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fitscalars.py
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fitscalars.py
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import losoto._version
import losoto._logging
from losoto.h5parm import h5parm, solWriter, solFetcher
import matplotlib
#matplotlib.use('GTK')
#import lofar.parmdb
import sys
import os
import scipy.ndimage
import time
import numpy
import math
import scipy.signal
from pylab import *
import numpy as np
import shutil
import progressbar
import logging
import pp
def fit_dclock_doffset(phases, freq):
c = 2.99792458e8
freq_old = numpy.copy(freq)
# NOTE THE *2 to use rr+ll instead of 0.5*(rr+ll)
par3complex = lambda p, freq, y: abs(numpy.cos((2.*pi*p[0]*freq) - (1.*8.44797245e9*p[1]/freq) + p[2]) - numpy.cos(y)) + abs(numpy.sin((2.*pi*p[0]*freq) - (1.*8.44797245e9*p[1]/freq) + p[2]) - numpy.sin(y))
par2complex = lambda p, freq, y: abs(numpy.cos((4.*pi*p[0]*freq) - (2.*8.44797245e9*p[1]/freq)) - numpy.cos(y)) + abs(numpy.sin((4.*pi*p[0]*freq) - (2.*8.44797245e9*p[1]/freq)) - numpy.sin(y))
rmwavcomplex = lambda RM, wav, y: abs(numpy.cos(2.*RM[0]*wav*wav) - numpy.cos(y)) + abs(numpy.sin(2.*RM[0]*wav*wav) - numpy.sin(y))
par2slope = lambda p, freq, y: abs(numpy.cos((2.*pi*p[0]*freq) + p[1]) - numpy.cos(y)) + abs(numpy.sin((2.*pi*p[0]*freq) + p[1]) - numpy.sin(y))
plotrm = lambda RM, wav: numpy.mod( (1.*RM*wav*wav) +1.0*pi, 2.*pi) -1.0*pi # notice the factor of 2
fitfuncfastplot = lambda p, freq: numpy.mod((2.*pi*p[0]*freq) - (1.*8.44797245e9*p[1]/freq) + (p[2])+ 1.0*pi, 2.*pi) -1.0*pi
phase = numpy.copy(phases)
pi = numpy.pi
chi_old=1e9
if len(freq) > 10:
# FIND INTIAL GUESS
for doffset in numpy.arange(-pi-0.1,pi+0.1,0.1):
for dclock in numpy.arange(-80e-9,80e-9,2e-9):
phase_model = numpy.mod ( (2.*pi*dclock*freq) + doffset, 2*pi) # NOTE THE *2 to use rr+ll instead of 0.5*(rr+ll)
phase_data = numpy.mod (phase, 2*pi)
angle = pi - numpy.abs(numpy.abs(phase_model - phase_data) - pi)
chi = numpy.sum(angle)
if chi < chi_old:
chi_old = chi
fitguess = [dclock,doffset]
#print 'Better fit 1', dclock, doffset
fitguess_1 = numpy.copy(fitguess)
#print 'iter 1', fitguess
for doffset in numpy.arange(fitguess_1[1]-0.2,fitguess_1[1]+ 0.2,0.01):
for dclock in numpy.arange(fitguess_1[0]-4e-9,fitguess_1[0]+ 4e-9,0.1e-10):
phase_model = numpy.mod ( (2.*pi*dclock*freq) + doffset, 2*pi) # NOTE THE *2 to use rr+ll instead of 0.5*(rr+ll)
phase_data = numpy.mod (phase, 2*pi)
angle = pi - numpy.abs(numpy.abs(phase_model - phase_data) - pi)
chi = numpy.sum(angle)
if chi < chi_old:
chi_old = chi
fitguess = [dclock,doffset]
#print 'Better fit 2', dclock, doffset
freq = freq.astype(numpy.float64)
phase= phase.astype(numpy.float64) #epsfcn=1e-7
#print 'hello', fitguess
fitresult, success = scipy.optimize.leastsq(par2slope, [fitguess[0], fitguess[1]], args=(freq, phase),maxfev=10000)
#print 'ok',fitresult
else:
print 'No valid data found'
fitresult = [0.0,0.0]
show_plot = False
if show_plot:
#if len(fitresult ==2):
# fitresult =[fitresult[0], fitresult[1],0]
# print 'Here'
# #fitresult = fitresult # [fitguess[0], fitguess[1], 0]
#fitresult = [[fitresultguess[0],fitresultguess[0],0.0]]
matplotlib.pyplot.plot(freq, numpy.mod(phase + 1.0*pi, 2.*pi) -1.0*pi, 'or' )
matplotlib.pyplot.plot(freq, numpy.mod(phase_diff + 1.0*pi, 2.*pi) -1.0*pi , '.', color='purple' )
TEC = numpy.mod((-8.44797245e9*(fitresult[1])/freq)+numpy.pi, 2*pi) - pi # notice factor of 2 because rr+ll
Clock = numpy.mod((2.*numpy.pi* 2.*fitresult[0]*freq )+numpy.pi, 2*pi) - pi # notice factor of 2 because rr+ll
phase_total = (2.*numpy.pi*2.*fitresult[0]*freq)+(-8.44797245e9*(2.*fitresult[1])/freq)+fitresult[2]
residual = numpy.mod(phase-phase_total+pi,2.*pi)-pi
matplotlib.pyplot.plot(freq, residual, '.', color='yellow')
idxl = int(min(freq_old)/1e4)
idxh = int(max(freq_old)/1e4)
bigfreqaxistmp = range(idxl, idxh)
bigfreqaxis = numpy.array([float(i) for i in bigfreqaxistmp])
bigfreqaxis = bigfreqaxis*1e4
matplotlib.pyplot.plot (bigfreqaxis, fitfuncfastplot(fitresult, bigfreqaxis[:]), "r-")
matplotlib.pyplot.plot (bigfreqaxis, plotrm(fitresultrm_wav, c/bigfreqaxis[:]), "-", color='purple')
#matplotlib.pyplot.plot (freq, fitfuncfastplot(fitresult, bigfreqaxis[:]), "r-")
matplotlib.pyplot.plot(freq, Clock, ',g')
matplotlib.pyplot.plot(freq, TEC, ',b')
matplotlib.pyplot.xlabel('freq')
matplotlib.pyplot.ylabel('phase')
matplotlib.pyplot.show()
return [fitresult[0], fitresult[1]]
def fit_dTEC_doffset(phases, freq):
c = 2.99792458e8
freq_old = numpy.copy(freq)
# NOTE THE *2 to use rr+ll instead of 0.5*(rr+ll)
par3complex = lambda p, freq, y: abs(numpy.cos((2.*pi*p[0]*freq) - (1.*8.44797245e9*p[1]/freq) + p[2]) - numpy.cos(y)) + abs(numpy.sin((2.*pi*p[0]*freq) - (1.*8.44797245e9*p[1]/freq) + p[2]) - numpy.sin(y))
par2complex = lambda p, freq, y: abs(numpy.cos((4.*pi*p[0]*freq) - (2.*8.44797245e9*p[1]/freq)) - numpy.cos(y)) + abs(numpy.sin((4.*pi*p[0]*freq) - (2.*8.44797245e9*p[1]/freq)) - numpy.sin(y))
rmwavcomplex = lambda RM, wav, y: abs(numpy.cos(2.*RM[0]*wav*wav) - numpy.cos(y)) + abs(numpy.sin(2.*RM[0]*wav*wav) - numpy.sin(y))
par2slope = lambda p, freq, y: abs(numpy.cos((-8.44797245e9*p[0]/freq) + p[1]) - numpy.cos(y)) + abs(numpy.sin((-8.44797245e9*p[0]/freq) + p[1]) - numpy.sin(y))
plotrm = lambda RM, wav: numpy.mod( (1.*RM*wav*wav) +1.0*pi, 2.*pi) -1.0*pi # notice the factor of 2
fitfuncfastplot = lambda p, freq: numpy.mod((2.*pi*p[0]*freq) - (1.*8.44797245e9*p[1]/freq) + (p[2])+ 1.0*pi, 2.*pi) -1.0*pi
phase = numpy.copy(phases)
pi = numpy.pi
chi_old=1e9
if len(freq) > 10:
# FIND INTIAL GUESS
for doffset in numpy.arange(-pi-0.1,pi+0.1,0.1):
for dclock in numpy.arange(-8.0,8.0,0.01):
phase_model = numpy.mod ( (2.*pi*dclock*freq) + doffset, 2*pi) # NOTE THE *2 to use rr+ll instead of 0.5*(rr+ll)
phase_data = numpy.mod (phase, 2*pi)
angle = pi - numpy.abs(numpy.abs(phase_model - phase_data) - pi)
chi = numpy.sum(angle)
if chi < chi_old:
chi_old = chi
fitguess = [dclock,doffset]
#print 'Better fit 1', dclock, doffset
fitguess_1 = numpy.copy(fitguess)
#print 'iter 1', fitguess
for doffset in numpy.arange(fitguess_1[1]-0.2,fitguess_1[1]+ 0.2,0.01):
for dclock in numpy.arange(fitguess_1[0]-0.1,fitguess_1[0]+ 0.1,0.001):
phase_model = numpy.mod ( (2.*pi*dclock*freq) + doffset, 2*pi) # NOTE THE *2 to use rr+ll instead of 0.5*(rr+ll)
phase_data = numpy.mod (phase, 2*pi)
angle = pi - numpy.abs(numpy.abs(phase_model - phase_data) - pi)
chi = numpy.sum(angle)
if chi < chi_old:
chi_old = chi
fitguess = [dclock,doffset]
#print 'Better fit 2', dclock, doffset
freq = freq.astype(numpy.float64)
phase= phase.astype(numpy.float64) #epsfcn=1e-7
#print 'hello', fitguess
fitresult, success = scipy.optimize.leastsq(par2slope, [fitguess[0], fitguess[1]], args=(freq, phase),maxfev=10000)
#print 'ok',fitresult
else:
print 'No valid data found'
fitresult = [0.0,0.0]
show_plot = False
if show_plot:
#if len(fitresult ==2):
# fitresult =[fitresult[0], fitresult[1],0]
# print 'Here'
# #fitresult = fitresult # [fitguess[0], fitguess[1], 0]
#fitresult = [[fitresultguess[0],fitresultguess[0],0.0]]
matplotlib.pyplot.plot(freq, numpy.mod(phase + 1.0*pi, 2.*pi) -1.0*pi, 'or' )
matplotlib.pyplot.plot(freq, numpy.mod(phase_diff + 1.0*pi, 2.*pi) -1.0*pi , '.', color='purple' )
#TEC = numpy.mod((-8.44797245e9*(2.*fitresult[1])/freq)+numpy.pi, 2*pi) - pi # notice factor of 2 because rr+ll
Clock = numpy.mod((2.*numpy.pi* 2.*fitresult[0]*freq )+numpy.pi, 2*pi) - pi # notice factor of 2 because rr+ll
phase_total = (2.*numpy.pi*2.*fitresult[0]*freq)+(-8.44797245e9*(2.*fitresult[1])/freq)+fitresult[2]
residual = numpy.mod(phase-phase_total+pi,2.*pi)-pi
matplotlib.pyplot.plot(freq, residual, '.', color='yellow')
idxl = int(min(freq_old)/1e4)
idxh = int(max(freq_old)/1e4)
bigfreqaxistmp = range(idxl, idxh)
bigfreqaxis = numpy.array([float(i) for i in bigfreqaxistmp])
bigfreqaxis = bigfreqaxis*1e4
matplotlib.pyplot.plot (bigfreqaxis, fitfuncfastplot(fitresult, bigfreqaxis[:]), "r-")
matplotlib.pyplot.plot (bigfreqaxis, plotrm(fitresultrm_wav, c/bigfreqaxis[:]), "-", color='purple')
#matplotlib.pyplot.plot (freq, fitfuncfastplot(fitresult, bigfreqaxis[:]), "r-")
matplotlib.pyplot.plot(freq, Clock, ',g')
matplotlib.pyplot.plot(freq, TEC, ',b')
matplotlib.pyplot.xlabel('freq')
matplotlib.pyplot.ylabel('phase')
matplotlib.pyplot.show()
return [fitresult[0], fitresult[1]]
def fit_dTEC_dclock_doffset(phases, freq):
c = 2.99792458e8
freq_old = numpy.copy(freq)
