plateform stringclasses 1
value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | mmagnuski/braintools-master | erp_from_table.m | .m | braintools-master/util/erp_from_table.m | 1,442 | utf_8 | b2977581d6f4e9fb85a5578ed554f041 | function erp = erp_from_table(trials, eeg, avgfun, byrow, ...
within, across)
if ~exist('byrow', 'var') || isempty(byrow)
byrow = 'subject';
end
if ~exist('across', 'var') || isempty(across)
across = 'subject';
end
across_grp = get_grouping(trials, byrow);
agrp = unique(across_grp);
erp = zeros(length(agrp), size(... |
github | brookstaylorjr/MACKtrack-master | checkMasks.m | .m | MACKtrack-master/CellScreen/checkMasks.m | 9,825 | utf_8 | d0994e88f816deaf5bde75eabc0ea7dd | function [] = checkMasks(track_folder, parameters)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% [] = showmasks(segmentation_folder)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
%... |
github | brookstaylorjr/MACKtrack-master | zeissbrowse.m | .m | MACKtrack-master/Utilities/zeissbrowse.m | 16,936 | utf_8 | 6f5d266aa5feea7fb7dc2c6b90bc9ba8 | function [] = zeissbrowse(start_dir)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% [] = zeissbrowse(start_dir)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% ZEISSBROWSE creates GUI allowing user to browse a sequential image ... |
github | brookstaylorjr/MACKtrack-master | terrorbar.m | .m | MACKtrack-master/Utilities/terrorbar.m | 10,679 | utf_8 | 1456a9ace1c5416f2d9e8a80fc0445d1 | function herrorbars=terrorbar(varargin)
%function herrorbars=terrorbar(x,val,lowererror,uppererror,errorbarwidth,errorbarunits)
%
%=========================
% terrorbar.m
% Draws error bars (just the error bars, not the lines) whose size can be
% controlled (which was otherwise a challenge in versions 2014b onwa... |
github | brookstaylorjr/MACKtrack-master | cmap.m | .m | MACKtrack-master/Utilities/cmap.m | 6,661 | utf_8 | 25c05282a4baf9e3e257af550fd9f7ab | %CMAP Create a custom colormap from RGB values
%
% FUNCTION: cmap_out = cmap(in_arg,in_size,in_cutd,in_cutl)
%
% Create custom colormaps from any number of colors
% Inputs: 1) in_arg = RGB triplets: [A B C] with values between 0-1
% 2) in_size (optional): Length of colorbar (i.e. 10 = 10 colors)
% ... |
github | brookstaylorjr/MACKtrack-master | xml2struct.m | .m | MACKtrack-master/Utilities/xml2struct.m | 6,955 | utf_8 | 58f0b998cc71b30b4a6a12b330cfe950 | function [ s ] = xml2struct( file )
%Convert xml file into a MATLAB structure
% [ s ] = xml2struct( file )
%
% A file containing:
% <XMLname attrib1="Some value">
% <Element>Some text</Element>
% <DifferentElement attrib2="2">Some more text</Element>
% <DifferentElement attrib3="2" attrib4="1">Even more t... |
github | brookstaylorjr/MACKtrack-master | linspecer.m | .m | MACKtrack-master/Utilities/linspecer.m | 5,876 | utf_8 | 080ead8e13635a6463b386592c36f674 | % function lineStyles = linspecer(N)
% This function creates an Nx3 array of N [R B G] colors
% These can be used to plot lots of lines with distinguishable and nice
% looking colors.
%
% lineStyles = linspecer(N); makes N colors for you to use: lineStyles(ii,:)
%
% colormap(linspecer); set your colormap to have eas... |
github | brookstaylorjr/MACKtrack-master | browse3i.m | .m | MACKtrack-master/Utilities/browse3i.m | 19,332 | utf_8 | 8442261d3da6033a968dc39f3d2c302f | function [] = browse3i(start_dir)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% [] = browse3i(start_dir)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% BROWSE3I creates GUI allowing user to browse a sequential image set insid... |
github | brookstaylorjr/MACKtrack-master | dscatter2.m | .m | MACKtrack-master/Utilities/dscatter2.m | 7,053 | utf_8 | 70dddb71e98d750631edf003e2dd313d | function [hAxes, h] = dscatter2(X,Y, varargin)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% function hAxes = dscatter2(X,Y, varargin)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ... |
github | brookstaylorjr/MACKtrack-master | browseXL.m | .m | MACKtrack-master/Utilities/browseXL.m | 20,918 | utf_8 | bd11236e0a7382771bb8845f64603342 | function [] = browseXL(start_dir)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% [] = browseXL(start_dir)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% BROWSEXL creates GUI allowing user to browse a sequential image set insid... |
github | brookstaylorjr/MACKtrack-master | multistack.m | .m | MACKtrack-master/Utilities/multistack.m | 3,651 | utf_8 | b0aca5e42c1afe8f34ccfeea1cbc2d50 | function [ ] = multistack(inputStack, bounds)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% MULTISTACK Create a GUI that allows the user to see a 3-D stack of images using a slider at bottom of figure.
%
% inputStack a 3-D matrix, e.g. formed by cat(3,img1,img2...)
%... |
github | brookstaylorjr/MACKtrack-master | phaseID.m | .m | MACKtrack-master/CellTrack/phaseID.m | 9,098 | utf_8 | 9ad03b4584cd2814d36b993a97420e81 | function [output, diagnos] = phaseID(phaseOrig,p,X)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% PHASEID: Edge and intensity method of determining background vs cell.
%
% phaseOrig original phase-contrast image
% p parameters struture
% X ... |
github | brookstaylorjr/MACKtrack-master | loadImages.m | .m | MACKtrack-master/CellTrack/loadImages.m | 8,798 | utf_8 | af3b7a6fcbd4e289d7edd419f372dfd5 | function handlesOut = loadImages(handles)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% handlesOut = loadImages(handles)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% LOADIMAGES: use nucleus/cell image expressions to load up... |
github | brookstaylorjr/MACKtrack-master | nucleusID.m | .m | MACKtrack-master/CellTrack/nucleusID.m | 12,812 | utf_8 | d3c29f7b979caba45a0ef4d1b5044032 | function [output, diagnos] = nucleusID(nuc_orig,p,data)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% [output, diagnos] = nucleusID(nuc_orig,p,data,~)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ... |
github | brookstaylorjr/MACKtrack-master | MACKtrack.m | .m | MACKtrack-master/CellTrack/MACKtrack.m | 77,986 | utf_8 | 4cfd72df0655b7c1d7a1c4a52209f446 | function varargout = MACKtrack(varargin)
% MACKTRACK MATLAB code for MACKtrack.fig
% MACKTRACK, by itself, creates a new MACKTRACK or raises the existing
% singleton*.
