diff --git a/dev/classes_idtxl.png b/dev/classes_idtxl.png index a17adede..8200e100 100644 Binary files a/dev/classes_idtxl.png and b/dev/classes_idtxl.png differ diff --git a/docs/doctrees/environment.pickle b/docs/doctrees/environment.pickle index de5ac3f8..299d9755 100644 Binary files a/docs/doctrees/environment.pickle and b/docs/doctrees/environment.pickle differ diff --git a/docs/doctrees/idtxl.doctree b/docs/doctrees/idtxl.doctree index 27d64c1d..96f6b0b3 100644 Binary files a/docs/doctrees/idtxl.doctree and b/docs/doctrees/idtxl.doctree differ diff --git a/docs/doctrees/idtxl_data_class.doctree b/docs/doctrees/idtxl_data_class.doctree index a6a4fc40..03ca570b 100644 Binary files a/docs/doctrees/idtxl_data_class.doctree and b/docs/doctrees/idtxl_data_class.doctree differ diff --git a/docs/doctrees/idtxl_estimators.doctree b/docs/doctrees/idtxl_estimators.doctree index ab40de08..fa56c64a 100644 Binary files a/docs/doctrees/idtxl_estimators.doctree and b/docs/doctrees/idtxl_estimators.doctree differ diff --git a/docs/doctrees/idtxl_results_class.doctree b/docs/doctrees/idtxl_results_class.doctree index 393896ed..552ddf56 100644 Binary files a/docs/doctrees/idtxl_results_class.doctree and b/docs/doctrees/idtxl_results_class.doctree differ diff --git a/docs/html/_modules/idtxl/active_information_storage.html b/docs/html/_modules/idtxl/active_information_storage.html index f7384a2c..fd3e7029 100644 --- a/docs/html/_modules/idtxl/active_information_storage.html +++ b/docs/html/_modules/idtxl/active_information_storage.html @@ -13,7 +13,6 @@ - @@ -679,7 +678,7 @@

Navigation

\ No newline at end of file diff --git a/docs/html/_modules/idtxl/bivariate_mi.html b/docs/html/_modules/idtxl/bivariate_mi.html index 78b2a098..1739d369 100644 --- a/docs/html/_modules/idtxl/bivariate_mi.html +++ b/docs/html/_modules/idtxl/bivariate_mi.html @@ -13,7 +13,6 @@ - @@ -316,7 +315,7 @@

Source code for idtxl.bivariate_mi

         # Main algorithm.
         print('\n---------------------------- (1) include source candidates')
         self._include_source_candidates(data)
-        print('\n---------------------------- (2) prune cadidates')
+        print('\n---------------------------- (2) prune candidates')
         self._prune_candidates(data)
         print('\n---------------------------- (3) final statistics')
         self._test_final_conditional(data)
@@ -389,7 +388,7 @@ 
Navigation
\ No newline at end of file diff --git a/docs/html/_modules/idtxl/bivariate_te.html b/docs/html/_modules/idtxl/bivariate_te.html index 95813ff7..f12fa53d 100644 --- a/docs/html/_modules/idtxl/bivariate_te.html +++ b/docs/html/_modules/idtxl/bivariate_te.html @@ -13,7 +13,6 @@ - @@ -321,7 +320,7 @@

Source code for idtxl.bivariate_te

         self._include_target_candidates(data)
         print('\n---------------------------- (2) include source candidates')
         self._include_source_candidates(data)
-        print('\n---------------------------- (3) prune cadidates')
+        print('\n---------------------------- (3) prune candidates')
         self._prune_candidates(data)
         print('\n---------------------------- (4) final statistics')
         self._test_final_conditional(data)
@@ -393,7 +392,7 @@ 
Navigation
\ No newline at end of file diff --git a/docs/html/_modules/idtxl/data.html b/docs/html/_modules/idtxl/data.html index c6675941..908ec465 100644 --- a/docs/html/_modules/idtxl/data.html +++ b/docs/html/_modules/idtxl/data.html @@ -13,7 +13,6 @@ - @@ -80,7 +79,7 @@

Source code for idtxl.data

         >>> data_2.set_data(data_new, 's')
 
     Note:
-        Realisations are stored as attribute 'data'. This can only be set via 
+        Realisations are stored as attribute 'data'. This can only be set via
         the 'set_data()' method.
 
     Args:
@@ -874,9 +873,9 @@ 
Source code for idtxl.data
 
         Generate example data and overwrite the instance's current data. The
         network is used as an example the paper on the MuTE toolbox (Montalto,
-        PLOS ONE, 2014, eq. 14). The network consists of five autoregressive
-        (AR) processes with model orders 2 and the following (non-linear)
-        couplings:
+        PLOS ONE, 2014, eq. 14) and was orginially proposed by Baccala &
+        Sameshima (2001). The network consists of five autoregressive (AR)
+        processes with model orders 2 and the following (non-linear) couplings:
 
         0 -> 1, u = 2 (non-linear)
         0 -> 2, u = 3
@@ -884,6 +883,16 @@ 
Source code for idtxl.data
         3 -> 4, u = 1
         4 -> 3, u = 1
 
+        References:
+
+        - Montalto, A., Faes, L., & Marinazzo, D. (2014) MuTE: A MATLAB toolbox
+          to compare established and novel estimators of the multivariate
+          transfer entropy. PLoS ONE 9(10): e109462.
+          https://doi.org/10.1371/journal.pone.0109462
+        - Baccala, L.A. & Sameshima, K. (2001). Partial directed coherence: a
+          new concept in neural structure determination. Biol Cybern 84:
+          463–474. https://doi.org/10.1007/PL00007990
+
         Args:
             n_samples : int
                 number of samples simulated for each process and replication
@@ -1115,7 +1124,7 @@ 
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\ No newline at end of file
diff --git a/docs/html/_modules/idtxl/estimator.html b/docs/html/_modules/idtxl/estimator.html
index 3ab7bd6c..8023a685 100644
--- a/docs/html/_modules/idtxl/estimator.html
+++ b/docs/html/_modules/idtxl/estimator.html
@@ -13,7 +13,6 @@
     
     
     
-    
     
     
 
@@ -416,7 +415,7 @@ 
Navigation

     

     
   
