1) ages_in_days was using entire array including expansion slots; 2) …

…Removed unneeded split_index 'min' calc; 3) Added expected_new_deaths_per_year since probably more useful to the way FUSION calculates population.

src/idmlaser/numpynumba/population.py

@@ -197,7 +197,7 @@ def eliminate_eulas(self, eula_age_in_years: float):
 
  # Calculate the age of each individual in days
  current_day = 0 # Adjust this if you have a simulation day tracker
- ages_in_days = current_day - self.__dict__['dob']
+ ages_in_days = current_day - self.__dict__['dob'][:self.count]
 
  # Calculate number of expansion slots
  birth_cap = (self.capacity-self.count) * 4 # hack coz right now seem to have "too many" births
@@ -210,9 +210,6 @@ def eliminate_eulas(self, eula_age_in_years: float):
  split_index = np.searchsorted(ages_in_days[sorted_indices], age_threshold_in_days)
  print( f"split_index = {split_index}" )
 
- # Ensure split_index does not exceed the size of the sorted_indices; probably unnecessary
- split_index = min(split_index, len(sorted_indices))
-
  # Keep only the individuals below the age threshold
  for key, value in self.__dict__.items():
  if isinstance(value, np.ndarray) and value.size == self._capacity:
@@ -254,6 +251,7 @@ def expected_pops_over_years(self, eula_age_in_years=5):
  unique_nodeids = np.unique(nodeid_filtered)
  nodeid_indices = {nodeid: i for i, nodeid in enumerate(unique_nodeids)}
  self.total_population_per_year = np.zeros((len(unique_nodeids), 20), dtype=int)
+ self.expected_new_deaths_per_year = np.zeros((len(unique_nodeids), 20), dtype=int)
 
  # Accumulate deaths by year and node
  for i in tqdm(range(len(death_year))):
@@ -263,6 +261,11 @@ def expected_pops_over_years(self, eula_age_in_years=5):
  # Convert deaths to populations by subtracting cumulative deaths from the initial population
  initial_population_counts = np.bincount(nodeid_filtered, minlength=len(unique_nodeids))
  cumulative_deaths = np.cumsum(self.total_population_per_year, axis=1)
+
+ # Calculate new deaths per year
+ self.expected_new_deaths_per_year[:, 0] = self.total_population_per_year[:, 0]
+ self.expected_new_deaths_per_year[:, 1:] = np.diff(cumulative_deaths, axis=1)
+
  self.total_population_per_year = initial_population_counts[:, None] - cumulative_deaths
 
  # Optional: print the resulting populations