Is there a way in Matlab to write the components of a vector starting from its modulus?. Maybe in a random way, but so that, by doing the reverse operation, I obtain the starting module
for example:
V_0 = 60 % starting modulus
perhaps I should use a for loop, But How?
I assume you're looking for a way, starting with the length of the vector to generate a vector with a random orientation but the specified length?
I'll consider a couple of cases here. First, for a 2-dimensional, real, vector. This is simply an application of polar-to-rectangular coordinate conversions.
v0 = 60; % Desired length
theta = 2 * pi * rand(1,1); % Random angle between 0 and 2pi
x0 = v0 * cos(theta);
y0 = v0 * sin(theta);
v = [x0 y0]; % Assemble the new vector
% Could also reduce line count by: v = v0 * [cos(theta) sin(theta)]
vLength = norm(v); % Should be the same as v0
The second case is if you're interested in complex numbers with the specified modulus. He we make use of Euler's formula.
v0 = 60; % Desired length
theta = 2 * pi * rand(1,1); % Random angle between 0 and 2pi
v = v0 * exp(1j * theta);
vLength = abs(v); % Should be the same as v0
For the third case, I'll consider arbitrary dimensioned vectors. This leverages a technique from What is a good method to find random points on the n-sphere when n is large?
v0 = 60; % Desired length
N = 7; % Number of dimensions
rndVector = randn(1, N); % Random, Gaussian, vector with N elements
rndVectorNorm = rndVector / norm(rndVector); % Normalized to unit length
v = v0 * rndVectorNorm; % Scale to desired length
vLength = norm(v); % Should be the same as v0
Related
For a given PMF p=f(\theta) for \theta between 0 and 2\pi, i computed the CDF in Matlab as
theta=0:2*pi/n:2*pi
for i=1:n
cdf(i)=trapz(theta(1:i),p(1:i));
end
and the result is verified.
I tried to do the same with cumsum as cdf=cumsum(p)*(2*pi)/n but the result is wrong. why?
How can i compute the CDF if the given PMF is in 2D asp=f(\theta,\phi) ? Can i do it without going into detail as explained here ?
In 1D case you can use cumsum to get the vectorized version of loop (assuming that both theta and p are column vectors):
n = 10;
theta = linspace(0, 2*pi, n).';
p = rand(n,1);
cdf = [0; 0.5 * cumsum((p(1:n-1) + p(2:n)) .* diff(theta(1:n)))];
In 2D case the function cumsum will be applied two times, in vertical and horizontal directions:
nthet = 10;
nphi = 10;
theta = linspace(0, 2*pi, nthet).'; % as column vector
phi = linspace(0, pi, nphi); % as row vector
p = rand(nthet, nphi);
cdf1 = 0.5 * cumsum((p(1:end-1, :) + p(2:end, :)) .* diff(theta), 1);
cdf2 = 0.5 * cumsum((cdf1(:, 1:end-1) + cdf1(:, 2:end)) .* diff(phi), 2);
cdf = zeros(nthet, nphi);
cdf(2:end, 2:end) = cdf2;
I am trying to solve the system of ODEs below. I am applying the finite differences to z, so I get a system of ODE that I can solve with a solver like ode45.
The problem is that under the conditions that interest me integration is too slow (time step is very small) and I get no result. I would like to know what is the underlying problem and if there is any way to solve it. I have tried:
using different solvers (tried all of them)
using dimensionless variables
using the pdepe function of Matlab to solve the PDE system directly without applying finite differences myself. Same problem occurs.
Full code (using the parameters that do not work)
function transport_model()
global parameter L Di epsb Q Cfeed K tfinal npz npt F ui h
parameter = [0.044511*432.75 432.75]; % isotherm parameters
L = 0.4; % m, column length
epsb = 0.367; % column bulk porosity
ui = 4e4; % m/s, velocity
Cfeed = 200; % feed concentration
K = 0.0782 % s-1, mass transfer coefficient
tfinal = 400; % min, final time for calculation
npz = 50; % number of discretization points in z
npt = 20;
F = (1-epsb)/epsb;
tspan = 0:tfinal/(npt-1):tfinal;
y0 = zeros(2*npz,1);
h = L/(npz-1);
sol = ode15s(#sedo, tspan, y0);
t = sol.x;
C1 = sol.y(1:npz,:);
q1 = sol.y((npz+1):end,:);
plot(t, C1(end,:))
xlim([0 tfinal])
ylim([0 Cfeed])
function DyDt = sedo(t,y)
global Cfeed K npz F ui h
N = npz;
y(1) = Cfeed;
DyDt = zeros(2*N,1);
% Forward finite differences
DyDt(1) = -ui * 1/(2*h)*(-y(3)+4*y(2)-3*y(1)) - F * K * (y(1) - ilangmuir(y(N+1)));
DyDt(N+1) = K * (y(1) - ilangmuir(y(N+1)));
% Central finite differences
for i=2:N-1
DyDt(i) = -ui * 1/(2*h)*(y(i+1)-y(i-1)) - F * K * (y(i) - ilangmuir(y(N+i)));
DyDt(N+i) = K * (y(i) - ilangmuir(y(N+i)));
end
% Backward finite differences
DyDt(N) = -ui * 1/(2*h)*(3*y(N)-4*y(N-1)+y(N-2)) - F * K * (y(N) - ilangmuir(y(N+N)));
DyDt(2*N) = K * (y(N) - ilangmuir(y(N+N)));
function c = ilangmuir(q)
% langmuir solved for c
global parameter
a = parameter(1);
b = parameter(2);
c = q /(a - b * q);
However, if I try to model a different case (different conditions) I get the correct response. So I am assuming this is a numerical problem caused by the conditions I am trying to model and not an error in the model. For instance, if I use the conditions below I get the correct response.
parameter = [0.044511*432.75 432.75]; % isotherm parameters
L = 0.4; % m, column length
epsb = 0.367; % column bulk porosity
ui = 0.0056; % m/s, velocity
Cfeed = 0.0025; % feed concentration
K = 0.0782 % s-1, mass transfer coefficient
tfinal = 4000; % min, final time for calculation
npz = 50; % number of discretization points in z
npt = 20;
I have already had a phase screen (a 2-D NxN matrix and LxL in size scale, ex: N = 256, L = 2 meters).
I would like to find phase structure function - D(r) defined by D(delta(r)) = <[x(r)-x(r+delta(r))]^2> (<.> is ensemble averaging, r is position in phase screen in meter, x is phase value at a point in phase screen, delta(r) is variable and not fix) in Matlab program. Do you have any suggestion for my purpose?
P/S: I tried to calculate D(r) via the autocorrelation (is defined as B(r)), but this calculation still remaining some approximations. Therefore, I want to calculate precisely the result of D(r). May you please see this image to better understand the definition of D(r) and B(r). Below is my function code to calculate B(r).
% Code copied from "Numerical Simulation of Optical Wave Propagation with Examples in Matlab",
% by Jason D. Schmidt, SPIE Press, SPIE Vol. No.: PM199
% listing 3.7, page 48.
% (Schmidt defines the ft2 and ift2 functions used in this code elswhere.)
function D = str_fcn2_ft(ph, mask, delta)
% function D = str_fcn2_ft(ph, mask, delta)
N = size(ph, 1);
ph = ph .* mask;
P = ft2(ph, delta);
S = ft2(ph.^2, delta);
W = ft2(mask, delta);
delta_f = 1/(N*delta);
w2 = ift2(W.*conj(W), delta_f);
D = 2 * ft2(real(S.*conj(W)) - abs(P).^2, delta) ./ w2 .*mask;`
%fire run
N = 256; %number of samples
L = 16; %grid size [m]
delta = L/N; %sample spacing [m]
F = 1/L; %frequency-domain grid spacing[1/m]
x = [-N/2 : N/2-1]*delta;
[x y] = meshgrid(x);
w = 2; %width of rectangle
%A = rect(x/2).*rect(y/w);
A = lambdaWrapped;
%A = phz;
mask = ones(N);
%perform digital structure function
C = str_fcn2_ft(A, mask, delta);
C = real(C);
One way of directly computing this function D(r) is through random sampling: you pick two random points on your screen, determine their distance and phase difference squared, and update an accumulator:
phi = rand(256,256)*(2*pi); % the data, phase
N = size(phi,1); % number of samples
L = 16; % grid size [m]
delta = L/N; % sample spacing [m]
D = zeros(1,sqrt(2)*N); % output function
count = D; % for computing mean
for n = 1:1e6 % find a good amount of points here, the more points the better the estimate
coords = randi(N,2,2);
r = round(norm(coords(1,:) - coords(2,:)));
if r<1
continue % skip if the two coordinates are the same
end
d = phi(coords(1,1),coords(1,2)) - phi(coords(2,1),coords(2,2));
d = mod(abs(d),pi); % you might not need this, depending on how A is constructed
D(r) = D(r) + d.^2;
count(r) = count(r) + 1;
end
I = count > 0;
D(I) = D(I) ./ count(I); % do not divide by 0, some bins might not have any samples
I = count < 100;
D(I) = 0; % ignore poor estimates
r = (1:length(D)) * delta;
plot(r,D)
If you need even more precision, consider interpolating. Compute random coordinates as floating-point values, and interpolate the phase to get the values in between samples. D then needs to be longer, indexed as round(r*10) or something like that. You will need many more random samples to fill up that much larger accumulator.
