Added initial age split functionality

src/pydisagg/age_split/age_var.py

@@ -0,0 +1,37 @@
+import numpy as np
+import pandas as pd
+
+age_group_id = [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19, 20, 30, 31, 32, 235]
+age_group_years_start = [0, 0.01917808, 0.07671233, 1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95]
+age_group_years_end = [0.01917808, 0.07671233, 1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90,95, 125]
+#TODO Handle low age groups more elegantly, we're just doing 1 year buckets for now, we should have an exception for age groups under 1, and explicitly model those as single
+
+age_mid = (np.array(age_group_years_start) + np.array(age_group_years_end)) / 2
+
+age_id_map = pd.DataFrame({
+    'age_group_id': age_group_id,
+    'age_group_years_start': age_group_years_start,
+    'age_group_years_end': age_group_years_end,
+    'age_mid': age_mid
+})
+
+rename_dict_dis = {
+    "year_start": "year_start", 
+    "year_end": "year_end", 
+    "location_id": "location_id",
+    "sex": "sex_id",
+    "age_start": "original_data_age_start",
+    "age_end": "original_data_age_end",
+    "mean": "value",
+    "standard_error": "SE" 
+}
+match_cols = [
+    "row_id",
+    "year_start",
+    "year_end",
+    "original_data_age_start",
+    "original_data_age_end",
+    "sex_id",
+    "value",
+    "SE",
+]

src/pydisagg/models.py

@@ -28,9 +28,6 @@ def fit_beta(
         observed_total: float,
         rate_pattern: NDArray,
         bucket_populations: NDArray,
-        lower_guess: Optional[float] = -50,
-        upper_guess: Optional[float] = 50,
-        verbose: Optional[int] = 0
     ) -> None:
         """
         Custom fit_beta for this model, as we can do it without rootfinding.