def testBaseline(self):
"""
Set up a baseline survey and run for a few days. A crude way to touch lots of code.
"""
nside = fs.set_default_nside(nside=32)
survey_length = 2.1 # days
# Define what we want the final visit ratio map to look like
target_map = fs.standard_goals(nside=nside)
filters = ['u', 'g', 'r', 'i', 'z', 'y']
surveys = []
for filtername in filters:
bfs = []
bfs.append(fs.M5_diff_basis_function(filtername=filtername, nside=nside))
bfs.append(fs.Target_map_basis_function(filtername=filtername,
target_map=target_map[filtername],
out_of_bounds_val=hp.UNSEEN, nside=nside))
bfs.append(fs.North_south_patch_basis_function(zenith_min_alt=50., nside=nside))
bfs.append(fs.Slewtime_basis_function(filtername=filtername, nside=nside))
bfs.append(fs.Strict_filter_basis_function(filtername=filtername))
weights = np.array([3.0, 0.3, 1., 3., 3.])
surveys.append(fs.Greedy_survey_fields(bfs, weights, block_size=1, filtername=filtername,
dither=True, nside=nside))
surveys.append(fs.Pairs_survey_scripted([], [], ignore_obs='DD'))
# Set up the DD
dd_surveys = fs.generate_dd_surveys()
surveys.extend(dd_surveys)
scheduler = fs.Core_scheduler(surveys, nside=nside)
observatory = Speed_observatory(nside=nside)
observatory, scheduler, observations = fs.sim_runner(observatory, scheduler,
survey_length=survey_length,
filename=None)
# Check that a second part of a pair was taken
assert('scripted' in observations['note'])
# Check that the COSMOS DD was observed
assert('DD:COSMOS' in observations['note'])
# And the u-band
assert('DD:u,COSMOS' in observations['note'])
# Make sure a few different filters were observed
assert(len(np.unique(observations['filter'])) > 3)
# Make sure lots of observations executed
assert(observations.size > 1000)
def testsched(self):
target_map = fs.standard_goals()['r']
bfs = []
bfs.append(fs.M5_diff_basis_function())
bfs.append(fs.Target_map_basis_function(target_map=target_map))
weights = np.array([1., 1])
survey = fs.Simple_greedy_survey_fields(bfs, weights)
scheduler = fs.Core_scheduler([survey])
observatory = Speed_observatory()
# Check that we can update conditions
scheduler.update_conditions(observatory.return_status())
# Check that we can get an observation out
obs = scheduler.request_observation()
assert (obs is not None)
# Check that we can flush the Queue
scheduler.flush_queue()
assert (len(scheduler.queue) == 0)
# Check that we can add an observation
scheduler.add_observation(obs)
from lsst.sims.speedObservatory import Speed_observatory
import healpy as hp
# Run a single-filter r-band survey.
# 5-sigma depth percentile
# standard target map (WFD, NES, SCP, GP)
# Slewtime
# mask lots of off-meridian space
# No pairs
# Greedy selection of opsim fields
if __name__ == "__main__":
survey_length = 365.25 * 10. # 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.Depth_percentile_basis_function(filtername=filtername))
bfs.append(
fs.Target_map_basis_function(target_map=target_map,
filtername=filtername,
out_of_bounds_val=hp.UNSEEN))
bfs.append(fs.North_south_patch_basis_function(zenith_min_alt=50.))