 
\ No newline at end of file
diff --git a/docs/html/_modules/idtxl/estimators_jidt.html b/docs/html/_modules/idtxl/estimators_jidt.html
index b7696e70..122d105b 100644
--- a/docs/html/_modules/idtxl/estimators_jidt.html
+++ b/docs/html/_modules/idtxl/estimators_jidt.html
@@ -13,7 +13,6 @@
     
     
     
-    
     
     
 
@@ -1851,7 +1850,7 @@ 
Navigation
\ No newline at end of file diff --git a/docs/html/_modules/idtxl/estimators_opencl.html b/docs/html/_modules/idtxl/estimators_opencl.html index 1b7a3f29..2e76e0f5 100644 --- a/docs/html/_modules/idtxl/estimators_opencl.html +++ b/docs/html/_modules/idtxl/estimators_opencl.html @@ -13,7 +13,6 @@ - @@ -149,6 +148,10 @@

Source code for idtxl.estimators_opencl

             raise RuntimeError('No OpenCL GPU device found.')
         my_gpu_devices = platform.get_devices(device_type=cl.device_type.GPU)
         context = cl.Context(devices=my_gpu_devices)
+        if gpuid > len(my_gpu_devices)-1:
+            raise RuntimeError(
+                'No device with gpuid {0} (available device IDs: {1}).'.format(
+                    gpuid, np.arange(len(my_gpu_devices))))
         queue = cl.CommandQueue(context, my_gpu_devices[gpuid])
         if self.settings['debug']:
             print("Selected Device: ", my_gpu_devices[gpuid].name)
@@ -814,7 +817,7 @@ 
Navigation
\ No newline at end of file diff --git a/docs/html/_modules/idtxl/estimators_pid.html b/docs/html/_modules/idtxl/estimators_pid.html index 1c638115..d54cd7d4 100644 --- a/docs/html/_modules/idtxl/estimators_pid.html +++ b/docs/html/_modules/idtxl/estimators_pid.html @@ -13,7 +13,6 @@ - @@ -51,7 +50,6 @@

Source code for idtxl.estimators_pid

 
 Bertschinger, N., Rauh, J., Olbrich, E., Jost, J., & Ay, N. (2014). Quantifying
 Unique Information. Entropy, 16(4), 2161–2183. http://doi.org/10.3390/e16042161
-
 """
 import numpy as np
 from . import synergy_tartu
@@ -63,9 +61,9 @@ 
Source code for idtxl.estimators_pid
 
[docs]class SydneyPID(Estimator):
     """Estimate partial information decomposition of discrete variables.
 
-    Fast implementation of the partial information decomposition (PID)
-    estimator for discrete data. The estimator does not require JAVA or GPU
-    modules to run.
+    Fast implementation of the BROJA partial information decomposition (PID)
+    estimator for discrete data (Bertschinger, 2014). The estimator does not
+    require JAVA or GPU modules to run.
 
     The estimator finds shared information, unique information and
     synergistic information between the two inputs s1 and s2 with respect to
@@ -80,6 +78,12 @@ 
Source code for idtxl.estimators_pid
     CMI decreases; and an outer loop which decreases the size of the
     probability mass increment the virtualised swapping utilises.
 
+    References
+
+    - Bertschinger, N., Rauh, J., Olbrich, E., Jost, J., & Ay, N. (2014).
+      Quantifying unique information. Entropy, 16(4), 2161–2183.
+      http://doi.org/10.3390/e16042161
+
     Args:
         settings : dict
             estimation parameters
@@ -701,7 +705,7 @@ 
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\ No newline at end of file
diff --git a/docs/html/_modules/idtxl/idtxl_exceptions.html b/docs/html/_modules/idtxl/idtxl_exceptions.html
index f153ab9d..26f76dbb 100644
--- a/docs/html/_modules/idtxl/idtxl_exceptions.html
+++ b/docs/html/_modules/idtxl/idtxl_exceptions.html
@@ -13,7 +13,6 @@
     
     
     
-    
     
     
 
@@ -121,7 +120,7 @@ 
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\ No newline at end of file
diff --git a/docs/html/_modules/idtxl/idtxl_io.html b/docs/html/_modules/idtxl/idtxl_io.html
index a536042a..47573b92 100644
--- a/docs/html/_modules/idtxl/idtxl_io.html
+++ b/docs/html/_modules/idtxl/idtxl_io.html
@@ -13,7 +13,6 @@
     
     
     
-    
     
     
 
@@ -52,6 +51,7 @@ 
Source code for idtxl.idtxl_io
 # import json
 import pickle
 import h5py
+import networkx as nx
 import numpy as np
 import copy as cp
 import itertools as it
@@ -421,7 +421,7 @@ 
Source code for idtxl.idtxl_io
     available (see documentation of method get_adjacency_matrix for details).
 
     Args:
-        adjacency_matrix : 2D numpy array
+        adjacency_matrix : AdjacencyMatrix instances
             adjacency matrix to be exported, returned by get_adjacency_matrix()
             method of Results() class
         weights : str
@@ -435,9 +435,9 @@ 
Source code for idtxl.idtxl_io
     """
     # use 'weights' parameter (string) as networkx edge property name and use
     # adjacency matrix entries as edge property values
-    custom_type = [(weights, type(adjacency_matrix[0, 0]))]
-    custom_npmatrix = np.matrix(adjacency_matrix, dtype=custom_type)
-    return nx.from_numpy_matrix(custom_npmatrix, create_using=nx.DiGraph())

+    G = nx.DiGraph()
+    G.add_weighted_edges_from(adjacency_matrix.get_edge_list(), weights)
+    return G

 
 
 
[docs]def export_networkx_source_graph(results, target, sign_sources=True, fdr=True):
@@ -456,7 +456,7 @@ 
Source code for idtxl.idtxl_io
         target : int
             target index
         sign_sources : bool [optional]
-            add only sources with significant information contribution
+            add sources with significant information contribution only
             (default=True)
         fdr : bool [optional]
             return FDR-corrected results (default=True)
@@ -540,7 +540,7 @@ 
Source code for idtxl.idtxl_io
       https://doi.org/10.1371/journal.pone.0068910
 
