Master index | Index for NoiseTools

# Index for NoiseTools

## Matlab files in this directory:

nt_banner | h=nt_banner(text,varargin) - annotate with text at head of figure
nt_bias_cluster | [c0,c1,A,todss,pwr0,pwr1]=nt_bias_cluster(x,dsr,flags) - cluster covariance
nt_bias_fft | [c0,c1]=nt_bias_fft(x,freq,nfft) - covariance with and w/o filter bias
nt_bias_filter | [c0,c1]=nt_bias_filter(x,B,A) - covariance with and w/o filter bias
nt_bsmean | [mn,sd,all]=nt_bsmean(x,N,w) - calculate mean, estimate sd using bootstrap
nt_bsmean_diff | [mn,sd]=nt_bsmean_diff(x1,x2,N) - calculate mean, estimate sd using bootstrap
nt_bsplot | nt_bsplot(x,sds,style,abscissa,zeroflag,rmsflag) - plot average with bootstrap standard deviation
nt_bsplot2 | nt_bsplot(x,percentile,style,abscissa,zeroflag,rmsflag) - plot average with confidence interval
nt_bsplot_diff | nt_bsplot_diff(x,y,sds,style,abscissa,zeroflag,rmsflag) - plot average difference with bootstrap standard deviation
nt_bsrms | [rms,sd,all]=nt_bsrms(x,N,w) - calculate rms, estimate sd using bootstrap
nt_bsrmsmean | [r,sd,all]=ft_bsrmsmean(x,N) - rms over channels of mean over trials, estimate sd using bootstrap
nt_cca | [A,B,R]=nt_cca(x,y,shifts,C,m,thresh,demeanflag) - canonical correlation
nt_cca_aad | [D,E,R]=nt_cca_match_aad(xA,xB,y,ssize) - calculate metrics for match-mismatch task
nt_cca_crossvalidate | [AA,BB,RR,iBest]=nt_cca_crossvalidate(xx,yy,shifts,ncomp,A0,B0) - CCA with cross-validation
nt_cca_crossvalidate2 | [A,B,RR]=nt_cca_crossvalidate2(xx,yy,shifts) - CCA with cross-validation
nt_cca_mm | [D,E,R]=nt_cca_match_mm3(x,y,ssize) - calculate metrics for match-mismatch task
nt_cluster1D | [C,A,score]=nt_cluster1D_b(x) - cluster 1D data into 2 clusters
nt_cluster_jd | [IDX,todss,SCORE,COVS]=nt_cluster_jd(x,dsr,smooth,flags,init,verbose) - cluster with joint diagonalization
nt_cov | [c,tw]=nt_cov(x,shifts,w) - time shift covariance
nt_cov2 | [c,tw]=nt_cov2(x,w) - weighted covariance
nt_cov_lags | [C,tw,m]=nt_cov_lags(x,y,shifts,nodemeanflag) - covariance of [x,y] with lags
nt_dataview | [p,data]=nt_dataview(data,p) - view data sets
nt_deboing | y=nt_deboing(x,events) - fit, remove ringing associated with events
nt_demean | [y,mn]=nt_demean(x,w) - remove weighted mean over cols
nt_demean2 | y=nt_demean2(x,w) - remove mean of each row and page
nt_destep | [y,stepList]=nt_destep(x,thresh,guard,depth,minstep) - remove step glitch from MEG data
nt_detrend | [y,w,r]=nt_detrend(x,order,w,basis,thresh,niter,wsize) - robustly remove trend
nt_dft_filter | y=nt_dft_filter(x,transfer,N) - apply filter using DFT
nt_double2int | nt_double2int() - recode/decode double as integer to save space
nt_dprime | [d,e]=nt_dprime(x,y,jd_flag) - calculate d' (discriminability) of two distributions
nt_dprime_old | [d,err_rate,perm]=nt_dprime(x,y,jd_flag) - calculate d' (discriminability) of two distributions
nt_dsample | [y,yy,yyy]=nt_dsample(x,dsr,method) - downsample by averaging neighboring samples
nt_dss0 | [todss,pwr1,pwr2]=nt_dss0(c0,c1,keep1,keep2) - dss from covariance
nt_dss1 | [todss,pwr0,pwr1]=nt_dss1(x,w,keep1,keep2) - evoked-biased DSS denoising
nt_dss_repeat_cluster | y=nt_dss_repeat_cluster(x,nkeep,cluster_size) - dss within clusters
nt_filter_peak | [B,A] = nt_filter_peak(Wo,Q) - second order resonator filter
nt_find_bad_channels | [iBad,toGood]=nt_find_bad_channels(x,proportion,thresh1,thresh2,thresh3) - find bad channels
nt_find_outlier_trials | [idx,d,mn,idx_unsorted]=nt_find_outlier_trials(x,criterion,plot,regress_flag) - find outlier trials
nt_fixsign | y=nt_fixsign(x) - flip signs to maximize inter-component correlation
nt_fold | y=fold(x,epochsize) - fold 2D to 3D
nt_greetings | nt_greetings - display message the first time the toolbox is used
nt_growmask | ww=nt_growmask(w,margin) - widen mask
nt_idx | i=nt_idx(x,scale,i) - index a data matrix
nt_idx_disp | nt_idx_disp(name,field,explainflag) - display contents of index file
