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About a value error called 'ValueError: A value in x_new is below the interpolation range'

Dear all,

I am trying to calculate correlation coefficients between one time series d=
ata and other time series. However,there are some missing values. So, I int=
erploated each time series with 1d interpolation in scipy and got correlati=
on coefficients between them. This code works well for some data sets, but =
doesn't for some others. Following is actual error I got:
0.0708904109589
0.0801369863014
0.0751141552511
0.0938356164384
0.0769406392694
Traceback (most recent call last):
  File "error_removed.py", line 56, in <module>
    i2 =3D interp(indices)
  File "/lustre/work/apps/python-2.7.1/lib/python2.7/site-packages/scipy/in=
terpolate/interpolate.py", line 394, in __call__
    out_of_bounds =3D self._check_bounds(x_new)
  File "/lustre/work/apps/python-2.7.1/lib/python2.7/site-packages/scipy/in=
terpolate/interpolate.py", line 449, in _check_bounds
    raise ValueError("A value in x_new is below the interpolation "
ValueError: A value in x_new is below the interpolation range.
--------------------------------------------------------------------
This time is 'x_new is below the interpolation range", but some times, it s=
hows=20
"above interpolation range.'

I would like to make some self-contained code, but, I am not sure how to ma=
ke it to represent my case well.
I just put all of my code here. I apologize for this inconvenience.
-----------------------------------------------------------------------
---------
a =3D []
c =3D 4
with open(filin1, 'r') as f1:
    arrays =3D [map(float, line.split()) for line in f1]
newa =3D [[x[1],x[2]] for x in arrays]

o =3D newa[58]
f =3D open(filin, "r")
percent1 =3D []
for columns in ( raw.strip().split() for raw in f ):
        a.append(columns[63])
        x =3D np.array(a, float)

not_nan =3D np.logical_not(np.isnan(x))
indices =3D np.arange(len(x))
interp =3D interp1d(indices[not_nan], x[not_nan])
#interp =3D np.interp(indices, indices[not_nan], x[not_nan])
i1 =3D interp(indices)

f.close
h1 =3D []
p1 =3D []
while c <278:
        c =3D c + 1
        d =3D c - 5
        b =3D []


        f.seek(0,0)
        for columns in ( raw.strip().split() for raw in f ):

                b.append(columns[c])
 y =3D np.array(b, float)
        h =3D haversine.distance(o, newa[d])
        n =3D len(y)
        l =3D b.count('nan')
        percent =3D l/8760.
        percent1 =3D percent1 + [percent]
       #print l, percent

        if percent < 0.1:
                not_nan =3D np.logical_not(np.isnan(y))
                indices =3D np.arange(len(y))

                interp =3D interp1d(indices[not_nan], y[not_nan])
                #interp =3D np.interp(indices, indices[not_nan], x[not_nan]=
)
                i2 =3D interp(indices)

                pearcoef =3D sp.pearsonr(i1,i2)
                p =3D pearcoef[0]
                p1 =3D p1 + [p]
                h1 =3D h1 + [h]
                print percent

print h1
print p1
print len(p1)
plt.plot(h1, p1, 'o')
plt.xlabel('Distance(km)')
plt.ylabel('Correlation coefficient')
plt.grid(True)
plt.show()
---------------------------------------------------------------
For any help or advice, I will really appreciate.

Best regards,

Isaac
0
winefrog (28)
2/6/2013 12:51:46 AM
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