如何使用scipy.interpolate.interpn函数和xarray(3d)来填充nan空白？当前错误[0维中的点必须严格升序]

fzsnzjdm 于 2022-11-09 发布在其他

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我有点沮丧，因为我找不到解决我的问题的方法，这在r中用包gapfill似乎很容易做到，但在python中就更难了。
我的问题来了：我有一个xarray（3d）以维度纬度、经度和时间。我想要的是在每个栅格/数组中插入nan值（由云和其他扭曲引起）。nan值形成块我的想法是不仅用每个时间步的相邻像素插值，而且用之前和之后的时间步插值。（假设几天前和几天后的像素值非常相似，因为土地覆盖变化不是很快）。我的目标是在相同的像素位置上对时间进行线性插值。（之前和之后有多少个时间步，我也不知道如何在interpn函数中定义？）
我找到了不同的方法来实现这一点，但是目前还没有。我找到的最有希望的方法是从包scipy中找到的interpolate.interpn函数。这个函数使用numpy数组而不是xarray。我的尝试是：


# change from xarray to numpy

my array_np = my array.to_numpy()

# lable dimensions (what is done when building a numpy with meshgrid)

x = array_np [0]
y = array_np [1]
z = array_np [2]

# get index of nan values

nanIndex= np.isnan(array_np ).nonzero()
nanIndex

# name dimensions of nan values

xc= nanIndex[0] 
yc= nanIndex[1]
zc= nanIndex[2] 

# For using the scipy interpolate. interpn function:

# points = the regular grid  - in my case x,y,z

# values = the data on the regular grid - in my case my array (my_array_np)

# point_nan = the point that is evaluate in the 3D grid - in my case xc, y,c, zy

points = (x, y, z) # dimensions
points_nan = (xc, yc, zc) #nandimensions

print(interpolate.interpn(points, my_array_np, points_nan))

我现在得到的错误是：

"The points in dimension 0 must be strictly ascending"

我哪里错了？提前谢谢你的帮助！如果你有其他的解决方案，也解决了我的问题，除了scipy，我也很乐意帮助！
这是我的数组的外观：

array([[[      nan,       nan,       nan, ..., 279.64   , 282.16998,
         279.66998],
        [277.62   , 277.52   , 277.88   , ..., 281.75998, 281.72   ,
         281.66   ],
        [277.38   , 277.75   , 277.88998, ..., 281.75998, 281.75998,
         280.91998],
        ...,
        [      nan,       nan,       nan, ..., 280.72998, 280.33   ,
         280.94   ],
        [      nan,       nan,       nan, ...,       nan,       nan,
               nan],
        [      nan,       nan,       nan, ...,       nan,       nan,
               nan]],
       [[      nan,       nan,       nan, ..., 272.22   , 271.54   ,
         271.02   ],
        [280.02   , 280.44998, 281.18   , ..., 271.47998, 271.88   ,
         272.03   ],
        [280.32   , 281.     , 281.27   , ..., 270.83   , 271.58   ,
         272.03   ],
        ...,
        [      nan,       nan,       nan, ..., 290.34   , 290.25   ,
         288.365  ],
        [      nan,       nan,       nan, ...,       nan,       nan,
               nan],
        [      nan,       nan,       nan, ...,       nan,       nan,
               nan]],

       [[      nan,       nan,       nan, ...,       nan,       nan,
               nan],
        [276.44998, 276.19998, 276.19   , ...,       nan,       nan,
               nan],
        [276.50998, 276.79   , 276.58   , ...,       nan,       nan,
               nan],
        ...,
        [      nan,       nan,       nan, ...,       nan,       nan,
               nan],
        [      nan,       nan,       nan, ...,       nan,       nan,
               nan],
        [      nan,       nan,       nan, ...,       nan,       nan,
               nan]],

       ...,

       [[      nan,       nan,       nan, ..., 276.38998, 276.44   ,
         275.72998],
        [      nan,       nan,       nan, ..., 276.55   , 276.81   ,
         276.72998],
        [      nan,       nan,       nan, ..., 279.74   , 277.11   ,
         276.97   ],
        ...,
        [      nan,       nan,       nan, ...,       nan,       nan,
               nan],
        [      nan,       nan,       nan, ...,       nan,       nan,
               nan],
        [      nan,       nan,       nan, ...,       nan,       nan,
               nan]],

