Py之Numpy:Numpy库中常用函数的简介、应用之详细攻略

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Numpy库中常用函数的简介、应用

1、X, Y = np.meshgrid(X, Y)


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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_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.


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)

    >>> 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)