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github | phtra2/hctsa-master | gauher.m | .m | hctsa-master/Toolboxes/gpml/util/gauher.m | 2,245 | utf_8 | 441ef6c145fe66f1b7ca9da6207f6003 | % compute abscissas and weight factors for Gaussian-Hermite quadrature
%
% CALL: [x,w] = gauher(N)
%
% x = base points (abscissas)
% w = weight factors
% N = number of base points (abscissas) (integrates an up to (2N-1)th order
% polynomial exactly)
%
% p(x)=exp(-x^2/2)/sqrt(2*pi), a =-Inf, b = Inf
%
% Th... |
github | phtra2/hctsa-master | elsympol.m | .m | hctsa-master/Toolboxes/gpml/util/elsympol.m | 699 | utf_8 | 33e751b982c07eb890d26629bf71f595 | % Evaluate the order R elementary symmetric polynomial Newton's identity aka
% the Newton–Girard formulae: http://en.wikipedia.org/wiki/Newton's_identities
%
% Copyright (c) by Carl Edward Rasmussen and Hannes Nickisch, 2010-01-10.
function E = elsympol(Z,R)
% evaluate 'power sums' of the individual terms in Z
sz = si... |
github | phtra2/hctsa-master | minimize.m | .m | hctsa-master/Toolboxes/gpml/util/minimize.m | 11,338 | utf_8 | ead014bd1c8c090ceaf16b20bf0cef66 | function [X, fX, i] = minimize(X, f, length, varargin)
% Minimize a differentiable multivariate function using conjugate gradients.
%
% Usage: [X, fX, i] = minimize(X, f, length, P1, P2, P3, ... )
%
% X initial guess; may be of any type, including struct and cell array
% f the name or pointer to the funct... |
github | phtra2/hctsa-master | minimize_v2.m | .m | hctsa-master/Toolboxes/gpml/util/minimize_v2.m | 11,952 | utf_8 | d8aad9cf50639371a892fbcc202eed7c | % minimize.m - minimize a smooth differentiable multivariate function using
% LBFGS (Limited memory LBFGS) or CG (Conjugate Gradients)
% Usage: [X, fX, i] = minimize(X, F, p, other, ... )
% where
% X is an initial guess (any type: vector, matrix, cell array, struct)
% F is the objective function (function poi... |
github | phtra2/hctsa-master | sq_dist.m | .m | hctsa-master/Toolboxes/gpml/util/sq_dist.m | 1,967 | utf_8 | 75b906d47729b33d7567f1353ced2f83 | % sq_dist - a function to compute a matrix of all pairwise squared distances
% between two sets of vectors, stored in the columns of the two matrices, a
% (of size D by n) and b (of size D by m). If only a single argument is given
% or the second matrix is empty, the missing matrix is taken to be identical
% to the fir... |
github | phtra2/hctsa-master | cov_deriv_sq_dist.m | .m | hctsa-master/Toolboxes/gpml/util/cov_deriv_sq_dist.m | 1,906 | utf_8 | 625e697b220630f920d967bce06884e7 | % Compute derivative k'(x^p,x^q) of a stationary covariance k(d2) (ard or iso)
% w.r.t. to squared distance d2 = (x^p - x^q)'*inv(P)*(x^p - x^q) measure. Here
% P is either diagonal with ARD parameters ell_1^2,...,ell_D^2 where D is the
% dimension of the input space or ell^2 times the unit matrix for isotropic
% covar... |
github | phtra2/hctsa-master | unwrap.m | .m | hctsa-master/Toolboxes/gpml/util/unwrap.m | 651 | utf_8 | 47d4deafec9cfdde0a4c291b3825c401 | % Extract the numerical values from "s" into the column vector "v". The
% variable "s" can be of any type, including struct and cell array.
% Non-numerical elements are ignored. See also the reverse rewrap.m.
function v = unwrap(s)
v = [];
if isnumeric(s)
v = s(:); % numeric values are re... |
github | phtra2/hctsa-master | glm_invlink_expexp.m | .m | hctsa-master/Toolboxes/gpml/util/glm_invlink_expexp.m | 427 | utf_8 | 99a5cdb9880a947109671401c7398199 | % Compute the log intensity for the inverse link function g(f) = exp(-exp(-f)).
%
% The function is used in GLM likelihoods such as likPoisson, likGamma, likBeta
% and likInvGauss.
%
% Copyright (c) by Hannes Nickisch, 2013-10-16.
function [lg,dlg,d2lg,d3lg] = glm_invlink_expexp(f)
lg = -exp(-f);
if nargout>1
... |
github | phtra2/hctsa-master | glm_invlink_logistic.m | .m | hctsa-master/Toolboxes/gpml/util/glm_invlink_logistic.m | 686 | utf_8 | b21f086f037b6560c290e0044e0beef5 | % Compute the log intensity for the inverse link function g(f) = log(1+exp(f))).
%
% The function is used in GLM likelihoods such as likPoisson, likGamma, likBeta
% and likInvGauss.
%
% Copyright (c) by Hannes Nickisch, 2013-10-16.
function [lg,dlg,d2lg,d3lg] = glm_invlink_logistic(f)
l1pef = max(0,f) + log(1+exp(-a... |
github | phtra2/hctsa-master | minimize_v1.m | .m | hctsa-master/Toolboxes/gpml/util/minimize_v1.m | 11,202 | utf_8 | cd58ba0b83b1121423ed9a53b33562a1 | function [X, fX, i] = minimize_old(X, f, length, varargin)
% Minimize a differentiable multivariate function using conjugate gradients.
%
% Usage: [X, fX, i] = minimize(X, f, length, P1, P2, P3, ... )
%
% X initial guess; may be of any type, including struct and cell array
% f the name or pointer to the f... |
github | phtra2/hctsa-master | rewrap.m | .m | hctsa-master/Toolboxes/gpml/util/rewrap.m | 1,014 | utf_8 | 64b6d7c0f51a8c77ddd012370a288b20 | % Map the numerical elements in the vector "v" onto the variables "s" which can
% be of any type. The number of numerical elements must match; on exit "v"
% should be empty. Non-numerical entries are just copied. See also unwrap.m.
function [s v] = rewrap(s, v)
if isnumeric(s)
if numel(v) < numel(s)
error('The ... |
github | phtra2/hctsa-master | solve_chol.m | .m | hctsa-master/Toolboxes/gpml/util/solve_chol.m | 994 | utf_8 | f4d6cd4b9e7b0a955c2c8709a4894dd3 | % solve_chol - solve linear equations from the Cholesky factorization.
% Solve A*X = B for X, where A is square, symmetric, positive definite. The
% input to the function is R the Cholesky decomposition of A and the matrix B.
% Example: X = solve_chol(chol(A),B);
%
% NOTE: The program code is written in the C language ... |
github | phtra2/hctsa-master | glm_invlink_logit.m | .m | hctsa-master/Toolboxes/gpml/util/glm_invlink_logit.m | 786 | utf_8 | b2fc9a03b835c7f6643f37b29eac8c0b | % Compute the log intensity for the inverse link function g(f) = 1/(1+exp(-f)).
%
% The function is used in GLM likelihoods such as likPoisson, likGamma, likBeta
% and likInvGauss.
%
% Copyright (c) by Hannes Nickisch, 2013-10-16.
function varargout = glm_invlink_logit(f)
varargout = cell(nargout, 1); % allocate th... |
github | phtra2/hctsa-master | minimize_lbfgsb_gradfun.m | .m | hctsa-master/Toolboxes/gpml/util/minimize_lbfgsb_gradfun.m | 2,390 | utf_8 | 0eca58fc12d068780d735fd5a83ebdfa | function G = minimize_lbfgsb_gradfun(X,varargin)
% extract input arguments
varargin = varargin{1}; strctX = varargin{2}; f = varargin{1};
% global variables serve as communication interface between calls
global minimize_lbfgsb_iteration_number
global minimize_lbfgsb_objective
global minimize_lbfgsb_gradie... |
github | phtra2/hctsa-master | minimize_lbfgsb.m | .m | hctsa-master/Toolboxes/gpml/util/minimize_lbfgsb.m | 4,476 | utf_8 | 10c2d1fef0bdc071cd35d3904c88f0ed | function [X, fX, i] = minimize_lbfgsb(X, f, length, varargin)
% Minimize a differentiable multivariate function using quasi Newton.
%
% Usage: [X, fX, i] = minimize_lbfgsb(X, f, length, P1, P2, P3, ... )
%
% X initial guess; may be of any type, including struct and cell array
% f the name or pointer to th... |
github | phtra2/hctsa-master | minimize_lbfgsb_objfun.m | .m | hctsa-master/Toolboxes/gpml/util/minimize_lbfgsb_objfun.m | 2,695 | utf_8 | d9bbd3614b193a06603c12f33f877104 | function y = minimize_lbfgsb_objfun(X,varargin)
% extract input arguments
varargin = varargin{1}; strctX = varargin{2}; f = varargin{1};
% global variables serve as communication interface between calls
global minimize_lbfgsb_iteration_number
global minimize_lbfgsb_objective
global minimize_lbfgsb_gradien... |
github | phtra2/hctsa-master | logsumexp2.m | .m | hctsa-master/Toolboxes/gpml/util/logsumexp2.m | 454 | utf_8 | aa7e4f12a67c8f2e12bc5d9113b9abd0 | % Compute y = log( sum(exp(x),2) ), the softmax in a numerically safe way by
% subtracting the row maximum to avoid cancelation after taking the exp
% the sum is done along the rows.
%
% Copyright (c) by Hannes Nickisch, 2013-10-16.
function [y,x] = logsumexp2(logx)
N = size(logx,2); max_logx = max(logx,[],2);
%... |
github | phtra2/hctsa-master | lik_epquad.m | .m | hctsa-master/Toolboxes/gpml/util/lik_epquad.m | 1,622 | utf_8 | 9f92aef26b02e08fcee8f74617ebd05d | % Compute infEP part of a likelihood function based on the infLaplace part using
% Gaussian-Hermite quadrature.
%
% The function is used in GLM likelihoods such as likPoisson, likGamma, likBeta
% and likInvGauss.
%
% Copyright (c) by Hannes Nickisch, 2013-10-16.
function varargout = lik_epquad(lik,hyp,y,mu,s2)
n = m... |
github | phtra2/hctsa-master | glm_invlink_exp.m | .m | hctsa-master/Toolboxes/gpml/util/glm_invlink_exp.m | 443 | utf_8 | af4bb74d42054f7b470ed8aecfcf4607 | % Compute the log intensity for the inverse link function g(f) = exp(f).
%
% The function is used in GLM likelihoods such as likPoisson, likGamma, likBeta
% and likInvGauss.
%
% Copyright (c) by Hannes Nickisch, 2013-10-16.
function [lg,dlg,d2lg,d3lg] = glm_invlink_exp(f)
lg = f;
if nargout>1
dlg = ones(size(f... |
github | phtra2/hctsa-master | covPeriodicNoDC.m | .m | hctsa-master/Toolboxes/gpml/cov/covPeriodicNoDC.m | 3,630 | utf_8 | 32d02bd08932f22fe8302ce97b797d39 | function K = covPeriodicNoDC(hyp, x, z, i)
% Stationary covariance function for a smooth periodic function, with period p:
%
% k(x,x') = sf^2 * [k0(pi*(x-x')/p) - f(ell)] / [1 - f(ell)]
% with k0(t) = exp( -2*sin^2(t)/ell^2 ) and f(ell) = \int 0..pi k0(t) dt.
%
% The constant (DC component) has been removed and... |
github | phtra2/hctsa-master | covGrid.m | .m | hctsa-master/Toolboxes/gpml/cov/covGrid.m | 7,051 | utf_8 | 45064e9c69d8e20f1122e20679e25085 | function [K,Mx,xe] = covGrid(cov, xg, hyp, x, z, i)
% covGrid - Kronecker covariance function based on a grid.
%
% The grid g is represented by its p axes xg = {x1,x2,..xp}. An axis xi is of
% size (ni,di) and the grid g has size (n1,n2,..,np,D), where D=d1+d2+..+dp.
% Hence, the grid contains N=n1*n2*..*np data point... |
github | phtra2/hctsa-master | covPERiso.m | .m | hctsa-master/Toolboxes/gpml/cov/covPERiso.m | 3,145 | utf_8 | 7308c3f2001744df0d77cd5dc190c637 | function K = covPERiso(cov, hyp, x, z, i)
% Stationary periodic covariance function for an isotropic stationary covariance
% function k0 such as covMaterniso, covPPiso, covRQiso and covSEiso.
% Isotropic stationary means that the covariance function k0(x,z) depends on the
% data points x,z only through the squared dis... |
github | phtra2/hctsa-master | covADD.m | .m | hctsa-master/Toolboxes/gpml/cov/covADD.m | 3,632 | utf_8 | 45875a6c0e52c3f98448f40f6b6fc599 | function K = covADD(cov, hyp, x, z, i)
% Additive covariance function using a 1d base covariance function
% cov(x^p,x^q;hyp) with individual hyperparameters hyp.
%
% k(x^p,x^q) = \sum_{r \in R} sf_r \sum_{|I|=r}
% \prod_{i \in I} cov(x^p_i,x^q_i;hyp_i)
%
% hyp = [ hyp_1
% hyp_2
% ...
... |
github | phtra2/hctsa-master | covPERard.m | .m | hctsa-master/Toolboxes/gpml/cov/covPERard.m | 3,588 | utf_8 | 47dfb8b9857ef5e9bc3693bb6e5c0aa9 | function K = covPERard(cov, hyp, x, z, i)
% Stationary periodic covariance function for a stationary covariance function
% k0 such as covMaternard, covPPard, covRQard and covSEard.
% Stationary means that the covariance function k0(x,z) depends on the
% data points x,z only through the squared distance
% dxz = (x-z)'*... |
github | phtra2/hctsa-master | infMCMC.m | .m | hctsa-master/Toolboxes/gpml/inf/infMCMC.m | 10,673 | utf_8 | 346201720f95a22a681c50bd2535b84c | function [post nlZ dnlZ] = infMCMC(hyp, mean, cov, lik, x, y, par)
% Markov Chain Monte Carlo (MCMC) sampling from posterior and
% Annealed Importance Sampling (AIS) for marginal likelihood estimation.
%
% The algorithms are not to be used as a black box, since the acceptance rate
% of the samplers need to be careful... |
github | phtra2/hctsa-master | infKL.m | .m | hctsa-master/Toolboxes/gpml/inf/infKL.m | 10,925 | utf_8 | 25a0bb16b3bced105beb3520acf7f57e | function [post nlZ dnlZ] = infKL(hyp, mean, cov, lik, x, y)
% Approximation to the posterior Gaussian Process by minimization of the
% KL-divergence. The function is structurally very similar to infEP; the
% only difference being the local divergence measure minimised.
% In infEP, one minimises KL(p,q) whereas in inf... |
github | phtra2/hctsa-master | infFITC_EP.m | .m | hctsa-master/Toolboxes/gpml/inf/infFITC_EP.m | 12,117 | utf_8 | a2c1fccebe29502421d32ed7ab5fcd14 | function [post nlZ dnlZ] = infFITC_EP(hyp, mean, cov, lik, x, y)
% FITC-EP approximation to the posterior Gaussian process. The function is
% equivalent to infEP with the covariance function:
% Kt = Q + G; G = diag(g); g = diag(K-Q); Q = Ku'*inv(Kuu + snu2*eye(nu))*Ku;
% where Ku and Kuu are covariances w.r.t.... |
github | phtra2/hctsa-master | infFITC_Laplace.m | .m | hctsa-master/Toolboxes/gpml/inf/infFITC_Laplace.m | 11,364 | utf_8 | 1de345e37242cee549b4d7841e348f84 | function [post nlZ dnlZ] = infFITC_Laplace(hyp, mean, cov, lik, x, y)
% FITC-Laplace approximation to the posterior Gaussian process. The function is
% equivalent to infLaplace with the covariance function:
% Kt = Q + G; G = diag(g); g = diag(K-Q); Q = Ku'*inv(Kuu + snu2*eye(nu))*Ku;
% where Ku and Kuu are covarian... |
github | phtra2/hctsa-master | infGrid.m | .m | hctsa-master/Toolboxes/gpml/inf/infGrid.m | 7,627 | utf_8 | cca2d1208eb8763b1f518d5106ec6fd5 | function [post nlZ dnlZ] = infGrid(hyp, mean, cov, lik, x, y, opt)
% Inference for a GP with Gaussian likelihood and covGrid covariance.
% The (Kronecker) covariance matrix used is given by:
% K = kron( kron(...,K{2}), K{1} ) = K_p x .. x K_2 x K_1.
%
% Compute a parametrization of the posterior, the negative log ma... |
github | phtra2/hctsa-master | infEP.m | .m | hctsa-master/Toolboxes/gpml/inf/infEP.m | 5,886 | utf_8 | bc70721e1eea7c28c46653d36d1cd851 | function [post nlZ dnlZ] = infEP(hyp, mean, cov, lik, x, y)
% Expectation Propagation approximation to the posterior Gaussian Process.
% The function takes a specified covariance function (see covFunctions.m) and
% likelihood function (see likFunctions.m), and is designed to be used with
% gp.m. See also infMethods.m.... |
github | phtra2/hctsa-master | infVB.m | .m | hctsa-master/Toolboxes/gpml/inf/infVB.m | 6,024 | utf_8 | ff46ca9c2cce23402f0058955fe60b93 | function [post, nlZ, dnlZ] = infVB(hyp, mean, cov, lik, x, y, opt)
% Variational approximation to the posterior Gaussian process.
% The function takes a specified covariance function (see covFunctions.m) and
% likelihood function (see likFunctions.m), and is designed to be used with
% gp.m. See also infMethods.m.
%
% ... |
github | phtra2/hctsa-master | infLaplace.m | .m | hctsa-master/Toolboxes/gpml/inf/infLaplace.m | 7,949 | utf_8 | 297780be514bf1d87f826763094f104f | function [post nlZ dnlZ] = infLaplace(hyp, mean, cov, lik, x, y, opt)
% Laplace approximation to the posterior Gaussian process.
% The function takes a specified covariance function (see covFunctions.m) and
% likelihood function (see likFunctions.m), and is designed to be used with
% gp.m. See also infMethods.m.
%
% C... |
github | phtra2/hctsa-master | MS_embed.m | .m | hctsa-master/Toolboxes/Michael_Small/MS_embed.m | 1,500 | utf_8 | 368575a3c663145eaa9cf32e6506d2eb | % [x,y] or x = MS_embed(z,lags) or MS_embed(z,dim,lag)
% embed z using given lags or dim and lag
% embed(z,dim,lag) == MS_embed(z,[0:lag:lag*(dim-1)])
% negative entries of lags are into future
%
% If return is [x,y], then x is the positive lags and y the negative lags
% Order of rows in x and y the same as sort(lags)
... |
github | phtra2/hctsa-master | MS_nearneigh.m | .m | hctsa-master/Toolboxes/Michael_Small/MS_nearneigh.m | 2,312 | utf_8 | 2540fcab7a7b3cedf587127fe1a237f7 | % function [d,i] = MS_nearneigh(X,tau,blocksize)
%
% calculate the nearest (RMS) neighbour of each embedded point
% represented as columns of X.
% tau points either side of each point are excluded (default tau=0);
% i is the index of the nearest neighbours and d are the distances.
%
% nearest neighbours are calculat... |
github | phtra2/hctsa-master | MS_firstzero.m | .m | hctsa-master/Toolboxes/Michael_Small/MS_firstzero.m | 1,065 | utf_8 | 27ea3a4955b3b8725386cbf98306c377 | % function tau = MS_firstzero(y);
%
% Find the first zero of the autocorrelation function of y.
%
% Michael Small
% michael.small@uwa.edu.au, http://school.maths.uwa.edu.au/~small/
% 3/3/2005
% For further details, please see M. Small. Applied Nonlinear Time Series
% Analysis: Applications in Physics, Physiology and Fi... |
github | phtra2/hctsa-master | MS_fnn.m | .m | hctsa-master/Toolboxes/Michael_Small/MS_fnn.m | 2,010 | utf_8 | 1cd69c6e7a88f85b85b540f448e41246 | % function nfnn = MS_fnn(y,de,tau,th,kth)
%
% determine the number of false nearest neighbours for the time
% series y embedded in dimension de with lag tau.
%
% for each pair of values (de,tau) the data y is embeded and the
% nearest neighbour to each point (excluding the immediate
% neighbourhood of n points) is de... |
github | phtra2/hctsa-master | MS_complexity.m | .m | hctsa-master/Toolboxes/Michael_Small/MS_complexity.m | 1,484 | utf_8 | e5f7f0d7194bf7f0f8b3368ba6246f31 | % cmp = MS_complexity(x,n);
%
% calculate the Lempel-Ziv complexity of the n-bit encoding of x.
%
% cmp is the normalised complexity, that is the number of distinct
% symbol sequences in x, divided by the expected number of distinct
% symbols for a noise sequence.
%
% Algorithm is implemented in complexitybs.c
%
% Mi... |
github | phtra2/hctsa-master | MS_unfolding.m | .m | hctsa-master/Toolboxes/Michael_Small/MS_unfolding.m | 3,108 | utf_8 | a42fccf6f4203969973bea0050013570 | % function [de,nfnn] = MS_unfolding(y,th,de,tau)
%
% Estimate the minimum unfolding dimension by calculating when the
% proportion of false nearest neighbours if first below th.
%
% The number of false nearest neighbours are calculated for the
% time series y embedded in dimension de with lag tau.
%
% For each pair of... |
github | phtra2/hctsa-master | MS_rms.m | .m | hctsa-master/Toolboxes/Michael_Small/MS_rms.m | 753 | utf_8 | b8873cc9f3c7a713aad1d30c4be94cb1 | % function e = MS_rms(y);
%
% e is the l2-norm of row vector y, for a n-by-m matrix e is the n-by-1 column
% vector which is the l2-norm of the n rows of y.;
%
% Michael Small
% michael.small@uwa.edu.au, http://school.maths.uwa.edu.au/~small/
% 3/3/2005
% For further details, please see M. Small. Applied Nonlinear Tim... |
github | phtra2/hctsa-master | ZG_hmm_cl.m | .m | hctsa-master/Toolboxes/ZG_hmm/ZG_hmm_cl.m | 2,918 | utf_8 | f7863d1e28a45ab314bace9d71b51d35 | % function [lik,likv] = hmm_cl(X,T,K,Mu,Cov,P,Pi);
%
% Calculate Likelihood for Hidden Markov Model
%
% X - N x p data matrix
% T - length of each sequence (N must evenly divide by T, default T=N)
% K - number of states
% Mu - mean vectors
% Cov - output covariance matrix (full, tied across states)
% P - state transi... |
github | phtra2/hctsa-master | ZG_hmm.m | .m | hctsa-master/Toolboxes/ZG_hmm/ZG_hmm.m | 4,477 | utf_8 | 759fbe16465ef5e964624980bde37aef | % function [Mu,Cov,P,Pi,LL] = ZG_hmm(X,T,K,cyc,tol);
%
% Gaussian Observation Hidden Markov Model
%
% X - N x p data matrix
% T - length of each sequence (N must evenly divide by T, default T=N)
% K - number of states (default 2)
% cyc - maximum number of cycles of Baum-Welch (default 100)
% tol - termination toleranc... |
github | phtra2/hctsa-master | ZG_rprod.m | .m | hctsa-master/Toolboxes/ZG_hmm/ZG_rprod.m | 1,615 | utf_8 | 14b0166c9cc9cade5ddd66d223841bd7 | % ZG_rprod
%
% row product
%
% Machine Learning Toolbox
% Version 1.0 01-Apr-96
% Copyright (c) by Zoubin Ghahramani
% http://mlg.eng.cam.ac.uk/zoubin/software.html
%
% ------------------------------------------------------------------------------
% The MIT License (MIT)
%
% Copyright (c) 1996, Zoubin Ghahramani
% ... |
github | phtra2/hctsa-master | ZG_rdiv.m | .m | hctsa-master/Toolboxes/ZG_hmm/ZG_rdiv.m | 1,726 | utf_8 | 57343e64a66f56fee5ff32c773be0125 | % function Z = ZG_rdiv(X,Y)
%
% row division: Z = X / Y row-wise
% Y must have one column
%
% Machine Learning Toolbox
% Version 1.0 01-Apr-96
% Copyright (c) by Zoubin Ghahramani
% http://mlg.eng.cam.ac.uk/zoubin/software.html
%
% ------------------------------------------------------------------------------
% The M... |
github | phtra2/hctsa-master | ZG_rsum.m | .m | hctsa-master/Toolboxes/ZG_hmm/ZG_rsum.m | 1,559 | utf_8 | 9134bc89fec347536ea1cff8b98506cd | % ZG_rsum(X)
% row sum
%
% Machine Learning Toolbox
% Version 1.0 01-Apr-96
% Copyright (c) by Zoubin Ghahramani
% http://mlg.eng.cam.ac.uk/zoubin/software.html
%
% ------------------------------------------------------------------------------
% The MIT License (MIT)
%
% Copyright (c) 1996, Zoubin Ghahramani
%
% Pe... |
github | phtra2/hctsa-master | opentstool.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/tstoolbox/gui/opentstool.m | 34,661 | UNKNOWN | 562f849f89e1cb9e2ba93e6ef39da6e6 | function tstool(varargin)
global TSTOOLdatapath TSTOOLpath TSTOOLfilter
% tstool is a matlab toolbox for nonlinear time series analysis
% which includes a graphical user interface (GUI)
%
% The command 'tstool' creates a GUI that allows
% the user to perform data manipulation and analysis
% with a wide range of cla... |
github | phtra2/hctsa-master | tsplot.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/tstoolbox/gui/private/tsplot.m | 3,555 | utf_8 | 3cc59c080ab7d923759639dcc211a901 | function tsplot(filename, varargin)
if nargin < 2
mode = 'large'; % im Modus 'large' wird eine eigene Figure gestartet
else
mode = varargin{1}; % im Modus 'small' wird in das Preview-Areal des tstool geplottet
end
if isunix % use greater fonts on Unix workstations
if strcmp(mode, 'small')
fontsize = 14;
else... |
github | phtra2/hctsa-master | sortdatafiles.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/tstoolbox/gui/private/sortdatafiles.m | 2,484 | utf_8 | d6d685053bc67bc69234cd4a986ea350 | function datafiles=sortdatafiles(datafiles)
% das ist ein Test
%
newdatafiles={};
new_n=0;
n=length(datafiles(:,1));
m=length(datafiles(1,:));
for i=1:n
equal_line=0;
for i1=1:i-1
equal=1;
for i2=1:m
if ~strcmp(char(datafiles(i,i2)),char(datafiles(i1,i2)))
equal=0;
end
end
% if equal... |
github | phtra2/hctsa-master | filterbank.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/tstoolbox/@core/filterbank.m | 11,025 | utf_8 | 623ac34c8f407ad76dc62ff43b93210c | function cout=filterbank(cin,h,g,order,basis)
%tstoolbox/@core/filterbank
% Syntax:
% * filterbank(cin,H,G,ORDER,BASIS)
%
% Input Arguments:
% * H - lowpass filter
% * G - highpass filter
% * ORDER - indicates the type of tree:
% + 0 - band sorting according to the filter bank
% +... |
github | phtra2/hctsa-master | scalogram.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/tstoolbox/@core/scalogram.m | 2,465 | utf_8 | 88f20c353031e76599846ca8edd42ee3 | function cout = scalogram(cin, smin, smax, sstep, tim)
%tstoolbox/@core/scalogram
% Syntax:
% * cout = scalogram(cin, smin, smax, sstep, tim)
%
% Copyright 1997-2001 DPI Goettingen, License http://www.physik3.gwdg.de/tstool/gpl.txt
x = data(cin);
lx = dlens(cin,1);
s = smin:sstep:smax;
sc = zeros(lx, length(s)... |
github | phtra2/hctsa-master | help_mex.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/tstoolbox/utils/help_mex.m | 219 | utf_8 | afa3a795ca56aa3160afd131bf079046 | function help_mex
d = dir('*.mexsg64'); % ".dll'
for i = 1:length(d)
n = d(i).name;
[path,name,ext] = fileparts(n);
myeval(name, 'disp(lasterr)');
end
function myeval(s1, s2)
disp(s1)
eval(s1, s2)
disp('')
|
github | phtra2/hctsa-master | TSTOOLpca.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/tstoolbox/utils/TSTOOLpca.m | 3,637 | utf_8 | 5eb90ef700324568d5a82672979b211f | function [rlvm, frvals, frvecs, trnsfrmd, mn, dv] = TSTL_pca(data, mode, maxpercent, sil)
% [rlvm, frvals, frvecs, trnsfrmd, mn, dv] = pca(data, mode, maxpercent, silent)
%
% principal component analysis of column orientated data set <data>
%
% input arguments :
%
% - each row of data is one 'observation', ... |
github | phtra2/hctsa-master | pauswahl.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/tstoolbox/utils/pauswahl.m | 7,308 | utf_8 | c3c8bd4889a85306b1904dfbf438dbbd | function [pol, train_fehler, test_fehler] = pauswahl(x, y, fracref, maxgrad)
% Polynomauswahlverfahren
% Monome werden nach einer Greedy-Heuristik aus einer vorgebenen Menge ausgewaehlt. Es
% wird dasjenige Monom gewaehlt, was den Fehler im aktullen Schritt am staeksten vermindert.
% Als Grad eines Monoms wird die Sum... |
github | phtra2/hctsa-master | infodim2.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/tstoolbox/@signal/infodim2.m | 1,441 | utf_8 | 3ea1dd8b9b9bb431cfdab1fe4ab767c4 | function [rs, s] = infodim2(s, n, kmax, past)
%tstoolbox/@signal/infodim2
% Syntax:
% * rs = infodim2(s, n, kmax, past)
%
% Input arguments:
% * n - number of randomly chosen reference points (n == -1 means :
% use all points)
% * kmax - maximal number of neighbors for each reference point
% ... |
github | phtra2/hctsa-master | filterbank.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/tstoolbox/@signal/filterbank.m | 3,799 | utf_8 | ff14b54270f1d4c981d3fb697209da05 | function rs = filterbank(s, depth, filterlen)
%tstoolbox/@signal/filterbank
% Syntax:
% * filterbank(s, depth, filterlen)
%
% Filter scalar signal s into 2^textdepth bands of equal bandwith, using
% maximally flat filters.
%
% Copyright 1997-2001 DPI Goettingen, License http://www.physik3.gwdg.de/tstool/gpl.... |
github | phtra2/hctsa-master | signal.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/tstoolbox/@signal/signal.m | 8,300 | utf_8 | ec2e1c14f23ebb4e243efe7f3eb44ef5 | function s = signal(argument, varargin)
%tstoolbox/@signal/signal
% Syntax:
% * s = signal(array)
% creates a new signal object from a data array array the data
% inside the object can be retrieved with x = data(s);
% * s = signal(array, achse1, achse2, ...)
% creates a new signal object fr... |
github | phtra2/hctsa-master | view.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/tstoolbox/@signal/view.m | 6,508 | utf_8 | c0172e02fb20774df5c9d0d156c46c90 | function view(s, fontsize, fhandle)
%tstoolbox/@signal/view
% Syntax:
% * view(signal) (fontsize=12)
% * view(signal, fontsize)
% * view(signal, fontsize, figurehandle)
%
% Signal viewer that decides from the signal's attributes which kind of
% plot to produce, using the signal's plothint entry to ge... |
github | phtra2/hctsa-master | addcommandlines.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/tstoolbox/@description/addcommandlines.m | 1,013 | utf_8 | bfd4fee3343b0b5120135767dc80c5a2 | function d = commandlines(d, commandname, varargin)
%tstoolbox/@description/addcommandlines
% adds new commandline to list of commands that have been applied to
% that signal
% example 1
% addcommandlines(s, 's = spec2(s', 512, 'Hanning' )) will add 's
% = spec2(s, 512, 'Hanning');' to the list... |
github | phtra2/hctsa-master | findlabel.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/tstoolbox/@unit/private/findlabel.m | 1,565 | utf_8 | 31c0ddaf800254da0b93f0b06880e536 | function [label, name, qeng, qger, dBScale, dBRef] = findlabel(factor, exponents)
% finds label and name for a given set of factors and exponents
%RESOURCES = get(0, 'UserData');
%TSTOOLunittab = RESOURCES{2};
load 'tstoolbox/units.mat';
if (exponents == [0 0 0 0 0 0 0 0]) | (factor == 0)
label = '';
name = '';
q... |
github | phtra2/hctsa-master | makemex.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/mex-dev/makemex.m | 4,543 | utf_8 | a5c5d656e3ecaf2639c8ba5655d69ed0 | function makemex(TSTOOLpath)
% compile and copy mex-files to destination directories
% Invoked by : makemex(TSTOOLpath)
% or: makemex
if nargin == 0
if which('units.mat')
TSTOOLpath = fileparts(which('units.mat'));
elseif exist(fullfile(pwd,'../tstoolbox','units.mat'))==2
TSTOOLpath=f... |
github | phtra2/hctsa-master | brute.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/mex-dev/NN/TestSuite/brute.m | 1,123 | utf_8 | d22cdf5bea5cc3dcbd66e4199d90d4d9 | function [indices, distances] = brute(points, refind, nnr, past)
% [indices, distances] = brute(points, refind, nnr, past)
%
% Brute force implementation of nearest neighbor search
%
% Input arguments :
%
% points - N by D matrix of N points of dimension D
% refind - integer reference indices
% nnr - number of neighb... |
github | phtra2/hctsa-master | test.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/mex-dev/NN/TestSuite/test.m | 6,469 | utf_8 | aeba8aef7698bae6251c4870d8a52f18 | function test(mode)
% test nearest neighbor search based mex files
% recompile
error_flag = 0;
if nargin < 1
mode = 'all';
end
disp('Fast nearest neighbor search routines test')
load points.dat
dat = points;
%dat = generate_chaotic_data(40000, 20);
%size(dat)
if strcmp(mode, 'delaunay2D') | strcmp(mode, 'all')
... |
github | phtra2/hctsa-master | pauswahl.m | .m | hctsa-master/Toolboxes/OpenTSTOOL/mex-dev/Polynomauswahl/pauswahl.m | 7,307 | utf_8 | f03f7cce7ca1ea159b5f43d27e308de3 | function [pol, train_fehler, test_fehler] = pauswahl(x, y, fracref, maxgrad)
% Polynomauswahlverfahren
% Monome werden nach einer Greedy-Heuristik aus einer vorgebenen Menge ausgewaehlt. Es
% wird dasjenige Monom gewaehlt, was den Fehler im aktullen Schritt am staeksten vermindert.
% Als Grad eines Monoms wird die Sum... |
github | phtra2/hctsa-master | RM_information.m | .m | hctsa-master/Toolboxes/Rudy_Moddemeijer/RM_information.m | 3,939 | utf_8 | 97af7638c51e84d7465823993914a331 | % RM_information Estimates the mutual information of two stationary signals with
% independent pairs of samples using various approaches.
% [ESTIMATE,NBIAS,SIGMA,DESCRIPTOR] = INFORMATION(X,Y) or
% [ESTIMATE,NBIAS,SIGMA,DESCRIPTOR] = INFORMATION(X,Y,DESCRIPTOR) or
% [ESTIMATE,NBIAS,SIGMA,DESCRIPTOR]... |
github | phtra2/hctsa-master | RM_entropy.m | .m | hctsa-master/Toolboxes/Rudy_Moddemeijer/RM_entropy.m | 3,130 | utf_8 | 6daa29bb6da5a10361b1632d7ce683b8 | % RM_entropy Estimates the entropy of stationary signals with
% independent samples using various approaches.
% [ESTIMATE,NBIAS,SIGMA,DESCRIPTOR] = ENTROPY(X) or
% [ESTIMATE,NBIAS,SIGMA,DESCRIPTOR] = ENTROPY(X,DESCRIPTOR) or
% [ESTIMATE,NBIAS,SIGMA,DESCRIPTOR] = ENTROPY(X,DESCRIPTOR,APPROACH) or
%... |
github | phtra2/hctsa-master | RM_histogram2.m | .m | hctsa-master/Toolboxes/Rudy_Moddemeijer/RM_histogram2.m | 2,490 | utf_8 | 827c5ebe29d9f1568a1cb0a5325dae0a | % RM_histogram2 Computes the two dimensional frequency histogram of two
% row vectors x and y.
% [RESULT,DESCRIPTOR] = HISTOGRAM2(X,Y) or
% [RESULT,DESCRIPTOR] = HISTOGRAM2(X,Y,DESCRIPTOR) or
%where
% DESCRIPTOR = [LOWERX,UPPERX,NCELLX;
% LOWERY,UPPERY,NCELLY]
%
% RESULT : A matr... |
github | phtra2/hctsa-master | RM_histogram.m | .m | hctsa-master/Toolboxes/Rudy_Moddemeijer/RM_histogram.m | 1,615 | utf_8 | 4ae2da9d29e8e01d9bfc62a918eeb0d0 | % RM_histogram Computes the frequency histogram of the row vector x.
% [RESULT,DESCRIPTOR] = HISTOGRAM(X) or
% [RESULT,DESCRIPTOR] = HISTOGRAM(X,DESCRIPTOR) or
% where
% DESCRIPTOR = [LOWER,UPPER,NCELL]
%
% RESULT : A row vector containing the histogram
% DESCRIPTOR: The used descriptor
%
% X : T... |
github | phtra2/hctsa-master | SQL_FlushKeywords.m | .m | hctsa-master/Database/SQL_FlushKeywords.m | 6,678 | utf_8 | e4f59fb4eb4e415d8daac572c2e5349c | function out = SQL_FlushKeywords(flushWhat)
% SQL_FlushKeywords
%
% Recomputes all keywords and linkage information in the database, for either
% time series ('ts') or operations ('ops').
%
% Useful for when there's a problem with the keyword relationships (e.g., when
% an SQL_add is interrupted).
% ------------------... |
github | phtra2/hctsa-master | TS_InspectQuality.m | .m | hctsa-master/PlottingAnalysis/TS_InspectQuality.m | 9,940 | utf_8 | d309c9428b8a19f0b3ed8ab7732b97df | function hadProblem = TS_InspectQuality(inspectWhat,customFile)
% TS_InspectQuality Statistics of quality labels from an hctsa analysis.
%
% This function loads the calculation quality information from HCTSA.mat,
% and plots a visualization of where different special-valued outputs are occurring.
%
% Useful for check... |
github | phtra2/hctsa-master | TS_TopFeatures.m | .m | hctsa-master/PlottingAnalysis/TS_TopFeatures.m | 15,180 | utf_8 | 64778ad6e3aaed1c5481acb94d932743 | function [ifeat, testStat, testStat_rand] = TS_TopFeatures(whatData,whatTestStat,doNull,varargin)
% TS_TopFeatures Top individual features for discriminating labeled time series
%
% This function compares each feature in an hctsa dataset individually for its
% ability to separate the labeled classes of time series a... |
github | phtra2/hctsa-master | TS_SimSearch.m | .m | hctsa-master/PlottingAnalysis/TS_SimSearch.m | 13,980 | utf_8 | 0ace00ecc1c05f6e3fd0f338af93a625 | function TS_SimSearch(varargin)
% TS_SimSearch Nearest neighbors of a given time series from an hctsa analysis.
%
% Nearest neighbors can provide a local context for a particular time series or
% operation.
%
%---INPUTS:
%
% targetID, the ID of the target time series or operation
% numNeighbors, the number of nearest ... |
github | phtra2/hctsa-master | TS_cluster.m | .m | hctsa-master/PlottingAnalysis/TS_cluster.m | 7,465 | utf_8 | 6392b53bab112941abb2098a4f087f0d | function TS_cluster(distanceMetricRow, linkageMethodRow, distanceMetricCol, linkageMethodCol, doSave, theFile)
% TS_cluster Linkage clustering for hctsa data.
%
% Reads in normalized data from HCTSA_N.mat, clusters the data matrix by
% reordering rows and columns with linkage clustering, and then saves the result
% ... |
github | phtra2/hctsa-master | TS_normalize.m | .m | hctsa-master/PlottingAnalysis/TS_normalize.m | 13,169 | utf_8 | 21548ded02bb38fd9c81e86c1333747c | function TS_normalize(normFunction,filterOptions,fileName_HCTSA,subs)
% TS_normalize Trims and normalizes data from an hctsa analysis.
%
% Reads in data from HCTSA.mat, writes a trimmed, normalized version to
% HCTSA_N.mat
% The normalization is all about a rescaling to the [0,1] interval for
% visualization and clust... |
github | APSDetectors/RoachFirmPy-master | ddrcontrol.m | .m | RoachFirmPy-master/Roach2DevelopmentTree/ddrcontrol.m | 6,725 | utf_8 | f581a83d4f9a10ae690e4a8b2c32cdd2 |
function [bramaddressout, dramaddressout, dramrst, dramrwn, dramcmdvld,dramrdack,dramoutmuxsel, stateout,syncout] =ddrcontrolb(...
syncin,startdac,startwrite,fsmreset, rdtoggle,wrtoggle, lutsize, offsetaddress,bramwritesize)
%
%
%
%
%
%
%
% Named states
%
idle = 0;
rdmode0 = 1;
rdmode1 = 2;
rdmode2 = 3;
rdmo... |
github | APSDetectors/RoachFirmPy-master | pfbfft_core_config.m | .m | RoachFirmPy-master/Roach2DevelopmentTree/pfbfft_core_config.m | 4,390 | utf_8 | b628407f526c856974eb784e65bf0c54 |
function pfbfft_core_config(this_block)
% Revision History:
%
% 06-Oct-2015 (14:53 hours):
% Original code was machine generated by Xilinx's System Generator after parsing
% /home/oxygen26/TMADDEN/ROACH2/projcts/pfbfft_core.vhd
%
%
this_block.setTopLevelLanguage('VHDL');
this_block.setE... |
github | APSDetectors/RoachFirmPy-master | qdrcontrol.m | .m | RoachFirmPy-master/Roach2DevelopmentTree/qdrcontrol.m | 3,659 | utf_8 | 1bce07b6c6af8662fc5bb572d8cd49a5 |
function [dramaddressout, sramrd ,stateout,syncout,bramaddressout,sramwr] =...
qdrcontrol(startdac,fsmreset, rdtoggle,lutsize,wrtoggle,startwrite,writelen,offsetaddress)
%
%
%sramrd,
%
%
% Named states
%
idle = 0;
rdmode0 = 1;
rdmode1 = 2;
wrmode0=3;
wrmode1 = 4;
wrmode2 = 5;
%
% persistent registers
%
per... |
github | APSDetectors/RoachFirmPy-master | ddrcontrolb.m | .m | RoachFirmPy-master/Roach2DevelopmentTree/ddrcontrolb.m | 6,538 | utf_8 | aa4ef267eac5881da8dcd6c81250b7bc |
function [bramaddressout, dramaddressout, dramrst, dramrwn, dramcmdvld,dramrdack,dramoutmuxsel, stateout,syncout] =ddrcontrolb(...
syncin,startdac,startwrite,fsmreset, rdtoggle,wrtoggle, lutsize, offsetaddress,bramwritesize)
%
%
%
%
%
%
%
% Named states
%
idle = 0;
rdmode0 = 1;
rdmode1 = 2;
rdmode2 = 3;
rdmo... |
github | APSDetectors/RoachFirmPy-master | fft_core_config.m | .m | RoachFirmPy-master/Roach2DevelopmentTree/fft_core_config.m | 4,378 | utf_8 | 6a67168a17087ef60c54534d765bdc53 |
function fft_core_config(this_block)
% Revision History:
%
% 10-Dec-2015 (15:12 hours):
% Original code was machine generated by Xilinx's System Generator after parsing
% /home/oxygen26/TMADDEN/ROACH2/projcts/fft_core.vhd
%
%
this_block.setTopLevelLanguage('VHDL');
this_block.setEntityN... |
github | APSDetectors/RoachFirmPy-master | getSetting.m | .m | RoachFirmPy-master/Roach2DevelopmentTree/mfiles/getSetting.m | 118 | utf_8 | 54acbd7ce46cd759d78dbdab8bd1efee |
function set= HDF_getSetting(filename,setname)
set=h5read(filename,strcat('/Settings/',setname),[1],[4096]);
end
|
github | APSDetectors/RoachFirmPy-master | HDF_getSetting.m | .m | RoachFirmPy-master/Roach2DevelopmentTree/mfiles/HDF_getSetting.m | 1,508 | utf_8 | 6526fcb73422d6e37b760b7a566788e5 |
function set= HDF_getSetting(filename,setname)
%get a vector of data from /Settings. A vector is returned, one element
%for each analuyzer sweep. Though the file always stores vectors of len
%4096, the returned vector here is only the elements where there is
%valid data, which could be much shorter...
%also we can re... |
github | APSDetectors/RoachFirmPy-master | fit_circle2.m | .m | RoachFirmPy-master/Roach2DevelopmentTree/mfiles/fit_circle2.m | 1,313 | utf_8 | 918a6e13017eb31ffd096f2a8fd33bae | function [ Circle ] = fit_circle2( x,y )
%Calculate center and radius of a circle given x,y
% Uses circle fitting routine from Gao dissertation
%From publication Chernov and Lesort, Journal of Mathematical Imaging and
%Vision 23: 239-252, 2005. Springer Science
% Updated: 01-09-2012 - alterted to work with 'Reso... |
github | APSDetectors/RoachFirmPy-master | HDF_readIQ.m | .m | RoachFirmPy-master/Roach2DevelopmentTree/mfiles/HDF_readIQ.m | 1,110 | utf_8 | 07123124b0e0cc914d9cf8b7c95747e3 |
function [i,q,freqs,f_cent,f_span,timestamp]=HDF_readIQ(filename, sweep_index)
%hdf file is a list of sweeps doen by net analuyzer. give filename
%and which sweep as an integer from 1 to 4096 (max size of file for now...)
%it returns i,q and freq vector as well as center freq, span and string
%timestamp as to when ... |
github | APSDetectors/RoachFirmPy-master | HDFR_getSetting.m | .m | RoachFirmPy-master/Roach2DevelopmentTree/mfiles/HDFR_getSetting.m | 1,050 | utf_8 | 9ff2ff38b6cc452814a75ad5d9a42739 |
function set= HDFR_getSetting(filename,resnum,setname)
%get a vector of data from /Settings. A vector is returned, one element
%for each analuyzer sweep. Though the file always stores vectors of len
%4096, the returned vector here is only the elements where there is
%valid data, which could be much shorter...
%also w... |
github | APSDetectors/RoachFirmPy-master | iq.m | .m | RoachFirmPy-master/Roach2DevelopmentTree/mfiles/iq.m | 1,400 | utf_8 | f8f69c4f9cd50d0ec4be7726b7a299c2 |
function outdata = iq(foffset,is_plot)
%is_plot = 1;
Cfreq=20000;
Ifreq=20;
Qfreq=20;
qchange = 0;
sigmoiderr=qchange * ((2./(1+exp( foffset)))-1);
Q=(5000-4900*abs(sigmoiderr));
Ffreq=Cfreq+Ifreq-2*Ifreq + foffset;
L=65536;
plotL=50;
w0=2 * (Ffreq/L);
[b,a]=iirnotch(w0,w0/Q);
%figure(1)
[fh,fw]=freqz(b,a,... |
github | APSDetectors/RoachFirmPy-master | HDFR_readIQ.m | .m | RoachFirmPy-master/Roach2DevelopmentTree/mfiles/HDFR_readIQ.m | 1,003 | utf_8 | 838dab99e8400dfb6e3d760ce03cd9be |
function [i,q,freqs]=HDFR_readIQ(filename,resnum, tracenum)
%hdf file is a list of sweeps doen by net analuyzer. give filename
%and which sweep as an integer from 1 to 4096 (max size of file for now...)
%it returns i,q and freq vector as well as center freq, span and string
%timestamp as to when the sweep was taken.
%... |
github | APSDetectors/RoachFirmPy-master | HDFR_Info.m | .m | RoachFirmPy-master/Roach2DevelopmentTree/mfiles/HDFR_Info.m | 5,442 | utf_8 | c80730891242dabd4ce74bfc698bbb7c | function [device_name, resnumbers ,numtraces,centfreqs,devgroup,resgroups,tracegroups,tracefields]=HDFR_Info(filename)
%**************************************************************************
%
% This example shows how to iterate over group members using
% H5Giterate.
%
% This file is intended for use with HDF5 L... |
github | APSDetectors/RoachFirmPy-master | get.m | .m | RoachFirmPy-master/ANLYellowBlocks/xps_library/@xps_dac_mkid_4x_r2/get.m | 1,964 | utf_8 | b339b0c993e6a4d4c7245ee5a81c130c | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Center for Astronomy Signal Processing and Electronics Research %
% http://seti.ssl.berkeley.edu/casper/ ... |
github | APSDetectors/RoachFirmPy-master | drc.m | .m | RoachFirmPy-master/ANLYellowBlocks/xps_library/@xps_dac_mkid_4x_r2/drc.m | 2,992 | utf_8 | 3ab77bfce809915097a39d56418ecd9a | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Center for Astronomy Signal Processing and Electronics Research %
% http://seti.ssl.berkeley.edu/casper/ ... |
github | APSDetectors/RoachFirmPy-master | xps_dac_mkid_4x_r2.m | .m | RoachFirmPy-master/ANLYellowBlocks/xps_library/@xps_dac_mkid_4x_r2/xps_dac_mkid_4x_r2.m | 8,080 | utf_8 | 5e52bd2e9b8efc333256a0be876cc449 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Center for Astronomy Signal Processing and Electronics Research %
% http://seti.ssl.berkeley.edu/casper/ ... |
github | APSDetectors/RoachFirmPy-master | set.m | .m | RoachFirmPy-master/ANLYellowBlocks/xps_library/@xps_dac_mkid_4x_r2/set.m | 1,837 | utf_8 | 36e88663abfc3840e2a2596d6628fb05 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Center for Astronomy Signal Processing and Electronics Research %
% http://seti.ssl.berkeley.edu/casper/ ... |
github | APSDetectors/RoachFirmPy-master | get.m | .m | RoachFirmPy-master/ANLYellowBlocks/xps_library/@xps_adc_mkid_4x_r2/get.m | 1,964 | utf_8 | b339b0c993e6a4d4c7245ee5a81c130c | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Center for Astronomy Signal Processing and Electronics Research %
% http://seti.ssl.berkeley.edu/casper/ ... |
github | APSDetectors/RoachFirmPy-master | drc.m | .m | RoachFirmPy-master/ANLYellowBlocks/xps_library/@xps_adc_mkid_4x_r2/drc.m | 1,751 | utf_8 | 805b22d398f2f8ef4c982d1cd1e67683 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Center for Astronomy Signal Processing and Electronics Research %
% http://seti.ssl.berkeley.edu/casper/ %
... |
github | APSDetectors/RoachFirmPy-master | gen_ucf.m | .m | RoachFirmPy-master/ANLYellowBlocks/xps_library/@xps_adc_mkid_4x_r2/gen_ucf.m | 3,032 | utf_8 | 26e3675df4b10667893187319a84bbc2 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Center for Astronomy Signal Processing and Electronics Research %
% http://seti.ssl.berkeley.edu/casper/ %
... |
github | APSDetectors/RoachFirmPy-master | set.m | .m | RoachFirmPy-master/ANLYellowBlocks/xps_library/@xps_adc_mkid_4x_r2/set.m | 1,837 | utf_8 | 36e88663abfc3840e2a2596d6628fb05 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Center for Astronomy Signal Processing and Electronics Research %
% http://seti.ssl.berkeley.edu/casper/ ... |
github | APSDetectors/RoachFirmPy-master | xps_adc_mkid_4x_r2.m | .m | RoachFirmPy-master/ANLYellowBlocks/xps_library/@xps_adc_mkid_4x_r2/xps_adc_mkid_4x_r2.m | 5,361 | utf_8 | 31719e618341a5d447fcf15c4d2e761a | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Center for Astronomy Signal Processing and Electronics Research %
% http://seti.ssl.berkeley.edu/casper/ %
... |
github | APSDetectors/RoachFirmPy-master | get.m | .m | RoachFirmPy-master/ANLYellowBlocks/mkid_dacadc_4x/xps_library/@xps_dac_mkid_4x_r2/get.m | 1,964 | utf_8 | b339b0c993e6a4d4c7245ee5a81c130c | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Center for Astronomy Signal Processing and Electronics Research %
% http://seti.ssl.berkeley.edu/casper/ ... |
github | APSDetectors/RoachFirmPy-master | drc.m | .m | RoachFirmPy-master/ANLYellowBlocks/mkid_dacadc_4x/xps_library/@xps_dac_mkid_4x_r2/drc.m | 2,992 | utf_8 | 3ab77bfce809915097a39d56418ecd9a | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Center for Astronomy Signal Processing and Electronics Research %
% http://seti.ssl.berkeley.edu/casper/ ... |
github | APSDetectors/RoachFirmPy-master | xps_dac_mkid_4x_r2.m | .m | RoachFirmPy-master/ANLYellowBlocks/mkid_dacadc_4x/xps_library/@xps_dac_mkid_4x_r2/xps_dac_mkid_4x_r2.m | 8,205 | utf_8 | 6d2009bb23c82ea1208463f0f35fad9f | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Center for Astronomy Signal Processing and Electronics Research %
% http://seti.ssl.berkeley.edu/casper/ ... |
github | APSDetectors/RoachFirmPy-master | set.m | .m | RoachFirmPy-master/ANLYellowBlocks/mkid_dacadc_4x/xps_library/@xps_dac_mkid_4x_r2/set.m | 1,837 | utf_8 | 36e88663abfc3840e2a2596d6628fb05 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Center for Astronomy Signal Processing and Electronics Research %
% http://seti.ssl.berkeley.edu/casper/ ... |
github | APSDetectors/RoachFirmPy-master | get.m | .m | RoachFirmPy-master/ANLYellowBlocks/mkid_dacadc_4x/xps_library/@xps_adc_mkid_4x_r2/get.m | 1,964 | utf_8 | b339b0c993e6a4d4c7245ee5a81c130c | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Center for Astronomy Signal Processing and Electronics Research %
% http://seti.ssl.berkeley.edu/casper/ ... |
github | APSDetectors/RoachFirmPy-master | drc.m | .m | RoachFirmPy-master/ANLYellowBlocks/mkid_dacadc_4x/xps_library/@xps_adc_mkid_4x_r2/drc.m | 1,751 | utf_8 | 805b22d398f2f8ef4c982d1cd1e67683 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Center for Astronomy Signal Processing and Electronics Research %
% http://seti.ssl.berkeley.edu/casper/ %
... |
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