🆕 opinion evolution plot

docs/reference/models/opinion/AlgorithmicBias.rst

@@ -17,6 +17,8 @@ This can be seen as a measure of the open-mindedness of individuals in a populat
 It defines a threshold on the distance between the opinion of the two individuals, beyond which communication between individuals is not possible due to conflicting views.
 Thus, if the distance between the opinions of the selected individuals is lower than epsilon, the two individuals adopt their average opinion. Otherwise nothing happens.
 
+Note: setting gamma=0 reproduce the results for the Deffuant model.
+
 --------
 Statuses
 --------

docs/reference/reference.rst

@@ -148,6 +148,7 @@ Pyplot Viz
 
    viz/mpl/DiffusionTrend.rst
    viz/mpl/DiffusionPrevalence.rst
+   viz/mpl/OpinionEvolution.rst
 
 
 **Model Comparison Visualizations**

docs/reference/viz/mpl/OpinionEvolution.rst

@@ -0,0 +1,47 @@
+*****************
+Opinion Evolution
+*****************
+
+The Opinion Evolution plot shows the node-wise opinion evolution in a continuous states model.
+
+.. autoclass:: ndlib.viz.mpl.OpinionEvolution.OpinionEvolution
+.. automethod:: ndlib.viz.mpl.OpinionEvolution.OpinionEvolution.__init__(model, trends)
+.. automethod:: ndlib.viz.mpl.OpinionEvolution.OpinionEvolution.plot(filename)
+
+
+Below is shown an example of Opinion Evolution description and visualization for the Algorithmic Bias model.
+
+.. code-block:: python
+
+    import networkx as nx
+    import ndlib.models.ModelConfig as mc
+    import ndlib.models.opinions as op
+    from ndlib.viz.mpl.OpinionEvolution import OpinionEvolution
+
+    # mMean field scenario
+    g = nx.complete_graph(100)
+
+    # Algorithmic Bias model
+    model = op.AlgorithmicBiasModel(g)
+
+    # Model configuration
+    config = mc.Configuration()
+    config.add_model_parameter("epsilon", 0.32)
+    config.add_model_parameter("gamma", 0)
+    model.set_initial_status(config)
+
+    # Simulation execution
+    iterations = model.iteration_bunch(100)
+
+    viz = OpinionEvolution(model, iterations)
+    viz.plot("opinion_ev.pdf")
+
+
+
+
+.. figure:: opinion_ev.png
+   :scale: 80 %
+   :align: center
+   :alt: Algorithmic Bias Opinion Evolution Example
+
+   SIR Diffusion Trend Example.

ndlib/test/test_mpl_viz.py

@@ -141,22 +141,23 @@ def test_trend_comparison(self):
         os.remove("trend_comparison.pdf")
 
     def test_opinion_viz(self):
-        g = nx.complete_graph(100)
+        g = nx.complete_graph(50)
 
         model = op.AlgorithmicBiasModel(g)
 
         # Model configuration
         config = mc.Configuration()
         config.add_model_parameter("epsilon", 0.32)
-        config.add_model_parameter("gamma", 1.2)
+        config.add_model_parameter("gamma", 0)
         model.set_initial_status(config)
 
         # Simulation execution
-        iterations = model.iteration_bunch(1000)
+        iterations = model.iteration_bunch(50)
 
         viz = OpinionEvolution(model, iterations)
-        viz.plot("opinion_ev.pdf")
+        viz.plot("opinion_ev.png")
         os.remove("opinion_ev.pdf")
 
+
 if __name__ == '__main__':
     unittest.main()

ndlib/viz/mpl/OpinionEvolution.py

@@ -1,5 +1,3 @@
-from ndlib.viz.mpl.DiffusionViz import DiffusionPlot
-from bokeh.palettes import Category20_9 as cols
 import os
 import matplotlib as mpl
 if os.environ.get('DISPLAY', '') == '':
@@ -25,7 +23,7 @@ def __init__(self, model, trends):
         self.srev = trends
         self.ylabel = "Opinion"
 
-    def plot(self, filename=None, percentile=90):
+    def plot(self, filename=None):
         """
         Generates the plot
 
@@ -44,30 +42,47 @@ def plot(self, filename=None, percentile=90):
         node2col = {}
 
         mx = 0
+
+        last_it = self.srev[-1]['iteration'] + 1
+        last_seen = {}
+
         for it in self.srev:
             sts = it['status']
+            its = it['iteration']
             for n, v in sts.items():
                 if n in nodes2opinions:
-                    nodes2opinions[n].append(v)
+                    last_id = last_seen[n]
+                    last_value = nodes2opinions[n][last_id]
+
+                    for i in range(last_id, its):
+                        nodes2opinions[n][i] = last_value
+
+                    nodes2opinions[n][its] = v
+                    last_seen[n] = its
                 else:
-                    nodes2opinions[n] = [v]
+                    nodes2opinions[n] = [0]*last_it
+                    nodes2opinions[n][its] = v
+                    last_seen[n] = 0
                     if v < 0.33:
                         node2col[n] = '#ff0000'
                     elif 0.33 <= v <= 0.66:
                         node2col[n] = '#00ff00'
                     else:
                         node2col[n] = '#0000ff'
 
+        mx = 0
         for k, l in future.utils.iteritems(nodes2opinions):
-            mx = len(l)
-            plt.plot(range(0, mx), l, lw=1, alpha=0.5, color=node2col[k])
+            if mx < last_seen[k]:
+                mx = last_seen[k]
+            x = list(range(0, last_seen[k]))
+            y = l[0:last_seen[k]]
+            plt.plot(x, y, lw=1, alpha=0.5, color=node2col[k])
 
-        # plt.grid(axis="y")
         plt.title(descr)
         plt.xlabel("Iterations", fontsize=24)
         plt.ylabel(self.ylabel, fontsize=24)
         plt.legend(loc="best", fontsize=18)
-        plt.xlim((0, mx))
+
 
         plt.tight_layout()
         if filename is not None: