Merge pull request #101 from ggrrll/percentage_infected_fixed

percentage_infected --> fraction_infected
GiulioRossetti · May 30, 2019 · afa1767 · afa1767
2 parents 00470c7 + af51643
commit afa1767
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diff --git a/docs/custom/compartments/CascadingComposition.rst b/docs/custom/compartments/CascadingComposition.rst
@@ -50,7 +50,7 @@ Indeed, heterogeneous compartment types can be mixed to build more complex scena
 
 	# Model initial status configuration
 	config = mc.Configuration()
-	config.add_model_parameter('percentage_infected', 0.1)
+	config.add_model_parameter('fraction_infected', 0.1)
 
 	# Simulation execution
 	model.set_initial_status(config)

diff --git a/docs/custom/compartments/ConditionalComposition.rst b/docs/custom/compartments/ConditionalComposition.rst
@@ -69,7 +69,7 @@ Indeed, heterogeneous compartment types can be mixed to build more complex scena
 
 	# Model initial status configuration
 	config = mc.Configuration()
-	config.add_model_parameter('percentage_infected', 0.1)
+	config.add_model_parameter('fraction_infected', 0.1)
 
 	# Simulation execution
 	model.set_initial_status(config)

diff --git a/docs/custom/compartments/CountDown.rst b/docs/custom/compartments/CountDown.rst
@@ -65,7 +65,7 @@ It requires activates after 10 iteration.
 
 	# Model initial status configuration
 	config = mc.Configuration()
-	config.add_model_parameter('percentage_infected', 0.1)
+	config.add_model_parameter('fraction_infected', 0.1)
 
 	# Simulation execution
 	model.set_initial_status(config)

diff --git a/docs/custom/compartments/EdgeCategoricalAttribute.rst b/docs/custom/compartments/EdgeCategoricalAttribute.rst
@@ -83,8 +83,8 @@ It restrain the rule evaluation to all those nodes connected trough a link havin
 
 	# Model initial status configuration
 	config = mc.Configuration()
-	config.add_model_parameter('percentage_infected', 0)
+	config.add_model_parameter('fraction_infected', 0)
 
 	# Simulation execution
 	model.set_initial_status(config)
-	iterations = model.iteration_bunch(100)
+	iterations = model.iteration_bunch(100)
diff --git a/docs/custom/compartments/EdgeNumericalAttribute.rst b/docs/custom/compartments/EdgeNumericalAttribute.rst
@@ -95,8 +95,8 @@ It restrain the rule evaluation to all those nodes connected at least to a "Susc
 
 	# Model initial status configuration
 	config = mc.Configuration()
-	config.add_model_parameter('percentage_infected', 0)
+	config.add_model_parameter('fraction_infected', 0)
 
 	# Simulation execution
 	model.set_initial_status(config)
-	iterations = model.iteration_bunch(100)
+	iterations = model.iteration_bunch(100)
diff --git a/docs/custom/compartments/EdgeStochastic.rst b/docs/custom/compartments/EdgeStochastic.rst
@@ -68,7 +68,7 @@ It requires a probability threshold - here set equals to 0.02 - and restrain the
 
 	# Model initial status configuration
 	config = mc.Configuration()
-	config.add_model_parameter('percentage_infected', 0.1)
+	config.add_model_parameter('fraction_infected', 0.1)
 
 	# Simulation execution
 	model.set_initial_status(config)
@@ -110,7 +110,7 @@ In case of an heterogeneous edge threshold distribution the same model can be ex
 		config.add_edge_configuration("threshold", e, np.random.random_sample())
 
 	config = mc.Configuration()
-	config.add_model_parameter('percentage_infected', 0.1)
+	config.add_model_parameter('fraction_infected', 0.1)
 
 	# Simulation execution
 	model.set_initial_status(config)

diff --git a/docs/custom/compartments/NodeCategoricalAttribute.rst b/docs/custom/compartments/NodeCategoricalAttribute.rst
@@ -69,7 +69,7 @@ It restrain the rule evaluation to all those nodes for which the attribute "Sex"
 
 	# Model initial status configuration
 	config = mc.Configuration()
-	config.add_model_parameter('percentage_infected', 0)
+	config.add_model_parameter('fraction_infected', 0)
 
