I have an issue in compiling my code in Matlab the error showing in the message is: Not enough input argument
I don't know how to solve it or where is the problem and I follow many documentations in the Mathworks website describes how to generalize problems 1-D with Neumann boundary conditions.
this is my code?
thank you in advance!
function [c,f,s] = oscpde(x,t,u,dudx)
c = 0;
f = dudx;
s = u-x*(x-1);
end
%----------------------------------------------
function u0 = oscic(x)
u0 = ((exp(1)*exp(1)+1))/(1-exp(1))*exp(0) + ((exp(1)*exp(1)+exp(1))/(1-exp(1)))*exp(0)+ 2;
end
%----------------------------------------------
function [pl,ql,pr,qr] = oscbc(xl,ul,xr,ur,t)
pl = 0;
ql = 1;
pr = 0;
qr = 1;
end
%----------------------------------------------
function [value, isterminal, direction] = pdevents(m,t,xmesh,umesh)
value = umesh;
isterminal = zeros(size(umesh));
direction = zeros(size(umesh));
end
**the error is:
Not enough input arguments.
Error in CODE (line 3)
f = dudx;
**
Related
I have an model to evaluate a thermodinamical process, it is made with Mathcad and I need it in MatLab.
It is a big equation that uses an integral thousand of times, each temperature uses an definite integral.
If I solve each time, the result is the same as Mathcad, but it takes hours, if I solve only once, it takes less than a second, but the result is wrong.
I split the equation and found that the error is in the part that is in the code.
How should I write my code to solve only one time the and get the right result?
clc
clear
FrCrist = FCrystFastWrong();
FrCrist = FCrystSlowRight();
function [Fvpa] = FCrystSlowRight()
tic;
syms T;
Part4 = (10^(-4.27+(3961/(T-751))));
i = 0;
for TempN = 996:1100
i = i+1;
TC(i) = TempN;
FintDef = int(Part4,T,[1000 TempN]);
Fvpa(i) = double(vpa(FintDef));
end
toc
plot(TC,Fvpa)
end
function [Fvpa] = FCrystFastWrong()
tic;
syms T;
Part4 = (10^(-4.27+(3961/(T-751))));
Fint = int(Part4,T);
qc = arrayfun(#char, Fint, 'uniform', 0);
qc = erase(qc,"int");
qc = erase(qc,", T");
qc = str2sym(qc);
f = inline(qc,'T');
i = 0;
for TempN = 996:1667
i = i+1;
TC(i) = TempN;
Solve(i) = f(TempN)-f(1000);
Fvpa(i) = Solve(i);
end
toc
plot(TC,Fvpa)
end
I am attempting to write a function called expSeries which uses another function factFunc to evaluate e^x. I have already written the function factFunc, as shown below:
function fact = factFunc(n)
f = 1;
for a = 1:b
f = f*a;
end
fact = f;
end
I am now attempting to write the function expSeries which evaulates e^x using the Taylor series. This is what I have so far:
function expo = exponentialFunc(x)
terms = input('Enter the number of terms');
b = 0;
for i = 1:terms
b = x/factFunc(terms);
end
expo = b;
end
And in the main program, I have
n = exponentialFunc(4);
disp(n);
Where in this instance I am trying to find e^4. However, the output is not what expected. Does anyone have any idea where I am going wrong?
Fix to factFunc:
function fact = factFunc(n)
f = 1;
for a = 1:n
f = f*a;
end
fact = f;
end
Fix to exponentialFunc
function expo = exponentialFunc(x)
terms = input('Enter the number of terms');
b = 0;
for i = 0:terms-1
b = b + x^i/factFunc(i);
end
expo = b;
end
Example
>> exponentialFunc(4)
Enter the number of terms10
ans =
54.1541
Note exp(4) = 54.59815...
I want to compile a MEX from a function that has the following code (MATLAB R2015a):
function r = MyFunc(x,type)
ind = randi(numel(x), 1);
getInd = #getIndFixed;
if strcmpi(type, 'random')
ind = numel(x);
getInd = #getIndRandom; % error here
end
x(getInd(ind)) = 1;
end
function k = getIndFixed(n)
k = n;
end
function k = getIndRandom(n)
k = randi(n, 1);
end
I get the type mismatch error between getIndFixed and getIndRandom at the line specified above:
Type mismatch: function_handle getIndFixed ~= getIndRandom.
Is there a way around this problem?
