id list | project string | origin_file list | test_list list | prob_info list | type list | node list | language string | toolfunc_count int64 | func_count int64 | pytest_info dict |
|---|---|---|---|---|---|---|---|---|---|---|
[
"cloudnetpy.cloudnetpy.utils.cumsumr",
"cloudnetpy.cloudnetpy.categorize.atmos_utils.calc_adiabatic_lwc"
] | cloudnetpy | [
"cloudnetpy/utils.py",
"cloudnetpy/categorize/atmos_utils.py"
] | [
"tests/unit/test_atmos_utils.py"
] | [
{
"class_start_lineno": 1,
"class_end_lineno": 1151,
"func_start_lineno": 532,
"func_end_lineno": 549,
"func_code": "def cumsumr(array: np.ndarray, axis: int = 0) -> np.ndarray:\n \"\"\"Finds cumulative sum that resets on 0.\n\n Args:\n array: Input array.\n axis: Axis where ... | [
"function_empty"
] | [
"cloudnetpy.utils.cumsumr",
"cloudnetpy.categorize.atmos_utils.calc_adiabatic_lwc"
] | Python | 2 | 2 | {
"total_num": 5,
"base_passed_num": 4
} |
[
"cloudnetpy.cloudnetpy.utils.binvec",
"cloudnetpy.cloudnetpy.utils.rebin_1d",
"cloudnetpy.cloudnetpy.utils.rebin_2d",
"cloudnetpy.cloudnetpy.cloudnetarray.CloudnetArray::rebin_data"
] | cloudnetpy | [
"cloudnetpy/utils.py",
"cloudnetpy/utils.py",
"cloudnetpy/utils.py",
"cloudnetpy/cloudnetarray.py"
] | [
"tests/unit/test_categorize.py"
] | [
{
"class_start_lineno": 1,
"class_end_lineno": 1151,
"func_start_lineno": 124,
"func_end_lineno": 140,
"func_code": "def binvec(x: np.ndarray | list) -> np.ndarray:\n \"\"\"Converts 1-D center points to bins with even spacing.\n\n Args:\n x: 1-D array of N real values.\n\n Return... | [
"function_empty"
] | [
"cloudnetpy.utils.binvec",
"cloudnetpy.utils.rebin_1d",
"cloudnetpy.utils.rebin_2d",
"cloudnetpy.cloudnetarray.CloudnetArray.rebin_data"
] | Python | 4 | 4 | {
"total_num": 4,
"base_passed_num": 0
} |
[
"cloudnetpy.cloudnetpy.utils.binvec",
"cloudnetpy.cloudnetpy.utils.rebin_2d",
"cloudnetpy.cloudnetpy.cloudnetarray.CloudnetArray::rebin_data"
] | cloudnetpy | [
"cloudnetpy/utils.py",
"cloudnetpy/utils.py",
"cloudnetpy/cloudnetarray.py"
] | [
"tests/unit/test_cloudnetarray.py"
] | [
{
"class_start_lineno": 1,
"class_end_lineno": 1151,
"func_start_lineno": 124,
"func_end_lineno": 140,
"func_code": "def binvec(x: np.ndarray | list) -> np.ndarray:\n \"\"\"Converts 1-D center points to bins with even spacing.\n\n Args:\n x: 1-D array of N real values.\n\n Return... | [
"function_empty"
] | [
"cloudnetpy.utils.binvec",
"cloudnetpy.utils.rebin_2d",
"cloudnetpy.cloudnetarray.CloudnetArray.rebin_data"
] | Python | 3 | 3 | {
"total_num": 17,
"base_passed_num": 15
} |
[
"cloudnetpy.cloudnetpy.concat_lib._Concat::_write_initial_data",
"cloudnetpy.cloudnetpy.concat_lib._Concat::concat_data"
] | cloudnetpy | [
"cloudnetpy/concat_lib.py",
"cloudnetpy/concat_lib.py"
] | [
"tests/unit/test_concat_lib.py",
"tests/unit/test_copernicus.py",
"tests/unit/test_galileo.py",
"tests/unit/test_mira.py"
] | [
{
"class_start_lineno": 122,
"class_end_lineno": 253,
"func_start_lineno": 173,
"func_end_lineno": 202,
"func_code": " def _write_initial_data(self, variables: list | None, ignore: list | None) -> None:\n for key in self.first_file.variables:\n if (\n variable... | [
"Development"
] | [
"cloudnetpy.concat_lib._Concat._write_initial_data",
"cloudnetpy.concat_lib._Concat.concat_data"
] | Python | 0 | 2 | {
"total_num": 71,
"base_passed_num": 32
} |
[
"cloudnetpy.cloudnetpy.datasource.DataSource::getvar",
"cloudnetpy.cloudnetpy.datasource.DataSource::_init_time"
] | cloudnetpy | [
"cloudnetpy/datasource.py",
"cloudnetpy/datasource.py"
] | [
"tests/unit/test_datasource.py"
] | [
{
"class_start_lineno": 16,
"class_end_lineno": 236,
"func_start_lineno": 60,
"func_end_lineno": 80,
"func_code": " def getvar(self, *args) -> np.ndarray:\n \"\"\"Returns data array from the source file variables.\n\n Returns just the data (and no attributes) from the original\n... | [
"function_empty",
"Development"
] | [
"cloudnetpy.datasource.DataSource.getvar",
"cloudnetpy.datasource.DataSource._init_time"
] | Python | 1 | 2 | {
"total_num": 9,
"base_passed_num": 6
} |
[
"cloudnetpy.cloudnetpy.instruments.disdrometer.parsivel._read_fmi",
