def first156274(runOffCorrection=True):
fi = os.listdir(data_directory+'/2012-05-17')
fi = filter(lambda x: 'RED.fits' in x , fi)
fi = ['2012-05-17/'+''.join(f.split('_RED')) for f in fi]
fg = {'SEP':10.1, 'PA':260.0, 'M0':0.0, }
doNotFit=[]
a = astromNew.fitListOfFiles(fi, data_directory,
firstGuess=fg, verbose=2,
doNotFit=doNotFit,
maxResiduals=3)
return a
def fitHD10360(ATPUP_scale=1.0):
"""
simple fit. ATPUP_scale=0.0 sets the pupil correction to 0
"""
fgNACO['ATPUP scale'] = ATPUP_scale
a = astromNew.fitListOfFiles(files_hd10360_NACO,
directory=data_directory,
firstGuess=fgNACO, plot=True,
doNotFit=['AT3', 'AT4', 'ATPUP scale'])
a['Delta DEC'] = a['BEST']['SEP']*np.cos(a['BEST']['PA']*np.pi/180)
a['Delta RA cos(DEC)'] = a['BEST']['SEP']*np.sin(a['BEST']['PA']*np.pi/180)
a['Delta RA'] = a['Delta RA cos(DEC)']/np.cos(a['DEC']['HD10360']*np.pi/180)
return a
def test_rotation():
"""
rotation modulation
"""
files = ['2012-03-18/PACMAN_OBJ_ASTRO_079_0001.fits',
'2012-03-18/PACMAN_OBJ_ASTRO_079_0004.fits',
'2012-03-18/PACMAN_OBJ_ASTRO_079_0005.fits',
'2012-03-18/PACMAN_OBJ_ASTRO_079_0006.fits',
'2012-03-18/PACMAN_OBJ_ASTRO_079_0007.fits',
'2012-03-18/PACMAN_OBJ_ASTRO_079_0008.fits',
'2012-03-18/PACMAN_OBJ_ASTRO_079_0009.fits',
'2012-03-18/PACMAN_OBJ_ASTRO_079_0010.fits',
'2012-03-18/PACMAN_OBJ_ASTRO_079_0011.fits',
'2012-03-18/PACMAN_OBJ_ASTRO_079_0012.fits',
'2012-03-18/PACMAN_OBJ_ASTRO_079_0013.fits',
'2012-03-18/PACMAN_OBJ_ASTRO_079_0014.fits',
'2012-03-18/PACMAN_OBJ_ASTRO_079_0015.fits',
'2012-03-18/PACMAN_OBJ_ASTRO_079_0016.fits',
'2012-03-18/PACMAN_OBJ_ASTRO_079_0020.fits',
'2012-03-18/PACMAN_OBJ_ASTRO_079_0021.fits',
'2012-03-18/PACMAN_OBJ_ASTRO_079_0022.fits',
'2012-03-18/PACMAN_OBJ_ASTRO_079_0024.fits',
'2012-03-18/PACMAN_OBJ_ASTRO_079_0026.fits',
'2012-03-18/PACMAN_OBJ_ASTRO_079_0027.fits',
'2012-03-18/PACMAN_OBJ_ASTRO_079_0028.fits',
'2012-03-18/PACMAN_OBJ_ASTRO_079_0029.fits',
]
# parameters for the fit:
fg = {'T:HD100286J SEP':10.0,
'T:HD100286J PA':210.0,
'T:HD100286J M0 MJD 56005.00 56005.14':0.0,
'T:HD100286J M0 MJD 56005.14 56005.99':0.0,
'T:HD129926 SEP':8.0,
'T:HD129926 PA':124.0,
'T:HD129926 M0 MJD 56005.00 56005.32':0.0,
'T:HD129926 M0 MJD 56005.32 56005.99':0.0,
}
doNotFit=[]
a = astromNew.fitListOfFiles(files,reduceData=False,
directory=data_directory,
firstGuess=fg, plot=True, verbose=2,
doNotFit=doNotFit, allX=False,
#maxResiduals=10,
#exportAscii='March12_Phase2.txt'
)
return
def test_rotation2():
"""
rotation modulation, second night
"""
files = ['2012-03-19/PACMAN_OBJ_ASTRO_080_0009.fits',
'2012-03-19/PACMAN_OBJ_ASTRO_080_0012.fits',
'2012-03-19/PACMAN_OBJ_ASTRO_080_0013.fits',
'2012-03-19/PACMAN_OBJ_ASTRO_080_0014.fits',
'2012-03-19/PACMAN_OBJ_ASTRO_080_0015.fits',
'2012-03-19/PACMAN_OBJ_ASTRO_080_0016.fits',
'2012-03-19/PACMAN_OBJ_ASTRO_080_0017.fits',
'2012-03-19/PACMAN_OBJ_ASTRO_080_0018.fits',