       [[      nan,       nan,       nan, ..., 277.38   , 278.08   ,
         277.79   ],
        [279.66998, 280.00998, 283.13   , ..., 277.34   , 277.41998,
         277.62   ],
        [      nan, 277.41   , 277.41   , ..., 277.825  , 277.31   ,
         277.52   ],
        ...,
        [      nan,       nan,       nan, ..., 276.52   ,       nan,
               nan],
        [      nan,       nan,       nan, ...,       nan,       nan,
               nan],
        [      nan,       nan,       nan, ...,       nan,       nan,
               nan]],

       [[      nan,       nan,       nan, ...,       nan,       nan,
               nan],
        [      nan,       nan,       nan, ...,       nan,       nan,
               nan],
        [      nan,       nan,       nan, ...,       nan,       nan,
               nan],
        ...,
        [      nan,       nan,       nan, ...,       nan,       nan,
               nan],
        [      nan,       nan,       nan, ...,       nan,       nan,
               nan],
        [      nan,       nan,       nan, ...,       nan,       nan,
               nan]]], dtype=float32)

scipy

来源：https://stackoverflow.com/questions/74150690/how-to-use-scipy-interpolate-interpn-function-with-xarray-3d-to-fill-nan-gaps

1条答案

按热度按时间

kninwzqo1#

interpn不能用于填充规则栅格中的间隙- interpn是用于将完整规则栅格（没有间隙）内插到不同坐标的快速方法。
为了用N维插值来填补缺失值，对非结构化的N维数据使用了简洁的插值方法。
由于您要对常规网格进行插值，因此我将演示scipy.interpolate.griddata的用法：

import xarray as xr, pandas as pd, numpy as np, scipy.interpolate

# create dummy data

x = y = z = np.linspace(0, 1, 5)
da = xr.DataArray(
    np.sin(x).reshape(-1, 1, 1) * np.cos(y).reshape(1, -1, 1) + z.reshape(1, 1, -1),
    dims=['x', 'y', 'z'],
    coords=[x, y, z],
)

# randomly fill with NaNs

da = da.where(np.random.random(size=da.shape) > 0.1)

如下所示

In [11]: da
Out[11]:
<xarray.DataArray (x: 5, y: 5, z: 5)>
array([[[0.        , 0.25      , 0.5       , 0.75      , 1.        ],
        [0.        , 0.25      , 0.5       , 0.75      , 1.        ],
        [0.        , 0.25      , 0.5       , 0.75      , 1.        ],
        [0.        , 0.25      , 0.5       , 0.75      , 1.        ],
        [0.        , 0.25      , 0.5       , 0.75      , 1.        ]],

       [[0.24740396, 0.49740396, 0.74740396, 0.99740396,        nan],
        [       nan, 0.48971277,        nan, 0.98971277, 1.23971277],
        [0.2171174 , 0.4671174 , 0.7171174 , 0.9671174 , 1.2171174 ],
        [0.18102272, 0.43102272, 0.68102272, 0.93102272, 1.18102272],
        [       nan, 0.38367293, 0.63367293, 0.88367293, 1.13367293]],

       [[0.47942554,        nan, 0.97942554, 1.22942554, 1.47942554],
        [0.46452136, 0.71452136, 0.96452136, 1.21452136, 1.46452136],
        [0.42073549, 0.67073549, 0.92073549, 1.17073549,        nan],
        [0.35079033, 0.60079033, 0.85079033, 1.10079033, 1.35079033],
        [       nan, 0.50903472, 0.75903472,        nan, 1.25903472]],

       [[0.68163876, 0.93163876, 1.18163876, 1.43163876, 1.68163876],
        [0.66044826, 0.91044826,        nan, 1.41044826, 1.66044826],
        [0.59819429, 0.84819429, 1.09819429, 1.34819429,        nan],
        [       nan, 0.74874749, 0.99874749, 1.24874749,        nan],
        [0.36829099, 0.61829099, 0.86829099, 1.11829099, 1.36829099]],

       [[0.84147098, 1.09147098,        nan, 1.59147098, 1.84147098],
        [0.81531169, 1.06531169, 1.31531169, 1.56531169, 1.81531169],
        [0.73846026, 0.98846026, 1.23846026, 1.48846026,        nan],
        [0.61569495, 0.86569495, 1.11569495, 1.36569495, 1.61569495],
        [0.45464871, 0.70464871, 0.95464871,        nan, 1.45464871]]])
Coordinates:
  * x        (x) float64 0.0 0.25 0.5 0.75 1.0
  * y        (y) float64 0.0 0.25 0.5 0.75 1.0
  * z        (z) float64 0.0 0.25 0.5 0.75 1.0

要使用非结构化Scipy插值器，必须将包含缺失值的网格化数据转换为不包含缺失值的1D点矢量：


# ravel all points and find the valid ones

points = da.data.ravel()
valid = ~np.isnan(points)
points_valid = points[valid]

# construct arrays of (x, y, z) points, masked to only include the valid points

xx, yy, zz = np.meshgrid(x, y, z)
xx, yy, zz = xx.ravel(), yy.ravel(), zz.ravel()
xxv = xx[valid]
yyv = yy[valid]
zzv = zz[valid]

# feed these into the interpolator, and also provide the target grid

interpolated = scipy.interpolate.griddata(np.stack([xxv, yyv, zzv]).T, points_valid, (xx, yy, zz), method="linear")

# reshape to match the original array and replace the DataArray values with

# the interpolated data

da.values = interpolated.reshape(da.shape)

这将导致数组被填充

In [32]: da
Out[32]:
<xarray.DataArray (x: 5, y: 5, z: 5)>
array([[[0.        , 0.25      , 0.5       , 0.75      , 1.        ],
        [0.        , 0.25      , 0.5       , 0.75      , 1.        ],
        [0.        , 0.25      , 0.5       , 0.75      , 1.        ],
        [0.        , 0.25      , 0.5       , 0.75      , 1.        ],
        [0.        , 0.25      , 0.5       , 0.75      , 1.        ]],

       [[0.24740396, 0.49740396, 0.74740396, 0.99740396, 1.23971277],
        [0.23226068, 0.48971277, 0.73226068, 0.98971277, 1.23971277],
        [0.2171174 , 0.4671174 , 0.7171174 , 0.9671174 , 1.2171174 ],
        [0.18102272, 0.43102272, 0.68102272, 0.93102272, 1.18102272],
        [0.12276366, 0.38367293, 0.63367293, 0.88367293, 1.13367293]],

       [[0.47942554, 0.71452136, 0.97942554, 1.22942554, 1.47942554],
        [0.46452136, 0.71452136, 0.96452136, 1.21452136, 1.46452136],
        [0.42073549, 0.67073549, 0.92073549, 1.17073549, 1.40765584],
        [0.35079033, 0.60079033, 0.85079033, 1.10079033, 1.35079033],
        [0.24552733, 0.50903472, 0.75903472, 1.00903472, 1.25903472]],

       [[0.68163876, 0.93163876, 1.18163876, 1.43163876, 1.68163876],
        [0.66044826, 0.91044826, 1.16044826, 1.41044826, 1.66044826],
        [0.59819429, 0.84819429, 1.09819429, 1.34819429, 1.57184545],
        [0.48324264, 0.74874749, 0.99874749, 1.24874749, 1.48324264],
        [0.36829099, 0.61829099, 0.86829099, 1.11829099, 1.36829099]],

       [[0.84147098, 1.09147098, 1.34147098, 1.59147098, 1.84147098],
        [0.81531169, 1.06531169, 1.31531169, 1.56531169, 1.81531169],
        [0.73846026, 0.98846026, 1.23846026, 1.48846026, 1.71550332],
        [0.61569495, 0.86569495, 1.11569495, 1.36569495, 1.61569495],
        [0.45464871, 0.70464871, 0.95464871, 1.20464871, 1.45464871]]])
Coordinates:
  * x        (x) float64 0.0 0.25 0.5 0.75 1.0
  * y        (y) float64 0.0 0.25 0.5 0.75 1.0
  * z        (z) float64 0.0 0.25 0.5 0.75 1.0

请注意，这填充了整个数组，因为可用点的船体覆盖了整个数组。如果不是这样，您可能需要使用最近邻或拟合样条来填充数据的第二步。

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如何使用scipy.interpolate.interpn函数和xarray(3d)来填充nan空白？当前错误[0维中的点必须严格升序]

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