Init readthedocs

.readthedocs.yaml

@@ -0,0 +1,32 @@
+# .readthedocs.yaml
+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+version: 2
+
+# Set the OS, Python version and other tools you might need
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.12"
+    # You can also specify other tool versions:
+    # nodejs: "19"
+    # rust: "1.64"
+    # golang: "1.19"
+
+# Build documentation in the "docs/" directory with Sphinx
+sphinx:
+   configuration: docs/conf.py
+
+# Optionally build your docs in additional formats such as PDF and ePub
+# formats:
+#    - pdf
+#    - epub
+
+# Optional but recommended, declare the Python requirements required
+# to build your documentation
+# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html
+# python:
+#    install:
+#    - requirements: docs/requirements.txt

docs/source/index.rst

@@ -6,6 +6,15 @@
 xesn Documentation
 ==================
 
+**xesn** is a python package for implementing Echo State Networks (ESNs), a
+particular form of Reservoir Computing originally discovered by
+[Jaeger_2001]_.
+The implementation makes use of 
+`numpy <https://numpy.org/>`_ and
+`scipy <https://scipy.org/>`_ for an efficient implementation on CPUs,
+and `cupy <https://cupy.dev/>`_ for GPUs.
+
+
 .. toctree::
    :maxdepth: 1
 

docs/source/references.rst

@@ -3,6 +3,9 @@ References
 
 .. [Arcomano_et_al_2020] Arcomano, T., Szunyogh, I., Pathak, J., Wikner, A., Hunt, B. R., & Ott, E. (2020). A Machine Learning-Based Global Atmospheric Forecast Model. Geophysical Research Letters, 47(9), e2020GL087776. https://doi.org/10.1029/2020GL087776
 
+.. [Jaeger_2001] Jaeger, H. (2001). The "echo state” approach to analysing and training recurrent neural networks – with an Erratum note. Bonn, Germany: German National Research Center for Information Technology GMD Technical Report, 148(34), 13.
+
+
 .. [Pathak_et_al_2018] Pathak, J., Hunt, B., Girvan, M., Lu, Z., & Ott, E. (2018). Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach. Physical Review Letters, 120(2), 024102. https://doi.org/10.1103/PhysRevLett.120.024102
 
 .. [Smith_et_al_2023] Smith, T. A., Penny, S. G., Platt, J. A., & Chen, T.-C. (2023, September 21). Temporal Subsampling Diminishes Small Spatial Scales in Recurrent Neural Network Emulators of Geophysical Turbulence. arXiv. Retrieved from http://arxiv.org/abs/2305.00100