Characterize

class pydaddy.Characterize(data, t=1.0, Dt=1, dt=1, bins=None, inc=None, inc_x=None, inc_y=None, slider_timescales=None, n_trials=1, show_summary=True, drift_threshold=None, diff_threshold=None, drift_degree=5, diff_degree=5, drift_alpha=0, diff_alpha=0, fit_functions=False, **kwargs)

Bases: object

Analyse a time series data and get drift and diffusion plots.

Parameters

  • data (list) – time series data to be analysed, data = [x] for scalar data and data = [x1, x2] for vector where x, x1 and x2 are of numpy.array object type

  • t (float, array, optional(default=1.0)) –

    float if its time increment between observation

    numpy.array if time stamp of time series

  • Dt (int,'auto', optional(default='auto')) –

    time scale for drift

    if ‘auto’ time scale is decided based of drift order.

  • dt (int, optional(default=1)) – time scale for difusion

  • inc (float, optional(default=0.01)) – increment in order parameter for scalar data

  • inc_x (float, optional(default=0.1)) – increment in order parameter for vector data x1

  • inc_y (float, optional(default=0.1)) – increment in order parameter for vector data x2

  • fft (bool, optional(default=True)) – if true use fft method to calculate autocorrelation else, use standard method

  • slider_timescales (list, optional(default=None)) – List of timescale values to include in slider.

  • n_trials (int, optional(default=1)) – Number of trials, concatenated timeseries of multiple trials is used.

  • show_summary (bool, optional(default=True)) – print data summary and show summary chart.

  • **kwargs – all the parameters for inherited methods.

Returns

output – object to access the analysed data, parameters, plots and save them.

Return type

pydaddy.output.Output