def make_darkhist(dateglob='20?????', plot=True, outroot=None, peak_norm=8.0, zodi_norm=False):
if plot:
plt.clf()
darkfiles = glob('aca_dark_cal/{}'.format(dateglob))
for darkdir in sorted(darkfiles):
date = re.search(r'(\d+)', darkdir).group(1)
if date not in cals_map:
print '{} not in cals map'.format(date)
continue
cal = cals_map[date]
print 'Processing', darkdir, date
hdus = fits.open(os.path.join(darkdir, 'imd.fits'))
dark = hdus[0].data.flatten()
hdus.close()
# Put in random gaussian noise to smooth things out at the low end
dark += np.random.normal(0.0, 0.8, size=1024 ** 2)
# Scale factor to adjust data to an effective temperature of t_ccd_ref.
# For t_ccds warmer than t_ccd_ref this scale factor is < 1, i.e. the
# observed dark current is made smaller to match what it would be at the
# lower reference temperature.
scale = 1.0 / dark_models.temp_scalefac(cal['temp'])
dark *= scale
if peak_norm is not None:
peak = get_peak(dark)
dark += peak_norm - peak
if zodi_norm:
dark -= cal['zodib']
print (' t_ccd={:.2f} scale={:.2f} peak={:.2f} zodib={:.2f}'
.format(cal['temp'], scale, peak, cal['zodib']))
y, xb = np.histogram(dark, darkbins.bins)
x = (xb[1:] + xb[:-1]) / 2
if plot:
plt.loglog(x, y)
plt.draw()
plt.show()
if outroot:
out = open('{}/{}.dat'.format(outroot, date), 'w')
for i in range(len(x)):
print >>out, x[i], y[i], 1 + y[i] * 0.1
out.close()
def draw_star(year=2014, t_ccd=-16.0, mag=10.3, figure=1, savefig=False):
year = int(year)
plt.close(figure)
plt.figure(figure, figsize=(6, 6))
img = dark_models.temp_scalefac(t_ccd) * DARKS[year][SL, SL]
nn = img.shape[0] / 2
img[nn - 3:nn + 3, nn - 3:nn + 3] += STAR_IMG * 10 ** ((STAR_MAG - mag) * 0.4)
plt.imshow(img, interpolation='nearest', cmap=cm.afmhot)
plt.colorbar()
plt.title('Year={} T_ccd={:.1f} Mag={:.1f}'.format(year, t_ccd, mag))
if savefig:
plt.savefig('star_{}_t{:.1f}_mag{:.1f}.png'.format(year, t_ccd, mag))
def plot_darkcal(darkfile, Tccd, plotsymcol='-', label=None, step=None):
"""
Plot a real dark cal, scaled from temp C{Tccd} to -19
@param darkfile: FITS file containing a dark current map image.
@param Tccd: CCD temperature (degC)
@param plotsymcol: Matplotlib symbol/color specifierr
@param label: Matplotlib plot label
@param step: Matplotlib plot step specifier
@return: C{(dark, x, y)}
- C{dark}: Dark current map (scaled to temperature C{Tccd})
- C{x, y}: Histogram values plotted
"""
hdus = pyfits.open(darkfile)
# Convert to an effective T_ccd = -19
dark = hdus[0].data.flatten() / dark_models.temp_scalefac(Tccd)
x, y = plot_dark(dark, label, plotsymcol, step=step)
return dark, x, y
def make_darkmaps(case='nominal', initial='pristine'):
"""Make dark current maps by degrading the CCD using the best-fit model
from fit_evol.py"""
date0 = DateTime('1999-05-23T18:00:00')
if initial == 'pristine':
# Start from a synthetic pristine CCD. Probably no good reason to use this,
# prefer starting from the pre-SSD-open dark cal on 1999223.
dark = dark_models.pristine_ccd()
darkcals = alldarkcals
elif re.match('from|zero', initial):
darkmap, year, doy = re.match(r'(\S+)(\d{4})(\d{3})', initial).groups()
yeardoy = year + ':' + doy
date0 = DateTime(yeardoy)
darkcals = alldarkcals[alldarkcals['date'] > yeardoy]
if darkmap == 'zero':
dark = dark_models.zero_dark_ccd()
else:
ok = alldarkcals['date'] == yeardoy
tccd = alldarkcals[ok]['tccd'][0]
scalefac = dark_models.temp_scalefac(tccd)
print 'Scaling dark cal', yeardoy, 'by', '%.2f' % scalefac
hdus = pyfits.open(os.path.join('aca_dark_cal', year+doy, 'imd.fits'))
dark = hdus[0].data.flatten() / scalefac
warmpix = dict(year=[], n100=[], n200=[], n2000=[])
print 'Calculating for', case, 'case starting from', initial, 'CCD'
# First evolve from date0 through last dark cal
dates = [x['date'] for x in darkcals] + [str(yr) + ':182' for yr in range(2009, 2020)]
datelast = date0
for datestr in dates:
date = DateTime(datestr)
datemx = date.mxDateTime
print date.date
# Evolve (degrade) the CCD dark map. 'dark' is for an effective T=-19 temperature.
dyear = (datemx - datelast.mxDateTime).days / yeardays
dark_models.degrade_ccd(dark, dyear)
# Determine actual or predicted CCD temperature
try:
ok = darkcals['date'] == datestr
tccd = darkcals[ok]['tccd'][0]
except IndexError:
if datemx.year > 2014:
if case == 'nominal':
tccd = -18
else:
tccd = -15
else:
tccd = -19
# Calculate dark map at actual temperature and possibly write out to file
scalefac = dark_models.temp_scalefac(tccd)
dark_tccd = dark * scalefac
outdir = os.path.join(case, initial)
if not os.path.exists(outdir):
os.makedirs(outdir)
outfile = os.path.join(outdir, '%04d%03d.fits' % (datemx.year, datemx.day_of_year))
if os.path.exists(outfile):
os.unlink(outfile)
hdu = pyfits.PrimaryHDU(np.float32(dark_tccd.reshape(1024,1024)))
hdu.writeto(outfile)
# Calculate the standard warm/hot pixel stats for prediction
warmpix['year'].append(datemx.year + datemx.day_of_year / yeardays)
warmpix['n100'].append(len(where(dark_tccd > 100)[0]))
warmpix['n200'].append(len(where(dark_tccd > 200)[0]))
warmpix['n2000'].append(len(where(dark_tccd > 2000)[0]))
datelast = date
## out = open('warmpix_' + case + '.dat', 'w')
## for i, n100 in enumerate(warmpix['n100']):
## print >>out, warmpix['year'][i], warmpix['n100'][i], warmpix['n200'][i], warmpix['n2000'][i]
## out.close()
return warmpix
