diff --git a/README.md b/README.md
index d86b3ab..da54e7c 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-[![Build Status](https://travis-ci.com/jajcayn/pygpso.svg?branch=master)](https://travis-ci.com/jajcayn/pygpso) ![](https://img.shields.io/github/v/release/jajcayn/pygpso) [![codecov](https://codecov.io/gh/jajcayn/pygpso/branch/master/graph/badge.svg)](https://codecov.io/gh/jajcayn/pygpso) [![PyPI license](https://img.shields.io/pypi/l/pygpso.svg)](https://pypi.python.org/pypi/pygpso/) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+[![Build Status](https://travis-ci.com/jajcayn/pygpso.svg?branch=master)](https://travis-ci.com/jajcayn/pygpso) ![](https://img.shields.io/github/v/release/jajcayn/pygpso) [![codecov](https://codecov.io/gh/jajcayn/pygpso/branch/master/graph/badge.svg)](https://codecov.io/gh/jajcayn/pygpso) [![PyPI license](https://img.shields.io/pypi/l/pygpso.svg)](https://pypi.python.org/pypi/pygpso/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/jajcayn/pygpso.git/master?filepath=examples)[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
 
 # pyGPSO
 *Optimise anything (but mainly large-scale biophysical models) using Gaussian Processes surrogate*
@@ -30,7 +30,7 @@ For those who want to optimise right away just
 ```bash
 pip install git+https://github.com/jajcayn/pygpso
 ```
-and go ahead! Make sure to check [example notebook](examples/optimisation_example.ipynb) how it works and what it can do.
+and go ahead! Make sure to check example notebooks in [the **examples** directory](examples/) to see how it works and what it can do. Or, alternatively, you can run interactive notebooks in binder: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/jajcayn/pygpso.git/master?filepath=examples).
 
 ### Go proper
 When you are the type of girl or guy who likes to install packages properly, start by cloning (or forking) this repository, then installing all the dependencies and finally install the package itself
@@ -38,7 +38,7 @@ When you are the type of girl or guy who likes to install packages properly, sta
 git clone https://github.com/jajcayn/pygpso
 cd pygpso/
 pip install -r requirements.txt
-# optionally
+# optionally, but recommended
 pip install -r requirements_optional.txt
 pip install .
 ```
@@ -48,7 +48,8 @@ pytest tests/
 ```
 
 ## Usage
-A guide on how to optimise using this package is given as a [jupyter notebook](examples/optimisation_example.ipynb).
+A guide on how to optimise and what can be done using this package is given as jupyter notebooks in [the **examples** directory](examples/). You can also try them out live thanks to binder: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/jajcayn/pygpso.git/master?filepath=examples).
+
 The basic idea is to initialise the parameter space in which the optimisation is to be run and then iteratively dig deeper and evaluate the objective function when necessary
 ```python
 from gpso.param_space import ParameterSpace
@@ -71,20 +72,19 @@ opt = GPSOptimiser(parameter_space=space, n_workers=4)
 best_point = opt.run(objective_function)
 ```
 
-The package offers plotting functions for visualising the results. Again, those are documented and showcased in the [example notebook](examples/optimisation_example.ipynb).
+The package also offers plotting functions for visualising the results. Again, those are documented and showcased in [the **examples** directory](examples/).
 
 ### Notes
 Gaussian Processes regression uses normalised coordinates within the bounds [0, 1]. All normalisation and de-normalisation is done automatically, however when you want to call `predict_y` on GPR model, do not forget to pass normalised coordinates. The normalisation is handled by `sklearn.MinMaxScaler` and `ParameterSpace` instance offers a convenience functions for this: `ParameterSpace.normalise_coords(orig_coords)` and `ParameterSpace.denormalise_coords(normed_coords)`.
 
 Plotting of the ternary tree (`gpso.plotting.plot_ternary_tree()`) requires `igraph` package, whose layout function is exploited. If you want to see the resulting beautiful tree, please install `python-igraph`.
+
+Support of saver (for saving models run, e.g. timeseries along with the optimisation) is provided by `PyTables` (and `pandas` if you're saving results to `DataFrame`s).
  
 ## Known bugs and future improvements
 * currently cannot be installed through `pip` from PyPI, since `GPFlow` 2.0 is not on PyPI yet
-* saving and resuming of the optimisation
-    * right now the parameter space and its state can be saved into binary `pkl` file
-    * however, the surrogate and its state cannot - this is mainly due to usage of `GPFlow` 2.0 which is not officially released as of now and hopefully this will change in the future (in `GPFlow` 2.0 the `saver` module from previous versions is missing)
-    
-    * the plan is to solve saving of the surrogate object and its state, then it will be easy to resume the optimisation from the saved state
+* saving of GP surrogate is now hacky, as `GPFlow` not yet officially supports saving / loading of the models due to [bug in `tensorflow`](https://github.com/tensorflow/tensorflow/issues/34908). The hacky way, unfortunately, only supports basic kernels and mean functions, i.e. no kernel operations (such as sum or multiplication) allowed (for now).
+* once `GPFlow` 2.0 is out there and supports saving, this package will be updated accordingly
 
 ## Final notes
 When you encounter a bug or have any idea for an improvement, please open an issue and/or contact me.
diff --git a/binder/postBuild b/binder/postBuild
new file mode 100755
index 0000000..a34b71f
--- /dev/null
+++ b/binder/postBuild
@@ -0,0 +1,6 @@
+#!/bin/bash
+
+# need to install also optional requirements for this...
+pip install -r requirements.txt
+pip install -r requirements_optional.txt
+python setup.py install
diff --git a/examples/0-basic-optimisation.ipynb b/examples/0-basic-optimisation.ipynb
new file mode 100644
index 0000000..b5b1ca4
--- /dev/null
+++ b/examples/0-basic-optimisation.ipynb
@@ -0,0 +1,619 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# GPSO basic optimisation example\n",
+    "\n",
+    "This notebook showcases the basic example of optimisation using the Gaussian Processes surrogate optimisation package. The method is described in Hadida et al. and reading the paper is recommended in order to grasp what is happening.\n",
+    "\n",
+    "**Reference:**\n",
+    "Hadida, J., Sotiropoulos, S. N., Abeysuriya, R. G., Woolrich, M. W., & Jbabdi, S. (2018). Bayesian Optimisation of Large-Scale Biophysical Networks. *NeuroImage*, 174, 219-236."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import logging\n",
+    "\n",
+    "from gpso.param_space import ParameterSpace\n",
+    "from gpso.optimisation import GPSOptimiser\n",
+    "from gpso.plotting import (\n",
+    "    plot_parameter_marginal_distributions,\n",
+    "    plot_conditional_surrogate_distributions,\n",
+    "    plot_ternary_tree,\n",
+    ")\n",
+    "from gpso.utils import set_logger\n",
+    "from gpflow.utilities import print_summary\n",
+    "\n",
+    "plt.style.use(\"seaborn-poster\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we define the objective function we would like to optimise. Note that this example is the same as in the original paper that introduced the GPSO method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define objective function - compare with paper Fig. 2\n",
+    "def obj(point, offset=0.0, rotate=True):\n",
+    "    x, y = point\n",
+    "    if rotate:\n",
+    "        ct = np.cos(np.pi / 4)\n",
+    "        st = np.sin(np.pi / 4)\n",
+    "        xn = ct * x + st * y\n",
+    "        yn = ct * y - st * x\n",
+    "        x = xn\n",
+    "        y = yn\n",
+    "    return (\n",
+    "        3 * (1 - x) ** 2.0 * np.exp(-(x ** 2) - (y + 1) ** 2)\n",
+    "        - 10 * (x / 5.0 - x ** 3 - y ** 5) * np.exp(-(x ** 2) - y ** 2)\n",
+    "        - 1 / 3 * np.exp(-((x + 1) ** 2) - y ** 2)\n",
+    "        - offset\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# bounds of the parameters we will optimise\n",
+    "x_bounds = [-3, 5]\n",
+    "y_bounds = [-3, 3]\n",
+    "# number of points per dimension for plotting\n",
+    "N_POINTS = 120"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we will plot the true objective function with its maximum point so we get ourselves familiar with the problem we want to optimise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 921.6x633.6 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# true objective - reference surface\n",
+    "x = np.linspace(x_bounds[0], x_bounds[1], N_POINTS)\n",
+    "y = np.linspace(y_bounds[0], y_bounds[1], N_POINTS)\n",
+    "x, y = np.meshgrid(x, y)\n",
+    "o = obj([x, y], rotate=True)\n",
+    "plt.imshow(o, vmax=8.0, vmin=-8.0, cmap=plt.get_cmap(\"cividis\"), origin=\"lower\")\n",
+    "cbar = plt.colorbar()\n",
+    "i, j = np.unravel_index(o.argmax(), o.shape)\n",
+    "plt.scatter(j, i, marker=\"d\", color=\"black\")\n",
+    "cbar.set_label(\"Objective-True\")\n",
+    "_ = plt.title(\"Reference surface\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now the party begins.\n",
+    "\n",
+    "## The party\n",
+    "\n",
+    "### Parameter Space\n",
+    "First we will initialise parameter space by calling `ParameterSpace` object which takes two arguments: parameter names as a list and parameter bounds as a list of lists or tuples. The order must agree, i.e. first parameter in names will have bounds as first tuple in the bounds argument.\n",
+    "\n",
+    "The `ParameterSpace` object initialises optimisation space, set-up normalisation (GP surrogate works with normalised coordinates between 0-1) and initialises ternary tree for partitioning with root leaf."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "space = ParameterSpace(\n",
+    "    parameter_names=[\"x\", \"y\"], parameter_bounds=[x_bounds, y_bounds]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Optimiser\n",
