Py之Numpy：Numpy库中常用函数的简介、应用之详细攻略

Py之Numpy：Numpy库中常用函数的简介、应用之详细攻略相关文章Py之Numpy：Numpy库简介、安装、使用方法、案例应用之详细攻略​​​​​​​Py之Numpy：Numpy库中常用函数的简介、应用之详细攻略Numpy库中常用函数的简介、应用1、X, Y = np.meshgrid(X, Y)meshgrid Found at: numpy.lib.function_baseReturn coordinate matrices from coordinate vectors.Make N-D coordinate arrays for vectorized evaluations of N-D scalar/vector fields over N-D grids, given  one-dimensional coordinate arrays x1, x2,..., xn... versionchanged:: 1.91-D and 0-D cases are allowed.Parameters----------x1, x2,..., xn : array_like1-D arrays representing the coordinates of a grid.indexing : {'xy', 'ij'}, optionalCartesian ('xy', default) or matrix ('ij') indexing of output.See Notes for more details... versionadded:: 1.7.0sparse : bool, optionalIf True a sparse grid is returned in order to conserve  memory. Default is False... versionadded:: 1.7.0copy : bool, optional. If False, a view into the original arrays are returned inorder to  conserve memory.  Default is True.  Please note that  ``sparse=False, copy=False`` will likely return noncontiguous arrays.  Furthermore, more than one element of a   broadcast array may refer to a single memory location.  If you need to  write to the arrays, make copies first... versionadded:: 1.7.0Returns-------X1, X2,..., XN : ndarrayFor vectors `x1`, `x2`,..., 'xn' with lengths ``Ni=len(xi)`` ,return ``(N1, N2, N3,...Nn)`` shaped arrays if indexing='ij'   or ``(N2, N1, N3,...Nn)`` shaped arrays if indexing='xy'  with the elements of `xi` repeated to fill the matrix along  the first dimension for `x1`, the second for `x2` and so on.Notes-----This function supports both indexing conventions  through the indexing keyword argument.  Giving the string 'ij' returns a  meshgrid with matrix indexing, while 'xy' returns a meshgrid with   Cartesian indexing.In the 2-D case with inputs of length M and N, the  outputs are of shape  (N, M) for 'xy' indexing and (M, N) for 'ij' indexing.  In the   3-D case with inputs of length M, N and P, outputs are of shape   (N, M, P) for   'xy' indexing and (M, N, P) for 'ij' indexing.  The  difference is  illustrated by the following code snippet::xv, yv = np.meshgrid(x, y, sparse=False, indexing='ij')for i in range(nx):for j in range(ny):# treat xv[i,j], yv[i,j]xv, yv = np.meshgrid(x, y, sparse=False, indexing='xy')for i in range(nx):for j in range(ny):# treat xv[j,i], yv[j,i]In the 1-D and 0-D case, the indexing and sparse  keywords have no effect.See Also--------index_tricks.mgrid : Construct a multi-dimensional    "meshgrid" using indexing notation.index_tricks.ogrid : Construct an open multi-dimensional   "meshgrid" using indexing notation.从坐标向量返回坐标矩阵。建立N-D坐标阵列，在N-D网格上对N-D标量/向量场进行向量化计算，给定一维坐标阵列x1, x2，…,xn。. .versionchanged:: 1.9允许1-D和0-D。参数----------x1, x2,…， xn: array_like表示网格坐标的一维数组。索引:{'xy'， 'ij'}，可选Cartesian ('xy'，默认)或矩阵('ij')索引的输出。参见注释了解更多细节。. .versionadded: 1.7.0稀疏:bool，可选如果为真，则返回一个稀疏网格以保存内存。默认是假的。. .versionadded: 1.7.0复制:bool，可选。如果为假，则返回原始数组的视图为了保存记忆。默认是正确的。请注意，' ' sparse=False, copy=False ' '将可能返回不相邻的数组。此外，广播数组中的多个元素可以引用单个内存位置。如果需要对数组进行写入，请首先进行复制。. .versionadded: 1.7.0返回-------X1, X2,…XN: ndarray对于向量“x1”，“x2”，…， 'xn'加上length ' ' ' Ni=len(xi) ' '，返回' ' (N1, N2, N3，…Nn) ' '形数组如果索引='ij'或' ' (N2, N1, N3，…Nn) ' '形数组如果索引='xy'与元素' xi '重复填充矩阵沿第一个维度为' x1 '，第二个为' x2 '，以此类推。笔记-----这个函数通过索引关键字参数支持两种索引约定。给出字符串'ij'返回一个带矩阵索引的meshgrid，而'xy'返回一个带笛卡尔索引的meshgrid。在输入长度为M和N的二维情况下，输出的形状为(N, M)，表示“xy”索引，(M, N)表示“ij”索引。在输入长度为M、N和P的3-D情况下，输出的形状(N、M、P)表示“xy”索引，(M、N、P)表示“ij”索引。区别如下面的代码片段所示::xv yv = np。meshgrid(x, y, sparse=False, index ='ij')i在range(nx)内:j in range(ny):治疗xv[i,j]， yv[i,j]xv yv = np。meshgrid(x, y, sparse=False, index ='xy')i在range(nx)内:j in range(ny):在1-D和0-D情况下，索引和稀疏关键字没有影响。。另请参阅--------index_tricks。mgrid:使用索引符号构造一个多维“meshgrid”。index_tricks。ogrid:使用索引符号构造一个开放的多维“meshgrid”。Examples-------->>> nx, ny = (3, 2)>>> x = np.linspace(0, 1, nx)>>> y = np.linspace(0, 1, ny)>>> xv, yv = np.meshgrid(x, y)>>> xvarray([[ 0. ,  0.5,  1. ],[ 0. ,  0.5,  1. ]])>>> yvarray([[ 0.,  0.,  0.],[ 1.,  1.,  1.]])>>> xv, yv = np.meshgrid(x, y, sparse=True)  # makesparse output arrays>>> xvarray([[ 0. ,  0.5,  1. ]])>>> yvarray([[ 0.],[ 1.]])`meshgrid` is very useful to evaluate functions on a grid.>>> x = np.arange(-5, 5, 0.1)>>> y = np.arange(-5, 5, 0.1)>>> xx, yy = np.meshgrid(x, y, sparse=True)>>> z = np.sin(xx**2 + yy**2) / (xx**2 + yy**2)>>> h = plt.contourf(x,y,z)