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lcsinglefit2.py
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lcsinglefit2.py
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from numpy import *
from scipy.optimize import leastsq
from lcspot2 import *
from scipy.cluster.vq import whiten
from scipy.cluster.vq import kmeans2
import matplotlib.pyplot as plt
plt.ion()
def vincfit(lctuple, p0s=[], initsteps=20, nclusters=30, threshratio=2, plsprint='some', plsplot=False):
time, intensity = lctuple
if plsprint != 'none':
print 'single spot fit'
print 'lightcurve:'
print intensity
print
#initial fits using leastsq, starting at various points in param space
opts,sses,p0s = vilcfits(time,intensity,p0s,initsteps)
#sort by sses
stups = sorted(zip(sses,opts,p0s), key=lambda x: x[0])
sses,opts,p0s = zip(*stups)
#cluster best fits
optsmat = whiten(array(opts[0:3*len(opts)/4]))
centroid,label = kmeans2(optsmat,nclusters,iter=20,minit='points')
ll = list(label)
newp0s = [opts[ll.index(i)] for i in range(0,nclusters) if i in ll]
#full fits
opts,sses,p1s = vilcfits(time,intensity,newp0s)
#sort by sses again
stups = sorted(zip(sses,opts,p1s), key=lambda x: x[0])
sses,opts,p1s = zip(*stups)
#find best fit(s)
threshold = sses[0]*threshratio
bestps = [boundparams(opts[0])]
bestsses = [sses[0]]
bestp1s = [p1s[0]]
for i in range(1,len(opts)):
if sses[i] > threshold:
break
if min([paramdist(boundparams(opts[i]),k,scalevals=array([45.0, 180.0, 90.0, 17.5, 25.0])) for k in bestps]) > 0.001:
bestps.append(boundparams(opts[i]))
bestsses.append(sses[i])
bestp1s.append(p1s[i])
#print best fit(s)
if plsprint != 'none':
print 'best fits:'
for i in range(0,len(bestps)):
print 'opt:',bestps[i],'sse:',bestsses[i],'p1:',bestp1s[i]
print
#print all fits
if plsprint == 'all':
print 'all fits:'
for i in range(0,len(opts)):
print 'opt:',opts[i],'sse:',sses[i],'p1:',p1s[i]
print
#plot
if plsplot:
colors = 'bgcmyk'
plt.figure()
plt.plot(time, intensity, 'r.')
for i in range(0,len(bestps)):
plt.plot(time, lcspot(time,bestps[i]), colors[i%len(colors)])
plt.title('single spot fit')
plt.xlabel('time')
plt.ylabel('intensity')
#plt.show()
return bestps
def fincfit(lctuple, inc, p0s=[], initsteps=20, nclusters=20, threshratio=2, plsprint='some', plsplot=False):
time, intensity = lctuple
if plsprint != 'none':
print 'fixed-inc fit, inc:', inc
print 'residual lightcurve:'
print intensity
print
#initial fits using leastsq, starting at various points in param space
opts,sses,p0s = filcfits(time,intensity,inc,p0s,initsteps)
#sort by sses
stups = sorted(zip(sses,opts,p0s), key=lambda x: x[0])
sses,opts,p0s = zip(*stups)
#cluster best fits
optsmat = whiten(array(opts[0:3*len(opts)/4]))
centroid,label = kmeans2(optsmat,nclusters,iter=20,minit='points')
ll = list(label)
newp0s = [opts[ll.index(i)] for i in range(0,nclusters) if i in ll]
#full fits
opts,sses,p1s = filcfits(time,intensity,inc,newp0s)
#sort by sses again
stups = sorted(zip(sses,opts,p1s), key=lambda x: x[0])
sses,opts,p1s = zip(*stups)
#find best fit(s)
threshold = sses[0]*threshratio
bestps = [boundparamsfi(opts[0])]
bestsses = [sses[0]]
bestp1s = [p1s[0]]
for i in range(1,len(opts)):
if sses[i] > threshold:
break
if min([paramdist(boundparamsfi(opts[i]),k,scalevals=array([180.0, 90.0, 17.5, 25.0])) for k in bestps]) > 0.001:
bestps.append(boundparamsfi(opts[i]))
bestsses.append(sses[i])
bestp1s.append(p1s[i])
#print best fit(s)
if plsprint != 'none':
print 'best fits:'
for i in range(0,len(bestps)):
print 'opt:',bestps[i],'sse:',bestsses[i],'p1:',bestp1s[i]
print
#print all fits
if plsprint == 'all':
print 'all fits:'
for i in range(0,len(opts)):
print 'opt:',opts[i],'sse:',sses[i],'p1:',p1s[i]
print
#plot
if plsplot:
colors = 'bgcmyk'
plt.figure()
plt.plot(time, intensity, 'r.')
