Merge pull request #66 from tee-lab/docs-minor

Updates to documentation

Author: arshednabeel

docs/source/citation.rst

@@ -1,6 +1,6 @@
 Citing PyDaddy
 ==============
 
-If you are using this package or any of the included code in your research, please cite the associated manuscript as follows:
+If you are using this package in your research, please cite the associated paper as follows:
 
-Nabeel, A., Karichannavar, A., Palathingal, S., Jhawar, J., Brückner, D., Danny Raj, M., & Guttal, V. (2022). PyDaddy: A Python package for discovering stochastic dynamical equations from timeseries data. arXiv preprint arXiv:2205.02645.
+Nabeel, A., Karichannavar, A., Palathingal, S., Jhawar, J., Brückner, D. B., Danny Raj, M., & Guttal, V., "Discovering stochastic dynamical equations from ecological time series data", arXiv preprint `arXiv:2205.02645 <https://arxiv.org/abs/2205.02645>`_, to appear in *The American Naturalist*.

docs/source/datasets.rst

@@ -10,15 +10,15 @@ PyDaddy comes pre-packaged with several sample datasets. These can be loaded eas
 The following sample datasets are available:
 
     - :code:`fish-data-etroplus`: Group polarization data from a fish schooling experiment [1]_.
-    - :code:`model-data-scalar-pairwise` and :code:`model-data-scalar-ternary`: Scalar (1-D) simulated datasets generated from a stochastic Gillespie simulation, with pairwise and ternary interaction models respectively [1]_ [2]_.
-    - :code:`model-data-vector-pairwise` and :code:`model-data-vector-ternary`: Vector (2-D) simulated datasets generated from a stochastic Gillespie simulation, with pairwise and ternary interaction models respectively [1]_ [2]_.
+    - :code:`cell-data-cellhopping`: Dataset from a confined cell migration experiment [2]_
+    - :code:`model-data-scalar-pairwise` and :code:`model-data-scalar-ternary`: Scalar (1-D) simulated datasets generated from a stochastic Gillespie simulation, with pairwise and ternary interaction models respectively [1]_ [3]_.
+    - :code:`model-data-vector-pairwise` and :code:`model-data-vector-ternary`: Vector (2-D) simulated datasets generated from a stochastic Gillespie simulation, with pairwise and ternary interaction models respectively [1]_ [3]_.
 
 The fish schooling dataset contains the time series of the group polarization vector $\mathbf m$ (2-dimensional), for a group of 15 fish (\emph{Etroplus suratensis}). The polarization time series is available at a uniform interval of 0.12 second. The dataset contains many missing data points [1]_.
 
 The simulated datasets were generated using a continuous-time stochastic simulation algorithm, with pairwise and ternary interaction models respectively. Each simulated time series was resampled to a suitable uniform sampling interval [1]_ [2]_. Simulated datasets are provided for both 1-D and 2-D.
 
 
-.. [1] Jhawar, J., Morris, R. G., Amith-Kumar, U. R., Danny Raj, M., Rogers, T., Rajendran, H., & Guttal, V. (2020). Noise-induced schooling of fish. Nature Physics, 16(4), 488-493.
-.. [2] Jhawar, J., & Guttal, V. (2020). Noise-induced effects in collective dynamics and inferring local interactions from data. Philosophical Transactions of the Royal Society B, 375(1807), 20190381.
-
-
+.. [1] Jhawar, J., Morris, R. G., Amith-Kumar, U. R., Danny Raj, M., Rogers, T., Rajendran, H., & Guttal, V. (2020). Noise-induced schooling of fish. Nature Physics, 16(4), 488-493 (`doi <https://doi.org/10.1038/s41567-020-0787-y>`_).
+.. [2] Brückner, D. B., Fink, A., Schreiber, C. et al. Stochastic nonlinear dynamics of confined cell migration in two-state systems. Nat. Phys. 15, 595–601 (2019) (`doi <https://doi.org/10.1038/s41567-019-0445-4>`_).
+.. [3] Jhawar, J., & Guttal, V. (2020). Noise-induced effects in collective dynamics and inferring local interactions from data. Philosophical Transactions of the Royal Society B, 375(1807), 20190381. (`doi <http://dx.doi.org/10.1098/rstb.2019.0381>`_)

docs/source/index.rst

@@ -33,7 +33,7 @@ Citation
 
 If you are using this package in your research, please cite the associated paper as follows:
 
-Nabeel, A., Karichannavar, A., Palathingal, S., Jhawar, J., Brückner, D. B., Danny Raj, M., & Guttal, V., "Discovering stochastic dynamical equations from ecological time series data", arXiv preprint [arXiv:2205.02645](https://arxiv.org/abs/2205.02645), to appear in *The American Naturalist*.
+Nabeel, A., Karichannavar, A., Palathingal, S., Jhawar, J., Brückner, D. B., Danny Raj, M., & Guttal, V., "Discovering stochastic dynamical equations from ecological time series data", arXiv preprint `arXiv:2205.02645 <https://arxiv.org/abs/2205.02645>`_, to appear in *The American Naturalist*.
 
 Contents
 --------

docs/source/tutorials.rst

@@ -1,4 +1,4 @@
-Tutorials (no installation required)
+Tutorials
 =========
 
 .. |colab-getting-started| image:: https://colab.research.google.com/assets/colab-badge.svg
@@ -49,6 +49,12 @@ Tutorials (no installation required)
 .. |github-cell| image:: https://img.shields.io/badge/Open_in_GitHub-grey?logo=github
     :target: https://www.github.com/tee-lab/PyDaddy/blob/master/notebooks/8_example_cell_migration.ipynb
 
+.. |colab-3d| image:: https://colab.research.google.com/assets/colab-badge.svg
+    :target: https://colab.research.google.com/github/tee-lab/PyDaddy/blob/colab/notebooks/9_higher_dimensions.ipynb
+
+.. |github-3d| image:: https://img.shields.io/badge/Open_in_GitHub-grey?logo=github
+    :target: https://www.github.com/tee-lab/PyDaddy/blob/master/notebooks/9_higher_dimensions.ipynb
+
 .. |colab| image:: https://colab.research.google.com/assets/colab-badge.svg
 
 .. |github| image:: https://img.shields.io/badge/Open_in_GitHub-grey?logo=github
@@ -116,4 +122,11 @@ Example analysis: Cell hopping
 
 An example analysis of a confined cell migration dataset (Brückner et. al., Nature Physics, 2019) using PyDaddy.
 
+Higher dimensions
+^^^^^^^^^^^^^^^^^
+|colab-3d| |github-3d|
+
+This notebooks demonstrates how to use the principles of SDE estimations and tools in PyDaddy to estimate SDEs for higher-dimensional systems. The notebook uses the example of a (3-dimensional) stochastic Lorenz system.
+
+