Coverage for python/lsst/sims/featureScheduler/basis_functions/rolling_funcs.py : 18%
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"""Basis function that tracks number of observations and tries to match a specified spatial distribution can enter multiple maps that will be used at different times in the survey
Parameters ---------- day_offset : np.array Healpix map that has the offset to be applied to each pixel when computing what season it is on. filtername : (string 'r') The name of the filter for this target map. nside: int (default_nside) The healpix resolution. target_maps : list of numpy array (None) healpix maps showing the ratio of observations desired for all points on the sky. Last map will be used for season -1. Probably shouldn't support going to season less than -1. 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. If using mulitple filters, see lsst.sims.featureScheduler.utils.calc_norm_factor for a utility that computes norm_factor. out_of_bounds_val : float (-10.) Reward value to give regions where there are no observations requested (unitless). season_modulo : int (2) The value to modulate the season by (years). max_season : int (None) For seasons higher than this value (pre-modulo), the final target map is used.
""" 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
# XXX--need to convert these features to track by season. 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
""" Parameters ---------- indx : list (None) Index values to compute, if None, full map is computed Returns ------- Healpix reward map """
result = self.result.copy() if indx is None: indx = self.all_indx
# Compute what season it is at each pixel seasons = utils.season_calc(conditions.night, offset=self.day_offset, modulo=self.season_modulo, max_season=self.max_season, season_length=self.season_length)
composite_target = self.result.copy()[indx] composite_nobs = self.result.copy()[indx]
composite_goal_N = self.result.copy()[indx]
for season in np.unique(seasons): season_indx = np.where(seasons == season)[0] composite_target[season_indx] = self.target_maps[season][season_indx] composite_nobs[season_indx] = self.survey_features['N_obs_%i' % season].feature[season_indx] composite_goal_N[season_indx] = composite_target[season_indx] * self.survey_features['N_obs_count_all_%i' % season].feature * self.norm_factor
result[indx] = composite_goal_N - composite_nobs[indx] result[self.out_of_bounds_area] = self.out_of_bounds_val
return result |