Better handle the pre-time path population distribution

ogcore/aggregates.py

@@ -133,8 +133,10 @@ def get_B(b, p, method, preTP):
         if preTP:
             part1 = b * np.transpose(p.omega_S_preTP * p.lambdas)
             omega_extended = np.append(p.omega_S_preTP[1:], [0.0])
-            imm_extended = np.append(p.imm_rates[0, 1:], [0.0])
-            pop_growth_rate = p.g_n[0]
+            imm_extended = np.append(
+                p.imm_rates_pre_TP[1:], [0.0]
+            )
+            pop_growth_rate = p.g_n_pretP
         else:
             part1 = b * np.transpose(p.omega_SS * p.lambdas)
             omega_extended = np.append(p.omega_SS[1:], [0.0])
@@ -189,8 +191,8 @@ def get_BQ(r, b_splus1, j, p, method, preTP):
     if method == "SS":
         if preTP:
             omega = p.omega_S_preTP
-            pop_growth_rate = p.g_n[0]
-            rho = p.rho[0, :]
+            pop_growth_rate = p.g_n_preTP
+            rho = p.rho_preTP
         else:
             omega = p.omega_SS
             pop_growth_rate = p.g_n_ss
@@ -206,7 +208,7 @@ def get_BQ(r, b_splus1, j, p, method, preTP):
             p.omega_S_preTP.reshape(1, p.S), p.omega[: p.T - 1, :], axis=0
         )
         rho = np.append(
-            p.rho[0, :].reshape(1, p.S), p.rho[: p.T - 1, :], axis=0
+            p.rho_preTP.reshape(1, p.S), p.rho[: p.T - 1, :], axis=0
         )
 
         if j is not None:

ogcore/default_parameters.json

@@ -4547,6 +4547,24 @@
             }
         }
     },
+    "g_n_preTP": {
+        "title": "Population growth rate from year before model start to start year",
+        "description": "Population growth rate from year before model start to start year.",
+        "section_1": "Demographic Parameters",
+        "notes": "",
+        "type": "float",
+        "value": [
+            {
+                "value": 0.0012907765315350872
+            }
+        ],
+        "validators": {
+            "range": {
+                "min": -1.0,
+                "max": 1.0
+            }
+        }
+    },
     "imm_rates": {
         "title": "Immigration rates over the time path.",
         "description": "Immigration rates over the time path.",
@@ -4568,6 +4586,45 @@
             }
         }
     },
+    "imm_rates_pre_TP": {
+        "title": "Immigration rates in period before model start year",
+        "description": "Immigration rates in period before model start year.",
+        "section_1": "Demographic Parameters",
+        "notes": "",
+        "type": "float",
+        "number_dims": 1,
+        "value": [
+            {
+                "value":
+                    [ 6.21124324e-03,  6.69539127e-03,  7.24160599e-03,  7.59057707e-03,
+        7.64614775e-03,  7.51951110e-03,  7.06681489e-03,  6.56429771e-03,
+        6.07319754e-03,  5.50700033e-03,  4.94732741e-03,  4.54958259e-03,
+        4.23425001e-03,  3.97403995e-03,  3.67752183e-03,  3.38021287e-03,
+        3.00514338e-03,  2.71916344e-03,  2.41686669e-03,  2.13548533e-03,
+        1.86850952e-03,  1.63930855e-03,  1.45713458e-03,  1.34212247e-03,
+        1.27164811e-03,  9.88138261e-04,  8.42267890e-04,  7.97801211e-04,
+        6.63231805e-04,  4.87948389e-04,  4.32979019e-04,  5.60040782e-04,
+        4.55369916e-04,  4.13717216e-04,  4.55029226e-04,  4.52104109e-04,
+        5.58645176e-04,  6.14631276e-04,  5.66684121e-04,  5.11408015e-04,
+        4.68968748e-04,  4.86541731e-04,  1.87084644e-04, -2.33525940e-05,
+        1.21315737e-03,  1.05324177e-03,  8.29011129e-07, -1.80456466e-04,
+        6.44696838e-04,  1.08838720e-03,  6.18137660e-04,  4.41473826e-04,
+        6.10186803e-04,  6.58633423e-04,  5.63207404e-04,  5.34295717e-04,
+        6.00114993e-04,  6.29050763e-04,  8.43098879e-04,  9.20493620e-04,
+        9.68908696e-04,  1.26374022e-03,  1.60616956e-03,  1.72840304e-03,
+        1.50429547e-03,  1.03459297e-03,  8.01449649e-04,  9.28625040e-04,
+        1.60993336e-03,  2.23227102e-03,  3.53451125e-03,  5.32480275e-03,
+        7.84622608e-03,  1.08220386e-02,  1.30810434e-02,  1.64519720e-02,
+        1.88010017e-02,  2.12725905e-02,  2.17955114e-02,  1.92400972e-02]
+    }
+        ],
+        "validators": {
+            "range": {
+                "min": -1.0,
+                "max": 1.0
+            }
+        }
+    },
     "rho": {
         "title": "Age-specific mortality rates.",
         "description": "Age-specific mortality rates.",
@@ -4587,6 +4644,44 @@
             }
         }
     },
+    "rho_preTP": {
+        "title": "Age-specific mortality rates.",
+        "description": "Age-specific mortality rates.",
+        "section_1": "Demographic Parameters",
+        "notes": "",
+        "type": "float",
+        "number_dims": 1,
+        "value": [
+            {
+                "value": [7.32853396e-04, 8.17119742e-04, 8.78872967e-04, 9.11652521e-04,
+       9.22980233e-04, 9.27340681e-04, 9.35710239e-04, 9.50095370e-04,
+       9.75973557e-04, 1.01035546e-03, 1.04974597e-03, 1.08965056e-03,
+       1.12905646e-03, 1.16544560e-03, 1.20432393e-03, 1.25117739e-03,
+       1.31100453e-03, 1.38282557e-03, 1.46963538e-03, 1.57292023e-03,
+       1.69015991e-03, 1.82531513e-03, 1.98785868e-03, 2.18224842e-03,
+       2.40545835e-03, 2.64949570e-03, 2.91235990e-03, 3.19652189e-03,
+       3.50144041e-03, 3.82658531e-03, 4.17597720e-03, 4.54600008e-03,
+       4.92938669e-03, 5.32344068e-03, 5.73216261e-03, 6.18294920e-03,
+       6.66506301e-03, 7.14289275e-03, 7.60533472e-03, 8.07929361e-03,
+       8.60718380e-03, 9.21954043e-03, 9.91697732e-03, 1.07137947e-02,
+       1.16186807e-02, 1.26511756e-02, 1.38062007e-02, 1.50692314e-02,
+       1.64399087e-02, 1.79462854e-02, 1.96828400e-02, 2.16430913e-02,
+       2.37466335e-02, 2.59837193e-02, 2.84282734e-02, 3.12585980e-02,
+       3.45111290e-02, 3.80870625e-02, 4.19924732e-02, 4.63268386e-02,
+       5.13310180e-02, 5.70418578e-02, 6.33201548e-02, 7.01769632e-02,
+       7.77688358e-02, 8.62903314e-02, 9.59061899e-02, 1.06724255e-01,
+       1.18798575e-01, 1.32125256e-01, 1.46682823e-01, 1.62432478e-01,
+       1.79326966e-01, 1.97313414e-01, 2.16339448e-01, 2.35357633e-01,
+       2.54059844e-01, 2.72119171e-01, 2.89195180e-01, 1.00000000e+00]
+    }
+        ],
+        "validators": {
+            "range": {
+                "min": 0.0,
+                "max": 1.0
+            }
+        }
+    },
     "etr_params": {
         "title": "Effective tax rate function parameters.",
         "description": "Effective tax rate function parameters.",

