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fit_clocktec_initialguess_losoto.py
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fit_clocktec_initialguess_losoto.py
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#!/usr/bin/python
import matplotlib
import lofar.parmdb
import sys
import os
import scipy
import time
import numpy
import math
import pyrap.tables
import pp
import scipy.signal
from pylab import *
import sys, os, glob, re
import numpy as np
import shutil
import progressbar
import logging
import pyrap.tables as pt
import lofar.parmdb
import losoto._version
import losoto._logging
from losoto.h5parm import h5parm, solWriter, solFetcher
import multiprocessing as mp
#import lofar.expion.fitting as fitting
import config
die=config.die
report=config.report
warn=config.warn
if len(sys.argv)<2:
die('Need a filename for config file')
filename=sys.argv[1]
if not(os.path.isfile(filename)):
die('Config file does not exist')
cfg=config.LocalConfigParser()
cfg.read(filename)
processedpath=cfg.get('paths','processed')
os.chdir(processedpath)
globaldbname = 'cal.h5' # input h5 parm file
t = pt.table('globaldb/OBSERVATION', readonly=True, ack=False)
calsource=t[0]['LOFAR_TARGET'][0]
ncpus = 16 # number of CPUs avaulable for parallel fitting
pi = numpy.pi
c = 2.99792458e8
ionmodel = h5parm(globaldbname ,readonly=True)
solsetNames = ionmodel.getSolsets()
for solsetName in solsetNames:
print solsetName
solset = ionmodel.getSolset(solsetName)
# solset name seems to always be sol000
soltabs = ionmodel.getSoltabs('sol000')
amptab = ionmodel.getSoltab('sol000','amplitude000')
phasetab = ionmodel.getSoltab('sol000','phase000')
rottab = ionmodel.getSoltab('sol000','rotation000')
anttab = ionmodel.getAnt('sol000')
source_id = 0 # source ID in global_db (usually 0)
def median_window_filter(ampl, half_window, threshold):
ampl_tot_copy = numpy.copy(ampl)
ndata = len(ampl)
flags = numpy.zeros(ndata, dtype=bool)
sol = numpy.zeros(ndata+2*half_window)
sol[half_window:half_window+ndata] = ampl
for i in range(0, half_window):
# Mirror at left edge.
idx = min(ndata-1, half_window-i)
sol[i] = ampl[idx]
# Mirror at right edge
idx = max(0, ndata-2-i)
sol[ndata+half_window+i] = ampl[idx]
#fix oct 2012
median_array = scipy.signal.medfilt(sol,int(half_window*2.-1))
sol_flag = numpy.zeros(ndata+2*half_window, dtype=bool)
sol_flag_val = numpy.zeros(ndata+2*half_window, dtype=bool)
for i in range(half_window, half_window + ndata):
# Compute median of the absolute distance to the median.
window = sol[i-half_window:i+half_window+1]
window_flag = sol_flag[i-half_window:i+half_window+1]
window_masked = window[~window_flag]
if len(window_masked) < math.sqrt(len(window)):
# Not enough data to get accurate statistics.
continue
median = numpy.median(window_masked)
q = 1.4826 * numpy.median(numpy.abs(window_masked - median))
# Flag sample if it is more than 1.4826 * threshold * the
# median distance away from the median.
if abs(sol[i] - median) > (threshold * q):
sol_flag[i] = True
mask = sol_flag[half_window:half_window + ndata]
for i in range(len(mask)):
if mask[i]:
ampl_tot_copy[i] = median_array[half_window+i] # fixed 2012
return ampl_tot_copy
def running_median(ampl,half_window) :
ampl_tot_copy = numpy.copy(ampl)
ndata = len(ampl)
flags = numpy.zeros(ndata, dtype=bool)
sol = numpy.zeros(ndata+2*half_window)
sol[half_window:half_window+ndata] = ampl
std = numpy.zeros(len(ampl))
for i in range(0, half_window):
