S+LEAF documentation ==================== S+LEAF is an `open-source `_ software that provides a flexible noise model with fast and scalable methods. It is largely inspired by the `celerite `_ / `celerite2 `_ model proposed by [1]_, [3]_. In particular the modeling of gaussian processes is similar, and uses the same semiseparable matrices representation as celerite. S+LEAF extends the celerite model in several ways: - It allows to account for close to diagonal (LEAF) noises such as instrument calibration errors (see [2]_). - It allows to model simulatenously several time series with the same Gaussian processes and their derivatives (see [4]_). - It provides an efficient implementation of the FENRIR stellar activity model (see [5]_) Please cite [2]_, [4]_, and/or [5]_ if you use S+LEAF in a publication. Installation ------------ Using conda ~~~~~~~~~~~ The S+LEAF package can be installed using conda with the following command: ``conda install -c conda-forge spleaf`` Using pip ~~~~~~~~~ It can also be installed using pip with: ``pip install spleaf`` Usage ----- S+LEAF covariance matrices are generated using the :doc:`_autosummary/spleaf.cov.Cov` class. The covariance matrix is modeled as the sum of different components (or terms), which split into two categories: noise terms and kernel terms (gaussian processes). See the :ref:`API reference` for a list of available terms. The low level implementation of S+LEAF matrices as defined by [2]_ is available as the :doc:`_autosummary/spleaf.Spleaf` class, but one typically does not need to directly deal with it. Examples -------- .. toctree:: calib multi .. _api_ref: API Reference ------------- .. autosummary:: :toctree: _autosummary :template: autosummary/custom_module.rst :recursive: spleaf.cov spleaf.term spleaf.fenrir spleaf Contribute ---------- Everyone is welcome to open issues and/or contribute code via pull-requests. A SWITCH edu-ID account is necessary to sign in to ``_. If you don't have an account, you can easily create one at ``_. Then you can sign in to ``_ by selecting "SWITCH edu-ID" as your organisation. References ---------- .. [1] `Foreman-Mackey et al., "Fast and Scalable Gaussian Process Modeling with Applications to Astronomical Time Series", 2017 `_. .. [2] `Delisle, J.-B., Hara, N., and Ségransan, D., "Efficient modeling of correlated noise. II. A flexible noise model with fast and scalable methods", 2020 `_. .. [3] `Gordon, T. A., Agol, E., Foreman-Mackey, D., "A Fast, Two-dimensional Gaussian Process Method Based on Celerite: Applications to Transiting Exoplanet Discovery and Characterization", 2020 `_. .. [4] `Delisle, J.-B., Unger, N., Hara, N., and Ségransan, D., "Efficient modeling of correlated noise. III. Scalable methods for jointly modeling several observables' time series with Gaussian processes", 2022 `_. .. [5] `Hara, N., and Delisle, J.-B., "A statistical model of stellar variability. I. FENRIR: a physics-based model of stellar activity, and its fast Gaussian process approximation", submitted `_.