diff --git a/README.md b/README.md
index 387b325..9b549ab 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,19 @@
-edtc-code
-=========
+Economic Dynamics, Theory and Computation
+============================================
+
+This is the code repository for the text [Economic Dynamics, Theory and Computation](http://johnstachurski.net/personal/edtc.html) by [John Stachurski](http://johnstachurski.net).
+
+### About
+
+This code repository is written in conjuction with the text [Economic
+Dynamics, Theory and Computation](http://johnstachurski.net/personal/edtc.html).  The Python code
+contained in this repository is explained in detail in the text.  The MATLAB
+code provides equivalents to the Python code for those who prefer MATLAB.
+
+### Downloading the Repository
+
+Either
+
+* Click the 'Zip' button at the top of this page, or
+* Use [Git](https://help.github.com) to clone the repository
 
-Code for Economic Dynamics, Theory and Computation
\ No newline at end of file
diff --git a/edtc_matlab.zip b/edtc_matlab.zip
deleted file mode 100644
index 821c98a..0000000
Binary files a/edtc_matlab.zip and /dev/null differ
diff --git a/edtc_matlab/cpdynam.m b/edtc_matlab/cpdynam.m
deleted file mode 100644
index 2b2b5d7..0000000
--- a/edtc_matlab/cpdynam.m
+++ /dev/null
@@ -1,76 +0,0 @@
-% Filename: cpdynam.m
-% Author: Andy Qi
-% Date: December 2008
-% Corresponds to: Listing 6.7
-
-% Note: All of the code is wrapped in a class definition so that it can
-% be put in one file.  See the comments in kurtzbellman.m.  An example
-% of usage is given below.
-
-classdef cpdynam < handle
-    properties
-        alpha = 0.8;
-        a = 5.0;
-        c = 2.0;
-        W;
-        D;
-        P;
-    end
-    
-    methods
-        function self = cpdynam
-            self.W = betarnd(5, 5, 1, 1000) * self.c + self.a; % Shock obs.
-            self.D = @(x) 1.0/x; % Demand curve
-            self.P = self.D;     % Inverse of 1/x is just 1/x
-        end
-
-        function fp = fix_point(self, h, lower, upper)
-            % Computes the fixed point of h on [upper,lower] using fzero, 
-            % which finds the  zeros (roots) of a univariate function.
-            % Parameters : h is a function and lower and upper are numbers 
-            % (floats or integers).
-            fp = fzero(@(x) x - h(x), [lower, upper]);
-        end
-        
-        function t = T(self, p, x)
-            % Computes Tp(x), where T is the pricing functional operator.
-            % Parameters : p is an instance of lininterp and x is a number.
-            y = self.alpha * mean(p.interp(self.W));
-            if y <= self.P(x)
-                t = self.P(x);
-                return;
-            end
-            h = @(r) self.alpha * +...
-                mean(p.interp(self.alpha * (x - self.D(r)) + self.W));
-            t = self.fix_point(h, self.P(x), y);
-        end
-    end
-end
-
-% The following test shows how to use the class.  Put the code in a
-% separate file, in the same directory as cpdynam.m and lininterp
-%
-% cp = cpdynam;
-% gridsize = 150;
-% grid = linspace(cp.a, 35, gridsize);
-% tol = 0.0005;
-% vals = zeros(1, gridsize);
-% new_vals = zeros(1, gridsize);
-% for i = 1:gridsize
-%     vals(i) = cp.P(grid(i));
-% end
-% while 1
-%     hold on;
-%     plot(grid, vals);
-%     p = lininterp(grid, vals);
-%     f = @(x) cp.T(p, x);
-%     for i = 1:gridsize
-%         new_vals(i) = f(grid(i));
-%     end
-%     if max(abs(new_vals - vals)) < tol
-%         break
-%     end
-%     vals = new_vals;
-% end
-% hold off;
-
diff --git a/edtc_matlab/ds.m b/edtc_matlab/ds.m
deleted file mode 100644
index 985c277..0000000
--- a/edtc_matlab/ds.m
+++ /dev/null
@@ -1,36 +0,0 @@
-% Filename: ds.m
-% Author: Andy Qi
-% Date: December 2008
-% Corresponds to: Listing 4.2
-
-classdef ds < handle
-
-    properties
-        % h is a function and x is a number
-        % in S representing the current state .
