersSummarizer(model_name_or_path="google/pegasus-xsum")
summary = summarizer.predict(documents=docs)
assert len(summary[0].meta) == len(check_output[0].meta)
assert len(summary[1].meta) - 1 == len(check_output[1].meta)
assert (
summary[0].meta["context"]
==
)
summary = summarizer.predict(documents=docs, generate_single_summary=True)
assert len(summary) == 1
assert not summary[0].meta # Metadata is not returned in case of a single summary
| test/nodes/test_summarizer.py | 273 | haystack | {
"docstring": "PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct.PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.",
"language": "en",
"n_whitespaces": 221,
"n_words": 222,
"vocab_size": 117
} | 122 | Python | 88 | 4d8f40425bc4e7346359b7609720a50ac10b8af9 | test_summarizer.py | 257,543 | 27 | 162 | add_metadata_summerizer | https://github.com/deepset-ai/haystack.git | Passing the meta-data in the summerizer response (#2179)
* Passing the all the meta-data in the summerizer
* Disable metadata forwarding if `generate_single_summary` is `True`
* Update Documentation & Code Style
* simplify tests
* Update Documentation & Code Style
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> | 304 | 0 | 75,094 | 13 |
|
2 | 14 | def dump_full(self) -> None:
styles = {}
if term.supports_colors(sys.stdout.fileno()):
styles[self] = term.Style16(color='magenta', bold=True)
print(self.root.pdebugformat(styles=styles))
| edb/ir/scopetree.py | 94 | edgedb | {
"docstring": "Do a debug dump of the root but hilight the current node.",
"language": "en",
"n_whitespaces": 11,
"n_words": 12,
"vocab_size": 11
} | 14 | Python | 13 | e59a77b36afa41b93518b8bc4128e6e90da08fda | scopetree.py | 176,140 | 6 | 56 | dump_full | https://github.com/edgedb/edgedb.git | Add a scopetree method to dump the root but to highlight the current node (#3330) | 53 | 0 | 41,716 | 12 |
|
1 | 8 | def local_devices_fixture():
return json.loads(load_fixture("awair/local_devices.json"))
@pytest.fixture(name="gen1_data", scope="session") | tests/components/awair/conftest.py | 54 | @pytest.fixture(name="gen1_data", scope="session") | core | {
"docstring": "Fixture representing devices returned by Awair local API.",
"language": "en",
"n_whitespaces": 7,
"n_words": 8,
"vocab_size": 8
} | 6 | Python | 6 | ebbff7b60e43f17d65ead811d314602b9daddfc4 | conftest.py | 303,763 | 2 | 15 | local_devices_fixture | https://github.com/home-assistant/core.git | Add Awair Local API support (#75535) | 11 | 1 | 102,572 | 10 |
1 | 2 | def root(self):
return self["root"]
| packages/python/plotly/plotly/graph_objs/_icicle.py | 22 | plotly.py | {
"docstring": "\n The 'root' property is an instance of Root\n that may be specified as:\n - An instance of :class:`plotly.graph_objs.icicle.Root`\n - A dict of string/value properties that will be passed\n to the Root constructor\n\n Supported dict properties:\n\n color\n sets the color of the root node for a\n sunburst/treemap/icicle trace. this has no\n effect when a colorscale is used to set the\n markers.\n\n Returns\n -------\n plotly.graph_objs.icicle.Root\n ",
"language": "en",
"n_whitespaces": 237,
"n_words": 63,
"vocab_size": 47
} | 4 | Python | 4 | 43e3a4011080911901176aab919c0ecf5046ddd3 | _icicle.py | 227,179 | 2 | 11 | root | https://github.com/plotly/plotly.py.git | switch to black .22 | 18 | 0 | 58,852 | 7 |
|
1 | 31 | def test_stream_slices_no_state_close_to_now(self, api, async_manager_mock, recent_start_date):
start_date = recent_start_date
end_date = pendulum.now()
stream = AdsInsights(api=api, start_date=start_date, end_date=end_date)
async_manager_mock.completed_jobs.return_value = [1, 2, 3]
slices = list(stream.stream_slices(stream_state=None, sync_mode=SyncMode.incremental))
assert slices == [{"insight_job": 1}, {"insight_job": 2}, {"insight_job": 3}]
async_manager_mock.assert_called_once()
args, kwargs = async_manager_mock.call_args
generated_jobs = list(kwargs["jobs"])
assert len(generated_jobs) == (end_date - start_date).days + 1
assert generated_jobs[0].interval.start == start_date.date()
assert generated_jobs[1].interval.start == start_date.date() + duration(days=1)
| airbyte-integrations/connectors/source-facebook-marketing/unit_tests/test_base_insight_streams.py | 259 | airbyte | {
"docstring": "Stream will use start_date when there is not state and start_date within 28d from now",
"language": "en",
"n_whitespaces": 14,
"n_words": 15,
"vocab_size": 14
} | 60 | Python | 44 | a3aae8017a0a40ff2006e2567f71dccb04c997a5 | test_base_insight_streams.py | 3,829 | 13 | 165 | test_stream_slices_no_state_close_to_now | https://github.com/airbytehq/airbyte.git | 🎉 🎉 Source FB Marketing: performance and reliability fixes (#9805)
* Facebook Marketing performance improvement
* add comments and little refactoring
* fix integration tests with the new config
* improve job status handling, limit concurrency to 10
* fix campaign jobs, refactor manager
* big refactoring of async jobs, support random order of slices
* update source _read_incremental to hook new state logic
* fix issues with timeout
* remove debugging and clean up, improve retry logic
* merge changes from #8234
* fix call super _read_increment
* generalize batch execution, add use_batch flag
* improve coverage, do some refactoring of spec
* update test, remove overrides of source
* add split by AdSet
* add smaller insights
* fix end_date < start_date case
* add account_id to PK
* add notes
* fix new streams
* fix reversed incremental stream
* update spec.json for SAT
* upgrade CDK and bump version
Co-authored-by: Dmytro Rezchykov <[email protected]>
Co-authored-by: Eugene Kulak <[email protected]> | 151 | 0 | 574 | 12 |
|
2 | 19 | def execute():
frappe.reload_doc("stock", "doctype", "purchase_receipt")
frappe.reload_doc("stock", "doctype", "purchase_receipt_item")
frappe.reload_doc("stock", "doctype", "delivery_note")
frappe.reload_doc("stock", "doctype", "delivery_note_item")
frappe.reload_doc("stock", "doctype", "stock_settings")
def update_from_return_docs(doctype):
for return_doc in frappe.get_all(
doctype, filters={"is_return": 1, "docstatus": 1, "return_against": ("!=", "")}
):
# Update original receipt/delivery document from return
return_doc = frappe.get_cached_doc(doctype, return_doc.name)
try:
return_doc.update_prevdoc_status()
except OverAllowanceError:
frappe.db.rollback()
continue
return_against = frappe.get_doc(doctype, return_doc.return_against)
return_against.update_billing_status()
frappe.db.commit()
# Set received qty in stock uom in PR, as returned qty is checked against it
frappe.db.sql(
)
for doctype in ("Purchase Receipt", "Delivery Note"):
update_from_return_docs(doctype)
| erpnext/patches/v13_0/update_returned_qty_in_pr_dn.py | 297 | erpnext | {
"docstring": " update `tabPurchase Receipt Item`\n\t\tset received_stock_qty = received_qty * conversion_factor\n\t\twhere docstatus = 1 ",
"language": "en",
"n_whitespaces": 13,
"n_words": 14,
"vocab_size": 13
} | 81 | Python | 63 | 494bd9ef78313436f0424b918f200dab8fc7c20b | update_returned_qty_in_pr_dn.py | 66,831 | 14 | 77 | execute | https://github.com/frappe/erpnext.git | style: format code with black | 56 | 0 | 14,353 | 15 |
|
6 | 32 | def geometric_edges(G, radius, p):
nodes_pos = G.nodes(data="pos")
try:
import scipy as sp
import scipy.spatial # call as sp.spatial
except ImportError:
# no scipy KDTree so compute by for-loop
radius_p = radius**p
edges = [
(u, v)
for (u, pu), (v, pv) in combinations(nodes_pos, 2)
if sum(abs(a - b) ** p for a, b in zip(pu, pv)) <= radius_p
]
return edges
# scipy KDTree is available
nodes, coords = list(zip(*nodes_pos))
kdtree = sp.spatial.cKDTree(coords) # Cannot provide generator.
edge_indexes = kdtree.query_pairs(radius, p)
edges = [(nodes[u], nodes[v]) for u, v in sorted(edge_indexes)]
return edges
@py_random_state(5)
@nodes_or_number(0) | networkx/generators/geometric.py | 252 | @py_random_state(5)
@nodes_or_number(0) | networkx | {
"docstring": "Returns edge list of node pairs within `radius` of each other.\n\n Parameters\n ----------\n G : networkx graph\n The graph from which to generate the edge list. The nodes in `G` should\n have an attribute ``pos`` corresponding to the node position, which is\n used to compute the distance to other nodes.\n radius : scalar\n The distance threshold. Edges are included in the edge list if the\n distance between the two nodes is less than `radius`.\n p : scalar\n The `Minkowski distance metric\n <https://en.wikipedia.org/wiki/Minkowski_distance>`_ use to compute\n distances.\n\n Returns\n -------\n edges : list\n List of edges whose distances are less than `radius`\n\n Notes\n -----\n Radius uses Minkowski distance metric `p`.\n If scipy is available, `scipy.spatial.cKDTree` is used to speed computation.\n\n Examples\n --------\n Create a graph with nodes that have a \"pos\" attribute representing 2D\n coordinates.\n\n >>> G = nx.Graph()\n >>> G.add_nodes_from([\n ... (0, {\"pos\": (0, 0)}),\n ... (1, {\"pos\": (3, 0)}),\n ... (2, {\"pos\": (8, 0)}),\n ... ])\n >>> p = 2 # Euclidean distance\n >>> nx.geometric_edges(G, radius=1, p=p)\n []\n >>> nx.geometric_edges(G, radius=4, p=p)\n [(0, 1)]\n >>> nx.geometric_edges(G, radius=6, p=p)\n [(0, 1), (1, 2)]\n >>> nx.geometric_edges(G, radius=9, p=p)\n [(0, 1), (0, 2), (1, 2)]\n ",
"language": "en",
"n_whitespaces": 364,
"n_words": 192,
"vocab_size": 112
} | 94 | Python | 70 | f6755ffa00211b523c6c0bec5398bc6c3c43c8b1 | geometric.py | 176,489 | 18 | 151 | geometric_edges | https://github.com/networkx/networkx.git | Update black (#5438)
* CI: sync up black dev requirements version with precommit
* Run black
Co-authored-by: Jarrod Millman <[email protected]> | 206 | 1 | 41,932 | 18 |
4 | 22 | def _proc_function_remote(self, *, fun, low, user, tag, jid, daemonize=True):
if daemonize and not salt.utils.platform.is_windows():
# Shutdown the multiprocessing before daemonizing
salt.log.setup.shutdown_multiprocessing_logging()
salt.utils.process.daemonize()
# Reconfigure multiprocessing logging after daemonizing
salt.log.setup.setup_multiprocessing_logging()
# pack a few things into low
low["__jid__"] = jid
low["__user__"] = user
low["__tag__"] = tag
try:
return self.cmd_sync(low)
except salt.exceptions.EauthAuthenticationError as exc:
log.error(exc)
| salt/client/mixins.py | 175 | salt | {
"docstring": "\n Run this method in a multiprocess target to execute the function on the\n master and fire the return data on the event bus\n ",
"language": "en",
"n_whitespaces": 45,
"n_words": 23,
"vocab_size": 19
} | 53 | Python | 47 | c78f1ee4f49df35ab04e921a45de0878716d8bf5 | mixins.py | 216,481 | 12 | 105 | _proc_function_remote | https://github.com/saltstack/salt.git | Implement ``__getstate__`` and ``__setstate__`` instead of using ``classmethod``
Signed-off-by: Pedro Algarvio <[email protected]> | 186 | 0 | 54,603 | 11 |
|
1 | 14 | def test_text_qtest(self, qtest_key, qtbot, key_tester):
with qtbot.wait_signal(key_tester.got_text):
qtbot.keyPress(key_tester, qtest_key.member)
info = keyutils.KeyInfo(qtest_key.member)
assert info.text() == key_tester.text.lower()
| tests/unit/keyinput/test_keyutils.py | 91 | qutebrowser | {
"docstring": "Make sure KeyInfo.text() lines up with QTest::keyToAscii.\n\n See key_data.py for inputs and expected values.\n ",
"language": "en",
"n_whitespaces": 28,
"n_words": 14,
"vocab_size": 14
} | 16 | Python | 16 | 623b06bc3dabfd53f637e611ec8d3e4feb521189 | test_keyutils.py | 321,569 | 5 | 56 | test_text_qtest | https://github.com/qutebrowser/qutebrowser.git | Fix remaining enum/flag issues | 55 | 0 | 117,802 | 10 |
|
1 | 11 | def mixin_head_parser(parser):
gp = add_arg_group(parser, title='Head')
gp.add_argument(
'--uses-before-address',
type=str,
help='The address of the uses-before runtime',
)
gp.add_argument(
'--uses-after-address',
type=str,
help='The address of the uses-before runtime',
)
gp.add_argument(
'--connection-list',
type=str,
help='dictionary JSON with a list of connections to configure',
)
gp.add_argument(
'--disable-reduce',
action='store_true',
default=False,
help='Disable the built-in reduce mechanism, set this if the reduction is to be handled by the Executor connected to this Head',
)
| jina/parsers/orchestrate/runtimes/head.py | 137 | jina | {
"docstring": "Mixing in arguments required by head pods and runtimes into the given parser.\n :param parser: the parser instance to which we add arguments\n ",
"language": "en",
"n_whitespaces": 29,
"n_words": 23,
"vocab_size": 21
} | 65 | Python | 44 | c7ad27e5614dfb2b1684f4718c5508840cd55de0 | head.py | 11,483 | 23 | 80 | mixin_head_parser | https://github.com/jina-ai/jina.git | refactor: add disable_reduce args (#4424) | 186 | 0 | 2,054 | 10 |
|
2 | 20 | def test_run_clm_no_trainer(self):
tmp_dir = self.get_auto_remove_tmp_dir()
testargs = f.split()
if torch.cuda.device_count() > 1:
# Skipping because there are not enough batches to train the model + would need a drop_last to work.
return
run_command(self._launch_args + testargs)
result = get_results(tmp_dir)
self.assertLess(result["perplexity"], 100)
self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0")))
self.assertTrue(os.path.exists(os.path.join(tmp_dir, "clm_no_trainer")))
| examples/pytorch/test_accelerate_examples.py | 180 | transformers | {
"docstring": "\n {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n --model_name_or_path distilgpt2\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --block_size 128\n --per_device_train_batch_size 5\n --per_device_eval_batch_size 5\n --num_train_epochs 2\n --output_dir {tmp_dir}\n --checkpointing_steps epoch\n --with_tracking\n ",
"language": "en",
"n_whitespaces": 149,
"n_words": 20,
"vocab_size": 18
} | 44 | Python | 39 | 99eb9b523f9b9ea6096323ce5610ce6633acc88a | test_accelerate_examples.py | 32,329 | 22 | 101 | test_run_clm_no_trainer | https://github.com/huggingface/transformers.git | Fix `no_trainer` CI (#18242)
* Fix all tests | 121 | 0 | 5,903 | 12 |
|
2 | 20 | def bokeh_chart(self, figure, use_container_width=False):
import bokeh
if bokeh.__version__ != ST_BOKEH_VERSION:
raise StreamlitAPIException(
f"Streamlit only supports Bokeh version {ST_BOKEH_VERSION}, "
f"but you have version {bokeh.__version__} installed. Please "
f"run `pip install --force-reinstall --no-deps bokeh=="
f"{ST_BOKEH_VERSION}` to install the correct version."
)
# Generate element ID from delta path
delta_path = self.dg._get_delta_path_str()
element_id = hashlib.md5(delta_path.encode()).hexdigest()
bokeh_chart_proto = BokehChartProto()
marshall(bokeh_chart_proto, figure, use_container_width, element_id)
return self.dg._enqueue("bokeh_chart", bokeh_chart_proto)
| lib/streamlit/elements/bokeh_chart.py | 153 | streamlit | {
"docstring": "Display an interactive Bokeh chart.\n\n Bokeh is a charting library for Python. The arguments to this function\n closely follow the ones for Bokeh's `show` function. You can find\n more about Bokeh at https://bokeh.pydata.org.\n\n Parameters\n ----------\n figure : bokeh.plotting.figure.Figure\n A Bokeh figure to plot.\n\n use_container_width : bool\n If True, set the chart width to the column width. This takes\n precedence over Bokeh's native `width` value.\n\n To show Bokeh charts in Streamlit, call `st.bokeh_chart`\n wherever you would call Bokeh's `show`.\n\n Example\n -------\n >>> import streamlit as st\n >>> from bokeh.plotting import figure\n >>>\n >>> x = [1, 2, 3, 4, 5]\n >>> y = [6, 7, 2, 4, 5]\n >>>\n >>> p = figure(\n ... title='simple line example',\n ... x_axis_label='x',\n ... y_axis_label='y')\n ...\n >>> p.line(x, y, legend_label='Trend', line_width=2)\n >>>\n >>> st.bokeh_chart(p, use_container_width=True)\n\n .. output::\n https://share.streamlit.io/streamlit/docs/main/python/api-examples-source/charts.bokeh_chart.py\n height: 700px\n\n ",
"language": "en",
"n_whitespaces": 389,
"n_words": 135,
"vocab_size": 102
} | 63 | Python | 57 | 72703b38029f9358a0ec7ca5ed875a6b438ece19 | bokeh_chart.py | 118,727 | 14 | 84 | bokeh_chart | https://github.com/streamlit/streamlit.git | Replace static apps with live Cloud apps (#4317)
Co-authored-by: kajarenc <[email protected]> | 208 | 0 | 26,384 | 13 |
|
3 | 30 | def matthews_corrcoef(y_true, y_pred, *, sample_weight=None):
y_type, y_true, y_pred = _check_targets(y_true, y_pred)
check_consistent_length(y_true, y_pred, sample_weight)
if y_type not in {"binary", "multiclass"}:
raise ValueError("%s is not supported" % y_type)
lb = LabelEncoder()
lb.fit(np.hstack([y_true, y_pred]))
y_true = lb.transform(y_true)
y_pred = lb.transform(y_pred)
C = confusion_matrix(y_true, y_pred, sample_weight=sample_weight)
t_sum = C.sum(axis=1, dtype=np.float64)
p_sum = C.sum(axis=0, dtype=np.float64)
n_correct = np.trace(C, dtype=np.float64)
n_samples = p_sum.sum()
cov_ytyp = n_correct * n_samples - np.dot(t_sum, p_sum)
cov_ypyp = n_samples**2 - np.dot(p_sum, p_sum)
cov_ytyt = n_samples**2 - np.dot(t_sum, t_sum)
if cov_ypyp * cov_ytyt == 0:
return 0.0
else:
return cov_ytyp / np.sqrt(cov_ytyt * cov_ypyp)
| sklearn/metrics/_classification.py | 336 | scikit-learn | {
"docstring": "Compute the Matthews correlation coefficient (MCC).\n\n The Matthews correlation coefficient is used in machine learning as a\n measure of the quality of binary and multiclass classifications. It takes\n into account true and false positives and negatives and is generally\n regarded as a balanced measure which can be used even if the classes are of\n very different sizes. The MCC is in essence a correlation coefficient value\n between -1 and +1. A coefficient of +1 represents a perfect prediction, 0\n an average random prediction and -1 an inverse prediction. The statistic\n is also known as the phi coefficient. [source: Wikipedia]\n\n Binary and multiclass labels are supported. Only in the binary case does\n this relate to information about true and false positives and negatives.\n See references below.\n\n Read more in the :ref:`User Guide <matthews_corrcoef>`.\n\n Parameters\n ----------\n y_true : array, shape = [n_samples]\n Ground truth (correct) target values.\n\n y_pred : array, shape = [n_samples]\n Estimated targets as returned by a classifier.\n\n sample_weight : array-like of shape (n_samples,), default=None\n Sample weights.\n\n .. versionadded:: 0.18\n\n Returns\n -------\n mcc : float\n The Matthews correlation coefficient (+1 represents a perfect\n prediction, 0 an average random prediction and -1 and inverse\n prediction).\n\n References\n ----------\n .. [1] `Baldi, Brunak, Chauvin, Andersen and Nielsen, (2000). Assessing the\n accuracy of prediction algorithms for classification: an overview\n <https://doi.org/10.1093/bioinformatics/16.5.412>`_.\n\n .. [2] `Wikipedia entry for the Matthews Correlation Coefficient\n <https://en.wikipedia.org/wiki/Matthews_correlation_coefficient>`_.\n\n .. [3] `Gorodkin, (2004). Comparing two K-category assignments by a\n K-category correlation coefficient\n <https://www.sciencedirect.com/science/article/pii/S1476927104000799>`_.\n\n .. [4] `Jurman, Riccadonna, Furlanello, (2012). A Comparison of MCC and CEN\n Error Measures in MultiClass Prediction\n <https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0041882>`_.\n\n Examples\n --------\n >>> from sklearn.metrics import matthews_corrcoef\n >>> y_true = [+1, +1, +1, -1]\n >>> y_pred = [+1, -1, +1, +1]\n >>> matthews_corrcoef(y_true, y_pred)\n -0.33...\n ",
"language": "en",
"n_whitespaces": 482,
"n_words": 283,
"vocab_size": 177
} | 93 | Python | 62 | 1fc86b6aacd89da44a3b4e8abf7c3e2ba4336ffe | _classification.py | 258,915 | 21 | 218 | matthews_corrcoef | https://github.com/scikit-learn/scikit-learn.git | MNT Update black to stable version (#22474) | 168 | 0 | 75,481 | 12 |
|
2 | 33 | def test_fed_filtering(self):
fed_hostname = self.hs.hostname + "2"
subspace = "#subspace:" + fed_hostname
# Create a few rooms which will have different properties.
public_room = "#public:" + fed_hostname
knock_room = "#knock:" + fed_hostname
not_invited_room = "#not_invited:" + fed_hostname
invited_room = "#invited:" + fed_hostname
restricted_room = "#restricted:" + fed_hostname
restricted_accessible_room = "#restricted_accessible:" + fed_hostname
world_readable_room = "#world_readable:" + fed_hostname
joined_room = self.helper.create_room_as(self.user, tok=self.token)
# Poke an invite over federation into the database.
self._poke_fed_invite(invited_room, "@remote:" + fed_hostname)
# Note that these entries are brief, but should contain enough info.
children_rooms = (
(
public_room,
{
"room_id": public_room,
"world_readable": False,
"join_rules": JoinRules.PUBLIC,
},
),
(
knock_room,
{
"room_id": knock_room,
"world_readable": False,
"join_rules": JoinRules.KNOCK,
},
),
(
not_invited_room,
{
"room_id": not_invited_room,
"world_readable": False,
"join_rules": JoinRules.INVITE,
},
),
(
invited_room,
{
"room_id": invited_room,
"world_readable": False,
"join_rules": JoinRules.INVITE,
},
),
(
restricted_room,
{
"room_id": restricted_room,
"world_readable": False,
"join_rules": JoinRules.RESTRICTED,
"allowed_room_ids": [],
},
),
(
restricted_accessible_room,
{
"room_id": restricted_accessible_room,
"world_readable": False,
"join_rules": JoinRules.RESTRICTED,
"allowed_room_ids": [self.room],
},
),
(
world_readable_room,
{
"room_id": world_readable_room,
"world_readable": True,
"join_rules": JoinRules.INVITE,
},
),
(
joined_room,
{
"room_id": joined_room,
"world_readable": False,
"join_rules": JoinRules.INVITE,
},
),
)
subspace_room_entry = _RoomEntry(
subspace,
{
"room_id": subspace,
"world_readable": True,
},
# Place each room in the sub-space.
[
{
"type": EventTypes.SpaceChild,
"room_id": subspace,
"state_key": room_id,
"content": {"via": [fed_hostname]},
}
for room_id, _ in children_rooms
],
)
| tests/handlers/test_room_summary.py | 544 | synapse | {
"docstring": "\n Rooms returned over federation should be properly filtered to only include\n rooms the user has access to.\n ",
"language": "en",
"n_whitespaces": 39,
"n_words": 17,
"vocab_size": 17
} | 218 | Python | 104 | 7754af24ab163a3666bc04c7df409e59ace0d763 | test_room_summary.py | 247,085 | 129 | 484 | test_fed_filtering | https://github.com/matrix-org/synapse.git | Remove the unstable `/spaces` endpoint. (#12073)
...and various code supporting it.
The /spaces endpoint was from an old version of MSC2946 and included
both a Client-Server and Server-Server API. Note that the unstable
/hierarchy endpoint (from the final version of MSC2946) is not yet
removed. | 1,598 | 0 | 71,495 | 14 |
|
1 | 25 | def test_tweedie_log_identity_consistency(p):
half_tweedie_log = HalfTweedieLoss(power=p)
half_tweedie_identity = HalfTweedieLossIdentity(power=p)
n_samples = 10
y_true, raw_prediction = random_y_true_raw_prediction(
loss=half_tweedie_log, n_samples=n_samples, seed=42
)
y_pred = half_tweedie_log.link.inverse(raw_prediction) # exp(raw_prediction)
# Let's compare the loss values, up to some constant term that is dropped
# in HalfTweedieLoss but not in HalfTweedieLossIdentity.
loss_log = half_tweedie_log.loss(
y_true=y_true, raw_prediction=raw_prediction
) + half_tweedie_log.constant_to_optimal_zero(y_true)
loss_identity = half_tweedie_identity.loss(
y_true=y_true, raw_prediction=y_pred
) + half_tweedie_identity.constant_to_optimal_zero(y_true)
# Note that HalfTweedieLoss ignores different constant terms than
# HalfTweedieLossIdentity. Constant terms means terms not depending on
# raw_prediction. By adding these terms, `constant_to_optimal_zero`, both losses
# give the same values.
assert_allclose(loss_log, loss_identity)
# For gradients and hessians, the constant terms do not matter. We have, however,
# to account for the chain rule, i.e. with x=raw_prediction
# gradient_log(x) = d/dx loss_log(x)
# = d/dx loss_identity(exp(x))
# = exp(x) * gradient_identity(exp(x))
# Similarly,
# hessian_log(x) = exp(x) * gradient_identity(exp(x))
# + exp(x)**2 * hessian_identity(x)
gradient_log, hessian_log = half_tweedie_log.gradient_hessian(
y_true=y_true, raw_prediction=raw_prediction
)
gradient_identity, hessian_identity = half_tweedie_identity.gradient_hessian(
y_true=y_true, raw_prediction=y_pred
)
assert_allclose(gradient_log, y_pred * gradient_identity)
assert_allclose(
hessian_log, y_pred * gradient_identity + y_pred**2 * hessian_identity
)
| sklearn/_loss/tests/test_loss.py | 255 | scikit-learn | {
"docstring": "Test for identical losses when only the link function is different.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | 174 | Python | 109 | 75a94f518f7bd7d0bf581ffb67d9f961e3c4efbc | test_loss.py | 259,434 | 25 | 155 | test_tweedie_log_identity_consistency | https://github.com/scikit-learn/scikit-learn.git | ENH migrate GLMs / TweedieRegressor to linear loss (#22548)
Co-authored-by: Olivier Grisel <[email protected]>
Co-authored-by: Thomas J. Fan <[email protected]> | 383 | 0 | 75,768 | 10 |
|
1 | 6 | def restart_subscription(name):
subscription = frappe.get_doc("Subscription", name)
subscription.restart_subscription()
@frappe.whitelist() | erpnext/accounts/doctype/subscription/subscription.py | 50 | @frappe.whitelist() | erpnext | {
"docstring": "\n\tRestarts a cancelled `Subscription`. The `Subscription` will 'forget' the history of\n\tall invoices it has generated\n\t",
"language": "en",
"n_whitespaces": 14,
"n_words": 16,
"vocab_size": 16
} | 8 | Python | 8 | 494bd9ef78313436f0424b918f200dab8fc7c20b | subscription.py | 65,075 | 3 | 21 | restart_subscription | https://github.com/frappe/erpnext.git | style: format code with black | 4 | 1 | 13,786 | 9 |
4 | 15 | def set_exception(self, exception):
if self._state != _PENDING:
raise exceptions.InvalidStateError(f'{self._state}: {self!r}')
if isinstance(exception, type):
exception = exception()
if type(exception) is StopIteration:
raise TypeError("StopIteration interacts badly with generators "
"and cannot be raised into a Future")
self._exception = exception
self._state = _FINISHED
self.__schedule_callbacks()
self.__log_traceback = True
| python3.10.4/Lib/asyncio/futures.py | 132 | XX-Net | {
"docstring": "Mark the future done and set an exception.\n\n If the future is already done when this method is called, raises\n InvalidStateError.\n ",
"language": "en",
"n_whitespaces": 42,
"n_words": 21,
"vocab_size": 17
} | 44 | Python | 36 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | futures.py | 220,514 | 12 | 70 | set_exception | https://github.com/XX-net/XX-Net.git | add python 3.10.4 for windows | 160 | 0 | 56,024 | 12 |
|
4 | 6 | def _move_into_position(self, width, height):
if self.should_center:
self.center()
if height is not None:
self.height = height
if width is not None:
self.width = width
| manim/mobject/svg/svg_mobject.py | 68 | manim | {
"docstring": "Uses the SVGMobject's config dictionary to set the Mobject's\n width, height, and/or center it. Use ``width``, ``height``, and\n ``should_center`` respectively to modify this.\n ",
"language": "en",
"n_whitespaces": 44,
"n_words": 23,
"vocab_size": 21
} | 23 | Python | 15 | 902e7eb4f0147b5882a613b67467e38a1d47f01e | svg_mobject.py | 189,467 | 7 | 42 | _move_into_position | https://github.com/ManimCommunity/manim.git | 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 | 84 | 0 | 46,075 | 9 |
|
1 | 11 | def upgrade():
with op.batch_alter_table("task_instance", schema=None) as batch_op:
batch_op.alter_column("pool_slots", existing_type=sa.Integer, nullable=False, server_default='1')
| airflow/migrations/versions/8646922c8a04_change_default_pool_slots_to_1.py | 70 | airflow | {
"docstring": "Change default pool_slots to 1 and make pool_slots not nullable",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 9
} | 11 | Python | 11 | 66c342d033bd3cb959b4dc4e7e4b8aad597aab63 | 8646922c8a04_change_default_pool_slots_to_1.py | 45,007 | 3 | 39 | upgrade | https://github.com/apache/airflow.git | Support generating SQL script for upgrades (#20962)
This PR attempts to add support for generating sql scripts for upgrade.
Example command:
`airflow db upgrade --revision-range e8d98d8ss99:78daisdu38d`
`airflow db upgrade --range 2.0.0:2.2.3` | 24 | 0 | 8,439 | 11 |
|
2 | 20 | async def test_setup_temporary_error(hass, aioclient_mock):
fake_async_add_entities = MagicMock()
errors = [HTTPStatus.TOO_MANY_REQUESTS, HTTPStatus.INTERNAL_SERVER_ERROR]
for error in errors:
aioclient_mock.get(re.compile("api.foobot.io/v2/owner/.*"), status=error)
with pytest.raises(PlatformNotReady):
await foobot.async_setup_platform(
hass, VALID_CONFIG, fake_async_add_entities
)
| tests/components/foobot/test_sensor.py | 104 | core | {
"docstring": "Expected failures caused by temporary errors in API response.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | 25 | Python | 23 | 8896229ea641a558161d8caed796895e9a78f457 | test_sensor.py | 304,817 | 9 | 63 | test_setup_temporary_error | https://github.com/home-assistant/core.git | Improve type hint in foobot sensor entity (#77164) | 88 | 0 | 103,612 | 12 |
|
1 | 17 | def test_constrained_layout22():
fig, ax = plt.subplots(layout="constrained")
fig.draw_without_rendering()
extents0 = np.copy(ax.get_position().extents)
fig.suptitle("Suptitle", y=0.5)
fig.draw_without_rendering()
extents1 = np.copy(ax.get_position().extents)
np.testing.assert_allclose(extents0, extents1)
| lib/matplotlib/tests/test_constrainedlayout.py | 129 | matplotlib | {
"docstring": "#11035: suptitle should not be include in CL if manually positioned",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | 18 | Python | 14 | ec4dfbc3c83866f487ff0bc9c87b0d43a1c02b22 | test_constrainedlayout.py | 107,161 | 8 | 77 | test_constrained_layout22 | https://github.com/matplotlib/matplotlib.git | ENH: implement and use base layout_engine for more flexible layout. | 42 | 0 | 22,616 | 11 |
|
1 | 25 | def test_syntax_highlight_ranges():
syntax = Syntax(
CODE,
lexer="python",
line_numbers=True,
word_wrap=False,
highlight_ranges=[
SyntaxHighlightRange(
# overline the 2nd char of the 1st line:
start=SyntaxPosition(1, 1),
end=SyntaxPosition(1, 2),
style=Style(overline=True),
),
SyntaxHighlightRange(
start=SyntaxPosition(1, len("def loop_")),
end=SyntaxPosition(1, len("def loop_first_last")),
style=Style(underline=True),
),
SyntaxHighlightRange(
start=SyntaxPosition(1, len("def loop_first")),
end=SyntaxPosition(3, len(" iter_values = iter")),
style=Style(bold=True),
),
SyntaxHighlightRange(
start=SyntaxPosition(9, len(" for ")),
end=SyntaxPosition(9, len(" for value in")),
style=Style(strike=True),
),
SyntaxHighlightRange(
start=SyntaxPosition(6, len(" except ")),
end=SyntaxPosition(6, len(" except StopIteration")),
style=Style(reverse=True),
),
# Those should be out of range, and have no impact:
SyntaxHighlightRange(
start=SyntaxPosition(1, 100), # `column_index` is out of range
end=SyntaxPosition(2, 2),
style=Style(bold=True),
),
SyntaxHighlightRange(
start=SyntaxPosition(1, 1),
end=SyntaxPosition(30, 2), # `line_number` is out of range
style=Style(bold=True),
),
],
)
rendered_syntax = render(syntax, True)
print(repr(rendered_syntax))
expected = '\x1b[1;38;2;227;227;221;48;2;39;40;34m \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 1 \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34md\x1b[0m\x1b[53;38;2;102;217;239;48;2;39;40;34me\x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34mf\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;166;226;46;48;2;39;40;34mloop_\x1b[0m\x1b[4;38;2;166;226;46;48;2;39;40;34mfirst\x1b[0m\x1b[1;4;38;2;166;226;46;48;2;39;40;34m_last\x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34m(\x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34mvalues\x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34m:\x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34mIterable\x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34m[\x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34mT\x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34m]\x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34m)\x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[1;38;2;249;38;114;48;2;39;40;34m-\x1b[0m\x1b[1;38;2;249;38;114;48;2;39;40;34m>\x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34mIterable\x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34m[\x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34mTuple\x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34m[\x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34mbool\x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34m,\x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34mbool\x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34m,\x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34mT\x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34m]\x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34m]\x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34m:\x1b[0m\n\x1b[1;38;2;227;227;221;48;2;39;40;34m \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 2 \x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[1;38;2;230;219;116;48;2;39;40;34m\x1b[0m\n\x1b[1;38;2;227;227;221;48;2;39;40;34m \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 3 \x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34miter_values\x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[1;38;2;249;38;114;48;2;39;40;34m=\x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[1;38;2;248;248;242;48;2;39;40;34miter\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m(\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mvalues\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m)\x1b[0m\n\x1b[1;38;2;227;227;221;48;2;39;40;34m \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 4 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34mtry\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m:\x1b[0m\n\x1b[1;38;2;227;227;221;48;2;39;40;34m \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 5 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mprevious_value\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;249;38;114;48;2;39;40;34m=\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mnext\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m(\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34miter_values\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m)\x1b[0m\n\x1b[1;38;2;227;227;221;48;2;39;40;34m \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 6 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34mexcept\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[7;38;2;166;226;46;48;2;39;40;34mStopIteration\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m:\x1b[0m\n\x1b[1;38;2;227;227;221;48;2;39;40;34m \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 7 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34mreturn\x1b[0m\n\x1b[1;38;2;227;227;221;48;2;39;40;34m \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 8 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mfirst\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;249;38;114;48;2;39;40;34m=\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34mTrue\x1b[0m\n\x1b[1;38;2;227;227;221;48;2;39;40;34m \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m 9 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34mfor\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[9;38;2;248;248;242;48;2;39;40;34mvalue\x1b[0m\x1b[9;38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[9;38;2;249;38;114;48;2;39;40;34min\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34miter_values\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m:\x1b[0m\n\x1b[1;38;2;227;227;221;48;2;39;40;34m \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m10 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34myield\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mfirst\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m,\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34mFalse\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m,\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mprevious_value\x1b[0m\n\x1b[1;38;2;227;227;221;48;2;39;40;34m \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m11 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mfirst\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;249;38;114;48;2;39;40;34m=\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34mFalse\x1b[0m\n\x1b[1;38;2;227;227;221;48;2;39;40;34m \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m12 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mprevious_value\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;249;38;114;48;2;39;40;34m=\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mvalue\x1b[0m\n\x1b[1;38;2;227;227;221;48;2;39;40;34m \x1b[0m\x1b[38;2;101;102;96;48;2;39;40;34m13 \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34myield\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mfirst\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m,\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;102;217;239;48;2;39;40;34mTrue\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m,\x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34m \x1b[0m\x1b[38;2;248;248;242;48;2;39;40;34mprevious_value\x1b[0m\n'
assert rendered_syntax == expected
| tests/test_syntax.py | 728 | rich | {
"docstring": "Iterate and generate a tuple with a flag for first and last value.",
"language": "en",
"n_whitespaces": 12,
"n_words": 13,
"vocab_size": 11
} | 193 | Python | 121 | ebc5d2797e7bfb595183fe61aac50be58c9a5174 | test_syntax.py | 161,775 | 48 | 291 | test_syntax_highlight_ranges | https://github.com/Textualize/rich.git | [syntax] add a `highlight_ranges` optional arg to the Syntax ctor
With this new API we can apply a style from (LINE A, COLUMN A) to (LINE B, COLUMN B) - which is something we will need to be able to add arbitrary ranges to Syntax | 833 | 0 | 39,061 | 18 |
|
2 | 18 | def str_presenter(dumper, data):
if len(data.splitlines()) > 1: # check for multiline string
return dumper.represent_scalar("tag:yaml.org,2002:str", data, style="|")
return dumper.represent_scalar("tag:yaml.org,2002:str", data)
yaml.add_representer(str, str_presenter)
yaml.representer.SafeRepresenter.add_representer(str, str_presenter)
deployment_app = PrefectTyper(
name="deployment", help="Commands for working with deployments."
