See changelog 21 jul 2021

changelog.md

@@ -1,9 +1,19 @@
 
 # Opqua Changelog
 
+## v0.2.2
+## 21 July 2021
+- change compositionPLot remove_legend behavior to fix bug
+- change pathogenDistanceDf seq_names behavior to fix bug
+- reduce mean inoculum from hosts into vectors to reflect malaria cycle
+- modify infectHost and infectVector inoculation behavior so that mean_inoculum
+does not affect overall transmission rate; each infection now results in at
+least 1 pathogen transfer (if not containing and not immune to the pathogen
+genome sampled)
+
 ## v0.2.1
 ## 1 June 2021
-- Update version tags
+- update version tags
 
 ## v0.2.0
 ## 1 June 2021
@@ -44,3 +54,5 @@
 - update tutorials. Done, tested
 
 ### TODO:
+- correctly update arguments in function documentation and README for
+compositionPlot family functions

opqua/internal/data.py

@@ -560,7 +560,8 @@ def pathogenDistanceDf(
 
     names = sequences
     if len(seq_names) > 0:
-        names = seq_names
+        new_names = seq_names * int( np.ceil( len(names) / len(seq_names) ) )
+        names = new_names[0:len(names)]
 
     dis_df = pd.DataFrame( dis_mat, index=names, columns=names )
 

opqua/internal/host.py

@@ -112,44 +112,50 @@ def infectHost(self, host):
         inoculum by drawing from a Poisson distribution with a mean equal to the
         mean inoculum size of the organism being infected weighted by each
         genome's fitness as a fraction of the total in the infector as the
-        probability of each trial. Each pathogen present in the inoculum will be
-        added to the infected organism, if it does not have protection from the
-        pathogen's genome. Fitnesses are computed for the pathogens' genomes in
-        the infected organism, and the organism is included in the poplation's
-        infected list if appropriate.
+        probability of each trial (minimum 1 pathogen transfered). Each pathogen
+        present in the inoculum will be added to the infected organism, if it
+        does not have protection from the pathogen's genome. Fitnesses are
+        computed for the pathogens' genomes in the infected organism, and the
+        organism is included in the poplation's infected list if appropriate.
 
         Arguments:
-        host -- the host to be infected (Host)
+        vector -- the vector to be infected (Vector)
 
         Returns:
         whether or not the model has changed state (Boolean)
         """
 
         changed = False
-        for genome,fitness in self.pathogens.items():
+
+        genomes = list( self.pathogens.keys() )
+        fitness_weights = [
+            self.pathogens[g] / self.sum_fitness for g in genomes
+            ]
+
+        for _ in range( max( np.random.poisson(
+                self.population.mean_inoculum_host
+                ), 1 ) ):
+            genome = np.random.choice( genomes, p=fitness_weights )
             if genome not in host.pathogens.keys() and not any(
                     [ p in genome for p in host.protection_sequences ]
-                    ) and ( np.random.poisson(
-                    self.population.mean_inoculum_host
-                    * fitness / self.sum_fitness, 1
-                    ) > 0 ):
+                    ):
                 host.acquirePathogen(genome)
                 changed = True
 
         return changed
 
     def infectVector(self, vector):
-        """Infect given host with a sample of this vector's pathogens.
+        """Infect given host with a sample of this host's pathogens.
 
         Each pathogen in the infector is sampled as present or absent in the
         inoculum by drawing from a Poisson distribution with a mean equal to the
         mean inoculum size of the organism being infected weighted by each
         genome's fitness as a fraction of the total in the infector as the
-        probability of each trial. Each pathogen present in the inoculum will be
-        added to the infected organism, if it does not have protection from the
-        pathogen's genome. Fitnesses are computed for the pathogens' genomes in
-        the infected organism, and the organism is included in the poplation's
-        infected list if appropriate.
+        probability of each trial (minimum 1 pathogen transfered). Each pathogen
+        present in the inoculum will be added to the infected organism, if it
+        does not have protection from the pathogen's genome. Fitnesses are
+        computed for the pathogens' genomes in the infected organism, and the
+        organism is included in the poplation's infected list if appropriate.
 
         Arguments:
         vector -- the vector to be infected (Vector)
@@ -159,13 +165,19 @@ def infectVector(self, vector):
         """
 
         changed = False
-        for genome,fitness in self.pathogens.items():
+
+        genomes = list( self.pathogens.keys() )
+        fitness_weights = [
+            self.pathogens[g] / self.sum_fitness for g in genomes
+            ]
+
+        for _ in range( max( np.random.poisson(
+                self.population.mean_inoculum_vector
+                ), 1 ) ):
+            genome = np.random.choice( genomes, p=fitness_weights )
             if genome not in vector.pathogens.keys() and not any(
                     [ p in genome for p in vector.protection_sequences ]
-                    ) and ( np.random.poisson(
-                    self.population.mean_inoculum_vector
-                    * fitness / self.sum_fitness, 1
-                    ) > 0 ):
+                    ):
                 vector.acquirePathogen(genome)
                 changed = True
 

