[SciPy-User] Value that compare two
Wed Apr 3 03:51:41 CDT 2013
Florian Lindner <mailinglists <at> xgm.de> writes:
> this is not exactly a scipy question... but I want to implement it with scipy.
> I have two datasets of shape: (n, 2), each row consists of a coordinate and a
> pressure value from experiments or simulations.
> I want to compare these two sets and get some kind of integral distance value.
> delta = abs(data2 - data1)
> delta[:,0] = data1[:,0] # I don't want to delta the coordinates ??
to compare two curves,
you could first interpolate both to the same fine grid,
say np.linspace( lo, hi, 1000 ).
from __future__ import division
import numpy as np
import pylab as pl
plot = 1
def compare_ab( A, B, xfine ):
""" compare A, B n x 2 on the same fine grid """
Ax, Ay = A.T
Bx, By = B.T
Afine = np.interp( xfine, Ax, Ay )
Bfine = np.interp( xfine, Bx, By )
absdiff = np.fabs( Afine - Bfine )
print "av |A - B|: %.3g" % absdiff.mean()
rms = np.sqrt( ((Afine - Bfine) ** 2) .mean() )
print "rms |A - B|: %.3g" % rms
pl.plot( xfine, Afine, label="A" )
pl.plot( xfine, Bfine, label="B" )
Which metric to use ? I don't know of *a priori* reasons
for choosing L1 vs L2 vs Lmax.
For distributions, you might ask on stats.stackexchange.com; and see
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