def reduce030(demo=False, max_GD_um=1.0, max_err_um=0.35, max_length_s=1.0,
sigma_clipping=4.0, onlyScans=False, min_scan_snr=3.0):
global astro_files030, data_directory030, scan_files030
if not onlyScans:
for f in astro_files030:
print '### reducing ', os.path.join(data_directory030,f) , '######'
# typical reduction
a = prima.drs(os.path.join(data_directory030,f))
a.astrometryFtk(writeOut=(not demo), overwrite=True,
max_length_s=max_length_s,plot=demo,
max_GD_um=max_GD_um, max_err_um=max_err_um,
sigma_clipping=sigma_clipping)
if demo:
break
a.raw.close()
for f in scan_files030:
print '### reducing ', os.path.join(data_directory030,f) , '########'
# typical reduction
a = prima.drs(os.path.join(data_directory030,f))
a.astrometryScan(writeOut=(not demo), overwrite=True,
plot=demo, min_snr=min_scan_snr,
sigma_clipping=sigma_clipping)
if demo:
break
a.raw.close()
print "#########################################################"
print " remember to copy reduced files to internal hard drive !"
print "#########################################################"
return
def analyse241_HD10268(reduceData=False, bootstrap=False):
"""
astrometric fit
"""
files = files_241_HD10268
if reduceData:
for f in files:
print '#'*5, f, '#'*12
a = prima.drs(data_directory+f)
a.astrometryFtk(max_err_um=5, max_GD_um=3.0, sigma_clipping=4.0,
writeOut=True, overwrite=True, max_length_s=1.0)
del a
selection = range(len(files))
selection=[0,1,2,4,5,6]
if bootstrap:
files = [files[k] for k in selection]
res = astrom.bootstrapListOfFiles(files, data_directory, N=500,
fit_param =[1, 1, 1],
plot=True,
first_guess=[6.0, 200.0,-3.92104],
maxResidualsMas=30, multi=True)
else:
astrom.interpListOfFiles(files, data_directory,
plot=True,
fit_param =[1, 1, 1],
first_guess=[6.0, 200.0,1.0],
fit_only_files=selection,maxResiduals=20)
res = []
return res
def analyse238_HD10360(reduceData=False, bootstrap=False):
"""
astrometric fit
"""
files = files_238_10360
if reduceData:
for f in files:
a = prima.drs(data_directory+f)
print '#'*5, f, '#'*12
a.astrometryFtk(max_err_um=2.0, max_GD_um=3.0, sigma_clipping=4.0,
writeOut=True, overwrite=True, max_length_s=1.0)
del a
a=[]
selection = range(len(files))
#selection = [0,1,2,3,5]
#selection = [1,3,5]
if bootstrap:
files = [files[k] for k in selection]
astrom.bootstrapListOfFiles(files, data_directory, N=500,
fit_param =[1, 1, 1], plot=True,
first_guess=[6.0, 200.0,-3.92104])
else:
res = astrom.interpListOfFiles(files, data_directory,
plot=True,quiet=False,
fit_param =[1, 1, 1],
first_guess=[6.0, 200.0,-3.92104],
fit_only_files=selection,
maxResiduals=5)
return
def analyse236_HD202730(reduceData=False, bootstrap=False):
"""
astrometric fit
"""
files = files_236
if reduceData:
for f in files:
print '#'*5, f, '#'*12
a = prima.drs(data_directory+f)
a.astrometryFtk(max_err_um=5, max_GD_um=3.0, sigma_clipping=3.0,
writeOut=True, overwrite=True, max_length_s=1.0)
del a
if f == '2011-08-23/PACMAN_OBJ_ASTRO_236_0007.fits':
tmp=pyfits.open(data_directory+\
'/2011-08-23/PACMAN_OBJ_ASTRO_236_0007_RED.fits',
mode='update')
tmp[0].header.update('HIERARCH ESO INS MODE', 'SWAPPED')
tmp.flush()
tmp.close()
selection = range(len(files))
#selection = [0,1,4,5,8,9]
if bootstrap:
files = [files[k] for k in selection]
astrom.bootstrapListOfFiles(files, data_directory, N=500,
fit_param =[1, 1, 1], plot=True,
first_guess=[6.0, 200.0,-3.92104],
maxResidualsMas=20, multi=True)
else:
astrom.interpListOfFiles(files, data_directory,
plot=True,
fit_param =[1, 1, 1],
first_guess=[6.0, 200.0,1.0],
fit_only_files=selection, maxResiduals=20 )
return
def analyse200_HD156274(reduction=False):
"""
astrometric fit
"""
files = files_200_HD156274
print len(files)
if reduction:
for f in files:
a = prima.drs(os.path.join(data_directory,f))
a.astrometryFtk(writeOut=True, max_err_um=4.0, max_GD_um=3.0,
correctA_B=True, overwrite=True, sigma_clipping=6.0)
del a
selection = [0,1,4,5,6,7,8,9,10,11,17,18,19] # normal
