def arces() :
ered=imred.Reducer(inst='ARCES',dir='UT191020/ARCES/')
ecomb=imred.Combiner(reducer=ered)
flat=ecomb.sum([11,15])
thar=ecomb.sum([19,20])
t=tv.TV()
apertures=np.loadtxt('newap')[:,1]
#traces=trace(flat,apertures/4)
traces=Trace()
traces.trace(flat,apertures/4,sc0=1024,thresh=1000,plot=t)
ec=traces.extract(thar,scat=True)
wcal=spectra.WaveCal(type='chebyshev2D',orders=54+np.arange(107),spectrum=ec)
wav=readmultispec.readmultispec('w131102.0004.ec.fits')['wavelen']
new=ec.data*0.
new[:,204:1855]=wav
wcal.identify(ec,wav=new,file=os.environ['PYVISTA_DIR']+'/data/lamps/thar_arces',xmin=201,xmax=1849,thresh=200,rad=3,plot=t)
wcal.fit()
wcal.identify(ec,file=os.environ['PYVISTA_DIR']+'/data/lamps/thar_arces',xmin=150,xmax=1900,thresh=200,rad=3,plot=t)
wcal.fit()
w=wcal.wave(image=np.array(ec.data.shape))
hr7950=ered.reduce(1,superflat=flat)
hr7950ec=traces.extract(hr7950,scat=True)
return flat,thar,traces,ec,wcal,w
def read_multispec_file(parameters):
image_name = parameters['MS_FITS_IMAGE']
wavelength_template = parameters['WAVE_TEMPLATE']
wavelength_grid = np.loadtxt(wavelength_template)[:, 0]
image = rmspec.readmultispec(image_name)
wavelengths = image['wavelen']
flux = image['flux']
# for now just make ids a np arange until I figure out how to hook the WOCS IDs
num_stars = len(flux)
ids = np.arange(num_stars)
ids = np.reshape(ids, (num_stars, 1))
output_array = np.zeros((num_stars, np.size(wavelength_grid)), dtype=np.float32)
for entry in range(num_stars):
current_fluxes = flux[entry]
current_wavelengths = wavelengths[entry]
function = ip.interp1d(current_wavelengths, current_fluxes, kind='linear')
interpolated_spectrum = function(wavelength_grid)
output_array[entry, :] = interpolated_spectrum
return ids, output_array
def read_multispec_file(parameters):
image_name = parameters['MS_FITS_IMAGE']
wavelength_template = parameters['WAVE_TEMPLATE']
wavelength_grid = np.loadtxt(wavelength_template)[:, 0]
image = rmspec.readmultispec(image_name)
wavelengths = image['wavelen']
flux = image['flux']
# for now just make ids a np arange until I figure out how to hook the WOCS IDs
num_stars = len(flux)
ids = np.arange(num_stars)
ids = np.reshape(ids, (num_stars, 1))
output_array = np.zeros((num_stars, np.size(wavelength_grid)), dtype=np.float32)
for entry in range(num_stars):
current_fluxes = flux[entry]
current_wavelengths = wavelengths[entry]
function = ip.interp1d(current_wavelengths, current_fluxes, kind='linear')
interpolated_spectrum = function(wavelength_grid)
output_array[entry, :] = interpolated_spectrum
if '/' in image_name:
image_name = image_name.split('/')[-1]
return ids, output_array, image_name
import sys
if __name__ == '__main__':
try:
if sys.argv[1] == 'long1d':
test_multi_spec = False
elif sys.argv[1] == 'multi':
test_multi_spec = True
else:
raise ValueError('python test.py [long1d/multi]\n'+\
'multi will be tested this time')
except:
test_multi_spec = True
app = QApplication(sys.argv)
window = MainWindow(dwvl_knots=10.0)
if test_multi_spec:
HD122563R = readmultispec.readmultispec('HD122563R.fits')
window.input_multi_data(\
HD122563R['wavelen'][0:5],HD122563R['flux'][0:5],\
output='HD122563Rn_testmulti.csv',
output_multi_head='HD122563_multi/')
else:
HD122563UVES = Table.read('./HD122563_UVES.fits')
test_region = (6400 < HD122563UVES['WAVE'][0]) & (
HD122563UVES['WAVE'][0] < 6800)
window.input_long1d(\
HD122563UVES['WAVE'][0][test_region],HD122563UVES['FLUX'][0][test_region],\
output='HD122563_UVESn_testlong1d.csv')
window.show()
sys.exit(app.exec_())
def ReadFits(datafile, errors=False, extensions=False, x=None, y=None, cont=None, return_aps=False, debug=False):
"""
Read a fits file. If extensions=False, it assumes IRAF's multispec format.
Otherwise, it assumes the file consists of several fits extensions with
binary tables, with the table names given by the x,y,cont, and errors keywords.
