__all__ = ("ModelObservatory",)
import numpy as np
from rubin_scheduler.scheduler.model_observatory import ModelObservatory as oMO
from rubin_scheduler.site_models import Almanac
from rubin_scheduler.utils import SURVEY_START_MJD, _healbin
# Take the model observatory from the scheduler and
# subclass to expand to include satellite constellations
[docs]
class ModelObservatory(oMO):
"""A class to generate a realistic telemetry stream for the scheduler
Parameters
----------
nside : `int`
The healpix nside resolution
mjd_start : `float`
The MJD to start the observatory up at.
Uses util to lookup default if None.
alt_min : `float`
The minimum altitude to compute models at (degrees).
lax_dome : `bool`
Passed to observatory model. If true, allows dome creep.
cloud_limit : `float`
The limit to stop taking observations if the cloud model
returns something equal or higher
sim_to_o : `sim_targetoO`
If one would like to inject simulated ToOs into the telemetry stream.
seeing_db : `str`
If one would like to use an alternate seeing database,
filename of sqlite file
park_after : `float`
Park the telescope after a gap longer than park_after (minutes)
init_load_length : `int`
The length of pre-scheduled sky brighntess to load initially (days).
alt_limit : `float`
Altitude limit for considering satellite streaks (degrees).
satellite_dt : `float`
The time step to use for computing satellite positions (seconds).
sat_nside : `int`
The HEALpix nside to use for satellite streak maps.
constellation : `rubin_sim.satellite_constellations.Constellation`
The satellite constellation to use.
"""
def __init__(
self,
nside=None,
mjd_start=SURVEY_START_MJD,
seed=42,
alt_min=5.0,
lax_dome=True,
cloud_limit=0.3,
sim_to_o=None,
seeing_db=None,
park_after=10.0,
init_load_length=10,
sat_nside=64,
satellite_dt=10.0,
constellation=None,
alt_limit=20.0,
):
# Add in the new satellite information
self.alt_limit = np.radians(alt_limit)
self.satelite_dt = satellite_dt / 3600.0 / 24.0 # Seconds to days
self.sat_nside = sat_nside
self.constellation = constellation
# Need to do a little fiddle with the MJD since
# self.mjd needs self.night set now.
self.mjd_start = mjd_start
self.almanac = Almanac(mjd_start=self.mjd_start)
self.night = -1
# Run the rest of the regular __init__ steps
super().__init__(
nside=None,
mjd_start=self.mjd_start,
seed=seed,
alt_min=alt_min,
lax_dome=lax_dome,
cloud_limit=cloud_limit,
sim_to_o=sim_to_o,
seeing_db=seeing_db,
park_after=park_after,
init_load_length=init_load_length,
)
[docs]
def return_conditions(self):
"""
Returns
-------
conditions: `rubin_sim.scheduler.features.conditions`
Current conditions as simulated by the ModelObservatory.
"""
# Spot to put in satellite streak prediction maps
self.conditions.satellite_mjds = self.sat_mjds
self.conditions.satellite_maps = self.satellite_maps
# Run the regular return conditions
super().return_conditions()
# I guess running super() means return statement gets skipped?
return self.conditions
@property
def mjd(self):
return self._mjd
@mjd.setter
def mjd(self, value):
self._mjd = value
self.almanac_indx = self.almanac.mjd_indx(value)
# Update night if needed
if self.almanac.sunsets["night"][self.almanac_indx] != self.night:
self.night = self.almanac.sunsets["night"][self.almanac_indx]
# Update the satellite prediction map for the night
self._update_satellite_maps()
def _update_satellite_maps(self):
"""Make the satellite prediction maps for the night.
Will set self.sat_mjds and self.satellite_maps that can then
be attached to a conditions object in self.return_conditions
"""
sunset = self.almanac.sunsets["sun_n12_setting"][self.almanac_indx]
sunrise = self.almanac.sunsets["sun_n12_rising"][self.almanac_indx]
self.sat_mjds = np.arange(sunset, sunrise, self.satelite_dt)
# Compute RA and decs for when sun is down
ras, decs, alts, illums = self.constellation.paths_array(self.sat_mjds)
below_limit = np.where(alts < self.alt_limit)
weights = np.zeros(ras.shape, dtype=int)
weights[illums] = 1
weights[below_limit] = 0
satellite_maps = []
for i, mjd in enumerate(self.sat_mjds):
spot_map = _healbin(
ras[:, i][illums[:, i]],
decs[:, i][illums[:, i]],
weights[:, i][illums[:, i]],
self.sat_nside,
reduce_func=np.sum,
dtype=int,
fill_val=0,
)
satellite_maps.append(spot_map)
self.satellite_maps = np.vstack(satellite_maps)
