Hey everybody...I am totally new to python...I found the script i wanted in the following link...but i am unable to understand it at all....please help me..

I know MATLAB...so either help me in converting it to MATLAB or help me understanding it n running it in python itself

I have a file with 3 columns on which i have to run the script.. TIME MAGNITUDE MAGNITUDE_ERROR

how to give these as input to these scripts...

please help me...i m really stuck in a very bad situation...

Thanks

Here is the script...

import agpy.mpfit as mpfit

import numpy as np

def powerfit(xax,data,err=None,alphaguess=-2.0,scaleguess=1.0,quiet=True):

"""

Fit a power law (a line in log-space) to data as a function of x

differs from 'plfit' because plfit fits a power law distribution,

this code simply fits a power law

"""

logdata = np.log10(data)

if err is None: err = np.ones(data.shape,dtype='float')

def mpfitfun(data,err):

def f(p,fjac=None): return [0,np.ravel(((np.log10(p[0])+np.log10(xax)*p[1])-data)/err)]

return f

mp = mpfit.mpfit(mpfitfun(logdata,err),xall=[scaleguess,alphaguess],quiet=quiet)

fitp = mp.params

return fitp,mp

def brokenpowerfit(xax, data, err=None, alphaguess1=0.0, alphaguess2=-2.0, scaleguess=1.0,

breakpoint=None, quiet=True):

"""

Fit a broken power law (a line in log-space) to data as a function of x

differs from 'plfit' because plfit fits a power law distribution,

this code simply fits a power law

This is a lot more intricate than the simple power law fit, since it involves

fitting two power laws with different slopes

Parameters:

p[0] - scale

p[1] - breakpoint

p[2] - power 1 (xax < breakpoint)

p[3] - power 2 (xax >= breakpoint)

There are 5 parameters (NOT 4) returned because there are two scales that are *NOT*

independent

returns: scale1,scale2,breakpoint,alpha1,alpha2

"""

logdata = np.log10(data)

if err is None: err = np.ones(data.shape,dtype='float')

def brokenpowerlaw(p):

lowerhalf = (np.log10(p[0]) + np.log10(xax)*p[2]) * (xax < p[1])

# find the location at which both functions must be equal

scale2loc = np.argmin(np.abs(xax - p[1]))

scale2 = np.log10(xax[scale2loc])*(p[2] - p[3]) + np.log10(p[0])

upperhalf = (scale2 + np.log10(xax)*p[3]) * (xax >= p[1])

# DEBUG print "scale1: %15g scale2: %15g xaxind: %5i xaxval: %15g lower: %15g upper: %15g" % (p[0],scale2,scale2loc,np.log10(xax[scale2loc]),lowerhalf[scale2loc-1],upperhalf[scale2loc])

return lowerhalf+upperhalf

def mpfitfun(data,err):

def f(p,fjac=None): return [0,np.ravel((brokenpowerlaw(p)-data)/err)]

return f

if breakpoint is None: breakpoint = np.median(xax)

parinfo = [{}, {'mpminstep':xax.min(),'mpmaxstep':xax.max(),'step':xax.min()}, {}, {}]

mp = mpfit.mpfit(mpfitfun(logdata,err),xall=[scaleguess,breakpoint,alphaguess1,alphaguess2],quiet=quiet,parinfo=parinfo)

fitp = mp.params

scale2loc = np.argmin(np.abs(xax - fitp[1]))

scale2 = 10**( np.log10(xax[scale2loc])*(fitp[2] - fitp[3]) + np.log10(fitp[0]) )

fitp = np.array( [fitp[0],scale2] + fitp[1:].tolist() )

return fitp,mp

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