# NOTE THE *2 to use rr+ll instead of 0.5*(rr+ll)
par3complex = lambda p, freq, y: abs(numpy.cos((2.*pi*p[0]*freq) - (1.*8.44797245e9*p[1]/freq) + p[2]) - numpy.cos(y)) + abs(numpy.sin((2.*pi*p[0]*freq) - (1.*8.44797245e9*p[1]/freq) + p[2]) - numpy.sin(y))
par2complex = lambda p, freq, y: abs(numpy.cos((4.*pi*p[0]*freq) - (2.*8.44797245e9*p[1]/freq)) - numpy.cos(y)) + abs(numpy.sin((4.*pi*p[0]*freq) - (2.*8.44797245e9*p[1]/freq)) - numpy.sin(y))
rmwavcomplex = lambda RM, wav, y: abs(numpy.cos(2.*RM[0]*wav*wav) - numpy.cos(y)) + abs(numpy.sin(2.*RM[0]*wav*wav) - numpy.sin(y))
par2slope = lambda p, freq, y: abs(numpy.cos((-8.44797245e9*p[0]/freq) + p[1]) - numpy.cos(y)) + abs(numpy.sin((-8.44797245e9*p[0]/freq) + p[1]) - numpy.sin(y))
plotrm = lambda RM, wav: numpy.mod( (1.*RM*wav*wav) +1.0*pi, 2.*pi) -1.0*pi # notice the factor of 2
fitfuncfastplot = lambda p, freq: numpy.mod((2.*pi*p[0]*freq) - (1.*8.44797245e9*p[1]/freq) + (p[2])+ 1.0*pi, 2.*pi) -1.0*pi
phase = numpy.copy(phases)
pi = numpy.pi
chi_old=1e9
if len(freq) > 10:
doffset = 0.0
# FIND INTIAL GUESS
# FIND INTIAL GUESS
for dTEC in numpy.arange(-1.0,1.0, 0.01):
for dclock in numpy.arange(-150e-9,150e-9,5e-9):
for doffset in numpy.arange(-pi-0.2,pi+0.2,0.2):
phase_model = numpy.mod ( (2.*pi*dclock*freq) - (8.44797245e9*dTEC/freq) + doffset, 2*pi) # NOTE THE *2 to use rr+ll instead of 0.5*(rr+ll)
phase_data = numpy.mod (phase, 2*pi)
angle = pi - numpy.abs(numpy.abs(phase_model - phase_data) - pi)
chi = numpy.sum(angle)
if chi < chi_old:
chi_old = chi
fitguess = [dclock,dTEC,doffset]
#print 'Better fit', dclock, dTEC
fitguess_1 = numpy.copy(fitguess)
#print 'iter 1', fitguess
for dTEC in numpy.arange(fitguess_1[1]-0.02,fitguess_1[1]+0.02, 0.004):
for dclock in numpy.arange(fitguess_1[0]-8e-9,fitguess_1[0]+8e-9,2e-9):
for doffset in numpy.arange(doffset+0.3,doffset-0.3,0.1):
phase_model = numpy.mod ( (2.*pi*dclock*freq) - (8.44797245e9*dTEC/freq)+doffset, 2*pi) # NOTE THE *2 to use rr+ll instead of 0.5*(rr+ll)
phase_data = numpy.mod (phase, 2*pi)
angle = pi - numpy.abs(numpy.abs(phase_model - phase_data) - pi)
chi = numpy.sum(angle)
if chi < chi_old:
chi_old = chi
fitguess = [dclock,dTEC,doffset]
#print 'Better fit', dclock, dTEC
freq = freq.astype(numpy.float64)
phase= phase.astype(numpy.float64) #epsfcn=1e-7
#print 'hello', fitguess
#fitresult, success = scipy.optimize.leastsq(par3complex, [fitguess[0], fitguess[1],0], args=(freq, phase),maxfev=10000)
#fitresult, success = scipy.optimize.leastsq(par3complex, [0,0,0], args=(freq, phase),maxfev=10000)
fitresult, success = scipy.optimize.leastsq(par3complex, [fitguess[0], fitguess[1],fitguess[2]], args=(freq, phase),maxfev=10000)
#print 'ok',fitresult
else:
print 'No valid data found'
fitresult = [0.0,0.0]
show_plot = False
if show_plot:
#if len(fitresult ==2):
# fitresult =[fitresult[0], fitresult[1],0]
# print 'Here'
# #fitresult = fitresult # [fitguess[0], fitguess[1], 0]
#fitresult = [[fitresultguess[0],fitresultguess[0],0.0]]
