spex60

IRTF SpeX instrument reduction for the 60 arcsec slit in low-resolution mode.

Nominal example

Process calibration (flats, arcs) data. Files will automatically be grouped and results saved to the directory "cal-YYYYMMDD":

from glob import glob
from spex60 import Prism60

spex = Prism60()
spex.process_cal(glob('bigdog/flat*.fits'))
spex.process_cal(glob('bigdog/arc*.fits'))

Reduce telluric calibrator.

import os
from glob import glob
from spex60 import Prism60

# generally, file lists need to be sorted (allows for ABBA sequence finding)
files = sorted(glob('bigdog/spec*.fits'))

spex = Prism60()
spex.load_flat('cal-20151229/flat-00029-00033.fits')
spex.load_wavecal('cal-20151229/wavecal-00034.fits')
stack, var, headers = spex.read_numbered(files, between=[19, 28], pair=True)

# median combine
im, var, header = spex.median_combine(stack, variances, headers, axis=0)

# save image
if not os.path.exists('rx-20151229'):
	os.mkdir('rx-20151229')

spex.save_image(im, var, header, 'rx-20151229/hd3266a.fits')

Extract spectrum of telluric calibrator:

rap = 30
bgap = 50, 100

spex.peak(im, ex_rap=rap, bgap=bgap)
spex.trace(im)
spex.extract(im, header, rap, bgap=bgap)

Reduce target images:

spex.load_flat('cal-20151229/flat-00013-00017.fits')
spex.load_wavecal('cal-20151229/wavecal-00018.fits')
stack, variances, headers = spex.read_numbered(
	files, between=[3, 12], pair=True)
im, var, header = spex.median_combine(stack, variances, headers, axis=0)
spex.save_image(im, var, header, 'rx-20151229/c2013x1.fits')

Check the aperture definitions for the object (and revise as needed):
```
bgap = 100, 130
spex.peak(stack[0], ex_rap=rap, bgap=bgap)
```

Extract target spectra, file-by-file, using the trace from the telluric standard. Append extracted spectra to the SpeX data object, keeping the already extracted telluric standard:

for i in range(len(stack)):
	spex.peak(stack[i], ex_rap=rap, bgap=bgap, plot=False)
	spex.extract(stack[i], headers[i], rap, var=variances[i], bgap=bgap, append=True)

Plot raw spectra:

import matplotlib.pyplot as plt

fig = plt.figure()
for i in range(len(spex.spec)):
	plt.plot(spex.wave[i], spex.spec[i])
plt.yscale('log')

Save profiles and raw spectra:

if not os.path.exists('spex-20151229'):
    os.mkdir('spec-20151229')
spex.save_extracted(path='spec-20151229')

Combine target spectra, plot, and save:

import numpy as np
wave = np.ma.mean(spex.wave[1:], 0)
spec, var, h = spex.median_combine(spex.spec[1:], spex.var[1:], spex.h[1:])
plt.plot(wave, spec, color='k')

mkelley/spex60

spex60

Nominal example