def __init__(self, frac_total, survey_name=''):
super(Fraction_of_obs_basis_function, self).__init__()
self.survey_name = survey_name
self.frac_total = frac_total
self.survey_features['Ntot'] = features.N_obs_survey()
self.survey_features['N_survey'] = features.N_obs_survey(
note=self.survey_name)
def __init__(self, fractions=[0.000, 0.009, 0.017], delays=[0., 0.5, 1.5], survey_name=None):
if len(fractions) != len(delays):
raise ValueError('fractions and delays must be same length')
super(Soft_delay_basis_function, self).__init__()
self.delays = delays
self.survey_name = survey_name
self.survey_features['last_obs_self'] = features.Last_observation(survey_name=self.survey_name)
self.fractions = fractions
self.survey_features['Ntot'] = features.N_obs_survey()
self.survey_features['N_survey'] = features.N_obs_survey(note=self.survey_name)
def __init__(self, basis_functions, RA, dec, sequence='rgizy',
nvis=[20, 10, 20, 26, 20],
exptime=30., u_exptime=30., nexp=2, ignore_obs=None, survey_name='DD',
reward_value=None, readtime=2., filter_change_time=120.,
nside=None, flush_pad=30., seed=42, detailers=None):
super(Deep_drilling_survey, self).__init__(nside=nside, basis_functions=basis_functions,
detailers=detailers, ignore_obs=ignore_obs)
random.seed(a=seed)
self.ra = np.radians(RA)
self.ra_hours = RA/360.*24.
self.dec = np.radians(dec)
self.survey_name = survey_name
self.reward_value = reward_value
self.flush_pad = flush_pad/60./24. # To days
self.filter_sequence = []
if type(sequence) == str:
self.observations = []
for num, filtername in zip(nvis, sequence):
for j in range(num):
obs = empty_observation()
obs['filter'] = filtername
if filtername == 'u':
obs['exptime'] = u_exptime
else:
obs['exptime'] = exptime
obs['RA'] = self.ra
obs['dec'] = self.dec
obs['nexp'] = nexp
obs['note'] = survey_name
self.observations.append(obs)
else:
self.observations = sequence
# Let's just make this an array for ease of use
self.observations = np.concatenate(self.observations)
order = np.argsort(self.observations['filter'])
self.observations = self.observations[order]
n_filter_change = np.size(np.unique(self.observations['filter']))
# Make an estimate of how long a seqeunce will take. Assumes no major rotational or spatial
# dithering slowing things down.
self.approx_time = np.sum(self.observations['exptime']+readtime*self.observations['nexp'])/3600./24. \
+ filter_change_time*n_filter_change/3600./24. # to days
if self.reward_value is None:
self.extra_features['Ntot'] = features.N_obs_survey()
self.extra_features['N_survey'] = features.N_obs_survey(note=self.survey_name)
def __init__(self, frac_total, aggressive_fraction, time_needed=30., RA=0.,
ha_limits=None, survey_name='', time_jump=44., sun_alt_limit=-18.):
super(Look_ahead_ddf_basis_function, self).__init__()
if aggressive_fraction > frac_total:
raise ValueError('aggressive_fraction should be less than frac_total')
self.survey_name = survey_name
self.frac_total = frac_total
self.ra_hours = RA/360.*24.
self.HA_limits = np.array(ha_limits)
self.sun_alt_limit = str(int(sun_alt_limit)).replace('-', 'n')
self.time_jump = time_jump / 60. / 24. # To days
self.time_needed = time_needed / 60. / 24. # To days
self.aggressive_fraction = aggressive_fraction
self.survey_features['Ntot'] = features.N_obs_survey()
self.survey_features['N_survey'] = features.N_obs_survey(note=self.survey_name)