Update uv lockfile

[larsevj]

Using CPython 3.14.6 interpreter at: /opt/hostedtoolcache/Python/3.14.6/x64/bin/python3.14
Resolved 292 packages in 4.17s
Updated aiohappyeyeballs v2.6.2 -> v2.7.1
Updated anyio v4.14.1 -> v4.14.2
Updated ast-serialize v0.5.0 -> v0.6.0
Updated asttokens v3.0.1 -> v3.0.2
Updated blosc2 v4.6.0 -> v4.9.1
Updated cffi v2.0.0 -> v2.1.0
Updated charset-normalizer v3.4.7 -> v3.4.9
Updated coverage v7.14.3 -> v7.15.2
Updated cwrap v1.6.14 -> v1.6.16
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Updated filelock v3.29.4 -> v3.31.1
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Added hpack v4.2.0
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Added hyperframe v6.1.0
Updated hypothesis v6.155.7 -> v6.157.0
Updated imageio v2.37.3 -> v2.37.4
Updated jupyter-builder v1.0.2 -> v1.1.1
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Updated python-utils v3.9.1 -> v4.0.0
Updated rpds-py v2026.5.1 -> v2026.6.3
Updated rust-just v1.54.0 -> v1.57.0
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Updated tifffile v2026.6.1 -> v2026.7.14
Updated tqdm v4.68.3 -> v4.69.1
Updated types-decorator v5.2.0.20260519 -> v5.2.0.20260712
Updated types-docutils v0.22.3.20260518 -> v0.22.3.20260712
Updated types-python-dateutil v2.9.0.20260518 -> v2.9.0.20260716
Updated types-requests v2.33.0.20260518 -> v2.33.0.20260712
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Updated uvicorn v0.49.0 -> v0.51.0
Updated virtualenv v21.5.1 -> v21.6.1
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Updated xarray v2026.4.0 -> v2026.7.0
Updated xtgeo v4.24.1 -> v4.24.5
Updated xtgeoviz v0.3.1 -> v0.3.2
Updated yarl v1.24.2 -> v1.24.5

[codecov-commenter]

❌ 1 Tests Failed:

Tests completed	Failed	Passed	Skipped
4115	1	4114	63

View the top 1 failed test(s) by shortest run time

tests/ert/ui_tests/cli/test_field_parameter.py::test_field_param_update_using_heat_equation_zero_var_params_and_adaptive_loc

Stack Traces | 3.03s run time

symlinked_heat_equation_storage_es = ErtConfig(substitutions={'<CONFIG_PATH>': '.../ert/ert/.pytest_cache.../d/heat_equation_data_es/test_data',...=40.5, north=25.5, radius=20.0), 3: CircleShapeConfig(shape_id=3, type='circle', east=25.5, north=40.5, radius=20.0)}))
caplog = <_pytest.logging.LogCaptureFixture object at 0x7f5f06ded400>

    @pytest.mark.timeout(600)
    @pytest.mark.xdist_group(name="uses_heat_equation_storage")
    def test_field_param_update_using_heat_equation_zero_var_params_and_adaptive_loc(
        symlinked_heat_equation_storage_es, caplog
    ):
        """Test field parameter updates with zero-variance regions and adaptive
        localization.
    
        This test verifies the behavior of the ensemble smoother update when dealing with
        field parameters that contain regions of zero variance (constant values across all
        realizations). Such scenarios have been reported to cause performance issues and
        numerical instabilities.
    
        Specifically, this test:
        1. Creates a field where the first 5 layers are set to constant values (1.0)
        2. Performs a smoother update with adaptive localization
        3. Verifies expected numerical warnings are raised due to zero variance
        4. Confirms the update still reduces overall parameter uncertainty
    
        The test documents known limitations with adaptive localization when handling
        zero-variance regions, particularly:
        - Runtime degradation
        - Numerical warnings from division by zero
        - Cross-correlation matrix instabilities
        """
        config = ErtConfig.from_file("config.ert")
        with open_storage(config.ens_path, mode="w") as storage:
            experiment = storage.get_experiment_by_name("es")
            prior = experiment.get_ensemble_by_name("iter-0")
            cond = prior.load_parameters("COND")
            init_temp_scale = prior.load_parameters("INIT_TEMP_SCALE")
            corr_length = prior.load_parameters("CORR_LENGTH")
    
            new_experiment = storage.create_experiment(
                experiment_config={
                    "parameter_configuration": [
                        pc.model_dump(mode="json")
                        for pc in config.ensemble_config.parameter_configuration
                    ],
                    "response_configuration": [
                        rc.model_dump(mode="json")
                        for rc in config.ensemble_config.response_configuration
                    ],
                    "observations": [
                        od.model_dump(mode="json") for od in config.observation_declarations
                    ],
                },
                name="exp-zero-var",
            )
            new_prior = storage.create_ensemble(
                new_experiment,
                ensemble_size=prior.ensemble_size,
                iteration=0,
                name="prior-zero-var",
            )
            cond["values"][:, :, :5, 0] = 1.0
            for real in range(prior.ensemble_size):
                new_prior.save_parameters(cond, "COND", real)
                new_prior.save_parameters(init_temp_scale, "INIT_TEMP_SCALE", real)
                new_prior.save_parameters(corr_length, "CORR_LENGTH", real)
    
            # Copy responses from existing prior to new prior.
            # Note that we ideally should generate new responses by running the
            # heat equation with the modified prior where parts of the field
            # are given a constant value.
            responses = prior.load_responses("summary", tuple(range(prior.ensemble_size)))
            for realization in range(prior.ensemble_size):
                df = responses.filter(pl.col("realization") == realization)
                new_prior.save_response("summary", df, realization)
    
            new_posterior = storage.create_ensemble(
                new_experiment,
                ensemble_size=config.runpath_config.num_realizations,
                iteration=1,
                name="new_ensemble",
                prior_ensemble=new_prior,
            )
    
            with warnings.catch_warnings(record=True) as record:
                warnings.simplefilter("always")  # Ensure all warnings are always recorded
                with caplog.at_level(logging.INFO):
                    es_settings = config.analysis_config.es_settings
                    strategy_map = build_strategy_map(
                        parameters=config.ensemble_config.parameters,
                        param_configs=new_prior.experiment.parameter_configuration,
                        enkf_truncation=es_settings.enkf_truncation,
                        correlation_threshold=es_settings.correlation_threshold,
                    )
                    smoother_update(
                        new_prior,
                        new_posterior,
                        experiment.observation_keys,
                        ObservationSettings(),
                        rng=np.random.default_rng(42),
                        strategy_map=strategy_map,
                    )
    
                    # Note that this used to fail since run time and user warnings were
                    # thrown because we tried updating parameters with zero variance.
>                   assert not record
E                   assert not [<warnings.WarningMessage object at 0x7f5f06d15a90>]

.../ui_tests/cli/test_field_parameter.py:455: AssertionError

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