Working with missing data

I’m trying to interpolate data which contains missing values using pyKrige. Is this possible? So far, I encountered this error while doing so:

import numpy as np
from pykrige.ok import OrdinaryKriging

data = np.array([[0.3, 1.2,np.nan],
                 [1.9, 0.6, np.nan],
                 [1.1, 3.2, np.nan],
                 [3.3, 4.4, 1.47],
                 [4.7, 3.8, 1.74]])

gridx = np.arange(0.0, 5.5, 0.5)
gridy = np.arange(0.0, 5.5, 0.5)

OK = OrdinaryKriging(data[:,0],data[:,1],data[:,2],variogram_model='linear',verbose=False)

 File "<ipython-input-40-17311a362b4a>", line 17, in <module>
    OK = OrdinaryKriging(data[:,0],data[:,1],data[:,2],variogram_model='linear',verbose=False)

  File "~/python3.6/site-packages/pykrige/ok.py", line 232, in __init__
    self.variogram_function, nlags, weight)

  File "~/python3.6/site-packages/pykrige/core.py", line 199, in initialize_variogram_model
    variogram_function, weight)

  File "~/python3.6/site-packages/pykrige/core.py", line 286, in calculate_variogram_model
    x0 = [(np.amax(semivariance) - np.amin(semivariance))/(np.amax(lags) - np.amin(lags)),

  File "~/python3.6/site-packages/numpy/core/fromnumeric.py", line 2252, in amax
    out=out, **kwargs)

  File "~/python3.6/site-packages/numpy/core/_methods.py", line 26, in _amax
    return umr_maximum(a, axis, None, out, keepdims)

ValueError: zero-size array to reduction operation maximum which has no identity

Is there a workaround for this?

Thanks!

[bsmurphy]

Sorry for the slow response on this. I do think you’re getting problems because you’re only feeding in two points in this example. Since you don’t specify any variogram model parameters, the code tries to automatically calculate the variogram model (linear in your case) from the data. However, since you only have two points, there is only one lag value (one distance pair) and the variogram model therefore can’t be calculated. (You can’t fit a line to one point.) If you look at OK.variogram_model_parameters, you’ll notice that they’re NaNs, because no model parameters could be calculated. So when you try to solve the kriging system, the matrix gets filled with NaNs due to the lack of a variogram model, hence the ValueError you’re getting. Hope that makes some sense…

[MuellerSeb]

As told: You filter out the points with nan values used for conditioning the kriging system and evaluate at these points.

In case of the linked example: xpred should then only hold the postions of interest (where nan values occurred) and y and x should be shrunk to hold only positions and data where no nan values occur.