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github | CyclotronResearchCentre/MRIquality-master | tbx_scfg_mriq_epi_qc.m | .m | MRIquality-master/tbx_scfg_mriq_epi_qc.m | 11,182 | utf_8 | bcec92fca293e172f5e8927a2f242e60 | function epiqc = tbx_scfg_mriq_epi_qc
%==========================================================================
% (Sub)configuration file for the MRIquality toolbox, partim EPI/QC.
% Quality control for EPI data currently includes:
% - sequential check: view a series of data to visually detect any obvious
% artefac... |
github | CyclotronResearchCentre/MRIquality-master | qa_snr_mb.m | .m | MRIquality-master/epi/qa/qa_snr_mb.m | 12,328 | utf_8 | 15feb37dc86f96a1167ba7f8ced44f4a | function SNR = qa_snr_mb(varargin)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% USAGE: eb_snr_mb(imfiles, noisefiles, PARAMS)
% imfiles list of file names of the images. The first few volume
% have already been discarded to avoid T1 saturation
% eff... |
github | CyclotronResearchCentre/MRIquality-master | spm_realign_fbirn.m | .m | MRIquality-master/epi/qa/spm_realign_fbirn.m | 21,234 | utf_8 | d2e1ee283215eaa33af3347079c73001 | function [P, Params] = spm_realign_fbirn(P,flags)
% Estimation of within modality rigid body movement parameters
% FORMAT P = spm_realign(P,flags)
%
% P - char array of filenames
% All operations are performed relative to the first image.
% ie. Coregistration is to the first image, and resampling
% ... |
github | CyclotronResearchCentre/MRIquality-master | myprctile.m | .m | MRIquality-master/epi/qa/myprctile.m | 5,614 | utf_8 | 6db9c325f377972d908cfb99ea4c8a86 | function y = myprctile(x,p,dim)
%PRCTILE Percentiles of a sample.
% Y = PRCTILE(X,P) returns percentiles of the values in X. P is a scalar
% or a vector of percent values. When X is a vector, Y is the same size
% as P, and Y(i) contains the P(i)-th percentile. When X is a matrix,
% the i-th row of Y contains... |
github | CyclotronResearchCentre/MRIquality-master | mriq_run_auto_qa.m | .m | MRIquality-master/epi/qa/mriq_run_auto_qa.m | 16,113 | utf_8 | 6f9219f48488ddbc89c89221e1f8db2e | function out = mriq_run_auto_qa(configfile)
% Main script for running automated QA. The argument is the defaults file
% containing the input directory (containing the DICOM (*.IMA) images as
% transferred after QA acquisition), the tmp, results and archiving
% directories to run the automated QA...
if (nargin==0)
... |
github | CyclotronResearchCentre/MRIquality-master | qc_check_sequential.m | .m | MRIquality-master/epi/qc/qc_check_sequential.m | 3,292 | utf_8 | 5ae673218af445c2a86c884bcb726e07 | %=========================================================================%
% This file is part of the MRI quality toolbox.
% Copyright (C) 2013-2018 - Cyclotron Research Centre
% University of Liege, Belgium
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU Genera... |
github | CyclotronResearchCentre/MRIquality-master | get_mosaic.m | .m | MRIquality-master/epi/qc/get_mosaic.m | 2,015 | utf_8 | 382856c02ab5c724a102ffefdf2ba83a | %=========================================================================%
% This file is part of the Quality Control Toolbox (TCQ)
% Copyright (C) 2013 - Cyclotron Research Centre
% University of Liege, Belgium
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU Ge... |
github | CyclotronResearchCentre/MRIquality-master | qc_spike_check.m | .m | MRIquality-master/epi/qc/qc_spike_check.m | 11,902 | utf_8 | 28ec814d1325f3227ca929d49a96fa95 | %=========================================================================%
% This file is part of the MRI quality toolbox.
% Copyright (C) 2013-2018 - Cyclotron Research Centre
% University of Liege, Belgium
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU Genera... |
github | CyclotronResearchCentre/MRIquality-master | manage_UI.m | .m | MRIquality-master/epi/qc/manage_UI.m | 2,599 | utf_8 | 620999023551b94595efc35d6de184f7 | %=========================================================================%
% This file is part of the Quality Control Toolbox (TCQ)
% Copyright (C) 2013 - Cyclotron Research Centre
% University of Liege, Belgium
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU Ge... |
github | CyclotronResearchCentre/MRIquality-master | eb_read_protocol.m | .m | MRIquality-master/coil/gmap_matlab_scripts/eb_read_protocol.m | 13,273 | utf_8 | 9c5eced0591273287b48d61c3d22b3ad | %=========================================================================%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later versi... |
github | CyclotronResearchCentre/MRIquality-master | mriq_run_config.m | .m | MRIquality-master/config/mriq_run_config.m | 962 | utf_8 | 77db24b7f33a6bd2854b20a93ca4031a | %==========================================================================
% (VOUT &) RUN SUBFUNCTION(S)
%==========================================================================
function out = mriq_run_config(job)
%==========================================================================
% PURPOSE
% Load standard ... |
github | theolivenbaum/XFOILinterface-master | createNACA4.m | .m | XFOILinterface-master/@Airfoil/createNACA4.m | 6,778 | utf_8 | 51efd0b7ae41d8ff4c61d9d61ff7a6a1 | function AF = createNACA4(Designation, NumPoints)
if nargin < 2
NumPoints = 100;
end
if nargin < 1
Designation = '0012';
end
iaf.designation=Designation;
iaf.n=NumPoints;
iaf.HalfCosineSpacing=1;
iaf.wantFile=0;
iaf.datFilePath='';
iaf.is_finiteTE=0;
... |
github | theolivenbaum/XFOILinterface-master | createNACA5.m | .m | XFOILinterface-master/@Airfoil/createNACA5.m | 7,062 | utf_8 | e1fb33bbc121ee5101ad20e4c4ea34bb | function AF = createNACA5(Designation, NumPoints)
if nargin < 2
NumPoints = 100;
end
if nargin < 1
Designation = '23012';
end
iaf.designation=Designation;
