< Master index Index for NoiseTools >

Index for NoiseTools

Matlab files in this directory:

 nt_bannerh=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_bias_mask[c0,c1]=bias_hi_amp(x,mask) - covariance of masked signal
 nt_bsmean[mn,sd,all]=nt_bsmean(x,N) - calculate mean, estimate sd using bootstrap
 nt_bsmean_diff[mn,sd]=nt_bsmean_diff(x1,x2,N) - calculate mean, estimate sd using bootstrap
 nt_bsplotnt_bsplot(x,sds,style,abscissa,zeroflag,rmsflag) - plot average with bootstrap standard deviation
 nt_bsplot2nt_bsplot(x,percentile,style,abscissa,zeroflag,rmsflag) - plot average with confidence interval
 nt_bsplot_diffnt_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) - 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_cluster_jd[A,todss]=nt_cluster_jd(x,dsr,flags) - cluster with joint diagonalization
 nt_cov[c,tw]=nt_cov(x,shifts,w) - time shift covariance
 nt_dataview[p,data]=nt_dataview(data,p) - view data sets
 nt_decimatey=nt_decimate(x,R) - apply matlab decimate function to columns of matrix
 nt_demean[y,mn]=nt_demean(x,w) - remove weighted mean over cols
 nt_demean2y=nt_demean2(x,w) - remove mean of each row and page
 nt_detrendy=nt_detrend(x,order,w,basis) - remove polynomial or sinusoidal trend
 nt_dft_filtery=nt_dft_filter(x,transfer,N) - apply filter using DFT
 nt_dsampley=nt_dsample(x,factor,resampel_flag) - 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,sns_flag) - evoked-biased DSS denoising
 nt_enforce_stationarityy=nt_enforce_stationarity(x,DSR,thresh) - locally project out non-stationary components
 nt_epochifyy=nt_epochify(x,tidx,bounds) - extract epochs based on trigger indices
 nt_filter_peak[B,A] = nt_filter_peak(Wo,Q) - second order resonator filter
 nt_find_clippedw=nt_find_clipped_trials(x,bounds) - find clipped trials
 nt_find_clipped_trials
 nt_find_outlier_trials[idx,d,mn,idx_unsorted]=nt_find_outlier_trials(x,criterion,plot,regress_flag) - find outlier trials
 nt_find_outlier_trials2[idx,d,mn,idx_unsorted]=nt_find_outlier_trials(x,criterion,mn,regress_flag) - find outlier trials
 nt_find_outlier_trials3[idx,d]=nt_find_outlier_trials3(x,criterion,norm_flag) - find outlier trials
 nt_find_outliersw=nt_find_outliers(x,toobig1,toobig2) - find outliers (glitches, etc.).
 nt_find_outliers2w=nt_find_outliers2(x,cutoff) - outliers based on mahalanobis distance
 nt_find_triggerstidx=nt_find_triggers(x,threshold,type,guard) - find triggers
 nt_fixsigny=nt_fixsign(x) - flip signs to maximize inter-component correlation
 nt_foldy=fold(x,epochsize) - fold 2D to 3D
 nt_greetingsnt_greetings - display message the first time the toolbox is used
 nt_growmaskww=nt_growmask(w,margin) - widen mask
 nt_hi_ampy=nt_hi_amp(x) - decompose into high-amplitude components
 nt_imagesccimagescc - plot image with symmetric scaling
 nt_kurtosis[todss,K]=nt_kurtosis(x,nIterations,exponent,w,smooth)- find high-kurtosis components
 nt_linecolorsnt_colorlines(h,permutation) - apply different colors to lines of plot
 nt_linestylesnt_stylelines(h,property,values) - apply different styles to lines of plot
 nt_lower_to_fullb=nt_lower_to_full(a,ind) - transform lower diagonal to full covariance
 nt_marknt_mark(idx,labels)
 nt_mat2trial[y]=nt_trial2mat(x) - convert 3D matrix to trial cell array
 nt_mmaty=nt_mmat(x,m) - matrix multiplication (with convolution)
 nt_mmx[y,abscissa]=mmx(x, N) - calculate min-max pairs
 nt_multiscalez=nt_multiscale(x,depth) - apply smoothing at multiple scales
 nt_multishiftz=nt_multishift(x,shifts,amplitudes) - apply multiple shifts to matrix
 nt_multishift2z=nt_multishift2(x,nshifts) - apply multiple shifts/smoothing to matrix
 nt_narrowband_scanA=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_normpagey=nt_normpage(x,w) - normalize each page so its weighted msq is 1
 nt_normpagecoly=nt_normpagecol(x,w) - normalize each column of each page so its weighted msq is 1
 nt_normrowy=nt_normcol(x) - normalize each row so its msq is 1
 nt_pca[z,idx]=nt_pca(x,shifts,nkeep,threshold,w) - time-shift pca
 nt_pca0[topcs,pwr]=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_pca_nodemean[z,idx]=nt_pca_nodemean(x,shifts,keep,threshold,w) - time-shift pca
 nt_pcarot[topcs,eigenvalues]=pcarot(cov,N) - PCA matrix from covariance
 nt_peaksignsgn=peaksign(x,dim) - sign of largest extremum
 nt_peaky[tocomps,ii]=nt_peaky(c,x,T,nSmooth) - find components that maximize peakiness
 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_regcovr=nt_regcov(cxy,cyy,keep,threshold) - regression matrix from cross covariance
 nt_repeatability[score]=nt_repeatability(x,demean_flag) - repeatability score
 nt_resampleRESAMPLE Change the sampling rate of a signal.
 nt_rmsRMS - Root-mean-square
 nt_sgram[s,f,t]=nt_sgram(x,window,noverlap,nfft,sr,flags) - spectrogram
 nt_smoothy=nt_smooth(x,T,nIterations,nodelayflag) - smooth by convolution with square window
 nt_snsy=nt_sns(x,nneigbors,skip,w) - sensor noise suppression
 nt_sns0r=nt_sns0(c,nneigbors,skip,wc) - sensor noise suppression
 nt_sns1y=nt_sns1(x,nneigbors,skip,w,threshold) - sensor noise suppression
 nt_spect_plotnt_spect_plot - plot power spectrum
 nt_spect_plot2nt_spect_plot2 - plot power spectrum
 nt_split[idx,score_vector,score]=nt_split(x,depth,thresh,guard) - split time series into intervals
 nt_split_jd[idx,score_vector,todss]=nt_split_dss(x,thresh,depth) - segmentation based on joint diagonalization
 nt_statmatrixstats=nt_statMatrix(x,plot_params) - calculate statistics arrays for each dim of matrix
 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_unfoldy=nt_fold(x) - unfold 3D to 2D
 nt_varplotvarplot(x,mode) - plot variance maps (time*trials, time*chans, chans*trials)
 nt_vecaddy=nt_vecadd(x,v) - add vector to all rows or columns of matrix
 nt_vecmulty=nt_vecmult(x,v) - multiply all rows or columns of matrix by vector
 nt_version
 nt_whosssize=nt_whoss - total Gbytes used by variables
 nt_wmeany=wmean(x,w,dim) - weighted average over columns
 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_yulewalk_whiten[y,B,A]=nt_yulewalk_whiten(x,order,freqs) - whiten spectrally
 test_nt_bias_ffttest nt_bias_fft
 zz_package

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