target_map=target_map[filtername],
out_of_bounds_val=hp.UNSEEN, nside=nside,
norm_factor=norm_factor))
if filtername2 is not None:
bfs.append(fs.Target_map_basis_function(filtername=filtername2,
target_map=target_map[filtername2],
out_of_bounds_val=hp.UNSEEN, nside=nside,
norm_factor=norm_factor))
bfs.append(fs.Slewtime_basis_function(filtername=filtername, nside=nside))
bfs.append(fs.Strict_filter_basis_function(filtername=filtername))
bfs.append(fs.Cadence_enhance_basis_function(enhance_window=[2.1, 5.], apply_area=cadence_area,
nside=nside))
bfs.append(fs.Zenith_shadow_mask_basis_function(nside=nside, shadow_minutes=60., max_alt=76.))
bfs.append(fs.North_south_patch_basis_function(zenith_min_alt=50., zenith_pad=20.,
nside=nside))
bfs.append(fs.Quadrant_basis_function(quadrants=['N', 'E', 'S'], azWidth=90.))
bfs.append(fs.Moon_avoidance_basis_function(nside=nside, moon_distance=40.))
bfs.append(fs.Bulk_cloud_basis_function(max_cloud_map=cloud_map, nside=nside))
weights = np.array([0., 0.3, 0.3, 3., 1., 1., 0., 0., 0., 0., 0.])
if filtername2 is None:
# Need to scale weights up so filter balancing still works properly.
weights = np.array([0., 0.6, 3., 1., 1., 0., 0., 0., 0., 0.])
if filtername2 is None:
survey_name = 'blob, %s' % filtername
else:
survey_name = 'blob, %s%s' % (filtername, filtername2)
surveys.append(fs.Blob_survey(bfs, weights, filtername=filtername,
filter2=filtername2,
survey_note=survey_name, az_range=180.,
search_radius=90, ignore_obs='DD'))
if __name__ == "__main__":
az_widths = [15., 30.]
for az_width in az_widths:
outdir = 'y_march_south_%i' % az_width
survey_length = 366 # days
# Define what we want the final visit ratio map to look like
target_map = fs.standard_goals()['y']
bfs = []
# Target number of observations
bfs.append(
fs.Target_map_basis_function(filtername='y',
target_map=target_map))
# Mask everything but the South
bfs.append(
fs.Quadrant_basis_function(quadrants=['S'], azWidth=az_width))
# throw in the depth percentile for good measure
bfs.append(fs.Depth_percentile_basis_function())
weights = np.array([1., 1., 1.])
survey = fs.Marching_army_survey(bfs, weights, npick=40)
scheduler = fs.Core_scheduler([survey])
observatory = Speed_observatory()
observatory, scheduler, observations = fs.sim_runner(
observatory,
scheduler,
survey_length=survey_length,
filename=outdir + '/y_marching_south_%i.db' % az_width)
title = 'az width %i' % az_width
import numpy as np
import lsst.sims.featureScheduler as fs
from lsst.sims.speedObservatory import Speed_observatory
import healpy as hp
if __name__ == "__main__":
survey_length = 5 # days
# Define what we want the final visit ratio map to look like
target_map = fs.standard_goals()['r']
bfs = []
bfs.append(fs.Depth_percentile_basis_function())
bfs.append(fs.Target_map_basis_function(target_map=target_map))
bfs.append(fs.Quadrant_basis_function())
bfs.append(fs.Slewtime_basis_function())
weights = np.array([.5, 1., 1., 1.])
survey = fs.Simple_greedy_survey_fields(bfs, weights, block_size=1)
scheduler = fs.Core_scheduler([survey])
observatory = Speed_observatory()
observatory, scheduler, observations = fs.sim_runner(
observatory,
scheduler,
survey_length=survey_length,
filename='marching_d%i.db' % survey_length,
delete_past=True)
# block_size=10, surveylength of 365 had runtime of 163 min. and got 0.26e6 observations. So, 10 years would be 27 hours.
# Going to block_size=1, runtime of 211 min, and 0.33e6 observations. So, 35 hours. Not too shabby!
# No pairs
# Greedy selection of opsim fields
if __name__ == "__main__":
survey_length = 365.25 # days
# Define what we want the final visit ratio map to look like
target_map = fs.standard_goals()['r']
filtername = 'r'
bfs = []
bfs.append(fs.M5_diff_basis_function(filtername=filtername, teff=False))
bfs.append(fs.Target_map_basis_function(target_map=target_map, filtername=filtername,
out_of_bounds_val=hp.UNSEEN))
bfs.append(fs.Quadrant_basis_function(quadrants='N', azWidth=15.))
bfs.append(fs.Slewtime_basis_function(filtername=filtername))
weights = np.array([1., 0.2, 1., 2.])
survey = fs.Greedy_survey_fields(bfs, weights, block_size=1, filtername=filtername)
scheduler = fs.Core_scheduler([survey])
observatory = Speed_observatory()
observatory, scheduler, observations = fs.sim_runner(observatory, scheduler,
survey_length=survey_length,
filename='one_filter_north.db',
delete_past=True)
# real 438m51.454s
# user 433m27.425s
# sys 2m3.193s
if __name__ == "__main__":
survey_length = 365.25 # days
# Define what we want the final visit ratio map to look like
target_map = fs.standard_goals()['r']
filtername = 'r'
bfs = []
bfs.append(fs.M5_diff_basis_function(filtername=filtername, teff=False))
bfs.append(
fs.Target_map_basis_function(target_map=target_map,
filtername=filtername,
out_of_bounds_val=hp.UNSEEN))
bfs.append(
fs.Quadrant_basis_function(quadrants='S', azWidth=15., maxAlt=75.))
bfs.append(fs.Slewtime_basis_function(filtername=filtername))
weights = np.array([1., 0.2, 1., 4.])
survey = fs.Greedy_survey_fields(bfs,
weights,
block_size=1,
filtername=filtername)
scheduler = fs.Core_scheduler([survey])
observatory = Speed_observatory()
observatory, scheduler, observations = fs.sim_runner(
observatory,
scheduler,
survey_length=survey_length,
filename='one_filter_south.db',