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chore(deps): bump actions/setup-python from 6 to 7 - #48

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Bumps actions/setup-python from 6 to 7.

Release notes

Sourced from actions/setup-python's releases.

v7.0.0

What's Changed

Enhancements

Bug Fix

Dependency Upgrade

New Contributors

Full Changelog: actions/setup-python@v6...v7.0.0

v6.3.0

What's Changed

Enhancement

Dependency update

Documentation

New Contributors

Full Changelog: actions/setup-python@v6.2.0...v6.3.0

v6.2.0

What's Changed

Dependency Upgrades

... (truncated)

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@dependabot
chore(deps): bump actions/setup-python from 6 to 7
e218184 
Bumps [actions/setup-python](https://github.com/actions/setup-python) from 6 to 7.
- [Release notes](https://github.com/actions/setup-python/releases)
- [Commits](actions/setup-python@v6...v7)

---
updated-dependencies:
- dependency-name: actions/setup-python
  dependency-version: '7'
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
@dependabot dependabot Bot added the dependencies Pull requests that update a dependency file label Jul 21, 2026
@codecov

codecov Bot commented Jul 21, 2026 •
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❌ 2 Tests Failed:

Tests completed Failed Passed Skipped
483 2 481 34
View the full list of 2 ❄️ flaky test(s) 
test/test_databases/test_cpsc2020.py::TestCPSC2020::test_locate_premature_beats

Flake rate in main: 100.00% (Passed 0 times, Failed 6 times)

Stack Traces | 0.002s run time 
self = <test_cpsc2020.TestCPSC2020 object at 0x7f533708ccb0>

    def test_locate_premature_beats(self):
>       premature_beat_intervals = reader.locate_premature_beats(0)
                                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

test/test_databases/test_cpsc2020.py:78: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
.../databases/cpsc_databases/cpsc2020.py:577: in locate_premature_beats
    premature_intervals = get_optimal_covering(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

total_interval = [0, np.int64(32774541)]
to_cover = array([ 1553625,  1575945,  1632144,  2144273,  2707955,  7186906,
       11018961, 11561557, 12435089, 16280655, 1628...567, 19748010, 24839791, 25130327,
       25420045, 28122639, 29253366, 29355057, 30297234, 31125726,
       32774540])
min_len = 4000, split_threshold = 4000, isolated_point_dist_threshold = 2000
traceback = False, kwargs = {}

    def get_optimal_covering(
        total_interval: Interval,
        to_cover: List[Union[int, float, Interval]],
        min_len: Union[int, float],
        split_threshold: Union[int, float],
        isolated_point_dist_threshold: Union[int, float] = 0,
        traceback: bool = False,
        **kwargs: Any,
    ) -> Union[GeneralizedInterval, Tuple[GeneralizedInterval, list]]:
        """Compute an optimal covering of `to_cover` by intervals.
    
        This function tries to find an optimal covering
        (disjoint union of intervals) that covers `to_cover` such that
        each interval in the covering is of length at least `min_len`,
        and any two intervals in the covering are at least `split_threshold`
        distance apart.
    
        Parameters
        ----------
        total_interval : Interval
            The total interval that the covering is picked from.
        to_cover : list
            A list of intervals or points to cover.
        min_len : int or float
            Minimun length (positive) of the intervals of the covering.
        split_threshold : int or float
            Minumun distance (positive) of intervals of the covering.
        isolated_point_dist_threshold : int or float, default 0.0
            The minimum distance (non-negative) of isolated points
            in `to_cover` to the interval boundaries of the interval
            containing the point in the covering.
            If one wants the isolated points to be centered in the
            interval containing the point,
            set `isolated_point_dist_threshold` to be ``min_len / 2``.
        traceback : bool, default False
            if True, a list containing the list of indices of the
            intervals in the original `to_cover`,
            that each interval in the covering covers.
    
        Raises
        ------
        If any of the intervals in `to_cover` exceeds the range of `total_interval`,
        ValueError will be raised
    
        Returns
        -------
        covering : GeneralizedInterval
            The covering that satisfies the given conditions
        ret_traceback : list, optional
            Contains the list of indices of the intervals in the original `to_cover`,
            that each interval in the covering covers.
            If `traceback` is False, this will not be returned.
    