nt_idxx | nt_idxx(fname,iname,bsize,chunksize,nfft) - create an index file to summarize large data file
nt_imagescc | nt_imagescc - plot image with symmetric scaling
nt_index | [status,p]=nt_index(name,p,forceUpdate) - index data files & directories
nt_inpaint | function y=nt_inpaint(x,w) - weighted interpolation based on correlation structure
nt_interpolate_bad_channels | y=interpolate_bad_channels(x,iBad,coordinates,n) - interpolate bad channels from good
nt_iplot | [hh,ii]=nt_iplot(fname) - plot data file based on index
nt_linecolors | nt_colorlines(h,permutation) - apply different colors to lines of plot
nt_linestyles | nt_stylelines(h,property,values) - apply different styles to lines of plot
nt_lower_to_full | b=nt_lower_to_full(a,ind) - transform lower diagonal to full covariance
nt_mark | nt_mark(idx,labels,line_params,text_params)
nt_mat2trial | [y]=nt_trial2mat(x) - convert 3D matrix to trial cell array
nt_mcca | [A,score,AA]=nt_mcca(C,N) - multiple cca
nt_mchanfilt | y=nt_mfilt(x,M,B,A,expand) - multichannel filter
nt_mmat | y=nt_mmat(x,m) - matrix multiplication (with convolution)
nt_mmx | [y,abscissa]=nt_mmx(x, N) - calculate min-max pairs
nt_morton | [iMorton,toMorton]=nt_morton(nrows,ncols) - indices for Morton scan of image
nt_multishift | z=nt_multishift(x,shifts,amplitudes) - apply multiple shifts to matrix
nt_multismooth | z=nt_multismooth(x,smooth,alignment,diff_flag) - apply multiple smoothing kernels
nt_multismooth_old | z=nt_multismooth(x,smooth,alignment,diff_flag) - apply multiple smoothing kernels
nt_narrowband_scan | A=nt_narrowband_scan(x,freqs,sr,Q,plotflag) - scan for narrowband components using DSS
nt_normcol | [y,norm]=nt_normcol(x,w) - normalize each column so its weighted msq is 1
nt_normpage | y=nt_normpage(x,w) - normalize each page so its weighted msq is 1
nt_normpagecol | y=nt_normpagecol(x,w) - normalize each column of each page so its weighted msq is 1
nt_normrow | y=nt_normcol(x) - normalize each row so its msq is 1
nt_outliers | [w,y]=nt_outliers(x,w,thresh,niter) - detect outliers based on weighted correlation structure
nt_pca | [z,idx]=nt_pca(x,shifts,nkeep,threshold,w) - time-shift pca
nt_pca0 | [topcs,pwr,y]=nt_pca0(x,shifts,nkeep,threshold,w) - time-shift pca
nt_pca_kmeans | [topcs,pwr]=nt_pca_kmeans(x,nkeep) - PCA preceded by kmeans for speed
nt_pcarot | [topcs,eigenvalues]=pcarot(cov,nkeep,threshold,N) - PCA matrix from covariance
nt_peaksign | sgn=peaksign(x,dim) - sign of largest extremum
nt_phase_scramble | y=nt_phase_scramble(x) - scramble time but preserve autocorrelation
nt_plot_mmx | nt_plot_mmx - plot data using min-max pairs
nt_plotxx | nt_plotxx(fname,bounds,chans) - plot using index file
nt_proximity | [closest,d]=nt_proximity(coordinates,N) - distance to neighboring channels
nt_qca | [squares,quads,D]=nt_qca(x,npcs,nsmooth,nquads) - maximize induced power using quadratic component analysis
nt_qca0 | [tosquares,quads,D]=nt_qca0(x,npcs,nsmooth,nquads) - maximize induced power using quadratic component analysis
nt_qca02 | [tosquare,quad,tosquare2,quad2,D]=nt_qca02(x,npcs,nsmooth) - maximize induced power using quadratic component analysis
nt_qca2 | [square,quad,square2,quad2,D]=nt_qca(x,npcs,nsmooth) - maximize induced power using quadratic component analysis
nt_qpca | [squares,quads]=nt_qpca(x,npcs,nsmooth,nquads) - quadratic PCA
nt_qpca0 | [tosquares,quads,D]=nt_qpca0(x,npcs,nsmooth,nquads) - quadratic PCA
nt_quad2square | [tosquare,D]=nt_quad2square(toquad,order) - quadratic to squared linear component
nt_read_data | [p,data]=nt_read_data(fname,flag) - read data from file
nt_read_header | [h,readwith]=nt_read_header(fname,flag) - read data from file
nt_regcov | r=nt_regcov(cxy,cyy,keep,threshold) - regression matrix from cross covariance
nt_regw | [b,z]=nt_regw(y,x,w) - weighted regression
nt_relshift | [xx,yy]=nt_relshift(x,y,shift,flag) - delay x relative to y
nt_rereference | [y,mn]=nt_rereference(x,w,factor) - rereference by subtracting robust mean
nt_resample | RESAMPLE Change the sampling rate of a signal.
