BaseMarkovSurvey¶
- class rubin_sim.scheduler.surveys.BaseMarkovSurvey(basis_functions, basis_weights, extra_features=None, smoothing_kernel=None, ignore_obs=None, survey_name=None, nside=None, seed=42, dither=True, detailers=None, camera='LSST', fields=None, area_required=None, npositions=7305)¶
Bases:
BaseSurvey
A Markov Decision Function survey object. Uses Basis functions to compute a final reward function and decide what to observe based on the reward. Includes methods for dithering and defaults to dithering nightly.
- Parameters:
- basis_functionlist of rubin_sim.schuler.basis_function objects
- basis_weightslist of float
Must be same length as basis_function
- seedhashable
Random number seed, used for randomly orienting sky tessellation.
- camerastr (‘LSST’)
Should be ‘LSST’ or ‘comcam’
- fieldsnp.array (None)
An array of field positions. Should be numpy array with columns of “RA” and “dec” in radians. If none, site_models.read_fields or utils.comcam_tessellate is used to read field positions.
- area_requiredfloat (None)
The valid area that should be present in the reward function (square degrees).
- npositionsint (7305)
The number of dither positions to pre-compute. Defaults to 7305 (so good for 20 years)
Methods Summary
calc_reward_function
(conditions)- Parameters:
generate_observations_rough
(conditions)- Returns:
If we want to smooth the reward function.
Methods Documentation
- calc_reward_function(conditions)¶
- Parameters:
- conditionsrubin_sim.scheduler.features.Conditions object
- Returns:
- rewardfloat (or array)
- generate_observations_rough(conditions)¶
- Returns:
- one of:
None
A list of observations
- smooth_reward()¶
If we want to smooth the reward function.