def simarc(arcdata, nspec=5000, nonuniform=False, testslit=False):
'''
Simulates an arc lamp exposure (CCD pixel to wavelength calibration).
Args:
arcdata: Table with columns VACUUM_WAVE and ELECTRONS
Options:
nspec: (int) number of spectra to simulate
nonuniform: (bool) include calibration screen non-uniformity
Returns: (wave, phot, fibermap)
wave: 1D[nwave] wavelengths in Angstroms
phot: 2D[nspec, nwave] photons observed by CCD (i.e. electrons)
fibermap: fibermap Table
Note: this bypasses specsim since we don't have an arclamp model in
surface brightness units; we only have electrons on the CCD. But it
does include the effect of varying fiber sizes.
TODO:
* add exptime support
* update inputs to surface brightness and DESI lamp lines (DESI-2674)
'''
wave = arcdata['VACUUM_WAVE']
phot = arcdata['ELECTRONS']
if testslit:
fibermap = astropy.table.Table(testslit_fibermap()[0:nspec])
else:
fibermap = astropy.table.Table(desispec.io.empty_fibermap(nspec))
fibermap.meta['FLAVOR'] = 'arc'
fibermap['OBJTYPE'] = 'ARC'
x = fibermap['X_TARGET']
y = fibermap['Y_TARGET']
r = np.sqrt(x**2 + y**2)
#- Determine ratio of fiber sizes relative to largest fiber
fiber_area = fiber_area_arcsec2(fibermap['X_TARGET'], fibermap['Y_TARGET'])
size_ratio = fiber_area / np.max(fiber_area)
#- Correct photons for fiber size
phot = np.tile(phot, nspec).reshape(nspec, len(wave))
phot = (phot.T * size_ratio).T
#- Apply calibration screen non-uniformity
if nonuniform:
ratio = _calib_screen_uniformity(radius=r)
assert np.all(ratio <= 1) and np.all(ratio > 0.99)
phot = (phot.T * ratio).T
return wave, phot, fibermap
def exposure_fiberflat(channel, expid, metric, outfile=None):
""" Generate an Exposure level plot of a FiberFlat metric
Args:
channel: str, e.g. 'b', 'r', 'z'
expid: int
metric: str, allowed entires are: ['meanflux']
Returns:
"""
from desispec.io.meta import find_exposure_night, findfile
from desispec.io.frame import read_meta_frame, read_frame
from desispec.io.fiberflat import read_fiberflat
from desimodel.focalplane import fiber_area_arcsec2
log = get_logger()
# Find exposure
night = find_exposure_night(expid)
# Search for frames with the input channel
frame0 = findfile('frame', camera=channel+'0', night=night, expid=expid)
if not os.path.exists(frame0):
log.fatal("No Frame 0 for channel={:s} and expid={:d}".format(channel, expid))
# Confirm frame is a Flat
fmeta = read_meta_frame(frame0)
assert fmeta['FLAVOR'].strip() == 'flat'
# Load up all the frames
x,y,metrics = [],[],[]
for wedge in range(10):
# Load
frame_file = findfile('frame', camera=channel+'{:d}'.format(wedge), night=night, expid=expid)
fiber_file = findfile('fiberflat', camera=channel+'{:d}'.format(wedge), night=night, expid=expid)
try:
frame = read_frame(frame_file)
except:
continue
else:
fiberflat = read_fiberflat(fiber_file)
fibermap = frame.fibermap
gdp = fiberflat.mask == 0
# X,Y
x.append([fibermap['X_TARGET']])
y.append([fibermap['Y_TARGET']])
area = fiber_area_arcsec2(x[-1], y[-1])
mean_area = np.mean(area)
# Metric
if metric == 'meanflux':
mean_norm = np.mean(fiberflat.fiberflat*gdp,axis=1) / (area / mean_area)
metrics.append([mean_norm])
# Cocatenate
x = np.concatenate(x)
y = np.concatenate(y)
metrics = np.concatenate(metrics)
# Plot
if outfile is None:
outfile='qa_{:08d}_{:s}_fiberflat.png'.format(expid, channel)
exposure_map(x,y,metrics, mlbl='Mean Flux',
title='Mean Flux for Exposure {:08d}, Channel {:s}'.format(expid, channel),
outfile=outfile)
def qa_fiberflat(param, frame, fiberflat):
""" Calculate QA on FiberFlat object
Args:
param: dict of QA parameters
frame: Frame
fiberflat: FiberFlat
Returns:
qadict: dict of QA outputs
Need to record simple Python objects for yaml (str, float, int)
"""
from desimodel.focalplane import fiber_area_arcsec2
log = get_logger()
# x, y, area
fibermap = frame.fibermap
x = fibermap['X_TARGET']
y = fibermap['Y_TARGET']
area = fiber_area_arcsec2(x, y)
mean_area = np.mean(area)
norm_area = area / mean_area
npix = fiberflat.fiberflat.shape[1]
# Normalize
norm_flat = fiberflat.fiberflat / np.outer(norm_area, np.ones(npix))
# Output dict
qadict = {}
# Check amplitude of the meanspectrum
qadict['MAX_MEANSPEC'] = float(np.max(fiberflat.meanspec))
if qadict['MAX_MEANSPEC'] < 100000:
log.warning("Low counts in meanspec = {:g}".format(
qadict['MAX_MEANSPEC']))
# Record chi2pdf
try:
qadict['CHI2PDF'] = float(fiberflat.chi2pdf)
except TypeError:
qadict['CHI2PDF'] = 0.
# N mask
qadict['N_MASK'] = int(np.sum(fiberflat.mask > 0))
if qadict['N_MASK'] > param['MAX_N_MASK']: # Arbitrary
log.warning("High rejection rate: {:d}".format(qadict['N_MASK']))
# Scale (search for low/high throughput)
gdp = fiberflat.mask == 0
rtio = (frame.flux / np.outer(norm_area, np.ones(npix))) / np.outer(
np.ones(fiberflat.nspec), fiberflat.meanspec)
scale = np.median(rtio * gdp, axis=1)
MAX_SCALE_OFF = float(np.max(np.abs(scale - 1.)))
fiber = int(np.argmax(np.abs(scale - 1.)))
qadict['MAX_SCALE_OFF'] = [MAX_SCALE_OFF, fiber]
if qadict['MAX_SCALE_OFF'][0] > param['MAX_SCALE_OFF']:
log.warning("Discrepant flux in fiberflat: {:g}, {:d}".format(
qadict['MAX_SCALE_OFF'][0], qadict['MAX_SCALE_OFF'][1]))
# Offset in fiberflat
qadict['MAX_OFF'] = float(np.max(np.abs(norm_flat - 1.)))
if qadict['MAX_OFF'] > param['MAX_OFF']:
log.warning("Large offset in fiberflat: {:g}".format(
qadict['MAX_OFF']))
# Offset in mean of fiberflat
#mean = np.mean(fiberflat.fiberflat*gdp,axis=1)
mean = np.mean(norm_flat * gdp, axis=1)
fiber = int(np.argmax(np.abs(mean - 1.)))
qadict['MAX_MEAN_OFF'] = [float(np.max(np.abs(mean - 1.))), fiber]
if qadict['MAX_MEAN_OFF'][0] > param['MAX_MEAN_OFF']:
log.warning("Discrepant mean in fiberflat: {:g}, {:d}".format(
qadict['MAX_MEAN_OFF'][0], qadict['MAX_MEAN_OFF'][1]))
# RMS in individual fibers
rms = np.std(gdp * (norm_flat - np.outer(mean, np.ones(fiberflat.nwave))),
axis=1)
#rms = np.std(gdp*(fiberflat.fiberflat-
# np.outer(mean, np.ones(fiberflat.nwave))),axis=1)
fiber = int(np.argmax(rms))
qadict['MAX_RMS'] = [float(np.max(rms)), fiber]
if qadict['MAX_RMS'][0] > param['MAX_RMS']:
log.warning("Large RMS in fiberflat: {:g}, {:d}".format(
qadict['MAX_RMS'][0], qadict['MAX_RMS'][1]))
# Return
return qadict
def qa_fiberflat(param, frame, fiberflat):
""" Calculate QA on FiberFlat object
Args:
param: dict of QA parameters
frame: Frame
fiberflat: FiberFlat
Returns:
qadict: dict of QA outputs
Need to record simple Python objects for yaml (str, float, int)
"""
from desimodel.focalplane import fiber_area_arcsec2
log = get_logger()
# x, y, area
fibermap = frame.fibermap
x = fibermap['DESIGN_X']
y = fibermap['DESIGN_Y']
area = fiber_area_arcsec2(x, y)
mean_area = np.mean(area)
norm_area = area / mean_area
npix = fiberflat.fiberflat.shape[1]
# Normalize
norm_flat = fiberflat.fiberflat / np.outer(norm_area, np.ones(npix))
# Output dict
qadict = {}
# Check amplitude of the meanspectrum
qadict['MAX_MEANSPEC'] = float(np.max(fiberflat.meanspec))
if qadict['MAX_MEANSPEC'] < 100000:
log.warning("Low counts in meanspec = {:g}".format(qadict['MAX_MEANSPEC']))
# Record chi2pdf
try:
qadict['CHI2PDF'] = float(fiberflat.chi2pdf)
except TypeError:
qadict['CHI2PDF'] = 0.
# N mask
qadict['N_MASK'] = int(np.sum(fiberflat.mask > 0))
if qadict['N_MASK'] > param['MAX_N_MASK']: # Arbitrary
log.warning("High rejection rate: {:d}".format(qadict['N_MASK']))
# Scale (search for low/high throughput)
gdp = fiberflat.mask == 0
rtio = (frame.flux / np.outer(norm_area, np.ones(npix))) / np.outer(np.ones(fiberflat.nspec),fiberflat.meanspec)
scale = np.median(rtio*gdp,axis=1)
MAX_SCALE_OFF = float(np.max(np.abs(scale-1.)))
fiber = int(np.argmax(np.abs(scale-1.)))
qadict['MAX_SCALE_OFF'] = [MAX_SCALE_OFF, fiber]
if qadict['MAX_SCALE_OFF'][0] > param['MAX_SCALE_OFF']:
log.warning("Discrepant flux in fiberflat: {:g}, {:d}".format(
qadict['MAX_SCALE_OFF'][0], qadict['MAX_SCALE_OFF'][1]))
# Offset in fiberflat
qadict['MAX_OFF'] = float(np.max(np.abs(norm_flat-1.)))
if qadict['MAX_OFF'] > param['MAX_OFF']:
log.warning("Large offset in fiberflat: {:g}".format(qadict['MAX_OFF']))
# Offset in mean of fiberflat
#mean = np.mean(fiberflat.fiberflat*gdp,axis=1)
mean = np.mean(norm_flat*gdp,axis=1)
fiber = int(np.argmax(np.abs(mean-1.)))
qadict['MAX_MEAN_OFF'] = [float(np.max(np.abs(mean-1.))), fiber]
if qadict['MAX_MEAN_OFF'][0] > param['MAX_MEAN_OFF']:
log.warning("Discrepant mean in fiberflat: {:g}, {:d}".format(
qadict['MAX_MEAN_OFF'][0], qadict['MAX_MEAN_OFF'][1]))
# RMS in individual fibers
rms = np.std(gdp*(norm_flat - np.outer(mean, np.ones(fiberflat.nwave))),axis=1)
#rms = np.std(gdp*(fiberflat.fiberflat-
# np.outer(mean, np.ones(fiberflat.nwave))),axis=1)
fiber = int(np.argmax(rms))
qadict['MAX_RMS'] = [float(np.max(rms)), fiber]
if qadict['MAX_RMS'][0] > param['MAX_RMS']:
log.warning("Large RMS in fiberflat: {:g}, {:d}".format(
qadict['MAX_RMS'][0], qadict['MAX_RMS'][1]))
# Return
return qadict
def frame_fiberflat(outfil, qaframe, frame, fiberflat):
""" QA plots for fiber flat
Args:
outfil:
qaframe:
frame:
fiberflat:
Returns:
Stuff?
