neuro_dot

Submodules

• neuro_dot.Analysis
• neuro_dot.DynamicFilter
• neuro_dot.File_IO
• neuro_dot.Light_Modeling
• neuro_dot.Matlab_Equivalent_Functions
• neuro_dot.Reconstruction
• neuro_dot.Spatial_Transforms
• neuro_dot.Temporal_Transforms
• neuro_dot.Visualizations

Package Contents

Functions

adjust_brain_pos(meshL, meshR[, params])	ADJUST_BRAIN_POS Repositions mesh orientations for display.
applycmap(overlay, underlay, params)	APPLYCMAP Performs color mapping and fuses images with anatomical models.
DrawColoredSynchPoints(info_in, subplt[, SPfr])	DRAWCOLOREDSYNCHPOINTS Draws vertical lines over a plot/imagesc type axis to delineate synchronization points on time traces.
nlrGrayPlots_220324(nlrdata, info[, mode])	NLRGRAYPLOTS_220324 This function generates a gray plot figure for measurement pairs
PlotInterpSurfMesh(volume, meshL, meshR, dim, params)	PLOTINTERPSURFMESH Interpolates volumetric data onto hemispheric meshes for display.
Plot_RawData_Cap_DQC(data, info_in[, params])	PLOT_RAWDATA_CAP_DQC Generates plots of cap data quality.
defineticks(start, end, steps[, blankLabels])	DEFINETICKS Defines tick marks based on a start point, end point, and step size.
PlotFalloffData(fall_data, separations[, params, ax])	PLOTFALLOFFDATA A basic falloff plotting function.
PlotFalloffLL(data, info[, params, ax])	PLOTFALLOFFLL A light-level falloff visualization.
PlotLRMeshes(meshL, meshR, params)	PLOTLRMESHES Renders a pair of hemispheric meshes.
Plot_RawData_Metrics_I_DQC(data, info[, params])	PLOT_RAWDATA_METRICS_I_DQC Generates a single-page data quality report including various metrics of the raw data quality.
Plot_RawData_Metrics_II_DQC(data, info[, params])	PLOT_RAWDATA_METRICS_II_DQC Generates a single-page data quality report including various metrics of the raw data quality.
Plot_RawData_Time_Traces_Overview(data, info_in[, params])	PLOT_RAWDATA_TIME_TRACES_OVERVIEW Generates a plot of raw data time traces.
cylinder(r[, h, a, nt, nv])	CYLINDER Parameterizes the cylinder of radius r, height h, and base point a.
PlotCapData(SrcRGB, DetRGB, info[, fig_axes, params])	PLOTCAPDATA A basic plotting function for generating and labeling cap grids.
PlotCapGoodMeas(info[, fig_axes, params])	PLOTCAPGOODMEAS A "Good Measurements" visualization overlaid on a cap grid.
PlotCapMeanLL(data, info[, fig_axes, params])	PLOTCAPMEANLL A visualization of mean light levels overlaid on a cap
PlotCapPhysiologyPower(data, info[, fig_axes, params])	PLOTCAPPHYSIOLOGYPOWER A visualization of band limited power OR band-referenced SNR for each optode.
PlotSlices_correct_orientation(m)	PLOTSLICES_CORRECT_ORIENTATION Used in PlotSlices to transpose the dimension, given by "m".
PlotSlices(underlay[, infoVol, params, overlay])	PLOTSLICES Creates an interactive 3D plot, including transverse, sagittal, and coronal slices.
PlotSlicesTimeTrace(underlay[, infoVol, params, ...])	PLOTSLICESTIMETRACE Creates an interactive 4D plot, including transverse, sagittal, and coronal slices, in addition to a voxel time trace.
PlotTimeTraceData(data, time[, params, fig_axes, ...])	PLOTTIMETRACEDATA A basic time traces plotting function.
vol2surf_mesh(Smesh, volume, dim[, params])	VOL2SURF_MESH Interpolates volumetric data onto a surface mesh.
check_keys(dict)	CHECK_KEYS Checks if entries in dictionary are mat-objects.
loadmat(filename)	LOADMAT Loads files with the *.mat extension.
loadmat7p3(filename)	LOADMAT7P3 Loads files with the *.mat extension in the "mat 7.3" format.
LoadVolumetricData(filename, pn, file_type)	LOADVOLUMETRICDATA Loads a volumetric data file
Make_NativeSpace_4dfp(header_in)	MAKE_NATIVESPACE_4DFP Calculates native space for an incomplete 4dfp header.
nifti_4dfp(header_in, img_in, mode)	NIFTI_4DFP Converts between nifti and 4dfp volume and header formats.
Read_4dfp_Header(filename, pn)	READ_4DFP_HEADER Reads the .ifh header of a 4dfp file.
SaveVolumetricData(volume, header, filename, pn, file_type)	SAVEVOLUMETRICDATA Saves a volumetric data file.
snirf2ndot(filename, pn[, save_file, output, dtype])	SNIRF2NDOT takes a file with the 'snirf' extension in the SNIRF format and converts it to NeuroDOT formatting.
todict(matobj)	TODICT Recursively constructs from matobjects nested dictionaries
Write_4dfp_Header(header, filename, pn)	WRITE_4DFP_HEADER Writes a 4dfp header to a .ifh file.
affine3d_img(imgA, infoA, infoB[, affine, interp_type])	AFFINE3D_IMG Transforms a 3D data set to a new space.
change_space_coords(coord_in, space_info[, output_type])	CHANGE_SPACE_COORDS Applies a look-up to change 3D coordinates into a new space.
GoodVox2vol(img, dim)	GOOD_VOX2VOL Transforms a VOX x TIME array into a volume, X x Y x Z x TIME, given a space described by "dim".
rotate_cap(tpos_in, dTheta)	ROTATE_CAP Rotates the cap in space.
rotation_matrix(direction, theta)	ROTATION_MATRIX Creates a rotation matrix.
detrend_tts(data_in)	DETREND_TTS Performs linear detrending.
nextpow2(N)	NEXTPOW2 Finds the next power of 2, given an integer input.
fft_tts(data, framerate)	FFT_TTS Computes the Fourier Transform of a time-domain input.
gethem(data, info[, sel_type, value])	GETHEM Calculates the mean across a set of measurements.
highpass(data_in, omegaHz, frate[, params])	HIGHPASS Applies a zero-phase digital highpass filter.
logmean(data_in)	LOGMEAN Computes the log-ratio of raw intensity data.
lowpass(data_in, omegaHz, frate[, params])	LOWPASS Applies a zero-phase digital lowpass filter.
regcorr(data_in, info, hem)	REGCORR Performs regression correction by wavelength.
resample_tts(data_in, info_in[, omega_resample, tol, ...])	RESAMPLE_TTS Resamples data while maintaining linear signal components.
rms_py(rms_input)	RMS_PY Computes the RMS value for each column of a row vector.
calc_NN(info_in, dr)	CALC_NN Calculates the Nearest Neigbor value for all measurement pairs.
Generate_pad_from_grid(grid, params, info)	GENERATE_PAD_FROM_GRID Generates info structures "optodes" and "pairs" from a given "grid".
makeFlatFieldRecon(A, iA)	MAKEFLATFIELDRECON Generates the flat field reconstruction of a given "A" sensitivity matrix.
DynamicFilter(input_data, info_in, params, mode[, ...])	This function is used to perform each step of the NeuroDOT PreProcessing Pipeline.
reconstruct_img(lmdata, iA)	RECONSTRUCT_IMG Performs image reconstruction by wavelength using the inverted A-matrix.
smooth_Amat(iA_in, dim, gsigma)	SMOOTH_AMAT Performs Gaussian smoothing on a sensitivity matrix.
spectroscopy_img(cortex_mu_a, E)	SPECTROSCOPY_IMG Completes the Beer-Lambert law from a reconstructed image.
Tikhonov_invert_Amat(A, lambda1, lambda2)	TIKHONOV_INVERT_AMAT Inverts a sensitivity "A" matrix.
BlockAverage(data_in, pulse, dt[, Tkeep])	BLOCKAVERAGE Averages data by stimulus blocks.
CalcGVTD(data)	CalcGVTD calculates the Root Mean Square across measurements (log-mean light levels or voxels) of the temporal derivative.
FindGoodMeas(data, info_in[, bthresh])	FINDGOODMEAS Performs "Good Measurements" analysis to return indices of measurements within a chosen threshold.
normcND(data)	NORMCND returns a column-normed matrix. It is assumed that the matrix is 2D.
normrND(data)	NORMRND returns a row-normed matrix. It is assumed that the matrix is 2D. Updated for broader compatability.

