ó ¦–Õ\c @`sëddlmZmZmZddlmZddlZddlZy¿ej ƒ­ej ej ddddƒej dddddej ƒƒ sØej ej dd dd ƒd ƒ sØej ej ddƒdƒ rçe d ƒ‚nej Z ejj ZWdQXWnJe k rOddd „Z ejjje ejjjƒddƒZnXeejƒdkrzd„Zd„Znd„Zd„ZeejƒedƒksÂeejƒedƒkrËeZneZdefd„ƒYZdS(i(tabsolute_importtdivisiontprint_function(t LooseVersionNgš™™™™™Ù?itcastingtunsafetdtypegà?ti8isIDivide not working with dtype: https://github.com/numpy/numpy/issues/3484cC`s7tj|||ƒ}|dk r3|j|ƒ}n|S(sÍImplementation of numpy.divide that works with dtype kwarg. Temporary compatibility fix for a bug in numpy's version. See https://github.com/numpy/numpy/issues/3484 for the relevant issue.N(tnptdividetNonetastype(tx1tx2toutRtx((s6lib/python2.7/site-packages/dask/array/numpy_compat.pyR s s1.15.0c C`stj|jtjƒs'tdƒ‚nt|ƒ|jkrKtdƒ‚nd|j}tt |ƒƒdgtt |d|jƒƒ}g}xrt ||ƒD]a\}}|dkrÉ|j |ƒq¡|| d||d}|j tj |ƒj|ƒƒq¡Wt|ƒS(Ns"`indices` must be an integer arrays;`indices` and `arr` must have the same number of dimensionsiiÿÿÿÿ(i(iÿÿÿÿ(Rt issubdtypeRtintegert IndexErrortlentndimt ValueErrortlisttrangeR tziptappendtarangetreshapettuple( t arr_shapetindicestaxist shape_onest dest_dimst fancy_indextdimtnt ind_shape((s6lib/python2.7/site-packages/dask/array/numpy_compat.pyt_make_along_axis_idx*s   #cC`sf|dkr-|j}t|ƒf}d}n%|dkrI|j|}n|j}|t|||ƒS(sŸ Take values from the input array by matching 1d index and data slices. This iterates over matching 1d slices oriented along the specified axis in the index and data arrays, and uses the former to look up values in the latter. These slices can be different lengths. Functions returning an index along an axis, like `argsort` and `argpartition`, produce suitable indices for this function. .. versionadded:: 1.15.0 Parameters ---------- arr: ndarray (Ni..., M, Nk...) Source array indices: ndarray (Ni..., J, Nk...) Indices to take along each 1d slice of `arr`. This must match the dimension of arr, but dimensions Ni and Nj only need to broadcast against `arr`. axis: int The axis to take 1d slices along. If axis is None, the input array is treated as if it had first been flattened to 1d, for consistency with `sort` and `argsort`. Returns ------- out: ndarray (Ni..., J, Nk...) The indexed result. Notes ----- This is equivalent to (but faster than) the following use of `ndindex` and `s_`, which sets each of ``ii`` and ``kk`` to a tuple of indices:: Ni, M, Nk = a.shape[:axis], a.shape[axis], a.shape[axis+1:] J = indices.shape[axis] # Need not equal M out = np.empty(Nk + (J,) + Nk) for ii in ndindex(Ni): for kk in ndindex(Nk): a_1d = a [ii + s_[:,] + kk] indices_1d = indices[ii + s_[:,] + kk] out_1d = out [ii + s_[:,] + kk] for j in range(J): out_1d[j] = a_1d[indices_1d[j]] Equivalently, eliminating the inner loop, the last two lines would be:: out_1d[:] = a_1d[indices_1d] See Also -------- take : Take along an axis, using the same indices for every 1d slice put_along_axis : Put values into the destination array by matching 1d index and data slices Examples -------- For this sample array >>> a = np.array([[10, 30, 20], [60, 40, 50]]) We can sort either by using sort directly, or argsort and this function >>> np.sort(a, axis=1) array([[10, 20, 30], [40, 50, 60]]) >>> ai = np.argsort(a, axis=1); ai array([[0, 2, 1], [1, 2, 0]]) >>> take_along_axis(a, ai, axis=1) array([[10, 20, 30], [40, 50, 60]]) The same works for max and min, if you expand the dimensions: >>> np.expand_dims(np.max(a, axis=1), axis=1) array([[30], [60]]) >>> ai = np.expand_dims(np.argmax(a, axis=1), axis=1) >>> ai array([[1], [0]]) >>> take_along_axis(a, ai, axis=1) array([[30], [60]]) If we want to get the max and min at the same time, we can stack the indices first: >>> ai_min = np.expand_dims(np.argmin(a, axis=1), axis=1) >>> ai_max = np.expand_dims(np.argmax(a, axis=1), axis=1) >>> ai = np.concatenate([ai_min, ai_max], axis=1) >>> ai array([[0, 1], [1, 0]]) >>> take_along_axis(a, ai, axis=1) array([[10, 30], [40, 60]]) iN(R tflatRRtshapeR&(tarrRRR((s6lib/python2.7/site-packages/dask/array/numpy_compat.pyttake_along_axisAsW     cC`sni|d6g|D]}|j|d^qd6g|D]}|j|d^q6d6|jd6}tj|ƒS(Ntnamesitformatsitoffsetstitemsize(tfieldsR.RR(Rtindextnametnew((s6lib/python2.7/site-packages/dask/array/numpy_compat.pyt_make_sliced_dtype_np_ge_16¥s %% cC`s0tjg|D]}|||f^q ƒ}|S(N(RR(RR0R1tdt((s6lib/python2.7/site-packages/dask/array/numpy_compat.pyt_make_sliced_dtype_np_lt_14¶s,s1.16.0s1.14.0t _RecursercB`s>eZdZd„Zd„d„d„d„Zdd„ZRS(s; Utility class for recursing over nested iterables cC`s ||_dS(N(t recurse_if(tselfR7((s6lib/python2.7/site-packages/dask/array/numpy_compat.pyt__init__ÌscK`s|S(N((Rtkwargs((s6lib/python2.7/site-packages/dask/array/numpy_compat.pytÑtcK`s|S(N((RR:((s6lib/python2.7/site-packages/dask/array/numpy_compat.pyR;ÒR<cK`s|S(N((R:((s6lib/python2.7/site-packages/dask/array/numpy_compat.pyR;ÓR<c `s(‡‡‡‡‡fd†‰ˆ||S(s{ Iterate over the nested list, applying: * ``f_map`` (T -> U) to items * ``f_reduce`` (Iterable[U] -> U) to mapped items For instance, ``map_reduce([[1, 2], 3, 4])`` is:: f_reduce([ f_reduce([ f_map(1), f_map(2) ]), f_map(3), f_map(4) ]]) State can be passed down through the calls with `f_kwargs`, to iterables of mapped items. When kwargs are passed, as in ``map_reduce([[1, 2], 3, 4], **kw)``, this becomes:: kw1 = f_kwargs(**kw) kw2 = f_kwargs(**kw1) f_reduce([ f_reduce([ f_map(1), **kw2) f_map(2, **kw2) ], **kw1), f_map(3, **kw1), f_map(4, **kw1) ]], **kw) c`sLˆj|ƒsˆ||Sˆ|‰ˆ‡‡fd†|Dƒ|SdS(Nc3`s|]}ˆ|ˆVqdS(N((t.0txi(tft next_kwargs(s6lib/python2.7/site-packages/dask/array/numpy_compat.pys ûs(R7(RR:(R?tf_kwargstf_maptf_reduceR8(R@s6lib/python2.7/site-packages/dask/array/numpy_compat.pyR?õs  ((R8RRBRCRAR:((R?RARBRCR8s6lib/python2.7/site-packages/dask/array/numpy_compat.pyt map_reduceÏs& cc`sq|j|ƒ}|||fV|s'dSxCt|ƒD]5\}}x&|j|||fƒD] }|VqZWq4WdS(sB Iterate over x, yielding (index, value, entering), where * ``index``: a tuple of indices up to this point * ``value``: equal to ``x[index[0]][...][index[-1]]``. On the first iteration, is ``x`` itself * ``entering``: bool. The result of ``recurse_if(value)`` N(R7t enumeratetwalk(R8RR0t do_recursetiR>tv((s6lib/python2.7/site-packages/dask/array/numpy_compat.pyRFs  ((t__name__t __module__t__doc__R9RDRF(((s6lib/python2.7/site-packages/dask/array/numpy_compat.pyR6Ãs  -(t __future__RRRtdistutils.versionRtnumpyRtwarningstcatch_warningstallcloseR tfloatt TypeErrortmat ma_divideR tcoret_DomainedBinaryOperationt_DomainSafeDividet __version__R&R*R3R5t_make_sliced_dtypetobjectR6(((s6lib/python2.7/site-packages/dask/array/numpy_compat.pyts6   %%     d