Export methods

Author: jiahao

src/FastHistogram.jl

@@ -1,3 +1,5 @@
+export hist_eig
+
 #Uses the method of Sturm sequences to compute the histogram of eigenvalues of a matrix
 #
 # Reference
@@ -25,7 +27,6 @@ function hist_eig{GridPoint <: Number}(M::AbstractMatrix, bins::Vector{GridPoint
         histogram[BinId] = sum([r < 0 for r in SturmRatioSequence])
     end
     histogram = int(diff([histogram; n]))
-    return histogram
 end
 
 
@@ -52,6 +53,5 @@ function hist_eig{GridPoint <: Number}(M::SymTridiagonal, bins::Vector{GridPoint
         histogram[BinId] = sum([SturmRatio < 0 for SturmRatio in r])
     end
     histogram = int(diff([histogram; n]))
-    return histogram
 end
 

src/FormalPowerSeries.jl

@@ -9,7 +9,11 @@
 #     Power Series---Integration---Conformal Mapping---Location of Zeros,
 #     Wiley-Interscience: New York, 1974
 
-import Base.inv, Base.length
+import Base.eye, Base.inv, Base.length, 
+       Base.==, Base.+, Base.-, Base.*, Base..*, Base.^
+export FormalPowerSeries, tovector, trim, isunit, isnonunit,
+       MatrixForm, reciprocal, derivative, isconstant, compose,
+       isalmostunit, FormalLaurentSeries
 
 #############################
 # Formal power series (fps) #
@@ -44,6 +48,20 @@ function tovector{T}(P::FormalPowerSeries{T}, n :: Integer)
 end
 
 
+# Basic housekeeping and properties
+
+# Remove extraneous zeros
+function trim{T}(P::FormalPowerSeries{T})
+    for (k,v) in P.c
+        if v==0
+            delete!(P.c, k)
+        end
+    end
+    return P
+end
+
+length{T}(P::FormalPowerSeries{T})=max([k for (k,v) in P.c])
+
 function =={T}(P::FormalPowerSeries{T}, Q::FormalPowerSeries{T})
     for (k,v) in P.c
         if v==0 #ignore explicit zeros
@@ -66,22 +84,6 @@ function =={T}(P::FormalPowerSeries{T}, Q::FormalPowerSeries{T})
     return true
 end
 
-
-
-# Basic housekeeping and properties
-
-# Remove extraneous zeros
-function trim{T}(P::FormalPowerSeries{T})
-    for (k,v) in P.c
-        if v==0
-            delete!(P.c, k)
-        end
-    end
-    return P
-end
-
-length{T}(P::FormalPowerSeries{T})=max([k for (k,v) in P.c])
-
 # Basic arithmetic [H, p.10]
 function +{T}(P::FormalPowerSeries{T}, Q::FormalPowerSeries{T})
     c = Dict{BigInt, T}()

src/GaussianEnsembleDistributions.jl

@@ -9,6 +9,8 @@
 # doi: 10.1063/1.1507823
 # arXiv: math-ph/0206043
 
+export VandermondeDeterminant, HermiteJPDF, LaguerreJPDF, JacobiJPDF 
+
 ###
 # Joint probability density functions
 #
@@ -101,5 +103,3 @@ function JacobiJPDF{Eigenvalue<:FloatingPoint}(lambda :: Vector{Eigenvalue}, n1
     return c * VandermondeDeterminant(lambda, beta) * Prod * exp(-Energy)
 end
 
-
-

src/GaussianEnsembleSamples.jl

@@ -8,6 +8,12 @@
 # doi: 10.1063/1.1507823
 # arXiv: math-ph/0206043
 
+using Distributions
+export GaussianHermiteMatrix, GaussianLaguerreMatrix, GaussianJacobiMatrix,
+       GaussianHermiteTridiagonalMatrix, GaussianLaguerreTridiagonalMatrix,
+       GaussianJacobiSparseMatrix,
+       GaussianHermiteSamples, GaussianLaguerreSamples, GaussianJacobiSamples
+
 #Generates a NxN symmetric Wigner matrix
 #Hermite ensemble
 function GaussianHermiteMatrix(n :: Integer, beta :: Integer)
@@ -57,7 +63,6 @@ end
 
 
 #A convenience function
-using Distributions
 chi(df) = sqrt(rand(Chisq(df)))
 
 #Generates a NxN tridiagonal Wigner matrix

src/RandomMatrices.jl

@@ -1,19 +1,21 @@
 require("Distributions")
 
 module RandomMatrices
-    using Distributions
-
+    importall Distributions
+    using Catalan
+    
     #Classical Gaussian matrix ensembles
     include("GaussianEnsembleSamples.jl")
+    include("GaussianEnsembleDistributions.jl")
+    
     # Classical univariate distributions
     ####################################
     include("densities/Semicircle.jl")
-    #Tracy-Widom distribution
     include("densities/TracyWidom.jl")
 
     #Fast histogrammer for matrix eigenvalues
     include("FastHistogram.jl")
 
     #Formal power series
     include("FormalPowerSeries.jl")
-end
+end #module

src/densities/TracyWidom.jl

@@ -1,8 +1,11 @@
 #Computes the Tracy-Widom distribution by directly solving the
 #Painlevé II equation
+export TracyWidom
 using ODE
+
 include("gradient.jl")
-function tracywidom(t::Real)
+
+function TracyWidom(t::Real)
     t0 = -8.0
 
     t>t0 ? return 0.0 : nothing