-        h;
-        x;
-    end
-    
-    methods
-        function self = ds(h,x)
-            self.h = h;
-            self.x = x;
-        end
-        
-        function update(self)
-            % Update the state of the system by applying h.
-            self.x = self.h(self.x);
-        end
-        
-        function traj = trajectory(self,n)
-            % Generate a trajectory of length n, starting 
-            % at the current state .
-            traj = zeros(1,n);
-            for i = 1:n
-                traj(i) = self.x;
-                self.update;
-            end
-        end
-    end
-end
diff --git a/edtc_matlab/edf.m b/edtc_matlab/edf.m
deleted file mode 100644
index ff424c2..0000000
--- a/edtc_matlab/edf.m
+++ /dev/null
@@ -1,29 +0,0 @@
-% Filename: edf.m
-% Author: Andy Qi
-% Date: December 2008
-% Corresponds to: Listing xxxx
-% Corresponds to Listing 6.3 in Economic Dynamics
-% Author: Andy Qi, Kyoto University
-% Date: Dec 22,2008
-
-classdef edf
-    properties
-        observations;
-    end
-    
-    methods
-        function obj = edf(observations)
-            obj.observations = observations;
-        end
-        
-        function f = p(obj,x)
-            counter = 0;
-            for obs = obj.observations
-                if obs <= x
-                    counter = counter + 1;
-                end
-            end
-            f = counter / length(obj.observations);
-        end
-    end
-end
diff --git a/edtc_matlab/fpi.m b/edtc_matlab/fpi.m
deleted file mode 100644
index 1d53e22..0000000
--- a/edtc_matlab/fpi.m
+++ /dev/null
@@ -1,111 +0,0 @@
-% Filename: fpi.m
-% Author: Andy Qi
-% Date: December 2008
-% Corresponds to: Listing 6.6
-
-% Note: All of the code is wrapped in a class definition so that it can
-% be put in one file.  See the comments in kurtzbellman.m.  An example
-% of usage is given below.
-
-classdef fpi < handle
-    properties
-        theta = 0.5;
-        alpha = 0.8;
-        rho = 0.9;
-        W;
-        gridmax = 8;
-        gridsize = 150;
-        grid;
-    end
-    
-    methods
-        function self = fpi
-            self.W = exp(randn(1, 1000)); % Draws of shock
-            self.grid = linspace(0, self.gridmax^(1e-1), self.gridsize).^10;
-        end
-        
-        function u = U(self, c)
-        % Utility function
-            u = 1 - exp(- self.theta * c);
-        end
-        
-        function pro = f(self, k, z)
-        % Production function
-            pro = (k^self.alpha) * z;
-        end
-        
-        function max = maximizer(self, h, a, b)
-            max = fminbnd(@(x) -h(x), a, b);
-        end
-        
-        function t = T(self, sigma, w)
-        % Implements the operator L T_sigma
-        % Parameters: sigma and w should be instances of lininterp
-            len = self.gridsize;
-            vals = zeros(1, len);
-            for i = 1:len
-                x = self.grid(i);
-                Tw_y = self.U(x - sigma.interp(x)) + ... 
-                self.rho * mean(w.interp(self.f(sigma.interp(x), self.W)));
-                vals(i) = Tw_y; 
-            end
-            t = lininterp(self.grid, vals);
-        end
-        
-        function g = get_greedy(self, w)
-        % Computes a w-greedy policy, where w is an instance of lininterp
-            len = self.gridsize;
-            vals = zeros(1, len);
-            for i = 1:len
-                x = self.grid(i);
-                h = @(k) self.U(x - k) + ...
-                self.rho * mean(w.interp(self.f(k, self.W)));
-                vals(i) = self.maximizer(h, 0, x);
-            end
-            g = lininterp(self.grid, vals);
-        end
-        
-        function v = get_value(self, sigma, v)
-        % Computes an approximation to v_sigma, the value of following 
-        % policy sigma. Function v is a guess of v_sigma. 