'2012-03-19/PACMAN_OBJ_ASTRO_080_0019.fits',
'2012-03-19/PACMAN_OBJ_ASTRO_080_0020.fits',
'2012-03-19/PACMAN_OBJ_ASTRO_080_0021.fits',
'2012-03-19/PACMAN_OBJ_ASTRO_080_0022.fits',
'2012-03-19/PACMAN_OBJ_ASTRO_080_0023.fits',
'2012-03-19/PACMAN_OBJ_ASTRO_080_0024.fits',
'2012-03-19/PACMAN_OBJ_ASTRO_080_0025.fits',
'2012-03-19/PACMAN_OBJ_ASTRO_080_0026.fits',
'2012-03-19/PACMAN_OBJ_ASTRO_080_0027.fits',
'2012-03-19/PACMAN_OBJ_ASTRO_080_0028.fits',
'2012-03-19/PACMAN_OBJ_ASTRO_080_0029.fits',
'2012-03-19/PACMAN_OBJ_ASTRO_080_0030.fits',
]
# parameters for the fit:
fg = {'T:HD100286J SEP':10.0,
'T:HD100286J PA':210.0,
'T:HD100286J M0 MJD 56006.00 56006.99':0.0,
}
doNotFit=[]
a = astromNew.fitListOfFiles(files,reduceData=False,
directory=data_directory,
firstGuess=fg, plot=True, verbose=2,
doNotFit=doNotFit, allX=False,
#maxResiduals=10,
#exportAscii='March12_Phase2.txt'
)
return
def analyseCorrection(files=files_hd65297_019):
# traditional fit:
titles = ['no corr.',
'blind corr.',
'adj. scale',
'adj. angle',
'adj. all']
firstGuess=[{'SEP':35, 'PA':135, 'M0':-0.03, 'PUPSCALE':0, 'PUPANGLE':0},
{'SEP':35, 'PA':135, 'M0':-0.03, 'PUPSCALE':1, 'PUPANGLE':0},
{'SEP':35, 'PA':135, 'M0':-0.03, 'PUPSCALE':1, 'PUPANGLE':0},
{'SEP':35, 'PA':135, 'M0':-0.03, 'PUPSCALE':1, 'PUPANGLE':0},
{'SEP':35, 'PA':135, 'M0':-0.03, 'PUPSCALE':1, 'PUPANGLE':0}]
doNotFit = [['PUPSCALE', 'PUPANGLE'],
['PUPSCALE', 'PUPANGLE'],
['PUPANGLE'],
['PUPSCALE'],
[]]
tmp = []
for k in range(len(titles)):
tmp.append(
astromNew.fitListOfFiles(files, directory=data_directory,
firstGuess=firstGuess[k],
doNotFit=doNotFit[k],
plot=False, verbose=0))
k_ = ['SEP', 'PA', 'M0', 'PUPSCALE', 'PUPANGLE']
print '| |',
for i in range(len(k_)):
print k_[i], '|',
print 'CHI2|RMS|'
print '|-'
for i in range(len(titles)):
print '|', titles[i], '|',
for k in k_:
print round(tmp[i][0][k], 5),'|',
print round(tmp[i][2], 2), '|',round(tmp[i][3],2), '|'
print '| |',
for k in k_:
print round(tmp[i][1][k], 5) if tmp[i][1][k]>0 else '','|',
print '| |'
def testHD66958_ALL(runOffCorrection=True):
fi = os.listdir(data_directory+'/comm18')
fi = filter(lambda x: 'RED.fits' in x , fi)
fi = ['comm18/'+''.join(f.split('_RED')) for f in fi]
fi = filter(lambda x: '019' in x , fi)
fg = {'SEP':35, 'PA':100,
#'SEP MJD 56000.0':35, 'PA MJD 56000.0':180, 'LINDIR':-142.0, 'LINRATE':-3844.0,
'M0 MJD 55945.00 55945.99':-0.034,
#'M0 MJD 55889.0 55889.999':-0.009,
#'M0 MJD 55890.0 55890.999':-0.010
}
if runOffCorrection:
for k in ATsPUP.keys(): #
fg[k] = ATsPUP[k]
if runOffCorrection=='blind':
doNotFit=['AT3', 'AT4', 'ATPUP scale', 'LIN']
else:
doNotFit=['AT3', 'AT4', 'LIN']
a = astromNew.fitListOfFiles(fi, data_directory,
firstGuess=fg, verbose=2,
doNotFit=doNotFit,
maxResiduals=30)
return a
def test_targetsRun2(runOffCorrection=False, allX=False):
"""
data collected after realignement of M4/AT3 by Stephane Guisard
to minimize pupil runout.
