如果我尝试运行下面的脚本，我得到的错误：LinAlgError: SVD did not converge in Linear Least Squares。我在一个类似的数据集上使用了完全相同的脚本，它在那里工作。我试图在我的数据集中搜索Python可能解释为NaN的值，但我什么也找不到。
我的数据集很大，不可能手工检查。（但我认为我的数据集是好的）。我还检查了stageheight_masked和discharge_masked的长度，但它们是相同的。有人知道为什么我的脚本中有一个错误，我可以做些什么吗？

import numpy as np
import datetime
import matplotlib.dates
import matplotlib.pyplot as plt
from scipy import polyfit, polyval

kwargs = dict(delimiter = '\t',\
     skip_header = 0,\
     missing_values = 'NaN',\
     converters = {0:matplotlib.dates.strpdate2num('%d-%m-%Y %H:%M')},\
     dtype = float,\
     names = True,\
     )

rating_curve_Gillisstraat = np.genfromtxt('G:\Discharge_and_stageheight_Gillisstraat.txt',**kwargs)

discharge = rating_curve_Gillisstraat['discharge']   #change names of columns
stageheight = rating_curve_Gillisstraat['stage'] - 131.258

#mask NaN
discharge_masked = np.ma.masked_array(discharge,mask=np.isnan(discharge)).compressed()
stageheight_masked = np.ma.masked_array(stageheight,mask=np.isnan(discharge)).compressed()

#sort
sort_ind = np.argsort(stageheight_masked)
stageheight_masked = stageheight_masked[sort_ind]
discharge_masked = discharge_masked[sort_ind]

#regression
a1,b1,c1 = polyfit(stageheight_masked, discharge_masked, 2)
discharge_predicted = polyval([a1,b1,c1],stageheight_masked)

print 'regression coefficients'
print (a1,b1,c1)

#create upper and lower uncertainty
upper = discharge_predicted*1.15
lower = discharge_predicted*0.85

#create scatterplot

plt.scatter(stageheight,discharge,color='b',label='Rating curve')
plt.plot(stageheight_masked,discharge_predicted,'r-',label='regression line')
plt.plot(stageheight_masked,upper,'r--',label='15% error')
plt.plot(stageheight_masked,lower,'r--')
plt.axhline(y=1.6,xmin=0,xmax=1,color='black',label='measuring range')
plt.title('Rating curve Catsop')
plt.ylabel('discharge')
plt.ylim(0,2)
plt.xlabel('stageheight[m]')
plt.legend(loc='upper left', title='Legend')
plt.grid(True)
plt.show()