# Mirror at left edge.
idx = min(ndata-1, half_window-i)
sol[i] = ampl[idx]
# Mirror at right edge
idx = max(0, ndata-2-i)
sol[ndata+half_window+i] = ampl[idx]
for i in range(len(ampl)):
#print i, i+half_window
std[i] = numpy.median(sol[i:i+(2*half_window)])
return std
def fit_dTEC_dclock_dFR(phases_rr, phases_ll, amp_rr, amp_ll, freq, distance_station):
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((4.*pi*p[0]*freq) - (2.*8.44797245e9*p[1]/freq) + p[2]) - numpy.cos(y)) + abs(numpy.sin((4.*pi*p[0]*freq) - (2.*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))
# NOTE THE *2 to use rr+ll instead of 0.5*(rr+ll)
par3complex_w = lambda p, freq, y: abs(numpy.cos((4.*pi*p[0]*freq) - (2.*8.44797245e9*p[1]/freq) + p[2]) - numpy.cos(y))*(freq/1e5) + abs(numpy.sin((4.*pi*p[0]*freq) - (2.*8.44797245e9*p[1]/freq) + p[2]) - numpy.sin(y))*(freq/1e5)
par2complex_w = lambda p, freq, y: abs(numpy.cos((4.*pi*p[0]*freq) - (2.*8.44797245e9*p[1]/freq)) - numpy.cos(y))*(freq/1e5) + abs(numpy.sin((4.*pi*p[0]*freq) - (2.*8.44797245e9*p[1]/freq)) - numpy.sin(y))*(freq/1e5)
rmwavcomplex_w = lambda RM, wav, y: abs(numpy.cos(2.*RM[0]*wav*wav) - numpy.cos(y))/wav + abs(numpy.sin(2.*RM[0]*wav*wav) - numpy.sin(y))/wav
plotrm = lambda RM, wav: numpy.mod( (2.*RM*wav*wav) +1.0*pi, 2.*pi) -1.0*pi # notice the factor of 2
fitfuncfastplot = lambda p, freq: numpy.mod((4.*pi*p[0]*freq) - (2.*8.44797245e9*p[1]/freq) + (p[2])+ 1.0*pi, 2.*pi) -1.0*pi
idx_rr = numpy.where(amp_rr != 1.0)
if len(idx_rr) != 0:
freq = freq[:][idx_rr]
phases_rr = phases_rr[:][idx_rr]
phases_ll = phases_ll[:][idx_rr]
amp_ll = amp_ll[:][idx_rr]
idx_ll = numpy.where(amp_ll != 1.0)
if len(idx_ll) != 0:
freq = freq[:][idx_ll]
phases_rr = phases_rr[:][idx_ll]
phases_ll = phases_ll[:][idx_ll]
if len(freq) < len(freq_old):
print 'Number of filtered out data points:', len(freq_old)-len(freq)
# ---------- end filter bad data -----------
if len(freq) != 0: # prepare and make arrays if there is valid data
freq = (freq[0:len(freq_old)])
phases_ll = (phases_ll[0: len(freq_old)])
phases_rr = (phases_rr[0: len(freq_old)])
phase = (phases_rr + phases_ll) # not divide by 2, then later fix this
#phase = (phases_ll + phases_ll) # temp, just fit RR for now
phase_diff = (phases_rr - phases_ll) # not divide by 2, then later fix this
wav = c/freq
pi = numpy.pi
chi_old=1e9
else:
phase = (phases_rr[:] + phases_ll[:])
phase_diff = (phases_rr[:] - phases_ll[:])
if len(freq) > 10:
# FIND INTIAL GUESS
for dTEC in numpy.arange(-1.0,1.0, 0.01):
for dclock in numpy.arange(-200e-9,200e-9,5e-9):
phase_model = numpy.mod ( (4.*pi*dclock*freq) - (2.*8.44797245e9*dTEC/freq), 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]
#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, 0.002):
for dclock in numpy.arange(fitguess_1[0]-8e-9,fitguess_1[0]+ 8e-9,1e-9):
phase_model = numpy.mod ( (4.*pi*dclock*freq) - (2.*8.44797245e9*dTEC/freq), 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]
#print 'Better fit', dclock, dTEC
#print 'iter 2', fitguess
chi_old = 1e9
for dFR in numpy.arange(-0.1,0.1,2e-4):
