working pipeline for india

src/disease_model/models/compartment_model.py

@@ -228,11 +228,10 @@ def predict_with_params(
             population_data, past_health_data, params)
 
         prediction = integrate.solve_ivp(
-            self.differential_equations,
+            fun=lambda t, y: self.differential_equations(t, y, tuple(self.parameter_config.flatten(params))),
             t_span=(0, forecast_length),
             t_eval=np.arange(1, forecast_length + 1),
-            y0=initial_state,
-            args=self.parameter_config.flatten(params))
+            y0=initial_state)
 
         return self.format_output(prediction.y)
 

src/disease_model/models/seir.py

@@ -75,7 +75,7 @@ def differential_equations(
         # pylint: disable=invalid-name,too-many-locals
         s, e, i, r, _ = compartments
         population = s + e + i + r
-        parameters = self.parameter_config.parse(args)
+        parameters = self.parameter_config.parse(args[0])
         beta = parameters['beta']
         gamma = parameters['gamma']
         sigma = parameters['sigma']

src/economic_model/data/sector_mapping.csv

@@ -1,25 +1,25 @@
-lockdown_sector,sector
-agriculture,
-chemical,Manufacturing
-commerce,"Trade, Hotels, Transport, Communication and Services Related to Broadcasting"
-construction,Construction
-education,Other services
-fin_prof_services,"Financial, Real Estate and Professional Services"
-food_consumables,
-healthcare,Other services
-hospitality_tourism,"Trade, Hotels, Transport, Communication and Services Related to Broadcasting"
-manufacturing,Manufacturing
-mining,Mining and quarrying
-media,Other services
-energy,"Electricity, gas, water supply & other utility services"
-telecom,"Transport, storage, communication & services related to broadcasting"
-public_admin,"Public Administration Defence, and Other Services"
-supply_chain_ship,"Transport, storage, communication & services related to broadcasting"
-forest_husb_fish,
-textiles,"Trade, Hotels, Transport, Communication and Services Related to Broadcasting"
-transportation ,"Transport, storage, communication & services related to broadcasting"
-utilities,"Electricity, gas, water supply & other utility services"
-open_border,"Transport, storage, communication & services related to broadcasting"
-air_travel,"Transport, storage, communication & services related to broadcasting"
-roal_rail_travel,"Transport, storage, communication & services related to broadcasting"
-public_transport,"Transport, storage, communication & services related to broadcasting"
+lockdown_sector,sector,id
+agriculture,"TTL_01T03: Agriculture, forestry and fishing",0
+chemical,TTL_20T21: Chemicals and pharmaceutical products,9
+commerce,TTL_69T82: Other business sector services,30
+construction,TTL_41T43: Construction,21
+education,TTL_85: Education,32
+fin_prof_services,TTL_64T66: Financial and insurance activities,28
+food_consumables,"TTL_10T12: Food products, beverages and tobacco",4
+healthcare,TTL_86T88: Human health and social work,33
+hospitality_tourism,"TTL_90T96: Arts, entertainment, recreation and other service activities",34
+manufacturing,TTL_24: Manufacture of basic metals,12
+mining,TTL_05T06: Mining and extraction of energy producing products,1
+media,"TTL_58T60: Publishing, audiovisual and broadcasting activities",25
+energy,TTL_05T06: Mining and extraction of energy producing products,1
+telecom,TTL_61: Telecommunications,26
+public_admin,TTL_84: Public administration and defence,31
+supply_chain_ship,TTL_49T53: Transportation and storage,23
+forest_husb_fish,"TTL_01T03: Agriculture, forestry and fishing",0
+textiles,"TTL_13T15: Textiles, wearing apparel, leather and related products",5
+transportation ,TTL_49T53: Transportation and storage,23
+utilities,"TTL_35T39: Electricity, gas, water supply, sewerage, waste and remediation services",20
+open_border,TTL_49T53: Transportation and storage,23
+air_travel,TTL_49T53: Transportation and storage,23
+roal_rail_travel,TTL_49T53: Transportation and storage,23
+public_transport,TTL_49T53: Transportation and storage,23

