Merge pull request #56 from mikhailsirenko/develop

Develop

unbreakable/analysis/analyzer.py

@@ -151,6 +151,18 @@ def prepare_outcomes(results: tuple, add_policies: bool, add_uncertainties: bool
             i += 1  # increase row index of the outcomes dataframe
     outcomes = pd.DataFrame(outcomes, columns=columns)
 
+    numeric_columns = ['total_population',
+                       'total_asset_loss', 'total_consumption_loss', 'tot_exposed_asset',
+                       'tot_asset_surv', 'expected_loss_frac', 'n_affected_people',
+                       'annual_average_consumption', 'poverty_line_adjusted', 'district_pml',
+                       'n_poor_initial', 'n_poor_affected', 'n_new_poor',
+                       'initial_poverty_gap', 'new_poverty_gap_initial', 'new_poverty_gap_all',
+                       'annual_average_consumption_loss',
+                       'annual_average_consumption_loss_pct', 'r', 'mean_recovery_rate',
+                       'tot_wellbeing_loss']
+
+    outcomes[numeric_columns] = outcomes[numeric_columns].apply(pd.to_numeric)
+
     # Rename a district
     outcomes['district'].replace(
         {'AnseLaRayeCanaries': 'Anse-La-Raye & Canaries'}, inplace=True)

unbreakable/data/writer.py

@@ -51,7 +51,8 @@ def get_outcomes(households: pd.DataFrame, tot_exposed_asset: float, expected_lo
     annual_average_consumption_loss, annual_average_consumption_loss_pct = calculate_average_annual_consumption_loss(
         affected_households, years_to_recover)
 
-    tot_wellbeing_loss = calc_tot_wellbeing_loss(households, consump_util)
+    tot_wellbeing_loss = calc_tot_wellbeing_loss(
+        affected_households, consump_util)
     r = calculate_resilience(affected_households, tot_wellbeing_loss)
 
     # Get the weighted average vulnerability by consumption quintile and decile
@@ -247,18 +248,18 @@ def calculate_resilience(affected_households: pd.DataFrame, tot_wellbeing_loss:
     Returns:
         float: Socio-economic resilience
     '''
-    total_consumption_loss = (
-        affected_households[['consumption_loss_NPV', 'popwgt']].prod(axis=1)).sum()
+    # total_consumption_loss = (
+    #     affected_households[['consumption_loss_NPV', 'popwgt']].prod(axis=1)).sum()
 
     total_asset_damage = (
         affected_households[['keff', 'v', 'popwgt']].prod(axis=1)).sum()
 
-    if total_consumption_loss == 0:
-        r = np.nan
+    # if total_consumption_loss == 0:
+    #     r = np.nan
 
-    else:
-        # r = total_asset_damage / total_consumption_loss
-        r = total_asset_damage / tot_wellbeing_loss
+    # else:
+    # r = total_asset_damage / total_consumption_loss
+    r = total_asset_damage / tot_wellbeing_loss
 
     return r
 
@@ -290,7 +291,7 @@ def get_weighted_vuln(affected_households: pd.DataFrame, quintile: bool) -> dict
         return average_v_by_d.to_dict()
 
 
-def calc_tot_wellbeing_loss(households: pd.DataFrame, consump_util: float) -> float:
+def calc_tot_wellbeing_loss(affected_households: pd.DataFrame, consump_util: float) -> float:
     '''Calculate wellbeing loss.
 
     Args:
@@ -300,8 +301,15 @@ def calc_tot_wellbeing_loss(households: pd.DataFrame, consump_util: float) -> fl
     Returns:
         float: Total wellbeing loss.
     '''
-    x = (households['aeexp'].multiply(households['popwgt'])
-         ).sum() / households['popwgt'].sum()
+    x = (affected_households['aeexp'].multiply(affected_households['popwgt'])
+         ).sum() / affected_households['popwgt'].sum()
+
     W = x**(-consump_util)
-    wellbeing_loss = - households['wellbeing'].sum() / W
-    return wellbeing_loss
+
+    affected_households = affected_households.assign(
+        cons_equiv_wellbeing=-affected_households['wellbeing'] / W)
+
+    tol_wellbeing_loss = affected_households['cons_equiv_wellbeing'].multiply(
+        affected_households['popwgt']).sum()
+
+    return tol_wellbeing_loss

unbreakable/modules/integrator.py

@@ -235,17 +235,17 @@ def calculate_wellbeing(households: pd.DataFrame, consump_util: float, discount_
                                 < poverty_line_adjusted, 'weeks_pov'] += 1
 
         # Integrate well-being
-        a = affected_households['c_t_unaffected']**(1 - consump_util)
-        b = (1 - consump_util) * dt
-        c = ((1 - ((affected_households['c_t_unaffected'] - affected_households['c_t']) / affected_households['c_t_unaffected'])
-              * np.e**(-affected_households['recovery_rate'] * _t))**(1 - consump_util) - 1)
-        d = np.e**(-discount_rate * _t)
-        e = a.div(b).mul(c).mul(d)
-        f = affected_households['c_t_unaffected']**(1 - consump_util)\
-            / (1 - consump_util) * dt\
-            * ((1 - ((affected_households['c_t_unaffected'] - affected_households['c_t']) / affected_households['c_t_unaffected'])
-                * np.e**(-affected_households['recovery_rate'] * _t))**(1 - consump_util) - 1)\
-            * np.e**(-discount_rate * _t)
+        # a = affected_households['c_t_unaffected']**(1 - consump_util)
+        # b = (1 - consump_util) * dt
+        # c = ((1 - ((affected_households['c_t_unaffected'] - affected_households['c_t']) / affected_households['c_t_unaffected'])
+        #       * np.e**(-affected_households['recovery_rate'] * _t))**(1 - consump_util) - 1)
+        # d = np.e**(-discount_rate * _t)
+        # e = a.div(b).mul(c).mul(d)
+        # f = affected_households['c_t_unaffected']**(1 - consump_util)\
+        #     / (1 - consump_util) * dt\
+        #     * ((1 - ((affected_households['c_t_unaffected'] - affected_households['c_t']) / affected_households['c_t_unaffected'])
+        #         * np.e**(-affected_households['recovery_rate'] * _t))**(1 - consump_util) - 1)\
+        #     * np.e**(-discount_rate * _t)
 
         affected_households['wellbeing'] += affected_households['c_t_unaffected']**(1 - consump_util)\
             / (1 - consump_util) * dt\