        TODO
        ----
        make positions of isolated points in the final covering as close as possible
        to the center of the interval that contains the point
    
        Examples
        --------
        >>> total_interval = [0, 100]
        >>> to_cover = [[7,33], 66, [82, 89]]
        >>> get_optimal_covering(total_interval, to_cover, 10, 5)
        [[7, 33], [56, 66], [82, 92]]
        >>> get_optimal_covering(total_interval, to_cover, 10, 5, traceback=True)
        ([[7, 33], [56, 66], [82, 92]], [[0], [1], [2]])
        >>> get_optimal_covering(total_interval, to_cover, 20, 5, traceback=True)
        ([[7, 33], [46, 66], [80, 100]], [[0], [1], [2]])
        >>> get_optimal_covering(total_interval, to_cover, 20, 13, traceback=True)
        ([[7, 33], [46, 66], [80, 100]], [[0], [1], [2]])
        >>> get_optimal_covering(total_interval, to_cover, 20, 14, traceback=True)
        ([[7, 33], [66, 89]], [[0], [1, 2]])
        >>> get_optimal_covering(total_interval, to_cover, 20, 13, isolated_point_dist_threshold=1)
        [[7, 33], [47, 67], [80, 100]]
        >>> get_optimal_covering(total_interval, to_cover, 20, 13, isolated_point_dist_threshold=2)
        [[7, 33], [64, 89]]
        >>> get_optimal_covering(total_interval, to_cover, 30, 3)
        [[3, 33], [36, 66], [70, 100]]
        >>> get_optimal_covering(total_interval, to_cover, 30, 4)
        [[3, 33], [59, 89]]
        >>> get_optimal_covering(total_interval, to_cover, 40, 5)
        [[0, 40], [60, 100]]
        >>> get_optimal_covering(total_interval, to_cover, 1000, 1, traceback=True)
        ([[0, 100]], [[0, 1, 2]])
    
        """
        assert validate_interval(total_interval)[0], "`total_interval` must be a valid interval (a sequence of two real numbers)"
        assert min_len > 0, "`min_len` must be positive"
        assert split_threshold > 0, "`split_threshold` must be positive"
        assert isolated_point_dist_threshold >= 0, "`isolated_point_dist_threshold` must be non-negative"
    
        if len(to_cover) == 0:
            return [] if not traceback else ([], [])
    
        start_time = time.time()
        verbose = kwargs.get("verbose", 0)
        tmp = sorted(total_interval)
        tot_start, tot_end = tmp[0], tmp[-1]
    
>       if (tot_start > min([item if isinstance(item, (int, float)) else item[0] for item in to_cover])) or (
                                                                         ^^^^^^^
            tot_end < max([item if isinstance(item, (int, float)) else item[-1] for item in to_cover])
        ):
E       IndexError: invalid index to scalar variable.

torch_ecg/utils/utils_interval.py:584: IndexError
test/test_models/test_loss.py::test_setup_criterion

Flake rate in main: 60.00% (Passed 2 times, Failed 3 times)

Stack Traces | 0.002s run time 
def test_setup_criterion():
        criterion = setup_criterion("WeightedBCELoss", pos_weight=torch.ones((1, 2)))
        criterion = setup_criterion("BCEWithLogitsWithClassWeightLoss", class_weight=class_weight)
        criterion = setup_criterion("FocalLoss", class_weight=class_weight)
        criterion = setup_criterion("AsymmetricLoss")
        criterion = setup_criterion("MaskedBCEWithLogitsLoss")
    
        for name in torch.nn.modules.loss.__all__:
>           criterion = setup_criterion(name)
                        ^^^^^^^^^^^^^^^^^^^^^

test/test_models/test_loss.py:152: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'LinearCrossEntropyLoss', kwargs = {}

    def setup_criterion(name: str, **kwargs: Any) -> nn.Module:
        """Setup the criterion (loss function).
    
        Parameters
        ----------
        name : str
            The name of the criterion.
        **kwargs : Any
            The keyword arguments for the criterion.
    
        Returns
        -------
        nn.Module
            The criterion (loss function).
    
        """
        if name in LOSSES:
            return LOSSES.build(name, **kwargs)
        if name.startswith("nn."):
            criterion = eval(name)(**kwargs)
        elif name in nn.modules.loss.__all__:
>           criterion = getattr(nn, name)(**kwargs)
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^
E           TypeError: LinearCrossEntropyLoss.__init__() missing 2 required positional arguments: 'in_features' and 'num_classes'

torch_ecg/models/loss.py:660: TypeError

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