climate.mutual_info¶
Provides classes for generating and analyzing complex climate networks.
- class pyunicorn.climate.mutual_info.MutualInfoClimateNetwork(data, threshold=None, link_density=None, non_local=False, node_weight_type='surface', winter_only=True, silence_level=0)[source]¶
Bases:
ClimateNetwork
Represents a mutual information climate network.
Constructs a static climate network based on mutual information at zero lag, as in [Ueoka2008].
Mutual information climate networks are undirected, since mutual information is a symmetrical measure. In contrast to Pearson correlation used in
TsonisClimateNetwork
, mutual information has the potential to detect nonlinear statistical interdependencies.- __cache_state__() Tuple[Hashable, ...] [source]¶
Hashable tuple of mutable object attributes, which will determine the instance identity for ALL cached method lookups in this class, in addition to the built-in object id(). Returning an empty tuple amounts to declaring the object immutable in general. Mutable dependencies that are specific to a method should instead be declared via @Cached.method(attrs=(…)).
NOTE: A subclass is responsible for the consistency and cost of this state descriptor. For example, hashing a large array attribute may be circumvented by declaring it as a property, with a custom setter method that increments a dedicated mutation counter.
- __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 MutualInfoClimateNework.
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.
- Possible choices for
- __rec_cache_state__() Tuple[object, ...] [source]¶
Similar to __cache_state__(), but lists attributes which are themselves instances of Cached. Empty by default.
- _cython_calculate_mutual_information(anomaly, n_bins=32)[source]¶
Calculate the mutual information matrix at zero lag.
The cython code is adopted from the Tisean 3.0.1 mutual.c module.
- Parameters:
anomaly (2D Numpy array (time, index)) – The anomaly time series.
n_bins (int) – The number of bins for estimating probability distributions.
fast (bool) – Indicates, whether fast or slow algorithm should be used.
- Return type:
2D array (index, index)
- Returns:
the mutual information matrix at zero lag.
- _set_winter_only(winter_only, dump=False)[source]¶
Toggle use of exclusively winter data points for network generation.
- Parameters:
winter_only (bool) – Indicates whether only winter months were used for network generation.
dump (bool) – Store MI in data file.
- calculate_similarity_measure(anomaly)[source]¶
Calculate the mutual information matrix.
Encapsulates calculation of mutual information with standard parameters.
- Parameters:
anomaly (2D Numpy array (time, index)) – The anomaly time series.
- Return type:
2D Numpy array (index, index)
- Returns:
the mutual information matrix at zero lag.
- data: ClimateData¶
The climate data used for network construction.
- local_mutual_information_weighted_vulnerability()[source]¶
Return mutual information weighted vulnerability.
- Return type:
1D Numpy array [index]
- Returns:
the mutual information weighted vulnerability sequence.
- mi_file¶
(string) - The name of the file for storing the mutual information matrix.
- mutual_information(anomaly=None, dump=True)[source]¶
Return mutual information matrix at zero lag.
- Check if mutual information matrix (MI) was already calculated before:
If yes, return MI from a data file.
If not, return MI from calculation and store in file.
- Parameters:
anomaly (2D Numpy array (time, index)) – The anomaly time series.
dump (bool) – Store MI in data file.
- Return type:
2D Numpy array (index, index)
- Returns:
the mutual information matrix at zero lag.
- mutual_information_weighted_average_path_length()[source]¶
Return mutual information weighted average path length.
- Return float:
the mutual information weighted average path length.
- mutual_information_weighted_closeness()[source]¶
Return mutual information weighted closeness.
- Return type:
1D Numpy array [index]
- Returns:
the mutual information weighted closeness sequence.
- set_winter_only(winter_only, dump=True)[source]¶
Toggle use of exclusively winter data points for network generation.
Also explicitly regenerates the instance of MutualInfoClimateNetwork.
- Parameters:
winter_only (bool) – Indicates whether only winter months were used for network generation.
dump (bool) – Store MI in data file.