"cloudnetpy.cloudnetpy.instruments.disdrometer.parsivel.parsivel2nc"
] | cloudnetpy | [
"cloudnetpy/instruments/disdrometer/parsivel.py",
"cloudnetpy/instruments/disdrometer/parsivel.py"
] | [
"tests/unit/test_disdrometer.py"
] | [
{
"class_start_lineno": 1,
"class_end_lineno": 713,
"func_start_lineno": 618,
"func_end_lineno": 657,
"func_code": "def _read_fmi(content: str):\n r\"\"\"Read format used by Finnish Meteorological Institute and University of\n Helsinki.\n\n Format consists of sequence of the following:\... | [
"function_empty"
] | [
"cloudnetpy.instruments.disdrometer.parsivel._read_fmi",
"cloudnetpy.instruments.disdrometer.parsivel.parsivel2nc"
] | Python | 2 | 2 | {
"total_num": 54,
"base_passed_num": 0
} |
[
"cloudnetpy.cloudnetpy.utils.l2norm_weighted",
"cloudnetpy.cloudnetpy.products.drizzle_error._calc_error"
] | cloudnetpy | [
"cloudnetpy/utils.py",
"cloudnetpy/products/drizzle_error.py"
] | [
"tests/unit/test_drizzle.py",
"tests/unit/test_drizzle_error.py"
] | [
{
"class_start_lineno": 1,
"class_end_lineno": 1151,
"func_start_lineno": 504,
"func_end_lineno": 529,
"func_code": "def l2norm_weighted(\n values: tuple,\n overall_scale: float,\n term_weights: tuple,\n) -> ma.MaskedArray:\n \"\"\"Calculates scaled and weighted Euclidean distance.\n... | [
"function_empty",
"Development"
] | [
"cloudnetpy.utils.l2norm_weighted",
"cloudnetpy.products.drizzle_error._calc_error"
] | Python | 1 | 2 | {
"total_num": 103,
"base_passed_num": 70
} |
[
"cloudnetpy.cloudnetpy.categorize.droplet.interpolate_lwp",
"cloudnetpy.cloudnetpy.categorize.droplet.find_liquid"
] | cloudnetpy | [
"cloudnetpy/categorize/droplet.py",
"cloudnetpy/categorize/droplet.py"
] | [
"tests/unit/test_droplet.py"
] | [
{
"class_start_lineno": 1,
"class_end_lineno": 245,
"func_start_lineno": 225,
"func_end_lineno": 238,
"func_code": "def interpolate_lwp(obs: ClassData) -> np.ndarray:\n \"\"\"Linear interpolation of liquid water path to fill masked values.\n\n Args:\n obs: The :class:`ClassData` ins... | [
"function_empty",
"Development"
] | [
"cloudnetpy.categorize.droplet.interpolate_lwp",
"cloudnetpy.categorize.droplet.find_liquid"
] | Python | 1 | 2 | {
"total_num": 18,
"base_passed_num": 15
} |
[
"cloudnetpy.cloudnetpy.categorize.atmos_utils.fill_clouds_with_lwc_dz",
"cloudnetpy.cloudnetpy.products.lwc.Lwc::_init_lwc_adiabatic",
"cloudnetpy.cloudnetpy.categorize.atmos_utils.calc_saturation_vapor_pressure",
"cloudnetpy.cloudnetpy.categorize.atmos_utils.calc_mixing_ratio",
"cloudnetpy.cloudnetpy.categ... | cloudnetpy | [
"cloudnetpy/categorize/atmos_utils.py",
"cloudnetpy/products/lwc.py",
"cloudnetpy/products/lwc.py",
"cloudnetpy/categorize/atmos_utils.py",
"cloudnetpy/categorize/atmos_utils.py",
"cloudnetpy/categorize/atmos_utils.py"
] | [
"tests/unit/test_lwc.py"
] | [
{
"class_start_lineno": 1,
"class_end_lineno": 357,
"func_start_lineno": 154,
"func_end_lineno": 172,
"func_code": "def fill_clouds_with_lwc_dz(\n temperature: np.ndarray, pressure: np.ndarray, is_liquid: np.ndarray\n) -> np.ndarray:\n \"\"\"Fills liquid clouds with lwc change rate at the ... | [
"function_empty",
"Development"
] | [
"cloudnetpy.categorize.atmos_utils.fill_clouds_with_lwc_dz",
"cloudnetpy.products.lwc.Lwc._init_lwc_adiabatic",
"cloudnetpy.products.lwc.Lwc.__init__",
"cloudnetpy.categorize.atmos_utils.calc_saturation_vapor_pressure",
"cloudnetpy.categorize.atmos_utils.calc_mixing_ratio",
"cloudnetpy.categorize.atmos_ut... | Python | 3 | 5 | {
"total_num": 37,
"base_passed_num": 0
} |
[
"cloudnetpy.cloudnetpy.utils.rebin_1d",
"cloudnetpy.cloudnetpy.cloudnetarray.CloudnetArray::rebin_data",
"cloudnetpy.cloudnetpy.utils.binvec"
] | cloudnetpy | [
"cloudnetpy/utils.py",
"cloudnetpy/cloudnetarray.py",
"cloudnetpy/categorize/mwr.py",
"cloudnetpy/utils.py"
] | [
"tests/unit/test_mwr.py"
] | [
{
"class_start_lineno": 1,
"class_end_lineno": 1151,
"func_start_lineno": 195,
"func_end_lineno": 231,
"func_code": "def rebin_1d(\n x_in: np.ndarray,\n array: np.ndarray | ma.MaskedArray,\n x_new: np.ndarray,\n statistic: str = \"mean\",\n *,\n mask_zeros: bool = True,\n) -> m... | [
"function_empty"
