Class Definitions
The Python port preserves the MATLAB-facing class names as canonical imports or compatibility adapters. Each class below lists its key public methods grouped by category.
Signal and Covariate Primitives
SignalObj (nstat.SignalObj)
Primary notebook: notebooks/SignalObjExamples.ipynb
Construction and metadata:
copySignal, setName, setXlabel, setYLabel, setUnits, setXUnits,
setYUnits, setSampleRate, setDataLabels, setPlotProps, with_metadata
Accessors:
getTime, getData, getOriginalData, getOrigDataSig, getPlotProps,
getValueAt, getSubSignalFromInd, getSubSignalFromNames, getSubSignal,
findNearestTimeIndex, findNearestTimeIndices, dataToMatrix,
dataToStructure, toStructure, signalFromStruct
Masking:
setDataMask, setMaskByInd, setMaskByLabels, setMask, resetMask,
restoreToOriginal, findIndFromDataMask, isMaskSet
Time windowing:
setMinTime, setMaxTime, getSigInTimeWindow, makeCompatible
Math and transforms:
abs, log, power, sqrt, median, mode, mean, std, max, min,
derivative, derivativeAt, integral, resample, resampleMe,
filter, filtfilt, merge
Shift and alignment:
shift, shiftMe, alignTime
Correlation:
autocorrelation, crosscorrelation, xcorr, xcov
Spectral analysis:
periodogram, MTMspectrum, spectrogram
Peak-finding:
findPeaks, findMaxima, findMinima, findGlobalPeak
Plotting:
plot
Covariate (nstat.Covariate)
Primary notebook: notebooks/CovariateExamples.ipynb
Inherits from SignalObj. Adds confidence interval support:
setConfInterval, mu, sigma
ConfidenceInterval (nstat.ConfidenceInterval)
Primary notebook: notebooks/ConfidenceIntervalOverview.ipynb
CovColl (nstat.CovColl)
Primary notebook: notebooks/CovCollExamples.ipynb
Collection management:
add, addCovariate, addCovCollection, addToColl, removeCovariate,
copy, get, getCov, getCovIndFromName, getCovIndicesFromNames,
isCovPresent
Time and sample rate:
findMinTime, findMaxTime, findMaxSampleRate, setMinTime, setMaxTime,
restrictToTimeWindow, setSampleRate, resample, enforceSampleRate
Masking:
resetMask, getCovDataMask, isCovMaskSet, flattenCovMask,
getSelectorFromMasks, generateSelectorCell, setMasksFromSelector,
setMask, nActCovar, maskAwayCov, maskAwayOnlyCov, maskAwayAllExcept
Shift and restore:
setCovShift, resetCovShift, restoreToOriginal
Data export:
getAllCovLabels, getCovLabelsFromMask, matrixWithTime, dataToMatrix,
dataToStructure, toStructure, fromStructure
Spiking Data Structures
nspikeTrain (nstat.nspikeTrain)
Primary notebook: notebooks/nSpikeTrainExamples.ipynb
nstColl (nstat.nstColl)
Primary notebook: notebooks/nstCollExamples.ipynb
Collection management:
addSingleSpikeToColl, addToColl, merge, length, get_nst, getNST,
getNSTnames, getUniqueNSTnames, toSpikeTrain
Time operations:
shiftTime, setMinTime, setMaxTime
Data export:
dataToMatrix, resample
PSTH:
psth, psthGLM, estimateVarianceAcrossTrials, psthBars
SSGLM (state-space GLM):
ssglm, ssglmFB
Basis generation:
generateUnitImpulseBasis
Plotting:
plot
History (nstat.History)
Primary notebook: notebooks/HistoryExamples.ipynb
Events (nstat.Events)
Primary notebook: notebooks/EventsExamples.ipynb
Experiment and Configuration Objects
Trial (nstat.Trial)
Primary notebook: notebooks/TrialExamples.ipynb
Partitioning:
getTrialPartition, setTrialPartition, setTrialTimesFor,
updateTimePartitions
Time and sample rate:
setMinTime, setMaxTime, setSampleRate, resample,
makeConsistentSampleRate, makeConsistentTime, isSampleRateConsistent,
findMaxSampleRate, findMinTime, findMaxTime
Covariate and neuron masks:
setCovMask, resetCovMask, setNeuronMask, resetNeuronMask,
setNeighbors, setHistory, resetHistory, setEnsCovHist, setEnsCovMask,
resetEnsCovMask, isNeuronMaskSet, isCovMaskSet, isMaskSet, isHistSet,
isEnsCovHistSet
Data access:
addCov, removeCov, getSpikeVector, get_covariate_matrix,
getDesignMatrix, getHistForNeurons, getHistMatrices,
getEnsembleNeuronCovariates, getEnsCovMatrix, getNeuronIndFromMask,
getNumUniqueNeurons, getNeuronNames, getUniqueNeuronNames,
getNeuronIndFromName, getAllCovLabels, getCovLabelsFromMask,
getHistLabels, getEnsCovLabels, getLabelsFromMask, flattenCovMask,
flattenMask
Utilities:
shiftCovariates, resampleEnsColl, restoreToOriginal, getAllLabels,
plot
TrialConfig (nstat.TrialConfig)
Primary notebook: notebooks/TrialConfigExamples.ipynb
ConfigColl (nstat.ConfigColl)
Primary notebook: notebooks/ConfigCollExamples.ipynb
Modeling and Inference
CIF (nstat.CIF)
Primary notebook: notebooks/PPSimExample.ipynb
Conditional intensity function (CIF) primitives and point-process simulation via thinning.
