The timesignal module

class pylife.stress.timesignal.TimeSignalGenerator(sample_rate, sine_set, gauss_set, log_gauss_set)

Generates mixed time signals

The generated time signal is a mixture of random sets of

• sinus signals
• gauss signals (not yet)
• log gauss signals (not yet)

For each set the user supplys a dict describing the set:

sinus_set = {
    'number': number of signals
    'amplitude_median':
    'amplitude_std_dev':
    'frequency_median':
    'frequency_std_dev':
    'offset_median':
    'offset_std_dev':
}

The amplitudes (\(A\)), fequencies (\(\omega\)) and offsets (\(c\)) are then norm distributed. Each sinus signal looks like

\(s = A \sin(\omega t + \phi) + c\)

where \(phi\) is a random value between 0 and \(2\pi\).

So the whole sinus \(S\) set is given by the following expression:

\(S = \sum^n_i A_i \sin(\omega_i t + \phi_i) + c_i\).

query(sample_num)

Gets a sample chunk of the time signal

Parameters:sample_num (int) – number of the samples requested Returns:samples – the requested samples Return type:1D numpy.ndarray

You can query multiple times, the newly delivered samples will smoothly attach to the previously queried ones.

reset()

Resets the generator

A resetted generator behaves like a new generator.

class pylife.stress.timesignal.TimeSignalPrep(df)

butter_bandpass(lowcut, highcut, fs, order=5)

Use the functonality of scipy

resample_acc(sample_rate_new=1)

Resampling the time series

Parameters:

• self (DataFrame) –
• time_col (str) – column name of the time column
• sample_rate_new (float) – sample rate of the resampled time series

Returns:

Return type:

DataFrame

running_stats_filt(col, window_length=2048, buffer_overlap=0.1, limit=0.05, method='rms')

Calculates the running statistics of one DataFrame column and drops the rejected data points from the whole DataFrame.

Attention: Reset_index is used

Parameters:

• self (DataFrame) –
• col (str) – column name of the signal for the runnings stats calculation
• window_length (int) – window length of the single time snippet, default is 2048
• buffer_overlap (float) – overlap parameter, 0.1 is equal to 10 % overlap of every buffer, default is 0.1
• limit (float) – limit value of skipping values, 0.05 for example skips all values which buffer method parameter is lower than 5% of the total max value, default is 0.05
• method (str) – method: ‘rms’, ‘min’, ‘max’, ‘abs’, default is ‘rms’

Returns:

Return type:

DataFrame