+    "\n",
+    "The main guest our party is, obviously, the optimiser itself. Since we want to make him comfortable, we offer number of user-defined arguments. Please note, that the defaults are very sensible, if you are not sure, what you are doing, just keep them at their default parameters.\n",
+    "\n",
+    "#### Arguments:\n",
+    "* `parameter_space`: parameter space to explore, which we initialisd in the previous step; the only required parameter\n",
+    "* `exploration_method`: method used for exploration of child leaves intervals; \"tree\" for growing tree up to `exploration_depth` and predicting at the center of each leaf, or \"sample\" for uniform sampling within the leaf's region; in both methods the best sample is recorded and represents the current leaf's score\n",
+    "* `exploration_depth`: depth of the exploration step, for \"tree\" method it is a depth to which the particular leaf is grown, for \"sample\" is this number multiplied by number of parameters (i.e. dimensionality of the optimisation problem) squared and this many samples are taken in each exploration steps\n",
+    "* `budget`: budget for the `stopping_condition`, i.e. how many iterations, evaluations of objective function, or up to which depth is the partition tree grown\n",
+    "* `stopping_condition`: to what stopping condition the `budget` refers to: either \"evaluations\" (algorithm stops after this many objective function evaluations), \"iterations\" (algorithms stops after this many iterations), or \"depth\" (algorithm stops after partition tree is grown to this depth)\n",
+    "* `update_cycle`: how often (in iterations) to update the GPR hyperparameters\n",
+    "* `varsigma`: expected probability that upper confidence bound (UCB) < f; it controls how \"optimistic\" we are during the exploration step; at a point x evaluated using GP, the UCB will be: $\\mathrm{UCB} = \\mu(x) + \\varsigma\\cdot\\sigma(x)$; $\\varsigma = \\mathrm{erfc}^{-1}(p/100)$ which corresponds to the upper bound of a $p$ confidence interval for Gaussian likelihood kernel\n",
+    "* `gp_lik_sigma`: initial standard deviation of Gaussian likelihood function (in normalised units)\n",
+    "* `n_workers`: number of workers to use where applicable (e.g. during initialisation or multiple objective function evaluations)\n",
+    "* `callbacks`: list of callbacks or None - more about that in `1-callbacks.ipynb`!\n",
+    "* `saver`: optional saver for results from objective function, as sometimes it is useful to track the output of the model, not just its score which we are maximising - more about saver in `2-saving-model-output.ipynb`!\n",
+    "* possible keyword arguments:\n",
+    "    * `gp_kernel`: covariance kernel for GPR, Matern52 by default\n",
+    "    * `gp_kernel_ARD`: whether to use ARD for lengthscales in the GPR kernel, by default False (usually worse, but for large dimensions might help)\n",
+    "    * `gp_meanf`: mean function for GPR, constant by default\n",
+    "    * `gp_mu`: initial value for constant mean function in GPR, 0.0 by default\n",
+    "    * `gp_length`: initial value for lengthscales in the kernel function in GPR, 0.25 by default (in normalised coordinates!)\n",
+    "    * `gp_var`: initial value for variance in the kernel function in GPR, 1.0 by default"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "opt = GPSOptimiser(\n",
+    "    parameter_space=space,\n",
+    "    exploration_method=\"tree\",\n",
+    "    exploration_depth=5,\n",
+    "    update_cycle=1,\n",
+    "    budget=50,\n",
+    "    stopping_condition=\"evaluations\",\n",
+    "    gp_lik_sigma=1.0e-3,\n",
+    "    n_workers=4,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run\n",
+    "\n",
+    "Finally run the optimisation! Fortunately, now running the optimisation is easy! Just call the `run` method of the `GPSOptimiser` instance. This method take two arguments:\n",
+    "* `objective_function`: this is obvious, the actual objective function we want to optimise for; must be callable and take un-normalised parameters as an argument and output scalar score (see example objective function in this notebook)\n",
+    "* `init_samples`: specify how to deal with initialisation of the optimisation problem; default is to evaluate two vertices per dimension, equally spaced from the center (i.e. within L1 ball of 0.25 of the domain size radius), or user can specify at which coordinates we should initialise\n",
+    "* `eval_repeats`: number of repetitions for objective evaluation function when it is stochastic and some statistics on the score is necessary; repeats are not counted towards the budget of objective evaluations; multiprocessing is used when n_workers > 1; 1 by default\n",
+    "* `eval_repeats_function`: function for aggregating multiple evaluations (see `eval_repeats`), has to take axis as an argument, good choices are mean, median or max; their nan-version can be used as well, when the stochastic evaluation might expectedly fail; mean by default"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2020-03-20 20:06:38] INFO: Starting 2-dimensional optimisation with budget of 50 objective function evaluations...\n",
+      "[2020-03-20 20:06:38] INFO: Sampling 2 vertices per dimension within L1 ball of 0.25 of the domain size radius in normalised coordinates using 4 worker(s)...\n",
+      "[2020-03-20 20:06:38] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:06:41] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:06:41] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:06:41] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:06:41] INFO: After 1th iteration: \n",
+      "\t number of obj. func. evaluations: 6 \n",
+      "\t highest score: 2.019346080880167 \n",
+      "\t highest UCB: 0.546413730058452\n",
+      "[2020-03-20 20:06:41] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:06:41] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:06:41] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:06:42] INFO: After 2th iteration: \n",
+      "\t number of obj. func. evaluations: 7 \n",
+      "\t highest score: 2.019346080880167 \n",
+      "\t highest UCB: 3.88313824695841\n",
+      "[2020-03-20 20:06:42] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:06:42] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:06:42] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:06:42] INFO: After 3th iteration: \n",
+      "\t number of obj. func. evaluations: 9 \n",
+      "\t highest score: 2.019346080880167 \n",
+      "\t highest UCB: 3.5729359051552176\n",
+      "[2020-03-20 20:06:42] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:06:42] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:06:42] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:06:43] INFO: After 4th iteration: \n",
+      "\t number of obj. func. evaluations: 12 \n",
+      "\t highest score: 2.019346080880167 \n",
+      "\t highest UCB: 3.259899162869712\n",
+      "[2020-03-20 20:06:43] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:06:43] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:06:43] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:06:43] INFO: After 5th iteration: \n",
+      "\t number of obj. func. evaluations: 15 \n",
+      "\t highest score: 6.822127355673395 \n",
+      "\t highest UCB: 7.162332354240296\n",
+      "[2020-03-20 20:06:43] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:06:43] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:06:43] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:06:44] INFO: After 6th iteration: \n",
+      "\t number of obj. func. evaluations: 19 \n",
+      "\t highest score: 6.822127355673395 \n",
+      "\t highest UCB: 6.965065385265492\n",
+      "[2020-03-20 20:06:44] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:06:44] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:06:44] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:06:45] INFO: After 7th iteration: \n",
+      "\t number of obj. func. evaluations: 23 \n",
+      "\t highest score: 6.822127355673395 \n",
+      "\t highest UCB: 7.586308159952344\n",
+      "[2020-03-20 20:06:45] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:06:45] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:06:45] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:06:45] INFO: After 8th iteration: \n",
+      "\t number of obj. func. evaluations: 27 \n",
+      "\t highest score: 6.822127355673395 \n",
+      "\t highest UCB: 7.071776374810146\n",
+      "[2020-03-20 20:06:45] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:06:46] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:06:46] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:06:46] INFO: After 9th iteration: \n",
+      "\t number of obj. func. evaluations: 31 \n",
+      "\t highest score: 6.822127355673395 \n",
+      "\t highest UCB: 6.375786411526237\n",
+      "[2020-03-20 20:06:46] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:06:47] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:06:47] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:06:47] INFO: After 10th iteration: \n",
+      "\t number of obj. func. evaluations: 36 \n",
+      "\t highest score: 7.880218798440746 \n",
+      "\t highest UCB: 7.62971639089788\n",
+      "[2020-03-20 20:06:47] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:06:48] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:06:48] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:06:48] INFO: After 11th iteration: \n",
+      "\t number of obj. func. evaluations: 40 \n",
+      "\t highest score: 8.04056301525883 \n",
+      "\t highest UCB: 8.036435700243205\n",
+      "[2020-03-20 20:06:48] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:06:48] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:06:48] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:06:49] INFO: After 12th iteration: \n",
+      "\t number of obj. func. evaluations: 45 \n",
+      "\t highest score: 8.04056301525883 \n",
+      "\t highest UCB: 8.105553226532628\n",
+      "[2020-03-20 20:06:49] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:06:49] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:06:49] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:06:50] INFO: After 13th iteration: \n",
+      "\t number of obj. func. evaluations: 52 \n",
+      "\t highest score: 8.102201204076508 \n",
+      "\t highest UCB: 8.074071892420118\n",
+      "[2020-03-20 20:06:50] INFO: Done. Highest evaluated score: 8.102201204076508\n"
+     ]
+    }
+   ],
+   "source": [
+    "# log_level INFO: reasonable amount of information on what is happening\n",
+    "# log_level DEBUG: a lot of information on what is happening\n",
+    "set_logger(log_level=logging.INFO)\n",
+    "# run vanilla, with default initialisation and just 1 repetition of objective function (since it's deterministic...)\n",
+    "best_point = opt.run(obj)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GPPoint(normed_coord=array([0.23662551, 0.68518519]), score_mu=8.102201204076508, score_sigma=0.0, score_ucb=0.0, label=<PointLabels.evaluated: 1>)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead>\n",
+       "<tr><th>name                   </th><th>class    </th><th>transform       </th><th>prior  </th><th>trainable  </th><th>shape  </th><th>dtype  </th><th>value                 </th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "<tr><td>GPR.mean_function.c    </td><td>Parameter</td><td>                </td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>0.19149976301992186   </td></tr>\n",
+       "<tr><td>GPR.kernel.variance    </td><td>Parameter</td><td>Softplus        </td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>3.163835951715155     </td></tr>\n",
+       "<tr><td>GPR.kernel.lengthscales</td><td>Parameter</td><td>Softplus        </td><td>       </td><td>True       </td><td>(1,)   </td><td>float64</td><td>[0.12871433]          </td></tr>\n",
+       "<tr><td>GPR.likelihood.variance</td><td>Parameter</td><td>Softplus + Shift</td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>1.0482618379448408e-06</td></tr>\n",
+       "</tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# WOHOOO, check the best point\n",
+    "print(best_point)\n",
+    "# and print summary of our trained GPR model\n",
+    "print_summary(opt.gp_surr.gpr_model, fmt=\"notebook\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Afterparty\n",
+    "\n",
+    "OK, optimiser told us we are done, let's check."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 921.6x633.6 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# first, just draw coordinates of the \"best point\" as returned by optimiser to the reference surface\n",
+    "plt.imshow(o, vmax=8.0, vmin=-8.0, cmap=plt.get_cmap(\"cividis\"), origin=\"lower\")\n",
+    "cbar = plt.colorbar()\n",
+    "i, j = np.unravel_index(o.argmax(), o.shape)\n",
+    "plt.scatter(j, i, marker=\"d\", color=\"black\")\n",
+    "ii, jj = best_point.normed_coord\n",
+    "plt.scatter(\n",
+    "    int(np.around(ii * N_POINTS)),\n",
+    "    int(np.around(jj * N_POINTS)),\n",
+    "    marker=\"o\",\n",
+    "    color=\"purple\",\n",
+    ")\n",
+    "cbar.set_label(\"Objective-True\")\n",
+    "_ = plt.title(\"Reference surface\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 921.6x633.6 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# interested how the surrogate surface actually looks like?\n",
+    "stk = np.hstack([x.reshape((-1, 1)), y.reshape((-1, 1))])\n",
+    "mean, var = opt.gp_surr.gpr_model.predict_y(opt.param_space.normalise_coords(stk))\n",
+    "mean = mean.numpy().reshape(x.shape)\n",
+    "var = var.numpy().reshape(x.shape)\n",
+    "plt.imshow(mean, vmax=8.0, vmin=-8.0, cmap=plt.get_cmap(\"cividis\"), origin=\"lower\")\n",
+    "cbar = plt.colorbar()\n",
+    "i, j = np.unravel_index(o.argmax(), o.shape)\n",
+    "plt.scatter(j, i, marker=\"d\", color=\"black\")\n",
+    "ii, jj = best_point.normed_coord\n",
+    "plt.scatter(\n",
+    "    int(np.around(ii * N_POINTS)),\n",
+    "    int(np.around(jj * N_POINTS)),\n",
+    "    marker=\"o\",\n",
+    "    color=\"purple\",\n",
+    ")\n",
+    "cbar.set_label(\"Objective-GP\")\n",
+    "_ = plt.title(\"Surrogate surface\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 921.6x633.6 with 8 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# now compare true objective function with a surrogate that was learnt by the GPR\n",
+    "plt.subplot(2, 2, 1)\n",
+    "plt.imshow(mean, vmax=8.0, vmin=-8.0, cmap=plt.get_cmap(\"cividis\"), origin=\"lower\")\n",
+    "cbar = plt.colorbar()\n",
+    "cbar.set_label(\"Objective-GP mean\")\n",
+    "plt.subplot(2, 2, 3)\n",
+    "plt.imshow(var, vmax=5.0, vmin=0.0, cmap=plt.get_cmap(\"plasma\"), origin=\"lower\")\n",
+    "cbar = plt.colorbar()\n",
+    "cbar.set_label(\"Objective-GP var\")\n",
+    "\n",
+    "plt.subplot(2, 2, 2)\n",
+    "plt.imshow(o, vmax=8.0, vmin=-8.0, cmap=plt.get_cmap(\"cividis\"), origin=\"lower\")\n",
+    "cbar = plt.colorbar()\n",
+    "cbar.set_label(\"Objective-True\")\n",
+    "plt.subplot(2, 2, 4)\n",
+    "plt.imshow(\n",
+    "    np.abs(o - mean), vmax=5.0, vmin=0, cmap=plt.get_cmap(\"magma\"), origin=\"lower\"\n",
+    ")\n",
+    "cbar = plt.colorbar()\n",
+    "cbar.set_label(\"abs(True - GP)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`GPSO` package itself contain some useful plotting methods.\n",
+    "\n",
+    "First is a look at the marginal distribution of the parameters. The method gathers all evaluated points, sorts them according to their score, selects $p$th percentile (50th in this example) and plots distribution of the values, either a kernel density estimate (`plot_type=\"kde\"`) or histogram (`plot_type=\"hist\"`).\n",
+    "\n",
+    "As we see from the plot, `x` around -1 and `y` around 1 lead to high score of the objective function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 921.6x633.6 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 921.6x633.6 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_parameter_marginal_distributions(opt, percentile=0.5, plot_type=\"kde\")\n",
+    "plot_parameter_marginal_distributions(opt, percentile=0.5, plot_type=\"hist\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another useful look at the data is to visualise the conditional surrogate distribution. This is easy in 2D (i.e. for optimisation of 2 parameters) - we already did it by hand. However, for more-than-2-D problems it becomes more challenges. `GPSO` offers `plot_conditional_surrogate_distributions` to the rescue.\n",
+    "\n",
+    "The resulting plot contains $n^{2}$ ($n$ being the number of optimised parameters) subplots arranged into a square - lower triangle contains conditional surrogate mean of a pair of parameters, upper triangle contains conditional surrogate variance, and the diagonal just states the mean and variance of scores from points evaluated within those parameter ranges."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 921.6x633.6 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_conditional_surrogate_distributions(\n",
+    "    opt, mean_limits=(-8, 8), var_limits=(0, 3), mean_cmap=\"cividis\", var_cmap=\"plasma\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, `GPSO` offers a view of the ternary tree: larger nodes with black edge represent leaves at whose center the objective function was evaluated, while the smaller nodes without an edge represent leaves that were only estimated using the surrogate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1080 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plotting tree requires python-igraph package\n",
+    "plot_ternary_tree(\n",
+    "    opt.param_space,\n",
+    "    cmap=\"cividis\",\n",
+    "    figsize=(20, 15),\n",
+    "    center_root_node=True,\n",
+    "    cmap_limits=[-8, 8],\n",
+    ")"
+   ]
+  }
+ ],
+ "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": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/1-callbacks.ipynb b/examples/1-callbacks.ipynb
new file mode 100644
index 0000000..af4876d
--- /dev/null
+++ b/examples/1-callbacks.ipynb
@@ -0,0 +1,639 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# GPSO optimisation example with callbacks\n",
+    "\n",
+    "This notebook shows the usage of callbacks during optimisation for the better experience!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "import os\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from shutil import rmtree\n",
+    "import logging\n",
+    "from gpflow.utilities import print_summary\n",
+    "\n",
+    "from gpso.param_space import ParameterSpace\n",
+    "from gpso.optimisation import GPSOptimiser, CallbackTypes, GPSOCallback\n",
+    "from gpso.callbacks import (\n",
+    "    PostIterationPlotting,\n",
+    "    PostUpdateLogging,\n",
+    "    PreFinaliseSave,\n",
+    "    GPFlowCheckpoints,\n",
+    ")\n",
+    "from gpso.utils import set_logger, make_dirs\n",
+    "\n",
+    "plt.style.use(\"seaborn-poster\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will use the same objective function as before, so no surprises there."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define objective function - compare with paper Fig. 2\n",
+    "def obj(point, offset=0.0, rotate=True):\n",
+    "    x, y = point\n",
+    "    if rotate:\n",
+    "        ct = np.cos(np.pi / 4)\n",
+    "        st = np.sin(np.pi / 4)\n",
+    "        xn = ct * x + st * y\n",
+    "        yn = ct * y - st * x\n",
+    "        x = xn\n",
+    "        y = yn\n",
+    "    return (\n",
+    "        3 * (1 - x) ** 2.0 * np.exp(-(x ** 2) - (y + 1) ** 2)\n",
+    "        - 10 * (x / 5.0 - x ** 3 - y ** 5) * np.exp(-(x ** 2) - y ** 2)\n",
+    "        - 1 / 3 * np.exp(-((x + 1) ** 2) - y ** 2)\n",
+    "        - offset\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# bounds of the parameters we will optimise\n",
+    "x_bounds = [-3, 5]\n",
+    "y_bounds = [-3, 3]\n",
+    "# number of points per dimension for plotting\n",
+    "N_POINTS = 120"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Definition of parameter space is the same as previously"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "space = ParameterSpace(\n",
+    "    parameter_names=[\"x\", \"y\"], parameter_bounds=[x_bounds, y_bounds]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now for the changes!\n",
+    "\n",
+    "### Callbacks\n",
+    "`pyGPSO` supports so-called callbacks. They are functions which run regularly after some events. Currently there exist 5 types of callbacks:\n",
+    "* **post-initialise callback**: runs just once, after initialisation of the problem\n",
+    "* **pre-iteration callback**: runs repeatedly just before the iteration loop\n",
+    "* **post-iteration callback**: runs repeatedly just after the iteration loop\n",
+    "* **post-update callback**: runs after each update of GPR\n",
+    "* **pre-finalise callback**: runs just once, just before the optimisation is finished\n",
+    "\n",
+    "`pyGPSO` comes with 4 already defined, hopefully useful, callbacks. However, they are very easy to create for yourself as we will see in a moment! Let's first check the predefined callbacks:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### `PostIterationPlotting` callback\n",
+    "Simple - plots conditional surrogate distribution, marginal parameter distribution and ternary tree after each iteration, so you can visually check the convergence of the algorithm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotting_dir = \"output\"\n",
+    "make_dirs(plotting_dir)\n",
+    "\n",
+    "plotting_callback = PostIterationPlotting(\n",
+    "    filename_pattern=os.path.join(plotting_dir, \"post_iter_plot\"),\n",
+    "    plot_ext=\".png\",\n",
+    "    gp_mean_limits=[-10, 10],\n",
+    "    gp_var_limits=[0, 5],\n",
+    "    marginal_plot_type=\"kde\",\n",
+    "    marginal_percentile=0.5,  # low percentile - just for showcase\n",
+    "    from_iteration=10,  # from 10th iteration, we do not care about first 4...\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### `PostUpdateLogging` callback\n",
+    "Even simpler callback - after each GPR update logs the current hyperparameters of GP regressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gp_logger = PostUpdateLogging()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### `PreFinaliseSave` callback\n",
+    "This callback saves parameter space (and its state, i.e. leaves) and GPR surrogate together with list of points that were either evaluated or (as centers of leaves) estimated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fin_saver = PreFinaliseSave(path=plotting_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### `GPFlowCheckpoints` callback\n",
+    "This callback saves checkpoints of the GPR model after each update. As such it might not be useful, but can be easily altered to provide more functionality, e.g. restoring GPR model on the go."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "checkpoints = GPFlowCheckpoints(path=plotting_dir, max_to_keep=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create your own callback!\n",
+    "OK, nice, you got me hooked, right? But I do not like your callbacks, I'd like to create my own!\n",
+    "\n",
+    "Well, no problemo! Lets say, you want a callback that will plot surface of GPR after each GPR update. Lets start!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we will subclass base callback\n",
+    "class GPRPlotSurface(GPSOCallback):\n",
+    "    # define type of callback - post-update\n",
+    "    callback_type = CallbackTypes.post_update\n",
+    "\n",
+    "    # do all settings here!\n",
+    "    def __init__(self, path):\n",
+    "        # always super init for a sanity check and validation\n",
+    "        super().__init__()\n",
+    "        self.path = path\n",
+    "        # set plotting data\n",
+    "        x = np.linspace(x_bounds[0], x_bounds[1], N_POINTS)\n",
+    "        y = np.linspace(y_bounds[0], y_bounds[1], N_POINTS)\n",
+    "        x, y = np.meshgrid(x, y)\n",
+    "        self.stk = np.hstack([x.reshape((-1, 1)), y.reshape((-1, 1))])\n",
+    "        self.im_shape = x.shape\n",
+    "        self.counter = 0\n",
+    "\n",
+    "    # this will be actually run when a callback is called\n",
+    "    # always only one argument - optimiser class itself\n",
+    "    def run(self, optimiser):\n",
+    "        # always super run for a sanity check and validation\n",
+    "        super().run(optimiser)\n",
+    "        # actual plotting\n",
+    "        plt.figure()\n",
+    "        mean, _ = optimiser.gp_surr.gpr_model.predict_y(\n",
+    "            optimiser.param_space.normalise_coords(self.stk)\n",
+    "        )\n",
+    "        mean = mean.numpy().reshape(self.im_shape)\n",
+    "        plt.imshow(\n",
+    "            mean, vmax=8.0, vmin=-8.0, cmap=plt.get_cmap(\"cividis\"), origin=\"lower\"\n",
+    "        )\n",
+    "        cbar = plt.colorbar()\n",
+    "        cbar.set_label(\"Objective-GP\")\n",
+    "        plt.title(\"Surrogate surface\")\n",
+    "        plt.savefig(os.path.join(self.path, f\"surr_surface_{self.counter}.png\"))\n",
+    "        plt.close()\n",
+    "        self.counter += 1\n",
+    "\n",
+    "\n",
+    "# now we need to init our brand new callback\n",
+    "surr_surface = GPRPlotSurface(path=plotting_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run with callbacks\n",
+    "After we initialise our callbacks, we run the optimiser as usual and just pass all callbacks as a list "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "opt = GPSOptimiser(\n",
+    "    parameter_space=space,\n",
+    "    exploration_method=\"tree\",\n",
+    "    exploration_depth=5,\n",
+    "    update_cycle=1,\n",
+    "    budget=50,\n",
+    "    stopping_condition=\"evaluations\",\n",
+    "    gp_lik_sigma=1.0e-3,\n",
+    "    n_workers=4,\n",
+    "    callbacks=[plotting_callback, gp_logger, fin_saver, checkpoints, surr_surface],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2020-03-20 20:09:07] INFO: Starting 2-dimensional optimisation with budget of 50 objective function evaluations...\n",
+      "[2020-03-20 20:09:07] INFO: Sampling 2 vertices per dimension within L1 ball of 0.25 of the domain size radius in normalised coordinates using 4 worker(s)...\n",
+      "[2020-03-20 20:09:07] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:09:10] INFO: Running PostUpdateLogging callback...\n",
+      "[2020-03-20 20:09:10] INFO: GPR summary:\n",
+      "name                     class      transform         prior    trainable    shape    dtype    value\n",
+      "-----------------------  ---------  ----------------  -------  -----------  -------  -------  ----------------------\n",
+      "GPR.mean_function.c      Parameter                             True         ()       float64  -0.9939317388570423\n",
+      "GPR.kernel.variance      Parameter  Softplus                   True         ()       float64  6.791967471710489\n",
+      "GPR.kernel.lengthscales  Parameter  Softplus                   True         (1,)     float64  [0.36047938]\n",
+      "GPR.likelihood.variance  Parameter  Softplus + Shift           True         ()       float64  1.0483157250592294e-06\n",
+      "[2020-03-20 20:09:10] INFO: Running GPFlowCheckpoints callback...\n",
+      "[2020-03-20 20:09:10] INFO: Running GPRPlotSurface callback...\n",
+      "[2020-03-20 20:09:10] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:09:10] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:09:10] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:09:11] INFO: Running PostUpdateLogging callback...\n",
+      "[2020-03-20 20:09:11] INFO: GPR summary:\n",
+      "name                     class      transform         prior    trainable    shape    dtype    value\n",
+      "-----------------------  ---------  ----------------  -------  -----------  -------  -------  ----------------------\n",
+      "GPR.mean_function.c      Parameter                             True         ()       float64  -0.4684727526208679\n",
+      "GPR.kernel.variance      Parameter  Softplus                   True         ()       float64  6.189678513431344\n",
+      "GPR.kernel.lengthscales  Parameter  Softplus                   True         (1,)     float64  [0.33173198]\n",
+      "GPR.likelihood.variance  Parameter  Softplus + Shift           True         ()       float64  1.0483157035209038e-06\n",
+      "[2020-03-20 20:09:11] INFO: Running GPFlowCheckpoints callback...\n",
+      "[2020-03-20 20:09:11] INFO: Running GPRPlotSurface callback...\n",
+      "[2020-03-20 20:09:11] INFO: After 1th iteration: \n",
+      "\t number of obj. func. evaluations: 6 \n",
+      "\t highest score: 2.019346080880167 \n",
+      "\t highest UCB: 0.546413730058452\n",
+      "[2020-03-20 20:09:11] INFO: Running PostIterationPlotting callback...\n",
+      "[2020-03-20 20:09:11] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:09:11] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:09:11] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:09:12] INFO: Running PostUpdateLogging callback...\n",
+      "[2020-03-20 20:09:12] INFO: GPR summary:\n",
+      "name                     class      transform         prior    trainable    shape    dtype    value\n",
+      "-----------------------  ---------  ----------------  -------  -----------  -------  -------  ----------------------\n",
+      "GPR.mean_function.c      Parameter                             True         ()       float64  -0.5856266206699724\n",
+      "GPR.kernel.variance      Parameter  Softplus                   True         ()       float64  4.646463600948689\n",
+      "GPR.kernel.lengthscales  Parameter  Softplus                   True         (1,)     float64  [0.29387037]\n",
+      "GPR.likelihood.variance  Parameter  Softplus + Shift           True         ()       float64  1.0483156926407187e-06\n",
+      "[2020-03-20 20:09:12] INFO: Running GPFlowCheckpoints callback...\n",
+      "[2020-03-20 20:09:12] INFO: Running GPRPlotSurface callback...\n",
+      "[2020-03-20 20:09:12] INFO: After 2th iteration: \n",
+      "\t number of obj. func. evaluations: 7 \n",
+      "\t highest score: 2.019346080880167 \n",
+      "\t highest UCB: 3.88313824695841\n",
+      "[2020-03-20 20:09:12] INFO: Running PostIterationPlotting callback...\n",
+      "[2020-03-20 20:09:12] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:09:12] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:09:12] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:09:13] INFO: Running PostUpdateLogging callback...\n",
+      "[2020-03-20 20:09:13] INFO: GPR summary:\n",
+      "name                     class      transform         prior    trainable    shape    dtype    value\n",
+      "-----------------------  ---------  ----------------  -------  -----------  -------  -------  ----------------------\n",
+      "GPR.mean_function.c      Parameter                             True         ()       float64  -0.22863654356883484\n",
+      "GPR.kernel.variance      Parameter  Softplus                   True         ()       float64  2.581293582205946\n",
+      "GPR.kernel.lengthscales  Parameter  Softplus                   True         (1,)     float64  [0.18603789]\n",
+      "GPR.likelihood.variance  Parameter  Softplus + Shift           True         ()       float64  1.0483156591119849e-06\n",
+      "[2020-03-20 20:09:13] INFO: Running GPFlowCheckpoints callback...\n",
+      "[2020-03-20 20:09:13] INFO: Running GPRPlotSurface callback...\n",
+      "[2020-03-20 20:09:13] INFO: After 3th iteration: \n",
+      "\t number of obj. func. evaluations: 9 \n",
+      "\t highest score: 2.019346080880167 \n",
+      "\t highest UCB: 3.5729359051552176\n",
+      "[2020-03-20 20:09:13] INFO: Running PostIterationPlotting callback...\n",
+      "[2020-03-20 20:09:13] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:09:14] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:09:14] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:09:14] INFO: Running PostUpdateLogging callback...\n",
+      "[2020-03-20 20:09:14] INFO: GPR summary:\n",
+      "name                     class      transform         prior    trainable    shape    dtype    value\n",
+      "-----------------------  ---------  ----------------  -------  -----------  -------  -------  ----------------------\n",
+      "GPR.mean_function.c      Parameter                             True         ()       float64  -0.17645620385163532\n",
+      "GPR.kernel.variance      Parameter  Softplus                   True         ()       float64  1.9015323713194445\n",
+      "GPR.kernel.lengthscales  Parameter  Softplus                   True         (1,)     float64  [0.13146083]\n",
+      "GPR.likelihood.variance  Parameter  Softplus + Shift           True         ()       float64  1.0483156604442525e-06\n",
+      "[2020-03-20 20:09:14] INFO: Running GPFlowCheckpoints callback...\n",
+      "[2020-03-20 20:09:14] INFO: Running GPRPlotSurface callback...\n",
+      "[2020-03-20 20:09:14] INFO: After 4th iteration: \n",
+      "\t number of obj. func. evaluations: 12 \n",
+      "\t highest score: 2.019346080880167 \n",
+      "\t highest UCB: 3.259899162869712\n",
+      "[2020-03-20 20:09:14] INFO: Running PostIterationPlotting callback...\n",
+      "[2020-03-20 20:09:14] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:09:15] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:09:15] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:09:15] INFO: Running PostUpdateLogging callback...\n",
+      "[2020-03-20 20:09:16] INFO: GPR summary:\n",
+      "name                     class      transform         prior    trainable    shape    dtype    value\n",
+      "-----------------------  ---------  ----------------  -------  -----------  -------  -------  ----------------------\n",
+      "GPR.mean_function.c      Parameter                             True         ()       float64  0.07026393922062586\n",
+      "GPR.kernel.variance      Parameter  Softplus                   True         ()       float64  4.948868327712322\n",
+      "GPR.kernel.lengthscales  Parameter  Softplus                   True         (1,)     float64  [0.14816661]\n",
+      "GPR.likelihood.variance  Parameter  Softplus + Shift           True         ()       float64  1.0483155747350392e-06\n",
+      "[2020-03-20 20:09:16] INFO: Running GPFlowCheckpoints callback...\n",
+      "[2020-03-20 20:09:16] INFO: Running GPRPlotSurface callback...\n",
+      "[2020-03-20 20:09:16] INFO: After 5th iteration: \n",
+      "\t number of obj. func. evaluations: 15 \n",
+      "\t highest score: 6.822127355673395 \n",
+      "\t highest UCB: 7.162332354240296\n",
+      "[2020-03-20 20:09:16] INFO: Running PostIterationPlotting callback...\n",
+      "[2020-03-20 20:09:16] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:09:16] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:09:16] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:09:17] INFO: Running PostUpdateLogging callback...\n",
+      "[2020-03-20 20:09:17] INFO: GPR summary:\n",
+      "name                     class      transform         prior    trainable    shape    dtype    value\n",
+      "-----------------------  ---------  ----------------  -------  -----------  -------  -------  ----------------------\n",
+      "GPR.mean_function.c      Parameter                             True         ()       float64  0.01975957008145603\n",
+      "GPR.kernel.variance      Parameter  Softplus                   True         ()       float64  4.48797842767545\n",
+      "GPR.kernel.lengthscales  Parameter  Softplus                   True         (1,)     float64  [0.16101731]\n",
+      "GPR.likelihood.variance  Parameter  Softplus + Shift           True         ()       float64  1.0483156115944418e-06\n",
+      "[2020-03-20 20:09:17] INFO: Running GPFlowCheckpoints callback...\n",
+      "[2020-03-20 20:09:17] INFO: Running GPRPlotSurface callback...\n",
+      "[2020-03-20 20:09:17] INFO: After 6th iteration: \n",
+      "\t number of obj. func. evaluations: 19 \n",
+      "\t highest score: 6.822127355673395 \n",
+      "\t highest UCB: 6.965065385265492\n",
+      "[2020-03-20 20:09:17] INFO: Running PostIterationPlotting callback...\n",
+      "[2020-03-20 20:09:17] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:09:19] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:09:19] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:09:19] INFO: Running PostUpdateLogging callback...\n",
+      "[2020-03-20 20:09:19] INFO: GPR summary:\n",
+      "name                     class      transform         prior    trainable    shape    dtype    value\n",
+      "-----------------------  ---------  ----------------  -------  -----------  -------  -------  ----------------------\n",
+      "GPR.mean_function.c      Parameter                             True         ()       float64  0.19144778543508212\n",
+      "GPR.kernel.variance      Parameter  Softplus                   True         ()       float64  3.4730013048222044\n",
+      "GPR.kernel.lengthscales  Parameter  Softplus                   True         (1,)     float64  [0.10565375]\n",
+      "GPR.likelihood.variance  Parameter  Softplus + Shift           True         ()       float64  1.0483155338788338e-06\n",
+      "[2020-03-20 20:09:19] INFO: Running GPFlowCheckpoints callback...\n",
+      "[2020-03-20 20:09:19] INFO: Running GPRPlotSurface callback...\n",
+      "[2020-03-20 20:09:20] INFO: After 7th iteration: \n",
+      "\t number of obj. func. evaluations: 23 \n",
+      "\t highest score: 6.822127355673395 \n",
+      "\t highest UCB: 7.586308159952344\n",
+      "[2020-03-20 20:09:20] INFO: Running PostIterationPlotting callback...\n",
+      "[2020-03-20 20:09:20] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:09:21] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:09:21] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:09:21] INFO: Running PostUpdateLogging callback...\n",
+      "[2020-03-20 20:09:21] INFO: GPR summary:\n",
+      "name                     class      transform         prior    trainable    shape    dtype    value\n",
+      "-----------------------  ---------  ----------------  -------  -----------  -------  -------  ----------------------\n",
+      "GPR.mean_function.c      Parameter                             True         ()       float64  0.3111087091292495\n",
+      "GPR.kernel.variance      Parameter  Softplus                   True         ()       float64  3.3853761677721703\n",
+      "GPR.kernel.lengthscales  Parameter  Softplus                   True         (1,)     float64  [0.10490081]\n",
+      "GPR.likelihood.variance  Parameter  Softplus + Shift           True         ()       float64  1.0483154985737434e-06\n",
+      "[2020-03-20 20:09:21] INFO: Running GPFlowCheckpoints callback...\n",
+      "[2020-03-20 20:09:21] INFO: Running GPRPlotSurface callback...\n",
+      "[2020-03-20 20:09:21] INFO: After 8th iteration: \n",
+      "\t number of obj. func. evaluations: 27 \n",
+      "\t highest score: 6.822127355673395 \n",
+      "\t highest UCB: 7.071776374810146\n",
+      "[2020-03-20 20:09:21] INFO: Running PostIterationPlotting callback...\n",
+      "[2020-03-20 20:09:21] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:09:22] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:09:22] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:09:22] INFO: Running PostUpdateLogging callback...\n",
+      "[2020-03-20 20:09:22] INFO: GPR summary:\n",
+      "name                     class      transform         prior    trainable    shape    dtype    value\n",
+      "-----------------------  ---------  ----------------  -------  -----------  -------  -------  ----------------------\n",