matplotlib.pyplot.plot(freq, numpy.mod(phase + 1.0*pi, 2.*pi) -1.0*pi, 'or' )
#matplotlib.pyplot.plot(freq, numpy.mod(phase_diff + 1.0*pi, 2.*pi) -1.0*pi , '.', color='purple' )
TEC = numpy.mod((-8.44797245e9*(fitresult[1])/freq)+numpy.pi, 2*pi) - pi # notice factor of 2 because rr+ll
Clock = numpy.mod((2.*numpy.pi*fitresult[0]*freq )+numpy.pi, 2*pi) - pi # notice factor of 2 because rr+ll
phase_total = (2.*numpy.pi*fitresult[0]*freq)+(-8.44797245e9*(fitresult[1])/freq)+fitresult[2]
residual = numpy.mod(phase-phase_total+pi,2.*pi)-pi
matplotlib.pyplot.plot(freq, residual, '.', color='purple')
idxl = int(min(freq_old)/1e4)
idxh = int(max(freq_old)/1e4)
bigfreqaxistmp = range(idxl, idxh)
bigfreqaxis = numpy.array([float(i) for i in bigfreqaxistmp])
bigfreqaxis = bigfreqaxis*1e4
matplotlib.pyplot.plot (bigfreqaxis, fitfuncfastplot(fitresult, bigfreqaxis[:]), "r-")
#matplotlib.pyplot.plot (bigfreqaxis, plotrm(fitresultrm_wav, c/bigfreqaxis[:]), "-", color='purple')
#matplotlib.pyplot.plot (freq, fitfuncfastplot(fitresult, bigfreqaxis[:]), "r-")
matplotlib.pyplot.plot(freq, Clock, ',g')
matplotlib.pyplot.plot(freq, TEC, ',b')
matplotlib.pyplot.xlabel('freq')
matplotlib.pyplot.ylabel('phase')
matplotlib.pyplot.show()
phase_total = (2.*numpy.pi*fitresult[0]*freq)+(-8.44797245e9*(fitresult[1])/freq)+fitresult[2]
#angle = pi - numpy.abs(numpy.abs(phase - phase_total) - pi)
#chi = numpy.sum(angle)
residual = numpy.mod(phase-phase_total+pi,2.*pi)-pi
#print residual
chi = numpy.sum(numpy.abs(residual))/len(residual)
return [fitresult[0], fitresult[1],fitresult[2], chi]
#import lofar.expion.fitting as fitting
pi = numpy.pi
c = 2.99792458e8
ionmodel = h5parm('scalarphase.h',readonly=True)
solsetNames = ionmodel.getSolsets()
for solsetName in solsetNames:
print solsetName
solset = ionmodel.getSolset(solsetName)
soltabs = ionmodel.getSoltabs('sol000')
scalarphasetab = ionmodel.getSoltab('sol000','scalarphase000')
anttab = ionmodel.getAnt('sol000')
print scalarphasetab.freq[:]
#print scalarphasetab.time[:]
#print scalarphasetab.val[:]
#print scalarphasetab.ant[:]
sphase = scalarphasetab.val[:]
print np.shape(sphase)
station = 52
ref_station = 0
time_id = 710
print scalarphasetab.ant[station]
phase = sphase[0,station,:,:] - sphase[0,ref_station,:,:]
matplotlib.pyplot.plot(scalarphasetab.freq[:]/1e6, np.mod(phase[:,710:715]+pi, 2.*pi) -pi, 'o')
matplotlib.pyplot.xlabel('freq [MHz]')
matplotlib.pyplot.ylabel('phase [rad]')
matplotlib.pyplot.ylim(-1.*pi,pi)
matplotlib.pyplot.show()
#sys.exit()
phase = numpy.copy(np.rot90(phase))
# time, freq
#phase = numpy.copy(scipy.ndimage.filters.minimum_filter(phase, (0,0)))
matplotlib.pyplot.subplot(1,2,0)
matplotlib.pyplot.imshow(np.cos(phase), vmin=-1.0, vmax=1.0, aspect='auto')
matplotlib.pyplot.subplot(1,2,1)