"""
files = [
###### March 2012 data ##############
# HD 134481
'2012-03-16/PACMAN_OBJ_ASTRO_077_0001.fits',
'2012-03-16/PACMAN_OBJ_ASTRO_077_0002.fits',
'2012-03-16/PACMAN_OBJ_ASTRO_077_0003.fits',
'2012-03-16/PACMAN_OBJ_ASTRO_077_0004.fits',
'2012-03-16/PACMAN_OBJ_ASTRO_077_0006.fits',
'2012-03-16/PACMAN_OBJ_ASTRO_077_0007.fits',
'2012-03-16/PACMAN_OBJ_ASTRO_077_0008.fits',
'2012-03-16/PACMAN_OBJ_ASTRO_077_0009.fits',
'2012-03-16/PACMAN_OBJ_ASTRO_077_0010.fits',
'2012-03-16/PACMAN_OBJ_ASTRO_077_0011.fits',
'2012-03-16/PACMAN_OBJ_ASTRO_077_0012.fits',
'2012-03-16/PACMAN_OBJ_ASTRO_077_0013.fits',
'2012-03-16/PACMAN_OBJ_ASTRO_077_0014.fits',
'2012-03-16/PACMAN_OBJ_ASTRO_077_0015.fits',
'2012-03-16/PACMAN_OBJ_ASTRO_077_0016.fits',
'2012-03-16/PACMAN_OBJ_ASTRO_077_0017.fits',
'2012-03-16/PACMAN_OBJ_ASTRO_077_0018.fits',
'2012-03-16/PACMAN_OBJ_ASTRO_077_0019.fits',
'2012-03-16/PACMAN_OBJ_ASTRO_077_0020.fits',
'2012-03-16/PACMAN_OBJ_ASTRO_077_0021.fits',
'2012-03-16/PACMAN_OBJ_ASTRO_077_0022.fits',
'2012-03-16/PACMAN_OBJ_ASTRO_077_0023.fits',
'2012-03-16/PACMAN_OBJ_ASTRO_077_0024.fits',
'2012-03-16/PACMAN_OBJ_ASTRO_077_0025.fits',
'2012-03-16/PACMAN_OBJ_ASTRO_077_0026.fits',
'2012-03-16/PACMAN_OBJ_ASTRO_077_0027.fits',
'2012-03-16/PACMAN_OBJ_ASTRO_077_0028.fits',
'2012-03-16/PACMAN_OBJ_ASTRO_077_0029.fits',
'2012-03-16/PACMAN_OBJ_ASTRO_077_0030.fits',
'2012-03-16/PACMAN_OBJ_ASTRO_077_0031.fits',
# HD 66598
'2012-03-17/PACMAN_OBJ_ASTRO_078_0002.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0003.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0004.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0005.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0006.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0007.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0008.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0009.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0010.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0011.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0012.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0013.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0014.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0015.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0016.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0017.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0018.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0019.fits',
# HD 129926
'2012-03-17/PACMAN_OBJ_ASTRO_078_0025.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0026.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0027.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0028.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0029.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0030.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0031.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0032.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0033.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0034.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0035.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0036.fits',