selection = range(len(files)) # ALL files
#selection = filter(lambda x: x!=6, selection) # merged
#selection = filter(lambda x: x!=9, selection) # merged
#selection = filter(lambda x: x!=20, selection) # merged
print len(selection)
astrom.interpListOfFiles(files, data_directory,
plot=True,
fit_param =[1, 1, 1, 0,0,0],
first_guess=[8,256,5.15523,0,0,0],
fit_only_files=selection,
maxResiduals=10)
return
def analyse201_HD156274(reReduce=False):
"""
astrometric fit
STS-AT4 rotation test
"""
files = files_201_HD156274
print len(files)
if reReduce:
for f in files:
a = prima.drs(os.path.join(data_directory,f))
a.astrometryFtk(writeOut=True, max_GD_um = 2.0,
overwrite=True, max_err_um=10, sigma_clipping=5.0)
del a
selection = range(len(files)) # ALL files
selection = filter(lambda x: x!=17, selection) # 17 has A-B overflow
selection = filter(lambda x: x!=1, selection) # 17 has A-B overflow
selection = filter(lambda x: x!=21, selection) # 17 has A-B overflow
#selection = [0,1,2,3,4,5,6,7,8,9,19,20,21] # normal
#selection = [0,1,5,9,19,20,21] # normal, no rotation
#selection = [10,11,12,13,14,15,16,18, 22, 23] # swapped, 17 has A-B overflow
#selection = [10,18,22,23] # swapped, no rotation
#selection = [0,1,5,9,19,20,21,10,18,22,23]
astrom.interpListOfFiles(files, data_directory,
plot=True,
fit_param =[1, 1, 1, 0,0,0],
first_guess=[10.08,258.02,5.15523,0,0,0],
fit_only_files=selection,
maxResiduals=35)
return
at4 = prima.pssRecorder('/Volumes/DATA/PRIMA/COMM15/pssRecorder/pssguiRecorder_lat4fsm_2011-07-19_23-49-45.dat')
at3 = prima.pssRecorder('/Volumes/DATA/PRIMA/COMM15/pssRecorder/pssguiRecorder_lat3fsm_2011-07-19_23-49-45.dat')
pyplot.figure(1)
pyplot.clf()
pyplot.plot(at4.mjd[::10], at4.data['Dermec.[deg]'][::10],
color='orange', linewidth=5, label='AT4 derot', alpha=0.9)
pyplot.plot(at3.mjd[::10], at3.data['Dermec.[deg]'][::10],
linestyle='dashed',
color='green', linewidth=2, label='AT3 derot')
pyplot.legend()
pyplot.xlim(5.5762e4+0.01, 5.5762e4+0.17)
pyplot.annotate('modulation\ncommand', xy=(5.5762e4+0.025, 108),
xycoords='data', size=12,
xytext=(5.5762e4+0.04, 120), textcoords='data',
arrowprops=dict(arrowstyle="->"))
pyplot.annotate('tracking\nwrapping', xy=(5.5762e4+0.1, 160),
xycoords='data', size=12,
xytext=(5.5762e4+0.06, 150), textcoords='data',
arrowprops=dict(arrowstyle="->"))
pyplot.annotate('swapping', xy=(5.5762e4+0.067, 60),
xycoords='data', size=12,
xytext=(5.5762e4+0.03, 50), textcoords='data',
arrowprops=dict(arrowstyle="->"))
pyplot.xlabel('MJD')
pyplot.ylabel('mechanical position (degrees)')
return
def exctractOpdFromFiles(files):
"""
run the .expectedOPD methon from the PRIMA drs and pickled it in a
'.opd' file.
"""
for f in files:
a = prima.drs(f)
opd = a.expectedOPD(plot=False)
g = open(f+'.opd', 'wb')
print 'saving:', f+'.opd'
cPickle.dump(opd, g, 2)
g.close
a.raw.close()
def doAll(files, directory='', reduceData=False, bootstrap=False,
selection=None, maxResiduals=None, pssRec=None,
firstGuess=[10.,90., 0.0], fittedParam=[1,1,1],
res2fits=False, max_GD_um=5.0, sigma_clipping=3.5,
max_err_um=2, max_length_s=1.0):
"""
astrometric data reduction and fit of a list of files. bootstrap=True also
provides a statistical estimate of the error bar in the separation vector.
"""
# -- data reduction
if reduceData:
for f in files:
print '#'*3, 'reducing:', f, '#'*5
a = prima.drs(directory+f, pssRec=pssRec)
if 'OBJ_ASTRO' in f: # reduce dual fringe tracking data
a.astrometryFtk(max_err_um=max_err_um, max_GD_um=max_GD_um,
sigma_clipping=sigma_clipping,
max_length_s=max_length_s, writeOut=True,
overwrite=True)
else: # reduce scanning data:
a.asrtrometryScan(max_err_um=max_err_um, max_GD_um=max_GD_um,
sigma_clipping=sigma_clipping,
writeOut=True,
overwrite=True)
a.raw.close()
a = ""
# -- data fitting (astrometry)
if selection is None:
selection = range(len(files))
if bootstrap: # boostrapped error bar
files = [files[k] for k in selection]
t0 = time.time()
res = bootstrapListOfFiles(files, directory, N=500,
fit_param =fittedParam, plot=True,
first_guess=firstGuess,
maxResidualsMas=maxResiduals,
multi=True)
print 'bootstraping performed in', round(time.time()-t0, 1), 's'
return res
else: # simple leastsquare fit of thge separation vector
interpListOfFiles(files, directory,
plot=True, quiet=False,
fit_param = fittedParam,
first_guess= firstGuess,
fit_only_files=selection,
maxResiduals=maxResiduals, res2fits=res2fits)
res = []
return
def reduce034(demo=False, max_GD_um=1.0, max_err_um=0.3, max_length_s=1.0,
sigma_clipping=5.0):
global astro_files034, data_directory034
for f in astro_files034:
if f.split('_')[2] == 'ASTRO':
print '### reducing ', f , '###################################'
# typical reduction
a = prima.drs(os.path.join(data_directory034,f))
a.astrometry(writeOut=(not demo), overwrite=True,
max_length_s=max_length_s,plot=demo,
max_GD_um=max_GD_um, max_err_um=max_err_um,
sigma_clipping=sigma_clipping)
if demo:
break
a.raw.close()
return
def main():
if not os.path.exists(sys.argv[1]):
print 'ERROR: file '+sys.argv[1]+' does not exist'
if len(sys.argv)>2:
if not sys.argv[2].split('=')[0]=='pssguiRecorderDir':
print 'ERROR: second argument should be pssguiRecorderDir=/xxx/xxx/xxx'
if not os.path.exists(sys.argv[2].split('=')[1]):
print 'ERROR: directory '+sys.argv[2].split('=')[1]+' does not exist'
else:
pssDir = sys.argv[2].split('=')[1]
else:
pssDir=None
a = prima.drs(sys.argv[1], pssRec=pssDir)
a.astrometryFtk(writeOut=1, overwrite=1, max_GD_um=2.5, max_err_um = 2.0,
sigma_clipping=3.5)
a=[]
def doAll(files, directory=data_directory, reduceData=False, bootstrap=False,
selection=None, maxResiduals=None, firstGuess=[10.,90., 0.0],
fittedParam=[1,1,1], res2fits=False):
"""
astrometric fit
"""
# -- data reduction
if reduceData:
for f in files:
print '#'*5, f, '#'*12
a = prima.drs(data_directory+f)
a.astrometryFtk(max_err_um=3, max_GD_um=5.0, sigma_clipping=4.0,
writeOut=True, overwrite=True, max_length_s=1.0)
a.raw.close()
del a
# correct a bug in this particular file
if f == '2011-08-23/PACMAN_OBJ_ASTRO_236_0007.fits':
tmp=pyfits.open(data_directory+\
'/2011-08-23/PACMAN_OBJ_ASTRO_236_0007_RED.fits',
mode='update')
tmp[0].header.update('HIERARCH ESO INS MODE', 'SWAPPED')
tmp.flush()
tmp.close()
# -- data fitting
if selection is None:
selection = range(len(files))
if bootstrap:
files = [files[k] for k in selection]
t0 = time.time()
res = astrom.bootstrapListOfFiles(files, data_directory, N=500,
fit_param =fittedParam, plot=True,
first_guess=firstGuess,
maxResidualsMas=maxResiduals,
multi=True)
print 'bootstraping performed in', round(time.time()-t0, 1), 's'
else:
astrom.interpListOfFiles(files, data_directory,
plot=True, quiet=False,
fit_param = fittedParam,
first_guess= firstGuess,
fit_only_files=selection,
maxResiduals=maxResiduals, res2fits=res2fits)
res = []
return
def analyseDark(sub_dir):
"""
sub routine for 'checkIfDarksAreContaminated'
"""
global data_directory
files = os.listdir(data_directory+sub_dir)
darksA = []
darksB = []
flatsA = []
flatsB = []
mjd_obs = []
ditA = []
ditB = []
for f in files:
if f[:16]=='PACMAN_OBJ_ASTRO':
print f
a = prima.drs(data_directory+sub_dir+f)
try:
darksA.append(a.fsu_calib[('FSUA', 'DARK')])
darksB.append(a.fsu_calib[('FSUB', 'DARK')])
flatsA.append(a.fsu_calib[('FSUA', 'FLAT')])
flatsB.append(a.fsu_calib[('FSUB', 'FLAT')])
mjd_obs.append(a.getKeyword('DATE-OBS'))
ditA.append(a.getKeyword('ISS PRI FSU1 DIT'))
ditB.append(a.getKeyword('ISS PRI FSU2 DIT'))
except:
print 'no calibs?'
a.__del__()
#--
darksA = numpy.array(darksA)
darksB = numpy.array(darksB)
flatsA = numpy.array(flatsA)
flatsB = numpy.array(flatsB)
ditA = numpy.array(ditA)
ditB = numpy.array(ditB)
# end analyseDark
return mjd_obs, darksA, darksB, flatsA, flatsB, ditA, ditB
def FSM_full_test(model=False, AT='AT4'):
"""
"""
directory = '/Volumes/DATA500/PRIMA/COMM16/'
### AT4
if AT=='AT4' or AT==4:
AT = 'AT4'; DT = .6/(24*3600)
#a = prima.drs(directory+'2011-08-29/PACMAN_OBJ_ASTRO_242_0014.fits')
#p = prima.pssRecorder(directory+'PSSRECORDER/pssguiRecorder_lat4vcm_2011-08-30_09-09-54.dat')
#f = open(directory+'PIEZOSCAN/pscsosfPiezoScan2_lat4fsm_2011-08-30T09_10_46.txt')
a = prima.drs(directory+'2011-08-29/PACMAN_OBJ_ASTRO_242_0018.fits')
p = prima.pssRecorder(directory+'PSSRECORDER/pssguiRecorder_lat4vcm_2011-08-30_09-49-23.dat')
f = open(directory+'PIEZOSCAN/pscsosfPiezoScan2_lat4fsm_2011-08-30T09_50_11.txt')
#a = prima.drs(directory+'2011-08-29/PACMAN_OBJ_ASTRO_242_0021.fits')
#p = prima.pssRecorder(directory+'PSSRECORDER/pssguiRecorder_lat4vcm_2011-08-30_10-24-43.dat')
#f = open(directory+'PIEZOSCAN/pscsosfPiezoScan2_lat4fsm_2011-08-30T10_25_14.txt')
#a = prima.drs(directory+'2011-08-29/PACMAN_OBJ_ASTRO_242_0023.fits')
#p = prima.pssRecorder(directory+'PSSRECORDER/pssguiRecorder_lat4vcm_2011-08-30_10-35-52.dat')
#f = open(directory+'PIEZOSCAN/pscsosfPiezoScan2_lat4fsm_2011-08-30T10_36_31.txt')
else:
### AT3
AT = 'AT3'; DT=0.75/(24.*3600)
#a = prima.drs(directory+'2011-08-29/PACMAN_OBJ_ASTRO_242_0016.fits')
#p = prima.pssRecorder(directory+'PSSRECORDER/pssguiRecorder_lat3vcm_2011-08-30_09-27-37.dat')
#f = open(directory+'PIEZOSCAN/pscsosfPiezoScan2_lat3fsm_2011-08-30T09_28_22.txt')
#a = prima.drs(directory+'2011-08-29/PACMAN_OBJ_ASTRO_242_0020.fits')
#p = prima.pssRecorder(directory+'PSSRECORDER/pssguiRecorder_lat3vcm_2011-08-30_10-09-40.dat')
#f = open(directory+'PIEZOSCAN/pscsosfPiezoScan2_lat3fsm_2011-08-30T10_10_09.txt')
#a = prima.drs(directory+'2011-08-29/PACMAN_OBJ_ASTRO_242_0024.fits')
#p = prima.pssRecorder(directory+'PSSRECORDER/pssguiRecorder_lat3vcm_2011-08-30_10-41-53.dat')
#f = open(directory+'PIEZOSCAN/pscsosfPiezoScan2_lat3fsm_2011-08-30T10_43_04.txt')
#a = prima.drs(directory+'2011-08-29/PACMAN_OBJ_ASTRO_242_0026.fits')
#p = prima.pssRecorder(directory+'PSSRECORDER/pssguiRecorder_lat3vcm_2011-08-30_10-53-32.dat')
#f = open(directory+'PIEZOSCAN/pscsosfPiezoScan2_lat3fsm_2011-08-30T10_54_20.txt')
a = prima.drs(directory+'2011-08-29/PACMAN_OBJ_ASTRO_242_0027.fits')
p = prima.pssRecorder(directory+'PSSRECORDER/pssguiRecorder_lat3vcm_2011-08-30_11-00-29.dat')
f = open(directory+'PIEZOSCAN/pscsosfPiezoScan2_lat3fsm_2011-08-30T11_00_58.txt')
lines = f.readlines()
f.close()
lines = filter(lambda x: not '#' in x and len(x)>10, lines)
mjd = [astro.tag2mjd('2011-08-30T'+x.split()[1])+DT for x in lines]
xmjd = np.linspace(min(mjd), max(mjd), 200)
FSM1X = [float(x.split()[2]) for x in lines]
FSM1Y = [float(x.split()[3]) for x in lines]
FSM2X = [float(x.split()[4]) for x in lines]
FSM2Y = [float(x.split()[5]) for x in lines]
print 'PCR START:', a.raw[0].header['ESO PCR ACQ START']
print 'PCR END :', a.raw[0].header['ESO PCR ACQ END']
min_mjd = min(mjd)
max_mjd = max(mjd)
if model:
param = np.array([0,0,.1,.1,.1,.1])
mjd_primet = 1e-6*a.raw['METROLOGY_DATA'].data.field('TIME')/\
(24*3600)+astro.tag2mjd(a.raw[0].header['ESO PCR ACQ START'])
primet = (a.raw['METROLOGY_DATA'].data.field('DELTAL')-
a.raw['METROLOGY_DATA'].data.field('DELTAL').mean())*1e6
print 'w_fit:'
w_fit = np.where((mjd_primet>min_mjd)*(mjd_primet<max_mjd))
#w_fit = np.where((mjd_primet>min_mjd))
w_fit = (w_fit[0][::200],)
print 'x_fit:'
X_fit = [mjd_primet[w_fit]-mjd_primet[w_fit].mean(),
interpByStep(mjd_primet[w_fit], mjd, np.array(FSM1X)-FSM1X[0]),
interpByStep(mjd_primet[w_fit], mjd, np.array(FSM1Y)-FSM1Y[0]),
interpByStep(mjd_primet[w_fit], mjd, np.array(FSM2X)-FSM2X[0]),
interpByStep(mjd_primet[w_fit], mjd, np.array(FSM2Y)-FSM2Y[0])]
Y_fit = primet[w_fit]
print 'fit:'
fit = myfit.fit(primetFsmBias, X_fit, param, Y_fit)
fit.leastsqfit()
print 'dOPD_um/(FSM1X_um - %5.3f) = %6.4f' %\
(FSM1X[0], fit.leastsq_best_param[2])
print 'dOPD_um/(FSM1Y_um - %5.3f) = %6.4f' %\
(FSM1Y[0], fit.leastsq_best_param[3])
print 'dOPD_um/(FSM2X_um - %5.3f) = %6.4f' %\
(FSM2X[0], fit.leastsq_best_param[4])
print 'dOPD_um/(FSM2Y_um - %5.3f) = %6.4f' %\
(FSM2Y[0], fit.leastsq_best_param[5])
pylab.figure(4, figsize=(17,3))
pylab.clf()
pylab.subplots_adjust(left=0.06, bottom=0.15, right=0.96, top=0.85,
wspace=0.15, hspace=0.01)
pylab.title(AT+' FSM test:'+a.filename)
pylab.plot(mjd_primet, primet, 'b-', label='PRIMET A-B')
pylab.plot(mjd_primet[w_fit[0]],
primetFsmBias(X_fit,fit.leastsq_best_param),
'r-', alpha=0.5, linewidth=3, label='linear model FSM')
pylab.plot(mjd_primet[w_fit[0]],
Y_fit-primetFsmBias(X_fit,fit.leastsq_best_param),
'-', color='g', alpha=0.5, linewidth=3, label='residuals')
pylab.hlines([0], min(mjd), max(mjd), color='k', linestyle='dashed',
linewidth=2)
pylab.legend(ncol=3, loc=('upper left' if AT=='AT4' else 'upper right'))
pylab.xlim(min_mjd, max_mjd)
pylab.ylabel('PRIMET A-B ($\mu$m)')
pylab.figure(3, figsize=(17,9))
pylab.subplots_adjust(left=0.06, bottom=0.07, right=0.96, top=0.96,
wspace=0.15, hspace=0.01)
pylab.clf()
ax1 = pylab.subplot(5,2,1)
pylab.title(AT+' FSM test:'+a.filename)
pylab.plot(1e-6*a.raw['METROLOGY_DATA'].data.field('TIME')/(24*3600)+
astro.tag2mjd(a.raw[0].header['ESO PCR ACQ START']),
(a.raw['METROLOGY_DATA'].data.field('DELTAL')-
a.raw['METROLOGY_DATA'].data.field('DELTAL').mean())*1e6,
'b-')
pylab.ylabel('PRIMET A-B ($\mu$m)')
pylab.subplot(5,2,3, sharex=ax1)
#pylab.plot(mjd, FSM1X, '-k', markersize=8, linestyle='steps')
pylab.plot(xmjd, interpByStep(xmjd, mjd, FSM1X), 'k-')
pylab.ylabel('FSM1 X ($\mu$m)')
pylab.subplot(5,2,5, sharex=ax1)
#pylab.plot(mjd, FSM1Y, '-k', markersize=8, linestyle='steps')
pylab.plot(xmjd, interpByStep(xmjd, mjd, FSM1Y), 'k-')
pylab.ylabel('FSM1 Y ($\mu$m)')
pylab.subplot(5,2,7, sharex=ax1)
#pylab.plot(mjd, FSM2X, '-k', markersize=8, linestyle='steps')
pylab.plot(xmjd, interpByStep(xmjd, mjd, FSM2X), 'k-')
pylab.ylabel('FSM2 X ($\mu$m)')
pylab.subplot(5,2,9, sharex=ax1)
#pylab.plot(mjd, FSM2Y, '-k', markersize=8, linestyle='steps')
pylab.plot(xmjd, interpByStep(xmjd, mjd, FSM2Y), 'k-')
pylab.ylabel('FSM2 Y ($\mu$m)')
pylab.xlabel('MJD')
pylab.subplot(5,2,2, sharex = ax1, sharey= ax1)
pylab.title(AT+' FSM test:'+a.filename)
pylab.plot(1e-6*a.raw['METROLOGY_DATA'].data.field('TIME')/(24*3600)+
astro.tag2mjd(a.raw[0].header['ESO PCR ACQ START']),
(a.raw['METROLOGY_DATA'].data.field('DELTAL')-
a.raw['METROLOGY_DATA'].data.field('DELTAL').mean())*1e6,
'b-')
pylab.subplot(5,2,4, sharex=ax1)
pylab.plot(p.mjd, p.data['VCM1X[um]'], '-k')
pylab.ylabel('VCM1 X ($\mu$m)')
pylab.subplot(5,2,6, sharex=ax1)
pylab.plot(p.mjd, p.data['VCM1Y[um]'], '-k')
pylab.ylabel('VCM1 Y ($\mu$m)')
pylab.subplot(5,2,8, sharex=ax1)
pylab.plot(p.mjd, p.data['VCM2X[um]'], '-k')
pylab.ylabel('VCM2 X ($\mu$m)')
pylab.subplot(5,2,10, sharex=ax1)
pylab.plot(p.mjd, p.data['VCM2Y[um]'], '-k')
pylab.ylabel('VCM2 Y ($\mu$m)')
pylab.xlim(min_mjd, max_mjd)
del a
return
def fitListOfFiles(
files=[],
directory="",
fitOnlyFiles=None,
firstGuess={"M0": 0.0, "SEP": 10.0, "PA": 90.0},
fitOnly=None,
doNotFit=None,
maxResiduals=None,
verbose=1,
reduceData=False,
pssRecDir=None,
max_err_um=1.0,
max_GD_um=2.5,
sigma_clipping=3.5,
plot=True,
randomize=False,
exportFits=None,
exportAscii=None,
rectified=True,
allX=False,
):
""" files, list of files
directory='', where the data are
fitOnlyFiles=None, list of index of files to fit
firstGuess={'M0':0.0, 'SEP':10.0, 'PA':90.0}, see dOPDfunc
fitOnly=None, doNotFit=None, list of names of variable to fit or not fit
verbose=1, 2 for max verbosity (list of files)
reduceData=False, : to forece reducing the data: relevant parameters:
pssRecDir=None,
max_err_um=2.0,
max_GD_um=4.0,
sigma_clipping=5.0,
exportFits=None, exportAscii=None : give a filename to activate
plot=True,
rectified=True, plot 'rectified' residuals,
maxResiduals=None, for the plot, for the MAX/min to this value (in um)
randomize=False : to be used for bootstrapping
"""
global dOPDfuncNiter, obs, obsFit
if len(files) == 0: # no files given means all of them!
files = os.listdir(directory)
files = filter(lambda x: ".fits" in x and not "RED" in x, files)
if reduceData:
for f in files:
print "#" * 3, "reducing:", f, "#" * 5
a = prima.drs(directory + f, pssRec=pssRecDir)
if "OBJ_ASTRO" in f: # reduce dual fringe tracking data
a.astrometryFtk(
max_err_um=max_err_um,
max_GD_um=max_GD_um,
sigma_clipping=sigma_clipping,
max_length_s=1.0,
writeOut=True,
overwrite=True,
)
else: # reduce scanning data:
a.astrometryScan(
max_err_um=max_err_um,
max_GD_um=max_GD_um,
sigma_clipping=sigma_clipping,
writeOut=True,
overwrite=True,
)
a = []
red = [] # tables of atom
# load all reduced files
if fitOnlyFiles is None:
fitOnlyFiles = range(len(files))
for k, f in enumerate(files):
# reduced file:
rf = f.split(".")[0] + "_RED.fits"
# load reduced file
a = atom(os.path.join(directory, rf))
# print the table of loaded files:
if not fitOnlyFiles == None:
if k in fitOnlyFiles:
fit = "X"
else:
fit = "_"
else:
fit = "X"
if verbose > 1: # pretty printing of files, in a table:
if k == 0:
N = len(rf)
print "|idx |fit|INS.MODE | TARGET | file" + " " * 20 + "| UT " + " " * 16 + " | MJD-OBS"
print "| %2d | %1s | %7s | %10s | %s | %s | %10.6f" % (
k,
fit,
a.insmode,
a.PS_ID[:10],
rf.split("OBJ_")[1],
a.date_obs[:19],
a.mjd_obs,
)
red.append(a) # list of reduced files
if verbose:
print ""
# prepare observation vector for the FIT
obs = []
for k in range(len(red)):
for i in range(len(red[k].d_al)):
obs.append(
{
"TARGET": red[k].data[0].header["ESO OCS PS ID"],
"FILENAME": red[k].filename.split("/")[-1],
"DOPD": red[k].d_al[i],
"eDOPD": red[k].d_al_err[i],
"LST": red[k].lst[i],
"MJD": red[k].mjd[i].min(),
"RA": red[k].radec[0],
"DEC": red[k].radec[1],
"SWAPPED": int(red[k].swapped[i]),
"FIT": k in fitOnlyFiles and red[k].d_al_err[i] > 0,
"B_XYZA": [
red[k].data[0].header["ESO ISS CONF T1X"] - red[k].data[0].header["ESO ISS CONF T2X"],
red[k].data[0].header["ESO ISS CONF T1Y"] - red[k].data[0].header["ESO ISS CONF T2Y"],
red[k].data[0].header["ESO ISS CONF T1Z"] - red[k].data[0].header["ESO ISS CONF T2Z"],
red[k].data[0].header["ESO ISS CONF A1L"] - red[k].data[0].header["ESO ISS CONF A2L"],
],
"ROT3": red[k].rot3[i],
"ROT4": red[k].rot4[i],
"AZ3": red[k].az3[i],
"AZ4": red[k].az4[i],
"ALT3": red[k].alt3[i],
"ALT4": red[k].alt4[i],
}
)
obsFit = filter(lambda x: x["FIT"], obs)
obsNoFit = filter(lambda x: not x["FIT"], obs)
t0 = time.time()
if not doNotFit is None:
# fitOnly = filter(lambda k: not k in doNotFit, firstGuess.keys())
fitOnly = filter(lambda k: not any([x in k for x in doNotFit]), firstGuess.keys())
dOPDfuncNiter = 0
if randomize > 0: # to be used by bootstrapping
if randomize != True:
np.random.seed(randomize)
w = np.random.randint(len(obsFit), size=len(obsFit))
tmp = []
for k in w:
tmp.append(obsFit[k])
obsFit = tmp
if "T:" in [x[:2] for x in firstGuess.keys()]:
### multiple targets case:
fit = dpfit.leastsqFit(
dOPDfuncMultiTargets,
obsFit,
firstGuess,
[o["DOPD"] for o in obsFit],
err=[o["eDOPD"] for o in obsFit],
fitOnly=fitOnly,
verbose=0,
epsfcn=1e-5,
)
best = fit["best"]
uncer = fit["uncer"]
chi2 = fit["chi2"]
model = fit["model"]
obs = dOPDfuncMultiTargets(obs, best, addIntermediate=True)
obsFit = dOPDfuncMultiTargets(obsFit, best, addIntermediate=True)
obsNoFit = dOPDfuncMultiTargets(obsNoFit, best, addIntermediate=True)
### compute a chi2 for each targets:
targets = list(set([x["TARGET"] for x in obsFit]))
chi2T = {}
rmsT = {}
for t in targets:
obsT = filter(lambda x: x["TARGET"] == t, obsFit)
tmp = dOPDfuncMultiTargets(obsT, best) - np.array([x["DOPD"] for x in obsT])
rmsT[t] = round(tmp.std() * 1e6, 2)
chi2T[t] = round((tmp ** 2 / np.array([x["eDOPD"] for x in obsT]) ** 2).mean(), 2)
if plot:
print "RMS (um):", rmsT
print "CHI2 :", chi2T
else:
fit = dpfit.leastsqFit(
dOPDfunc,
obsFit,
firstGuess,
[o["DOPD"] for o in obsFit],
err=[o["eDOPD"] for o in obsFit],
fitOnly=fitOnly,
verbose=0,
epsfcn=1e-5,
)
best = fit["best"]
uncer = fit["uncer"]
chi2 = fit["chi2"]
model = fit["model"]
obs = dOPDfunc(obs, best, addIntermediate=True)
obsFit = dOPDfunc(obsFit, best, addIntermediate=True)
obsNoFit = dOPDfunc(obsNoFit, best, addIntermediate=True)
RMSum = (np.array([o["DOPD"] for o in obsFit]) - model).std() * 1e6
if plot:
print "residuals= %4.2f (um)" % (RMSum)
print "CHI2= %4.2f" % (chi2)
# print result
tmp = best.keys()
tmp.sort()
if best.has_key("AT scale") and best["AT scale"] == 0:
tmp = filter(lambda x: not "AT4" in x and "AT3" not in x, tmp)
units = {
"M0": "mm",
"SEP": "arcsec",
"PA": "deg",
"AT3 phi": "deg",
"AT4 phi": "deg",
"LINRATE": "uas/day",
"LINDIR": "deg",
}
if plot:
for k in tmp:
if uncer[k] > 0:
nd = int(-np.log10(uncer[k]) + 2)
form = "%" + str(max(nd, 0) + 2) + "." + str(max(nd, 0)) + "f"
form = k + " = " + form + " +/- " + form
print form % (round(best[k], nd), round(uncer[k], nd)),
else:
print k + " = " + " %f" % (best[k]),
u = ""
for z in units.keys():
if z in k:
u = "(" + units[z] + ")"
print u
# export to Ascii:
if not exportAscii is None:
f = open(exportAscii, "w")
tmp = best.keys()
tmp.sort()
for k in tmp:
f.write("# " + k + " =" + str(best[k]) + "\n")
if uncer[k] != 0:
f.write("# ERR " + k + " =" + str(uncer[k]) + "\n")
tmp = obs[0].keys()
tmp.sort()
f.write("# " + "; ".join([t.replace(" ", "_") for t in tmp]) + "\n")
for o in obs:
f.write("" + "; ".join([str(o[k]) for k in tmp]) + "\n")
f.close()
# export to FITS:
if not exportFits is None:
# create fits object
hdu0 = pyfits.PrimaryHDU(None)
tmp = best.keys()
tmp.sort()
for k in tmp:
hdu0.header.update("HIERARCH " + k, best[k])
if uncer[k] != 0:
hdu0.header.update("HIERARCH ERR " + k, uncer[k])
tmp = obs[0].keys()
tmp.sort()
cols = []
for k in tmp:
if k == "MJD":
form = "F12.5"
elif (
type(obs[0][k]) == float
or type(obs[0][k]) == np.float
or type(obs[0][k]) == np.float32
or type(obs[0][k]) == np.float64
):
form = "F12.9"
elif type(obs[0][k]) == str:
form = "A" + str(max([len(o[k]) for o in obs]))
elif (
type(obs[0][k]) == bool
or type(obs[0][k]) == np.bool
or type(obs[0][k]) == np.bool_
or type(obs[0][k]) == int
):
form = "I"
else:
print "DEBUG:", k, obs[0][k], type(obs[0][k])
form = ""
if k != "B_XYZA":
cols.append(pyfits.Column(name=k.replace(" ", "_"), format=form, array=[o[k] for o in obs]))
else:
cols.append(pyfits.Column(name="B_X", format="F12.8", array=[o[k][0] for o in obs]))
cols.append(pyfits.Column(name="B_Y", format="F12.8", array=[o[k][1] for o in obs]))
cols.append(pyfits.Column(name="B_Z", format="F12.8", array=[o[k][2] for o in obs]))
cols.append(pyfits.Column(name="B_A", format="F12.8", array=[o[k][3] for o in obs]))
hducols = pyfits.ColDefs(cols)
hdub = pyfits.new_table(hducols)
hdub.header.update("EXTNAME", "ASTROMETRY REDUCED", "")
thdulist = pyfits.HDUList([hdu0, hdub])
outfile = exportFits
if os.path.exists(outfile):
os.remove(outfile)
print "writting ->", outfile
thdulist.writeto(outfile)
return
# return fitting result
res = {
"BEST": best,
"UNCER": uncer,
"CHI2": chi2,
"RMS": RMSum,
"MJD_MIN": min([o["MJD"] for o in obsFit]),
"MJD_MAX": max([o["MJD"] for o in obsFit]),
"MJD_MEAN": np.array([o["MJD"] for o in obsFit]).mean(),
}
res["RA"] = {}
res["DEC"] = {}
for o in obs:
if not res["RA"].has_key(o["TARGET"]):
res["RA"][o["TARGET"]] = o["RA"]
res["DEC"][o["TARGET"]] = o["DEC"]
if not plot:
return res
# =============== PLOT =============================
if "T:" in [x[:2] for x in best.keys()]:
modelNoFit = dOPDfuncMultiTargets(obsNoFit, best)
else:
modelNoFit = dOPDfunc(obsNoFit, best)
pyplot.figure(0, figsize=(18, 6))
pyplot.clf()
pyplot.subplots_adjust(left=0.05, bottom=0.07, right=0.99, top=0.95, wspace=0.01)
if allX:
# display all sorts of X axis
Xs = [
lambda o: (o["LST"] - o["RA"] + 12) % 24 - 12,
lambda o: o["ROT4"],
lambda o: (o["AZ4"] - o["ALT4"] - 2 * o["ROT4"]) % 360,
lambda o: o["AZ4"],
lambda o: o["MJD"],
]
Xlabels = ["hour angle (h)", "ROT4 (degrees)", "AZ4 - ALT4 - 2*ROT4 (degrees)", "AZ4 (degrees)", "MJD"]
else:
# display only Hour Angle
Xs = [lambda o: (o["LST"] - o["RA"] + 12) % 24 - 12]
Xlabels = ["hour angle (h)"]
for i in range(len(Xs)):
X = Xs[i]
Xlabel = Xlabels[i]
if i == 0:
ax0 = pyplot.subplot(1, len(Xs), i + 1)
else:
pyplot.subplot(1, len(Xs), i + 1, sharey=ax0)
if not rectified:
### plot data used for the fit
pyplot.plot(
[X(o) if not o["SWAPPED"] else None for o in obsFit],
[(obsFit[k]["DOPD"] - model[k]) * 1e6 for k in range(len(obsFit))],
"ob",
alpha=0.3,
label="NORMAL",
)
pyplot.plot(
[X(o) if o["SWAPPED"] else None for o in obsFit],
[(obsFit[k]["DOPD"] - model[k]) * 1e6 for k in range(len(obsFit))],
"or",
alpha=0.3,
label="SWAPPED",
)
if len(obsNoFit) > 1:
### plot data NOT used for the fit
pyplot.plot(
[X(o) if not o["SWAPPED"] else None for o in obsNoFit],
