def fit_mod(fc=0.01, mmax = 1e7):
t0 = 0.1
delta_t = 0.5
mmin = log10(200.)
age_slope = -0.9
cmf_slope = -2.0
starting = array([t0, delta_t, mmin, cmf_slope, age_slope])
parinfo = [{'value':0., 'fixed':0, 'limited':[0,0], 'limits':[0.,0.], \
'step':0.}
for i in range(5)]
fac = 0.5
print starting
parinfo[0]['limited'][0]=1
parinfo[0]['limited'][1]=1
parinfo[0]['limits'][0]=0.
parinfo[0]['limits'][1]=10.
parinfo[0]['value']=0.1
parinfo[0]['step']=0.01*fac
parinfo[1]['limited'][0]=1
parinfo[1]['limited'][1]=1
parinfo[1]['limits'][0]=0.
parinfo[1]['limits'][1]=10.
parinfo[1]['value']=0.5
parinfo[1]['step']=0.01*fac
parinfo[2]['limited'][0]=1
parinfo[2]['limited'][1]=1
parinfo[2]['limits'][0]=2.
parinfo[2]['limits'][1]=4.
parinfo[2]['value']=2.2
parinfo[2]['step']=0.01*fac
parinfo[3]['limited'][0]=1
parinfo[3]['limited'][1]=1
parinfo[3]['limits'][0]=-2.2
parinfo[3]['limits'][1]=-1.8
parinfo[3]['value']=-2.
parinfo[3]['step']=0.01*fac
parinfo[4]['limited'][0]=1
parinfo[4]['limited'][1]=1
parinfo[4]['limits'][0]=-0.2
parinfo[4]['limits'][1]=0.2
parinfo[4]['value']=0
parinfo[4]['step']=0.01*fac
sss = call_wrap2(starting)
return mpfit(call_wrap2, parinfo = parinfo, \
functkw={'mmax':mmax, 'fc':fc})
def multigaussfit(xax,
data,
ngauss=1,
err=None,
params=[1, 0, 1],
fixed=[False, False, False],
limitedmin=[False, False, True],
limitedmax=[False, False, False],
minpars=[0, 0, 0],
maxpars=[0, 0, 0],
quiet=True,
shh=True):
"""
An improvement on onedgaussfit. Lets you fit multiple gaussians.
Inputs:
xax - x axis
data - y axis
ngauss - How many gaussians to fit? Default 1 (this could supercede onedgaussfit)
err - error corresponding to data
These parameters need to have length = 3*ngauss. If ngauss > 1 and length = 3, they will
be replicated ngauss times, otherwise they will be reset to defaults:
params - Fit parameters: [amplitude, offset, width] * ngauss
If len(params) % 3 == 0, ngauss will be set to len(params) / 3
fixed - Is parameter fixed?
limitedmin/minpars - set lower limits on each parameter (default: width>0)
limitedmax/maxpars - set upper limits on each parameter
quiet - should MPFIT output each iteration?
shh - output final parameters?
