diff --git a/bayes_opt/__init__.py b/bayes_opt/__init__.py
index 1a9c88672..b4f2058e2 100644
--- a/bayes_opt/__init__.py
+++ b/bayes_opt/__init__.py
@@ -1,5 +1,11 @@
 from .bayesian_optimization import BayesianOptimization, Events
 from .util import UtilityFunction
-from .observer import ScreenLogger
+from .observer import ScreenLogger, JSONLogger
 
-__all__ = ["BayesianOptimization", "UtilityFunction", "Events", "ScreenLogger"]
+__all__ = [
+    "BayesianOptimization",
+    "UtilityFunction",
+    "Events",
+    "ScreenLogger",
+    "JSONLogger",
+]
diff --git a/bayes_opt/bayesian_optimization.py b/bayes_opt/bayesian_optimization.py
index 7604858c0..c280d106c 100644
--- a/bayes_opt/bayesian_optimization.py
+++ b/bayes_opt/bayesian_optimization.py
@@ -61,7 +61,7 @@ def dispatch(self, event):
 
 
 class BayesianOptimization(Observable):
-    def __init__(self, f, pbounds, random_state=None, verbose=1):
+    def __init__(self, f, pbounds, random_state=None, verbose=2):
         """"""
         self._random_state = ensure_rng(random_state)
 
@@ -96,16 +96,18 @@ def max(self):
     def res(self):
         return self._space.res()
 
-    def register(self, x, target):
+    def register(self, params, target):
         """Expect observation with known target"""
-        self._space.register(x, target)
+        self._space.register(params, target)
+        self.dispatch(Events.OPTMIZATION_STEP)
 
-    def probe(self, x, lazy=True):
+    def probe(self, params, lazy=True):
         """Probe target of x"""
         if lazy:
-            self._queue.add(x)
+            self._queue.add(params)
         else:
-            self._space.probe(x)
+            self._space.probe(params)
+            self.dispatch(Events.OPTMIZATION_STEP)
 
     def suggest(self, utility_function):
         """Most promissing point to probe next"""
@@ -166,9 +168,7 @@ def maximize(self,
                 iteration += 1
 
             self.probe(x_probe, lazy=False)
-            self.dispatch(Events.OPTMIZATION_STEP)
 
-        # Notify about finished optimization
         self.dispatch(Events.OPTMIZATION_END)
 
     def set_bounds(self, new_bounds):
diff --git a/bayes_opt/observer.py b/bayes_opt/observer.py
index a8f2e3f2a..6269162bc 100644
--- a/bayes_opt/observer.py
+++ b/bayes_opt/observer.py
@@ -55,7 +55,7 @@ class ScreenLogger(_Tracker):
     _default_cell_size = 9
     _default_precision = 4
 
-    def __init__(self, verbose=0):
+    def __init__(self, verbose=2):
         self._verbose = verbose
         self._header_length = None
         super(ScreenLogger, self).__init__()
@@ -101,11 +101,11 @@ def _step(self, instance, colour=Colours.black):
         res = instance.res[-1]
         cells = []
 
-        cells.append(self._format_number(self._iterations))
+        cells.append(self._format_number(self._iterations + 1))
         cells.append(self._format_number(res["target"]))
 
-        for val in res["params"].values():
-            cells.append(self._format_number(val))
+        for key in instance.space.keys:
+            cells.append(self._format_number(res["params"][key]))
 
         return "| " + " | ".join(map(colour, cells)) + " |"
 
@@ -120,21 +120,26 @@ def _header(self, instance):
         self._header_length = len(line)
         return line + "\n" + ("-" * self._header_length)
 
+    def _is_new_max(self, instance):
+        if self._previous_max is None:
+            self._previous_max = instance.max["target"]
+        return instance.max["target"] > self._previous_max
+
     def update(self, event, instance):
         if event == Events.OPTMIZATION_START:
-            line = self._header(instance)
+            line = self._header(instance) + "\n"
         elif event == Events.OPTMIZATION_STEP:
-            colour = (
-                Colours.purple if
-                self._previous_max is None or
-                instance.max["target"] > self._previous_max else
-                Colours.black
-            )
-            line = self._step(instance, colour=colour)
+            is_new_max = self._is_new_max(instance)
+            if self._verbose == 1 and not is_new_max:
+                line = ""
+            else:
+                colour = Colours.purple if is_new_max else Colours.black
+                line = self._step(instance, colour=colour) + "\n"
         elif event == Events.OPTMIZATION_END:
-            line = "=" * self._header_length
+            line = "=" * self._header_length + "\n"
 
-        print(line)
+        if self._verbose:
+            print(line, end="")
         self._update_tracker(event, instance)
 
 class JSONLogger(_Tracker):
diff --git a/bayes_opt/target_space.py b/bayes_opt/target_space.py
index a250d9568..7ad4b499b 100644
--- a/bayes_opt/target_space.py
+++ b/bayes_opt/target_space.py
@@ -182,7 +182,7 @@ def probe(self, x):
         x = self._as_array(x)
 
         try:
-            y = self._cache[_hashable(x)]
+            target = self._cache[_hashable(x)]
         except KeyError:
             params = dict(zip(self._keys, x))
             target = self.target_func(**params)
diff --git a/bayes_opt/util.py b/bayes_opt/util.py
index 2bcc589b5..f4c217e10 100644
--- a/bayes_opt/util.py
+++ b/bayes_opt/util.py
@@ -1,5 +1,3 @@
-from __future__ import print_function
-from __future__ import division
 import warnings
 import numpy as np
 from scipy.stats import norm
@@ -129,6 +127,35 @@ def _poi(x, gp, y_max, xi):
         return norm.cdf(z)
 
