API Documentation¶

This is the API reference for the neurodsp module.

Table of Contents¶

Filtering ¶

Functions and utilities in the filt module, for filtering time series.

General Filter Function ¶

filter_signal(sig, fs, pass_type, f_range[, ...])

Apply a bandpass, bandstop, highpass, or lowpass filter to a neural signal.

FIR Filters ¶

`filter_signal_fir`(sig, fs, pass_type, f_range)	Apply an FIR filter to a signal.
`design_fir_filter`(fs, pass_type, f_range[, ...])	Design an FIR filter.

IIR Filters ¶

`filter_signal_iir`(sig, fs, pass_type, ...[, ...])	Apply an IIR filter to a signal.
`design_iir_filter`(fs, pass_type, f_range, ...)	Design an IIR filter.

Check Filter Properties ¶

`check_filter_definition`(pass_type, f_range)	Check a filter definition for validity, and get filter frequency range of the filter.
`check_filter_properties`(filter_coefs, ...[, ...])	Check a filters properties, including pass band and transition band.

Filter Utilities ¶

`compute_frequency_response`(filter_coefs, ...)	Compute the frequency response of a filter.
`compute_pass_band`(fs, pass_type, f_range)	Compute the pass bandwidth of a filter.
`compute_transition_band`(f_db, db[, low, high])	Compute transition bandwidth of a filter.
`compute_nyquist`(fs)	Compute the Nyquist frequency.
`remove_filter_edges`(sig, filt_len)	Drop the edges, by making NaN, from a filtered signal, to avoid edge artifacts.

Time-Frequency Analyses ¶

Functions and utilities in the timefrequency module, for time-frequency analyses.

Hilbert Methods ¶

`robust_hilbert`(sig)	Compute the Hilbert transform, ignoring any boundaries that are NaN.
`phase_by_time`(sig, fs[, f_range, remove_edges])	Compute the instantaneous phase of a time series.
`amp_by_time`(sig, fs[, f_range, remove_edges])	Compute the instantaneous amplitude of a time series.
`freq_by_time`(sig, fs[, f_range, remove_edges])	Compute the instantaneous frequency of a time series.

Wavelet Methods ¶

`compute_wavelet_transform`(sig, fs, freqs[, ...])	Compute the time-frequency representation of a signal using morlet wavelets.
`convolve_wavelet`(sig, fs, freq[, n_cycles, ...])	Convolve a signal with a complex wavelet.

Spectral Analyses ¶

Functions and utilities in the spectral module, for spectral analyses.

Spectral Power ¶

`compute_spectrum`(sig, fs[, method, avg_type])	Compute the power spectral density of a time series.
`compute_spectrum_welch`(sig, fs[, avg_type, ...])	Compute the power spectral density using Welch's method.
`compute_spectrum_wavelet`(sig, fs, freqs[, ...])	Compute the power spectral density using wavelets.
`compute_spectrum_medfilt`(sig, fs[, ...])	Compute the power spectral density as a smoothed FFT.

Spectral Measures ¶

`compute_absolute_power`(freqs, powers, band)	Compute absolute power for a given frequency band.
`compute_relative_power`(freqs, powers, band)	Compute relative power for a given frequency band.
`compute_band_ratio`(freqs, powers, low_band, ...)	Calculate band ratio measure between two predefined frequency ranges.

Spectral Variance ¶

`compute_scv`(sig, fs[, window, nperseg, ...])	Compute the spectral coefficient of variation (SCV) at each frequency.
`compute_scv_rs`(sig, fs[, window, nperseg, ...])	Compute a resampled version of the spectral coefficient of variation (SCV).
`compute_spectral_hist`(sig, fs[, window, ...])	Compute the distribution of log10 power at each frequency from the signal spectrogram.

Spectral Utilities ¶

`trim_spectrum`(freqs, power_spectra, f_range)	Extract a frequency range of interest from power spectra.
`trim_spectrogram`(freqs, times, spg[, ...])	Extract a frequency or time range of interest from a spectrogram.

Burst Detection ¶

Functions and utilities in the burst module, for detection bursts in time series.

Burst Detection Algorithms ¶

detect_bursts_dual_threshold(sig, fs, ...[, ...])

Detect bursts in a signal using the dual threshold algorithm.

Burst Utilities ¶

compute_burst_stats(bursting, fs)

Compute statistics of bursts.

