diff --git a/climada/engine/unsequa/input_var.py b/climada/engine/unsequa/input_var.py
index 32a81cbaf..cf5a401f4 100644
--- a/climada/engine/unsequa/input_var.py
+++ b/climada/engine/unsequa/input_var.py
@@ -222,7 +222,7 @@ def var_to_inputvar(var):
         return InputVar(func=lambda: var, distr_dict={})
 
     @staticmethod
-    def haz(haz, n_ev=None, bounds_int=None, bounds_freq=None):
+    def haz(haz_list, n_ev=None, bounds_int=None, bounds_frac=None, bounds_freq=None):
         """
         Helper wrapper for basic hazard uncertainty input variable
 
@@ -234,22 +234,33 @@ def haz(haz, n_ev=None, bounds_int=None, bounds_freq=None):
         HI: scale the intensity of all events (homogeneously)
             The instensity of all events is multiplied by a number
             sampled uniformly from a distribution with (min, max) = bounds_int
+        HA: scale the fraction of all events (homogeneously)
+            The fraction of all events is multiplied by a number
+            sampled uniformly from a distribution with (min, max) = bounds_frac
         HF: scale the frequency of all events (homogeneously)
             The frequency of all events is multiplied by a number
             sampled uniformly from a distribution with (min, max) = bounds_freq
+        HL: sample uniformly from hazard list
+            From the provided list of hazard is elements are uniformly
+            sampled. For example, Hazards outputs from dynamical models
+            for different input factors.
 
         If a bounds is None, this parameter is assumed to have no uncertainty.
 
         Parameters
         ----------
-        haz : climada.hazard.Hazard
-            The base hazard
+        haz : List of climada.hazard.Hazard
+            The list of base hazard. Can be one or many to uniformly sample
+            from.
         n_ev : int, optional
             Number of events to be subsampled per sample. Can be equal or
             larger than haz.size. The default is None.
         bounds_int : (float, float), optional
             Bounds of the uniform distribution for the homogeneous intensity
             scaling. The default is None.
+        bounds_frac : (float, float), optional
+            Bounds of the uniform distribution for the homogeneous fraction
+            scaling. The default is None.
         bounds_freq : (float, float), optional
             Bounds of the uniform distribution for the homogeneous frequency
             scaling. The default is None.
@@ -260,16 +271,21 @@ def haz(haz, n_ev=None, bounds_int=None, bounds_freq=None):
             Uncertainty input variable for a hazard object.
 
         """
-        kwargs = {'haz': haz, 'n_ev': n_ev}
+        n_haz = len(haz_list)
+        kwargs = {'haz_list': haz_list, 'n_ev': n_ev}
         if n_ev is None:
             kwargs['HE'] = None
         if bounds_int is None:
             kwargs['HI'] = None
+        if bounds_frac is None:
+            kwargs['HA'] = None
         if bounds_freq is None:
             kwargs['HF'] = None
+        if n_haz == 1:
+            kwargs['HL'] = 0
         return InputVar(
             partial(_haz_uncfunc, **kwargs),
-            _haz_unc_dict(n_ev, bounds_int, bounds_freq)
+            _haz_unc_dict(n_ev, bounds_int, bounds_frac, bounds_freq, n_haz)
             )
 
     @staticmethod
@@ -327,7 +343,7 @@ def exp(exp_list, bounds_totval=None, bounds_noise=None):
             )
 
     @staticmethod
-    def impfset(impf_set, haz_id_dict= None, bounds_mdd=None, bounds_paa=None,
+    def impfset(impf_set_list, haz_id_dict= None, bounds_mdd=None, bounds_paa=None,
                     bounds_impfi=None):
         """
         Helper wrapper for basic impact function set uncertainty input variable.
@@ -347,13 +363,20 @@ def impfset(impf_set, haz_id_dict= None, bounds_mdd=None, bounds_paa=None,
             The value intensity are all summed with a random number
             sampled uniformly from a distribution with
             (min, max) = bounds_int
+        IL: sample uniformly from impact function set list
+            From the provided list of impact function sets elements are uniformly
+            sampled. For example, impact functions obtained from different
+            calibration methods.
+
 
         If a bounds is None, this parameter is assumed to have no uncertainty.
 
         Parameters
         ----------
-        impf_set : climada.entity.impact_funcs.impact_func_set.ImpactFuncSet
-            The base impact function set.
+        impf_set_list : list of ImpactFuncSet
+            The list of  base impact function set. Can be one or many to
+            uniformly sample from. The impact function ids must identical
+            for all impact function sets.
         bounds_mdd : (float, float), optional
             Bounds of the uniform distribution for the homogeneous mdd
             scaling. The default is None.
@@ -375,7 +398,8 @@ def impfset(impf_set, haz_id_dict= None, bounds_mdd=None, bounds_paa=None,
             Uncertainty input variable for an impact function set object.
 
         """
-        kwargs = {}
+        n_impf_set = len(impf_set_list)
+        kwargs = {'impf_set_list': impf_set_list}
         if bounds_mdd is None:
             kwargs['MDD'] = None
         if bounds_paa is None:
@@ -383,17 +407,19 @@ def impfset(impf_set, haz_id_dict= None, bounds_mdd=None, bounds_paa=None,
         if bounds_impfi is None:
             kwargs['IFi'] = None
         if haz_id_dict is None:
-            haz_id_dict = impf_set.get_ids()
+            haz_id_dict = impf_set_list[0].get_ids()
+        if n_impf_set == 1:
+            kwargs['IL'] = 0
         return InputVar(
             partial(
-                _impfset_uncfunc, impf_set=impf_set, haz_id_dict=haz_id_dict,
+                _impfset_uncfunc, haz_id_dict=haz_id_dict,
                 **kwargs
                 ),
-            _impfset_unc_dict(bounds_impfi, bounds_mdd, bounds_paa)
+            _impfset_unc_dict(bounds_impfi, bounds_mdd, bounds_paa, n_impf_set)
         )
 
     @staticmethod
-    def ent(impf_set, disc_rate, exp_list, meas_set, haz_id_dict,
+    def ent(impf_set_list, disc_rate, exp_list, meas_set, haz_id_dict,
             bounds_disc=None, bounds_cost=None, bounds_totval=None,
             bounds_noise=None, bounds_mdd=None, bounds_paa=None,
             bounds_impfi=None):
@@ -436,6 +462,10 @@ def ent(impf_set, disc_rate, exp_list, meas_set, haz_id_dict,
             The value intensity are all summed with a random number
             sampled uniformly from a distribution with
             (min, max) = bounds_int
+        IL: sample uniformly from impact function set list
+            From the provided list of impact function sets elements are uniformly
+            sampled. For example, impact functions obtained from different
+            calibration methods.
 
 
         If a bounds is None, this parameter is assumed to have no uncertainty.
@@ -464,8 +494,10 @@ def ent(impf_set, disc_rate, exp_list, meas_set, haz_id_dict,
         bounds_impfi : (float, float), optional
             Bounds of the uniform distribution for the homogeneous shift
             of intensity. The default is None.
-        impf_set : climada.engine.impact_funcs.impact_func_set.ImpactFuncSet
-            The base impact function set.
+        impf_set_list : list of ImpactFuncSet
+            The list of  base impact function set. Can be one or many to
+            uniformly sample from. The impact function ids must identical
+            for all impact function sets.
         disc_rate : climada.entity.disc_rates.base.DiscRates
             The base discount rates.
         exp_list : [climada.entity.exposures.base.Exposure]
@@ -485,6 +517,7 @@ def ent(impf_set, disc_rate, exp_list, meas_set, haz_id_dict,
 
         """
         n_exp = len(exp_list)
+        n_impf_set = len(impf_set_list)
 
         kwargs = {}
         if bounds_mdd is None:
@@ -493,6 +526,8 @@ def ent(impf_set, disc_rate, exp_list, meas_set, haz_id_dict,
             kwargs['PAA'] = None
         if bounds_impfi is None:
             kwargs['IFi'] = None
+        if n_impf_set== 1:
+            kwargs['IL'] = 0
         if bounds_disc is None:
             kwargs['DR'] = None
         if bounds_cost is None:
@@ -506,18 +541,19 @@ def ent(impf_set, disc_rate, exp_list, meas_set, haz_id_dict,
 
         return InputVar(
             partial(_ent_unc_func,
-                    impf_set=impf_set, haz_id_dict=haz_id_dict,
+                    impf_set_list=impf_set_list, haz_id_dict=haz_id_dict,
                     disc_rate=disc_rate, bounds_noise=bounds_noise,
                     exp_list=exp_list, meas_set=meas_set, **kwargs
                     ),
             _ent_unc_dict(bounds_totval=bounds_totval, bounds_noise=bounds_noise,
-                          bounds_impfi=bounds_impfi, bounds_mdd=bounds_mdd,
+                          bounds_impfi=bounds_impfi, n_impf_set=n_impf_set,
+                          bounds_mdd=bounds_mdd,
                           bounds_paa=bounds_paa, bounds_disc=bounds_disc,
-                          bounds_cost=bounds_cost, n_exp=n_exp)
+                          bounds_cost=bounds_cost, n_exp=n_exp,)
         )
 
     @staticmethod
-    def entfut(impf_set, exp_list, meas_set, haz_id_dict,
+    def entfut(impf_set_list, exp_list, meas_set, haz_id_dict,
                bounds_cost=None, bounds_eg=None, bounds_noise=None,
                bounds_impfi=None, bounds_mdd=None, bounds_paa=None,
                ):
@@ -556,6 +592,10 @@ def entfut(impf_set, exp_list, meas_set, haz_id_dict,
             The value intensity are all summed with a random number
             sampled uniformly from a distribution with
             (min, max) = bounds_impfi
+        IL: sample uniformly from impact function set list
+            From the provided list of impact function sets elements are uniformly
+            sampled. For example, impact functions obtained from different
+            calibration methods.
 
 
         If a bounds is None, this parameter is assumed to have no uncertainty.
@@ -581,8 +621,10 @@ def entfut(impf_set, exp_list, meas_set, haz_id_dict,
         bounds_impfi : (float, float), optional
             Bounds of the uniform distribution for the homogeneous shift
             of intensity. The default is None.
-        impf_set : climada.engine.impact_funcs.impact_func_set.ImpactFuncSet
-            The base impact function set.
+        impf_set_list : list of ImpactFuncSet
+            The list of  base impact function set. Can be one or many to
+            uniformly sample from. The impact function ids must identical
+            for all impact function sets.
         exp_list : [climada.entity.exposures.base.Exposure]
             The list of base exposure. Can be one or many to uniformly sample
             from.
@@ -600,6 +642,7 @@ def entfut(impf_set, exp_list, meas_set, haz_id_dict,
 
         """
         n_exp = len(exp_list)
+        n_impf_set = len(impf_set_list)
 
         kwargs = {}
         if bounds_mdd is None:
@@ -608,6 +651,8 @@ def entfut(impf_set, exp_list, meas_set, haz_id_dict,
             kwargs['PAA'] = None
         if bounds_impfi is None:
             kwargs['IFi'] = None
+        if n_impf_set == 1:
+            kwargs['IL'] = 0
         if bounds_cost is None:
             kwargs['CO'] = None
         if bounds_eg is None:
@@ -619,38 +664,46 @@ def entfut(impf_set, exp_list, meas_set, haz_id_dict,
 
         return InputVar(
             partial(_entfut_unc_func, haz_id_dict=haz_id_dict,
-                    bounds_noise=bounds_noise, impf_set=impf_set,
+                    bounds_noise=bounds_noise, impf_set_list=impf_set_list,
                     exp_list=exp_list, meas_set=meas_set, **kwargs),
             _entfut_unc_dict(bounds_eg=bounds_eg, bounds_noise=bounds_noise,
-                             bounds_impfi=bounds_impfi, bounds_paa=bounds_paa,
+                             bounds_impfi=bounds_impfi, n_impf_set=n_impf_set,
+                             bounds_paa=bounds_paa,
                              bounds_mdd=bounds_mdd, bounds_cost=bounds_cost,
                              n_exp=n_exp)
         )
 
 
 #Hazard
-def _haz_uncfunc(HE, HI, HF, haz, n_ev):
-    haz_tmp = copy.deepcopy(haz)
+def _haz_uncfunc(HE, HI, HA, HF, HL, haz_list, n_ev):
+    haz_tmp = copy.deepcopy(haz_list[int(HL)])
     if HE is not None:
         rng = np.random.RandomState(int(HE))
         event_id = list(rng.choice(haz_tmp.event_id, int(n_ev)))
         haz_tmp = haz_tmp.select(event_id=event_id)
     if HI is not None:
         haz_tmp.intensity = haz_tmp.intensity.multiply(HI)
+    if HA is not None:
+        haz_tmp.fraction = haz_tmp.fraction.multiply(HA)
     if HF is not None:
         haz_tmp.frequency = np.multiply(haz_tmp.frequency, HF)
     return haz_tmp
 
-def _haz_unc_dict(n_ev, bounds_int, bounds_freq):
+def _haz_unc_dict(n_ev, bounds_int, bounds_frac, bounds_freq, n_haz):
     hud = {}
     if n_ev is not None:
         hud['HE'] = sp.stats.randint(0, 2**32 - 1) #seed for rnd generator
     if bounds_int is not None:
         imin, idelta = bounds_int[0], bounds_int[1] - bounds_int[0]
         hud['HI'] = sp.stats.uniform(imin, idelta)
+    if bounds_frac is not None:
+        amin, adelta = bounds_frac[0], bounds_frac[1] - bounds_frac[0]
+        hud['HA'] = sp.stats.uniform(amin, adelta)
     if bounds_freq is not None:
         fmin, fdelta = bounds_freq[0], bounds_freq[1] - bounds_freq[0]
         hud['HF'] = sp.stats.uniform(fmin, fdelta)
+    if n_haz > 1:
+        hud['HL'] = sp.stats.randint(0, n_haz)
     return hud
 
 #Exposure
@@ -676,8 +729,8 @@ def _exp_unc_dict(bounds_totval, bounds_noise, n_exp):
     return eud
 
 #Impact function set
-def _impfset_uncfunc(IFi, MDD, PAA, impf_set, haz_id_dict):
-    impf_set_tmp = copy.deepcopy(impf_set)
+def _impfset_uncfunc(IFi, MDD, PAA, IL, impf_set_list, haz_id_dict):
+    impf_set_tmp = copy.deepcopy(impf_set_list[int(IL)])
     for haz_type, fun_id_list in haz_id_dict.items():
         for fun_id in fun_id_list:
             if MDD is not None:
@@ -700,7 +753,7 @@ def _impfset_uncfunc(IFi, MDD, PAA, impf_set, haz_id_dict):
                 impf_set_tmp.get_func(haz_type=haz_type, fun_id=fun_id).intensity = new_int
     return impf_set_tmp
 
-def _impfset_unc_dict(bounds_impfi, bounds_mdd, bounds_paa):
+def _impfset_unc_dict(bounds_impfi, bounds_mdd, bounds_paa, n_impf_set):
     iud = {}
     if bounds_impfi is not None:
         xmin, xdelta = bounds_impfi[0], bounds_impfi[1] - bounds_impfi[0]
@@ -711,6 +764,8 @@ def _impfset_unc_dict(bounds_impfi, bounds_mdd, bounds_paa):
     if bounds_mdd is not None:
         xmin, xdelta = bounds_mdd[0], bounds_mdd[1] - bounds_mdd[0]
         iud['MDD'] = sp.stats.uniform(xmin, xdelta)
+    if n_impf_set > 1:
+        iud['IL'] = sp.stats.randint(0, n_impf_set)
     return iud
 
 #Entity
@@ -738,36 +793,36 @@ def _meas_set_unc_dict(bounds_cost):
     cmin, cdelta = bounds_cost[0], bounds_cost[1] - bounds_cost[0]
     return {'CO': sp.stats.uniform(cmin, cdelta)}
 
-def _ent_unc_func(EN, ET, EL, IFi, MDD, PAA, CO, DR, bounds_noise,
-                 impf_set, haz_id_dict, disc_rate, exp_list, meas_set):
+def _ent_unc_func(EN, ET, EL, IFi, IL, MDD, PAA, CO, DR, bounds_noise,
+                 impf_set_list, haz_id_dict, disc_rate, exp_list, meas_set):
     ent = Entity()
     ent.exposures = _exp_uncfunc(EN, ET, EL, exp_list, bounds_noise)
-    ent.impact_funcs = _impfset_uncfunc(IFi, MDD, PAA, impf_set=impf_set,
+    ent.impact_funcs = _impfset_uncfunc(IFi, MDD, PAA, IL, impf_set_list=impf_set_list,
                                             haz_id_dict=haz_id_dict)
     ent.measures = _meas_set_uncfunc(CO, meas_set=meas_set)
     ent.disc_rates = _disc_uncfunc(DR, disc_rate)
     return ent
 
 def _ent_unc_dict(bounds_totval, bounds_noise, bounds_impfi, bounds_mdd,
-                  bounds_paa, bounds_disc, bounds_cost, n_exp):
+                  bounds_paa, n_impf_set, bounds_disc, bounds_cost, n_exp):
     ent_unc_dict = _exp_unc_dict(bounds_totval, bounds_noise, n_exp)
-    ent_unc_dict.update(_impfset_unc_dict(bounds_impfi, bounds_mdd, bounds_paa))
+    ent_unc_dict.update(_impfset_unc_dict(bounds_impfi, bounds_mdd, bounds_paa, n_impf_set))
     ent_unc_dict.update(_disc_unc_dict(bounds_disc))
     ent_unc_dict.update(_meas_set_unc_dict(bounds_cost))
     return  ent_unc_dict
 
-def _entfut_unc_func(EN, EG, EL, IFi, MDD, PAA, CO, bounds_noise,
-                 impf_set, haz_id_dict, exp_list, meas_set):
+def _entfut_unc_func(EN, EG, EL, IFi, IL, MDD, PAA, CO, bounds_noise,
+                 impf_set_list, haz_id_dict, exp_list, meas_set):
     ent = Entity()
     ent.exposures = _exp_uncfunc(EN=EN, ET=EG, EL=EL, exp_list=exp_list, bounds_noise=bounds_noise)
-    ent.impact_funcs = _impfset_uncfunc(IFi, MDD, PAA, impf_set=impf_set,
+    ent.impact_funcs = _impfset_uncfunc(IFi, MDD, PAA, IL, impf_set_list=impf_set_list,
                                             haz_id_dict=haz_id_dict)
     ent.measures = _meas_set_uncfunc(CO, meas_set=meas_set)
     ent.disc_rates = DiscRates() #Disc rate of future entity ignored in cost_benefit.calc()
     return ent
 
 def _entfut_unc_dict(bounds_impfi, bounds_mdd,
-                  bounds_paa, bounds_eg, bounds_noise,
+                  bounds_paa, n_impf_set, bounds_eg, bounds_noise,
                   bounds_cost, n_exp):
     eud = {}
     if bounds_eg is not None:
@@ -777,7 +832,7 @@ def _entfut_unc_dict(bounds_impfi, bounds_mdd,
         eud['EN'] = sp.stats.randint(0, 2**32 - 1) #seed for rnd generator
     if n_exp > 1:
         eud['EL'] = sp.stats.randint(0, n_exp)
-    eud.update(_impfset_unc_dict(bounds_impfi, bounds_mdd, bounds_paa))
+    eud.update(_impfset_unc_dict(bounds_impfi, bounds_mdd, bounds_paa, n_impf_set))
     if bounds_cost is not None:
         eud.update(_meas_set_unc_dict(bounds_cost))
     return eud
diff --git a/climada/engine/unsequa/test/test_unsequa.py b/climada/engine/unsequa/test/test_unsequa.py
index a6361175c..cd3d59a97 100755
--- a/climada/engine/unsequa/test/test_unsequa.py
+++ b/climada/engine/unsequa/test/test_unsequa.py
@@ -122,7 +122,7 @@ def make_costben_iv():
 
     entdem = ent_dem()
     ent_iv = InputVar.ent(
-        impf_set = entdem.impact_funcs,
+        impf_set_list = [entdem.impact_funcs],
         disc_rate = entdem.disc_rates,
         exp_list = [entdem.exposures],
         meas_set = entdem.measures,
@@ -134,7 +134,7 @@ def make_costben_iv():
 
