import functools import sys import math import warnings import numpy as np from .._utils import set_module import numpy.core.numeric as _nx from numpy.core.numeric import ScalarType, array from numpy.core.numerictypes import issubdtype import numpy.matrixlib as matrixlib from .function_base import diff from numpy.core.multiarray import ravel_multi_index, unravel_index from numpy.core import overrides, linspace from numpy.lib.stride_tricks import as_strided array_function_dispatch = functools.partial( overrides.array_function_dispatch, module='numpy') __all__ = [ 'ravel_multi_index', 'unravel_index', 'mgrid', 'ogrid', 'r_', 'c_', 's_', 'index_exp', 'ix_', 'ndenumerate', 'ndindex', 'fill_diagonal', 'diag_indices', 'diag_indices_from' ] def _ix__dispatcher(*args): return args @array_function_dispatch(_ix__dispatcher) def ix_(*args): """ Construct an open mesh from multiple sequences. This function takes N 1-D sequences and returns N outputs with N dimensions each, such that the shape is 1 in all but one dimension and the dimension with the non-unit shape value cycles through all N dimensions. Using `ix_` one can quickly construct index arrays that will index the cross product. ``a[np.ix_([1,3],[2,5])]`` returns the array ``[[a[1,2] a[1,5]], [a[3,2] a[3,5]]]``. Parameters ---------- args : 1-D sequences Each sequence should be of integer or boolean type. Boolean sequences will be interpreted as boolean masks for the corresponding dimension (equivalent to passing in ``np.nonzero(boolean_sequence)``). Returns ------- out : tuple of ndarrays N arrays with N dimensions each, with N the number of input sequences. Together these arrays form an open mesh. See Also -------- ogrid, mgrid, meshgrid Examples -------- >>> a = np.arange(10).reshape(2, 5) >>> a array([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]) >>> ixgrid = np.ix_([0, 1], [2, 4]) >>> ixgrid (array([[0], [1]]), array([[2, 4]])) >>> ixgrid[0].shape, ixgrid[1].shape ((2, 1), (1, 2)) >>> a[ixgrid] array([[2, 4], [7, 9]]) >>> ixgrid = np.ix_([True, True], [2, 4]) >>> a[ixgrid] array([[2, 4], [7, 9]]) >>> ixgrid = np.ix_([True, True], [False, False, True, False, True]) >>> a[ixgrid] array([[2, 4], [7, 9]]) """ out = [] nd = len(args) for k, new in enumerate(args): if not isinstance(new, _nx.ndarray): new = np.asarray(new) if new.size == 0: # Explicitly type empty arrays to avoid float default new = new.astype(_nx.intp) if new.ndim != 1: raise ValueError("Cross index must be 1 dimensional") if issubdtype(new.dtype, _nx.bool_): new, = new.nonzero() new = new.reshape((1,)*k + (new.size,) + (1,)*(nd-k-1)) out.append(new) return tuple(out) class nd_grid: """ Construct a multi-dimensional "meshgrid". ``grid = nd_grid()`` creates an instance which will return a mesh-grid when indexed. The dimension and number of the output arrays are equal to the number of indexing dimensions. If the step length is not a complex number, then the stop is not inclusive. However, if the step length is a **complex number** (e.g. 5j), then the integer part of its magnitude is interpreted as specifying the number of points to create between the start and stop values, where the stop value **is inclusive**. If instantiated with an argument of ``sparse=True``, the mesh-grid is open (or not fleshed out) so that only one-dimension of each returned argument is greater than 1. Parameters ---------- sparse : bool, optional Whether the grid is sparse or not. Default is False. Notes ----- Two instances of `nd_grid` are made available in the NumPy namespace, `mgrid` and `ogrid`, approximately defined as:: mgrid = nd_grid(sparse=False) ogrid = nd_grid(sparse=True) Users should use these pre-defined instances instead of using `nd_grid` directly. """ def __init__(self, sparse=False): self.sparse = sparse def __getitem__(self, key): try: size = [] # Mimic the behavior of `np.arange` and use a data type # which is at least as large as `np.int_` num_list = [0] for k in range(len(key)): step = key[k].step start = key[k].start stop = key[k].stop if start is None: start = 0 if step is None: step = 1 if isinstance(step, (_nx.complexfloating, complex)): step = abs(step) size.append(int(step)) else: size.append( int(math.ceil((stop - start) / (step*1.0)))) num_list += [start, stop, step] typ = _nx.result_type(*num_list) if self.sparse: nn = [_nx.arange(_x, dtype=_t) for _x, _t in zip(size, (typ,)*len(size))] else: nn = _nx.indices(size, typ) for k, kk in enumerate(key): step = kk.step start = kk.start if start is None: start = 0 if step is None: step = 1 if isinstance(step, (_nx.complexfloating, complex)): step = int(abs(step)) if step != 1: step = (kk.stop - start) / float(step - 1) nn[k] = (nn[k]*step+start) if self.sparse: slobj = [_nx.newaxis]*len(size) for k in range(len(size)): slobj[k] = slice(None, None) nn[k] = nn[k][tuple(slobj)] slobj[k] = _nx.newaxis return nn except (IndexError, TypeError): step = key.step stop = key.stop start = key.start if start is None: start = 0 if isinstance(step, (_nx.complexfloating, complex)): # Prevent the (potential) creation of integer arrays step_float = abs(step) step = length = int(step_float) if step != 1: step = (key.stop-start)/float(step-1) typ = _nx.result_type(start, stop, step_float) return _nx.arange(0, length, 1, dtype=typ)*step + start else: return _nx.arange(start, stop, step) class MGridClass(nd_grid): """ An instance which returns a dense multi-dimensional "meshgrid". An instance which returns a dense (or fleshed out) mesh-grid when indexed, so that each returned argument has the same shape. The dimensions and number of the output arrays are equal to the number of indexing dimensions. If the step length is not a complex number, then the stop is not inclusive. However, if the step length is a **complex number** (e.g. 5j), then the integer part of its magnitude is interpreted as specifying the number of points to create between the start and stop values, where the stop value **is inclusive**. Returns ------- mesh-grid `ndarrays` all of the same dimensions See Also -------- ogrid : like `mgrid` but returns open (not fleshed out) mesh grids meshgrid: return coordinate matrices from coordinate vectors r_ : array concatenator :ref:`how-to-partition` Examples -------- >>> np.mgrid[0:5, 0:5] array([[[0, 0, 0, 0, 0], [1, 1, 1, 1, 1], [2, 2, 2, 2, 2], [3, 3, 3, 3, 3], [4, 4, 4, 4, 4]], [[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]]]) >>> np.mgrid[-1:1:5j] array([-1. , -0.5, 0. , 0.5, 1. ]) """ def __init__(self): super().__init__(sparse=False) mgrid = MGridClass() class OGridClass(nd_grid): """ An instance which returns an open multi-dimensional "meshgrid". An instance which returns an open (i.e. not fleshed out) mesh-grid when indexed, so that only one dimension of each returned array is greater than 1. The dimension and number of the output arrays are equal to the number of indexing dimensions. If the step length is not a complex number, then the stop is not inclusive. However, if the step length is a **complex number** (e.g. 5j), then the integer part of its magnitude is interpreted as specifying the number of points to create between the start and stop values, where the stop value **is inclusive**. Returns ------- mesh-grid `ndarrays` with only one dimension not equal to 1 See Also -------- mgrid : like `ogrid` but returns dense (or fleshed out) mesh grids meshgrid: return coordinate matrices from coordinate vectors r_ : array concatenator :ref:`how-to-partition` Examples -------- >>> from numpy import ogrid >>> ogrid[-1:1:5j] array([-1. , -0.5, 0. , 0.5, 1. ]) >>> ogrid[0:5,0:5] [array([[0], [1], [2], [3], [4]]), array([[0, 1, 2, 3, 4]])] """ def __init__(self): super().__init__(sparse=True) ogrid = OGridClass() class AxisConcatenator: """ Translates slice objects to concatenation along an axis. For detailed documentation on usage, see `r_`. """ # allow ma.mr_ to override this concatenate = staticmethod(_nx.concatenate) makemat = staticmethod(matrixlib.matrix) def __init__(self, axis=0, matrix=False, ndmin=1, trans1d=-1): self.axis = axis self.matrix = matrix self.trans1d = trans1d self.ndmin = ndmin def __getitem__(self, key): # handle matrix builder syntax if isinstance(key, str): frame = sys._getframe().f_back mymat = matrixlib.bmat(key, frame.f_globals, frame.f_locals) return mymat if not isinstance(key, tuple): key = (key,) # copy attributes, since they can be overridden in the first argument trans1d = self.trans1d ndmin = self.ndmin matrix = self.matrix axis = self.axis objs = [] # dtypes or scalars for weak scalar handling in result_type result_type_objs = [] for k, item in enumerate(key): scalar = False if isinstance(item, slice): step = item.step start = item.start stop = item.stop if start is None: start = 0 if step is None: step = 1 if isinstance(step, (_nx.complexfloating, complex)): size = int(abs(step)) newobj = linspace(start, stop, num=size) else: newobj = _nx.arange(start, stop, step) if ndmin > 1: newobj = array(newobj, copy=False, ndmin=ndmin) if trans1d != -1: newobj = newobj.swapaxes(-1, trans1d) elif isinstance(item, str): if k != 0: raise ValueError("special directives must be the " "first entry.") if item in ('r', 'c'): matrix = True col = (item == 'c') continue if ',' in item: vec = item.split(',') try: axis, ndmin = [int(x) for x in vec[:2]] if len(vec) == 3: trans1d = int(vec[2]) continue except Exception as e: raise ValueError( "unknown special directive {!r}".format(item) ) from e try: axis = int(item) continue except (ValueError, TypeError) as e: raise ValueError("unknown special directive") from e elif type(item) in ScalarType: scalar = True newobj = item else: item_ndim = np.ndim(item) newobj = array(item, copy=False, subok=True, ndmin=ndmin) if trans1d != -1 and item_ndim < ndmin: k2 = ndmin - item_ndim k1 = trans1d if k1 < 0: k1 += k2 + 1 defaxes = list(range(ndmin)) axes = defaxes[:k1] + defaxes[k2:] + defaxes[k1:k2] newobj = newobj.transpose(axes) objs.append(newobj) if scalar: result_type_objs.append(item) else: result_type_objs.append(newobj.dtype) # Ensure that scalars won't up-cast unless warranted, for 0, drops # through to error in concatenate. if len(result_type_objs) != 0: final_dtype = _nx.result_type(*result_type_objs) # concatenate could do cast, but that can be overriden: objs = [array(obj, copy=False, subok=True, ndmin=ndmin, dtype=final_dtype) for obj in objs] res = self.concatenate(tuple(objs), axis=axis) if matrix: oldndim = res.ndim res = self.makemat(res) if oldndim == 1 and col: res = res.T return res def __len__(self): return 0 # separate classes are used here instead of just making r_ = concatentor(0), # etc. because otherwise we couldn't get the doc string to come out right # in help(r_) class RClass(AxisConcatenator): """ Translates slice objects to concatenation along the first axis. This is a simple way to build up arrays quickly. There are two use cases. 1. If the index expression contains comma separated arrays, then stack them along their first axis. 2. If the index expression contains slice notation or scalars then create a 1-D array with a range indicated by the slice notation. If slice notation is used, the syntax ``start:stop:step`` is equivalent to ``np.arange(start, stop, step)`` inside of the brackets. However, if ``step`` is an imaginary number (i.e. 100j) then its integer portion is interpreted as a number-of-points desired and the start and stop are inclusive. In other words ``start:stop:stepj`` is interpreted as ``np.linspace(start, stop, step, endpoint=1)`` inside of the brackets. After expansion of slice notation, all comma separated sequences are concatenated together. Optional character strings placed as the first element of the index expression can be used to change the output. The strings 'r' or 'c' result in matrix output. If the result is 1-D and 'r' is specified a 1 x N (row) matrix is produced. If the result is 1-D and 'c' is specified, then a N x 1 (column) matrix is produced. If the result is 2-D then both provide the same matrix result. A string integer specifies which axis to stack multiple comma separated arrays along. A string of two comma-separated integers allows indication of the minimum number of dimensions to force each entry into as the second integer (the axis to concatenate along is still the first integer). A string with three comma-separated integers allows specification of the axis to concatenate along, the minimum number of dimensions to force the entries to, and which axis should contain the start of the arrays which are less than the