+      "GPR.mean_function.c      Parameter                             True         ()       float64  0.26938405446172387\n",
+      "GPR.kernel.variance      Parameter  Softplus                   True         ()       float64  3.011974679743116\n",
+      "GPR.kernel.lengthscales  Parameter  Softplus                   True         (1,)     float64  [0.10531179]\n",
+      "GPR.likelihood.variance  Parameter  Softplus + Shift           True         ()       float64  1.0483154521664233e-06\n",
+      "[2020-03-20 20:09:22] INFO: Running GPFlowCheckpoints callback...\n",
+      "[2020-03-20 20:09:22] INFO: Running GPRPlotSurface callback...\n",
+      "[2020-03-20 20:09:23] INFO: After 9th iteration: \n",
+      "\t number of obj. func. evaluations: 31 \n",
+      "\t highest score: 6.822127355673395 \n",
+      "\t highest UCB: 6.375786411526237\n",
+      "[2020-03-20 20:09:23] INFO: Running PostIterationPlotting callback...\n",
+      "[2020-03-20 20:09:23] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:09:23] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:09:23] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:09:24] INFO: Running PostUpdateLogging callback...\n",
+      "[2020-03-20 20:09:24] INFO: GPR summary:\n",
+      "name                     class      transform         prior    trainable    shape    dtype    value\n",
+      "-----------------------  ---------  ----------------  -------  -----------  -------  -------  ----------------------\n",
+      "GPR.mean_function.c      Parameter                             True         ()       float64  0.2689861009698867\n",
+      "GPR.kernel.variance      Parameter  Softplus                   True         ()       float64  3.136405684359817\n",
+      "GPR.kernel.lengthscales  Parameter  Softplus                   True         (1,)     float64  [0.11201374]\n",
+      "GPR.likelihood.variance  Parameter  Softplus + Shift           True         ()       float64  1.0483151619541386e-06\n",
+      "[2020-03-20 20:09:24] INFO: Running GPFlowCheckpoints callback...\n",
+      "[2020-03-20 20:09:24] INFO: Running GPRPlotSurface callback...\n",
+      "[2020-03-20 20:09:24] INFO: After 10th iteration: \n",
+      "\t number of obj. func. evaluations: 36 \n",
+      "\t highest score: 7.880218798440746 \n",
+      "\t highest UCB: 7.62971639089788\n",
+      "[2020-03-20 20:09:24] INFO: Running PostIterationPlotting callback...\n",
+      "[2020-03-20 20:09:31] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:09:32] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:09:32] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:09:32] INFO: Running PostUpdateLogging callback...\n",
+      "[2020-03-20 20:09:32] INFO: GPR summary:\n",
+      "name                     class      transform         prior    trainable    shape    dtype    value\n",
+      "-----------------------  ---------  ----------------  -------  -----------  -------  -------  ----------------------\n",
+      "GPR.mean_function.c      Parameter                             True         ()       float64  0.23676298471900195\n",
+      "GPR.kernel.variance      Parameter  Softplus                   True         ()       float64  2.9740338613214536\n",
+      "GPR.kernel.lengthscales  Parameter  Softplus                   True         (1,)     float64  [0.11314123]\n",
+      "GPR.likelihood.variance  Parameter  Softplus + Shift           True         ()       float64  1.0483144174386143e-06\n",
+      "[2020-03-20 20:09:32] INFO: Running GPFlowCheckpoints callback...\n",
+      "[2020-03-20 20:09:32] INFO: Running GPRPlotSurface callback...\n",
+      "[2020-03-20 20:09:32] INFO: After 11th iteration: \n",
+      "\t number of obj. func. evaluations: 40 \n",
+      "\t highest score: 8.04056301525883 \n",
+      "\t highest UCB: 8.036435700243205\n",
+      "[2020-03-20 20:09:32] INFO: Running PostIterationPlotting callback...\n",
+      "[2020-03-20 20:09:39] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:09:40] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:09:40] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:09:40] INFO: Running PostUpdateLogging callback...\n",
+      "[2020-03-20 20:09:40] INFO: GPR summary:\n",
+      "name                     class      transform         prior    trainable    shape    dtype    value\n",
+      "-----------------------  ---------  ----------------  -------  -----------  -------  -------  ----------------------\n",
+      "GPR.mean_function.c      Parameter                             True         ()       float64  0.24542508590354975\n",
+      "GPR.kernel.variance      Parameter  Softplus                   True         ()       float64  3.1520687163346235\n",
+      "GPR.kernel.lengthscales  Parameter  Softplus                   True         (1,)     float64  [0.12050097]\n",
+      "GPR.likelihood.variance  Parameter  Softplus + Shift           True         ()       float64  1.0483094391988122e-06\n",
+      "[2020-03-20 20:09:40] INFO: Running GPFlowCheckpoints callback...\n",
+      "[2020-03-20 20:09:40] INFO: Running GPRPlotSurface callback...\n",
+      "[2020-03-20 20:09:40] INFO: After 12th iteration: \n",
+      "\t number of obj. func. evaluations: 45 \n",
+      "\t highest score: 8.04056301525883 \n",
+      "\t highest UCB: 8.105553226532628\n",
+      "[2020-03-20 20:09:40] INFO: Running PostIterationPlotting callback...\n",
+      "[2020-03-20 20:09:47] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:09:48] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:09:48] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:09:48] INFO: Running PostUpdateLogging callback...\n",
+      "[2020-03-20 20:09:48] INFO: GPR summary:\n",
+      "name                     class      transform         prior    trainable    shape    dtype    value\n",
+      "-----------------------  ---------  ----------------  -------  -----------  -------  -------  ----------------------\n",
+      "GPR.mean_function.c      Parameter                             True         ()       float64  0.19149976301992186\n",
+      "GPR.kernel.variance      Parameter  Softplus                   True         ()       float64  3.163835951715155\n",
+      "GPR.kernel.lengthscales  Parameter  Softplus                   True         (1,)     float64  [0.12871433]\n",
+      "GPR.likelihood.variance  Parameter  Softplus + Shift           True         ()       float64  1.0482618379448408e-06\n",
+      "[2020-03-20 20:09:48] INFO: Running GPFlowCheckpoints callback...\n",
+      "[2020-03-20 20:09:48] INFO: Running GPRPlotSurface callback...\n",
+      "[2020-03-20 20:09:49] INFO: After 13th iteration: \n",
+      "\t number of obj. func. evaluations: 52 \n",
+      "\t highest score: 8.102201204076508 \n",
+      "\t highest UCB: 8.074071892420118\n",
+      "[2020-03-20 20:09:49] INFO: Running PostIterationPlotting callback...\n",
+      "[2020-03-20 20:09:55] INFO: Done. Highest evaluated score: 8.102201204076508\n",
+      "[2020-03-20 20:09:55] INFO: Running PreFinaliseSave callback...\n",
+      "[2020-03-20 20:09:55] WARNING: output could not be created: [Errno 17] File exists: 'output'\n"
+     ]
+    }
+   ],
+   "source": [
+    "# log_level INFO: reasonable amount of information on what is happening\n",
+    "# log_level DEBUG: a lot of information on what is happening\n",
+    "set_logger(log_level=logging.INFO)\n",
+    "# run vanilla, with default initialisation and just 1 repetition of objective function (since it's deterministic...)\n",
+    "best_point = opt.run(obj)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GPPoint(normed_coord=array([0.23662551, 0.68518519]), score_mu=8.102201204076508, score_sigma=0.0, score_ucb=0.0, label=<PointLabels.evaluated: 1>)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead>\n",
+       "<tr><th>name                   </th><th>class    </th><th>transform       </th><th>prior  </th><th>trainable  </th><th>shape  </th><th>dtype  </th><th>value                 </th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "<tr><td>GPR.mean_function.c    </td><td>Parameter</td><td>                </td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>0.19149976301992186   </td></tr>\n",
+       "<tr><td>GPR.kernel.variance    </td><td>Parameter</td><td>Softplus        </td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>3.163835951715155     </td></tr>\n",
+       "<tr><td>GPR.kernel.lengthscales</td><td>Parameter</td><td>Softplus        </td><td>       </td><td>True       </td><td>(1,)   </td><td>float64</td><td>[0.12871433]          </td></tr>\n",
+       "<tr><td>GPR.likelihood.variance</td><td>Parameter</td><td>Softplus + Shift</td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>1.0482618379448408e-06</td></tr>\n",
+       "</tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# WOHOOO, check the best point\n",
+    "print(best_point)\n",
+    "# and print summary of our trained GPR model\n",
+    "print_summary(opt.gp_surr.gpr_model, fmt=\"notebook\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Aftermath\n",
+    "\n",
+    "So we can see that the results are the same as before (duh), but, we can see:\n",
+    "1. overview table of GPR hyperparameters in the log after each update (thanks to `PostUpdateLogging` callback)\n",
+    "2. plots after each iteration (thanks to `PostIterationPlotting` callback)\n",
+    "3. saved parameter space and GPR surrogate (thanks to `PreFinaliseSave` callback)\n",
+    "4. checkpoints while training the GPR (courtesy of `GPFlowCheckpoints` callback)\n",
+    "\n",
+    "but mainly\n",
+    "\n",
+    "5. plots of surrogate mean surface after each update, so we can monitor how the training \"helps\" the surrogate to get the correct shape (courtesy of our brand new `GPRPlotSurface` callback)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# cleaning - run after you check the results!\n",
+    "rmtree(plotting_dir)"
+   ]
+  }
+ ],
