用掩盖数据进行Scipy插值? [英] Scipy interpolation with masked data?

问题描述

我正在尝试插值包含被屏蔽数据的2D数组.我已经使用了一些可用的SciPy模块的方法，包括interp2d，bisplrep/bisplev和RectBivariateSpline.另外，我的数据是一个规则数组，这意味着网格具有相同的尺寸(在本例中为1ºX1º).

I am trying to interpolate a 2D array that contents masked data. I have used some of the SciPy module's methods available, including interp2d, bisplrep/bisplev, as well as RectBivariateSpline. As an additional information, my data is a regular array, which means that grids have the same dimension (in this case 1ºX1º).

话虽这么说，有没有什么方法可以插值，从而避免使用Python在数组中屏蔽数据?我仍然是使用Python和NumPy/SciPy模块的新手.

Having said that, is there any way to interpolate avoiding masked data in an array with Python? I am still new using Python and NumPy/SciPy modules.

推荐答案

您实际上可以使用接受x, y, z的每个函数( mgrid :

You can actually use every function that accepts x, y, z (which is the case for interp2d and probably the others as well) with your masked data. But you need to explicitly create a mgrid:

z = ... # Your data
x, y = np.mgrid[0:z.shape[0], 0:z.shape[1]]

然后，您需要删除所有这些坐标中的所有掩码值:

Then you need to delete all masked values in all of these coordinates:

x = x[~z.mask]
y = y[~z.mask]
z = z[~z.mask]

使用这些最终的x, y, z，您可以调用每个指定的函数(接受不完整的网格，因此RectBivariateSpline将不起作用).但是请注意，其中一些使用插值框，因此，如果由于遮罩而丢弃数据的区域太大，则插值将在那里失败(结果为np.nan或0).但是，如果发生这种情况，您可以调整参数以弥补这一点.

With these final x, y, z you can call every of your specified functions (that accepts incomplete grids, so RectBivariateSpline won't work). Notice however that some of these use interpolation boxes so if there is a too big region where you discarded the data because of your mask the interpolation will fail there (resulting in np.nan or 0). But you might tweak the parameters to compensate for that, if it happens.

data = np.random.randint(0, 10, (5,5))
mask = np.random.uniform(0,1,(5,5)) > 0.5
z = np.ma.array(data, mask=mask)
x, y = np.mgrid[0:z.shape[0], 0:z.shape[1]]
x1 = x[~z.mask]
y1 = y[~z.mask]
z1 = z[~z.mask]
interp2d(x1, y1, z1)(np.arange(z.shape[0]), np.arange(z.shape[1]))

array([[  1.1356716 ,   2.45313727,   3.77060294,   6.09790177, 9.31328935],
       [  3.91917937,   4.        ,   4.08082063,   3.98508121, 3.73406764],
       [ 42.1933738 ,  25.0966869 ,   8.        ,   0.        , 0.        ],
       [  1.55118338,   3.        ,   4.44881662,   4.73544593, 4.        ],
       [  5.        ,   8.        ,  11.        ,   9.34152525, 3.58619652]])

您可以看到0的小区域，因为该遮罩中有许多被遮罩的值:

you can see the small area of 0's because the mask had there many masked values:

mask
array([[False,  True,  True,  True, False],
       [False, False,  True, False, False],
       [ True,  True, False,  True,  True],
       [False,  True, False,  True,  True],
       [False,  True, False, False,  True]], dtype=bool)

data
array([[2, 4, 4, 5, 5],
       [1, 4, 1, 3, 8],
       [9, 1, 8, 0, 9],
       [7, 2, 0, 3, 4],
       [9, 6, 0, 4, 4]])

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