Changed 'New Infections' to New_Infections like a good unix boy and t…

…o make post-processing easier.

jb/src/post_proc.py

@@ -40,9 +40,9 @@ def get_cases(node_id=0):
         # Calculate the week number based on Timestep
         df_filtered['Week'] = df_filtered['Timestep'] // 7
 
-        # Group by Week and sum the New Infections for each week
-        weekly_new_infections = df_filtered.groupby('Week')['New Infections'].sum().reset_index()
-        return weekly_new_infections["New Infections"].to_numpy()
+        # Group by Week and sum the New_Infections for each week
+        weekly_new_infections = df_filtered.groupby('Week')['New_Infections'].sum().reset_index()
+        return weekly_new_infections["New_Infections"].to_numpy()
 
     def log_transform(x, debug=1):
         """
@@ -79,7 +79,7 @@ def analyze_ccs():
     cases_df = cases_df[cases_df["Timestep"] > burnin]
 
     # Set the parameters for the binomial distribution
-    num_trials = cases_df['New Infections']  # Number of trials (events)
+    num_trials = cases_df['New_Infections']  # Number of trials (events)
     prob_success = 0.5  # Probability of success (observation)
 
     cases_df['Observed Infections'] = binom.rvs(num_trials, prob_success)
@@ -171,7 +171,7 @@ def analyze():
     # 1) Total new infections per year...
 
     # Calculate the total number of new infections
-    total_new_infections = df["New Infections"].sum()
+    total_new_infections = df["New_Infections"].sum()
 
     # Calculate the number of years
     num_years = ( df["Timestep"].max()-burnin) / 365  # Assuming 365 timesteps per year
@@ -183,7 +183,7 @@ def analyze():
     df_london = df[df["Node"] == 0]
 
     # Calculate the total number of new infections in London
-    total_new_infections_london = df_london["New Infections"].sum()
+    total_new_infections_london = df_london["New_Infections"].sum()
 
     # Calculate the average number of new infections in London per year
     average_new_infections_per_year_london = total_new_infections_london / num_years

jb/src/report.py

@@ -40,7 +40,7 @@ def init():
         else:
             csvfile = open( settings.report_filename, 'w', newline='', buffering=int(1024*1024))  
         csvwriter = csv.writer(csvfile)
-        csvwriter.writerow(['Timestep', 'Node', 'Susceptible', 'Infected', 'New Infections', 'Recovered', 'Births'])
+        csvwriter.writerow(['Timestep', 'Node', 'Susceptible', 'Infected', 'New_Infections', 'Recovered', 'Births'])
     if publish_report:
         global client_sock
         client_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)

jb/src/sir_numpy.py

@@ -412,7 +412,7 @@ def calculate_new_infections( data, inf, sus, totals ):
     ret_ni = np.zeros(settings.num_nodes).astype( np.uint32 )
     for node in range(settings.num_nodes):
         ret_ni[node] = int(sus[node]*inf[node]*settings.base_infectivity*totals[node])
-    #print( f"New Infections: {new_infections} = {np.array(sorted(sus.values()))} * {np.array(sorted(inf.values()))} * {settings.base_infectivity}" )
+    #print( f"New_Infections: {new_infections} = {np.array(sorted(sus.values()))} * {np.array(sorted(inf.values()))} * {settings.base_infectivity}" )
     return ret_ni
 
 def handle_transmission_by_node( data, new_infections, node=0 ):

jb/src/sir_numpy_c.py

@@ -405,7 +405,7 @@ def cni_c():
     return new_infections 
 
 def handle_transmission_by_node( data, new_infections, susceptible_counts, node=0 ):
-    # print( f"DEBUG: New Infections: {new_infections}" )
+    # print( f"DEBUG: New_Infections: {new_infections}" )
     # print( f"DEBUG: susceptible_counts: {susceptible_counts}" )
     if new_infections[node]>susceptible_counts[node]:
         raise ValueError( f"ERROR: Asked for {new_infections[node]} new infections but only {susceptible_counts[node]} susceptibles exist in node {node}." )
@@ -445,7 +445,7 @@ def handle_new_infections_np(new_infections):
 
