def obsid_probabilities():
per_obsid_two = {-7: [], -11: [], -14: []}
per_obsid_three = {-7: [], -11: [], -14: []}
per_obsid_four = {-7: [], -11: [], -14: []}
target_year = 2018
acq_stars = np.load("/proj/sot/ska/data/acq_stat_reports/acq_stats_with_temp_and_warmpix.npy")
warm_threshold = 100
# acq_mag_limit = {}
for t_ccd_max, offset, color in izip((-14, -11, -7), (0, 0.0025, 0.005), ("r", "g", "b")):
# load these from np save files if they exists
if (
os.path.exists("acq_two_{}.npy".format(t_ccd_max))
& os.path.exists("acq_three_{}.npy".format(t_ccd_max))
& os.path.exists("acq_four_{}.npy".format(t_ccd_max))
):
per_obsid_two[t_ccd_max] = np.load("acq_two_{}.npy".format(t_ccd_max))
per_obsid_three[t_ccd_max] = np.load("acq_three_{}.npy".format(t_ccd_max))
per_obsid_four[t_ccd_max] = np.load("acq_four_{}.npy".format(t_ccd_max))
continue
dates, t_ccds = dark_models.ccd_temperature_model(
t_ccd_max, start="{}:001".format(target_year), stop="{}:001".format(target_year), n=1
)
warm_frac = dark_models.get_warm_fracs(warm_threshold, date=dates.secs[0], T_ccd=t_ccds[0]) + offset
for time, t_ccd in izip(dates.secs, t_ccds):
warm_frac = dark_models.get_warm_fracs(warm_threshold, date=time, T_ccd=t_ccd)
# mag_limit = optimize.brentq(acq_fail_prob, 6.0, 12.0, args=(warm_frac + offset, 0.5))
# acq_mag_limit[t_ccd_max] = mag_limit
obsids = np.unique(acq_stars["obsid"])
for obsid in np.unique(obsids):
# ignore cal for the time being
if obsid > 40000:
continue
acq = acq_stars[(acq_stars["obsid"] == obsid)]
# ignore multi-obi
if len(np.unique(acq["obi"])) > 1:
continue
if len(acq) < 8:
continue
acq_probs = [1 - acq_fail_prob(star["mag"], warm_frac) for star in acq]
prob_n = prob_n_stars(acq_probs)
cum = np.cumsum(prob_n)
per_obsid_two[t_ccd_max].append(cum[2])
per_obsid_three[t_ccd_max].append(cum[3])
per_obsid_four[t_ccd_max].append(cum[4])
np.save("acq_two_{}".format(t_ccd_max), per_obsid_two[t_ccd_max])
np.save("acq_three_{}".format(t_ccd_max), per_obsid_three[t_ccd_max])
np.save("acq_four_{}".format(t_ccd_max), per_obsid_three[t_ccd_max])
return per_obsid_two, per_obsid_three, per_obsid_four
def plot_warm_frac_future():
"""
Plot the future warm pixel fraction from the 2013 baseline dark current model
for the N100 case. Include observed fractions from dark current calibrations.
"""
warm_threshold = 100.
dates, pred_warm_fracs, obs_warm_fracs = get_warm_frac(warm_thresholds=[warm_threshold])
plt.close(1)
plt.figure(1, figsize=(6, 4))
color = 'g'
plot_cxctime(dates, obs_warm_fracs[warm_threshold], color + '.')