Returns:
Fit parameters
Model
Fit errors
chi2
"""
if len(params) != ngauss and (len(params) / 3) > ngauss:
ngauss = len(params) / 3
# make sure all various things are the right length; if they're not, fix them using the defaults
for parlist in (params, fixed, limitedmin, limitedmax, minpars, maxpars):
if len(parlist) != 3 * ngauss:
# if you leave the defaults, or enter something that can be multiplied by 3 to get to the
# right number of gaussians, it will just replicate
if len(parlist) == 3:
parlist *= ngauss
elif parlist == params:
parlist[:] = [1, 0, 1] * ngauss
elif parlist == fixed or parlist == limitedmax:
parlist[:] = [False, False, False] * ngauss
elif parlist == limitedmin:
parlist[:] = [False, False, True] * ngauss
elif parlist == minpars or parlist == maxpars:
parlist[:] = [0, 0, 0] * ngauss
def mpfitfun(x, y, err):
if err == None:
def f(p, fjac=None):
return [0, (y - n_gaussian(pars=p)(x))]
else:
def f(p, fjac=None):
return [0, (y - n_gaussian(pars=p)(x)) / err]
return f
if xax == None:
xax = numpy.arange(len(data))
parnames = {0: "SHIFT", 1: "WIDTH", 2: "AMPLITUDE"}
parinfo = [{
'n': ii,
'value': params[ii],
'limits': [minpars[ii], maxpars[ii]],
'limited': [limitedmin[ii], limitedmax[ii]],
'fixed': fixed[ii],
'parname': parnames[ii / 3] + str(ii / 3),
'error': ii
} for ii in range(len(params))]
mp = mpfit(mpfitfun(xax, data, err), parinfo=parinfo, quiet=quiet)
mpp = mp.params
mpperr = mp.perror
chi2 = mp.fnorm
if mp.status == 0:
raise Exception(mp.errmsg)
if not shh:
for i, p in enumerate(mpp):
parinfo[i]['value'] = p
print(parinfo[i]['parname'], p, " +/- ", mpperr[i])
print("Chi2: ", mp.fnorm, " Reduced Chi2: ", mp.fnorm / len(data),
" DOF:",
len(data) - len(mpp))
return mpp, n_gaussian(pars=mpp)(xax), mpperr, chi2
def onedgaussfit(xax,
data,
err=None,
params=[0, 1, 0, 1],
fixed=[False, False, False, False],
limitedmin=[False, False, False, True],
limitedmax=[False, False, False, False],
minpars=[0, 0, 0, 0],
maxpars=[0, 0, 0, 0],
quiet=True,
shh=True):
"""
Inputs:
xax - x axis
data - y axis
err - error corresponding to data
params - Fit parameters: Height of background, Amplitude, Shift, Width
fixed - Is parameter fixed?
limitedmin/minpars - set lower limits on each parameter (default: width>0)
limitedmax/maxpars - set upper limits on each parameter
quiet - should MPFIT output each iteration?
shh - output final parameters?
Returns:
Fit parameters
Model
Fit errors
chi2
"""
def mpfitfun(x, y, err):
if err == None:
def f(p, fjac=None):
return [0, (y - onedgaussian(x, *p))]
else:
def f(p, fjac=None):
return [0, (y - onedgaussian(x, *p)) / err]
return f
if xax == None:
xax = numpy.arange(len(data))
parinfo = [{
'n': 0,
'value': params[0],
'limits': [minpars[0], maxpars[0]],
'limited': [limitedmin[0], limitedmax[0]],
'fixed': fixed[0],
'parname': "HEIGHT",
'error': 0
}, {
'n': 1,
'value': params[1],
'limits': [minpars[1], maxpars[1]],
'limited': [limitedmin[1], limitedmax[1]],
'fixed': fixed[1],
'parname': "AMPLITUDE",
'error': 0
}, {
'n': 2,
'value': params[2],
'limits': [minpars[2], maxpars[2]],
'limited': [limitedmin[2], limitedmax[2]],
'fixed': fixed[2],
'parname': "SHIFT",
'error': 0
}, {
'n': 3,
'value': params[3],
'limits': [minpars[3], maxpars[3]],
'limited': [limitedmin[3], limitedmax[3]],
'fixed': fixed[3],
'parname': "WIDTH",
'error': 0
}]
mp = mpfit(mpfitfun(xax, data, err), parinfo=parinfo, quiet=quiet)
mpp = mp.params
mpperr = mp.perror
chi2 = mp.fnorm
if mp.status == 0:
raise Exception(mp.errmsg)
if not shh:
for i, p in enumerate(mpp):
parinfo[i]['value'] = p
print(parinfo[i]['parname'], p, " +/- ", mpperr[i])
print("Chi2: ", mp.fnorm, " Reduced Chi2: ", mp.fnorm / len(data),
" DOF:",
len(data) - len(mpp))
return mpp, onedgaussian(xax, *mpp), mpperr, chi2
def gaussfit(data,
err=None,
params=[],
autoderiv=1,
return_all=0,
circle=0,
fixed=numpy.repeat(False, 7),
limitedmin=[False, False, False, False, True, True, True],
limitedmax=[False, False, False, False, False, False, True],
usemoment=numpy.array([], dtype='bool'),
minpars=numpy.repeat(0, 7),
maxpars=[0, 0, 0, 0, 0, 0, 360],
rotate=1,
vheight=1,
quiet=True,
returnmp=False,
returnfitimage=False,
**kwargs):
"""
Gaussian fitter with the ability to fit a variety of different forms of
2-dimensional gaussian.
