Файловый менеджер - Редактировать - /home/digitalm/venv/lib/python3.7/site-packages/numpy/lib/__pycache__/twodim_base.cpython-37.pyc
Назад
B �5�g q � @ s� d Z ddlZddlmZmZmZmZmZmZm Z m Z m Z m Z m Z mZmZmZmZmZmZmZ ddlmZmZ ddlmZ ddlmZ ddlm Z dd d d d d dddddddddgZ ej ej dd�Z ee �Z ee �Z!ee �Z"dd� Z#dd � Z$e e$�d d � �Z%e e$�d d � �Z&d>dd �d d!�Z'eed�dde(d"fdd �d#d ���Z)e e'�e)�Z*d?d$d%�Z+e e+�d@d&d��Z,e e+�dAd'd ��Z-dBdd �d(d)�Z.eed�dde(fdd �d*d ���Z/e e.�e/�Z0dCd+d,�Z1e e1�dDd-d��Z2e e1�dEd.d��Z3dFd/d0�Z4e e4�dGd2d��Z5dHd3d4�Z6e e6�dId6d��Z7ed�dJd7d��Z8ed�dKd8d��Z9dLd9d:�Z:e e:�dMd;d��Z;ed�dNd<d��Z<e e:�dOd=d��Z=dS )Pz- Basic functions for manipulating 2d arrays � N)� asanyarray�arange�zeros� greater_equal�multiply�ones�asarray�where�int8�int16�int32�int64�intp�empty� promote_types�diagonal�nonzero�indices)�set_array_function_like_doc� set_module)� overrides)�iinfo)� broadcast_to�diag�diagflat�eye�fliplr�flipud�tri�triu�tril�vander� histogram2d� mask_indices� tril_indices�tril_indices_from� triu_indices�triu_indices_from�numpy)�modulec C sL |t jkr| t jkrtS |tjkr0| tjkr0tS |tjkrH| tjkrHtS tS )z# get small int that fits the range ) �i1�max�minr �i2r �i4r r )�low�high� r1 �K/home/digitalm-up/venv/lib/python3.7/site-packages/numpy/lib/twodim_base.py�_min_int s r3 c C s | fS )Nr1 )�mr1 r1 r2 �_flip_dispatcher+ s r5 c C s0 t | �} | jdk rtd��| dd�ddd�f S )ad Reverse the order of elements along axis 1 (left/right). For a 2-D array, this flips the entries in each row in the left/right direction. Columns are preserved, but appear in a different order than before. Parameters ---------- m : array_like Input array, must be at least 2-D. Returns ------- f : ndarray A view of `m` with the columns reversed. Since a view is returned, this operation is :math:`\mathcal O(1)`. See Also -------- flipud : Flip array in the up/down direction. flip : Flip array in one or more dimesions. rot90 : Rotate array counterclockwise. Notes ----- Equivalent to ``m[:,::-1]`` or ``np.flip(m, axis=1)``. Requires the array to be at least 2-D. Examples -------- >>> A = np.diag([1.,2.,3.]) >>> A array([[1., 0., 0.], [0., 2., 0.], [0., 0., 3.]]) >>> np.fliplr(A) array([[0., 0., 1.], [0., 2., 0.], [3., 0., 0.]]) >>> A = np.random.randn(2,3,5) >>> np.all(np.fliplr(A) == A[:,::-1,...]) True � zInput must be >= 2-d.N���)r �ndim� ValueError)r4 r1 r1 r2 r / s 0 c C s, t | �} | jdk rtd��| ddd�df S )aw Reverse the order of elements along axis 0 (up/down). For a 2-D array, this flips the entries in each column in the up/down direction. Rows are preserved, but appear in a different order than before. Parameters ---------- m : array_like Input array. Returns ------- out : array_like A view of `m` with the rows reversed. Since a view is returned, this operation is :math:`\mathcal O(1)`. See Also -------- fliplr : Flip array in the left/right direction. flip : Flip array in one or more dimesions. rot90 : Rotate array counterclockwise. Notes ----- Equivalent to ``m[::-1, ...]