#'2012-03-17/PACMAN_OBJ_ASTRO_078_0037.fits',# BAD
#'2012-03-17/PACMAN_OBJ_ASTRO_078_0038.fits',# BAD
#'2012-03-17/PACMAN_OBJ_ASTRO_078_0039.fits',# BAD
'2012-03-17/PACMAN_OBJ_ASTRO_078_0040.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0041.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0042.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0043.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0044.fits',
'2012-03-17/PACMAN_OBJ_ASTRO_078_0045.fits',
]
# parameters for the fit:
fg = {'T:HD129926 SEP':8,
'T:HD129926 PA':124,
'T:HD129926 M0 MJD 56004.00 56004.35':0.00,
'T:HD129926 M0 MJD 56004.35 56004.99':0.00,
'T:HD66598 SEP':35,
'T:HD66598 PA':134,
'T:HD66598 M0 MJD 56004.00 56004.99':0.00,
'T:HD134481 SEP': 26,
'T:HD134481 PA': 144,
'T:HD134481 M0 MJD 56003.00 56003.30':0.00,
'T:HD134481 M0 MJD 56003.30 56003.99':0.00,
}
doNotFit=[]
### March 2012 pupil runout parameters:
if runOffCorrection:
for k in Bonnet1.keys(): # add
fg[k] = Bonnet1[k]
doNotFit.extend(['scale',
#'AT4 ct', 'AT4 st', 'AT4 cd', 'AT4 sd', # fit 2ROT
#'AT4 ct', 'AT4 st', 'AT4 c2d', 'AT4 s2d', # fit ROT
#'AT4 c2d', 'AT4 s2d', #'AT4 c2d', 'AT4 s2d', # fit THETA
'AT4',
'AT3'])
a = astromNew.fitListOfFiles(files,reduceData=False,
directory=data_directory,
firstGuess=fg, plot=True, verbose=2,
doNotFit=doNotFit, allX=allX,
maxResiduals=10,
#exportAscii='March12_Phase2.txt'
)
return
def test_targetsBegRun(runOffCorrection=False, allX=False):
files_hd66958 = [
###### March 2012 data ##############
'2012-03-13/PACMAN_OBJ_ASTRO_074_0001.fits',
'2012-03-13/PACMAN_OBJ_ASTRO_074_0002.fits',
'2012-03-13/PACMAN_OBJ_ASTRO_074_0003.fits',
'2012-03-13/PACMAN_OBJ_ASTRO_074_0004.fits',
'2012-03-13/PACMAN_OBJ_ASTRO_074_0005.fits',
'2012-03-13/PACMAN_OBJ_ASTRO_074_0006.fits',
'2012-03-13/PACMAN_OBJ_ASTRO_074_0007.fits',
'2012-03-13/PACMAN_OBJ_ASTRO_074_0008.fits',
'2012-03-13/PACMAN_OBJ_ASTRO_074_0009.fits',
'2012-03-13/PACMAN_OBJ_ASTRO_074_0010.fits',
'2012-03-13/PACMAN_OBJ_ASTRO_074_0011.fits',
'2012-03-13/PACMAN_OBJ_ASTRO_074_0012.fits',
'2012-03-13/PACMAN_OBJ_ASTRO_074_0013.fits',
'2012-03-13/PACMAN_OBJ_ASTRO_074_0014.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0001.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0002.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0003.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0004.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0005.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0006.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0007.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0008.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0009.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0010.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0011.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0012.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0013.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0014.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0015.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0016.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0017.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0018.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0019.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0020.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0021.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0022.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0023.fits',
