Merge pull request #763 from sentinel-hub/develop

Release 1.5.1

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v4.5.0
     hooks:
       - id: end-of-file-fixer
       - id: requirements-txt-fixer
@@ -13,13 +13,13 @@ repos:
       - id: debug-statements
 
   - repo: https://github.com/psf/black
-    rev: 23.7.0
+    rev: 23.9.1
     hooks:
       - id: black
         language_version: python3
 
   - repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: "v0.0.282"
+    rev: "v0.0.292"
     hooks:
       - id: ruff
 

CHANGELOG.md

@@ -1,3 +1,10 @@
+## [Version 1.5.1] - 2023-10-17
+
+- `MorphologicalFilterTask` adapted to work on boolean values.
+- Added `temporal_subset` method to `EOPatch`, which can be used to extract a subset of an `EOPatch` by filtering out temporal slices. Also added a corresponding `TemporalSubsetTask`.
+- `EOExecutor` now has an option to treat `TemporalDimensionWarning` as an exception.
+- String representation of `EOPatch` objects was revisited to avoid edge cases where the output would print enormous objects.
+
 ## [Version 1.5.0] - 2023-09-06
 
 The release focuses on making `eo-learn` much simpler to install, reducing the number of dependencies, and improving validation of soundness of `EOPatch` data.

docs/source/conf.py

@@ -134,7 +134,7 @@
 # When Sphinx documents class signature it prioritizes __new__ method over __init__ method. The following hack puts
 # EOTask.__new__ method to the blacklist so that __init__ method signature will be taken instead. This seems the
 # cleanest way even though a private object is accessed.
-sphinx.ext.autodoc._CLASS_NEW_BLACKLIST.append("{0.__module__}.{0.__qualname__}".format(EOTask.__new__))  # noqa: SLF001
+sphinx.ext.autodoc._CLASS_NEW_BLACKLIST.append(f"{EOTask.__module__}.{EOTask.__new__.__qualname__}")  # noqa: SLF001
 
 
 EXAMPLES_FOLDER = "./examples"

eolearn/__init__.py

@@ -1,5 +1,5 @@
 """Main module of the `eolearn` package."""
-__version__ = "1.5.0"
+__version__ = "1.5.1"
 
 import importlib.util
 import warnings

eolearn/core/__init__.py

@@ -19,6 +19,7 @@
     RemoveFeatureTask,
     RenameFeatureTask,
     SaveTask,
+    TemporalSubsetTask,
     ZipFeatureTask,
 )
 from .eodata import EOPatch

eolearn/core/core_tasks.py

@@ -413,6 +413,30 @@ def execute(self, src_eopatch: EOPatch, dst_eopatch: EOPatch) -> EOPatch:
         return dst_eopatch
 
 
+class TemporalSubsetTask(EOTask):
+    """Extracts a temporal subset of the EOPatch."""
+
+    def __init__(
+        self, timestamps: None | list[dt.datetime] | list[int] | Callable[[list[dt.datetime]], Iterable[bool]] = None
+    ):
+        """
+        :param timestamps: Input for the `temporal_subset` method of EOPatch. Can also be provided in execution
+            arguments. Value in execution arguments takes precedence.
+        """
+        self.timestamps = timestamps
+
+    def execute(
+        self,
+        eopatch: EOPatch,
+        *,
+        timestamps: None | list[dt.datetime] | list[int] | Callable[[list[dt.datetime]], Iterable[bool]] = None,
+    ) -> EOPatch:
+        timestamps = timestamps if timestamps is not None else self.timestamps
+        if timestamps is None:
+            raise ValueError("Value for `timestamps` must be provided on initialization or as an execution argument.")
+        return eopatch.temporal_subset(timestamps)
+
+
 class MapFeatureTask(EOTask):
     """Applies a function to each feature in input_features of a patch and stores the results in a set of
     output_features.

eolearn/core/eodata.py

@@ -455,36 +455,36 @@ def __repr__(self) -> str:
 
     @staticmethod
     def _repr_value(value: object) -> str:
-        """Creates a representation string for different types of data.
-
-        :param value: data in any type
-        :return: representation string
-        """
+        """Creates a representation string for different types of data."""
         if isinstance(value, np.ndarray):
             return f"{EOPatch._repr_value_class(value)}(shape={value.shape}, dtype={value.dtype})"
 
         if isinstance(value, gpd.GeoDataFrame):
             crs = CRS(value.crs).ogc_string() if value.crs else value.crs
             return f"{EOPatch._repr_value_class(value)}(columns={list(value)}, length={len(value)}, crs={crs})"
 