%
% H = MACKTRACK returns the handle to a new MACKTRACK or the handle to
% the existing singleton*.
%
% MACKTRACK('CALLBACK',hO... |
github | brookstaylorjr/MACKtrack-master | testImages.m | .m | MACKtrack-master/CellTrack/testImages.m | 8,518 | utf_8 | 9906c8da789b4fbeaa0cf9cce1787a56 | function [] = testImages(handles)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% [] = testImages(handles)
%
% TESTIMAGES Once parameters are loaded/set, segment specified images and display diagnostic figure
%
% handles master structure with parameters and naming dat
%- -... |
github | brookstaylorjr/MACKtrack-master | memoryCheck.m | .m | MACKtrack-master/CellTrack/memoryCheck.m | 23,998 | utf_8 | 9c8936544f2e5aa6f615d152b28de8f5 | function [CellData_out, queue_out] = memoryCheck(CellData, queue, cell_img, curr_frame, p)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% MEMORYCHECK checks each cell's history to identify inconsistencies in segmentation/tracking
%
% CellData flat structure with cell ... |
github | brookstaylorjr/MACKtrack-master | setLocations.m | .m | MACKtrack-master/CellTrack/setLocations.m | 9,781 | utf_8 | fb064edf2bf0ccdf4a526cfde33abbe9 | function varargout = setLocations(varargin)
% SETLOCATIONS MATLAB code for setLocations.fig
% SETLOCATIONS, by itself, creates a new SETLOCATIONS window or raises the existing
% singleton*.
%
% H = SPECIFYLOCATIONS returns the handle to a new SPECIFYLOCATIONS or the handle to
% the existing singleto... |
github | brookstaylorjr/MACKtrack-master | trackNuclei.m | .m | MACKtrack-master/CellTrack/trackNuclei.m | 17,450 | utf_8 | d1a8966a5245ab7bbc3b608d0ff85f91 | function [CellDataOut, queue_out] = trackNuclei(queue_in,CellData,curr_frame, p)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% [CellDataOut, queue_out] = trackNuclei(queue_in,CellData,curr_frame, p)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -... |
github | brookstaylorjr/MACKtrack-master | perimetersplit.m | .m | MACKtrack-master/CellTrack/subfunctions/perimetersplit.m | 5,079 | utf_8 | 72690a9e525b3674090a065e4e5e39a8 | function [cut_lines, all_pts] = perimetersplit(mask1,p)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% [cut_lines, all_pts] = perimetersplit(mask1,p)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% PERIMTE... |
github | brookstaylorjr/MACKtrack-master | calculatejump.m | .m | MACKtrack-master/CellTrack/subfunctions/calculatejump.m | 2,695 | utf_8 | c56f49048fe85bde208b232a234fe764 |
function [image_jump, r_jumps, c_jumps, maxes] = calculatejump(old_img, new_img, n)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% [image_jump] = calculatejump(old_img, new_img)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% ... |
github | brookstaylorjr/MACKtrack-master | linkblock.m | .m | MACKtrack-master/CellTrack/subfunctions/linkblock.m | 4,774 | utf_8 | 164e37dd3a33d875c2aa354dff7044d8 | function links = linkblock(block1, blocks, start_pt, labeldata, p)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% links = linkblock(block1, blocks, start_pt, labeldata, p)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ... |
github | brookstaylorjr/MACKtrack-master | bridgenuclei.m | .m | MACKtrack-master/CellTrack/subfunctions/bridgenuclei.m | 12,186 | utf_8 | e0284b20e5e707d14e5e1a8f17faacf8 | function [label_out] = bridgenuclei(subobj_in,obj_cc, cutoff, shapedef, verbose)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% [label_out] = bridgenuclei(label_in,cutoff,verbose)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
%... |
github | brookstaylorjr/MACKtrack-master | checkDynamics.m | .m | MACKtrack-master/CellQuery/checkDynamics.m | 20,532 | utf_8 | f191f5414456aa17cdfb3f43943b8f60 | function varargout = checkDynamics(varargin)
% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% [] = checkDynamics(varargin)
% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% CHECKDYNAMICS creates a figure showing tracked/outlined ... |
github | brookstaylorjr/MACKtrack-master | checknfkb.m | .m | MACKtrack-master/CellQuery/checknfkb.m | 13,831 | utf_8 | b2ecb108aa900c0d09a52e865d5320ea | function varargout = checknfkb(varargin)
% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% [] = checknfkb(id)
% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% CHECKNFKB creates a figure showing tracked/outlined cells that corrres... |
github | brookstaylorjr/MACKtrack-master | MACKquery.m | .m | MACKtrack-master/CellQuery/MACKquery.m | 35,209 | utf_8 | 99f8f34f2f970f3592972422ce3fd14a | function varargout = MACKquery(varargin)
% MACKquery M-file for MACKquery.fig
% MACKQUERY, by itself, creates a new MACKQUERY or raises the existing
% singleton*.
%
% H = MACKQUERY returns the handle to a new MACKQUERY or the handle to
% the existing singleton*.
%
% MACKQUERY('CALLBACK',hObject... |
github | brookstaylorjr/MACKtrack-master | colormapStack.m | .m | MACKtrack-master/CellQuery/subfunctions/colormapStack.m | 5,194 | utf_8 | 3abe943e0881ea1080fdb42b71d7720a | function h = colormapStack(measure1, CellData, options, fig_handle)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% Make stacked-colormap plot of cells
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% Make figure (if not specifi... |
github | brookstaylorjr/MACKtrack-master | scatterPlot.m | .m | MACKtrack-master/CellQuery/subfunctions/scatterPlot.m | 8,764 | utf_8 | 72f236b0217ee98d03db5a7a8c950388 | function scatterPlot(handles)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% SCATTERPLOT Display scatter plot from handles.Export data
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% Initialize axes and slider and set properti... |
github | brookstaylorjr/MACKtrack-master | linePlot.m | .m | MACKtrack-master/CellQuery/subfunctions/linePlot.m | 4,305 | utf_8 | 30ccf61ca68c112a76f8a47cc96f7432 | function linePlot(measure1, CellData, options, GroupingVector, fig_handle)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% Plot cell trajectories on x/y axes
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% Make figure (if not ... |
github | brookstaylorjr/MACKtrack-master | visualizeCell.m | .m | MACKtrack-master/CellQuery/subfunctions/visualizeCell.m | 7,276 | utf_8 | 2355e47e79da29b1d0dda05e00aff587 | function visualizeCell(handles)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% visualizeCell(handles)
% VISUALIZECELL makes new figure to show/cycle outlined cell on user's choice of images
%
% INPUT:
% handles main data structure provided by MACKquery GUI
%- - - - -... |
github | brookstaylorjr/MACKtrack-master | histogramSeries.m | .m | MACKtrack-master/CellQuery/subfunctions/histogramSeries.m | 10,568 | utf_8 | 47ae40f9e569209d801f31dd1ca3bbb3 | function histogramSeries(handles)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% Make histogram for frame- initialize "cycle frame" slider
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% Initialize axes and slider and set prop... |
github | brookstaylorjr/MACKtrack-master | actinModule.m | .m | MACKtrack-master/CellMeasure/actinModule.m | 5,520 | utf_8 | e9e553f4e865fecd8e4e22887a527c6d |
function [CellMeasurements, ModuleData] = actinModule(CellMeasurements, parameters, labels, AuxImages, ModuleData)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% [CellMeasurements, ModuleData] = actinModule(CellMeasurements, parameters, labels, AuxImages, ModuleData)
%- ... |
github | brookstaylorjr/MACKtrack-master | saveFeatures.m | .m | MACKtrack-master/CellMeasure/saveFeatures.m | 6,648 | utf_8 | e8a3bddda5ea8edbeff86d8b2fecfc0b | function featuresOut = saveFeatures(imageIn,featuresIn)
%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% SAVEFEATURES allows user selection of features in a given image for use in featureModule
% Features are saved from images in the featuresOut structure, named feature001, ... |
github | makarandtapaswi/Video_ShotThread_SceneDetect-master | cvhci_process_options.m | .m | Video_ShotThread_SceneDetect-master/utilities/cvhci_process_options.m | 2,233 | windows_1250 | 45e9f6b48e40046de62f3e4d7fd8a844 | function [ options ] = cvhci_process_options(args, varargin)
%CVHCI_PROCESS_OPTIONS parses varargin options
% OPT = CVHCI_PROCESS_OPTIONS(ARGS, DEFAULT_OPTS) expects a cell array or struct
% ARGS. If ARGS is a cell array, it parses 'OptionName1', OptionValue1, ...
% pairs. If ARGS is a struct, field names ar... |
github | makarandtapaswi/Video_ShotThread_SceneDetect-master | transitivity_cliques.m | .m | Video_ShotThread_SceneDetect-master/utilities/transitivity_cliques.m | 1,525 | utf_8 | 7e7fb74205264c01faf2990d92861c71 | function [cliqs, t_in_cliq] = transitivity_cliques(aff_mat)
%TRANSITIVITY_CLIQUES Applies transitivity, gets maximal cliques
%
% Finds cliques in a graph given an affinity matrix (0 for no edge, 1 for
% edge). Automatically applies transitivity rules first to ensure that if
% A-B and B-C, then A-C.
%
% NOTE: Input aff... |
github | makarandtapaswi/Video_ShotThread_SceneDetect-master | visualize_threads_via_htmlrender.m | .m | Video_ShotThread_SceneDetect-master/visualization/visualize_threads_via_htmlrender.m | 3,072 | utf_8 | 5896836ec85063d9d992c57d74c1af23 | function Threads = visualize_threads_via_htmlrender(VideoStruct, cell_threads, shot_assigned)
%VISUALIZE_THREADS_VIA_HTMLRENDER Creates the HTML files showing shot threading
% Reach until this point using
% SS = shot_similarity(VideoStruct, params);
% [cell_threads, shot_assigned] = similarity_to_threads(SS... |
github | makarandtapaswi/Video_ShotThread_SceneDetect-master | visualize_scenes_via_htmlrender.m | .m | Video_ShotThread_SceneDetect-master/visualization/visualize_scenes_via_htmlrender.m | 2,055 | utf_8 | 4730feebeaa58d957ebe558d1eb088f0 | function visualize_scenes_via_htmlrender(VideoStruct, scene_breaks, params)
%VISUALIZE_SCENES_VIA_HTMLRENDER Show automatically generated scenes in HTML
% Method = ncuts
% This creates the scenes with auto-scene count of diff1 (may need to change
% based on series)
%
%
% Then call the python tool on command line in cas... |
github | makarandtapaswi/Video_ShotThread_SceneDetect-master | project_corners.m | .m | Video_ShotThread_SceneDetect-master/threading/project_corners.m | 1,274 | utf_8 | d1b3b039a653c52b406c9dd3abd5d836 | function [corners1, corners2] = project_corners(im1, im2, H)
%PROJECT_CORNERS - Summary
% Description
%
% TODO:
% - Write documentation
% - "Verify" visually
if nargin < 3
[ ~, ~, ~, H ] = calculate_homography(im1, im2);
end
corners1 = initialize_corners(im1);
corners2 = initialize_corners(im2);
for i = 1:numel(... |
github | makarandtapaswi/Video_ShotThread_SceneDetect-master | calculate_homography.m | .m | Video_ShotThread_SceneDetect-master/threading/calculate_homography.m | 3,474 | utf_8 | 460ab8de8bf8312053ce1ba7f776349f | function [ has_homography,resX1,resX2,H,ok ] = calculate_homography(im1,im2)
% Extracted and adapted from the vlfeat.org example under
% https://github.com/vlfeat/vlfeat/blob/master/apps/sift_mosaic.m
%% --------------------------------------------------------------------
% ... |
github | makarandtapaswi/Video_ShotThread_SceneDetect-master | are_images_similar.m | .m | Video_ShotThread_SceneDetect-master/threading/are_images_similar.m | 1,798 | utf_8 | a8c7894e6cf7e663411215face54d5f0 | function [decision, data] = are_images_similar(im1, im2, params, debug)
%ARE_SHOTS_SIMILAR - Shot threading based on homography via SIFT
% Based on the code used by Sebastian Geiger to thread shots
%
% Last modified: 13-02-2013
if ~exist('debug', 'var')
debug = false;
end
% Compute homography
[has_homography, x1,... |
github | greengirl/NCBIminer-master | readjobs_old.m | .m | NCBIminer-master/readjobs_old.m | 6,121 | utf_8 | 7910ff6ee84c903aaba10bb84df6ab87 | function [featuretype,featurename,Refseq,ExpectValue,seqlen,Tax,timeout,Alignments,...