 
+def load_logs(optimizer, logs):
+    """Load previous ...
+
+    """
+    import json
+
+    if isinstance(logs, str):
+        logs = [logs]
+
+    for log in logs:
+        with open(log, "r") as j:
+            while True:
+                try:
+                    iteration = next(j)
+                except StopIteration:
+                    break
+
+                iteration = json.loads(iteration)
+                try:
+                    optimizer.register(
+                        x=iteration["params"],
+                        target=iteration["target"],
+                    )
+                except KeyError:
+                    pass
+
+    return optimizer
+
+
 def unique_rows(a):
     """
     A function to trim repeated rows that may appear when optimizing.
diff --git a/examples/advanced-tour.ipynb b/examples/advanced-tour.ipynb
new file mode 100644
index 000000000..ed7f85a1a
--- /dev/null
+++ b/examples/advanced-tour.ipynb
@@ -0,0 +1,341 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Advanced tour of the Bayesian Optimization package"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bayes_opt import BayesianOptimization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Suggest-Evaluate-Register Paradigm\n",
+    "\n",
+    "Internally the `maximize` method is simply a wrapper around the methods `suggest`, `probe`, and `register`. If you need more control over your optimization loops the Suggest-Evaluate-Register paradigm should give you that extra flexibility.\n",
+    "\n",
+    "For an example of running the `BayesianOptimization` in a distributed fashion (where the function being optimized is evaluated concurrently in different cores/machines/servers), checkout the `async_optimization.py` script in the examples folder."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Lets start by definying our function, bounds, and instanciating an optimization object.\n",
+    "def black_box_function(x, y):\n",
+    "    return -x ** 2 - (y - 1) ** 2 + 1\n",
+    "\n",
+    "optimizer = BayesianOptimization(\n",
+    "    f=black_box_function,\n",
+    "    pbounds={'x': (-2, 2), 'y': (-3, 3)},\n",
+    "    verbose=2,\n",
+    "    random_state=1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One extra ingredient we will need is an `UtilityFunction` instance. In case it is not clear why, take a look at the literature to understand better how this method works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bayes_opt import UtilityFunction\n",
+    "\n",
+    "utility = UtilityFunction(kind=\"ucb\", kappa=2.5, xi=0.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `suggest` method of our optimizer can be called at any time. What you get back is a suggestion for the next parameter combination the optimizer wants to probe.\n",
+    "\n",
+    "Notice that while the optimizer hasn't observed any points, the suggestions will be random. However they will stop being random and improve in quality the more points are observed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Next point to probe is: {'x': -0.331911981189704, 'y': 1.3219469606529488}\n"
+     ]
+    }
+   ],
+   "source": [
+    "next_point_to_probe = optimizer.suggest(utility)\n",
+    "print(\"Next point to probe is:\", next_point_to_probe)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You are now free to evaluate you function at the suggested point however/whenever you like"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found the target value to be: 0.7861845912690542\n"
+     ]
+    }
+   ],
+   "source": [
+    "target = black_box_function(**next_point_to_probe)\n",
+    "print(\"Found the target value to be:\", target)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Last thing left to do is to tell the optimizer what target value was observed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer.register(\n",
+    "    params=next_point_to_probe,\n",
+    "    target=target,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And that's it. By repeating the steps above you recreate the internals of the `maximize` method. This should give you all the flexibility you need to log progress, hault execution, concurrent evaluations, etc."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.6991696847348962 {'x': 0.35745335256298433, 'y': 1.4160017019275122}\n",
+      "0.99784805556957 {'x': 0.04141972554410957, 'y': 1.0208890106582527}\n",
+      "0.9746135479061905 {'x': -0.15019450712870294, 'y': 0.9468204727157574}\n",
+      "0.9931443154738931 {'x': -0.0648518091817296, 'y': 1.0514774452742501}\n",
+      "0.9970772367740163 {'x': 0.004366164063039053, 'y': 0.9461140107508605}\n",
+      "{'target': 0.99784805556957, 'params': {'x': 0.04141972554410957, 'y': 1.0208890106582527}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "for _ in range(5):\n",
+    "    next_point = optimizer.suggest(utility)\n",
+    "    target = black_box_function(**next_point)\n",
+    "    optimizer.register(params=next_point, target=target)\n",
+    "    \n",
+    "    print(target, next_point)\n",
+    "print(optimizer.max)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dealing with discrete parameters\n",
+    "\n",
+    "**There is not principled way of dealing with discrete parameters using this package.**\n",
+    "\n",
+    "Ok, now that we got that out of the way, how do you do it? You're bound to be in a situation where some of your function parameters may only take on discrete values. Unfortunately the nature of how bayesian optimization with gaussian processes is implement doesn't allow for an easy/intuitive way of dealing with discrete parameters. But that doesn't mean it is impossible."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def func_with_discrete_params(x, y, d):\n",
+    "    assert type(d) == int\n",
+    "    \n",
+    "    return  (x ** (1 / (d + y + 5))) / (1.1 ** (1.2 * x * d / y))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def function_to_be_optimized(x, y, w):\n",
+    "    d = int(w)\n",
+    "    return func_with_discrete_params(x, y, d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = BayesianOptimization(\n",
+    "    f=function_to_be_optimized,\n",
+    "    pbounds={'x': (0, 10), 'y': (.1, 4), 'w': (0, 5)},\n",
+    "    verbose=2,\n",
+    "    random_state=1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|   iter    |  target   |     w     |     x     |     y     |\n",
+      "-------------------------------------------------------------\n",
+      "| \u001b[0m 1       \u001b[0m | \u001b[0m 9.924e-0\u001b[0m | \u001b[0m 2.085   \u001b[0m | \u001b[0m 7.203   \u001b[0m | \u001b[0m 0.1004  \u001b[0m |\n",
+      "| \u001b[95m 2       \u001b[0m | \u001b[95m 0.7368  \u001b[0m | \u001b[95m 1.512   \u001b[0m | \u001b[95m 1.468   \u001b[0m | \u001b[95m 0.4601  \u001b[0m |\n",
+      "| \u001b[95m 3       \u001b[0m | \u001b[95m 1.205   \u001b[0m | \u001b[95m 0.9313  \u001b[0m | \u001b[95m 3.456   \u001b[0m | \u001b[95m 1.647   \u001b[0m |\n",
+      "| \u001b[0m 4       \u001b[0m | \u001b[0m 0.8194  \u001b[0m | \u001b[0m 2.694   \u001b[0m | \u001b[0m 4.192   \u001b[0m | \u001b[0m 2.772   \u001b[0m |\n",
+      "| \u001b[0m 5       \u001b[0m | \u001b[0m 0.01104 \u001b[0m | \u001b[0m 1.022   \u001b[0m | \u001b[0m 8.781   \u001b[0m | \u001b[0m 0.2068  \u001b[0m |\n",
+      "| \u001b[0m 6       \u001b[0m | \u001b[0m 0.0     \u001b[0m | \u001b[0m 0.0     \u001b[0m | \u001b[0m 0.0     \u001b[0m | \u001b[0m 4.0     \u001b[0m |\n",
+      "| \u001b[95m 7       \u001b[0m | \u001b[95m 1.227   \u001b[0m | \u001b[95m 0.0     \u001b[0m | \u001b[95m 6.317   \u001b[0m | \u001b[95m 4.0     \u001b[0m |\n",
+      "| \u001b[0m 8       \u001b[0m | \u001b[0m 0.2822  \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 10.0    \u001b[0m | \u001b[0m 4.0     \u001b[0m |\n",
+      "| \u001b[95m 9       \u001b[0m | \u001b[95m 1.292   \u001b[0m | \u001b[95m 0.0     \u001b[0m | \u001b[95m 10.0    \u001b[0m | \u001b[95m 4.0     \u001b[0m |\n",
+      "| \u001b[0m 10      \u001b[0m | \u001b[0m 0.0     \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 0.0     \u001b[0m | \u001b[0m 4.0     \u001b[0m |\n",
+      "| \u001b[95m 11      \u001b[0m | \u001b[95m 1.307   \u001b[0m | \u001b[95m 0.0     \u001b[0m | \u001b[95m 3.919   \u001b[0m | \u001b[95m 0.1     \u001b[0m |\n",
+      "| \u001b[0m 12      \u001b[0m | \u001b[0m 1.167   \u001b[0m | \u001b[0m 0.0     \u001b[0m | \u001b[0m 4.019   \u001b[0m | \u001b[0m 4.0     \u001b[0m |\n",
+      "| \u001b[0m 13      \u001b[0m | \u001b[0m 1.255   \u001b[0m | \u001b[0m 0.0     \u001b[0m | \u001b[0m 4.898   \u001b[0m | \u001b[0m 2.007   \u001b[0m |\n",
+      "| \u001b[0m 14      \u001b[0m | \u001b[0m 1.193   \u001b[0m | \u001b[0m 0.0     \u001b[0m | \u001b[0m 2.91    \u001b[0m | \u001b[0m 1.062   \u001b[0m |\n",
+      "| \u001b[0m 15      \u001b[0m | \u001b[0m 1.269   \u001b[0m | \u001b[0m 0.0     \u001b[0m | \u001b[0m 8.518   \u001b[0m | \u001b[0m 4.0     \u001b[0m |\n",
+      "| \u001b[0m 16      \u001b[0m | \u001b[0m 0.9458  \u001b[0m | \u001b[0m 1.184   \u001b[0m | \u001b[0m 10.0    \u001b[0m | \u001b[0m 4.0     \u001b[0m |\n",
+      "| \u001b[0m 17      \u001b[0m | \u001b[0m 3.523e-0\u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 2.615   \u001b[0m | \u001b[0m 0.1     \u001b[0m |\n",
+      "| \u001b[0m 18      \u001b[0m | \u001b[0m 1.269   \u001b[0m | \u001b[0m 0.0     \u001b[0m | \u001b[0m 4.155   \u001b[0m | \u001b[0m 0.9822  \u001b[0m |\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
+      "\u001b[0;32m~/Dropbox/Projects/Packages/BayesianOptimization/bayes_opt/bayesian_optimization.py\u001b[0m in \u001b[0;36mmaximize\u001b[0;34m(self, init_points, n_iter, acq, kappa, xi, **gp_params)\u001b[0m\n\u001b[1;32m    164\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 165\u001b[0;31m                 \u001b[0mx_probe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_queue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    166\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Dropbox/Projects/Packages/BayesianOptimization/bayes_opt/bayesian_optimization.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     25\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Queue is empty, no more objects to retrieve.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     27\u001b[0m         \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_queue\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mStopIteration\u001b[0m: Queue is empty, no more objects to retrieve.",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-88-332b7e51f4b3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaximize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgp_params\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"alpha\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m1e-3\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/Dropbox/Projects/Packages/BayesianOptimization/bayes_opt/bayesian_optimization.py\u001b[0m in \u001b[0;36mmaximize\u001b[0;34m(self, init_points, n_iter, acq, kappa, xi, **gp_params)\u001b[0m\n\u001b[1;32m    165\u001b[0m                 \u001b[0mx_probe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_queue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 167\u001b[0;31m                 \u001b[0mx_probe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuggest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mutil\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    168\u001b[0m                 \u001b[0miteration\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Dropbox/Projects/Packages/BayesianOptimization/bayes_opt/bayesian_optimization.py\u001b[0m in \u001b[0;36msuggest\u001b[0;34m(self, utility_function)\u001b[0m\n\u001b[1;32m    119\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcatch_warnings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m             \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimplefilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_space\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_space\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m         \u001b[0;31m# Finding argmax of the acquisition function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/venvs/bo/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    245\u001b[0m                     optima.append(\n\u001b[1;32m    246\u001b[0m                         self._constrained_optimization(obj_func, theta_initial,\n\u001b[0;32m--> 247\u001b[0;31m                                                        bounds))\n\u001b[0m\u001b[1;32m    248\u001b[0m             \u001b[0;31m# Select result from run with minimal (negative) log-marginal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m             \u001b[0;31m# likelihood\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/venvs/bo/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py\u001b[0m in \u001b[0;36m_constrained_optimization\u001b[0;34m(self, obj_func, initial_theta, bounds)\u001b[0m\n\u001b[1;32m    474\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizer\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"fmin_l_bfgs_b\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    475\u001b[0m             \u001b[0mtheta_opt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc_min\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvergence_dict\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 476\u001b[0;31m                 \u001b[0mfmin_l_bfgs_b\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitial_theta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbounds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbounds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    477\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mconvergence_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"warnflag\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    478\u001b[0m                 warnings.warn(\"fmin_l_bfgs_b terminated abnormally with the \"\n",
+      "\u001b[0;32m~/venvs/bo/lib/python3.7/site-packages/scipy/optimize/lbfgsb.py\u001b[0m in \u001b[0;36mfmin_l_bfgs_b\u001b[0;34m(func, x0, fprime, args, approx_grad, bounds, m, factr, pgtol, epsilon, iprint, maxfun, maxiter, disp, callback, maxls)\u001b[0m\n\u001b[1;32m    197\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m     res = _minimize_lbfgsb(fun, x0, args=args, jac=jac, bounds=bounds,\n\u001b[0;32m--> 199\u001b[0;31m                            **opts)\n\u001b[0m\u001b[1;32m    200\u001b[0m     d = {'grad': res['jac'],\n\u001b[1;32m    201\u001b[0m          \u001b[0;34m'task'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'message'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/venvs/bo/lib/python3.7/site-packages/scipy/optimize/lbfgsb.py\u001b[0m in \u001b[0;36m_minimize_lbfgsb\u001b[0;34m(fun, x0, args, jac, bounds, disp, maxcor, ftol, gtol, eps, maxfun, maxiter, iprint, callback, maxls, **unknown_options)\u001b[0m\n\u001b[1;32m    333\u001b[0m             \u001b[0;31m# until the completion of the current minimization iteration.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    334\u001b[0m             \u001b[0;31m# Overwrite f and g:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 335\u001b[0;31m             \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc_and_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    336\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mtask_str\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mb'NEW_X'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    337\u001b[0m             \u001b[0;31m# new iteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/venvs/bo/lib/python3.7/site-packages/scipy/optimize/lbfgsb.py\u001b[0m in \u001b[0;36mfunc_and_grad\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    283\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    284\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mfunc_and_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 285\u001b[0;31m             \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    286\u001b[0m             \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjac\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    287\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/venvs/bo/lib/python3.7/site-packages/scipy/optimize/optimize.py\u001b[0m in \u001b[0;36mfunction_wrapper\u001b[0;34m(*wrapper_args)\u001b[0m\n\u001b[1;32m    291\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfunction_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mwrapper_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    292\u001b[0m         \u001b[0mncalls\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 293\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrapper_args\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    294\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    295\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mncalls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunction_wrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/venvs/bo/lib/python3.7/site-packages/scipy/optimize/optimize.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x, *args)\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m         \u001b[0mfg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjac\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/venvs/bo/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py\u001b[0m in \u001b[0;36mobj_func\u001b[0;34m(theta, eval_gradient)\u001b[0m\n\u001b[1;32m    222\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0meval_gradient\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m                     lml, grad = self.log_marginal_likelihood(\n\u001b[0;32m--> 224\u001b[0;31m                         theta, eval_gradient=True)\n\u001b[0m\u001b[1;32m    225\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mlml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/venvs/bo/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py\u001b[0m in \u001b[0;36mlog_marginal_likelihood\u001b[0;34m(self, theta, eval_gradient)\u001b[0m\n\u001b[1;32m    429\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_marginal_likelihood_value_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 431\u001b[0;31m         \u001b[0mkernel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclone_with_theta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtheta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    432\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0meval_gradient\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/venvs/bo/lib/python3.7/site-packages/sklearn/gaussian_process/kernels.py\u001b[0m in \u001b[0;36mclone_with_theta\u001b[0;34m(self, theta)\u001b[0m\n\u001b[1;32m    208\u001b[0m         \"\"\"\n\u001b[1;32m    209\u001b[0m         \u001b[0mcloned\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 210\u001b[0;31m         \u001b[0mcloned\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtheta\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtheta\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    211\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcloned\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/venvs/bo/lib/python3.7/site-packages/sklearn/gaussian_process/kernels.py\u001b[0m in \u001b[0;36mtheta\u001b[0;34m(self, theta)\u001b[0m\n\u001b[1;32m    276\u001b[0m                              \u001b[0;34m\" Should be %d; given are %d\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    277\u001b[0m                              % (i, len(theta)))\n\u001b[0;32m--> 278\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    279\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    280\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/venvs/bo/lib/python3.7/site-packages/sklearn/gaussian_process/kernels.py\u001b[0m in \u001b[0;36mset_params\u001b[0;34m(self, **params)\u001b[0m\n\u001b[1;32m    176\u001b[0m             \u001b[0;31m# Simple optimisation to gain speed (inspect is slow)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m         \u001b[0mvalid_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miteritems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0msplit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'__'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/venvs/bo/lib/python3.7/site-packages/sklearn/gaussian_process/kernels.py\u001b[0m in \u001b[0;36mget_params\u001b[0;34m(self, deep)\u001b[0m\n\u001b[1;32m    143\u001b[0m         \u001b[0mcls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m         \u001b[0minit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'deprecated_original'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 145\u001b[0;31m         \u001b[0minit_sign\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msignature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    146\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvarargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    147\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mparameter\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minit_sign\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/python/3.7.1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/inspect.py\u001b[0m in \u001b[0;36msignature\u001b[0;34m(obj, follow_wrapped)\u001b[0m\n\u001b[1;32m   3073\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msignature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfollow_wrapped\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3074\u001b[0m     \u001b[0;34m\"\"\"Get a signature object for the passed callable.