Py之Numpy：Numpy库中常用函数的简介、应用之详细攻略
Py之Numpy：Numpy庫中常用函數的簡介、應用之詳細攻略
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目錄
Numpy庫中常用函數的簡介、應用
1、X, Y = np.meshgrid(X, Y)
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Py之Numpy：Numpy庫簡介、安裝、使用方法、案例應用之詳細攻略???????
Py之Numpy：Numpy庫中常用函數的簡介、應用之詳細攻略
Numpy庫中常用函數的簡介、應用
1、X, Y = np.meshgrid(X, Y)
| meshgrid Found at: numpy.lib.function_base Return 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:: 1.9 ? ? 1-D and 0-D cases are allowed. ? ?? ? ? Parameters ? ? ---------- ? ? x1, x2,..., xn : array_like ? ? 1-D arrays representing the coordinates of a grid. ? ? indexing : {'xy', 'ij'}, optional ? ? Cartesian ('xy', default) or matrix ('ij') indexing of output. ? ? See Notes for more details. ? ?? ? ? .. versionadded:: 1.7.0 ? ? sparse : bool, optional ? ? If True a sparse grid is returned in order to conserve? memory. Default is False. ? ?? ? ? .. versionadded:: 1.7.0 ? ? copy : bool, optional.?If False, a view into the original arrays are returned in? ? ? ?order 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:: 1.7.0 ? ?? ? ? Returns ? ? ------- ? ? X1, X2,..., XN : ndarray ? ? For 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. | 從坐標向量返回坐標矩陣。
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| ? ? Examples ? ? -------- ? ? >>> nx, ny = (3, 2) ? ? >>> x = np.linspace(0, 1, nx) ? ? >>> y = np.linspace(0, 1, ny) ? ? >>> xv, yv = np.meshgrid(x, y) ? ? >>> xv ? ? array([[ 0. , ?0.5, ?1. ], ? ? [ 0. , ?0.5, ?1. ]]) ? ? >>> yv ? ? array([[ 0., ?0., ?0.], ? ? [ 1., ?1., ?1.]]) ? ? >>> xv, yv = np.meshgrid(x, y, sparse=True) ?# make? ? ? ?sparse output arrays ? ? >>> xv ? ? array([[ 0. , ?0.5, ?1. ]]) ? ? >>> yv ? ? array([[ 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) | ? |
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