Entrezs,fprefix,grpdist,location,extseq,database]=readjobs_old(varargin)
% varargin: input file name with full path
if numel(varargin)
% fid=fopen(varargin{1});
FileName=varargin{1};
else
[FileName,PathName]=ui... |
github | Filios92/Viterbi-Decoder-master | viterbi.m | .m | Viterbi-Decoder-master/viterbi.m | 6,390 | utf_8 | dc6de50382f6e5c13329afcda43c9aad | %% viterbi: Viterbi algorithm
% PARAMETERS:
% - Convolutional Code Generation Matrix (e.g. [1 1 1; 1 0 1])
% - Input Sequence (Sequence to decode) (as binary [ 1 0 0 1 0 0] or string '10 01 00')
% - 0 to not print info at the end
%
% OUTPUT:
% - Decoded Sequence
% - Number of Errors betwe... |
github | ihpdep/samf-master | run_tracker.m | .m | samf-master/run_tracker.m | 6,850 | utf_8 | 09a33433d8aebdcc3345d970683acf98 |
%
% High-Speed Tracking with Kernelized Correlation Filters
%
% Joao F. Henriques, 2014
% http://www.isr.uc.pt/~henriques/
%
% Main interface for Kernelized/Dual Correlation Filters (KCF/DCF).
% This function takes care of setting up parameters, loading video
% information and computing precisions. For the actua... |
github | nrafidi/compEEG-master | filtfilthd.m | .m | compEEG-master/Preprocessing/filtfilthd.m | 6,966 | utf_8 | 388f8b385779a492be2e8875b0112ace | function x=filtfilthd(varargin)
% FILTFILTHD Zero-phase digital filtering with dfilt objects.
%
% FILTFILLTHD provides zero phase filtering and accepts dfilt objects on
% input. A number of end-effect minimization methods are supported.
%
% Examples:
% x=FILTFILTHD(Hd, x)
% x=FILTFILTHD(Hd, x, method)
% where Hd is a d... |
github | nrafidi/compEEG-master | getAlpha.m | .m | compEEG-master/Preprocessing/old/getAlpha.m | 499 | utf_8 | 710f285e0a55073714254e466391755e | %Gets the average alpha band power of the given trial and returns it in
%alpha. The width dimension of trial is channel, and the length is time.
%Dependencies: none
function alpha = getAlpha(trial)
start = 8; %Start of alpha band
bw = 4; %Bandwidth to be examined
[length width] = size(trial);
alpha = zeros(1... |
github | nrafidi/compEEG-master | getTheta.m | .m | compEEG-master/Preprocessing/old/getTheta.m | 497 | utf_8 | 5bedd46b3cff414efbb2a345d7adc58e | %Gets the average theta band power of the given trial and returns it in
%theta. The width dimension of trial is channel, and the length is time
%Dependencies: none
function theta = getTheta(trial)
start = 4; %Starting frequency
bw = 3; %Bandwidth to be examined
[length width] = size(trial);
theta = zeros(1, ... |
github | nrafidi/compEEG-master | getBeta.m | .m | compEEG-master/Preprocessing/old/getBeta.m | 510 | utf_8 | 247560b330501266a47fbeb3e2c0a7ee | %Gets the average beta band power of the given trial and returns it in
%betaa. The width dimension of trial is channel, and the length is time
%Dependencies: none
function beta = getBeta(trial)
start = 13; %Start of the beta band
%Beta band is counted as anything 13 Hz or greater
[length width] = size(trial);
... |
github | nrafidi/compEEG-master | getVav.m | .m | compEEG-master/Preprocessing/old/getVav.m | 317 | utf_8 | 4a5ebea04a7c69ab3786b44969c2c721 | %Gets the average voltage of the given trial and returns it in vAv
%The width dimension of trial is channel, and the length is time
%Dependencies: none
function vAv = getVav(trial)
[length width] = size(trial);
vAv = zeros(1, width);
for i = 1:width
vAv(1, i) = mean(trial(:, i));
end
end |
github | nrafidi/compEEG-master | feature_extractor.m | .m | compEEG-master/Preprocessing/old/feature_extractor.m | 5,905 | utf_8 | b0f3526418d0a0705907e94a438cafae | %Extracts the features of the EEG timecourse stored in data, assumed to be
%of width 15, with the first column as time in ms (0 value at stimulus
%onset), the next 14 containing data for 14 channels. Stimlabel contains
%class labels of each data point (1 for comp, 2 for non-comp). labels is a
%cell array of strings... |
github | nrafidi/compEEG-master | PresentStim.m | .m | compEEG-master/Experiment/PresentStim.m | 6,521 | utf_8 | 15eae8c83cd05f3bab6723aa6098ede2 | function RTs = PresentStim(par)
KbName('UnifyKeyNames');
state.esc=KbName('escape');
state.res=KbName('space');
state.atscanner = par.atscanner;
state.pportTime = par.pportTime;
eventfilename = sprintf('%s_%s_events.txt',par.subject,par.expname);
state.eventfile = fopen(eventfilename,'a');
state.bgcolor =... |
github | nrafidi/compEEG-master | PresentStimQ.m | .m | compEEG-master/Experiment/PresentStimQ.m | 6,519 | utf_8 | 5ce1d30f542ed7257e158d0ef3196c55 | function [RTs, corr] = PresentStimQ(par)
KbName('UnifyKeyNames');
state.esc=KbName('escape');
state.res=KbName('space');
state.atscanner = par.atscanner;
state.pportTime = par.pportTime;
eventfilename = sprintf('%s_%s_events.txt',par.subject,par.expname);
state.eventfile = fopen(eventfilename,'a');
state.bgcolor ... |
github | nrafidi/compEEG-master | imrotate.m | .m | compEEG-master/Experiment/expr/imrotate.m | 7,604 | utf_8 | 1c8d44acd34d56a4df043ece2aee45b7 | function varargout = imrotate(varargin)
%IMROTATE Rotate image, allowing for a non-black FILL.
% Modified by Daniel Drucker.
% B = IMROTATE(A,ANGLE) rotates image A by ANGLE degrees in a
% counterclockwise direction around its center point. To rotate the image
% clockwise, specify a negative value for ANGLE. I... |
github | nrafidi/compEEG-master | implace.m | .m | compEEG-master/Experiment/expr/implace.m | 3,014 | utf_8 | e28ca1e9784b3b7297c12b70c7278015 | % IMPLACE - place image at specified location within larger image
%
% Usage: newim = implace(im1, im2, roff, coff)
%
% Arguments:
%
% im1 - Image that im2 is to be placed in.