Input Parameters:
data - 2-dimensional data array
err=None - error array with same size as data array
params=[] - initial input parameters for Gaussian function.
(height, amplitude, x, y, width_x, width_y, rota)
if not input, these will be determined from the moments of the system,
assuming no rotation
autoderiv=1 - use the autoderiv provided in the lmder.f function (the
alternative is to us an analytic derivative with lmdif.f: this method
is less robust)
return_all=0 - Default is to return only the Gaussian parameters.
1 - fit params, fit error
returnfitimage - returns (best fit params,best fit image)
returnmp - returns the full mpfit struct
circle=0 - default is an elliptical gaussian (different x, y widths),
but can reduce the input by one parameter if it's a circular gaussian
rotate=1 - default allows rotation of the gaussian ellipse. Can remove
last parameter by setting rotate=0. numpy.expects angle in DEGREES
vheight=1 - default allows a variable height-above-zero, i.e. an
additive constant for the Gaussian function. Can remove first
parameter by setting this to 0
usemoment - can choose which parameters to use a moment estimation for.
Other parameters will be taken from params. Needs to be a boolean
array.
Output:
Default output is a set of Gaussian parameters with the same shape as
the input parameters
Can also output the covariance matrix, 'infodict' that contains a lot
more detail about the fit (see scipy.optimize.leastsq), and a message
from leastsq telling what the exit status of the fitting routine was
Warning: Does NOT necessarily output a rotation angle between 0 and 360 degrees.
"""
usemoment = numpy.array(usemoment, dtype='bool')
params = numpy.array(params, dtype='float')
if usemoment.any() and len(params) == len(usemoment):
moment = numpy.array(moments(data, circle, rotate, vheight, **kwargs),
dtype='float')
params[usemoment] = moment[usemoment]
elif params == [] or len(params) == 0:
params = (moments(data, circle, rotate, vheight, **kwargs))
if vheight == 0:
vheight = 1
params = numpy.concatenate([[0], params])
fixed[0] = 1
# mpfit will fail if it is given a start parameter outside the allowed range:
for i in range(len(params)):
if params[i] > maxpars[i] and limitedmax[i]: params[i] = maxpars[i]
if params[i] < minpars[i] and limitedmin[i]: params[i] = minpars[i]
if err == None:
errorfunction = lambda p: numpy.ravel((twodgaussian(p,circle,rotate,vheight)\
(*numpy.indices(data.shape)) - data))
else:
errorfunction = lambda p: numpy.ravel((twodgaussian(p,circle,rotate,vheight)\
(*numpy.indices(data.shape)) - data)/err)
def mpfitfun(data, err):
if err == None:
def f(p, fjac=None): return [0,numpy.ravel(data-twodgaussian(p,circle,rotate,vheight)\
(*numpy.indices(data.shape)))]
else:
def f(p, fjac=None): return [0,numpy.ravel((data-twodgaussian(p,circle,rotate,vheight)\
(*numpy.indices(data.shape)))/err)]
return f
parinfo = [{
'n': 1,
'value': params[1],
'limits': [minpars[1], maxpars[1]],
'limited': [limitedmin[1], limitedmax[1]],
'fixed': fixed[1],
'parname': "AMPLITUDE",
'error': 0
}, {
'n': 2,
'value': params[2],
'limits': [minpars[2], maxpars[2]],
'limited': [limitedmin[2], limitedmax[2]],
'fixed': fixed[2],
'parname': "XSHIFT",
'error': 0
}, {
'n': 3,
'value': params[3],
'limits': [minpars[3], maxpars[3]],
'limited': [limitedmin[3], limitedmax[3]],
'fixed': fixed[3],
'parname': "YSHIFT",
'error': 0
}, {
'n': 4,
'value': params[4],
'limits': [minpars[4], maxpars[4]],
'limited': [limitedmin[4], limitedmax[4]],
'fixed': fixed[4],
'parname': "XWIDTH",
'error': 0
}]
if vheight == 1:
parinfo.insert(
0, {
'n': 0,
'value': params[0],
'limits': [minpars[0], maxpars[0]],
'limited': [limitedmin[0], limitedmax[0]],
'fixed': fixed[0],
'parname': "HEIGHT",
'error': 0
})
if circle == 0:
parinfo.append({
'n': 5,
'value': params[5],
'limits': [minpars[5], maxpars[5]],
'limited': [limitedmin[5], limitedmax[5]],
'fixed': fixed[5],
'parname': "YWIDTH",
'error': 0
})
if rotate == 1:
parinfo.append({
'n': 6,
'value': params[6],
'limits': [minpars[6], maxpars[6]],
'limited': [limitedmin[6], limitedmax[6]],
'fixed': fixed[6],
'parname': "ROTATION",
'error': 0
})
if autoderiv == 0:
# the analytic derivative, while not terribly difficult, is less
# efficient and useful. I only bothered putting it here because I was
# instructed to do so for a class project - please ask if you would
# like this feature implemented
raise ValueError("I'm sorry, I haven't implemented this feature yet.")