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,
wfd = fs.WFD_healpixels(nside=nside)
ra, dec = fs.ra_dec_hp_map(nside=nside)
rolling_mask1 = np.zeros(ra.size, dtype=bool)
rolling_mask1[np.where((wfd == 1) & (dec > dec_limits[0]))] = True
rolling_mask2 = np.zeros(ra.size, dtype=bool)
rolling_mask2[np.where((dec < dec_limits[0]) & (dec > dec_limits[1]))] = True
rolling_mask3 = np.zeros(ra.size, dtype=bool)
rolling_mask3[np.where((dec < dec_limits[1]) & (wfd == 1))] = True
observatory = Speed_observatory(nside=nside)
# Define what we want the final visit ratio map to look like
years = np.round(survey_length/365.25)
target_map = fs.standard_goals(nside=nside)
filters = ['u', 'g', 'r', 'i', 'z', 'y']
surveys = []
for filtername in filters:
bfs = []
bfs.append(fs.M5_diff_basis_function(filtername=filtername, nside=nside))
bfs.append(fs.Target_map_basis_function(filtername=filtername,
target_map=target_map[filtername],
out_of_bounds_val=hp.UNSEEN, nside=nside))
bfs.append(fs.North_south_patch_basis_function(zenith_min_alt=50., nside=nside))
bfs.append(fs.Rolling_mask_basis_function(rolling_mask1, year_mod=3, year_offset=0,
mjd_start=observatory.mjd, nside=nside))
bfs.append(fs.Rolling_mask_basis_function(rolling_mask1, year_mod=3, year_offset=1,
mjd_start=observatory.mjd, nside=nside))
import lsst.sims.featureScheduler as fs from lsst.sims.speedObservatory import Speed_observatory import matplotlib.pylab as plt import healpy as hp import time from rolling_slair import Target_map_modulo_basis_function from drive_cadence import Cadence_enhance_basis_function # Try out the rolling cadence survey_length = 365.25 * 10 # days nside = fs.set_default_nside(nside=32) years = np.round(survey_length / 365.25) t0 = time.time() target_map = fs.standard_goals(nside=nside) # Set up a map of where to drive the cadence cadence_area = target_map['r'] * 0 cadence_area[np.where(target_map['r'] == 1)] = 1. hp.mollview(cadence_area, title='Where to drive cadence') even_year_target = fs.standard_goals(nside=nside) odd_year_target = fs.standard_goals(nside=nside) up = 2.0 down = 0. # Let's find the healpix that divides the WFD area in half wfd = even_year_target['r'] * 0 wfd[np.where(even_year_target['r'] == 1)] = 1 wfd_accum = np.cumsum(wfd)
import numpy as np
import lsst.sims.featureScheduler as fs
from lsst.sims.speedObservatory import Speed_observatory
import matplotlib.pylab as plt
import healpy as hp
from numpy.lib.recfunctions import append_fields
if __name__ == '__main__':
nside = fs.set_default_nside(nside=32)
survey_length = 365.25*10 # days
# Define what we want the final visit ratio map to look like
years = np.round(survey_length/365.25)
target_map = fs.standard_goals(nside=nside)
filters = ['u', 'g', 'r', 'i', 'z', 'y']
surveys = []
for filtername in filters:
bfs = []
bfs.append(fs.M5_diff_basis_function(filtername=filtername, nside=nside))
bfs.append(fs.Target_map_basis_function(filtername=filtername,
target_map=target_map[filtername],
out_of_bounds_val=hp.UNSEEN, nside=nside))
bfs.append(fs.North_south_patch_basis_function(zenith_min_alt=50., nside=nside))
#bfs.append(fs.Zenith_mask_basis_function(maxAlt=78., penalty=-100, nside=nside))
bfs.append(fs.Slewtime_basis_function(filtername=filtername, nside=nside))
bfs.append(fs.Strict_filter_basis_function(filtername=filtername))
weights = np.array([3.0, 0.4, 1., 2., 3.])
surveys.append(fs.Greedy_survey_fields(bfs, weights, block_size=1, filtername=filtername,
def generate_slair_scheduler():
nside = fs.set_default_nside(nside=32)
# get rid of silly northern strip.
target_map = fs.standard_goals(nside=nside)
norm_factor = fs.calc_norm_factor(target_map)
# List to hold all the surveys (for easy plotting later)
surveys = []
# Set up observations to be taken in blocks
filter1s = ['u', 'g', 'r', 'i', 'z', 'y']
filter2s = [None, 'g', 'r', 'i', None, None]
pair_surveys = []
for filtername, filtername2 in zip(filter1s, filter2s):
bfs = []
bfs.append(
fs.M5_diff_basis_function(filtername=filtername, nside=nside))
if filtername2 is not None:
bfs.append(
fs.M5_diff_basis_function(filtername=filtername2, nside=nside))
bfs.append(
fs.Target_map_basis_function(filtername=filtername,
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.Zenith_shadow_mask_basis_function(nside=nside,
shadow_minutes=60.,
max_alt=76.))
