Merge pull request #22 from kbonney/kld-doccs

Documentation updates

docs/index.rst

@@ -17,7 +17,50 @@ Python environment.
 
 To get started, we recommend exploring the pyrocs :ref:`tutorials<tutorials>`.
 
-The :ref:`API documentation<apidoc>` provides additional details about the functions included in the package.
+Datasets can be analyzed in multiple ways using using functions within pyRoCS. 
+While most of these datasets are in array formats, they reflect different underlying 
+structures, with some representing counts of entries and
+sequences and other representing summaries of graph-based structures. The below table summarizes
+current functionality. Letters in parentheticals indicate which module the function is located:
+information theory \(I), bioscience \(B), or complex systems \(C).
+
+.. list-table:: (Module) Functions and Primary Data Inputs
+   :widths: 25 75
+   :header-rows: 1
+
+   * - (Module) Function
+     - Primary Data Inputs
+   * - \(I) discrete_entropy
+     - Unique entries in a process and associated counts
+   * - \(I) kl_divergence
+     - Two arrays of probability distributions
+   * - \(I) novelty_transience_resonance
+     - An array of probability distribution
+   * - \(I) mutual_info
+     - Two arrays of entries (optional: counts)
+   * - \(B) affinity
+     - Matrix of co-occurring variables (optional: weights)
+   * - \(B) functional_redundancy
+     - Array of relative abundance and array of symmetric similarities
+   * - \(B) hill_diversity
+     - Proportion of individuals in group
+   * - \(B) hill_shannon
+     - Proportion of individuals in group
+   * - \(B) hill_simpson
+     - Proportion of individuals in group
+   * - \(C) causal_complexity
+     - Adjacency matrix of graph structure
+   * - \(C) cyclomatic_complexity
+     - Adjacency matrix of graph structure
+   * - \(C) feedback_density
+     - Adjacency matrix of graph structure
+   * - \(C) fluctuation_complexity
+     - Array of sequenced events
+   * - \(C) grc
+     - Adjacency matrix of graph structure
+
+The :ref:`API documentation<apidoc>` provides additional details about the functions 
+included in the package.
 
 .. toctree::
     :maxdepth: 1

pyrocs/biosciences/affinity.py

@@ -23,8 +23,8 @@ def affinity(data: np.ndarray, weights=None, to_bool=bool) -> float:
     results in a binary implementation of affinity within this software.
     
     Args:
-        data (array) 
-        weights (optional array) 
+        data (array): Matrix of co-occurring variables
+        weights (optional array): weights for each variable
         to_bool: function or type to convert array values to boolean
         
     Returns:

pyrocs/complex_systems/causal_complexity.py

@@ -23,7 +23,7 @@ def cyclomatic_complexity(A : np.ndarray, directed : bool = False) -> float:
     higher cyclomatic complexity values).     
     
     Args:
-        A (array)
+        A (array): Adjacency matrix of graph structure
     Returns:
         float
     '''
@@ -64,7 +64,7 @@ def feedback_density(A : np.ndarray, directed : bool = False) -> float:
     edges are included in one or more feedback loops.
     
     Args:
-        A (array)
+        A (array): Adjacency matrix of graph structure
     Returns:
         float
     '''
@@ -124,7 +124,7 @@ def causal_complexity(A: np.ndarray, directed : bool = False) -> float:
     of causal complexity than those systems with lower feedback density.
     
     Args:
-        A (array)
+        A (array): Adjacency matrix of graph structure
     Returns:
         float
     '''

pyrocs/complex_systems/grc.py

@@ -23,7 +23,7 @@ def grc(A : np.ndarray, directed : bool) -> float:
     versa :cite:p:`lakkaraju_complexity_2019`.
 
     Args:
-        A (array): Square matrix of adjacencies in the network
+        A (array): Adjacency matrix of graph structure
         directed (bool): If true, assume A represents a directed graph (row -> column).
             If false, assume A represents an undirected graph.
     Returns:

pyrocs/information_theory/kl_divergence.py

@@ -79,7 +79,7 @@ def novelty_transience_resonance(
         thetas_arr (array): rows are topic mixtures
         window (int): positive integer defining scale or scale size
     Returns:
-        tuple(array): novelties, transiences, resonances   
+        tuple(array) [novelties, transiences, resonances] 
     """
 
     # Find the first and last center speech offset, given window size.