nptyping.array.Array.dump() - Code Metrics - Inspection of "Added array methods to Array for proper hints." - ramonhagenaars/nptyping - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Pull Request — master (#4)

by Ramon

created 2019-06-04 19:31 UTC

nptyping.array.Array.dump() A

↳ Parent: nptyping.array

Complexity

Conditions

Size

Total Lines	2
Code Lines	2

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	2
nop	1
dl	0
loc	2
rs	10
c	0
b	0
f	0

"""
The array module: support for typing Numpy ndarrays.
"""
import functools
import numpy as np
from nptyping import _array_meta


class Array(metaclass=_array_meta.meta()):
    """
    A representation of the `numpy.ndarray`.

    Example of an array with an undefined generic type and shape:
        `Array`

    Example of an array with a defined generic type:
        `Array[int]`

    Example of an array with a defined generic type and shape (rows):
        `Array[int, 3]`
        `Array[int, 3, ...]`
        `Array[int, 3, None]`

    Examples of an array with a defined generic type and shape (cols):
        `Array[int, None, 2]`
        `Array[int, ..., 2]`

    Example of an array with a defined generic type and shape (rows and cols):
        `Array[int, 3, 2]`

    """
    @functools.wraps(np.ndarray.__abs__)
    def __abs__(self): ...

    @functools.wraps(np.ndarray.__add__)
    def __add__(self): ...

    @functools.wraps(np.ndarray.__and__)
    def __and__(self): ...

    @functools.wraps(np.ndarray.__array__)
    def __array__(self): ...

    @functools.wraps(np.ndarray.__array_finalize__)
    def __array_finalize__(self): ...

    @functools.wraps(np.ndarray.__array_function__)
    def __array_function__(self): ...

    @functools.wraps(np.ndarray.__array_interface__)
    def __array_interface__(self): ...

    @functools.wraps(np.ndarray.__array_prepare__)
    def __array_prepare__(self): ...

    @functools.wraps(np.ndarray.__array_priority__)
    def __array_priority__(self): ...

    @functools.wraps(np.ndarray.__array_struct__)
    def __array_struct__(self): ...

    @functools.wraps(np.ndarray.__array_ufunc__)
    def __array_ufunc__(self): ...

    @functools.wraps(np.ndarray.__array_wrap__)
    def __array_wrap__(self): ...

    @functools.wraps(np.ndarray.__bool__)
    def __bool__(self): ...

    @functools.wraps(np.ndarray.__complex__)
    def __complex__(self): ...

    @functools.wraps(np.ndarray.__contains__)
    def __contains__(self): ...

    @functools.wraps(np.ndarray.__copy__)
    def __copy__(self): ...

    @functools.wraps(np.ndarray.__deepcopy__)
    def __deepcopy__(self): ...

    @functools.wraps(np.ndarray.__delitem__)
    def __delitem__(self): ...

    @functools.wraps(np.ndarray.__divmod__)
    def __divmod__(self): ...

    @functools.wraps(np.ndarray.__float__)
    def __float__(self): ...

    @functools.wraps(np.ndarray.__floordiv__)
    def __floordiv__(self): ...

    @functools.wraps(np.ndarray.__getitem__)
    def __getitem__(self): ...

    @functools.wraps(np.ndarray.__iadd__)
    def __iadd__(self): ...

    @functools.wraps(np.ndarray.__iand__)
    def __iand__(self): ...

    @functools.wraps(np.ndarray.__ifloordiv__)
    def __ifloordiv__(self): ...

    @functools.wraps(np.ndarray.__ilshift__)
    def __ilshift__(self): ...

    @functools.wraps(np.ndarray.__imatmul__)
    def __imatmul__(self): ...

    @functools.wraps(np.ndarray.__imod__)
    def __imod__(self): ...

    @functools.wraps(np.ndarray.__imul__)
    def __imul__(self): ...

    @functools.wraps(np.ndarray.__index__)
    def __index__(self): ...

    @functools.wraps(np.ndarray.__int__)
    def __int__(self): ...

    @functools.wraps(np.ndarray.__invert__)
    def __invert__(self): ...

    @functools.wraps(np.ndarray.__ior__)
    def __ior__(self): ...

    @functools.wraps(np.ndarray.__ipow__)
    def __ipow__(self): ...

    @functools.wraps(np.ndarray.__irshift__)
    def __irshift__(self): ...

    @functools.wraps(np.ndarray.__isub__)
    def __isub__(self): ...

    @functools.wraps(np.ndarray.__iter__)
    def __iter__(self): ...

    @functools.wraps(np.ndarray.__itruediv__)
    def __itruediv__(self): ...

    @functools.wraps(np.ndarray.__ixor__)
    def __ixor__(self): ...

    @functools.wraps(np.ndarray.__len__)
    def __len__(self): ...

    @functools.wraps(np.ndarray.__lshift__)
    def __lshift__(self): ...

    @functools.wraps(np.ndarray.__matmul__)
    def __matmul__(self): ...

    @functools.wraps(np.ndarray.__mod__)
    def __mod__(self): ...

    @functools.wraps(np.ndarray.__mul__)
    def __mul__(self): ...

    @functools.wraps(np.ndarray.__neg__)
    def __neg__(self): ...

    @functools.wraps(np.ndarray.__or__)
    def __or__(self): ...

    @functools.wraps(np.ndarray.__pos__)
    def __pos__(self): ...

    @functools.wraps(np.ndarray.__pow__)
    def __pow__(self): ...

    @functools.wraps(np.ndarray.__radd__)
    def __radd__(self): ...

    @functools.wraps(np.ndarray.__rand__)
    def __rand__(self): ...

    @functools.wraps(np.ndarray.__rdivmod__)
    def __rdivmod__(self): ...

    @functools.wraps(np.ndarray.__rfloordiv__)
    def __rfloordiv__(self): ...

    @functools.wraps(np.ndarray.__rlshift__)
    def __rlshift__(self): ...

    @functools.wraps(np.ndarray.__rmatmul__)
    def __rmatmul__(self): ...

    @functools.wraps(np.ndarray.__rmod__)
    def __rmod__(self): ...

    @functools.wraps(np.ndarray.__rmul__)
    def __rmul__(self): ...

    @functools.wraps(np.ndarray.__ror__)
    def __ror__(self): ...

    @functools.wraps(np.ndarray.__rpow__)
    def __rpow__(self): ...

    @functools.wraps(np.ndarray.__rrshift__)
    def __rrshift__(self): ...

    @functools.wraps(np.ndarray.__rshift__)
    def __rshift__(self): ...

    @functools.wraps(np.ndarray.__rsub__)
    def __rsub__(self): ...

    @functools.wraps(np.ndarray.__rtruediv__)
    def __rtruediv__(self): ...

    @functools.wraps(np.ndarray.__rxor__)
    def __rxor__(self): ...

    @functools.wraps(np.ndarray.__setitem__)
    def __setitem__(self): ...

    @functools.wraps(np.ndarray.__setstate__)
    def __setstate__(self): ...

    @functools.wraps(np.ndarray.__sub__)
    def __sub__(self): ...

    @functools.wraps(np.ndarray.__truediv__)
    def __truediv__(self): ...

    @functools.wraps(np.ndarray.__xor__)
    def __xor__(self): ...

    @functools.wraps(np.ndarray.all)
    def all(self): ...

    @functools.wraps(np.ndarray.any)
    def any(self): ...

    @functools.wraps(np.ndarray.argmax)
    def argmax(self): ...

    @functools.wraps(np.ndarray.argmin)
    def argmin(self): ...

    @functools.wraps(np.ndarray.argpartition)
    def argpartition(self): ...

    @functools.wraps(np.ndarray.argsort)
    def argsort(self): ...

    @functools.wraps(np.ndarray.astype)
    def astype(self): ...

    @functools.wraps(np.ndarray.base)
    def base(self): ...

    @functools.wraps(np.ndarray.byteswap)
    def byteswap(self): ...

    @functools.wraps(np.ndarray.choose)
    def choose(self): ...

    @functools.wraps(np.ndarray.clip)
    def clip(self): ...

    @functools.wraps(np.ndarray.compress)
    def compress(self): ...

    @functools.wraps(np.ndarray.conj)
    def conj(self): ...

    @functools.wraps(np.ndarray.conjugate)
    def conjugate(self): ...

    @functools.wraps(np.ndarray.copy)
    def copy(self): ...

    @functools.wraps(np.ndarray.ctypes)
    def ctypes(self): ...

    @functools.wraps(np.ndarray.cumprod)
    def cumprod(self): ...

    @functools.wraps(np.ndarray.cumsum)
    def cumsum(self): ...

    @functools.wraps(np.ndarray.data)
    def data(self): ...

    @functools.wraps(np.ndarray.diagonal)
    def diagonal(self): ...