     entfutdem = ent_fut_dem()
     entfut_iv = InputVar.entfut(
-        impf_set = entfutdem.impact_funcs,
+        impf_set_list = [entfutdem.impact_funcs],
         exp_list = [entfutdem.exposures],
         meas_set = entfutdem.measures,
         bounds_eg=[0.8, 1.5],
diff --git a/doc/tutorial/climada_engine_unsequa_helper.ipynb b/doc/tutorial/climada_engine_unsequa_helper.ipynb
index f55e14a8b..d4f57c721 100644
--- a/doc/tutorial/climada_engine_unsequa_helper.ipynb
+++ b/doc/tutorial/climada_engine_unsequa_helper.ipynb
@@ -41,8 +41,8 @@
    "id": "e446c327-d6cf-457f-b181-fef150bdbe81",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:29.292044Z",
-     "start_time": "2022-01-10T20:10:29.289389Z"
+     "end_time": "2022-07-07T13:17:40.632751Z",
+     "start_time": "2022-07-07T13:17:40.629637Z"
     }
    },
    "outputs": [],
@@ -96,8 +96,8 @@
    "id": "0fd2b3ec-c0e8-4ae6-8439-39b4501e6196",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:32.886682Z",
-     "start_time": "2022-01-10T20:10:29.504756Z"
+     "end_time": "2022-07-07T13:13:32.066932Z",
+     "start_time": "2022-07-07T13:13:28.979968Z"
     }
    },
    "outputs": [
@@ -105,7 +105,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2022-01-10 21:10:32,810 - climada.entity.exposures.base - INFO - Reading /Users/ckropf/climada/demo/data/exp_demo_today.h5\n"
+      "2022-07-07 15:13:32,000 - climada.entity.exposures.base - INFO - Reading /Users/ckropf/climada/demo/data/exp_demo_today.h5\n"
      ]
     }
    ],
@@ -122,8 +122,8 @@
    "id": "b062b610-787c-41ab-a355-bbb6291361bd",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:33.458079Z",
-     "start_time": "2022-01-10T20:10:32.888874Z"
+     "end_time": "2022-07-07T13:13:32.552786Z",
+     "start_time": "2022-07-07T13:13:32.068811Z"
     }
    },
    "outputs": [],
@@ -140,8 +140,8 @@
    "id": "87cc5928-3a53-4cef-acc9-097e85da398a",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:33.471516Z",
-     "start_time": "2022-01-10T20:10:33.459502Z"
+     "end_time": "2022-07-07T13:13:32.566201Z",
+     "start_time": "2022-07-07T13:13:32.554801Z"
     }
    },
    "outputs": [
@@ -169,8 +169,8 @@
    "id": "53e56665-c74f-49f3-a355-135fd0e5ed40",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:33.696309Z",
-     "start_time": "2022-01-10T20:10:33.474333Z"
+     "end_time": "2022-07-07T13:13:32.776385Z",
+     "start_time": "2022-07-07T13:13:32.568822Z"
     }
    },
    "outputs": [
@@ -210,8 +210,8 @@
    "id": "802ac379-39a0-476d-b068-36520d03a459",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:33.703989Z",
-     "start_time": "2022-01-10T20:10:33.697855Z"
+     "end_time": "2022-07-07T13:13:32.783469Z",
+     "start_time": "2022-07-07T13:13:32.778810Z"
     }
    },
    "outputs": [],
@@ -242,8 +242,8 @@
    "id": "ac28063d-83aa-43f8-a75d-f91fe20e44e8",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:33.740545Z",
-     "start_time": "2022-01-10T20:10:33.706259Z"
+     "end_time": "2022-07-07T13:13:32.811623Z",
+     "start_time": "2022-07-07T13:13:32.785563Z"
     }
    },
    "outputs": [
@@ -251,7 +251,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2022-01-10 21:10:33,708 - climada.hazard.base - INFO - Reading /Users/ckropf/climada/demo/data/tc_fl_1990_2004.h5\n"
+      "2022-07-07 15:13:32,787 - climada.hazard.base - INFO - Reading /Users/ckropf/climada/demo/data/tc_fl_1990_2004.h5\n"
      ]
     }
    ],
@@ -262,7 +262,7 @@
     "value_unit = 'people'\n",
     "litpop_kwargs = {\n",
     "    'countries' : ['CUB'],\n",
-    "    'res_arcsec' : 300, \n",
+    "    'res_arcsec' : 150, \n",
     "    'reference_year' : 2020,\n",
     "    'fin_mode' : 'norm',\n",
     "    'total_values' : [tot_pop]\n",
@@ -281,8 +281,8 @@
    "id": "a0370352-db8d-4507-90e2-931108fd0854",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:38.921653Z",
-     "start_time": "2022-01-10T20:10:33.742286Z"
+     "end_time": "2022-07-07T13:13:39.652566Z",
+     "start_time": "2022-07-07T13:13:32.813253Z"
     }
    },
    "outputs": [
@@ -293,48 +293,177 @@
       "\n",
       " Computing litpop for m=0, n=0 \n",
       "\n",
-      "2022-01-10 21:10:33,998 - climada.entity.exposures.litpop.litpop - INFO - \n",
+      "2022-07-07 15:13:33,055 - climada.entity.exposures.litpop.litpop - INFO - \n",
       " LitPop: Init Exposure for country: CUB (192)...\n",
       "\n",
-      "2022-01-10 21:10:33,999 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
-      "2022-01-10 21:10:35,544 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
-      "2022-01-10 21:10:35,545 - climada.entity.exposures.base - INFO - category_id not set.\n",
-      "2022-01-10 21:10:35,545 - climada.entity.exposures.base - INFO - cover not set.\n",
-      "2022-01-10 21:10:35,546 - climada.entity.exposures.base - INFO - deductible not set.\n",
-      "2022-01-10 21:10:35,546 - climada.entity.exposures.base - INFO - centr_ not set.\n",
-      "2022-01-10 21:10:35,549 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
-      "2022-01-10 21:10:35,551 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
-      "2022-01-10 21:10:35,577 - climada.util.interpolation - WARNING - Distance to closest centroid is greater than 100km for 77 coordinates.\n",
+      "2022-07-07 15:13:34,051 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,082 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,109 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,135 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,163 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,188 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,223 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,289 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,316 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,325 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:13:34,326 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,355 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,385 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,410 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,435 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,459 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,487 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,508 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,519 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:13:34,520 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,543 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,570 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,597 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,621 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,645 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,685 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,710 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,742 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,768 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,791 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,830 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,862 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,899 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,933 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,955 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:34,981 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:35,019 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:35,043 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:35,068 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:35,090 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:35,119 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:35,142 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:35,173 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
+      "2022-07-07 15:13:35,173 - climada.entity.exposures.base - INFO - category_id not set.\n",
+      "2022-07-07 15:13:35,174 - climada.entity.exposures.base - INFO - cover not set.\n",
+      "2022-07-07 15:13:35,174 - climada.entity.exposures.base - INFO - deductible not set.\n",
+      "2022-07-07 15:13:35,175 - climada.entity.exposures.base - INFO - centr_ not set.\n",
+      "2022-07-07 15:13:35,179 - climada.entity.exposures.base - INFO - Matching 5524 exposures with 2500 centroids.\n",
+      "2022-07-07 15:13:35,181 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
+      "2022-07-07 15:13:35,189 - climada.util.coordinates - WARNING - Distance to closest centroid is greater than 100km for 332 coordinates.\n",
       "\n",
       " Computing litpop for m=0, n=1 \n",
       "\n",
-      "2022-01-10 21:10:35,824 - climada.entity.exposures.litpop.litpop - INFO - \n",
+      "2022-07-07 15:13:35,416 - climada.entity.exposures.litpop.litpop - INFO - \n",
       " LitPop: Init Exposure for country: CUB (192)...\n",
       "\n",
-      "2022-01-10 21:10:35,825 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
-      "2022-01-10 21:10:37,238 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
-      "2022-01-10 21:10:37,239 - climada.entity.exposures.base - INFO - category_id not set.\n",
-      "2022-01-10 21:10:37,239 - climada.entity.exposures.base - INFO - cover not set.\n",
-      "2022-01-10 21:10:37,239 - climada.entity.exposures.base - INFO - deductible not set.\n",
-      "2022-01-10 21:10:37,240 - climada.entity.exposures.base - INFO - centr_ not set.\n",
-      "2022-01-10 21:10:37,243 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
-      "2022-01-10 21:10:37,245 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
-      "2022-01-10 21:10:37,269 - climada.util.interpolation - WARNING - Distance to closest centroid is greater than 100km for 77 coordinates.\n",
+      "2022-07-07 15:13:36,256 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:36,284 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:36,309 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:36,335 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:36,367 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:36,392 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:36,424 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:36,494 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:36,519 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:36,529 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:13:36,530 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:36,556 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:36,586 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:36,610 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:36,637 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:36,666 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:36,691 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:36,716 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:36,725 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:13:36,726 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:36,754 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:36,786 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:36,818 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:36,843 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:36,872 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:36,909 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:36,936 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:36,965 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2022-07-07 15:13:36,989 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:37,013 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:37,052 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:37,087 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:37,123 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:37,156 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:37,177 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:37,201 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:37,241 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:37,263 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:37,288 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:37,311 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:37,343 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:37,367 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:37,400 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
+      "2022-07-07 15:13:37,400 - climada.entity.exposures.base - INFO - category_id not set.\n",
+      "2022-07-07 15:13:37,401 - climada.entity.exposures.base - INFO - cover not set.\n",
+      "2022-07-07 15:13:37,401 - climada.entity.exposures.base - INFO - deductible not set.\n",
+      "2022-07-07 15:13:37,402 - climada.entity.exposures.base - INFO - centr_ not set.\n",
+      "2022-07-07 15:13:37,406 - climada.entity.exposures.base - INFO - Matching 5524 exposures with 2500 centroids.\n",
+      "2022-07-07 15:13:37,407 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
+      "2022-07-07 15:13:37,415 - climada.util.coordinates - WARNING - Distance to closest centroid is greater than 100km for 332 coordinates.\n",
       "\n",
       " Computing litpop for m=0, n=2 \n",
       "\n",
-      "2022-01-10 21:10:37,499 - climada.entity.exposures.litpop.litpop - INFO - \n",
+      "2022-07-07 15:13:37,637 - climada.entity.exposures.litpop.litpop - INFO - \n",
       " LitPop: Init Exposure for country: CUB (192)...\n",
       "\n",
-      "2022-01-10 21:10:37,501 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
-      "2022-01-10 21:10:38,888 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
-      "2022-01-10 21:10:38,889 - climada.entity.exposures.base - INFO - category_id not set.\n",
-      "2022-01-10 21:10:38,889 - climada.entity.exposures.base - INFO - cover not set.\n",
-      "2022-01-10 21:10:38,890 - climada.entity.exposures.base - INFO - deductible not set.\n",
-      "2022-01-10 21:10:38,891 - climada.entity.exposures.base - INFO - centr_ not set.\n",
-      "2022-01-10 21:10:38,894 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
-      "2022-01-10 21:10:38,895 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
-      "2022-01-10 21:10:38,919 - climada.util.interpolation - WARNING - Distance to closest centroid is greater than 100km for 77 coordinates.\n"
+      "2022-07-07 15:13:38,561 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:38,589 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:38,615 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:38,638 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:38,665 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:38,689 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:38,720 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:38,784 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:38,808 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:38,820 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:13:38,820 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:38,844 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:38,873 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:38,897 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:38,920 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:38,944 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:38,968 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:38,990 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:38,999 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:13:39,000 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:39,022 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:39,047 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:39,074 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:39,097 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:39,123 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:39,161 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:39,187 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:39,217 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:39,242 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:39,265 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:39,304 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:39,334 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:39,370 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:39,402 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:39,423 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:39,448 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:39,487 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:39,510 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:39,532 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:39,554 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:39,580 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:39,603 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:13:39,633 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
+      "2022-07-07 15:13:39,634 - climada.entity.exposures.base - INFO - category_id not set.\n",
+      "2022-07-07 15:13:39,634 - climada.entity.exposures.base - INFO - cover not set.\n",
+      "2022-07-07 15:13:39,635 - climada.entity.exposures.base - INFO - deductible not set.\n",
+      "2022-07-07 15:13:39,635 - climada.entity.exposures.base - INFO - centr_ not set.\n",
+      "2022-07-07 15:13:39,639 - climada.entity.exposures.base - INFO - Matching 5524 exposures with 2500 centroids.\n",
+      "2022-07-07 15:13:39,641 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
+      "2022-07-07 15:13:39,649 - climada.util.coordinates - WARNING - Distance to closest centroid is greater than 100km for 332 coordinates.\n"
      ]
     }
    ],
@@ -352,8 +481,8 @@
    "id": "cfa54889-cb6c-4b4d-afc7-81a20dd77eb8",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:38.926915Z",
-     "start_time": "2022-01-10T20:10:38.923116Z"
+     "end_time": "2022-07-07T13:13:39.658265Z",
+     "start_time": "2022-07-07T13:13:39.654050Z"
     }
    },
    "outputs": [],
@@ -370,8 +499,8 @@
    "id": "ee5cfd3d-9f71-4ccc-95e1-dc8e6ea9d8b3",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:38.934028Z",
-     "start_time": "2022-01-10T20:10:38.928323Z"
+     "end_time": "2022-07-07T13:13:39.666418Z",
+     "start_time": "2022-07-07T13:13:39.660259Z"
     }
    },
    "outputs": [],
@@ -386,8 +515,8 @@
    "id": "15c78c6a-d1ee-4eb0-982a-2920eda04f25",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:38.949269Z",
-     "start_time": "2022-01-10T20:10:38.937644Z"
+     "end_time": "2022-07-07T13:13:39.681827Z",
+     "start_time": "2022-07-07T13:13:39.670420Z"
     }
    },
    "outputs": [
@@ -423,51 +552,51 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>1383</th>\n",
-       "      <td>1713.015083</td>\n",
-       "      <td>POINT (-78.29167 22.45833)</td>\n",
-       "      <td>22.458333</td>\n",
-       "      <td>-78.291667</td>\n",
+       "      <th>5519</th>\n",
+       "      <td>92.974926</td>\n",
+       "      <td>POINT (-80.52083 23.18750)</td>\n",
+       "      <td>23.187500</td>\n",
+       "      <td>-80.520833</td>\n",
        "      <td>192</td>\n",
        "      <td>1</td>\n",
-       "      <td>431</td>\n",
+       "      <td>619</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1384</th>\n",
-       "      <td>1085.168934</td>\n",
-       "      <td>POINT (-79.20833 22.62500)</td>\n",
-       "      <td>22.625000</td>\n",
-       "      <td>-79.208333</td>\n",
+       "      <th>5520</th>\n",
+       "      <td>131.480741</td>\n",
+       "      <td>POINT (-80.47917 23.18750)</td>\n",
+       "      <td>23.187500</td>\n",
+       "      <td>-80.479167</td>\n",
        "      <td>192</td>\n",
        "      <td>1</td>\n",
-       "      <td>476</td>\n",
+       "      <td>619</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1385</th>\n",
-       "      <td>950.764517</td>\n",
-       "      <td>POINT (-79.62500 22.79167)</td>\n",
-       "      <td>22.791667</td>\n",
-       "      <td>-79.625000</td>\n",
+       "      <th>5521</th>\n",
+       "      <td>77.695093</td>\n",
+       "      <td>POINT (-80.68750 23.18750)</td>\n",
+       "      <td>23.187500</td>\n",
+       "      <td>-80.687500</td>\n",
        "      <td>192</td>\n",
        "      <td>1</td>\n",
-       "      <td>524</td>\n",
+       "      <td>618</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1386</th>\n",
-       "      <td>1129.619078</td>\n",
-       "      <td>POINT (-79.45833 22.70833)</td>\n",
-       "      <td>22.708333</td>\n",
-       "      <td>-79.458333</td>\n",
+       "      <th>5522</th>\n",
+       "      <td>43.122163</td>\n",
+       "      <td>POINT (-80.89583 23.14583)</td>\n",
+       "      <td>23.145833</td>\n",
+       "      <td>-80.895833</td>\n",
        "      <td>192</td>\n",
        "      <td>1</td>\n",
-       "      <td>475</td>\n",
+       "      <td>617</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1387</th>\n",
-       "      <td>300.552289</td>\n",
-       "      <td>POINT (-80.79167 23.20833)</td>\n",
-       "      <td>23.208333</td>\n",
-       "      <td>-80.791667</td>\n",
+       "      <th>5523</th>\n",
+       "      <td>106.033524</td>\n",
+       "      <td>POINT (-80.85417 23.14583)</td>\n",
+       "      <td>23.145833</td>\n",
+       "      <td>-80.854167</td>\n",
        "      <td>192</td>\n",
        "      <td>1</td>\n",
        "      <td>617</td>\n",
@@ -477,19 +606,19 @@
        "</div>"
       ],
       "text/plain": [
-       "            value                    geometry   latitude  longitude  \\\n",
-       "1383  1713.015083  POINT (-78.29167 22.45833)  22.458333 -78.291667   \n",
-       "1384  1085.168934  POINT (-79.20833 22.62500)  22.625000 -79.208333   \n",
-       "1385   950.764517  POINT (-79.62500 22.79167)  22.791667 -79.625000   \n",
-       "1386  1129.619078  POINT (-79.45833 22.70833)  22.708333 -79.458333   \n",
-       "1387   300.552289  POINT (-80.79167 23.20833)  23.208333 -80.791667   \n",
+       "           value                    geometry   latitude  longitude  region_id  \\\n",
+       "5519   92.974926  POINT (-80.52083 23.18750)  23.187500 -80.520833        192   \n",
+       "5520  131.480741  POINT (-80.47917 23.18750)  23.187500 -80.479167        192   \n",
+       "5521   77.695093  POINT (-80.68750 23.18750)  23.187500 -80.687500        192   \n",
+       "5522   43.122163  POINT (-80.89583 23.14583)  23.145833 -80.895833        192   \n",
+       "5523  106.033524  POINT (-80.85417 23.14583)  23.145833 -80.854167        192   \n",
        "\n",
-       "      region_id  impf_TC  centr_TC  \n",
-       "1383        192        1       431  \n",
-       "1384        192        1       476  \n",
-       "1385        192        1       524  \n",
-       "1386        192        1       475  \n",
-       "1387        192        1       617  "
+       "      impf_TC  centr_TC  \n",
+       "5519        1       619  \n",
+       "5520        1       619  \n",
+       "5521        1       618  \n",
+       "5522        1       617  \n",
+       "5523        1       617  "
       ]
      },
      "execution_count": 11,
@@ -507,14 +636,21 @@
    "id": "b11722a1-0609-4fdc-9f92-e735a14f5c56",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:41.243291Z",
-     "start_time": "2022-01-10T20:10:38.951093Z"
+     "end_time": "2022-07-07T13:13:45.556216Z",
+     "start_time": "2022-07-07T13:13:39.684061Z"
     }
    },
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2022-07-07 15:13:39,690 - climada.util.plot - WARNING - Error parsing coordinate system 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]'. Using projection PlateCarree in plot.\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 648x936 with 2 Axes>"
       ]
@@ -533,8 +669,8 @@
    "id": "4d6d5ea3-4733-4bff-88c8-e9fc14090497",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:41.251711Z",
-     "start_time": "2022-01-10T20:10:41.244959Z"
+     "end_time": "2022-07-07T13:13:45.565152Z",
+     "start_time": "2022-07-07T13:13:45.558121Z"
     }
    },
    "outputs": [],
@@ -549,8 +685,8 @@
    "id": "ced52885-d233-4c55-b84b-b2821847f281",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:41.264409Z",
-     "start_time": "2022-01-10T20:10:41.253039Z"
+     "end_time": "2022-07-07T13:13:45.578591Z",
+     "start_time": "2022-07-07T13:13:45.567310Z"
     }
    },
    "outputs": [
@@ -586,51 +722,51 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>1383</th>\n",
-       "      <td>56.491498</td>\n",
-       "      <td>POINT (-78.29167 22.45833)</td>\n",
-       "      <td>22.458333</td>\n",
-       "      <td>-78.291667</td>\n",
+       "      <th>5519</th>\n",
+       "      <td>0.567593</td>\n",
+       "      <td>POINT (-80.52083 23.18750)</td>\n",
+       "      <td>23.187500</td>\n",
+       "      <td>-80.520833</td>\n",
        "      <td>192</td>\n",
        "      <td>1</td>\n",
-       "      <td>431</td>\n",
+       "      <td>619</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1384</th>\n",
-       "      <td>22.670204</td>\n",
-       "      <td>POINT (-79.20833 22.62500)</td>\n",
-       "      <td>22.625000</td>\n",
-       "      <td>-79.208333</td>\n",
+       "      <th>5520</th>\n",
+       "      <td>1.135089</td>\n",
+       "      <td>POINT (-80.47917 23.18750)</td>\n",
+       "      <td>23.187500</td>\n",
+       "      <td>-80.479167</td>\n",
        "      <td>192</td>\n",
        "      <td>1</td>\n",
-       "      <td>476</td>\n",
+       "      <td>619</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1385</th>\n",
-       "      <td>17.402300</td>\n",
-       "      <td>POINT (-79.62500 22.79167)</td>\n",
-       "      <td>22.791667</td>\n",
-       "      <td>-79.625000</td>\n",
+       "      <th>5521</th>\n",
+       "      <td>0.396363</td>\n",
+       "      <td>POINT (-80.68750 23.18750)</td>\n",
+       "      <td>23.187500</td>\n",
+       "      <td>-80.687500</td>\n",
        "      <td>192</td>\n",
        "      <td>1</td>\n",
-       "      <td>524</td>\n",
+       "      <td>618</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1386</th>\n",
-       "      <td>24.565452</td>\n",
-       "      <td>POINT (-79.45833 22.70833)</td>\n",
-       "      <td>22.708333</td>\n",
-       "      <td>-79.458333</td>\n",
+       "      <th>5522</th>\n",
+       "      <td>0.122097</td>\n",
+       "      <td>POINT (-80.89583 23.14583)</td>\n",
+       "      <td>23.145833</td>\n",
+       "      <td>-80.895833</td>\n",
        "      <td>192</td>\n",
        "      <td>1</td>\n",
-       "      <td>475</td>\n",
+       "      <td>617</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1387</th>\n",
-       "      <td>1.739005</td>\n",
-       "      <td>POINT (-80.79167 23.20833)</td>\n",
-       "      <td>23.208333</td>\n",
-       "      <td>-80.791667</td>\n",
+       "      <th>5523</th>\n",
+       "      <td>0.738231</td>\n",
+       "      <td>POINT (-80.85417 23.14583)</td>\n",
+       "      <td>23.145833</td>\n",
+       "      <td>-80.854167</td>\n",
        "      <td>192</td>\n",
        "      <td>1</td>\n",
        "      <td>617</td>\n",
@@ -640,19 +776,19 @@
        "</div>"
       ],
       "text/plain": [
-       "          value                    geometry   latitude  longitude  region_id  \\\n",
-       "1383  56.491498  POINT (-78.29167 22.45833)  22.458333 -78.291667        192   \n",
-       "1384  22.670204  POINT (-79.20833 22.62500)  22.625000 -79.208333        192   \n",
-       "1385  17.402300  POINT (-79.62500 22.79167)  22.791667 -79.625000        192   \n",
-       "1386  24.565452  POINT (-79.45833 22.70833)  22.708333 -79.458333        192   \n",
-       "1387   1.739005  POINT (-80.79167 23.20833)  23.208333 -80.791667        192   \n",
+       "         value                    geometry   latitude  longitude  region_id  \\\n",
+       "5519  0.567593  POINT (-80.52083 23.18750)  23.187500 -80.520833        192   \n",
+       "5520  1.135089  POINT (-80.47917 23.18750)  23.187500 -80.479167        192   \n",
+       "5521  0.396363  POINT (-80.68750 23.18750)  23.187500 -80.687500        192   \n",
+       "5522  0.122097  POINT (-80.89583 23.14583)  23.145833 -80.895833        192   \n",
+       "5523  0.738231  POINT (-80.85417 23.14583)  23.145833 -80.854167        192   \n",
        "\n",
        "      impf_TC  centr_TC  \n",
-       "1383        1       431  \n",
-       "1384        1       476  \n",
-       "1385        1       524  \n",
-       "1386        1       475  \n",
-       "1387        1       617  "
+       "5519        1       619  \n",
+       "5520        1       619  \n",
+       "5521        1       618  \n",
+       "5522        1       617  \n",
+       "5523        1       617  "
       ]
      },
      "execution_count": 14,
@@ -670,14 +806,21 @@
    "id": "33d4b6b5-643e-47e3-bf62-a28a104c6bc0",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:43.527411Z",
-     "start_time": "2022-01-10T20:10:41.265824Z"
+     "end_time": "2022-07-07T13:13:50.931372Z",
+     "start_time": "2022-07-07T13:13:45.580862Z"
     }
    },
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2022-07-07 15:13:45,584 - climada.util.plot - WARNING - Error parsing coordinate system 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]'. Using projection PlateCarree in plot.\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
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Tnzp1iqZNm/Lcc8/x7bffcuzYMU6dOsXBgwc9Im5BX0t3i8TERPr27cvOnTuZNWsWefLkAfQfNCkzBtjtdmRZ9lDe7rf9/cBd2XX9IHU4HOTIkSNT8kqv/fHjx8mfP7/HWk/Pr7dNmzZs3ryZVatWcfr0aQ4dOsStW7dSjZUe8ubNayqpLrm4fB5dx1xr9uTJk2aAjQuVK1dmzZo1gO4P63IpAH3Nubs/3O/nmRf/TXgVwH8gsmbNylNPPZXu+bSiQ10fWq5XWZY9lDeXEpHSwueOnDlz3nFcgHPnzpGYmEhUVBRnzpwhLCzMPJdSGdE0zeSQFmcXJ03TKF++PBs3buSXX37hl19+4YsvvuDbb79Nd2vc5dPjQsr+VVVlwIABqVK6ZGb7MzN807smMyhUqBBBQUHs378/TWV8zJgxFCpUiO7du5M1a1bi4uLS7CetgKCCBQuaSniRIkUoV64c1apVY+3atab1ITM4ceIEzz//PNWrV6dKlSq0bduWffv28d13393Fnd4ZLuXPhWLFigH6FmSePHlSRQ5fvnwZi8VC9uzZ75pvSEgIS5cuZefOnWzdupUffviBuXPn8s0331CyZMkMubZq1Yq3336b+Ph41q5dy/DhwwF9HebLly/N6Pzs2bNz5coV2rdvT5EiRahTpw5Nmzblxo0b5lavC3erWMXFxfHaa69x9OhRZs2a5WHVzpMnj4fPHMCVK1c8lPf7bX+/SMvyKoTItLzSa5/Z5xt0F5xt27bRpk0bWrZsyfDhw2nTpk2m26f1WZqWuwOkPb+apqGqKqBH5qf8oed+j/f7eebFfxNeH8B/IUJDQ9m3b5/HB8aff/6J1WqlYMGCgO7Q7dp+AN2p3sfH575+MSYlJfH+++/TpUsXWrRowfvvv29+gAEcPXrUDEQB2Ldvn/nlGhoamsr/bu/evWTPnp1s2bIxZ84c9uzZQ7NmzRg3bhzff/89x48f5+jRo1itVhISEjx+mUdGRt6Ra2hoKBcuXOCpp54y/1atWsXq1aszda8Z8b0fKIpCixYtmD9/fqqAhtOnT/P999+bXxilS5cmMjIyld/bjRs3OH36NGXLlr3jWLIsI4TwmKfMYNmyZZQsWZIvv/ySF198kapVq3LhwoUMfcAyi/Xr11OtWjUzRyHogU2yLFO4cGEqVqzIzZs3OXnypHl+z549lC5dOs0v04z4bt26lW+++YYaNWowZMgQ1q5dS2BgIL/88kum+NaoUYOAgABze9plVQoNDeXy5csEBASY68xisTB27FiuX7/Ozz//jMViYc6cOfTo0YNatWqZ25H3KktVVXnjjTc4deoU33zzTSqXhooVK3L8+HEP/8Y9e/aY191v+4eJ+5VXkSJFuHjxIjdv3jSPLVq0iE6dOnlcFxcXx/Lly/nkk0/o378/zZs3x2azcevWrQe2xt1RtGjRVJ8nf/75pxkEFRYWxr59+8xzdrvd4/P7fj/PvPhvwqsAPiYcPHjQtGa5/tJyXg8ICODs2bNcunQp0327fObGjBnDyZMn2bp1K+PGjaNNmzYeFqFhw4Zx8OBBduzYwfjx43nuuefumIsrNjaWK1eupPpzWaAmTZpEQkIC77zzDu+99x5nz571iPS9cuUKH374IadOnWLZsmUsXrzY3NZ45ZVX+PXXX5k6dSqnT59m1apV/O9//6Nbt25IksSlS5cYPXo0u3bt4vz58yxbtozAwECKFClC+fLlSUpKYsaMGURGRjJ9+vQMHbV79uzJwoUL+e677zh37hyLFi1i6tSpHls0d0JGfO8Xb775Jk6nkxdffJHffvuN8+fPs27dOnr06GFG+YH+ZVy+fHneeecddu3aRWRkJNu3b+eNN96gVKlSqbYer1+/bs7bqVOnGDFiBJqmeeSZzAyCgoI4deoUu3fv5vz588yZM4clS5Z4bMv6+/tz/Phxjy/bzCIiIgKLxcKQIUM4efIkO3bsYOjQobRv357cuXOTP39+6tevz+DBgzl06BA//fQTM2fOTLWNllm+mqYxceJEVq1aZaaYuXLlihmIc/PmzTsGhCiKQrNmzZgxYwZNmzY1rb81a9akePHi9OvXjwMHDnDkyBEGDhzIhQsXyJ8/P0FBQVy9epUtW7YQGRnJsmXLzG28tHwZXbhy5Uq6iZMXLFjAH3/8wZgxY8iRI4c5365t24iICEJDQ3nvvfc4duwYixYtYtWqVXTr1u2BtHfxu5+sAunhXuXlQq1atShUqBDvv/8+x44d448//mDq1KnUq1fP4zofHx/8/PxYv34958+fZ8+ePbz99tsIITI1zt2iZ8+eLF68mO+++44zZ86wYMECli5dSvfu3QF46aWXWLRoEcuXL+fUqVOMGjWKGzdueLS/0+eZ3W7nypUrD4W7F/9gCC8eOcLCwtL8W7FihTh//rwICwsTR48eFUIIsWXLFlGtWjVRtWpVoaqq6Nq1qxg3blyGY+zatUs899xzokyZMqJWrVris88+E0lJSUIIIXbs2CHCwsLE7NmzRbVq1US1atXEJ598Iux2+11zDgsLE8OHDxf79+8XpUqVEuvWrTPbzJ8/X5QvX16cPn1aTJo0SbRs2VKMHDlSVKhQQTRq1EgsW7bMY4z169eLFi1aiDJlyoiGDRuKWbNmCU3ThBBCJCQkiBEjRogaNWqIsmXLig4dOohdu3aZbWfPni1q1qwpKlasKAYOHCgmTJgg3n77bSGEEEuXLhVVq1ZNdU/ff/+9aNKkiShTpoxo0qSJWLRoUbr3v2nTJhEWFpZpvpMmTRJt27ZNtz8hhBg0aJDJMS1cuXJFjBo1StSvX1+ULVtWNGrUSEycOFHcunXL47qYmBgxYsQIUadOHVGmTBlRu3ZtMWLECHHz5k3zGtecu/+Fh4eLl156yUOO7khPbkIIER8fL/r37y8qV64sIiIiRPfu3cWiRYtEiRIlRFRUlBBCiMmTJ4vy5cuLN954445ySLnmXTh8+LB48cUXRXh4uHj66afFmDFjzDUshBA3btwQb7/9tihfvryoXbu2mD17drpjZIbvnDlzRMOGDUWZMmVEo0aNxMKFC832Xbt2FYMGDbrjfezbt0+EhYWJHTt2eByPiooSb731lggPDxeVK1cWb731ljmmqqpi9OjRomrVqiI8PFw899xzYvXq1aJEiRJi9+7d6comLCxMTJo0KU0enTp1SvM5dZ/L8+fPi5deekmUK1dONGrUSKxcufKBtXfxW7p0aZr8jh49KsLCwsT58+eFEMlrMy4uzmy7adMm83r38xnJK6P2Qghx9uxZ0bNnT1G+fHlRs2ZN8fnnnwtVVYUQQtSvX1988803Qgj9mW/atKkoV66caNiwoZg0aZLo1auXGDp0qBBC3PGzOOXzn9Y8vv322x5r6vvvvxeNGzcWZcqUEc2aNRPLly/36HPhwoWifv36okKFCmLo0KHilVde8VgDd/o8c8kg5dr04r8NSYiHYM/24onGzp076d69O3v37vUIyniYmDx5Mps3b/YodfZfx+DBg4mPj083D97jxrJly/jkk0+8ucOA8+fPM2PGDD788MN0r9m4cSOjR49m06ZN6fp6/VewdOlSfH19ad68+eOm4oUXXqSD//anlBdePGa4tmZSBq08bly/fj1TOfD+K5g5cybPPPNMmufOnz/PmjVr+Pzzz3nuuef+88qfpmksX778vupIe+GFFw8f3hAhL7x4jNi8eTO1atVixYoVd6yZ+6jRpk0bLl269EASTv8bMHTo0HRzAkZHRzN06FAiIiLSTc78X4Isy8yePTtTORS98MKLxwfvFrAXXnjhhRdeeOHFfwz/7b0KL7zwwgsvvPDCi/8gvAqgF1544YUXXnjhxX8MXgXQCy/+w7h48SJt27Y1g1COHz/OCy+8QIUKFWjevHmmEyG7sHTpUp599lkqVqxoltJy4aeffmLIkCEPlH9K3L59mxIlStxz5HK3bt345JNP7rrdqlWraNeu3T2NeS+YPHnyAxuvRIkSaf41btw4U+0HDx6cqhavF1548eTDGwTihRf/YYwePZpevXphsVhITEykV69e1K9fn9GjR/PTTz/x9ttvs2rVKrOCzJ2wceNGRo0axYcffkilSpXYtGkTb731FgsXLqRcuXI0bdqUGTNmsGvXLqpUqfII7u7uMXny5Lsun/X7778zbNgws2rDPxHjx4+nRo0aHsfuVBbSCy+8+OfDawH0wov/KA4dOsT+/fvN9Cbr1q3D6XQydOhQQkNDeeONNyhfvjwLFy7MVH+LFy+mbdu2tGnThkKFCvHSSy9RtWpVfvzxR/OaLl268OWXXz6U+3kQCAoKIjAwMNPXf/LJJ7z66quZUpCfZGTNmpWcOXN6/IWEhDxuWl54cVew2+20bNnSY+chI6iqysSJE6lZsyYRERH069fvnqoX/RPhVQC9+NeiRIkSLFu2jHbt2lGuXDnatm3L33//bZ6/evUqgwYNokaNGlSqVIk+ffqY5fgiIyMpUaIEU6ZMoWrVqvTu3ZudO3dSs2ZNVq9eTZ06dQgPD2fMmDGcOXOGLl26UKFCBTp27MiZM2cyzfHXX3+lTZs2lC9fnubNm7N06VLz3Msvv0zbtm3NOr2rVq2ibNmyHDlyhJ07dxIREcGSJUuoVasWERERDB8+3KP8VoMGDRg8eHC6Y3/zzTc0atTIzFu3d+9eKlas6GEBq1KlCnv27MnUvbzxxhu8/PLLqY671zNu0KABe/bs4cSJE5nq89q1awwaNIjq1atTtmxZGjVqxJIlS8zzCQkJZgqWWrVqsXbtWo/23bp1Y/r06fTu3Zvy5cvTrFkzDh48yPTp06levTrVqlXzUEjdt4AnT57MW2+9xbhx46hatSpVqlRh7NixHjWn9+zZw7x582jSpEmm7iclvvvuO5o2bWquHfd6sPPmzeOZZ56hbNmyVK1alYEDBxIfH59mPzt37uT555+nQoUKNG7c2ENGGa2DjOB0Ovniiy9o2LAhZcqUoUaNGnz00Udp1o+Oi4tjwIABVKtWjYoVK9KjRw+P52H//v288MILlC9fniZNmjBjxgwPeXrhxb0iMTGRvn37cuzYsbtqN3HiRJYvX86nn37KN998w5kzZxgxYsRDYvmE4fEWIvHCi4eHsLAwUbduXbFlyxZx+PBh8cILL5jlmRwOh2jevLno3Lmz+Pvvv8Xff/8tnn/+edGhQwehaZpZuqlz587i9OnT4vjx42LHjh2idOnS4qWXXhLHjh0TK1euFGFhYaJ+/fpi/fr14vDhw6JVq1birbfeyhS/EydOiIoVK4pFixaJs2fPitWrV4sqVaqI1atXCyH08lEVK1YU8+bNE1evXhXVqlUT//vf/4QQemmnUqVKiWbNmok9e/aIP/74QzRo0EAMHjzY7P/atWsiNjY23fGrV68u1q9fb75/7bXXxKhRozyuWbBggahXr17mBJ4Chw4dEiVLlhQ//PCDx/FOnTqJGTNmZKqPl19+WfTo0UMcOXJEnDlzRnz88ceiTJky4sqVK0IIvZxe06ZNxZ49e8Rff/0lWrZs6VHyqmvXrqJChQpi+fLl4vTp06Jz586iSpUqok+fPuLEiRNizpw5IiwsTBw+fNi83lXea9KkSaJMmTJi2LBh4uTJk2Lx4sWiZMmSYuPGjal4Zqb0X0osW7ZMlC9fXixZssS8t4iICBETEyN+/PFHERERIbZu3SoiIyPFhg0bRKVKlcxyd+7jnTp1SpQtW1aMHz9enDp1SixfvlyUKVPGlEFG6yBl6bSU+Oqrr0T9+vXFrl27xPnz58Xy5ctF6dKlxc8//2zOgauk4ZgxY0Tr1q3F4cOHxalTp0SvXr1Ep06dTB4RERHif//7nzhz5ozYsmWLqFevnrmmvfDiXnHgwAHRvHlz0apVqwzXsztu3bolypYt6/E5uH37dtG0adM7lkb9t8CrAP7H8NRTn4vJk3emOn769A0BI8X+/ZceA6uHg7CwMI8vlw0bNoiwsDBht9vFpk2bROnSpUV0dLR5PioqSpQuXVps27bNVADXrl1rnnfV0zx48KB5rHr16uKjjz4y33/99deiSZMmmeI3aNAgMXz4cI9jX331lWjXrp35ft68eSIiIkK8+uqr4vnnnxdOp9ODy549e8xr169fL8qUKZOqVnBaiIyMFGFhYeLUqVPmse7du4uxY8d6XLd48WJRo0aNTN2PO6Kjo0XDhg3F888/LxwOh8e5YcOGiXfeeSdT/cydO1ecO3fOfH/z5k0RFhYmdu3aJW7duiXKlCkjNm/ebJ7fu3dvKgWwd+/e5vkFCxaIsLAwERMTYx6rWLGiWcs2pQJYqVIlj/rDbdq0EZ999lkqnveiALZv3158/PHH5nun0ynGjRsnzp07J7Zv3+7xpSSEEL179xZDhgxJNd4nn3wi2rRp43HtvHnzxO+//54pHmFhYaJcuXKiYsWKHn+uZ2P9+vVi+/btHm1atmwpvvzySyGEpwLYu3dv0blzZ3MNRkdHm2t00qRJ4pVXXvHo58cffxTVqlXLFE8vvEgPX3/9tfjss89EQkJCKgXw6tWrom/fviI8PFzUrFlTvP/+++YPos2bN4sKFSr8J5S9tOANAvHiX43ChQub/3f5djkcDk6cOEH+/PnJnTu3eT5Pnjzkz5+f48eP89RTTwGQP3/+VH0WKFDA/L+vr6/Hex8fHxwOR6a4HTt2jGPHjrFy5UrzmNPp9NiC7dq1K6tWreKXX35hzZo1Ho75VquVihUrmu/LlSuHw+Hg1KlTlC9f/o5jX7t2DYDg4GCPe7Hb7R7X2e12fH19M3U/Lpw/f54ePXpgtVqZOnVqqqCKoKAgTp8+nam+XnjhBdatW8fcuXM5c+YMBw8eBHS/nVOnTuFwOChdurR5fbly5VKVYks5X9myZSNr1qzmMZvNluq+XciXLx82m818HxgYmO61d4sTJ07w0ksvme8VRWHQoEEAFCxYkIMHDzJp0iROnTrF8ePHOXnyJG3atEmzn3Llynkc69at211xGTZsWKrSbTly5ACgUaNG/PHHH0ycOJHTp09z5MgRzp8/n2aU8Ouvv06vXr2oUaMGVatWpUGDBrRu3RrQI8x37NhBeHi4eb2maSQmJnLjxg2PteiFF3eDHj16pHvurbfeIk+ePHz//fckJiYyfvx4+vXrx9dff83Zs2fJkycPGzduZOrUqdy4cYPatWszZMgQsmTJ8gjv4PHAqwB68a9GWuWohBAeX+ru0DQN4VYcx8fHJ9U1KaMj77X2q6qqdOvWjU6dOqV7TWxsLJGRkUiSxB9//EGRIkU8xnUf28X7bqI33f2vcufOzdWrVz3OX7lyxUNJzggupSY4OJjZs2enGUigqiqSJGXYlxCCXr16ERUVRfPmzXnuuecoVqwYzz77bKrrXFAUJdX9p1RAMzO2Cw+znNmdoo1XrFjBiBEjaNeuHTVr1uS1115j8uTJd91PZpEzZ07zR09KTJkyhTlz5tC+fXsaNWpEv379GDBgQJrXli9fno0bN/LLL7/wyy+/8MUXX/Dtt9+yZMkSnE4nTZo0oW/fvqna/Re+bL149Ni5cydHjhxh7ty55mf+hAkTqFOnDsePH+f27dtcvnyZadOmMWTIEBRF4eOPP6Z///7MmDHjMbN/+PAGgXiRCrdv23n77TUUKPAZVutoChb8nI8//hWAjRtPYbWO5tq1ZGf0qKhbKMqH7N0bhdOpMWLEZooW/QKrdTS5c0+gb991qKquaIwcuYV27b5nwICfCAn5hODgT+jf/yc0Tf8Sz6j9g0JoaCgXL17k0qVL5rHo6GiioqIeWTqP0NBQzp49y1NPPWX+7dixg/nz55vXfPTRR+TOnZtRo0Yxfvx4oqOjzXNJSUkcP37cfP/XX3/h4+PjoSSmh1y5cgFw48YN81h4eDh//fWXh3P/7t27PSw2d0J0dDQvvfQSefPm5ZtvvjEtSClx48YNcubMmWF/J0+e5Pfff2fGjBm8/fbbNGnShLi4OEBX+ooWLYrVavUI7Dl69GimLbCPG0WKFOHQoUPmeyEETZs2ZcOGDcyfP58ePXowcuRIOnbsSMmSJTl79qyHsptePwADBw7k008/fSA858+fz6BBgxg8eDBt2rShYMGCXLx4MU0uc+bMYc+ePTRr1oxx48bx/fffc/z4cY4ePUpoaCinTp3yWO/Hjx9n8uTJ9/wjygsv7oTjx4+TkJBAtWrVCA8PJzw83Mx6cOrUKSwWC7dv32bcuHFUr16dqlWr8vHHH/PLL79w9uzZx8z+4cP71HmRCv36/cT27ZGsWNGJo0ffok+fagwduok//4yifv0i5Mzpz4oVR8zrlyw5RPHiIVSqlJfx439j3rx9zJvXluPH3+bTTxszZcoufvzxqHn9qlXHuHXLzvbtPZg4sQlffLGT1av1yK3MtH8QqFGjBmFhYQwYMIADBw6wf/9++vfvT+HChalevfoDG+fKlSsekbnueOWVV9iyZQvTpk3j7Nmz/PTTT4wbN860uG3dupVVq1YxatQoOnToQFhYGB988IFHH8OGDePgwYPs2LGD8ePH89xzz+Hv7w/A9evXuXXrVppj586dm5w5c3LkSPI8NmnSBE3TGDFiBCdPnuSrr75i//79poVSVVWuXLmS7hbomDFjUFWVcePG4XA4uHLlCleuXCE2NtbjuqNHj5pblomJiVy5ciXN/rJkyYKiKKxevZoLFy7w22+/mVukdrudwMBAOnbsyNixY9m5cycHDx5kxIgRd2Xhe5x46aWX+O6771i1ahVnz57lk08+IS4ujoiICLJly8bOnTs5ceIEx48fZ/jw4Zw4cSJN2Xfu3JmjR48yefJkzpw5w4oVK1i7di3169cH7rwOMoNs2bKxdetWzp49y8GDB+nTpw8xMTFpcrl06RKjR49m165dnD9/nmXLlhEYGEiRIkXo0qULZ8+eZcyYMZw6dYrff/+dkSNHkiVLFq8C6MVDgdPpJF++fKxYscLj7+eff6ZmzZrmD+FixYqZbVwGgIsXLz4Wzo8S3qfuP4gBA34mMPBjj78yZaaa52vWLMj06S2JiMhH0aLBvPtuDbJl8+HAgcvIskSnTmVZvDjZ4rBo0SFeeEH/Qi9dOiezZ7emVq1CFC4cRPfuFShdOicHDlw2r/fzs/Lll80oUSIHr7wSToUKudmxIzLT7R8EZFlmypQpBAUF0a1bN15++WXy5MnDnDlz0t0evhfUqlWLNWvWpHmubNmyTJo0iTVr1tC8eXPGjRtH79696dGjB3FxcXzwwQd07tyZcuXKIUkSH3zwAdu2beOHH34w+3j22Wfp0aMHffv25ZlnnjEVJIAOHTrw0UcfpTm2JEnUrl2bXbt2mccCAgKYMWMGx44do02bNqxZs4apU6eaOe6ioqKoVauWR6oSF+x2Oxs3buT69es0a9aMWrVqmX/Dhg0zr7t16xZHjx6lXr16AKxZs4ZatWqlyTF37tx8+OGHLFq0iGeffZYxY8bwwgsvUKJECdMXcMiQITRo0IC3336bHj160Lp1a/z8/NLs70lD8+bN6dOnD5999hmtWrXi77//ZsaMGQQFBTF06FAkSaJ9+/a8/PLLJCUl8dprr6Wy9IHu4zht2jQ2bdpEy5YtmTZtGp988gkRERHAnddBZjBu3DgiIyNp2bIlb775Jvnz56dDhw7mHLijT58+1KxZk759+9KsWTO2b9/O//73P7JmzUqePHn4+uuvOXDgAK1bt2bgwIG0bNnyoVeH8eK/i9DQUC5fvkxAQIBpdbZYLIwdO5br169TuXJlAI/nyrWr4u47/G+FJNKy43vxr0Xhwv/HSy9VpGtXzyCBCxdiqVdvLvv3v06ZMjlZteoYW7ee5dixa+zdG8WFC7eYPbs1L71Ukd27L1K9+kyioweQlKRSsODnHDnyJsWLZwdg69YzrFt3gqNHr7Fv3yVOnbrBBx/UZeTIeowcuYVlyw7z99+vm2PXqzeHKlXy8emnTTJs/0/D0qVL8fX1pXnz5g+03507d9K9e3f27t1LQEDAPfXx999/8+qrr/Lrr79m2tdt0qRJ1KlTxyP45G6wcOFC1q5dy9y5c81jr7zyCrNmzbqn/rzwwgsv3FGiRAmmTZtG/fr10TSN9u3bExgYyKBBg7BYLIwePZrY2FhWrFiBoij06dOHU6dOMXr0aCwWC8OHDydnzpxMnz79cd/KQ4fXAvgfRI4c/hQrFuLx99RTQeb5nj1/5PXXV2OzKbzwQjl+++0VgoKSI0EjIvIRGhrMihVHWLLkEJUr5zWVv9Gjt9KmzffY7Spt2pRk9eoXqFgxj8f4NlvqIAXXz5DMtP+nQNM0li9fniq68klB+fLlKVOmTKrkyekhLi6O7du3e0Td3g2EECxcuJDXX09W/nfv3k2ePP/M+fXCCy+ebMiyzFdffUVQUBDdu3ena9euhISEMGPGDDNYbNy4cVSuXJlXX32VF198keLFizNx4sTHzPzRwBsF7IUHhBDMmbOP9eu70aCBHkwQHR1HTEyih9N3585l+eGHo9y4kWhu/wJMnvwHEyc24ZVX9MCBpCQnZ8/eTNNhPC3cb/snCbIsM3v27IcaSXq/GDlyJG+99RbNmjXLMJo0MDCQefPm3fP9rFu3jrCwMJ5++mnzWMWKFc1tmH8T3nzzTX7//fd0zzdr1uy+tmW98MKLtHH0qKe/eJ48edKNoAfw8/Nj5MiRjBw58iEze/LgVQC9SIWAACvLlh2mSJEgLl68xcCBGxACkpKSo0O7dCnPJ5/8htOpsWhRB/N4SIgfq1cfp06dp4iNTWLUqK3cuJHo0fZOuN/2TxoelvJXrVq1VB9094KCBQt6+BRmhPu5n2effTZVCpcHkcLkScQHH3xAQkJCuufvddveCy+88OJB4d/56euGmzdvmqkjvNAjOW/evElkZKTH8agoPUrw0qVLTJpUlzFj/uDrr/eSO7c/7dsXw9dX8Msvx2nRQt+u8/WFEiWCCQy0oqoxREbGADB+fA2GDv2dsmWnkj27L02bPkWnTmH8/vtpIiMjiY2NxW53eIyflJTErVu3iIyMzLC9F178U3CnfIyJiYne9eyFF+g7C0FBQWmee9zf33fi9m/AvzoI5ObNmzRp0oSYmJjHTcULL7zwwgsvvEiBbNmy8fPPP6dStG7evElERMRdJbZ/0EiP278F/2oLYFxcHDExMSxYsOAf4WiemJh412W3/s1wyePChQu0abOUypVjKRnqT1hIfio//TSHD8Xx8fS9rFrxHJIsIckyQtOQFQXV6USxWlAdTmRFRnVqWH2sOO0OLDYrVy5fISr6InFxcdy+fZtr169z+fIl8ubJi4+PDwLQVA1JkVCdKpIsIVQBMgT6BxIQGMDtuNucPnuarFmz0qxZszSrXoDuV3n69GlOnTrFtWvXOHDggHnu1VdfNdOsZEYekiTx448/eiQ/Br2kmyRJ9OvXL822p06d0v0RLVby5stLr169TG5JSUnEx8fz+eefe7R58cUXKVasGHFxcWzatAmHw0GrVq2eGJ9G7/PiCa88PPFfkMfly5dZuXIlZ86cAaBOnTpomsahQ4eIjY2lbdu2ZhqpJ1Ee0dHRdOnShbi4uFRKVlxcHIqicO7cOZxO5yPnZrFYKFSoUJrc/i144ApgdHQ0H3/8MTt37sRisVC3bl0GDx5M1qxZzSSgu3fvJiAggLZt29KnTx/TD2j16tVMmDCBvHnzMnHiRPLmzUtkZCQNGzakS5cujBgxwmOszZs307t37wx9ofLkyfOPyOmTkJDwj8lh9igQExPD77//zubNm7l8OYBF347Cz8+GeisRJasfZaol8eK7OylcTA9WkWQJoYlUr0iA8DxfoGABKkmV0hlZgJR2XylfNaGxbt06Fi9ezOjRo80e4uLiiI6OJjo62qydGhERQZkyZTh58iQAbdq0oXLlyunmHbTb7URGRuLr60vOnDk5deoUX3zxBQBVq1bl6WpPkzVbVk6ePIndbqdevXpmveOUyJ8/P7///jvnzp2jY8eOqZ6HvXv34ufnR40aNahbty5ZsmQxP/QOHTrEgQMHsFqtrF27lpdffplp06ZRtmzZNOvBPip4nxdPeOXhif+CPAoUKEB4eDh///03//vf/9i1axfZs2cnISEBm83G7t27OXLkCFWrVqV06dLp/kh9kqGqTlT10SuA/5B88veFB6oAaprGm2++SXBwMPPmzSMpKYmRI0cyaNAgvvzyS3r16kXp0qVZunQpV69e5b333sNqtfLOO++QlJTEtGnT+Pzzzzly5AiTJk1i7NixZt/fffcdrVu3pkKFCg+SshdPKJYvX86ePXsokK8Ab3XvzeoVq/ix5xwqDK/JquWLefPF/mz5/TwlioeQeOU2so+CJEugSEiShOpQsQXYsN+2Ywv0wX7bjk+AD0m3k7D4WNBUDcUi61Y+WdYVRSGQLbq1ULEoqA4nis2CM8mJYlVQ7U6sflYciQ6svjYcCXZki0L92vVYt24dmqaxYMEC9u3bh6Zp5M6Vm1w5c1H4qcK0a9GObDmyYb9tZ9zH4zh88DB7/97L8ePHee2111IFQ6xdu5bVq1eTL18+7El2Ll+5TGhoKEWKFKF3794etVOLFi3K9evXOXXqFBcuXODSpUs4HA5y5sxJgQIFsFgs7Nu3j3PnzlGkSBHKli2bSt6VKlVi6tSpqY4DlC5dmhdeeIFly5Zx4MABvvzyS27F3mLD+g0kJiZSuHBhihcv/sRZF7zw4r8ASZKoUKECX3zxBStWrGDz5s0A5MiRgzZt2nDjxg0OHz7Mhg0byJ07N40bN/aofPGkQzL+PY5x/+14oArg4cOHOXDgANu2bTNrfQ4dOpQXXniBy5cvU6pUKT788EOyZs1K0aJFeeaZZ9i5cycADocDX19fwsLCAFKlUMibNy/Dhw9n2bJl/9rIwf86HA6HWUTeYrHQ952+5MiVA2fMbYaOrM+gwT8z63IgXXq8wfZD13hryCZGj2mAT4g/LqudJEsggcXHiqRI2AJ9kBUZH7dXJFCEAhLIFjnVTz3JIiNJEpJiRZIlbIoNWZFRrLqSafWTkBUZW4De14VLF8iVK5e5zTt8+HACAwJ1LoblUVM1c3w/xY8qT1ehcrXKzJgxg3nz5vHSSy+Z5bDi4+PZuHEjH3zwAdmzZ0dogkuXLzFnzhwGDRrE1StX+fPPP4mMjOTixYtcvHgRHx8f8uXLR/78+QkLC8NmtXHp8iX27tmLU3VSuHBh6tWrx4EDBzh//nymt51dqFWrFmFhYUyfPp3g4GAiIyMJDQ3l559/RlVVXnzxRUqXLs3x48epWLGit7SXF148YlitVjp27Ej9+vU5ceIEkZGRbN68mcjISCRJoly5cuTPn5+ZM2dSo0YNmjdv/o94TmVJ/3sc4/7b8UA1qXz58jFjxow0C71bLBZz+wrgyJEjbNy4kdatWwN6tE2NGjWoUqUKgYGBzJw506P9+++/T//+/Zk1axavvvrqg6TtxUOEK8YoZX1Wh8PB9evXuXHjBidPnuTYsWNcuXKFQoUK0bVTV54q/BQx56/h8E0gavhCqr8UQcNat+nRYwnnziVQomAgAxvnoV29QiSev4kcYNUteVbZtPDJNgU10YnFz4oz0WFY75xYfa04kxwoVguapnnwkiRJ9yO0KKgOFcUi40xyYvXXrYlWPyuq3bAIOnUr4vcLvyc+Pp4VK1ZQrkxZ/H38PayGLiuixceSikfXTl2YMftr5s6dS9u2bcmaNSuzZ8+matWqZAvIiubUUB0quXLkJHeu3PTp04csgVkoUaIE+fPmI7xCRfLkykOWLFnQ3LeoJf1eVFXz4JE7R25mz57N+++/f9c/pHLlymWWdYuNjWX79u1kyZKF8uXLU6lSJb788ksOHz5My5YtU6V78cILLx4NcuTIQY4cOcz3Qghu3LjBxo0b2bt3L++++y5z585lwYIFdOnS5YlXAiXj73GM+2/HA1UAg4ODqVOnjsexOXPm8NRTT3kohc2bN+fEiROUKVOGl19+2Tzer18/Xn75Zfz9/VP5RRUqVIjXX3+dKVOm8Oyzz961BcOLBwNN04iKiiJXrlwIIVAUBUVREEKwfPlyzp45i8VqIUeOHCQlJXHkyBFu3bqF1WrFx8cHH5sPdoed+Ph4goKCCAkOIV/+fDRu3JisWbJSoGABhCaQFRm/XIFYsviSZ0BbLPmDmP5lceSsPmhJKrIEWrwDKdCGcGjIfhZczn6uYBBJkVEsuqXP6mdDtsim9c4iWZEVGVlL8eEnAULRFShZv1aSZd0S6G9DUiRk2Wac0xWuNq3bsG//PvLny09ElQgsNos+viwjyxKSIiMrss7DX7cmunj4Zwmgd+/erF61mjFjxuDn60f2HNlp27YtiqyYipxsken0fCe6duuKRbYgKYZ1UZIQQujXiGR/RyR9C0M25OC6l1p1arH/0H42b958X/57WbNmpWnTph7H/P39AVi5cqVXAfTCiycEkiQREhJC8+bNWbhwIcuWLeONN95gypQpLFq0iOeffz7VD/QnCZIkIT8Gfk+yTB4UHupe6vTp01m/fj3Tpk3zOD5+/HhiY2P5+OOPefPNNz3qgt4p2qZnz56sXr2akSNHprIQevFgoGkax44dIzY2lrCwMIKCgnA4HBw7dozz58/z999/c+PGDRRF4fr16wQGBjJ27Fj+/vtvDh48SNvWbVE1lcuXLuPr60PDeg3Jky8P8bfiUVFJuB2Pj48vgYGBnuH9huKiOTRUp4rFx4L9VhK+Nh8S9kXiK0kknriKb4X8qAkO5PgktLgk5NxZ0ewqslGqTva1ICtCtwBqwrS8qQ4VCxacduO93YlQFDRN83jQXQoVEmhOTbfaGW2cSQ4Um8W0pmmqhizLFC1SlGKhxRCGNVF1ONE0gWIRqM40eNj06GSs+nubYqNNy9Y0adSEmNgYcuXMhaRJqKqazEPSefj6++JMMHg4VBSrYiqbuuXP4I/+f00TKAJUh4qw6Dw6duzIhAkTKFGiBIUKFcrUujh//jy//vor169fp2bNmoSHh6e6pkePHnTu3Jk///zzntefF1548XAgSRLdunXj888/Z9++fbzxxhtMmjSJZcuW0b59+8dNL10oj8kEqPz79b+HpwBOmTKFSZMmMWzYMOrVq+dxrkyZMgB8/PHHdOjQgWPHjpm+f3eC1Wpl1KhRdOnShZUrV6Yb8ZgSiYmJd8zK/6QgKSnpsY5//Nhx1m9Yj9ViJSg4iFWrVlG9enX27N5D9hzZyZc3Hw0bNqRE8RJEXohk8ZLFxMfHs2HDBg4ePEiL5i0oXLgwskUmtGgoskVGc2o4nA4Uq4JFseBj80GSJVShoqmaxxax0ASSor+qdhXhC3bNgVTzKVQ/K3JgLlQfUCUJzd8XsvugaYBNQciariypDtAMnzshowkNzamhqfpWqqZqOJOcoIEkVNN6hhC64SwtHpqGmqSiaRqqU9PfOzWEEUCCAFRA0rdbJEObdd6Jh1ND0vTINknoSpvVx0qOHNlRNf18Sh6qcJKUmJTMQ9VQJRWhCtPfUO8QvbiycV9OzanzQOeRJUsWnuv4nPnDK2fOnOTPl59qT1fzCC65efMmkyZNws/PD6vVSuVKldm/fz+LFi2iZMmSaa4hSZKoVKnSI3neHvfz8qTBKw9PeOXhCZc86taty7FjxyhfvjxNmzbl+++/59lnn30sFq/ExMQMr9F3QR4BmTTG/bfjoSiAH330Ed988w0jRoygS5cugJ6v6K+//qJJkybmda5IpBs3bmS678qVK/Pcc88xduxYBg4cmKk2vr6+/5h0AI+ap6ZpHD58mF9++YWoi1G0b9ee0qVKkZiUxNDhQ1m9ejXNnmlG02eaojpUZMMztljRUNq1bsuUaVNZt24ddevUpWzpMggNFJuCiorio6DixOJjxYkD2aKY27t6UIRuoULgdkxGkzVki4wjwYFVtiAJsCo2HAhsFhuOJEDVdIXHrpeIk30tehCIVUYCNEkYPJwoPhadhy0NHpqGLEloIi0eKrJFQXOoSIqU3KekYrHqPoJSCk9h4Uo3I0QqHhYfC8774OG0O7FZbWiSljEPMPuTpJQ8rFQoW57wiuHcuHad6CuXOHTwEJMmTaJfv35kz54d0JW5XLlycfz4cbIEZuHQoUNmUnXXOl23bh0Oh4NatWoRHBwMQGRkJFarldy5cwN6UMumTZs4deoU+fLlo2zZsuzYsYObN2/Srl27TFsh08I/5bl+VPDKwxNeeXjCz8+PEiVKsGTJEtq3b4/T6SQ2NhabzfZYgiszkzlAeUwaoPIf0AAf+Ix/8cUXLFiwgHHjxtGmTRvz+JkzZ3j77bfZtGkT+fPnB2D//v3IskxoaOhdjfHuu++yadOmOxZ49iJjqKrK7NmziY6KplatmrzU7SV8/HzQnCqqULFarYSFhbHu53U0faYpikX22GosWaoUk7+YnOyHJku6VczwWQNdkdFfFRQjxYoZfQvIbnZ2WZFNnzdJklAsMoqvVfehs8pY/G0gS1gDbToPQ1kShpJjBkAoMkguHkoyD0nn4bJM6u8NHkim71wyDyMaWDb6EbqiJcuSrmBZFDcfPEP5g2Q5pOAhhD6+7o+XOR6urVyXH4xskcGQdWZ4yC4eGDwURU93oygoFoXgkBCy585BqZKlyJUnFx999BE5c+Q0FcrQokV5/fXXzbyGZcqWMa22CQkJ/PjjjxQoUICtW7dSrmw5qlStwuLFi7l06RLdunajeo3qrFu3jg0bNgB68NfRo0e5cOEC2bJlY/369fTo0eNhLXEvvPAiBYKCgggPD2flypU8//zzbNmyhaNHj5o7c08aZHgsW8BPdmjMg8EDVQAPHTrEtGnT6NmzJzVr1uTKlSvmuUqVKlGmTBmGDBnCsGHDuH79OiNGjKBTp04eEUuZQdasWXn//ffTrXrgReawcuVK4uPjGTBggG5ZUvUtUaEJsmbJQt06dTl46CD9+/QHIdA0gSwJ3dfNCLQAwLRgyWhC9zlzKYmaJpDQ2whh5Nu7IyTj2mRrmKYJZIF+3FDyZEno/nuu6w0FSR9fJCuHLr84TT8mhAZ34CEhGdcZ4yGZ42uaQEZ/VWRdTqYcTIVUgCkH1zXuPDQ0Tco0D8C4RqSSh6YJZFlAGjyEEG7zYfBQ8Lg3nUeynGrXrE3l8Mpcuao/t5qqse23bXz00UfUrFmTQoUK8dRTT3Ht2jVmzZrFgQMHKFeuHG1atsE/0J/ff/+ddevWkTt3bp7v+Dyz58ymYnhFypQpw+bNm3nlpVeIuhSFn58fixcvJiYmJlXQmKZpT3xUohde/NPRunVrRo8eTb169QgNDeXcuXNPrgL4mCyAjyPw5FHjgSqAP/30E5qmMX36dKZPn+5xbuXKlXz11Vd89NFHdOnSBUVRaN26NQMGDLinsZo1a8YPP/zAli1bHgDzfz/sdjvzv5nP1WtXad68OXnz5uXXX39l2NBhWBWL/nxJRsJNWUJo8GyTZ2jRojmqQzX9yfTKGq5ACf0BcSk3CPRrJGFaqXT/XffrPC1krgTMkiIbPnX6+C6rl9CE4VGnR/fKioyqufnugWHhwjymGX268wBMPzkPHu6BE0IY965HN6uGP5/JQ0oeQ5JlNFU1fe3MktpScnSuZihuLp+8u+EhyUYAh8GDdHjIsqwHi6TgIbnzcN0bLh4KYMha6Mq5i4e/vz+Fnyps8ggNLcqJEyf5a99f/PyTnvMvKCiIYqHFaNuqDdmCg8xglEYNG9G4UXJkcdmyZVm1ahUdO3Y0rfXhhJOUlMTJkye5dOkSW7ds5eTJkzzzzDNcvHiRMWPGMGLEiH9E6UYvvPinIiAggDJlyjBq1CieeeYZbt68+bgppQvlMfkAevMA3iX69euXoVVu0qRJd9VngQIF0i319r///e+u+vovQdM0li5dyrVr10hISKBgwYIkJSXRoF59pkyZAkCrlq3IljWrqdRJkp5E2XyVDfXN2AZ1vZrBBi64px4xlEjTJ83oC7e+dSVT99VzefhKSMn/dyl2JCtMuiLmNsYdeAjXtinJ92R+gnjwcHtP8quHHPB8Nc+TBg93Oeh2T88+UvIgHR6uu0/JAzfu7sflO/PQnQrTmA+Th2Qq66l4SBJFixalWPFiyT6JTr1GslC1VHJydyRv2bIlo8eMxmKx0KBBA/z8/FAUheXLl7N3714ALly4gDCIL168GICZM2fy4osv/iPKN3rhxT8VzZs35+jRo5w5c+aJrnUryxKSePTaWEq/6n8jvCU1/qU4fPgwhw8fpn7d+nz3/XdcvnyZ/n37ERwUzOuvvY7Nx0ZokaKmtQxNGGXRJDSnhuKjoKkC2QKaKkDWI3YlIZnbdK4kyrKcHLAghNs2o6pHsrq2X/XExKquWgiBcCkLrv+7eDgNHpoKMmiOO/PQt2o9eWjGNqhAIJyaubXtitJVNRUJOTla1uCBpOnb3aqWLA9Vw2KxoDoEsqJvjequeil4qCl4WGTT+iaEfl8ePEx5GFueWnLkLiINHkbKGlUVyBZJt0C6rIEZ8DAtnCl4YPgNqobMU/EwuGqqZip+pjzM9aEl85CSeQT6BzD4vcGsW7+OUaNGkZiYSPaQ7CTZk/Dz86N169ZIkkTNmjUBqFGjhukf+PHHH/P6669Trly5R/TEeOHFfwshISG0b9+eGTNmmAUZHgZiYmIICAi45yATRYLHoP95o4C9+OcgLi6OLVu2cPLkSRISErhy5Qrt27enakRVatSsgdPhxGq1AsLw9Ug2Fwl0pUg2EhYLi7G9aAZOuEqjGQERGImFJdksdWYec726EiCjt3dtcUqKbPh4uZvvpGQexrj69qaSKR5IMlK6PORkHrKMUETycQ8ebmYzd8ud7Dk+ssHD+FXqkoNurUvBQ5KQDa5mMug78pA85gMlBQ/ZjYd0Fzxc8yHLSIpIg4eMLCdvUaeaD1e/kGo+MuIRkj2ELi904YVOL7Dnzz04khxcv3k9Tf+/KlWqUKBAAb5fuJBjx4+za9curwLohRcPEeHh4UyaNMkzJ+sDwrlz55gxYwbx8fFYrVZatWpF+fLl8ff3vys/X9fOz6NGRgqg3W6nffv29O/fn/r166d5za1btxg5ciRbtmzBz8+Pl156iZ49e2b6/MOGVwH8B+Pq1atcuXKFfPnyMWTIEABe7fEqWYOyYpEt5CuYDzVJRbEoyGYAQnK5MHD5nxnWO1VDk3QLD0JPYqxbfFQUWU88LFv06zwcJDR0RVBIaE4VRbLgdKhYZElvo8i6HyGY1rqUPGTDwiUremJjoUloQk8KrbnzUNLhYYwvNAEaCKeG4pOCh+zJQ8LwwzNkYPJQ9VJwmhHRrKkaAjfLpIuHUzXldice+OjjS0paPAQSWuZ4aHoOQeFMtkwqiqLzkGU9sERKm4fm1NzmIz0eKeUhzC3flPIAkZzKR9VQZJ1Hqm0TNx7hFcLRnBrXY67z+eefsyJgBbnz5CZbtmwUK1YMm81G3rx5eePNN7lw4QIFCxZk//797N+/n1u3blG+fHmefvppj21mL7zw4v7wsNK/HD58mKSkJD799FPOnz/PihUrWLZsGVarla5du2Y66ORxpYG505iJiYn079+fY8eO3bGLoUOHcuXKFRYsWMCZM2cYMmQIefLkoUWLFpk6/7DhVQD/YXA6nezatYutW7dy/fp18uXLx7lz5wBo3Lgx5cqXM9OLSLKEZJOMvHSGtUkzlBUjgFeSQWi6dQgp2cokGRYjWZHBavFI6yKU9H+9uSxEFpsFSZaw2CwgYb7H8FeTNPQ4e1echCyB5mblkmW9zJxFQZbceCgyWO/Aw/ghKwzuHjxkTx6mHCSjndCtYciePACjBJxsyMHgITLgIaXgIaXFQ2SOh1HWTrEqep/uaXZcPET6PGQ3HqTgoVtotfR5uNYFyel7dCusbl1VrG7H0/ObkfU/WZbJnSc3ffv0ZdeeXRw/fpyrV69y+fJlOnToQOXKlbHZbBQpUgRN0/jqq6/MLvbt28eNGzdo1qxZ2mN44YUXTwwaNGjAnj17+O2336hduzZ9+vQB4NixY8yaNYsePXpkKk+jAo/FApgeDh48yKBBgzK0ml64cIGff/6ZlStXUrx4cUqWLMmJEyeYO3cuLVq0yPD8o4BXAXzCIYTA4XAQGxvLvn372LhhI3ly56bZM80IK1Zct5gZ1imrzYIj0Y7Vz4YjwY7Fz4pqlCBz2nVLoMufzGML2LC8qU7dsqY5VCy+VhyJDqw+VhxJDiy+FtQkJ4rVYqRfMfiBK8jVUDxVhFBwJjnS4GHFaXe68cDV2uQhLIpp4XMmORF+wuBhwZHkxOpr1UuypcXDiJL14JHowOqv87D62nDaHW48ZDRVePCQJN3XTRgJlhWLjNOuYvXT5aGXhLtfHlacdtUsM5eKhxBIsl7tQ3HnkeRE+AuTh2p3YsGKM8mJYlVMn0wpLR4ONx4Bxrz4WpNL09md+nVGlHG6PISi37+bPEwexty6rLyePDQUi4LTiBgOyRpMy+YtsMfr6+PUiVMsXLyQPXv20KFDB/766y9UVaV169bIskyZMmX4v//7P1atWoWPjw8NGzZ8OA+cF1548UBgtVrp0aMHEydOJDQ0lHz58gEQFhZGpUqV+Omnn6hdu3aG/bgyVDxypDPkjh07aNiwIa+//joVKlRIt/lff/1F1qxZKV68uHksIiKCKVOmkJSUlOF5Hx+fB3Yr6cGrAD6hiIuL488//2T58uU4nU6yZMlC0aJFefW1Vz0qJ5hbdlb91WaR9WTDAXqyZNMiZ7wqQklOWWJC0a1EFt2XSzP81ax+Vr1Pxapbgfz0V0lzi/I1vuldPFw+c7IipcPDkjEPw6fM4mtJk4f1HngQYEOWZSyyNQUPUvGQLZ48pHTkcX88UsrDnYfeSUoeFl8LspzMQzZ5GFVQhFsUrhsPV/u0eCT7OGaGh2H1M+4rFQ+jGosQrqhudx6GBTIFD6vBI7R4KEOGDGHZsmWMGDGCEiVKcOPGDVq0aEHlypUB6N69O1OnTmXp0qUkJCRQt27du36uvPDCi0eH3Llz07ZtW2bOnMmgQYOw2WyAHvD1448/Mm/evAz7kB/TDrBwS6zgjswmrr906RK5cuXyOJYzZ040TePy5csZni9YsOC9Us80vArgY0R8fDy//PIL/v7+ZiSkqqps2LCBH3/8ESEEQ4cMJUdIDrOKhqzIOBMdAEYOON2yojqcKIYlR7FoOJKc2PyTLXDORAcWm8WI2PRUElxbsKpdt+zYExzY/G3Yb9uxBdqwxyVh8bHqvl5uPISml2OTjWPuPJxJThSbhjMxmYfVz6ZbjWxKpng4Ehxovhr223Z8Amwk3fbkofsGGlHAApNbKh5WTS+jZvR5Jx6mX6IEmkPF6mfDHm/HFmDDfjsJnwBfDx4uOUhyGjysim4ds+nWS8Wq6e/9rDjdeZjWO8lDDkLTuakO1ZChA81Pc+Nhx+JjSYOHHrmbcl1YfCw4ElPwSHRg9dV5KFYlOTF1WjzsqiEHd3n4mDySczumz8NlPXUk2nVeSU5TDlYfC62btaJW9VrkyJGDTZs3cezoMVMBLFu2LKNGjeKDDz5gw4YN7N69mzx58tCkSROKFi368B9YL7zw4q7x9NNPc/DgQebPn0+RIkWoVq0aBQoU4PXXX2fjxo2sWrXqju2VFOmlHhWEJOG8j/YJCQmmwuuC673dbs/w/KOAVwF8jBg1ahS3bt0C9FD50qVLs2TJEnx9fHn//fe5desW+QrkM9N/uCwxCMP3QAJZ6Mcki9XDoiNZdJ8xJJtucfHXf0Yl94HZhysVimzVI3RtAfoDZzOsM7ZAH318IafPwzgmKdYUFieZ02dOs2TJEi5fvkyOHDkoXrw4xYsXJzQ0lAD/AFRVJcmehL+fH0gSV29c5Vb0LXLkyKFb3AJsaAgStSSyWGxYrBaThxBCz1oi9F9sik33G5QUyeShWPSya7o89OM2f2u68nClMxE2xbh/myEPHyRFSiWPZDkopjIrWxQPC6A1BQ/J5GHzmEedhyups96/YvCw+ltT8LClwwM3HjZPHn535uGxd5sOD1MegT5Ibu+FkI38gZnhYdN5KK550S2dNkUmb0BeEBBWMoyFCxd6PDM5c+bk1Vdf5fvvv6do0aJcvXqVCRMm0LJlSxo1amREunvhhRdPCiRJolSpUixYsIDdu3ezc+dOmjVrRmhoKCVLlsyw/eO0AN4PfH19Uylyrvd+fn4Znn8U8CqAjwmaptGxY0euX7/OihUrWLt2LQf2H6D6009TrXI1bAE+ZM+aHc2p4kxymspOyjXp8rFSHSoWXwvORMN6Fm+HABuOeN3S4vLzMyM3NWEEDxjRpoqi+6tZk33wVLvTtA65fABlRTZ1hPR4WH0tukXHz0birQRmzpxJu3btKFOmDNHR0Rw7doxff/2VuXPnki1rNmJiYwDw9/MnS9YsxMTEEBISwq1bt1AUhcSEROJux+Hn54emaeTLkxdNCGJiYrgZc9NjC9disVCkcBGKhxanbLmy5MqeE/x0OSTJgsiz57kWc52YmzHcvh1H7K1bJCQkYLfbyZM7D5XCK1GoYEEsNsM/zqrgtOvyd92b0+XPmOQwU6qklINrrlSnljwf/joPyd/HbV6EOR/ulUBc8yEb82GxKjgTnaiKas61u1+lnCL4woOHBKpDS7UuMstDdaqmD6DFZtHv25SHLoeUPFL5AJo8dKuqPr4tjXWazCNf7nxcvXqV+Ph4/P39zXurWLEixYoV47fffsPpdJIzZ05WrlzJzz//TPXq1Wnbtm2mFUFVVfn5559RFAVfX1/8/Pzw8/Mje/bs5M2bN1N9eOGFF3eGn58fNWvWpFWrVvz1119s2bKFOXPmpLKApQVXvN6jhnafY+bJk8ejHC7A5cuXsVgsZM+ePcPzjwJeBfAxYfXq1axdu5bcuXNTvnx5evTogT3Jjn+Avxmo4fKxsvrajEhety9n3L9YpWSrl5/+tFj9bYbVSLcAuvqULUZUqZvFS7YoegEIN6uZy3rn4et1FzxsBo+Y+FhsNhtVqlQBoGjRohQtWpRnnnkGVVWJiooiJCQEX19frl69yo0bNyhatChWq5Vbt25x8+ZNAgICyJYtG4qicPPmTaKjo5FlmeDgYLJly+bxIRIfH8+xY8c4evQo07+eTkBAAP7+/ly6dIn4+Hhy5cpF7ty5CQoKIku2rOQrkN9MUnrmzBkWLV1EfHw8FStWJLxiOEVDi2LxtZqy033xdEuc1c/mce8e8tAEkiKhGNuiNn+bOS+SIpnz4kohI3tYZF3zYuQ9dPkL+lh0K5zBQ/LVrb5Wg88deVh0HlZ/nXNmeUhp8ZBSyyM1D8zqc3rpPxcPxeThvk5t/jazHCCShCyBRVgoXLgwixYtolXLVoRkDzHnOTAwkDp16uDn54fT6WTGjBlcu3aN8+fPM2PGDHr37p0q11h8fDzx8fEEBASYv7BVVWXlypVpPqNdunQxXTO88MKLe8PNmzf5+uuvadKkCVmyZKF27drUrl0bVVXZtWsXy5cvv2N7RZIeS13e+912rlixIjdv3uTkyZOEhoYCsGfPHkqXLo2Pj0+G5x8FvArgY0BcXBx79+7llZdeQVVVypYui4yE0+5E89V0fz3DP8sVZer6knb5WAG6n5VxTFM1FM3ln+eyCOoRohabgmpXTStiWpAkCdXpsvA4DWuiOw8FzQgo0Gv2GnnnBOb4LkVTEYqZ9+7G1ese1ht3KIriUe4rV65cBAYG0qdPHwYNGkSuXLlSOcIGBQXdsWyRv78/FStWpGLFinTs2JFjx46hqip58uQhODj4jglIS5cuTbNmzYiKimLv3r18++23qKpKh3YdKFOujOkv53TzlzN93qTkahsp50Oxgupw6sedmi5jwzdQdTiNRNCp58M1V5qqmdHHwk/cPQ8hozoMHnYnksXFQ18X2PTcimmlcNF56P6JmtNYU4kO3fLn9ipbFdCEuS5cPIQQKdaFHknudEUdOzUzklixpubxXIfn+O233xj3yTh69+6dpq+fxWLh1VdfZeXKlWzZsgW73c6PP/5ImzZtzGtOnjzJxIkTAT35ba9evQDd52bKlCmmpTEhIYF169Zx7NixR/Yr3Asv/q2Iiooy64BXqlTJ41zKz//0IPF4ssDcy5g3b95EURSyZMlC/vz5qV+/PoMHD2bUqFGcP3+emTNnMmbMGIAMzz8KZEoBjI6O5uOPP2bnzp1YLBbq1q3L4MGDyZo16x3PpYU1a9akqhdcvHhx0xF0zpw5zJo1i1KlSjFhwgSyZMnCzp076d69OwMHDkwVgTN//nxmzZrFpk2b7uX+HzlmzJjBoUOHyJs3L6XLlMbXzxeh6l+SLt82xaZHe1psFmSrguTEyAunW+8wcr8hYR6TQP8SBiSLnrtNkiUUq4IkyyhmPkCjuoQQnttzsqelKW0eRgoZFw+SecjuPCwyly9fZs+fe8yQ+czC9ctn26/baNe+3X3JWpblTPmYpETevHlp3rw5zZo14/Dhw8yfP5/KpyrTslkLJEXPoWcmRpYlfT5cMOWhO64oGJZVoSRb0hR9XpBBMfIDmiXpXPNiWM1cypNrHiT08SVFwuJjMbf1TR4ee69uPCwGD6s7DxnZisFDScEDvRKILCFUfV1oRhUXi48VZBcP/b27ldl9XaTkIRsWYsWdh5zsi5mShyuKMKxkGDNmzGD48OFp/qBQFIU2bdrQpEkTvvvuO37++WcPBbBIkSJERESQM2dOnn32WY+2kiSRM2dO871LyczM9pQXXniRNnbv3s2iRYuIi4ujdu3aHtkr7gaypJeDe+S4hzHffvtt8ufPz7hx4wAYN24cI0aMoHPnzmTLlo0+ffrQvHlz8/qMzj9sZKgAaprGm2++SXBwMPPmzSMpKYmRI0cyaNAgpkyZku459wSu7jh58iS1atUyBQTJmcgjIyNZu3YtX331FRs2bGDu3Lm89dZb5nWTJ0+madOm/6gi8TExMURHR3Px4kWuX7/OgQMHGPfROGw+VjRVIFRhVFYATVWNmqu6FUR16ttyulUPDx/AlD5WmnmtiiLr1hq9ioOqf5kLgYxLmZCMGrUkKxjGK6CPb7Q1eRgWQlmWPHwA0+Kxa89uli5bStWqVenRowdFihTJtLxUVa9MERUVdd+yv19IkkTp0qV5//33mTt3LpO+nMQLL3QhJFsINvkO8nDzAdSrZEiGTI15kXXZyigePm+ylHpeQJ8PJF02mlM1aiInz4vmVoEjTd+7FDxc6yIVD2NdJPsAunionjycKrLmUvhkVON4ZnmYcnDxcL1a0udRplQZwsLC+O2332jcuHG6c+bv70+PHj3o1q2bx3FZlnnllVcyNe9exc8LL+4Pu3btYtmyZZQqVYrIyEjatbv3H/MKkl4N5JEj4zGPHj3q8f6bb77xeB8UFMSkSZPSbZ/R+YeNDBXAw4cPc+DAAbZt22b+Sh46dCgvvPDCHc/FxsamaQU8fvw4YWFhHr+4XYiPjyckJISwsDDOnDnDgQMHPM6HhIQwatQoZsyYcU83+ziwb98+Fi5cSOnSpYmMjMTX1xebr/4FI1v0mMnk+rdGxQvDIicbP3sUi2ym1XD/MnWlLJFkt37cIioBZEWvHqE6VdOfC0B2W9yyRTbbmxVALCl4SOj+W64x0+ChCY0fVv/AgQMH6NOnzz0p6g6HnuImb74nxwE/MDCQ119/nXXr1jH5y8nIssyHoz40o2jdfe+S5aGZVTRc8yKhz4cuU8XcAtUjk/X5cM2Lfq2ruoar9rJkWHl1RcsVcCG78ZBdxjMXD8NFAHdLm3DjYXHjIUtu47vzUDx5yHrEuN4Rd89DSsFDSp+HjGQaJRvUa8DUaVOpWrUq2bJlu+OcPQ4lzm6343A48Pf3z9B/KD4+ngMHDmC1WilatGiG9+OFF/8krFu3jsKFC3PmzBn69et3Xz5timTmFXi0eBw65yNGhgpgvnz5mDFjRpoKW44cOdI9l5SUlGZ/J06cSDf7d1hYGFmyZKFChQrkyZMnlTb9wQcf8Nprr7FmzZp/TDmoypUrs3TpUnr16IUmNJISk5JzrYGRZkNDM2rfCoRuDRJ6vVxFNt5LQvcFlJN9AEHWj0kymhAoQhg1XY1oToRR/9eosiGl7wOIZFwjjJq8QkqbB66yYfqrw+lkz5497D+wn6ioKPLkzcOgQYPS9fvLCH5+ftStW5emTZveU/uHBVmWadasGREREUycOFH3b9NE8ny48gCCPh/GnAlNV5KEJhCSLltJM/zqhOy5FtwhJeff0zRNr7NsWMPc50WW0+Dh8r1LsS7QXDw0j3WBlnkeKdeFOw80YcohFQ9jDQkj+lxoGpqEseZkD37uSH4nkT9fPurVrcekSZPo3bs3Pj4++Pr6PpYcYQCHDh1i165dnDp1itdff51PP/2UhIQEZFkmZ86cPPXUUzRt2jRVNPHFixf58ssvKVSoEJqmsWDBAgoWLMirr776yNI/eOHFw0JSUhLR0dEoisI777xzR5/tzEBC8jBYPCpo/wENMEMFMDg4mDp16ngcmzNnDk899RS5c+cmd+7caZ5LSyl0OBycPXuWHTt2MHPmTJKSkqhduzbvvfceWbJkAWD8+PEMHjzYjPp0R0REBB06dODjjz+mVq1a6foZPkn45ZdfKFumLIosY1EsWC1u6SlcvneSXjlBkiTQkn94SBK6A4Qq3JoIMylSsj8f+peqBKBHW0oqyd+ewrDuuawyrmTHKXjg4iJLCA8eEsmN9WNJSUls376djZs3ki9fPp5++mkzaON+vpBlWeb5558H9ESaTxrOnDlD4acKG0m4VfO4a16MKXAL4jBkJpIttW6N9OhrN0tuSgVIlmVzGxSzb8M/T9Jr9KpaCh5grgvJta3swUPBLAZtdOryAXW37iafBUVxKXTJ47jm2Z2HuRXudt6VUNrkYRyTFT1IxjWWyASPpk2boqkaEyZMwGaz4XA46NevX6rPoUeBDRs2cOTIEUCP3ktISCAoKIgaT9egQsUKHDx4kIkTJ5r34XQ68bH54HA66NihI1WqVEE4NVQ0+g/ozxdffMHgwYMf+X144cWDhNVqpUuXLlSpUuWB5OV8XBbA+80D+E/AXUcBT58+nfXr1zNt2rS7Ogf6l6fT6cRisfD5559z+fJlxo0bR9++fZk5c6Z5XUhISJrtAd577z02b97MhAkT+PDDD++W/iOBqqpcvnyZQ4cOsWXLFt7t/66h5KF/e7sWlgZ65IRkvBjn3L5Yza1Z40s2TSR3kfylK7mUOdkI/tBjqSREujzcv9BdfZiXSsk89h3Yz9KlSylQsACvvfYaTz311AOS3JOPhPh4/Hx9gWR5kHJeJJekhacsjbnV28m4ZG7OB8lrQELPRG+uC9BTIUie60IfLhM8SIeH0cZT2XfbAnYb3+Th+qEg4cEvlTxc/aTHA3ceLjlkzKNpk6Y88+wzJNyOZ9fe3cyZM4d33303w+LsDxpNmzYlNDSUp59+mnPnzgG6u8CadWto2KghjRo2omGDBty+HY8s6wmv7Ul2/Pz9jB+CulXYZrPRuVNnVq5aycWLF82aqV548U+ELMvUqFHjgfWnSBLKY7DGice0s3C38PixfZe4KwVwypQpTJo0iWHDhlGvXr1Mn3OhePHi7Nixg+DgYABKlSpFSEgIHTp00C0rhQtnyCFbtmwMGTKEd999l9atW2eKd2Ji4iOzJu3YsYOff/6Z4KBg8uXLx6u9XsXf35/4hATduV1Lniwh9K07V6WPxMREPejCoaI43BLwOlUURS8XlnKaBZh9yK4gEKueXkRxWoxABdljLFewQXo8NM2tnJpTT82hWBScTifbd25n//79dO3W1ZyvhyXb9NwIHhdiYmL4fft2KpavSGJCAs405sU96ME9gld1OrGoevoTxWFBU1U9gteYN/cgEN1vTja3/DVVD8pJTEwygy7MdWExEkYriptrQDo8HC4eVpx2R2oekmfwRXo8NFe5PaeKxWrB6XSm4uEeBOLqN10eToOHnBEP4xo3eVSpXIVjR4+xYcOGVDsVDxuFChUyIxtLlizJhAkTuHHjBmvXruWT8Z+QO3ducmTPQcXyFciTNy9CE1gs+rPpsDtIsiehSDI2Xx8qlCuPj48Ps2bNonPnzvekBD5pz8vjhlcenngS5ZGYmJjhNRKQfvKuh4cnVf1LSEhgxYoVbN26lf3793Pjxg0zL265cuWoX78+zZs3z5QbVqYVwI8++ohvvvmGESNG0KVLl0yfSwmX8udCsWLFAL1wcmYUQIAWLVqwfPlyRowYQYcOHTK83pXh/1Hg559/JiYmhvcHv09glkAz6jFl4ASQKt0HAvz8/dAcGrJVRjg1ZKtiRFfK5jZhcmM8tg4l2ajqYTjTu14Vi4ymirTTwKTBQ3WoyFajrVVBc+gVKbZu2cLu3bvp06ePuWX/sPE4faKuX7/O3r17uXTpErGxsZw6dYomTZpQv049PT+jkfbkjvPiinpVhT4f1uSIa9mSIrDHY148lTdJ1t0D/AL8PNaFexoYV1/u6Vc8eSSPq1mTE4TLxvZummlg0uChOVOuT83jvL7GU/LQTOVRfkA80MA/0J82rVozfeYMrl69ynPPPfdYy8H5+/vTs2dPEhISOH/+PGdOn+Hr2TMpVbIUhZ4qhEVRcDicnD17lt17dhNaNJR+/fqh2TQiqkTgcDiYNm0a/fv3v6dKJF4fQk945eGJJ00evsZuyp3wuLaA77cSyIOG0+lk+vTpzJkzhwIFClCnTh1atGhBUFAQqqpy48YNjh49yvfff8+ECRPo1q0br7766h0D4jKlAH7xxRcsWLCAcePGeeTWyuhcSqxfv55hw4axZcsWcyEePHgQWZYzrfy5MHLkSFq2bMn8+fPvqt3DxNatW/Hx8WHkByOxWWxoqqZbfDKVgFlBVVWEJvTkwBa9tJYiMBJBk3EiaFcUpRBGQl29nJkkcVeJoFUjZYlqdyIpeoLqxLjbrFm7lr59+z4y5e9+EBcXx19//UVsbCwWiwWr1Wr+xcXFcerUKSRJwtfXl5s3b5KYkEihpwoREBDA9evXuXTpEtHR0VQoX4Gc2XNQsmRJ2rZoQ668uXEk6vUaPRIwa2kkYDZ83BTJKLMnBJpTt7Lq8yJlmAhaUzUzEbRq9OG0O7HKMs4kJ4pVZJwI2uLiIaXgAZrJ406JoPUAJM2h6SXoHE5kxWrwkHAmpUxIbVQC0XSfQfOHiEgeT3O48XClycmQh2bw0BNSq04VNAjKGkT//v2ZP28+n332Ga+++mqqH5qPGn5+foSFhREWFkb1GtXZvXs3ly9fRlV1q3HuPLrP4oWLF9i4aSNnTp3hXOQ5YmNjcTgc7Nmzh7CwMP78808uXbpE9uzZCQgIoHLlyqmSo3vhxb8ZCo9nC/hJCwJ57rnnePrpp1mxYkWGOwSnT5/m22+/pWPHjvzwww/pXpehAnjo0CGmTZtGz549qVmzpkftuqioqHTPBQcHY7FYPDJjR0REYLFYGDJkCG+//TZXrlzhgw8+oH379nftxF2wYEHefPNNJkyYQP78+e+q7cPAxYsXWb16Nf369iNnrpymRcMiWfQvNHdzG3hYR1wl2Cw2i/5qlNQyS5Bphh+fIhtfhIbflpR8TJZlPfhDkvRgDqOUm54sODkNSHo8JHceUmoeP69dSYXyFf4R/knnz5/nyy+/JCwsjFy5cpGYmIjT6cThcOBwOPD19aVixYqAvi0S4B+Ar58vFy9e5Pbt2xQpUoSqVatSqGAhfP18PSxsyJhyAcz5SFmCzZwPYy4kyaKnN3HNiyFj1/G01oUZxGFUgnEl6Lb4Wk0eru39DHlIkpGGxeJR6g/JlYKINHkkG+MkNEUz1wWy5PHqyUMk34cRACIbcnDJICUPyUgqLbl8YNOQg+v+NLe17ZKDVbbRo2cPNm3exPjx42nevDnFihVDkqRMf7YIIbh06RI3btzAarUSFBREtmzZ7tuimCVLFurXr+9xbNeuXQDkzJGTq1evUqZsGVq0asGKFSvYv38/a9as4bfffqN27doEBgaan6NTpkyhSpUqtG3b9o5Vbbzw4t8CSXpMW8BPlv7HV199lenPsiJFijB06FAuXbp0x+syVAB/+uknNE1j+vTpTJ8+3eNc06ZN0z23cuVKwsLCPDJjBwcHM3PmTMaNG0fHjh3x8fGhRYsWvPfee5m6qZR4+eWXWbVqFbdu3bqn9g8KV69eZcWKFdSvX5/gLEFoqoYj3o7F5irjphgKoe7rBbojvWZWelARFgWn3YlqceJ0OLH62XAkOLD66iXAXNa7tHwAJcOyo/hYcCY6sfpZccTbsQbYcMTbUWwWRFo8BPr/VeOcU0VY9TJuskVGtTux+tq4GHmRP/74g+HDhz9y2d4Ldu/eTXiFcDp27Igj0a7LMt6Ozd8He3wSio8FYSa/Tk7EHPpUqDkfskWXg+ZQcdoNeThUcz4sPnqZPTlFinqPxMdOUB2aPh8Jnjwc8XaDR/L4qRIwO/Wtd9WhW4idSU5Ui2qU+bOgunjYnWYuvgx5uK0Lk4fN4pGQOpmH0CueONx5WHAmOUz5WH2tOJMcqXgINyKm8izp8rT627DftmNz8QiwYc8ED9Vh+D46kksWqlYV1a7zUJOcNKzXgEL5C7Fx80Z++uknEhMTadKkCTlz5uTChQvkz5+fQoUKERUVxfz588mbNy8RERF65Zo9e9A0jZw5cmK324mJjSE2NhY/Pz9TGQwKCsJms5GYmEiJEiUoVKgQa9asoXDhwtStWzfTgSgRERFUrFjRQ7nUNI3IyEjz/OHDhyldojQF8uc3fCKhdo1azJ4zm28XfEvXbl3v5fHw4iEiMTGR3bt3U6tWrcdN5V8DhcezBfxYcg/eAekpfzExMWTJkiXNH4QZKYySSDcx3D8fkZGRNGzYkI0bNz606iGJiYmMGjWKUqVK0aFDB3xsPqaDvKwkB1a4+zQBuFJy4HLUl2Xi4+Px9/f3qMzh4fN0Byui0ESqNmnzSKsPTx7JTv/6uNOnT6dw4cI888wzD0WG6SEhIeGefFYuXLjAF198Qa5cuciWNRsvvfwSsiR7yMOV285THslykFLIQ5IkDxl7+lMaA6e0Wrl8MlPMx73yiI+Px8/P7yHxEKnaZVoervcu3z/uzCPl+BnxcEVTp5LH7Xj8/P3S5yHBtWvXWLJkCZGRkVSuXJmoqCgiIyPRNI0OHTpgt9s5fvw4ISEhVKxYkXx58+kuGwYPTdWIux1HTEwMMTEx3Lx5E4fDgaZprF27ln79+jF27FgAypYtS8+ePe87CbVrm3jMmDE4nU4iIiKoWaMm2YKygYDEpETGjh1L165dKVGihNnuXp+XfysehzzOnTvHuHHjGDBgAKGhoY907IzwJK6PO31Hu86VSryCTajp9PDwYJcUDvvmfKj6w/3g66+/ZubMmcTExPDTTz8xadIksmXLxuDBg83qahnhrtPAeOGJ9evXU6JECZ5v9xyKrHhYNnTLj8O0bKRlvpNk3dfO4mfFmeBA89HMPuy37Vh8LB5+XKbSAEYuOlfEropiU1ANn0NHktPkYfGz4kx0oFhT8Ejx5Xw7No5Nv27CkeSkeMni+Cq+7Du0j+vXr2e6jNaTgPz58/Phhx9y7tw5fvjhB77//nsqVahE8bBiqEmqbolLdBjVTvCQR7K1SvdXs/rp1il9PpLwCfD1mBfXfEiuRNCCZJ87qz4fFpsFR5ITzaqh2p1grAsPHsb47vPhUg5Vh4rN34YjXt++zpiHnoDZjOS2WXQePhYcick8hLEurL6Z5GFXzXWZLA8fk4e7j6okueQhUKwKTnuy9RBfK/ZEh+5vaneCIQerjx4lLSmZ4+FIcODn66fzCbRhjzN4uM1HUJYgerzYI5U8nElOrL66ZbZqRFXUJKc5H5IPHjz8bf4E5A4gb848Zr+SJLHvL/3Z6NmzJ6tXr8bPx4/333+fsmXLUqlSJUqVKnVP28cuK+LFixcBOHXqFLt27WLwu4OxWC1YUGhQrwHbt2/3UAC9ePxwKQobNmx44hTAfyq8UcCp8fXXX/Ptt98ydOhQhg0bBug7sqNGjcLHxyfTu6peBfA+IITgjz/+oFevXlj9rHp5L6usl7cKsCHJMrYAOQ3LCh7HFKtelsvqp0dE+gTakBUFn0AbSBKKMHy1jDJv7sZpWRil3yyS4c+VNg/Z/848LkZdZOHChVy/fp06derw22+/kZiYSJ48eejbt+8/rj6qr68vYWFh9O7dm+XLl7Nk+RKyZMlC586dyZ0lNzZ/W4bzgk33U/MJ1GXoE+hjvnefl+T5UMy2ySX59PnQ/SsVFNe8GCX37sxD78Ni8LD5676HqXmkXhfJPGyePGRPHvI98HCNq/OQzfe48xCAlCwPm1FGULJIKIqCTyp5gCTLWJU7rdPU8kDGbX7S4+EpD1d5Q31c/VlTLJnhoXjwqFQpnP/9738oikKrVq1o2KABMbGx7PvrLzZu3MjcuXMpU6YMDRo0uOsgN4A6depw/vx5fH19cTqdyDbdig0KBQoVYNce3Y8wLi4OVVXTfEYdDofuN5pJi4AX9wdZlvnkk0/QNC3ji70gPj4+w2v0Ff/o1bEnbQvYHYsXL2bUqFHUrl3bdM1q1KgR/v7+DB482KsAPgpERUUhNEHePHmxxzuw+qFb/vzdfO9UTfejUlXT5wxS+97JVgVHkgObK7rSP30fwFS6imElsvhadIuO4WtmDbDhuJ2OD6AbD4fdwZrVa0hMTKRXr14UKVLkkW/3PixkyZKF7t27o2kaGzdu5LPPPiNvnrxUiahCeMWKWA2FIF3fO6vumykb1iOrrxVHotP0fZMVOaUR0SMQQ3NqqdaF7ovoPi+eaXoQAllxywNp8HAkOrEqafgAuvEAz3XhWjOqU021Ljx5CHNd3JFHZn0AM7Lexdux+btbV914yJ55AFP5ANr1+3YkOrFakn0AnUm6VdHpUD18ET22jzWBJOtR8Snnw+O51ZLlYPLQhMe6qFyhMklJdq5fu06NajWwxzvIGpiV6lWqU7tmba5fu8Hf+/cx5cspvNb7NTPlVWbRqVMnQLcEzpo1iw8++ICsWbPi7+dPiRIluHLlCocOHeL3339n7969VKxYkW7dupnbfH/++SczZsxAURQGDhzojR5+RPgnZEl4EvDDDz+wZs2aDK/z+gCmRnR0dJo/KvPmzUtMTEym+/EqgPeBc+fOUaRoESxWC4qsgAxWf92KZ/O36dthqr4dJVt0i4LstiMkWxQ9UlPRIyStNguKjwXJIhsWQatpGUyrPJb5xSZLphVR9pOTecgZ87h29Sozvv4af39/Xn/9dbJnz/4YJPnwIcsyjRs3pl69ehw4cIDt27ez/IflVKhQgapVq1KsWDFkyYiotrgiq/V5sbiiVd3mRVZkrLItlW8aJG8ju3IrSoqEzd8HZIz5kMx5uX3rNj+u+BGbzUajRo308oZG1GsqHj4WFJti8pCM6FurX8Y8FNVijoskpeIhVD3C2bUukl1D0+AhGdHpBg9XFLDViI5Ok4chB4tVQTKeD2RjfFnG5u+j58t08YBUUciykoY8rIp+3J2HImfIQ7G4ycOcl9Q8MHkk9+fiERiUhabPNPXIxSkrMlbJiiTLZM+VnQaNGpIrT26mT59O48aNadCgwV1XLMmXLx9Dhw7l+vXr3L59m5iYGDZs2MDt27f58ssvAT3tTEJCAvv376dq1aoAlC9fnhYtWrBq1So+++wzxo0bh4+Pzz0/Q1548aAQGxvLTz/9lKnvG0WSsDwGC6DzSQsDdkPZsmX56aef6Nmzp8fxBQsWULp06Uz341UA7wM3b94kW5ZsaA7V9M9THU4svrpvlcsqktk8gI4kJz6+mt7WzW/PlQbEpcQJoelfSimDPoy8c5pQcSQ58M8aoEes2nQfrLR4TJv2P2rUrEGDBg3M1Cb/ZlitVsLDwwkPDyc2NpZdu3axePFikpKS6PRcJ0qWKKnLFz3hscuq6ppTq6/NmBdFr94hu/neCd0HMOV86NYzPd+f5tRz+jkN38Dt23/n+vXrZM+ena+mfkW/Pv1QFMXMe6fz0PPeOZOcCD/hwUO2KqAJc3w9D+AdeNidSEZePte6sNgsGeQjTMEj0aFb3Nxe0+YhkpM3G8EeTpcV0e408wNafHRrpmKzZJgHMKU88MXjecmYR/J8KFajMovhs+mSg15Jx5OHa3yXbyOyZOYjdJ8PSxryKBlWgr7v9GXRkkVs27aNSpUqUahQIfLkyUNwcLC+rZ+BYiZJEtmzZze/MMuWLcsff/zBhg0buHDhAgkJCTgcDo8Pf0VRaNasGdu3b8dut7Nr1y5vdOojhNPpZMyYMQwYMMBrFUyBo0ePAhAeHs7SpUvveK3E4/HHe5K/DQcPHkyPHj3YsWMHDoeD//u//+PUqVOcOXPGo6xuRvAqgPcIIQR///03jRs1RlJkFEC2KnoKM1lCceX0s1k8KoEgkjfqBMLwBdOtCi4rnmI18r3ZLHqfTv0a4crl5+YSa9ZRlSTdsgFMmzaNixcvMmbMGMNComDxkTytiJrG2XNniYmNoX79+v8J5S8lsmbNSsOGDWnQoAFHjhxh5syZfDD8A/z9/XVlQdGd/RWr7nPmmkuLj1W3fjldUbiuOsrJQSWueVEkjCovimnZNS22ikT2HDlQjxymU6dOTJ06lZ82/ESL5i2MyheSwUO3OCmGxdji48bDIxo4HR7GupBkCdmqmBY9SZZRrOjnJUuqaHUz+lZLwcOq5y7Uech34EEqHhaDh+LOQ9J56TyUzPOw6FZ3i49u+cssD1nR/QTdeeCaL8NX0WJTzPGTo5ElTx5GnW1dHpjjp8UjT548vPP2O5w5fYYDBw+wY/t2Ll26zOUrl8mWLRujRo26Kz9bSZKoVq0a1apV48yZM4wfPx4fHx+WLVtGuXLlyJIlCzabjeDgYEqXLs3vv//O3/v+9iqAjxAJCQlcvnyZ8+fP35VV5t+KqKgoYmJi8PHxYdu2bUiSxNatWzNsp/B4LICPw+8wsyhXrhzr1q1jwYIF+Pv7k5iYSO3atZk2bdpd5VT2KoD3iJUrV5KQkEDpkqX16gROFWTdF0+W9fQRgO7jheF7J7vymwEeaUVUs9qC0IT5xaG69albnDQkLbUPoIzxZYPEzp07uHL5CoqisGf3HsIrhBs8klOHbPvtVxYvXQJAxw4d//MJZSVJolSpUpQqWZK169bStnUbAL06hSITeT6S3Hlzo0gyUZei2fbrNspVKEdo4VCsNiuapnHz5k0sikJglizJKVIkl8+n7renKMkVQDRVVy5LFC/Bgm8XcPv2bVq2bMnkyZNp/kwzkCTTkVxzqrrVTtMQql7lBfS+kEiuCeyqEIOb75uLhyaQJaHn9FNkk4dQNYSEng/RtT7Ncm7oli5jLatOPR+iuT5dVUScOg/NqedPvDMPo9avwUMzLG1C1esHa27bqZ48dCuiBw83eUiGHNLkkcIHkJQ8ZDm5QommIbvzSFGTOBUPPOXhmg/XvMkWvTayZvAoWKAghQoVQmgCxSJzIfICY8ePY9asWfTu3fuu167dbmft2rU0aNCA4KBgLFYLM2bMAHQLoKqqvP322/z5558cOHiAnTt3Uq1atbsex4u7R5YsWShVqhRffvklkyZN+k8H4ly/fp1PPvmEvHnzcvbsWQCyZctGq1atWL9+/R3ben0A00ZISAhvv/32ffXx312R94k9e/Zw/fp1VFXF6mMBl3+SIusKlWxswUGy35IsIUt6fjL3L0dJlpPbQfK1bn1KssQfO//A7rBTp5Ze9D7JnsTVK1e5dv0ahQoU4vcdv/P79t955513+O6775g7by6RDSPJlzcfwdmCkGSZW3Gx/LLtV8LDwylXrhxPP/30Y5Phk4bnO3Xi888/Z/kPK4iIiABg2bJlnDp9CpvNRkBAALdv3yY8PJwffviBmJgYwiuGExISws/rfwagTu065MqVCwTkzZeX/HnzGfMrPObSNbe+fj4EBwdz8+ZNbt26RcGCBfWKGICE7FZJxKjq4WprrC+PPl2WYQkkN4sXRhJhV18YVsmo6ChkWeby5ctYFAulSpdKXp/CtU4lIzLWfT0mr09kg5fkyTN9HpIHD1cbSZF1OUlGGbm0eKSQnftzkjEPyZMHukVSr06C2bckZM/nVLi9ghvX5PF0eaSeDxcPFNlTDlKyUpondx7Gjx/PmDFj2LdvHxUqVLirNdu/f38KFSzEyy+/jDPJgV+gP067k6XLl6KquiIaGBjI66+/zqeffsrcuXNJiE+gXv169/yceJF5HD58GIDffvuNunXrPmY2jw/Xr1/HZrMRFxdHhQoVKF68OBEREcTGxmbYVtY/CR8By9TjPklo3759pnfqlixZkqnrvArgPWLUqFFMmTKFXbt3Uat2LcO3Tvc1EpowrHWSx3FIjjbV8+wKfSPYdcx43blrJ1t//YW4uDh8fX3x9fHFYrVw4sQJQC8jde3qNRKTEsmePTvR0dGA7k8xcOBAgoOD6d+/P1FRUezZs4fDhw9z/fp1kCAwIJC6detSr169xyO4JxiBgYG88847/PjDj3z//fdcjLqIqqr07duXQoUKcfv2bfz8/PD39wf0CjBfffUVwUFB9OvXDx8fHzas38ChQ4cQCFauWknWrFno3bs3/j7++qQb/mhC1RApAnvi4uLw9/dHqBrIsse6kJDQhJbc3rVe0K27QhKeSaXB2EbWcxoKowINRp8IwbT/TdPXhYEXOr9A1YgqKBaLGfXqwcOwimmaQBIp1i2YvnaZ5oGbPDRDHm5Jyc127jxw42HK4s48XP148HCTgycPYfJI1U6k4OGaF00gpZgPlMzxAPD38+eV7i/zzXfzTR/BgICATOURbN++PT+t+4lDhw5RrEgxEPqPkMoRlZk3bx7Xrl9j//79VKxYkTfffJMpU6awaPEi6tar+590+3jUaNasGWvWrCEhIeFxU3mskCSJuLg42rZtS/Xq1c3jmVMAH4817knbF6tfv/4D79OrAN4HmjdvztSpU9HQiAiPwF/2N6x6xme7EUopjIPml58LkqSfEwKB/kWxYdMGtm3bRvfu3QkJCSExMZHExEQcDgcFChTg6tWrOJ1OcuTIQdasWZFlmaioKAIDA1M5GufNm5cWLVo8Eln8W5AtWza6de+W5jlfX1+P9zly5EhVHq9T507m/zVNY/bs2ezZu5faNWqb869pGrv27OLvA/sJrxBuKoDFixdnyZIl3I6PJzAw0NySRBPJT6qxZlxt9KTEyeObx0XyVqoiy6iui4w+hcG/XLlytGvXjpMnTzJp0iRy58pNkcJFPDygTR6GBS95HP28y31BSm6QDg8FVRLmNULVkq1ihn+de/40M2DJNSa6a4VIxUNkioer/KJicedhuGuYPDCUOs38BjC3gSGZh5aCh3x/PIoULcKw94exefNmjh49yrWr15j/zXzCSoQREhJCQEAAlStXJkeOHB7rrX79+hQqVIivvvqKnj16Yr1uZdOmTYQWDeW1Xq9x4tQJtmzZzKZNm5gwYQJTp07Fi0eH5s2bU6tWLbJly/a4qTxWFClShJEjR+q7I3cJBekx5QF8sn4gvfXWWw+8zweu5EZHR/POO+9QrVo1atasyfvvv29q+Xc6B7B69Wrq16/PCy+8QFRUFKCXgylRogQffvhhqrE2b978WDPhFy5cmHfeeYdjx44x6P1BjBo9ysg/ZmzryjKSJHsEBXj8mdtB+lbRmbNn2bZtGwMGDKB48eJkz56d/PnzExoaSsmSJQkMDKRw4cIUK1aMoKAgc4srb9683iizJxCyLFO1alX+/PNPPXhHAkmWOXDwAD+t/5nixYuzeOli0zrgKke2ecvm5DXj2roleVtSlt1f9bUmma8ulwNjO1JyXZOsqMjG9mmPHj2IjY1lwoQJaJqGj48Ph48cJiExwewvmYdk3oOcxmvK8ZN5SGZ7jHtJ7lM2/vDgl9ZzIhmyc/WJRJrykPDkYQZ+uMvBg4dspP+RPY+n+Zy68UDylAP3zkMyeFitVho1bET37t3p804f3nvvPUqW0J/7c+fOsXDhwjTXWWhoKJ07d2bJ0iVMnDiR69evs+6ndVy9eoWSJUoiywrPPvvsI1jxXqSEJEkEBQX9562tsizfk/IHyVvAj+PvScaKFSt4/vnnCQ8PJyIigq5du7Jhw4a76uOBKoCapvHmm28SHx/PvHnz+Oqrrzhy5AiDBg264zmApKQkpk2bxueff06rVq2YNGmSR9/fffcd+/bte5B0HwgKFCjAa6+9Rr169cieIzuqU0Ug0FRV/7+x1aSpmvnq/n/X1pXQBCdOnqD609UJCgp63LflxQNCqVKliIqKYsf27Rw+fIix48fyyy+/UKxYMRo2bEjJkiW5du2aaT165pln2PbbNq5evWoEXyRbjjQh0BwamupyM9ADEzSnqgcSafraEppmBEhoehtVRXPqFivNqYIkoTo1AvwC6Na5G/nz5+evv/6ia9eurPtpHYOGDGLnzp1oqpq8TjG2mo1xXTzQ9IAJTB5aCh7GdrfJQzX/r1vfVIRwb6tv+WqqZr7qVj/MNuY1QqA59TFMHkafHjwM2SXz0Dx5QPK9uniIZB5mm/R4aO48hFsdYU8eZJqHPk/BQcFUq1qNhvUa0KZVG06ePMkbb7zB6tWrU62zypUr89Zbb1G6dGkSExMJDw/no3EfM3DwQC5cvIDD4WD79u0kJiY+/EXvhRcPEK4t4Ef9l55y5HA4+PDDD80o/PHjx5v+tu6YPHkyJUqUSPPPVeZxzZo1qc5lZtdu7ty5fPDBB1SoUIHRo0czYsQIwsLCGDBgAD/88EOG7V14oFvAhw8f5sCBA2zbto2cOXMCMHToUF544YU7nouNjUWWZbN8F8Dvv//u0XfevHkZPnw4y5YteyKjqSpVqsSsWbM4duI4RYsV5VLUJQoXecpM3SLjci7Xl5UkuR1DRkiC4yeO06hho8d8J15kFrGxsRw7dowyZcqkW2TdYrHQq1cvflr3E8eOH6Nx48Y4HA7TIdy1peda09mzZ6d5s+ZM/GwiRYsWpWiRIjxdrTpZsmZBcU/krchmnjnZgqeFwXA9QJKQzW1KzUjerJcxM1OoWC20b9+ekSNH0rRpUz777DP69+/P5q2bqVatmrk+ZcUVoCSDIpKDOGQJxRXAlCkeRtoXV6k8t1d3i5vresVwoZAUCRkleRxZNuUhkYKH4s7DI5s0sqFom0nYDTmk4mHwVERy+9Q8hBngk8xDRrYITx5uclDS4EEqHvp7WZHNbebs2UP4YMQHbNu2jZ9//pm9e/ditVpp166d+Znp4+ND4cKFOXToEPHx8VSoUIHExETOnz9PTEwMZ86cYe3atbRt25bDhw+bCdBVVcVut6e7hr3w4nHCwuNJA5PemJ999hnbt29n+vTp3L59m4EDB5ItWzZee+01j+teeeUVs5oP6AayXr16UbhwYfLlywfAyZMnqVWrFuPGjUseNxP6zezZsxk9ejStWrUyj7Vq1YqyZcsydepUWrduncl7fIDIly8fM2bMMBU8d+TIkSPdc0lJSeTMmZMaNWpQpUoVAgMDUyUzfP/99+nfvz+zZs3i1VdffZC0MwVVVbl27RpBQUEe+bqcTidOp5NixYoRGhrKjes3eO+997BZbUycMBHV4URWjATMqX8kIGkSmqoyd/5cbt68SeytjJ1ivXgysHnzZn766Sd8fHwYOXJkun4+rl927j5joH8g/PyzHj2cJ08e83j9BvUpX6E8p0+f5vDhw3w45kMqVapE4YKFyRaSjQAff0JyZCcxPpG427e4GRNDTGwMMTH63+3bt/Gx+eAf4I+vry8BAQH4+vhis9mwJyXh1FScDiea0K3UxYuHUbhQYYYPH25WqagcXllPb2IEMumpjlxJrFWsfhKq3Yksy3qScdPfT/asWCP09EOqqumpapx6+hvNqVu6VCOXouZU00zArCtPIAnJDK4yeThU0ND5KBJOR3o8tLR5yMk8kt8biaKdqsdcmalgUvLwdcnBpifVViScdhXZItzK4CXzEEKYiad1Hvr9ag6XtdVIR2PIyeQhQYBfAM80eYZGDRpx4eIFzp47y3fffsew4cPMeQsODqZQwUJ06dKF7b9vR8uq8XzH58mRPQdCCFauXskPK36gUrj+g7V48eJERUURFRXFa6++RtlyZR/U4+GFFw8EipBQxJPhA5iUlMR3333H//3f/5kR+wMGDGDixIn06tXLI6VaQEAAAQEB5vs5c+Zw5coV5s+fbx47fvw4YWFhaepFd0JMTAxlypRJdTw8PJzLly9nup8HqgAGBwdTp04dj2Nz5szhqaeeInfu3KkSFLrOuW6+X79+vPzyy/j7+6dKilqoUCFef/11pkyZwrPPPvtI61oeP36c+fPnExcXR0JCAmPHjiU2NpY1a9Zw5MgRAKpUqUKJEiVYvnw5oGvjurVGTyJsepCnhKRbFS5cvEj79u0zFRXlxZOBSpUqcejgIc5HnicqKipDR++UfkCSJDFgwAAURUlVHsxV9SEiIoIWLVqwa9cuTp05xdU9V4mNiSX2ViyKrBAUFETWbFkJCgoiW9ZshBYLJcA/gKSkJBISEoiPjyf2VizR0dE4VScWxYLFasGiWPQchqrGwkULiY6OJiAgAFmWaVC/AY0bNzZIYpZC02TNSHRsAUlPRK0nQLZ4WKvMCFeBqQSlTEHjei4kxbAuGonT3YMx3MdPycOVmNs1PpKRON3k4VmPOF0etpQ8XOlcgDR4JBsTDR4p5CCl4JGSP8JlPZVMOciKDD4uHpIph/R42Cw2ioYWoXCRwhw5qicw79GjBwBPP/00Z06fYe68uQzoNwAkGDtuLMWLF6d1q9a0aduGNm3agBA0btqYX3/9lZIlSxISHMLMWTMZO3bsXZeq88KLhwmJx5OSJa0RDx8+TEJCgpkmDCAiIoIrV65w7ty5NOvzAty6dYuvvvqK/v376+U+DZw4cYLatWvfNbf27dvzf//3f3z66admcKKqqsyePZt27dplup+Hupc6ffp01q9fz7Rp0zJ97k7+bz179mT16tWMHDnyrsqd3CsSEhJY+eNK/vzzTzp26EhYseIsWb6UDz74gMDAQGrXqs3z7Z5DtiksWrSI9evXmw79xYsWQ01yGmXcjETQimT6S7kiAV2JoOvXq8/SpUuJiYn5z1bm+KehYMGCDHl/CA6HI8N0HWlBkiRCQ0MzvC44OJgmTZqQkJDwULbp2ol2xMTEEBAQwF9//cWSxYsJrxBOULYgo1ybntBYdahYsOBMcuiJlp2qXu4wKbnsoawY69ZQeoTQgzU0IZCcKqpDw+pnxZFgx+Znw5Fgx+pvw5Ggl9czo2Q1geweOWtR0IyE6O7l5FSbqpdkM8Z3lbfz5CF59Juah4/Owy9tHq7cgS4eqkPF4s4jpTzsTixWJdma6a68CoGsaqbfoOpQ74IHps+wi0ePl3sw+cvJ7Ni+g0qVK6EoCl26dmHq1Kl8//33HD1+lJiYGK5evUrWwCw0e6aZycMqW6hXqx6SLJGUkITdbsfhcHgVQC+eKOhRwI9n3JS4dOkS/v7+BAYGmsdcBqzo6Oh0FcDvvvsOf39/2rdvbx5zOBycPXuWHTt2MHPmTJKSkqhduzbvvfdehkGdFy5cYNu2bdStW5fSpUtjsVg4evQoV65coUSJEnTo0MG89k45AR+aAjhlyhQmTZrEsGHDqFevXqbP3QlWq5VRo0bRpUsXVq5c6TEJDxKRkZH88ssv7NmzhwoVKjDk/SFkCcyCEILu3bvTRe2i12s1qiTIikz37t2ZOWsmDRs2pHLlyvj6+Oq/4DXN8DWSPHx/kCQki5EYV5Gw2qz4+/vTunVrr/L3D8O9KH9PElyRiqBbsm/cuMGUaVN4++23CQkJ8YiIlRUZi2Q1/All873+aklz7UqyhGzUS5Yt+vNi9bMhWxT9VZax+hnl01zPi5vVzFUKzsXB4uLhYzFKHcrm+PqrlKYVMX0exquSzMOVuy89HrIiJ/NIJQ/dmqfIqXnILkukpiEbPox3zUN4lpBs2rQpS5ctpXyF8uZQnTp1YuPGjTRu3JiLFy+yf/9+SpUujWKzpMlj+87tFCtWLFWqIy+8eNxQhIQlvR20hzluGgpgQkJCqt1J13u73Z5mP5qmsXDhQrp16+bh33fmzBmcTicWi4XPP/+cy5cvM27cOPr27ZuhgatMmTKptoDvNok8PCQF8KOPPuKbb75hxIgRdOnSJdPnMoPKlSvz3HPPMXbsWAYOHJipNomJiXdMxCmE4OrVq5w8eZIjR45w/fp1KlWsRP93+pMtKBuqU+X2rbjk0lKyXv5JcfMpUh0q3V7oitOu+/LE3byl+yklGcXl1TSK3BsfwonxCaxZs4ZXXnmFvHnz3lfS0HXr1hETE0PhwoUpVaqUh7n5n4akpKTHTeGJwqOSR+3atVEUhVmzZtG1c1cCAwPA8Euz+lpxJDg8rHj2BAeKTUGoGpKhtLh88IQQekkyp269U+2q8Vw4kK1KsvUuybDeOVSkFFbEZF86XRnSDOvd7djbSEjYE+xY/a044g0e5vgPjodHeTrNKP1md1kRHYYc7Fh8LKh2FdnqOb6rvdCEPr7LiujGwzxu8FBsaVgRzfFlQ94SBfMWpHDhwqxbt466deuSJUsW/Pz8aNGiBdHR0cTGxhIcHMzadWupU6sOsiQTEhJMcEiIPp7NwvYd22nVotW/KmGx9/PDE0+iPDITla6nZHn0SGvb2dfXN5Wi53qf3u7MX3/9RVRUlEfABkDx4sXZsWMHwcHBgJ4xIiQkhA4dOnDmzJl0rYnw4HICPnAF8IsvvmDBggWMGzdO9zXJ5Lm7wbvvvsumTZuYPHlypq739fXFz88Ph8PBmTNnKFCgAH5+fsTFxbFp0yZ27NiBLMuUKlWK6tWrU6Z0GT1KUoAmNG7euMnNW7fJkSOHOcnuH+ouHz/395qvbmHQ/PRXBDicDg4fPkxsbCyhoaHkyZ0HJPD180WWZa5cuULRokXvWS5CCE6fPs2JEyf4448/KFGiBH369Lnn/jLCtWvX2Lx5M76+vgQHBRMcEkxQtiBCsodkaEk4c+YMS5cuxWKx/D975x0mRbG18V+Hmdm87JKWnJdFJEcJElVEFBTUq6joBUyYwICiIogKKoigXAmihKt+6jUgICIKqEQFBATJsATJGTbMTIfvj+rumdkAiwK7aL/PMzSdqt6uqdk5c+qc99C6dWvq16+fZ01iNzMxEhdrPDp06ICu60x4dwKPPPwISclJzhx35naU8HJHRUfGuuVMdoHIz4usyJgxodrUkiw5nxN7PzJm1g6+I8IQAoiOjcYX5RNtRpmR/dsZvIRxKwAPp3/7mc7Cwx4Pm4ftLTwbD7vfAvEgxMX2RIbzuPnmm5k3bx5vvPEG/fr1c2Kkp0+f7lQKKlWqFAt+WEAgEOD48eM88cQTzJ07l2rVqrFz506OHj9Krehaf2XaFDm4fz8iUdTGoyAeZ5EEchHI5Ow3DwMwJSWFzMxMMjIynASPQ4cOAeTKcbDxww8/0KBBg1wi7oBj/NmoXr06IJaaz2QA2u1OmDCBbdu2oes6VatWpVevXlx33XVnfTYb59UA/P333xk/fjx9+vShZcuWzsAA7Nu3L99zSUlJ5yTtkpCQwKBBg+jfv3+Brrc9d/3798cwDFJSUqhfvz4//fQT9evX5/7e91G2Qjnxq9unsDt9N1t3bOH33zeQvjOdqKgo4uLiOHLkCJUqVuL+vvchK0IKwo7t0wKaiN3JDKBGiV/wnijhAZQUmdWrf2XWnNkkJSVRvHhxvv76ayqUr8AVza9g3759ZGdn5zuBCgpJkhgwYMBfauNc8O233/LTTz8BkFojFROTY8eOceLECXw+H1WqVKFatWpUrVqVSpUqRSyVejwedu7cSXJyMu+++y7JyckMGzbMXf4uQrj66quRJIkxY8fw8EMPkxATj+IVc9r2fAmPl+bEAioexYlzdWxCSXitFFV41hSPgpYdxBvjJZAZQI32CI+cFdcnlj7NUAKEteSp2xm6AU3wyNYwo8wIHopHFXG3cije1sngzZeHj0CmHzXKgx7UUVQZQw/7gSeFtWHxEN47zRkPrxW3lxcP0zDDxsFqVzfAel7Fq6BlnYWHaTpyN4Yh4gL1sFhIn9dHu1btqFihIm+//Tb33XcfVatWZf/+/fh8Pp5++mlKlizp/Mj69NNPGTx4MCDq1F5++eUkJSWRnZ3NV199xf79++ncubPzheTCRWFBMWVU8+JbgHkZgGlpaURHR7Ny5Uon4XXFihWUKFGCihUr5tnO6tWradKkSa7j8+bN47nnnmPhwoWOYb5+/XpkWT6r8ffll1/y3HPP0aNHD2677TYMw2DVqlU8/fTT6Lqey9uYH86rATh37lwMw2DixIlMnDgx4tw111yT77mZM2c6WlYFRefOnZkxYwYLFy4867Wff/45jzzyCG2ubMP2HdvZuXMnp06e4qknnqREyZIYhkEgGGDpL0tZsGABkiRRq1Yt2rZrS82aNZ03xzAMxo8fz7fz59HlOiHWaMf+CD0wEcODDOs3refbed9y7NgxTNOkePHi3H777aSlpQHCbbx48WIWLVlEfHw8jz/++CW1XJuVleUsKaSlpXHP3fcQGxeLqZtIisTRI0dJ35nOtq3b+HTlpxw8eJDKlSuTllqTps2bUa5cOZ5++mmWLFlCRkYG5cuXd42/Igg7G3jsW2Pp26cvFSpWcOLgPFEeMfcl8RlQJY+lb5kDEmAqSBIiPk+RrM+J2EqKhOQV8WweK6M2wvNmybpIivCWOTF4PpEF7PCwjku6dBYeUg4ehHhY2bmyaub2AObgoVrPLUnSGXnkggQYipPJnBcPOwNYVsnFQzKFzqCduRweE1m/QX1UVWX8+PEMHToUENmB8fHxER72bt26sXLlSgzD4OGHH6ZChQrous7bb71NXHwcTRs3ZdKkSVzf5XpatW5V0OniwsV5h0TeGbkXo9+ciIqKokePHgwbNowRI0YQCAQYNWoUvXr1AuD48eMoihKRxLFp0yZuvvnmXG01btwYVVV55plnePjhhzl06BAvvPAC3bt3P6szaMKECTzzzDMRYXRdu3YlNTWVSZMmFdgAlEyzEEzri4Q9e/bQoUMHbr31Vo4fP07x4sW58447KZtSBtXjcWJofv11NV/M+IKqVavSoUMHqlSpkm+bP/74I7t27eLWHreKbDrd8iQEdFCEMbti5Qri4+Lp2q0rFSpUwDTNAlX3uFBZnhcCEyZMIDo6mpu63oQvyhdaDgvqKFbsox0D6YnycPLYSbbv3M7vv//OmrVr6NWrF5dffmbNsUtpPC4GCnM8fvnlFz755BPqXF6Hy9JqUatWLTyKx9EFdLTz5MilzvDQCCRCGn5+DY/PQ9AfxBOlEszWUFRZrJZGxNtZWoJh2nl6UEfxKmScOk1MTIyIx/WK2Lu8eIR7787EQ40SXsVcPMI1DVUrjlANeSJtrU9D13PzcISg83gum4fVr81D8ShO5nXO6+0kECWchz0eJ08TExeLoel8/NknSJLE0qVLaXNlG9J3ptO2bVtKly5NpUqVAKFhKkmSk/W7+tfVTJw0kWs7XcvVHa7myLEjvD7qdV577bVLMtHJ/fsRiaI4HvZ39Pfff0/58uXzPHf3aT8JhWClnJRgSpwvFze/389LL73E7Nmz8fl89OjRg/79+yPLMnfeKSor2cLOuq5Tu3ZtJk2alKfcy8aNGxkxYgRr167F5/PRpUsXoSOcI9EkJ+rVq8fMmTNzeR137tzJ9ddfz9q1awv0jEWvpMYFQJ9/96FkyRJ4vT7rF7mIyTl05Aifff4Zh48c5t///neBlju2btlKtWrVHE+Erd2VkXWS96a8R0xMDE888USe6/1/B2RnZ/PJJ59w4MABnnryKTweT8TSll2rVfUKT4viEZ6OuIQ4GjRoQL06dalZsybz588/qwHoouigSZMmVKtWjTVr1rB42RK+mj2Tf9/9bypWruhUJ1E8SsRyq1M9Q5YwJVPIIMmiTrHiUcLukVE9ipBS0Q3HowYmKHKoioZHcryNwsumIHtE5QzbE58/D/E5NW3pJUkP8ZDFPfKZeEghnUMlvGqIIiGbitP3WXlIefCQIsdDUYW3VMQEmjl4hNpTPMJwc8ZDtXggdEhHvzGapk2acuONN/LjTz/y29q1fLZlC3379KV6jeq5wm5qptVkwIABTJkyBcM06HJdF1JSUli4cCEdO3Z0PfQuCgWyIRdKDGDOnE0bPp+PYcOGMWzYsFznpk+fHrGvKIqjFZwX0tLSmDJlyjlzq1y5MkuWLMllAC5dupQyZcoUuJ1/hAG4YdMGiie3FDU5TZMjR46wavWvLFiwgPbt23PvffcWKAbx0KFDbNi4gR7de4jYP0QM4Lat25gydQpXtr6STtd2yjOZ4e8A0zR58803KVu2LE8MeAJFtvQN7VhIazyQrH2TUIWFsOuOHz9OsbOIJrsoekhOTqZdu3a0a9eOX375hbfHvU10TDSVK1amQcMG1EqrhdfrFbFvjoYfueaF8JyLuNzweYEsOZUwHOTU8LOrZChSqKa2pgtjSTOQVdm6Xj4DD0LJFvnyMCL1lyWr/7C4QNGf4CPLVv+KlINHTk3DvHjIOXiINpDy4RH2fPZ42HqiWJ+5+Lg4Bg0c5PBo27oNRqsrmb9gPsuXL6d6jdw/dqOjo6levToDBw7k3XffZfLkydz+r9v44KMP2bhxIz179iQ5Ofn8TywXLs6AwssCLrq47777GDhwIFu2bKFBgwYA/Prrr3z66ac8//zzBW7nH2EA/vzzz8yZMwfFKsnm8/lITU3lqYFPnVMJljlz5nBl6yuJjYt1Mv1+XPwj8+bN46677uKyyy67gE9R+Dh69KjIHhzwRK6lvvAqDs5LkUVNV1kSX4AIr8epU6fQDSPPbFEXlwaaNGlCo0aNOHjwIJs3b2bhDwuZOm0qZcqU4Ybrb6Bmak3hEQ6fH/b7LQsvsayKesbIOBmvsqLkquIhkh9kJNNwahJLCE+ifY/wyMlOJm2oTxweUo5jWPMybx5ybh6y5ZXD5iGik2zRadmu8BHBwzp/Rh7k4CGdmQd2W5LTDhLIsuLcA3nwkCWqVK7CipUrrASVvL/i4uPjefjhh3nrrbdYsmwpDRs0ID19J8899xx39LyDK1pc4X5uXVw0qKaMahRCv0XYAuzcuTOGYfD+++/z2WefOUmXo0ePpkOHDgVu5x9hAN5zzz2ULl2aYDCIaZrExMSc8x+wAwcOsG7dOp4b9BymIbJdv5jxBUePHuXJJ5+kePHiF4h90cGuXbucWAjTCPPJ29rWWPVOkZzzhmEKT6Bpoljbq6++mrfHvc27777LHXfcUeTiUlwUDLIsk5KSQkpKCldeeSWBQIB1a9fx3vvvMfSFofh8PsCeF2I+iJi+yHkh2/viAMjWciuhSiB2jV9Mqw3FlloxrFq/cuh4+NyM6N/SEnRiC63+CfEwDRPy42GYmLJpXYPoF1lkG8tGJD9ZeMwxzswjYhzy4mGKO+22gdzjYO9jYFj1hp2xtnkAmFCpUiXiYuN48cUXqVevHpmZmTRo0IBSpUpx8OBBjh07RvHixUlNTeW+++7j/fffZ/u27ew/IKRkZsyYwdrf1nLTTTdRqlSpCzGtXLiIgGwW0hJwEc+O6NKlC126dPlLbfwjDEAQsiN/JZD50KFDxMTEsHTZUg4ePMja39bSsmVL7rnnnksyQPrPoEKFCuzevVsEu6uqExgvKZYorSyJeCxTbE3DtJwWlpSGLIEOUb4oHn34Ub748gtefull0mql0b17d9cQvMTh9Xpp2LghC39cyI70HaTVTHMkXAxDCDEHNR2FkDSM/TtMxI7K6LpuubtChouIo7OXP42I85IiY2qWlWWa1rJoDukXu4ybLGEAiiSjS6G2nXg/cUDwMCJ5iGQO0/G2GYZhnwaE103TtBAP03A84fnykGVMhwe5eUgWjzDY5+16x4YRZvCZwig/Ew9FUXjwgQdJT09nw8YNxEbH8tGHH2GYBiVLlqRYYjF+2PcDhm7Q+brO6LrO7j27nf4TiyXi9XgZ+fpIkpOTuaz2ZbRs2fIf8QPYReFANSRU4+J7nFW7iHgRxZo1a3j//ffZsWMH48ePZ9asWVSsWJFrrrmmwG38YwzAv4ratWuze/duTp48SUpKabrdOPis9fr+bihRogSJiYmsW7+e+vXrYa35IllbrC8aOxje3kpISFLYErEs4fV6ufXWW9m+fTsLFi5g7ty5f0kc3EXRQfny5fn111/5/IvPSSqWRO3atSmWWIzDRw7z5YwvGfjUQMqVKWcZJqF5ATiJVUCkGLMkPGhijVPMLdm6R5Yi5xqyZBmWknOf2M1jflqGliEZoeOE5mk4D0mWQ/3anM/Ew/pY5MuDUBuSJId4EMkvPJPYaUcCrCVcOXw8JGufSB7O+EkgmZLQ6KxeDUM3uKHrDRiageKR0YMGkgwrV67i3XffpWrVqvTs2ZMff/yR3bt3s2fPHlGyavgI0nek89u633j11Vfp1atXrtJULlycD0im5VS46P1CUTUAf/jhBx599FFuuOEGduzYgaZpBAIBHn/8cTIyMrjpppsK1I5rABYQkiRx7bXXFjaNQkf37t2ZPn06NVNTnSU+0w5S0o1QcL1moPgUDN1EVhEB6pLluTAlJwapcqXKXHvNtYx5a8zf0gA0TREu8E8Knu/QoQPDhg0jEAiwf/9+ipcozqbNm0hMTCQxMZENGzeQUjIFWfVg6BqyGorbi5gflhfZNHFi2URSgwGGiWGaGJpI7JINq8qHYiUe5ZU4YXnLDN2ISCQJb9uwvIln5GEIWRpMAwyxbIvDw8zNQ5LATmCxr7F52P3qehgPKZKHBKZuOu2YJqF7LKkYm4ehGdayshnyEubBw1nSzoOHqnqoX7c+o0aNwp/pJy4+jhLJJRjz1hjq1q3L9u3b+WPPHqpVrUaN1BrUrVOH96dOoW3btlx99dV/2yQ4F4UDxVAKxQOoYBIRw1GEMGbMGAYNGsQtt9zCzJkzAejXrx8lS5Zk0qRJrgHo4sKgZs2aVKtWjW+/m0fXG7oSKfgmsrVkRcZUTUeyQwTkK47QrkgKEQK6EhK/rPyFKpWrFO6DXQDs2bOHDz/8kPT0dB555BFHBPzvjuLFi/PMwKeZv2AB9RvUp1atUHmxMWPG8NVXX9GieQt279vNB//9ACSoUrkKaWlp1KhRg5iYGEfoWLKlZBQJGVl4t2RLeN0SXwcc8WVZlkEJeRSdeWktw8rWkmu4gDVyKInJllQRXraweZqLhwyKGepXtXnIyIpZAB6yw8OWlEG290MJHOHLzzmldexqRA6PiPGweUhgJZJEfE7z4xGWUKOqKh7Fg6zIJBZLJCEhgT59+vDt3G9ZvGQJlW+vgixLVK9enaeefIpp06exaNEi7rzzTmrWrHkhp5iLfxAUU0IxLv6PCkUqmsYfwPbt27niiityHb/iiit46aWXCtyOawC6OGfcdNNNvPTSSzRu2JiUlBRx0AyrZWrLfJgmui0Do+koiooe1IUXx5Lb0DSN77777qKWsLsY2LRpE+PGjUPTNGqm1sy3TNDfFaXLpHDb7bflOl6pUiU2bdrEl19+QfqunXS8qiOVKlViy5YtrFy1kk8+/QS/34+u66iqSt26dbnz9jtRTOHpMgxLPFmW0DUhe6IHRZycHhTxhTnrdJuGAZhIkpBOUVTFklkBPaAhSR4hP2NJuNgizrJseRsJi+UzJYuH8CKqiijJJsuyw8PQdBE9VEAeks0DD4YlHi2OS7l52O3lxSPHeBiajmkS2b+9lGxVNQnx0JGwSlpKaoiH9XlOLpZMmTJlWLlyJW3atmH48OG8P+V9ulzXheLJycTHxfNA3wfYsn0LU96fQpOmTbj++uv/MfHRLi4cJENGLgQDsCgnuqekpLBhwwan5reNxYsXU7Zs2QK34xqALs4ZiYmJXHvttXz6+ac8+uijyJIsAvpl2ZL4CIni2iXy8Kpi6xHeBdPyVNhB7k7geiFh3759/PDDD2zYsIGBAwcSExNzzm1kZ2ezadMmKlSowJgxYwBo1bIVt9x6yznVuv47o1u3bnTs2JGffvwRb5SPK664AlmWKV++PO3atXOuM00Tv9/Pm2++ycrVK2nevLntvEK3vMiqR5SCs8upybIcqpoRXk5O6CULT54hhbx9iizi5mQJBWt+esO8gbKMaeb44pEAQ8xtQxbGkerNi0ekdMuZeNhl9ZAlFEkN88blw0PJh0fYeNgexPDYwZzhTBE8ENy9ss0jzHtvfZ6bNWvmfMH079+fxYsXM2nyJJ4Z+IwwImWDy2pfxtMDn+b/Pvk/XnzxRerUqcPu3bu54447/nKtcxf/TCiGUkgewKJrAT744IM8//zzpKenYxgGCxYs4I8//uD//u//eO655wrcjvut5CJPmKbJ4cOHKVGiRJ6SOW3atGH58uUsX7qcJo2bYBoGsmKXp5LRArootZUdxBPtJZgdxBPlQfMHUQwVw7CC3U2JHjf14L333iM2NpZBgwZdcK/BokWL+OGHH3jggQeYOXMmKSkpzJgxA4By5coRFRV1Tu1pmoaiKEybNo2NGzeSnZ0NCPmhxo0bu5ppORAXF8e1nTuf8RpJkoiKiuK2225jwoQJfPvtt1SvVp2OHTrwxZdfIisy/+p+K0RHE8wKoEZ5hAdQtapnhHveTNPRztNtD59Vxk2UglMJWiULNX8Q1auiWW2Zhinsp/BsXt3gdNZpli5dxqnTp2hcvxFVa1TLn0dYFjCIJA1dM1A9CloYj/DSiZo/aJWZy4OH9VyGbgiuAU2Uo/QHc4+HFbMYPg62HSjLwnup+kL92Tz0gIZKJI+6teuweeNmpk6dyrGjx6hXvx779+/n5PGTJCYlovk1FK+CT/HSp08ftm7cSvrudBZuW+jGBbr401AMGUVXLn6/RfjP9g033EDx4sUZP3480dHRvPXWW1SrVo1Ro0bRsWPHArdTIANw//79vPLKKyxfvhxVVWnTpg1PP/00CQkJzjWBQIDu3bszYMCAiF/yOfH111/Tv3//iGM1atRg1qxZAEyZMoX33nuPWrVqMXLkSOLj41m+fDl33XUXTz31FL17946497///S/vvfce8+fPL/BDuzg7jh49ygsvvECrVq3o0aNHrtqEiqLwr3/9iwkTJpBSNoWyZcqieuRQDJVVqN5+2VmNnmivk4loG0Zt27clNS2VV155xTEMzzcyMjJYtGgR69atY9u2bYCQ0Fi+fLlzTd++falfv/45GWz79u3jpZdecr7cY2JiuPLKK+natasra3MeUKlSJV566SX27t3L6l9X8/qokc7y8Jb0rTRu3BhvrM/JHpYVGTmsbi5hIXhOrJ0EsuINlXKUJbzWvkcW89PjeM9MdMPgxx9/ZO/evZw8eZK9e/cSCASoXbs2pUuX5t2pk2nYsCE33XiTKONmc1HJg4cVg2fx8ITxCP+s2DxyefHCn8UQ16uS8F56or3IqhwxHpIsCT2znFM6J4/os/Pw4OGOO+8ACU6ePMmgQYMAmPvdXOo3qM+WzVuoUrUKaTXTUBSFGmk1SL0slSNHj/Dpp5/Sp08fVFV1jUEX5wTJlJFzeuIvSr9FMwPYRsuWLWnZsuVfauOsBqBhGPTr14+kpCSmTZuG3+9nyJAhDBw4kHfeeQcQS18DBgxg8+bNZ+1w27ZttGrVyimWDDjLY3v27GHOnDm88847fPfdd0ydOpWHHnrIue6tt97immuuyVU02sX5R3JyMsWLF2fVqlVs3ryZXr16UaVKZKJGlSpVuPrqqxk5ciQArw1/DVVS8ER7CGQG8cZ6CWQErK0f1efB0A3hHdEMoeFmiHJepUuUomH9hrzwwgvnVVjbNE1Wr17NJ598wokTJwChCXnHHXdQokQJnnrqKTZu3Ejjxo3/VP3mzMxMx/hr1KgRHTuKmDYX5w/2EnH58uW5ss2VqKrKoUOH+OCDD1j0008US0wiMzuTY0ePYWLS+eprubxOHXGzlTARGQcnoQd0Z376dT/btmxDQyP7dDY7du1g3fp1lEkpQ1KxJE6ePknAH6Blq5bEx8eTkpJCyZIlkSSJAwcO0LBhQ6ZNm8bcOd/Q6dprCWYHHS+aZIU6hHiIUAmxldD8uvP58MVG4c/wO7xUn2LxtkSuLUPQNAzhlQtoqN6Q9zKYFUBTPGhBDU+UN5KHHDIkI3joIhta8+t442wePvz25zYzgOrNzcMjCS993bp1OXX6FG+99Ra1atVi85bNfPThh7Rq1ZpmjZpRrHgiDeo24K3/vMXjjz9O6dKluffee10RaRcFhlgCLgQPYNHNAQHgl19+4aOPPmLLli3IskytWrW44447uPzyywvcxlkNwA0bNrBu3ToWLVrklE179tlnuf322zl58iS7d+9m4MCBKErB3qAtW7aQmpqaZwm2zMxMkpOTSU1NJT09nXXr1kWcT05OZujQoUyaNKlAfbn485AkibZt23LgwAFqptbkP//5DwMHDowwknbt2sUPP/zg7Ks+FY/HgyzLeGMtz0qsF1kRXglH5kKWkdXIwChJlmjSrAmrVq9y5GX+Kk6ePMlLL73E6dOnAahXrx5t2rQhNTXV8UJUrlyZypUr/+k+qlWrxltvvVXg+e/ir8FedahUqRIPPfQQO3fu5MSJE8TFxZGUlERGRgbTp09nzfq11KtXj9TUVGJ8MZYYuYRpigxexaOwb/8+vv/+e3777TcqVapETEwMiqJQrnw56tWvR1RUFMePHycQCNCgQYOIFQ8bo0eP5uTJk4AooSarMp5oj+NFyxmDZyJby7iWd1xVnM8HMmGfF2/Ysm24pg2OEScrIsbWI0soqoIa5UGN8iBZ8X/nzEO2eYR9bmNy8LDuV30qg54ZxMIfFjo/oCRJ4uqrryY6OpqFCxcyYtQIunXrxgcffADAiOEj+PmXn3n77bd58skn/3E6qi7+HBRTQTEvfrRaYVQfKShmz57Nk08+Sbt27ejSpQuGYbBmzRr+9a9/8c4779C6desCtXPWUS1btiyTJk3K02Dz+/0sW7aMDh068MADD1CvXr2zdrh169Z8yaWmphIfH0+9evVISUlh+vTpEedfeOEF7rvvPr7++ms6nyWGyMVfR506dZg7dy7XtL+GLl26MGLECOrXr0/x4sXJzMxk+fLldO/WnaXLl9KgfgNUWUX3a5iq4mT76gE9FFtlxRoJDbVQP4ZhsHjpYubO+5aBAwcSFxd3XvhrmoaqqlxxxRV06NDhnLKjzgWu8Vc48Hg81LE9fWF4/vnnWbZsGUuXLmX69OmklC5N2bLlSEhIIOAPcOr0SbZv34Fu6LRp04YePXoQGxv7pzh07NiRxYsXc/DgQXbt3MlHH37EVe2uolhysVDVDyNcwsVEtj4fqldB81uxsv4giipiaFWfBy2goXrEdXb4hF0NxWlXN5FkAz2oQYwXLTuIEWUQzAqIuNusIIpH1D+XLQ1CyTYAI3iozuczmB0U/YbxUDwKRh48SpUoxb9uvpVt27Zz4OABgoEgEyZM4PXXX+euu+5i69atvPHGGwD869Z/4VE8tLqiFceOHmP8+PE8+uijuUJLXLjICcmUkAtFCLroBgGOHTuWgQMH0qtXr4jjkydP5vXXXz9/BmBSUhJXXnllxLEpU6ZQqVIlSpYsmSsm70wIBoPs3LmTZcuWMXnyZPx+P61bt474Nfjaa6/x9NNPk5iYmOuLtXHjxvTo0YNXXnmFVq1a5fmL3MX5Q6lSpWjRogVvT3ib22+/nacHPs2atWs4dfIUiqzwUL+HKF++PE2aNwmrliBZBewtLTNZRlIkVJ/HynYMeRT2H9jPggUL2LhxI/Hx8Tz++OPndWkoOTmZV1555by15+LSQExMDO3bt6d9+/YEg0G2b9/OgQMHOHXqFNEx0ZSvWJ5rO3cmJSXlLyfodOzYkY4dO2IYBocPH2bRokWMfns0Dz/8MKVLlnK0MYFwGT7nM6L6pNDnQ7FiZ1UZ1cpKlvLK5LVK4Mm64VwvyzJqlAdkEc8X7gE0dSMfHlIYD9G/JypvHnlmFFs8qqdWp3rN6nzxxRckJCQ4Y1q9enUGDhzI6NGjadmqpaisIEnc0PUGpk6dypQpU+jTp48bE+jijFBQUQohX7Uo/6w/cOBALrsMhAj/2LFjC9zOOY/qxIkTmTdvHuPHjz/XW0lPT3e8MqNHjxYlhUaM4LHHHmPy5MnOdWeqmvDkk0+yYMECRo4cyYsvvnjOHFycG7p27UqlSpWYPHkyXTp34cpWV0Z47/SgHvGFIjwHIjNRleysRtXJJjR1g/RdO5n33bfs2r2btm3bct9991GuXDk3W9bFeYfH46FmzZoXXJhYlmVKlSrFTTfdROnSpXnnnXd4ov8TEYlATiZxWCyiETRQo8XnwxPlFZ44SXjebE+5k0Ec5r1TLL0/xYPwAFrZwIbHEFV3PCJzFw/olqZf+Oc0Lx6eCB7W57eAPE6ePsmCBQsYNmxYhEF3YP8BUmukggFaGI/bb72d/0z4D1988QXdu3e/oO+Ni0sbMgpyIRiAchEtAwfC0Js+fTqDBw+OOD5z5kw6dOhQ4HbOaVTHjRvH2LFjee6552jbtu253AqIbN9ly5aRlJQEQK1atUhOTqZHjx6kp6cXKBYrMTGRZ555hieeeIKuXbsWqN/s7GyysrLOme/Fht/vL2wKeSItLY0HH3yQqVOnUqp0KcdYs5MfHGkKRWz1gC62fh3DMNADOrquk75pC4sXL+bEiRO0aNGCO+6805F8saVTwlFUx6Ow4I5HJIrqeDRs2JB9+/Yxb/48ru10rZNxaxtNkiJh6tbWNND9OqZhoAeMsK2JpFvXOcab3Y6JZmiYpknQH8Q0TGR0NEMjoAcwNAMZXWzNsGoiZ+JhGKHPq9/moZ+RhxnGIzommrS0NNavX0+pUqWIi4vD6/Xy6+pfSUtLwx/wR/BHkuh1Vy+mTJ3CDz/8QNOmTc/7+1BU50dhoSiOR15/93NCllQUqRAMQKnoGoCxsbF8+umnLFu2jIYNG6KqKuvXr2fdunW0bNmSRx991LnW1qTNCwUe1ZdfftmxOHv27PmnidvGn43q1asDwqVZ0GD8Ll268MUXXzB48GB69Ohx1uujoqIuGUmOosqzYsWKdO3alXfffZfy5csjKzI1qteg7uV18UX5kJCIjo1G8YigdkPSkRSZvQf2snrdalatXIXqUbnmmmto2LBhgePmiup4FBbc8YhEUR2Pa665hqFDh9LuyrbE28uiZlg9YVkPxeB5VDRJsz43hhP7J3uUiLrGJjixfKEYQFFlx46vjfJGoRFE8anoWFV3wmMALX4OD0n0o0sWD6zazLLhxP7Jlsjz2Xh0bNeBTz//H0eOHCEYDBITE0O9uvVo2qgpXo83Fw9vQiJ33HYHY94eg6ZptG/f/ryvAhTV+VFYKGrjURDNVUmSkaSLvyArSfpF77OgCAaDdOnSBQBdFw6W6tWrO/ZUQVEgA3DMmDF88MEHjBghsrr+LObNm8dzzz3HwoULnYm4fv16ZFk+50zMIUOGcP311/Pf//73T/NxcW6oW7cuj/V/jGPHjhEMBlmzZg0/LfoJTdMcbbaWLVty+vRpTpw4wf79+/H7/TRs2JBed/eicuXK7jKvi38E4uPjqVGjBlt3bKNRw0Yh+RUnBlABE6u2sdiKpVYTLD0+TDtWMOQ8A5ANUetXMo1Q7V8JZFl8ScqKEorBtTLvkSVkJCcG0U7kkFXrHpuHEuJh6wKKNnPzkMJ5qDI1aqTy/HPPs/ePvSxZuoS2bdpSvERxkfmvhMo/hvMoWaok/R/rz/tT3mfFLyvoeFVHateufc5i7C7+vlAUtXCygIuwDszw4cPPSztnHdXff/+d8ePH06dPH1q2bMmhQ4ecc0lJSWctcXX8+HEURSE+Pp7GjRujqirPPPMMDz/8MIcOHeKFF16ge/fu51wmqEKFCvTr14+RI0dSrly5c7rXxZ9H2bJlnWza+vXrR5zbuHEjmzdvJiUlhZo1a1K8eHHhLXSDvF38A1GxYkV2pu+kYf2G1hE7bMKKvbMyhGWTvLeyCYYJsvDi2SFJIvbOFJp8iqj1K04bGIbhXGuXxDNNEyK+y0LhG2fjIdqw+5Zy8XD6sZaVDcOgVImS3HjjjZi63YdowzBNpJw8DIPiycXp/8hjrPt9PT/+9CPTp08nKSmJyy+/nBtuuMGtJ/wPhywryEohLAHLRcsD+Oyzz/LII48U2Fbau3cvb7311hmNxbOO6ty5czEMg4kTJzJx4sSIczNnziQ1NfWM9z/88MOUK1eOESNGkJSUxOTJkxkxYgQ333wzPp+PLl268OSTTxbogXLinnvuYdasWZw6depP3e/i/CItLY20tLTCpuHCRZFAgwYNeGPUG3To0IHEhETLYLLi5wBFljElM2SM2bF6WN45WUY3dLFsa91nn3eik0zxj22MyaospJgMq01L/zA8ZvdceCBJyJLFI8x7H+JhRvCwIcsyuhbOg7x5OO3J1K9fnzq162BKJn/s+YPXR73OmjVrXM3AfzgkRUEqhJxcqYjJe7Vu3ZqePXtSr149rrrqKlq1apVLMu3IkSP88ssvfPXVV6xfv56BAweesU3JDP8U/s2wZ88eOnTowPfff39JVA/JysoqcjEahQl3PCLhjkckLoXx+PTTT8GEm266ybGPJEnCMMSyqKEZSKqEqRki1k4Ty6n2sqktnG5iRkiwSJLlpbOq6siKTMbpDGLjY9EDOopH1CKWrWXnXBIuefCQVSnUv2bmwUMK2Xln4KEHdRSvgh7QkT0y5tl4WHWNnfFQJPxZ2Xw5cwaLFi0CRAJhjRo18Pl8tGnTpkD6gZfC/LiYKIrjcabvaPvcW4frU8q4+CEBB+VsHi6xOhe3YDDI8OHDmT17NgDdu3fn8ccfzzOu/Wylb0+dOsWQIUOcsLi7776bPn365MspMzOTTz75hA8++IA9e/ZQsmRJkpKSMAyDY8eOceTIEcqXL8+tt97Kv/71r7Nq6l58v6oLFy5c/EPQoEEDvvjiC2uZ1krCsEqwmbIwwBRDsRIpTAyrNJuhG8JbphuYklgqtb1odjKFaZgYmo4RDIuxMxD3KPY9shCidqp5hPx0cg4eksNDiLNLZo7+JSWSh2EZdoaJoRkiYcTmYSpiaykDmJA/D4SBKckSCxYu4Jtvv0HTNILBIACjRo5i4+8b2LJtKzvTdzJ79mzKpJShbr26XHPNNW6Iyd8cskdFNgpjCTjvPt944w2WLl3KxIkTycjI4KmnniIxMZH77rsv17VnKn0LYlnXLmuZnp7OM888Q0pKipPgkRMxMTHcfffd3H333WzdupV169Zx+PBhJEmiZMmS1K5dm2rVqhX4GV0D0IULFy4uEFJSUti3bx+6oePxeJzlVbv0GipIioRsCMF0hdBxJ8FDlkCSRfJEmAyLLAtNPsU6LquKSOTwKEiyjGyCpMgockj+xU4D/lM8JAmUMB5yTh6K4OERW8XhYZyVh6IqBAIBZs6eyaBBg9i9ezfvv/8+LVu0JCoqirr16lG/YQMM3SA7O5vde3Yz55s5+P3+v5SY6KLoQ5KVQsoCzt2n3+/no48+4s0333Qqnz3++OOMGjWKvn375voxcqbSt3/88QfffvstM2fOpEaNGqSlpbF161amTp2arwEYjj+T9ZsTrgHowoULFxcIcXFxpKSksH3rdmrUqIFpGkiSjGmIhA0jqKNGe9CDGrJiCUFbpRNljyKycQ05Il7ONE0URUbXhFSLHtRRZCHCbgQMdF13RNkVSQqVk7MgvIh/goeUg4dhonisJV+PihbUUSRLHN4UXkFFkgRPQLe8mvnxkCWZUiVLCd0yq58mDRtjGAaaP8j2nTv4es7X3NLjFnbu3MnevXvZtWsXl19+OZUrVz5rQqKLSxOyqiAXQhawnIcBuGHDBrKysmjcuLFzrHHjxhw6dIhdu3blUjM5U+nb1atXk5CQQI0aNSLaGjduHH6/H5/Pd34e5AxwPzEuXLhwcQFRrVo1ZsyaQcsWLWnevDmKqmCaogSbYZdis8q4qVGifJsa5RFLtJaHzLTq+JphcXOSVWZRklQkRcbjVZG9MrIhSrlJklXWTRISLyHnoeT0/6d5ABhY7QseHouHLElC7sUqZSdJklNeDiJlaMJ5qJLEPffcw6nTpyhfrjy79+ymWo3qoqxdlIf0Xels276N4a8Np0njJmRkZFCqVCn+97//cezYMVq3bs3hw4cpX748u3btIiYmBl0XnldVVfF6vVSpUoVatWq5y8aXECRVReLiZ4JLBHMdO3DgADExMRGxdbZ3b//+/REG4NlK3x44cCBX6dOSJUtiGAYHDx6kQoUKF+bBwuAagC5cuHBxAdG1a1f+7//+jx9+/AFD02nZqpUlAK2Ikok+D8HsoBCADhN1Vryq471zcifsGEBHgBn0gI4n1kswO0hUVDSBzACeGC/BzIAov5hH7J3w3llC1F5RAi5PHgHNuS5PHnpICFoPaHhjvAQyA3hjvSEeWQEUjxppRObkERAlJEsUK07p0qXRsjWqV6mOlh0U/II6NVNrsiR5CUeOHiErO4sKFSpQqVIlrmp/FRmZp3lt5OsA7Nq5i6uvuppgIIAhmfgz/ZiySeapTL766ivmzp1L+/btqVOnToEF6V0UHiRVLZRScFIefWZlZeVKQLL3A4FAxPGzlb49l7YuFFwD0IULFy4uII4fP86SJUuQZZlyFcs7As1I4FG8SLKEN8Yj4uk8MpIs45G9Ip7Oug6IzJ6VQFaFJ1BU31HwRHuQVRlvrFeIQcd6kWRZSLHkkX0b3r4n2oMky3nw8ISug1ztSLKEbBmYikdGVhSnf4dHjDcy9i8sBtB5lhw85BgJJMmKI5TwKDKVq1Rm6NChGKbBvHnzmDlzJrt372bRokUMHDiQrl27MmPGDLL92cQnxlOsWDHKli0rlpcVBUPXud68np9//pl58+bx3Xff0adPH4oVK3bB54CLvwBZBqkQPLamHFH3HkTlkpzGmb2fM8P6bKVvz6WtCwXXAHThwoWLC4hjx45RtmxZHnv4Mbwej1VjV2S9an5deMsy/Phio/BnWN6zjACqT7G8d5FGnGkYwjsY0FC9KkG/hicKgllBNCWIFtTwRAmPoOoT10myHGG0if5Fxq6kWDzivARO+/HF+s6Jh+bX8ER5CGYHhNcwO+S99ESZaH7N4ZsfDyEDI3iI/v1443wEToe8iapHEd5DWaZDmw60u7Kd8Kq8PoJXX30Vn89Hv379OHXqFF9//TVHjx7lxIkTjBoxCm+U5c30qjSq14iGdRvy/YLveeWVV2jUqBFNmzZ1KxUVUciqiixd/CVg2VTJuQqckpJCZmYmGRkZxMbGAjjFMfISaD5T6duUlJSIwhoABw8eRFVVihcvflZ+c+bMYfLkyaSnp/PFF18wffp0ypQpwz333FPgZ3QNQBcuXLi4gKhataooj3j6BGVSUixvmGx52kS2rTfOBxJ447zIsowvzvaaWS6yvDxvimx5xyQURUGN9qBGeZA8MrIs44kR8XuOFzHCAyifgYf053jEeFEURWQRKwqyxUOVpbPyUMJ4SLKEN86HJEn44oQX0xebN4/ylcrT4ooWLFm6BJ/Px9atWzl+/Dh79uyhXr16tGvXTngiZRlVieRxbedradq8Kb/8/AtTp07FMAwaN25M06ZNSUlJubiTxEW+kFQVKR9Jlgvar5HbAExLSyM6OpqVK1dy5ZVXArBixQpKlChBxYoVI649W+nb8uXLc/z4cbZt2+ZIt6xcuZLLLrvsrAkgn3/+Oa+++ir//ve/+c9//gOIWONXX30VTdPo27dvgZ7RNQBduHDh4gJCURSuuuoqZs6cyb/vvAfZib1T0Pz5xACGxd7Jcqj6R+4YQBF7R6wXLSuI4TMInin2zvHemWE8ChYDWBAekhV7SKyXYIbNI4hie+/CYgBBaAPmjEVUfCpatujX0ERsoBbQxX5QF7WSneeRuKXHLdzcvQcnjp9k9NjRlClThheefwEFmbhi8WfkUSwukavad6R9m/bs27+X5T//zOg3RlO/QX26detW5IST/5GQJbEMfNGR2xscFRVFjx49GDZsGCNGjCAQCDBq1Ch69eoFnHvp23bt2vH0008zdOhQdu/ezeTJk3nppZfOyuz9999nyJAhXHvttYwfPx6AW2+9lWLFivHqq6+6BqALFy5cFBW0bt2aWbNmETCCxKpe4TWTJNQoSXjPoj0iycKKD1RRkRXZuc40w+L4TEuzT7f09VQRr2dn8HpiRPveaC+SIipxIBFRvcPOwpXDsn/PziPkgMuLh6yKrF9PjBdJkvDGiP690Z58eISMuPx4yKrlRfRKZ+VRwlucV4a/4lQVyczItOIrz85DMU0qV61CpSqV6XJ9F6ZNm8Z3333H9ddfXxjTxUUYJEVFKoRawJKed59PPvkkfr+fvn374vP56NGjh1O941xL344YMYLBgwdz2223kZiYyKOPPsp11113Vm62/FFO1KpVi8OHDxf4GV0D0IULF38LHD58mJ07d1KsWLFzUsO/GPB6vZQtW5Y9u/dQvVp1TNMASXL097TsIJ4or8h69anomoFpmiJGLqf+nmmimDJ60EDxgB4UsW1aQMPwGBiaDlYMnuIBXctdxzdcfw85bx6KT8VweIS8ZpE8lFA/AQ3TKgGnqCLpQvHYPEx0LR8dQCsW0eZh9++x+Dg87GoqOXk4XkRN6BEGNOFdDWgYHh1DN0I8VNPRIww3QMN5+Lw+mjdpxo9Lfjqn99g0TRYtWsQvv/xC69atqVSpEpmZmSiKQsmSJYmKuvjlzP4OkDxK4RiAct4Z4j6fj2HDhjFs2LBc56ZPnx6xn5aWxpQpU/Lto1ixYowdO/acuVWtWpWff/45l1TMnDlzqFq1aoHbOe+jun//fl555RWWL1+Oqqq0adOGp59+moSEBOeaQCBA9+7dGTBgAO3atXOOz549m5EjR1KmTBlGjRpFmTJlnHqAPXv2ZPDgwRF9LViwgPvvv59Nmzad78dw4cLFJYZJkyaxe/duQAiq3nXXXUVKHDgmJgZ/0I+sypiG8GgZsoyEhOJRQQbFoyLJMooqPIOmbahgIuWoBKKoICsSpqk41TRkj4hvk2TJ8gxKqB7FcfsJx5kkWpOkSB5SJA9ZlpFsHrJp3ZcXD+GZM604QjwgqzYPQh5Kj+RkA9vtmGboOQUPyekfi08EDzOcBxDuRVQVZEV2KpCI8RDPLikizlBSRExiBA9rHMJ5JBdP5vTp06Snp/P999/jUT1c0+maiED/NavXMHPWTDyqSpOmTVn32zoyszJpc2UbPvn4EzxeD4kJiQSCAY4cOULnaztz9TVXF87ku5QhyYWzBGwUXa3IAQMG8Mgjj7B27Vp0Xeejjz5i165dLFy4UAipFxDn9a+jYRj069ePpKQkpk2bht/vZ8iQIQwcOJB33nkHgOzsbAYMGMDmzZsj7vX7/YwfP57Ro0ezceNGxo4dy/Dhw53zH330EV27dnXKr7hw4cJFOB599FGmTZ3G2t/WsmLFCgKBAL1798bjufgZhHkhKiqK7Mxsp4YvkvDOCW+ZgazaNXER1TNUxfLQWRU7CIu9kyRRW9cQXkJTljB0HQwwLI+fYZjIGBg5Y+9k2Wk3gge5eRiagVxQHnZdYU3UBBY8lLPzIJyHqB4SzgMZzDPxCKtJbNo8JGs8TNPiwRl5GIaBjGx5S6FUydL4s/2MGTOGrjd05eTJkwwdOpS6deuydu1aEhISkCWZ2/71L3TdYOK7EyldujTPDHwaCYlmTZuhBTUUVXgmT2WeYviIETRu0pj4+PgiMycvBUiqgqQWQik4iq5GZOvWrfnf//7Hu+++S40aNViyZAnVqlXj448/pnbt2gVu57wagBs2bGDdunUsWrTIUcd+9tlnuf322zl58iS7d+9m4MCBeYpvBoNBoqKiSE1NBWDJkiUR58uUKcPzzz/P559/XqR+1btw4aJoICYmhltuvYW1v60FYO3ataxatYpmzZoVMjOBhIQEjp84LmLsrJg3WZatTFrhoRMZtbIlfSY8ZICo+gHCq2ZatX8lrKVQyakMgkxoa1oVOazr7fusLI7cPBQJ2bAMO4uHpBSABzhL2g4PyeYhIZnk5mEj7B6bh9AFDPGQZRkjJw9FFu1KWMu5Ng/T8fgJHuHb/HlIFg/Zutbr8/DUU08SyA5QLDkJUzf5Zu43rF27llatWnF1x6tJSEhAURUwYUD/ASQVS0JV1YilakmRkHSZpKRkaqbWZMTwEWRmZVK7dm3uvPPOiIoSLvKBqiB5CuM7v+gagCCyfsOdZH8G53VUy5Yty6RJk/IsfOz3+1m2bBkdOnTggQceyOXJi4uLo0WLFjRp0oS4uDgmT54ccX7QoEEMGDCA9957j3vvvfd80nbhwsXfBMnJybz55pukp6cTHR1N+fLlC5uSg3LlyrFk8RKaN2tOTEwMgBPLZlrLkaH9yK0NsWxqe7AILcWahOrEWfuRFUAit3n2Y1pN5Hc+Px72wRz9Y5pg5M0jdEvufiTTutbMi58Z2b/dXkSWcx488hmP/HlATGwsUd4op9+333qb48eOEx8f7yw/2+eqVKkiDD/7GXLyAG7qdhNZ/kySEpP5cuaXfPDBB9x7772u9uDZIBfSEnARLhf42muvnfH8U089VaB2zqsBmJSU5Gjj2JgyZQqVKlWiZMmS9O7d+4z39+/fn3vuuYeYmJhcJVIqVqzIAw88wLhx47j22msvSp08Fy5cXHrwer3OSkJRQqNGjdiyZQsjXhvBbbfexmWXXRZp+EgKpqk5+4ZuiGTVsKQH23iTEUuviqqgSyFDxtAMxysGwj40DFO4x5x+rHhAQyyBGroR0b4iK2iaxcM4Aw9r6TXEQyRqYLWJFb/o8MhpRIbxQBIhROHGrSRLmJpI8zUNE0M6Cw8Ty4NnOMaYYUTysO+x+7eFqAvCw8QkLjYu9EzhPAybhxziQSSPhIQEktRi6JpB95u6M3LUSJYsWULLli3/zHT6x6DQloDNousB/O233yL2dV1nz549nDx5ks6dOxe4nQvqV504cSLz5s1zdGoKgjOV5enTpw+zZ89myJAhuTyELly4cJEfNm/ezJtvvunsP/744xc9U9jn89GrVy82b97MlPencF2X62jWuKlY8pSlXFtJlsAUy5uO+DEgieA3cd5aKhbLoLJjgNiJIEiSaNPS8LNlYLCWXLGXXM3c/YutdZ2UBw9y8hD9m7JlCGE4/JAkZCl/Hs7Sbx7jAJaAsyyHvGthPAjnIeGMgyyJlyEbznhIYTzs+L98eZDj/XB4SHb+i/VPOA+7HyKWtW0D1ebpVby0uKIFv/32m2sAng2KAoUR9mUUXQMwZ7axjZdffvmcQuQu2KiOGzeOsWPH8txzz9G2bdvz0qbH42Ho0KH07NmTmTNnFjh+Ijs7m6ysrPPC4ULC7/cXNoUiBXc8IuGORyTOZTxKlixJ8eLFHc/Wu+++S+NGjbi2c+eLvgRXoUIF7n/gfqZOnUq1KtVISIhHC1qJE4aBElSsRAoFw9CxTJVIAWZJcpIZDF1H9XrIzs5CVmS0YNApvSYrSuT1hMqbyrKMoevIqiX8HFTRglo+PAwgPIQwfx5aIOhshfxKiEdOIWgTE1lWIngoAbF1eKh58MjRv+2JdHh4rPFQJUvMWsjDKIocwd+GaZooioKuiWt1WwBb0wU/03CkbWT5TDwE53AeWjAoRK4tHnpQx5RMVq1axXVdrrto301F8e9Hdnb2Wa8pNA9gETYA88Ndd91F9+7dGThwYIGuvyAG4Msvv8z06dMZPHgwPXv2PK9tN2rUiFtuuYXhw4cXeJ07KirqklF0v1R4Xiy44xEJdzwiUdDxiI6O5oUXXmDnzp2MGjUKgO/nz+f4iRP06tXroieWVaxYkdq1a/PhRx9Sp04dDF2nbv16lCpRUhg6lpixYWnWRZZQC8W8gVXVw8qYjYqJwtC8Yt8j2nDcVc4WJ8Ei1I8wwIygjqQKseTI8+E8ItvLxcPjjdxqOXngPM8ZeSii9vDZeERoA0oI7qowxKKiozE8hjDmvMKoDF8GtyG4W4amKq61K4/k5mFVPykoj7D3Q/EoaP4gk99/j9jYWNLS0i7qD5Ci9vejQNqItqf6YuMSjM1cuXKlFX5QMJz3v3pjxozhgw8+YMSIEXTr1u18Nw/AE088wfz583nrrbcuSPsuXLj4+0FVVapVq8YzzzzjZM+tXLkSRVHo1avXRfcEdu3alYULF3Ls2DEMw2D06NG0b9OeDld1wAgYQtPPNPMWglZtIWhhtEiyhK7pGEEjJANjCS/nLQRtYgtNG4YhhKBl4S3zyF5RGi5KlIKTVTmickckD1sI2irTJoEe1MEEQw/nQb5C0KYppFtsHh5JFjyiPQXjkUMIWg/qVn86RlB3YhiNoA6EZHIcb6TlPRRxlyaGYQqvpTUe6p/h4bV5SJw4dgIkE4/qITY+jj/27mXvvr288MILbgJIQaAq4nWxoRddD2D37t1zzZ3Tp0+zc+fOc0qSPa8G4O+//8748ePp06cPLVu25NChQ865pKSk8/YrOyEhgUGDBtG/f//z0p4LFy7+OahQoQKPPfaYExP4888/06BBA+rWrXtRv5AVRaFDhw7Ofrt27Zg2bRrrN67njjvuoFTJUrm8SuGlz4TMiAySKNemelVkjyXnYpVMs2Ve8isFZ5rivBFWig0Z1CiPaNOnRsTemWHeuxAPInjYkiq5ech58LBK3SkhHnb/WHxy8oj0hiKMZEVGklQh9BwxHkIcWlKEJI0dj0eEIQqygSXHI5JGZOt6NcrjjMs58/Cp7PljD6+++ioA9erWpU/fvsxfMJ/atWvnKYfmIg8ohWQAakX3/Wnbtm2uv1Uej4e6detyxRVXFLid82oAzp07F8MwmDhxIhMnTow4N3PmzPOamde5c2dmzJjBwoULz1ubLly4+GcgNTWV5557jgkTJnDkyBEmTJhAQkICV199NdWrV6dcuXIX/Qu6ZMmS9O/fn++++45x48bx7DPPghFKSIiIvdOtZUjZQA9oeGJF+TSioglmBvDEeAlmBVE8OWLv7KVXw0T2CO+d6lWFh8vnIZgdRLVi31Sfx4pbs5ZCC8DDG+MlmBnAG+stEA/D8ZqFeCg+FS1bc7ybqk9FC+iRPCxj1Im9040cPHxo2UHMqCiCWQE8UV6CWYFcPOxMEtMwhNfOr6FGqWh+LcTDq2AEDVFezvZ2arqTXWzaMYi6iawYloi2jB7Q+PbbbwG48cYb+eKLL3jyySeJj4/n6aefvqhz61KGZCdEFUK/RRUPP/zweWnnvBqA/fv3L7BXrqDl28qXL5/vtRMmTCgwNxcuXLgIR9myZRk6dCi7d+/mvffeIysrixUrVrBkyRJOnTp1zhl15wOyLHP11Vfz22+/seznZbRu1Qon4zXc2ySBrFpeK4+MoijCS6XKeGK9yLKMJ8YjvFVGHt4qywVnexLVaI9zjyRJSB7hkfP4VOE5U6QC8ZAVBU+MV2wLwEPJh4cUY2XsOjykfHnIefKQUaM8Qqg5xubhFTGA4Tws2EvBkuwR5fNy8lAtHvIZeNj9W3qDskemcePGbN++nQ0bNgAi6SEqKooXX3yR9u3bc+WVV+Lz+S7onLrkocqF4wFUi5YO4KOPPlrgawtaDs4tqeHChYt/NCpUqMCzzz7Ld999x7fffkt2djaJCYkcOHAATdOoVKnSRefUs2dPUWzegGpVq1GqZKkIo0WySq6pXpWgX8P0mQSzgmheDc0fFB6v7CCqz8oGVhVHo0+SQ/p3pm4gKRKaX8cb5yVw2o8v1kcgI4An2hN2r3V9DuNJkiX0oI4nykMwO4Dp8xDMClrxexoe25vmPTMPO7lC8+tW/368cT4Cp8/CA8BKJNECFo+sAGaUJ2w8NDw+KwvXis0Ty7iG8CLKduk5xSljpwXCeNjjEWXdaz2DZJWfs3mYuuG8H54oD0cOHqZi+YpUqlAJ1aPy+KOPc+LUCQ4eOIDq8bBhwwY2bNjAI488ctHm1SWJwloCLmJL9LZ4/PmEawC6cOHiHw9VVenUqRMdO3ZkwYIFxMfHM3r0aAKBACNHjswlTH+hkZKSQsOGDfl1za/MnjObdu3acfVVVzvJC0hgqMLT5ZFFzJo31oviUaxYNhlPtMcq6WbFtJlgmIaogoEVG2gqIIHiUUECb6wPZAlvrPCWKapiVSiRrftMR5bG5iHbPKLte+zSa6Jf1Y4JlD25eZiCn5IHD1mW8+URciOGeEiKxcPy9OUcD1WWnOVEh4cl2SJby8KKx+LhDeOhhPHwqCJpxRmHEA8QRq39fowa+wanTp1CURSioqLYkb6DcuXKUbx4cVasWEHv3r2ZOHEiX331Fddff72bEJIfwnUsL3a/RQh/texbXnANQBcuXLiwoKoqV111Fd9++y1JxZL4Y+8fPPbYY6iqSlpaGvfcc89Fk9KwKyedOHGCd955B3+Wn+s6dw7FvhkG2Pp2HkXE3nm86AHhGdQDGljxfIadyAGO50qSZRHrZ8feeYW3zhstvGeqT3i8FMfzZnu8hOFjGqJmr6EZYOvceRW0rCBqtCfEI6hZ3jUDQzby5hHURMxhGA9PzBl4hGWj2Dx0zQjT27PGQ/U68YV58QArntEwkO3xsGIhPT7Lm5drPOSIsnL58SiWWIy77+pF9dQaZGRkkJiY6Bh5ZcuWZfLkyXg8Hr755hvKli1L5cqVKVGixEWZW5cUlEJaAlaK1hJwOEzT5Pvvv2fLli3ouu4cDwQCrF+/vsCFMlwD0IULFy5yoGPHjmRmZpKVncXRo0fRNI1169axYMGCcyq1dD6QmJjIQw89xIsvvkjzFs2d5WDZUJBkUL0qkiKh+iyPn1fE1Cn2cau6RThMS7TYztRV8ThxbsgSHiueULIyZw1Dyu2hMgEZ4U2TJIeHJ9orYvq8InYOVKefnHB4yOLacB6S9Nd4qL5QW2fnITvZwrIqO/16LO9lbh6RkjZCZwYki8fxEyc4euwoNWulIctyRIWrRYsWkZCQwCOPPMKrr75K8eLFWbliJZ988gmDBg06YzWsfyIkVUYqhHi8wuizoBg+fDgffvgh1apVY/PmzaSlpbF3715OnjxJjx49CtyOawC6cOHCRQ7Isky3bt3o1q0bpmmSlZXFnDlziI6O5tNPPyUxMZEWLVoUuBrRX0VcXBzXXnstEyZMoFmTZjRp0oSE+ASxFKwZItYuqIGB0K6zY95UOVTPNjxuzjBBFVp5mIS084IasiGEqME6rwrtvIgybo73ztpaOn8eK3PYY2nmqdKZeZjhPKz+QUEPWILQefEIE8aO5GHrHlo8ghqYZsF5WOMQ4mHfkz+PXONg8Thy+DDJycm5RHm3bNnCjBkzUBSFnj17EhUVxXXXXkf16tX54ccf+OKLL7jnnnsu3ES6FCErhROPJxetGMBwzJ49m5dffpmuXbty1VVX8cYbb1CuXDkee+yxgolrW3ANQBcuXLg4AyRJIiYmhu7du7Nv3z6GDRtG6dKl+f7777n55ptp3LhxgdoxDIOlS5Zy/MRxypYtS7169c5Jtb9t27YkJyezbt06Ro8Zzf333kdCYiIx0TFIkoiTE3F0CoFAgDVrVnM6Uyw9VqpQkRIlSorYtbCqFfY9tu6drChCgBqh8SdbMXqyRwkr42YteVrZu6YmEiiQhESL4hFfnIpHQZZlVI/iVBeRJECW8+YByIpieeHIn0dELKLgIZaiieShKoCE6lGQZQnVqzgJJPny8ITzEG2ciQeYVpUKE1MS9+kaHD9xglOnTqFpGqoq4gYXLFjA3Llz6XVXL3bt3sWWLVu49957+eabb/jsi89QVZUTJ07g9XrZtnUb2dnZxMbGUq9+PTp06EB0dDSGYZCRkYGu62RlZVGyZMmLnql+0eHGAObCiRMnaNKkCQBpaWmsXbuWKlWq0K9fPx5++GGeffbZArXzN585Lly4cHH+UKZMGR588EGmTZuGJEnMmDGDhg0bntGQO3LkCB9//DH79u3jyJEjzvHnnnuOsmXLFrhvSZKoV68e9erV49tvv+WVEcNRVZUXXxhKQmKiqKShG+z94w/efX8ypUqVIiUlhR07dvDpp59S+7LaXHPV1SQXTxaizGHtiixYOcfWiu8DR+su43QGK39dxbZtWzl46BCmaXL5ZbW55pprUCQFw8qoBZEVa1pVNmTDtMq4EVKitpZSI+vn5uYhhfEIL7tmWu2IMDyrDcO6R5ac8TANAwxLp08K8XC0APPjoeTgoYSeLaIMXgQPA1M3aNigIT/+9COffvIpXbt1Zc2aNSxevJgBAwaQnJjMosWLWLt2LXv27KFTp06kpKRw8uRJJkyYwOLFi6lYoSL333c/x48d56vZX/H111/TuXNnvv/+exRFITMzExDiv2XKlKFdu3Y0a9Ys37kTDAYLPM+KHFSpcCRZ1KJrAKakpHDgwAHKli1LlSpV2LhxI127diU+Pp6jR48WuB3XAHThwoWLc8Dll1/O0KFD2bhxI9WrVz+rF2/jxo2sW7cOgBIlSlCjRg0aNGhwTsZfTlx11VWUKVOGtWvXMvSlF4mNjSUmJoZjx44BcNNNN0VUBMjOzmbWrFmMeXusk5kaDAapWrUqycnJ6LpO48aNUWWFb7+bh2EYNGrYiMtrX05sXCzr16/n9w2/s27dOi677DIur1OHlJQUdF1n3rx5TJ0+jbvvvBtFDunm6VbCiC36LGL0sGL2JKecmiRLSGYoMxdMZEVy6gRLSE6msaEbztKr3Y5kreSG1/mVwngYAeuYhCMZE76EHMHDkJxSb8IzavGwYwmtTGPzLDyQ4O4772bOt3N44YUXyMzM5JGHHqZkiZIYms5dd97Fr6t+5YOPPmDjxo2iXn1UNJqmAfDH3j9ILpFMclISWVlZAHz99dcAtG/fnjat25CQmIAe1Hhi4JNMnTqVo0ePEhUVRWJiIsFgkNmzZuH1+Th29Bj+gJ/UGqnUrVeX1q1bX1pVSFwZmFy4/vrreeKJJxg+fDitW7emX79+1KhRg4ULF1K1atUCt+MagC5cuHBxjoiOjqZBgwa5ju/bt489e/ZQtWpVp95uy5YtadiwITt37iQ1NfWcln3zgyRJ1KlThzp16vCvf/2LY8eOoes6MTExREdH51oWjIqKokePHvTo0QPTNAkEApimSXp6OidPnsQ0TWbMmIEsy3Tq1InY2Fh++fkXvpn7DdlZ2VSvXp3SKaW59957c1V0qlSpEqNHj+bHn36kSrUqJMQmUKJUCatunBVfJ4sYOyH9Yjo6gmb4NQrWmFlLstb4OdeZoesdHUJCXjjRD6F+c9zjZOxay9h58gi7x35JZiQ/IWNjZTITWhZ3eFj3JMTHc/vtt9OxbQcOHztC1cpVAdGm1+ulSZMmqKrKjvQd3NjtRg4cPECxxGJohsaJoyeQJRnd1LnlllsYM2YMycnJ1K5dm/nz5zN//nyeeuJJypcrz0P9HmLt2rXs2LGDYsWK8emnnzrvzeP9B1A8uTix8XGs+20dX3/zNevWrSMmJoYyZcpw9dVXF31jUIos3XdR+80DwWCQ4cOHM3v2bEDU5X388cfzHMetW7cyfPhw1qxZQ2xsLJ07d+axxx5zxL+//vrrXMUzatSowaxZs3K1tXTpUudH3UMPPURUVBSnTp2iQ4cO9OrVi5EjR5KYmOiUHiwIXAPQhQsXLs4TVq9ezcyZM6lYsSK7du0C4D//+Q/R0dGkpaVdkD4VRaFEiRJkZWUVSKJGkiTnCyicU84lxNq1axeof1VV6dWrF2+88Qb+b/yoqkqTJk249uprifWqIEnIkoRuGLl4YJpOTJ5hGGFl2hDxepqGZXFhSIaTHRxusJmGyCQ2TFBkGV0yRJuINsUSLUAkj/DYvzx5yDYPCVMzz4mHLMtiOdgwKV68OCVLl8TQTTAlZzxMSaJRo0Y0btwY0zQpV7acSGRRZOJj4x0eNarXYOSrI1nx6woO7j9It27diPL4KF06BSSJ1BqppNYIGeXlypSjRmoNvB4vycWThYi2LFOtWjX6PdCP1atXExUVxc8rfmbbtm3ccccdRTvzWJULaQk47z7feOMNli5dysSJE8nIyOCpp54iMTGR++67L+K6jIwM+vbtS7Nmzfj44485dOgQzz77LMFgkOeeew6Abdu20apVK0aMGBHqNp+Yzn//+9+UKlWKG264gW7dukX0169fP/r163fuj1iQi/bv388rr7zC8uXLUVWVNm3a8PTTT5OQkHBO1jCc3eKdMmUK7733HrVq1WLkyJHEx8ezfPly7rrrLp566ilHG8vGf//7X9577z3mz59/zg/vwoULF+cTnTp1onnz5ni9Xn744QcqVKiQ6xrTNNE0DY/HUwgMLwxKlSpF//79OXjwIFWqVOG1114jJjqGzp07W7V7hdETkUlsJWII6RmxbCpbdX4jtoSWdAGrApsUKW0j4dQIliRZSMtIstBLlMPPWzxs8Wik/HmQFw8Z2TIMC8IjfDlasnlY/TttSoS2srXknWM8vF4PrVq1wrAqkRiaYS1Pk0vip2XLls5yuSQJQ1qynjs2MZ5WrVohKzINGzTg67lzGDp0KLIsk5SURLFixahcuTKtW7cmMTHxvM6RPw2lkGIAldweQL/fz0cffcSbb75JvXr1AHj88ccZNWoUffv2jfDuL168mFOnTvHiiy/i9XqpVq0ajz76KMOGDXMMwC1btpCamkrJkiXPSmfBggXMmjWLr7/+mkmTJnHZZZfRrVs3rrvuOooXL/6nHvGsBqBhGPTr14+kpCSmTZuG3+9nyJAhDBw4kHfeeafA1rCNM1m8e/bsYc6cObzzzjt89913TJ06lYceesi57q233uKaa66hfPnyf+phXbhw4eJCQpIkkpKSAPLVC/zPf/7D+vXradKkCT179rzoVUYuFEqXLk1SUhKLFy8mEAjQtHETTNOMTCQxQsunJlYuiCpjaDqKR0E3TCTDFN4yycAwQkvBEkQkkpiGKTxplvfNNERfhi7KuRmGkGoxNM0qnRfGA5ySdKZ9zCoFlxcP07CXhA1M05KFkWxB7rPwsBNJND03DwmMPHjomo7qUSN5EM5DLJVH8LASdUzDxJQED93moYoEFxCyQZJlfF7fuQudO3UmKzOT48ePc/TYMT753yd8/fXXjBs3rmhUJylCS8AbNmwgKysrIvO/cePGHDp0iF27dlG5cmXneL169Rg3blyuz3cgEHCSjrZu3Urr1q0LRCclJYU+ffrQp08fduzYwezZs/n444957bXXaNmyJV27dqVjx47n9PfkrGb1hg0bWLduHcOHD6dmzZrUrVuXZ599lvnz53Po0CE++ugjBg4cSL169WjRogWPP/4406dPdyZbToRbvPbL/oOZmZlJcnIyqampVK9enYyMjIh7k5OTGTp0aIEfzoULFy6KGurXr090dDS//PILI0eOJBgMsmPHjnz/Zl5K+Oqrr1ixYgUPPvggycWLOwLPdvKEpMgoioysKkJyRhHeOlkVEi2KdZ19j6Ja5y1ZFtGOuE+xhZvDyt2J/hRLGNqSuFGVCB7h94V4KBYPOU8esqrk4qGEPc8ZeaiK8PipSuSzOfcpkS+rf7st5x41nIeci4fo134GKXSNI/WjOIkx4e+HqirEJyZQoWJF6tWrR7eu3YiNjS3sqeRAUmRHDPqivvIQDD9w4AAxMTER+p+2927//v0R15YuXToirELXdaZNm+aoBgSDQXbu3MmyZcvo1KkT7dq1Y/DgwZw6deqsY1KlShUeeughZs+ezaeffkqNGjUYM2YMLVq0cLyLBcFZDcCyZcsyadKkPF2Uf/zxxxmt4bywdevWfLNUUlNTiY+Pp169eowaNYo77rgj4vwLL7zATz/95GRDuXDhwsWlhpYtW/Laa6/Rvn179uzZw6OPPsrrr7/Oe++9V9jU/jJ8Ph+J8YlE+aLYuGEjWkDD0ITnSg/qGJqOruni/7qQadE1HT2gYegGWlDHCIp9MNH89vWa5VWz7gnq6JrYGkEdw/o/JhhBHVMz0ANWH5qOqQv5F9PuI5yHIbxkuqajWfdoAR0jaKAHNEyEmHRuHmYED93hITJ59aCOqZlofuse+2XxCI2HaDuChzU+ekAL8TBz8AhaY6HnHgeHhyVubeqmGI98eIjnEG03bNAQ0zD5v4/+z8kqL1QocuG9ciArKyuXh83eDwQCZ3yMF198kU2bNvHkk08CkJ6e7uhEjh49miFDhvDLL7/w2GOPndPw1KpVi/vvv5+HH36YihUr8tlnnxX43rMuASclJXHllVdGHJsyZQqVKlU6qzUc7g4FIizeyZMn4/f7ad26NU8++STx8fEAvPbaazz99NMkJibmiiNs3LgxPXr04JVXXqFVq1YkJCQU+EFduHDhoqhAURS6du3K6tWrOXr0KB3ad2DlqpXs3bv3L8nDFDbat2/Phx98yJChQwBRwaRly5Z06tQJj0cViR2YjsyKLcFie808sgckUCWPKOvmUx2PlS0f6Kwfh0GSJSTdkpKRVKd8m6IqTvsipk70k5uHnC8PySovZ/Nw+j8LD9UneHhlqzSe7ZWz4/IsLjYP8Xw5eXgdHrIi580jvH8pJFbt8LBL9EV5UDxKvjxEhrJo4/nnn2f27Nm88sor1KpVi3//+9/neaacA4qQEHRUVFQuQ8/ezy8BS9d1hg4dymeffcabb77JZZddBojch2XLljkroLVq1SI5OZkePXqQnp6ey37KidOnTzN//nzmzJnD4sWLKVOmDNdffz1jxowp8COecxbwxIkTmTdvHuPHj+f48ePnZA3ntHgPHjzIiBEjeOyxxyKKFycnJ+fb/5NPPsmCBQsYOXIkL7744rnSd+HChYsiAY/HwwsvvMD69euZPXs2x48fv+SXgWNjY+l7b1+2bduGJEnExcXx1VdfMWL4CB649wGSk5Os+DWc8mmyJdosyRJaQMcX4yWQGcAb5yWYFUCN8qAHRTydU27NjLR8ZFlCD+qoPg/B7CCqTyWYHcDr9aIHNFSvihbQUbwKelBHsTQDQzws4WdNZOFqAQ1fjI9AVgBvTMF4mLph9aNF8ND8QVSfBz2goXhV0b8V5ye0D8+Rh091zkeU9wvn4VMJZouSfMHsAB6Ljz0e4TwMTUdSJEv+RvCI9kZxS4+bueG6Gxg5eiQffvghN954Y4GyzM87ilASSEpKCpmZmWRkZDjL5IcOHQLEkm9OBINBHn/8cebPn8+YMWPo2LFjxHnb+LNRvXp1QCw152UAZmRk8P333/PNN9+waNEiYmJEotW0adOoX7/+OT/iORmA48aNY+zYsTz33HO0bduWb7755pys4b9q8YIojP7MM8/wxBNP0LVr13Oh78KFCxdFCh6Ph/r163P55Zdz4sSJP53NV9RQrVo15/99+vThq6++4tMvPuX+++9Hzpk5C8imYpWRU5EVGV+cT3ipYn1OZq2UnxfIFm22Yt28MV7h+bI8Xo43zfYC5vTiAWCVgFNtHuI+X+y58/Ao3gge4bF89v8F33PjIckSXpuHJUx9Jh7eGJuHzxoPNWI8InjYsJs0xVjGqDE8NfApPv/8c1588UVuuukmpwTZRUM+y7EXpd8cSEtLIzo6mpUrVzoroytWrKBEiRJUrFgx1/XPPvssP/74IxMmTKBly5YR5+bNm8dzzz3HwoULHXtp/fr1yLKcpy30wAMPsGTJEkzTpG3btowePZo2bdr8pVKABb7z5ZdfZvr06QwePJiePXsC524Nw7lbvHmhS5cufPHFFwwePJgePXqc9frs7GxHTb0ow+/3FzaFIgV3PCLhjkck/m7jERMT85f+ThXl8Wjfvj3vvvsuPy//mdq1ajuGhqmbIus2oDveMsWnovs1ZI+CETRQvQpa0MrODeqOzArhXjNZRteFNIoeMPBGe8k4KZIIg1lBPNEetOwgsmpVC5EjvWcF4eF4D1UFXdcdiRfHGxjBQ0YP6HijvQSyAniiPYJHlAfNb/MglwFo6iaqR0EL46H6VIJ+DeWsPAzxXHb/ETw8ofHIDuLxWTw8SmgccvLwqmh+DcWrcH2nLjRp3IQvvvgCn9dHjdQa52VeZGdnn/2iImQA2oLqw4YNY8SIEQQCAUaNGkWvXr0AOH78OIqiEB8fz/fff8+MGTMYPHgwqampjm0EoiJQ48aNUVWVZ555hocffphDhw7xwgsv0L179zztp1OnTvHcc8/RqVMnJ2Tur6JABuCYMWP44IMPGDFiBN26dXOOn6s1fK4W75kwZMgQrr/+ev773/+e9dqoqKjCcV3/CVwqPC8W3PGIhDsekXDHIxJFeTwqVqzIgQMHaN68OXbQmyP8bBtRMTm2Ro6tVXUjHI5oc45rkSAqWvztl2QJMzrsvOM9+3M88hLgy49HVHQUSIR4xOTmYWILYF84HkhS5Hjk5CERJsRtbcOuSSieSKdOnfhyxpcMHDjQERP/K4iKijr7ReH6kRcT+XhYn3zySfx+P3379sXn89GjRw/69OkDwMMPP0y5cuUYMWIEc+bMAUTyR85wtVWrVpGUlMTkyZMZMWIEN998Mz6fjy5dujhJIjlREFvnXHFWA/D3339n/Pjx9OnTh5YtW0ZYsUlJSWe0hiHSIj5Xi/dMqFChAv369WPkyJGUK1funO514cKFCxcXD0eOHGH58uX0u/9BtIAmyrHJloaetZUkEcfnjfYQyAriifEQzAw63jPFowidPMsgcbxVhomiCi+h8FoJ710wO4hH9WBoIjZPD1jePC23986wq3joIhZR8MjhvXO8iIrgb1cCCfciei3vnTfMe5ctvGiGZqB6rNg/S8xZspaVw8fB4aFIZ/QiKo73zjIELR6G7b2zYx+tGMRgVmg8FGsczo2Hh5rValKpUiU++ugjevXqdVF0AiVFylOS5WL0mxd8Ph/Dhg1j2LBhuc5Nnz7d+f/IkSMZOXLkGftIS0tjypQpf4nnX8FZDcC5c+diGAYTJ05k4sSJEedmzpx5RmsYIi3ic7V4z4Z77rmHWbNmFUg3x4ULFy5cFA6++eYbmjVtRvlKFSIyVyXTFDFp1nKlrU3niRLZt55okf3qifYgyzKmXdrNaQAnk9hjVc9QfR4kRWTuKp6Qvh+ojnZeToR4hMXrWZmzkhzioUadgYchjCiPT7IymD2hNhQpxEOScvGQ8+Mh58NDysEjbAlXcXioETxUn4rqVTFkIUwtFYSHZukGyuJ98UZ7ufXWW3njjTf4/PPPuemmmy68EahIoBaCBzAfA/DvhLMagP37989Vui0n8rOGIdIihj9n8TZr1oxNmzblOq6qKjNmzDintly4cOHCxcXFqVOnSK2e6mgC2gaLaZqOVh1eLA094UVTVKvihgd0TQdVbMPr8OaMwROVPwwR4xYMaeepkvCIyaocFjt4Nh7iHkWVRSUOh4dV3eQMPCRJQrd5BDQRz5cXj5xeRCMHj8AZeCimVQkkZyyitbW9mWHjITQOBQ/bI5pnDKDDI/R+2NwVVeH+vvczbryoctGlS5cLawQWoUogfzf8+fQRFy5cuHDh4gwwTZOVK1eyfft2rrn6GlHb1pI+CY+9Uzxh2a