specified number of dimensions. In other words the third integer allows you to specify where the 1's should be placed in the shape of the arrays that have their shapes upgraded. By default, they are placed in the front of the shape tuple. The third argument allows you to specify where the start of the array should be instead. Thus, a third argument of '0' would place the 1's at the end of the array shape. Negative integers specify where in the new shape tuple the last dimension of upgraded arrays should be placed, so the default is '-1'. Parameters ---------- Not a function, so takes no parameters Returns ------- A concatenated ndarray or matrix. See Also -------- concatenate : Join a sequence of arrays along an existing axis. c_ : Translates slice objects to concatenation along the second axis. Examples -------- >>> np.r_[np.array([1,2,3]), 0, 0, np.array([4,5,6])] array([1, 2, 3, ..., 4, 5, 6]) >>> np.r_[-1:1:6j, [0]*3, 5, 6] array([-1. , -0.6, -0.2, 0.2, 0.6, 1. , 0. , 0. , 0. , 5. , 6. ]) String integers specify the axis to concatenate along or the minimum number of dimensions to force entries into. >>> a = np.array([[0, 1, 2], [3, 4, 5]]) >>> np.r_['-1', a, a] # concatenate along last axis array([[0, 1, 2, 0, 1, 2], [3, 4, 5, 3, 4, 5]]) >>> np.r_['0,2', [1,2,3], [4,5,6]] # concatenate along first axis, dim>=2 array([[1, 2, 3], [4, 5, 6]]) >>> np.r_['0,2,0', [1,2,3], [4,5,6]] array([[1], [2], [3], [4], [5], [6]]) >>> np.r_['1,2,0', [1,2,3], [4,5,6]] array([[1, 4], [2, 5], [3, 6]]) Using 'r' or 'c' as a first string argument creates a matrix. >>> np.r_['r',[1,2,3], [4,5,6]] matrix([[1, 2, 3, 4, 5, 6]]) """ def __init__(self): AxisConcatenator.__init__(self, 0) r_ = RClass() class CClass(AxisConcatenator): """ Translates slice objects to concatenation along the second axis. This is short-hand for ``np.r_['-1,2,0', index expression]``, which is useful because of its common occurrence. In particular, arrays will be stacked along their last axis after being upgraded to at least 2-D with 1's post-pended to the shape (column vectors made out of 1-D arrays). See Also -------- column_stack : Stack 1-D arrays as columns into a 2-D array. r_ : For more detailed documentation. Examples -------- >>> np.c_[np.array([1,2,3]), np.array([4,5,6])] array([[1, 4], [2, 5], [3, 6]]) >>> np.c_[np.array([[1,2,3]]), 0, 0, np.array([[4,5,6]])] array([[1, 2, 3, ..., 4, 5, 6]]) """ def __init__(self): AxisConcatenator.__init__(self, -1, ndmin=2, trans1d=0) c_ = CClass() @set_module('numpy') class ndenumerate: """ Multidimensional index iterator. Return an iterator yielding pairs of array coordinates and values. Parameters ---------- arr : ndarray Input array. See Also -------- ndindex, flatiter Examples -------- >>> a = np.array([[1, 2], [3, 4]]) >>> for index, x in np.ndenumerate(a): ... print(index, x) (0, 0) 1 (0, 1) 2 (1, 0) 3 (1, 1) 4 """ def __init__(self, arr): self.iter = np.asarray(arr).flat def __next__(self): """ Standard iterator method, returns the index tuple and array value. Returns ------- coords : tuple of ints The indices of the current iteration. val : scalar The array element of the current iteration. """ return self.iter.coords, next(self.iter) def __iter__(self): return self @set_module('numpy') class ndindex: """ An N-dimensional iterator object to index arrays. Given the shape of an array, an `ndindex` instance iterates over the N-dimensional index of the array. At each iteration a tuple of indices is returned, the last dimension is iterated over first. Parameters ---------- shape : ints, or a single tuple of ints The size of each dimension of the array can be passed as individual parameters or as the elements of a tuple. See Also -------- ndenumerate, flatiter Examples -------- Dimensions as individual arguments >>> for index in np.ndindex(3, 2, 1): ... print(index) (0, 0, 0) (0, 1, 0) (1, 0, 0) (1, 1, 0) (2, 0, 0) (2, 1, 0) Same dimensions - but in a tuple ``(3, 2, 1)`` >>> for index in np.ndindex((3, 2, 1)): ... print(index) (0, 0, 0) (0, 1, 0) (1, 0, 0) (1, 1, 0) (2, 0, 0) (2, 1, 0) """ def __init__(self, *shape): if len(shape) == 1 and isinstance(shape[0], tuple): shape = shape[0] x = as_strided(_nx.zeros(1), shape=shape, strides=_nx.zeros_like(shape)) self._it = _nx.nditer(x, flags=['multi_index', 'zerosize_ok'], order='C') def __iter__(self): return self def ndincr(self): """ Increment the multi-dimensional index by one. This method is for backward compatibility only: do not use. .. deprecated:: 1.20.0 This method has been advised against since numpy 1.8.0, but only started emitting DeprecationWarning as of this version. """ # NumPy 1.20.0, 2020-09-08 warnings.warn( "`ndindex.ndincr()` is deprecated, use `next(ndindex)` instead", DeprecationWarning, stacklevel=2) next(self) def __next__(self): """ Standard iterator method, updates the index and returns the index tuple. Returns ------- val : tuple of ints Returns a tuple containing the indices of the current iteration. """ next(self._it) return self._it.multi_index # You can do all this with slice() plus a few special objects, # but there's a lot to remember. This version is simpler because # it uses the standard array indexing syntax. # # Written by Konrad Hinsen # last revision: 1999-7-23 # # Cosmetic changes by T. Oliphant 2001 # # class IndexExpression: """ A nicer way to build up index tuples for arrays. .. note:: Use one of the two predefined instances `index_exp` or `s_` rather than directly using `IndexExpression`. For any index combination, including slicing and axis insertion, ``a[indices]`` is the same as ``a[np.index_exp[indices]]`` for any array `a`. However, ``np.index_exp[indices]`` can be used anywhere in Python code and returns a tuple of slice objects that can be used in the construction of complex index expressions. Parameters ---------- maketuple : bool If True, always returns a tuple. See Also -------- index_exp : Predefined instance that always returns a tuple: `index_exp = IndexExpression(maketuple=True)`. s_ : Predefined instance without tuple conversion: `s_ = IndexExpression(maketuple=False)`. Notes ----- You can do all this with `slice()` plus a few special objects, but there's a lot to remember and this version is simpler because it uses the standard array indexing syntax. Examples -------- >>> np.s_[2::2] slice(2, None, 2) >>> np.index_exp[2::2] (slice(2, None, 2),) >>> np.array([0, 1, 2, 3, 4])[np.s_[2::2]] array([2, 4]) """ def __init__(self, maketuple): self.maketuple = maketuple def __getitem__(self, item): if self.maketuple and not isinstance(item, tuple): return (item,) else: return item index_exp = IndexExpression(maketuple=True) s_ = IndexExpression(maketuple=False) # End contribution from Konrad. # The following functions complement those in twodim_base, but are # applicable to N-dimensions. def _fill_diagonal_dispatcher(a, val, wrap=None): return (a,) @array_function_dispatch(_fill_diagonal_dispatcher) def fill_diagonal(a, val, wrap=False): """Fill the main diagonal of the given array of any dimensionality. For an array `a` with ``a.ndim >= 2``, the diagonal is the list of locations with indices ``a[i, ..., i]`` all identical. This function modifies the input array in-place, it does not return a value. Parameters ---------- a : array, at least 2-D. Array whose diagonal is to be filled, it gets modified in-place. val : scalar or array_like Value(s) to write on the diagonal. If `val` is scalar, the value is written along the diagonal. If array-like, the flattened `val` is written along the diagonal, repeating if necessary to fill all diagonal entries. wrap : bool For tall matrices in NumPy version up to 1.6.2, the diagonal "wrapped" after N columns. You can have this behavior with this option. This affects only tall matrices. See also -------- diag_indices, diag_indices_from Notes ----- .. versionadded:: 1.4.0 This functionality can be obtained via `diag_indices`, but internally this version uses a much faster implementation that never constructs the indices and uses simple slicing. Examples -------- >>> a = np.zeros((3, 3), int) >>> np.fill_diagonal(a, 5) >>> a array([[5, 0, 0], [0, 5, 0], [0, 0, 5]]) The same function can operate on a 4-D array: >>> a = np.zeros((3, 3, 3, 3), int) >>> np.fill_diagonal(a, 4) We only show a few blocks for clarity: >>> a[0, 0] array([[4, 0, 0], [0, 0, 0], [0, 0, 0]]) >>> a[1, 