`` or ``np.flip(m, axis=0)``. Requires the array to be at least 1-D. Examples -------- >>> A = np.diag([1.0, 2, 3]) >>> A array([[1., 0., 0.], [0., 2., 0.], [0., 0., 3.]]) >>> np.flipud(A) array([[0., 0., 3.], [0., 2., 0.], [1., 0., 0.]]) >>> A = np.random.randn(2,3,5) >>> np.all(np.flipud(A) == A[::-1,...]) True >>> np.flipud([1,2]) array([2, 1]) � zInput must be >= 1-d.Nr7 .)r r8 r9 )r4 r1 r1 r2 r e s 2 )�likec C s |fS )Nr1 )�N�M�k�dtype�orderr; r1 r1 r2 �_eye_dispatcher� s rA �Cc C s� |dk r t | |||||d�S |dkr(| }t| |f||d�}||krF|S |dkrT|}n | | }d|d|| � j|d|d �< |S )a Return a 2-D array with ones on the diagonal and zeros elsewhere. Parameters ---------- N : int Number of rows in the output. M : int, optional Number of columns in the output. If None, defaults to `N`. k : int, optional Index of the diagonal: 0 (the default) refers to the main diagonal, a positive value refers to an upper diagonal, and a negative value to a lower diagonal. dtype : data-type, optional Data-type of the returned array. order : {'C', 'F'}, optional Whether the output should be stored in row-major (C-style) or column-major (Fortran-style) order in memory. .. versionadded:: 1.14.0 ${ARRAY_FUNCTION_LIKE} .. versionadded:: 1.20.0 Returns ------- I : ndarray of shape (N,M) An array where all elements are equal to zero, except for the `k`-th diagonal, whose values are equal to one. See Also -------- identity : (almost) equivalent function diag : diagonal 2-D array from a 1-D array specified by the user. Examples -------- >>> np.eye(2, dtype=int) array([[1, 0], [0, 1]]) >>> np.eye(3, k=1) array([[0., 1., 0.], [0., 0., 1.], [0., 0., 0.]]) N)r= r> r? r@ r; )r? r@ r r: )�_eye_with_liker �flat)r< r= r> r? r@ r; r4 �ir1 r1 r2 r � s 1 c C s | fS )Nr1 )�vr> r1 r1 r2 �_diag_dispatcher� s rG c C s� t | �} | j}t|�dkrv|d t|� }t||f| j�}|dkrH|}n | | }| |d|| � j|d|d �< |S t|�dkr�t| |�S td��dS )a� Extract a diagonal or construct a diagonal array. See the more detailed documentation for ``numpy.diagonal`` if you use this function to extract a diagonal and wish to write to the resulting array; whether it returns a copy or a view depends on what version of numpy you are using. Parameters ---------- v : array_like If `v` is a 2-D array, return a copy of its `k`-th diagonal. If `v` is a 1-D array, return a 2-D array with `v` on the `k`-th diagonal. k : int, optional Diagonal in question. The default is 0. Use `k>0` for diagonals above the main diagonal, and `k<0` for diagonals below the main diagonal. Returns ------- out : ndarray The extracted diagonal or constructed diagonal array. See Also -------- diagonal : Return specified diagonals. diagflat : Create a 2-D array with the flattened input as a diagonal. trace : Sum along diagonals. triu : Upper triangle of an array. tril : Lower triangle of an array. Examples -------- >>> x = np.arange(9).reshape((3,3)) >>> x array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) >>> np.diag(x) array([0, 4, 8]) >>> np.diag(x, k=1) array([1, 5]) >>> np.diag(x, k=-1) array([3, 7]) >>> np.diag(np.diag(x)) array([[0, 0, 0], [0, 4, 0], [0, 0, 8]]) r: r Nr6 zInput must be 1- or 2-d.) r �shape�len�absr r? rD r r9 )rF r> �s�n�resrE r1 r1 r2 r � s 7 c C s� y | j }W n tk r" d}Y nX t| ��� } t| �}|t|� }t||f| j�}|dkr�td|| t d�}|| || }n"td|| t d�}||| | }| |j |< |s�|S ||�S )a� Create a two-dimensional array with the flattened input as a diagonal. Parameters ---------- v : array_like Input data, which is flattened and set as the `k`-th diagonal of the output. k : int, optional Diagonal to set; 0, the default, corresponds to the "main" diagonal, a positive (negative) `k` giving the number of the diagonal above (below) the main. Returns ------- out : ndarray The 2-D output array. See Also -------- diag : MATLAB work-alike for 1-D and 2-D arrays. diagonal : Return specified diagonals. trace : Sum along diagonals. Examples -------- >>> np.diagflat([[1,2], [3,4]]) array([[1, 0, 0, 0], [0, 2, 0, 0], [0, 0, 3, 0], [0, 0, 0, 4]]) >>> np.diagflat([1,2], 1) array([[0, 1, 0], [0, 0, 2], [0, 0, 0]]) Nr )r? ) Z__array_wrap__�AttributeErrorr ZravelrI rJ r r? r r rD )rF r> �wraprK rL rM rE �fir1 r1 r2 r 2 s" ( c C s |fS )Nr1 )r< r= r> r? r; r1 r1 r2 �_tri_dispatchern s rQ c C sn |dk rt | ||||d�S |dkr&| }t�t| td| �d�t| || t| || �d��}|j|dd�}|S )a\ An array with ones at and below the given diagonal and zeros elsewhere. Parameters ---------- N : int Number of rows in the array. M : int, optional Number of columns in the array. By default, `M` is taken equal to `N`. k : int, optional The sub-diagonal at and below which the array is filled. `k` = 0 is the main diagonal, while `k` < 0 is below it, and `k` > 0 is above. The default is 0. dtype : dtype, optional Data type of the returned array. The default is float. ${ARRAY_FUNCTION_LIKE} .. versionadded:: 1.20.0 Returns ------- tri : ndarray of shape (N, M) Array with its lower triangle filled with ones and zero elsewhere; in other words ``T[i,j] == 1`` for ``j <= i + k``, 0 otherwise. Examples -------- >>> np.tri(3, 5, 2, dtype=int) array([[1, 1, 1, 0, 0], [1, 1, 1, 1, 0], [1, 1, 1, 1, 1]]) >>> np.tri(3, 5, -1) array([[0., 0., 0., 0., 0.], [1., 0., 0., 0., 0.], [1., 1., 0., 0., 0.]]) N)r= r> r? r; r )r? F)�copy)�_tri_with_liker �outerr r3 Zastype)r< r= r> r? r; r4 r1 r1 r2 r r s *"c C s | fS )Nr1 )r4 r> r1 r1 r2 �_trilu_dispatcher� s rU c C s6 t | �} t| jdd� |td��}t|| td| j��S )a� Lower triangle of an array. Return a copy of an array with elements above the `k`-th diagonal zeroed. Parameters ---------- m : array_like, shape (M, N) Input array. k : int, optional Diagonal above which to zero elements. `k = 0` (the default) is the main diagonal, `k < 0` is below it and `k > 0` is above. Returns ------- tril : ndarray, shape (M, N) Lower triangle of `m`, of same shape and data-type as `m`. See Also -------- triu : same thing, only for the upper triangle Examples -------- >>> np.tril([[1,2,3],[4,5,6],[7,8,9],[10,11,12]], -1) array([[ 0, 0, 0], [ 4, 0, 0], [ 7, 8, 0], [10, 11, 12]]) ���N)r> r? r: )r r rH �boolr r r? )r4 r> �maskr1 r1 r2 r � s !c C s: t | �} t| jdd� |d td��}t|td| j�| �S )a� Upper triangle of an array. Return a copy of an array with the elements below the `k`-th diagonal zeroed. Please refer to the documentation for `tril` for further details. See Also -------- tril : lower triangle of an array Examples -------- >>> np.triu([[1,2,3],[4,5,6],[7,8,9],[10,11,12]], -1) array([[ 1, 2, 3], [ 4, 5, 6], [ 0, 8, 9], [ 0, 0, 12]]) rV Nr: )r> r? )r r rH rW r r r? )r4 r> rX r1 r1 r2 r � s c C s | fS )Nr1 )�xr< � increasingr1 r1 r2 �_vander_dispatcher� s r[ Fc C s� t | �} | jdkrtd��|dkr*t| �}tt| �|ft| jt�d�}|s`|dd�ddd�f n|}|dkr|d|dd�df< |dkr�| dd�df |dd�dd�f< tj |dd�dd�f |dd�dd�f dd� |S )ar Generate a Vandermonde matrix. The columns of the output matrix are powers of the input vector. The order of the powers is determined by the `increasing` boolean