)
app.add_typer(deployment_app)
| src/prefect/cli/deployment.py | 135 | prefect | {
"docstring": "\n configures yaml for dumping multiline strings\n Ref: https://stackoverflow.com/questions/8640959/how-can-i-control-what-scalar-form-pyyaml-uses-for-my-data\n ",
"language": "en",
"n_whitespaces": 18,
"n_words": 8,
"vocab_size": 8
} | 34 | Python | 30 | 36d9870433a22fff3944fa07f8e2feeb1b622bd9 | deployment.py | 57,811 | 4 | 42 | str_presenter | https://github.com/PrefectHQ/prefect.git | Working YAML generation with lots of bells and whistles | 49 | 0 | 11,712 | 11 |
|
2 | 27 | def test_lookup_using_custom_divider(self):
jane = Employee.objects.create(name="Jane,Green", department=self.design)
modeladmin = EmployeeCustomDividerFilterAdmin(Employee, site)
employees = [jane, self.jack]
request = self.request_factory.get(
"/", {"name__in": "|".join(e.name for e in employees)}
)
# test for lookup with custom divider
request.user = self.alfred
changelist = modeladmin.get_changelist_instance(request)
# Make sure the correct queryset is returned
queryset = changelist.get_queryset(request)
self.assertEqual(list(queryset), employees)
# test for lookup with comma in the lookup string
request = self.request_factory.get("/", {"name": jane.name})
request.user = self.alfred
changelist = modeladmin.get_changelist_instance(request)
# Make sure the correct queryset is returned
queryset = changelist.get_queryset(request)
self.assertEqual(list(queryset), [jane])
| tests/admin_filters/tests.py | 259 | django | {
"docstring": "\n Filter __in lookups with a custom divider.\n ",
"language": "en",
"n_whitespaces": 22,
"n_words": 7,
"vocab_size": 7
} | 85 | Python | 48 | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | tests.py | 207,137 | 16 | 156 | test_lookup_using_custom_divider | https://github.com/django/django.git | Refs #33476 -- Reformatted code with Black. | 229 | 0 | 51,875 | 13 |
|
1 | 4 | def blend_soft_light(self, rgb, intensity):
return 2 * intensity * rgb + (1 - 2 * intensity) * rgb**2
| lib/matplotlib/colors.py | 44 | matplotlib | {
"docstring": "\n Combine an RGB image with an intensity map using \"soft light\" blending,\n using the \"pegtop\" formula.\n\n Parameters\n ----------\n rgb : ndarray\n An MxNx3 RGB array of floats ranging from 0 to 1 (color image).\n intensity : ndarray\n An MxNx1 array of floats ranging from 0 to 1 (grayscale image).\n\n Returns\n -------\n ndarray\n An MxNx3 RGB array representing the combined images.\n ",
"language": "en",
"n_whitespaces": 164,
"n_words": 60,
"vocab_size": 38
} | 18 | Python | 14 | 9b6abd0b4933811e0a45c2535ab8fd107db65dd9 | colors.py | 110,264 | 2 | 28 | blend_soft_light | https://github.com/matplotlib/matplotlib.git | DOC: improve grammar and consistency | 32 | 0 | 24,006 | 10 |
|
1 | 3 | def __call__(self):
return list(self)
| lib/matplotlib/cm.py | 21 | matplotlib | {
"docstring": "\n Return a list of the registered colormap names.\n\n This exists only for backward-compatibility in `.pyplot` which had a\n ``plt.colormaps()`` method. The recommended way to get this list is\n now ``list(colormaps)``.\n ",
"language": "en",
"n_whitespaces": 66,
"n_words": 30,
"vocab_size": 28
} | 4 | Python | 4 | 686c9e5a413e31c46bb049407d5eca285bcab76d | cm.py | 108,461 | 2 | 11 | __call__ | https://github.com/matplotlib/matplotlib.git | Fix spelling errors | 18 | 0 | 23,201 | 7 |
|
4 | 8 | def get_custom_object_name(obj):
if hasattr(obj, "name"): # Accept `Loss` instance as `Metric`.
return obj.name
elif hasattr(obj, "__name__"): # Function.
return obj.__name__
elif hasattr(obj, "__class__"): # Class instance.
return generic_utils.to_snake_case(obj.__class__.__name__)
else: # Unrecognized object.
return None
| keras/engine/compile_utils.py | 95 | keras | {
"docstring": "Returns the name to use for a custom loss or metric callable.\n\n Args:\n obj: Custom loss of metric callable\n\n Returns:\n Name to use, or `None` if the object was not recognized.\n ",
"language": "en",
"n_whitespaces": 50,
"n_words": 31,
"vocab_size": 26
} | 34 | Python | 25 | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | compile_utils.py | 271,050 | 9 | 53 | get_custom_object_name | https://github.com/keras-team/keras.git | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 81 | 0 | 80,685 | 12 |
|
1 | 14 | def permute_dimensions(x, pattern):
return tf.compat.v1.transpose(x, perm=pattern)
@keras_export("keras.backend.resize_images")
@tf.__internal__.dispatch.add_dispatch_support
@doc_controls.do_not_generate_docs | keras/backend.py | 66 | @keras_export("keras.backend.resize_images")
@tf.__internal__.dispatch.add_dispatch_support
@doc_controls.do_not_generate_docs | keras | {
"docstring": "Permutes axes in a tensor.\n\n Args:\n x: Tensor or variable.\n pattern: A tuple of\n dimension indices, e.g. `(0, 2, 1)`.\n\n Returns:\n A tensor.\n\n Example:\n\n >>> a = tf.constant([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])\n >>> a\n <tf.Tensor: shape=(4, 3), dtype=int32, numpy=\n array([[ 1, 2, 3],\n [ 4, 5, 6],\n [ 7, 8, 9],\n [10, 11, 12]], dtype=int32)>\n >>> tf.keras.backend.permute_dimensions(a, pattern=(1, 0))\n <tf.Tensor: shape=(3, 4), dtype=int32, numpy=\n array([[ 1, 4, 7, 10],\n [ 2, 5, 8, 11],\n [ 3, 6, 9, 12]], dtype=int32)>\n\n ",
"language": "en",
"n_whitespaces": 238,
"n_words": 87,
"vocab_size": 57
} | 9 | Python | 9 | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | backend.py | 269,576 | 2 | 23 | permute_dimensions | https://github.com/keras-team/keras.git | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 12 | 1 | 80,199 | 9 |
1 | 9 | def get_sal_struct(company, currency, salary_slip_based_on_timesheet, condition):
return frappe.db.sql_list(
.format(
condition=condition
),
{
"company": company,
"currency": currency,
"salary_slip_based_on_timesheet": salary_slip_based_on_timesheet,
},
)
| erpnext/payroll/doctype/payroll_entry/payroll_entry.py | 68 | erpnext | {
"docstring": "\n\t\tselect\n\t\t\tname from `tabSalary Structure`\n\t\twhere\n\t\t\tdocstatus = 1 and\n\t\t\tis_active = 'Yes'\n\t\t\tand company = %(company)s\n\t\t\tand currency = %(currency)s and\n\t\t\tifnull(salary_slip_based_on_timesheet,0) = %(salary_slip_based_on_timesheet)s\n\t\t\t{condition}",
"language": "en",
"n_whitespaces": 17,
"n_words": 26,
"vocab_size": 19
} | 19 | Python | 17 | 494bd9ef78313436f0424b918f200dab8fc7c20b | payroll_entry.py | 66,913 | 20 | 43 | get_sal_struct | https://github.com/frappe/erpnext.git | style: format code with black | 8 | 0 | 14,378 | 10 |
|
1 | 8 | def gelu_new(x):
return 0.5 * x * (1 + paddle.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * paddle.pow(x, 3))))
| modules/image/text_to_image/disco_diffusion_cnclip_vitb16/cn_clip/clip/modeling_bert.py | 72 | PaddleHub | {
"docstring": " Implementation of the gelu activation function currently in Google Bert repo (identical to OpenAI GPT).\n Also see https://arxiv.org/abs/1606.08415\n ",
"language": "en",
"n_whitespaces": 29,
"n_words": 18,
"vocab_size": 18
} | 19 | Python | 15 | f4d6e64cdc132ae868699a0ba442f4ab1d304a14 | modeling_bert.py | 49,732 | 2 | 49 | gelu_new | https://github.com/PaddlePaddle/PaddleHub.git | add disco_diffusion_cnclip_vitb16 module | 25 | 0 | 9,898 | 16 |
|
4 | 19 | def clean_stale_components(self):
with self._components_lock:
stale_components = []
stale_component_ids = []
for id, component in self._components.items():
elapsed = time.monotonic() - component.last_reported_time
if elapsed > self._component_timeout_s:
stale_component_ids.append(id)
logger.info(
"Metrics from a worker ({}) is cleaned up due to "
"timeout. Time since last report {}s".format(id, elapsed)
)
for id in stale_component_ids:
stale_components.append(self._components.pop(id))
return stale_components
# TODO(sang): add start and end timestamp | python/ray/_private/metrics_agent.py | 154 | ray | {
"docstring": "Clean up stale components.\n\n Stale means the component is dead or unresponsive.\n\n Stale components won't be reported to Prometheus anymore.\n ",
"language": "en",
"n_whitespaces": 41,
"n_words": 20,
"vocab_size": 19
} | 59 | Python | 52 | 073e7bc04d989607848552537f9f5ac91fa07d85 | metrics_agent.py | 136,720 | 15 | 90 | clean_stale_components | https://github.com/ray-project/ray.git | [Dashboard] Remove opencensus from agent proxy export (#30469)
This PR removes the Opencensus usage on proxy export. Previously, OpenCensus APIs we are using for proxy export deepcopies the whole data {labels -> data} whenever there's a new export which causes O(N^2) write on metrics record. See the below section for more details on removing Opencensus.