plot_cxctime(dates, pred_warm_fracs[warm_threshold], color + '-')
for t_ccd_max, offset, color in izip((-14, -11, -7), (0, 0.005, 0.01), ('r', 'g', 'b')):
dates, t_ccds = dark_models.ccd_temperature_model(t_ccd_max, start='2014:001',
stop='2018:001', n=20)
warm_fracs = []
for time, t_ccd in izip(dates.secs, t_ccds):
warm_frac = dark_models.get_warm_fracs(warm_threshold, date=time, T_ccd=t_ccd)
warm_fracs.append(warm_frac + offset)
plot_cxctime(dates.secs, warm_fracs, color + '-',
label='T_ccd < {} C'.format(t_ccd_max), linewidth=1.5)
x0, x1 = plt.xlim()
dx = (x1 - x0) * 0.03
plt.xlim(x0 - dx, x1 + dx)
plt.grid()
plt.legend(loc='best', fontsize=12)
plt.title('Warm pixel fraction vs. time')
plt.ylabel('N > 100 warm pixel fraction')
plt.tight_layout()
plt.savefig('warm_frac_future.png')
def get_warm_frac(dateglob='20?????', outroot=None, warm_thresholds=None):
"""
Get the warm pixel fraction from the 2013 baseline dark current model.
"""
if warm_thresholds is None:
warm_thresholds = [50., 100., 200.]
darkfiles = glob('aca_dark_cal/{}'.format(dateglob))
pred_warm_fracs = {x: list() for x in warm_thresholds}
obs_warm_fracs = {x: list() for x in warm_thresholds}
dates = []
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
print 'Processing', darkdir, date
hdus = fits.open(os.path.join(darkdir, 'imd.fits'))
dark = hdus[0].data.flatten()
hdus.close()
cal = cals_map[date]
warm_fracs = dark_models.get_warm_fracs(warm_thresholds, date=cal['day'], T_ccd=cal['temp'])
dates.append(DateTime(cal['day']).secs)
for warm_threshold, warm_frac in zip(warm_thresholds, warm_fracs):
pred_warm_fracs[warm_threshold].append(warm_frac)
obs_warm_fracs[warm_threshold].append(np.sum(dark > warm_threshold) / 1024.0 ** 2)
return dates, pred_warm_fracs, obs_warm_fracs
def plot_limiting_mag():
"""
Plot the future limiting magnitude for three cases of max T_ccd (-14, -11, -7).
"""
warm_threshold = 100.
plt.close(1)
plt.figure(1, figsize=(6, 4))
color = 'g'
for t_ccd_max, offset, color in izip((-14, -11, -7), (0, 0.0025, 0.005), ('r', 'g', 'b')):
dates, t_ccds = dark_models.ccd_temperature_model(t_ccd_max, start='2014:001',
stop='2018:001', n=20)
warm_fracs = []
for time, t_ccd in izip(dates.secs, t_ccds):
warm_frac = dark_models.get_warm_fracs(warm_threshold, date=time, T_ccd=t_ccd)
warm_fracs.append(warm_frac + offset)
n100 = np.array(warm_fracs)
mag_limit = np.log10(0.5 / n100) / 1.18 + 10.09
plot_cxctime(dates.secs, mag_limit, color + '-',
label='T_ccd < {} C'.format(t_ccd_max), linewidth=1.5)
x0, x1 = plt.xlim()
dx = (x1 - x0) * 0.03
plt.xlim(x0 - dx, x1 + dx)
plt.grid()
plt.legend(loc='best', fontsize=12)
plt.title('ACA Limiting magnitude vs. time')
plt.ylabel('ACA Mag')
plt.tight_layout()
plt.savefig('limiting_mag.png')
def plot_acq_mag_limit():
"""
Plot the future acq star limiting magnitude for three cases of
max T_ccd (-14, -11, -7).
"""
warm_threshold = 100.
plt.close(1)
plt.figure(1, figsize=(6, 4))
color = 'g'
for t_ccd_max, offset, color in izip((-14, -11, -7), (0, 0.0025, 0.005), ('r', 'g', 'b')):
dates, t_ccds = dark_models.ccd_temperature_model(t_ccd_max, start='2014:001',
stop='2018:001', n=20)
warm_fracs = []
for time, t_ccd in izip(dates.secs, t_ccds):
warm_frac = dark_models.get_warm_fracs(warm_threshold, date=time, T_ccd=t_ccd)
warm_fracs.append(warm_frac + offset)
mag_limits = []
for warm_frac in warm_fracs:
mag_limit = optimize.brentq(acq_success_prob, 6.0, 12.0, args=(warm_frac, 0.5))
mag_limits.append(mag_limit)
plot_cxctime(dates.secs, mag_limits, color + '-',
label='T_ccd < {} C'.format(t_ccd_max), linewidth=1.5)
x0, x1 = plt.xlim()
dx = (x1 - x0) * 0.03
plt.xlim(x0 - dx, x1 + dx)
plt.grid()
plt.legend(loc='best', fontsize=12)
plt.title('Acquisition magnitude limit vs. time')
plt.ylabel('ACA Mag')
plt.tight_layout()
plt.savefig('acq_mag_limit.png')
def save_stars_per_catalog():
target_year = 2018
guide_stars = np.load('/proj/sot/ska/data/gui_stat_reports/guide_stats_with_temp_and_warmpix.npy')
acq_stars = np.load('/proj/sot/ska/data/acq_stat_reports/acq_stats_with_temp_and_warmpix.npy')
warm_threshold = 100
# guide stars
gui_mag_limit = {}
for t_ccd_max, offset, color in izip((-14, -11, -7), (0, 0.0025, 0.005), ('r', 'g', 'b')):
dates, t_ccds = dark_models.ccd_temperature_model(t_ccd_max,
start='{}:001'.format(target_year),
stop='{}:001'.format(target_year), n=1)
warm_frac = (dark_models.get_warm_fracs(warm_threshold, date=dates.secs[0], T_ccd=t_ccds[0])
+ offset)
gui_mag_limit[t_ccd_max] = np.log10(0.5 / warm_frac) / 1.18 + 10.09
per_obsid_guide = { -7: [],
-11: [],
-14: [] }
obsids = np.unique(guide_stars['obsid'])
for obsid in obsids:
# ignore cal for the time being
if obsid > 40000:
continue
gs = guide_stars[(guide_stars['obsid'] == obsid) & (guide_stars['type'] != 'FID') & (guide_stars['type'] != 'MON')]
# ignore multi-obi
if len(np.unique(gs['obi'])) > 1:
continue
for mag in gui_mag_limit:
n_bright_stars = len(np.flatnonzero(gs['mag_exp'] < gui_mag_limit[mag]))
per_obsid_guide[mag].append(n_bright_stars)
print obsid, mag, n_bright_stars
per_obsid_acq = { -7: [],
-11: [],
-14: [] }
acq_mag_limit = {}
for t_ccd_max, offset, color in izip((-14, -11, -7), (0, 0.0025, 0.005), ('r', 'g', 'b')):
dates, t_ccds = dark_models.ccd_temperature_model(t_ccd_max,
start='{}:001'.format(target_year),
stop='{}:001'.format(target_year), n=1)
warm_frac = (dark_models.get_warm_fracs(warm_threshold, date=dates.secs[0], T_ccd=t_ccds[0])
+ offset)
for time, t_ccd in izip(dates.secs, t_ccds):
warm_frac = dark_models.get_warm_fracs(warm_threshold, date=time, T_ccd=t_ccd)
mag_limit = optimize.brentq(acq_success_prob, 6.0, 12.0, args=(warm_frac + offset, 0.5))
acq_mag_limit[t_ccd_max] = mag_limit
obsids = np.unique(acq_stars['obsid'])
for obsid in obsids:
# ignore cal for the time being
if obsid > 40000:
continue
acq = acq_stars[(acq_stars['obsid'] == obsid)]
# ignore multi-obi
if len(np.unique(acq['obi'])) > 1:
continue
if len(acq) < 8:
continue
for mag in acq_mag_limit:
n_bright_stars = len(np.flatnonzero(acq['mag'] < acq_mag_limit[mag]))
per_obsid_acq[mag].append(n_bright_stars)
print obsid, mag, n_bright_stars
for mag in [-7, -11, -14]:
np.save('guide_{}_bright'.format(mag), per_obsid_guide[mag])
np.save('acq_{}_bright'.format(mag), per_obsid_acq[mag])