Merge pull request #59 from CurricularAnalytics/development

Version 0.5.2

Project.toml

@@ -1,7 +1,7 @@
 name = "CurricularAnalytics"
 uuid = "593ffa3d-269e-5d81-88bc-c3b6809c35a6"
 authors = ["Greg Heileman <[email protected]>", "Hayden Free <[email protected]>", "Orhan Abar <[email protected]>", "Will Thompson <[email protected]>"]
-version = "0.5.1"
+version = "0.5.2"
 
 [deps]
 Blink = "ad839575-38b3-5650-b840-f874b8c74a25"

docs/make.jl

@@ -19,6 +19,7 @@ makedocs(
         "Metrics"                                   => "metrics.md",
         "Creating Degree Plans"                     => "degreeplans.md",
         #"Simulating Student Flows"                  => "simulating.md",
+        "Graph Algorithms"                          => "graph_algs.md",
         "Contributing"                              => "contributing.md",
         "License Information"                       => "license.md",
         "Citing CurricularAnalytics.jl"             => "citing.md"

docs/src/contributing.md

@@ -3,6 +3,7 @@
 We welcome all contributors and ask that you read these guidelines before starting to work on this project. Following these guidelines will facilitate collaboration and improve the speed at which your contributions gets merged.
 
 ## Bug reports
+
 If you encounter code in this toolbox that does not function properly please file a bug report. The report should be raised as a github issue with a minimal working example that reproduces the error message or erroneous result. The example should include any data needed, and if the issue is incorrectness, please post the correct result along with the incorrect result produced by the toolbox.
 
 Please include version numbers of all relevant libraries and Julia itself.

docs/src/graph_algs.md

@@ -0,0 +1,17 @@
+# Graph Algorithms
+
+This toolbox makes use of a number of the graph algorithms provided in the [LightGraphs](https://juliagraphs.github.io/LightGraphs.jl/latest/index.html) package.  In addition, we have implemented a number of graph algorithms that you may find useful when developing analytics around curriculum graphs.  The functions implementing these algorithms are described next.
+
+```@docs
+dfs
+gad
+all_paths
+longest_path
+reachable_from
+reachable_from_subgraph
+reachable_to
+reachable_to_subgraph
+reach
+reach_subgraph
+topological_sort
+```

docs/src/metrics.md

@@ -1,6 +1,6 @@
 # Metrics
 
-A number of predefined metrics that have been defined for curricula and degree plans are described below. You may also define your own metrics for curricula and degree plans.  Each of these data types has a `metrics` dictionary where you may write these user-defined metrics.
+A number of predefined metrics for analyzing curricula and degree plans are described below. You may also define your own metrics for curricula and degree plans.  Each of these data types has a `metrics` dictionary where you may write these user-defined metrics.
 
 ## Curricular Metrics
 
@@ -58,16 +58,28 @@ As an example of the structural complexity metric, consider the same four-course
 complexity
 ```
 
+### Basic Metrics (Curriculum)
+
+All of the predefined metrics for a given curriculum described above will be computed and stored in the curriculum's `metric` dictionary by using the following function.
+
+```@docs
+basic_metrics(::Curriculum)
+```
+
 ## Degree Plan Metrics
 
-The aforementioned curricular complexity metrics are independent of how a curriculum is layed out as a degree plan.  That is, the curricular metrics will not change as different degree plans are created.  Degree plan metrics are related to the manner in which courses in the curriculum are laid out across the terms in the degree plan.  These metrics are used in the creation of optimal degree plans as described in [Optimzed Degree Plans]@ref.
+The aforementioned curricular complexity metrics are independent of how a curriculum is layed out as a degree plan.  That is, the curricular metrics will not change as different degree plans are created.  Degree plan metrics are related to the manner in which courses in the curriculum are laid out across the terms in the degree plan.  These metrics are used in the creation of optimal degree plans as described in [Optimized Degree Plans](@ref).
+
+### Basic Metrics (Degree Plans)
 
 A set of basic statistics associated with the distribution of credit hours in a degree plan can be obtained by using:
 
 ```@docs
-basic_metrics
+basic_metrics(::DegreePlan)
 ```
 
+### Requisite Distance
+
 A degree plan metric that is based upon the separation of courses and their pre- and co-requisites in a degree plan is described next.
 
 ```@docs

docs/src/persistence.md

@@ -6,7 +6,7 @@ The ability to read/write curricula and degree plans to disk is greatly facilita
 
 The *CSV file format* stores data as comma-separated values in a text file, allowing data to be presented in tabular form. You can open CSV files with either a text editor or by using your favorite spreadsheet program. The sections below describe the CSV file formats used for curricula and degree plans, as well as functions that can be used to read/write these CSV files.
 
-### Curricula
+### Curricula Files
 
 The CSV file format used to store curricula is shown below:
 ![file format for curricula](./curriculum-format.png)
@@ -38,7 +38,7 @@ Below is an example curriculum file that uses the aforedescribed format:
 A link to this CSV file can be found [here](./curriculum-ex.csv), and a visualization of this curriculum, created using the function described in [Visualization Functions](@ref), is as follows:
 ![visualization of example curriculum](./curriculum-ex-viz.png)
 
-### Degree Plans
+### Degree Plan Files
 
 The CSV file format used to store degree plans is shown below:
 ![file format for curricula](./degree-plan-format.png)