For instance, a way to specify that both functions have the same signature?
In C, the signature of the function would be:
int (*getInd)(int);
int getIndFixed(int);
int getIndRandom(int);
//...
getInd = getIndFixed;
getInd = getIndRandom;
You cannot change function handles to different functions after they are assigned in code generation. Unlike "C" these function calls are resolved at compile time. If your type input is constant then you can write your code as
function r = MyFunc(x,type)
if strcmpi(type, 'random')
ind = numel(x);
getInd = #getIndRandom; % error here
else
ind = randi(numel(x), 1);
getInd = #getIndFixed;
end
x(getInd(ind)) = 1;
end
If type is not constant you need to move everything inside if or else branch.
`sol = pdepe(m,#ParticleDiffusionpde,#ParticleDiffusionic,#ParticleDiffusionbc,x,t);
% Extract the first solution component as u.
u = sol(:,:,:);
function [c,f,s] = ParticleDiffusionpde(x,t,u,DuDx)
global Ds
c = 1/Ds;
f = DuDx;
s = 0;
function u0 = ParticleDiffusionic(x)
global qo
u0 = qo;
function [pl,ql,pr,qr] = ParticleDiffusionbc(xl,ul,xr,ur,t,x)
global Ds K n
global Amo Gc kf rhop
global uavg
global dr R nr
sum = 0;
for i = 1:1:nr-1
r1 = (i-1)*dr; % radius at i
r2 = i * dr; % radius at i+1
r1 = double(r1); % convert to double precision
r2 = double(r2);
sum = sum + (dr / 2 * (r1*ul+ r2*ur));
end;
uavg = 3/R^3 * sum;
ql = 1;
pl = 0;
qr = 1;
pr = -((kf/(Ds.*rhop)).*(Amo - Gc.*uavg - ((double(ur/K)).^2).^(n/2) ));`
dq(r,t)/dt = Ds( d2q(r,t)/dr2 + (2/r)*dq(r,t)/dr )
q(r, t=0) = 0
dq(r=0, t)/dr = 0
dq(r=dp/2, t)/dr = (kf/Ds*rhop) [C(t) - Cp(at r = dp/2)]
q = solid phase concentration of trace compound in a particle with radius dp/2
C = bulk liquid concentration of trace compound
Cp = trace compound concentration at particle surface
I want to solve the above pde with initial and boundary conditions given. Tried Matlab's pdepe, but does not work satisfactorily. Maybe the boundary conditions is creating problem for me. I also used this isotherm equation for equilibrium: q = K*Cp^(1/n). This is convection-diffusion equation but i could not find any write ups that addresses solving this type of equation properly.
There are two problems with the current implementation.
Incorrect Source Term
The PDE you are attempting to solve has the form
which has the equivalent form
where the last term arises due to the factor of 2 in the original PDE.
The last term needs to be incorporated into pdepe via a source term.
Calculation of q average
The current implementation attempts to calculate the average value of q using the left and right values of q passed to the boundary condition function.
This is incorrect.
The average value of q needs to be calculated from a vector of up-to-date values of the quantity.
However, we have the complication that the only function to receive all mesh values is ParticleDiffusionpde; however, the mesh values passed to that function are not guaranteed to be from the mesh we provided.
Solution: use events (as described in the pdepe documentation).
This is a hack since the event function is meant to detect zero-crossings, but it has the advantage that the function is given all values of q on the mesh we provide.
So, the working example below (you'll notice I set all of the parameters to 1 since I didn't know better) uses the events function to update a variable qStore that can be accessed by the boundary condition function (see here for an explanation), and the boundary condition function performs a vectorized trapezoidal integration for the average calculation.