neuro_dot.adjust_brain_pos(meshL, meshR, params=None)

ADJUST_BRAIN_POS Repositions mesh orientations for display.

[Lnodes, Rnodes] = ADJUST_BRAIN_POS(meshL, meshR) takes the left and right hemispheric meshes “meshL” and “meshR”, respectively, and repositions them to the proper perspective for display.

[Lnodes, Rnodes] = ADJUST_BRAIN_POS(meshL, meshR, params) allows the user to specify parameters for plot creation.

Params:

ctx:

Defines inflation of mesh. std: standard pial mesh Default inf: inflated mesh vinf: very inflated mesh flat: flat mesh

orientation:

Select orientation of volume. t: transverse Default s: for sagittal

view:

Sets the view perspective. lat: lateral view Default post: posterior view dorsal: dorsal view

Dependencies: ROTATE_CAP, ROTATION_MATRIX.

See Also: PLOTLRMESHES.

neuro_dot.applycmap(overlay, underlay, params)

APPLYCMAP Performs color mapping and fuses images with anatomical models.

mapped = APPLYCMAP(overlay) fuses the N-D image array “overlay” with a default flat gray background and applies a number of other default settings to create a scaled and colormapped N-D x 3 RGB image array “mapped”.

mapped = APPLYCMAP(overlay, underlay) fuses the image array “overlay” with the anatomical atlas volume input “underlay” as the background.

mapped = APPLYCMAP(overlay, underlay, params) allows the user to specify parameters for plot creation.

Params:

TC:

Direct map integer data values to defined color map (“True Color”). Default Value: 0

DR:

Dynamic range. Default Value: 1000

Scale:

Maximum value to which image is scaled. Default Value: 90% max

PD:

If PD = 1, Sets the entirety of the colormap to run from zero to the Scale value. If PD = 0, Colormap is centered around zero. Default Value: 0

BG:

Background color, as an RGB triplet. Default Value: [0.5, 0.5, 0.5]

Saturation:

Sets the transparency of the colors. Size: equal to the size of the data. Must be within range [0, 1]. Default Value: none

Cmap.P:

Colormap for positive data values. Default Value: jet

Cmap.N:

Colormap for negative data values. Default Value: none

Cmap.flipP:

Logical, flips the positive colormap. Default Value: 0

Cmap.flipN:

Logical, flips the negative colormap. Default Value: 0

Th.P:

Value of min threshold to display positive data values. Default Value: 25% max

Th.N:

Value of max threshold to display negative data values. Default Value: -Th.P

returns:

mapped, map_out, params

See Also: PLOTSLICES, PLOTINTERPSURFMESH, PLOTLRMESHES, PLOTCAPMEANLL.

neuro_dot.DrawColoredSynchPoints(info_in, subplt, SPfr=0)

DRAWCOLOREDSYNCHPOINTS Draws vertical lines over a plot/imagesc type axis to delineate synchronization points on time traces. This function assumes the standard NeuroDOT info structure and paradigm format

Info:

info.paradigm:

Contains all paradigm timing information

info.paradigm.synchpts:

Contains sample points corresponding to stimulus time points of interest (e.g. onsets, rest, etc.)

info.paradigm.synchtype:

Optional field that contains a marker to distinguish between synchronization types, traditionally as sound boops of differing frequencies, i.e., 25, 30, 35 (Hz).

info.paradigm.Pulse_1:

Indices of synchpts that denote ‘bookends’ of data collection as well as resting (or OFF) periods of a given stimulus.

info.paradigm.Pulse_2:

Indices of synchpts that correspond to the start of a given stimulus epoch (e.g., the start of a flickering checkerboard)

info.paradigm.Pulse_3:

Indices of synchpts of a 2nd stimulus type

info.paradigm.Pulse_4:

Indices of synchpts of a 3rd stimulus type

The 2nd input, SPfr, selects whether or not to adjust synchpoint timing based on framerate of data. (Default=0).

neuro_dot.nlrGrayPlots_220324(nlrdata, info, mode='auto')

NLRGRAYPLOTS_220324 This function generates a gray plot figure for measurement pairs using only clean data from wavelength 2. Input data “nlrdata” is assumed to be filtered and resampled. The data is grouped into info.pairs.r2d<20, 20<=info.pairs.r2d<30, and 30<=info.pairs.r2d<40.

neuro_dot.PlotInterpSurfMesh(volume, meshL, meshR, dim, params)

PLOTINTERPSURFMESH Interpolates volumetric data onto hemispheric meshes for display.

PLOTINTERPSURFMESH(volume, meshL, meshR, dim) takes functional imaging data “volume” and interpolates it onto the left and right hemispheric surface meshes given in “meshL” and “meshR”, respectively, using the spatial information in “dim”. The result is a bilateral sagittal view of the activations overlain onto the surface of the brain represented by the meshes.

PLOTINTERPSURFMESH(volume, meshL, meshR, dim, params) allows the user to specify parameters for plot creation.

Params:

Scale:

(90% max) Maximum value to which image is scaled.

Th.P:

(25% max) Value of min threshold to display positive data values.

Th.N:

(-Th.P) Value of max threshold to display negative data values.

Dependencies: VOL2SURF_MESH, PLOTLRMESHES, ADJUST_BRAIN_POS, ROTATE_CAP, ROTATION_MATRIX.

See Also: PLOTSLICES.

neuro_dot.Plot_RawData_Cap_DQC(data, info_in, params=None)

PLOT_RAWDATA_CAP_DQC Generates plots of cap data quality.

Includes: Relative average light levels for 2 sets of distances of source-detector measurements, the cap good measurements plot, and a measure of the pulse power at each optode location.

neuro_dot.defineticks(start, end, steps, blankLabels=False)

DEFINETICKS Defines tick marks based on a start point, end point, and step size.

See: PLOT_RAWDATA_METRICS_I_DQC

neuro_dot.PlotFalloffData(fall_data, separations, params=None, ax=None)

PLOTFALLOFFDATA A basic falloff plotting function.

PLOTFALLOFFDATA(data, info) takes one input array “fall_data” and plots it against another “separations” to create a falloff plot.

PLOTFALLOFFDATA(data, info, params) allows the user to specify parameters for plot creation.

h = PLOTFALLOFFDATA(…) passes the handles of the plot line objects created.

Params:

fig_size:

[200, 200, 560, 420] Default figure position vector.

fig_handle:

(none) Specifies a figure to target. If empty, spawns a new figure.

xlimits:

‘auto’ Limits of x-axis.

xscale:

‘linear’ Scaling of x-axis.

ylimits:

‘auto’ Limits of y-axis.

yscale:

‘log’ Scaling of y-axis.

See Also: PLOTFALLOFFLL.

neuro_dot.PlotFalloffLL(data, info, params=None, ax=None)

PLOTFALLOFFLL A light-level falloff visualization.

PLOTFALLOFFLL(data, info) takes a light-level array “data” of the MEAS x TIME format, and generates a plot of each channel’s temporal mean against its source-detector distance, in the specified groupings.

PLOTFALLOFFLL(data, info, params) allows the user to specify parameters for plot creation.

Params:

fig_size:

[200, 200, 560, 420] Default figure position vector.

fig_handle:

(none) Specifies a figure to target. If empty, spawns a new figure.

dimension:

‘2D’ Dimension of pair radii used.

rlimits:

(all R2D) Limits of pair radii displayed.

Nnns:

(all NNs) Number of NNs displayed.

Nwls:

(all WLs) Number of WLs displayed.

useGM:

0 Use Good Measurements.

xlimits:

[0, 60] Limits of x-axis.

xscale:

‘linear’ Scaling of x-axis.

ylimits:

[1e-6, 1e1] Limits of y-axis.

yscale:

‘log’ Scaling of y-axis.

Dependencies: PLOTFALLOFFDATA

neuro_dot.PlotLRMeshes(meshL, meshR, params)

PLOTLRMESHES Renders a pair of hemispheric meshes.

PLOTLRMESHES(meshL, meshR) renders the data in a pair of left and right hemispheric meshes “meshL.data” and “meshR.data”, respectively, and applies full color mapping to them. If no data is present for either mesh, a default gray mesh will be plotted.

PLOTLRMESHES(meshL, meshR, params) allows the user to specify parameters for plot creation.

Params:

fig_size:

[20, 200, 960, 420] Default figure position vector.

fig_handle:

(none) Specifies a figure to target. If empty, spawns a new figure.

Scale:

(90% max) Maximum value to which image is scaled.

PD:

0 Declares that input image is positive definite.

cblabels:

([-90% max, 90% max]) Colorbar axis labels. Min defaults to 0 if PD==1, both default to +/- Scale if supplied.

cbticks:

(none) Specifies positions of tick marks on colorbar axis.

alpha:

1. Transparency of mesh.

view:

‘lat’ Sets the view perspective.

Dependencies: ADJUST_BRAIN_POS, ROTATE_CAP, ROTATION_MATRIX, APPLYCMAP.

See Also: PLOTINTERPSURFMESH, VOL2SURF_MESH.

neuro_dot.Plot_RawData_Metrics_I_DQC(data, info, params=None)

PLOT_RAWDATA_METRICS_I_DQC Generates a single-page data quality report including various metrics of the raw data quality. Light fall off as a function of Rsd, SNR plots vs. mean light level, Source-detector mean light-level plots, Power spectra for 830nm at 2 Rsd, and Histogram for measurement noise.

neuro_dot.Plot_RawData_Metrics_II_DQC(data, info, params=None)

PLOT_RAWDATA_METRICS_II_DQC Generates a single-page data quality report including various metrics of the raw data quality.

Zoomed raw time traces, Light fall off as a function of Rsd, Power spectra for 830nm at 2 Rsd, and Histogram for measurement noise.

neuro_dot.Plot_RawData_Time_Traces_Overview(data, info_in, params=None)

PLOT_RAWDATA_TIME_TRACES_OVERVIEW Generates a plot of raw data time traces. Time Traces are separated by wavelength (columns). The top row shows time traces for all measurements within a source-detector distance range (defaults as 0.1 - 5.0 cm). The bottom row shows the same measurements but including only measurements passing a variance threshold (default: 0.075) as well as vertical lines corresponding to the stimulus paradigm.

neuro_dot.cylinder(r, h=1, a=0, nt=100, nv=50)

CYLINDER Parameterizes the cylinder of radius r, height h, and base point a.

See: PlotCapData

neuro_dot.PlotCapData(SrcRGB, DetRGB, info, fig_axes=None, params=None)

PLOTCAPDATA A basic plotting function for generating and labeling cap grids.

PLOTCAPDATA(SrcRGB, DetRGB, info) plots the input RGB information in one of three modes:

Modes:

text:

• Optode numbers are arranged in a cap grid and colored with the RGB input.

patch:

• Optodes are plotted as patches and colored with the RGB input.

textpatch:

• Optodes are plotted as patches and colored with the RGB input, with optode numbers overlain in white.