+ "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": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/2-saving-model-output.ipynb b/examples/2-saving-model-output.ipynb
new file mode 100644
index 0000000..6591904
--- /dev/null
+++ b/examples/2-saving-model-output.ipynb
@@ -0,0 +1,425 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# GPSO optimisation example with saving the model output\n",
+    "\n",
+    "So in the real world, usually you want to, say, optimise the parameters of the model AND save the model results, right? So after the optimisation you can check plethora of other stuff on your model.\n",
+    "Don't worry, we got your back!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "import os\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from shutil import rmtree\n",
+    "import logging\n",
+    "from gpflow.utilities import print_summary\n",
+    "\n",
+    "from gpso.param_space import ParameterSpace\n",
+    "from gpso.optimisation import GPSOptimiser\n",
+    "from gpso.utils import set_logger, make_dirs\n",
+    "from gpso.saving_helper import TableSaver, table_reader\n",
+    "\n",
+    "plt.style.use(\"seaborn-poster\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "again, objective function and parameter space are without change"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define objective function - compare with paper Fig. 2\n",
+    "def obj(point, offset=0.0, rotate=True):\n",
+    "    x, y = point\n",
+    "    if rotate:\n",
+    "        ct = np.cos(np.pi / 4)\n",
+    "        st = np.sin(np.pi / 4)\n",
+    "        xn = ct * x + st * y\n",
+    "        yn = ct * y - st * x\n",
+    "        x = xn\n",
+    "        y = yn\n",
+    "    return (\n",
+    "        3 * (1 - x) ** 2.0 * np.exp(-(x ** 2) - (y + 1) ** 2)\n",
+    "        - 10 * (x / 5.0 - x ** 3 - y ** 5) * np.exp(-(x ** 2) - y ** 2)\n",
+    "        - 1 / 3 * np.exp(-((x + 1) ** 2) - y ** 2)\n",
+    "        - offset\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# bounds of the parameters we will optimise\n",
+    "x_bounds = [-3, 5]\n",
+    "y_bounds = [-3, 3]\n",
+    "# number of points per dimension for plotting\n",
+    "N_POINTS = 120"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "space = ParameterSpace(\n",
+    "    parameter_names=[\"x\", \"y\"], parameter_bounds=[x_bounds, y_bounds]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Saving the output\n",
+    "\n",
+    "It's easy - just initialise the `TableSaver` with filename of HDF file and we are good! And yes, the objective function now needs to return **both**: the model results and score which we are optimising"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output_dir = \"output\"\n",
+    "make_dirs(output_dir)\n",
+    "\n",
+    "# init saver\n",
+    "saver = TableSaver(os.path.join(output_dir, \"results.h5\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def obj_w_return(point, offset=0.0, rotate=True):\n",
+    "    # we re-use the original objective function\n",
+    "    score = obj(point, offset, rotate)\n",
+    "    # and create dummy timeseries - 100 samples long with 2 features\n",
+    "    np.random.seed(int(np.abs(point[0])))\n",
+    "    timeseries = np.random.rand(100, 2)\n",
+    "    # return first timeseries, then score\n",
+    "    return timeseries, score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "opt = GPSOptimiser(\n",
+    "    parameter_space=space,\n",
+    "    exploration_method=\"tree\",\n",
+    "    exploration_depth=5,\n",
+    "    update_cycle=1,\n",
+    "    budget=50,\n",
+    "    stopping_condition=\"evaluations\",\n",
+    "    gp_lik_sigma=1.0e-3,\n",
+    "    n_workers=4,\n",
+    "    saver=saver,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2020-03-20 20:11:21] INFO: Starting 2-dimensional optimisation with budget of 50 objective function evaluations...\n",
+      "[2020-03-20 20:11:21] INFO: Sampling 2 vertices per dimension within L1 ball of 0.25 of the domain size radius in normalised coordinates using 4 worker(s)...\n",
+      "[2020-03-20 20:11:21] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:11:24] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:11:24] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:11:24] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:11:24] INFO: After 1th iteration: \n",
+      "\t number of obj. func. evaluations: 6 \n",
+      "\t highest score: 2.019346080880167 \n",
+      "\t highest UCB: 0.546413730058452\n",
+      "[2020-03-20 20:11:24] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:11:24] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:11:24] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:11:24] INFO: After 2th iteration: \n",
+      "\t number of obj. func. evaluations: 7 \n",
+      "\t highest score: 2.019346080880167 \n",
+      "\t highest UCB: 3.88313824695841\n",
+      "[2020-03-20 20:11:24] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:11:25] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:11:25] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:11:25] INFO: After 3th iteration: \n",
+      "\t number of obj. func. evaluations: 9 \n",
+      "\t highest score: 2.019346080880167 \n",
+      "\t highest UCB: 3.5729359051552176\n",
+      "[2020-03-20 20:11:25] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:11:25] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:11:25] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:11:25] INFO: After 4th iteration: \n",
+      "\t number of obj. func. evaluations: 12 \n",
+      "\t highest score: 2.019346080880167 \n",
+      "\t highest UCB: 3.259899162869712\n",
+      "[2020-03-20 20:11:25] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:11:26] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:11:26] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:11:26] INFO: After 5th iteration: \n",
+      "\t number of obj. func. evaluations: 15 \n",
+      "\t highest score: 6.822127355673395 \n",
+      "\t highest UCB: 7.162332354240296\n",
+      "[2020-03-20 20:11:26] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:11:26] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:11:26] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:11:27] INFO: After 6th iteration: \n",
+      "\t number of obj. func. evaluations: 19 \n",
+      "\t highest score: 6.822127355673395 \n",
+      "\t highest UCB: 6.965065385265492\n",
+      "[2020-03-20 20:11:27] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:11:27] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:11:27] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:11:28] INFO: After 7th iteration: \n",
+      "\t number of obj. func. evaluations: 23 \n",
+      "\t highest score: 6.822127355673395 \n",
+      "\t highest UCB: 7.586308159952344\n",
+      "[2020-03-20 20:11:28] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:11:28] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:11:28] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:11:28] INFO: After 8th iteration: \n",
+      "\t number of obj. func. evaluations: 27 \n",
+      "\t highest score: 6.822127355673395 \n",
+      "\t highest UCB: 7.071776374810146\n",
+      "[2020-03-20 20:11:28] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:11:29] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:11:29] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:11:29] INFO: After 9th iteration: \n",
+      "\t number of obj. func. evaluations: 31 \n",
+      "\t highest score: 6.822127355673395 \n",
+      "\t highest UCB: 6.375786411526237\n",
+      "[2020-03-20 20:11:29] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:11:30] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:11:30] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:11:30] INFO: After 10th iteration: \n",
+      "\t number of obj. func. evaluations: 36 \n",
+      "\t highest score: 7.880218798440746 \n",
+      "\t highest UCB: 7.62971639089788\n",
+      "[2020-03-20 20:11:30] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:11:31] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:11:31] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:11:31] INFO: After 11th iteration: \n",
+      "\t number of obj. func. evaluations: 40 \n",
+      "\t highest score: 8.04056301525883 \n",
+      "\t highest UCB: 8.036435700243205\n",
+      "[2020-03-20 20:11:31] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:11:32] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:11:32] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:11:32] INFO: After 12th iteration: \n",
+      "\t number of obj. func. evaluations: 45 \n",
+      "\t highest score: 8.04056301525883 \n",
+      "\t highest UCB: 8.105553226532628\n",
+      "[2020-03-20 20:11:33] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:11:33] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:11:33] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:11:34] INFO: After 13th iteration: \n",
+      "\t number of obj. func. evaluations: 52 \n",
+      "\t highest score: 8.102201204076508 \n",
+      "\t highest UCB: 8.074071892420118\n",
+      "[2020-03-20 20:11:34] INFO: Done. Highest evaluated score: 8.102201204076508\n"
+     ]
+    }
+   ],
+   "source": [
+    "# log_level INFO: reasonable amount of information on what is happening\n",
+    "# log_level DEBUG: a lot of information on what is happening\n",
+    "set_logger(log_level=logging.INFO)\n",
+    "# run vanilla, with default initialisation and just 1 repetition of objective function (since it's deterministic...)\n",
+    "best_point = opt.run(obj_w_return)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GPPoint(normed_coord=array([0.23662551, 0.68518519]), score_mu=8.102201204076508, score_sigma=0.0, score_ucb=0.0, label=<PointLabels.evaluated: 1>)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead>\n",