     Args:
-        adjacency_matrix : 2D numpy array
+        adjacency_matrix : AdjacencyMatrix instance
             adjacency matrix to be exported, returned by get_adjacency_matrix()
             method of Results() class
         mni_coord : numpy array
@@ -562,15 +562,13 @@ 
Source code for idtxl.idtxl_io
     """
     # Check input and get default settings for plotting. The default for
     # node labels is a list of '-' (no labels).
-    n_nodes = adjacency_matrix.shape[0]
-    n_edges = np.sum(adjacency_matrix > 0)
+    n_nodes = adjacency_matrix.n_nodes()
+    n_edges = adjacency_matrix.n_edges()
     labels = kwargs.get('labels', ['-' for i in range(n_nodes)])
     node_color = kwargs.get('node_color', np.ones(n_nodes))
     node_size = kwargs.get('node_size', np.ones(n_nodes))
     if n_edges == 0:
         Warning('No edges in results file. Nothing to plot.')
-    assert adjacency_matrix.shape[0] == adjacency_matrix.shape[1], (
-        'Adjacency matrix must be quadratic.')
     assert mni_coord.shape[0] == n_nodes and mni_coord.shape[1] == 3, (
         'MNI coordinates must have shape [n_nodes, 3].')
     assert len(labels) == n_nodes, (
@@ -597,7 +595,7 @@ 
Source code for idtxl.idtxl_io
     with open('{0}.edge'.format(file_name), 'w') as text_file:
         for i in range(n_nodes):
             for j in range(n_nodes):
-                print('{0}\t'.format(adjacency_matrix[i, j]),
+                print('{0}\t'.format(adjacency_matrix._edge_matrix[i, j]),
                       file=text_file, end='')
             print('', file=text_file)

 

@@ -638,7 +636,7 @@ 
Navigation

     

     
   
 
\ No newline at end of file
diff --git a/docs/html/_modules/idtxl/idtxl_utils.html b/docs/html/_modules/idtxl/idtxl_utils.html
index bbfbf5f7..528e42e8 100644
--- a/docs/html/_modules/idtxl/idtxl_utils.html
+++ b/docs/html/_modules/idtxl/idtxl_utils.html
@@ -13,7 +13,6 @@
     
     
     
-    
     
     
 
@@ -399,7 +398,7 @@ 
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\ No newline at end of file
diff --git a/docs/html/_modules/idtxl/multivariate_mi.html b/docs/html/_modules/idtxl/multivariate_mi.html
index fcdb30ad..3029dfdd 100644
--- a/docs/html/_modules/idtxl/multivariate_mi.html
+++ b/docs/html/_modules/idtxl/multivariate_mi.html
@@ -13,7 +13,6 @@
     
     
     
-    
     
     
 
@@ -387,7 +386,7 @@ 
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\ No newline at end of file
diff --git a/docs/html/_modules/idtxl/multivariate_te.html b/docs/html/_modules/idtxl/multivariate_te.html
index 82aabf1b..4094d2b7 100644
--- a/docs/html/_modules/idtxl/multivariate_te.html
+++ b/docs/html/_modules/idtxl/multivariate_te.html
@@ -13,7 +13,6 @@
     
     
     
-    
     
     
 
@@ -392,7 +391,7 @@ 
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\ No newline at end of file
diff --git a/docs/html/_modules/idtxl/network_analysis.html b/docs/html/_modules/idtxl/network_analysis.html
index 90050068..4d054693 100644
--- a/docs/html/_modules/idtxl/network_analysis.html
+++ b/docs/html/_modules/idtxl/network_analysis.html
@@ -13,7 +13,6 @@
     
     
     
-    
     
     
 
@@ -349,7 +348,8 @@ 
Source code for idtxl.network_analysis
             cond = self.settings['add_conditionals']
             if type(cond) is tuple:  # easily add single variable
                 cond = [cond]
-            candidate_set = list(set(candidate_set).difference(set(cond)))
+            cond_idx = self._lag_to_idx(cond)            
+            candidate_set = list(set(candidate_set).difference(set(cond_idx)))
         return candidate_set
 
     def _append_selected_vars_idx(self, idx):
@@ -583,7 +583,7 @@ 
Navigation

     

     
   
 
\ No newline at end of file
diff --git a/docs/html/_modules/idtxl/network_comparison.html b/docs/html/_modules/idtxl/network_comparison.html
index ba9bbecd..d10184a7 100644
--- a/docs/html/_modules/idtxl/network_comparison.html
+++ b/docs/html/_modules/idtxl/network_comparison.html
@@ -13,7 +13,6 @@
     
     
     
-    
     
     
 
@@ -175,8 +174,8 @@ 
Source code for idtxl.network_comparison
             settings=self.settings,
             union_network=self.union,
             results={
-                'cmi_diff_abs': {link_a[1]: [np.abs(self.cmi_diff)],
-                                 link_b[1]: [np.abs(self.cmi_diff)]},
+                'cmi_diff_abs': {link_a[1]: np.abs(self.cmi_diff),
+                                 link_b[1]: np.abs(self.cmi_diff)},
                 'a>b': {link_a[1]: [te_a > te_b], link_b[1]: [te_a > te_b]},
                 'pval': {link_a[1]: [pvalue], link_b[1]: [pvalue]},
                 'cmi_surr': self.cmi_surr,
@@ -1063,7 +1062,7 @@ 
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\ No newline at end of file
diff --git a/docs/html/_modules/idtxl/network_inference.html b/docs/html/_modules/idtxl/network_inference.html
index e24fa783..0f8b1d93 100644
--- a/docs/html/_modules/idtxl/network_inference.html
+++ b/docs/html/_modules/idtxl/network_inference.html
@@ -13,7 +13,6 @@
     
     
     
-    
     
     
 