matplotlib.pyplot.imshow(np.sin(phase), vmin=-1.0, vmax=1.0, aspect='auto')
matplotlib.pyplot.xlabel('block')
matplotlib.pyplot.ylabel('time')
#matplotlib.pyplot.show()
########################
refantenna_id = 0
source_id = 0 # source ID in global_db (usually 0)
ncpus = 24*2
clockfit = 0.*numpy.copy(scalarphasetab.time[:])
phaseoffset = 0.*numpy.copy(scalarphasetab.time[:])
TECfit = 0.*numpy.copy(scalarphasetab.time[:])
chifit = 0.*numpy.copy(scalarphasetab.time[:])
clockarray = numpy.zeros([len(scalarphasetab.time),len(scalarphasetab.ant)])
phaseoffsetarray = numpy.zeros([len(scalarphasetab.time),len(scalarphasetab.ant)])
TECarray = numpy.zeros([len(scalarphasetab.time),len(scalarphasetab.ant)])
chiarray = numpy.zeros([len(scalarphasetab.time),len(scalarphasetab.ant)])
print 'REF STATION:', scalarphasetab.ant[refantenna_id]
print '# TIMESLOTS ', len(scalarphasetab.time)
print '# FREQUENCIES', len(scalarphasetab.freq)
ppservers = ()
# Creates jobserver with ncpus workers
job_server = pp.Server(ncpus, ppservers=ppservers)
print "Starting pp with", job_server.get_ncpus(), "workers"
phases_all = numpy.copy(scalarphasetab.val)
start_time_id = 0
stop_time_id = len(scalarphasetab.time)
print numpy.shape(phases_all)
phases_freq = numpy.copy(scalarphasetab.freq[:])
for antenna_id in range(1, len(scalarphasetab.ant[:])) :
#for antenna_id in range(1, 3) :
if antenna_id != refantenna_id:
stationspos = anttab[scalarphasetab.ant[refantenna_id]] - anttab[scalarphasetab.ant[antenna_id]]
distance_station = numpy.sqrt(stationspos[0]**2 + stationspos[1]**2 + stationspos[2]**2)
print 'Distance stations to reference station', distance_station/1e3, ' km'
jobs = []
for time_id in range(start_time_id,stop_time_id):
#for time_id in range(0,10):
phases = numpy.copy((phases_all[source_id,antenna_id,:,time_id] -phases_all[source_id,refantenna_id,:,time_id]))
#jobs.append(job_server.submit(fit_dclock_doffset,(phases,phases_freq),(), ("numpy","scipy.optimize","matplotlib.pyplot",)))
jobs.append(job_server.submit(fit_dTEC_dclock_doffset,(phases,phases_freq),(), ("numpy","scipy.optimize","matplotlib.pyplot",)))
print 'Submitting job #', time_id
#print phases, phases_freq
i= 0
for job in jobs:
fitresult = job()
#print job()
#print numpy.shape(clockfit)
clockfit[start_time_id+i] = fitresult[0]
TECfit[start_time_id+i] = fitresult[1]
phaseoffset[start_time_id+i] = fitresult[2]
chifit[start_time_id+i] = fitresult[3]
clockarray[start_time_id+i,antenna_id] = fitresult[0]
TECarray[start_time_id+i,antenna_id] = fitresult[1]
phaseoffsetarray[start_time_id+i,antenna_id] = fitresult[2]
chiarray[start_time_id+i,antenna_id] = fitresult[3]
print 'TIME_ID', start_time_id+i, 'FIT (dclock,offset)', fitresult
i = i + 1
numpy.save('ScPclock.npy', clockarray)
numpy.save('ScPoffset.npy', phaseoffsetarray)
numpy.save('ScPTEC.npy', TECarray)
numpy.save('ScPchi.npy', chiarray)