% im2 - Image to be placed.
% roff - Row and column offset of placement of im2 relative
% coff to im1, (0,0) aligns ... |
github | nrafidi/compEEG-master | odds.m | .m | compEEG-master/Classification/odds.m | 7,260 | utf_8 | 13f79ed5d3793ffc2c3a1dbf05f2aa83 | function [risk, odd]=odds(varargin)
%ODDS
% This function calculates the Risk Ratio and the Odds Ratio (OR) on a 2x2
% input matrix. Both ratios are computed with confidence intervals. If
% confidence interval of OR doesn't encompass the value OR=1, then the
% function computes the Bayesian Credibility Assessment ... |
github | nrafidi/compEEG-master | bootstrapCluster_KRTGM.m | .m | compEEG-master/Classification/bootstrapCluster_KRTGM.m | 13,893 | utf_8 | fea14196a80de496514bdd9e0116c0f2 | %Bootstrap cluster value
function [trueClusterT, permClusterT, bootGrid] = bootstrapCluster_KRTGM(clusterToUse, computationToPlot, pValThresh)
dataRootR = '/Users/nrafidi/Documents/MATLAB/compEEG-data-rep/';
load(sprintf('%s/results/clusters_pVals_KRTGM.mat', dataRootR));
numPerms = 1000;
numSubjects = size(Individual... |
github | nrafidi/compEEG-master | clusterPermTestPooledSub_fullTime_Corr.m | .m | compEEG-master/Classification/clusterPermTestPooledSub_fullTime_Corr.m | 13,385 | utf_8 | aa13ea51dc9061343466e756193c8fe5 | function [clusters, monteCarloPvals, permutationClusters, ...
permutationHist, sizePermClusters] = ...
clusterPermTestPooledSub_fullTime_Corr(dataX, dataY, varargin)
% CLUSTERPERMTEST: runs the cluster permutation test described in Maris &
% Oostenveld 2007 for two conditions in a within-subjects MEG study
%
%
... |
github | nrafidi/compEEG-master | runCompClass_PermTest_cortex.m | .m | compEEG-master/Classification/runCompClass_PermTest_cortex.m | 3,210 | utf_8 | 0a38b51689a5537f6172eb03802cc7c7 | % Runs cross-validated classification within subject for the competition
% data
function runCompClass_PermTest_cortex(sub, winToUse)
addpath ~/compEEG/Classification/logisticRegression/
% loadFname = sprintf('~/CompEEG/Data/CompEEG_%s_Vis_BP2-200_N60_Ref_Hilbert-theta_Epochs_Features_Overlap_Time.mat', sub);
loadFna... |
github | nrafidi/compEEG-master | analysisPipeline_KR_Perm_PLOS.m | .m | compEEG-master/Classification/analysisPipeline_KR_Perm_PLOS.m | 5,447 | utf_8 | e614ae7552766ce52fecb75c2fc83491 | % Full KR Analysis pipeline
function analysisPipeline_KR_Perm_PLOS
addpath ./logisticRegression/
subjects = {'AA', 'BB', 'DD', 'EE', 'F', 'GG', 'HH', 'JJ', ...
'K', 'M', 'N', 'O', 'R', 'S', 'T', 'U', 'V', 'X', 'Y', 'Z'};
numSub = length(subjects);
eegDataRoot = '/Users/nrafidi/Documents/MATLAB/compEEG-data/preproc-... |
github | nrafidi/compEEG-master | bootstrapCluster_KRTGM_behav.m | .m | compEEG-master/Classification/bootstrapCluster_KRTGM_behav.m | 13,875 | utf_8 | 902fbee8c532690a5be7e940c1109eff | %Bootstrap cluster value
function [trueClusterT, permClusterT, bootGrid] = bootstrapCluster_KRTGM_behav(clusterToUse, computationToPlot, behav_str)
dataRootR = '/Users/nrafidi/Documents/MATLAB/compEEG-data-rep/';
load(sprintf('%s/results/clusters_pVals_KRTGM_behav%s.mat', dataRootR, behav_str));
numPerms = 1000;
numSu... |
github | nrafidi/compEEG-master | clusterPermTestPooledSub_fullTime.m | .m | compEEG-master/Classification/clusterPermTestPooledSub_fullTime.m | 14,580 | utf_8 | e06f312db97a477e6f0e2b0e168dee82 | function [clusters, monteCarloPvals, permutationClusters, ...
permutationHist, sizePermClusters] = ...
clusterPermTestPooledSub_fullTime(pooledData, varargin)
% CLUSTERPERMTEST: runs the cluster permutation test described in Maris &
% Oostenveld 2007 for two conditions in a within-subjects MEG study
%
% Inputs:... |
github | nrafidi/compEEG-master | logReg.m | .m | compEEG-master/Classification/logisticRegression/logReg.m | 4,841 | utf_8 | f5992b717f3b1a2dc339f39f1bcf1d24 | %NOTE: this code is a work in progress. Feel free to edit it, but please
%comment what you change and sign with your initials -NSR
% TO DO: different lambdas for each feature? Uncertain
function [weights, lambda] = logReg(X, Y, chooseLambda, regBias, varargin)
% logReg learns a logistic regression between X and Y.
% c... |
github | nrafidi/compEEG-master | logRegMult.m | .m | compEEG-master/Classification/logisticRegression/logRegMult.m | 4,848 | utf_8 | 9e7d33e92db654131171aeaf4fa2330d | %NOTE: this code is a work in progress. Feel free to edit it, but please
%comment what you change and sign with your initials -NSR
% TO DO: different lambdas for each feature? Uncertain
function [weights, lambda] = logRegMult(X, Y, chooseLambda, regBias)
% logRegMult learns a multiple logistic regression between X and... |
github | nrafidi/compEEG-master | doLRCrossValWinZ.m | .m | compEEG-master/Classification/logisticRegression/doLRCrossValWinZ.m | 1,970 | utf_8 | a5d591f9d18eaba669fb9c049209942b | function errs = doLRCrossValWinZ(X, Y, chooseLambda, regBias, folds, ...
numFolds, doSave, fname)
%doLRCrossValWinZ runs cross validated logistic regression with L2 penalty
% and returns errors on each fold, zscoring X within each cross-validation
% fold
% X = data feature set (NxF)
% Y = data labels (Nx?) If Y is ... |
github | nrafidi/compEEG-master | logReg_SAG.m | .m | compEEG-master/Classification/logisticRegression/logReg_SAG.m | 3,528 | utf_8 | d25b9b43bb55e9cfec41532f22598928 | %NOTE: this code is a work in progress. Feel free to edit it, but please
%comment what you change and sign with your initials -NSR
% TO DO: different lambdas for each feature? Uncertain
function [weights, lambda] = logReg_SAG(X, Y, chooseLambda, regBias)
% logReg learns a logistic regression between X and Y.