iaf.n=NumPoints;
iaf.HalfCosineSpacing=1;
iaf.wantFile=0;
iaf.datFilePath='';
iaf.is_finiteTE=0;
... |
github | deb1996/Rank-Order-Distance-based-clustering-master | dbscandistance.m | .m | Rank-Order-Distance-based-clustering-master/dbscandistance.m | 2,827 | utf_8 | 4132e30b5d396497fd9c5c0ab6da413b | function [class,type,clustermat,no]=dbscandistance(rank_order,k,Eps) % x = dataset, k = no. of %points within the radius & Eps as the radius
[m,n]=size(rank_order);
if nargin<3 || isempty(Eps)
[Eps]=epsilon(x,k);
end
%x = [[1:m]' x];
%[m,n] = size(x);
type = zeros(1,m);
no = 1;
touched = zeros(m,1);
... |
github | tsairuthvik10/Device-to-Device-Communication-master | algorithm3.m | .m | Device-to-Device-Communication-master/algorithm3.m | 1,063 | utf_8 | a97f17a0f11b4b09d188015e103d28b0 | %hd_kb is channel gain of d2d to eNB in which Idon't knw what the hell is G
%hd_cb is channel gain of cellular to eNB
function capacity3=algorithm3(PCmax,PDmax,K,M,Nu,Nd)
%PDmax=250; %will have an array of elements
%PCmax=250; %will have an array of elements
noise=(3.981*10^(-18)*3*10^(6)*10^(-3))/20;%noise spectr... |
github | tsairuthvik10/Device-to-Device-Communication-master | algorithm1.m | .m | Device-to-Device-Communication-master/algorithm1.m | 1,349 | utf_8 | 0fddad1d15b8ae719e54bad1d81554cc | %T1(k),T2(k) are capacities of d2d to eNB and d2d pair respectively
%hd_kb is channel gain of d2d to eNB in which Idon't knw what the hell is G
%hd_kk is channel gain of d2d pair
function capacity=algorithm1(PDmax,PCmax,K,M,Nu,Nd)
%PDmax=250; %will have an array of elements
noise=(3.981*10^(-18)*3*10^(6))/20;%nois... |
github | tsairuthvik10/Device-to-Device-Communication-master | power_matrix.m | .m | Device-to-Device-Communication-master/power_matrix.m | 903 | utf_8 | 7a23198d041db718ed4fde8698497a3d | %for eta and lambda, we are creating a power matrix of size kXm
%hd_kb is channel gain of d2d to eNB in which Idon't knw what the hell is G
%hd_cb is channel gain of cellular to eNB
%hd_kk is channel gain of d2d pair
function [Pmi,Pmj]=power_matrix(Pd,Pc,k,m)
Emin=10000;%in milliwatts
noise=(3.981*10^(-18)*3*10^(... |
github | tsairuthvik10/Device-to-Device-Communication-master | munkres.m | .m | Device-to-Device-Communication-master/munkres.m | 7,171 | utf_8 | b44ad4f1a20fc5d03db019c44a65bac3 | function [assignment,cost] = munkres(costMat)
% MUNKRES Munkres (Hungarian) Algorithm for Linear Assignment Problem.
%
% [ASSIGN,COST] = munkres(COSTMAT) returns the optimal column indices,
% ASSIGN assigned to each row and the minimum COST based on the assignment
% problem represented by the COSTMAT, where the... |
github | robwalton/mason-master | mason.m | .m | mason-master/mason.m | 17,854 | utf_8 | 16834d44b018bd69759ae6c776e32e2d | function [Num,Den] = mason(NetFile,Start,Stop)
% mason.m
% This function takes a netfile describing a signal flow graph
% with symbolic coefficients and generates an equation representing
% the equivilent term between an independent input node, and dependent
% output node. Please see the *readme* file for a full descri... |
github | FaceAR/OpenNPD-master | ReadList.m | .m | OpenNPD-master/tmp/npd_ori/NPDFaceDetector/ReadList.m | 860 | utf_8 | 75824658d174c61c8bc39fcae5f42fd7 | function [numItems, filenames] = ReadList( listFile )
%{
function [numItems, filenames] = ReadList( listFile )
input :
listFile : list of image filenames
output :
numItems : number of total items contained in the list file
filenames : cell array of image filenames read from listFile
au... |
github | FaceAR/OpenNPD-master | Partition.m | .m | OpenNPD-master/tmp/npd_ori/NPDFaceDetector/Partition.m | 1,780 | utf_8 | dd53dad942b9c1f76a3db634aed69afc | function [label, nGroups] = Partition(A)
%% function label = Partition(A)
% Partition of N elements in a set according to the adjacency matrix A,
% return labels of each element, and the number of groups.
%
% example:
% A = [1 0 1 0 0 0 0 0 0; 0 1 0 1 0 0 0 0 0; 1 0 1 0 0 0 0 0 0; 0 1 0 1 1 0 0 0 1;
% 0 ... |
github | FaceAR/OpenNPD-master | DetectFace.m | .m | OpenNPD-master/tmp/npd_ori/NPDFaceDetector/DetectFace.m | 5,776 | utf_8 | bb688ab3930f39a439731382a8837a07 | function rects = DetectFace(model, I, options)
%% function rects = DetectFace(model, I, options)
% Face detection function for the NPD method
%
% Input:
% <model>: the learned model for the NPD face detector
% <I>: the input image to be detected
% [optioins]: optional parameters. A structure containing any... |
github | FaceAR/OpenNPD-master | TestDQT.m | .m | OpenNPD-master/tmp/npd_ori/NPDFaceDetector_Train/src/TestDQT.m | 784 | utf_8 | 492519b93e41b58a50059196956ce146 | function score = TestDQT(tree, x)
% function to test the learned DQT based weak classifier.
n = size(x,1);
score = zeros(n,1,'single');
if isempty(x)
score(:) = repmat(tree.fit, size(x,1), 1);
else
score = TestSubTree(tree, x, 0);
end
end
function score = TestSubTree(tree, x, node)
if isempt... |
github | FaceAR/OpenNPD-master | LearnGAB.m | .m | OpenNPD-master/tmp/npd_ori/NPDFaceDetector_Train/src/LearnGAB.m | 9,694 | utf_8 | a826d5e3f8372dc2c6ea799b27027700 | function [model, negPassIndex, posFx, negFx] = LearnGAB(posX, negX, model, options)
%% [model, negPassIndex, posFx, negFx] = LearnGAB(posX, negX, model, options)
% Train a soft cascade based Gentle AdaBoost classifier, with deep quadratic
% tree (DQT) based weak classifiers.
%
% Input:
% <posX>: features of th... |
github | FaceAR/OpenNPD-master | TrainDetector.m | .m | OpenNPD-master/tmp/npd_ori/NPDFaceDetector_Train/src/TrainDetector.m | 13,102 | utf_8 | c1c45d18ba4cf4e1e91d361d6cf18281 | function model = TrainDetector(faceDBFile, nonfaceDBFile, outFile, options)
%% function model = TrainDetector(faceDBFile, nonfaceDBFile, outFile, options)
% Train a Nomalized Pixel Difference (NPD) based face detector.
%
% Input:
% <faceDBFile>: MAT file for the face images. It contains an array FaceDB
% of s... |
github | icopavan/modulationclassification_matlab-master | fkmeans.m | .m | modulationclassification_matlab-master/k-means/fast k-means/fkmeans.m | 7,426 | utf_8 | 6b8627b1d5fb32be996ca6818b925363 | function [label, centroid, dis] = fkmeans(X, k, options)
% FKMEANS Fast K-means with optional weighting and careful initialization.
% [L, C, D] = FKMEANS(X, k) partitions the vectors in the n-by-p matrix X
% into k (or, rarely, fewer) clusters by applying the well known batch
% K-means algorithm. Rows of X correspo... |
github | sverma88/Tensor-Factorization-HOSVD-iterative-master | Reconstruct_Tensor.m | .m | Tensor-Factorization-HOSVD-iterative-master/Reconstruct_Tensor.m | 751 | utf_8 | a466861403db7eb72be2096daf7c9c76 | % Function to Reconstruct Tensor, given its factors Matrices and core
% tensor
function [Reconstructed_Tensor]=Reconstruct_Tensor(Core_Tensor,Singular_Factors)
%Input
% Core_Tensor : Core Tensor of the original Tensor obtained using coupled
% HOSVD
% Singular_Factors : Singular ... |
github | sverma88/Tensor-Factorization-HOSVD-iterative-master | find_svd_mode.m | .m | Tensor-Factorization-HOSVD-iterative-master/find_svd_mode.m | 591 | utf_8 | 6b7791e007d03ccd9abc2c03155c9056 | % Function to find left singular vectors of a matrix
function [Singular_factor]=find_svd_mode(Tensor_A,Mode,Rank)
% Inputs
% Tensor_A : Tensor A of mode N
% Mode : Specifying the matricization Mode of tensor A
% Rank : Rank reduction in SVD
%
% Outputs
% Singular_factor : Singula... |
github | sverma88/Tensor-Factorization-HOSVD-iterative-master | Tensor_Multiply_Specific_Factors.m | .m | Tensor-Factorization-HOSVD-iterative-master/Tensor_Multiply_Specific_Factors.m | 792 | utf_8 | d9f6afc3e2d632a2fa962317049675d5 | % Function to Multiply Tensor in all Modes excluding specified Modes
function [Ten_A]=Tensor_Multiply_Specific_Factors(Tensor_A,Exclude_Modes,Singular_Factors_A)
%Input
% Tensor_A : Tensor 'A' of Mode 'N'
% Exclude_Modes : row vector specific modes in which tensor has not
% ... |
github | sverma88/Tensor-Factorization-HOSVD-iterative-master | find_Core_Tensor.m | .m | Tensor-Factorization-HOSVD-iterative-master/find_Core_Tensor.m | 745 | utf_8 | 630c2651cc8c70b952f03a035908c3bd | % Function to find core tensor utilizing Factors Matrices of each Mode of
% Tensor and the Tensor itself
function [Core_Tensor]=find_Core_Tensor(Tensor_A,Singular_Factors_A)
% Inputs
% Tensor_A : Tensor 'A' of Mode 'n'
% Singular_Factors_A : Left Singular values obtained by using svd on each
% ... |
github | sverma88/Tensor-Factorization-HOSVD-iterative-master | Reconstruct_Uncoupled_Modes_Tensor.m | .m | Tensor-Factorization-HOSVD-iterative-master/Reconstruct_Uncoupled_Modes_Tensor.m | 1,169 | utf_8 | e1f55ac24273062592358b8ae07cf43e |
% Function to reconstruct uncoupled Modes of the Tensor
function [S_FactorsA]=Reconstruct_Uncoupled_Modes_Tensor(Tensor_A,Singular_Factors_A,Rank_A,Coupled_Modes_A,S_Factors_A)
%Input
% Tensor_A : Tensor 'A' of Mode 'N'
% Rank : Rank reduction in SVD
% Coupled_Modes_A : index to t... |
github | sverma88/Tensor-Factorization-HOSVD-iterative-master | Decompose_Tensor_Coupled_HOSVD_iteratively.m | .m | Tensor-Factorization-HOSVD-iterative-master/Decompose_Tensor_Coupled_HOSVD_iteratively.m | 2,066 | utf_8 | 51c6fb5c7220d3139b5b4d1e4a8be477 | %% Function to find iterative HOSVD of Single Tensor
function [Core_Tensor_A,Singular_Factors_A,Singular_FA]=Decompose_Tensor_Coupled_HOSVD_iteratively(Tensor_A,Rank_A,Error_Threshold,Max_iterations)
%Input
% Tensor_A : Tensor 'A' of Mode 'N'
% Rank_A : Rank reduction in SVD
% Error_Thres... |
github | sverma88/Tensor-Factorization-HOSVD-iterative-master | Project_Images.m | .m | Tensor-Factorization-HOSVD-iterative-master/Project_Images.m | 1,639 | utf_8 | ab768dd0cbd2384854160a24d9159a67 | % Function to project training images on lower dimensions
function [Projected_Images]=Project_Images(Tensor_A,Row_Projection,Col_Projection)
% Inputs
% Tensor_A : Tensor 'A' of Mode 'n'
% Row_Projection : Low rank Singular Factors of rows computed from
% Tensor A using Iterati... |
github | sverma88/Tensor-Factorization-HOSVD-iterative-master | Decompose_Tensor_HOSVD_Cou_UnCo.m | .m | Tensor-Factorization-HOSVD-iterative-master/Decompose_Tensor_HOSVD_Cou_UnCo.m | 1,193 | utf_8 | b18693825ff62d5d4e56185219abe7ad | % Function to decompose tensor HOSVD of a single complete tensor coupled in
% other modes with another tensor or it might not be coupled at all
function [Singular_Factors_A]=Decompose_Tensor_HOSVD_Cou_UnCo(Tensor_A,Rank_A,Coupled_Modes)
%Input
% Tensor_A : Tensor 'A' of Mode 'N' having 'O' modes coupled
... |
github | eeGuoJun/AAAI2016-master | generateH_hybrid.m | .m | AAAI2016-master/JunGuo_AAAI_2016_code/files/generateH_hybrid.m | 1,946 | utf_8 | a3f59d12446433fa7f41bbb2e633f9db | function [H,T] = generateH_hybrid(Hinit, nFea)
% concatenating the Kron-form spectral codes
% to the sequency Walsh-ordered Hadamard codes
% Input:
% Hinit -spectral matrix, each column has only one non-zero position
% nFea -feature dimension of original data Y
% Output:
% H -Kron-form spectral cod... |
github | eeGuoJun/AAAI2016-master | DADL.m | .m | AAAI2016-master/JunGuo_AAAI_2016_code/files/DADL.m | 1,974 | utf_8 | 4ad57c605449771139db8c2587b0c89f | function [Omega] = DADL(Y,W,H,lamda1,lamda2,lamda3,sigma,T)
% 'DADL.m' implements DADL in Algorithm 1
% Input:
% Y -each column is a training sample
% W -the weighting matrix (local topology)
% H -each column is a target code (reflects y's catergory)
% lamda1 -regulariza... |
github | PhilipCastiglione/learning-machines-master | submit.m | .m | learning-machines-master/miscellany/ml-stanford-ng/machine-learning-ex2/ex2/submit.m | 1,605 | utf_8 | 9b63d386e9bd7bcca66b1a3d2fa37579 | function submit()
addpath('./lib');
conf.assignmentSlug = 'logistic-regression';
conf.itemName = 'Logistic Regression';
conf.partArrays = { ...
{ ...
'1', ...
{ 'sigmoid.m' }, ...
'Sigmoid Function', ...
}, ...
{ ...
'2', ...
{ 'costFunction.m' }, ...
'Logistic R... |
github | PhilipCastiglione/learning-machines-master | submitWithConfiguration.m | .m | learning-machines-master/miscellany/ml-stanford-ng/machine-learning-ex2/ex2/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | PhilipCastiglione/learning-machines-master | savejson.m | .m | learning-machines-master/miscellany/ml-stanford-ng/machine-learning-ex2/ex2/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | PhilipCastiglione/learning-machines-master | loadjson.m | .m | learning-machines-master/miscellany/ml-stanford-ng/machine-learning-ex2/ex2/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | PhilipCastiglione/learning-machines-master | loadubjson.m | .m | learning-machines-master/miscellany/ml-stanford-ng/machine-learning-ex2/ex2/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | PhilipCastiglione/learning-machines-master | saveubjson.m | .m | learning-machines-master/miscellany/ml-stanford-ng/machine-learning-ex2/ex2/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | PhilipCastiglione/learning-machines-master | submit.m | .m | learning-machines-master/miscellany/ml-stanford-ng/machine-learning-ex1/ex1/submit.m | 1,876 | utf_8 | 8d1c467b830a89c187c05b121cb8fbfd | function submit()
addpath('./lib');
conf.assignmentSlug = 'linear-regression';
conf.itemName = 'Linear Regression with Multiple Variables';
conf.partArrays = { ...
{ ...
'1', ...
{ 'warmUpExercise.m' }, ...
'Warm-up Exercise', ...
}, ...
{ ...
'2', ...
{ 'computeCost.m... |
github | PhilipCastiglione/learning-machines-master | submitWithConfiguration.m | .m | learning-machines-master/miscellany/ml-stanford-ng/machine-learning-ex1/ex1/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | PhilipCastiglione/learning-machines-master | savejson.m | .m | learning-machines-master/miscellany/ml-stanford-ng/machine-learning-ex1/ex1/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | PhilipCastiglione/learning-machines-master | loadjson.m | .m | learning-machines-master/miscellany/ml-stanford-ng/machine-learning-ex1/ex1/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | PhilipCastiglione/learning-machines-master | loadubjson.m | .m | learning-machines-master/miscellany/ml-stanford-ng/machine-learning-ex1/ex1/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | PhilipCastiglione/learning-machines-master | saveubjson.m | .m | learning-machines-master/miscellany/ml-stanford-ng/machine-learning-ex1/ex1/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | jschoormans/CS_sims-master | fnlCg.m | .m | CS_sims-master/sparseMRI_v0.2/fnlCg.m | 4,150 | utf_8 | 8b609952730b6286877607035d03ec18 | function x = fnlCg(x0,params)
%-----------------------------------------------------------------------
%
% res = fnlCg(x0,params)
%
% implementation of a L1 penalized non linear conjugate gradient reconstruction
%
% The function solves the following problem:
%
% given k-space measurments y, and a fourier operator F the... |
github | jschoormans/CS_sims-master | adjD.m | .m | CS_sims-master/sparseMRI_v0.2/@TVOP/private/adjD.m | 475 | utf_8 | c4e2e12cdb9c4e0c441f6ca464f65b5a | function res = adjD(y)
res = zeros(size(y,1),size(y,2));
%y1 = ones(imsize)*y(1)/sqrt(prod(imsize));
%yx = (reshape(y(2:prod(imsize)+1), imsize(1), imsize(2)));
%yy = (reshape(y(prod(imsize)+2:end), imsize(1), imsize(2)));
res = adjDx(y(:,:,1)) + adjDy(y(:,:,2));
return;
function res = adjDy(x)
res = x(:,[1,1:end... |
github | jschoormans/CS_sims-master | angioSynth.m | .m | CS_sims-master/sparseMRI_v0.2/simulation/angioSynth.m | 937 | utf_8 | 7ec402c9db03dbd487eb72348b776594 | function res = angioSym(imSize, xradVec, yradVec, ampVec)
% res = angioSym(imSize, xradVec, yradVec, ampVec)
res = zeros(imSize);
map = zeros(imSize);
for n=1:length(xradVec)
tmp = ellipsoid(imSize,xradVec(n),yradVec(n));
while sum((tmp(:) + map(:))>1)
tmp = ellipsoid(imSize,xradVec(n),yradVec(n));
end
map = t... |
github | jschoormans/CS_sims-master | minTimeGradient.m | .m | CS_sims-master/sparseMRI_v0.2/utils/minTimeGradient.m | 7,132 | utf_8 | 3f4990d1e1e0c3cce63bcd06cabe380b | function [C,time,g,s,k, phi, sta, stb] = minTimeGradient(C,g0, gfin, gmax, smax,T, show)
% [C,time,g,s,k] = minTimeGradient(C, g0, gfin, gmax, smax,T,show)
%
% Given a k-space trajectory C(n), gradient and slew constraints. This
% function will return a new parametrization that will meet these
% constraint while getti... |
github | jschoormans/CS_sims-master | invD.m | .m | CS_sims-master/sparseMRI_v0.2/threshold/invD.m | 309 | utf_8 | 797904ae4b64c59f923711af7612395e | function res = invD(y,imsize)
[res, flag, relres, iter] = lsqr(@afun,y,[],150,[],[],[],imsize);
res = reshape(res,imsize(1),imsize(2));
function res = afun(x, imsize, istranspose)
if nargin==2
x = reshape(x,imsize(1), imsize(2));
res = D(x);
else
res = adjD(x,imsize);
res = res(:);
end
|
github | jschoormans/CS_sims-master | adjD.m | .m | CS_sims-master/sparseMRI_v0.2/threshold/adjD.m | 459 | utf_8 | 1e4854a073fd4b0cad9cfc2b199e6d93 | function res = adjD(y,imsize)
res = zeros(imsize);
y1 = ones(imsize)*y(1)/sqrt(prod(imsize));
yx = (reshape(y(2:prod(imsize)+1), imsize(1), imsize(2)));
yy = (reshape(y(prod(imsize)+2:end), imsize(1), imsize(2)));
res = y1 + adjDx(yx) + adjDy(yy);
return;
function res = adjDy(x)
res = x(:,[1,1:end-1]) - x;
res(:,... |
github | jschoormans/CS_sims-master | sus.m | .m | CS_sims-master/EvolvingKspace/sus.m | 1,319 | utf_8 | 7a9a88175f65a298e8c0f32db39e111c | % SUS.M (Stochastic Universal Sampling)
%
% This function performs selection with STOCHASTIC UNIVERSAL SAMPLING.
%
% Syntax: NewChrIx = sus(FitnV, Nsel)
%
% Input parameters:
% FitnV - Column vector containing the fitness values of the
% individuals in the population.
% Nsel ... |
github | jschoormans/CS_sims-master | nl_conjgrad_fluor.m | .m | CS_sims-master/chemical_shift_fluor19_sims/nl_conjgrad_fluor.m | 2,416 | utf_8 | ec4113724a9383948f82bbb26ec8fd4b | function [x] = nl_conjgrad_fluor(A, b, x,niter,varargin)
n1=varargin{3}
n2=varargin{4};
rr = @(I) reshape(I,[n1,n2])
T= MakeWaveletOp(x,n1,n2)
if nargin>4
realI=varargin{1};
realflag=1;
lambda=varargin{2}
else
realflag=0;
lambda=0; disp('no lambda used')
end
figure(100);
% x=zeros(size(A'*b)... |
github | jschoormans/CS_sims-master | lassoADMM.m | .m | CS_sims-master/Code/ADDMLASSO/lassoADMM.m | 2,940 | utf_8 | 062fdade86c815d66d8a69e34178c075 | function [z, history] = lassoADMM(A, b, lambda, rho, alpha)
% lasso Solve lasso problem via ADMM
%
% [z, history] = lasso(A, b, lambda, rho, alpha);
%
% Solves the following problem via ADMM:
%
% minimize 1/2*|| Ax - b ||_2^2 + \lambda || x ||_1
%
% The solution is returned in the vector x.
%
% history is a structur... |
github | jschoormans/CS_sims-master | fnlCg_test_oud.m | .m | CS_sims-master/Code/old_uitzoeken/fnlCg_test_oud.m | 7,505 | utf_8 | 692798e5d853aef063f0707ee1e7209f | function x = MCfnlCg_test(x0,params)
%-----------------------------------------------------------------------
%
% res = fnlCg(x0,params)
%
% implementation of a L1 penalized non linear conjugate gradient reconstruction
%
% The function solves the following problem:
%
% given k-space measurments y, and a fourier operato... |
github | jschoormans/CS_sims-master | calctrajBART.m | .m | CS_sims-master/Code/old_uitzoeken/calctrajBART.m | 1,396 | utf_8 | 5d3bf622dfb98c35acab7a8a1b3005af | function [kspace, coordsdyns]=calctrajBART(K);
%function to transform a Cartesian k-space with multiple dynamics into one list of sampled k-lines
%undersampled lines will be removed
%input:K(fe,pe1,pe2,nc,ndyn)
%output: coordinates and reshaped k-space with only sampled points (BART
%toolbox input)
%3-6-2016: TO DO... |
github | jschoormans/CS_sims-master | Recon_varNSA.m | .m | CS_sims-master/Code/old_uitzoeken/Recon_varNSA.m | 1,830 | utf_8 | 475b1dc166b265b9a663577483abae3b | classdef Recon_varNSA < MRecon
properties
% No additional properties needed
end
methods
function MR = Recon_varNSA( filename )
% Create an MRecon object MR upon creation of My_Recon
MR = MR@MRecon(filename);
end
% Overload (overwrite) the existing Perform function of MRecon
function Perf... |
github | jschoormans/CS_sims-master | SigmoidFitting.m | .m | CS_sims-master/Code/abstractGraz/SigmoidFitting.m | 1,814 | utf_8 | 6d9ee53fe3fb5492f57cc5e4d35f40e4 | function [width] = SigmoidFitting(Y,Center,visualize,normalizeoption)
if ~exist('normalizeoption')
normalizeoption=0;
end
% Fits a Sigmoid function to an Image Edge
X=[1:length(Y)];
% normalize Y(0 to 1?)
Yn=Normalize(Y,normalizeoption);
%%FIND CENTER (CAN ONLY BE DONE WITH ENOUGH SNR!)
if isempty(Center)
Ce... |
github | jschoormans/CS_sims-master | MCfnlCg_test.m | .m | CS_sims-master/Code/general_recon/MCfnlCg_test.m | 6,061 | utf_8 | 46064d50b1047866e93a368ae24f257c | function x = MCfnlCg_test(x0,params)
%-----------------------------------------------------------------------
%
% res = fnlCg(x0,params)
%
% implementation of a L1 penalized non linear conjugate gradient reconstruction
%
% The function solves the following problem:
%
% given k-space measurments y, and a fourier operato... |
github | jschoormans/CS_sims-master | reconVarNSA.m | .m | CS_sims-master/Code/general_recon/reconVarNSA.m | 6,706 | utf_8 | eeb18e1a76fc0ab46c429ee6df2d895c | function P=reconVarNSA(K,P,rr)
% JASPER SCHOORMANS 25-10-2016
% RECONSTRUCTION OF VARIABLE NSA CS MEASUREMENTS
% INPUT: K a k-space matrix [nx ny nz nc nNSA]
disp('params...')
P=setParams(K,P);
P.xfmWeight=rr*P.xfmWeight;
% K=FFTmeas(K,P);
disp('mask...')
[data,P.mask,P.MNSA,P.pdf]=makemask(K,P);
disp('recon...'... |
github | jschoormans/CS_sims-master | adjD.m | .m | CS_sims-master/Code/general_recon/functions/@TVOP/private/adjD.m | 475 | utf_8 | c4e2e12cdb9c4e0c441f6ca464f65b5a | function res = adjD(y)
res = zeros(size(y,1),size(y,2));
%y1 = ones(imsize)*y(1)/sqrt(prod(imsize));
%yx = (reshape(y(2:prod(imsize)+1), imsize(1), imsize(2)));
%yy = (reshape(y(prod(imsize)+2:end), imsize(1), imsize(2)));
res = adjDx(y(:,:,1)) + adjDy(y(:,:,2));
return;
function res = adjDy(x)
res = x(:,[1,1:end... |
github | jschoormans/CS_sims-master | adjD.m | .m | CS_sims-master/Code/general_recon/functions/@TV2op/private/adjD.m | 351 | utf_8 | 8d7e947bb2c258fff60450026bc5e2cc | function res = adjD(y)
res = zeros(size(y,1),size(y,2));
res = adjDx(y(:,:,1)) + adjDy(y(:,:,2));
return;
function res = adjDy(x)
res = x(:,[1,1:end-1]) - 2.*x + x(:,[2:end,end]);
res(:,1) = -x(:,1);
res(:,end) = x(:,end-1);
function res = adjDx(x)
res = x([1,1:end-1],:) - 2.*x + x([2:end,end],:);
res(1,:) = -x(1,... |
github | jschoormans/CS_sims-master | MultiCoil_CG.m | .m | CS_sims-master/Code/ISMRM/MultiCoil_CG.m | 4,871 | utf_8 | fd8ae9063fc461da3afe3d9c35c89da1 | function x=MultiCoil_CG(x0,params)
% MODIFICATION OF CONJUGATE GRADIENT ALGO BY M LUSTIG
% USING BART TOOLBOX
% SIMPLIFIED VERSION
% non-linear conjugate gradient algorithm that uses variable density
% sampling
% dependencies: BART toolbox
x = x0;
% line search parameters
maxlsiter = params.lineSearchItn... |
github | jschoormans/CS_sims-master | reconVarNSA2D.m | .m | CS_sims-master/Code/ISMRM_abstract/reconVarNSA2D.m | 6,103 | utf_8 | 6ab14381c5f905039d3bac73a775c1df | function P=reconVarNSA2D(K,P)
% JASPER SCHOORMANS 25-10-2016
% RECONSTRUCTION OF VARIABLE NSA CS MEASUREMENTS
% INPUT: K a k-space matrix [nx ny nz nc nNSA]
addpath(genpath('L:\basic\divi\Projects\cosart\CS_simulations\Code'))
addpath(genpath('C:\Users\jschoormans\Dropbox\phD\bart-0.3.01'));
addpath(genpath('L... |
github | jschoormans/CS_sims-master | multicoil_fnlCg_experimental.m | .m | CS_sims-master/Code/ISMRM_abstract/multicoil_fnlCg_experimental.m | 5,541 | utf_8 | 49af7947c03a242dcf83564329609423 | function x = multicoil_fnlCg_experimental(x0,params)
%-----------------------------------------------------------------------
%
% res = fnlCg(x0,params)
%
% implementation of a L1 penalized non linear conjugate gradient reconstruction
%
% The function solves the following problem:
%
% given k-space measurments y, and a... |
github | jschoormans/CS_sims-master | reconVarNSA51.m | .m | CS_sims-master/experiments/VNSA_51_retro/reconVarNSA51.m | 7,889 | utf_8 | 5726a86ea6b46bb2ddaad3ae3d3ed260 | function P=reconVarNSA51(K,P)
% JASPER SCHOORMANS 25-10-2016
% RECONSTRUCTION OF VARIABLE NSA CS MEASUREMENTS
% INPUT: K a k-space matrix [nx ny nz nc nNSA]
%GET K_SPACE
%{
MR=MRecon
MR.Parameter.Parameter2Read.typ = 1;
MR.Parameter.Recon.CoilCombination='yes'
MR.Parameter.Recon.ACNrVirtualChannels=5;
% Produ... |
github | jschoormans/CS_sims-master | adjD.m | .m | CS_sims-master/experiments/VNSA_51_retro/ReconCode/functions/@TVOP/private/adjD.m | 475 | utf_8 | c4e2e12cdb9c4e0c441f6ca464f65b5a | function res = adjD(y)
res = zeros(size(y,1),size(y,2));
%y1 = ones(imsize)*y(1)/sqrt(prod(imsize));
%yx = (reshape(y(2:prod(imsize)+1), imsize(1), imsize(2)));
%yy = (reshape(y(prod(imsize)+2:end), imsize(1), imsize(2)));
res = adjDx(y(:,:,1)) + adjDy(y(:,:,2));
return;
function res = adjDy(x)
res = x(:,[1,1:end... |
github | jschoormans/CS_sims-master | adjD.m | .m | CS_sims-master/experiments/VNSA_51_retro/ReconCode/functions/@TV2op/private/adjD.m | 351 | utf_8 | 8d7e947bb2c258fff60450026bc5e2cc | function res = adjD(y)
res = zeros(size(y,1),size(y,2));
res = adjDx(y(:,:,1)) + adjDy(y(:,:,2));
return;
function res = adjDy(x)
res = x(:,[1,1:end-1]) - 2.*x + x(:,[2:end,end]);
res(:,1) = -x(:,1);
res(:,end) = x(:,end-1);
function res = adjDx(x)
res = x([1,1:end-1],:) - 2.*x + x([2:end,end],:);
res(1,:) = -x(1,... |
github | jschoormans/CS_sims-master | MCfnlCg_test.m | .m | CS_sims-master/experiments/VNSA_51_retro/ReconCode/general_recon/MCfnlCg_test.m | 5,917 | utf_8 | a27626913145833ecc21a39568faff89 | function x = MCfnlCg_test(x0,params)
%-----------------------------------------------------------------------
%
% res = fnlCg(x0,params)
%
% implementation of a L1 penalized non linear conjugate gradient reconstruction
%
% The function solves the following problem:
%
% given k-space measurments y, and a fourier operato... |
github | jschoormans/CS_sims-master | reconVarNSA2D.m | .m | CS_sims-master/experiments/VNSA_51_retro/ReconCode/ISMRM_abstract/reconVarNSA2D.m | 6,103 | utf_8 | 6ab14381c5f905039d3bac73a775c1df | function P=reconVarNSA2D(K,P)
% JASPER SCHOORMANS 25-10-2016
% RECONSTRUCTION OF VARIABLE NSA CS MEASUREMENTS
% INPUT: K a k-space matrix [nx ny nz nc nNSA]
addpath(genpath('L:\basic\divi\Projects\cosart\CS_simulations\Code'))
addpath(genpath('C:\Users\jschoormans\Dropbox\phD\bart-0.3.01'));
addpath(genpath('L... |
github | jschoormans/CS_sims-master | multicoil_fnlCg_experimental.m | .m | CS_sims-master/experiments/VNSA_51_retro/ReconCode/ISMRM_abstract/multicoil_fnlCg_experimental.m | 5,541 | utf_8 | 49af7947c03a242dcf83564329609423 | function x = multicoil_fnlCg_experimental(x0,params)
%-----------------------------------------------------------------------
%
% res = fnlCg(x0,params)
%
% implementation of a L1 penalized non linear conjugate gradient reconstruction
%
% The function solves the following problem:
%
% given k-space measurments y, and a... |
github | jschoormans/CS_sims-master | mrics.m | .m | CS_sims-master/mrics/mrics.m | 3,913 | utf_8 | 30147306422a7bd1e29df458dee6914f | % mrics.m by Tom Goldstein (TomGoldstein1@gmail.com)
% This file contains methods for performing compressed sensing
% recontructions of images from k-space data using the Split Bregman
% method.