1] array([[0, 0, 0], [0, 4, 0], [0, 0, 0]]) >>> a[2, 2] array([[0, 0, 0], [0, 0, 0], [0, 0, 4]]) The wrap option affects only tall matrices: >>> # tall matrices no wrap >>> a = np.zeros((5, 3), int) >>> np.fill_diagonal(a, 4) >>> a array([[4, 0, 0], [0, 4, 0], [0, 0, 4], [0, 0, 0], [0, 0, 0]]) >>> # tall matrices wrap >>> a = np.zeros((5, 3), int) >>> np.fill_diagonal(a, 4, wrap=True) >>> a array([[4, 0, 0], [0, 4, 0], [0, 0, 4], [0, 0, 0], [4, 0, 0]]) >>> # wide matrices >>> a = np.zeros((3, 5), int) >>> np.fill_diagonal(a, 4, wrap=True) >>> a array([[4, 0, 0, 0, 0], [0, 4, 0, 0, 0], [0, 0, 4, 0, 0]]) The anti-diagonal can be filled by reversing the order of elements using either `numpy.flipud` or `numpy.fliplr`. >>> a = np.zeros((3, 3), int); >>> np.fill_diagonal(np.fliplr(a), [1,2,3]) # Horizontal flip >>> a array([[0, 0, 1], [0, 2, 0], [3, 0, 0]]) >>> np.fill_diagonal(np.flipud(a), [1,2,3]) # Vertical flip >>> a array([[0, 0, 3], [0, 2, 0], [1, 0, 0]]) Note that the order in which the diagonal is filled varies depending on the flip function. """ if a.ndim < 2: raise ValueError("array must be at least 2-d") end = None if a.ndim == 2: # Explicit, fast formula for the common case. For 2-d arrays, we # accept rectangular ones. step = a.shape[1] + 1 # This is needed to don't have tall matrix have the diagonal wrap. if not wrap: end = a.shape[1] * a.shape[1] else: # For more than d=2, the strided formula is only valid for arrays with # all dimensions equal, so we check first. if not np.all(diff(a.shape) == 0): raise ValueError("All dimensions of input must be of equal length") step = 1 + (np.cumprod(a.shape[:-1])).sum() # Write the value out into the diagonal. a.flat[:end:step] = val @set_module('numpy') def diag_indices(n, ndim=2): """ Return the indices to access the main diagonal of an array. This returns a tuple of indices that can be used to access the main diagonal of an array `a` with ``a.ndim >= 2`` dimensions and shape (n, n, ..., n). For ``a.ndim = 2`` this is the usual diagonal, for ``a.ndim > 2`` this is the set of indices to access ``a[i, i, ..., i]`` for ``i = [0..n-1]``. Parameters ---------- n : int The size, along each dimension, of the arrays for which the returned indices can be used. ndim : int, optional The number of dimensions. See Also -------- diag_indices_from Notes ----- .. versionadded:: 1.4.0 Examples -------- Create a set of indices to access the diagonal of a (4, 4) array: >>> di = np.diag_indices(4) >>> di (array([0, 1, 2, 3]), array([0, 1, 2, 3])) >>> a = np.arange(16).reshape(4, 4) >>> a array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15]]) >>> a[di] = 100 >>> a array([[100, 1, 2, 3], [ 4, 100, 6, 7], [ 8, 9, 100, 11], [ 12, 13, 14, 100]]) Now, we create indices to manipulate a 3-D array: >>> d3 = np.diag_indices(2, 3) >>> d3 (array([0, 1]), array([0, 1]), array([0, 1])) And use it to set the diagonal of an array of zeros to 1: >>> a = np.zeros((2, 2, 2), dtype=int) >>> a[d3] = 1 >>> a array([[[1, 0], [0, 0]], [[0, 0], [0, 1]]]) """ idx = np.arange(n) return (idx,) * ndim def _diag_indices_from(arr): return (arr,) @array_function_dispatch(_diag_indices_from) def diag_indices_from(arr): """ Return the indices to access the main diagonal of an n-dimensional array. See `diag_indices` for full details. Parameters ---------- arr : array, at least 2-D See Also -------- diag_indices Notes ----- .. versionadded:: 1.4.0 Examples -------- Create a 4 by 4 array. >>> a = np.arange(16).reshape(4, 4) >>> a array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15]]) Get the indices of the diagonal elements. >>> di = np.diag_indices_from(a) >>> di (array([0, 1, 2, 3]), array([0, 1, 2, 3])) >>> a[di] array([ 0, 5, 10, 15]) This is simply syntactic sugar for diag_indices. >>> np.diag_indices(a.shape[0]) (array([0, 1, 2, 3]), array([0, 1, 2, 3])) """ if not arr.ndim >= 2: raise ValueError("input array must be at least 2-d") # For more than d=2, the strided formula is only valid for arrays with # all dimensions equal, so we check first. if not np.all(diff(arr.shape) == 0): raise ValueError("All dimensions of input must be of equal length") return diag_indices(arr.shape[0], arr.ndim)