[(obsNoFit[k]["DOPD"] - modelNoFit[k]) * 1e6 for k in range(len(obsNoFit))],
"+c",
alpha=0.2,
label="NORMAL, not fitted",
)
pyplot.plot(
[X(o) if o["SWAPPED"] else None for o in obsNoFit],
[(obsNoFit[k]["DOPD"] - modelNoFit[k]) * 1e6 for k in range(len(obsNoFit))],
"+y",
alpha=0.2,
label="SWAPPED, not fitted",
)
pyplot.ylabel("$O-C$ ($\mu$m)")
else:
### plot data used for the fit
pyplot.plot(
[X(o) if not o["SWAPPED"] else None for o in obsFit],
[(-1) ** obsFit[k]["SWAPPED"] * (obsFit[k]["DOPD"] - model[k]) * 1e6 for k in range(len(obsFit))],
"ob",
alpha=0.3,
label="NORMAL",
)
pyplot.plot(
[X(o) if o["SWAPPED"] else None for o in obsFit],
[(-1) ** obsFit[k]["SWAPPED"] * (obsFit[k]["DOPD"] - model[k]) * 1e6 for k in range(len(obsFit))],
"or",
alpha=0.3,
label="SWAPPED",
)
if len(obsNoFit) > 1:
### plot data NOT used for the fit
pyplot.plot(
[X(o) if not o["SWAPPED"] else None for o in obsNoFit],
[
(-1) ** obsNoFit[k]["SWAPPED"] * (obsNoFit[k]["DOPD"] - modelNoFit[k]) * 1e6
for k in range(len(obsNoFit))
],
"+c",
alpha=0.2,
label="NORMAL, not fitted",
)
pyplot.plot(
[X(o) if o["SWAPPED"] else None for o in obsNoFit],
[
(-1) ** obsNoFit[k]["SWAPPED"] * (obsNoFit[k]["DOPD"] - modelNoFit[k]) * 1e6
for k in range(len(obsNoFit))
],
"+y",
alpha=0.2,
label="SWAPPED, not fitted",
)
# -- plot pupil bias correction
# pyplot.plot([X(o) for o in obs],
# [(-1)**o['SWAPPED']*o['fit PUPbias']*1e6 for o in obs],
# '.k', label='pup runout correction')
if i == 0:
pyplot.ylabel("$(-1)^\mathrm{swapped}(O-C)$ ($\mu$m)")
pyplot.text(
min([X(o) for o in obs]),
0,
"RMS=" + str(round(RMSum, 2)) + "$\mu$m",
va="center",
ha="left",
bbox=dict(boxstyle="round", ec=(0.4, 0.2, 0.0), fc=(1.0, 0.8, 0.0), alpha=0.2),
)
pyplot.legend(ncol=4, loc="upper left")
pyplot.xlabel(Xlabel)
if not maxResiduals is None:
pyplot.ylim(-maxResiduals, maxResiduals)
return res
def OPDmodel(loadData=False, bin=10, do_fit=False):
"""
"""
global opd_model_data
try:
print len(opd_model_data)
except:
loadData=True
if loadData:
files = ['2011-11-21/PACMAN_OBJ_ASTRO_326_00%s.fits'%(f) for f in
['04', '05', '06', '07', '08', '09', '11', '12',
'13', '14', '15', '16', '17', '18', '19', '20']]
opd_model_data = []
for f in files:
a = prima.drs(data_directory+f)
opd_model_data.append(a.expectedOPD(bin=bin))
a = []
B = opd_model_data[0]['XYZ']
fit = np.array([1,1,1,1])
p_fit = np.array(B)[np.where(np.array(fit))]
p_fixed = np.array(B)[np.where(1-np.array(fit))]
obsN = filter(lambda x: x['INSMODE']=='NORMAL', opd_model_data)
obsS = filter(lambda x: x['INSMODE']=='SWAPPED', opd_model_data)
plsq = leastsq(leastsqOPDModel, p_fit, args=(fit, p_fixed, obsN), epsfcn=1e-3)
Bf = np.array(B)
Bf[np.where(fit)] = plsq[0]
print np.array(B)-np.array(Bf)
for x in opd_model_data:
x['ORIGINAL']=prima.projBaseline(x['XYZA'], [x['RA'], x['DEC']],
x['LST'])['opd']/x['AIRN']-x['DL2-DL1']
x['RESIDUALS']=prima.projBaseline(Bf, [x['RA'], x['DEC']],
x['LST'])['opd']/x['AIRN']-x['DL2-DL1']
obsN = filter(lambda x: x['INSMODE']=='NORMAL', opd_model_data)
obsS = filter(lambda x: x['INSMODE']=='SWAPPED', opd_model_data)
obs = opd_model_data
pyplot.figure(0)
pyplot.clf()
pyplot.subplot(221)
pyplot.plot([x['AZ'] for x in obsN], [x['RESIDUALS'].mean() for x in obsN], 'ob',
label='NORMAL')
pyplot.plot([x['AZ'] for x in obsS], [x['RESIDUALS'].mean() for x in obsS], 'or',
label='SWAPPED')
pyplot.plot([x['AZ'] for x in obsN], [x['ORIGINAL'].mean() for x in obsN], '+b',
label='NORMAL')
pyplot.plot([x['AZ'] for x in obsS], [x['ORIGINAL'].mean() for x in obsS], '+r',
label='SWAPPED')
#pyplot.legend()
pyplot.xlabel('AZ')
pyplot.ylabel('residuals')
pyplot.subplot(222)
pyplot.plot([x['ALT'] for x in obsN], [x['RESIDUALS'].mean() for x in obsN], 'ob',
label='NORMAL')
pyplot.plot([x['ALT'] for x in obsS], [x['RESIDUALS'].mean() for x in obsS], 'or',
label='SWAPPED')
pyplot.plot([x['ALT'] for x in obsN], [x['ORIGINAL'].mean() for x in obsN], '+b',
label='NORMAL')
pyplot.plot([x['ALT'] for x in obsS], [x['ORIGINAL'].mean() for x in obsS], '+r',
label='SWAPPED')
#pyplot.legend()
pyplot.xlabel('ALT')
pyplot.ylabel('residuals')
pyplot.subplot(223)
pyplot.plot(range(len(obs)), [x['RESIDUALS'].mean() for x in obs], 'ok',
label='NORMAL')
pyplot.plot(range(len(obs)), [x['ORIGINAL'].mean() for x in obs], '+k',
label='NORMAL')
#pyplot.legend()
pyplot.xlabel('N')
pyplot.ylabel('residuals')
return opd_model_data