Instead of using their APIs, we will store the aggregation data in memory and export them using a custom Prometheus exporter (0 deepcopies, purely done by lock). Below is the flamegraph for the same workload (100 actors + submitting 1000 tasks per second + 1 second metrics export). Before this fix, the CPU usage was > 100% all the time. With this fix, the CPU usage is only about 10~15% with the same workload. | 279 | 0 | 30,979 | 17 |
|
1 | 20 | def _annotate_pose(cls, image, face):
center = np.array((face.aligned.size / 2,
face.aligned.size / 2)).astype("int32").reshape(1, 2)
center = np.rint(face.aligned.transform_points(center, invert=True)).astype("int32")
points = face.aligned.pose.xyz_2d * face.aligned.size
points = np.rint(face.aligned.transform_points(points, invert=True)).astype("int32")
cv2.line(image, tuple(center), tuple(points[1]), (0, 255, 0), 2)
cv2.line(image, tuple(center), tuple(points[0]), (255, 0, 0), 2)
cv2.line(image, tuple(center), tuple(points[2]), (0, 0, 255), 2)
| tools/alignments/jobs.py | 291 | faceswap | {
"docstring": " Annotate the pose onto the frame.\n\n Parameters\n ----------\n image: :class:`numpy.ndarray`\n The frame that pose is to be annotated on to\n face: :class:`lib.align.AlignedFace`\n The aligned face loaded for head centering\n ",
"language": "en",
"n_whitespaces": 87,
"n_words": 29,
"vocab_size": 25
} | 47 | Python | 29 | 5e73437be47f2410439a3c6716de96354e6a0c94 | jobs.py | 101,251 | 9 | 196 | _annotate_pose | https://github.com/deepfakes/faceswap.git | lib.align updates:
- alignments.py
- Add typed dicts for imported alignments
- Explicitly check for presence of thumb value in alignments dict
- linting
- detected_face.py
- Typing
- Linting
- Legacy support for pre-aligned face
- Update dependencies to new property names | 129 | 0 | 20,671 | 16 |
|
3 | 7 | def filter_on_submodules(all_modules, submodule):
filtered_modules = [
mod for mod in all_modules if PACKAGE + submodule in mod.__name__
]
return filtered_modules
| keras/tests/keras_doctest.py | 43 | keras | {
"docstring": "Filters all the modules based on the module flag.\n\n The module flag has to be relative to the core package imported.\n For example, if `submodule=keras.layers` then, this function will return\n all the modules in the submodule.\n\n Args:\n all_modules: All the modules in the core package.\n submodule: Submodule to filter from all the modules.\n\n Returns:\n All the modules in the submodule.\n ",
"language": "en",
"n_whitespaces": 75,
"n_words": 60,
"vocab_size": 38
} | 20 | Python | 17 | a449efe29b092e658a29cd847e0494979a47d252 | keras_doctest.py | 268,868 | 5 | 27 | filter_on_submodules | https://github.com/keras-team/keras.git | Add a keras doctest modeled on tensorflow doctest
PiperOrigin-RevId: 424672415 | 29 | 0 | 79,737 | 10 |
|
1 | 15 | def transform(self, X):
check_is_fitted(self)
X = self._validate_data(
X, accept_sparse=("csr", "csc"), dtype=[np.float64, np.float32], reset=False
)
W = self._solve_W(X, self.components_, self._transform_max_iter)
return W
| sklearn/decomposition/_nmf.py | 96 | scikit-learn | {
"docstring": "Transform the data X according to the fitted MiniBatchNMF model.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Data matrix to be transformed by the model.\n\n Returns\n -------\n W : ndarray of shape (n_samples, n_components)\n Transformed data.\n ",
"language": "en",
"n_whitespaces": 111,
"n_words": 40,
"vocab_size": 31
} | 21 | Python | 19 | 69132ebbd39f070590ca01813340b5b12c0d02ab | _nmf.py | 259,702 | 7 | 62 | transform | https://github.com/scikit-learn/scikit-learn.git | FEA Online implementation of non-negative matrix factorization (#16948)
Co-authored-by: Tom Dupré la Tour <[email protected]>
Co-authored-by: jeremie du boisberranger <[email protected]>
Co-authored-by: Thomas J. Fan <[email protected]>
Co-authored-by: Jérémie du Boisberranger <[email protected]> | 74 | 0 | 75,877 | 11 |
|
1 | 10 | def kubernetes_manifest():
template = Template(
(prefect.__module_path__ / "cli" / "templates" / "kubernetes.yaml").read_text()
)
manifest = template.substitute(
{
"image_name": get_prefect_image_name(),
}
)
print(manifest)
| src/prefect/cli/orion.py | 83 | prefect | {
"docstring": "\n Generates a kubernetes manifest for to deploy Orion to a cluster.\n\n Example:\n $ prefect orion kubernetes-manifest | kubectl apply -f -\n ",
"language": "en",
"n_whitespaces": 38,
"n_words": 21,
"vocab_size": 19
} | 22 | Python | 18 | 23365cf7727c45f38ad983d610ffec5c15ceca21 | orion.py | 53,269 | 10 | 44 | kubernetes_manifest | https://github.com/PrefectHQ/prefect.git | Add kubernetes manifest commands | 72 | 0 | 10,764 | 15 |
|
1 | 10 | def active_count(self):
return self.order_by().exclude(inventory_sources__source='controller').values(name_lower=Lower('name')).distinct().count()
| awx/main/managers.py | 68 | awx | {
"docstring": "Return count of active, unique hosts for licensing.\n Construction of query involves:\n - remove any ordering specified in model's Meta\n - Exclude hosts sourced from another Tower\n - Restrict the query to only return the name column\n - Only consider results that are unique\n - Return the count of this query\n ",
"language": "en",
"n_whitespaces": 105,
"n_words": 51,
"vocab_size": 37
} | 4 | Python | 4 | f52ef6e9677b01c111b012a8725da43a2580d8f1 | managers.py | 80,945 | 2 | 37 | active_count | https://github.com/ansible/awx.git | Fixes case sensitive host count | 18 | 0 | 17,116 | 15 |
|
1 | 18 | def test_partial_fit_weight_class_balanced(klass):
# partial_fit with class_weight='balanced' not supported | sklearn/linear_model/tests/test_sgd.py | 121 | @pytest.mark.parametrize("klass", [SGDClassifier, SparseSGDClassifier]) | scikit-learn | {
"docstring": "\n regex = (\n r\"class_weight 'balanced' is not supported for \"\n r\"partial_fit\\. In order to use 'balanced' weights, \"\n r\"use compute_class_weight\\('balanced', classes=classes, y=y\\). \"\n r\"In place of y you can use a large enough sample \"\n r\"of the full training set target to properly \"\n r\"estimate the class frequency distributions\\. \"\n r\"Pass the resulting weights as the class_weight \"\n r\"parameter\\.\"\n )\n with pytest.raises(ValueError, match=regex):\n klass(class_weight=\"balanced\").partial_fit(X, Y, classes=np.unique(Y))\n\n\[email protected](\"klass\", [SGDClassifier, SparseSGDClassifier])",
"language": "en",
"n_whitespaces": 140,
"n_words": 69,
"vocab_size": 57
} | 8 | Python | 8 | 7f0b57e626d36a7c6d8f417261c6bbfe05376a98 | test_sgd.py | 260,298 | 13 | 59 | test_partial_fit_weight_class_balanced | https://github.com/scikit-learn/scikit-learn.git | MAINT parameter validation in SGD*, PassiveAgressive* and Perceptron (#23521)
Co-authored-by: jeremie du boisberranger <[email protected]>
Co-authored-by: Guillaume Lemaitre <[email protected]>
Co-authored-by: Meekail Zain <[email protected]> | 10 | 1 | 76,168 | 13 |
1 | 9 | def test_naive_all_pairs_lowest_common_ancestor6(self):
G = self.DG.copy()
G.add_node(-1)
gen = naive_all_pairs_lca(G, [(-1, -1), (-1, 0)])
assert dict(gen) == {(-1, -1): -1}
| networkx/algorithms/tests/test_lowest_common_ancestors.py | 101 | networkx | {
"docstring": "Test that pairs with no LCA specified emits nothing.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | 19 | Python | 18 | b2f91c34a23058dd70b41784af0d87890216026a | test_lowest_common_ancestors.py | 177,019 | 5 | 63 | test_naive_all_pairs_lowest_common_ancestor6 | https://github.com/networkx/networkx.git | Naive lowest common ancestor implementation (#5736)
* Add naive lca methods
* Naive algorithm implementation for LCA
* Modify naive lca functions
* Correct parameters of nx.ancestors
* Update lowest_common_ancestors.py
* Parametrize tests
* Apply suggestions from code review
Co-authored-by: Dan Schult <[email protected]>
* Yield instead of append
* Tests for naive lca
* Correct test cases for naive lca algorithms
* Apply suggestions from code review
Co-authored-by: Mridul Seth <[email protected]>
* Fix function name -when calling
* Make requested changes
* Inlining _get_a_lowest_common_ancestor
Co-authored-by: dtuncturk <[email protected]>
Co-authored-by: Dan Schult <[email protected]>
Co-authored-by: Mridul Seth <[email protected]> | 54 | 0 | 42,231 | 11 |
|
5 | 19 | def get_columns(salary_slips):
columns = [
_("Salary Slip ID") + ":Link/Salary Slip:150",
_("Employee") + ":Link/Employee:120",
_("Employee Name") + "::140",
_("Date of Joining") + "::80",
_("Branch") + ":Link/Branch:-1",
_("Department") + ":Link/Department:-1",
_("Designation") + ":Link/Designation:120",
_("Company") + ":Link/Company:120",
_("Start Date") + "::80",
_("End Date") + "::80",
_("Leave Without Pay") + ":Float:50",
_("Payment Days") + ":Float:120",
]
salary_components = {_("Earning"): [], _("Deduction"): []}
for component in frappe.db.sql(
% (", ".join(["%s"] * len(salary_slips))),
tuple([d.name for d in salary_slips]),
as_dict=1,
):
salary_components[_(component.type)].append(component.salary_component)
columns = (
columns
+ [(e + ":Currency:120") for e in salary_components[_("Earning")]]
+ [_("Gross Pay") + ":Currency:120"]
+ [(d + ":Currency:120") for d in salary_components[_("Deduction")]]
+ [
_("Loan Repayment") + ":Currency:120",
_("Total Deduction") + ":Currency:120",
_("Net Pay") + ":Currency:120",
]
)
return columns, salary_components[_("Earning")], salary_components[_("Deduction")]
| erpnext/payroll/report/salary_register/salary_register.py | 483 | erpnext | {
"docstring": "\n\tcolumns = [\n\t _(\"Salary Slip ID\") + \":Link/Salary Slip:150\",\n\t _(\"Employee\") + \":Link/Employee:120\",\n\t _(\"Employee Name\") + \"::140\",\n\t _(\"Date of Joining\") + \"::80\",\n\t _(\"Branch\") + \":Link/Branch:120\",\n\t _(\"Department\") + \":Link/Department:120\",\n\t _(\"Designation\") + \":Link/Designation:120\",\n\t _(\"Company\") + \":Link/Company:120\",\n\t _(\"Start Date\") + \"::80\",\n\t _(\"End Date\") + \"::80\",\n\t _(\"Leave Without Pay\") + \":Float:130\",\n\t _(\"Payment Days\") + \":Float:120\",\n\t _(\"Currency\") + \":Link/Currency:80\"\n\t]\n\tselect distinct sd.salary_component, sc.type\n\t\tfrom `tabSalary Detail` sd, `tabSalary Component` sc\n\t\twhere sc.name=sd.salary_component and sd.amount != 0 and sd.parent in (%s)",
"language": "en",
"n_whitespaces": 161,
"n_words": 75,
"vocab_size": 58
} | 121 | Python | 79 | 494bd9ef78313436f0424b918f200dab8fc7c20b | salary_register.py | 66,968 | 37 | 267 | get_columns | https://github.com/frappe/erpnext.git | style: format code with black | 87 | 0 | 14,394 | 17 |
|
1 | 4 | def serialize(metric):
return serialize_keras_object(metric)
@keras_export("keras.metrics.deserialize") | keras/metrics/__init__.py | 32 | @keras_export("keras.metrics.deserialize") | keras | {
"docstring": "Serializes metric function or `Metric` instance.\n\n Args:\n metric: A Keras `Metric` instance or a metric function.\n\n Returns:\n Metric configuration dictionary.\n ",
"language": "en",
"n_whitespaces": 39,
"n_words": 20,
"vocab_size": 17
} | 5 | Python | 5 | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | __init__.py | 274,614 | 2 | 11 | serialize | https://github.com/keras-team/keras.git | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 10 | 1 | 81,240 | 7 |
4 | 23 | def get_data(filters):
data = []
component_types = frappe.db.sql()
component_types = [comp_type[0] for comp_type in component_types]
if not len(component_types):
return []
conditions = get_conditions(filters)
entry = frappe.db.sql( % (conditions , ", ".join(['%s']*len(component_types))), tuple(component_types), as_dict=1)
for d in entry:
data.append({
"employee": d.employee,
"employee_name": d.employee_name,
"it_comp": d.salary_component,
"posting_date": d.posting_date,
"it_amount": d.amount,
"gross_pay": d.gross_pay
})
return data
| erpnext/payroll/report/income_tax_deductions/income_tax_deductions.py | 220 | erpnext | {
"docstring": " select name from `tabSalary Component`\n\t\twhere is_income_tax_component = 1 select sal.employee, sal.employee_name, sal.posting_date, ded.salary_component, ded.amount,sal.gross_pay\n\t\tfrom `tabSalary Slip` sal, `tabSalary Detail` ded\n\t\twhere sal.name = ded.parent\n\t\tand ded.parentfield = 'deductions'\n\t\tand ded.parenttype = 'Salary Slip'\n\t\tand sal.docstatus = 1 %s\n\t\tand ded.salary_component in (%s)\n\t",
"language": "en",
"n_whitespaces": 38,
"n_words": 44,
"vocab_size": 31
} | 53 | Python | 43 | 3936d8b70e4847dddd49bf467fcbc6e2fcd106c5 | income_tax_deductions.py | 64,417 | 26 | 133 | get_data | https://github.com/frappe/erpnext.git | refactor: remove India specific code | 35 | 0 | 13,631 | 15 |
|
3 | 10 | def generator(self):
K = self.module.number_field
return K.ext.alias if K and K.ext.is_aliased else self.T.gen
| sympy/polys/numberfields/modules.py | 53 | sympy | {
"docstring": "\n Return a :py:class:`~.Symbol` to be used when expressing this element\n as a polynomial.\n\n If we have an associated :py:class:`~.AlgebraicField` whose primitive\n element has an alias symbol, we use that. Otherwise we use the variable\n of the minimal polynomial defining the power basis to which we belong.\n ",
"language": "en",
"n_whitespaces": 89,
"n_words": 46,
"vocab_size": 36
} | 13 | Python | 12 | d37a3c05b98c8144d401fa264af687a525b5e39c | modules.py | 197,803 | 3 | 33 | generator | https://github.com/sympy/sympy.git | Improve printing for `PrimeIdeal`
* Support latex printing
* Rename `_pretty()` --> `repr()` since this is not 2D printing.
* Provide a `__str__()` method, which prints less info than the `__repr__()` method. | 34 | 0 | 48,701 | 9 |
|
1 | 16 | def test_disabling_background_update_sleep(self):
self.get_success(
self.store.db_pool.simple_insert(
"background_updates",
values={"update_name": "test_update", "progress_json": '{"my_key": 1}'},
)
)
self.update_handler.side_effect = self.update
self.update_handler.reset_mock()
self.updates.start_doing_background_updates(),
# 2: advance the reactor very little
self.reactor.pump([0.025])
# check that an update has run
self.update_handler.assert_called()
| tests/storage/test_background_update.py | 133 | synapse | {
"docstring": "\n Test that disabling sleep in the config results in bg update not sleeping\n ",
"language": "en",
"n_whitespaces": 28,
"n_words": 13,
"vocab_size": 12
} | 33 | Python | 31 | ef3619e61d84493d98470eb2a69131d15eb1166b | test_background_update.py | 247,568 | 12 | 77 | test_disabling_background_update_sleep | https://github.com/matrix-org/synapse.git | Add config settings for background update parameters (#11980) | 155 | 0 | 71,746 | 13 |
|
1 | 11 | def test_get_global_no_mutability(self) -> None:
# First add some account data to set up the test.
self.get_success(
self._store.add_account_data_for_user(
self.user_id, "test.data", {"wombat": True}
)
)
# Now request that data and then mutate it (out of negligence or otherwise).
the_data = self.get_success(
self._account_data_mgr.get_global(self.user_id, "test.data")
)
with self.assertRaises(TypeError):
# This throws an exception because it's a frozen dict.
the_data["wombat"] = False
| tests/module_api/test_account_data_manager.py | 114 | synapse | {
"docstring": "\n Tests that modules can't introduce bugs into Synapse by mutating the result\n of `get_global`.\n ",
"language": "en",
"n_whitespaces": 36,
"n_words": 14,
"vocab_size": 14
} | 58 | Python | 51 | 85ca963c1add5ca12f59238a50dfc63df4846bb7 | test_account_data_manager.py | 247,972 | 15 | 64 | test_get_global_no_mutability | https://github.com/matrix-org/synapse.git | Add Module API for reading and writing global account data. (#12391) | 184 | 0 | 72,031 | 12 |
|
1 | 5 | def size(self) -> Size:
return Size(self.width, self.height)
| src/textual/geometry.py | 32 | textual | {
"docstring": "Get the size of the region.\n\n Returns:\n Size: Size of the region.\n\n ",
"language": "en",
"n_whitespaces": 37,
"n_words": 12,
"vocab_size": 8
} | 7 | Python | 7 | 6ee4d41bb7a39238a18949f5648773562c6a1c9b | geometry.py | 184,573 | 8 | 19 | size | https://github.com/Textualize/textual.git | docs | 21 | 0 | 44,676 | 8 |
|
1 | 4 | async def test_remove_order():
removals: list[str] = []
| tests/test_widget_removing.py | 27 | textual | {
"docstring": "The removal of a top-level widget should cause bottom-first removal.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | 7 | Python | 7 | 9748850657337ba31f220387e4a7777a87ec019a | test_widget_removing.py | 185,774 | 10 | 87 | test_remove_order | https://github.com/Textualize/textual.git | Add a unit test for removal ordering via Widget.remove | 13 | 0 | 45,174 | 8 |
|
4 | 9 | def createPreModuleLoadCode(self, module):
# This is only relevant on standalone mode for Windows
if not isStandaloneMode():
return
full_name = module.getFullName()
if full_name == self.binding_name and isWin32Windows():
code =
yield (
code,
"Adding binary folder to runtime 'PATH' environment variable for proper Qt loading.",
)
| nuitka/plugins/standard/PySidePyQtPlugin.py | 78 | Nuitka | {
"docstring": "Method called when a module is being imported.\n\n Notes:\n If full name equals to the binding we insert code to include the dist\n folder in the 'PATH' environment variable (on Windows only).\n\n Args:\n module: the module object\n Returns:\n Code to insert and descriptive text (tuple), or (None, None).\n import os\npath = os.environ.get(\"PATH\", \"\")\nif not path.startswith(__nuitka_binary_dir):\n os.environ[\"PATH\"] = __nuitka_binary_dir + \";\" + path\n",
"language": "en",
"n_whitespaces": 136,
"n_words": 64,
"vocab_size": 54
} | 44 | Python | 40 | 6b317645a6edf73a8628229c540555142725478d | PySidePyQtPlugin.py | 178,696 | 14 | 43 | createPreModuleLoadCode | https://github.com/Nuitka/Nuitka.git | Plugins: Minor cleanups | 154 | 0 | 42,792 | 10 |
|
1 | 10 | def ledoit_wolf(X, *, assume_centered=False, block_size=1000):
estimator = LedoitWolf(
assume_centered=assume_centered,
block_size=block_size,
store_precision=False,
).fit(X)
return estimator.covariance_, estimator.shrinkage_
| sklearn/covariance/_shrunk_covariance.py | 70 | scikit-learn | {
"docstring": "Estimate the shrunk Ledoit-Wolf covariance matrix.\n\n Read more in the :ref:`User Guide <shrunk_covariance>`.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Data from which to compute the covariance estimate.\n\n assume_centered : bool, default=False\n If True, data will not be centered before computation.\n Useful to work with data whose mean is significantly equal to\n zero but is not exactly zero.\n If False, data will be centered before computation.\n\n block_size : int, default=1000\n Size of blocks into which the covariance matrix will be split.\n This is purely a memory optimization and does not affect results.\n\n Returns\n -------\n shrunk_cov : ndarray of shape (n_features, n_features)\n Shrunk covariance.\n\n shrinkage : float\n Coefficient in the convex combination used for the computation\n of the shrunk estimate.\n\n Notes\n -----\n The regularized (shrunk) covariance is:\n\n (1 - shrinkage) * cov + shrinkage * mu * np.identity(n_features)\n\n where mu = trace(cov) / n_features\n ",
"language": "en",
"n_whitespaces": 263,
"n_words": 145,
"vocab_size": 103
} | 15 | Python | 15 | 9a90af51510c0722ab880061107e5cfdcf09192f | _shrunk_covariance.py | 261,798 | 7 | 46 | ledoit_wolf | https://github.com/scikit-learn/scikit-learn.git | MAINT Parameters validation for covariance.ledoit_wolf (#24870)
Co-authored-by: Guillaume Lemaitre <[email protected]>
Co-authored-by: jeremiedbb <[email protected]> | 48 | 0 | 77,003 | 11 |
|
3 | 15 | def dist_get_direct_url(dist):
# type: (Distribution) -> Optional[DirectUrl]
if not dist.has_metadata(DIRECT_URL_METADATA_NAME):
return None
try:
return DirectUrl.from_json(dist.get_metadata(DIRECT_URL_METADATA_NAME))
except (
DirectUrlValidationError,
json.JSONDecodeError,
UnicodeDecodeError,
) as e:
logger.warning(
"Error parsing %s for %s: %s",
DIRECT_URL_METADATA_NAME,
dist.project_name,
e,
)
return None
| .venv/lib/python3.8/site-packages/pip/_internal/utils/direct_url_helpers.py | 100 | transferlearning | {
"docstring": "Obtain a DirectUrl from a pkg_resource.Distribution.\n\n Returns None if the distribution has no `direct_url.json` metadata,\n or if `direct_url.json` is invalid.\n ",
"language": "en",
"n_whitespaces": 29,
"n_words": 20,
"vocab_size": 17
} | 36 | Python | 32 | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | direct_url_helpers.py | 61,169 | 17 | 62 | dist_get_direct_url | https://github.com/jindongwang/transferlearning.git | upd; format | 154 | 0 | 12,422 | 11 |
|
1 | 2 | def cmax(self):
return self["cmax"]
| packages/python/plotly/plotly/graph_objs/_cone.py | 22 | plotly.py | {
"docstring": "\n Sets the upper bound of the color domain. Value should have the\n same units as u/v/w norm and if set, `cmin` must be set as\n well.\n\n The 'cmax' property is a number and may be specified as:\n - An int or float\n\n Returns\n -------\n int|float\n ",
"language": "en",
"n_whitespaces": 111,
"n_words": 45,
"vocab_size": 40
} | 4 | Python | 4 | 43e3a4011080911901176aab919c0ecf5046ddd3 | _cone.py | 226,571 | 2 | 11 | cmax | https://github.com/plotly/plotly.py.git | switch to black .22 | 18 | 0 | 58,244 | 7 |
|
4 | 20 | def tcp_pseudoheader(tcp):
# type: (TCP) -> bytes
if isinstance(tcp.underlayer, IP):
plen = len(bytes(tcp))
return in4_pseudoheader(socket.IPPROTO_TCP, tcp.underlayer, plen)
elif conf.ipv6_enabled and _is_ipv6_layer(tcp.underlayer):
plen = len(bytes(tcp))
return raw(scapy.layers.inet6.in6_pseudoheader(
socket.IPPROTO_TCP, tcp.underlayer, plen))
else:
raise ValueError("TCP packet does not have IP or IPv6 underlayer")
| scapy/layers/inet.py | 142 | scapy | {
"docstring": "Pseudoheader of a TCP packet as bytes\n\n Requires underlayer to be either IP or IPv6\n ",
"language": "en",
"n_whitespaces": 21,
"n_words": 15,
"vocab_size": 15
} | 40 | Python | 35 | 20ac1d00389d0735e6d8cd1347f0a53f478144ba | inet.py | 209,126 | 10 | 88 | tcp_pseudoheader | https://github.com/secdev/scapy.git | Support TCP-MD5 and TCP-AO (#3358)
Support TCP-MD5 and TCP-AO | 101 | 0 | 52,615 | 14 |
|
2 | 19 | def get_collection_version_metadata(self, namespace, name, version):
api_path = self.available_api_versions.get('v3', self.available_api_versions.get('v2'))
url_paths = [self.api_server, api_path, 'collections', namespace, name, 'versions', version, '/']
n_collection_url = _urljoin(*url_paths)
error_context_msg = 'Error when getting collection version metadata for %s.%s:%s from %s (%s)' \
% (namespace, name, version, self.name, self.api_server)
data = self._call_galaxy(n_collection_url, error_context_msg=error_context_msg, cache=True)
self._set_cache()
signatures = data.get('signatures') or []
return CollectionVersionMetadata(data['namespace']['name'], data['collection']['name'], data['version'],
data['download_url'], data['artifact']['sha256'],
data['metadata']['dependencies'], data['href'], signatures)
| lib/ansible/galaxy/api.py | 262 | ansible | {
"docstring": "\n Gets the collection information from the Galaxy server about a specific Collection version.\n\n :param namespace: The collection namespace.\n :param name: The collection name.\n :param version: Version of the collection to get the information for.\n :return: CollectionVersionMetadata about the collection at the version requested.\n ",
"language": "en",
"n_whitespaces": 86,
"n_words": 43,
"vocab_size": 29
} | 62 | Python | 53 | 43e55db20821a1341d21ffa1e4e7e6185b244105 | api.py | 266,587 | 12 | 163 | get_collection_version_metadata | https://github.com/ansible/ansible.git | ansible-galaxy - add signature verification of the MANIFEST.json (#76681)
* ansible-galaxy collection install|verify:
- Support verifying the origin of the MANIFEST.json when the Galaxy server has provided signatures.
