DRY

Signed-off-by: zethson <[email protected]>

ehrapy/_compat.py

@@ -1,7 +1,6 @@
 # Since we might check whether an object is an instance of dask.array.Array
 # without requiring dask installed in the environment.
-# This would become obsolete should dask become a requirement for ehrapy
-
+from collections.abc import Callable
 
 try:
     import dask.array as da
@@ -11,6 +10,12 @@
     DASK_AVAILABLE = False
 
 
+def _raise_array_type_not_implemented(func: Callable, type_: type) -> NotImplementedError:
+    return NotImplementedError(
+        f"{func.__name__} does not support array type {type_}. Must be of type {func.registry.keys()}."  # type: ignore
+    )
+
+
 def is_dask_array(array):
     if DASK_AVAILABLE:
         return isinstance(array, da.Array)

ehrapy/preprocessing/_normalization.py

@@ -6,7 +6,7 @@
 import numpy as np
 import sklearn.preprocessing as sklearn_pp
 
-from ehrapy._compat import is_dask_array
+from ehrapy._compat import _raise_array_type_not_implemented
 
 try:
     import dask.array as da
@@ -77,7 +77,7 @@ def _scale_func_group(
 
 @singledispatch
 def _scale_norm_function(arr):
-    raise NotImplementedError(f"scale_norm does not support data to be of type {type(arr)}")
+    _raise_array_type_not_implemented(_scale_norm_function, type(arr))
 
 
 @_scale_norm_function.register
@@ -135,7 +135,7 @@ def scale_norm(
 
 @singledispatch
 def _minmax_norm_function(arr):
-    raise NotImplementedError(f"minmax_norm does not support data to be of type {type(arr)}")
+    _raise_array_type_not_implemented(_minmax_norm_function, type(arr))
 
 
 @_minmax_norm_function.register
@@ -194,7 +194,7 @@ def minmax_norm(
 
 @singledispatch
 def _maxabs_norm_function(arr):
-    raise NotImplementedError(f"maxabs_norm does not support data to be of type {type(arr)}")
+    _raise_array_type_not_implemented(_scale_norm_function, type(arr))
 
 
 @_maxabs_norm_function.register
@@ -243,7 +243,7 @@ def maxabs_norm(
 
 @singledispatch
 def _robust_scale_norm_function(arr, **kwargs):
-    raise NotImplementedError(f"robust_scale_norm does not support data to be of type {type(arr)}")
+    _raise_array_type_not_implemented(_robust_scale_norm_function, type(arr))
 
 
 @_robust_scale_norm_function.register
@@ -303,7 +303,7 @@ def robust_scale_norm(
 
 @singledispatch
 def _quantile_norm_function(arr):
-    raise NotImplementedError(f"robust_scale_norm does not support data to be of type {type(arr)}")
+    _raise_array_type_not_implemented(_quantile_norm_function, type(arr))
 
 
 @_quantile_norm_function.register
@@ -362,7 +362,7 @@ def quantile_norm(
 
 @singledispatch
 def _power_norm_function(arr, **kwargs):
-    raise NotImplementedError(f"power_norm does not support data to be of type {type(arr)}")
+    _raise_array_type_not_implemented(_power_norm_function, type(arr))
 
 
 @_power_norm_function.register

pyproject.toml

@@ -72,7 +72,7 @@ medcat = [
     "medcat",
 ]
 dask = [
-    "dask",
+    "anndata[dask]",
     "dask-ml",
 ]
 dev = [

tests/preprocessing/test_normalization.py

@@ -89,11 +89,6 @@ def test_vars_checks(adata_to_norm):
         ep.pp.scale_norm(adata_to_norm, vars=["String1"])
 
 
-# TODO: list the supported array types centrally?
-norm_scale_supported_types = [np.asarray, da.asarray]
-norm_scale_unsupported_types = [sparse.csc_matrix]
-
-
 # TODO: check this for each function, with just default settings?
 @pytest.mark.parametrize(
     "array_type,expected_error",