def nes_light_footprints(nside=None):
"""
A quick function to generate the "standard" goal maps. This is the traditional WFD/mini survey footprint.
"""
NES_scaledown = 2.
SCP_scaledown = 1.5
result = {}
result['u'] = generate_goal_map(nside=nside, NES_fraction=0./NES_scaledown,
WFD_fraction=0.31, SCP_fraction=0.15/SCP_scaledown,
GP_fraction=0.15,
wfd_dec_min=-62.5, wfd_dec_max=3.6)
result['g'] = generate_goal_map(nside=nside, NES_fraction=0.2/NES_scaledown,
WFD_fraction=0.44, SCP_fraction=0.15/SCP_scaledown,
GP_fraction=0.15,
wfd_dec_min=-62.5, wfd_dec_max=3.6)
result['r'] = generate_goal_map(nside=nside, NES_fraction=0.46/NES_scaledown,
WFD_fraction=1.0, SCP_fraction=0.15/SCP_scaledown,
GP_fraction=0.15,
wfd_dec_min=-62.5, wfd_dec_max=3.6)
result['i'] = generate_goal_map(nside=nside, NES_fraction=0.46/NES_scaledown,
WFD_fraction=1.0, SCP_fraction=0.15/SCP_scaledown,
GP_fraction=0.15,
wfd_dec_min=-62.5, wfd_dec_max=3.6)
result['z'] = generate_goal_map(nside=nside, NES_fraction=0.4/NES_scaledown,
WFD_fraction=0.9, SCP_fraction=0.15/SCP_scaledown,
GP_fraction=0.15,
wfd_dec_min=-62.5, wfd_dec_max=3.6)
result['y'] = generate_goal_map(nside=nside, NES_fraction=0./NES_scaledown,
WFD_fraction=0.9, SCP_fraction=0.15/SCP_scaledown,
GP_fraction=0.15,
wfd_dec_min=-62.5, wfd_dec_max=3.6)
return result
def gp_smooth(nside=32):
# Treat the galactic plane as just part of the WFD
result = {}
result['u'] = generate_goal_map(nside=nside,
NES_fraction=0.,
WFD_fraction=0.31,
SCP_fraction=0.15,
GP_fraction=0.31)
result['g'] = generate_goal_map(nside=nside,
NES_fraction=0.2,
WFD_fraction=0.44,
SCP_fraction=0.15,
GP_fraction=0.44)
result['r'] = generate_goal_map(nside=nside,
NES_fraction=0.46,
WFD_fraction=1.0,
SCP_fraction=0.15,
GP_fraction=1.0)
result['i'] = generate_goal_map(nside=nside,
NES_fraction=0.46,
WFD_fraction=1.0,
SCP_fraction=0.15,
GP_fraction=1.0)
result['z'] = generate_goal_map(nside=nside,
NES_fraction=0.4,
WFD_fraction=0.9,
SCP_fraction=0.15,
GP_fraction=0.9)
result['y'] = generate_goal_map(nside=nside,
NES_fraction=0.,
WFD_fraction=0.9,
SCP_fraction=0.15,
GP_fraction=0.9)
return result
def __init__(self, filtername='r', nside=default_nside, target_map=None,
survey_features=None, condition_features=None, norm_factor=0.00010519,
out_of_bounds_val=-10., mod_year=2, offset=0, mjd0=59580.035):
"""
Parameters
----------
filtername: (string 'r')
The name of the filter for this target map.
nside: int (default_nside)
The healpix resolution.
target_map : numpy array (None)
A healpix map showing the ratio of observations desired for all points on the sky
norm_factor : float (0.00010519)
for converting target map to number of observations. Should be the area of the camera
divided by the area of a healpixel divided by the sum of all your goal maps. Default
value assumes LSST foV has 1.75 degree radius and the standard goal maps.
out_of_bounds_val : float (-10.)
Point value to give regions where there are no observations requested
"""
if nside is None:
nside = utils.set_default_nside()
self.norm_factor = norm_factor
if survey_features is None:
survey_features = {}
# Map of the number of observations in filter
survey_features['N_obs'] = N_observations_mod(filtername=filtername,
mod_year=mod_year,
offset=offset,
mjd0=mjd0, nside=nside)
# Count of all the observations
survey_features['N_obs_count_all'] = N_obs_count_mod(filtername=None,
mod_year=mod_year,
offset=offset,
mjd0=mjd0)
if condition_features is None:
condition_features = {}
condition_features['Current_mjd'] = features.Current_mjd()
super(Target_map_modulo_basis_function, self).__init__(survey_features=survey_features,
condition_features=condition_features)
self.nside = nside
if target_map is None:
self.target_map = utils.generate_goal_map(filtername=filtername)
else:
self.target_map = target_map
self.out_of_bounds_area = np.where(self.target_map == 0)[0]
self.out_of_bounds_val = out_of_bounds_val
self.mjd0 = mjd0
self.mod_year = mod_year
self.offset = offset
def no_gp_north(nside=32):
result = {}
gl1 = 40.
gl2 = 290.
result['u'] = generate_goal_map(nside=nside,
NES_fraction=0.,
WFD_fraction=0.31,
SCP_fraction=0.15,
GP_fraction=0.15,
WFD_upper_edge_fraction=0.,
gp_long1=gl1,
gp_long2=gl2)
result['g'] = generate_goal_map(nside=nside,
NES_fraction=0.2,
WFD_fraction=0.44,
SCP_fraction=0.15,
GP_fraction=0.15,
WFD_upper_edge_fraction=0.,
gp_long1=gl1,
gp_long2=gl2)
result['r'] = generate_goal_map(nside=nside,
NES_fraction=0.46,
WFD_fraction=1.0,
SCP_fraction=0.15,
GP_fraction=0.15,
WFD_upper_edge_fraction=0.,
gp_long1=gl1,
gp_long2=gl2)
result['i'] = generate_goal_map(nside=nside,
NES_fraction=0.46,
WFD_fraction=1.0,
SCP_fraction=0.15,
GP_fraction=0.15,
WFD_upper_edge_fraction=0.,
gp_long1=gl1,
gp_long2=gl2)
result['z'] = generate_goal_map(nside=nside,
NES_fraction=0.4,
WFD_fraction=0.9,
SCP_fraction=0.15,
GP_fraction=0.15,
WFD_upper_edge_fraction=0.,
gp_long1=gl1,
gp_long2=gl2)
result['y'] = generate_goal_map(nside=nside,
NES_fraction=0.,
WFD_fraction=0.9,
SCP_fraction=0.15,
GP_fraction=0.15,
WFD_upper_edge_fraction=0.,
gp_long1=gl1,
gp_long2=gl2)
return result
def big_wfd(nside=32):
"""
A quick function to generate the "standard" goal maps. This is the traditional WFD/mini survey footprint.