PLOTCAPDATA(SrcRGB, DetRGB, info, params) allows the user to specify parameters for plot creation.

Params:

fig_size:

[20, 200, 1240, 420] Default figure position vector.

fig_handle:

(none) Specifies a figure to target. If empty, spawns a new figure.

dimension:

‘2D’ Specifies either a 2D or 3D plot rendering.

mode:

‘textpatch’ Display mode.

See Also: PLOTCAP, PLOTCAPGOODMEAS, PLOTCAPMEANLL.

neuro_dot.PlotCapGoodMeas(info, fig_axes=None, params=None)

PLOTCAPGOODMEAS A “Good Measurements” visualization overlaid on a cap grid.

PLOTCAPGOODMEAS(info) plots a visualization of the Good Measurements determined by FINDGOODMEAS and arranges them based on the metadata in “info.optodes”. Good channels are depicted as green lines, bad channels red lines; sources and detectors are given lettering in light blue and red.

The plot title provides tallies for all specified groupings. The next line of the title lists how many optodes for which only 33% of their measurements are good. These optodes are surrounded with white circles.

PLOTCAPGOODMEAS(info, params) allows the user to specify parameters for plot creation.

Params:

fig_size:

[20, 200, 1240, 420] Default figure position vector.

fig_handle:

(none) Specifies a figure to target. If empty, spawns a new figure.

dimension:

‘2D’ Specifies either a 2D or 3D plot rendering.

rlimits:

(all R2D) Limits of pair radii displayed.

Nnns:

(all NNs) Number of NNs displayed.

Nwls:

(all WLs) Number of WLs averaged and displayed.

mode:

‘good’ Display mode. ‘good’ displays channels above noise threhsold, ‘bad’ below.

Dependencies: PLOTCAPDATA, ISTABLEVAR.

See Also: FINDGOODMEAS, PLOTCAP, PLOTCAPMEANLL.

neuro_dot.PlotCapMeanLL(data, info, fig_axes=None, params=None)

PLOTCAPMEANLL A visualization of mean light levels overlaid on a cap grid.

PLOTCAPMEANLL(data, info) plots an intensity map of the mean light levels for specified measurement groupings of each optode on the cap and arranges them based on the metadata in “info.optodes”.

PLOTCAPMEANLL(data, info, params) allows the user to specify parameters for plot creation.

Params:

fig_size:

[20, 200, 1240, 420] Default figure position vector.

fig_handle:

(none) Specifies a figure to target. If empty, spawns a new figure.

dimension:

‘2D’ Specifies either a 2D or 3D plot rendering.

rlimits:

(all R2D) Limits of pair radii displayed.

Nnns:

(all NNs) Number of NNs displayed.

Nwls:

(all WLs) Number of WLs averaged and displayed.

useGM:

0 Use Good Measurements.

Cmap.P:

‘hot’ Default color mapping.

Dependencies: PLOTCAPDATA, ISTABLEVAR, APPLYCMAP.

See Also: PLOTCAP, PLOTCAPGOODMEAS.

neuro_dot.PlotCapPhysiologyPower(data, info, fig_axes=None, params=None)

PLOTCAPPHYSIOLOGYPOWER A visualization of band limited power OR band-referenced SNR for each optode.

Params:

fig_size:

[20, 200, 1240, 420] Default figure position vector.

fig_handle:

(none) Specifies a figure to target. If empty, spawns a new figure.

dimension:

‘2D’ Specifies either a 2D or 3D plot rendering.

rlimits:

(all R2D) Limits of pair radii displayed.

Nnns:

(all NNs) Number of NNs displayed.

Nwls:

(all WLs) Number of WLs averaged and displayed.

useGM:

0 Use Good Measurements.

Cmap.P:

‘hot’ Default color mapping.

Dependencies: PLOTCAPDATA, ISTABLEVAR, APPLYCMAP.

See Also: PLOTCAP, PLOTCAPGOODMEAS.

neuro_dot.PlotSlices_correct_orientation(m)

PLOTSLICES_CORRECT_ORIENTATION Used in PlotSlices to transpose the dimension, given by “m”.

neuro_dot.PlotSlices(underlay, infoVol=None, params=None, overlay=None)

PLOTSLICES Creates an interactive 3D plot, including transverse, sagittal, and coronal slices.

PLOTSLICES(underlay) takes a 3D voxel space image “underlay” and generates views along the three canonical axes.

In interactive mode, left-click on any point to move to those slices. To reset to the middle of the volume, right-click anywhere. To cancel interactive mode, press “Q”, “Esc”, or the middle mouse button.

PLOTSLICES(underlay, infoVol) uses the volumetric data in “infoVol” to display spatial coordinates of the slices in question.

PLOTSLICES(underlay, infoVol, params) allows the user to specify parameters for plot creation.

Params:

fig_size:

[20, 200, 1240, 420] Default figure position vector.

fig_handle:

(none) Specifies a figure to target. If empty, spawns a new figure.

CH:

1 Turns crosshairs on (1) and off (0).

Scale:

(90% max) Maximum value to which image is scaled.

PD:

0 Declares that input image is positive definite.

cbmode:

0 Specifies whether to use custom colorbar axis labels.

cboff:

0 If set to 1, no colorbar is displayed

cblabels:

([-90% max, 90% max]) Colorbar axis labels. When cbmode==1, min defaults to 0 if PD==1, both default to +/- Scale if supplied. When cbmode==0, then cblabels dictates colorbar axis limits.

cbticks:

(none) When cbmode==1, specifies positions of tick marks on colorbar axis.

slices:

(center frames) Select which slices are displayed. If empty, activates interactive navigation.

slices_type:

‘idx’ Use MATLAB indexing (‘idx’) for slices, or spatial coordinates (‘coord’) as provided by invoVol.

orientation:

‘t’ Select orientation of volume. ‘t’ for transverse, ‘s’ for sagittal.

Note: APPLYCMAP has further options for using “params” to specify parameters for the fusion, scaling, and colormapping process.

PLOTSLICES(underlay, infoVol, params, overlay) overlays the image provided by “overlay”. When this is done, all color mapping is applied to the overlay image, and the underlay is rendered as a grayscale image times the RGB triplet in “params.BG”.

Dependencies: APPLYCMAP.

