MAINT: use functools for memoization

- replace `@cached_var` with `@functools.lru_cache`
  and `@cached_const` with `@functools.cached_property`
- refactor calls of any `cached_property`, now not callable anymore,
  substitute with `self.set_link_attribute(property)` where necessary
- remove `cache_helper` and `cache` attribute in `core.network`
- adapt `clear_cache()` methods accordingly,
  introduce `clear_cache_startswith()`
- remove `Network.clear_link_attribute()`
- adapt `Network.{del|set}_link_attribute()` accordingly,
  drop clearing cache of `path_lengths()` in `set_link_attribute()`
- related issue: #148

- locally disable pylint false positive `not-callable` on
  `multiprocess.Pool()`

src/pyunicorn/climate/climate_network.py

@@ -20,6 +20,9 @@
 #  Import essential packages
 #
 
+# Import decorator for memoization
+from functools import cached_property
+
 #  Import NumPy for the array object and fast numerics
 import numpy as np
 
@@ -28,7 +31,6 @@
 
 #  Import GeoNetwork and GeoGrid classes
 from ..core import GeoNetwork, GeoGrid
-from ..core.network import cached_const
 
 
 #
@@ -626,7 +628,7 @@ def set_link_density(self, link_density):
         threshold = self.threshold_from_link_density(link_density)
         self.set_threshold(threshold)
 
-    @cached_const('base', 'correlation_distance')
+    @cached_property
     def correlation_distance(self):
         """
         Return correlation weighted distances between nodes.
@@ -658,14 +660,14 @@ def correlation_distance(self):
         """
         return self.similarity_measure() * self.grid.angular_distance()
 
-    @cached_const('base', 'inv_correlation_distance')
+    @cached_property
     def inv_correlation_distance(self):
         """
         Return correlation weighted distances between nodes.
 
         :rtype: 2D matrix [index, index]
         """
-        m = self.correlation_distance()
+        m = self.correlation_distance
         np.fill_diagonal(m, np.inf)
         self.set_link_attribute('inv_correlation_distance', 1 / m)
         return 1 / m
@@ -694,7 +696,8 @@ def correlation_distance_weighted_closeness(self):
         :rtype: 1D Numpy array [index]
         :return: the correlation distance weighted closeness sequence.
         """
-        self.inv_correlation_distance()
+        self.set_link_attribute(
+            'inv_correlation_distance', self.inv_correlation_distance)
         return self.closeness('inv_correlation_distance')
 
     def local_correlation_distance_weighted_vulnerability(self):
@@ -717,5 +720,6 @@ def local_correlation_distance_weighted_vulnerability(self):
         :return: the local correlation distance weighted vulnerability
                  sequence.
         """
-        self.inv_correlation_distance()
+        self.set_link_attribute(
+            'inv_correlation_distance', self.inv_correlation_distance)
         return self.local_vulnerability('inv_correlation_distance')

src/pyunicorn/climate/tsonis.py

@@ -20,12 +20,14 @@
 #  Import essential packages
 #
 
+# Import decorator for memoization
+from functools import cached_property
+
 #  Import NumPy for the array object and fast numerics
 import numpy as np
 
 from .climate_network import ClimateNetwork
 from .climate_data import ClimateData
-from ..core.network import cached_const
 
 
 #
@@ -197,7 +199,7 @@ def calculate_similarity_measure(self, anomaly):
         """
         return self._calculate_correlation(anomaly)
 
-    @cached_const('base', 'correlation')
+    @cached_property
     def correlation(self):
         """
         Return the correlation matrix at zero lag.
@@ -289,7 +291,7 @@ def correlation_weighted_average_path_length(self):
 
         :return float: the correlation weighted average path length.
         """
-        self.correlation()
+        self.set_link_attribute('correlation', self.correlation)
         return self.average_path_length('correlation')
 
     def correlation_weighted_closeness(self):
@@ -305,7 +307,7 @@ def correlation_weighted_closeness(self):
         :rtype: 1D Numpy array [index]
         :return: the correlation weighted closeness sequence.
         """
-        self.correlation()
+        self.set_link_attribute('correlation', self.correlation)
         return self.closeness('correlation')
 
     def local_correlation_weighted_vulnerability(self):
@@ -321,5 +323,5 @@ def local_correlation_weighted_vulnerability(self):
         :rtype: 1D Numpy array [index]
         :return: the correlation weighted vulnerability sequence.
         """
-        self.correlation()
+        self.set_link_attribute('correlation', self.correlation)
         return self.local_vulnerability('correlation')

src/pyunicorn/core/__init__.py

@@ -38,7 +38,7 @@
 #  Import classes
 #
 
-from .network import Network, NetworkError, nz_coords, cached_const
+from .network import Network, NetworkError, nz_coords
 from .spatial_network import SpatialNetwork
 from .geo_network import GeoNetwork
 from .grid import Grid

src/pyunicorn/core/interacting_networks.py

@@ -768,7 +768,7 @@ def internal_global_clustering(self, node_list):
         :return float: the internal global clustering coefficient for a
             subnetwork.
         """
-        clustering = self.local_clustering()
+        clustering = self.local_clustering
         internal_clustering = clustering[node_list].mean()
         return internal_clustering