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Output standard extraction as well #119

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a357963
Output standard extraction as well
joshwalawender May 22, 2018
dcd435d
shorten label to minimize overlap
joshwalawender May 22, 2018
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62 changes: 52 additions & 10 deletions MOSFIRE/Extract.py
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Original file line number Diff line number Diff line change
Expand Up @@ -254,10 +254,9 @@ def plot_apertures(self):
facecolor='y', alpha=0.3,
)
pl.text(ap['position']-ap['width']+1,
self.ydata.max() + 0.05*yspan,
'position={:.0f}\nwidth={:.0f}'.format(ap['position'],
ap['width']),
)
self.ydata.max() + 0.05*yspan,
f"pos={ap['position']:.0f}\nwidth={ap['width']:.0f}",
)
pl.draw()
pl.show()

Expand Down Expand Up @@ -497,6 +496,8 @@ def optimal_extraction(image, variance_image, aperture_table,

spectra = []
variances = []
spectra_std = []
variances_std = []
for i,row in enumerate(aperture_table):
pos = row['position']
width = int(row['width'])
Expand All @@ -511,6 +512,8 @@ def optimal_extraction(image, variance_image, aperture_table,
mask=np.isnan(spectra2D[ymin:ymax,:]))
info(' Performing standard extraction')
f_std, V_std = standard_extraction(DmS, V)
spectra_std.append(f_std)
variances_std.append(V_std)
info(' Forming initial spatial profile')
P_init_data = np.array([row/f_std for row in DmS])
P_init = np.ma.MaskedArray(data=P_init_data,\
Expand All @@ -533,19 +536,41 @@ def optimal_extraction(image, variance_image, aperture_table,
spectra = np.ma.MaskedArray(data=np.array(spectra), mask=mask)
variances = np.ma.MaskedArray(data=np.array(variances), mask=mask)

mask_std = np.isnan(np.array(spectra_std)) | np.isnan(np.array(variances_std))
spectra_std = np.ma.MaskedArray(data=np.array(spectra_std), mask=mask_std)
variances_std = np.ma.MaskedArray(data=np.array(variances_std), mask=mask_std)

for i,row in enumerate(aperture_table):
hdulist = fits.HDUList([])
if plotfileout:
fig = pl.figure(figsize=(16,6))
fig = pl.figure(figsize=(16,10))
wunit = getattr(u, w.to_header()['CUNIT1'])

sp = spectra[i]
hdulist.append(fits.PrimaryHDU(data=sp.filled(0), header=header))
hdulist[0].header['APPOS'] = row['position']
hdulist[0].header['COMMENT'] = 'OPTIMALLY EXTRACTED SPECTRUM'

var = variances[i]
hdulist.append(fits.ImageHDU(data=var.filled(0), header=header))
hdulist[1].header['APPOS'] = row['position']
hdulist[1].header['COMMENT'] = 'VARIANCE DATA'

sp_std = spectra_std[i]
hdulist.append(fits.PrimaryHDU(data=sp_std.filled(0), header=header))
hdulist[2].header['APPOS'] = row['position']
hdulist[2].header['COMMENT'] = 'STANDARD EXTRACTION SPECTRUM'

var_std = variances_std[i]
hdulist.append(fits.ImageHDU(data=var_std.filled(0), header=header))
hdulist[3].header['APPOS'] = row['position']
hdulist[3].header['COMMENT'] = 'VARIANCE DATA FOR STANDARD EXTRACTION'

if plotfileout:
sigma = np.sqrt(variances[i])
pix = np.arange(0,sp.shape[0],1)
wavelengths = w.wcs_pix2world(pix,1)[0]*wunit.to(u.micron)*u.micron
pl.subplot(2,1,1)
sigma = np.sqrt(variances[i])
fillmin = sp-sigma
fillmax = sp+sigma
mask = np.isnan(fillmin) | np.isnan(fillmax) | np.isnan(sp)
Expand All @@ -557,21 +582,38 @@ def optimal_extraction(image, variance_image, aperture_table,
pl.plot(wavelengths, sp, 'k-',
label='Spectrum for Aperture {} at {:.0f}'.format(i,
row['position']))
pl.title('Optimal Spectral Extraction')
pl.xlabel('Wavelength (microns)')
pl.ylabel('Flux (e-/sec)')
pl.xlim(wavelengths.value.min(),wavelengths.value.max())
pl.ylim(0,1.05*sp.max())
pl.legend(loc='best')

pl.subplot(2,1,2)
sigma_std = np.sqrt(variances_std[i])
fillmin_std = sp_std-sigma_std
fillmax_std = sp_std+sigma_std
mask_std = np.isnan(fillmin_std) | np.isnan(fillmax_std) | np.isnan(sp_std)
pl.fill_between(wavelengths[~mask_std], fillmin_std[~mask_std], fillmax_std[~mask_std],\
label='uncertainty',\
facecolor='black', alpha=0.2,\
linewidth=0,\
interpolate=True)
pl.plot(wavelengths, sp_std, 'k-',
label='Spectrum for Aperture {} at {:.0f}'.format(i,
row['position']))
pl.title('Standard Extraction')
pl.xlabel('Wavelength (microns)')
pl.ylabel('Flux (e-/sec)')
pl.xlim(wavelengths.value.min(),wavelengths.value.max())
pl.ylim(0,1.05*sp_std.max())
pl.legend(loc='best')

bn, ext = os.path.splitext(plotfileout)
plotfilename = '{}_{:02d}{}'.format(bn, i, ext)
pl.savefig(plotfilename, bbox_inches='tight')
pl.close(fig)

var = variances[i]
hdulist.append(fits.ImageHDU(data=var.filled(0), header=header))
hdulist[1].header['APPOS'] = row['position']
hdulist[1].header['COMMENT'] = 'VARIANCE DATA'
if fitsfileout:
bn, ext = os.path.splitext(fitsfileout)
fitsfilename = '{}_{:02d}{}'.format(bn, i, ext)
Expand Down