Add misfit plot for breakthrough observations

[SAKavli]

Issue
Resolves #12968

The strategy has been to tie breakthrough observations plotting functionality close up to summary observations.
To easily achieve this, it's beneficial if breakthrough observations and responses can be matched on dates.

(Screenshot of new behavior in GUI if applicable)

• PR title captures the intent of the changes, and is fitting for release notes.
• Added appropriate release note label
• Commit history is consistent and clean, in line with the contribution guidelines.
• Make sure unit tests pass locally after every commit (git rebase -i main --exec 'just rapid-tests')

When applicable

• When there are user facing changes: Updated documentation
• New behavior or changes to existing untested code: Ensured that unit tests are added (See Ground Rules).
• Large PR: Prepare changes in small commits for more convenient review
• Bug fix: Add regression test for the bug
• Bug fix: Add backport label to latest release (format: 'backport release-branch-name')

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 90.51%. Comparing base (e980540) to head (71e4389).
⚠️ Report is 4 commits behind head on main.
✅ All tests successful. No failed tests found.

Additional details and impacted files

@@            Coverage Diff             @@
##             main   #12987      +/-   ##
==========================================
+ Coverage   90.40%   90.51%   +0.10%     
==========================================
  Files         453      453              
  Lines       31155    31164       +9     
==========================================
+ Hits        28165    28207      +42     
+ Misses       2990     2957      -33     

Flag	Coverage Δ
cli-tests	37.24% <0.00%> (-0.01%)	⬇️
gui-tests	67.93% <60.00%> (+0.08%)	⬆️
performance-and-unit-tests	77.05% <80.00%> (-0.01%)	⬇️
test	46.16% <0.00%> (-0.02%)	⬇️

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[codspeed-hq]

Merging this PR will not alter performance

✅ 35 untouched benchmarks

---

_{Comparing SAKavli:breakthrough-misfit (05c5f54) with main (e980540)}

[eivindjahren]

Looks good! Just some small readability comments. We could consider refactoring the visualization snapshot tests a bit. I had a thought that we might want to have:

def _iter_keys(key_model, data_model):
    for key_index in range(key_model.rowCount()):
        item = data_model.itemAt(data_model.index(key_index, 0))
        assert item is not None
        yield item, key_model.index(key_index, 0)


def select_plotter_figure(plot_window: PlotWindow, key: str, plot_tab_name: str):
    """the figure of the given key in the given plot tab"""
    central_tab = plot_window._central_tab
    data_types = get_child(plot_window, DataTypeKeysWidget)
    key_list = data_types.data_type_keys_widget

    found_selected_key = False
    for key_def, key_index in _iter_keys(key_list.model(), data_types.model):
        if key_def.key == key:
            key_list.setCurrentIndex(key_index)
            for tab_index, tab in enumerate(plot_window._plot_widgets):
                if tab.name == plot_tab_name:
                    found_selected_key = True
                    assert central_tab.isTabEnabled(tab_index)
                    central_tab.setCurrentWidget(tab)
                    assert key_def.dimensionality == tab._plotter.dimensionality
                    if plot_tab_name == STD_DEV:
                        # we need a better resolution for box plots
                        tab._figure.set_size_inches(
                            2000 / tab._figure.get_dpi(),
                            1000 / tab._figure.get_dpi(),
                        )
                    yield tab._figure.figure
    assert found_selected_key

def summary_response(realization: int) -> pl.DataFrame:
    num_points = 14
    center = 6.0 + 0.35 * realization
    steepness = 1.0 + 0.1 * (realization % 3)

    values = [
        0.01 + 0.94 / (1.0 + exp(-(day - center) / steepness))
        for day in range(1, num_points + 1)
    ]

    return pl.DataFrame(
        {
            "response_key": ["WWCT:OP1"] * num_points,
            "time": [datetime(2000, 1, day) for day in range(1, num_points + 1)],
            "values": pl.Series(values, dtype=pl.Float32),
        }
    )


@contextmanager
def open_plotter(config: ErtConfig, qtbot: QtBot):
    log_handler = GUILogHandler()
    with ErtServerController.init_service(project=Path(config.ens_path)):
        args_mock = Mock()
        args_mock.config = "breakthrough.ert"
        gui = _setup_main_window(config, args_mock, log_handler, config.ens_path)
        qtbot.addWidget(gui)

        button_plot_tool = get_child(gui, QToolButton, name="button_Create_plot")

        qtbot.mouseClick(button_plot_tool, Qt.MouseButton.LeftButton)
        plot_window = get_child(gui, PlotWindow)

        yield plot_window

        plot_window.close()


def setup_storage(config):
    def dump_all(configurations):
        return [c.model_dump(mode="json") for c in configurations]

    ens_config = config.ensemble_config
    num_reals = config.runpath_config.num_realizations
    with open_storage(config.ens_path, mode="w") as storage:
        experiment = storage.create_experiment(
            experiment_config={
                "parameter_configuration": dump_all(ens_config.parameter_configuration),
                "response_configuration": dump_all(ens_config.response_configuration),
                "derived_response_configuration": dump_all(
                    ens_config.derived_response_configuration
                ),
                "observations": dump_all(config.observation_declarations),
                "ert_templates": config.ert_templates,
            },
            name="breakthrough-experiment",
        )
        ensemble = experiment.create_ensemble(ensemble_size=num_reals, name="prior")
        bt_config = experiment.derived_response_configuration["breakthrough"]
        for r in range(num_reals):
            ensemble.save_response("summary", summary_response(r), r)
            breakthrough_response = bt_config.derive_from_storage(0, r, ensemble)
            ensemble.save_response("breakthrough", breakthrough_response, r)


def create_breakthrough_figure(plot_tab_name: str):
    @pytest.fixture
    def plot_figure(use_tmpdir, qtbot: QtBot):
        config = _breakthrough_config()

        setup_storage(config)

        open_storage(config.ens_path, mode="r")

        with open_plotter(config, qtbot) as plot_window:
            yield from select_plotter_figure(
                plot_window, "BREAKTHROUGH:WWCT:OP1", plot_tab_name
            )

    return plot_figure


plot_figure = create_breakthrough_figure(ENSEMBLE)

Note that select_plotter_figure is made a bit more general as it could be reused in other tests.

[SAKavli]

Great suggestion, @eivindjahren ! I will steal your snipped if you don't mind 💰