ó ØÆ÷Xc@`s—ddlmZmZmZddddddgZdd lmZdd lmZm Z d „Z d „Z d „Z d„Z d„Zdd„ZdS(i(tdivisiontabsolute_importtprint_functiont atleast_1dt atleast_2dt atleast_3dtvstackthstacktstacki(tnumeric(t asanyarraytnewaxiscG`sg}xT|D]L}t|ƒ}t|jƒdkrF|jdƒ}n|}|j|ƒq Wt|ƒdkrw|dS|SdS(s% Convert inputs to arrays with at least one dimension. Scalar inputs are converted to 1-dimensional arrays, whilst higher-dimensional inputs are preserved. Parameters ---------- arys1, arys2, ... : array_like One or more input arrays. Returns ------- ret : ndarray An array, or list of arrays, each with ``a.ndim >= 1``. Copies are made only if necessary. See Also -------- atleast_2d, atleast_3d Examples -------- >>> np.atleast_1d(1.0) array([ 1.]) >>> x = np.arange(9.0).reshape(3,3) >>> np.atleast_1d(x) array([[ 0., 1., 2.], [ 3., 4., 5.], [ 6., 7., 8.]]) >>> np.atleast_1d(x) is x True >>> np.atleast_1d(1, [3, 4]) [array([1]), array([3, 4])] iiN(R tlentshapetreshapetappend(tarystrestarytresult((s4/tmp/pip-build-X4mzal/numpy/numpy/core/shape_base.pyR s'  cG`s°g}x…|D]}}t|ƒ}t|jƒdkrI|jddƒ}n4t|jƒdkrw|tdd…f}n|}|j|ƒq Wt|ƒdkr¨|dS|SdS(s` View inputs as arrays with at least two dimensions. Parameters ---------- arys1, arys2, ... : array_like One or more array-like sequences. Non-array inputs are converted to arrays. Arrays that already have two or more dimensions are preserved. Returns ------- res, res2, ... : ndarray An array, or list of arrays, each with ``a.ndim >= 2``. Copies are avoided where possible, and views with two or more dimensions are returned. See Also -------- atleast_1d, atleast_3d Examples -------- >>> np.atleast_2d(3.0) array([[ 3.]]) >>> x = np.arange(3.0) >>> np.atleast_2d(x) array([[ 0., 1., 2.]]) >>> np.atleast_2d(x).base is x True >>> np.atleast_2d(1, [1, 2], [[1, 2]]) [array([[1]]), array([[1, 2]]), array([[1, 2]])] iiN(R R R RR R(RRRR((s4/tmp/pip-build-X4mzal/numpy/numpy/core/shape_base.pyR=s%  cG`síg}xÂ|D]º}t|ƒ}t|jƒdkrL|jdddƒ}nnt|jƒdkr}|tdd…tf}n=t|jƒdkr´|dd…dd…tf}n|}|j|ƒq Wt|ƒdkrå|dS|SdS(só View inputs as arrays with at least three dimensions. Parameters ---------- arys1, arys2, ... : array_like One or more array-like sequences. Non-array inputs are converted to arrays. Arrays that already have three or more dimensions are preserved. Returns ------- res1, res2, ... : ndarray An array, or list of arrays, each with ``a.ndim >= 3``. Copies are avoided where possible, and views with three or more dimensions are returned. For example, a 1-D array of shape ``(N,)`` becomes a view of shape ``(1, N, 1)``, and a 2-D array of shape ``(M, N)`` becomes a view of shape ``(M, N, 1)``. See Also -------- atleast_1d, atleast_2d Examples -------- >>> np.atleast_3d(3.0) array([[[ 3.]]]) >>> x = np.arange(3.0) >>> np.atleast_3d(x).shape (1, 3, 1) >>> x = np.arange(12.0).reshape(4,3) >>> np.atleast_3d(x).shape (4, 3, 1) >>> np.atleast_3d(x).base is x.base # x is a reshape, so not base itself True >>> for arr in np.atleast_3d([1, 2], [[1, 2]], [[[1, 2]]]): ... print(arr, arr.shape) ... [[[1] [2]]] (1, 2, 1) [[[1] [2]]] (1, 2, 1) [[[1 2]]] (1, 1, 2) iiNi(R R R RR R(RRRR((s4/tmp/pip-build-X4mzal/numpy/numpy/core/shape_base.pyRqs1  "cC`s)tjg|D]}t|ƒ^q dƒS(sæ Stack arrays in sequence vertically (row wise). Take a sequence of arrays and stack them vertically to make a single array. Rebuild arrays divided by `vsplit`. This function continues to be supported for backward compatibility, but you should prefer ``np.concatenate`` or ``np.stack``. The ``np.stack`` function was added in NumPy 1.10. Parameters ---------- tup : sequence of ndarrays Tuple containing arrays to be stacked. The arrays must have the same shape along all but the first axis. Returns ------- stacked : ndarray The array formed by stacking the given