def acq_success_prob(date=None, t_ccd=-19.0, mag=10.0, color=0.6, spoiler=False):
"""
Return probability of acquisition success for given date, temperature and mag.
Any of the inputs can be scalars or arrays, with the output being the result of
the broadcasted dimension of the inputs.
This is based on the dark model and acquisition success model presented
in the State of the ACA 2013, and subsequently updated to use a Probit
transform and separately fit B-V=1.5 stars. This is available in the
state_of_aca repo as fit_sota_model_probit.ipynb.
:param date: Date(s) (scalar or np.ndarray, default=NOW)
:param t_ccd: CD temperature(s) (degC, scalar or np.ndarray, default=-19C)
:param mag: Star magnitude(s) (scalar or np.ndarray, default=10.0)
:param color: Star color(s) (scalar or np.ndarray, default=0.6)
:param spoil: Star spoiled (boolean or np.ndarray, default=False)
:returns: Acquisition success probability(s)
"""
from mica.archive.aca_dark.dark_model import get_warm_fracs
date = DateTime(date).secs
is_scalar, dates, t_ccds, mags, colors, spoilers = broadcast_arrays(date, t_ccd, mag,
color, spoiler)
spoilers = spoilers.astype(bool)
warm_fracs = []
for date, t_ccd in zip(dates.ravel(), t_ccds.ravel()):
warm_frac = get_warm_fracs(WARM_THRESHOLD, date=date, T_ccd=t_ccd)
warm_fracs.append(warm_frac)
warm_frac = np.array(warm_fracs).reshape(dates.shape)
probs = model_acq_success_prob(mags, warm_fracs, colors)
p_0p7color = .4294 # probability multiplier for a B-V = 0.700 star (REF?)
p_spoiler = .9241 # probability multiplier for a search-spoiled star (REF?)
# If the star is brighter than 8.5 or has a calculated probability
# higher than the max_star_prob, clip it at that value
max_star_prob = .985
probs[mags < 8.5] = max_star_prob
probs[colors == 0.7] *= p_0p7color
probs[spoilers] *= p_spoiler
probs = probs.clip(1e-6, max_star_prob)
# Return probabilities. The [()] getitem at the end will flatten a
# scalar array down to a pure scalar.
return probs[0] if is_scalar else probs
def get_interval_data(intervals, times, ccd_temp, obsreqs=None):
"""
Determine the max temperature and mean offsets over each interval.
If the OR list is supplied (in the obsreqs dictionary) the ACA offsets will
also be calculated for each interval and included in the returned data.
:param intervals: list of dictionaries describing obsid/catalog intervals
:param times: times of the temperature samples
:param ccd_temp: ccd temperature values
:param obsreqs: optional dictionary of OR list from parse_cm.or_list
:returns: dictionary (keyed by obsid) of intervals with max ccd_temps
"""
obstemps = {}
for interval in intervals:
# treat the model samples as temperature intervals
# and find the max during each obsid npnt interval
obs = {'ccd_temp': None}
obs.update(interval)
stop_idx = 1 + np.searchsorted(times, interval['tstop'])
start_idx = -1 + np.searchsorted(times, interval['tstart'])
ok_temps = ccd_temp[start_idx:stop_idx]
ok_times = times[start_idx:stop_idx]
# If there are no good samples, put the times in the output dict and go to the next interval
if len(ok_temps) == 0:
obstemps[str(interval['obsid'])] = obs
continue
obs['ccd_temp'] = np.max(ok_temps)
obs['n100_warm_frac'] = dark_model.get_warm_fracs(
100, interval['tstart'], np.max(ok_temps))
# If we have an OR list, the obsid is in that list, and the OR list has zero-offset keys
if (obsreqs is not None and interval['obsid'] in obsreqs
and 'chip_id' in obsreqs[interval['obsid']]):
obsreq = obsreqs[interval['obsid']]
ddy, ddz = get_aca_offsets(obsreq['detector'],
obsreq['chip_id'],
obsreq['chipx'],
obsreq['chipy'],
time=ok_times,
t_ccd=ok_temps)
obs['aca_offset_y'] = np.mean(ddy)
obs['aca_offset_z'] = np.mean(ddz)
obstemps[str(interval['obsid'])] = obs
return obstemps
