ntrfc.timeseries package

Submodules

ntrfc.timeseries.integral_scales module

ntrfc.timeseries.integral_scales.get_self_correlating_frequencies(timesteps, signal)

Calculate the self-correlating frequencies in a signal.

Parameters:

• timesteps (ndarray) – An array of uniformly spaced timesteps, corresponding to the time intervals between samples in the signal.

• signal (ndarray) – The input signal to analyze.

Returns:

frequencies – An array of self-correlating frequencies, in units of inverse timesteps.

Return type:

ndarray

Notes

This function calculates the autocorrelation of the input signal, and finds the local maxima in the autocorrelation signal. These local maxima correspond to the self-correlating frequencies in the signal.

ntrfc.timeseries.integral_scales.integralscales(signal, timesteparray)

ntrfc.timeseries.stationarity module

ntrfc.timeseries.stationarity.estimate_error_jacknife(timeseries, block_size=20, n_samples=4000)

Estimates the errors of the mean, variance, and autocorrelation of a given time series using jackknife resampling method.

Parameters:

• timeseries (array-like) – The input time series.

• block_size (int, optional) – The block size used in the jackknife resampling method (default is 20).

• n_samples (int, optional) – The number of jackknife samples to generate (default is 4000).

Returns:

A tuple of three floats representing the error estimates of the mean, variance, and autocorrelation, respectively.

Return type:

tuple

Notes

The jackknife resampling method is used to estimate the errors of the mean, variance, and autocorrelation of the

input time series.

The function generates n_samples jackknife samples by randomly selecting blocks from the time series calculates

the mean, variance, and autocorrelation of each jackknife sample.

It also generates n_samples noise samples with the same block size as the original time series and calculates the

mean, variance, and autocorrelation of each noise sample.

The standard deviation of the jackknife estimates for mean, variance, and autocorrelation are calculated, and each

is multiplied by a factor of 16 to obtain the final error estimates.

Choosing an appropriate block size is crucial for obtaining reliable and accurate estimates of the errors of the

mean, variance, and autocorrelation of a given time series using the jackknife resampling method.

ntrfc.timeseries.stationarity.estimate_stationarity(timeseries, verbose=False)

ntrfc.timeseries.stationarity.optimal_bin_width(sample1, sample2)

Compute the optimal bin width using cross-validation estimator for mean integrated squared error.

Parameters:

datanumpy array

One-dimensional array of data points.

Returns:

hfloat

Optimal bin width.

ntrfc.timeseries.stationarity.optimal_window_size(time_series, min_interval=0.05, max_interval=0.25, verbose=False)

Determines the optimal window size for a given time series.

Parameters:

time_seriesarray-like

The time series to analyze.

verbosebool, optional

If True, a plot of the correlation coefficient and KS test results for each window size will be displayed.

Returns:

int or bool

The optimal window size for the time series. If no suitable window size is found, False is returned.

Notes:

The function normalizes the input time series using the minmax_normalize() function. The window size is chosen based on a cumulative score that takes into account the correlation coefficient and

KS test p-value.

The function returns False if no suitable window size is found, meaning the input time series does not exhibit the

necessary periodicity.

ntrfc.timeseries.stationarity.smd_probability_compare(sample1, sample2, verbose=False)

Compare the probability distribution of two signals using the Freedman-Diaconis rule to determine the number of bins.

Parameters:

• sample1 (numpy.ndarray) – First signal.

• sample2 (numpy.ndarray) – Second signal.

Returns:

Mean squared error between the probability distribution densities

of the two signals. A value of 0 indicates that the probability distributions are not alike, while a value of 1 indicates that they are equal.

Return type:

float

Module contents