See Also: PLOTINTERPSURFMESH, PLOTSLICESMOV, PLOTSLICESTIMETRACE.

neuro_dot.PlotSlicesTimeTrace(underlay, infoVol=None, params=None, overlay=None, info=None)

PLOTSLICESTIMETRACE Creates an interactive 4D plot, including transverse, sagittal, and coronal slices, in addition to a voxel time trace.

PLOTSLICESTIMETRACE(underlay, infoVol, params, overlay) uses the same basic inputs as the function PLOTSLICES to create an interactive 3-axis plot of a 4D volume, plus an axis for the time trace of the selected voxel.

PLOTSLICESTIMETRACE(…, info) allows the user to input an “info” structure to provide information about the 4D volume’s native framerate. The default framerate is 1 Hz.

Params:

fig_size:

[20, 200, 840, 720] Default figure position vector.

fig_handle:

(none) Specifies a figure to target. If empty, spawns a new figure.

CH:

1 Turns crosshairs on (1) and off (0).

Scale:

(90% max) Maximum value to which image is scaled.

PD:

0 Declares that input image is positive definite.

cbmode:

0 Specifies whether to use custom colorbar axis labels.

cblabels:

([-90% max, 90% max]) Colorbar axis labels. When cbmode==1, min defaults to 0 if PD==1, both default to +/- Scale if supplied. When cbmode==0, then cblabels dictates colorbar axis limits.

cbticks:

(none) When cbmode==1, specifies positions of tick marks on colorbar axis.

slices:

(center frames) Select which slices are displayed. If empty, activates interactive navigation.

slices_type:

‘idx’ Use MATLAB indexing (‘idx’) for slices, or spatial coordinates (‘coord’) as provided by invoVol.

orientation:

‘t’ Select orientation of volume. ‘t’ for transverse, ‘s’ for sagittal.

kernel:

[1] A sampling kernel for the time trace plot. Other options: ‘gaussian’ | ‘cube’ | ‘sphere’.

Note: APPLYCMAP has further options for using “params” to specify parameters for the fusion, scaling, and colormapping process.

Dependencies: APPLYCMAP.

See Also: PLOTINTERPSURFMESH, PLOTSLICES.

neuro_dot.PlotTimeTraceData(data, time, params=None, fig_axes=None, coordinates=[1, 1])

PLOTTIMETRACEDATA A basic time traces plotting function.

PLOTTIMETRACEDATA(data, time) takes a light-level array “data” of the MEAS x TIME format, and plots its time traces.

PLOTTIMETRACEDATA(data, time, params) allows the user to specify parameters for plot creation.

h = PLOTTIMETRACEDATA(…) passes the handles of the plot line objects created.

Params:

fig_size:

[200, 200, 560, 420] Default figure position vector.

fig_handle:

(none) Specifies a figure to target. If empty, spawns a new figure.

xlimits:

‘auto’ Limits of x-axis.

xscale:

‘linear’ Scaling of x-axis.

ylimits:

‘auto’ Limits of y-axis.

yscale:

‘linear’ Scaling of y-axis.

See Also: PLOTTIMETRACEALLMEAS, PLOTTIMETRACEMEAN.

neuro_dot.vol2surf_mesh(Smesh, volume, dim, params=None)

VOL2SURF_MESH Interpolates volumetric data onto a surface mesh.

Smesh = VOL2SURF_MESH(mesh_in, volume, dim) takes the mesh “Smesh” and interpolates the values of the volumetric data “volume” at the mesh’s surface, using the spatial information in “dim”. These values are output as “Smesh”.

Smesh = VOL2SURF_MESH(Smesh, volume, dim, params) allows the user to specify parameters for plot creation.

Params:

OL:

0 If “overlap” data is presented (OL==1), this sets the interpolation method to “nearest”. Default is “linear”.

See Also: PLOTINTERPSURFMESH, GOOD_VOX2VOL, AFFINE3D_IMG.

neuro_dot.check_keys(dict)

CHECK_KEYS Checks if entries in dictionary are mat-objects. If entries are mat objects, todict is called to change them to nested dictionaries

neuro_dot.loadmat(filename)

LOADMAT Loads files with the *.mat extension.

Function written by ‘mergen’ on Stack Overflow: https://stackoverflow.com/questions/7008608/scipy-io-loadmat-nested-structures-i-e-dictionaries

This function should be called instead of direct spio.loadmat as it cures the problem of not properly recovering python dictionaries from mat files.

Loadmat calls the function check_keys to cure all entries which are still mat-objects.

NOTE:This function does not work for .mat -v7.3 files.

neuro_dot.loadmat7p3(filename)

LOADMAT7P3 Loads files with the *.mat extension in the “mat 7.3” format.

Function written by ‘mergen’ on Stack Overflow: https://stackoverflow.com/questions/7008608/scipy-io-loadmat-nested-structures-i-e-dictionaries

This function should be called instead of direct spio.loadmat as it cures the problem of not properly recovering python dictionaries from mat files.

Loadmat7p3 calls the function check_keys to cure all entries which are still mat-objects.

NOTE:This function is to be used for .mat -v7.3 files only.

neuro_dot.LoadVolumetricData(filename, pn, file_type)

LOADVOLUMETRICDATA Loads a volumetric data file

[volume, header] = LOADVOLUMETRICDATA(filename, pn, file_type) loads a file specified by “filename”, path “pn”, and “file_type”, and returns it in two parts: the raw data file “volume”, and the header “header”, containing a number of key-value pairs in 4dfp format.

[volume, header] = LOADVOLUMETRICDATA(filename) supports a full filename input, as long as the extension is included in the file name and matches a supported file type.

Supported File Types/Extensions: ‘.4dfp’ 4dfp, ‘nii’ NIFTI.

NOTE: This function uses the NIFTI_Reader toolbox available on MATLAB Central. This toolbox has been included with NeuroDOT 2.

Dependencies: READ_4DFP_HEADER, READ_NIFTI_HEADER, MAKE_NATIVESPACE_4DFP.

See Also: SAVEVOLUMETRICDATA.

neuro_dot.Make_NativeSpace_4dfp(header_in)

MAKE_NATIVESPACE_4DFP Calculates native space for an incomplete 4dfp header.

header_out = MAKE_NATIVESPACE_4DFP(header_in) checks whether “header_in” contains the “mmppix” and “center” fields (these are not always present in 4dfp files). If either is absent, a default called the “native space” is calculated from the other fields of the volume.

See Also: LOADVOLUMETRICDATA, SAVEVOLUMETRICDATA.

neuro_dot.nifti_4dfp(header_in, img_in, mode)

NIFTI_4DFP Converts between nifti and 4dfp volume and header formats.