weights = np.array([3.0, 3.0, .3, .3, 3., 3., 0.])
if filtername2 is None:
# Need to scale weights up so filter balancing still works properly.
weights = np.array([6.0, 0.6, 3., 3., 0.])
# XXX-
# This is where we could add a look-ahead basis function to include m5_diff in the future.
# Actually, having a near-future m5 would also help prevent switching to u or g right at twilight?
# Maybe just need a "filter future" basis function?
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))
pair_surveys.append(surveys[-1])
# Let's set up some standard surveys as well to fill in the gaps. This is my old silly masked version.
# It would be good to put in Tiago's verion and lift nearly all the masking. That way this can also
# chase sucker holes.
filters = ['u', 'g', 'r', 'i', 'z', 'y']
#filters = ['i', 'z', 'y']
greedy_surveys = []
for filtername in filters:
bfs = []
bfs.append(
fs.M5_diff_basis_function(filtername=filtername, nside=nside))
bfs.append(
fs.Target_map_basis_function(filtername=filtername,
target_map=target_map[filtername],
out_of_bounds_val=hp.UNSEEN,
nside=nside,
norm_factor=norm_factor))
bfs.append(
fs.North_south_patch_basis_function(zenith_min_alt=50.,
nside=nside))
bfs.append(
fs.Slewtime_basis_function(filtername=filtername, nside=nside))
bfs.append(fs.Strict_filter_basis_function(filtername=filtername))
bfs.append(
fs.Zenith_shadow_mask_basis_function(nside=nside,
shadow_minutes=60.,
max_alt=76.))
weights = np.array([3.0, 0.3, 1., 3., 3., 0.])
# Might want to try ignoring DD observations here, so the DD area gets covered normally--DONE
surveys.append(
fs.Greedy_survey_fields(bfs,
weights,
block_size=1,
filtername=filtername,
dither=True,
nside=nside,
ignore_obs='DD'))
greedy_surveys.append(surveys[-1])
# Set up the DD surveys
dd_surveys = fs.generate_dd_surveys()
surveys.extend(dd_surveys)
survey_list_o_lists = [dd_surveys, pair_surveys, greedy_surveys]
# put in as list-of-lists so pairs get evaluated first.
scheduler = fs.Core_scheduler(survey_list_o_lists, nside=nside)
return scheduler
import numpy as np
import healpy as hp
import matplotlib.pylab as plt
import lsst.sims.featureScheduler as fs
from lsst.sims.featureScheduler.observatory import Speed_observatory
if __name__ == "__main__":
survey_length = 5.2 # days
# Define what we want the final visit ratio map to look like
target_maps = fs.standard_goals()
filters = ['r', 'i']
weights = {}
weights['r'] = [1., 1., 1.]
weights['i'] = [1.5, 1., 0.]
surveys = []
for filtername in filters:
bfs = []
bfs.append(fs.Depth_percentile_basis_function(filtername=filtername))
bfs.append(fs.Target_map_basis_function(target_map=target_maps[filtername], filtername=filtername))
bfs.append(fs.Filter_change_basis_function(filtername=filtername))
weight = weights[filtername]
surveys.append(fs.Simple_greedy_survey_fields(bfs, weight, filtername=filtername))
scheduler = fs.Core_scheduler(surveys)
observatory = Speed_observatory()
observatory, scheduler, observations = fs.sim_runner(observatory, scheduler, survey_length=survey_length,
filename='two_filt.db')