src/economic_model/models/basic_lockdown_model.py

@@ -3,7 +3,9 @@
 """
 from typing import Dict
 import attr
+import os
 import numpy as np
+import pandas as pd
 from help_project.src.economic_model.utils import gva_data
 from help_project.src.exitstrategies import lockdown_policy as lockdown
 from help_project.src.economic_model.models.economy_simulation import simulate_economy
@@ -34,19 +36,32 @@ def _get_economic_vector_for_single_policy(self, lockdown_policy: lockdown.Lockd
                     sector_mappings.iloc[i]['lockdown_sector'])
 
         baseline_gva = gva.get_gvas()
-        adjusted_gva = {}
+        india_input_output = pd.read_csv(os.path.dirname(__file__) + '/../data/india_input.csv')
+        india_input_output.columns = india_input_output.iloc[0]
+        india_input_output = india_input_output.iloc[1:]
+        india_input_output = india_input_output.set_index('To: (sector in column)')
+        india_input_output = india_input_output.drop(
+            ['TXS_IMP_FNL: Taxes less subsidies on intermediate and final products (paid in foreign countries)',
+             'TXS_INT_FNL: Taxes less subsidies on intermediate and final products (paid in domestic agencies, includes duty on imported products)',
+             'TTL_INT_FNL: Total intermediate consumption at purchasers’ prices',
+             'TTL_97T98: Private households with employed persons'], axis=0)
+        india_input_output = india_input_output.drop(columns=india_input_output.columns[-8:])
+        india_input_output = india_input_output.drop(columns=[india_input_output.columns[-2]])
+        sectors = india_input_output.index.values[:-2]
+        labor_shocks = {}
+        for sector in sectors:
+            labor_shocks[sector]=1
         for key in mapping_dict:
-            if key not in baseline_gva:
+            if key not in sectors:
                 continue
             weights = []
             for sector in mapping_dict[key]:
                 if sector in lockdown_policy:
                     weights.append(lockdown_policy[sector])
             if len(weights) > 0:
-                adjusted_gva[key] = baseline_gva[key] * np.mean(weights)
-            else:
-                adjusted_gva[key] = baseline_gva[key]
-        return adjusted_gva
+                labor_shocks[key] = np.mean(weights)
+        adjusted_gvas = simulate_economy(labor_shocks)
+        return adjusted_gvas
 
     def get_economic_vector(
             self, lockdown_policy: lockdown.LockdownTimeSeries) -> Dict[str, float]:

src/economic_model/models/economy_simulation.py

@@ -79,7 +79,7 @@ def simulate_economy(shocks_to_labor_supply=None):
                   'rounds': 500,}
 
     # Cleaning input output table for india
-    india_input_output = pd.read_csv('india_input.csv')
+    india_input_output = pd.read_csv(os.path.dirname(__file__) + '/../data/india_input.csv')
     india_input_output.columns = india_input_output.iloc[0]
     india_input_output = india_input_output.iloc[1:]
     india_input_output = india_input_output.set_index('To: (sector in column)')
@@ -121,13 +121,18 @@ def simulate_economy(shocks_to_labor_supply=None):
     labor_alphas = [labor_dict, labor_requirement]
     for i in range(number_sectors):
         agent_parameters[i]['output_input_ratio']=output_input_ratio[i]
-
-
+    shocks_array=[]
+    for i in range(len(india_input_output)-2):
+        if india_input_output.index[i] in shocks_to_labor_supply.keys():
+            shocks_array.append(0.5 + 0.5*(shocks_to_labor_supply[india_input_output.index[i]]))
+        else:
+            shocks_array.append(1)
+    shocks_to_labor_supply = shocks_array
     if shocks_to_labor_supply is None:
         shocks_to_labor_supply = [random.uniform(0.5,1) for i in range(number_sectors)]
     shocks =  main(parameters,agent_parameters,labor_alphas, shocks_to_labor_supply)
-    GVAs=[]
+    GVAs={}
     for i in range(number_sectors):
-        GVAs.append(shocks[i]*india_input_output.iloc[-1,i])
+        GVAs[india_input_output.index[i]]=(shocks[i]*india_input_output.iloc[-1,i])
     return GVAs