nt_resample_interp1 | y=resample_interp1(x,p,q,method) - Resample using interp1 (no antialiasing)
nt_rms | RMS - Root-mean-square
nt_same_clim | nt_same_clim(h) - harmonize color limits of plots within figure
nt_sgram | [s,f,t]=nt_sgram(x,window,noverlap,nfft,sr,flags) - spectrogram
nt_smooth | y=nt_smooth(x,T,nIterations,nodelayflag) - smooth by convolution with square window
nt_sns | y=nt_sns(x,nneigbors,skip,w) - sensor noise suppression
nt_sns0 | r=nt_sns0(c,nneigbors,skip,wc) - sensor noise suppression
nt_sns1 | y=nt_sns1(x,nneigbors,skip,w,threshold) - sensor noise suppression
nt_sns_cluster | y=nt_sns_cluster(x,nneigbors,cluster_size) - sensor noise suppression within clusters
nt_sparse_filter | y=nt_sparse_filter(x,T,A) - convolve multichannel data with sparse impulse response
nt_spect_plot | nt_spect_plot - plot power spectrum
nt_spect_plot2 | nt_spect_plot2 - plot power spectrum
nt_split | [idx,score_vector,score]=nt_split(x,depth,thresh,guard,minstep) - split time series into intervals
nt_split_jd | [idx,score_vector,todss]=nt_split_dss(x,thresh,depth) - segmentation based on joint diagonalization
nt_squeeze_all | y=nt_squeeze_all(x) - squeeze structs and cell arrays
nt_star | [y,w,ww]=nt_star(x,thresh,closest,depth) - sensor noise suppression
nt_star2 | [y,w,ww]=nt_star2(x,thresh,closest,w) - sensor noise suppression
nt_statmatrix | stats=nt_statMatrix(x,plot_params) - calculate statistics arrays for each dim of matrix
nt_test |
nt_topoplot | nt_topoplot(cfg,data) - simple topoplot
nt_trial2mat | [y,w]=nt_trial2mat(x,max_nsamples) - convert trial cell array to 3D matrix
nt_tsr | [y,idx,w]=nt_tsr(x,ref,shifts,wx,wref,keep,thresh) - time-shift regression (TSPCA)
nt_tsr_nodemean | [y,idx,w]=nt_tsr_nodemean(x,ref,shifts,wx,wref,keep,thresh) - time-shift regression (TSPCA)
nt_tsregress | [z,idx]=nt_tsregress(x,y,shifts,xw,yw,keep,threshold) - time-shift regression
nt_tsxcov | [c,tw]=nt_tsxcov(x,y,shifts,w) - cross-covariance of X and time-shifted Y
nt_unfold | y=nt_fold(x) - unfold 3D to 2D
nt_unique | [C,IA,IC,N] = nt_unique(A, varargin) - unique with counts
nt_vecadd | y=nt_vecadd(x,v) - add vector to all rows or columns of matrix
nt_vecmult | y=nt_vecmult(x,v) - multiply all rows or columns of matrix by vector
nt_version |
nt_video_sns | y=nt_video_sns(x,nneighbors) - apply SNS locally
nt_whiten | [A,y]=nt_whiten(x,N) - whiten spectrally using pca
nt_whoss | size=nt_whoss - total Gbytes used by variables
nt_wmean | y=nt_wmean(x,w,dim) - weighted average
nt_wpwr | [y,tweight]=nt_wpwr(x,w) - weighted power
nt_xcov | [c,tw]=nt_xcov(x,y,shifts,w) - cross-covariance of X and time-shifted Y
nt_xprod | [y,ind]=nt_xprod(x,flag,dsratio,normrow_flag) - form all crossproducts
nt_xprod2 | [y,ind]=nt_xprod(x1,x2,dsratio) - form all crossproducts
nt_xxcorr | [C,idx]=nt_xxcorr(A,B,MAXLAG) - true unbiased cross-correlation
nt_zapline | [y,yy]=nt_zapline(x,fline,nremove,p,plotflag) - remove power line artifact
zz_package |
zz_upload_data |

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