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3075\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mSignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfollow_wrapped\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfollow_wrapped\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3076\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3077\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/python/3.7.1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/inspect.py\u001b[0m in \u001b[0;36mfrom_callable\u001b[0;34m(cls, obj, follow_wrapped)\u001b[0m\n\u001b[1;32m   2823\u001b[0m         \u001b[0;34m\"\"\"Constructs Signature for the given callable object.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2824\u001b[0m         return _signature_from_callable(obj, sigcls=cls,\n\u001b[0;32m-> 2825\u001b[0;31m                                         follow_wrapper_chains=follow_wrapped)\n\u001b[0m\u001b[1;32m   2826\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2827\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/python/3.7.1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/inspect.py\u001b[0m in \u001b[0;36m_signature_from_callable\u001b[0;34m(obj, follow_wrapper_chains, skip_bound_arg, sigcls)\u001b[0m\n\u001b[1;32m   2280\u001b[0m         \u001b[0;31m# If it's a pure Python function, or an object that is duck type\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2281\u001b[0m         \u001b[0;31m# of a Python function (Cython functions, for instance), then:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2282\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_signature_from_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msigcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2283\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2284\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_signature_is_builtin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/python/3.7.1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/inspect.py\u001b[0m in \u001b[0;36m_signature_from_function\u001b[0;34m(cls, func)\u001b[0m\n\u001b[1;32m   2130\u001b[0m     \u001b[0mfunc_code\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__code__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2131\u001b[0m     \u001b[0mpos_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc_code\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mco_argcount\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2132\u001b[0;31m     \u001b[0marg_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc_code\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mco_varnames\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2133\u001b[0m     \u001b[0mpositional\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg_names\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mpos_count\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2134\u001b[0m     \u001b[0mkeyword_only_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc_code\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mco_kwonlyargcount\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "optimizer.maximize(gp_params={\"alpha\": 1e-3})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Changing the utility function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tuning the underlying Gaussian Process"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Observers Continued"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/async_optimization.py b/examples/async_optimization.py
new file mode 100644
index 000000000..570d60fa6
--- /dev/null
+++ b/examples/async_optimization.py
@@ -0,0 +1,136 @@
+import time
+import random
+
+from bayes_opt import BayesianOptimization
+from bayes_opt.util import UtilityFunction, Colours
+
+import asyncio
+import threading
+
+try:
+    import json
+    import tornado.ioloop
+    import tornado.httpserver
+    from tornado.web import RequestHandler
+    import requests
+except ImportError:
+    raise ImportError(
+        "In order to run this example you must have the libraries: " +
+        "`tornado` and `requests` installed."
+    )
+
+
+def black_box_function(x, y):
+    """Function with unknown internals we wish to maximize.
+
+    This is just serving as an example, however, for all intents and
+    purposes think of the internals of this function, i.e.: the process
+    which generates its outputs values, as unknown.
+    """
+    time.sleep(random.randint(1, 7))
+    return -x ** 2 - (y - 1) ** 2 + 1
+
+
+class BayesianOptimizationHandler(RequestHandler):
+    """Basic functionality for NLP handlers."""
+    _bo = BayesianOptimization(
+        f=black_box_function,
+        pbounds={"x": (-4, 4), "y": (-3, 3)}
+    )
+    _uf = UtilityFunction(kind="ucb", kappa=3, xi=1)
+
+    def post(self):
+        """Deal with incoming requests."""
+        body = tornado.escape.json_decode(self.request.body)
+
+        try:
+            self._bo.register(
+                x=body["params"],
+                target=body["target"],
+            )
+            print("BO has registered: {} points.".format(len(self._bo.space)), end="\n\n")
+        except KeyError:
+            pass
+        finally:
+            suggested_params = self._bo.suggest(self._uf)
+
+        self.write(json.dumps(suggested_params))
+
+
+def run_optimization_app():
+    asyncio.set_event_loop(asyncio.new_event_loop())
+    handlers = [
+        (r"/bayesian_optimization", BayesianOptimizationHandler),
+    ]
+    server = tornado.httpserver.HTTPServer(
+        tornado.web.Application(handlers)
+    )
+    server.listen(9009)
+    tornado.ioloop.IOLoop.instance().start()
+
+
+def run_optimizer():
+    global optimizers_config
+    config = optimizers_config.pop()
+    name = config["name"]
+    colour = config["colour"]
+
+    register_data = {}
+    max_target = None
+    for _ in range(10):
+        status = name + " wants to register: {}.\n".format(register_data)
+
+        resp = requests.post(
+            url="http://localhost:9009/bayesian_optimization",
+            json=register_data,
+        ).json()
+        target = black_box_function(**resp)
+
+        register_data = {
+            "params": resp,
+            "target": target,
+        }
+
+        if max_target is None or target > max_target:
+            max_target = target
+
+        status += name + " got {} as target.\n".format(target)
+        status += name + " will to register next: {}.\n".format(register_data)
+        print(colour(status), end="\n")
+
+    global results
+    results.append((name, max_target))
+    print(colour(name + " is done!"), end="\n\n")
+
+
+if __name__ == "__main__":
+    ioloop = tornado.ioloop.IOLoop.instance()
+    optimizers_config = [
+        {"name": "optimizer 1", "colour": Colours.red},
+        {"name": "optimizer 2", "colour": Colours.green},
+        {"name": "optimizer 3", "colour": Colours.blue},
+    ]
+
+    app_thread = threading.Thread(target=run_optimization_app)
+    app_thread.daemon = True
+    app_thread.start()
+
+    targets = (
+        run_optimizer,
+        run_optimizer,
+        run_optimizer
+    )
+    optimizer_threads = []
+    for target in targets:
+        optimizer_threads.append(threading.Thread(target=target))
+        optimizer_threads[-1].daemon = True
+        optimizer_threads[-1].start()
+
+    results = []
+    for optimizer_thread in optimizer_threads:
+        optimizer_thread.join()
+
+    for result in results:
+        print(result[0], "found a maximum value of: {}".format(result[1]))
+
+    ioloop.stop()
diff --git a/examples/basic-tour.ipynb b/examples/basic-tour.ipynb
new file mode 100644
index 000000000..0148b29c2
--- /dev/null
+++ b/examples/basic-tour.ipynb
@@ -0,0 +1,494 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Basic tour of the Bayesian Optimization package\n",
+    "\n",
+    "This is a constrained global optimization package built upon bayesian inference and gaussian process, that attempts to find the maximum value of an unknown function in as few iterations as possible. This technique is particularly suited for optimization of high cost functions, situations where the balance between exploration and exploitation is important.\n",
+    "\n",
+    "Bayesian optimization works by constructing a posterior distribution of functions (gaussian process) that best describes the function you want to optimize. As the number of observations grows, the posterior distribution improves, and the algorithm becomes more certain of which regions in parameter space are worth exploring and which are not, as seen in the picture below.\n",
+    "\n",
+    "As you iterate over and over, the algorithm balances its needs of exploration and exploitation taking into account what it knows about the target function. At each step a Gaussian Process is fitted to the known samples (points previously explored), and the posterior distribution, combined with a exploration strategy (such as UCB (Upper Confidence Bound), or EI (Expected Improvement)), are used to determine the next point that should be explored (see the gif below).\n",
+    "\n",
+    "This process is designed to minimize the number of steps required to find a combination of parameters that are close to the optimal combination. To do so, this method uses a proxy optimization problem (finding the maximum of the acquisition function) that, albeit still a hard problem, is cheaper (in the computational sense) and common tools can be employed. Therefore Bayesian Optimization is most adequate for situations where sampling the function to be optimized is a very expensive endeavor. See the references for a proper discussion of this method."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Specifying the function to be optimized\n",
+    "\n",
+    "This is a function optimization package, therefore the first and most important ingreedient is, of course, the function to be optimized.\n",
+    "\n",
+    "**DISCLAIMER:** We know exactly how the output of the function below depends on its parameter. Obviously this is just an example, and you shouldn't expect to know it in a real scenario. However, it should be clear that you don't need to. All you need in order to use this package (and more generally this technique) is a function f that takes a known set of parameters and outputs a real number."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def black_box_function(x, y):\n",
+    "    \"\"\"Function with unknown internals we wish to maximize.\n",
+    "\n",
+    "    This is just serving as an example, for all intents and\n",
+    "    purposes think of the internals of this function, i.e.: the process\n",
+    "    which generates its output values, as unknown.\n",
+    "    \"\"\"\n",
+    "    return -x ** 2 - (y - 1) ** 2 + 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Getting Started\n",
+    "\n",
+    "All we need to get started is to instanciate a `BayesianOptimization` object specifying a function to be optimized `f`, and its parameters with their corresponding bounds, `pbounds`. This is a constrained optimization technique, so you must specify the minimum and maximum values that can be probed for each parameter in order for it to work"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bayes_opt import BayesianOptimization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Bounded region of parameter space\n",
+    "pbounds = {'x': (2, 4), 'y': (-3, 3)}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = BayesianOptimization(\n",
+    "    f=black_box_function,\n",
+    "    pbounds=pbounds,\n",
+    "    verbose=2, # verbose = 1 prints only when a maximum is observed, verbose = 0 is silent\n",
+    "    random_state=1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The BayesianOptimization object will work all of the box without much tuning needed. The main method you should be aware of is `maximize`, which does exactly what you think it does.\n",
+    "\n",
+    "There are many parameters you can pass to maximize, nonetheless, the most important ones are:\n",
+    "- `n_iter`: How many steps of bayesian optimization you want to perform. The more steps the more likely to find a good maximum you are.\n",
+    "- `init_points`: How many steps of **random** exploration you want to perform. Random exploration can help by diversifying the exploration space."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|   iter    |  target   |     x     |     y     |\n",
+      "-------------------------------------------------\n",
+      "| \u001b[0m 1       \u001b[0m | \u001b[0m-7.135   \u001b[0m | \u001b[0m 2.834   \u001b[0m | \u001b[0m 1.322   \u001b[0m |\n",
+      "| \u001b[0m 2       \u001b[0m | \u001b[0m-7.78    \u001b[0m | \u001b[0m 2.0     \u001b[0m | \u001b[0m-1.186   \u001b[0m |\n",
+      "| \u001b[0m 3       \u001b[0m | \u001b[0m-19.0    \u001b[0m | \u001b[0m 4.0     \u001b[0m | \u001b[0m 3.0     \u001b[0m |\n",
+      "| \u001b[0m 4       \u001b[0m | \u001b[0m-16.3    \u001b[0m | \u001b[0m 2.378   \u001b[0m | \u001b[0m-2.413   \u001b[0m |\n",
+      "| \u001b[95m 5       \u001b[0m | \u001b[95m-4.441   \u001b[0m | \u001b[95m 2.105   \u001b[0m | \u001b[95m-0.005822\u001b[0m |\n",
+      "=================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "optimizer.maximize(\n",
+    "    init_points=2,\n",
+    "    n_iter=3,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The best combination of parameters and target value found can be accessed via the property `bo.max`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'target': -4.441293113411222, 'params': {'x': 2.104665051994087, 'y': -0.005822117636089974}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(optimizer.max)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "While the list of all parameters probed and their corresponding target values is available via the property `bo.res`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration 0: \n",
+      "\t{'target': -7.135455292718879, 'params': {'x': 2.8340440094051482, 'y': 1.3219469606529488}}\n",
+      "Iteration 1: \n",
+      "\t{'target': -7.779531005607566, 'params': {'x': 2.0002287496346898, 'y': -1.1860045642089614}}\n",
+      "Iteration 2: \n",
+      "\t{'target': -19.0, 'params': {'x': 4.0, 'y': 3.0}}\n",
+      "Iteration 3: \n",
+      "\t{'target': -16.29839645063864, 'params': {'x': 2.3776144540856503, 'y': -2.412527795983739}}\n",
+      "Iteration 4: \n",
+      "\t{'target': -4.441293113411222, 'params': {'x': 2.104665051994087, 'y': -0.005822117636089974}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i, res in enumerate(optimizer.res):\n",
+    "    print(\"Iteration {}: \\n\\t{}\".format(i, res))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1 Changing bounds\n",
+    "\n",
+    "During the optimization process you may realize the bounds chosen for some parameters are not adequate. For these situations you can invoke the method `set_bounds` to alter them. You can pass any combination of **existing** parameters and their associated new bounds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer.set_bounds(new_bounds={\"x\": (-2, 3)})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|   iter    |  target   |     x     |     y     |\n",
+      "-------------------------------------------------\n",
+      "| \u001b[0m 6       \u001b[0m | \u001b[0m-5.145   \u001b[0m | \u001b[0m 2.115   \u001b[0m | \u001b[0m-0.2923  \u001b[0m |\n",
+      "| \u001b[0m 7       \u001b[0m | \u001b[0m-5.379   \u001b[0m | \u001b[0m 2.337   \u001b[0m | \u001b[0m 0.04125 \u001b[0m |\n",
+      "| \u001b[95m 8       \u001b[0m | \u001b[95m-3.581   \u001b[0m | \u001b[95m 1.874   \u001b[0m | \u001b[95m-0.03426 \u001b[0m |\n",
+      "| \u001b[95m 9       \u001b[0m | \u001b[95m-2.624   \u001b[0m | \u001b[95m 1.702   \u001b[0m | \u001b[95m 0.1472  \u001b[0m |\n",
+      "| \u001b[95m 10      \u001b[0m | \u001b[95m-1.762   \u001b[0m | \u001b[95m 1.442   \u001b[0m | \u001b[95m 0.1735  \u001b[0m |\n",
+      "=================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "optimizer.maximize(\n",
+    "    init_points=0,\n",
+    "    n_iter=5,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Guiding the optimization\n",
+    "\n",
+    "It is often the case that we have an idea of regions of the parameter space where the maximum of our function might lie. For these situations the `BayesianOptimization` object allows the user to specify specific points to be probed. By default these will be explored lazily (`lazy=True`), meaning only the next time you call `maximize` that these points will be evaluated. This probing process happens before the gaussian process takes over.\n",
+    "\n",
+    "Parameters can be passed as dictionaries such as below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer.probe(\n",
+    "    x={\"x\": 0.5, \"y\": 0.7},\n",
+    "    lazy=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or as an iterable. Beware that the order has to be alphabetical. You can usee `bo.space.keys` for guidance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer.probe(\n",
+    "    x=[-0.3, 0.1],\n",
+    "    lazy=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|   iter    |  target   |     x     |     y     |\n",
+      "-------------------------------------------------\n",
