Subset callable using multiple variables (#361)

* sevaral variables can be passed to the callable returning the mask

* add some examples. update typings.

* add raise TypeError

* consistent typing. add unit tests

* minor changes

* docstring edits

* refactor and add tests. revert np.all. small updates to the docstring

* docstring edits

* Added one Exception to validate all variable share the same dimension

* bump version

* Update pyproject.toml

Add myself as an author for conda forge invite

* black v24

---------

Co-authored-by: Vadim Bertrand <vadim.bertrand@univ-grenoble-alpes.fr>
Co-authored-by: Philippe Miron <philippe.miron@dtn.com>
Co-authored-by: Shane Elipot <selipot@miami.edu>
Co-authored-by: Kevin Santana <kevinsantana11@gmail.com>

Author: 4 people

clouddrift/ragged.py

@@ -545,20 +545,21 @@ def subset(
     obs_dim_name: str = "obs",
     full_trajectories=False,
 ) -> xr.Dataset:
-    """Subset a ragged array dataset as a function of one or more criteria.
+    """Subset a ragged array xarray dataset as a function of one or more criteria.
     The criteria are passed with a dictionary, where a dictionary key
     is a variable to subset and the associated dictionary value is either a range
     (valuemin, valuemax), a list [value1, value2, valueN], a single value, or a
-    masking function applied to every row of the ragged array using ``apply_ragged``.
+    masking function applied to any variable of the dataset.
 
     This function needs to know the names of the dimensions of the ragged array dataset
     (`traj_dim_name` and `obs_dim_name`), and the name of the rowsize variable (`rowsize_var_name`).
-    Default values are provided for these arguments (see below), but they can be changed if needed.
+    Default values corresponds to the clouddrift convention ("traj", "obs", and "rowsize") but should
+    be changed as needed.
 
     Parameters
     ----------
     ds : xr.Dataset
-        Dataset stored as ragged arrays.
+        Xarray dataset composed of ragged arrays.
     criteria : dict
         Dictionary containing the variables (as keys) and the ranges/values/functions (as values) to subset.
     id_var_name : str, optional
@@ -570,19 +571,19 @@ def subset(
     obs_dim_name : str, optional
         Name of the observation dimension (default is "obs").
     full_trajectories : bool, optional
-        If True, it returns the complete trajectories (rows) where at least one observation
-        matches the criteria, rather than just the segments where the criteria are satisfied.
-        Default is False.
+        If True, the function returns complete rows (trajectories) for which the criteria
+        are matched at least once. Default is False which means that only segments matching the criteria
+        are returned when filtering along the observation dimension.
 
     Returns
     -------
     xr.Dataset
-        subset Dataset matching the criterion(a)
+        Subset xarray dataset matching the criterion(a).
 
     Examples
     --------
-    Criteria are combined on any data or metadata variables part of the Dataset.
-    The following examples are based on NOAA GDP datasets which can be accessed with the
+    Criteria are combined on any data (with dimension "obs") or metadata (with dimension "traj") variables
+    part of the Dataset. The following examples are based on NOAA GDP datasets which can be accessed with the
     ``clouddrift.datasets`` module.
 
     Retrieve a region, like the Gulf of Mexico, using ranges of latitude and longitude:
@@ -607,7 +608,7 @@ def subset(
 
     >>> subset(ds, {"rowsize": (0, 1000)})
 
-    Retrieve specific drifters from their IDs:
+    Retrieve specific drifters using their IDs:
 
     >>> subset(ds, {"id": [2578, 2582, 2583]})
 
@@ -637,10 +638,32 @@ def subset(
     >>> func = (lambda arr: ((arr - arr[0]) % 2) == 0)
     >>> subset(ds, {"time": func})
 