    @functools.wraps(np.ndarray.dot)
    def dot(self): ...

    @functools.wraps(np.ndarray.dtype)
    def dtype(self): ...

    @functools.wraps(np.ndarray.dump)
    def dump(self): ...

    @functools.wraps(np.ndarray.dumps)
    def dumps(self): ...

    @functools.wraps(np.ndarray.fill)
    def fill(self): ...

    @functools.wraps(np.ndarray.flags)
    def flags(self): ...

    @functools.wraps(np.ndarray.flat)
    def flat(self): ...

    @functools.wraps(np.ndarray.flatten)
    def flatten(self): ...

    @functools.wraps(np.ndarray.getfield)
    def getfield(self): ...

    @functools.wraps(np.ndarray.imag)
    def imag(self): ...

    @functools.wraps(np.ndarray.item)
    def item(self): ...

    @functools.wraps(np.ndarray.itemset)
    def itemset(self): ...

    @functools.wraps(np.ndarray.itemsize)
    def itemsize(self): ...

    @functools.wraps(np.ndarray.max)
    def max(self): ...

    @functools.wraps(np.ndarray.mean)
    def mean(self): ...

    @functools.wraps(np.ndarray.min)
    def min(self): ...

    @functools.wraps(np.ndarray.nbytes)
    def nbytes(self): ...

    @functools.wraps(np.ndarray.ndim)
    def ndim(self): ...

    @functools.wraps(np.ndarray.newbyteorder)
    def newbyteorder(self): ...

    @functools.wraps(np.ndarray.nonzero)
    def nonzero(self): ...

    @functools.wraps(np.ndarray.partition)
    def partition(self): ...

    @functools.wraps(np.ndarray.prod)
    def prod(self): ...

    @functools.wraps(np.ndarray.ptp)
    def ptp(self): ...

    @functools.wraps(np.ndarray.put)
    def put(self): ...

    @functools.wraps(np.ndarray.ravel)
    def ravel(self): ...

    @functools.wraps(np.ndarray.real)
    def real(self): ...

    @functools.wraps(np.ndarray.repeat)
    def repeat(self): ...

    @functools.wraps(np.ndarray.reshape)
    def reshape(self): ...

    @functools.wraps(np.ndarray.resize)
    def resize(self): ...

    @functools.wraps(np.ndarray.round)
    def round(self): ...

    @functools.wraps(np.ndarray.searchsorted)
    def searchsorted(self): ...

    @functools.wraps(np.ndarray.setfield)
    def setfield(self): ...

    @functools.wraps(np.ndarray.setflags)
    def setflags(self): ...

    @functools.wraps(np.ndarray.shape)
    def shape(self): ...

    @functools.wraps(np.ndarray.size)
    def size(self): ...

    @functools.wraps(np.ndarray.sort)
    def sort(self): ...

    @functools.wraps(np.ndarray.squeeze)
    def squeeze(self): ...

    @functools.wraps(np.ndarray.std)
    def std(self): ...

    @functools.wraps(np.ndarray.strides)
    def strides(self): ...

    @functools.wraps(np.ndarray.sum)
    def sum(self): ...

    @functools.wraps(np.ndarray.swapaxes)
    def swapaxes(self): ...

    @functools.wraps(np.ndarray.take)
    def take(self): ...

    @functools.wraps(np.ndarray.tobytes)
    def tobytes(self): ...

    @functools.wraps(np.ndarray.tofile)
    def tofile(self): ...

    @functools.wraps(np.ndarray.tolist)
    def tolist(self): ...

    @functools.wraps(np.ndarray.tostring)
    def tostring(self): ...

    @functools.wraps(np.ndarray.trace)
    def trace(self): ...

    @functools.wraps(np.ndarray.transpose)
    def transpose(self): ...

    @functools.wraps(np.ndarray.var)
    def var(self): ...

    @functools.wraps(np.ndarray.view)
    def view(self): ...