] | [
"cloudnetpy.utils.rebin_1d",
"cloudnetpy.cloudnetarray.CloudnetArray.rebin_data",
"cloudnetpy.categorize.mwr.Mwr.rebin_to_grid",
"cloudnetpy.utils.binvec"
] | Python | 3 | 3 | {
"total_num": 4,
"base_passed_num": 3
} |
[
"cloudnetpy.cloudnetpy.utils.append_data",
"cloudnetpy.cloudnetpy.instruments.radiometrics.RadiometricsCombined::__init__"
] | cloudnetpy | [
"cloudnetpy/utils.py",
"cloudnetpy/instruments/radiometrics.py"
] | [
"tests/unit/test_radiometrics.py"
] | [
{
"class_start_lineno": 1,
"class_end_lineno": 1151,
"func_start_lineno": 911,
"func_end_lineno": 925,
"func_code": "def append_data(data_in: dict, key: str, array: np.ndarray) -> dict:\n \"\"\"Appends data to a dictionary field (creates the field if not yet present).\n\n Args:\n da... | [
"function_empty",
"Development"
] | [
"cloudnetpy.utils.append_data",
"cloudnetpy.instruments.radiometrics.RadiometricsCombined.__init__"
] | Python | 1 | 2 | {
"total_num": 17,
"base_passed_num": 0
} |
[
"cloudnetpy.cloudnetpy.utils.binvec",
"cloudnetpy.cloudnetpy.utils.rebin_2d",
"cloudnetpy.cloudnetpy.utils.rebin_1d"
] | cloudnetpy | [
"cloudnetpy/utils.py",
"cloudnetpy/utils.py",
"cloudnetpy/utils.py"
] | [
"tests/unit/test_utils.py"
] | [
{
"class_start_lineno": 1,
"class_end_lineno": 1151,
"func_start_lineno": 124,
"func_end_lineno": 140,
"func_code": "def binvec(x: np.ndarray | list) -> np.ndarray:\n \"\"\"Converts 1-D center points to bins with even spacing.\n\n Args:\n x: 1-D array of N real values.\n\n Return... | [
"function_empty"
] | [
"cloudnetpy.utils.binvec",
"cloudnetpy.utils.rebin_2d",
"cloudnetpy.utils.rebin_1d"
] | Python | 3 | 3 | {
"total_num": 160,
"base_passed_num": 151
} |
[
"d3rlpy.d3rlpy.models.encoders.DefaultEncoderFactory::create",
"d3rlpy.d3rlpy.models.builders.create_discrete_q_function"
] | d3rlpy | [
"d3rlpy/models/encoders.py",
"d3rlpy/models/builders.py"
] | [
"tests_copy/envs/test_wrappers.py",
"tests_copy/models/test_builders.py"
] | [
{
"class_start_lineno": 209,
"class_end_lineno": 265,
"func_start_lineno": 224,
"func_end_lineno": 238,
"func_code": " def create(self, observation_shape: Shape) -> Encoder:\n factory: Union[PixelEncoderFactory, VectorEncoderFactory]\n if len(observation_shape) == 3:\n ... | [
"Development"
] | [
"d3rlpy.models.encoders.DefaultEncoderFactory.create",
"d3rlpy.models.builders.create_discrete_q_function"
] | Python | 0 | 2 | {
"total_num": 42,
"base_passed_num": 15
} |
[
"d3rlpy.d3rlpy.dataset.transition_pickers.BasicTransitionPicker::__call__",
"d3rlpy.d3rlpy.metrics.evaluators.make_batches",
"d3rlpy.d3rlpy.metrics.evaluators.TDErrorEvaluator::__call__",
"d3rlpy.d3rlpy.models.encoders.DefaultEncoderFactory::create",
"d3rlpy.d3rlpy.models.builders.create_discrete_q_function... | d3rlpy | [
"d3rlpy/dataset/transition_pickers.py",
"d3rlpy/metrics/evaluators.py",
"d3rlpy/metrics/evaluators.py",
"d3rlpy/models/encoders.py",
"d3rlpy/models/builders.py"
] | [
"tests_copy/metrics/test_evaluators.py"
] | [
{
"class_start_lineno": 43,
"class_end_lineno": 72,
"func_start_lineno": 49,
"func_end_lineno": 72,
"func_code": " def __call__(self, episode: EpisodeBase, index: int) -> Transition:\n _validate_index(episode, index)\n\n observation = retrieve_observation(episode.observations, i... | [
"Development"
] | [
"d3rlpy.dataset.transition_pickers.BasicTransitionPicker.__call__",
"d3rlpy.metrics.evaluators.make_batches",
"d3rlpy.metrics.evaluators.TDErrorEvaluator.__call__",
"d3rlpy.models.encoders.DefaultEncoderFactory.create",
"d3rlpy.models.builders.create_discrete_q_function"
] | Python | 0 | 5 | {
"total_num": 19,
"base_passed_num": 0
} |
[
"d3rlpy.d3rlpy.models.torch.q_functions.ensemble_q_function._gather_quantiles_by_indices",
"d3rlpy.d3rlpy.models.torch.q_functions.ensemble_q_function._reduce_quantile_ensemble",
"d3rlpy.d3rlpy.models.torch.q_functions.mean_q_function.DiscreteMeanQFunctionForwarder::compute_error",
"d3rlpy.d3rlpy.models.torch... | d3rlpy | [
"d3rlpy/models/torch/q_functions/ensemble_q_function.py",
"d3rlpy/models/torch/q_functions/ensemble_q_function.py",
"d3rlpy/models/torch/q_functions/mean_q_function.py",
