Created
December 5, 2014 19:42
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Fit a charge-charge interaction tail
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#!/usr/bin/env python | |
''' fit a simple equation ''' | |
import os, sys | |
from math import * | |
fnin = None # input file | |
exponent = -1 | |
xmin = -1e30 | |
xmax = 1e30 | |
def usage(): | |
''' print usage and die ''' | |
print '''fit.py [OPTIONS] input.dat | |
Linear regression | |
Options: | |
-e: fit a + b x^e | |
-x, -xmin=: minimal x for fitting | |
-X, -xmax=: maximal x for fitting | |
-v: be verbose | |
-vv: be more verbose | |
-h, --help: print this message | |
Examples: | |
fit.py -e-1 my.dat | |
''' | |
exit(1) | |
def doargs(): | |
''' handle command line options ''' | |
import getopt, glob | |
try: | |
opts, args = getopt.gnu_getopt(sys.argv[1:], "e:x:X", | |
["xmin=", "xmax=", "verbose=", "help"]) | |
except getopt.GetoptError, err: | |
print str(err) | |
usage() | |
global fnin, exponent, xmin, xmax | |
for o, a in opts: | |
if o in ("-e",): | |
exponent = float(a) | |
elif o in ("-x", "--xmin"): | |
xmin = float(a) | |
elif o in ("-X", "--xmax"): | |
xmax = float(a) | |
elif o in ("-v",): | |
verbose += 1 | |
elif o in ("-h", "--help",): | |
usage() | |
ls = args | |
if len(ls) > 0: | |
fnin = ls[0] | |
else: | |
fnin = glob.glob("*.dat")[0] | |
def fitlow(ls): | |
n = len(ls) | |
sz = sy = szz = syy = szy = 0 | |
for i in range(n): | |
x, y = ls[i] | |
z = pow(x, exponent) | |
sy += y | |
sz += z | |
szz += z*z | |
szy += z*y | |
syy += y*y | |
''' now solve | |
n a + sz b = sy --> sz a + sz*sz/n b = sy*sz/n | |
sz a + szz b = szy | |
''' | |
var = szz - sz*sz/n | |
cov = szy - sy*sz/n | |
b = cov/var | |
a = (sy - b*sz)/n | |
vary = syy - sy*sy/n | |
res = (vary - cov*cov/var)/n | |
print "best fit: %g%+g*x^(%s), residue %g (%d points)" % ( | |
a, b, exponent, sqrt(res), n) | |
unitb = 0.00013893545782*1e7 | |
invepsr = b / unitb | |
print invepsr | |
def dofit(fn): | |
ls = [] | |
for ln in open(fn).readlines(): | |
xy = ln.strip() | |
if not xy: continue | |
xy = xy.split() | |
xi = float(xy[0]) | |
yi = float(xy[1]) | |
if xi < xmin or xi > xmax: continue | |
ls += [(xi, yi),] | |
fitlow(ls) | |
if __name__ == "__main__": | |
doargs() | |
dofit(fnin) |
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