phase_model = numpy.mod (2.*dFR*wav*wav, 2*pi) # notice the factor of 2
phase_data = numpy.mod (phase_diff, 2*pi)
angle = pi - numpy.abs(numpy.abs(phase_model - phase_data) - pi)
chi = numpy.sum(angle)
if chi < chi_old:
chi_old = chi
fitrmguess = dFR
#print 'Better fit', fitrmguess
fitrmguess_1 = numpy.copy(fitrmguess)
for dFR in numpy.arange(fitrmguess_1-5e-4,fitrmguess_1+5e-4,0.5e-5):
phase_model = numpy.mod (2.*dFR*wav*wav, 2*pi) # notice the factor of 2
phase_data = numpy.mod (phase_diff, 2*pi)
angle = pi - numpy.abs(numpy.abs(phase_model - phase_data) - pi)
chi = numpy.sum(angle)
if chi < chi_old:
chi_old = chi
fitrmguess = dFR
#print 'Better fit', fitrmguess
# DO THE FITTING
# SOLVE Clock-TEC anticorrelation problem on short baselines
freq = freq.astype(numpy.float64)
phase= phase.astype(numpy.float64) #epsfcn=1e-7
if distance_station < 0. : #15.0*1e3: DOES NOT WORK, NEED 3 par FIT
fitresult, success = scipy.optimize.leastsq(par2complex, fitguess, args=(freq, phase))
#fitresult = fitguess
else:
fitresult, success = scipy.optimize.leastsq(par3complex, [fitguess[0], fitguess[1], 0.0], args=(freq, phase),maxfev=10000)
#fitresult = [fitguess[0], fitguess[1], 0.0]
#print fitresult, success
fitresultrm_wav, success = scipy.optimize.leastsq(rmwavcomplex, [fitrmguess], args=(wav, phase_diff))
else:
print 'No valid data found'
fitresult = [0.0,0.0, 0.0]
fitresultrm_wav= 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],fitresult[2], fitresultrm_wav]
time_id = 100
pol_id = 0
#antenna_id = 29
refantenna_id = 0
source_id = 0
goodstartidx = 0
CScorrect = False
if CScorrect:
csclockvals = numpy.load('../CS_clocks.npy')
A = numpy.zeros((len(amptab.freq[:]), 2), dtype = float)
A[:,0] = amptab.freq[:]*2*pi
A[:,1] = -8.44797245e9/amptab.freq[:]
sol = numpy.zeros((len(amptab.ant), 2))
clockfit = 0.*numpy.copy(amptab.time)
TECfit = 0.*numpy.copy(amptab.time)
RMfit = 0.*numpy.copy(amptab.time)
phaseoffset = 0.*numpy.copy(amptab.time)
clockarray = numpy.zeros([len(amptab.time),len(amptab.ant)])
tecarray = numpy.zeros([len(amptab.time),len(amptab.ant)])
rmarray = numpy.zeros([len(amptab.time),len(amptab.ant)])
phaseoffsetarray = numpy.zeros([len(amptab.time),len(amptab.ant)])
print 'REF STATION:', amptab.ant[refantenna_id]
print '# TIMESLOTS ', len(amptab.time)
print '# FREQUENCIES', len(amptab.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(phasetab.val)
#phases_all = numpy.load('../phases_3C196.npy')
start_time_id = 0
stop_time_id = len(phasetab.val[0,source_id,0,0,:])
if CScorrect:
# CORRECT CS CLOCKS
for time_id in range(start_time_id,stop_time_id):
print time_id
phases_rr = numpy.copy(phases_all[0,source_id,:,:,time_id])
phases_ll = numpy.copy(phases_all[1,source_id,:,:,time_id])
#RR correct
for ss in range(0,len(phasetab.ant[:])):
sol[ss,0] = (csclockvals[ss,0]) # clock RR
sol[ss,1] = 0.0 # TEC
#print 'RR', sol[ss,0]
phases_rr = phases_rr - dot(A, sol.T)
#LL correct
for ss in range(0,len(phasetab.ant[:])):
sol[ss,0] = (csclockvals[ss,1]) # clock LL
sol[ss,1] = 0.0 # TEC
#print 'LL', sol[ss,0]
phases_ll = phases_ll - dot(A, sol.T)
phases_all[time_id,:,:,source_id,0] = numpy.copy(phases_rr)
phases_all[time_id,:,:,source_id,1] = numpy.copy(phases_ll)
# count number of RS stations
N_RS = 0
for ss in range(0,len(phasetab.ant[:])):
sname = phasetab.ant[ss]
if sname[0:2] == 'RS':
N_RS=N_RS+1
#for antenna_id in range(3, 4):
#for antenna_id in range(len(ionmodel.stations[:])-N_RS, len(ionmodel.stations[:])) :
freq = numpy.copy(phasetab.freq)
for antenna_id in range(1, len(phasetab.ant[:])) :
if antenna_id != refantenna_id:
stationspos = anttab[phasetab.ant[refantenna_id]] - anttab[phasetab.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(3050,3051):
phases_rr = numpy.copy((phases_all[0,source_id,antenna_id,:,time_id] \
-phases_all[0,source_id,refantenna_id,:,time_id]))
phases_ll = numpy.copy((phases_all[1,source_id,antenna_id,:,time_id] \
-phases_all[1,source_id,refantenna_id,:,time_id]))
# -------- filter bad data: where amplitudes equal 1.0 -----------
amp_rr = numpy.copy(amptab.val[0,source_id,antenna_id,:,time_id])
amp_ll = numpy.copy(amptab.val[0,source_id,antenna_id,:,time_id])
jobs.append(job_server.submit(fit_dTEC_dclock_dFR,(phases_rr, phases_ll, amp_rr, amp_ll, freq, distance_station),(), ("numpy","scipy.optimize","matplotlib.pyplot",)))
print 'Submitting job #', time_id
i= 0
for job in jobs:
fitresult = job()
clockfit[start_time_id+i] = fitresult[0]
TECfit[start_time_id+i] = fitresult[1]
RMfit[start_time_id+i] = fitresult[3]
phaseoffset[start_time_id+i] = fitresult[2]
clockarray[start_time_id+i,antenna_id] = fitresult[0]
tecarray[start_time_id+i,antenna_id] = fitresult[1]
rmarray[start_time_id+i,antenna_id] = fitresult[3]
phaseoffsetarray[start_time_id+i,antenna_id] = fitresult[2]
print 'TIME_ID', start_time_id+i, 'FIT (dclock, dTEC, offset, dRM)', fitresult
i = i + 1
os.system('rm -f ' + 'fitted_data_dclock_' + calsource + '_1st.npy')
os.system('rm -f ' + 'fitted_data_dTEC_' + calsource + '_1st.npy')
numpy.save('fitted_data_dclock_' + calsource + '_1st.npy', clockarray)
numpy.save('fitted_data_dTEC_' + calsource + '_1st.npy', tecarray)
for antenna_id in range(0, len(amptab.ant[:])):
print 'Cleaning up Clock and TEC values for: ', amptab.ant[antenna_id]
clockfit = clockarray[:,antenna_id]
TECfit = tecarray[:,antenna_id]
clockfit = median_window_filter(clockfit, 7, 5)
TECfit = median_window_filter(TECfit, 5, 5)
clockfit = median_window_filter(clockfit, 5, 3)
TECfit = median_window_filter(TECfit, 5, 3)
clockfit = median_window_filter(clockfit, 3, 3)
TECfit = median_window_filter(TECfit, 3, 3)
clockfit = running_median(clockfit, 3)
TECfit = running_median(TECfit, 3)
clockarray[:,antenna_id] = clockfit
tecarray[:,antenna_id] = TECfit
#matplotlib.pyplot.plot(clockarray[:,antenna_id])
os.system('rm -f ' + 'fitted_data_dclock_' + calsource + '_1st.sm.npy')
os.system('rm -f ' + 'fitted_data_dTEC_' + calsource + '_1st.sm.npy')
numpy.save('fitted_data_dclock_' + calsource + '_1st.sm.npy', clockarray)
numpy.save('fitted_data_dTEC_' + calsource + '_1st.sm.npy', tecarray)
#matplotlib.pyplot.savefig('fit_clocktec.png')