for i in range(0,len(bestps)):
plt.plot(time, lcspotfi(time,inc,bestps[i]), colors[i%len(colors)])
plt.xlabel('time')
plt.ylabel('residual intensity')
plt.title('fixed-inc fit, inc: ' + str(inc))
#plt.show()
return bestps
def fstarfit(lctuple, inc, teq, alpha, p0s=[], initsteps=20, nclusters=20, threshratio=2, plsprint='some', plsplot=False, snum=-1):
time, intensity = lctuple
if plsprint != 'none':
print 'fixed-star fit, inc:', inc, 'teq:', teq, 'alpha:', alpha, 'spot #', snum
print 'residual lightcurve:'
print intensity
print
#initial fits using leastsq, starting at various points in param space
opts,sses,p0s = fstarfits(time,intensity,inc,teq,alpha,p0s,initsteps)
#sort by sses
stups = sorted(zip(sses,opts,p0s), key=lambda x: x[0])
sses,opts,p0s = zip(*stups)
#cluster best fits
optsmat = whiten(array(opts[0:3*len(opts)/4]))
centroid,label = kmeans2(optsmat,nclusters,iter=20,minit='points')
ll = list(label)
newp0s = [opts[ll.index(i)] for i in range(0,nclusters) if i in ll]
#full fits
opts,sses,p1s = fstarfits(time,intensity,inc,teq,alpha,newp0s)
#sort by sses again
stups = sorted(zip(sses,opts,p1s), key=lambda x: x[0])
sses,opts,p1s = zip(*stups)
#find best fit(s)
threshold = sses[0]*threshratio
bestps = [boundparamsfstar(opts[0])]
bestsses = [sses[0]]
bestp1s = [p1s[0]]
for i in range(1,len(opts)):
if sses[i] > threshold:
break
if min([paramdist(boundparamsfstar(opts[i]),k,scalevals=array([180.0, 90.0, 17.5])) for k in bestps]) > 0.001:
bestps.append(boundparamsfstar(opts[i]))
bestsses.append(sses[i])
bestp1s.append(p1s[i])
#print best fit(s)
if plsprint != 'none':
print 'best fits:'
for i in range(0,len(bestps)):
print 'opt:',bestps[i],'sse:',bestsses[i],'p1:',bestp1s[i]
print
#print all fits
if plsprint == 'all':
print 'all fits:'
for i in range(0,len(opts)):
print 'opt:',opts[i],'sse:',sses[i],'p1:',p1s[i]
print
#plot
if plsplot:
colors = 'bgcmyk'
plt.figure()
plt.plot(time, intensity, 'r.')
for i in range(0,len(bestps)):
plt.plot(time, lcspotfi(time,inc,bestps[i]), colors[i%len(colors)])
plt.xlabel('time')
plt.ylabel('residual intensity')
plt.title('inc: ' + str(inc) + ', teq: ' + str(teq) + ', alpha: ' + str(alpha) + ', spot #' + str(snum))
#plt.show()
return bestps
def vilcfits(time,intensity,p0s=[],mfev=0):
if not p0s:
p0s = spacedparams(5)
opts = []
sses = []
p0s2 = []
for p0 in p0s:
try:
fps, iem = leastsq(lcspotdiffs3, p0, args=(time,intensity), maxfev=mfev)
opts.append(fps)
sses.append(lcspotsse(fps,time,intensity))
p0s2.append(p0)
except RuntimeError as e:
print "Runtime Error({0}): {1}".format(e.errno, e.strerror)
return opts,sses,p0s2
def filcfits(time,intensity,inc,p0s=[],mfev=0):
if not p0s:
p0s = spacedparamsfi(5)
opts = []
sses = []
p0s2 = []
for p0 in p0s:
try:
fps, iem = leastsq(lcspotdiffsfi, p0, args=(time,intensity,inc), maxfev=mfev)
opts.append(fps)
sses.append(lcspotssefi(fps,time,intensity,inc))
p0s2.append(p0)
except RuntimeError as e:
print "Runtime Error({0}): {1}".format(e.errno, e.strerror)
return opts,sses,p0s2
def fstarfits(time,intensity,inc,teq,alpha,p0s=[],mfev=0):
if not p0s:
p0s = spacedparamsspot(5)
opts = []
sses = []
p0s2 = []
for p0 in p0s:
try:
fps, iem = leastsq(lcspotdiffsfstar, p0, args=(time,intensity,inc,teq,alpha), maxfev=mfev)
opts.append(fps)
sses.append(lcspotssefstar(fps,time,intensity,inc,teq,alpha))
p0s2.append(p0)
except RuntimeError as e:
print "Runtime Error({0}): {1}".format(e.errno, e.strerror)
return opts,sses,p0s2