1			"""
2			The array module: support for typing Numpy ndarrays.
3			"""
4			import functools
5			import numpy as np
6			from nptyping import _array_meta
7
8
9			class Array(metaclass=_array_meta.meta()):
10			"""
11			A representation of the `numpy.ndarray`.
12
13			Example of an array with an undefined generic type and shape:
14			`Array`
15
16			Example of an array with a defined generic type:
17			`Array[int]`
18
19			Example of an array with a defined generic type and shape (rows):
20			`Array[int, 3]`
21			`Array[int, 3, ...]`
22			`Array[int, 3, None]`
23
24			Examples of an array with a defined generic type and shape (cols):
25			`Array[int, None, 2]`
26			`Array[int, ..., 2]`
27
28			Example of an array with a defined generic type and shape (rows and cols):
29			`Array[int, 3, 2]`
30
31			"""
32			@functools.wraps(np.ndarray.__abs__)
33			def __abs__(self): ...
34
35			@functools.wraps(np.ndarray.__add__)
36			def __add__(self): ...
37
38			@functools.wraps(np.ndarray.__and__)
39			def __and__(self): ...
40
41			@functools.wraps(np.ndarray.__array__)
42			def __array__(self): ...
43
44			@functools.wraps(np.ndarray.__array_finalize__)
45			def __array_finalize__(self): ...
46
47			@functools.wraps(np.ndarray.__array_function__)
48			def __array_function__(self): ...
49
50			@functools.wraps(np.ndarray.__array_interface__)
51			def __array_interface__(self): ...
52
53			@functools.wraps(np.ndarray.__array_prepare__)
54			def __array_prepare__(self): ...
55
56			@functools.wraps(np.ndarray.__array_priority__)
57			def __array_priority__(self): ...
58
59			@functools.wraps(np.ndarray.__array_struct__)
60			def __array_struct__(self): ...
61
62			@functools.wraps(np.ndarray.__array_ufunc__)
63			def __array_ufunc__(self): ...
64
65			@functools.wraps(np.ndarray.__array_wrap__)
66			def __array_wrap__(self): ...
67
68			@functools.wraps(np.ndarray.__bool__)
69			def __bool__(self): ...
70
71			@functools.wraps(np.ndarray.__complex__)
72			def __complex__(self): ...
73
74			@functools.wraps(np.ndarray.__contains__)
75			def __contains__(self): ...
76
77			@functools.wraps(np.ndarray.__copy__)
78			def __copy__(self): ...
79
80			@functools.wraps(np.ndarray.__deepcopy__)
81			def __deepcopy__(self): ...
82
83			@functools.wraps(np.ndarray.__delitem__)
84			def __delitem__(self): ...
85
86			@functools.wraps(np.ndarray.__divmod__)
87			def __divmod__(self): ...
88
89			@functools.wraps(np.ndarray.__float__)
90			def __float__(self): ...
91
92			@functools.wraps(np.ndarray.__floordiv__)
93			def __floordiv__(self): ...
94
95			@functools.wraps(np.ndarray.__getitem__)
96			def __getitem__(self): ...
97
98			@functools.wraps(np.ndarray.__iadd__)
99			def __iadd__(self): ...
100
101			@functools.wraps(np.ndarray.__iand__)
102			def __iand__(self): ...
103
104			@functools.wraps(np.ndarray.__ifloordiv__)
105			def __ifloordiv__(self): ...
106
107			@functools.wraps(np.ndarray.__ilshift__)
108			def __ilshift__(self): ...
109
110			@functools.wraps(np.ndarray.__imatmul__)
111			def __imatmul__(self): ...
112
113			@functools.wraps(np.ndarray.__imod__)
114			def __imod__(self): ...
115
116			@functools.wraps(np.ndarray.__imul__)
117			def __imul__(self): ...
118
119			@functools.wraps(np.ndarray.__index__)
120			def __index__(self): ...
121
122			@functools.wraps(np.ndarray.__int__)
123			def __int__(self): ...
124
125			@functools.wraps(np.ndarray.__invert__)
126			def __invert__(self): ...
127
128			@functools.wraps(np.ndarray.__ior__)
129			def __ior__(self): ...
130
131			@functools.wraps(np.ndarray.__ipow__)
132			def __ipow__(self): ...
133
134			@functools.wraps(np.ndarray.__irshift__)
135			def __irshift__(self): ...
136
137			@functools.wraps(np.ndarray.__isub__)
138			def __isub__(self): ...
139
140			@functools.wraps(np.ndarray.__iter__)
141			def __iter__(self): ...
142
143			@functools.wraps(np.ndarray.__itruediv__)
144			def __itruediv__(self): ...
145
146			@functools.wraps(np.ndarray.__ixor__)
147			def __ixor__(self): ...
148
149			@functools.wraps(np.ndarray.__len__)
150			def __len__(self): ...
151
152			@functools.wraps(np.ndarray.__lshift__)
153			def __lshift__(self): ...
154
155			@functools.wraps(np.ndarray.__matmul__)
156			def __matmul__(self): ...
157
158			@functools.wraps(np.ndarray.__mod__)
159			def __mod__(self): ...
160
161			@functools.wraps(np.ndarray.__mul__)
162			def __mul__(self): ...
163
164			@functools.wraps(np.ndarray.__neg__)
165			def __neg__(self): ...
166
167			@functools.wraps(np.ndarray.__or__)
168			def __or__(self): ...
169
170			@functools.wraps(np.ndarray.__pos__)
171			def __pos__(self): ...
172
173			@functools.wraps(np.ndarray.__pow__)
174			def __pow__(self): ...
175
176			@functools.wraps(np.ndarray.__radd__)
177			def __radd__(self): ...
178
179			@functools.wraps(np.ndarray.__rand__)
180			def __rand__(self): ...
181
182			@functools.wraps(np.ndarray.__rdivmod__)
183			def __rdivmod__(self): ...
184
185			@functools.wraps(np.ndarray.__rfloordiv__)
186			def __rfloordiv__(self): ...
187
188			@functools.wraps(np.ndarray.__rlshift__)
189			def __rlshift__(self): ...
190
191			@functools.wraps(np.ndarray.__rmatmul__)
192			def __rmatmul__(self): ...
193
194			@functools.wraps(np.ndarray.__rmod__)
195			def __rmod__(self): ...
196
197			@functools.wraps(np.ndarray.__rmul__)
198			def __rmul__(self): ...
199
200			@functools.wraps(np.ndarray.__ror__)
201			def __ror__(self): ...
202
203			@functools.wraps(np.ndarray.__rpow__)
204			def __rpow__(self): ...
205
206			@functools.wraps(np.ndarray.__rrshift__)
207			def __rrshift__(self): ...
208
209			@functools.wraps(np.ndarray.__rshift__)
210			def __rshift__(self): ...
211
212			@functools.wraps(np.ndarray.__rsub__)
213			def __rsub__(self): ...
214
215			@functools.wraps(np.ndarray.__rtruediv__)
216			def __rtruediv__(self): ...
217
218			@functools.wraps(np.ndarray.__rxor__)
219			def __rxor__(self): ...
220
221			@functools.wraps(np.ndarray.__setitem__)
222			def __setitem__(self): ...
223
224			@functools.wraps(np.ndarray.__setstate__)
225			def __setstate__(self): ...
226
227			@functools.wraps(np.ndarray.__sub__)
228			def __sub__(self): ...
229
230			@functools.wraps(np.ndarray.__truediv__)
231			def __truediv__(self): ...
232
233			@functools.wraps(np.ndarray.__xor__)
234			def __xor__(self): ...
235
236			@functools.wraps(np.ndarray.all)
237			def all(self): ...
238
239			@functools.wraps(np.ndarray.any)
240			def any(self): ...
241
242			@functools.wraps(np.ndarray.argmax)
243			def argmax(self): ...
244
245			@functools.wraps(np.ndarray.argmin)
246			def argmin(self): ...
247
248			@functools.wraps(np.ndarray.argpartition)
249			def argpartition(self): ...
250
251			@functools.wraps(np.ndarray.argsort)
252			def argsort(self): ...
253
254			@functools.wraps(np.ndarray.astype)
255			def astype(self): ...
256
257			@functools.wraps(np.ndarray.base)
258			def base(self): ...
259
260			@functools.wraps(np.ndarray.byteswap)
261			def byteswap(self): ...
262
263			@functools.wraps(np.ndarray.choose)
264			def choose(self): ...
265
266			@functools.wraps(np.ndarray.clip)
267			def clip(self): ...
268
269			@functools.wraps(np.ndarray.compress)
270			def compress(self): ...
271
272			@functools.wraps(np.ndarray.conj)
273			def conj(self): ...
274
275			@functools.wraps(np.ndarray.conjugate)
276			def conjugate(self): ...
277
278			@functools.wraps(np.ndarray.copy)
279			def copy(self): ...
280
281			@functools.wraps(np.ndarray.ctypes)
282			def ctypes(self): ...
283
284			@functools.wraps(np.ndarray.cumprod)
285			def cumprod(self): ...
286
287			@functools.wraps(np.ndarray.cumsum)
288			def cumsum(self): ...
289
290			@functools.wraps(np.ndarray.data)
291			def data(self): ...
292