"""
wfd_dec_max = 20
result = {}
result['u'] = generate_goal_map(nside=nside,
NES_fraction=0.,
WFD_fraction=0.31,
SCP_fraction=0.15,
GP_fraction=0.15,
wfd_dec_min=-62.5,
wfd_dec_max=wfd_dec_max)
result['g'] = generate_goal_map(nside=nside,
NES_fraction=0.2,
WFD_fraction=0.44,
SCP_fraction=0.15,
GP_fraction=0.15,
wfd_dec_min=-62.5,
wfd_dec_max=wfd_dec_max)
result['r'] = generate_goal_map(nside=nside,
NES_fraction=0.46,
WFD_fraction=1.0,
SCP_fraction=0.15,
GP_fraction=0.15,
wfd_dec_min=-62.5,
wfd_dec_max=wfd_dec_max)
result['i'] = generate_goal_map(nside=nside,
NES_fraction=0.46,
WFD_fraction=1.0,
SCP_fraction=0.15,
GP_fraction=0.15,
wfd_dec_min=-62.5,
wfd_dec_max=wfd_dec_max)
result['z'] = generate_goal_map(nside=nside,
NES_fraction=0.4,
WFD_fraction=0.9,
SCP_fraction=0.15,
GP_fraction=0.15,
wfd_dec_min=-62.5,
wfd_dec_max=wfd_dec_max)
result['y'] = generate_goal_map(nside=nside,
NES_fraction=0.,
WFD_fraction=0.9,
SCP_fraction=0.15,
GP_fraction=0.15,
wfd_dec_min=-62.5,
wfd_dec_max=wfd_dec_max)
return result
def u_double_footprint(nside=None):
"""
"""
result = {}
result['u'] = generate_goal_map(nside=nside,
NES_fraction=0.,
WFD_fraction=0.67,
SCP_fraction=0.30,
GP_fraction=0.30,
wfd_dec_min=-62.5,
wfd_dec_max=3.6)
result['g'] = generate_goal_map(nside=nside,
NES_fraction=0.2,
WFD_fraction=0.44,
SCP_fraction=0.15,
GP_fraction=0.15,
wfd_dec_min=-62.5,
wfd_dec_max=3.6)
result['r'] = generate_goal_map(nside=nside,
NES_fraction=0.46,
WFD_fraction=1.0,
SCP_fraction=0.15,
GP_fraction=0.15,
wfd_dec_min=-62.5,
wfd_dec_max=3.6)
result['i'] = generate_goal_map(nside=nside,
NES_fraction=0.46,
WFD_fraction=1.0,
SCP_fraction=0.15,
GP_fraction=0.15,
wfd_dec_min=-62.5,
wfd_dec_max=3.6)
result['z'] = generate_goal_map(nside=nside,
NES_fraction=0.4,
WFD_fraction=0.9,
SCP_fraction=0.15,
GP_fraction=0.15,
wfd_dec_min=-62.5,
wfd_dec_max=3.6)
result['y'] = generate_goal_map(nside=nside,
NES_fraction=0.,
WFD_fraction=0.9,
SCP_fraction=0.15,
GP_fraction=0.15,
wfd_dec_min=-62.5,
wfd_dec_max=3.6)
return result
def bluer_footprint(nside=32):
"""Try a bluer filter dist. May want to turn this into a larger parameter search.
"""
result = {}
result['u'] = generate_goal_map(nside=nside,
NES_fraction=0.,
WFD_fraction=0.31,
SCP_fraction=0.15,
GP_fraction=0.15,
WFD_upper_edge_fraction=0.)
# Turn up the g WFD
result['g'] = generate_goal_map(nside=nside,
NES_fraction=0.2,
WFD_fraction=0.9,
SCP_fraction=0.15,
GP_fraction=0.15,
WFD_upper_edge_fraction=0.)
result['r'] = generate_goal_map(nside=nside,
NES_fraction=0.46,
WFD_fraction=1.0,
SCP_fraction=0.15,
GP_fraction=0.15,
WFD_upper_edge_fraction=0.)
result['i'] = generate_goal_map(nside=nside,
NES_fraction=0.46,
WFD_fraction=1.0,
SCP_fraction=0.15,
GP_fraction=0.15,
WFD_upper_edge_fraction=0.)
# Turn down the z and y WFD
result['z'] = generate_goal_map(nside=nside,
NES_fraction=0.4,
WFD_fraction=0.7,
SCP_fraction=0.15,
GP_fraction=0.15,
WFD_upper_edge_fraction=0.)
result['y'] = generate_goal_map(nside=nside,
NES_fraction=0.,
WFD_fraction=0.7,
SCP_fraction=0.15,
GP_fraction=0.15,
WFD_upper_edge_fraction=0.)
return result
def __init__(self,
day_offset=None,
filtername='r',
nside=None,
target_maps=None,
norm_factor=None,
out_of_bounds_val=-10.,
season_modulo=2,
max_season=None,
season_length=365.25):
super(Target_map_modulo_basis_function,
self).__init__(nside=nside, filtername=filtername)
if norm_factor is None:
warnings.warn(
'No norm_factor set, use utils.calc_norm_factor if using multiple filters.'
)
self.norm_factor = 0.00010519
else:
self.norm_factor = norm_factor
self.survey_features = {}
# Map of the number of observations in filter
for i, temp in enumerate(target_maps[0:-1]):
self.survey_features['N_obs_%i' %
i] = features.N_observations_season(
i,
filtername=filtername,
nside=self.nside,
modulo=season_modulo,
offset=day_offset,
max_season=max_season,
season_length=season_length)
# Count of all the observations taken in a season
self.survey_features['N_obs_count_all_%i' %
i] = features.N_obs_count_season(
i,
filtername=None,
season_modulo=season_modulo,
offset=day_offset,
nside=self.nside,
max_season=max_season,
season_length=season_length)
# Set the final one to be -1
self.survey_features['N_obs_%i' % -1] = features.N_observations_season(
-1,
filtername=filtername,
nside=self.nside,
modulo=season_modulo,
offset=day_offset,
max_season=max_season,
season_length=season_length)
self.survey_features['N_obs_count_all_%i' %
-1] = features.N_obs_count_season(
-1,
filtername=None,
season_modulo=season_modulo,
offset=day_offset,
nside=self.nside,
max_season=max_season,
season_length=season_length)
if target_maps is None:
self.target_maps = utils.generate_goal_map(filtername=filtername,
nside=self.nside)
else:
self.target_maps = target_maps
# should probably actually loop over all the target maps?
self.out_of_bounds_area = np.where(self.target_maps[0] == 0)[0]
self.out_of_bounds_val = out_of_bounds_val
self.result = np.zeros(hp.nside2npix(self.nside), dtype=float)
self.all_indx = np.arange(self.result.size)
# For computing what day each healpix is on
if day_offset is None:
self.day_offset = np.zeros(hp.nside2npix(self.nside), dtype=float)
else:
self.day_offset = day_offset
self.season_modulo = season_modulo
self.max_season = max_season
self.season_length = season_length
filtername1=filtername,
filtername2=filtername2,
survey_note=survey_name,
ignore_obs='DD',
dither=True))
pair_surveys.append(surveys[-1])
#filters = ['u', 'g', 'r', 'i', 'z', 'y']
filters = ['i', 'z', 'y']
greedy_target_map = standard_goals(nside=nside)
# Let's take out the NES area on the target maps. This way we won't
# take images in the NES that aren't paired.
temp_map = generate_goal_map(nside=nside,
NES_fraction=1.,
WFD_fraction=0,
SCP_fraction=0,
GP_fraction=0,
WFD_upper_edge_fraction=0.)
nes_pix = np.where(temp_map == 1)
for filtername in greedy_target_map:
greedy_target_map[filtername][nes_pix] = 0
greedy_surveys = []
for filtername in filters:
bfs = []
bfs.append(
basis_functions.M5_diff_basis_function(filtername=filtername,
nside=nside))
bfs.append(
basis_functions.Target_map_basis_function(
filtername=filtername,
for arg in sys.argv:
exec_command += ' ' + arg
extra_info['exec command'] = exec_command
try:
extra_info['git hash'] = subprocess.check_output(
['git', 'rev-parse', 'HEAD'])
except subprocess.CalledProcessError:
extra_info['git hash'] = 'Not in git repo'
extra_info['file executed'] = os.path.realpath(__file__)
fileroot = 'wfd_vary_fp%i' % fp
file_end = 'v1.4_'
gp_only = generate_goal_map(NES_fraction=.0,
WFD_fraction=0.,
SCP_fraction=0.0,
GP_fraction=0.2)
nes_scp = generate_goal_map(NES_fraction=.1,
WFD_fraction=0.,
SCP_fraction=0.1,
GP_fraction=0.0)
gp_pix = np.where(gp_only > 0)[0]
nes_pix = np.where(nes_scp > 0)[0]
# Baseline footprint
if fp == 1:
footprints = standard_goals()
# Let's wipe out the GP
if fp == 2:
