Added getting started info

Author: jcass11

README.rst

@@ -8,9 +8,20 @@ diffusive_distinguishability
         :alt: Documentation Status
 
 
-Simulation of homogeneous diffusion, bayesian estimation of underlying diffusion constant and analysis of distinguishability between diffusivities (ndim_homogeneous_distinguishability.py in diffusive_distinguisbaility)
+Simulation of homogeneous diffusion, bayesian estimation of underlying diffusion constant and analysis of distinguishability between diffusivities 
 
-Jupyter notebook providing examples of how to interact with submodules of above .py, and example analysis (ndim_diffusion_analysis_tutorial.ipynb)
+
+Getting Started
+---------------
+
+The python package ``ndim_homogeneous_distinguishability.py`` contains the meat of this project, as a set of functions which can be used to:
+1) Simulate diffusive trajectories (pure diffusion with a homogeneous diffusion constant)
+2) Use Bayesian inference to estimate the diffusion constant used to generate a trajectory by producing a posterior diffusivity distribution
+3) Analyze the dependence of diffusivity estimation error, and the ability to distinguish between trajectories with differing diffusivities, conditional on model parameters
+
+Examples of how to use these functions, as well as some of our own analysis of diffusivity distinguishability, are provided in the Jupyter notebook ``ndim_diffusion_analysis_tutorial.ipynb``.
+
+Also included are some stored pre-calculated numpy arrays used in the provided Jupyter notebook example analysis (in the directory ``loc_error_saved_files``) and another Jupyter notebook containing a toy model quantifying the relative impact of localization error on diffusion estimates conditional on number of spatial dimensions (``test_overestimation.ipynb``). 
 
 
 * Free software: Allen Institute Software License