@@ -279,8 +278,8 @@ 
Source code for idtxl.network_inference
                 self.settings['max_lag_sources'] < 0):
             raise RuntimeError('max_lag_sources has to be an integer >= 0.')
         if (type(self.settings['tau_sources']) is not int or
-                self.settings['tau_sources'] < 1):
-            raise RuntimeError('tau_sources must be an integer > 0.')
+                self.settings['tau_sources'] < 0):
+            raise RuntimeError('tau_sources must be an integer >= 0.')
         if self.settings['min_lag_sources'] > self.settings['max_lag_sources']:
             raise RuntimeError('min_lag_sources ({0}) must be smaller or equal'
                                ' to max_lag_sources ({1}).'.format(
@@ -378,7 +377,7 @@ 
Source code for idtxl.network_inference
                                '(''max_lag_sources'') needs to be specified.')
         if 'min_lag_sources' not in self.settings:
             raise RuntimeError('The minimum lag for source embedding '
-                               '(''max_lag_sources'') needs to be specified.')
+                               '(''min_lag_sources'') needs to be specified.')
         self.settings.setdefault('max_lag_target', settings['max_lag_sources'])
 
         if (type(self.settings['min_lag_sources']) is not int or
@@ -391,8 +390,8 @@ 
Source code for idtxl.network_inference
                 self.settings['max_lag_target'] <= 0):
             raise RuntimeError('max_lag_target must be an integer > 0.')
         if (type(self.settings['tau_sources']) is not int or
-                self.settings['tau_sources'] < 1):
-            raise RuntimeError('tau_sources must be an integer > 0.')
+                self.settings['tau_sources'] < 0):
+            raise RuntimeError('tau_sources must be an integer >= 0.')
         if (type(self.settings['tau_target']) is not int or
                 self.settings['tau_target'] < 1):
             raise RuntimeError('tau_sources must be an integer > 0.')
@@ -572,7 +571,7 @@ 
Source code for idtxl.network_inference
                     # For now we don't need a stack trace:
                     # traceback.print_tb(aee.__traceback__)
                     break
-                    
+
                 # Test max CMI for significance with maximum statistics.
                 te_max_candidate = max(temp_te)
                 max_candidate = candidate_set[np.argmax(temp_te)]
@@ -720,7 +719,7 @@ 
Source code for idtxl.network_inference
                     # For now we don't need a stack trace:
                     # traceback.print_tb(aee.__traceback__)
                     break
-                
+
                 # Find variable with minimum MI/TE. Test min TE/MI for
                 # significance with minimum statistics. Build conditioning set
                 # for minimum statistics by removing the minimum candidate.
@@ -759,7 +758,7 @@ 
Source code for idtxl.network_inference
                     # For now we don't need a stack trace:
                     # traceback.print_tb(aee.__traceback__)
                     break
-                
+
                 # Remove the minimum it is not significant and test the next
                 # min. candidate. If the minimum is significant, break. All
                 # other sources will be significant as well (b/c they have
@@ -1105,7 +1104,7 @@ 
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diff --git a/docs/html/_modules/idtxl/partial_information_decomposition.html b/docs/html/_modules/idtxl/partial_information_decomposition.html
index ee39baf8..83ad622e 100644
--- a/docs/html/_modules/idtxl/partial_information_decomposition.html
+++ b/docs/html/_modules/idtxl/partial_information_decomposition.html
@@ -13,7 +13,6 @@
     
     
     
-    
     
     
 
@@ -389,7 +388,7 @@ 
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\ No newline at end of file
diff --git a/docs/html/_modules/idtxl/results.html b/docs/html/_modules/idtxl/results.html
index 576c8d6c..9bae6705 100644
--- a/docs/html/_modules/idtxl/results.html
+++ b/docs/html/_modules/idtxl/results.html
@@ -13,7 +13,6 @@
     
     
     
-    
     
     
 
@@ -45,10 +44,15 @@ 
Navigation

             
   
Source code for idtxl.results
 """Provide results class for IDTxl network analysis."""
+import sys
+import warnings
 import copy as cp
 import numpy as np
 from . import idtxl_utils as utils
 
+warnings.simplefilter(action='ignore', category=FutureWarning)
+MIN_INT = -sys.maxsize - 1  # minimum integer for initializing adj. matrix
+
 
 
[docs]class DotDict(dict):
     """Dictionary with dot-notation access to values.
@@ -95,6 +99,70 @@ 
Source code for idtxl.results
         # self.__dict__ = self
 
 
+
[docs]class AdjacencyMatrix():
+    """Adjacency matrix representing inferred networks."""
+    def __init__(self, n_nodes, weight_type):
+        self._edge_matrix = np.zeros((n_nodes, n_nodes), dtype=bool)
+        self._weight_matrix = np.zeros((n_nodes, n_nodes), dtype=weight_type)
+        if np.issubdtype(weight_type, np.integer):
+            self._weight_type = np.integer
+        elif np.issubdtype(weight_type, np.float):
+            self._weight_type = np.float
+        elif weight_type is bool:
+            self._weight_type = weight_type
+        else:
+            raise RuntimeError('Unknown weight data type {0}.'.format(
+                weight_type))
+
+
[docs]    def n_nodes(self):
+        """Return number of nodes."""
+        return self._edge_matrix.shape[0]

+
+
[docs]    def n_edges(self):
+        return self._edge_matrix.sum()

+
+
[docs]    def add_edge(self, i, j, weight):
+        """Add weighted edge (i, j) to adjacency matrix."""
+        if not np.issubdtype(type(weight), self._weight_type):
+            raise TypeError(
+                'Can not add weight of type {0} to adjacency matrix of type '
+                '{1}.'.format(type(weight), self._weight_type))
+        self._edge_matrix[i, j] = True
+        self._weight_matrix[i, j] = weight

+
+
[docs]    def add_edge_list(self, i_list, j_list, weights):
+        """Add multiple weighted edges (i, j) to adjacency matrix."""
+        if len(i_list) != len(j_list):
+            raise RuntimeError(
+                'Lists with edge indices must be of same length.')
+        if len(i_list) != len(weights):
+            raise RuntimeError(
+                'Edge weights must have same length as edge indices.')
+        for i, j, weight in zip(i_list, j_list, weights):
+            self.add_edge(i, j, weight)

+
+
[docs]    def print_matrix(self):
+        """Print weight and edge matrix."""
+        print(self._edge_matrix)
+        print(self._weight_matrix)

+
+
[docs]    def get_edge_list(self):
+        """Return list of weighted edges.
+
+        Returns
+            list of tuples
+                each entry represents one edge in the graph: (i, j, weight)
+        """
+        edge_list = np.zeros(self.n_edges(), dtype=object)  # list of tuples
+        ind = 0
+        for i in range(self.n_nodes()):
+            for j in range(self.n_nodes()):
+                if self._edge_matrix[i, j]:
+                    edge_list[ind] = (i, j, self._weight_matrix[i, j])
+                    ind += 1
+        return edge_list

+
+
 
[docs]class Results():
     """Parent class for results of network analysis algorithms.
 
@@ -127,19 +195,15 @@ 
Source code for idtxl.results
 
     def _print_edge_list(self, adjacency_matrix, weights):
         """Print edge list to console."""
-        link_found = False
-        for s in range(self.data_properties.n_nodes):
-            for t in range(self.data_properties.n_nodes):
-                if adjacency_matrix[s, t]:
-                    link_found = True
-                    if weights == 'binary':
-                        print('\t{0} -> {1}'.format(
-                            s, t, weights, adjacency_matrix[s, t]))
-                    else:
-                        print('\t{0} -> {1}, {2}: {3}'.format(
-                            s, t, weights, adjacency_matrix[s, t]))
-
-        if not link_found:
+        edge_list = adjacency_matrix.get_edge_list()
+        if edge_list.size > 0:
+            for e in edge_list:
+                if weights == 'binary':
+                    print('\t{0} -> {1}'.format(e[0], e[1]))
+                else:
+                    print('\t{0} -> {1}, {2}: {3}'.format(
+                        e[0], e[1], weights, e[2]))
+        else:
             print('No significant links found in the network.')
 
     def _check_result(self, process, settings):
@@ -229,12 +293,12 @@ 
Source code for idtxl.results
     e.g., estimation of active information storage.
 
     Note that for convenience all dictionaries in this class can additionally
-    be accessed using dot-notation: 
+    be accessed using dot-notation:
 
     >>> res_network.settings.cmi_estimator
-    
-    or 
-    
+
+    or
+
     >>> res_network.settings['cmi_estimator'].
 
     Attributes:
@@ -482,11 +546,11 @@ 
Source code for idtxl.results
     MultivariateTE or Bivariate TE.
 
     Note that for convenience all dictionaries in this class can additionally
-    be accessed using dot-notation: 
-    
+    be accessed using dot-notation:
+
     >>> res_network.settings.cmi_estimator
-    
-    or 
+
+    or
 
     >>> res_network.settings['cmi_estimator'].
 
@@ -618,10 +682,11 @@ 
Source code for idtxl.results
 
             fdr : bool [optional]
                 return FDR-corrected results (default=True)
+
+        Returns:
+            AdjacencyMatrix instance
         """
-        adjacency_matrix = np.zeros(
-            (self.data_properties.n_nodes, self.data_properties.n_nodes),
-            dtype=int)
+        adjacency_matrix = AdjacencyMatrix(self.data_properties.n_nodes, int)
 
         if weights == 'max_te_lag':
             for t in self.targets_analysed:
@@ -629,26 +694,38 @@ 
Source code for idtxl.results
                 delays = self.get_target_delays(target=t,
                                                 criterion='max_te',
                                                 fdr=fdr)
-                if sources.size:
-                    adjacency_matrix[sources, t] = delays
+                adjacency_matrix.add_edge_list(
+                    sources, np.ones(len(sources), dtype=int) * t, delays)
         elif weights == 'max_p_lag':
             for t in self.targets_analysed:
                 sources = self.get_target_sources(target=t, fdr=fdr)
                 delays = self.get_target_delays(target=t,
                                                 criterion='max_p',
                                                 fdr=fdr)
-                if sources.size:
-                    adjacency_matrix[sources, t] = delays
+                adjacency_matrix.add_edge_list(
+                    sources, np.ones(len(sources), dtype=int) * t, delays)
         elif weights == 'vars_count':
             for t in self.targets_analysed:
                 single_result = self.get_single_target(target=t, fdr=fdr)
-                for s in single_result.selected_vars_sources:
-                    adjacency_matrix[s[0], t] += 1
+                sources = np.zeros(len(single_result.selected_vars_sources))
+                weights = np.zeros(len(single_result.selected_vars_sources))
+                for i, s in enumerate(single_result.selected_vars_sources):
+                    sources[i] = s[0]
+                    weights[i] += 1
+                adjacency_matrix.add_edge_list(
+                    sources, np.ones(len(sources), dtype=int) * t, weights)
         elif weights == 'binary':
             for t in self.targets_analysed:
                 single_result = self.get_single_target(target=t, fdr=fdr)
-                for s in single_result.selected_vars_sources:
-                    adjacency_matrix[s[0], t] = 1
+                sources = np.zeros(
+                    len(single_result.selected_vars_sources), dtype=int)
+                weights = np.zeros(
+                    len(single_result.selected_vars_sources), dtype=int)
+                for i, s in enumerate(single_result.selected_vars_sources):
+                    sources[i] = s[0]
+                    weights[i] = 1
+                adjacency_matrix.add_edge_list(
+                    sources, np.ones(len(sources), dtype=int) * t, weights)
         else:
             raise RuntimeError('Invalid weights value')
         return adjacency_matrix

@@ -688,12 +765,12 @@ 
Source code for idtxl.results
     algorithms.
 
     Note that for convenience all dictionaries in this class can additionally
-    be accessed using dot-notation: 
-    
+    be accessed using dot-notation:
+
     >>> res_pid._single_target[2].source_1
-    
-    or 
-    
+
+    or
+
     >>> res_pid._single_target[2].['source_1'].
 
     Attributes:
@@ -833,43 +910,43 @@ 
Source code for idtxl.results
                    connectivity for significant links
                 - 'diff_abs': absolute difference
 
+        Returns:
+            AdjacencyMatrix instance
         """
         # Note: right now, the network comparison work on the uncorrected
         # networks only. This may have to change in the future, in which case
         # the value for 'fdr' when accessing single target results or adjacency
         # matrices has to be taken from the analysis settings.
         if weights == 'comparison':
-            adjacency_matrix = np.zeros(
-                (self.data_properties.n_nodes, self.data_properties.n_nodes),
-                dtype=bool)
+            adjacency_matrix = AdjacencyMatrix(
+                self.data_properties.n_nodes, int)
             for t in self.targets_analysed:
                 sources = self.get_target_sources(t)
-                for (i, s) in enumerate(sources):
-                        adjacency_matrix[s, t] = self.ab[t][i]
+                for i, s in enumerate(sources):
+                    adjacency_matrix.add_edge(s, t, int(self.ab[t][i]))
         elif weights == 'union':
-            adjacency_matrix = np.zeros(
-                (self.data_properties.n_nodes, self.data_properties.n_nodes),
-                dtype=int)
+            adjacency_matrix = AdjacencyMatrix(
+                self.data_properties.n_nodes, int)
             for t in self.targets_analysed:
                 sources = self.get_target_sources(t)
-                if sources.size:
-                    adjacency_matrix[sources, t] = 1
+                adjacency_matrix.add_edge_list(
+                    sources, np.ones(len(sources), dtype=int) * t,
+                    np.ones(len(sources), dtype=int))
         elif weights == 'diff_abs':
-            adjacency_matrix = np.zeros(
-                (self.data_properties.n_nodes, self.data_properties.n_nodes),
-                dtype=float)
+            adjacency_matrix = AdjacencyMatrix(
+                self.data_properties.n_nodes, float)
             for t in self.targets_analysed:
                 sources = self.get_target_sources(t)
                 for (i, s) in enumerate(sources):
-                    adjacency_matrix[s, t] = self.cmi_diff_abs[t][i]
+                    print(self.cmi_diff_abs)
+                    adjacency_matrix.add_edge(s, t, self.cmi_diff_abs[t][i])
         elif weights == 'pvalue':
-            adjacency_matrix = np.ones(
-                (self.data_properties.n_nodes, self.data_properties.n_nodes),
-                dtype=float)
+            adjacency_matrix = AdjacencyMatrix(
+                self.data_properties.n_nodes, float)
             for t in self.targets_analysed:
                 sources = self.get_target_sources(t)
                 for (i, s) in enumerate(sources):
-                    adjacency_matrix[s, t] = self.pval[t][i]
+                    adjacency_matrix.add_edge(s, t, self.pval[t][i])
         else:
             raise RuntimeError('Invalid weights value')
 
@@ -973,7 +1050,7 @@ 
Navigation

     

     
   
 
\ No newline at end of file
diff --git a/docs/html/_modules/idtxl/single_process_analysis.html b/docs/html/_modules/idtxl/single_process_analysis.html
index 162f6655..5fb7a0d4 100644
--- a/docs/html/_modules/idtxl/single_process_analysis.html
+++ b/docs/html/_modules/idtxl/single_process_analysis.html
@@ -13,7 +13,6 @@
     
     
     
-    
     
     
 
@@ -89,7 +88,7 @@ 
Navigation

     

     
   
 
\ No newline at end of file
diff --git a/docs/html/_modules/idtxl/stats.html b/docs/html/_modules/idtxl/stats.html
index d4a9f826..952839a6 100644
--- a/docs/html/_modules/idtxl/stats.html
+++ b/docs/html/_modules/idtxl/stats.html
@@ -13,7 +13,6 @@
     
     
     
-    
     
     
 
@@ -1660,7 +1659,7 @@ 
Navigation

     

     
   
 
\ No newline at end of file
diff --git a/docs/html/_modules/idtxl/synergy_tartu.html b/docs/html/_modules/idtxl/synergy_tartu.html
index d9e9fcb0..0cfefc85 100644
--- a/docs/html/_modules/idtxl/synergy_tartu.html
+++ b/docs/html/_modules/idtxl/synergy_tartu.html
@@ -13,7 +13,6 @@
     
     
     
-    
     
     
 
@@ -93,9 +92,6 @@ 
Source code for idtxl.synergy_tartu
 
[docs]def q_vidx(i):
     return 3*i+2

 
-
[docs]class BROJA_2PID_Exception(Exception):
-    pass

-
 
 
[docs]class Solve_w_ECOS:
     # (c) Abdullah Makkeh, Dirk Oliver Theis
@@ -630,7 +626,7 @@ 
Navigation

     

     
   
 
\ No newline at end of file
diff --git a/docs/html/_modules/idtxl/visualise_graph.html b/docs/html/_modules/idtxl/visualise_graph.html
index bf7d3dc7..beb3eea4 100644
--- a/docs/html/_modules/idtxl/visualise_graph.html
+++ b/docs/html/_modules/idtxl/visualise_graph.html
@@ -13,7 +13,6 @@
     
     
     
-    
     
     
 
@@ -132,7 +131,7 @@ 
Source code for idtxl.visualise_graph
         target : int
             index of target process
         sign_sources : bool [optional]
-            add only sources significant information contribution
+            plot sources with significant information contribution only
             (default=True)
         display_edge_labels : bool [optional]
             display TE value on edge lables (default=False)
@@ -213,13 +212,16 @@ 
Source code for idtxl.visualise_graph
     # https://stackoverflow.com/questions/25500541/
     # matplotlib-bwr-colormap-always-centered-on-zero
     if diverging:
-        max_val = np.max(abs(adj_matrix))
+        max_val = np.max(abs(adj_matrix._weight_matrix))
         min_val = -max_val
     else:
-        max_val = np.max(adj_matrix)
-        min_val = -np.min(adj_matrix)
-    plt.imshow(adj_matrix, cmap=mat_color, interpolation='nearest',
-               vmin=min_val, vmax=max_val)
+        max_val = np.max(adj_matrix._weight_matrix)
+        min_val = -np.min(adj_matrix._weight_matrix)
+
+    adj_matrix_masked = np.ma.masked_where(
+        np.invert(adj_matrix._edge_matrix), adj_matrix._weight_matrix)
+    plt.imshow(adj_matrix_masked, cmap=mat_color,
+               interpolation='nearest', vmin=min_val, vmax=max_val)
 
     # Set the colorbar and make colorbar match the image in size using the
     # fraction and pad parameters (see https://stackoverflow.com/a/26720422).
@@ -233,7 +235,8 @@ 
Source code for idtxl.visualise_graph
     elif cbar_label == 'binary':
         cbar_label = 'Binary adjacency matrix'
     elif cbar_label == 'p-value':
-        cbar_ticks = np.arange(0, 1.001, 0.1)
+        # cbar_ticks = np.arange(0, 1.001, 0.1)
+        cbar_ticks = np.arange(0, max_val * 1.01, max_val / 5)
     else:
         cbar_ticks = np.arange(min_val, max_val + 0.01 * max_val,
                                cbar_stepsize)
@@ -241,8 +244,8 @@ 
Source code for idtxl.visualise_graph
     cbar.set_label(cbar_label, rotation=90)
 