+KjOIJZclKSkjbTvEIXT8R+yb8VaZkWtIp4n5dErImiqogIaFaXkBbD8/UTfHdbknBSPnysLJwVRkkI4yHjOyRzsoDm4fFOV8e0hl4eHLzcBJZFBnFiu0TNfVy8CAHD0sTUfUq4n3wCp1CU7J5SHnwUEHG0Q+0eSQkJvDoo48ydsxYAoEA7dq1O6N821+CKheODExh9HmR4RqALly4cOHigmDSpEkcPnyYvn36Ur5secdjJerg2vVrQ7F3hmbr4OkhPbxc9XNz6O9ZniunjbCtXe0Ca19GduoJS2H1c3PGABqaLvY140/yiHwmoAA8ZKdGbN48pBAP3RBOOzMsBtDhYebNwx4P21NaUB6mFPl+WDxiY2J58P4H+ezLz3j11VeJiYnhhhtuoH79+ufXIygXUhaw7BqALly4cOHCxTnjxIkT/P777wx/eTg+nzdC9kRksIpdSZaQTCsWzbJjbIFkUbEiVL/WDFvyJMz2kRA1b01Ats7LVv1fwzaOLGdZuA6gbGWX2kkQkik5y7NCM9AyVtVz4SGqjPw5HuIeyciLByEeVqwkFg/y4qHkwUOJ5JFTFzEvHooa/r6IfmweCYkJ9P53bwxdZ9Pmzfzvs/+xZcsWevTogRxmQJ06dYpvv/mWDZs24PP66HJ9F2rVqlWgeSTJ4nWxURh9Xmy4BqALFy5cFAFomsHIkUt4//3VpKcfJzHRR8eOVRk2rB3Vqp2f5bVgUGfSpFU8+KAQ8x0yZCGzZm1mxYp7z0v7M2fOZM6cOdx9990sXbqU1q1bo6oqphVnJyE58XYQir0zDRMUeGrgd6z8dR/ffdMTQzYdD5tpmCCbGKYpwtQch1cohs40hIdt6/ajvDZyGXPnbuX4cT9VqxbjvnsbcW/fBmfmYfdjQvXUtxkwoDkP3NsQSTat/m2+IR5OO1gxiHZ1DhnhTcPaWs8oKXZ/RPTv8LDGwRkX08A07bg+zomH3W/41u5DUnDG8Ww8DMMUPMibh31/zdSaDHhsAG+OfZOhQ4eSmZlJ8eLFSU5OZsuWLTSo14CePXty5NARpk6dStu2bZ2yaGeEIhWSDqAbA+jChQsXLi4CBg36ns8+28DYsZ2oXbsUBw6cZsiQH2jd+n1+/70fxYoVQDPtLPjww98YPHiBYwA+8UQLHn646V9uF2DXrl0sXbqU1BqpzPl6DvXr1efazteGGRfhLiopQgPP+o/9DyagyDK6pgljw7k9lHgR0Z4pPGRrVh/g6mv+y3Wda/DfqddTvmIyC+bv4MmB37N161FGjbw6bx5myNgMz1I2AUmWQdPCjLK8eZiAZO0qsowpGZh6mJGZo21sQWqrX6HFZ/MIPXOIhwSaMMbCeYjrcvMQnlYZSTLCjDzT4eHkfNjjYSFfHkbePMRBcX90dDQ3dbuRX1evptM1nTh27BhHjhyhW9duJCUWQ1YVyqWUo2LlivzfR//H/Pnzzz6x3CXgCwbXAHThwoWLIoDJk3/lzTev4brrUgGoXLkY//vfzZQuPZIvv9zI3XfX/8t9mGbkflycl7g4b94XFxCZmZmsX7+ehQsX0rZtW9q3aY/skTGCIl4sJEQsCxFnJbS0KVmyzk6JNUkcsYWJxXGQTcmyMSTnPmTrblkkiGDA3ffM4Pouqbz/flcyT2cSEx9DlUrFiIn10uvuGTxwf2Oq10iO4CFJiDaQwngIQ0g+Bx6yxUMy7PtzlFyzZGps4xHFGg9HyiacR9hSbw4eyJJIO7H7tzN4ZdGuTB48JMnqX8qHR8hAzclDlmVMxRRJMLq1ROzwkHLxSEurRa3LLsM0TJKLJ1OlchUUjyyquFj9lyhRggfue4CP//fx2ZU8zlR+70KiMPq8yPj7m7guXLhwcQlAliXmz09Hs5IGAGJjvaxefT833pgGiGXiwYMXULXqGDyeYZQuPZLHHvsGXRf3DBmykJtu+pjHH59LcvKrJCW9yoABczEMk4UL07nnnhkcOZKFJA1l4cJ0hgxZSOPGIX3Xjz76jQYNJhAV9RIJCcPp2vX/OHDgtHN+/vwd1K8/nujol2nefAI9erxNzZrDWfHLChrUa0BCXCpXXfNfihV7lQqV3uTpZ77Hnx3ENEwMTUfXdUwnGcFkzpwt1Ks/gfjEV7nhho84flyUBjN04V36+ec/uLLNVOISX6Vm2jhee20xumZgGKINXdcxNB3TMFm2bA/r1x/i8f7NRX+GjhEU195y82V8M/s2KlRIwNB0FM8wZs3cLBIjDJMFC3ageIZx6qRfeB1Nky1bjtLyyinExLzClW2nsnnTEQxDLIlu3nyYHj0+pUSpkcTEDqdOvXeY+dVGq1/xbIYuPG+GHuKBKZJBTFNIyei67lxr2PdY9+uaLuRgTNDD7hHXm6G+9LDx0HR0Tbf6NcAQ/Yd46PnwMM6NR9j1Ng/D5mHk5mHoYeOhWe+ZadLyisj6uHlCkQrv9TeHawC6cOHCRRHA449fwZQpqylf/g3uvvtLpk5dzYEDp6lePZnERLH8+9pri5k2bQ3Tpt3Ili0P8/rrVzFu3C989VVIJ3XWrM2cOhVg6dLejBp1NWPGLGf27M20aFGBN9+8huTkaPbte5wWLSpE9L9kyW7uvnsGTz3Vgk2bHuLLL//F6tX7eeWVnwDYseMY1133IS1alOTpp4sRG7uTL788QsWKFbn/gfupUKEeHa/+kLS0Eixd+m/Gj+/CBx/8xuChPwpRY1VBURVnu3XbMW7q8Sndu9di5Yq+tGhZgSlT14AEqkfh0OEMrrvhY667rgarV93Lm292YvzEVbwxehmK1YbdnqzIrP3tEPHxXmpfXgpZlVFUFdkjo6gKHq9K+w5ViY7xCmkUhBdLse6VrS97O8lDkiQmTlpJ3z4NWLmyL+XLJ3DVNR+g6cKbeWP3T4lP8LFk8T2sWtmXunVL0/f+r9F002lT8djbEA97HMQ2dCy0lZFU2eGhehSQxXhIkoTiVVAsoWpZkVGU0HhGjIks+pfs/m0+HuXP87DeFyEHozoc7Pvyejn9qmE8vAqyIs6r3rMvQkqWCPVFf/0DloD//k/owoULF5cAnn66Ff/7381cfnkpPvzwN+6+ewblyr3Bo4/OcTx8l11Wkvff70qrVhWpXLkYd91Vj8suK8m6dQeddqKjPbz9dmdq1izBv//dgHr1SrNs2R68XoXExCgkCVJS4vB6lYj+fT6FSZOu57bb6lCpUjHat69Ct241WbdOlP+cNGkV5cp5SUxcQZvWtfnq80HcdGMtVEUIBI9/5xcqVy7GG691oFZaCTp1rMyokVfx1ls/c+JYpuX5MdCDGrqm8+67q6hbpxTPP9ea6lUSeeqJFnRoX8URbx731s80blSGp55oQZVKCXS6uirDhrRh1BtL0YM6elDD0AzhWQzqHD2SSUKCD80fxNRFG6ZpovmDGIaB5g+iBTQMTQcIecyCOnpQjK/u1zANEVP373vqc+ftl5NaPZn/jO3E8ePZzPhyI6dP+el1V11GvtqBGtWSSK2exIDHmnP4cCZ795xE13S0gBbi4Rce0Lx4iL51h4cW0Kx7LO5B3XkW+1pDN5ytFtSccYgYD+sV4mGgBfQQD0sQWg/qZ+VhGEZo3+GhYWphPAJhPALWVgu1qQU0gtn284fel/1795/9g+F6AC8Y3BhAFy5cuCgi6N79Mrp3v4zTpwMsXJjOtGlrGDv2Z8qUiefpp1vRrVsaP/yQzjPPfMemTUdYs+YA27cfc5aIASpVSoww7hISfAQC+ln7btSoLAkJPl566Uc2bjzM+vWH+O23A7RqVZFgMMj8+euJjz/F0wOfJiEhAdM0adGyIl9+sRHVq7Jxy1GaNy+PL8YLkihf1vrKSgSDBjt2nqRevdJW4oTwsP2+4TANG5UVosuSKHnWvHl5flq0C0+Ul/UbDrPwh50kJr3qcDQMk6wsjROnAiQnR0e0V7J0HMePZ+OJ8oAMHp+KLFtl5RQZT5RXCB9bgZCyKkSZTUxHeFn1eRyJk+ZXVMAT5QUZkkvGUaNGMpu3HuXWYtE88kgzPvxoHb/+up9NGw+zctU+QVAWJeiQQFFkwcPpPzcPSZIwMcNi8ISHzSNZZeTscnKSR8SkqYoVjxgZXyiygOXQeISdEzxEaTg5nIfksXjIDg+wQvly8JCt9ycXDyWsL4h4P+S8eFjzIiMrk+nTp7N+/fqzfyhkCice7x/gHvsHPKILFy5cFG2sXXuARx+d4+zHxXnp0iWVTz65mVtvrc0332wFYNiwH+jW7WMCAZ1u3dKYPft26tdPiWgrp2cPcid/5IX583dQp847IvatZQXeeec6HnywCadOneKFF14gKyuDtJq1iPFFE/QHhUfHjvvSDDxWhYpAVhA9oBPMDqJZhmcwKDxAQX8QPaAR9AfBNNEtj5EWCKL5NRRJpKcGMv1omkG3G1JZuaIvy3+6m1+W/5sVy3vz+7r7iYlSnHaC2UF0TadBnZJkZARZ8+s+DN2w+hf9BrKCdLr2v8z7divB7CAAhmZYfHT8WeKYzUMIPQsepmESyPRj6CaKDCePZdL8islMGL+SMqVjeahfYz79uLvVpk4wK4Cpi3EwdINAZsDhoQd10aemoznjoRPM1tADOppfixiPQKZftJUZEDwy/Bia1aZfE543vxgHzR6PrEAuHqY9Hv4cPII6mj/EQ3N4iPdJ84fzMM7MI5AXD3GNoRsELD4/LvyR4cOHU6ZMGZ544omzzkt3CfjC4bw/4f79+3nkkUdo1qwZLVu2ZNCgQZw8eRKAYDDIiy++SLNmzWjWrBmvvfYauh76ZTp79mzatWvH7bffzr594hfVnj17qFmzJi+++GKuvhYsWEDNmjXP9yO4cOHCxUWFrhuMHfszP/yQnutcQoKPkiVjAXjrrZ8ZNepqRo26hrvuqkeVKsXYufN4REWKM+FMBRrGj1/BLbfUZurUbjzwQBOaNy/PihXbOXz4CH369KFLl6Zs2nwSxaei+lQ8UR5W/bpfZL8qErVrl+Lnn/fiifaI89Felv28F49HpnqN4niixHHFp6J6VerWK83KVfuQPQqqT5xbs+4QSOCN9XHZZSXZsu0YNdNKUqtuCjVrlWLT1mO89MoivNGeCB6yKtOkeQXq1CnFmHErRNxftBfVK3h89MnvzPtuBxUqJeKJ8uDxyJzODKJ6VRSvws49pwAcHpIk8du6g3hjfSiqQkbAYMvWo1xeJ4Wfluxh8+aj/PjT3Tz7/JV0vbEWR46J5BUUCU+0F0mR8MZ4UVQFb6zP4aF4FIevao+HV8mxVR0e3lgfsio7PJz9GK+43qs642C/RP9yJA+POG6fd3h4ZNGXN9S/7BXvh+JVHY6iXyWCh9O2zTtPHuIaWZHxxvg4fvI4s76ZTd++fbnxxhvxeDxnn7OKVGivvzvOqwFoGAb9+vUjMzOTadOm8c4777Bx40YGDhwIwBtvvMHSpUuZOHEio0eP5quvvuLdd98FwO/3M378eEaPHs0NN9zA2LFjI9r+6KOPWLNmzfmk68KFCxdFAg0alOGmm2px882fMnHiSrZtO8rq1fsZOXIJH3zwGwMGNAcgOTma2bO3sHXrUVat2sctt/yPY8ey8fvPvsQLwrN4+nSA338/RHa2FnEuOTman3/+g08/XcSKFTvo1WsaS5ceJjm5JOVKleXfverx+++HePaZ79mw/iD/GfczH3+8HtM00QMavXvVZceOYzz04Gx+//0gMz7/nSee+Jaet11OfLRqeZWEl0kLaPy7Vz22bDnKU0/OY/2a/bw1djmzZ28RXsQMP/f2bsCWLUd5uN9s1q/Zx7y52+j34NfEx3kxNSOHt0p4HN8ecw2ffvo799w9g5+X7mbjhkOMem0x/fp9zRP9m1G9cjE0f5Amjcsy/p0VrPvtAPPmbmXUyCUAwluliWzVt9/+hWlTfmX1r3vpddeXVK6UyFXtKlIsIYpAQOej/65l+5ajfPbJOgb0/xaArIyA8LAFdPynsjF1E/+pbAzdEFvNIJDhR/drlrcvNB6aXxNes6Du8Ahk+NGDGv4Mv/BkZgaE9y4rgBG0vHr2OGQHna0R1EM8TmdjaLrjifOf9ofatryOWkDLzcPy3ulB3eKh4z8teNjnHR5ZuXkE8+CxdetWKlasSJUqVQr+4bAkZwrl9TfHeY0B3LBhA+vWrWPRokWULFkSgGeffZbbb7+dQ4cO8dFHH/Hmm29Sr149AB5//HFGjRpF3759CQaDREVFkZoqNLCWLFkS0XaZMmV4/vnn+fzzz1FVN3TRhQsXfy98+OFNvP76EsaMWc5jj32Dosg0a1aOb77pyRVXiIzdKVO68eCDs6lT5x1KloyhW7c0evduwMqV+wrUR4cOVWnQoAwNGkzgww9vijg3dGhbevb8lNtvn4eqQrVqXp54vA4TJm5Cl2SqVE/ms//dwpNPzeONN5fTvHk57rijDn/8cQrV56FKjeJ8/fXtPPXUdzRsOIkSJWLo1asezz/XGsWnOhU47IC36jVLMOfr2+k/4FveHvcLV1xRngcfbMLq1fvxxHipXC2Kr2ffxjOD5tOkxfskJ0dz222X8/Ir7Z2YPQDTFJmohm7Qum0VFi64i+EjFnNrz684fTpIjerJvPnmNdxzdz1RHs00eeuta7n/gdk0aTaZyy4rwfARHbj55v/hiQnFAD75ZAveHPsLGzYepmXLCsz46l/4Yn20bF2Rl4a147kXfuDkyW9JTU3mtdc7cv/9s+nU+SP27TtNrVol5FHIRAAAG3ZJREFUeObpltzesy7eOB+SLOGN84kYvJiwGMBwHT8RfIcky3gUoZfnsb2Iliagk6Ush/ZDAYRiTAxFwjAN4c2zrhEeQRHz5431npWHadUE9sR4HH1D4fkT9xqqOHbk2AkOHDjA6dOnOXr0KLt372b37t2cPn0aj8dDQkIC1atXJyUlhaysLH788Ud69+59Tp8Lewn4YuOfsAQsmQVdOygAjh07xm+//caVV17pHFu5ciW33347H3/8MbfeeisrV64kLi4OgN27d9OxY0fmzp1L5cqVGT16NO+++y5xcXFMnjyZyy+/nD179tChQwfGjRvHgAEDeOihh7j3XlG2aMGCBdx///1s2rQpTz72vd9//z3ly5c/X495wZCVlUV0dHRh0ygycMcjEu54RMIdj0icj/E4duwYL730Ejff2IPGzZqgZWsoHgXTNB2vYaNGZZzybQ89Nhe/X+O9yV3R/EEUn4qWraF6FHRNR/V50AKiDUPTHYFn0xQ1ew3TRJYlDEteRQ9qeKO9BDIDeGK9BDODeKI9BLOCDg+7codtsJiGSOLQgjqqV3V4ZJzIICY+BkMzUL0KWkDPwcOq8ytLGEZePHwEsvx4or0Es4J4ojwEswIoXjUXj//7eD2Dh/zApAldaN6kDMtW7KfvvTMZ9mI7enRNRfGqGJrgpwd15DPyEMaspEjogXx4ZAdRPYooBecYgLBv/z7e/s84Eosl8sSjj4vx9wdRfSqnT2QQExeDoRvOOMiqgqEZIEEwEODw0SMcPHiAffv2c+z4MRLiEyhdujQyEmvWrwUDSpUuxd69e9m1axeSJFGhYgXi4uIoVqwYFSpUoGLFiiQkJKBpGkeOHGHr1q0cPnwYwzC48sorKVOmjDPfzvQdbZ/7dNxHlCkVGed6MbDv4H5u7nfbJWM//BmcV1daUlJShPEHMGXKFCpVqsSBAweIiYlxjD/A8RLu37+fypUr079/f+655x5iYmLweiPV6StWrMgDDzzAuHHjuPbaa6lQIVLDyoULFy5c/Hns2rWLd955h2uuuYbGzZqgqApStPA0mbpB+u6T3HXXl3z4wU3UvrwkP/+8l4/+bz2ffNxDaMXZGatRHiuGytKQ84Y04wgzVgBkuxybYoY8WrKEGuVBkiQ80R4RPxYtMldN3YgMZLTakyQJj1Xhwuah+lShQSfbPCRkRcqXh6TISLoRxgORhSuHeHhivKIyhmHxsDxmr76+lHcn3UD79lUwDYP27aswaeL1PNZ/Lrf963JkVcZwxkDEl8l5eLUkU4yDpEuhMcuDhzfaI6p0GMIA3bRpE0uXLWX9+vWOU8WUhdZh+HioPhVDMxytQ0mRWLN2DXO+mcOhQ4dISkqiTJkypKSkUK16NY4fP87GzRvJzMykQYMGeL1eDh06xBUtruC2228jKSnJKUeXE16vl5iYmL/8XS1s5Iu/HFsIXV50XNC11IkTJzJv3jzGjx/P8ePHcxl19n4gEHCOFStWLN/2+vTpw+zZsxkyZAiTJ0++IJxduHDh4p+GYDDIu+++y/XXXU+zpk1F2S4kUQHCA7qm06VzDQY+1YKHHprD/gOnqVy5GGPevIaO7SqhBTWMoI4nyovmF1472xNnWjF1pmGG6ueGQZYldM0Q/QQ0FI/wkiGBEdTB2lc8ImvYXsa1DT/TFF47UzdAlkI8AhpmjNCyU7yKqHxhyCH5lTAepmmimKAHNfCqaH4t5K2TwAha/IIapuU1C/Ew2bDhMFc0K4OuCV08SZZo1iiFDRsOE8wOGw+viqEbyEaYERmqyivqHnsKwEPTURTT6Wva9GnUrl2bJk2a0LhxY7799luC/gCqoqCHjYet2ad4FAxdZ+6cb/nll1+47fbbSEtLKxRD62worISMf0ISyAUzAMeNG8fYsWN57rnnaNu2Ld98802EoQchw6+gyxYej4ehQ4fSs2dPZs6cGeFNPBOys7PJyso6twcoBPj9/sKmUKTgjkck3PGIhDsekfiz42GaJrNmzqJ69erUqVMHf1AkC8gI4WbZFMaKZMj0f6wxjz3W2NKdE4ZXQAtaNX8N9ICBoevofqs0WUCUCpP0fHTvEEac6E8O9asb6AHd2Tc1A82URVuGWHoVZlOYAWiYETw0Q8Pv9zvCyY4BikkwEGDXnt3s2rWL9PR0Tpw4QTAYRFEUEhISiI+PJzExkeTkZMqXK0/JkiXxIpJPJNNqyxQeQN3QqVkzmYU/ptO2bSUxVorMT0t2UrNmcTRDw7DHJWiNh7V6HB53J0niGTRTCCnnGgd7awpPqGbITp3l5ORk1q9fT82aNXn77be59tprkVWZgC7El+3xOHrsKLt37SYQDLBlyxYOHz7MI48+QmxsLNnZ2edlHp4LCtKnpIiqJBcbhRF3eLFxQQzAl19+menTpzN48GB69uwJQEpKCpmZmWRkZBAbKyQNDh0SCvOlS5cucNuNGjXilltuYfjw4Tz11FMFuicqKuqSiRW6VHheLLjjEQl3PCLhjkck/sx4fPzxx2zbuo1HHnkEr+Jx4tUUVUWXdbF8qRkiRs0UxptkLYNKsixiyTwKuqSjqAo6OqpPRZM0ZFUYK7KiONeHPF+hZANDN8S9koasKuhBLYyHgi4bDg9JltB1nazMTDxeLx5VRVGtaz0KmqQT1INs2bqFLVu2cOzYMSfBwjBMjh0/xokTJyhfvjw1a9bk6quvpnTp0ni9XgKBACdOnODYsWMcP36cffv2sXTpUg4ePEj58uWpUqkKVatVxefzsXTZUtJ3pHP8xHEqVvBxx52f8NBDFWndsgLr1p/itdc3ceed5Vj+83Lq1qtLYlxiaDxUBcMwkC1PpGniGLGyLKHLBqpHjRwPj2qNkyw8oapMdlY27777Ljt27ADg559/BuC7ed/RtnVbVI+KhvD4BZUgo94cRfHk4sQnxFOpUiVuueUWfD7f+Zp+54yoqKizX6TI4nWx4RqA544xY8bwwQcfMGLECLp16+YcT0tLIzo6mpUrVzpxgitWrKBEiRJUrFjxnPp44oknmD9/Pm+99db5pO7ChQsX/yicOHGCH374gRdfGEpMbAy6X0OSpbBVSdPKTBWyGJIRWnY1Ecu3yJKIJ7MSPMKrXMiyjG7YHjosb53seO0wCLvHQNd11qxfy5FDRyhdpjTxsXH8vnEDe/fu5fTp084rKyuLqKgodF0nEAgQFRXl/NCXZZlDhw5x+eWXU716dRo0bIAshypVFCtWjBIlSuSpQRcdHU1iYmKu76Ts7GzS09PZvn07i5cs5vTp0zRp0oQuXbo4cXDjxv3Im2+u5KWXdlGunJdbby1Fy5YJ7D+4n29e/4b69etzw/U3EOWLcpZaTaxsUzupRDLFeJpiPEysJVBNEuOlm47xvHv3bka/ORpZllFVlWLFinHq1Cn8fj+nM05HvB+SDNt2bMXn8/HoY49eqOl0YSAVUjze338F+PwagL///jvjx4+nT58+tGzZ0vHwgUgQ6dGjB8OGDWPEiBEEAgFGjRpFr169zrmfhIQEBg0aRP/+/c8nfRcuXLj4R8AwDGbNmsWqVau4+uqrSYhPOPtN+ehFmKZJVlYWqqyCAek700lKTmLP7j2UKVuG5KRk5/YzfaeePHmSKVOnEtSCVKpYic1bN3Pi+AmqVa9G06ZNiYuLc16xsbGOUWcYhhPmk52djaZppKSkYBjGefMQR0VFkZaWRlpaWr7XPPZYBx57rEOe52688UZmzZrFK8NfoftN3WlQv/5f5vT9/O8JBkUFk2rVqpGRkUGJEiVISkpi27ZtfPTxR8TFx1ErtRbbd25n1apV9Oje4y/3e7HhysBcOJxXA3Du3LkYhsHEiROZOHFixLmZM2fy5JNP4vf76du3Lz6fjx49etCnT58/1Vfnzp2ZMWMGCxcuPA/MXbhw4eKfg82bN7No0SLuvvNuaqbVxNB1x8CzZUHEvuTE6NllA+ylXEyTdevW8cmnn3Dq1Cl0XSc6OhpVVTlx4gRVqlTh6NGjJCQk0KxpM6pWqYLPF0W2P5tAIEDAH8Dv9xMI+tm9Zw8rVqzgyiuv5LrrrnOMu4JAlmViYmKIiYmJOF6U4r5jYmK45ZZbqF+/Pp988gnfffcdtWvX5vTp0zRq2MgRRjZNOHHsOPPmzSOtVhqVylckToknIzMT/wk/WRlZZPkz2Zm+iy1btjjtly9fnl9++YU2bdqwfPlysrKyWLZ8GWXKlGHHjh1UqFCB2267jerVqxfWEPxp2BnlhdHv3x3nVQewqMHVAby04Y5HJNzxiIQ7HpE4l/F49dVX6dChA/Xr1ndi6xRVZIzKiizi0+zYP1t+RRaZC5quo2sahw8fYcRrIwB4/vnnKVGiBFu2bKFixYooikJ0dDSGYbB582aWLVvG7l27CWpBvF4vUT4fXq8Pr9eLL8pHuXLlaNiwISVKlCiU8biYMAyDX1f+ys5dO4mOjmbp0qVkZGYQHx+PruucPHmSRg0bcfz4cXbu3Em2P5soXxRR0VFER0UTHRNN+XLlqVCxAlWrViUlJYUDBw7w/fffExMTQ/HixdE0jebNm0c8f1Ecj4LoAM746AvKppS96Nz27t9L19tuvGTshz+Df0RJjT/++KOwKRQI2dnZBQuK/YfAHY9IuOMRCXc8IlHQ8TBNk82bN9Pt+q7s2b0HSRZev4zsTA4fOERsQhyGppORlckfe/awZ+8f/PHHH/j9fjRNc2LO4uPjqV+/PhUqVCAQCHDgwAESEhI4fvx4RH9xcXF07NixQPz37NnzZx8/z/aK6vwoXaY0pcuI5Mc6des4CZKSJJGYmOjEJ9r+mfzkWTRNc8YspwbvkSNHIvaL4njs37//rNccOnK4UJZjDx05fNH7vNj4WxuACQkiruWuu+4qZCYuXLhwUbSwaNGiwqbgwgWJiYl5SrrFxcWRmJhIn4f6FgIrgfy4/V3wt14CBhFYfPLkycKm4cKFCxcuXLjIAbuMXF44fvw4p0+fvriEwnAmbn8H/O0NQBcuXLhw4cKFCxeR+PvnObtw4cKFCxcuXLiIgGsAunDhwoULFy5c/MPgGoDnEadPn2bQoEE0b96c5s2bM3DgQFGGKAfeeOMN2rdvH3Fs9uzZtGvXjttvv519+/ah6zqNGjXigw8+iLju448/pmbNmowdOzbi+MyZM6lbt26uessXA2d67v379/PII4/QrFkzWrZsyaBBg84Yk/n1119Ts2bNiFeXLl2c81OmTOHKK6/kvvvu49SpUxw9epSaNWvm0oN84403qFmzJp999lnE8fHjx9OhQ95irRcaBZkfgUCA66+/ngULFkQcv1Tnx+eff57r/bRfv/zyC8ePH+eJJ56gWbNmtG7dmrfffhvDMPJt7+86P842TmebO3/X+aHrOqNGjaJly5Y0btyY/v3758oyDsc/dX6sXbs21/EGDRo491+q88PFhYVrAJ5HDB06lC1btjB58mQmT57Mli1beP755yOuWbduHZMnT4445vf7GT9+PKNHj+aGG25g7NixKIpCo0aNWLNmTcS1S5cupXTp0ixdujTi+K+//kqDBg3wer0X5uHOgPye2zAM+vXrR2ZmJtOmTeOdd95h48aNDBw4MN+2tm3bRqtWrVi0aJHzmj59OiB0oebMmcM777zDZZddxtSpU0lOTqZ69ernNE7NmjU7/4NQAJxtfmRnZ/PYY4+xefPmiPsu5fnRuXPniPdy0aJFtG7dmvr169OgQQMefvhhNm7cyDvvvMO4ceP49ttvGT16dL7t/V3nx9nG6Uxz5+88P0aNGsUXX3zB66+/zvTp00lPT2fw4MH5tvdPnR9bt26levXqEee/++474NKeHy4uLFwD8Dxi/vz59OrVi9q1a1O7dm3uueceFi9e7JwPBAI888wzEb/MAILBIFFRUaSmppKWlkZGRgYATZs2jfiAmqbJ8uXL6d27N2vXro3IjipMwya/596wYQPr1q1j+PDh1KxZk7p16/Lss88yf/78fL2AW7ZsITU1lZIlSzqvpKQkADIzM0lOTiY1NZXq1avnO06nTp1i/fr19O7dm2XLlkW0v3r16iI3TgDr16+nR48eeWpWXsrzIyoqKuK9XL16NcuXL2fkyJFs2rSJn3/+mddff52GDRtSt25dhg0bxpQpU/Kt4vB3nR9nGidVVc84d/6u8yM7O5vp06czZMgQWrRoQa1atRg4cCCbN292SqDlxD91fmzZsoVq1apFXFO8eHHg0p4fLi4sXAPwPCIpKYlZs2Zx6tQpTp8+zaxZs7j88sud8+PGjaNixYp06tQp4r64uDhatGhBkyZNuO+++7j33nsB8QFNT093ljw2bdpEZmYm//rXv4iLi2PFihWA+MO2adMmmjdvfnEeNAfye+6yZcsyadIkSpYsmesev9+fZ1tbt26latWqeZ5LTU0lPj6eevXqMWrUKO644w4AmjRpwtq1ax3R1OXLl1O2bFm6devGkSNHnJJJ27Zt4/jx40VunACWLVtGhw4d+Pjjj3Pdd6nPDxuapjFy5EjuueceKlSowK5du4iKiqJWrVrONTVr1iQQCPDbb7/l2cbfeX7YyDlOcOa583edHytWrEBRFNq0aeNc07x5c7755htHKDkn/qnz40zP/XeZHy7OP1wD8DzixRdfZN26dTRp0oTGjRuzfft2Zzlr/fr1fPLJJwwZMiTPe/v378/ixYv56aefnD/stWvXJjY21vmVtmTJEho1aoTP56Np06YsWbIEgDVr1uDz+ahTp86Ff8g8kN9zJyUl5VKnnzJlCpUqVcrTKAwGg+zcuZNly5bRqVMn2rVrx+DBgzl16pRzzWuvvcaiRYuYN28e5cqVA6BZs2acOnWK7du3A2IZ44orriAxMZFatWo54/Trr79SuXJlSpcufaGG4ow40/zo3bs3/fv3z1ep/1KeHzbmzp3LwYMH6d27NwAlSpQgOzubo0ePOtfYHtDwYzb+7vPDRs5xgjPPHfh7zo+dO3eSkpLC999/zw033EDr1q0ZNGhQxPsdjn/y/Ni6dSubN292xmnAgAEcPHjQOf93mB8uzj9cA/A8YseOHVSpUoXp06fz/vvvExUVxaBBgwgEAjz99NM89dRTeRo+NooVKxYRY2HHaaxduxYQf5jsX2HNmzd34jRWrVpFo0aN8v1VfKGR33PnxMSJE5k3b16e5wDS09PRNA1VVRk9ejRDhgzhl19+4bHHHou4Ljk5GUVRnP3ixYtTrVo15w+Z/QccxB/38HEqzF+xBR2n/HCpzg8bH374ITfddBOJiYkA1KtXjwoVKvDCCy9w4sQJjh07xiuvvIKqqnku8f3d54eNnOMEBZs7f7f5kZGRwcGDBxk/fjzPPPMMo0aN4vfff2fAgAF53v9PnR8ZGRns27ePQCDASy+9xKhRo9i7dy+9e/eO+Bxd6vPDxQWA6eK8YOfOnWZaWpq5Y8cO59iWLVvM1NRUs3fv3mbv3r2d49OnTzfbtWtXoHYnTJhg9u7d2wwEAmb9+vXNtWvXmqZpmlu3bjVr1qxpnjhxwuzdu7c5ceLE8/o8BcWZnnvNmjXOsbfffttMTU01p02bdsb2jh49GrG/du1aMzU1NaL9vDB48GBz8ODB5oEDB8yaNWuahw8fNk3TNBcuXGg2bdrUNAzD7NSpkzl79uxze8DzhIKOk2maZmpqqjl//vwCtVvU54eN/fv35/msGzZsMK+99lqzZs2aZv369c13333XvOKKK/J9/r/r/LCR1zidy9zJiUt5fkyYMMFMTU01N2zY4Bxbv369mZqaaqanp+fZzj9xfpimaZ48edIMBoPO/qFDh8y0tDRz0aJFZ2zvUpkfLi4MXA/gecK6devwer1UrlzZOVa9enWioqLYvn07y5cvp0GDBjRo0IARI0awd+9eGjRo4MRZ5IdmzZrx22+/sWbNGlRVpXbt2gBUq1aNEiVKsGrVKtauXVtov0zP9Nx2kfKXX36Zt956i8GDB3PnnXeesT07YDu8LYADBw6c8T57nJYtW0ZqaqoTAN24cWMyMjJYtWoV6enpRXqc/gyK+vyw8eOPP1KmTBnq1q0bcTwtLY2vv/6axYsXs2zZMm677TaOHDnixDblxN91ftjIa5z+yty5lOdHqVKlgNB7DDhxbnv37s2znX/i/ACIj49HVVVnv0SJEhQrVqzAz13U54eLCwPXADxPKF26NNnZ2aSnpzvH9uzZQ3Z2NiNGjGDWrFl8+eWXfPnll9x3332UKlWKL7/8MiJJJC/Url2bQCDAzJkzadasGbIcesuaNWvGnDlzMAyDyy677EI92hlxpueuWLEiY8aM4YMPPmDEiBH07NnzjG3NmzePZs2aRWSArl+/HlmWI7788kLTpk3ZsmVLxPINQGxsLJdffjmffPIJ1atXJzk5+U8951/F2cbpz6Kozw8bv/76K40bN444duLECW6//Xb27t1L8eLF8fl8LFy4kJIlS1KtWrVcbfyd54eNvMbpr8ydS3l+NGrUCIDff//dOWYnZJQvXz5XG//U+fHbb7/RoEEDdu/e7Rzbu3cvR48ezfNzFI5LZX64uDBwDcDzhHr16lG7dm2eeeYZ1q9fz7p163jiiSdo2rQpTZs2pVKlSs4rKSkJVVWpVKlSvkH/NlRVpWHDhnz11VcRf5hAxGnMnTuXJk2aRMS0XEyc6bllWWb8+PH07t2bli1bcujQIeelaRogin3bQdqNGzdGVf+/nTtmTR0Kwzj+CLrq4qLi4ODsINQiYlfHggidFEScaqljCQgOgouQoYsBv4eunRxLcdDBwQ8guKjYwXbojVxpr73gLV45/x9kyYEDeXkIb06S49XDw4Om06mGw6Esy1I+n//2w+tgMKhIJKJ+v//HOp1yG4NDdfruIeCQ/z0frslkong8vncuEAhos9mo3W5rNpvp6elJzWZTt7e38ng8kszJh+urOh2TnXPORzQaVS6Xk2VZen5+1mg0UqPR0NXV1W6FmHx8rKKHQiFZlqXxeKyXlxfd398rnU4rkUgcnO9c8oGfQQP4j3i9XnW7XYXDYVUqFVWrVcViMT0+Ph49dyqV0mq1+rQMf3l5qfV6fdIb06HrHgwG2m63chxHmUxm73D/uKvVamq1WpI+Xt/0ej0tFgsVCgXV63Vls9mDG7/+7uLiQq+vr5+ekN06nfI1hqn5cM3n872fGly2bWu5XOr6+lrNZlN3d3e6ubnZjZuSD9dXdTo2O+ecj3a7rWQyqWq1qlKppHg8rk6nsxsnH5LP55PjOPL7/SoWiyqXy4rFYrJt+6/mPId84Gd43t5+bX4EAAAAI7ACCAAAYBgaQAAAAMPQAAIAABiGBhAAAMAwNIAAAACGoQEEAAAwDA0gAACAYWgAAQAADEMDCAAAYJh3ppJaZi0D6HkAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 648x936 with 2 Axes>"
       ]
@@ -696,8 +839,8 @@
    "id": "2294a2ae-b312-483e-bab7-f8c30469d6be",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:43.739566Z",
-     "start_time": "2022-01-10T20:10:43.529732Z"
+     "end_time": "2022-07-07T13:13:51.141418Z",
+     "start_time": "2022-07-07T13:13:50.933853Z"
     }
    },
    "outputs": [
@@ -747,8 +890,12 @@
     "> For each sub-sample, n_ev events are sampled with replacement. HE is the value of the seed for  the uniform random number generator.\n",
     "- HI: scale the intensity of all events (homogeneously)\n",
     "> The instensity of all events is multiplied by a number sampled uniformly from a distribution with (min, max) = bounds_int\n",
+    "- HA: scale the fraction of all events (homogeneously)\n",
+    "> The fraction of all events is multiplied by a number sampled uniformly from a distribution with (min, max) = bounds_frac\n",
     "- HF: scale the frequency of all events (homogeneously)\n",
     "> The frequency of all events is multiplied by a number sampled uniformly from a distribution with (min, max) = bounds_freq\n",
+    "- HL: sample uniformly from hazard list\n",
+    "> From the provided list of hazard is elements are uniformly sampled. For example, Hazards outputs from dynamical models for different input factors.\n",
     "\n",
     "If a bounds is None, this parameter is assumed to have no uncertainty."
    ]
@@ -759,8 +906,8 @@
    "id": "21876972-5120-44ce-81f3-6bb52a32cc97",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:43.765948Z",
-     "start_time": "2022-01-10T20:10:43.741273Z"
+     "end_time": "2022-07-07T13:13:51.170646Z",
+     "start_time": "2022-07-07T13:13:51.143728Z"
     }
    },
    "outputs": [
@@ -768,7 +915,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2022-01-10 21:10:43,742 - climada.hazard.base - INFO - Reading /Users/ckropf/climada/demo/data/tc_fl_1990_2004.h5\n"
+      "2022-07-07 15:13:51,145 - climada.hazard.base - INFO - Reading /Users/ckropf/climada/demo/data/tc_fl_1990_2004.h5\n"
      ]
     }
    ],
@@ -785,8 +932,8 @@
    "id": "0adedfaf-bd2b-4ec4-9d28-28c0c69d56c4",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:43.772087Z",
-     "start_time": "2022-01-10T20:10:43.767951Z"
+     "end_time": "2022-07-07T13:13:51.177209Z",
+     "start_time": "2022-07-07T13:13:51.173049Z"
     }
    },
    "outputs": [],
@@ -795,7 +942,7 @@
     "bounds_freq = [0.9, 1.1] #+- 10% noise on the frequency of all events\n",
     "bounds_int = None #No uncertainty on the intensity\n",
     "n_ev = None \n",
-    "haz_iv = InputVar.haz(haz_base, n_ev=n_ev, bounds_freq=bounds_freq, bounds_int=bounds_int)"
+    "haz_iv = InputVar.haz([haz_base], n_ev=n_ev, bounds_freq=bounds_freq, bounds_int=bounds_int)"
    ]
   },
   {
@@ -804,8 +951,8 @@
    "id": "187b9c74-3ab3-403d-95ec-20ab148f0b4e",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:43.780479Z",
-     "start_time": "2022-01-10T20:10:43.774073Z"
+     "end_time": "2022-07-07T13:13:51.188466Z",
+     "start_time": "2022-07-07T13:13:51.180776Z"
     }
    },
    "outputs": [
@@ -832,16 +979,19 @@
    "id": "040bcec7-406e-4007-94a1-0650c3686920",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:43.786970Z",
-     "start_time": "2022-01-10T20:10:43.782373Z"
+     "end_time": "2022-07-07T13:13:51.196611Z",
+     "start_time": "2022-07-07T13:13:51.190590Z"
     }
    },
    "outputs": [],
    "source": [
     "bounds_freq = [0.9, 1.1] #+- 10% noise on the frequency of all events\n",
     "bounds_int = None #No uncertainty on the intensity\n",
+    "bounds_frac = [0.7, 1.1] #noise on the fraction of all events\n",
     "n_ev = round(0.8 * haz_base.size) #sub-sample with re-draw events to obtain hazards with n=0.8*tot_number_events\n",
-    "haz_iv = InputVar.haz(haz_base, n_ev=n_ev, bounds_freq=bounds_freq, bounds_int=bounds_int)"
+    "haz_iv = InputVar.haz(\n",
+    "    [haz_base], n_ev=n_ev, bounds_freq=bounds_freq, bounds_int=bounds_int, bounds_frac=bounds_frac\n",
+    ")"
    ]
   },
   {
@@ -858,8 +1008,8 @@
    "id": "3cdd89a1-831d-49a6-bcdd-6d650150ecb8",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:43.792766Z",
-     "start_time": "2022-01-10T20:10:43.788430Z"
+     "end_time": "2022-07-07T13:13:51.204357Z",
+     "start_time": "2022-07-07T13:13:51.199302Z"
     }
    },
    "outputs": [],
@@ -876,8 +1026,8 @@
    "id": "c21c99a6-0e13-4e56-bab3-36fd5af1f387",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:43.797524Z",
-     "start_time": "2022-01-10T20:10:43.794056Z"
+     "end_time": "2022-07-07T13:13:51.210906Z",
+     "start_time": "2022-07-07T13:13:51.206889Z"
     }
    },
    "outputs": [
@@ -903,16 +1053,16 @@
    "id": "afc1d32a-63cb-4a8f-911c-a9e539b21a61",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:43.992504Z",
-     "start_time": "2022-01-10T20:10:43.799061Z"
+     "end_time": "2022-07-07T13:13:51.503718Z",
+     "start_time": "2022-07-07T13:13:51.214496Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<Figure size 1152x360 with 2 Axes>"
+       "<Figure size 1728x360 with 3 Axes>"
       ]
      },
      "metadata": {},
@@ -939,8 +1089,8 @@
    "id": "4f7252dd-b1ba-43c4-ba56-160faf16de43",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:44.005540Z",
-     "start_time": "2022-01-10T20:10:43.994260Z"
+     "end_time": "2022-07-07T13:13:51.517317Z",
+     "start_time": "2022-07-07T13:13:51.505856Z"
     }
    },
    "outputs": [
@@ -957,7 +1107,7 @@
    ],
    "source": [
     "#The number of events per sample is equal to n_ev\n",
-    "haz_sub = haz_iv.evaluate(HE=928165924, HI=None, HF = 1.1)\n",
+    "haz_sub = haz_iv.evaluate(HE=928165924, HI=None, HF = 1.1, HA=None)\n",
     "#The number for HE is irrelevant, as all samples have the same n_Ev\n",
     "haz_sub.size - n_ev"
    ]
@@ -987,18 +1137,21 @@
     "> The value of paa at each intensity is multiplied by a number sampled uniformly from a distribution with (min, max) = bounds_paa\n",
     "- IFi: shift the intensity (homogeneously)\n",
     "> The value intensity are all summed with a random number sampled uniformly from a distribution with (min, max) = bounds_int\n",
+    "- IL: sample uniformly from impact function set list\n",
+    "> From the provided list of impact function sets elements are uniformly sampled. For example, impact functions obtained from different calibration methods.\n",
+    "\n",
     "\n",
     "If a bounds is None, this parameter is assumed to have no uncertainty."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 2,
    "id": "1b44d6e2-1905-4191-a91d-f4dd80c3d4ae",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:44.010158Z",
-     "start_time": "2022-01-10T20:10:44.007159Z"
+     "end_time": "2022-07-07T13:17:56.457258Z",
+     "start_time": "2022-07-07T13:17:53.260369Z"
     }
    },
    "outputs": [],
@@ -1019,12 +1172,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 3,
    "id": "c9d1c458-6af7-4103-8b8c-99edd9306e89",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:44.017097Z",
-     "start_time": "2022-01-10T20:10:44.012007Z"
+     "end_time": "2022-07-07T13:17:56.937145Z",
+     "start_time": "2022-07-07T13:17:56.459327Z"
     }
    },
    "outputs": [],
@@ -1033,7 +1186,7 @@
     "bounds_impfi = [-10, 10] #-10 m/s ; +10m/s uncertainty on the intensity\n",
     "bounds_mdd = [0.7, 1.1] #-30% - +10% uncertainty on the mdd\n",
     "bounds_paa = None #No uncertainty in the paa\n",
-    "impf_iv = InputVar.impfset(impf_set_base,\n",
+    "impf_iv = InputVar.impfset(impf_set_list=[impf_set_base],\n",
     "                           bounds_impfi=bounds_impfi,\n",
     "                           bounds_mdd=bounds_mdd,\n",
     "                           bounds_paa=bounds_paa,\n",
@@ -1042,18 +1195,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 4,
    "id": "376e5b42-5a5f-4a3b-8316-90bd1ac638e1",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:46.868271Z",
-     "start_time": "2022-01-10T20:10:44.019021Z"
+     "end_time": "2022-07-07T13:17:59.935968Z",
+     "start_time": "2022-07-07T13:17:56.940333Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1119,12 +1272,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 5,
    "id": "3b83130a-4f80-445b-9be8-b7851560cc03",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:47.187916Z",
-     "start_time": "2022-01-10T20:10:46.870375Z"
+     "end_time": "2022-07-07T13:18:00.296882Z",
+     "start_time": "2022-07-07T13:17:59.939044Z"
     }
    },
    "outputs": [
@@ -1132,10 +1285,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2022-01-10 21:10:47,184 - climada.entity.exposures.base - INFO - category_id not set.\n",
-      "2022-01-10 21:10:47,185 - climada.entity.exposures.base - INFO - geometry not set.\n",
-      "2022-01-10 21:10:47,185 - climada.entity.exposures.base - INFO - region_id not set.\n",
-      "2022-01-10 21:10:47,185 - climada.entity.exposures.base - INFO - centr_ not set.\n"
+      "2022-07-07 15:18:00,292 - climada.entity.exposures.base - INFO - category_id not set.\n",
+      "2022-07-07 15:18:00,293 - climada.entity.exposures.base - INFO - geometry not set.\n",
+      "2022-07-07 15:18:00,294 - climada.entity.exposures.base - INFO - region_id not set.\n",
+      "2022-07-07 15:18:00,294 - climada.entity.exposures.base - INFO - centr_ not set.\n"
      ]
     }
    ],
@@ -1149,19 +1302,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 6,
    "id": "402cf2f7-8b50-450f-a878-321272b3fd64",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:47.205620Z",
-     "start_time": "2022-01-10T20:10:47.198309Z"
+     "end_time": "2022-07-07T13:18:00.307821Z",
+     "start_time": "2022-07-07T13:18:00.298504Z"
     }
    },
    "outputs": [],
    "source": [
     "from climada.engine.unsequa import InputVar\n",
     "ent_iv = InputVar.ent(\n",
-    "    impf_set = ent.impact_funcs,\n",
+    "    impf_set_list = [ent.impact_funcs],\n",
     "    disc_rate = ent.disc_rates,\n",
     "    exp_list = [ent.exposures],\n",
     "    meas_set = ent.measures,\n",
@@ -1178,12 +1331,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 7,