+        repr_str = str(value)
+        if len(repr_str) <= MAX_DATA_REPR_LEN:
+            return repr_str
+
         if isinstance(value, (list, tuple, dict)) and value:
-            repr_str = str(value)
-            if len(repr_str) <= MAX_DATA_REPR_LEN:
-                return repr_str
+            lb, rb = ("[", "]") if isinstance(value, list) else ("(", ")") if isinstance(value, tuple) else ("{", "}")
 
-            l_bracket, r_bracket = ("[", "]") if isinstance(value, list) else ("(", ")")
-            if isinstance(value, (list, tuple)) and len(value) > 2:
-                repr_str = f"{l_bracket}{value[0]!r}, ..., {value[-1]!r}{r_bracket}"
+            if isinstance(value, dict):  # generate representation of first element or (key, value) pair
+                some_key = next(iter(value))
+                repr_of_el = f"{EOPatch._repr_value(some_key)}: {EOPatch._repr_value(value[some_key])}"
+            else:
+                repr_of_el = EOPatch._repr_value(value[0])
 
-            if len(repr_str) > MAX_DATA_REPR_LEN and isinstance(value, (list, tuple)) and len(value) > 1:
-                repr_str = f"{l_bracket}{value[0]!r}, ...{r_bracket}"
+            many_elements_visual = ", ..." if len(value) > 1 else ""  # add ellipsis if there are multiple elements
+            repr_str = f"{lb}{repr_of_el}{many_elements_visual}{rb}"
 
             if len(repr_str) > MAX_DATA_REPR_LEN:
                 repr_str = str(type(value))
 
-            return f"{repr_str}, length={len(value)}"
+            return f"{repr_str}<length={len(value)}>"
 
-        return repr(value)
+        return str(type(value))
 
     @staticmethod
     def _repr_value_class(value: object) -> str:
@@ -726,6 +726,7 @@ def merge(
             self, *eopatches, features=features, time_dependent_op=time_dependent_op, timeless_op=timeless_op
         )
 
+    @deprecated_function(EODeprecationWarning, "Please use the method `temporal_subset` instead.")
     def consolidate_timestamps(self, timestamps: list[dt.datetime]) -> set[dt.datetime]:
         """Removes all frames from the EOPatch with a date not found in the provided timestamps list.
 
@@ -750,6 +751,45 @@ def consolidate_timestamps(self, timestamps: list[dt.datetime]) -> set[dt.dateti
 
         return remove_from_patch
 
+    def temporal_subset(
+        self, timestamps: Iterable[dt.datetime] | Iterable[int] | Callable[[list[dt.datetime]], Iterable[bool]]
+    ) -> EOPatch:
+        """Returns an EOPatch that only contains data for the temporal subset corresponding to `timestamps`.
+
+        For array-based data appropriate temporal slices are extracted. For vector data a filtration is performed.
+
+        :param timestamps: Parameter that defines the temporal subset. Can be a collection of timestamps, a
+            collection of timestamp indices. It is possible to also provide a callable that maps a list of timestamps
+            to a sequence of booleans, which determine if a given timestamp is included in the subset or not.
+        """
+        timestamp_indices = self._parse_temporal_subset_input(timestamps)
+        new_timestamps = [ts for i, ts in enumerate(self.get_timestamps()) if i in timestamp_indices]
+        new_patch = EOPatch(bbox=self.bbox, timestamps=new_timestamps)
+
+        for ftype, fname in self.get_features():
+            if ftype.is_timeless() or ftype.is_meta():
+                new_patch[ftype, fname] = self[ftype, fname]
+            elif ftype.is_vector():
+                gdf: gpd.GeoDataFrame = self[ftype, fname]
+                new_patch[ftype, fname] = gdf[gdf[TIMESTAMP_COLUMN].isin(new_timestamps)]
+            else:
+                new_patch[ftype, fname] = self[ftype, fname][timestamp_indices]
+
+        return new_patch
+
+    def _parse_temporal_subset_input(
+        self, timestamps: Iterable[dt.datetime] | Iterable[int] | Callable[[list[dt.datetime]], Iterable[bool]]
+    ) -> list[int]:
+        """Parses input into a list of timestamp indices. Also adds implicit support for strings via `parse_time`."""
+        if callable(timestamps):
+            accepted_timestamps = timestamps(self.get_timestamps())
+            return [i for i, accepted in enumerate(accepted_timestamps) if accepted]
+        ts_or_idx = list(timestamps)
+        if all(isinstance(ts, int) for ts in ts_or_idx):
+            return ts_or_idx  # type: ignore[return-value]
+        parsed_timestamps = {parse_time(ts, force_datetime=True) for ts in ts_or_idx}  # type: ignore[call-overload]
+        return [i for i, ts in enumerate(self.get_timestamps()) if ts in parsed_timestamps]
+
     def plot(
         self,
         feature: Feature,