 def handle_transmission( data_in, new_infections_in, susceptible_counts ):
     # We want to do this in parallel;
-    #print( f"DEBUG: New Infections: {new_infections_in}" )
+    #print( f"DEBUG: New_Infections: {new_infections_in}" )
     update_ages_lib.handle_new_infections_mp(
         unborn_end_idx, # we waste a few cycles now coz the first block is immune from maternal immunity
         dynamic_eula_idx,

jb/tests/test_calc_new_inf_c.py

@@ -459,8 +459,8 @@ def test_calculate_new_infections(self):
 
             # Compare with reference results
             #self.assert( new_infections_timestep == ref_results[timestep], f"Results mismatch at timestep {timestep}" )
-            #print( f"New Infections [test] @ {timestep} = {new_infections_timestep}\n" )
-            #print( f"New Infections [ref][ @ {timestep} = {NI[timestep]}\n" )
+            #print( f"New_Infections [test] @ {timestep} = {new_infections_timestep}\n" )
+            #print( f"New_Infections [ref][ @ {timestep} = {NI[timestep]}\n" )
             self.assertAlmostEqual( new_infections_timestep, NI[timestep], delta=10 ) # , "Results mismatch at timestep " + str(timestep)
 
         # Call run_ref_model to get reference results
@@ -505,8 +505,8 @@ def test_calculate_new_infections(self):
 
             # Compare with reference results
             #self.assert( new_infections_timestep == ref_results[timestep], f"Results mismatch at timestep {timestep}" )
-            #print( f"New Infections [test] @ {timestep} = {new_infections_timestep}\n" )
-            #print( f"New Infections [ref][ @ {timestep} = {NI[timestep]}\n" )
+            #print( f"New_Infections [test] @ {timestep} = {new_infections_timestep}\n" )
+            #print( f"New_Infections [ref][ @ {timestep} = {NI[timestep]}\n" )
             self.assertAlmostEqual( new_infections_timestep, NI[timestep], delta=10 ) # , "Results mismatch at timestep " + str(timestep)
 
 

jb/utils/add_observed_inc.py

@@ -9,7 +9,7 @@
 df = pd.read_csv(input_file)
 
 # Set the parameters for the binomial distribution
-num_trials = df['New Infections']  # Number of trials (events)
+num_trials = df['New_Infections']  # Number of trials (events)
 prob_success = 0.5  # Probability of success (observation)
 
 # Generate the binomial distribution

jb/utils/folium_animate_from_sqlite.py

@@ -9,7 +9,8 @@
 from folium.plugins import HeatMapWithTime
 
 # Connect to the SQLite database (or create it)
-conn = sqlite3.connect(':memory:')
+#conn = sqlite3.connect(':memory:')
+conn = sqlite3.connect('ew.db')
 cursor = conn.cursor()
 
 def preproc( sim_report_file ):
@@ -34,7 +35,7 @@ def preproc( sim_report_file ):
     # Create the view
     cursor.execute("""
     CREATE VIEW cases AS
-    SELECT Timestep, Node, Name, Latitude, Longitude, "New Infections"
+    SELECT Timestep, Node, Name, Latitude, Longitude, New_Infections
     FROM engwal, cities
     WHERE engwal.Node = cities.ID
     """)
@@ -48,7 +49,7 @@ def process( output_file ):
     m = folium.Map(location=(birmingham_location[0],birmingham_location[1]), zoom_start=8) # Create a list to store the data for HeatMapWithTime
 
     start_time=800
-    cursor.execute(f'SELECT CAST(Timestep AS INT), Latitude, Longitude, CAST("New Infections" AS INT) FROM cases WHERE (CAST(Timestep AS INT)>{start_time})')
+    cursor.execute(f'SELECT CAST(Timestep AS INT), Latitude, Longitude, CAST(New_Infections AS INT) FROM cases WHERE (CAST(Timestep AS INT)>{start_time})')
     raw_data = cursor.fetchall()
 
     # Group the data by timestep