The exact solution of the damped harmonic oscillator
$$x'' + 2\gamma x' + \omega^2 x = 0, \quad x(0)=x_0, \quad x'(0)=-\gamma x_0$$
with $0 < \gamma < \omega$ is
$$x(t)= x_0 e^{-\gamma t} \cos(\beta t) \quad \text{where} \quad \beta:=\sqrt{\omega^2 - \gamma^2}$$
Notice that this second order ODE can be written as a first order system by making the substitutions:
$x' = y$ and,
$y' = -2\gamma y - \omega^2 x$
I want to solve the system using the method:
$$\dfrac{ x_{n+1} - x_{n-1} }{2h} = y_n \quad \quad \dfrac{y_{n+1} - y_{n-1}}{2h} = -2\gamma y_n - \omega^2 x_n.$$
which is an explicit midpoint rule. This is the code that I constructed for the problem, but it is not giving me the correct result. My plot has no harmonic behavior as I would anticipate.
function resonance
omega = 1; % resonant frequency = sqrt(k/m)
a = 0.2; % drag coeficient per unit mass
b = 0.1; % driving amplitude per unit mass
omega0 = 1.2; % driving frequency
tBegin = 0; % time begin
tEnd = 80; % time end
x0 = 0.2; % initial position
v0 = 0.8; % initial velocity
a = omega^2; % calculate a coeficient from resonant frequency
% Use Runge-Kutta 45 integrator to solve the ODE
[t,w] = ode45(#derivatives, [tBegin tEnd], [x0 v0]);
x = w(:,1); % extract positions from first column of w matrix
v = w(:,2); % extract velocities from second column of w matrix
plot(t,x);
title('Damped, Driven Harmonic Oscillator');
ylabel('position (m)');
xlabel('time (s)');
% Function defining derivatives dx/dt and dv/dt
% uses the parameters a, b, A, omega0 in main program but changeth them not
function derivs = derivatives(tf,wf)
xf = wf(1); % wf(1) stores x
vf = wf(2); % wf(2) stores v
dxdt = vf; % set dx/dt = velocity
dvdt = xf + 2 * b * vf + a * tf; % set dv/dt = acceleration
derivs = [dxdt; dvdt]; % return the derivatives
end
end
Also, my apologies about the formatting. I am use to math stackexchange, and the LaTeX style formatting doesn't seem to be applicable here and I do not know how to put my math in the math environment.
You missed a sign, it should be
dvdt = - ( xf + 2 * b * vf + a * tf ); % set dv/dt = acceleration
However, the whole expression is at odds with the previously stated equation,
x'' + 2*b*x' * a*x = 0
should result in
dvdt = - ( 2*b*vf + a*xf ); % set dv/dt = acceleration
But then again you have defined a twice, so change w2=omega^2 to get
dvdt = - ( 2*b*vf + w2*xf + a ); % set dv/dt = acceleration
I tried to apply the method posted in Find positive solutions to undetermined linear system of equations to the set
A=[0 0.0992 0.315 0.619 1; 0 0.248 0.315 0.248 0]; b=[0.1266 0.4363].
It is supposed that there exists a positive and constrained solution to this problem. The worst thing is that I have an answer code but I can't make it work because my Matlab version doesn`t recognize the anonymous function call and some instructions are obscure for me.
Here is the code:
% example_pt_source_atmos_setup.m
% determine geometry
D2 = 0.5; % diameter of the observation aperture [m]
wvl = 1e-6; % optical wavelength [m]
k = 2*pi / wvl; % optical wavenumber [rad/m]
Dz = 50e3; % propagation distance [m]
% use sinc to model pt source
DROI = 4 * D2; % diam of obs-plane region of interest [m]
D1 = wvl*Dz / DROI; % width of central lobe [m]
R = Dz; % wavefront radius of curvature [m]
% atmospheric properties
Cn2 = 1e-16; % structure parameter [m^-2/3], constant
% SW and PW coherence diameters [m]
r0sw = (0.423 * k^2 * Cn2 * 3/8 * Dz)^(-3/5);
r0pw = (0.423 * k^2 * Cn2 * Dz)^(-3/5);
p = linspace(0, Dz, 1e3);
% log-amplitude variance
rytov = 0.563 * k^(7/6) * sum(Cn2 * (1-p/Dz).^(5/6) ...
.* p.^(5/6) * (p(2)-p(1)));
% screen properties
nscr = 11; % number of screens
A = zeros(2, nscr); % matrix
alpha = (0:nscr-1) / (nscr-1);
A(1,:) = alpha.^(5/3);
A(2,:) = (1 - alpha).^(5/6) .* alpha.^(5/6);
b = [r0sw.^(-5/3); rytov/1.33*(k/Dz)^(5/6)];
% initial guess
x0 = (nscr/3*r0sw * ones(nscr, 1)).^(-5/3);
% objective function
fun = #(X) sum((A*X(:) - b).^2);
% constraints
x1 = zeros(nscr, 1);
rmax = 0.1; % maximum Rytov number per partial prop
x2 = rmax/1.33*(k/Dz)^(5/6) ./ A(2,:);
x2(A(2,:)==0) = 50^(-5/3)
[X,fval,exitflag,output] ...
= fmincon(fun,x0,[],[],[],[],x1,x2)
% check screen r0s
r0scrn = X.^(-3/5)
r0scrn(isinf(r0scrn)) = 1e6;
% check resulting r0sw & rytov
bp = A*X(:); [bp(1)^(-3/5) bp(2)*1.33*(Dz/k)^(5/6)]
[r0sw rytov]
Thanks for your attention.
Carolina Rickenstorff