opqua/internal/plot.py

@@ -309,7 +309,7 @@ def compositionPlot(
 
         if remove_legend:
             pd.DataFrame(
-                labels, columns=['Groups']
+                labs, columns=['Groups']
                 ).to_csv(file_name.split('.')[0]+'_labels.csv')
         else:
             handles, labels = ax.get_legend_handles_labels()

opqua/internal/vector.py

@@ -110,17 +110,17 @@ def acquirePathogen(self, genome):
             self.population.infected_vectors.append(self)
 
     def infectHost(self, host):
-        """Infect given host with a sample of this host's pathogens.
+        """Infect given host with a sample of this vector's pathogens.
 
         Each pathogen in the infector is sampled as present or absent in the
         inoculum by drawing from a Poisson distribution with a mean equal to the
         mean inoculum size of the organism being infected weighted by each
         genome's fitness as a fraction of the total in the infector as the
-        probability of each trial. Each pathogen present in the inoculum will be
-        added to the infected organism, if it does not have protection from the
-        pathogen's genome. Fitnesses are computed for the pathogens' genomes in
-        the infected organism, and the organism is included in the poplation's
-        infected list if appropriate.
+        probability of each trial (minimum 1 pathogen transfered). Each pathogen
+        present in the inoculum will be added to the infected organism, if it
+        does not have protection from the pathogen's genome. Fitnesses are
+        computed for the pathogens' genomes in the infected organism, and the
+        organism is included in the poplation's infected list if appropriate.
 
         Arguments:
         vector -- the vector to be infected (Vector)
@@ -130,13 +130,19 @@ def infectHost(self, host):
         """
 
         changed = False
-        for genome,fitness in self.pathogens.items():
+
+        genomes = list( self.pathogens.keys() )
+        fitness_weights = [
+            self.pathogens[g] / self.sum_fitness for g in genomes
+            ]
+
+        for _ in range( max( np.random.poisson(
+                self.population.mean_inoculum_host
+                ), 1 ) ):
+            genome = np.random.choice( genomes, p=fitness_weights )
             if genome not in host.pathogens.keys() and not any(
                     [ p in genome for p in host.protection_sequences ]
-                    ) and ( np.random.poisson(
-                    self.population.mean_inoculum_host
-                    * fitness / self.sum_fitness, 1
-                    ) > 0 ):
+                    ):
                 host.acquirePathogen(genome)
                 changed = True
 
@@ -149,11 +155,11 @@ def infectVector(self, vector):
         inoculum by drawing from a Poisson distribution with a mean equal to the
         mean inoculum size of the organism being infected weighted by each
         genome's fitness as a fraction of the total in the infector as the
-        probability of each trial. Each pathogen present in the inoculum will be
-        added to the infected organism, if it does not have protection from the
-        pathogen's genome. Fitnesses are computed for the pathogens' genomes in
-        the infected organism, and the organism is included in the poplation's
-        infected list if appropriate.
+        probability of each trial (minimum 1 pathogen transfered). Each pathogen
+        present in the inoculum will be added to the infected organism, if it
+        does not have protection from the pathogen's genome. Fitnesses are
+        computed for the pathogens' genomes in the infected organism, and the
+        organism is included in the poplation's infected list if appropriate.
 
         Arguments:
         vector -- the vector to be infected (Vector)
@@ -163,13 +169,19 @@ def infectVector(self, vector):
         """
 
         changed = False
-        for genome,fitness in self.pathogens.items():
+
+        genomes = list( self.pathogens.keys() )
+        fitness_weights = [
+            self.pathogens[g] / self.sum_fitness for g in genomes
+            ]
+
+        for _ in range( max( np.random.poisson(
+                self.population.mean_inoculum_vector
+                ), 1 ) ):
+            genome = np.random.choice( genomes, p=fitness_weights )
             if genome not in vector.pathogens.keys() and not any(
                     [ p in genome for p in vector.protection_sequences ]
-                    ) and ( np.random.poisson(
-                    self.population.mean_inoculum_vector
-                    * fitness / self.sum_fitness, 1
-                    ) > 0 ):
+                    ):
                 vector.acquirePathogen(genome)
                 changed = True
 

opqua/model.py

@@ -363,7 +363,7 @@ def newSetup(
             mean_inoculum_host = \
                 1e2 if mean_inoculum_host is None else mean_inoculum_host
             mean_inoculum_vector = \
-                1e2 if mean_inoculum_vector is None else mean_inoculum_vector
+                1 if mean_inoculum_vector is None else mean_inoculum_vector
             recovery_rate_host = \
                 1e-1 if recovery_rate_host is None else recovery_rate_host
             recovery_rate_vector = \