"d3rlpy/models/torch/q_functions/iqn_q_function.py",
"d3rlpy/models/torch/q_functions/ensemble_q_function.py",
"d3rlpy/models/torch/q_fu... | [
"tests_copy/models/torch/q_functions/test_ensemble_q_function.py"
] | [
{
"class_start_lineno": 1,
"class_end_lineno": 367,
"func_start_lineno": 35,
"func_end_lineno": 52,
"func_code": "def _gather_quantiles_by_indices(\n y: torch.Tensor, indices: torch.Tensor\n) -> torch.Tensor:\n # TODO: implement this in general case\n if y.dim() == 3:\n # (N, bat... | [
"Development"
] | [
"d3rlpy.models.torch.q_functions.ensemble_q_function._gather_quantiles_by_indices",
"d3rlpy.models.torch.q_functions.ensemble_q_function._reduce_quantile_ensemble",
"d3rlpy.models.torch.q_functions.mean_q_function.DiscreteMeanQFunctionForwarder.compute_error",
"d3rlpy.models.torch.q_functions.iqn_q_function.D... | Python | 0 | 5 | {
"total_num": 30,
"base_passed_num": 4
} |
[
"d3rlpy.d3rlpy.models.torch.q_functions.iqn_q_function.DiscreteIQNQFunction::forward",
"d3rlpy.d3rlpy.models.torch.q_functions.iqn_q_function.DiscreteIQNQFunctionForwarder::compute_error"
] | d3rlpy | [
"d3rlpy/models/torch/q_functions/iqn_q_function.py",
"d3rlpy/models/torch/q_functions/base.py",
"d3rlpy/models/torch/q_functions/iqn_q_function.py"
] | [
"tests_copy/models/torch/q_functions/test_iqn_q_function.py"
] | [
{
"class_start_lineno": 65,
"class_end_lineno": 119,
"func_start_lineno": 92,
"func_end_lineno": 115,
"func_code": " def forward(self, x: TorchObservation) -> QFunctionOutput:\n h = self._encoder(x)\n\n if self.training:\n n_quantiles = self._n_quantiles\n else... | [
"Development"
] | [
"d3rlpy.models.torch.q_functions.iqn_q_function.DiscreteIQNQFunction.forward",
"d3rlpy.models.torch.q_functions.base.DiscreteIQNQFunction.__call__",
"d3rlpy.models.torch.q_functions.iqn_q_function.DiscreteIQNQFunctionForwarder.compute_error"
] | Python | 0 | 2 | {
"total_num": 8,
"base_passed_num": 4
} |
[
"datachain.src.datachain.lib.convert.python_to_sql.python_to_sql",
"datachain.src.datachain.lib.signal_schema.SignalSchema::get_column_type",
"datachain.src.datachain.lib.dc.DataChain::mutate",
"datachain.src.datachain.lib.signal_schema.SignalSchema::mutate"
] | datachain | [
"datachain/lib/convert/python_to_sql.py",
"datachain/func/func.py",
"datachain/lib/signal_schema.py",
"datachain/func/func.py",
"datachain/lib/dc.py",
"datachain/lib/signal_schema.py"
] | [
"tests/unit/test_func.py"
] | [
{
"class_start_lineno": 1,
"class_end_lineno": 117,
"func_start_lineno": 37,
"func_end_lineno": 82,
"func_code": "def python_to_sql(typ): # noqa: PLR0911\n if inspect.isclass(typ):\n if issubclass(typ, SQLType):\n return typ\n if issubclass(typ, Enum):\n r... | [
"function_empty",
"Development"
] | [
"datachain.lib.convert.python_to_sql.python_to_sql",
"datachain.func.func.Func.get_column",
"datachain.lib.signal_schema.SignalSchema.get_column_type",
"datachain.func.func.get_db_col_type",
"datachain.lib.dc.DataChain.mutate",
"datachain.lib.signal_schema.SignalSchema.mutate"
] | Python | 1 | 4 | {
"total_num": 94,
"base_passed_num": 39
} |
[
"datachain.src.datachain.query.session.Session::_cleanup_temp_datasets",
"datachain.src.datachain.query.session.Session::__exit__"
] | datachain | [
"datachain/query/session.py",
"datachain/query/session.py"
] | [
"tests/unit/test_listing.py",
"tests/unit/test_session.py"
] | [
{
"class_start_lineno": 19,
"class_end_lineno": 195,
"func_start_lineno": 103,
"func_end_lineno": 110,
"func_code": " def _cleanup_temp_datasets(self) -> None:\n prefix = self.get_temp_prefix()\n try:\n for dataset in list(self.catalog.metastore.list_datasets_by_prefi... | [
"Development"
] | [
"datachain.query.session.Session._cleanup_temp_datasets",
"datachain.query.session.Session.__exit__"
] | Python | 0 | 2 | {
"total_num": 24,
"base_passed_num": 7
} |
[
"datachain.src.datachain.lib.signal_schema.SignalSchema::_get_flat_tree",
"datachain.src.datachain.lib.convert.python_to_sql.python_to_sql",
"datachain.src.datachain.lib.signal_schema.SignalSchema::db_signals",
"datachain.src.datachain.query.session.Session::_cleanup_temp_datasets",