- Allow supplemental signatures to use during verification on the CLI/requirements file.
* ansible-galaxy collection install:
- Support disabling signature verification. This silences the warning provided by ansible-galaxy if the Galaxy server provided signatures it cannot use because no keyring is configured.
- Store Galaxy server metadata alongside installed collections for provenance. This is used by 'ansible-galaxy collection verify --offline'.
* Add unit tests for method that gets signatures from a Galaxy server
* Add integration tests for user-provided signature sources
- Test CLI option combinations
- Test installing collections with valid/invalid signature sources
- Test disabling GPG verification when installing collections
- Test verifying collections with valid/invalid signature sources
* Make signature verification advisory-by-default if signatures are provided by the Galaxy server
- Make the default keyring None
- Warn if the keyring is None but the Galaxy server provided signatures
- Error if the keyring is None but the user supplied signatures
- Error if the keyring is not None but is invalid
* changelog
* add ansible-galaxy user documentation for new options
Co-authored-by: Matt Martz <[email protected]>
Co-authored-by: Sviatoslav Sydorenko <[email protected]>
Co-authored-by: Martin Krizek <[email protected]>
Co-authored-by: Sandra McCann <[email protected]>
Co-authored-by: Andy Mott <[email protected]>
Co-authored-by: John R Barker <[email protected]> | 232 | 0 | 78,486 | 11 |
|
6 | 22 | def get_args(tp):
if isinstance(tp, _AnnotatedAlias):
return (tp.__origin__,) + tp.__metadata__
if isinstance(tp, (typing._GenericAlias, GenericAlias)):
if getattr(tp, "_special", False):
return ()
res = tp.__args__
if get_origin(tp) is collections.abc.Callable and res[0] is not Ellipsis:
res = (list(res[:-1]), res[-1])
return res
return ()
# 3.10+
if hasattr(typing, 'TypeAlias'):
TypeAlias = typing.TypeAlias
# 3.9
elif sys.version_info[:2] >= (3, 9): | pipenv/patched/notpip/_vendor/typing_extensions.py | 203 | pipenv | {
"docstring": "Get type arguments with all substitutions performed.\n\n For unions, basic simplifications used by Union constructor are performed.\n Examples::\n get_args(Dict[str, int]) == (str, int)\n get_args(int) == ()\n get_args(Union[int, Union[T, int], str][int]) == (int, str)\n get_args(Union[int, Tuple[T, int]][str]) == (int, Tuple[str, int])\n get_args(Callable[[], T][int]) == ([], int)\n ",
"language": "en",
"n_whitespaces": 121,
"n_words": 45,
"vocab_size": 36
} | 54 | Python | 39 | f3166e673fe8d40277b804d35d77dcdb760fc3b3 | typing_extensions.py | 20,897 | 11 | 101 | get_args | https://github.com/pypa/pipenv.git | 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 | 166 | 0 | 3,611 | 16 |
|
1 | 23 | def test_following_previous_schedule(self):
local_tz = pendulum.timezone('Europe/Zurich')
start = local_tz.convert(datetime.datetime(2018, 10, 28, 2, 55), dst_rule=pendulum.PRE_TRANSITION)
assert start.isoformat() == "2018-10-28T02:55:00+02:00", "Pre-condition: start date is in DST"
utc = timezone.convert_to_utc(start)
assert utc.isoformat() == "2018-10-28T00:55:00+00:00", "Pre-condition: correct DST->UTC conversion"
dag = DAG('tz_dag', start_date=start, schedule_interval='*/5 * * * *')
_next = dag.following_schedule(utc)
next_local = local_tz.convert(_next)
assert _next.isoformat() == "2018-10-28T01:00:00+00:00"
assert next_local.isoformat() == "2018-10-28T02:00:00+01:00"
prev = dag.previous_schedule(utc)
prev_local = local_tz.convert(prev)
assert prev_local.isoformat() == "2018-10-28T02:50:00+02:00"
prev = dag.previous_schedule(_next)
prev_local = local_tz.convert(prev)
assert prev_local.isoformat() == "2018-10-28T02:55:00+02:00"
assert prev == utc
| tests/models/test_dag.py | 284 | airflow | {
"docstring": "\n Make sure DST transitions are properly observed\n ",
"language": "en",
"n_whitespaces": 22,
"n_words": 7,
"vocab_size": 7
} | 81 | Python | 50 | 2fdc23333909096d427171002582e2906f8bbc0a | test_dag.py | 43,883 | 18 | 167 | test_following_previous_schedule | https://github.com/apache/airflow.git | Fix remaining mypy issues in "core" Airflow (#20795)
Co-authored-by: Josh Fell <[email protected]>
Co-authored-by: Tzu-ping Chung <[email protected]>
Co-authored-by: Jarek Potiuk <[email protected]> | 207 | 0 | 8,084 | 10 |
|
3 | 21 | def close(self) -> None:
if not self.has_closed:
# update the run.txt with stopping time
self.run_txt_data["stop_time"] = datetime.datetime.now().isoformat(sep=" ")
with open(os.path.join(self.submit_config.run_dir, "run.txt"), "w") as f:
pprint.pprint(self.run_txt_data, stream=f, indent=4, width=200, compact=False)
self.has_closed = True
# detach the global singleton
global _run_context
if _run_context is self:
_run_context = None
| reconstruction/ostec/external/stylegan2/dnnlib/submission/run_context.py | 163 | insightface | {
"docstring": "Close the context and clean up.\n Should only be called once.",
"language": "en",
"n_whitespaces": 17,
"n_words": 11,
"vocab_size": 11
} | 46 | Python | 37 | 7375ee364e0df2a417f92593e09557f1b2a3575a | run_context.py | 9,376 | 11 | 97 | close | https://github.com/deepinsight/insightface.git | initialize ostec | 167 | 0 | 1,590 | 16 |
|
7 | 20 | def wrapCommandForDebuggerForExec(*args):
gdb_path = getExecutablePath("gdb")
# Windows extra ball, attempt the downloaded one.
if isWin32Windows() and gdb_path is None:
from nuitka.Options import assumeYesForDownloads
mingw64_gcc_path = getCachedDownloadedMinGW64(
target_arch=getArchitecture(),
assume_yes_for_downloads=assumeYesForDownloads(),
)
with withEnvironmentPathAdded("PATH", os.path.dirname(mingw64_gcc_path)):
lldb_path = getExecutablePath("lldb")
if gdb_path is None and lldb_path is None:
lldb_path = getExecutablePath("lldb")
if lldb_path is None:
general.sysexit("Error, no 'gdb' or 'lldb' binary found in path.")
if gdb_path is not None:
args = (gdb_path, "gdb", "-ex=run", "-ex=where", "-ex=quit", "--args") + args
else:
args = (lldb_path, "lldb", "-o", "run", "-o", "bt", "-o", "quit", "--") + args
return args
| nuitka/utils/Execution.py | 254 | Nuitka | {
"docstring": "Wrap a command for system debugger to call exec\n\n Args:\n args: (list of str) args for call to be debugged\n Returns:\n args tuple with debugger command inserted\n\n Notes:\n Currently only gdb and lldb are supported, but adding more\n debuggers would be very welcome.\n ",
"language": "en",
"n_whitespaces": 83,
"n_words": 43,
"vocab_size": 36
} | 90 | Python | 60 | 98badaaafd4e56529378947358acae489035fa1e | Execution.py | 178,724 | 19 | 142 | wrapCommandForDebuggerForExec | https://github.com/Nuitka/Nuitka.git | Windows: Make running in debugger work with cmd files as well | 214 | 0 | 42,804 | 14 |
|
9 | 29 | def sort_bbox(end2end_xywh_bboxes, no_match_end2end_indexes):
groups = []
bbox_groups = []
for index, end2end_xywh_bbox in zip(no_match_end2end_indexes,
end2end_xywh_bboxes):
this_bbox = end2end_xywh_bbox
if len(groups) == 0:
groups.append([index])
bbox_groups.append([this_bbox])
else:
flag = False
for g, bg in zip(groups, bbox_groups):
# this_bbox is belong to bg's row or not
if is_abs_lower_than_threshold(this_bbox, bg[0]):
g.append(index)
bg.append(this_bbox)
flag = True
break
if not flag:
# this_bbox is not belong to bg's row, create a row.
groups.append([index])
bbox_groups.append([this_bbox])
# sorted bboxes in a group
tmp_groups, tmp_bbox_groups = [], []
for g, bg in zip(groups, bbox_groups):
g_sorted, bg_sorted = sort_line_bbox(g, bg)
tmp_groups.append(g_sorted)
tmp_bbox_groups.append(bg_sorted)
# sorted groups, sort by coord y's value.
sorted_groups = [None] * len(tmp_groups)
sorted_bbox_groups = [None] * len(tmp_bbox_groups)
ys = [bg[0][1] for bg in tmp_bbox_groups]
sorted_ys = sorted(ys)
for g, bg in zip(tmp_groups, tmp_bbox_groups):
idx = sorted_ys.index(bg[0][1])
sorted_groups[idx] = g
sorted_bbox_groups[idx] = bg
# flatten, get final result
end2end_sorted_idx_list, end2end_sorted_bbox_list \
= flatten(sorted_groups, sorted_bbox_groups)
# check sorted
#img = cv2.imread('/data_0/yejiaquan/data/TableRecognization/singleVal/PMC3286376_004_00.png')
#img = drawBboxAfterSorted(img, sorted_groups, sorted_bbox_groups)
return end2end_sorted_idx_list, end2end_sorted_bbox_list, sorted_groups, sorted_bbox_groups
| ppstructure/table/table_master_match.py | 411 | PaddleOCR | {
"docstring": "\n This function will group the render end2end bboxes in row.\n :param end2end_xywh_bboxes:\n :param no_match_end2end_indexes:\n :return:\n ",
"language": "en",
"n_whitespaces": 31,
"n_words": 15,
"vocab_size": 14
} | 162 | Python | 98 | ddaa2c2552e19635cd6cdf38619f1f176c358f89 | table_master_match.py | 24,501 | 36 | 260 | sort_bbox | https://github.com/PaddlePaddle/PaddleOCR.git | add SLANet | 534 | 0 | 4,748 | 16 |
|
20 | 17 | def process_directive(self, directive):
# Parse the line: split it up, make sure the right number of words
# is there, and return the relevant words. 'action' is always
# defined: it's the first word of the line. Which of the other
# three are defined depends on the action; it'll be either
# patterns, (dir and patterns), or (dirpattern).
action, patterns, thedir, dirpattern = self._parse_directive(directive)
# OK, now we know that the action is valid and we have the
# right number of words on the line for that action -- so we
# can proceed with minimal error-checking.
if action == 'include':
for pattern in patterns:
if not self._include_pattern(pattern, anchor=True):
logger.warning('no files found matching %r', pattern)
elif action == 'exclude':
for pattern in patterns:
found = self._exclude_pattern(pattern, anchor=True)
#if not found:
# logger.warning('no previously-included files '
# 'found matching %r', pattern)
elif action == 'global-include':
for pattern in patterns:
if not self._include_pattern(pattern, anchor=False):
logger.warning('no files found matching %r '
'anywhere in distribution', pattern)
elif action == 'global-exclude':
for pattern in patterns:
found = self._exclude_pattern(pattern, anchor=False)
#if not found:
# logger.warning('no previously-included files '
# 'matching %r found anywhere in '
# 'distribution', pattern)
elif action == 'recursive-include':
for pattern in patterns:
if not self._include_pattern(pattern, prefix=thedir):
logger.warning('no files found matching %r '
'under directory %r', pattern, thedir)
elif action == 'recursive-exclude':
for pattern in patterns:
found = self._exclude_pattern(pattern, prefix=thedir)
#if not found:
# logger.warning('no previously-included files '
# 'matching %r found under directory %r',
# pattern, thedir)
elif action == 'graft':
if not self._include_pattern(None, prefix=dirpattern):
logger.warning('no directories found matching %r',
dirpattern)
elif action == 'prune':
if not self._exclude_pattern(None, prefix=dirpattern):
logger.warning('no previously-included directories found '
'matching %r', dirpattern)
else: # pragma: no cover
# This should never happen, as it should be caught in
# _parse_template_line
raise DistlibException(
'invalid action %r' % action)
#
# Private API
#
| .venv/lib/python3.8/site-packages/pip/_vendor/distlib/manifest.py | 437 | transferlearning | {
"docstring": "\n Process a directive which either adds some files from ``allfiles`` to\n ``files``, or removes some files from ``files``.\n\n :param directive: The directive to process. This should be in a format\n compatible with distutils ``MANIFEST.in`` files:\n\n http://docs.python.org/distutils/sourcedist.html#commands\n ",
"language": "en",
"n_whitespaces": 105,
"n_words": 36,
"vocab_size": 30
} | 307 | Python | 136 | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | manifest.py | 62,044 | 36 | 247 | process_directive | https://github.com/jindongwang/transferlearning.git | upd; format | 1,158 | 0 | 12,851 | 16 |
|
1 | 8 | def hex6(self) -> str:
r, g, b, a = self.clamped
return f"#{r:02X}{g:02X}{b:02X}"
| src/textual/color.py | 54 | textual | {
"docstring": "The color in CSS hex form, with 6 digits for RGB. Alpha is ignored.\n\n Returns:\n str: A CSS hex-style color, e.g. \"#46b3de\"\n\n ",
"language": "en",
"n_whitespaces": 47,
"n_words": 22,
"vocab_size": 21
} | 12 | Python | 12 | 6f7d3b5ad711aa7df62ca6b3fca5cd638dcec665 | color.py | 184,969 | 9 | 22 | hex6 | https://github.com/Textualize/textual.git | text color | 33 | 0 | 44,871 | 8 |
|
10 | 16 | def is_matching(G, matching):
if isinstance(matching, dict):
matching = matching_dict_to_set(matching)
nodes = set()
for edge in matching:
if len(edge) != 2:
raise nx.NetworkXError(f"matching has non-2-tuple edge {edge}")
u, v = edge
if u not in G or v not in G:
raise nx.NetworkXError(f"matching contains edge {edge} with node not in G")
if u == v:
return False
if not G.has_edge(u, v):
return False
if u in nodes or v in nodes:
return False
nodes.update(edge)
return True
| networkx/algorithms/matching.py | 185 | networkx | {
"docstring": "Return True if ``matching`` is a valid matching of ``G``\n\n A *matching* in a graph is a set of edges in which no two distinct\n edges share a common endpoint. Each node is incident to at most one\n edge in the matching. The edges are said to be independent.\n\n Parameters\n ----------\n G : NetworkX graph\n\n matching : dict or set\n A dictionary or set representing a matching. If a dictionary, it\n must have ``matching[u] == v`` and ``matching[v] == u`` for each\n edge ``(u, v)`` in the matching. If a set, it must have elements\n of the form ``(u, v)``, where ``(u, v)`` is an edge in the\n matching.\n\n Returns\n -------\n bool\n Whether the given set or dictionary represents a valid matching\n in the graph.\n\n Raises\n ------\n NetworkXError\n If the proposed matching has an edge to a node not in G.\n Or if the matching is not a collection of 2-tuple edges.\n\n ",
"language": "en",
"n_whitespaces": 257,
"n_words": 152,
"vocab_size": 86
} | 75 | Python | 44 | 28b3014d68d2b4e40d3e02219770296a827bd55c | matching.py | 176,367 | 18 | 111 | is_matching | https://github.com/networkx/networkx.git | Update matching functions for error validation and speed (#4897)
* First steps to update matching functions for #4644
Expand tests
Change API to raise NetworkXError when matching involves nodes not in G
Update is_*_matching to 100+ times faster.