else:
# p, cov, infodict, errmsg, success = optimize.leastsq(errorfunction,\
# params, full_output=1)
mp = mpfit(mpfitfun(data, err), parinfo=parinfo, quiet=quiet)
if returnmp:
returns = (mp)
elif return_all == 0:
returns = mp.params
elif return_all == 1:
returns = mp.params, mp.perror
if returnfitimage:
fitimage = twodgaussian(mp.params, circle, rotate,
vheight)(*numpy.indices(data.shape))
returns = (returns, fitimage)
return returns
def multigaussfit(xax, data, ngauss=1, err=None, params=[1,0,1],
fixed=[False,False,False], limitedmin=[False,False,True],
limitedmax=[False,False,False], minpars=[0,0,0], maxpars=[0,0,0],
quiet=True, shh=True, veryverbose=False):
"""
An improvement on onedgaussfit. Lets you fit multiple gaussians.
Inputs:
xax - x axis
data - y axis
ngauss - How many gaussians to fit? Default 1 (this could supersede onedgaussfit)
err - error corresponding to data
These parameters need to have length = 3*ngauss. If ngauss > 1 and length = 3, they will
be replicated ngauss times, otherwise they will be reset to defaults:
params - Fit parameters: [amplitude, offset, width] * ngauss
If len(params) % 3 == 0, ngauss will be set to len(params) / 3
fixed - Is parameter fixed?
limitedmin/minpars - set lower limits on each parameter (default: width>0)
limitedmax/maxpars - set upper limits on each parameter
quiet - should MPFIT output each iteration?
shh - output final parameters?
Returns:
Fit parameters
Model
Fit errors
chi2
"""
if len(params) != ngauss and (len(params) / 3) > ngauss:
ngauss = len(params) / 3
if isinstance(params,np.ndarray): params=params.tolist()
# make sure all various things are the right length; if they're not, fix them using the defaults
for parlist in (params,fixed,limitedmin,limitedmax,minpars,maxpars):
if len(parlist) != 3*ngauss:
# if you leave the defaults, or enter something that can be multiplied by 3 to get to the
# right number of gaussians, it will just replicate
if len(parlist) == 3:
parlist *= ngauss
elif parlist==params:
parlist[:] = [1,0,1] * ngauss
elif parlist==fixed or parlist==limitedmax:
parlist[:] = [False,False,False] * ngauss
elif parlist==limitedmin:
parlist[:] = [False,False,True] * ngauss
elif parlist==minpars or parlist==maxpars:
parlist[:] = [0,0,0] * ngauss
def mpfitfun(x,y,err):
if err is None:
def f(p,fjac=None): return [0,(y-n_gaussian(pars=p)(x))]
else:
def f(p,fjac=None): return [0,(y-n_gaussian(pars=p)(x))/err]
return f
if xax == None:
xax = np.arange(len(data))
parnames = {0:"AMPLITUDE",1:"SHIFT",2:"WIDTH"}
parinfo = [ {'n':ii, 'value':params[ii],
'limits':[minpars[ii],maxpars[ii]],
'limited':[limitedmin[ii],limitedmax[ii]], 'fixed':fixed[ii],
'parname':parnames[ii%3]+str(ii%3), 'error':ii}
for ii in xrange(len(params)) ]
if veryverbose:
print "GUESSES: "
print "\n".join(["%s: %s" % (p['parname'],p['value']) for p in parinfo])
mp = mpfit(mpfitfun(xax,data,err),parinfo=parinfo,quiet=quiet)
mpp = mp.params
mpperr = mp.perror
chi2 = mp.fnorm
if mp.status == 0:
raise Exception(mp.errmsg)
if not shh:
print "Final fit values: "