 	# Simulation execution
 	model.set_initial_status(config)

diff --git a/docs/custom/compartments/NodeNumericalAttribute.rst b/docs/custom/compartments/NodeNumericalAttribute.rst
@@ -95,8 +95,8 @@ It restrain the rule evaluation to all those nodes connected at least to a "Susc
 
 	# Model initial status configuration
 	config = mc.Configuration()
-	config.add_model_parameter('percentage_infected', 0)
+	config.add_model_parameter('fraction_infected', 0)
 
 	# Simulation execution
 	model.set_initial_status(config)
-	iterations = model.iteration_bunch(100)
+	iterations = model.iteration_bunch(100)
diff --git a/docs/custom/compartments/NodeStochastic.rst b/docs/custom/compartments/NodeStochastic.rst
@@ -73,7 +73,7 @@ Since such transition is not tied to neighbors statuses the only parameter requi
 
 	# Model initial status configuration
 	config = mc.Configuration()
-	config.add_model_parameter('percentage_infected', 0.1)
+	config.add_model_parameter('fraction_infected', 0.1)
 
 	# Simulation execution
 	model.set_initial_status(config)

diff --git a/docs/custom/compartments/NodeThreshold.rst b/docs/custom/compartments/NodeThreshold.rst
@@ -64,7 +64,7 @@ It requires a threshold - here set equals to 0.2.
 
 	# Model initial status configuration
 	config = mc.Configuration()
-	config.add_model_parameter('percentage_infected', 0.1)
+	config.add_model_parameter('fraction_infected', 0.1)
 
 	# Simulation execution
 	model.set_initial_status(config)
@@ -103,7 +103,7 @@ In case of an heterogeneous node threshold distribution the same model can be ex
 		config.add_node_configuration("threshold", i, np.random.random_sample())
 
 	config = mc.Configuration()
-	config.add_model_parameter('percentage_infected', 0.1)
+	config.add_model_parameter('fraction_infected', 0.1)
 
 	# Simulation execution
 	model.set_initial_status(config)

diff --git a/docs/custom/custom.rst b/docs/custom/custom.rst
@@ -142,8 +142,8 @@ SIR
 
 	# Model initial status configuration
 	config = mc.Configuration()
-	config.add_model_parameter('percentage_infected', 0.1)
+	config.add_model_parameter('fraction_infected', 0.1)
 
 	# Simulation execution
 	model.set_initial_status(config)
-	iterations = model.iteration_bunch(5)
+	iterations = model.iteration_bunch(5)
diff --git a/docs/developer/general/WGit.rst b/docs/developer/general/WGit.rst
@@ -10,4 +10,4 @@ Contents:
    intro.rst
    install.rst
    latest_source.rst
-   patch.rst
+   patch.rst
diff --git a/docs/developer/general/install.rst b/docs/developer/general/install.rst
@@ -26,4 +26,4 @@ There are good instructions here: http://book.git-scm.com/2_installing_git.html
 
 .. [#] https://git-for-windows.github.io
 .. [#] https://code.google.com/archive/p/git-osx-installer/downloads
-.. [#] http://help.github.com/
+.. [#] http://help.github.com/
diff --git a/docs/developer/general/intro.rst b/docs/developer/general/intro.rst
@@ -8,4 +8,4 @@ There are several different workflows here, for different ways of working with N
 
 This is not a comprehensive git reference, it’s just a workflow for our own project. It’s tailored to the github hosting service. You may well find better or quicker ways of getting stuff done with git, but these should get you started.
 
-For general resources for learning git, see git resources.
+For general resources for learning git, see git resources.
diff --git a/docs/installing.rst b/docs/installing.rst
@@ -121,4 +121,4 @@ These are extra packages you may consider using with ``NDlib``
 IPython, interactive Python shell, http://ipython.scipy.org/
 
 .. _pypl: https://pypi.python.org/pypi/ndlib/
-.. _GitHub: https://github.com/GiulioRossetti/ndlib/
+.. _GitHub: https://github.com/GiulioRossetti/ndlib/
diff --git a/docs/reference/mconf/Mconf.rst b/docs/reference/mconf/Mconf.rst
@@ -37,7 +37,7 @@ Model parameters can be setted as in the following example:
     config = mc.Configuration()
     config.add_model_parameter("beta", 0.15)
 
-The only model parameter common to all the diffusive approaches is ``percentage_infected`` that allows to specify the ratio of infected nodes at the beginning of the simulation.
+The only model parameter common to all the diffusive approaches is ``fraction_infected`` that allows to specify the ratio of infected nodes at the beginning of the simulation.
 
 
 ------------------
@@ -110,6 +110,6 @@ Node statuses can be set as in the following example:
     infected_nodes = [0, 1, 2, 3, 4, 5]
     config.add_model_initial_configuration("Infected", infected_nodes)
 
-Explicit status specification takes priority over the percentage specification expressed via model definition (e.g. ``percentage_infected``).
+Explicit status specification takes priority over the percentage specification expressed via model definition (e.g. ``fraction_infected``).
 
 Only the statuses implemented by the chosen model can be used to specify initial configurations of nodes.
diff --git a/docs/reference/models/dynamics/dKThreshold.rst b/docs/reference/models/dynamics/dKThreshold.rst
@@ -38,7 +38,7 @@ threshold           Node   float in [0, 1]   0.1     False      Individual thres
 
 The initial infection status can be defined via:
 
-    - **percentage_infected**: Model Parameter, float in [0, 1]
+    - **fraction_infected**: Model Parameter, float in [0, 1]
     - **Infected**: Status Parameter, set of nodes
 
 The initial blocked nodes can be defined via:
@@ -105,7 +105,7 @@ In the code below is shown an example of instantiation and execution of a Kertes
     config = mc.Configuration()
     config.add_model_parameter('adopter_rate', 0.4)
     config.add_model_parameter('percentage_blocked', 0.1)
-    config.add_model_parameter('percentage_infected', 0.1)
+    config.add_model_parameter('fraction_infected', 0.1)
 
     # Setting node parameters
     threshold = 0.25

diff --git a/docs/reference/models/dynamics/dProfile.rst b/docs/reference/models/dynamics/dProfile.rst
@@ -45,7 +45,7 @@ adopter_rate  Model  float in [0, 1]   0       False      Autonomous adoption
 
 The initial infection status can be defined via:
 
-    - **percentage_infected**: Model Parameter, float in [0, 1]
+    - **fraction_infected**: Model Parameter, float in [0, 1]
     - **Infected**: Status Parameter, set of nodes
 
 The two options are mutually exclusive and the latter takes precedence over the former.
@@ -107,7 +107,7 @@ In the code below is shown an example of instantiation and execution of a Profil
     config = mc.Configuration()
     config.add_model_parameter('blocked', 0)
     config.add_model_parameter('adopter_rate', 0)
-    config.add_model_parameter('percentage_infected', 0.1)
+    config.add_model_parameter('fraction_infected', 0.1)
 
     # Setting nodes parameters
     profile = 0.15
@@ -120,4 +120,4 @@ In the code below is shown an example of instantiation and execution of a Profil
     # Simulate snapshot based execution
     iterations = model.execute_snapshots()
 
-.. [#] Milli, L., Rossetti, G., Pedreschi, D., & Giannotti, F. (2018). Active and passive diffusion processes in complex networks. Applied network science, 3(1), 42.
+.. [#] Milli, L., Rossetti, G., Pedreschi, D., & Giannotti, F. (2018). Active and passive diffusion processes in complex networks. Applied network science, 3(1), 42.
diff --git a/docs/reference/models/dynamics/dProfileThreshold.rst b/docs/reference/models/dynamics/dProfileThreshold.rst
@@ -42,7 +42,7 @@ adopter_rate  Model  float in [0, 1]   0       False      Autonomous adoption
 
 The initial infection status can be defined via:
 
-    - **percentage_infected**: Model Parameter, float in [0, 1]
+    - **fraction_infected**: Model Parameter, float in [0, 1]
     - **Infected**: Status Parameter, set of nodes
 
 The two options are mutually exclusive and the latter takes precedence over the former.
@@ -103,7 +103,7 @@ In the code below is shown an example of instantiation and execution of a Profil
     config = mc.Configuration()
     config.add_model_parameter('blocked', 0)
     config.add_model_parameter('adopter_rate', 0)
-    config.add_model_parameter('percentage_infected', 0.1)
+    config.add_model_parameter('fraction_infected', 0.1)
 
     # Setting nodes parameters
     threshold = 0.15
@@ -118,4 +118,4 @@ In the code below is shown an example of instantiation and execution of a Profil
     # Simulate snapshot based execution
     iterations = model.execute_snapshots()
 
