Structural Design Pattern

seirsplus/networks.py

@@ -7,9 +7,43 @@
 
 import matplotlib.pyplot as pyplot
 
+from abc import ABCMeta, abstractmethod
 
 
+class INetwork(metaclass = ABCMeta):    #class IBuilder(metaclass = ABCMeta)
+    "Creational Pattern: The Builder Interface"
 
+@staticmethod
+@abstractmethod
+def generate_workplace_contact_network():
+    "Build a workplace contact network"
+
+@staticmethod
+@abstractmethod
+def generate_demographic_contact_network():
+    "Build a demographic contact network"
+
+@staticmethod
+@abstractmethod
+def household_country_data():
+    "Household data for the United States"
+
+@staticmethod
+@asbtractmethod
+def custom_exponential_graph():
+    "Provides the Graph "
+
+
+@staticmethod
+@abstractmethod
+def get_result():
+    "Return all the values"   
+
+
+
+class Network(INetwork):        #class Builder(IBuilder):
+    "The Concrete Builder"
+
 def generate_workplace_contact_network(num_cohorts=1, num_nodes_per_cohort=100, num_teams_per_cohort=10,
                                         mean_intracohort_degree=6, pct_contacts_intercohort=0.2,
                                         farz_params={'alpha':5.0, 'gamma':5.0, 'beta':0.5, 'r':1, 'q':0.0, 'phi':10, 
@@ -692,7 +726,33 @@ def plot_degree_distn(graph, max_degree=None, show=True, use_seaborn=True):
         pyplot.show()
 
 
+    #The Product
+    class SIERSApp():
+        "SEIRS Network Models"
+
+        def __init__(self):
+            self.parts = []
+
 
+    #The Director Class
+    class NetworkDirector:
+        "Builds the complex representation"   
+
+        @staticmethod
+        def construct():
+            return Builder()\
+                   .generate_workplace_contact_network()\
+                   .generate_demographic_contact_network()\
+                   .household_data()\
+                   .custom_exponential_graph()
+
+                   #Omitted plot_degree_distn()  
+
+
+
+    #The Client
+        SEIRSApp = NetworkDirector.construct()
+        print(APP.parts)
 
 
 

seirsplus/utilities.py

@@ -1,69 +1,115 @@
 import numpy
 import matplotlib.pyplot as pyplot
+from abc import ABCMeta, abstractmethod
+
+
+class Target(metaclass = ABCMeta):
+        "Target Interface"
+
+	@abstractmethod 
+        def targetRequest(self):
+                return "Behaviour of the target"
+
+        def __init__(self, adaptee: Adaptee):
+        	self.adaptee = adaptee    
+
+
+class Adaptee:
+
+        def specificRequest(self):
+                pass 
+
+
+class Adapter(Target):
+
+	def targetRequest(self):
+		self.adaptee.specificRequest()
+
+
+
+
 
 def gamma_dist(mean, coeffvar, N):
-	scale = mean*coeffvar**2
-	shape = mean/scale
-	return numpy.random.gamma(scale=scale, shape=shape, size=N)
+        scale = mean*coeffvar**2
+        shape = mean/scale
+        return numpy.random.gamma(scale=scale, shape=shape, size=N)
 
 
 def dist_info(dists, names=None, plot=False, bin_size=1, colors=None, reverse_plot=False):
-    dists  = [dists] if not isinstance(dists, list) else dists
-    names  = [names] if(names is not None and not isinstance(names, list)) else (names if names is not None else [None]*len(dists))
-    colors = [colors] if(colors is not None and not isinstance(colors, list)) else (colors if colors is not None else pyplot.rcParams['axes.prop_cycle'].by_key()['color'])
-    
-    for i, (dist, name) in enumerate(zip(dists, names)):
+        dists  = [dists] if not isinstance(dists, list) else dists
+        names  = [names] if(names is not None and not isinstance(names, list)) else (names if names is not None else [None]*len(dists))
+        colors = [colors] if(colors is not None and not isinstance(colors, list)) else (colors if colors is not None else pyplot.rcParams['axes.prop_cycle'].by_key()['color'])
+
+for i, (dist, name) in enumerate(zip(dists, names)):
         print((name+": " if name else "")+" mean = %.2f, std = %.2f, 95%% CI = (%.2f, %.2f)" % (numpy.mean(dist), numpy.std(dist), numpy.percentile(dist, 2.5), numpy.percentile(dist, 97.5)))
         print()
 
         if(plot):
-            pyplot.hist(dist, bins=numpy.arange(0, int(max(dist)+1), step=bin_size), label=(name if name else False), color=colors[i], edgecolor='white', alpha=0.6, zorder=(-1*i if reverse_plot else i))
+                pyplot.hist(dist, bins=numpy.arange(0, int(max(dist)+1), step=bin_size), label=(name if name else False), color=colors[i], edgecolor='white', alpha=0.6, zorder=(-1*i if reverse_plot else i))
 
-    if(plot):
-        pyplot.ylabel('num nodes')
-        pyplot.legend(loc='upper right')
-        pyplot.show()
+        if(plot):
+                pyplot.ylabel('num nodes')
+                pyplot.legend(loc='upper right')
+                pyplot.show()
 
 
 def network_info(networks, names=None, plot=False, bin_size=1, colors=None, reverse_plot=False):
-    import networkx
-    networks = [networks] if not isinstance(networks, list) else networks
-    names    = [names] if not isinstance(names, list) else names
-    colors = [colors] if(colors is not None and not isinstance(colors, list)) else (colors if colors is not None else pyplot.rcParams['axes.prop_cycle'].by_key()['color'])
+        import networkx
+        networks = [networks] if not isinstance(networks, list) else networks
+        names    = [names] if not isinstance(names, list) else names
+        colors = [colors] if(colors is not None and not isinstance(colors, list)) else (colors if colors is not None else pyplot.rcParams['axes.prop_cycle'].by_key()['color'])
 
-    for i, (network, name) in enumerate(zip(networks, names)):
+        for i, (network, name) in enumerate(zip(networks, names)):
 
-        degree        = [d[1] for d in network.degree()]
+                degree        = [d[1] for d in network.degree()]
 
         if(name):
-            print(name+":")
-        print("Degree: mean = %.2f, std = %.2f, 95%% CI = (%.2f, %.2f)\n        coeff var = %.2f" 
-              % (numpy.mean(degree), numpy.std(degree), numpy.percentile(degree, 2.5), numpy.percentile(degree, 97.5), 
-                 numpy.std(degree)/numpy.mean(degree)))
-        r = networkx.degree_assortativity_coefficient(network)
-        print("Assortativity:    %.2f" % (r))
-        c = networkx.average_clustering(network)
-        print("Clustering coeff: %.2f" % (c))
-        print()
+                print(name+":")
+                print("Degree: mean = %.2f, std = %.2f, 95%% CI = (%.2f, %.2f)\n        coeff var = %.2f" 
+                      % (numpy.mean(degree), numpy.std(degree), numpy.percentile(degree, 2.5), numpy.percentile(degree, 97.5), 
+                         numpy.std(degree)/numpy.mean(degree)))
+                r = networkx.degree_assortativity_coefficient(network)
+                print("Assortativity:    %.2f" % (r))
+                c = networkx.average_clustering(network)
+                print("Clustering coeff: %.2f" % (c))
+                print()
 
         if(plot):
-            pyplot.hist(degree, bins=numpy.arange(0, int(max(degree)+1), step=bin_size), label=(name+" degree" if name else False), color=colors[i], edgecolor='white', alpha=0.6, zorder=(-1*i if reverse_plot else i))
+                    pyplot.hist(degree, bins=numpy.arange(0, int(max(degree)+1), step=bin_size), label=(name+" degree" if name else False), color=colors[i], edgecolor='white', alpha=0.6, zorder=(-1*i if reverse_plot else i))
 
-    if(plot):
-        pyplot.ylabel('num nodes')
-        pyplot.legend(loc='upper right')
-        pyplot.show()
+        if(plot):
+                pyplot.ylabel('num nodes')
+                pyplot.legend(loc='upper right')
+                pyplot.show()
 
 
 def results_summary(model):
-    print("total percent infected: %0.2f%%" % ((model.total_num_infected()[-1]+model.total_num_recovered()[-1])/model.numNodes * 100) )
-    print("total percent fatality: %0.2f%%" % (model.numF[-1]/model.numNodes * 100) )
-    print("peak  pct hospitalized: %0.2f%%" % (numpy.max(model.numH)/model.numNodes * 100) )
+        print("total percent infected: %0.2f%%" % ((model.total_num_infected()[-1]+model.total_num_recovered()[-1])/model.numNodes * 100) )
+        print("total percent fatality: %0.2f%%" % (model.numF[-1]/model.numNodes * 100) )
+        print("peak  pct hospitalized: %0.2f%%" % (numpy.max(model.numH)/model.numNodes * 100) )
+
+
+
+
+def client(target:Target):
+        print(target.targetRequest())
+
 
+        if __name__ == "main":
+                print("Client ")
+                client(target)
+                print("\n")
 
 
+                adaptee = Adaptee()
+                print("Client")
+                print("Adaptee")
+                print("Client")
 
 
+        adapter = Adapter(adaptee)
+        adapter.targetRequest()
+        client = (adapter)