for i,p in enumerate(mpp):
parinfo[i]['value'] = p
print parinfo[i]['parname'],p," +/- ",mpperr[i]
print "Chi2: ",mp.fnorm," Reduced Chi2: ",mp.fnorm/len(data)," DOF:",len(data)-len(mpp)
return mpp,n_gaussian(pars=mpp)(xax),mpperr,chi2
def onedgaussfit(xax, data, err=None,
params=[0,1,0,1],fixed=[False,False,False,False],
limitedmin=[False,False,False,True],
limitedmax=[False,False,False,False], minpars=[0,0,0,0],
maxpars=[0,0,0,0], quiet=True, shh=True,
veryverbose=False,
vheight=True, negamp=False,
usemoments=False):
"""
Inputs:
xax - x axis
data - y axis
err - error corresponding to data
params - Fit parameters: Height of background, Amplitude, Shift, Width
fixed - Is parameter fixed?
limitedmin/minpars - set lower limits on each parameter (default: width>0)
limitedmax/maxpars - set upper limits on each parameter
quiet - should MPFIT output each iteration?
shh - output final parameters?
usemoments - replace default parameters with moments
Returns:
Fit parameters
Model
Fit errors
chi2
"""
def mpfitfun(x,y,err):
if err is None:
def f(p,fjac=None): return [0,(y-onedgaussian(x,*p))]
else:
def f(p,fjac=None): return [0,(y-onedgaussian(x,*p))/err]
return f
if xax == None:
xax = np.arange(len(data))
if vheight is False:
height = params[0]
fixed[0] = True
if usemoments:
params = onedmoments(xax,data,vheight=vheight,negamp=negamp, veryverbose=veryverbose)
if vheight is False: params = [height]+params
if veryverbose: print "OneD moments: h: %g a: %g c: %g w: %g" % tuple(params)
parinfo = [ {'n':0,'value':params[0],'limits':[minpars[0],maxpars[0]],'limited':[limitedmin[0],limitedmax[0]],'fixed':fixed[0],'parname':"HEIGHT",'error':0} ,
{'n':1,'value':params[1],'limits':[minpars[1],maxpars[1]],'limited':[limitedmin[1],limitedmax[1]],'fixed':fixed[1],'parname':"AMPLITUDE",'error':0},
{'n':2,'value':params[2],'limits':[minpars[2],maxpars[2]],'limited':[limitedmin[2],limitedmax[2]],'fixed':fixed[2],'parname':"SHIFT",'error':0},
{'n':3,'value':params[3],'limits':[minpars[3],maxpars[3]],'limited':[limitedmin[3],limitedmax[3]],'fixed':fixed[3],'parname':"WIDTH",'error':0}]
mp = mpfit(mpfitfun(xax,data,err),parinfo=parinfo,quiet=quiet)
mpp = mp.params
mpperr = mp.perror
chi2 = mp.fnorm
if mp.status == 0:
raise Exception(mp.errmsg)
if (not shh) or veryverbose:
print "Fit status: ",mp.status
for i,p in enumerate(mpp):
parinfo[i]['value'] = p
print parinfo[i]['parname'],p," +/- ",mpperr[i]
print "Chi2: ",mp.fnorm," Reduced Chi2: ",mp.fnorm/len(data)," DOF:",len(data)-len(mpp)
return mpp,onedgaussian(xax,*mpp),mpperr,chi2
def gaussfit(data,err=None,params=(),autoderiv=True,return_all=False,circle=False,
fixed=np.repeat(False,7),limitedmin=[False,False,False,False,True,True,True],
limitedmax=[False,False,False,False,False,False,True],
usemoment=np.array([],dtype='bool'),
minpars=np.repeat(0,7),maxpars=[0,0,0,0,0,0,360],
rotate=1,vheight=1,quiet=True,returnmp=False,
returnfitimage=False,**kwargs):
"""
Gaussian fitter with the ability to fit a variety of different forms of
2-dimensional gaussian.
Input Parameters:
data - 2-dimensional data array
err=None - error array with same size as data array
params=[] - initial input parameters for Gaussian function.
(height, amplitude, x, y, width_x, width_y, rota)
if not input, these will be determined from the moments of the system,
assuming no rotation
autoderiv=1 - use the autoderiv provided in the lmder.f function (the
alternative is to us an analytic derivative with lmdif.f: this method
is less robust)
return_all=0 - Default is to return only the Gaussian parameters.
1 - fit params, fit error
returnfitimage - returns (best fit params,best fit image)
returnmp - returns the full mpfit struct
circle=0 - default is an elliptical gaussian (different x, y widths),
but can reduce the input by one parameter if it's a circular gaussian
rotate=1 - default allows rotation of the gaussian ellipse. Can remove
last parameter by setting rotate=0. np.expects angle in DEGREES
vheight=1 - default allows a variable height-above-zero, i.e. an
additive constant for the Gaussian function. Can remove first
parameter by setting this to 0
usemoment - can choose which parameters to use a moment estimation for.
Other parameters will be taken from params. Needs to be a boolean
array.
Output:
Default output is a set of Gaussian parameters with the same shape as
the input parameters
If returnfitimage=True returns a np array of a gaussian
contructed using the best fit parameters.
If returnmp=True returns a `mpfit` object. This object contains
a `covar` attribute which is the 7x7 covariance array
generated by the mpfit class in the `mpfit_custom.py`
module. It contains a `param` attribute that contains a
list of the best fit parameters in the same order as the
optional input parameter `params`.
Warning: Does NOT necessarily output a rotation angle between 0 and 360 degrees.
"""
usemoment=np.array(usemoment,dtype='bool')
params=np.array(params,dtype='float')
if usemoment.any() and len(params)==len(usemoment):
moment = np.array(moments(data,circle,rotate,vheight,**kwargs),dtype='float')
params[usemoment] = moment[usemoment]
elif params == [] or len(params)==0:
params = (moments(data,circle,rotate,vheight,**kwargs))
if vheight==0:
vheight=1
params = np.concatenate([[0],params])
fixed[0] = 1
# mpfit will fail if it is given a start parameter outside the allowed range:
for i in xrange(len(params)):
if params[i] > maxpars[i] and limitedmax[i]: params[i] = maxpars[i]
if params[i] < minpars[i] and limitedmin[i]: params[i] = minpars[i]
if err is None:
errorfunction = lambda p: np.ravel((twodgaussian(p,circle,rotate,vheight)\
(*np.indices(data.shape)) - data))
else:
errorfunction = lambda p: np.ravel((twodgaussian(p,circle,rotate,vheight)\
(*np.indices(data.shape)) - data)/err)
def mpfitfun(data,err):
if err is None:
def f(p,fjac=None): return [0,np.ravel(data-twodgaussian(p,circle,rotate,vheight)\
(*np.indices(data.shape)))]
else:
def f(p,fjac=None): return [0,np.ravel((data-twodgaussian(p,circle,rotate,vheight)\
(*np.indices(data.shape)))/err)]
return f
parinfo = [
{'n':1,'value':params[1],'limits':[minpars[1],maxpars[1]],'limited':[limitedmin[1],limitedmax[1]],'fixed':fixed[1],'parname':"AMPLITUDE",'error':0},
{'n':2,'value':params[2],'limits':[minpars[2],maxpars[2]],'limited':[limitedmin[2],limitedmax[2]],'fixed':fixed[2],'parname':"XSHIFT",'error':0},
{'n':3,'value':params[3],'limits':[minpars[3],maxpars[3]],'limited':[limitedmin[3],limitedmax[3]],'fixed':fixed[3],'parname':"YSHIFT",'error':0},
{'n':4,'value':params[4],'limits':[minpars[4],maxpars[4]],'limited':[limitedmin[4],limitedmax[4]],'fixed':fixed[4],'parname':"XWIDTH",'error':0} ]
if vheight == 1:
parinfo.insert(0,{'n':0,'value':params[0],'limits':[minpars[0],maxpars[0]],'limited':[limitedmin[0],limitedmax[0]],'fixed':fixed[0],'parname':"HEIGHT",'error':0})
if circle == 0:
parinfo.append({'n':5,'value':params[5],'limits':[minpars[5],maxpars[5]],'limited':[limitedmin[5],limitedmax[5]],'fixed':fixed[5],'parname':"YWIDTH",'error':0})
if rotate == 1:
parinfo.append({'n':6,'value':params[6],'limits':[minpars[6],maxpars[6]],'limited':[limitedmin[6],limitedmax[6]],'fixed':fixed[6],'parname':"ROTATION",'error':0})
if autoderiv == 0:
# the analytic derivative, while not terribly difficult, is less
# efficient and useful. I only bothered putting it here because I was
# instructed to do so for a class project - please ask if you would
# like this feature implemented
raise ValueError("I'm sorry, I haven't implemented this feature yet.")