Input:

header_in:

‘struct’ of either a nifti or 4dfp header

mode:

flag to indicate direction of conversion between nifti and 4dfp formats

• ‘n’: 4dfp to Nifti

• ‘4’: Nifti to 4dfp

Output:

header_out:

header in Nifti or 4dfp format

neuro_dot.Read_4dfp_Header(filename, pn)

READ_4DFP_HEADER Reads the .ifh header of a 4dfp file.

header = READ_4DFP_HEADER(filename, pn) reads an .ifh text file specified by “filename”, containing a number of key-value pairs. The specific pairs are parsed and stored as fields of the output structure “header”.

See Also: LOADVOLUMETRICDATA.

neuro_dot.SaveVolumetricData(volume, header, filename, pn, file_type)

SAVEVOLUMETRICDATA Saves a volumetric data file.

SAVEVOLUMETRICDATA(volume, header, filename, pn, file_type) saves volumetric data defined by “volume” and “header” into a file specified by “filename”, path “pn”, and “file_type”.

SAVEVOLUMETRICDATA(volume, header, filename) supports a full filename input, as long as the extension is included in the file name and matches a supported file type.

• Supported File Types/Extensions: ‘.4dfp’ 4dfp, ‘.nii’ NIFTI.

Dependencies: WRITE_4DFP_HEADER, NIFTI_4DFP.

See Also: LOADVOLUMETRICDATA, MAKE_NATIVESPACE_4DFP.

neuro_dot.snirf2ndot(filename, pn, save_file=0, output=None, dtype=[])

SNIRF2NDOT takes a file with the ‘snirf’ extension in the SNIRF format and converts it to NeuroDOT formatting.

This function depends on the “Snirf” class from pysnirf2.

Inputs:

Filename:

the name of the file to be converted, followed by the .snirf extension.

Save_file:

flag which determines whether the data will be saved to a ‘mat’ file in NeuroDOT format. (default = 1)

Output:

the filename (without extension) of the .mat file to be saved.

Type:

optional - the only currently acceptable value is “snirf”

Outputs:

data:

NeuroDOT formatted data (# of channels x # of samples)

info:

NeuroDOT formatted metadata “info”

neuro_dot.todict(matobj)

TODICT Recursively constructs from matobjects nested dictionaries

neuro_dot.Write_4dfp_Header(header, filename, pn)

WRITE_4DFP_HEADER Writes a 4dfp header to a .ifh file.

WRITE_4DFP_HEADER(header, filename) writes the input “header” in 4dfp format to an .ifh file specified by “filename”.

See Also: SAVEVOLUMETRICDATA.

neuro_dot.affine3d_img(imgA, infoA, infoB, affine=np.eye(4), interp_type='nearest')

AFFINE3D_IMG Transforms a 3D data set to a new space.

imgB = AFFINE3D_IMG(imgA, infoA, infoB, affine) takes a reconstructed, VOX x TIME image “imgA” and transforms it from its initial voxel space defined by the structure “infoA” into a target voxel space defined by the structure “infoB” and using the transform matrix “affine”. The output is a VOX x TIME matrix “imgB” in the target voxel space.

imgB = AFFINE3D_IMG(imgA, infoA, infoB, affine, interp_type) allows the user to specify an interpolation method for the INTERP3 function that AFFINE3D_IMG uses. Other methods that can be used (input as strings) are ‘nearest’, ‘spline’, and ‘cubic’. The default value is ‘linear’.

See Also: SPECTROSCOPY_IMG, CHANGE_SPACE_COORDS, INTERP3.

neuro_dot.change_space_coords(coord_in, space_info, output_type='coord')

CHANGE_SPACE_COORDS Applies a look-up to change 3D coordinates into a new space.

coord_out = CHANGE_SPACE_COORDS(coord_in, space_info, output_type) takes a set of coordinates “coord_in” of the initial space “output_type”, and converts them into the new space defined by the structure “space_info”, which is then output as “coord_out”.

See Also: AFFINE3D_IMG.

neuro_dot.GoodVox2vol(img, dim)

GOOD_VOX2VOL Transforms a VOX x TIME array into a volume, X x Y x Z x TIME, given a space described by “dim”.

imgvol = GOOD_VOX2VOL(img, dim) reshapes a VOX x TIME array “img” into an X x Y x Z x TIME array “imgvol”, according to the dimensions of the space described by “dim”.

See Also: SPECTROSCOPY_IMG.

neuro_dot.rotate_cap(tpos_in, dTheta)

ROTATE_CAP Rotates the cap in space.

tpos_out = ROTATE_CAP(tpos_in, dTheta) rotates the cap grid given by “tpos_in” by the rotation vector “dTheta” (in degrees) and outputs it as “tpos_out”.

Dependencies: ROTATION_MATRIX.

See Also: PLOTLRMESHES, SCALE_CAP.

neuro_dot.rotation_matrix(direction, theta)

ROTATION_MATRIX Creates a rotation matrix.

rot = ROTATION_MATRIX(direction, theta) generates a rotation matrix “rot” for the vector “direction” given an angle “theta” (in radians).

See Also: ROTATE_CAP.

neuro_dot.detrend_tts(data_in)

DETREND_TTS Performs linear detrending.

data_out = DETREND_TTS(data_in) takes a raw light-level data array “data_in” of the format MEAS x TIME and removes the straight-line fit along the TIME dimension from each measurement, returning it as “data_out”.

See Also: LOGMEAN.

neuro_dot.nextpow2(N)

NEXTPOW2 Finds the next power of 2, given an integer input.

neuro_dot.fft_tts(data, framerate)

FFT_TTS Computes the Fourier Transform of a time-domain input. ftmag = FFT_TTS(data, framerate) takes a data array “data” of the MEAS x TIME format, pads it timewise to the next highest power of two (for better performance), performs the fast Fourier transform of each channel using the built-in MATLAB function FFT, normalizes by the padded time length, and takes the first half of the transformed data (which is the positive half of the frequency domain). The result is output into “ftmag”.

[ftmag, ftdomain] = FFT_TTS(data, framerate) also returns the corresponding normalized frequency domain “ftdomain”, which extends from 0 to the Nyquist frequency, which is calculated from the input “framerate”.

[ftmag, ftdomain, ftpower] = FFT_TTS(data, framerate) also returns the power spectrum, which is the absolute value of the magnitude, squared.

[ftmag, ftdomain, ftpower, ftphase] = FFT_TTS(data, framerate) also returns the phase at each frequency, as calculated by the MATLAB ANGLE function.

