Summary

Modern astronomical surveys have multiple competing scientific goals. Optimizing the observation schedule for these goals presents significant computational and theoretical challenges, and state-of-the-art methods rely on expensive human inspection of simulated telescope schedules. Automated methods, such as reinforcement learning, have recently been explored to accelerate scheduling. DeepSurveySim provides methods for tracking and approximating sky conditions for a set of observations from a user-supplied telescope configuration.

Documentation

ReadTheDocs

Build locally

First install the package from source, then run

pip install sphinx
cd docs
make html

The folder docs/_build/html will be populated with the documentation. Navigate to file:///<path to local install>/docs/_build/html/index.html in any web browser to view.

Installation

Install from pip

Simply run

pip install DeepSurveySim

This will install the project with all its mandatory requirements.

If you wish to include the optional skybright, use the command:

pip install DeepSurveySim[skybright]

Not installing this will result in loss of the variables sky_magintude, tau, and teff, but will work on most (if not all) machines.

Install from source

The project is built with poetry, and this is the recommended install method. All dependencies are resolved in the poetry.lock file, so you can install immediately from the command

git clone https://github.com/deepskies/DeepSurveySim.git
poetry shell
poetry install --all-extras

Assuming you have poetry installed on your base environment. This will use lock file to install all the correct versions. To use the installed environment, use the command poetry shell to enter it. The command exit will take you out of this environment as it would for any other type of virtual environment.

Otherwise, you can use the pyproject.toml with your installer of choice.

To verify all the depedencies are properly installed - run python run pytest.

Example:

To run as a live envoriment for RL

from DeepSurveySim.Survey.survey import Survey
from DeepSurveySim.IO.read_config import ReadConfig

seo_config = ReadConfig(
        observator_configuration="DeepSurveySim/settings/SEO.yaml"
    )()

survey_config = ReadConfig(
        observator_configuration="DeepSurveySim/settings/equatorial_survey.yaml",
        survey=True
    )()

env = Survey(seo_config, survey_config)
observation = env._observation_calculation()

stop = True
while not stop:
    action = model.predict_action(observation)
    observation, reward, stop, log = env.step()

To generate observations

from DeepSurveySim.Survey.survey import Survey
from DeepSurveySim.IO.read_config import ReadConfig

seo_config = ReadConfig(
        observator_configuration="DeepSurveySim/settings/SEO.yaml"
    )()

survey_config = ReadConfig(
        observator_configuration="DeepSurveySim/settings/equatorial_survey.yaml",
        survey=True
    )()

env = Survey(seo_config, survey_config)
observations = env()

Acknowledgement

This work was produced by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics. Publisher acknowledges the U.S. Government license to provide public access under the DOE Public Access Plan DOE Public Access Plan.

We acknowledge the Deep Skies Lab as a community of multi-domain experts and collaborators who’ve facilitated an environment of open discussion, idea-generation, and collaboration. This community was important for the development of this project.

We thank Franco Terranova and Shohini Rhae for their assistance in testing the preliminary version of the package, and Eric Neilsen Jr. for his domain expertise.

Citation

If this package is useful for your work, we request you cite us:

If the skybright option is used, we also encourage its citation:

@misc{skybright_Neilsen:2019,
    author = "Neilsen, Eric",
    title = "{skybright}",
    reportNumber = "FERMILAB-CODE-2019-01",
    doi = "10.11578/dc.20190212.1",
    month = "2",
    year = "2019"
}