feat!: remove verbose parameter

xeofs/data_container/data_container.py

@@ -1,7 +1,6 @@
 from typing import Dict
 
 import dask
-from dask.diagnostics.progress import ProgressBar
 from typing_extensions import Self
 
 try:
@@ -52,13 +51,9 @@ def deserialize(cls, dt: DataTree) -> Self:
             container._allow_compute[key] = node.attrs["allow_compute"]
         return container
 
-    def compute(self, verbose=False, **kwargs):
+    def compute(self, **kwargs):
         computed_data = {k: v for k, v in self.items() if self._allow_compute[k]}
-        if verbose:
-            with ProgressBar():
-                (computed_data,) = dask.compute(computed_data, **kwargs)
-        else:
-            (computed_data,) = dask.compute(computed_data, **kwargs)
+        (computed_data,) = dask.compute(computed_data, **kwargs)
         for k, v in computed_data.items():
             self[k] = v
 

xeofs/models/_base_model.py

@@ -5,7 +5,6 @@
 
 import dask.base
 import xarray as xr
-from dask.diagnostics.progress import ProgressBar
 from typing_extensions import Self
 
 from .._version import __version__
@@ -54,13 +53,11 @@ def get_serialization_attrs(self) -> dict:
         """Get the attributes to serialize."""
         raise NotImplementedError
 
-    def compute(self, verbose: bool = False, **kwargs):
+    def compute(self, **kwargs):
         """Compute and load delayed model results.
 
         Parameters
         ----------
-        verbose : bool
-            Whether or not to provide additional information about the computing progress.
         **kwargs
             Additional keyword arguments to pass to `dask.compute()`.
         """
@@ -74,11 +71,7 @@ def compute(self, verbose: bool = False, **kwargs):
             if data_is_dask(v) and v.attrs.get("allow_compute", True)
         }
 
-        if verbose:
-            with ProgressBar():
-                (data_objs,) = dask.base.compute(data_objs, **kwargs)
-        else:
-            (data_objs,) = dask.base.compute(data_objs, **kwargs)
+        (data_objs,) = dask.base.compute(data_objs, **kwargs)
 
         for k, v in data_objs.items():
             dt[k] = DataTree(v)

xeofs/models/_base_model_cross_set.py

@@ -1,4 +1,3 @@
-import warnings
 from abc import abstractmethod
 from typing import Any, Hashable, Sequence
 
@@ -80,19 +79,11 @@ def __init__(
         alpha: Sequence[float] | float = 1.0,
         solver: Sequence[str] | str = "auto",
         compute: bool = True,
-        verbose: bool = False,
         sample_name: str = "sample",
         feature_name: Sequence[str] | str = "feature",
         random_state: Generator | int | None = None,
         solver_kwargs: dict[str, Any] = {},
     ):
-        if verbose:
-            warnings.warn(
-                "The 'verbose' parameter is deprecated and will be removed in a future release.",
-                category=DeprecationWarning,
-                stacklevel=3,
-            )
-
         super().__init__()
 
         # Process parameters
@@ -134,7 +125,6 @@ def __init__(
             "sample_name": sample_name,
             "feature_name": feature_name,
             "random_state": random_state,
-            "verbose": verbose,
             "compute": compute,
             "solver": solver,
         }
@@ -145,7 +135,6 @@ def __init__(
             "solver": solver,
             "random_state": random_state,
             "compute": compute,
-            "verbose": False,
             "solver_kwargs": solver_kwargs,
         }
 

xeofs/models/_base_model_single_set.py

@@ -1,4 +1,3 @@
-import warnings
 from abc import abstractmethod
 from typing import (
     Hashable,
@@ -49,8 +48,6 @@ class _BaseModelSingleSet(_BaseModel):
         If True, four pieces of the fit will be computed sequentially: 1) the
         preprocessor scaler, 2) optional NaN checks, 3) SVD decomposition, 4) scores
         and components.
-    verbose: bool, default=False
-        Whether to show a progress bar when computing the decomposition.
     random_state: int | None, default=None
         Seed for the random number generator.
     solver: {"auto", "full", "randomized"}, default="auto"
@@ -70,18 +67,10 @@ def __init__(
         sample_name="sample",
         feature_name="feature",
         compute=True,
-        verbose=False,
         random_state=None,
         solver="auto",
         solver_kwargs={},
     ):
-        if verbose:
-            warnings.warn(
-                "The 'verbose' parameter is deprecated and will be removed in a future release.",
-                category=DeprecationWarning,
-                stacklevel=3,
-            )
-
         super().__init__()
 
         self.n_modes = n_modes
@@ -98,7 +87,6 @@ def __init__(
             "sample_name": sample_name,
             "feature_name": feature_name,
             "random_state": random_state,
-            "verbose": verbose,
             "compute": compute,
             "solver": solver,
             "solver_kwargs": solver_kwargs,
@@ -108,7 +96,6 @@ def __init__(
             "solver": solver,
             "random_state": random_state,
             "compute": compute,
-            "verbose": verbose,
             "solver_kwargs": solver_kwargs,
         }
 

xeofs/models/_np_classes/_sparse_pca.py

@@ -619,9 +619,6 @@ def compute_rspca(
     tol : float, (default ``tol = 1e-5``).
         Stopping tolerance for reconstruction error.
 
-    verbose : bool ``{'True', 'False'}``, optional (default ``verbose = True``).
-        Display progress.
-
     oversample : integer, optional (default: 10)
         Controls the oversampling of column space. Increasing this parameter
         may improve numerical accuracy.

xeofs/models/_np_classes/_svd.py

@@ -54,8 +54,6 @@ class _SVD:
         and optionally truncated afterwards.
     random_state : np.random.Generator | int | None, default=None
         Seed for the random number generator.
-    verbose: bool, default=False
-        Whether to show a progress bar when computing the decomposition.
     solver_kwargs : dict, default={}
         Additional keyword arguments passed to the SVD solver.
     """
@@ -67,7 +65,6 @@ def __init__(
         flip_signs: bool = True,
         solver: str = "auto",
         random_state: np.random.Generator | int | None = None,
-        verbose: bool = False,
         solver_kwargs: dict = {},
     ):
         sanity_check_n_modes(n_modes)
@@ -83,7 +80,6 @@ def __init__(
         self.n_modes_precompute = n_modes
         self.init_rank_reduction = init_rank_reduction
         self.flip_signs = flip_signs
-        self.verbose = verbose
         self.solver = solver
         self.random_state = random_state
         self.solver_kwargs = solver_kwargs

xeofs/models/cpcca.py

@@ -132,7 +132,6 @@ def __init__(
         pca_init_rank_reduction: Sequence[float] | float = 0.3,
         check_nans: Sequence[bool] | bool = True,
         compute: bool = True,
-        verbose: bool = False,
         sample_name: str = "sample",
         feature_name: Sequence[str] | str = "feature",
         solver: str = "auto",
@@ -151,7 +150,6 @@ def __init__(
             pca_init_rank_reduction=pca_init_rank_reduction,
             alpha=alpha,
             compute=compute,
-            verbose=verbose,
             sample_name=sample_name,
             feature_name=feature_name,
             solver=solver,
@@ -1128,7 +1126,6 @@ def __init__(
         n_pca_modes: Sequence[float | int | str] | float | int | str = 0.999,
         pca_init_rank_reduction: Sequence[float] | float = 0.3,
         compute: bool = True,
-        verbose: bool = True,
         sample_name: str = "sample",
         feature_name: Sequence[str] | str = "feature",
         solver: str = "auto",
@@ -1146,7 +1143,6 @@ def __init__(
             pca_init_rank_reduction=pca_init_rank_reduction,
             alpha=alpha,
             compute=compute,
-            verbose=verbose,
             sample_name=sample_name,
             feature_name=feature_name,
             solver=solver,
@@ -1402,7 +1398,6 @@ def __init__(
         n_pca_modes: Sequence[float | int | str] | float | int | str = 0.999,
         pca_init_rank_reduction: Sequence[float] | float = 0.3,
         compute: bool = True,
-        verbose: bool = True,
         sample_name: str = "sample",
         feature_name: Sequence[str] | str = "feature",
         solver: str = "auto",
@@ -1420,7 +1415,6 @@ def __init__(
             pca_init_rank_reduction=pca_init_rank_reduction,
             alpha=alpha,
             compute=compute,
-            verbose=verbose,
             sample_name=sample_name,
             feature_name=feature_name,
             solver=solver,

xeofs/models/decomposer.py

@@ -5,7 +5,6 @@
 import xarray as xr
 from dask.array import Array as DaskArray  # type: ignore
 from dask.array.linalg import svd_compressed as dask_svd
-from dask.diagnostics.progress import ProgressBar
 from scipy.sparse.linalg import svds as complex_svd  # type: ignore
 from sklearn.utils.extmath import randomized_svd
 