+      "| \u001b[95m 11      \u001b[0m | \u001b[95m 0.66    \u001b[0m | \u001b[95m 0.5     \u001b[0m | \u001b[95m 0.7     \u001b[0m |\n",
+      "| \u001b[0m 12      \u001b[0m | \u001b[0m 0.1     \u001b[0m | \u001b[0m-0.3     \u001b[0m | \u001b[0m 0.1     \u001b[0m |\n",
+      "=================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "optimizer.maximize(init_points=0, n_iter=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Saving, loading and restarting\n",
+    "\n",
+    "By default you can follow the progress of your optimization by setting `verbose>0` when instanciating the `BayesianOptimization` object. If you need more control over logging/alerting you will need to use an observer. For more information about observers checkout the advanced tour notebook. Here we will only see how to use the native `JSONLogger` object to save to and load progress from files.\n",
+    "\n",
+    "### 4.1 Saving progress"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bayes_opt.observer import JSONLogger\n",
+    "from bayes_opt.event import Events"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The observer paradigm works by:\n",
+    "1. Instantiating an observer object.\n",
+    "2. Tying the observer object to a particular event fired by an optimizer.\n",
+    "\n",
+    "`BayesianOptimization` object fire a number of internal events during optimization, in particular, everytime it probes the function and obtains a new parameter-target combination it will fire an `Events.OPTIMIZATION_STEP` event, which our logger will listen to.\n",
+    "\n",
+    "**Caveat:** The logger will not look back at previously probed points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger = JSONLogger(path=\"./logs.json\")\n",
+    "optimizer.subscribe(Events.OPTMIZATION_STEP, logger)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|   iter    |  target   |     x     |     y     |\n",
+      "-------------------------------------------------\n",
+      "| \u001b[0m 13      \u001b[0m | \u001b[0m-12.48   \u001b[0m | \u001b[0m-1.266   \u001b[0m | \u001b[0m-2.446   \u001b[0m |\n",
+      "| \u001b[0m 14      \u001b[0m | \u001b[0m-3.854   \u001b[0m | \u001b[0m-1.069   \u001b[0m | \u001b[0m-0.9266  \u001b[0m |\n",
+      "| \u001b[0m 15      \u001b[0m | \u001b[0m 0.3932  \u001b[0m | \u001b[0m 0.3099  \u001b[0m | \u001b[0m 0.2853  \u001b[0m |\n",
+      "| \u001b[95m 16      \u001b[0m | \u001b[95m 0.8768  \u001b[0m | \u001b[95m 0.02198 \u001b[0m | \u001b[95m 0.6497  \u001b[0m |\n",
+      "| \u001b[95m 17      \u001b[0m | \u001b[95m 0.9446  \u001b[0m | \u001b[95m 0.198   \u001b[0m | \u001b[95m 0.8727  \u001b[0m |\n",
+      "=================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "optimizer.maximize(\n",
+    "    init_points=2,\n",
+    "    n_iter=3,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.2 Loading progress\n",
+    "\n",
+    "Naturally, if you stored progress you will be able to load that onto a new instance of `BayesianOptimization`. The easiest way to do say is by invoking the `load_logs` function, from the `util` submodule. )\n",
+    "\n",
+    "ps: In the advanced tour you will encounter another way, even more general, or loading values onto an instance of `BayesianOptimization`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bayes_opt.util import load_logs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "new_optimizer = BayesianOptimization(\n",
+    "    f=black_box_function,\n",
+    "    pbounds={\"x\": (-2, 2), \"y\": (-2, 2)},\n",
+    "    verbose=2,\n",
+    "    random_state=7,\n",
+    ")\n",
+    "print(len(new_optimizer.space))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load_logs(new_optimizer, logs=[\"./logs.json\"]);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New optimizer is now aware of 5 points.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"New optimizer is now aware of {} points.\".format(len(new_optimizer.space)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|   iter    |  target   |     x     |     y     |\n",
+      "-------------------------------------------------\n",
+      "| \u001b[0m 1       \u001b[0m | \u001b[0m 0.6131  \u001b[0m | \u001b[0m 0.5571  \u001b[0m | \u001b[0m 0.7233  \u001b[0m |\n",
+      "| \u001b[0m 2       \u001b[0m | \u001b[0m 0.8609  \u001b[0m | \u001b[0m-0.3295  \u001b[0m | \u001b[0m 1.175   \u001b[0m |\n",
+      "| \u001b[0m 3       \u001b[0m | \u001b[0m 0.3762  \u001b[0m | \u001b[0m 0.2407  \u001b[0m | \u001b[0m 1.752   \u001b[0m |\n",
+      "| \u001b[0m 4       \u001b[0m | \u001b[0m-0.7841  \u001b[0m | \u001b[0m-0.897   \u001b[0m | \u001b[0m 1.99    \u001b[0m |\n",
+      "| \u001b[0m 5       \u001b[0m | \u001b[0m 0.1402  \u001b[0m | \u001b[0m-0.8733  \u001b[0m | \u001b[0m 0.6883  \u001b[0m |\n",
+      "| \u001b[0m 6       \u001b[0m | \u001b[0m-1.798   \u001b[0m | \u001b[0m 1.545   \u001b[0m | \u001b[0m 1.642   \u001b[0m |\n",
+      "| \u001b[0m 7       \u001b[0m | \u001b[0m 0.9332  \u001b[0m | \u001b[0m 0.0556  \u001b[0m | \u001b[0m 1.252   \u001b[0m |\n",
+      "| \u001b[0m 8       \u001b[0m | \u001b[0m-5.369   \u001b[0m | \u001b[0m 2.0     \u001b[0m | \u001b[0m-0.5392  \u001b[0m |\n",
+      "| \u001b[95m 9       \u001b[0m | \u001b[95m 0.97    \u001b[0m | \u001b[95m-0.1587  \u001b[0m | \u001b[95m 0.9304  \u001b[0m |\n",
+      "| \u001b[0m 10      \u001b[0m | \u001b[0m 0.7718  \u001b[0m | \u001b[0m 0.4293  \u001b[0m | \u001b[0m 1.21    \u001b[0m |\n",
+      "=================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "new_optimizer.maximize(\n",
+    "    init_points=0,\n",
+    "    n_iter=10,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Next Steps\n",
+    "\n",
+    "This tour should be enough to cover most usage scenarios of this package. If, however, you feel like you need to know more, please checkout the `advanced-tour` notebook. There you will be able to find other, more advanced features of this package that could be what you're looking for. Also, browse the examples folder for implementation tips and ideas."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/exploitation vs exploration.ipynb b/examples/exploitation vs exploration.ipynb
index d93641307..0e14def00 100644
--- a/examples/exploitation vs exploration.ipynb	
+++ b/examples/exploitation vs exploration.ipynb	
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -18,10 +18,7 @@
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "from bayes_opt import BayesianOptimization, Observer, Events\n",
-    "\n",
-    "# use sklearn's default parameters for theta and random_start\n",
-    "gp_params = {\"alpha\": 1e-5, \"n_restarts_optimizer\": 2}"
+    "from bayes_opt import BayesianOptimization"
    ]
   },
   {
@@ -33,12 +30,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -52,9 +49,11 @@
    "source": [
     "np.random.seed(42)\n",
     "xs = np.linspace(-2, 10, 10000)\n",
-    "f = np.exp(-(xs - 2)**2) + np.exp(-(xs - 6)**2/10) + 1/ (xs**2 + 1)\n",
     "\n",
-    "plt.plot(f)\n",
+    "def f(x):\n",
+    "    return np.exp(-(x - 2) ** 2) + np.exp(-(x - 6) ** 2 / 10) + 1/ (x ** 2 + 1)\n",
+    "\n",
+    "plt.plot(xs, f(xs))\n",
     "plt.show()"
    ]
   },
@@ -67,23 +66,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
     "def plot_bo(f, bo):\n",
-    "    xs = [x[\"x\"] for x in bo.res[\"all\"][\"params\"]]\n",
-    "    ys = bo.res[\"all\"][\"values\"]\n",
+    "    x = np.linspace(-2, 10, 10000)\n",
+    "    xs = [res[\"params\"] for res in bo.res]\n",
+    "    ys = [res[\"target\"] for res in bo.res]\n",
     "\n",
-    "    mean, sigma = bo.gp.predict(np.arange(len(f)).reshape(-1, 1), return_std=True)\n",
+    "    mean, sigma = bo._gp.predict(x.reshape(-1, 1), return_std=True)\n",
     "    \n",
     "    plt.figure(figsize=(16, 9))\n",
-    "    plt.plot(f)\n",
-    "    plt.plot(np.arange(len(f)), mean)\n",
-    "    plt.fill_between(np.arange(len(f)), mean+sigma, mean-sigma, alpha=0.1)\n",
-    "    plt.scatter(bo.X.flatten(), bo.Y, c=\"red\", s=50, zorder=10)\n",
-    "    plt.xlim(0, len(f))\n",
-    "    plt.ylim(f.min()-0.1*(f.max()-f.min()), f.max()+0.1*(f.max()-f.min()))\n",
+    "    plt.plot(x, f(x))\n",
+    "    plt.plot(x, mean)\n",
+    "    plt.fill_between(x, mean+sigma, mean-sigma, alpha=0.1)\n",
+    "    plt.scatter(bo.space.x.flatten(), bo.space.target, c=\"red\", s=50, zorder=10)\n",
     "    plt.show()"
    ]
   },
@@ -105,45 +103,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initialization completed\n"
-     ]
-    },
-    {
-     "ename": "TypeError",
-     "evalue": "'BayesianOptimization' object is not subscriptable",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-10-d4d486ba80af>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mbo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEvents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFIT_STEP_DONE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobserver\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mbo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaximize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minit_points\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0macq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ucb\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkappa\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mgp_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0mbo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaximize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minit_points\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0macq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ucb\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkappa\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mgp_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/BayesianOptimization/bayes_opt/bayesian_optimization.py\u001b[0m in \u001b[0;36mmaximize\u001b[0;34m(self, init_points, n_iter, acq, kappa, xi, **gp_params)\u001b[0m\n\u001b[1;32m    313\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    314\u001b[0m             \u001b[0;31m# Notify about finished iteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 315\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEvents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFIT_STEP_DONE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    316\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    317\u001b[0m         \u001b[0;31m# Print a final report if verbose active.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/BayesianOptimization/bayes_opt/observer.py\u001b[0m in \u001b[0;36mdispatch\u001b[0;34m(self, event)\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0msubscriber\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_subscribers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/BayesianOptimization/bayes_opt/observer.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, event, instance)\u001b[0m\n\u001b[1;32m      9\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Initialization completed\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mevent\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mEvents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFIT_STEP_DONE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Optimization step finished, current max: \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minstance\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'max'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mevent\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mEvents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFIT_DONE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Optimization finished, maximum value at: \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minstance\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'max'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: 'BayesianOptimization' object is not subscriptable"
-     ]
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x648 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
+    "bo = BayesianOptimization(\n",
+    "    f=f,\n",
+    "    pbounds={\"x\": (-2, 10)},\n",
+    "    verbose=0,\n",
+    "    random_state=987234,\n",
+    ")\n",
     "\n",
-    "observer = Observer()\n",
-    "\n",
-    "bo = BayesianOptimization(f=lambda x: f[int(x)],\n",
-    "                          pbounds={\"x\": (0, len(f)-1)},\n",
-    "                          verbose=0)\n",
-    "\n",
-    "bo.register(Events.INIT_DONE, observer)\n",
-    "bo.register(Events.FIT_DONE, observer)\n",
-    "bo.register(Events.FIT_STEP_DONE, observer)\n",
-    "\n",
-    "bo.maximize(init_points=2, n_iter=3, acq=\"ucb\", kappa=1, **gp_params)\n",
-    "bo.maximize(init_points=2, n_iter=25, acq=\"ucb\", kappa=1, **gp_params)\n",
+    "bo.maximize(n_iter=10, acq=\"ucb\", kappa=0.1)\n",
     "\n",
     "plot_bo(f, bo)"
    ]
@@ -159,15 +143,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x648 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "bo = BayesianOptimization(f=lambda x: f[int(x)],\n",
-    "                          pbounds={\"x\": (0, len(f)-1)},\n",
-    "                          verbose=0)\n",
+    "bo = BayesianOptimization(\n",
+    "    f=f,\n",
+    "    pbounds={\"x\": (-2, 10)},\n",
+    "    verbose=0,\n",
+    "    random_state=987234,\n",
+    ")\n",
     "\n",
-    "bo.maximize(init_points=2, n_iter=25, acq=\"ucb\", kappa=10, **gp_params)\n",
+    "bo.maximize(n_iter=10, acq=\"ucb\", kappa=10)\n",
     "\n",
     "plot_bo(f, bo)"
    ]
@@ -190,36 +190,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 45,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/fmfnogueira/venvs3/general/lib/python3.5/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
-      "  if self._edgecolors == str('face'):\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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KPRv2pdIfom7vBbzwQphUKl3s8kRERERKmgKriJSUXC5HY2OOt+17OHu3kwmF/MUuaYtV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x6HSy+FmprsaJguFhchf2hYwPj/3s+MRS9yaM+hLLn/a8768hvO2+BxY4Fbggfx3Gvv8oc/bHq9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\nTkSuQmCC\n",
+      "image/png": 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RERHpHxSUiki/1vVEMLLrT3hNO7gtdQbnzcg2BfI9Nx+31iuyATYsnz6C13YeZG/1pXj1G/H2re+0zlpIZpTCKyIiIoVPQamI9FvWWpLdUndvI2liPBddxIJJlcDASd1t43suy6ePAOCBYAHWuMQ23NVtXdeOxCIiIiKFaGDt1ERkUEmkw04Njsgk8TfczcPBXJacMhHPdbLNgZyBkbrbxvccJlaVMHlYCfdvSZGaeHY2KO2SrpsKuqQ2i4iIiBQgBaUi0m8lupyS+tsew00e4PbMGXygrevuADslhezXZMim8L78zgH2T7wIt/EdvD0vdlvb9XskIiIiUmgG3m5NRAaFILSkuow98TfcTZMzhDeLTmfmuPLsYwMwKDXGEPUclk8fgQUezMzFulFiG/7Qba2CUhERESl0A2+3JiKDQreRJ5kEfu3DPJCZx9Jpo3GMwXUMnjswX+Z8z2Xy8BImVBbz4JZWkpOWZ4NS2zlQ18xSERERKXQDc7cmIgNet9Tdt5/ASce5LzOfZdOyTYAG4ilpm7YxN+dMG86L2w9wYPLFuPE9RHY+122tTktFRESkkA3cHZuIDFjdZpMC/qb7iDulvBadxZwJQ7OPDaBRMF25jsFzDGdNHU4QWh4P52K9opxdeBNpnZSKiIhI4VJQKiL9TiLT5eQvSOFvfpBHgrksOWk0npud5RkdwCelAH7EZcbYIVQUR3i8Nk5y8rnENt4DYabTutBaUppZKiIiIgVqYO/YRGTAsdZ2S0eNbn8aJ9XI3emOqbsD95S0je85OMZw5tThrKmto3nqJTitdUS3r+62tlsNroiIiEiBUFAqIv1KMhN2HcdJbNO9JJxiXvBmM7+6AhjY9aRtIm5bUDqMeDLgOXcuYaQEf9M93dYmMwG26zdOREREpAAM/F2biAwoya71kUEaf/MDPB6ezoIpo/E9F8PgCEohm6K8oLoS33N4YksTqckriW1+AMLOJ6PWZgN6ERERkUIzOHZtIjIghKEl2aWeNLrjWZxEA39IzWfZtOFA9gTRGJOPW+xzvucQi7gsqK7k6U37SdRciNNal7MLb7eAXkRERKQAKCgVkX4jmQnpmoDqb7qPlImx1pnN4pqq7GORwfPS5nsOBjjrpOHsaUywvnQR1vWJbb6v21ql8IqIiEghGjw7NxHp97rN2wwD/M338RRzmDV5NMVRDxgcTY7aGGOIuA5n1FRhgCe2tpCcdA7+pvvpWnxrUQqviIiIFB4FpSLSLwShJRV0DqgiO5/DbdnPncnDXXddx+A6gyN1t40fcagq9Tl1bDlPbdpPcurFuM278Pa81G2tUnhFRESk0CgoFZF+oWstKWS77qZNlKeYw9Ipw4DB0+Coo6ib/ZrPOmkYG/Y0sWP42VjHUwqviIiI9AuDb/cmIv1SousJnw3xN93HGjOHUyaOZkhRBBhcqbttPNfBdbLzSgGe3J4mNX4p/qb7lMIrIiIiBe+YQakx5tfGmL3GmNePcP0TxphXjTGvGWOeNcbM6vnbFJHBLAgt6a6pu7tfwI3v4fbEXJZNz6buGpMdkTIYRT2HSVXFjB1axDNb9pOcehHega14+9/stlYpvCIiIlJIjmf3di1w/lGubwXOttaeBvw/4Jc9cF8iIu26NTgC/E33EhiPP4anc9bUQ6m77uA7JW3je9kxOGdMqWLdtgYOTvwAFpM9Le0iGSiFV0RERArHMYNSa+1TQP1Rrj9rrW049Ne1wLgeujcRESBHUGotsY338idnNjXjx1BV6gODaxRMV1E3OxpmSc0wkpmQF/ZHSI9dhJ+jrtRaujWNEhEREcmXnt7BfQF4oIc/p4gMYpkgJBN2PtXz3n0Zt2kHtyXmcs604e2PtzX8GYzaRsPMmTAU33N4ZvN+ElMvJLL/TdyG2m7ru9XoioiIiORJj+3gjDHLyAal3z7Kmi8ZY9YZY9bt27evp55aRAawRI6mPLFN9xIYl0eCuZxzaBRMxHVwBtkomK78iEMs4jJ/UiXPbqkjUZOtvPC3PNhtba5uxiIiIiL50CNBqTFmJnAN8CFrbd2R1llrf2mtnWetnTd8+PAjLRMRaZcrddffeC8vuzMZO3oMo8pjwOAcBdNV20nxkpoqdh5oZVumivTwGfhbHuq21lpIqQuviIiIFIAT3sUZYyYAdwCfstZuPPFbEhHJSgchQdfU3X3r8Q5u49bWLqm7CkrxXAfHGJZMqQLg2S11JGvOJ7LreUxr9/cLdVoqIiIiheB4RsLcBKwBphljdhhjvmCMudIYc+WhJd8FqoCfG2NeNsas68X7FZFB5Ehdd0McHg7msexQ6q5zqJ5Ssim8o8uLmDyshGc27ydZcx7Ghvi1j3Zbq7pSERERKQTesRZYaz92jOv/C/hfPXZHIiKHdAuarCW28R7WR06lomw0E6qKAZ2SdhR1HVoJWDKlit8//w4Hy89kaMko/C0PkZixqtPa0GbnvyqgFxERkXzSTkREClI6CAm7zNJ06zbgNWzmlpbDDY5A9aQd+d7h0TCZ0LLu7QMka84j+vYfIZPotj6pulIRERHJM+3kRKQg5UrdjW26D4vhwWAey6Zn60kNCko7ahsNM2tcOSW+yzNb9pOsOR8n3UJ0++pu65M5vs8iIiIifUk7OREpSLnqHf1N97Ahegp+xRimDC8FsqNgjBnco2C6inoOnuuwsLqKZzfXkRy3hDBSgl/bvQtvJrTdmkmJiIiI9CUFpSJScHKm7jZsIbL/TW5tyXbdbQtEVU/aXdv3ZElNFfuak2yqT5OatAx/y8Nguwf76sIrIiIi+aTdnIgUnCN13QW4PzO/vesuKHU3l+zpcTYoBbKnpTXn48b34L37arf1SXXhFRERkTzSbk5ECk6u1N3YxnvZHJ1OpmwMp4wZAmRHwXjqHJuT77pUlfpMH1WWrSutXoE1Dv6WB7utTQch1iqFV0RERPJDuzkRKSg5U3cPbCOy91Vub53LsmnDcQ6l7voRvYQdSdv3ZklNFa/vPMhBykiPWYi/pXtdqUVdeEVERCR/tKMTkYKSO3X3PgDuyczXKJjjFD10grxochWhhT9tqydZcx6R/W/gHNzebb2CUhEREckX7ehEpKDk7rp7L29Hp9IcG8us8eVAdhRMVKm7R+Q4Bs8xzBg7hFLfY21tNigFcp6WqtmRiIiI5It2dCJSMHKl7jqNO4jueZE7EnM5e9pwPCf7shX1NArmWKKeg+c4zJ9UwdraOjJDq8lUnpRzNIy1kNJpqYiIiOSBglIRKRi5Undjm7Opu3en53POtOHtj2sUzLG1fY8W11SxtynJ1v1xkpPPJbpjLSbV3G19KlBQKiIiIn1PuzoRKRi5U3fvY6c/mb3RccyfVHn4cc/ty1vrl6KugyFbVwpkU3irV2LCNNG3n+y2PpnjTQERERGR3qagVEQKQs7U3eY9RHY+zx+S8zhzynAih2pIXcfgOkrdPRZjDFHPYeSQGNXDSlhTW0d6zHxCfwh+7SPd1mdCSxBqNIyIiIj0LQWlIlIQcnbd3Xw/BssdyXmdUnfVdff4taXwLppcycvbD5AIHVITzyG67TGw3U+mVVcqIiIifU07OxEpCLlSd2Ob7uVdfyLvuBNYXFPV/rhSd49fx9EwqSDkxe0NJCefixvfi7f3tW7r1YVXRERE+pqCUhHJu1ypu6ZlH5Eda7gnNY/FNVXEItlA1BiIuErdPV6e6+AYw5wJQ/E9hzVb6khOWo7F4Nc+2m19KhNirVJ4RUREpO8oKBWRvMvddfcBjA25PTGPZdNGtD/uu65GwbxHUc/B91xOn1DBc7X12OJhpEfNwd/ava7Uoi68IiIi0rcUlIpI3iVz1DH6m+5jf3Qcm8xElk4Z1v64RsG8d36HutK361vYdaCV1ORziex5CSe+t9t61ZWKiIhIX9LuTkTyKh2E3Tq+mtZ6otuf5v5gPvOrqyiNee3X1OTovetYVwqwtraOZPXK7LVtj3dbn+tNAhEREZHeot2diORVzq67Wx7C2IBbW+ayfPrh1N2I6+BoFMx75jgGzzFMrCpmdHmMtbX1ZEacRlAyMmddaaDRMCIiItKHFJSKSF7lOpWLbbqXA9FRvMFkzp56eBSMUnffv6jnYIxhYXUlf9pWTya0pKpXEH37CQjS3dYrhVdERET6inZ4IpI3OVN3k41Etz/FQ+ECTp9YQXlxpP2aUnffv7aAfnFNFS2pgNd2HiRZfS5OqonIzue6rddoGBEREekr2uGJSN7kbHC05SFMkOL38bmduu46xhBx9ZL1fkVdBwPMm1iJ6xjW1NaRmngW1ongb9VoGBEREckf7fBEJG9yjoLZeDcHoyN52U7hnGlK3e0pxhiinkNpzOO0seWsra3HRktJjVucMyi1QDpQUCoiIiK9T7s8EcmL3Km7TUTffoJH7UJmja+gqtRvv6bU3RPXnsI7uYoNe5qoa06SmrwSr34T7oFt3dYrhVdERET6gnZ5IpIXOVN3a7Opu79rPp1lHU5JDQpKe0LbaJiFkysBeG5rPcnqc7PXjpDCKyIiItLbtMsTkbzIOQpm4700RYfzkp3Csi6jYIzRKJgT5bkOjjFMG1VGRXGE52rrCSomkxk6OedomExoCTUaRkRERHqZglIR6XM5U3dTzfjbHucxFnHKmKGMHBJrv+ZH9FLVU6JeNjBdWF3F2to6QmtJTl5JdMezkI53W58KdFoqIiIivUs7PRHpczlPSWsfxgRJbmia0+mUFMD33L66tQGvLQ16UU0lB1rTbNjTRKp6JSZI4m9/utv6ZFpBqYiIiPQuBaUi0udy1pNuvJfmyDBesCexvMMoGM8xuI5Sd3tKe11pdRUAa2vrSI1bTBgpIZojhTcZqNmRiIiI9C4FpSLSp3Kn7sbxtz7GH91FTB05hLEVRe3X/IhOSXuS4xg8x1BZEmXaqDLWbKkDN0pq4tn4Wx+DLrNJrc3+NxMRERHpLQpKRaRP5UrdjW59FBMkuP7gHJZN65q6q5epntZxNMzrOxtpTmRIVq/Ebd6Ft//NbuvVhVdERER6k3Z7ItKnEjlqFGMb7yEeqWKdndapntQxhoirl6me1haULppcSWAtf9pWT6p6RfaaRsOIiIhIH9NuT0T6TDoICbukh5KO4299lCfcRUwePoTqYSXtl9R1t3dEXQcDnDa2nBLfZU1tHWHpKNIjTsum8HaRDkJs1/9uIiIiIj1EOz4R6TM5u+5ufRyTaeX6xjmsPEWpu33BGEPUc/Bch/mTKllbW4e1lmT1CiK7/oRJHOi03qLRMCIiItJ7tOMTkT6TO3X3buKRSp4Pp7Py5JHtjxsOd4qVntexrvTdxiRb98ezo2FsQPTtJ7qtVwqviIiI9Bbt+ESkT6Qy3VN3TaoZf8vDPOYsZtrocsZXFrdf8z0XYzQKpre0BfyLJreNhqknPep0wlhFzhReBaUiIiLSWxSUikifSGRypO5ufhATJLiucV6nU1JQPWlv81wHxxhGlceoHlbCmto6cFySk5YdGg3TOQjNhJYwVF2piIiI9Dzt+kSkT+SqJ41tuJOD0dG8aKd2C0qVutv7OqbwvrS9gdZUQKp6JU5rHd6el7utV12piIiI9Abt+kSk1yUzAV2bt5rWOqJvP8EDLOG0cRWMKo+1X4u6Do6j1N3e1tZIalFNJenA8uL2BpKTlmGNg59jNExSKbzSDwWhJZEOiCczNCXSNCbSNCXSNCczJNKBukuLiBQAL983ICIDX+4GR/diwgzXNc3jwoVK3c2HttPo2eOH4nsOa2vrOGPKNNKj5+JvfYz4km91Wq+6UukvUpmQRCYgmc4xhioHA0Rch6jnEIu4uHpTTESkT2nnJyK9ylpLMlfq7lt3si82iQ1MYMXJXUfBuH11e4Oa4xgiroPvucydWJGtKwVS1SuIvPsyTnxvp/WhtWSUwisFLJEOqI+naGhJ0ZoKjisghcNjj5qTGfY3J6mPZz9eJ6giIn1DQamI9KpkJqTrts5p2kVk51r+ECzm9AmVVJX67dcirqNTij7Usa70nfpWdjS0kKxemb227fFu61VXKoUoE4Q0xFMcbE2T7oH/R9NBSGMizb7mJM3JjJp8iYj0MgWlIu9BEFqSmYBEOvtPp1OjpiYD7xmLAAAgAElEQVS45hr49rezfzY15e9GC0juBkd/wGC5vnk+K0/pkrrr6WWpL7WPhqk5PBomM/xUgpKR+LXd60qVwiuFJp7MUB9P9cobJtZmP/9+BaciIr1KNaUix2CtpSUV0JoOCHJsSIyB4ufXUnLZJZgwhHgcSkrgG9+A+++HpUvzcNeFIQxtziAm9tYdvFN0MrvSo1k+vXPqbiyi1N2+FPUcjIHxFUWMHVrE2to6PjJ3HKnqFfgb74EgDW6kfb2CUikUYWhpTKT7pAGXJRuctqQylPoeRRHNURYR6UkKSkWOIpEOaEykO3WODcOQza8+S8vr91N6YAOxeAOzf/Q8JtXhRDAez/554YWwaxeUlvbtjReIXKm7bsMWIntf5WbzGc6YMozyosMBj+cYpe7mQdR1SNqQRZMruf+1PaQyIcnqcyl6/UYiu/5EevyS9rWWbGAa1Ym25FEQWhpaUjnfKOxN1kJTIkNLKqDU9/QmmohID9GuQiQHay0HW9McbO0QkFpL7TO30fzTMzjz8Q9z7ru/pjpTy/g338V0C70OCUO4+eY+u+9C05ordfeN27A43NK6gAtPG93pmq8NXl60NZZaXFNFazrg1R0HSE08C+tEco6GUV2p5FMmCKmLJ/s8IO0oCLO/IxriKTX/EhHpAQpKRbqw1nKgJd2pFjLVuI9dV69i8XNfpci28Mz0/8vOL76K+/WXKJl4GU7qCJuSeBz72uuDstY0CG33hiM2pOiNW1hfNIdk0QiWHKpjbBPT6VtetJ16zp1YgecY1tTWYaOlpMYtyh2UKoVX8iQdhNS3pLrNPc6XVBBSH0/RlEirU6+IyAlQ+q5IB9ZaGlo6d2+M71hPyW1XMCOo45GxVzL9sr9liu/jOoZS38M/ZVq2hrQtZbcjF+x//xSiMUxLy6CqNc3V4CjyzrO4TTv4TXAp584cScQ9HIR6jsFzFZTmg3sobbo46jF7/FDWbqnnL5ZDqnolZU/+PU7jO4RDxrevzwQh1lrV1EmfygQhDQUUkLaxQEsqIJEOKYsppVdE5P3QDlCkg67jBJJbn2P4LR/ECZI8ufQGZl7xj0R9nxLfo6okSiziYq64ApzcP0o2ACcTZgNSyAauTU3ZWtPm5r74kvImV+pu0Ru3kHJLuS89lwtOHdXpmlJ386vttHRRTRWb9zWztynRPhrG3/pYp7VtMx1F+koYZt8wLLSAtKPwUNlHfTzVPUtE3dlFRI5KQanIIc3JTKcujundr1Nx18dpsCW8dv5tzFy4HAOUF0Uo9b3Dp0RlZdmTz7Ky7EkoZP/0fUwslvvJBnitaSoTdqv3Mqk4/qZ7eDJyBiMqK5gxZkin60rdzS+/w7xSyI6GCSpqyJRPxK99pNt6pfBKX7HWcqA1TVjIEWkH6UMpvQdb09nXwdWrYexY+PrX4d/+Lfvn2LHZxzsIQ0smCNv/UTqwiAwmSt8VAZKZgHgy0/5307Sb0lsupyX0eH35dcydMTMbkBZH2pvCdLJ0abbL7s03w+bNMGUKrF8PP/lJ7ieMx7PrBqhEpvspqb/5Ppx0C//TvJALzhzVKfVTqbv5F3UdDFAzvIThpT5rt9RxyawxpKpXUPT6TZBuhUhR+3oFpdJXGlsz3U8e+4FEOiBVf4BhF1yIae5wMnqo1MNeeCGNW94mU1RMENqc7fKMgYjjEPEcfM/pVPIgIjKQKCiVQS881EWxXZAic/OnKc00c9esX3HBnNMBGFJ0hIC0TWkpfOELh/9+zTVHrDW1xcWYKVN66ksoKNbanPWksfU3U++PZV1iGv+3S+quarDyzxhDxHVIBSGLaip5YsM+MmF2NEzxy78mumMNqerl7eszoSUMLY5G+Egvakllcr7JVRDCDE5rPaa1HifRgNPagEk0EKZbicdbaWltofSh5wjTCXK9wgWpJFv//q/ZfvGluMUVxCpGU1lRwfAyvz34tDabKp8KQuLJbP13UcSlKOLqZ09EBhQFpTLoNSUyneqU0g9+h3GNr/Lfw/6WD6/I1tS9r3l0q1ZlmxrlYDMJGi84j/L3e9MFLJkJu9V9OY07iL7zDNe5q1gwqYoxQ4s6XVdQWhiiXjYoXTy5inte2c0buxqZOW4x1ivC3/pIp6AUspvlmKP/dtI70kFIcyJz7IW9KQxwD27D2/cmXt1buAffxm3cgdv4Dk7TLozNHTBXtP3LGwlIpnOu8ZIpZr96PbNH3Nr+WIMtZZetYp8znKbYaFrLp2BGTKdk/GnUTBjP0OIozckM8WSGoqhLSdQ7HJw2NWWzdTZtgqlTs7+Dysp68JshItJ7FJTKoJbMBJ3ehXe2PsG4Ddfye3MB517+5xhj8D2HEv99/Ki01ZpeeGG2hjQeh5ISMmGAs8rl7Zv/N1P//DaK3s/nLmCtqdwNjgyW37Ys4iuzx3S6FnEdXL3jXxB8z6E5CfMnVeIYWLOljpnjakhNWEp062PZY5sOadepINQbCtIr2mZF92lVpbU4TTuI7nyeyK51RPa8gLd/AyZIZC9jiPsj2GmGsyU1iU3p03nXVtBgy8j4QykdOpziocMZOqScqqFlVA4p5ST3Tqa8/F3ctmZ3HYRFRdQt/woHViwkjDeQadwDB3cQbd7J1MQeKpLrKX73HngXeA322qFsdmvYN3QWjJvPuFPPZNzIYZTEIpQ8vwZz0UWdftcMlk7vIjIwmHwV0s+bN8+uW7cuL88tAtlNT1081d6QxySb8K9eyv6E4eWL7mHpyeMxBoaV+CeWJtXc3LnWdNUq7rvhn7hoz1W8UvMVpl3xzwNmYx+Elv3Nyc4PhgHDfrWAt9Ij+ETy/+Oev1ja3ukVoCzmURwdWIF5f7avKUloLV/87ToygeU3n5tP0SvXMuSxb7P/s6sJKqe2r3Udw7BSP493KwPVwdZ0zjKAnmYSB4lufxJ/62NE334St3k3AGGkhMTIOWyP1LCudRQP7qtkXXwECXwqS6LMGlfOaePKmTqijJrhJVTl+DkwQCzZQlnNJEyObru2rIz41u0k/KJujeGyCyxO8y4SO16nacfrhHvWU9HwGmMz2wEIrGGDU8Pu4nmc/ffX47W2dv8cZWXZfgelpSf0fRIReb+MMS9Ya+cda512gjJotaSCThsB8+h3KU2+y8/H/ZTPn5ydyTgkFjnxup2utabA8s/9E4//+0aWb7mKN9fMo2bJn3UK1PqrXGNgom8/gdu0g6vSH+bCuaO7fZ2xo9XpSp/zIw6tqYBFk6u4+qlaGuIpnOoV2WtbH6OlQ1AahJYgtDrplh6VSAe9GpA68b34G+8htvEeIruex9iA0C8nNfFsGkYt5JlUDbdsH8JztQfJhJYS32VhdRXfrKlizvihjKsoOuaM3qjrUBbz8IbEcmbM4DiY+++ntGoopWRTlZOZkFQmPNzUyRjCsrFETx5L1cnntX/ud1sbOLhpLQc3rmbInjUse+p6nEyOgBSwYUjqdzeS+eznO506G8AxBmOyby55jtHcYRHJKwWlMiiFoe3Ubdfb8zIVG27iOnshl1z8ISAbLPXWCWaR7zHl879k01XLGP34X7Fv0mxGjp3c7zvQ5kzdffW3xCOVPJSYy/VdUnd9z1GzjgITdR1aCVg8uYpfPlXL89vqOW/GeDJV04jWPkLL3Cs7rU9lQoqiemNBekYYWpp6o4403Ups0z3E3ryN6PanMTYkXTWd+IK/IDFxOU/FJ3Lf+n2sfnw/yUzIqCEJLp8/nqVThjFrXPlxvzYboCwW6fwzkas7+6pVnU4vI+6hzrp+NosnHVgyYUgmtASBJbSW0GavUVRB+cwLKJ95AQaI7/o/lKX/O/f9xOOk39rQ6ffdkbhOttmZf6jTr4JUEelLCkplUGpOZQ6/a2wtzoPfps4O4cD8bzCs1McApbHe/fGYMHIYT5/3C8Y8eBmJmz7Hga88SGVpcb8N0pKZoNscQadpN37tI/zOvYRTxlVRPayk0/WBkrY8kPhedjTM9NFlDC2KsGZLHefNGEWyeiXFL/4Sk2zC+oebp6SCkKKcvUVF3rumZKZH55G6DVsoeuU6itbfjJM8QKZ8IvEFf0li2mW8G6vm7pd3cdddO3m3cT0VxREunjma82aM4rRx5TjvMSiLuA7lRZHcmQM5MmaOxBhD1DNEj3eU/OzZR+z0TgRs7W/Zeqtl5NlfpHhE9RE/TTbzIXtKbQDfcymKugMii0dECt8xX2mMMb82xuw1xrx+hOvGGPNTY8xmY8yrxpjTe/42RXpOEFoSHU70/Ddvo6L+ZX4R+RQfPuNkAEp8r09SEpcsXML9E79FTeur7L37HzqPpulncp6Srr8JYwOujp/JpXPGdrqW3fRos1No2kbDOMawcHIla2vrCK0lWb0SE6aJbn+q03rNK5WeksqEPZa2G9n1J4be9UmG/WYJxS//itTEs6j/6B3Uff45/lT9Vb79VJpL/usZ/uepWiZWlvCvf3Ya9/7FUr51/nRmjR/6ngPSEt+jsiSan1T2VavAyf1amnFjbJp1Mgu2/5qJ1y+m7uo/4+BbT9CtRXoXluy86YaWFPXxFMlCHcsjIgPG8RwFXQv8DPjtEa5fAEw99M9C4KpDf4oUpHjHU9J0K5E/fo+Xw8lMXP4FfM/FdQzFfZSO6DqGD1zxNR76jzWs3Hw12zacj3fKWZTFIn3y/D0lCC3JrsFJGFD02g2sj51OgxnH8ukjOl32I67SwwpU22iYRZOreGj9u2zY08TJY+YT+kPwtz5KcupF7WtDa8kEYb9PPZf8stbSlDjxN+Wi25+iZO1PiO54ljBWSfPiv6Z15qcJioezpraO63/3Ii9uP0BZzOOKBeO5bM5YxlcWv+/nMwbKjzXDurcdodM7jkPmD3czbu4i1m15i/1PX82iuruovH8VtY9Np3X+nzNs3kfgGGOd0kHIgZYQ3wsoix3hJFhE5AQdMyi11j5ljJl0lCUfAn5rs2181xpjhhpjRltrd/fQPYr0mK6npLFXrqMkuZdrS77Gt04ZDWRnkvZlsFReHGHkR3/C7huWU3LfV2kZv5qIW9GvUltzNjja9hhu005+nv4wly4Y2+3rKepHX99g0zYaZtHkKgzw7JY6Th5dTWriOUccDaOgVE5Eazogk6sD7XHy9r5O6dPfw3/7SYKSUTSd/Y+0nvYpwkgxT2zYxzWrn2fz3maGl/l8bcVUPjR7zPsb9dXBUdN1+9oR6lZjpaU4mZBJU6YzsebH7Kz/O1Y/eg2z3rmBGav/kl1rf0jjom9SMX8VmKP/DCczIanmZPeaWRGRHtATRXNjgXc6/H3Hoce6BaXGmC8BXwKYMGFCDzy1yHvT0uGU1KTi+Gv/g9XBDOad/cH2DoT5CAZPnTyOe+f8K5e89EXevONbmI/9HM8x/WKjb63Nmbpb/NI1NEaG8WhyHjef3jl113WM6pQKmHcofbeyJMqMsUN4ZvN+vrC0mmT1CmIb78bb9zqZEae1r09nLETzeMPSr4Whpfl9NjdymnZR+sy/EnvjVmxsKE3nfI+WmZ/FulHW1tbziyfX89aeJiZWFvPdi0/hAzNGZhsKnaBYxGVIrG/fwDymI9StRj2HqhKfAy0pRlRWMOLyv6a59S+57eEbmLn5Kqav/jrvPPefxJd8i4rTL+v0hlNXFmhMpEllQoYUFdjXLyL9Wp/uCq21v7TWzrPWzhs+fHhfPrUIYdg5eIq9eA2xVAO/L/00K07Oppb2dnOjI/FchxXnX8o9pR9mxu7baXrt/uzg+DzNEX4vkpmwW2MSd/9b+G8/ybXpc1k8dTSjy4s6XdcpaeHzI9lfD2fUDGP9rkbqmpOkJi3PXtv6aKe1yUD1ZvL+NSUzvOdXujBD8Yv/Q9W1S4lt+AMt87/K/s8/R8vpX+aV3a18+foX+PrNL3OwNc13Lz6FG7+0kItmju6RgLQs5lFeFOlXAZnrZN9kaqvjLy3yOfNDX8BcuZrbq/+BIJVg+pNfIfGL5bTWPnfMz5fIBJ3mfIuInKieCEp3AuM7/H3cocdECkprOuhwStqM/6ef8Xgwm8VnX4Bj2lrh5y9YKvU9ai7/Fzba8VQ+9g3SzftoOo42/vnWcoRT0ozj85vE2Vw+f3y36/0pNXmwih7avC+dOgzIpvCGJSNIj5xNtLZzUGoth2crirwHmeC9Nzfy9rxM5Y3nU/bEd0mPW8z+zz5N85nfYXfS5+/uep0vXf8COw+08tfnTePWKxdz0czReEdoBPReGAMVxVGKo/1zcIExhqHF0U6pt0NKYiy97Cskv/wst475FkUtu5h01yXsv/6zZA7sOurnC0JLXTypn30R6RE9EZTeDXz6UBfeRcBB1ZNKobHWdgqeil67AT/dyE1FH2PZoQY8JX5+AyVjDFPGDOO1hT+kJGik6bav05rq3SHyJyodhN02JKa1nqI3buVh92wqh4/m9AlDO133PacwarDkqNpGw0wdUcrwMp9nNu8HIFm9ksjuFzCtdZ3Wa2Mq78d7mkkapCl55vtU3nQBTnwvBy6+hgOX3kBL8TiuebqWy/9nDU9t3McXllZz25VL+MjccT1yMgrgOYaqEn9AlB0MiUUo7VJPO7S0mLOu+CbbPv4UfyhdxZS9j1Dx68Xsf+D7EBy5AZW10BBPqQu3iJyw4xkJcxOwBphmjNlhjPmCMeZKY0zbBPX7gVpgM3A18Oe9drci71OnFNMgTeT5q1gbnszMhSvaa0nz2j3xkFjEZfnZy7mj/FNM3f8IzetupjGRLtgUqVynpEWv3YAJEvxH8wquWDChW4qbTkn7h+ysRAdjDEunDOO5rfWkg5Bk9QoMFn/bHzut16ZU3qtkJiB1nG9muPWbqPz9RZQ+9xMSp1xO3WdXkzzpgzy5aT+X/88arn56K2dOHcbNX17El86a3KONeGKem79xL72kxPcYkqPL+8TRI1n0pZ/y1AfuY507mxlv/oT0VWfSUrv2iJ/LAgdaFJiKyIk5ZlBqrf2YtXa0tTZirR1nrf2VtfYX1tpfHLpurbVftdbWWGtPs9au6/3bFnlvOgZPsQ13EWvdw2+dD3HxrGzH3RPtwtiThhRFOfWj3+EVO5URT/8tYeOegpxfGoSWZNdT3CBF8cu/5pXIbBpKp3DBqaM6XXZMfhpJyfvTdip0xpQqWlIBL20/QGbUbMKiqmwX3g60IZX3Kp48jiwQayl67QaqbjgX9+A7HPjgr2k87z/Zm/L59u2v8q3bXqXMj/CLT57OP192Wrf69RNV4nuUF/ev+tHjVRR1GVocIddXNvO02VR/9U5unfpveMmDTLzzUt69+S+xyaacn0uBqYicqP6fhyJyDJ1STK0l+tx/sSEcz7DZF1Mc9XDz1HH3SFzHMGnEUDaf8UO8MEXLbX9OOhMQL7D60o6djNsUvXELbvNufhQ/n08snNAtda6v5r9Kz2jLHpg/qRLfc1i9eT8Yh2T1Cvxtf4TwcFBhUWAqxy+RDo6d8p1uZcjDX2fII98kNXYBdZ9+gtYpF3LHiztY9cs1PLu5jq+cU8N1n5/PnAkVPXp/huz80a5prgON77lUlERxcgTdUc/hrA9+hp0ff4KHii/m1B234F61mIPrH8vxmQ4Fpq0pMkrlF5H3QUGpDHgdZ2hGtz1OUcMGrgkv4qOHGvAUYqBUEnU5c9Fibqv4ApMbnqF57W9oTmYKpm7PWtt9NmmYofj5/2JrdCqvROfwodljOl02qOtuf9NxTNLciRU8s3k/1lqS1StwEg1E9rzYaX2h/P8phe9Yb7I5B9+m8uYPUrT+9zQv/AYHLruJ7ekyrrz+BX7w4AZOHjWE331xIZ9dMqnHR2e1daotpDcre1PEdY6anjxu9EjmXHk1983/Dc2Bx0kPfZw9t3wDm27tttZaaGhJExZoyYmIFC4FpTKgWWtJdBkDs5cKGqd8iBFlMYwpzEDJGMOQoghzP/ptnmcGo9d8D9vwdsGMiWlJBXS9jdjGu/EObuP7zRfz0XkTunWo9D0XZwDVZA0WbSm8S6cMY0dDK9vrW0hNPAdrXKJdRsPopFSORyIdkDlK0BLZsYaq352He3A7DZdeT/OSb3HnK3v45DXPs2VfnL+76GR+9vE5TKgs7vF78z2HyuJov5gR3ZNcx1BZHG3vut2VMYYFZ11A02f/yIPFH2TWjt+RvupsGre90G1taC0HCuR3lYj0H4PrVVcGnUQ6bE8xdQ9sI/b2H7kxs4xLTp8EZAPSQq0VikVcxlSUsOucHxNYSN72ZYIgoPF9DpnvKdZa4qku92BDip/7T3ZGJrLaW8Dl88Z1+7jiPHc3lvenLYX3jCnZ0TCrN+/HxoaSHjMfv8tomHQQaiMqx9R8lFPS2Bu3UXHbRwmLh1H/iYfZPeJsvnnrK3z/gbeYMWYIN35xIR+cNaZXXrdLfI+hxdFB++aZ4xiGFkeO2iRqZFUFs758NXef+lP8dCOT7vggux/4IV3fpUwHYd5/V4lI/6KgVAa0jimmRa/+lhDDU6UXMXditv6o0OfNlcUiLDl9DneO+N9MbHqJpif+i0Q6v2NiWtPdT0n9zQ8QqXuLH8YvYtX8iQwtjna6HnGdHhvNIH0r24EXRpXHmDK8lNWbOoyG2fc6TtPhCWAWSAcKSuXIEukgdzdxaylZ8yPKH/wq6bELqL/iXh7dU8zHrn6Oddsa+KuVU/mvj89h5JBYj9+TMTC0eODXjx4PYwxDYhHKYkf+XjjGsPADq9hxxWM8H1nA7Dd/xP5rPky6ub7TukQ6oKXrG5giIkegXaIMWJmODY7SrURf/R0PBfM4c+5MjDHEPLfgW/y7jqHY91j84b/kaTOX8S/9iGDvWzQm8lOzY63t3jEzzFD6zL+y05vAE9Ez+fjCCd0+rhDrduX4tZ+WTq3ilR0HaUqkSVWvyF7b1qULr+pK5Shy1pKGAUMe/itK1/yQ1lNWseeS3/GDJ/bwN3e8xujyGNd9fgFXLJiQsxnPiYq4DlUlfkGMBCskxVEv25n3KN/ysWPHMuHK27l79NeY2rgW7+qzqdvwbKc1zYnC6YUgIoVNQakMWIkO9W2xTfcQSR3g9/YDXHRadgxMT86x600lUZeKUp/4ef9Oi40S3HElNsjkZUxMSyo4PO/1kNgbt+DVb+J7LR/mY4smU9Zl9l2hdTeW987vUFcahJbnauvJDDuZoGysRsPIcctZSxqkKL//SorW30Tzwr/i9fn/yhdveI3bX9zJxxdM4JrPzKN6WEmv3E9x1KWiOFLwb07mi++5VJX4R81yiUZcFn7sb3lq6Q0E1jL13o+w/f4ft6fzZkfFqL5URI5NQakMWK0dGhz5L11LrR1D8bRllB/ahLQ1cCl0xhjKYh5zTpnOPeO+yfiWNzn4yA9JBWGfpkblrCXNJCh99ods8E7ieX8xq+aN7/ZxJQWeIi3H5nsOBpgxppzyosih0TCGZPUKom8/CZlk+9qM6krlCDrOiwYg3crQuz9HbOPdNJ3199xV8Tk+85s/sftgKz/66Ey+tnJqr6T9OyZbO1kWG5jzR3uS6xgqjlFnCnDawuU0fPJRXvTnMf+tf2P3bz5FJhUHso2PGluVxisiR9c/duUi71EqE7af6Hn71hN79wWuz6zg0jljgf6XTup7LrGIy1mXfZlH3aVUr/8ZyR0v92lqVDxHx93il67Bbd7FP7R8lD9fNrXbxsUxhlhELzP9nTHZN3Fcx7C4popnt9QRhJZU9QqcdJzorufa11qUwivdJTNd5pKm41Tc9QmiWx+jftkP+Pt9y/m7u15n8vASrv/CQs6cOrxX7iPmuVSVRJWu+x601ZmWFx09nXf4iFGMu/JOHhjxRWY2PEriF+fRvHcbAIlMfnshiEjh025RBqREpsMYmPU3k8FjTelKZo0fiiG7MelvynyPYt8l8sEf02BLMXd+mTCd6JMxMUFoaelSC+Y07aZk7b/ztJlL/YhFXDRzdLePK/ELt7uxvDdtmQVnTR3GwdY0r+44QHLCmVg3SrRWo2Hk6Fo61qKnW6m461NEdqxhx7L/4NOvzuCOl3byiYUT+J9PzmVUee80MyovilBeHBm03XVPVCziMqzEP+rvT89zOf2T/8Sjs3/CyPQOKm74ALte+yMAja3p3E2uRERQUCoDkLX28DuyQRr/jdt4JDidpaedhGMMfqR/zst0nOy71dMnT+KZU/6B8elt7Ln9WwShpekYg+hPVHMiQ9etROnT3yPMpPm/iU/yzXNP6laX5RhTkDNg5f1pO1laNLmKiGt4auN+iJSQGrcEv0tdqTrwSkfpIDx8ep5JMvSezxN551nWL/gBlz09ls17m/mXy07lL1dM7ZX5oO3BlF6PTpjjGMqLs6emR2s8NWvFx3jjojtopogZD3+CLQ/9HEs2MBURyUVBqQw4yUzYnmbqb30UL1HHLcHZXHDaKIB+HSjFIi4xz2Xx+VfwYOllzN51M/vX3kRrqvdSo5KZoNPJM0DknWcpeusOrspczCkzZjFr/NBuH1fqezolHUBcxxBxHUp8j/mTKnli416stSSrV+I1bMY9sK19reaVSkftp6RBmvL7voS/7XGemv4dLn16HL7n8qvPzGfFySN7/Hk9x1BRHM0GUP3wjchCFou4DCuNUhx1OdJ3duL002n97CO8Hp3NkvX/SO2N3yCZTmtMjIjkpKBUBpxkukPX3fW/p85UcGD0mYyrKO5XDY6OpCzm4ToOkz/+77xqpjHp2b+hZecbNCZ6PjXKWktT1wHo6ThDHv4rdjsjudH7M76x8qRuH+c6pt90N5bj19aF95xpw9l14P9n784Doyqvxo9/n3vvLJmZJOz7DmFVFkVRAbEKFsGltu6te92q9qV2UVtt1fftqtXqz1prrXXFXXFXRBFEcQUVFNl3lCUhyWTWO/c+vz8moSwJZJnJJJPz+QdJ7tw5IJl7z33Oc06cVdurSPafDIB37Z4lvAkp4RWkS//jKVXAjoYAACAASURBVAdch+LXr8S/+nWe6zaD8z8byqF92/OfCw9jUJdQRt/TqN4D2THka/Wf9y1Zuglf+u+5rlXodh260OXyWcwrPoUjv32MzfefTVl5BSnZdy6E2It8Wou8orUmUb2qp6Lb8a6Zw9P2eE4Y1Qto3aukNQxDpfdGhQJUnvQv4tqL+cz52NFKyqPJjK5QRZL7DrovfPf/sCrWMSN2KVdPHUlxwLPP62QIfX7afTSMAuYt347Tvj+p9gNrKeGVm05BelVMawrn/Rb/8hd4OHQR16w7nHOP6MsdZ46muGDfz4/GUir92dMp5JWHYs3IrL4mdQx68Xv2XTn1eLwMufBe3u5zNYdWzSN833Q2bd4E4TDcfz9ce23613A4J/ELIVoGSUpFXkmk3F17HwuWPYuhU7zIMRw3NF0ali97irxWuoyyZNAQPj3sNnqkNlH+8I+w7SSVe69sNlIy5e4z6N67fh6Bzx7gQWcqoSGTai2585pG3vw9iz1ZZroDb8eQj4N7FTNvxXaA9GiYje+BHdl1rDQ7Eq6riSUdAp/+g8Di+3nKOplbyqbwu5OGc9WxgzI2H9SoHpvVOeQjKNsGcsYyDYoLPHQK+Qj5rD3+/yrDYMRpNzBv1G0MsFdRcNME3O7dYMYM+Mtf0r/27AkLFuTwTyCEyCVJSkVe2b101/flkyxhED0HjyHkt/BVj7TIFyFf+s806uhTeL3vLxgR+ZBvHv8p8WRqjxmtjeG6moq9GlIY4S0UvnIFa+nFw4HzuH7a0FpfW+iXVdJ8VrNaOmlwZ1ZsrWJLeYxk/8koJ5FOTKulXI0rnTbbtJjt4Fv2LIXzb+YNjuT3qXP4f2ePYdrB+3bqbgxvdRLUudBHwCvJaEthGIqgz6JTyEf7QHrfac21d+hx5/LFEffS+bFNGJEoRKofZEUi6ZXSadOgqiqH0QshckWSUpE3di/dNXcsw7vjK562xzN1RLrBUT6u3hX5PZiG4tAfXMNrxWcxZttzrH3+fwnH7SatVFXG7V1zXgFIJSh6+RLsRJTLkjP47fcPo9C/b9ldwGtmpXumaDlqfo4mDU7PkZy/YjvJnkfgegL49h4NIyW8bU91Saa+9lrUn35F6IWr+dAdxp/9M7j//HEc0rd9k05v7p7wVJeLipbLaxkU+tOrpx2DXgr9FiPW7kCbdYz9cV148snmDVII0SLIkobIG7uX7vqXz8LFYL5nPJcO6IBS/13hySdGdXfJ0kiCkef/lffu38b4dX/noxct+p3yazoEvA1OEsNxe88mNdql6PWr8H3zCT9J/pSzpk1mWPeifV5nGkr2krYBHtPAUIreHQIM7Bxk3ortnHV4H5J9JuFb8yZhrdOb+0gnpZI0tCELFqRXulwXFYlQ4AFXGbx++Wn84ydH0S7gbfApFel/c17LwGcZ8tCrFbPM6v9/69dCLFb7QZEIia+X4yRTGEphGgpTKemeLEQbIJ/uIm/sSqS0xrfseRbqgzh4SAme6j2O+VraZVYnph6PRe+LHuQ9/yQOX3UnK57+HeXRZINKKKPJFNHdS3+1JvT2rylY8SJ/sM+mx1FnM31k7aV3RX5P3v4diz35POlLx9GDO/PZxnLKo0kSA7+LWbUFa9uSXcfJvtI2pKb0MhzeVZKpbDCTLjc+cDPt3WS9TmMaCr9lEvJZtA946VyYXhEN+ixJSPNFSQkEg7V+yw0EifcdQDieoiJmUxZJsr0qwdbKONvDCcoiSSqiNuG4TSzpkEy5sk1AiDwhn/Aib9SU7lrfLsZTuZ5ZqSOYMry6wZGV36s1HtOgfcCL3+ejz48fZWHwOCZsuJe1/76QbeWV6Yv2ATodRhKpPce/uClCb/wPwc//wz9T06kYcwU/nti/1vcP+iwZvdCG1Pw8TRrcGVfDglU7SAyYglYGvlWv7TrOkX2lbceTT6ZLL2vjanzPPYNS/51367dMCrzp5LO4wEOHoJcuhT46hXwUBzy7PlPkQVceOvNMMGq/XriuTfx736/9e1pjOy7xlEM06VAZt9kZTSet28MJKqLpGajS+VuI1knuIkVeSKQcarZA+pc/j42HD7xHcWjf9nkxm7Q+PKZBh4CXAr+Pfpc8wpwuF3BY+atU3DOZTY8+gO7Zs9ZOh1qnmxpV7dZp14hsI/TUGQS/epI77B+weez1/GzK4FpvEL2mIWW7bUw6WYCh3QrpWuRj3ort6EAn7B6H41v9+h7Hyr7S/OK61YmB7VQ/yLKpiNrEvlr+36Y1ezGiEYo2raNLoZ9OIR8dgl6KAx6K/Onk0+8x8ZiSgLYZhYXw6qvpX6tXTHUgiOOzsM62WPHi79B1PeCog6vT83DD8RRlkSTbwnEqYvauh9VCiJZP7iRFXthVuus6+L5+gbl6NIcP749ltK3xJJZp0DHopTJucPCP/sz8tw9i1Ac3UnTJJajkbitW1TePeto0SlesxQlUl1JpF9/Sx/HN+19IRrg2dTmDT7iMc0b2qPX9aubTibbH7zGJJR0mDe7MC59tIZpMERh0AoXzfodRsR63uC8g+0pbs5TjYjsa23VJOZqU61LXGORojz6YXg/epL3vN4NBVElJdoMVrcuECbBlS3qFfdUq1KBBlJ1wEsuf/hUTt83k3UeSlJz7/1B1rKgeiNYQtx3itoOhUhR4TQo8Zl514Bci3+T/8pFoE2pGwXg2LcSKbmWWfSSTh9WU7ratf+ZKpRPF9gEvB005n6rglbh1/Khrx8X7xKNYW7+ABbfj/8dhtHvzGr6Md+aaojs46YJfcGIdCamh0ntZpQFF21TTOOzYoV1IpFzeW1VKYuBUAPy7rZbKvtLWw6meK1oeTa80lUaSVFbv3bOduhPSLeUx/rcKPNSSkEK6VPPMM7MXuGidQiG4+GL44x/h4osp7tKJQeffw7sdz2Bi6TOsevByXLfpK52u1kQSKXZUJaiI2aSkekOIFklWSkWrZzvurvEl/uXPE1MFfFYwjt/0bodlqDbbHMNrGXSwvDjhMGYdc0uNaISiZ34F5en2/B+7g3kt8Cv6TDybG0d0r/Opcjoh9chT5zbMZ5koZTOyVzs6hbzMWbaVKcNHYnccim/V60QPuQxIJzqOq+XfSgvluJq47ZBIuY3ai7dmexW/nvkuj3AXyYt64310J0rrdDVGMJhOSF99NZ2ACLEfXsugwOuh5Nw7eXeml4nbHuW9B1L0v/BfmGZmqi1qVk8LvCYhryUPVYVoQSQpFa3ertJdx8a34mVedg5lwkF9MA1FgVfKBs3Bg9M3h7Xs90p5LRb1msTSbseT7HUEBw8dyqWdg/vd22UZinYBryQZIl3Cqx2OHdplVwlvcNAJBD+6ExUrRRd0BNIPjkxDfhZbkkTKIZZ09hz/1EDLvw3z05mLuFPdTQ9VRvmMWXj/MhqeegpWrYJBg9IrpJKQinoK+S0SKYeSc25lwZMWE755kPf/fQF9L3oAy8rcVpFYMp2chnwWAa/cCgvRErTNJSSRV2rKA70b38NMlPNSahxTqkt3fXnedbde9tPp0PAW0O/2mUw/5ypOPXosg7qE9puQFnhNOgQlIRVpNV14a0p4F6zcQWLgVJR28a2Zs+u4piQ+InO0Tpfn7qhKUB61m/T/ZenmCq6cuYgfmy8xSX9C1aTf4el7BKqwcI+STElIRUOYhiLgs1CGQcnZf+bdXpdyVNVsNj1wPk6qjvLwRtIawvEUOyNJHOkSLkTOSVIqWrWaTpAAvpUvE1d+vg6MZUTPIrymIckT1NrpkGAQCgtRr75CUef2eA5Q4uyzDDoEvTKLVOzBaxkYSjGqd7qE961l20h1HYUT6r7HaBjZV5pbWmuiyRQ7qtJ7RJt6A754w06ufnwxk7zLudKdSbzkJGJjLqFAGlqJDAh6TWouM4PP+F/m97mScVVvsfmB83Cd1P5f3AhJx6U0kiBuS6deIXJJahZEq7ZH191Vr/GGM5ojh/fEUEo6fu5ur06HNWV1KhTCT7oM03E1yZSb7rBJet+oZSi8piH7bkSd/B6DaFJz7NAuzFq8hUjSoXDgVAq+fALsKHgCuFr2leZK3HaoSqQythL0wZpSfvXMFwwvinOHvgsn2I/K4+/Aa5ltdv++yCylFIU+D5Xx9MrokNN+y9ynNN/ZdA8fPngxfS58ACPD2wG0hoqYje24FPqlo7wQuSBXENGq1cwg82z5CDO2g1dSh3PMkM4o/tsdVFTbq9Ph3mV1NXtwC/3p+YGh6vmBkpCK/al5+HPcsK4kHZcFq3aQGHQCKhXDt2H+ruNktbR5pRyXnZEkFbGmr4zWeHfldn7x9Of0be/nkY4PYiUrqTjxX2hfoezfFxlV4N1zfMvwM37H3O4/ZlzF66x78LIGzzGtr2h192ldV6tpIUTWyF27aLW01rtudH0rX8FWHhZ5xzK6T7t0WaEkU0Jknae6TH5kr2I6h3y8tWwbyV5H4vqK8K2S0TDNTWtNOG5TGkmSzODoi7eWbeXaZ5dQ0qWQR0d+RmjjO4Qn3USq8wgMpeQhoMi4kG/PYr5hZ/4v87qex5HlL7H6kSuzlpgmUi5lkSSu7DMVolnJVUS0WkknXWaK1vhWvsK77igOHdwbyzCkdFeIZuT3mBhK8Z2hnVm4upSqlEGi33H41syG6jmDCUf2a2VbMuVSGkkSrWMEVGO9tWwrN876khE9ivjn8V46f/AHEgOOJzbqQiC9qiV7zUWm+T3mHv0OlGEw5Ow/M7/TWYwvfY5Vj12TtcQ05WrKotIASYjmJEmpaLVqVl6srZ9hVW3hFXss3xnSRUp3hWhm/uqft8k1Jbwr0yW8RqwUz5aPgfSeLRlanz1ViRQ7s3AT/fbX27hx1pcc1LOIu34whO5vXoXrb0/Fd/9GTTcaaXAksmXv1VJlGAz+0R282+H7TNj+OGueuj794ZIFjquz8jMlhKid3LmLVqumyZF/5cs4mLxvHcZh/dvjtQx5ai9EM7JMA8tQHNyrmC6FPmZ/9S3JfseiTS++Va/uOi6T5aQizXE1ZZEkkUTmu5LO/XobN8xayoieRdxx5mi6LLwFq2wFlVP/364ZtNLlXGST1zL2ecisDINB5/2d+UUnctSWB1k365asvX9NYiqlvEJknySlolVy3HQ3z5rS3Y8YwYiBffFZppTuCpEDBd50Ce/Ug7rxweoyylI+kn0n4V/5yq6VDNlXmlmJlENpJLFrLFYmvbN8G7+ZtZTh3dMJaYcNswl88TCRsVeS7Dtp13HS4EhkW9C376AIwzAYeME/WRA4jnFr72Hd63dl7f0dV1Mes6X5kRBZJkmpaJVqbm7N0q+xytfysj2WSYOl664QueK30snJ1BHdcLRmzrKtxEtOwgxvwvp2MSArpZlUlUhRHrWzUrk4b8V2fv38UoZ1L+RvZ42myC6l6M1rsLuOomr8dbuOU0o+b0X2eUxj1+fL7izLovcFD/CRdxyHffkHNs5/JGsx2I5LRczO2vmFEJKUilaqZhSMf+UruCje5jCOGtQJnyUNN4TIBcNId2Ad2CVESZcQry39lsTA76IND/6VLwPpBdNsrOq1JVpryqPZKdcFmL9iO79+bglDuxVy55ljCHlNiub8HGXHqDjh72B6dx1b4JHPW9E8gr7aV+T9fj+dLniMpdZwRn18LZs/fiFrMSRS7q7ZqUKIzJOkVLRKNSsuvlWvsUQNoX+/AYR8Fj6P/JMWIldqSuenHtSNL7dUsj7qJdnnaHwrX5IS3gyo2T+ayNLf4YKVO7j+uSUM7lrInWeNJuS38H/5BL41bxKe+BucDiV7HC8NjkRzsUyjzlLxYKgQ77lPstboy7B3r+Lbpe9kLY5Y0iGW4e7WQog0uYMXrU4y5aI1GOHNeLYv5dXkGOm6K0QL4LMMlILjR3RFAW8s/Zb44BOxKjZgbVsCSFLaWLaTnp2YylLDlfdX7+C6576gpGuIu84eTaHfg1G5kcJ3biDZ6yhiY368x/Ee08Ay5fNWNJ+g16Kudfn2HTqTPPspttGR/rMvomzNoqzFEY7b8jkmRBbIFUW0OrtWSVfPBuBtfQgTS6R0V4hcU0rh95h0KfQztl97Xv/yW+IDvotWJv4VLwLp5EoahjRMIuWwM5LEzdLf26frd3Lds0sY0CnEXWeNodDvAe1S9MYM0Lp6/Muetwt+qUoRzcw01H4ba3Xu1psdpz5JDB9dXzib6LcrsxKHBipitnTkFSLD5KoiWp2aJ5S+NbPZbHQn2GM47YNeKd0VogUI7FbCu2lnjCVlFsk+E/GtfBm0RgO2Izdz9RW3nXRDoyydf+nmCn7x9Od0L/Zz51mjKSrwAFDw+YP4Ni6gatLNuMV993iNglobzwiRbftbLQXo1X8IK45/GOWmKHjiB9iV32YlDldraXwkRIbJXbxoVVxXYzsuKhnBs2EBryVHM0G67grRYlimgcc0OGZIF3yWwWtLvyFRciJW+VqsHV8B0oW3vqLJVFZvfFdsDTPjyc9oH/By9zmH0D6YbmJk7lxD4fxbSPQ7ltjBP9rndT7LxJDZpCIHDEMRqGVEzO4GH3QYHx55L8XOTlIPn45OVGUllqTjZq3hmBBtkdzFi1al5mbWu34ehpvkLfcQji7phNcypHRXiBaiwGMS8lkcPbgzb361lcp+6RJeX3UJr+zHOrCqRIpwPHs3vOtLI/z08cX4PSZ3nzOGzoW+9Ddch6I3foo2vVROuT0992Uvfq/cOojcCXjM2v5Z7mHMUVN4Y/if6J1YSflDPwQ3Oz9LkURKOooLkSFyZRGtyq79pGveoEqF+KZ4NH07Bnd1/RRC5J7fY6CAk0Z1pzKeYu5Gl2Tvo/CvSHfhTcm+0v0Kx+2srsBsKY9x1cz07Ni/nzOGHu0Kdn0v8Om9eLd8TPjYP+AWdt/ntYZS+KR0V+SQYSiC3v2vlgIcMfUcZvX6BUOrPmD74z8hG0N9NVAZs+XzTIgMkKRUtCrJlAuug3fNm8x1RnJUSXcp3RWihVFK4feaHNavA92L/bz0+TckSk7C2rkas/RrNFLCW5fKuE00iyMntocTXDVzMTHb4a6zx9C3Y3DX98ydqwm9/xfiA08gPvQHtb5+f41mhGguAe+BV0uVUhx1xs95qfiHHLT1Bba+dHNWYkm5moiMiRGiyeROXrQajqtxXI3n20WYsVJmpw5hgpTuCtEiBTwmhlKcOLI7H60rY32X76CVgX+5lPDWpTJuZ3UGYnk0ydWPL2ZnNMnfzhzN4K6F//2mdima/XO05SN83J9rLdsF8MsDQNECKFW/1VJDKUad9xfe8k1m5Kp/sH3+v7ISj5TxCtF0cnURrcaurrur38DB5FPvoYzqVSylu0K0QDUNj04a1QMFPLfCJtl7PP7ls0BrSUr3ku2EtCqe4qdPfMaW8hi3nT6Kg3oW7/H9gi8ewbt5IeFJN+GGutZ6DplNKlqSgDf94OtAfB6L3uf/i4+M0Qz95LdUfvFKVuKplG68QjSJXF1Eq1FzE+tdM5tPGcpBA/rgMQ28cpMkRIsU8Jp0LfJzxMCOvPzFN8QGn4pVvgZr6+ekXC1z/qplOyGNJR1+9tRnrN5WxR+/fzCH9m2/x/eN8BZC795Cos9E4iPOrvM8BfIAULQgSimCvvr9mywOBTDPfoSV9KXXnCtIbvgk4/GkXC3deIVoArmbF61GwnEwy9fhKV3O63a6dNdjGjKaQIgWymcZGEpx8qgebA8neNc6Am168X/9HCD7SiH7CWki5fDLZz5n6eYKbjllBOMHddrzAK0pmvNLlOsQnnxbnWW7inQDKyFakgJP/VZLAXp17cKW6Q9TqgsJPfcjdPnGjMcTSaRw5GGbEI0iVxjRKtiOi9bpVVKAufpQjhzQUUp3hWjBlFIEvCYTSzrRPuDh2a8iJPodly7hdR0SbbyEN5zlhDTluPzm+aV8vG4nN0wfznHD9i3L9S9/Ht/aOVRNuB6nXb86z+WzTNm7L1ocpRShA8wt3d1BQwbzwRH3YjpxnMfOgkQ4o/Fo0j/XQoiGk6RUtAr2rlEws1ln9KZD7yEUFXik664QLVyBx8RrGkwf2Z13V+5ga7+TMCNb8Wx6v03vKw1nucuu42pueukr3l25g19+dwjTR+473kVFd1A49waS3Q4hOvrH+z2fzCYVLZXfY2A2oGJq4viJzBr0e7rG1xCdeQG4mf05TKRcEinpxitEQ8lVRrQKyZSLSoTxblzIa8nRTCzphFdKd4Vo8QwjPR7m+2N64WrNY2XDcD1BCr5+DlfrNlnqVpVIZTUh1Vrz59e/5s2vtnLldwZy2qG9aj2u8J0bUYlKKo+/HYy6q05kNqloyRq6Wgow5eRzmNnhSvrvXEDli9dmPKZwPCWzS4VoIElKRauQTLl4N8xH6RRzndFMGNQJn+xvEqJVCHhMerYvYPygTjz9+Q5iA6fhW/kypBJtbrU0kkhltRmK1pq/zVnJC59t4cKj+nHekf1qPc67ZjYFXz9HZNwMnE7D9ntO2UsqWjq/x2zQaqmhFEf/8Hqe955MyZpHCC+4N6PxOK4mZstqqRANIVca0eIlUy4a8K59i6gKUNp+FL07BOTJvRCthGUa+C2T08f2YmfUZmHwOxiJSnzr3mpTSWk0maIqy90575u/hic+3siZh/XmskkDaj1GJcIUzbkWu+NQIof/9IDnlK67ojVo6Gppgddk0Ll/4111KP0+upnE17MzGk9VIiUdxoVoAElKRYtnOy5ojXfd28x3DuLIwd3wmA3bQyKEyK2Az+Tw/h3o0yHAXWt74hZ0xP/18ySctrGaEEs6hOPZTUgf+WA9D7y3jpNH9eBnk0vqbEwUWvB/GJFvqTz+DjC9+z2nZSiZTSpaBb/HxGrgfUGX4iDqtPtZpXvR8bXL0NuWZSwerSGSlBExQtSXXGlEi5dMuZilX2NVfcPbzqh06a40OBKiVfFUr5aedmgvvtgS4ZteJ+BbPRvi4V2NzPJV3HaozHJHzmc+3cTdb69iyvCuXHfC0DoTUs+mhQQ+f5DomEtIdT/kgOct8MoqqWg9gg1cLQUo6d2DLyfdR8T14nnyLFR0e8biiSWdNrlvXojGkDt70aJprbEdF9/atwFY7DmEg3sVyygYIVqhoM9i+sHdKfCYPB47AuXE8a18Ka9LeBMph8pYdhPSV5d8w61vLGdiSSduOml43VUkdoyi2deQKu5D1fj6NXfxyzYJ0Yr4PSaeRqzsTxg7hueG3EYgWYYz84eQSmQkHg1UZblCQoh8IUmpaNFsR6MBz7q3WUEfBg0agt9qWEMDIUTL4LUMOgS9TB/ZnX+u7UCieCAFXz6Rt0lpIuVQEbXJ5jrJ219v439f/orD+rXn96cetN9S29AHt2GVryE85a/gCR7w3D5LOpyL1qehe0trnDztRP7V4ed0r/yc5Es/T9ffZkA85eR9NYgQmSBJqWjRko6LSlbh3fQhb6dGMbGkk6ySCtGKBX0W5xzeB8eFeYEpeDd/iLtjdd6NT0im3KwnpO+v3sGNs5ZyUM9i/nLayP02f7O2fk7gk38QO+gckn2Ortf55bNWtEZey8DbiNVS01BMP/sqHrFOo/fap7E/+GfGYspmx20h8kW9fmqVUlOVUsuVUquUUtfV8v0+Sqm5SqnFSqkvlFLTMh+qaIvslIt3w7sY2maBHsW4AR1lP6kQrZjXMhjQOchxw7ryxy2j0MrA99UTJPNoJcF2XMpjyawmpIvW7+S6Z5cwsHOI288YRcC7n9Uhx6Zo9jW4gU6Ej76pXudXCvmsFa1WyN+41dKQ32LIOX9irj6U7gtvhjXzMhJPIuXKaqkQB3DAK45SygT+DpwADAfOVkoN3+uwG4CntNZjgLOAezIdqGh7avaTete9TRQ/Tq9xFBd4pBOkEK1cyGdx7hF9WZsoZm3xOAq+fIpkMrv7LptLynHZGU1mqvKvVl9uqeDnT39O92I/d541mkK/Z7/HBz69B8/2pYSP/RPaX1yv9/BZZp3NkoRo6Tym0eiHKn07FRKedg+r3e4EX7wYY+e6jMQke0uF2L/6/MQeDqzSWq/RWieBJ4BT9jpGA0XV/10MbMlciKKtsh2N1hpzzVu86xzEkYO7SzmZEHnAMg1G9W7H4f07cF/FkZhVW3AztCKRSynHpSzLCenKbWFmPPEZ7QNe7j7nENoH9z/SxSxbRWjhX4mXnEiipP5FTDKbVLR2jd1bCjBuWD/eOeROUo4LT5yDSlY1OZ6k4+bt/nkhMqE+SWlPYONuv99U/bXd3QT8SCm1CXgVuDoj0Yk2Lem4mGUr8VVt5h1XRsEIkU9CPovzjujL87GRJKxCvEseb9WjE9IrpHZWE9INpVGunrkYn8fk7nPG0LnQt/8XaJeiN3+OtvyEj/1jvd/HUAqvfNaKVs4yjSY9yD75OxN4oPtv6RBdQ+qZS0E3PaGUvaVC1C1TV52zgQe11r2AacAjSql9zq2UulQp9YlS6pPt2zM3B0rkJzvl4luXHgWzuugI+nQMNKrVuxCi5TENxTFDOjOweydedsfjX/UayaqduQ6rURxXszNq42YxI91SHuPKmYsAuPvsMfRoV3DA1xR88QjezR8QnnQTbrBLvd9LZpOKfBHyWTS2CF0pxQ9OP5d/+i+ix7dv4c6t/4OduiQd2VsqRF3qc4e/Gei92+97VX9tdxcDTwForRcCfqDT3ifSWt+ntR6rtR7buXPnxkUs2oSa/aTmmrdY6fZk8JBhskoqRJ4J+iwumzSAh2LjUU4cljyT65AazHE1ZZFkVhPSbeE4Vz++mJjtcNfZY+jX6cDjXIzwFkLv3kKiz0TiI85u0Pv55bNW5AnTUE16yOL3mIw7+zfM4hi6f3YXxrIXmhyTrJYKUbv6XHk+BkqUUv2VUl7SjYxe3OuYDcBxAEqpYaSTUlkKFY1mOxptR/BtXshcd3R16a48vRcinyil+O6IrrjdR7NS9cX8DknlBAAAIABJREFU7KGMzQZsDinHzXpCuqMqwZWPLaYskuSOM0czuGvhgV+kNUVvXYtyHcKTb0u30q0nj2lIMzmRV4JeqyE/Avvo1SEI0+9gkTuIwtd/irl9WZPiSaTc9F5VIcQeDnjl0VqngKuAN4BlpLvsfqmUukUpdXL1YT8HLlFKfQ48Dlyg823onGhWtuPi3fgepmvzsTWGUb3byR4nIfKQ32Nx9XGDeSh5LN7tS7E3fJLrkOqlZg9pNhPS0qoEVz62iB1VCe48azQH96xn59wVL+BbM5uqo67FadevQe/p98jnrMgvhqEI7m9kUj0cOaQH7465nbDrw3rmPFSisknniyScJr1eiHxUr6uP1vpVrfVgrfVArfXvq7/2W631i9X//ZXWerzWepTWerTWenY2gxb5L5ly8a59iyg+PP3HE2xCFz0hRMs2eWgXVnebThQ/qY/uz3U4B2RXd9nNZkJaFkly5czFbK1McPsZoxjZq129XqdiZRS9/WvsrqOJHnJJg95TAX6pSBF5KOA1MZo44uiMYw/nns43Eoxuhucvb1Ljo3jKadWN3YTIBnkkKlocrTV2ysFYNYf3nBGMK+khN0pC5DHTNPjJ1NHMSh2F9dmzcM+dcO21cP/9EA7nOrw9JFPZn0O6M5LkqpmL2FIe4/YzRjGmT/t6v7Zw3k2oRAWVx98ORsMe5nktA8OQ2aQi/yilmjQiBtJdqc85/Sz+7jmfLlveQr17e5POF0nK3lIhdidJqWhxbEdjlK+hILKJ+e4ojhzYUUp3hchzE0s6E02MxHNbKfrnv4K//AVmzICePWHBglyHB0Ai5VCe5YS0PJrkqscXs2lnOiE9pG/9E1Lvunco+OpJIoddRarziAa/t8yBFvmswGtiNvGhS1GBh0PPuJ6X3PF0+uQ2zDVvNfpc8aSDK6ulQuwid/qixbGddOkuwLddJtCt2J/jiIQQWRcOc9G9t0MSVDyZ/lokkl4pnTYNqpo+vL4pYkmH8qhNNm8hK6I2Vz++mI1lUW47fRRj+3Wo92tVMkLRnF+Qaj+IyLifNfi9lUI6nIu8V+hv+lagwd2KqJh8G8vdXgReuhyjYn2jzqOBqC17S4WoIVcg0eLYjotaNYfVbncGDz1IbpSEaAuefBKjriVI14Unn2zeeHZTlUhRGbez+h6lVQl+8tgi1u2I8pfTRnJ4//onpADB9/+EWbkxXbZrNfxBnt9jopq4506Ils5nmXgz0F16yugBvDD4z6RSKcynzgM71qjzRJMppC+oEGlyty9anGQ8QsGWD3jHHc2EEhkFI0SbsHJlemW0NpEIrFrVvPGQ3t9eEbWzPldwWzjOFY8uYlN5lNvPGMURAzo26PWezR8RWPQvoqMuxO45rlExyL590VaEMrBaCvCj6d/hb8W/pEP4a9QrP2vUOCutIW7LeBghQJJS0cLYjotn4/tYboIlBWMZ1q0o1yEJIZpDSQkEg7V/LxiEQYOaNRzH1ZRFksRT2S2v21Ie4/JHFrG9KsHfzhzNYQ1cIcWOUvTG/+AW9aJq4g2NisE0lOzbF22GxzQysn/aYxp8/8yLuZfT6bLmecxF/27UeaLS8EgIQJJS0cLYjou5eg4x7cU/6GgKvPL0Xog24cwzwajjkmQY6e83k7jtUBpJkMpyE5INZVEuf/RTKuM2d58zpkFddmuE3vsTVvkaKo6/A+0NNSoOaXAk2ppCn0UmitU7F/rofepNzHVG027e77A2f9zgc6RcTSLLD7+EaA0kKRUtip3SGKvfYqE7nCOH9MzI3g8hRCtQWAivvpr+tXrFVHsg7g/gvPwKhBqXcDWE1prKuE1FzM5qh12AVduquPyRT0nYLvf88BBG9Chu8Dk8mz8ksOg+oqMuwO4zsdGxFEhSKtoYw1AZm38+tn8nPjvsL2x2O+CbdTEqVtrgc8SSkpQKIXf8okVJla4mFFnP+2o0Rw7oKI03hGhLJkyALVvgzjtxrzgPNdXP7Vf9kJeLBmA72d13lUy5lEaSzXJzuGj9Ti575FMMpfjHjw5hcNfChp9kj7Ld3zY6Fq9pNHlMhhCtUSADI2JqnDVpJP/ociPeeCnWrMtBN+zzKpFycWQ8jGjjJCkVLUbKcfGsmQNAec9JFBV4chyREKLZhUJw8cUY9zxEYvp4Lgi9w/+9uIQ126qysvfKdTUVMZud0WSz3BS+/fU2/ueJz+gU8nL/+WMZ0LlxK8Ch9/6IVb6WiuP/hvbWsRe3HqR0V7RVSilCGVotNZTiwtO+x1/Ni+jwzXys9//W4HPI3lLR1klSKloM29GkVsxhrduVwcNGySgYIdo4d9wV9NBbOTz5Ab9/bRmVMZudkSSpDKyauq6mKpFiR1WCeDPNCnzm0038+rklDOlWyH3njm30DGbPpg/+2223z4RGx6MAv0c+Z0Xb5feYGbvXaBfwMvYH1/CicxTtP7wVz4Z3G/T6mO3IeBjRpsnVSLQYyXiUwm/e5x13NN8Z0kVKd4Vo44zhJ5Iq7sMN7WYzf8V2nl+8maSTLrOtjNuNSk5tx6UybrOjKkEkkaI5bgFdrfn73FXc+sZyJpR04u5zxlAcaGQliB2laPYMnOLeVE28sUlx+SyZTSpEKENNjwBG9m7PhqP+wFq3K74XLsWIbKv3a2U8jGjrJCkVLYZe/x4eN8Hq4iPo3SGQ63CEEDnm9XiJHnY13au+5NIe6/nr7BUs2VQBpBuDlEaSlEWSRBIpbMetdZXBcTVx2yFcnYiWVe8bba71iEgixa+e+YKHF67n1DE9+dMPDm5SyWzh/JuxytdS2cSyXQC/V24BhLBMg0CGyngBTjtqKP/ucTNGsgrzuR+DW/9KDCnhFW2ZXJFEi+C4GnfFbBLaQ2jwMVK6K4RAKYUeeQ5OsBsz/C/StcjPdc99wY6qxK5jbMelKpGiLJJkWzjBtnCc7eEE28MJtlXG2VGVoCJmE006zd5IZEt5jEsf/pT3V5Xyi+MHc+3UIVh1jb2pB++aNwl8/iCRQ6/A7j2+SbEZSuGzZD+pEABBr4mRoaoBpRQXfX8at3kvo8P2DzHn/6ner025mmRKVktF2yR3/qJFsB0XY83bfOAOY+LwPlJSJoQAwFdQQOSwKwls+YB/HB2nqnrlsa4uuVqny2VdrZttNbQ2n67fyUUPfsy3lXHuOGsUp4/t3aTPNRXdTtHsGdidhlM1/vomxyd7SYX4L6UUhf7MrZYW+j1MPO2nPO0eQ6dFd+FZ+1a9Xxtrpj3uQrQ0clUSLYJduo52kbV8bB3CmD7tch2OEKKF8JoGsYN/hFvQkSHL7+OWkw9i2TeVXP/8kow0PMo0V2seWLCWq2Yuosjv4YELxjKuf8emnVRrimf/DCMRpmLaPWD5mhynzCYVYk9+j4k/g9UDw7oXsXPS71nm9sH/0hUY4c31el3CdnBlPIxogyQpFS2Cu2I2ALF+36HAKzdLQog0w1B4/CEih16Bb/1cjivayLVTh7JwdSm3vPxVi5rttzOSZMYTn/HP+WuYMrwrD150GH07Nm3fJ0DBkkfwrXmT8MQbcDoNa/L5PKaBZcrlX4i9hfwWmSzUOnnsQB7rcwuuncB69iJw7AO+RiOrpaJtkquSyDnX1cSXzWaD25nhIw6V0l0hxB58lkFs9IW4BR0JvfdHvjemJ1ccM5A3vtzKjbOWYreAFdP5K7Zzzv0fsnhDOdedMJSbTx5BwNv0ckCzbBWF7/yWRN9jiI35cQYildJdIepiGopCX+ZmpCuluPh7U7jNdxXtyz7Deuf/6vU6SUpFWyRXJpFzyUSMdlsXMl+PZtLQLrkORwjRwvgsA+0NUTVuBr4N8/Gun8cFR/Xj6mMH8dbX2/jVM1/krGtlOG5z80tf8stnvqBj0MsDF47l1DE9M/NwLRWn+NXL0Zafyu/eCarpl2wFGS1RFCLfFHhNvBmsJAh4LSafcTkzncl0/Pzeeu0vdaThkWiDJCkVOeeuX4jPjbG503g6Br25DkcI0cJYpoFpKGIjz8cp7EVowe9Ba350RF+uO2EoH6wp5ccPfcKmndFmi0lrzWtLv+Gs+z7gjaVbuWh8P/5z4WGUdCnM2HsUzrsJz7YlVHz3TtxQt4yc02eZGIZUowixP4X+zM0uBSjpUkjVMTfztdsb30tXYlRtPeBr6mrmJkS+kqRU5FzV0tdIaIv2IyZL6a4QolZeywDLR9VRv8Kz9XN8K18G4NQxPfnbWaPZHk5w4X8+Zs5XB77Za6pl31Ry6SOfctOLX9Gl0M+/LxjLZZMG4sng6opv+QsEPv8PkUN/QnLgdzN2XplNKsSBWaZBKIPdeCG9v/TRXjdh2BHMWZcecH5pIiUNj0TbIlcnkVNaa4w1b/GRO5RjDu6X63CEEC1Uzezi+LDTSHUcQmjBH8BJAjCuf0cevPBwerUP8JtZS7n+uSVsrYxnPIbl34b55TOfc8F/PmZjWZTfTB/Gvy8Yy7DuRRl9H3PnWorevIZk90OpmvDrjJ1XZpMKUX8Br5XRB01KKS783lTu8PyYDts+wHz/zv0er4F4SlZLRdshSanIqWTZBjpF17CkYCwlXUK5DkcI0UL5LDPdFdMwCU+8Eat8DYHF/971/Z7tC/jX+Ydy5XcG8u7K7Zx+70L+PncVZZFkk9435bq8s3wbVz++mPMe+IhF68u5ZGJ/nrn8KE4e1QMj09UdqTjFr1wCyqJi+n1gZq7pijQ4EqJhigs8GS3jDfktjvzBDF5yjqT9R7dibfpwv8dLCa9oSzJbmyBEAyWWvYEPcAdI6a4QYv98pkk85ZAcMIVE/8kEF95KfOj3cUNdAbAMg/OO7MfkYV355/w1PLJwPTM/3MBxw7pw/IhujO3bHn895nMmUg5fbKxg7vJtzFuxnR1VSToX+rjs6AGcPrYXhf7MJYp70JrCt3+NZ9sSdp7yMG5Rr4yeXmaTCtEwpqEo9HuojB94lEt9De9ZzDNH/YFNC8+k/QuX4lz0Drqgfa3HplyN7bgZXbEVoqVSWuemXn3s2LH6k08+ycl7i5Zj073fh28+Z805Czl6iHTeFULULW47VMTSN4fmzjV0fHgS8cGnUHnC3bUev25HhGcXbeKVJd8QSTj4LIOh3Qop6VpItyI/7QIeDKWwHZeySJJvK+Os2lbF8m/DpFyNzzI4ckBHTji4GxNKOmEZ2b0xLPjsPxS9fR1Vh/8PkQyW7UJ6NmkHaSQnRKNURO2MltJqrbnnsae5YesMKnofi3v6I9Q1ILXAa1KUrQdhQjQDpdSnWuuxBzxOklKRKzqVIP6HfrzsHsX3bngKj+x1EkLsh9aa7eEENVet0ILfE/zoLsrOfAm75+F1vi6Zclm0YSfvry5l2TeVrNpWRbSWsrgOQS99OwQ4qGcxo3oXc1i/DvVaWc0Ez6aFtH/mNJJ9j6H8e49kZPzL7or8Hgq88hkrRGO4rqY0ksTN4D1zRdTm1ft+wwz3IXYc/XucsbXPIVYKOod8Uk0mWq36JqVSvityJrFmIQVulJ09JklCKoQ4IKUUXssgUT2/L3L4DPzLnqXozZ9T+qM5YPlqfZ3XMjhiQEeOGNARSCe3MduhPGqjNVimorjA02wJ6N6Myk20e+linOJ+VEz7R8YTUoXsJxWiKQwj/RmxM9q0Peq7Kw54GHrqtcx96nMmvHsTFX2OwOly0D7HaQ2JlJuzzychmotcpUTObFv8Mklt0nPM1FyHIoRoJXbvHqu9QSon34ZVtoLgB3+t9zmUUgS8Fj3aFdCzfQFdi/w5u+FTySravXgBOEnKT3kI7ctsJ18An8eUVRYhmshrGRRmeEzMmL4d+eKwP1LmBrGeuxjsSK3HScMj0RZIUipyxrv2bT7VQ5g0ckCuQxFCtBI1o2FqJPsfS2zEWQQ/vhtr6xc5iqqRHJvily/B2v4VFdPvw+kwKCtvIw2OhMiMgNfCn+HKrtOPHsM/OlxHUWQ96tVraz0m6bg4MrNU5DlJSkVO6IpNdIuvZm27owj5pIpcCFE/hqH26UQZnnQzbqATRW/8FFKZn0+aFVpT9Nav8K17m8rJt5Lsf2xW3sY00iXPQojMKCqwMI3MVR6YhuKM08/h3+pUuqx+GmPZC7UeF7dltVTkN7lSiZz4dtErAARGSOmuEKJh9t4fqf3tqDz+Djw7llE476bcBNVAwQ/voGDpTKrGXUP84B9m7X1klVSIzFJK0a7AU1ez3EbpGPLR7ZTf8Zk7kOAb12CEN+9zTEySUpHnJCkVORFZ+hpbdAfGjRuf61CEEK2Mr5byuWT/44gc+hMCn/8H34qXchBV/RUs+heh9/9MbPiZRI76VdbeRyFJqRDZYJkGxQWZHdNy+MBuzD/oj2jHxnnmUnD3TEIdV5OsbvImRD6SpFQ0P8eme9mHfO47jO7tArmORgjRypi1lPACVE24nmS3Qyia/TPMnWtyENmBFXzxMEXv3EB80HQqp/y1ztmEmeCzTIwMlhkKIf7LZ2V+fugPjp/I/YVX0G3nJ6TevXOf72dyVqoQLY0kpaLZVaxYQFBHs7aHSgiR//ZueASA6aVi+j/B8NBu1o9Q8fLmD2w//Esfp2jOL0kMmELF9HvBzOwN7T7v55VLvBDZVOA1CWawL4ZlGEw64394Qx9B109vQ21ZvMf347aDzuCsVCFaErliiWa3+eMXsbVJyREn5joUIUQrVdcIF7e4D+Un/wezYgPFL10Mjt3MkdWuYPH9FM+eQaLvMZSfeD+Y3qy+n2moWsuchRCZFfJZFHgz97PWvV2A8ORb2aaLMZ6/ZI8xMTUzS4XIR5KUimZXuOkdvjCGMaxfz1yHIoRopUxDYdVRmmr3OoLKKX/Ft3EBRbNn7LM3q1lpTfD9Wyma+xviA6dSfspDYPmz/rayl1SI5lPk92R0VMzEUYN5ts+NdIhvIv7SdXt8T7rwinwlSaloVvHSjfROrmF7t4kyzF0I0SR1rZYCxEecSXj89RQse4aiOb8AnYPVBSdJ4ZxfEPrgNmIjzqLipH83S0IqDY6EaH7Fgcwmpid/70ye8J5K33VPYS/975iYRMrFlZmlIg9JUiqaRzgM999P1WUXwaIkXQbLflIhRNPsLykFiI6bQdW4ayhYOpOiN38ObqqZIgMV3U77Z04nsORRIof/lMrj/wZG88xklgZHQuRGccBzwM+l+vJ7TPr84P9Y4g6geM7PUeFvdn1PGh6JfNQ8V0jRti1YANOmgevSKRLB9SjGvHMavPoqTJiQ6+iEEK1UTRde26l7FbRm5Erow9sxItuomH4f2hvMalyeTR9Q/NpPMKKlVEz7B/Gh38/q++1NGhwJkTvF1TNMY8mmJ44lPTry5uF/YeDHPyT+9KX4LnwBlEHcdglkd1u6EM1Orlwiu8LhdEIaDkMkvVnfsDWq5utVVTkOUAjRmvk9B7iMKUVk/LVUTr4V77q5tH/iRMyyldkJxkkSXPAH2j99KtrwUHbWi82ekEqDIyFyr8jvIZShrryTJ07g8Q5X0Kf8IyLz7gLAdlxS+3kYJ0RrJEmpyK4nnwS3jg9O101/XwghGqm+CVhs5HmUn/oYZmQrHR+dQsEXj2R0n6l3/Tw6PnIsoY/uJD7iLMrOfYtU11EZO399BTLYBVQI0XhBn5VeNW3ieZRSTDjjGuaqw+m16FaczZ8DEJcuvCLPSFIqsmvlyl0rpPuIRGDVquaNRwiRV0xD4TXrdylL9vsOpee+jd39EIrm/IL2T5yI9e3iA79wP6xvF9Nu1rm0f/YMcGx2fu9RKo+/A+0NNem8jaEgo41WhBBN4/eYdAh6MZu4x7td0Ic97W/s1CHU85dCKi5deEXekaRUZFdJCQTr2L8VDMKgQc0bjxAi7zSksYgb6sbO056h4rt3YVasp+PMqbR75nS8q98AJ1m/k9gR/F89Q7unf0DHmVPxbP6I8PjrKT1/HskBUxr5p2g6v1caHAnR0limQcegt8kNkEYNGcibJb+le3IdZS/cgONqkrJaKvKI0jo3baXHjh2rP/nkk5y8t2hG4TD07Jn+dW+FhbBlC4Saf0VBCJE/XFezoypBQ69mKhGm4IuHCCy6DzOyFddXTLLP0djdDyHVfiBuoDMYFioVxwhvxtq5Gs/mD/Bu/hjlxHGKehMdeT6x0RfmZGV0bx2CXjz1XDUWQjS/uO1QGbdp7K13ynH57N4fMz3xCitPmEnXUcdT5PdkNkghMkwp9anWeuwBj5OkVGTdggWkpkzGchJgk14hNQzpviuEyJiKqN34MQlOEu/6efhXvIR30/uYlRvrPNTuNJxk7/EkBk3D7nUEqJaRBHpMgw5BaccpREvnupqqZKrR3Xm/3V5K8cPHETBTOJctoFuXrjL3XbRokpSKFmXh747n4M8WERr243RJ75lnygqpECJj4rZDRczOyLlUdAdmxQaM2A6UdtGmDzfYlVS7fuAJZOQ9Mq24IHPzEYUQ2ZdyXKoSKRKNKMFd9P6bTFl4Hks7fpehVzwmP/uiRatvUipzSkXWfbMzzAiWsH7qCYy44k+5DkcIkYd8loFSNLosbnc60IlUoFPTT9RMDKXkplSIVsYyDdoFvNiOSzThNKjS45CjpvDmynM5ofQhlrz8Lw4utdKNJWse+hcWZjFyIbJDklKRdYvfm800FaXD6Gm5DkUIkadUdWKWiYH1rY2MgRGi9fKYBsUBg5BrEbcdYraD4x746dqwM25h/a/f4KA//gRtFaCi0fT2qGuuke1RolWSpFRknb3sNVKYdB8zPdehCCHyWEEbTEoV6T+3EKJ1Mw1F0GcR9FmkHJdEysV2XJKOW2sFSCBl0/GJTaikhmQ0/cWaEXzTpu23kaTrahytcbVGa3C1xq3+VWtAg0bv8b5KgUKBAkOl4zWUwjIUpqFkX6toMklKRVaVRZIMC7/PxnZj6O8vynU4Qog85jENLEORqscqQ76QMTBC5B/LNLB266TtuBrbcXFqkklX45n1bDpJrIV2XRKPziRx/oVQnWzunoRmJWZDYZkGXtPAaxlNns0q2h5JSkVWLfj4Y042NrNx2EW5DkUI0QYUeE3C8VSuw2g2AVklFSLvmYbCNPb6Wd+wFqKRWo9XkQipFSuJ281XOZJyNSnX2fWelqHweUz81p4JthB1kX8lIqsqPn8FgJ6HfS/HkQgh2gK/ZdaxdpB/fHKzJ0TbVVKS3kNaCzcQxBkwsJkD2lPK1UQSKUojScoiSWJJh1xN/BCtg1zNRNaE4zb9St9lu68PRqdBuQ5HCNEGGIbCZ7WN1cOAV4qdhGizzjwzPfO9NoZB4vunNW88+2E7LpVxm+1VCaoSKdw2tMVC1J8kpSJr3lmylsPVV8T6Tc51KEKINqSgDXSj9VTv2xJCtFGFhekuu4WFu1ZMHa+J9sJHN/8W3QJnwWsNkUSKHZEEkURKVk7FHuQxq8iajZ++ik+l6HLoybkORQjRhtQ02ajPWIXWSsbACCGYMCHdZffJJ2HVKiJdOpHcfhedKv7Dt6U/pFvH9rmOsFZaQ1UiRTTpUOi3ZM6yACQpFVkSTabo8s07xMwg/gHjcx2OEKKNCeRxwyPTUHITJ4RIC4Xg4osBsJIptn0ykEGzz2fWk9fT6fJ7sOoq8W0BXK2piNnEbYdCv0c69rZxLfdfqmjV3vl6K0ezmLLuE1GWN9fhCCHamHxueBTyyfNkIcS+/JZJ4UFTWd77DE6OzWL2q8/nOqR6SaRcSqsSzdotWLQ8kpSKrFj6yXy6qHKKRp6Y61CEEG2QUT2OIN8YSlZJhRC1MwyF1zRof8qfKPV259ivb+Kz1ZtyHVa9aKAiZlMRtWWvaRslSanIuHDcJrRhDi4Kz9Dv5jocIUQbFczDfZeySiqE2B+/x0R7g6ROvJvexnYqX/wN5dFkrsOqt3jKoTSSJOW4uQ5FNLN6JaVKqalKqeVKqVVKqevqOOYMpdRXSqkvlVIzMxumaE3eWb6NCe6nlLUfhbewc67DEUK0UZZp4M2jOZ7pVdL8+fMIITLPZxkowOg/nm+Gns/p+nWee3Zmq1p9dFxNWTRJIiXlvG3JAa9uSikT+DtwAjAcOFspNXyvY0qA64HxWusRwIwsxCpaAdfVLPzsS0Yaa/ENn4Yhm9aFEDmUT+NhQj4LpeQzVQhRN8NQu8ZFWVNuoqygL+duu5VZH3yd48gaRmsoj9rEkpKYthX1eeR6OLBKa71Ga50EngBO2euYS4C/a613Amitt2U2TNFaVMRsPGvmAKCGTM1xNEKIts7vMfOio6Oskgoh6mvXvnNPAfqUf9BDldHhvZtZsTWc28AaoTJuU5XIz07qYk/1ucL1BDbu9vtN1V/b3WBgsFLqPaXUB0opyUbaqHeWb2e8+wnRgu5Y3UbkOhwhhCDobf37MGWVVAhRXzUlvACpHoeyc/QVnGHM5eVnHmyVK4+RRIrKuJ3rMESWZeqxqwWUAMcAZwP/Ukq12/sgpdSlSqlPlFKfbN++PUNvLVqKRMph7pcbmGAuxR10PF4rf8rmhBCtl99jYLTihM40VF6VIQshskup/5bwAqSOvpbKohKuid/Nva9/ksPIGi+WdCQxzXP1SUo3A713+32v6q/tbhPwotba1lqvBVaQTlL3oLW+T2s9Vms9tnNnaYCTbyqiNvaquQRI4Aw+QfaTCiFaBKUUgVac1EnHXSFEQ+0xOsryYZ90D51UmHHL/8ybX23NXWBNIIlpfqtPUvoxUKKU6q+U8gJnAS/udcws0qukKKU6kS7nXZPBOEUL57ia+Su3c7TzEbYVhH4Tcx2SEELsEvCatMbFUo9pyFxSIUSD7V7CC5DqOpLIuJ9xqvkeH732EFvKYzmLrSliSYewJKZ56YBJqdY6BVwFvAEsA57SWn+plLpFKXVy9WFvAKVKqa+AucAvtdal2QpatDwx2+Gtr77heGsR9oDJ+PwFuQ5JCCF2UUq1yr2lskoqhGiMvUt4AWK7kNUwAAAgAElEQVRHzCDa8SB+x7+49fn3Wu0s0GjSISLNj/JOvfaUaq1f1VoP1loP1Fr/vvprv9Vav1j931prfY3WerjW+mCt9RPZDFq0POXRJJWrFtKRCpKDTsCTR7MBhRD5obWtlvotc5+bSiGEqK99qixMD9Hpd9POiPHDHXdy3/zVuQksA6oSqVbZtEnUTa52osnitsPC1aVMdD7CVRbOwMl5MYJBCJFfWtNqqQJC/tYRqxCiZdq7hBfA6TSM6PhfMd38iLIPn+DjtWU5iS0TKuM2iZQkpvlCklLRZLGkw1tfbWWq9SnJ3uOxAvs0XhZCiBYh4DVbRSfegM+Sh3tCiCaprYQXIDr2J8S7Hsr/eR/i/724gJ2RZA6iy4yKmN1qy5DFniQpFf+/vfuOk6q8/jj+ee6durOVooKAVBUQEcUSe4siEgux90RNVaOJ0SQa/cUUW2KiJhqNJjGJGktQLNhrNDaUXlSK0ouUrdPv8/tjF2TZXdiF2Sk73/frlVdg7r0zB7jgnHvOc57tkkp71MSSLJk3lf4sJzFkLEG1m4lInjLG5P06TccYIgU8LVhE8kerg9IcH3XH3UGpm+Dq5N388plZWGuzH1wGWAvro0k8rzDjly8pe5Dt0pBM899Pv+DQ9PsAxAceq/WkIpLXwgEXXx5XIctCPkwBVHNFJP+11sILkO42mIZDruFI5yN6ffYEj3ywOOuxZUras1RHNZG30Cl7kG1mrSWWSPPCrBWM9X9EYse9oLy3Ws5EJO+Vhfy5DqFVIZ+rLWBEJGPaauEFaBh1MYmdD+CGwD95/LX3+HhFbZajy5xE2tNWMQVOSalss2gyzfpokvnzP2UPPiU++DhNihSRghDwOYR8+ZX8GdNYJRURyaQ2H3QZh5pjbyfkwq2Bv3DtEzNoSBTuVisNiTSxpAYfFSplELLNGhJpXp27iiPMhwDEBx+n9aQiUjBKQ75W29pypTzkx1GniYhkWFstvADpyv7UHXY9B9hpHFzzDL978ZOsxpZpNbEkaa0vLUjKIGSbxFNp0p7lhZkrODE0hVTlANLddtV6UhEpGK5j8mbbFbXtikhn2VILL0B0z/OJ9zuU64MPM3XGdF6YtSKL0WWWtY0TeQt1cFMxUwYh2ySaSLOyJsani5exjzeD+KDjcBxH60lFpKCUBHwEcvwwzTFGbbsi0qmCW1quYAw1x/wev8/hrtL7ufm52SxdF81ecBmWTHvUJ9TGW2iUlEqHpT1LPOXx4qyVHOlMwbUprScVkYJVHvaTy2G3FWG17YpI59pSCy+AV96H2sNuYM/kdM5yXuLnE2cW9P6f9fEUiVThxl+MlEVIh21YBP/CrBWcEfmIdGRHkr1Haz2piBQk1zGU52gab1nIpwd6ItLpHMdsdYlVbI+ziPc/iqvdh6hb/gn3vLkgS9F1DrXxFhb9l1A6xFpLNJFmweo6lq76gv3SHxEfcjwYR+tJRaRghfwu4UB213SG/C4lAbXtikh2bHXdujHUfPV3OL4Af638G/98ZyHvLVyTneA6gWctNbHCnSZcbJRFSIdEk2ks8MKslRzlTsXnxYntegKuY7SeVEQKWlnQl7WHawHXoVzrSEUki9rT0eaV9aL2iF8zKDqDK8tf5f+ems3a+kQWouscsaS2iSkUSkqlQxoSaTxreWHWCs4qm0K6pCfJ3vup/UxECp4xhsqwv9MfsPkcQ2WJH5PLhawiUnQcx7RrsFts6KnEBh3Ld9IP0jO2iBueno1XwG2wtbEUnraJyXvKJKTdYsnGbWCmLlrPuupqRic+aGzdddycT68UEckExzFUlQRwOilh9DW9vxJSEcmFdm09ZQy1R/8W4y/hgW5/470Fq/n3+4s7P7hO4llLbVxtvPlOmYS0W7RpvPazM5ZzbGAGfi9GbMg4ACWlItJluI6hWyTzianfdRoTXi11EJEcae9QSi+yAzVH3shOtTO4cafX+dNr85izvKaTo+s8sWSaeEptvPlMmYS0SzLtkUh7NCRSvDJnFedVTMULdyfZ5yv4HKMvWSLSpbiOoXskkLE1piG/S1WJtn4RkdxqbwsvQHy3k4gNGcepNf9gdMkKrnliJnUFPDioJprSNN48pqRU2qUh3vh06fWPV+Mlo4yMvkts8HHg+PBrPamIdEGNrbz+7ZrKa4DykJ+KsNaQikh+CPrb+b3NGGqOuhmCZdxbdh9fVNfxm0lzCjax86ylTm28eUvZhGxV2rMbWx6emb6ck8vn4ks1EN/1BECtuyLSdRnTuIdpZYkfXwernCGfS/fSYNa3mhER2ZKgr/3/JtmSHtQcdQvl62Zx3+C3eGXuKiZ8tLQTo+tcDYk0qbSX6zCkFcomZKsaEikssLw6yoefr+Ocsil4oSoSfQ4E2r8+QUSkUAWbEsyKsJ+gz6Gt9NQxhnDApXskQEVJ50/yFRHpKNcxHVqaEN91HLHdTuLgpX/l9H7r+cPLn/LJytpOjLBzae/S/KRsQrbIWku0aX+nSTNWECDJ0Jq3iQ8aA64fv+uoJU1EikbI71JZEqBnWZBukQAV4cbW3MoSPz1Kg/QsC1Ie8uNTB4mI5LFQe1t4m9QceSNeqJJfpP9I9xD87IkZ1BdoK2wy7Wnv0jyk/2rKFkWTaaxtTE4nzVjOhTvOw03WEdvQuqsqqYgUIWMaKw0hv0vI7xL0uaqKikjB6EgLL4ANd6P2q78ltGY2/9z1TZaui3LTc3MLdn1pTSxZsLF3VcooZIvqmwYcTVtSzZJ1UU4LvYsX7k6i3yGA1pOKiIiIFJqOtvACxAeNITrsNAbNvYef75PgxdkrmTh1WSdF2LmsRUOP8owyCmlTLJnGa3qK9Oz05fQIJNjli/82VkldPwbwu6oMiIiIiBSabZkJUnv4L/FKenLO8hs5sH8pt730CZ+uKsz1pRp6lF+UlEqbNqwViCbSvDxnJZf0/gQnHSO2+8lAY+uu1pOKiIiIFJ6Qv+OTwW2okppjbsO/5mPu2Ol5SoM+rpkwk4ZEYVYdVS3NH0pKpVXxVJqU11glfWnOShoSaY7nLdJlfUj23hfQelIRERGRQuU6psNbXQEkBhxFdI+z6D7tz9xxSIrF6xq45fmPC3KNZjzlbdz2UHJLWYW0qiH+5V/QJ6csZWS3JD1Wvk1st5PANN42Wk8qIiIiUri2pVoKUHvYDXilvdh/2rV8+8BePDdzBc9MX57h6LKjVlvE5AVlFdJCMu2RaOqx/3RVLbOW1fCDXrMxNk1s6HigcS8+bXkgIiIiUri2da95Gyyj5pjf41s3j+/bRxi9SxW3vvAxC1bXZTjCzpf2LNGEqqW5pqxCWti0SjpxyjL8ruGAhtdJdduVVI9hgFp3RURERAqdz3W2qYUXILHLYTSMvIDIR/dw6/71lARcfvbEzIJM8Grj2iIm15RZSDNpzxJr6q2PJdM8P2sFXx9kKVn+XuOAo6bBRtv6ZE1ERERE8kdwG1t4AeoOuY50RV/6vvljfn38QD77op7fvvhxBqPLDmuhvgCT6a5EmYU0U7/J9LRX566iNpbi/PIpAMR2O3njMa0nFRERESl8oe0oNNhAhJpjbse3fiGHL76LCw7qzzPTlzNpRuGtL22Ip/A8VUtzRZmFbOR5ltgmT4memLKUvt3CDF7xLMkd9yJdNQAAn2NwtrHVQ0RERETyh891cLfje12y74E0jLqIkqn3873+yxnVt5Kbn59bcOtLLVBXoFvbdAVKSmWjhmSaDc+HFqyuY/qSai4aUo//i9lEh5++8TytJxURERHpOrZ1Cu8GtQdfQ6pyAN1eupxfj+1P2O/y0wkzCm7/0lgiTVrV0pxQdiEAWGub/cMxceoyfI5hnPca1vE3bgXTJOjbvn+4RERERCR/bPesEH8JNcfegVOzhP5TbuZXJ+3BorUN3DhpbkENELJAXbywEumuQkmpABBNptnwb0YsmWbSzOUcOaSKqnlPEh94DDbcDQAD+F217oqIiIh0Ff7tbOEFSO68Hw37fIeS6Q9woJnBtw8dxIuzV/Kfj5ZmKMrsiCXTpJq2RpTsUVIqWGup32QbmBdnr6QmmuLbvRfgRNe0aN01RkmpiIiISFeSiZ0V6g68mlS3IZS/eAXn71PFgYO68/uXPmHWsuoMRJg9m34vluxQUirEkh5eU5nUWstjkxczqGeE4aufxQt3J9H/yI3nqnVXREREpOvZ3nWlAPjDVB97O07dcireuJ7/O2E4PcuC/GzCTKobktv//lkSS6VJqlqaVUpKpdk2MDOWVvPJyjrO3rOU4IIXiQ79Orj+jcc15EhERESk6/G7Dk4GuuFSvfahYd9LCM96mJ7L3+DG8SNYUx/n+qdnbSyCFIJ6rS3NKmUYRS6WbD5l7LHJSygN+jjBfQfjJYkNP2PjMZ9jtnu9gYiIiIjkp5A/M6lB3QFXkuy+O+Uv/YhhVR5XHL0r78xfw9/f/iwj758N8ZSnamkWKSktcps+BVpTF+fVuasYt2cvKj5+nGTP4aR6Dt94XFVSERERka4rY8u0fEFqxtyJ07CasteuZfzeOzNm+E7c++YC3l+4NjOfkQWqlmaPsowiFk+lSW1SJX1y6jJSnuWcgfX4V04lNuy0ZudrPamIiIhI1xXwZaaFFyC1457U73854TmPEZr/PFcftxv9e0S4buJMVtXGMvIZnU3V0uxRUlrENp0slkp7PPHRUg4Y2I2Bix7HOn6iQ0/ZeNwYVUpFREREurpMtfAC1O9/Ocmee1D28o+JpGu4cfwIYkmPa56YWTDbrqhamh3KMopUYrMnP298sprVdXFO36snodmPER8yFlvSY+PxoKsqqYiIiEhXl9HOODdAzZg7cGLrKX/1pwzoEeFnY3dn+pJq/vTa/Mx9TieKp7yCSaALmZLSItWQaP7U59HJS+hdGeLw9P9w4tU0jDiv2fFgBp+aiYiIiEh+ymQLL0Cq53Dqv/IjQh8/SfCTpzlm+E6cuk8fHnp/Ea/OXZWxz+lM2re08ynTKELJtEc89eUTn9nLapi6eD2nje5LZOa/SFUOINn3oGbXBFzdKiIiIiLFINPFiPp9LyW5416Uv3I1pmE1Pzh6CMN7l/PLZ2bz+Zr6jH5WZ4il0qqWdjJlGkWoYbOnPQ++9zmRoMv4vnUElr5HdMS5jYtImwRcB0dbwYiIiIgUhVCmh1s6PqrH3IFJ1FL+8tX4HcON40cQcB2uenx6QazbrE+oWtqZlJQWmVTaI5b68i/V8uoor81dzUl77Uz3jx9uHHA0fLOpu2rdFRERESkamW7hBUh33426A68mNO9ZQh8/wY7lIX598h4sWtvADc/Mxlq79TfJoXgyTdrL7xgLmbKNIrP5U55HPlgMBs4Y1ZPwrEeJDz4OW9Kz2TnaCkZERESkuHRGUaJhn++S6LUPZa/8FKduJaP7d+OSIwfz+ser+cc7n2f88zLJAvWJ/K/oFiolpUUk7VliyS+T0rpYiolTl3H00B3ot/JlnPh6onue2+wan2Nw1borIiIiUlQy3sIL4LjUHHsHJhWj/OUrwVrO2q8fXx22I39+Yz7vLliT+c/MoFgijadqaadQUlpENn+6M3HaUhoSac7crx/hqX8lVTmQRN+Dm50T9KtKKiIiIlJsOqOFFyDdbTB1h/yM4IIXCc1+BGMM14wdysAepfz8yZksWx/N+GdmigUaklpb2hmUlBYJz7PENmndTaU9HvlgMXv3q2SEmU9g+Yc07PVNMM1viaBPt4iIiIhIMeqsuSINoy4m0edAyl79Ge76zwgHXG4+ZQQWuPo/05t19uWbhkQq79e/FiJlHEWiPpFi078+r8xdxcqaOGft34+SKffj+SPEhp/R7BrHGPzaCkZERESkKHVKCy+Acagecyc4LuXPXwpeij5VJfzihOF8urKOG5+bm7eJn7UQzeOkuVAp4ygCnmeJblIltdbyz3c+Z5duJRzSyyP08ZPEhp+BDZY1uy6kqbsiIiIiRauzWngBvPI+1B55E4Fl7xP54E4ADhrcg4sPHcjzM1fw6OQlnfK5mVAfV1Kaaco6ikBDMt2sSvr2/DV8uqqO8w/sT2TGvzBekoZRF7a4TlN3RURERIpbZ24NGBv6dWK7nUTknd/iWzEVgG8c1J9DhvTg9lc+ZcqidZ322dvDszavW4wLkZLSLs5aS8MmA46stfz97c/oVRHi2N2rCE9/gHj/I0lXDWp2nWMMAa0nFRERESlqndbC26TmqJvxSnag4rnvQbIexxj+72vD2bkyzE8nzGBVbaxTP39b1ce1PUwmKevo4hoSaTZtyf9o0XpmLK3mnAN2oXTBJNz6lTSMuqjFdZ35VExERERECkNntvAC2FAl1WPuxF23gLI3bwCgNOTj5q+PIJ7y+Ml/ZhBP5V9VMuXZvIyrUCnzaEMq7eXtAuv2sta22Abm7//7jG6RAONG7ETJh/c0bgPT/4gW13b2UzERERERKQydPWck2e9gGvb5LiXT/k5gwUsADOxZynXjhjFrWQ03P/9xXn4v33Rmi2wfJaVt8LrAZK1osnmVdPayGt5fuJaz9u9H2cr38K+cSsPo77bYBkatuyIiIiKyQTbmjNQd9BOSPYZR/uLlmIbVAByx+w5cdPAAnp2+nH9/sLjTY+ioeMojlfZyHUaXoMxjCxoK+OmHtbbFZLC//+8zykI+xo/amcgHfyJd0oPo0FNbXKupuyIiIiKyQWe38ALgC1I99i6ceC0VL/6QDZWVCw8ZwOG79eSOVz7l3QVrOjeGbdBQ4EWsfNGu7MMYM8YY87ExZp4x5idbOO/rxhhrjBmduRBzJ+0V7mStWNLD26RMumB1HW98sprTRvelouZjgp+9QsOoi8EfbnFtyK/WXRERERH5UjaKFukeQ6k95FqCC14kPOOfQGMH3/VfG8bAnqVc++RMFq1p6PQ4OiKWSON5+ddaXGi2encZY1zgT8BxwDDgTGPMsFbOKwN+ALyX6SBzqVCrpXWbTQS7778LKQm4nD66LyWT78LzlxAdeUGL61zH4HdVKRURERGRL2WraBEddRHxXQ6j7PXrcNfNB6Ak4OPWU/bENYYrH5tGXSx/Jt9aCn/JXz5oT/axHzDPWrvAWpsA/g2c2Mp5vwRuBvJzbvM2SqY9EqnC6hWPJdPNqqSfrqrllbmrOH3fvlSlVhCa+wTREediQ5UtrlWVVEREREQ253cdXKeTW3gBjEPNsXdgfSEqJn0P0gkAeleGuXH8CJasj/LziTNJ51F1snG3i/yJpxC1JyndGdh0ZfGSptc2MsbsDfS11j6bwdjyRkMif57GtMfmVdK/vLmQ0qCPs/brR8lH94IxNOz9rVavDWnAkYiIiIi0IlvFC690J2q++jv8K6dS+vbNG1/fe5cqrjxmV/43fw13vz4/K7G0h2ct8QIrYuWb7c5AjDEOcBvwo3ac+y1jzGRjzOTVq1dv70dnTSFN1ool082eHM1ZXsMbn6zmzP36Uumto2T6P4ntPh6vvE+La32OwafWXRERERFpRTaLF/Ehx9Ow5/lEJv+RwGevb3x9/N59+PreO/PPdz/n+ZkrshbP1tTHC6uIlW/ac2ctBfpu8vM+Ta9tUAbsAbxujPkMOAB4qrVhR9bae621o621o3v27LntUedAfYGsLd38L8Rf/ruA8pCPM/btR8nkuyAdp37/K1q9NhxQ666IiIiItM6XrRbeJrWH/YJk990pf/4SnPpVG1//4Vd3Ze9+lfz62TnMXlaTtXi2JOXZglvyl0/ak5R+AAwxxgwwxgSAM4CnNhy01lZba3tYa/tba/sD7wInWGsnd0rEORLfrAKZj+KpNKlNYpy5tJq3563h7AN2oTy9lpJpfyc29BTSVQNbXGuAUBb2oBIRERGRwpXV+SP+MNXH34MTr6X8+UvBNiZ9PtfhNyePoHtpgKsen87q2nj2YtqCaIEUsfLRVpNSa20KuAR4AZgDPGqtnWWMucEYc0JnB5gvLFCf52tLN9+X9N43F1AZ9nPqPn22WiUN+BycLD75EhEREZHCk+35I+keu1N7xC8Jfv46JZPv3vh6VSTArafuSV08xZWPTcuLhDCWyv8iVr5q111lrZ1krd3VWjvIWvvrpteus9Y+1cq5h3e1KukG+bwPUSLlkdxk3euURet4b+FazvnKLpSlmqqku3+91SopaOquiIiIiGydz3XwZbmQER1xLrEh4yh9+zf4ln+08fUhO5Txq5P24JOVtfzfU7Oa7T6RK9oeZttoqk0H5HO1dNO1pNZa7nx1Hj3Lgs2rpAe0XiU1RkmpiIiIiLRP1r83GkPNV3+HF9mJiknfwcRrNx46eEgPLjtqCK9/spq7Xsv9RN6GRErbw2wDJaUdFM3Damky7ZHYpEr66txVzFpWw7cPHUhJbAUlU//atJZ0UKvXh5WQioiIiEg75aKYYUOVVB//Z9yaJZS9chVskvidsW/fjRN5n5q6LOuxbcpatD3MNlBS2kH5WC3dtEqaSnvc9fp8BvWMMHZEL0r/dwtgqTvwqjavV1IqIiIiIu3lOgZ/DrYRTPbel/oDf0x47gRCsx/Z+Loxhh8esyv7D+jGTc/PZfJna7Me26Ya8mB9a6FRUroN8qlamkp7zZ7GPDFlKUvWRfneEYMJrplDaNYjNOx1IV5531avD7iO9iYVERERkQ7JVVGjft/LSPQ9iPJXfoK75uONr/ucxom8/bqV8JMJM/h8TX1O4oPGLsZNZ73I1ikb2Qb5VC3ddOJuXTzF/W8tZO9+lRw0qDulb/0aGyyjfr/L2rxee5OKiIiISEcFfQ452bfBcak+7m68QCmVT1+ESXyZfJaGfNx22kh8juGHj05jfUMiFxECqpZ2lJLSbRRN5H7kc9qzxFJf3vAPvvs56xqSXHrkEAJL3iG48GXq970MG+7W6vWOMRpwJCIiIiId5jiGQJa3h9nAK92R6rF/xl03j7KXr2y2vrR3ZZhbTxnJqpo4V/9nBokcre+MJ/Ons7IQKCndRvlQLd3081fWxHjo/UUcPXQHhu0UoeyN60mX9qJh1EVtXl+iKqmIiIiIbKNcFjeS/Q6m/sCrCM+dQHj6P5odG9GngmvHDWXq4vXc+NycnEzDtWh7mI5QUrodook0qRz1i6c9S2yTtoA/vjoPz4PvHzGY8MwH8a+aTt2h14E/3Or1Bg04EhEREZFtl7MW3ib1+/2AeP+jKHv9WnwrpzU7duzwnbj4kAFMmrGC+/67MCfxqYW3/ZSUbqe6eG6qpQ2JFBue+UxdvJ4XZ6/k7AP6sXMwRulbN5LY+SvEdju5zeuDfhcnyxsfi4iIiEjXYYwh6MthkcM4VB/3R7ySnlQ+fSEmtr7Z4QsPHsDxI3px31sLeWpa9reK8awlnlJi2h5KSrdTPOVlvVfd8yzRpicvac9y24ufsENZkPO/0p/S/92Cia+n9shfg2k76YyodVdEREREtlMokNt0woa7sX7cX3DqVlDx/KVgv/xebozhZ2N3Z78B3bhp0lzemb8m6/FFVS1tFyWlGZDtamlDMr2xSvr0tGV8vLKWS44cTFn1XMLT/050z/NJ9Rze5vVBn7aBEREREZHtF/S5W6qDZEWq1z7UHno9wQUvUjL5rmbHfK7DjeNHMGiHCD+dMIO5K2qyGls85eV8OGohUGaSAcm0RyxLC5mttTQ0DTiqjSW5+/X5jOxTwTG796DspSuxoSrqDrx6i+9REvBlI1QRERERKQL5sJtDdNRFxHY9gdK3foN/8dvNjpUGfdx22l5UhP1c8cg0lq2PZjc2DTzaKiWlGVIbS2VlsldDIr1x6vV9/11IdTTJj47Zjci0+wms+Ijaw3+JDVe1eX3AdXI2vltEREREup68GJ5pDDVfvY101UAqn7kYp2ZJs8M9y4L8/vSRJNMel/97KtXRZNZCa0hkJ08oZMpOMsSzttPbeBurpI1PWj5ZWctjk5dw4l69GRpeS+lbNxIfcDSx3cdv8T0iQVVJRURERCRz/K6DmwcDNG2wjPUn/B3SCSqf+gYkm1dEB/Ys5dZT9mRZdZQrH5uWtSFE1ja28UrblJRmUEMnbxETS3p41pL2LDc9N5fysI/vHT6I8pd/jDUONUffssXhRqqSioiIiEhnyItqKZDuNpjqsXfhWzWD8pevhM0qlKP6VfGLE4YzfUk110+chZelCqYGHm2ZMpQMq4l1XrW0vmkt6RNTljJrWQ2XH70rO817hODnb1B3yM/xynbe4vWqkoqIiIhIZ8iHdaUbJAYeQ/2BVxGe8zglU+5tcfyooTty+dFDeO3j1fz+pU+y0lqbSHudWrwqdEpKMyyZ9jYOIsqkWDJN2rOsro1z1+vz2K9/N8b2rqPs9Z8T73cI0ZHnb/H6oE9VUhERERHpHK5jCOTR7g71+19ObNBxlL7xC/yL3mpx/Mz9+nHGvn15dPISHvjf51mJSQOP2pY/d04XUhdLZXz0c33TetU/vPwJyZTlqmMGUvnc97G+EDXH3glmy3+UpaqSioiIiEgnyqdqKcah5rg/ku42mMpnv4VTs7jFKT84eghjhu/E3W/MZ8JHS1p5k8yKJtMaeNQGJaWdwAI1GZzoFUumSXmWd+av4eU5q7jgoP7sPvdP+FdOpebo3+KV9dri9eGAq31JRURERKRThfwOuR939CUbKG0cfOQlqZx4ASZR3+y4Yww/HzeUgwZ355bnP+aVOSs7Nx4NPGqTMpVOkshgG29DIk1dPMWNz82hf/cSLuq1kMj7dxAdfgbxXcdt8VpjoFT7koqIiIhIJzPGEPTlUbUUSFcNpHrsPfi+mE35c98Fr3kLrc91+M3JIxjRp4LrJs7ivYVrOjWeBg08apWS0k5UF0uR3M4FzYmURzLtcecrn7K6Ns6vjiinxwvfI9V9N2qO/M1Wry8P+XHyYES3iIiIiHR9oUD+pReJAUdSe/ivCM1/gdL//qrF8ZDf5XenjmyNo74AABp5SURBVKR/9whXPz6DWcuqOy2WpAYetSr/7pouxALV0eR29Y43JFK8t3ANT05dxnn77sj+H1wBXorqE/4G/sgWrw24Tn719ouIiIhIlxb0uThb2KIwV6KjLqRh5DeJfHgX4Rn/anG8POzn9jP3oiri54pHprHwi/pW3iVDsWjgUQtKSjtZ2rNUb+P60mTaY019gt88O5ddqsJcmbwH/8ppjYu2qwZu8VpjGv9yiYiIiIhkU8ifnylG7RG/JL7LEZS9cjX+Rf9tcbxHaZA7zxyF6xgue3gKK6pjnRKHBh61lJ93TBcTT3nUxTu+vrQhnuaPr85jZU2M+we9SemcR6k74Erig8Zs9drykB9XbbsiIiIikmXhfO3Uc3xUj7uXdNUgKp++EHftvBan9Kkq4fYz9qIhkeayh6ewrj6R8TA08KglJaVZUh9PEetAqT6V9njj01U8MWUpN+06l8Ezf0906KnUf+XKrV4bDrhq2xURERGRnPC5Dv483fnBBstZd9I/wfFR+cTZmIYvWpyz645l/PbUPVlRE+PSh6dkdFeNDaIaeNRMft4tXVRNNEk81b4bcOn6KDc8PZvxlZ9y2pKbSPQ5kJpjbmvsy90Cv+tQpj1JRURERCSH8rZaCngVu7D+xAdw61ZQ9cTZLbaKARjVr4pbTtmTz9bUc/kjU7ep63FLEhp41IyS0iyyQHXD1hPTVNrjuomzGBydwa3Jm0hVDWT9CX8DN7DF6xxjqAz7MXm4uFxEREREike+7Vm6uWTvfakedw++VdOpeOZiSLeshh4wsDs3jh/B3BW1/OjRaRmvbmrg0ZeUlGbZhsS0RStvbS3cdx9cfTXvXnMLiZmv8o/grdiKPqw/5TFsqHKL7+sYQ1WJtn8RERERkdwzxhDM42opQHzQGGqPuoXgZ69Q/tKPGhd7buaQIT254YThTF+ynqsen97ursf20MCjL6nPMwc2bBWT9iyRoA/eegvGjgXPg/p6RvsDPGSSpL81kHUXPY4X2WGL77chIfXlae++iIiIiBSfsN/t0EyVXIjueS5O/SpK37kFL7IDdYdc2+Kco4ftSCLt8YunZ/PTCTO4+et7ZmTN7IaBR5oFo0ppTtXFU6xfuQY7dmxjpbS+sZ89lExgEhbnbyuwlG7xPfyuQ/dIQAmpiIiIiOSVgM8piN0g6g/4IQ17nkfkgzsJf/SXVs8ZO6IXV4/ZjbfnreG6ibNIeZlZD6qBR42UyeSY8+ij2LYWOVsITni81UMGiAR9atkVERERkbyVzwOPNjKG2iNvIjZ4LOWvX0to1iOtnjZ+7z5cfvQQXp27il89MwcvA623ibRH2lMLr9p3c8xdMB+noeXELwCnoR53wfxmrxkg6HOJBF1VR0VEREQkr4X9LvXxFHmfdjku1WPvxkw8j/IXL8f6gsR3O6nFaWfu14940uPuN+bjOoZrjh+Ks51DRqPJNKVFvntGcf/q84DXuzs24GASLaulNhLBDB5M0OfgOIaA6xBwHVVGRURERKQgOI4h6HOJZXBAUKfxhVh/wt+pmnAWFc99n2o3SHzwcS1Ou+Cg/qQ8j7/8dyEWuGbs0O1qU44mlJSq1JZDobkTKKn/E7Tx7Mg4DpHzzqayJEB5yE/I7yohFREREZGCEgoUUMrhL2H9yf8iucOeVDz7LQKfvdbqaRcdMpCLDxnAs9OX8+tn52xXC65nbUan+haiArpDug6ndjkVz1xMxaTvsijUl0tPvYpUpBQikcYTIhEoK4NJk6B0y4OORERERETyWdDnbneLazbZQCnrxz9MqtuuVE68AP+it1o976JDBvKtQwfy7Izl/OrZ2duVmBb7wKPirhNnWypOyZS/EHn3dxgvzeQB3+P0OV/h3NMG4d59DTz6KMybB4MHw+mnKyEVERERkS6hJOBSF0/lOox2s6FK1n39Ebo9Np6qJ85m/YkPkOh/eIvzLjx4AAa4580FWAs/Hzdsm1p5EykPz7NF2xWppDQbUjHCMx8i8v4duHXLiQ06lg92/THfmLiafQZUccXRQzAlAbjwwlxHKiIiIiKScQUz8GgTtqQHa0+dQNV/TqNy4rmsH3cfiUHHtjjvmwcPwBj48xsLsMB125CYWhoHHkWKdG1pcf6qs8SpWUx4xr8Iz3wIt34ViZ33p2bMHSyq2I/L/vY+O1WE+M3Je1Ae9uc6VBERERGRTlNQA482YUt6sO6U/1A14Qwqn/4m1WP/THzXr7U47xsHDcBguPuN+Vhrue5rw/A5HVspqaRUMsap/pzg/BcIzXsO/5J3AEgMOIqafb5Nou8hxFIeV/3jQ+Ipj7vPGclOFWFMAfXYi4iIiIhsi3Cg8JJSABuuYt0pj1H5xFlUPPstalJ3Eht2SovzLjioPxi4+/X5JNOWG04cjr8DWzimPUsi5RHwFd/YHyWlbTCznyRgIqRLd8KL7Ij1l4DTVNFMRXHitTjRL3DXf4a7fiH+ldPwL5uMW7cMgGT33ak/4EdE9zgTr7wPANZafvnMbD5ZWctvTxvJwB4RSgphQ2ERERERke0U8Dm4jtmugUC5YoPlrB//CJVPnkPF89/HiX5Bwz7faXHeBQf2J+hz+MPLnxJNprlp/AhCHfi+H02mlZRKE8/D9+TFVHktF2Nb42Bsyz1F02V9SO68H/W99yXR/yjSVQNanHP/Wwt5ec4qvn/EIA4e3INQQFu8iIiIiEjxKAm41MYKZ+DRpmwgwrrxD1Mx6XuUvXE9Tt1y6g69HkzzJPLM/foR9rvc9NxcrnhkKr89dWS723LjyTQ25Cu6Tkolpa0xhuR33qVu9WLc+hU49asgFcOkk2BTWH8EGyzHC3cjXdGfdGV/bLB8i2/59LRl/OW/Czl+RC/OPWAXACIB/faLiIiISPEI+13qYoU18KgZX4jqcX/Be+1aIh/+GaduJTXH3g6+YLPTThq1M+GAyy+ems2lD0/hD6fv1a45MhaIJT3CgeLqplRW1BpjoNsgkqG+JDPwdu8uWMONz81lvwHd+NnY3THGEPK72zQuWkRERESkUBljCAXcwt6X03GpPfI3pMt6U/bWr3AbVrF+3P3YcFWz044dvhMhv8s1T8zguw9+xB1n7EX30mAbb/qlaDJddElp8TUsZ9knK2v56YQZDOwR4cbxI/A1LXaOFNmNJiIiIiICdI2ZKsbQsN+lVI/5I/5lH9DtoTG4X8xtcdphu/bkd6eNZPHaBr7zr49YWRPb6lsn0x6pdMvlgl2ZktJOtKI6xhWPTKU06OO200dS2tRLHvQ5G5NTEREREZFi4nMdAl3ku3Bs2KmsO3UCJllPt4fHEpj/Qotz9h/QnTvOHMWa+jjf/ueHLFrTsNX3jSYLuJK8DbrG3ZCH1tTFufThKcSSHn84fS92KAttPFaitaQiIiIiUsS6Untqsve+rD37RdLdBlM58Xwi7/0eNhuMulffSv501t5EE2ku/sdkZi+r2eJ7RpNprC3YlbcdpqS0E9REk1z276msqo1x22kjGbRD6cZjftcpyjHPIiIiIiIbdLX5Kl5Zb9aeNpHY7idT+vZNVE44C9Owutk5Q3uV85fzR1MSdPnegx/x7oI1bb6ftRBPFU8Lr7KjDGtIpLji0al8vqaeW07Zk5F9K5sdL+lCT4VERERERLZVl/te7A9Tc9xd1Bx1C4El/6P7P4/Cv/jtZqf061bCfeeNpk9VmB8+Oo3nZ65o8+1iRdTCq6Q0g+KpND9+bDpzltXyq5P2YP8B3Zsddx3Toc1zRURERES6qrDfpcttx2kM0ZHns/as57CBMqoe+zqRt2+CdGLjKd1Lg/z5nH0Y2aeC65+axUPvLWr1reIpj7RXHC28SkozJJHy+OmEGUz+fB3XjhvK4bvt0OIc7UsqIiIiItLIGEO4ixZsUj2Hs/bsF4kNO43S935PtwePxbdqxsbjpSEft58xiiN334HbX/mUO175FK+VNaTFUi1VUpoB8VSaq/4znbfnreHqMbsxdkSvFuc4xhDy67dbRERERGSDrjwA1AYi1Iy5g/UnPoAT/YJuD40h8vbNkIoDEPA5/OqkPThlnz48+N4ifvHUbBKbrSMtlim8ypK2UyzZ2LL77vw1/Gzs7ozfu0+r55UEXEyX608QEREREdl2rmMI+bpmtXSD+KAxrDnvTWK7nUzpe7fR/R+HE1jwMtD467/ymF357mGDeH7WCi57eArV0eTGa9OeJZ7q+ompktLtEEum+dGj03h/4VquOX4oJ+61c6vnGdMFF3KLiIiIiGRASbDrf0+24Spqjvsj68b/G4yh6smzqXzyHNx1CzHGcMFB/bnhxOHMXFbNRQ9MZvHaL/cyjSW6/hReJaXbqC6e4opHpvLh5+u47mvD+NrI3m2e27iIW1VSEREREZHN+V2HgFscaUmi/xGsOe91ag+5Dv/i/9H9gYMpe/nHOLXLOHb4TvzxrL2pjia56IHJTFu8HmhcKuh18YFHxfGnn2Fr6xN878GPmLa4ml+cOLzVNaQbGDTgSERERERkS4qhWrqRG6Bh3++z5hv/IzriXMIzH6bHXw+g7LVr2LuijvvPH01Z2MclD03hxVkrsECsi7fwGtvKlKdsGD16tJ08eXJOPrs9EimPdQ2JFq8vWx/lsn9PYVVNnN+MH8HBg3ts8X3CAZfykL+zwhQRERER6RLW1idIprt+q+rmnOpFlL57G6HZjwIQH3I8q4ZfyGVv+Zm6eD3fPWwQF47sTo9JE+HTT2HIEDj9dCgry3HkW2eM+dBaO3qr5ykpbV1rSen8VXVc9u8pxFMevzt1JCP7Vm71fbpHAviKpB1BRERERGRbxZLpZkN+io1Ts5iSKfcTnvEvnEQtiR578KQ9lBfeC/Cn//yWoGsw9fUQiYDjwKRJcPDBuQ57i5SUbqfNk9Kpi9dz5WPTCPlcbj9jLwbtULrV9wj5XCpKVCUVEREREWmPL+ripLv4+smtMYk6QrMfJTzrEfyLpmBvq8W0bOBsrJQuWwalW89LcqW9SalKeO3w3MzlXPLQR1SVBLj3vH3alZBCkfXGi4iIiIhsp9KgZrHYQCnRvb7J2rNfoLbiCjBtFLk8Dx55JLvBdZJ2JaXGmDHGmI+NMfOMMT9p5fgPjTGzjTHTjTGvGGN2yXyo2Wet5Z435vN/T81mzz6V3Hf+aHpXhtt1bcB18KttV0RERESk3UJ+F9fRrhUbOCvrMfE2Wprr62HevOwG1Em2mjUZY1zgT8BxwDDgTGPMsM1OmwKMttbuCTwO3JLpQLMtnkzz84mz+Ovbn/G1kb24/Yy9qAi3vxVXVVIRERERkY5TtfRL6YGD8EoirR+MRGDw4OwG1EnaU8rbD5hnrV1grU0A/wZO3PQEa+1r1toNO7y+C/TJbJjZVd2Q5Ly/vs9Ls1dyyRGDuWbs0A5VPX2OIehTUioiIiIi0lGqln4pNv6UxqFGrXGcxim8XUB7Mq2dgcWb/HxJ02ttuRB4bnuCyrVI0GWH8iA3jR/BuV/ZBWM69pcioqc7IiIiIiLbTNXSRrasjOr/PIEtK2usjELj/5eVNU7fzeMhRx2R0T9tY8w5wGjgsDaOfwv4FkC/fv0y+dEZ5XMd7jxz71b3Kd0a1zGE/KqSioiIiIhsq5DfpT6eIlXkk3gBgocfhlm2rHGo0bx5jS27p5/eZRJSaF9SuhTou8nP+zS91owx5mjgGuAwa228tTey1t4L3AuNW8J0ONoCEAnoqY6IiIiIyPaKBH1FvW8pgGMMYb8LgVK48MJch9Np2tO++wEwxBgzwBgTAM4Antr0BGPMKOAe4ARr7arMh1kYHGMI+TVxV0RERERke4X8btHvZlEW8nV4KWEh2uqfsrU2BVwCvADMAR611s4yxtxgjDmh6bRbgVLgMWPMVGPMU228XZdWEnCL4qYREREREcmGYl5b6iuiZYHt+lO21k4CJm322nWb/PjoDMdVcIxpTEpFRERERCQzAj6HoM8hnvJyHUrWlYaKJyEv7np4BpUEiqO0LiIiIiKSTcVYLQ36nKLaYlJJaQYYoKRISusiIiIiItnkcx3CRdaRWGyJuJLSDAgFXBxt8CsiIiIi0ilKAz6KpSmxJODiK7IBT8X1q+0EBm0DIyIiIiLSmRzHFEX10JjizC2UlG6noN/FVZVURERERKRTlQR8+Lr49+6yoL8oOzCVlG6nSJH1t4uIiIiI5EpZyJ/rEDqNvwjXzm6gpHQ7hHzF1+8tIiIiIpIrAV/XTdzKi2gLmM0po9oOJcGu+RdCRERERCRflQZ8OF1s6lEk6CvqYlfx/sq3U8B18BfxjSMiIiIikguOYyjrQlVF1zFFvyRQWdU2ihTB9C8RERERkXwU8ruEfF0jkSsP+TFdrPLbUUpKt4HfdQj49FsnIiIiIpIrZaHC37u0JOAqr0BJ6TaJaC2piIiIiEhOOY6hIly403h9RbL3ansoKe0gn2MIdpFWARERERGRQhb0uQU5jdcAFWG17W6gpLSDtJZURERERCR/lAV9+JzCSu7KQv6inra7Of1OdIDrGEL+wnsSIyIiIiLSVRljmqqOuY6kfUL+wqzudiYlpR2gnm8RERERkfzjcx3KQ/m/vtTnGMq70HY2maKktJ1UJRURERERyV8hv5vXRSRjoLIkoHWkrVBS2k6RQP7e4CIiIiIi0jj/JR8LSQaoDAdwC2zta7YoKW0HxxhCfv1WiYiIiIjku/KQj0CeDREqD/u1H+kW6HemHUqDPpXZRUREREQKgDGGyhI//jxJTMtD/rys3uaT/PiTymOqkoqIiIiIFBZjDFV5kJiWhXyatNsOyra2IhJ0VSUVERERESkwGxLTXLXylof8lGguTbsoKd0CxxjCKrWLiIiIiBSkDa28IV/2vtMboCLsV4W0A5SUboGqpCIiIiIihc0YQ0WJPyvbxbiOoSoS0BrSDlI9uQ2uY/C7uplERERERLqCSNCH33WojibxrM34+4d8LmUhH462fekwJaVt0B5CIiIiIiJdS8Dn0KM0QG08RTSRzsh7OsZQFsrP/VELhZJSEREREREpGsaYxiFEfpf6eJpYatuSU2MgEvBREtCSv+2lpFRERERERIqOz3WoKHEo9XxEk2liyTRpb8ttvQbwuw7hgEvQ5ygZzRAlpSIiIiIiUrRcx1Aa9FEa9JH2LMm0R9qzeNZiaWzPdQz4HAe/a5SIdgIlpSIiIiIiIjQmqK6jtaHZpi1hREREREREJGeUlIqIiIiIiEjOKCkVERERERGRnFFSKiIiIiIiIjmjpFRERERERERyRkmpiIiIiIiI5IySUhEREREREckZJaUiIiIiIiKSM0pKRUREREREJGeUlIqIiIiIiEjOKCkVERERERGRnFFSKiIiIiIiIjmjpFRERERERERyRkmpiIiIiIiI5IySUhEREREREckZJaUiIiIiIiKSM0pKRUREREREJGeUlIqIiIiIiEjOKCkVERERERGRnFFSKiIiIiIiIjmjpFRERERERERyxlhrc/PBxqwGPs/Jh7dfD+CLXAcheUf3hbRF94a0RfeGtEX3hrRG94W0pdDujV2stT23dlLOktJCYIyZbK0dnes4JL/ovpC26N6QtujekLbo3pDW6L6QtnTVe0PtuyIiIiIiIpIzSkpFREREREQkZ5SUbtm9uQ5A8pLuC2mL7g1pi+4NaYvuDWmN7gtpS5e8N7SmVERERERERHJGlVIRERERERHJGSWlW2GMudUYM9cYM90Y84QxpjLXMUnuGGPGGGM+NsbMM8b8JNfxSH4wxvQ1xrxmjJltjJlljPlBrmOS/GGMcY0xU4wxz+Q6FskfxphKY8zjTd8x5hhjvpLrmCQ/GGOuaPpvyUxjzMPGmFCuY5LcMMb81Rizyhgzc5PXuhljXjLGfNr0/1W5jDFTlJRu3UvAHtbaPYFPgJ/mOB7JEWOMC/wJOA4YBpxpjBmW26gkT6SAH1lrhwEHAN/XvSGb+AEwJ9dBSN65HXjeWrs7MBLdIwIYY3YGLgNGW2v3AFzgjNxGJTn0d2DMZq/9BHjFWjsEeKXp5wVPSelWWGtftNammn76LtAnl/FITu0HzLPWLrDWJoB/AyfmOCbJA9ba5dbaj5p+XEvjl8udcxuV5ANjTB/geOC+XMci+cMYUwEcCtwPYK1NWGvX5zYqySM+IGyM8QElwLIcxyM5Yq19E1i72csnAg80/fgB4KSsBtVJlJR2zDeB53IdhOTMzsDiTX6+BCUeshljTH9gFPBebiORPPEH4CrAy3UgklcGAKuBvzW1dt9njInkOijJPWvtUuC3wCJgOVBtrX0xt1FJntnRWru86ccrgB1zGUymKCkFjDEvN/Xtb/6/Ezc55xoaW/QezF2kIpLPjDGlwH+Ay621NbmOR3LLGDMOWGWt/TDXsUje8QF7A3dba0cB9XSRFjzZPk3rA0+k8cFFbyBijDknt1FJvrKN26h0ia1UfLkOIB9Ya4/e0nFjzAXAOOAoqz10itlSoO8mP+/T9JoIxhg/jQnpg9baCbmOR/LCQcAJxpixQAgoN8b8y1qrL5iyBFhird3QUfE4Skql0dHAQmvtagBjzATgQOBfOY1K8slKY0wva+1yY0wvYFWuA8oEVUq3whgzhsbWqxOstQ25jkdy6gNgiDFmgDEmQOPggadyHJPkAWOMoXFt2Bxr7W25jkfyg7X2p9baPtba/jT+e/GqElIBsNauABYbY3ZreukoYHYOQ5L8sQg4wBhT0vTflqPQECxp7ing/KYfnw9MzGEsGaNK6db9EQgCLzX+28C71trv5DYkyQVrbcoYcwnwAo3T8P5qrZ2V47AkPxwEnAvMMMZMbXrtZ9baSTmMSUTy26XAg00PORcA38hxPJIHrLXvGWMeBz6icdnYFODe3EYluWKMeRg4HOhhjFkCXA/cBDxqjLkQ+Bw4LXcRZo5RN6qIiIiIiIjkitp3RUREREREJGeUlIqIiIiIiEjOKCkVERERERGRnFFSKiIiIiIiIjmjpFRERERERERyRkmpiIiIiIiI5IySUhEREREREckZJaUiIiIiIiKSM/8PniWOtN/SqjsAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x112d49320>"
+       "<Figure size 1152x648 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "bo = BayesianOptimization(f=lambda x: f[int(x)],\n",
-    "                          pbounds={\"x\": (0, len(f)-1)},\n",
-    "                          verbose=0)\n",
+    "bo = BayesianOptimization(\n",
+    "    f=f,\n",
+    "    pbounds={\"x\": (-2, 10)},\n",
+    "    verbose=0,\n",
+    "    random_state=987234,\n",
+    ")\n",
     "\n",
-    "bo.maximize(init_points=2, n_iter=25, acq=\"ei\", xi=1e-4, **gp_params)\n",
+    "bo.maximize(n_iter=10, acq=\"ei\", xi=1e-4)\n",
     "\n",
     "plot_bo(f, bo)"
    ]
@@ -235,34 +232,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 46,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/fmfnogueira/venvs3/general/lib/python3.5/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
-      "  if self._edgecolors == str('face'):\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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b2WyK1+tlQt9jeXb1Y0QiUavLEREREckqCqwiYqlUKsVdb/yHYe4Tyc/fMZotfZ3NZuOy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\n8m8iHA4SDoeIxWLru9dnvnM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Xsmup+PNpNLaFNvl4j8eDw1FMbW2QcDjcnaWLiEUUWKVL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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10f38d278>"
+       "<Figure size 1152x648 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "bo = BayesianOptimization(f=lambda x: f[int(x)], pbounds={\"x\": (0, len(f)-1)}, verbose=0)\n",
+    "bo = BayesianOptimization(\n",
+    "    f=f,\n",
+    "    pbounds={\"x\": (-2, 10)},\n",
+    "    verbose=0,\n",
+    "    random_state=987234,\n",
+    ")\n",
     "\n",
-    "bo.maximize(init_points=2, n_iter=25, acq=\"ei\", xi=0.1, **gp_params)\n",
+    "bo.maximize(n_iter=10, acq=\"ei\", xi=1e-1)\n",
     "\n",
     "plot_bo(f, bo)"
    ]
@@ -285,34 +281,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/fmfnogueira/venvs3/general/lib/python3.5/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
-      "  if self._edgecolors == str('face'):\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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rpsAqIlXHsizi8QTvvDNEOh0iFOrgf//XzfnnO13ZpjMM+Oxn4eqrna5ERKpNMBgmGJxJ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\nWZljerrmocBmazQ8PEy9Pkqr5WdZe80FK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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x112d3b390>"
+       "<Figure size 1152x648 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "bo = BayesianOptimization(f=lambda x: f[int(x)],\n",
-    "                          pbounds={\"x\": (0, len(f)-1)},\n",
-    "                          verbose=0)\n",
+    "bo = BayesianOptimization(\n",
+    "    f=f,\n",
+    "    pbounds={\"x\": (-2, 10)},\n",
+    "    verbose=0,\n",
+    "    random_state=987234,\n",
+    ")\n",
     "\n",
-    "bo.maximize(init_points=2, n_iter=25, acq=\"poi\", xi=1e-4, **gp_params)\n",
+    "bo.maximize(n_iter=10, acq=\"poi\", xi=1e-4)\n",
     "\n",
     "plot_bo(f, bo)"
    ]
@@ -328,51 +321,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/fmfnogueira/venvs3/general/lib/python3.5/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
-      "  if self._edgecolors == str('face'):\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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U0+WIiIiIZB0FVhFxXCwW49a5z/KT5M8pbV/gdDnN6qqxx7EmMI8vV61yuhQRERGRrKPA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\nAElFTkSuQmCC\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x112d1ccc0>"
+       "<Figure size 1152x648 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "bo = BayesianOptimization(f=lambda x: f[int(x)], pbounds={\"x\": (0, len(f)-1)}, verbose=0)\n",
+    "bo = BayesianOptimization(\n",
+    "    f=f,\n",
+    "    pbounds={\"x\": (-2, 10)},\n",
+    "    verbose=0,\n",
+    "    random_state=987234,\n",
+    ")\n",
     "\n",
-    "bo.maximize(init_points=2, n_iter=25, acq=\"poi\", xi=0.1, **gp_params)\n",
+    "bo.maximize(n_iter=10, acq=\"poi\", xi=1e-1)\n",
     "\n",
     "plot_bo(f, bo)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "BayesianOptimization",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "bayesian_optimization"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -384,9 +367,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.7.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 2
 }
diff --git a/examples/sklearn_example.py b/examples/sklearn_example.py
index 608dcbe25..e4e5d88e0 100644
--- a/examples/sklearn_example.py
+++ b/examples/sklearn_example.py
@@ -1,59 +1,121 @@
-from __future__ import print_function
-from __future__ import division
-
 from sklearn.datasets import make_classification
-from sklearn.cross_validation import cross_val_score
+from sklearn.model_selection import cross_val_score
 from sklearn.ensemble import RandomForestClassifier as RFC
 from sklearn.svm import SVC
 
