numpy · 20revsined · Apr 8, 2024 · Apr 9, 2024 · Apr 9, 2024 · Apr 11, 2024
diff --git a/issue_25589_tests.py b/issue_25589_tests.py
@@ -0,0 +1,66 @@
+import numpy as np
+from numpy.ma.testutils import assert_equal
+
+array1 = np.ma.array([3, 4, 5, 6], mask = [False, False, False, False])
+result1_array1 = array1 ** 3.5
+assert_equal(result1_array1.data, np.array([46.76537180435969, 128.0, 279.5084971874737, 529.0897844411664]))
+assert_equal(len(result1_array1.mask), 4)
+
+result2_array1 = array1 ** 0.9
+assert_equal(result2_array1.data, np.array([2.6878753795222865, 3.4822022531844965, 4.256699612603923, 5.015752812467621]))
+assert_equal(len(result2_array1.mask), 4)
+
+result3_array1 = array1 ** 1.75
+assert_equal(result3_array1.data, np.array([6.8385211708643325, 11.313708498984761, 16.71850762441055, 23.00195175286581]))
+assert_equal(len(result3_array1.mask), 4)
+
+array2 = np.ma.array([10, 10.7, 1.5, 19.3412], mask = [False])
+result1_array2 = array2 ** 1.2391
+assert_equal(result1_array2.data, np.array([17.342032668438673, 18.858599562101034, 1.6527024811874533, 39.271871725802804]))
+assert_equal(len(result1_array2.mask), 4)
+
+result2_array2 = array2 ** -0.6783
+assert_equal(result2_array2.data, np.array([0.20974904879785425, 0.20034060622191774, 0.7595516280961806, 0.1340844142280547]))
+assert_equal(len(result2_array2.mask), 4)
+
+result3_array2 = array2 ** 5.1948
+assert_equal(result3_array2.data, np.array([156602.9720686047, 222557.8146985335, 8.217862539806553, 4819744.856544941]))
+assert_equal(len(result3_array2.mask), 4)
+
+array3 = np.ma.array([3.4, 1.2], mask = [False, False])
+result1_array3 = array3 ** 7.8
+
+assert_equal(result1_array3.data, np.array([13980.91363494161, 4.145851281415363]))
+assert_equal(len(result1_array3.mask), 2)
+
+result2_array3 = array3 ** 1.2
+assert_equal(result2_array3.data, np.array([4.342848711597634, 1.2445647472039776]))
+assert_equal(len(result2_array3.mask), 2)
+
+result3_array3 = np.ma.power(array3, 6.7)
+assert_equal(result3_array3.data, np.array([3638.3857677420656, 3.3924569758842456]))
+assert_equal(len(result3_array3.mask), 2)
+
+array4 = np.ma.array([7.1205], mask = False)
+result1_array4 = np.ma.power(array4, 4.57)
+assert_equal(result1_array4, np.array([7869.967571527348]))
+assert_equal(len(result1_array4.mask), 1)
+
+result2_array4 = np.ma.power(array4, 0)
+assert_equal(result2_array4, np.array([1]))
+assert_equal(len(result2_array4.mask), 1)
+
+array5 = np.ma.array([6.7], mask = [False])
+result1_array5 = array5 ** 2.1
+assert_equal(result1_array5, np.array([54.294655975192626]))
+assert_equal(len(result1_array5.mask), 1)
+
+result2_array5 = array5 ** -0.345
+assert_equal(result2_array5, np.array([0.518805047913752]))
+assert_equal(len(result2_array5.mask), 1)
+
+result3_array5 = np.ma.power(array5, 1.24)
+assert_equal(result3_array5, np.array([10.576275504399648]))
+assert_equal(len(result3_array5.mask), 1)
+
+print("All tests passed!")
diff --git a/issue_25677_tests.py b/issue_25677_tests.py
@@ -0,0 +1,64 @@
+import numpy as np
+from numpy.ma.testutils import assert_equal
+
+float16_1 = np.ma.array([230.12, 5601, 1027.8, 12389.4219], mask = [0, 0, 1, 0], dtype = "float16")
+assert_equal(float16_1.filled(), np.array([230.12, 5601, 65504, 12389.4219], dtype = np.float16))
+
+float16_2 = np.ma.array([7.8, 1.204, -193], mask = [0, 0, 0], dtype = np.float16)
+assert_equal(float16_2.filled(), np.array([7.8, 1.204, -193], dtype = np.float16))
+
+float16_3 = np.ma.array([2951], mask = [1], dtype = np.float16)
+assert_equal(float16_3.filled(), np.array([65504], dtype = np.float16))
+
+float16_4 = np.ma.array([65504, 65504, 221.4612, 43391, -14583], mask = [1, 0, 1, 0, 1], dtype = np.float16)
+assert_equal(float16_4.filled(), np.array([65504, 65504, 65504, 43391, 65504], dtype = np.float16))
+
+int8_1 = np.ma.array([1, 2, 6, 7], mask = [1, 1, 0, 1], dtype = np.int8)
+assert_equal(int8_1.filled(), np.array([127, 127, 6, 127], dtype = np.int8))
+
+int8_2 = np.ma.array([127, 127], mask = [1, 0], dtype = np.int8)
+assert_equal(int8_2.filled(), np.array([127, 127], dtype = np.int8))
+
+int8_3 = np.ma.array([100], mask = [1], dtype = np.int8)
+assert_equal(int8_3.filled(), np.array([127], dtype = np.int8))
+
+int8_4 = np.ma.array([-14, 123, 104, -98], mask = [0, 0, 0, 1], dtype = np.int8)
+assert_equal(int8_4.filled(), np.array([-14, 123, 104, 127], dtype = np.int8))
+
+uint8_1 = np.ma.array([1, 5], mask = [0, 1], dtype = np.uint8)
+assert_equal(uint8_1.filled(), np.array([1, 255], dtype = np.uint8))
+
+uint8_2 = np.ma.array([19, 156], mask = [1, 1], dtype = np.uint8)
+assert_equal(uint8_2.filled(), np.array([255, 255], dtype = np.uint8))
+
+uint8_3 = np.ma.array([92, 57, 194, 1, 0, 12], mask = [1, 1, 1, 0, 1, 0], dtype = np.uint8)
+assert_equal(uint8_3.filled(), np.array([255, 255, 255, 1, 255, 12], dtype = np.uint8))
+
+uint8_4 = np.ma.array([1, 4, 12, 96, 45], mask = [0, 0, 0, 0, 1], dtype = np.uint8)
+assert_equal(uint8_4.filled(), np.array([1, 4, 12, 96, 255], dtype = np.uint8))
+
+int16_1 = np.ma.array([0, 120], mask = [1, 1], dtype = "int16")
+assert_equal(int16_1.filled(), np.array([32767, 32767], dtype = np.int16))
+
+int16_2 = np.ma.array([240], mask = [1], dtype = np.int16)
+assert_equal(int16_2.filled(), np.array([32767], dtype = np.int16))
+
+int16_3 = np.ma.array([-1, 42, 123, 50, 81, 217], mask = [0, 1, 0, 0, 0, 0], dtype = "int16")
+assert_equal(int16_3.filled(), np.array([-1, 32767, 123, 50, 81, 217], dtype = np.int16))
+
+int16_4 = np.ma.array([10, 128, -19, 1], mask = [0, 0, 1, 0], dtype = np.int16)
+assert_equal(int16_4.filled(), np.array([10, 128, 32767, 1], dtype = np.int16))
+
+uint16_1 = np.ma.array([1, 5], mask = [0, 1], dtype = "uint16")
+assert_equal(uint16_1.filled(), np.array([1, 65535], dtype = np.uint16))
+
+uint16_2 = np.ma.array([9, 79, 31, 111], mask = [1, 1, 1, 1], dtype = "uint16")
+assert_equal(uint16_2.filled(), np.array([65535, 65535, 65535, 65535], dtype = np.uint16))
+
+uint16_3 = np.ma.array([57, 12, 100], mask = [0, 0, 0], dtype = np.uint16)
+assert_equal(uint16_3.filled(), np.array([57, 12, 100], dtype = np.uint16))
+
+uint16_4 = np.ma.array([2, 56, 6, 1, 123, 61, 1], mask = [0, 0, 0, 1, 0, 1, 1], dtype = "uint16")
+assert_equal(uint16_4.filled(), np.array([2, 56, 6, 65535, 123, 65535, 65535], dtype = np.uint16))
+
+print("All tests passed!")
diff --git a/numpy/lib/_function_base_impl.py b/numpy/lib/_function_base_impl.py
@@ -3867,8 +3867,34 @@ def median(a, axis=None, out=None, overwrite_input=False, keepdims=False):
     >>> assert not np.all(a==b)
 
     """
-    return _ureduce(a, func=_median, keepdims=keepdims, axis=axis, out=out,
-                    overwrite_input=overwrite_input)
+
+    # handle edge case where a is a Python list
+    if isinstance(a, list):
+        a = np.array(a)
+
+    if a.size > 0 and len(a.shape) > 0 and all(isinstance(i, np.datetime64) for i in a) and axis is None:
+        a = sorted(a)
+        if len(a) % 2 != 0:
+            return a[len(a)//2]
+        else:
+            second = a[len(a)//2]
+            first = a[len(a)//2 - 1]
+            days_between = (second - first)//2
+            median_date = np.datetime64(first + days_between)
+
+            if out is not None:
+                np.append(out, [median_date])
+                return out
+
+            else:
+                return median_date
+    elif a.size > 0 and len(a.shape) > 0 and all(isinstance(i, np.datetime64) for i in a) and axis is not None:
+        if isinstance(axis, int):
+            dates = a[:,axis]
+
+    else:
+        return _ureduce(a, func=_median, keepdims=keepdims, axis=axis, out=out,
+                        overwrite_input=overwrite_input)
 
 
 def _median(a, axis=None, out=None, overwrite_input=False):

diff --git a/numpy/ma/core.py b/numpy/ma/core.py
@@ -167,11 +167,16 @@ class MaskError(MAError):
 # b: boolean - c: complex - f: floats - i: integer - O: object - S: string
 default_filler = {'b': True,
                   'c': 1.e20 + 0.0j,
-                  'f': 1.e20,
-                  'i': 999999,
+                  "int8": 127,
+                  "uint8": 255,
+                  "int16": 32767,
+                  "uint16": 65535,
+                  "float16": 65504,
+                  "float32": 1.e20,
+                  "float64": 1.e20,
+                  "i": 999999,
                   'O': '?',
                   'S': b'N/A',
-                  'u': 999999,
                   'V': b'???',
                   'U': 'N/A'
                   }
@@ -298,7 +303,10 @@ def _scalar_fill_value(dtype):
         if dtype.kind in 'Mm':
             return default_filler.get(dtype.str[1:], '?')
         else:
-            return default_filler.get(dtype.kind, '?')
+            if default_filler.get(str(dtype)) is not None:
+                return default_filler.get(str(dtype), '?')
+            else:
+                return default_filler.get(dtype.kind, '?')
 
     dtype = _get_dtype_of(obj)
     return _recursive_fill_value(dtype, _scalar_fill_value)
@@ -7085,7 +7093,7 @@ def power(a, b, third=None):
     # Get the masks
     ma = getmask(a)
     mb = getmask(b)
-    m = mask_or(ma, mb)
+    m = mask_or(ma, mb, shrink=False)
     # Get the rawdata
     fa = getdata(a)
     fb = getdata(b)
@@ -7112,6 +7120,7 @@ def power(a, b, third=None):
         elif result._mask is nomask:
             result._mask = invalid
         result._data[invalid] = result.fill_value
+    # result.mask = a.mask
     return result
 
 argmin = _frommethod('argmin')

diff --git a/numpy/ma/tests/test_core.py b/numpy/ma/tests/test_core.py
@@ -4455,6 +4455,74 @@ def test_power(self):
         assert_almost_equal(x, y)
         assert_almost_equal(x._data, y._data)
 
+        # Adding custom test cases for issue 25589
+        array1 = np.ma.array([3, 4, 5, 6], mask = [False, False, False, False])
+        result1_array1 = array1 ** 3.5
+        assert_equal(result1_array1.data, np.array([46.76537180435969, 128.0, 279.5084971874737, 529.0897844411664]))
+        assert_equal(len(result1_array1.mask), 4)
+
+        result2_array1 = array1 ** 0.9
+        assert_equal(result2_array1.data, np.array([2.6878753795222865, 3.4822022531844965,
+                                                     4.256699612603923, 5.015752812467621]))
+        assert_equal(len(result2_array1.mask), 4)
+
+        result3_array1 = array1 ** 1.75
+        assert_equal(result3_array1.data, np.array([6.8385211708643325, 11.313708498984761,
+                                                      16.71850762441055, 23.00195175286581]))
+        assert_equal(len(result3_array1.mask), 4)
+
+        array2 = np.ma.array([10, 10.7, 1.5, 19.3412], mask = [False])
+        result1_array2 = array2 ** 1.2391
+        assert_equal(result1_array2.data, np.array([17.342032668438673, 18.858599562101034,
+                                                        1.6527024811874533, 39.271871725802804]))
+        assert_equal(len(result1_array2.mask), 4)
+
+        result2_array2 = array2 ** -0.6783
+        assert_equal(result2_array2.data, np.array([0.20974904879785425, 0.20034060622191774,
+                                                         0.7595516280961806, 0.1340844142280547]))
+        assert_equal(len(result2_array2.mask), 4)
+
+        result3_array2 = array2 ** 5.1948
+        assert_equal(result3_array2.data, np.array([156602.9720686047, 222557.8146985335,
+                                                        8.217862539806553, 4819744.856544941]))
+        assert_equal(len(result3_array2.mask), 4)
+
+        array3 = np.ma.array([3.4, 1.2], mask = [False, False])
+        result1_array3 = array3 ** 7.8
+
+        assert_equal(result1_array3.data, np.array([13980.91363494161, 4.145851281415363]))
+        assert_equal(len(result1_array3.mask), 2)
+
+        result2_array3 = array3 ** 1.2
+        assert_equal(result2_array3.data, np.array([4.342848711597634, 1.2445647472039776]))
+        assert_equal(len(result2_array3.mask), 2)
+
+        result3_array3 = np.ma.power(array3, 6.7)
+        assert_equal(result3_array3.data, np.array([3638.3857677420656, 3.3924569758842456]))
+        assert_equal(len(result3_array3.mask), 2)
+
+        array4 = np.ma.array([7.1205], mask = False)
+        result1_array4 = np.ma.power(array4, 4.57)
+        assert_equal(result1_array4, np.array([7869.967571527348]))
+        assert_equal(len(result1_array4.mask), 1)
+
+        result2_array4 = np.ma.power(array4, 0)
+        assert_equal(result2_array4, np.array([1]))
+        assert_equal(len(result2_array4.mask), 1)
+
+        array5 = np.ma.array([6.7], mask = [False])
+        result1_array5 = array5 ** 2.1
+        assert_equal(result1_array5, np.array([54.294655975192626]))
+        assert_equal(len(result1_array5.mask), 1)
+
+        result2_array5 = array5 ** -0.345
+        assert_equal(result2_array5, np.array([0.518805047913752]))
+        assert_equal(len(result2_array5.mask), 1)
+
+        result3_array5 = np.ma.power(array5, 1.24)
+        assert_equal(result3_array5, np.array([10.576275504399648]))
+        assert_equal(len(result3_array5.mask), 1)
+
     def test_power_with_broadcasting(self):
         # Test power w/ broadcasting
         a2 = np.array([[1., 2., 3.], [4., 5., 6.]])

diff --git a/test_cases.py b/test_cases.py
@@ -0,0 +1,94 @@
+import numpy as np
+
+# bug 25677
+arr = np.ma.array([1, 2, 3], mask=[1, 0, 1], dtype=np.int8)
+arr.filled()
+
+arr = np.ma.array([1, 2, 3], mask=[1, 0, 1], dtype=np.float16)
+arr.filled()
+
+arr = np.ma.array([1.e4, 1.e4, 1.e4], mask = [1,1,1], dtype = np.float16)
+arr.filled()
+
+f16 = np.ma.array([230.12, 5601, 1027.8, 12389.4219], mask = [0, 0, 1, 0], dtype = "float16")
+f16.filled()
+
+f32 = np.ma.array([-230.12, 5601, 1027.8, 12389.4219, -9999.5321, 567.1034827], mask = [0, 0, 1, 0, 1, 1], dtype = "float32")
+f32.filled()
+
+f64 = np.ma.array([3.141592, -230.12, 5601, 1027.8, 12389.4219, -9999.5321, 567.1034827], mask = [0, 0, 1, 0, 1, 1, 1], dtype = "float64")
+f64.filled()
+
+i8 = np.ma.array([1, 2, 6, 7], mask = [1, 1, 0, 1], dtype = np.int8)
+i8.filled()
+
+ui8 = np.ma.array([1, 5], mask = [0, 1], dtype = np.uint8)
+ui8.filled()
+
+i16 = np.ma.array([5000, 120], mask = [1, 1], dtype = "int16")
+i16.filled()
+
+ui16 = np.ma.array([1, 5], mask = [0, 1], dtype = "uint16")
+ui16.filled()
+
+i32 = np.ma.array([11111, 5500, -9182], mask = [0, 1, 1], dtype = np.int32)
+i32.filled()
+
+ui32 = np.ma.array([11111, 5500, 9182, 4040], mask = [1, 0, 1, 1], dtype = np.uint32)
+ui32.filled()
+
+i64 = np.ma.array([pow(2, 34), pow(-2, 40), pow(2, 9)], mask = [0, 0, 0], dtype = np.int64)
+i64.filled()
+
+ui64 = np.ma.array([57, 120, pow(2, 34), pow(2, 40), pow(2, 41)], mask = [1, 1, 0, 0, 0], dtype = np.uint64)
+ui64.filled()
+
+i64 = np.ma.array([19675483920], mask=True, dtype = np.int64)
+i64.filled()
+
+a = np.ma.array([([0, 1],)], mask = np.ma.array([([False, False],)], dtype=[('A', '?', (2,))]), dtype=[('A', '>i2', (2,))])
+
+ilist = [1, 2, 3, 4, 5]
+flist = [1.1, 2.2, 3.3, 4.4, 5.5]
+slist = [b'one', b'two', b'three', b'four', b'five']
+ddtype = [('a', int), ('b', float), ('c', '|S8')]
+mask = [0, 1, 0, 0, 1]
+base = np.ma.array(list(zip(ilist, flist, slist)), mask=mask, dtype=ddtype)
+
+# bug 26216
+a = np.array([np.datetime64('2005-02-25'), np.datetime64(1, 'Y')])
+print(np.median(a))
+
+# bug 25589
+a = np.array((1, 2, 3))  # arbitrary data
+b = np.zeros(3)  # all-False mask
+m = np.ma.masked_array(a, b)
+m * 4
+m ** 2
+m ** 3
+m ** 1.2
+m ** -0.4
+
+array1 = np.ma.array([3, 4, 5, 6], mask = [False, False, False, False])
+array1 ** 3.5
+array1 ** 0.9
+array1 ** 1.75
+
+array2 = np.ma.array([10, 10.7, 1.5, 19.3412], mask = [False])
+array2 ** 1.2391
+array2 ** -0.6783
+array2 ** 5.1948
+
+array3 = np.ma.array([3.4, 1.2], mask = [False, False])
+array3 ** 7.8
+array3 ** 1.2
+np.ma.power(array3, 6.7)
+
+array4 = np.ma.array([7.1205], mask = False)
+np.ma.power(array4, 4.57)
+np.ma.power(array4, 0)
+
+array5 = np.ma.array([6.7], mask = [False])
+array5 ** 2.1
+array5 ** -0.345
+np.ma.power(array5, 1.24)