Encrypt/decrypt image with AES256 in Matlab - matlab

I have to encrypt and decrypt an image with AES256. I'm working on the program below, which encrypts plaintext.
AES is an algorithm that has fixed length input in 128 bit. It work in four different steps every round; AES256 has 14 rounds, as the program shows for a different kind of this algorithm.
I'm looking to adapt the main program for any image that surely will have a length larger than 128bit. Should I divide it into many blocks that have the same size, or do you have other suggestions?
function [output] = aes(s, oper, mode, input, iv, sbit)
% AES Encrypt/decrypt array of bytes by AES.
% output = aes(s, oper, mode, input, iv, sbit)
% Encrypt/decrypt array of bytes by AES-128, AES-192, AES-256.
% All NIST SP800-38A cipher modes supported (e.g. ECB, CBC, OFB, CFB, CTR).
% Usage example: out = aesdecrypt(s, 'dec', 'ecb', data)
% s: AES structure (generated by aesinit)
% oper: operation:
% 'e', 'enc', 'encrypt', 'E',... = encrypt
% 'd', 'dec', 'decrypt', 'D',... = decrypt
% mode: operation mode
% 'ecb' = Electronic Codebook Mode
% 'cbc' = Cipher Block Chaining Mode
% 'cfb' = Cipher Feedback Mode
% 'ofb' = Output Feedback Mode
% 'ctr' = Counter Mode
% For counter mode you need external AES_GET_COUNTER()
% counter function.
% input: plaintext/ciphertext byte-vector with length
% multiple of 16
% iv: initialize vector - some modes need it
% ending initialize vector is stored in s.iv, so you
% can use aes() repetitively to encode/decode
% large vector:
% out = aes(s, 'enc', 'cbc', input1, iv);
% out = [out aes(s, 'enc', 'cbc', input1, s.iv)];
% ...
% sbit: bit-width parameter for CFB mode
% output: ciphertext/plaintext byte-vector
%
% See
% Morris Dworkin, Recommendation for Block Cipher Modes of Operation
% Methods and Techniques
% NIST Special Publication 800-38A, 2001 Edition
% for details.
error(nargchk(4, 6, nargin));
validateattributes(s, {'struct'}, {});
validateattributes(oper, {'char'}, {});
validateattributes(mode, {'char'}, {});
validateattributes(input, {'numeric'}, {'real', 'vector', '>=', 0, '<', 256});
if (nargin >= 5)
validateattributes(iv, {'numeric'}, {'real', 'vector', '>=', 0, '<', 256});
if (length(iv) ~= 16)
error('Length of ''iv'' must be 16.');
end
end
if (nargin >= 6)
validateattributes(sbit, {'numeric'}, {'real', 'scalar', '>=', 1, '<=', 128});
end
if (mod(length(input), 16))
error('Length of ''input'' must be multiple of 16.');
end
switch lower(oper)
case {'encrypt', 'enc', 'e'}
oper = 0;
case {'decrypt', 'dec', 'd'}
oper = 1;
otherwise
error('Bad ''oper'' parameter.');
end
blocks = length(input)/16;
input = input(:);
switch lower(mode)
case {'ecb'}
% Electronic Codebook Mode
% ------------------------
output = zeros(1,length(input));
idx = 1:16;
for i = 1:blocks
if (oper)
% decrypt
output(idx) = aesdecrypt(s,input(idx));
else
% encrypt
output(idx) = aesencrypt(s,input(idx));
end
idx = idx + 16;
end
case {'cbc'}
% Cipher Block Chaining Mode
% --------------------------
if (nargin < 5)
error('Missing initialization vector ''iv''.');
end
output = zeros(1,length(input));
ob = iv;
idx = 1:16;
for i = 1:blocks
if (oper)
% decrypt
in = input(idx);
output(idx) = bitxor(ob(:), aesdecrypt(s,in)');
ob = in;
else
% encrypt
ob = bitxor(ob(:), input(idx));
ob = aesencrypt(s, ob);
output(idx) = ob;
end
idx = idx + 16;
end
% store iv for block passing
s.iv = ob;
case {'cfb'}
% Cipher Feedback Mode
% --------------------
% Special mode with bit manipulations
% sbit = 1..128
if (nargin < 6)
error('Missing ''sbit'' parameter.');
end
% get number of bits
bitlen = 8*length(input);
% loop counter
rounds = round(bitlen/sbit);
% check
if (rem(bitlen, sbit))
error('Message length in bits is not multiple of ''sbit''.');
end
% convert input to bitstream
inputb = reshape(de2bi(input,8,2,'left-msb')',1,bitlen);
% preset init. vector
ib = iv;
ibb = reshape(de2bi(ib,8,2,'left-msb')',1,128);
% preset output binary stream
outputb = zeros(size(inputb));
for i = 1:rounds
iba = aesencrypt(s, ib);
% convert to bit, MSB first
ibab = reshape(de2bi(iba,8,2,'left-msb')',1,128);
% strip only sbit MSB bits
% this goes to xor
ibab = ibab(1:sbit);
% strip bits from input
inpb = inputb((i - 1)*sbit + (1:sbit));
% make xor
outb = bitxor(ibab, inpb);
% write to output
outputb((i - 1)*sbit + (1:sbit)) = outb;
if (oper)
% decrypt
% prepare new iv - bit shift
ibb = [ibb((1 + sbit):end) inpb];
else
% encrypt
% prepare new iv - bit shift
ibb = [ibb((1 + sbit):end) outb];
end
% back to byte ary
ib = bi2de(vec2mat(ibb,8),'left-msb');
% loop
end
output = bi2de(vec2mat(outputb,8),'left-msb');
% store iv for block passing
s.iv = ib;
case {'ofb'}
% Output Feedback Mode
% --------------------
if (nargin < 5)
error('Missing initialization vector ''iv''.');
end
output = zeros(1,length(input));
ib = iv;
idx = 1:16;
for i = 1:blocks
% encrypt, decrypt
ib = aesencrypt(s, ib);
output(idx) = bitxor(ib(:), input(idx));
idx = idx + 16;
end
% store iv for block passing
s.iv = ib;
case {'ctr'}
% Counter Mode
% ------------
if (nargin < 5)
iv = 1;
end
output = zeros(1,length(input));
idx = 1:16;
for i = (iv):(iv + blocks - 1)
ib = AES_GET_COUNTER(i);
ib = aesencrypt(s, ib);
output(idx) = bitxor(ib(:), input(idx));
idx = idx + 16;
end
s.iv = iv + blocks;
otherwise
error('Bad ''oper'' parameter.');
end
the aesencrypt function :
function [out] = aesencrypt(s, in)
% AESENCRYPT Encrypt 16-bytes vector.
% Usage: out = aesencrypt(s, in)
% s: AES structure
% in: input 16-bytes vector (plaintext)
% out: output 16-bytes vector (ciphertext)
if (nargin ~= 2)
error('Bad number of input arguments.');
end
validateattributes(s, {'struct'}, {});
validateattributes(in, {'numeric'}, {'real','uint8'});
% copy input to local
% 16 -> 4 x 4
state = reshape(in, 4, 4);
% Initial round
% AddRoundKey keyexp(1:4)
state = bitxor(state, (s.keyexp(1:4, :))');
% Loop over (s.rounds - 1) rounds
for i = 1:(s.rounds - 1)
% SubBytes - lookup table
state = s.s_box(state + 1);
% ShiftRows
state = shift_rows(state, 0);
% MixColumns
state = mix_columns(state, s);
% AddRoundKey keyexp(i*4 + (1:4))
state = bitxor(state, (s.keyexp((1:4) + 4*i, :))');
end
% Final round
% SubBytes - lookup table
state = s.s_box(state + 1);
% ShiftRows
state = shift_rows(state, 0);
% AddRoundKey keyexp(4*s.rounds + (1:4))
state = bitxor(state, (s.keyexp(4*s.rounds + (1:4), :))');
% copy local to output
% 4 x 4 -> 16
out = reshape(state, 1, 16);
function aesinit:
function s = aesinit(key)
% AESINIT Generate structure with s-boxes, expanded key, etc.
% Usage: s = aesinit([23 34 168 ... 39])
% key: 16 (AES-128), 24 (AES-192), and 32 (AES-256)
% items array with bytes of key
% s: AES structure for AES parameters and tables
% Stepan Matejka, 2011, matejka[at]feld.cvut.cz
% $Revision: 1.1.0 $ $Date: 2011/10/12 $
validateattributes(key,...
{'numeric'},...
{'real', 'vector', '>=', 0, '<=', 255});
key = key(:);
lengthkey = length(key);
switch (lengthkey)
case 16
rounds = 10;
case 24
rounds = 12;
case 32
rounds = 14;
otherwise
error('Only AES-128, AES-192, and AES-256 are supported.');
end
% fill s structure
s = {};
s.key = key;
s.bytes = lengthkey;
s.length = lengthkey * 8;
s.rounds = rounds;
% irreducible polynomial for multiplication in a finite field 0x11b
% bin2dec('100011011');
s.mod_pol = 283;
% s-box method 1 (slow)
% ---------------------
% % multiplicative inverse table
% % first is zero, calculate rest
% inverse = zeros(1,256);
% for i = 2:256
% inverse(i) = find_inverse(i - 1, s.mod_pol);
% end
%
% % generate s-box
% s_box = zeros(1,256);
% for i = 1:256
% % affine transformation
% s_box(i) = aff_trans(inverse(i));
% end
% s.s_box = s_box;
%
% % generate inverse s-box
% inv_s_box(s_box(1:256) + 1) = (1:256) - 1;
% s.inv_s_box = inv_s_box;
% s-box method 2 (faster)
% -----------------------
% first build logarithm lookup table and it's inverse
aes_logt = zeros(1,256);
aes_ilogt = zeros(1,256);
gen = 1;
for i = 0:255
aes_logt(gen + 1) = i;
aes_ilogt(i + 1) = gen;
gen = poly_mult(gen, 3, s.mod_pol);
end
% store log tables
s.aes_logt = aes_logt;
s.aes_ilogt = aes_ilogt;
% build s-box and it's inverse
s_box = zeros(1,256);
loctable = [1 2 4 8 16 32 64 128 1 2 4 8 16 32 64 128];
for i = 0:255
if (i == 0)
inv = 0;
else
inv = aes_ilogt(255 - aes_logt(i + 1) + 1);
end
temp = 0;
for bi = 0:7
temp2 = sign(bitand(inv, loctable(bi + 1)));
temp2 = temp2 + sign(bitand(inv, loctable(bi + 4 + 1)));
temp2 = temp2 + sign(bitand(inv, loctable(bi + 5 + 1)));
temp2 = temp2 + sign(bitand(inv, loctable(bi + 6 + 1)));
temp2 = temp2 + sign(bitand(inv, loctable(bi + 7 + 1)));
temp2 = temp2 + sign(bitand(99, loctable(bi + 1)));
if (rem(temp2,2))
temp = bitor(temp, loctable(bi + 1));
end
end
s_box(i + 1) = temp;
end
inv_s_box(s_box(1:256) + 1) = (0:255);
% table correction (must be)
s_box(1 + 1) = 124;
inv_s_box(124 + 1) = 1;
inv_s_box(99 + 1) = 0;
s.s_box = s_box;
s.inv_s_box = inv_s_box;
% tables for fast MixColumns
mix_col2 = zeros(1,256);
mix_col3 = mix_col2;
mix_col9 = mix_col2;
mix_col11 = mix_col2;
mix_col13 = mix_col2;
mix_col14 = mix_col2;
for i = 1:256
mix_col2(i) = poly_mult(2, i - 1, s.mod_pol);
mix_col3(i) = poly_mult(3, i - 1, s.mod_pol);
mix_col9(i) = poly_mult(9, i - 1, s.mod_pol);
mix_col11(i) = poly_mult(11, i - 1, s.mod_pol);
mix_col13(i) = poly_mult(13, i - 1, s.mod_pol);
mix_col14(i) = poly_mult(14, i - 1, s.mod_pol);
end
s.mix_col2 = mix_col2;
s.mix_col3 = mix_col3;
s.mix_col9 = mix_col9;
s.mix_col11 = mix_col11;
s.mix_col13 = mix_col13;
s.mix_col14 = mix_col14;
% expanded key
s.keyexp = key_expansion(s.key, s.s_box, s.rounds, s.mod_pol, s.aes_logt, s.aes_ilogt);
% poly & invpoly
s.poly_mat = [...
2 3 1 1;...
1 2 3 1;...
1 1 2 3;...
3 1 1 2];
s.inv_poly_mat =[...
14 11 13 9;...
9 14 11 13;...
13 9 14 11;...
11 13 9 14];
% end of aesinit.m
% ------------------------------------------------------------------------
function p = poly_mult(a, b, mod_pol)
% Multiplication in a finite field
% For loop multiplication - slower than log/ilog tables
% but must be used for log/ilog tables generation
p = 0;
for counter = 1 : 8
if (rem(b, 2))
p = bitxor(p, a);
b = (b - 1)/2;
else
b = b/2;
end
a = 2*a;
if (a > 255)
a = bitxor(a, mod_pol);
end
end
% ------------------------------------------------------------------------
function inv = find_inverse(in, mod_pol)
% Multiplicative inverse for an element a of a finite field
% very bad calculate & test method
% Not used in faster version
% loop over all possible bytes
for inv = 1 : 255
% calculate polynomial multiplication and test to be 1
if (1 == poly_mult(in, inv, mod_pol))
% we find it
break
end
end
inv = 0;
% ------------------------------------------------------------------------
function out = aff_trans(in)
% Affine transformation over GF(2^8)
% Not used for faster s-box generation
% modulo polynomial for multiplication in a finite field
% bin2dec('100000001');
mod_pol = 257;
% multiplication polynomial
% bin2dec('00011111');
mult_pol = 31;
% addition polynomial
% bin2dec('01100011');
add_pol = 99;
% polynomial multiplication
temp = poly_mult(in, mult_pol, mod_pol);
% xor with addition polynomial
out = bitxor(temp, add_pol);
% ------------------------------------------------------------------------
function expkey = key_expansion(key, s_box, rounds, mod_pol, aes_logt, aes_ilogt)
% Expansion of key
% This is old version for AES-128 (192?, 256? not tested):
% rcon = ones(1,rounds);
% for i = 2:rounds
% rcon(i) = poly_mult(rcon(i - 1), 2, mod_pol);
% end
% % fill bytes 2, 3, and 4 by 0
% rcon = [rcon(:), zeros(rounds, 3)];
%
% kcol = length(key)/4;
% expkey = (reshape(key, kcol, 4))';
% for i = (kcol + 1):(4*rounds + 4)
% % copy the previous row of the expanded key into a buffer
% temp = expkey(i - 1, :);
% % each fourth row
% if (mod(i, 4) == 1)
% % shift temp
% temp = temp([2 3 4 1]);
% % s-box transform
% temp = s_box(temp + 1);
% % compute the current round constant
% r = rcon((i - 1)/4, :);
% % xor
% temp = bitxor(temp, r);
% else
% if ((kcol > 6) && (mod(i, kcol) == 0))
% temp = s_box(temp);
% end
% end
% % generate new row of the expanded key
% expkey(i, :) = bitxor(expkey(i - 4, :), temp);
% end
% This is new faster version for all AES:
rcon = 1;
kcol = length(key)/4;
expkey = (reshape(key,4,kcol))';
% traverse for all rounds
for i = kcol:(4*(rounds + 1) - 1)
% copy the previous row of the expanded key into a buffer
temp = expkey(i, :);
% each kcol row
if (mod(i, kcol) == 0)
% rotate word
temp = temp([2 3 4 1]);
% s-box transform
temp = s_box(temp + 1);
% xor
temp(1) = bitxor(temp(1), rcon);
% new rcon
% 1. classic poly_mult
% rcon = poly_mult(rcon, 2, mod_pol);
% 2. or faster version with log/ilog tables
% note rcon is never zero here
% rcon = aes_ilogt(mod((aes_logt(rcon + 1) + aes_logt(2 + 1)), 255) + 1);
rcon = aes_ilogt(mod((aes_logt(rcon + 1) + 25), 255) + 1);
else
if ((kcol > 6) && (mod(i, kcol) == 4))
temp = s_box(temp + 1);
end
end
% generate new row of the expanded key
expkey(i + 1, :) = bitxor(expkey(i - kcol + 1, :), temp);
end

You are limited to 128 bits because you tried to encrypt using aesencrypt, one of the low level functions which work on 4x4 blocks. If you instead use the aes function you can encode any "byte-vector with length multiple of 16". It will repeatedly call aesenrypt for you until all 4x4 blocks are processed.

Related

Berlekamp Massey Algorithm for BCH simplified binary version

I am trying to follow Lin, Costello's explanation of the simplified BM algorithm for the binary case in page 210 of chapter 6 with no success on finding the error locator polynomial.
I'm trying to implement it in MATLAB like this:
function [locator_polynom] = compute_error_locator(syndrome, t, m, field, alpha_powers)
%
% Initial conditions for the BM algorithm
polynom_length = 2*t;
syndrome = [syndrome; zeros(3, 1)];
% Delta matrix storing the powers of alpha in the corresponding place
delta_rho = uint32(zeros(polynom_length, 1)); delta_rho(1)=1;
delta_next = uint32(zeros(polynom_length, 1));
% Premilimnary values
n_max = uint32(2^m - 1);
% Initialize step mu = 1
delta_next(1) = 1; delta_next(2) = syndrome(1); % 1 + S1*X
% The discrepancy is stored in polynomial representation as uint32 numbers
value = gf_mul_elements(delta_next(2), syndrome(2), field, alpha_powers, n_max);
discrepancy_next = bitxor(syndrome(3), value);
% The degree of the locator polynomial
locator_degree_rho = 0;
locator_degree_next = 1;
% Update all values
locator_polynom = delta_next;
delta_current = delta_next;
discrepancy_rho = syndrome(1);
discrepancy_current = discrepancy_next;
locator_degree_current = locator_degree_next;
rho = 0; % The row with the maximum value of 2mu - l starts at 1
for i = 1:t % Only the even steps are needed (so make t out of 2*t)
if discrepancy_current ~= 0
% Compute the correction factor
correction_factor = uint32(zeros(polynom_length, 1));
x_exponent = 2*(i - rho);
if (discrepancy_current == 1 || discrepancy_rho == 1)
d_mu_times_rho = discrepancy_current * discrepancy_rho;
else
alpha_discrepancy_mu = alpha_powers(discrepancy_current);
alpha_discrepancy_rho = alpha_powers(discrepancy_rho);
alpha_inver_discrepancy_rho = n_max - alpha_discrepancy_rho;
% The alpha power for dmu * drho^-1 is
alpha_d_mu_times_rho = alpha_discrepancy_mu + alpha_inver_discrepancy_rho;
% Equivalent to aux mod(2^m - 1)
alpha_d_mu_times_rho = alpha_d_mu_times_rho - ...
n_max * uint32(alpha_d_mu_times_rho > n_max);
d_mu_times_rho = field(alpha_d_mu_times_rho);
end
correction_factor(x_exponent+1) = d_mu_times_rho;
correction_factor = gf_mul_polynoms(correction_factor,...
delta_rho,...
field, alpha_powers, n_max);
% Finally we add the correction factor to get the new delta
delta_next = bitxor(delta_current, correction_factor(1:polynom_length));
% Update used data
l = polynom_length;
while delta_next(l) == 0 && l>0
l = l - 1;
end
locator_degree_next = l-1;
% Update previous maximum if the degree is higher than recorded
if (2*i - locator_degree_current) > (2*rho - locator_degree_rho)
locator_degree_rho = locator_degree_current;
delta_rho = delta_current;
discrepancy_rho = discrepancy_current;
rho = i;
end
else
% If the discrepancy is 0, the locator polynomial for this step
% is passed to the next one. It satifies all newtons' equations
% until now.
delta_next = delta_current;
end
% Compute the discrepancy for the next step
syndrome_start_index = 2 * i + 3;
discrepancy_next = syndrome(syndrome_start_index); % First value
for k = 1:locator_degree_next
value = gf_mul_elements(delta_next(k + 1), ...
syndrome(syndrome_start_index - k), ...
field, alpha_powers, n_max);
discrepancy_next = bitxor(discrepancy_next, value);
end
% Update all values
locator_polynom = delta_next;
delta_current = delta_next;
discrepancy_current = discrepancy_next;
locator_degree_current = locator_degree_next;
end
end
I'm trying to see what's wrong but I can't. It works for the examples in the book, but not always. As an aside, to compute the discrepancy S_2mu+3 is needed, but when I have only 24 syndrome coefficients how is it computed on step 11 where 2*11 + 3 is 25?
Thanks in advance!
It turns out the code is ok. I made a different implementation from Error Correction and Coding. Mathematical Methods and gives the same result. My problem is at the Chien Search.
Code for the interested:
function [c] = compute_error_locator_v2(syndrome, m, field, alpha_powers)
%
% Initial conditions for the BM algorithm
% Premilimnary values
N = length(syndrome);
n_max = uint32(2^m - 1);
polynom_length = N/2 + 1;
L = 0; % The curent length of the LFSR
% The current connection polynomial
c = uint32(zeros(polynom_length, 1)); c(1) = 1;
% The connection polynomial before last length change
p = uint32(zeros(polynom_length, 1)); p(1) = 1;
l = 1; % l is k - m, the amount of shift in update
dm = 1; % The previous discrepancy
for k = 1:2:N % For k = 1 to N in steps of 2
% ========= Compute discrepancy ==========
d = syndrome(k);
for i = 1:L
aux = gf_mul_elements(c(i+1), syndrome(k-i), field, alpha_powers, n_max);
d = bitxor(d, aux);
end
if d == 0 % No change in polynomial
l = l + 1;
else
% ======== Update c ========
t = c;
% Compute the correction factor
correction_factor = uint32(zeros(polynom_length, 1));
% This is d * dm^-1
dd_sum = modulo(alpha_powers(d) + n_max - alpha_powers(dm), n_max);
for i = 0:polynom_length - 1
if p(i+1) ~= 0
% Here we compute d*d^-1*p(x_i)
ddp_sum = modulo(dd_sum + alpha_powers(p(i+1)), n_max);
if ddp_sum == 0
correction_factor(i + l + 1) = 1;
else
correction_factor(i + l + 1) = field(ddp_sum);
end
end
end
% Finally we add the correction factor to get the new locator
c = bitxor(c, correction_factor);
if (2*L >= k) % No length change in update
l = l + 1;
else
p = t;
L = k - L;
dm = d;
l = 1;
end
end
l = l + 1;
end
end
The code comes from this implementation of the Massey algorithm

Fast approach in matlab to estimate linear regression with AR terms

I am trying to estimate regression and AR parameters for (loads of) linear regressions with AR error terms. (You could also think of this as a MA process with exogenous variables):
, where
, with lags of length p
I am following the official matlab recommendations and use regArima to set up a number of regressions and extract regression and AR parameters (see reproducible example below).
The problem: regArima is slow! For 5 regressions, matlab needs 14.24sec. And I intend to run a large number of different regression models. Is there any quicker method around?
y = rand(100,1);
r2 = rand(100,1);
r3 = rand(100,1);
r4 = rand(100,1);
r5 = rand(100,1);
exo = [r2 r3 r4 r5];
tic
for p = 0:4
Mdl = regARIMA(3,0,0);
[EstMdl, ~, LogL] = estimate(Mdl,y,'X',exo,'Display','off');
end
toc
Unlike the regArima function which uses Maximum Likelihood, the Cochrane-Orcutt prodecure relies on an iteration of OLS regression. There are a few more particularities when this approach is valid (refer to the link posted). But for the aim of this question, the appraoch is valid, and fast!
I modified James Le Sage's code which covers only AR lags of order 1, to cover lags of order p.
function result = olsc(y,x,arterms)
% PURPOSE: computes Cochrane-Orcutt ols Regression for AR1 errors
%---------------------------------------------------
% USAGE: results = olsc(y,x)
% where: y = dependent variable vector (nobs x 1)
% x = independent variables matrix (nobs x nvar)
%---------------------------------------------------
% RETURNS: a structure
% results.meth = 'olsc'
% results.beta = bhat estimates
% results.rho = rho estimate
% results.tstat = t-stats
% results.trho = t-statistic for rho estimate
% results.yhat = yhat
% results.resid = residuals
% results.sige = e'*e/(n-k)
% results.rsqr = rsquared
% results.rbar = rbar-squared
% results.iter = niter x 3 matrix of [rho converg iteration#]
% results.nobs = nobs
% results.nvar = nvars
% results.y = y data vector
% --------------------------------------------------
% SEE ALSO: prt_reg(results), plt_reg(results)
%---------------------------------------------------
% written by:
% James P. LeSage, Dept of Economics
% University of Toledo
% 2801 W. Bancroft St,
% Toledo, OH 43606
% jpl#jpl.econ.utoledo.edu
% do error checking on inputs
if (nargin ~= 3); error('Wrong # of arguments to olsc'); end;
[nobs nvar] = size(x);
[nobs2 junk] = size(y);
if (nobs ~= nobs2); error('x and y must have same # obs in olsc'); end;
% ----- setup parameters
ITERMAX = 100;
converg = 1.0;
rho = zeros(arterms,1);
iter = 1;
% xtmp = lag(x,1);
% ytmp = lag(y,1);
% truncate 1st observation to feed the lag
% xlag = x(1:nobs-1,:);
% ylag = y(1:nobs-1,1);
yt = y(1+arterms:nobs,1);
xt = x(1+arterms:nobs,:);
xlag = zeros(nobs-arterms,arterms);
for tt = 1 : arterms
xlag(:,nvar*(tt-1)+1:nvar*(tt-1)+nvar) = x(arterms-tt+1:nobs-tt,:);
end
ylag = zeros(nobs-arterms,arterms);
for tt = 1 : arterms
ylag(:,tt) = y(arterms-tt+1:nobs-tt,:);
end
% setup storage for iteration results
iterout = zeros(ITERMAX,3);
while (converg > 0.0001) & (iter < ITERMAX),
% step 1, using intial rho = 0, do OLS to get bhat
ystar = yt - ylag*rho;
xstar = zeros(nobs-arterms,nvar);
for ii = 1 : nvar
tmp = zeros(1,arterms);
for tt = 1:arterms
tmp(1,tt)=ii+nvar*(tt-1);
end
xstar(:,ii) = xt(:,ii) - xlag(:,tmp)*rho;
end
beta = (xstar'*xstar)\xstar' * ystar;
e = y - x*beta;
% truncate 1st observation to account for the lag
et = e(1+arterms:nobs,1);
elagt = zeros(nobs-arterms,arterms);
for tt = 1 : arterms
elagt(:,tt) = e(arterms-tt+1:nobs-tt,:);
end
% step 2, update estimate of rho using residuals
% from step 1
res_rho = (elagt'*elagt)\elagt' * et;
rho_last = rho;
rho = res_rho;
converg = sum(abs(rho - rho_last));
% iterout(iter,1) = rho;
iterout(iter,2) = converg;
iterout(iter,3) = iter;
iter = iter + 1;
end; % end of while loop
if iter == ITERMAX
% error('ols_corc did not converge in 100 iterations');
print('ols_corc did not converge in 100 iterations');
end;
result.iter= iterout(1:iter-1,:);
% after convergence produce a final set of estimates using rho-value
ystar = yt - ylag*rho;
xstar = zeros(nobs-arterms,nvar);
for ii = 1 : nvar
tmp = zeros(1,arterms);
for tt = 1:arterms
tmp(1,tt)=ii+nvar*(tt-1);
end
xstar(:,ii) = xt(:,ii) - xlag(:,tmp)*rho;
end
result.beta = (xstar'*xstar)\xstar' * ystar;
e = y - x*result.beta;
et = e(1+arterms:nobs,1);
elagt = zeros(nobs-arterms,arterms);
for tt = 1 : arterms
elagt(:,tt) = e(arterms-tt+1:nobs-tt,:);
end
u = et - elagt*rho;
result.vare = std(u)^2;
result.meth = 'olsc';
result.rho = rho;
result.iter = iterout(1:iter-1,:);
% % compute t-statistic for rho
% varrho = (1-rho*rho)/(nobs-2);
% result.trho = rho/sqrt(varrho);
(I did not adapt in the last 2 lines the t-test for rho vectors of length p, but this should be straight forward to do..)

Last plot in subplot becomes over-sized

I am using subplot function of MATLAB. Surprisingly the last plot in each subplot set becomes over-sized. Can anybody help me to resolve this issue? I have experimented with the parameters a little, but no luck. I am not able to post the plot figure.
function plotFluxVariabilityByGene(cRxn,KeggID,geneName)
load iJO1366; % Load the model iJO1366
%Find 'Gene' associated reactions from 'model'
reactions = rxnNamesFromKeggID(model,KeggID);
nCheck = 0; % Initialize counter
% Determine initial subplot dimensions
[R C setSize] = subplotSize(numel(reactions));
for n = 1 : numel(reactions)
% Get the name of nth reaction
rxn = reactions{n};
% Define the array for control reaction fluxes
cRxnArray = getCrxnArray(model,cRxn);
% Initialize storage for lower and upper limit-values
L = []; U = []; Avg = [];
% Get the fluxVariability values
for i = 1 : numel(cRxnArray)
modelMod = changeRxnBounds(model,cRxn,cRxnArray(i),'b');
[L(i) U(i)] = fluxVariability(modelMod,100,'max',{rxn});
Avg(i) = (L(i) + U(i))/2;
%fprintf('mthfcFlux = %f; Li = %f; Ui = %f\n',array(i),L(i),U(i));
end
% adjust the subplot number
nCheck = nCheck + 1;
% Determine the range of n to be saved in one file
if nCheck == 1
start = n;
elseif nCheck == setSize;
stop = n;
end
subplot(R,C,nCheck)
plot(cRxnArray,L,'-r','LineWidth',1); hold on;
plot(cRxnArray,L,'^r','MarkerSize',3,'LineWidth',2);
plot(cRxnArray,U,'-.b','LineWidth',1);
plot(cRxnArray,U,'^b','MarkerSize',2,'LineWidth',2);
plot(cRxnArray,Avg,'-','Color',[0.45,0.45,0.45],'LineWidth',2.5);
% Label X and Y axes
%xlabel([cRxn ' Flux']);
%ylabel(['fluxVariability ' char(rxn)]);
xlabel('Flux');
ylabel('fluxVariability');
hold off;
% Adjust X and Y axes limits
%xmn = min(cRxnArray) - ((max(cRxnArray) - min(cRxnArray))*0.05);
%xmx = max(cRxnArray) + ((max(cRxnArray) - min(cRxnArray))*0.05);
%ymn = min([U L]) - ((max([U L]) - min([U L]))*0.05);
%ymx = max([U L]) + ((max([U L]) - min([U L]))*0.05);
%if xmn ~= xmx
% xlim([xmn xmx]);
%end
%if ymn ~= ymx
% ylim([ymn ymx]);
%end
% Print which reactions are done
fprintf('\n......done for %s',char(rxn));
% If 'setSize' subplots are done then save the set in a file
if nCheck == setSize
saveas(gcf,['TEST/' cRxn 'flux-Vs-' geneName '_fluxVariability' num2str(start) '-' num2str(stop) '.fig']);
saveas(gcf,['TEST/' cRxn 'flux-Vs-' geneName '_fluxVariability' num2str(start) '-' num2str(stop) '.eps']); close(gcf);
% Determine initial subplot dimensions
[R C setSize] = subplotSize(numel(reactions)-n);
% Return nCheck to zero;
nCheck = 0;
end
end
% If nCheck is not equal to 16 then there are subplot that is not saved
% inside the for loop. Let's save it here.
if nCheck ~= setSize
stop = n;
saveas(gcf,['TEST/' cRxn 'flux-Vs-' geneName '_fluxVariability' num2str(start) '-' num2str(stop) '.fig']);
saveas(gcf,['TEST/' cRxn 'flux-Vs-' geneName '_fluxVariability' num2str(start) '-' num2str(stop) '.eps']); close(gcf);
end
fprintf('\nAll done\n');
end
%####################################################
%# Other functions ##
%####################################################
function rxnNames = rxnNamesFromKeggID(model,KeggID)
% Find 'Gene' associated reactions from 'model'
associatedRxns = findRxnsFromGenes(model,KeggID);
% Extract the reaction details from the structure to a cell
rxnDetails = eval(sprintf('associatedRxns.%s',KeggID));
% Extract only the reaction names from the cell
rxnNames = rxnDetails(:,1);
end
%####################################################
function cRxnArray = getCrxnArray(model,cRxn)
% Define the solver
changeCobraSolver('glpk');
% Find solution for the model
sol = optimizeCbModel(model);
% Change the objective of the default model to 'cRxn'
tmpModel = changeObjective(model,cRxn);
% Find slution for the changed model. This gives the maximum and
% minimum possible flux through the reaction 'cRxn' when the model is
% still viable
%solMax = optimizeCbModel(tmpModel,'max');
solMin = optimizeCbModel(tmpModel,'min');
% Create an array of 20 euqally spaced flux values between 'solMin' and
% 'sol.x'
%array = linspace(solMin.f,solMax.f,10);
cRxnArray = linspace(solMin.f,sol.x(findRxnIDs(model,cRxn)),20);
end
%####################################################
function [R C setSize] = subplotSize(remainingPlots)
% Sets number of columns and rows to 3 if total subplot >= 9
if remainingPlots > 7
R = 3; C = 3; setSize = 9;
elseif remainingPlots < 7
R = 2; C = 3; setSize = 6;
elseif remainingPlots < 5
R = 2; C = 2; setSize = 4;
elseif remainingPlots < 4
R = 1; C = 3; setSize = 3;
elseif remainingPlots < 3
R = 1; C = 2; setSize = 2;
end
end
%####################################################
My subplot looks like this:
I suspect its because you are calling subplotSize a second time inside your loop. This could be changing your R and C variables.
I would advise to check the R and C variables at the subplot command on each loop.

How does the choice of the wavelet function impact the speed of cwt()?

In cwt() I can specify which wavelet function to use. How does that impact the speed of cwt()?
Here is a benchmark, which I run with the -singleCompThread option when starting MATLAB to force it to use a single computational thread. cwt() was passed a 1,000,000-sample signal and asked to compute scales 1 to 10. My CPU is an i7-3610QM.
Code used:
clear all
%% Benchmark parameters
results_file_name = 'results_scale1-10.csv';
number_of_random_runs = 10;
scales = 1:10;
number_of_random_samples = 1000000;
%% Construct a cell array containing all the wavelet names
wavelet_haar_names = {'haar'};
wavelet_db_names = {'db1'; 'db2'; 'db3'; 'db4'; 'db5'; 'db6'; 'db7'; 'db8'; 'db9'; 'db10'};
wavelet_sym_names = {'sym2'; 'sym3'; 'sym4'; 'sym5'; 'sym6'; 'sym7'; 'sym8'};
wavelet_coif_names = {'coif1'; 'coif2'; 'coif3'; 'coif4'; 'coif5'};
wavelet_bior_names = {'bior1.1'; 'bior1.3'; 'bior1.5'; 'bior2.2'; 'bior2.4'; 'bior2.6'; 'bior2.8'; 'bior3.1'; 'bior3.3'; 'bior3.5'; 'bior3.7'; 'bior3.9'; 'bior4.4'; 'bior5.5'; 'bior6.8'};
wavelet_rbior_names = {'rbio1.1'; 'rbio1.3'; 'rbio1.5'; 'rbio2.2'; 'rbio2.4'; 'rbio2.6'; 'rbio2.8'; 'rbio3.1'; 'rbio3.3'; 'rbio3.5'; 'rbio3.7'; 'rbio3.9'; 'rbio4.4'; 'rbio5.5'; 'rbio6.8'};
wavelet_meyer_names = {'meyr'};
wavelet_dmeyer_names = {'dmey'};
wavelet_gaus_names = {'gaus1'; 'gaus2'; 'gaus3'; 'gaus4'; 'gaus5'; 'gaus6'; 'gaus7'; 'gaus8'};
wavelet_mexh_names = {'mexh'};
wavelet_morl_names = {'morl'};
wavelet_cgau_names = {'cgau1'; 'cgau2'; 'cgau3'; 'cgau4'; 'cgau5'};
wavelet_shan_names = {'shan1-1.5'; 'shan1-1'; 'shan1-0.5'; 'shan1-0.1'; 'shan2-3'};
wavelet_fbsp_names = {'fbsp1-1-1.5'; 'fbsp1-1-1'; 'fbsp1-1-0.5'; 'fbsp2-1-1'; 'fbsp2-1-0.5'; 'fbsp2-1-0.1'};
wavelet_cmor_names = {'cmor1-1.5'; 'cmor1-1'; 'cmor1-0.5'; 'cmor1-1'; 'cmor1-0.5'; 'cmor1-0.1'};
% Concatenate all wavelet names into a single cell array
wavelet_categories_names = who('wavelet*names');
wavelet_names = {};
for wavelet_categories_number=1:size(wavelet_categories_names,1)
temp = wavelet_categories_names(wavelet_categories_number);
temp = eval(temp{1});
wavelet_names = vertcat(wavelet_names, temp);
end
%% Prepare data
random_signal = rand(number_of_random_runs,number_of_random_samples);
%% Run benchmarks
result_file_ID = fopen(results_file_name, 'w');
for wavelet_number = 1:size(wavelet_names,1)
wavelet_name = wavelet_names(wavelet_number,:)
% Compute wavelet on a random signal
tic
for run = 1:number_of_random_runs
cwt(random_signal(run, :),scales,wavelet_name{1});
end
run_time_random_test = toc
fprintf(result_file_ID, '%s,', wavelet_name{1})
fprintf(result_file_ID, '%d\n', run_time_random_test)
end
size(wavelet_names,1)
fclose(result_file_ID);
If you want to see the impact of the choice of the scale:
Code used:
clear all
%% Benchmark parameters
results_file_name = 'results_sym2_change_scale.csv';
number_of_random_runs = 10;
scales = 1:10;
number_of_random_samples = 10000000;
% wavelet_names = {'sym2', 'sym3'}%, 'sym4'};
output_directory = 'output';
wavelet_names = get_all_wavelet_names();
%% Prepare data
random_signal = rand(number_of_random_runs,number_of_random_samples);
%% Prepare result folder
if ~exist(output_directory, 'dir')
mkdir(output_directory);
end
%% Run benchmarks
result_file_ID = fopen(results_file_name, 'w');
for wavelet_number = 1:size(wavelet_names,1)
wavelet_name = wavelet_names{wavelet_number}
if wavelet_number > 1
fprintf(result_file_ID, '%s\n', '');
end
fprintf(result_file_ID, '%s', wavelet_name)
run_time_random_test_scales = zeros(size(scales,2),1);
for scale_number = 1:size(scales,2)
scale = scales(scale_number);
% Compute wavelet on a random signal
tic
for run = 1:number_of_random_runs
cwt(random_signal(run, :),scale,wavelet_name);
end
run_time_random_test = toc
fprintf(result_file_ID, ',%d', run_time_random_test)
run_time_random_test_scales(scale_number) = run_time_random_test;
end
figure
bar(run_time_random_test_scales)
title(['Run time on random signal for ' wavelet_name])
xlabel('Scale')
ylabel('Run time (seconds)')
save_figure( fullfile(output_directory, ['run_time_random_test_' wavelet_name]) )
close all
end
size(wavelet_names,1)
fclose(result_file_ID);
With 3 functions:
get_all_wavelet_names.m:
function [ wavelet_names ] = get_all_wavelet_names( )
%GET_ALL_WAVELET_NAMES Get a list of available wavelet functions
%% Construct a cell array containing all the wavelet names
wavelet_haar_names = {'haar'};
wavelet_db_names = {'db1'; 'db2'; 'db3'; 'db4'; 'db5'; 'db6'; 'db7'; 'db8'; 'db9'; 'db10'};
wavelet_sym_names = {'sym2'; 'sym3'; 'sym4'; 'sym5'; 'sym6'; 'sym7'; 'sym8'};
wavelet_coif_names = {'coif1'; 'coif2'; 'coif3'; 'coif4'; 'coif5'};
wavelet_bior_names = {'bior1.1'; 'bior1.3'; 'bior1.5'; 'bior2.2'; 'bior2.4'; 'bior2.6'; 'bior2.8'; 'bior3.1'; 'bior3.3'; 'bior3.5'; 'bior3.7'; 'bior3.9'; 'bior4.4'; 'bior5.5'; 'bior6.8'};
wavelet_rbior_names = {'rbio1.1'; 'rbio1.3'; 'rbio1.5'; 'rbio2.2'; 'rbio2.4'; 'rbio2.6'; 'rbio2.8'; 'rbio3.1'; 'rbio3.3'; 'rbio3.5'; 'rbio3.7'; 'rbio3.9'; 'rbio4.4'; 'rbio5.5'; 'rbio6.8'};
wavelet_meyer_names = {'meyr'};
wavelet_dmeyer_names = {'dmey'};
wavelet_gaus_names = {'gaus1'; 'gaus2'; 'gaus3'; 'gaus4'; 'gaus5'; 'gaus6'; 'gaus7'; 'gaus8'};
wavelet_mexh_names = {'mexh'};
wavelet_morl_names = {'morl'};
wavelet_cgau_names = {'cgau1'; 'cgau2'; 'cgau3'; 'cgau4'; 'cgau5'};
wavelet_shan_names = {'shan1-1.5'; 'shan1-1'; 'shan1-0.5'; 'shan1-0.1'; 'shan2-3'};
wavelet_fbsp_names = {'fbsp1-1-1.5'; 'fbsp1-1-1'; 'fbsp1-1-0.5'; 'fbsp2-1-1'; 'fbsp2-1-0.5'; 'fbsp2-1-0.1'};
wavelet_cmor_names = {'cmor1-1.5'; 'cmor1-1'; 'cmor1-0.5'; 'cmor1-1'; 'cmor1-0.5'; 'cmor1-0.1'};
% Concatenate all wavelet names into a single cell array
wavelet_categories_names = who('wavelet*names');
wavelet_names = {};
for wavelet_categories_number=1:size(wavelet_categories_names,1)
temp = wavelet_categories_names(wavelet_categories_number);
temp = eval(temp{1});
wavelet_names = vertcat(wavelet_names, temp);
end
end
save_figure.m:
function [ ] = save_figure( output_graph_filename )
% Record aa figure as PNG and fig files
% Create the folder if it doesn't exist already.
[pathstr, name, ext] = fileparts(output_graph_filename);
if ~exist(pathstr, 'dir')
mkdir(pathstr);
end
h = gcf;
set(0,'defaultAxesFontSize',18) % http://www.mathworks.com/support/solutions/en/data/1-8XOW94/index.html?solution=1-8XOW94
boldify(h);
print('-dpng','-r600', [output_graph_filename '.png']);
print(h,[output_graph_filename '.pdf'],'-dpdf','-r600')
saveas(gcf,[output_graph_filename '.fig'], 'fig')
end
and boldify.m:
function boldify(h,g)
%BOLDIFY Make lines and text bold for standard viewgraph style.
% BOLDIFY boldifies the lines and text of the current figure.
% BOLDIFY(H) applies to the graphics handle H.
%
% BOLDIFY(X,Y) specifies an X by Y inch graph of the current
% figure. If text labels have their 'UserData' data property
% set to 'slope = ...', then the 'Rotation' property is set to
% account for changes in the graph's aspect ratio. The
% default is MATLAB's default.
% S. T. Smith
% The name of this function does not represent an endorsement by the author
% of the egregious grammatical trend of verbing nouns.
if nargin < 1, h = gcf;, end
% Set (and get) the default MATLAB paper size and position
set(gcf,'PaperPosition','default');
units = get(gcf,'PaperUnits');
set(gcf,'PaperUnits','inches');
fsize = get(gcf,'PaperPosition');
fsize = fsize(3:4); % Figure size (X" x Y") on paper.
psize = get(gcf,'PaperSize');
if nargin == 2 % User specified graph size
fsize = [h,g];
h = gcf;
end
% Set the paper position of the current figure
set(gcf,'PaperPosition', ...
[(psize(1)-fsize(1))/2 (psize(2)-fsize(2))/2 fsize(1) fsize(2)]);
fsize = get(gcf,'PaperPosition');
fsize = fsize(3:4); % Graph size (X" x Y") on paper.
set(gcf,'PaperUnits',units); % Back to original
% Get the normalized axis position of the current axes
units = get(gca,'Units');
set(gca,'Units','normalized');
asize = get(gca,'Position');
asize = asize(3:4);
set(gca,'Units',units);
ha = get(h,'Children');
for i=1:length(ha)
% if get(ha(i),'Type') == 'axes'
% changed by B. A. Miller
if strcmp(get(ha(i), 'Type'), 'axes') == 1
units = get(ha(i),'Units');
set(ha(i),'Units','normalized');
asize = get(ha(i),'Position'); % Axes Position (normalized)
asize = asize(3:4);
set(ha(i),'Units',units);
[m,j] = max(asize); j = j(1);
scale = 1/(asize(j)*fsize(j)); % scale*inches -normalized units
set(ha(i),'FontWeight','Bold');
set(ha(i),'LineWidth',2);
[m,k] = min(asize); k = k(1);
if asize(k)*fsize(k) > 1/2
set(ha(i),'TickLength',[1/8 1.5*1/8]*scale); % Gives 1/8" ticks
else
set(ha(i),'TickLength',[3/32 1.5*3/32]*scale); % Gives 3/32" ticks
end
set(get(ha(i),'XLabel'),'FontSize',18); % 14-pt labels
set(get(ha(i),'XLabel'),'FontWeight','Bold');
set(get(ha(i),'XLabel'),'VerticalAlignment','top');
set(get(ha(i),'YLabel'),'FontSize',18); % 14-pt labels
set(get(ha(i),'YLabel'),'FontWeight','Bold');
%set(get(ha(i),'YLabel'),'VerticalAlignment','baseline');
set(get(ha(i),'Title'),'FontSize',18); % 16-pt titles
set(get(ha(i),'Title'),'FontWeight','Bold');
% set(get(ha(i), 'FontSize',20, 'XTick',[]));
end
hc = get(ha(i),'Children');
for j=1:length(hc)
chtype = get(hc(j),'Type');
if chtype(1:4) == 'text'
set(hc(j),'FontSize',17); % 12 pt descriptive labels
set(hc(j),'FontWeight','Bold');
ud = get(hc(j),'UserData'); % User data
if length(ud) 8
if ud(1:8) == 'slope = ' % Account for change in actual slope
slope = sscanf(ud,'slope = %g');
slope = slope*(fsize(2)/fsize(1))/(asize(2)/asize(1));
set(hc(j),'Rotation',atan(slope)/pi*180);
end
end
elseif chtype(1:4) == 'line'
set(hc(j),'LineWidth',2);
end
end
end
Bonus: correlation between all wavelets on a random signal with 1000000 samples with the first 10 scales:
Code used:
%% PRE-REQUISITE: You need to download http://www.mathworks.com/matlabcentral/fileexchange/24253-customizable-heat-maps , which gives the function heatmap()
%% Benchmark parameters
scales = 1:10;
number_of_random_samples = 1000000;
% wavelet_names = {'sym2'; 'sym3'; 'sym4'; 'sym5'; 'sym6'; 'sym7'; 'sym8'};
% wavelet_names = {'cgau1'; 'cgau2'; 'cgau3'; 'cgau4'; 'cgau5'};
wavelet_names = {'db2'; 'sym2'};
OUTPUT_FOLDER = 'output_corr';
% wavelet_names = get_all_wavelet_names(); % WARNING: you need to remove all complex wavelets, viz. cgau1, shan, fbsp and cmor, and the heatmap will be pissed to see complex values coming to her.
%% Prepare data
random_signal = rand(1,number_of_random_samples);
results = zeros(size(wavelet_names,1), number_of_random_samples);
%% Prepare result folder
if ~exist(OUTPUT_FOLDER, 'dir')
mkdir(OUTPUT_FOLDER);
end
%% Run benchmarks
for scale_number = 1:size(scales,2)
scale = scales(scale_number);
for wavelet_number = 1:size(wavelet_names,1)
wavelet_name = wavelet_names{wavelet_number}
% Compute wavelet on a random signal
run = 1;
results(wavelet_number, :) = cwt(random_signal(run, :),scale,wavelet_name);
if wavelet_number == 999
break
end
end
correlation_results = corrcoef(results')
heatmap(correlation_results, [], [], '%0.2f', 'MinColorValue', -1.0, 'MaxColorValue', 1.0, 'Colormap', 'jet',...
'Colorbar', true, 'ColorLevels', 64, 'UseFigureColormap', false);
title(['Correlation matrix for scale ' num2str(scale)]);
xlabel(['Wavelet 1 to ' num2str(size(wavelet_names,1)) ' for scale ' num2str(scale)]);
ylabel(['Wavelet 1 to ' num2str(size(wavelet_names,1)) ' for scale ' num2str(scale)]);
snapnow
print('-dpng','-r600',fullfile(OUTPUT_FOLDER, ['scalecorr' num2str(scale) '.png']))
end
Correlation for each wavelet between different scales (1 to 100):
Code used:
%% PRE-REQUISITE: You need to download http://www.mathworks.com/matlabcentral/fileexchange/24253-customizable-heat-maps , which gives the function heatmap()
%% Benchmark parameters
scales = 1:100;
number_of_random_samples = 1000000;
% wavelet_name = 'gaus2';
% wavelet_names = {'sym2', 'sym3'}%, 'sym4'};
OUTPUT_FOLDER = 'output_corr';
wavelet_names = get_all_wavelet_names(); % WARNING: you need to remove all complex wavelets, viz. cgau1, shan, fbsp and cmor, and the heatmap will be pissed to see complex values coming to her.
%% Prepare data
random_signal = rand(1,number_of_random_samples);
results = zeros(size(scales,2), number_of_random_samples);
%% Prepare result folder
if ~exist(OUTPUT_FOLDER, 'dir')
mkdir(OUTPUT_FOLDER);
end
%% Run benchmarks
for wavelet_number = 1:size(wavelet_names,1)
wavelet_name = wavelet_names{wavelet_number}
run_time_random_test_scales = zeros(size(scales,2),1);
for scale_number = 1:size(scales,2)
scale = scales(scale_number);
run = 1;
% Compute wavelet on a random signal
results(scale_number, :) = cwt(random_signal(run, :),scale,wavelet_name);
end
correlation_results = corrcoef(results')
heatmap(correlation_results, [], [], '%0.2f', 'MinColorValue', -1.0, 'MaxColorValue', 1.0, 'Colormap', 'jet',...
'Colorbar', true, 'ColorLevels', 64, 'UseFigureColormap', false);
title(['Correlation matrix for wavelet ' wavelet_name]);
xlabel(['Scales 1 to ' num2str(max(scales)) ' for wavelet ' wavelet_name]);
ylabel(['Scales 1 to ' num2str(max(scales)) ' for wavelet ' wavelet_name]);
snapnow
print('-dpng','-r600',fullfile(OUTPUT_FOLDER, [wavelet_name '_scalecorr_scale1to' num2str(max(scales)) '.png']))
end

Matlab error: Undefined function 'dmod' for input arguments of type 'double'. How come there is no dmod function as it should come with Matlab?

I have been working on some exam and need to do an exercise involving some frequency shift keying. That is why I use dmod function from Matlab - it comes with Matlab. But as I write in my console
yfsk=dmod([1 0], 3, 0.5, 100,'fsk', 2, 1);
it gives me this
`Undefined function 'dmod' for input arguments of type 'double'.
I have also tried doc dmod and it says 'page not found' in matlab help window.
Do you know wheter this is because I didn't install all the matlab packages or this function is not suported with matlab 2012a?
Thank you
This is gonna be helpful:
function [y, t] = dmod(x, Fc, Fd, Fs, method, M, opt2, opt3)
%DMOD
%
%WARNING: This is an obsolete function and may be removed in the future.
% Please use MODEM.PAMMOD, MODEM.QAMMOD, MODEM.GENQAMMOD, FSKMOD,
% MODEM.PSKMOD, or MODEM.MSKMOD instead.
% Y = DMOD(X, Fc, Fd, Fs, METHOD...) modulates the message signal X
% with carrier frequency Fc (Hz) and symbol frequency Fd (Hz). The
% sample frequency of Y is Fs (Hz), where Fs > Fc and where Fs/Fd is
% a positive integer. For information about METHOD and subsequent
% parameters, and about using a specific modulation technique,
% type one of these commands at the MATLAB prompt:
%
% FOR DETAILS, TYPE MODULATION TECHNIQUE
% dmod ask % M-ary amplitude shift keying modulation
% dmod psk % M-ary phase shift keying modulation
% dmod qask % M-ary quadrature amplitude shift keying
% % modulation
% dmod fsk % M-ary frequency shift keying modulation
% dmod msk % Minimum shift keying modulation
%
% For baseband simulation, use DMODCE. To plot signal constellations,
% use MODMAP.
%
% See also DDEMOD, DMODCE, DDEMODCE, MODMAP, AMOD, ADEMOD.
% Copyright 1996-2007 The MathWorks, Inc.
% $Revision: 1.1.6.5 $ $Date: 2007/06/08 15:53:47 $
warnobsolete(mfilename, 'Please use MODEM.PAMMOD, MODEM.QAMMOD, MODEM.GENQAMMOD, FSKMOD, MODEM.PSKMOD, or MODEM.MSKMOD instead.');
swqaskenco = warning('off', 'comm:obsolete:qaskenco');
swapkconst = warning('off', 'comm:obsolete:apkconst');
swmodmap = warning('off', 'comm:obsolete:modmap');
swamod = warning('off', 'comm:obsolete:amod');
opt_pos = 6; % position of 1st optional parameter
if nargout > 0
y = []; t = [];
end
if nargin < 1
feval('help','dmod')
return;
elseif isstr(x)
method = lower(deblank(x));
if length(method) < 3
error('Invalid method option for DMOD.')
end
if nargin == 1
% help lines for individual modulation method.
addition = 'See also DDEMOD, DMODCE, DDEMODCE, MODMAP, AMOD, ADEMOD.';
if method(1:3) == 'qas'
callhelp('dmod.hlp', method(1:4), addition);
else
callhelp('dmod.hlp', method(1:3), addition);
end
else
% plot constellation, make a shift.
opt_pos = opt_pos - 3;
M = Fc;
if nargin >= opt_pos
opt2 = Fd;
else
modmap(method, M);
return;
end
if nargin >= opt_pos+1
opt3 = Fs;
else
modmap(method, M, opt2);
return;
end
modmap(method, M, opt2, opt3); % plot constellation
end
return;
end
if (nargin < 4)
error('Usage: Y = DMOD(X, Fc, Fd, Fs, METHOD, OPT1, OPT2, OPT3) for passband modulation');
elseif nargin < opt_pos-1
method = 'samp';
else
method = lower(method);
end
len_x = length(x);
if length(Fs) > 1
ini_phase = Fs(2);
Fs = Fs(1);
else
ini_phase = 0; % default initial phase
end
if ~isfinite(Fs) | ~isreal(Fs) | Fs<=0
error('Fs must be a positive number.');
elseif length(Fd)~=1 | ~isfinite(Fd) | ~isreal(Fd) | Fd<=0
error('Fd must be a positive number.');
else
FsDFd = Fs/Fd; % oversampling rate
if ceil(FsDFd) ~= FsDFd
error('Fs/Fd must be a positive integer.');
end
end
if length(Fc) ~= 1 | ~isfinite(Fc) | ~isreal(Fc) | Fc <= 0
error('Fc must be a positive number. For baseband modulation, use DMODCE.');
elseif Fs/Fc < 2
warning('Fs/Fc must be much larger than 2 for accurate simulation.');
end
% determine M
if isempty(findstr(method, '/arb')) & isempty(findstr(method, '/cir'))
if nargin < opt_pos
M = max(max(x)) + 1;
M = 2^(ceil(log(M)/log(2)));
M = max(2, M);
elseif length(M) ~= 1 | ~isfinite(M) | ~isreal(M) | M <= 0 | ceil(M) ~= M
error('Alphabet size M must be a positive integer.');
end
end
if isempty(x)
y = [];
return;
end
[r, c] = size(x);
if r == 1
x = x(:);
len_x = c;
else
len_x = r;
end
% expand x from Fd to Fs.
if isempty(findstr(method, '/nomap'))
if ~isreal(x) | all(ceil(x)~=x)
error('Elements of input X must be integers in [0, M-1].');
end
yy = [];
for i = 1 : size(x, 2)
tmp = x(:, ones(1, FsDFd)*i)';
yy = [yy tmp(:)];
end
x = yy;
clear yy tmp;
end
if strncmpi(method, 'ask', 3)
if isempty(findstr(method, '/nomap'))
% --- Check that the data does not exceed the limits defined by M
if (min(min(x)) < 0) | (max(max(x)) > (M-1))
error('An element in input X is outside the permitted range.');
end
y = (x - (M - 1) / 2 ) * 2 / (M - 1);
else
y = x;
end
[y, t] = amod(y, Fc, [Fs, ini_phase], 'amdsb-sc');
elseif strncmpi(method, 'fsk', 3)
if nargin < opt_pos + 1
Tone = Fd;
else
Tone = opt2;
end
if (min(min(x)) < 0) | (max(max(x)) > (M-1))
error('An element in input X is outside the permitted range.');
end
[len_y, wid_y] = size(x);
t = (0:1/Fs:((len_y-1)/Fs))'; % column vector with all the time samples
t = t(:, ones(1, wid_y)); % replicate time vector for multi-channel operation
osc_freqs = pi*[-(M-1):2:(M-1)]*Tone;
osc_output = (0:1/Fs:((len_y-1)/Fs))'*osc_freqs;
mod_phase = zeros(size(x))+ini_phase;
for index = 1:M
mod_phase = mod_phase + (osc_output(:,index)*ones(1,wid_y)).*(x==index-1);
end
y = cos(2*pi*Fc*t+mod_phase);
elseif strncmpi(method, 'psk', 3)
% PSK is a special case of QASK.
[len_y, wid_y] = size(x);
t = (0:1/Fs:((len_y-1)/Fs))';
if findstr(method, '/nomap')
y = dmod(x, Fc, Fs, [Fs, ini_phase], 'qask/cir/nomap', M);
else
y = dmod(x, Fc, Fs, [Fs, ini_phase], 'qask/cir', M);
end
elseif strncmpi(method, 'msk', 3)
M = 2;
Tone = Fd/2;
if isempty(findstr(method, '/nomap'))
% Check that the data is binary
if (min(min(x)) < 0) | (max(max(x)) > (1))
error('An element in input X is outside the permitted range.');
end
x = (x-1/2) * Tone;
end
[len_y, wid_y] = size(x);
t = (0:1/Fs:((len_y-1)/Fs))'; % column vector with all the time samples
t = t(:, ones(1, wid_y)); % replicate time vector for multi-channel operation
x = 2 * pi * x / Fs; % scale the input frequency vector by the sampling frequency to find the incremental phase
x = [0; x(1:end-1)];
y = cos(2*pi*Fc*t+cumsum(x)+ini_phase);
elseif (strncmpi(method, 'qask', 4) | strncmpi(method, 'qam', 3) |...
strncmpi(method, 'qsk', 3) )
if findstr(method,'nomap')
[y, t] = amod(x, Fc, [Fs, ini_phase], 'qam');
else
if findstr(method, '/ar') % arbitrary constellation
if nargin < opt_pos + 1
error('Incorrect format for METHOD=''qask/arbitrary''.');
end
I = M;
Q = opt2;
M = length(I);
% leave to the end for processing
CMPLEX = I + j*Q;
elseif findstr(method, '/ci') % circular constellation
if nargin < opt_pos
error('Incorrect format for METHOD=''qask/circle''.');
end
NIC = M;
M = length(NIC);
if nargin < opt_pos+1
AIC = [1 : M];
else
AIC = opt2;
end
if nargin < opt_pos + 2
PIC = NIC * 0;
else
PIC = opt3;
end
CMPLEX = apkconst(NIC, AIC, PIC);
M = sum(NIC);
else % square constellation
[I, Q] = qaskenco(M);
CMPLEX = I + j * Q;
end
y = [];
x = x + 1;
% --- Check that the data does not exceed the limits defined by M
if (min(min(x)) < 1) | (max(max(x)) > M)
error('An element in input X is outside the permitted range.');
end
for i = 1 : size(x, 2)
tmp = CMPLEX(x(:, i));
y = [y tmp(:)];
end
ind_y = [1: size(y, 2)]';
ind_y = [ind_y, ind_y+size(y, 2)]';
ind_y = ind_y(:);
y = [real(y) imag(y)];
y = y(:, ind_y);
[y, t] = amod(y, Fc, [Fs, ini_phase], 'qam');
end
elseif strncmpi(method, 'samp', 4)
% This is for converting an input signal from sampling frequency Fd
% to sampling frequency Fs.
[len_y, wid_y] = size(x);
t = (0:1/Fs:((len_y-1)/Fs))';
y = x;
else % invalid method
error(sprintf(['You have used an invalid method.\n',...
'The method should be one of the following strings:\n',...
'\t''ask'' Amplitude shift keying modulation;\n',...
'\t''psk'' Phase shift keying modulation;\n',...
'\t''qask'' Quadrature amplitude shift-keying modulation, square constellation;\n',...
'\t''qask/cir'' Quadrature amplitude shift-keying modulation, circle constellation;\n',...
'\t''qask/arb'' Quadrature amplitude shift-keying modulation, user defined constellation;\n',...
'\t''fsk'' Frequency shift keying modulation;\n',...
'\t''msk'' Minimum shift keying modulation.']));
end
if r==1 & ~isempty(y)
y = y.';
end
warning(swqaskenco);
warning(swapkconst);
warning(swmodmap);
warning(swamod);
% [EOF]
I do believe that dmod function (Communication Toolbox) has been abandoned in the latest MATLAB releases.
It looks like dmod is no more :-)
Here is the link that says it was removed:
http://www.mathworks.com/help/comm/release-notes.html?searchHighlight=dmod
It should be replaced wit with comm.FSKModulator System object
I think you may be looking for the demodulation function (demod) from the signal processing toolbox.
http://www.mathworks.com.au/help/signal/ref/demod.html