My 3rd attempt at implementing multivariate models with missing data

.github/workflows/tutorials.yml

@@ -20,16 +20,19 @@ jobs:
           fetch-depth: 0
 
       - name: Set up Python
-        uses: actions/setup-python@v2
+        uses: conda-incubator/setup-miniconda@v2
         with:
           python-version: 3.8
+          auto-update-conda: true
 
       - name: Install dependencies
+        shell: bash -l {0}
         run: |
           python -m pip install -U pip
-          python -m pip install --use-feature=2020-resolver ".[tutorials]"
+          python -m pip install ".[tutorials]"
 
       - name: Get theano compiledir
+        shell: bash -l {0}
         id: compiledir
         run: |
           python -c "import theano; print('::set-output name=compiledir::' + theano.config.compiledir.split('/')[-1])"
@@ -44,6 +47,7 @@ jobs:
             tutorials-
 
       - name: Execute the notebooks
+        shell: bash -l {0}
         run: |
           jupytext --to ipynb --execute docs/tutorials/*.py
 

python/celerite2/__init__.py

@@ -1,17 +1,17 @@
 # -*- coding: utf-8 -*-
 
-__all__ = ["__version__", "terms", "GaussianProcess"]
-from . import terms
+__all__ = ["__version__", "terms", "latent", "GaussianProcess"]
+from . import latent, terms
 from .celerite2_version import __version__
-from .numpy import GaussianProcess
+from .core import GaussianProcess
 
 __uri__ = "https://celerite2.readthedocs.io"
 __author__ = "Daniel Foreman-Mackey"
 __email__ = "[email protected]"
 __license__ = "MIT"
 __description__ = "Fast and scalable Gaussian Processes in 1D"
 __bibtex__ = __citation__ = r"""
-@article{celerite1,
+@article{celerite2:foremanmackey17,
    author = {{Foreman-Mackey}, D. and {Agol}, E. and {Ambikasaran}, S. and
              {Angus}, R.},
     title = "{Fast and Scalable Gaussian Process Modeling with Applications to
@@ -25,7 +25,7 @@
    adsurl = {http://adsabs.harvard.edu/abs/2017AJ....154..220F},
   adsnote = {Provided by the SAO/NASA Astrophysics Data System}
 }
-@article{celerite2,
+@article{celerite2:foremanmackey18,
    author = {{Foreman-Mackey}, D.},
     title = "{Scalable Backpropagation for Gaussian Processes using Celerite}",
   journal = {Research Notes of the American Astronomical Society},
@@ -38,4 +38,19 @@
    adsurl = {http://adsabs.harvard.edu/abs/2018RNAAS...2a..31F},
   adsnote = {Provided by the SAO/NASA Astrophysics Data System}
 }
+@article{celerite2:gordon20,
+   author = {{Gordon}, Tyler A. and {Agol}, Eric and {Foreman-Mackey}, Daniel},
+    title = "{A Fast, Two-dimensional Gaussian Process Method Based on
+              Celerite: Applications to Transiting Exoplanet Discovery and
+              Characterization}",
+  journal = {\aj},
+     year = 2020,
+    month = nov,
+   volume = 160,
+   number = 5,
+    pages = {240},
+      doi = {10.3847/1538-3881/abbc16},
+   adsurl = {https://ui.adsabs.harvard.edu/abs/2020AJ....160..240G},
+  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
+}
 """