-        % Parameters: sigma and v are both instances of lininterp
-            tol = 1e-2; % Error tolerance
-            while 1
-                new_v = self.T(sigma, v);
-                err = max(abs(new_v.Y - v.Y));
-                if err < tol
-                    v = new_v;
-                    return;
-                end
-                v = new_v;
-            end
-        end
-    end
-end
-
-% The following test shows how to use the class.  Put the code in a
-% separate file, in the same directory as fpi.m and lininterp
-%
-% tol = 0.005;
-% f = fpi;
-% % Choose a guess of the optimal policy
-% sigma = f.get_greedy(lininterp(f.grid, f.U(f.grid)));
-% Compute v_sigma using U as a starting point for the interative
-% procedure in get_value
-% v = f.get_value(sigma, lininterp(f.grid, f.U(f.grid)));
-% while 1
-%     hold on;
-%     plot(f.grid, sigma.Y);
-%     sigma_new = f.get_greedy(v);
-%     % Compute v_sigma_new using v as a starting point for the iteration
-%     v_new = f.get_value(sigma_new, v);
-%     err = max(abs(v_new.Y - v.Y));
-%     if err < tol
-%         break
-%     else
-%         v = v_new;
-%         sigma = sigma_new;
-%     end
-% end
-% hold off;
-
diff --git a/edtc_matlab/fvi.m b/edtc_matlab/fvi.m
deleted file mode 100644
index 2dddf1d..0000000
--- a/edtc_matlab/fvi.m
+++ /dev/null
@@ -1,70 +0,0 @@
-% Filename: fvi.m
-% Author: Andy Qi
-% Date: December 2008
-% Corresponds to: Listing 6.5
-
-% Note: All of the code is wrapped in a class definition so that it can
-% be put in one file.  See the comments in kurtzbellman.m.  An example
-% of usage is given below.
-
-classdef fvi < handle
-    properties
-        theta = 0.5;
-        alpha = 0.8;
-        rho = 0.9;
-        W;
-        gridmax = 8;
-        gridsize = 150;
-        grid;
-    end
-    
-    methods
-        function self = fvi
-            self.W = exp(randn(1,1000)); % Draws of shock
-            self.grid = linspace(0,self.gridmax^(1e-1),self.gridsize).^10;
-        end
-        
-        function u = U(self,c)      % Utility function
-            u = 1 - exp(- self.theta * c);
-        end
-        
-        function pro = f(self,k,z)  % Production function
-            pro = (k^self.alpha) * z;
-        end
-        
-        function m = maximum(self,h,a,b)
-            m = h(fminbnd(@(x) -h(x),a,b));
-        end
-        
-        function li = bellman(self,w)
-            % The approximate Bellman operator.
-            % Parameters : w is an instance of lininterp (see the file
-            %              lininterp.m)
-            % Returns : An instance of lininterp
-            
-            len = length(self.grid);
-            vals = zeros(1,len);
-            for i = 1:len
-                y = self.grid(i);
-                h = @(k) U(self,(y - k)) +... 
-                self.rho * mean(w.interp(f(self,k,self.W)));
-                vals(i) = self.maximum(h,0,y);
-            end
-            li = lininterp(self.grid,vals);
-        end
-    end
-end
-
-% The following test shows how to use the class.  Put the code in a
-% separate file, in the same directory as fvi.m and lininterp
-%
-% f = fvi;
-% v = lininterp(f.grid, f.U(f.grid));
-% 
-% for i = 1:10
-%     hold on;
-%     plot(f.grid, v.Y);
-%     w = f.bellman(v);
-%     v = w;
-% end
-% hold off;
diff --git a/edtc_matlab/kurtzbellman.m b/edtc_matlab/kurtzbellman.m
deleted file mode 100644
index 68a2df5..0000000
--- a/edtc_matlab/kurtzbellman.m
+++ /dev/null
@@ -1,84 +0,0 @@
-% Filename: kurtzbellman.m
-% Author: Andy Qi
-% Date: December 2008
-% Corresponds to: Listing 5.1
-
-% The MATLAB code for this listing is put in a class definition
-% because MATLAB can only have one function or class definition
-% per file.  By putting the code in a class definition, we can
-% have all the code in one file.  At the bottom of the file is
-% a test of the code which shows how to use the class.
