From c8256a7adb823c10ed6507b5138d168b6addbd9d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Fabian=20Fr=C3=B6hlich?= <fabian@schaluck.com>
Date: Tue, 14 Jan 2020 18:17:32 -0500
Subject: [PATCH 01/23] Fixes pysb (#902)

* dont assume strippedsyms to be reals as this will break derivatives

* fix nested total derivatives, fixes #901

* adress review comments

* add comment
---
 python/amici/ode_export.py | 26 ++++++++++++++++----------
 1 file changed, 16 insertions(+), 10 deletions(-)

diff --git a/python/amici/ode_export.py b/python/amici/ode_export.py
index 8e611962ad..e9ea048586 100644
--- a/python/amici/ode_export.py
+++ b/python/amici/ode_export.py
@@ -708,8 +708,8 @@ class ODEModel:
         computed for an equation, key defines the name and values should be
         arguments for ODEModel.multiplication() @type dict
 
-        _lock_total_derivative: set this to true when computing a total
-        derivative from a partial derivative call to enforce a partial
+        _lock_total_derivative: add chainvariables to this set when computing
+        tota derivative from a partial derivative call to enforce a partial
         derivative in the next recursion. prevents infinite recursion
 
         _simplify: If not None, this function will be used to simplify symbolic
@@ -812,7 +812,7 @@ def __init__(self, simplify: Optional[Callable] = sp.powsimp):
             },
         }
 
-        self._lock_total_derivative = False
+        self._lock_total_derivative = list()
         self._simplify = simplify
 
     def import_from_sbml_importer(self, si):
@@ -1170,8 +1170,12 @@ def _generateSymbol(self, name):
                 comp.get_id()
                 for comp in getattr(self, component)
             ])
+            # this gives us access to the "stripped" symbols that were
+            # generated by pysb (if compiling a pysb model). To ensure
+            # correctness of derivatives, the same assumptions as in pysb
+            # have to be used (currently no assumptions)
             self._strippedsyms[name] = sp.Matrix([
-                sp.Symbol(comp.get_name(), real=True)
+                sp.Symbol(comp.get_name())
                 for comp in getattr(self, component)
             ])
             if name == 'y':
@@ -1328,9 +1332,10 @@ def _compute_equation(self, name):
         elif name in self._total_derivative_prototypes:
             args = self._total_derivative_prototypes[name]
             args['name'] = name
-            self._lock_total_derivative = True
+            self._lock_total_derivative += args['chainvars']
             self._total_derivative(**args)
-            self._lock_total_derivative = False
+            for cv in args['chainvars']:
+                self._lock_total_derivative.remove(cv)
 
         elif name in self._multiplication_prototypes:
             args = self._multiplication_prototypes[name]
@@ -1452,7 +1457,7 @@ def _compute_equation(self, name):
         if name in ['Jy', 'dydx']:
             # do not transpose if we compute the partial derivative as part of
             # a total derivative
-            if not self._lock_total_derivative:
+            if not len(self._lock_total_derivative):
                 self._eqs[name] = self._eqs[name].transpose()
 
         if self._simplify:
@@ -1500,7 +1505,7 @@ def _derivative(self, eq, var, name=None):
         }
         for cv in ['w', 'tcl']:
             if var_in_function_signature(eq, cv) \
-                    and not self._lock_total_derivative \
+                    and cv not in self._lock_total_derivative \
                     and var is not cv \
                     and min(self.sym(cv).shape) \
                     and (
@@ -1510,9 +1515,10 @@ def _derivative(self, eq, var, name=None):
                 chainvars.append(cv)
 
         if len(chainvars):
-            self._lock_total_derivative = True
+            self._lock_total_derivative += chainvars
             self._total_derivative(name, eq, chainvars, var)
-            self._lock_total_derivative = False
+            for cv in chainvars:
+                self._lock_total_derivative.remove(cv)
             return
 
         # this is the basic requirement check

From b859bd7f1801fdaac269c93e70be758c4bf4825c Mon Sep 17 00:00:00 2001
From: FFroehlich <fabian@schaluck.com>
Date: Wed, 15 Jan 2020 15:38:17 -0500
Subject: [PATCH 02/23] update notebook

---
 .../ExampleSteadystate.ipynb                  | 1224 +++++++++--------
 1 file changed, 638 insertions(+), 586 deletions(-)

diff --git a/python/examples/example_steadystate/ExampleSteadystate.ipynb b/python/examples/example_steadystate/ExampleSteadystate.ipynb
index 82cd95a3b9..158a8b4a26 100644
--- a/python/examples/example_steadystate/ExampleSteadystate.ipynb
+++ b/python/examples/example_steadystate/ExampleSteadystate.ipynb
@@ -13,17 +13,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<Logger amici.ode_export (WARNING)>\n",
-      "<Logger amici.sbml_import (WARNING)>\n",
-      "<Logger amici.pysb_import (WARNING)>\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# SBML model we want to import\n",
     "sbml_file = 'model_steadystate_scaled.xml'\n",
@@ -201,53 +191,40 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-01-14 12:02:36.343 - amici.sbml_import - INFO - Finished processing SBML parameters            (1.24E-03s)\n",
-      "2020-01-14 12:02:36.422 - amici.sbml_import - INFO - Finished processing SBML species               (7.57E-02s)\n",
-      "2020-01-14 12:02:36.525 - amici.sbml_import - INFO - Finished processing SBML reactions             (8.92E-02s)\n",
-      "2020-01-14 12:02:36.528 - amici.sbml_import - INFO - Finished processing SBML compartments          (5.43E-04s)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<Logger amici.sbml_import (INFO)>\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2020-01-14 12:02:37.140 - amici.sbml_import - INFO - Finished processing SBML rules                 (6.08E-01s)\n",
-      "2020-01-14 12:02:37.776 - amici.sbml_import - INFO - Finished processing SBML observables           (4.67E-01s)\n",
-      "2020-01-14 12:02:38.077 - amici.ode_export - INFO - Finished writing J.cpp                          (2.54E-01s)\n",
-      "2020-01-14 12:02:38.157 - amici.ode_export - INFO - Finished writing JB.cpp                         (7.84E-02s)\n",
-      "2020-01-14 12:02:38.170 - amici.ode_export - INFO - Finished writing JDiag.cpp                      (1.11E-02s)\n",
-      "2020-01-14 12:02:38.285 - amici.ode_export - INFO - Finished writing JSparse.cpp                    (1.05E-01s)\n",
-      "2020-01-14 12:02:38.506 - amici.ode_export - INFO - Finished writing JSparseB.cpp                   (3.61E-02s)\n",
-      "2020-01-14 12:02:38.616 - amici.ode_export - INFO - Finished writing Jy.cpp                         (9.61E-02s)\n",
-      "2020-01-14 12:02:40.056 - amici.ode_export - INFO - Finished writing dJydsigmay.cpp                 (1.44E+00s)\n",
-      "2020-01-14 12:02:40.397 - amici.ode_export - INFO - Finished writing dJydy.cpp                      (3.35E-01s)\n",
-      "2020-01-14 12:02:40.439 - amici.ode_export - INFO - Finished writing dwdp.cpp                       (2.37E-02s)\n",
-      "2020-01-14 12:02:40.448 - amici.ode_export - INFO - Finished writing dwdx.cpp                       (4.64E-03s)\n",
-      "2020-01-14 12:02:40.515 - amici.ode_export - INFO - Finished writing dxdotdw.cpp                    (6.32E-02s)\n",
-      "2020-01-14 12:02:40.596 - amici.ode_export - INFO - Finished writing dxdotdp_explicit.cpp           (7.23E-02s)\n",
-      "2020-01-14 12:02:40.732 - amici.ode_export - INFO - Finished writing dydx.cpp                       (1.10E-01s)\n",
-      "2020-01-14 12:02:40.804 - amici.ode_export - INFO - Finished writing dydp.cpp                       (6.94E-02s)\n",
-      "2020-01-14 12:02:40.823 - amici.ode_export - INFO - Finished writing dsigmaydp.cpp                  (1.57E-02s)\n",
-      "2020-01-14 12:02:40.845 - amici.ode_export - INFO - Finished writing sigmay.cpp                     (2.00E-02s)\n",
-      "2020-01-14 12:02:40.856 - amici.ode_export - INFO - Finished writing w.cpp                          (6.74E-03s)\n",
-      "2020-01-14 12:02:41.030 - amici.ode_export - INFO - Finished writing x0.cpp                         (5.22E-02s)\n",
-      "2020-01-14 12:02:41.077 - amici.ode_export - INFO - Finished writing x0_fixedParameters.cpp         (2.48E-03s)\n",
-      "2020-01-14 12:02:41.085 - amici.ode_export - INFO - Finished writing sx0.cpp                        (6.79E-03s)\n",
-      "2020-01-14 12:02:41.097 - amici.ode_export - INFO - Finished writing sx0_fixedParameters.cpp        (1.01E-02s)\n",
-      "2020-01-14 12:02:41.162 - amici.ode_export - INFO - Finished writing xdot.cpp                       (6.18E-02s)\n",
-      "2020-01-14 12:02:41.172 - amici.ode_export - INFO - Finished writing y.cpp                          (8.70E-03s)\n",
-      "2020-01-14 12:02:41.183 - amici.ode_export - INFO - Finished writing x_rdata.cpp                    (5.12E-03s)\n",
-      "2020-01-14 12:02:41.189 - amici.ode_export - INFO - Finished writing total_cl.cpp                   (4.86E-03s)\n",
-      "2020-01-14 12:02:41.194 - amici.ode_export - INFO - Finished writing x_solver.cpp                   (3.44E-03s)\n",
-      "2020-01-14 12:02:41.344 - amici.ode_export - INFO - Finished generating cpp code                    (3.53E+00s)\n",
-      "2020-01-14 12:03:41.052 - amici.ode_export - INFO - Finished compiling cpp code                     (5.97E+01s)\n"
+      "2020-01-15 15:30:05.015 - amici.sbml_import - INFO - Finished processing SBML parameters            (7.95E-04s)\n",
+      "2020-01-15 15:30:05.018 - amici.sbml_import - INFO - Finished processing SBML species               (2.23E-03s)\n",
+      "2020-01-15 15:30:05.033 - amici.sbml_import - INFO - Finished processing SBML reactions             (1.43E-02s)\n",
+      "2020-01-15 15:30:05.034 - amici.sbml_import - INFO - Finished processing SBML compartments          (2.22E-04s)\n",
+      "2020-01-15 15:30:05.116 - amici.sbml_import - INFO - Finished processing SBML rules                 (8.09E-02s)\n",
+      "2020-01-15 15:30:05.192 - amici.sbml_import - INFO - Finished processing SBML observables           (6.24E-02s)\n",
+      "2020-01-15 15:30:05.249 - amici.ode_export - INFO - Finished writing J.cpp                          (3.75E-02s)\n",
+      "2020-01-15 15:30:05.261 - amici.ode_export - INFO - Finished writing JB.cpp                         (1.06E-02s)\n",
+      "2020-01-15 15:30:05.267 - amici.ode_export - INFO - Finished writing JDiag.cpp                      (5.27E-03s)\n",
+      "2020-01-15 15:30:05.276 - amici.ode_export - INFO - Finished writing JSparse.cpp                    (8.72E-03s)\n",
+      "2020-01-15 15:30:05.285 - amici.ode_export - INFO - Finished writing JSparseB.cpp                   (7.53E-03s)\n",
+      "2020-01-15 15:30:05.302 - amici.ode_export - INFO - Finished writing Jy.cpp                         (1.52E-02s)\n",
+      "2020-01-15 15:30:05.397 - amici.ode_export - INFO - Finished writing dJydsigmay.cpp                 (9.41E-02s)\n",
+      "2020-01-15 15:30:05.434 - amici.ode_export - INFO - Finished writing dJydy.cpp                      (3.66E-02s)\n",
+      "2020-01-15 15:30:05.443 - amici.ode_export - INFO - Finished writing dwdp.cpp                       (7.90E-03s)\n",
+      "2020-01-15 15:30:05.447 - amici.ode_export - INFO - Finished writing dwdx.cpp                       (2.91E-03s)\n",
+      "2020-01-15 15:30:05.454 - amici.ode_export - INFO - Finished writing dxdotdw.cpp                    (5.28E-03s)\n",
+      "2020-01-15 15:30:05.467 - amici.ode_export - INFO - Finished writing dxdotdp_explicit.cpp           (1.17E-02s)\n",
+      "2020-01-15 15:30:05.484 - amici.ode_export - INFO - Finished writing dydx.cpp                       (1.27E-02s)\n",
+      "2020-01-15 15:30:05.498 - amici.ode_export - INFO - Finished writing dydp.cpp                       (1.28E-02s)\n",
+      "2020-01-15 15:30:05.504 - amici.ode_export - INFO - Finished writing dsigmaydp.cpp                  (5.58E-03s)\n",
+      "2020-01-15 15:30:05.508 - amici.ode_export - INFO - Finished writing sigmay.cpp                     (3.23E-03s)\n",
+      "2020-01-15 15:30:05.513 - amici.ode_export - INFO - Finished writing w.cpp                          (4.27E-03s)\n",
+      "2020-01-15 15:30:05.516 - amici.ode_export - INFO - Finished writing x0.cpp                         (1.98E-03s)\n",
+      "2020-01-15 15:30:05.517 - amici.ode_export - INFO - Finished writing x0_fixedParameters.cpp         (7.01E-04s)\n",
+      "2020-01-15 15:30:05.520 - amici.ode_export - INFO - Finished writing sx0.cpp                        (2.19E-03s)\n",
+      "2020-01-15 15:30:05.524 - amici.ode_export - INFO - Finished writing sx0_fixedParameters.cpp        (3.26E-03s)\n",
+      "2020-01-15 15:30:05.532 - amici.ode_export - INFO - Finished writing xdot.cpp                       (7.64E-03s)\n",
+      "2020-01-15 15:30:05.536 - amici.ode_export - INFO - Finished writing y.cpp                          (2.85E-03s)\n",
+      "2020-01-15 15:30:05.538 - amici.ode_export - INFO - Finished writing x_rdata.cpp                    (1.54E-03s)\n",
+      "2020-01-15 15:30:05.540 - amici.ode_export - INFO - Finished writing total_cl.cpp                   (1.33E-03s)\n",
+      "2020-01-15 15:30:05.544 - amici.ode_export - INFO - Finished writing x_solver.cpp                   (2.67E-03s)\n",
+      "2020-01-15 15:30:05.564 - amici.ode_export - INFO - Finished generating cpp code                    (3.66E-01s)\n",
+      "2020-01-15 15:30:17.005 - amici.ode_export - INFO - Finished compiling cpp code                     (1.14E+01s)\n"
      ]
     }
    ],
@@ -673,20 +650,14 @@
       " numrhsevals:  [  0 114 160 193 212 227 237 247 256 261 265 268 272 276 281 287 298 304\n",
       " 309 315 319 322 326 329 332 335 338 341 345 348 351 354 358 361 365 368\n",
       " 372 375 378 381 384 387 389 392 396 399 403 407 410 413 416 420 423 427\n",
-      " 431 435 438 442 445 448]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      " 431 435 438 442 445 448]\n",
       "numerrtestfails:  [0 1 1 3 3 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5\n",
       " 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5]\n",
       "numnonlinsolvconvfails:  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
       "       order:  [0 5 5 5 5 5 4 4 5 5 5 5 5 5 5 4 5 4 4 4 4 4 4 4 4 5 5 4 4 4 4 4 4 5 5 5 5\n",
       " 5 5 5 4 4 4 5 5 5 5 5 4 4 5 5 5 4 4 3 3 3 3 4]\n",
-      "    cpu_time:  1.7509999999999977\n",
+      "    cpu_time:  1.2739999999999994\n",
       "   numstepsB:  None\n",
       "numrhsevalsB:  None\n",
       "numerrtestfailsB:  None\n",
@@ -748,7 +719,9 @@
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -758,7 +731,9 @@
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -786,7 +761,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Log-likelihood -93.244712\n"
+      "Log-likelihood -102.648113\n"
      ]
     }
    ],
@@ -829,7 +804,9 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1145,13 +1122,7 @@
       "  [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
       "    1.00000000e+00  0.00000000e+00]\n",
       "  [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
-      "    0.00000000e+00  0.00000000e+00]]]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "    0.00000000e+00  0.00000000e+00]]]\n",
       "     ssigmay:  [[[0. 0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0. 0.]\n",
@@ -1256,13 +1227,7 @@
       "     ssigmaz:  None\n",
       "        sllh:  [nan nan nan nan nan nan nan nan]\n",
       "       s2llh:  None\n",
-      "      status:  0.0\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "      status:  0.0\n",
       "        sres:  [[0. 0. 0. 0. 0. 0. 0. 0.]\n",
       " [0. 0. 0. 0. 0. 0. 0. 0.]\n",
       " [0. 0. 0. 0. 0. 0. 0. 0.]\n",
@@ -1370,9 +1335,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Log-likelihood: -965.036808\n",
-      "Gradient: [-1.56345665e+02 -1.47156924e+02  3.44690906e+02  3.33801311e+01\n",
-      "  5.62586648e+02 -3.89449157e+00  4.26602370e+00  1.67034469e+04]\n"
+      "Log-likelihood: -1540.615428\n",
+      "Gradient: [ 2.00932843e+02  1.83428692e+02 -4.64936420e+02 -5.31266369e+01\n",
+      " -7.09252643e+02 -7.16051763e-02 -5.63883590e-01  2.82926928e+04]\n"
      ]
     }
    ],
@@ -1387,7 +1352,6 @@
     "\n",
     "solver = model.getSolver()\n",
     "solver.setMaxSteps(10**4) # Set maximum number of steps for the solver\n",
-    "solver.setRelativeTolerance(1e-12) # Lower relative tolerances to ensure accurate results\n",
     "\n",
     "# simulate time-course to get artificial data\n",
     "rdata = amici.runAmiciSimulation(model, solver)\n",
@@ -1427,31 +1391,31 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "sllh: |error|_2: 0.027890\n",
+      "sllh: |error|_2: 42.592180\n",
       "\n",
-      "sllh: p[0]: |error|_2: 0.009140\n",
-      "sllh: p[1]: |error|_2: 0.013789\n",
-      "sllh: p[2]: |error|_2: 0.003467\n",
-      "sllh: p[3]: |error|_2: 0.018971\n",
-      "sllh: p[4]: |error|_2: 0.010865\n",
-      "sllh: p[5]: |error|_2: 0.000002\n",
-      "sllh: p[6]: |error|_2: 0.000010\n",
-      "sllh: p[7]: |error|_2: 0.003771\n",
+      "sllh: p[0]: |error|_2: 0.031244\n",
+      "sllh: p[1]: |error|_2: 0.016243\n",
+      "sllh: p[2]: |error|_2: 0.018955\n",
+      "sllh: p[3]: |error|_2: 0.010088\n",
+      "sllh: p[4]: |error|_2: 0.016982\n",
+      "sllh: p[5]: |error|_2: 0.000280\n",
+      "sllh: p[6]: |error|_2: 0.001050\n",
+      "sllh: p[7]: |error|_2: 42.592175\n",
       "\n",
-      "sy: p[0]: |error|_2: 0.000398\n",
-      "sy: p[1]: |error|_2: 0.001700\n",
-      "sy: p[2]: |error|_2: 0.000158\n",
-      "sy: p[3]: |error|_2: 0.006592\n",
-      "sy: p[4]: |error|_2: 0.001237\n",
-      "sy: p[5]: |error|_2: 0.000001\n",
+      "sy: p[0]: |error|_2: 0.002974\n",
+      "sy: p[1]: |error|_2: 0.002717\n",
+      "sy: p[2]: |error|_2: 0.001308\n",
+      "sy: p[3]: |error|_2: 0.000939\n",
+      "sy: p[4]: |error|_2: 0.006106\n",
+      "sy: p[5]: |error|_2: 0.000000\n",
       "sy: p[6]: |error|_2: 0.000000\n",
       "sy: p[7]: |error|_2: 0.000000\n",
       "\n",
-      "sx: p[0]: |error|_2: 0.000021\n",
-      "sx: p[1]: |error|_2: 0.000725\n",
-      "sx: p[2]: |error|_2: 0.000105\n",
-      "sx: p[3]: |error|_2: 0.002510\n",
-      "sx: p[4]: |error|_2: 0.000579\n",
+      "sx: p[0]: |error|_2: 0.001033\n",
+      "sx: p[1]: |error|_2: 0.001076\n",
+      "sx: p[2]: |error|_2: 0.000121\n",
+      "sx: p[3]: |error|_2: 0.000439\n",
+      "sx: p[4]: |error|_2: 0.001569\n",
       "sx: p[5]: |error|_2: 0.000000\n",
       "sx: p[6]: |error|_2: 0.000000\n",
       "sx: p[7]: |error|_2: 0.000000\n",
@@ -1513,7 +1477,8 @@
     "    verbose and print(res)\n",
     "    return res\n",
     "\n",
-    "err_norm = check_grad(func, grad, p_orig, 'llh')\n",
+    "epsilon = 1e-4\n",
+    "err_norm = check_grad(func, grad, p_orig, 'llh', epsilon=epsilon)\n",
     "print('sllh: |error|_2: %f' % err_norm)\n",
     "# assert err_norm < 1e-6\n",
     "print()\n",
@@ -1521,31 +1486,119 @@
     "for ip in range(model.np()):\n",
     "    plist = [ip]\n",
     "    p = p_orig.copy()\n",
-    "    err_norm = check_grad(func, grad, p[plist], 'llh', p, [ip])\n",
+    "    err_norm = check_grad(func, grad, p[plist], 'llh', p, [ip], epsilon=epsilon)\n",
     "    print('sllh: p[%d]: |error|_2: %f' % (ip, err_norm))\n",
     "\n",
     "print()\n",
     "for ip in range(model.np()):\n",
     "    plist = [ip]\n",
     "    p = p_orig.copy()\n",
-    "    err_norm = check_grad(func, grad, p[plist], 'y', p, [ip])\n",
+    "    err_norm = check_grad(func, grad, p[plist], 'y', p, [ip], epsilon=epsilon)\n",
     "    print('sy: p[%d]: |error|_2: %f' % (ip, err_norm))\n",
     "\n",
     "print()\n",
     "for ip in range(model.np()):\n",
     "    plist = [ip]\n",
     "    p = p_orig.copy()\n",
-    "    err_norm = check_grad(func, grad, p[plist], 'x', p, [ip])\n",
+    "    err_norm = check_grad(func, grad, p[plist], 'x', p, [ip], epsilon=epsilon)\n",
     "    print('sx: p[%d]: |error|_2: %f' % (ip, err_norm))\n",
     "\n",
     "print()\n",
     "for ip in range(model.np()):\n",
     "    plist = [ip]\n",
     "    p = p_orig.copy()\n",
-    "    err_norm = check_grad(func, grad, p[plist], 'sigmay', p, [ip])\n",
+    "    err_norm = check_grad(func, grad, p[plist], 'sigmay', p, [ip], epsilon=epsilon)\n",
     "    print('ssigmay: p[%d]: |error|_2: %f' % (ip, err_norm))\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 8 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "------\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 8 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "eps=1e-4\n",
+    "op=model.getParameters()\n",
+    "\n",
+    "\n",
+    "solver.setSensitivityMethod(amici.SensitivityMethod_forward) # forward sensitivity analysis\n",
+    "solver.setSensitivityOrder(amici.SensitivityOrder_first) # first-order sensitivities\n",
+    "model.requireSensitivitiesForAllParameters()\n",
+    "solver.setRelativeTolerance(1e-12)\n",
+    "rdata = amici.runAmiciSimulation(model, solver, edata)\n",
+    "\n",
+    "def fd(x0, ip, eps, symbol='llh'):\n",
+    "    p = list(x0[:])\n",
+    "    old_parameters = model.getParameters()\n",
+    "    solver.setSensitivityOrder(amici.SensitivityOrder_none)\n",
+    "    p[ip]+=eps\n",
+    "    model.setParameters(p)\n",
+    "    rdata_f = amici.runAmiciSimulation(model, solver, edata)\n",
+    "    p[ip]-=2*eps\n",
+    "    model.setParameters(p)\n",
+    "    rdata_b = amici.runAmiciSimulation(model, solver, edata)\n",
+    "    \n",
+    "    model.setParameters(old_parameters)\n",
+    "    return (rdata_f[symbol]-rdata_b[symbol])/(2*eps)\n",
+    "\n",
+    "def plot_sensitivities(symbol, eps):\n",
+    "    fig, axes = plt.subplots(4,2, figsize=(15,10))\n",
+    "    for ip in range(4):\n",
+    "        fd_approx = fd(model.getParameters(), ip, eps, symbol=symbol)\n",
+    "\n",
+    "        axes[ip,0].plot(edata.getTimepoints(), rdata[f's{symbol}'][:,ip,:], 'r-')\n",
+    "        axes[ip,0].plot(edata.getTimepoints(), fd_approx, 'k--')\n",
+    "        axes[ip,0].set_ylabel(f'sensitivity {symbol}')\n",
+    "        axes[ip,0].set_xlabel('time')\n",
+    "\n",
+    "\n",
+    "        axes[ip,1].plot(edata.getTimepoints(), np.abs(rdata[f's{symbol}'][:,ip,:]-fd_approx), 'k-')\n",
+    "        axes[ip,1].set_ylabel('difference to fd')\n",
+    "        axes[ip,1].set_xlabel('time')\n",
+    "        axes[ip,1].set_yscale('log')\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "plot_sensitivities('x', eps)\n",
+    "print('------')\n",
+    "plot_sensitivities('y', eps)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1557,7 +1610,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1567,7 +1620,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1613,19 +1666,19 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>0</td>\n",
+       "      <th>0</th>\n",
        "      <td>0.0</td>\n",
        "      <td>data</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>-0.580906</td>\n",
-       "      <td>1.138852</td>\n",
-       "      <td>2.548393</td>\n",
-       "      <td>-0.328878</td>\n",
-       "      <td>3.430770</td>\n",
-       "      <td>0.265899</td>\n",
+       "      <td>-0.979183</td>\n",
+       "      <td>0.192956</td>\n",
+       "      <td>1.072430</td>\n",
+       "      <td>0.048621</td>\n",
+       "      <td>1.944063</td>\n",
+       "      <td>-0.727894</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1634,19 +1687,19 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1</td>\n",
+       "      <th>1</th>\n",
        "      <td>0.5</td>\n",
        "      <td>data</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.061815</td>\n",
-       "      <td>2.587035</td>\n",
-       "      <td>0.052490</td>\n",
-       "      <td>0.495810</td>\n",
-       "      <td>3.480146</td>\n",
-       "      <td>0.057071</td>\n",
+       "      <td>1.318564</td>\n",
+       "      <td>0.893174</td>\n",
+       "      <td>0.119221</td>\n",
+       "      <td>0.667590</td>\n",
+       "      <td>2.991938</td>\n",
+       "      <td>-0.606037</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1655,19 +1708,19 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2</td>\n",
+       "      <th>2</th>\n",
        "      <td>1.0</td>\n",
        "      <td>data</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>-0.364329</td>\n",
-       "      <td>-0.076008</td>\n",
-       "      <td>0.770576</td>\n",
-       "      <td>2.179266</td>\n",
-       "      <td>4.011141</td>\n",
-       "      <td>0.968669</td>\n",
+       "      <td>1.998418</td>\n",
+       "      <td>0.319199</td>\n",
+       "      <td>1.230149</td>\n",
+       "      <td>-0.398608</td>\n",
+       "      <td>3.856450</td>\n",
+       "      <td>-0.913528</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1676,19 +1729,19 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>3</td>\n",
+       "      <th>3</th>\n",
        "      <td>1.5</td>\n",
        "      <td>data</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.002332</td>\n",
-       "      <td>1.337097</td>\n",
-       "      <td>-0.756828</td>\n",
-       "      <td>0.599910</td>\n",
-       "      <td>4.724282</td>\n",
-       "      <td>1.996087</td>\n",
+       "      <td>-0.014750</td>\n",
+       "      <td>-0.427097</td>\n",
+       "      <td>-0.279452</td>\n",
+       "      <td>1.433348</td>\n",
+       "      <td>4.788364</td>\n",
+       "      <td>-0.074601</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1697,19 +1750,19 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>4</td>\n",
+       "      <th>4</th>\n",
        "      <td>2.0</td>\n",
        "      <td>data</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>2.469387</td>\n",
-       "      <td>2.777323</td>\n",
-       "      <td>-0.917034</td>\n",
-       "      <td>2.146876</td>\n",
-       "      <td>4.111527</td>\n",
-       "      <td>2.106783</td>\n",
+       "      <td>0.407775</td>\n",
+       "      <td>0.463747</td>\n",
+       "      <td>0.119668</td>\n",
+       "      <td>1.180980</td>\n",
+       "      <td>3.065187</td>\n",
+       "      <td>-0.101223</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1718,19 +1771,19 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>5</td>\n",
+       "      <th>5</th>\n",
        "      <td>2.5</td>\n",
        "      <td>data</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>-0.690338</td>\n",
-       "      <td>1.233353</td>\n",
-       "      <td>-0.617748</td>\n",
-       "      <td>0.443415</td>\n",
-       "      <td>5.329311</td>\n",
-       "      <td>1.042524</td>\n",
+       "      <td>1.481813</td>\n",
+       "      <td>0.293208</td>\n",
+       "      <td>-0.159899</td>\n",
+       "      <td>0.218286</td>\n",
+       "      <td>3.183441</td>\n",
+       "      <td>0.933746</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1739,19 +1792,19 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>6</td>\n",
+       "      <th>6</th>\n",
        "      <td>3.0</td>\n",
        "      <td>data</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>-0.090433</td>\n",
-       "      <td>3.113311</td>\n",
-       "      <td>-0.246096</td>\n",
-       "      <td>0.139758</td>\n",
-       "      <td>2.567485</td>\n",
-       "      <td>1.001862</td>\n",
+       "      <td>-0.064053</td>\n",
+       "      <td>1.484332</td>\n",
+       "      <td>0.119981</td>\n",
+       "      <td>1.719267</td>\n",
+       "      <td>4.822919</td>\n",
+       "      <td>1.856944</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1760,19 +1813,19 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>7</td>\n",
+       "      <th>7</th>\n",
        "      <td>3.5</td>\n",
        "      <td>data</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.369380</td>\n",
-       "      <td>0.579885</td>\n",
-       "      <td>-0.851098</td>\n",
-       "      <td>0.000604</td>\n",
-       "      <td>4.472670</td>\n",
-       "      <td>0.904686</td>\n",
+       "      <td>1.411443</td>\n",
+       "      <td>1.836223</td>\n",
+       "      <td>1.359105</td>\n",
+       "      <td>0.048072</td>\n",
+       "      <td>4.722903</td>\n",
+       "      <td>-2.202143</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1781,19 +1834,19 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>8</td>\n",
+       "      <th>8</th>\n",
        "      <td>4.0</td>\n",
        "      <td>data</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>-1.099564</td>\n",
-       "      <td>3.210831</td>\n",
-       "      <td>1.263742</td>\n",
-       "      <td>0.448372</td>\n",
-       "      <td>4.271006</td>\n",
-       "      <td>-0.690721</td>\n",
+       "      <td>0.805056</td>\n",
+       "      <td>0.163220</td>\n",
+       "      <td>0.146697</td>\n",
+       "      <td>0.425173</td>\n",
+       "      <td>3.338153</td>\n",
+       "      <td>-1.097586</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1802,19 +1855,19 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>9</td>\n",
+       "      <th>9</th>\n",
        "      <td>4.5</td>\n",
        "      <td>data</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.064880</td>\n",
-       "      <td>-0.812373</td>\n",
-       "      <td>0.581151</td>\n",
-       "      <td>0.178842</td>\n",
-       "      <td>4.296181</td>\n",
-       "      <td>1.771814</td>\n",
+       "      <td>2.785217</td>\n",
+       "      <td>-0.098704</td>\n",
+       "      <td>-0.560119</td>\n",
+       "      <td>2.580439</td>\n",
+       "      <td>4.363146</td>\n",
+       "      <td>1.110953</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1823,19 +1876,19 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>10</td>\n",
+       "      <th>10</th>\n",
        "      <td>5.0</td>\n",
        "      <td>data</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.398187</td>\n",
-       "      <td>-0.764161</td>\n",
-       "      <td>-1.909564</td>\n",
-       "      <td>-0.135744</td>\n",
-       "      <td>3.378465</td>\n",
-       "      <td>0.877764</td>\n",
+       "      <td>0.117663</td>\n",
+       "      <td>-0.734754</td>\n",
+       "      <td>-0.664079</td>\n",
+       "      <td>0.541150</td>\n",
+       "      <td>3.478977</td>\n",
+       "      <td>-1.436971</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1844,19 +1897,19 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>11</td>\n",
+       "      <th>11</th>\n",
        "      <td>5.5</td>\n",
        "      <td>data</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.399516</td>\n",
-       "      <td>1.470097</td>\n",
-       "      <td>-0.238305</td>\n",
-       "      <td>-0.305426</td>\n",
-       "      <td>4.508414</td>\n",
-       "      <td>1.382739</td>\n",
+       "      <td>1.059940</td>\n",
+       "      <td>0.499436</td>\n",
+       "      <td>-0.805301</td>\n",
+       "      <td>1.440611</td>\n",
+       "      <td>2.165131</td>\n",
+       "      <td>-0.749951</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1865,19 +1918,19 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>12</td>\n",
+       "      <th>12</th>\n",
        "      <td>6.0</td>\n",
        "      <td>data</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.963333</td>\n",
-       "      <td>-0.402086</td>\n",
-       "      <td>0.775235</td>\n",
-       "      <td>1.567235</td>\n",
-       "      <td>3.003864</td>\n",
-       "      <td>0.148294</td>\n",
+       "      <td>2.044621</td>\n",
+       "      <td>0.089834</td>\n",
+       "      <td>-0.030406</td>\n",
+       "      <td>0.931452</td>\n",
+       "      <td>4.924059</td>\n",
+       "      <td>1.285210</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1886,19 +1939,19 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>13</td>\n",
+       "      <th>13</th>\n",
        "      <td>6.5</td>\n",
        "      <td>data</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.860035</td>\n",
-       "      <td>1.720437</td>\n",
-       "      <td>-0.257288</td>\n",
-       "      <td>-0.008972</td>\n",
-       "      <td>4.657872</td>\n",
-       "      <td>0.009823</td>\n",
+       "      <td>1.136111</td>\n",
+       "      <td>-1.453566</td>\n",
+       "      <td>0.570666</td>\n",
+       "      <td>1.460973</td>\n",
+       "      <td>1.416084</td>\n",
+       "      <td>-0.236831</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1907,19 +1960,19 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>14</td>\n",
+       "      <th>14</th>\n",
        "      <td>7.0</td>\n",
        "      <td>data</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>-0.268294</td>\n",
-       "      <td>0.790623</td>\n",
-       "      <td>1.565493</td>\n",
-       "      <td>0.390212</td>\n",
-       "      <td>2.289319</td>\n",
-       "      <td>2.018072</td>\n",
+       "      <td>-0.077502</td>\n",
+       "      <td>1.456230</td>\n",
+       "      <td>1.537391</td>\n",
+       "      <td>0.465748</td>\n",
+       "      <td>5.488972</td>\n",
+       "      <td>-0.039388</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1928,19 +1981,19 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>15</td>\n",
+       "      <th>15</th>\n",
        "      <td>7.5</td>\n",
        "      <td>data</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.376917</td>\n",
-       "      <td>0.094986</td>\n",
-       "      <td>-1.076985</td>\n",
-       "      <td>0.047076</td>\n",
-       "      <td>3.485641</td>\n",
-       "      <td>1.122709</td>\n",
+       "      <td>1.736106</td>\n",
+       "      <td>-2.389954</td>\n",
+       "      <td>-0.401374</td>\n",
+       "      <td>1.470112</td>\n",
+       "      <td>4.389249</td>\n",
+       "      <td>0.794856</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1949,19 +2002,19 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>16</td>\n",
+       "      <th>16</th>\n",
        "      <td>8.0</td>\n",
        "      <td>data</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>2.327584</td>\n",
-       "      <td>0.272424</td>\n",
-       "      <td>0.786510</td>\n",
-       "      <td>0.857738</td>\n",
-       "      <td>4.751938</td>\n",
-       "      <td>-1.117641</td>\n",
+       "      <td>-0.882210</td>\n",
+       "      <td>2.627972</td>\n",
+       "      <td>0.680587</td>\n",
+       "      <td>1.609892</td>\n",
+       "      <td>3.058685</td>\n",
+       "      <td>0.085670</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1970,19 +2023,19 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>17</td>\n",
+       "      <th>17</th>\n",
        "      <td>8.5</td>\n",
        "      <td>data</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.212276</td>\n",
-       "      <td>0.653588</td>\n",
-       "      <td>-0.085610</td>\n",
-       "      <td>2.219086</td>\n",
-       "      <td>2.986550</td>\n",
-       "      <td>1.023628</td>\n",
+       "      <td>1.328443</td>\n",
+       "      <td>0.587181</td>\n",
+       "      <td>-0.485711</td>\n",
+       "      <td>1.499816</td>\n",
+       "      <td>4.450632</td>\n",
+       "      <td>1.079241</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1991,19 +2044,19 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>18</td>\n",
+       "      <th>18</th>\n",
        "      <td>9.0</td>\n",
        "      <td>data</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.817425</td>\n",
-       "      <td>0.707867</td>\n",
-       "      <td>1.353827</td>\n",
-       "      <td>0.159925</td>\n",
-       "      <td>3.563772</td>\n",
-       "      <td>0.916139</td>\n",
+       "      <td>-0.655710</td>\n",
+       "      <td>-0.594330</td>\n",
+       "      <td>-1.469235</td>\n",
+       "      <td>0.276059</td>\n",
+       "      <td>2.635020</td>\n",
+       "      <td>0.752944</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -2012,19 +2065,19 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>19</td>\n",
+       "      <th>19</th>\n",
        "      <td>9.5</td>\n",
        "      <td>data</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>-0.643787</td>\n",
-       "      <td>2.453173</td>\n",
-       "      <td>-0.375571</td>\n",
-       "      <td>0.045604</td>\n",
-       "      <td>4.114438</td>\n",
-       "      <td>1.291537</td>\n",
+       "      <td>0.560821</td>\n",
+       "      <td>-0.423914</td>\n",
+       "      <td>0.248592</td>\n",
+       "      <td>1.040715</td>\n",
+       "      <td>3.336944</td>\n",
+       "      <td>0.784157</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -2033,19 +2086,19 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>20</td>\n",
+       "      <th>20</th>\n",
        "      <td>10.0</td>\n",
        "      <td>data</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.660324</td>\n",
-       "      <td>0.137157</td>\n",
-       "      <td>-0.152916</td>\n",
-       "      <td>2.334600</td>\n",
-       "      <td>2.762463</td>\n",
-       "      <td>1.414332</td>\n",
+       "      <td>0.872973</td>\n",
+       "      <td>1.334567</td>\n",
+       "      <td>-1.307793</td>\n",
+       "      <td>2.725420</td>\n",
+       "      <td>2.947031</td>\n",
+       "      <td>-1.336964</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -2059,74 +2112,73 @@
       ],
       "text/plain": [
        "    time datatype  t_presim   k0  k0_preeq  k0_presim  observable_x1  \\\n",
-       "0    0.0     data       0.0  1.0       NaN        NaN      -0.580906   \n",
-       "1    0.5     data       0.0  1.0       NaN        NaN       0.061815   \n",
-       "2    1.0     data       0.0  1.0       NaN        NaN      -0.364329   \n",
-       "3    1.5     data       0.0  1.0       NaN        NaN       1.002332   \n",
-       "4    2.0     data       0.0  1.0       NaN        NaN       2.469387   \n",
-       "5    2.5     data       0.0  1.0       NaN        NaN      -0.690338   \n",
-       "6    3.0     data       0.0  1.0       NaN        NaN      -0.090433   \n",
-       "7    3.5     data       0.0  1.0       NaN        NaN       0.369380   \n",
-       "8    4.0     data       0.0  1.0       NaN        NaN      -1.099564   \n",
-       "9    4.5     data       0.0  1.0       NaN        NaN       1.064880   \n",
-       "10   5.0     data       0.0  1.0       NaN        NaN       0.398187   \n",
-       "11   5.5     data       0.0  1.0       NaN        NaN       1.399516   \n",
-       "12   6.0     data       0.0  1.0       NaN        NaN       0.963333   \n",
-       "13   6.5     data       0.0  1.0       NaN        NaN       1.860035   \n",
-       "14   7.0     data       0.0  1.0       NaN        NaN      -0.268294   \n",
-       "15   7.5     data       0.0  1.0       NaN        NaN       0.376917   \n",
-       "16   8.0     data       0.0  1.0       NaN        NaN       2.327584   \n",
-       "17   8.5     data       0.0  1.0       NaN        NaN       0.212276   \n",
-       "18   9.0     data       0.0  1.0       NaN        NaN       1.817425   \n",
-       "19   9.5     data       0.0  1.0       NaN        NaN      -0.643787   \n",
-       "20  10.0     data       0.0  1.0       NaN        NaN       0.660324   \n",
+       "0    0.0     data       0.0  1.0       NaN        NaN      -0.979183   \n",
+       "1    0.5     data       0.0  1.0       NaN        NaN       1.318564   \n",
+       "2    1.0     data       0.0  1.0       NaN        NaN       1.998418   \n",
+       "3    1.5     data       0.0  1.0       NaN        NaN      -0.014750   \n",
+       "4    2.0     data       0.0  1.0       NaN        NaN       0.407775   \n",
+       "5    2.5     data       0.0  1.0       NaN        NaN       1.481813   \n",
+       "6    3.0     data       0.0  1.0       NaN        NaN      -0.064053   \n",
+       "7    3.5     data       0.0  1.0       NaN        NaN       1.411443   \n",
+       "8    4.0     data       0.0  1.0       NaN        NaN       0.805056   \n",
+       "9    4.5     data       0.0  1.0       NaN        NaN       2.785217   \n",
+       "10   5.0     data       0.0  1.0       NaN        NaN       0.117663   \n",
+       "11   5.5     data       0.0  1.0       NaN        NaN       1.059940   \n",
+       "12   6.0     data       0.0  1.0       NaN        NaN       2.044621   \n",
+       "13   6.5     data       0.0  1.0       NaN        NaN       1.136111   \n",
+       "14   7.0     data       0.0  1.0       NaN        NaN      -0.077502   \n",
+       "15   7.5     data       0.0  1.0       NaN        NaN       1.736106   \n",
+       "16   8.0     data       0.0  1.0       NaN        NaN      -0.882210   \n",
+       "17   8.5     data       0.0  1.0       NaN        NaN       1.328443   \n",
+       "18   9.0     data       0.0  1.0       NaN        NaN      -0.655710   \n",
+       "19   9.5     data       0.0  1.0       NaN        NaN       0.560821   \n",
+       "20  10.0     data       0.0  1.0       NaN        NaN       0.872973   \n",
        "\n",
        "    observable_x2  observable_x3  observable_x1_scaled  \\\n",
-       "0        1.138852       2.548393             -0.328878   \n",
-       "1        2.587035       0.052490              0.495810   \n",
-       "2       -0.076008       0.770576              2.179266   \n",
-       "3        1.337097      -0.756828              0.599910   \n",
-       "4        2.777323      -0.917034              2.146876   \n",
-       "5        1.233353      -0.617748              0.443415   \n",
-       "6        3.113311      -0.246096              0.139758   \n",
-       "7        0.579885      -0.851098              0.000604   \n",
-       "8        3.210831       1.263742              0.448372   \n",
-       "9       -0.812373       0.581151              0.178842   \n",
-       "10      -0.764161      -1.909564             -0.135744   \n",
-       "11       1.470097      -0.238305             -0.305426   \n",
-       "12      -0.402086       0.775235              1.567235   \n",
-       "13       1.720437      -0.257288             -0.008972   \n",
-       "14       0.790623       1.565493              0.390212   \n",
-       "15       0.094986      -1.076985              0.047076   \n",
-       "16       0.272424       0.786510              0.857738   \n",
-       "17       0.653588      -0.085610              2.219086   \n",
-       "18       0.707867       1.353827              0.159925   \n",
-       "19       2.453173      -0.375571              0.045604   \n",
-       "20       0.137157      -0.152916              2.334600   \n",
+       "0        0.192956       1.072430              0.048621   \n",
+       "1        0.893174       0.119221              0.667590   \n",
+       "2        0.319199       1.230149             -0.398608   \n",
+       "3       -0.427097      -0.279452              1.433348   \n",
+       "4        0.463747       0.119668              1.180980   \n",
+       "5        0.293208      -0.159899              0.218286   \n",
+       "6        1.484332       0.119981              1.719267   \n",
+       "7        1.836223       1.359105              0.048072   \n",
+       "8        0.163220       0.146697              0.425173   \n",
+       "9       -0.098704      -0.560119              2.580439   \n",
+       "10      -0.734754      -0.664079              0.541150   \n",
+       "11       0.499436      -0.805301              1.440611   \n",
+       "12       0.089834      -0.030406              0.931452   \n",
+       "13      -1.453566       0.570666              1.460973   \n",
+       "14       1.456230       1.537391              0.465748   \n",
+       "15      -2.389954      -0.401374              1.470112   \n",
+       "16       2.627972       0.680587              1.609892   \n",
+       "17       0.587181      -0.485711              1.499816   \n",
+       "18      -0.594330      -1.469235              0.276059   \n",
+       "19      -0.423914       0.248592              1.040715   \n",
+       "20       1.334567      -1.307793              2.725420   \n",
        "\n",
        "    observable_x2_offsetted  observable_x1withsigma  observable_x1_std  \\\n",
-       "0                  3.430770                0.265899                1.0   \n",
-       "1                  3.480146                0.057071                1.0   \n",
-       "2                  4.011141                0.968669                1.0   \n",
-       "3                  4.724282                1.996087                1.0   \n",
-       "4                  4.111527                2.106783                1.0   \n",
-       "5                  5.329311                1.042524                1.0   \n",
-       "6                  2.567485                1.001862                1.0   \n",
-       "7                  4.472670                0.904686                1.0   \n",
-       "8                  4.271006               -0.690721                1.0   \n",
-       "9                  4.296181                1.771814                1.0   \n",
-       "10                 3.378465                0.877764                1.0   \n",
-       "11                 4.508414                1.382739                1.0   \n",
-       "12                 3.003864                0.148294                1.0   \n",
-       "13                 4.657872                0.009823                1.0   \n",
-       "14                 2.289319                2.018072                1.0   \n",
-       "15                 3.485641                1.122709                1.0   \n",
-       "16                 4.751938               -1.117641                1.0   \n",
-       "17                 2.986550                1.023628                1.0   \n",
-       "18                 3.563772                0.916139                1.0   \n",
-       "19                 4.114438                1.291537                1.0   \n",
-       "20                 2.762463                1.414332                1.0   \n",
-
+       "0                  1.944063               -0.727894                1.0   \n",
+       "1                  2.991938               -0.606037                1.0   \n",
+       "2                  3.856450               -0.913528                1.0   \n",
+       "3                  4.788364               -0.074601                1.0   \n",
+       "4                  3.065187               -0.101223                1.0   \n",
+       "5                  3.183441                0.933746                1.0   \n",
+       "6                  4.822919                1.856944                1.0   \n",
+       "7                  4.722903               -2.202143                1.0   \n",
+       "8                  3.338153               -1.097586                1.0   \n",
+       "9                  4.363146                1.110953                1.0   \n",
+       "10                 3.478977               -1.436971                1.0   \n",
+       "11                 2.165131               -0.749951                1.0   \n",
+       "12                 4.924059                1.285210                1.0   \n",
+       "13                 1.416084               -0.236831                1.0   \n",
+       "14                 5.488972               -0.039388                1.0   \n",
+       "15                 4.389249                0.794856                1.0   \n",
+       "16                 3.058685                0.085670                1.0   \n",
+       "17                 4.450632                1.079241                1.0   \n",
+       "18                 2.635020                0.752944                1.0   \n",
+       "19                 3.336944                0.784157                1.0   \n",
+       "20                 2.947031               -1.336964                1.0   \n",
        "\n",
        "    observable_x2_std  observable_x3_std  observable_x1_scaled_std  \\\n",
        "0                 1.0                1.0                       1.0   \n",
@@ -2175,7 +2227,7 @@
        "20                          1.0                         NaN  "
       ]
      },
-     "execution_count": 21,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2188,7 +2240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2198,7 +2250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -2237,298 +2289,298 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>0</td>\n",
+       "      <th>0</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.680906</td>\n",
-       "      <td>0.738852</td>\n",
-       "      <td>1.848393</td>\n",
-       "      <td>0.528878</td>\n",
-       "      <td>0.030770</td>\n",
-       "      <td>1.658994</td>\n",
+       "      <td>1.079183</td>\n",
+       "      <td>0.207044</td>\n",
+       "      <td>0.372430</td>\n",
+       "      <td>0.151379</td>\n",
+       "      <td>1.455937</td>\n",
+       "      <td>8.278943</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1</td>\n",
+       "      <th>1</th>\n",
        "      <td>0.5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.477552</td>\n",
-       "      <td>1.902356</td>\n",
-       "      <td>0.139001</td>\n",
-       "      <td>0.582924</td>\n",
-       "      <td>0.204532</td>\n",
-       "      <td>4.822960</td>\n",
+       "      <td>0.779197</td>\n",
+       "      <td>0.208496</td>\n",
+       "      <td>0.072270</td>\n",
+       "      <td>0.411145</td>\n",
+       "      <td>0.692741</td>\n",
+       "      <td>11.454045</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2</td>\n",
+       "      <th>2</th>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.944402</td>\n",
-       "      <td>0.809295</td>\n",
-       "      <td>0.674152</td>\n",
-       "      <td>1.019122</td>\n",
-       "      <td>0.277854</td>\n",
-       "      <td>3.885968</td>\n",
+       "      <td>1.418346</td>\n",
+       "      <td>0.414088</td>\n",
+       "      <td>1.133725</td>\n",
+       "      <td>1.558753</td>\n",
+       "      <td>0.123162</td>\n",
+       "      <td>14.936001</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>3</td>\n",
+       "      <th>3</th>\n",
        "      <td>1.5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.431933</td>\n",
-       "      <td>0.606445</td>\n",
-       "      <td>0.832904</td>\n",
-       "      <td>0.540889</td>\n",
-       "      <td>0.993630</td>\n",
-       "      <td>14.256878</td>\n",
+       "      <td>0.585149</td>\n",
+       "      <td>1.157749</td>\n",
+       "      <td>0.355528</td>\n",
+       "      <td>0.292550</td>\n",
+       "      <td>1.057712</td>\n",
+       "      <td>6.450000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>4</td>\n",
+       "      <th>4</th>\n",
        "      <td>2.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.908853</td>\n",
-       "      <td>2.061487</td>\n",
-       "      <td>0.986728</td>\n",
-       "      <td>1.025807</td>\n",
-       "      <td>0.395691</td>\n",
-       "      <td>15.462488</td>\n",
+       "      <td>0.152759</td>\n",
+       "      <td>0.252089</td>\n",
+       "      <td>0.049974</td>\n",
+       "      <td>0.059911</td>\n",
+       "      <td>0.650649</td>\n",
+       "      <td>6.617580</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>5</td>\n",
+       "      <th>5</th>\n",
        "      <td>2.5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.243394</td>\n",
-       "      <td>0.534602</td>\n",
-       "      <td>0.684049</td>\n",
-       "      <td>0.662697</td>\n",
-       "      <td>1.630560</td>\n",
-       "      <td>4.894680</td>\n",
+       "      <td>0.928757</td>\n",
+       "      <td>0.405543</td>\n",
+       "      <td>0.226200</td>\n",
+       "      <td>0.887826</td>\n",
+       "      <td>0.515310</td>\n",
+       "      <td>3.806906</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>6</td>\n",
+       "      <th>6</th>\n",
        "      <td>3.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.637304</td>\n",
-       "      <td>2.431349</td>\n",
-       "      <td>0.309829</td>\n",
-       "      <td>0.953983</td>\n",
-       "      <td>1.114478</td>\n",
-       "      <td>4.549911</td>\n",
+       "      <td>0.610923</td>\n",
+       "      <td>0.802369</td>\n",
+       "      <td>0.056249</td>\n",
+       "      <td>0.625525</td>\n",
+       "      <td>1.140957</td>\n",
+       "      <td>13.100735</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>7</td>\n",
+       "      <th>7</th>\n",
        "      <td>3.5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.171979</td>\n",
-       "      <td>0.086224</td>\n",
-       "      <td>0.912605</td>\n",
-       "      <td>1.082116</td>\n",
-       "      <td>0.806561</td>\n",
-       "      <td>3.633264</td>\n",
+       "      <td>0.870083</td>\n",
+       "      <td>1.170115</td>\n",
+       "      <td>1.297599</td>\n",
+       "      <td>1.034648</td>\n",
+       "      <td>1.056794</td>\n",
+       "      <td>27.435032</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>8</td>\n",
+       "      <th>8</th>\n",
        "      <td>4.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.635844</td>\n",
-       "      <td>2.559529</td>\n",
-       "      <td>1.204247</td>\n",
-       "      <td>0.624189</td>\n",
-       "      <td>0.619704</td>\n",
-       "      <td>12.270016</td>\n",
+       "      <td>0.268776</td>\n",
+       "      <td>0.488083</td>\n",
+       "      <td>0.087202</td>\n",
+       "      <td>0.647388</td>\n",
+       "      <td>0.313150</td>\n",
+       "      <td>16.338665</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>9</td>\n",
+       "      <th>9</th>\n",
        "      <td>4.5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.533342</td>\n",
-       "      <td>1.449888</td>\n",
-       "      <td>0.523497</td>\n",
-       "      <td>0.884234</td>\n",
-       "      <td>0.658666</td>\n",
-       "      <td>12.402753</td>\n",
+       "      <td>2.253679</td>\n",
+       "      <td>0.736219</td>\n",
+       "      <td>0.617772</td>\n",
+       "      <td>1.517363</td>\n",
+       "      <td>0.725632</td>\n",
+       "      <td>5.794149</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>10</td>\n",
+       "      <th>10</th>\n",
        "      <td>5.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.128904</td>\n",
-       "      <td>1.388843</td>\n",
-       "      <td>1.965524</td>\n",
-       "      <td>1.189927</td>\n",
-       "      <td>0.246216</td>\n",
-       "      <td>3.506723</td>\n",
+       "      <td>0.409429</td>\n",
+       "      <td>1.359436</td>\n",
+       "      <td>0.720039</td>\n",
+       "      <td>0.513033</td>\n",
+       "      <td>0.145704</td>\n",
+       "      <td>19.640623</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>11</td>\n",
+       "      <th>11</th>\n",
        "      <td>5.5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.876601</td>\n",
-       "      <td>0.857365</td>\n",
-       "      <td>0.292705</td>\n",
-       "      <td>1.351255</td>\n",
-       "      <td>0.895681</td>\n",
-       "      <td>8.598242</td>\n",
+       "      <td>0.537026</td>\n",
+       "      <td>0.113297</td>\n",
+       "      <td>0.859701</td>\n",
+       "      <td>0.394782</td>\n",
+       "      <td>1.447601</td>\n",
+       "      <td>12.728659</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>12</td>\n",
+       "      <th>12</th>\n",
        "      <td>6.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.444344</td>\n",
-       "      <td>1.003688</td>\n",
-       "      <td>0.722275</td>\n",
-       "      <td>0.529257</td>\n",
-       "      <td>0.597738</td>\n",
-       "      <td>3.706950</td>\n",
+       "      <td>1.525632</td>\n",
+       "      <td>0.511769</td>\n",
+       "      <td>0.083366</td>\n",
+       "      <td>0.106527</td>\n",
+       "      <td>1.322456</td>\n",
+       "      <td>7.662203</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>13</td>\n",
+       "      <th>13</th>\n",
        "      <td>6.5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.344736</td>\n",
-       "      <td>1.129208</td>\n",
-       "      <td>0.308918</td>\n",
-       "      <td>1.039571</td>\n",
-       "      <td>1.066643</td>\n",
-       "      <td>5.054765</td>\n",
+       "      <td>0.620811</td>\n",
+       "      <td>2.044795</td>\n",
+       "      <td>0.519037</td>\n",
+       "      <td>0.430374</td>\n",
+       "      <td>2.175145</td>\n",
+       "      <td>7.521306</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>14</td>\n",
+       "      <th>14</th>\n",
        "      <td>7.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.780124</td>\n",
-       "      <td>0.209067</td>\n",
-       "      <td>1.515094</td>\n",
-       "      <td>0.633448</td>\n",
-       "      <td>1.292236</td>\n",
-       "      <td>15.062423</td>\n",
+       "      <td>0.589332</td>\n",
+       "      <td>0.874674</td>\n",
+       "      <td>1.486992</td>\n",
+       "      <td>0.557912</td>\n",
+       "      <td>1.907417</td>\n",
+       "      <td>5.512185</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>15</td>\n",
+       "      <th>15</th>\n",
        "      <td>7.5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.131651</td>\n",
-       "      <td>0.477543</td>\n",
-       "      <td>1.126244</td>\n",
-       "      <td>0.970059</td>\n",
-       "      <td>0.086888</td>\n",
-       "      <td>6.141412</td>\n",
+       "      <td>1.227539</td>\n",
+       "      <td>2.962483</td>\n",
+       "      <td>0.450634</td>\n",
+       "      <td>0.452977</td>\n",
+       "      <td>0.816720</td>\n",
+       "      <td>2.862886</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>16</td>\n",
+       "      <th>16</th>\n",
        "      <td>8.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.822083</td>\n",
-       "      <td>0.291679</td>\n",
-       "      <td>0.738307</td>\n",
-       "      <td>0.153263</td>\n",
-       "      <td>1.187835</td>\n",
-       "      <td>16.231417</td>\n",
+       "      <td>1.387711</td>\n",
+       "      <td>2.063869</td>\n",
+       "      <td>0.632384</td>\n",
+       "      <td>0.598891</td>\n",
+       "      <td>0.505418</td>\n",
+       "      <td>4.198303</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>17</td>\n",
+       "      <th>17</th>\n",
        "      <td>8.5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.290340</td>\n",
-       "      <td>0.097354</td>\n",
-       "      <td>0.132834</td>\n",
-       "      <td>1.213856</td>\n",
-       "      <td>0.569684</td>\n",
-       "      <td>5.210123</td>\n",
+       "      <td>0.825828</td>\n",
+       "      <td>0.030947</td>\n",
+       "      <td>0.532935</td>\n",
+       "      <td>0.494585</td>\n",
+       "      <td>0.894398</td>\n",
+       "      <td>5.766254</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>18</td>\n",
+       "      <th>18</th>\n",
        "      <td>9.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.317523</td>\n",
-       "      <td>0.158986</td>\n",
-       "      <td>1.307512</td>\n",
-       "      <td>0.839878</td>\n",
-       "      <td>0.014890</td>\n",
-       "      <td>4.162367</td>\n",
+       "      <td>1.155612</td>\n",
+       "      <td>1.143211</td>\n",
+       "      <td>1.515550</td>\n",
+       "      <td>0.723745</td>\n",
+       "      <td>0.913861</td>\n",
+       "      <td>2.530417</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>19</td>\n",
+       "      <th>19</th>\n",
        "      <td>9.5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.141136</td>\n",
-       "      <td>1.911165</td>\n",
-       "      <td>0.421041</td>\n",
-       "      <td>0.949096</td>\n",
-       "      <td>0.572429</td>\n",
-       "      <td>7.941869</td>\n",
+       "      <td>0.063471</td>\n",
+       "      <td>0.965922</td>\n",
+       "      <td>0.203122</td>\n",
+       "      <td>0.046015</td>\n",
+       "      <td>0.205065</td>\n",
+       "      <td>2.868075</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>20</td>\n",
+       "      <th>20</th>\n",
        "      <td>10.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.165375</td>\n",
-       "      <td>0.398424</td>\n",
-       "      <td>0.197602</td>\n",
-       "      <td>1.344701</td>\n",
-       "      <td>0.773118</td>\n",
-       "      <td>9.193829</td>\n",
+       "      <td>0.378024</td>\n",
+       "      <td>0.798986</td>\n",
+       "      <td>1.352478</td>\n",
+       "      <td>1.735521</td>\n",
+       "      <td>0.588551</td>\n",
+       "      <td>18.319130</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2536,76 +2588,76 @@
       ],
       "text/plain": [
        "    time  t_presim   k0  k0_preeq  k0_presim  observable_x1  observable_x2  \\\n",
-       "0    0.0       0.0  1.0       NaN        NaN       0.680906       0.738852   \n",
-       "1    0.5       0.0  1.0       NaN        NaN       0.477552       1.902356   \n",
-       "2    1.0       0.0  1.0       NaN        NaN       0.944402       0.809295   \n",
-       "3    1.5       0.0  1.0       NaN        NaN       0.431933       0.606445   \n",
-       "4    2.0       0.0  1.0       NaN        NaN       1.908853       2.061487   \n",
-       "5    2.5       0.0  1.0       NaN        NaN       1.243394       0.534602   \n",
-       "6    3.0       0.0  1.0       NaN        NaN       0.637304       2.431349   \n",
-       "7    3.5       0.0  1.0       NaN        NaN       0.171979       0.086224   \n",
-       "8    4.0       0.0  1.0       NaN        NaN       1.635844       2.559529   \n",
-       "9    4.5       0.0  1.0       NaN        NaN       0.533342       1.449888   \n",
-       "10   5.0       0.0  1.0       NaN        NaN       0.128904       1.388843   \n",
-       "11   5.5       0.0  1.0       NaN        NaN       0.876601       0.857365   \n",
-       "12   6.0       0.0  1.0       NaN        NaN       0.444344       1.003688   \n",
-       "13   6.5       0.0  1.0       NaN        NaN       1.344736       1.129208   \n",
-       "14   7.0       0.0  1.0       NaN        NaN       0.780124       0.209067   \n",
-       "15   7.5       0.0  1.0       NaN        NaN       0.131651       0.477543   \n",
-       "16   8.0       0.0  1.0       NaN        NaN       1.822083       0.291679   \n",
-       "17   8.5       0.0  1.0       NaN        NaN       0.290340       0.097354   \n",
-       "18   9.0       0.0  1.0       NaN        NaN       1.317523       0.158986   \n",
-       "19   9.5       0.0  1.0       NaN        NaN       1.141136       1.911165   \n",
-       "20  10.0       0.0  1.0       NaN        NaN       0.165375       0.398424   \n",
+       "0    0.0       0.0  1.0       NaN        NaN       1.079183       0.207044   \n",
+       "1    0.5       0.0  1.0       NaN        NaN       0.779197       0.208496   \n",
+       "2    1.0       0.0  1.0       NaN        NaN       1.418346       0.414088   \n",
+       "3    1.5       0.0  1.0       NaN        NaN       0.585149       1.157749   \n",
+       "4    2.0       0.0  1.0       NaN        NaN       0.152759       0.252089   \n",
+       "5    2.5       0.0  1.0       NaN        NaN       0.928757       0.405543   \n",
+       "6    3.0       0.0  1.0       NaN        NaN       0.610923       0.802369   \n",
+       "7    3.5       0.0  1.0       NaN        NaN       0.870083       1.170115   \n",
+       "8    4.0       0.0  1.0       NaN        NaN       0.268776       0.488083   \n",
+       "9    4.5       0.0  1.0       NaN        NaN       2.253679       0.736219   \n",
+       "10   5.0       0.0  1.0       NaN        NaN       0.409429       1.359436   \n",
+       "11   5.5       0.0  1.0       NaN        NaN       0.537026       0.113297   \n",
+       "12   6.0       0.0  1.0       NaN        NaN       1.525632       0.511769   \n",
+       "13   6.5       0.0  1.0       NaN        NaN       0.620811       2.044795   \n",
+       "14   7.0       0.0  1.0       NaN        NaN       0.589332       0.874674   \n",
+       "15   7.5       0.0  1.0       NaN        NaN       1.227539       2.962483   \n",
+       "16   8.0       0.0  1.0       NaN        NaN       1.387711       2.063869   \n",
+       "17   8.5       0.0  1.0       NaN        NaN       0.825828       0.030947   \n",
+       "18   9.0       0.0  1.0       NaN        NaN       1.155612       1.143211   \n",
+       "19   9.5       0.0  1.0       NaN        NaN       0.063471       0.965922   \n",
+       "20  10.0       0.0  1.0       NaN        NaN       0.378024       0.798986   \n",
        "\n",
        "    observable_x3  observable_x1_scaled  observable_x2_offsetted  \\\n",
-       "0        1.848393              0.528878                 0.030770   \n",
-       "1        0.139001              0.582924                 0.204532   \n",
-       "2        0.674152              1.019122                 0.277854   \n",
-       "3        0.832904              0.540889                 0.993630   \n",
-       "4        0.986728              1.025807                 0.395691   \n",
-       "5        0.684049              0.662697                 1.630560   \n",
-       "6        0.309829              0.953983                 1.114478   \n",
-       "7        0.912605              1.082116                 0.806561   \n",
-       "8        1.204247              0.624189                 0.619704   \n",
-       "9        0.523497              0.884234                 0.658666   \n",
-       "10       1.965524              1.189927                 0.246216   \n",
-       "11       0.292705              1.351255                 0.895681   \n",
-       "12       0.722275              0.529257                 0.597738   \n",
-       "13       0.308918              1.039571                 1.066643   \n",
-       "14       1.515094              0.633448                 1.292236   \n",
-       "15       1.126244              0.970059                 0.086888   \n",
-       "16       0.738307              0.153263                 1.187835   \n",
-       "17       0.132834              1.213856                 0.569684   \n",
-       "18       1.307512              0.839878                 0.014890   \n",
-       "19       0.421041              0.949096                 0.572429   \n",
-       "20       0.197602              1.344701                 0.773118   \n",
+       "0        0.372430              0.151379                 1.455937   \n",
+       "1        0.072270              0.411145                 0.692741   \n",
+       "2        1.133725              1.558753                 0.123162   \n",
+       "3        0.355528              0.292550                 1.057712   \n",
+       "4        0.049974              0.059911                 0.650649   \n",
+       "5        0.226200              0.887826                 0.515310   \n",
+       "6        0.056249              0.625525                 1.140957   \n",
+       "7        1.297599              1.034648                 1.056794   \n",
+       "8        0.087202              0.647388                 0.313150   \n",
+       "9        0.617772              1.517363                 0.725632   \n",
+       "10       0.720039              0.513033                 0.145704   \n",
+       "11       0.859701              0.394782                 1.447601   \n",
+       "12       0.083366              0.106527                 1.322456   \n",
+       "13       0.519037              0.430374                 2.175145   \n",
+       "14       1.486992              0.557912                 1.907417   \n",
+       "15       0.450634              0.452977                 0.816720   \n",
+       "16       0.632384              0.598891                 0.505418   \n",
+       "17       0.532935              0.494585                 0.894398   \n",
+       "18       1.515550              0.723745                 0.913861   \n",
+       "19       0.203122              0.046015                 0.205065   \n",
+       "20       1.352478              1.735521                 0.588551   \n",
        "\n",
        "    observable_x1withsigma  \n",
-       "0                 1.658994  \n",
-       "1                 4.822960  \n",
-       "2                 3.885968  \n",
-       "3                14.256878  \n",
-       "4                15.462488  \n",
-       "5                 4.894680  \n",
-       "6                 4.549911  \n",
-       "7                 3.633264  \n",
-       "8                12.270016  \n",
-       "9                12.402753  \n",
-       "10                3.506723  \n",
-       "11                8.598242  \n",
-       "12                3.706950  \n",
-       "13                5.054765  \n",
-       "14               15.062423  \n",
-       "15                6.141412  \n",
-       "16               16.231417  \n",
-       "17                5.210123  \n",
-       "18                4.162367  \n",
-       "19                7.941869  \n",
-       "20                9.193829  "
+       "0                 8.278943  \n",
+       "1                11.454045  \n",
+       "2                14.936001  \n",
+       "3                 6.450000  \n",
+       "4                 6.617580  \n",
+       "5                 3.806906  \n",
+       "6                13.100735  \n",
+       "7                27.435032  \n",
+       "8                16.338665  \n",
+       "9                 5.794149  \n",
+       "10               19.640623  \n",
+       "11               12.728659  \n",
+       "12                7.662203  \n",
+       "13                7.521306  \n",
+       "14                5.512185  \n",
+       "15                2.862886  \n",
+       "16                4.198303  \n",
+       "17                5.766254  \n",
+       "18                2.530417  \n",
+       "19                2.868075  \n",
+       "20               18.319130  "
       ]
      },
-     "execution_count": 23,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2617,7 +2669,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -2663,7 +2715,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>0</td>\n",
+       "      <th>0</th>\n",
        "      <td>0.0</td>\n",
        "      <td>simulation</td>\n",
        "      <td>0.0</td>\n",
@@ -2684,7 +2736,7 @@
        "      <td>0.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1</td>\n",
+       "      <th>1</th>\n",
        "      <td>0.5</td>\n",
        "      <td>simulation</td>\n",
        "      <td>0.0</td>\n",
@@ -2705,7 +2757,7 @@
        "      <td>0.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2</td>\n",
+       "      <th>2</th>\n",
        "      <td>1.0</td>\n",
        "      <td>simulation</td>\n",
        "      <td>0.0</td>\n",
@@ -2726,7 +2778,7 @@
        "      <td>0.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>3</td>\n",
+       "      <th>3</th>\n",
        "      <td>1.5</td>\n",
        "      <td>simulation</td>\n",
        "      <td>0.0</td>\n",
@@ -2747,7 +2799,7 @@
        "      <td>0.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>4</td>\n",
+       "      <th>4</th>\n",
        "      <td>2.0</td>\n",
        "      <td>simulation</td>\n",
        "      <td>0.0</td>\n",
@@ -2768,7 +2820,7 @@
        "      <td>0.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>5</td>\n",
+       "      <th>5</th>\n",
        "      <td>2.5</td>\n",
        "      <td>simulation</td>\n",
        "      <td>0.0</td>\n",
@@ -2789,7 +2841,7 @@
        "      <td>0.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>6</td>\n",
+       "      <th>6</th>\n",
        "      <td>3.0</td>\n",
        "      <td>simulation</td>\n",
        "      <td>0.0</td>\n",
@@ -2810,7 +2862,7 @@
        "      <td>0.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>7</td>\n",
+       "      <th>7</th>\n",
        "      <td>3.5</td>\n",
        "      <td>simulation</td>\n",
        "      <td>0.0</td>\n",
@@ -2831,7 +2883,7 @@
        "      <td>0.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>8</td>\n",
+       "      <th>8</th>\n",
        "      <td>4.0</td>\n",
        "      <td>simulation</td>\n",
        "      <td>0.0</td>\n",
@@ -2852,7 +2904,7 @@
        "      <td>0.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>9</td>\n",
+       "      <th>9</th>\n",
        "      <td>4.5</td>\n",
        "      <td>simulation</td>\n",
        "      <td>0.0</td>\n",
@@ -2873,7 +2925,7 @@
        "      <td>0.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>10</td>\n",
+       "      <th>10</th>\n",
        "      <td>5.0</td>\n",
        "      <td>simulation</td>\n",
        "      <td>0.0</td>\n",
@@ -2894,7 +2946,7 @@
        "      <td>0.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>11</td>\n",
+       "      <th>11</th>\n",
        "      <td>5.5</td>\n",
        "      <td>simulation</td>\n",
        "      <td>0.0</td>\n",
@@ -2915,7 +2967,7 @@
        "      <td>0.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>12</td>\n",
+       "      <th>12</th>\n",
        "      <td>6.0</td>\n",
        "      <td>simulation</td>\n",
        "      <td>0.0</td>\n",
@@ -2936,7 +2988,7 @@
        "      <td>0.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>13</td>\n",
+       "      <th>13</th>\n",
        "      <td>6.5</td>\n",
        "      <td>simulation</td>\n",
        "      <td>0.0</td>\n",
@@ -2957,7 +3009,7 @@
        "      <td>0.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>14</td>\n",
+       "      <th>14</th>\n",
        "      <td>7.0</td>\n",
        "      <td>simulation</td>\n",
        "      <td>0.0</td>\n",
@@ -2978,7 +3030,7 @@
        "      <td>0.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>15</td>\n",
+       "      <th>15</th>\n",
        "      <td>7.5</td>\n",
        "      <td>simulation</td>\n",
        "      <td>0.0</td>\n",
@@ -2999,7 +3051,7 @@
        "      <td>0.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>16</td>\n",
+       "      <th>16</th>\n",
        "      <td>8.0</td>\n",
        "      <td>simulation</td>\n",
        "      <td>0.0</td>\n",
@@ -3020,7 +3072,7 @@
        "      <td>0.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>17</td>\n",
+       "      <th>17</th>\n",
        "      <td>8.5</td>\n",
        "      <td>simulation</td>\n",
        "      <td>0.0</td>\n",
@@ -3041,7 +3093,7 @@
        "      <td>0.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>18</td>\n",
+       "      <th>18</th>\n",
        "      <td>9.0</td>\n",
        "      <td>simulation</td>\n",
        "      <td>0.0</td>\n",
@@ -3062,7 +3114,7 @@
        "      <td>0.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>19</td>\n",
+       "      <th>19</th>\n",
        "      <td>9.5</td>\n",
        "      <td>simulation</td>\n",
        "      <td>0.0</td>\n",
@@ -3083,7 +3135,7 @@
        "      <td>0.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>20</td>\n",
+       "      <th>20</th>\n",
        "      <td>10.0</td>\n",
        "      <td>simulation</td>\n",
        "      <td>0.0</td>\n",
@@ -3224,7 +3276,7 @@
        "20                          1.0                         0.1  "
       ]
      },
-     "execution_count": 24,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3236,7 +3288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -3272,7 +3324,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>0</td>\n",
+       "      <th>0</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -3283,7 +3335,7 @@
        "      <td>0.700000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1</td>\n",
+       "      <th>1</th>\n",
        "      <td>0.5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -3294,7 +3346,7 @@
        "      <td>0.191491</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2</td>\n",
+       "      <th>2</th>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -3305,7 +3357,7 @@
        "      <td>0.096424</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>3</td>\n",
+       "      <th>3</th>\n",
        "      <td>1.5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -3316,7 +3368,7 @@
        "      <td>0.076076</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>4</td>\n",
+       "      <th>4</th>\n",
        "      <td>2.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -3327,7 +3379,7 @@
        "      <td>0.069694</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>5</td>\n",
+       "      <th>5</th>\n",
        "      <td>2.5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -3338,7 +3390,7 @@
        "      <td>0.066301</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>6</td>\n",
+       "      <th>6</th>\n",
        "      <td>3.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -3349,7 +3401,7 @@
        "      <td>0.063733</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>7</td>\n",
+       "      <th>7</th>\n",
        "      <td>3.5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -3360,7 +3412,7 @@
        "      <td>0.061506</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>8</td>\n",
+       "      <th>8</th>\n",
        "      <td>4.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -3371,7 +3423,7 @@
        "      <td>0.059495</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>9</td>\n",
+       "      <th>9</th>\n",
        "      <td>4.5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -3382,7 +3434,7 @@
        "      <td>0.057653</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>10</td>\n",
+       "      <th>10</th>\n",
        "      <td>5.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -3393,7 +3445,7 @@
        "      <td>0.055960</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>11</td>\n",
+       "      <th>11</th>\n",
        "      <td>5.5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -3404,7 +3456,7 @@
        "      <td>0.054400</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>12</td>\n",
+       "      <th>12</th>\n",
        "      <td>6.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -3415,7 +3467,7 @@
        "      <td>0.052960</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>13</td>\n",
+       "      <th>13</th>\n",
        "      <td>6.5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -3426,7 +3478,7 @@
        "      <td>0.051629</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>14</td>\n",
+       "      <th>14</th>\n",
        "      <td>7.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -3437,7 +3489,7 @@
        "      <td>0.050399</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>15</td>\n",
+       "      <th>15</th>\n",
        "      <td>7.5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -3448,7 +3500,7 @@
        "      <td>0.049259</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>16</td>\n",
+       "      <th>16</th>\n",
        "      <td>8.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -3459,7 +3511,7 @@
        "      <td>0.048203</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>17</td>\n",
+       "      <th>17</th>\n",
        "      <td>8.5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -3470,7 +3522,7 @@
        "      <td>0.047224</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>18</td>\n",
+       "      <th>18</th>\n",
        "      <td>9.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -3481,7 +3533,7 @@
        "      <td>0.046315</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>19</td>\n",
+       "      <th>19</th>\n",
        "      <td>9.5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -3492,7 +3544,7 @@
        "      <td>0.045471</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>20</td>\n",
+       "      <th>20</th>\n",
        "      <td>10.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -3531,7 +3583,7 @@
        "20  10.0       0.0  1.0       NaN        NaN  0.494949  0.535581  0.044686"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }

From bad607e0bc2ac03247e07fc1724296c83c270ad8 Mon Sep 17 00:00:00 2001
From: Daniel Weindl <daniel.weindl@helmholtz-muenchen.de>
Date: Wed, 15 Jan 2020 19:28:09 +0100
Subject: [PATCH 03/23] Fix Matlab compilation error if AMICI or model path
 contains blanks

Quote sensitive strings. Closes #910.
---
 matlab/@amimodel/compileAndLinkModel.m      | 12 ++++++------
 matlab/auxiliary/compileAMICIDependencies.m |  8 ++++----
 2 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/matlab/@amimodel/compileAndLinkModel.m b/matlab/@amimodel/compileAndLinkModel.m
index b5c4f23340..5d26d91c72 100644
--- a/matlab/@amimodel/compileAndLinkModel.m
+++ b/matlab/@amimodel/compileAndLinkModel.m
@@ -121,11 +121,11 @@ function compileAndLinkModel(modelname, modelSourceFolder, coptim, debug, funs,
     if(numel(funsForRecompile))
         fprintf('ffuns | ');
 
-        sources = cellfun(@(x) fullfile(modelSourceFolder,[modelname '_' x '.cpp']),funsForRecompile,'UniformOutput',false);
+        sources = cellfun(@(x) ['"' fullfile(modelSourceFolder,[modelname '_' x '.cpp']) '"'],funsForRecompile,'UniformOutput',false);
         sources = strjoin(sources,' ');
 
         eval(['mex ' DEBUG COPT ...
-            ' -c -outdir ' modelObjectFolder ' ' ...
+            ' -c -outdir "' modelObjectFolder '" ' ...
             sources ' ' ...
             includesstr ]);
         cellfun(@(x) updateFileHashSource(modelSourceFolder, modelObjectFolder, [modelname '_' x]),funsForRecompile,'UniformOutput',false);
@@ -143,8 +143,8 @@ function compileAndLinkModel(modelname, modelSourceFolder, coptim, debug, funs,
     % compile the wrapfunctions object
     fprintf('wrapfunctions | ');
     eval(['mex ' DEBUG COPT ...
-        ' -c -outdir ' modelObjectFolder ' ' ...
-        fullfile(modelSourceFolder,'wrapfunctions.cpp') ' ' ...
+        ' -c -outdir "' modelObjectFolder '" "' ...
+        fullfile(modelSourceFolder,'wrapfunctions.cpp') '" ' ...
         includesstr]);
     objectsstr = [objectsstr, ' "' fullfile(modelObjectFolder,['wrapfunctions' objectFileSuffix]) '"'];
 
@@ -173,7 +173,7 @@ function compileAndLinkModel(modelname, modelSourceFolder, coptim, debug, funs,
 
     mexFilename = fullfile(modelSourceFolder,['ami_' modelname]);
     eval(['mex ' DEBUG ' ' COPT ' ' CLIBS ...
-        ' -output ' mexFilename ' ' objectsstr])
+        ' -output "' mexFilename '" ' objectsstr])
 end
 
 function [objectStrAmici] = compileAmiciBase(amiciRootPath, objectFolder, objectFileSuffix, includesstr, DEBUG, COPT)
@@ -203,7 +203,7 @@ function compileAndLinkModel(modelname, modelSourceFolder, coptim, debug, funs,
             baseFilename = fullfile(amiciSourcePath, sourcesForRecompile{j});
             sourceStr  = [sourceStr, ' "', baseFilename, '.cpp"'];
         end
-        eval(['mex ' DEBUG COPT ' -c -outdir ' objectFolder ...
+        eval(['mex ' DEBUG COPT ' -c -outdir "' objectFolder '" ' ...
             includesstr ' ' sourceStr]);
         cellfun(@(x) updateFileHashSource(amiciSourcePath, objectFolder, x), sourcesForRecompile);
         updateHeaderFileHashes(amiciIncludePath, objectFolder);
diff --git a/matlab/auxiliary/compileAMICIDependencies.m b/matlab/auxiliary/compileAMICIDependencies.m
index d433ce45bb..d20285ed0d 100644
--- a/matlab/auxiliary/compileAMICIDependencies.m
+++ b/matlab/auxiliary/compileAMICIDependencies.m
@@ -33,13 +33,13 @@
     sourcesToCompile = '';
     for j=1:length(sources_sundials)
         if(del_sundials || ~exist(fullfile(objectFolder,objects_sundials{j}), 'file'))
-            sourcesToCompile = [sourcesToCompile, ' ', fullfile(sundials_path,sources_sundials{j})];
+            sourcesToCompile = [sourcesToCompile, ' "', fullfile(sundials_path,sources_sundials{j}), '"'];
         end
     end
     sources_ssparse = getSourcesSSparse();
     for j=1:length(sources_ssparse)
         if(del_ssparse || ~exist(fullfile(objectFolder,objects_ssparse{j}), 'file'))
-            sourcesToCompile = [sourcesToCompile, ' ', fullfile(ssparse_path,sources_ssparse{j})];
+            sourcesToCompile = [sourcesToCompile, ' "', fullfile(ssparse_path,sources_ssparse{j}), '"'];
         end
     end
 
@@ -48,8 +48,8 @@
 
     % compile
     if(~strcmp(sourcesToCompile, ''))
-        eval(['mex ' DEBUG ' ' COPT ' -c -outdir ' ...
-            objectFolder ...
+        eval(['mex ' DEBUG ' ' COPT ' -c -outdir "' ...
+            objectFolder '" ' ...
             includesstr ' ' sourcesToCompile ]);
     end
 

From 242126d2cfaca825cf4818a7a3183d30f3d5f188 Mon Sep 17 00:00:00 2001
From: Daniel Weindl <daniel.weindl@helmholtz-muenchen.de>
Date: Wed, 15 Jan 2020 20:43:41 +0100
Subject: [PATCH 04/23] Don't pass empty include strings to mex

---
 matlab/@amimodel/compileAndLinkModel.m | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/matlab/@amimodel/compileAndLinkModel.m b/matlab/@amimodel/compileAndLinkModel.m
index 5d26d91c72..e460289cf3 100644
--- a/matlab/@amimodel/compileAndLinkModel.m
+++ b/matlab/@amimodel/compileAndLinkModel.m
@@ -63,7 +63,10 @@ function compileAndLinkModel(modelname, modelSourceFolder, coptim, debug, funs,
     dependencyPath = fullfile(amiciRootPath, 'ThirdParty');
     gslPath = fullfile(dependencyPath, 'gsl');
     [objectsstr, includesstr] = compileAMICIDependencies(dependencyPath, objectFolder, objectFileSuffix, COPT, DEBUG);
-    includesstr = strcat(includesstr,' -I"', modelSourceFolder, '"', ' -I"', gslPath, '"');
+    includesstr = strcat(includesstr, ' -I"', gslPath, '"');
+    if (~isempty(modelSourceFolder))
+        includesstr = strcat(includesstr,' -I"', modelSourceFolder, '"');
+    end
 
     %% Recompile AMICI base files if necessary
     [objectStrAmici] = compileAmiciBase(amiciRootPath, objectFolder, objectFileSuffix, includesstr, DEBUG, COPT);

From 8a28b7fd56f642b4c31961a4d626430892ebc4be Mon Sep 17 00:00:00 2001
From: FFroehlich <fabian@schaluck.com>
Date: Thu, 16 Jan 2020 09:25:47 -0500
Subject: [PATCH 05/23] add section about integration tolerances

---
 .../ExampleSteadystate.ipynb                  | 1027 +++++++++--------
 1 file changed, 552 insertions(+), 475 deletions(-)

diff --git a/python/examples/example_steadystate/ExampleSteadystate.ipynb b/python/examples/example_steadystate/ExampleSteadystate.ipynb
index 158a8b4a26..99c3d9d145 100644
--- a/python/examples/example_steadystate/ExampleSteadystate.ipynb
+++ b/python/examples/example_steadystate/ExampleSteadystate.ipynb
@@ -191,40 +191,40 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-01-15 15:30:05.015 - amici.sbml_import - INFO - Finished processing SBML parameters            (7.95E-04s)\n",
-      "2020-01-15 15:30:05.018 - amici.sbml_import - INFO - Finished processing SBML species               (2.23E-03s)\n",
-      "2020-01-15 15:30:05.033 - amici.sbml_import - INFO - Finished processing SBML reactions             (1.43E-02s)\n",
-      "2020-01-15 15:30:05.034 - amici.sbml_import - INFO - Finished processing SBML compartments          (2.22E-04s)\n",
-      "2020-01-15 15:30:05.116 - amici.sbml_import - INFO - Finished processing SBML rules                 (8.09E-02s)\n",
-      "2020-01-15 15:30:05.192 - amici.sbml_import - INFO - Finished processing SBML observables           (6.24E-02s)\n",
-      "2020-01-15 15:30:05.249 - amici.ode_export - INFO - Finished writing J.cpp                          (3.75E-02s)\n",
-      "2020-01-15 15:30:05.261 - amici.ode_export - INFO - Finished writing JB.cpp                         (1.06E-02s)\n",
-      "2020-01-15 15:30:05.267 - amici.ode_export - INFO - Finished writing JDiag.cpp                      (5.27E-03s)\n",
-      "2020-01-15 15:30:05.276 - amici.ode_export - INFO - Finished writing JSparse.cpp                    (8.72E-03s)\n",
-      "2020-01-15 15:30:05.285 - amici.ode_export - INFO - Finished writing JSparseB.cpp                   (7.53E-03s)\n",
-      "2020-01-15 15:30:05.302 - amici.ode_export - INFO - Finished writing Jy.cpp                         (1.52E-02s)\n",
-      "2020-01-15 15:30:05.397 - amici.ode_export - INFO - Finished writing dJydsigmay.cpp                 (9.41E-02s)\n",
-      "2020-01-15 15:30:05.434 - amici.ode_export - INFO - Finished writing dJydy.cpp                      (3.66E-02s)\n",
-      "2020-01-15 15:30:05.443 - amici.ode_export - INFO - Finished writing dwdp.cpp                       (7.90E-03s)\n",
-      "2020-01-15 15:30:05.447 - amici.ode_export - INFO - Finished writing dwdx.cpp                       (2.91E-03s)\n",
-      "2020-01-15 15:30:05.454 - amici.ode_export - INFO - Finished writing dxdotdw.cpp                    (5.28E-03s)\n",
-      "2020-01-15 15:30:05.467 - amici.ode_export - INFO - Finished writing dxdotdp_explicit.cpp           (1.17E-02s)\n",
-      "2020-01-15 15:30:05.484 - amici.ode_export - INFO - Finished writing dydx.cpp                       (1.27E-02s)\n",
-      "2020-01-15 15:30:05.498 - amici.ode_export - INFO - Finished writing dydp.cpp                       (1.28E-02s)\n",
-      "2020-01-15 15:30:05.504 - amici.ode_export - INFO - Finished writing dsigmaydp.cpp                  (5.58E-03s)\n",
-      "2020-01-15 15:30:05.508 - amici.ode_export - INFO - Finished writing sigmay.cpp                     (3.23E-03s)\n",
-      "2020-01-15 15:30:05.513 - amici.ode_export - INFO - Finished writing w.cpp                          (4.27E-03s)\n",
-      "2020-01-15 15:30:05.516 - amici.ode_export - INFO - Finished writing x0.cpp                         (1.98E-03s)\n",
-      "2020-01-15 15:30:05.517 - amici.ode_export - INFO - Finished writing x0_fixedParameters.cpp         (7.01E-04s)\n",
-      "2020-01-15 15:30:05.520 - amici.ode_export - INFO - Finished writing sx0.cpp                        (2.19E-03s)\n",
-      "2020-01-15 15:30:05.524 - amici.ode_export - INFO - Finished writing sx0_fixedParameters.cpp        (3.26E-03s)\n",
-      "2020-01-15 15:30:05.532 - amici.ode_export - INFO - Finished writing xdot.cpp                       (7.64E-03s)\n",
-      "2020-01-15 15:30:05.536 - amici.ode_export - INFO - Finished writing y.cpp                          (2.85E-03s)\n",
-      "2020-01-15 15:30:05.538 - amici.ode_export - INFO - Finished writing x_rdata.cpp                    (1.54E-03s)\n",
-      "2020-01-15 15:30:05.540 - amici.ode_export - INFO - Finished writing total_cl.cpp                   (1.33E-03s)\n",
-      "2020-01-15 15:30:05.544 - amici.ode_export - INFO - Finished writing x_solver.cpp                   (2.67E-03s)\n",
-      "2020-01-15 15:30:05.564 - amici.ode_export - INFO - Finished generating cpp code                    (3.66E-01s)\n",
-      "2020-01-15 15:30:17.005 - amici.ode_export - INFO - Finished compiling cpp code                     (1.14E+01s)\n"
+      "2020-01-16 09:24:10.771 - amici.sbml_import - INFO - Finished processing SBML parameters            (6.90E-04s)\n",
+      "2020-01-16 09:24:10.776 - amici.sbml_import - INFO - Finished processing SBML species               (3.02E-03s)\n",
+      "2020-01-16 09:24:10.791 - amici.sbml_import - INFO - Finished processing SBML reactions             (1.44E-02s)\n",
+      "2020-01-16 09:24:10.792 - amici.sbml_import - INFO - Finished processing SBML compartments          (2.46E-04s)\n",
+      "2020-01-16 09:24:10.876 - amici.sbml_import - INFO - Finished processing SBML rules                 (8.26E-02s)\n",
+      "2020-01-16 09:24:10.962 - amici.sbml_import - INFO - Finished processing SBML observables           (7.26E-02s)\n",
+      "2020-01-16 09:24:11.015 - amici.ode_export - INFO - Finished writing J.cpp                          (3.75E-02s)\n",
+      "2020-01-16 09:24:11.024 - amici.ode_export - INFO - Finished writing JB.cpp                         (8.14E-03s)\n",
+      "2020-01-16 09:24:11.028 - amici.ode_export - INFO - Finished writing JDiag.cpp                      (3.66E-03s)\n",
+      "2020-01-16 09:24:11.036 - amici.ode_export - INFO - Finished writing JSparse.cpp                    (7.40E-03s)\n",
+      "2020-01-16 09:24:11.045 - amici.ode_export - INFO - Finished writing JSparseB.cpp                   (7.93E-03s)\n",
+      "2020-01-16 09:24:11.061 - amici.ode_export - INFO - Finished writing Jy.cpp                         (1.48E-02s)\n",
+      "2020-01-16 09:24:11.161 - amici.ode_export - INFO - Finished writing dJydsigmay.cpp                 (9.81E-02s)\n",
+      "2020-01-16 09:24:11.199 - amici.ode_export - INFO - Finished writing dJydy.cpp                      (3.70E-02s)\n",
+      "2020-01-16 09:24:11.208 - amici.ode_export - INFO - Finished writing dwdp.cpp                       (8.14E-03s)\n",
+      "2020-01-16 09:24:11.212 - amici.ode_export - INFO - Finished writing dwdx.cpp                       (2.15E-03s)\n",
+      "2020-01-16 09:24:11.218 - amici.ode_export - INFO - Finished writing dxdotdw.cpp                    (5.14E-03s)\n",
+      "2020-01-16 09:24:11.237 - amici.ode_export - INFO - Finished writing dxdotdp_explicit.cpp           (1.69E-02s)\n",
+      "2020-01-16 09:24:11.259 - amici.ode_export - INFO - Finished writing dydx.cpp                       (1.61E-02s)\n",
+      "2020-01-16 09:24:11.270 - amici.ode_export - INFO - Finished writing dydp.cpp                       (1.09E-02s)\n",
+      "2020-01-16 09:24:11.277 - amici.ode_export - INFO - Finished writing dsigmaydp.cpp                  (5.89E-03s)\n",
+      "2020-01-16 09:24:11.281 - amici.ode_export - INFO - Finished writing sigmay.cpp                     (3.75E-03s)\n",
+      "2020-01-16 09:24:11.286 - amici.ode_export - INFO - Finished writing w.cpp                          (3.71E-03s)\n",
+      "2020-01-16 09:24:11.289 - amici.ode_export - INFO - Finished writing x0.cpp                         (2.25E-03s)\n",
+      "2020-01-16 09:24:11.290 - amici.ode_export - INFO - Finished writing x0_fixedParameters.cpp         (8.19E-04s)\n",
+      "2020-01-16 09:24:11.293 - amici.ode_export - INFO - Finished writing sx0.cpp                        (2.54E-03s)\n",
+      "2020-01-16 09:24:11.297 - amici.ode_export - INFO - Finished writing sx0_fixedParameters.cpp        (3.07E-03s)\n",
+      "2020-01-16 09:24:11.304 - amici.ode_export - INFO - Finished writing xdot.cpp                       (5.76E-03s)\n",
+      "2020-01-16 09:24:11.307 - amici.ode_export - INFO - Finished writing y.cpp                          (2.37E-03s)\n",
+      "2020-01-16 09:24:11.309 - amici.ode_export - INFO - Finished writing x_rdata.cpp                    (1.36E-03s)\n",
+      "2020-01-16 09:24:11.310 - amici.ode_export - INFO - Finished writing total_cl.cpp                   (7.47E-04s)\n",
+      "2020-01-16 09:24:11.313 - amici.ode_export - INFO - Finished writing x_solver.cpp                   (1.82E-03s)\n",
+      "2020-01-16 09:24:11.326 - amici.ode_export - INFO - Finished generating cpp code                    (3.59E-01s)\n",
+      "2020-01-16 09:24:22.266 - amici.ode_export - INFO - Finished compiling cpp code                     (1.09E+01s)\n"
      ]
     }
    ],
@@ -657,7 +657,7 @@
       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
       "       order:  [0 5 5 5 5 5 4 4 5 5 5 5 5 5 5 4 5 4 4 4 4 4 4 4 4 5 5 4 4 4 4 4 4 5 5 5 5\n",
       " 5 5 5 4 4 4 5 5 5 5 5 4 4 5 5 5 4 4 3 3 3 3 4]\n",
-      "    cpu_time:  1.2739999999999994\n",
+      "    cpu_time:  1.268999999999999\n",
       "   numstepsB:  None\n",
       "numrhsevalsB:  None\n",
       "numerrtestfailsB:  None\n",
@@ -761,7 +761,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Log-likelihood -102.648113\n"
+      "Log-likelihood -92.752605\n"
      ]
     }
    ],
@@ -785,6 +785,75 @@
     "print('Log-likelihood %f' % rdata['llh'])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simulation tolerances\n",
+    "Numerical error tolerances are often critical to get accurate results. For the state variables, integration errors can be controlled using `setRelativeTolerance` and `setAbsoluteTolerance`. Similar functions exist for sensitivies, steadstates and quadratures. We initially compute a reference "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "solver.setRelativeTolerance(1e-16)\n",
+    "solver.setAbsoluteTolerance(1e-16)\n",
+    "solver.setSensitivityOrder(amici.SensitivityOrder_none)\n",
+    "rdata_ref = amici.runAmiciSimulation(model, solver, edata)\n",
+    "\n",
+    "def get_simulation_error(solver):\n",
+    "    rdata = amici.runAmiciSimulation(model, solver, edata)\n",
+    "    return np.mean(np.abs(rdata['x']-rdata_ref['x'])), np.mean(np.abs(rdata['llh']-rdata_ref['llh']))\n",
+    "    \n",
+    "def get_errors(tolfun, tols):\n",
+    "    solver.setRelativeTolerance(1e-16)\n",
+    "    solver.setAbsoluteTolerance(1e-16)\n",
+    "    x_errs = []\n",
+    "    llh_errs = []\n",
+    "    for tol in tols:\n",
+    "        getattr(solver, tolfun)(tol)\n",
+    "        x_err, llh_err = get_simulation_error(solver)\n",
+    "        x_errs.append(x_err)\n",
+    "        llh_errs.append(llh_err)\n",
+    "    return x_errs, llh_errs\n",
+    "    \n",
+    "atols = np.logspace(-5,-15, 100)\n",
+    "atol_x_errs, atol_llh_errs = get_errors('setAbsoluteTolerance', atols)\n",
+    "\n",
+    "rtols = np.logspace(-5,-15, 100)\n",
+    "rtol_x_errs, rtol_llh_errs = get_errors('setRelativeTolerance', rtols)\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
+    "\n",
+    "def plot_error(tols, x_errs, llh_errs, tolname, ax):\n",
+    "    ax.plot(tols, x_errs, 'r-', label='x')\n",
+    "    ax.plot(tols, llh_errs, 'b-', label='llh')\n",
+    "    ax.set_xscale('log')\n",
+    "    ax.set_yscale('log')\n",
+    "    ax.set_xlabel(f'{tolname} tolerance')\n",
+    "    ax.set_ylabel('average numerical error')\n",
+    "    ax.legend()\n",
+    "\n",
+    "plot_error(atols, atol_x_errs, atol_llh_errs, 'absolute', axes[0])\n",
+    "plot_error(rtols, rtol_x_errs, rtol_llh_errs, 'relative', axes[1])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -803,7 +872,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {
     "scrolled": true
    },
@@ -1326,7 +1395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {
     "scrolled": true
    },
@@ -1335,9 +1404,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Log-likelihood: -1540.615428\n",
-      "Gradient: [ 2.00932843e+02  1.83428692e+02 -4.64936420e+02 -5.31266369e+01\n",
-      " -7.09252643e+02 -7.16051763e-02 -5.63883590e-01  2.82926928e+04]\n"
+      "Log-likelihood: -1205.195087\n",
+      "Gradient: [ 1.13739826e+01  1.23993091e+01 -3.54960044e+01 -9.76603519e+00\n",
+      " -6.22459540e+01  6.68794579e-01 -6.46531682e+00  2.13965462e+04]\n"
      ]
     }
    ],
@@ -1384,23 +1453,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "sllh: |error|_2: 42.592180\n",
+      "sllh: |error|_2: 32.261882\n",
       "\n",
-      "sllh: p[0]: |error|_2: 0.031244\n",
-      "sllh: p[1]: |error|_2: 0.016243\n",
-      "sllh: p[2]: |error|_2: 0.018955\n",
-      "sllh: p[3]: |error|_2: 0.010088\n",
-      "sllh: p[4]: |error|_2: 0.016982\n",
+      "sllh: p[0]: |error|_2: 0.010290\n",
+      "sllh: p[1]: |error|_2: 0.023725\n",
+      "sllh: p[2]: |error|_2: 0.035811\n",
+      "sllh: p[3]: |error|_2: 0.007622\n",
+      "sllh: p[4]: |error|_2: 0.095420\n",
       "sllh: p[5]: |error|_2: 0.000280\n",
       "sllh: p[6]: |error|_2: 0.001050\n",
-      "sllh: p[7]: |error|_2: 42.592175\n",
+      "sllh: p[7]: |error|_2: 32.261730\n",
       "\n",
       "sy: p[0]: |error|_2: 0.002974\n",
       "sy: p[1]: |error|_2: 0.002717\n",
@@ -1513,43 +1582,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x720 with 8 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x720 with 8 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "eps=1e-4\n",
     "op=model.getParameters()\n",
@@ -1592,10 +1629,50 @@
     "        axes[ip,1].set_yscale('log')\n",
     "    \n",
     "    plt.tight_layout()\n",
-    "    plt.show()\n",
-    "\n",
-    "plot_sensitivities('x', eps)\n",
-    "print('------')\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 8 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_sensitivities('x', eps)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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RFKVA7dixgy1btjBgwACMRqNrzo1evXpx7NgxqlSpwrZt2wgKCuKHH364bj3PPfcc+/fvp2nTpvTt25enn36a6Ojo65ZXFEVR7h0quHGv03XSBg4koEQJZu3fz8GMDDw0jSa+vvR4+mk4fJhgm42TNhtfnzlDtzlz8AwMLOpWK4qiKIpyH5k2bRo+Pj785z//ISoqil27duHl5cXYsWNp2bIlM2bMYPjw4aSlpdGuXTs6dOiAruu51hUcHMzq1auZOXMmv/76KzVq1MgzIKIoiqLcG1Rw4x72ba9e4OeH+8yZtNY0RjZpQnpiIpd1nU3x8bz8ww+O5VQVRVEURXHRNC1U07TvNU1bqGnasKJuz73u7NmzLFu2jF69euHr60v//o510tLS0mjVqhUArVq14ujRo6xevZoSJUrw7bffEhQUxN69e3Ot02Aw0L9/f3bt2kVwcDDt2rXj1VdfJTk5udCel6IoilK41Jwb96CfRo/mlfHjuaDrTAGGvPgifPQRqJnDFUVRlEJQlHNuaJq2EGgDxIhI9Szb/w3MwLFa+QIRmZRHHU8BxURkiaZpS0Xk+Rsd9345xygIw4cP54MPPuD48eOEhITg5eWFpmnY7XaOHj1KmTJlmDJlCuPHj8dgMDBy5Eh2797NsmXL0DSNN998k/fff/+69aenpzN69GgmT55MWFgYS5YsoUGDBoX4DBXl/qbrOmlpaaSkpLhSampqtt/zs+3hhx+mf//+GAzq+vz9LK9zDBXcuIfs/fpruvTsyeGMDACeCAjgiz/+IFCNzlAURVEKUREHNx4FkoDPM4MbmqYZgaNAS+AssAPogiPQMTFHFf8B7MByQIDFIvLpjY57r59jFJTk5GTKli3L448/zrfffsuIESOYOHEibm5utG3blmXLlrnKRkREMGjQIFauXEnVqlV54YUXGDduHKmpqYSHh7N+/XqCg4Ove6z169fTo0cPoqKiGD16NMOHD8dsNhfG01SU+8bvv//OwIEDuXLliisokZaWdkt1ubu74+HhgaenJ0ajkcjISJ555hk+++wzfH1973DLlX8KFdxwumdPPKKiyOjUCc8tW7ADtdzd+WrZMqq2aVPULVMURVHuQ0W9WoqmaQ8AP2UJbjQCxohIK+fvwwFEJGdgI3P/ocB2EdmoadpyEelwo2Pes+cYBWzu3Ln07duXTZs20aRJE/z9/UlISEBE2Lp1Kw0bNrxmn//973/079+fU6dO0aFDByIiIti5cycmk4nZs2fTu3fv6x4vLi6O119/nS+//JJ69erx+eefU7Vq1YJ8iopy3zh37hy1a9fG19eXZs2auQITWVPObdf73cPDI9sIDRFhxowZDB06lPDwcL7//nsqqwu49yUV3HC61048Ui5dYmCDBnx46hTuwHu+vjwyejT/GjKkqJumKIqi3MfuwuBGB+DfItLL+fsLQEMR6Xed/asDY4BLQJKIDL1Oud5Ab4DQ0NC6p0+fvrNP5B6n6zrVqlXDx8eH7du38/XXX9O1a1fc3d2pX78+GzduvO6+qampvP/++0yaNAk3NzdatmzJypUrsdvtPProo6xevRpPT8/r7r9s2TL69OlDcnIyEydOZMCAAWqou6LcBpvNRvPmzdm5cyc7d+4ssKDh77//TqdOncjIyGDx4sW0bdu2QI6j3L3UUrD3GN1mY0jduviXKMGCU6d412KBRYsYGR+vAhuKoiiKcptE5G8R6SAir10vsOEsNx8YC+x2U/Na3bSff/6ZI0eOMGjQIDRNY+TIkYBjItE333wzz309PDwYM2YMBw4coEmTJnz33XeEhYURHBzMxo0bCQoKYu3atdfdv2PHjvz999+0aNGCwYMH07x5cyIiIu7k01OU+8rYsWPZuHEjc+fOLdDRUI8//ji7du0iPDycdu3aMXbs2OuunKTcf1Rw4x/mw44d8TWb+b/duzEA7zz2GO8lJUHPnkXdNEVRFEW5W50Dymb5vYxzm1KEpk2bRnBwMB07dmTv3r2cPHkSd3d3qlSpwlNPPZWvOipWrMj//vc/VqxYQWpqKlFRUVStWpWUlBSeeOIJevTocd2OT6lSpfjhhx/45JNP2LlzJzVr1mThwoXcT6Oa72eRkZHs2bOH48ePExMTQ2pqqvrb36Jff/2V8ePH89JLL9GjR48CP15oaCibNm2iR48ejBkzhmeeeYb4+PgCP65y91O3pfxT/PQTtpdfxjsmBivQIyyMj/bswc3bu6hbpiiKoijZ3IW3pZhwTCjaHEdQYwfQVUQO3Klj/qPPMYrA/v37qVmzJhMmTGD48OE0bdqUzZs3A/Dxxx/Tq1evm64zOTmZCRMmMHnyZCwWC7quk5KSQqlSpVi3bl2eV5MjIiJ48cUX2bBhA23atOHjjz+mVKlSt/z8lLuXzWZjwoQJjBs3DpvNli3PZDLh4+ODj48Pvr6+2X7md1tgYCDFihUromdX+M6fP0/t2rUJDAxk+/bteHl5FdqxRYTZs2czePBgKlSowIoVK6hWrVqhHV8pGmrODad/4onH7i+/5OX//Icf09MpA6yuU4f6X32lVkBRFEVR7lpFvFrKV0AzIBCIBt4RkU80TXsSmI5jhZSFIjL+Dh3vaeDpsLCwV44dO3Ynqrwv9OrViy+//JIzZ86gaRqBgYGYzWaKFStGREQE7u7ut1z3kSNH6NevH7/++it+fn7Ex8djMBgYPXo077zzznX303WdmTNnMnz4cLy8vJg3bx4dOtxwLlnlH+To0aP06NGDbdu20bVrVzp06EBiYiKJiYkkJCRc8zjnz8yUV/9J0zQ6d+7MqFGj7vnJau12Oy1btmTr1q3s2LGDBx98sEjasXHjRjp27EhKSgqff/45zz77bJG0QykcKrjh9E8KbpzdsYMuLVqwOSEBgHdKlmTMhg2gghqKoijKXa6oR24UhX/SOUZRi4mJITQ0lBdffJF58+bRs2dPPv/8cwDGjx/PiBEjAPjiiy8YOXIkw4cPz3MFlNyICMuXL2fw4MGcO3cOo9GI3W6nevXq/P777wQGBl5330OHDtGjRw927txJ165dmTVr1n11Jf5eJCLMmzePoUOHYrFYmDt3Ls8///wt1ZU5Iii3wEdCQgL79u1j7ty5pKSk3PNBjrFjxzJmzBgWLlzISy+9dMv16LpOXFwcV65cITY2ltjYWOLi4oiLiyMhIcGVWrVqxZNPPplrHWfPnuW5555j+/btjBw5kjFjxmA0Gm+5TcrdK89zDBG5b1LdunXlbmePj5cOISGigQASajTK79OmFXWzFEVRFCXfgJ1yF3zvF0YCngbmh4WF3amX7543duxYAeTQoUNit9vFYrGI0WgUT09PuXz5sqtc8eLFBef5UFBQkHz99dc3fazExER58803xWg0itFoFEDMZrMsXrw4z/0yMjJk7NixYjKZJDg4WH7++eebPvb9IDExUQ4ePChr1qyRBQsWyNSpU+XMmTNF3axsoqKipHXr1gLIE088IWfPni3wY8bExMh///tf8fLyEk3TpEuXLnLw4MECP25hWrdunWiaJi+88ILouu7avn//fmnatKnUqFFDwsPDJTQ0VEqVKiUBAQHi4+MjHh4e4ubmJkajUTRNc/2P5ze99NJL121Tamqq/Oc//xFAWrduLbGxsYXxUiiFLK9zDDVy426h6zBkCMyaRaDNhl3TmNGrFz3mzy/qlimKoijKTVEjN5TrSU9Pp1y5cjz00EOsWrWKiRMnMmLECDRNo3///syYMQOAH374gXbt2tGoUSN8fHxYu3YtIkLZsmX56KOPaN269U0d98CBA/Tr14/169e7tjVr1oz//e9/eS4Zu2vXLnr06MHBgwd57bXXmDx5Mt73yXxn6enpnD17ljNnzlw3Xbly5Zr9LBYLffv2ZdiwYQQFBRVBy69atmwZr732GqmpqUyePJm+ffuiaVqhHf/ixYtMnTqVWbNm3VMjOaKjo6lduzb+/v7s2LHD9T+ROcdNYmIimqZhMBgwGAwYjUZMJhNmsxk3Nzfc3NywWCy4u7vj7u6Op6cnHh4eeHl54e3tjbe3N76+vnh7e+Pn54e/vz9ms5k+ffpw5coVqlevzp9//pnr/6KI8NFHHzFgwABCQ0P5/vvvqV69emG/REoBUiM37vKRG9OeeUZ8QTaBiLu7nB86VOxWa1E3S1EURVFuCfkcuQEE5JXyU0dRJ9TIjZuyaNEiAWTNmjUiIlKiRAkBRNM0OXXqlKtc1apVBXBd7Y6IiJDGjRu7rt6GhYXJpk2bburYuq7LF1984TomIJ6envLjjz/muV9qaqoMGTJENE2TChUqyObNm2/uSd+FbDabREZGypYtW+Trr7+WyZMny4ABA+TZZ5+VevXqScmSJXO9ah4QECC1atWSNm3aSJ8+fWTChAmyePFi2bBhg5w8eVKOHTsmL730khgMBvHy8pK3335brly5UujP78qVK9K9e3cBpH79+nL48OFCb0NWOUdydO3aVQ4dOlSkbbpVdrtdWrZsKe7u7rJv375seQ0bNhRA+vfvXyDHTk1NlXr16gkgPj4+8tdff1237ObNm6VUqVLi5eUl33zzTYG0RykaeZ1jFPlJQWGmuy24sXzoUCnhHI5lBJn58MMi6elF3SxFURRFuS03Edw4BZx0/rQDl4DLzsen8lPH3ZLutnOMu5Gu61KrVi158MEHRdd1+fHHHx3nQEajPP/8865yR48eFUAqVap0TR1///231KlTx9XZrlGjhuzdu/em2nHlyhV5+eWXs3Xa27VrJ9YbXFjasGGDlC9fXjRNk7feekvS0tJu6rh3iz179kiFChWuCVz4+PhItWrVpFWrVtKrVy8ZO3asLFy4UNauXSuHDx+WpKSkfB/j0KFD0qlTJwHE399fJkyYcFP7347ffvtNypYtK0ajUcaMGSMZGRmFctz8uBeCHO+9954AMn/+/GzbM283q1KlSoG3oX///gKIwWCQjz/++Lrlzp07J40aNRJA/vvf/4rNZivwtv1T2Gw2SUlJkbi4OLl8+XK2W4vudiq4cZedeNg3b5bKZrPjSgXIkyVKyOUTJ4q6WYqiKIpyR+Q3uJGZgI+BJ7P83hr46GbqKOp0t5xj3M3WrVsngKszkjk6A5CdO3e6yrVq1UoAWb58+XXr2rZtm1SpUsW1f4MGDeTo0aM31Z5ffvlFSpcu7arD19f3hqNBEhISpHfv3gJI9erVZc+ePTd1zKL25ZdfioeHh4SEhMjs2bNl1apVsn//fomLiyuQ4+3Zs0fatGnjmjdlxowZBRYUSk1NlcGDB7sCY9u2bSuQ49wJMTEx8tZbb4mnp+c/KsixYcMGMRgM0qVLl2yd4R07doimaeLu7p5t3pyCtHTpUjGZTALICy+8cN1yaWlp8uqrr7rmXCms9hWEb775Rvr37y+9e/eWF198Ubp27SodOnSQtm3bSqtWreTxxx+Xxo0bS/369aVmzZpSpUoVqVChgpQpU0ZKlCghfn5+4uHh4Zp/KGsqXry4tGzZUt566y356quv5MiRI2K324v6Kecqr3MMNedGIYrZsYOg11+HHTuoA5g8Pfn6xx+p+K9/FVmbFEVRFOVOu9k5NzRN2y8iNW607W6kloLNv3bt2vHHH38QGRnJ2bNnqVSpEkajkUcffZR169YBjnv2fX198fX1JTY29oZ1/vbbb/Tq1YuIiAjAMY/G4sWLKVOmTL7alJSUxLBhw5g9e7ZrW8+ePVm4cCEGg+G6+61atYpevXpx8eJFxowZw3//+19MJlO+jlkUbDYbw4YNY+rUqTRp0oTly5dTsmTJQjv+n3/+yYgRI1i/fj1ly5Zl9OjRvPjii3fsNduzZw/du3fn4MGDvP7663zwwQd5zqWyZMkSXnvtNdLS0lxzQWTOAeHp6Ymnpyc+Pj74+Pjg7++Pv78/xYoVo0SJEgQGBlKyZElKlixJqVKlCAwMzPO9kpeLFy8yZcoUZs2aRVpaGl26dGHUqFFUvgtXR7x48SK1a9fGy8uLXbt24ePjA0BaWholS5YkISGBVatW3fR8OLfjxIkTNGzYkMuXL1OlShW2bduGr69vrmUXLFjA66+/TkhICCtWrKBWrVqF1s47Yf78+bz66hmXirIAACAASURBVKv4+Pjg6enpmrckcw6TG/2eV56IcPDgQfbs2cP+/fuxWq0AeHt7U7t2berUqUOdOnV46KGHqFatGmazuUhfCzXnRhFfVbly6pQ8VaKEaCDzQKRiRZE//iiStiiKoihKQePmR26sAUYCDzjT28Cam6mjqJMauZG3Y8eOiaZpMnLkSBEReeKJJ1xXDFetWuUqN2DAAAFk1KhRN1X/d9995xqFoWmatGnT5qau0G7cuFHKlCnjalNgYOANR2VcvnxZOnfuLIA0bNiwyOd1uJ5Lly5JixYtBJC+fftKehHdAq3ruqxdu1YaNGjgmjflyy+/vK2rwzabTSZMmCBms1lKly59w1VtUlNTpXnz5q5bGipUqCClS5eWgIAA8fLycq3iATe3ggfO26ssFov4+/vLgAEDbnibU1ZZR3IYDAbp1q3bXfV+stvt8u9//1ssFss1/xeZc+G89tprRdK29PR0efjhhwUQb29v2bVr13XLbt26VYKDg8XDw0O++uqrQmzl7fnyyy9F0zR58sknC/z/Nz09Xfbs2SOffPKJ9OvXTxo3bixeXl6u97mbm5s89NBD0qtXL5k9e7b8+eefkpycXKBtyimvc4wiPxkozFTYJx7piYnySuXKYsz8otQ0+XHYsEJtg6IoiqIUtlsIbgQAM4A9wG5gOv+QCUUzkwpu5K1fv35iNpslKipKEhMTxWAwiNFodM2/kcnb21vMZvNNdQyzWrRokWsJWYPBIM8//7wkJibma9+UlBQZOnRotg7roEGDbrjf119/LQEBAeLh4SGTJk0qsuBBbvbu3Svly5cXNzc3+eSTT+54/UlJSbJq1SoZOXKktG3bVho2bHjDAIOu67Jy5UqpUaOGa96UlStX3vQ9/8ePH5dHHnlEAOnUqdMNg1m//PKLeHt7CyDlypWTOXPmyPz582X79u25/s3sdrtER0fLnj175Oeff5bFixfL1KlTZcSIEfLaa69J586dpXXr1tK4cWOpVauWhIeHS0hIiJidt55bLBYZNGjQPRHkmDRpkgAyZ86cbNsnTpwogISHhxdRy67KvCXJYDBc086szp8/L02aNBFAhgwZcsufNYVl5cqVYjQa5bHHHpOUlJQiaYPNZpPDhw/LV199JW+++aa0aNFCAgICXJ+TBoNBqlatKt26dZMpU6bIunXrCnQZXhXcKOwTD7td7O+8I36ZM3GDTHn66cI5tqIoiqIUsfwGN4DFzp8D81P+bkyo1VJu6MqVK+Ll5SU9evQQEZE+ffq4Too//fRTV7kFCxYIIM8999xtH3PGjBni6+srgJhMJnnllVfyHXTYvn27hIaGutpYpkwZOX78eJ77REVFyTPPPCOAVKtWTdavX3/bz+F2ff311+Lp6SnBwcGydevWW67HarXK5s2bZeLEidKpUyepXbu2lChRwtWJzy098sgjEh0dnWe9drtdvvrqKwkPD3fNm7J27dobBjl0XZf58+eLl5eX+Pn5yRdffJHnPna7Xbp27eoa1dOxY0cpV65criMvfH19JTQ0VBo0aCAdOnSQ4cOHy5IlS+TQoUP5HmGSnp4uAwYMEIvFcseCHN27d5cjR47ke/87adOmTWI0GqVjx47ZXuddu3aJpmlisVjk4sWLRdK2nL777jvXPBydO3e+7t8sPT1dXn/9dQHkX//6l1y6dKmQW5o/v/76q1gsFqlfv74kJCQUdXOy0XVdTp8+Ld9//72MHj1ann766Wyj3wCpUKFCgQR7/5HBDeDfwBHgODAsl3wLsNSZvw144EZ1FkZwY9nrr4vV11cEpKemyYBatcSamlrgx1UURVGUu8VNBDcOAsHAX0Ax/oFLwWYmNXLj+iZPniyA7N69W+x2u2sSxdKlS2c78c3scJ47d+6OHNdut8u7774rHh4eruHUb7zxRr46mWlpaTJixAjRnKvaaZomY8eOveF+P/74ozzwwAOuSQ4vXLhwJ57KTbHZbPLWW2+5ggznz5+/4T52u1327dsns2bNkhdffFEaNmwopUuXdnXQcyZN08RoNIrRaBSDwXBNXmawYOjQoTcMClitVlmwYIGULVtWAGnWrJls2bIl17Lnz593TVDavHlziYyMzLPuPXv2uJb+LVasmOuWGECaNm0qo0ePls6dO8sjjzwi5cuXFz8/P1fnOLdkNpulWLFiUrFiRWnSpIl0795dxo0bJ99//72cOXMm27Ezgxxubm63HOSIjo6WN998Uzw9PcVkMsmgQYMK9Ip4TpcuXZIyZcpIxYoVs006m56eLv7+/gLIypUrC609+XHy5EnX3zw8PDzPpYg//fRTsVgsEh4eLseOHSvEVt7Yn3/+KV5eXlK9evUCCb5ERETI0qVLZcSIEdKrVy85e/bsHak3JiZG1qxZI5MmTZIBAwbckTpz+scFNwAjcAKoALg5T3qq5SjTF5jnfNwZWHqjegvyxOO3KVOkrPMevQEg0qGDSD6HQSqKoijKveQmghsDgENAOleXhM1MJ/NTx92SVHAjd1arVUJDQ+Wxxx4TEZHZs2e7OoqTJk1yldu+fbsA8tBDD93xNthsNhkyZIirk+nh4SHTpk3L17579+51BStwzhNxo2BBcnKyvP3222I2m8XPz09mz55daEtQXr582TWfyWuvvXbdq6YfffSRPPbYY1KuXDlX8Odmk8FgEC8vLwkJCZG6devKM888I23atLkm2FG8ePEb3qoi4ggozZgxQ4KCggSQp556Ktv8Dt99950EBgaKu7u7TJ8+/YZBk6FDh7oCLVWqVHHNpREcHCwbN27Mc1+r1Sr79u2TRYsWyVtvvSXPPvus1K1bV8qUKSPe3t7XPMesycPDQ3r27Omah+BOBDkuXLggvXv3FoPBIMWLF5fZs2cX+O0UdrtdnnrqKXFzc7tmHotHH31UAOnVq1eBtuFWpaenu2498fLyku3bt1+37ObNm6V48eISGBgof9wlcyLu3btX/P39JSwsTKKiom56f7vdLocPH5bPPvtMhg4dKm3atJGaNWtKqVKlxN3dPdf3rcVikXXr1hXAs7nzCiS4AXwHPAUYbrWOPOpuRJaJxIDhwPAcZdYAjZyPTcAlcKz+cr1UECcef3/1ldTM8iZp6usrZ/L4B1IURVGUe11+gxty9Tt97s2UvxuTCm7kbunSpQLI999/LyIiwcHBrg5H1iuqjRo1EkA2bNhQYG1JT0+X3r17u67MN2vWLF9Dpq1Wq4waNcrVUTYYDDJz5swb7nf48GHX5JX16tWTHTt23ImncV379u2TChUqiNlslvnz51+33HvvvXfDwIWmaeLh4SGlSpWSGjVqSOvWraVfv34ye/Zs2bx5c54TCF6+fFnatm3rer0yU35uVRFxzOMxYcIE18iAjh07Ss+ePV3BrwMHDuS5/+nTp6VChQquDpuPj49r5M77779/w+PnV0pKivzxxx8yZ84c6devn7Ru3Vpq1KjhChaZTCbp0aNHtiBH//79swU5Bg8efFNBir1790qzZs0EHEsRr1279o49n5wyR1x9+OGH2bZ/8MEHAkjFihUL7NhZxcfHy9ChQyUkJERq166d50iMnDJHMGmaluf/7NGjRyUsLEzc3d3zXIK6MBw5ckSCgoKkTJkyEhERkWsZq9Uqe/bskY8++kj69+8vrVq1kmrVqkmJEiVc76/rBTCCgoKkRo0a8tRTT8ngwYPl008/ldGjR4umaaJpmkyZMqWQn/HNy+sc45aXgtU0rQXwEvAwsAz4VESO3FJl19bdAfi3iPRy/v4C0FBE+mUp87ezzFnn7yecZS5dr947vRSsXrw4pthYBHAHqnp4UNzDg1IeHixu0AAsFqafOMHBxEQsZrNj2R03N4L9/en36KNgsfD9oUNcTEvD4uGBxd0dNw8PSgQG0qRBA/Dz40JGBpbAQHxDQjC6ud2xtiuKUkREEF1Ht9vRbTZE1xG7Hd1ux+LmhgHISE8nLSUFPUue6DoB/v4YgMTERJKSklz1iK4juk7Z0qXRRLh0+TLxCQmIiOsYAJUfeAB0nbPnz3MlPt7xRaDriAhGoEZYGIhwPDKS2Ph4V57oOm4mE3UrVQIR9h8/TqyzftF1dF3Hy2Lh4apVQdfZdugQVxITr+4vgr+nJ42rVgURft+3j/jk5Gz5Qb6+NK1SBUT4aedOktLSsrWvbEAAjzqXxlu6dStpGRnZ8sOCgng0PBxE+GTzZux2e7Yvu+qlS9O0YkXsdjtzN2265suwXtmyNClfntSMDOZu2XJNftMHHqBRaChxqanM3bo1a6ccEaFVWBj1g4OJTkxk3s6d1+Q/W7kydUqVIjIujvm7dzvOMrKU6VatGtUDAzl6+TIL9u27emzn2+aV6tWpEhDAXzExLDpwwLU9c/9BtWpR3teXrefPs+To0WzHBni7Th1CvLz4/dw5vj5+3LX/2Mceo/S8eXf8bX6zS8HeC4p6ufm71SOPPEJ0dDRHjx5ly5YtPPbYYwC88cYbTJ06FYBLly4RFBREqVKliIqKKvA2xcTE0KhRI06ePElAQAAbNmygevXqN9zv8OHDPPnkk5w6dQqAmjVrsmHDBvz9/a+7j4iwdOlSBg8eTHR0NH369GH8+PF57nMrli9fzosvvoivry/ffvstjRo1yrXczJkzGThwIAD+/v6ULl2akJAQypcvT5UqVahVqxZ169a9pfbFxcWxa9cuGjdujLu7O1FRUbzwwguuZX4BDAYDb7zxBu+///4Nl0+Ni4tj6tSpTJs2jdTUVEaMGMGoUaNwy+N8ePr06QwdOhS73Y63tzdJSUkAPPvssyxZsiTP5WHvFF3Xeffdd5kyZQrJycmYTCa6dOnCnDlz8Pb2JiMjg6FDh/LRRx+RkZGBxWKhb9++fPDBB/laGldE+P777xk6dCgnT57k6aefZurUqYSHh9+x57B161aaNm1K27ZtWb58OZqmAbBv3z7q1KmDyWTizJkzBAUF3bFjZqXrOp988gnTp0/n0KFDjk6rpiEieHl5sWXLlnwv5frDDz/QoUMHrFYrzz33HN98802u771Lly7Rtm1btm7dyuTJk3njjTdcz7uwnD59mqZNm5KWlsamTZuuWRK4bt267N27F915XpeTwWDAbDa7ljbOutyrpmnYbDZsNhtWqxWbzYbdbsdut6PrOr6+vkRFRaHrOu3atWPFihWF/vzzK69zjFsObmSp3A/ogmPZtjPAx8ASEbHeRp13LLihaVpvoDdAaGho3dOnT99qs66xo0YNGvz9NwANvLzwNJtJt9kIMBj4KTQU0tN54dw5fk1LI12EDBHSgcrA3846HgH+zFFvAxyTiADUAvY5H/sAfkYjLX18WPjgg+Dnx+ATJ0gzGvHz9cXPzw//YsWoFhbGY40agZ8fhy9fxqtkSfxCQvAJDka7xXW4lYKV2QnVrVbMBgPY7aQkJpKemordakW3WrFbrWgilCpeHOx2os6fJykxEbvVit1mQ7fZcDMYqBIaCrrO30ePEp+Q4PjgslrRbTZ83N1pEB4Ous76v/4iLjHR0cm227HbbJT09aVZlSpgt7N82zYSnB1su82GruuUDwjg35Urg64ze+NGUtLT0Z2dW13XqREURFtn/e/8/jtW5wdmZnqkdGnaV6yIzWZj4IYNju2ZHWQRWgcH81xoKInp6by2dSu6iCM587uGhPBccDAxqan8Z+9ex76ZZUToGxLCs8WLczI5mZ5HjmTPB0aVKsXTvr78lZxMj8hIRx44ygHTAwNp5enJpuRkel686NquO/9GS/z8aObmxk+pqbyUmOjY7txfB37x8OBhTWOJ1corVuvVfZ0/9wI1gA9xjMXP6SRQHpiA4wM1p4tAIDACmJhLfhqOyYj6A7Ny5JmBDOfjF4HPcuQHAJedj5/DMTQvq1Ag89PzCWBtjvwHyftz7eEs22pkKZupJfCL8/EDWY6VqT3wrfNxcSA2R35PYJHzsRuQ8wvodRyvSQaO1yinYThe08s4XuOcxuN43SNw/I1ymonjdd8P1Mwl/1PgRYOBrSI0cX7valnSVyYT7Y1GftN1nnauL581f4W7Oy3MZn6w2XghNdWVD6BpGj97e9PQZOKrjAz6p6RkzwfW+/tTzWzmk7Q0RjpP9jVgQ3g44YcP59Li23M/BTc0TXsaeDosLOyVY8eOFXVz7irbtm3j4YcfZsaMGQwYMIC6deuye/duDAYDp06dIjQ0FIAePXqwePFiZs6cSf/+/Qulbbqu85///IfPPvsMg8HA9OnT83Vsu93OhAkTeOeddxARTCYTixYtolu3bnnuFx8fz+jRo5k1axaBgYFMnTqVbt263XbnwW63M2rUKCZOnEijRo1Yvnw5wcHBuZYdPXo048aNAxyv+Wef5fwmuHlxcXGsXLmSZcuW8csvv2C1WilTpgxjxoyhZ8+emEwmTpw4Qbdu3di2bZtrPz8/P5YuXUqrVq1ueIxLly5x+fLlazp6OdvRokULdu3ahcFgcHX+KleuzLfffsuDDz5428/1Zum6zrhx45g8eTLJyckYjUa6dOnC3Llz70iQIz09nenTp/Pee++Rnp7OgAEDGDVqFH5+frfV7tjYWOrUqYPRaGT37t2uQFdGRgalSpXiypUrfPfddzz77LO3dZzcbN68mXfeeYdNmzZhdX4XlitXjldeeYUhQ4YwevRoJk+ejNFoZNGiRXTv3j1f9UZGRtKgQQOio6OpUKECO3bsICAg4Jpyqamp9OzZk2XLltG3b19mzJiRr7/FnXDhwgWaNm3KxYsXWb9+PbVr186Wv2DBAl555ZVbqlvTNFcyGAyuZDQaMRqNiAiJiYnkjAuEhoZSs2ZNmjVrRvv27SlfPrczoMJXYMENTdOKA92BF4Ao4AugCVBDRJrdRr2NgDEi0sr5+3AAEZmYpcwaZ5k/NU0zAReAEpLHEyqIqyp1PDzYm5aGEVg/cyZN8vHFqFutGGw2SE8nNjqalPh40hMTSU9OJiMlBTcRqgUFQXw8y9au5ez588THxRGfkEB8UhKV3dz4b8mSEB/Pw3//zYmMDOJFXCfzXYAvnY99gUTnYw3HCJNXLBZmFCuGuLlR9fx53IxG3I1GLM7UuUwZeoWHk2Yy0Wf3btydI04sbm64Wyw0r1yZR6tUIUXT+GLvXtwslmxfzvUrVKBqSAhxycn8tHcv5PjifjgsjLBSpbiUmMgv+/dnf3E0jcaVKlGuRAnOx8by6/79ro63ruvY7XZaV61KqL8/xy9c4Ednfmaeruu8UKsWZb292RsVxfIDB7Ll2XWdobVrE+zhwfrISL44etS1PTNNr1WLILOZbyMjWRQZmS3PLsL3Vavip2nMi4rik5gY7CKupIuwNzQUN11nzOXLLExKcuQBdhE0IMbHB+x2eqelschux46j4wuO2fQyO20duNqZy5S1g9mKq53BTNWAA87HuXUwGwJbnY9r4uiIZdWCq53W8jg6clk9y9VObyBXO8OZenC10+yFo4NpyJJ6m838n6cnVk0jOD4eDTBomiNf03jd25vhxYoRJ0L9qCg0wKhprjIDAgN5JTCQ83Y7T588eXV/ZxoUEkLHoCAi0tN5+ehRx4d4ZhmDgcHly9OyZEmOJCfz34MHHR/umR/2msbgypV5OCiI/XFxTD582LU98+egGjWoHhjI3suX+ThLvsFgQNM0+teuTYWAAHbGxPDNkSOu7Zl19GvQgFK+vmw/d441J09m+6LRNI3XGjXC39OTbWfOsOX06WvyezVujIfFwraICPacPevY7szTNI0XmzbFaDSy/dQpDp8/j8FodOUZjUaeb9wYNI0dJ05w+tKlq/saDFjMZp6sXx80jV0nTnAhLu7q8Y1GPNzdebRmTdA09p44QWxS0tW2GQx4eXhQt0oVMBjYf/IkSamp2er39vSkWsWKoGkcioggLSMjW76Plxfly5YFTeNYZCQ2uz37/l5eBJcqBZpGxNmz6CKOfGcZb29vigcEgKZx7sIFgKv5BgOenp74+voiwOUrV67Jd3d3x8PDA12EpORkcB438/huFgtmNzcExwmeK98ZMDaaTBiMRseICOdn3v0cTL6fghuZ1MiNa3Xu3JnVq1dz9uxZ4uPjKVu2LJqm0bVrV5YsWQI4OoCenp4YDAaSkpJueDX/Tlu2bBndunXDarXSqlUrfvrpp3x1Zk6ePEmLFi1cozgaNWrEunXrcHd3z3O/PXv20KdPH7Zt28Zjjz3GnDlzqFat2i21/cqVK3Tr1o3Vq1fzyiuv8OGHH2KxXBu+1XWd9u3bs3LlSgD69OnDnDlzbumY4AjUZAY01qxZg9VqJTQ0lE6dOlGvXj2mTZvGtm3bqFy5MuPHj6d9+/ZomsbevXt54YUX+Pvvq+HtWrVq8csvv9zWCIClS5fSs2dP0tPTXVf3vb29mTVrFj179rzleu8UXdcZP34877//vivI0blzZ+bNm+cKcgwZMoT58+ffUpDjwoULjBw5koULFxIYGMi4cePo1asXRqPxptsqIjzzzDOsXr2aLVu2UL9+fVde8+bNWbduHT179mTRokU3Xff1nDlzhtGjR7NixQri4+MBR/Crffv2vPvuu5QpUyZb+aVLl9KtWzfsdjsDBw5k+vTp+TqOzWajZcuWrF+/Hg8PD3777bdcRzjpus6wYcOYPHkybdq04auvvsLb2/v2n2geYmNjadasGSdPnmTt2rXXtGvt2rU88cQTADz55JO0b98eT09PvLy88PLyyvbYx8cHb29v3N3db+rzVNd1tm3bxooVK5g3bx6JiYnXlDGZTJQoUYJq1arRpEkT2rZtS+3atQv9c7tAghuapq3AMQhhMbBIRM5nydt5Oyc1zmDFUaA5cA7YAXQVkQNZyryOI4jymqZpnYH2ItIpr3oL4sRj5+efU9/5wWkE1k2fzqPO4X6FSXSdtLg44s+eRUtMpKTJBPHxLF+zhrhLl4iLjSUhPp7UtDQaFC9Ox9BQbKmpdF+/nnSbjXSbjTSrlXS7nS7+/vTz8yMuOZmakZGk6zppzlEn6cB7OK4qRwLlcmnLNGAQjinwc4uTLwBexjE65eFc8pcCnYBfcVzNzWkV0BpYgeNqbk6bcETYluC4mmvE0bHO/LnF25uabm4ssloZkZzs6Dzj6EQbNY1fy5alnLs7n8bH82FsrGu70WDAqGmsrFGDYu7ufHbhAktjYq7mOdPiRo2wuLnxRUQEv0ZHX80zGjEbjcx4/HEwGll2/Di7Y2JcUVOjwYCnxcLQxx4Do5GfjhzheGwsRqPREV01mfD19KRro0ZgNPLb4cNcSEjAaDI5ktGIr7c3LR56CAwGth07RkJaGobMfJMJXx8falWuDEYjByMiSLfZMJrNGIxGDEYj3j4+hJYpAwYDZ6KjEU3D4Oy0Gd3ccPfwwM/fH4xGEpOTHXlmMwaTCc1gwOjmhtFsviagpSjK/eVWghuappUEMs+kt4tIzJ1vWcFRwY3szpw5Q/ny5Rk4cCBTp06lffv2rFixAnB08DOvSr7//vsMGzaMXr168fHHHxdJW8+dO0ejRo04c+YMJUqUYPPmzVSqVOmG+4kIEyZMYNSoUYgIbm5ufPHFF3To0CHP/XRdZ8GCBQwbNozExESGDBnCqFGj8PLyynebDxw4QLt27YiMjOTDDz/k1VdfzbVcbGws9evX5+TJkwB06tSJpUuX5vs4mRISEvjhhx/45ptvWLNmDRkZGYSGhtKxY0c6depE/fr1XRe6RISVK1cyYsQIDh06RL169Zg0aRLNmzcHYMuWLXTt2pXIyEhX/b1792bu3Lk31UnKyMigbdu2rFmzxrXNYDDw8ssvM2fOnDwDAyLCjh07XCNNSpcuneftLneCrutMnDiRSZMmkZSU5Ljo8PzzzJs3Dx8fn1yDHK+//jrvv/9+voIcu3fvZtCgQWzatImaNWsyffp0Hn/88Ztq4/Tp0xk8eDDTpk1j0KBBru0zZsxg0KBBPPDAA5w4ceK2O7Pp6elMnjyZBQsWkDmq3mw28+ijjzJmzBiaNGmS5/4HDhzgkUceISEhgSZNmvD777/ne4TF22+/zYQJE9A0jSlTpvDGG2/kWm7OnDn079+f2rVr89NPP1G6dOmbe5L5lJiYSIsWLdi7dy+rVq1y/Z9k2rZtG40bN8ZutwOQkpKCh4dHgbQlq759+7r+Jzt37kxsbCwHDhzgwoULrlE14PifCwgIIDw8nEaNGvHUU0/RrFmzAg145HmOcb3JOG6UgMdvdd981v8kjgDHCeBt57Z3gbbOx+445vo4DmwHKtyozoKa7KtNyZLiBaKBNAaR338vkOPcDXS7XeypqSLx8WKNipIz27fLid9/v5rWrZMru3eLHD8u6QcPyrFff82e1q6VuF27RI4elZS//pIjP/98Na1eLUdWr5aEnTtFDh2S5L175divv8rJDRskYvNmObN9u5zbtUvSIiJEYmIkPSpKrkRESMLZs5IUHS0ply9LemKi2NPTRfK5FrmiKIpy53HzE4p2wjEw7TPgcxyrpXS4mTqKOqkJRbN76623xGAwyKlTpyQ9PV1MJpNomiYtWrTIVi4oKEg0TZP4+PgiaqmD3W6X559/XnAuYfrRRx/le9/IyMhsK6rUq/f/7J15WFTVG8e/584GDAz7JrhD7qaSuWuumaam4pJpaVSGZWqaGv5yKzXXTMvM1NLMxKWy1Fyzcl8wzQ13TEWUfYfZ3t8fA1fAAWaGWVDP53nOM8O995zzzjAD53zvuzxjUvnV+/fv04gRIwgAVatWjX755RfS6/Xl9tuyZQsplUoKCAiggwcPlnrd0aNHycXFpZhd5pCenk7r1q2j3r17i0kKq1atSu+//z4dPXq0XFu1Wi19++23YonXLl26FKtasWPHDvL09BTtk8lk9MMPP5hk2/79+8nV1bVYssTmzZs/VI61JAkJCTR37lx66qmnHkqg6u/vEjzbIQAAIABJREFUT2FhYdSnTx965513aM6cOfT999/T/v376cqVK5STk2OSbeWh0+lo1qxZov0SiYQGDx4sfgfy8/Pp3XffLZZ41NQSxnq9njZu3CiWVe7bty9du3bNJLuOHz9OMpmM+vTpU+x3e+7cORIEgWQyWYXLNG/atInCwsLEajOMMWrYsCGtXLmy3Oo3JcnIyKDQ0FCxAo45ZZd37Nghvr99+vQpde5t27aRUqmkatWq0blz58yyzxRycnKoY8eOJJFIxKTLRTl37hzJZDLxc1qjRg2r21AW3377rfi7mjx5snj81q1btHTpUurXr5+YiLXo9wkAubu7U9OmTSnTBtVDy1pjOHwxYM9mq4XHye+/JyVAnzVpQhqASCIhnQ2zF3M4HA6HU5mxQNw4A8CvyM++AM6YM4ajGxc3HpCZmUkeHh4UHh5ORA8qFgCgXbt2idft3LmTAIhlYisDa9euFaup9O7d2+QNl16vFyt6oKCiyoQJE0zqf+DAAWrYsCEBoBdffJGuX79u9DqdTkdTpkwhANSiRQu6fft2qWMuWbKkWLWSgIAAkyrDZGRk0A8//EB9+vQhhUJBACg4OJjGjRtHR44cMXsDSkSUm5tLn332Gfn4+BAA6t+/P128eFE8v2bNGnEuAOTl5UWXLl0q9T149dVXi22ivL29aceOHaXOr9Vqafv27dS3b1/xd9uuXTv67rvvaNeuXbRq1SqaMWMGvfnmm2LFk6KiS8m5nn76aerRowe99dZbNHPmTFq9ejXt3r2bLly4QBkZGSa/LzqdjmbPni1Wc5FIJDRo0KBiIseoUaPETbiTkxMtXLjQpLFzcnJo1qxZpFQqSS6X06RJk8oUEFNTU6lGjRpUrVo1Sk5OFo9rNBry9vYmABQdHW3S3BcvXqR33nmH/vjjD9Lr9XTmzBnq3bt3sQ1wYGAgTZw40az3yxg6nY5eeukl8f0pS+wrya1btygwMJAAUGhoaKkVgGJiYiggIIDc3d1p3759FbK3KGq1mnr27EmMMVq3bt1D569fvy6+Z4XC65gxY6w2v6nExMSQUqkkANS5c+dS/wakpqbS999/T8OGDaMGDRqQq6srSaVSm9jExQ07LDzSX3iByMODaNUq+gsgN4D2zZ9vs/k4HA6Hw6msWCBunC3xs1DymD0bDCmMNgL4ylQPEi5uPOCLL74gAHTo0CEiIlKpVGLpyqJ3hBs1akQA6MyZM44y1ShxcXHipicgIKBUsaEkOp2O+vfvL3ohACAfHx/au3dvuX3VajUtWLCAlEolOTs706xZsygvL088n5qaSj179iQAFBERUexcSRvCw8MJBaVICzd98fHxpc6dkZFB69evp5deekkUGYKCgmjs2LF0+PBhiwQNY6Snp9O0adPI1dWVBEGgiIgI+u+//8TzU6dOFe8SA6D69esX23CePXu2mOggkUjoo48+KtW+Gzdu0EcffURBQUEEgPz8/GjixIkUGxtrkr1ZWVl06dIl2rdvH61Zs4ZmzZpFkZGR1KtXL2rWrBn5+fkZFUCqVKlCMTExJr8vOp2OPv3002Iix8CBAyktLY2IDCJHZGQkSSQSURjYb6KX+J07d0TRzd/fn1auXElarbbYNXq9nvr160dSqZSOHDlS7Fy3bt0IAA0ZMsSk+ZKSkqhmzZrFfkeFz5VKJQ0YMMDk998cCssbM8boyy+/NLmfRqOhtm3biqLazZs3jV538+ZNatCgAUmlUvruu+8qbK9WqxUFi+XLlz90/u7du6Jnz5w5c+jZZ58lAKXaZ2tSUlJE77SqVavS/fv3Tepnrb8dJeHihj0WHqdOkR6g62PH0qZx44gBJAFo77x5tpuTw+FwOJxKiAXixnwAu2Ao5jMcwO8A5pkzRpGxVgO4D+BciePdAVyCIZx1cjljjAfQruD5r6bMy8UNAzqdjkJDQ6l58+ak1+vp+++/Fzc3a9euFa+Li4sjAFS7dm0HWls6Re8IS6VSWrNmjUn9cnJyqFWrVqRQKKhdu3bia3/uuecoNTW13P63bt0SxYk6derQ3r176fz58xQaGkpSqZSWLVtWajhIamoqhYSEiB4GEomEGGOiyFSUzMxM+vHHH6lv377i3eEqVarQmDFj6NChQzbblBAZwnHGjh1LcrmcFAoFjR8/npKSksTzhe97YXvxxRfp3XffLXasa9eu4ua/KHl5eRQdHU1du3YlxhgJgkA9evSgLVu2mOS5Yi55eXl0/fp1+vvvv2n9+vU0b948qlq1KgUHB5sVJkFk+MzNnTtXFDkEQaABAwaIn5vk5GTq3Lmz+B60aNGi3DCcQo4fP06tW7cmANS0aVP6+++/xXNLly4lADS/xE3ZwuNVq1Y16fOg0WioVatWxTyGCptcLqfXX3+9WFiStdmxY4coKg4fPtysvoXhYQqFgo4ePWr0mrS0NPH9nzZtmkkhZMbQ6/X0xhtvEACaZ2SfmJqaKop4EydOJL1eT3K5nLy8vCyaz1rodDp64YUXCAA5OzuX+j7ZA5uIGzAUTegJQLB0DHs3Wy88JoWEkAdjlBoXR5vef58YQAJAu+fMsem8HA6Hw+FUJswVNwxd0A/AooLW19z+RcZpD6BZUXEDhpzS1wDUgqFa8JkC74xGALaVaH4F7csC0eWQKfNyccPAb7/9RgBo/fr1RETiXdzAwEBSq9XidS+++GKx6yor33zzjXj3ecCAASZt8hITEykkJIS8vb1py5YtYs4JmUxGs2fPNmneHTt2UK1atcQNl5+fX7ENaUmOHz8uuo63adNG9JZZsWLFQ9deuHBB9GYIDAyk9957jw4ePGhTQcMYcXFxNHz4cBIEgVQqFc2cOVOMz8/OzqY6deo8tEn29fU1ukE+d+4cjR07VgyhqF69Os2cObOYZ4i9OHXqFDk7O1Pbtm0tElR0Oh3NmzdP/B0KgkDh4eGiyBETEyN+NgRBoNdee82kefR6Pf3444/i53HAgAH0888/k1wup549exb7/cfGxop5NkwRUOLi4qhKlSri76lq1aq0YMECUqvVFBMTQ2+++aaY/yUsLIxWrlxZahhIRbh+/Tp5eXmJIk5pHk7GmDt3rvielvZ3KT8/n4YPH04A6NVXXzX796vX6+n9998nAPS///3vofPZ2dkUEBBAAGjkyJFERLRr1y5CQThXZWDq1Kni+2TM68Qe2Erc6AJD6ddrAD4FUMfSsezVbL3wOLV+PQGg6c89R0REmydMEAWOfZ9+atO5ORwOh8OpLFjguTHXlGNmjFejhLjRCsCuIj9/COBDE8aRANhaxvm3AJwEcLJatWo2eCcfPTp16kRBQUHipqZws7NgwQLxmtzcXJJIJOTu7u5AS03n8uXL5OvrK27aTNnsXblyhXx8fKh27dp0//59mjNnjnhXuWrVqnTixIlyx8jJyaFp06ZR7969y5zzyy+/FO+WT5w4UUyWOWrUqIeu/eeff8jX15f8/f1p7969dhc0jHHu3DnRW8PPz4+WLFkibkpPnTpF7u7uJJVKi32GiAzeJytXrqSWLVuK4tGAAQNo9+7dDn9dGzZsIAD01ltvWXyHX6fT0YIFC4qJHO3ataOTJ08SEdHq1avF0AVnZ2daunSpSeNmZ2fTjBkzRLEhODi4mOeMRqMRP+/liY937twp5k2iVCqNJsYkMng+LF26lOrXr0+AIeHke++9RxcuXDDxHTGNnJwcatKkiejBFBcXZ3LfzZs3i2Lm9OnTjV6j1+tp5syZBIA6duxokkdWITNmzCAANHr06Ic+FxqNRkwEO2jQIPF4oRD8119/mTyPrdm6dasY9hYREWH3+W0ibtCDf+zuAN4GcAvAYQAjAMgqOq4tmj3uqrwUGEjuAKUWfJF++uAD8gMoDiDaudPm83M4HA6H42gsEDdOGTn2rzljlOhbUtwIB7CyyM/DAHxRTv8VBTdx2poyJ/fcIDpz5owYI05E1KZNGwJALi4uxZIZTpgwQdyIPypotVrRJVsmk9GGDRvK7XP48GFycnKili1bUk5ODqWnpxfbCPbp04dyc3MttqlohReZTEZbt26lXr16EQBq27btQ9cfPXqUPDw8qGrVqnT58mWL57UVR44coQ4dOhBgqAqxdu1ao/khjh49Sm+88Ya4sa9Xrx4tXLjQ5DwA9uLDDz8kALRs2bIKjaPX62nhwoXFcnxUq1aNvvrqK9JoNDRq1ChxQx4cHEwHDhwwadxbt27R+PHjH8oPUvg5L7rBLsm9e/fohRdeKBaC0rBhQ5Mruvz11180ePBgUfDr0KEDbdiwwaqhQ4WJZ2UyGe00Yw8WExNDzs7OBIBefvnlUq9bu3YtyWQyql+/vkkCymeffSaGzJQU33Q6HdWrV48A0PPPP1/snEqlIoVCYbFIZiuuXr0qhs+EhYWZ5SVTUWwmbgDwBjCm4K7FrwAGAVgK4M+KjGurZo+Fx+noaEMsVtHM3z/9RMQYJQP019y5NreBw+FwOBxHYqq4ASASwFkA2QD+LdJuAFhnyhiljFshccPMuXoBWBESEmKbN/MRYsSIEeTi4kLJycmUnJwsbnw++OCDYtepVCqSSqU2yYFga5YuXSomvRw2bFi5G44tW7YQY4z69u0rbtT3798v3hl3cnKyaPObnp4uemj4+flRXFwcffTRRwQYkoGW3GQWlk6tXbu2WXey7Y1er6edO3dS06ZNxQ3z1q1bKSkpiRYvXixWlXFxcaHXX3+dDh8+XOk2fYVotVrq2bMnSaVS+vPPP60y5t69e6lZs2aioODs7EwRERF09epVeu6558TjrVu3NqkccUmWL18ufoaMeb+kpKRQnz59xO+2l5cXubu7U61atYp5f5jKvXv3aM6cOWKySn9/f4qKirLaZ7Ro1SBTQ8KIDAk9C7+jzz77bKmizb59+8jd3Z0CAgLK9MZauXKlGFpSciydTkfNmzcnANSqVati5y5duiT+PisjOTk51LhxY4u8ZCqCTcQNAD8DuACDa2dgiXNmx9rao9nrrkq/KlWoniCQruiX/JdfqHpBiMqOGTPsYgeHw+FwOI7ADHHDvUCI+BFA9SLNy5T+ZYxbUtywKCzFxLm4uEFECQkJJJfLKTIykoiIXnnlFQIM1RKKliwtTDDau3dvR5laYc6fPy/G9desWbPcxJGFd2zHjh0rHtPpdDRx4kTxjntoaKjJVSROnDgh5tdo27YtaTQa2rx5s7jpL+nB8Pvvv5OTkxPVr1+/zKoplQmdTkfR0dEUGhoqhmQUbjRXrFhRZlnTykRaWhrVrVuXfHx86MaNG1Yb9+7duzR48GCxug1jjFq3bk2rVq0SQxsEQaA33njDJG8KIkP4lUQiIalU+lBVjoyMDBowYID4e/D09KQlS5ZQ8+bNydXVlc6dO1eh11NYrvfFF18kxhgxxqhnz560bdu2h7x3zOXAgQNi0ty+ffuaLIbl5uaKITTBwcGlhp+cP3+eqlevTi4uLvTrr78+dH7Dhg3EGKPu3bsb9W7o0qULAaBGjRo9JCiNHDmSANDKlStNstlRDB06VPSS+f33320+n63EjR5GjiksHc8ezV7ixt19+ygXIProo2LHf4mKEnNwbC8ljovD4XA4nEcdR9/kMCJuSAFcB1ATDxKKNrDmnE96WMq0adMIAMXGxpJOpyO5XE4oSLpXlMJEiI4qaWgt1Go1derUiQBDJYjS8gwUMmbMGAJAixcvLnb87t274l1bxhi9+uqrZW5Gly1bJm4wCz1izp49SxKJhARBEPMxFLJlyxaSyWTUtGlTSkxMtPDVOg61Wk3ffPMNTZ48udKVDDaVS5cukbu7Oz399NOUlZVl1bF1Oh3Nnz9fTEJZ6HUxcOBAMaeGUqksN/GjTqcjf39/AlCsMlBWVhYNHTpUFOFUKhV99tlnpNfradiwYQSAfv75Z6u+pri4OJoyZYpoT/Xq1Wn27NlmV58pSnx8vPge1alTR0xcWx46nY569OhBAMjNza1UAfLu3bsUFhZGgiDQF198IR7ftm0bSaVSateundEEqv369SMAVKtWLaPf+6CgIGKM2TXkw1KKesl88sknNp3LVuKGsfjYh45VpmbXhUf//qR2c6PsEgmgtkZFkVAocEydaj97OBwOh8OxE44UNwq8QO4C0AC4DSCi4HgPAJdhSIQ+xYrzPfGeG8ePHycfHx/q0aMHERF98skn4kbr33//Fa8rzMnRuHFjR5lqdebPny+KDW+++Wapd4W1Wi317duXGGP0008/PXT+p59+EhNHurq6UnR09EPXDBkyRLw7WiimpKWliaVDi5baJTJ4yUgkEmrVqpVZSQ851uf3338XS7vaKozm77//pubNm4sbTIVCQaGhoeLns3r16nT48GGjfXv37k0AqF+/fkRkKHEbEREhJo1UKpU0Z84c0bNg4cKFBIBm2NAbPT8/n6Kjo6ljx47i537SpEkWv39qtVrMA6RSqej8+fMm9x07dqxow969e41ek5WVJea8GT9+PO3bt4+cnJwoLCzMqKdRYfnZwMBAo7l3UlJSCAA99dRTpr9IB1PUS6ZPnz42S+xrVXEDQACAMAAXATSFodxaMwDPAYg1dzx7NnuKG5lHj1JNgP5nJKHTbx99RAJATgBptmyxm00cDofD4dgDR3tuOKI9iZ4bCQkJ4gI9ICBATExYWI6zS5cuxa5v3749AaDdu3c7wlyb8c8//5CHh4coTIwZM4ZycnIeui47O5tatGhBTk5OdOTIkYfO63Q6evPNN8XNadOmTenWrVuUnp5OdevWJcBQCvX69evi9YW5CsaPH19srK+//poYY9SxY0eT71JzbMu8efMIAM2aNcum8yQmJtKwYcPETWbh57Lwefv27enevXvi9YX5IAIDAykvL49GjRolJvp0dnamadOmFduk7tq1iwRBoP79+9utKs3FixfFULeS3k/m8u6774ohc5s3bza537Jly8SQmdI8YbRarTg+Y4zq169v1GOqsBysl5dXqSFWs2fPJgAUFRVlso2Vgbt371JgYCABoJCQEJuEkFlb3HgNwH4AmQWPhe1XAP3MHc+ezd4Lj/5BQeQGUPLVqw+d2z59Om0GiBgjKseVkcPhcDicRwlLxA0Ycm10KXjuDMDN3DEc0Z5Ezw21Wk0LFy4klUpFMpmMPvjgA3EBu3XrVnETtW/fPrFPamoqMcbIz8/PUWbblNzcXBo4cKC4KZRIJNSjR4+H8izcv3+fateuTT4+PnTVyPqQyFCFoDDWXxAEMbygVatWxZKwdu3alQBQ586di/VftGgRAaCePXsaFVk4jkGv19Mrr7xCjDGjuRmsjU6no8WLF1NQUJD4nSz04hAEgSIjI+nq1askkUhIIpHQ66+/LubwcHJyokmTJj2U7+Ly5cvk4eFBjRo1srtoptPp6KWXXiJBEGjHjh0VGmvNmjXiezF58mST++3evVv8jr///vtGr9Hr9fT555/Tc889R3fu3Hno/McffyyGuRQVmUpSmDzX2BiVHY1GQ+3btycnJyeLEtuWh63CUvpb2tdRzd7ixr9bthAAmtKmjfELtm8nHUDdAPrlww/tahuHw+FwOLbCXHEDwJsATgC4VvBzKIB95ozh6PakeG7s2rVL9CLo3r37QzHohefq1atXzH389ddfJwA0b948e5tsV9RqNUVFRYmeHChIFFjUW+XSpUvk7e1NoaGhZebBWL16tViScty4ccXOFZbTrVGjhnj3XK/X08yZMwkAhYeHP5LVaB53cnJyKCwsjNzc3MwKi6gohw8fplatWhUr3Vq0FYafyOVyGjt2rNH8D+np6VSvXj3y9vYWvYfsTWZmJj399NOkUqnowoULFRrr1KlTYmLeadOmmdwvNjZW9ITp2bOnWd4rS5YsET1iyqoskp+fT4IgkK+vr8ljV0ZsFQ5nbc+NoQWP4wG8X7KZO549myMWHgOCg8kVoKRS6olf+PprEgBiAP30CNV753A4HA6nNCwQN07DkOjznyLHzpozhqPak+K5ce3aNerTpw8BoNq1a9Nvv/32UOx7bGysuFlav369eFyn05GTkxMpFAq7ubFXBn788UeqXbu2+J74+/vTggULSKfT0aFDh0ihUFDr1q3L9K7QaDQP3bldt26dGGqQkpJCRAZhY+LEiYSCJK6mVsjg2J9bt26Rv78/hYSEiL8/e5GSkkIjRowoFrKCglwSb7/9dqmJK3U6HfXq1YskEgn98ccfdrW5JDdv3iQ/Pz+Ly88WJSUlhSQSCXl6eprVLzk5mYKDgwkANWjQwGjOjJKsWbNGFJDKE2Y2bNhAAOiVV14xy64nBWuLGyMLHqcZa+aOZ8/mCHHj7M8/EwNoUQmXwaLs/OSTBwJHiVrwHA6Hw+E8alggbhwrePyn4FEK4F9zxnB0e1w9N7KysmjKlCmkUCjEpIKlbYAKSxr6+PgU21wXlkItWTnlSeH06dPUrl078a65k5MTRURE0Jo1a4gxRuHh4SaLPjExMSQIAkkkEjp79iwRGTae77zzDgGgyMjIJ0pAelQ5dOgQyWQy6tatm0OEKJ1OR19++SU1aNCAhg8fXm740pQpUwgALV261E4Wls3hw4dJLpdThw4dKuyh1LlzZwJQaqLQ0tBoNGKlIz8/vzLDL3755RdijJFUKqXjx4+XO3ZhEtWjR4+aZVNZ6PV60uv1pNPpSKvVklarJY1GQxqNpsLldu2NrcJSfC3t66jmqIXHsS5dSKdUEpXherh7zhxR4NhcIikUh8PhcDiPEhaIG/MARAGIBdAVwM8AZpkzhqPa4+q5odfr6ccffxTvTg4dOpRu375d6vWZmZni5v2zzz4rdi4gIIAYY5ScnGxrsys1KSkp9Nprr4l5DQRBED07SiYENUZSUpKYf2Pjxo1EZNhgDR8+nADQhAkTbFaJg2N9ChN5mvK7dyTR0dEEgCIiIirV5+v7778nlFOlyBROnjxJAKhly5YW9R88eLAYalKYVLko+/btI0EQSBCEYnmISkOv15OTkxM5OzsXe12JiYnUvn17ksvlJJPJSCaTkVQqJalUKuZNEQRBTHpqLPyotKZUKmnPnj0WvX5HUNYagxnOmw9j7DKAOADRAH4iolSLBrIjzzzzDJ08edL+E1+4ADRsCM2ECZDNm1fqZXs+/RQvfPgh3gOwKDoaGDjQfjZyOBwOh2MlGGMxRPSMGdcLACIAdAPAAOwCsJIsXaQ4AIetMWzAmTNnMHr0aBw4cADNmjXDkiVL0KZNmzL7REZGYvny5XByckJiYiJcXV0BAPv370enTp3Qpk0bHDx40B7mV3p0Oh0WLFiAhQsXIjExUTweERGBlStXltqnRo0auH37NqKiojBr1iyo1WoMHToUmzZtwvTp0zF16lQwxuz1MjhWYPTo0fjiiy+wdu1aDBs2zNHmPMTp06fRpk0bNGnSBH/88QcUCoWjTSpGVFQU5syZg8WLF2PMmDEWjxMQEID79+8jIyND/NtlDjNmzMD06dMhkUiwadMm9O3bFwBw4sQJtG7dGjqdDj///DP69OlT7lhHjhxB69at0bFjR/zxxx8AgNu3b6Nbt264ceMGIiMjoVAoxO+6sUdzz61evRpKpRKnT5+GRCIx+/XbmzLXGKWpHqY0AM8CWATgOoBtKMjHUVmbI11GN7dpQ0GMUeLFi2Vel/zTT0QSCRFAx994w07WcTgcDodjPWC+54YSgKTIzxIALuaM4ej2OISlJCUlUWRkJAmCQD4+PrRixQqT3JVTU1PFGP5JkyYVO9e0aVMCYPSOJodo+/btYmUUFOTRmDZt2kPve2EZ3Z49exKRoTrLiy++SABowYIFjjCdYwXUajV17NiRFAoFHTt2zNHmFOP+/ftUvXp1CgoKsknFC2tgrQoqU6dOFb2fLGXdunViBZZ58+bRhQsXSC6XEwBas2aNyeMMGTKkWN6iK1euUPXq1cnNzY3++usvi+0ri0LvHHPsdCRlrTGs8g8dgA+AtQB01hjPVs2RC4/zv/5KDKDJprg8HT5MXxZkLX7Rz490PCkUh8PhcB4hLBA3jgJwLfKzK4DD5ozh6PYoixsajYa+/PJL8vT0JIlEQu+9955JiQ51Oh1FRUWJlRYEQaD4+Hjx/K1btwgAVa9e3YbWPx6cOXOGVCpVsQSPgwcPpvv379O7775LACg0NJR0Oh1lZmZSp06dCAB99dVXjjadU0ESExOpRo0aVKVKlWLfH0eiVqvFUp4nTpxwtDllYo0KKjk5OSSRSMjLy6tCthw+fFgMOyv8u7hkyRKzxvD29iZBECgnJ4fOnDlD/v7+5O3tTSdPnqyQbWWh0+koLCyMqlWrZlJyVEdjE3EDgArAawB+B3AZwFwAYZaOZ4/m6IXHy9WrkxKg+yZ88e6dO0dBBepfiFRK6bdu2cFCDofD4XAqjgXixmlTjlXGhkc858aff/5JjRs3JgDUqVMnMUlleWzfvp28vb3F7P9SqZRef/31Ytf07duXANDq1attYfpjx71796hGjRpivD0AMXZepVJRRkYGpaamUuvWrUkQhEfmLiunfM6cOUMuLi7UsmXLUhP22pPIyEgCQOvWrXO0KSZx8+ZN8vf3r1AFlULvqIpWg7l58yZ5eXkRAJoxY4ZZfa9du0YAqH79+nTkyBHy8PCgoKCgCpe9NYW9e/cSAFq0aJHN56oothI3bgD4DEArS8ewd3O0uHFh2zZiAE169lmTrtfk5lIHd3cCQG4And2yxcYWcjgcDodTcSwQNw4BaFbk5zAAR8wZw9HN0WsMc/nvv/9o0KBBBICqVatGmzdvNikp361btygsLEz0MOjbty999dVXBKCYW31+fj5JpVJyc3Oz5ct47IiNjSUvLy966qmnaP78+RQUFESurq4UGxtLiYmJ1KxZM5LJZLR582ZHm8qxMps3byYANGLECIcm7ly+fDkBoA8esQqOhV4TllZQOXAD80iBAAAgAElEQVTgAAGg1q1bV9iW/Px8iwSJSZMmEQAaPnw4KZVKCgkJoRs3blTYHlPp2rUreXl5UVpamt3mtARbiRvM0r6OapVh4TGkwHsj7coVk/uMa9aMANBLAFGRuvEcDofD4VRGLBA3mgO4BuAAgIMArlZ2b9CSrTKsMUwhNzeXPv74Y3J2diYnJyeaPn06ZWdnl9tPo9FQRESEGFNep04dOnfuHBERPf/881SzZs1iG7KoqCgCQOPGjbPZa3lcOXDgACkUCmrbtq3oIh4fH08NGjQghUJB27dvd7CFHFtRmPvh888/d8j8f//9N0mlUurevfsjVx6UyJD3AhWooOLj40OCIFB6eroNrCufWrVqiWFpjRo1snuuk5iYGAJAUVFRdp3XXKwqbgBYXPD4G4BfSzZzx7NnqwwLj2t79tBxxojMTFizd/Jk0jFGBNDViAgbWcfhcDgcTsUxV9wwdIEMQMOCJjO3v6NbZVhjmMJbb71FACg8PJzi4uJM6rN69WpSKpViwsui4RCJiYkkkUgeSiTq4eFBEonkkYjfroxs2LCBANDAgQPpxo0bFBISQkqlssIu85zKjU6noz59+pBEIqG9e/fade6bN2+Sr68vhYaGUmpqql3ntiYffvghAaDFixeb3bfQc6Lk3zN7kJycLHrEtWzZ0qS8R7Zg8ODB5OzsXGnyvxjD2uJGWMFjB2PN3PHs2SrNwmPoUCJnZ9InJJjX78wZOuDkRAygTh4ePNEoh8PhcColFoobrQEMAfBqYTN3DEe2SrPGKIdu3bpR8+bNTbr27NmzFBoaKiYLHTlyJGlKrD2+/vprAkCnTp0Sj23atIkA0AsvvGBV25805s2bRwDI2dmZ3N3d6fDhw442iWMHMjIyqEGDBuTl5UXXrl2zy5zZ2dnUtGlTUqlUdLGcyo6VnYpUUElNTSXGGHl5edk9NCg8PJwAUJUqVSgzM9Oucxfl6tWrJJVKaeTIkQ6zoTzKWmMIMBMiiil42oSI/iraADQxd7wnEfrf//BWbi6ievc2r2Pjxmh48SJqSqX4Iy0NNZyckHTpkm2M5HA4HA7HTjDGvgewAEBbGEJUmgMwXsO+ksEY68UYW5Genu5oU0xGIpGUeT4rKwt9+vRBo0aNcOXKFTz77LO4desWli9fDqlUWuza6OhohISEoEmTB0vAqKgoAMDSpUutb/wTxIQJEzB27Fh4enpi//79aNWqlaNN4tgBNzc3bN26FUSEPn36IDMz06bzEREiIiJw+vRprF+/HnXr1rXpfLZGEAR8//33aNSoEQYPHoyLFy+a3NfDwwMtWrRASkoK/vzzT9sZWQQiwsyZM7F582YAhr+prq6udpnbGLVr18bIkSOxcuVKXL582WF2WExpqkd5DcApI8f+sXQ8e7TKdFdlWK1a5AxQwr//mt1Xp9FQD19fAkAuAB3/9lvrG8jhcDgcjoXA/JwbF/EI5vIq2irTGqMsunXrRi3LKEs/e/ZsksvlBIB8fX1p586dpV6bkJBAgiDQlClTxGPnz58Xs/1zrINOp3O0CRwHsGfPHhIEgfr27WvTz8CcOXMIAM2ZM8dmczgCSyuo7Ny5kwBQmzZtbGidAZ1OR2PHjhUrIzk7O1eK73tCQgIplUoKDw93tClGKWuNYbbnBmPsZcbYbwBqMsZ+LdL2A0ixgt7yRPC/r75CPoB5I0aY3VeQSrH9/n181K4dcgCMGDECWLXK6jZyOBwOh2MnzgEIcLQRTzJ//vknAgMDERUVBSLCtGnTcP/+fTz//POl9tmyZQv0ej0GDRokHhs9ejQAYO7cuTa3+UlBEMxernMeA7p06YKFCxfi559/xscff2zVsYkIarUaW7duRVRUFAYPHoxJkyZZdQ5HU61aNfzyyy+4c+cO+vfvD7VabVK/bt26wd3dHYcPH0ZKiu22tlqtFhEREVi8eDF69eoFIkK7du0qxffd398f48ePx+bNm3H8+HFHm2MWzCB+mNGBseoAagKYA2BykVOZAP4lIq31zLMuzzzzDJ08edLRZoi8Vrs2Nl2/jutnziCgcWOLxtjx0Ud4dvZs+Oj1SHrtNfh89511jeRwOBwOx0wYYzFEZHJYScENkiYAjgPILzxORGbGbzqOyrbGKI1u3bohMzMTR44cAQAkJCSgf//+OHz4MACge/fuiI6OhkqlKnesDh06IDExEefPnwdjDFlZWXB3d4eXlxcSExNt+jo4nCcBIsKIESOwZs0azJo1C76+vsjLy0Nubm6FHwv3gE2bNsXBgwfh4uLi4FdrG3744QcMHToUb775Jr7++mswxsrtM3bsWHz++ef48MMPMXv2bKvblJ+fjyFDhuCnn37C9OnTcfz4cezYsQObNm1CeHi41eezhIyMDISEhKBhw4bYt2+fSe+bvShrjWG2uPEoU9kWHlf37UPdLl0wplkzLIyJKb9DaVy6hPjmzVErMxNNlUocSEqC1MnJeoZyOBwOh2MGFogbHYwdJ0M+L5vDGKsFYAoAdyIKLzimBLAMgBrAn0T0Q1ljVLY1Rml069YNWVlZOHjwIMaMGYNly5ZBr9ejZs2a2Lx5M5o1a2bSOPHx8QgODsa0adMwbdo0AEBkZCSWL1+OTz75BFOmTLHly+Bwnhjy8vLQqVMnUZAsikwmg7OzM5ycnCx6VCqVGDJkCHx9fR3wyuzHlClTMHv2bCxevBhjxowp9/qEhAQEBgbC09MTycnJVt3YZ2dno2/fvtizZw8WL16M0aNHw83NDXl5eUhPT3dovo2SLFmyBGPGjMHOnTvL9OKzN1YVNxhjB4moLWMsE4ZyNeIpAERE5Uv9DqIyLjzWtmuH506cQLUbN4DAQIvHyUpIQPPq1RGrVsOfMZw6eRJVTFygcDgcDucRR68HtFqoMzKQlpgIdVYWNLm5yMvIgCYvDzV8fKCSyRB/9y7OxMZCk58PdU4Ouj3zDFQvv2x1c8wVNwr6VAcQSkR7GWMuACREVG4mPcbYagAvArhPRA2LHO8O4HMAEgAriehTE8baXETcGAYgjYh+Y4xFE9GgsvpWxjWGMbp164a4uDjcu3cPGRkZcHZ2xsKFCxEZGWnWOJ9//jnGjh2Lixcvom7dutDr9XBzc4NWq0Vubm6lcK3mcB4XtFot4uLiiokTTk5O5SYH5hjQ6/UIDw/H1q1bsW3bNrzwwgvl9mnWrBn++ecf7NmzB126dLGKHampqejZsyeOHTuGVatWYfjw4Th27BhatmyJxo0b48yZM1aZx1rk5+ejXr16UKlUOHXqVKX5u17WGkNq7GBZEFHbgke3ihrGAV797jugTh1g7lxg8WKLx3ENCMD57GwMqlkTm2/fRq2wMOxevBjtTVAnORwO55FHrwfUaiAnBwn//YfctDTkpKcjPzMTuVlZcJdK0bBKFSA3F5sPHEB2VhbycnOhzs+HOj8fIR4e6BMSAqjVmLhnDzRaLdRqNTRaLTRaLVp5eeGtGjWQk5eHQceOQavXG5pOB61ej54eHpjs54fbubnoce0atETQE0FX0F5xccHHKhVO5+Whe0oK9AB0MNwh0BNhpEyGuXI5fler0U+jQWGxe33B4/sA5gkCVuj1eBvF7ywAwIcAZsMQLzrdyNszD8AHAOYCWFLk+HYAPWwgbpgLY+xNAG8B8AJQG0AQgOUAOpvQ/TsAXwBYW2Q8CYAvAXQFcBvACcbYrzAIHXNK9H+diO4bGTcYwNmC5zpTX0tl5+zZs0hISABjDK+++iq++eYbyOVys8eJjo5G48aNxcoKy5cvR05ODl5++eVKswDmcB4XpFIpQkJCHG3GI4sgCFi7di3atWuHwYMH4+jRo6hXr16ZfaZMmYLw8HBMnz7dKuJGQkICnn/+ecTGxmLTpk3o168fAGD16tUAgFdeeaXCc1gbhUKBjz/+GEOHDsWGDRswZMgQR5tULhaHpTDGagO4TUT5jLHnADQGsJaI0qxon1WprHdVLvbrhw+2bsU3J08isGnTCo83r0cPTP79d3gBSFq8GOACB4fDMYI6KwtZ9+8jJzkZuWlpqO3rCyE3F+cvXsSNmzeRl5WFvJwc5GVnQ5Ofj8hnnwVyc/FjTAz+uXMH+Wq1oWk0kBLhmyZNAI0GE8+exam0NGgKNv8aIqgYw96aNQGtFi/evImLajW0hZt/AL6M4V93d0CvR730dNwigh4PNvf+AP6TSAAiuOv1KOk66AOgMMJfiod3okEw7HABQMDD4kAIgCsFz405nzYGcAZAGgBPI+fbAjhQMEadIuMUtgGMYb1MhpMA2qvVEAqOCwAExvC2QoE5rq44qNViYEaGeFwCQMIYxnt6ItLbG3/m5WFMQgIkjD1ogoBJVauid0AA/kxPx7ybNyERBMgkEkgkEsgEAeMaNEDzgAAcSknB+mvXIJVKIZdKMaZ9ewQvXGjkFVUMC8JSTgN4FsAxImpacOwsETUysX8NANsKPTcYY60ATCei5wt+/hAAiKiksFFynJKeG6lEtI0xtoGIBpfVt7KuMUoil8uh1Wpx7do11KxZ06Ix/vvvP1SvXh2zZs0Sy74GBwcjPj4eCQkJ8PPzs6bJHA6HYxVu3bqF5s2bQ6lU4vjx4/D29i71Wq1WCw8PD+Tk5ODu3bvw9/e3eN6bN2+iS5cuiI+Pxy+//IKuXbuK5wICAnDv3j3ExcWhevXqFs9hK/R6PZo1a4aMjAzExsZaJIZbG5vk3ChYiDwDoAaAHQC2AmhARD0stLNwXC8A0QXjxgEYSESpJa5pAuArACoY1rCziCi6vLEr68Lj+v79eKpTJ7zz9NP4/PRpq4y559NPofroI7TQapEzaBBcNmywyrgcDqcM9HogLw95SUm4GxeH7KQkZCYlISc9HdlpaXgmOBhVnJxw8epVbDt9WkzslZefj7z8fLwTEoIGLi749cYNrLh2DWqdDvlaLTR6PdR6PVZUqYJmMhnm3b+P5Wlp0ALFBII/lEo0Zgyv5+TgR52umDhAAC7DcEu8HYCDRsxPh+GPamM8uF1dlML/FiEArpU4xwrmAgy3u++UOC8HkF8Qs1qTCLfxYHPPAPgwhltKJSAIaJKdjdt6vbixlzCGmlIp/g4OBiQSdLl9G/f1esgKNvYyxlBfqcQ3DRsCUileO3cOmXo95FIppBIJZFIpnvb1xdiwMEAux8zjx6EXBMjkcjg5OUGuUOCpKlXwfNOmgFyOLf/8A5lCAYWLCxRKJRSurqhSpQqq16wJvVyOhPR0OKlUcPLwgNzVlec4MoIF4sYxImrBGPuHiJoyxqQwlJw3Kdu2EXEjHEB3Inqj4OdhAFoQ0bul9PcGMAsGT4+VRDSnIOfGFwDyABw0lnODMfYWDB4nqFatWtjNmzdNfckOQyaTQa/XQ6ez3BllwYIF+OCDD3Dp0iVs2rQJn3/+ORITE9GiRQscPXrUitZyOByOdTl69Ciee+45tGzZErt37y5zsz5y5EisWLECkydPxpw5ZWrjpRIbG4uuXbsiKysLO3bsQKtWrcRzV69eRWhoKAIDAxEfH2/R+PZg165d6N69O5YsWSJWxHIkthI3ThFRM8bYBwDyiGhp4aKkgsbOA5BCRJ8yxiYD8CSiSSWueQqG/B5XGGNVAMQAqFee10hlFTcAIOKpp/DDlSu4duIEgp4xK0y5dG7ehLpZM1RLSYG/kxOO3b0LJw8P64zN4TgYvVaLnKQkZNy5g8x795CZmAhPQUBtlQp5KSlYu28fcrKzkZ2ZieycHOTm5aG9ry/6+vsjPjkZkTExyNNoDAKCTge1Toeh7u4Y6+6Ok5mZ6HfvHrREhgZAR4R3pVLMkkqxUaPBKwXiQaFwAABjAXwGYD6AiUZsng5gGgwhBMaC/5cAGA0gEgZ//JJEAxjIGCKJsBIodvdfAoO40Vwuxwd5eViXnw9pwZ1/GWOQCgJ21ayJakolPk1MxPb0dMgEAXKJBDKJBHKpFN+3bg0XpRJr4+JwMiUFCrkccoUCCoUCzs7O+KB7d0ChwMGbNxGfnQ0XlQrObm5wcnODUqVCk6ZNAaUSWXo9BBcXOHl4QKgECj/H/lggbsyDwTHmVRi+BqMAXCAik7JSVlTcqAiMsV4AeoWEhLx55cqVcq93NNYQN5o0aYL//vsP2dnZUKvVEAQB7du3x5YtW+Dl5WVFazkcDsf6mFpBpVB88PDwQHJystkhd6dOncLzzz8PiUSC3bt3o3GJ6pgff/wxpk6dilGjRuHLL7+0+PXYGiJC586dce7cOVy7dg1ubo7NTmErceMYgMUwZBfvRUQ3GGPniibzsnDcSwCeI6K7jLFAGDKU1ymnzxkA4URU5qqiMosb1//8E3U6dkRk48ZYYsVkMuqsLLT280NMbi68GMPkF15A+KRJqNm+vdXm4DyZ5KWlIeX6daTHxyMtPh4ZiYlQaLV4rlo1ICsLy/buxd2UFGTn5CAnNxc5eXmopVBgeq1aQF4eusfEIEmthlqvR36BZ0JTmQw/qVSARgPv1FTkweCaVeiB0BTAiYLnxlJohQE4CSAehjCEkrQH8BeA0wVjlaQ/gM2CgINE6EQECR4IBxLGMNrJCTPd3fG3VovhqamQCgLkBU0mCBgfHIyBwcE4lpODuXFxUMhkUMhkcJLLoVAo8FqTJmhWowYuZWVh940bcHZxgdLdHS5ubnBRqRD29NPwCgxEFmPIYgyufn5w8fGBIDU7PRKH41AsEDcEABEAusGg2e2CwYPCpEWKtcJSKkJlXmMUpSLixsGDBzFq1CicPWvw7XJxccGrr76KefPmOXyxy+FwOOZgagWVBg0a4MKFC9i+fTt69DA9QOHvv/9Gr1694OnpiT179iA0NPSha+rWrYtLly7h8OHDxTw6KiMnTpzAs88+i2nTpmH69OkOtcVW4kZ9AG8DOEJEPzLGasIQQjLXclMBxlgaEXkUPGcwxLuW6m7AGHsWwBoYQmL0Rs4/Mi6jb9Spg3WXL1vXe6OA1596Ct8WuaO0UhAQERiI3/39cdrHB+ETJiC0SPwX59FCnZVlEBpu30Ydd3cgPR1/Hz2K81euID0tDZmZmcjKygI0GnzeuDGQk4P3T5/GibQ05Gq1UOt0yNPr4QrglI8PoNEgLDUVl3Q66PBAYHADUBgj5gogu4QdbgAyCp47Acgvcd4bQFLBcxkALR7kJBAANGQM/zg7AxIJArOyoAMgZQxyxiBjDM8plfimRg1AoUDHS5cglUigkEjgLJfDWS5Hh+BgRDRtCrVCgXmnTsFFqYTS1RWu7u5w9fBA3aeeQp0GDaCVyxGfkwNVYCBcAwJ4WAGHY2XMETcKkn+uJSKLs6kZETekMERidYYhSuoEgCFEdN7SOcqY+7H23CAiLF26FHPnzi3mNv3JJ58gKirKqiUSORwOx14UraBSr149MMbAGIMgCMWeJycnIy4uDkqlEo0aNTJ6nbF+Bw4cQI0aNbBnzx4EBwc/NH9SUhJ8fX3h4uKCjIyMR6LyzYABA/D777/j2rVrFcpBUlFsIm5UBMbYXgABRk5NAbCmqJjBGEslImP521Do2QHgNSIqN8izst9ViTt4ED906ID3IiLgtmKF1ce/8fffiJ49G3+dPIlvZTIEJCWhjVaLwwXnJQD8BAENPDyw6e234TF0KFBOJmFO2eSlpSHpyhWk3LyJlNu3kXr3LjpXqwaVRoM9MTH4/cIFZGZlISsvD5l5echRq7GhVi346XSY+N9/2JiRATUR1ETQFIRH3JXJoCIq9rsrSuE3+ik8SJBo7HxVPEiwWCgwKAFkODkBEgla5ObiGhFkBcKCgjEEy+XYX7cu4OSEt27cQLxWC2e5HEonJzgrFKjl44MPOnYElEpEx8ZCI5fDzdMTbt7eUPn6wjc4GNXr1wc8PAAuKHA4jy0WeG4cBNCJiNQWzPUjgOdgyCt7D8A0IlrFGOsBg4epBMBqIppl7tgmzv9YihtpaWkYP348fvzxR+Tm5oIxhhYtWiA5ORne3t44cuSInSzmcDgc25CdnY3JkycjPj4eRAQigl6vF58TETQaDfbu3Qu9Xo/27dtDLpcXO1/y+sKfg4KCsGzZMvj6+hqde9WqVXjjjTfQs2dPbNu2zc6v3DIuX76M+vXrIzIyEkuXLnWYHbby3GgDQwh5dRiS0zMY8mDUstDOwnFNCkthjKlgEDZmE9FmU8au7OIGAOCtt4A1a4CrV4GqVW0+3c0DB7Bx7lzsP3YM51JTkaDTGZIUwnAnvRWAOEFAPZUK7Rs3Rr/Ro9E4PNzmdtmLrIQEJF+5gsQbN5B86xbSEhLwrK8vaspkOBUbi29OnkRmTg6y8vKQlZ+PbI0Gs3x90UkqxbLERMxMT4caEIUHHYAfYQhvGArgoexzMGTffQFALwDG/pQdB9CcMfQjwjYUhETAkDdBBuCcnx/8XFzwQVoa9ubmwkkqhbNUCmeZDC4KBaI7d4agUmHz7du4kp0Ndw8PqLy8oPLxgVdAANp26AB4eEDt4gKpSsVDHjgcjtWxQNxYC6AegF9RxCmMiBbZwDyb8EisMVC+uBETE4OxY8fi0KFDICIoFAoMHDgQixYtQkpKCurUqYNFixZh3Lhxdracw+FwHMOwYcOwbt06TJo0CZ9+aixrmvm0a9cOBw8exMaNGzFgwACrjGkP3n77baxatQqxsbGoXbu2Q2ywlbgRC2AcDMk8xf+QRJRs0YAPxp0PILlIQlEvIppY4ho5gN8B/EZEi00d+5FYeMTFYUvt2rj8zDP48Ngxh5iQdPo0fP78E9i/H2137sQJtRpFb6WpAKT7+QH16mGVmxuCW7aEQqmEIJFAEAT4enqiTs2agCDg1OXL0BNBkEohCAIEqRRenp4IDgiAHsC127chyGTieSaRQKVSwcPTE9r0dPxz7BhS7txBemIi0pKSkJ6WhjBPT3Ty9UV8QgImHDqEnALRIVejQa5Wi1fd3DDG1RUHMzLQPzkZauBBUkgYMtUthKHczigjr38CDAkhZwH4n5HzcwBMFgR8CmCmXg8pIHo2yBnDN4GB6OrtjR+ys7E6KQkucjlcnZzg6uwMlVKJMe3bo1r16jiflYWrOTnwDAyEV1AQvGrUgFetWjzpK4fDeeSxQNyYZuw4Ec2wnlW24XHw3CAirF69Gh9//DEKw3f9/f0xfvx4jBs3DtICEbww+d2tW7eMullzOBzO48ipU6cQFhYGd3d3JCUliX8TLSU3NxcqlQp6vR6pqalQqVRWstT23L17FyEhIejTpw/Wr1/vEBtsllCUiFpUyDLj43oD2AigGoCbMOTxSGGMPQPgbSJ6gzE2FMC3AIrGzg4nojLrqD4S4gaA0Y0bY9nZszi5fj2avvyyo80BAMSfOoWf5szBvoMHocjMxAatFsjPhwQPyj8WUh2GGr6AwZ2nJHUBXET5SSFvwlAPuCQdAfxRcE1zI+cHAdggkeBvxtBdq4UMD/I2yBnDOE9PjA0Kwim9HpNu34ZSoYCrkxNUSiXcXV0x4Jln0KxBAyQIAs6kpMA7KAi+tWrBt25duPj4lPk+cTgcDsd8caNIPxciyrGFTbbmUVljFBU3srKyEBUVhdWrVyM72+Aw06RJE8yfPx9dunR5qG/Dhg3h6emJAwcO2NtsDofDcSi1a9fG9evX8csvv6BPnz4VGuu3335D79690aRJE/zzzz9WstB+FCZjjYmJQbNmzew+v63EjU9h2Jv+hCJ5A4nolEUD2oFHZeGReuMG6tSujdpKJQ6lplbasAF9QgK+HjcOZ86cgU6nAwHQE+FpT0+MqVcPIMLrhw5BrdM9iEUD0NLLC2NCQ6HX6TDg6FFDKU293vBIhE7e3hhTsybyJBK8dfYsXF1c4OriApVKBTeVCi0bNECLZs2gVioRm5YGj6pV4VWzJq8qweFwOJUECzw3WgFYBcCViKoxxp4GMJKIjDnYVSoeVc+NTp064Y8//oBer4dUKkXv3r3x2WefoVq1akb7nT9/Hg0bNsTSpUvx7rtWr6jL4XA4lZolS5ZgzJgxaNWqFQ4fNpb1znQGDhyITZs2Yf78+ZgwYYKVLLQf6enpqFWrFsLCwrB79267z28rcWO/kcNERJ0sGtAOPCriBgCsefNNDF+5EquGD8fr337raHM4HA6HwzEZC8SNYwDCAfxKRE0LjlW4vLw9eVTWGIIgoHDt5+npidGjRyMqKgoKhaLMflOnTsWsWbNw584dBAQYywnP4XA4jy+pqanw9fWFTqfDtWvXUKuWZWkmdTodPDw8kJWVhatXrzosb0VFWbRoEcaPH489e/YY9fSzJWWtMQRLByWijkZapRU2HjVeXb4cbVUqTFqzBmnXrzvaHA6Hw+FwbAoR3SpxyLRapRyzKBQ2tmzZguTkZMyYMaNcYYOIEB0djQ4dOnBhg8PhPJF4enqid+/eAIBly5ZZPM6xY8eQlZWFKlWqPLLCBgCMGjUK1apVw+TJk6HXl0xS4DgsFjcYY/6MsVWMsd8Lfq7PGIuwnmlPNkwiwbLVq/EZAHcrZeXlcDgcDqeScosx1hoAMcZkjLEJMKRnqvQwxnoxxlakp6c72hSz6NevHxgzlhnrYc6cOYPLly9j0KBBNraKw+FwKi+jR48GAKxYsQJqtdmVywEAGzduBAD079/fanY5AicnJ8ycORMxMTHYvNmkwqV2wWJxA8B3AHYBqFLw82UAYytqEOcBjfr3x9AxY8BWrgQ5qHIKh8PhcDh24G0A7wAIAnAHQJOCnys9RPQbEb3l7u7uaFNsRnR0NCQSySO/GOdwOJyK0KFDBwQGBiIzMxM///yzRWNs2rQJABAeHm5N0xzC0KFD0bBhQ0yZMgUajcbR5gComLjhQ0QbUVAsg4gKK21yrMmMGVitUqFz167QWagQcjgcDodTGWGMzS142pGIXiEifyLyI6KhFS0tz7EOhSEpnTt3hg+vGMbhcJ5gBEHAO+8YdPdFixaZ3fiQZuEAACAASURBVP/SpUuIj4+Hs7MzWrdubW3z7I5EIsGcOXNw9epVrFy50tHmAKiYuJFdULaVAIAx1hLAo+WT+SigUsF52DDsz8zEitdec7Q1HA6Hw+FYkx7MEBvxoaMNsZRHNSzFVE6ePIkbN27wkBQOh8MBMHz4cDDGcPz4ccTGxprVt9Dbo1u3bpA+JhUee/bsiXbt2mHGjBnIyspytDkVEjfeB/ArgNqMsUMA1gIYbRWrOMUY/Pnn6OjhgajoaNw/f97R5nA4HA6HYy12AkgF0JgxlsEYyyz66GjjTOFxD0vZuHEjZDIZ+vbt62hTOBwOx+EEBQWhc+fOAICvvvrKrL4//PADADxWYjFjDHPnzsW9e/ewePFiR5tToWoppwB0ANAawEgADYjoX2sZxnkAEwR8+f33yCbCpJdecrQ5HA6Hw+FYi/8RkQeA7USkIiK3oo+ONu5Jh4iwceNGdOvWDZ6eno42h8PhcCoFhaEpq1atQm5urkl97t27h3PnzkEQBDz//PO2NM/utGrVCi+99BLmzZuHxMREh9pSkWopAwA4E9F5AC8BiGaMNbOaZZxi1HvxRbzfogXWXL2KKxs2ONocDofD4XCswZGCx0fCS+NJ4+jRo/jvv/8eq7uMHA6HU1F69uwJT09PZGdni9VPyuO3334DADRt2hReXl62NM8hzJ49G9nZ2Zg9e7ZD7ahIWMpHRJTJGGsLoDOAVQDM883hmMX/fvkFB3x9ETpnDqDVOtocDofD4XAqipwxNgRAa8ZYv5LN0cY96URHR0Mul6N3796ONoXD4XAqDTKZDBEREQCAJUuWmNRnQ8HN6cdVLK5Xrx5GjBiBZcuWIS4uzmF2VETcKKyM0hPAN0S0HYC84iZxSsM1IABtvvoK+PdfpM2f72hzOBwOh8OpKG8DaAfAA0CvEu1FB9plMo9rQlG9Xo9NmzbhhRdewOOaT4TD4XAs5Y033gAAnDp1CqdPny7z2uzsbPz1118AgF69etncNkcxffp0CIKAqVOnOsyGiogbdxhjXwMYBGAHY0xRwfE4ptCvH76pXx+1oqJwt5wvEofD4XA4lRkiOkhEkQAmEtGIEu11R9tnCo9rQtGDBw8iPj7+sb3LyOFwOBWhTp06aNmyJRhjWL58eZnX7t69G1qtFlWqVEGdOnXsZKH9CQ4OxnvvvYd169bh338dk4qzImLEQAC7ADxPRGnA/9m77/gqq/uB459z90hys0lImDJkioooDlygICC4raM4ClpHW6tVaW2pm1b8aWudFUWqIpUiQ9yA0AIKuFhlDwkkZN2su8f5/XEvFDGElZtLku/79TqvPPd5nvs83xxC8s035zmHTOA3jRKVODilOO/ZZ/EA98nkokIIIZoxpdQF8U23PJZyfJk+fTp2u71F/5VRCCGOxe23347WmqlTp1JbW3vQ82bMmAHAFVdcQWz185brwQcfxOVyMX58clZ4P5bVUrxa65la603x18Va608aLzRxMF2HDOGBc87h7R07+PyZZ5IdjhBCCHG0zo1/PPCRlGbzWEpLFA6HmTFjBsOHDyclJSXZ4QghxHHpyiuvxOFw4PP5ePvtt+s9JxwO75tMdNSoUU0ZXlJkZGQwfvx4Pvjgg32P4jQlpbVu8psmS//+/fXKlSuTHUaj8FVW0qtNG+wGA99WVmJ2OpMdkhBCCAGAUuorrXX/ZMfRlJpLjrH3r4YN5X/z589n8ODBvPvuu1x55ZVNFZoQQjQ748aNY/LkyfTs2ZNVq1b9aGTG4sWLOffcc7Hb7VRVVWGxtPwpKn0+H127dqWwsJBly5Y1+miVhnIMmSOjmbJnZvLX8ePZEgyy/P77kx2OEEIIccSUUr9uqDVhHJ2VUpOVUjMa2tdaTJ8+HafTySWXXJLsUIQQ4rg2duxYotEoa9asYfny5T86PmvWLAAuvvjiVlHYALDb7Tz88MNkZGTQ1JNtS3GjGRvxyCNsGzKEs954A4qKkh2OEEIIcaRS460/8HOgIN5uB045nAsopV5TSpUqpdYcsH+oUmqDUmqzUurBhq6htd6qtb71UPtag1AoxL/+9S8uvfRSHA5HssMRQojjWv/+/enVqxcGg+FHE4tqrXn33XcBuOyyy5IRXtLccsstfPjhh6SnpzfpfaW40czlv/wyRCIsHzMm2aEIIYQQR0Rr/bDW+mGgEDhFa32v1vpe4FSg/WFeZgowdP8dSikj8DwwDOgJ/EQp1VMp1Ucp9f4BLbfRPqEWYP78+VRWVsoqKUIIcRiUUvtGb7z99tu43e59x9atW0dRURFKKYYNG5bEKJtesiZOleJGc9epE9NGjuT0BQv45Mknkx2NEEIIcTTaAMH9Xgfj+w5Ja70YqDxg9wBgc3z0RRB4BxiltV6ttR5xQCttjE+gpfjnP/9JWloaQ4cOPfTJQgghuOGGGzCbzQSDQaZOnbpv/+zZswE45ZRTyMnJSVZ4rYoUN1qAy199la5mM3dNmECgpibZ4QghhBBHaiqwXCn1R6XUH4EviY3IOFoFwM79XhfF99VLKZWllHoJOFkpNf5g++p53zil1Eql1MqysrJjCPf4EAwGee+99xg9ejRWqzXZ4QghRLOQlZXF5Zdfjslk4oUXXtg3YfPeR1KuuOKKZIbXqkhxowWwpqXxt4cfZlMoxFOXX57scIQQQogjorV+HLgZcMfbzVrrJhuOqLWu0FrfrrU+Ye9969tXz/teAR4Gvm4JE8V98sknVFVVySMpQghxhG699VbC4TAbN25k8eLF7N69m2+//RaAESNkZfOmIsWNFuKi8eO5sqCAx+fPZ9u//53scIQQQogjorX+Wmv9l3j75hgvtwtot9/rwvg+0YDp06eTkZHB4MGDkx2KEEI0KxdeeCHt27fHZDLx0ksvMXfuXADy8/Pp3bt3kqNrPaS40YI8M3Mm+Uqx9YEHkh2KEEIIkUwrgK5KqU5KKQtwLTAnETfSWs/VWo9zuVyJuHyT8fv9zJ49m8suu6zVLFcohBCNxWAw7Bu9MWPGDF599VWUUowePTppk2u2RqZkByAaT+GAAWx64gmM48fD+++DDIESQogWRUejhLxeAjU1BGprCdTWkudyYQyH2b1zJzt37iTg9RLweGIffT5Gn3QSxlCI/6xdy1dbtxLw+wkEAgQCAX51zjlkN/PJqJVS04DzgGylVBEwQWs9WSl1F/AxYARe01qvTdD9RwIju3TpkojLN5kPP/yQ2tpaeSRFCCGO0k033cQf//hHwuEwK1euBODSSy9NclSti9o74Ulr0L9/f733C63FCgYJn3QSr5WVcePGjdgzM5MdkRBCtAg6GiVYW4u/qgpfdTX+mhr81dW0TUsjzWSirKSEFatW4fd4CPh8sY9+PyO7daPAbmfV9u288913+OOFBX8wiD8Y5LETT6STycScnTt5avt2AuEw/mgUfySCPxplUXo6nUIh/s/j4b5IhAN/ahcDecAfgEfribsWSAF+DTyz334DsDovj57FxY3eV0qpr7TW/Rv9wsex5pJj7P0L4oH537XXXsv8+fMpLi7GZJK/fQkhxNEYNmwYCxcuJBAIYLPZcLvd2Gy2ZIfVojSUY8hPr5bGYmH5nXdy2913UzR6NI8sXpzsiIQQotGFvF58lZX4qqqwRaO4zGaCNTWs/PZbfDU1+Gpr8dXV4auro1+bNvTNyqKyooJnFy3C5/PhCwTwxwsMPy0o4CKXi42Vldy8di2+cHhfYcEXjfJXm42rolH+7fdzbj2xzAJGEXsOYng9x7sQW6bjv8AkwArYlMJqMGBTilqTCVwujMEgFqORVKsVm8mEzWzGarFgPeMMyMxkQHk5D+3cidVqjTWbDavNRso554DLxfXl5ZxRUYHV4cDqdO5r9p49weHg4VCIhwwGrKmpWNPSMEmy1ShawsgNj8fD3LlzufHGG6WwIYQQx+DWW2/lo48+wmAwcNFFF0lho4nJT7AW6My77uK6SZP407//zY2ffkrXIUOSHZIQojWIRPCUl+OtqMBbWYk3PsLBCXTPyQGvlxkLF1JdVYXX48Hr8eDzeumVns5V7dqB18sNCxZQGwjgCwbxhkJ4w2GudDr5rcNBwOslu7wcHxDZ77YPAk8SG6FwVj1hPQb0jR9/FLABdqVizWDg4lAI8vIwAw6zmUy7HZvZjN1qxWaxUNi7N3TqROdQiCfWrcNmt2Oz27E7ndgcDk7t2xcKCxkYDrOspAR7WhrWlBRsaWlYU1PJysuDtDSutli4xmg8aPcNp/7iyF5nx9vBdI+3g0lt4Jg4elrrucDc/v37j012LEdr3rx5eL1eeSRFCCGO0aWXXkp2djbl5eXySEoSSHGjhZo0axbvn3wyd//kJ3xYWooyyNyxQrR6kQh1e/ZQU1KCJ16A8FRVofx+BnbsCB4Pc5ctY+uuXXg9HjweD16fj1yTiQe7dQOPhztWrGB1TU2s8BCJ4IlE6G8wMNNggECAk4AtB9x2JP+byfFuoOSA4zcajVzlcoHDwdqyMrRSOEymWKHB4SCroAD69MFis/Gz5cux2+3YbbbYR4eD/t26Qa9euCwWPlq/Hnta2v+ay0V227bQpg3tbTaiVutBvx92Aj5toPsKgfENHM8AzmjguEwn1jK1hJEb06dPJy8vj0GDBiU7FCGEaNYsFgtjxozh2Wef5ZJLLkl2OK2OzLnRgv3l8sv51XvvMeO++7jiqaeSHY4Q4jCFvF7q9uyhtqSEurIy6ioqOK1DB5TXy4rvvmPVpk3U1dTgqavD4/EQ9Pt56uSToa6OSatWMa+4GE8oRF0ohCcSwQmsM5kgEGAUP14yohOwNb49BPgsvq0AJ3Cq2cznhYXgdPKzPXvYGgrhsFhwWq04bTZ65uRw39lng8PB62vX4gUcKSk4UlOxp6RQUFDAqf36gd3O1ooKzKmpODIysGdmYktPxyDD4FscmXPj+HXgnBu1tbXk5ubys5/9jOeeey6ZoQkhRIvg8/nYsGED/fr1S3YoLZLMudFK3fn22yzLyyNv6lT44x/B6Ux2SEK0ONFIBE9pKfZwGJPPx54dO1i/bh21lZXUVVVR63ZTV1PDzT17kh6J8PG6dby1bh11Ph+1gQB1wSC1oRCLc3LI9Pn4Q1UVj0YiP7qPB3AAbwF/2W+/EUhTij8XF6NSUvDX1RGJRsmy22mfnk6KzUZWSgpcdBGkpHDb999zic+HIzUVZ1oaTpeL9OxsOO00cDqZ5vdjSEnBkZWFNS3tR6McXj1Ef9x8iOOdD9mjQoimNHfuXPx+vzySIoQQjcRut0thI0mOu+KGUioTmA50BLYDV2ut3Qc5Nw1YB8zSWt/VVDE2FyabjXfmzYOzz4ZHH4WJE5MdkhDHhWg4jKe0lJrdu6ktLaWmtJTOaWlkGwx8v20bc5Yto7amhtqaGmrq6qj1eLi/Uyd6KcXH33/PPdu3UxMOUxuNUgto4EtgADAXqO/B+4tmzybdamWn2cx//H5SjEZSzGZSLRbapqURPfNMyM7m/IoKLOXlpKSmkhovPqRmZGCOTxr5u2CQXxsMpOTk4MzNxZKS8oMCxEPxdjCHGiCZfWRdKUSr19wfS5k+fToFBQWceeaZyQ5FCCGEOCbHXXGD2Nxw87XWE5VSD8ZfP3CQcx8FZDmQhpx1FjXXXceEP/+ZcWefTY8RI5IdkRBHT2uiXi+lW7ZQU1xMdUkJNaWl1JSX0yM9nRMdDvbs3s1T8+dT4/FQ7fFQ7fNR4/dzf0YGo7Xmy8pKBnq9P1pO8x3gGmATsXkhIPYNMk0pUg0Gbo5GoW1b0jMy6O31kmq3k+p0kpaaSmpqKgXnnAPt2jEsGGR+ZSUpWVmkZmeTkpNDal4eaXl5YLXyM+BnDXyK58fbweQcbd8JIRKiOU8oWlVVxUcffcSdd96JQebmEkII0cwdj8WNUcB58e03gM+pp7ihlDoVaAN8BLSq53qPlP+3v2XKtGl8d+ONzK+okMlFRfKEw1BdzdbVq6natYuqkhKq9uyhurycLk4n52RmEigv57aPP6bK46HK56M6GKQ6FOI2i4UHgkFKw2Hy67n0E8Qme/QCLwJpBgMuk4k0sxmX1Yq5XTvo3p32JhMPrV9PWloaaenppGZkkJqZGZsTolMnzrZY2KM1aQUF9T6WcTrwzwY+xYJ4E0KI493s2bMJBoPySIoQQogW4XgsbrTRWhfHt0uIFTB+QCllAJ4GbgAGN3QxpdQ4YBxA+/btGzfSZiK3Vy+euOYa7njnHYbl5nL72LEM//3vMTscyQ5NNDdaQ00NO9eupXzHDty7d+MuKcFdWkqOUowqLISqKn7+ySfsrK6myuejKhikOhxmmFK8Eg4D0BvwHXDpscA5gDktjUUeT6woYbHQLi2NXnY7nbt1g1NOIdPp5IU1a0jLzMSVnU1aTg6uNm0o7NIFOnakU2oqHrP5oJ9CPvBIA5+iFcg9pk4SQrQmzfmxlOnTp9OhQwcGDBiQ7FCEEEKIY5aU4oZS6jMgr55Dv9v/hdZaK6XqW87lDuADrXXR3lm/D0Zr/QrwCsRmMj+6iJu/cW+8QUVZGS8uXMhlEyfy0HPP8egvfwm33gqdZYq/VkVr6oqLqdi2DXdREe5du6gsLsbo8zG6Y0dwu5k4fz7f7t6N2+ul0ufDHQrRHZgXjUIkwjBg7QGXvRAYZTSCy8UGr5dqpUi32eiemorL6WRAhw4QnzdiysaNWF0u0tu0IT0vD1dBAVkdOkDbthiMRrY1EL4F+HmCukYIIY5Uc30spaKigk8//ZR77rmHQ+VSQgghRHOQlOKG1vqgoy2UUnuUUvla62KlVD5QWs9pA4FzlFJ3ACmARSlVp7V+MEEhN3tGi4WHPvuMB/1+Pnz8cXovXQoTJ/LZE0/wp8xMxv70p4x+9FEsKSnJDlUcAW95OZXbtlGxbRvVu3czqLAQKip4b/FilmzYQEV1NRW1tVR6vRjDYRalpIDbzU/CYd4/4FqdgNEARiNfGY2s0poMi4Vsh4OuTic927SBIUMgI4M/ff89IbudjPx8MgoKyGjXjswOHaBNG1CKBYeI++qE9IYQQojD9d577xEOh+WRFCGEEC2G2rvO+fFCKfUUULHfhKKZWuv7Gzj/JqD/4ayW0lzWoG8yRUXM/M1v+PW777IjEiFbKW469VTGPvYY3S6+ONnRtSo6GqV6505S/X6MlZWs/fprln/1FRWlpVRUVFBRVUVlbS1vnXACVreb32/fzqTaWvz7XUMBIWJLg94GvAlkGo1kmc1k2e3kp6by5rBhkJnJB3v2UBKNkpGbS0ZeHhnxkROFPXtCSgrIX/GEEMegoTXoW6rmkmPsHaUxePBgtm3bxqZNm2TkhhBCiGajoRzjeJxzYyLwT6XUrcAO4n/kVUr1B27XWje00IA4EoWFXD5tGqPeeINP//xn/v7iizy7ciVvDx3K94MGYRw3Dn355Si7PdmRNjvBmhpK16+nbMsWynfsoLyoiPKSEq7t0IEcn4/3v/uOp9eto9znozwUojwaJQxsI7YG8lxik2NC7D9plsFAltlMXWEh1s6dOa1NG+6uriYzM5Os3Fwy27Qhq6AAzjsPcnJ4IT2dl53Og8Z3qOVAhRBCtGwLFizgwQcflMKGEEKIFuO4G7mRSM3lryrJVLJqFRuff55B8+cT3rKFk41GLuzTh7F//CO9Ro1KdnjJoTWe0lLKNm4kIxjE5fOxY9063lm4kNKyMsrcbspqaij3evlrWhoDa2uZ7vFwbT2X+g9wVnY2c6xWnqqtJcfpJNvlIjsjg+ycHMZcfDFZnTpRbjJRa7WS3aULKXl5ssKNEKJZaU0jN/abUHTspk2bkh3OIe1fzPjuu+/o27dvEqMRQgghjkxDOYYUN0T9olHKZ8/m7l/9ipnff08QGJiSwrhrruHqiRNxZGcnO8JjEqmro+i77yjbsoXSbdsoKyqidPduzs3MZIDRyMZt27hh5UpKAwHKIhG88fdNBW4kVqQ4B7ADuUYjOVYrOU4nE049ldN79GC7ycSnJSVk5+eT06ED2R07kn3CCWR27oyhgZU8hBCiJWhNxY29mkuOsbe4ceKJJ7Ju3ToZuSGEEKJZaW6PpYjjgcFA9mWXMe2yyyhfv56p99/PKx9/zM2TJ1MwbRpDxowhdPPNmE87Lalh6miUupISKrZupWL7dlyhEF1sNgIlJUycM4eKysrYpJp1dVT4fIyx27nL76fE46FjPdebaDQyoG1bnC4XWQ4HPfLyyMnIIDc3l5y8PM466yzo04fTMzKoc7lw5ta/aGhHYkubCiGEEMeja665RgobQgghWhQpbohDyj7xRH49Zw73RKMsfeklBi5bBq+/zvgXX2SRw8GVgwbhTEnBYDSS6XRy7emng8HAvNWrKfN4MBgMGIxGDAYD2S4XF/XrBwYD89esoTYQwGAwYDSbY8fT0zmtRw9Qio/+/W+Kd+6korSU8vJyKqqqOMlq5a70dKiooNP69eyORAjuF+ttwEvEvrAfAVKBLJOJLKuVbIcDV9eucMYZ5GZm8urmzeQUFJDbqRM5nTuTe+KJpOTlgcFAAfBhA31ijjchhBCiOZJVUoQQQrQ08liKODpuN1PuvpunZ8xgTSCwb3dPYG18+yxg6QFvOx34Ir7dF1h9wPHBwKfx7U7A9vi2mdikmpe7XDzfpw9kZXHvpk2YnU6ysrLIys0lKz+fbj170uPUUyEri3BaGiabrVE+XSGEEIdPHks5fu0drdGa8j8hhBAthzyWIhpfRgY3vfkmY6ZOpXrnTiLBIJFQCEP8GNEo/yopwe/zEQ2HiUYiRMNhLCYT5OdDNMq7W7fi8/mIhMP7zkm126F9e4hGmVdSgj0vj6zOnUnNz//RpJpPHyJE+eIWQghxOJRSnYHfAS6t9ZXxfaOB4UAaMFlr/UkSQxRCCCHEIcjvf+KYKIOB9A4d6j2WV1DQ4Hu79+7d4PGeRx2VEEKI1kIp9RowAijVWvfeb/9Q4C+AEXhVaz3xYNfQWm8FblVKzdhv3yxgllIqA5gESHFDCCGEOI5JcUMIIYQQzdkU4G/EFrQCQCllBJ4HhgBFwAql1BxihY4nD3j/LVrr0gau/1D8WkIIIYQ4jklxQwghhBDNltZ6sVKq4wG7BwCb4yMyUEq9A4zSWj9JbJTHIanY5BQTgQ+11l8f5JxxwDiA9u3bH1X8QgghhGgchkOfIoQQQgjRrBQAO/d7XRTfVy+lVJZS6iXgZKXU+Pjuu4nNc32lUur2+t6ntX5Fa91fa90/JyenkUIXQgghxNGQkRtCCCGEaNW01hXA7Qfs+yvw10O9Vyk1EhjZpUuXBEUnhBBCiMPRqoobX331VblSakcjXzYbKG/ka4oY6dvEkH5NHOnbxJG+TYxE9Wv9M003nV1Au/1eF8b3NTqt9VxgrlLqsuaUY+xdErYVk+8piSN9mxjSr4kjfZsYTZ5jtKrihta60ceMKqVWHmydXXFspG8TQ/o1caRvE0f6NjFacL+uALoqpToRK2pcC1yXyBtKjtG8SN8mjvRtYki/Jo70bWIko19lzg0hhBBCNFtKqWnAMqC7UqpIKXWr1joM3AV8DPwX+KfWem0y4xRCCCFEYrWqkRtCCCGEaFm01j85yP4PgA+aOBwhhBBCJImM3Dh2ryQ7gBZM+jYxpF8TR/o2caRvE0P69fgm/z6JI32bONK3iSH9mjjSt4nR5P2qtNZNfU8hhBBCCCGEEEKIRiMjN4QQQgghhBBCCNGsSXHjGCilhiqlNiilNiulHkx2PC2BUqqdUmqhUmqdUmqtUuqXyY6ppVFKGZVS3yil3k92LC2JUipdKTVDKbVeKfVfpdTAZMfUEiil7ol/L1ijlJqmlLIlO6bmSin1mlKqVCm1Zr99mUqpT5VSm+IfM5IZo/gfyTESQ/KMxJIcIzEkx0gMyTEaz/GSY0hx4ygppYzA88AwoCfwE6VUz+RG1SKEgXu11j2BM4A7pV8b3S+JrR4gGtdfgI+01icCJyF9fMyUUgXAL4D+WuvegJHYkp7i6EwBhh6w70Fgvta6KzA//lokmeQYCSV5RmJJjpEYkmM0MskxGt0UjoMcQ4obR28AsFlrvVVrHQTeAUYlOaZmT2tdrLX+Or5dS+ybd0Fyo2o5lFKFwHDg1WTH0pIopVzAIGAygNY6qLWuSm5ULYYJsCulTIAD2J3keJotrfVioPKA3aOAN+LbbwCjmzQocTCSYySI5BmJIzlGYkiOkVCSYzSS4yXHkOLG0SsAdu73ugj54diolFIdgZOBL5MbSYvyLHA/EE12IC1MJ6AMeD0+HPdVpZQz2UE1d1rrXcAk4HugGKjWWn+S3KhanDZa6+L4dgnQJpnBiH0kx2gCkmc0OskxEkNyjASQHKNJNHmOIcUNcVxSSqUA/wJ+pbWuSXY8LYFSagRQqrX+KtmxtEAm4BTgRa31yYAHGd5/zOLPZo4ilti1BZxKqRuSG1XLpWPLp8kSaqJVkDyjcUmOkVCSYySA5BhNq6lyDCluHL1dQLv9XhfG94ljpJQyE0s43tJaz0x2PC3IWcClSqntxIY4X6CUejO5IbUYRUCR1nrvX/9mEEtExLEZDGzTWpdprUPATODMJMfU0uxRSuUDxD+WJjkeESM5RgJJnpEQkmMkjuQYiSE5RuI1eY4hxY2jtwLoqpTqpJSyEJuAZk6SY2r2lFKK2DOF/9Va/1+y42lJtNbjtdaFWuuOxL5eF2itpULdCLTWJcBOpVT3+K4LgXVJDKml+B44QynliH9vuBCZRK2xzQHGxLfHALOTGIv4H8kxEkTyjMSQHCNxJMdIGMkxEq/JcwxTom/QUmmtw0qpu4CPic2u+5rWem2Sw2oJzgJuBFYrpb6N7/ut1vqDJMYkxOG4G3gr/ovIVuDmJMfT7Gmtv1RKzQC+JrbCwTfAK8mNqvlSSk0DzgOylVJFwARgCfJDyQAAIABJREFUIvBPpdStwA7g6uRFKPaSHCOhJM8QzZHkGI1McozGdbzkGCr2+IsQQgghhBBCCCFE8ySPpQghhBBCCCGEEKJZk+KGEEIIIYQQQgghmjUpbgghhBBCCCGEEKJZk+KGEEIIIYQQQgghmjUpbgghhBBCCCGEEKJZk+KGEKLJKaXSlVJ3xLfbxpfiEkIIIYQ4JpJjCNF6yVKwQogmp5TqCLyvte6d5FCEEEII0YJIjiFE62VKdgBCiFZpInCCUupbYBPQQ2vdWyl1EzAacAJdgUmABbgRCACXaK0rlVInAM8DOYAXGKu1Xt/0n4YQQgghjjOSYwjRSsljKUKIZHgQ2KK17gf85oBjvYHLgdOAxwGv1vpkYBnw0/g5rwB3a61PBe4DXmiSqIUQQghxvJMcQ4hWSkZuCCGONwu11rVArVKqGpgb378a6KuUSgHOBN5VSu19j7XpwxRCCCFEMyM5hhAtmBQ3hBDHm8B+29H9XkeJfc8yAFXxv8gIIYQQQhwuyTGEaMHksRQhRDLUAqlH80atdQ2wTSl1FYCKOakxgxNCCCFEsyU5hhCtlBQ3hBBNTmtdASxRSq0BnjqKS1wP3KqU+g5YC4xqzPiEEEII0TxJjiFE6yVLwQohhBBCCCGEEKJZk5EbQgghhBBCCCGEaNakuCGEEEIIIYQQQohmTYobQgghhBBCCCGEaNakuCGEEEIIIYQQQohmTYobQgghhBBCCCGEaNakuCGEEEIIIYQQQohmTYobQgghhBBCCCGEaNakuCGEEEIIIYQQQohmTYobQgghhBBCCCGEaNakuCGEEEIIIYQQQohmTYobQgghhBBCCCGEaNZMyQ6gKWVnZ+uOHTsmOwwhhBCiRfvqq6/KtdY5yY6jKUmOIYQQQiReQzlGqypudOzYkZUrVyY7DCGEEKJFU0rtSHYMTU1yDCGEECLxGsox5LEUIYQQQgghhBBCNGtS3BBCCCGEOEpKqZFKqVeqq6uTHYoQQgjRqklxQwghhBDiKGmt52qtx7lcrmSHIoQQQrRqrWrODdEArSESIRIM4q+rIxIMEgmFiAQChINBMlNTsRgM1FRVsae0lEgwSDh+jsFopGufPtjy8yElBZRK9mcjhBBCCCFEk9Jas3PnTtq1a4eSfFiIJifFjRbOvXUrn/zlL5yxaxcdVq7kk8pKrq6tJQxE9mufABcC7wI/qec6XwIDgOnAuHqOrwF6AS8YDEzQmgyTiQyLhXSbjQyHg79dcAHZ+fksr6vju+pqMnJyyGjThoyCAtILCujYpw+G9HQwGhPRDUIIIYQQQiTUhAkTePTRRxkwYAD33Xcfl112GSaT/LolRFOR/20tTDQc5ptp0/hwyhQ+XLGCL2priQL/Z7dzz/DhFJrN3PTf/2I0GDAajRiNRkxGIx1POQVycuhXWcmfNmyIHTOZMJlMGE0mOvTvDxkZnF9ezj+2bcNkNseOm81EwmE6dOoEPh8nfvMNV373HVV1dbg9Htx+P9tqajC+/z5UV/NeOMzEeuL2AnaleMhi4a1IhAyzmUybjeyUFLJdLp677DJUdjYrqqtxG41kd+hAVseOZHfpgiMnB2WQJ6yEEEIIIURyvPzyyzz66KMMHTqUTZs2cfXVV9OpUyfuuecebrnlFpxOZ7JDFKLFU1rrZMfQZPr3769b4jJtFZs2UTp7Nj1WraL6ww/JLi8nAvR3OBh66qkMu+EGBowZg9FqTW6gWuMrL6d861aqiopw796Nu7iYqtJSxvTqBW43b37xBR9v3ozb46HS66U8ECAcibA1GgXgGuCfB1y2A7C9bVvIzmZCTQ3rw2GyXS6yMjLIzsmhQ8eOjLroIsjOplRrbPn5pLZtKwURIYRIEKXUV1rr/smOoym11BxDCHFoc+bM4bLLLmPo0KHMnj0bpRSzZ8/mqaee4osvviAjI4M77riDu+66i7y8vGSHK0Sz1lCOIcWNZigaDrPyH//gw6lT+WjlSpbX1XE2sCgzEy6+mI8KCjjlppvI7dUr2aE2nnAY3G6+X72anevXU15URPnu3ZSXlmL0ermvc2eoqOCOL79kgdtNeShEpdZo4GTg6/hlBgAriA1ZylSKTLOZczMyeGnAAMjM5KmtWwlarWRlZ5OZl0dWfj7tu3ena79+kJkpc4oIIcRhkOKGEKK1+OKLL7jgggvo3bs3Cxcu/NEIjaVLlzJp0iRmzZqF2Wzmxhtv5N5776VHjx5JiliI5k2KG3HNOfGo3roV17Jl8OGHXPHuu8wMBlHAAKeTof37M3zMGE776U9lzor9RIJBqnbswFdcTKHJBBUV/Oujj9j+/fdUVFRQWVVFRW0tXY1GnsjIgIoKuhUVsemA/xNXADPi2wVA1GAg02wm02ol0+FgeOfOjDvjDMjI4KU1a0jNzCSjTRvS8/LIKCgg74QTyOjUCWy2pu4CIYRIitZU3FBKjQRGdunSZeymTZuSHY4Qoglt2rSJM888E5fLxdKlS8nNzT3ouRs3buSZZ55hypQp+P1+RowYwX333cegQYNk8lEhjoAUN+KaU3EjEgyyYupUPvzHP/joq6/42uNhD5CZnc2Hffrg7tGDi+6+m+wTT0x2qC2Ov7oa97ZtVGzfTuXOnaSGQpycng4VFfzuvfcoc7uprK2l0uOhwu9nlNnMI5EIQa+X+h78uQ94CqixWOgWDpMen2w1w24n3eHgul69GHHyydTZ7byzcSMZubmkt2lDRn4+rvx88k44AWfbtmA2N3FPCCHE0WlNxY29mlOOIYQ4dnv27OHMM8+kpqaGZcuW0aVLl8N6X1lZGS+88AJ/+9vfKC8vp3///tx3331cccUVMvmoEIdBihtxzSLx2LqVf91+O7d99hkVWqOA01NSGHbaadwxfjzZF14IMlfEcUkHApRt3hybT6SoiKqSEtx79tAtJYVTUlOp3rOH33z8cWyyVa+XKr8fdzDIfVYrt/v9bIhEqK9U9SJwO7DKYuGicJg0oxGX2YzLaiXNbufek07irG7d+B54d8sW0jIycGVl4crOJi03l559++IqLCSSkoIhNVXmGhFCJJwUN4QQLVldXR3nn38+a9euZeHChZx++ulHfA2fz8fUqVN5+umn2bRpEx07dtw3+WhKSkoCohaiZZDiRtzxnHhs+vRTfH/7G33nzaPYYOCBwkKGDR/ORb/4BVlduyY7PJFoWhOuqqJ4wwbcRUW4d+3CXVJCTUUFp+fm0t1mY+v33/Onf/+bGq+Xap+Par+fmmCQp9PSuCgQ4KOaGobVc+mPgIuBmcBVQJpSpBoMpJpMpJpM/L1PH/rk57PU5+PtXbtIdTpJTU0lNS2N1PR0Rg8aRHpeHntCIcqCQVJzc0nNzyc1Px+z3d6k3SSEaB6kuCGEaKnC4TCjRo3io48+Yvbs2YwYMeKYrheNRpk7dy5PPfUUS5YsIT09nZ///Ofcfffd5OfnN1LUQrQcUtyIOx4Tj/++/z6P330307Zv50KDgU9+8Qv4zW+gbdtkhyaamWg4TF1JCdW7dlFTUkJ1SQnVe/ZwWn4+2VqzZsMGpi9fTnVNDbVeL7U+H7V+P8+1a0e3cJh/FBfzq8pKarUmtN91NwDdgEnAbw64pxXYmplJ2/R0XgkEeKOmBqfZTIrVitNmI8Vu55mLLsLmcrG4tJS1bjcpLhfOtDRSMjJwZmRw5hlnoFJTqdUalZKCIzsbgwzLFKJZk+KGEKIl0lozduxYJk+ezMsvv8y4ceMa9frLli3j6aefZubMmZjNZq6//nruvfdeerWkRQKEOEYN5RjyG0SSrJk5k0d/9Sve3bkTO/Dr/v25d/Jk6Ns32aGJZspgMpFWWEhaYWG9x3vH28HcGG8AgZoaaouLqd2zh0KnE/x+Rq1fT/s1a6itqoq1mhpq6+pI79sXfD4s69Zh9/moDQYp9njwRCLURSL8dds28PmYDrxwwD3NQDC+fTfwRnzbATiVosBs5ptu3cDh4OHSUlZ4vTgtFhxWKw67nbbp6fzuggvA6WTuli2UBYM4UlNxulw40tLIbtOGk/r1A4eDCr8fY2oqzpwczA7H0XSxEEIIIVqxRx55hMmTJ/PQQw81emEDYODAgcyYMYPNmzfz7LPP8tprr/H6669zySWXcO2115KdnU16evoPms1mkwlJhYiTkRtN7dtv4dFH+b+ZM5kA3D1wIPdMnkyOLAclWrJoFG95OTUlJdSVlVFXXo6nshJ/TQ0Xdu0KdXV8vGIFq7Zupa6uDo/HQ53XizkS4blevcDr5f61a1lQWYk3HMYTieCNRikAvo1/DzsXWHzAbfdfBrg/8FV82wTYgfMtFmbn54PDwZXFxZREIjjMZuxmM3aLhdPz8rinf3+w23nq228JGY3YHY5Yczrp1qEDA/v1A7ud5du2YXY6saWmYktLw+ZykZadjTMrC6xWWUJYtCoyckMI0dJMnjyZn/3sZ9x000289tprTVJQKC8v58UXX+S5556jrKys3nMsFsuPCh6H21wuF9FoFJ/Ph8/nw+/3/+DjofYd7Hh6ejp/+ctfyMvLS3gfidZHHkuJS2biseKNN3j0gQe4Ys8exrhceO+4A/+tt5J5wglJiUeIFiMaBb8f965d1JSW4nW78brdeKqqsITDnNG+PXi9/HPxYnaVleGpq8Pr9eLz++los/HLLl3A5+OWL75ge10dvnA41iIRzrVYeMVuB6+XXK+XA9OKG4Gp8W0bEDjg+M+JjVaJACnxc2wGw742Ljube9q3p85s5ur167GZTNgsln1tVPfuXHziidRozUvffovVZsNqs2Gz27E6HPTv3p2unTrhiUb5ZudOrE4ntpQUrCkp2FJTyc7Lw5GZSdRsBqtVHvcRTUaKG0KIluSDDz7g0ksvZciQIcyZMwfzQVawe+utt7jnnnsYMWIEzz77LGlpaY1y/0AgwNatW6murqaqquqgrb7jfr+/UWI4kNFoxG63Y7fbsdlsP9hevXo1hYWFLFiwgIKCgoTcX7ReUtyIS0biseyVV3j0oYf4sKyMDKWYOHIk4954A9LTmzQOIcSx0dEowbo6fG433spKfFVV2LWmbWoq+Hx8/O9/46urw+/x4Pd68Xu99MjM5NyCAkIeDw99+in+QCDWgkH8wSCX5uRwfXY2lbW1DF29Gl84jD8a3dd+azZzTyTClkiE+haY+xtwJ7AKOKme468DNwFLgLOJPQZkBaxKYVWKV3NyGJaezrJwmF+WlGA1GrEYjbGPJhMP9+nDSW3a8FVNDa9t2YLFYsFqsez7eNMZZ1CQk8MGt5sl33+P1WbDsl8755RTSHG5KKmtZbfbjcXhiDWnE4vDQZuCAow2G2GDAWW1YrRYEvOPJ5pccy9uKKXaA38FKoGNWuuJh3qPFDeESAytNeFw+KAFhURbsWIF5513HieeeCKLFi066Eomn3/+ORdccAF7f7cyGAwMHjyYl156iU6dOjVlyD/g9/sbLIoYjcYfFSfqK1gcuN3QsrVLlixh2LBh5ObmsmDBAtq3b9+En7Fo6aS4Edekicfixdx1/fU8X1REtlLce9FF3PHqqwedD0EIIQ5Gh8N43W4CNTUEamsJ1NURqK0lx+kk02aj1u3mi2++IRAvqgS8Xvw+H4M6dqRrWho7SkqYsnw5/kCAQCBAIBgkEAxyZ5cunJySwoqSEv6wbh3BSIRAOEwwGiUQifD3Nm0YYDAwq7qasW43Qa0J8L8RKiuIPe7zCnBbPXH/FzgR+D/g3nqO7wQKgUeACYABsBArwliUYmtuLmk2G3+uq+ONujrMSmE2GGLNaGRh//4YLBZe3rWLzyoqMJtMmI1GzCYTKVYrz55/PpjN/HPzZtZUVmI2m7FYLJgtFlxOJ7cOGgRmM59v3kxxbS0miwWz1YrZYiEtLY1zTj4ZTCbWFRXhDYcxW62YbDbMNhvO1FQK2rUDs5lqrxdMJswOR+y4w9Hql1xOZnFDKfUaMAIo1Vr33m//UOAvgBF4taGChVJqOJChtX5TKTVda33Noe4rxQ0hGl9VVRVXXXUVK1as4De/+Q2//OUvm3SZ1C1btjBw4ECcTifLli076GMWGzZsoE+fPoTDYd5//31WrVrFpEmTqKioAKBfv34899xznH322U0We7J9+eWXXHzxxaSnp7Nw4cKkFnhEy9IsixuHSkKUUlZiI8JPBSqAa7TW2xu6ZqITDx2NsvCZZzj1vfdwLVnC3PR0Npx9Nre/+iopbdok7L5CCNGUdDRK2O/HGA5jCIfxuN2Ul5QQqKsj6PUS8vkI+nz07dABu1Js3bGDNVu2EPT7Yy0QIOj3c8Mpp+AAFm/YwKLNmwkGg7EWChEMhfi/AQOwRiJMXb+eOUVFhCKRfS0cibCwZ08IhXh4506mu92EtCYUjRLSGhuwJTUVQiGu9/l4+4CfdW2BXfHt4cAHB3yO3YH18e365nM5Fdj70+QU4JsDjp8PLLBYwGymv9/P9mgUk1KYAJNSXOR08krbtmAyMWT7dqqiUUwGA6Z48WZIdjbju3UDk4nrv/mGMMSOG40YjUbOLyjgxh490AYDv166FJPRiMlkih03mTirY0eGdO9OEHj+yy8xmUwYTabYOWYzp3TqRL8TTgCnE0aPPqqvg4YkubgxCKgDpu4tbiiljMBGYAhQRKw29xNiOcaTB1ziFmJPk80ANPAPrfXrh7qvFDeEaFzbtm1j+PDhbN68mbPPPpuFCxeSk5PDb3/7W26//XZsNltC719WVsaZZ56J2+1myZIldO/evd7zysvL6dSpE3V1dbz00kvcdtv/yv0zZszggQceYOvWrQC0a9eOxx57jJ/+9KcJjf148fXXXzNkyBAcDgcLFiyga9euyQ5JNJFp06Yxb948pkyZ0uAon6PR7IobB0tCtNbr9jvnDqCv1vp2pdS1wGWH+stKohIPHY3yycSJPDpxIktqa3kqLY37HnkExo4FWZVBCCGSTkejRIJBQh4PQY+HSCBAZkoKhELs3rWLmqoqwn4/oUCAkN+PGTi5c2cIhfhi1SrK3G7CgQChYJBQMEi6xcLwXr0gHObtZcsoqaoiFAoRCgaJRCK0T0nh5vjxR5csYY/HQzgcJhQOE45E6Jeezj1dukAoxI3Ll1MZDMaKNtEo4WiUwWlp/CEvD8Jh+q9fT100SkRrwvF2g83Gkw4H4VCIzKoqIkA43qLElm3+M1AFZNTTH48Avwdo0wZKShq9v5P9WIpSqiPw/n7FjYHAH7XWF8dfjwfQWh9Y2Nj7/vuA5VrrxUqpGVrrKw9y3jhgHED79u1P3bFjR2N/KkK0SsuWLWPUqFGEw2Hee+89zj33XL744gt+97vfsWDBAgoLC/nDH/7ATTfdlJDHVbxeLxdccAHfffcdCxYsYODAgfWe5/f76dSpEyUlJdx///386U9/qve8FStWcOedd7JixQoAXC4Xv/rVr3jooYca/Re/4813333H4MGDMZvNzJ8/nx6yiEKLFwwGyczMxOv1sm3bNjp06NCo128wx9BaH3cNGAh8vN/r8cD4A875GBgY3zYB5cSLNQdrp556qm5U27frWX376gFOpwZ0odGon7/mGu1zuxv3PkIIIcRhikYiOhIIaO3z6WhNjXZv367LN27UJatX66KVK/WOpUu1+5tvtN64MdYSAFipk5tHdATW7Pf6SmKjQPe+vhH4WwPv701s5MZLwKTDuWej5xhCtFLvvPOOtlqt+oQTTtDr16//0fH58+frM844QwO6S5cu+q233tKRSKTR7h8KhfTIkSO1wWDQ77333kHPi0QiumfPnhrQV1111WFde+fOnfrSSy/VRqNRA9pisegxY8Zodwv/3WHNmjW6TZs2Ojc3V69evTrZ4YgEu/baazWgf/7znyfk+g3lGEkvZNQb1GEkIcAaoHC/11uA7Iau29iJR9Rs1sSGrOqx3btrf01No15fCCGEaI6ae3HjCO81EnilS5cujduJQrQy0WhUP/744xrQZ599ti4rK2vw3Dlz5ui+fftqQPfp00fPmjVLR6PRY47htttu04B+/vnnGzx3yJAhGtCnn376Ed/H4/HoO+64Q9vtdg1og8GgBw8erDdv3ny0oR/31q9fr9u2bauzsrL0119/nexwRIKsXr1aAzojI6NRi477ayjHaPEznimlximlViqlVh5sfeijVTdpEntXuP77hg20dbl4//e/b9R7CCGEEOKY7QLa7fe6kP9Nu3JMtNZztdbjXC5XY1xOiFYpGAxyyy238Lvf/Y7rrruOzz77jOzs7IOer5Ri5MiRfPPNN0ybNg2/38/o0aM544wz+Oyzz/YWHo/YE088wcsvv8z48eO54447DnreuHHj+PTTT+nYsSP/+c9/jvg+DoeD559/nrq6OiZNmkRmZiafffYZXbp04aSTTuLzzz8/qviPZ927d2fx4sU4nU4uuOCCfY/oiJbl0ksvBeDNN9/EkITJ1Y/X4sbhJCH7zlFKmQAXsYlFf0Br/YrWur/Wun9OTk6jBpn6i1/g3rGDNipW4qjUmpGPPcZVNhu8916j3ksIIYQQR20F0FUp1UkpZQGuBeY0xoWVUiOVUq9UV1c3xuWEaHXcbjdDhw5lypQpTJgwgTfffBOr1XpY7zUYDFx77bWsW7eOV199leLiYoYMGcIFF1zA0qVLjyiOKVOm8NBDD3HjjTfy+OOPH/S8J598kr///e9kZGSwevXqY5ozw2AwcO+991JWVsbMmTPp0qULq1at4vzzz6ddu3a8/voh5zI+7pSWljJ9+nQWLVrEzp07iUaj+46dcMIJLFq0iIyMDAYPHsyyZcuSGKlobE8//TTbtm3jrLPO4pJLLklKDMfrhKImYhOKXkisiLECuE5rvXa/c+4E+uj/TSh6udb66oaum6gJRWuKirigWze+8vkAeBb4JTAnM5PQLbdwxVNPNfo9hRBCiONVkldLmQacB2QDe4AJWuvJSqlLiP2INgKvaa0P/tvLUZDVUoQ4clu2bGH48OFs27aNyZMnc8MNNxzT9QKBAC+//DKPP/44paWlDB8+nMcee4x+/fo1+L6PP/6YESNGcN555zFv3jwsFku9502bNo3rrrsOm83Gpk2bKCwsPKZ46/P1119z55138sUXXwCQlpbGL37xCyZMmHBcTj5aUlLC5MmTmTdvHmvWrKG2tvZH5yilMJvNWK1W7HY7drudkpISwuEw5557Lr1796agoICCggI6dOhA586dycvLS8pf/sXRqampIScnh2g0yp49e8jMzEzYvZrdaikA9SUhSqlHiD1jM0cpZQP+AZwMVALXaq23NnTNRCYetbt3M7JHDy6tqeHXBgPRaBQn4AfaGgw8c/fdXP3sswm5txBCCHE8SfZqKU1JKTUSGNmlS5exmzZtSnY4QjQbS5YsYfTo0USjUWbNmsU555zTaNf2eDz89a9/5c9//jNVVVVcffXVPPLII/Uu5/r1118zaNAgunbtyqJFi0hLS6v3mv/5z38499xzUUqxfPlyTjnllEaLtz67d+/mzjvvZO7cuUQiEQCMRuMPigQOh4PU1FRcLhcZGRlkZmaSk5NDbm4ueXl5FBQU0K5dO9q1a3fQgs2R8Pv9rFu3jtdff50FCxawdetW/H7/vuMWi4WsrCy6dOlC165dKSsro6KigqqqKmpqavB4PPj9foLxlcUOxWAwYDKZsNls2O12bDYbxviS6HubyWT6wfbBmtls3vdx/7a3YBSNRolEIkQiEaLR6L7Xe7cbeq21JhKJoLXed8xgMJCZmUl2dvYP/j0KCwvp0KEDqampx/zvcTwZMmQIn332GQ8//DB/+MMfEnqvZlncSIRE/1VF19Sghg+HpUt5ITOTO8vLf3A832DgmTvu4JrnnktYDEIIIUSytabixl4yckOIwzdt2jRuvvlm2rdvz7x58+jatWtC7lNVVcWkSZN49tln8fl8jBkzhgkTJuxbmnLbtm0MHDgQq9XKsmXLaNu2bb3X2bJlCz179iQUCjFr1qx98wo0Bb/fzwMPPMCiRYuoq6vD4/Hg8/kIBAKEQqHDKhLstbc4YrFY9hVHotEowWAwtlx5KLTvmvv/In80vy+efvrpLF26tMHRF5s3b2bYsGHs2LGDsWPH4nK5KCkpobS0lMrKStxuN7W1tfuKInsLCPqHkzof9Rwryba3eGO1WrHZbDgcDlJSUuotVrVt25a2bdtyzjnnNEqhqjF9/vnnnH/++RQUFFBUVJTw+0lxI65JEo+6OhaeeSaXrF7NmB49mPLf/xIArEAAOB9YkJcHkybB9dcnNhYhhBAiCaS4IYSoj9aaxx9/nN///vcMGjSImTNnkpWVlfD7lpaW8uSTT/Liiy8SjUa57bbbuP3227niiisoLS1lyZIl9OjRo973VlZW0rFjR2pra3nuuee46667Eh7vkQoGgxQXF1NUVMSuXbsoLi5mz549+0ZOuN1uqqur9xUK9i+OKKX2/ZJtNBoxGAyEQqF9BY/9f1c0Go24XC4KCgro2bMnmZmZpKSk7GtpaWmkpqbyyCOPsG7dOk4++WRWrlzZYIGjvLycIUOGsG7dOv71r38xYsSIY+6PcDiM3+/f1wKBAMFgkEAgUG8zGAw/GAVy4Ov9P+5/bG/b+5799/n9foqKiigqKqK4uHhf0aa8vJzKykqqqqqora2lrq4On8+H3+8nFAr9qM8PlJ6ezs6dO/l/9s47LKprfdvPnkbvHVGDCtgLIGKPiiF2sccW/UwsiS3GeKxHjSXR2AKWeI4tJ2oSu7FhjBKDEhN7b6igIiIiInUYZp7vD4b9A0GkDEWz7+taFzO7rPXuYcpaz36Lubl5qV8nQ6DT6eDs7Iz4+Hj8/fffaNq0aZmPWegc41VlVN7GVl416JNjY9nWyooygP/98EO2sbLKrmUN8IGjIwnwC4BOgsAfRo4sF5skJCQkJCTKC1RwKdgMYAjpAAAgAElEQVTybJBKwUpIFAm1Ws0PP/yQADh48GBmZGQU6bzPPvuMPj4+PH36dKltuH//Pj/++GPK5XICoJGREcPDwwu12dXVlQA4adKkUo9fGbl16xanTJnCxo0b09jYmADEZmVlxbZt23LZsmVMSEgocp9arZaNGzcmANavX58ajabQ4xMSEujr60ulUsldu3aV9pLeeLRaLR89esS//vqLO3fu5MqVKzljxgy2b99eLH1cWZg8eTIBsHv37uU2ZmFzjAqfFJRnKy9xgyRT4uL4rrU1ZQB/GD2a2ydN4mKlkgSYamfH7lZWFPRfHI6CwE0jRpSbbRISEhISEmVJUcUNALaFtaL0UVlaec4xJCTeNBISEti2bVsC4Ny5c6nT6V57jlarFc/JaT4+Prx27Vqp7bl9+zbHjBnDAwcOFDp+gwYNCIBBQUGlHrMycfbsWXbp0oUmJiZ5Xl8bGxu2a9eOwcHBTExMLNUYWq2Wfn5+BEAvL6/XChzPnz9n8+bNKZfL+dNPP5Vq7LeZpk2bEgCHDRtW0abw0aNHlMvlNDY2Znp6ermNK4kbFTTxSI2PZztrawoA/54zh0xPJzt3ZiBAAWD/qlXZQb8fAOvK5eR//1uuNkpISEhISBiaYogb9wDc1f/VAniK7LLuWgD3itJHZWmSuPHPJjIykg0bNuTx48cr2pRKx+3bt+np6UmVSsUtW7YU6ZykpCS6u7sTAGvWrMnQ0FDWqlVLXIS3bt2a0dHRZWp3p06dREHlbeDJkyf85JNPaGdnJ76OZmZmDAgI4KpVq5iUlGTwMbVaLVu1akUArFGjBtVqdaHHv3jxgm3atKFMJuP//vc/g9vzNpCRkUFbW1sC4Lp16yrUFh8fHwLg2rVry3VcSdyowIlHanw8v/Pyog4gN20iSYbOn09L/ZeKnSBwz7RpDLSz4ziABJhgY8N57dsz8d69crdXQkJCQkKitBQ3LAXAfwF0zvW8E4C1xemjopoUliKRnp5OK30IsoWFBbVabUWbVGkIDw+nnZ0d7ezsCg3/yM2tW7doaWlJAAwICMjzeu7bt49ubm4EQEEQGBgYyLi4OIPb/cknnxAAq1Wr9lqPg8qMRqPhkiVLWLNmTVHQkMvlbNWqFQ8ePFhuduSEU1SvXv21d/hTUlLYvn17CoLA9evXl5OFbxaRkZFUKBSUyWQ8f/58hdiwfft20SunvJHEjQoUN0iSaWlkx468DnCzPseGJj2dffRfzgA4vlYt8sED8v332SeXe5iDILBPlSo8umRJxdguISEhISFRTEogblwuyrbK3N6Wu7sSxScnt0BO69evX0WbVCnYvHkzVSoVPT09efv27SKdExoaSqVSSQAcN27cK4/bunUrHRwcCIAymYy9e/c2mOfB4sWLCYDW1tZl4s1QHuzfv58tWrQQc4sAoIeHB1esWFFhYk2OJ0yVKlWYnJxc6LFpaWkMDAwkAK5Zs6acLHyz2LFjBwHQ3Ny83N+nGo2GFhYWFAShyJ9tQyKJG5Vh4pGWxiEuLhQArssVIxW+ahXtBYHrANLSkgwNZcLt25zTti0bGhtTmevH8rxcTjZqxL9HjWJCBbyRJCQkJCQkikIJxI3DAGYCeEffZgA4XJw+KrpJ4sY/k2HDhonztIULF1IQBALg7t27K9q0CkOn03HOnDkEwLZt2xY5EWVwcDAFQaAgCPzuu++KdM7q1atFrxm5XM5hw4aVKvY/5260kZERo6KiStxPRXD9+nX26dOHZmZm4nvS3t6eY8eOZXx8fEWbR5Ls2bMnAdDJyem1C/L09HR27dqVAPjtt9+Wk4VvFpMmTSIA1q5du1zHHTJkCAFwRCE5I2NjY7lt27YyGV8SNyrJxCPt2TMG2tsTAP87dOj/7dBqyeHDSUGgN8DODg5Mz5XE57fFiznK3Z10cCABOud8YQkCe7m68tevvqqAq5GQkJCQkCiYEogbtgC+BXAewDkAK96UhKJSWMo/lzVr1oiLyE6dOlGn03HWrFmiN8GxY8cq2sRyJyMjg4MHDyYADh069LU5FnIYNWpUdmVBlYpHjx4t1pharZYLFy6kqakpAVCpVHL8+PHF9lCIiIigTCajXC7n33//XaxzK4qkpCROnjyZzs7O4nvR2NiY3bt354ULFyravALp37+/KLy8TvhSq9Xs1asXAXDx4sXlZOGbRYsWLQiAAwcOLJfxrl+/TkEQaGlp+coQvJMnT9LV1ZWWlpbFqrJTVCRxo5KIGySZnpjI9/UCx9pBg/LsU//1F11ksuyYTYCnv/8+fwcJCVwSGMhGL3l1OANkgwbknDlMf/CgnK5GQkJCQkIiP8VIKPqD/u+EohxfmVtlmGNIlB+nTp0SvTSsra0ZGxsr7nN0dBQXmX/88UcFWlm+3L59m61btyYAzp8/v8gVUdq0aSO+jnfv3i3x+FqtllOmTKGRkZH4+s+aNatIOVDu3r0rnlfZS5FqtVquXbuWdevWFd+DMpmMvr6+3L59e0WbVySGDh1KALS1tX2tV0lmZqYoiAwdOpSbN29mZGRkkd5fFUVsbCz//PNPXrhwgbdu3WJMTAwTExOZmZlp8LHUarUYorVq1SqD9/8yOYl99+zZk2+fTqfjypUrqVQqWbNmTV68eLFMbJDEjUo28Uh//pydHRzYFmDW6tV59mk1Gn5Sv372FxXA4N69C+3r6JIl7OPmxlHGxiTARH0lFjtBYJCLC0PnzaP2DU6EJCEhISHx5lEMceMaAFcAFwHYQCoFK/EGEB8fn6eE5vbt23njxg3279+fU6ZM4dKlS0UPAnNzc0ZERFS0yWVKTEwMR40aRYVCQVNTU27durVI5yUmJrJ69epiPojX5WEoKmq1mmPGjKFCoRBzEixdurRQO3JCW5YvX16qsR8/fsyJEyfmEbsMxfHjx9muXTsxJwkAvvPOO1ywYAEzMjIMPl5Z8/HHHxMAraysXvt6aTQafvrppzQ3N88TctO1a1fOnz+fv/32W4XlR3n06BH37dvHOXPmsFu3bnR1dc2Tg+flplAoaGVlRRcXF9aqVYsNGzZk8+bN2aFDB3bv3p0DBgzgiBEjOG7cOE6dOpVffvkllyxZwjVr1vD7779nZGRkPhuioqKoVCopk8nK1Oto5cqVBMBmzZrl25eamiqGq3Tp0oXPnj0rMzsKm2MI2fv/Gfj6+vLMmTMVbQYAQP3iBTT9+8M8NBRZISFQjB2bZ/8vM2agz8KF0ACI6tgR1UNDAZms8E6fP8ed+fPRd/VqXE1PR6Z+swLATEdHzB45EimtW0Ph5wdja+uyuCwJCQkJCQkIgnCWpG8RjhsPYAyAGgBiAAi5dpNkjTIy0eBUpjmGRNmh0+ng7u6O+/fvQyaTYcCAAfjggw8waNAgkER6ejqysrIgk8mg0+lgbm4OADh69Cj8/Pwq2HrD8uzZM3z99dcICQmBVqvFyJEjMXPmTDg7O7/23Js3b6Jp06ZITk7Ge++9h0OHDkH2unluMUlLS8PIkSPx448/QqfTwdbWFkuWLMHw4cPFY7KysuDu7o6HDx9i3LhxCA4OLtWY06ZNw9dff4369esjLCwM9vb2JepHp9PhyJEj2LFjByIiInD37l1kZGQAAKytrdG7d2/MnTsXVapUKZW9Fc348eMREhICCwsLXLlyBdWqVSv0eK1Wi6tXr+LUqVNiu379OgBAEATUq1cPzZs3h7+/P/z9/VG7dm2Dvq8ePXqEs2fP5mmxsbHi+F5eXvDx8YGPjw88PT2hVquRlpaG1NTUPH8L2vaqv5mZmXlsMDIywuzZszF58mQolUpx+y+//IIePXrAzMwMDx8+hLWB13qpqamws7NDVlYWHj16BEdHR3Hf3bt30atXL1y6dAlz5szBzJkzDf55zk2hc4xXqR6vawB2AegCQFbSPsq7Vbq7KhkZTO7Uia0AruzbN9/uB3//zRUWFiRAVqvGpJs3i9X98eBg9q9alY6CwO/1ZWYn5ZSBAmgFsJZCwXetrXk+KIhcsYKJhw8z/Q3NDC0hISEhUTlA8XNurCnO8ZWxVbo5hkSZ8N577xEAzczM6OLiwunTp1MQBHp7ezM6OppJSUncvXu3mHcip8lkMg4ePJi//fbbG3mXPTfJycmcP38+LS0tKQgCBw8ezDt37hT5/IMHD4peFePHjy9DS7NJTExkjx49xBAOZ2dn7tixgyTZpEkTAmDXrl1LPU5WVhZdXV1Zr149GhkZ0dvbm4m5cugVxoMHD7ho0SK2b9+eDg4Ooq05zdbWloGBgTx58mSp7axsTJ48WfxMlSQsKTExkYcPH+acOXP4/vvv09raWnzdLC0t2bFjR86aNYsHDhzg06dPi9SnTqfjw4cPuXfvXv773/9mly5d8uQ1EQSBderU4eDBg7l8+XL+8ccffPHiRbFtLwoajYZJSUmMjY3llStX2KdPHwJg48aNefbs2TzHTp06lQBYs2ZNg5ekzql2M3369DzbDx06RBsbG1pbW/PAgQMGHfNVFDbHKI24EQBgC4A7AL4G4FXSvsqrVcaJhzo5mT30H5YCQ1C0WjIwkEMAGgElTx6alESuWMEd777LVpaWdJfLaaEPfQHA3/TiR48clymA1oJAD6WS7a2tGTN4MBkSwvjQUKoN5DYoISEhIfF2Ulxx401ukBKK/mOYOXNmdl40CwsCYMuWLQmAgwYNYlpaWr7jc5JkNmzYkMbGxuLCyMzMjN26dePq1atLlWOivMnIyGBwcLCYU6R79+68dOlSsfpYvny5WBFl7dq1ZWRpwcTExLBDhw55Fr45i0RDLAQPHTpEANyxYwcPHDhApVJJf3//fItejUbDffv2cdiwYfTy8hJzfeQ0IyMjenp6cujQody7d2+Z5GmobEyfPp0AaGJiwlu3bpWqL61Wyxs3bnDTpk0cPXo0GzduTJk+pyH0IVBDhw7l6tWree7cOWo0Gj548IB79uzhrFmz2LlzZzo5OeURJuvVq8ehQ4fy22+/5YkTJwwWQlVSdu3aRWdnZ8rlcv7rX//K8/2Tk8OmT58+Bhvv5MmTojCYg1ar5ZdffklBENioUaMCw2XKijIRN/h/P+pWAEYDeAAgAsBwAMrS9lsWrTKKG2S2wNHTxYUAuCIoqMBjFnXqREH/IZveooVBx0968ICa0FBy0SKubdaMLczNWf0l8SNBL3545xI/bASBnkolO1hbUzt8OLloEa+sXs2Yl1RECQkJCYl/Fv8kcSOnVdY5hoRh2Lt3r7jwBEAbGxvKZDIuXbr0lYkNtVotzc3NKZPJGB4eThcXF1pYWHDAgAF0d3cXF09eXl6cOHEiQ0NDS1XGtKzIysripk2bxPwY7777bonyiOTkWFCpVAwLCzO8oUUkMjKS/v7+BMCqVasWu6rKq+jfvz/t7OzECjE7d+6kXC5ns2bNOHv2bLZt25Z2dnZ5vDIEQaC9vT3btWvHhQsXvnHlZw3JvHnzxESwV65cMWjfycnJDAsL41dffcUePXqIAh2QXUI4t5BRv359fvjhhwwODubJkyeZkpJiUFsMxbNnzzhixAhRsDl+/DjJbPEsR5wpbQ6ZHHK8VnI8hxITE9mtWzcC4ODBg5mammqQcYpKmYkbAOwATABwBsAvAPoDCAHwe2n6LatWmScemamp7KVPQLO2gBAVkjyxZg1N9R++lhYW5eZBkRQdTYaGkgsXcmGjRmyuFz/M9eKHoBc+CLBKbuVZn9jU28iIbNOGHDKEW/v04Y7Jk3n3+HEp0amEhITEW4okbki8TURGRlKhUFAQBFHUsLGx4ZEjR1577vfff08A9PX15c2bN+ni4kInJydeu3aNN27c4IoVKxgYGCiKJiYmJuzcuTODg4N5+/btcri6V6PT6bhr1y7WrVuXAOjj48PDhw8Xu0qFVqtlq1atRFGosizgExISDOa6/+zZMxoZGXHkyJHcsWMHhwwZQg8PDzH8JqcZGxuzdu3aHDFiBA8cOGAwYeVtYfHixaIAdv78+TIbR6fT8d69e/zxxx85ZcoUhoSEMCIiotwX6YbgyJEjolg6evRoJiUl8cGDB1SpVBQE4bWhTDqdjteuXXvl5zrHq6ZTp04kyUuXLrFWrVpUKBQMCQmpkKo1ZRWWshvZWc6nAXB5aV+lnNRU9olHZmoqP6lRgzcBcsYMsgD1PvHePXrpsySvMDUlS+m6ZQhSY2LIsDAyOJgL/f3Zyd6eDY2N6SqT0QygXS7xw+yljMFKgHVkMrJGDbJFC07y8uLMVq24dexYXvnlF2qKWB9dQkJCQqLyUJJ5AAAnAF31zbG451d0q+xzDImSkZ6eTltbW7EyRU48e3FyTHh4eBAADx06xGvXrtHR0ZEuLi553O9TU1N58OBBjhs3TjweAGvVqsUxY8Zw06ZNvHr1KrOyssriMvNx9OhR+vn5iZ4l27dvL9EiJjExkdWqVSMAenp6vpGLx5eJiori5s2bOXHiRAYGBtLT0zNP9ZzcXhmOjo6iOPTee+/9I0JMSsuKFSuy1whKZZlW/nibSElJ4aRJkyiTyejm5sb9+/eLYVImJiavzDUSGRnJjh07EgA3bNiQb39cXBzlcjmNjIyYkpLCrVu30tTUlC4uLjxx4kRZX9YrKStxo11Jz62o9kZMPDIzyaFDqQM4zNyc2z77jLoCVOVNzZtn//vkckYWUt6q0pCaSp46xR+GD+dn3t7s4exMHxMTVpXL6SeTkXI5mSsMJnfzAkhLS7JaNbY1N2cPZ2d+5u3NtYMGMWLtWqa+pj62hISEhET5UlxxA0A/ANEAvgfwPwD3APQpTh8V3d6IOYZEsWncuDEBiG7eXl5exXZTv3TpEgHQwcGBJHn58mXa2dnRzc3tlTk3IiMjGRISwi5duuQpf2lubs62bdty8uTJ/Pnnn3n37l2D3jn9+++/GRAQQAB0c3PjunXrSuxdcO3aNTE/yfvvv2/wBIdlRVZWFs+dO8eVK1dyxIgRbNWqFatXr04zM7N8iT5zQhlkMhmVSiXbtm3LTz75hEeOHMlzvTklNPv161duAtWbzHfffZcdBq9QVOgi+k3j1KlTrFevHgFw4MCB/Pzzz0VhNvf7Ua1Wc8GCBTQ2NqaFhQWrVKnC+vXr5/suadasWfYN9RUrOHHiRAJgq1at+OjRo/K+tDyUac6NN6m9SROPpzt3sqE++VQrS0ue/v77/Adt2MDV+i/Z4R4e5W+koVGref7HH7lpxAhO8fNjnypV2MzMjCPMzEhra6pzxcTlbvUBUiZjqrExXWQyNjAyYqCdHUfVqcOl3bvz4urVZFxcRV+dhISExD+GEogbF3N7awBwAHCxOH1UdHuT5hgSRWPYsGFiKEGOwFHSnBhdu3YlAM6ePZskef78edrY2LB69eqvDdPIysri1atXuWnTJn766af08/OjSqUS50F2dnZ8//33OWvWLP7yyy+MjY0ttn3Xrl1jr169CID29vZctmxZqfJ/HDhwQAzJmDRpUon7KUsuXrzIBQsWsH///vTx8aGzs3O+5J45TaFQ0NbWlnXq1GHnzp05ZcoUbtu2jY8ePeLVq1cJgMuWLSt0vG+++YYAOHTo0DdG6KlINmzYQEEQKJfLKzRHy5uGWq3mnDlzqFQqaW9vz/r16xPITgBMkuHh4aI3UZ8+fRgTE8P169cTAI8ePSr2s2fPHlEYyUlSOn78+GJ5H0VHRxv8+khJ3HhjJx5ZajX/M3gwHfUCxtCaNfn08uU8x1zbt482+v21VSomPXhQQdaWD1qNhpFHj3L7pEmc264dh7i7M6RmTdLTkzesrWmkzwGS+wcpUB8ScypXHpBaCgVbWliwf9Wq/LV/f3LDBqpPnJC8QCQkJCQMQAnEjcsvPZe9vK2ytzdtjiFROGvWrBFDCxQKBeVyealyACQnJ1OpVFKpVIqVFs6cOUMrKyvWqFGDD4o5f1Or1Txz5gzXrFnD//f//h8bNGiQpyKEm5sbg4KCuHDhQh45cuSVJUmjoqI4bNgwymQyWlhYcM6cOUxKSirxdZLk0qVLxYoo69atK1VfZcGpU6dYu3btfAKGkZERnZ2d6e3tzX79+nH+/Pk8cuTIaz11pkyZQoVCwbgi3EibO3cuAXDUqFEVkqvgTWPz5s0UBIEymYyhoaEVbc4bxZUrV0TPixzRLifUrFq1aty3b594bHp6Ou3t7UUBRKvV0srKigDo6OhIExMTbt68uchjJycns379+lQqlWUicEjixhs+8Ui6f5//ataM7gCTTUzIefPIXCV/0hMT2czMjNDntDhVCX9Iypu4K1cYOn8+l3TrxrBu3cjAQIa5u9NZnwckd/jLGL34sSrHvRCgCUBHQWBtlYq76tUjhwxh5Lhx3DpuHC/v2kVNJcxmLiEhIVFZKIG48Q2AwwCG6dshAIuL00dFNUilYMuMuLg4fvfdd+VeLjUiIkIMP8gJR1mwYEGp+81Z2Hbp0kXcdurUKVpYWNDDw6PUrt4pKSkMDw/nsmXL+MEHH7BWrVp5Fu8eHh4cOHAgly9fzrCwME6YMIEqlYpGRkacNGkS4w1wgyeneoORkRHDw8NL3Z8huXDhAhs1aiS+Hg0aNGBwcDDPnj1b4lARjUZDZ2dncVH4OnQ6Hf/1r38RACdOnCgJHEVgx44dosCxdu1aKQlrMcjKyuKyZcuo1OdrzAmNKkiwmzFjBgVBYGRkpPg5FgSBNWvW5MWLF4s85unTp8VQOl9f3zKpAFVWOTd2AegCQFbSPsq7vaniRg4ZV6+SvXpRDTDAyIhbx47Nk4/ji6ZNCYCdAHLJkgq09M1AnZzMi9u3MyYkhJw1i2EdOrCtpSW9lEo6CAJN9F4gIXrxY9xLCr9cLybts7YmGzTgjgYNOLRGDc5q04bfjxzJvzdtkjxBJCQk/pEUV9zIPgW9ACzTt6Dinl/R7U2fY1RGevToIf7m+vj48Ouvv2ZkZGSZjnn//n2xNGSTJk1obW1Nf3//1y6oNBoNly9fLpZjfBUODg4EwEuXLonbTpw4QTMzM9auXZuPHz82yHXk8OzZM/76669csGABe/bsySpVquTJFTFixAjev3+/1ONs3bqV9vb2BEBbW9tKUxGFJG/evCnesYY+b8rrKkgUlQMHDhAAd+/eXeRzdDodx48fTwCcMWOGQex429m7d28ezyRbW1u2atWKs2fP5o0bNyraPJLZuXTmzJnD9u3b09nZmcbGxjQ3N6eNjQ2dnJxYtWpVenh4sEGDBvTz82O7du3YpUsXDhgwgB999BEnTZrEOXPmcPny5dy0aRP37NnD8PBwXrlyhXFxcSUSdSIjI/nee+8RAE1NTcXPfUGlnGNiYqhQKDh48GDxde7UqROfPXtW5PGCg4PF/9Nnn31WbHuLSlmJGwEAtgC4A+BrAF4l7au82tsy8Yj++Wc20Wdlbm5unsdT48Q331CjUJAATzRvLpVbNQTJyWREBM/OnMkvmjZlnypV2NzcnDUVCtoKAiOUSlIQ2KWAGE0APAGQRkYcb2REd4WCvqam7OrkxDH16nFxly5M376djIwkpfhLCQmJt4QSeG4sKsq2ytzeljlGZeHXX38lAH7xxRf85ptv8ixOmzRpwgULFuSpNmIIoqKixFwWbdq0YWBgIE1MTF47TkxMDF1cXET7VCoVGzduzPnz5+cLBzl8+LDoRZGb48eP08TEhPXq1eOTJ08Mel0vc/z4cVpaWlKlUtHZ2Zk+Pj7s378/FyxYwKNHjxbrTmtoaCirVq0q3uXt3LlzpamIEhUVxdatW4v/F3d3d/72228GHaNv3760t7enupjV/XQ6HT/++GMC4Pz58w1q09vKlStXOGrUKNavXz9fdRqlUkl3d3f279+fmzdvLvP34MWLFzl79my2a9eOzs7OeYSXnO8AJycn2tnZ0dLSkiYmJlSpVJTL5QUmpS1Oc3R0fG2SVbVazYULF4oJQ0NCQqjRaBgUFCT2s3DhwnyCSffu3cX9ffv2LXJuGK1Wy549e4r/i71795b4tS0KZRqWAsAKwGgADwBEABgOQFnafsuivU0Tjyy1mhuGD6ez/sM02N2dL65fz94ZE8MrDg4UALrJ5Yw5e7Zijf2HkBwTw4i1a7lh+HBOb9GCA6tX57vW1kz09CRdXNhDLqeigC+pZL1niE8ubxBnmYy1VSq+a2VF9ulDTpzIgyNHcs/06bx24ADVlWTiICEhIVEQJRA3zhWw7VJx+qjo9jbNMSoajUbDunXrsmbNmszIyBC3R0VFcenSpWzevLn4G9qwYUPOmzeP13PmQCUkPDxcdN1u0qSJWK1h5cqVhZ7366+/ivHs77//PoOCgkQPhpzm4ODA3r17iwvrpnpP202bNuXp67fffqOxsTEbNWrEhISEUl3PqwgLCxOv09HRsdAEmnZ2dqxXrx67d+/O6dOnc/fu3WJJyb///pteXl7i8a1atSqz5IHF5dGjRwwICBAXkW5ubmWy2EpISKBKpeKECRNKdH5WVpZ4l3zpm1D5sJIRFxfH4OBgdurUiS4uLvkEBktLSzZt2pRffPEFz5ZiLXT+/HnOmjWL7777Lp2cnAoUMt555x326NGDwcHBjImJKVK/arWaMTExvHz5MsPCwrhr1y6uW7eOS5cu5axZszhx4kQOHz6cffv2ZadOndi2bVs2aNAgj/hQkDfHyZMnxSSivXv35sOHD/Psb9++vdiHt7e3mEvo0KFD4veBvb19kV+fuLg4sdyzi4tLsfMHlYQyEzcA2AGYAOAMgF8A9AcQAuD30vRbVu1tnHi8iInh9BYt2EIQqDU2JmfPpi45mVqNhp31ro8qgAfnzq1oUyX0aDUaRkdE8MDcuQzp1YscN47s2ZNjHB1ZU6GgnSDQWB8SI9MLHwTo8tLEQwBoC5A2NmT16hxkbc2OtrYcWqMGp7dowf8OGcKIlSvJ2FjJK0RCQqJcKaq4AWAMgMsAUgFcygbbRAUAACAASURBVNXuAdhclD4qS3sb5xgVRUhICAFwz549rzzm/v37XLFiBVu2bCn+LtarV49z5szhlStXijyWTqfjmjVrxAWLi4sLb926RTMzMwYEBBR65zInh4YgCPkWp/Hx8Zw7dy4bNWqUJ95doVCIySxNTU3z5XoIDQ2lSqWij4/PK5OAlpQtW7ZQJpNREASuWbNG3J6VlcUzZ84wODiYw4cPZ4sWLVi1alWampq+9i6zubk5Bw4cyO+//5737t0zqL3FJSEhgd26dcuTL+XHH38ss/FyyrteuHChxH1oNBr27t2bALh69WoDWvfPQ6vVMiIighMnTqS3t3eeEsoAKJfL6ebmxh49enDt2rUFfr7Onj3LmTNnsm3btnR0dCxQyHB3d2fPnj0ZEhJSZCHDkISHh9POzo4AaGFhwQMHDpDMDkEbNWoUAbBq1ar85ZdfCjxfq9WKYoS5uTnlcrnodZHz2XF3dy+S10ZucTcwMLDcqgCVibgBYDeAawCmAXB5aV+xY21znWsL4AiA2/q/NgUc0xjAnwCu6idB/YvS99s88dDeuUP268enABsqFPxh9GhqNRou6dZNrB7yha9vRZspUUw0qanktWvknj384YMPOL5RI/Z2dWUrS0vWVqnoo1CQFhakUkmrgiYducQRB71XiIMgsKZCQR9TU35StSo5dCg5dSp/+PBDHpw7l7dCQ7PHlZCQkCghxRA3rAC8A+BHANVzNduinF+Z2ts8xyhPnj59ShsbGwYEBBQ52eLDhw8ZHBzMNm3aiJPzOnXqcNasWbx06dIr+8nIyBBDAwDQxMSEcXFxbNmyJa2srF6Zi0Kr1TIwMJBAdpnYoiTOPHr0KPv27UtHR8c8v9NGRkbs0aMHDxw4IC4M9u/fT6VSST8/v1JXLsnhq6++Ehd4+/fvL9a5kZGRXL58eZ58HTnVUF6edwiCQHt7e7Zv356LFy8udZLUopCUlMR+/fqJC1FbW9tyqdLi6+vLxo0bl7oftVotlgp+2ZtHonQkJSVx3bp1DAoKYrVq1cTyxDnNzMyMderUKVDIMDIyYo0aNRgUFMRVq1aVqMRyWaHVavnRRx+Jn8EGDRqI1zBp0iSxItOrePLkiVjiOue7zM3NjQDE92LuaioFMXPmTPEz/9VXXxny8l5LWYkbnQvYZlTS/nL1sRjAVP3jqSg4DtcTgIf+sSuAWADWr+v7nzDxuLFlC331CWP8zMwYsXYtT61bRzOAVQBqq1UjXxOnJfHmknjvHk+tW8cfRo/m3HbtuLhpU7JzZ9LXl41VKtrpE6XK9V/crrnEj5dL6ArIDpWhpSXp4sIGKhX9zc3Z1dGRH3l6cnabNjz6+efk0aPU3rsn5XeRkJAQKc1Njje1/RPmGOXBJ598QrlcXizvi9w8evSIq1at4rvvvisuVjw9PTljxgyeP39eFDoePXokhrfIZDLKZDL+9ddfXLRoEQHwf//7X4H9x8XFiTkmqlWrVqIKI4mJiZw/f34+cUChULBu3bqcPn06N27cSIVCwRYtWrx2ofI6xowZIwoxZ86cKbatPXv2FG11cXHJE+IRFxfHHTt2cOrUqezatSs9PT3zhbrk5BIZPXo0f//9d4Pd3U1NTeWHH34oJoC1tLTkt99+a5C+X8fly5cJgCtWrDBIf+np6QwICKBMJuNPP/1kkD4lCubixYucNm0a/f39aW1tnUfI6NWrF1evXl2ksr6VgYMHD4qfN0EQOG/evCKf+8cff1AQBBoZGXHz5s0EskPoMjIyWKVKFQYEBBR4nlqtZqtWrUTvs9fl/ygLykrcKCg+Nt+2EvR7M8cTBIALgJtFOOdijthRWPunTDy0Gg2///hjuup/1AdUq8b4P/5grK8vCfAnvfARVclKdEmUP6nR0WREBLl5M7967z1+Ur8+e7m6srWlJesZGXGYqSlpbc1UlSpP+dyc5qsXRmJyCSIqgBYAnQSBk62sSG9vxrZqxQHVqnF8o0b8KjCQP4wezeMhIUy4dEkKmZGQeAuRxA2JknDp0iXKZDKOGzfOIP09fvyYa9asYYcOHUSho1atWpw0aRJdXFxoYmJCMzMzAuDatWt56dIlqlQq9urVq0Bvj+PHj4t3Ozt37lzqRfq6desIgHXr1uUHH3xAZ2fnfHeVBUFg7dq1S7TY0ul04l1Ya2vrYlUwSU9P57Bhw0ThwMbGhhs2bCjy+VFRUZw/fz7btGlDW1vbPNclk8no4uLCLl26cM2aNcUOv1Gr1Rw9erQY7mNmZsaFCxeWm0s8SX7++edUKBQGTf6akpLC1q1bUy6XFxqS9U/kzp07/OKLLzh06FDOnz+f27Zt48WLFytN8tryJjMzk1999ZVYkSX3d5yfn1+Rc/YsXrw4z2fz999/J0kuWLCAAPKJzJGRkWJOIQ8PD4OHzhUVg4obAJwB+AC4DqAJAG99exfAjeL2V0D/z3M9FnI/f8XxfnpbXluS9p828Uh5/Jj/bt2aA2Uy0tiYuunTeXfVKnbTZxgWAAa5uDC5ErlZSVRutBoNo0+d4tGlS7lu2DAeHTqU/PhjPggIoL+5OeuoVHSTy2mj9xD5UC9+HCxAGAHAAfr9e3OJIo6CwBoKBZuYmPCnevXIvn15bcAAzm7blt8NHMi9M2fy/M8/M7GCY3slJCRezZsubgCoC2AbgDUA+hTlnH/aHMPQ6HQ6tmvXjra2tmWSTPPJkyf8z3/+w44dO1Iul9Pd3Z2enp4EwOHDh1OtVrNRo0Z0dHQscMG6ePFiMRTDkNUtatSoQQBistGkpCQuWbKEvr6++TwgzM3N6e3tzbFjx/K3334rdDGv0Wjo7e0tupsXNbxFq9Vy8uTJYtUYMzMzLlq0qNTXqVaruW3bNg4YMIA1atTIk4ck59p8fHw4efJkMcFhDmFhYVyyZAk1Gg0///xz0TZjY2POmDGjXEUNMnth6eTkxKCgIIP3nZSURD8/P6pUKh46dMjg/b9J6HQ6HjlyhN27d6cgCJTL5XlCo3JatWrVGBAQwE8//ZTffvstQ0NDeffu3Xz5bN4WIiIixIShQUFBYgLPu3fvit9pSqWyyF5FOSW3c3tqxMfH09jYmCNHjhS3bd26VQzrGTRoULl/7nJjaHHjQwBhAJL1f3PaLwB6FbGP3wBcKaD1eFnMAJBYSD8uek8P/0KOGYnshKdnqlWrVlavcaVGFxVFDhzIOzlfAnI5W1tY0DTHDRLgtKZNpTvoEmWGOj6eF7dv597p07mqXz9Ob9GCIzw8eKBpU7JFC4a6ubFqLlEkJ2xmql78mPsKcWQ+QMpkXCyT0RzZ+USqy+VsYGTElhYWPNWyJTlsGE8NGsQl3bpx69ixDF+5klHh4VLFGQmJMqQk4gayc20E6B+bALAobh/6czcAeALgykvb39fPGSKhD38tpI/PAbTWP/6lKONK4kbp2LlzJwFw1apVZT5WUlIShwwZQiC7MgpJzpgxg0D+JKZarVac/BsZGRm8lOi5c+cIgM7OzgXuP3XqFDt37lxgjgtBEGhra8sWLVpw6tSpPH36tHh9OQkDGzVqVGBFhZfRarX86quvaKoPbVapVPziiy/KdAFz+fJlTp06lX5+frS0tMwXolO9enX26dNHzAWQI2qoVCpOnDixSNdVFuzbt48Ayqzc5bNnz9i4cWMaGxszLCysTMaozKSkpHDNmjWsW7euGCoxY8YMcRGfkpLC8+fP86effuKXX37JQYMGsWnTpvneQ0ZGRqxXrx579erFqVOncuPGjTx58qRY8aeykpaWxnv37vHPP//k7t27uWbNGs6ZM4ejR49mp06dKAgC3dzcXunds3jxYlGEqFOnzmurGGm1Wu7atSufGDRixAiamJgwISFBzE0kl8uL5cFVVhQ2xxCy9xcfQRB6k9xZopML7/cmgHdJxgqC4ILsyiteBRxnCeB3AAtJ7ihK376+vjxz5oxB7X2TSPjrL/y8fDl+P3kSv8fEIF7/v1cAaA5gl0qF1PHjUf2bbyrUTgmJHHQvXkD24AEenjuHI8eO4dGDB4iLj8fT58+RkJKCfzs6oiWApY8eYWFyMjJIZALQIvuX7WcA/QAM0D9+mV8BdJTL8TGJn3U6GAsCTGQymMnlsFAqsdvbG67OztiZlIQLaWlwdHKCk5sbXD08UKVBA1T384PM2LjcXg8JiTcFQRDOkvQtxvEfI/tmhC3JmoIgeAD4jmSHEozdBkAKgP+RrK/fJgdwC0BHAA8BnAbwAQA5gK9e6uL/6f/OBpAGoAXJlq8b958+xygNGRkZqFOnDiwsLHDu3DkoFIoyG0un02Hx4sWYNm0abG1tERMTgwsXLqBly5YYOnQoNm7cKB777Nkz+Pr64t69e3B1dcXp06fh6upqcJvef/99HD58GAsXLsS0adMKPObPP/9Ely5doFKpMHHiRFy5cgVnzpxBdHQ0MjIyxONkMpk4yff09MT+/fvh4eFR6Pjr16/H5MmT8fz5c8jlcgwbNgyrV6+GSqUy6HW+juTkZGzbtg379u3DmTNnEBsbC51Ol+eYkSNHIiQkpNxty02fPn0QHh6Ohw8fQqlUlskY8fHxePfddxEdHY0jR46gefPmZTJOZeLevXtYtWoV1q9fj+fPn6NJkyaYMGEC+vfvD+MizLVI4smTJ7h58yZu3ryJW7duiX/v3LmDrKws8VhbW1t4eXnB09MTnp6esLa2homJCUxMTGBsbCw+ftVzY2NjyGSyIl9bVlYW4uPj8fjxY7HFxcXleZ7TkpKSCuzD3t4eTk5O6NSpE/7973/DwsLileM9efIEnTt3xtmzZyGTyTBjxgx8+eWXRbYXAC5fvoyGDRvCyckJcXFxsLa2xokTJ1CvXr1i9VMWFDbHKLa4IQjCYJKbBUH4HNnrhzyQXFYyM8X+vwGQQPJrQRCmInuiM+WlY1QADgHYR3JFUfuWJh7/B3U6XNu3D2GbNyMoJQVmR49io0aDScie6QXY2mJg795oN2IEqjZrVtHmSkgUG11mJvD4MWQPHuBSRASORUQg7vFjxCUk4FlyMp6np2OzmxvcMjMx/tEjbE5Lg5qEBkAWsr/cHgBwA+AL4GwBY6QCMAXQGtkrJSUAI0GAsSDAUi7HtXr1AAsLfPn4MW5mZsLawgJ2NjZwcHSEa7Vq6N21K+DiggwbG6js7SErwwm9hER5UgJx4wKyw0z/ItlEv+0yyQYlHP8dAPtziRvNAcwhGah/Pg0ASL4sbLzcjxzALpI9XrF/JLJFGVSrVs0nOjq6JOb+41m4cCFmzJiBo0ePon379nn2ZWRkFGlh8ypI4vjx49i4cSOOHz+OBw8eQKfTQalU4saNG3B2dkbjxo2hVqtx6dIlWFlZAcgWEzp06ID09HR06NABoaGhZSa6JCUlwd7eHjKZDImJiTA1NS3wuKtXryIwMBApKSnYt28fWrduDQB4+vQpdu7ciZ9//hm///47Xp7bKxQKuLq6onHjxujYsSP69esHR0dH7N27F6NHj8bjx48hCAKCgoKwceNGWFpalsl1FhedTocOHTrgzz//hL+/P44fP45t27ahb9++FWbT06dP4erqinHjxmHp0qVlOlZsbCzatGmDJ0+e4NixY/Dx8SnT8SoCkggLC0NwcDB++eUXyGQy9O7dG+PHj0eLFi0gCIJBxtFoNIiKisonety8eROxsbEl6tPIyKhQAQTIFhkeP36M+Pj4fJ9LALCwsICzs3Oe5uTklG+bo6NjiYS0jRs3YsyYMVCr1ahevToOHTqEOnXqFOncM2fOwN/fH1qtFr6+vggPDy/Vd7EhMbS4MYrkWkEQZhe0n+TcEtiYu387ZMe5VgMQDaAfyWeCIPgCGE3yI0EQBgPYiOxSsDkMI3mhsL4lcaMQdDrc+fFHDBgxAmfUanGzDEDSO+/AvEMHXK5ZEzYBAXBr2rTi7JSQKE+ePwcePsSl8HBcOH8ecTExiH/yBE+fP8eL1FTs8PQEXrzA0MhIHE1NRQYpCiQyAOn6bqoAePRS13JkiygA4ADgKbKTDMmR7U3lJAiIsrcHjI3RJSEBsSTMlEqYq1SwMDZGXUdHzOnQAbCxwY6oKAiWlnB65x041KwJp9q1YenmJoklEhVGCcSNv0g2EwThPMkmgiAokJ2kvGEJx38HecWNPgDeJ/mR/vkQAM1Iji3k/OkAzACsIXnidWNKc4ySERMTAy8vLwQGBmLnzrwOwX369MHOnTthZ2eHLl26YPr06fDyyufMm4/Tp09j/fr1OHr0KO7duwetVgsg26uhatWqaNu2LWbMmAFPT0+MGzcOK1euxLFjx9CuXTsAQEhICCZMmACSmDlzJubNm2f4C3+JGTNmYOHChejZsyd27979yuPu37+P9957D9HR0fj555/RvXt3AMChQ4fQrVs3aLVaLFiwAIMGDcK2bdtw9OhRXL58GXFxceLrAAAqlQqZmZkAgPbt2+OHH34oE6+U0vDgwQO4u7tj8uTJWLBgARo3boz09HRcu3atwjw3QkJCMH78eFy8eBENG5bo66lY3L9/H23atEFycjKOHTuGRo0alfmY5UFqaiq2bNmC4OBgXL16FXZ2dhg1ahTGjBkDNze3crclOTkZGRkZSE9PR3p6ep7HxX2e81in0+URKl4WLZycnGBmZlbm15eSkoLu3bsjLCwMgiBg9OjRWLlyZaGeJyEhIZg4caLoOVXRouLLFDrHeFW8yusaAIeSnltRTYqHLRoXt29nbX1cIwAOMTIiraz4rv55LaWSH3l5ccsnnzDm7NmKNldConKj0TDu8mWe3bKFB+bM4aYRI7i4c2cuatuW/Phjsk8fjqpShf7m5mxgZMQaCgVdZDLWlclIU1NSqaRdAflGrPT5SKhPxvryfgeAFARSLqc1QDOA1oJAZ5mM1eVyDrSwIL29yVat2MPBgb1dXTmsVi2Ob9yYs9u04b6PPiI3bSL37mXEmjW8cegQE+7ckUr+ShQJFDPnBrLLwE8HcAPZoSO7ASwoTh8v9fcOcuXcANAHwLpcz4cAWFnS/l8aqxuA/9SqVassXsq3nsGDB9PIyIh37tzJsz2n1KZKpRLjxwHQ1taWgwYNypPF/+rVq5w4cSLr1q2bJ1GlIAh0dXVl3759uW/fvnz5I44cOUIAnDBhAsns2PO+ffuKCfkOHDhQ9i+AHq1WSzs7OwLg1atXCz02Pj6eTZs2pUwm4/r16/nf//5XzMnxqhK2ZPZrOmvWLLZp04aOjo708/PjrVu3DH0pBmPGjBmUyWS8p08gfvDgQQJgcHBwhdnk7e1Nb2/vch0zMjKSrq6uFASBLVq04JIlS/J9Xt4U7t27x8mTJ9PGxoYA2LhxY27YsIFpaWkVbdpbz549e8TqUA4ODjx16lS+Y7RaLXv27Cl+B+7evZvu7u5s2bJlBVj8agqbY5Tmx/wWskPWRwCwKWk/5dkkcaN47Jk+nY6CwOEAKZfzr/btuaRLF3Z3dqaVfuIQAJC1apFDhnDzhx/yzObNzJQSNUpIlAlajYbxN27w8q5dPL92Lfnzz+Tq1VzRqRMneXtzhKcne1epwvdsbTnW2Zls1Ij08KCHXE4HQaAlQBOASoCNcokjLwsjAFhfvy/9FfvbAaRCwRiViuYAbQWBLjIZ35HLWVup5DxnZ7J5cz5o2ZLdnZz4QbVq/NjLi5O8vTmnbVuemjCB3LCBSVu3MmzFCl7cuZMPzpxhegWVFZMwLCUQN2QAPgawHcAO/WOhOH281N/L4kZzAIdzPZ8GYFpJ+y+oSXOM4hMREUEAnD59er59OVn/oa/YUbVqVdrY2IjlDoHskqK5n+dM2rt168Yff/yRmZmZrxw7MTGRbm5u9PLyYlpaGpOSkujh4UEAdHR0fG0SvrIgJ1Fl7dq1X3tscnIyO3bsmCcBp6GTnVYkGRkZdHBwYI8ePcRtOp2O7du3p52dHZ8/f17uNl28eLHCxJUHDx7wyy+/ZJMmTcT/eaNGjTh37lxevny5wNLFlQWdTsdjx46xZ8+elMlklMvl7Nu3L8PDwyu13W8jarVaFC8AcMCAAWJi3ri4ODERsYuLi5jAdfny5QQgJiyuDBQ2xyhxQlEAEATBD9m5+noCuAbgJ5KbS9xhGSO5jJYM3ZIlkP373+iVno5QAPO7d8eE7dtxcft2aP7+G82io5EcEQGr+HgQgDEAX0tL+Ht5oW/v3vAbPBioUqWCr0JCQqJAdDo8vXMH8bdvIyEqCgkxMXgWG4vqRkZo7+CAjIQEjPn1VySnpSFFrUZKZibSsrLQxcQE8ywscCc1FU0TEqAhkYXsUBsdsn8UdgI4AuC9AoYdAOBHZCd6HVDA/rEAQmQybBEEfKTVQg5AKQhQ6v8utLHBh/b22J+RgXlPn8JEoYCpUglTlQpmxsb4zNsbjd3dcSklBcdiY2FhZQUrOztYOTjA2tkZ9Zo0gWmVKoC1NSCF7xicEoSlmAHIIKnVP5cDMCKZVsLx30HesBQFsm/KdAAQg+w0OQNJXn1VH8UYqxuAbrVq1fr49u3bpe3uH4NOp4O/vz9iYmJw8+ZNmJubi/s2b96MIUOGAAA6duyI6tWrIzw8HPfv30d6enqB/QmCAFdXVwQEBMDf3x/u7u5wd3dH9erVYWRklO/4Dz/8EFu2bEFERAQUCgXatGmD1NRUtGrVCmFhYWWa1LQwvL29cf78eWzevBmDBg0q9NihQ4fihx9+AJB9PRs2bChWksPKTM574MiRIwgICBC3nzt3Dj4+Ppg2bRoWLlxYrjZNmjQJK1euRGxsLOzs7Mp17Nzcu3cPu3fvxq5duxAREQGS8PDwQK9evRAUFISmTZtWivdBWlqaGHpy5coV2NnZYeTIkRgzZgyqVq1a0eb9o/njjz8QFBSEZ8+ewdLSElOmTMG8efOgVqsRGBiIgwcPiu+hpKQkuLm5oWfPnuL3TUVj0JwbrxjAHsAyAINIykvdYRkhiRulICsLX/j6YvnFi9ACcBQE/HfaNHRfsABAdoLS+3/+iVPbtuFUeDj+un0bZ1NSsBTZi5T7zs74XC6Hf5MmaNapE3wGDICJrW1FXpGEhEQ5kPXiBR5fv45n0dF49vAhEmJj8Tw+Hk3t7dHQ3By3o6OxOCICKRkZSMnIQFpmJtI0Gky0s0N/Y2PsfPYMY589g4YUBRQtgEUyGSYA+LdOh4Ki4YMBjAMwBsB3BezfDGCQvm3VbxOQ7T4gB3BMpUJLpRKfZWbih6wsKAUBqpwmk+GwhwfcLCzw7dOnOPz8OUxUKpioVDDVJxhb1LEjjC0tcSw2FpFJSTCztISFjQ3MbW1haW8PX39/wMICmcbGUFhavnX5UUogbpxCdhnYFP1zcwC/kmxRgrF/BPAuAHsAcQBmk1wvCEJnACuQ/S/eQHJBcfsuDGmOUTy+//57DBs2DD/88AMGDx4sbtfpdLC2tkZycjIsLS1BEsnJyeJ+S0tL1K1bF82bN0f9+vXx+++/47fffsPjx4/xqjmtq6urKHa4u7uDJObPn49Zs2bBzc0NY8aMgU6nw+TJk/FNBVeMi46Ohru7OywsLJCYmFjgIlWn0yEgIABhYWGws7ND9+7dsXHjRgwZMgTr168vswoe5Ym/vz+eP3+O69ev50sqOXjwYOzcuRO3b98ut/wMGo0GVapUQZs2bbBjR5GKNJYLjx8/xt69e7Fr1y4cO3YMWVlZqFKlCoKCgtCrVy+0bt263IQ6jUaDe/fu4datW/jjjz+wbt06JCYmolGjRpgwYQIGDBgAExOTcrFF4vXodDp89NFH2LRpU7bHgyDg66+/xpQpU/IdO378eHz33XeIjo6Gi4tLBViblzIRN/SlWIOQfdOtJrLjY7eRLKioQKVAmniUnmd372JQs2YIffoUANDZzAwHIiKAApIqqV+8QNa5czC7dAkR+/Zh8O+/456+DJMCQCNTU/yna1d4d+8OjY8PFJ6eECqB0iwhIfHmkZWWhufR0UiIjsbzmBh4WVvDWqvFlevX8eu5c0hOSkJKaipS0tKQmp6OWTVrwkMux7p797D+0SOotVqotVpk6nRQkzhkbY16AEa/eIHNGg20yBZVdPp2G9k/fM0BnCrAniQAlgAaArhcwP6cX14PAJG5tssAqACkK5WAXI7majWukZALAhQAFIIAO7kcl6tWBZRKDIiNxW2NBiqZDCq5HEYKBdxMTbGhWTPAyAhfXr+OeI1GzORuamKCqo6OGNS6NWBmBlhZAV27GuA/kJeSVEsh2fh12yojkudG8UlOToanpyfeeecdnDx5Ms8CPifBZw5yuRy+vr7o3r07PvroIzg6Or6y3/3792PZsmWIiIiAWp8cXaVSwcnJCfb29nj27JlYLcXb2xt16tTBli1boFAosG3bNgQFBZXdRReDHI+MsWPHIiQkJM8+tVoNb29vXLt2DTVq1MClS5dgamqKBQsWYNasWejcuTO2b9/+yoorbwKnT5+Gn58fQkJCMHZs/py/UVFR8PLywqBBg7Bhw4ZysWnv3r3o2bMn9u3bh65l8J1pCBITE7F//37s2rULhw8fRnp6uih+9erVCwEBAaWudpGVlYX79+/j1q1buH37dp4WFRUlJq2Vy+UICgrC+PHj0apVK4NVPZEwPBcuXMDnn3+OL7/8Ei1bFlz1/Pbt2/Dy8sKsWbMwd26paocYhLISN+4B2INsQePPUthXbkjihuG4GRqKfkFBGJ6RgYkAzlStihtdumDwmjWFnhd35Qr++vFHnAoLw6nr17FRrUb19HSsBDBHEODv4AD/Bg3g/957aDpgAKyqVSuX65GQkJAoCZkpKXgRE4OkmBgkPXqE548fIyUxEV1r14YsPR0H//4b56KikJaWhtS0NKRnZCArKwsbGjcGMjIw7coVnExMRIZWi0y9uKIEcNHZGdBo0O7pU1zIyoKWFMUVUwAJCgWg08FVp8PLReyM8X+VeswAqU1/5gAAIABJREFUvBzXYQHgRc4TQQD02dANSQnEjZMAxpE8p3/ug+yEn80NblwZIc0xis7UqVOxaNEi/PXXX/Dz8xO3P3nyBC4uLmKGfmNjY5w5cwb16tUr9hiHDh3CsmXLcPLkSTGUxczMDG3btkW/fv2waNEiXL9+HXZ2dvjrr79Qs2ZNw1ycAcjMzIS1tTUyMzPx6NEjUdBJSEhA/fr18fjxY/j7++PEiROQy//PYXrt2rUYM2YMmjdvjn379sH2DfWQHTZsGHbu3ImYmJhXlqWdPHkyli1bhosXL6JBgxJVjC4WQUFB+PPPP/Hw4cMKC1kqDqmpqTh8+DB27dqFffv24cWLFzA3N0eXLl3Qq1cvdOrUCRYWFgWeq9Pp8PDhwwIFjLt370Kj0YjHmpubw8PDI1+rXbv2G/v+kyiYrl274vTp07h//36BoX7lSVmJGwINEdNSjkgTjzLg6FFg3Di0uH4dfyJ7Iv2Blxe+CQ2F9TvvvP58rRa4dg3HNm3C5n378Nf9+7imv9uiAPDcywtmLVrgjLMz5A0bokHPnlBUkhrLEhISEpUJXVYW0p4+RcbTp7BXKoHUVJw5cwZPHj9GWlISUl+8QGpyMqzkcgyqUwdISwPkcqAMylyWQNxoCuAnZFdNFgA4A+hfmb1Bc5A8N4pHZGQk6tWrhw8++ACbNm3Ks69Fixb488/s+2Wmpqa4dOmSQUSHI0eOYOnSpfjjjz/y5Oxo2rQpTpw4UWFlRQtj9erV+PTTT9GqVSuEh4fjzp07aNKkCZKTk9G7d+9Xhkbs3LkTAwcOhIeHBw4fPowqb1jOs/j4eFStWhUfffRRHg+el0lMTETNmjXRrFkzHDp06P+zd99xVZftA8c/N3tvEREBFQeCe+eqnKllZcOWZZZlWq7SyuepfmW2zMyGDUtLy540LVeae+QeKQoqIohskSWbw7l/f3A0MydyOIDX+/U6LzjfcX+v8xUPF9e5h9lj8vf3Z+zYsRYftlQexcXFbNiwgcWLF/Prr7+SlpaGvb09vXv3ZtCgQQD/KGTExMRQWFh4/nxHR0dCQkIuWcTw8/OTXhk3iTVr1tCnTx/mzp3L448/btFYKrS4oZSaobUeq5Raxt89a8/TWt9VvjDNT4ob5hO/fTvj77uPZUlJFFOWmXZxcWHLihXQvft1tZV18iS7fvyR49u28ZzWsHMnfdPT+YOyTyzburnRsXFjevTqxcBRo6CS18MWQghxZddb3DCdYws0MT09qrUuudLxVY3kGNfm7rvvZt26dRw7duwfY7f//PNPunbtCpQVNqKiogg0Q+/NjRs3MmPGDFq1asUbb7xR4e1XpODgYE6ePMnMmTN58cUXKS4uZty4cUyfPv2K523YsIFBgwbh6enJH3/8QZMmTa54fFXy7rvv8sorrxAZGUloaOgVj/3www958cUX/zXpaEX7+OOPGTt2LBEREYSHh5vtOpWhtLSUbdu2nZ+Q9OTJk0DZ8K2GDRv+o3DRuHFjGjVqhL+/f5WYoFRYltaa8PBw7O3t2bt3r0WLWhVd3Girtd6rlOpxqf1a603liLFSSOJhfkaDgY/uuYePVq7E3mgkBqBWLWZ16MDwn37C7oLZ0K+Z1sRt2cL2hQvZuXUrO6Kj2Z+XRxdgPYC/Py86OVErJISOffvSbsgQXPz8KvR1CSGEuHblLG7cQtkSruf7fGutv6/g0MxGcoyrO/fJ37vvvsukSZP+sc/e3p7i4mLs7OyIj4+ndu3aFoqy6ti1axcdO3Y8/3zGjBmMGTPmms7dt28f/fr1Q2vNypUrad++vbnCrDAGg+H8H9hr16696vGFhYXnhz/s2bPHbH+At2rVCltbW3bv3m2W9i1Fa01UVBROTk7Uq1fvH0OchLiUL7/8kmeffZbNmzfTrVs3i8VxxRzjcmvEXu0BjLmWbVXpIWvQV66Sbdu07t5dLzq3Bjvo/rVq6WN//HHDbRdmZ+uEZcu0njlTGx56SIfa2v697j3o5g4O+qtu3bSePVvriAhtKCqqgFckhBDiWnCFNegv9QDmAduAz4FPTI+Z19OGpR7AncBXISEhFXsTa5ji4mLdrFkz3bBhQ11YWPiPfU2bNtWAVkrp1NRUC0VYNQ0ePFjb2dnpRYsWXfe5x44d08HBwdrZ2Vn/UQG5l7ktWbJEA3rJkiXXfM78+fM1oOfPn2+WmPbv368B/dlnn5mlfSGqk7y8PO3p6akHDx5s0TiulGPcyJwb+7TWbS7atl9r3bpcDVYC+VTFMnLT0vhvv37M2b+fbNO2hjY2/Pzqq7SpwBl3048eZddPP7Fz/Xp2Hj7MvQUFjMjPJxFoCrT38KBj06a079aNdvfeS70OHWR1FiGEMINyzLkRBTTT5U1KqgDJMa7sk08+4YUXXuDXX389P84foE+fPqxZswYoGzLSo8clOwaLckpOTqZfv35ERUUxf/58HnjgAUuHdFm9evU6P+fDtU7aaTQaad++PWfOnOHIkSM3vBrIxcaOHcusWbNITk6WCTKFACZNmsS0adM4ceIEQUFBFonhSjnGdf9lp5R6yDTfRn2l1NILHhuAjBsNVtQ8Lr6+fLRvH1las3jiRMLt7Yk1GPB5801wdmZ1nz5kHD9+9YauwqdJE/q//jr/t2kTq9LTGZGbC0ePUjp9OkPDw8kpLmbajh0M/uADgjp35n+enjBgAAnjxrHstddIOXiwAl6tEEKIcjhE2SSiogZKT0/ntddeo3fv3tx1V9nUbEajkR49epwvbNx6661S2DCDOnXqsGnTJjp16sSQIUP47LPPLB3SJUVFRbFu3TpGjhx5XauRWFlZ8cEHH3Dy5MkrTkBaHsXFxfzwww8MGjRIChtCmIwaNQqlVJV9LynPnBtBQH3gHeDlC3adBQ5qrQ0VF17Fkk9Vqo78Y8dweu01jIsX41xSQhHQxsmJ9954g54vvWS26xZmZXHgl1/Ys3o1g4CAyEi+iozkGdP/g7pWVrSvXZt2YWE8O2IE3rffDt7eZotHCCFqonL03NgAtAJ2AUXntusqPEn5xSTHuLxRo0bx5ZdfcuDAAcLCwjAajXTq1On8HAbW1tYUFRXJmH8zKigo4MEHH2TZsmW89tprvPHGG1VqlYvRo0cze/ZsTp06Ra1ata77/P79+7N9+3ZiYmIqrBCxZMkS7r33XlauXMkdd9xRIW0KURM88MADrFmzhoSEBJydnSv9+mZZCrY6ksSj6jEaDHzywANMW7qUhNJSALyV4o0+fRi9dClUwvJseamp/PXLL+xes4Y9f/3F7sREoktKOAN4Ap97e7PB0ZH2zZvTrmdP2t5/P+5mmMFdCCFqinIUN6rdJOXnyFKwV3bw4EFat27NqFGjmDlzJqWlpbRu3ZqIiAiUUmitmTVrFs8++6ylQ63xDAYDI0aMYM6cOYwcOZJPPvmkShSUcnJyqFu3LoMHD/7X8sDXKiIigpYtWzJ+/HimTZtWIXENGjSI3bt3Ex8ff129SYSo6c6tbmWp9+6KXi1lq9a6q1LqLP9cClYBWmvtVv5QzUuKG1Xbvh9/ZMKoUWzOyuJJ4GsrK442bszWDh0Y9s03WFXiL5azCQm4HjsGe/bwwQ8/MCsykljD352SWtnbs+/ee1EdOnDC35/at96Ks69vpcUnhBBVWTlXSwkCGmmt1yqlnABrrfVZ80RY8STH+DetNT179uTAgQNER0fj6upKeHg4x44dw9PTk8zMTOrWrUtCQoKlQ71paK155ZVXeO+99xg1alSFD+Uoj08//ZTnn3+e3bt3067ddb1t/MOTTz7JDz/8wNGjRwkODr6hmFJTU6lbty4TJkzgvffeu6G2hKhptNa0b9+e/Px8Dh8+XOm9wKTnhokkHtVDfno6vPUWTgsXcm9yMksoWxewnYsL44YP575p0yq10HHOmWPH2LNwIXs2buRsXBzvFhRAYiKdKOtH3djOjla1a9OqWTO69O5Nt0cfBVnKTghxEypHz42ngRGAl9a6oVKqEfCF1rqn2YKsYJJj/NvixYsZPHgwn332GcOGDSM0NJSTJ0/SokULDprmuYqMjCQ0NNTCkd58xo0bx4wZM1i7di09e1ruv5nWmtDQUDw8PNixY8cNtZWQkEDjxo255557+OGHH26orenTpzNhwgT5+RTiMubNm8fQoUNZvXo1ffr0qdRrm6W4oZRqCCRorYuUUrcCLYDvtdZZ5Y7UzCTxqH7it2/nrSee4NfoaNJNP6v2QEqXLnj897/Qt69lA0xOZtVXX7Fz40b+OnqUv06fJs5g4F7gFwA/Px6zsiIgMJDWnTrRql8/Qnr2tEhxRgghKks5iht/AR2AnedWXVNKRWitm5srxoomOcY/FRYWEhoaiqurK5s3byYsLIykpCR69erFvn37yMjIYMCAASxfvtzSod6UCgoKaNWqFUVFRURERODq6mqRONauXUvv3r2ZN28ejz766A23N3nyZKZOncqePXto27ZtudrQWtOiRQucnZ1vuOAiRE1VVFREUFAQbdq0YeXKlZV67QpdLeUCvwClSqkQ4CugHvDjDbQnxL8Edu7M10ePctpoJHLFCh4KDCTMygqPP/+Efv1obWVF/1q1+POLLywTYJ069Hv9dV7fsIElSUnElpSQGRvLRz/9BB99REHPnkRkZDBtxw4enDGDJv364WZry7TgYHjuOQxffMHu776jIEMWGhJC3NSKtNbF554opWz459BXUc1Mnz6duLg43nzzTRo3bkxSUhL33HMPQUFBZGRkYGNjw88//2zpMG9ajo6OzJkzh/j4eF5++eWrn2Amn376KbVq1eL++++vkPYmTpyIj48PL730EuX9AHf//v0cOnSIJ554okJiEqImsre3Z+TIkfz+++8cPXrU0uH8TWtdrgewz/T1JeB50/f7y9teZTzatm2rRQ2xe7cuGDhQu5YlvxrQrqAH+/vrAwsXWjq6fynMztb7FyzQ3w4bpl9o2VIvDwvT2s1NHzLFbg06zN5ePxIcrD8YMEDHfP+91qdPWzpsIYQoF2CPvr6c4n3gVeAI0BtYArx9PW1Y6gHcCXwVEhJSsTexGktISNDOzs66f//+2tPTUwP60Ucf1StXrjz/O3vKlCmWDlNorcePH68BvX79+kq/dmxsrLaystKTJ0+u0HZnzpypAb1y5cpynT969Ghtb2+vMzIyKjQuIWqalJQUbWdnp0eNGlWp171SjnEjw1J2AjOAycCdWutYpdQhrXV4uRqsBNJltGbaOGMG77z1FpszMigE+gG/e3qS1bcvZ55+moa3327pEC9Na7IPHmTdDz/w144d/HXsGPvT00koLWUF0B/Y4u3NFCA8KIjwli0J79GDZnfcIZOXCiGqtHIMS7EChgN9KJugfDUwW5c3SbEAyTH+9thjj/Hzzz9ja2tLXl4eI0aMYOrUqTRt2pT09HRq1apFWlqapcMUQH5+Pq1atcJgMHDw4EFcXFwq7dovv/wy06ZNIzY2lnr16lVYu8XFxYSFheHg4MBff/11XSvCFBUV4e/vT58+fViwYEGFxSRETfX444/zyy+/kJCQgIeHR6Vc84o5xuWqHld7AM2AmcBDpuf1gUnlba8yHtJzo+b77dVX9aE2bbS2tdUvmD4d8lVKjwwL04l791o6vGty+sgRnb9ihdYffKB/79VLt3Vy0o4X9FBRoCMCArS+6y69Z9gw/ePo0frgokW66OxZS4cuhBBa6+vruQFYAz9c6/FV9SE5Rplt27ZpQNva2mpAjx07VhuNRn3fffed/z22atUqS4cpLrBlyxatlNKjR4+utGvm5+drLy8vPXjwYLO0v3DhQg3ob7755rrOW7RokfyMCnEd9u7dqwH94YcfVto1r5RjyGopomYyGvl90iRe+/xz9ufnU2raXNfKir+GDsXn1VehUSOLhng9SouLid2yhUNr13Jo925e9PTEISqKCZGRTDf9H7YBGtvbE+7ry/dPPol9q1ZkBwXhEhaGtZ2dZV+AEOKmUo6eG1uB2/UF825UN5JjgNFopGXLlhw6dAgom9xxypQpzJ8/n8ceewyAdu3asXv3bkuGKS7h3OopGzdupEePHma/3pw5c3jyySfZsGEDt956a4W3r7Wmc+fOnDp1iujoaJycnK7pvDvvvJP9+/dz8uTJ6+rxIcTNrFu3biQkJHD8+PFK+X9jrtVSugBvAEGU/V2lAK21blDOOM1OEo+bk6GwkK+HDWPWkiWcLCoi27S9t7U1tp6ePDdyJP1fe61armBSlJPDsTVrOLRxI4f27SMiJoaUrCx2FRUBMARYCjRzciK8Th3CmzaldZcu9Hz4YahXD6xuZE5hIYS4tHIUN74HQil7y8o7t11rPd0M4ZmF5Bjwf//3f7zxxhsATJ06lVdeeYVTp04RHh5Obm4uWmvi4+MJCAiwbKDiX/Lz82nRogVaaw4ePIizs7PZrqXLejpRXFxMREQESimzXGfLli10796dt99+m1dfffWqx6ekpBAQEMDEiROZOnWqWWISoiZatGgR999/P0uWLOHuu+82+/XMVdw4AowD9sL5D8bRWp8pV4OVQBIPgcEA8+bBt9/itXUrmabNNkC4gwMjBwxgxNy5UIljTs0iNxeiovj1++/ZvG0bh0+e5FBmJklGI62BfQCOjjzp6EiBiwtNGzSgaYsWNO3alcY9e+Lo5WXhFyCEqM7KUdx4/VLbtdb/V3FRmdfNnmOsWbOGPn36APDRRx8xduxYjEYjvXv3ZsuWLZSUlPDEE08wZ84cC0cqLmfz5s306NGDF154gY8//ths19m+fTu33HILs2bN4tlnnzXbdQDuvvtu1q9fT0xMDLVq1brisdOmTeOll17iyJEjNGnSxKxxCVGTGAwGGjZsSIMGDdiwYYPZr2eu4sZOrXXHG4qskt3siYf4t53ffMPHb77J2lOnOK01bYE9QKG/P+Pc3Bj70Uc06dfP0mFWmIyYGE7v2EGT/Hw4coRHfvqJ7WlpxBkM59dcvBNYWr8+NG3KG5mZ+DdsSNOOHWl6++3UCg1FSW8PIcRVXG9x44LznLTW+eaIydxu5hwjOjqaJk2aoLU+PxQFYMaMGYwbNw6lFI6OjmRnZ2NTDXtJ3kxeeOEFPvnkEzZt2kT37t3Nco1HHnmE5cuXk5iYaPYJTI8cOUJ4eDjPPfccM2fOvOxxWmuaN2+Om5sb27ZtM2tMQtREH3zwARMnTuTAgQO0aNHCrNcyV3HjXcomAVsMFJ3brrXeV64G/27XC/gfEAzEAQ9orTMvc6wbEAn8qrUefbW2b+bEQ1xd2uHDZH7yCU02beLzI0cYZdruCtzi7c0zw4cz6O23q+XwlaspyMggesMGjmzdildGBr1KSiiKjMT7wIG/+4cDnkrxSr16vNSzJ4ZGjfg9P5+m3btTv1s3bBwcLBa/EKJqKUfPjc7AN4CL1jpQKdUSeEZr/ZzZgqwgSqk7gTtDQkKejo6OtnQ4lU5rjb+/PykpKXTp0oWtW7cCEBkZSZs2bXB2diYjI4PPP/+ckSNHWjhacTV5eXm0aNECpRQHDx685rkqrlVKSgqBgYE899xzzJgxo0LbvpyRI0cye/ZsIiMjaXSZ+dZ2795Nhw4d+PLLLxkxYkSlxCVETZKZmUlAQABDhgzhm2++Meu1zFXcuFSfE621vqF1N5VS7wMZWut3lVIvA55a60mXOfZjoJbpeCluiApjKCzkp3HjmL1gAbuzszn3MeInVlaMDgsjtm9fvMeMwa2Gjxs2Ggwk7N7NkY0bidq9myNHj9JbKe49c4bolBQam46zBRra2dHI05OxXbtye8+eFNSrR6qHB/U6dJAJTYW4yZSjuLETuA9YqrVubdpWpZeXv9jNmmM8//zzfPrpp9ja2pKQkICvry/FxcV06tSJ2NhYsrKyCAoKIi4uztKhimu0ceNGbrvtNsaOHctHH31UoW2/9dZbvPbaaxw9epTGjRtf/YQKkJKSQkhICHfccQcLFy685DGjRo3i22+/JSUlBXd390qJS4iaZuTIkcyZM4dTp05ddRjYjTDLUrDmegBHgTqm7+sARy9zXFvgJ+AJ4NNraVuWaRPltfeHH/TQBg10nq+v1qDbmZazq2NlpYc1aqQjfvnF0iFWusKUFL1j9mw996mn9KSOHfU9dero5g4Oeqmdndag15nukR3opnZ2eqCvrx7Xpo0+9vrrWv/xhy46ckQbioos/TKEEGbAdSwFW3Y4O01f91+w7cD1tGHpx82YYxw4cOD88q4LFy48v33y5Mka0N7e3hrQu3btsmCUojxGjRqllVJ6y5YtFdZmcXGx9vf313379q2wNq/VG2+8oQG9ffv2f+0rKCjQnp6e+uGHH670uISoSSIjIzWgp0yZYtbrXCnHuJGeG7WBqYC/1voOpVQzoLPW+ob6oSilsrTWHqbvFZB57vkFx1gB64FHgV5AOy09N0Rlycjgk0ce4Yv16zlaXHx+Nl0PILN1axg4kMInnsChQZVdOMi8jEZITiZx2zZ+X76c6CNHOJ6QwPGMDI4XFrIB6AB8DzwFNLCzo5GHByF16xLSqBEPP/AAnm3aQGAgyDJsQlRL5ei5sQiYDnwKdATGUPa7fYiZQqxwN1uOYTQa8fHxITMzk759+7Jq1SoAtm3bRrdu3WjXrh27du3i9ttvZ926dRaOVlyv3Nxcmjdvjq2tLX/99VeFDE9ZuHAhDzzwAMuWLWPgwIEVEOW1y83NJSQkhEaNGrF58+Z/rNByLq4//viD3r17V2pcQtQ0ffv25dChQ8TFxWFra2uWa5hrWMrvwBxgsta6pVLKhrJPXJpfw7lrAb9L7JoMfHdhMUMplam19rzo/NGAk9b6faXUE1yhuKGUGgGMAAgMDGx78uTJa3uBQlwDo8HAwgkTmPPDDxiys1lrMABgb3q0cnNjcM+eDJs5s8YPYbkW2mhEJyVhFRPD3rVrWbhmDccTEog+c4bjhYXkA0mUddn6wMqK2dbWNPTwoH7t2tQPCqJ+aCh33Xcfto0bg6fnVa4mhLCUchQ3fICPKfvAQgF/AGN0FV6B7WI3W3Fj6NChzJs3DycnJ1JTU3FxcSE3N5dWrVphMBhISUnBYDCQlpaGl6zAVS2tX7+enj17MmHCBKZNm3bD7fXo0YNTp04RHR2NtQU+vPjyyy959tln+fXXXxk0aND57f379+fQoUPExsZaJC4hapKVK1cyYMAAfvzxRx566CGzXMNcxY3dWuv2Sqn9+u/xsX9prVvdQKwopY4Ct2qtk5VSdYCNWusmFx3zA9ANMAIugB3wudb65Su1fbMlHsICsrIonDWLnlOnciA39x+TcQ6wtWV5794YhwzBeP/9MgHnRbTRSMqBA/jl5KBiYli0bBk/7d5NbEYGsYWFZGqNLVBA2UzGL9rZsc7amvoeHtT396d+gwaEhIfT7/77ITgYHB0t+4KEuIlda3FDKfWe1nqSUup+rfWlB8NXEzdTjrFjxw46d+4MlM3P0KNHDwCeeeYZvv76a3r27MnatWsZP348H374oSVDFTfoueee44svvmDr1q3ccsst5W7n4MGDtGzZkmnTpjFhwoQKjPDaGQwGmjdvjtaaiIgIbG1tSUpKol69erzyyivnV/kRQpSf0WgkNDQUDw8Pdu7caZZrmKu4sREYDKzRWrdRSnUC3tNa9yh3pGXtfgCc0X9PKOqltZ54heOfQIaliCoqfvt2vnzxRZbv28eg4mLeNBpZDtwF+FtZ0T0ggGEjR9LzxRdr5CosFSn75EkS9+yhmVIQG8tnS5eyMiqK2Oxs4oqLKaBsiaVY0/FDHRyItrGhgbc39QMCqB8SQrO2bel8550QEAByv4Uwm+sobkQALYC9Wus25o/MfG6WHKO0tBQPDw9yc3N55JFHmD9/PgArVqxg4MCBjBgxgq+//ho3NzcyMjKwkuXDq7WzZ8/SvHlzHBwc2L9/P47l/ODgmWeeYd68eSQkJFi0J8/SpUsZNGgQs2bN4tlnn+X9999n0qRJHDt27LIrqQghrs+nn37K888/z/bt2+nUqVOFt2+u4kYb4BMgHDhE2aol92mtD5Y3UFO73sDPQCBwkrKlYDOUUu2AZ7XWT110/BNIcUNUF/v28ft//8sLf/xBrMFwfr4Oa2Cury+PDh6M8amnsGpTrXP8SqeNRlIPHeLMwYOEmYofr//yC1vj4ojNzSXedK+7AlsArK3pa2tLvp0dgZ6e1KtTh8CgIFq1a8ctffuWzfchs6ULUW7XUdz4AHiasl6Y+ZQNSdHnvmqt3cwa6N9xNKBsaKy71vo+0zZn4HOgmLJepD9cqY2bJce4++67+e233/D09CQlJQU7OzvOnDlDWFgYtWvXBso+pf/pp5948MEHLRytqAjr1q2jV69evPTSS7z//vvXff65JSIffvhhvv76azNEeO201vTo0YOjR49y/PhxOnTogLe39/kljIUQN+7s2bMEBATQv39/FixYUOHtm6W4YWrYBmhCWRJyVGtdUu7GKsHNkniI6sFoMLBu2jTmzJrF5oQENhmNNAT6UzZbbhMHB/qGhzP05ZcJHzzYwtFWb4aCAk7t3k1hTAyhRiPExjJq4UIiU1OJz8vjlMFACfAwcO6vlyClcLOzI9DNjcBatQgMCKB7p0506dULAgMx+PpiI0NfhLik6yhu2Guti5RSv2mtB13t+Mu08S0wEEjTFywdq5TqR9k8HtbAbK31u9fQ1qILihuPAVla62VKqf9pra/4l/rNkGOc+yMXygoYzZuXTbM2dOhQFixYwLRp0xg7diyhoaFERkZaMlRRwZ555hlmz57Nn3/+ed2fxH700UeMHz+e/fv306rVDY1erxA7d+6kU6dODBw4kOXLlzN79myGDx9u6bCEqFHGjx/PV199RVJSEm5uFfs5hbl6btwPrNJan1VK/QdoA0zRWu8rf6jmdTP/kcnwAAAgAElEQVQkHqIaMxjgf/9jwn/+w3cnT3Lmgv+b9kBu/frYdO1KdOfONBw+HCs7O8vFWsMYS0pIiYig9NQp6hUVYYiNZcKCBcSnpnIyO5v4wkLOaM0k4F0gB/AE/K2tCXRyItDTk0A/P+7u2pXOXbtSUrs2aXZ2+LVogbX8O4mb0HUUN/aZhrbO01o/Vs5rdQdyge/PFTeUUtbAMaA3kADsBh6irNDxzkVNPKm1TjOdd2Fx4xXgd631X0qpH7XWD18pjpqeYxQXF+Pu7k5hYSHjxo1j+vTpAPz+++/079+fyZMn88UXX5CRkcHhw4cJDQ21cMSiIuXk5BAeHo6zszP79+/H4RrnDTMajTRu3Jg6deqwZcsWM0d57R588EF+/vlnHB0dSUlJqfA/voS42aWlpaG1Pt+jryKZq7hxUGvdQinVFXgLmAa8prXuWP5QzaumJx6iZslNSeGniRP5ddUqzmRlsd1gAK1xoyyL91WK1t7e3Hn77Tz83nt4BAdbOOKaLS8tDcPJk7hnZpJ15AjTFy0iPimJ+DNniM/N5ZTBwIfAaOAwZeP1rAE/KyvqOjoS4O7O2E6d6NahA5keHhzIz6duWBh1W7fGqVYti742ISradRQ3DlG2rPxbwEsX79daL77G6wUDyy8obnQG3tBa9zU9f8XU3sWFjYvbubjnRqbWerlS6qerLUtb03OM22+/nQ0bNuDv78+pU6ewsrI6/wevq6srd999N1OnTuWee+5h8eJr+mcT1cwff/xB3759mTRpEu++e9WOUMDfxa+qNkwpJiaG0NBQhgwZwvfff2/pcIQQ18FcxY39WuvWSql3gAit9Y8XrpxSFdX0xEPUcEYjrFnDyDFjWHfiBCdLSig27fIDkl1doWlT3nV25p5Jk2jSr58lo73pGA0GDMnJ2J0+zenISH5ZsYLEU6dISE0lMSODxLw8pllZcUdBAauBC/91PJSirp0dX7dsSeewMKIdHVmflUXdhg2pGxqKf/Pm1GrSBCszrRcuREW7juJGV+AR4AFg6UW7tdb6yWu8XjD/LG7cB/Q7N0+XqVDR8QrLxnsDb1PW02O21vod05wbnwKFwNZLzblxsyw3/9tvv3H33XejlCIuLo7AwEDg75U0Vq9ezYABA1BKkZmZiZOTk4UjFuby9NNP8+2337J9+3Y6dOhw1eMHDBjA/v37iYuLw66K9WTcv38/QUFBslSxENWMuYoby4FEyhKBNpSt0LhLa92yvIGamxQ3RE0TtXw586ZOxTchgbGZmUTl5tLMtM8OCLa1pVtwMM+OGUO7kSNBZq23vNxcMiMj2b9lC4nR0STExpKYnExiejpTvb0JzchgdnIyT1/03mwN7PfxoXlgIKusrVmUnU0dX1/q+PvjFxREnZAQ2t56K3aBgSDLDAsLu9bixgXHD9daf3MD1wvmBoobFaGm5hj5+fl4eHhQUlLCO++8w8svvwzA5s2b6dGjB+PGjWPTpk3s27eP9957j4kTL7vAnagBsrOzCQ8Px83Njb17915xeMrx48dp3Lgxr7/+Oq+//nolRimEqMnMVdxwouzDxwitdbRSqg7QXGv9R/lDNa+amngIcU5uQgJzx49n+fr17M/I4LTWaOAx4Hul2OnlxVtWVvTt3p37//Mf/KrAxF7i3wyFhaRERJB48CCJR4+SHBdHclISE+rXxzMjgy8iIvi/xETSjEaMF5yXRtmyVe85ODDXaKSOkxN13N3x8/Ghjp8fY+6/H9t69Ui3t8fa3x+PoCCUFLyEGVxHz43btdbrlVL3Xmp/ZQ9LKQ+l1J3AnSEhIU9HR0dXdPMW1759e/bs2UPjxo05cuQISikKCgpo2bIlpaWlPPnkk/znP/+hefPmHDx4QwvmiWpi1apV3HHHHbzyyitMnTr1ssdNmDCBmTNnEh8fT506dSoxQiFETWa21VKqGyluiJuNobCQlW+9RejhwzSKiGBMbCwzL5qoNMDGhrldutD10UfhvvvAw8NyAYvrUlpczOkjR0iOjCT52DH6+ftjlZbGgk2b+OXwYZJzckgpKCDZYMBIWfc6BTwJzAFsAV8rK3zt7Ql2dWVx375QuzYrzpwh3doa38BAfBs0wLdRI3ybNsVeJlwT1+g6ihv/p7V+XSk15xK7b2RYig1lE4r2pKyX6W7gYa314Wt8CdetJuYYc+fOZdiwYVhbW5Oamoq3tzcAEydO5IMPPuDHH3/ksccew8rKiqSkJHx8fCwcsagsw4cPZ+7cuezYsYP27dv/a39eXh4BAQH069fPLEtBCiFuXlLcMKmJiYcQ1+vwb7+x6KOP2HjgAJHZ2aRrzV6gFXAPsBoIsrWlbd26DBg4kEGvv46TJKzVmjYayU1OxjU3F5KT2bR+PfsOHuR0Whqp6emkZWdjU1zMEnd3SE2lT2Ehay5qozFw1MMDfH0Zc/YsCUpR29MTXx8ffP38aNykSdkSkbVqkWNvj0tAgMwRchO73mEpN3itBcCtgA+QCryutf5GKdUfmEHZqK5vtdZvm+n6NbLnRnZ2Nt7e3pSWlvLtt98ybNgwAPbs2UPHjh0ZPnw4a9euJTY2lrlz5/L4449bOGJRmbKysggPD8fDw4O9e/dib2//j/1ff/01I0aMYOvWrXTp0sVCUQohaiIpbphIcUOIfzMaDFjt3w+LF/PC3Ln8mJpKhmk4yzl5jo44hYTwnYcHbrfcwoD//Ac7FxeLxSzMSGtyU1JIjYoi7fhx0uLiSI2Px76ggMfr1oXUVIZt3syuzEzSios5Y/pZuR1YZ2qiEXAC8FIKH1tbfBwc6OPvz3+7dgUfH76MjsbB05NadeviExSET/36+DZujEudOqCUxV66qDjX0XNj/JX2a62nV1xU5lXTcozQ0FCOHDlC+/bt2bVrF1C2HGy7du04c+YMgwYNYtasWdx2222sX7/ewtEKS1i5ciUDBgxg8uTJTJky5fx2rTWtWrXCysqKffv2oeR9XQhRgaS4YVLTEg8hzMVoMLD5009ZMncucXFx/AaQnY0HkG06xg0IcXSkb7NmTH3nHbjtNrCxsVjMwjIMhYWkHztGcVISgVZWkJ7O10uXkpCUxOmMDNKzs0nPzaWzrS1v29hAejrOJSXkX9TOcGC2rS3a25u2WVl4Ojjg7eKCj7s7Pl5e3NaiBbd17Eipuzu7U1PxCgzEKzgYj6AgbGQC1SrnOoob52YZbAK05+8VU+6kbJLyR80UYoWpiT03ZsyYwbhx47C1tSUrK+v86idvvvkmr7/+OtOnT2fChAk4OTmRnp5+xUklRc02bNgw5s2bx86dO2nbti0AW7ZsoXv37syePZvhw4dbOEIhRE0jxQ0TKW4IcQMMBlZPncqSn35ie2wsJwoLyaVsAss00yFBSuFqZ0dLPz96dO3KwPHj8W/TxoJBiypHa7JPnSL9+HHSY2NJP3WK9MRE6tva0t3dncKUFIasWkV6fj7phYWkl5SQoTX/Ad6k7Get9kVNugNve3szKjiYVBcXxsbG4uXmhpeHB97e3nj5+tKlXTsahodT7OJClrU1nvXrYyvLVZpNOVZL2QwM0FqfNT13BVZorbubK8aKVlNyjLS0NPz8/NBas3TpUu68804ADh8+TOvWrRk8eDBr1qzhzJkzrFq1ir59+1o4YmFJmZmZhIWF4ePjw549e7Czs+PBBx9kzZo1JCQkyLLAQogKJ8UNk5qSeAhRVeRnZBA7Zw5hBw9SvHcvvocPn+/ZcU5HYEedOhhCQni1qIi+999Pj9Gj5dN2cc1Ki4sxnDmDfV4eBUlJbNy4kYyUFDLS0sg4c4aMzEzu8vGhp709xxISGBgVRYbBQKbW51eTmQM8AewAOpu2uQIe1tZ42NryUaNG9Kxfnyil+PLkSTzd3fHw8sLD2xuPWrXo0qkTPsHBFDo6UuLsjIufn6w0cwXlKG4cBVporYtMz+2Bg1rrJuaKsaLUtJ4bQUFBxMfH06dPH1avXg1AaWkpXbp0ISYmhi5duvDbb78xZMgQmShSALBs2TLuuusuXnvtNUaMGEFwcDBjx47lgw8+sHRoQogaSIobJlLcEML8jAYD22fPZuW8eeyMiqK7wcBrhYWsLymh5wXHuQABdnZMbNmSYU88AXfdBQEBFopa1ERGg4HshAQyYmPx1hqPkhKSYmJYsnHj+aJIZk4OWXl5vFqnDh2MRn5PSmLI6dPkXNTWJqA78CPwCGUzVLorhae1NR52dsxv2ZKmdeuytbCQ31JScHNzw93DA3cvL9y8vOjTvTvOfn5kW1lRZGeHe716NXr1mXIUNyYDDwBLTJvuBv5njqVbzaUm5Bj//e9/mTJlCo6OjuTk5GBjGmp4bhjKxIkTef/99/Hx8SE1NRUrKfAJk6FDh7JgwQLuuusulixZwvHjx2nQoIGlwxJC1EBS3DCpCYmHENVVblwcv0yZwoYNG9iflMTJwkJygAnAB8DnwBjAx8qKRi4utA8Joc+993LbmDEyeamodIaiInISEsg6dYqsxEQaubnhWlxMZGQkK3fuJCsri6ycnLLiSH4+s+rVI7CggFmJiUzIyaHgovbigXrAFOC/pm12lBVI3Gxs2NOsGR5eXvyYk8MfWVm4u7jg5uqKu7s7bp6ePNW/P1bu7pzIzSVHa1x9fXHz88O1Th3s3dyqXC+S8qyWopRqA3QzPd2std5f8ZGZT3XPMeLj4wkKCgL4xwoXMTExNG/enFtvvZVNmzZRUFDAvn37aNWqlSXDFVVMRkYGYWFhpKSkMHDgQJYtW2bpkIQQNZQUN0yqe+IhRE1jNBgwbN+O3bp1zP/lFyZHRZFSWkrxBcd8DTzl4MDnDg78z2ikZXAwXW+7jV6jR+MVEmKp0IW4opL8fHISE8lOTCQnNZUwHx9s8/LYd+AA2yMiyM7KIjs7m+zcXHLy8pgbGopdbi5To6P5Mj2dHKORHNOwGhugGFDAk5QNsbmQF3DGywtcXZmUl8eWwkJc7e1xdXDAzcmJAE9P3uzdG1xdWXnyJGeMRlw9PXHz8aFDx464dO1a4a+/MpeCtbSaMCxFa03t2rU5ffr0P4abaK3p2bMne/fupVmzZuzYsYPx48fz4YcfWjhiURUtW7aMIUOGsGLFCm699VZLhyOEqKGkuGEixQ0hqoeM48f5ffp01q1dy1RHR/xSUhh4+jQrLnq/sgGOeHnRsEEDFjk7c6ZuXfo88wz1u1ebOQiFuCxtNJKbksLZpCT8nZzg7FkO/vUXx2NiOJuZydmsLHJycqCoiFfDw+HsWd7es4eNKSmcLSribEkJZ0tL8QX2aA1GI7cBGy+4RqSnJ6EZGRUe+81U3DinOucYo0aN4vPPP8fd3Z3MzMzzS3d+9dVXPPPMMzz++ON89913NGjQgJiYGAtHK6qywsJCWT1HCGFWUtwwqc6JhxACsuLiWPvJJ2xZt46DcXHE5+YSbW2NVXExjYDjpuOsADelCLSzY3///lh16kRSeDh+ffpgJcvVipuR1lBQQHpcHJmJiZxNS+NsejodGjfG8Y47KvxyUtyoPiIjIwkLCzv/fWhoKAAJCQmEhYXRokULdu7cidFo5MSJEwQGBloyXCGEEDe5K+UYkuULIaoNj+Bg7vvwQ+67eEdxMfO//poV//sf+44c4VhWFsklJZwoKsJqyRJYsoTmQAbgDNS2sSHYxYUuISG8OXo0dO8O9etX+usRotIoBU5O+DRrhk+zZpaORlQRWmtuueUWAJ5//vnzhQ2tNSNHjqSkpITU1FRKSkr48MMPpbAhhBCiSpOeG0KImstohEOHYO1aRn7+OdsSE0koKiJba0oxzVVgOtQV0ICPtTVBzs6E1q1L3x49uOeFF6BJE6hiEzYKUZXdTD03qvOcGw899BA//fQTvr6+pKamnt++YMECHn74Yfr168eqVato27Ytkj8JIYSoCmRYiokUN4QQ52QcP07CihW0SE2FiAharVnDqeJicrTGYDomBIgGjIAH4GFtTT1HR5rUqUObli3p++ijNLrzTil8CHGRm6m4cU51yzF27NhB586dUUoRHx9PgGkp7tOnT9OsWTMCAgI4ePAgtra2pKSk4OHhYeGIhRBCCBmWIoQQ/+IVEoLXmDHnn/91wb6chAT+nDMHu+hoyM0lLSoK+6NHSSkt5VRuLtuio5kTHU23RYvYDMTa2dHVYMDfwYH6Xl40bdCA1p060W3oUHxMY9mFEKKqKC0tpWfPngC88cYb5wsbAC+88ALZ2dlYW1tjNBr57rvvpLAhhBCiWpCeG0IIcR3y09PZ8d13bF+1itYFBfTPz2f1iRPclZ39jyVsAYYC3ynFShsbni4tpY69PUGenjQNDqZlx470GjYMr+bNLfEyhDAr6blRtfXr14/Vq1cTHBxMbGzs+e1Lly5l0KBBtG/fnt27d9O3b19WrVplwUiFEEKIf5JhKSbVKfEQQlQ/hsJCIpcvZ9fSpRw8cID77O3pnpfHlydP8kJe3r+KH1OAyUrxsbU17xmN1La3J9jdncZBQbTq0IEBTz2FW3i4DHsR1Y4UN6quVatWcccdd2BlZcXp06fx8vICICsri7CwMJycnDh+/Diurq6kp6djZ2dn4YiFEEKIv8mwFCGEqAQ2Dg60uO8+Wtz3z/VcnjE9jAYDUStWsGvZMg7u28d9rq6QkUHaiRNk5OeTXFDAXwUFkJICO3fy2yefcBfwvJUVC7TGy8aGOk5OBHl5ERIczNhhw3Dr2hWCgqQAIoSFXDChqKVDuSZDhgwBYObMmecLGwAvvfQSycnJuLi4AGW9OKSwIYQQojqRnhtCCFFFGA0GYjZsYOfixRzYs4fJPj54JCXxbHQ08wsKKKBsctNzEgF/oAewHXBWCi9ra2o7OBDo6cn8Rx7BJjyctIAAvDp2xMbBwRIvS9yEpOdG1WVnZ0dpaSmlpaXnt61bt45evXoREhLC8ePHeeKJJ5gzZ44FoxRCCCEurVoNS1FKeQH/A4KBOOABrXXmJY4LBGYD9ShbwbG/1jruSm1Xl8RDCCEuJz89nQO//krExo2M8PWFEyd47s8/WZ6ZSVZpKfnAuT9Zzr27BwMnKeuq50TZqi8NHB3Z0KMHBAbyS24uNvXqEd6zJ/W7d8fKRjr1iRsjxY2q6+LiRl5eHs2bN6ewsJDk5GT8/PxITEzESnqDCSGEqIKqW3HjfSBDa/2uUuplwFNrPekSx20E3tZar1FKuQBGrXX+ldquLomHEELcCENhITGrV9MkOxuiopi0eDGbExNJLSwk02gkT2tsgTzT8e5AzgXnWwOBSnGidm3w9OSJjAxKHBwI8venUdOmNO3UibCBA3Hz96/01yaqByluVF0XFzfGjRvHjBkzsLOzo6SkhIiICMJklSchhBBVVHUrbhwFbtVaJyul6gAbtdZNLjqmGfCV1rrr9bRdXRIPIYQwO6MREhLg8GHmzJvH/sOHOZWWRvLZs5wuKqI2sM3GBoqKsNUaw0Wn1wLSlAI7O4KKi7GyssLH3h5fZ2fqeHpya1gYjw4aBCEhZNWujUc1mY9AVAwpblRdFxY3duzYwS233IKvry+pqam8+uqrvP3225YOUQghhLis6lbcyNJae5i+V0DmuecXHHM38BRQDNQH1gIva61LL9HeCGAEQGBgYNuTJ0+a+RUIIUTNkp+eTuTKlUT++SfHo6KIS0ggSGvecnGBjAw8kpLIg38UQJoCUaZttqZtNoA9ZXODDHZ15fMmTcj38mJMbCz1/P0JbtSIhm3aENqzJ16NGlXuixQVSoobVde54kZ+fj6tW7cmOTmZrKwsmjRpwpEjRywdnhBCCHFFVW61FKXUWsDvErsmX/hEa62VUpeqvtgA3YDWQDxlc3Q8AXxz8YFa66+Ar6As8bihwIUQ4ibk5ONDu6FDaTd06CX3Z5m+Gg0GUg4e5NjmzTinpYG1NYUnTtB39WrS8/PJKC4mx2jkrNak5uTA7t0coWzyJI4dg40bz7fZH1hhY8M+GxvuKCrCxdoaD1tbPB0cqOXqyhOtW9P3llvI8vQkqrSU+rfcgm+zZjJfiKh01W21lHMfak2ZMoWoqCisra2xsbFh/fr1Fo5MCCGEuDEWyQK11r0ut08plaqUqnPBsJS0SxyWAPyltT5hOudXoBOXKG4IIYSoHFY2Nvi3aYN/mzbnt7kAqy53gtFIsxMnWPfbb5w4cIDYmBgSU1NJzsriXmdnsLYmNT2ds4WFnDEYOGEwQEEBZGbiHh9P399+YwHw3IUxUNZTZKq9PeO9vFiuFG9lZ+Pt6EgtNzf8vL0JqFePe/r2JaBdOwz+/lj5+EhRRJSb1noZsKxdu3ZPWzqW6/Huu+/i5uZGTk4On3/+Of4yh44QQohqripmc0uBx4F3TV9/u8QxuwEPpVQtrfVp4Hag6vcFFUII8TcrKxxCQrh9wgRuv8whdwAXzhSdk5DAia1b8SsshLNn6bx3L49v3UpadjZnCgrILi4mp7QUXysryMhgZ1ERuwDy8iA9HU6cgN27sV68mOeA0cCXgKLsF6Id4KAUi3x8uNXHh28KC1mYk4OXszM+np7UrlULv7p1uf/OO3Fr0gRjYCBWbm5mvElCVDyj0YiDgwM5OTl07tyZkSNHWjokIYQQ4oZVxTk3vIGfgUDKVi98QGudoZRqBzyrtX7KdFxv4EPKctK9wAitdfGV2q4u42GFEEJUHKPBQPrRo8T8+SfxERHEnzjBg35+BBYUMD8igk9jY8kpKSG3tJR8o5FCrVljbU1no5EBWrPyEm3uBdoAtwEbKesxcm5OEXuliPLzw8fdnTeys9lcUIC7gwMeLi54ubnh4+PDpMGDsfLzI14prPz88GveHBsHh0q7J+Ymc25UXdbW1hiNRpRSODg4kJaWhouLi6XDEkIIIa5JtZpQ1JyqS+IhhBCiajAaDGTFxRG3axcJhw+THBdHSmIiLzVqhNPZs7y7Zw+/JCdz9lxxRGuKtOaMjQ0ORiMtjUYOXqLdc795GwInLthuBTgBZ52dwcGBQbm5RBmNOFtb42Jnh4udHfVcXfmqZ0/w8GB+XBxnbW3x9vPDJyAA3/r18WvWDJ9GjcDKyty357KkuFF1lc3VXmbx4sXcc889FoxGCCGEuD5VbkJRIYQQojqwsrHBKyQEr5AQ2lxi/8umx+UcAAqzskg9dIikyEhSYmLISE6Gxo3hzBme3rSJP5OSyCks5GxxMbkGA7Zag4MDFBVxrKiIE0BpSQkUFgJgl57OV7NnAzAKyLnomk5Anun7WkA2fw+5sVOKZra2bAwKAicn8t3dcdq0qZx3R1RngwYNksKGEEKIGkWKG0IIIYQZOXh4ENS1K0Fdu/5r35UKI1C2nO45RoOBjNhYsk+cAEdHSEvj6z/+IObkSTIzM8nKySEnPx9vpSAwEPLyaHrkCPHFxRQajRQDeVqTWlwMMTFgNFKsFE4V+WJFtbFo0SJLhyCEEEJUKCluCCGEENWAlY0NPo0alQ05MXngvvuueM6Wq7TpYTRWQGSiOrKRFYKEEELUMJYbkCuEEEIIy7LgvBxViVKqgVLqG6XUoittE0IIIUTVJVmNEEIIIaotpdS3Sqk0pdShi7b3U0odVUodV0pdcQSQ1vqE1nr41bYJIYQQouqSPolCCCGEqM7mAp8C35/boJSyBj4DegMJwG6l1FLAGnjnovOf1FqnVU6oQgghhDAXKW4IIYQQotrSWm9WSgVftLkDcFxrfQJAKfUTMEhr/Q4wsHIjFEIIIURluKmKG3v37k1XSp2s4GZ9gPQKblOUkXtrHnJfzUfurfnIvTUPc93XIDO0eT3qAqcueJ4AdLzcwUopb+BtoLVS6hWt9TuX2naJ80YAI0xPc5VSRyvsFZQx28+9UsoczVYn8p5iPnJvzUPuq/nIvTWPSs8xbqrihta6VkW3qZTao7VuV9HtCrm35iL31Xzk3pqP3FvzkPtaRmt9Bnj2atsucd5XwFfmikv+fcxH7q35yL01D7mv5iP31jwscV9lQlEhhBBC1DSJQL0LngeYtgkhhBCihpLihhBCCCFqmt1AI6VUfaWUHTAEWGrhmIQQQghhRlLcuHFm644q5N6aidxX85F7az5yb82j2t9XpdQCYDvQRCmVoJQarrU2AKOB1UAU8LPW+rAl4yynav/vU4XJvTUfubfmIffVfOTemkel31elta7sawohhBBCCCGEEEJUGOm5IYQQQgghhBBCiGpNihtCCCGEEEIIIYSo1qS4cQOUUv2UUkeVUseVUi9bOp6aQClVTym1QSkVqZQ6rJQaY+mYahqllLVSar9SarmlY6lJlFIeSqlFSqkjSqkopVRnS8dUEyilxpneCw4ppRYopRwsHVN1pZT6VimVppQ6dME2L6XUGqVUtOmrpyVjFH+THMM8JM8wL8kxzENyDPOQHKPiVJUcQ4ob5aSUsgY+A+4AmgEPKaWaWTaqGsEATNBaNwM6AaPkvla4MZRNsCcq1sfAKq11U6Alco9vmFKqLvAC0E5rHQ5YU7bqhSifuUC/i7a9DKzTWjcC1pmeCwuTHMOsJM8wL8kxzENyjAomOUaFm0sVyDGkuFF+HYDjWusTWuti4CdgkIVjqva01sla632m789S9uZd17JR1RxKqQBgADDb0rHUJEopd6A78A2A1rpYa51l2ahqDBvAUSllAzgBSRaOp9rSWm8GMi7aPAj4zvT9d8DdlRqUuBzJMcxE8gzzkRzDPCTHMCvJMSpIVckxpLhRfnWBUxc8T0B+OVYopVQw0BrYadlIapQZwETAaOlAapj6wGlgjqk77myllLOlg6rutNaJwDQgHkgGsrXWf1g2qhqnttY62fR9ClDbksGI8yTHqASSZ1Q4yTHMQ3IMM5Aco1JUeo4hxQ1RJSmlXIBfgLFa6xxLx1MTKKUGAmla672WjqUGsgHaALO01q2BPKR7/w0zjc0cRFli5w84K+WlmwcAACAASURBVKUetWxUNZcuWxte1ocXNwXJMyqW5BhmJTmGGUiOUbkqK8eQ4kb5JQL1LngeYNombpBSypayhOMHrfViS8dTg3QB7lJKxVHWxfl2pdR8y4ZUYyQACVrrc5/+LaIsERE3phcQq7U+rbUuARYDt1g4ppomVSlVB8D0Nc3C8YgykmOYkeQZZiE5hvlIjmEekmOYX6XnGFLcKL/d8P/s3XlcVGX7+PHPYRgYlmEHAREX3MMtMdsezcjdTM2nTFMzK6uvrZbtWpal6aOVS1lmVpZZapm7j6al2VO5hrnkjgiyrwMDs9y/P8D5iaKCAgN4vV+v++Us59znOgOM97nOvdBM07TGmqa5UTwBzY9OjqnW0zRNo3hM4QGl1Axnx1OXKKVeUkpFKKUaUfz7+pNSSjLUlUApdQY4pWlai5KXYoH9TgyprogHbtQ0zbPkuyEWmUStsv0IjCx5PBJY4cRYxP8nbYwqIu2MqiFtjKojbYwqI22MqlftbQzXqj5AXaWUsmqaNhZYT/HsuguUUn87Oay64BZgOBCnadqektdeVkqtcWJMQpTHE8BXJRcix4BRTo6n1lNK/a5p2lJgF8UrHOwGPnZuVLWXpmmLgduAIE3TEoCJwBTgW03TRgMngXucF6E4S9oYVUraGaI2kjZGJZM2RuWqKW0MrXj4ixBCCCGEEEIIIUTtJMNShBBCCCGEEEIIUatJckMIIYQQQgghhBC1miQ3hBBCCCGEEEIIUatJckMIIYQQQgghhBC1miQ3hBBCCCGEEEIIUatJckMIUe00TfPTNO3xksfhJUtxCSGEEEJcFWljCHHtkqVghRDVTtO0RsAqpVS0k0MRQgghRB0ibQwhrl2uzg5ACHFNmgJEaZq2BzgMtFJKRWua9gAwAPACmgHTATdgOFAI9FFKZWiaFgXMAYKBfOBhpdTB6j8NIYQQQtQw0sYQ4holw1KEEM7wInBUKdUeeP6896KBQUAnYDKQr5TqAPwGjCjZ5mPgCaVUR+A5YG61RC2EEEKImk7aGEJco6TnhhCiptmslMoFcjVNywZWlrweB7TVNM0buBn4TtO0s/u4V3+YQgghhKhlpI0hRB0myQ0hRE1TeM5j+znP7RR/Z7kAWSV3ZIQQQgghykvaGELUYTIsRQjhDLmA8Up2VErlAMc1Tfs3gFasXWUGJ4QQQohaS9oYQlyjJLkhhKh2Sql04FdN0/YB066gimHAaE3T9gJ/A3dVZnxCCCGEqJ2kjSHEtUuWghVCCCGEEEIIIUStJj03hBBCCCGEEEIIUatJckMIIYQQQgghhBC1miQ3hBBCCCGEEEIIUatJckMIIYQQQgghhBC1miQ3hBBCCCGEEEIIUatJckMIIYQQQgghhBC1miQ3hBBCCCGEEEIIUatJckMIIYQQQgghhBC1miQ3hBBCCCGEEEIIUatJckMIIYQQQgghhBC1miQ3hBBCCCGEEEIIUau5OjuA6hQUFKQaNWrk7DCEEEKIOm3nzp1pSqlgZ8dRnaSNIYQQQlS9S7UxrqnkRqNGjdixY4ezwxBCCCHqNE3TTjo7huombQwhhBCi6l2qjSHDUoQQQgghhBBCCFGr1cjkhqZpCzRNS9E0bd9F3tc0TftA07Qjmqb9pWna9dUdoxBCCCGEEEIIIWqGGpncABYCvS7xfm+gWUl5BPiwGmISQgghhBBCCCFEDVQj59xQSv2iaVqjS2xyF/CFUkoB/9M0zU/TtDClVFK1BCiuiLWggLyUFPIzMopLVhb5WVlEh4XhrRSHjx7l17/+Ij8vjwKTCRedDm8fH+7u0oWA0FCSioo4bTJhDArCOzgY75AQvEND0bm5OfvUhBBCCCGuabm5uXz11Vd4e3vToEEDIiMjqV+/Pm7SThNCVJMamdwoh/rAqXOeJ5S8JsmNamK3Wvlr2TI2LVrE7UAHg4E/EhMZ//ff5FutxcVmI99u51uDgS5FRSyzWhlSRl3/AzoDW4HRZbzf5csvCQAWA+PKeD/e3Z0GPj7MsduZZzLhrddjdHPD290db4OBD++4A09/f35KSSEuK4vAevUIql+foMhIgpo0oWH79mheXqBplfb5CCGEuDZomnYncGfTpk2dHYoQTvPHH39w3333cezYsVKva5pGaGgokZGRjoTH2X/PPg4JCUGTNpgQohLU1uRGuWma9gjFQ1eIjIx0cjS1mFLk7t3L19OmsemXX9h8+jRpSgGwLDCQDqGhuCiFAgI9PGjg5oanuzueBgOBMTEQGcn1+fnMPH4cTy8vPI3G4uLjQ4sOHaBePe622bjdYsHTzw8Pf39sVit5KSnUMxjAbGbg4cM0O3CAvKys4pKTQ25uLgFt20JREUFxcTQ9epTcwkJyCgtJzMsjz2rFdelSMJn4vrCQ2eedlg6wABgMvKjXs95qJcjDg2CjkSBfX+qHhPDCwIEQHMwBkwmr0UhQVBSBzZrh5uVVvT8DIYQQNY5SaiWwMiYm5mFnxyJEdbPZbLz77rtMmDCB8PBwfvrpJ8LDwzl16hTx8fHEx8c7HsfFxbF69WoKCgpK1eHu7k5ERESZiY+z/xqNRiedoRCiNtFUyQVqTVMyLGWVUiq6jPfmAVuUUotLnh8CbrvcsJSYmBgly7SVX9KePWyaNw//I0fo+88/ZMbHEwSE63TENmpE7B13cPtDD1E/JsbZoZaLJT+f3KQk0o8dI+34cdJOnSI3JYWhUVGQlsasX37hvydOkGoykVZYSJrViq9SnCjZvzew7pz6fIAYDw82tW0LwcFMT04mTaejXr16hNSvT0hkJJGtWtHihhsgKAhc63wuUQghANA0badSqnb851BJpI1RO9jtdiZMmEB+fj5PPvkkjRo1cnZItdbp06cZPnw4mzdv5t///jfz5s3D39//kvsopcjIyCiV9Dj38alTpzh9+jR2u73Ufu3atWPFihU0bNiwKk9JCFELXKqNUVuTG32BsUAfikc0fKCUuuFydUrD4zKyslg9fTrrfvyRTYcOcaCoCIC79Hp+6N8fYmM52bIlkV27ornU1LloK5fNbEaXnQ2pqez89VeO//MPqYmJpKWkkJaejq/NxqTwcEhJofuBA/xcWFjcE6REV2ALgKZxi05HposL9Tw9CTEaCfHz48YWLRjWvTuEhLAzIwPfhg0JadECY3j4NfMZCyHqHkluiJrIarXy4IMP8uWXX+Li4oKmadx7772MHz+edu3aOTu8WmXFihU8+OCDmM1mZs2axahRoyptaInVaiUpKcmR7Dh+/DhTp07Fy8uL9evXEx19waWBEOIaUuuSG5qmLQZuA4KAZGAioAdQSn2kFX97zqZ4RZV8YJRS6rItCml4lFaQkcGv8+dzaONG/i8rC3bupJvdzh/AvwIDie3cmdihQ2l3zz3o9Hpnh1srKLudrJMnSTl4kJSjR9Hn5nKjry+kpDB+xQqOpaaSnJtLSkEBKVYrfZTiq5J9fYDckscGIESnY1RICK936IAKCuLlQ4cIDAoiJDyckMhIQho3pmGbNgQ2bw4Gg3NOWAghyiDJDVHTFBUVMWzYMJYuXcpbb73FiBEjeO+99/j444/Jy8ujZ8+ejB8/nm7dusn8D5dQUFDAuHHj+PDDD7n++uv5+uuvadGiRZUfNy4ujp49e1JQUMCqVau45ZZbqvyYQoiaqdYlN6rKNd/wsFrZ/913LP/0Uzbt3Mn2rCyKKL6QTr/xRjx79CChTRtCevXCzdvb2dFeE+wFBbhkZKDOnGH92rWkxMeTkphIcnIyyRkZdPX0ZLSbG3nJyQSdPk3hefu/DEwGMoxGOpvNhBgMBHt5EeLnR0hgIHfecAOdO3XC7OfHkYICgps3J7BpU1wlGSKEqEKS3BA1idlsZvDgwaxevZr//Oc/PPvss473MjMz+eijj3j//fdJTk4mJiaG8ePHM2jQIHQ6nROjrnni4uIYMmQI+/fvZ9y4cUyePBl3d/dqO/6JEyfo0aMHp06d4rvvvqNfv37VdmwhRM0hyY0S13TDY9s2GDSImampPAu09/AgtnVrYvv351+PPIJ3aKizIxSXoex28pKSHL1CUk6cIMrNjWi9npQTJ3h6wwZSzvYKKSoi1W5nNvAY8BdwtsOtBvhrGsF6PdOiorizRQtOGAx8Eh9PUHAwwWFhBEdEENyoEc2vvx7vhg3B09Np5y2EqH0kuSFqCpPJxIABA9i4cSNz587lscceK3M7s9nMl19+ybRp0zh8+DBRUVGMGzeOBx54AA8Pj2qOumZRSjFnzhyee+45/Pz8+OKLL+jRo4dTYklJSaFPnz7s2bOH+fPn88ADDzglDiGE80hyo8S12vBY99ZbZE6axH2NG5P54ovYbr6ZoGroQiicy261YktNRZ+VRebRo2z86SdSEhJISU4mNT2dtOxsngoO5haLhU0JCfTMysJ2Xh1rKJ5IdbW7O/9ntRLs7k6QpyfBPj4E+/nxZGwsDZs357SmcaKoiMCGDQls3JiAqCh0sq69ENcsSW6ImiAnJ4e+ffuyfft2FixYwMiRIy+7j81mY8WKFUydOpU//viD4OBgnnzySR5//HECAgKqIeqaJTU1lQcffJBVq1bRp08fPvvsM0JCQpwaU25uLoMGDWLjxo28++67PP/8806NRwhRvSS5UeJabHh89+yzDJs5k/aenvx25Ai6sDBnhyRqKLvVSvapU6T+8w9px4+TGh/PzcHBBJvN/O+vv5j7xx+k5uSQlp9PamEhqTYbvwFtgbnA/51Tlwb4aRp/RkYSFRrKCquV5ZmZBPr5ERgQQGBICIGhodzZoweGsDBMnp64hoTg7uPjlHMXQlQuSW4IZ8vIyKBnz57s2bOHr776invuuadC+yul2Lp1K++++y6rV6/Gy8uLhx56iGeeeeaaWbFj48aNjBgxgvT0dKZNm8YTTzxRY+YjKSwsZOTIkSxZsoRx48bx7rvv4iITsQtxTZDkRolrreGxYNQoHl64kJuMRlbFxeF3jfxnLKqPMpnQ0tNJPHCAuB07SE9MJD05mfS0NNIyMpjcrBm+ubnMPnCAaUlJpNtsmM7ZPwcwAs8D0wEvIEinI9DNjUAPD9becQe64GBWp6dz2GwmIDgY/3r1CAgPJzAykpYxMeDvD9JLRIgaRZIbwplSUlLo3r07Bw8eZOnSpdx5551XVV9cXBzTp0/n66+/RinFfffdx/PPP0/btm0rKeKapaioiFdffZVp06bRqlUrFi9eXCNXk7Hb7Tz11FPMnj2b4cOH8+mnn6KXCfBFDbBq1Sp+++03nn/+efz8/JwdTp0jyY0S11LDY+aAATy7YgU9AgNZvn8/Xk7uQijEWeasLDKOHiXt+HHaBASgZWSwZft2tsfFkZaRQXpWFum5uZjMZjbXqwcZGQzLyODr8+oJAlJLHg/X6fgZCNDrCTAYCPDyomlQEFN69oSAANYlJmJycyMgLIyAiAj8GzQgoFGj4rlmashdKCHqktqe3NA0LRL4AMgA/lFKTbncPtdSG6MmO336NLGxscTHx7NixQq6d+9eaXWfOnWq1AorvXr14oUXXqBr1641pkfD1Tp8+DD33XcfO3fuZMyYMcyYMQPPGjzvllKKyZMn89prr9GnTx++/fZbvLy8nB2WuAYdPnyYV155hTVr1mAyFd/K0zSNLl26MHv2bFnCuBJJcqPENdHwUAreeIPX3niDA/Xr89X+/dLVX9R6Z4fMZJ48SUZ8PBmnT1OUmUm/Ro0gI4MPt2zhj5MnycjLIyM/n4zCQsKUYiNAURE3Af87r84bgN91OvDzo39BAUmAn8GAv6cnft7edIyMZMxtt4G/P6uPHcPV1xe/0FD8IyLwi4jAv1Ej9NKAEqJMzkxuaJq2AOgHpCilos95vRfwPqAD5l8qYaFpWl/AXym1SNO0JUqpey933GuijVHDnThxgtjYWFJSUli9ejVdunSpkuNkZmby4Ycf8v7775OSkkKnTp0YP348AwcOrLUrrCil+Pzzzxk7dixubm7Mnz+fQYMGOTuscvv444957LHHuOGGG1i9evU1OT+KqH75+fm89dZbLFy4kKSkJAAMBgM9e/akffv2fPjhh6SkpADQtGlTJk+eXOEhcgDZ2dls3bqVX375BRcXFzp27EjHjh1p3LhxnUmsVoQkN0rU9YaH3Wrl1COP0PCzz1APPID9o4/QVeMSXULUOEpBQQFJBw6Qevw4GQkJZCQmkpGcjK/dzr8bNoTMTJ7etIl/MjLIKiggs6iITKuVLsC3djsAoUDyeVUPARZ7eoK/PzdmZKDX6/EzGPD19MTX25vbmzXj7htvRPn48O2BA/gGBeFbrx6+oaH4hocT0LAhHkFB0nNE1ElOTm50AfKAL84mNzRN0wH/AN2BBOBP4D6KEx3vnFfFg4ANWAoo4Eul1GeXO25db2PUdP/88w+xsbHk5eWxfv16brjhhio/ptls5osvvmDatGkcOXKEJk2a0KFDB+rVq0e9evUICQm54LG3t3eNuxjJysriscce45tvvqFr164sWrSIiIiICtVht9sZNmwY3377LS4uLnh6euLj40NQUBD16tUjIiKCxo0b07x5c6677jqaN2+Oq6trpZ7H8uXLGTp0KFFRUaxfv77C5yDqvoKCArKyskqV3NxcGjduTJs2bTAYDJetw263s3jxYqZNm8Zff/2FUgoXFxc6dOjA+PHjL0herF27lvHjx7Nv3z4A/Pz8GDt2LBMnTrzo30BOTg5bt25ly5YtbNmyhV27dmG323Fzc0MphcViAcDf35/rr7/ekezo2LEjTZo0qXHfMZVNkhsl6nLDw2o283CbNqw6coS4hx8m9KOPQCZWEuLqmM2QmcnBXbvISEgg68wZMpOTyUxLo4nBQJ+QEFRGBv9ev56M/HyyCgvJtljIttkYBUxTinyK5xI530vA2zodmUYjHfPy8NPr8XV3x7ckQTI0Opqe7dqR4+7Ot//8g09AAD6BgfgEB+MTEkJk8+b41K8PRiPU0juFou5y9rAUTdMaAavOSW7cBLyulOpZ8vwlAKXU+YmNs/s/B/yhlPpF07SlSqnBlztmXW5j1HT79u3jjjvuwGaz8d///pf27dtX6/HPrrDyySefcPLkSZKTk8nIyChzWw8PjzKTHmW9FhAQUOWTZG7fvp2hQ4eSkJDApEmTeOGFFyrc++T333+nd+/eZGZmVmg/FxcXDAYD3t7e+Pv7ExISQnh4OI0aNSIqKopWrVrRpk0bfH19y13nli1buOuuu/D19WX9+vW0atWqQjFdraysLLZv346/vz/h4eGEhYXhVoPmBbNYLCQlJZGQkEBCQgJnzpzhlltuoWPHjs4OrVwKCwsdSYnMzMwLEhWXK4WFhRetW6fT0bp1azp06OAo7du3d/z+7dmzh1deeYVNmzY56omIiGD06NG8+OKLl02MHD16lCeeeIINGzZgs9nQ6/XcddddzJo1Cy8vL7Zt28bmzZvZsmULO3fudCQzbrzxRm677Ta6detG586dcXFxYd++fezcudNR/vrrL0fCw8/Pj+uvv75U0iMqKuqi3yX5+fmsWrUKk8nk2EbTtAseV/Q9FxcX9Hp9pQ4NPEuSGyXqasOjMCeHYa1asSwxkddvu40JmzahSWJDCOdSCvLzsWVkcGj3brLPnCE7JYXs1FSy09Np5+dHZ19f0s+c4amffiI7P59ss5msoiKyLRZecXPjEbOZv5WirFGanwAPUXz7uRvg4+KCr6srPno9Pu7uTIiO5l9NmnDEbufzY8fw8fHBx8/PkSTp3LEjQQ0akO/qSp6mYQwLw+DnJ98dolLUwOTGYKCXUuqhkufDgc5KqbEX2T8aeB1IA/KUUs9dZLtHgEcAIiMjO548ebJyT0Rc1q5du+jRowdubm5s2rSp2i9mL8ZisZCamkpycjLJycmkpKSU+Tg5OZnU1FRstvMXYy++2AoODiYoKAhvb2+8vb3x8vK64N+yXrvYv2fvFNtsNiZPnsykSZOIjIzk66+/5sYbb6zQOdrtdh599FE++eQTx2sNGzYkJCSE5ORkMjMzyc/PL/PcgFJ3ly91PaJpGj4+Prz22muMGzfusnHt2bOHXr16YbFYWLNmDZ07d67AWVXc2aTawoUL+eGHHy64gA4ODiY8PJz69esTHh7uKOc+Dw4OvuohTWazmdOnTzsSF+eWs6+fOXOmzM/6pptuYuzYsQwePLhGJWMA0tLS+Pzzz/nkk084dOjQJbfV6/X4+/vj5+fnKOc/P794enpy+PBhdu3axe7du9m9ezdnzpxx1Onr60tBQQFFRUUAeHl50b9/fyZPnkzjxo0rfD75+fmMGzeOhQsXYjabL4j/bDLjtttu46abbsLDw+OydRYVFZWZ8Dgbs6+vLx06dKBjx460atWK+Ph4tm3bxp49ey6aiK0sVZFrkORGibqY3DClpDCodWs2pKczc8AAnv7+e2eHJISoLHY71sxMzhw9Ss6ZM2SfOUNOaio56el0DAmhibs7x0+eZPbWreTk5ZGTn0+O2Uy22cw7QUF0tdlYk55Ov/x8zv+m3wTcDiyheIgNgCvgrWkYXVxY2bAh7YKD2VBUxEdnzmD08MDo5VVcjEbG3H47gaGhHM3P52h2NsagIHxCQvAODsY7JAT/iAhcytG9U9RNtT25cSXqYhujpvvtt9/o3bs3vr6+bNq0iaZNm5ZrvxUrVmC32xk4cGAVR1g+drudjIyMMhMfKSkppKWlYTKZMJlM5OXlXfC4Itzd3fHy8sLFxYW0tDSGDh3K3LlzK9Q7Aop7y/To0cMxzwDAs88+y3/+858LtjWbzezevZs9e/awf/9+jh49SkJCAqmpqWRnZ2M2my96AaTT6dA0DavVCkBAQADTpk3jwQcfvGR8R48epWfPniQlJbFs2TJ69epVofMrj7///pvPP/+cRYsWkZSUREBAAEOHDmXQoEGYzWYSExNJTEzk9OnTpR4nJydfcL46nY7Q0NBLJkB0Ot0lkxdpaWkXxOjr60tERMRFi5+fH8uWLWPOnDkcPnyY0NBQxowZwyOPPEJ4eHilf2bldXYp5nnz5rF06VKKioq45ZZb6N27NwEBARdNVBgMhqsekmG325k2bRqzZ88mISGhzG3CwsJK9fC4/vrradSo0UWPnZeXx6+//sqWLVvYvHkzO3bswGazodPp0Ol0jiRE/fr1mTBhAg8//PBVn0dRURF///03P//8M99++y1xcXHk5eWV2kbTNAIDA+nQoQPBwcG4urqi0+lwdXVFr9ej0+kcPTDO9sg4+7urlMJut1/w/OxjpRSurq7MnDnzqs6jLJLcKFHnGh5ZWbwcHc3U06f5ZNQoHlywwNkRCSFqILvVSn5aGjmJieQkJZGTnEyLgAB87XYOHz7Mhp07yc3JITc3l9y8PHJNJt5o1oxIm41vT5xgUnw8uVYruXY7uUphBY4BjYEpFA+xOV8yEKLX845Ox1yLBW+dDqNej7dej7e7O9917Yq7ry8/JiezIzOz+K6kjw/ePj4Y/f0ZGBsL3t6cNpko1OvxCgrCKygIz6AgXCp5nLaofDUwuVGhYSkVPNadwJ1NmzZ9+PDhw1dbnSinLVu20K9fP8LCwti0aRORkZHl2u+tt97itddeAyAwMJCXXnqJZ555psqHf1QVu91OQUHBBQmPspIg575mMpno0aMHQ4YMufxBzvP8888zffp0x3MfHx/WrVvHTTfddMXnkZKSwo4dO/jrr784ePAgJ06cIDExkfT0dHJycrBarXh6epKfnw9AaGgoc+bMueSkp8nJyfTq1Yt9+/axcOFChg0bdsXxnZWens4333zDwoUL2bFjB66urvTp04cHHniAvn37lqvXg9VqJTk5uczEx9nHiYmJl72jHhQUdMnERf369fH29i7XedntdjZs2MCsWbNYu3YtOp2OwYMHM3bsWG6++eZqm8PhxIkTzJ07l8WLF5OQkIC7uztNmzalXr162Gw27HY7Pj4+jh4ZgYGBBAYGEhISQkhICGFhYYSFheHj41PhmLdt28aECRPYunWrI5kWFRXFY489xlNPPYXJZGLPnj2lengcOHDA0TPJz8+P9u3bOxIegYGBbNu2jS1btvDnn39itVpxdXXlhhtuoFu3bo6eGV5eXvz222889dRT7NixA6UUXl5ejBo1iqlTp1Z4taKUlBQWLFjAqlWriIuLIycnx/Geh4cHkZGRNGnSBD8/P44ePcrevXsvOVznXO7u7nh6euLh4VGqlPWah4cHRqORt99+u0Lxl4ckN0rUqeRGSgr07EnBvn1sf/llYt94w9kRCSGuAcpupzAnB7fCQlxMJpKOHePoP/+Qm5ZGbmYmeVlZ5OXkMKZdO9zNZpbv2cOqI0fIKygg12wmr6iIPIuFnfXr42Iy8WRaGrMtllI9SzyBs/ci7we+Oi+GSE3jZGAgeHnxdG4uvxUW4qXX4+Xmhpe7O438/Jhy223g7c3iI0dItljwMhqLi68v9UJDubFTp+LkSU4OLl5eeAYG4hkUhL4c3T/F5dXA5IYrxROKxgKnKR7RNVQp9XdlHbNOtTFquHXr1jFw4ECaNGnCxo0bCQsLK9d+L730ElOmTMHDw4Obb76ZzZs3Y7fb8fT0ZMyYMUyZMqXGdcmvSY4fP063bt04d/jV7bffztq1a6v0c7Pb7cTGxrJlyxaCgoJo1aoVW7duBaBRo0Z8+umn3H777WXum52dzYABA9iyZQszZszgmWeeqfDxLRYL69atY+HChaxcuRKLxUL79u0ZOXIkQ4cOJSQk5KrO72IKCgpISkpyJDusVqsjcREeHl6uyS+heHWfXbt2ERcXx8GDBzl+/Lijp8fZC9+zd+r1ej2aplFQUEBubi52ux0vLy8iIyNp3LgxRqMRT09Px1Anb2/v4mGvJcXX1xc/Pz/c3d0dvUqSkpJISkoiNTWV9PR0ieDBzAAAIABJREFUsrKyyMnJIS8vj/z8fAoLC7FYLI67/pVF0zRHLwQ3Nzfc3d0xGAx4enri7e2N0WjEx8cHFxcXNm/eTHZ2NlA8SefgwYOZNGkSoaGhlzxGQUEB+/btK5Xw+OuvvxzDTVxdXenUqZNjzoybb775kksVnzlzhqeffprly5djsVjQ6XR0796d2bNnExUVVeY+CQkJLFiwgDVr1vD333+X6p3h5eVF69at6dOnD6NGjaJhw4YX7G+xWDhy5Ag5OTkUFBQ4Sn5+fqnnV/KaTqcrs0fR1ZLkRom60vCI/+03nu/Rg3lWK34//AA9ezo7JCGEuGLKbqcgI4O85GTyUlIoyMjgutBQyMvjt507+ef4cUy5ueTl5mLKy8PNZuOVNm3AZOLNHTv4NSUFk8VSXKxWIlxc2OThAXl53GK1sv2843UC/ih53B7Ye857rkAvV1dWhoSApyd3JieTphSeJckTTzc3bgoL46kbbgBPT/6zZw92V1c8vb3xMhrxNBpp2rAh17dpA56e/HXqFO4+Pnj6++Ph749nQAAGP7863/vEyaulLAZuA4Io7kQ0USn1qaZpfYD3KF4hZYFSanIlHU96blSjH374gXvuuYfrrruODRs2EBwcXK79nnjiCWbPno2XlxdxcXE0btyYrKwsxo4dy7fffovFYsHV1ZXBgwczZ84cWUr0PG+++SYTJ050dEHX6XTMmTOHMWPGVFsM9913H9988w1Go5EVK1bw0ksv8fvvvwPQunVrvvjiizInxjSbzQwbNozly5fzwgsv8M4775Trrv7evXv5/PPP+eqrr0hJSSE4OJj777+fkSNH0q5du0o/vyuRlZXFzp07L5q4uNiQH03TcHd3d6zeY7FYsFqtWK1W7Ha7o5dEVV0nnk06nB1yZLVacXFxITw8nOuuu45mzZoRGhpKeHg4kZGRjqLT6UhJSSEpKckxbOts0iQjI4PMzExycnIcyROTyURBQQFms5mioiIsFovj3M6l1+vp2rUrkyZNuqoeSFDcM+fQoUOkpqYSExNT7t4z59fx1ltv8cEHHzgm6m3dujXTp0+nVatWzJ8/n3Xr1nHgwAFHbyYAo9FIdHQ0ffv2ZdSoUU4dWlSVJLlRoi4kN/5Zv547+vYl22bjp/nz6Th6tLNDEkKIGqsoLw9TaiqmtLTikpGBq8VC29BQMJlYsXUrZ1JTMeXlkW8ykZ+fTyMPDx6JigKTidHbtnHKZCLfYikuNhuxbm7McXOD/Hz8zGayzzvmKODsIEE9YD3v/bHALIOBQg8PorKz8dDp8NTp8HB1xcPVlZGRkTzQvDm5rq48v3cvHgYDnh4eGAwGPDw8uK11a2JatsQEbDh0CIO3Nx5GIx4+Pnj4+NCgUSP8Q0OxublhBgz+/uj0+qr9oM/j7J4bzlAX2hg13eLFixk+fDgxMTGsXbsWf3//cu03atQoFi5ciK+vL/v377+gwV9UVMSrr77K3LlzMZlMaJrG7bffzrx58y56t/RakZiYSGxsLAcPHnS8FhkZyS+//FLmXeCqNm7cOGbMmIG7uzvbtm1Dp9MxYsQIxzKbnTp1YtGiRTRv3rzUfjabjf/7v/9j3rx5PPjgg8ybN6/MZThTUlL4+uuvWbhwIXv37kWv19O/f39GjhxJr1690F/Fd6nVaiXn7BDQ3Nwyhwzl5+c77nqfe0e8sLCQ9PR0EhISyp24MBqNBAcHExYWRqNGjWjZsiXR0dF07Nix3ElBKJ4/Yd26dXzwwQesX78egM6dO9OtWzciIiLIy8sjNze31LCnoqIixzLA4eHhREREEBkZScOGDdm/fz/z5s3jm2++oaCggJiYGMaMGcOQIUOuKBFwpYqKijhz5gxZWVlER0fX2KFpy5Yt46WXXqKs5Lmvry9t27blzjvvZNSoUQQFBTkhwup3yTbG2Qk/roXSsWNHVZvt/uYbFaJpKljT1K6vv3Z2OEIIcc2zWSwqLzlZJe/bp45v3ar2/fCDOrlypVK//KLsa9eq5S+8oL56/HH1yYgR6oO771ZTevdWG+6/X6nx41X+o4+q0c2bq/saNlQDwsJUj8BA9S8fH/VJw4ZKtW6tEhs0UCEuLsoIyhUUJWV68Vo86uA5r51bPip5f8c5r+lB+YCq5+KilgcHK9WqldrZsqU6etttVfK5ADtUDfh/vzpLbW9j1HSffvqp0jRNde3aVeXk5JR7v8GDBytABQYGqtTU1Etua7PZ1HvvvaeCg4MdfzsdOnRQ27dvv9rwa6X3339fubq6lvp+GT16tLLZbE6Na/r06QpQOp1OrVmzRiml1JYtW1RUVJQjzttuu00lJCSU2s9ut6sJEyYoQPXv31/l5+crpZQqLCxUy5YtU/3793ecb0xMjJo9e7ZKS0u7bDw2m02tX79eDR8+XDVq1Ei5ubkpnU6nXFxclKZpZX5PX2nRNE0ZDAYVHBysWrdurWJjY9Xo0aPVtGnT1Nq1a1VKSkrlf+DnOHnypHrxxRdVYGCgAlTr1q3V3LlzVW5u7iX3y87OVnPnzlXt2rVTgPLy8lKPPPKI2rlzZ5XGW5fs27dP9ezZU3Xr1k299957KjMz09khOc2l2hjSc6OW+GP+fHo8/DBGnY6NK1fSondvZ4ckhBCiGlnNZgoyM3G1WPAAzFlZHDxwgIKcHApycjDn5VGQl0eH8HCaGI0knTnDl7/9RkF+PmazmYKS8lCTJnTy8mLXmTPU8/Oj/o8/Vnqs11LPDRmWUvVmz57NE088QY8ePfj+++/LPcFenz59WLt2LaGhoRw6dAgfH59yH3PZsmWMHz+eY8eOAdC4cWPeffddBg8efEXnUJtkZGTQvXt3du3a5XjN09OTH374ge7duzsxsv/vq6++Yvjw4QB89tlnjBw5EoAff/yRxx57jMTERDRNo1+/fixcuLDUMKM5c+bwxBNPcMstt9C+fXsWL15Meno6YWFhDB8+nJEjR9K6deuLHttut7Nq1Sq+/vprtm/fzunTpx3DHFxcXAgJCcHLy8sxh8XZ+R7OnffBYDDg7u5e5qSMZ5f3PXduC6PR6FgytiYoKChgyZIlzJo1i127duHr68uoUaN4/PHHadasmWO7HTt2MG/ePBYvXozJZKJ9+/aMGTOGoUOHVujvUYhzSc+N2n5XZcMGFW8wqDs8PNSJbducHY0QQghxSUjPDVFJpk6dqgB11113KbPZXK59bDab6tq1qwJUgwYNlMlkuuLjb9++XV1//fWOO+fBwcFq5syZTu+9UFU+++wz5ebm5uglAKhOnTpd9s68M2zcuNHR0+Ltt98u9d7ChQsdvQtcXFzUsGHDSv0eLFmyROn1euXu7q6GDBmi1q5dqywWS5nHsVgs6ttvv1V33323Cg8PL9Ubw8XFRTVs2FANGzZMrVmzps7+XlyM3W5Xv/76q7rvvvscP4vevXuradOmqY4dOypAeXh4qAcffFD9/vvvym63OztkUQdcqo3h9MZAdZba2PD435QpyqrXK9W2rVJnzjg7HCGEEOKyJLkhrtavv/6q+vfvrwA1ZMgQVVRUVK79bDabiomJUYBq2rSpKiwsrJR4jh07prp37+64sPXy8lLPPvtspdXvbLm5uerWW28tNQTCxcVFTZ061dmhXdLu3buVwWBQgBo7duwF78+cOVMZjUYFKFdXV/X44487fpeOHz9eZtd+s9msvvjiC9W/f38VGhpaKpmh0+lUkyZN1AMPPKA2bdp0zSUzLiUxMVG9/vrrKjQ0VAGqTZs2avbs2SorK8vZoYk6RpIbtbTh8fnDDysdqGmRkUplZDg7HCGEEKJcrqXkBnAn8HHTpk0r6+O7ZtlsNrVy5UrHRXZAQICaNGmSslqt5drfYrGo6OhoBajo6OiL3om/GpmZmWrYsGFKr9cXz2ej16uhQ4eq9PT0Sj9WdVm6dKny8PBwJDQAFRISovbv3+/s0Mrl5MmTytfXVwFq0KBBF7xvs9nUhAkTHEkQd3d39corrzgSEyaTSc2fP1/17t271HwrZ3++zZs3V4888ojaJr2ny6WwsFAdPnxYemlUMpvNptasWaM2bNhQJd9ttcml2hgy50YNNWvwYJ5ctoxYf39+2L8f78ussyyEEELUFOWdc0PTtEuut6mUyqi8qKpWbWpj1DRFRUUsXryYadOm8ffffxMZGcmzzz7L6NGjy716QlFREdHR0Rw+fJiYmBh+//33Kl39oKioiFdeeYUPP/zQscJKt27d6Nq1K1FRUbRo0YKWLVtW6+oPFVVUVMRdd93FunXrSr0+ePBglixZUmNXjyhLVlYWrVu3JikpiZtvvpmtW7deEL/VamXcuHF8+OGHWCwWvLy8MBgMpKenO7Zxc3MjKiqK2NhYRo4cSUzMNTF1kKiB7HY769atY9GiRWzbtq3U3C4Afn5+REVFcdNNN9GvXz9iY2PLXAGoLpKlYEvUlobHzAEDeHbFCgaEhbF4/34Mfn7ODkkIIYQotwokN45TfIdUAyKBzJLHfkC8UqpxlQZaiWpLG6MmycvL45NPPmHGjBkkJCQQHR3NCy+8wL333luhJTfz8/Np1aoV8fHxdO3alZ9++qnaLsztdjuzZs1i8uTJpKamXvC+pmno9Xo8PDwwGo34+/s7lueMjIykUaNGNG/enJYtWxJaDTeyrFYrx44dY9u2bTz11FPk5eWh1+uxWCy4u7uzaNGiWjtpqtlspn379hw6dIgWLVqwZ88eDAbDBdsVFBTw6KOPsnjxYlxcXGjevDndu3dn1KhRREdHOyFy57Db7aSlpREUFFSrElllyc/P5+TJkyQkJJCYmEhSUhIpKSmkpaWRnp5OVlYW2dnZ5OXlOZbYLSoqwmq1YrfbMRgM1K9fn7Zt29KtWzcGDhxIREREtZ6D3W5n/fr1jmRGQkJCqYlqIyMj6dKlC5qmsWPHDk6cOIHJZCpVh6+vL02bNqVz587069eP7t2718mEhyQ3StSGhoclL48Qo5Ebg4JYeeoUrmV8KQshhBA1WUVXS9E07RPge6XUmpLnvYEBSqkxVRVjZZHVUiouJSWFWbNmMWfOHDIzM+nSpQsvvPACvXv3RtO0CtWVk5NDixYtOHPmDL1792bNmjWX3efw4cMYDAYaNGhwpadQph07drBr1y6OHz/OqVOnSEpKIjU1lYyMDPLy8igoKMBisXCptrerqyvu7u4YjUb8/PwIDAwkODgYHx8ffH198fX1xd/fH39/fwIDAwkMDCQgIACbzUZ8fDxHjhy54PhZWVmYTCbMZnOpO78AOp0Om83Gddddxy+//FJqVZHayG6306VLF3799VfCwsLYt29ftZxTUVERM2bMYN68ecTHx+Pq6oqXlxf+/v6EhIRQv359mjRpQvPmzbnuuuto06ZNlfbqsdvtHD58mD179vD3339z9OhR4uPjOXPmjOP3saioCACDwcC9997L9OnTCQoKqrKYrsbff//N+PHj2b9/f6nkhMViueB3+mI0TcPV1RW9Xo/BYMDDwwNvb28MBgOJiYmkp6eXqsvV1ZXQ0FBat27Nv/71LwYMGFCpyS+73c5///tfvvzyS7Zt28apU6dKJTMaNGhA165dGTFiBN26dSszAZWfn8+qVatYu3YtO3bs4Pjx42UmPJo0aULnzp3p27cvPXr0wM3NrdLOwxkkuVGiNiQ3+P57jg4ahH3+fJqNHu3saIQQQogKu4LkRpxSqs3lXqvJakUbw8mOHTvG9OnT+eyzzygsLOSuu+7ihRde4MYbb7yi+tLS0mjZsiXp6ekMHjyY77777rL7mEwmmjRp4rj7Wd13Z6G4x8qBAwc4dOgQR48e5dSpU5w+fZqUlBQyMjLIyckhPz+fwsLCSyZCKkLTNHQ6nePCTq/Xk5KSgqZpvPjii7z99tuVcpyaYvDgwSxbtgxfX1/27t1Lw4YNq+Q4a9eu5c033+SPP/7AZrOhaRoNGzbEYrGQnZ1NQUEBNputzH01TcNgMODt7U1AQAChoaFERETQpEkTWrRoQbt27WjZsuUFd96LiorYv38/e/fuZf/+/Rw9epSEhASSk5PJzMzEZDJhtVrLPKZOp8PDwwNfX19CQkLw9fXlf//7H2azGU3TiImJYcaMGdx6662V/llVlN1uZ+bMmbz//vucOnUKKE44nF1S9+xSuWcTgf7+/gQFBREcHExoaChhYWFERETQoEEDQkJCytU7Zc+ePaxYsYJt27Zx4MABkpOTS32WLi4uBAUF0bx5c8dwkFtvvbVcddvtdjZv3sznn3/OL7/8UmYy49Zbb2XkyJHExsZecW+a/Px81qxZw5o1axwJj7y8vFLb+Pj4OBIeffr0oVevXrUq4SHJjRK1ouExeDBs3QqnT0Md7EYkhBCi7ruC5MZ6YCuwqOSlYUAXpVTPqoivKtSKNoaT7N69m6lTp/Ldd9+h0+kYMWIEzz33HC1btrziOhMTE2ndujXZ2dk88MADfPbZZ+Xab9q0aYwfPx6DwUDbtm35+eefyxy64Gxr167l1VdfZdeuXWW+r9PpHHeh9Xo9rq6u6HQ6NE1D0zSUUthsNiwWi6P7/dku+Gfb/gEBAaxfv77OzivxxBNPMHv2bAwGA7/99hvt27evlHqPHz/OK6+8wsqVKx0XjcHBwdx///1MmDABv/OGk1utVg4fPkxcXBwHDhzg2LFjnDp1ijNnzpCenk5ubi6FhYUX7YGg0+kwGAxomnbJZImrqyuenp6O3iIRERFERUXRsmVL2rVrR3R0dJm/63a7nTlz5jB16lROnz4NQGhoKM888wzPPfdctQ9ZOXToEOPGjWPDhg1YLBY0TaNDhw68+eab9OnTp1pjATh58iTff/89W7ZsIS4ujtOnT1NYWOh4X9M0fH19iYqKolOnTvTq1YuePXvi5ubGli1b+OKLL/j555+Jj48vlcyIiIjg1ltv5f7776dnz55V+jmbzWZWr17N2rVr+eOPP8pMeLi4uODi4oJOp7vg++VsQsnd3d3R68XDwwNPT088PT0xGo2ORNPZ4uvri5+fHz17Vv5/45LcKFHTGx7Z8fE83LgxL99zD+0XL3Z2OEIIIcQVuYLkRgAwEehC8RwcvwCTZELR2kspxU8//cTUqVP573//i9Fo5NFHH+Xpp58mPDz8quo+efIk0dHR5OXlMXbsWGbNmlWu/XJzc2ncuDExMTGMGTOGQYMG8eCDDzJ//vwKD4epCnl5eUyYMIGFCxeSmZkJQEREBPfffz9t27alVatWtGzZslKSMVartU6OxT/fO++8w8svv4yrqytr1qyhe/fuV1SP2Wzm3XffZf78+Y5eBO7u7nTv3p3JkyfTtm3bq47VbDYTFxfHvn37+Oeffzh69KijR09mZiZ2ux1/f39CQ0OJjIykSZMmtG7dmvbt29OiRYtK+Xn+/vvvjBs3ju3bt6OUws3Njbvuuov33nvvqv9uL+Xs3DUzZ87k5MmTABiNRoYOHcrbb79d44ZLZWRksGLFCjZu3Mju3buJj4+/YDiIi4tLqWRG/fr1HcmMXr16OX2eE7PZzNq1a1mzZg27d+/GZDJRWFhIUVGRY8iPxWLBZrNhs9mw2+2lkqPlVRW5hku2MS62jEpdLDV9KdgFo0YpQP1v/nxnhyKEEEJcMcq5FCzwZcm/T5Vn+5pYkKVgS7FarWrJkiWqY8eOClChoaFqypQpKisrq1LqP3jwoGPZ0vHjx1do37fffru4nfW//ymllHr11VcVoObMmVMpsV2prVu3qptvvtmxDKtOp1M9evRQe/fudWpcdcWCBQuUpmlK0zS1aNGiCu27fPlyFRMT4/jZaJqm2rZtqxYtWuRYSrYuyszMVA8//LDy9PR0LIvbtm1btW7duko9zpEjR9SAAQOUm5tbqeP88MMPlXqc6lBQUKCWL1+uxowZo66//nrVuHFjNWTIELVy5co697tis9lUZmamOnbsmPrzzz/Vxo0b1dKlS9WCBQvUe++9p9544w31/PPPq6effrpKjn+pNobTGwXVWWp6ciPW319Fuboqex37AxBCCHFtqUByYz8QDuwF/IGAc0t56qgppaa3Mapafn6++vDDD1VUVJQCVLNmzdTHH3+sCgoKKu0Yu3fvVu7u7gpQb775ZoX2zc7OVv7+/qpv376O12w2m+rXr59ydXVVP//8c6XFWR6FhYXqtddeU8HBwY6Lunr16qk33nhDFRYWVmss14I1a9YonU6nADV9+vRLbvvPP/+owYMHl7qwDw0NVS+++KLKzc2tpohrjvnz56tGjRo5PovAwED12muvKYvFckX12Ww2NXfuXNWkSRNHnd7e3mr06NEqNTW1kqMXdZHTkhvAcqAv4FKVxylvqckNj9M7dyoN1IR//cvZoQghhBBXpQLJjSeBA0AhcAw4fk45Vp46akqpyW2MqpaSkqIiIyMVoDp16qSWLl2qrFZrpR5j+/btSq/XK0DNnDmzwvtPmjRJAerPP/8s9XpWVpZq3ry5CgkJUfHx8ZUV7kXt2rVL3X777Y4LbRcXF/Wvf/1Lbd++vcqPfa37888/HcmxZ599ttR7JpNJvfzyyyosLMxxwe3h4aEGDRqk9u/f76SIa5a4uDh1++23O3qxuLq6qv79+6tjx46Va/8TJ06ou+++2/EzAFR0dLRasmRJFUcu6hpnJjfuAL4CjgJTgBZVebzLlZrc8JjRv78C1ME1a5wdihBCCHFVypvcOFuADyuyfU0sNbmNUdUeffRRpdPp1Nq1a5Xdbq/0+jdt2uRIBsybN6/C+2dmZio/Pz/Vv3//Mt/fv3+/MhqNqlOnTpXa0+Qsi8WipkyZosLDwx0XdQEBAer5559XJpOp0o8nLu7YsWPKaDQqQA0ZMkR98803qn379krTNMewk44dO6rvvvvO2aHWWCaTST399NPKx8fH8fvcsmVLtXTp0gu2tdls6pNPPlFNmzZ1bOvp6alGjBihkpKSnBC9qAucPiwF8AUeBU4B24FRgL46jn1uqckNj/mRkWqwn5+zwxBCCCGuWkWTG3Wh1OQ2RlXau3evcnFxUU8++WSV1L9y5Url4uJyRfMlnDVx4kQFqF27dl10mx9++EEB6oEHHqi0BM2BAwdU3759HT1ONE1TnTp1Uhs3bqyU+sWVSU1NVSEhIY6LbUDVr19fvfbaa5JsqqCvv/5aNW/e3PE5+vr6queee04dOXJE3XvvvcpgMJRKgFzp37AQ57pUG6PKV0vRNC0QuB8YDiRS3JPjVqCNUuq2Kj34eWrsTOYHD0KrVjBjBjzzjLOjEUIIIa5KRVdLqQtqbBujCimluOOOO9izZw+HDx+u1BUNrFYrEydO5J133kHTNJYuXcrAgQMrXE9GRgaNGzfmjjvuYNmyZZfcduLEiUyaNIlZs2YxduzYK4rbbrfz0UcfMW3aNE6cOAGAj48P999/P2+//Ta+vr5XVK+oXGazmX79+hEUFMTkyZOJiopydki12uHDh3nqqafYsGFDqaVqPTw8GDhwINOmTavS1VbEteVSbYwqXQNK07TvgRbAl8CdSqmkkreWaJp2bbUALuHQBx/QWNNwGzLE2aEIIYQQogI0TbsTuLNp06bODqXarVixgp9++onZs2dXWmIjLy+Pp59+mkWLFlFYWIirqyurVq2iZ8+eV1TfjBkzyMnJ4fXXX7/sthMnTmT37t0888wztG3bli5dupT7OHa7nREjRrB06VIKCwsBaNu2LW+88QYDBgy4othrm+zsbF5//XViYmLo378/RqPR2SFdlMFgYOPGjc4Oo85o1qwZa9asoaioiNdff53NmzfzyCOPMHLkSKcveSquLVXac0PTtG5Kqc1VdoAKqol3VZTdTlN3d9r6+/N9SoqzwxFCCCGu2pX03NA0rR7QqeTpH0qpWvWfYk1sY1SlwsJCrrvuOgwGA3v27MHV9erulyUmJvLYY4+xevVqbDYbBoOBUaNGMX36dDw9Pa+ozrS0NBo3bkyfPn1YsmRJufbJzs6mc+fOZGRksHPnTho0aFCu/V588UWmTp2Kp6cn99xzD1OnTiUkJOSK4q6t3nnnHV5++WWgOHnQp08f7rnnHvr164eXl5eToxNC1BWXamNUaSqtJiU2aqrfFyzgmNVK/379nB2KEEII4RSapt0D/AH8G7gH+F3TtMHOjUpcyvvvv8/Ro0eZOXPmVSU29u3bxy233EJERAQ//vgjRqORN998E5PJRM+ePXnzzTevuO7p06djMpmYOHFiuffx9fXlhx9+wGw2M3DgQAoKCi67j91u54MPPkCv15Oens5nn312zSU2LBYLc+bMITY2lm3btvHwww+zfft2hgwZQkhICPfeey/Lly8v1+cphBBXSvoJOdlXH3yAOzCoHN0lhRBCiDrqFaCTUmqkUmoEcAPwmpNjEheRnJzMW2+9Rb9+/ejevfsV1fHTTz9x3XXX0aZNG7Zv305YWBjz588nKSmJ3NxcAgMDGTBgAFOmTOE///lPhetPSUlh9uzZ3HfffbRu3bpC+7Zs2ZJFixaxc+dOHn30US7Xy/n111+noKCAESNGYDAYKhxrXbBs2TJOnz7NM888wy233MIHH3xAQkICmzdvZuTIkWzevJm7776bkJAQhg0bxooVKxzDd4QQotJcbKZRZxagF3AIOAK8WMb7DwCpwJ6S8lB56q1pM5kXmUwqWNPU4Pr1nR2KEEIIUWmo+FKwcec9dzn/tZpealoboyo99NBDSq/Xq0OHDlV43y+//FI1aNDAsYJCixYt1Jo1a9TBgwdVz549HUu+6nQ6x3Oj0ahsNluFjvPcc88pFxcXdfDgwQrHeNbrr7+uAPX+++9fdBubzaa8vLyUq6vrNb3Sxo033qiaNWt20Z+TxWJRGzduVA8//LAKCAhQgPLx8VEjRoxQq1atUoWFhdUcsRCitrpUG6NKe25omrZc07S+mqaV+ziapumAOUCJnrksAAAgAElEQVRvoDVwn6ZpZaXclyil2peU+ZUUcrXa8t57pCrFsOHDnR2KEEII4UzrNE1b///YO+/wqKqtD79nanojhF4iHZReBYNKR5rAFQFRES6Iil5BinIpVgQBP0QURSxcRcEIgoKgXi5NkC4dAgESQhohbVImk5lZ3x8ziZQQEshkEjjv8+xnzpy1z16/GUKyzzprr60oytOKojwNrAd+cbMmlQI4ePAgy5YtY/z48dSvX79I19jtdt59912CgoIYMWIEFy5coF27dhw8eJDp06czfvx4GjZsyKZNm/Dz82Pq1KlkZmayceNGHnvsMUwmE2+//XaRNcbHx7N48WKGDx9OgwYNbvWjMn36dPr378+ECRPYsmVLgX1mz55NZmYmQ4cOveXaIOWdP//8kz///JMXX3zxhsUjdTodXbp04dNPPyU+Pp6NGzcyaNAg1q1bR58+fahcuTKjRo1i06ZN5ObmlvInUFFRuVNwdUHRrsBIoD3wPfCFiJy6yTUdgFki0sP5/lUAEZl9RZ+ngdYiUqx9uspasS954gl2rl1L64sXMfr5uVvOLZF47BjHf/uNrLQ07q9dmwCdjr8iIth58iRms5mcvJaTw8RmzQjWall18iQ/nz+PxWol12bLf13RsCFBisIb586xLiUFBTBoNBh1OgxaLavbtcPLy4tPo6PZk5qK0WDAw2DAw8MDLy8vpvXqBZ6e7LhwgdisLLz8/PDy98e3QgV8KlSgUdOm4O2N3c8PjcHg7q9ORUVF5Y7lFguKDsSxVTzAdhFZU/LKXEdZm2O4AhHhwQcf5Pjx45w+fZqAgIBC+5vNZqZMmcLSpUvJzs5Go9HQo0cP5s+fz4cffsjy5cvJyMgAoEmTJrzzzjv069fvqjEyMjIIDAzEYDBgMpmKtPPCyy+/zKJFizhx4gT16tW79Q8MpKen065dO5KSkti/fz81a9bMt9ntdvz9/cnOziY1NRUfH5/b8lVeGTp0KL/88gsxMTHF/g4sFgu//fYbK1eu5Mcff8xfkjRo0CAee+wxOnfufNvFalVUVO4sCp1j3CiloyQb4A88C1wAduIIeOhv0Hcw8NkV70cAH17T52kgDjgMhAM1CvE9BtgH7KtZs2bJ5MKUBBkZIt7eIqNHu1tJoWSnpMj2Dz+U+X37yplHHxW5/355x99ffEAUZ0ppXgsHEZDB15zPa1ud9u43sJ902h+4gT3Tab/3BnZxtnsKsClX2KtdcU4LYgCpCCJ+fiIVKshDBoPU0mqlgV4vzTw8pL2PjwytVEmkTx+RoUPl9Vat5OWWLWVW587y/oAB8vnIkfLf118X2bpV5OhRSTl1Smy5ue7+p1NRUVFxGxR/Wcqcopwry+1uWJby/fffCyBLliwptN/ly5dlyJAhotPpBBC9Xi9PPvmkbN26VcLCwkSj0eSff/TRR+X8+fOFjjdy5EgBZOrUqTfVePHiRTEajTJy5MhifbbCOHnypPj5+UnLli0lKysr//zcuXMFkMcff7zEfJU3Lly4IDqdTiZMmHDbY2VnZ8uPP/4oQ4cOFW9vbwEkJCRExo0bJ4cPHy4BtSoqKncChc0xXJq5AaAoSgXgCRxBiljgGxxPZu4TkQcL6D8Y6Ckio53vRwDt5IosDeeYGSKSoyjKWGCIiDx8My1l6anKuldeYdP8+by7fj2+vXu7TYfdauXUxo38uXo1LbKzaZ6czKqjRxkXF0e6CNYr+s4EZgELNRpeF6GiTkctX19qBgfjaTQyoU0bQitVYldCAttjYzF6eODh4YGHtzcenp70at8ev6AgotPTScjKwtPPD6OfH54BAXgFBhJQtSoaDw+45qmMJSODrMRE/DQaNNnZnIuI4GJ0NJlpaWSkpZGdkUFOVhajWraErCy+2bOHQxcvYs7JwZyTQ05uLlq7nc+bNwezmXGHDrE/PR2z1Uq2zUaOCD7AcT8/yM2lfmYm50WwAXanBi8g84rja2t9+wOpzmMjYHEeK4AWqA8c8/YGg4H66enkAB4aDZ5aLV46HZ0qVGBuy5bg68ukw4cxeHkREBBAYIUKBFaqRMPGjWnSpg0EB2MNCEB3lxYsU1FRKR8UN3NDUZQDItLymnOHRaRpyasrWRRF6Qv0rVu37j9Pnz7tbjkuw2w206hRI/z8/Dhw4ABarfa6PpGRkYwdO5bNmzcjInh7e/Pcc89Rs2ZN3nvvPaKjowEICQlh/PjxTJ06tUhP5c1mM35+fmi1WkwmU6HXjB8/niVLlnDq1CnuueeeW//A1/Dzzz/Tr18/hg8fzvLly1EUhYCAAEwmE8nJyfj7+5eYr/LEa6+9xpw5czhz5gyhoaElNm52djYbNmxg5cqV/Pzzz3h4eBAXF4fRaCwxHyoqKuWTwuYYrl6WsgZoAPwH+FJE4q6w7StIVFGWpVzTXwski8hN/6qUpeBG30qVOHj5MtFmM5pSSLezZGSQPncuwcePc+DoUR45fZpUux3zFX2GAiuAn4HHgQpaLdU9PakbEkLTRo0YPGIEtfr3h7vwxtqSno4hIwOSkti9YwexUVGkXrpEWkoKaSkpVFAUXqhTB9LTGbZjBxczM8nMzSXLZsNss9FIp2O9jw9YLASYTGTBVcGTe4BI57FSgP8mwFHADHhe0U+DI3jSR6PhB19fEnU6WqWm4qEoeGq1eOh0eOv1PF69OmPvvZckvZ65J07g5++Pf0AA/kFBBFWuTNPmzanZqBH2ChWw+/mpwRMVFZXboqjBDUVRxgHPcfWvQQBf4A8RecJFEkucsjTHcAXvvPMO06ZNY/PmzTz00ENX2axWK2FhYezatQuAihUr8vLLL3Pq1ClWrVqVv/1ny5YtmTt3Ll26dCm2/+eff56PPvqIf/3rX7z//vsF9omJiaFOnTo89dRTfPrpp8X2cTPefPNNZsyYwfvvv49Go+Gll15i0KBBhIeHl7iv8kBWVhY1a9YkLCyM1atXu8zPL7/8Qu/evfnpp5/o06ePy/yoqKiUD9wZ3OgtIhuuOWcUkRvu/aQoig6IALoAF4G9wDAROXZFnyp5gRJFUR4FpohI+5vpKSsTj6RTp6jSsCH/atOG9/bscamv9JgYXnzwQVZERtIK2AVE4Yg4BSgKlY1GQgMDaRwayqABA2g5YgRUruxSTSpXY7dasaamYkhLg6Qkft6wgeS4OFKSkkhNTibNZKK5jw9PVqlCRkoKfXbvJjM3l2xn4MRst9NPr+cjo5FTFgtNzeb8wEne/+7eOKrz/QZ0L0DD48C3wErnMVwdPJmi0/GGlxfrRBiXmYlRUTBqtXhptXjqdLxWty69a9dmb3Y2n0dH4+Plha+vryOIEhhIj44dqVqnDqkaDWlaLRXq1cMrOLhIa6dVVFTKH8UIbvgDgcBsYOoVJpOIJLtKnysoK3MMVxAbG0v9+vXp3r17gTexY8aMYenSpVSvXp0xY8awfv169uzZg4hgNBoZPHgwCxYsICQk5JY1WCwW/Pz8EBFMJhOGAmpnPffcc3z22WecPn2aWrVq3bKvG2G32xk0aBA//fQTXl5eZGZmcunSJYKCgkrcV3lg6dKljBkzhq1btxIWFuYyPxaLhZCQEAYMGMCXX37pMj8qKirlg8LmGK5OGXgL2HDNuV1AywL6AiAiVkVRXgA24biv+lxEjimK8gaO9TXrgBcVRekHWIFkHDU4yg3fz5qFFRg+caLLfMQeOMDYXr3YkJiIHcfT/odatYIvv6RW48aY1ZvKMoNGp8MQHAzBwVCnDn3atbthXx9gSyFjNQCujRyaU1OxJyZCVhbtoqNZuXkzqUlJpKWkkJ6eTlp6On0rVwY/P+rExhJ29ChZziU7ZueyncrO5UKXsrK4bLc7gic2W37myb6DB+l98CCrgSUF6Prgiy8YD7wGfHyNTQF+0Gh4VK9nss3GJ1YrekXBkNc0GtbUrk0Tf38+SU1ldXIyXgYDXkYj3h4e+Hh7M6NLFwIqVmRvcjJnMjLwDwrCv3Jl/CtVIqBGDao2aoTmLi30pqJSlhGRNCANR/KgShnltddeIzc3l/fee+86W3x8PMuWLcNgMJCbm8uMGTMAqFatGq+88kqhO2gUB4PBwPjx45k3bx4vvvgiS5Zc/dcmKiqKzz77jNGjR7sksAGg0WhYvnw59evXJz4+nq5du961gQ0RYeHChbRo0YIHHnjApb4MBgP9+/dn7dq1WCyWAgNbKioqKuCizA1FUSoD1YCvgWH8nWnvBywRkYYl7rQIlJWnKg/4+5Ock8PRrCyUkg4ynDgBY8dSZ/t2zuLIzpjcrRtT1q8vleUvKncfdqsVMjLQpKSQGBnJof37SUtMJDUpifTUVNJTUxlRpw51dDp+PnWKzyIiyLRYyLJY8gMoy4ODaa0ovJqczBKzmVxnzRMrjgyUP4E2QFfgvwVoOAPUwbEt0+4C7Gk4fvm0xVFdWMPfWSkGIM3fH3Q6Bmdmsjc3Nz+oYtRqCdLr+b1VK/D05I3z5zmTnY2Xhwfenp54e3tTOSiI53r0AF9f/oiJwazV4lexIj4hIfhXrkxAzZp4BQVdV0tGReVO5lZ2SynvlJU5Rkmzd+9e2rZty+TJk5kzZ8519k6dOvHHH38AoCgKHTp0YP78+bRvf9OE2mJjtVrx8/MjNzeXtLS0q7ZeHTNmDF999RWRkZFUr169xH1fSVBQECkpKdx7773s3r37rtwC9vfff6dbt258+eWXPPXUUy7399NPP9GvXz82btxIjx49XO5PRUWl7FLqy1IURXkKRzZFaxz3EnmYcNTecN3CvEIoCxMPW2Qkz9Wty719+jD+p59KbNw/lixh3Msv847ZTB9gQ2Agcf37M+qLL0rMh4qKu7FbraRHR5MSE0NKTAypcXGkJSbySP36GLKy2LB3L9tPnyYjM5OM7GwyzWaycnJYd++9aHJyeOn0aX5PSyPHbsciQq5zD504Pz+wWmmXlcVBEez8vaxHC/mFdSsBiddo0vN3AdlA/i4sm4cnkOU8DsIRaNFc0WorCif8/ECno2laGiki+Vkreq2Wll5efNmoERiNDD1+nBxFwUOvx8NoxNNopGW1aoxq2xa8vPjk4EF0np74+Pvj7e+PT1AQtUJDCW3YEHx9ydLp8AgKUgOdKi5HDW7cGYgInTp1IjIykoiICPyu2bZ+x44dPPDAA2g0GkSEyMjIEi0qWRAzZ87kjTfeYMSIESxfvhyAs2fP0qBBA5599lkWLVrkUv9ffPEFzzzzDK1ateLAgQMMGzaM//znPyhKQRWz7lz69u3Lnj17iI6OLpUin2azmZCQEIYMGcLSpUtd7k9FRaXs4s6aG4NE5AeXOSgmZWLi8c47MG0anDsHtWvf9nBrpkzhlQULOGt13H497evLFytWgFpwSUWl5LBYIDWV6BMnSIyKIi0xkfRLlzClpKC1WBjeuDFkZDB/yxbOXL5MltlMtsVCtsVCRZ2Oz+vUAbOZnidOEJWTkx9YyRWhtqKwy8cHbDYqZWSQClcFV6oD0U4ZGv6uo5JHbeCc87igqXUj4DiOAMy1008FeBj4XacjHgi1WtHiCOhoFQUd8LiHBx8EBxMpQu+EBAwaDXqNBoNGg0Gr5clq1RgdGso5q5VpJ0/iYTTiYTA4Xo1G+jZuTIcGDYjNyeGXM2fw8vV1ND8/PP38aNioEcHVq2PR6zHrdHgFB6sFbe8AbiW4oShKLaCeiPyuKIonoBMRk2sUljxlYo5Rwnz77bcMGzaMzz77jFGjRl1nr1GjBjExMQB06dKF33//3eWa7HY7fn5+mM1mkpOT8fPz45lnnmHFihWcPXuWqlWrutR/pUqVuHTpEhcvXmTZsmVMnz6d+fPnM2HCBJf6LUucPn2a+vXrM3PmTGbNmlVqfocPH86mTZuIj48v0i47KioqdyalXnNDUZQnRORroLaiKNf9theRBa7wW+YR4a9ly2h2//0otxnYsC9ZQu3nn+eC3VH1oKWnJx8tWkS7AiYfKioqt4nBACEh1AwJoWYh3W5WRWfjTewJhRnNZuIjIzElJGBKTCTj8mVMyckE6/VQpQpkZjJvwwYyMjLIys4m22wm22ymdUAA1KiBPSODLnv2kGOzYclrdjstvbwgIAC72UxAYiJW5xbQVhEsQHpODiQkkGS1csZuR7g6wBKSns7oEyc4gKMo7bXE79pFBxy7MI0twD4BmA8sAF69xqYAc4BJWi2z7XZmiVxV6FYDfOntzaOeniwwm1mQlYVOUdAqCnpFQafR8GXNmrT292dZSgpfJyWh12oxaLUYdDqMej3vtW5N9aAg1l28yJaEBIzOwIzRaMTDw4MxDz+Ml58ff8XHcy41FS9fXzx9ffF0BmcaN22Kxtsbs0aDxscHg1rb5ZZQFOWfwBgcCU51cMT1luAoLq7iBrKyspg8eTItWrTg6aefvs6+aNEiYmJi0Ov1WK3W/CwKV6PRaJg+fTpTp05l9OjRvPPOOyxfvpzx48e7PLDxzTffkJiYSNeuXalSpQqvvfYaBw8eZNKkSTRr1uyWdoEpjyxatAiDwcCzzz5bqn4HDx7MihUr2Lp1613zXauoqBQPV4U9vZ2v6izvCg6Hh9Pi7Fm+7NSJW1mdaLda+ah/f57bvh2NyURtoE5AAJ+uWkW9bt1KWK2KikqZwsODkCZNCGnS5IZdJj733I0vBwp7ploViCvE3g7H9sVXYrdasWdlQVYWvS5dYv/Ro2SnpZGVmkpWejpZJhMtK1WCgAC6xcTw+p49mM1mzDk5jmax8GitWuDvT/uLF+keEYHFbsditWKx28m122lSsaLjsycnUyk1FasINmdNFhugtdkgO5uEnByS7Parsl4ESDx9GnDsFrSlgM/1YnQ01YEPKLieS/916wgFnqXgei6ZgBeOmjBHr7FpAJuigKLQym7nsPNcXoDGC0jy9QWNhocyMzlmszkCN84ATbBWy4GaNUGn4x+xsZyxWNBqNOicS5ZqGI2saNYM9HpePHmS2Jwc9DodBp0OvU5HaEAA09q1A6OR+fv3Y7LbMRqNGAwGjB4e1K5UiT4dO8LQMlHL83kcZXF2A4jIaUVRbn1rDZXb5r333iMmJoYVK1ag1WqvslksFqZMmYKiKOTm5tK7d2+XBxauZNKkScyePZsffvgBRVEwGAxMmTKlVPwCfP7554Aj0PLll1/Svn17hgwZwr59+6hdAlm5ZZnU1FQ+//xzHn/8cSqX8u56PXv2xNvbm/DwcDW4oaKiUiCuXpZSUUQuucxBMXF3yuiUdu1YsGcPcSdPEtygQZGvM6emMiksjM+OHMEMfKAojB8wAJYsgdvYVk1FRUXlbsJqNpOVlERWcjKZSUlkpqbSsFIlDLm5nIqI4HhkJOasLLIyMsjJzsZsNvNcixZ4iPDDoUP8ER1NjsWCxWJxBGCsVr5p0QKN1cobJ06wJTmZXLudXJsNq92OAuy95x6wWhkWE8NOsxk75AdoPBWF8/7+YLfT1mTi+BXBGTuOpwQpWi2IUMVuJ/6az3NlPRfvK47z8MNR4wUcS5Is19grAomKAnY7JU1xl6UoirJbRNopinJQRFo4t4U/ICJNS1yci3D3HKMkiYmJoX79+vTp04dVq1ZdZ3/66af56quvMBqNWCwW4uPjb2ub11th0aJFvPjiiwC88sorBe7kUpKEh4fzj3/8g86dO7Nly5arbKdPn6ZNmzbUrl2bnTt33tEFRhcsWMDEiRPZv38/LVvecPNDlzFkyBC2bNlCbGzsdUE3FRWVuwN31tyIAM4DK4HVIpLiMmdFwJ0TD7vVSm0PD5pWqMDPCYUmn+eTfv48Yzt3Jjw6GiuOXR2erF+f97duxaeUo+UqKioqKmULq9mMzmoFs5nYc+fITE0l22QiJz2dnMxMPIDWtWpBTg5r//iD5NRULDk5mLOzseTkUNXLi+GtWsHUqSWu7RaCG3Nx1ON9EhgPPAccF5FpJS6uaHoaA7OAy8B/RST8ZtfcScGNJ554gvDwcE6ePHldJkJMTAw1a9bEYDCQk5PDgAEDWLNmjVt05gVXjhw5wr333utSX3n1RSIjI7nnnnuus//yyy/07t2bWbNmMXPmTJdqcRc2m426detSo0YNtm3b5hYN33//PY899hhbtmyhc+fObtGgoqLiXkq95kYeIlJfUZS2wOPANEVRjgPfOetx3FVsX7yYCzYbcx577Oado6NhzBismzaxEkfq8qQOHXhj82a10J6KioqKCsDffw98fKgaHFxo3/6DBpWCottiKjAKOIKjPMsG4LNbGUhRlM+BPkCiiNx7xfmewEIcJVs+E5F3CxmmF7BIRLYrirIOuGlw407hzz//5JtvvmHatGkFLrEYNGgQIoKiKGg0Gr5w065sx48fx2Jx5CNNnDiRTZs2uczXunXriImJ4f777y8wsAHQq1cvHn30URYsWMD48eMJCgpymR53sW7dOs6fP8/8+fPdpqFXr154enoSHh6uBjdUVFSuw6WZG1c5UpRgHDXjhouIW/LI3PlU5fkmTfjq+HESEhLwLiR1M/nMGbrXr8+3ItSrUIF9Tz5Jy7lz1a0bVVRUVFTKDbeQueENmEXE5nyvBYwicu1qm6KMFQZkAMvzghvO8SKAbkAMsBcYiiPQMfuaIZ5xvs7EsdrnfhHpeDO/d0Lmht1up0OHDly4cIGIiAh8rimQu3nzZrp06UJQUBDJyckMGTKE7777zi1ahwwZwoYNG/D09CQpKYlz585Rq1Ytl/iqXbs2UVFRnDp1ivr169+w35EjR2jWrBmvvvoqb7/9tku0uJMHH3yQ8+fPExkZ6dYlIYMGDWLXrl3ExMSg0WjcpkNFRcU9FDbHcOlvBEVR/BRFeUpRlF+AnTjq1bV1pc8ySU4O8y5eZHPPnoUGNgAevO8+9ouw5uGHISmJ1gsWqIENFRUVFZU7nf/iKCOShyeF18C9ISKyDUi+5nRb4IyInBURC/Ad0F9EjohIn2taorM9jyOjJOlGvhRFGaMoyj5FUfZdulRmSozdMt988w179uxh9uzZ1wU2wLFcBRw7qWi1Wj799NPSlgg4ggjff/89L730EgsXLkREePLJJ13ia+PGjURFRdG2bdtCAxsA9913H4899hgLFy7kTvh5uJK//vqLrVu3Mn78eLfXuhg8eDBxcXHs2rXLrTpUVFTKHq4Odx4CmgNviEh9EZkiIvtd7LPs8csveKal0dZZ+OpGvP7ggxwxm2np6cnk/xZUt19FRUVFReWOxENEMvLeOI9LsipjNeDCFe9jnOcKRFGU2oqifAosB25YqVJEPhWR1iLSumLFiiUm1h1kZGQwdepU2rRpw4gRI66zL1iwgLi4OEJDQzGbzQwbNgw/Pz83KIXXX38dX19fJkyYwNChQ6lWrRrbtm0jIiKixH298MILACxbtqxI/WfOnEl2djZz584tcS3uZOHChXh7ezNq1Ch3S+GRRx7BaDQSHn7XrBZTUVEpIq4ObtwjIi+LyF0dWp0yZQof+vhAIdu1Hlu7lte3bsUD+O/x46UnTkVFRUVFxf1kKoqSv/WCoiitgGx3iRGR8yIyRkSGi8iOwvoqitJXUZRP09LSCutW5pkzZw6xsbH83//933Wp/haLhWnTpqHVaomLi0On07FkyRK36Pzrr7/44YcfePnll/PrWixevBigxLM3Nm/eTGRkJC1btixywdJGjRoxbNgwFi9eTHz8tXsclU8SEhJYsWIFTz/9NAEBAe6Wg5+fHz169CA8PBy7C3Z7UlFRKb+4JLihKMr/OQ/XKYpyXXOFz7JKekwMH0REcCo0FG60vMRup9/AgQiwYvJkAu7wPdJVVFRUVFSu4V/A94qibFcUZQeOXdZeKMHxLwI1rnhf3XnuthGRn0RkjL+/f0kM5xaioqKYN28eQ4cO5f7777/OPnLkSMxmMy1atMBsNjNy5Ei3bXc6a9YsAgIC+Ne//pV/rn///tSqVYvdu3dz9OjREvM1btw4oOhZG3nMnDkTi8XC7NnXlnMpn3zyySdYLJb8rXfLAoMGDSImJoa9e/e6W4qKikoZwlWZG/9xvs4D5hfQ7hpWz5qFGRhe2B+EYcNYa7fz7wYNeHTOnFLTpqKioqKiUhYQkb1AQ2Ac8CzQqISXse4F6imKEqooigHHLm4l8rDlTsjcmDx5MoqiMKeAOUhUVBTffvstfn5+HD58GL1ez6JFi9ygEvbv38/atWuZOHHidRkEefU/Sip7Y8eOHURERNC0aVOaN29erGvr1q3LU089xZIlS4iJiSkRPe4iJyeHjz76iN69e9+05khp0rdvX/R6vbo0RaVMkpuby+XLl90t467EJcGNKyYkzUVk65UNRw2Ou4Zv1qyhjk5Hu2eeKdB+9MMPsaxcyb3BwbypLkdRUVFRUbl7aQM0BVoCQxVFuaW7VEVRvgV2AQ0URYlRFGWUiFhxZIJsAk4Aq0TkWEmILu+ZG9u3b2fVqlVMnjyZGjVqXGfP2/q1Q4cOWCwWxo4di9FodINSR0ZEUFBQgRkE3bt3p169ehw8eLBEnuaPHTsWgM8+u6UdiZk+fToiUu53TVm1ahUJCQlXZcqUBQIDA+natSvh4eGU1s6PKio3wmw2s23bNt566y26d+9OYGAgwcHBtGnThrfffptjx46pP6elhEu3glUU5YCItLzm3EERaeEyp4VQ2tu0xf31F9VbtGBap068sX37dfb0mBgq1ahBBSDm0CFo2rTUtKmoqKioqLiKW9gK9j9AHeAvwOY8LSJSdvLgb0J53ArWbrfTpk0bEhMTOXXq1HVLTTZt2kTPnj2pV68eUVFRKIqCyWRCr9eXutY9e/bQrl07Zs+ezdSpUwvss337dsLCwmjSpMltLU/ZvXs37du3p3Hjxhw7dusxsHHjxrFs2TIiIiKoXQ6XHIsIrVq1Iicnh6NHj6IoirslXcXnn3/OqFGj2L9/Py1btrz5BUyfebkAACAASURBVCoqJYTJZGLXrl1s27aNbdu2sXv3biwWC+DYNSksLIxKlSqxfv16du/eDTgyugYMGMCAAQNo376923cdKs8UOscQkRJvOPaO/wlIwZH2mdf+B/zXFT6L0lq1aiWlyYkpU6QPyIn16wu0t/byEkD+3alTqepSUVFRUVFxJcA+Kd684QTOBy7lrQF9gU/r1q1bcl9gKbFs2TIB5Ouvvy7QHhISIoAMHjxYAHnllVdKWeHf9OzZU4KDg8VkMhXar3HjxgLI9u3bb9nXfffdJ4Ds2LHjlscQEblw4YIYjUZ55plnbmscd7Ft2zYB5JNPPnG3lAJJSkoSrVYrr776qrulqNzhXL58WdauXSsTJ06UNm3aiFarFUC0Wq20bdtWXnnlFVm3bp1cvnz5umsvXrwoH3/8sfTo0UP0er0AEhISIqNHj5aff/5ZsrOz3fCJyjeFzTFckrmhKEotIBSYjWOP+DxMwGFxpIeWOqX+VKVNG7DbYf/1y4bnPvIIUzZsoLHBwLGcnNLTpKKioqKi4mJuIXPje+BFEYlzoSyXUt4yN0wmE/Xq1SM0NJSdO3de91R+9uzZvPbaa3Tv3p3Nmzej1+sxmUxuedq4c+dOOnbsyNy5c5k0aVKhfffu3Uvbtm2pV6/eLW0Ne+DAAVq1akX9+vU5derUrUrO56WXXmLx4sWcPHmSunXr3vZ4pcngwYP53//+x4ULF9xWQPZmdO/enXPnzhEREVHmMktUyi9xcXFs3749PzPjyJEjABiNRtq1a0dYWBhhYWF06NABHx+fIo+blpbGL7/8wo8//siGDRswmUx4e3vTq1cvBgwYQO/evQkMDHTVx7pjKPXMjbLaSjNzI2HnTokCkfnzr7NF/PabaEAMIJdOniw1TSoqKioqKqUBxc/c+B+ObM9NXJHxWZwx3N1KOzv0dpk6daoAsnv37uts2dnZYjQaRafTybBhwwSQadOmuUGlg65du0pISIhkZGQUqX/z5s0FkF9//bXYvlq0aCGAbN68udjXFkRcXJx4enrKE088USLjlRbnzp0TjUYjU6dOdbeUQvnkk08EkEOHDrlbiko5xW63y7lz5+Srr76SUaNGSb169QQQQLy9vaV79+7y1ltvybZt20o0y8JsNsvGjRvl2WeflSpVqgggOp1OunbtKh9++KFER0eXmK87jcLmGC75Aw/scL6agPQrmglId4XPorTSnHjMCAsTLcilI0euNths8mNQkBhAvh43rtT0qKioqKiolBa3ENzoXFArzhjuapTDZSmRkZFiMBhkxIgRBdrzlqG8/PLLotVqxdvbW2w2WymrdLB161YBZMGCBUW+5siRIwJI7dq1i+Ur77o6deoUV2ahvPLKK6Ioihw/frxEx3Ulr7zyimi12jJ/g5WQkCAajUamT5/ubikq5YjExET59ttv5ZlnnpEaNWrkBzMCAwOlX79+Mm/ePNmzZ49YLJZS0WOz2eTPP/+UqVOnSsOGDfP1tG7dWt588005cuSI2O32UtFSHihsjuHSgqJljdJKGRW7nfoeHtT08eG/yclXG595Br74AkuPHhg2bnS5FhUVFRUVldKmuMtSnNfUAuqJyO+KongBWhExuUZhyVOelqUMGjSIjRs3EhERQbVq1a6yRUZGUq9ePQICAujevTsrV67krbfeYtq0aTccLy0tjY4dO5Keno6fnx/+/v5XvRZ0rqBXDw+P65YWPPTQQ5w8eZKzZ8/i6elZ5M/Yvn17du/ezY8//kj//v2LdE3btm3Zu3cvmzZtonv37kX2dTMuXbpEaGgojzzyCCtXriyxcV1FRkYG1atXp2fPnnz33XfulnNTHn74YeLj4zmu7jqocgPMZjN//PEHv/32G7/99hsHDhwAICAggC5duvDQQw/lFyPWaFyymWixOHnyJGvXruXHH3/kzz//BKBOnToMGDCAMWPGlKltmd2B25al4Kh8bnQePwi8CAS40mdhrbQyN3Y7C3Qte/rpq87/PneutAJJ8fMTyckpFS0qKioqKiqlDcXP3PgnsBeIdL6vhxsLkN9KKy/LUv73v/8JIG+++WaB9mbNmgkgX331lWg0GvH19b1p1saHH34ogAwZMkQGDhwoXbp0kTZt2kiDBg2kcuXK4uUsoH6zptPppEKFChIaGirNmzeXDh06CCAffPBBsT9nRESEAFKtWrUi9T9x4oQAUqtWrWL7KgqvvfaaAHL48GGXjF+SLF68WADZtWuXu6UUiTy9x44dc7eUckdsbKzbsrJcid1ul8OHD8v8+fOlR48e4unpmf87JiwsTN588035888/xWq1ulvqTYmNjZUlS5ZIz549Ra/Xi4+Pj6y/wWYVdwuFzTFcvRXsX0BroDawAVgLNBGR3i5zWgil9VTlpebN+eTQIRKiovCvWROArKQkQipWJAvY/fnntBk50uU6VFRUVFRU3MEtFBT9C2gL7BbndvGKohwRkftcpbGkKS+ZG48++ii7d+8mMjLyukyIn3/+mb59+9K4cWMaNmzI6tWrmTdvHhMnTrzheCJC06ZNMRqNFPb5c3NzMZlMpKWlkZ6env965XFBr/7+/qxcuRIPD49if9awsDC2b9/Ot99+y+OPP15o344dO7Jz507WrVtH3759i+3rZiQnJxMaGkqXLl1YvXp1iY9fUtjtdho1akRAQED+FpZlnbi4OKpVq8asWbOYMWOGW7VYrVb++9//0qJFC0JCQtyqpSBEhMOHD7N69WrWrFnDkSNHmDRpEnPnznW3tNsmLi6O33//PT87Iz4+HoCGDRvSvXt3unXrRufOnfH19XWz0lsnJiaGfv36cejQId5//33Gjx9/VxbSdWfmxgHn6yRgvPP4oCt9FtZK46mKLSdHqmk0MuiaJwUP+Pk51q+2aOFyDSoqKioqKu6E4mdu7JYr5giADsfuam7PyCiC9nJVc6N79+7Svn37Am3BwcECyB9//CEajUYCAgJuus57x44dAsjSpUtdIfe2OH/+vCiKIpUqVSq035kzZwSQGjVquFTPrFmzBJD9+/e71M/tsGHDBgFkxYoV7pZSLB544AG577773C1DZs+enZ+J1KpVK5k2bZps375dcnNz3abJZrPJH3/8IRMnTpTQ0FABRKPRSFhYmDz00EOi1Wrl6NGjbtN3q2RmZsovv/wiEyZMyN++GZAKFSrI448/LsuWLSvzNWNuhYyMDBkwYIAAMm7cuFKrC1KWKGyO4eo/+LuBocBRINR57qgrfRbWSiVldONGiQc5vXhx/qnFQ4Y4ClTpdK73r6KioqKi4mZuIbgxF3gNOAl0A9YAbxdnDHe38rIs5UbBjbwb7379+skjjzwigCy+Yi5zI4YPHy5+fn5F3smktOnatasA8tlnn92wT1hYmAASHh7uUi2pqakSGBgojzzyiEv93A7du3eXqlWrlrsbpoULFwogJ924C2FiYqL4+vpK165d5a233pJOnTqJVqsVQPz9/WXgwIHy6aeflsoNt8VikV9//VWeffZZqVy5sgCi1+ulV69esnTpUklISBARkUuXLklgYKA89NBDZb5gpc1mk/3798u7774rDz/8sBgMBgHEYDDIww8/LO+++67s37//jlxmcy02m02mTJkigHTr1k1SUlLcLalUcWdwozHwATDU+T4UmOJKn4W1Upl4jBgh4u8v4twqKDcyUowgOpALe/a43r+KioqKioqbuYXghgZH3Y3vgXDnsVKcMdzdynNwIzMzUwwGg+j1ejl9+rQoiiIVKlS46ViXLl0Sg8EgL7zwgqvk3jaxsbGFfp6oqCgBpEqVKqWi55133imz9SyOHTsmgLz99tvullJsLly44Hbtzz//vGi1Wjlx4kT+uZSUFAkPD5fRo0dL9erV87MLGjduLBMmTJBff/21xLYXzczMlDVr1siIESMkICAgfyvTwYMHy4oVKyQ1NbXA6z766CMB5LvvvisRHa7AZrNJr1698r+/++67TyZMmCAbN26UzMxMd8tzG59//rno9Xpp2LChnDlzxt1ySg23BTfKWnP1xCPz0iXprdXKtisj8vfcI+tB/jNypEt9q6ioqKiolBWKE9wAtMA3Re1fVlt5Dm7kpThPnz5dunXrdtNMhzzee+89Acp8SnufPn0EkEWLFl1n69KliwDyzTfflIoWk8kkFStWlG7dupWKv+IwduxY8fDwkEuXLrlbyi3Rvn17aeGm5d8nT54UrVYrzz333A372O12OXr0qMyfP1+6deuWn3ng6ekpvXv3lg8++EAiIiKKlUGRkpIiX3/9tQwcODC/cG9gYKA89dRTsnbtWsnKyrrpGFarVVq2bCnVqlUTk8lUZN+lyccffyyAzJgxQ2JjY90tp0yxZcsWCQoKkgoVKsi2bdvcLadUcGfmRkfgNyACOAucA8660mdhzdUTj+9efFEA2Tx/voiI7Bg4UGwg8uCDLvWroqKioqJSlriFzI0dgKE415SVVt5rbpw8eTI/s+HcuXOiKIqEhITcdBybzSZ16tSRBx54wJVyS4SkpCTRaDTi7+9/1Y1jTExMkT9vSTJv3jwBytSNyOXLl8XT01NGjx7tbim3TN736o4n2P379xdfX9/85R5FISMjQ9avXy/jx4+XevXq5Wcl3HPPPTJu3DhZu3atpKenX3ddfHy8fPLJJ9KjRw/R6/UCSNWqVeW5556T33///ZaWFO3cuVMAmTx5crGvdTVRUVHi6+srXbp0KfNLZ9zF6dOnpUGDBqLX6+Wrr75ytxyX487gxkmgFxACVMhrrvRZWHN1cKNvpUpSTaMRW26u7HBGGLtotflLVFRUVFRUVO4GbiG4sRzHVrDTgQl5rThjuLuV18yNJk2aCCBr166Vzp07CyBff/31Tcf59ddfSzXj4XYZPHiwADJnzpz8cz169BBAPv/881LVkpmZKZUrV5bOnTuXmZu1d999t9xsVXsjzp07d92/cWmwZcsWAeSdd965rXEiIyPlo48+kn79+om3t3d+nYyHHnpI5syZI/Pnz5dOnTqJoiiOWn516sikSZNk165dJVJnYuTIkaLT6a5aVuNu7Ha79OzZU7y8vOTs2bPullOmSU5Ozs9Ee/XVV+/o2iNuLSjqyvGL21w58UiKiBAdyCutW0uOySR+zujr/95/32U+VVRUVFRUyiK3ENyYWVArzhjubuUxuBEeHp6/fj0yMrJYtScGDhwowcHBYjabXSm3xEhNTRWtVis+Pj5is9kkISFBFEWR4OBgt+j54IMPBJDff//dLf6vxGKxSPXq1aVLly7ulnLbtG7dWtq2bVtq/mw2m7Ru3VqqV69epCUgRSUnJ0c2b94skydPlqZNm+ZndTRr1kxmzZolhw8fLvHAWEJCggQEBEjXrl3LTNDtq6++EkAWLlzobinlAovFImPGjBFABg4ceMfWI3FncONd4D2gA9Ayr7nSZ2HNlROPJcOGCSAHVqyQbkFBAsg/GzRwmT8VFRUVFZWySnGDG3kN8LqV68pCKy/BjW7dukn79u3FZrNJYGCgKIoiERERcv/99xd5x5CLFy+KVquVSZMmlYLikuOJJ54QQGbOnJlfh2PJkiVu0ZKdnS3Vq1eXDh06uP1GcuXKlQLIunXr3KqjJMjLQDl//nyp+Pvmm28EkOXLl7vUz8WLF0vlMy1atEgA+f77713u62bExcVJYGCgdOzY8Y7OQihp7Ha7LFiwQBRFkVatWsnFixfdLanEKWyOoTjsrkFRlP8VcFpE5GGXOS2E1q1by759+1wydnijRnx/8SK9HnuMkcuWUUOr5bzZjEanc4k/FRUVFRWVsoqiKPtFpHUx+ncAlgE+IlJTUZRmwFgRec5lIksYV84xSpLu3buTkZHBgw8+yOzZsxk4cCBvvfUWjRs3pkaNGkRHR990jDfeeIOZM2dy5swZ6tSpUwqqS4bMzEwCAgLQ6XRYLBb8/f1JTk52m55PPvmEZ599lg0bNtCrVy+36bj//vtJTEwkIiICjUbjNh0lwZkzZ6hXrx4LFizg5Zdfdqkvs9lMgwYNqFChAvv27Sv33x2A1WqldevWJCcnc+LECby9vd2mZdCgQaxfv55Dhw7RoEEDt+kor/z0008MHTqUgIAAfvrpJ1q0aOFuSSVGYXMMl/4vFJGHCmhuCWy4lKgoBp88ycrnn+fiF1/gCWzdvFkNbKioqKioqBSN/wN6AJcBROQQEOZWRUVEUZS+iqJ8mpaW5m4pRcZms/Hee+9hMBj4z3/+w5NPPgnARx99dNNrrVYrS5cupXv37uUqsAHg7e3NyJEjMZvN2O12Zs2a5VY9I0eOpHbt2syYMQNXPmwsjD179rBr1y5eeumlO+LmvG7dujRv3pzw8HCX+/rggw+Ijo5m3rx5d8R3B6DT6Vi8eDEXLlzg7bffdpuO8PBwVq9ezeuvv64GNm6Rvn378scff6DRaOjUqRM//vijuyWVCi79n6goSiVFUZYpivKL831jRVFGudKnOzj6/vukA6xaxTS7nYz58wkNKxdzMhUVFRUVlTKBiFy45pTNLUKKiYj8JCJj/P393S2lyERERGC1Wpk+fTqnT59m3759hIaG0qdPn5teu2HDBmJiYnj22WdLQWnJs2jRIoxGI4GBgYwfP96tWgwGAzNmzGDfvn2sW7fOLRoWLlyIn58fTz/9tFv8u4LBgwezc+dOLl686DIfSUlJvP322/Tp04eHH76zntt27NiRJ598knnz5hEREVHq/i9fvszzzz9Pq1atmDhxYqn7v5No1qwZe/bs4d5772XgwIG89957bguklhauDjN+CWwCqjrfRwD/crHPUmfokiXcp9HwyNmzWNu0QTNhgrslqaioqKiolCcuKIpyPyCKougVRXkFOOFuUXciGRkZpKamUrFiRf7973/z1FNPAbB06dIiXf/xxx9TtWpV+vbt60qZLsNoNHLu3DkiIiJQFMXdchgxYgR169ZlxowZ2O32UvUdGxvLqlWrGDVqFL6+vqXq25UMHjwYgNWrV7vMxxtvvEFmZiZz5851mQ93MmfOHDw9PXnxxRdL/Wb4X//6F8nJySxbtgydmgV/21SuXJktW7bwj3/8g8mTJzN69GgsFou7ZbkMV//EBIvIKkVRXgUQEauiKOXiSUxRORweztGcHAAuAeZ16/BxrySXIHY71pwcrNnZaO12DBoNNrOZ+NhYbLm5V/UNDAjA18eH3NxcEpKS4JrJQ2BAAN5eXlgsFpJSUq52pCgE+vvj6emJxWolLSMDjU6HRq9Hq9Oh0enw8PZGZzBgB0SjQaPVotwh6YAqKioqdynPAguBasBF4FfgebcqukM5cuQIAF999RV79+7l0KFD1KtXjy5dutz02rNnz7Jp0yZmzJhRrm86qlSp4m4J+eh0OmbOnMmIESP44Ycf+Mc//lFqvj/66CNsNhsvvPBCqfksDRo0aMC9995LeHi4S7JzIiIi+Pjjjxk9ejSNGjUq8fHLApUrV+b111/n5ZdfZu3atQwYMKBU/K5fv56vv/6aGTNm0KxZs1LxeTfg6enJt99+S4MGDXjzzTc5e/YsP/zwA0FBQS7zmZubS3x8PDVq1HCZj4JwdUHRLcAg4DcRaakoSntgjoh0LsK1PXFMdLTAZyLy7jV2I7AcaIVjje4QETlf2JiuKPY1qU0b5jnHXD9rFr1nzizR8a9F7HasGRnozWYwmTj611+kJiRgSkoiIzUVU0oKtby86FK1KphMvLRhA6mZmZiys8nIycFis9EvMJAJISFYLRZanjxJrt2OVST/9VlPT/5tMJCSm0vVjAysgPUKDW8B04BooFYBGt/HkZ5zHGhSgP0zYBSwG2hfgH0l8BjwO9CtAPsGoBfwI/Co85yCIw1JC2w2GOio0/Gd3c5YsxmtouTbNIrCb8HB3OvhwdfZ2cxKTUULaBUlv/1Uty41PD35OjmZJYmJaDUaxxjO1+9atCDQw4MVcXH8EBfnsF/RPu3UCaPBwKpz59gSH+84r9Wi1WrRa7W8+/DDoNWy9swZ/kpIQKvVotPp0Gq1eBmNPB8WBlotv0VEcDY52RHU0WjQ6nT4enkxsF070GrZERFBosmEVqfLb77e3nRq1gw0Gg6dO0dGTg6aPLtej4+PDw3uuQe0Ws7FxmKx2dDq9Y4Akk6Hl5cXIZUqgUbD5bQ00GjQ6PVotFq0BgN6oxGjlxdoNNgBRQ0sqaioFEBRC4oqijJHRKYoivIPEfm+NLS5ivJSUNRgMGCz2bDZbDRp0oTjx4+zfft2OnXqdNNrX331VebOnUtUVBTVq1cvBbV3Bzabjfvuuw9wBJ+0Wq3LfWZnZ1OzZk06derEmjVrXO6vtHn99dd5/fXXiY2NpXLlyiU69sCBA/ntt984ffp0iY9dlrBarbRo0QKTycTx48fx8vJyqb+0tDSaNGlCQEAABw4cwGAwuNTf3co333zDM888Q61atfj555+pX7/+LY+VlZXF2bNnOXPmDJGRkURGRuYfR0VF4e3tTWpqaolnyRU2x3B12H0CsA6ooyjKH0BFYPDNLlIURQssxnFvGwPsVRRlnYgcv6LbKCBFROoqivI4MAcYUtIfoDDsVisfOicyw2rVuj6wIUKOyYQpPp6MxEQykpLIuHwZrdlMm+rVwWTi+61biYqLIyMjA1NGBhlZWdQyGHitVi0wmRh48CAnsrIw2Wxk2Gxk4Lix/8npohsQf42uIUAXAIOBdVYroij46nR463QYdToUvR4CA9HqdNRJSECv1aJz3njrdToa1qoFDRrgCYzfvRudXo9ep0Ov16PT6QirVw/q1aOCzcanBw+i0Wiu+qFtGxoK1apRJTOTpfv3X/eddKpXDypXJjQtjU/++uu677VVw4YQHEyDy5f58NAh7HY7NpsNu92O3WajQePGEBhIw4QEXj982HE+r48I1Zo2BV9f6sTGMvL4cYddJL9PQPPm4OFBpQsXaHfmDDa7/e8mgr5yZdDr0ZnNGHU6bHY7uU7fNrsdnNkolxMSOHX5MjaR/GYXQXJywG7nUHo632dlYQOHHUcA5t2DB8Fm40e7nS+v+eyBwPPOScYnwA/X2GsAA53Hb+J4tHkljYFjzuPngJ3X2NsBfzqP+wNHrrF3BX5zHrcBzl1jfxTIS/KsBCThCCxpnZ/tCa2WZR4eoNFQKSODXOd5DY7A0jOenrwTGIhVUagXF5d/Pu/1n87AW7oID0ZGOmxX2MdWqcJTlSqRYLUy9OTJv+3ONrZWLfpXqUJ0djYvHTuGRqP5267RMLZuXR6sUoUzGRm8eeRIvl3JszduTKuQEE6mpbH46NH8n+28fv9s1owGwcEcu3yZFSdO/G1z9hvZsiU1AwM5nJjI+lOnUPL8O+1PtmlDRT8/DsXGsu3sWRRFcfRx2oe1a4eflxeHL15kX1SUIysp73qNhsFt2+JhNHL4wgVOxMbmn1ec1/dv1w6tTsfR6GjOXbqUP67iDLB1b90aNBqOR0cTn5KSb1M0Ggx6PR3uuw8UhVPR0SSbTPnjarRajEYjTevXB0UhMiYGU3Z2vk1RFDw8PKhbuzYoClGxsZgtlr+1aTR4enhQrUoVUBRiExPJtVqvsnt4eBBcoQIoCknJydhErtJnNBrx9fMDRSEtPR2Bq+x6gwEPT09QFLLNZoCrxs8LAAqQF9LPs6m4ld6KokwFXgXKdXCjvLFjxw6OHz9O48aNixTYyMnJYdmyZfTt21cNbJQwWq2WWbNmMWTIEL777juGDx/ucp8rVqwgKSmJl156yeW+3MHgwYOZNWsWa9asYdy4cSU27o4dO1izZg1vvvnmHR3YgL+Li3bu3Jl3332XN954w6X+Jk2aRFxcHKtXr1YDGy5k+PDh1K5dm0cffZR27drxww8/FFo3Jjk5+brARd5xXFzcVX0DAgKoW7cubdq04fHHH6du3brY7fZSCdjm4dLMDQBFUXRAAxz3QKdEJPcml+RtCTdLRHo43+cta5l9RZ9Nzj67nD7igYpSyAcq6acq3zduzGMnTqAFOvn5kWm1kmG1EgT84eUFGRl0s1r5/Zrr7uXvm8r7gV3OY2/AR6Oho6cnP9StC76+PB8dzSW7HR9PT3y9vPDx9qZJzZoM69QJfH3ZdPo0Gm9vfIOD8Q0OxqdiRQKrVcOvalVQfzGUbUSwW63YLBZsubnYLBbsubn4enmBzUZqcjJZGRnYrFbsubnYcnPRALWrVAGbjXPR0aSnpzuutVqxW60YtVqa16kDdjt7jx0jNT3d8YQuNxe7zYa/pydhDRuC3c4v+/aRkpGB3WZzBG6sVqr4+9OzcWOw2Vi+axdpmZl/B45sNuoGBTGgUSOw23lv61YyLZb8wJPNZqNlSAiP1a8PdjsTt2wh1xlwygtAda5cmaGhoeRarYzeseNvmzMw1L9yZYZVr44pJ4fh+/djs9sRyO/zdJUqDAsJId5s5rFjxxxLk5zX2kWYUKkSQwICOJ2dzeBz5xznId/+ToUKDPL2Zn9WFoMSEx1jO+0CfOrnRx+9nv/l5DDYZLrKZgd+NBjoqtGwxmrlMavV4Z+/b5Z3AB1xFBsaWcA/+SGgKbAIeLEA+1kgFJgNvFaA/RIQ7LTNLsBuBozAeODDa2w6IO+X79PAV9fYA4G8DREH8XcQK4+aQJTzuDt/B8HyaAIcdR5f+Xstj/ZXnLvvir55dOPvYF3tK3zlMZC/g30VrtCax1OQHyw0AteuJn0ex3dicdqvZSowW1G4DFR2/hlRnA3gDa2WKXo9USL8P3v3HR5VtT18/LtnJskkISRAwBAChN4JSBFQEASkKNVYEFGxIF4V9V71CldFBZRXRUQRBSmK/EAQkC5wRQVRQWmXKib00EJIJW3K2e8fmUSIoc9kMsn6PM95Zubsc/ZZcwjJnjW7NHYNRTy//N2AAIYHBLDXMLg5M/Nv5ZODgxlktbLFbueO9PS8cqUKq1mRYgAAIABJREFUjvksNJQ7rFY22Gzc7xqud3757EqVuDUwkNXZ2fzDtXRlft0KmB8RQSurlcXnzvHy2bN/Xd9Vx5Jq1WhgtfJ/aWm8dV75spgYam/eXMQduT5X0XPjXeBxoByQ5QpL5z9qrcu7PTgP8ZWeG35+fhiGQd26dfnzzz/57bffaNOmzWXP++qrrxg0aBCrV6+mR48exRBp2WIYBi1btiQ7O5u9e/d6bNhPZmYms2bNYuzYsURERLB9+/YSMfeIu2mtady4MZGRkaxbt85tdbZr146EhATi4uI83pOhpBg8eDCLFi1iz549Hlshad26dXTr1o0XXniBd9991yPXEBc6dOgQd955J3/++SeTJk2iadOmRSYxUgpNIVC1alXq1q1LnTp1qFOnzgXPPTnM5XyXamN4eljK3cBqrXWGUuoV4EZgrNZ622XOiwV6aq0fc70eAtyktX76vGN2u45JcL0+4DomqVBdw4BhADVq1Gh15EjhJvO1y6pQgeDUVAAa+vlRKyyMcgEBRJYvzwfdukG5ciw8dIiTNhvlypenXFgY5cLCCI+IyGtIhISQbLfjFxZGcOXKsnSsED5KGwbaMFBaowCnw4HDZsNwOtGuxJDhdBIcGIjZZCInK4vMzEwMh6PgXMPppHLFilhMJtLS0khNTc1L3LiOMQyD2tWqYTaZSExKIuns2bxyp7OgjmZ162ICjp48SeLZswX1aq3BMGjXpAlozf7DhznlOl8bBlprzMCtzZuD1uyIj+dUcnJBmdYaq58fXZs1A635ee9eEl3xaVeCqrzVSg/X+f/duZMzaWkF5VprwoOD6eUqX7J1K8nnzl1QXi00lN5Nm4LWzNm0ifScnAuuX6dSJXo3bgzApxs3km23XxB/kxtuoFeDBqA1723YgMP1vvO31pGR9KhbF6fTybgNGy4o01rTsUYNuteqRZbNxriNG/P2A7jKb4+O5rYaNUjOzubtTZv+KievwTugTh06RkZyIiOD/7d16191u455oF492kVEcDAtjXe3by8oy/8b/ETDhrQKD2dPcjITd+/+W/k/GzemWYUK/H7mDB/98UfB/vzH15o2pX758qw/fZqpcXEF5+Zf/93mzakRFMS3J04w8/Dhgvc26ZZbqDZ/vtv/T1xFciNAa52rlFqqte7n9kCKkS8lN5yu/x8xMTHsKKIHZVE6d+7M0aNHiY+PLzXLXpY0S5YsYcCAAcycOZOhQ4tKkV+7M2fOMHnyZD7++GPOnj1L+/bt+eijj2jVqpVbr1OSvPrqq7z11lucOnWKypUrX3d98+fP57777mPWrFmlanWZyzlx4gQNGjTg1ltvZcWKFW6vPzMzk2bNmmGxWPjf//5HYGCg268hipaWlsZ9993H6tWrC/aZzWZq1qx5QdIiP4lRu3btEpHUu2Qbo3ADz50bsNP1eAvwA3AHsPkKzoslb56N/NdDgMmFjtkNRJ33+gB5E5hetN5WrVppd/vs3nu1yfXl7T+aNnV7/UIIIYSvAbboK2snbHM9fnklx5fEDegDTKtbt677bqAHWSyW/A5neseOHVd0zt69ezWgx48f7+HoyjbDMHSrVq10dHS0zs3NdUudcXFxevjw4dpqtWpA9+3bV2/cuNEtdZd0O3bs0ICeNm3addeVk5Ojo6OjdUxMjHY4HG6Izre89957GtDLli1ze93PPvusBvT69evdXre4PLvdrhcvXqxXr16t4+Pjtc1m83ZIl3WpNoanU+/5K6PcAXymtV4JXMlYiePkTS+QL8q1r8hjXMNSQsmbWLRYPfbVV2xftIhQYMru3bQMDCQrKemy5wkhhBACf6XU/UAHpdTAwpu3g7sSWuvlWuthoaGh3g7limhXb5/WrVtf8WoEn376KX5+fjzyyCOeDK3MU0rx5ptvcvjwYWbNmnVddW3evJnY2Fjq16/PzJkzeeCBB9i3bx9Lly7l5ptvdlPEJVvz5s2pU6cOCxcuvO66Jk+ezOHDh3nvvfeKdf6AkmLEiBE0btyYZ599luzsbLfV+/PPP/Phhx/y1FNP0alTJ7fVK66cxWJhwIAB9OjRgzp16uDn5+ftkK6Lp5Mbx5VSU8mb43KVa4WTK7nm70A9pVQtpZQ/cB95E5Oebxl5w6whr6fH9zr/L3Yxaz5wICfOnKFlYCDHcnKw1K4NO3d6IxQhhBDClwwHOgJh5PWAOH+704txlVpOZ973TrNnz76i47Oysvjiiy+IjY11S9d+cWm9evWiXbt2jB07lhzX5MhXyjAMVqxYQadOnWjXrh3r1q1j5MiRHDlyhM8++4yGDRt6KOqSSSlFbGws33//PcnJhWdqunJnz55l7Nix9OrVi27durkxQt/h5+fH5MmTOXToEO+8845b6szJyeHRRx+levXqvP12UTOJCXH1PJ3cuAdYA/TQWqcCFYEXL3eS1toBPO06dx+wQGu9Ryn1plKqr+uwGUAlpVQ8eauyvOyJN3ClgsLD2ZaVxdHHHsM/I4MdMTFMHzLEmyEJIYQQJZrWeqPW+kngJa310EKbdBPwoEaNGl3RcfPnzyctLY3hw4d7OCIBeR/Ix4wZQ0JCAp999tkVnZObm8vMmTNp2rQpffr04ciRI0ycOJGjR48ybty4Ur+qx6XExsbicDhYtqzwd6RXbuzYsaSnp7vtQ72v6tKlC/feey/jx4/n0KHC6+ldvTfeeIP9+/fz2WefERIS4oYIhSiG1VJKkmKb7GvhQmrdfTeHgUE1azInPl4mCxVCCFFmXMWEordprb+/2BAUrXXhhXtKLF+ZUDR/ZYwrbf+1bduWzMxMdu/eXSpX1SiJtNZ07tyZP//8kwMHDlx0Ar/U1FSmTp3KpEmTOHnyJC1atODFF1/k7rvv9vmu5e6itaZWrVo0bdr0mibDjI+Pp3Hjxjz88MNMmzbNAxH6loSEBBo2bEjXrl1ZunTpNdezbds22rZty4MPPsjMmTPdGKEoCy7VxpDprj0hNpbv16+nslLMO3KEhkFBpB4+7O2ohBBCiJLmVtdj4SEpMiylBNi6dSu///47w4cPl8RGMcrvvXHq1Ck++eSTv5UfO3aMf/3rX1SvXp2XX36Zpk2bsnbtWrZt28b9998viY3z5A9NWbt2LWlpaVd9/siRI/H39+eNN97wQHS+Jyoqitdee41ly5axatWqa6rDZrMxdOhQqlSpwoQJE9wcoSjrJLnhIbU6dSIhPZ2O5csTZ7dTrVYttlzn5FBCCCFEaaK1Hu16LDwkRYallABTp04lMDCQITLMtth16tSJbt26MX78eM6dOwfAzp07GTJkCLVr12bSpEn069eP7du3s3btWrp37y4JqIuIjY3FbrezfPnyqzrvl19+YeHChbz00ktUrVrVQ9H5nueee46GDRsyYsSIq54XBuD//b//x86dO/nkk0+oUKGCByIUZZkMSykG/7n5Zj745Rf2AjXfeQdevOy0I0IIIYTPuophKf+8VLnW+n33ReVZpW1YSlpaGtWqVePee+9lxowZxRGaKGTTpk20b9+eoUOHcuLECdasWUNwcDDDhg3jueeeo0aNGt4O0ScYhkHNmjVp1aoVS5YsuaJztNZ06NCBI0eOEBcXR3BwsIej9C3fffcd3bt3Z8yYMbzyyitXfN6ePXto2bIlAwcO5KuvvvJghKI0k2EpXjbu559JW7GCmgEBpL70Eo9Vr47hcHg7LCGEEMLbQlxba+BJoJprGw7c6MW4yrw5c+aQmZnJk08+6e1Qyqx27drRu3dvZs2axf/+9z/eeustjh07xvvvvy+JjatgMpm46667WL16NRkZGVd0zsKFC9m0aRNjxoyRxEYRunXrRmxsLG+99RZHjhy5onOcTiePPPIIoaGhfPTRRx6OUJRV0nOjOJ06xQO1a/N/2dlEmc1s3rKFyBYtvBePEEII4QFX2nPjvOM3AHdorTNcr0OAlVrrTp6K0d283sa4QlfSc0NrTfPmzQkICMAX3lNpdvLkSX7++Wf69OlDQECAt8PxWRs3bqRjx47MmzeP++6775LH5ubm0rhxY4KCgtixYwdms7mYovQtR48epVGjRvTs2ZNFixZd9vgJEybwwgsvMHfuXAYNGlQMEYrSSnpulBQREcxOT6d35cokOJ3UbtmSNePGeTsqIYQQwttuAGznvba59hULpVRtpdQMpdTC8/YFK6W+UEp9ppQaXFyxlAS//PILu3fvluVfS4CqVasSGxsriY3r1KFDB6pWrcrChQsve+yUKVM4ePAg7733niQ2LqFGjRr85z//YfHixaxdu/aSx8bFxfHKK6/Qt2/fyyaXhLgektwoZiaLhZWJibzTuzc2oOcrrzC+SxdvhyWEEEJ402zgN6XU60qp14HNwOdXcqJSaqZSKlEptbvQ/p5Kqf1KqXil1MuXqkNrfVBr/Wih3QOBhVrrx4G+V/pGSoNPPvmE8uXLy7erotQwmUwMHDiQVatWkZmZedHjkpOTGTNmDLfffjs9evQoxgh907/+9S/q1avHM888Q25ubpHHGIbBY489RkBAAJ988olMfCs8SpIbXvLiypX89MknhAHNf/wROnUCm+1ypwkhhBCljtZ6HDAUSHFtQ7XWb1/h6Z8DPc/foZQyAx8DvYDGwCClVGOlVDOl1IpCW5WL1BsFHHM9d17dO/JdSUlJfP311zz44IMy14AoVWJjY8nOzubbb7+96DHjxo0jNTWVd999txgj810BAQF8+OGH/Pnnn0ycOLHIYz799FM2bNjA+++/T2RkZDFHKMoaSW540c3Dh5OSkkLvevUwfvqJ/iEhHNqwwdthCSGEEMVOa71Naz3JtW2/ivM2AMmFdrcF4l09MmzAV0A/rfUurfWdhbbEi1SdQF6CA8pQe+nzzz/HZrPxxBNPeDsUIdyqY8eOVK5c+aJDUw4ePMjkyZMZOnQozZs3L+bofFfPnj3p378/Y8aM4dixYxeUHTlyhH//+990796doUOHeilCUZaUmT/WJVZYGPzxB0s7dmSpzUb9W29l4b/+5e2ohBBCCF9Wjb96XUBeoqLaxQ5WSlVSSn0KtFRKjXTtXgzcpZT6BFh+kfOGKaW2KKW2nDlzxk2he49hGEydOpVbbrmFpk2bejscIdzKbDYzcOBAVqxYQXZ29t/KR40ahcVi4c033/RCdL5t4sSJGIbBv877DKO1ZtiwYWitmTZtmgxHEcVCkhslgcnEgA0bmDpkCAZw9/vv079qVeL++19vRyaEEEKUelrrs1rr4VrrOvnDYbTWmVrroVrrJ7XW/3eR86ZprVtrrVtXrly5eIP2gHXr1hEfHy/Lv4pSKzY2lszMTNasWXPB/k2bNjF//nxeeOEFqlW7aB5UXER0dDSjRo3i66+/5rvvvgPgiy++YO3atYwfP57o6GjvBijKDFkKtoTZuXAhne+5hxStCQIyq1aFBx7A8fLLWCpW9HZ4QgghxGVd7VKwbrheNLBCa93U9bo98LrWuofr9UiAq5jH42qu3QfoU7du3cfj4uLcXb3bXWop2LvuuosNGzaQkJAgq3OIUslutxMREUGvXr2YM2cOkPd/oWPHjsTHxxMfH0+5cuW8HKVvysnJoWnTpvj5+bF69WpatGhB06ZNWb9+PSaTfJ8u3EeWgvUhzWNjSTYM5j/7LOOiouD0aU69+y7WSpVoHBDA1MGDMRwOb4cphBBClGS/A/WUUrWUUv7AfcAyT1xIa71caz0sNDTUE9UXmxMnTrB06VKGDh0qiQ1Ravn5+dG/f3+WL19esLrHN998w88//8yYMWMksXEdrFYrkyZN4o8//uCmm24iJyeHGTNmSGJDFCv5aSuh7vngA547dgwyM0l48kmizGb22WwMnzuXAD8/OoeFsWPaNG+HKYQQQniVUmoe8CvQQCmVoJR6VGvtAJ4G1gD7gAVa6z0eun4fpdS0tLQ0T1RfbKZPn47T6WTYsGHeDkUIj4qNjSU9PZ3vvvsOm83Gv//9b5o0aSITXrrBHXfcQZ8+fTh9+jRvvvkm9evX93ZIooyRYSk+5MS2bbw2aBCL4+JI0Zr5wD0hIWxu354q//kPtTp18naIQgghRLEPSykJfKWNUdSwFIfDQa1atWjcuPHf5iIQorSx2WxUqVKFAQMG0LJlS5599llWrlxJ7969vR1aqXD69GkWL17M448/jsVi8XY4ohSSYSmlROSNNzJ9/36SDYPts2dzT8+eYLNx/9q11L71VqqZzYzq0IFzp055O1QhhBBC+IhVq1aRkJDA8OHDvR2KEB7n7+9Pv379WLJkCW+88QZdu3alV69e3g6r1Ljhhht48sknJbEhvEKSGz6qxZAh8O23kJXFW089RcvAQE4aBm//+ivlq1alc3AwTJ8OhuHtUIUQQohSqzQMS/n000+JjIykT58+3g5FiGIRGxtLamoqKSkpvPfee7JMqRClhCQ3fJ3JxL2TJ7MtK4uslBRev/VWqpvNOLKy4PHHsfn7c2d4OBs++sjbkQohhBCljq9PKHro0CFWr14tXchFmdK9e3fCw8MZOnQoLVq08HY4Qgg3keRGKWINC2P0jz9yxOFg45Ej8PDDfOXvz8qzZ7l1xAhClaJDSAhPNWvGvg8+gPR0b4cshBBC+DRf77kxbdo0lFI89thj3g5FiGJjtVrZt28fn376qbdDEUK4kSQ3SqsaNWDWLB7MymLjJ5/QvWJFbMCv584xZfdufnj+eQgN5W2LhRoWC10rVODf7dqx7t13sZ075+3ohRBCCJ/gyz03bDYbM2bMoE+fPkRFRXk7HCGKVXh4OH5+ft4OQwjhRpLcKANuHj6ctWfPkq01GSdPsvill7jvscegQweO+ftz0unk+9RU3tm8mW4vvURASAjbrFaoV485LVsyrnt3ts2di+FwePutCCGEEMJNFi9ezJkzZ3jyySe9HYoQQghx3WQpWAHAqZ07+faDD9jw00/sOn6cTQEBWNLTqW8YxLmOUUAIUDMggB39+mHq2JFzt95KuWbNvBi5EEKIkkaWgi25zl8KtnPnzhw9epT4+HhMJvm+SwghRMl3qTaGzBwlAIho3pyhM2cytND+b9euZcUnn7Dx99/Zc+YMCTYbB3JzMS1YAAsWUAs4C1iBYKWoYDbTJCSEb7p0gRo1WJCWRmidOjTp2ZPImBhMMlmZEEKIUkQp1QfoU7duXW+HclX27dvH+vXrGT9+vCQ2hBBClArSc0NcPcOA33+HNWt4cOpUfjtzhhSHg3NakwuEkpfwAAgAbOedagGamEzsqFoVKlbk7lOnCAoJoWa1atRv0oSG7dvTuHdvgsLDi/tdCSGEcBPpuVFy5ffcePbZZ5kyZQoJCQlUqVLFy1EJIYQQV+ZSbQxJbgi3M2w2TIcOwd69TJo1iz8OHODYmTOcOneOJJuNBkqxxmQCux1VxM9fbeCAUhhmM6EOB/5KYVWKYLOZYLOZflWq8Hrz5uSUL8/IffuoVLEiVSIiiKhZk6r16lGnTRsqNmgA0ktECCG8QpIbJVd+ciMsLIxevXoxd+5cL0ckhBBCXDkZliKKlcnfHxo0gAYNeHbAgEsem5aQwJ5Vq/hj0ybi9+/n8PHjxJhMYLWSk55OwIkT5GpNptacNgycdjtBR4/y+tGjHAM+KKLOrsB3wBagHXk/5P5AgFIEmkz8IzSUl6tVY6fTyTMJCQT6+REcEEBwQADlgoK4v3lzbmncmASnk3XHjhEWHk5oRAQVIyMJq16diAYN8A8LA+nGK4QQwkelpqYyfPhwb4chhBBCuI0kN4RXlY+Kov2wYbQfNuxvZUFAUlEnORxw4gTVDx1i8apVJCYkcObUKc6cPUtKejo9QkOhXDn8ExOJPniQLMMgR2uytCbN6eRYcjKkpLBTazYUUb3as4dbgIXA80WUjwTeAt4G/gOYyVt2yOR6PtVqZXBgIB/ZbIzPzsaiFH5K4Wcy4WcyMbVmTdpXqsS81FRmJyZitViw+vtj9fMjMCCAUR06EFWlCj+fOcOmkycJDAoiIDAQa1AQ1uBgerVvT1BYGAkZGSRlZxNcqRKBYWEEVahAUHg4/uXKydwmQghRTHx1zo3GjRvTsWNHb4chhBBCuI18AhK+x2KBGjWw1qjBgFtvvehhzYH4S1TzAHC/w0Hq4cOcPXSI1OPHST5+nEYVKoDFwh0HD5L8yy+cy8wk49w5MnNyyMzN5fZq1cBqpc6JEzQ8cYJcw8CmNQ6tsWuN1TXk5kxuLmcNAwMwAO3aTuzfD8BSYHURccXu3UsUMPYi5buApsAgYGMR5aeBKuT1WvnNtU+5NhNg8/cHk4muNhubDaMgMWNSCitwIjwcTCb6paayzeHADJiVwuyaMHZz7dpgNvNgQgL7cnOxmExYTCbMSlE1IIB5MTFgNjPijz84mpOD2WTC7DqmZkgIb7dpAxYLb2zfTpLNhsViwWI2Y7FYqFWxIsPatgWLhalbt5LpdOLn54fZbMZssVCjcmV6tWwJZjOLtm7FqTVmPz8s/v74+fsTGR5Oi4YNwWzm1z/+wOTnV1Bu8fenYqVKREZGYphMJCQmYrJYsAQEYPLzw+LnR1D58liDgjBMJhyGgcVqlUSR8BjD4SjYTOT9QTYcDtJTU/P2O50Fj+WsVsoFBeGw2TiWkPBXudOJYbdzQ4UKVCxfnqzMTP44cADD6UQbBg67HcPppE5EBBEVKpCans7WffswDAOHw8HNMTGU79vX27fCp2mtlwPLW7du/bi3Y7kaw4cPLxiiIoQQQpQGMueGEF5iO3eO9OPHyTh9mozERDKSkjiXksLNNWtSzjDYvGsXm+LiyM7KIjcnh1ybjdzcXEa3bk15rfli717WHDuGzeEg1+HA7nRidzpZ2aQJVqeTlw4dYk1aGg7DwOFKvmitORgRAYbBgLNn2Wi3FyRfDK3xA5KCg8EwaJWTw26tC5IymrxVcc65GsORWnOy0HsKAHJcz0OAc4XKg8/bZwVyC5VX5K/JaP0AR6HyqsAJ13OTK6bz1QYOuJ4X1WRvBux0xRBSRPlNwCYgDqhfRPntwBpgM3nJo8LuB/5PKVZoTVEfF58CPjKZmGMYPFREnK8qxWizmSmGwbOG8bfy900mnjabGe90MtpVfr6ZZjODLRZG2e28X6hcAYv9/OhlNjPCbuczp/Nv5//k709rk4mhNhtfFVH/bn9/6phM3G2zsbKI8lP+/pQ3mehls/FjEdfPsloB6Jiby++F/vb4A+kBAQDcaLOx97xyTd7PTrK/PwANbDYOFbp2GJDoSkRFORycLlReGThhNgMQ7nSSel7dAFHAEddQs3KGQVah82sD8a6ffT+t//az2RDY53pe1M9mc+B/XPxnrx3wKxf/2esG/Je8hGZR37UPBBa5ttgiyocCM4FpwBPn7V8O3OmBdoDMuVFy5Sc0UlJSCAsL83I0QgghxNWROTeEKIH8y5UjvEEDwhs0KLL8Jtd2MQ+5tot5x7VdzDeXiW/rZcpPFHptOBw4slwfCW02Dpw4QXZ6OrbsbGxZWdizs/FTCqKiwG5n3Y4dpKWlYc/NzdtsNipardCwITgcTNuwgbSsLJwOB06nE6fdTnRYGDRpAk4no3/8kSybDcPpxGEYOJ1OmlaoAI0agdPJYz/+iN0wMAwDp2FgaE2HihWhdm38HQ76bt6M4Ur45D/2qFQJatSgQm4uXXbtQgOGYeQ9ak2vSpUgIoIq2dm0iY9Hu87LL29XsSKEhxOZmUmThATAlRhyHdO0QgWoUIHIc+eoe/p0wQfg/PLosDAoX54bzp0j6uzZv31ArhIWBkFB3HDuHDekpxfszz+uQmgoBAZSJSOD8HPn/qrf9RgSEgIBAVTMyCAsq/DHd7CGhICfH+Hp6YTl5Pyt3K98ebBYCE9LIzS3cGoKTK65aCqnphJm+2udpPxeQ1SoAEBESgqVbLYLzrUqBa5VkqolJZFot19QHmYyQZUqoBQ1ExPJdjgKEj8KuMFshqpVQSnqnTqFv90OShUcU9NigWrVQCkaJyRw6rzkjgIa+vtD9eoAxBw5QorTmdfjyVVHy8DAvHKlaBMfT5ZhXFDeoVw5qFEDlKLdvn04XfXml3cJDYXoaPy1puPu3X+Vucp7hYdDdDSV7HZu37XrgjKlFAMiIqBmTaJzc+l/frlru7t6dahZkxaZmdzvKjcBJpMJpRSD6tSB6tXpmJ7OE7t2Fexvfsstf/t3FGWDJDaEEEKUNtJzQwghhBBuJT03Sq78nhtlqf0nhBCi9LhUG0OWexBCCCGEEEIIIYRPk+SGEEIIIcQ1Ukr1UUpNS0tL83YoQgghRJkmyQ0hhBBCiGuktV6utR4WGhrq7VCEEEKIMk2SG0IIIYQQQgghhPBpZWpCUaXUGeCIm6sNB5LcXKfII/fWM+S+eo7cW8+Re+sZnrqvNbXWlT1Qb4klbQyfI/fWc+TeeobcV8+Re+sZxd7GKFPJDU9QSm0pazPCFxe5t54h99Vz5N56jtxbz5D7WrLJv4/nyL31HLm3niH31XPk3nqGN+6rDEsRQgghhBBCCCGET5PkhhBCCCGEEEIIIXyaJDeu3zRvB1CKyb31DLmvniP31nPk3nqG3NeSTf59PEfurefIvfUMua+eI/fWM4r9vsqcG0IIIYQQQgghhPBp0nNDCCGEEEIIIYQQPk2SG0IIIYQQQgghhPBpkty4Dkqpnkqp/UqpeKXUy96OpzRQSlVXSv2glNqrlNqjlHrW2zGVNkops1Jqu1JqhbdjKU2UUmFKqYVKqT+UUvuUUu29HVNpoJR63vW7YLdSap5SyurtmHyVUmqmUipRKbX7vH0VlVL/VUrFuR4reDNG8RdpY3iGtDM8S9oYniFtDM+QNob7lJQ2hiQ3rpFSygx8DPQCGgODlFKNvRtVqeAA/qW1bgy0A56S++p2zwL7vB1EKTQJWK21bgjEIPf4uimlqgEjgNZa66aAGbjwJTPlAAAgAElEQVTPu1H5tM+BnoX2vQys01rXA9a5XgsvkzaGR0k7w7OkjeEZ0sZwM2ljuN3nlIA2hiQ3rl1bIF5rfVBrbQO+Avp5OSafp7U+qbXe5nqeQd4v72rejar0UEpFAXcA070dS2milAoFOgEzALTWNq11qnejKjUsQKBSygIEASe8HI/P0lpvAJIL7e4HfOF6/gXQv1iDEhcjbQwPkXaG50gbwzOkjeFR0sZwk5LSxpDkxrWrBhw773UC8sfRrZRS0UBLYLN3IylVPgBeAgxvB1LK1ALOALNc3XGnK6WCvR2Ur9NaHwfeA44CJ4E0rfVa70ZV6tygtT7pen4KuMGbwYgC0sYoBtLOcDtpY3iGtDE8QNoYxaLY2xiS3BAlklKqHLAIeE5rne7teEoDpdSdQKLWequ3YymFLMCNwCda65ZAJtK9/7q5xmb2I69hFwkEK6Ue8G5UpZfOWxte1ocXZYK0M9xL2hgeJW0MD5A2RvEqrjaGJDeu3XGg+nmvo1z7xHVSSvmR1+D4P631Ym/HU4rcDPRVSh0mr4vzbUqpOd4NqdRIABK01vnf/i0kryEirk834JDW+ozW2g4sBjp4OabS5rRSqiqA6zHRy/GIPNLG8CBpZ3iEtDE8R9oYniFtDM8r9jaGJDeu3e9APaVULaWUP3kT0Czzckw+TymlyBtTuE9r/b634ylNtNYjtdZRWuto8n5ev9daS4baDbTWp4BjSqkGrl1dgb1eDKm0OAq0U0oFuX43dEUmUXO3ZcBDrucPAUu9GIv4i7QxPETaGZ4hbQzPkTaGx0gbw/OKvY1h8fQFSiuttUMp9TSwhrzZdWdqrfd4OazS4GZgCLBLKbXDtW+U1nqVF2MS4ko8A/yf64PIQWCol+PxeVrrzUqphcA28lY42A5M825UvkspNQ/oDIQrpRKA0cB4YIFS6lHgCHCP9yIU+aSN4VHSzhC+SNoYbiZtDPcqKW0MlTf8RQghhBBCCCGEEMI3ybAUIYQQQgghhBBC+DRJbgghhBBCCCGEEMKnSXJDCCGEEEIIIYQQPk2SG0IIIYQQQgghhPBpktwQQgghhBBCCCGET5PkhhCi2CmlwpRS/3A9j3QtxSWEEEIIcV2kjSFE2SVLwQohip1SKhpYobVu6uVQhBBCCFGKSBtDiLLL4u0AhBBl0nigjlJqBxAHNNJaN1VKPQz0B4KBesB7gD8wBMgFemutk5VSdYCPgcpAFvC41vqP4n8bQgghhChhpI0hRBklw1KEEN7wMnBAa90CeLFQWVNgINAGGAdkaa1bAr8CD7qOmQY8o7VuBbwATCmWqIUQQghR0kkbQ4gySnpuCCFKmh+01hlAhlIqDVju2r8LaK6UKgd0AL5WSuWfE1D8YQohhBDCx0gbQ4hSTJIbQoiSJve858Z5rw3yfmeZgFTXNzJCCCGEEFdK2hhClGIyLEUI4Q0ZQMi1nKi1TgcOKaXuBlB5YtwZnBBCCCF8lrQxhCijJLkhhCh2WuuzwM9Kqd3Au9dQxWDgUaXU/4A9QD93xieEEEII3yRtDCHKLlkKVgghhBBCCCGEED5Nem4IIYQQQgghhBDCp0lyQwghhBBCCCGEED5NkhtCCCGEEEIIIYTwaZLcEEIIIYQQQgghhE+T5IYQQgghhBBCCCF8miQ3hBBCCCGEEEII4dMkuSGEEEIIIYQQQgifJskNIYQQQgghhBBC+DRJbgghhBBCCCGEEMKnSXJDCCGEEEIIIYQQPk2SG0IIIYQQQgghhPBpFm8HUJzCw8N1dHS0t8MQQgghSrWtW7cmaa0rezuO4iRtDCGEEMLzLtXGKFPJjejoaLZs2eLtMIQQQohSTSl1xNsxFDdpYwghhBCed6k2hgxLEUIIIYQQQgghhE+T5IYQQgghhBBCCCF8miQ3hBBCCCGukVKqj1JqWlpamrdDEUIIIcq0MjXnhs+YMiXv8R//8G4cQgghhLgkrfVyYHnr1q0f93YsQojSx+l08u6775KRkcGYMWMwmeS76csxDIPU1FTOnj1LcnIyycnJpKamkpqaSlpaGunp6WRkZJCRkYHdbmfkyJE0bdrU22ELN/BqckMp1ROYBJiB6Vrr8YXKOwEfAM2B+7TWC88rcwK7XC+Paq37Fk/UHmYYnHz1VZ5MTubNKlVoHhvr7YiEEEIIIYQQxez48eMMHjyY9evXA5CZmcnEiRNRSnk5suL36quvsnDhQnJycsjNzcVms2G323E4HDgcDpxOJ4ZhoLW+6rrnzZvHU089xaRJkyR55OO8ltxQSpmBj4HuQALwu1JqmdZ673mHHQUeBl4ooopsrXULjwdazE6vX8/+5GQ2Ak8+8gg/9e+PySIdbIQQQgghhCgrVq1axUMPPUR2djZffPEF27dv54MPPqBSpUq8+uqr3g6vWI0bN46xY8eilMJsNmM2m7FYLPj5+REcHIy/vz9Wq7VgCwoKIigoiODgYMqVK0e5cuUoX748oaGhhIaGEhYWRlhYGBUrVuTgwYMMGzaMyZMns2jRItasWUOzZs28/ZbFNfLmp+a2QLzW+iCAUuoroB9QkNzQWh92lRneCNAbvvrwQ54D3uzShdd++IFZjz3Go59/7u2whBBCCCGEEB5ms9kYNWoUEyZMICYmhvnz59OgQQMeeOABUlJSeO2116hYsSJPPfWUt0MtFkuXLuWVV17BarVy4MABIiMj3Vr/TTfdxF133cWAAQNYtWoVMTEx0ovDDaZOncq6deuYPn065cuXL7brevNfrBpw7LzXCa59V8qqlNqilNqklOp/sYOUUsNcx205c+bMtcZabL7/9Vfq+Pnxyn//S8fy5Xlp9myS9u/3dlhCCCGEEEIIDzp48CC33HILEyZM4KmnnmLTpk00aNAAAJPJxPTp0+nXrx9PP/00c+fO9XK0nrdv3z5iY2MxmUx8//33bktspKSkMGfOHO6++24iIiJ4/PHH+fjjj1mxYgXBwcFMnjyZqKgodu3adfnKRJFGjhzJokWLcDgcxXpdX05H1dRatwbuBz5QStUp6iCt9TStdWutdevKlSsXb4RXyZGTw4+nT3NbnToos5kpn39OutaMGTjQ26EJIYQQQgghPGT+/Pm0bNmSuLg4Fi9ezOTJk7FarRccY7FY+Oqrr+jcuTMPPfQQq1at8lK0npeamspNN92Ew+Fg+vTptG/f/rrqO3LkCB9++CFdu3alcuXKDBkyhI0bN9KhQwcWLFhA/fr1WbNmDXv37qV3796cPHmSmJgYnnnmGQyjzAwicIupU6eSkpJCnz59qFixYrFe25vJjeNA9fNeR7n2XRGt9XHX40HgR6ClO4Pzhu3z55MO3Hb77QA0HTCAr/v35/W9e+Hnn70bnBBCCCGEEMKtsrKyGDZsGPfddx9NmjRhx44dDBgw4KLHW61Wli5dSkxMDHfddRc//fRTMUZbPAzDoGXLlmRkZPDcc88xdOjQq65Da8327dsZPXo0LVq0IDo6mmeffZaTJ0/y4osv8uuvv3L8+HEWL15MXFwcDz/8MFOmTKFRo0bceOONzJ8/X3pxXKNXXnkFpRTTp08v9mt7M7nxO1BPKVVLKeUP3Acsu5ITlVIVlFIBrufhwM2cN1eHr/r+q68A6PL4X6vJ9f/ySypUr45j+HAcOTneCk0IIYQQQgjhRnv27KFt27ZMnz6dkSNHsn79emrWrHnZ88qXL8+3335LzZo1ufPOO9mxY0cxRFt8br/9dg4fPkzXrl2ZOHHiFZ9nt9v57rvveOaZZ4iOjubGG29kzJgxlCtXjnfffZc///yTvXv38vbbb9OuXbuCOTWioqKYNm0ae/fu5Y477mDs2LE8+eSTvPLKK/Ts2VN6cVyFWbNmkZSURI8ePQgPDy/266trWS7HbRdXqjd5S72agZla63FKqTeBLVrrZUqpNsA3QAUgBziltW6ilOoATAUM8hI0H2itZ1zueq1bt9Zbtmzx1Nu5buldu7LlwAFuO3z4gv2pc+bQecgQHuzbl38uXeqd4IQQQogrpJTa6ho6WmaU9DaGEKLk0FozY8YMRowYQUhICHPmzKF79+5XXc+xY8e4+eabyc3NZePGjdSrV88D0Rav559/ng8++IBatWoRHx9/2Uk909PTWb16NUuXLmXlypWkpaVhtVq5/fbb6devH3feeSdVqlS5qhi2bt3KqFGjWLt2LVFRUQwYMICZM2eSmZlJ1apVZUWVS4iIiCAxMZGEhAS3T/6a71JtDK8mN4pbiW542GxQoQI8+ih8+OEFRdow6FO1KusTE9n3229EtWnjpSCFEEKIy/P15IZSqgbwIZAM/Km1Hn+5c0p0G0MIUWKkp6fzxBNP8NVXX9GtWze+/PJLIiIirrm+/fv307FjR4KCgti4cSNRUVFujLZ4zZgxg8cee4yQkBASEhIuusrG8ePHWbZsGUuXLuX777/HbrcTHh7OnXfeSf/+/enevTtBQUHXHc8PP/zAyJEj2bx5M/Xr1yc4OJjt27ejlJIVVYowb9487r//frp27cp3333nsetcqo0h/xolxM65cxmTlcXZ1n//d1ImEx99/TVO4LlLjMETQgghyjql1EylVKJSaneh/T2VUvuVUvFKqZcvU00zYKHW+hFKwZxeQoiSYcuWLbRs2ZKvv/6acePGsWbNmutKbAA0aNCA1atXk5yczO23305SUpKboi1ev/76K8OGDcNisbB58+a/JTYOHz7MuHHjaNu2LVFRUfzjH/8gPj6eESNGsGHDBk6dOsWsWbPo16+fWxIbAF26dOHXX3/lm2++wWKxsH37durWrUtAQIDMxVGEF154AYCZM2d6LQZJbpQQS2bPZjRg6tixyPJanTrxSrduLDp+nG/ffLN4gxNCCCF8x+dAz/N3KKXMwMdAL6AxMEgp1Vgp1UwptaLQVgXYBDyqlPoeWF3M8QshShmtNRMnTqRDhw7Y7XbWr1/PqFGj3Pat/4033sjy5cs5dOgQvXv3JiMjwy31FpcTJ05w2223YRgGixYtolGjRgVlTqeTCRMm0KhRo4KJKseNG8fu3buJi4vjvffeo2PHjpjNZo/EppSif//+7Ny5k88//xybzUZOTg6VKlWSuTjO880333DixAk6depEjRo1vBaHDEspITqHhZFhs7E1K+uix+SmpxNTuTL1LRaWJSVBYGAxRiiEEEJcGW8PS1FKRQMrtNZNXa/bA69rrXu4Xo8E0Fq/fZHzXwB+01pvUEot1FrHXuS4YcAwgBo1arQ6cuSIu9+KEMLHJSUlMXToUFasWEH//v2ZMWOGx5bHXL58OQMGDKBz586sXLmSgIAAj1zHnWw2G9WrVycxMZG33nqLkSNHFpTt3buXRx55hM2bN9O3b18+/PDDK5pw1ZNyc3OZOnUqY8eO5cyZM5jNZpxOZ5mfi6NmzZocPXqU+Ph46tSp49FrybCUEi4rKYlf09K4rUmTSx4XUL48386cyaKsLHi7yPaYEEIIIf6uGnDsvNcJrn0XsxoYoZT6FDh8sYO01tO01q211q0rV67slkCFEKXHhg0baNGiBWvXruWjjz5i8eLF15TY+Oabb5g2bdplj+vTpw+zZs1i3bp13H///TgcjmsJu1i1b9+exMRE7rvvvoLEht1uZ9y4cbRs2ZL4+Hjmzp3LkiVLvJ7YAAgICGDEiBEcOHCAN954g0DXl81luRfHqlWrOHr0KO3atfN4YuNyJLlRAvwycyY24LY+fS57bK3Bg/EbPJj08eM5XgrXtRZCCCG8TWu9W2sdq7UerrV+4VLHKqX6KKWmpaWlFVd4QogSzul08uabb9KlSxeCgoLYtGkTTz/9NEqpq6rHMAwGDBjAwIEDeeKJJ3jjjTcue86QIUOYNGkSixcv5oknnqAk99IfPHgw27ZtIyYmhnnz5gGwY8cO2rZtyyuvvEL//v3Zu3cvgwYNuup752khISG89tprHDp0iH/+85/4+fmhtWby5MlERkaWqbk4nnnmGcC7c23kk+RGCXBk/XpCgFsee+yKjjfeeYf2TicP9++PLmOZQSGEEOIaHAeqn/c6yrXvummtl2uth4WGhrqjOiGEjztx4gTdunVj9OjRDB48mK1bt9Ky5dXPS7x//36qVq3KkiVLiIiIIDAwkNdff50pU6Zc9twRI0YwevRoZs6cyYsvvlgiExzvvPMOc+fOpXLlymzatInc3Fxee+012rRpw8mTJ1m0aBHz58+/6mVci1t4eDgTJkwgPj6ehx56CIDTp08TExNDWUh6r1u3joMHD9KqVasL5krx1s+czLlRErRvj0MpLL/8csWnTLn3Xp5asIB5zzzDfYWWjhVCeI/hcGDk5GBxODCysjgSH09Oejq5586Rm5lJblYWNwQG0qBKFWyZmSzYsIHcnBxsOTnYcnOx5ebS6oYbuC0qiuS0NMZt3Ijd4cDhdOJwOHAYBndERnJXtWocSkvjxe3bcRoGDsPA6doerlqVQZUrsyU1lWfj43FqjVNrDNfjC+HhPFC+PN+mpfF8YiIGeX+E8h/HhIUxODiY2efOMSo1FQ0Fm6E1U0JCuMvfnwlZWbyVnV2wP/+vyTyrld4WCyNzc/nIbi+4N/nla/39udlsZpjNxpdO5wVlAFstFpqYTMTa7Swr4m/UUZOJCJOJ251Ofiii3G6xANDB4eC3QmVmINc16Vhzp5O9hcoDgExXeQOnk4OFykOAZFf90Q7H3z4dhwMn/fwAiLTbSQLO/66pOhDvGgN9Q24u6eeVKaC+UuywWkEpKmdlkXXe+Qq40WRifXBwXnl6OnYo+DZLAZ0sFpaEhIBSVElOLtivXMf19PdnVoUKJGtN88REvqlWjTZHj/7tHl6vEjjnhgX4E+hKXlLjd+B+rfUeN1yrD9Cnbt26j8fFxV1vdUIUC4fDgdlsLnHfhvsqp9PJpk2bWLp0KbNmzSIrK4spU6YUfNi9Wh999BHPPfcchmFw1113sWDBAvbt20fLli2x2+3Mnz+fe+6555J1aK0ZMWIEkydP/ttcFt62atUq7rjjDgICAoiPj+fEiRM88sgj7NmzhwcffJCJEyd6bF4ST9u3bx+9e/fm8OHDDB06tET0ZvCk+vXrExcXx//+9z+aN29esH/ixIksWLCAdevWuW31mnyXamNY3HolcfXS0+H337Fc5S+cJ778klkrVvD8xx/T64UXCPXirLRCFBdHTg7pCQnkJCcTGRQEGRls/v13EhISyExNJTMjg6xz5whRimFNmkBWFq/99BOHU1PJttnIttnIdTqp7ufHzFq1wG6n6x9/cNJux6E1DsPAATQ0m1kbFgZOJ1HJyaS7Pvg7yfsQ3gTYajaD1lgMA2ehOBsC+1zPaxfxPloBW4AkYEgR5Z2A24CjwPtFlCfGxXEXeZ/UFhVRHp6czCBgP1BUynRbQgIPKMUxrYnjwg/PAMdTUiA9nWSnkyRX7zB1XnlmdjY4ndjsduxaF5SZlMp7bjaDvz9WhwOra7zv+ef7BwVBQADh584Rlp19wfUVYA0PBz8/aqakUPW88nz+VauCxUK9pCTis7NdJ6uC44yoKEwmE43PnCExv9xVt59SEB0NQLNTp8jJyQH++oahnMkE1fO+4G908iQOm+2Ca1c0mSAqCrSm3smTmFzJm/wUSzWzGapWBSD65Eks5yVvtNZUs1igUiUAqiQmYjaMgnO11lSyWCAkJC+WnByUK2mUf4zVZAJXcgWlwJW0yr9GrmGAzYbhdJJ53v78+pNsNkhJweb6t808e5bSRik1D+gMhCulEoDRWusZSqmngTXk5bhmuiOxAXk9N4DlrVu3ftwd9QnhaceOHePWW2+lWrVqLFiwgKqu31ni6mRmZvLf//6XpUuXsmLFCpKSkrBYLHTr1o2JEyfSsGHDq67TZrPRs2dPfvjhB/z8/Jg9ezaDBw8GoEmTJqxfv56OHTsyaNAgKlWqRNeuXS9al1KKSZMmkZKSwqhRo6hYsSJPPPHENb9fd9m/fz/9+vVDKcXKlSv58MMPmTBhApGRkaxcuZLevXsXSxxHjhzhvffe44cffqBv376MHTvWLavXNGrUiFmzZtGlSxe++OILXnrppWv6WfAFGzduJC4ujpiYmAsSGwCLFy8mJyfH7YmNy5GeG1727Rtv8NrrrzP/yy+p/cADV3XultmzafvQQzwTE8OkHTs8FKEQF+FwQHIyJ/bsIX73blJPnSI9KYn01FTOpafzQosWmLKymLplCz8mJJCdm0uW3U62w4HT6eSXWrUgN5e7ExL4OTcXm9Z5CQbAD0ixWMAwqG0YHCp0aQuQ3x+gIpBSqNwK5H+kLQdkFioPgYJvzEOg4NtxRd5YvTpKsddqBbOZqMxMslzXNCuFGWgTEMA3kZFgsdD6yBEMpbCYTPgphZ/ZTMcKFRjToAFYLNy1bRtmsxk/sxk/iyWvPCqKB5s3x6YUY377DT8/P/z9/fG3WvH396dVnTq0b9SIHGDl7t0EBAbiFxCAn9WKv9VKdI0aRFWrhg04lJiIf1AQfoGB+AcF4R8cTFBYGP7lyv31IViIYubtnhveUBLbGEIUlpSURMeOHTl+/DhOp5OwsDAWLVpEu3btvB2aTzh16hTLly9n2bJlfPfdd+Tk5BAaGsodd9xB37596dmzJ9c6RG3btm107dqV1NRUoqOj+fnnn4mMjPzbcatWreLOO+/EbDbz66+/0rr1pX/V2u12Bg4cyMqVK5k3bx733nvvNcXnDunp6VSvXp309HRefPFFlixZQlxcHMOGDeOdd9655nt3pTZv3syECRNYt24dya7ejfnKlSvHmDFjeO655677OoZhYLFYMJlMNGvWjE2bNvnEyjVXq3Hjxuzbt4/ff//9gp/DxMREIiIiGD16NKNHj3b7dS/VxpDkhpf9q3VrPt66lZSzZwm8hu5XTzdrxqHdu1n222+Y27TxQITCpxkGWadOcWTbNk7Hx5N4+DBnT58mOSmJh+rWJQpYtHs3s+PiOJebS6bdTrbTSbbTydKKFWmkNU+mpvKlzYaDvJ4L+b0X/gTqAe2BTUVcOgUIA2KAnUWUOwGT2czNTifbyPsq1ULeN+shJhMHo6LA35+HExPZZbMRYDYTYLEQYDZzQ1AQX3ToAFYr7//5J8dzcwkKDCQoOJig4GAiwsO5t0sXCA5m24kTGIGBhFSuTMgNN1C+WjWCwsMxyQd/ITymLCU3ZFiK8BXp6el07dqV3bt3s3btWsLCwujfvz/Hjh3j448/5vHHpfNRYVpr9u7dy9KlS1m2bBmbN28GIDo6mr59+9KvXz86duyIn2s44rV68803ef3119Fa8+ijjzJt2rRL9iKYPXs2Dz30EAEBAezatYt69epdsv7s7Gx69OjBpk2bWLZsGT179ryueK+FYRjUr1+fAwcO0Lx5c3bt2kXNmjWZPn36JXugXO81Fy5cyKeffsqmTZvIdvXm9PPzo0WLFjz66KMMHjyYp556ijlz5mAYBpUqVWLSpEkFPWauVaVKlTh37hw2m41//vOfTJgwwR1vqcT47bffuOmmm2jSpAm7d+++oGz69Ok8/vjj7Nixg5iYGLdf+5JtDK11mdlatWqlS5qWgYG6c1jYNZ+fe/q0NqpU0bptW60dDjdGJrzFnp2tj23apFPWrNF6yRJ9+O239ds9eugX27TRj9Wvr2OrVdO3V6yoVzVsqHWzZnpOlSo60mTSFZXS5UAHgDaD/hS0Bv0kF0yZULB94SofXGi/cp2/zmLROjBQP+/vrysppSNNJl3bYtGN/f11q8BAfaRtW6179tRftG6th9arp0fExOhRHTrot3v00B/fc4/OnTVL66VLdfzcuXr7vHn68E8/6bRjx7TTbvf2LRZCeBiwRZeAv/vFuZXENoYQ+bKzs3WXLl20xWLRK1asKNh/9uxZ3aNHDw3oJ554Qufk5HgxypLBbrfrH374QT///PO6Tp06Be2jNm3a6DFjxuidO3dqwzDccq2MjAzdqlUrDWir1aqXL19+xedO+P/snXd8FMX7xz97/dJJIyEhgUDokBAIvdfQpEoEpEkRkS6gCCJN+IIg0ouIKKAIoQgCShGR3gQEkQ6RhBBCer1c+fz+SLIS0pNL0d++X695JTczO/Ps3t7dzLNPWbaMAGhpacmwsLA8+8fExNDX15darZZnzpwpitiFolOnTuJ5CoLACRMmMD4+3uzzJCcnc9myZfT19aVCoRDfPysrKwYEBPDgwYPZHhcZGcnu3btTEAQCYMWKFXnkyJFCy9G0aVMC4MiRIwmAhw8fLvRYZZF69eoRAE+fPp2lrVu3bqxcubLZPievktsao9QXAyVZytrC48XduxQAzmvXLlP9BB8fVlcq2cDCgqu8vcnevRkWGMhp/v5c0rUrt4wYwZ/mz+f1XbsY/+gR+c03fAzw8LhxpXMiEiIZiomzGzbw7qpV5Jo1DH/vPY6tU4dveHiws4MDm1hZsbZazcV2dqSzM49otaJC4mUlw8h05cP6HJQTkwBSELhWEKgGaAXQPl0JUUWh4B4PD7JpU/7k58d+bm4cWa0ap/n7c2GnTlw/cCDDNmwgjx1j5C+/MPjsWSZHR5f25ZOQkPiPICk3JCTKDnq9nj179iQAbtu2LUu7wWDgjBkzCIBNmzbl06dPS0HK0iUuLo67du3im2++yXLlyhEA1Wo1u3btyvXr1zM0NNTsc544cYKWlpYEwNq1azMyMrLAY2S8b/b29oyNjc2zf3h4OL29vWlnZ8fr168XRuxCMW7cOHH96u3tzVOnTpl1/LCwME6dOpWVK1cWlRMA6OzszMGDBxfoXIODg9miRQtxjBo1avDSpUsFlum9994jAK5fv5516tShs7Mznz17VuBxyiJXr14lAFarVi1LW1xcHFUqFSdPnlxs80vKjTK68Ng9bVqaxmvdOrEuMSKCwksb2L7pG9x9OWxwh6S3+6a/1qfPN0UAACAASURBVAC0BegsCKwkl3NLhQpkkyY86+/PwIoVOc3fn5uGDuXFr76SNrPZoE9O5uNTp3hq9WpeWriQXL6cxunTOdzbmz1dXNjKxoY+Gg29FAqO0GhIW1uGqVRZFBMA2Cr9vbmeTZsA8A1BILVaXrGyoku6QsJHo2FLGxt2d3bmvjZtyAkTGDplCte8/jq/nziRJ5Yt4639+xn96JFkASEhIVFm+f+k3ADQA8DGqlWrmuvySUiYDaPRyKFDhxIAV61alWvfXbt20dLSkq6urqXyZL+kCQ0N5dq1axkQEECVSkUAdHBw4NChQ7l79+5isSrIYPLkyeKacMqUKUUaK8MywM3NjTqdLs/+jx8/ppubG11cXHj//v0izZ0fpkyZIp7rhAkTmJSUZJZxr1+/zkGDBtHJyemf9bUg0MvLi9OmTcuXNUtu3LhxQ7ROAMBGjRoV6HqdOnWKAPjGG2/wxo0bVKvV7NKlS7FZM5QkGdZGx48fz9K2c+dOAuDJkyeLbX5JucGyqdz4tVcv9pPLqUtIEOvmtm1LAJzVogVJ0hgfT96/z+gff+T3EydyVb9+/KhlS46tXZtveHjwYP36ZNOm3OjsnEm5oQWoALgofYM9KwflyDKAtLDgFzY2rKVSsX25chzu7c2FnTpx34wZjP/jD9JoLK1LVCB08fF8fOoUz37xBffNmMED775LLlhATp7MCTVqsKeLC9vY2bG+VsuqCgW7qtWknR2p1VKbzbWpnH7tmE2bHGBbmYy0s6O+fPksiokhXl7c0aULuXAhdevX8+CcObz63XeMuH1bUkpISEj85/n/pNzIKGVtjSEhYTKZOGnSJALg3Llz83XMjRs3WKVKFSqVSm7YsKGYJSwd7t27x7feekt0WfD29ubUqVP522+/UV/Ma7TIyEjWqFFDdCcxlwVDr169xCfpxnys22/dukUHBwdWrlyZW7Zs4Q8//MCTJ0/y+vXrfPz4MWNiYvI1Tm5ERkaya9eu4tp5+/btRRrPaDTywIED7Ny5M62srMRxFQoF69evz+XLlzM5OblIc2THyZMn6eXlJc7XsWNHhoeH50teQRBE64bVq1cTAFesWGF2GUuSmzdvEgC9vLyybR84cCAdHR1pKMZwCbmtMaSAoqVJrVqApydw+LBYVV+txrXUVMQ+eQIbd/cCDTezeXMsPHsWv37+OVpPnPhPQ3pQyftnz+LPkyfx1/XrePjkCZ5ERWGprS38k5PxbnQ01hmNePVuOACgO4B3BAHfkbCTy+GkVsPdxgaVXF3x8eDBsKtRAzcfP8bDp09h1OvTisEAk8GAAY0bA0Yjfrl5E/eePYPRYBCLHMD4pk0BoxHfXbuGO8+fw2gywZCeTUNpNOKTatWAmBgMvXIFV+PjkWgwINlkQgoJWwCPlErAYICtySRmv8jg5YwYGgC6l9rkAKoKAm7b2ABaLRpGRcEgk8FWpUI5CwvYW1ujSeXKGN2lC+DqinPR0ahQpw5cfX3TslBISEhISOTI/6eAohmUuTWGxP97FixYgI8++ggTJ07E8uXLIQgCfvzxR3z++ec4f/48GjdujOPHj2c5Ljo6GgMHDsRPP/2EUaNGYdWqVf+JTA9//fUXPvnkE3z33XdQqVQYPXo0xowZgxo1akAQXk06bn7279+P/v37Q6fToVGjRjhx4oRZ02S2bNkSp0+fhr+/Py5evJhn/0uXLqFTp06IiYnJtl0QBNja2sLOzk78m1Feff1q3dWrVzFmzBi8ePECAPD999+jf//+hT63H3/8EQMHDkR8fDwAwMLCAk2aNMHYsWPRu3dvs6RwzYu9e/di7NixePbsGWQyGfr27YvNmzfDKpd9gaOjIxITE5GcnAySeO2113DkyBFcunQpS+rUfwtNmjTBhQsXcOjQIXTp0iVTW2pqKpydndG3b198+eWXxSaDFFC0DD5Vib9/n6EAuWTJP5XR0TQCPOnmVqgxEyMiWEmhYE2VirpCmtJFP3rEE8uXc1W/fhxfrx4jAwLIBg041tKSNunWIHiphKZbNtTPwTIkw/KhWh7tFbNpk73U7op/Al1qAFoD9JbLyYoVyerVOaBcObaxs2NPFxcOrVKFk+rX59oePcgNG8h9+3hj61Y+PnWq0NdFQkJCQiL/IJ+WG0jL5pxjyc8YpV0guaUUGwkJCTx8+HCRnyD/f2TNmjUEwDfffJM7duxgy5YtqVar/7FAlcsJgLNnz872+JfjcDRp0qRYYk6UFNeuXWO/fv0oCAItLS05derUIrssFASj0chhw4aJbhPz588vtnnq1KlDAAwICMjXMYmJibx//z6vXLnC48ePc/fu3fzyyy+5bNkyzp49mxMmTOCQIUP42muvsVWrVvTx8aGnpydtbW2zXde/XDIsY/JrNZQdycnJYiBSQRDYu3dvXrx4sdDjmYMNGzaI569QKPjOO+/kaPHTrFkzAmBUVBRJ8vnz53RxcWGtWrWYmJhYkmKbhbt37xIAPT09s23/+eefCYD79+8vVjlyW2NIlhulxHfjx2Pg6tW49u238BkwIK3y/feBJUuAhQuBGTMKNe6Ps2fjx/nzsXTuXFjNnm1Gif/BZDDgwYkT+OPnn9HXzg54+hTfPXiAi+HhkMnlkMtkkCsUEGQyLGzfHpDL8cOdO/grMhJyhQJyuRxyhQIqpRLvtGkDyOU4fucOQmJjIVcqxT4OLi5o37UrUKECYG8PlIBWVkJCQkKi6OTXckMQhEdIWwgLADyQlkVaQFom6b9JVi5WQc1IWVpj/FeYNWsWPvnkEzRr1gybNm1CzZo1S1ukfwXbt2/Hm2++CTs7OyQkJMBgMAAA7Ozs0LFjR3zwwQfw8vJChQoVkJKSgnPnzqFx48bZjhUUFIRhw4bB2toau3fvRrNmzUryVIrEpUuXsGDBAuzfvx82NjYYP348Jk2aBEdHxxKTISQkBC1atEBwcDDKlSuHY8eOwc/Pr9jmMxgMqFq1KoKDgzF48GB88803xTaX0WhEfHw8YmJiEBsbi5iYGLHMmzcPDx8+RN++fREUFFSo8Xfu3Ilhw4YhOTkZ7u7uOHz4MOrUqWPmsygcJpMJCxcuxMKFC5GcnAyNRoNp06Zhzpw5maxIpk+fjk8//RSbN2/G8OHDAQBHjx5Fp06d8M4772Dt2rWldQqFIsM6aN++fejZs2eW9rFjx+Kbb75BREQEtFptsckhWW6UQcuNkdWr0xag4aXAP3WUSjYHyKL6i/XqRVpYkI8fF1FKCQkJCQmJgoMCxtwA8AWAri+97gJgQ0HGKO1SltYY/xVq1qzJKlWq0N7eniqVivPmzctXwMT/j+j1eq5ZsyZTbACkB8gcPHgwb968meWYI0eOEACtra1zfYp88+ZNVq1alUqlkuvXry/zARFPnTolpre1t7fnvHnzGF0KQfS3bt0qWi+0b9++xO7dxMREMcimOTNW6HQ63rhxg0FBQVy0aBHHjBnD7t2709/fn15eXnRwcKBGo6FMJiMA1q1bt1DzxMfHs1WrVqK1xtSpU812DuZGr9dzwoQJVCqV4mfp5cC9p0+fJgAGBgZmOm5aelKJffv2lbTIhebx48di4NrsMBqNdHV1Zd++fYtdltzWGKW6EAAQAOAOgPsAPsimvRWA3wEYAPR7pW0ogHvpZWh+5itLC48qCgV7uriIr8PTg7PUVquLPnhwMM+r1ZwumchKSEhISJQChVBu3MhPXVkuZWmN8V/g9u3bBMCVK1cyPDycgYGB4obpwoULpS1emSA5OZlLlixh7dq1xQ0l0k3lhw4dyocPH+Y5xvjx4wmAjRs3zrVfVFQUu3TpQgAcOXIkU1JSzHUaZsFkMvHYsWNs06YNgbQUoIsXL2ZcXFyJy2I0GsUAn3K5PM8sNcVBREQEbWxsCIALFy7MsZ9Op+Mff/zBHTt28JNPPuHo0aPZrVs3NmzYkJUrV6a9vX0mhUVORS6X08LCgs7OzqxWrRq7d+9eqOCeX3/9teg+VblyZd69e7col6HESExM5KBBg8Tr5OTkxF27dolBRb29vTP11+l09PPzo729PUNCQkpJ6oLRNj3pxY4dO7JtP3/+PAFw69atxS5LbmuMUnNLEQRBDuAugI4AQgBcAjCA5K2X+lQCYANgKoD9JIPS6+0BXAbQEGkfqisAGpCMzm3OsmIyGnzmDCq1aIHPe/fGxD17AAATfHyw6o8/sLJvX4wvpPnWyyzp1g3vHzqE/TNnoseCBUUeT0JCQkIif9BkgslohEmvh8lggDH9r1alglwQkJKUhPi4OJjSAy+bDAaYjEa4OjhAIZMhKioKERERaWMYjWJ77cqVoZDJEBIWhqfPn8OUMU96aVGnDmQA7v79N/5+9kxsp8kEmkzo2qABYDLh93v38Pj5c5iMRnTy94fNG2+Y/RoUNKCoIAg/AzgFYFt61SAArUh2NrtwxURZWWP8V1i8eDE++OAD/P3336hYsSIA4MCBA3jnnXcQFhaGiRMnYv78+bC0tCxlSUuWhIQEfPbZZ9i+fTvu3buXoQiEs7MzYmJiULFiRZw7dw5OTk75HrNWrVr466+/MHfuXMzOxaXZaDTi448/xieffILGjRtj9+7dcHNzK/I5FQWSOHz4MBYsWIBz586hQoUKmD59OkaNGmXWYJ35wWAwYMuWLZg5cyaeP38OFxcX/Pbbb/D29i5ROTIIDg5GjRo1kJKSgg0bNmD06NGZ2o8ePYqAgACYTKZsj5fL5dBoNLCysoKdnR0cHR3h6uoKd3d3eHl5oWrVqqhVqxYqVqxY5ICeUVFR6Nq1Ky5cuACZTIaZM2di3rx5RRqzNHjx4gWGDBmCn376CSSxbds2TJw4UQwq+jJ3795F/fr10aRJExw9erREgqIWlpCQEHh4eKB8+fIICwvLts+MGTOwdOlSPH/+HOXKlcOSJUvw66+/4ttvv4WdnZ1Z5cltjVGayo2mAOZkLFwEQZgBACQXZdN3C4AfX1JuDADQhuTb6a83APiV5He5zVlWFh5bRo7E8C+/xB+7d6Nunz4AAFe5HM9NJuiSk6HQaIo8hz4pCfXt7ZFgMODPp09h6exc5DElJCQkAAAk9Ckp0CclpZXkZBhSUqASBNhbWQEGA/66cwd6nQ6GlBQYUlNhSE2Fs7U1qrq4wJSaisPnz6fV6/Xi35rly8PP3R0pSUnY9NtvMBgMae0GAwwGA1p7eKClmxuiExLwv7NnYTQaYTAaxb+vV6qEduXLIyQ2Fu9fuQKDyQRjejEYjRjv4YFO9va4FRuLsbdvw2AywUTCmF4Wubqio4UFziUk4K3QULHeSMII4Bs7O7RVKHAwORlD4uPF+ozyi1yO5iYTviExNJvLdg2AD4A1AMZl0/4AgBeAxQA+yKY9HIAzgFkAPsmmPQmAFsBEACtfaZMjzQQSAN4C8FX6/7fs7VEzMjLbt7koFEK5YQ/gY6RZbBLAbwDmkYwyu3BmRhCEHgB6VK1addS9e/dKW5z/DE2bNoVer8er67bY2FjMmDED69atQ+XKlbFhwwZ07NixlKQsGaKiovDpp5/i+++/x6NHj8R6T09PBAYGok+fPnjttdegVqtx+vRpeHh45DpeSkoKmjVrBg8PD+zZswcxMTFwc3ODTqfDhQsX4O/vn+vxe/bswdChQ2FpaYmgoCC0aNHCLOdZEEwmE3744QcsWLAAv//+Ozw9PfHBBx9g2LBh0JhhHZ1foqKisHz5cuzatQv37t0TFQW9e/dGUFBQqW9Yb968CT8/PxgMBuzatQt9+/YFkJY5pl69ejAajejbty88PT1RuXJleHt7o2bNmnBzcysx2detW4dJkyYhNTUV1atXx88//wxPT88Smbu4+PPPP1GvXj0oFAr4+Pjg0qVLiIyMhL29faZ+mzdvxogRI7B48WJMnz69lKTNm4CAAPz888/4+uuvMWTIkGz71KhRAxUrVsTRo0cBAB4eHggNDYVOp4NCoTCrPLmtMcw7U8FwA/DkpdchALKPZpS/Y7NVHQuCMBrAaAB5ftmXFJ1jYvC1tTVqpwdiCT59Gs9MJvhptWZRbACA0sIC65YuRavx47GgZ08sOnfOLONKSEiYF5PBAF1cHFLj46FPSoKjlRWg0+FJcDBePH8OXWIiUpOToU9JgcxoRNuaNQG9HieuXkVweDj0Oh1SdTqkpqbCRqHACF9fIDUV6y5cwL0XL5Cq10Ov1yPVYEBFjQbzatYEUlMx7upV3ElIgN5ohN5kgt5kgp9Gg/WuroBej7bBwbiv18NAQp9eAuRyfC8IgMEANwARr5zLIPzz2L0BgORX2scAWJf+f/dsrsVUAH5IS9s8Ppv2uQBaCgIS5XKsMBigQNqmXSEIkAPwDQlBOxsbpJC4EB4OhUwGuSCktQsCkqKjAYUCQmoqAECtUEAuCJAJAuQyGdTlywPOzrCKj0ddvT4tOPJLxaFuXcDJCe7R0Rhw+3ZafUYQZbkcFfz8AAcH+L54gbl37kAuk0Eml0OW3u7asCFgZ4dWz55h1YMHkMvlae3pfRwbNQKsrNAjLAweT5780yaXQxAE2Pj7AxoNBj99imbPnmVpV9WvDyiVmPD0KV6Pjk6rl8kgk8kgUyiAOnUAmQxznj3DpMRECDIZKletWrAb1swIgrCV5GAAg0lOzPOAMgjJAwAONGzYcFRpy/JfISwsDOfPn8eCbCxPbW1tsXbtWgwYMAAjR45Ep06dMHToUHz22WdZNg7/dlJTUxEQEIATJ04ASEvNWbVqVQwcOBBTpkyBra0tQkND0bx5c5hMJhw9ejTPta7JZEK9evVw7949XL16FR4eHrh8+TL27NmDrl27okOHDggPD89VQdCnTx/UqFEDvXr1Qtu2bbFy5UqMGTOmRNKpGo1GBAUFYcGCBbh58yaqVKmCL7/8EoMHD4ZSqSz2+YG0jeuyZctw+PBhPHv2TKyvWLEiunfvjmnTpqFy5bIRC7lOnTr45Zdf0Lp1a/Tv3x/Hjx+Hj48PGjduDIPBgI0bN2LUqNL56nr+/Dk6d+6Ma9euQS6XY9GiRfjgg+xU+/8+ateujU8//RTvvfceHjx4ACAtleyIESMy9Rs+fDh++uknzJw5E+3atUPDhmUvg/rz589x5MgRODk55ajYuH37Nu7cuYPx49NWbzExMXjy5Alq1KhhdsVGXpSmcqNEILkRwEYgzXKjlMUBSLieP48hXbsCcjkAIHrZMtQEMHngQLNO1XLcOAxbsQJLz5/H0B9/RI3u2W0nJCT+H2IyQZ+YiITISOji4pASF4eU+HikxMWhhqsrNCQePX6MG3fvIiUpCSmJidAlJyMlORlv1a8PS5MJx/76Cz/dvYuU1FSk6HRISU2FTq/HNz4+0BoMWPPoEbaHhUFnNCLVZILOZEKqyYRHjo4QUlMxLj4eGwwG8Wk6AFgBiE//fzqAHa+I7Qrgafr/nwH48ZV2bwAjdu8GBAH7AJwjoRQEqNKLr0YDmEyASoX4xEQk6fVQyGTQKhSwkctRzs4OqFYNUCrhL5ejsl4PhVwOpUIBpUKBeuXLA35+gFKJmZcvI8VkglKphFKlgkKpRPUKFYB69QCFAluvXAHkcrFNoVajoosLUKUKZAoFLty5A6VaDcVLxd7BAXBygrVMhoiEBCg0GijUashVKig0GshVKkChgDuAlFze3qpIC+SUEzUB/JpLe10AO3Np9wGwOpf2euklt/Hr5tJeK73kRPX0khNV0ktOeKSXMkIDQRAqAHhLEIRvkJYpReTfYLkhYX5++OEHAECvXr1y7NOyZUtcv34d8+fPx+LFi3H48GGsXr0a/fr1K5FNdnHzxx9/oHXr1qJVxbvvvouJEydmcrWIjIxEp06dEBUVhRMnTqB69dy+GdIUG40aNcK9e/fQtGlT2Nvb4+DBg6hUqRIOHjyId955B+vWrUP79u1x5syZXMeqVasWLl68iEGDBmHs2LG4fPky1qxZU2xWEwaDAd9++y0WLlyIO3fuoGbNmti2bRsCAwNLZPN06NAhrF69GqdPn0Z8fNovtVwuR926dTFgwACMHz8eVlZWxS5HYWjRogX27t2LXr16oWPHjrC3t0d8fDymTp1aaoqNzz77DO+//z4MBgPq1q2LI0eOwMXFpVRkKS6mTJmCoKAgnEt/wHzkyJEsyg1BELBhwwacP38eAwYMwNWrV8vcfTRixAiQzFbZnMG+ffsAQMygsmLFCgDAoEGDil/AV5DcUkqYJydP4lCbNnh92TLYT5mSVunkBMTEADqd2dOdRvz1F75u0AAT/P2h+vVX4D/wgy/x34MmE5Kjo5EUGQkLEhYk4iMicP3GDSTFxiI5Ph7JCQlITkhA28qVUcnSEveePMHXly4hOSUlreh0SNbpMLNKFdRVKnE8LAzvP3yIZKMxrZhMSDaZcEypREO9HpsAZPeT/ifSNpYrAEzKpj0YaRvDRYKAT0hoBAFqQYBGJoNaJsP56tVhY2GBDZGRCIqKglqhgEouh1qphFqpxJdt2kCu1WLv33/j0osXUKtUUKvVUKnVsNBqMaZDB0CtxsW//0ZYQgKUGg3UFhZQabWwsLZGg7p1AZUKodHRSAWgsrBIK5aWUFlZQWtrKypOJSRKiwKkgp0A4B2keeSEIrNygyS9iklEs1MW1hj/FQICAvDgwQPcvXs3X4qKa9euYcSIEfj999/Rs2dPrFmzptRjQRSFJUuWYMaMGTCZTBg+fDi+/PLLLNchPj4eHTp0wPXr1/Hzzz+jdevWANIsG8LDwxEaGoqQkBCEhISI/x86dAjR0dEQBAEk8frrr6Nhw4bi0/I5c+Zg+/btuHv3Lj755BN8+OGHecpqMpkwZ84czJ8/H40aNcKePXuyXHuS0Ov1SEpKQnJyMpKSkgpcjh8/jkePHsHHxwezZs1Cnz59itVtIjU1FZs2bcJXX32F69evQ6/XAwC0Wi0aNWqEt99+G4GBgaXudlIQvvrqK7z11lsAgLZt2+KXX34pcRlCQkLQuXNn3Lp1C0qlEsuWLROf9v8XSUpKgpOTE5KSkuDu7o4nT55k2+/UqVNo06YNhg4dis2bN5ewlDkTFRUFJycn2NnZITIXF9bGjRuDJC5evAgAqFmzJm7fvo3Y2FjY2NiYXa4ixdwQBGEPgC8BHCaZfcSZwgmlQFpA0fZIW9BcAjCQ5J/Z9N2CzMoNe6QFEc1IFP070gKK5vqEpywsPNYPGoR3vv0Wd48cgXfHjnh07Bh+6dgRwxs1guzCheKZdONG4O23wa+/hpCDOZGERG7QZIIuPh6Jz59DkZICW5kM+thYnDp3DglRUUiIiUFifDwS4+PR2MUFTR0cEBUVhQ9//RVJKSlI1OmQlJqKJL0e79rbo79Gg9uxsWgZFoYkEkkvzbUZwHAA5wE0zUaW7wH0B3AMaemWtAA0ggCtTAatTIZNHh5o6eiIM6mp+CQkBFqlElqVClq1Glq1GpOaNIGXiwtuxsbiWHAwNBoNNBYWUFtYQGNhgfb+/rCxt8ezxESExsVBY2UFjbU1NDY2UNvYoJyLC+QWFkAJm9lJSPybKETMjXUk3ylOmYqbsrDG+C8QGxsLJycnTJo0CUuWLMn3cQaDAcuXL8fs2bOhUqmwZMkSjBo16l+1+UxNTUWHDh1w6tQpqFQq7Ny5U3wSCgA6nQ5Pnz7Fw4cPMWnSJNy6dQvdunWDWq0WFRlhYWEwGo2ZxlWpVFAqlUhMTISlpSXefvttREREYOvWrdizZw+cnJzQqVMnJCcno3Xr1jh37pwY78TPz+9VMbNl7969GDJkCJRKJSpUqJBFOfGqTPlBpVLBwsICFhYW8PLywrRp09CjR49is8x5/vw5li1bhj179uDBgwdisFYHBwe0a9cOkydPRtOm2a1M/h28+eab2L59OwDAysoKDx48gHMJxuRbsGAB5syZA6PRiIYNG+Lw4cNwdHQssflLi+PHj6NDhw4A0j7DKpUq236zZ8/G/PnzsWPHDgQGBpakiDnSr18/7N69GytXrsxRCRUaGgp3d3dRIZqamgqNRgN3d3f8/fffxSJXUZUbHZC212gCYBeAr0jeMZNgXQF8jjS36c0kPxEEYR7S0rvsFwTBH8BeAOWQZoX8jGTt9GPfApChUv6E5FdZZ8hMWVh4BHp44OzTp/g7NRWCTIZBlSrh2+BgBE2Zgr7LlhXPpCYTjtWpg+n37uH7Q4fg/R8PvCUBwGgE4uMRevcuYsLCEBcejriICMRFRqK8UolWbm5AQgLmHD6M6Ph4JCQlISElBQkpKWhvbY0p5crBFB8Pj/v3kWAyIQFpARMB4D0AS5HmPpGdLnY2gLkyGcK1WvgkJ8NCJoOFXA5LhQIWSiXGVq6M16tUQbhMhrl//gkLjQYWFhawtLSEhaUl2vv4oFbVqoglcfHvv6G1sfmn2NmhvLs7LBwcQI0GgkolWSNJSJRBCqrc+DcjBRQ1L9999x0GDhyIM2fOoFmzZgU+/v79+xg9ejROnDiB1q1bY+PGjahWrVoxSGperl69inbt2iEmJgZVq1bFmTNnoNVqsXLlSuzevRshISGIiHg10lHaJtXd3R3u7u5wc3PL9v958+Zh9erVcHZ2xqNHj2BhYQG9Xg9/f388f/4cf/31F/R6PRo2bIjg4GDY29sjKioKtra2ePbsWb5dTW7duoW5c+fCaDSKSolXi1arzbHt1X4l4W5y7do1LF26FEePHsXz588BpLkKeHh4oHfv3pg8eXKZidlXFObPn4/Zs2fDyckJb775JpYvXw4HBwc8fvy42N0gHj16hE6dOuH+/ftQq9VYs2ZNFveM/BAfH48bN25Ap9NBp9MhJSWlUP+/WieXy/HZZ58Va2BcFxcXhIeHo1WrVjh58mS2fQwGA1q1aoVbt27h2rVrqFSpUrHJkx9iY2Nhb28Pa2trxMTE5Nhv7dq1ePfdd3Hr1i3UrFkT69atw9ixjaGXSQAAIABJREFUYzFhwgTRPcXc5LrGyClH7KsFgC3S4sE9AXAWaQoPZX6PLwultHPQG/V6OgkCB3t5iXV2gkAVQKNeX6xzX92xg+r0XNT+lpb8rGdPhly6VKxzShQcg07HuOBg8sED8soVXlq/nj98+CG3jhnDNYGBXNS5MzcEBJDjxpFDhnBc5cpsV64cG1pYsJpSSVeZjL3lchIgAXpkk4u8T3obAboAtBMEusnlrK5SsYGFBRdUqkR260YGBnJ09eqc4OPDD5s148JOnbiib1+enjGD3LmTxgMHeHLlSl7Zto13fvqJTy5eZNTDh0xNSCBNptK+lBISEqUIcslB/18tpb3G+K/Qv39/li9fnkajsdBjmEwmbtq0iba2tlSr1Vy0aBFTU1PNKKV5WbRoEQVBIACOHDmSSUlJXLp0KR0dHQmArVu35ujRozl37ly2aNGCAPjBBx8wNjY2z7Hnz59PALS1tWVkZGSmtkuXLlEmk3HMmDEkSaPRyNdee40AKJPJCIAtW7YslnMuTYxGIz/++GPa2NiIayOFQkFfX18uXbqUiYmJpS1iJhITEzl69Gi+8cYbTElJKfDxO3bsIABqtVqGhYWRJIcNG0YArFixIkNDQ4v0ecuNGTNmiPdSixYt8nXPZseTJ0/o6emZZU2bUxEEgRqNhnZ2dixfvjw9PDxYrVo11q1bl/7+/mzRogXbt2/Pbt260c3NjZ6enoyLizPz2f/D9OnTRdm++OKLHPs9fPiQ1tbWbN68OfXFvDfMizfeeIMAuGTJklz7dezYkdWqVaMpfe2fHuOST548KTbZcltj5Fex4YC07HKXAewHEAhgFdLiXJT6giK/pbQXHn8EBREAvxoxgiR5Zfv2tB8tW9sSmf/v8+e5pGtX+mm1BMBqAE0tW5Jr1zIpOLhEZPivYzIamRQZSYaGkn/+yb+2b+ePH3/MrWPGcFW/fpzXrh3nNm1KDh9O9u7N9ytWpK9WS0+5nLbpX3rVXlI+tM7mC9sPIO3tSU9PDrS1ZXNra3ZxcmJgxYocVb06V7VtS86ZQ372GYPGjOHOSZN4eP58nlm/njf27GHYxYvkixdkSoqkhJCQkCgWJOWGRGFITk6mlZUVR48ebZbxnj59yj59+hAAfX19eeXKFbOMay6Sk5PZvHlzAqBarWZQUBDXrl3LChUqEAA7duzICxcukExT2Lz33nsEwNmzZ+dr/LVr14qb2uAc1nlTpkwhAP72229i3dKlS0VlCwAuXry46CdbBtDr9Zw2bRq16etguVzO9u3bMygoqNg290Xl3r17rFevnvh+9O/fv0CyXrx4kTKZjHK5nJcvX87U1r179ywKAYVCQa1Wy3LlyrFChQr09vZmgwYN2LZtW/bp04ejRo3ihx9+yM8//5zff/89T58+zeDg4Cwb8Vu3btHDw4MAqNFo+O233xb6GkRERLBmzZq0trbmd999xxMnTvDs2bO8cuUK//zzT96/f58hISGMiIhgXFwcU1NTxY12fjhz5gwFQeDYsWMLLWNeXLhwIdM1zm3jvz19fzhnzpxikycv4uPjKZfLaWVllev9Fh0dTYVCwenTp5NMUxwqFAo6OjoyNja22BSFRVJuIM0t5BaAGQBcX2n7Vy1eSnvh8fXAgQTAx2fOkCR7u7oSAA989FGJy3Ln8GH+MmwYWaMGdQCdAHZ2cOCWESMYIyk60jCZmBQezocnT/LiV1/x4Jw5/HrUKC7r0YOG6dPJESO4rl49NrO2ZnWViuVlMqoAKgGa0pUTw7NRTrgKAunuTtapwzkeHuxRvjyHeHlxgo8PP27dmhv79ye3bCH37eMfX37JK9u28d6xY3x24waTIiNpKqM/wBISEhIZFGZ9AKA80rIEdwfgXNDjS7uU9hrjv8DBgwcJgIcOHRLrzPH0MigoiC4uLpTL5Zw+fXqZeDJ/+fJl2traEgC9vb25cuVKVqpUiQDYvHlz/vrrr5n6L1y4kAA4bty4fG3cvv/+ewKgUqnkzZs3c+yXkJDASpUqsUaNGkxOThbrT506RY1GI65dfv/998KfbCmj0+k4btw4qtVq8ZoMHz68TNwHubF//37a2tqyXLlyPHz4MJcsWVKgeyAkJER8D4OCgrLt8/HHH7Nr165s3rw569aty0qVKtHZ2ZnW1tZUq9WUy+X5tpbIUBhpNBpRGdO+ffsiXee4uDj6+/tTrVZn+UyYk0mTJhEAT5w4USzjG41GCoJAJycnAmDlypVz7T9kyBDKZDKePn26WOTJiyFDhhAA582bl2u/bdu2EQDPnTtHktyzZw8BcNCgQZw1axadnJwKba2TG0VVbrTNq8+/pZT6wqNnT76oVEl8WQ6gJi0afOlhMjHm1CnOaNqUlRQKAqAKYC9XV15etIgs41/8BcGg0/HZjRu8uXcvk376idy5k7/PmMF57dpxfL16HODpyY729vTVavnUxYVUqzk3hy/vCIWCdHXlmgoV2K5cOb7u7s7RNWrw/caNuahzZxrWriV37ODtTZt4YfNm3j1yhM9v3WLqf+h6SkhISOREQZUbSIsTHAzgawDfAHgEoF9BxijtUuprjP8AI0eOpLW1tWh6/8477xAAu3btmmnjXRiioqI4YsQIAmCVKlV46tQpc4hcKBYsWJBp81ejRg0CYIMGDXj48OEsG9f169eLG4b8PLU/duyY+LQ+P5ujn3/+mQD40SsP2yIiIujs7Cw+cX706FGBzrO0SUxM5IgRI6hUKkXrmLFjxxb5XsoNvV7PW7duFWkMg8HAmTNnplnr+vlluu4Z1jsLFizIdYykpCTRrWnhwoVFkodMszK6e/cuT5w4wW3btnHp0qWcNm0ahw0bxp49e7JVq1b08fGhl5cXXVxc6Obmxj179hR5znbt2lEul3P//v1FPofcSExMZJUqVejl5cWEhIRimcPJyYlqtZpt27YlANEdLDvi4uLo5eVFT09PRkdHF4s8OZGcnEyFQkELC4s8v2/69etHV1dXsV+7du0IgBcuXKCjoyNfe+21YpGxyG4p/5VSqgsPg4G0tSVHjUp7feoUowEeKUOLIZPRyPObNnGiry9dZTKeBkhLS/7ZtSv3z5rFlGLQvJmDhPBw3j92jKfWrOGuKVO4ql8/Pn73XfKtt3jM35/1tVq6yGSUvaScuJBuWfFl+mtbgFUUCjaxsmJ3Z2f+/frr5LRpvDppEjcPH84DH33Ec198wfvHjzMmOFiynpCQkJDIhUIoN66/bK0BwAnA9YKMUdpFUm4UDYPBQGdnZwYGBop15cqVE3+3LSwsuGPHjiLPc/z4cXp5eVGhUHD16tUFMl8vKsnJyWzWrJloPeDl5UUArFWrFnfv3p2tLF988QUFQWC3bt3yFTfk0qVLVCgUFASBBw4cyLdsgwcPpkKh4I0bNzLVG41G0b1AEAT+9NNP+R6ztIiNjeXAgQOpSH9op9VqOXXq1GKLYZCamsrVq1fTx8dHjC9RpUoVPnjwoMBjRUREsGPHjgTAESNGZFHEGI1GvvnmmwTAjRs3ZjuG0WhkzZo1CYCDBw8u1DmVNnq9nr169SIAbt26tUTm/PXXXwmAkyZNKpbxW7ZsSQAMCQkR473kZily4cIFKhQKBgYGluj31KhRowiAH374Ya79kpOTaWlpmUlJo9VqaWVlxY0bNxarJYyk3CgDC4/ft21jV4C3ly1Lq+jSJe3ynzxZajLlhkGno+n4cXL0aE5JN2mzEwS+5e3No4sXU1+MWm+SNKWmks+eMf7MGR6eP5+bhw/nwk6dOMHHh6+7u/OXunVJb2/+8pLJ5Mtlj0xGVqjAc9Wrs6uTE0dUq8aZzZtzVb9+3DFxIiP27CGvX2fKo0fUxccX67lISEhI/H+jEMqNG6+8lr1aV9aLpNwoGqdPnyYAUYFx//59AmCTJk04ZcoU0dKhadOmRX6SGRMTw27duokbyMIEaSwoFy9eFDc0Ga4CXl5e3Lp1Kw0GQ5b+ycnJHDlyJAGwU6dOTEpKynOO+/fvi64XX3/9dYHki4iIoKOjIxs3bpxFHpPJRHt7e3GN9fHHHxdo7JIiMjKSffv2FV0pLC0t+dFHHxVLPI3U1FSuWrWK9erVExUaAOjq6sp69eoRSAvKmhGLID9cuHCBFStWpFqtzjXoZGpqKrt06UKZTMa9e/dmae/SpYv4Wfk3YjQaxYCnK1asKNG5x44dS0EQeCY9hIA5mTFjBgFw/fr1PHv2rHiP5ua2k+GOtmXLFrPLkx06nY5KpZIajSbPz82BAwcIQFR4ZpxT165dWatWLfr6+habUkZSbpSBhcfi9C+asD/+IElWFASOkMuz7zx+PB/37VvsGVTyS2pCAg/NncvBXl60Tv/yrimX0zR2LHn6NGk00mQ0MiE8nGHXr/PukSMM/ekn8tdfqd+3j9+OG8cNgwbx0+7dObtVK0728+P+jh3JwEBGdujAljY29NVqWUWhoJMgUAPw03TLijuvKC1sAVZXqRhUqxYZGMino0bxfwEB/HrUKP68cCGv79rF8Js3y8y1k5CQkPj/SCGUG58C+BnAsPRyGMCSgoxR2kVSbhSN9957j0qlUvTPfvvttwmAmzdvJpm2ca9atapo9bBq1aoizWcwGPjhhx+Km8CMLBLFwdy5czMF6HR3d+eGDRtytMR49OgRGzRoQACcOXNmtsqPVwkLC6OlpSUBcFnGg7QCkuE/v3LlymzHz7CEAMB27dqVejaHDMLCwtitWzdRyWBjY8NFixaZXamRodCoW7duFoXGuHHjGBISIvb97rvvxMCl7u7uWSxiXsZkMnH9+vVUqVT09PTMEvgzOxISEti4cWOq1WqefOlBaUbsCA8PjzLz/hQEk8nEyZMnEyidgJpxcXH09PRk9erV86VQLAgXL14kAPbt25fkP+9VixYtcjzGYDCwTZs2tLS05L1798wqT3aMGzeOADh16tQ8+44YMYI2NjbU6XQkKQZvzlDIfPPNN8UmZ1FjbuwB0A2ALK++Zb2U5sKjs4MDa6nVJMlD8+YRAHu5uGTteOUKwwGqAXopFIwuY/6NSZGRDJo6lev9/EiNhiaAdQQhk8sHAL6TrpzQZ2NVYQlwjpUV6e3NeB8ftrG1ZY/y5TnQ05Nv16zJqQ0b8sSwYeTq1dTt2MEz69fz4cmTTIyIKO3Tl5CQkJDIBwVVbqQdgj4APksvvQt6fGkVAD0AbKxatao5L+H/K0wmE6tUqcKAgACxrkKFCpTJZJw1a1amYJaLFi0SN9m1atUqcrrBnTt30sLCgm5ubrx48WKRxnqVpKQk1qlT558HNLa2XL58ea4xHw4fPkx7e3va2trmO85AbGysaFnxwQcfFFpek8nEgIAAWllZZZtdJSg961/Gxt7V1ZWhoaGFnq+oBAcHs0OHDqLiyN7enp9//rlZ50hNTeXKlSuzKDQqVKjA8ePH8+nTpzkem5iYKLqYCILAMWPGZFG4JCUlcejQoQTAgIAAvnjxIt+yvXjxgjVq1KCtrS2vX78uZsexsbFhTExMoc+5NFmwYAEBcPz48SXqivEyR44cIQC+//77Zh3XZDJRJpOxSpUqYp23tzcB5HrfPnnyhOXKlWPDhg1FRUJxoNfrqVarqVar81SMGQwGOjk5ccCAAWKdnZ0d1Wo1O3XqRFdX12KVtajKjQ4AtgN4AOB/AKrndUxZLaWl3NDFx9MC4Li6dUmSbe3sCICXsjEZ/MLNjYkA26Rr3y0BXilC+qRiJS6OMRs2cFL9+pzZvDn/FxDANYGB/Obtt3l56VLy2DHywgX+dfAgQy5dYuyTJzQU440uISEhIVE2KITlxuL81JXlIlluFJ4//viDALhhwwaSae4FGZvVjM2kr68vV61axcjISIaHh4uWDRkKkKJw7do1enp6Uq1WF9idIyd27NghKmEyZIzPxQ3WaDSKFh716tXL91NanU5HNzc30cWmqDx69IgWFhbs1q1btpvLjCwKGVkfVCpVpuw2JcH9+/fF+AUA6OzsLN475kCn03HFihWFUmhkx/79+2llZSXKmqFEe/DgAX19fUVXn/xY6LxKcHAw3dzcaG9vT0EQqFQqeefOnQKPUxbIUM68+eabpZ6ad+TIkZTJZGZXeGYEFc0gLCyMSqWScrmcDx8+zPG43bt3F1l5mRcZwWrHjRuXZ9/ffvuNAPj999+TJO/evUsArF+/PoG8A94WFbO4pQCwBTAGwBMAZwEMB6DM7/FloZTWwuN0+od19/TpNOr1VKW7V2Tpt24dAbC5VkuSnNqwYdqPIsAtZvjBkpCQkJCQKAkKodz4PZu6PwoyRmkXSblReObNm0dBEETXkAx3EUdHR7Zu3Zpr1qyhn58fgbSMF2+88QaPHDnCTZs2ifErKlasmGvK07yIiIgQsxhMnjy50Cb9Dx48oI+Pj7ghbtiwYZ4xQiIjI8U4CUOGDMl36kyj0chq1aoRAHv27FkoebNj+fLlmeKfvDpnRsraXr16iVYTr2ZaKQ5u3rzJRo0aZVI2fGumB4A6nY6ff/4569Spk0WhMWHChAIrNLIbPyNAJgC2bduWtra2tLOz48GDB4s09o8//iiOu3PnziKNVVp8++23FASBPXr0yFfg3OImJiaGbm5urF27tllj8mQo5cLDw8W6rVu3EgDd3NxyVeqMHj2agiDw+PHjZpMnA4PBQI1GQ6VSmS+LiylTplClUoluhBnxgVq1akWNRsOIYra2L7JyA4ADgIkALgPYDyAQwCoAv+bn+LJSSmvhcWL4cDYH+OLePQZNnUoAfMPDI0u/aumpqi5+9ZVYt2vKFCoAWgE0jh5dglJLSEhISEgUjvwqNwC8A+AGgEQAf7xUHgHYlp8xykqRlBuFx8/Pj82aNRNfZ8TWAMDly5eL9VevXuX48ePFLCoeHh6cMWOGmIFEEASOGjWq0E99U1NTOX78eAJghw4dGBkZma/joqOjeeDAATEIIgDK5XLxqWZuXLlyhZUqVaJSqeS6desKZIqfsdHPzWe/MBgMBvr7+9PJySlbN4nQ0FAqlUrKZDJu375djC3Rtm3bQiuFkpOTGRoayhs3bvDkyZPct28ft2zZws8//5xz584VLRwA0NPTs8hpRsk0hcPy5cuzKDTc3Nw4ceLEYonDcvToUTHoq1wu57Zt24o0XnR0NK2trYn0WDQNGzZkXFycmaQtGQ4ePEiFQsFWrVqZPc5FUchQGplTcZeR4nfdunWZ6rt27UoAHDp0aI7HJiQksHr16qxatarZFUAZCuXR+dhrmkwmenl5sWvXrmKdq6srZTIZVSpVvsYoKkV1S9kL4BaAGQBcX2krsE9taZZSW3i0aUP6+ZEkv69WjS4Ab+7bl6nLkf/9L83sUqPJcvjtw4d53sqKBBjh4yNl95CQkJCQKNMUQLlhC6ASgO8AeL5U7PNzfFkqknKjcDx+/JgAuGTJEpJpm1xBEOjg4EAA/Ouvv7Ick5yczB07drBTp04UBIGCINDHx0e04nBwcOCpU6cKLdPmzZupUqno5eWVbSDI8PBwBgUFccKECfT19c0ULBQAq1WrxqioqDzn+fLLL6lWq+nu7s7z588XSMaAgAACYJ06dYrFhP/atWtUKBQcNmxYtu07d+4Ur3V4eLiY1tbJyYktW7ZkgwYNWLt2bVapUoXu7u50dnamnZ0draysxCfEMpksy7XLrQiCQAsLC1pbW9PKyooWFhbUarXUaDRUq9VUqVRUKpVUKBSUy+WUy+WUyWTiPLnNlaHQePbsmdmvZQYvXrxg586dCUC8XhkWMIWJT6DX68U0vZMmTeKBAwcol8vZoUOHYo13YE5OnTpFjUZDPz+/MhknJCNF8tWrV80y3uXLlwmAffr0yVSv1+tFpW1ubl4HDx4kAK5evdos8pBp1lgWFhZUKBS5xgPK4Pr16wT+SUUcERFBACxfvjwB8NatW2aTLSeKqtzomk2dOq/jymIpjYVHamwsE5RK8r33SKORVChIB4cs/Sqlp626vmtX9gMlJtJYuzZdAToKAh8X4UdbQkJCQkKiOPm3PfwwR5GUG4VjxYoVBMC7d++SJD///HMCYNWqVenh4ZGnJcPjx485d+5c0VVCmW4FC4C9e/cu9Cbv3LlzdHV1paWlJTdu3Mjt27fz7bffZs2aNcXxtVot27dvzx49eogb5wkTJuQ59stpXjt06MDnz58XSLbBgweLFgzFmREjI3Xl0aNHs20fNGgQAbBz5840Go3s27dvFmWEXC6nSqWihYUFbWxs6ODgQFdXVzEjhY+PD5s0acJ27doxICCAfn5+LF++fKb3MeNaZ7hxlCtXjg4ODnRycqKzszNdXFxYoUIFuru708PDg56envTy8mLVqlVZrVo11qhRg7Vr12bdunXp6+tLPz8/NmzYkE2aNOGkSZOKVaGRwaVLl+jp6UmVSsUNGzbQZDLxwoULYuwSKysr/vjjjwUas2nTpgSQ6Qn6V199lWYh/sYbpR63Ii+uXr1KW1tbVq9evcCfgZIiMjKSLi4u9PX1NYu1REZQUS8vryxtV65coSAI1Gq1ortHdse3bduWTk5OOfYpKF988QWRHuskP2TEBsr43HzwwQfiPdylSxezyJQXRVVuZOcHm6Xu31BKY+FxfOlSKgGe/vRTXpw9m6cA8hUtePT27dQAbGRpmed4gzJ+vAEemjevuMSWkJCQkJAoNJJyQyK/tG3blrVr1xZfZ7gfWFtbc9SoUfkex2g08tixYxwwYABVKpW4KVapVNy6dWu+xzGZTHzw4AG/+uor9u/fX3QheHnx/r///Y9nz56lTqcTFQ0KhYJ79+7Nc/zCpHl9mYw0mU5OTvmOzVFYkpKS6O3tTS8vr2znMhqNouVAxpNknU6X70210Wjkvn372KtXL3GT//K1btGiBVeuXJlrINZ/A1988QVVKhU9PDyyBKg0Go0cM2aMqBzr2LFjvlwzMu67mjVrZrne/0u3Bp8wYUKpZRzJi7t379LZ2Znu7u7ZZuYpS+zdu5cAOH/+fLOM5+zsnCmo6MtkuIfk9nty6dIlAihyIOUMWrRoQQC5BjR9GV9fXzZv3lx8XaVKFfFze+TIEbPIlBeFUm4AcAHQAMBfAOoD8EsvbQDczum4slxKY+Exs3lzygHGhoSwgYUFATD03LnMndzdqQMYmU+TxFX9+lFIv4nmtm1bDFJLSEhISEgUHkm5IZEfXrx4QblczpkzZ5JM2+jJ5XLRPDsoKKhQ40ZFRXHlypV0dHQUF91OTk7cu3dvlo2gyWTirVu3uH79eg4cOFDMPAKkZWvp0aMH/f39CYCvvfaaGM8gPj6etWrVEl0z8rMxKEya15dZuHChqPgp7oB9GZw4cYIAOH369Gzbnzx5ImZ7uH37dp7jXb16lW+//Ta9vLwyxblQKpWsXbs233//fT5+/Njcp1EqJCUl8a233hKVFrm9Zzdu3KC7u7topZJdMNcMMtKlOjo6ZqsIMZlMohJs4cKFZjkXc/LkyRN6enrS0dExW7ezskhgYCCVSmW2bmoFpXXr1gSQY0yXjPTRub13AwYMoFarNUsq5gyrqvzw6NEjAuCnn35K8h83QpVKxTp16pSYMq2wyo2hAE4AiE//m1H2A+iT03FluZTGwqOplRWbWFlRn5xMOcDygpCp/eKCBXwCkC1bFmjcsxs2UIu0TCoRHTqkubxISEhISEiUAQqj3EBarI0O6f9rAVgXdIzSLJJyo+Bs2bKFAHj58mWSaelTAbBevXqUy+V5ZhnJD7t37xYDLgJguXLl+PHHH3PFihXs27dvJosBFxcXBgYGcs2aNbx586aoCDGZTFyxYgXlcjlr1arFgwcP0sbGhgDYqFGjPLMpFDbN68ts2LCBAKjRaEp88z9y5EjK5XL+/vvv2bZ/++23ogLpVTeZZ8+ecc6cOaxfv34mKxhBEFixYkUOGTKEp0+fLonTKFEePnwoZviZNWtWvi10pk2bJip9mjdvniUOxa5du8T7ICQkJMdxjEaj6Da0adOmIp2LOYmIiGDNmjVpbW0tfu7/DTx//pyOjo709/cvsivYrFmzCIBr1qzJtj0yMpJqtZoymSzHDFAPHz6kUqkscvrna9euia5l+SHDbTDjO2zZsmXiZ7ok77OiuqX0zavPv6WU9MIjLjSUcoAfNmvGNf37EwDH1qmTqU95mYwKgLp0X9OCEH7zJoPs7UmAeg8PRj94YC7RJSQkJCQkCk1BlRsARgG4BOBB+mtvAMcLMkZpF0m5UXB69uxJd3d38Wlfq1atCKQFyXzZ7NkczJo1K5OlAABWqlSJQ4YM4aZNm3jv3r08nzoeP36clpaW4vHjx4/Pc97IyEgxE0JB0ry+TFBQEAVBoEKh4PXr1wt8fFGJioqii4sL/fz8ctzYBQYGipukTZs2sX379rS1tc10ve3t7dmlSxdu3769WGOFlDaHDh1iuXLlCm2h8+DBA9HUX6VSiYEbL126JAZJfdW9JTt0Oh07d+5MmUzGH374ocBymJu4uDj6+/tTrVbz119/LW1xCkyG8nXx4sVFGuf3338XYwLlRFBQEAHQ2dk5RzevyZMnUyaTFcmaJMOyKD+Zncg0q5M6L+1lM9JeOzg45CsYqbkorOXGm+l/3wMw5dWS03FluZT0wuPgnDkEwGOffsq66drqsJd+lDalpwzrYG9f+EmMRrJjR7YEaAHw0tdfm0FyCQkJCQmJwlMI5cY1ACoAV1+qu1GQMcxZANQCsBPAOgD98nOMpNwoGImJidRqtRw3bpxYp9FoaG1tTUEQOK8Y4oqF/h975x0eZZU18N+dnkoCaRACGAi9J3REitJBUPwsu2BB0FVw1bU3wFWkrBR11VVEgVVWpQSUqhARXGQhQUIJvQYIhEASQiaZdr4/ZjIGDKSQQvT9Pc99ZuY9t5yZJJN7z3vKyZPSqlUrbxjEihUrSjV+7Nixl3keTJ8+/Zqs1a+iAAAgAElEQVQGkaSkpDKXeS3g+++/91b82LBhQ6nHlxcFHgMF7uhX4nQ6vWEVBc3Hx0c6duwoU6ZMKXFZ3epITk6OrFq1Sp555hlp166dANKmTRs5ePDgdc37+uuvi95TcKBdu3bekrslPYiKuMOnOnbsKBaLRX788cfr0ud6sFqt0rt3b9Hr9TeEoaUsuFwuGT58uJjN5hKFYF1rHp1OJzfddNM1+40YMUIAGTFiRJHyc+fOSY0aNWTQoEFl1qVu3bqi0+lKlCcnPT1ddDqdN9dHQRghIBMmTCizDmWhrMaNRzyPE4pqVxt3I7fK3ngcHTNGpur1knPihOhA6ur1l8lrKSUK5GRi4nWv9aKnxrsO5OORI697Pg0NDQ0NjbJSBuPGFs/jds+jAUguzRyF5poLnAV2XXG9P7APOAi8UMwcfwNu9jxfXpJ1NeNG6ShI0rdu3ToR+TW3Q4Erf0nuTJeVmTNnehM4lmRTnpub6z20BgUFydatW70Hjz/96U9F5j2YO3dumcu8FjBhwgRvCdP4+PgyzVFeuFwuuf3228XHx0cOXcVT+Pjx49K9e3cZP358pZSDrCpsNpv89NNPMmnSJOnRo4e3sovJZJKePXvKW2+9VW7JXlNTU6VFixZeg1FZklqmp6dLkyZNJCgoSJKTk8tFr9Jgt9tl2LBhAsj8+fMrff3y5PTp0xIcHCxdu3YtdTLgwoSHh4vJZLpmH6fTKWFhYdfMPzRt2jQBZP369aXWITc3VwBp0qRJifoXVOIpCCf697//7T576nSVUnGoMNcblhJaXJ+ytuI2GoAZ+NIj3wI08FxvAFg9d3p+AT4syXqVvvFo316kZ0+RqVNlD8i6//s/r+hdzz/FwWFh5bZc/IsvisHz5Te6ceNym1dDQ0NDQ6M0lMG4MQ14CdgL3AYsBd4szRyF5uqBOwH6rkLX9MAhINrjIbLD453RCvj2ihbmaf8EpgM/lWRdzbhROkaNGiXBwcHe8opDhw71hjXUqlXrug4OJeG///2v9074bbfddtU7l/v27fMmOG3Tpo330OpyueSNN94QpZTExsbK8ePHRcR9h3rMmDFuz9wylHkVcYcT3HzzzV7vh4SEhDK/z/IkNTVVAgIC5NZbb71hq3BUBC6XS5KTk2XmzJkyaNAg8ff393rvxMbGynPPPSdr1qyp0Oo1H374oUybNq3M448ePSp16tSROnXqVGrOFqfTKQ94PNVnz55daetWJPPnzxdAZs2aVeY5evbsKYCcOnXqmv327NkjOp1OzGZzkd5PVqtV6tWrJ3FxcaUu/VtQhvtqyYKvZOjQoRIVFeX92+/cuXOx4TUVxfUaN/YDa4HRQHBx/UvarrbRuKLPYwWGC+Ae4Ev51bixq7RrVubGI/PIEVkMkvXSSyKNG7s/6oJfSqdTOuv17mSg1+HWVBQH162TYM/diA2NGolUYvyThoaGhoaGSJmMGzrceTe+BhZ5nqvSzHHFfJftE4AuwJpCr18EXizBPHpg2TXkY4FtwLZ69epV0Kf5+8Nut0twcLCMGjXKey0wMFAsFotERETIPffcUyl6pKenS/369QWQqKgoOXPmzGXyL7/8UgwGg/um0VUS9y1fvlwCAgIkLCxMvvzyy+sq8yriLpFZq1YtAaRRo0Y3XDjH+++/L4B89tlnVa1KhXLkyBGZM2eO3Hvvvd6754DExMTIX/7yF1m0aNEN97Mpjp07d0pQUJA0bty4UqrtuFwuefrpp6skbKEicblcMmjQIPHx8Slz6NFrr70m8GsJ5WtRUCWpVatWRcoLjC0LFy4slQ6dOnUSQE6cOFFs35ycHLFYLJflGSr4bqwKb6Br7TGUW35tlFIdPcaFYcAe4D8i8u9iB157zi7ARBHp53n9IoCIvFWozxpPn81KKQOQBoTizqj+rYi0LM2acXFxsm3btutRu8Qsff557pg2jTWTJzPqpZd4LjiYp8+fdwunT4fnniOlb1+arVlT7mvnZWYyr1UrHklNhZo1yV63jsC2bct9HQ0NjRsIlwscDlx5eWSePYstNxdHfj52qxVbbi61/PwI8fMj9+JFknbtwmG3Y8/L8z62iorippAQzp47x9odO7Dn5+N0OHDa7dgdDvo0akSzkBAOpKXx1S+/4HQ6cTgc3sc/NWtG6+Bgtp46xfw9e3C4XDidTlwiOF0unmrWjNaBgaxKTWXe4cM4XS5cIrg8j5ObNKGFnx+fp6Yy7+RJt6xQ+7RRIxqazcw6eZIvMjJwuU3GuACXCCsbNKCO0cirp0+zJDsbF27jfcHj9qgo/JXi0bNnWW21/hoUDijgeHg4iHDX+fNsstvd83vkJiC1Zk0Q4dbsbJKcTgr+c4oI/kqR6u8PQNylS+x1ubw/FgGCgVQfHwAa5+VxotD/XQHCgWMmEwC1bTYyCskAooDDBgOIUMPp5FKhH7sADYH9Oh0AFpcLe6Gx4HZP2KUUAHoR7/sGWKnXM8DhKPGvWUlRSiWKSFwp+vsBeSLi9LzWA2YRyS3j+g0otE9QSo0A+ovIw57XI4FOIjLuGuNfAvyAD0RkU3FrVuYeo7qzfv16+vTpw5IlSxg+fDi7d++mZcuWxMbGkpiYyKeffsoDDzxQKbq4XC6GDBnCypUrsVgsfPfdd3Tv3p0nn3yS2bNno9Pp+Pjjj3nooYeuOkdKSgq33347Bw4coEaNGixYsIAhQ4aUWpf58+fz0EMP4XQ6+dOf/sT8+fPRef62bxRcLhc9evQgJSWFlJQUwsLCqlqlcuHcuXOsX7+edevWsW7dOg4dOgRAREQEffr04dZbb6VPnz5ERUVVsabXx6ZNm7jtttto1aoV69evx9/zv6simDx5Mi+//DLjx49n9uzZKM//od8DqamptGjRgnbt2rF+/fpS/53u2LGDtm3bcvvttxMfH19s/7i4OBITE3nllVf4+9//fpnM5XLRvn17srOzSUlJwWw2l0gHHx8fLBYLFy5cKLbv0qVLueOOO1i3bh29e/dm9erVDBgwgJCQENLT00u0XnlyzT3G1aweRTUgBJgPOEsz7ipzjQDmFHo9Enjvij67gLqFXh/y6NAAuARsBzbgiYu9yjpVcldlXKtW4uuplALIxFtuERERp90uWy0WEb1eJCurYpV44AF5CsQIsuyllyp2LQ2NSsRpt8ul9HTJOHBATm7bJgfXrZO933wjsnWryKZNkvj++7Ji4kRZ9Mwz8sW4cTL3wQfli4ceEnnnHZFp02TOsGEyoUcPebFLF3kmLk6eaNNG3ujYUWTMGJEHHpCnmzaVu6Ki5I46dWRoeLgMCAmRx6KiRHr3FunRQwYHB0tHPz+J9fGRNhaLtDSb5Z6AALeXVsOG0tJolHp6vdTV66WOTicROp0MNplEQkJEataUEKUkECQAxM+TDPg2nU7EYpF8g0GMIAYQvafpQHqDiFJyjMvO5d7WG0RAfr6KfJhHvuwq8lEe+dyryJ/wyKddRT7RI3/hKvJ3PfJHryL/j0d+z1Xk33vkQ68i3+X5fPqBKM9npiv0GaYrJWIwSB+lxOj5XjR5mhlEzGYRi0VuUUp8QHw8PxdfkCAQ8fMT8feX7jqdBIAEgtTwtEilRIKDRYKDpateL7WUklpKSYhSEqqUNNXrRcLCRMLCpLPRKBGe34naOp3U0emko9EoEhkpUreudDSZJEqvl/qe1kCvl74Wi0h0tEh0tHQwm6WRwSAxBoPEGI0SYzTKCH9/kSZNRJo2lVizWZqaTNLMZJLmJpO0MJtldFCQSKtWIq1aSazFIq0tFmljsUhbi0V2tG1bIX+jlN5z42fAv9Brf+C/pZnjivkacLnnRrF7jutYawjwUaNGjcr/g/ydMn78eLFYLJKTkyMiIvfff78Acu+995bIVbsi+Pvf/+4NNSjw5ggICChxdZILFy7I66+/XqYyr06nU0aOHCmA6PX6G94rYvfu3WIymeTee++talXKzJkzZ+Tbb7+VZ555Rtq2bev9XxIYGChDhw6V2bNny+7du3+X4TfLli0TvV4vffv2lfz8/ApZo8DD589//nOpwyWqC3PmzBFA3n///VKPLUgq2qBBgxL1z8rKEh8fH1FKSWIRuRrXrl0rgMyYMaNE8/38888ClDgZ6ciRIyU4ONhb5aggB9H1hEpdD9faYxTruaGUCgSG4/bcaIg7DvYrEUm85sBiKMldFKXULk+fVM/rQ0An4CLuTVCGUioWiAdaiEj2tdaszLsqLSwWovz92Z+ZyVGnk+zTp/GPiGDiLbcw6ccfeb9jR/6yZUuF6/HRn//MXz7/HBfwSvfu/H3jxgpfU+PGx+VwYMvJIefMGS6lp5Obmcml8+dpGhaGvwgHDh1iy+7d5F68iDU3F2tuLnlWK4+2bEmEycSylBTiDx0i32Yj3+Eg327H5nQyv0kTInQ63jp2jEUZGdhFsLtcOEVwuFwkRUQQJML9GRmstNlw4r7r7sS9q8ixWMDloqvNxv/49eQK7rvrTs/zesCJK96TrpA8DLjSjmwC8j3Pg4CsK+S+4L0j7os7qU9hgoAC27YZsBXSC9wnqcM6HShFgNNJvkdW0NopxX99fUEpQnJycCqF8uitA24xmfi6Zk1cQIOzZ9EDOqXQK4UOGBQQwNt16pDpctH7yBF0SnnleqW4IzSUp+vX55TdzkMpKeh1Ogw6nbuPTsdd9epxd3Q0x/LymLRzJ3qlMBgMGPR6dDodw2Ni6HnTTRy7eJE5u3a5xxuN6PV69AYDA5s3p2W9ehzPymJVSgoGo9E93mTCYDTSrUUL6oaHcyo7mx3HjrnHGo2YzGb0RiPNGjYkKDiY8zk5nL5wAYPZ7B5rMqE3mwkLD8fk60uew4EDMFgsGCwWdAYDOoMBjepFGTw3fhGRtsVdK8V8Dbjcc6NYb9HrRfPcKBkiQv369Wnfvr33jmV4eDjnz5+ne/funD9/nh07dlSJbgsWLGDUqFEABAQEcOLECWrUqFGha54/f57OnTtz4MABatWqxU8//USTJk0qdM3yYNKkSUycOJEVK1YwcODAqlbnmpw7d47ExEQSExPZtm0b27Zt48QJ9y7CZDLRtWtXr2dGXFwchj/A/5y5c+cyevRo7rvvPmbMmIHVaiU3N7fU7Wrjjhw5wqBBg1iyZAlGo7Gq326FICL069ePzZs3s2vXLurXr1+q8REREVy4cIH8/PziOwOrVq1i4MCBREREcPr06d/I+/Xrx9atWzl06BDBwcHXnGvkyJH8+9//ZtmyZQwdOvSafe12O+Hh4QwZMoR58+YhIhgMBkQEh8NRJd5l19pjlMS4cQS38eArEdlcjkqVOSxFrlBaKfUD8IyIXHNXUVkbj7SdO6ndujUTevRg0o8/0sRoZK/NhsvhwN9oxA5kpafjGxJS4boAbP30U3o+9BC5uA9lY/z8eLdTJ7JbtmRLnTrc8vjjmCrQLU3DjcvhwJWbiyEnh7zTp9memEh2ejpZ6elczMwkJzubruHhdAgK4kBqKlP/9z9y8/LIdzjIs9vJdzh4NDycEQEBfJeRwV9TU7G7XG4DgggOEd708eFho5H3rVaeKTAeeJoA7wDjgPHAe0XoOA8Y5WkLipCvAAbijk9bVoT8f0AHoC/wHZcf7hVwRK+nrsHAcLud71wu78FerxR64FRoKAajkQczM9mYn49BKQw6HXqlsOh0bGnZEgwGnjt+nKRLlzDq9Rh0OkwGAzVMJuZ26wYmE7P27uXwpUsYDQbMZjMmo5GQwEDG9ewJJhPL9uwh027H5OOD0WTCZLFQMziY7nFxYDaTcuoULoMBS0AA5oAALP7+WGrUwD8kBAwGuMHchDU0bjTKYNz4CRgvIkme17G4PSu6lHH9Blxu3DDgziHWBzgJbAXuE5HdZZm/KDTjRslITEwkLi7OG3py6tQpIiMjad26NSkpKTz55JNMmzat0vVavnw5I0aMwG63Y7FYyMvLIyYmhm3bthEYGFgha/7www8MHDgQq9XKzTffzPfff4/JE6J2o5Ofn0/79u25ePEiu3fvJiAgoKpVAuDChQskJSV5jRjbtm3j6NGjXnnjxo2JjY0lLi6O2NhYOnTogK+vb9UpXIVMmTKFF198sVRjdDodfn5++Pr6XrNFRUXxyiuv4OMJyfy9cuzYMVq2bEmXLl1Ys2ZNqUJvevfuTUJCAqmpqURGRpZozIABA1i9ejWzZs3ir3/962WyHTt20K5dO5599lmmTp16zXlq165Neno6NputWOPElWGE8+fP5/777ycmJob9+/eXSO/y5lp7jJKYJqOvNCaUE1uBGKXUTbg3GvcA913RZzlwP7AZt0vpehERpVQocF5EnEqpaCAGOFwBOpaJTXPnAnDw+HEARt92GwAvdO2KFRjTpEmlGTYAOjz4ICduvpn/69iR3VlZBOfmwvr1fL1+PQ8DvPACvkBtg4GmwcG8OGwY3caOhfbt/xCHuJy0NDIOHiTj+HF88vJoFhgI58/z9vLlZGZlkX3xItmXLpFjtdIlIICnw8Pdhog9e7A6neSLkO9y4QAG6/XMM5k4YLfT3G73GhUK/oD6A6uA/+LeYV/JCNzZ9HYAnxQhb3DhAiOA47hLCuiuaFl2OxiNBJpMBNrtGJTCqBRGnQ6TTkd0nToQGsqt2dnsSUvDZDBgNhgwGQxYjEZi27eH2rV55Nw56qemYrFY8PH1dcfl+frSuVs3CA3lnZwcns/Jwa9mTXcLCcG3Zk38w8LAZGJtMZ/50mLknxYjL27b+2Qx8tuLkTcrRq6hoVHuPAl8rZQ6hdsWGgHcXZaJlFILgZ5AiFIqFXf5+k+UUuOANbiThM4tL8OGUmoIMKRRo0blMd3vnvj4eHQ6nTcnxdtvvw1Ap06dSE5Opn///t6+drudn376ibZt2xIUFFRhOr344otMmTIFnU7HP//5T8aOHUvv3r3ZuHEjkZGRbNy4kbblnLts0qRJTJo0CYAJEyYwceLEcp2/ojGbzcyZM4du3brxyiuvMHv27ErXITs7+zJDRmJiIgcPHvTKo6Oj6dixI4899hhxcXG0b9++wj1xqhPPP/88TZo04fTp08UaKwqa0Wj8XeXOuF7q16/P1KlTefzxx73eMCWlR48eJCQksGTJEsaPH1+iMZ9//jlhYWHeXCaFDRNt2rRh1KhRzJ49m8cff5x69eoVOUd2djZpaWk0b968RF4XS5cuxcfHh379+gHwyiuvAPDaa6+VSOfK5qqeG0qpWSLypFLqG349m3kRkWv7sJRkcaUGArP4daPxplLqddxxNMuVUhbcN5DbAeeBe0TksFLqTuB1wI77pvQEEfmmuPUq666KjBnDvv/8hy+AD3NyOH7hAjqDgcCAAAS4ePFi1XtKpKWxe84c/jFvHsknT3I0L49McSffewf3nf1JwAygrslEi7AwunXowMC//IUYj7GmssnLzCT78GHCXC44e5ala9Zw7PhxzmdkkJmVRebFi9TW65larx7k5HDzrl2cstmwulzki2ATIUYpkvR6cLkwegwShbmJX61kRX11Nwd24w5JsODxOvA0AzDYaOSLoCDO6fV0SE/HpNNh1ukw6fVYDAbujohgfOPGpCnFC3v24Ofj426+vgTUqEGPFi2Ia9WKHL2epDNnCAwNxa9WLQLCwwmsUwdLUJDmoq+hoXHDU1rPDc8YI1Dgj79PROzlr1nFoXlulIyWLVsSGhpKQkICAA0aNOD48eOMHTuWBQsWcP78eW9CvP79+7PGk3hdr9fj7+9PWFgY9evXp3nz5rRv357u3bsTHR1dpgOXw+Hg1ltvZcOGDfj6+vLDDz/QoUMHr/zZZ5/lH//4Bzqdjo8++qhUB5erYbPZuPXWW9m4cSM+Pj6sXLmSnj17Xve8VcW4ceN4//332bx5M506daqwdXJycti+fftloSX79u3zyuvVq0dcXJy3xcbGUrNmzQrTR0OjAJfLRZ8+fUhKSmL37t3UrVu3RON27txJ69atS5xUtIAxY8YwZ84cnnzySWbOnHmZ7MSJE8TExHD33Xczb968IsdPnz6d5557rsjkpFciItSrV4/Y2Fji4+M5fPgwDRs2RClVZSEpUMawFKVUrIgkKqVuKUouIhvKUcdKodI2HjEx0LAhrFkDrVpBcjKf9O7NwwkJPNG2LbO3b694HcrIge++IzwpicDERCYmJDDz3Dkucrl1ax3Qu2ZN3vP1JdFioWXTpthsNqxWKw6bjckdOoDVyttJSWw+c4Y8hwObw0G+04kJ+K5RI7DZuOvIEZLy8rwhFQ4RApTikL8/OJ3E5OZytFA+BgAfoCB1vl+h5wUEAAWJVwI9cgNgBExK0cZoZH2dOmCx0PPECWxK4WcyuZvZTMfISP7arRsEBvLBzp34BQURFBZGrchIatatS+2mTQlq0MAdmqChoaGhUSRlNG50xZ2+xvsFKyLzy1m1cqeQ58aYAwcOVLU6NzQHDhygcePGXpfqnJwcAgICiImJQURo0qQJ3377LQBnz54lIiICf39/YmNjOX78OOnp6Vy6dAlXoYpEAEopfHx8qFmzJlFRUcTExNCmTRu6dOlCbGxskaEeqampdOjQgbS0NKKjo9m2bVuRceqLFy/mnnvuweFwMHr0aObMmXNd779Lly5kZGQQExPDzz//XO0P4NnZ2bRo0YKgoCASExOvO6zGbrdz8OBBdu3adVk7ePCg9+ceGRn5G0NGaGhoebwdDY0ycejQIVq1akWvXr349ttvS2RsLchdERUVdVnoVHHYbDZq1KiBw+EgIyPjN2FzL7zwAtOmTSMpKalIj7OCyitnzpwpttrRtm3b6NChgzeM8OGHH+aTTz6hZcuW7Ny5s8Q6lzfXVS0F+GtJrlWHFhsbKxXN8c2b5X6QeTExcgxE/vlPEatVxGiUDUaj2K3WCtehvHHa7bL1s8/kjT59ZHBYmOTfdJOIr680v0rVAvG0RkXIVIFcKWmKuyKEEcTiqUpQWymRoCCRWrXkVpNJog0GaWE2S0c/P+kdFCSj69UTGTlSZNw4+degQTJz2DCZN2aMrJg4Uf736adyYvNmkUuXqvoj09DQ0PhDQ+mrpSzAHbH3PvCup71TmjmqulXGHqO6M336dAHk6NGjIiIyefJkAeSxxx4TQN555x1v39tvv10A+fDDD38zz5kzZ2TRokXywgsvyJAhQ6RFixZSq1YtMRqNRe5LjEajhIaGSuvWrWXYsGHy1FNPiclkEkCGDx9ebDWH/fv3S3BwsADStm1bsZZhLzdv3jzR6/UCyMiRI39XFSSWL18ugLzxxhslHuN0OuXQoUOybNkyefPNN+Xee++V1q1be38ugOh0OmncuLEMHz5cJkyYIN98802VVNLR0CgJM2fOFEDmz59f4jERERFiMplKvdYbb7zh/f66kgsXLkjNmjWlb9++RY41mUxSq1atEq3z8ssvi06nk3PnzklmZqb3O/bdd98ttc7lybX2GCVJKJokIu2vuLZdRNoVb1e5sagMz43PHn6YBz/5hFpKkS1CXn4+J0aOpP5XX8GLL8LkyRW6fmViy8lh4wcfsGPTJnx8ffELCMA3IIARt94Kfn4cy84mz2gkIDQU/4gI/MPCtJAKDQ0NjT8AZUgomgI0l+I2JTcwWlhK8XTr1g2r1UpSUhLgDlHZvXs3U6dO5fnnn2f//v3ExMRw8eJFgoKCCAgIIDMzs1Rr5OXlsWXLFrZs2cKOHTs4ePAgqampnD9/nry8PG8/pRTTp0/nb3/7W4nn7dq1K9u3byc4OJgtW7YQExNT7DiXy8UDDzzAggUL0Ov1zJ0711uR5ffE3XffTXx8PMnJyZdVexERTp8+/RtPjN27d5Ob+6v/bb169WjZsuVlrWnTpr/7hJQavx+cTic9evQgJSWFPXv2EBERUeyYPn36sH79ek6cOFHicBZwf6+EhoZy4cIFjhw58ptKLbNmzeKpp55izZo19O3b13t9w4YN9OzZk+HDh7NkyZJi1ykcRjhjxgz+9re/oZQiLy+vSpMflzUs5V7cCT67A4XrhwYALhEpKh/iDU1lbDzub9iQbw8f5jwQ6+vLD4cOEVy7Nu2VYovD8YdI0KmhoaGh8cemDMaNr4EnROS39e1ucLSwlJKRlpZGnTp1mDRpEq+++ioOhwOz2Uzt2rWJjY1l586dHDp0CKUUo0aNYsGCBUyePLnU1RyuhcvlIiUlhc2bN9O9e3eaNm1a6jkK4t0NBgMLFy5kxIgRV+1bXcu8loW0tDSaNWtG8+bNue+++7wGjF27dnHhwgVvv/Dw8N8YMZo3b15hFWk0NCqTvXv30rZtWwYOHMjixYuLDU95/fXXmTBhAjNnzmT06NGcOXPG286ePfub5+np6fTv35+3336br776ivvuu4/OnTuzefPlBU3z8/Np1qwZgYGBJCYmotfrAbcR8quvvmLt2rXcVkwOxcJhhI8//jjR0dHenB5VVSWlgDKFpQD1cWcb3wzcUqi1BwxXG3cjt4p2GXU5nVJXr5cGHrfDeWPGyL316gkgf+/Tp0LX1tDQ0NDQuFGg9GEpCcAF3NVMlhe00sxR1U0LS7k2//rXvwSQ5ORkERGZM2eOADJmzBjx9/eXRx99VERE8vLyxGAwiK+v7w0bujF37lzR6XQCyNNPP11kn4SEBPHx8RFAevToIfn5+ZWsZeXzySefeENKgoKCpHv37vLoo4/Ke++9JwkJCXL27NmqVlFDo8KZOnWqALJw4UJJT0+X3bt3y/r162XhwoUya9Yseemll2T06NEyePBgadWqlTcEiyJC6gCpVauWNGvWTHr27Cl9+/YVQO69916x2WwSHR0tgPz888+/0WPhwoXu8+i8ed5roaGhYjAYxOVyFfs+pk2b5g0j/Oqrr7z6vPrqq+X6eZWFa+0xqnwzUJmtojce+7/7TgDx8+SSyDhwQHQgASBOu71C19bQ0NDQ0PKZvxUAACAASURBVLhRKINx45aiWmnmqOqmGTeuzYABAyQ6Otq7qe7cubMA8sUXXwgg8fHxIiIybtw4AeS5556rSnWLZfv27eLv7y+AdO/eXeyF9nkTJ04UpZQopWTixIlVqGXl4nK5ZOvWrZKamlqiw5OGxu8Ru90uHTp0uKqxQq/XS+3ataVt27bSr18/UUpJYGCgTJ8+XebNmyerV6+W7du3y8mTJ8Vms/1m/ilTpnjzbaxfv14AadSo0W/6OZ1OiYuLk6ioKMnNzZWMjAwBpE2bNiV6H127dpV27dqJiEiXLl28+TYyMjKu7wMqB661x7hqAgSl1CYR6a6UurJYhnI7fIjmP3YFaWvW0AA4CnT19+fhHj1wAROHDtVyTWhoaGhoaFwFEdmglKoPxIjI90opX9xVtjV+B2RnZ7Nu3TrGjx/vddNOSkqiZs2aJCcnYzAY6NWrF06nkzlz5mAymXjjjTeqWOtr07ZtW06ePElcXBybNm2ibt26bNmyhVGjRvHjjz/+Lsq8lhalFHFxpSqSpKHxu8NgMLBkyRI+++wzAgMDCQ8PJzw8nLCwMMLDwwkODr6shGrt2rXJyMjgmWeeKdH8zz//PL6+vjzxxBPk5eURGxtLYmIiixYtuixMTqfTMX36dHr16sW7776LzWYD4K677ip2jbS0NDZv3szEiRPZsmULmzdvRilFZGTkDV/h6aonbhHp7nkMqDx1qjc3nzjBER8fvrVaCXviCW6ePJlgpXh62bKqVk1DQ0NDQ+OGRSk1BhgL1AQaApHAh8ANn9+rUM6NqlblhmXVqlXYbDaGDRsGwMqVK7HZbPTp04c1a9bQtWtXAgMDefXVV8nLy2Ps2LEYjcYq1rp4AgMD2bt3L3fffTeLFi2iQYMGAL+bMq8aGhplo27durzyyisl6tuiRQvWrVvHiRMniIqKKtGY8ePHY7FYeOSRR+jUqRNKKR599NHf5ADq2bMngwcPZvLkyURGRgLw2GOPFTv/8uXLERGGDx/Om2++iY+PD1arlaFDh5ZIv6qk2OyWSqmGSimz53lPpdQTSqmgileteiEuF65168DhYLDZTMekJPYCS558sqpV09DQ0NDQuNF5HOgGZAOIyAEgrEo1KiEi8o2IjK1Ro0ZVq3LDEh8fT1hYGF26dAHgnXfeAeChhx5i+/bt9OvXDxFh1qxZGAwGZs2aVZXqlgqdTsfXX3/NzJkz8fHxYeTIkezdu1czbGhoaJSIW265BYDFixeXatyYMWOYP38+W7duJSgoiIyMDKZMmfKbflOmTCE7O5u9e/cSFhZGcHBwsXPHx8cTHR1NYGAgixYtIijIffR/9tlnS6VjVVCS0h2LAadSqhHwERAFfFGhWlVDdi9bRvC5c4Tb7SyKisKxejU3hYbSc8aMqlZNQ0NDQ0PjRidfRGwFL5RSBi4PidWopuTn57NixQqGDh3qzdi/adMm/Pz8SE9PB6B///7MmDGDnJwcRowYUS3Lfz755JPk5uYyf/78y1zONTQ0NK7FnXfeCcC6detKPfbPf/4zX375JdnZ2QBMmjQJh8NxWZ8WLVrQt29fXC4XHTp0KHbOgjDCYcOG8d577wFw7tw5atasyU033VRqHSubknz7ukTEAQwH3hWRZ4HaFatW9SPh88/JBs4Cf09NJRA4O2FCFWuloaGhoaFRLdiglHoJ8FFK3QZ8DXxTxTpplAMJCQlcvHjRG5Kybds2Ll26RNeuXVmzZg2hoaG0bduWN998E51OxwcffFDFGmtoaGhUHs2bN0ev15OcnFym8XfeeSfLli1Dr9eTl5fH6NGjf9OnINeR1Wotdr6CMMJ+/frx8ccf06VLF+x2e7GlY28USmLcsCul7gXuB771XLvxAyErmfX//S8KsADJeXkE6XSEPf54VauloaGhoaFRHXgBSAd2Ao8AK4GSBSxXMUqpIUqpj7KysqpalRuS+Ph4/P396dPHnT7l7bffBmDcuHGsWbOGvn378tlnn3HhwgUGDBjgdX/W0NDQ+KMQFhbG6dOnyzx+0KBBfPut+5g+f/589uzZc5l869at6PV61q9fz9atW685V3x8PKGhoaSkpJCVleVNRPq3v/2tzPpVJiUxbjwIdAHeFJEjSqmbgAUVq1b1wmmz8f3p0whu4wbAu08/XZUqaWhoaGhoVAuUUnpggYh8LCJ3icgIz/NqEZai5dy4Oi6Xi2XLljFgwAAsFvcOae3atRiNRmrXrs25c+fo378/L7/8MkopPvrooyrWWENDQ6PyadGiBXa7nWPHjpV5jv79+/O458Z6XFycd64zZ86QkZFBq1atCA0N5dlnn+Vq/14LhxG+++67dOnShd27d+Pn51eikJYbgWKNGyKyR0SeEJGFntdHRGRqxatWffjlq6/I8TzPBKL0eu6cPr0qVdLQ0NDQ0KgWiIgTqK+UMlW1Lhrly5YtW0hLS2P48OEAHDt2jPPnz9O+fXu+++47ABwOB2lpafTo0YM6depUpboaGhoaVUJZk4peyXvvvUdQUBBWq5UuXbpw8OBBb96M++67jwkTJrBhwwZWrlxZ5PiCMMLatWtz6NAhhg4dyqVLl+jWrdt16VWZlKRaSjel1HdKqf1KqcNKqSNKqcOVoVx1IWjXLhoDBbuyD197rSrV0dDQ0NDQqG4cBn5SSr2qlHq6oFW1UhrXR3x8PEajkYEDBwIwdar73tjDDz/M6tWradeuHZMmTQLQvDY0NDT+sBQkFV2/fv11zzV37lwA0tPT6dGjBwsXLgTgkUceYezYscTExPDcc8/9JvEo/BpGuGHDBurXr09SUhLgLj1bXShJWMonwAygO9ABiPM8anho+P337AMuAG/XqsVAzbihoaGhoaFRGg7hzuulAwIKNY1qioiwdOlSevXqRUHIzvLly9HpdNxxxx1s3ryZZs2acfToUWJjY2ncuHEVa6yhoaFRNTRr1uy6kooWZvjw4cTExOBwOMjLy+PQoUOEhIQQGBiI0WhkypQp7Nmzh3nz5l02riCMsHPnzmzcuJHx48ezbt26ywzU1YGSGDeyRGSViJwVkYyCVuGaVRPsubm8m5jIOcAXeHrRoqpWSUNDQ0NDo1ohIpNEZBIwveC55/UNj5ZQtGhSUlI4cOCANyQlMzOTkydP0rRpU3788UccDgc//fQToHltaGhoaISHh19XUtHCfP7558CvVVKysrL43//+B7iNH126dOG1117j0qVL3jEFYYRWqxV/f39uu+02bxhhdSpvXRJNE5RS05VSXZRS7QtahWtWTdjy6ac8AYQCM0JDoWfPKtZIQ0NDQ0OjeuHZY+wB9npet1FKvV/FapUILaFo0SxduhSAoUOHAjBjxgzAHfe9Zs0afH19OXbsGM2aNaN9e21bqaGh8cemRYsWOBwOjhw5ct1zdejQga5du3L+/HkAQkNDufXWW9m4cSNKKaZPn86pU6eYOXOmd8zSpUsxGAz8/PPPjB49mg8//BBwhxFWJ0pi3OiEOxRlMvC2p/2jIpWqTrw39dfcql0mT65CTTQ0NDQ0NKots4B+QAaAiOwAelSpRhrXRXx8PJ07d/YmCf3yyy8BeOKJJ1i9ejVGoxGAf/7zn1Wmo4aGhsaNQk/PDfLrTSpaQMF3LsDPP/9MnTp16N+/P99//z3dunVj+PDhTJ06lbNnz3rDCKOionC5XDzxxBPeMMIHHnigXPSpLEpSLaVXEa13eSyulOqvlNqnlDqolHqhCLlZKfWlR75FKdWgkOxFz/V9Sql+5aFPWfjmxAkAmhiNdKlmli0NDQ0NDY0bBRE5ccUlZ5UoonHdnDhxgm3btjFs2DAAbDYbBw4coH79+pw+fZqjR4+SlZVFgwYN6NWrVxVrq6GhoVH1FCQVTUhIKPe5P/30UzZs2EDDhg0ZPHgwK1as4K233sJqtfL666+TkpLCwYMHOXPmDMOGDSMoKMgbRmgwGMpdn4qkJNVSwpVSnyilVnleN1dKjb7ehT117f8JDACaA/cqpZpf0W00cEFEGgEzgakFOgD3AC2A/sD7nvkqlUvp6eR6nv9n/vzKXl5DQ0NDQ+P3wgmlVFdAlFJGpdQzQEpVK6VRNpYtWwbgNW589NFHiAjDhw9nzZo13n6FXaI1NDQ0/sg0adIEvV7Pzp07y2W+d955BwCdTsfkyZMJDg4mISGBli1bMnz4cHbt2sXYsWP517/+xbRp0wDIzc3lqaeeYtasWYA7jLC6URJTzGfAp8DLntf7gS9xV1G5HjoCB0XkMIBS6j/A7cCeQn1uByZ6ni8C3lPuzCi3A/8RkXzgiFLqoGe+zdepU6kY3dxtiwkE2t5zT2UuraGhoVHtEZcLl8OBOJ0Y9HpwucjLzcVhs+FyOHA5nbgcDvQ6HTX8/cHlIj09HbvNhjiduDzjLSYT4bVqgcvF4WPHsNtsuJxO9/xOJ4F+ftQLDweXi1/27sVht3vl4nIRWqMGDSMjweVi4y+/4PJcd7lciNNJVGgojSMjcdjtrE1M9I4TEVxOJ03q1KFpnTpY8/L49kq5y0W7evVoVqcO2bm5LN227Tfybg0b0iwigvTsbBYnJV0mExH6Nm5Mk7AwUi9c4KtffvHKC9odLVrQqGZNDp47x1fJyZfJRIRRbdrQoEYNktPS+HrPnstkT/XsSeiNEVL5KDAbiAROAmuBx6tUI40yEx8fT7NmzWjSpAkAn332GQDPPvssI0eOBCAiIsJr/NDQ0NDQcH8vlldS0QIj87hx43jnnXd49NFHmTt3LuvWrWPgwIHcfffdvPPOO1gsFubNm4fFYqFFixZ0796d0aPdfgx//etfy0WXyqQkxo0QEflKKfUigIg4lFLl4SoaCRR2QU3Fnd+jyD6edbOAWp7rP18xNrKoRZRSY4GxAPXq1SsHtX/l1awslgIPtW1brvNqaGhUAC4XOJ24bDZyL17EabP92ux2Anx88DObyc/N5XhqKk67/bJWPzSUmv7+ZGVlsWP/fvd1hwOn3Y7DbicuOprwwEBOnzvHxt27cXoO506HA6fDQb/mzakTGMiB06dZs2sXTqcTp9Pp7uN0MqpNG2r7+ZGYmsqylJRf5Z5D9nOxsYSZzaw7dozFhw7hcrlwemQul4sZsbEEGwwsPnaMr44dwyXilovgEuE/bdrgqxQfHT/Ol2lp3usFbWPz5uhEeOvkSRadP+++DrhEMCvFtvr1weXiqbNnWXbpklfuFCFEp2NHzZrgcnFfdjar7HZcIgjgAqKVItlkApeL2xwO1nvGFtAOSPI8vxnYdsWP7mbgx0LP910hHwisKCQ/dYX8buA/nuc9gItXyB8GPi4kv5IncbsO5gGDipC/CrwOZAL/V4R8GtAMSAMeKEL+gUd+AvhLEfJ/A01w10r9WxHypqtW0Qh3Js6Xi5Df/MMPNMB95+BNQBVqIw8frlLjhlJqqog8D/QSkT9VmSIa5cb58+f54YcfeO655wB3ecEdO3YQGhpKrVq12LBhAwBvvvlmVaqpoaGhccPRokULTp48yeHDh4mOji7zPE6nk4MHDxIZGcnMmTP55JNPmDdvHtOmTSMkJIQ1a9YwZMgQxo0bx9ChQ1m2bBl5eXk89dRT2O12Dh48SP369fH39y/Hd1c5lMS4cUkpVQsQAKVUZ6Da1DsTkY+AjwDi4uKkPOdulptL24AAvkpO5o2zZ/ELCyvP6TU0KgYRHHl52C5dwpGXh91qxW61opxOwoOCwG7n0OHDXMzKwp6XhyM/H3t+Pv5GI+1vugkcDr7fto0LWVnY8/Nx2O3YbTbqBAQwoGlTcDj418aNZF665JY5HDgcDprXrMl9jRuDw8EzGzaQk5+Pw+n0th4hIYytXx8cDgZv3ozd6cThOcA7XC7uqlmTv4aGYrXZ6LhvH04RHCLex/F+fjzj48NZm40mmZnu64ADd+D+W8BzwGEgpoiP5X3cB8s9QFF5+xcAfwZ2ALcUIY/H7VKWhPswfSXfA3U88vFFyHutXk1tYDvwd0DvaTrP48N79xJmNHLAZmOR1YpOKbfM85hns4HJxJmLF0nOzLxcrhTO9HQwmbDn5ZHvcKBTCr1Oh0GnQymFBAaCXk9gTg517HZ0Op17DqWwGAzQti3odMQcPEj3jAy3zNMnyGyGrl1Bp6NnSgqhFy6gCslD/PygSxdQinuSk+mcne2WeVpEQAB07Ag6HU/88gtncnLQ6fXoPLpFBgVBbCwoxcTERLLy871jlVJEhYRAq1agFO8lJpLndLrX1+tRSlEvNBSaNgWl+E9SEi5Aecbq9HrqhoZCdDQoxffbt6N0OvdYzxqRYWFQty4+Lhc/79vnHlvQRylqh4VBWBghDgc7jx1Dea4XzBFaqxYEB3OTw8GhM2d+Ha/TofR6goOCwN+fVg4HpzIzf5V55AEBAWCx0M3pJDM31zu+oJl9fMBoZIDL5X7vV8h1ej3odNyDO57zBmOgJ+fWi8DXVa2MxvWzYsUKnE6ntwTskiVLcDgc9O/fn5UrV+J0OvHz8+PBBx+sYk01NDQ0bix69erF2rVrWbx4Mc8++2yZ51m8eDEul4t+/fqh0+mYMmUK48eP57777mPt2rX4+/uzcuVK7rjjDpYtW0ZAQAB+fn7cddddl4URVkeUyLXP+56yr+8CLYFduKuejhCR5OtaWKkuwEQR6ed5XeAZ8lahPms8fTYrpQy4b3yFAi8U7lu437XWjIuLk23brrwneH389MEHdH/sMSb27MmECkgAo1F9cNps6BwOlM1GzvnzXEhPJz8nh/ycHGy5ueRfukTcTTdhcLnYc+AAKUeOYMvLI99qJd9qxZafz+MdO6JzOPgmOZmfjh7FZrdjt9uxORzgdPKvjh3BZmNWSgrfnzmD3enE5nRidzrxV4rV0dFgt/NYaiqrLl3C5nJhE8EuQqRS7NbrweHgVmDdFfq3xn1wB7cL1f+ukHcDNnmet+Dy+DFwlzlY7Xlej8vdsvTACJ2O//j4gMFAo4sXycFtXTUohUEp7goM5K3atcFgoNOBAyjP4duo06HX6bgzPJzHoqOxKcU9SUkY9Hr34Vyvx6DTMaRBA+5o1Igcl4uX/vc/93W9Hr3BgEGvp29MDDdHR5Npt/Pxtm3oDQb0Hrler6dH06a0iIriQl4eK3budF8v1GIbNyYqIoJMq5XEQ4fQG41umdGIwWgkpkEDgoODuZiXx/GzZ71yncGA3mQiPDwcHz8/8mw2Llqt6E0mdx+jEZ3RiMXPD53RiHgOpBoa1RmlVKKIxJWg33RgDOAP5OJ2KJGCRxEJrFBFywGl1BBgSKNGjcYcOHCgqtWpcu688062bNnC8ePH0el09O7dm4SEBHbu3MmIESPYt28fb775Ji+99FJVq6qhoaFxQ3HgwAEaN25M//79WbVqVZnn6devH2vXruWXX36hTZs2ANSuXZu0tDT27NlDs2bNAMjPz+fuu+9m2bJlvPXWW7zwwgvExsaSlJTEiRMnqFu3brm8r/LmWnuMYo0bngkMuL1iFbBPROzloJQBd/6OPrjja7cC94nI7kJ9HgdaicijSql7gDtE5P+UUi2AL3Dn2aiD+5wWIyLXDJepCOMGwP9FRbEiNZUDiYnU0Wq133A4rFayTpwgMzWVcKMRfyDt5Em2JCeTe/Ei1pwcrJcuYc3N5d5mzYg0Gtly+DCfJidjzc/HarORa7Nhtdv5uEEDokWYn5bGK+npWF0urCLk4fYOOAI0wF03uSj38HQgBHgJtyfBlVgBC/BX3K7qZsCoFCal8FGKI/Xrg9HIaxcusOLiRUx6PUadDqNeT02Tia87dQKjkdlHjpCUmYnRYMBkNGIyGgn19+flHj3AaGThnj0cz8nB6DmYG41GQoOCGNG5MxiNrN+7l6z8fIxmMwaTCaPZTM3gYNo1bw5GI3tTU3EARosFo48PBrMZv4AAaoWHg8FATl4eerMZg8WC3mRCV80yLWtoaFwfpTBumEUkXym1TERurwzdKoqK2mNUJ6xWKyEhITzwwAPeEq++vr4YDAZOnz6Nv78/er0em82GTjPiamhoaPwGo9FIREQEJ05cWUCs5NSoUQO73U5ubq732sqVKxk0aBCtW7dmx44d3ut2u51vv/2WQYMGYTAYMJvNBAcHc/bs2et6HxXJtfYYxZ44lFJ3AatFZLdS6hWgvVLqDRFJKm7stfDk0BgHrMF9Y3euZ43XgW0ishx30tIFnoSh5/F41Hr6fYX75rEDeLw4w0ZFMmXBApb16sWchx/mtaTr+lg0isCem+s1TtQEaopw7vhx4jdsIDMjg8zMTDKzs8m8eJG/1K5NN52O/546xd3Hj5PpdJJTaK4VuOPztwBFpTGLW7GCSIOBEwYDS/Pz8dHp8NHr8dHr8TUYsFssEBJCZI0a9Pbzw8dsxtfHB4vZjNlsJrB7dwgOZuDZs4SdOoXZx8fdLBZMPj4EdOwI/v6My87mntxczH5+mPz8MPv7u1tEBFgszDYama3UVT+T1z3tahSX/ufeYuTF1Xpu2qHDNeXVL0JPQ0OjitiMOxIsu6oV0bh+vvvuO3Jzc73uzJs2bcJqtTJ48GAeeeQRAG655RbNsKGhoaFxFcLDw0lLSyvz+EOHDpGdnU337t0vuz5w4ECaN29OcnIy69ato0+fPoDbmFLwnb1o0SJvGGF1pSS3U18Vka+VUt1xe1n8A/dN5SuTf5YaEVkJrLzi2muFnucBd11l7Ju4c6NVOdE9e7Jl1Chaz58P27dDu3ZVrVK1Iuv4cfYnJLB/yxYa22x0uHSJg8nJ9Ny7l0yXi0uF+r6DO19BGm4/ZnDnJAhSiiC9njt1OmjQgNCYGG4zGAgKCCCoRg2CgoMJqlWL1p06Qb163OJ0kpiRgU+NGvgEBXmbX0gIWCyMAEZcQ+c+nnY12nra1ajjaRoaGhp/cExKqfuArkqpO64UisiSKtBJo4zEx8cTFBTELbe4MxO9/fbbADz22GMMGTIEgKlTp1aZfhoaGho3Oi1btuTkyZMcPHiQRo0alXr8u+++C+CtTFWYhQsX0qZNGx544IEiPUMKPO4KEkJXR0pi3CjwiBgEfCwiK5RSb1SgTtWStrNnw8qVnH/iCYI3bNBi5q8gPzubQz/8gDp2jGZWK/kpKdy2ZAn7c3I44/q1bsJTQIfoaCKjo+nvcFCjsHEiJISObdpAy5Y09vPjuN1OUFQU/hERv/m8Y4C519AniKKTRmpoaGhoVCqPAn/C/bU85AqZAJpxo5rgcDhYvnw5gwcPxmg0ApCQkIDZbCYhIQGn04mPjw+xsbFVrKmGhobGjUuvXr1Ys2YNixcv5vnnny/1+G+++QalFPfff/9vZK1bt6Znz5788MMPfPLJJ96SrwVs2bKFgIAAWrZsWWb9q5qSGDdOKqX+BdwGTFVKmXHfLNcoTFAQvzz8MLdMmcKCV19l6B+wxJnL4SBz925qnj0L+/fz4qefsv3oUfZnZXHM4cCFu0zil4A5PJwaBgODGzWiccOGNGnblsZduxLdowcEBuIDzLnGWiYgqjLelIaGhoZGhSEim4BNSqltIvJJVeujUXZ++uknMjIyGDbMHfS5b98+srKy6N69O++99x4Ad9xxB+oaIZcaGhoaf3TuvPNOXnjhBRISEkpt3LDb7Rw5coR69ephNpuL7LNw4UIiIyN5+umnefDBB71hggVhhIMGFVX0vvpQEuPG/wH9gX+ISKZSqjZQ9to0v2NaTphA5IwZPDN9Ov1ffBFTNawNXBqs58/z5XPP8c2qVezPyOBgfj7tgP965Jt1Oi6azXSqU4eRDRrQuGVL2tx8MwwYADVq8E1VKq+hoaGhUeUopXqLyHrgghaWUr1ZunQpZrOZfv36Ab+Gn9SuXRur1QpQ7TfNGhoaGhVNo0aNMBgM7Nq1q9RjFy5ciIgwYMCAq/aJiIjgnnvu4YsvvuCVV15h8uTJwK9hhE899VTZFL9BKFG1lN8LlZHJfOWkSQyaOJHZd9zBE4sXV+haVcbRo/DBB9wxaxZLbTYaGAy0Dgmhcd26tG3Thj+NHAmNG0NEBGh3aDQ0NDT+cJSiWsokEZmglPq0CLGIyEMVoF6F8EeqliIiZGVlcerUKU6ePMmpU6d4+eWXad++PcuXLwfcRo309HR8fX3JyclBREhPTyckJKSKtdfQ0NC4sYmKiiLt/9u7++ioyrPf499rEiQCSkBBICCRF+MDCHIK1pdVVECrpyKPgCgtiopYKCot1jcerT6lKj1QRCrahSIgVgUMKvh4HEoVcJ1qEZXKm2h5DxDAIChBgiHX+SMTRCQYwuzZM8Pvs9asmdmzZ88vt65w5Zq977uwkG++OboFSiuW3j54udfD2bt3L3Xr1gVg165dZGVlkZ2dzd69e9m7d+8xZU+EY1otRY7OFQ88wKXjx/PQK6/Qf/Vq6rdsGXakuCgrLWXe6NE8OX48EwoLycnI4O6f/ITbe/Tg4l//WnOMiIjIUXP3B2P3N4WdRcrt2bOHzZs3H7hVNC8OfV5xNkYFM2PcuHEAbNu2jcLCQnJycti0aRP16tWjZcuWamyIiFRBu3btKCgo4NNPP+XMM8+s8vsWLVpE7dq1j9jYAMjKyuLOO+/k0UcfZeDAgdx///0HLiNMdWpuxJlFIvxp4kQ69unDnF/9igHRaNiRjsmuDRuY+pvfMGHOHD795hsamvHJz39OzqhRnNdMs16IiEj1mdnwI73u7mMTleV4sX37dt55551Kmxc7d+783ntOPPFEcnJyaNKkCZ07d6ZJkyYHnlc8bty4MbVq1QJgzJgxAOzcuRMzY+fOnSm9tKCISCJ17dqVN998k/z8fO67774qvWflypUUFxdzySWXVGn/P/zhD0yYMIGXXnqJkpISAAYPHlztzMlCzY0AnN27N59eey0t8vPh00/LL9FINcuW8dVjj9H82WfZBZxXpw7P33ILfUaNoubJJ4edTkREjud1ZAAAFhdJREFU0sNJsfs8oDMwO/a8B7AoUSHMrAXwX0Bdd+8T21YbeBLYB8x3978mKk+QBg8ezKxZ5VOZZGZm0rhxY5o0aUJeXh5du3Y90LA4uIFRt27do5oIND92WW5xcTFt27Zl+fLlB+biEBGRI+vduzd333038+fPr3JzY/z48QCHXSXlcCKRCGPHjuWWW24hPz+fjIwM+vXrV+3MyUJzbgSlsBBat2bDBRdweoqcvfHNnj289sADLJ45k1EbN0LNmkzo2JHzhg7lR/37hx1PRERSRFXn3Dho/4XAz9z9q9jzk4D/cfcuVXjvs8CVwDZ3b3fQ9suBx4EM4Bl3H1WFY718UHPjemCnu88xs+nufu2R3psqc2789Kc/pbCwkLlz59KgQYMDM+XHy549e6hTpw6RSIT9+/dzzTXXEI1G+fzzzw8sESsiIkdWo0YNGjZsyKZNm6q0f25uLhs2bKCkpOSoftc2a9aMgoIC2rdvz7/+9a/qxk2oI9UYmighKI0a8Wbv3rSYO5f5sWtQk1Xhxx8zsls3ck86iWvGjmXGli189fvfQ0EBQ999V40NEREJ2mmUnyFRYV9sW1VMoXxVtwPMLAOYAFwBtAH6mVkbMzvbzF4/5NawkuM2BTbGHu+vYpaUUKtWLU477bS4NzYAJkyYgLuzf/9+OnfuzHvvvUf37t3V2BAROQqNGjVi69atVdq3pKSEDRs2cMYZZxz179qpU6dSs2ZN7r333urETDpqbgToorFjycnIYPiIEZSVloYd57vc4d13mXPRRZzeoQO/e+stzq5Xj9n3389nxcWc9MADoIm/REQkMZ4DFpnZQ2b2EPBPypsWP8jdFwI7Dtl8LvBvd1/j7vuAl4Ce7r7U3a885LatkkMXUN7gANVLVTZt2rQDj++55x42btyoS1JERI5S+/bt2b9/P6tWrfrBfadOnYq7c+WVVx7153Tt2pW9e/emxSUpoH+sA3Vi/fo8+stf8tHXXzNtyJCw4wDw9Y4dPHvTTfzfVq3gggu44MMPGXrOOax6803e/PxzeowcScYJJ4QdU0REjiPu/jBwE/BF7HaTuz96DIfM4duzLqC8UZFT2c5mdoqZ/QXoaGYVFzjPAnqb2VPAnEred6uZLTazxdu3bz+GuOmhrKyM5cuXA9C2bVs2bNgAoOaGiMhRqpgYtGIOoyOpaCrfcccdgWZKBWpuBOy6xx/n3Nq1GfHssxRvq+zLoeCVfPkl951/Pk1PPZWBU6bwQlERPPUUp2zZwmMffcSZKjxERCRE7v6huz8eu32U4M8ucvfB7t6yoqni7sXufpO7D6lsMlF3n+jundy9U4MGDRIZOSk9//zzlJWVAfDEE08QjUY566yzaN68ecjJRERSS+/evQGYP3/+D+77wQcfcPLJJ9OyZcuAUyU/NTcCFsnMZOzo0RSVlfHub38bSobibdu4qkULRr33Hpc0acL8ceN4bscOGDwY6tQJJZOIiEiANgEHr1feNLYt7sysh5lN3LVrVxCHTyl/+tOfAMjJyeHHP/4xCxYs0FkbIiLVcMYZZ5CZmXngbLjKLFmyhK+//przzjsvQcmSm5obCXDhkCFsvOoquufnQxVnvI2bXbt4o0sX5hUVMXngQF4uKOCiYcOwACYRExERSRLvA63N7AwzOwG4jm+XmY0rd5/j7rfWrVs3iMOnlKVLlwLlZ20sXLiQvXv3qrkhIlJNjRs3ZuvWrRxpddM///nPAAwcODBRsZKa/sJNkAaPPQalpSwdOjRhn+nbtkHXrlyzZg0rxo3jxmeeSdhni4iIJIKZvQi8C+SZWYGZDXT3UuA2IAqsBGa4+5G//qr+56fUmRtHKpKPxeTJk3F3srKy6NmzJ9FolJo1a3LRRRcF8nkiIumuYlLRlStXVrrP3LlziUQiBy5jOd6puZEoLVrw9MUX0+G11/jwr4e9dDeuNi1ezI+bN+fdZcvgtdfIGzYs8M8UERFJNHfv5+6N3b2Guzd190mx7W+4+5mxeTQeDvDzdeYGcNdddwEwfPhwzIxoNEqXLl2oVatWyMlERFJT165dAZg1a9ZhXy8uLqagoIBWrVqRkZGRyGhJS82NBOo7cSKnmHHnbbfhsQm3grBm/nx+cv75fLJ3L9+MGQNXXBHYZ4mIiBzPUu3MDQAzi9uxCgsL6d+/P0VFRQCMHDmSjRs3smLFCl2SIiJyDHr16gXAggULDvv6pEmTAOjZs2fCMiW7zLADHE/qNm/Of/fty9Dp05l9//30fOSRuH/Gitmz6X711ZS489bUqXS64Ya4f4aIiIiUc/c5wJxOnToNCjtLvH355Zd8/PHHLF++nFWrVrFu3To2bdrEtm3b2LFjB7t37z6wOgpAu3btiEQiRKNRQEvAiogci9zcXGrUqFHppKIvvPACALfffnsiYyW1UJobZlYfmA7kAuuAvu7+xWH2GwDcH3v6B3efGts+H2gMfB177TJ3D2+d1aNw65QpPPHKK9w1ZgxXjBjBCXFcreTfr75Kl169qGHGgvx82l19ddyOLSIiIumhrKyMJUuW8PHHH7Nq1SrWrl1LQUEBhYWFfPHFF+zevZuSkpJK5+cws++81qhRIy677LIDq6VEo1FycnJo27ZtQn4eEZF01bhxYzZt2oS7f+esO3dnyZIlZGdn06xZsyMc4fgS1pkb9wJ/d/dRZnZv7Pk9B+8Qa4A8CHQCHPjAzGYf1AT5hbsvTmToeMjMymLMiBHc+NBDrBo5krP/+Mf4HPidd8i94QZ+XqcOd7zyCq26dYvPcUVERKRSZtYD6NGqVauwo1TJokWL2LVrFx07dvzea5FIhKysLLKzs6lfvz6NGzemWbNmtGzZkoYNG/LGG28we/Zs3J1IJELfvn159NFHyc3NPXCM0tJS5s2bR69eveJ6+YuIyPGoffv2bNiwgRUrVnynYbxo0SJKSkro3r17iOmST1jNjZ7AxbHHU4H5HNLcAH4K/M3ddwCY2d+Ay4EXExMxOFc88ABrFy6k9tNPwz33QP36x3S8t/74R8566CGaNG/O+HnzoGnTOCUVERGRI0m1y1L27NkDwHXXXUeLFi3Iy8ujXbt2tGnThqysrO/tv379ekaMGMGDDz5IWVkZkUiEfv368cgjj9C8efPv7b9o0SJ27typS1JEROKga9euvP766+Tn53+nufHEE08AMGhQSvzTkzBhNTdOc/ctsceFwGmH2ScH2HjQ84LYtgqTzWw/kE/5JSuHPXfSzG4FbgU4/fTTjzV3XFgkQu1x4yjt0IF/DhnChdOnV/tYs+6+m+tGj6ZPdjYvLFwIDRvGMamIiIikm0gkwosvHvm7orVr13LfffcxY8aMA2dq9O/fn4cffviI9VQ0GiUSiejbRBGROOjduzfDhw9n4cKF39k+b948MjIy6NGjR0jJklNgq6WY2TwzW3aY23emc401JY520fVfuPvZwE9it+sr29HdJ7p7J3fv1KBBg6P+OQJz9tn8/pxzuHjGDD6NTbx1tJ679VauGT2aTnXq8OSSJWpsiIiIyBFVNo9GhdWrV3PttdfSsmVLpk+fjplx/fXXs3btWqZNm/aDXxRFo1HOPfdc6h/jWakiIlL+5fyhk4ru2rWLwsJC8vLyiES0+OnBAhsNd+/u7u0Oc3sN2GpmjQFi94ebDHQTcPDsKE1j23D3ivuvgBeAc4P6OYL0qylTyALuuemmo37vhL59GfD001xSrx5zP/uM7MOcGioiIiLBSsWlYA/ns88+o0+fPrRu3ZoZM2ZgZgwYMIB169bx3HPPVens16KiIhYtWqRLUkRE4qhJkyZs27btQHN64sSJwLdLxcq3wmr1zAYGxB4PAF47zD5R4DIzq2dm9YDLgKiZZZrZqQBmVgO4EliWgMxx16h9e+679FJe3bKF+ePGVfl9JSNH8peZM+nZqBGvr1lDnUaNAkwpIiIilXH3Oe5+a926dcOOUi2rVq3i6quv5swzzyQ/P59IJMKNN97I+vXrmTJlylHNwj9v3jzcXc0NEZE4at++PWVlZSxbVv4n7/TYlAZDhw4NM1ZSCqu5MQq41Mw+A7rHnmNmnczsGYDYRKIjgfdjt9/HttWkvMnxMbCE8rM5nk78jxAfv3npJZplZDB8xAjKSkuPuK+XlfHN3XdT83e/461evZi5ejVZ2dkJSioiIiLpYuXKlVx11VWcddZZvPrqq2RkZHDzzTezbt06Jk+eTNNqTE4ejUapV68enTt3DiCxiMjxqWIOo1mzZuHuLF26lPr169NIX3B/TyjNDXcvcvdu7t46dvnKjtj2xe5+y0H7PevurWK3ybFtxe7+I3dv7+5t3X2Yu+8P4+eIhxPr12fUL3/JN19/zZbYrLeHU1Zaym0dOnDt6NGUDhpEg5kzqVGrVgKTioiISDooKyujTZs2zJkzh4yMDAYOHMj69euZNGlStZoaUD6XRzQapXv37mRmhjVfvYhI+qm4/GTBggUsWLCAffv20aVLl5BTJSfNQJIE+o0fz0edO5MzejQUF3/v9dK9e7kxL48nly2jZefOZPzlL6DJY0REREKXanNulMbOEs3IyGDQoEFs2LCBZ555hpycnB9455EtW7aMzZs365IUEZE4a9q0KTVq1GDlypU89dRTAAwePDjkVMlJfyEnAcvIIPOxx9i1eTPzhgz5zmslX35J3xYtmLZmDSO7deP/vPcepsaGiIhIUkjVOTfGjRtHr169KCgoYPXq1XzxxReUlZVV+3jR2Mpvam6IiMRfxaSib7/9NpmZmVx66aVhR0pKOm8wWVx4IcNzc3lp2jQ+veMOcjp1guJiBuTl8UphIY/36sUd+flhpxQREZE0cPvtt39vWyQSoV69epxyyinUr1+/yvcnnXQS0WiUtm3bVvuyFhERqVyHDh1Yv34927dvp3379loCthJqbiSR/5o8mecvuYT7+/Vj8uLF8LOf8eutW7n85pu5cdKksOOJiIhImvjkk08oKipix44dld5v3ryZZcuWUVRUxO7duys9VmZmJqWlpQwfPjyBP4GIyPGjW7duzJ49G4A+ffqEnCZ5qbmRRFpcfDHDOndm9PvvUyc3lz8XF3Pe9Omcd801YUcTERGRNJKXl3dU++/bt48dO3Z8rwlS8Xj37t1allBEJCC9e/dm2LBhAAw5ZBoD+ZaaG0lmxMyZjM7N5YmdO7lryhROV2NDREREQnbCCSfQqFEjLT0oIhKCnJwcTjzxRLKzszn11FPDjpO01NxIMtnNm7N01iwsEuH0nj3DjiMiIiJHYGY9gB6tWrUKO4qIiKSxf/zjH9SpUyfsGElNzY0k1O7qq8OOICIiIlXg7nOAOZ06dRoUdhYREUlf55xzTtgRkp6mWRURERERERGRlKbmhoiIiIiIiIikNDU3RERERERERCSlqbkhIiIiIiIiIinN3D3sDAljZtuB9XE+7KnA53E+ppTT2AZD4xocjW1wNLbBCGpcm7t7gwCOm7RUY6QcjW1wNLbB0LgGR2MbjITXGMdVcyMIZrbY3TuFnSMdaWyDoXENjsY2OBrbYGhck5v++wRHYxscjW0wNK7B0dgGI4xx1WUpIiIiIiIiIpLS1NwQERERERERkZSm5saxmxh2gDSmsQ2GxjU4GtvgaGyDoXFNbvrvExyNbXA0tsHQuAZHYxuMhI+r5twQERERERERkZSmMzdEREREREREJKWpuXEMzOxyM1tlZv82s3vDzpMOzKyZmb1tZivMbLmZDQs7U7oxswwz+8jMXg87Szoxs2wze9nMPjGzlWZ2ftiZ0oGZ/Sb2u2CZmb1oZllhZ0pVZvasmW0zs2UHbatvZn8zs89i9/XCzCjfUo0RDNUZwVKNEQzVGMFQjRE/yVJjqLlRTWaWAUwArgDaAP3MrE24qdJCKXCnu7cBzgOGalzjbhiwMuwQaehx4E13PwvogMb4mJlZDnAH0Mnd2wEZwHXhpkppU4DLD9l2L/B3d28N/D32XEKmGiNQqjOCpRojGKox4kw1RtxNIQlqDDU3qu9c4N/uvsbd9wEvAT1DzpTy3H2Lu38Ye/wV5b+8c8JNlT7MrCnwM+CZsLOkEzOrC3QBJgG4+z533xluqrSRCZxoZplALWBzyHlSlrsvBHYcsrknMDX2eCrwnwkNJZVRjREQ1RnBUY0RDNUYgVKNESfJUmOouVF9OcDGg54XoH8c48rMcoGOwD/DTZJWxgF3A2VhB0kzZwDbgcmx03GfMbPaYYdKde6+CRgDbAC2ALvcfW64qdLOae6+Jfa4EDgtzDBygGqMBFCdEXeqMYKhGiMAqjESIuE1hpobkpTMrA6QD/za3b8MO086MLMrgW3u/kHYWdJQJvC/gKfcvSNQjE7vP2axazN7Ul7YNQFqm1n/cFOlLy9fPk1LqMlxQXVGfKnGCJRqjACoxkisRNUYam5U3yag2UHPm8a2yTEysxqUFxx/dfdZYedJIxcCV5nZOspPce5qZs+HGyltFAAF7l7x7d/LlBcicmy6A2vdfbu7fwPMAi4IOVO62WpmjQFi99tCziPlVGMESHVGIFRjBEc1RjBUYwQv4TWGmhvV9z7Q2szOMLMTKJ+AZnbImVKemRnl1xSudPexYedJJ+5+n7s3dfdcyv9/fcvd1aGOA3cvBDaaWV5sUzdgRYiR0sUG4DwzqxX73dANTaIWb7OBAbHHA4DXQswi31KNERDVGcFQjREc1RiBUY0RvITXGJlBf0C6cvdSM7sNiFI+u+6z7r485Fjp4ELgemCpmS2JbRvh7m+EmEmkKm4H/hr7Q2QNcFPIeVKeu//TzF4GPqR8hYOPgInhpkpdZvYicDFwqpkVAA8Co4AZZjYQWA/0DS+hVFCNESjVGZKKVGPEmWqM+EqWGsPKL38REREREREREUlNuixFRERERERERFKamhsiIiIiIiIiktLU3BARERERERGRlKbmhoiIiIiIiIikNDU3RERERERERCSlqbkhIglnZtlm9qvY4yaxpbhEREREjolqDJHjl5aCFZGEM7Nc4HV3bxdyFBEREUkjqjFEjl+ZYQcQkePSKKClmS0BPgP+w93bmdmNwH8CtYHWwBjgBOB6oAT43+6+w8xaAhOABsAeYJC7f5L4H0NERESSjGoMkeOULksRkTDcC6x293OAuw55rR3QC+gMPAzscfeOwLvADbF9JgK3u/uPgN8CTyYktYiIiCQ71RgixymduSEiyeZtd/8K+MrMdgFzYtuXAu3NrA5wATDTzCreUzPxMUVERCTFqMYQSWNqbohIsik56HHZQc/LKP+dFQF2xr6REREREakq1RgiaUyXpYhIGL4CTqrOG939S2CtmV0DYOU6xDOciIiIpCzVGCLHKTU3RCTh3L0I+H9mtgwYXY1D/AIYaGb/ApYDPeOZT0RERFKTagyR45eWghURERERERGRlKYzN0REREREREQkpam5ISIiIiIiIiIpTc0NEREREREREUlpam6IiIiIiIiISEpTc0NEREREREREUpqaGyIiIiIiIiKS0tTcEBEREREREZGUpuaGiIiIiIiIiKS0/w8xg2nKQCrfjAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 8 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
     "plot_sensitivities('y', eps)"
    ]
   },
@@ -1610,7 +1687,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1620,7 +1697,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1673,12 +1750,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>-0.979183</td>\n",
-       "      <td>0.192956</td>\n",
-       "      <td>1.072430</td>\n",
-       "      <td>0.048621</td>\n",
-       "      <td>1.944063</td>\n",
-       "      <td>-0.727894</td>\n",
+       "      <td>0.854770</td>\n",
+       "      <td>1.145550</td>\n",
+       "      <td>-0.059872</td>\n",
+       "      <td>-1.677553</td>\n",
+       "      <td>2.485344</td>\n",
+       "      <td>0.190391</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1694,12 +1771,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.318564</td>\n",
-       "      <td>0.893174</td>\n",
-       "      <td>0.119221</td>\n",
-       "      <td>0.667590</td>\n",
-       "      <td>2.991938</td>\n",
-       "      <td>-0.606037</td>\n",
+       "      <td>-0.053593</td>\n",
+       "      <td>0.241636</td>\n",
+       "      <td>1.593800</td>\n",
+       "      <td>2.498764</td>\n",
+       "      <td>2.743638</td>\n",
+       "      <td>-0.660179</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1715,12 +1792,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.998418</td>\n",
-       "      <td>0.319199</td>\n",
-       "      <td>1.230149</td>\n",
-       "      <td>-0.398608</td>\n",
-       "      <td>3.856450</td>\n",
-       "      <td>-0.913528</td>\n",
+       "      <td>1.171368</td>\n",
+       "      <td>1.410101</td>\n",
+       "      <td>1.438133</td>\n",
+       "      <td>1.021609</td>\n",
+       "      <td>3.051800</td>\n",
+       "      <td>0.610429</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1736,12 +1813,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>-0.014750</td>\n",
-       "      <td>-0.427097</td>\n",
-       "      <td>-0.279452</td>\n",
-       "      <td>1.433348</td>\n",
-       "      <td>4.788364</td>\n",
-       "      <td>-0.074601</td>\n",
+       "      <td>-0.205662</td>\n",
+       "      <td>2.391259</td>\n",
+       "      <td>0.054255</td>\n",
+       "      <td>-1.032851</td>\n",
+       "      <td>2.960908</td>\n",
+       "      <td>0.133051</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1757,12 +1834,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.407775</td>\n",
-       "      <td>0.463747</td>\n",
-       "      <td>0.119668</td>\n",
-       "      <td>1.180980</td>\n",
-       "      <td>3.065187</td>\n",
-       "      <td>-0.101223</td>\n",
+       "      <td>0.759200</td>\n",
+       "      <td>0.067206</td>\n",
+       "      <td>-1.456685</td>\n",
+       "      <td>2.600566</td>\n",
+       "      <td>2.878851</td>\n",
+       "      <td>-0.193766</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1778,12 +1855,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.481813</td>\n",
-       "      <td>0.293208</td>\n",
-       "      <td>-0.159899</td>\n",
-       "      <td>0.218286</td>\n",
-       "      <td>3.183441</td>\n",
-       "      <td>0.933746</td>\n",
+       "      <td>-0.311092</td>\n",
+       "      <td>0.141907</td>\n",
+       "      <td>-0.228872</td>\n",
+       "      <td>-0.193889</td>\n",
+       "      <td>4.811703</td>\n",
+       "      <td>0.994076</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1799,12 +1876,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>-0.064053</td>\n",
-       "      <td>1.484332</td>\n",
-       "      <td>0.119981</td>\n",
-       "      <td>1.719267</td>\n",
-       "      <td>4.822919</td>\n",
-       "      <td>1.856944</td>\n",
+       "      <td>0.865276</td>\n",
+       "      <td>0.838981</td>\n",
+       "      <td>0.668306</td>\n",
+       "      <td>1.623928</td>\n",
+       "      <td>3.328777</td>\n",
+       "      <td>0.075325</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1820,12 +1897,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.411443</td>\n",
-       "      <td>1.836223</td>\n",
-       "      <td>1.359105</td>\n",
-       "      <td>0.048072</td>\n",
-       "      <td>4.722903</td>\n",
-       "      <td>-2.202143</td>\n",
+       "      <td>-0.163654</td>\n",
+       "      <td>2.215009</td>\n",
+       "      <td>-0.771913</td>\n",
+       "      <td>3.161547</td>\n",
+       "      <td>4.519341</td>\n",
+       "      <td>1.115471</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1841,12 +1918,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.805056</td>\n",
-       "      <td>0.163220</td>\n",
-       "      <td>0.146697</td>\n",
-       "      <td>0.425173</td>\n",
-       "      <td>3.338153</td>\n",
-       "      <td>-1.097586</td>\n",
+       "      <td>0.395456</td>\n",
+       "      <td>0.612326</td>\n",
+       "      <td>0.976628</td>\n",
+       "      <td>-0.055129</td>\n",
+       "      <td>2.919909</td>\n",
+       "      <td>0.113068</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1862,12 +1939,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>2.785217</td>\n",
-       "      <td>-0.098704</td>\n",
-       "      <td>-0.560119</td>\n",
-       "      <td>2.580439</td>\n",
-       "      <td>4.363146</td>\n",
-       "      <td>1.110953</td>\n",
+       "      <td>0.717336</td>\n",
+       "      <td>1.043128</td>\n",
+       "      <td>1.064387</td>\n",
+       "      <td>-0.143616</td>\n",
+       "      <td>5.751210</td>\n",
+       "      <td>2.882783</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1883,12 +1960,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.117663</td>\n",
-       "      <td>-0.734754</td>\n",
-       "      <td>-0.664079</td>\n",
-       "      <td>0.541150</td>\n",
-       "      <td>3.478977</td>\n",
-       "      <td>-1.436971</td>\n",
+       "      <td>-1.204150</td>\n",
+       "      <td>0.689827</td>\n",
+       "      <td>1.082176</td>\n",
+       "      <td>1.904679</td>\n",
+       "      <td>2.756413</td>\n",
+       "      <td>1.600066</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1904,12 +1981,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.059940</td>\n",
-       "      <td>0.499436</td>\n",
-       "      <td>-0.805301</td>\n",
-       "      <td>1.440611</td>\n",
-       "      <td>2.165131</td>\n",
-       "      <td>-0.749951</td>\n",
+       "      <td>0.805529</td>\n",
+       "      <td>0.522137</td>\n",
+       "      <td>0.670364</td>\n",
+       "      <td>2.454703</td>\n",
+       "      <td>3.018521</td>\n",
+       "      <td>1.862394</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1925,12 +2002,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>2.044621</td>\n",
-       "      <td>0.089834</td>\n",
-       "      <td>-0.030406</td>\n",
-       "      <td>0.931452</td>\n",
-       "      <td>4.924059</td>\n",
-       "      <td>1.285210</td>\n",
+       "      <td>0.667126</td>\n",
+       "      <td>0.783559</td>\n",
+       "      <td>-1.014303</td>\n",
+       "      <td>-0.101997</td>\n",
+       "      <td>1.940476</td>\n",
+       "      <td>-1.256017</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1946,12 +2023,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.136111</td>\n",
-       "      <td>-1.453566</td>\n",
-       "      <td>0.570666</td>\n",
-       "      <td>1.460973</td>\n",
-       "      <td>1.416084</td>\n",
-       "      <td>-0.236831</td>\n",
+       "      <td>0.349566</td>\n",
+       "      <td>-0.937770</td>\n",
+       "      <td>0.449607</td>\n",
+       "      <td>0.239118</td>\n",
+       "      <td>4.411886</td>\n",
+       "      <td>-0.570199</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1967,12 +2044,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>-0.077502</td>\n",
-       "      <td>1.456230</td>\n",
-       "      <td>1.537391</td>\n",
-       "      <td>0.465748</td>\n",
-       "      <td>5.488972</td>\n",
-       "      <td>-0.039388</td>\n",
+       "      <td>-1.199949</td>\n",
+       "      <td>0.312773</td>\n",
+       "      <td>1.263932</td>\n",
+       "      <td>1.544323</td>\n",
+       "      <td>3.262497</td>\n",
+       "      <td>0.091539</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1988,12 +2065,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.736106</td>\n",
-       "      <td>-2.389954</td>\n",
-       "      <td>-0.401374</td>\n",
-       "      <td>1.470112</td>\n",
-       "      <td>4.389249</td>\n",
-       "      <td>0.794856</td>\n",
+       "      <td>1.100669</td>\n",
+       "      <td>0.046852</td>\n",
+       "      <td>-1.349183</td>\n",
+       "      <td>2.652128</td>\n",
+       "      <td>1.339296</td>\n",
+       "      <td>0.999152</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -2009,12 +2086,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>-0.882210</td>\n",
-       "      <td>2.627972</td>\n",
-       "      <td>0.680587</td>\n",
-       "      <td>1.609892</td>\n",
-       "      <td>3.058685</td>\n",
-       "      <td>0.085670</td>\n",
+       "      <td>-0.882725</td>\n",
+       "      <td>0.565985</td>\n",
+       "      <td>-0.286692</td>\n",
+       "      <td>1.673683</td>\n",
+       "      <td>0.860009</td>\n",
+       "      <td>0.185171</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -2030,12 +2107,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.328443</td>\n",
-       "      <td>0.587181</td>\n",
-       "      <td>-0.485711</td>\n",
-       "      <td>1.499816</td>\n",
-       "      <td>4.450632</td>\n",
-       "      <td>1.079241</td>\n",
+       "      <td>0.093720</td>\n",
+       "      <td>0.850729</td>\n",
+       "      <td>0.528986</td>\n",
+       "      <td>0.980290</td>\n",
+       "      <td>6.252438</td>\n",
+       "      <td>1.571740</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -2051,12 +2128,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>-0.655710</td>\n",
-       "      <td>-0.594330</td>\n",
-       "      <td>-1.469235</td>\n",
-       "      <td>0.276059</td>\n",
-       "      <td>2.635020</td>\n",
-       "      <td>0.752944</td>\n",
+       "      <td>1.995882</td>\n",
+       "      <td>2.575455</td>\n",
+       "      <td>0.444333</td>\n",
+       "      <td>0.765691</td>\n",
+       "      <td>3.344862</td>\n",
+       "      <td>-0.876842</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -2072,12 +2149,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.560821</td>\n",
-       "      <td>-0.423914</td>\n",
-       "      <td>0.248592</td>\n",
-       "      <td>1.040715</td>\n",
-       "      <td>3.336944</td>\n",
-       "      <td>0.784157</td>\n",
+       "      <td>0.146899</td>\n",
+       "      <td>0.926665</td>\n",
+       "      <td>1.004243</td>\n",
+       "      <td>1.257351</td>\n",
+       "      <td>2.845607</td>\n",
+       "      <td>-0.663591</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -2093,12 +2170,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.872973</td>\n",
-       "      <td>1.334567</td>\n",
-       "      <td>-1.307793</td>\n",
-       "      <td>2.725420</td>\n",
-       "      <td>2.947031</td>\n",
-       "      <td>-1.336964</td>\n",
+       "      <td>0.688457</td>\n",
+       "      <td>-0.191403</td>\n",
+       "      <td>-1.501904</td>\n",
+       "      <td>0.003456</td>\n",
+       "      <td>3.982428</td>\n",
+       "      <td>1.368897</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -2112,73 +2189,73 @@
       ],
       "text/plain": [
        "    time datatype  t_presim   k0  k0_preeq  k0_presim  observable_x1  \\\n",
-       "0    0.0     data       0.0  1.0       NaN        NaN      -0.979183   \n",
-       "1    0.5     data       0.0  1.0       NaN        NaN       1.318564   \n",
-       "2    1.0     data       0.0  1.0       NaN        NaN       1.998418   \n",
-       "3    1.5     data       0.0  1.0       NaN        NaN      -0.014750   \n",
-       "4    2.0     data       0.0  1.0       NaN        NaN       0.407775   \n",
-       "5    2.5     data       0.0  1.0       NaN        NaN       1.481813   \n",
-       "6    3.0     data       0.0  1.0       NaN        NaN      -0.064053   \n",
-       "7    3.5     data       0.0  1.0       NaN        NaN       1.411443   \n",
-       "8    4.0     data       0.0  1.0       NaN        NaN       0.805056   \n",
-       "9    4.5     data       0.0  1.0       NaN        NaN       2.785217   \n",
-       "10   5.0     data       0.0  1.0       NaN        NaN       0.117663   \n",
-       "11   5.5     data       0.0  1.0       NaN        NaN       1.059940   \n",
-       "12   6.0     data       0.0  1.0       NaN        NaN       2.044621   \n",
-       "13   6.5     data       0.0  1.0       NaN        NaN       1.136111   \n",
-       "14   7.0     data       0.0  1.0       NaN        NaN      -0.077502   \n",
-       "15   7.5     data       0.0  1.0       NaN        NaN       1.736106   \n",
-       "16   8.0     data       0.0  1.0       NaN        NaN      -0.882210   \n",
-       "17   8.5     data       0.0  1.0       NaN        NaN       1.328443   \n",
-       "18   9.0     data       0.0  1.0       NaN        NaN      -0.655710   \n",
-       "19   9.5     data       0.0  1.0       NaN        NaN       0.560821   \n",
-       "20  10.0     data       0.0  1.0       NaN        NaN       0.872973   \n",
+       "0    0.0     data       0.0  1.0       NaN        NaN       0.854770   \n",
+       "1    0.5     data       0.0  1.0       NaN        NaN      -0.053593   \n",
+       "2    1.0     data       0.0  1.0       NaN        NaN       1.171368   \n",
+       "3    1.5     data       0.0  1.0       NaN        NaN      -0.205662   \n",
+       "4    2.0     data       0.0  1.0       NaN        NaN       0.759200   \n",
+       "5    2.5     data       0.0  1.0       NaN        NaN      -0.311092   \n",
+       "6    3.0     data       0.0  1.0       NaN        NaN       0.865276   \n",
+       "7    3.5     data       0.0  1.0       NaN        NaN      -0.163654   \n",
+       "8    4.0     data       0.0  1.0       NaN        NaN       0.395456   \n",
+       "9    4.5     data       0.0  1.0       NaN        NaN       0.717336   \n",
+       "10   5.0     data       0.0  1.0       NaN        NaN      -1.204150   \n",
+       "11   5.5     data       0.0  1.0       NaN        NaN       0.805529   \n",
+       "12   6.0     data       0.0  1.0       NaN        NaN       0.667126   \n",
+       "13   6.5     data       0.0  1.0       NaN        NaN       0.349566   \n",
+       "14   7.0     data       0.0  1.0       NaN        NaN      -1.199949   \n",
+       "15   7.5     data       0.0  1.0       NaN        NaN       1.100669   \n",
+       "16   8.0     data       0.0  1.0       NaN        NaN      -0.882725   \n",
+       "17   8.5     data       0.0  1.0       NaN        NaN       0.093720   \n",
+       "18   9.0     data       0.0  1.0       NaN        NaN       1.995882   \n",
+       "19   9.5     data       0.0  1.0       NaN        NaN       0.146899   \n",
+       "20  10.0     data       0.0  1.0       NaN        NaN       0.688457   \n",
        "\n",
        "    observable_x2  observable_x3  observable_x1_scaled  \\\n",
-       "0        0.192956       1.072430              0.048621   \n",
-       "1        0.893174       0.119221              0.667590   \n",
-       "2        0.319199       1.230149             -0.398608   \n",
-       "3       -0.427097      -0.279452              1.433348   \n",
-       "4        0.463747       0.119668              1.180980   \n",
-       "5        0.293208      -0.159899              0.218286   \n",
-       "6        1.484332       0.119981              1.719267   \n",
-       "7        1.836223       1.359105              0.048072   \n",
-       "8        0.163220       0.146697              0.425173   \n",
-       "9       -0.098704      -0.560119              2.580439   \n",
-       "10      -0.734754      -0.664079              0.541150   \n",
-       "11       0.499436      -0.805301              1.440611   \n",
-       "12       0.089834      -0.030406              0.931452   \n",
-       "13      -1.453566       0.570666              1.460973   \n",
-       "14       1.456230       1.537391              0.465748   \n",
-       "15      -2.389954      -0.401374              1.470112   \n",
-       "16       2.627972       0.680587              1.609892   \n",
-       "17       0.587181      -0.485711              1.499816   \n",
-       "18      -0.594330      -1.469235              0.276059   \n",
-       "19      -0.423914       0.248592              1.040715   \n",
-       "20       1.334567      -1.307793              2.725420   \n",
+       "0        1.145550      -0.059872             -1.677553   \n",
+       "1        0.241636       1.593800              2.498764   \n",
+       "2        1.410101       1.438133              1.021609   \n",
+       "3        2.391259       0.054255             -1.032851   \n",
+       "4        0.067206      -1.456685              2.600566   \n",
+       "5        0.141907      -0.228872             -0.193889   \n",
+       "6        0.838981       0.668306              1.623928   \n",
+       "7        2.215009      -0.771913              3.161547   \n",
+       "8        0.612326       0.976628             -0.055129   \n",
+       "9        1.043128       1.064387             -0.143616   \n",
+       "10       0.689827       1.082176              1.904679   \n",
+       "11       0.522137       0.670364              2.454703   \n",
+       "12       0.783559      -1.014303             -0.101997   \n",
+       "13      -0.937770       0.449607              0.239118   \n",
+       "14       0.312773       1.263932              1.544323   \n",
+       "15       0.046852      -1.349183              2.652128   \n",
+       "16       0.565985      -0.286692              1.673683   \n",
+       "17       0.850729       0.528986              0.980290   \n",
+       "18       2.575455       0.444333              0.765691   \n",
+       "19       0.926665       1.004243              1.257351   \n",
+       "20      -0.191403      -1.501904              0.003456   \n",
        "\n",
        "    observable_x2_offsetted  observable_x1withsigma  observable_x1_std  \\\n",
-       "0                  1.944063               -0.727894                1.0   \n",
-       "1                  2.991938               -0.606037                1.0   \n",
-       "2                  3.856450               -0.913528                1.0   \n",
-       "3                  4.788364               -0.074601                1.0   \n",
-       "4                  3.065187               -0.101223                1.0   \n",
-       "5                  3.183441                0.933746                1.0   \n",
-       "6                  4.822919                1.856944                1.0   \n",
-       "7                  4.722903               -2.202143                1.0   \n",
-       "8                  3.338153               -1.097586                1.0   \n",
-       "9                  4.363146                1.110953                1.0   \n",
-       "10                 3.478977               -1.436971                1.0   \n",
-       "11                 2.165131               -0.749951                1.0   \n",
-       "12                 4.924059                1.285210                1.0   \n",
-       "13                 1.416084               -0.236831                1.0   \n",
-       "14                 5.488972               -0.039388                1.0   \n",
-       "15                 4.389249                0.794856                1.0   \n",
-       "16                 3.058685                0.085670                1.0   \n",
-       "17                 4.450632                1.079241                1.0   \n",
-       "18                 2.635020                0.752944                1.0   \n",
-       "19                 3.336944                0.784157                1.0   \n",
-       "20                 2.947031               -1.336964                1.0   \n",
+       "0                  2.485344                0.190391                1.0   \n",
+       "1                  2.743638               -0.660179                1.0   \n",
+       "2                  3.051800                0.610429                1.0   \n",
+       "3                  2.960908                0.133051                1.0   \n",
+       "4                  2.878851               -0.193766                1.0   \n",
+       "5                  4.811703                0.994076                1.0   \n",
+       "6                  3.328777                0.075325                1.0   \n",
+       "7                  4.519341                1.115471                1.0   \n",
+       "8                  2.919909                0.113068                1.0   \n",
+       "9                  5.751210                2.882783                1.0   \n",
+       "10                 2.756413                1.600066                1.0   \n",
+       "11                 3.018521                1.862394                1.0   \n",
+       "12                 1.940476               -1.256017                1.0   \n",
+       "13                 4.411886               -0.570199                1.0   \n",
+       "14                 3.262497                0.091539                1.0   \n",
+       "15                 1.339296                0.999152                1.0   \n",
+       "16                 0.860009                0.185171                1.0   \n",
+       "17                 6.252438                1.571740                1.0   \n",
+       "18                 3.344862               -0.876842                1.0   \n",
+       "19                 2.845607               -0.663591                1.0   \n",
+       "20                 3.982428                1.368897                1.0   \n",
        "\n",
        "    observable_x2_std  observable_x3_std  observable_x1_scaled_std  \\\n",
        "0                 1.0                1.0                       1.0   \n",
@@ -2227,7 +2304,7 @@
        "20                          1.0                         NaN  "
       ]
      },
-     "execution_count": 22,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2240,7 +2317,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2250,7 +2327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -2295,12 +2372,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.079183</td>\n",
-       "      <td>0.207044</td>\n",
-       "      <td>0.372430</td>\n",
-       "      <td>0.151379</td>\n",
-       "      <td>1.455937</td>\n",
-       "      <td>8.278943</td>\n",
+       "      <td>0.754770</td>\n",
+       "      <td>0.745550</td>\n",
+       "      <td>0.759872</td>\n",
+       "      <td>1.877553</td>\n",
+       "      <td>0.914656</td>\n",
+       "      <td>0.903906</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -2309,12 +2386,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.779197</td>\n",
-       "      <td>0.208496</td>\n",
-       "      <td>0.072270</td>\n",
-       "      <td>0.411145</td>\n",
-       "      <td>0.692741</td>\n",
-       "      <td>11.454045</td>\n",
+       "      <td>0.592960</td>\n",
+       "      <td>0.443043</td>\n",
+       "      <td>1.402310</td>\n",
+       "      <td>1.420030</td>\n",
+       "      <td>0.941041</td>\n",
+       "      <td>11.995462</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -2323,12 +2400,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.418346</td>\n",
-       "      <td>0.414088</td>\n",
-       "      <td>1.133725</td>\n",
-       "      <td>1.558753</td>\n",
-       "      <td>0.123162</td>\n",
-       "      <td>14.936001</td>\n",
+       "      <td>0.591296</td>\n",
+       "      <td>0.676814</td>\n",
+       "      <td>1.341709</td>\n",
+       "      <td>0.138536</td>\n",
+       "      <td>0.681487</td>\n",
+       "      <td>0.303567</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -2337,12 +2414,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.585149</td>\n",
-       "      <td>1.157749</td>\n",
-       "      <td>0.355528</td>\n",
-       "      <td>0.292550</td>\n",
-       "      <td>1.057712</td>\n",
-       "      <td>6.450000</td>\n",
+       "      <td>0.776061</td>\n",
+       "      <td>1.660607</td>\n",
+       "      <td>0.021821</td>\n",
+       "      <td>2.173649</td>\n",
+       "      <td>0.769744</td>\n",
+       "      <td>4.373485</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -2351,12 +2428,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.152759</td>\n",
-       "      <td>0.252089</td>\n",
-       "      <td>0.049974</td>\n",
-       "      <td>0.059911</td>\n",
-       "      <td>0.650649</td>\n",
-       "      <td>6.617580</td>\n",
+       "      <td>0.198665</td>\n",
+       "      <td>0.648630</td>\n",
+       "      <td>1.526379</td>\n",
+       "      <td>1.479497</td>\n",
+       "      <td>0.836985</td>\n",
+       "      <td>7.543005</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
@@ -2365,12 +2442,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.928757</td>\n",
-       "      <td>0.405543</td>\n",
-       "      <td>0.226200</td>\n",
-       "      <td>0.887826</td>\n",
-       "      <td>0.515310</td>\n",
-       "      <td>3.806906</td>\n",
+       "      <td>0.864147</td>\n",
+       "      <td>0.556844</td>\n",
+       "      <td>0.295173</td>\n",
+       "      <td>1.300000</td>\n",
+       "      <td>1.112952</td>\n",
+       "      <td>4.410204</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
@@ -2379,12 +2456,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.610923</td>\n",
-       "      <td>0.802369</td>\n",
-       "      <td>0.056249</td>\n",
-       "      <td>0.625525</td>\n",
-       "      <td>1.140957</td>\n",
-       "      <td>13.100735</td>\n",
+       "      <td>0.318405</td>\n",
+       "      <td>0.157019</td>\n",
+       "      <td>0.604574</td>\n",
+       "      <td>0.530187</td>\n",
+       "      <td>0.353186</td>\n",
+       "      <td>4.715461</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
@@ -2393,12 +2470,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.870083</td>\n",
-       "      <td>1.170115</td>\n",
-       "      <td>1.297599</td>\n",
-       "      <td>1.034648</td>\n",
-       "      <td>1.056794</td>\n",
-       "      <td>27.435032</td>\n",
+       "      <td>0.705014</td>\n",
+       "      <td>1.548900</td>\n",
+       "      <td>0.833419</td>\n",
+       "      <td>2.078828</td>\n",
+       "      <td>0.853233</td>\n",
+       "      <td>5.741110</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
@@ -2407,12 +2484,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.268776</td>\n",
-       "      <td>0.488083</td>\n",
-       "      <td>0.087202</td>\n",
-       "      <td>0.647388</td>\n",
-       "      <td>0.313150</td>\n",
-       "      <td>16.338665</td>\n",
+       "      <td>0.140824</td>\n",
+       "      <td>0.038976</td>\n",
+       "      <td>0.917134</td>\n",
+       "      <td>1.127690</td>\n",
+       "      <td>0.731393</td>\n",
+       "      <td>4.232125</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
@@ -2421,12 +2498,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>2.253679</td>\n",
-       "      <td>0.736219</td>\n",
-       "      <td>0.617772</td>\n",
-       "      <td>1.517363</td>\n",
-       "      <td>0.725632</td>\n",
-       "      <td>5.794149</td>\n",
+       "      <td>0.185797</td>\n",
+       "      <td>0.405613</td>\n",
+       "      <td>1.006734</td>\n",
+       "      <td>1.206693</td>\n",
+       "      <td>2.113695</td>\n",
+       "      <td>23.512450</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
@@ -2435,12 +2512,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.409429</td>\n",
-       "      <td>1.359436</td>\n",
-       "      <td>0.720039</td>\n",
-       "      <td>0.513033</td>\n",
-       "      <td>0.145704</td>\n",
-       "      <td>19.640623</td>\n",
+       "      <td>1.731241</td>\n",
+       "      <td>0.065145</td>\n",
+       "      <td>1.026216</td>\n",
+       "      <td>0.850497</td>\n",
+       "      <td>0.868269</td>\n",
+       "      <td>10.729745</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
@@ -2449,12 +2526,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.537026</td>\n",
-       "      <td>0.113297</td>\n",
-       "      <td>0.859701</td>\n",
-       "      <td>0.394782</td>\n",
-       "      <td>1.447601</td>\n",
-       "      <td>12.728659</td>\n",
+       "      <td>0.282614</td>\n",
+       "      <td>0.090596</td>\n",
+       "      <td>0.615964</td>\n",
+       "      <td>1.408874</td>\n",
+       "      <td>0.594212</td>\n",
+       "      <td>13.394799</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
@@ -2463,12 +2540,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.525632</td>\n",
-       "      <td>0.511769</td>\n",
-       "      <td>0.083366</td>\n",
-       "      <td>0.106527</td>\n",
-       "      <td>1.322456</td>\n",
-       "      <td>7.662203</td>\n",
+       "      <td>0.148137</td>\n",
+       "      <td>0.181956</td>\n",
+       "      <td>1.067263</td>\n",
+       "      <td>1.139975</td>\n",
+       "      <td>1.661126</td>\n",
+       "      <td>17.750062</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
@@ -2477,12 +2554,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.620811</td>\n",
-       "      <td>2.044795</td>\n",
-       "      <td>0.519037</td>\n",
-       "      <td>0.430374</td>\n",
-       "      <td>2.175145</td>\n",
-       "      <td>7.521306</td>\n",
+       "      <td>0.165734</td>\n",
+       "      <td>1.528999</td>\n",
+       "      <td>0.397978</td>\n",
+       "      <td>0.791481</td>\n",
+       "      <td>0.820657</td>\n",
+       "      <td>10.854981</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
@@ -2491,12 +2568,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.589332</td>\n",
-       "      <td>0.874674</td>\n",
-       "      <td>1.486992</td>\n",
-       "      <td>0.557912</td>\n",
-       "      <td>1.907417</td>\n",
-       "      <td>5.512185</td>\n",
+       "      <td>1.711779</td>\n",
+       "      <td>0.268782</td>\n",
+       "      <td>1.213533</td>\n",
+       "      <td>0.520663</td>\n",
+       "      <td>0.319058</td>\n",
+       "      <td>4.202913</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
@@ -2505,12 +2582,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.227539</td>\n",
-       "      <td>2.962483</td>\n",
-       "      <td>0.450634</td>\n",
-       "      <td>0.452977</td>\n",
-       "      <td>0.816720</td>\n",
-       "      <td>2.862886</td>\n",
+       "      <td>0.592101</td>\n",
+       "      <td>0.525677</td>\n",
+       "      <td>1.398442</td>\n",
+       "      <td>1.634992</td>\n",
+       "      <td>2.233233</td>\n",
+       "      <td>4.905843</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
@@ -2519,12 +2596,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.387711</td>\n",
-       "      <td>2.063869</td>\n",
-       "      <td>0.632384</td>\n",
-       "      <td>0.598891</td>\n",
-       "      <td>0.505418</td>\n",
-       "      <td>4.198303</td>\n",
+       "      <td>1.388225</td>\n",
+       "      <td>0.001882</td>\n",
+       "      <td>0.334895</td>\n",
+       "      <td>0.662683</td>\n",
+       "      <td>2.704094</td>\n",
+       "      <td>3.203293</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
@@ -2533,12 +2610,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.825828</td>\n",
-       "      <td>0.030947</td>\n",
-       "      <td>0.532935</td>\n",
-       "      <td>0.494585</td>\n",
-       "      <td>0.894398</td>\n",
-       "      <td>5.766254</td>\n",
+       "      <td>0.408896</td>\n",
+       "      <td>0.294495</td>\n",
+       "      <td>0.481762</td>\n",
+       "      <td>0.024940</td>\n",
+       "      <td>2.696204</td>\n",
+       "      <td>10.691248</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
@@ -2547,12 +2624,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.155612</td>\n",
-       "      <td>1.143211</td>\n",
-       "      <td>1.515550</td>\n",
-       "      <td>0.723745</td>\n",
-       "      <td>0.913861</td>\n",
-       "      <td>2.530417</td>\n",
+       "      <td>1.495980</td>\n",
+       "      <td>2.026573</td>\n",
+       "      <td>0.398018</td>\n",
+       "      <td>0.234113</td>\n",
+       "      <td>0.204019</td>\n",
+       "      <td>13.767435</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
@@ -2561,12 +2638,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.063471</td>\n",
-       "      <td>0.965922</td>\n",
-       "      <td>0.203122</td>\n",
-       "      <td>0.046015</td>\n",
-       "      <td>0.205065</td>\n",
-       "      <td>2.868075</td>\n",
+       "      <td>0.350451</td>\n",
+       "      <td>0.384657</td>\n",
+       "      <td>0.958773</td>\n",
+       "      <td>0.262651</td>\n",
+       "      <td>0.696401</td>\n",
+       "      <td>11.609403</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
@@ -2575,12 +2652,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.378024</td>\n",
-       "      <td>0.798986</td>\n",
-       "      <td>1.352478</td>\n",
-       "      <td>1.735521</td>\n",
-       "      <td>0.588551</td>\n",
-       "      <td>18.319130</td>\n",
+       "      <td>0.193508</td>\n",
+       "      <td>0.726984</td>\n",
+       "      <td>1.546589</td>\n",
+       "      <td>0.986442</td>\n",
+       "      <td>0.446847</td>\n",
+       "      <td>8.739479</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2588,76 +2665,76 @@
       ],
       "text/plain": [
        "    time  t_presim   k0  k0_preeq  k0_presim  observable_x1  observable_x2  \\\n",
-       "0    0.0       0.0  1.0       NaN        NaN       1.079183       0.207044   \n",
-       "1    0.5       0.0  1.0       NaN        NaN       0.779197       0.208496   \n",
-       "2    1.0       0.0  1.0       NaN        NaN       1.418346       0.414088   \n",
-       "3    1.5       0.0  1.0       NaN        NaN       0.585149       1.157749   \n",
-       "4    2.0       0.0  1.0       NaN        NaN       0.152759       0.252089   \n",
-       "5    2.5       0.0  1.0       NaN        NaN       0.928757       0.405543   \n",
-       "6    3.0       0.0  1.0       NaN        NaN       0.610923       0.802369   \n",
-       "7    3.5       0.0  1.0       NaN        NaN       0.870083       1.170115   \n",
-       "8    4.0       0.0  1.0       NaN        NaN       0.268776       0.488083   \n",
-       "9    4.5       0.0  1.0       NaN        NaN       2.253679       0.736219   \n",
-       "10   5.0       0.0  1.0       NaN        NaN       0.409429       1.359436   \n",
-       "11   5.5       0.0  1.0       NaN        NaN       0.537026       0.113297   \n",
-       "12   6.0       0.0  1.0       NaN        NaN       1.525632       0.511769   \n",
-       "13   6.5       0.0  1.0       NaN        NaN       0.620811       2.044795   \n",
-       "14   7.0       0.0  1.0       NaN        NaN       0.589332       0.874674   \n",
-       "15   7.5       0.0  1.0       NaN        NaN       1.227539       2.962483   \n",
-       "16   8.0       0.0  1.0       NaN        NaN       1.387711       2.063869   \n",
-       "17   8.5       0.0  1.0       NaN        NaN       0.825828       0.030947   \n",
-       "18   9.0       0.0  1.0       NaN        NaN       1.155612       1.143211   \n",
-       "19   9.5       0.0  1.0       NaN        NaN       0.063471       0.965922   \n",
-       "20  10.0       0.0  1.0       NaN        NaN       0.378024       0.798986   \n",
+       "0    0.0       0.0  1.0       NaN        NaN       0.754770       0.745550   \n",
+       "1    0.5       0.0  1.0       NaN        NaN       0.592960       0.443043   \n",
+       "2    1.0       0.0  1.0       NaN        NaN       0.591296       0.676814   \n",
+       "3    1.5       0.0  1.0       NaN        NaN       0.776061       1.660607   \n",
+       "4    2.0       0.0  1.0       NaN        NaN       0.198665       0.648630   \n",
+       "5    2.5       0.0  1.0       NaN        NaN       0.864147       0.556844   \n",
+       "6    3.0       0.0  1.0       NaN        NaN       0.318405       0.157019   \n",
+       "7    3.5       0.0  1.0       NaN        NaN       0.705014       1.548900   \n",
+       "8    4.0       0.0  1.0       NaN        NaN       0.140824       0.038976   \n",
+       "9    4.5       0.0  1.0       NaN        NaN       0.185797       0.405613   \n",
+       "10   5.0       0.0  1.0       NaN        NaN       1.731241       0.065145   \n",
+       "11   5.5       0.0  1.0       NaN        NaN       0.282614       0.090596   \n",
+       "12   6.0       0.0  1.0       NaN        NaN       0.148137       0.181956   \n",
+       "13   6.5       0.0  1.0       NaN        NaN       0.165734       1.528999   \n",
+       "14   7.0       0.0  1.0       NaN        NaN       1.711779       0.268782   \n",
+       "15   7.5       0.0  1.0       NaN        NaN       0.592101       0.525677   \n",
+       "16   8.0       0.0  1.0       NaN        NaN       1.388225       0.001882   \n",
+       "17   8.5       0.0  1.0       NaN        NaN       0.408896       0.294495   \n",
+       "18   9.0       0.0  1.0       NaN        NaN       1.495980       2.026573   \n",
+       "19   9.5       0.0  1.0       NaN        NaN       0.350451       0.384657   \n",
+       "20  10.0       0.0  1.0       NaN        NaN       0.193508       0.726984   \n",
        "\n",
        "    observable_x3  observable_x1_scaled  observable_x2_offsetted  \\\n",
-       "0        0.372430              0.151379                 1.455937   \n",
-       "1        0.072270              0.411145                 0.692741   \n",
-       "2        1.133725              1.558753                 0.123162   \n",
-       "3        0.355528              0.292550                 1.057712   \n",
-       "4        0.049974              0.059911                 0.650649   \n",
-       "5        0.226200              0.887826                 0.515310   \n",
-       "6        0.056249              0.625525                 1.140957   \n",
-       "7        1.297599              1.034648                 1.056794   \n",
-       "8        0.087202              0.647388                 0.313150   \n",
-       "9        0.617772              1.517363                 0.725632   \n",
-       "10       0.720039              0.513033                 0.145704   \n",
-       "11       0.859701              0.394782                 1.447601   \n",
-       "12       0.083366              0.106527                 1.322456   \n",
-       "13       0.519037              0.430374                 2.175145   \n",
-       "14       1.486992              0.557912                 1.907417   \n",
-       "15       0.450634              0.452977                 0.816720   \n",
-       "16       0.632384              0.598891                 0.505418   \n",
-       "17       0.532935              0.494585                 0.894398   \n",
-       "18       1.515550              0.723745                 0.913861   \n",
-       "19       0.203122              0.046015                 0.205065   \n",
-       "20       1.352478              1.735521                 0.588551   \n",
+       "0        0.759872              1.877553                 0.914656   \n",
+       "1        1.402310              1.420030                 0.941041   \n",
+       "2        1.341709              0.138536                 0.681487   \n",
+       "3        0.021821              2.173649                 0.769744   \n",
+       "4        1.526379              1.479497                 0.836985   \n",
+       "5        0.295173              1.300000                 1.112952   \n",
+       "6        0.604574              0.530187                 0.353186   \n",
+       "7        0.833419              2.078828                 0.853233   \n",
+       "8        0.917134              1.127690                 0.731393   \n",
+       "9        1.006734              1.206693                 2.113695   \n",
+       "10       1.026216              0.850497                 0.868269   \n",
+       "11       0.615964              1.408874                 0.594212   \n",
+       "12       1.067263              1.139975                 1.661126   \n",
+       "13       0.397978              0.791481                 0.820657   \n",
+       "14       1.213533              0.520663                 0.319058   \n",
+       "15       1.398442              1.634992                 2.233233   \n",
+       "16       0.334895              0.662683                 2.704094   \n",
+       "17       0.481762              0.024940                 2.696204   \n",
+       "18       0.398018              0.234113                 0.204019   \n",
+       "19       0.958773              0.262651                 0.696401   \n",
+       "20       1.546589              0.986442                 0.446847   \n",
        "\n",
        "    observable_x1withsigma  \n",
-       "0                 8.278943  \n",
-       "1                11.454045  \n",
-       "2                14.936001  \n",
-       "3                 6.450000  \n",
-       "4                 6.617580  \n",
-       "5                 3.806906  \n",
-       "6                13.100735  \n",
-       "7                27.435032  \n",
-       "8                16.338665  \n",
-       "9                 5.794149  \n",
-       "10               19.640623  \n",
-       "11               12.728659  \n",
-       "12                7.662203  \n",
-       "13                7.521306  \n",
-       "14                5.512185  \n",
-       "15                2.862886  \n",
-       "16                4.198303  \n",
-       "17                5.766254  \n",
-       "18                2.530417  \n",
-       "19                2.868075  \n",
-       "20               18.319130  "
+       "0                 0.903906  \n",
+       "1                11.995462  \n",
+       "2                 0.303567  \n",
+       "3                 4.373485  \n",
+       "4                 7.543005  \n",
+       "5                 4.410204  \n",
+       "6                 4.715461  \n",
+       "7                 5.741110  \n",
+       "8                 4.232125  \n",
+       "9                23.512450  \n",
+       "10               10.729745  \n",
+       "11               13.394799  \n",
+       "12               17.750062  \n",
+       "13               10.854981  \n",
+       "14                4.202913  \n",
+       "15                4.905843  \n",
+       "16                3.203293  \n",
+       "17               10.691248  \n",
+       "18               13.767435  \n",
+       "19               11.609403  \n",
+       "20                8.739479  "
       ]
      },
-     "execution_count": 24,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2669,7 +2746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -3276,7 +3353,7 @@
        "20                          1.0                         0.1  "
       ]
      },
-     "execution_count": 25,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3288,7 +3365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -3583,7 +3660,7 @@
        "20  10.0       0.0  1.0       NaN        NaN  0.494949  0.535581  0.044686"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }

From ddd206e9b1e6838a597dd31f5eae92ef6b79568d Mon Sep 17 00:00:00 2001
From: Daniel Weindl <daniel.weindl@helmholtz-muenchen.de>
Date: Thu, 16 Jan 2020 10:10:44 +0100
Subject: [PATCH 06/23] Fix(matlab) Compile CalcMD5 on demand (Fixes #914)

---
 matlab/amiwrap.m                   | 16 ----------------
 matlab/auxiliary/CalcMD5/CalcMD5.m | 18 ++++++++++++++++--
 2 files changed, 16 insertions(+), 18 deletions(-)

diff --git a/matlab/amiwrap.m b/matlab/amiwrap.m
index c8d889b01b..fa820871bb 100644
--- a/matlab/amiwrap.m
+++ b/matlab/amiwrap.m
@@ -75,22 +75,6 @@ function amiwrap( varargin )
     addpath(genpath(fullfile(matlabRootPath,'auxiliary')));
     addpath(fullfile(matlabRootPath,'symbolic'));
     
-    % compile CalcMD5 if necessary
-    try
-        CalcMD5('TEST','char','hex');
-    catch
-        try
-            addpath(fullfile(matlabRootPath,'auxiliary','CalcMD5'))
-            CalcMD5('TEST','char','hex');
-        catch
-            disp('CalcMD5 has not been compiled yet. Compiling now!')
-            tmpdir = pwd;
-            cd(fullfile(matlabRootPath,'auxiliary','CalcMD5'))
-            mex(fullfile(matlabRootPath,'auxiliary','CalcMD5','CalcMD5.c'))
-            addpath(fullfile(matlabRootPath,'auxiliary','CalcMD5'))
-            cd(tmpdir);
-        end
-    end
     
     % try to load
     if(~isstruct(symfun))
diff --git a/matlab/auxiliary/CalcMD5/CalcMD5.m b/matlab/auxiliary/CalcMD5/CalcMD5.m
index 687f1138e2..80d4d0c416 100644
--- a/matlab/auxiliary/CalcMD5/CalcMD5.m
+++ b/matlab/auxiliary/CalcMD5/CalcMD5.m
@@ -1,4 +1,4 @@
-function MD5 = CalcMD5(Data, InClass, OutClass)  %#ok<STOUT,INUSD>
+function MD5 = CalcMD5(Data, varargin)  %#ok<STOUT,INUSD>
 % 128 bit MD5 checksum: file, string, byte stream [MEX]
 % This function calculates a 128 bit checksum for arrays and files.
 % Digest = CalcMD5(Data, [InClass], [OutClass])
@@ -67,5 +67,19 @@
 
 % If the current Matlab path is the parent folder of this script, the
 % MEX function is not found - change the current directory!
-error(['JSim:', mfilename, ':NoMex'], 'Cannot find MEX script.');
 
+% If a CalcMD5 call ends up here, this means CalcMD5 is not compiled or
+% not found in the matlab path.
+% Therefore, compile CalcMD5
+disp('CalcMD5 has not been compiled yet. Compiling now!')
+md5_path=fileparts(mfilename('fullpath'));
+addpath(fullfile(md5_path))
+tmpdir = pwd;
+cd(fullfile(md5_path))
+mex(fullfile(md5_path,'CalcMD5.c'))
+addpath(md5_path)
+cd(tmpdir);
+
+% Make actual call to mex file
+MD5 = CalcMD5(Data, varargin{:});
+end

From ea5db4b87932f987c89309104fe70cd554cd483f Mon Sep 17 00:00:00 2001
From: FFroehlich <fabian@schaluck.com>
Date: Thu, 16 Jan 2020 11:57:23 -0500
Subject: [PATCH 07/23] Update ExampleSteadystate.ipynb

---
 .../ExampleSteadystate.ipynb                  | 867 +++++++++---------
 1 file changed, 439 insertions(+), 428 deletions(-)

diff --git a/python/examples/example_steadystate/ExampleSteadystate.ipynb b/python/examples/example_steadystate/ExampleSteadystate.ipynb
index 99c3d9d145..c6717c3c18 100644
--- a/python/examples/example_steadystate/ExampleSteadystate.ipynb
+++ b/python/examples/example_steadystate/ExampleSteadystate.ipynb
@@ -184,47 +184,47 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "scrolled": false
+    "scrolled": true
    },
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-01-16 09:24:10.771 - amici.sbml_import - INFO - Finished processing SBML parameters            (6.90E-04s)\n",
-      "2020-01-16 09:24:10.776 - amici.sbml_import - INFO - Finished processing SBML species               (3.02E-03s)\n",
-      "2020-01-16 09:24:10.791 - amici.sbml_import - INFO - Finished processing SBML reactions             (1.44E-02s)\n",
-      "2020-01-16 09:24:10.792 - amici.sbml_import - INFO - Finished processing SBML compartments          (2.46E-04s)\n",
-      "2020-01-16 09:24:10.876 - amici.sbml_import - INFO - Finished processing SBML rules                 (8.26E-02s)\n",
-      "2020-01-16 09:24:10.962 - amici.sbml_import - INFO - Finished processing SBML observables           (7.26E-02s)\n",
-      "2020-01-16 09:24:11.015 - amici.ode_export - INFO - Finished writing J.cpp                          (3.75E-02s)\n",
-      "2020-01-16 09:24:11.024 - amici.ode_export - INFO - Finished writing JB.cpp                         (8.14E-03s)\n",
-      "2020-01-16 09:24:11.028 - amici.ode_export - INFO - Finished writing JDiag.cpp                      (3.66E-03s)\n",
-      "2020-01-16 09:24:11.036 - amici.ode_export - INFO - Finished writing JSparse.cpp                    (7.40E-03s)\n",
-      "2020-01-16 09:24:11.045 - amici.ode_export - INFO - Finished writing JSparseB.cpp                   (7.93E-03s)\n",
-      "2020-01-16 09:24:11.061 - amici.ode_export - INFO - Finished writing Jy.cpp                         (1.48E-02s)\n",
-      "2020-01-16 09:24:11.161 - amici.ode_export - INFO - Finished writing dJydsigmay.cpp                 (9.81E-02s)\n",
-      "2020-01-16 09:24:11.199 - amici.ode_export - INFO - Finished writing dJydy.cpp                      (3.70E-02s)\n",
-      "2020-01-16 09:24:11.208 - amici.ode_export - INFO - Finished writing dwdp.cpp                       (8.14E-03s)\n",
-      "2020-01-16 09:24:11.212 - amici.ode_export - INFO - Finished writing dwdx.cpp                       (2.15E-03s)\n",
-      "2020-01-16 09:24:11.218 - amici.ode_export - INFO - Finished writing dxdotdw.cpp                    (5.14E-03s)\n",
-      "2020-01-16 09:24:11.237 - amici.ode_export - INFO - Finished writing dxdotdp_explicit.cpp           (1.69E-02s)\n",
-      "2020-01-16 09:24:11.259 - amici.ode_export - INFO - Finished writing dydx.cpp                       (1.61E-02s)\n",
-      "2020-01-16 09:24:11.270 - amici.ode_export - INFO - Finished writing dydp.cpp                       (1.09E-02s)\n",
-      "2020-01-16 09:24:11.277 - amici.ode_export - INFO - Finished writing dsigmaydp.cpp                  (5.89E-03s)\n",
-      "2020-01-16 09:24:11.281 - amici.ode_export - INFO - Finished writing sigmay.cpp                     (3.75E-03s)\n",
-      "2020-01-16 09:24:11.286 - amici.ode_export - INFO - Finished writing w.cpp                          (3.71E-03s)\n",
-      "2020-01-16 09:24:11.289 - amici.ode_export - INFO - Finished writing x0.cpp                         (2.25E-03s)\n",
-      "2020-01-16 09:24:11.290 - amici.ode_export - INFO - Finished writing x0_fixedParameters.cpp         (8.19E-04s)\n",
-      "2020-01-16 09:24:11.293 - amici.ode_export - INFO - Finished writing sx0.cpp                        (2.54E-03s)\n",
-      "2020-01-16 09:24:11.297 - amici.ode_export - INFO - Finished writing sx0_fixedParameters.cpp        (3.07E-03s)\n",
-      "2020-01-16 09:24:11.304 - amici.ode_export - INFO - Finished writing xdot.cpp                       (5.76E-03s)\n",
-      "2020-01-16 09:24:11.307 - amici.ode_export - INFO - Finished writing y.cpp                          (2.37E-03s)\n",
-      "2020-01-16 09:24:11.309 - amici.ode_export - INFO - Finished writing x_rdata.cpp                    (1.36E-03s)\n",
-      "2020-01-16 09:24:11.310 - amici.ode_export - INFO - Finished writing total_cl.cpp                   (7.47E-04s)\n",
-      "2020-01-16 09:24:11.313 - amici.ode_export - INFO - Finished writing x_solver.cpp                   (1.82E-03s)\n",
-      "2020-01-16 09:24:11.326 - amici.ode_export - INFO - Finished generating cpp code                    (3.59E-01s)\n",
-      "2020-01-16 09:24:22.266 - amici.ode_export - INFO - Finished compiling cpp code                     (1.09E+01s)\n"
+      "2020-01-16 11:54:47.121 - amici.sbml_import - INFO - Finished processing SBML parameters            (6.30E-04s)\n",
+      "2020-01-16 11:54:47.125 - amici.sbml_import - INFO - Finished processing SBML species               (2.22E-03s)\n",
+      "2020-01-16 11:54:47.139 - amici.sbml_import - INFO - Finished processing SBML reactions             (1.32E-02s)\n",
+      "2020-01-16 11:54:47.140 - amici.sbml_import - INFO - Finished processing SBML compartments          (2.36E-04s)\n",
+      "2020-01-16 11:54:47.219 - amici.sbml_import - INFO - Finished processing SBML rules                 (7.89E-02s)\n",
+      "2020-01-16 11:54:47.300 - amici.sbml_import - INFO - Finished processing SBML observables           (6.63E-02s)\n",
+      "2020-01-16 11:54:47.353 - amici.ode_export - INFO - Finished writing J.cpp                          (3.82E-02s)\n",
+      "2020-01-16 11:54:47.362 - amici.ode_export - INFO - Finished writing JB.cpp                         (8.50E-03s)\n",
+      "2020-01-16 11:54:47.366 - amici.ode_export - INFO - Finished writing JDiag.cpp                      (3.08E-03s)\n",
+      "2020-01-16 11:54:47.375 - amici.ode_export - INFO - Finished writing JSparse.cpp                    (7.90E-03s)\n",
+      "2020-01-16 11:54:47.384 - amici.ode_export - INFO - Finished writing JSparseB.cpp                   (7.47E-03s)\n",
+      "2020-01-16 11:54:47.399 - amici.ode_export - INFO - Finished writing Jy.cpp                         (1.41E-02s)\n",
+      "2020-01-16 11:54:47.498 - amici.ode_export - INFO - Finished writing dJydsigmay.cpp                 (9.77E-02s)\n",
+      "2020-01-16 11:54:47.535 - amici.ode_export - INFO - Finished writing dJydy.cpp                      (3.71E-02s)\n",
+      "2020-01-16 11:54:47.546 - amici.ode_export - INFO - Finished writing dwdp.cpp                       (9.46E-03s)\n",
+      "2020-01-16 11:54:47.549 - amici.ode_export - INFO - Finished writing dwdx.cpp                       (1.88E-03s)\n",
+      "2020-01-16 11:54:47.556 - amici.ode_export - INFO - Finished writing dxdotdw.cpp                    (5.38E-03s)\n",
+      "2020-01-16 11:54:47.569 - amici.ode_export - INFO - Finished writing dxdotdp_explicit.cpp           (1.13E-02s)\n",
+      "2020-01-16 11:54:47.589 - amici.ode_export - INFO - Finished writing dydx.cpp                       (1.53E-02s)\n",
+      "2020-01-16 11:54:47.601 - amici.ode_export - INFO - Finished writing dydp.cpp                       (1.05E-02s)\n",
+      "2020-01-16 11:54:47.605 - amici.ode_export - INFO - Finished writing dsigmaydp.cpp                  (3.87E-03s)\n",
+      "2020-01-16 11:54:47.609 - amici.ode_export - INFO - Finished writing sigmay.cpp                     (2.44E-03s)\n",
+      "2020-01-16 11:54:47.612 - amici.ode_export - INFO - Finished writing w.cpp                          (2.75E-03s)\n",
+      "2020-01-16 11:54:47.615 - amici.ode_export - INFO - Finished writing x0.cpp                         (1.83E-03s)\n",
+      "2020-01-16 11:54:47.616 - amici.ode_export - INFO - Finished writing x0_fixedParameters.cpp         (5.91E-04s)\n",
+      "2020-01-16 11:54:47.619 - amici.ode_export - INFO - Finished writing sx0.cpp                        (2.14E-03s)\n",
+      "2020-01-16 11:54:47.623 - amici.ode_export - INFO - Finished writing sx0_fixedParameters.cpp        (2.99E-03s)\n",
+      "2020-01-16 11:54:47.629 - amici.ode_export - INFO - Finished writing xdot.cpp                       (5.83E-03s)\n",
+      "2020-01-16 11:54:47.635 - amici.ode_export - INFO - Finished writing y.cpp                          (4.59E-03s)\n",
+      "2020-01-16 11:54:47.638 - amici.ode_export - INFO - Finished writing x_rdata.cpp                    (1.87E-03s)\n",
+      "2020-01-16 11:54:47.640 - amici.ode_export - INFO - Finished writing total_cl.cpp                   (9.08E-04s)\n",
+      "2020-01-16 11:54:47.643 - amici.ode_export - INFO - Finished writing x_solver.cpp                   (1.97E-03s)\n",
+      "2020-01-16 11:54:47.657 - amici.ode_export - INFO - Finished generating cpp code                    (3.50E-01s)\n",
+      "2020-01-16 11:54:57.708 - amici.ode_export - INFO - Finished compiling cpp code                     (1.00E+01s)\n"
      ]
     }
    ],
@@ -657,7 +657,7 @@
       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
       "       order:  [0 5 5 5 5 5 4 4 5 5 5 5 5 5 5 4 5 4 4 4 4 4 4 4 4 5 5 4 4 4 4 4 4 5 5 5 5\n",
       " 5 5 5 4 4 4 5 5 5 5 5 4 4 5 5 5 4 4 3 3 3 3 4]\n",
-      "    cpu_time:  1.268999999999999\n",
+      "    cpu_time:  1.147\n",
       "   numstepsB:  None\n",
       "numrhsevalsB:  None\n",
       "numerrtestfailsB:  None\n",
@@ -761,7 +761,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Log-likelihood -92.752605\n"
+      "Log-likelihood -100.615231\n"
      ]
     }
    ],
@@ -790,7 +790,7 @@
    "metadata": {},
    "source": [
     "### Simulation tolerances\n",
-    "Numerical error tolerances are often critical to get accurate results. For the state variables, integration errors can be controlled using `setRelativeTolerance` and `setAbsoluteTolerance`. Similar functions exist for sensitivies, steadstates and quadratures. We initially compute a reference "
+    "Numerical error tolerances are often critical to get accurate results. For the state variables, integration errors can be controlled using `setRelativeTolerance` and `setAbsoluteTolerance`. Similar functions exist for sensitivies, steadstates and quadratures. We initially compute a reference solution using extremely low tolerances and then assess the influence on integration error for different levels of absolute and relative tolerance."
    ]
   },
   {
@@ -800,7 +800,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1080x360 with 2 Axes>"
       ]
@@ -851,7 +851,11 @@
     "    ax.legend()\n",
     "\n",
     "plot_error(atols, atol_x_errs, atol_llh_errs, 'absolute', axes[0])\n",
-    "plot_error(rtols, rtol_x_errs, rtol_llh_errs, 'relative', axes[1])"
+    "plot_error(rtols, rtol_x_errs, rtol_llh_errs, 'relative', axes[1])\n",
+    "\n",
+    "# reset relative tolerance to default value\n",
+    "solver.setRelativeTolerance(1e-8)\n",
+    "solver.setRelativeTolerance(1e-16)"
    ]
   },
   {
@@ -1404,9 +1408,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Log-likelihood: -1205.195087\n",
-      "Gradient: [ 1.13739826e+01  1.23993091e+01 -3.54960044e+01 -9.76603519e+00\n",
-      " -6.22459540e+01  6.68794579e-01 -6.46531682e+00  2.13965462e+04]\n"
+      "Log-likelihood: -1088.566890\n",
+      "Gradient: [-5.70730678e+01 -7.76339486e+01  1.27121046e+02  1.42311847e+01\n",
+      "  2.71899987e+02 -1.64169706e+00  3.56056940e+00  1.91288570e+04]\n"
      ]
     }
    ],
@@ -1460,16 +1464,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "sllh: |error|_2: 32.261882\n",
+      "sllh: |error|_2: 28.864869\n",
       "\n",
-      "sllh: p[0]: |error|_2: 0.010290\n",
-      "sllh: p[1]: |error|_2: 0.023725\n",
-      "sllh: p[2]: |error|_2: 0.035811\n",
-      "sllh: p[3]: |error|_2: 0.007622\n",
-      "sllh: p[4]: |error|_2: 0.095420\n",
+      "sllh: p[0]: |error|_2: 0.001781\n",
+      "sllh: p[1]: |error|_2: 0.002541\n",
+      "sllh: p[2]: |error|_2: 0.026482\n",
+      "sllh: p[3]: |error|_2: 0.003422\n",
+      "sllh: p[4]: |error|_2: 0.085271\n",
       "sllh: p[5]: |error|_2: 0.000280\n",
       "sllh: p[6]: |error|_2: 0.001050\n",
-      "sllh: p[7]: |error|_2: 32.261730\n",
+      "sllh: p[7]: |error|_2: 28.864726\n",
       "\n",
       "sy: p[0]: |error|_2: 0.002974\n",
       "sy: p[1]: |error|_2: 0.002717\n",
@@ -1750,12 +1754,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.854770</td>\n",
-       "      <td>1.145550</td>\n",
-       "      <td>-0.059872</td>\n",
-       "      <td>-1.677553</td>\n",
-       "      <td>2.485344</td>\n",
-       "      <td>0.190391</td>\n",
+       "      <td>-0.414816</td>\n",
+       "      <td>-0.876073</td>\n",
+       "      <td>1.227374</td>\n",
+       "      <td>-2.444445</td>\n",
+       "      <td>2.570303</td>\n",
+       "      <td>-0.957382</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1771,12 +1775,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>-0.053593</td>\n",
-       "      <td>0.241636</td>\n",
-       "      <td>1.593800</td>\n",
-       "      <td>2.498764</td>\n",
-       "      <td>2.743638</td>\n",
-       "      <td>-0.660179</td>\n",
+       "      <td>2.451814</td>\n",
+       "      <td>-1.136215</td>\n",
+       "      <td>-1.698820</td>\n",
+       "      <td>1.238130</td>\n",
+       "      <td>2.486213</td>\n",
+       "      <td>0.362012</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1792,12 +1796,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.171368</td>\n",
-       "      <td>1.410101</td>\n",
-       "      <td>1.438133</td>\n",
-       "      <td>1.021609</td>\n",
-       "      <td>3.051800</td>\n",
-       "      <td>0.610429</td>\n",
+       "      <td>0.471712</td>\n",
+       "      <td>-0.151082</td>\n",
+       "      <td>-1.797739</td>\n",
+       "      <td>0.288818</td>\n",
+       "      <td>2.313434</td>\n",
+       "      <td>-0.686996</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1813,12 +1817,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>-0.205662</td>\n",
-       "      <td>2.391259</td>\n",
-       "      <td>0.054255</td>\n",
-       "      <td>-1.032851</td>\n",
-       "      <td>2.960908</td>\n",
-       "      <td>0.133051</td>\n",
+       "      <td>0.704033</td>\n",
+       "      <td>0.896003</td>\n",
+       "      <td>0.451385</td>\n",
+       "      <td>1.046007</td>\n",
+       "      <td>5.043385</td>\n",
+       "      <td>-0.671759</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1834,12 +1838,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.759200</td>\n",
-       "      <td>0.067206</td>\n",
-       "      <td>-1.456685</td>\n",
-       "      <td>2.600566</td>\n",
-       "      <td>2.878851</td>\n",
-       "      <td>-0.193766</td>\n",
+       "      <td>0.901247</td>\n",
+       "      <td>0.895383</td>\n",
+       "      <td>-1.412566</td>\n",
+       "      <td>1.901419</td>\n",
+       "      <td>3.837156</td>\n",
+       "      <td>0.147422</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1855,12 +1859,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>-0.311092</td>\n",
-       "      <td>0.141907</td>\n",
-       "      <td>-0.228872</td>\n",
-       "      <td>-0.193889</td>\n",
-       "      <td>4.811703</td>\n",
-       "      <td>0.994076</td>\n",
+       "      <td>1.242644</td>\n",
+       "      <td>1.337338</td>\n",
+       "      <td>-1.103457</td>\n",
+       "      <td>0.432559</td>\n",
+       "      <td>2.887246</td>\n",
+       "      <td>2.096051</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1876,12 +1880,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.865276</td>\n",
-       "      <td>0.838981</td>\n",
-       "      <td>0.668306</td>\n",
-       "      <td>1.623928</td>\n",
-       "      <td>3.328777</td>\n",
-       "      <td>0.075325</td>\n",
+       "      <td>-0.808071</td>\n",
+       "      <td>2.087179</td>\n",
+       "      <td>-0.791739</td>\n",
+       "      <td>3.218673</td>\n",
+       "      <td>3.837424</td>\n",
+       "      <td>0.190466</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1897,12 +1901,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>-0.163654</td>\n",
-       "      <td>2.215009</td>\n",
-       "      <td>-0.771913</td>\n",
-       "      <td>3.161547</td>\n",
-       "      <td>4.519341</td>\n",
-       "      <td>1.115471</td>\n",
+       "      <td>0.083943</td>\n",
+       "      <td>1.977401</td>\n",
+       "      <td>-0.003218</td>\n",
+       "      <td>0.692508</td>\n",
+       "      <td>5.517687</td>\n",
+       "      <td>2.463217</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1918,12 +1922,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.395456</td>\n",
-       "      <td>0.612326</td>\n",
-       "      <td>0.976628</td>\n",
-       "      <td>-0.055129</td>\n",
-       "      <td>2.919909</td>\n",
-       "      <td>0.113068</td>\n",
+       "      <td>-0.678246</td>\n",
+       "      <td>0.868593</td>\n",
+       "      <td>1.344468</td>\n",
+       "      <td>0.650529</td>\n",
+       "      <td>3.649025</td>\n",
+       "      <td>1.408445</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1939,12 +1943,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.717336</td>\n",
-       "      <td>1.043128</td>\n",
-       "      <td>1.064387</td>\n",
-       "      <td>-0.143616</td>\n",
-       "      <td>5.751210</td>\n",
-       "      <td>2.882783</td>\n",
+       "      <td>1.623963</td>\n",
+       "      <td>0.095821</td>\n",
+       "      <td>1.552055</td>\n",
+       "      <td>0.295860</td>\n",
+       "      <td>4.188810</td>\n",
+       "      <td>0.128069</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1960,12 +1964,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>-1.204150</td>\n",
-       "      <td>0.689827</td>\n",
-       "      <td>1.082176</td>\n",
-       "      <td>1.904679</td>\n",
-       "      <td>2.756413</td>\n",
-       "      <td>1.600066</td>\n",
+       "      <td>0.035316</td>\n",
+       "      <td>0.673437</td>\n",
+       "      <td>0.068617</td>\n",
+       "      <td>1.458583</td>\n",
+       "      <td>2.803158</td>\n",
+       "      <td>1.455018</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1981,12 +1985,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.805529</td>\n",
-       "      <td>0.522137</td>\n",
-       "      <td>0.670364</td>\n",
-       "      <td>2.454703</td>\n",
-       "      <td>3.018521</td>\n",
-       "      <td>1.862394</td>\n",
+       "      <td>0.912310</td>\n",
+       "      <td>2.633523</td>\n",
+       "      <td>-0.515745</td>\n",
+       "      <td>0.620131</td>\n",
+       "      <td>4.286741</td>\n",
+       "      <td>0.179958</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -2002,12 +2006,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.667126</td>\n",
-       "      <td>0.783559</td>\n",
-       "      <td>-1.014303</td>\n",
-       "      <td>-0.101997</td>\n",
-       "      <td>1.940476</td>\n",
-       "      <td>-1.256017</td>\n",
+       "      <td>-0.036992</td>\n",
+       "      <td>0.530859</td>\n",
+       "      <td>-1.114001</td>\n",
+       "      <td>1.962801</td>\n",
+       "      <td>4.852431</td>\n",
+       "      <td>1.744331</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -2023,12 +2027,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.349566</td>\n",
-       "      <td>-0.937770</td>\n",
-       "      <td>0.449607</td>\n",
-       "      <td>0.239118</td>\n",
-       "      <td>4.411886</td>\n",
-       "      <td>-0.570199</td>\n",
+       "      <td>-0.402563</td>\n",
+       "      <td>0.933044</td>\n",
+       "      <td>1.046995</td>\n",
+       "      <td>0.395994</td>\n",
+       "      <td>2.288115</td>\n",
+       "      <td>1.817704</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -2044,12 +2048,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>-1.199949</td>\n",
-       "      <td>0.312773</td>\n",
-       "      <td>1.263932</td>\n",
-       "      <td>1.544323</td>\n",
-       "      <td>3.262497</td>\n",
-       "      <td>0.091539</td>\n",
+       "      <td>-0.505098</td>\n",
+       "      <td>2.559202</td>\n",
+       "      <td>-0.145338</td>\n",
+       "      <td>0.352249</td>\n",
+       "      <td>5.320218</td>\n",
+       "      <td>1.215976</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -2065,12 +2069,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.100669</td>\n",
-       "      <td>0.046852</td>\n",
-       "      <td>-1.349183</td>\n",
-       "      <td>2.652128</td>\n",
-       "      <td>1.339296</td>\n",
-       "      <td>0.999152</td>\n",
+       "      <td>1.538138</td>\n",
+       "      <td>0.302181</td>\n",
+       "      <td>-2.137766</td>\n",
+       "      <td>1.630837</td>\n",
+       "      <td>3.806037</td>\n",
+       "      <td>0.093093</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -2086,12 +2090,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>-0.882725</td>\n",
-       "      <td>0.565985</td>\n",
-       "      <td>-0.286692</td>\n",
-       "      <td>1.673683</td>\n",
-       "      <td>0.860009</td>\n",
-       "      <td>0.185171</td>\n",
+       "      <td>1.764999</td>\n",
+       "      <td>0.527020</td>\n",
+       "      <td>0.853451</td>\n",
+       "      <td>1.278411</td>\n",
+       "      <td>4.361549</td>\n",
+       "      <td>-0.308099</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -2107,12 +2111,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.093720</td>\n",
-       "      <td>0.850729</td>\n",
-       "      <td>0.528986</td>\n",
-       "      <td>0.980290</td>\n",
-       "      <td>6.252438</td>\n",
-       "      <td>1.571740</td>\n",
+       "      <td>0.723098</td>\n",
+       "      <td>0.539088</td>\n",
+       "      <td>1.028430</td>\n",
+       "      <td>0.059518</td>\n",
+       "      <td>3.805920</td>\n",
+       "      <td>1.652130</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -2128,12 +2132,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.995882</td>\n",
-       "      <td>2.575455</td>\n",
-       "      <td>0.444333</td>\n",
-       "      <td>0.765691</td>\n",
-       "      <td>3.344862</td>\n",
-       "      <td>-0.876842</td>\n",
+       "      <td>1.761027</td>\n",
+       "      <td>1.024605</td>\n",
+       "      <td>0.443612</td>\n",
+       "      <td>1.453533</td>\n",
+       "      <td>2.976122</td>\n",
+       "      <td>0.269625</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -2149,12 +2153,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.146899</td>\n",
-       "      <td>0.926665</td>\n",
-       "      <td>1.004243</td>\n",
-       "      <td>1.257351</td>\n",
-       "      <td>2.845607</td>\n",
-       "      <td>-0.663591</td>\n",
+       "      <td>0.075774</td>\n",
+       "      <td>0.587967</td>\n",
+       "      <td>0.095493</td>\n",
+       "      <td>-0.258109</td>\n",
+       "      <td>2.943583</td>\n",
+       "      <td>0.289256</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -2170,12 +2174,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.688457</td>\n",
-       "      <td>-0.191403</td>\n",
-       "      <td>-1.501904</td>\n",
-       "      <td>0.003456</td>\n",
-       "      <td>3.982428</td>\n",
-       "      <td>1.368897</td>\n",
+       "      <td>-0.002435</td>\n",
+       "      <td>-1.179119</td>\n",
+       "      <td>0.299502</td>\n",
+       "      <td>-0.345324</td>\n",
+       "      <td>5.717243</td>\n",
+       "      <td>-0.411100</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
@@ -2189,73 +2193,73 @@
       ],
       "text/plain": [
        "    time datatype  t_presim   k0  k0_preeq  k0_presim  observable_x1  \\\n",
-       "0    0.0     data       0.0  1.0       NaN        NaN       0.854770   \n",
-       "1    0.5     data       0.0  1.0       NaN        NaN      -0.053593   \n",
-       "2    1.0     data       0.0  1.0       NaN        NaN       1.171368   \n",
-       "3    1.5     data       0.0  1.0       NaN        NaN      -0.205662   \n",
-       "4    2.0     data       0.0  1.0       NaN        NaN       0.759200   \n",
-       "5    2.5     data       0.0  1.0       NaN        NaN      -0.311092   \n",
-       "6    3.0     data       0.0  1.0       NaN        NaN       0.865276   \n",
-       "7    3.5     data       0.0  1.0       NaN        NaN      -0.163654   \n",
-       "8    4.0     data       0.0  1.0       NaN        NaN       0.395456   \n",
-       "9    4.5     data       0.0  1.0       NaN        NaN       0.717336   \n",
-       "10   5.0     data       0.0  1.0       NaN        NaN      -1.204150   \n",
-       "11   5.5     data       0.0  1.0       NaN        NaN       0.805529   \n",
-       "12   6.0     data       0.0  1.0       NaN        NaN       0.667126   \n",
-       "13   6.5     data       0.0  1.0       NaN        NaN       0.349566   \n",
-       "14   7.0     data       0.0  1.0       NaN        NaN      -1.199949   \n",
-       "15   7.5     data       0.0  1.0       NaN        NaN       1.100669   \n",
-       "16   8.0     data       0.0  1.0       NaN        NaN      -0.882725   \n",
-       "17   8.5     data       0.0  1.0       NaN        NaN       0.093720   \n",
-       "18   9.0     data       0.0  1.0       NaN        NaN       1.995882   \n",
-       "19   9.5     data       0.0  1.0       NaN        NaN       0.146899   \n",
-       "20  10.0     data       0.0  1.0       NaN        NaN       0.688457   \n",
+       "0    0.0     data       0.0  1.0       NaN        NaN      -0.414816   \n",
+       "1    0.5     data       0.0  1.0       NaN        NaN       2.451814   \n",
+       "2    1.0     data       0.0  1.0       NaN        NaN       0.471712   \n",
+       "3    1.5     data       0.0  1.0       NaN        NaN       0.704033   \n",
+       "4    2.0     data       0.0  1.0       NaN        NaN       0.901247   \n",
+       "5    2.5     data       0.0  1.0       NaN        NaN       1.242644   \n",
+       "6    3.0     data       0.0  1.0       NaN        NaN      -0.808071   \n",
+       "7    3.5     data       0.0  1.0       NaN        NaN       0.083943   \n",
+       "8    4.0     data       0.0  1.0       NaN        NaN      -0.678246   \n",
+       "9    4.5     data       0.0  1.0       NaN        NaN       1.623963   \n",
+       "10   5.0     data       0.0  1.0       NaN        NaN       0.035316   \n",
+       "11   5.5     data       0.0  1.0       NaN        NaN       0.912310   \n",
+       "12   6.0     data       0.0  1.0       NaN        NaN      -0.036992   \n",
+       "13   6.5     data       0.0  1.0       NaN        NaN      -0.402563   \n",
+       "14   7.0     data       0.0  1.0       NaN        NaN      -0.505098   \n",
+       "15   7.5     data       0.0  1.0       NaN        NaN       1.538138   \n",
+       "16   8.0     data       0.0  1.0       NaN        NaN       1.764999   \n",
+       "17   8.5     data       0.0  1.0       NaN        NaN       0.723098   \n",
+       "18   9.0     data       0.0  1.0       NaN        NaN       1.761027   \n",
+       "19   9.5     data       0.0  1.0       NaN        NaN       0.075774   \n",
+       "20  10.0     data       0.0  1.0       NaN        NaN      -0.002435   \n",
        "\n",
        "    observable_x2  observable_x3  observable_x1_scaled  \\\n",
-       "0        1.145550      -0.059872             -1.677553   \n",
-       "1        0.241636       1.593800              2.498764   \n",
-       "2        1.410101       1.438133              1.021609   \n",
-       "3        2.391259       0.054255             -1.032851   \n",
-       "4        0.067206      -1.456685              2.600566   \n",
-       "5        0.141907      -0.228872             -0.193889   \n",
-       "6        0.838981       0.668306              1.623928   \n",
-       "7        2.215009      -0.771913              3.161547   \n",
-       "8        0.612326       0.976628             -0.055129   \n",
-       "9        1.043128       1.064387             -0.143616   \n",
-       "10       0.689827       1.082176              1.904679   \n",
-       "11       0.522137       0.670364              2.454703   \n",
-       "12       0.783559      -1.014303             -0.101997   \n",
-       "13      -0.937770       0.449607              0.239118   \n",
-       "14       0.312773       1.263932              1.544323   \n",
-       "15       0.046852      -1.349183              2.652128   \n",
-       "16       0.565985      -0.286692              1.673683   \n",
-       "17       0.850729       0.528986              0.980290   \n",
-       "18       2.575455       0.444333              0.765691   \n",
-       "19       0.926665       1.004243              1.257351   \n",
-       "20      -0.191403      -1.501904              0.003456   \n",
+       "0       -0.876073       1.227374             -2.444445   \n",
+       "1       -1.136215      -1.698820              1.238130   \n",
+       "2       -0.151082      -1.797739              0.288818   \n",
+       "3        0.896003       0.451385              1.046007   \n",
+       "4        0.895383      -1.412566              1.901419   \n",
+       "5        1.337338      -1.103457              0.432559   \n",
+       "6        2.087179      -0.791739              3.218673   \n",
+       "7        1.977401      -0.003218              0.692508   \n",
+       "8        0.868593       1.344468              0.650529   \n",
+       "9        0.095821       1.552055              0.295860   \n",
+       "10       0.673437       0.068617              1.458583   \n",
+       "11       2.633523      -0.515745              0.620131   \n",
+       "12       0.530859      -1.114001              1.962801   \n",
+       "13       0.933044       1.046995              0.395994   \n",
+       "14       2.559202      -0.145338              0.352249   \n",
+       "15       0.302181      -2.137766              1.630837   \n",
+       "16       0.527020       0.853451              1.278411   \n",
+       "17       0.539088       1.028430              0.059518   \n",
+       "18       1.024605       0.443612              1.453533   \n",
+       "19       0.587967       0.095493             -0.258109   \n",
+       "20      -1.179119       0.299502             -0.345324   \n",
        "\n",
        "    observable_x2_offsetted  observable_x1withsigma  observable_x1_std  \\\n",
-       "0                  2.485344                0.190391                1.0   \n",
-       "1                  2.743638               -0.660179                1.0   \n",
-       "2                  3.051800                0.610429                1.0   \n",
-       "3                  2.960908                0.133051                1.0   \n",
-       "4                  2.878851               -0.193766                1.0   \n",
-       "5                  4.811703                0.994076                1.0   \n",
-       "6                  3.328777                0.075325                1.0   \n",
-       "7                  4.519341                1.115471                1.0   \n",
-       "8                  2.919909                0.113068                1.0   \n",
-       "9                  5.751210                2.882783                1.0   \n",
-       "10                 2.756413                1.600066                1.0   \n",
-       "11                 3.018521                1.862394                1.0   \n",
-       "12                 1.940476               -1.256017                1.0   \n",
-       "13                 4.411886               -0.570199                1.0   \n",
-       "14                 3.262497                0.091539                1.0   \n",
-       "15                 1.339296                0.999152                1.0   \n",
-       "16                 0.860009                0.185171                1.0   \n",
-       "17                 6.252438                1.571740                1.0   \n",
-       "18                 3.344862               -0.876842                1.0   \n",
-       "19                 2.845607               -0.663591                1.0   \n",
-       "20                 3.982428                1.368897                1.0   \n",
+       "0                  2.570303               -0.957382                1.0   \n",
+       "1                  2.486213                0.362012                1.0   \n",
+       "2                  2.313434               -0.686996                1.0   \n",
+       "3                  5.043385               -0.671759                1.0   \n",
+       "4                  3.837156                0.147422                1.0   \n",
+       "5                  2.887246                2.096051                1.0   \n",
+       "6                  3.837424                0.190466                1.0   \n",
+       "7                  5.517687                2.463217                1.0   \n",
+       "8                  3.649025                1.408445                1.0   \n",
+       "9                  4.188810                0.128069                1.0   \n",
+       "10                 2.803158                1.455018                1.0   \n",
+       "11                 4.286741                0.179958                1.0   \n",
+       "12                 4.852431                1.744331                1.0   \n",
+       "13                 2.288115                1.817704                1.0   \n",
+       "14                 5.320218                1.215976                1.0   \n",
+       "15                 3.806037                0.093093                1.0   \n",
+       "16                 4.361549               -0.308099                1.0   \n",
+       "17                 3.805920                1.652130                1.0   \n",
+       "18                 2.976122                0.269625                1.0   \n",
+       "19                 2.943583                0.289256                1.0   \n",
+       "20                 5.717243               -0.411100                1.0   \n",
        "\n",
        "    observable_x2_std  observable_x3_std  observable_x1_scaled_std  \\\n",
        "0                 1.0                1.0                       1.0   \n",
@@ -2372,12 +2376,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.754770</td>\n",
-       "      <td>0.745550</td>\n",
-       "      <td>0.759872</td>\n",
-       "      <td>1.877553</td>\n",
-       "      <td>0.914656</td>\n",
-       "      <td>0.903906</td>\n",
+       "      <td>0.514816</td>\n",
+       "      <td>1.276073</td>\n",
+       "      <td>0.527374</td>\n",
+       "      <td>2.644445</td>\n",
+       "      <td>0.829697</td>\n",
+       "      <td>10.573821</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -2386,12 +2390,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.592960</td>\n",
-       "      <td>0.443043</td>\n",
-       "      <td>1.402310</td>\n",
-       "      <td>1.420030</td>\n",
-       "      <td>0.941041</td>\n",
-       "      <td>11.995462</td>\n",
+       "      <td>1.912447</td>\n",
+       "      <td>1.820894</td>\n",
+       "      <td>1.890310</td>\n",
+       "      <td>0.159396</td>\n",
+       "      <td>1.198465</td>\n",
+       "      <td>1.773556</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -2400,12 +2404,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.591296</td>\n",
-       "      <td>0.676814</td>\n",
-       "      <td>1.341709</td>\n",
-       "      <td>0.138536</td>\n",
-       "      <td>0.681487</td>\n",
-       "      <td>0.303567</td>\n",
+       "      <td>0.108360</td>\n",
+       "      <td>0.884369</td>\n",
+       "      <td>1.894163</td>\n",
+       "      <td>0.871327</td>\n",
+       "      <td>1.419854</td>\n",
+       "      <td>12.670683</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -2414,12 +2418,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.776061</td>\n",
-       "      <td>1.660607</td>\n",
-       "      <td>0.021821</td>\n",
-       "      <td>2.173649</td>\n",
-       "      <td>0.769744</td>\n",
-       "      <td>4.373485</td>\n",
+       "      <td>0.133633</td>\n",
+       "      <td>0.165351</td>\n",
+       "      <td>0.375309</td>\n",
+       "      <td>0.094792</td>\n",
+       "      <td>1.312732</td>\n",
+       "      <td>12.421583</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -2428,12 +2432,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.198665</td>\n",
-       "      <td>0.648630</td>\n",
-       "      <td>1.526379</td>\n",
-       "      <td>1.479497</td>\n",
-       "      <td>0.836985</td>\n",
-       "      <td>7.543005</td>\n",
+       "      <td>0.340712</td>\n",
+       "      <td>0.179547</td>\n",
+       "      <td>1.482260</td>\n",
+       "      <td>0.780350</td>\n",
+       "      <td>0.121320</td>\n",
+       "      <td>4.131129</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
@@ -2442,12 +2446,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.864147</td>\n",
-       "      <td>0.556844</td>\n",
-       "      <td>0.295173</td>\n",
-       "      <td>1.300000</td>\n",
-       "      <td>1.112952</td>\n",
-       "      <td>4.410204</td>\n",
+       "      <td>0.689589</td>\n",
+       "      <td>0.638587</td>\n",
+       "      <td>1.169758</td>\n",
+       "      <td>0.673553</td>\n",
+       "      <td>0.811504</td>\n",
+       "      <td>15.429952</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
@@ -2456,12 +2460,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.318405</td>\n",
-       "      <td>0.157019</td>\n",
-       "      <td>0.604574</td>\n",
-       "      <td>0.530187</td>\n",
-       "      <td>0.353186</td>\n",
-       "      <td>4.715461</td>\n",
+       "      <td>1.354942</td>\n",
+       "      <td>1.405217</td>\n",
+       "      <td>0.855471</td>\n",
+       "      <td>2.124931</td>\n",
+       "      <td>0.155462</td>\n",
+       "      <td>3.564042</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
@@ -2470,12 +2474,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.705014</td>\n",
-       "      <td>1.548900</td>\n",
-       "      <td>0.833419</td>\n",
-       "      <td>2.078828</td>\n",
-       "      <td>0.853233</td>\n",
-       "      <td>5.741110</td>\n",
+       "      <td>0.457417</td>\n",
+       "      <td>1.311292</td>\n",
+       "      <td>0.064725</td>\n",
+       "      <td>0.390211</td>\n",
+       "      <td>1.851578</td>\n",
+       "      <td>19.218570</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
@@ -2484,12 +2488,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.140824</td>\n",
-       "      <td>0.038976</td>\n",
-       "      <td>0.917134</td>\n",
-       "      <td>1.127690</td>\n",
-       "      <td>0.731393</td>\n",
-       "      <td>4.232125</td>\n",
+       "      <td>1.214527</td>\n",
+       "      <td>0.217291</td>\n",
+       "      <td>1.284974</td>\n",
+       "      <td>0.422031</td>\n",
+       "      <td>0.002277</td>\n",
+       "      <td>8.721648</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
@@ -2498,12 +2502,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.185797</td>\n",
-       "      <td>0.405613</td>\n",
-       "      <td>1.006734</td>\n",
-       "      <td>1.206693</td>\n",
-       "      <td>2.113695</td>\n",
-       "      <td>23.512450</td>\n",
+       "      <td>1.092425</td>\n",
+       "      <td>0.541694</td>\n",
+       "      <td>1.494402</td>\n",
+       "      <td>0.767217</td>\n",
+       "      <td>0.551295</td>\n",
+       "      <td>4.034688</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
@@ -2512,12 +2516,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.731241</td>\n",
-       "      <td>0.065145</td>\n",
-       "      <td>1.026216</td>\n",
-       "      <td>0.850497</td>\n",
-       "      <td>0.868269</td>\n",
-       "      <td>10.729745</td>\n",
+       "      <td>0.491775</td>\n",
+       "      <td>0.048756</td>\n",
+       "      <td>0.012657</td>\n",
+       "      <td>0.404401</td>\n",
+       "      <td>0.821524</td>\n",
+       "      <td>9.279264</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
@@ -2526,12 +2530,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.282614</td>\n",
-       "      <td>0.090596</td>\n",
-       "      <td>0.615964</td>\n",
-       "      <td>1.408874</td>\n",
-       "      <td>0.594212</td>\n",
-       "      <td>13.394799</td>\n",
+       "      <td>0.389395</td>\n",
+       "      <td>2.020790</td>\n",
+       "      <td>0.570145</td>\n",
+       "      <td>0.425698</td>\n",
+       "      <td>0.674008</td>\n",
+       "      <td>3.429569</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
@@ -2540,12 +2544,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.148137</td>\n",
-       "      <td>0.181956</td>\n",
-       "      <td>1.067263</td>\n",
-       "      <td>1.139975</td>\n",
-       "      <td>1.661126</td>\n",
-       "      <td>17.750062</td>\n",
+       "      <td>0.555981</td>\n",
+       "      <td>0.070744</td>\n",
+       "      <td>1.166961</td>\n",
+       "      <td>0.924822</td>\n",
+       "      <td>1.250828</td>\n",
+       "      <td>12.253422</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
@@ -2554,12 +2558,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.165734</td>\n",
-       "      <td>1.528999</td>\n",
-       "      <td>0.397978</td>\n",
-       "      <td>0.791481</td>\n",
-       "      <td>0.820657</td>\n",
-       "      <td>10.854981</td>\n",
+       "      <td>0.917862</td>\n",
+       "      <td>0.341815</td>\n",
+       "      <td>0.995366</td>\n",
+       "      <td>0.634604</td>\n",
+       "      <td>1.303114</td>\n",
+       "      <td>13.024050</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
@@ -2568,12 +2572,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.711779</td>\n",
-       "      <td>0.268782</td>\n",
-       "      <td>1.213533</td>\n",
-       "      <td>0.520663</td>\n",
-       "      <td>0.319058</td>\n",
-       "      <td>4.202913</td>\n",
+       "      <td>1.016928</td>\n",
+       "      <td>1.977647</td>\n",
+       "      <td>0.195737</td>\n",
+       "      <td>0.671411</td>\n",
+       "      <td>1.738663</td>\n",
+       "      <td>7.041457</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
@@ -2582,12 +2586,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.592101</td>\n",
-       "      <td>0.525677</td>\n",
-       "      <td>1.398442</td>\n",
-       "      <td>1.634992</td>\n",
-       "      <td>2.233233</td>\n",
-       "      <td>4.905843</td>\n",
+       "      <td>1.029570</td>\n",
+       "      <td>0.270348</td>\n",
+       "      <td>2.187025</td>\n",
+       "      <td>0.613702</td>\n",
+       "      <td>0.233507</td>\n",
+       "      <td>4.154744</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
@@ -2596,12 +2600,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.388225</td>\n",
-       "      <td>0.001882</td>\n",
-       "      <td>0.334895</td>\n",
-       "      <td>0.662683</td>\n",
-       "      <td>2.704094</td>\n",
-       "      <td>3.203293</td>\n",
+       "      <td>1.259499</td>\n",
+       "      <td>0.037083</td>\n",
+       "      <td>0.805247</td>\n",
+       "      <td>0.267410</td>\n",
+       "      <td>0.797446</td>\n",
+       "      <td>8.135988</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
@@ -2610,12 +2614,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.408896</td>\n",
-       "      <td>0.294495</td>\n",
-       "      <td>0.481762</td>\n",
-       "      <td>0.024940</td>\n",
-       "      <td>2.696204</td>\n",
-       "      <td>10.691248</td>\n",
+       "      <td>0.220483</td>\n",
+       "      <td>0.017146</td>\n",
+       "      <td>0.981206</td>\n",
+       "      <td>0.945712</td>\n",
+       "      <td>0.249686</td>\n",
+       "      <td>11.495145</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
@@ -2624,12 +2628,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.495980</td>\n",
-       "      <td>2.026573</td>\n",
-       "      <td>0.398018</td>\n",
-       "      <td>0.234113</td>\n",
-       "      <td>0.204019</td>\n",
-       "      <td>13.767435</td>\n",
+       "      <td>1.261125</td>\n",
+       "      <td>0.475724</td>\n",
+       "      <td>0.397297</td>\n",
+       "      <td>0.453729</td>\n",
+       "      <td>0.572759</td>\n",
+       "      <td>2.302771</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
@@ -2638,12 +2642,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.350451</td>\n",
-       "      <td>0.384657</td>\n",
-       "      <td>0.958773</td>\n",
-       "      <td>0.262651</td>\n",
-       "      <td>0.696401</td>\n",
-       "      <td>11.609403</td>\n",
+       "      <td>0.421576</td>\n",
+       "      <td>0.045959</td>\n",
+       "      <td>0.050022</td>\n",
+       "      <td>1.252809</td>\n",
+       "      <td>0.598425</td>\n",
+       "      <td>2.080936</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
@@ -2652,12 +2656,12 @@
        "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.193508</td>\n",
-       "      <td>0.726984</td>\n",
-       "      <td>1.546589</td>\n",
-       "      <td>0.986442</td>\n",
-       "      <td>0.446847</td>\n",
-       "      <td>8.739479</td>\n",
+       "      <td>0.497384</td>\n",
+       "      <td>1.714701</td>\n",
+       "      <td>0.254816</td>\n",
+       "      <td>1.335222</td>\n",
+       "      <td>2.181662</td>\n",
+       "      <td>9.060495</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2665,73 +2669,73 @@
       ],
       "text/plain": [
        "    time  t_presim   k0  k0_preeq  k0_presim  observable_x1  observable_x2  \\\n",
-       "0    0.0       0.0  1.0       NaN        NaN       0.754770       0.745550   \n",
-       "1    0.5       0.0  1.0       NaN        NaN       0.592960       0.443043   \n",
-       "2    1.0       0.0  1.0       NaN        NaN       0.591296       0.676814   \n",
-       "3    1.5       0.0  1.0       NaN        NaN       0.776061       1.660607   \n",
-       "4    2.0       0.0  1.0       NaN        NaN       0.198665       0.648630   \n",
-       "5    2.5       0.0  1.0       NaN        NaN       0.864147       0.556844   \n",
-       "6    3.0       0.0  1.0       NaN        NaN       0.318405       0.157019   \n",
-       "7    3.5       0.0  1.0       NaN        NaN       0.705014       1.548900   \n",
-       "8    4.0       0.0  1.0       NaN        NaN       0.140824       0.038976   \n",
-       "9    4.5       0.0  1.0       NaN        NaN       0.185797       0.405613   \n",
-       "10   5.0       0.0  1.0       NaN        NaN       1.731241       0.065145   \n",
-       "11   5.5       0.0  1.0       NaN        NaN       0.282614       0.090596   \n",
-       "12   6.0       0.0  1.0       NaN        NaN       0.148137       0.181956   \n",
-       "13   6.5       0.0  1.0       NaN        NaN       0.165734       1.528999   \n",
-       "14   7.0       0.0  1.0       NaN        NaN       1.711779       0.268782   \n",
-       "15   7.5       0.0  1.0       NaN        NaN       0.592101       0.525677   \n",
-       "16   8.0       0.0  1.0       NaN        NaN       1.388225       0.001882   \n",
-       "17   8.5       0.0  1.0       NaN        NaN       0.408896       0.294495   \n",
-       "18   9.0       0.0  1.0       NaN        NaN       1.495980       2.026573   \n",
-       "19   9.5       0.0  1.0       NaN        NaN       0.350451       0.384657   \n",
-       "20  10.0       0.0  1.0       NaN        NaN       0.193508       0.726984   \n",
+       "0    0.0       0.0  1.0       NaN        NaN       0.514816       1.276073   \n",
+       "1    0.5       0.0  1.0       NaN        NaN       1.912447       1.820894   \n",
+       "2    1.0       0.0  1.0       NaN        NaN       0.108360       0.884369   \n",
+       "3    1.5       0.0  1.0       NaN        NaN       0.133633       0.165351   \n",
+       "4    2.0       0.0  1.0       NaN        NaN       0.340712       0.179547   \n",
+       "5    2.5       0.0  1.0       NaN        NaN       0.689589       0.638587   \n",
+       "6    3.0       0.0  1.0       NaN        NaN       1.354942       1.405217   \n",
+       "7    3.5       0.0  1.0       NaN        NaN       0.457417       1.311292   \n",
+       "8    4.0       0.0  1.0       NaN        NaN       1.214527       0.217291   \n",
+       "9    4.5       0.0  1.0       NaN        NaN       1.092425       0.541694   \n",
+       "10   5.0       0.0  1.0       NaN        NaN       0.491775       0.048756   \n",
+       "11   5.5       0.0  1.0       NaN        NaN       0.389395       2.020790   \n",
+       "12   6.0       0.0  1.0       NaN        NaN       0.555981       0.070744   \n",
+       "13   6.5       0.0  1.0       NaN        NaN       0.917862       0.341815   \n",
+       "14   7.0       0.0  1.0       NaN        NaN       1.016928       1.977647   \n",
+       "15   7.5       0.0  1.0       NaN        NaN       1.029570       0.270348   \n",
+       "16   8.0       0.0  1.0       NaN        NaN       1.259499       0.037083   \n",
+       "17   8.5       0.0  1.0       NaN        NaN       0.220483       0.017146   \n",
+       "18   9.0       0.0  1.0       NaN        NaN       1.261125       0.475724   \n",
+       "19   9.5       0.0  1.0       NaN        NaN       0.421576       0.045959   \n",
+       "20  10.0       0.0  1.0       NaN        NaN       0.497384       1.714701   \n",
        "\n",
        "    observable_x3  observable_x1_scaled  observable_x2_offsetted  \\\n",
-       "0        0.759872              1.877553                 0.914656   \n",
-       "1        1.402310              1.420030                 0.941041   \n",
-       "2        1.341709              0.138536                 0.681487   \n",
-       "3        0.021821              2.173649                 0.769744   \n",
-       "4        1.526379              1.479497                 0.836985   \n",
-       "5        0.295173              1.300000                 1.112952   \n",
-       "6        0.604574              0.530187                 0.353186   \n",
-       "7        0.833419              2.078828                 0.853233   \n",
-       "8        0.917134              1.127690                 0.731393   \n",
-       "9        1.006734              1.206693                 2.113695   \n",
-       "10       1.026216              0.850497                 0.868269   \n",
-       "11       0.615964              1.408874                 0.594212   \n",
-       "12       1.067263              1.139975                 1.661126   \n",
-       "13       0.397978              0.791481                 0.820657   \n",
-       "14       1.213533              0.520663                 0.319058   \n",
-       "15       1.398442              1.634992                 2.233233   \n",
-       "16       0.334895              0.662683                 2.704094   \n",
-       "17       0.481762              0.024940                 2.696204   \n",
-       "18       0.398018              0.234113                 0.204019   \n",
-       "19       0.958773              0.262651                 0.696401   \n",
-       "20       1.546589              0.986442                 0.446847   \n",
+       "0        0.527374              2.644445                 0.829697   \n",
+       "1        1.890310              0.159396                 1.198465   \n",
+       "2        1.894163              0.871327                 1.419854   \n",
+       "3        0.375309              0.094792                 1.312732   \n",
+       "4        1.482260              0.780350                 0.121320   \n",
+       "5        1.169758              0.673553                 0.811504   \n",
+       "6        0.855471              2.124931                 0.155462   \n",
+       "7        0.064725              0.390211                 1.851578   \n",
+       "8        1.284974              0.422031                 0.002277   \n",
+       "9        1.494402              0.767217                 0.551295   \n",
+       "10       0.012657              0.404401                 0.821524   \n",
+       "11       0.570145              0.425698                 0.674008   \n",
+       "12       1.166961              0.924822                 1.250828   \n",
+       "13       0.995366              0.634604                 1.303114   \n",
+       "14       0.195737              0.671411                 1.738663   \n",
+       "15       2.187025              0.613702                 0.233507   \n",
+       "16       0.805247              0.267410                 0.797446   \n",
+       "17       0.981206              0.945712                 0.249686   \n",
+       "18       0.397297              0.453729                 0.572759   \n",
+       "19       0.050022              1.252809                 0.598425   \n",
+       "20       0.254816              1.335222                 2.181662   \n",
        "\n",
        "    observable_x1withsigma  \n",
-       "0                 0.903906  \n",
-       "1                11.995462  \n",
-       "2                 0.303567  \n",
-       "3                 4.373485  \n",
-       "4                 7.543005  \n",
-       "5                 4.410204  \n",
-       "6                 4.715461  \n",
-       "7                 5.741110  \n",
-       "8                 4.232125  \n",
-       "9                23.512450  \n",
-       "10               10.729745  \n",
-       "11               13.394799  \n",
-       "12               17.750062  \n",
-       "13               10.854981  \n",
-       "14                4.202913  \n",
-       "15                4.905843  \n",
-       "16                3.203293  \n",
-       "17               10.691248  \n",
-       "18               13.767435  \n",
-       "19               11.609403  \n",
-       "20                8.739479  "
+       "0                10.573821  \n",
+       "1                 1.773556  \n",
+       "2                12.670683  \n",
+       "3                12.421583  \n",
+       "4                 4.131129  \n",
+       "5                15.429952  \n",
+       "6                 3.564042  \n",
+       "7                19.218570  \n",
+       "8                 8.721648  \n",
+       "9                 4.034688  \n",
+       "10                9.279264  \n",
+       "11                3.429569  \n",
+       "12               12.253422  \n",
+       "13               13.024050  \n",
+       "14                7.041457  \n",
+       "15                4.154744  \n",
+       "16                8.135988  \n",
+       "17               11.495145  \n",
+       "18                2.302771  \n",
+       "19                2.080936  \n",
+       "20                9.060495  "
       ]
      },
      "execution_count": 27,
@@ -3669,6 +3673,13 @@
     "# look at the States in rdata as DataFrame \n",
     "amici.getSimulationStatesAsDataFrame(model, [edata], [rdata])"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From 9e3f31cd34d360b6b1619704dcd4089f4e4d6140 Mon Sep 17 00:00:00 2001
From: FFroehlich <fabian@schaluck.com>
Date: Thu, 16 Jan 2020 13:12:23 -0500
Subject: [PATCH 08/23] change to cxxcode printer

---
 python/amici/ode_export.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/python/amici/ode_export.py b/python/amici/ode_export.py
index e9ea048586..7ca251de1f 100644
--- a/python/amici/ode_export.py
+++ b/python/amici/ode_export.py
@@ -21,7 +21,7 @@
 
 from typing import Callable, Optional
 from string import Template
-import sympy.printing.ccode as ccode
+import sympy.printing.cxxcode as cxxcode
 from sympy.matrices.immutable import ImmutableDenseMatrix
 from sympy.matrices.dense import MutableDenseMatrix
 
@@ -2564,7 +2564,7 @@ def _printWithException(self, math):
 
         """
         try:
-            ret = ccode(math)
+            ret = cxxcode(math, standard='c++11')
             ret = re.sub(r'(^|\W)M_PI(\W|$)', r'\1amici::pi\2', ret)
             return ret
         except:

From a41d56c16aebf7d19f0f35a90a39a310a3b352ca Mon Sep 17 00:00:00 2001
From: Daniel Weindl <daniel.weindl@helmholtz-muenchen.de>
Date: Thu, 30 Jan 2020 22:34:42 +0100
Subject: [PATCH 09/23] Fix(python) Always recompile clibs and extensions
 (Closes #700)

Also fix swig interface generation for 'python setup.py install'
---
 python/sdist/custom_commands.py | 15 +++++++++++++++
 scripts/installAmiciSource.sh   |  8 --------
 2 files changed, 15 insertions(+), 8 deletions(-)

diff --git a/python/sdist/custom_commands.py b/python/sdist/custom_commands.py
index 6aee6a70e7..6564da8d7b 100644
--- a/python/sdist/custom_commands.py
+++ b/python/sdist/custom_commands.py
@@ -34,6 +34,7 @@ def finalize_options(self):
         install.finalize_options(self)
 
     def run(self):
+        generateSwigInterfaceFiles()
         install.run(self)
 
 
@@ -75,6 +76,15 @@ def _single_compile(obj):
 class my_build_clib(build_clib):
     """Custom build_clib"""
 
+    def run(self):
+        # Always force recompilation. The way setuptools/distutils check for
+        # whether sources require recompilation is not reliable and may lead
+        # to crashes or wrong results. We rather compile once too often...
+        self.force = True
+
+        build_clib.run(self)
+
+
     def build_libraries(self, libraries):
         no_clibs = self.get_finalized_command('develop').no_clibs
         no_clibs |= self.get_finalized_command('install').no_clibs
@@ -181,6 +191,11 @@ def run(self):
                 copyfile(libfilenames[0],
                          os.path.join(target_dir, os.path.basename(libfilenames[0])))
 
+        # Always force recompilation. The way setuptools/distutils check for
+        # whether sources require recompilation is not reliable and may lead
+        # to crashes or wrong results. We rather compile once too often...
+        self.force = True
+
         # Continue with the actual extension building
         build_ext.run(self)
 
diff --git a/scripts/installAmiciSource.sh b/scripts/installAmiciSource.sh
index 65caa5ab47..b07c792d1f 100755
--- a/scripts/installAmiciSource.sh
+++ b/scripts/installAmiciSource.sh
@@ -11,14 +11,6 @@ AMICI_PATH=$(cd $SCRIPT_PATH/.. && pwd)
 #make python-wheel
 #pip3 install --user --prefix= `ls -t ${AMICI_PATH}/build/python/amici-*.whl | head -1`
 
-rm -f ${AMICI_PATH}/python/sdist/amici/*.cxx
-rm -f ${AMICI_PATH}/python/sdist/amici/*.so
-rm -f ${AMICI_PATH}/python/sdist/amici/amici.py
-rm -f ${AMICI_PATH}/python/sdist/amici/amici_without_hdf5.py
-rm -f ${AMICI_PATH}/python/sdist/amici/libs/*
-rm -rf ${AMICI_PATH}/python/sdist/build/
-
-
 # test install from setup.py
 set +e
 python3 -m venv ${AMICI_PATH}/build/venv --clear

From 3f35bf2e63ac34404fd739b02cbce32ad6f1c6fe Mon Sep 17 00:00:00 2001
From: FFroehlich <fabian@schaluck.com>
Date: Mon, 3 Feb 2020 09:58:09 -0500
Subject: [PATCH 10/23] implement getName function for models

---
 include/amici/model.h           | 7 +++++++
 src/model.cpp                   | 4 ++++
 src/model_header.ODE_template.h | 4 ++++
 3 files changed, 15 insertions(+)

diff --git a/include/amici/model.h b/include/amici/model.h
index 21733ea9bf..4dba877e8b 100644
--- a/include/amici/model.h
+++ b/include/amici/model.h
@@ -418,6 +418,13 @@ class Model : public AbstractModel {
     int setFixedParametersByNameRegex(std::string const &par_name_regex,
                                       realtype value);
 
+    /**
+     * @brief Returns the model name
+     * @return model name
+     */
+    virtual std::string getName() const;
+    
+    
     /**
      * @brief Reports whether the model has parameter names set.
      * Also returns true if the number of corresponding variables is just zero.
diff --git a/src/model.cpp b/src/model.cpp
index 977c569372..decb8376d9 100644
--- a/src/model.cpp
+++ b/src/model.cpp
@@ -483,6 +483,10 @@ int Model::setFixedParametersByNameRegex(std::string const &par_name_regex,
                              par_name_regex, "fixedParameters", "name");
 }
 
+std::string Model::getName() const {
+    return "";
+}
+
 bool Model::hasParameterNames() const {
     return np() == 0 || !getParameterNames().empty();
 }
diff --git a/src/model_header.ODE_template.h b/src/model_header.ODE_template.h
index df0523811f..a3f8d7f340 100644
--- a/src/model_header.ODE_template.h
+++ b/src/model_header.ODE_template.h
@@ -733,6 +733,10 @@ class Model_TPL_MODELNAME : public amici::Model_ODE {
     TPL_X_SOLVER_IMPL
 
     TPL_TOTAL_CL_IMPL
+    
+    virtual std::string getName() const override {
+        return "TPL_MODELNAME"
+    }
 
     /**
      * @brief Get names of the model parameters

From b3b6bcdc8a96024ff56beaced3dbab5c1fd21cfd Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Fabian=20Fr=C3=B6hlich?= <fabian@schaluck.com>
Date: Mon, 3 Feb 2020 10:02:51 -0500
Subject: [PATCH 11/23] Update src/model_header.ODE_template.h

Co-Authored-By: Daniel Weindl <dweindl@users.noreply.github.com>
---
 src/model_header.ODE_template.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/model_header.ODE_template.h b/src/model_header.ODE_template.h
index a3f8d7f340..3f72923a50 100644
--- a/src/model_header.ODE_template.h
+++ b/src/model_header.ODE_template.h
@@ -734,7 +734,7 @@ class Model_TPL_MODELNAME : public amici::Model_ODE {
 
     TPL_TOTAL_CL_IMPL
     
-    virtual std::string getName() const override {
+    std::string getName() const override {
         return "TPL_MODELNAME"
     }
 

From cc9c34e0ff4ce69dcce946a26337a28775e57e79 Mon Sep 17 00:00:00 2001
From: FFroehlich <fabian@schaluck.com>
Date: Mon, 3 Feb 2020 17:44:21 -0500
Subject: [PATCH 12/23] add getName call to example notebook, fix ODE_template

---
 python/examples/example_steadystate/ExampleSteadystate.ipynb | 1 +
 src/model_header.ODE_template.h                              | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/python/examples/example_steadystate/ExampleSteadystate.ipynb b/python/examples/example_steadystate/ExampleSteadystate.ipynb
index c6717c3c18..f9c37c07ac 100644
--- a/python/examples/example_steadystate/ExampleSteadystate.ipynb
+++ b/python/examples/example_steadystate/ExampleSteadystate.ipynb
@@ -282,6 +282,7 @@
    "source": [
     "model = model_module.getModel()\n",
     "\n",
+    "print(\"Model name:\", model.getName())\n",
     "print(\"Model parameters:\", model.getParameterIds())\n",
     "print(\"Model outputs:   \", model.getObservableIds())\n",
     "print(\"Model states:    \", model.getStateIds())"
diff --git a/src/model_header.ODE_template.h b/src/model_header.ODE_template.h
index 3f72923a50..48ae0028bf 100644
--- a/src/model_header.ODE_template.h
+++ b/src/model_header.ODE_template.h
@@ -735,7 +735,7 @@ class Model_TPL_MODELNAME : public amici::Model_ODE {
     TPL_TOTAL_CL_IMPL
     
     std::string getName() const override {
-        return "TPL_MODELNAME"
+        return "TPL_MODELNAME";
     }
 
     /**

From 5b32260cce0e290f4882075b60acc405ac27ac63 Mon Sep 17 00:00:00 2001
From: Daniel Weindl <daniel.weindl@helmholtz-muenchen.de>
Date: Fri, 7 Feb 2020 19:04:56 +0100
Subject: [PATCH 13/23] Build optimized AMICI and sundials by default (Closes
 #934)

---
 scripts/buildAmici.sh    | 18 +++++++++++++----
 scripts/buildSundials.sh | 43 +++++++++++++++++++++++-----------------
 2 files changed, 39 insertions(+), 22 deletions(-)

diff --git a/scripts/buildAmici.sh b/scripts/buildAmici.sh
index 49bcb5e17f..e50eaa95f9 100755
--- a/scripts/buildAmici.sh
+++ b/scripts/buildAmici.sh
@@ -7,13 +7,23 @@ CMAKE=${CMAKE:-cmake}
 MAKE=${MAKE:-make}
 
 SCRIPT_PATH=$(dirname $BASH_SOURCE)
-AMICI_PATH=$(cd $SCRIPT_PATH/.. && pwd)
+AMICI_PATH=$(cd "$SCRIPT_PATH"/.. && pwd)
+
+mkdir -p "${AMICI_PATH}"/build
+cd "${AMICI_PATH}"/build
 
-mkdir -p ${AMICI_PATH}/build
-cd ${AMICI_PATH}/build
 CPPUTEST_BUILD_DIR=${AMICI_PATH}/ThirdParty/cpputest-master/build/
+
+if [[ $TRAVIS = true ]]; then
+  # Running on CI server
+  build_type="Debug"
+else
+  build_type="RelWithDebInfo"
+fi
+
 CppUTest_DIR=${CPPUTEST_BUILD_DIR} \
-  ${CMAKE} -DCMAKE_BUILD_TYPE=Debug -DPython3_EXECUTABLE=$(which python3) ..
+  ${CMAKE} -DCMAKE_BUILD_TYPE=$build_type -DPython3_EXECUTABLE="$(command -v python3)" ..
+
 ${MAKE}
 
 ${MAKE} python-sdist
diff --git a/scripts/buildSundials.sh b/scripts/buildSundials.sh
index 173e7c7b33..c9908e8770 100755
--- a/scripts/buildSundials.sh
+++ b/scripts/buildSundials.sh
@@ -5,7 +5,7 @@
 set -e
 
 SCRIPT_PATH=$(dirname $BASH_SOURCE)
-AMICI_PATH=$(cd $SCRIPT_PATH/.. && pwd)
+AMICI_PATH=$(cd "$SCRIPT_PATH"/.. && pwd)
 
 SUITESPARSE_ROOT="${AMICI_PATH}/ThirdParty/SuiteSparse"
 SUNDIALS_BUILD_PATH="${AMICI_PATH}/ThirdParty/sundials/build/"
@@ -13,6 +13,13 @@ SUNDIALS_BUILD_PATH="${AMICI_PATH}/ThirdParty/sundials/build/"
 CMAKE=${CMAKE:-cmake}
 MAKE=${MAKE:-make}
 
+if [[ $TRAVIS = true ]]; then
+  # Running on CI server
+  build_type="Debug"
+else
+  build_type="RelWithDebInfo"
+fi
+
 # enable SuperLUMT support if library exists
 SuperLUMT=""
 if [[ -f ${AMICI_PATH}/ThirdParty/SuperLU_MT_3.1/lib/libsuperlu_mt_PTHREAD.a ]]
@@ -22,25 +29,25 @@ then
          -DSUPERLUMT_LIBRARY_DIR=${AMICI_PATH}/ThirdParty/SuperLU_MT_3.1/lib/"
 fi
 
-mkdir -p ${SUNDIALS_BUILD_PATH}
-cd ${SUNDIALS_BUILD_PATH}
+mkdir -p "${SUNDIALS_BUILD_PATH}"
+cd "${SUNDIALS_BUILD_PATH}"
 
 ${CMAKE} -DCMAKE_INSTALL_PREFIX="${SUNDIALS_BUILD_PATH}" \
--DCMAKE_BUILD_TYPE=Debug \
--DCMAKE_POSITION_INDEPENDENT_CODE=ON \
--DBUILD_ARKODE=OFF \
--DBUILD_CVODE=OFF \
--DBUILD_IDA=OFF \
--DBUILD_KINSOL=OFF \
--DBUILD_SHARED_LIBS=ON \
--DBUILD_STATIC_LIBS=ON \
--DEXAMPLES_ENABLE_C=OFF \
--DEXAMPLES_INSTALL=OFF \
--DKLU_ENABLE=ON \
--DKLU_LIBRARY_DIR="${SUITESPARSE_ROOT}/lib" \
--DKLU_INCLUDE_DIR="${SUITESPARSE_ROOT}/include" \
-${SuperLUMT} \
-..
+  -DCMAKE_BUILD_TYPE=$build_type \
+  -DCMAKE_POSITION_INDEPENDENT_CODE=ON \
+  -DBUILD_ARKODE=OFF \
+  -DBUILD_CVODE=OFF \
+  -DBUILD_IDA=OFF \
+  -DBUILD_KINSOL=OFF \
+  -DBUILD_SHARED_LIBS=ON \
+  -DBUILD_STATIC_LIBS=ON \
+  -DEXAMPLES_ENABLE_C=OFF \
+  -DEXAMPLES_INSTALL=OFF \
+  -DKLU_ENABLE=ON \
+  -DKLU_LIBRARY_DIR="${SUITESPARSE_ROOT}/lib" \
+  -DKLU_INCLUDE_DIR="${SUITESPARSE_ROOT}/include" \
+  "${SuperLUMT}" \
+  ..
 
 ${MAKE}
 ${MAKE} install

From 5156c8c4ce3e6babe8bb4836a725417d27f8b93f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Fabian=20Fr=C3=B6hlich?= <fabian@schaluck.com>
Date: Fri, 7 Feb 2020 23:42:58 -0500
Subject: [PATCH 14/23] Add logo, fixes #628 (#935)

* add logo, fixes #628

* add CC0 license

* change wording in graphics readme
---
 README.md                           |   6 +-
 documentation/gfx/LICENSE.md        | 121 ++++++++++
 documentation/gfx/README.md         |   1 +
 documentation/gfx/banner.png        | Bin 0 -> 138875 bytes
 documentation/gfx/logo.png          | Bin 0 -> 85197 bytes
 documentation/gfx/logo_template.svg | 344 ++++++++++++++++++++++++++++
 documentation/gfx/logo_text.png     | Bin 0 -> 97163 bytes
 7 files changed, 471 insertions(+), 1 deletion(-)
 create mode 100644 documentation/gfx/LICENSE.md
 create mode 100644 documentation/gfx/README.md
 create mode 100644 documentation/gfx/banner.png
 create mode 100644 documentation/gfx/logo.png
 create mode 100644 documentation/gfx/logo_template.svg
 create mode 100644 documentation/gfx/logo_text.png

diff --git a/README.md b/README.md
index 3270e6608d..5d869087e4 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,8 @@
-# About AMICI
+# AMICI - Advanced Multilanguage Interface for CVODES and IDAS
+
+![AMICI banner](https://raw.githubusercontent.com/ICB-DCM/AMICI/develop/documentation/gfx/banner.png)
+
+## About
 
 AMICI provides a multi-language (Python, C++, Matlab) interface for the
 [SUNDIALS](https://computing.llnl.gov/projects/sundials/) solvers
diff --git a/documentation/gfx/LICENSE.md b/documentation/gfx/LICENSE.md
new file mode 100644
index 0000000000..ccdb359217
--- /dev/null
+++ b/documentation/gfx/LICENSE.md
@@ -0,0 +1,121 @@
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diff --git a/documentation/gfx/README.md b/documentation/gfx/README.md
new file mode 100644
index 0000000000..a7956ac702
--- /dev/null
+++ b/documentation/gfx/README.md
@@ -0,0 +1 @@
+The graphics in this directory are under CC0 1.0 license. See LICENSE.md file.
diff --git a/documentation/gfx/banner.png b/documentation/gfx/banner.png
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literal 0
HcmV?d00001


From 05b0b36f54072ca7a431561b9092d2169c58fb11 Mon Sep 17 00:00:00 2001
From: Daniel Weindl <dweindl@users.noreply.github.com>
Date: Sat, 8 Feb 2020 17:27:38 +0100
Subject: [PATCH 15/23] Deploy AMICI python package source distribution to
 github (Closes #809) (#940)

---
 .github/workflows/deploy_sdist.yml | 31 ++++++++++++++++++++++++++++++
 1 file changed, 31 insertions(+)
 create mode 100644 .github/workflows/deploy_sdist.yml

diff --git a/.github/workflows/deploy_sdist.yml b/.github/workflows/deploy_sdist.yml
new file mode 100644
index 0000000000..dbe4b7331e
--- /dev/null
+++ b/.github/workflows/deploy_sdist.yml
@@ -0,0 +1,31 @@
+name: Deploy Python source distribution
+on: push
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v1
+      with:
+        fetch-depth: 20
+
+    - name: apt
+      run: |
+        sudo apt-get install -y swig3.0 \
+          && sudo ln -s /usr/bin/swig3.0 /usr/bin/swig
+    - name: pip
+      run: |
+        pip3 install --upgrade --user wheel \
+          && pip3 install --upgrade --user setuptools
+
+    - name: Create AMICI sdist
+      run: |
+        cd python/sdist && /usr/bin/python3 setup.py sdist
+
+    - name: Archive sdist
+      uses: actions/upload-artifact@v1
+      with:
+        name: sdist
+        path: python/sdist/dist

From a5f8c35d768055a8c4e31762be6065fbb799cd5b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Fabian=20Fr=C3=B6hlich?= <fabian@schaluck.com>
Date: Sat, 8 Feb 2020 16:13:01 -0500
Subject: [PATCH 16/23] enable multithreading in swig (#938)

---
 swig/amici.i | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/swig/amici.i b/swig/amici.i
index 359520677c..b76868f3af 100644
--- a/swig/amici.i
+++ b/swig/amici.i
@@ -1,4 +1,4 @@
-%module amici
+%module("threads=1") amici
 
 %include <exception.i>
 %exception {

From 69d8d2348e6e490ea0ffae739da27d643ef512b4 Mon Sep 17 00:00:00 2001
From: "Thomas S. Ligon" <tom@thomassligon.info>
Date: Sun, 9 Feb 2020 01:41:05 +0100
Subject: [PATCH 17/23] Enable MSVC compilation of Python extensions (#847)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Enable building AMICI Python package with MSVC (Closes #424)
* Implement setting compiler-specific build options (Closes #812)
* Add Windows OpenBLAS download script
* Set up basic Travis-CI Windows workflow (#424)

Co-authored-by: Fabian Fröhlich <fabian@schaluck.com>
Co-authored-by: Daniel Weindl <dweindl@users.noreply.github.com>
Co-authored-by: Thomas S. Ligon <tom@thomassligon.info>
---
 .travis.yml                                   | 29 ++++++
 CMakeLists.txt                                |  2 +-
 include/amici/defines.h                       |  1 +
 include/amici/misc.h                          | 12 ---
 models/model_calvetti/CMakeLists.txt          |  2 +-
 models/model_dirac/CMakeLists.txt             |  2 +-
 models/model_events/CMakeLists.txt            |  2 +-
 models/model_jakstat_adjoint/CMakeLists.txt   |  2 +-
 .../model_jakstat_adjoint_o2/CMakeLists.txt   |  2 +-
 models/model_nested_events/CMakeLists.txt     |  2 +-
 models/model_neuron/CMakeLists.txt            |  2 +-
 models/model_neuron_o2/CMakeLists.txt         |  2 +-
 models/model_robertson/CMakeLists.txt         |  2 +-
 models/model_steadystate/CMakeLists.txt       |  2 +-
 python/sdist/custom_commands.py               | 98 ++++++++++++++++---
 python/sdist/setup.py                         | 31 +++---
 python/sdist/setup_clibs.py                   | 15 ++-
 scripts/installOpenBLAS.ps1                   | 17 ++++
 src/CMakeLists.template.cmake                 |  2 +-
 src/solver.cpp                                |  1 +
 20 files changed, 173 insertions(+), 55 deletions(-)
 create mode 100644 scripts/installOpenBLAS.ps1

diff --git a/.travis.yml b/.travis.yml
index a49e266a39..60b4c0b66f 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -131,6 +131,33 @@ matrix:
         on:
           branch: master
 
+    - os: windows
+      # language python currently not supported on Windows
+      language: cpp
+      compiler: msvc
+      git:
+        # clone after enabled symlinks below
+        clone: false
+      env: PATH=/c/Python37:/c/Python37/Scripts:$PATH
+      before_install:
+        - export -f travis_fold travis_nanoseconds travis_time_start travis_time_finish
+        # allow PowerShell to run scripts
+        - powershell -Command Set-ExecutionPolicy Unrestricted -Force
+        # Enable Windows developer mode to support symlinks
+        - powershell -Command New-ItemProperty -Path "HKLM:\SOFTWARE\Microsoft\Windows\CurrentVersion\AppModelUnlock" -Name AllowDevelopmentWithoutDevLicense -Value 1  -PropertyType DWord
+        # stick to python 3.7 until there is a 3.8 wheel for windows
+        # as installation from sdist fails because of reasons...
+        - choco install python --version 3.7.5
+        - choco install swig
+        - python -m pip install --upgrade pip
+        - pip install --user -U numpy
+        - git clone -c core.symlinks=true https://github.com/ICB-DCM/AMICI.git && cd AMICI
+        - if [[ "$TRAVIS_PULL_REQUEST" == "false" ]]; then git checkout -qf $TRAVIS_COMMIT; elif [[ "$TRAVIS_PULL_REQUEST" != "false" ]]; then git fetch origin pull/$TRAVIS_PULL_REQUEST/head:$TRAVIS_BRANCH && git checkout $TRAVIS_BRANCH; fi
+        # run BLAS installation script
+        - if [[ "$TRAVIS_OS_NAME" == "windows" ]]; then powershell -File 'C:\Users\travis\build\AMICI\scripts\installOpenBLAS.ps1';export BLAS_LIBS BLAS_CFLAGS; fi
+        # define Windows environment variables in BLAS because PowerShell definition didn't do the trick
+        - if [[ "$TRAVIS_OS_NAME" == "windows" ]]; then export BLAS_LIBS='/LIBPATH:C:\\BLAS\\OpenBLAS-0.3.6-x64\\lib libopenblas.lib' BLAS_CFLAGS='/IC:\\BLAS\\OpenBLAS-0.3.6-x64\\include'; fi
+
 install:
     - export BASE_DIR=`pwd`
     # Build swig4.0 (not yet available with apt) to include pydoc in source distribution for pypi
@@ -146,6 +173,8 @@ install:
     - if [[ "$CI_BUILD" == "TRUE" ]]; then ./scripts/buildAmici.sh; fi
     - if [[ "$CI_ARCHIVE" == "TRUE" ]]; then ./scripts/installAmiciArchive.sh; fi
     - if [[ "$CI_PYTHON" == "TRUE" ]]; then ./scripts/installAmiciSource.sh; fi
+    - if [[ "$TRAVIS_OS_NAME" == "windows" ]]; then cd python/sdist && python setup.py sdist; fi
+    - if [[ "$TRAVIS_OS_NAME" == "windows" ]]; then pip install $(ls -t dist/amici-*.tar.gz | head -1); fi
 
 script:
     - export -f travis_fold travis_nanoseconds travis_time_start travis_time_finish
diff --git a/CMakeLists.txt b/CMakeLists.txt
index cf40b33264..a427873fef 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -18,7 +18,7 @@ project(amici)
 
 set(CMAKE_MODULE_PATH ${CMAKE_SOURCE_DIR}/cmake)
 set(CMAKE_POSITION_INDEPENDENT_CODE ON)
-set(CMAKE_CXX_STANDARD 11)
+set(CMAKE_CXX_STANDARD 14)
 if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
     # require at least gcc 4.9, otherwise regex wont work properly
     if (CMAKE_CXX_COMPILER_VERSION VERSION_LESS 4.9)
diff --git a/include/amici/defines.h b/include/amici/defines.h
index 1c90baa92c..780b4a62dc 100644
--- a/include/amici/defines.h
+++ b/include/amici/defines.h
@@ -3,6 +3,7 @@
 
 #include <cmath>
 #include <functional>
+#include <string>
 
 namespace amici {
 
diff --git a/include/amici/misc.h b/include/amici/misc.h
index 6284a1d8f9..b13534ad47 100644
--- a/include/amici/misc.h
+++ b/include/amici/misc.h
@@ -95,16 +95,4 @@ std::string printfToString(const char *fmt, va_list ap);
 
 } // namespace amici
 
-#ifndef __cpp_lib_make_unique
-// custom make_unique while we are still using c++11
-namespace std {
-template<typename T, typename... Args>
-std::unique_ptr<T> make_unique(Args&&... args)
-{
-    return std::unique_ptr<T>(new T(std::forward<Args>(args)...));
-}
-}
-#endif
-
 #endif // AMICI_MISC_H
-
diff --git a/models/model_calvetti/CMakeLists.txt b/models/model_calvetti/CMakeLists.txt
index 26b63a33a9..20718b82e1 100644
--- a/models/model_calvetti/CMakeLists.txt
+++ b/models/model_calvetti/CMakeLists.txt
@@ -9,7 +9,7 @@ endif(POLICY CMP0065)
 
 project(model_calvetti)
 
-set(CMAKE_CXX_STANDARD 11)
+set(CMAKE_CXX_STANDARD 14)
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
 set(CMAKE_POSITION_INDEPENDENT_CODE ON)
 
diff --git a/models/model_dirac/CMakeLists.txt b/models/model_dirac/CMakeLists.txt
index f73a171551..d0b6d8781d 100644
--- a/models/model_dirac/CMakeLists.txt
+++ b/models/model_dirac/CMakeLists.txt
@@ -9,7 +9,7 @@ endif(POLICY CMP0065)
 
 project(model_dirac)
 
-set(CMAKE_CXX_STANDARD 11)
+set(CMAKE_CXX_STANDARD 14)
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
 set(CMAKE_POSITION_INDEPENDENT_CODE ON)
 
diff --git a/models/model_events/CMakeLists.txt b/models/model_events/CMakeLists.txt
index 150dd43b02..d28423efe1 100644
--- a/models/model_events/CMakeLists.txt
+++ b/models/model_events/CMakeLists.txt
@@ -9,7 +9,7 @@ endif(POLICY CMP0065)
 
 project(model_events)
 
-set(CMAKE_CXX_STANDARD 11)
+set(CMAKE_CXX_STANDARD 14)
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
 set(CMAKE_POSITION_INDEPENDENT_CODE ON)
 
diff --git a/models/model_jakstat_adjoint/CMakeLists.txt b/models/model_jakstat_adjoint/CMakeLists.txt
index 76484be708..b7a43019fb 100644
--- a/models/model_jakstat_adjoint/CMakeLists.txt
+++ b/models/model_jakstat_adjoint/CMakeLists.txt
@@ -9,7 +9,7 @@ endif(POLICY CMP0065)
 
 project(model_jakstat_adjoint)
 
-set(CMAKE_CXX_STANDARD 11)
+set(CMAKE_CXX_STANDARD 14)
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
 set(CMAKE_POSITION_INDEPENDENT_CODE ON)
 
diff --git a/models/model_jakstat_adjoint_o2/CMakeLists.txt b/models/model_jakstat_adjoint_o2/CMakeLists.txt
index 92516553d0..199af7804c 100644
--- a/models/model_jakstat_adjoint_o2/CMakeLists.txt
+++ b/models/model_jakstat_adjoint_o2/CMakeLists.txt
@@ -9,7 +9,7 @@ endif(POLICY CMP0065)
 
 project(model_jakstat_adjoint_o2)
 
-set(CMAKE_CXX_STANDARD 11)
+set(CMAKE_CXX_STANDARD 14)
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
 set(CMAKE_POSITION_INDEPENDENT_CODE ON)
 
diff --git a/models/model_nested_events/CMakeLists.txt b/models/model_nested_events/CMakeLists.txt
index 120e5a08ed..3c6dcc0a35 100644
--- a/models/model_nested_events/CMakeLists.txt
+++ b/models/model_nested_events/CMakeLists.txt
@@ -9,7 +9,7 @@ endif(POLICY CMP0065)
 
 project(model_nested_events)
 
-set(CMAKE_CXX_STANDARD 11)
+set(CMAKE_CXX_STANDARD 14)
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
 set(CMAKE_POSITION_INDEPENDENT_CODE ON)
 
diff --git a/models/model_neuron/CMakeLists.txt b/models/model_neuron/CMakeLists.txt
index 0c63349ca8..eb41751c03 100644
--- a/models/model_neuron/CMakeLists.txt
+++ b/models/model_neuron/CMakeLists.txt
@@ -9,7 +9,7 @@ endif(POLICY CMP0065)
 
 project(model_neuron)
 
-set(CMAKE_CXX_STANDARD 11)
+set(CMAKE_CXX_STANDARD 14)
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
 set(CMAKE_POSITION_INDEPENDENT_CODE ON)
 
diff --git a/models/model_neuron_o2/CMakeLists.txt b/models/model_neuron_o2/CMakeLists.txt
index 7c2121cb0f..110b901d92 100644
--- a/models/model_neuron_o2/CMakeLists.txt
+++ b/models/model_neuron_o2/CMakeLists.txt
@@ -9,7 +9,7 @@ endif(POLICY CMP0065)
 
 project(model_neuron_o2)
 
-set(CMAKE_CXX_STANDARD 11)
+set(CMAKE_CXX_STANDARD 14)
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
 set(CMAKE_POSITION_INDEPENDENT_CODE ON)
 
diff --git a/models/model_robertson/CMakeLists.txt b/models/model_robertson/CMakeLists.txt
index 7af14aba5b..6e5ddf1a7a 100644
--- a/models/model_robertson/CMakeLists.txt
+++ b/models/model_robertson/CMakeLists.txt
@@ -9,7 +9,7 @@ endif(POLICY CMP0065)
 
 project(model_robertson)
 
-set(CMAKE_CXX_STANDARD 11)
+set(CMAKE_CXX_STANDARD 14)
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
 set(CMAKE_POSITION_INDEPENDENT_CODE ON)
 
diff --git a/models/model_steadystate/CMakeLists.txt b/models/model_steadystate/CMakeLists.txt
index 0b14584260..d91835a801 100644
--- a/models/model_steadystate/CMakeLists.txt
+++ b/models/model_steadystate/CMakeLists.txt
@@ -9,7 +9,7 @@ endif(POLICY CMP0065)
 
 project(model_steadystate)
 
-set(CMAKE_CXX_STANDARD 11)
+set(CMAKE_CXX_STANDARD 14)
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
 set(CMAKE_POSITION_INDEPENDENT_CODE ON)
 
diff --git a/python/sdist/custom_commands.py b/python/sdist/custom_commands.py
index 6564da8d7b..65042677e1 100644
--- a/python/sdist/custom_commands.py
+++ b/python/sdist/custom_commands.py
@@ -2,18 +2,22 @@
 
 import glob
 import os
-import sys
 import subprocess
+import sys
 from shutil import copyfile
+from typing import Dict, List, Tuple
 
+from amici.setuptools import generateSwigInterfaceFiles
+from setuptools.command.build_clib import build_clib
 from setuptools.command.build_ext import build_ext
-from setuptools.command.sdist import sdist
-from setuptools.command.install_lib import install_lib
 from setuptools.command.develop import develop
 from setuptools.command.install import install
-from setuptools.command.build_clib import build_clib
+from setuptools.command.install_lib import install_lib
+from setuptools.command.sdist import sdist
+from distutils import log
 
-from amici.setuptools import generateSwigInterfaceFiles
+# typehints
+Library = Tuple[str, Dict[str, List[str]]]
 
 
 class my_install(install):
@@ -39,8 +43,8 @@ def run(self):
 
 
 def compile_parallel(self, sources, output_dir=None, macros=None,
-            include_dirs=None, debug=0, extra_preargs=None,
-            extra_postargs=None, depends=None):
+                     include_dirs=None, debug=0, extra_preargs=None,
+                     extra_postargs=None, depends=None):
     """Parallelized version of distutils.ccompiler.compile"""
     macros, objects, extra_postargs, pp_opts, build = \
         self._setup_compile(output_dir, macros, include_dirs, sources,
@@ -84,8 +88,7 @@ def run(self):
 
         build_clib.run(self)
 
-
-    def build_libraries(self, libraries):
+    def build_libraries(self, libraries: List[Library]):
         no_clibs = self.get_finalized_command('develop').no_clibs
         no_clibs |= self.get_finalized_command('install').no_clibs
 
@@ -96,6 +99,10 @@ def build_libraries(self, libraries):
         import distutils.ccompiler
         distutils.ccompiler.CCompiler.compile = compile_parallel
 
+        # Work-around for compiler-specific build options
+        set_compiler_specific_library_options(
+            libraries, self.compiler.compiler_type)
+
         build_clib.build_libraries(self, libraries)
 
 
@@ -141,7 +148,6 @@ def run(self):
                     subprocess.run(['dsymutil',os.path.join(search_dir,file),
                                     '-o',os.path.join(search_dir,file + '.dSYM')])
 
-
         # Continue with the actual installation
         install_lib.run(self)
 
@@ -149,8 +155,15 @@ def run(self):
 class my_build_ext(build_ext):
     """Custom build_ext to allow keeping otherwise temporary static libs"""
 
+    def build_extension(self, ext):
+        # Work-around for compiler-specific build options
+        set_compiler_specific_extension_options(ext, self.compiler.compiler_type)
+
+        build_ext.build_extension(self, ext)
+
     def run(self):
-        """Copy the generated clibs to the extensions folder to be included in the wheel
+        """Copy the generated clibs to the extensions folder to be included in
+        the wheel
 
         Returns:
 
@@ -168,7 +181,6 @@ def run(self):
                 # get the previously built static libraries
                 build_clib = self.get_finalized_command('build_clib')
                 libraries = build_clib.get_library_names() or []
-                library_dirs = build_clib.build_clib
 
             # Module build directory where we want to copy the generated libs
             # to
@@ -189,7 +201,8 @@ def run(self):
                     "Found unexpected number of files: " % libfilenames
 
                 copyfile(libfilenames[0],
-                         os.path.join(target_dir, os.path.basename(libfilenames[0])))
+                         os.path.join(target_dir,
+                                      os.path.basename(libfilenames[0])))
 
         # Always force recompilation. The way setuptools/distutils check for
         # whether sources require recompilation is not reliable and may lead
@@ -243,3 +256,62 @@ def saveGitVersion(self):
                                  '--always', '--tags'],
                                  stdout=f)
         assert(sp.returncode == 0)
+
+
+def set_compiler_specific_library_options(
+        libraries: List[Library],
+        compiler_type: str) -> None:
+    """Set compiler-specific library options.
+
+    C/C++-libraries for setuptools/distutils are provided as dict containing
+    entries for 'sources', 'macros', 'cflags', etc.
+    As we don't know the compiler type at the stage of calling
+    ``setuptools.setup`` and as there is no other apparent way to set
+    compiler-specific options, we elsewhere extend the dict with additional
+    fields ${original_field}_${compiler_class}, and add the additional
+    compiler-specific options here, at a stage when the compiler has been
+    determined by distutils.
+
+    Arguments:
+        libraries:
+            List of libraries as passed as ``libraries`` argument to
+            ``setuptools.setup`` and ``setuptools.build_ext.build_extension``.
+            This is modified in place.
+        compiler_type:
+            Compiler type, as defined in
+            ``distutils.ccompiler.compiler.compiler_class``, (e.g. 'unix',
+            'msvc', 'mingw32').
+    """
+
+    for lib in libraries:
+        for field in ['cflags', 'sources', 'macros']:
+            try:
+                lib[1][field] += lib[1][f'{field}_{compiler_type}']
+                log.info(f"Changed {field} for {lib[0]} with {compiler_type} "
+                         f"to {lib[1][field]}")
+            except KeyError:
+                # No compiler-specific options set
+                pass
+
+
+def set_compiler_specific_extension_options(
+        ext: 'setuptools.Extension',
+        compiler_type: str) -> None:
+    """Set compiler-specific extension build options.
+
+    Same game as in ``set_compiler_specific_library_options``, except that
+    here we look for compiler-specific class attributes.
+
+    Arguments:
+        ext: setuptools/distutils extension object
+        compiler_type: Compiler type
+    """
+    for attr in ['extra_compile_args', 'extra_link_args']:
+        try:
+            new_value = getattr(ext, attr) + \
+                getattr(ext, f'{attr}_{compiler_type}')
+            setattr(ext, attr, new_value)
+            log.info(f"Changed {attr} for {compiler_type} to {new_value}")
+        except AttributeError:
+            # No compiler-specific options set
+            pass
diff --git a/python/sdist/setup.py b/python/sdist/setup.py
index 735dacfc1b..67dfec52ce 100755
--- a/python/sdist/setup.py
+++ b/python/sdist/setup.py
@@ -113,6 +113,18 @@ def main():
         extra_compiler_flags=cxx_flags + ['-DDLONG']
     )
 
+    # Readme as long package description to go on PyPi
+    # (https://pypi.org/project/amici/)
+    with open("README.md", "r", encoding="utf-8") as fh:
+        long_description = fh.read()
+
+    # Remove the "-Wstrict-prototypes" compiler option, which isn't valid for
+    # C++ to fix warnings.
+    cfg_vars = sysconfig.get_config_vars()
+    for key, value in cfg_vars.items():
+        if type(value) == str:
+            cfg_vars[key] = value.replace("-Wstrict-prototypes", "")
+
     # Build shared object
     amici_module = Extension(
         name='amici._amici',
@@ -135,21 +147,14 @@ def main():
             *blaspkgcfg['library_dirs'],
             'amici/libs',  # clib target directory
         ],
-        extra_compile_args=['-std=c++11', *cxx_flags],
+        extra_compile_args=cxx_flags,
         extra_link_args=amici_module_linker_flags
     )
-
-    # Readme as long package description to go on PyPi
-    # (https://pypi.org/project/amici/)
-    with open("README.md", "r", encoding="utf-8") as fh:
-        long_description = fh.read()
-
-    # Remove the "-Wstrict-prototypes" compiler option, which isn't valid for
-    # C++ to fix warnings.
-    cfg_vars = sysconfig.get_config_vars()
-    for key, value in cfg_vars.items():
-        if type(value) == str:
-            cfg_vars[key] = value.replace("-Wstrict-prototypes", "")
+    # Monkey-patch extension (see
+    # `custom_commands.set_compiler_specific_extension_options`)
+    amici_module.extra_compile_args_mingw32 = ['-std=c++14']
+    amici_module.extra_compile_args_unix = ['-std=c++14']
+    amici_module.extra_compile_args_msvc = ['/std:c++14']
 
     # Install
     setup(
diff --git a/python/sdist/setup_clibs.py b/python/sdist/setup_clibs.py
index ea33f37b69..7e8a99f031 100644
--- a/python/sdist/setup_clibs.py
+++ b/python/sdist/setup_clibs.py
@@ -8,7 +8,6 @@
 import glob
 import re
 
-
 def getSundialsSources():
     """Get list of Sundials source files"""
     srcs = [
@@ -160,7 +159,9 @@ def getLibSundials(extra_compiler_flags=None):
                          'amici/ThirdParty/SuiteSparse/BTF/Include/',
                          'amici/ThirdParty/SuiteSparse/SuiteSparse_config',
                          'amici/ThirdParty/SuiteSparse/include'],
-        'cflags': ['-Wno-misleading-indentation', *extra_compiler_flags]
+        'cflags': [*extra_compiler_flags],
+        'cflags_mingw32': ['-Wno-misleading-indentation'],
+        'cflags_unix': ['-Wno-misleading-indentation']
     })
     return libsundials
 
@@ -183,8 +184,9 @@ def getLibSuiteSparse(extra_compiler_flags=None):
                          'amici/ThirdParty/SuiteSparse/SuiteSparse_config',
                          'amici/ThirdParty/SuiteSparse/include'
                          ],
-        'cflags': ['-Wno-unused-but-set-variable', *extra_compiler_flags]
-
+        'cflags': [*extra_compiler_flags],
+        'cflags_mingw32': ['-Wno-unused-but-set-variable'],
+        'cflags_unix': ['-Wno-unused-but-set-variable']
     })
     return libsuitesparse
 
@@ -218,7 +220,10 @@ def getLibAmici(extra_compiler_flags=None, h5pkgcfg=None, blaspkgcfg=None):
                          'amici/ThirdParty/sundials/src',
                          'amici/ThirdParty/gsl/',
                          ],
-        'cflags': ['-std=c++11', *extra_compiler_flags],
+        'cflags': [*extra_compiler_flags],
+        'cflags_mingw32': ['-std=c++14'],
+        'cflags_unix': ['-std=c++14'],
+        'cflags_msvc': ['/std:c++14'],
         'macros': [],
     })
 
diff --git a/scripts/installOpenBLAS.ps1 b/scripts/installOpenBLAS.ps1
new file mode 100644
index 0000000000..4b5ceb6421
--- /dev/null
+++ b/scripts/installOpenBLAS.ps1
@@ -0,0 +1,17 @@
+Write-Host 'script installOpenBLAS.ps1 started'
+$VerbosePreference = "Continue" # display verbose messages
+New-Item -Path 'C:\BLAS' -ItemType Directory -Force # create directory
+# Enforce stronger cryptography
+[System.Net.ServicePointManager]::SecurityProtocol = [System.Net.SecurityProtocolType]::Tls12
+$uri = 'https://sourceforge.net/projects/openblas/files/v0.3.6/OpenBLAS-0.3.6-x64.zip/download'
+$output = 'C:\BLAS\OpenBLAS-0.3.6-x64.zip'
+# Invoke-WebRequest $uri -OutFile $output
+$webclient = New-Object System.Net.WebClient
+$webclient.DownloadFile($uri,"$output")
+Expand-Archive -Path 'C:\BLAS\OpenBLAS-0.3.6-x64.zip' -DestinationPath 'C:\BLAS\OpenBLAS-0.3.6-x64' -Force # expand zip file
+New-Item -Path Env:BLAS_LIBS -Value "/LIBPATH:C:\BLAS\OpenBLAS-0.3.6-x64\lib libopenblas.lib" -Force # create environment variable
+New-Item -Path Env:BLAS_CFLAGS -Value "/IC:\BLAS\OpenBLAS-0.3.6-x64\include" -Force # create environment variable
+Get-ChildItem 'C:\BLAS\OpenBLAS-0.3.6-x64' -Recurse # check for files
+Get-Item -Path Env:BLAS_* # check environment variables
+$VerbosePreference = "SilentlyContinue" # don't display verbose messages
+Write-Host 'script installOpenBLAS.ps1 completed'
diff --git a/src/CMakeLists.template.cmake b/src/CMakeLists.template.cmake
index f6995c174a..05b99ae6b2 100644
--- a/src/CMakeLists.template.cmake
+++ b/src/CMakeLists.template.cmake
@@ -13,7 +13,7 @@ endif(POLICY CMP0074)
 
 project(TPL_MODELNAME)
 
-set(CMAKE_CXX_STANDARD 11)
+set(CMAKE_CXX_STANDARD 14)
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
 set(CMAKE_POSITION_INDEPENDENT_CODE ON)
 
diff --git a/src/solver.cpp b/src/solver.cpp
index f86a896fa9..3a462b1211 100644
--- a/src/solver.cpp
+++ b/src/solver.cpp
@@ -9,6 +9,7 @@
 #include <cstdio>
 #include <cstring>
 #include <ctime>
+#include <memory>
 
 namespace amici {
 

From 1abfab9d211986c3a2d987512dc7742968395db0 Mon Sep 17 00:00:00 2001
From: Daniel Weindl <dweindl@users.noreply.github.com>
Date: Sun, 9 Feb 2020 21:40:30 +0100
Subject: [PATCH 18/23] Extend PEtab support (#921)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Closes #890

* Update PEtab model import (now using YAML files and observable table)
* Add `simulate_petab(...)` to compute cost function / simulate model based on PEtab data
* Add tests for PEtab models based on benchmark collection to run as github action

Co-authored-by: Yannik Schälte <31767307+yannikschaelte@users.noreply.github.com>
Co-authored-by: Fabian Fröhlich <fabian@schaluck.com>
---
 .../test-benchmark-collection-models.yml      |  54 ++
 .gitignore                                    |   4 +
 python/amici/ode_export.py                    |   2 +-
 python/amici/petab_import.py                  | 330 ++++----
 python/amici/petab_objective.py               | 723 ++++++++++++++----
 python/amici/sbml_import.py                   |  78 +-
 python/sdist/setup.py                         |   2 +-
 tests/benchmark-models/benchmark_models.yaml  |  83 ++
 .../test_benchmark_collection.sh              | 109 +++
 tests/benchmark-models/test_petab_model.py    | 139 ++++
 10 files changed, 1216 insertions(+), 308 deletions(-)
 create mode 100644 .github/workflows/test-benchmark-collection-models.yml
 create mode 100644 tests/benchmark-models/benchmark_models.yaml
 create mode 100755 tests/benchmark-models/test_benchmark_collection.sh
 create mode 100755 tests/benchmark-models/test_petab_model.py

diff --git a/.github/workflows/test-benchmark-collection-models.yml b/.github/workflows/test-benchmark-collection-models.yml
new file mode 100644
index 0000000000..9b141dd251
--- /dev/null
+++ b/.github/workflows/test-benchmark-collection-models.yml
@@ -0,0 +1,54 @@
+name: Benchmark collection
+on:
+  push:
+    branches:
+      - develop
+      - master
+      - dw_misc
+
+  pull_request:
+    branches:
+      - master
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v1
+      with:
+        fetch-depth: 20
+
+    # install dependencies
+    - name: apt
+      run: |
+        sudo apt-get install -y swig3.0 libatlas-base-dev \
+          && sudo ln -s /usr/bin/swig3.0 /usr/bin/swig
+    - name: pip
+      run: |
+        pip3 install --upgrade --user wheel \
+          && pip3 install --upgrade --user setuptools
+    - run: pip3 install shyaml petab>=0.1.1
+    - run: |
+        echo ::add-path::${HOME}/.local/bin/
+        echo ::add-path::${GITHUB_WORKSPACE}/tests/performance/
+    # install AMICI
+    - name: Create AMICI sdist
+      run: |
+        cd python/sdist \
+          && check_time.sh create_sdist /usr/bin/python3 setup.py sdist
+    - name: Install AMICI sdist
+      run: |
+        AMICI_PARALLEL_COMPILE=2 check_time.sh \
+            install_sdist pip3 install -v --user \
+            $(ls -t python/sdist/dist/amici-*.tar.gz | head -1)
+
+    # retrieve test models
+    - name: Download and test benchmark collection
+      # TODO do something more efficient than cloning this big repo
+      run: |
+        git clone --depth 1 https://github.com/LeonardSchmiester/Benchmark-Models.git \
+          && cd Benchmark-Models && git checkout hackathon && cd .. \
+          && export BENCHMARK_COLLECTION="$(pwd)/Benchmark-Models/hackathon_contributions_new_data_format/" \
+          && tests/benchmark-models/test_benchmark_collection.sh
diff --git a/.gitignore b/.gitignore
index 772a153645..f8c19ef397 100644
--- a/.gitignore
+++ b/.gitignore
@@ -131,6 +131,7 @@ tests/sbml-test-suite/
 tests/sedml-test-suite/
 */sbml-semantic-test-cases/*
 tests/SBMLTestModels/
+tests/benchmark-models/test_bmc
 /python/test/amici-SBMLTest*/
 
 python/examples/example_steadystate/model_steadystate_scaled/*
@@ -154,6 +155,7 @@ AMICI_guide.pdf
 ThirdParty/bionetgen.tar.gz
 ThirdParty/BioNetGen-*
 ThirdParty/cpputest-master*
+ThirdParty/doxygen/*
 ThirdParty/mtocpp-master*
 ThirdParty/sundials/build/*
 ThirdParty/SuiteSparse/lib/*
@@ -161,3 +163,5 @@ ThirdParty/SuiteSparse/share/*
 ThirdParty/SuperLU_MT_3.1/
 ThirdParty/superlu_mt_3.1.tar.gz
 ThirdParty/swig-*
+
+_untracked/
diff --git a/python/amici/ode_export.py b/python/amici/ode_export.py
index 7ca251de1f..0fdc0b7deb 100644
--- a/python/amici/ode_export.py
+++ b/python/amici/ode_export.py
@@ -635,7 +635,7 @@ def __init__(self, identifier, name, value):
 
 
 class ODEModel:
-    """An ODEModel defines an Ordinay Differential Equation as set of
+    """An ODEModel defines an Ordinary Differential Equation as set of
     ModelQuantities. This class provides general purpose interfaces to
     compute arbitrary symbolic derivatives that are necessary for model
     simulation or sensitivity computation
diff --git a/python/amici/petab_import.py b/python/amici/petab_import.py
index f5e8dff22a..24111d8b65 100644
--- a/python/amici/petab_import.py
+++ b/python/amici/petab_import.py
@@ -1,24 +1,24 @@
 """
 Import a model in the PEtab (https://github.com/ICB-DCM/PEtab/) format into
-AMICI
+AMICI.
 """
 
 import argparse
 import logging
 import math
 import os
-import time
-from typing import List, Dict, Union, Optional
+from typing import List, Dict, Union, Optional, Tuple
 
 import amici
 import libsbml
 import pandas as pd
 import petab
-from colorama import Fore
-from colorama import init as init_colorama
+import sympy as sp
+from amici.logging import get_logger, log_execution_time
+from petab.C import *
 
 
-logger = logging.getLogger(__name__)
+logger = get_logger(__name__, logging.WARNING)
 
 
 def get_fixed_parameters(
@@ -27,7 +27,7 @@ def get_fixed_parameters(
         const_species_to_parameters: bool = False) -> List[str]:
     """Determine, set and return fixed model parameters
 
-    Parameters specified in `condition_file_name` are turned into constants.
+    Parameters specified in `condition_df` are turned into constants.
     Only global SBML parameters are considered. Local parameters are ignored.
 
     Arguments:
@@ -45,19 +45,27 @@ def get_fixed_parameters(
         List of IDs of parameters which are to be considered constant.
     """
 
-    # column names are model parameter names that should be made constant
-    # except for any overridden parameters
+    # Column names are model parameter IDs, compartment IDs or species IDs.
+    # Thereof, all parameters except for any overridden ones should be made
+    # constant.
     # (Could potentially still be made constant, but leaving them might
     # increase model reusability)
+
+    # handle parameters in condition table
     if condition_df is not None:
         fixed_parameters = list(condition_df.columns)
+        # get rid of conditionName column
         try:
-            fixed_parameters.remove('conditionName')
+            fixed_parameters.remove(CONDITION_NAME)
         except ValueError:
             pass
-        # remove overridden parameters
+
+        logger.debug(f'Condition table: {condition_df.shape}')
+
+        # remove overridden parameters (`object`-type columns)
         fixed_parameters = [p for p in fixed_parameters
-                            if condition_df[p].dtype != 'O']
+                            if condition_df[p].dtype != 'O'
+                            and sbml_model.getParameter(p) is not None]
         # must be unique
         if len(fixed_parameters) != len(set(fixed_parameters)):
             raise AssertionError(
@@ -65,23 +73,16 @@ def get_fixed_parameters(
     else:
         fixed_parameters = []
 
-    # States occurring as column names of the condition table need to be
-    #  converted to parameters
-    # TODO https://github.com/ICB-DCM/PEtab/issues/181
-    species_to_convert = [x for x in fixed_parameters
-                          if sbml_model.getSpecies(x)]
-    species_to_parameters(species_to_convert, sbml_model)
-
     # Others are optional
     if const_species_to_parameters:
         # Turn species which are marked constant in the SBML model into
         # parameters
         constant_species = constant_species_to_parameters(sbml_model)
 
-        logger.log(logging.INFO, "Constant species converted to parameters "
-                   + str(len(constant_species)))
-        logger.log(logging.INFO, "Non-constant species "
-                   + str(len(sbml_model.getListOfSpecies())))
+        logger.debug("Constant species converted to parameters: "
+                     + str(len(constant_species)))
+        logger.info("Non-constant species "
+                    + str(len(sbml_model.getListOfSpecies())))
 
         # ... and append them to the list of fixed_parameters
         for species in constant_species:
@@ -94,12 +95,36 @@ def get_fixed_parameters(
         # check global parameters
         if not sbml_model.getParameter(fixed_parameter) \
                 and not sbml_model.getSpecies(fixed_parameter):
-            logger.log(logging.WARN,
-                       f"{Fore.YELLOW}Parameter or species '{fixed_parameter}'"
-                       " provided in condition table but not present in"
-                       " model.")
+            logger.warning(f"Parameter or species '{fixed_parameter}'"
+                           " provided in condition table but not present in"
+                           " model. Ignoring.")
             fixed_parameters.remove(fixed_parameter)
 
+    if condition_df is None:
+        return fixed_parameters
+
+    # initial concentrations for species or initial compartment sizes in
+    # condition table will need to be turned into fixed parameters
+
+    # if there is no initial assignment for that species, we'd need
+    # to create one. to avoid any naming collision right away, we don't allow
+    # that for now
+
+    # we can't handle them yet
+    compartments = [col for col in condition_df
+                    if sbml_model.getCompartment(col) is not None]
+    if compartments:
+        raise NotImplementedError("Can't handle initial compartment sizes "
+                                  "at the moment. Consider creating an "
+                                  f"initial assignment for {compartments}")
+
+    species = [col for col in condition_df
+               if sbml_model.getSpecies(col) is not None]
+    if species:
+        raise NotImplementedError("Can't handle species in condition table."
+                                  "Consider creating an initial assignment for"
+                                  f" {species}")
+
     return fixed_parameters
 
 
@@ -185,13 +210,14 @@ def constant_species_to_parameters(sbml_model: 'libsbml.Model') -> List[str]:
     return species_to_parameters(transformables, sbml_model)
 
 
+@log_execution_time('Importing PEtab model', logger)
 def import_model(sbml_model: Union[str, 'libsbml.Model'],
                  condition_table: Optional[Union[str, pd.DataFrame]] = None,
-                 measurement_table: Optional[Union[str, pd.DataFrame]] = None,
+                 observable_table: Optional[Union[str, pd.DataFrame]] = None,
                  model_name: Optional[str] = None,
-                 model_output_dir: str = None,
+                 model_output_dir: Optional[str] = None,
                  verbose: bool = True,
-                 allow_reinit_fixpar_initcond: bool = False,
+                 allow_reinit_fixpar_initcond: bool = True,
                  **kwargs) -> None:
     """Create AMICI model from PEtab problem
 
@@ -202,8 +228,8 @@ def import_model(sbml_model: Union[str, 'libsbml.Model'],
             PEtab condition table. If provided, parameters from there will be
             turned into AMICI constant parameters (i.e. parameters w.r.t. which
             no sensitivities will be computed).
-        measurement_table:
-            PEtab measurement table.
+        observable_table:
+            PEtab observable table.
         model_name:
             Name of the generated model. If model file name was provided,
             this defaults to the file name without extension, otherwise
@@ -214,24 +240,27 @@ def import_model(sbml_model: Union[str, 'libsbml.Model'],
         verbose:
             Print/log extra information.
         allow_reinit_fixpar_initcond:
-            See amici.ode_export.ODEExporter
+            See amici.ode_export.ODEExporter. Must be enabled if initial
+            states are to be reset after preequilibration.
         **kwargs:
             Additional keyword arguments to be passed to
             ``amici.sbml_importer.sbml2amici``.
     """
-
-    # Color output
-    init_colorama(autoreset=True)
-
     if verbose:
-        logger.log(logging.INFO, f"{Fore.GREEN}Importing model ...")
+        logger.setLevel(verbose)
 
-    # Create a copy, because it will be modified by SbmlImporter
-    sbml_doc = sbml_model.getSBMLDocument().clone()
-    sbml_model = sbml_doc.getModel()
+    logger.info(f"Importing model ...")
 
-    sbml_importer = amici.SbmlImporter(sbml_model)
+    # Get PEtab tables
+    observable_df = petab.get_observable_df(observable_table)
+    # to determine fixed parameters
+    condition_df = petab.get_condition_df(condition_table)
+
+    if observable_df is None:
+        raise NotImplementedError("PEtab import without observables table "
+                                  "is currently not supported.")
 
+    # Model name from SBML ID or filename
     if model_name is None:
         if isinstance(sbml_model, libsbml.Model):
             model_name = sbml_model.getId()
@@ -241,68 +270,64 @@ def import_model(sbml_model: Union[str, 'libsbml.Model'],
     if model_output_dir is None:
         model_output_dir = os.path.join(os.getcwd(), model_name)
 
-    sbml_model = sbml_importer.sbml
-
-    if verbose:
-        logger.log(logging.INFO,
-                   f"{Fore.GREEN}Model name is '{model_name}' "
-                   f"Writing model code to '{model_output_dir}'")
-        show_model_info(sbml_model)
-
-    # Read PEtab observables and sigmas
-    observables = petab.get_observables(sbml_importer.sbml, remove=True)
-    sigmas = petab.get_sigmas(sbml_importer.sbml, remove=True)
-
-    # Read PEtab error model
-    if measurement_table is not None:
-        if isinstance(measurement_table, str):
-            measurement_df = petab.get_measurement_df(measurement_table)
-        else:
-            measurement_df = measurement_table
+    logger.info(f"Model name is '{model_name}'. "
+                f"Writing model code to '{model_output_dir}'.")
 
-        noise_distrs = petab_noise_distributions_to_amici(
-            petab.get_noise_distributions(measurement_df))
+    # Load model
+    if isinstance(sbml_model, str):
+        # from file
+        sbml_reader = libsbml.SBMLReader()
+        sbml_doc = sbml_reader.readSBMLFromFile(sbml_model)
+        sbml_model = sbml_doc.getModel()
     else:
-        noise_distrs = {}  # use default
+        # Create a copy, because it will be modified by SbmlImporter
+        sbml_doc = sbml_model.getSBMLDocument().clone()
+        sbml_model = sbml_doc.getModel()
 
-    # Replace observableIds occurring in error model definition
-    import sympy as sp
-    for observable_id, formula in sigmas.items():
-        repl = sp.sympify(formula).subs(
-            observable_id, observables[observable_id]['formula'])
-        sigmas[observable_id] = str(repl)
+    show_model_info(sbml_model)
 
-    if verbose:
-        logger.log(logging.INFO, f'Observables {len(observables)}')
-        logger.log(logging.INFO, f'Sigmas {len(sigmas)}')
+    sbml_importer = amici.SbmlImporter(sbml_model)
+    sbml_model = sbml_importer.sbml
+
+    if observable_df is not None:
+        observables, noise_distrs, sigmas = \
+            get_observation_model(observable_df)
+
+    logger.info(f'Observables: {len(observables)}')
+    logger.info(f'Sigmas: {len(sigmas)}')
 
     if not len(sigmas) == len(observables):
         raise AssertionError(
             f'Number of provided observables ({len(observables)}) and sigmas '
             f'({len(sigmas)}) do not match.')
 
-    if condition_table is not None:
-        # get the condition dataframe before parsing fixed parameters
-        if isinstance(condition_table, str):
-            condition_df = petab.get_condition_df(condition_table)
-        else:
-            condition_df = condition_table
-        logger.log(logging.INFO, f'Condition table: {condition_df.shape}')
-    else:
-        condition_df = None
+    # TODO: adding extra output parameters is currently not supported,
+    #  so we add any output parameters to the SBML model.
+    #  this should be changed to something more elegant
+    # <BeginWorkAround>
+    formulas = {val['formula'] for val in observables.values()}
+    formulas |= set(sigmas.values())
+    output_parameters = set()
+    for formula in formulas:
+        for free_sym in sp.sympify(formula).free_symbols:
+            sym = str(free_sym)
+            if sbml_model.getElementBySId(sym) is None:
+                output_parameters.add(sym)
+    logger.debug(f"Adding output parameters to model: {output_parameters}")
+    for par in output_parameters:
+        petab.add_global_parameter(sbml_model, par)
+    # <EndWorkAround>
 
     fixed_parameters = get_fixed_parameters(sbml_model=sbml_model,
                                             condition_df=condition_df)
 
-    if verbose:
-        logger.log(logging.INFO,
-                   f"Overall fixed parameters {len(fixed_parameters)}")
-        logger.log(logging.INFO, "Non-constant global parameters "
-                   + str(len(sbml_model.getListOfParameters())
-                         - len(fixed_parameters)))
+    logger.debug(f"Fixed parameters are {fixed_parameters}")
+    logger.info(f"Overall fixed parameters: {len(fixed_parameters)}")
+    logger.info("Variable parameters: "
+                + str(len(sbml_model.getListOfParameters())
+                      - len(fixed_parameters)))
 
     # Create Python module from SBML model
-    start = time.time()
     sbml_importer.sbml2amici(
         modelName=model_name,
         output_dir=model_output_dir,
@@ -311,48 +336,101 @@ def import_model(sbml_model: Union[str, 'libsbml.Model'],
         sigmas=sigmas,
         allow_reinit_fixpar_initcond=allow_reinit_fixpar_initcond,
         noise_distributions=noise_distrs,
+        verbose=verbose,
         **kwargs)
-    end = time. time()
 
-    if verbose:
-        logger.log(logging.INFO, f"{Fore.GREEN}Model imported successfully in "
-                                 f"{round(end - start, 2)}s")
 
+def get_observation_model(observable_df: pd.DataFrame
+                          ) -> Tuple[Dict[str, Dict[str, str]],
+                                     Dict[str, str],
+                                     Dict[str, Union[str, float]]]:
+    """
+    Get observables, sigmas, and noise distributions from PEtab observation
+    table in a format suitable for `sbml2amici`.
 
-def petab_noise_distributions_to_amici(noise_distributions: Dict) -> Dict:
+    Arguments:
+        observable_df: PEtab observables table
+
+    Returns:
+        Tuple of dicts with observables, noise distributions, and sigmas.
+    """
+
+    if observable_df is None:
+        return {}, {}, {}
+
+    observables = {}
+    sigmas = {}
+
+    for _, observable in observable_df.iterrows():
+        oid = observable.name
+        name = observable.get(OBSERVABLE_NAME, "")
+        formula_obs = observable[OBSERVABLE_FORMULA]
+        formula_noise = observable[NOISE_FORMULA]
+        observables[oid] = {'name': name, 'formula': formula_obs}
+        sigmas[oid] = formula_noise
+
+    # Replace observableIds occurring in error model definition
+    for observable_id, formula in sigmas.items():
+        repl = sp.sympify(formula).subs(
+            observable_id, observables[observable_id]['formula'])
+        sigmas[observable_id] = str(repl)
+
+    noise_distrs = petab_noise_distributions_to_amici(observable_df)
+
+    return observables, noise_distrs, sigmas
+
+
+def petab_noise_distributions_to_amici(observable_df: pd.DataFrame) -> Dict:
     """
     Map from the petab to the amici format of noise distribution
     identifiers.
 
     Arguments:
-        noise_distributions: as obtained from `petab.get_noise_distributions`
+        observable_df: PEtab observable table
 
     Returns:
-        Dictionary of obserable_id => AMICI noise-distributions
+        Dictionary of observable_id => AMICI noise-distributions
     """
     amici_distrs = {}
-    for id_, val in noise_distributions.items():
+    for _, observable in observable_df.iterrows():
         amici_val = ''
 
-        if val['observableTransformation']:
-            amici_val += val['observableTransformation'] + '-'
+        if OBSERVABLE_TRANSFORMATION in observable \
+                and isinstance(observable[OBSERVABLE_TRANSFORMATION], str) \
+                and observable[OBSERVABLE_TRANSFORMATION]:
+            amici_val += observable[OBSERVABLE_TRANSFORMATION] + '-'
 
-        if val['noiseDistribution']:
-            amici_val += val['noiseDistribution']
-
-        amici_distrs[id_] = amici_val
+        if NOISE_DISTRIBUTION in observable \
+                and isinstance(observable[NOISE_DISTRIBUTION], str) \
+                and observable[NOISE_DISTRIBUTION]:
+            amici_val += observable[NOISE_DISTRIBUTION]
+        else:
+            amici_val += 'normal'
+        amici_distrs[observable.name] = amici_val
 
     return amici_distrs
 
 
+def petab_scale_to_amici_scale(scale_str: str) -> int:
+    """Convert PEtab parameter scaling string to AMICI scaling integer"""
+
+    if scale_str == petab.LIN:
+        return amici.ParameterScaling_none
+    if scale_str == petab.LOG:
+        return amici.ParameterScaling_ln
+    if scale_str == petab.LOG10:
+        return amici.ParameterScaling_log10
+
+    raise ValueError(f"Invalid parameter scale {scale_str}")
+
+
 def show_model_info(sbml_model: 'libsbml.Model'):
     """Log some model quantities"""
 
-    logger.log(logging.INFO, f'Species: {len(sbml_model.getListOfSpecies())}')
-    logger.log(logging.INFO, 'Global parameters: '
-               + str(len(sbml_model.getListOfParameters())))
-    logger.log(logging.INFO,
-               f'Reactions: {len(sbml_model.getListOfReactions())}')
+    logger.info(f'Species: {len(sbml_model.getListOfSpecies())}')
+    logger.info('Global parameters: '
+                + str(len(sbml_model.getListOfParameters())))
+    logger.info(f'Reactions: {len(sbml_model.getListOfReactions())}')
 
 
 def parse_cli_args():
@@ -383,38 +461,23 @@ def parse_cli_args():
                         help='Conditions table')
     parser.add_argument('-p', '--parameters', dest='parameter_file_name',
                         help='Parameter table')
+    parser.add_argument('-b', '--observables', dest='observable_file_name',
+                        help='Observable table')
 
-    group = parser.add_mutually_exclusive_group()
-    group.add_argument('-y', '--yaml', dest='yaml_file_name',
+    parser.add_argument('-y', '--yaml', dest='yaml_file_name',
                        help='PEtab YAML problem filename')
 
-    # or with model name, following default naming
-    group.add_argument('-n', '--model-name', dest='model_name',
-                        help='Model name where all files are in the working '
-                        'directory and follow PEtab naming convention. '
-                        'Specifying -[smcp] will override defaults')
+    parser.add_argument('-n', '--model-name', dest='model_name',
+                        help='Name of the python module generated for the '
+                             'model')
 
     args = parser.parse_args()
 
-    if args.model_name:
-        if not args.sbml_file_name:
-            args.sbml_file_name = petab.get_default_sbml_file_name(
-                args.model_name)
-        if not args.measurement_file_name:
-            args.measurement_file_name = \
-                petab.get_default_measurement_file_name(args.model_name)
-        if not args.condition_file_name:
-            args.condition_file_name = petab.get_default_condition_file_name(
-                args.model_name)
-        if not args.parameter_file_name:
-            args.parameter_file_name = petab.get_default_parameter_file_name(
-                args.model_name)
-
-    if not args.model_name and not args.yaml_file_name \
+    if not args.yaml_file_name \
             and not all((args.sbml_file_name, args.condition_file_name,
-                         args.measurement_file_name)):
+                         args.observable_file_name)):
         parser.error('When not specifying a model name or YAML file, then '
-                     'SBML, condition and measurement file must be specified')
+                     'SBML, condition and observable file must be specified')
 
     return args
 
@@ -433,7 +496,8 @@ def main():
             sbml_file=args.sbml_file_name,
             condition_file=args.condition_file_name,
             measurement_file=args.measurement_file_name,
-            parameter_file=args.parameter_file_name)
+            parameter_file=args.parameter_file_name,
+            observable_files=args.observable_file_name)
 
     # First check for valid PEtab
     petab.lint_problem(pp)
@@ -441,7 +505,7 @@ def main():
     import_model(model_name=args.model_name,
                  sbml_model=pp.sbml_model,
                  condition_table=pp.condition_df,
-                 measurement_table=pp.measurement_df,
+                 observable_table=pp.observable_df,
                  model_output_dir=args.model_output_dir,
                  compile=args.compile,
                  verbose=args.verbose)
diff --git a/python/amici/petab_objective.py b/python/amici/petab_objective.py
index 8fc9cd4402..a96659c8c1 100644
--- a/python/amici/petab_objective.py
+++ b/python/amici/petab_objective.py
@@ -1,173 +1,588 @@
-"""Functionality related to evaluating the objective function for a PEtab
-problem"""
+"""Functionality related to running simulations or evaluating the objective
+function as defined by a PEtab problem"""
 
-import numbers
-import numpy as np
-import pandas as pd
 import copy
+import logging
+import numbers
+from typing import (List, Sequence, Optional, Dict, Tuple, Union, Any,
+                    Collection, Iterator)
 
-from typing import List, Sequence
 import amici
+import numpy as np
+import pandas as pd
 import petab
+from amici.logging import get_logger, log_execution_time
+from petab.C import *
+
+
+LLH = 'llh'
+SLLH = 'sllh'
+FIM = 'fim'
+S2LLH = 's2llh'
+RES = 'res'
+SRES = 'sres'
+RDATAS = 'rdatas'
+
+logger = get_logger(__name__)
+
+
+@log_execution_time('Simulating PEtab model', logger)
+def simulate_petab(
+        petab_problem: petab.Problem,
+        amici_model: amici.Model,
+        solver: Optional[amici.Solver] = None,
+        problem_parameters: Optional[Dict[str, float]] = None,
+        simulation_conditions: Union[pd.DataFrame, Dict] = None,
+        parameter_mapping: List[petab.ParMappingDictTuple] = None,
+        parameter_scale_mapping: List[petab.ScaleMappingDictTuple] = None,
+        scaled_parameters: Optional[bool] = False,
+        log_level: int = logging.WARNING
+) -> Dict[str, Any]:
+    """Simulate PEtab model
+
+    Arguments:
+        petab_problem:
+            PEtab problem to work on.
+        amici_model:
+            AMICI Model assumed to be compatible with ``petab_problem``.
+        solver:
+            An AMICI solver. Will use default options if None.
+        problem_parameters:
+            Run simulation with these parameters. If None,
+            PEtab `nominalValues` will be used). To be provided as dict,
+            mapping PEtab problem parameters to SBML IDs.
+        simulation_conditions:
+            Result of petab.get_simulation_conditions. Can be provided to save
+            time if this has be obtained before.
+        parameter_mapping:
+            Optional precomputed PEtab parameter mapping for efficiency.
+        parameter_scale_mapping:
+            Optional precomputed PEtab parameter scale mapping for efficiency.
+        scaled_parameters:
+            If True, problem_parameters are assumed to be on the scale provided
+            in the PEtab parameter table and will be unscaled. If False, they
+            are assumed to be in linear scale.
+        log_level:
+            Log level, see `logging` module.
+    Returns:
+        Dictionary of
+
+        * cost function value (LLH),
+        * const function sensitivity w.r.t. parameters (SLLH),
+          (**NOTE**: Sensitivities are computed for the non-scaled parameters)
+        * list of `ReturnData`s (RDATAS),
+
+        corresponding to the different simulation conditions.
+        For ordering of simulation conditions, see
+        `petab.Problem.get_simulation_conditions_from_measurement_df`.
+    """
+    logger.setLevel(log_level)
+
+    if solver is None:
+        solver = amici_model.getSolver()
+
+    # Get parameters
+    if problem_parameters is None:
+        # Use PEtab nominal values as default
+        problem_parameters = {t.Index: getattr(t, NOMINAL_VALUE) for t in
+                              petab_problem.parameter_df.itertuples()}
+
+    # Get parameter mapping
+    if parameter_mapping is None:
+        parameter_mapping = \
+            petab_problem.get_optimization_to_simulation_parameter_mapping(
+                warn_unmapped=False)
+
+    # Generate ExpData with all condition-specific information
+    edatas = edatas_from_petab(
+        model=amici_model,
+        petab_problem=petab_problem,
+        problem_parameters=problem_parameters,
+        simulation_conditions=simulation_conditions,
+        parameter_mapping=parameter_mapping,
+        parameter_scale_mapping=parameter_scale_mapping,
+        scaled_parameters=scaled_parameters)
+
+    # Simulate
+    rdatas = amici.runAmiciSimulations(amici_model, solver, edata_list=edatas)
+
+    # Compute total llh
+    llh = sum(rdata['llh'] for rdata in rdatas)
+    # Compute total sllh
+    sllh = aggregate_sllh(amici_model=amici_model, rdatas=rdatas,
+                          parameter_mapping=parameter_mapping)
+
+    # TODO: implement me
+    # # Compute total fim
+    # fim = None
+    # # Compute total s2llh
+    # s2llh = None
+    # # Compute total res
+    # res = None
+    # # Compute total sres
+    # sres = None
+
+    # log results
+    sim_cond = petab_problem.get_simulation_conditions_from_measurement_df()
+
+    for i, rdata in enumerate(rdatas):
+        logger.debug(f"Condition: {sim_cond.iloc[i, :].values}, status: "
+                     f"{rdata['status']}, llh: {rdata['llh']}")
+
+    return {
+        LLH: llh,
+        SLLH: sllh,
+        # FIM: fim,
+        # S2LLH: s2llh,
+        # RES: res,
+        # SRES: sres,
+        RDATAS: rdatas
+    }
 
 
 def edatas_from_petab(
-        model: amici.Model, measurement_df: pd.DataFrame,
-        condition_df: pd.DataFrame,
-        simulation_conditions=None) -> List[amici.ExpData]:
+        model: amici.Model,
+        petab_problem: petab.Problem,
+        problem_parameters: Dict[str, numbers.Number],
+        simulation_conditions: Union[pd.DataFrame, Dict] = None,
+        parameter_mapping: List[petab.ParMappingDictTuple] = None,
+        parameter_scale_mapping: List[petab.ScaleMappingDictTuple] = None,
+        scaled_parameters: Optional[bool] = False
+) -> List[amici.ExpData]:
     """
-    Create list of amici.ExpData objects for PEtab problem.
+    Create list of ``amici.ExpData`` objects for PEtab problem.
+
+    Sets timepoints, fixed parameters (including preequilibration),
+    non-fixed parameters, and observed data and sigmas.
 
     Arguments:
         model:
             AMICI model.
-        measurement_df:
-            PEtab measurement table.
-        condition_df:
-            PEtab condition table.
+        petab_problem:
+            PEtab problem
+        problem_parameters:
+            Dictionary mapping parameter names of the PEtab problem to
+            parameter values
         simulation_conditions:
             Result of petab.get_simulation_conditions. Can be provided to save
             time if this has be obtained before.
+        parameter_mapping:
+            Optional precomputed PEtab parameter mapping for efficiency.
+        parameter_scale_mapping:
+            Optional precomputed PEtab parameter scale mapping for efficiency.
+        scaled_parameters:
+            If True, problem_parameters are assumed to be on the scale provided
+            in the PEtab parameter table and will be unscaled. If False, they
+            are assumed to be in linear scale.
 
     Returns:
-        List with one ExpData per simulation condition.
+        List with one ``ExpData`` per simulation condition.
     """
 
-    condition_df = condition_df.reset_index()
-
     # number of amici simulations will be number of unique
     # (preequilibrationConditionId, simulationConditionId) pairs.
-    # Can be improved by checking for identical condition vectors.
+    # Can be optimized by checking for identical condition vectors.
     if simulation_conditions is None:
-        simulation_conditions = petab.get_simulation_conditions(
-            measurement_df)
+        simulation_conditions = \
+            petab_problem.get_simulation_conditions_from_measurement_df()
 
-    observable_ids = model.getObservableIds()
+    # Get parameter mapping if not user-provided
+    if parameter_mapping is None:
+        parameter_mapping = \
+            petab_problem.get_optimization_to_simulation_parameter_mapping(
+                warn_unmapped=False)
 
-    fixed_parameter_ids = model.getFixedParameterIds()
+    if parameter_scale_mapping is None:
+        parameter_scale_mapping = \
+            petab_problem.get_optimization_to_simulation_scale_mapping(
+                mapping_par_opt_to_par_sim=parameter_mapping)
 
-    edatas = []
-    for _, condition in simulation_conditions.iterrows():
-        # amici.ExpData for each simulation
+    observable_ids = model.getObservableIds()
 
-        # extract rows for condition
-        df_for_condition = petab.get_rows_for_condition(
-            measurement_df, condition)
+    logger.debug(f"Problem parameters: {problem_parameters}")
 
-        # make list of all timepoints for which measurements exist
-        timepoints = sorted(
-            df_for_condition.time.unique().astype(float))
-
-        # init edata object
-        edata = amici.ExpData(model)
-
-        # find replicate numbers of time points
-        timepoints_w_reps = []
-        for time in timepoints:
-            # subselect for time
-            df_for_time = df_for_condition[df_for_condition.time == time]
-            # rep number is maximum over rep numbers for observables
-            n_reps = max(df_for_time.groupby(
-                ['observableId', 'time']).size())
-            # append time point n_rep times
-            timepoints_w_reps.extend([time] * n_reps)
-
-        # set time points in edata
-        edata.setTimepoints(timepoints_w_reps)
-
-        # handle fixed parameters
-        _fixed_parameters_to_edata(edata, condition_df,
-                                   fixed_parameter_ids, condition)
-
-        # prepare measurement matrix
-        y = np.full(shape=(edata.nt(), edata.nytrue()), fill_value=np.nan)
-        # prepare sigma matrix
-        sigma_y = y.copy()
-
-        # add measurements and sigmas
-        # iterate over time points
-        for time in timepoints:
-            # subselect for time
-            df_for_time = df_for_condition[df_for_condition.time == time]
-            time_ix_0 = timepoints_w_reps.index(time)
-
-            # remember used time indices for each observable
-            time_ix_for_obs_ix = {}
-
-            # iterate over measurements
-            for _, measurement in df_for_time.iterrows():
-                # extract observable index
-                observable_ix = observable_ids.index(
-                    f'observable_{measurement.observableId}')
-
-                # update time index for observable
-                if observable_ix in time_ix_for_obs_ix:
-                    time_ix_for_obs_ix[observable_ix] += 1
-                else:
-                    time_ix_for_obs_ix[observable_ix] = time_ix_0
-
-                # fill observable and possibly noise parameter
-                y[time_ix_for_obs_ix[observable_ix],
-                  observable_ix] = measurement.measurement
-                if isinstance(measurement.noiseParameters, numbers.Number):
-                    sigma_y[time_ix_for_obs_ix[observable_ix],
-                            observable_ix] = measurement.noiseParameters
-
-        # fill measurements and sigmas into edata
-        edata.setObservedData(y.flatten())
-        edata.setObservedDataStdDev(sigma_y.flatten())
-
-        # append edata to edatas list
+    edatas = []
+    for (_, condition), cur_parameter_mapping, cur_parameter_scale_mapping \
+            in zip(simulation_conditions.iterrows(),
+                   parameter_mapping, parameter_scale_mapping):
+        # Create amici.ExpData for each simulation
+        edata = get_edata_for_condition(
+            condition=condition, amici_model=model, petab_problem=petab_problem,
+            problem_parameters=problem_parameters,
+            observable_ids=observable_ids,
+            parameter_mapping=cur_parameter_mapping,
+            parameter_scale_mapping=cur_parameter_scale_mapping,
+            scaled_parameters=scaled_parameters
+        )
         edatas.append(edata)
 
     return edatas
 
 
-def _fixed_parameters_to_edata(
-        edata: amici.ExpData, condition_df: pd.DataFrame,
-        fixed_parameter_ids: Sequence[str], condition) -> None:
+def subset_dict(full: Dict[Any, Any],
+                *args: Collection[Any]) -> Iterator[Dict[Any, Any]]:
+    """Get subset of dictionary based on provides keys
+
+    Arguments:
+        full: Dictionary to subset
+        *args: Collections of keys to be contained in the different subsets
     """
-    Apply fixed parameters for a given simulation condition to the
-    corresponding ExpData.
 
-    Parameters:
-        edata:
-            ExpData to set fixed parameters on.
-        condition_df:
-            The conditions table.
-        fixed_parameter_ids:
-            Ids of parameters that are to be considered constant (in correct
-            AMICI order).
+    for keys in args:
+        yield {key: val for (key, val) in full.items() if key in keys}
+
+
+def get_edata_for_condition(
+        condition: Union[Dict, pd.Series],
+        problem_parameters: Dict[str, numbers.Number],
+        amici_model: amici.Model,
+        petab_problem: petab.Problem,
+        observable_ids: List[str],
+        parameter_mapping: Optional[petab.ParMappingDictTuple] = None,
+        parameter_scale_mapping: Optional[petab.ScaleMappingDictTuple] = None,
+        scaled_parameters: Optional[bool] = False
+) -> amici.ExpData:
+    """Get ``amici.ExpData`` for the given PEtab condition
+
+    Sets timepoints, fixed parameters (including preequilibration),
+    variable parameters, and observed data and sigmas.
+
+    Arguments:
         condition:
-            The current condition, as created by
-            petab.get_simulation_conditions.
+            pandas.DataFrame row with preequilibrationConditionId and
+            simulationConditionId.
+        problem_parameters:
+            PEtab problem parameters as parameterId=>value dict. Only
+            parameters included here will be set. Remaining parameters will
+            be used currently set in `amici_model`.
+        amici_model:
+            AMICI model
+        petab_problem:
+            Underlying PEtab problem
+        observable_ids:
+            List of observable IDs
+        parameter_mapping:
+            PEtab parameter mapping for current condition
+        parameter_scale_mapping:
+            PEtab parameter scale mapping for current condition
+        scaled_parameters:
+            If True, problem_parameters are assumed to be on the scale provided
+            in the PEtab parameter table and will be unscaled. If False, they
+            are assumed to be in linear scale.
+
+    Returns:
+        ExpData instance
     """
 
-    if len(fixed_parameter_ids) == 0:
-        # nothing to be done
-        return
-
-    # find fixed parameter values
-    fixed_parameter_vals = condition_df.loc[
-        condition_df.conditionId == condition.simulationConditionId,
-        fixed_parameter_ids].values
-    # fill into edata
-    edata.fixedParameters = fixed_parameter_vals.astype(
-        float).flatten()
-
-    # same for preequilibration if necessary
-    if ('preequilibrationConditionId' in condition
-           and condition.preequilibrationConditionId):
-        fixed_preequilibration_parameter_vals = condition_df.loc[
-            condition_df.conditionId == condition.preequilibrationConditionId,
-            fixed_parameter_ids].values
-        edata.fixedParametersPreequilibration = \
-            fixed_preequilibration_parameter_vals.astype(float) \
-                                                 .flatten()
+    # extract measurement table rows for condition
+    measurement_df = petab.get_rows_for_condition(
+        measurement_df=petab_problem.measurement_df, condition=condition)
+
+    if amici_model.nytrue != len(observable_ids):
+        raise AssertionError("Number of AMICI model observables does not"
+                             "match number of PEtab observables.")
+
+    edata = amici.ExpData(amici_model)
+
+    ##########################################################################
+    # parameter mapping
+
+    # get mapping if required
+    if parameter_mapping is None:
+        # TODO petab.get_parameter_mapping_for_condition
+        raise NotImplementedError()
+
+    if parameter_scale_mapping is None:
+        # TODO petab.get_parameter_scale_mapping_for_condition
+        raise NotImplementedError()
+
+    condition_map_preeq, condition_map_sim = parameter_mapping
+    condition_scale_map_preeq, condition_scale_map_sim = \
+        parameter_scale_mapping
+
+    logger.debug(f"PEtab mapping: {parameter_mapping}")
+
+    if len(condition_map_preeq) != len(condition_scale_map_preeq) \
+            or len(condition_map_sim) != len(condition_scale_map_sim):
+        raise AssertionError("Number of parameters and number of parameter "
+                             "scales do not match.")
+    if len(condition_map_preeq) \
+            and len(condition_map_preeq) != len(condition_map_sim):
+        logger.debug(f"Preequilibration parameter map: {condition_map_preeq}")
+        logger.debug(f"Simulation parameter map: {condition_map_sim}")
+        raise AssertionError("Number of parameters for preequilbration "
+                             "and simulation do not match.")
+
+    # PEtab parameter mapping may contain parameter_ids as values, these *must*
+    # be replaced
+
+    def _get_par(model_par, value):
+        """Replace parameter IDs in mapping dicts by values from
+        problem_parameters where necessary"""
+        if isinstance(value, str):
+            # estimated parameter
+            # (condition table overrides have been handled by PEtab
+            # parameter mapping)
+            return problem_parameters[value]
+        if model_par in problem_parameters:
+            # user-provided
+            return problem_parameters[model_par]
+        # constant value
+        return value
+
+    condition_map_sim = {key: _get_par(key, val)
+                         for key, val in condition_map_sim.items()}
+    condition_map_preeq = {key: _get_par(key, val)
+                           for key, val in condition_map_preeq.items()}
+
+    # separate fixed and variable AMICI parameters, because we may have
+    # different fixed parameters for preeq and sim condition, but we cannot
+    # have different variable parameters. without splitting,
+    # merge_preeq_and_sim_pars_condition below may fail.
+    variable_par_ids = amici_model.getParameterIds()
+    fixed_par_ids = amici_model.getFixedParameterIds()
+
+    condition_map_preeq_var, condition_map_preeq_fix = \
+        subset_dict(condition_map_preeq, variable_par_ids, fixed_par_ids)
+
+    condition_scale_map_preeq_var, condition_scale_map_preeq_fix = \
+        subset_dict(condition_scale_map_preeq, variable_par_ids, fixed_par_ids)
+
+    condition_map_sim_var, condition_map_sim_fix = \
+        subset_dict(condition_map_sim, variable_par_ids, fixed_par_ids)
+
+    condition_scale_map_sim_var, condition_scale_map_sim_fix = \
+        subset_dict(condition_scale_map_sim, variable_par_ids, fixed_par_ids)
+
+    logger.debug("Fixed parameters preequilibration: "
+                 f"{condition_map_preeq_fix}")
+    logger.debug("Fixed parameters simulation: "
+                 f"{condition_map_sim_fix}")
+    logger.debug("Variable parameters preequilibration: "
+                 f"{condition_map_preeq_var}")
+    logger.debug("Variable parameters simulation: "
+                 f"{condition_map_sim_var}")
+
+    petab.merge_preeq_and_sim_pars_condition(
+        condition_map_preeq_var, condition_map_sim_var,
+        condition_scale_map_preeq_var, condition_scale_map_sim_var,
+        condition)
+
+    logger.debug(f"Merged: {condition_map_sim_var}")
+
+    # If necessary, bring parameters to linear scale
+    if scaled_parameters:
+        # For dynamic parameters we could also change ExpData.pscale, but since
+        # we need to do it for fixed parameters anyways, we just do it for all
+        # and set pscale to linear. we can skip preequilibration parameters,
+        # because they are identical with simulation parameters, and only the
+        # latter are used from here on
+        unscale_parameters_dict(condition_map_preeq_fix,
+                                condition_scale_map_preeq_fix)
+        unscale_parameters_dict(condition_map_sim_fix,
+                                condition_scale_map_sim_fix)
+        unscale_parameters_dict(condition_map_sim_var,
+                                condition_scale_map_sim_var)
+
+    ##########################################################################
+    # variable parameters and parameter scale
+
+    # parameter list from mapping dict
+    parameters = [condition_map_sim_var[par_id]
+                  for par_id in amici_model.getParameterIds()]
+
+    edata.parameters = parameters
+
+    edata.pscale = amici.parameterScalingFromIntVector(
+        [amici.ParameterScaling_none] * len(parameters))
+
+    ##########################################################################
+    # timepoints
+
+    # find replicate numbers of time points
+    timepoints_w_reps = _get_timepoints_with_replicates(
+        df_for_condition=measurement_df)
+
+    edata.setTimepoints(timepoints_w_reps)
+
+    ##########################################################################
+    # initial states
+    # initial states have been set during model import. if they were
+    # overwritten in the PEtab condition table, they would be handled as fixed
+    # model parameters below
+
+    ##########################################################################
+    # fixed parameters preequilibration
+    if condition_map_preeq:
+        fixed_pars_preeq = [condition_map_preeq_fix[par_id]
+                            for par_id in amici_model.getFixedParameterIds()]
+        edata.fixedParametersPreequilibration = fixed_pars_preeq
+
+    ##########################################################################
+    # fixed parameters simulation
+    fixed_pars_sim = [condition_map_sim_fix[par_id]
+                      for par_id in amici_model.getFixedParameterIds()]
+    edata.fixedParameters = fixed_pars_sim
+
+    ##########################################################################
+    # measurements and sigmas
+    y, sigma_y = _get_measurements_and_sigmas(
+        df_for_condition=measurement_df, timepoints_w_reps=timepoints_w_reps,
+        observable_ids=observable_ids)
+    edata.setObservedData(y.flatten())
+    edata.setObservedDataStdDev(sigma_y.flatten())
+
+    return edata
+
+
+def unscale_parameter(value: numbers.Number,
+                      petab_scale: str) -> numbers.Number:
+    """Parameter to linear scale
+
+    Arguments:
+        value:
+            Value to unscale
+        petab_scale:
+            Current scale of ``value``
+
+    Returns:
+        ``value`` on linear scale
+    """
+    if petab_scale == LIN:
+        return value
+    if petab_scale == LOG10:
+        return np.power(10, value)
+    if petab_scale == LOG:
+        return np.exp(value)
+    raise ValueError(f"Unknown parameter scale {petab_scale}. "
+                     f"Must be from {(LIN, LOG, LOG10)}")
+
+
+def unscale_parameters(values: Sequence[numbers.Number],
+                       petab_scales: Sequence[str]) -> List[numbers.Number]:
+    """Parameters to linear scale
+
+    Arguments:
+        values:
+            Values to unscale
+        petab_scales:
+            Current scales of ``values``
+
+    Returns:
+        List of ``values`` on linear scale
+    """
+    return [unscale_parameter(value, scale)
+            for value, scale in zip(values, petab_scales)]
+
+
+def unscale_parameters_dict(
+        value_dict: Dict[Any, numbers.Number],
+        petab_scale_dict: Dict[Any, str]) -> None:
+    """Parameters to linear scale
+
+    Bring values in ``value_dict`` from current scale provided in
+    ``petab_scale_dict`` to linear scale (in-place).
+    Both arguments are expected to have the same length and matching keys.
+
+    Arguments:
+        value_dict:
+            Values to unscale
+        petab_scale_dict:
+            Current scales of ``values``
+    """
+    if not value_dict.keys() == petab_scale_dict.keys():
+        raise AssertionError("Keys don't match.")
+
+    for key, value in value_dict.items():
+        value_dict[key] = unscale_parameter(value, petab_scale_dict[key])
+
+
+def _get_timepoints_with_replicates(
+        df_for_condition: pd.DataFrame) -> List[numbers.Number]:
+    """
+    Get list of timepoints including replicate measurements
+
+    Arguments:
+        df_for_condition:
+            PEtab measurement table subset for a single condition.
+
+    Returns:
+        Sorted list of timepoints, including multiple timepoints accounting
+        for replicate measurements.
+    """
+    # create sorted list of all timepoints for which measurements exist
+    timepoints = sorted(df_for_condition[TIME].unique().astype(float))
+
+    # find replicate numbers of time points
+    timepoints_w_reps = []
+    for time in timepoints:
+        # subselect for time
+        df_for_time = df_for_condition[df_for_condition.time == time]
+        # rep number is maximum over rep numbers for observables
+        n_reps = max(df_for_time.groupby(
+            [OBSERVABLE_ID, TIME]).size())
+        # append time point n_rep times
+        timepoints_w_reps.extend([time] * n_reps)
+
+    return timepoints_w_reps
+
+
+def _get_measurements_and_sigmas(
+        df_for_condition: pd.DataFrame,
+        timepoints_w_reps: Sequence[numbers.Number],
+        observable_ids: Sequence[str]) -> Tuple[np.array, np.array]:
+    """Get measurements and sigmas
+
+    Generate arrays with measurements and sigmas in AMICI format from a
+    PEtab measurement table subset for a single condition.
+
+    Arguments:
+        df_for_condition:
+            Subset of PEtab measurement table for one condition
+        timepoints_w_reps:
+            Timepoints for which there exist measurements, including replicates
+        observable_ids:
+            List of observable IDs for mapping IDs to indices.
+    """
+    # prepare measurement matrix
+    y = np.full(shape=(len(timepoints_w_reps), len(observable_ids)),
+                fill_value=np.nan)
+    # prepare sigma matrix
+    sigma_y = y.copy()
+
+    timepoints = sorted(df_for_condition[TIME].unique().astype(float))
+
+    for time in timepoints:
+        # subselect for time
+        df_for_time = df_for_condition[df_for_condition[TIME] == time]
+        time_ix_0 = timepoints_w_reps.index(time)
+
+        # remember used time indices for each observable
+        time_ix_for_obs_ix = {}
+
+        # iterate over measurements
+        for _, measurement in df_for_time.iterrows():
+            # extract observable index
+            observable_ix = observable_ids.index(measurement[OBSERVABLE_ID])
+
+            # update time index for observable
+            if observable_ix in time_ix_for_obs_ix:
+                time_ix_for_obs_ix[observable_ix] += 1
+            else:
+                time_ix_for_obs_ix[observable_ix] = time_ix_0
+
+            # fill observable and possibly noise parameter
+            y[time_ix_for_obs_ix[observable_ix],
+              observable_ix] = measurement[MEASUREMENT]
+            if isinstance(measurement[NOISE_PARAMETERS], numbers.Number):
+                sigma_y[time_ix_for_obs_ix[observable_ix],
+                        observable_ix] = measurement[NOISE_PARAMETERS]
+    return y, sigma_y
 
 
 def rdatas_to_measurement_df(
-        rdatas: List[amici.ReturnData], model: amici.Model,
+        rdatas: Sequence[amici.ReturnData],
+        model: amici.Model,
         measurement_df: pd.DataFrame) -> pd.DataFrame:
     """
     Create a measurement dataframe in the PEtab format from the passed
@@ -175,7 +590,7 @@ def rdatas_to_measurement_df(
 
     Parameters:
         rdatas:
-            A list of rdatas with the ordering of
+            A sequence of rdatas with the ordering of
             `petab.get_simulation_conditions`.
         model:
             AMICI model used to generate `rdatas`.
@@ -186,20 +601,15 @@ def rdatas_to_measurement_df(
         A dataframe built from the rdatas in the format of `measurement_df`.
     """
 
-    # initialize dataframe
     df = pd.DataFrame(columns=list(measurement_df.columns))
 
-    # get simulation conditions
     simulation_conditions = petab.get_simulation_conditions(
         measurement_df)
 
-    # get observable ids
     observable_ids = model.getObservableIds()
 
     # iterate over conditions
-    for data_idx, condition in simulation_conditions.iterrows():
-        # current rdata
-        rdata = rdatas[data_idx]
+    for (_, condition), rdata in zip(simulation_conditions.iterrows(), rdatas):
         # current simulation matrix
         y = rdata['y']
         # time array used in rdata
@@ -219,9 +629,8 @@ def rdatas_to_measurement_df(
             row_sim = copy.deepcopy(row)
 
             # extract simulated measurement value
-            timepoint_idx = t.index(row.time)
-            observable_idx = observable_ids.index(
-                "observable_" + row.observableId)
+            timepoint_idx = t.index(row[TIME])
+            observable_idx = observable_ids.index(row[OBSERVABLE_ID])
             measurement_sim = y[timepoint_idx, observable_idx]
 
             # change measurement entry
@@ -231,3 +640,39 @@ def rdatas_to_measurement_df(
             df = df.append(row_sim, ignore_index=True)
 
     return df
+
+
+def aggregate_sllh(
+        amici_model: amici.Model,
+        rdatas: Sequence[amici.ReturnDataView],
+        parameter_mapping: Optional[List[petab.ParMappingDictTuple]],
+) -> Union[None, Dict[str, float]]:
+    """Aggregate likelihood gradient for all conditions, according to PEtab
+    parameter mapping.
+
+    Arguments:
+        amici_model:
+            AMICI model from which ``rdatas`` were obtained.
+        rdatas:
+            Simulation results.
+        parameter_mapping:
+            PEtab parameter mapping to condition-specific
+            simulation parameters.
+    """
+    sllh = {}
+    model_par_ids = amici_model.getParameterIds()
+    for (_, par_map_sim), rdata in zip(parameter_mapping, rdatas):
+        if rdata['status'] != amici.AMICI_SUCCESS \
+                or 'sllh' not in rdata\
+                or rdata['sllh'] is None:
+            return None
+
+        for model_par_id, problem_par_id in par_map_sim.items():
+            if isinstance(problem_par_id, str):
+                model_par_idx  = model_par_ids.index(model_par_id)
+                cur_par_sllh = rdata['sllh'][model_par_idx]
+                try:
+                    sllh[problem_par_id] += cur_par_sllh
+                except KeyError:
+                    sllh[problem_par_id] = cur_par_sllh
+    return sllh
diff --git a/python/amici/sbml_import.py b/python/amici/sbml_import.py
index bf0f931b6a..82d063bbcd 100644
--- a/python/amici/sbml_import.py
+++ b/python/amici/sbml_import.py
@@ -315,7 +315,7 @@ def checkSupport(self):
                                 'are currently not supported!')
 
         if hasattr(self.sbml, 'all_elements_from_plugins') \
-                and len(self.sbml.all_elements_from_plugins) > 0:
+                and self.sbml.all_elements_from_plugins.getSize() > 0:
             raise SBMLException('SBML extensions are currently not supported!')
 
         if len(self.sbml.getListOfEvents()) > 0:
@@ -796,7 +796,7 @@ def processRules(self):
         # rules
         for variable in assignments.keys():
             self.replaceInAllExpressions(
-                sp.sympify(variable, locals=self.local_symbols),
+                sp.Symbol(variable, real=True),
                 assignments[variable]
             )
         for comp, vol in zip(self.compartmentSymbols, self.compartmentVolume):
@@ -910,9 +910,19 @@ def processObservables(self, observables: Dict[str, Dict[str, str]],
                     )
                     observables[observable]['formula'] = repl
 
+            def replace_assignments(formula):
+                """Replace assignment rules in observables"""
+                formula = sp.sympify(formula, locals=self.local_symbols)
+                for s in formula.free_symbols:
+                    r = self.sbml.getAssignmentRuleByVariable(str(s))
+                    if r is not None:
+                        formula = formula.replace(s, sp.sympify(
+                            sbml.formulaToL3String(r.getMath()),
+                             locals=self.local_symbols))
+                return formula
+
             observableValues = sp.Matrix([
-                sp.sympify(observables[observable]['formula'],
-                           locals=self.local_symbols)
+                replace_assignments(observables[observable]['formula'])
                 for observable in observables
             ])
             observableNames = [
@@ -1351,54 +1361,54 @@ def assignmentRules2observables(sbml_model,
 
 def noise_distribution_to_cost_function(
         noise_distribution: str) -> Callable[[str], str]:
-    """
-    Parse cost string to a cost function definition amici can work with.
+    """Parse noise distribution string to a cost function definition amici can
+    work with.
 
     Arguments:
 
     noise_distribution: A code specifying a noise model. Can be any of
     [normal, log-normal, log10-normal, laplace, log-laplace, log10-laplace].
-    @type str
 
     Returns:
 
     A function that takes a strSymbol and then creates a cost function string
-    from it, which can be sympified.
+    (negative log-likelihood) from it, which can be sympified.
 
     Raises:
+        ValueError: in case of invalid ``noise_distribution``
     """
     if noise_distribution in ['normal', 'lin-normal']:
-        llhYString = lambda strSymbol: \
-            f'0.5*log(2*pi*sigma{strSymbol}**2) ' \
-            f'+ 0.5*(({strSymbol} - m{strSymbol}) ' \
-            f'/ sigma{strSymbol})**2'
+        nllh_y_string = lambda str_symbol: \
+            f'0.5*log(2*pi*sigma{str_symbol}**2) ' \
+            f'+ 0.5*(({str_symbol} - m{str_symbol}) ' \
+            f'/ sigma{str_symbol})**2'
     elif noise_distribution == 'log-normal':
-        llhYString = lambda strSymbol: \
-            f'0.5*log(2*pi*sigma{strSymbol}**2*m{strSymbol}**2) ' \
-            f'+ 0.5*((log({strSymbol}) - log(m{strSymbol})) ' \
-            f'/ sigma{strSymbol})**2'
+        nllh_y_string = lambda str_symbol: \
+            f'0.5*log(2*pi*sigma{str_symbol}**2*m{str_symbol}**2) ' \
+            f'+ 0.5*((log({str_symbol}) - log(m{str_symbol})) ' \
+            f'/ sigma{str_symbol})**2'
     elif noise_distribution == 'log10-normal':
-        llhYString = lambda strSymbol: \
-            f'0.5*log(2*pi*sigma{strSymbol}**2*m{strSymbol}**2) ' \
-            f'+ 0.5*((log({strSymbol}, 10) - log(m{strSymbol}, 10)) ' \
-            f'/ sigma{strSymbol})**2'
+        nllh_y_string = lambda str_symbol: \
+            f'0.5*log(2*pi*sigma{str_symbol}**2*m{str_symbol}**2) ' \
+            f'+ 0.5*((log({str_symbol}, 10) - log(m{str_symbol}, 10)) ' \
+            f'/ sigma{str_symbol})**2'
     elif noise_distribution in ['laplace', 'lin-laplace']:
-        llhYString = lambda strSymbol: \
-            f'log(2*sigma{strSymbol}) ' \
-            f'+ Abs({strSymbol} - m{strSymbol}) ' \
-            f'/ sigma{strSymbol}'
+        nllh_y_string = lambda str_symbol: \
+            f'log(2*sigma{str_symbol}) ' \
+            f'+ Abs({str_symbol} - m{str_symbol}) ' \
+            f'/ sigma{str_symbol}'
     elif noise_distribution == 'log-laplace':
-        llhYString = lambda strSymbol: \
-            f'log(2*sigma{strSymbol}*m{strSymbol}) ' \
-            f'+ Abs(log({strSymbol}) - log(m{strSymbol})) ' \
-            f'/ sigma{strSymbol}'
+        nllh_y_string = lambda str_symbol: \
+            f'log(2*sigma{str_symbol}*m{str_symbol}) ' \
+            f'+ Abs(log({str_symbol}) - log(m{str_symbol})) ' \
+            f'/ sigma{str_symbol}'
     elif noise_distribution == 'log10-laplace':
-        llhYString = lambda strSymbol: \
-            f'log(2*sigma{strSymbol}*m{strSymbol}) ' \
-            f'+ Abs(log({strSymbol}, 10) - log(m{strSymbol}, 10)) ' \
-            f'/ sigma{strSymbol}'
+        nllh_y_string = lambda str_symbol: \
+            f'log(2*sigma{str_symbol}*m{str_symbol}) ' \
+            f'+ Abs(log({str_symbol}, 10) - log(m{str_symbol}, 10)) ' \
+            f'/ sigma{str_symbol}'
     else:
         raise ValueError(
-            f"Cost type {cost_code} not reconized.")
+            f"Cost type {noise_distribution} not recognized.")
 
-    return llhYString
+    return nllh_y_string
diff --git a/python/sdist/setup.py b/python/sdist/setup.py
index 67dfec52ce..b634c47895 100755
--- a/python/sdist/setup.py
+++ b/python/sdist/setup.py
@@ -198,7 +198,7 @@ def main():
         setup_requires=['setuptools>=40.6.3'],
         python_requires='>=3.6',
         extras_require={
-            'petab': ['petab>=0.0.1', 'colorama']
+            'petab': ['petab>=0.1.1']
         },
         package_data={
             'amici': ['amici/include/amici/*',
diff --git a/tests/benchmark-models/benchmark_models.yaml b/tests/benchmark-models/benchmark_models.yaml
new file mode 100644
index 0000000000..94e2affbaa
--- /dev/null
+++ b/tests/benchmark-models/benchmark_models.yaml
@@ -0,0 +1,83 @@
+Bachmann_MSB2011:
+  llh: -478.459689232875
+  note: unchecked
+
+Becker_Science2010:
+  llh: -364.118614198023
+
+#Beer_MolBioSystems2014    None
+
+Boehm_JProteomeRes2014:
+  llh: -138.22199693517703
+  note: benchmark collection reference ignores factor 1/2
+
+Borghans_BiophysChem1997:
+  llh: -3471.740659965799
+  note: benchmark collection reference value does not match, but model outputs do. maybe due to D2D data normalization
+
+Brannmark_JBC2010:
+  llh: 283.778227541074
+  note: unchecked
+
+#Bruno_JExpBio2016:    None
+
+Chen_MSB2009:
+  llh: -115700.1192629893
+  note: benchmark collection reference ignores factor 1/2
+
+#Crauste_CellSystems2017:    None
+
+Elowitz_Nature2000:
+  llh: -4249.322017350108
+  note: benchmark collection reference value does not match, but model outputs do. maybe due to D2D data normalization
+
+Fiedler_BMC2016:
+  llh: -117.16780323362
+  note: unchecked
+
+Fujita_SciSignal2010:
+  llh: -3975.060953726485
+  note: benchmark collection reference value does not include condition data7..data12
+
+# Hass_PONE2017    None
+
+Isensee_JCB2018:
+  llh: 7950.84181339651
+  note: unchecked
+
+Lucarelli_CellSystems2018:
+  llh: 91.8885069265334
+  note: unchecked
+
+Merkle_PCB2016:
+  llh: -1388.59682706751
+  note: unchecked
+
+Raia_CancerResearch2011:
+  llh: 690.619495552297
+  note: unchecked
+
+Schwen_PONE2014:
+  llh: -705.4660173266402
+  note: benchmark collection reference value does not match, but model outputs do. maybe due to D2D data normalization
+
+Sneyd_PNAS2002:
+  llh: 319.79177818768756
+  note: benchmark collection reference ignores factor 1/2
+
+Sobotta_Frontiers2017:
+  llh: -1346.75391686389
+  note: unchecked
+
+Swameye_PNAS2003:
+  llh: -142.118024712038
+  note: unchecked
+
+Weber_BMC2015:
+  llh: 592.403584529373
+  note: unchecked
+
+Zheng_PNAS2012:
+  llh: 278.33353271001477
+  note: benchmark collection reference ignores factor 1/2
+
diff --git a/tests/benchmark-models/test_benchmark_collection.sh b/tests/benchmark-models/test_benchmark_collection.sh
new file mode 100755
index 0000000000..aecb6e9121
--- /dev/null
+++ b/tests/benchmark-models/test_benchmark_collection.sh
@@ -0,0 +1,109 @@
+#!/bin/bash
+# Import and run selected benchmark models with nominal parameters and check
+# agreement with reference values
+#
+# Expects environment variable BENCHMARK_COLLECTION to provide path to
+# benchmark collection model directory
+
+# Confirmed to be working
+models="
+Boehm_JProteomeRes2014
+Borghans_BiophysChem1997
+Elowitz_Nature2000
+Schwen_PONE2014
+Chen_MSB2009
+Fujita_SciSignal2010
+Sneyd_PNAS2002
+Zheng_PNAS2012"
+
+# Not matching reference for unclear reasons
+# Lucarelli_CellSystems2018
+# Weber_BMC2015
+#
+# PEtab needs fixing: Bachmann_MSB2011
+#
+# Unsupported:
+#
+# Becker_Science2010: multiple models
+#
+# no reference value:
+# Alkan_SciSignal2018
+# Beer_MolBioSystems2014
+# Blasi_CellSystems2016
+# Crauste_CellSystems2017
+# Hass_PONE2017
+# Korkut_eLIFE2015
+# Perelson_Science1996
+# Bruno_JExpBio2016
+#
+# Timepoint-specific parameter overrides
+# Fiedler_BMC2016
+# Brannmark_JBC2010
+# Isensee_JCB2018
+# Sobotta_Frontiers2017
+#
+# yaml missing:
+# Casaletto_PNAS2019
+#
+# Model missing:
+# Merkle_PCB2016
+#
+# SBML extensions:
+# Parmar_PCB2019
+#
+# Events:
+# Swameye_PNAS2003
+#
+# state-dependent sigmas:
+# Raia_CancerResearch2011
+
+set -e
+
+[[ -n "${BENCHMARK_COLLECTION}" ]] && model_dir="${BENCHMARK_COLLECTION}"
+
+function show_help() {
+  echo "-h: this help; -n: dry run, print commands; -b path_to_models_dir"
+}
+
+OPTIND=1
+while getopts "h?nb:" opt; do
+  case "$opt" in
+  h | \?)
+    show_help
+    exit 0
+    ;;
+  n)
+    dry_run=1
+    ;;
+  b)
+    model_dir=$OPTARG
+    ;;
+  esac
+done
+
+script_path=$(dirname "$BASH_SOURCE")
+script_path=$(cd "$script_path" && pwd)
+
+for model in $models; do
+  yaml="${model_dir}"/"${model}"/"${model}".yaml
+  amici_model_dir=test_bmc/"${model}"
+  mkdir -p "$amici_model_dir"
+  cmd_import="amici_import_petab --verbose -y ${yaml} -o ${amici_model_dir} -n ${model}"
+  cmd_run="$script_path/test_petab_model.py --verbose -y ${yaml} -d ${amici_model_dir} -m ${model} -c"
+
+  printf '=%.0s' {1..40}
+  printf "   %s   " "${model}"
+  printf '=%.0s' {1..40}
+  echo
+
+  if [[ -z "$dry_run" ]]; then
+    $cmd_import && $cmd_run
+  else
+    echo "$cmd_import"
+    echo "$cmd_run"
+  fi
+
+  printf '=%.0s' {1..100}
+  echo
+  echo
+done
diff --git a/tests/benchmark-models/test_petab_model.py b/tests/benchmark-models/test_petab_model.py
new file mode 100755
index 0000000000..41e9cfcc41
--- /dev/null
+++ b/tests/benchmark-models/test_petab_model.py
@@ -0,0 +1,139 @@
+#!/usr/bin/env python3
+
+"""
+Simulate a PEtab problem and compare results to reference values
+"""
+
+import argparse
+import importlib
+import logging
+import os
+import sys
+
+import petab
+import yaml
+from amici.logging import get_logger
+from amici.petab_objective import (simulate_petab, rdatas_to_measurement_df,
+                                   LLH, RDATAS)
+from petab.visualize import plot_petab_problem
+
+logger = get_logger(f"amici.{__name__}", logging.WARNING)
+
+
+def parse_cli_args():
+    """Parse command line arguments
+
+    Returns:
+        Parsed CLI arguments from ``argparse``.
+    """
+
+    parser = argparse.ArgumentParser(
+        description='Simulate PEtab-format model using AMICI.')
+
+    # General options:
+    parser.add_argument('-v', '--verbose', dest='verbose', action='store_true',
+                        help='More verbose output')
+    parser.add_argument('-c', '--check', dest='check', action='store_true',
+                        help='Compare to reference value')
+    parser.add_argument('-p', '--plot', dest='plot', action='store_true',
+                        help='Plot measurement and simulation results')
+
+    # PEtab problem
+    parser.add_argument('-y', '--yaml', dest='yaml_file_name',
+                        required=True,
+                        help='PEtab YAML problem filename')
+
+    # Corresponding AMICI model
+    parser.add_argument('-m', '--model-name', dest='model_name',
+                        help='Name of the AMICI module of the model to '
+                             'simulate.', required=True)
+    parser.add_argument('-d', '--model-dir', dest='model_directory',
+                        help='Directory containing the AMICI module of the '
+                             'model to simulate. Required if model is not '
+                             'in python path.')
+
+    parser.add_argument('-o', '--simulation-file', dest='simulation_file',
+                        help='File to write simulation result to, in PEtab'
+                        'measurement table format.')
+
+    args = parser.parse_args()
+
+    return args
+
+
+def main():
+    """Simulate the model specified on the command line"""
+
+    args = parse_cli_args()
+
+    if args.verbose:
+        logger.setLevel(logging.DEBUG)
+
+    logger.info(f"Simulating '{args.model_name}' "
+                f"({args.model_directory}) using PEtab data from "
+                f"{args.yaml_file_name}")
+
+    # load PEtab files
+    problem = petab.Problem.from_yaml(args.yaml_file_name)
+
+    # load model
+    if args.model_directory:
+        sys.path.insert(0, args.model_directory)
+    model_module = importlib.import_module(args.model_name)
+    amici_model = model_module.getModel()
+
+    res = simulate_petab(
+        petab_problem=problem, amici_model=amici_model,
+        log_level=logging.DEBUG)
+    rdatas = res[RDATAS]
+    llh = res[LLH]
+
+    # create simulation PEtab table
+    sim_df = rdatas_to_measurement_df(rdatas=rdatas, model=amici_model,
+                                      measurement_df=problem.measurement_df)
+    sim_df.rename(columns={petab.MEASUREMENT: petab.SIMULATION}, inplace=True)
+
+    if args.simulation_file:
+        sim_df.to_csv(index=False, sep="\t")
+
+    if args.plot:
+        try:
+            # visualize fit
+            ax = plot_petab_problem(petab_problem=problem, sim_data=sim_df)
+
+            # save figure
+            fig_path = os.path.join(args.model_directory,
+                                    args.model_name + "_vis.png")
+            logger.info(f"Saving figure to {fig_path}")
+            ax.flatten()[0].get_figure().savefig(fig_path, dpi=150)
+
+        except NotImplementedError:
+            pass
+
+    if args.check:
+        references_yaml = os.path.join(os.path.dirname(__file__),
+                                       "benchmark_models.yaml")
+        with open(references_yaml) as f:
+            refs = yaml.full_load(f)
+
+        try:
+            ref_llh = refs[args.model_name]["llh"]
+            logger.info(f"Reference llh: {ref_llh}")
+
+            if abs(ref_llh - llh) < 1e-3:
+                logger.info(f"Computed llh {llh} matches reference "
+                            f"{ref_llh}. Absolute difference is "
+                            f"{ref_llh - llh}.")
+            else:
+                logger.error(f"Computed llh {llh} does not match reference "
+                             f"{ref_llh}. Absolute difference is "
+                             f"{ref_llh - llh}."
+                             f" Relative difference is {llh / ref_llh}")
+                sys.exit(1)
+        except KeyError:
+            logger.error("No reference likelihood found for "
+                         f"{args.model_name} in {references_yaml}")
+
+
+if __name__ == "__main__":
+    main()

From 0ad439ec67dc8361781bd34c3f9ca69061b6ad5c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Fabian=20Fr=C3=B6hlich?= <fabian@schaluck.com>
Date: Sun, 9 Feb 2020 17:32:33 -0500
Subject: [PATCH 19/23] Update README.md: Smaller AMICI banner (#942)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Co-authored-by: Fabian Fröhlich <fabian@schaluck.com>
Co-authored-by: Daniel Weindl <dweindl@users.noreply.github.com>
---
 README.md | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 5d869087e4..c2bfb1d1d1 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,8 @@
-# AMICI - Advanced Multilanguage Interface for CVODES and IDAS
+<img src="./documentation/gfx/banner.png" height="60" align="left">
 
-![AMICI banner](https://raw.githubusercontent.com/ICB-DCM/AMICI/develop/documentation/gfx/banner.png)
+## Advanced Multilanguage Interface for CVODES and IDAS
 
-## About
+## About 
 
 AMICI provides a multi-language (Python, C++, Matlab) interface for the
 [SUNDIALS](https://computing.llnl.gov/projects/sundials/) solvers
@@ -103,7 +103,13 @@ and/or
   Parameter estimation for dynamical systems with discrete events and logical
   operations. Bioinformatics, 33(7), 1049-1056.
   doi:[10.1093/bioinformatics/btw764](https://doi.org/10.1093/bioinformatics/btw764)
+  
+When presenting work that employs AMICI, feel free to use one of the icons in 
+[documentation/gfx/](https://github.com/ICB-DCM/AMICI/tree/master/documentation/gfx), which are available under a [CC0](documentation/gfx/LICENSE.md) license:
 
+<p align="center">
+  <img src="./documentation/gfx/logo_text.png" height="75">
+</p>
 
 ## Status of SBML support in Python-AMICI
 

From 85bac087cef1697b42edae4e07374282518a2f47 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Yannik=20Sch=C3=A4lte?=
 <31767307+yannikschaelte@users.noreply.github.com>
Date: Mon, 10 Feb 2020 07:36:35 +0100
Subject: [PATCH 20/23] add simplified petab import function (#939)

* add simplified petab import function

* adapt docstring format

* add return types; use existing variable name

* Update python/amici/petab_import.py

Co-Authored-By: Daniel Weindl <dweindl@users.noreply.github.com>

Co-authored-by: Daniel Weindl <dweindl@users.noreply.github.com>
---
 python/amici/petab_import.py | 127 +++++++++++++++++++++++++++++++++++
 1 file changed, 127 insertions(+)

diff --git a/python/amici/petab_import.py b/python/amici/petab_import.py
index 24111d8b65..4ef9561424 100644
--- a/python/amici/petab_import.py
+++ b/python/amici/petab_import.py
@@ -7,6 +7,10 @@
 import logging
 import math
 import os
+import time
+import sys
+import shutil
+import importlib
 from typing import List, Dict, Union, Optional, Tuple
 
 import amici
@@ -210,6 +214,129 @@ def constant_species_to_parameters(sbml_model: 'libsbml.Model') -> List[str]:
     return species_to_parameters(transformables, sbml_model)
 
 
+def import_petab_problem(
+        petab_problem: petab.Problem,
+        model_output_dir: str = None,
+        model_name: str = None,
+        force_compile: bool = False,
+        **kwargs) -> amici.Model:
+    """
+    Import model from petab problem.
+
+    Arguments:
+        petab_problem:
+            A petab problem containing all relevant information on the model.
+        model_output_dir:
+            Directory to write the model code to. Will be created if doesn't
+            exist. Defaults to current directory.
+        model_name:
+            Name of the generated model. If model file name was provided,
+            this defaults to the file name without extension, otherwise
+            the SBML model ID will be used.
+        force_compile:
+            Whether to compile the model even if the target folder is not
+            empty, or the model exists already.
+        **kwargs:
+            Additional keyword arguments to be passed to
+            ``amici.sbml_importer.sbml2amici``.
+
+    Returns
+        model:
+            The imported model.
+    """
+    # generate folder and model name if necessary
+    if model_output_dir is None:
+        model_output_dir = _create_model_output_dir_name(petab_problem.sbml_model)
+    if model_name is None:
+        model_name = _create_model_name(model_output_dir)
+
+    # create folder
+    if not os.path.exists(model_output_dir):
+        os.makedirs(model_output_dir)
+
+    # add to path
+    if model_output_dir not in sys.path:
+        sys.path.insert(0, model_output_dir)
+
+    # check if compilation necessary
+    if not _can_import_model(model_name) or force_compile:
+        # check if folder exists
+        if os.listdir(model_output_dir) and not force_compile:
+            raise ValueError(
+                f"Cannot compile to {model_output_dir}: not empty. Please assign a "
+                "different target or set `force_compile`.")
+
+        # remove folder if exists
+        if os.path.exists(model_output_dir):
+            shutil.rmtree(model_output_dir)
+
+        logger.info(f"Compiling model {model_name} to {model_output_dir}.")
+
+        # compile the model
+        import_model(sbml_model=petab_problem.sbml_model,
+                     condition_table=petab_problem.condition_df,
+                     observable_table=petab_problem.observable_df,
+                     model_name=model_name,
+                     model_output_dir=model_output_dir,
+                     **kwargs)
+
+    # load module
+    model_module = importlib.import_module(model_name)
+
+    # import model
+    model = model_module.getModel()
+
+    logger.info(f"Successfully loaded model {model_name} from {model_output_dir}.")
+
+    return model
+
+
+def _create_model_output_dir_name(sbml_model: 'libsbml.Model') -> str:
+    """
+    Find a folder for storing the compiled amici model.
+    If possible, use the sbml model id, otherwise create a random folder.
+    The folder will be located in the `amici_models` subfolder of the current
+    folder.
+    """
+    BASE_DIR = os.path.abspath("amici_models")
+
+    # create base directory
+    if not os.path.exists(BASE_DIR):
+        os.makedirs(BASE_DIR)
+
+    # try sbml model id
+    sbml_model_id = sbml_model.getId()
+    if sbml_model_id:
+        model_output_dir = os.path.join(BASE_DIR, sbml_model_id)
+    else:
+        # create random folder name
+        model_output_dir = tempfile.mkdtemp(dir=BASE_DIR)
+
+    return model_output_dir
+
+
+def _create_model_name(folder: str) -> str:
+    """
+    Create a name for the model.
+    Just re-use the last part of the folder.
+    """
+    return os.path.split(os.path.normpath(folder))[-1]
+
+
+def _can_import_model(model_name: str) -> bool:
+    """
+    Check whether a module of that name can already be imported.
+    """
+    # try to import (in particular checks version)
+    try:
+        importlib.import_module(model_name)
+    except ModuleNotFoundError:
+        return False
+
+    # no need to (re-)compile
+    return True
+
+
 @log_execution_time('Importing PEtab model', logger)
 def import_model(sbml_model: Union[str, 'libsbml.Model'],
                  condition_table: Optional[Union[str, pd.DataFrame]] = None,

From fb1df1afcaeb153b8f5489bc4a549c679b47ab07 Mon Sep 17 00:00:00 2001
From: Daniel Weindl <daniel.weindl@helmholtz-muenchen.de>
Date: Mon, 10 Feb 2020 12:27:04 +0100
Subject: [PATCH 21/23] PEtab: fix parameter mapping errors in presence of
 LocalParameters

---
 python/amici/petab_objective.py | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/python/amici/petab_objective.py b/python/amici/petab_objective.py
index a96659c8c1..5e0b43329c 100644
--- a/python/amici/petab_objective.py
+++ b/python/amici/petab_objective.py
@@ -8,6 +8,7 @@
                     Collection, Iterator)
 
 import amici
+import libsbml
 import numpy as np
 import pandas as pd
 import petab
@@ -87,6 +88,18 @@ def simulate_petab(
         problem_parameters = {t.Index: getattr(t, NOMINAL_VALUE) for t in
                               petab_problem.parameter_df.itertuples()}
 
+    # Because AMICI globalizes all local parameters during model import,
+    # we need to do that here as well to prevent parameter mapping errors
+    # (PEtab does currently not care about SBML LocalParameters)
+    if petab_problem.sbml_document:
+        converter_config = libsbml.SBMLLocalParameterConverter()\
+            .getDefaultProperties()
+        petab_problem.sbml_document.convert(converter_config)
+    else:
+        logger.debug("No petab_problem.sbml_document is set. Cannot convert "
+                     "SBML LocalParameters. If the model contains "
+                     "LocalParameters, parameter mapping will fail.")
+
     # Get parameter mapping
     if parameter_mapping is None:
         parameter_mapping = \

From 54e58bcb337865241251bc22a291d555accfc656 Mon Sep 17 00:00:00 2001
From: Daniel Weindl <dweindl@users.noreply.github.com>
Date: Mon, 10 Feb 2020 21:39:41 +0100
Subject: [PATCH 22/23] Fix PEtab import for performance test model (#946)

* Fix PEtab import for performance test model

* PEtab: handle initial concentrations from condition table

* Performance test:
  * Increase reference time for model import and compilation, as now also sx0 is computed
  * compute forward sensitivities only for 10 parameters (unclear what has been computed there until now)
---
 .../test-benchmark-collection-models.yml      |  1 -
 .github/workflows/test-large-model.yml        | 10 +++---
 python/amici/petab_import.py                  | 17 ++++-----
 python/amici/petab_objective.py               | 35 +++++++++++++++++--
 tests/performance/reference.yml               |  6 ++--
 tests/performance/test.py                     |  6 ++--
 6 files changed, 52 insertions(+), 23 deletions(-)

diff --git a/.github/workflows/test-benchmark-collection-models.yml b/.github/workflows/test-benchmark-collection-models.yml
index 9b141dd251..6f1963fc17 100644
--- a/.github/workflows/test-benchmark-collection-models.yml
+++ b/.github/workflows/test-benchmark-collection-models.yml
@@ -4,7 +4,6 @@ on:
     branches:
       - develop
       - master
-      - dw_misc
 
   pull_request:
     branches:
diff --git a/.github/workflows/test-large-model.yml b/.github/workflows/test-large-model.yml
index 0772a53a04..8409098b95 100644
--- a/.github/workflows/test-large-model.yml
+++ b/.github/workflows/test-large-model.yml
@@ -4,7 +4,7 @@ on:
     branches:
       - develop
       - master
-      - fix_896_perftestfiles
+      - fix_945
 
   pull_request:
     branches:
@@ -52,12 +52,10 @@ jobs:
       run: |
         cd CS_Signalling_ERBB_RAS_AKT \
           && check_time.sh \
-          petab_import amici_import_petab.py -v \
+          petab_import amici_import_petab -v \
             -n 'CS_Signalling_ERBB_RAS_AKT_petab' \
-            -s 'FroehlichKes2018/PEtab/CS_Signalling_ERBB_RAS_AKT_petab.xml' \
-            -c 'FroehlichKes2018/PEtab/conditions_petab.tsv' \
-            -m 'FroehlichKes2018/PEtab/measurements_petab.tsv' \
-            -p 'FroehlichKes2018/PEtab/parameters_petab.tsv' --no-compile
+            -y 'FroehlichKes2018/PEtab/FroehlichKes2018.yaml' \
+            --no-compile
 
     # install model package
     - name: Install test model
diff --git a/python/amici/petab_import.py b/python/amici/petab_import.py
index 4ef9561424..54d297b29f 100644
--- a/python/amici/petab_import.py
+++ b/python/amici/petab_import.py
@@ -4,24 +4,24 @@
 """
 
 import argparse
+import importlib
 import logging
 import math
 import os
-import time
-import sys
 import shutil
-import importlib
+import sys
+import tempfile
 from typing import List, Dict, Union, Optional, Tuple
 
 import amici
 import libsbml
+import numpy as np
 import pandas as pd
 import petab
 import sympy as sp
 from amici.logging import get_logger, log_execution_time
 from petab.C import *
 
-
 logger = get_logger(__name__, logging.WARNING)
 
 
@@ -123,11 +123,12 @@ def get_fixed_parameters(
                                   f"initial assignment for {compartments}")
 
     species = [col for col in condition_df
-               if sbml_model.getSpecies(col) is not None]
+               if not np.issubdtype(condition_df[col].dtype, np.number)
+               and sbml_model.getSpecies(col) is not None]
     if species:
-        raise NotImplementedError("Can't handle species in condition table."
-                                  "Consider creating an initial assignment for"
-                                  f" {species}")
+        raise NotImplementedError(
+            "Can't handle parameterized initial concentrations in condition "
+            f"table. Consider creating an initial assignment for {species}")
 
     return fixed_parameters
 
diff --git a/python/amici/petab_objective.py b/python/amici/petab_objective.py
index 5e0b43329c..8e4958473a 100644
--- a/python/amici/petab_objective.py
+++ b/python/amici/petab_objective.py
@@ -420,9 +420,38 @@ def _get_par(model_par, value):
 
     ##########################################################################
     # initial states
-    # initial states have been set during model import. if they were
-    # overwritten in the PEtab condition table, they would be handled as fixed
-    # model parameters below
+    # Initial states have been set during model import based on the SBML model.
+    # If initial states were overwritten in the PEtab condition table, they are
+    # applied here. We never change amici_model.x0 here (and assume it contains
+    # the original values; we only change ExpData.x0.
+
+    species = [col for col in petab_problem.condition_df
+               if petab_problem.sbml_model.getSpecies(col) is not None]
+    if species:
+        x0 = amici_model.getInitialStates()
+        species_ids = amici_model.getStateIds()
+        for species_id in species:
+            if not np.issubdtype(petab_problem.condition_df[species_id].dtype,
+                                 np.number):
+                raise NotImplementedError(
+                    "Support for parametric overrides for initial states "
+                    "is not yet implemented.")
+
+            species_idx = species_ids.find(species_id)
+
+            if condition[PREEQUILIBRATION_CONDITION_ID]:
+                condition_id = condition[PREEQUILIBRATION_CONDITION_ID]
+            else:
+                condition_id = condition[SIMULATION_CONDITION_ID]
+
+            x0[species_idx] = petab_problem.condition_df.loc(condition_id,
+                                                             species_id)
+
+        edata.x0 = x0
+
+    # TODO: depends on #924: In case of parametric overrides, they would have
+    #  to be handled as fixed model parameters below. These cases are filtered
+    #  out at import stage.
 
     ##########################################################################
     # fixed parameters preequilibration
diff --git a/tests/performance/reference.yml b/tests/performance/reference.yml
index 7a40daaea8..9152736bfa 100644
--- a/tests/performance/reference.yml
+++ b/tests/performance/reference.yml
@@ -1,10 +1,10 @@
 # Reference wall times (seconds) with some buffer
 create_sdist: 25
 install_sdist: 140
-petab_import: 1920 # ^= 32'
-install_model: 420
+petab_import: 2500
+install_model: 470
 forward_simulation: 2
-forward_sensitivities: 6
+forward_sensitivities: 35
 adjoint_sensitivities: 4
 forward_simulation_non_optimal_parameters: 2
 adjoint_sensitivities_non_optimal_parameters: 5
diff --git a/tests/performance/test.py b/tests/performance/test.py
index 535a18f727..1a0bc8cadd 100755
--- a/tests/performance/test.py
+++ b/tests/performance/test.py
@@ -15,11 +15,13 @@ def main():
     edata = amici.ExpData(model)
     edata.setTimepoints([1e8])
     edata.setObservedData([1.0])
+    edata.setObservedDataStdDev([1.0])
 
     if arg == 'forward_simulation':
         solver.setSensitivityMethod(amici.SensitivityMethod_none)
         solver.setSensitivityOrder(amici.SensitivityOrder_none)
     elif arg == 'forward_sensitivities':
+        model.setParameterList(list(range(100)))
         solver.setSensitivityMethod(amici.SensitivityMethod_forward)
         solver.setSensitivityOrder(amici.SensitivityOrder_first)
     elif arg == 'adjoint_sensitivities':
@@ -27,12 +29,12 @@ def main():
         solver.setSensitivityOrder(amici.SensitivityOrder_first)
     elif arg == 'forward_simulation_non_optimal_parameters':
         tmpPar = model.getParameters()
-        model.setParameters([0.1 for iPar in tmpPar])
+        model.setParameters([0.1 for _ in tmpPar])
         solver.setSensitivityMethod(amici.SensitivityMethod_none)
         solver.setSensitivityOrder(amici.SensitivityOrder_none)
     elif arg == 'adjoint_sensitivities_non_optimal_parameters':
         tmpPar = model.getParameters()
-        model.setParameters([0.1 for iPar in tmpPar])
+        model.setParameters([0.1 for _ in tmpPar])
         solver.setSensitivityMethod(amici.SensitivityMethod_adjoint)
         solver.setSensitivityOrder(amici.SensitivityOrder_first)
     else:

From 1fa9433791a8adbdb39c0e2b2f5239f25eaf43ba Mon Sep 17 00:00:00 2001
From: Daniel Weindl <daniel.weindl@helmholtz-muenchen.de>
Date: Mon, 10 Feb 2020 21:41:31 +0100
Subject: [PATCH 23/23] Bump version number (0.10.18)

---
 .travis.yml | 2 +-
 version.txt | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 60b4c0b66f..b26cf57e0f 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -152,7 +152,7 @@ matrix:
         - python -m pip install --upgrade pip
         - pip install --user -U numpy
         - git clone -c core.symlinks=true https://github.com/ICB-DCM/AMICI.git && cd AMICI
-        - if [[ "$TRAVIS_PULL_REQUEST" == "false" ]]; then git checkout -qf $TRAVIS_COMMIT; elif [[ "$TRAVIS_PULL_REQUEST" != "false" ]]; then git fetch origin pull/$TRAVIS_PULL_REQUEST/head:$TRAVIS_BRANCH && git checkout $TRAVIS_BRANCH; fi
+        - if [[ "$TRAVIS_PULL_REQUEST" == "false" ]]; then git checkout -qf $TRAVIS_COMMIT; elif [[ "$TRAVIS_PULL_REQUEST" != "false" ]]; then git fetch --update-head-ok origin pull/$TRAVIS_PULL_REQUEST/head:$TRAVIS_BRANCH && git checkout $TRAVIS_BRANCH; fi
         # run BLAS installation script
         - if [[ "$TRAVIS_OS_NAME" == "windows" ]]; then powershell -File 'C:\Users\travis\build\AMICI\scripts\installOpenBLAS.ps1';export BLAS_LIBS BLAS_CFLAGS; fi
         # define Windows environment variables in BLAS because PowerShell definition didn't do the trick
diff --git a/version.txt b/version.txt
index f00339d71c..3971e7e2d8 100644
--- a/version.txt
+++ b/version.txt
@@ -1 +1 @@
-0.10.17
+0.10.18