Analysis (nstat.Analysis)
Primary notebook: notebooks/AnalysisExamples.ipynb
Core fitting:
GLMFit, run_analysis_for_neuron, run_analysis_for_all_neurons,
RunAnalysisForNeuron, RunAnalysisForAllNeurons
PSTH:
psth
Diagnostics:
computeKSStats, computeInvGausTrans, computeFitResidual,
KSPlot, plotFitResidual, plotInvGausTrans, plotSeqCorr, plotCoeffs
History and model selection:
computeHistLag, computeHistLagForAll, compHistEnsCoeff,
compHistEnsCoeffForAll
Network and Granger causality:
computeGrangerCausalityMatrix, computeNeighbors, spikeTrigAvg
FitResult (nstat.FitResult)
Primary notebook: notebooks/FitResultExamples.ipynb
Coefficient access:
getCoeffs, getHistCoeffs, getCoeffIndex, getHistIndex, getParam,
getCoeffsWithLabels, computePlotParams, getPlotParams
Lambda (conditional intensity) access:
lambdaSignal, lambda_obj, lambda_model, lambda_result, lambdaObj,
lambdaCov, lambda_sig, lambda_data, lambda_values, lambda_time,
lambda_rate, evalLambda
Diagnostics and statistics:
computeKSStats, computeInvGausTrans, computeFitResidual
Plotting:
plotResults, KSPlot, plotResidual, plotInvGausTrans, plotSeqCorr,
plotCoeffs, plotCoeffsWithoutHistory, plotHistCoeffs, plotValidation
Serialization:
toStructure, fromStructure, CellArrayToStructure, mergeResults
FitResSummary (nstat.FitResSummary)
Primary notebook: notebooks/FitResSummaryExamples.ipynb
Alias of FitSummary. Aggregates multiple FitResult objects.
Information criterion:
getDiffAIC, getDiffBIC, getDifflogLL
Coefficient extraction:
getCoeffs, getHistCoeffs, getSigCoeffs, binCoeffs, setCoeffRange,
mapCovLabelsToUniqueLabels
Plotting:
plotIC, plotAIC, plotBIC, plotlogLL, plotResidualSummary,
plotSummary, boxPlot, plotAllCoeffs, plot3dCoeffSummary,
plot2dCoeffSummary, plotKSSummary
DecodingAlgorithms (nstat.DecodingAlgorithms)
Primary notebook: notebooks/DecodingExample.ipynb
Point-process decode filters:
PPDecodeFilterLinear, PPDecodeFilter, PPHybridFilterLinear,
ComputeStimulusCIs, computeSpikeRateCIs
Kalman and unscented Kalman filters:
kalman_filter, PP_fixedIntervalSmoother, ukf
Helper methods:
PPDecode_predict, PPDecode_updateLinear
State-space GLM (SSGLM) — EM algorithm:
PPSS_EStep, PPSS_MStep, PPSS_EM, PPSS_EMFB
SSGLM utilities:
estimateInfoMat, prepareEMResults
See Examples for the full help-style index and API Reference for the module layout.