arrays. See Also -------- stack : Join a sequence of arrays along a new axis. hstack : Stack arrays in sequence horizontally (column wise). dstack : Stack arrays in sequence depth wise (along third dimension). concatenate : Join a sequence of arrays along an existing axis. vsplit : Split array into a list of multiple sub-arrays vertically. Notes ----- Equivalent to ``np.concatenate(tup, axis=0)`` if `tup` contains arrays that are at least 2-dimensional. Examples -------- >>> a = np.array([1, 2, 3]) >>> b = np.array([2, 3, 4]) >>> np.vstack((a,b)) array([[1, 2, 3], [2, 3, 4]]) >>> a = np.array([[1], [2], [3]]) >>> b = np.array([[2], [3], [4]]) >>> np.vstack((a,b)) array([[1], [2], [3], [2], [3], [4]]) i(t_nxt concatenateR(ttupt_m((s4/tmp/pip-build-X4mzal/numpy/numpy/core/shape_base.pyR´s6cC`sVg|D]}t|ƒ^q}|djdkrBtj|dƒStj|dƒSdS(s Stack arrays in sequence horizontally (column wise). Take a sequence of arrays and stack them horizontally to make a single array. Rebuild arrays divided by `hsplit`. This function continues to be supported for backward compatibility, but you should prefer ``np.concatenate`` or ``np.stack``. The ``np.stack`` function was added in NumPy 1.10. Parameters ---------- tup : sequence of ndarrays All arrays must have the same shape along all but the second axis. Returns ------- stacked : ndarray The array formed by stacking the given arrays. See Also -------- stack : Join a sequence of arrays along a new axis. vstack : Stack arrays in sequence vertically (row wise). dstack : Stack arrays in sequence depth wise (along third axis). concatenate : Join a sequence of arrays along an existing axis. hsplit : Split array along second axis. Notes ----- Equivalent to ``np.concatenate(tup, axis=1)`` Examples -------- >>> a = np.array((1,2,3)) >>> b = np.array((2,3,4)) >>> np.hstack((a,b)) array([1, 2, 3, 2, 3, 4]) >>> a = np.array([[1],[2],[3]]) >>> b = np.array([[2],[3],[4]]) >>> np.hstack((a,b)) array([[1, 2], [2, 3], [3, 4]]) iiN(RtndimRR(RRtarrs((s4/tmp/pip-build-X4mzal/numpy/numpy/core/shape_base.pyRìs/cC`s g|D]}t|ƒ^q}|s4tdƒ‚ntd„|Dƒƒ}t|ƒdkrktdƒ‚n|djd}| |ko”|knsºdj||ƒ}t|ƒ‚n|dkrÓ||7}ntdƒf|t j f}g|D]}||^q÷}t j |d|ƒS( sþ Join a sequence of arrays along a new axis. The `axis` parameter specifies the index of the new axis in the dimensions of the result. For example, if ``axis=0`` it will be the first dimension and if ``axis=-1`` it will be the last dimension. .. versionadded:: 1.10.0 Parameters ---------- arrays : sequence of array_like Each array must have the same shape. axis : int, optional The axis in the result array along which the input arrays are stacked. Returns ------- stacked : ndarray The stacked array has one more dimension than the input arrays. See Also -------- concatenate : Join a sequence of arrays along an existing axis. split : Split array into a list of multiple sub-arrays of equal size. Examples -------- >>> arrays = [np.random.randn(3, 4) for _ in range(10)] >>> np.stack(arrays, axis=0).shape (10, 3, 4) >>> np.stack(arrays, axis=1).shape (3, 10, 4) >>> np.stack(arrays, axis=2).shape (3, 4, 10) >>> a = np.array([1, 2, 3]) >>> b = np.array([2, 3, 4]) >>> np.stack((a, b)) array([[1, 2, 3], [2, 3, 4]]) >>> np.stack((a, b), axis=-1) array([[1, 2], [2, 3], [3, 4]]) s need at least one array to stackcs`s|]}|jVqdS(N(R (t.0tarr((s4/tmp/pip-build-X4mzal/numpy/numpy/core/shape_base.pys Ysis)all input arrays must have the same shapeis"axis {0} out of bounds [-{1}, {1})taxisN( R t ValueErrortsetR Rtformatt IndexErrortslicetNoneRR R(tarraysRRtshapest result_ndimtmsgtsltexpanded_arrays((s4/tmp/pip-build-X4mzal/numpy/numpy/core/shape_base.pyR"s3  N(t __future__RRRt__all__tR RR R RRRRRR(((s4/tmp/pip-build-X4mzal/numpy/numpy/core/shape_base.pyts  4 4 C 8 6