% To use the method, simply add this "m" file to your current directory,
% and then call the follow... |
github | jschoormans/CS_sims-master | mrics_cov.m | .m | CS_sims-master/mrics/mrics_cov.m | 3,963 | utf_8 | 9e91bd4c0ac94c7ec6abd26dd6734436 | % mrics.m by Tom Goldstein (TomGoldstein1@gmail.com)
%MODIFIED TO INCLUDE A COVARIANCE WEIGHTING!!
% This file contains methods for performing compressed sensing
% recontructions of images from k-space data using the Split Bregman
% method.
% To use the method, simply add this "m" file to your c... |
github | jschoormans/CS_sims-master | mricswaveletTV.m | .m | CS_sims-master/mrics/mricswaveletTV.m | 4,729 | utf_8 | 0ba02ecc177ac627d0ac2cc1f9f7c33b | % modification of mrics.m by Tom Goldstein (TomGoldstein1@gmail.com)
% to include wavelets. Jasper Schoormans
% This file contains methods for performing compressed sensing
% recontructions of images from k-space data using the Split Bregman
% method.
% To use the method, simply add this "m" file to yo... |
github | TUC-ProAut/ros_octomap-master | rosmsg_check.m | .m | ros_octomap-master/octomap_pa_matlab/scripts/rosmsg_check.m | 4,318 | ibm852 | 4f741793ce397200d68264b3b0a2d7bc | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% rosmsg_check.m %
% ============== %
... |
github | meshonline/rhubarb-lip-sync-master | rtpAnalyze.m | .m | rhubarb-lip-sync-master/lib/webrtc-8d2248ff/tools/matlab/rtpAnalyze.m | 7,892 | utf_8 | 46e63db0fa96270c14a0c205bbab42e4 | function rtpAnalyze( input_file )
%RTP_ANALYZE Analyze RTP stream(s) from a txt file
% The function takes the output from the command line tool rtp_analyze
% and analyzes the stream(s) therein. First, process your rtpdump file
% through rtp_analyze (from command line):
% $ out/Debug/rtp_analyze my_file.rtp my_f... |
github | Walabot-Projects/Walabot-BigBrother-master | STL_Import.m | .m | Walabot-BigBrother-master/STL_Import.m | 6,645 | utf_8 | 2e387456fd8576332b337722a812ebf6 | function varargout=STL_Import(filename,mode)
% STL_Import is a tool designed to import into MATLAB both binary and ASCII STL files.
%
% This scprit is mainly a collage betwwen file axchange fileid 22409 and 3642, plus
% some other features that can be considered new on FEX.
%
% SYNOPSIS:
%
%
% %mode 1 (default)
% [... |
github | jgte/orb-master | slr.m | .m | orb-master/slr.m | 49,567 | utf_8 | aad1a640b46e0f252de76b2830b23eeb | classdef slr < gravity
properties(Constant)
data_options={...
'~/data/SLR';...
'./data/SLR';...
};
%default value of parameters
%NOTICE: needs updated when adding a new parameter
parameter_list={...
'verbose', false, @islogical;...
'C20mean',-4.8416945732000E-... |
github | jgte/orb-master | attitude.m | .m | orb-master/attitude.m | 69,816 | UNKNOWN | e7d9799406529e128038827a96a19b5c | classdef attitude
%static
properties(Constant,GetAccess=private)
%list of data fields
%NOTICE: needs updated when adding a new data type
data_type_list={'quat','ang','angr','anga'};
%default value of parameters
%NOTICE: needs updated when adding a new parameter
%NOTICE: it's not a bad idea t... |
github | jgte/orb-master | orbit.m | .m | orb-master/orbit.m | 68,006 | utf_8 | 7dce7d95d63e33fe5bc9c6e3e4370c83 | classdef orbit
%static
properties(Constant,GetAccess=private)
%list of data fields
%NOTICE: needs updated when adding a new data type
data_type_list=struct(...
'pos',struct(...
'label','position',...
'size',3,...
'xyz',struct('units',{{'m', 'm', 'm'}},'names',{{'x', 'y',... |
github | jgte/orb-master | grace.m | .m | orb-master/grace.m | 56,465 | utf_8 | b7e75d2ebbc6f66436e99a7a685b78ac | classdef grace
properties(Constant)
l1bdir_options={...
'~/data/grace';...
'./data/grace';...
};
% data_name, version, ...
l1b_data={...
'KBR1B','03';...
'SCA1B','03';...
'AHK1B','02';...
'GNV1B','02';...
'MAS1B','02';...
'THR1B','02';...
'CLK1B','... |
github | jgte/orb-master | nrtdm.m | .m | orb-master/nrtdm.m | 10,641 | utf_8 | af0c4b5d5c26d14cdede052fbd22e2e6 | classdef nrtdm
%static
properties(Constant)
%default value of some internal parameters
default_list=struct(...
'time_format','yyyy-MM-dd hh:mm:ss.sss',...
'debug',true...
