timescales.fit.ACF

class timescales.fit.ACF(lags=None, corrs=None, fs=None)[source]

Autocorrelation function class.

Parameters
lags1d array

Time lag definitions.

corrs1d or 2d array

Autocorrelation coefficients.

fsfloat

Sampling rate, in Hz.

Notes

Parameters may be set on initialization or using the compute_acf method.

Attributes
corrs_fit1d array

Autocorrelation full fit.

params1d array

Optimized parameters.

param_nameslist of str

Parameter names in order of params.

rsqfloat

R-squared of the full fit.

guesslist, optional, default: None

Estimated parameters as either: [tau, height, offset] when with_cos is False, or [exp_tau, osc_tau, osc_gamma, osc_freq, amp_ratio, height, offset].

boundslist, optional, default: None

Parameters bounds as [(*lower_bounds), (*upper_bounds)].

__init__(lags=None, corrs=None, fs=None)[source]

Initialize object.

Methods

__init__([lags, corrs, fs])

Initialize object.

compute_acf(sig, fs[, nlags, normalize, ...])

Compute autocorrelation.

fit([lags, corrs, gen_fits, gen_components, ...])

Fit without an oscillitory component.

gen_corrs_fit([gen_components])

Generate fit and r-squared.

plot([ax, title])

Plot ACF.
compute_acf(sig, fs, nlags=None, normalize=True, from_psd=False, psd_kwargs=None, n_jobs=- 1, progress=None)[source]

Compute autocorrelation.

Parameters
sig1d or 2d array

Voltage time series or spike counts.

fsfloat

Sampling rate, in Hz.

nlagsint, optional, default: None

Number of lags to compute. None defaults to the sampling rate, fs.

normalizebool, optional, default: True

Normalizes from zero to one when True.

from_psdbool, optional, default: False

Compute correlations from the inverse FFT of the PSD.

psd_kwargsdict, optional, default: None

Compute spectrum kwargs. Only used if from_psd is True.

n_jobsint, optional, default: -1

Number of jobs to run in parralel, when corrs is 2d. Default is equal to multiprocessing’s cpu_count().

progress{None, ‘tqdm’, ‘tqdm.notebook’}

Specify whether to display a progress bar. Uses ‘tqdm’, if installed.

fit(lags=None, corrs=None, gen_fits=True, gen_components=False, with_cos=False, guess=None, bounds=None, maxfev=1000, n_jobs=- 1, progress=None)[source]

Fit without an oscillitory component.

Parameters
lags1d array

Time lag definitions.

corrs1d or 2d array

Autocorrelation coefficients.

gen_fitsbool, optional, default: False

Generates fit array and r-squared when True. Does not generate full fits when False to prevent OOM.

gen_componentsbool, optional, default: False

When gen_fits and with_cos are True, the exponential decay and cosine components are be generated separately when this parameter is True.

with_cosbool, optional, default: False

Includes oscillatory component as a damped cosine.

guesslist, optional, default: None

Estimated parameters as either: [tau, height, offset] when with_cos is False, or [exp_tau, osc_tau, osc_gamma, osc_freq, amp_ratio, height, offset].

boundslist, optional, default: None

Parameters bounds as [(*lower_bounds), (*upper_bounds)].

maxfevint

Maximum number of fitting iterations.

n_jobsint

Number of jobs to run in parralel, when corrs is 2d.

progress{None, ‘tqdm’, ‘tqdm.notebook’}

Specify whether to display a progress bar. Uses ‘tqdm’, if installed.

gen_corrs_fit(gen_components=False)[source]

Generate fit and r-squared.

Parameters
gen_componentsbool, optional, default: True

Generates oscillatory and exponential components separately, in additon to combined, when True.

plot(ax=None, title=None)[source]

Plot ACF.

Parameters
axAxesSubplot, optional, default: None

Axis to plot on.