def getMedian(files, param):
list = []
for file in files:
[wght, flux], keys = tb.readlist(file, ['w', 'aper_flux'])
list.append(float(keys[str(param)]))
median = np.median(list)
print 'median is ', median
return median
def extractData(file, inputype):
print 'reading ', file
if (inputype=='obs'):
[wght, flux], keys = tb.readlist(file, ['w', 'aper_flux'])
airmass = keys['AIRMASS']
exptime = keys['EXPTIME']
Object = keys['OBJECT']
if (inputype=='simu'):
wght, flux, airmass, Object = fromSimu(file)
exptime = 1.
return wght, flux, airmass, Object, exptime
import pylab as pl
import toolbox as tb
import croaks
import scipy.interpolate as interp
if __name__ == "__main__":
outfile = sys.argv[1]
files = sys.argv[2:]
values = []
fig = pl.figure()
for file in files:
print file
name = os.path.basename(file)
value, keys = tb.readlist(file, ('wavelength', 'contamination'))
values.append(value)
pl.plot(value[0], value[1], label = name)
pl.ylim(0.001, .02)
pl.legend()
pl.xlabel('wavelength (nm)')
pl.ylabel('Contamination (fraction)')
pl.title('Ronchi 200 - 2nd order light contamination')
fig.savefig("contamination.pdf")
pl.show()
### I guess I will fit a constant value
tb.DumpTuple(('wavelength', 'contamination'),(second[:,0], interp/second[:,1])
, outfile )
img = []
seeing = []
zenith = []
files = sys.argv[1:]
reference = []
colors = iter(cm.rainbow(np.linspace(0, 1, len(files) + 1)))
fig, ax1 = pl.subplots(figsize=(7, 5))
#pl.xlim([350,1000])
pl.xlim([700, 1000])
pl.ylim([0.9, 1.1])
pl.ylabel('aper_flux / aper_100')
pl.xlabel('wght (nm)')
for file in files:
values, keys = tb.readlist(file, ['pixel', 'w', 'aper_flux'])
filters = keys['FILTERS']
obj = re.split('[ /]', keys['IMG_NAME'])
img = obj[-2]
print
print 'raw file : ', obj[0], img, filters
print file
aperture = file.split('/')[-2].split('_')[-1].split('aper')[-1]
print aperture
if (str(aperture) == '100'):
print 'reference is aper 100'
reference = values[2]
wght = values[0]
print len(wght), len(values[0])
color = next(colors)
inputype = args.Type #''' Either obs or simu '''
files = args.files
outfile = args.outfile
colors = iter(cm.rainbow(np.linspace(0, 1, len(files))))
Range = [350., 950.]
W = []
Z = []
F = []
O = []
fig = pl.figure()
ax = fig.add_subplot(111, projection='3d')
i = 1
for file in files:
print 'reading ', file
if (inputype == 'obs'):
[wght, flux], keys = tb.readlist(file, ['w', 'aper_flux'])
airmass = keys['AIRMASS']
exptime = keys['EXPTIME']
Object = keys['OBJECT']
if (inputype == 'simu'):
wght, flux, airmass, Object, exptime = fromSimu(file)
'''
Selected a clean range
'''
if (airmass >= 3.1):
print 'airmass is above 3.1'
continue
out = np.array([wght, flux]).transpose()
out = out[(out[:, 0] >= 350)]
[email protected] ''' import os, sys, re import numpy as np import pylab as pl import toolbox as tb import scipy.interpolate as interp if __name__ == "__main__": if(len(sys.argv)<2): usage = 'give a list of spectrum with pixel and flux columns' sys.exit(usage) spectra = sys.argv[1:] total = [] air = [] fig = pl.figure(1) for spectrum in spectra : [ wgth,flux], keys = tb.readlist(spectrum, ['pixel', 'flux']) airmass = keys['AIRMASS'] air.append(airmass) total.append(sum(flux)) print 'airmass : ', airmass pl.plot(wgth, flux, color='black') fig.savefig("resp.pdf") fig = pl.figure(2) pl.plot(air, total, 'r^') pl.show() pl.clf()
logging.info('INFO mode')
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'''
param_file = os.path.join(input_rep, file)
if not os.path.isfile(param_file):
continue
array, keys = tb.readlist(param_file,
['pixel', 'w',\
'aper_flux',\
'psf_gauss_flux', 'psf_gauss_sigma','psf_gauss_mu'])
target = tb.STDname(keys.get('OBJECT'))
seeing = keys.get('SEEING') # This is currently not a good measurement
pix2wgth = keys.get('PIX2WGTH')
ronchi = keys.get('RONCHI')
array = np.array(array).transpose()
array = array[(array[:, 1] <=
1100)] # Need a better model for the fitting
pixel = array[:, 0]
w = array[:, 1]
aper_flux = array[:, 2]
psf_gauss_flux = array[:, 3]
psf_gauss_sigma = array[:,
4] # now use for a better seeing determination as a function of wght
psf_gauss_mu = array[:, 5]