% chooseL... |
github | nrafidi/compEEG-master | doLRCrossValNoZ.m | .m | compEEG-master/Classification/logisticRegression/doLRCrossValNoZ.m | 1,781 | utf_8 | 00b4072114bf54f43f7dde0f461ebdc8 | function errs = doLRCrossValNoZ(X, Y, chooseLambda, regBias, folds, ...
numFolds, doSave, fname)
% doLRCrossValNoZ runs cross validated logistic regression with L2 penalty
% and returns errors on each fold
% X = data feature set (NxF)
% Y = data labels (Nx?) If Y is a matrix and not a vector, multiple
% logistic re... |
github | nrafidi/compEEG-master | loggaussian.m | .m | compEEG-master/Classification/GNB/loggaussian.m | 1,581 | utf_8 | eaacd7a34254d9a4b1fa9821476d20da | % loggaussian(x,mu,sigma,<seleectedFeatures 0>,<ignoreNormalizer 0>)
%
% Inputs:
% x: row vector
% mu: row vector of means
% signma: row vector of std deviations
% <seleectedFeatures> = (optional) a subset of the indices of mu, in which case only these will be
% used to calculate result. If 0 or unsuppli... |
github | nrafidi/compEEG-master | nbayes_train.m | .m | compEEG-master/Classification/GNB/nbayes_train.m | 2,940 | utf_8 | bc2af0ad990176803b51f406686c857b | % nbayesModel = nbayes_train(exampleMatrix, labels, poolVarEstimate, <classProbabilities)
%
% Train a Gaussian Naive Bayes classifier
%
% INPUTS:
% examples : a mxn matrix with one training example per row
% labels : a 1xm column vector of integer labels from 1 to L
%
% alternatively, you can input probabilistic lab... |
github | nrafidi/compEEG-master | vectorizeMatrix.m | .m | compEEG-master/Classification/GNB/vectorizeMatrix.m | 197 | utf_8 | 8dd42430ae8f4a415c2c7280c0da7a35 | % function vec = vectorizeMatrix(M)
%
% given an input rxc matrix, return a rc x 1 column vector with the same values
function vec = vectorizeMatrix(M)
vec=reshape(M,(size(M,1)*size(M,2)),1);
|
github | nrafidi/compEEG-master | nbayes_apply.m | .m | compEEG-master/Classification/GNB/nbayes_apply.m | 1,439 | utf_8 | 02abde4d7976a2506c6ab95f4a05dd68 | % rslt = nbayes_apply(examples, model, <selectedFeatures>)
%
% Apply a given Gaussian Naive Bayes classifier to a set of examples
%
% INPUTS:
% examples : a mxn matrix with one training example per row
% model : a NBayes classifier trained using nbayes_train
% <selectedFeatures> : (optional), if provided, a set of ind... |
github | nrafidi/compEEG-master | gaussian.m | .m | compEEG-master/Classification/GNB/gaussian.m | 511 | utf_8 | dde063ddcb71382a521682c3790a4bc4 | % gaussian(x,mu,sigma)
%
% Inputs:
% x: row vector
% mu: row vector of means
% signma: row vector of std deviations
% Output:
% probability of x under a naive Gaussian distribution (with a diagonal covariance matrix)
%
% Example: gaussian([2 3], [3 3], [1 1])
%
% History:
% Created 3/2014 by Tom
function pr = gaus... |
github | nrafidi/compEEG-master | matricizeVector.m | .m | compEEG-master/Classification/GNB/matricizeVector.m | 340 | utf_8 | 0835a24ed274c7dfdeffa0fb396290c4 | % function M = matricizeVector(v,nrows)
%
% given an input rx1 matrix, v, plus a target number of rows, nrows, return a nrows x (r/nrows)
% matrix M with the same values
%
% Note this is the inverse of vectorizeMatrix.m
%
% History:
% created 3/12/14 by Tom
function M = matricizeVector(v,nrows)
M=reshape(v,nrows,... |
github | nrafidi/compEEG-master | rankAccuracy.m | .m | compEEG-master/Classification/GNB/rankAccuracy.m | 930 | utf_8 | d9bed6a41cf55a3bbdce9e5efcbdc804 | % rankAcc = rankAccuracy(logprobs, correctLabels, GNBmodel)
%
% Input: logprobs: a mxn array, where the i,j entry gives the log probability of label j for example
% i correctLabels: a mx1 column vector that contains the correct labels for the m examples (e.g.,
% [2 1 4 3]') GNBmodel: a GNB model, as trained by nbayes... |
github | nrafidi/compEEG-master | sortGNBfeaturesByMuDifference.m | .m | compEEG-master/Classification/GNB/sortGNBfeaturesByMuDifference.m | 2,150 | utf_8 | 1b5ce4107bbca03b85957fa28459a987 | % sortedFeats = sortGNBfeaturesByMuDifference(GNBmodel, <useSigmas (default=0)>)
%
% Inputs:
% GNBmodel = a GNBmodel trained using the function nbayes_train.m
% useSigmas = an optional argument (default=0). If 0, ranks features by distance between class-conditional
% means. If 1, it also uses class co... |
github | romi1502/NMF-matlab-master | NMF.m | .m | NMF-matlab-master/NMF.m | 3,649 | utf_8 | 76d44445b3e0e57b8dfeec51608ca77e | function [W, H, bDsave] = NMF(V,R,Niter,beta,initialV)
% [W,H, bDsave] = NMF(V,R,Niter,beta,initialV)
% NMF with beta divergence cost function.
%Input :
% - V : power spectrogram to factorize (a MxN matrix)
% - R : number of templates
% - Niter : number of iterations
% - beta (optional): beta used for... |
github | ericpony/polynomial-loop-invariant-synthesis-master | lagrange.m | .m | polynomial-loop-invariant-synthesis-master/lagrange.m | 4,226 | utf_8 | bb924696f639de4942bcd4ae0c1f8656 | function result = Lagrange(degree, num_var, num_term, samples, nomials)
result = LagrangeBasis(degree, num_var, num_term, samples, nomials);
if(iscell(result))
lagrangeBasis = result{1};
detVandermonde = result{2};
printf('%d ', detVandermonde);
result = lagrangeBasis;
els... |
github | facebookarchive/C3D-master | read_binary_blob.m | .m | C3D-master/C3D-v1.0/examples/c3d_feature_extraction/script/read_binary_blob.m | 840 | utf_8 | a322780be077a4c7623fe76154839a4c | %
% Licensed under the Creative Commons Attribution-NonCommercial 3.0
% License (the "License"). You may obtain a copy of the License at
% https://creativecommons.org/licenses/by-nc/3.0/.
% Unless required by applicable law or agreed to in writing, software
% distributed under the License is distributed on an "... |
github | facebookarchive/C3D-master | read_binary_blob_preserve_shape.m | .m | C3D-master/C3D-v1.0/examples/c3d_feature_extraction/script/read_binary_blob_preserve_shape.m | 2,064 | utf_8 | ca6b67177d4ff394b626e4970aecaa5a | %
% Licensed under the Creative Commons Attribution-NonCommercial 3.0
% License (the "License"). You may obtain a copy of the License at
% https://creativecommons.org/licenses/by-nc/3.0/.
% Unless required by applicable law or agreed to in writing, software
% distributed under the License is distributed on an "... |
github | facebookarchive/C3D-master | prepare_batch.m | .m | C3D-master/C3D-v1.0/matlab/caffe/prepare_batch.m | 1,298 | utf_8 | 68088231982895c248aef25b4886eab0 | % ------------------------------------------------------------------------
function images = prepare_batch(image_files,IMAGE_MEAN,batch_size)
% ------------------------------------------------------------------------
if nargin < 2
d = load('ilsvrc_2012_mean');
IMAGE_MEAN = d.image_mean;
end
num_images = length... |
github | facebookarchive/C3D-master | matcaffe_demo.m | .m | C3D-master/C3D-v1.0/matlab/caffe/matcaffe_demo.m | 3,344 | utf_8 | 669622769508a684210d164ac749a614 | function [scores, maxlabel] = matcaffe_demo(im, use_gpu)
% scores = matcaffe_demo(im, use_gpu)
%
% Demo of the matlab wrapper using the ILSVRC network.
%
% input
% im color image as uint8 HxWx3
% use_gpu 1 to use the GPU, 0 to use the CPU
%
% output
% scores 1000-dimensional ILSVRC score vector
%
% You m... |
github | facebookarchive/C3D-master | classification_demo.m | .m | C3D-master/C3D-v1.1/matlab/demo/classification_demo.m | 5,466 | utf_8 | 45745fb7cfe37ef723c307dfa06f1b97 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | jdonley/SoundZone_Tools-master | buildDocumentation.m | .m | SoundZone_Tools-master/buildDocumentation.m | 6,380 | utf_8 | 2ea47098932dfcfbdb2d8837d80bc491 | function docFiles = buildDocumentation( WorkingDir, DocDir, MainFile, DocFiles, RuntimeDependencies, ThirdPartyHTML )
% Generates documentation HTML and builds MATLAB search database for dependencies of a main file
%
% Syntax: DOCFILES = BUILDDOCUMENTATION( WORKINGDIR, DOCDIR, MAINFILE, DOCFILES, RUNTIMEDEPENDENCIES, ... |
github | jdonley/SoundZone_Tools-master | invFIR.m | .m | SoundZone_Tools-master/invFIR.m | 7,610 | utf_8 | 160ddaed092f532d3ad9ca381d211837 | function [ih]=invFIR(type,h,Nfft,Noct,L,range,reg,window)
% Design inverse filter (FIR) from mono or stereo impulse response
% ------------------------------------------------------------------------------
% description: design inverse filter (FIR) from mono or stereo impulse response
% -------------------------------... |
github | jdonley/SoundZone_Tools-master | pesq_mex_fast_vec.m | .m | SoundZone_Tools-master/pesq_mex_fast_vec.m | 2,604 | utf_8 | 462c9f4408cc9b3515ed7047eca7ea5b | function [ res ] = pesq_mex_fast_vec( reference_sig, degraded_sig, Fs, modeOfOperation )
% Accepts vectors for a mex compiled version of the objective Perceptual Evaluation of Speech Quality measure
%
% Syntax: [ res ] = pesq_mex_vec( reference_sig, degraded_sig, Fs )
%
% Inputs:
% reference_sig - Reference (clea... |
github | jdonley/SoundZone_Tools-master | stoi.m | .m | SoundZone_Tools-master/stoi.m | 7,625 | utf_8 | 05de4359befcfe571b9ed4b30c396080 | function d = stoi(x, y, fs_signal)
% The Short-Time Objective Intelligibility measure
% d = stoi(x, y, fs_signal) returns the output of the short-time
% objective intelligibility (STOI) measure described in [1, 2], where x
% and y denote the clean and processed speech, respectively, with sample
% rate fs_sign... |
github | jdonley/SoundZone_Tools-master | interpVal_2D.m | .m | SoundZone_Tools-master/interpVal_2D.m | 3,217 | utf_8 | c655f1ae466106d52105f1b8a737d74d | function [ interpolated_values ] = interpVal_2D( values, index_values1, index_values2, desired_index_values1, desired_index_values2, interpolation_type )
% This function will interpolate from desired abitrarily spaced index values in a 2D array
%
% Syntax: [ interpolated_values ] = interpVal_2D( ...
% va... |
github | jdonley/SoundZone_Tools-master | LTASS.m | .m | SoundZone_Tools-master/LTASS.m | 2,496 | utf_8 | 0c57716dc3c824fc6fd79298a6049db5 | function [ spect, frqs ] = LTASS( speech_folder_OR_vec, nfft, fs )
% Computes the Long-Term Average Speech Spectrum from a folder of speech files or vector of speech samples
%
% Syntax: [ spect, frqs ] = LTASS( speech_folder_OR_vec, nfft )
%
% Inputs:
% speech_folder_OR_vec - The path to the folder containing the spee... |
github | jdonley/SoundZone_Tools-master | estoi.m | .m | SoundZone_Tools-master/estoi.m | 6,941 | utf_8 | 5cde4afea2dd65d2fd5584eb04c96ed1 | function d = estoi(x, y, fs_signal)
% Implementation of the Extended Short-Time Objective Intelligibility (ESTOI) predictor
% d = estoi(x, y, fs_signal) returns the output of the extended short-time
% objective intelligibility (ESTOI) predictor.