-.. [#] Milli, L., Rossetti, G., Pedreschi, D., & Giannotti, F. (2018). Active and passive diffusion processes in complex networks. Applied network science, 3(1), 42.
+.. [#] Milli, L., Rossetti, G., Pedreschi, D., & Giannotti, F. (2018). Active and passive diffusion processes in complex networks. Applied network science, 3(1), 42.
diff --git a/docs/reference/models/dynamics/dSI.rst b/docs/reference/models/dynamics/dSI.rst
@@ -37,7 +37,7 @@ beta   Model  float in [0, 1]           True       Infection probability
 
 The initial infection status can be defined via:
 
-    - **percentage_infected**: Model Parameter, float in [0, 1]
+    - **fraction_infected**: Model Parameter, float in [0, 1]
     - **Infected**: Status Parameter, set of nodes
 
 The two options are mutually exclusive and the latter takes precedence over the former.
@@ -98,7 +98,7 @@ In the code below is shown an example of instantiation and execution of an DynSI
     # Model Configuration
     config = mc.Configuration()
     config.add_model_parameter('beta', 0.01)
-    config.add_model_parameter("percentage_infected", 0.1)
+    config.add_model_parameter("fraction_infected", 0.1)
     model.set_initial_status(config)
 
     # Simulate snapshot based execution

diff --git a/docs/reference/models/dynamics/dSIR.rst b/docs/reference/models/dynamics/dSIR.rst
@@ -39,7 +39,7 @@ gamma  Model  float in [0, 1]           True       Removal probability
 
 The initial infection status can be defined via:
 
-    - **percentage_infected**: Model Parameter, float in [0, 1]
+    - **fraction_infected**: Model Parameter, float in [0, 1]
     - **Infected**: Status Parameter, set of nodes
 
 The two options are mutually exclusive and the latter takes precedence over the former.
@@ -101,7 +101,7 @@ In the code below is shown an example of instantiation and execution of an DynSI
     config = mc.Configuration()
     config.add_model_parameter('beta', 0.01)
     config.add_model_parameter('gamma', 0.01)
-    config.add_model_parameter("percentage_infected", 0.1)
+    config.add_model_parameter("fraction_infected", 0.1)
     model.set_initial_status(config)
 
     # Simulate snapshot based execution

diff --git a/docs/reference/models/dynamics/dSIS.rst b/docs/reference/models/dynamics/dSIS.rst
@@ -38,7 +38,7 @@ lambda  Model  float in [0, 1]           True       Recovery probability
 
 The initial infection status can be defined via:
 
-    - **percentage_infected**: Model Parameter, float in [0, 1]
+    - **fraction_infected**: Model Parameter, float in [0, 1]
     - **Infected**: Status Parameter, set of nodes
 
 The two options are mutually exclusive and the latter takes precedence over the former.
@@ -101,7 +101,7 @@ In the code below is shown an example of instantiation and execution of an DynSI
     config = mc.Configuration()
     config.add_model_parameter('beta', 0.01)
     config.add_model_parameter('lambda', 0.01)
-    config.add_model_parameter("percentage_infected", 0.1)
+    config.add_model_parameter("fraction_infected", 0.1)
     model.set_initial_status(config)
 
     # Simulate snapshot based execution
@@ -112,4 +112,4 @@ In the code below is shown an example of instantiation and execution of an DynSI
 
 
 
-.. [#] W. O. Kermack and A. McKendrick, “A Contribution to the Mathematical Theory of Epidemics,” Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, vol. 115, no. 772, pp. 700–721, Aug. 1927
+.. [#] W. O. Kermack and A. McKendrick, “A Contribution to the Mathematical Theory of Epidemics,” Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, vol. 115, no. 772, pp. 700–721, Aug. 1927
diff --git a/docs/reference/models/epidemics/GeneralisedThreshold.rst b/docs/reference/models/epidemics/GeneralisedThreshold.rst
@@ -40,7 +40,7 @@ mu         Model  int                        True      Exogenous timescale
 
 The initial infection status can be defined via:
 
-    - **percentage_infected**: Model Parameter, float in [0, 1]
+    - **fraction_infected**: Model Parameter, float in [0, 1]
     - **Infected**: Status Parameter, set of nodes
 
 The two options are mutually exclusive and the latter takes precedence over the former.
@@ -95,7 +95,7 @@ In the code below is shown an example of instantiation and execution of a Thresh
         
     # Model Configuration
     config = mc.Configuration()
-    config.add_model_parameter('percentage_infected', 0.1)
+    config.add_model_parameter('fraction_infected', 0.1)
     config.add_model_parameter('tau', 5)
     config.add_model_parameter('mu', 5)
 

diff --git a/docs/reference/models/epidemics/IndependentCascades.rst b/docs/reference/models/epidemics/IndependentCascades.rst
@@ -38,7 +38,7 @@ Edge threshold  Edge   float in [0, 1]   0.1     False      Edge threshold
 
 The initial infection status can be defined via:
 
-    - **percentage_infected**: Model Parameter, float in [0, 1]
+    - **fraction_infected**: Model Parameter, float in [0, 1]
     - **Infected**: Status Parameter, set of nodes
 
 The two options are mutually exclusive and the latter takes precedence over the former.
@@ -93,7 +93,7 @@ In the code below is shown an example of instantiation and execution of an Indep
         
     # Model Configuration
     config = mc.Configuration()
-    config.add_model_parameter('percentage_infected', 0.1)
+    config.add_model_parameter('fraction_infected', 0.1)
     
     # Setting the edge parameters
     threshold = 0.1
@@ -106,4 +106,4 @@ In the code below is shown an example of instantiation and execution of an Indep
     iterations = model.iteration_bunch(200)
 
 
-.. [#] D. Kempe, J. Kleinberg, and E. Tardos, “Maximizing the spread of influence through a social network,” in Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ser. KDD ’03, 2003, pp. 137–146.
+.. [#] D. Kempe, J. Kleinberg, and E. Tardos, “Maximizing the spread of influence through a social network,” in Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ser. KDD ’03, 2003, pp. 137–146.
diff --git a/docs/reference/models/epidemics/KThreshold.rst b/docs/reference/models/epidemics/KThreshold.rst
@@ -38,7 +38,7 @@ threshold           Node   float in [0, 1]   0.1     False      Individual thres
 
 The initial infection status can be defined via:
 
-    - **percentage_infected**: Model Parameter, float in [0, 1]
+    - **fraction_infected**: Model Parameter, float in [0, 1]
     - **Infected**: Status Parameter, set of nodes
 
 The initial blocked nodes can be defined via:
@@ -99,7 +99,7 @@ In the code below is shown an example of instantiation and execution of a Kertes
     config = mc.Configuration()
     config.add_model_parameter('adopter_rate', 0.4)
     config.add_model_parameter('percentage_blocked', 0.1)
-    config.add_model_parameter('percentage_infected', 0.1)
+    config.add_model_parameter('fraction_infected', 0.1)
 
     # Setting node parameters
     threshold = 0.25
@@ -113,4 +113,4 @@ In the code below is shown an example of instantiation and execution of a Kertes
 
 
 .. [#] Z. Ruan, G. In ̃iguez, M. Karsai, and J. Kertész, “Kinetics of social contagion,” Phys. Rev. Lett., vol. 115, p. 218702, Nov 2015.
-.. [#] D. J. Watts, “A simple model of global cascades on random networks,” Proceedings of the National Academy of Sciences, vol. 99, no. 9, pp. 5766–5771, 2002.
+.. [#] D. J. Watts, “A simple model of global cascades on random networks,” Proceedings of the National Academy of Sciences, vol. 99, no. 9, pp. 5766–5771, 2002.
diff --git a/docs/reference/models/epidemics/Profile.rst b/docs/reference/models/epidemics/Profile.rst
@@ -47,7 +47,7 @@ adopter_rate  Model  float in [0, 1]   0       False      Autonomous adoption
 
 The initial infection status can be defined via:
 
-    - **percentage_infected**: Model Parameter, float in [0, 1]
+    - **fraction_infected**: Model Parameter, float in [0, 1]
     - **Infected**: Status Parameter, set of nodes
 
 The two options are mutually exclusive and the latter takes precedence over the former.
@@ -101,7 +101,7 @@ In the code below is shown an example of instantiation and execution of a Profil
     config = mc.Configuration()
     config.add_model_parameter('blocked', 0)
     config.add_model_parameter('adopter_rate', 0)
-    config.add_model_parameter('percentage_infected', 0.1)
+    config.add_model_parameter('fraction_infected', 0.1)
 
     # Setting nodes parameters
     profile = 0.15