argument. Specifically, when `increasing` is False, the `i`-th output column is the input vector raised element-wise to the power of ``N - i - 1``. Such a matrix with a geometric progression in each row is named for Alexandre- Theophile Vandermonde. Parameters ---------- x : array_like 1-D input array. N : int, optional Number of columns in the output. If `N` is not specified, a square array is returned (``N = len(x)``). increasing : bool, optional Order of the powers of the columns. If True, the powers increase from left to right, if False (the default) they are reversed. .. versionadded:: 1.9.0 Returns ------- out : ndarray Vandermonde matrix. If `increasing` is False, the first column is ``x^(N-1)``, the second ``x^(N-2)`` and so forth. If `increasing` is True, the columns are ``x^0, x^1, ..., x^(N-1)``. See Also -------- polynomial.polynomial.polyvander Examples -------- >>> x = np.array([1, 2, 3, 5]) >>> N = 3 >>> np.vander(x, N) array([[ 1, 1, 1], [ 4, 2, 1], [ 9, 3, 1], [25, 5, 1]]) >>> np.column_stack([x**(N-1-i) for i in range(N)]) array([[ 1, 1, 1], [ 4, 2, 1], [ 9, 3, 1], [25, 5, 1]]) >>> x = np.array([1, 2, 3, 5]) >>> np.vander(x) array([[ 1, 1, 1, 1], [ 8, 4, 2, 1], [ 27, 9, 3, 1], [125, 25, 5, 1]]) >>> np.vander(x, increasing=True) array([[ 1, 1, 1, 1], [ 1, 2, 4, 8], [ 1, 3, 9, 27], [ 1, 5, 25, 125]]) The determinant of a square Vandermonde matrix is the product of the differences between the values of the input vector: >>> np.linalg.det(np.vander(x)) 48.000000000000043 # may vary >>> (5-3)*(5-2)*(5-1)*(3-2)*(3-1)*(2-1) 48 r: z.x must be a one-dimensional array or sequence.N)r? r7 r )�outZaxis) r r8 r9 rI r r r? �intr � accumulate)rY r< rZ rF �tmpr1 r1 r2 r! � s I 0c c sV | V |V y t |�}W n tk r0 d}Y nX |dkrF|E d H n|V |V d S )Nr: r6 )rI � TypeError)rY �y�bins�range�normed�weights�densityr< r1 r1 r2 �_histogram2d_dispatcherX s rg � c C s� ddl m} y t|�}W n tk r0 d}Y nX |dkrV|dkrVt|� } } | | g}|| |g|||||�\} } | | d | d fS )aK Compute the bi-dimensional histogram of two data samples. Parameters ---------- x : array_like, shape (N,) An array containing the x coordinates of the points to be histogrammed. y : array_like, shape (N,) An array containing the y coordinates of the points to be histogrammed. bins : int or array_like or [int, int] or [array, array], optional The bin specification: * If int, the number of bins for the two dimensions (nx=ny=bins). * If array_like, the bin edges for the two dimensions (x_edges=y_edges=bins). * If [int, int], the number of bins in each dimension (nx, ny = bins). * If [array, array], the bin edges in each dimension (x_edges, y_edges = bins). * A combination [int, array] or [array, int], where int is the number of bins and array is the bin edges. range : array_like, shape(2,2), optional The leftmost and rightmost edges of the bins along each dimension (if not specified explicitly in the `bins` parameters): ``[[xmin, xmax], [ymin, ymax]]``. All values outside of this range will be considered outliers and not tallied in the histogram. density : bool, optional If False, the default, returns the number of samples in each bin. If True, returns the probability *density* function at the bin, ``bin_count / sample_count / bin_area``. normed : bool, optional An alias for the density argument that