    "id": "50b9cd64-1c42-42ef-9955-762615fefb02",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:47.840005Z",
-     "start_time": "2022-01-10T20:10:47.206997Z"
+     "end_time": "2022-07-07T13:18:00.947057Z",
+     "start_time": "2022-07-07T13:18:00.309713Z"
     }
    },
    "outputs": [
@@ -1212,12 +1365,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 8,
    "id": "f9bcf449-5d5d-4fac-a10a-e28cf1581773",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:47.847448Z",
-     "start_time": "2022-01-10T20:10:47.842240Z"
+     "end_time": "2022-07-07T13:18:00.952942Z",
+     "start_time": "2022-07-07T13:18:00.948371Z"
     }
    },
    "outputs": [],
@@ -1244,12 +1397,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 9,
    "id": "9adcdda7-5488-42cc-abfd-d098e359d0f2",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:47.874958Z",
-     "start_time": "2022-01-10T20:10:47.849449Z"
+     "end_time": "2022-07-07T13:18:00.986469Z",
+     "start_time": "2022-07-07T13:18:00.954689Z"
     }
    },
    "outputs": [
@@ -1257,7 +1410,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2022-01-10 21:10:47,851 - climada.hazard.base - INFO - Reading /Users/ckropf/climada/demo/data/tc_fl_1990_2004.h5\n"
+      "2022-07-07 15:18:00,956 - climada.hazard.base - INFO - Reading /Users/ckropf/climada/demo/data/tc_fl_1990_2004.h5\n"
      ]
     }
    ],
@@ -1280,12 +1433,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 10,
    "id": "6afb089a-a268-40dd-ae7d-8e12e23ae925",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:55.048825Z",
-     "start_time": "2022-01-10T20:10:47.877094Z"
+     "end_time": "2022-07-07T13:18:09.287329Z",
+     "start_time": "2022-07-07T13:18:00.988977Z"
     }
    },
    "outputs": [
@@ -1296,57 +1449,222 @@
       "\n",
       " Computing litpop for m=1, n=0 \n",
       "\n",
-      "2022-01-10 21:10:48,109 - climada.entity.exposures.litpop.litpop - INFO - \n",
+      "2022-07-07 15:18:01,386 - climada.entity.exposures.litpop.litpop - INFO - \n",
       " LitPop: Init Exposure for country: CUB (192)...\n",
       "\n",
-      "2022-01-10 21:10:48,111 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
-      "2022-01-10 21:10:49,491 - climada.util.finance - WARNING - No data available for country. Using non-financial wealth instead\n",
-      "2022-01-10 21:10:50,565 - climada.util.finance - INFO - GDP CUB 2020: 1.074e+11.\n",
-      "2022-01-10 21:10:50,570 - climada.util.finance - WARNING - No data for country, using mean factor.\n",
-      "2022-01-10 21:10:50,582 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
-      "2022-01-10 21:10:50,583 - climada.entity.exposures.base - INFO - category_id not set.\n",
-      "2022-01-10 21:10:50,583 - climada.entity.exposures.base - INFO - cover not set.\n",
-      "2022-01-10 21:10:50,584 - climada.entity.exposures.base - INFO - deductible not set.\n",
-      "2022-01-10 21:10:50,584 - climada.entity.exposures.base - INFO - centr_ not set.\n",
-      "2022-01-10 21:10:50,589 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
-      "2022-01-10 21:10:50,592 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
-      "2022-01-10 21:10:50,624 - climada.util.interpolation - WARNING - Distance to closest centroid is greater than 100km for 77 coordinates.\n",
+      "2022-07-07 15:18:01,968 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:01,997 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,022 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,044 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,069 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,093 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,125 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,187 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,210 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,220 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:02,221 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,233 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:02,234 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,248 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:02,249 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,264 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:02,265 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,278 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:02,279 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,303 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,330 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,341 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:02,342 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,351 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:02,351 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,363 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:02,364 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,391 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,417 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,443 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,466 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,504 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,528 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,559 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,585 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,596 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:02,597 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,634 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,666 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,700 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,734 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,758 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,768 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:02,769 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,809 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,821 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:02,822 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,845 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,869 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,896 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,919 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:02,930 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:02,959 - climada.util.finance - WARNING - No data available for country. Using non-financial wealth instead\n",
+      "2022-07-07 15:18:04,013 - climada.util.finance - INFO - GDP CUB 2020: 1.074e+11.\n",
+      "2022-07-07 15:18:04,017 - climada.util.finance - WARNING - No data for country, using mean factor.\n",
+      "2022-07-07 15:18:04,028 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
+      "2022-07-07 15:18:04,029 - climada.entity.exposures.base - INFO - category_id not set.\n",
+      "2022-07-07 15:18:04,030 - climada.entity.exposures.base - INFO - cover not set.\n",
+      "2022-07-07 15:18:04,031 - climada.entity.exposures.base - INFO - deductible not set.\n",
+      "2022-07-07 15:18:04,032 - climada.entity.exposures.base - INFO - centr_ not set.\n",
+      "2022-07-07 15:18:04,037 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
+      "2022-07-07 15:18:04,039 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
+      "2022-07-07 15:18:04,046 - climada.util.coordinates - WARNING - Distance to closest centroid is greater than 100km for 78 coordinates.\n",
       "\n",
       " Computing litpop for m=0, n=1 \n",
       "\n",
-      "2022-01-10 21:10:50,872 - climada.entity.exposures.litpop.litpop - INFO - \n",
+      "2022-07-07 15:18:04,291 - climada.entity.exposures.litpop.litpop - INFO - \n",
       " LitPop: Init Exposure for country: CUB (192)...\n",
       "\n",
-      "2022-01-10 21:10:50,874 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
-      "2022-01-10 21:10:52,432 - climada.util.finance - WARNING - No data available for country. Using non-financial wealth instead\n",
-      "2022-01-10 21:10:52,863 - climada.util.finance - INFO - GDP CUB 2020: 1.074e+11.\n",
-      "2022-01-10 21:10:52,867 - climada.util.finance - WARNING - No data for country, using mean factor.\n",
-      "2022-01-10 21:10:52,877 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
-      "2022-01-10 21:10:52,878 - climada.entity.exposures.base - INFO - category_id not set.\n",
-      "2022-01-10 21:10:52,878 - climada.entity.exposures.base - INFO - cover not set.\n",
-      "2022-01-10 21:10:52,879 - climada.entity.exposures.base - INFO - deductible not set.\n",
-      "2022-01-10 21:10:52,879 - climada.entity.exposures.base - INFO - centr_ not set.\n",
-      "2022-01-10 21:10:52,884 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
-      "2022-01-10 21:10:52,886 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
-      "2022-01-10 21:10:52,914 - climada.util.interpolation - WARNING - Distance to closest centroid is greater than 100km for 77 coordinates.\n",
+      "2022-07-07 15:18:04,812 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:04,842 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:04,869 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:04,894 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:04,923 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:04,953 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:04,987 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,049 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,075 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,086 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:05,087 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,098 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2022-07-07 15:18:05,099 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,114 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:05,115 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,131 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:05,132 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,148 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:05,149 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,174 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,199 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,208 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:05,209 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,220 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:05,221 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,233 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:05,234 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,261 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,287 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,310 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,333 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,368 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,392 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,420 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,444 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,455 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:05,456 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,492 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,523 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,557 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,588 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,611 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,622 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:05,623 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,659 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,671 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:05,671 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,693 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,716 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,742 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,764 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:05,775 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:05,801 - climada.util.finance - WARNING - No data available for country. Using non-financial wealth instead\n",
+      "2022-07-07 15:18:06,617 - climada.util.finance - INFO - GDP CUB 2020: 1.074e+11.\n",
+      "2022-07-07 15:18:06,621 - climada.util.finance - WARNING - No data for country, using mean factor.\n",
+      "2022-07-07 15:18:06,630 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
+      "2022-07-07 15:18:06,631 - climada.entity.exposures.base - INFO - category_id not set.\n",
+      "2022-07-07 15:18:06,631 - climada.entity.exposures.base - INFO - cover not set.\n",
+      "2022-07-07 15:18:06,632 - climada.entity.exposures.base - INFO - deductible not set.\n",
+      "2022-07-07 15:18:06,632 - climada.entity.exposures.base - INFO - centr_ not set.\n",
+      "2022-07-07 15:18:06,636 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
+      "2022-07-07 15:18:06,637 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
+      "2022-07-07 15:18:06,643 - climada.util.coordinates - WARNING - Distance to closest centroid is greater than 100km for 78 coordinates.\n",
       "\n",
       " Computing litpop for m=1, n=1 \n",
       "\n",
-      "2022-01-10 21:10:53,159 - climada.entity.exposures.litpop.litpop - INFO - \n",
+      "2022-07-07 15:18:06,884 - climada.entity.exposures.litpop.litpop - INFO - \n",
       " LitPop: Init Exposure for country: CUB (192)...\n",
       "\n",
-      "2022-01-10 21:10:53,161 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
-      "2022-01-10 21:10:54,584 - climada.util.finance - WARNING - No data available for country. Using non-financial wealth instead\n",
-      "2022-01-10 21:10:54,997 - climada.util.finance - INFO - GDP CUB 2020: 1.074e+11.\n",
-      "2022-01-10 21:10:55,001 - climada.util.finance - WARNING - No data for country, using mean factor.\n",
-      "2022-01-10 21:10:55,011 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
-      "2022-01-10 21:10:55,012 - climada.entity.exposures.base - INFO - category_id not set.\n",
-      "2022-01-10 21:10:55,012 - climada.entity.exposures.base - INFO - cover not set.\n",
-      "2022-01-10 21:10:55,013 - climada.entity.exposures.base - INFO - deductible not set.\n",
-      "2022-01-10 21:10:55,013 - climada.entity.exposures.base - INFO - centr_ not set.\n",
-      "2022-01-10 21:10:55,018 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
-      "2022-01-10 21:10:55,020 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
-      "2022-01-10 21:10:55,046 - climada.util.interpolation - WARNING - Distance to closest centroid is greater than 100km for 77 coordinates.\n"
+      "2022-07-07 15:18:07,423 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:07,449 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:07,473 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:07,496 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:07,521 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:07,544 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:07,574 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:07,637 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:07,660 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:07,670 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:07,670 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:07,682 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:07,683 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:07,697 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:07,698 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:07,710 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:07,711 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:07,723 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:07,724 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:07,748 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:07,773 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:07,783 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:07,784 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:07,793 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:07,794 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2022-07-07 15:18:07,805 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:07,806 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:07,832 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:07,859 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:07,882 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:07,907 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:07,943 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:07,968 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:07,997 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:08,021 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:08,032 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:08,032 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:08,071 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:08,102 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:08,136 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:08,167 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:08,189 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:08,200 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:08,201 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:08,240 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:08,252 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:08,253 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:08,276 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:08,298 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:08,323 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:08,345 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:08,357 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:08,383 - climada.util.finance - WARNING - No data available for country. Using non-financial wealth instead\n",
+      "2022-07-07 15:18:09,253 - climada.util.finance - INFO - GDP CUB 2020: 1.074e+11.\n",
+      "2022-07-07 15:18:09,257 - climada.util.finance - WARNING - No data for country, using mean factor.\n",
+      "2022-07-07 15:18:09,267 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
+      "2022-07-07 15:18:09,268 - climada.entity.exposures.base - INFO - category_id not set.\n",
+      "2022-07-07 15:18:09,268 - climada.entity.exposures.base - INFO - cover not set.\n",
+      "2022-07-07 15:18:09,269 - climada.entity.exposures.base - INFO - deductible not set.\n",
+      "2022-07-07 15:18:09,270 - climada.entity.exposures.base - INFO - centr_ not set.\n",
+      "2022-07-07 15:18:09,275 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
+      "2022-07-07 15:18:09,277 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
+      "2022-07-07 15:18:09,283 - climada.util.coordinates - WARNING - Distance to closest centroid is greater than 100km for 78 coordinates.\n"
      ]
     }
    ],
@@ -1360,12 +1678,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 11,
    "id": "c3bff058-b814-4059-8893-9de07fb8b360",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:55.144154Z",
-     "start_time": "2022-01-10T20:10:55.050611Z"
+     "end_time": "2022-07-07T13:18:09.405351Z",
+     "start_time": "2022-07-07T13:18:09.292279Z"
     }
    },
    "outputs": [
@@ -1373,10 +1691,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2022-01-10 21:10:55,139 - climada.entity.exposures.base - INFO - category_id not set.\n",
-      "2022-01-10 21:10:55,140 - climada.entity.exposures.base - INFO - geometry not set.\n",
-      "2022-01-10 21:10:55,140 - climada.entity.exposures.base - INFO - region_id not set.\n",
-      "2022-01-10 21:10:55,141 - climada.entity.exposures.base - INFO - centr_ not set.\n"
+      "2022-07-07 15:18:09,400 - climada.entity.exposures.base - INFO - category_id not set.\n",
+      "2022-07-07 15:18:09,401 - climada.entity.exposures.base - INFO - geometry not set.\n",
+      "2022-07-07 15:18:09,402 - climada.entity.exposures.base - INFO - region_id not set.\n",
+      "2022-07-07 15:18:09,402 - climada.entity.exposures.base - INFO - centr_ not set.\n"
      ]
     }
    ],
@@ -1390,19 +1708,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 12,
    "id": "99939159-2a7c-4f32-8f78-8fcbe067b1b5",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:55.154796Z",
-     "start_time": "2022-01-10T20:10:55.146177Z"
+     "end_time": "2022-07-07T13:18:09.424434Z",
+     "start_time": "2022-07-07T13:18:09.408541Z"
     }
    },
    "outputs": [],
    "source": [
     "from climada.engine.unsequa import InputVar\n",
     "ent_iv = InputVar.ent(\n",
-    "    impf_set = ent.impact_funcs,\n",
+    "    impf_set_list = [ent.impact_funcs],\n",
     "    disc_rate = ent.disc_rates,\n",
     "    exp_list = litpop_list,\n",
     "    meas_set = ent.measures,\n",
@@ -1419,24 +1737,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 13,
    "id": "d786ada6-bb2d-4542-894b-dff7ba12a377",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:57.415665Z",
-     "start_time": "2022-01-10T20:10:55.157499Z"
+     "end_time": "2022-07-07T13:18:11.834519Z",
+     "start_time": "2022-07-07T13:18:09.427602Z"
     }
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "<GeoAxesSubplot:title={'center':\"LitPop Exposure for ['CUB'] at 300 as, year: 2020, financial mode: pc,\\nexp: (0.5, 1), admin1_calc: False\"}>"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2022-07-07 15:18:09,448 - climada.util.plot - WARNING - Error parsing coordinate system 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]'. Using projection PlateCarree in plot.\n"
+     ]
     },
     {
      "data": {
@@ -1481,6 +1796,8 @@
     "> The value of paa at each intensity is multiplied by a number sampled uniformly from a distribution with (min, max) = bounds_paa\n",
     "- IFi: shift the impact function intensity (homogeneously)\n",
     "> The value intensity are all summed with a random number sampled uniformly from a distribution with (min, max) = bounds_impfi\n",
+    "- IL: sample uniformly from impact function set list\n",
+    "> From the provided list of impact function sets elements are uniformly sampled. For example, impact functions obtained from different calibration methods.\n",
     "\n",
     "\n",
     "If a bounds is None, this parameter is assumed to have no uncertainty."
@@ -1496,12 +1813,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 14,
    "id": "36d59a4c-eb0a-4b11-a19b-89b968240c29",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:57.511248Z",
-     "start_time": "2022-01-10T20:10:57.417175Z"
+     "end_time": "2022-07-07T13:18:11.940658Z",
+     "start_time": "2022-07-07T13:18:11.836643Z"
     }
    },
    "outputs": [
@@ -1509,10 +1826,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2022-01-10 21:10:57,507 - climada.entity.exposures.base - INFO - category_id not set.\n",
-      "2022-01-10 21:10:57,507 - climada.entity.exposures.base - INFO - geometry not set.\n",
-      "2022-01-10 21:10:57,508 - climada.entity.exposures.base - INFO - region_id not set.\n",
-      "2022-01-10 21:10:57,509 - climada.entity.exposures.base - INFO - centr_ not set.\n"
+      "2022-07-07 15:18:11,936 - climada.entity.exposures.base - INFO - category_id not set.\n",
+      "2022-07-07 15:18:11,937 - climada.entity.exposures.base - INFO - geometry not set.\n",
+      "2022-07-07 15:18:11,938 - climada.entity.exposures.base - INFO - region_id not set.\n",
+      "2022-07-07 15:18:11,938 - climada.entity.exposures.base - INFO - centr_ not set.\n"
      ]
     }
    ],
@@ -1527,18 +1844,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 15,
    "id": "46115ff5-ddea-4ceb-9653-578b74c07297",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:57.519160Z",
-     "start_time": "2022-01-10T20:10:57.513470Z"
+     "end_time": "2022-07-07T13:18:11.948329Z",
+     "start_time": "2022-07-07T13:18:11.942392Z"
     }
    },
    "outputs": [],
    "source": [
     "entfut_iv = InputVar.entfut(\n",
-    "    impf_set = ent_fut.impact_funcs,\n",
+    "    impf_set_list = [ent_fut.impact_funcs],\n",
     "    exp_list = [ent_fut.exposures],\n",
     "    meas_set = ent_fut.measures,\n",
     "    bounds_cost=[0.6, 1.2],\n",
@@ -1560,12 +1877,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 16,
    "id": "5766a082-03dc-4e8c-896a-88e6411774a9",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:57.525368Z",
-     "start_time": "2022-01-10T20:10:57.521104Z"
+     "end_time": "2022-07-07T13:18:11.954599Z",
+     "start_time": "2022-07-07T13:18:11.950148Z"
     }
    },
    "outputs": [],
@@ -1592,12 +1909,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 17,
    "id": "8f505ca7-1133-450b-96a0-e8133ba285ee",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:10:57.551457Z",
-     "start_time": "2022-01-10T20:10:57.527285Z"
+     "end_time": "2022-07-07T13:18:11.985438Z",
+     "start_time": "2022-07-07T13:18:11.957060Z"
     }
    },
    "outputs": [
@@ -1605,7 +1922,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2022-01-10 21:10:57,529 - climada.hazard.base - INFO - Reading /Users/ckropf/climada/demo/data/tc_fl_1990_2004.h5\n"
+      "2022-07-07 15:18:11,958 - climada.hazard.base - INFO - Reading /Users/ckropf/climada/demo/data/tc_fl_1990_2004.h5\n"
      ]
     }
    ],
@@ -1628,12 +1945,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 18,
    "id": "6f6bc7a9-3779-4ae7-94e2-91808cad51d8",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:11:03.982167Z",
-     "start_time": "2022-01-10T20:10:57.552955Z"
+     "end_time": "2022-07-07T13:18:19.888504Z",
+     "start_time": "2022-07-07T13:18:11.987390Z"
     },
     "scrolled": true
    },
@@ -1645,60 +1962,354 @@
       "\n",
       " Computing litpop for m=1, n=0 \n",
       "\n",
-      "2022-01-10 21:10:57,798 - climada.entity.exposures.litpop.litpop - INFO - \n",
+      "2022-07-07 15:18:12,244 - climada.entity.exposures.litpop.litpop - INFO - \n",
       " LitPop: Init Exposure for country: CUB (192)...\n",
       "\n",
-      "2022-01-10 21:10:57,799 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
-      "2022-01-10 21:10:57,800 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
-      "2022-01-10 21:10:59,239 - climada.util.finance - WARNING - No data available for country. Using non-financial wealth instead\n",
-      "2022-01-10 21:10:59,680 - climada.util.finance - INFO - GDP CUB 2020: 1.074e+11.\n",
-      "2022-01-10 21:10:59,683 - climada.util.finance - WARNING - No data for country, using mean factor.\n",
-      "2022-01-10 21:10:59,693 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
-      "2022-01-10 21:10:59,693 - climada.entity.exposures.base - INFO - category_id not set.\n",
-      "2022-01-10 21:10:59,694 - climada.entity.exposures.base - INFO - cover not set.\n",
-      "2022-01-10 21:10:59,694 - climada.entity.exposures.base - INFO - deductible not set.\n",
-      "2022-01-10 21:10:59,695 - climada.entity.exposures.base - INFO - centr_ not set.\n",
-      "2022-01-10 21:10:59,698 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
-      "2022-01-10 21:10:59,700 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
-      "2022-01-10 21:10:59,726 - climada.util.interpolation - WARNING - Distance to closest centroid is greater than 100km for 77 coordinates.\n",
+      "2022-07-07 15:18:12,773 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:12,773 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:12,803 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:12,804 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:12,830 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:12,831 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:12,854 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:12,854 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:12,881 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:12,881 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:12,906 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:12,907 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:12,937 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:12,938 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,001 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,002 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,025 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,025 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,034 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:13,035 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,035 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,047 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:13,048 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,048 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,062 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:13,063 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,064 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,077 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:13,078 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,078 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,090 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:13,090 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,091 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,117 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,118 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,144 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,145 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,155 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:13,156 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,157 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,165 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:13,166 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,166 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,178 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:13,178 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,179 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,202 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,203 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,231 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,232 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,256 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,257 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,281 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,281 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,316 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,316 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,344 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,344 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,374 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,375 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,399 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,400 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,412 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:13,412 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2022-07-07 15:18:13,413 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,451 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,452 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,483 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,483 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,521 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,522 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,554 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,555 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,579 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,579 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,590 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:13,591 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,592 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,630 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,630 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,643 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:13,644 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,644 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,666 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,667 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,690 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,691 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,718 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,718 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,742 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:13,742 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:13,754 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:13,780 - climada.util.finance - WARNING - No data available for country. Using non-financial wealth instead\n",
+      "2022-07-07 15:18:14,384 - climada.util.finance - INFO - GDP CUB 2020: 1.074e+11.\n",
+      "2022-07-07 15:18:14,388 - climada.util.finance - WARNING - No data for country, using mean factor.\n",
+      "2022-07-07 15:18:14,396 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
+      "2022-07-07 15:18:14,397 - climada.entity.exposures.base - INFO - category_id not set.\n",
+      "2022-07-07 15:18:14,397 - climada.entity.exposures.base - INFO - cover not set.\n",
+      "2022-07-07 15:18:14,398 - climada.entity.exposures.base - INFO - deductible not set.\n",
+      "2022-07-07 15:18:14,398 - climada.entity.exposures.base - INFO - centr_ not set.\n",
+      "2022-07-07 15:18:14,401 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
+      "2022-07-07 15:18:14,403 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
+      "2022-07-07 15:18:14,409 - climada.util.coordinates - WARNING - Distance to closest centroid is greater than 100km for 78 coordinates.\n",
       "\n",
       " Computing litpop for m=0, n=1 \n",
       "\n",
-      "2022-01-10 21:10:59,981 - climada.entity.exposures.litpop.litpop - INFO - \n",
+      "2022-07-07 15:18:14,640 - climada.entity.exposures.litpop.litpop - INFO - \n",
       " LitPop: Init Exposure for country: CUB (192)...\n",
       "\n",
-      "2022-01-10 21:10:59,982 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
-      "2022-01-10 21:10:59,983 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
-      "2022-01-10 21:11:01,391 - climada.util.finance - WARNING - No data available for country. Using non-financial wealth instead\n",
-      "2022-01-10 21:11:01,816 - climada.util.finance - INFO - GDP CUB 2020: 1.074e+11.\n",
-      "2022-01-10 21:11:01,819 - climada.util.finance - WARNING - No data for country, using mean factor.\n",
-      "2022-01-10 21:11:01,829 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
-      "2022-01-10 21:11:01,829 - climada.entity.exposures.base - INFO - category_id not set.\n",
-      "2022-01-10 21:11:01,829 - climada.entity.exposures.base - INFO - cover not set.\n",
-      "2022-01-10 21:11:01,830 - climada.entity.exposures.base - INFO - deductible not set.\n",
-      "2022-01-10 21:11:01,830 - climada.entity.exposures.base - INFO - centr_ not set.\n",
-      "2022-01-10 21:11:01,833 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
-      "2022-01-10 21:11:01,835 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
-      "2022-01-10 21:11:01,861 - climada.util.interpolation - WARNING - Distance to closest centroid is greater than 100km for 77 coordinates.\n",
+      "2022-07-07 15:18:15,172 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,173 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,199 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,200 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,223 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,224 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,249 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,250 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,279 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,279 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,301 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,302 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,330 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,331 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,390 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,391 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,417 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,418 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,428 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:15,429 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,429 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,441 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:15,441 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,442 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,457 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:15,457 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,458 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2022-07-07 15:18:15,471 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:15,472 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,472 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,483 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:15,484 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,485 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,512 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,513 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,539 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,540 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,550 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:15,551 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,552 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,562 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:15,562 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,563 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,575 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:15,575 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,576 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,601 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,602 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,633 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,634 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,661 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,662 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,688 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,689 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,729 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,730 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,757 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,758 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,787 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,787 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,812 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,813 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,824 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:15,824 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,825 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,862 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,862 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,893 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,894 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,928 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,929 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,961 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,962 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,984 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:15,985 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:15,999 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:16,000 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:16,000 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:16,040 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:16,040 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:16,051 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:16,052 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:16,052 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:16,076 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:16,077 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:16,103 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:16,103 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:16,131 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:16,132 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:16,156 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:16,156 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:16,167 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2022-07-07 15:18:16,195 - climada.util.finance - WARNING - No data available for country. Using non-financial wealth instead\n",
+      "2022-07-07 15:18:17,186 - climada.util.finance - INFO - GDP CUB 2020: 1.074e+11.\n",
+      "2022-07-07 15:18:17,190 - climada.util.finance - WARNING - No data for country, using mean factor.\n",
+      "2022-07-07 15:18:17,200 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
+      "2022-07-07 15:18:17,201 - climada.entity.exposures.base - INFO - category_id not set.\n",
+      "2022-07-07 15:18:17,201 - climada.entity.exposures.base - INFO - cover not set.\n",
+      "2022-07-07 15:18:17,202 - climada.entity.exposures.base - INFO - deductible not set.\n",
+      "2022-07-07 15:18:17,203 - climada.entity.exposures.base - INFO - centr_ not set.\n",
+      "2022-07-07 15:18:17,208 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
+      "2022-07-07 15:18:17,210 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
+      "2022-07-07 15:18:17,216 - climada.util.coordinates - WARNING - Distance to closest centroid is greater than 100km for 78 coordinates.\n",
       "\n",
       " Computing litpop for m=1, n=1 \n",
       "\n",
-      "2022-01-10 21:11:02,111 - climada.entity.exposures.litpop.litpop - INFO - \n",
+      "2022-07-07 15:18:17,454 - climada.entity.exposures.litpop.litpop - INFO - \n",
       " LitPop: Init Exposure for country: CUB (192)...\n",
       "\n",
-      "2022-01-10 21:11:02,113 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
-      "2022-01-10 21:11:02,113 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
-      "2022-01-10 21:11:03,498 - climada.util.finance - WARNING - No data available for country. Using non-financial wealth instead\n",
-      "2022-01-10 21:11:03,929 - climada.util.finance - INFO - GDP CUB 2020: 1.074e+11.\n",
-      "2022-01-10 21:11:03,933 - climada.util.finance - WARNING - No data for country, using mean factor.\n",
-      "2022-01-10 21:11:03,944 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
-      "2022-01-10 21:11:03,945 - climada.entity.exposures.base - INFO - category_id not set.\n",
-      "2022-01-10 21:11:03,945 - climada.entity.exposures.base - INFO - cover not set.\n",
-      "2022-01-10 21:11:03,945 - climada.entity.exposures.base - INFO - deductible not set.\n",
-      "2022-01-10 21:11:03,946 - climada.entity.exposures.base - INFO - centr_ not set.\n",
-      "2022-01-10 21:11:03,950 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
-      "2022-01-10 21:11:03,951 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
-      "2022-01-10 21:11:03,979 - climada.util.interpolation - WARNING - Distance to closest centroid is greater than 100km for 77 coordinates.\n"
+      "2022-07-07 15:18:17,967 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:17,967 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:17,997 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:17,998 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,024 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,024 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,050 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,050 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,079 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,080 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,111 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,111 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,155 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,155 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,223 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,224 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,250 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,251 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,261 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:18,262 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,263 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,275 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:18,275 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,276 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,291 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:18,291 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,292 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,304 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:18,305 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,305 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,316 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:18,317 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,317 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,344 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,345 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,371 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,372 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,385 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:18,386 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,387 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,399 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:18,399 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,400 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,413 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:18,414 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,414 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,440 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,441 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,468 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,469 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,492 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,493 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,516 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,516 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,553 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2022-07-07 15:18:18,553 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,577 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,578 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,607 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,607 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,631 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,632 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,644 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:18,644 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,645 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,681 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,682 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,714 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,714 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,748 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,748 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,782 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,782 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,804 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,804 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,816 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:18,817 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,818 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,855 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,856 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,866 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:18,867 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,867 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,891 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,891 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,914 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,914 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,942 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,943 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,965 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
+      "2022-07-07 15:18:18,966 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
+      "2022-07-07 15:18:18,978 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
+      "2022-07-07 15:18:19,006 - climada.util.finance - WARNING - No data available for country. Using non-financial wealth instead\n",
+      "2022-07-07 15:18:19,855 - climada.util.finance - INFO - GDP CUB 2020: 1.074e+11.\n",
+      "2022-07-07 15:18:19,859 - climada.util.finance - WARNING - No data for country, using mean factor.\n",
+      "2022-07-07 15:18:19,869 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
+      "2022-07-07 15:18:19,870 - climada.entity.exposures.base - INFO - category_id not set.\n",
+      "2022-07-07 15:18:19,870 - climada.entity.exposures.base - INFO - cover not set.\n",
+      "2022-07-07 15:18:19,871 - climada.entity.exposures.base - INFO - deductible not set.\n",
+      "2022-07-07 15:18:19,872 - climada.entity.exposures.base - INFO - centr_ not set.\n",
+      "2022-07-07 15:18:19,875 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
+      "2022-07-07 15:18:19,878 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
+      "2022-07-07 15:18:19,884 - climada.util.coordinates - WARNING - Distance to closest centroid is greater than 100km for 78 coordinates.\n"
      ]
     }
    ],
@@ -1712,12 +2323,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 19,
    "id": "c7d61fe7-7496-4e89-9393-6e8ca5e9f080",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:11:04.107072Z",
-     "start_time": "2022-01-10T20:11:03.984164Z"
+     "end_time": "2022-07-07T13:18:19.993383Z",
+     "start_time": "2022-07-07T13:18:19.890454Z"
     }
    },
    "outputs": [
@@ -1725,10 +2336,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2022-01-10 21:11:04,102 - climada.entity.exposures.base - INFO - category_id not set.\n",
-      "2022-01-10 21:11:04,103 - climada.entity.exposures.base - INFO - geometry not set.\n",
-      "2022-01-10 21:11:04,103 - climada.entity.exposures.base - INFO - region_id not set.\n",
-      "2022-01-10 21:11:04,104 - climada.entity.exposures.base - INFO - centr_ not set.\n"
+      "2022-07-07 15:18:19,989 - climada.entity.exposures.base - INFO - category_id not set.\n",
+      "2022-07-07 15:18:19,989 - climada.entity.exposures.base - INFO - geometry not set.\n",
+      "2022-07-07 15:18:19,990 - climada.entity.exposures.base - INFO - region_id not set.\n",
+      "2022-07-07 15:18:19,990 - climada.entity.exposures.base - INFO - centr_ not set.\n"
      ]
     }
    ],
@@ -1743,12 +2354,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 20,
    "id": "b63f8e63-052d-4ac7-9d9b-56e070e54990",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2022-01-10T20:11:04.117569Z",
-     "start_time": "2022-01-10T20:11:04.109427Z"
+     "end_time": "2022-07-07T13:18:20.002393Z",
+     "start_time": "2022-07-07T13:18:19.995393Z"
     },
     "tags": []
    },
@@ -1756,7 +2367,7 @@
    "source": [
     "from climada.engine.unsequa import InputVar\n",
     "entfut_iv = InputVar.entfut(\n",
-    "    impf_set = ent_fut.impact_funcs,\n",
+    "    impf_set_list = [ent_fut.impact_funcs],\n",
     "    exp_list = litpop_list,\n",
     "    meas_set = ent_fut.measures,\n",
     "    bounds_cost=[0.6, 1.2],\n",
@@ -1772,9 +2383,9 @@
  "metadata": {
   "hide_input": false,
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python [conda env:climada_310]",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-climada_310-py"
   },
   "language_info": {
    "codemirror_mode": {