ZeroDivisionError：浮点除以零使用 scipy.interpolate.rbf('cubic")

Question

i am trying to interpolate missing values using scipy library and specifically rbf('cubic'). But i get the following error:

Traceback (most recent call last):
  File "C:\Users\St\Desktop\py_magn\inter.py", line 89, in <module>
    rbfp = Rbf(xn ,yn, magn, function='cubic') #PARAMETERS
  File "C:\Users\St\AppData\Local\Programs\Python\Python38\lib\site-packages\scipy\interpolate\rbf.py", line 239, in __init__
    self.epsilon = np.power(np.prod(edges)/self.N, 1.0/edges.size)
ZeroDivisionError: float division by zero

my code is the following:

x, y, mag = df[:,0], df[:,1], df[:,3]



emptyInd = np.where(np.isnan(mag))
print(emptyInd[0])
#----------------------------------------------------------------------------------------------------

def eucl_dist(pnt,x,y,mag):
    value = []
    dist = 0.0
    typ = [('Eucli Dist', float), ('x', float), ('y', float), ('Magn', float)]
    for i in range(len(x)):
        dist = sqrt((pnt[0]-x[i])**2 + (pnt[1]-y[i])**2)
        res = [dist, x[i], y[i], mag[i]]
        value.append(res)
    
    value = np.vstack(value)
    
    return value,typ

#--------------------------------------------------------------------------------

                    
xinter=[]
yinter=[]
magInter=[]
neigh = []
if len(emptyInd)!=0 :
    listOfEMPval = list(zip(emptyInd[0])) #, emptyInd[1]))
    for ind in listOfEMPval:
        xn=[]
        yn=[]
        magn=[]
        xinter = np.take(x,ind)
        yinter = np.take(y,ind)
        
        edist, typ = eucl_dist((xinter, yinter), x, y, mag)
        edist = rf.unstructured_to_structured(edist, np.dtype(typ))
        indx = np.argsort(edist, order='Eucli Dist')
        edist = np.reshape([edist[i] for i in indx],(len(edist), 1))
        
        sz=182
        for k in range(sz):
            if np.isnan(edist[k][0][3]):
                k+=1
                sz+=1
            else:
                xn = np.append(xn, edist[k][0][1])
                yn = np.append(yn, edist[k][0][2])
                magn = np.append(magn, edist[k][0][3])
           
        rbfp = Rbf(xn ,yn, magn, function='cubic') #PARAMETERS
        magInter = np.append(magInter, rbfp(xinter,yinter)) #INTERPOLATION
        

    for i in range(len(listOfEMPval)):
        np.put(mag, listOfEMPval[i], magInter[i])
    

 

i take in consideration a specific amount of data points to use for interpolation. Also, is there any other way to make it faster? thanks

UPDATE As i wrote in the "ANSWER" section, i solved my problem. the only think that remained is if there is any way to make it faster. i think the problem is the interpolation part

Answer 1

So i realised the problem and i fixed it. In my code i calculate the distance between the nan point (which i want to interpolate) and all the other. At first i didn't have in mind about so many nan values in my dataset and also i thought that each time i fell on a 'nan' value, by increasing the value by 1 of the variable, which i use in a for-loop to pick the points for interpolation ( specifically: for i in range(nval): ), would maintain the amount of points for interpolation. But it didnt. So i just added the following code in order to build an array that from a sorted list kept only the points with values(not nan)

ex=[0,0,0]
for i in range(len(edist)):
    if np.isnan(edist[i][0][3])==False:
        ex=np.vstack((ex,[edist[i][0][1],edist[i][0][2],edist[i][0][3]]))
        
ex=ex[1:,:]