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 HD10360():
"""
"""
NACO_sep = 11448.3 # Damien
#NACO_sep *=0.999
NACO_PA = 187.0 # Damien
NACO_PA +=0.9
data = [{'DEC_mas': -11327.459991981585,
'MJD': 55799.38788483571, 'PCA': ([-0.78351315847945402,
-0.62137519301107402],
[-0.78351315847945402, -0.62137519301107402]),
'RA_mas': -1586.2834801192896, 'errs_uas': (784.90506323494469,
56.755250740679948), 'NOTE':'ACOMM2'},
{'DEC_mas': -11328.434517473423,
'MJD': 55886.23025809787, 'PCA': ([-0.47704928581135631,
-0.87887654360943956],
[-0.47704928581135631, -0.87887654360943956]),
'RA_mas': -1573.9752919650998, 'errs_uas': (636.66710179387803,
97.443812507475542) , 'NOTE':'ACOMM3'},
{'DEC_mas': -11325.155618568057,
'MJD': 55889.144962967606, 'PCA': ([-0.5851692703101341,
-0.81091116966330268],
[-0.5851692703101341, -0.81091116966330268]),
'RA_mas': -1572.8284251763607, 'errs_uas': (1128.5886751363144,
95.502764113947521), 'NOTE':'ACOMM3'},
#{'DEC_mas':NACO_sep*np.cos(NACO_PA*np.pi/180),
# 'RA_mas':NACO_sep*np.sin(NACO_PA*np.pi/180),
# 'MJD': 55886.2, 'errs_uas':(1000,1000),
# 'PCA':([1,0],[0,1]), 'NOTE':'NACO'},
#{'DEC_mas': -11321.057996449183,
{'DEC_mas': -11322.677253993401,
'MJD': 55891.11655027233,
'PCA': ([-0.78812707963177309, -0.61551255580296094],
[-0.78812707963177309, -0.61551255580296094]),
'RA_mas': -1571.7671378904256,
'errs_uas': (582.6031352344354, 38.92080197079315),'NOTE':'ACOMM3'},
{'DEC_mas': -11325.519043937573,
'MJD': 55891.22297829576,
'PCA': ([-0.36045943416341675, -0.93277489048676132],
[-0.36045943416341675, -0.93277489048676132]),
'RA_mas': -1571.9855632046058,
'errs_uas': (152.81098660659865, 28.057280342744438),
'NOTE':'ACOMM3'}
]
cork = [55800, -1586.0, -11327,-1, 1, 12, 1., 1, 1, 1]
fit = np.array([0, 1, 1, 1, 1, 0, 1, 1, 1,1])
obs = [(x['MJD'], x['RA_mas'], x['DEC_mas']) for x in data]
p_fit = np.array(cork)[np.where(np.array(fit))]
p_fixed = np.array(cork)[np.where(1-np.array(fit))]
plsq = leastsq(leastsqCorkScrew, p_fit, args=(fit,p_fixed,obs,False), epsfcn=1e-3)
corkF = np.array(cork)
corkF[np.where(fit)] = plsq[0]
print corkF
t=np.linspace(55795, 55900, 2000)
model = leastsqCorkScrew(plsq[0], fit, p_fixed,
[(tau,0,0) for tau in t], model=True)
# linear fit to displacement:
f = myfit.fit(myfit.PolyN,
[d['MJD']-55800 for d in data], [0.,1.],
[d['RA_mas'] for d in data],
err=[d['errs_uas'][1] for d in data])
f.leastsqfit()
cRA = f.leastsq_best_param
f = myfit.fit(myfit.PolyN,
[d['MJD']-55800 for d in data], [0.,1.],
[d['DEC_mas'] for d in data],
err=[d['errs_uas'][1] for d in data])
f.leastsqfit()
cDEC = f.leastsq_best_param
pyplot.figure(10, figsize=(10,5))
pyplot.clf()
pyplot.axes().set_aspect('equal', 'datalim')
th = np.linspace(0, 2*np.pi, 100)
colors=['r', 'g', 'b', 'y', 'm', 'c']
for k,d in enumerate(data):
### observation:
pyplot.plot(d['RA_mas']/1000., d['DEC_mas']/1000., 'o'+colors[k],
label='MJD:%8.2f (%s)'%(d['MJD'], d['NOTE']), markersize=8)
### error to linear fit:
pyplot.plot([d['RA_mas']/1000, myfit.PolyN(d['MJD']-55800, cRA)/1000],
[d['DEC_mas']/1000, myfit.PolyN(d['MJD']-55800, cDEC)/1000],
color=colors[k], linewidth=2, alpha=0.5, linestyle='dotted')
### error to corkScrew:
#pyplot.plot([d['RA_mas']/1000, leastsqCorkScrew(plsq[0],fit,p_fixed,
# [(d['MJD'],0,0)],
# model=True)[0][0]/1000],
# [d['DEC_mas']/1000, leastsqCorkScrew(plsq[0],fit,p_fixed,
# [(d['MJD'],0,0)],
# model=True)[0][1]/1000],
# color=colors[k], linewidth=2)
### error ellipse:
pyplot.plot(d['RA_mas']/1000.+
d['errs_uas'][0]*d['PCA'][0][1]*np.cos(th)/1e6+
d['errs_uas'][1]*d['PCA'][0][0]*np.sin(th)/1e6,
d['DEC_mas']/1000.+
-d['errs_uas'][1]*d['PCA'][0][1]*np.sin(th)/1e6+
d['errs_uas'][0]*d['PCA'][0][0]*np.cos(th)/1e6,
color=colors[k], linewidth=3, alpha=0.5)
#pyplot.plot([m[0]/1000. for m in model], [m[1]/1000 for m in model], '-',
# linewidth=3, alpha=0.5, color='0.5')
pyplot.plot(myfit.PolyN(t-55800, cRA)/1000.,
myfit.PolyN(t-55800, cDEC)/1000., 'k-',
label='linear fit', alpha=0.3, linewidth=2, markersize=10)
print 'computed linear movement: %6.2f uas/day' %\
(np.sqrt(cRA[1]**2+cDEC[1]**2)*1000)
# some best obtained astrometric results:
#pyplot.errorbar(-1.578, -11.326, xerr=0.001, marker='o',
# alpha=0.5, color='0.6',
# label='GLAO perfo:$\pm$1.0mas\n(Meyer et al. 2011)')
#pyplot.errorbar(-1.578, -11.3265, xerr=0.0005, marker='o',
# alpha=0.5, color='0.4',
# label='HST perfo:$\pm$0.5mas\n(McLaughlin et al. 2009)')
pyplot.xlabel('$\Delta$ in E direction (arcsec)')
pyplot.ylabel('$\Delta$ in N direction (arcsec)')
pyplot.xlim(pyplot.xlim()[1], pyplot.xlim()[0])
pyplot.title('HD10360 - HD10360J')
pyplot.legend(numpoints=1, prop={'size':8})
return