'2012-03-15/PACMAN_OBJ_ASTRO_076_0002.fits',
'2012-03-15/PACMAN_OBJ_ASTRO_076_0003.fits',
'2012-03-15/PACMAN_OBJ_ASTRO_076_0004.fits',
'2012-03-15/PACMAN_OBJ_ASTRO_076_0005.fits',
'2012-03-15/PACMAN_OBJ_ASTRO_076_0006.fits',
'2012-03-15/PACMAN_OBJ_ASTRO_076_0007.fits',
'2012-03-15/PACMAN_OBJ_ASTRO_076_0008.fits',
'2012-03-15/PACMAN_OBJ_ASTRO_076_0009.fits',
'2012-03-15/PACMAN_OBJ_ASTRO_076_0010.fits',
'2012-03-15/PACMAN_OBJ_ASTRO_076_0011.fits',
'2012-03-15/PACMAN_OBJ_ASTRO_076_0012.fits',
'2012-03-15/PACMAN_OBJ_ASTRO_076_0013.fits',
'2012-03-15/PACMAN_OBJ_ASTRO_076_0014.fits',
'2012-03-15/PACMAN_OBJ_ASTRO_076_0015.fits',
'2012-03-15/PACMAN_OBJ_ASTRO_076_0016.fits',
'2012-03-15/PACMAN_OBJ_ASTRO_076_0017.fits',
'2012-03-15/PACMAN_OBJ_ASTRO_076_0018.fits',
'2012-03-15/PACMAN_OBJ_ASTRO_076_0019.fits',
'2012-03-15/PACMAN_OBJ_ASTRO_076_0020.fits',
'2012-03-15/PACMAN_OBJ_ASTRO_076_0021.fits',
'2012-03-15/PACMAN_OBJ_ASTRO_076_0022.fits',
'2012-03-15/PACMAN_OBJ_ASTRO_076_0023.fits',
'2012-03-15/PACMAN_OBJ_ASTRO_076_0024.fits',
'2012-03-15/PACMAN_OBJ_ASTRO_076_0025.fits',
'2012-03-15/PACMAN_OBJ_ASTRO_076_0026.fits',]
fg = {'T:HD66598 SEP':35,
'T:HD66598 PA':134,
'T:HD66598 M0 MJD 56000.00 56000.07':0.00,
'T:HD66598 M0 MJD 56000.07 56000.99':0.00,
'T:HD66598 M0 MJD 56001.00 56001.05':0.00,
'T:HD66598 M0 MJD 56001.05 56001.99':0.00,
'T:HD134481 SEP': 26,
'T:HD134481 PA': 144,
'T:HD134481 M0 MJD 56002.00 56002.32':0.00,
'T:HD134481 M0 MJD 56002.32 56002.35':0.00,
'T:HD134481 M0 MJD 56002.35 56002.99':0.00,
}
doNotFit=['LIN']
### Jan 2012 parameters:
#for k in ATsPUP.keys(): # add
# fg[k] = ATsPUP[k]
### March 2012 parameters:
if runOffCorrection:
for k in Bonnet0.keys(): # add
fg[k] = Bonnet0[k]
doNotFit.extend(['scale','AT3', 'AT4'])
a = astromNew.fitListOfFiles(files_hd66958,reduceData=False,
directory=data_directory,
firstGuess=fg, plot=True, verbose=2,
doNotFit=doNotFit, allX=allX,
maxResiduals=20,
#exportAscii='March12_Phase1.txt'
)
deltaDec = a['BEST']['T:HD134481 SEP']*np.cos(a['BEST']['T:HD134481 PA']*np.pi/180)
deltaRA_cosDec = a['BEST']['T:HD134481 SEP']*np.sin(a['BEST']['T:HD134481 PA']*np.pi/180)
deltaRA = deltaRA_cosDec/np.cos(a['DEC']['HD134481']*np.pi/180)
print 'HD134481: dRA', deltaRA, 'dDEC=', deltaDec
return
def testHD66958(allX=False, runOffCorrection=True):
"""
- functionnal test;
- astrometric fit on HD 66598 to see the amplitude of the
residuals compared to january
"""
files_hd66958 = [
### March 2012 data
'2012-03-13/PACMAN_OBJ_ASTRO_074_0001.fits',
'2012-03-13/PACMAN_OBJ_ASTRO_074_0002.fits',
'2012-03-13/PACMAN_OBJ_ASTRO_074_0003.fits',
'2012-03-13/PACMAN_OBJ_ASTRO_074_0004.fits',
'2012-03-13/PACMAN_OBJ_ASTRO_074_0005.fits',
# no 0006
'2012-03-13/PACMAN_OBJ_ASTRO_074_0007.fits',
'2012-03-13/PACMAN_OBJ_ASTRO_074_0008.fits',
'2012-03-13/PACMAN_OBJ_ASTRO_074_0009.fits',
'2012-03-13/PACMAN_OBJ_ASTRO_074_0010.fits',