* improve matching_dict_to_set and docs for min_weight_matching
* fix sphinx error | 201 | 0 | 41,853 | 13 |
|
2 | 14 | def test_google_type_conversion(mock_fields_meta_data):
desired_mapping = {
"accessible_bidding_strategy.target_impression_share.location": "string", # "ENUM"
"campaign.name": ["string", "null"], # STRING
"campaign.end_date": ["string", "null"], # DATE
"campaign.optimization_score": ["number", "null"], # DOUBLE
"campaign.resource_name": ["string", "null"], # RESOURCE_NAME
"campaign.shopping_setting.campaign_priority": ["integer", "null"], # INT32
"campaign.shopping_setting.merchant_id": ["integer", "null"], # INT64
"campaign_budget.explicitly_shared": ["boolean", "null"], # BOOLEAN
"bidding_strategy.enhanced_cpc": ["string", "null"], # MESSAGE
"segments.date": ["string", "null"], # autoadded, should be DATE
}
# query is select field of each type
query =
instance = stream_instance(query=query, api_mock=mock_fields_meta_data)
final_schema = instance.get_json_schema()
schema_properties = final_schema.get("properties")
for prop, value in schema_properties.items():
assert desired_mapping[prop] == value.get("type"), f"{prop} should be {value}"
| airbyte-integrations/connectors/source-google-ads/unit_tests/test_source.py | 285 | airbyte | {
"docstring": "\n query may be invalid (fields incompatibility did not checked).\n But we are just testing types, without submitting the query and further steps.\n Doing that with all possible types.\n \n SELECT\n accessible_bidding_strategy.target_impression_share.location,\n campaign.name,\n campaign.end_date,\n campaign.optimization_score,\n campaign.resource_name,\n campaign.shopping_setting.campaign_priority,\n campaign.shopping_setting.merchant_id,\n campaign_budget.explicitly_shared,\n bidding_strategy.enhanced_cpc\n FROM campaign\n ",
"language": "en",
"n_whitespaces": 174,
"n_words": 40,
"vocab_size": 39
} | 91 | Python | 60 | d4f8b25b8e3e109db866352cf1dcec0d73c92cbd | test_source.py | 5,061 | 31 | 142 | test_google_type_conversion | https://github.com/airbytehq/airbyte.git | Source Google Ads: Improve unit and integration tests (#12651)
* #12650 source Googel ads: tests
* #12650 source google ads: add changelog item
* #12650 source google ads: add comments to tests
* auto-bump connector version
Co-authored-by: Octavia Squidington III <[email protected]> | 206 | 0 | 714 | 11 |
|
1 | 5 | def has_refs(self) -> bool:
return len(self._session_report_run_counts) > 0
| lib/streamlit/forward_msg_cache.py | 30 | streamlit | {
"docstring": "True if this Entry has references from any AppSession.\n\n If not, it can be removed from the cache.\n ",
"language": "en",
"n_whitespaces": 40,
"n_words": 18,
"vocab_size": 17
} | 8 | Python | 8 | 704eab3478cf69847825b23dabf15813a8ac9fa2 | forward_msg_cache.py | 118,557 | 6 | 17 | has_refs | https://github.com/streamlit/streamlit.git | Rename and refactor `Report` machinery (#4141)
This refactor renames (almost) everything related to the outdated "report" concept with more precise concepts that we use throughout our code, primarily "script run", "session", and "app". | 30 | 0 | 26,290 | 9 |
|
1 | 10 | def test_multi_part_language_bad_format(self, m):
m.return_value = ["chi_sim", "eng"]
msgs = check_default_language_available(None)
self.assertEqual(len(msgs), 1)
self.assertEqual(msgs[0].level, ERROR)
| src/paperless_tesseract/tests/test_checks.py | 78 | paperless-ngx | {
"docstring": "\n GIVEN:\n - An OCR language which is multi part (ie chi-sim)\n - The language is correctly NOT formatted\n WHEN:\n - Installed packages are checked\n THEN:\n - No errors are reported\n ",
"language": "en",
"n_whitespaces": 103,
"n_words": 30,
"vocab_size": 24
} | 14 | Python | 13 | 55ef0d4a1b62c3abe8500cad97ddeecf9f746b84 | test_checks.py | 320,360 | 5 | 47 | test_multi_part_language_bad_format | https://github.com/paperless-ngx/paperless-ngx.git | Fixes language code checks around two part languages | 49 | 0 | 117,148 | 9 |
|
1 | 15 | def simple_test(self, feats, img_metas, **kwargs):
all_cls_scores, all_mask_preds = self(feats, img_metas)
mask_cls_results = all_cls_scores[-1]
mask_pred_results = all_mask_preds[-1]
# upsample masks
img_shape = img_metas[0]['batch_input_shape']
mask_pred_results = F.interpolate(
mask_pred_results,
size=(img_shape[0], img_shape[1]),
mode='bilinear',
align_corners=False)
return mask_cls_results, mask_pred_results
| mmdet/models/dense_heads/maskformer_head.py | 125 | mmdetection | {
"docstring": "Test without augmentaton.\n\n Args:\n feats (list[Tensor]): Multi-level features from the\n upstream network, each is a 4D-tensor.\n img_metas (list[dict]): List of image information.\n\n Returns:\n tuple: A tuple contains two tensors.\n\n - mask_cls_results (Tensor): Mask classification logits,\\\n shape (batch_size, num_queries, cls_out_channels).\n Note `cls_out_channels` should includes background.\n - mask_pred_results (Tensor): Mask logits, shape \\\n (batch_size, num_queries, h, w).\n ",
"language": "en",
"n_whitespaces": 191,
"n_words": 55,
"vocab_size": 49
} | 33 | Python | 27 | 4bb184bae070f37febb10f82bee3a217dc1ad7c5 | maskformer_head.py | 244,108 | 11 | 80 | simple_test | https://github.com/open-mmlab/mmdetection.git | [Enhance] MaskFormer refactor (#7471)
* maskformer refactor
update docstring
update docstring
update unit test
update unit test
update unit test
* remove redundant code
* update unit test | 133 | 0 | 70,242 | 11 |
|
1 | 12 | def test_query_by_embedding_excluded_meta_data_return_embedding_true(self, mocked_document_store):
mocked_document_store.return_embedding = True
mocked_document_store.excluded_meta_data = ["foo", "embedding"]
mocked_document_store.query_by_embedding(self.query_emb)
_, kwargs = mocked_document_store.client.search.call_args
# we expect "embedding" was removed from the final query
assert kwargs["body"]["_source"] == {"excludes": ["foo"]}
| test/document_stores/test_opensearch.py | 102 | haystack | {
"docstring": "\n Test that when `return_embedding==True` the field should NOT be excluded even if it\n was added to `excluded_meta_data`\n ",
"language": "en",
"n_whitespaces": 39,
"n_words": 17,
"vocab_size": 17
} | 30 | Python | 28 | e7627c3f8b241654b61f8523479c81f855102f0a | test_opensearch.py | 257,656 | 6 | 57 | test_query_by_embedding_excluded_meta_data_return_embedding_true | https://github.com/deepset-ai/haystack.git | Use opensearch-py in OpenSearchDocumentStore (#2691)
* add Opensearch extras
* let OpenSearchDocumentStore use opensearch-py
* Update Documentation & Code Style
* fix a bug found after adding tests
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: Sara Zan <[email protected]> | 79 | 0 | 75,108 | 10 |
|
1 | 3 | def id() -> str:
return _distro.id()
| pipenv/patched/notpip/_vendor/distro/distro.py | 25 | pipenv | {
"docstring": "\n Return the distro ID of the current distribution, as a\n machine-readable string.\n\n For a number of OS distributions, the returned distro ID value is\n *reliable*, in the sense that it is documented and that it does not change\n across releases of the distribution.\n\n This package maintains the following reliable distro ID values:\n\n ============== =========================================\n Distro ID Distribution\n ============== =========================================\n \"ubuntu\" Ubuntu\n \"debian\" Debian\n \"rhel\" RedHat Enterprise Linux\n \"centos\" CentOS\n \"fedora\" Fedora\n \"sles\" SUSE Linux Enterprise Server\n \"opensuse\" openSUSE\n \"amzn\" Amazon Linux\n \"arch\" Arch Linux\n \"cloudlinux\" CloudLinux OS\n \"exherbo\" Exherbo Linux\n \"gentoo\" GenToo Linux\n \"ibm_powerkvm\" IBM PowerKVM\n \"kvmibm\" KVM for IBM z Systems\n \"linuxmint\" Linux Mint\n \"mageia\" Mageia\n \"mandriva\" Mandriva Linux\n \"parallels\" Parallels\n \"pidora\" Pidora\n \"raspbian\" Raspbian\n \"oracle\" Oracle Linux (and Oracle Enterprise Linux)\n \"scientific\" Scientific Linux\n \"slackware\" Slackware\n \"xenserver\" XenServer\n \"openbsd\" OpenBSD\n \"netbsd\" NetBSD\n \"freebsd\" FreeBSD\n \"midnightbsd\" MidnightBSD\n \"rocky\" Rocky Linux\n \"aix\" AIX\n ============== =========================================\n\n If you have a need to get distros for reliable IDs added into this set,\n or if you find that the :func:`distro.id` function returns a different\n distro ID for one of the listed distros, please create an issue in the\n `distro issue tracker`_.\n\n **Lookup hierarchy and transformations:**\n\n First, the ID is obtained from the following sources, in the specified\n order. The first available and non-empty value is used:\n\n * the value of the \"ID\" attribute of the os-release file,\n\n * the value of the \"Distributor ID\" attribute returned by the lsb_release\n command,\n\n * the first part of the file name of the distro release file,\n\n The so determined ID value then passes the following transformations,\n before it is returned by this method:\n\n * it is translated to lower case,\n\n * blanks (which should not be there anyway) are translated to underscores,\n\n * a normalization of the ID is performed, based upon\n `normalization tables`_. The purpose of this normalization is to ensure\n that the ID is as reliable as possible, even across incompatible changes\n in the OS distributions. A common reason for an incompatible change is\n the addition of an os-release file, or the addition of the lsb_release\n command, with ID values that differ from what was previously determined\n from the distro release file name.\n ",
"language": "en",
"n_whitespaces": 754,
"n_words": 359,
"vocab_size": 208
} | 6 | Python | 6 | c69d55f7c82d5ae2cce542bcfb98d043ca4836a0 | distro.py | 21,509 | 79 | 13 | id | https://github.com/pypa/pipenv.git | Vendor in pip 22.1.2 | 12 | 0 | 3,889 | 7 |
|
1 | 33 | def test_execute_task_instances_backfill_tasks_wont_execute(self, dag_maker):
dag_id = 'SchedulerJobTest.test_execute_task_instances_backfill_tasks_wont_execute'
task_id_1 = 'dummy_task'
with dag_maker(dag_id=dag_id):
task1 = EmptyOperator(task_id=task_id_1)
self.scheduler_job = SchedulerJob(subdir=os.devnull)
session = settings.Session()
dr1 = dag_maker.create_dagrun(run_type=DagRunType.BACKFILL_JOB)
ti1 = TaskInstance(task1, run_id=dr1.run_id)
ti1.refresh_from_db()
ti1.state = State.SCHEDULED
session.merge(ti1)
session.flush()
assert dr1.is_backfill
self.scheduler_job._critical_section_execute_task_instances(session)
session.flush()
ti1.refresh_from_db()
assert State.SCHEDULED == ti1.state
session.rollback()
| tests/jobs/test_scheduler_job.py | 222 | airflow | {
"docstring": "\n Tests that backfill tasks won't get executed.\n ",
"language": "en",
"n_whitespaces": 22,
"n_words": 7,
"vocab_size": 7
} | 43 | Python | 31 | 49e336ae0302b386a2f47269a6d13988382d975f | test_scheduler_job.py | 47,528 | 19 | 131 | test_execute_task_instances_backfill_tasks_wont_execute | https://github.com/apache/airflow.git | Replace usage of `DummyOperator` with `EmptyOperator` (#22974)
* Replace usage of `DummyOperator` with `EmptyOperator` | 180 | 0 | 9,148 | 11 |
|
1 | 24 | def rand_series_with_duplicate_datetimeindex() -> Series:
dates = [
datetime(2000, 1, 2),
datetime(2000, 1, 2),
datetime(2000, 1, 2),
datetime(2000, 1, 3),
datetime(2000, 1, 3),
datetime(2000, 1, 3),
datetime(2000, 1, 4),
datetime(2000, 1, 4),
datetime(2000, 1, 4),
datetime(2000, 1, 5),
]
return Series(np.random.randn(len(dates)), index=dates)
# ----------------------------------------------------------------
# Scalars
# ----------------------------------------------------------------
@pytest.fixture(
params=[
(
Interval(left=0, right=5, inclusive="right"),
IntervalDtype("int64", inclusive="right"),
),
(
Interval(left=0.1, right=0.5, inclusive="right"),
IntervalDtype("float64", inclusive="right"),
),
(Period("2012-01", freq="M"), "period[M]"),
(Period("2012-02-01", freq="D"), "period[D]"),
(
Timestamp("2011-01-01", tz="US/Eastern"),
DatetimeTZDtype(tz="US/Eastern"),
),
(Timedelta(seconds=500), "timedelta64[ns]"),
]
) | pandas/conftest.py | 360 | @pytest.fixture(
params=[
(
Interval(left=0, right=5, inclusive="right"),
IntervalDtype("int64", inclusive="right"),
),
(
Interval(left=0.1, right=0.5, inclusive="right"),
IntervalDtype("float64", inclusive="right"),
),
(Period("2012-01", freq="M"), "period[M]"),
(Period("2012-02-01", freq="D"), "period[D]"),
(
Timestamp("2011-01-01", tz="US/Eastern"),
DatetimeTZDtype(tz="US/Eastern"),
),
(Timedelta(seconds=500), "timedelta64[ns]"),
]
) | pandas | {
"docstring": "\n Fixture for Series with a DatetimeIndex that has duplicates.\n ",
"language": "en",
"n_whitespaces": 16,
"n_words": 9,
"vocab_size": 9
} | 78 | Python | 43 | f538568afc2c76c2d738d32e3544cf9fe6742960 | conftest.py | 167,613 | 17 | 120 | rand_series_with_duplicate_datetimeindex | https://github.com/pandas-dev/pandas.git | TYP: misc return type annotations (#47558) | 290 | 1 | 40,065 | 13 |
2 | 5 | def execute():
name = frappe.db.sql(
)
if not name:
frappe.db.sql(
"update `tabProduction Order` pro \
set \
description = (select description from tabItem where name=pro.production_item) \
where \
ifnull(description, '') = ''"
)
| erpnext/patches/v5_7/update_item_description_based_on_item_master.py | 54 | erpnext | {
"docstring": " select name from `tabPatch Log` \\\n\t\twhere \\\n\t\t\tpatch like 'execute:frappe.db.sql(\"update `tabProduction Order` pro set description%' ",
"language": "en",
"n_whitespaces": 15,
"n_words": 16,
"vocab_size": 15
} | 33 | Python | 24 | 494bd9ef78313436f0424b918f200dab8fc7c20b | update_item_description_based_on_item_master.py | 66,864 | 14 | 26 | execute | https://github.com/frappe/erpnext.git | style: format code with black | 22 | 0 | 14,363 | 10 |
|
2 | 11 | def iter_mapped_dependencies(self) -> Iterator["Operator"]:
from airflow.models.xcom_arg import XComArg
for ref in XComArg.iter_xcom_args(self._get_expansion_kwargs()):
yield ref.operator
| airflow/models/mappedoperator.py | 62 | airflow | {
"docstring": "Upstream dependencies that provide XComs used by this task for task mapping.",
"language": "en",
"n_whitespaces": 11,
"n_words": 12,
"vocab_size": 11
} | 14 | Python | 14 | 197cff3194e855b9207c3c0da8ae093a0d5dda55 | mappedoperator.py | 47,756 | 5 | 37 | iter_mapped_dependencies | https://github.com/apache/airflow.git | Ensure TaskMap only checks "relevant" dependencies (#23053)
When looking for "mapped dependants" of a task, we only want a task if
it not only is a direct downstream of the task, but also it actually
"uses" the task's pushed XCom for task mapping. So we need to peek into
the mapped downstream task's expansion kwargs, and only count it as a
mapped dependant if the upstream is referenced there. | 46 | 0 | 9,245 | 9 |
|
6 | 16 | def get_expiry_date(self, **kwargs):
try:
modification = kwargs['modification']
except KeyError:
modification = timezone.now()
# Same comment as in get_expiry_age
try:
expiry = kwargs['expiry']
except KeyError:
expiry = self.get('_session_expiry')
if isinstance(expiry, datetime):
return expiry
elif isinstance(expiry, str):
return datetime.fromisoformat(expiry)
expiry = expiry or self.get_session_cookie_age()
return modification + timedelta(seconds=expiry)
| django/contrib/sessions/backends/base.py | 152 | django | {
"docstring": "Get session the expiry date (as a datetime object).\n\n Optionally, this function accepts `modification` and `expiry` keyword\n arguments specifying the modification and expiry of the session.\n ",
"language": "en",
"n_whitespaces": 47,
"n_words": 26,
"vocab_size": 22
} | 46 | Python | 30 | 436862787cbdbd68b0ba20ed8c23b295e3679df3 | base.py | 203,048 | 15 | 89 | get_expiry_date | https://github.com/django/django.git | Refs #29708 -- Made SessionBase store expiry as string. | 182 | 0 | 50,223 | 12 |
|
2 | 27 | def evaluate(model, criterion, metric, data_loader):
model.eval()
metric.reset()
losses = []
for batch in data_loader:
input_ids, token_type_ids, labels = batch
logits = model(input_ids, token_type_ids)
loss = criterion(logits, labels)
probs = F.sigmoid(logits)
losses.append(loss.numpy())
metric.update(probs, labels)
auc, f1_score = metric.accumulate()
print("eval loss: %.5f, auc: %.5f, f1 score: %.5f" %
(np.mean(losses), auc, f1_score))
model.train()
metric.reset()
| examples/text_classification/multi_label/train.py | 187 | PaddleNLP | {
"docstring": "\n Given a dataset, it evals model and computes the metric.\n\n Args:\n model(obj:`paddle.nn.Layer`): A model to classify texts.\n criterion(obj:`paddle.nn.Layer`): It can compute the loss.\n metric(obj:`paddle.metric.Metric`): The evaluation metric.\n data_loader(obj:`paddle.io.DataLoader`): The dataset loader which generates batches.\n ",
"language": "en",
"n_whitespaces": 72,
"n_words": 34,
"vocab_size": 30
} | 51 | Python | 41 | 621357338437ee420eabbbf5ab19065bc85e73a5 | train.py | 322,167 | 16 | 116 | evaluate | https://github.com/PaddlePaddle/PaddleNLP.git | Update neural search readme and Add Paddle Serving Support (#1558)