-
-classdef kurtzbellman < handle
-    properties
-        beta = 0.5;
-        rho = 0.9;
-        B = 10;
-        M = 5;
-        S;  % State space = 0,...,B + M
-        Z;  % Shock space = 0,...,B
-    end
-
-    methods
-        function self = kurtzbellman
-            self.S = 0:(self.B + self.M);
-            self.Z = 0:self.B;
-        end
-        
-        function u = U(self,c)
-            % Utility function
-            u = c^self.beta;
-        end
-
-        function p = phi(self,z)
-            % Probability mass function,uniform distribution
-            if 0 <= z && z <= self.B
-                p = 1.0 / length(self.Z);
-            else
-                p = 0;
-            end
-        end
-
-        function g = Gamma(self,x)
-            % The correspondence of feasible actions .
-            g = 0:min(x,self.M);
-        end
-
-        function Tv = T(self,v)
-            % An implementation of the Bellman operator.
-            % Parameters: v is a sequence representing a
-            % function defined on S. Returns: Tv,a list.
-            Tv = zeros(1,length(self.S));
-            for x = self.S
-                % Compute the value of the objective function for each
-                % a in Gamma (x), and store the result in vals
-                vals = zeros(1,length(Gamma(self,x)));
-                for a = Gamma(self,x)
-                    Sum = 0;
-                    for z = self.Z
-                        Sum = Sum + v(a + z + 1) * self.phi(z);
-                    end
-                    y = U(self,(x - a)) + self.rho * Sum;
-                    vals(a + 1) = y;
-                end
-                Tv(x + 1) = max(vals);
-            end
-        end
-    end
-end
-
-% The following test shows how to use the class.  Put the code in a
-% separate file, in the same directory as kurtzbellman.m
-%
-% kb = kurtzbellman;
-% w = zeros(1, length(kb.S));
-% err = 1;
-% while err > 0.001
-%     v = kb.T(w);
-%     err = max(abs(v - w));
-%     w = v;
-%     hold on;
-%     plot(w);
-% end
-% hold off;
-
diff --git a/edtc_matlab/kurtzvsigma.m b/edtc_matlab/kurtzvsigma.m
deleted file mode 100644
index 8ef490e..0000000
--- a/edtc_matlab/kurtzvsigma.m
+++ /dev/null
@@ -1,45 +0,0 @@
-% Filename: kurtzvsigma.m
-% Author: Andy Qi
-% Date: December 2008
-% Corresponds to: Listing 5.2
-
-% Note: See the comments below for an example of usage.
-
-function v_sigma = kurtzvsigma(kb,sigma)
-    % Computes the value of following policy sigma.  Here kb
-    % is an instance of the class kurtzbellman, as defined in
-    % kurtzbellman.m
-    
-    % Set up the stochastic kernel p_sigma as a 2D array :
-    len = length(kb.S);
-    p_sigma = zeros(len,len);
-    for x = kb.S
-        for y = kb.S
-            p_sigma(x + 1,y + 1) = phi(kb,(y - sigma(x + 1)));
-        end
-    end
-    % Create the right Markov operator M_sigma :
-    M_sigma = @(h)p_sigma * h;
-    
-    % Set up the function r_sigma as an array :
-    % Initialize r_sigma into a column vector
-    r_sigma = zeros(len,1); 
-    for x = kb.S
-        r_sigma(x + 1) = U(kb,(x - sigma(x + 1)));
-    end
-    v_sigma = zeros(len,1);
-    discount = 1;
-    for i = 1:50
-        v_sigma = v_sigma + discount * r_sigma;
-        r_sigma = M_sigma(r_sigma);
-        discount = discount * kb.rho;
-    end
-end
-
-% Example of usage (put this in a separate file in the same 
-% directory as kurtzbellman.m and kurtzvsigma.m
-%
-% BO = kurtzbellman;
-% m = ones(1, 16);
-% kurtzvsigma(BO, m)
-
diff --git a/edtc_matlab/lininterp.m b/edtc_matlab/lininterp.m
deleted file mode 100644
index cd429ae..0000000
--- a/edtc_matlab/lininterp.m
+++ /dev/null
@@ -1,32 +0,0 @@
-% Filename: lininterp.m
-% Author: Andy Qi
-% Date: December 2008
-% Corresponds to: Listing 6.4
-
-classdef lininterp
-    % Provides linear interpolation in one dimension
-    properties
-        % Parameters : X and Y are sequences or arrays
-        % containing the (x,y) interpolation points
-        X;
-        Y;
-    end
-    methods
-        function self = lininterp(x,y)
-            self.X = x;
-            self.Y = y;
-        end
-        
-        function i = interp(self,z)
-            % Uses MATLAB's interp1 function for interpolation.
-            % The z values are truncated so that they lie inside
-            % the grid points.  The effect is that evaluation of
-            % a point to the left of X(1) returns Y(1), while 
-            % evaluation of a point to the right of X(end) returns
-            % Y(end)
-            z = max(z, self.X(1));
-            z = min(z, self.X(end));
-            i = interp1(self.X, self.Y, z);
-        end
-    end
-end
diff --git a/edtc_matlab/mc.m b/edtc_matlab/mc.m
deleted file mode 100644
index 3d6730a..0000000
--- a/edtc_matlab/mc.m
+++ /dev/null
@@ -1,43 +0,0 @@
-% Filename: mc.m
-% Author: Andy Qi
-% Date: December 2008
-% Corresponds to: Listing 4.4
-
-classdef mc < handle
-    
-    properties
-        % p[x] is a sequence
-        % of length N for each x, and represents p(x,dy).
-        % The parameter X is an integer in S.
-        p;
-        X;
-    end
-
-    methods
-        function self = mc(p,X)
-            % Create an instance with stochastic kernel
-            % p and current state X.Here p[x] is an array of length N
-            % for each x, and represents p(x,dy).
-            % The parameter X is an integer in S.
-            self.p = p;
-            self.X = X;
-        end
-
-        function update(self)
-            % Update the state by drawing from p(X,dy).
-            px = self.p((self.X),:);
-            len = length(px);
-            self.X = randsample(1:len,1,true,px);
-        end
-
-        function path = sample_path(self,n)
-            % Generate a sample path of length n, starting
-            % from the current state .
-            path = zeros(1,n);
-            for i = 1:n
-                path(i) = self.X;
-                self.update;
-            end
-        end
-    end
-end
diff --git a/edtc_matlab/p2srs.m b/edtc_matlab/p2srs.m
deleted file mode 100644
index c7306b3..0000000
--- a/edtc_matlab/p2srs.m
+++ /dev/null
@@ -1,34 +0,0 @@
-% Filename: p2srs.m
-% Author: Andy Qi
-% Date: December 2008
-% Corresponds to: Listing 5.3
-
-% Note: The code below gives an example of usage
-
-function r = p2srs(p)
-    % Takes a kernel p on S = {1,...,N} and returns a function 
-    % F(x,z) which represents it as an SRS.
-    % Parameters : p is a matrix
-    % Returns : A function F with arguments (x,z).
-    S = 1:length(p(1,:));
-    function f = F(x,z)
-        a = 0;
-        for y = S
-            if a < z && z <= a + p(x,y)
-                f = y;
-                return;
-            end
-            a = a + p(x,y);
-        end
-    end
-    r = @F;
-end
-
-% Example of usage (put this in a separate file in the same directory).
-%
-%
-% pH = [0.971,0.029,0.000;  
-%       0.145,0.778,0.077;
-%       0.000,0.508,0.492];
-% F = p2srs(pH);
-% F(2, 0.7)  % should return 2
diff --git a/edtc_matlab/polyclass.m b/edtc_matlab/polyclass.m
deleted file mode 100644
index bfaac91..0000000
--- a/edtc_matlab/polyclass.m
+++ /dev/null
@@ -1,36 +0,0 @@
-% Filename: polyclass.m
-% Author: Andy Qi
-% Date: December 2008
-% Corresponds to: Listing 2.2
-
-classdef polyclass
-    properties
-        coef;
-    end
-    
-    methods
-        function self = polyclass(coef)
-            % Creates an instance p of the Polynomial class,
-            % where p(x) = coef[0] x^0 + ... + coef[N] x^N.
-            self.coef = coef;
-        end
-        
-        function y = evaluate(self,x)
-            % Reverse the order of coef, and then use MATLAB's 
-            % polyval function to evaluate
-            y = polyval(self.coef(end:-1:1),x);
-        end
-        
-        function self = differentiate(self)
-            len = length(self.coef);
-            new_coef = zeros(1,len);
-            for i = 1:len
-                new_coef(i) = (i-1) * self.coef(i);  
-            end
-            % Remove the first element, which is zero
-            new_coef(1) = [];
-            % And reset coefficients data to new values
-            self.coef = new_coef;
-        end
-    end
-end
diff --git a/edtc_matlab/srs.m b/edtc_matlab/srs.m
deleted file mode 100644
index 5e539c2..0000000
--- a/edtc_matlab/srs.m
+++ /dev/null
@@ -1,38 +0,0 @@
-% Filename: srs.m
-% Author: Andy Qi
-% Date: December 2008
-% Corresponds to: Listing 6.1
-
-classdef srs < handle
-    properties
-        % Parameters : F and phi are functions, where
-        % phi() returns a draw from phi. X is a
-        % number representing the initial condition
-        F;
-        phi;
-        X;
-    end
-    
-    methods
-        function self = srs(F,phi,X)
-            % Represents X_{t + 1} = F(X_t,W_{t + 1}); W ~ phi
-            self.F = F;
-            self.phi = phi;
-            self.X = X;
-        end
-        
-        function update(self)
-            % Update the state according to X = F(X, W)
-            self.X = self.F(self.X,self.phi());
-        end
-        
-        function path = sample_path(self,n)
-            % Generate path of length n from current state
-            path = zeros(1,n);
-            for i = 1:n
-                path(i) = self.X;
-                self.update;
-            end
-        end
-    end
-end
diff --git a/edtc_matlab/testds.m b/edtc_matlab/testds.m
deleted file mode 100644
index e7d34d0..0000000
--- a/edtc_matlab/testds.m
+++ /dev/null
@@ -1,11 +0,0 @@
-% Filename: testds.m
-% Author: Andy Qi
-% Date: December 2008
-% Corresponds to: Listing 4.3
-
-quadmap = @(x)4 * x * (1 - x);
-q = ds(quadmap,0.1);          % Create an instance q of ds
-T1 = q.trajectory(100);       % T1 holds trajectory from 0.1
-
-q.x = 0.2;                    % Reset current state to 0.2
-T2 = q.trajectory(100);       % T2 holds trajectory from 0.2
diff --git a/edtc_matlab/testmc.m b/edtc_matlab/testmc.m
deleted file mode 100644
index 856bb8d..0000000
--- a/edtc_matlab/testmc.m
+++ /dev/null
@@ -1,21 +0,0 @@
-% Filename: testmc.m
-% Author: Andy Qi
-% Date: December 2008
-% Corresponds to: Listing 4.5
-
-pH = [0.971,0.029,0.000;
-      0.145,0.778,0.077;
-      0.000,0.508,0.492];
-
-psi = [0.3,0.4,0.3];                  % Initial condition
-len = length(psi);
-rs = randsample(1:len,1,true,psi);    % Use randsample fuction returns i with
-                                      % probability phi[i], where phi is a
-                                      % sequence
-h = mc(pH,rs);                        % Create an instance of class mc
-T1 = sample_path(h,1000);             % Series is Markov -(p, psi)
-
-psi2 = [0.8,0.1,0.1];                 % Alternative initial cond.