Rhythm Analyses ¶

Functions and utilities in the rhythm module, for finding and analyzing rhythmic and recurring patterns in time series.

Sliding Window Matching ¶

sliding_window_matching(sig, fs, win_len, ...)

Find recurring patterns in a time series using the sliding window matching algorithm.

Lagged Coherence ¶

compute_lagged_coherence(sig, fs, freqs[, ...])

Compute lagged coherence, reflecting the rhythmicity across a frequency range.

Aperiodic Analyses ¶

Functions and utilities in the aperiodic module, for analyzing aperiodic activity in time series.

Auto-correlation Analyses ¶

compute_autocorr(sig[, max_lag, lag_step, ...])

Compute the signal autocorrelation (lagged correlation).

Fluctuation Analyses ¶

`compute_fluctuations`(sig, fs[, n_scales, ...])	Compute a fluctuation analysis on a signal.
`compute_rescaled_range`(sig, win_len)	Compute rescaled range of a given time series at a given scale.
`compute_detrended_fluctuation`(sig, win_len)	Compute detrended fluctuation of a time series at the given window length.

Signal Decomposition ¶

`compute_irasa`(sig, fs[, f_range, hset, thresh])	Separate aperiodic and periodic components using IRASA.
`fit_irasa`(freqs, psd_aperiodic)	Fit the IRASA derived aperiodic component of the spectrum.

Conversions ¶

`convert_exponent_alpha`(exponent)	Convert a powerlaw exponent to the expected DFA alpha value.
`convert_alpha_exponent`(alpha)	Convert a DFA alpha value to the expected powerlaw exponent.
`convert_exponent_hurst`(exponent, ...)	Convert a powerlaw exponent to the expected Hurst exponent value.
`convert_hurst_exponent`(hurst, fractional_class)	Convert a Hurst exponent value to the expected powerlaw exponent.
`convert_exponent_hfd`(exponent)	Convert exponent to expected Higuchi fractal dimension value.
`convert_hfd_exponent`(hfd)	Convert Higuchi fractal dimension value to expected 1/f exponent value.

Simulations ¶

Functions and utilities in the sim module, for simulating time series.

Periodic Signals ¶

`sim_oscillation`(n_seconds, fs, freq[, ...])	Simulate an oscillation.
`sim_bursty_oscillation`(n_seconds, fs, freq)	Simulate a bursty oscillation.
`sim_variable_oscillation`(n_seconds, fs, freqs)	Simulate an oscillation that varies in frequency and cycle parameters.
`sim_damped_oscillation`(n_seconds, fs, freq, ...)	Simulate a damped relaxation oscillation.

Aperiodic Signals ¶

`sim_powerlaw`(n_seconds, fs[, exponent, f_range])	Simulate a power law time series, with a specified exponent.
`sim_poisson_pop`(n_seconds, fs[, n_neurons, ...])	Simulate a Poisson population.
`sim_synaptic_current`(n_seconds, fs[, ...])	Simulate a signal as a synaptic current, which has 1/f characteristics with a knee.
`sim_knee`(n_seconds, fs, exponent1, ...)	Simulate a signal whose power spectrum has a 1/f structure with a knee.
`sim_random_walk`(n_seconds, fs[, theta, mu, ...])	Simulate a mean-reverting random walk, as an Ornstein-Uhlenbeck process.
`sim_frac_gaussian_noise`(n_seconds, fs[, ...])	Simulate a timeseries as fractional gaussian noise.
`sim_frac_brownian_motion`(n_seconds, fs[, ...])	Simulate a timeseries as fractional brownian motion.

Transients ¶

`sim_synaptic_kernel`(n_seconds, fs, tau_r, tau_d)	Simulate a synaptic kernel with specified time constants.
`sim_action_potential`(n_seconds, fs, centers, ...)	Simulate an action potential as the sum of skewed gaussians.
`sim_damped_erp`(n_seconds, fs, amp, freq[, ...])	Simulate an ERP complex as a decaying (damped) sine wave.