"datachain.src.datachain... | datachain | [
"datachain/lib/signal_schema.py",
"datachain/lib/signal_schema.py",
"datachain/lib/convert/python_to_sql.py",
"datachain/lib/signal_schema.py",
"datachain/query/session.py",
"datachain/query/session.py"
] | [
"tests/unit/lib/test_arrow.py"
] | [
{
"class_start_lineno": 135,
"class_end_lineno": 751,
"func_start_lineno": 630,
"func_end_lineno": 639,
"func_code": " def _get_flat_tree(\n self, tree: dict, prefix: list[str], depth: int\n ) -> Iterator[tuple[list[str], DataType, bool, int]]:\n for name, (type_, substree) i... | [
"function_empty",
"Development"
] | [
"datachain.lib.signal_schema.SignalSchema._get_flat_tree",
"datachain.lib.signal_schema.SignalSchema.get_flat_tree",
"datachain.lib.convert.python_to_sql.python_to_sql",
"datachain.lib.signal_schema.SignalSchema.db_signals",
"datachain.query.session.Session._cleanup_temp_datasets",
"datachain.query.sessio... | Python | 1 | 5 | {
"total_num": 32,
"base_passed_num": 31
} |
[
"datachain.src.datachain.lib.image.convert_image",
"datachain.src.datachain.lib.image.convert_images"
] | datachain | [
"datachain/lib/image.py",
"datachain/lib/image.py"
] | [
"tests/unit/lib/test_clip.py",
"tests/unit/lib/test_image.py"
] | [
{
"class_start_lineno": 1,
"class_end_lineno": 81,
"func_start_lineno": 7,
"func_end_lineno": 46,
"func_code": "def convert_image(\n img: Image.Image,\n mode: str = \"RGB\",\n size: Optional[tuple[int, int]] = None,\n transform: Optional[Callable] = None,\n encoder: Optional[Calla... | [
"function_empty"
] | [
"datachain.lib.image.convert_image",
"datachain.lib.image.convert_images"
] | Python | 2 | 2 | {
"total_num": 41,
"base_passed_num": 13
} |
[
"datachain.src.datachain.lib.file.File::get_destination_path",
"datachain.src.datachain.lib.file.File::export",
"datachain.src.datachain.lib.file.File::ensure_cached",
"datachain.src.datachain.lib.file.File::_symlink_to"
] | datachain | [
"datachain/lib/file.py",
"datachain/lib/file.py",
"datachain/lib/file.py",
"datachain/lib/file.py"
] | [
"tests/unit/lib/test_file.py"
] | [
{
"class_start_lineno": 125,
"class_end_lineno": 468,
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"base_passed_num": 2
} |
[
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"langchain.libs.langchain.langchain.retrievers.ensemble.EnsembleRetriever::weighted_reciprocal_rank"
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"langchain/retrievers/ensemble.py"
] | [
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"langchain.retrievers.ensemble.EnsembleRetriever.weighted_reciprocal_rank"
] | Python | 1 | 2 | {
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"base_passed_num": 0
} |
[
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"langchain.libs.langchain.langchain.smith.evaluation.runner_utils._get_messages",
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] | langchain | [
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"langchain/smith/evaluation/runner_utils.py",
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] | [
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] | [
"langchain.smith.evaluation.runner_utils._get_prompt",
"langchain.smith.evaluation.runner_utils._get_messages",
"langchain.smith.evaluation.runner_utils._run_llm"
] | Python | 3 | 3 | {
"total_num": 43,
"base_passed_num": 36
} |
[
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"open-iris.src.iris.nodes.normalization.nonlinear_normalization.NonlinearNormalization::_generate_correspondences"
] | open-iris | [
"iris/utils/math.py",
"iris/nodes/normalization/nonlinear_normalization.py"
] | [
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] | [
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] | [
"iris.utils.math.estimate_diameter",
"iris.nodes.normalization.nonlinear_normalization.NonlinearNormalization._generate_correspondences"
] | Python | 2 | 2 | {
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} |
[
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"open-iris.src.iris.nodes.vectorization.contouring.filter_polygon_areas",
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] | open-iris | [