Dependencies: NORMALIZE2RANGE_TTS.

See Also: LOGMEAN, FFT, POW2, NEXTPOW2, ANGLE.

neuro_dot.gethem(data, info, sel_type='r2d', value=[10, 16])

GETHEM Calculates the mean across a set of measurements.

hem = GETHEM(data, info) takes a light-level array “data” of the format MEAS x TIME, and using the scan metadata in “info.pairs” averages the measurements for each wavelength present. The result is referred to as the “hem” of a measurement set. If there is a good measurements logical vector present in “info.MEAS.GI”, it will be applied to the data; otherwise, “info.MEAS.GI” will be set to true for all measurements (i.e., all measurements are assumed to be good). The variable “hem” is output in the format WL x TIME.

hem = GETHEM(data, info, sel_type, value) allows the user to set the criteria for determining shallow measurements. “sel_type” can be ‘r2d’, ‘r3d’, or ‘NN’, corresponding to the columns of the “info.pairs” table, and “value” can either take the form of a two-element “[min, max]” vector (for ‘r2d’ and ‘r3d’), or a scalar or vector containing all nearest neighbor numbers to be averaged. By default, this function averages the first nearest neighbor.

See Also: REGCORR, DETREND_TTS.

neuro_dot.highpass(data_in, omegaHz, frate, params=None)

HIGHPASS Applies a zero-phase digital highpass filter. data_out = HIGHPASS(data_in, omegaHz, frate) takes a light-level array “data_in” in the MEAS x TIME format and applies to it a forward-backward zero-phase digital highpass filter at a Nyquist cutoff frequency of “omegaHz * (2 * frate)”, returning it as “data_out”.

This function also removes the linear component of the input data.

See Also: LOWPASS, LOGMEAN, FILTFILT.

neuro_dot.logmean(data_in)

LOGMEAN Computes the log-ratio of raw intensity data.

data_out = LOGMEAN(data_in) takes a light-level data array “data_in” of the format MEAS x TIME, and takes the negative log of each element of a row divided by that row’s average. The result is output into “data_out” in the same MEAS x TIME format.

The formal equation for the LOGMEAN operation is:

Y_{out} = -log(phi_{in} / <phi_{in}>)

If the raw optical data phi is complex (as in the frequency domain case), Y behaves a bit differently. Phi can be defined in terms of Real and Imaginary parts: Phi = Re(Phi) + 1i.*Im(Phi), or in terms of it’s magnitude (A) and phase (theta): Phi = A.*exp(1i.*theta). The temporal average of Phi (what we use for baseline) is best calculated on the Real/Imaginary decription:

Phi_o=<phi>=mean(data_in,2) = A_o*exp(i*(th_o));

Taking the logarithm of complex ratio:

Y_Rytov=-log(phi/<phi>)=-log[A*exp(i*th)/A_o*exp(i*th_o)]

=-[log(A/A_o) + i(th-th_o)];

Y_Rytov_Re=-log(abs(data_in/Phi_o)); Y_Rytov_Im=-angle(data_in/Phi_o);

Though this looks like 1 complex number, these components of Y should not mix, so the imaginary component will be tacked onto the end of the measurement list to keep them separate.

Example: If data = [1, 10, 100; exp(1), 10*exp(1), 100*exp(1)];

then LOGMEAN(data) yields [3.6109, 1.3083, -.9943; 3.6109, 1.3083, -.9943].

See Also: LOWPASS, HIGHPASS.

neuro_dot.lowpass(data_in, omegaHz, frate, params=None)

LOWPASS Applies a zero-phase digital lowpass filter.

data_out = LOWPASS(data_in, omegaHz, frate) takes a light-level array “data_in” in the MEAS x TIME format and applies to it a forward-backward zero-phase digital lowpass filter at a Nyquist cutoff frequency of “omegaHz * (2 * frate)”, returning it as “data_out”.

This function also removes the linear component of the input data.

See Also: HIGHPASS, LOGMEAN.

neuro_dot.regcorr(data_in, info, hem)

REGCORR Performs regression correction by wavelength.

[data_out, R] = REGCORR(data_in, info, hem) takes a light-level data array “data_in” of the format MEAS x TIME, and using the scan metadata in “info.pairs” and a WL x MEAS “hem” array generated by GETHEM, performs a regression correction for each wavelength of the data, which is returned in the MEAS x TIME array “data_out”. The corresponding correlation coefficients for each measurement are returned in “R” as a MEAS x 1 array.

If y_{r} is the signal to be regressed out and y_{in} is a data time trace (either source-detector or imaged), then the output is the least-squares regression:

y_{out} = y_{in} - y_{in}(<y_{in},y_{r}>/|y_{r}|^2).

Additionally, the correlation coefficient is given by:

R = (<y_{in},y_{r}>/(|y_{in}|*|y_{r}|)).

See Also: GETHEM, DETREND_TTS.

neuro_dot.resample_tts(data_in, info_in, omega_resample=1, tol=0.001, framerate=0)

RESAMPLE_TTS Resamples data while maintaining linear signal components.

[data_out, info_out] = RESAMPLE_TTS(data_in, info_in, tHz, tol, framerate) takes a raw light-level data array “data_in” of the format MEAS x TIME, and resamples it (typically downward) to a new frequency using the built-in MATLAB function RESAMPLE. The new sampling frequency is calculated as the ratio of input “omega_resample” divided by “framerate” (both scalars), to within the tolerance specified by “tol”.

This function is needed because the linear signal components, which can be important in other NeuroDOT pipeline calculations, can be inadvertently removed by downsampling using RESAMPLE alone.

Note: This function resamples synch points in addition to data. Be sure to take care that your data and synch points match after running this function! “info.paradigm.init_synchpts” stores the original synch points if you need to restore them.

See Also: DETREND_TTS, RESAMPLE.

neuro_dot.rms_py(rms_input)

RMS_PY Computes the RMS value for each column of a row vector.

For matrices (N x M), rms_py(rms_input) is a row vector containing the RMS value from each column

For complex input, rms_py separates the real and imaginary portions of each column of rms_input, squares them, and then takes the square root of the mean of that value

For real input, the root mean square is calculated as follows:

neuro_dot.calc_NN(info_in, dr)

CALC_NN Calculates the Nearest Neigbor value for all measurement pairs.

