⬆️ tqdm progress bar flag in iteration_bunch

ndlib/models/ContinuousModel.py

@@ -288,22 +288,17 @@ def iteration(self, node_status=True):
             return {"iteration": self.actual_iteration - 1, "status": {},
                     "status_delta": copy.deepcopy(status_delta)}
 
-    def iteration_bunch(self, bunch_size, node_status=True, tqdm=True):
+    def iteration_bunch(self, bunch_size, node_status=True, progress_bar=False):
         """
         Execute bunch_size of model iterations and save the result if save_file is set
 
         :param bunch_size: integer number of iterations to execute
         :param node_status: boolean indicating whether to keep the statuses of the nodes
-        :param tqdm: boolean indicating whether to use tqdm to show the estimated duration
+        :param progress_bar: boolean indicating whether to use tqdm to show the estimated duration
 
         :return: list of outputs for every iteration
         """
-        if tqdm:
-            iterations = super().iteration_bunch(bunch_size, node_status)
-        else:
-            iterations = []
-            for _ in range(bunch_size):
-                iterations.append(self.iteration(node_status))
+        iterations = super().iteration_bunch(bunch_size, node_status, progress_bar=progress_bar)
 
         if self.save_file:
             split = self.save_file.split('/')

ndlib/models/ContinuousModelRunner.py

@@ -72,7 +72,7 @@ def analyze_sensitivity(self, sa_type, initial_status, bounds, n, iterations, se
                 self.model.constants[name] = param_values[i,j]
             # Set intial values
             self.model.set_initial_status(initial_status, self.config)
-            outputs.append(self.model.iteration_bunch(iterations, node_status=self.node_status, tqdm=False))
+            outputs.append(self.model.iteration_bunch(iterations, node_status=self.node_status, progress_bar=False))
 
         # Parse the outputs for every simulation (TODO: Optimize)
         print('Parsing outputs...')

ndlib/models/DiffusionModel.py

@@ -187,17 +187,18 @@ def clean_initial_status(self, valid_status=None):
             if s not in valid_status:
                 self.status[n] = 0
 
-    def iteration_bunch(self, bunch_size, node_status=True):
+    def iteration_bunch(self, bunch_size, node_status=True, progress_bar=False):
         """
         Execute a bunch of model iterations
 
         :param bunch_size: the number of iterations to execute
         :param node_status: if the incremental node status has to be returned.
+        :param progress_bar: whether to display a progress bar, default False
 
         :return: a list containing for each iteration a dictionary {"iteration": iteration_id, "status": dictionary_node_to_status}
         """
         system_status = []
-        for it in tqdm.tqdm(past.builtins.xrange(0, bunch_size)):
+        for it in tqdm.tqdm(past.builtins.xrange(0, bunch_size), disable=not progress_bar):
             its = self.iteration(node_status)
             system_status.append(its)
         return system_status

ndlib/models/opinions/AlgorithmicBiasModel.py

@@ -180,20 +180,21 @@ def iteration(self, node_status=True):
             return {"iteration": self.actual_iteration - 1, "status": {},
                     "node_count": node_count.copy(), "status_delta": status_delta.copy()}
 
-    def steady_state(self, max_iterations, nsteady=1000, sensibility=0.00001, node_status=True):
+    def steady_state(self, max_iterations, nsteady=1000, sensibility=0.00001, node_status=True, progress_bar=False):
         """
         Execute a bunch of model iterations
 
         :param max_iterations: the maximum number of iterations to execute
         :param nsteady: number of required stable states
         :param sensibility: sensibility check for a steady state
         :param node_status: if the incremental node status has to be returned.
+        :param progress_bar: whether to display a progress bar, default False
 
         :return: a list containing for each iteration a dictionary {"iteration": iteration_id, "status": dictionary_node_to_status}
         """
         system_status = []
         steady_it = 0
-        for it in tqdm.tqdm(range(0, max_iterations)):
+        for it in tqdm.tqdm(range(0, max_iterations), disable=not progress_bar):
             its = self.iteration(node_status)
 
             if it > 0:

ndlib/test/test_continuous_model.py

@@ -59,7 +59,7 @@ def self_confidence_impact(node, graph, status, attributes, constants):
         addiction_model.set_initial_status(initial_status, config)
 
         # Simulation
-        iterations = addiction_model.iteration_bunch(50, node_status=True, tqdm=False)
+        iterations = addiction_model.iteration_bunch(50, node_status=True, progress_bar=False)
         self.assertEqual(len(iterations), 50)
 
     def test_constants(self):
@@ -210,7 +210,7 @@ def update(node, graph, status, attributes, constants):
         model.set_initial_status(initial_status, config)
 
         # Simulation
-        iterations = model.iteration_bunch(10, node_status=True, tqdm=False)
+        iterations = model.iteration_bunch(10, node_status=True, progress_bar=False)
         self.assertEqual(len(iterations), 10)
         self.assertTrue(os.path.isfile(output_path + '.npy'))
         os.remove(output_path + '.npy')

ndlib/test/test_ndlib.py

@@ -21,7 +21,7 @@ def get_graph(er=False):
     if not er:
         g = nx.complete_graph(100)
     else:
-        g = nx.erdos_renyi_graph(1000, 0.1)
+        g = nx.erdos_renyi_graph(100, 0.1)
     gi = ig.Graph(directed=False)
     gi.add_vertices(list(g.nodes()))
     gi.add_edges(list(g.edges()))
@@ -137,10 +137,10 @@ def test_algorithmic_bias_model(self):
             model.set_initial_status(config)
             iterations = model.iteration_bunch(10)
             self.assertEqual(len(iterations), 10)
-            iterations = model.iteration_bunch(100, node_status=False)
-            self.assertEqual(len(iterations), 100)
+            iterations = model.iteration_bunch(10, node_status=False)
+            self.assertEqual(len(iterations), 10)
 
-            _ = model.steady_state(max_iterations=100)
+            _ = model.steady_state(max_iterations=10000)
 
     def test_voter_model(self):
         for g in get_graph():