"iris/utils/math.py",
"iris/nodes/vectorization/contouring.py",
"iris/nodes/vectorization/contouring.py"
] | [
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"iris.utils.math.area",
"iris.nodes.vectorization.contouring.filter_polygon_areas",
"iris.nodes.vectorization.contouring.ContouringAlgorithm._filter_contours"
] | Python | 3 | 3 | {
"total_num": 12,
"base_passed_num": 9
} |
[
"open-iris.src.iris.utils.math.cartesian2polar",
"open-iris.src.iris.nodes.geometry_estimation.linear_extrapolation.LinearExtrapolation::_estimate"
] | open-iris | [
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"iris/nodes/geometry_estimation/linear_extrapolation.py"
] | [
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"iris.nodes.geometry_estimation.linear_extrapolation.LinearExtrapolation._estimate"
] | Python | 1 | 2 | {
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} |
[
"open-iris.src.iris.callbacks.pipeline_trace.PipelineCallTraceStorage::get",
"open-iris.src.iris.callbacks.pipeline_trace.PipelineCallTraceStorage::__getitem__"
] | open-iris | [
"iris/callbacks/pipeline_trace.py",
"iris/callbacks/pipeline_trace.py"
] | [
"tests/unit_tests/callbacks/test_pipeline_trace.py"
] | [
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"func_code": " def get(self, result_name: str) -> Any:\n \"\"\"Get result_name result.\n\n Args:\n result_name (str): Result name.\n\n Raises:\n Pipe... | [
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] | [
"iris.callbacks.pipeline_trace.PipelineCallTraceStorage.get",
"iris.callbacks.pipeline_trace.PipelineCallTraceStorage.__getitem__"
] | Python | 2 | 2 | {
"total_num": 8,
"base_passed_num": 2
} |
[
"open-iris.src.iris.utils.math.cartesian2polar",
"open-iris.src.iris.nodes.eye_properties_estimation.occlusion_calculator.OcclusionCalculator::_get_quantile_points"
] | open-iris | [
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"iris/nodes/eye_properties_estimation/occlusion_calculator.py"
] | [
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] | [
{
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] | [
"iris.utils.math.cartesian2polar",
"iris.nodes.eye_properties_estimation.occlusion_calculator.OcclusionCalculator._get_quantile_points"
] | Python | 1 | 2 | {
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} |
[
"open-iris.src.iris.nodes.eye_properties_estimation.bisectors_method.BisectorsMethod::_calculate_perpendicular_bisectors",
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] | open-iris | [
"iris/nodes/eye_properties_estimation/bisectors_method.py",
"iris/nodes/eye_properties_estimation/bisectors_method.py"
] | [
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] | [
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"iris.nodes.eye_properties_estimation.bisectors_method.BisectorsMethod._find_center_coords"
] | Python | 1 | 2 | {
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} |
[
"open-iris.src.iris.utils.math.cartesian2polar",
"open-iris.src.iris.nodes.geometry_refinement.smoothing.Smoothing::_cut_into_arcs",
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"iris/nodes/geometry_refinement/smoothing.py",
"iris/nodes/geometry_refinement/smoothing.py",
"iris/nodes/geometry_refinement/smoothing.py"
] | [
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"iris.nodes.geometry_refinement.smoothing.Smoothing._smooth_arc",
"iris.nodes.geometry_refinement.smoothing.Smoothing._smooth_circular_shape"
] | Python | 3 | 4 | {
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} |
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"open-iris.src.iris.pipelines.iris_pipeline.IRISPipeline::instanciate_node... | open-iris | [
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"iris/pipelines/iris_pipeline.py",
"iris/pipelines/iris_pipeline.py",
"iris/pipelines/iris_pipeline.py"
] | [
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] | [
{
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"func_code": " def instanciate_class(self, class_name: str, kwargs: Dict[str, Any]) -> Callable:\n \"\"\"Instanciate a class from its string definition and its kwargs.\n\n This... | [