* 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
Co-authored-by: Zeyu Chen <[email protected]>
Co-authored-by: tianxin <[email protected]> | 129 | 0 | 118,077 | 11 |
|
1 | 2 | def ygap(self):
return self["ygap"]
| packages/python/plotly/plotly/graph_objs/_heatmap.py | 22 | plotly.py | {
"docstring": "\n Sets the vertical gap (in pixels) between bricks.\n\n The 'ygap' property is a number and may be specified as:\n - An int or float in the interval [0, inf]\n\n Returns\n -------\n int|float\n ",
"language": "en",
"n_whitespaces": 84,
"n_words": 32,
"vocab_size": 31
} | 4 | Python | 4 | 43e3a4011080911901176aab919c0ecf5046ddd3 | _heatmap.py | 226,903 | 2 | 11 | ygap | https://github.com/plotly/plotly.py.git | switch to black .22 | 18 | 0 | 58,576 | 7 |
|
1 | 3 | def __enter__(self):
raise NotImplementedError()
| python3.10.4/Lib/asyncio/unix_events.py | 20 | XX-Net | {
"docstring": "Enter the watcher's context and allow starting new processes\n\n This function must return self",
"language": "en",
"n_whitespaces": 20,
"n_words": 14,
"vocab_size": 14
} | 4 | Python | 4 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | unix_events.py | 220,938 | 2 | 10 | __enter__ | https://github.com/XX-net/XX-Net.git | add python 3.10.4 for windows | 18 | 0 | 56,170 | 7 |
|
1 | 2 | def adapt_streams_if_testing(func):
| airbyte-integrations/connectors/source-mixpanel/source_mixpanel/testing.py | 13 | airbyte | {
"docstring": "\n Due to API limitations (60 requests per hour) there is unavailable to make acceptance tests in normal mode,\n so we're reducing amount of requests by, if `is_testing` flag is set in config:\n\n 1. Take time range in only 1 month\n 2. Patch Funnels, so we download data only for one Funnel entity\n 3. Removing RPS limit for faster testing\n ",
"language": "en",
"n_whitespaces": 78,
"n_words": 59,
"vocab_size": 51
} | 2 | Python | 2 | d79b319819650f99fae2ab8c6c8d3ab25d474cf1 | testing.py | 5,687 | 4 | 15 | adapt_streams_if_testing | https://github.com/airbytehq/airbyte.git | :tada: Source Mixpanel: Beta preparation (#13372)
* Add extra mode to Source, to allow run acceptance tests
* move streams into distinct modules
* Add property name transformation for Export stream for avoiding collisions
* Update doc
* Add `date_window_size` | 5 | 0 | 808 | 6 |
|
1 | 6 | def getsource(object):
lines, lnum = getsourcelines(object)
return ''.join(lines)
# --------------------------------------------------- class tree extraction | python3.10.4/Lib/inspect.py | 40 | XX-Net | {
"docstring": "Return the text of the source code for an object.\n\n The argument may be a module, class, method, function, traceback, frame,\n or code object. The source code is returned as a single string. An\n OSError is raised if the source code cannot be retrieved.",
"language": "en",
"n_whitespaces": 54,
"n_words": 44,
"vocab_size": 32
} | 13 | Python | 13 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | inspect.py | 218,386 | 3 | 21 | getsource | https://github.com/XX-net/XX-Net.git | add python 3.10.4 for windows | 21 | 0 | 55,274 | 8 |
|
2 | 11 | def set_floatx(value):
global _FLOATX
accepted_dtypes = {"float16", "float32", "float64"}
if value not in accepted_dtypes:
raise ValueError(
f"Unknown `floatx` value: {value}. "
f"Expected one of {accepted_dtypes}"
)
_FLOATX = str(value)
@keras_export("keras.backend.image_data_format")
@tf.__internal__.dispatch.add_dispatch_support | keras/backend_config.py | 98 | @keras_export("keras.backend.image_data_format")
@tf.__internal__.dispatch.add_dispatch_support | keras | {
"docstring": "Sets the default float type.\n\n Note: It is not recommended to set this to float16 for training, as this\n will likely cause numeric stability issues. Instead, mixed precision, which\n is using a mix of float16 and float32, can be used by calling\n `tf.keras.mixed_precision.set_global_policy('mixed_float16')`. See the\n [mixed precision guide](\n https://www.tensorflow.org/guide/keras/mixed_precision) for details.\n\n Args:\n value: String; `'float16'`, `'float32'`, or `'float64'`.\n\n Example:\n >>> tf.keras.backend.floatx()\n 'float32'\n >>> tf.keras.backend.set_floatx('float64')\n >>> tf.keras.backend.floatx()\n 'float64'\n >>> tf.keras.backend.set_floatx('float32')\n\n Raises:\n ValueError: In case of invalid value.\n ",
"language": "en",
"n_whitespaces": 140,
"n_words": 76,
"vocab_size": 65
} | 31 | Python | 29 | f3cafc77c269f7ecbf80bb4cf4b54e28c153f4e6 | backend_config.py | 277,755 | 9 | 37 | set_floatx | https://github.com/keras-team/keras.git | resolve line-too-long in root directory | 80 | 1 | 82,185 | 12 |
3 | 45 | def test_fetch_final_taken_task(business_client):
config = dict(
title='test_label_races',
is_published=True,
label_config=
)
annotation_result = json.dumps([{
'from_name': 'text_class',
'to_name': 'text',
'type': 'choices',
'value': {'choices': ['class_A']}
}])
project = make_project(config, business_client.user)
project.sampling = Project.SEQUENCE
project.save()
ann1 = make_annotator({'email': '[email protected]'}, project, True)
ann2 = make_annotator({'email': '[email protected]'}, project, True)
# create tasks
tasks = []
num_tasks = 2
for i in range(num_tasks):
tasks.append({'data': {'text': f'this is {str(i)}'}})
r = business_client.post(
f'/api/projects/{project.id}/tasks/bulk/', data=json.dumps(tasks), content_type='application/json')
assert r.status_code == 201
# set max annotations
r = business_client.patch(
f'/api/projects/{project.id}/',
data=json.dumps({'maximum_annotations': 2}),
content_type='application/json'
)
assert r.status_code == 200
print('ann1 takes any task and complete it')
r = ann1.get(f'/api/projects/{project.id}/next')
task_id = json.loads(r.content)['id']
ann1.post(f'/api/tasks/{task_id}/annotations/', data={'task': task_id, 'result': annotation_result})
print('ann2 takes the same task (because of depth-first) but just lock it - don\'t complete')
r = ann2.get(f'/api/projects/{project.id}/next')
assert json.loads(r.content)['id'] == task_id
print('ann1 takes another task')
r = ann1.get(f'/api/projects/{project.id}/next')
another_task_id = json.loads(r.content)['id']
assert another_task_id != task_id
print('ann1 should never take task_id since he has completed it')
for i in range(3):
r = ann1.get(f'/api/projects/{project.id}/next')
assert json.loads(r.content)['id'] == another_task_id
@pytest.mark.skipif(not redis_healthcheck(), reason='Multi user locks only supported with redis enabled')
@pytest.mark.django_db | label_studio/tests/test_next_task.py | 662 | @pytest.mark.skipif(not redis_healthcheck(), reason='Multi user locks only supported with redis enabled')
@pytest.mark.django_db | label-studio | {
"docstring": "\n <View>\n <Text name=\"text\" value=\"$text\"></Text>\n <Choices name=\"text_class\" choice=\"single\" toName=\"text\">\n <Choice value=\"class_A\"></Choice>\n <Choice value=\"class_B\"></Choice>\n </Choices>\n </View>",
"language": "en",
"n_whitespaces": 105,
"n_words": 14,
"vocab_size": 13
} | 172 | Python | 119 | aaa022d8acbeb002eab2930965da276e9298cd54 | test_next_task.py | 177,569 | 52 | 330 | test_fetch_final_taken_task | https://github.com/heartexlabs/label-studio.git | [ext] Add video interpolation by param (DEV-74) (#1735)
* Add video interpolation by param
* Change label-studio-tools commit
* Fix typo and add some comments
* Fix context field
* Fix label-studio-tools link
* fix link to ext dep
* Update requirements for label_studio_tools
* Change label-studio-tools commit with refactoring
* Change label-studio-tools requirement
* Change label-studio-tools version to dev3
* Change base settings
* Add interpolate_key_frames option in ExportMixin
* Change serializer options to context
* Add serializer for Export
- Add serializer for Export
- Switch to is_video_object_tracking and new extract_key_frames logic
- Change label-studio-tools requirement
* Fix serializer fields
* Fix export type in serializer
* Add exportType to support both export params
* Move to parsed_config in is_video_object_tracking
* Add interpolate_key_frames to SerializationOptionsSerializer
* Change label-studio-tools to version with sequence
* Change label-studio-tools with time fix
* Add parse_label_config to Project model
* Fix new project condition
* Change from presave signal to save method
* Fix input data for tests
* Upgrade label-studio-tools version
* Change label-studio-tools version with key frames order fix
Co-authored-by: Sergey Zhuk <[email protected]>
Co-authored-by: Max Tkachenko <[email protected]>
Co-authored-by: Sergei Ivashchenko <[email protected]> | 363 | 1 | 42,442 | 17 |
2 | 16 | def create_central_storage_strategy():
compute_devices = (
["cpu:0", "gpu:0"]
if (tf.config.list_logical_devices("GPU"))
else ["cpu:0"]
)
return tf.distribute.experimental.CentralStorageStrategy(
compute_devices, parameter_device="cpu:0"
)
TESTCASES = (
{"testcase_name": "base", "strategy_fn": default_strategy_fn},
{"testcase_name": "distribute", "strategy_fn": create_mirrored_strategy},
)
@test_combinations.generate(test_combinations.combine(mode=["graph", "eager"])) | keras/mixed_precision/layer_test.py | 156 | @test_combinations.generate(test_combinations.combine(mode=["graph", "eager"])) | keras | {
"docstring": "Create a CentralStorageStrategy, using a GPU if it is available.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 9
} | 31 | Python | 25 | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | layer_test.py | 274,999 | 9 | 44 | create_central_storage_strategy | https://github.com/keras-team/keras.git | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 77 | 1 | 81,282 | 12 |
1 | 21 | def decode_predictions(preds, top=5):
return imagenet_utils.decode_predictions(preds, top=top)
preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
mode='',
ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF,
error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC)
decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
DOC =
setattr(ResNetRS50, '__doc__', ResNetRS50.__doc__ + DOC)
setattr(ResNetRS152, '__doc__', ResNetRS152.__doc__ + DOC)
setattr(ResNetRS200, '__doc__', ResNetRS200.__doc__ + DOC)
setattr(ResNetRS270, '__doc__', ResNetRS270.__doc__ + DOC)
setattr(ResNetRS350, '__doc__', ResNetRS350.__doc__ + DOC)
setattr(ResNetRS420, '__doc__', ResNetRS420.__doc__ + DOC)
| keras/applications/resnet_rs.py | 205 | keras | {
"docstring": "\n\n Reference:\n [Revisiting ResNets: Improved Training and Scaling Strategies](\n https://arxiv.org/pdf/2103.07579.pdf)\n\n For image classification use cases, see\n [this page for detailed examples](\n https://keras.io/api/applications/#usage-examples-for-image-classification-models).\n\n For transfer learning use cases, make sure to read the\n [guide to transfer learning & fine-tuning](\n https://keras.io/guides/transfer_learning/).\n\n Note: each Keras Application expects a specific kind of input preprocessing.\n For ResNetRs, by default input preprocessing is included as a part of the\n model (as a `Rescaling` layer), and thus\n `tf.keras.applications.resnet_rs.preprocess_input` is actually a\n pass-through function. In this use case, ResNetRS models expect their inputs\n to be float tensors of pixels with values in the [0-255] range.\n At the same time, preprocessing as a part of the model (i.e. `Rescaling`\n layer) can be disabled by setting `include_preprocessing` argument to False.\n With preprocessing disabled ResNetRS models expect their inputs to be float\n tensors of pixels with values in the [-1, 1] range.\n\n Args:\n depth: Depth of ResNet network.\n dropout_rate: dropout rate before final classifier layer.\n bn_momentum: Momentum parameter for Batch Normalization layers.\n bn_epsilon: Epsilon parameter for Batch Normalization layers.\n activation: activation function.\n block_args: list of dicts, parameters to construct block modules.\n se_ratio: Squeeze and Excitation layer ratio.\n model_name: name of the model.\n drop_connect_rate: dropout rate at skip connections.\n include_top: whether to include the fully-connected layer at the top of\n the network.\n weights: one of `None` (random initialization), `'imagenet'`\n (pre-training on ImageNet), or the path to the weights file to be loaded.\n Note: one model can have multiple imagenet variants depending on\n input shape it was trained with. For input_shape 224x224 pass\n `imagenet-i224` as argument. By default, highest input shape weights are\n downloaded.\n input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to\n use as image input for the model.\n input_shape: optional shape tuple. It should have exactly 3 inputs\n channels, and width and height should be no smaller than 32.\n E.g. (200, 200, 3) would be one valid value.\n pooling: optional pooling mode for feature extraction when `include_top`\n is `False`.\n - `None` means that the output of the model will be\n the 4D tensor output of the\n last convolutional layer.\n - `avg` means that global average pooling\n will be applied to the output of the\n last convolutional layer, and thus\n the output of the model will be a 2D tensor.\n - `max` means that global max pooling will\n be applied.\n classes: optional number of classes to classify images into, only to be\n specified if `include_top` is True, and if no `weights` argument is\n specified.\n classifier_activation: A `str` or callable. The activation function to\n use on the \"top\" layer. Ignored unless `include_top=True`. Set\n `classifier_activation=None` to return the logits of the \"top\" layer.\n include_preprocessing: Boolean, whether to include the preprocessing layer\n (`Rescaling`) at the bottom of the network. Defaults to `True`.\n\n Returns:\n A `keras.Model` instance.\n",
"language": "en",
"n_whitespaces": 883,
"n_words": 450,
"vocab_size": 256
} | 47 | Python | 30 | c223693db91473c9a71c330d4e38a751d149f93c | resnet_rs.py | 268,893 | 2 | 20 | decode_predictions | https://github.com/keras-team/keras.git | KERAS application addition of Resnet-RS model | 48 | 0 | 79,758 | 8 |
|
4 | 2 | def all_nodes(self):
| jina/serve/runtimes/gateway/graph/topology_graph.py | 13 | jina | {
"docstring": "\n The set of all the nodes inside this Graph\n\n :return: A list of nodes\n ",
"language": "en",
"n_whitespaces": 36,
"n_words": 14,
"vocab_size": 12
} | 2 | Python | 2 | ef662b529b2a2eecea7bb99759a9f7b9d86d3062 | topology_graph.py | 12,505 | 11 | 69 | all_nodes | https://github.com/jina-ai/jina.git | feat: add grpc health checking (#4779) | 9 | 0 | 2,324 | 6 |
|
11 | 60 | def filtered_queryset(self):
qs = self.model.objects.all()
# FIXME: the following fields will be attached to the wrong object
# if they are included in prefetch_related because of
# https://github.com/django-polymorphic/django-polymorphic/issues/68
# 'job_template', 'job', 'project', 'project_update', 'workflow_job',
# 'inventory_source', 'workflow_job_template'
q = Q(user=self.user)
inventory_set = Inventory.accessible_pk_qs(self.user, 'read_role')
if inventory_set:
q |= (
Q(ad_hoc_command__inventory__in=inventory_set)
| Q(inventory__in=inventory_set)
| Q(host__inventory__in=inventory_set)
| Q(group__inventory__in=inventory_set)
| Q(inventory_source__inventory__in=inventory_set)
| Q(inventory_update__inventory_source__inventory__in=inventory_set)
)
credential_set = Credential.accessible_pk_qs(self.user, 'read_role')
if credential_set:
q |= Q(credential__in=credential_set)
auditing_orgs = (
(Organization.accessible_objects(self.user, 'admin_role') | Organization.accessible_objects(self.user, 'auditor_role'))
.distinct()
.values_list('id', flat=True)
)
if auditing_orgs:
q |= (
Q(user__in=auditing_orgs.values('member_role__members'))
| Q(organization__in=auditing_orgs)
| Q(notification_template__organization__in=auditing_orgs)
| Q(notification__notification_template__organization__in=auditing_orgs)
| Q(label__organization__in=auditing_orgs)
| Q(role__in=Role.objects.filter(ancestors__in=self.user.roles.all()) if auditing_orgs else [])
)
project_set = Project.accessible_pk_qs(self.user, 'read_role')
if project_set:
q |= Q(project__in=project_set) | Q(project_update__project__in=project_set)
jt_set = JobTemplate.accessible_pk_qs(self.user, 'read_role')
if jt_set:
q |= Q(job_template__in=jt_set) | Q(job__job_template__in=jt_set)
wfjt_set = WorkflowJobTemplate.accessible_pk_qs(self.user, 'read_role')
if wfjt_set:
q |= (
Q(workflow_job_template__in=wfjt_set)
| Q(workflow_job_template_node__workflow_job_template__in=wfjt_set)
| Q(workflow_job__workflow_job_template__in=wfjt_set)
)
team_set = Team.accessible_pk_qs(self.user, 'read_role')
if team_set:
q |= Q(team__in=team_set)
app_set = OAuth2ApplicationAccess(self.user).filtered_queryset()
if app_set:
q |= Q(o_auth2_application__in=app_set)
token_set = OAuth2TokenAccess(self.user).filtered_queryset()
if token_set:
q |= Q(o_auth2_access_token__in=token_set)
return qs.filter(q).distinct()
| awx/main/access.py | 665 | awx | {
"docstring": "\n The full set is returned if the user is:\n - System Administrator\n - System Auditor\n These users will be able to see orphaned activity stream items\n (the related resource has been deleted), as well as the other\n obscure cases listed here\n\n Complex permissions omitted from the activity stream of a normal user:\n - host access via group\n - permissions (from prior versions)\n - notifications via team admin access\n\n Activity stream events that have been omitted from list for\n normal users since 2.4:\n - unified job templates\n - unified jobs\n - schedules\n - custom inventory scripts\n ",
"language": "en",
"n_whitespaces": 224,
"n_words": 95,
"vocab_size": 71
} | 167 | Python | 99 | e87e041a2a2a6d168a84d3eeea6664985f1c8ab8 | access.py | 82,255 | 53 | 404 | filtered_queryset | https://github.com/ansible/awx.git | Break up and conditionally add the RBAC checks for ActivityStream (#13279)
This should vastly improve the queries executed when accessing any of
the activity stream endpoints as a normal user, in many cases. | 753 | 0 | 17,336 | 21 |
|
11 | 35 | def read_packet(self, size=CAN_MTU):
# type: (int) -> Optional[Packet]
line = self.f.readline()
line = line.lstrip()
if len(line) < 16:
raise EOFError
is_log_file_format = orb(line[0]) == orb(b"(")
fd_flags = None
if is_log_file_format:
t_b, intf, f = line.split()
if b'##' in f:
idn, data = f.split(b'##')
fd_flags = orb(data[0])
data = data[1:]
else:
idn, data = f.split(b'#')
le = None
t = float(t_b[1:-1]) # type: Optional[float]
else:
h, data = line.split(b']')
intf, idn, le = h.split()
t = None
if self.ifilter is not None and \
intf.decode('ASCII') not in self.ifilter:
return None
data = data.replace(b' ', b'')
data = data.strip()
if len(data) <= 8 and fd_flags is None:
pkt = CAN(identifier=int(idn, 16), data=hex_bytes(data))
else:
pkt = CANFD(identifier=int(idn, 16), fd_flags=fd_flags,
data=hex_bytes(data))
if le is not None:
pkt.length = int(le[1:])
else:
pkt.length = len(pkt.data)
if len(idn) > 3:
pkt.flags = 0b100
if t is not None:
pkt.time = t
return pkt
| scapy/layers/can.py | 500 | scapy | {
"docstring": "Read a packet from the specified file.\n\n This function will raise EOFError when no more packets are available.\n\n :param size: Not used. Just here to follow the function signature for\n SuperSocket emulation.\n :return: A single packet read from the file or None if filters apply\n ",
"language": "en",
"n_whitespaces": 93,
"n_words": 45,
"vocab_size": 40
} | 146 | Python | 77 | ada91610ad55339bce4d84bc7d5e44ee1cab0c6f | can.py | 209,950 | 40 | 312 | read_packet | https://github.com/secdev/scapy.git | Add support of CANFD (#3782)
* Add support of CANFD
Co-authored-by: superuserx
* fix tests
* fix flake
* fix test
* fix test for python2
* fix test for python2
* fix test for python2
Co-authored-by: superuserx <[email protected]>
Co-authored-by: Nils Weiss <[email protected]> | 554 | 0 | 52,837 | 14 |
|
3 | 9 | def _set_speaker_encoder_paths_from_tts_config(self):
if hasattr(self.tts_config, "model_args") and hasattr(
self.tts_config.model_args, "speaker_encoder_config_path"
):
self.encoder_checkpoint = self.tts_config.model_args.speaker_encoder_model_path
self.encoder_config = self.tts_config.model_args.speaker_encoder_config_path
| TTS/utils/synthesizer.py | 82 | TTS | {
"docstring": "Set the encoder paths from the tts model config for models with speaker encoders.",
"language": "en",
"n_whitespaces": 13,
"n_words": 14,
"vocab_size": 13
} | 16 | Python | 15 | 8fd1ee1926a956a146188179baee143ef11a003d | synthesizer.py | 261,841 | 6 | 49 | _set_speaker_encoder_paths_from_tts_config | https://github.com/coqui-ai/TTS.git | Print urls when BadZipError | 70 | 0 | 77,026 | 11 |
|
1 | 13 | def alg_field_from_poly(self, poly, alias=None, root_index=-1):
r
from sympy.polys.rootoftools import CRootOf
root = CRootOf(poly, root_index)
alpha = AlgebraicNumber(root, alias=alias)
return self.algebraic_field(alpha, alias=alias)
| sympy/polys/domains/domain.py | 81 | sympy | {
"docstring": "\n Convenience method to construct an algebraic extension on a root of a\n polynomial, chosen by root index.\n\n Parameters\n ==========\n\n poly : :py:class:`~.Poly`\n The polynomial whose root generates the extension.\n alias : str, optional (default=None)\n Symbol name for the generator of the extension.\n E.g. \"alpha\" or \"theta\".\n root_index : int, optional (default=-1)\n Specifies which root of the polynomial is desired. The ordering is\n as defined by the :py:class:`~.ComplexRootOf` class. The default of\n ``-1`` selects the most natural choice in the common cases of\n quadratic and cyclotomic fields (the square root on the positive\n real or imaginary axis, resp. $\\mathrm{e}^{2\\pi i/n}$).\n\n Examples\n ========\n\n >>> from sympy import QQ, Poly\n >>> from sympy.abc import x\n >>> f = Poly(x**2 - 2)\n >>> K = QQ.alg_field_from_poly(f)\n >>> K.ext.minpoly == f\n True\n >>> g = Poly(8*x**3 - 6*x - 1)\n >>> L = QQ.alg_field_from_poly(g, \"alpha\")\n >>> L.ext.minpoly == g\n True\n >>> L.to_sympy(L([1, 1, 1]))\n alpha**2 + alpha + 1\n\n ",
"language": "en",
"n_whitespaces": 397,
"n_words": 154,
"vocab_size": 107
} | 21 | Python | 19 | 1af5040d2466d2e6455eb07454f7da8dd345a9b8 | domain.py | 197,778 | 41 | 55 | alg_field_from_poly | https://github.com/sympy/sympy.git | Support `alias` for prim. elt. of `AlgebraicField` | 55 | 0 | 48,688 | 9 |
|
14 | 35 | def _update_defaults(self, defaults):
# type: (Dict[str, Any]) -> Dict[str, Any]
# Accumulate complex default state.
self.values = optparse.Values(self.defaults)
late_eval = set()
# Then set the options with those values
for key, val in self._get_ordered_configuration_items():
# '--' because configuration supports only long names
option = self.get_option("--" + key)
# Ignore options not present in this parser. E.g. non-globals put
# in [global] by users that want them to apply to all applicable
# commands.
if option is None:
continue
assert option.dest is not None
if option.action in ("store_true", "store_false"):
try:
val = strtobool(val)
except ValueError:
self.error(
"{} is not a valid value for {} option, " # noqa
"please specify a boolean value like yes/no, "
"true/false or 1/0 instead.".format(val, key)
)
elif option.action == "count":
with suppress(ValueError):
val = strtobool(val)
with suppress(ValueError):
val = int(val)
if not isinstance(val, int) or val < 0:
self.error(
"{} is not a valid value for {} option, " # noqa
"please instead specify either a non-negative integer "
"or a boolean value like yes/no or false/true "
"which is equivalent to 1/0.".format(val, key)
)
elif option.action == "append":
val = val.split()
val = [self.check_default(option, key, v) for v in val]
elif option.action == "callback":
assert option.callback is not None
late_eval.add(option.dest)
opt_str = option.get_opt_string()
val = option.convert_value(opt_str, val)
# From take_action
args = option.callback_args or ()
kwargs = option.callback_kwargs or {}
option.callback(option, opt_str, val, self, *args, **kwargs)
else:
val = self.check_default(option, key, val)
defaults[option.dest] = val
for key in late_eval:
defaults[key] = getattr(self.values, key)
self.values = None
return defaults
| .venv/lib/python3.8/site-packages/pip/_internal/cli/parser.py | 518 | transferlearning | {
"docstring": "Updates the given defaults with values from the config files and\n the environ. Does a little special handling for certain types of\n options (lists).",
"language": "en",
"n_whitespaces": 37,
"n_words": 24,
"vocab_size": 22
} | 254 | Python | 148 | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | parser.py | 60,539 | 47 | 308 | _update_defaults | https://github.com/jindongwang/transferlearning.git | upd; format | 1,029 | 0 | 12,199 | 18 |
|
1 | 21 | def test_start_new_processes_with_same_filepath(self):
manager = DagFileProcessorManager(
dag_directory='directory',
max_runs=1,
processor_timeout=timedelta(days=365),
signal_conn=MagicMock(),
dag_ids=[],
pickle_dags=False,
async_mode=True,
)
file_1 = 'file_1.py'
file_2 = 'file_2.py'
file_3 = 'file_3.py'
manager._file_path_queue = [file_1, file_2, file_3]
# Mock that only one processor exists. This processor runs with 'file_1'
manager._processors[file_1] = MagicMock()
# Start New Processes
manager.start_new_processes()
# Because of the config: '[scheduler] parsing_processes = 2'
# verify that only one extra process is created
# and since a processor with 'file_1' already exists,
# even though it is first in '_file_path_queue'
# a new processor is created with 'file_2' and not 'file_1'.
assert file_1 in manager._processors.keys()
assert file_2 in manager._processors.keys()
assert [file_3] == manager._file_path_queue
| tests/dag_processing/test_manager.py | 185 | airflow | {
"docstring": "\n Test that when a processor already exist with a filepath, a new processor won't be created\n with that filepath. The filepath will just be removed from the list.\n ",
"language": "en",
"n_whitespaces": 50,
"n_words": 28,
"vocab_size": 22
} | 105 | Python | 71 | 18da1217d7ae593ff33c681353b027fac9252523 | test_manager.py | 46,452 | 19 | 110 | test_start_new_processes_with_same_filepath | https://github.com/apache/airflow.git | Replace timedelta.max with year long timdelta in test_manager (#22527)
Timedelta.max used in tests is not realistic and in some
circumstances, when it is added to date, it might cause
date OverflowError. Using long (but not 999999999 days long)
timedelta solves the problem. | 315 | 0 | 8,892 | 12 |
|
1 | 8 | def search(self, value, user=None, object_types=None, lookup=DEFAULT_LOOKUP_TYPE):
raise NotImplementedError
| netbox/netbox/search/backends.py | 33 | netbox | {
"docstring": "\n Search cached object representations for the given value.\n ",
"language": "en",
"n_whitespaces": 23,
"n_words": 8,
"vocab_size": 8
} | 8 | Python | 8 | 9628dead07ccef9608b32906aa8194bc948e5a09 | backends.py | 265,890 | 2 | 22 | search | https://github.com/netbox-community/netbox.git | Closes #10560: New global search (#10676)
* Initial work on new search backend
* Clean up search backends
* Return only the most relevant result per object
* Clear any pre-existing cached entries on cache()
* #6003: Implement global search functionality for custom field values
* Tweak field weights & document guidance
* Extend search() to accept a lookup type
* Move get_registry() out of SearchBackend
* Enforce object permissions when returning search results
* Add indexers for remaining models
* Avoid calling remove() on non-cacheable objects
* Use new search backend by default
* Extend search backend to filter by object type
* Clean up search view form
* Enable specifying lookup logic
* Add indexes for value field
* Remove object type selector from search bar
* Introduce SearchTable and enable HTMX for results
* Enable pagination
* Remove legacy search backend
* Cleanup
* Use a UUID for CachedValue primary key
* Refactoring search methods
* Define max search results limit
* Extend reindex command to support specifying particular models
* Add clear() and size to SearchBackend
* Optimize bulk caching performance
* Highlight matched portion of field value
* Performance improvements for reindexing
* Started on search tests
* Cleanup & docs
* Documentation updates
* Clean up SearchIndex
* Flatten search registry to register by app_label.model_name
* Clean up search backend classes
* Clean up RestrictedGenericForeignKey and RestrictedPrefetch
* Resolve migrations conflict | 22 | 0 | 78,230 | 6 |
|
7 | 59 | def call_load(self, other_args):
parser = argparse.ArgumentParser(
add_help=False,
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
prog="load",
description=,
)
parser.add_argument(
"-c",
"--coin",
help="Coin to get. Must be coin symbol (e.g., btc, eth)",
dest="coin",
type=str,
required="-h" not in other_args,
)
parser.add_argument(
"-s",
"--start",
type=valid_date,
default=(datetime.now() - timedelta(days=1100)).strftime("%Y-%m-%d"),
dest="start",
help="The starting date (format YYYY-MM-DD) of the crypto",
)
parser.add_argument(
"--exchange",
help="Exchange to search",
dest="exchange",
type=str,
default="binance",
choices=self.exchanges,
)
parser.add_argument(
"-e",
"--end",
type=valid_date,
default=datetime.now().strftime("%Y-%m-%d"),
dest="end",
help="The ending date (format YYYY-MM-DD) of the crypto",
)
parser.add_argument(
"-i",
"--interval",
action="store",
dest="interval",
type=str,
default="1440",
choices=["1", "5", "15", "30", "60", "240", "1440", "10080", "43200"],
help="The interval of the crypto",
)
parser.add_argument(
"--vs",
help="Quote currency (what to view coin vs). e.g., usdc, usdt, ... if source is ccxt, usd, eur, ... otherwise", # noqa
dest="vs",
default="usdt",
type=str,
)
if other_args and "-" not in other_args[0][0]:
other_args.insert(0, "-c")
ns_parser = self.parse_known_args_and_warn(
parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED
)
if ns_parser:
if ns_parser.source in ("YahooFinance", "CoinGecko"):
if ns_parser.vs == "usdt":
ns_parser.vs = "usd"
(self.current_df) = cryptocurrency_helpers.load(
symbol=ns_parser.coin.lower(),
vs_currency=ns_parser.vs,
end_date=ns_parser.end.strftime("%Y-%m-%d"),
start_date=ns_parser.start.strftime("%Y-%m-%d"),
interval=ns_parser.interval,
source=ns_parser.source,
exchange=ns_parser.exchange,
)
if not self.current_df.empty:
self.vs = ns_parser.vs
self.exchange = ns_parser.exchange
self.source = ns_parser.source
self.current_interval = ns_parser.interval
self.current_currency = ns_parser.vs
self.symbol = ns_parser.coin.lower()
cryptocurrency_helpers.show_quick_performance(
self.current_df,
self.symbol,
self.current_currency,
ns_parser.source,
ns_parser.exchange,
self.current_interval,
)
export_data(
ns_parser.export,
os.path.dirname(os.path.abspath(__file__)),
"load",
self.current_df.copy(),
)
| openbb_terminal/parent_classes.py | 791 | OpenBBTerminal | {
"docstring": "Process load command.Load crypto currency to perform analysis on.\n Yahoo Finance is used as default source.\n Other sources can be used such as 'ccxt' or 'cg' with --source.\n If you select 'ccxt', you can then select any exchange with --exchange.\n You can also select a specific interval with --interval.",
"language": "en",
"n_whitespaces": 92,
"n_words": 49,
"vocab_size": 40
} | 198 | Python | 141 | 46141766d7250671b7bc75872e2034afe4938374 | parent_classes.py | 286,499 | 99 | 486 | call_load | https://github.com/OpenBB-finance/OpenBBTerminal.git | Sdk dates (#3354)
* example changes in slopes
* change lettering size and side bar capitalization
* revert back to Fira
* start automatic website generation
* this was autogen
* add examples to slopes model
* generate slopes doc
* change to _index.md
* allow italic formatting
* fix regex
* option to regenerate paths
* update alt docs
* fix generate
* update alt
* fix generate
* update common
* target italic only for types
* format alt
* format italic common
* add sig indentation
* update sig indent alt
* update common ident
* add todo
* generate docstrings for all menus
* fix maxdd
* fix returns font size
* fix keys docs
* fix more docstrings
* escape literal symbols
* escape literal symbols
* reformat keys
* format opt
* remove literal escape
* remove another literal escape
* remove another literal escape
* unindent returns
* update docs return unindent
* add comma in last arg
* fix funcs without params
* fix signature
* compact some code
* refactor some more code
* refactor some code
* some final cleanup
* write docstrings
* change main
* move futures paths
* generate futures docs
* add external axes references
* fix typo
* revert to double docstring
* fix small bug
* remove docs folder
* generate.py in website folder
* add forecast to docs
* clear some warnings
* fix underscore
* remove cite
* refresh website docs
* fix forecast docstrings
* fix po
* fix po docs and remove italic
* fix more docstrings
* remove last warning
* codespell
* flake8
* exclude website contente from flake
* noqa on optimizer
* update website
* fix mypy
* remove setup from mypy
* mypy to openbbterminal
* update precommit
* pylint
* try to remove sdk loading issue
* fix dates active command
* fix crypto.change formats
* fix eb formats
* nonzero fix
* format dates crypto.load
* format supply transac
* format hr altindex
* format load crypto
* regenerate docs
* format ba trend dates
* regenerate docs
* format ba trend
* candle defaults
* fix sentiment test
* remove unused import
* shopt
* shopt again
* revert crypto helpers
* test shopt
* fix some tests
* skip trending test
* fix alcoin test
* helpers
* write docs
* rewrite helper
Co-authored-by: Jeroen Bouma <[email protected]> | 1,328 | 0 | 85,834 | 16 |