Working Example
function [] = ParticleDiffusion()
% Parameters
Ds = 1;
q0 = 0;
K = 1;
n = 1;
Amo = 1;
Gc = 1;
kf = 1;
rhop = 1;
% Space
rMesh = linspace(0,1,10);
rMesh = rMesh(:) ;
dr = rMesh(2) - rMesh(1) ;
% Time
tSpan = linspace(0,1,10);
% Vector to store current u-value
qStore = zeros(size(rMesh));
options.Events = #(m,t,x,y) events(m,t,x,y);
% Solve
[sol,~,~,~,~] = pdepe(1,#ParticleDiffusionpde,#ParticleDiffusionic,#ParticleDiffusionbc,rMesh,tSpan,options);
% Use the events function to update qStore
function [value,isterminal,direction] = events(m,~,~,y)
qStore = y; % Value of q on rMesh
value = m; % Since m is constant, it will never be zero (no event detection)
isterminal = 0; % Continue integration
direction = 0; % Detect all zero crossings (not important)
end
function [c,f,s] = ParticleDiffusionpde(r,~,~,DqDr)
% Define the capacity, flux, and source
c = 1/Ds;
f = DqDr;
s = DqDr./r;
end
function u0 = ParticleDiffusionic(~)
u0 = q0;
end
function [pl,ql,pr,qr] = ParticleDiffusionbc(~,~,R,ur,~)
% Calculate average value of current solution
qL = qStore(1:end-1);
qR = qStore(2: end );
total = sum((qL.*rMesh(1:end-1) + qR.*rMesh(2:end))) * dr/2;
qavg = 3/R^3 * total;
% Left boundary
pl = 0;
ql = 1;
% Right boundary
qr = 1;
pr = -(kf/(Ds.*rhop)).*(Amo - Gc.*qavg - (ur/K).^n);
end
end
i have question,about which i am too much interested,suppose that i have two M-file in matlab, in the first one i have described following function for calculating peaks and peaks indeces
function [peaks,peak_indices] = find_peaks(row_vector)
A = [0 row_vector 0];
j = 1;
for i=1:length(A)-2
temp=A(i:i+2);
if(max(temp)==temp(2))
peaks(j) = row_vector(i);
peak_indices(j) = i;
j = j+1;
end
end
end
and in second M-file i have code for describing sinusoidal model for given data sample
function [ x ]=generate(N,m,A3)
f1 = 100;
f2 = 200;
T = 1./f1;
t = (0:(N*T/m):(N*T))';
wn = rand(length(t),1).*2 - 1;
x = 20.*sin(2.*pi.*f1.*t) + 30.*cos(2.*pi.*f2.*t) + A3.*wn;
end
my question is how to combine it together?one solution would be just create two M-file into folder,then call function from one M-file and made operation on given vector and get result,and then call second function from another M file on given result and finally get what we want,but i would like to build it in one big M-file,in c++,in java,we can create classes,but i am not sure if we can do same in matlab too,please help me to clarify everything and use find_peaks function into generate function
UPDATED:
ok now i would like to show simple change what i have made in my code
function [ x ] = generate(N,m,A3)
f1 = 100;
f2 = 200;
T = 1./f1;
t = (0:(N*T/m):(N*T))'; %'
wn = rand(length(t),1).*2 - 1;
x = 20.*sin(2.*pi.*f1.*t) + 30.*cos(2.*pi.*f2.*t) + A3.*wn;
[pks,locs] = findpeaks(x);
end
i used findpeaks built-in function in matlab,but i am getting following error
generate(1000,50,50)
Undefined function 'generate' for input arguments of type 'double'.
also i am interested what would be effective sampling rate to avoid alliasing?
You can simply put both in one file. The file must have the same name as the first function therein, and you will not be able to access subsequently defined functions from outside that file. See the MATLAB documentation on functions http://www.mathworks.co.uk/help/matlab/ref/function.html (particularly the examples section).
Also note that MATLAB has a built-in function findpeaks().
(By the way, you're still sampling at too low a frequency and will most certainly get aliasing - see http://en.wikipedia.org/wiki/Aliasing#Sampling_sinusoidal_functions )
Edit: As you requested it, here is some more information on the sampling theorem. A good and simple introduction to these basics is http://www.dspguide.com/ch3/2.htm and for further reading you should search for the Shannon/Nyquist sampling theorem.
Try with this, within a single MATLAB script
function test()
clc, clear all, close all
x = generate(1000,50,50);
[p,i] = find_peaks(x)
end
function x = generate(N,m,A3)
f1 = 100;
f2 = 200;
T = 1./f1;
t = (0:(N*T/m):(N*T))'; %'
wn = rand(length(t),1).*2 - 1;
x = 20.*sin(2.*pi.*f1.*t) + 30.*cos(2.*pi.*f2.*t) + A3.*wn;
end
function [peaks,peak_indices] = find_peaks(row_vector)
A = [0;row_vector;0];
j = 1;
for i=1:length(A)-2
temp=A(i:i+2);
if(max(temp)==temp(2))
peaks(j) = row_vector(i);
peak_indices(j) = i;
j = j+1;
end
end
end