);
end
%read only
properties(SetAccess=private)
metadata
start
stop
ts
file_list
end
method... |
github | jgte/orb-master | nrtdm_product.m | .m | orb-master/nrtdm_product.m | 2,731 | utf_8 | 12fec5008418eb510b9c1bb60a1b69c8 | %This object defines details of the product
classdef nrtdm_product
properties(GetAccess = 'private', SetAccess = 'private')
sep_index
debug
end
properties(Dependent)
category
name
sat
field
file
end
properties(GetAccess = 'public', SetAccess = 'private')
str
end
methods(Sta... |
github | jgte/orb-master | gravity.m | .m | orb-master/gravity.m | 132,664 | utf_8 | 075ce0a205cc46b1fe6234bab3a33c4c | classdef gravity < simpletimeseries
%static
properties(Constant)
%this is used to define the epoch of static fields (in the gravity.load method)
static_select_date=time.zero_date;
static_start_date=time.zero_date;
static_stop_date=time.inf_date;
% Supported functionals are the following,
% '... |
github | jgte/orb-master | file.m | .m | orb-master/file.m | 48,359 | utf_8 | 3181b9ef60e9c83aa0b571aa3283bb2e | classdef file
properties(Constant)
%NOTICE: this used to be called 'DATE_PLACE_HOLDER'
dateplaceholder='DATE_PLACEHOLDER';
archivedfilesext={'.gz','.gzip','.z','.zip','.tgz','.tar.gz','.tar'};
%NOTICE: these need to be hard-coded because the point is to translate paths with any
% of these s... |
github | jgte/orb-master | m2html.m | .m | orb-master/packages/m2html/m2html.m | 59,376 | utf_8 | 5931c54cfe25b61fd2328729ae23cdf2 | function m2html(varargin)
%M2HTML - Documentation Generator for Matlab M-files and Toolboxes in HTML
% M2HTML by itself generates an HTML documentation of the Matlab M-files found
% in the direct subdirectories of the current directory. HTML files are
% written in a 'doc' directory (created if necessary). All the o... |
github | jgte/orb-master | mdot.m | .m | orb-master/packages/m2html/private/mdot.m | 2,679 | utf_8 | 99805833e4e1e95e0377c8bde4d71014 | function mdot(mmat, dotfile,f)
%MDOT - Export a dependency graph into DOT language
% MDOT(MMAT, DOTFILE) loads a .mat file generated by M2HTML using option
% ('save','on') and writes an ascii file using the DOT language that can
% be drawn using <dot> or <neato> .
% MDOT(MMAT, DOTFILE,F) builds the graph containing... |
github | jgte/orb-master | doxysearch.m | .m | orb-master/packages/m2html/private/doxysearch.m | 7,892 | utf_8 | f4b7765f57ab9a6828fd9cdec0f4bb5d | function result = doxysearch(query,filename)
%DOXYSEARCH Search a query in a 'search.idx' file
% RESULT = DOXYSEARCH(QUERY,FILENAME) looks for request QUERY
% in FILENAME (Doxygen search.idx format) and returns a list of
% files responding to the request in RESULT.
%
% See also DOXYREAD, DOXYWRITE
% Copyright (C)... |
github | jgte/orb-master | doxywrite.m | .m | orb-master/packages/m2html/private/doxywrite.m | 3,710 | utf_8 | 827b350c36516ee3fd48170e72f1421b | function doxywrite(filename, kw, statinfo, docinfo)
%DOXYWRITE Write a 'search.idx' file compatible with DOXYGEN
% DOXYWRITE(FILENAME, KW, STATINFO, DOCINFO) writes file FILENAME
% (Doxygen search.idx. format) using the cell array KW containing the
% word list, the sparse matrix (nbword x nbfile) with non-null value... |
github | jgte/orb-master | mlxparse.m | .m | orb-master/packages/m2html/private/mlxparse.m | 7,366 | utf_8 | 8bb8a65ee64ac6b7394c558a171acac4 | function [mlx, rels, out] = mlxparse(filename)
% Parse Live Script (*.mlx) files
% FORMAT [mlx, rels, out] = mlxparse(filename)
%
% https://www.mathworks.com/help/matlab/matlab_prog/live-script-file-format.html
% Copyright (C) 2020 Guillaume Flandin <Guillaume@artefact.tk>
mlx = struct('properties',{},'content',{});
... |
github | jgte/orb-master | doxyread.m | .m | orb-master/packages/m2html/private/doxyread.m | 3,219 | utf_8 | a3ac719498b1dba5e9db25e5b8471c8e | function [statlist, docinfo] = doxyread(filename)
%DOXYREAD Read a 'search.idx' file generated by DOXYGEN
% STATLIST = DOXYREAD(FILENAME) reads FILENAME (Doxygen search.idx
% format) and returns the list of keywords STATLIST as a cell array.
% [STATLIST, DOCINFO] = DOXYREAD(FILENAME) also returns a cell array
% con... |
github | jgte/orb-master | mergeimports.m | .m | orb-master/packages/+yaml/mergeimports.m | 5,187 | utf_8 | 389eed38355535a1716f4fce46a255b1 | % Walks through a tree structure data. Whenever it finds a structure, which have field named 'import' it assumes that in that field is a cell array and merges all structures found in that array.
% Parameter verb is used for debugging purposes.
function result = mergeimports(data, verb)
if ~exist('verb','var')
... |
github | jgte/orb-master | ReadYamlRaw.m | .m | orb-master/packages/+yaml/ReadYamlRaw.m | 8,002 | utf_8 | 5ef33bd0141cabef057b76efd63032f1 | % Reads YAML file, converts YAML sequences to MATLAB cell columns and YAML mappings to MATLAB structs
%
% filename ... name of yaml file to be imported
% verbose ... verbosity level (0 or absent = no messages,
% 1 = notify imports)
function result = ReadYamlRaw(filename... |
github | jgte/orb-master | makematrices.m | .m | orb-master/packages/+yaml/makematrices.m | 5,667 | utf_8 | a54d79b3b9f32e0850cf7f684a5dcc9e | % Recursively walks through a Matlab hierarchy and substitutes cell vectors by a matrix when possible.
% Specifically substitutes cell objects like
%
% {{1,2,3},{4,5,6}}
%
% by
%
% {1,2,3;4,5,6}
%
% It leaves other objects unchanged except that it may change cell
% orientations (from column to row, et... |
github | jgte/orb-master | deflateimports.m | .m | orb-master/packages/+yaml/deflateimports.m | 1,700 | utf_8 | 300493cb34b569bf5a149e2fd759016a | % Transforms structures:
% - import: A, B
% - import: C
% - import: D, E, F
%
% into:
% - import: A, B, C, D, F, F
%
function result = deflateimports(r)
result = recurse(r, 0, []);
end
function result = recurse(data, level, addit)
if iscell(data) && ~yaml.ismymatrix(data)
result =... |
github | jgte/orb-master | dosubstitution.m | .m | orb-master/packages/+yaml/dosubstitution.m | 935 | utf_8 | 2bb62ce20a4be9c1a47a83f9e0f62ae7 | function result = dosubstitution(r, dictionary)
if ~exist('dictionary','var')
dictionary = {};
end;
result = recurse(r, 0, dictionary);
end
function result = recurse(data, level, dictionary)
if iscell(data) && ~yaml.ismymatrix(data)
result = iter_cell(data, level, dictionary);
... |
github | jgte/orb-master | merge_struct.m | .m | orb-master/packages/+yaml/merge_struct.m | 1,288 | utf_8 | 7153ad97047d5fcc98c52574b17d91a0 | % Does merge of two structures. The result is structure which is union of fields of p and s.
% If there are equal field names in p and s, fields in p are overwriten with their peers from s.
function result = merge_struct(p, s, donotmerge, deep)
if ~( isstruct(p) && isstruct(s) )
error('Only structures c... |
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