Cycles ¶

`sim_cycle`(n_seconds, fs, cycle_type[, phase])	Simulate a single cycle of a periodic pattern.
`sim_sine_cycle`(n_seconds, fs)	Simulate a cycle of a sine wave.
`sim_asine_cycle`(n_seconds, fs, rdsym[, side])	Simulate a cycle of an asymmetric sine wave.
`sim_sawtooth_cycle`(n_seconds, fs, width)	Simulate a cycle of a sawtooth wave.
`sim_gaussian_cycle`(n_seconds, fs, std[, center])	Simulate a cycle of a gaussian.
`sim_skewed_gaussian_cycle`(n_seconds, fs, ...)	Simulate a cycle of a skewed gaussian.
`sim_exp_cos_cycle`(n_seconds, fs, exp[, ...])	Simulate an exponential cosine cycle.
`sim_asym_harmonic_cycle`(n_seconds, fs, phi, ...)	Simulate an asymmetrical cycle as a sum of harmonics.

Combined Signals ¶

`sim_combined`(n_seconds, fs, components[, ...])	Simulate a signal by combining multiple component signals.
`sim_peak_oscillation`(sig_ap, fs, freq, bw, ...)	Simulate a signal with an aperiodic component and a specific oscillation peak.
`sim_modulated_signal`(n_seconds, fs, ...)	Simulate an amplitude modulated signal.

Utilities ¶

`rotate_spectrum`(freqs, spectrum, delta_exponent)	Rotate the power law exponent of a power spectrum.
`rotate_timeseries`(sig, fs, delta_exp[, ...])	Rotate a timeseries of data, changing it's 1/f exponent.
`modulate_signal`(sig, modulation[, fs, ...])	Apply amplitude modulation to a signal.

Random Seed ¶

set_random_seed([seed_val])

Set the random seed value.

Plots ¶

Functions in the plts module, for plotting time series and analysis outputs.

Time Series ¶

`plot_time_series`(times, sigs[, labels, ...])	Plot a time series.
`plot_instantaneous_measure`(times, sigs[, ...])	Plot an instantaneous measure, of phase, amplitude or frequency.
`plot_bursts`(times, sig, bursting[, ax])	Plot a time series, with labeled bursts.

Spectral ¶

`plot_power_spectra`(freqs, powers[, labels, ...])	Plot power spectra.
`plot_scv`(freqs, scv[, ax])	Plot spectral coefficient of variation.
`plot_scv_rs_lines`(freqs, scv_rs[, ax])	Plot spectral coefficient of variation, from the resampling method, as lines.
`plot_scv_rs_matrix`(freqs, t_inds, scv_rs[, ax])	Plot spectral coefficient of variation, from the resampling method, as a matrix.
`plot_spectral_hist`(freqs, power_bins, ...[, ...])	Plot spectral histogram.

Filter ¶

`plot_filter_properties`(f_db, db, fs, ...[, ...])	Plot filter properties, including frequency response and filter kernel.
`plot_frequency_response`(f_db, db[, ax])	Plot the frequency response of a filter.
`plot_impulse_response`(fs, impulse_response)	Plot the impulse response of a filter.

Rhythm ¶

`plot_swm_pattern`(pattern[, ax])	Plot the resulting pattern from a sliding window matching analysis.
`plot_lagged_coherence`(freqs, lcs[, ax])	Plot lagged coherence values across frequencies.

Time Frequency ¶

plot_timefrequency(times, freqs, powers[, ...])

Plot a time-frequency representation of data.

Utilities ¶

Functions in the utils module, providing general utilities.

Normalization ¶

`normalize_sig`(sig[, mean, variance])	Normalize the mean and variance of a signal.
`demean`(array[, mean])	Demean an array, updating to specified mean.
`normalize_variance`(array[, variance])	Normalize the variance of an array, updating to specified variance.

Data ¶

`create_freqs`(freq_start, freq_stop[, freq_step])	Create an array of frequencies.
`create_times`(n_seconds, fs[, start_val])	Create an array of time indices.
`create_samples`(n_samples[, start_val])	Create an array of sample indices.
`compute_nsamples`(n_seconds, fs)	Calculate the number of samples for a given time definition.
`compute_nseconds`(sig, fs)	Compute the length, in time, of a signal.
`compute_cycle_nseconds`(freq[, fs])	Compute the length, in seconds, for a single cycle at a particular frequency.
`split_signal`(sig, n_samples)	Split a signal into non-overlapping segments.

Outliers ¶

`remove_nans`(sig)	Drop any NaNs on the edges of an array.
`restore_nans`(sig, sig_nans[, dtype])	Restore NaN values to the edges of an array.
`discard_outliers`(data, outlier_percent)	Discard outlier arrays with high values.