'2012-03-13/PACMAN_OBJ_ASTRO_074_0011.fits',
'2012-03-13/PACMAN_OBJ_ASTRO_074_0012.fits',
'2012-03-13/PACMAN_OBJ_ASTRO_074_0013.fits',
'2012-03-13/PACMAN_OBJ_ASTRO_074_0014.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0001.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0002.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0003.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0004.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0005.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0006.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0007.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0008.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0009.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0010.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0011.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0012.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0013.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0014.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0015.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0016.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0017.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0018.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0019.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0020.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0021.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0022.fits',
'2012-03-14/PACMAN_OBJ_ASTRO_075_0023.fits',
#'2012-03-14/PACMAN_OBJ_ASTRO_075_0024.fits'
]
fg = {'SEP':35, 'PA':134,
'M0 MJD 56000.0 56000.07':0.00,
'M0 MJD 56000.07 56000.99':0.00,
'M0 MJD 56001.0 56001.05':0.00,
'M0 MJD 56001.05 56001.99':0.00,
}
doNotFit=['LIN','AT3', 'AT4']
if runOffCorrection=='blind':
doNotFit.extend('scale')
### Jan 2012 parameters:
#for k in ATsPUP.keys(): # add
# fg[k] = ATsPUP[k]
### March 2012 parameters:
if runOffCorrection:
for k in Bonnet1.keys(): # add
fg[k] = Bonnet1[k]
a = astromNew.fitListOfFiles(files_hd66958,reduceData=False,
directory=data_directory,
firstGuess=fg, plot=True, verbose=1,
doNotFit=doNotFit, allX=allX,
maxResiduals=10,
exportAscii='hd66598.txt'
)
return
def fitAllHD10360():
global pup
HD10360corr = []
HD10360 = []
for k, f in enumerate(files_hd10360):
print k+1, '/', len(files_hd10360)
fg = {'SEP':8., 'PA':135, 'M0':0.0}
for k in ATsPUP.keys():
fg[k] = ATsPUP[k]
HD10360corr.append(astromNew.fitListOfFiles(f, directory=data_directory,
firstGuess=fg,
doNotFit=['AT3','AT4','PUP'],
plot=False, verbose=False))
print HD10360corr[-1]['MJD_MIN'], '-', HD10360corr[-1]['MJD_MAX']
HD10360.append(astromNew.fitListOfFiles(f, directory=data_directory,
firstGuess={'SEP':8., 'PA':135, 'M0':-0.0},
plot=False, verbose=False))
pyplot.figure(10)
pyplot.clf()
pyplot.axes().set_aspect('equal', 'datalim')
th = np.linspace(0, 2*np.pi, 100)
colors=['r', 'g', 'b', 'y', 'm', 'c']
### data reduced without correction: ####################
XYT = []
for k,d in enumerate(HD10360):
### observation:
X = d['BEST']['SEP']*np.sin(d['BEST']['PA']*np.pi/180)
Y = d['BEST']['SEP']*np.cos(d['BEST']['PA']*np.pi/180)
XYT.append((X,Y, d['MJD_MEAN']))
pyplot.plot(X, Y, 's'+colors[k], markersize=10, alpha=0.5,
