def Extraction(name, wavelength, flux):
data = np.array([wavelength, flux]).transpose()
li = Lines.Lines(data)
wg_lines, flux_lines, sflux_lines = li.ExtractLines()
li.Plot(write=True, name=str(name + '.pdf'))
tb.DumpTuple(['wg_lines', 'flux_lines', 'sflux_lines'],
[wg_lines, flux_lines, sflux_lines], str(name + '.list'))
def Atmo(self, wavelength, signal, mkcalib=True, name='', **kwargs):
if self.sed.calspec:
fig = pl.figure()
signal = np.array(signal)
wg, atmo = self.sed.EstimateAtmoT(wavelength, signal, mkcalib=True)
tb.DumpTuple(['wg', 'atmo'], [wg, atmo],
os.path.join(self.directory,
str('Tatmo' + name + '.list')))
if self.plot is True:
pl.plot(wg, atmo, '-k', label=str('Tatmo' + name))
pl.xlabel('wavelength (nm)')
pl.ylabel('Atmospheric transmission (norm@max)')
ymin = np.nanmin(atmo)
ymax = np.nanmax(atmo)
xmin = np.nanmin(wg)
xmax = np.nanmax(wg)
xmax = min(xmax, 1100)
pl.xlim(xmin, xmax)
pl.ylim(ymin, ymax)
pl.legend()
return
def ExtractProfile(self,footprint, mask, direction):
Y =[]
if(direction=="y"):
footprint = footprint.T
mask = mask.T
rows, cols = len(footprint), len(footprint[0])
for i in range(0,rows):
keep = footprint[i , :]
remo = mask[i , :]
keep = keep[remo==0]
if len(keep) is 0 :
flux = Y[-1]
else:
flux = np.median(keep)
Y.append(flux)
rows = np.arange(0,rows)
file = str("background"+self.spectrum.order+".list")
tb.DumpTuple(('rows', 'level'),(rows, Y)
, os.path.join(self.spectrum.out_dir,file) )
return Y
edges.append(item[2][0])
for i in EW:
print 'EW : ', i
#ew_w, ew_f = equWidth(wght, signal, continuum, i) #abrupt version
ew_w, ew_f = equWidth2(data[:,0],data[:,1], i, plot=plot) #fitted edges
print Object, jd, airmass, ew_w, ew_f, mean_seeing
outlist.append([Object, jd, airmass,parallactic_angle, ew_w, ew_f, mean_seeing, pwv])
if plot:
pl.figure
pl.plot(wght, continuum, 'g', lw=1, label='continuum')
pl.plot(data[:,0],data[:,1], 'r^',label='raw flux')
pl.plot(continuum_w, continuum_f, 'bo',label='spline input')
pl.legend()
pl.figure()
pl.plot(edges, np.ones(len(EW)*2), 'r|')
pl.plot(wght, (signal) / continuum, label='signal/continuum')
pl.legend()
pl.show()
names =['object', 'jd', 'airmass', 'parallactic','ew_w','ew_f', 'mean_seeing', 'pwv']
tb.DumpTuple(names,
zip(*outlist),
outfile)
print 'writing : ', outfile
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 )
c = FK5(Angle(looking_ra, u.degree), Angle(looking_dec, u.degree))
idx, d2d, d3d = match_coordinates_sky(c, catalog)
print idx, ' distance ~ ', (
d2d.degree)[0] * 3600 / 0.8, #mode rebin 2*2
print ' object is at number : ', sources_nb[idx]
found = objects[objects.field('NUMBER') == sources_nb[idx]]
print 'Found : ', found, ' => ', found.field(
'X_IMAGE'), found.field('Y_IMAGE')
pixcrd = np.array(
[[found.field('X_IMAGE')[0],
found.field('Y_IMAGE')[0]]], np.float_)
world = w.wcs_pix2world(pixcrd, 1)
ra = world[0][0]
dec = world[0][1]
name = reference[0]
data.append(
list([
name, date, exptime, band, ra, dec, airmass, outhum,
outpress, wndspeed
]) + list(found[0]))
if item == 0:
columns = ('name', 'date', 'exptime', 'band', 'ra', 'dec',
'airmass', 'outhum', 'outpress',
'wndspeed') + found.dtype.names
print 'output entries : ', columns
print 'writing ', outcat
tb.DumpTuple(columns, zip(*data), outcat)
if __name__ == "__main__":
step = 1.
wght = np.arange(450, 901, step)
qe = np.loadtxt(sys.argv[1])
cbp = np.loadtxt(sys.argv[2])
QE = interp.griddata(qe[:, 0], qe[:, 1], wght)
CBP = interp.griddata(cbp[:, 0], cbp[:, 1], wght)
out = QE / CBP
filename = 'Oct2017QE.list'
outdir = os.path.dirname(sys.argv[1])
tb.DumpTuple(('wght', 'qe'), (wght, out), os.path.join(outdir, filename))
pl.figure()
pl.plot(qe[:, 0], qe[:, 1], 'r^', label='file1')
pl.legend()
pl.figure()
pl.plot(cbp[:, 0], cbp[:, 1], 'bo', label='file2')
pl.legend()
pl.figure()
pl.plot(wght, out, 'r^', label='cbp Norm')
#pl.plot(wght, CBP, b'o')
pl.legend()
pl.show()
else:
wneg.append(i)
neg.append(abs(j))
pl.plot(wpos, pos, color='red', marker='+', label="pos.")
pl.plot(wneg, neg, color='blue', marker='+', label="neg.")
#pl.errorbar(Wreg,Freg0, sFreg0, color='red', marker='+', linestyle='None', label= "Extrapolation to z=0 ")
pl.title('0 airmass')
pl.xlabel('wavelength (nm)')
pl.ylabel('-2.5*log10(flux) (mag)')
pl.legend(loc='upper left')
fig3.savefig("regressionOairmassP0.pdf")
fig4 = pl.figure()
pl.errorbar(Wreg,
Freg1,
sFreg1,
color='red',
marker='+',
linestyle='None',
label="regression ")
pl.xlabel('wavelength (nm)')
pl.legend(loc='upper right')
fig4.savefig("regressionOairmassP1.pdf")
pl.show()
outlist = [Wreg, Freg0, sFreg0, Freg1, sFreg1]
names = ['wght', 'intercept', 'sintercept', 'slope', 'sslope']
tb.DumpTuple(names, outlist, outfile)
print 'writing : ', outfile
#!/usr/bin/env python ''' dump spectrum from stis fitsimage Author: Augustin Guyonnet [email protected] ''' import os, sys import astropy.io.fits as pf import toolbox as tb if __name__ == "__main__": Image = sys.argv[1] fits = pf.open(Image) target = fits[0].header.get('TARGETID') tbdata = fits[1].data # the table is in extension 1 cols = tbdata.columns # names of the columns tbdata = tbdata[tbdata.field(0) < 11000] # Select lambda<1100nm wavelength = tbdata.field(0) / 10 # Convert in nm flux = tbdata.field(1) staterror = tbdata.field(2) syserror = tbdata.field(3) tb.DumpTuple(('wavelength', 'flux', 'staterror', 'syserror'),\ [wavelength, flux, staterror, syserror], str(target)+'.list')
if __name__ == "__main__":
if len(sys.argv) <= 1:
usage()
files = sys.argv[1:]
data = []
names = zeroth(files[0]).dtype.names
for file in files:
dirname = os.path.dirname(file)
img = dirname.split("_")[-1]
fits = os.path.join(dirname,'segmentation.fits')
filters = pf.open(fits)[0].header.get('FILTERS')
comments = str(pf.open(fits)[0].header.get('COMMENT'))
wght = comments.split("WAVE")[1].split(" ")[1]
print fits, 'wght = ', wght
obj = zeroth(file)
if (len(obj)!=1):
continue
obj = list(obj[0].tolist())
obj = [int(img)] + [float(wght)] + obj
data.append(obj)
names = ['img']+['wght']+list(names)
tb.DumpTuple(names, zip(*data), 'cbp.list')
#!/usr/bin/env python ''' dump a atmospheric trnasmission profile Author: Augustin Guyonnet [email protected] ''' import os, sys import toolbox as tb import scipy.ndimage.filters as filt if __name__ == "__main__": wavelength, flux = tb.AtmoTransmission() flux = filt.gaussian_filter1d(flux, sigma=2.46) tb.DumpTuple(('wavelength', 'flux'), [wavelength, flux], 'atmo.list')
print "A*x*np.exp(-(x)**2/(2.*sigma**2)) / sigma**2"
print "A, sigma = ", coeff
fit = rayleigh(x, *coeff)
distrib = "Rayleigh"
pl.text(10, 0.1, 'A, sigma = %s' % (coeff), color='red', fontsize=8)
if method == 'G':
gg_init = models.Gaussian1D(amplitude=1, mean=260,
stddev=10.) + models.Gaussian1D(
amplitude=1, mean=290, stddev=10.)
fitter = fitting.SLSQPLSQFitter()
gg_fit = fitter(gg_init, x, n)
print gg_fit
fit = gg_fit(x)
distrib = "Two gaussian"
pl.text(220,
0.02,
'%s \n %s' % (gg_fit.param_names, gg_fit.parameters),
color='red',
fontsize=8)
pl.plot(x, fit, 'b-', label=distrib + ' distribution')
pl.xlabel(name)
pl.legend()
pl.show()
outname = name + '_distrib'
names = ['bin', 'value']
tb.DumpTuple(names, [x, nTO3_distrib.list * bin_width], outname + '.list')
print 'writing : ', outname + '.list'
wght_recal)
outfile = str('spectrum' + name + '.list')
tb.DumpFile(keys, ('pixel', 'wght', 'wght_recal', 'flux', 'sed_flux',
'sed_flux_noqe'),
(pixel, w, wght_recal, best_flux, sed_flux, sed_flux_noqe),
os.path.join(input_rep, outfile))
'''Measurement of the EW based on the position of the lines'''
'''From the template +/- the resolution at this position '''
sigmas = []
segments = []
for i in ew_position:
sigmas.append(mod.Resolution(i))
ew_flux, segments = EW(wght_recal, best_flux, ew_position, sigmas)
outfile = str('ew_extraction' + name + '.pdf')
Plot(wght_recal,
best_flux,
segments,
name=os.path.join(input_rep, outfile))
outfile = str('ew_extraction' + name + '.list')
tb.DumpTuple(['wg_lines', 'flux_lines', 'sflux_lines'],
[ew_position, ew_flux,
np.zeros(len(ew_position))],
os.path.join(input_rep, outfile))
'''Replot after having rescaled the wght solution'''
mod.ForwardModeling(wght_recal, best_flux, name='_2')
mod.Atmo(wght_recal, best_flux, mkcalib=True, name='_2')
if plot:
pl.show()
pl.clf()
Y.append(flux)
return X, Y
if __name__ == "__main__":
amps = ('11', '12', '21', '22')
direction = sys.argv[1] # 'x' is default, 'y' transpose the array
Image = sys.argv[2:]
for img in Image:
figOp = pl.figure(1)
inst = instru.telinst(img)
data = inst.Image(img)
(filepath, filename) = os.path.split(img)
name = filename.split('.')[0] + str(direction)
colors = iter(cm.rainbow(np.linspace(0, 1, len(amps))))
for amp in amps:
overscan = inst.OverscanRegion(data, amp)
X, Y = extract_profile(overscan, direction)
# Draw raw profile and dump tuple:
graph(X, Y, Color=next(colors), Linestyle='-', Name=str(amp))
tb.DumpTuple(['X', 'Y'], [X, Y], name + str(amp) + '.list')
pl.xlabel('position (pixel)')
pl.ylabel('counts (ADU)')
pl.title('Overscans' + name)
pl.legend(loc='upper left')
name += '.pdf'
figOp.savefig(name)
