Removed Schema.jl and added some docs (#4)

src/AlgebraicRelations.jl

@@ -1,5 +1,4 @@
 module AlgebraicRelations
-  include("Schema.jl")
   include("Query.jl")
   include("SQL.jl")
   include("Interface.jl")

src/Interface.jl

@@ -1,17 +1,24 @@
 module Interface
   export init_tables, prepare, execute, Connection, format_form
   using Catlab
-  using AlgebraicRelations.SchemaLib, AlgebraicRelations.QueryLib, AlgebraicRelations.SQL
+  using AlgebraicRelations.QueryLib, AlgebraicRelations.SQL
   using LibPQ, DataFrames
   import LibPQ:
     Connection, Result, Statement
 
 
+  # init_tables:
+  # This function will initialize a database with the tables required for the
+  # given schema argument
+
   function init_tables(conn::Connection, types, tables, schema)
     st = sql(types, tables, schema)
     result = LibPQ.execute(conn, st)
   end
 
+  # upload_csv:
+  # This function uploads data from a CSV to the connected database
+
   function upload_csv(conn::Connection, table::String, filename::String)
     LibPQ.execute(conn, "COPY '$table' FROM '$filename' DELIMITER ',' CSV HEADER;")
   end
@@ -27,11 +34,18 @@ module Interface
     Statement(conn, uid, query, res, length(q.wd.input_ports))
   end
 
+  # execute:
+  # This function gets the results running the provided query on the connected
+  # database
+
   function execute(conn::Connection, q::Query)::DataFrame
     query = sql(q)
     DataFrame(LibPQ.execute(conn, query))
   end
 
+  # execute(Statement, Array):
+  # This function runs a prepared SQL statement using the input array as
+  # arguments
   function execute(st::Statement, input::AbstractArray)::DataFrame
     DataFrame(LibPQ.execute(st, input))
   end

src/Query.jl

@@ -19,8 +19,6 @@ import Catlab.Programs: @program
 
 using AutoHashEquals
 
-import AlgebraicRelations.SchemaLib: Schema
-
 @auto_hash_equals struct Types
   ports::Ports
 end
@@ -30,6 +28,9 @@ end
 This structure holds the relationship graph between fields in a query
 
 # Fields
+- `types::Dict{Symbol, Tuple{Array{String,1}, Array{T,1} where T}}`:
+          The mapping between a type symbols and their fundamental types along
+          with the field names for those types if the SQL type is composite.
 - `tables::Dict{Symbol, Tuple{Array{String,1}, Array{String,1}}}`:
           The mapping between a table symbols and their column names for domain
           and codomain.

src/SQL.jl

@@ -1,13 +1,14 @@
 module SQL
 export sql, present_sql, to_sql
 using Catlab.Theories, Catlab.Present, Catlab.WiringDiagrams
-using AlgebraicRelations.QueryLib, AlgebraicRelations.SchemaLib
-import AlgebraicRelations.SchemaLib: Schema
+using AlgebraicRelations.QueryLib
 
 TypeToSql = Dict(String => "text",
                  Int64 => "int",
                  Float64 => "float4")
 
+# Generates a new array where all strings are unique. The new strings are based
+# off of the strings provided in the original array.
 uniquify(a::Array{String,1}) = begin
   a_n = Array{String,1}()
   # Fill a_n with unique values
@@ -27,6 +28,7 @@ uniquify(a::Array{String,1}) = begin
   return a_n
 end
 
+# Adds aliases for the SQL table names
 function add_aliases(names::Array{String,1})
   aliases = map(names) do name
               p_ind = findfirst(".", name)[1]
@@ -36,13 +38,16 @@ function add_aliases(names::Array{String,1})
   new_names = [n*" AS "*a for (n,a) in zip(names, aliases)]
   return new_names
 end
+
+# Converts a Julia datatype to its SQL equivalent
 function to_sql(t)
   if t isa DataType
     return TypeToSql[t]
   end
   return t
 end
 
+# Evaluates the connections between ports (primarily concerned with Junction nodes)
 function evaluate_ports(q::Query)
   wd = q.wd
   tables = q.tables
@@ -122,7 +127,7 @@ function evaluate_ports(q::Query)
   aliases, port_val
 end
 
-
+# Generates an SQL query from a provided Query object
 function sql(q::Query)::String
 
   tables = q.tables
@@ -166,7 +171,8 @@ function sql(q::Query)::String
   return select*from*condition
 end
 
-
+# Generates the SQL needed to generate the tables and types necessary for a
+# schema
 sql(types_dict, tables, schema) = begin
   primitives = map(collect(types_dict)) do (key,val)
     names = val[1]
@@ -236,7 +242,7 @@ sql(types_dict, tables, schema) = begin
 end
 
 
-# Need to generate a wrapper call around this to insert parameters
+# Generates a wrapper call around this to insert parameters
 # If the original query accepts a person's name (p_name) and returns
 # their manager's name (m_name) and the person's salary (salary), then
 # the wrapped query would look like this: