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authorSebastian Berg <sebastian@sipsolutions.net>2022-09-09 12:51:52 +0200
committerGitHub <noreply@github.com>2022-09-09 12:51:52 +0200
commit397b6e51b446647839f548529d06dc80bf0a4c53 (patch)
tree59b9bf9721bd614976556c42cae4f7e5e6d1378e /numpy
parentb63d475ace5e003e95cbb4844869e7a3c12ab54a (diff)
parentcf94f4f1ec8fd47e0e8a7c1e5c51d61ed38de8df (diff)
downloadnumpy-397b6e51b446647839f548529d06dc80bf0a4c53.tar.gz
Merge pull request #22228 from bsipocz/ma_cleanup_old_deprecations
MAINT: Remove long deprecated functionality from np.ma
Diffstat (limited to 'numpy')
-rw-r--r--numpy/ma/core.py84
-rw-r--r--numpy/ma/tests/test_deprecations.py5
2 files changed, 2 insertions, 87 deletions
diff --git a/numpy/ma/core.py b/numpy/ma/core.py
index 93eb74be3..ba387e585 100644
--- a/numpy/ma/core.py
+++ b/numpy/ma/core.py
@@ -5804,74 +5804,6 @@ class MaskedArray(ndarray):
np.copyto(out, np.nan, where=newmask)
return out
- # unique to masked arrays
- def mini(self, axis=None):
- """
- Return the array minimum along the specified axis.
-
- .. deprecated:: 1.13.0
- This function is identical to both:
-
- * ``self.min(keepdims=True, axis=axis).squeeze(axis=axis)``
- * ``np.ma.minimum.reduce(self, axis=axis)``
-
- Typically though, ``self.min(axis=axis)`` is sufficient.
-
- Parameters
- ----------
- axis : int, optional
- The axis along which to find the minima. Default is None, in which case
- the minimum value in the whole array is returned.
-
- Returns
- -------
- min : scalar or MaskedArray
- If `axis` is None, the result is a scalar. Otherwise, if `axis` is
- given and the array is at least 2-D, the result is a masked array with
- dimension one smaller than the array on which `mini` is called.
-
- Examples
- --------
- >>> x = np.ma.array(np.arange(6), mask=[0 ,1, 0, 0, 0 ,1]).reshape(3, 2)
- >>> x
- masked_array(
- data=[[0, --],
- [2, 3],
- [4, --]],
- mask=[[False, True],
- [False, False],
- [False, True]],
- fill_value=999999)
- >>> x.mini()
- masked_array(data=0,
- mask=False,
- fill_value=999999)
- >>> x.mini(axis=0)
- masked_array(data=[0, 3],
- mask=[False, False],
- fill_value=999999)
- >>> x.mini(axis=1)
- masked_array(data=[0, 2, 4],
- mask=[False, False, False],
- fill_value=999999)
-
- There is a small difference between `mini` and `min`:
-
- >>> x[:,1].mini(axis=0)
- masked_array(data=3,
- mask=False,
- fill_value=999999)
- >>> x[:,1].min(axis=0)
- 3
- """
-
- # 2016-04-13, 1.13.0, gh-8764
- warnings.warn(
- "`mini` is deprecated; use the `min` method or "
- "`np.ma.minimum.reduce instead.",
- DeprecationWarning, stacklevel=2)
- return minimum.reduce(self, axis)
-
def max(self, axis=None, out=None, fill_value=None, keepdims=np._NoValue):
"""
Return the maximum along a given axis.
@@ -6719,15 +6651,9 @@ class _extrema_operation(_MaskedUFunc):
self.compare = compare
self.fill_value_func = fill_value
- def __call__(self, a, b=None):
+ def __call__(self, a, b):
"Executes the call behavior."
- if b is None:
- # 2016-04-13, 1.13.0
- warnings.warn(
- f"Single-argument form of np.ma.{self.__name__} is deprecated. Use "
- f"np.ma.{self.__name__}.reduce instead.",
- DeprecationWarning, stacklevel=2)
- return self.reduce(a)
+
return where(self.compare(a, b), a, b)
def reduce(self, target, axis=np._NoValue):
@@ -8090,12 +8016,6 @@ def asanyarray(a, dtype=None):
# Pickling #
##############################################################################
-def _pickle_warn(method):
- # NumPy 1.15.0, 2017-12-10
- warnings.warn(
- f"np.ma.{method} is deprecated, use pickle.{method} instead",
- DeprecationWarning, stacklevel=3)
-
def fromfile(file, dtype=float, count=-1, sep=''):
raise NotImplementedError(
diff --git a/numpy/ma/tests/test_deprecations.py b/numpy/ma/tests/test_deprecations.py
index 3e0e09fdd..40c8418f5 100644
--- a/numpy/ma/tests/test_deprecations.py
+++ b/numpy/ma/tests/test_deprecations.py
@@ -39,11 +39,6 @@ class TestArgsort:
class TestMinimumMaximum:
- def test_minimum(self):
- assert_warns(DeprecationWarning, np.ma.minimum, np.ma.array([1, 2]))
-
- def test_maximum(self):
- assert_warns(DeprecationWarning, np.ma.maximum, np.ma.array([1, 2]))
def test_axis_default(self):
# NumPy 1.13, 2017-05-06