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"Development"
] | [
"iris.pipelines.iris_pipeline.IRISPipeline.instanciate_class",
"iris.pipelines.iris_pipeline.IRISPipeline.instanciate_pipeline",
"iris.pipelines.iris_pipeline.IRISPipeline.instanciate_node",
"iris.pipelines.iris_pipeline.IRISPipeline.instanciate_nodes"
] | Python | 3 | 4 | {
"total_num": 33,
"base_passed_num": 0
} |
[
"open-iris.src.iris.io.validators.is_binary",
"open-iris.src.iris.nodes.binarization.specular_reflection_detection.SpecularReflectionDetection::run"
] | open-iris | [
"iris/io/validators.py",
"iris/nodes/binarization/specular_reflection_detection.py"
] | [
"tests/unit_tests/nodes/binarization/test_specular_reflection_detection.py"
] | [
{
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"func_code": "def is_binary(cls: type, v: np.ndarray, field: fields.ModelField) -> np.ndarray:\n \"\"\"Check if array has only boolean values, i.e. is binary.\n\n Args:\n cls (type): ... | [
"function_empty"
] | [
"iris.io.validators.is_binary",
"iris.nodes.binarization.specular_reflection_detection.SpecularReflectionDetection.run"
] | Python | 2 | 2 | {
"total_num": 4,
"base_passed_num": 0
} |
[
"open-iris.src.iris.nodes.eye_properties_estimation.bisectors_method.BisectorsMethod::_calculate_perpendicular_bisectors",
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] | open-iris | [
"iris/nodes/eye_properties_estimation/bisectors_method.py",
"iris/nodes/eye_properties_estimation/bisectors_method.py"
] | [
"tests/unit_tests/nodes/eye_properties_estimation/test_pupil_iris_property_calculator.py"
] | [
{
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"func_code": " def _calculate_perpendicular_bisectors(\n self, polygon: np.ndarray, min_distance_between_sector_points_in_px: float\n ) -> Tuple[np.ndarray, np.ndarray]:\n \"... | [
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"iris.nodes.eye_properties_estimation.bisectors_method.BisectorsMethod._find_center_coords"
] | Python | 1 | 2 | {
"total_num": 13,
"base_passed_num": 8
} |
[
"rdt.rdt.transformers.utils.strings_from_regex",
"rdt.rdt.transformers.id.RegexGenerator::__setstate__"
] | rdt | [
"rdt/transformers/utils.py",
"rdt/transformers/id.py"
] | [
"tests/unit/transformers/test___init__.py"
] | [
{
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"func_code": "def strings_from_regex(regex, max_repeat=16):\n \"\"\"Generate strings that match the given regular expression.\n\n The output is a generator that produces regular expression... | [
"Development"
] | [
"rdt.transformers.utils.strings_from_regex",
"rdt.transformers.id.RegexGenerator.__setstate__"
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} |
[
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"rdt.rdt.transformers.null.NullTransformer::fit",
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"rdt/transformers/null.py",
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"func_code": " def _get_missing_value_replacement(self, data):\n \"\"\"Get the fill value to use for the given data.\n\n Args:\n data (pd.Series):\n The... | [
"function_empty",
"Development"
] | [
"rdt.transformers.null.NullTransformer._get_missing_value_replacement",
"rdt.transformers.null.NullTransformer.fit",
"rdt.transformers.boolean.BinaryEncoder._fit"
] | Python | 2 | 3 | {
"total_num": 16,
"base_passed_num": 13
} |
[
"rdt.rdt.transformers.utils.check_nan_in_transform",
"rdt.rdt.transformers.categorical.UniformEncoder::_reverse_transform",
"rdt.rdt.transformers.categorical.OneHotEncoder::_reverse_transform",
"rdt.rdt.transformers.categorical.LabelEncoder::_reverse_transform"
] | rdt | [
"rdt/transformers/utils.py",
"rdt/transformers/categorical.py",
"rdt/transformers/categorical.py",
"rdt/transformers/categorical.py"
] | [
"tests/unit/transformers/test_categorical.py"
] | [
{
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"func_code": "def check_nan_in_transform(data, dtype):\n \"\"\"Check if there are null values in the transformed data.\n\n Args:\n data (pd.Series or numpy.ndarray):\n Da... | [