"""
from desimodel.focalplane import fiber_area_arcsec2
# Setup
fibermap = frame.fibermap
gdp = fiberflat.mask == 0
nfiber = len(frame.fibers)
xfiber = np.zeros(nfiber)
yfiber = np.zeros(nfiber)
for ii,fiber in enumerate(frame.fibers):
mt = np.where(fiber == fibermap['FIBER'])[0]
xfiber[ii] = fibermap['X_TARGET'][mt]
yfiber[ii] = fibermap['Y_TARGET'][mt]
area = fiber_area_arcsec2(xfiber,yfiber)
mean_area = np.mean(area)
jet = cm = plt.get_cmap('jet')
# Tile plot(s)
fig = plt.figure(figsize=(8, 5.0))
gs = gridspec.GridSpec(2,2)
# Mean Flatfield flux in each fiber
ax = plt.subplot(gs[0,0])
ax.xaxis.set_major_locator(plt.MultipleLocator(100.))
mean_flux = np.mean(frame.flux*gdp, axis=1) / fiber_area_arcsec2(xfiber,yfiber)
rms_mean = np.std(mean_flux)
med_mean = np.median(mean_flux)
#from xastropy.xutils import xdebug as xdb
#pdb.set_trace()
mplt = ax.scatter(xfiber, yfiber, marker='o', s=9., c=mean_flux, cmap=jet)
mplt.set_clim(vmin=med_mean-2*rms_mean, vmax=med_mean+2*rms_mean)
cb = fig.colorbar(mplt)
cb.set_label('Mean Flux')
# Mean
ax = plt.subplot(gs[0,1])
ax.xaxis.set_major_locator(plt.MultipleLocator(100.))
mean_norm = np.mean(fiberflat.fiberflat*gdp,axis=1) / (area/mean_area)
m2plt = ax.scatter(xfiber, yfiber, marker='o', s=9., c=mean_norm, cmap=jet)
#m2plt.set_clim(vmin=0.98, vmax=1.02)
cb = fig.colorbar(m2plt)
cb.set_label('Mean of Fiberflat')
# RMS
ax = plt.subplot(gs[1,0])
ax.xaxis.set_major_locator(plt.MultipleLocator(100.))
rms = np.std(gdp*(fiberflat.fiberflat-
np.outer(mean_norm, np.ones(fiberflat.nwave))),axis=1)
rplt = ax.scatter(xfiber, yfiber, marker='o', s=9., c=rms, cmap=jet)
#rplt.set_clim(vmin=0.98, vmax=1.02)
cb = fig.colorbar(rplt)
cb.set_label('RMS in Fiberflat')
# Meta text
ax2 = plt.subplot(gs[1,1])
ax2.set_axis_off()
show_meta(ax2, qaframe, 'FIBERFLAT', outfil)
"""
xlbl = 0.05
ylbl = 0.85
i0 = outfil.rfind('/')
ax2.text(xlbl, ylbl, outfil[i0+1:], color='black', transform=ax2.transAxes, ha='left')
yoff=0.10
for key in sorted(qaframe.data['FIBERFLAT']['METRICS'].keys()):
if key in ['QA_FIG']:
continue
# Show
ylbl -= yoff
ax2.text(xlbl+0.05, ylbl, key+': '+str(qaframe.data['FIBERFLAT']['METRICS'][key]),
transform=ax2.transAxes, ha='left', fontsize='x-small')
"""
# Finish
plt.tight_layout(pad=0.1,h_pad=0.0,w_pad=0.0)
_ = makepath(outfil)
plt.savefig(outfil)
plt.close()
print('Wrote QA SkyRes file: {:s}'.format(outfil))
