1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
|
from typing import TypeAlias, TypeVar
import numpy as np
import numpy.typing as npt
from numpy._typing import _Shape
_ScalarT = TypeVar("_ScalarT", bound=np.generic)
MaskedArray: TypeAlias = np.ma.MaskedArray[_Shape, np.dtype[_ScalarT]]
MAR_1d_f8: np.ma.MaskedArray[tuple[int], np.dtype[np.float64]]
MAR_b: MaskedArray[np.bool]
MAR_c: MaskedArray[np.complex128]
MAR_td64: MaskedArray[np.timedelta64]
AR_b: npt.NDArray[np.bool]
MAR_1d_f8.shape = (3, 1) # type: ignore[assignment]
MAR_1d_f8.dtype = np.bool # type: ignore[assignment]
np.ma.min(MAR_1d_f8, axis=1.0) # type: ignore[call-overload]
np.ma.min(MAR_1d_f8, keepdims=1.0) # type: ignore[call-overload]
np.ma.min(MAR_1d_f8, out=1.0) # type: ignore[call-overload]
np.ma.min(MAR_1d_f8, fill_value=lambda x: 27) # type: ignore[call-overload]
MAR_1d_f8.min(axis=1.0) # type: ignore[call-overload]
MAR_1d_f8.min(keepdims=1.0) # type: ignore[call-overload]
MAR_1d_f8.min(out=1.0) # type: ignore[call-overload]
MAR_1d_f8.min(fill_value=lambda x: 27) # type: ignore[call-overload]
np.ma.max(MAR_1d_f8, axis=1.0) # type: ignore[call-overload]
np.ma.max(MAR_1d_f8, keepdims=1.0) # type: ignore[call-overload]
np.ma.max(MAR_1d_f8, out=1.0) # type: ignore[call-overload]
np.ma.max(MAR_1d_f8, fill_value=lambda x: 27) # type: ignore[call-overload]
MAR_1d_f8.max(axis=1.0) # type: ignore[call-overload]
MAR_1d_f8.max(keepdims=1.0) # type: ignore[call-overload]
MAR_1d_f8.max(out=1.0) # type: ignore[call-overload]
MAR_1d_f8.max(fill_value=lambda x: 27) # type: ignore[call-overload]
np.ma.ptp(MAR_1d_f8, axis=1.0) # type: ignore[call-overload]
np.ma.ptp(MAR_1d_f8, keepdims=1.0) # type: ignore[call-overload]
np.ma.ptp(MAR_1d_f8, out=1.0) # type: ignore[call-overload]
np.ma.ptp(MAR_1d_f8, fill_value=lambda x: 27) # type: ignore[call-overload]
MAR_1d_f8.ptp(axis=1.0) # type: ignore[call-overload]
MAR_1d_f8.ptp(keepdims=1.0) # type: ignore[call-overload]
MAR_1d_f8.ptp(out=1.0) # type: ignore[call-overload]
MAR_1d_f8.ptp(fill_value=lambda x: 27) # type: ignore[call-overload]
MAR_1d_f8.argmin(axis=1.0) # type: ignore[call-overload]
MAR_1d_f8.argmin(keepdims=1.0) # type: ignore[call-overload]
MAR_1d_f8.argmin(out=1.0) # type: ignore[call-overload]
MAR_1d_f8.argmin(fill_value=lambda x: 27) # type: ignore[call-overload]
np.ma.argmin(MAR_1d_f8, axis=1.0) # type: ignore[call-overload]
np.ma.argmin(MAR_1d_f8, axis=(1,)) # type: ignore[call-overload]
np.ma.argmin(MAR_1d_f8, keepdims=1.0) # type: ignore[call-overload]
np.ma.argmin(MAR_1d_f8, out=1.0) # type: ignore[call-overload]
np.ma.argmin(MAR_1d_f8, fill_value=lambda x: 27) # type: ignore[call-overload]
MAR_1d_f8.argmax(axis=1.0) # type: ignore[call-overload]
MAR_1d_f8.argmax(keepdims=1.0) # type: ignore[call-overload]
MAR_1d_f8.argmax(out=1.0) # type: ignore[call-overload]
MAR_1d_f8.argmax(fill_value=lambda x: 27) # type: ignore[call-overload]
np.ma.argmax(MAR_1d_f8, axis=1.0) # type: ignore[call-overload]
np.ma.argmax(MAR_1d_f8, axis=(0,)) # type: ignore[call-overload]
np.ma.argmax(MAR_1d_f8, keepdims=1.0) # type: ignore[call-overload]
np.ma.argmax(MAR_1d_f8, out=1.0) # type: ignore[call-overload]
np.ma.argmax(MAR_1d_f8, fill_value=lambda x: 27) # type: ignore[call-overload]
MAR_1d_f8.all(axis=1.0) # type: ignore[call-overload]
MAR_1d_f8.all(keepdims=1.0) # type: ignore[call-overload]
MAR_1d_f8.all(out=1.0) # type: ignore[call-overload]
MAR_1d_f8.any(axis=1.0) # type: ignore[call-overload]
MAR_1d_f8.any(keepdims=1.0) # type: ignore[call-overload]
MAR_1d_f8.any(out=1.0) # type: ignore[call-overload]
MAR_1d_f8.sort(axis=(0,1)) # type: ignore[arg-type]
MAR_1d_f8.sort(axis=None) # type: ignore[arg-type]
MAR_1d_f8.sort(kind='cabbage') # type: ignore[arg-type]
MAR_1d_f8.sort(order=lambda: 'cabbage') # type: ignore[arg-type]
MAR_1d_f8.sort(endwith='cabbage') # type: ignore[arg-type]
MAR_1d_f8.sort(fill_value=lambda: 'cabbage') # type: ignore[arg-type]
MAR_1d_f8.sort(stable='cabbage') # type: ignore[arg-type]
MAR_1d_f8.sort(stable=True) # type: ignore[arg-type]
MAR_1d_f8.take(axis=1.0) # type: ignore[call-overload]
MAR_1d_f8.take(out=1) # type: ignore[call-overload]
MAR_1d_f8.take(mode="bob") # type: ignore[call-overload]
np.ma.take(None) # type: ignore[call-overload]
np.ma.take(axis=1.0) # type: ignore[call-overload]
np.ma.take(out=1) # type: ignore[call-overload]
np.ma.take(mode="bob") # type: ignore[call-overload]
MAR_1d_f8.partition(['cabbage']) # type: ignore[arg-type]
MAR_1d_f8.partition(axis=(0,1)) # type: ignore[arg-type, call-arg]
MAR_1d_f8.partition(kind='cabbage') # type: ignore[arg-type, call-arg]
MAR_1d_f8.partition(order=lambda: 'cabbage') # type: ignore[arg-type, call-arg]
MAR_1d_f8.partition(AR_b) # type: ignore[arg-type]
MAR_1d_f8.argpartition(['cabbage']) # type: ignore[arg-type]
MAR_1d_f8.argpartition(axis=(0,1)) # type: ignore[arg-type, call-arg]
MAR_1d_f8.argpartition(kind='cabbage') # type: ignore[arg-type, call-arg]
MAR_1d_f8.argpartition(order=lambda: 'cabbage') # type: ignore[arg-type, call-arg]
MAR_1d_f8.argpartition(AR_b) # type: ignore[arg-type]
np.ma.ndim(lambda: 'lambda') # type: ignore[arg-type]
np.ma.size(AR_b, axis='0') # type: ignore[arg-type]
MAR_1d_f8 >= (lambda x: 'mango') # type: ignore[operator]
MAR_1d_f8 > (lambda x: 'mango') # type: ignore[operator]
MAR_1d_f8 <= (lambda x: 'mango') # type: ignore[operator]
MAR_1d_f8 < (lambda x: 'mango') # type: ignore[operator]
MAR_1d_f8.count(axis=0.) # type: ignore[call-overload]
np.ma.count(MAR_1d_f8, axis=0.) # type: ignore[call-overload]
MAR_1d_f8.put(4, 999, mode='flip') # type: ignore[arg-type]
np.ma.put(MAR_1d_f8, 4, 999, mode='flip') # type: ignore[arg-type]
np.ma.put([1,1,3], 0, 999) # type: ignore[arg-type]
np.ma.compressed(lambda: 'compress me') # type: ignore[call-overload]
np.ma.allequal(MAR_1d_f8, [1,2,3], fill_value=1.5) # type: ignore[arg-type]
np.ma.allclose(MAR_1d_f8, [1,2,3], masked_equal=4.5) # type: ignore[arg-type]
np.ma.allclose(MAR_1d_f8, [1,2,3], rtol='.4') # type: ignore[arg-type]
np.ma.allclose(MAR_1d_f8, [1,2,3], atol='.5') # type: ignore[arg-type]
MAR_1d_f8.__setmask__('mask') # type: ignore[arg-type]
MAR_b *= 2 # type: ignore[arg-type]
MAR_c //= 2 # type: ignore[misc]
MAR_td64 **= 2 # type: ignore[misc]
MAR_1d_f8.swapaxes(axis1=1, axis2=0) # type: ignore[call-arg]
|