else:
# p, cov, infodict, errmsg, success = optimize.leastsq(errorfunction,\
# params, full_output=1)
mp = mpfit(mpfitfun(data,err),parinfo=parinfo,quiet=quiet)
if returnmp:
returns = (mp)
elif return_all == 0:
returns = mp.params
elif return_all == 1:
returns = mp.params,mp.perror
if returnfitimage:
fitimage = twodgaussian(mp.params,circle,rotate,vheight)(*np.indices(data.shape))
returns = (returns,fitimage)
return returns
def onedgaussfit(xax,
data,
err=None,
params=[0, 1, 0, 1],
fixed=[False, False, False, False],
limitedmin=[False, False, False, True],
limitedmax=[False, False, False, False],
minpars=[0, 0, 0, 0],
maxpars=[0, 0, 0, 0],
quiet=True,
shh=True,
veryverbose=False,
vheight=True,
negamp=False,
usemoments=False):
"""
Inputs:
xax - x axis
data - y axis
err - error corresponding to data
params - Fit parameters: Height of background, Amplitude, Shift, Width
fixed - Is parameter fixed?
limitedmin/minpars - set lower limits on each parameter (default: width>0)
limitedmax/maxpars - set upper limits on each parameter
quiet - should MPFIT output each iteration?
shh - output final parameters?
usemoments - replace default parameters with moments
Returns:
Fit parameters
Model
Fit errors
chi2
"""
def mpfitfun(x, y, err):
if err is None:
def f(p, fjac=None):
return [0, (y - onedgaussian(x, *p))]
else:
def f(p, fjac=None):
return [0, (y - onedgaussian(x, *p)) / err]
return f
if xax == None:
xax = np.arange(len(data))
if vheight is False:
height = params[0]
fixed[0] = True
if usemoments:
params = onedmoments(xax,
data,
vheight=vheight,
negamp=negamp,
veryverbose=veryverbose)
if vheight is False: params = [height] + params
if veryverbose:
print "OneD moments: h: %g a: %g c: %g w: %g" % tuple(params)
parinfo = [{
'n': 0,
'value': params[0],
'limits': [minpars[0], maxpars[0]],
'limited': [limitedmin[0], limitedmax[0]],
'fixed': fixed[0],
'parname': "HEIGHT",
'error': 0
}, {
'n': 1,
'value': params[1],
'limits': [minpars[1], maxpars[1]],
'limited': [limitedmin[1], limitedmax[1]],
'fixed': fixed[1],
'parname': "AMPLITUDE",
'error': 0
}, {
'n': 2,
'value': params[2],
'limits': [minpars[2], maxpars[2]],
'limited': [limitedmin[2], limitedmax[2]],
'fixed': fixed[2],
'parname': "SHIFT",
'error': 0
}, {
'n': 3,
'value': params[3],
'limits': [minpars[3], maxpars[3]],
'limited': [limitedmin[3], limitedmax[3]],
'fixed': fixed[3],
'parname': "WIDTH",
'error': 0
}]
mp = mpfit(mpfitfun(xax, data, err), parinfo=parinfo, quiet=quiet)
mpp = mp.params
mpperr = mp.perror
chi2 = mp.fnorm
if mp.status == 0:
raise Exception(mp.errmsg)
if (not shh) or veryverbose:
print "Fit status: ", mp.status
for i, p in enumerate(mpp):
parinfo[i]['value'] = p
print parinfo[i]['parname'], p, " +/- ", mpperr[i]
print "Chi2: ", mp.fnorm, " Reduced Chi2: ", mp.fnorm / len(
data), " DOF:", len(data) - len(mpp)
return mpp, onedgaussian(xax, *mpp), mpperr, chi2
def call_mpfit(FUNCTION, INIT_PARAM, x, y, error, with_plot = False):
"""
This method makes fit of function(par,x) to experimental data
contained in X,Y arrays.