eolearn/core/eoexecution.py

@@ -27,7 +27,7 @@
 
 from .eonode import EONode
 from .eoworkflow import EOWorkflow, WorkflowResults
-from .exceptions import EORuntimeWarning
+from .exceptions import EORuntimeWarning, TemporalDimensionWarning
 from .utils.fs import get_base_filesystem_and_path, get_full_path, pickle_fs, unpickle_fs
 from .utils.logging import LogFileFilter
 from .utils.parallelize import _decide_processing_type, _ProcessingType, parallelize
@@ -36,8 +36,7 @@
 class _HandlerWithFsFactoryType(Protocol):
     """Type definition for a callable that accepts a path and a filesystem object"""
 
-    def __call__(self, path: str, filesystem: FS, **kwargs: Any) -> Handler:
-        ...
+    def __call__(self, path: str, filesystem: FS, **kwargs: Any) -> Handler: ...
 
 
 # pylint: disable=invalid-name
@@ -56,6 +55,7 @@ class _ProcessingData:
     filter_logs_by_thread: bool
     logs_filter: Filter | None
     logs_handler_factory: _HandlerFactoryType
+    raise_on_temporal_mismatch: bool
 
 
 @dataclass(frozen=True)
@@ -87,6 +87,7 @@ def __init__(
         filesystem: FS | None = None,
         logs_filter: Filter | None = None,
         logs_handler_factory: _HandlerFactoryType = FileHandler,
+        raise_on_temporal_mismatch: bool = False,
     ):
         """
         :param workflow: A prepared instance of EOWorkflow class
@@ -109,6 +110,7 @@ def __init__(
               object.
 
             The 2nd option is chosen only if `filesystem` parameter exists in the signature.
+        :param raise_on_temporal_mismatch: Whether to treat `TemporalDimensionWarning` as an exception.
         """
         self.workflow = workflow
         self.execution_kwargs = self._parse_and_validate_execution_kwargs(execution_kwargs)
@@ -117,6 +119,7 @@ def __init__(
         self.filesystem, self.logs_folder = self._parse_logs_filesystem(filesystem, logs_folder)
         self.logs_filter = logs_filter
         self.logs_handler_factory = logs_handler_factory
+        self.raise_on_temporal_mismatch = raise_on_temporal_mismatch
 
         self.start_time: dt.datetime | None = None
         self.report_folder: str | None = None
@@ -194,6 +197,7 @@ def run(self, workers: int | None = 1, multiprocess: bool = True, **tqdm_kwargs:
                 filter_logs_by_thread=filter_logs_by_thread,
                 logs_filter=self.logs_filter,
                 logs_handler_factory=self.logs_handler_factory,
+                raise_on_temporal_mismatch=self.raise_on_temporal_mismatch,
             )
             for workflow_kwargs, log_path in zip(self.execution_kwargs, log_paths)
         ]
@@ -264,7 +268,10 @@ def _execute_workflow(cls, data: _ProcessingData) -> WorkflowResults:
             data.logs_handler_factory,
         )
 
-        results = data.workflow.execute(data.workflow_kwargs, raise_errors=False)
+        with warnings.catch_warnings():
+            if data.raise_on_temporal_mismatch:
+                warnings.simplefilter("error", TemporalDimensionWarning)
+            results = data.workflow.execute(data.workflow_kwargs, raise_errors=False)
 
         cls._try_remove_logging(data.log_path, logger, handler)
         return results

eolearn/core/eoworkflow.py

@@ -307,12 +307,10 @@ def get_nodes(self) -> list[EONode]:
         return self._nodes[:]
 
     @overload
-    def get_node_with_uid(self, uid: str, fail_if_missing: Literal[True] = ...) -> EONode:
-        ...
+    def get_node_with_uid(self, uid: str, fail_if_missing: Literal[True] = ...) -> EONode: ...
 
     @overload
-    def get_node_with_uid(self, uid: str, fail_if_missing: Literal[False] = ...) -> EONode | None:
-        ...
+    def get_node_with_uid(self, uid: str, fail_if_missing: Literal[False] = ...) -> EONode | None: ...
 
     def get_node_with_uid(self, uid: str, fail_if_missing: bool = False) -> EONode | None:
         """Returns node with give uid, if it exists in the workflow."""

eolearn/core/extra/ray.py

@@ -61,7 +61,7 @@ def _get_processing_type(*_: Any, **__: Any) -> _ProcessingType:
 def _ray_workflow_executor(workflow_args: _ProcessingData) -> WorkflowResults:
     """Called to execute a workflow on a ray worker"""
     # pylint: disable=protected-access
-    return RayExecutor._execute_workflow(workflow_args)
+    return RayExecutor._execute_workflow(workflow_args)  # noqa: SLF001
 
 
 def parallelize_with_ray(

eolearn/core/utils/parallelize.py

@@ -49,9 +49,7 @@ def _decide_processing_type(workers: int | None, multiprocess: bool) -> _Process
     """
     if workers == 1:
         return _ProcessingType.SINGLE_PROCESS
-    if multiprocess:
-        return _ProcessingType.MULTIPROCESSING
-    return _ProcessingType.MULTITHREADING
+    return _ProcessingType.MULTIPROCESSING if multiprocess else _ProcessingType.MULTITHREADING
 
 
 def parallelize(
@@ -74,10 +72,7 @@ def parallelize(
     :return: A list of function results.
     """
     if not params:
-        raise ValueError(
-            "At least 1 list of parameters should be given. Otherwise it is not clear how many times the"
-            "function has to be executed."
-        )
+        return []
     processing_type = _decide_processing_type(workers=workers, multiprocess=multiprocess)
 
     if processing_type is _ProcessingType.SINGLE_PROCESS:
@@ -105,7 +100,6 @@ def execute_with_mp_lock(function: Callable[..., OutputType], *args: Any, **kwar
     :param function: A function
     :param args: Function's positional arguments
     :param kwargs: Function's keyword arguments
-    :return: Function's results
     """
     if multiprocessing.current_process().name == "MainProcess" or MULTIPROCESSING_LOCK is None:
         return function(*args, **kwargs)
@@ -165,10 +159,7 @@ def join_futures_iter(
     """
 
     def _wait_function(remaining_futures: Collection[Future]) -> tuple[Collection[Future], Collection[Future]]:
-        done, not_done = concurrent.futures.wait(
-            remaining_futures, timeout=float(update_interval), return_when=FIRST_COMPLETED
-        )
-        return done, not_done
+        return concurrent.futures.wait(remaining_futures, timeout=float(update_interval), return_when=FIRST_COMPLETED)
 
     def _get_result(future: Future) -> Any:
         return future.result()
@@ -184,8 +175,6 @@ def _base_join_futures_iter(
 ) -> Generator[tuple[int, OutputType], None, None]:
     """A generalized utility function that resolves futures, monitors progress, and serves as an iterator over
     results."""
-    if not isinstance(futures, list):
-        raise ValueError(f"Parameters 'futures' should be a list but {type(futures)} was given")
     remaining_futures: Collection[FutureType] = _make_copy_and_empty_given(futures)
 
     id_to_position_map = {id(future): index for index, future in enumerate(remaining_futures)}
@@ -195,9 +184,8 @@ def _base_join_futures_iter(
             done, remaining_futures = wait_function(remaining_futures)
             for future in done:
                 result = get_result_function(future)
-                result_position = id_to_position_map[id(future)]
                 pbar.update(1)
-                yield result_position, result
+                yield id_to_position_map[id(future)], result
 
 
 def _make_copy_and_empty_given(items: list[T]) -> list[T]:

eolearn/features/extra/clustering.py

@@ -88,7 +88,7 @@ def execute(self, eopatch: EOPatch) -> EOPatch:
 
         # All connections to masked pixels are removed
         if self.mask_name is not None:
-            mask = eopatch.mask_timeless[self.mask_name].squeeze()
+            mask = eopatch.mask_timeless[self.mask_name].squeeze(axis=-1)
             graph_args["mask"] = mask
             data = data[np.ravel(mask) != 0]
 

eolearn/geometry/morphology.py

@@ -48,7 +48,7 @@ def __init__(
         self.no_data_label = no_data_label
 
     def execute(self, eopatch: EOPatch) -> EOPatch:
-        feature_array = eopatch[(self.mask_type, self.mask_name)].squeeze().copy()
+        feature_array = eopatch[(self.mask_type, self.mask_name)].squeeze(axis=-1).copy()
 
         all_labels = np.unique(feature_array)
         erode_labels = self.erode_labels if self.erode_labels else all_labels
@@ -148,6 +148,10 @@ def __init__(
     def map_method(self, feature: np.ndarray) -> np.ndarray:
         """Applies the morphological operation to a raster feature."""
         feature = feature.copy()
+        is_bool = feature.dtype == bool
+        if is_bool:
+            feature = feature.astype(np.uint8)
+
         morph_func = partial(cv2.morphologyEx, kernel=self.struct_elem, op=self.morph_operation)
         if feature.ndim == 3:
             for channel in range(feature.shape[2]):
@@ -158,4 +162,4 @@ def map_method(self, feature: np.ndarray) -> np.ndarray:
         else:
             raise ValueError(f"Invalid number of dimensions: {feature.ndim}")
 
-        return feature
+        return feature.astype(bool) if is_bool else feature