Update random growth examples to latest Julia. randomgrowth2 now uses PyPlot

Author: andreasnoack

demos/book/8/randomgrowth.jl

@@ -7,15 +7,15 @@ function random_growth(M, N, q)
 
     G[1,1] = true
     # update the possible sets
-    imagesc((0,N), (M,0), G)
+    display(imagesc((0,N), (M,0), G))
     for t = 1:T
         sets = next_possible_squares(G)
         ## Highlights all the possible squares
         for i = 1:length(sets)
             idx = sets[i]::(Int,Int)
             G[idx[1], idx[2]] = 0.25
         end
-        imagesc((0,N), (M,0), G)
+        display(imagesc((0,N), (M,0), G))
         sleep(.01)
 
         ## Actual growth
@@ -26,12 +26,12 @@ function random_growth(M, N, q)
                 G[idx[1], idx[2]] = ison
             end
         end
-        imagesc((0,N), (M,0), G)
+        display(imagesc((0,N), (M,0), G))
         G[G .== 0.5] = 1
         G[G .== 0.25] = 0
         sleep(.01)
     end
-    G
+    return G
 end
 
 function next_possible_squares(G)

demos/book/8/randomgrowth2.jl

@@ -1,13 +1,14 @@
 #randomgrowth2.jl
-using Winston
+using PyPlot
 using ODE
-include("../1/gradient.jl")
 
 function randomgrowth2()
     num_trials = 2000
     g = 1
     q = 0.7
-    Ns = 80 # [50, 100, 200]
+    Ns = 80
+    # Ns = [50, 100, 200]
+    clf()
     # [-1.771086807411  08131947928329]
     # Gap = c d^N
     for jj = 1:length(Ns)
@@ -23,24 +24,25 @@ function randomgrowth2()
         end
         C = (B - N*om(g,q)) / (sigma_growth(g,q)*N^(1/3))
         d = 0.2
-        x, n = hist(C - exp(-N*0.0025 - 3), -6:d:4)
-        p = FramedPlot()
-        h = Histogram(n/(d*num_trials), step(x))
-        h.x0 = first(x)
-        add(p, h)
+        subplot(1,length(Ns),jj)
+        plt.hist(C - exp(-N*0.0025 - 3), normed = true)
+        xlim(-6, 4)
 
         ## Theory
         t0 = 4
         tn = -6
         dx = 0.005
         deq = (t, y) -> [y[2]; t*y[1]+2*y[1]^3; y[4]; y[1]^2]
         y0 = [airy(t0); airy(1,t0); 0; airy(t0)^2]      # boundary conditions
-        t, y = ode23(deq, t0:-dx:tn, y0)                # solve
-        F2 = exp(-y[:,3])                               # the distribution
+        t, y = ode23(deq, y0, t0:-dx:tn)                # solve
+        F2 = Float64[exp(-y[i][3]) for i = 1:length(y)]                               # the distribution
         f2 = gradient(F2, t)                            # the density
-       
-        add(p, Curve(t, f2, "color", "red", "linewidth", 3))
-        Winston.display(p)
+
+        # add(p, Curve(t, f2, "color", "red", "linewidth", 3))
+        # Winston.display(p)
+        subplot(1,length(Ns),jj)
+        plot(t, f2, "r", linewidth = 3)
+        ylim(0, 0.6)
         println(mean(C))
     end
 end
@@ -49,7 +51,7 @@ function G(N, M, q)
     # computes matrix G[N,M] for a given q
     GG = floor(log(rand(N,M))/log(q))
     #float(rand(N,M) < q)
-    
+
     # Compute the edges: the boundary
     for ii = 2:N
         GG[ii, 1] = GG[ii, 1] + GG[ii-1, 1]
@@ -64,7 +66,7 @@ function G(N, M, q)
     ## Plot
     # imagesc((0,N),(M,0),GG)
     # sleep(.01)
-    
+
     return  GG[N, M]
 end