👌 Improve Project API data handling (#1257)

This PR improves the data handling of the Project API in notebook workflows.

Since notebooks are often executed as a whole (`Run All`) the default of `ignore_existing=False` in `project.import_data` leads to using `ignore_existing=True` each time calling it in an actual case study, which makes the code less readable and adds a lot of redundancy and a default of `ignore_existing=True` is more sensible (IMHO `ignore_existing=False` only makes sense in a CLI or GUI context). 

Having to import each dataset one by one also clutters the code quite a lot.
```py
project.import_data("measured_data/Npq2_220219_800target3fasea.ascii", dataset_name="TA")
project.import_data(guide_s1, dataset_name="guide_s1")
project.import_data(guide_s2, dataset_name="guide_s2")
project.import_data(guide_s3, dataset_name="guide_s3")
project.import_data(guide_s4, dataset_name="guide_s4")
project.import_data(guide_s5, dataset_name="guide_s5")
project.import_data(guide_s6, dataset_name="guide_s6")
project.import_data(guide_s7, dataset_name="guide_s7")
project.import_data(guide_s8, dataset_name="guide_s8")
```
This is why it is a lot more convenient to allow the use of a mapping (especially since that mapping could have been defined and used for plotting even before importing glotaran at all)
```py
my_datasets ={
    "TA":"measured_data/Npq2_220219_800target3fasea.ascii"
    "guide_s1":guide_s1,
    "guide_s2":guide_s2,
    "guide_s3":guide_s3,
    "guide_s4":guide_s4,
    "guide_s5":guide_s5,
    "guide_s6":guide_s6,
    "guide_s7":guide_s7,
    "guide_s8":guide_s8,
}
project.import_data(my_datasets)
```

Lastly, users might want to switch out datasets in the optimization without touching the model definition for example to use an averaged dataset to have a quicker feedback loop or to use some other kind of preprocessing/correcting on the data and compare results with the exact same model. 

```py
project.optimize("my_model", "my_parameters", data_lookup_overwrite={"TA": averaged_data})
```

* 👌 Change default of ignore_existing in import_data to True

This allow using a notbook workflow w/o cluttering it with 'ignore_existing=True' all over the place

* 👌 Allow importing data from a mapping

* 👌 Add data_overwrite option to Result.optimize and Result.create_scheme

* 👌 Changed data_overwrite to data_lookup_overwrite to clarify that data are not overwritten

* 🚧📚 Added change to changelog

* 🩹 Fix allow_overwrite=True not having any effect if ignore_existing=True

* 🧹 Renamed data_lookup_overwrite to data_lookup_override

* 👌 Apply suggestions from code review

Co-authored-by: Joris Snellenburg <jsnel@users.noreply.github.com>

---------

Co-authored-by: Joris Snellenburg <jsnel@users.noreply.github.com>

changelog.md

@@ -23,6 +23,7 @@
 - ✨ Allow usage of subfolders in project API for parameters, models and data (#1232)
 - ✨ Allow import of xarray objects in project API import_data (#1235)
 - 🩹 Add number_of_clps to result and correct degrees_of_freedom calculation (#1249)
+- 👌 Improve Project API data handling (#1257)
 
 ### 🩹 Bug fixes
 

glotaran/project/project.py

@@ -35,6 +35,8 @@
 if TYPE_CHECKING:
     from collections.abc import Hashable
 
+    from glotaran.typing.types import LoadableDataset
+
 TEMPLATE = "version: {gta_version}"
 
 PROJECT_FILE_NAME = "project.gta"
@@ -203,31 +205,36 @@ def load_data(
 
     def import_data(
         self,
-        dataset: str | Path | xr.Dataset | xr.DataArray,
+        dataset: LoadableDataset | Mapping[str, LoadableDataset],
         dataset_name: str | None = None,
         allow_overwrite: bool = False,
-        ignore_existing: bool = False,
+        ignore_existing: bool = True,
     ):
-        """Import a dataset.
+        """Import a dataset by saving it as an .nc file in the project's data folder.
 
         Parameters
         ----------
-        dataset : str | Path | xr.Dataset | xr.DataArray
+        dataset : LoadableDataset
             Dataset instance or path to a dataset.
         dataset_name : str | None
             The name of the dataset (needs to be provided when dataset is an xarray instance).
             Defaults to None.
         allow_overwrite: bool
             Whether to overwrite an existing dataset.
         ignore_existing: bool
-            Whether to ignore import if the dataset already exists.
+            Whether to skip import if the dataset already exists and allow_overwrite is False.
+            Defaults to ``True``.
         """
-        self._data_registry.import_data(
-            dataset,
-            dataset_name=dataset_name,
-            allow_overwrite=allow_overwrite,
-            ignore_existing=ignore_existing,
-        )
+        if not isinstance(dataset, Mapping) or isinstance(dataset, (xr.Dataset, xr.DataArray)):
+            dataset = {dataset_name: dataset}
+
+        for key, value in dataset.items():
+            self._data_registry.import_data(
+                value,
+                dataset_name=key,
+                allow_overwrite=allow_overwrite,
+                ignore_existing=ignore_existing,
+            )
 
     @property
     def has_models(self) -> bool:
@@ -623,6 +630,7 @@ def create_scheme(
         parameters_name: str,
         maximum_number_function_evaluations: int | None = None,
         clp_link_tolerance: float = 0.0,
+        data_lookup_override: Mapping[str, LoadableDataset] | None = None,
     ) -> Scheme:
         """Create a scheme for optimization.
 
@@ -636,18 +644,33 @@ def create_scheme(
             The maximum number of function evaluations.
         clp_link_tolerance : float
             The CLP link tolerance.
+        data_lookup_override: Mapping[str, LoadableDataset] | None
+            Allows to bypass the default dataset lookup in the project ``data`` folder and use a
+            different dataset for the optimization without changing the model. This is especially
+            useful when working with preprocessed data. Defaults to ``None``.
 
         Returns
         -------
         Scheme
             The created scheme.
         """
+        if data_lookup_override is None:
+            data_lookup_override = {}
         loaded_model = self.load_model(model_name)
-        data = {dataset: self.load_data(dataset) for dataset in loaded_model.dataset}
+        data_lookup_override = {
+            dataset_name: dataset_value
+            for dataset_name, dataset_value in data_lookup_override.items()
+            if dataset_name in loaded_model.dataset
+        }
+        data = {
+            dataset_name: self.data[dataset_name]
+            for dataset_name in loaded_model.dataset
+            if dataset_name not in data_lookup_override
+        }
         return Scheme(
             model=loaded_model,
             parameters=self.load_parameters(parameters_name),
-            data=data,
+            data=data | data_lookup_override,
             maximum_number_function_evaluations=maximum_number_function_evaluations,
             clp_link_tolerance=clp_link_tolerance,
         )
@@ -659,6 +682,7 @@ def optimize(
         result_name: str | None = None,
         maximum_number_function_evaluations: int | None = None,
         clp_link_tolerance: float = 0.0,
+        data_lookup_override: Mapping[str, LoadableDataset] | None = None,
     ) -> Result:
         """Optimize a model.
 
@@ -674,6 +698,10 @@ def optimize(
             The maximum number of function evaluations.
         clp_link_tolerance : float
             The CLP link tolerance.
+        data_lookup_override: Mapping[str, LoadableDataset] | None
+            Allows to bypass the default dataset lookup in the project ``data`` folder and use a
+            different dataset for the optimization without changing the model. This is especially
+            useful when working with preprocessed data. Defaults to ``None``.
 
         Returns
         -------
@@ -683,7 +711,11 @@ def optimize(
         from glotaran.optimization.optimize import optimize
 
         scheme = self.create_scheme(
-            model_name, parameters_name, maximum_number_function_evaluations, clp_link_tolerance
+            model_name=model_name,
+            parameters_name=parameters_name,
+            maximum_number_function_evaluations=maximum_number_function_evaluations,
+            clp_link_tolerance=clp_link_tolerance,
+            data_lookup_override=data_lookup_override,
         )
         result = optimize(scheme)
 

glotaran/project/project_data_registry.py

@@ -73,6 +73,6 @@ def import_data(
             dataset = load_dataset(dataset)
 
         data_path = self.directory / f"{dataset_name}.nc"
-        if data_path.exists() and ignore_existing:
+        if data_path.exists() and ignore_existing and allow_overwrite is False:
             return
         save_dataset(dataset, data_path, allow_overwrite=allow_overwrite)

glotaran/project/test/test_project.py

@@ -16,6 +16,7 @@
 
 from glotaran import __version__ as gta_version
 from glotaran.builtin.io.yml.utils import load_dict
+from glotaran.io import load_dataset
 from glotaran.io import load_parameters
 from glotaran.io import save_dataset
 from glotaran.io import save_parameters
@@ -30,6 +31,7 @@
 from glotaran.testing.simulated_data.sequential_spectral_decay import (
     PARAMETERS as example_parameter,
 )
+from glotaran.typing.types import LoadableDataset
 from glotaran.utils.io import chdir_context
 
 
@@ -248,11 +250,10 @@ def test_import_data(tmp_path: Path, name: str | None):
     save_dataset(example_dataset, test_data)
 
     project.import_data(test_data, dataset_name=name)
-    with pytest.raises(FileExistsError):
-        project.import_data(test_data, dataset_name=name)
+    project.import_data(test_data, dataset_name=name)
 
-    project.import_data(test_data, dataset_name=name, allow_overwrite=True)
-    project.import_data(test_data, dataset_name=name, ignore_existing=True)
+    with pytest.raises(FileExistsError):
+        project.import_data(test_data, dataset_name=name, ignore_existing=False)
 
     data_folder = tmp_path / "test_project/data"
 
@@ -288,6 +289,41 @@ def test_import_data_xarray(tmp_path: Path, data: xr.Dataset | xr.DataArray):
     assert project.load_data("test_data").equals(xr.Dataset({"data": xr.DataArray([1])}))
 
 
+def test_import_data_allow_overwrite(existing_project: Project):
+    """Overwrite data when ``allow_overwrite==True``."""
+
+    dummy_data = xr.Dataset({"data": xr.DataArray([1])})
+
+    assert not existing_project.load_data("dataset_1").equals(dummy_data)
+
+    existing_project.import_data(dummy_data, dataset_name="dataset_1", allow_overwrite=True)
+
+    assert existing_project.load_data("dataset_1").equals(dummy_data)
+
+
+@pytest.mark.parametrize(
+    "data",
+    (
+        xr.DataArray([1]),
+        xr.Dataset({"data": xr.DataArray([1])}),
+    ),
+)
+def test_import_data_mapping(tmp_path: Path, data: xr.Dataset | xr.DataArray):
+    """Import data as a mapping"""
+    project = Project.open(tmp_path)
+
+    test_data = tmp_path / "import_data.nc"
+    save_dataset(example_dataset, test_data)
+
+    project.import_data({"test_data_1": data, "test_data_2": test_data})
+
+    assert (tmp_path / "data/test_data_1.nc").is_file() is True
+    assert (tmp_path / "data/test_data_2.nc").is_file() is True
+
+    assert project.load_data("test_data_1").equals(xr.Dataset({"data": xr.DataArray([1])}))
+    assert project.load_data("test_data_2").equals(load_dataset(test_data))
+
+
 def test_create_scheme(existing_project: Project):
     scheme = existing_project.create_scheme(
         model_name="test_model",
@@ -300,6 +336,31 @@ def test_create_scheme(existing_project: Project):
     assert scheme.maximum_number_function_evaluations == 1
 
 
+@pytest.mark.parametrize(
+    "data",
+    (xr.DataArray([1]), xr.Dataset({"data": xr.DataArray([1])}), "file"),
+)
+def test_create_scheme_data_lookup_override(
+    tmp_path: Path, existing_project: Project, data: LoadableDataset
+):
+    """Test data_lookup_override functionality."""
+
+    if data == "file":
+        data = tmp_path / "file_data.nc"
+        save_dataset(xr.Dataset({"data": xr.DataArray([1])}), data)
+
+    scheme = existing_project.create_scheme(
+        model_name="test_model",
+        parameters_name="test_parameters",
+        data_lookup_override={"dataset_1": data},
+    )
+
+    assert len(scheme.data) == 1
+    assert "dataset_1" in scheme.data
+    assert "dataset_1" in scheme.model.dataset
+    assert scheme.data["dataset_1"].equals(xr.Dataset({"data": xr.DataArray([1])}))
+
+
 @pytest.mark.parametrize("result_name", ["test", None])
 def test_run_optimization(existing_project: Project, result_name: str | None):
     assert existing_project.has_models