Inputs:

info_in:

info structure containing data measurement list

dr:

minimum separation for sources and detectors to be grouped into. (default = 10 mm) NOTE: distances are in millimeters

Outputs:

info_out:

info structure containing updated data measurement list with “info.pairs.NN”

neuro_dot.Generate_pad_from_grid(grid, params, info)

GENERATE_PAD_FROM_GRID Generates info structures “optodes” and “pairs” from a given “grid”.

The input: “grid” must have fields that list the spatial locations of sources and detectors in 3D: spos3, dpos3.

The input: “params” can be used to pass in mod type (default is ‘CW’ but can be the modulation frequency if fd) and wavelength(s) of the data in field ‘lambda’.

Params:

dr:

Minimum separation for sources and detectors to be grouped into different neighbors.

lambda:

Wavelengths of the light. Any number of comma-separated values is allowed. Default: [750,850].

Mod:

Modulation type or frequency. Can be ‘CW or ‘FD’ or can be the actual modulation frequency (e.g., 0 or 200) in MHz.

pos2:

Flag which determines NN classification. Defaults to 0, where 3D coordinates are used. If set to 1, 2D coordinates will be used for NN classification.

CapName:

Name for your pad file.

neuro_dot.makeFlatFieldRecon(A, iA)

MAKEFLATFIELDRECON Generates the flat field reconstruction of a given “A” sensitivity matrix.

Inputs:

A:

“A” sensitivity matrix

iA:

inverted “A” sensitivity matrix

Outputs:

Asens:

flat field reconstruction of a given “A” sensitivity matrix

neuro_dot.DynamicFilter(input_data, info_in, params, mode, save='no', pathToSave='./')

This function is used to perform each step of the NeuroDOT PreProcessing Pipeline.

The input “mode” is used to decide which step of the pipeline to perform, as well as which figures to generate.

Modes:

fft_lml:

display Fourier transform of the logmean light levels

lml:

display time traces of logmean light levels

fft_dt:

display Fourier transform of detrended data

fft_dt_hp:

display Fourier transform of detrended and high-pass filtered data

fft_superficial_signal:

display Fourier transform of data which has undergone superficial signal regression

high_pass:

display high-pass filtered data time traces

low_pass:

display low-pass filtered data time traces

low_pass_fft:

display Fourier transform of low-pass filtered data

fft_lowpass_2:

display Fourier transform of twice-low-pass filtered data

superficial_signal:

display time traces of data which has undergone superficial signal regression

fft_resample:

display Fourier transform of resampled data

resample:

display time traces of resampled data

ba:

display time traces of block-averaged data

fft_ba:

display Fourier transform of block-averaged data

neuro_dot.reconstruct_img(lmdata, iA)

RECONSTRUCT_IMG Performs image reconstruction by wavelength using the inverted A-matrix.

img = RECONSTRUCT_IMG(data, iA) takes the inverted VOX x MEAS sensitivity matrix “iA” and right-multiplies it by the logmeaned MEAS x TIME light-level matrix “lmdata” to reconstruct an image in voxel space.

The image is output in a VOX x TIME matrix “img”.

See Also: TIKHONOV_INVERT_AMAT, SMOOTH_AMAT, SPECTROSCOPY_IMG, FINDGOODMEAS.

neuro_dot.smooth_Amat(iA_in, dim, gsigma)

SMOOTH_AMAT Performs Gaussian smoothing on a sensitivity matrix.

iA_out = SMOOTH_AMAT(iA_in, dim, gsigma) takes the inverted VOX x MEAS sensitivity matrix “iA_in” and performs Gaussian smoothing in the 3D voxel space on each of the concatenated measurement matrices within, returning it as “iA_out”. The user specifies the Gaussian filter half-width “gsigma”.

See Also: TIKHONOV_INVERT_AMAT, RECONSTRUCT_IMG, FINDGOODMEAS.

neuro_dot.spectroscopy_img(cortex_mu_a, E)

SPECTROSCOPY_IMG Completes the Beer-Lambert law from a reconstructed image.

img_out = SPECTROSCOPY_IMG(img_in, E) takes the reconstructed VOX x TIME x WL mua image “img_in” and multiplies it by the inverse of the extinction coefficient matrix “E” to create an output VOX x TIME x HB matrix “img_out”, where HB 1 and 2 are the voxel-space time series images for HbO and HbR, respectively.

See Also: RECONSTRUCT_IMG, AFFINE3D_IMG.

neuro_dot.Tikhonov_invert_Amat(A, lambda1, lambda2)

TIKHONOV_INVERT_AMAT Inverts a sensitivity “A” matrix.

iA = TIKHONOV_INVERT_AMAT(A, lambda1, lambda2) allows the user to specify the values of the “lambda1” and “lambda2” parameters in the inversion calculation. lambda2 for spatially-variant regularization, is optional.

See Also: SMOOTH_AMAT, RECONSTRUCT_IMG, FINDGOODMEAS.

neuro_dot.BlockAverage(data_in, pulse, dt, Tkeep=0)

BLOCKAVERAGE Averages data by stimulus blocks.

data_out = BLOCKAVERAGE(data_in, pulse, dt) takes a data array “data_in” and uses the pulse and dt information to cut that data timewise into blocks of equal length (dt), which are then averaged together and output as “data_out”.

Tkeep is a temporal mask. Any time points with a zero in this vector is set to NaN.

neuro_dot.CalcGVTD(data)

CalcGVTD calculates the Root Mean Square across measurements (log-mean light levels or voxels) of the temporal derivative.

The data is assumed to have measurements in the first and time in the last dimension.

Any selection of measurement type or voxel index must be done outside of this function.

neuro_dot.FindGoodMeas(data, info_in, bthresh=0.075)

FINDGOODMEAS Performs “Good Measurements” analysis to return indices of measurements within a chosen threshold.

info_out = FINDGOODMEAS(data, info_in) takes a light-level array “data” in the MEAS x TIME format, and calculates the std of each channel as its noise level.

These are then thresholded by the default value of 0.075 to create a logical array, and both are returned as MEAS x 1 columns of the “info.MEAS” table.

If pulse synch point information exists in “info.system.synchpts”, then FINDGOODMEAS will crop the data to the start and stop pulses.

info_out = FINDGOODMEAS(data, info_in, bthresh) allows the user to specify a threshold value.

See Also: PLOTCAPGOODMEAS, PLOTHISTOGRAMSTD.

neuro_dot.normcND(data)

NORMCND returns a column-normed matrix. It is assumed that the matrix is 2D.

neuro_dot.normrND(data)

NORMRND returns a row-normed matrix. It is assumed that the matrix is 2D. Updated for broader compatability.