@@ -991,7 +991,7 @@ def get_pop_objs(
991991 assert np .allclose (pop_counter_2D , pop_2D )
992992
993993 # """"
994- # CHANGE - in OG-Core, we are implicitLy assuming pre-TP rates of mortality,
994+ # CHANGE - in OG-Core, we are implicitly assuming pre-TP rates of mortality,
995995 # fertility, and immigration are the same as the period 0 rates.
996996
997997 # So let's just infer the pre-pop_dist from those.
@@ -1062,17 +1062,17 @@ def get_pop_objs(
10621062 newborns = (fert_rates [0 , :] * pre_pop_EpS [:]).sum ()
10631063 initial_pop_counter [0 ] = (
10641064 1 - infmort_rates [0 ]
1065- ) * newborns + imm_rates_orig [0 , 0 ] * pre_pop_EpS [0 ]
1065+ ) * newborns + imm_rates_orig [0 , 0 ] * pre_pop_EpS [0 ]
10661066 initial_pop_counter [1 :] = (
10671067 pre_pop_EpS [:- 1 ] * (1 - mort_rates [0 , :- 1 ])
10681068 + pre_pop_EpS [1 :] * imm_rates_orig [0 , 1 :]
1069- )
1069+ )
10701070
10711071 max_diff = np .abs (pop_2D [0 , :] - initial_pop_counter ).max ()
10721072 print (
10731073 "Pre-period population verification: Max diff = " ,
10741074 max_diff
1075- )
1075+ )
10761076
10771077 if max_diff > 100_000 :
10781078 print (
@@ -1081,7 +1081,7 @@ def get_pop_objs(
10811081 + "This may indicate inconsistencies in the data or "
10821082 + "immigration rate calculations, but using UN historical "
10831083 + "data as it is more reliable than backward-solved estimates."
1084- )
1084+ )
10851085
10861086 # Create the transition matrix for the population distribution
10871087 # from T0 going forward (i.e., past when we have data on forecasts)
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