ogcore/demographics.py

@@ -775,9 +775,12 @@ def get_pop_objs(
                 path, length T + S
 
     """
+    start_data_year = initial_data_year - 1  # grab data from one year
+    T = T + 1  # add one period to T to account for period -1 pop
+    # before initial so have pre-start year population distribution
     # TODO: this function does not generalize with T.
     # It assumes one model period is equal to one calendar year in the
-    # time dimesion (it does adjust for S, however)
+    # time dimension (it does adjust for S, however)
     T0 = (
         final_data_year - initial_data_year + 1
     )  # number of periods until constant fertility and mortality rates
@@ -788,8 +791,8 @@ def get_pop_objs(
         final_data_year,
     )
     assert E + S <= max_age - min_age + 1
-    assert initial_data_year >= 2011 and initial_data_year <= 2100 - 1
-    assert final_data_year >= 2011 and final_data_year <= 2100 - 1
+    assert initial_data_year >= 2012 and initial_data_year <= 2100 - 1
+    assert final_data_year >= 2012 and final_data_year <= 2100 - 1
     # Ensure that the last year of data used is before SS transition assumed
     # Really, it will need to be well before this
     assert final_data_year > initial_data_year
@@ -810,7 +813,7 @@ def get_pop_objs(
             min_age,
             max_age,
             country_id,
-            initial_data_year,
+            start_data_year,
             final_data_year,
             download_path=download_path,
         )
@@ -839,7 +842,7 @@ def get_pop_objs(
             min_age,
             max_age,
             country_id,
-            initial_data_year,
+            start_data_year,
             final_data_year,
             download_path=download_path,
         )
@@ -890,7 +893,7 @@ def get_pop_objs(
                 initial_pop,
                 pre_pop_dist,
                 country_id,
-                initial_data_year,
+                start_data_year,
                 final_data_year,
                 download_path=download_path,
             )
@@ -901,7 +904,7 @@ def get_pop_objs(
                 min_age,
                 max_age,
                 country_id=country_id,
-                start_year=initial_data_year,
+                start_year=start_data_year,
                 end_year=final_data_year,
                 download_path=download_path,
             )
@@ -933,7 +936,7 @@ def get_pop_objs(
             infmort_rates,
             pop_2D,
             country_id,
-            initial_data_year,
+            start_data_year,
             final_data_year,
             download_path=download_path,
         )
@@ -1145,7 +1148,6 @@ def get_pop_objs(
         omega_path_lev[0, -S:].sum() - pre_pop_EpS[-S:].sum()
     ) / pre_pop_EpS[-S:].sum()
     g_n_path[fixper + 1 :] = g_n_SS
-    omega_S_preTP = pre_pop_EpS[-S:] / pre_pop_EpS[-S:].sum()
     imm_rates_mat = np.concatenate(
         (
             imm_rates_orig[:fixper, E:],
@@ -1156,6 +1158,10 @@ def get_pop_objs(
         ),
         axis=0,
     )
+    # compute pop objects for the year before the model start year
+    # population distribution
+    # TODO: can delete this and proobalby any "pre_pop" calculations above
+    omega_S_preTP = pre_pop_EpS[-S:] / pre_pop_EpS[-S:].sum()
 
     if GraphDiag:
         # Check whether original SS population distribution is close to
@@ -1304,13 +1310,16 @@ def get_pop_objs(
 
     # Return objects in a dictionary
     pop_dict = {
-        "omega": omega_path_S,
+        "omega": omega_path_S[1:, :],
         "g_n_ss": g_n_SS,
         "omega_SS": omega_SSfx[-S:] / omega_SSfx[-S:].sum(),
-        "rho": mort_rates_S,
-        "g_n": g_n_path,
-        "imm_rates": imm_rates_mat,
-        "omega_S_preTP": omega_S_preTP,
+        "rho": mort_rates_S[1:, :],
+        "g_n": g_n_path[1:],
+        "imm_rates": imm_rates_mat[1:, :],
+        "omega_S_preTP": omega_path_S[0, :],
+        "imm_rates_preTP": imm_rates_mat[0, :],
+        "rho_preTP": mort_rates_S[0, :],
+        "g_m_preTP": g_n_path[0],
     }
 
     return pop_dict

ogcore/parameters.py

@@ -329,6 +329,8 @@ def compute_default_params(self):
                 np.reshape(self.omega_SS, (1, self.S)), (self.T + self.S, 1)
             )
             self.omega_S_preTP = self.omega_SS
+            self.imm_rates_preTP = np.zeros(self.S)
+            self.rho_preTP = self.rho[0, :]
 
         # Create time series of stationarized UBI transfers
         self.ubi_nom_array = self.get_ubi_nom_objs()

tests/test_demographics.py

@@ -558,10 +558,10 @@ def test_data_download(tmpdir):
         T,
         0,
         99,
-        fert_rates=fert_rates,
-        mort_rates=mort_rates,
-        infmort_rates=infmort_rates,
-        imm_rates=imm_rates,
+        fert_rates=fert_rates[1:, :],
+        mort_rates=mort_rates[1:, :],
+        infmort_rates=infmort_rates[1:],
+        imm_rates=imm_rates[1:, :],
         infer_pop=True,
         pop_dist=pop_dist[0, :].reshape(1, E + S),
         pre_pop_dist=pre_pop_dist,