对于想了解对具有多个维度的numpy.argsort进行不变排序的读者，本文将提供新的信息，我们将详细介绍在多维分析中如果对多个维度进行限定，并且为您提供关于API:numpy.argsort、Arg

对于想了解对具有多个维度的numpy.argsort进行不变排序的读者，本文将提供新的信息，我们将详细介绍在多维分析中如果对多个维度进行限定，并且为您提供关于API: numpy.argsort、Argsort.argsort - 它在做什么？、Argsort无法与numba结合使用、arrays – Perl：基于特定列对具有多个列的2D数组进行排序的有价值信息。

• 对具有多个维度的numpy.argsort进行不变排序（在多维分析中如果对多个维度进行限定）
• API: numpy.argsort
• Argsort.argsort - 它在做什么？
• Argsort无法与numba结合使用
• arrays – Perl：基于特定列对具有多个列的2D数组进行排序

对具有多个维度的numpy.argsort进行不变排序（在多维分析中如果对多个维度进行限定）

numpy.argsort
docs状态

返回：
index_array：ndarray，int沿指定轴对a进行排序的索引数组。如果a是一维的，则a[index_array]产生排序的a。

我如何应用numpy.argsort多维数组的结果以返回已排序的数组？（不只是一维或二维数组；它可以是一个N维数组，其中N仅在运行时才知道）

>>> import numpy as np>>> np.random.seed(123)>>> A = np.random.randn(3,2)>>> Aarray([[-1.0856306 ,  0.99734545],       [ 0.2829785 , -1.50629471],       [-0.57860025,  1.65143654]])>>> i=np.argsort(A,axis=-1)>>> A[i]array([[[-1.0856306 ,  0.99734545],        [ 0.2829785 , -1.50629471]],       [[ 0.2829785 , -1.50629471],        [-1.0856306 ,  0.99734545]],       [[-1.0856306 ,  0.99734545],        [ 0.2829785 , -1.50629471]]])

对我来说，这不仅仅是使用的问题sort()；我有另一个数组B，我想B使用np.argsort(A)沿适当轴的结果进行排序。考虑以下示例：

>>> A = np.array([[3,2,1],[4,0,6]])>>> B = np.array([[3,1,4],[1,5,9]])>>> i = np.argsort(A,axis=-1)>>> BsortA = ???             # should result in [[4,1,3],[5,1,9]]# so that corresponding elements of B and sort(A) stay together

似乎此功能已经是numpy中的增强请求。

答案1

该numpy的问题＃8708具有take_along_axis的样本实现，做什么，我需要;
我不确定大型阵列是否有效，但似乎可行。

def take_along_axis(arr, ind, axis):    """    ... here means a "pack" of dimensions, possibly empty    arr: array_like of shape (A..., M, B...)        source array    ind: array_like of shape (A..., K..., B...)        indices to take along each 1d slice of `arr`    axis: int        index of the axis with dimension M    out: array_like of shape (A..., K..., B...)        out[a..., k..., b...] = arr[a..., inds[a..., k..., b...], b...]    """    if axis < 0:       if axis >= -arr.ndim:           axis += arr.ndim       else:           raise IndexError(''axis out of range'')    ind_shape = (1,) * ind.ndim    ins_ndim = ind.ndim - (arr.ndim - 1)   #inserted dimensions    dest_dims = list(range(axis)) + [None] + list(range(axis+ins_ndim, ind.ndim))    # could also call np.ix_ here with some dummy arguments, then throw those results away    inds = []    for dim, n in zip(dest_dims, arr.shape):        if dim is None:            inds.append(ind)        else:            ind_shape_dim = ind_shape[:dim] + (-1,) + ind_shape[dim+1:]            inds.append(np.arange(n).reshape(ind_shape_dim))    return arr[tuple(inds)]

产生

>>> A = np.array([[3,2,1],[4,0,6]])>>> B = np.array([[3,1,4],[1,5,9]])>>> i = A.argsort(axis=-1)>>> take_along_axis(A,i,axis=-1)array([[1, 2, 3],       [0, 4, 6]])>>> take_along_axis(B,i,axis=-1)array([[4, 1, 3],       [5, 1, 9]])

API: numpy.argsort

参考网站: http://docs.scipy.org/doc/numpy/reference/index.html

摘自: 《NumPy Reference, Release 1.7.0》

Argsort.argsort - 它在做什么？

x = numpy.array([-1,5,-2,0])

print x.argsort().argsort()

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x = numpy.array([-1,5,-2,0])
print(x.argsort())  # [2 0 3 1]

x.argsort().argsort()

x.argsort()
# array([2,3,1])
x.argsort().argsort()
# array([1,2])
# To sort the array x.argsort(),you start with index 1 which hold the value 0,and so on

@jit
def sortVertices(coord):
    yr = coord[:,1]
    xc = coord[:,0]
    xl = np.array([xc.size])
    yl = np.array([yr.size])
    center_xc = np.sum(xc)/xl
    center_yr = np.sum(yr)/yl
    theta = np.arctan2(yr-center_yr,xc-center_xc) * 180.0 / np.pi
    indices = np.argsort(theta)
    x = xc[indices]
    y = yr[indices]
    coord_new = np.vstack((x,y)).T
    return coord_new

NumbaWarning: 
Compilation is falling back to object mode WITH looplifting enabled because Function sortVertices Failed at nopython mode lowering due to: Failed in nopython mode pipeline (step: nopython frontend)
No implementation of function Function(<function make_quicksort_impl.<locals>.run_quicksort at 0x1298bc9d8>) found for signature:
 
run_quicksort(array(float64,1d,C))
 
There are 2 candidate implementations:
  - Of which 2 did not match due to:
  Overload in function ''register_jitable.<locals>.wrap.<locals>.ov_wrap'': File: numba/core/extending.py: Line 150.
    With argument(s): ''(array(float64,C))'':
   Rejected as the implementation raised a specific error:
     AttributeError: ''function'' object has no attribute ''get_call_template''
  raised from /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/core/types/functions.py:511\

my @unsortedArray = { ["Harry",10],["Tim",8],["Joe",3]};
my @sortedArray = ?????

sortedArray should be { ["Joe",3],["Harry",10] };

my @unsorted = ( ["Harry",3] );

my @sorted = sort { $a->[1] <=> $b->[1] } @unsorted;