behaves identically. To avoid confusion with the broken normed argument to `histogram`, `density` should be preferred. weights : array_like, shape(N,), optional An array of values ``w_i`` weighing each sample ``(x_i, y_i)``. Weights are normalized to 1 if `normed` is True. If `normed` is False, the values of the returned histogram are equal to the sum of the weights belonging to the samples falling into each bin. Returns ------- H : ndarray, shape(nx, ny) The bi-dimensional histogram of samples `x` and `y`. Values in `x` are histogrammed along the first dimension and values in `y` are histogrammed along the second dimension. xedges : ndarray, shape(nx+1,) The bin edges along the first dimension. yedges : ndarray, shape(ny+1,) The bin edges along the second dimension. See Also -------- histogram : 1D histogram histogramdd : Multidimensional histogram Notes ----- When `normed` is True, then the returned histogram is the sample density, defined such that the sum over bins of the product ``bin_value * bin_area`` is 1. Please note that the histogram does not follow the Cartesian convention where `x` values are on the abscissa and `y` values on the ordinate axis. Rather, `x` is histogrammed along the first dimension of the array (vertical), and `y` along the second dimension of the array (horizontal). This ensures compatibility with `histogramdd`. Examples -------- >>> from matplotlib.image import NonUniformImage >>> import matplotlib.pyplot as plt Construct a 2-D histogram with variable bin width. First define the bin edges: >>> xedges = [0, 1, 3, 5] >>> yedges = [0, 2, 3, 4, 6] Next we create a histogram H with random bin content: >>> x = np.random.normal(2, 1, 100) >>> y = np.random.normal(1, 1, 100) >>> H, xedges, yedges = np.histogram2d(x, y, bins=(xedges, yedges)) >>> # Histogram does not follow Cartesian convention (see Notes), >>> # therefore transpose H for visualization purposes. >>> H = H.T :func:`imshow <matplotlib.pyplot.imshow>` can only display square bins: >>> fig = plt.figure(figsize=(7, 3)) >>> ax = fig.add_subplot(131, title='imshow: square bins') >>> plt.imshow(H, interpolation='nearest', origin='lower', ... extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]]) <matplotlib.image.AxesImage object at 0x...> :func:`pcolormesh <matplotlib.pyplot.pcolormesh>` can display actual edges: >>> ax = fig.add_subplot(132, title='pcolormesh: actual edges', ... aspect='equal') >>> X, Y = np.meshgrid(xedges, yedges) >>> ax.pcolormesh(X, Y, H) <matplotlib.collections.QuadMesh object at 0x...> :class:`NonUniformImage <matplotlib.image.NonUniformImage>` can be used to display actual bin edges with interpolation: >>> ax = fig.add_subplot(133, title='NonUniformImage: interpolated', ... aspect='equal', xlim=xedges[[0, -1]], ylim=yedges[[0, -1]]) >>> im = NonUniformImage(ax, interpolation='bilinear') >>> xcenters = (xedges[:-1] + xedges[1:]) / 2 >>> ycenters = (yedges[:-1] + yedges[1:]) / 2 >>> im.set_data(xcenters, ycenters, H) >>> ax.images.append(im) >>> plt.show() r )� histogramddr: r6 )r( ri rI r` r ) rY ra rb rc rd re rf ri r< ZxedgesZyedges�hist�edgesr1 r1 r2 r" j s { c C s$ t | | ft�}|||�}t|dk�S )a� Return the indices to access (n, n) arrays, given a masking function. Assume `mask_func` is a function that, for a square array a of size ``(n, n)`` with a possible offset argument `k`, when called as ``mask_func(a, k)`` returns a new array with zeros in certain locations (functions like `triu` or `tril` do precisely this). Then this function returns the indices where the non-zero values would be located. Parameters ---------- n : int The returned indices will be valid to access arrays of shape (n, n). mask_func : callable A function whose call signature is similar to that of `triu`, `tril`. That is, ``mask_func(x, k)`` returns a boolean array, shaped like `x`. `k` is an optional argument to the function. k : scalar An optional argument which is passed through to `mask_func`. Functions like `triu`, `tril` take a second argument that is interpreted as an offset. Returns ------- indices : tuple of arrays. The `n` arrays of indices corresponding to the locations where ``mask_func(np.ones((n, n)), k)`` is True. See Also -------- triu, tril, triu_indices, tril_indices Notes ----- .. versionadded:: 1.4.0 Examples -------- These are the indices that would allow you to access the upper triangular part of any 3x3 array: >>> iu = np.mask_indices(3, np.triu) For example, if `a` is a 3x3 array: >>> a = np.arange(9).reshape(3, 3) >>> a array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) >>> a[iu] array([0, 1, 2, 4, 5, 8]) An offset can be passed also to the masking function. This gets us the indices starting on the first diagonal right of the main one: >>> iu1 = np.mask_indices(3, np.triu, 1) with which we now extract only three elements: >>> a[iu1] array([1, 2, 5]) r )r r] r )rL Z mask_funcr> r4 �ar1 r1 r2 r# � s B c s0 t | ||td�� t� fdd�t� jdd�D ��S )ap Return the indices for the lower-triangle of an (n, m) array. Parameters ---------- n : int The row dimension of the arrays for which the returned indices will be valid. k : int, optional Diagonal offset (see `tril` for details). m : int, optional .. versionadded:: 1.9.0 The column dimension of the arrays for which the returned arrays will be valid. By default `m` is taken equal to `n`. Returns ------- inds : tuple of arrays The indices for the triangle. The returned tuple contains two arrays, each with the indices along one dimension of the array. See also -------- triu_indices : similar function, for upper-triangular. mask_indices : generic function accepting an arbitrary mask function. tril, triu Notes ----- .. versionadded:: 1.4.0 Examples -------- Compute two different sets of indices to access 4x4 arrays, one for the lower triangular part starting at the main diagonal, and one starting two diagonals further right: >>> il1 = np.tril_indices(4) >>> il2 = np.tril_indices(4, 2) Here is how they can be used with a sample 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]]) Both for indexing: >>> a[il1] array([ 0, 4, 5, ..., 13, 14, 15]) And for assigning values: >>> a[il1] = -1 >>> a array([[-1, 1, 2, 3], [-1, -1, 6, 7], [-1, -1, -1, 11], [-1, -1, -1, -1]]) These cover almost the whole array (two diagonals right of the main one): >>> a[il2] = -10 >>> a array([[-10, -10, -10, 3], [-10, -10, -10, -10], [-10, -10, -10, -10], [-10, -10, -10, -10]]) )r> r? c 3 s | ]}t |� j�� V qd S )N)r rH )�.0�inds)�tri_r1 r2 � <genexpr>� s z tril_indices.<locals>.<genexpr>T)�sparse)r rW �tupler rH )rL r> r4 r1 )ro r2 r$ : s N c C s | fS )Nr1 )�arrr> r1 r1 r2 � _trilu_indices_form_dispatcher� s rt c C s, | j dkrtd��t| jd || jd d�S )a� Return the indices for the lower-triangle of arr. See `tril_indices` for full details. Parameters ---------- arr : array_like The indices will be valid for square arrays whose dimensions are the same as arr. k : int, optional Diagonal offset (see `tril` for details). See Also -------- tril_indices, tril Notes ----- .. versionadded:: 1.4.0 r6 zinput array must be 2-drV r7 )r> r4 )r8 r9 r$ rH )rs r> r1 r1 r2 r% � s c s6 t | ||d td� � t� fdd�t� jdd�D ��S )a� Return the indices for the upper-triangle of an (n, m) array. Parameters ---------- n : int The size of the arrays for which the returned indices will be valid. k : int, optional Diagonal offset (see `triu` for details). m : int, optional .. versionadded:: 1.9.0 The column dimension of the arrays for which the returned arrays will be valid. By default `m` is taken equal to `n`. Returns ------- inds : tuple, shape(2) of ndarrays, shape(`n`) The indices for the triangle. The returned tuple contains two arrays, each with the indices along one dimension of the array. Can be used to slice a ndarray of shape(`n`, `n`). See also -------- tril_indices : similar function, for lower-triangular. mask_indices : generic function accepting an arbitrary mask function. triu, tril Notes ----- .. versionadded:: 1.4.0 Examples -------- Compute two different sets of indices to access 4x4 arrays, one for the upper triangular part starting at the main diagonal, and one starting two diagonals further right: >>> iu1 = np.triu_indices(4) >>> iu2 = np.triu_indices(4, 2) Here is how they can be used with a sample 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]]) Both for indexing: >>> a[iu1] array([ 0, 1, 2, ..., 10, 11, 15]) And for assigning values: >>> a[iu1] = -1 >>> a array([[-1, -1, -1, -1], [ 4, -1, -1, -1], [ 8, 9, -1, -1], [12, 13, 14, -1]]) These cover only a small part of the whole array (two diagonals right of the main one): >>> a[iu2] = -10 >>> a array([[ -1, -1, -10, -10], [ 4, -1, -1, -10], [ 8, 9, -1, -1], [ 12, 13, 14, -1]]) r: )r> r? c 3 s | ]}t |� j�� V qd S )N)r rH )rm rn )ro r1 r2 rp s z triu_indices.<locals>.<genexpr>T)rq )r rW rr r rH )rL r> r4 r1 )ro r2 r& � s P c C s, | j dkrtd��t| jd || jd d�S )a Return the indices for the upper-triangle of arr. See `triu_indices` for full details. Parameters ---------- arr : ndarray, shape(N, N) The indices will be valid for square arrays. k : int, optional Diagonal offset (see `triu` for details). Returns ------- triu_indices_from : tuple, shape(2) of ndarray, shape(N) Indices for the upper-triangle of `arr`. See Also -------- triu_indices, triu Notes ----- .. versionadded:: 1.4.0 r6 zinput array must be 2-drV r7 )r> r4 )r8 r9 r& rH )rs r> r1 r1 r2 r' s )NNNN)N)r )r )NNN)N)r )r )NN)NF)NNNNN)rh NNNN)r )r N)N)r )r N)r )>�__doc__� functoolsZnumpy.core.numericr r r r r r r r r r r r r r r r r r Znumpy.core.overridesr r Z numpy.corer r Znumpy.lib.stride_tricksr �__all__�partialZarray_function_dispatchr* r- r. r3 r5 r r rA �floatr rC rG r r rQ r rS rU r r r[ r! rg r" r# r$ rt r% r& r' r1 r1 r1 r2 �<module> s| P 68 > G ;7 & Z F S U
| ver. 1.4 |
Github
|
.
| PHP 8.3.23 | Генерация страницы: 0.05 |
proxy
|
phpinfo
|
Настройка