     # Set x- and y-ticks.
-    plt.xticks(np.arange(adj_matrix.shape[1]))
-    plt.yticks(np.arange(adj_matrix.shape[0]))
+    plt.xticks(np.arange(adj_matrix.n_nodes()))
+    plt.yticks(np.arange(adj_matrix.n_nodes()))
     ax = plt.gca()
     ax.xaxis.tick_top()
     return cbar
@@ -326,19 +329,22 @@ 
Source code for idtxl.visualise_graph
         cbar_label = 'A != B'
     elif results.settings.tail_comp == 'one':
         cbar_label = 'A > B'
-    _plot_adj_matrix(results.get_adjacency_matrix('comparison').astype(int),
+    adj_matrix_comparison = results.get_adjacency_matrix('comparison')
+    _plot_adj_matrix(adj_matrix_comparison,
                      mat_color='OrRd', cbar_label=cbar_label, cbar_stepsize=1)
     ax.set_title('Comparison {0}'.format(cbar_label), y=1.1)
 
     ax = plt.subplot(235)  # plot abs. differences adjacency matrix
-    _plot_adj_matrix(results.get_adjacency_matrix('diff_abs'),
+    adj_matrix_diff = results.get_adjacency_matrix('diff_abs')
+    _plot_adj_matrix(adj_matrix_diff,
                      mat_color='BuGn', cbar_label='norm. CMI diff [a.u.]',
                      cbar_stepsize=0.1)
     ax.set_title('CMI diff abs (A - B)', y=1.1)
 
-    ax = plt.subplot(236) # plot p-value adjacency matrix
-    _plot_adj_matrix(results.get_adjacency_matrix('pvalue'), mat_color='gray',
-                     cbar_label='p-value', cbar_stepsize=0.05)
+    ax = plt.subplot(236)  # plot p-value adjacency matrix
+    adj_matrix_pval = results.get_adjacency_matrix('pvalue')
+    _plot_adj_matrix(adj_matrix_pval, mat_color='Greys',
+                     cbar_label='p-value')
     ax.set_title('p-value [%]', y=1.1)
     return graph_union, fig

 

@@ -379,7 +385,7 @@ 
Navigation

     

     
   
 
\ No newline at end of file
diff --git a/docs/html/_modules/index.html b/docs/html/_modules/index.html
index 43109b78..b479aa95 100644
--- a/docs/html/_modules/index.html
+++ b/docs/html/_modules/index.html
@@ -13,7 +13,6 @@
     
     
     
-    
     
     
 
@@ -102,7 +101,7 @@ 
Navigation

     

     
   
 
\ No newline at end of file
diff --git a/docs/html/genindex.html b/docs/html/genindex.html
index 064aeaa2..2ac27951 100644
--- a/docs/html/genindex.html
+++ b/docs/html/genindex.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
     
 
@@ -72,6 +71,12 @@ 
A

 

   
 

@@ -295,6 +300,8 @@ 
G

       
   
   
    
+      
  • get_edge_list() (idtxl.results.AdjacencyMatrix method)
+
    • 
       
  • get_realisations() (idtxl.data.Data method)
 
    • 
       
  • get_significant_processes() (idtxl.results.ResultsSingleProcessAnalysis method)
@@ -495,6 +502,10 @@ 
    M
    
 
    N
    
 
-  
+  
    
   
   
+      
  • print_matrix() (idtxl.results.AdjacencyMatrix method)
+
    • 
       
  • processes_analysed (idtxl.results.ResultsSingleProcessAnalysis attribute)
 
    • 
       
  • provide_marginals() (idtxl.synergy_tartu.Solve_w_ECOS method)
@@ -709,7 +722,7 @@ 
    Navigation
    
     
     
   
 
\ No newline at end of file
diff --git a/docs/html/idtxl.html b/docs/html/idtxl.html
index d776e29c..f34eed53 100644
--- a/docs/html/idtxl.html
+++ b/docs/html/idtxl.html
@@ -13,7 +13,6 @@
     
     
     
-    
     
     
     
@@ -831,14 +830,24 @@ 
    idtxl package
+
  • Montalto, A., Faes, L., & Marinazzo, D. (2014) MuTE: A MATLAB toolbox
+to compare established and novel estimators of the multivariate
+transfer entropy. PLoS ONE 9(10): e109462.
+https://doi.org/10.1371/journal.pone.0109462
    • 
+
  • Baccala, L.A. & Sameshima, K. (2001). Partial directed coherence: a
+new concept in neural structure determination. Biol Cybern 84:
+463–474. https://doi.org/10.1007/PL00007990
    • 
+
 
    
 
    Args:
    
 
    
@@ -3331,9 +3340,9 @@ 
    idtxl packageclass idtxl.estimators_pid.SydneyPID(settings)[source]
 
    Bases: idtxl.estimator.Estimator
    
 
    Estimate partial information decomposition of discrete variables.
    
-
    Fast implementation of the partial information decomposition (PID)
-estimator for discrete data. The estimator does not require JAVA or GPU
-modules to run.
    
+
    Fast implementation of the BROJA partial information decomposition (PID)
+estimator for discrete data (Bertschinger, 2014). The estimator does not
+require JAVA or GPU modules to run.
    
 
    The estimator finds shared information, unique information and
 synergistic information between the two inputs s1 and s2 with respect to
 the output t.
    
@@ -3345,6 +3354,12 @@ 
    idtxl package
+
  • Bertschinger, N., Rauh, J., Olbrich, E., Jost, J., & Ay, N. (2014).
+Quantifying unique information. Entropy, 16(4), 2161–2183.
+http://doi.org/10.3390/e16042161
    • 
+
 
    
 
    Args:
    
 
    
@@ -3584,7 +3599,7 @@ 
    idtxl package
 
    Args:
    
 
    
-
    adjacency_matrix : 2D numpy array
    
+
    adjacency_matrix : AdjacencyMatrix instance
    
 
    adjacency matrix to be exported, returned by get_adjacency_matrix()
 method of Results() class
    
 
    mni_coord : numpy array
    
@@ -3619,7 +3634,7 @@ 
    idtxl package
 
    Args:
    
 
    
-
    adjacency_matrix : 2D numpy array
    
+
    adjacency_matrix : AdjacencyMatrix instances
    
 
    adjacency matrix to be exported, returned by get_adjacency_matrix()
 method of Results() class
    
 
    weights : str
    
@@ -3652,7 +3667,7 @@ 
    idtxl package: int
 
    target index
    
 
    sign_sources : bool [optional]
    
-
    add only sources with significant information contribution
+
    add sources with significant information contribution only
 (default=True)
    
 
    fdr : bool [optional]
    
 
    return FDR-corrected results (default=True)
    
@@ -4997,6 +5012,56 @@ 
    idtxl package
 
    idtxl.results module
    
 
    Provide results class for IDTxl network analysis.
    