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"rdt.transformers.categorical.UniformEncoder._reverse_transform",
"rdt.transformers.categorical.OneHotEncoder._reverse_transform",
"rdt.transformers.categorical.LabelEncoder._reverse_transform"
] | Python | 3 | 4 | {
"total_num": 95,
"base_passed_num": 85
} |
[
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"rdt.rdt.transformers.null.NullTransformer::fit",
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"rdt/transformers/null.py",
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"rdt/transformers/datetime.py",
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] | [
"tests/unit/transformers/test_datetime.py"
] | [
{
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"func_code": " def _get_missing_value_replacement(self, data):\n \"\"\"Get the fill value to use for the given data.\n\n Args:\n data (pd.Series):\n The... | [
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] | [
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"rdt.transformers.datetime.UnixTimestampEncoder._convert_to_datetime",
"rdt.transformers.datetime.UnixTimestampEncoder._transform_helper",
"rdt.transformers.datetime.UnixTimestampEncoder._tra... | Python | 3 | 4 | {
"total_num": 32,
"base_passed_num": 21
} |
[
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"rdt.rdt.transformers.null.NullTransformer::fit"
] | rdt | [
"rdt/transformers/null.py",
"rdt/transformers/null.py"
] | [
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] | [
{
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"func_end_lineno": 93,
"func_code": " def _get_missing_value_replacement(self, data):\n \"\"\"Get the fill value to use for the given data.\n\n Args:\n data (pd.Series):\n The... | [
"function_empty"
] | [
"rdt.transformers.null.NullTransformer._get_missing_value_replacement",
"rdt.transformers.null.NullTransformer.fit"
] | Python | 2 | 2 | {
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} |
[
"rdt.rdt.transformers.null.NullTransformer::_get_missing_value_replacement",
"rdt.rdt.transformers.null.NullTransformer::fit",
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] | rdt | [
"rdt/transformers/null.py",
"rdt/transformers/null.py",
"rdt/transformers/null.py",
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"tests/unit/transformers/test_numerical.py"
] | [
{
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"class_end_lineno": 194,
"func_start_lineno": 60,
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"func_code": " def _get_missing_value_replacement(self, data):\n \"\"\"Get the fill value to use for the given data.\n\n Args:\n data (pd.Series):\n The... | [
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"rdt.transformers.null.NullTransformer.fit",
"rdt.transformers.null.NullTransformer.reverse_transform",
"rdt.transformers.numerical.FloatFormatter._reverse_transform"
] | Python | 4 | 4 | {
"total_num": 90,
"base_passed_num": 61
} |
[
"transformers.src.transformers.image_utils.infer_channel_dimension_format",
"transformers.src.transformers.image_transforms.to_channel_dimension_format",
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] | transformers | [
"transformers/image_utils.py",
"transformers/image_transforms.py",
"transformers/image_utils.py",
"transformers/image_transforms.py",
"transformers/image_transforms.py"
] | [
"tests/test_image_transforms.py"
] | [
{
"class_start_lineno": 1,
"class_end_lineno": 811,
"func_start_lineno": 220,
"func_end_lineno": 254,
"func_code": "def infer_channel_dimension_format(\n image: np.ndarray, num_channels: Optional[Union[int, Tuple[int, ...]]] = None\n) -> ChannelDimension:\n \"\"\"\n Infers the channel d... | [
"function_empty"
] | [
"transformers.image_utils.infer_channel_dimension_format",
"transformers.image_transforms.to_channel_dimension_format",
"transformers.image_utils.get_image_size",
"transformers.image_transforms.flip_channel_order",
"transformers.image_transforms.get_resize_output_image_size"
] | Python | 3 | 3 | {
"total_num": 24,
"base_passed_num": 5
} |
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