@@ -38,8 +37,6 @@ class Decomposer:
         and optionally truncated afterwards.
     random_state : np.random.Generator | int | None, default=None
         Seed for the random number generator.
-    verbose: bool, default=False
-        Whether to show a progress bar when computing the decomposition.
     component_dim_name : str, default='mode'
         Name of the component dimension in the output DataArrays.
     solver_kwargs : dict, default={}
@@ -54,7 +51,6 @@ def __init__(
         compute: bool = True,
         solver: str = "auto",
         random_state: np.random.Generator | int | None = None,
-        verbose: bool = False,
         component_dim_name: str = "mode",
         solver_kwargs: dict = {},
     ):
@@ -72,7 +68,6 @@ def __init__(
         self.init_rank_reduction = init_rank_reduction
         self.flip_signs = flip_signs
         self.compute = compute
-        self.verbose = verbose
         self.solver = solver
         self.random_state = random_state
         self.component_dim_name = component_dim_name
@@ -299,10 +294,5 @@ def _compute_svd_result(self, U, s, VT):
             case False:
                 pass
             case True:
-                match self.verbose:
-                    case True:
-                        with ProgressBar():
-                            U, s, VT = dask.compute(U, s, VT)
-                    case False:
-                        U, s, VT = dask.compute(U, s, VT)
+                U, s, VT = dask.compute(U, s, VT)
         return U, s, VT

xeofs/models/eof.py

@@ -37,8 +37,6 @@ class EOF(_BaseModelSingleSet):
         If True, four pieces of the fit will be computed sequentially: 1) the
         preprocessor scaler, 2) optional NaN checks, 3) SVD decomposition, 4) scores
         and components.
-    verbose: bool, default=False
-        Whether to show a progress bar when computing the decomposition.
     random_state : Optional[int], default=None
         Seed for the random number generator.
     solver: {"auto", "full", "randomized"}, default="auto"
@@ -64,7 +62,6 @@ def __init__(
         sample_name: str = "sample",
         feature_name: str = "feature",
         compute: bool = True,
-        verbose: bool = False,
         random_state: Optional[int] = None,
         solver: str = "auto",
         solver_kwargs: Dict = {},
@@ -79,7 +76,6 @@ def __init__(
             sample_name=sample_name,
             feature_name=feature_name,
             compute=compute,
-            verbose=verbose,
             random_state=random_state,
             solver=solver,
             solver_kwargs=solver_kwargs,
@@ -278,8 +274,6 @@ class ComplexEOF(EOF):
         computation. If True, four pieces of the fit will be computed
         sequentially: 1) the preprocessor scaler, 2) optional NaN checks, 3) SVD
         decomposition, 4) scores and components.
-    verbose: bool, default=False
-        Whether to show a progress bar when computing the decomposition.
     random_state : Optional[int], default=None
         Seed for the random number generator.
     solver: {"auto", "full", "randomized"}, default="auto"
@@ -318,7 +312,6 @@ def __init__(
         sample_name: str = "sample",
         feature_name: str = "feature",
         compute: bool = True,
-        verbose: bool = False,
         random_state: Optional[int] = None,
         solver: str = "auto",
         solver_kwargs: Dict = {},
@@ -333,7 +326,6 @@ def __init__(
             sample_name=sample_name,
             feature_name=feature_name,
             compute=compute,
-            verbose=verbose,
             random_state=random_state,
             solver=solver,
             solver_kwargs=solver_kwargs,
@@ -485,8 +477,6 @@ class HilbertEOF(ComplexEOF):
         If True, four pieces of the fit will be computed sequentially: 1) the
         preprocessor scaler, 2) optional NaN checks, 3) SVD decomposition, 4) scores
         and components.
-    verbose: bool, default=False
-        Whether to show a progress bar when computing the decomposition.
     random_state : Optional[int], default=None
         Seed for the random number generator.
     solver: {"auto", "full", "randomized"}, default="auto"
@@ -522,7 +512,6 @@ def __init__(
         sample_name: str = "sample",
         feature_name: str = "feature",
         compute: bool = True,
-        verbose: bool = False,
         random_state: Optional[int] = None,
         solver: str = "auto",
         solver_kwargs: Dict = {},
@@ -537,7 +526,6 @@ def __init__(
             sample_name=sample_name,
             feature_name=feature_name,
             compute=compute,
-            verbose=verbose,
             random_state=random_state,
             solver=solver,
             solver_kwargs=solver_kwargs,