See ReadExtensionFits for a convenience function that assumes my standard names
"""
if debug:
print "Reading in file %s: " % datafile
if extensions:
# This means the data is in fits extensions, with one order per extension
# At least x and y should be given (and should be strings to identify the field in the table record array)
if type(x) != str:
x = raw_input("Give name of the field which contains the x array: ")
if type(y) != str:
y = raw_input("Give name of the field which contains the y array: ")
orders = []
hdulist = pyfits.open(datafile)
if cont == None:
if not errors:
for i in range(1, len(hdulist)):
data = hdulist[i].data
xypt = DataStructures.xypoint(x=data.field(x), y=data.field(y))
orders.append(xypt)
else:
if type(errors) != str:
errors = raw_input("Give name of the field which contains the errors/sigma array: ")
for i in range(1, len(hdulist)):
data = hdulist[i].data
xypt = DataStructures.xypoint(x=data.field(x), y=data.field(y), err=data.field(errors))
orders.append(xypt)
elif type(cont) == str:
if not errors:
for i in range(1, len(hdulist)):
data = hdulist[i].data
xypt = DataStructures.xypoint(x=data.field(x), y=data.field(y), cont=data.field(cont))
orders.append(xypt)
else:
if type(errors) != str:
errors = raw_input("Give name of the field which contains the errors/sigma array: ")
for i in range(1, len(hdulist)):
data = hdulist[i].data
xypt = DataStructures.xypoint(x=data.field(x), y=data.field(y), cont=data.field(cont),
err=data.field(errors))
orders.append(xypt)
else:
# Data is in multispec format rather than in fits extensions
# Call Rick White's script
try:
retdict = multispec.readmultispec(datafile, quiet=not debug)
except ValueError:
warnings.warn("Wavelength not found in file %s. Using a pixel grid instead!" % datafile)
hdulist = pyfits.open(datafile)
data = hdulist[0].data
hdulist.close()
numpixels = data.shape[-1]
numorders = data.shape[-2]
wave = np.array([np.arange(numpixels) for i in range(numorders)])
retdict = {'flux': data,
'wavelen': wave,
'wavefields': np.zeros(data.shape)}
# Check if wavelength units are in angstroms (common, but I like nm)
hdulist = pyfits.open(datafile)
header = hdulist[0].header
hdulist.close()
wave_factor = 1.0 #factor to multiply wavelengths by to get them in nanometers
for key in sorted(header.keys()):
if "WAT1" in key:
if "label=Wavelength" in header[key] and "units" in header[key]:
waveunits = header[key].split("units=")[-1]
if waveunits == "angstroms" or waveunits == "Angstroms":
# wave_factor = u.nm/u.angstrom
wave_factor = u.angstrom.to(u.nm)
if debug:
print "Wavelength units are Angstroms. Scaling wavelength by ", wave_factor
if errors == False:
numorders = retdict['flux'].shape[0]
else:
numorders = retdict['flux'].shape[1]
orders = []
for i in range(numorders):
wave = retdict['wavelen'][i] * wave_factor
if errors == False:
flux = retdict['flux'][i]
err = np.ones(flux.size) * 1e9
err[flux > 0] = np.sqrt(flux[flux > 0])
else:
if type(errors) != int:
errors = int(raw_input("Enter the band number (in C-numbering) of the error/sigma band: "))
flux = retdict['flux'][0][i]
err = retdict['flux'][errors][i]
cont = FittingUtilities.Continuum(wave, flux, lowreject=2, highreject=4)
orders.append(DataStructures.xypoint(x=wave, y=flux, err=err, cont=cont))
if return_aps:
# Return the aperture wavefields too
orders = [orders, retdict['wavefields']]
return orders
def MakeXYpoints(datafile, errors=False, extensions=False, x=None, y=None, cont=None):
print "Reading in file %s: " % datafile
if extensions:
#This means the data is in fits extensions, with one order per extension
#At least x and y should be given (and should be strings to identify the field in the table record array)
if type(x) != str:
x = raw_input("Give name of the field which contains the x array: ")
if type(y) != str:
y = raw_input("Give name of the field which contains the y array: ")
orders = []
hdulist = pyfits.open(datafile)
if cont == None:
if not errors:
for i in range(1, len(hdulist)):
data = hdulist[i].data
xypt = DataStructures.xypoint(x=data.field(x), y=data.field(y))
orders.append(xypt)
else:
if type(errors) != str:
errors = raw_input("Give name of the field which contains the errors/sigma array: ")
for i in range(1, len(hdulist)):
data = hdulist[i].data
xypt = DataStructures.xypoint(x=data.field(x), y=data.field(y), err=data.field(errors))
orders.append(xypt)
elif type(cont) == str:
if not errors:
for i in range(1, len(hdulist)):
data = hdulist[i].data
xypt = DataStructures.xypoint(x=data.field(x), y=data.field(y), cont=data.field(cont))
orders.append(xypt)
else:
if type(errors) != str:
errors = raw_input("Give name of the field which contains the errors/sigma array: ")
for i in range(1, len(hdulist)):
data = hdulist[i].data
xypt = DataStructures.xypoint(x=data.field(x), y=data.field(y), cont=data.field(cont),
err=data.field(errors))
orders.append(xypt)
else:
#Data is in multispec format rather than in fits extensions
#Call Rick White's script
retdict = multispec.readmultispec(datafile)
#Check if wavelength units are in angstroms (common, but I like nm)
header = pyfits.getheader(datafile)
wave_factor = 1.0 #factor to multiply wavelengths by to get them in nanometers
for key in sorted(header.keys()):
if "WAT1" in key:
if "label=Wavelength" in header[key] and "units" in header[key]:
units = header[key].split("units=")[-1]
if units == "angstroms" or units == "Angstroms":
wave_factor = units.angstroms.to(units.nm)
print "Wavelength units are Angstroms. Scaling wavelength by ", wave_factor
if errors == False:
numorders = retdict['flux'].shape[0]
else:
numorders = retdict['flux'].shape[1]
print "There are %i orders" % numorders
orders = []
for i in range(numorders):
wave = retdict['wavelen'][i] * wave_factor
if errors == False:
flux = retdict['flux'][i]
err = np.ones(flux.size) * 1e9
err[flux > 0] = np.sqrt(flux[flux > 0])
else:
if type(errors) != int:
errors = int(raw_input("Enter the band number (in C-numbering) of the error/sigma band: "))
flux = retdict['flux'][0][i]
err = retdict['flux'][errors][i]
cont = FittingUtilities.Continuum(wave, flux, lowreject=2, highreject=4)
orders.append(DataStructures.xypoint(x=wave, y=flux, err=err, cont=cont))
return orders
