"""
import sys
import matplotlib.pyplot as plt
from matplotlib import cm
from astropy.io import fits
sys.path.insert(0, '../dark_current_model')
import dark_models
if not 'DARKS' in globals():
DARK0 = dark_models.get_dark_map('2013191')
STAR = fits.open('obs890_adat41.fits')[1].data
DARKS = {}
DARKS[2014] = dark_models.degrade_ccd(DARK0, 0.5, inplace=False)
for year in range(2015, 1999 + 25 + 1):
print "Creating year {} image".format(year)
DARKS[year] = dark_models.degrade_ccd(DARKS[year - 1], 1.0, inplace=False)
# 237 == 1
# 279 == 100
# dt = 42 degC => 10*2 => 21 degC per decade
STAR_IMG = STAR[400]['img_corr'] / 1.7 # e-/sec from a 1.7 sec readout
STAR_MAG = 10.3
SL = slice(400, 440)
def draw_star(year=2014, t_ccd=-16.0, mag=10.3, figure=1, savefig=False):
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