 from bayes_opt import BayesianOptimization
+from bayes_opt.util import Colours
 
-# Load data set and target values
-data, target = make_classification(
-    n_samples=1000,
-    n_features=45,
-    n_informative=12,
-    n_redundant=7
-)
-
-def svccv(C, gamma):
-    val = cross_val_score(
-        SVC(C=C, gamma=gamma, random_state=2),
-        data, target, 'f1', cv=2
-    ).mean()
-
-    return val
-
-def rfccv(n_estimators, min_samples_split, max_features):
-    val = cross_val_score(
-        RFC(n_estimators=int(n_estimators),
-            min_samples_split=int(min_samples_split),
-            max_features=min(max_features, 0.999),
-            random_state=2
-        ),
-        data, target, 'f1', cv=2
-    ).mean()
-    return val
+def get_data():
+    """Synthetic binary classification dataset."""
+    data, targets = make_classification(
+        n_samples=1000,
+        n_features=45,
+        n_informative=12,
+        n_redundant=7,
+        random_state=134985745,
+    )
+    return data, targets
 
-if __name__ == "__main__":
-    gp_params = {"alpha": 1e-5}
 
-    svcBO = BayesianOptimization(svccv,
-        {'C': (0.001, 100), 'gamma': (0.0001, 0.1)})
-    svcBO.explore({'C': [0.001, 0.01, 0.1], 'gamma': [0.001, 0.01, 0.1]})
+def svc_cv(C, gamma, data, targets):
+    """SVC cross validation.
+
+    This function will instantiate a SVC classifier with parameters C and
+    gamma. Combined with data and targets this will in turn be used to perform
+    cross validation. The result of cross validation is returned.
+
+    Our goal is to find combinations of C and gamma that maximizes the roc_auc
+    metric.
+    """
+    estimator = SVC(C=C, gamma=gamma, random_state=2)
+    cval = cross_val_score(estimator, data, targets, scoring='roc_auc', cv=4)
+    return cval.mean()
+
+
+def rfc_cv(n_estimators, min_samples_split, max_features, data, targets):
+    """Random Forest cross validation.
+
+    This function will instantiate a random forest classifier with parameters
+    n_estimators, min_samples_split, and max_features. Combined with data and
+    targets this will in turn be used to perform cross validation. The result
+    of cross validation is returned.
+
+    Our goal is to find combinations of n_estimators, min_samples_split, and
+    max_features that minimzes the log loss.
+    """
+    estimator = RFC(
+        n_estimators=n_estimators,
+        min_samples_split=min_samples_split,
+        max_features=max_features,
+        random_state=2
+    )
+    cval = cross_val_score(estimator, data, targets,
+                           scoring='neg_log_loss', cv=4)
+    return cval.mean()
+
 
-    rfcBO = BayesianOptimization(
-        rfccv,
-        {'n_estimators': (10, 250),
-        'min_samples_split': (2, 25),
-        'max_features': (0.1, 0.999)}
+def optimize_svc(data, targets):
+    """Apply Bayesian Optimization to SVC parameters."""
+    def svc_crossval(expC, expGamma):
+        """Wrapper of SVC cross validation.
+
+        Notice how we transform between regular and log scale. While this
+        is not technically necessary, it greatly improves the performance
+        of the optimizer.
+        """
+        C = 10 ** expC
+        gamma = 10 ** expGamma
+        return svc_cv(C=C, gamma=gamma, data=data, targets=targets)
+
+    optimizer = BayesianOptimization(
+        f=svc_crossval,
+        pbounds={"expC": (-3, 2), "expGamma": (-4, -1)},
+        random_state=1234,
+        verbose=2
+    )
+    optimizer.maximize(n_iter=10)
+
+    print("Final result:", optimizer.max)
+
+
+def optimize_rfc(data, targets):
+    """Apply Bayesian Optimization to Random Forest parameters."""
+    def rfc_crossval(n_estimators, min_samples_split, max_features):
+        """Wrapper of RandomForest cross validation.
+
+        Notice how we ensure n_estimators and min_samples_split are casted
+        to integer before we pass them along. Moreover, to avoid max_features
+        taking values outside the (0, 1) range, we also ensure it is capped
+        accordingly.
+        """
+        return rfc_cv(
+            n_estimators=int(n_estimators),
+            min_samples_split=int(min_samples_split),
+            max_features=max(min(max_features, 0.999), 1e-3),
+            data=data,
+            targets=targets,
+        )
+
+    optimizer = BayesianOptimization(
+        f=rfc_crossval,
+        pbounds={
+            "n_estimators": (10, 250),
+            "min_samples_split": (2, 25),
+            "max_features": (0.1, 0.999),
+        },
+        random_state=1234,
+        verbose=2
     )
+    optimizer.maximize(n_iter=10)
+
+    print("Final result:", optimizer.max)
+
+if __name__ == "__main__":
+    data, targets = get_data()
 
-    svcBO.maximize(n_iter=10, **gp_params)
-    print('-' * 53)
-    rfcBO.maximize(n_iter=10, **gp_params)
+    print(Colours.yellow("--- Optimizing SVM ---"))
+    optimize_svc(data, targets)
 
-    print('-' * 53)
-    print('Final Results')
-    print('SVC: %f' % svcBO.res['max']['max_val'])
-    print('RFC: %f' % rfcBO.res['max']['max_val'])
+    print(Colours.green("--- Optimizing Random Forest ---"))
+    optimize_rfc(data, targets)
diff --git a/examples/usage.py b/examples/usage.py
deleted file mode 100644
index ad53771aa..000000000
--- a/examples/usage.py
+++ /dev/null
@@ -1,55 +0,0 @@
-"""Example of how to use this bayesian optimization package."""
-
-import sys
-sys.path.append("./")
-from bayes_opt import BayesianOptimization
-
-# Lets find the maximum of a simple quadratic function of two variables
-# We create the bayes_opt object and pass the function to be maximized
-# together with the parameters names and their bounds.
-bo = BayesianOptimization(lambda x, y: -x ** 2 - (y - 1) ** 2 + 1,
-                          {'x': (-4, 4), 'y': (-3, 3)})
-
-# One of the things we can do with this object is pass points
-# which we want the algorithm to probe. A dictionary with the
-# parameters names and a list of values to include in the search
-# must be given.
-bo.explore({'x': [-1, 3], 'y': [-2, 2]})
-
-# Additionally, if we have any prior knowledge of the behaviour of
-# the target function (even if not totally accurate) we can also
-# tell that to the optimizer.
-# Here we pass a dictionary with 'target' and parameter names as keys and a
-# list of corresponding values
-bo.initialize(
-    {
-        'target': [-1, -1],
-        'x': [1, 1],
-        'y': [0, 2]
-    }
-)
-
-# Once we are satisfied with the initialization conditions
-# we let the algorithm do its magic by calling the maximize()
-# method.
-bo.maximize(init_points=5, n_iter=15, kappa=2)
-
-# The output values can be accessed with self.res
-print(bo.res['max'])
-
-# If we are not satisfied with the current results we can pickup from
-# where we left, maybe pass some more exploration points to the algorithm
-# change any parameters we may choose, and the let it run again.
-bo.explore({'x': [0.6], 'y': [-0.23]})
-
-# Making changes to the gaussian process can impact the algorithm
-# dramatically.
-gp_params = {'kernel': None,
-             'alpha': 1e-5}
-
-# Run it again with different acquisition function
-bo.maximize(n_iter=5, acq='ei', **gp_params)
-
-# Finally, we take a look at the final results.
-print(bo.res['max'])
-print(bo.res['all'])
diff --git a/examples/visualization.ipynb b/examples/visualization.ipynb
index 66aab1f37..6e2635ae2 100644
--- a/examples/visualization.ipynb
+++ b/examples/visualization.ipynb
@@ -13,9 +13,7 @@
     "\n",
     "import matplotlib.pyplot as plt\n",
     "from matplotlib import gridspec\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# plt.style.use(['dark_background'])"
+    "%matplotlib inline"
    ]
   },
   {
@@ -533,9 +531,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.7.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 2
 }
diff --git a/examples/xgboost_example.py b/examples/xgboost_example.py
deleted file mode 100644
index a66180300..000000000
--- a/examples/xgboost_example.py
+++ /dev/null
@@ -1,79 +0,0 @@
-"""
-Baysian hyperparameter optimization [https://github.com/fmfn/BayesianOptimization]
-for Mean Absoulte Error objective
-on default features for https://www.kaggle.com/c/allstate-claims-severity
-"""
-
-__author__ = "Vladimir Iglovikov"
-
-import pandas as pd
-import xgboost as xgb
-from sklearn.preprocessing import LabelEncoder
-from sklearn.metrics import mean_absolute_error
-from bayes_opt import BayesianOptimization
-from tqdm import tqdm
-
-
-def xgb_evaluate(min_child_weight,
-                 colsample_bytree,
-                 max_depth,
-                 subsample,
-                 gamma,
-                 alpha):
-
-    params['min_child_weight'] = int(min_child_weight)
-    params['cosample_bytree'] = max(min(colsample_bytree, 1), 0)
-    params['max_depth'] = int(max_depth)
-    params['subsample'] = max(min(subsample, 1), 0)
-    params['gamma'] = max(gamma, 0)
-    params['alpha'] = max(alpha, 0)
-
-
-    cv_result = xgb.cv(params, xgtrain, num_boost_round=num_rounds, nfold=5,
-             seed=random_state,
-             callbacks=[xgb.callback.early_stop(50)])
-
-    return -cv_result['test-mae-mean'].values[-1]
-
-
-def prepare_data():
-    train = pd.read_csv('../input/train.csv')
-    categorical_columns = train.select_dtypes(include=['object']).columns
-
-    for column in tqdm(categorical_columns):
-        le = LabelEncoder()
-        train[column] = le.fit_transform(train[column])
-
-    y = train['loss']
-
-    X = train.drop(['loss', 'id'], 1)
-    xgtrain = xgb.DMatrix(X, label=y)
-
-    return xgtrain
-
-
-if __name__ == '__main__':
-    xgtrain = prepare_data()
-
-    num_rounds = 3000
-    random_state = 2016
-    num_iter = 25
-    init_points = 5
-    params = {
-        'eta': 0.1,
-        'silent': 1,
-        'eval_metric': 'mae',
-        'verbose_eval': True,
-        'seed': random_state
-    }
-
-    xgbBO = BayesianOptimization(xgb_evaluate, {'min_child_weight': (1, 20),
-                                                'colsample_bytree': (0.1, 1),
-                                                'max_depth': (5, 15),
-                                                'subsample': (0.5, 1),
-                                                'gamma': (0, 10),
-                                                'alpha': (0, 10),
-                                                })
-
-    xgbBO.maximize(init_points=init_points, n_iter=num_iter)
-