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github | alpapado/food-101-master | extractImageFeatures.m | .m | food-101-master/src/common/extractImageFeatures.m | 3,255 | utf_8 | 40147bf0b39d36bb5f4749c3234091fe | function [features, badSegments, goodSegments] = extractImageFeatures(I, L, params, ignoreSmallSegments)
%extractSuperpixelFeatures Extracts SURFs and Lab values for every
% superpixel in image
if ~exist('ignoreSmallSegments', 'var')
ignoreSmallSegments = true;
end
% Preallocate space for result
spIndices = uniq... |
github | alpapado/food-101-master | extractfield.m | .m | food-101-master/src/common/extractfield.m | 2,863 | utf_8 | 51fdc7b90e4171b3266de9bd41f58402 | function A = extractfield(S, name)
%EXTRACTFIELD Field values from structure array
%
% A = EXTRACTFIELD(S, NAME) returns the field values specified by the
% fieldname NAME in the 1-by-N output array A. N is the total number
% of elements in the field NAME of structure S:
%
% N = numel([S(:).(na... |
github | alpapado/food-101-master | nodeSplit.m | .m | food-101-master/src/forest/nodeSplit.m | 3,995 | utf_8 | dcda38b673ad2b6272cb1f1285d1a98d | function [left, right, svm] = nodeSplit(trset, trsetInd)
%nodeSplitSerial Splits the input data in two parts
% Generates n binary SVMs as decision functions on random binary partitions
% of the class labels in data. Keeps the one that maximizes the
% information gain criterion.
% trset: The total training set on ... |
github | alpapado/food-101-master | nodeSplitParallel.m | .m | food-101-master/src/forest/nodeSplitParallel.m | 2,844 | utf_8 | cd86999b70d683182e7c547c5bc961fe | function [left, right, svm] = nodeSplitParallel(trset, trsetInd)
%nodeSplitSerial Splits the input data in two parts
% Generates n binary SVMs as decision functions on random binary partitions
% of the class labels in data. Keeps the one that maximizes the
% information gain criterion.
% trset: The total training... |
github | alpapado/food-101-master | errorCheck.m | .m | food-101-master/src/forest/errorCheck.m | 3,500 | utf_8 | a07f0e4aa93787a0a4b1774cab600f7d | function errorCheck( rtree )
%errorCheck Summary of this function goes here
% Detailed explanation goes here
checkSpliting(rtree);
checkLeaves(rtree);
visualize(rtree);
end
function checkSpliting(rtree)
iterator = rtree.depthfirstiterator;
hasError = false;
for i = 1:size(iterator, 2);
nodeId = iterator(i);
... |
github | alpapado/food-101-master | randomTree.m | .m | food-101-master/src/forest/randomTree.m | 3,484 | utf_8 | 5f1cc540e416ad8703c601dd190d7171 | function rtree = randomTree(rtree, parentId, trset )
%randomTree Grows a random binary tree on the given training set.
%
% Grows a binary tree with the following procedure:
% On each node, a number of linear SVMs is generated on random binary
% partitions of the class labels, to be used as decision functions.
% Among... |
github | alpapado/food-101-master | leafMetrics.m | .m | food-101-master/src/componentMining/leafMetrics.m | 3,238 | utf_8 | 26f57e004871137c980730eb86c5d3f3 | function metrics = leafMetrics(leaves, params)
%leafMetrics Computes all the leaf metrics
% The function computes the leaf metrics, classDistribution,
% classConfidence, delta and distinctiveness for set of leaves as defined
% by the first input for forest parameters given by the second input.
classDist = class... |
github | alpapado/food-101-master | mineComponents.m | .m | food-101-master/src/componentMining/mineComponents.m | 3,679 | utf_8 | 181ba2b0b425e9a6b530c3232bf5105c | function components = mineComponents(leaves, metrics, vset, params)
%mineComponents Mine discriminative components using the forest leaves
% The process is as follows: First, the leaves are sorted based on their
% distinctiveness. Those that contain too much similar information are
% filtered out. Then for each c... |
github | GoldbergLab/RodentJoystick-master | dirrec.m | .m | RodentJoystick-master/Utilities/dirrec.m | 6,754 | utf_8 | 7aa3b1a0c81a20fc273e6759120fe01f | function [varargout] = dirrec(reper,ext)
%
% Find files recursively in a given folder.
%
% C=dirrec('c:\windows') returns a cell C with the full pathname of all
% files in the c:\windows folder and all its sub-folders.
%
% C=dirrec('c:\windows','.exe') idem but returns only the files with
% extension ... |
github | GoldbergLab/RodentJoystick-master | subplottitle.m | .m | RodentJoystick-master/Utilities/subplottitle.m | 3,795 | utf_8 | 9dfec6e1da50acfcc34993a1c90fae1f | %MTIT creates a major title in a figure with many axes
%
% MTIT
% - creates a major title above all
% axes in a figure
% - preserves the stack order of
% the axis handles
%
%SYNTAX
%-------------------------------------------------------------------------------
% P = MTIT(TXT,[OPT1,...,OPTn])
% P = MTIT(FH,... |
github | GoldbergLab/RodentJoystick-master | rdir.m | .m | RodentJoystick-master/Utilities/rdir.m | 11,997 | utf_8 | c12efe1d6178d62af7ffb6db8365e7c1 | function [varargout] = rdir(rootdir,varargin)
% RDIR - Recursive directory listing
%
% D = rdir(ROOT)
% D = rdir(ROOT, TEST)
% D = rdir(ROOT, TEST, RMPATH)
% D = rdir(ROOT, TEST, 1)
% D = rdir(ROOT, '', ...)
% [D, P] = rdir(...)
% rdir(...)
%
%
% *Inputs*
%
% * ROOT
%
% rdir(ROOT) lists the specified files.
% R... |
github | GoldbergLab/RodentJoystick-master | uipickfiles.m | .m | RodentJoystick-master/Utilities/uipickfiles.m | 46,772 | utf_8 | a3096fe428fd1d87d96a7860e10930cd | function out = uipickfiles(varargin)
%uipickfiles: GUI program to select files and/or folders.
%
% Syntax:
% files = uipickfiles('PropertyName',PropertyValue,...)
%
% The current folder can be changed by operating in the file navigator:
% double-clicking on a folder in the list or pressing Enter to move further
% dow... |
github | GoldbergLab/RodentJoystick-master | pdftops.m | .m | RodentJoystick-master/Utilities/export_fig/pdftops.m | 3,574 | utf_8 | 92ff676904575e16046dfff010b4e145 | function varargout = pdftops(cmd)
%PDFTOPS Calls a local pdftops executable with the input command
%
% Example:
% [status result] = pdftops(cmd)
%
% Attempts to locate a pdftops executable, finally asking the user to
% specify the directory pdftops was installed into. The resulting path is
% stored for future refere... |
github | GoldbergLab/RodentJoystick-master | crop_borders.m | .m | RodentJoystick-master/Utilities/export_fig/crop_borders.m | 3,666 | utf_8 | ebb9c61581b6f0d4a2db2fd1d9e30685 | function [A, vA, vB, bb_rel] = crop_borders(A, bcol, padding)
%CROP_BORDERS Crop the borders of an image or stack of images
%
% [B, vA, vB, bb_rel] = crop_borders(A, bcol, [padding])
%
%IN:
% A - HxWxCxN stack of images.
% bcol - Cx1 background colour vector.
% padding - scalar indicating how much padding to ha... |
github | GoldbergLab/RodentJoystick-master | isolate_axes.m | .m | RodentJoystick-master/Utilities/export_fig/isolate_axes.m | 4,721 | utf_8 | 253cd7b7d8fc7cb00d0cc55926f32de5 | function fh = isolate_axes(ah, vis)
%ISOLATE_AXES Isolate the specified axes in a figure on their own
%
% Examples:
% fh = isolate_axes(ah)
% fh = isolate_axes(ah, vis)
%
% This function will create a new figure containing the axes/uipanels
% specified, and also their associated legends and colorbars. The objects
%... |
github | GoldbergLab/RodentJoystick-master | im2gif.m | .m | RodentJoystick-master/Utilities/export_fig/im2gif.m | 6,048 | utf_8 | 5a7437140f8d013158a195de1e372737 | %IM2GIF Convert a multiframe image to an animated GIF file
%
% Examples:
% im2gif infile
% im2gif infile outfile
% im2gif(A, outfile)
% im2gif(..., '-nocrop')
% im2gif(..., '-nodither')
% im2gif(..., '-ncolors', n)
% im2gif(..., '-loops', n)
% im2gif(..., '-delay', n)
%
% This function converts a mu... |
github | GoldbergLab/RodentJoystick-master | read_write_entire_textfile.m | .m | RodentJoystick-master/Utilities/export_fig/read_write_entire_textfile.m | 924 | utf_8 | 779e56972f5d9778c40dee98ddbd677e | %READ_WRITE_ENTIRE_TEXTFILE Read or write a whole text file to/from memory
%
% Read or write an entire text file to/from memory, without leaving the
% file open if an error occurs.
%
% Reading:
% fstrm = read_write_entire_textfile(fname)
% Writing:
% read_write_entire_textfile(fname, fstrm)
%
%IN:
% fname - Pathn... |
github | GoldbergLab/RodentJoystick-master | pdf2eps.m | .m | RodentJoystick-master/Utilities/export_fig/pdf2eps.m | 1,471 | utf_8 | a1f41f0c7713c73886a2323e53ed982b | %PDF2EPS Convert a pdf file to eps format using pdftops
%
% Examples:
% pdf2eps source dest
%
% This function converts a pdf file to eps format.
%
% This function requires that you have pdftops, from the Xpdf suite of
% functions, installed on your system. This can be downloaded from:
% http://www.foolabs.com/xpdf ... |
github | GoldbergLab/RodentJoystick-master | print2array.m | .m | RodentJoystick-master/Utilities/export_fig/print2array.m | 9,200 | utf_8 | 3e36ba8333a926bb8107fcbe946b3ae9 | function [A, bcol] = print2array(fig, res, renderer, gs_options)
%PRINT2ARRAY Exports a figure to an image array
%
% Examples:
% A = print2array
% A = print2array(figure_handle)
% A = print2array(figure_handle, resolution)
% A = print2array(figure_handle, resolution, renderer)
% A = print2array(figure_handle... |
github | GoldbergLab/RodentJoystick-master | append_pdfs.m | .m | RodentJoystick-master/Utilities/export_fig/append_pdfs.m | 2,678 | utf_8 | 949c7c4ec3f5af6ff23099f17b1dfd79 | %APPEND_PDFS Appends/concatenates multiple PDF files
%
% Example:
% append_pdfs(output, input1, input2, ...)
% append_pdfs(output, input_list{:})
% append_pdfs test.pdf temp1.pdf temp2.pdf
%
% This function appends multiple PDF files to an existing PDF file, or
% concatenates them into a PDF file if the output fi... |
github | GoldbergLab/RodentJoystick-master | using_hg2.m | .m | RodentJoystick-master/Utilities/export_fig/using_hg2.m | 1,002 | utf_8 | b1620dd31f4d0b8acea2723e354a3518 | %USING_HG2 Determine if the HG2 graphics engine is used
%
% tf = using_hg2(fig)
%
%IN:
% fig - handle to the figure in question.
%
%OUT:
% tf - boolean indicating whether the HG2 graphics engine is being used
% (true) or not (false).
% 19/06/2015 - Suppress warning in R2015b; cache result for improved per... |
github | GoldbergLab/RodentJoystick-master | eps2pdf.m | .m | RodentJoystick-master/Utilities/export_fig/eps2pdf.m | 7,661 | utf_8 | ab0c84a2a57942e7e121faef8e0742df | function eps2pdf(source, dest, crop, append, gray, quality, gs_options)
%EPS2PDF Convert an eps file to pdf format using ghostscript
%
% Examples:
% eps2pdf source dest
% eps2pdf(source, dest, crop)
% eps2pdf(source, dest, crop, append)
% eps2pdf(source, dest, crop, append, gray)
% eps2pdf(source, dest, crop... |
github | GoldbergLab/RodentJoystick-master | export_fig.m | .m | RodentJoystick-master/Utilities/export_fig/export_fig.m | 53,417 | utf_8 | c5da360a74572b2d0b97489763902d75 | function [imageData, alpha] = export_fig(varargin)
%EXPORT_FIG Exports figures in a publication-quality format
%
% Examples:
% imageData = export_fig
% [imageData, alpha] = export_fig
% export_fig filename
% export_fig filename -format1 -format2
% export_fig ... -nocrop
% export_fig ... -transparent
% ex... |
github | GoldbergLab/RodentJoystick-master | ghostscript.m | .m | RodentJoystick-master/Utilities/export_fig/ghostscript.m | 7,492 | utf_8 | 7a1e094c8bf153e1b239765ff6fd43df | function varargout = ghostscript(cmd)
%GHOSTSCRIPT Calls a local GhostScript executable with the input command
%
% Example:
% [status result] = ghostscript(cmd)
%
% Attempts to locate a ghostscript executable, finally asking the user to
% specify the directory ghostcript was installed into. The resulting path
% is s... |
github | GoldbergLab/RodentJoystick-master | fix_lines.m | .m | RodentJoystick-master/Utilities/export_fig/fix_lines.m | 6,290 | utf_8 | 8437006b104957762090e3d875688cb6 | %FIX_LINES Improves the line style of eps files generated by print
%
% Examples:
% fix_lines fname
% fix_lines fname fname2
% fstrm_out = fixlines(fstrm_in)
%
% This function improves the style of lines in eps files generated by
% MATLAB's print function, making them more similar to those seen on
% screen. Grid ... |
github | GoldbergLab/RodentJoystick-master | circ_kuipertest.m | .m | RodentJoystick-master/Utilities/CircStat2012a/circ_kuipertest.m | 2,964 | utf_8 | 3665581dc83ba07d40cd68faa8c71d8f | function [pval, k, K] = circ_kuipertest(alpha1, alpha2, res, vis_on)
% [pval, k, K] = circ_kuipertest(sample1, sample2, res, vis_on)
%
% The Kuiper two-sample test tests whether the two samples differ
% significantly.The difference can be in any property, such as mean
% location and dispersion. It is a circula... |
github | GoldbergLab/RodentJoystick-master | circ_clust.m | .m | RodentJoystick-master/Utilities/CircStat2012a/circ_clust.m | 3,346 | utf_8 | 09f16d972b35b7b7b55b361710748587 | function [cid, alpha, mu] = circ_clust(alpha, numclust, disp)
%
% [cid, alpha, mu] = circClust(alpha, numclust, disp)
% Performs a simple agglomerative clustering of angular data.
%
% Input:
% alpha sample of angles
% numclust number of clusters desired, default: 2
% disp show plot at each ste... |
github | GoldbergLab/RodentJoystick-master | circ_raotest.m | .m | RodentJoystick-master/Utilities/CircStat2012a/circ_raotest.m | 4,132 | utf_8 | a088b9d557b94032992d192ef76b8fd4 | function [p U UC] = circ_raotest(alpha)
% [p U UC] = circ_raotest(alpha)
% Calculates Rao's spacing test by comparing distances between points on
% a circle to those expected from a uniform distribution.
%
% H0: Data is distributed uniformly around the circle.
% H1: Data is not uniformly distributed around th... |
github | GoldbergLab/RodentJoystick-master | circ_cmtest.m | .m | RodentJoystick-master/Utilities/CircStat2012a/circ_cmtest.m | 2,037 | utf_8 | f669b42437259c370cf3d42eca50e470 | function [pval med P] = circ_cmtest(varargin)
%
% [pval, med, P] = circ_cmtest(alpha, idx)
% [pval, med, P] = circ_cmtest(alpha1, alpha2)
% Non parametric multi-sample test for equal medians. Similar to a
% Kruskal-Wallis test for linear data.
%
% H0: the s populations have equal medians
% HA: the s populations... |
github | GoldbergLab/RodentJoystick-master | circ_wwtest.m | .m | RodentJoystick-master/Utilities/CircStat2012a/circ_wwtest.m | 4,511 | utf_8 | a4c542bbf886b77a383a8ff18bfbec64 | function [pval table] = circ_wwtest(varargin)
% [pval, table] = circ_wwtest(alpha, idx, [w])
% [pval, table] = circ_wwtest(alpha1, alpha2, [w1, w2])
% Parametric Watson-Williams multi-sample test for equal means. Can be
% used as a one-way ANOVA test for circular data.
%
% H0: the s populations have equal means
%... |
github | GoldbergLab/RodentJoystick-master | calib_gui.m | .m | RodentJoystick-master/calib_gui/calib_gui.m | 10,917 | utf_8 | eee8f1279db5d87d2a3f6477fd2d3d5a | function varargout = calib_gui(varargin)
%
% calib_gui('Verbose', 1) will have calib_gui print out any errors and
% the corresponding full stack trace
%
% Calib_gui is used for calibrating our joysticks - currently, it is
% dependent on ppscript, converting recorded data files to .mat files
% that are loaded... |
github | GoldbergLab/RodentJoystick-master | calib_gui.backup.m | .m | RodentJoystick-master/calib_gui/backup/calib_gui.backup.m | 10,920 | utf_8 | f99c2e2e3a1316b9bc70fa8ea8845231 | function varargout = calib_gui(varargin)
%
% calib_gui('Verbose', 1) will have calib_gui print out any errors and
% the corresponding full stack trace
%
% Calib_gui is used for calibrating our joysticks - currently, it is
% dependent on ppscript, converting recorded data files to .mat files
% that are loaded... |
github | GoldbergLab/RodentJoystick-master | find_sector.m | .m | RodentJoystick-master/PostProcessing/find_sector.m | 1,986 | utf_8 | fa31db4770bb49dfb4dcfbd3842cfa10 | %[targsec, distr, fh, angle_distr]
% find_sector(stats) or
% find_sector(stats, [reward_rate, thresh, pflag])
% OPTIONAL ARG ORDER:
% reward_rate, thresh, pflag
% computes the required target sector (angles) required for 'reward_rate'
% percentage of trials to be rewarded looking only at portions of t... |
github | GoldbergLab/RodentJoystick-master | perform_sector_analysis.m | .m | RodentJoystick-master/PostProcessing/TrajectoryAnalysis/perform_sector_analysis.m | 9,497 | utf_8 | 91a6e35dd41a75feaa325221b64d65eb | %[targsec, cumulative_distr, angle_distr, labels, line] =
% perform_sector_analysis(stats, [reward_rate, thresh, plotflag, ax])
% computes the required target sector (angles) required for 'reward_rate'
% a percentage of trials to be rewarded, looking only at portions of
% trajectories with magnitude greater th... |
github | GoldbergLab/RodentJoystick-master | video_trajectory_analysis.m | .m | RodentJoystick-master/PostProcessing/TrajectoryAnalysis/video_trajectory_analysis.m | 1,127 | utf_8 | 59ce5de31635e51a6e43c46e5288a2ca | %multi_trajectory_analysis writes a video file with name fname to the
%current directory. The video is an animation of all the structs in jslist
%using the script trajectory_analysis to generate plots
function video_trajectory_analysis (jslist, fname, varargin)
default = {1, 10,[400 1400], [300 30 60]};
numvarargs = l... |
github | GoldbergLab/RodentJoystick-master | getmaxcontlength.m | .m | RodentJoystick-master/PostProcessing/TrajectoryAnalysis/getmaxcontlength.m | 1,534 | utf_8 | b5a6f6a463929baec4b78dc3def890cd | % getmaxcontlength(magtraj, thresh) finds the maximum length of time for a
% given trajectory that the joystick deviation (magnitude) stayed within a
% threshold
% ARGUMENTS:
% magtraj :: a vector corresponding to the magnitude of a trajectory over
% time
% thresh :: threshold for reward - a number in the r... |
github | GoldbergLab/RodentJoystick-master | multi_sector_analysis.m | .m | RodentJoystick-master/PostProcessing/TrajectoryAnalysis/multi_sector_analysis.m | 2,547 | utf_8 | 06fe02f7e2edd4d9260bc493ac979582 | %[labels] =
% multi_sector_analysis(dir_list, [reward_rate, thresh, plotflag, ax])
% computes the required target sector (angles) required for 'reward_rate'
% a percentage of trials to be rewarded, looking only at portions of
% trajectories with magnitude greater than the threshold thresh
% EXAMPLE:
% ... |
github | GoldbergLab/RodentJoystick-master | detect_sharpturns.m | .m | RodentJoystick-master/PostProcessing/TrajectoryAnalysis/detect_sharpturns.m | 1,979 | utf_8 | 9fb1aeb66a9d158f7146dccf1c010eaf | function [redir_points, quality, data] = detect_sharpturns(traj)
%take in a single trajectory, and fit a smoothing spline - then examine
%redirection points.
[redir_points,quality, data] = zero_points(traj.traj_x_seg, traj.traj_y_seg);
end
% zero_points(x, y, r_scale)
%
% takes in x-y data and computes all local r... |
github | GoldbergLab/RodentJoystick-master | trajectory_analysis.m | .m | RodentJoystick-master/PostProcessing/TrajectoryAnalysis/trajectory_analysis.m | 7,976 | utf_8 | 29bf850e49188e01e44e90618b79a234 | function [bin_summary, labels, lhandle] = trajectory_analysis(stats, varargin)
%[bin_summary, labels, graphgroups] =
% trajectory_analysis(stats, [derivflag, PLOT_RANGE,TIME_RANGE, CONTL,
% pflag, axeslst, color, multiflag])
%
% plots trajectory profiles from stats using the (optional)
% arguments for a h... |
github | GoldbergLab/RodentJoystick-master | js_touch_dist.m | .m | RodentJoystick-master/PostProcessing/TrajectoryAnalysis/js_touch_dist.m | 4,229 | utf_8 | ec991fe7773a1a26a6ea1629bf3df6bf | % js_touch_dist(stats, [interv, targ_time,targ_reward,dist_thresh,
% all_traj_flag, plotflag, smoothparam, ax, color])
%
% takes the stats structure, a target hold time, a target reward
% percentage, and a distance threshold and computes a recommended hold
% threshold while also generating a hold time di... |
github | GoldbergLab/RodentJoystick-master | save_gui_plots.m | .m | RodentJoystick-master/PostProcessing/pp_gui/save_gui_plots.m | 3,402 | utf_8 | c6592c071e2a5dbd874747a2549c2dfa | function [handles] = save_gui_plots(handles)
%helper function that handles all saving routines
axeslst =[handles.axes1; handles.axes2; handles.axes3; handles.axes4; ...
handles.axes5; handles.axes6];
root = handles.guisavedirloc;
t = now; date = datestr(t, 'mm_dd_yyyy'); time = datestr(t, 'HH_MM_SS');
if ... |
github | GoldbergLab/RodentJoystick-master | pp_gui.m | .m | RodentJoystick-master/PostProcessing/pp_gui/pp_gui.m | 42,012 | utf_8 | a58b17bfe35b91deb25d75e967c117c2 | function varargout = pp_gui(varargin)
% PP_GUI MATLAB code for pp_gui.fig
% PP_GUI, by itself, creates a new PP_GUI or raises the existing
% singleton*.
%
% H = PP_GUI returns the handle to a new PP_GUI or the handle to
% the existing singleton*.
%
% PP_GUI('CALLBACK',hObject,eventData,handles,... |
github | GoldbergLab/RodentJoystick-master | trajectory_variability_heat_map.m | .m | RodentJoystick-master/PostProcessing/VariabilityAnalysis/trajectory_variability_heat_map.m | 4,836 | utf_8 | fb53df4b0e1a3c1dfa04eae990bd6bec | function [ output_args ] = trajectory_variability_heat_map(statslist, varargin)
%[ output_args ] = trajectory_variability_heat_map(stats, [numgroups, min_ht, axlst])
%
% trajectory_variability_heat_map analyzes the inherent variability in
% various trajectories by aligning each trajectory by its end point, and
% ... |
github | GoldbergLab/RodentJoystick-master | start_to_target_distr.m | .m | RodentJoystick-master/PostProcessing/VariabilityAnalysis/start_to_target_distr.m | 2,869 | utf_8 | ccd4511fc021f1c0db6d4cd709767fa5 | function start_to_target_distr(traj_struct, varargin)
% start_to_target_distr(traj_struct, [hist_bin, smoothparam, numplots, ht_range, rw_filter, axlst, color])
%
% generates plots of the distribution of distances from a trajectory's
% start to its end point. start_to_target_distribution takes a stats
% structur... |
github | GoldbergLab/RodentJoystick-master | generate_time_distr.m | .m | RodentJoystick-master/PostProcessing/TimeDistributions/generate_time_distr.m | 6,636 | utf_8 | a053f8890614313b51180bca666e5648 |
% [np_plot, rew_plot, day, times]
% = generate_time_distr(jstruct, [interval, plotflag, ax, color])
% takes in the jstruct as an argument and either generates
% (1) plots of the nose poke and reward distributions over time
% or(2) data of the nose poke, reward, and times for further
% manipulation or p... |
github | GoldbergLab/RodentJoystick-master | get_rewardandht_times.m | .m | RodentJoystick-master/PostProcessing/TimeDistributions/get_rewardandht_times.m | 4,159 | utf_8 | 658846e90c7378af9c6913ebb7cccc6e | function [data, dates, statistics] = get_rewardandht_times(dirlist, varargin )
%data =
% get_rewardandht_times(dirlist, [hist_int, TIME_RANGE, combineflag])
% returns histogram data for all valid trajectories for all days in dirlist -
% it gives time distributions of:
% all hold times, rewarded trajectory hold time... |
github | GoldbergLab/RodentJoystick-master | multi_time_distr.m | .m | RodentJoystick-master/PostProcessing/TimeDistributions/multi_time_distr.m | 3,647 | utf_8 | 81af75a2b28851c1014df7733469791a | % multi_time_distr(dirlist[, interval, layout, combineflag, lim, ax]) generates
%
% histogram time distributions of nosepokes and rewards for all jstructs
% in the list dirlist - only dirlist is a required argument, rest
% are optional
%
% ARGUMENTS:
%
% dirlist :: a list of files referring to saved (and post... |
github | GoldbergLab/RodentJoystick-master | dpsimplify.m | .m | RodentJoystick-master/PostProcessing/PPUtilities/dpsimplify.m | 6,519 | utf_8 | 2c3d643ff3a28521c5c11e9295c5be59 | function [ps,ix] = dpsimplify(p,tol)
% ORIGINAL GOAL: use in decomposing into motor primitives, but turned out
% to be unnecessary - unused now
%
% Recursive Douglas-Peucker Polyline Simplification, Simplify
% Source: Wolfgang Schwanghart - MATLAB file exchange
%
% [ps,ix] = dpsimplify(p,tol)
%
% dpsimplify uses t... |
github | GoldbergLab/RodentJoystick-master | get_stats_with_trajid.m | .m | RodentJoystick-master/PostProcessing/PPUtilities/get_stats_with_trajid.m | 1,774 | utf_8 | e74c188139afaa219218a0044f2a1783 | % [stats] = get_stats_with_trajid(stats,trajid, varargin)
%
% get_stats_with_trajid modifies the stats structures trajectories based
% on trajid. Filters trajectories depending on whether they were laser
% trajectories or not.
%
% ARGS :
%
% stats :: standard stats structure
%
% trajid :: an id in [... |
github | GoldbergLab/RodentJoystick-master | load_stats.m | .m | RodentJoystick-master/PostProcessing/PPUtilities/load_stats.m | 9,246 | utf_8 | ba1faa524c4f60cf1891b471d1c206df | function [statslist, dates, days, errlist] = load_stats(dirlist,varargin)
%[statslist, dates, days] = load_stats(dirlist, combineflag) attempts
% to load the stats structures from the directories in dirlist.
%
% OUTPUTS:
%
% statslist :: struct array of stats structures - not structs containing
% filenames li... |
github | GoldbergLab/RodentJoystick-master | get_stats_with_day.m | .m | RodentJoystick-master/PostProcessing/PPUtilities/get_stats_with_day.m | 781 | utf_8 | fbe617b62397a9e49efd11e59fa4597d | % [stats] = get_stats_with_trajid(stats,datenum_input, varargin)
%
% get_stats_with_day modifies the stats structures trajectories based
% on the matlab datenum. Filters trajectories depending on the day of the trajectory.
%
% ARGS :
%
% stats :: standard stats structure
% datenum_input :: date input in ... |
github | GoldbergLab/RodentJoystick-master | sort_traj_into_bins.m | .m | RodentJoystick-master/PostProcessing/PPUtilities/sort_traj_into_bins.m | 3,425 | utf_8 | db3bd2aaf507521d5a3b5c0e5e2c87aa | function sortedtraj = sort_traj_into_bins(tstruct, bins, varargin)
% sortedtraj = sort_traj_into_bins(tstruct, bins ,[rwfilter, ht_definition])
%
% generates a struct representation of a trajectory struct binned by hold
% time. Also supports filtering by reward (returns only rewarded
% trajectories).
%
% OUTPUTS... |
github | GoldbergLab/RodentJoystick-master | hist2d.m | .m | RodentJoystick-master/PostProcessing/PPUtilities/hist2d.m | 1,662 | utf_8 | 6d0587d838dec8f9489501e824d68ce6 | %function mHist = hist2d ([vY, vX], vYEdge, vXEdge)
%2 Dimensional Histogram
%Counts number of points in the bins defined by vYEdge, vXEdge.
%size(vX) == size(vY) == [n,1]
%size(mHist) == [length(vYEdge) -1, length(vXEdge) -1]
%
%EXAMPLE
% mYX = rand(100,2);
% vXEdge = linspace(0,1,10);
% vYEdge = linspace(0,1,20... |
github | GoldbergLab/RodentJoystick-master | onsets.m | .m | RodentJoystick-master/PostProcessing/SensorDistributions/onsets.m | 1,564 | utf_8 | d7a8ea29fa4e676638bbc5f61a1195fa | % [distr] = onsets(jstruct, index, [interv, end_time])
%
% returns and plots the distribution of sensor onset to offset times
% The sensor is selected via index.
%
% OUTPUTS:
%
% dist :: distribution of sensor onset to offset times
%
% ARGUMENTS:
%
% jstruct :: standard jstruct data structure
%
% index :: ... |
github | GoldbergLab/RodentJoystick-master | gaps.m | .m | RodentJoystick-master/PostProcessing/SensorDistributions/gaps.m | 1,377 | utf_8 | a612b7bc4d5d76351a42e8d2c42ab133 | % [distr] = gaps(jstruct, index, [interv, end_time])
%
% returns and plots the distribution of sensor onset to offset times
% The sensor is selected via index.
%
% OUTPUTS:
%
% dist :: distribution of sensor onset to offset times
%
% ARGUMENTS:
%
% jstruct :: standard jstruct data structure
%
% index :: in... |
github | GoldbergLab/RodentJoystick-master | multi_doAll.m | .m | RodentJoystick-master/PostProcessing/CoreAnalysis/multi_doAll.m | 2,897 | utf_8 | c62edc0de024a5551b95c4b2187d6372 | % [report, newdirs, skipped_dat] = multi_doAll(dir_list, varargin)
%
% a robust post processing function that can perform exactly
% what doAllpp does, but for a list of directories. It will not crash on
% single day failure, instead generating a log of analysis attempts
% Automatically excludes erroneous addit... |
github | GoldbergLab/RodentJoystick-master | xy_makestruct.m | .m | RodentJoystick-master/PostProcessing/CoreAnalysis/xy_makestruct.m | 5,595 | utf_8 | f1272bcaf3a478e0888a8228b539e219 | %[jstruct] = xy_makestruct(working_dir)
%
% xy_makestruct(working_dir) takes all the .mat files in working_dir and
% processes all of them to create a single jstruct that stores a summary of
% all the data from a single day:
% the resulting jstruct is a vector where each entry is a structure with
% numerous ... |
github | GoldbergLab/RodentJoystick-master | doAllpp.m | .m | RodentJoystick-master/PostProcessing/CoreAnalysis/doAllpp.m | 3,723 | utf_8 | e31ca6412ac0d1b9ae6a069cc2c1e43e | % [failedflag, err] = doAllpp(working_dir, [statflag, combinecont])
%
% does all standard post processing analysis, based on the combination
% of flags:
% always: combines .dat files, generates jstruct
% if statflag: calls doAllstats
% if combinecont: combines contingency folder if possible
%
% OUTPUT:
% ... |
github | GoldbergLab/RodentJoystick-master | xy_getstats.m | .m | RodentJoystick-master/PostProcessing/CoreAnalysis/xy_getstats.m | 16,433 | utf_8 | 63fccd118c9a5f9f1f2c1a748cabf3c2 | % stats = xy_getstats(jspath/jstruct_d, [jstruct_x, jstruct_y, savedir])
%
% generates a struct containing several fields describing an entire day's
% (folder's) trajectories.
%
% OUTPUT:
%
% stats has the following fields:
%
% np_count :: nose poke count for the day
%
% js_r_count :: number of... |
github | GoldbergLab/RodentJoystick-master | addstatsinfolder.m | .m | RodentJoystick-master/PostProcessing/CoreAnalysis/addstatsinfolder.m | 732 | utf_8 | ce67fba2189e791bdd1b22ee0ccc608f | %No longer used - can be archived
function addstatsinfolder(working_dir, varargin)
try
default = {'off'};
numvarargs = length(varargin);
if numvarargs > 1
error('too many arguments (> 2), only one required and one optional.');
end
[default{1:numvarargs}] = varargin{:};
disp(working_dir);
if (numel(working_dir)==0... |
github | GoldbergLab/RodentJoystick-master | ppscript.m | .m | RodentJoystick-master/PostProcessing/CoreAnalysis/ppscript.m | 2,970 | utf_8 | 207d4964ac1d237b73b7bcfadd128433 | % ppscript(working_dir,filespec,numField)
%
% takes all .dat files from the directory working_dir, makes a new
% subdirectory called comb, and combines the .dat files into .mat data
% files
%
% ARGUMENTS:
%
% working_dir :: string representation of directory to be analyzed
%
% filespec :: file format ... |
github | GoldbergLab/RodentJoystick-master | compute_vel_accel_distr.m | .m | RodentJoystick-master/PostProcessing/ActivityDistributions/compute_vel_accel_distr.m | 6,010 | utf_8 | 97f8f144cee8589cf3eee1afce636518 | function [data, rawdata] = compute_vel_accel_distr(stats,varargin)
%[median, variation, accel, accelv] = get_vel_accel_distr(stats)
% returns the relative velocity and acceleration distributions
% (their medians, and the differences between their 75th and 25th
% percentiles)
%
% OUTPUTS: all structures are 201x... |
github | GoldbergLab/RodentJoystick-master | rolling_reward_rate.m | .m | RodentJoystick-master/PostProcessing/LearningAnalysis/rolling_reward_rate.m | 3,064 | utf_8 | e46d38703aa07915a6d383e9c52ad261 | function rolling_reward_rate(dirlist, window, varargin)
% rolling_reward_rate(dirlist, window, [filter_ht, ax])
%
% rolling_reward_rate combines all data from dirlist, and then generates a
% rolling reward rate using <window> trajectories. seemed to have
% mediocre results - not sure if significant
%
% ARGUMENTS ... |
github | GoldbergLab/RodentJoystick-master | xy_anlys_gui.m | .m | RodentJoystick-master/xy_anlys_gui/xy_anlys_gui.m | 21,345 | utf_8 | 29764115cbf101bf8ef36253ec6fd767 | function varargout = xy_anlys_gui(varargin)
% This GUI is a tool to analyze individual trajectories.
% xy_anlys_gui displays raw data, and allows cycling through individual
% trajectories.
% Can be called as xy_anlys_gui
% xy_anlys_gui('Verbose', 1) will display both error stack traces and
% occasional... |
github | GoldbergLab/RodentJoystick-master | xy_anlys_gui_2017a.m | .m | RodentJoystick-master/xy_anlys_gui/xy_anlys_gui_2017a.m | 21,772 | utf_8 | e37afc361231c7a2e19350cd8e8ff826 | function varargout = xy_anlys_gui_2017a(varargin)
% This GUI is a tool to analyze individual trajectories.
% xy_anlys_gui_2017a displays raw data, and allows cycling through individual
% trajectories.
% Can be called as xy_anlys_gui_2017a
% xy_anlys_gui_2017a('Verbose', 1) will display both error stack trace... |
github | GoldbergLab/RodentJoystick-master | plot_raw_data.m | .m | RodentJoystick-master/xy_anlys_gui/plot_raw_data.m | 4,651 | utf_8 | f51433893b13b8541d5afb729f542e59 | function [handles] = plot_raw_data(handles, axnum)
%plot_raw_data plots raw sensor information onto axes axnum. np, js, post,
%x, y, dev, are all 1/0 flags instructing whether or not to plot raw data.
% also plots analog information
npons = [handles.np1, handles.np2, handles.np3, handles.np4, handles.np5];
npon = get(n... |
github | GoldbergLab/RodentJoystick-master | indiv_trajectory_plot.m | .m | RodentJoystick-master/xy_anlys_gui/indiv_trajectory_plot.m | 3,362 | utf_8 | 5a7a6aa53927e5478a41c4cc2b3a2b16 | function [handles] = indiv_trajectory_plot(handles)
% for use in xy_anlys_gui - there's a small rectangular plot at the bottom
% that can plot deviation, velocity, speed, acceleration,... for a specific
% trajectory. This function is called to plot data on that axis.
[success, retrieval] = retrieve_data(handles);
if s... |
github | GoldbergLab/RodentJoystick-master | plot_traj_xy.m | .m | RodentJoystick-master/xy_anlys_gui/plot_traj_xy.m | 4,604 | utf_8 | 4bab4b44c1f7a18bbf19e80697f6af4b | function [handles] = plot_traj_xy(handles)
%This function plots the trajectory on x-y coordinate space on the large
%square center axis for xy_anlys_gui
axes(handles.axes6); cla; axis manual; axis square;
% only continue if actually if there are trajectories
if(numel(handles.traj_struct))>0
%BOX CIRCLE RADIUS
... |
github | GoldbergLab/RodentJoystick-master | motor_primitives.m | .m | RodentJoystick-master/xy_anlys_gui/motor_primitives.m | 12,651 | utf_8 | 4c88f3b04f772b2442c29e485059f015 | function varargout = motor_primitives(varargin)
% MOTOR_PRIMITIVES MATLAB code for motor_primitives.fig
% MOTOR_PRIMITIVES, by itself, creates a new MOTOR_PRIMITIVES or raises the existing
% singleton*.
%
% H = MOTOR_PRIMITIVES returns the handle to a new MOTOR_PRIMITIVES or the handle to
% the exis... |
github | GoldbergLab/RodentJoystick-master | behavior_report.m | .m | RodentJoystick-master/Automation/behavior_report.m | 1,353 | utf_8 | 269b468b52956a03f4b9da0b463f0e42 | %[report] = behavior_report(dirlist)
%
% takes a directory list and generates a string cell array report
% of the pellet counts and success rates of all directory entries in
% dirlist. Statistics are separated by directory (not necessarily box)
%
% ARGUMENTS
%
% dirlist :: dirlist is a cell array of direct... |
github | GoldbergLab/RodentJoystick-master | scheduled_analysis.m | .m | RodentJoystick-master/Automation/scheduled_analysis.m | 2,941 | utf_8 | 4ea225d3e95d0d5d3b5a2ff3fbacb9fb | % scheduled_analyis(experiment_directory)
%
% runs the entire core analysis pipeline on any subdirectories of
% experiment_directory containing raw, unprocessed data
%
% ARGUMENTS:
%
% experiment_directory :: the desired experiment directory for running
% automated analysis
%
function pp_report = scheduled_... |
github | GoldbergLab/RodentJoystick-master | auto_anlys_gui.m | .m | RodentJoystick-master/Automation/auto_anlys_gui.m | 51,641 | utf_8 | 9f37d94df0bf5f9bf617dbef00fe7de9 | %auto_anlys_gui is a GUI used to run all our automated analysis. It can
%handle both automated post processing analysis and the automated
%contingency updates separately. Both these analyses are run every 24
%hours. While the times can be anything, it probably makes the most sense
%to have contingency updates run short... |
github | GoldbergLab/RodentJoystick-master | update_all_boxes_anlys_gui.m | .m | RodentJoystick-master/Automation/update_all_boxes_anlys_gui.m | 5,590 | utf_8 | ba1f786495da21e723b970655bc79cf0 | % updates all information for all boxes in the GUI, including basic
% statistics and current contingency information.
% update_all_boxes_anlys_gui(handles) also puts recommendations based on
% post processing analysis scripts for new contingency changes.
function handles = update_all_boxes_anlys_gui(handles)
% hObjec... |
github | GoldbergLab/RodentJoystick-master | recommend_contigencies.m | .m | RodentJoystick-master/Automation/recommend_contigencies.m | 3,735 | utf_8 | dcd6e51d51a6553d0f8fc76c185eb0b5 | %[thresh, holdtime, centerhold, sector, oldcont]
% = recommend_contigencies(handles, exptdir, dirlist, boxnum)
% takes in an experiment director, the list of directories for which to
% generate its analysis, and a box number as an identifier.
% handles is a set of handles for an instance of a valid automated analys... |
github | GoldbergLab/RodentJoystick-master | write_out_all_contingencies_anlys_gui.m | .m | RodentJoystick-master/Automation/write_out_all_contingencies_anlys_gui.m | 5,059 | utf_8 | 1b5dacd534fd824657f9ac84c08b825d | %[handles, failures, attachments]
%= write_out_all_contingencies_anlys_gui(handles, manual)
%writes out all the contingency information from the
%automation analysis gui wherever possible (i.e. if a box has information
%available). If it cannot do so for a box, it will display a failure
%message to the MATLAB console... |
github | GoldbergLab/RodentJoystick-master | init_auto_contingency_update.m | .m | RodentJoystick-master/Automation/init_auto_contingency_update.m | 3,033 | utf_8 | 7aa83fb07f7e1d22203b8fb3f584f437 | %autotimer = init_auto_contingency_update(handles, [start_time, period]);
%This function returns a timer that handles automatically updating the
%automated contingency update feature on the GUI. The GUI must be running
%for this function to work (note the handles argument). This function has a
%subfunction that contain... |
github | SergMa/free-nross-master | looper.m | .m | free-nross-master/matlab/looper.m | 533 | utf_8 | 7fcc5146474283c70f9ec845b3a7a9aa | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Loop signal
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% INPUT:
% x = vector 1xN of input signal samples
% M = needed number of samples
% OUTPUT:
% y = vector 1xM of looped signal
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | SergMa/free-nross-master | my_delay_init.m | .m | free-nross-master/matlab/my_delay_init.m | 660 | utf_8 | e7bbf70d32d5f237b66d5c796e4675d2 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Delay of signal (initialization)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% INPUT:
% N = number of delay buffers (number of inputs/outputs)
% len = number of taps in delay buffer
% OUTPUT:
% state = created state o... |
github | SergMa/free-nross-master | filter_bank_fft_init.m | .m | free-nross-master/matlab/filter_bank_fft_init.m | 801 | utf_8 | e12847bf2e857f8b2f8c2917c19f807e | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Filters bank (initialization)
% FFT-based version
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% INPUT: fftsize = length of FFT transformation vector: 32,64,128,...
% OUTPUT: state = created state of filters bank
% state.N
% ... |
github | SergMa/free-nross-master | hf100.m | .m | free-nross-master/matlab/hf100.m | 833 | utf_8 | 84562d1b3db7aa4c654bc212bd976c49 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% High-pass IIR filter (cut frequency is about 100 Hz)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% INPUT: x = input sample
% xdelay = previous value of x
% ydelay = previous value of y
% OUTPUT: y = output sam... |
github | SergMa/free-nross-master | my_energy_init.m | .m | free-nross-master/matlab/my_energy_init.m | 753 | utf_8 | 8c783fa136e9bdde05a0cba048cad268 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Measurer of signal energy (Initialization)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Calculate average signal "energy" as sum of abs values of signal
% samples over len samples.
%
% INPUT:
% len = number of signal sample... |
github | SergMa/free-nross-master | energy_bank.m | .m | free-nross-master/matlab/energy_bank.m | 818 | utf_8 | d2648a173bc0463c0697ea9059ab1e02 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Measurers of energies bank
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% INPUT: vy = vector 1xN = filters outputs
% state = current state of measurers bank
% state.N
% state.LEN
% ... |
github | SergMa/free-nross-master | filter_bank_5.m | .m | free-nross-master/matlab/filter_bank_5.m | 1,325 | utf_8 | 4956a3f6a315438239a4dd6edc5c8e75 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Filters bank
% version 5
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% INPUT: x = input sample of signal
% state = current state of filters bank
% state.N
% state.freqs
% state.taps
% ... |
github | SergMa/free-nross-master | filter_bank_7.m | .m | free-nross-master/matlab/filter_bank_7.m | 1,325 | utf_8 | 8655e795b4350b9f7c337be4f6b494cf | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Filters bank
% version 7
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% INPUT: x = input sample of signal
% state = current state of filters bank
% state.N
% state.freqs
% state.taps
% ... |
github | SergMa/free-nross-master | lin2db.m | .m | free-nross-master/matlab/lin2db.m | 94 | utf_8 | e39761ff997962a91556a1fba73f1b92 | % Convert linear value to decibells
function db = lin2db(lin)
db = 20*log2(lin);
return |
github | SergMa/free-nross-master | energy_bank_init.m | .m | free-nross-master/matlab/energy_bank_init.m | 689 | utf_8 | 99e2eb47797fba47018e066148952993 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Measurers of energies bank (Initialization)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% INPUT: N = number of measurers
% LEN = length of measurers (number of samples to
% measure energy)
% OUTPUT: state = cr... |
github | SergMa/free-nross-master | filter_bank_fft.m | .m | free-nross-master/matlab/filter_bank_fft.m | 1,233 | utf_8 | a2d32b521d4718fefb97227ecc1e8c41 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Filters bank
% FFT-based version
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% INPUT: x = input sample of signal
% state = current state of filters bank
% state.N
% state.freqs
% state.... |
github | SergMa/free-nross-master | my_filter.m | .m | free-nross-master/matlab/my_filter.m | 839 | utf_8 | 6dacf017c11ea8e3caa577af9be7a3fc | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% FIR-filter
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% INPUT:
% x = input signal sample
% state = current FIR-filter state:
% state.len
% state.coeff
% state.delay
% state.out
% OU... |
github | SergMa/free-nross-master | spectrogram.m | .m | free-nross-master/matlab/spectrogram.m | 2,183 | utf_8 | c816fa57df2c7b36eb5d11968770fe10 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% function [] = spectrogram(x, width)
% Plot spectrogramm of defined signal
% INPUTS: x = 1xN - signal samples
% width - width of spectrogram, must be power of 2 (16,32,64,128,256,512,1024,2048,4096)
% fs - sample frequency, Hz
% ... |
github | SergMa/free-nross-master | my_energy.m | .m | free-nross-master/matlab/my_energy.m | 1,113 | utf_8 | 027ea93a7d6281383732384fb4033412 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Measurer of signal energy
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Calculate average signal "energy" as sum of abs values of signal
% samples over len samples.
%
% INPUT:
% x = input sample of signal
% state = curren... |
github | SergMa/free-nross-master | filter_bank_6.m | .m | free-nross-master/matlab/filter_bank_6.m | 2,033 | utf_8 | 864c8a7b458bd3ef29d80d9204bb7535 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Filters bank
% version 6
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% INPUT: x = input sample of signal
% state = current state of filters bank
% state.N
% state.freqs
% state.taps
% ... |
github | SergMa/free-nross-master | autoscale.m | .m | free-nross-master/matlab/autoscale.m | 255 | utf_8 | 128737a3542f9518400aeb3386277482 | % Autoscale signal
function y = autoscale( x, normx )
%Calculate max amplitude of signal
maxx = max( max( abs(x) ) );
%Scale signal to maxx = normx
if(maxx>0)
y = (normx/maxx) * x;
else
y = x;
end
return
|
github | SergMa/free-nross-master | mixer.m | .m | free-nross-master/matlab/mixer.m | 662 | utf_8 | 93b41d373d05ede7712925f06cbff82e | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Mix two signals
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% INPUT:
% x1 = vector 1xN1 of input signal-1 samples
% amp1 = amplitude of signal-1, dB
% x2 = vector 1xN2 of input signal-2 samples
% amp2 = amplitude ... |
github | SergMa/free-nross-master | db2lin.m | .m | free-nross-master/matlab/db2lin.m | 95 | utf_8 | 0074dadd84acab34849c2fafdea0df5d | % Convert decibells to linear value
function lin = db2lin( db )
lin = 10^(db/20);
return |
github | SergMa/free-nross-master | filter_bank_6_init.m | .m | free-nross-master/matlab/filter_bank_6_init.m | 17,664 | utf_8 | 609081686c1fd12bc8e199bb25d1bbf4 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Filters bank (initialization)
% version 6
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% INPUT: ---
% OUTPUT: state = created state of filters bank:
% state.N
% state.freqs
% state.taps
% ... |
github | SergMa/free-nross-master | filter_bank_5_init.m | .m | free-nross-master/matlab/filter_bank_5_init.m | 5,544 | utf_8 | 2c311cb956c06d6299622b3a2e8c5016 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Filters bank (initialization)
% version 5
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% INPUT: ---
% OUTPUT: state = created state of filters bank
% state.N
% state.freqs
% state.taps
% ... |
github | SergMa/free-nross-master | filter_bank_7_init.m | .m | free-nross-master/matlab/filter_bank_7_init.m | 7,432 | utf_8 | af2aadbabf391424488507032ab80c6e | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Filters bank (initialization)
% version 7
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% INPUT: ---
% OUTPUT: state = created state of filters bank:
% state.N
% state.freqs
% state.taps
% ... |
github | SergMa/free-nross-master | noisegate.m | .m | free-nross-master/matlab/noisegate.m | 1,053 | utf_8 | 0eff05112ce9b9330b22325d659c88ba | % Noise Gate
%
% ex = energy of input signal
% en = estimate of energy of noise
% T1 = Highest noisegate threshold (if signal is higher of T1, transmit full signal)
% T2 = noisegate threshold 2
% T3 = noisegate threshold 3
% T4 = noisegate threshold 4
% T5 = noisegate threshold 5
% T6 = noisegate threshold 6
% T7 = Low... |
github | SergMa/free-nross-master | my_delay.m | .m | free-nross-master/matlab/my_delay.m | 811 | utf_8 | b04f7a45c23457fced4f331c930fe96d | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Delay of signal
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% INPUT:
% x = vector 1xN of input signal samples
% state = current state of delay
% state.N
% state.len
% state.delay
% OUTPUT:
... |
github | wmvanvliet/ERP-beamformer-master | st_lcmv_apply.m | .m | ERP-beamformer-master/matlab/st_lcmv_apply.m | 1,071 | utf_8 | 37e4c654716d756c0f167f1d4c48bf78 | function X_trans = st_lcmv_apply(X, W, varargin)
% Apply a spatio-temporal LCMV beamformer to the data.
%
% Required parameters
% -------------------
% X : 3D matrix (n_channels x n_samples x n_trials)
% The trials.
% W : row vector (1 x (n_channels * n_sampels))
% The filter weights, obtained through the st... |
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