Analysis of the dynamics

This module sets up api functions for dynamical correlation analysis.

tunacell.stats.api.compute_bivariate(row_univariate, col_univariate, size=None)

Computes cross-correlation between observables defiend in univs.

This functions handles conditions and time-window binning:

  • all conditions provided in cset are applied independently, in addition to the computation with unconditioned data (labelled ‘master’)
  • A time-binning window is provided with a given offset and a period. Explicitely a given time value t found in data will be rounded up to closest offset_t + k * delta_t, where k is an integer.
Parameters:
  • univs (couple of Univariate instances) –
  • size (int (default None)) – limit the iterator to size Lineage instances (used for testing)
Returns:

Return type:

TwoObservable instance

tunacell.stats.api.compute_stationary(univ, region, options, size=None)

Computes stationary autocorrelation. API level.

Parameters:
  • univ (Univariate instance) – the stationary autocorr is based on this object
  • region (Region instance) –
  • options (CompuParams instance) – set the ‘adjust_mean’ and ‘disjoint’ options
  • size (int (default None)) – limit number of parsed Lineages
tunacell.stats.api.compute_stationary_bivariate(row_univariate, col_univariate, region, options, size=None)

Computes stationary cross-correlation function from couple of univs

Need to compute stationary univariates as well.

tunacell.stats.api.compute_univariate(exp, obs, region='ALL', cset=[], times=None, size=None)

Computes one-point and two-point functions of statistical analysis.

This functions handles conditions and time-window binning:

  • all conditions provided in cset are applied independently, in addition to the computation with unconditioned data (labelled ‘master’)
  • A time-binning window is provided with a given offset and a period. Explicitely a given time value t found in data will be rounded up to closest offset_t + k * delta_t, where k is an integer.
Parameters:
  • exp (Experiment instance) –
  • obs (Observable instance) –
  • region (Region instance or str (default ‘ALL’)) – in case of str, must be the name of a registered region
  • cset (list of FilterSet instances) –
  • times (1d ndarray, or str (default None)) – array of times at which process is evaluated. Default is to use the ‘ALL’ region with the period taken from experiment metadata. User can opt for a specific time array, or for the label of a region as a string
  • size (int (default None)) – limit the iterator to size Lineage instances (used for testing)
Returns:

Return type:

Univariate instance

tunacell.stats.api.load_bivariate(row_univariate, col_univariate)

Initialize a StationaryBivariate instance from its dynamical one.

Parameters:
  • row_univariate (Univariate instance) –
  • col_univariate (Univariate instance) –
Returns:

set up with empty arrays

Return type:

Bivariate instance

tunacell.stats.api.load_stationary(univ, region, options)

Initialize a StationaryUnivariate instance from its dynamical one.

Parameters:
  • univ (Univariate instance) –
  • region (Region instance) –
  • options (CompuParams instance) –
Returns:

set up with empty arrays

Return type:

StationaryInstance instance

tunacell.stats.api.load_stationary_bivariate(row_univariate, col_univariate, region, options)

Initialize a StationaryBivariate instance from its dynamical one.

Parameters:
  • row_univariate (Univariate instance) –
  • col_univariate (Univariate instance) –
  • region (Region instance) –
  • options (CompuParams instance) –
Returns:

set up with empty arrays

Return type:

StationaryBivariate instance

tunacell.stats.api.load_univariate(exp, obs, region='ALL', cset=[])

Initialize an empty Univariate instance.

Such a Univariate instance is bound to an experiment (through exp), an observable, and a set of conditions.

Parameters:
  • exp (Experiment instance) –
  • obs (Observable instance) –
  • region (Region instance or str (default ‘ALL’)) – in case of str, must be the name of a registered region
  • cset (sequence of FilterSet instances) –
Returns:

initialized, nothing computed yet

Return type:

Univariate instance

Raises:

UnivariateIOError – when importing fails (no data corresponds to input params)