No BSD License
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sampler - function to sample density and velocities
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...
sampler - function to sample density and velocities
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FNewt(x,a)
Function used by the N-variable Newton's method
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NaiveGE(a,b)
Forward elimination
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bess(m_max,x)
Function to calculate of Bessel function
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bess(m_max,x)
Function to calculate of Bessel function
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colide(v,cell_n,...
colide - Function to process collisions in cells
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colide(v,cell_n,...
colide - Function to process collisions in cells
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fnewt(x,a)
Function used by the N-variable Newton's method
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fund(x,n)
Return function value or derivative
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fund(x,n)
Return function value or derivative
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gravrk(s,time,GM)
The time is not used in this version
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gravrk(s,time,GM)
The time is not used in this version
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intrpf(xi,x,y)
Function to interpolate between data points
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intrpf(xi,x,y)
Function to interpolate between data points
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legndr(n,x)
Legendre polynomials
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legndr(n,x)
Legendre polynomials
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linreg(x,y,sigma)
Function to perform linear regression (fit a line)
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linreg(x,y,sigma)
Function to perform linear regression (fit a line)
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lorzrk(a,time,param)
Function to define the Lorenz model equations
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lorzrk(a,time,param)
Function to define the Lorenz model equations
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mover(x,v,npart, ...
mover - Function to move particles by free flight
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mover(x,v,npart, ...
mover - Function to move particles by free flight
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my_f(x,param)
Error function integrand
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my_f(x,param)
Error function integrand
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naivege(a,b)
x=naivege(a,b) performs naive (no pivoting) Gaussian elimination
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pollsf(x, y, sigma, M)
Function to fit a polynomial to data
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pollsf(x, y, sigma, M)
Function to fit a polynomial to data
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rk4(x,t,tau,derivsRK,param)
Runge-Kutta integrator (4th order)
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rk4(x,t,tau,derivsRK,param)
Runge-Kutta integrator (4th order)
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rkA(x,t,tau,err,derivsRK,para...
Adaptive Runge-Kutta routine
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rka(x,t,tau,err,derivsRK,para...
Adaptive Runge-Kutta routine
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rombf(a,b,N,func,param)
Function to compute integrals by Romberg algorithm
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rombf(a,b,N,func,param)
Function to compute integrals by Romberg algorithm
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sorter(x,npart,ncell,L)
sorter - Function to sort particles into cells
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sorter(x,npart,ncell,L)
sorter - Function to sort particles into cells
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spinview(nviews,wait)
spinview - Routine to rotate a 3D plot
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sprrk(a,time,param)
Function to compute 3 mass-spring system
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sprrk(a,time,param)
Function to compute 3 mass-spring system
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tri_GE(a,b)
Function to solve b = a*x by Gaussian elimination where
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tri_GE(a,b)
Function to solve b = a*x by Gaussian elimination where
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yt=sft(y)
Slow Fourier transform function
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yt=sft(y)
Slow Fourier transform function
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zeroj(m_order,n_zero)
Function which returns the zeros of the Bessel function J(x)
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zeroj(m_order,n_zero)
Function which returns the zeros of the Bessel function J(x)
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aftcs.m
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aftcs.m
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aftcs_p.m
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balle.m
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balle.m
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balle_p.m
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contents.m
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contents.m
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contents.m
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contents.m
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deriv.m
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deriv.m
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deriv_p.m
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dftcs.m
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dftcs.m
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dftcs_p.m
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dsmceq.m
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dsmceq.m
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dsmceq_p.m
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dsmcne.m
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dsmcne.m
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dsmcne_p.m
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factn.m
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factn.m
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facts.m
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facts.m
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fftpoi.m
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fftpoi.m
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fftpoi_p.m
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galrkn.m
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galrkn.m
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galrkn_p.m
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interp.m
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interp.m
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interp_p.m
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jacobi.m
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jacobi.m
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jacobi_p.m
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lorenz.m
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lorenz.m
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lorenz_p.m
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lsftest.m
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lsftest.m
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lsftst_p.m
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newtn.m
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newtn.m
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newtn_p.m
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orbe.m
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orbe.m
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orbe_p.m
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orbec.m
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orbec.m
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orbrk.m
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orbrk.m
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orbrka.m
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orbrka.m
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orthog.m
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orthog.m
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pendul.m
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pendul.m
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pendul_p.m
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pendulv.m
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pendulv.m
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rndoff.m
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rndoff.m
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rndoff_p.m
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schro.m
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schro.m
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schro_p.m
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schrot.m
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schrot.m
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sftdem_p.m
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sftdemo.m
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sftdemo.m
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sprfft.m
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sprfft.m
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sprfft_p.m
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traffic.m
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traffic.m
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trafic_p.m
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View all files
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| pollsf(x, y, sigma, M)
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function [a_fit, sig_a, yy, chisqr] = pollsf(x, y, sigma, M)
% Function to fit a polynomial to data
% Inputs -
% x - Independent variable
% y - Dependent variable
% sigma - Estimate error in y
% M - Number of parameters used to fit data
% Outputs -
% a_fit - Fit parameters; a(1) is intercept, a(2) is slope
% sig_a - Estimated error in the parameters a()
% yy - Curve fit to the data
% chisqr - Chi squared statistic
N = length(x);
b = y./sigma; % Form the vector b
for i=1:N
for j=1:M
A(i,j) = x(i)^(j-1)/sigma(i); % Form the design matrix
end
end
C = inv(A.' * A); % Compute the correlation matrix
a_fit = C * A.' * b.'; % Compute the best fit parameters
for j=1:M
sig_a(j) = sqrt(C(j,j)); % Find the estimated error for
end % the fit parameters a(j)
% Compute chi-square
yy = zeros(1,N);
for j=1:M
yy = yy + a_fit(j)*x.^(j-1); % yy is the curve fit
end
chisqr = sum( ((y-yy)./sigma).^2 );
return;
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