label = 'no correction' if k==0 else None)
# linear fit
Xb, Xe, chi2, Xm =\
dpfit.leastsqFit(dpfit.polyN, [x[2]-55885 for x in XYT],
{'A0':0, 'A1':1},
[x[0] for x in XYT])
Yb, Ye, chi2, Ym =\
dpfit.leastsqFit(dpfit.polyN, [x[2]-55885 for x in XYT],
{'A0':0, 'A1':1},
[x[1] for x in XYT])
# display linear fit
MJD = np.linspace(55885, 55890, 100)
pyplot.plot(dpfit.polyN(MJD-55885, Xb), dpfit.polyN(MJD-55885, Yb),
'k-', linewidth=3, alpha=0.3)
# distance to fit
for k,d in enumerate(HD10360):
X = d['BEST']['SEP']*np.sin(d['BEST']['PA']*np.pi/180)
Y = d['BEST']['SEP']*np.cos(d['BEST']['PA']*np.pi/180)
pyplot.plot(Xm[k], Ym[k], 'x'+colors[k], markersize=6)
pyplot.plot([X, Xm[k]], [Y, Ym[k]], '-'+colors[k],
linewidth=2, alpha=0.5)
### data reduced with correction ############################
XYT=[]
for k,d in enumerate(HD10360corr):
### observation:
X = d['BEST']['SEP']*np.sin(d['BEST']['PA']*np.pi/180)
Y = d['BEST']['SEP']*np.cos(d['BEST']['PA']*np.pi/180)
XYT.append((X,Y, d['MJD_MEAN']))
pyplot.plot(X, Y, 'o'+colors[k], markersize=10,
label = 'scaled correction' if k==0 else None)
# linear fit
Xb, Xe, chi2, Xm =\
dpfit.leastsqFit(dpfit.polyN, [x[2]-55885 for x in XYT],
{'A0':0, 'A1':1},
[x[0] for x in XYT])
Yb, Ye, chi2, Ym =\
dpfit.leastsqFit(dpfit.polyN, [x[2]-55885 for x in XYT],
{'A0':0, 'A1':1},
[x[1] for x in XYT])
print 'linrate:', np.sqrt(Xb['A1']**1+Yb['A1']**2)*1e6, 'uas/day'
# display linear fit
MJD = np.linspace(55885, 55892, 10)
pyplot.plot(dpfit.polyN(MJD-55885, Xb), dpfit.polyN(MJD-55885, Yb),
'k-', linewidth=3, alpha=0.3)
# distance to fit
for k,d in enumerate(HD10360corr):
X = d['BEST']['SEP']*np.sin(d['BEST']['PA']*np.pi/180)
Y = d['BEST']['SEP']*np.cos(d['BEST']['PA']*np.pi/180)
pyplot.plot(Xm[k], Ym[k], 'x'+colors[k], markersize=6)
pyplot.plot([X, Xm[k]], [Y, Ym[k]], '-'+colors[k],
linewidth=2, alpha=0.5)
pyplot.legend(loc='upper left')
pyplot.xlabel('$\delta$ E (\")')
pyplot.ylabel('$\delta$ N (\")')
return
# -----------------------------------------------------------------------
files_hd18622 = ['2012-01-14/PACMAN_OBJ_ASTRO_015_0003.fits',
'2012-01-14/PACMAN_OBJ_ASTRO_015_0004.fits',
'2012-01-14/PACMAN_OBJ_ASTRO_015_0005.fits',
'2012-01-14/PACMAN_OBJ_ASTRO_015_0006.fits',
'2012-01-14/PACMAN_OBJ_ASTRO_015_0007.fits',
'2012-01-16/PACMAN_OBJ_ASTRO_017_0001.fits',
'2012-01-16/PACMAN_OBJ_ASTRO_017_0002.fits',
'2012-01-16/PACMAN_OBJ_ASTRO_017_0003.fits',
'2012-01-16/PACMAN_OBJ_ASTRO_017_0004.fits',
'2012-01-18/PACMAN_OBJ_ASTRO_019_0001.fits',
'2012-01-18/PACMAN_OBJ_ASTRO_019_0002.fits',
'2012-01-18/PACMAN_OBJ_ASTRO_019_0003.fits',
'2012-01-18/PACMAN_OBJ_ASTRO_019_0004.fits']
"""
astromNew.fitListOfFiles(comm18.files_hd18622, directory=comm18.data_directory,
maxResiduals=3,
firstGuess={'SEP':8.3, 'PA':91.3,
'M0 MJD 55941.00 55941.99':-0.017,
'M0 MJD 55943.00 55943.99':-0.022,
'M0 MJD 55945.00 55945.99':-0.022 })
"""
"""
astromNew.fitListOfFiles(files_hd18622_014, directory=data_directory,
maxResiduals=3,
firstGuess={'SEP':8.3, 'PA':91.3,
'M0 MJD 55941.00 55941.99':-0.017,
'M0 MJD 55943.00 55943.99':-0.022},
fitOnly=['SEP','PA','M0 MJD 55941.00 55941.99','M0 MJD 55943.00 55943.99'])