%
% Implementation of the Extended Short-Time Objective
% Intelligib... |
github | jdonley/SoundZone_Tools-master | ConcatTIMITtalkers.m | .m | SoundZone_Tools-master/ConcatTIMITtalkers.m | 1,818 | utf_8 | ead55a80d11663e5f96a9806dc8db2d2 | function ConcatTIMITtalkers( TIMITdir, OutDir )
% Concatenates all the talkers from the TIMIT corpus into individual speech files
%
% Syntax: CONCATTIMITTALKERS( TIMITDIR, OUTDIR )
%
% Inputs:
% TIMITdir - The directory of the TIMIT corpus
% OutDir - The output directory to save the concatenated speech files
%
%... |
github | jdonley/SoundZone_Tools-master | getAllFiles.m | .m | SoundZone_Tools-master/getAllFiles.m | 2,043 | utf_8 | 5ed34f78f3950903b89944cda11cf5be | function fileList = getAllFiles(dirPath)
% Retrieves a list of all files within a directory
%
% Syntax: fileList = getAllFiles(dirName)
%
% Inputs:
% dirPath - The relative or full path of the directory to recursivley
% search.
%
% Outputs:
% fileList - A cell array list of the full path for each ... |
github | jdonley/SoundZone_Tools-master | pesq_mex_vec.m | .m | SoundZone_Tools-master/pesq_mex_vec.m | 2,728 | utf_8 | 0c4f532121cacc2d3960ce2d9c4cbc5f | function [ res ] = pesq_mex_vec( reference_sig, degraded_sig, Fs, modeOfOperation )
% Accepts vectors for a mex compiled version of the objective Perceptual Evaluation of Speech Quality measure
%
% Syntax: [ res ] = pesq_mex_vec( reference_sig, degraded_sig, Fs )
%
% Inputs:
% reference_sig - Reference (clean, ta... |
github | jdonley/SoundZone_Tools-master | invimplms.m | .m | SoundZone_Tools-master/invimplms.m | 2,067 | utf_8 | e69a35d9a212e8765eab31cf988ed6ba | function inv=invimplms(den,n,d)
% Inverse impulse using the Levinson-Durbin algorithm
% syntax inv=invimplms(den,n,d)
% den - denominator impulse
% n - length of result
% d - delay of result
% inv - inverse impulse response of length n with delay d
%
% Levinson-Durbin algorithm from ... |
github | jdonley/SoundZone_Tools-master | interpFromVal_2D.m | .m | SoundZone_Tools-master/interpFromVal_2D.m | 3,745 | utf_8 | 32fc38275dac19f437ef1b3fc06ac278 | function [ interpolated_index_values2 ] = interpFromVal_2D( values, index_values1, index_values2, desired_index_values1, desired_values2 )
% This function will interpolate from desired z-axis values and return the interpolation indices for them in the y-axis
%
% Syntax: [ interpolated_values ] = interpFromVal_2D( valu... |
github | jdonley/SoundZone_Tools-master | keepFilesFromFolder.m | .m | SoundZone_Tools-master/keepFilesFromFolder.m | 1,763 | utf_8 | 1daadb0f307af33f2caad31fbdce09f9 | function [Files] = keepFilesFromFolder( FileList, KeepFromFolder )
% Keeps files and file paths in a cell array if the file names in a given folder are found in the path string
%
% Syntax: [Files] = KEEPFILESFROMFOLDER(FileList,KeepFromFolder)
%
% Inputs:
% FileList - A list of files to filter
% KeepFromFol... |
github | jdonley/SoundZone_Tools-master | IRcompactingKirkebyFilter.m | .m | SoundZone_Tools-master/IRcompactingKirkebyFilter.m | 1,649 | utf_8 | 23f6721f7e3b8dcc1de9330eb3350b85 | function [ filt ] = IRcompactingKirkebyFilter( ir, ir_len, f_band, fs, reg )
% Compacting Kirkeby Filter
% Regularisation parameter
ereg = epsreg(ir_len*fs,f_band,fs,reg);
% Time-packing filtering
H = fft(ir, ir_len*fs);
C = conj(H) ./ (conj(H).*H + ereg);
filt = ifft(C);
end
function ereg = epsreg(Nfft, f_band, ... |
github | xiufeng/lte_monitor-master | lte_code_block_deconcatenation.m | .m | lte_monitor-master/octave/lte_code_block_deconcatenation.m | 3,563 | utf_8 | 09684caa216372d91800484101164c8f | %
% Copyright 2012 Ben Wojtowicz
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU Affero General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% This progra... |
github | xiufeng/lte_monitor-master | lte_generate_ul_rs.m | .m | lte_monitor-master/octave/lte_generate_ul_rs.m | 11,949 | utf_8 | 1c78e98b09ad2c8df569fb85c40fb233 | %
% Copyright 2013 Ben Wojtowicz
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU Affero General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% This progra... |
github | xiufeng/lte_monitor-master | lte_rate_match_turbo.m | .m | lte_monitor-master/octave/lte_rate_match_turbo.m | 4,814 | utf_8 | eb83a574a2875b005c81013a8b164437 | %
% Copyright 2012, 2014 Ben Wojtowicz
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU Affero General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% This ... |
github | xiufeng/lte_monitor-master | lte_ulsch_channel_decode.m | .m | lte_monitor-master/octave/lte_ulsch_channel_decode.m | 3,025 | utf_8 | fbbf034c63179ef85f77b3a35d44cdbe | %
% Copyright 2014 Ben Wojtowicz
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU Affero General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% This progra... |
github | xiufeng/lte_monitor-master | lte_rate_unmatch_conv.m | .m | lte_monitor-master/octave/lte_rate_unmatch_conv.m | 4,510 | utf_8 | 51b5c572e966d3c85c2510243cff6c4c | %
% Copyright 2012 Ben Wojtowicz
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU Affero General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% This progra... |
github | xiufeng/lte_monitor-master | lte_dlsch_channel_decode.m | .m | lte_monitor-master/octave/lte_dlsch_channel_decode.m | 2,741 | utf_8 | a95f8f4bbb993b26820bfd1f9922560b | %
% Copyright 2012 Ben Wojtowicz
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU Affero General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% This progra... |
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