-len2 = length(psi2);
-h.X = randsample(1:len2,1,true,psi2); % Reset the current state
-T2 = sample_path(h,1000);             % Series is Markov -(p, psi2)
diff --git a/edtc_matlab/testsrs.m b/edtc_matlab/testsrs.m
deleted file mode 100644
index b9b5b4d..0000000
--- a/edtc_matlab/testsrs.m
+++ /dev/null
@@ -1,17 +0,0 @@
-% Filename: testsrs.m
-% Author: Andy Qi
-% Date: December 2008
-% Corresponds to: Listing 6.2
-
-alpha = 0.5;
-sigma = 0.2;
-s = 0.5;
-delta = 0.1;
-% Define F(k, z) = s * k ^ alpha * z + (1 - delta ) * k
-F = @(k,z) s * (k^alpha) * z + (1 - delta) * k;
-lognorm = @() lognrnd(0,sigma);
-
-solow_srs = srs(F,lognorm,1.0);
-P1 = sample_path(solow_srs,500); % Generate Path from X = 1
-solow_srs.X = 60;                % Reset the current state
-P2 = sample_path(solow_srs,500); % Generate Path from X = 60
diff --git a/edtc_matlab_rev/ar1.m b/edtc_matlab_rev/ar1.m
deleted file mode 100644
index bce6d6e..0000000
--- a/edtc_matlab_rev/ar1.m
+++ /dev/null
@@ -1,12 +0,0 @@
-% Filename: ar1.m
-% Author: Andy Qi
-% Date: December 2008
-% Corresponds to: Listing 8.1
-
-a = 0.5;
-b = 1;
-X = zeros(1, 101);    % Create an empty array to store path
-X(1) = normrnd(0, 1); % X_0 has dist N(0, 1)
-for t = 1:100
-    X(t + 1) = normrnd(a * X(t) + b, 1);
-end
diff --git a/edtc_matlab_rev/ecdf.m b/edtc_matlab_rev/ecdf.m
deleted file mode 100644
index dcd580f..0000000
--- a/edtc_matlab_rev/ecdf.m
+++ /dev/null
@@ -1,2 +0,0 @@
-
-% Please use the MATLAB built in ecdf function
diff --git a/edtc_matlab_rev/fphamilton.m b/edtc_matlab_rev/fphamilton.m
deleted file mode 100644
index 79850cf..0000000
--- a/edtc_matlab_rev/fphamilton.m
+++ /dev/null
@@ -1,13 +0,0 @@
-% Filename: fphamilton.m
-% Author: Andy Qi
-% Date: December 2008
-% Corresponds to: Listing 4.6
-
-pH = [0.971,0.029,0.000;  % Hamilton's kernel
-      0.145,0.778,0.077;
-      0.000,0.508,0.492];
-I = eye(3);               % 3 by 3 identity matrix
-Q = ones(3);              % Matrix of ones
-b = ones(3,1);            % Vector of ones
-A = transpose(I - pH + Q);
-solution = A \ b;
diff --git a/edtc_matlab_rev/polyclass0.m b/edtc_matlab_rev/polyclass0.m
deleted file mode 100644
index 5f0572c..0000000
--- a/edtc_matlab_rev/polyclass0.m
+++ /dev/null
@@ -1,7 +0,0 @@
-
-% Filename: polyclass0.m
-%
-% The MATLAB version of the Python code in polyclass0.py is omitted, since
-% polyclass0.py is not real Python, but rather for illustration purposes
-% only.  Please see polyclass.m
-%
diff --git a/edtc_matlab_rev/quadmap1.m b/edtc_matlab_rev/quadmap1.m
deleted file mode 100644
index d5f72d0..0000000
--- a/edtc_matlab_rev/quadmap1.m
+++ /dev/null
@@ -1,12 +0,0 @@
-% Filename: quadmap1.m
-% Author: Andy Qi
-% Date: December 2008
-% Corresponds to: Listing 4.1
-
-datapoints = zeros(1,200);
-x = 0.11;
-for t = 1:200
-    datapoints(t) = x;
-    x = 4 * x * (1-x);
-end
-plot(datapoints)
diff --git a/edtc_python.zip b/edtc_python.zip
deleted file mode 100644
index f25cb15..0000000
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diff --git a/edtc_python/testgenfinitemc.py b/python_code/testgenfinitemc.py
similarity index 100%
rename from edtc_python/testgenfinitemc.py
rename to python_code/testgenfinitemc.py
diff --git a/edtc_python/testsrs.py b/python_code/testsrs.py
similarity index 100%
rename from edtc_python/testsrs.py
rename to python_code/testsrs.py