293			@functools.wraps(np.ndarray.diagonal)
294			def diagonal(self): ...
295
296			@functools.wraps(np.ndarray.dot)
297			def dot(self): ...
298
299			@functools.wraps(np.ndarray.dtype)
300			def dtype(self): ...
301
302			@functools.wraps(np.ndarray.dump)
303			def dump(self): ...
304
305			@functools.wraps(np.ndarray.dumps)
306			def dumps(self): ...
307
308			@functools.wraps(np.ndarray.fill)
309			def fill(self): ...
310
311			@functools.wraps(np.ndarray.flags)
312			def flags(self): ...
313
314			@functools.wraps(np.ndarray.flat)
315			def flat(self): ...
316
317			@functools.wraps(np.ndarray.flatten)
318			def flatten(self): ...
319
320			@functools.wraps(np.ndarray.getfield)
321			def getfield(self): ...
322
323			@functools.wraps(np.ndarray.imag)
324			def imag(self): ...
325
326			@functools.wraps(np.ndarray.item)
327			def item(self): ...
328
329			@functools.wraps(np.ndarray.itemset)
330			def itemset(self): ...
331
332			@functools.wraps(np.ndarray.itemsize)
333			def itemsize(self): ...
334
335			@functools.wraps(np.ndarray.max)
336			def max(self): ...
337
338			@functools.wraps(np.ndarray.mean)
339			def mean(self): ...
340
341			@functools.wraps(np.ndarray.min)
342			def min(self): ...
343
344			@functools.wraps(np.ndarray.nbytes)
345			def nbytes(self): ...
346
347			@functools.wraps(np.ndarray.ndim)
348			def ndim(self): ...
349
350			@functools.wraps(np.ndarray.newbyteorder)
351			def newbyteorder(self): ...
352
353			@functools.wraps(np.ndarray.nonzero)
354			def nonzero(self): ...
355
356			@functools.wraps(np.ndarray.partition)
357			def partition(self): ...
358
359			@functools.wraps(np.ndarray.prod)
360			def prod(self): ...
361
362			@functools.wraps(np.ndarray.ptp)
363			def ptp(self): ...
364
365			@functools.wraps(np.ndarray.put)
366			def put(self): ...
367
368			@functools.wraps(np.ndarray.ravel)
369			def ravel(self): ...
370
371			@functools.wraps(np.ndarray.real)
372			def real(self): ...
373
374			@functools.wraps(np.ndarray.repeat)
375			def repeat(self): ...
376
377			@functools.wraps(np.ndarray.reshape)
378			def reshape(self): ...
379
380			@functools.wraps(np.ndarray.resize)
381			def resize(self): ...
382
383			@functools.wraps(np.ndarray.round)
384			def round(self): ...
385
386			@functools.wraps(np.ndarray.searchsorted)
387			def searchsorted(self): ...
388
389			@functools.wraps(np.ndarray.setfield)
390			def setfield(self): ...
391
392			@functools.wraps(np.ndarray.setflags)
393			def setflags(self): ...
394
395			@functools.wraps(np.ndarray.shape)
396			def shape(self): ...
397
398			@functools.wraps(np.ndarray.size)
399			def size(self): ...
400
401			@functools.wraps(np.ndarray.sort)
402			def sort(self): ...
403
404			@functools.wraps(np.ndarray.squeeze)
405			def squeeze(self): ...
406
407			@functools.wraps(np.ndarray.std)
408			def std(self): ...
409
410			@functools.wraps(np.ndarray.strides)
411			def strides(self): ...
412
413			@functools.wraps(np.ndarray.sum)
414			def sum(self): ...
415
416			@functools.wraps(np.ndarray.swapaxes)
417			def swapaxes(self): ...
418
419			@functools.wraps(np.ndarray.take)
420			def take(self): ...
421
422			@functools.wraps(np.ndarray.tobytes)
423			def tobytes(self): ...
424
425			@functools.wraps(np.ndarray.tofile)
426			def tofile(self): ...
427
428			@functools.wraps(np.ndarray.tolist)
429			def tolist(self): ...
430
431			@functools.wraps(np.ndarray.tostring)
432			def tostring(self): ...
433
434			@functools.wraps(np.ndarray.trace)
435			def trace(self): ...
436
437			@functools.wraps(np.ndarray.transpose)
438			def transpose(self): ...
439
440			@functools.wraps(np.ndarray.var)
441			def var(self): ...
442
443			@functools.wraps(np.ndarray.view)
444			def view(self): ...
445

ramonhagenaars / nptyping

Pull Request — master (#4)

nptyping.array.Array.dump() A

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