(1)the FUNCTION must be defined as follows:
def func(param,x):
value = some_formula(param[0],param[1]..., x)
return value
(2)INIT_PARAM -- is a list of initial values of parameters
(3) X exp. data
(4) Y experimental data
Return: [list of fited parameters], [list of parameters errors],
Chi2-value
"""
def mpfitfunc(x,y,err):
if err == None:
def f(p,fjac=None):
return [0,(y-FUNCTION(p,x))]
else:
def f(p,fjac=None):
return [0,(y-FUNCTION(p,x))/err]
return f
mpfitparaminfo = generate_mpfit_info(INIT_PARAM)
mp2 = mpfit(mpfitfunc(x,y,error),
parinfo=mpfitparaminfo,quiet=0)
fit_params = mp2.params
fit_par_error= mp2.perror
chi2 = mp2.fnorm
if len(fit_params)>0:
print()
print("MPFIT ALGORITHM RESULT:")
N_params = len(INIT_PARAM)
p_cnt=-1
output_string = ' '
#for a report about obtained param value into a string:
for p in fit_params:
p_cnt+=1
string = str()
if fit_par_error!=None:
string = "PARAM "+str(p_cnt)+":\t"+str(p)+"+/-("+str(fit_par_error[p_cnt])+")\n"
print(string)
else:
string = "PARAM "+str(p_cnt)+":\t"+str(p)+"\n"
print(string)
output_string+=string
#end for
output_string += 'Chi2: '+str(chi2)
#write that string to the file:
param_file = open("params.mpfit.txt", 'w')
param_file.write(output_string)
param_file.close()
#write fited function values at X into output file.
FUNC_VAL = numpy.zeros(len(x))
FUNC_VAL = FUNCTION(fit_params, x)
write_arrays( [x, FUNC_VAL], "output.mpfit.txt")
print ("Chi2: "+str( chi2))
if with_plot:
PLT.plot(x,y,'+', x,FUNC_VAL,'-');
PLT.legend(['data','formula approx.'],loc='best')
PLT.show()
# end if with_plot
if fit_par_error!=None:
return fit_params,chi2
else:
return fit_params, fit_par_error,chi2
return fit_params
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
def func(x, a, b, c):
#return a * np.exp(-b * x) + c
return a * np.log(b * x) + c
x = np.linspace(1,5,50) # changed boundary conditions to avoid division by 0
y = func(x, 2.5, 1.3, 0.5)
yn = y + 0.2*np.random.normal(size=len(x))
popt, pcov = curve_fit(func, x, yn)
plt.figure()
plt.plot(x, yn, 'ko', label="Original Noised Data")
plt.plot(x, func(x, *popt), 'r-', label="Fitted Curve")
plt.legend()
plt.show()
"""
import mpfit
import Numeric
x = Numeric.arange(100, Numeric.float)
p0 = [5.7, 2.2, 500., 1.5, 2000.]
y = ( p[0] + p[1]*[x] + p[2]*[x**2] + p[3]*Numeric.sqrt(x) +
p[4]*Numeric.log(x))
fa = {'x':x, 'y':y, 'err':err}
m = mpfit('myfunct', p0, functkw=fa)
print 'status = ', m.status
if (m.status <= 0): print 'error message = ', m.errmsg
print 'parameters = ', m.params
