climate.coupled_tsonis

Provides classes for generating and analyzing complex coupled climate networks.

class pyunicorn.climate.coupled_tsonis.CoupledTsonisClimateNetwork(data_1, data_2, threshold=None, link_density=None, non_local=False, node_weight_type='surface', selected_months=None, silence_level=0)[source]

Bases: CoupledClimateNetwork

Encapsulates a coupled similarity network embedded on a spherical surface.

Particularly provides functionality to generate a complex network from the Pearson correlation matrix of time series from two different observables (temperature, pressure), vertical levels etc.

Construct a static climate network following Tsonis et al. from the Pearson correlation matrix at zero lag [Tsonis2004].

Hence, coupled Tsonis climate networks are undirected due to the symmetry of the correlation matrix.

The idea of coupled climate networks is based on the concept of coupled patterns, for a review refer to [Bretherton1992].

Note

The two observables (layers) need to have the same time grid (temporal sampling points).

__init__(data_1, data_2, threshold=None, link_density=None, non_local=False, node_weight_type='surface', selected_months=None, silence_level=0)[source]

Initialize an instance of CoupledTsonisClimateNetwork.

Note

Either threshold OR link_density have to be given!

Possible choices for node_weight_type:

  • None (constant unit weights)

  • "surface" (cos lat)

  • "irrigation" (cos**2 lat)

Parameters:

  • data_1 (ClimateData) – The climate data for the first layer.

  • data_2 (ClimateData) – The climate data for the second layer.

  • threshold (float) – The threshold of similarity measure, above which two nodes are linked in the network.

  • link_density (float) – The networks’s desired link density.

  • non_local (bool) – Determines, whether links between spatially close nodes should be suppressed.

  • node_weight_type (str) – The type of geographical node weight to be used.

  • ]selected_months ([int) – The months for which to calculate the correlation matrix. The full time series are used for default value None.

  • silence_level (int) – The inverse level of verbosity of the object.

__str__()[source]

Return a string representation of CoupledClimateNetwork object.

_calculate_correlation(anomaly_1, anomaly_2)[source]

Return the correlation matrix at zero lag.

Parameters:

  • anomaly_1 (2D Numpy array (time, index_1)) – the first set of anomaly time series from which to calculate the correlation matrix at zero lag.

  • anomaly_2 (2D Numpy array (time, index_2)) – the second set of anomaly time series from which to calculate the correlation matrix at zero lag.

Return type:

2D Numpy array (index, index)

Returns:

the correlation matrix at zero lag.

calculate_similarity_measure(anomaly_1, anomaly_2)[source]

Encapsulate the calculation of the correlation matrix at zero lag.

Parameters:

  • anomaly_1 (2D Numpy array (time, index_1)) – the first set of anomaly time series from which to calculate the correlation matrix at zero lag.

  • anomaly_2 (2D Numpy array (time, index_2)) – the second set of anomaly time series from which to calculate the correlation matrix at zero lag.

Return type:

2D Numpy array (index, index)

Returns:

the correlation matrix at zero lag.

correlation()[source]

Return the coupled correlation matrix at zero lag.

Return type:

2D Numpy array (index, index)

Returns:

the correlation matrix at zero lag.

silence_level: int

(string) - The inverse level of verbosity of the object.