+
    
+
    
+class idtxl.results.AdjacencyMatrix(n_nodes, weight_type)[source]
    
+
    Bases: object
    
+
    Adjacency matrix representing inferred networks.
    
+
    
+
    
+add_edge(i, j, weight)[source]
    
+
    Add weighted edge (i, j) to adjacency matrix.
    
+
    
+
+
    
+
    
+add_edge_list(i_list, j_list, weights)[source]
    
+
    Add multiple weighted edges (i, j) to adjacency matrix.
    
+
    
+
+
    
+
    
+get_edge_list()[source]
    
+
    Return list of weighted edges.
    
+
    
+
    Returns
    
+
    
+
    list of tuples
    
+
    each entry represents one edge in the graph: (i, j, weight)
    
+
    
+
    
+
    
+
    
+
+
    
+
    
+n_edges()[source]
    
+
    
+
+
    
+
    
+n_nodes()[source]
    
+
    Return number of nodes.
    
+
    
+
+
    
+
    
+print_matrix()[source]
    
+
    Print weight and edge matrix.
    
+
    
+
+
    
+
 
    
 
    
 class idtxl.results.DotDict[source]
    
@@ -5239,6 +5304,8 @@ 
    idtxl package
 
    idtxl.synergy_tartu module
    
-
    
-
    
-exception idtxl.synergy_tartu.BROJA_2PID_Exception[source]
    
-
    Bases: Exception
    
-
    
-
 
    
 
    
 idtxl.synergy_tartu.I_X_Y(p)[source]
    
@@ -6466,7 +6529,7 @@ 
    idtxl package: int
 
    index of target process
    
 
    sign_sources : bool [optional]
    
-
    add only sources significant information contribution
+
    plot sources with significant information contribution only
 (default=True)
    
 
    display_edge_labels : bool [optional]
    
 
    display TE value on edge lables (default=False)
    
@@ -6598,7 +6661,7 @@ 
    Navigation
    
     
         
   
 
\ No newline at end of file
diff --git a/docs/html/idtxl_data_class.html b/docs/html/idtxl_data_class.html
index de68eedf..7890e057 100644
--- a/docs/html/idtxl_data_class.html
+++ b/docs/html/idtxl_data_class.html
@@ -13,7 +13,6 @@
     
     
     
-    
     
     
     
@@ -153,14 +152,24 @@ 
    The Data Class
+
  • Montalto, A., Faes, L., & Marinazzo, D. (2014) MuTE: A MATLAB toolbox
+to compare established and novel estimators of the multivariate
+transfer entropy. PLoS ONE 9(10): e109462.
+https://doi.org/10.1371/journal.pone.0109462
    • 
+
  • Baccala, L.A. & Sameshima, K. (2001). Partial directed coherence: a
+new concept in neural structure determination. Biol Cybern 84:
+463–474. https://doi.org/10.1007/PL00007990
    • 
+
 
    
 
    Args:
    
 
    
@@ -953,7 +962,7 @@ 
    Navigation
    
     
     
   
 
\ No newline at end of file
diff --git a/docs/html/idtxl_estimators.html b/docs/html/idtxl_estimators.html
index 8e8df232..33207c3f 100644
--- a/docs/html/idtxl_estimators.html
+++ b/docs/html/idtxl_estimators.html
@@ -13,7 +13,6 @@
     
     
     
-    
     
     
     
@@ -1623,9 +1622,9 @@ 
    PID Estimatorsclass idtxl.estimators_pid.SydneyPID(settings)[source]
 
    Estimate partial information decomposition of discrete variables.
    
-
    Fast implementation of the partial information decomposition (PID)
-estimator for discrete data. The estimator does not require JAVA or GPU
-modules to run.
    
+
    Fast implementation of the BROJA partial information decomposition (PID)
+estimator for discrete data (Bertschinger, 2014). The estimator does not
+require JAVA or GPU modules to run.
    
 
    The estimator finds shared information, unique information and
 synergistic information between the two inputs s1 and s2 with respect to
 the output t.
    
@@ -1637,6 +1636,12 @@ 
    PID Estimators
+
  • Bertschinger, N., Rauh, J., Olbrich, E., Jost, J., & Ay, N. (2014).
+Quantifying unique information. Entropy, 16(4), 2161–2183.
+http://doi.org/10.3390/e16042161
    • 
+
 
    
 
    Args:
    
 
    
@@ -1877,7 +1882,7 @@ 
    Navigation
    
     
     
   
 
\ No newline at end of file
diff --git a/docs/html/idtxl_helper.html b/docs/html/idtxl_helper.html
index 49198360..2d007d8f 100644
--- a/docs/html/idtxl_helper.html
+++ b/docs/html/idtxl_helper.html
@@ -13,7 +13,6 @@
     
     
     
-    
     
     
     
@@ -873,7 +872,7 @@ 
    Navigation
    
     
     
   
 
\ No newline at end of file
diff --git a/docs/html/idtxl_network_comparison.html b/docs/html/idtxl_network_comparison.html
index 219b6ed6..c36d7de3 100644
--- a/docs/html/idtxl_network_comparison.html
+++ b/docs/html/idtxl_network_comparison.html
@@ -13,7 +13,6 @@
     
     
     
-    
     
     
     
@@ -331,7 +330,7 @@ 
    Navigation
    
     
     
   
 
\ No newline at end of file
diff --git a/docs/html/idtxl_network_inference.html b/docs/html/idtxl_network_inference.html
index 2969c025..e5a1433e 100644
--- a/docs/html/idtxl_network_inference.html
+++ b/docs/html/idtxl_network_inference.html
@@ -13,7 +13,6 @@
     
     
     
-    
     
     
     
@@ -1060,7 +1059,7 @@ 
    Navigation
    
     
     
   
 
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