+    The filtering function can accept several input variables passed as a tuple. For example, retrieve
+    drifters released in the Mediterranean Sea, but exclude those released in the Bay of Biscay and the Black Sea:
+
+    >>> def mediterranean_mask(lon: xr.DataArray, lat: xr.DataArray) -> xr.DataArray:
+    >>>     # Mediterranean Sea bounding box
+    >>>     in_med = np.logical_and(-6.0327 <= lon, np.logical_and(lon <= 36.2173,
+    >>>                                                            np.logical_and(30.2639 <= lat, lat <= 45.7833)))
+    >>>     # Bay of Biscay
+    >>>     in_biscay = np.logical_and(lon <= -0.1462, lat >= 43.2744)
+    >>>     # Black Sea
+    >>>     in_blacksea = np.logical_and(lon >= 27.4437, lat >= 40.9088)
+    >>>     return np.logical_and(in_med, np.logical_not(np.logical_or(in_biscay, in_blacksea)))
+    >>> subset(ds, {("start_lon", "start_lat"): mediterranean_mask})
+
     Raises
     ------
     ValueError
-        If one of the variable in a criterion is not found in the Dataset
+        If one of the variable in a criterion is not found in the Dataset.
+    TypeError
+        If one of the `criteria` key is a tuple while its associated value is not a `Callable` criterion.
+    TypeError
+        If variables of a `criterion` key associated to a `Callable` do not share the same dimension.
+
+    See Also
+    --------
+    :func:`apply_ragged`
     """
     mask_traj = xr.DataArray(
         data=np.ones(ds.sizes[traj_dim_name], dtype="bool"), dims=[traj_dim_name]
@@ -650,19 +673,30 @@ def subset(
     )
 
     for key in criteria.keys():
-        if key in ds or key in ds.dims:
-            if ds[key].dims == (traj_dim_name,):
+        if np.all(np.isin(key, ds.variables) | np.isin(key, ds.dims)):
+            if isinstance(key, tuple):
+                criterion = [ds[k] for k in key]
+                if not all(c.dims == criterion[0].dims for c in criterion):
+                    raise TypeError(
+                        "Variables passed to the Callable must share the same dimension."
+                    )
+                criterion_dims = criterion[0].dims
+            else:
+                criterion = ds[key]
+                criterion_dims = criterion.dims
+
+            if criterion_dims == (traj_dim_name,):
                 mask_traj = np.logical_and(
                     mask_traj,
                     _mask_var(
-                        ds[key], criteria[key], ds[rowsize_var_name], traj_dim_name
+                        criterion, criteria[key], ds[rowsize_var_name], traj_dim_name
                     ),
                 )
-            elif ds[key].dims == (obs_dim_name,):
+            elif criterion_dims == (obs_dim_name,):
                 mask_obs = np.logical_and(
                     mask_obs,
                     _mask_var(
-                        ds[key], criteria[key], ds[rowsize_var_name], obs_dim_name
+                        criterion, criteria[key], ds[rowsize_var_name], obs_dim_name
                     ),
                 )
         else:
@@ -769,7 +803,7 @@ def unpack(
 
 
 def _mask_var(
-    var: xr.DataArray,
+    var: Union[xr.DataArray, list[xr.DataArray]],
     criterion: Union[tuple, list, np.ndarray, xr.DataArray, bool, float, int, Callable],
     rowsize: xr.DataArray = None,
     dim_name: str = "dim_0",
@@ -778,8 +812,8 @@ def _mask_var(
 
     Parameters
     ----------
-    var : xr.DataArray
-        DataArray to be subset by the criterion
+    var : xr.DataArray or list[xr.DataArray]
+        DataArray or list of DataArray (only applicable if the criterion is a Callable) to be used by the criterion
     criterion : array-like or scalar or Callable
         The criterion can take four forms:
         - tuple: (min, max) defining a range
@@ -815,26 +849,41 @@ def _mask_var(
     array([False, True, False,  True, False])
     Dimensions without coordinates: dim_0
 
+    >>> y = xr.DataArray(data=np.arange(0, 5)+2)
+    >>> rowsize = xr.DataArray(data=[2, 3])
+    >>> _mask_var([x, y], lambda var1, var2: ((var1 * var2) % 2) == 0, rowsize, "dim_0")
+    <xarray.DataArray (dim_0: 5)>
+    array([True, False, True,  False, True])
+    Dimensions without coordinates: dim_0
+
     Returns
     -------
     mask : xr.DataArray
         The mask of the subset of the data matching the criteria
     """
+    if not callable(criterion) and isinstance(var, list):
+        raise TypeError(
+            "The `var` parameter can be a `list` only if the `criterion` is a `Callable`."
+        )
+
     if isinstance(criterion, tuple):  # min/max defining range
         mask = np.logical_and(var >= criterion[0], var <= criterion[1])
     elif isinstance(criterion, (list, np.ndarray, xr.DataArray)):
         # select multiple values
         mask = np.isin(var, criterion)
     elif callable(criterion):
         # mask directly created by applying `criterion` function
-        if len(var) == len(rowsize):
-            mask = criterion(var)
+        if not isinstance(var, list):
+            var = [var]
+
+        if len(var[0]) == len(rowsize):
+            mask = criterion(*var)
         else:
             mask = apply_ragged(criterion, var, rowsize)
 
         mask = xr.DataArray(data=mask, dims=[dim_name]).astype(bool)
 
-        if not len(var) == len(mask):
+        if not len(var[0]) == len(mask):
             raise ValueError(
                 "The `Callable` function must return a masked array that matches the length of the variable to filter."
             )

pyproject.toml

@@ -4,11 +4,12 @@ build-backend = "hatchling.build"
 
 [project]
 name = "clouddrift"
-version = "0.30.0"
+version = "0.31.0"
 authors = [
   { name="Shane Elipot", email="selipot@miami.edu" },
   { name="Philippe Miron", email="philippemiron@gmail.com" },
-  { name="Milan Curcic", email="mcurcic@miami.edu" }
+  { name="Milan Curcic", email="mcurcic@miami.edu" },
+  { name="Kevin Santana", email="kevinsantana11@gmail.com" }
 ]
 description = "Accelerating the use of Lagrangian data for atmospheric, oceanic, and climate sciences"
 readme = "README.md"

tests/ragged_tests.py

@@ -714,13 +714,42 @@ def test_subset_callable(self):
         self.assertTrue(all(ds_sub["id"] == [1, 2]))
         self.assertTrue(all(ds_sub["rowsize"] == [5, 4]))
 
+    def test_subset_callable_tuple(self):
+        func = lambda arr1, arr2: np.logical_and(
+            arr1 >= 0, arr2 >= 30
+        )  # keep positive longitude and latitude larger or equal than 30
+        ds_sub = subset(self.ds, {("lon", "lat"): func})
+        self.assertTrue(all(ds_sub["id"] == [1, 2]))
+        self.assertTrue(all(ds_sub["rowsize"] == [2, 2]))
+        self.assertTrue(all(ds_sub["lon"] >= 0))
+        self.assertTrue(all(ds_sub["lat"] >= 30))
+
     def test_subset_callable_wrong_dim(self):
         func = lambda arr: [arr, arr]  # returns 2 values per element
         with self.assertRaises(ValueError):
             subset(self.ds, {"time": func})
         with self.assertRaises(ValueError):
             subset(self.ds, {"id": func})
 
+    def test_subset_callable_wrong_type(self):
+        rows = [0, 2]  # test extracting first and third rows
+        with self.assertRaises(TypeError):  # passing a tuple when a string is expected
+            subset(self.ds, {("traj",): rows})
+
+    def test_subset_callable_tuple_unknown_var(self):
+        func = lambda arr1, arr2: np.logical_and(
+            arr1 >= 0, arr2 >= 30
+        )  # keep positive longitude and latitude larger or equal than 30
+        with self.assertRaises(ValueError):
+            subset(self.ds, {("a", "lat"): func})
+
+    def test_subset_callable_tuple_not_same_dimension(self):
+        func = lambda arr1, arr2: np.logical_and(
+            arr1 >= 0, arr2 >= 30
+        )  # keep positive longitude and latitude larger or equal than 30
+        with self.assertRaises(TypeError):
+            subset(self.ds, {("id", "lat"): func})
+
 
 class unpack_tests(unittest.TestCase):
     def test_unpack(self):