climate.spearman

Provides classes for generating and analyzing complex climate networks.

class pyunicorn.climate.spearman.SpearmanClimateNetwork(data, threshold=None, link_density=None, non_local=False, node_weight_type='surface', winter_only=True, silence_level=0)[source]

Bases: TsonisClimateNetwork

Encapsulate a Spearman climate network.

The Spearman climate network is constructed from the Spearman rank order correlation matrix (Spearman’s rho). Spearman’s rho is more robust with respect to ouliers and non-gaussian data distributions than the Pearson correlation coefficient used in TsonisClimateNetwork.

Hence, Spearman climate networks are undirected due to the symmetry of the Spearman’s rho matrix.

__init__(data, threshold=None, link_density=None, non_local=False, node_weight_type='surface', winter_only=True, silence_level=0)[source]

Initialize an instance of SpearmanClimateNetwork.

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 (ClimateData) – The climate data used for network construction.

  • 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.

  • winter_only (bool) – Determines, whether only data points from the winter months (December, January and February) should be used for analysis. Possibly, this further suppresses the annual cycle in the time series.

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

__str__()[source]

Returns a string representation of SpearmanClimateNetwork.

_calculate_correlation(anomaly)[source]

Return Spearman’s rho matrix at zero lag.

Parameters:

anomaly (2D Numpy array (time, index)) – the anomaly time series from to calculate the correlation matrix at zero lag.

Return type:

2D Numpy array (index, index)

Returns:

the Spearman’s rho matrix at zero lag.

static rank_time_series(anomaly)[source]

Return rank time series.

Ranks are generated individually for each time series.

Parameters:

anomaly (2D Numpy array [time, index]) – The anomaly time series to be converted into ranks.

Return type:

2D Numpy array [time, index]

Returns:

the rank time series.