def fromObs(file, colname):
dict, values, names = tb.readcat(file)
#print 'Dict : ' ,dict.items()
latitude = ' '.join(dict.get('LATITUDE'))
ha = ' '.join(dict.get('HA'))
dec = dict.get('DEC')
parallactic_angle = tb.ParallacticAngle(latitude, ha, dec)
parallactic_angle = parallactic_angle[0]
print parallactic_angle
jd = ' '.join(dict.get('OBJECT'))
Object = dict.get('JD')[0]
airmass = dict.get('AIRMASS')[0]
data = np.array([values.field('w'),
values.field(str(colname))]).transpose()
#data = np.array([values.field('w'), values.field('psf_gauss_flux')]).transpose()
return data, airmass, Object, jd, parallactic_angle
def fromObs(file):
dict, values, names = tb.readcat(file)
meanseeing = meanSeeing(values.field('w'), values.field('psf_gauss_sigma'))
#print 'Dict : ' ,dict.items()
latitude = ' '.join(dict.get('LATITUDE'))
ha = ' '.join(dict.get('HA'))
dec = dict.get('DEC')
parallactic_angle = tb.ParallacticAngle(latitude, ha, dec)
parallactic_angle = parallactic_angle[0]
print 'parallactic_angle ', parallactic_angle
Object = ' '.join(dict.get('OBJECT'))
jd = dict.get('JD')[0]
airmass = dict.get('AIRMASS')[0]
#data = np.array([values.field('w'), values.field('aper_flux')]).transpose()
data = np.array([values.field('w'), values.field('psf_gauss_flux')]).transpose()
print 'flux estimator is Gauss PSF'
return data, airmass, Object, jd, parallactic_angle, meanseeing
return args
if __name__ == "__main__":
args = grabargs()
plot = args.plot
outfile = args.outfile
inputype = args.Type #''' Either obs or simu '''
files = args.files
seeing = args.seeing
select = args.select
if ((inputype=='simu') and (seeing is not None)):
dict, values, names = tb.readcat(seeing)
seeing_x = values.field('w')
seeing_y = values.field('psf_gauss_sigma')
pix2wght = float(dict.get('PIX2WGTH')[0])
mean_seeing = meanSeeing(seeing_x, seeing_y)
print 'MEAN SEEING ' , mean_seeing
wmin = 680.
wmax = 1200.
width = 2.
outlist = []
for file in files:
print 'opening ', file
if (inputype=='obs'):
data, airmass, Object, jd, parallactic_angle, mean_seeing = fromObs(file)
name = 0
date = 0
band = 0
ra = 0
dec = 0
exptime = 0
targets = buildTargetCoords(references)
print targets
'''Searching for matches'''
for item, rep in enumerate(reps):
file = os.path.join(rep, 'se.list')
img = os.path.join(rep, 'calibrated.fits')
print 'reading : ', file
print 'opening img : ', img
dict, objects, names = tb.readcat(file)
if extern:
hdulist = fits.open(extern)
else:
hdulist = fits.open(img)
date = hdulist[0].header.get('DATE')
date = dateutil.parser.parse(date)
date = (time.Time(date)).jd
band = hdulist[0].header.get('FILTERS').replace(" ", "_")
exptime = hdulist[0].header.get('EXPTIME')
airmass = hdulist[0].header.get('AIRMASS')
outhum = hdulist[0].header.get('OUTHUM')
outpress = hdulist[0].header.get('OUTPRESS')
wndspeed = hdulist[0].header.get('WNDSPEED')
'''Maybe match keyword name and reference name...unreliable'''
tb.ProdInstall()
template_path = os.environ['TEMPLATE_PREFIX']
if not os.path.exists(template_path):
os.makedirs(template_path)
for input_rep in input_reps:
logging.info('\n' + 'Analyzing : ' + input_rep)
'''observed spectrum'''
input_file = os.path.join(input_rep, file)
if not os.path.isfile(input_file):
continue
print 'reading ', input_file
dict, array, names = tb.readcat(input_file)
target = ' '.join(dict.get('OBJECT'))
print target
if not target:
continue
filters = ' '.join(dict.get('FILTERS'))
if (str(filters).find('RG715') >= 0):
print 'RG715 filter in place'
tel_t = 'rg715'
else:
print 'Telescope set up open'
tel_t = 'open'
seeing = float(
dict.get('SEEING')[0]) # This is currently not a good measurement
recarray doc :
https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.recarray.html
'''
import os, sys, re
import numpy as np
import toolbox as tb
if __name__ == "__main__":
show_stuff = True
files = sys.argv[1:]
for file in files:
dict, values, names = tb.readcat(file)
columns = ('NUMBER', 'FLUX_ISO', 'FLUXERR_ISO', 'FLUX_ISOCOR',
'FLUXERR_ISOCOR', 'FLUX_APER1', 'FLUX_APER2', 'FLUX_APER3',
'FLUX_APER4', 'FLUX_APER5', 'FLUXERR_APER1',
'FLUXERR_APER2', 'FLUXERR_APER3', 'FLUXERR_APER4',
'FLUXERR_APER5', 'MAG_APER', 'MAGERR_APER', 'FLUX_AUTO',
'FLUXERR_AUTO', 'FLUX_BEST', 'FLUXERR_BEST', 'THRESHOLD',
'FLUX_MAX', 'X_IMAGE', 'Y_IMAGE', 'X2_IMAGE', 'Y2_IMAGE',
'XY_IMAGE', 'CXX_IMAGE', 'CYY_IMAGE', 'CXY_IMAGE',
'A_IMAGE', 'B_IMAGE', 'FLAGS')
data = np.array(values)
outcat = file
print 'writing ', outcat
print 'output entries : ', columns
tb.DumpTuple(columns, zip(*data), outcat)