+       "<tr><th>name                   </th><th>class    </th><th>transform       </th><th>prior  </th><th>trainable  </th><th>shape  </th><th>dtype  </th><th>value                 </th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "<tr><td>GPR.mean_function.c    </td><td>Parameter</td><td>                </td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>0.19149976301992186   </td></tr>\n",
+       "<tr><td>GPR.kernel.variance    </td><td>Parameter</td><td>Softplus        </td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>3.163835951715155     </td></tr>\n",
+       "<tr><td>GPR.kernel.lengthscales</td><td>Parameter</td><td>Softplus        </td><td>       </td><td>True       </td><td>(1,)   </td><td>float64</td><td>[0.12871433]          </td></tr>\n",
+       "<tr><td>GPR.likelihood.variance</td><td>Parameter</td><td>Softplus + Shift</td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>1.0482618379448408e-06</td></tr>\n",
+       "</tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# WOHOOO, check the best point\n",
+    "print(best_point)\n",
+    "# and print summary of our trained GPR model\n",
+    "print_summary(opt.gp_surr.gpr_model, fmt=\"notebook\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# do not forget to close the saver\n",
+    "saver.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### After\n",
+    "\n",
+    "so yes, the results are the same, nice...\n",
+    "\n",
+    "but what about our saved timeseries?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2020-03-20 20:11:34] INFO: Loading output/results.h5...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# read the data\n",
+    "results, scores, parameters, extras = table_reader(\n",
+    "    os.path.join(output_dir, \"results.h5\")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All returned objects is a list as per run of the model.\n",
+    "* `results` - list of actual results, i.e. dummy timeseries in our case\n",
+    "* `scores` - list of scores per run\n",
+    "* `parameters` - list of parameters per run\n",
+    "* `extras` - here the additional data is stored as a dictionary passed to the `TableSaver` at the initialisation, useful for storing other parameters of your model against which you are not optimising, but it's good to know what are they"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[array(8.07790009)]\n",
+      "{'x': array(-1.17283951), 'y': array(1.11111111)}\n",
+      "(100, 2)\n",
+      "None\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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51hY7WOy6+KFvuxIAsDvy8Tf31qfVH3i0AJjQxVKYXd0F4OiNwW1aAOR19H1v9hgAWtDQ70LAIQY7g4u9lNVuukjrny30Ga3BhLTvrcdUJEAtug7ozgMtulbXDtqMrLsAME2Rog1SKwGymDnoTiAtkgGpMWxNDQNTC4BWdX5mDJsKAwAA/+BlsQyozjSg4aQlQJdOxWbXYy8FOqFxjEqAhjksm3qhn4UCgJ5TdBIgenE1WNTIDIDhIq2MBKANkBgA6wEIbjeZAtRSg7k6AwCYbQkQDSSpWgBwrhz/ph4AKgGyJmCLWYPuRNqqAkA+ATeVBKR+jh0G1hzoFOCVsAC46bpDuPrgEgDgtuPncWK9HtnLYBwvmM5vDZtPd9Lp/wFFApSzoFcf98d7f+InZQDUBCAgjgEFjC72ozKLtD5NAdlLBYD1AIBzhQFoUgLUUgZAnQIMzHYBQBslVQv2wSVZQmW6/9gYUIuZhu5AaFMBMCEPQBMMwIn1nXZdQFoCSQIUFgCOwyIWgHPgw3fWwwLQQm7o+dhsMEoWgKz/v+oV8e0iJmAdQ7CHWIBzW4PkdF76N+kkQFRfayQBKpECtEUlQHuoky4VAHuocKkTXDmPNskAtNUDMHcMAPUAVDxeafcfKJcCZBkAi5lD6xkA+cS/1ZQJWPmcqgv3v/zGk/jOt38OP/o7t9qZAgpoAbC6GHdVJBnQnSdq+d5UJudC0zKgp2+Pb0sMAJUA5ZiAdY/vESPwFx46g9e8/XN4/X/5PM5skgVLrgSovAfAeJHWnwUJ0JwyAOrfPalBYG1igueNAZA8ABV/b6r/BzAcGvrBbAyoxUyj7R4AhQG4NCEPQNUC4NP3B4uN+05eqk3OMivY0kiAAOD6y1bx0msOAAAeObOFe05sVP4stQBo1AjsjYGTdwS3V68A9l8dP0YXvbkMgK4AaD8DsL49xC/9j7sxHPtY3x7hjifW4wdzTcAFPQBkkWYcA7q1RyVAdFu5H8xUmDeoBUCDv99IMpi36LvWMQB7XRqYhTo9AEoB8MxFwzWOlQBZzDR0B1ZLpgFzzqfmAahq/mr1OPkpQ40Bpah7JsBAKQDONckAnLk/Xqhf/UqAsfixbgEGYFR/AfCVR8/he37zC/j1TzYXf/mWj96Ps5txgSWxalIMaHUPQDkGgEiAGtSQ1w61IOJ7SL5UFxIMQHO/nyQva9O5e97mANTpAVAkQL5p4WQlQBYzjRYzALp878Y8AOocgIoMgBRTaH0AEtQYUIof+bZj0br568cvVP6sBAPQZAEg5f/fJD82ZQnQH9/yOB4728fvf/HRRli0T99/Gh9SfBvS4imPAajiAZh1BkA9R8+jDEjVgDfJAPhycdkaCefcSYDq9ADIDIBjHANqJUAWs4wWewB0nb3mPABqAVDtpE8X/TZRSIYcAyqfVA+u9NBzg1MaTfApi6GyOLzQpAToaZoA9Er5sW6BOQC6AsBkeFgGxKKfc2B3WG8HeX17iF/50D2J+6VjqmYPwEiZEJ4Lbwxsny/0Ga2BLQAm6wHwVD9YSwqAuTMB1zgHYFMuAJhpAWAHgVnMNKowABUXJXnQFQCbg73hAaDbri5C5x1yDKibeLwbFgB1XHjVIqJRCZBgAJgDHHuZ/JjEAJTxAFRjAKgUynhwliF+lUh/RPEGKPn8uTGg5RkAo/1k+zwA8ry9ZAJWFyu2AGjsO/B9DvXwUAMipoa5ZgDqNQEz0/3HswWAxSxD6wEwKABu/a/A264GPv7v6t+mEKosB2hOAqSe5Kue9GkB0KokiRZAjgFNnlQ7bqABMpZ2ZCBpAm7ogjnsA+cfDm5f/uKkzEWaA5AzCKwBE/BgFBdCuuOqLD5z/2l8MJT+rPRc/NPXXR89Ji3M82JAqQfAaBBYQQagr8QA7qVFtLrI20sRpnVhQgyAzvTbbgZgDxWyRTGkBcA4oC/LQvEAuNxKgCwsyjMAd/xpkEhx55/Xv00hdCfjJkzAuq7PcFzRBFy0QzlHSIsBFeg4IQNQQ6daNQGf32pIAkSPmbUrko9XlQBVLACGUm5+PQXp+vYQv0ykP7/yQy/CtYdjpkNamFM6XycBkjwA+Qtc+jcY/T2JHPA9tHCyEqCJpQDpTL+t8XDpGIBxg5LGaUNdh1QpfEszAHQSsC0ALGYNWg+AQQpQ/1zwf6+5E9CkTMCeprNQmQGwJuBUbOVKgBpkAJqSANELcWch+fi0JUCj+Hsw6pgb4Fc/Fkt/XvvcI/jZm69Fx4mTjyQJXy4DQD0A+Yu7wilANAHI8DNaA1sAJBd/DaUA6QqA1iQBzVsK0EBhSsvu974HbJ+T7nJMGQD6mbYAsJg5lJkE7HvAbpjR3mAu9aRMwLoFUdWTPmUvrAdAhvgNe66DhU6yAIglQA0wAE1JgKROkSYuTmIASsSAVvTbSB6AGr7Xz9x/OoppXem5ePubbwRjDB3iAZAKuGEOA1DUBEwLAJO/J8EA7KFFdEICtIe2vS5MiAHQS4Bacv7WFgB7qJAtigQDUPJv7Z9LTJJ2yngArATIYuZQJgVoZx2Soa6hC5KuA7zZQIShrgCouminixKbAiRDMAA6+Q8AdCMJUP0MwIX+AL5Bx3g49qVM+1zkMgDUAzANEzDxAFRkADjneMvH7ov+/Ss/9CJcfTBgONIZgPBvcntAR1Mg0QLAYFFTWAK0Vz0Avp/M/bcegMYWvloGoGbTfGmMdR6AGZYAqV6pssesIv8BingArATIYpZRxgOwo+SzN6XH1Jx4+0OvNglD1udU7T7TbWxNB6klEAyAOgRMoE4GQC3kfA6s72Tvr4Oxhx/4rS/hO/7zZ/C39z1j9kFeTgHQWQjSgYCScwAqmoDH9XkANgdjPHUhKEheeMUafvbma6PHXFIAaD0Auu4/UNwDUNQEvLVHJUC67dwrxUudmBQDoDlXt+b8PXcSIGUdUpa1U9k/AA43fC+x3zFHDipoEWwBYFEeZVKAttUCYHJ6TKB+I7BuAVH1pD+STMAtuYC0AJxzbA2D328lrQAIGYA6hvDo2Jc8I/DdT2/g+Lk+fA589O9Omn0QnSzpagoAxmIZUJ4ESJcSVKEA4JxL30PVApq+1+X7FsHIxGPh3wBUBiA8pyykFAAFPQCjoiZ7HQPQlgFPWdAt8OaxAFAXf015ABq4FtQGLQOwRwrZouBcIwGaIgPQ0inAgC0AZhOcAx/9ReD3Xgs8c29znyPl3IYLl6IMgOnJ+PFbgHffDHzmLUZPT+tU1l0A6D6n3jkAe2ChMSFsD71o3bWWUgCkLiJLQDdMLG8WwMn1uNN2akNz0dVBYgBSLhbCCJwrAdI8XkECpLIgVT0AtACguf8A4DoaDwDnMauRxgAU9gAULGhUBsDwc6YO3QJvL2x33ZhYClDyvN9KE/DCvuD/s5oCNOxDkhkD5X9zbQFgKKMTx19L9f+ALQBmE2cfBG5/L3D6HuAb72nuc+hBtXwo+P9wM9vYW5YBuO33gHMPAbe8E9i5mPv0tBNv3T4APQNQ3yRgOwcgRl4EKADFSFo/A3AhxwhMF/20GMhEHgMAAN2wAJjwJGDVCF0nA7DQkS8/HZ0EaDyIzxEmBYCRB6CgxE5lAAw/Z+qwBUCASXkAdHLQtgwC0xUAe2EfLgMdC1qaAUge+8YMgPhMV3+tagNsATCL2DxFbicr2NpATyBLh+LbWTKF7fPyv00r84hZ0NB7GqR1f+tOAtJHv9VoAm4LhdwCyBGg+QxAVSOwVgLUz+6a0UX/6Uu7ZvtCngcAiBe/w362/KRmEzCNAAWqL2jo/tzLKACi45f+PboIUKCwB6AQA+D7cWyxdP8eWEhrPQDWBDxJ2WnVmTC1IZIAsfg4mlUPgG59UNoDEPu4Bjw4z7imHgArAbKYCmiX3aBbXhr0RLpMCoCsBXrCBGxqqCEXLgPqMm3hVfcsgCY8AJIJ2DIAEWjxlmYC7tbJAGh+x3wJUMwA+Bw4Y5IGRPfntItFNAuAZy/oRfdrYX98X40SoDpZFVUC1KHyLfG5dKJnTR4AdQ5Apldk50IySQfYGwWA9QAESMwBmFwMaOsYgO5SLDP0BnvDy1IUuvVHDQzASR6scVwYxpdbCZDFVEC77OqCu05IDMDB+HZWAaBIgLyxKZ1GnmewoKGLaOIzxGbtHgBNAVBRJkEvJK0xkbUAlAFYS5MAOZpFZAn4PtdKufJMwKc2djL/rQVdqKUxAF0yDCxLBiQeWzlC7qsgARrJi6fqvor4N+l2mPSY5AHQMgBpBQBJ2DDxACh/QyYLoJEABC/aA/IJXddzLgsAVQLUTOe71YPABAPQXZJlhrO4P2gLgGoegF3exUWsFXs/cY5oaQQoYAuA2QQtAFTNfZ3QeQCAQgzAeGR4MqYXXAMGgF7k9y3GB+AkPABVFp6ez6WmjDUBx5AkQL18D0CVQox2vo+sxhfMvGnAqvGXMgKpMGIA6CyAlAW9N44v9Iv74veqIgGq2QNAC9qeK0fjaT0Ag5whYIDcYTPxAKisRtbfpNP/A3sjCtQyAAESfzNvRAqlO++3RsIpzgGdJfkcM4syoAY8AGf5AYxAvUYG35tvCwCLaUBiAC42R/OJDhNzgEUiORhcytg2WZJkzgCQA3hswgDEJ96Dy/EBWLsHQJsCVP77Vjv+lgGIIUmA0gaB6WQkJUAXvlcdWIxuZ5mAd0de4vHaGABaAKQxACOlW95dyn6+AdQCoLIHgEqAVA8A9W+IY6iwBKjYHAAgpwDQJQABe4QB0OyrDU4xvu/kBt712YdxwtT8PinoFn8N/H66hkNrGIBIArQoL0hnMQmoLg/AcDtay5zFfgx5sbABKwGymA5oAcC97AV5FfhkB18g9FgBBqBcAWDAAJAT74HluOPRdg+A+n52EnAMSQKUMwcAqFaI0QjQgyu9KLHmXIYJWBf7WZwBMJAApTEAqmG2axgdmgF1/6vVA5CZAiQ8AEVNwAZzAJQixsv6m1IZgGY76SYTp/PfZHISoLHn4xfe+w2849Pfwv/9kQajp8tA+z3UXwBoY0Db4AHgPG6adZbkJsNeKGSLoi4PADn2z/IDGIMwlnnfG53CbRkAi4lCTdppyghMNW4iWgzILAD4dkkJED2AC3oAJAZgEh6ACgWAusCyDEAMsxhQmiRT/rtTzaqHV4IiMksCdErT+TRjAEzmABhIgOhCnxYAlSRAcke9cgwo2Z/VGFCtB0CSAK1Bi4IegMQxlrWfUA/AMvFVNFgAvOdLj+Hb3vIp/MEXH632RhOUAN19YgOnLwX78WNny3tOGoGOFWqCAdAUklUjoWsBbTB0F2dfAlSXB2CLFgD7ZQlQ3vvRx20BYDFRqLr/pnwA4mLidMwYAM6TDIDpibioB8CjEqDJMgBVuqTqYqQ1GtIWwCgG1KknBUjtVB8OfQAbO6PUouykhgEwGgZWZA4AkC7podrX7rI8O6CkDDAZA9pgCpBk4C4QAyp5AEwKgAK+hj6RAO27kryouc7pe7/yOLYGY/zRLcervdEE5wDc+nAclVp3k6UyJsSENDEUshZQyWxHkQDNYgFQlweADAELPACUAcj53qQhqTYG1GKSmAoDYFAADPtgyoHjj0wlQDQGNH9RRS/qsgSo3gu3Nvu5VgagBR2klsAkBlTWkVdgAKROtYvDq/E+dDHFB6BjAIwkQEUmAQOGEqBVOTrU4JjRIWkCnowHIE4BKuoBKJ4ClLmfUAZg31Xkc5orAHbC5KVLVc9V2gKgmTkAtzwSFwDbw5bNGpiQB6CJmTC1YESO/e6y3GSYxQKgLg8ALQCgmoBz3o9+r44dBGYxKXA+uQIg1QOQ4jnQRJIaMwD0gmuwmKEn4yYlQLUzAMoFYzhu2cV0ijCJAZXmAFToVtPOd6/j4PBKfNFMmwVAGQA37Gaf2xokZDQJmDAANAEnlQFQPQBL5DXlZEDqtlctSAcZg8DcXA9AigSooAegGANACoC1K8iLmutyi3PA7sivtoDUDgKrf7u3h2Pc8WR8jekPx9mzFSaNCXkAdIVkKxo4lAFQTcAzWQDUxQDEx/4ZfgDjIilA9POsBMhiYhhuJXfOxhgAMurahAHQSJH8cZkYUIMCQGIAaAxo8ylAVbTn6mKkFReQlsBEAkRlJO3Z0J8AACAASURBVPUxAA6OEAYgbRow1fvfcGXsiTm9kSNZM5kEbGICHqWYgLNekwPVBFzZA0DnACgSoG6uByBNAlTNA2CUArR0UP4+G2QA6PZtjyo0ACbkAfja8QvSeYrzmMVoBbQegCYkQJpmUBtMwLT4V2NATa+/ewm6BmQpDwCVAO3HkNPzTM770WPPSoAsJga1+w806AGgDICBCVjHAIxNJwFTE7BJASB3Gpd7wcE7CQagSna/esFohYa0JTCTANXvAVjoODi0QgqAFAbgVCj3cRjw7dfEsbgn84zAhSYBo3gKEFCBAWjQA6AyAK7OA0AKgFQJUDEPgOqzSd1POI89ACuXF5YalQU9p/SrnK8m5AGg+n+BVvkAJpwCdAXO4woE1+FWNHAkCdCikgI0gwWA1gNQoiDdlD0AMgOQVwCQx60EyGJi0BUAbfEA6BgA0xOQFANaTALUcZxIMlK7B6DmQWDqBcPGgMaQUoDSTMBNpAAREzAQyHp0EAv9o/sWcfXBePGdmwQ0NmEADOYAZEqAykWBqhKgqppmeRBYVgyopgBIZQCKLcyTDEDK37RzMV4orl6uSCcalACR7ekPqjAAkykAqP5fYLvKdteNSc0B8Diew07g1oV/jVsX/jWew060o4EzVhmAWZcA6TwA1RiAc1BSgHJjQK0EyGIa0HX7NZ33yuC8+BwATSHil2EAjCRA8Ym347Jowdj2OQA2BjQdogBY7rmSXpyiiTkAPdeRTMC6YWBbg3G0b125fxFX7o+Hh+Uagb2iDIBBClCCAShZADSYAqTGgHYkCVD4PJMYUMkDkH18c84Tf0MqA0ATgFYuk5mGhiRAvjIJPMEADLfNGzpaCVC9C/OzmwM8+EzyfD+XDIDv47uce+AyDpdxvMa5rx2DwFQGYOZjQOv1AKzzFQzRVeYA5KUAWQmQxTQwKQaAXkjcTrDYYOHuVIgBKBMDWswDEDAAwcV7MPZr7arr5wDUJwGyMaAxxKIiTf8PKEkylQoAmQE4QkzAOgkQTQC68sASjh2Iu+8n8yajjgtOAtZR3IAyB2BVLhpKFgDq/lfnHICsQWBxCpBJDKi5Nld3bKYWNTQBaFWRADUUA6rKk/pDsnDZOgu840XAb74IOH1f/ptNIP7yK48mu/9Ay5KAJuQBGHkcq4iP9WUM2tHAkUzAc5oCVLTg4zxiAM7yAwCATo98b3nHkZUAWUwFk/IA0APK6QKMxSxAgRQgbnIh5TyeqgcUlgC5DpNSY+rsTuliESsxAKoJeNyCDlJLIDwAaVOAgWYkQAsdmQHQmYBpAtAxhQHInQXgmUwCLigBonMAgNLTgCfpAXAcBhb+fAkPAHNkSZP0QtqZz154ak37accrTQBauSxodESf00wBoBatkgTosc8Du+vBgu7hT+W/2QRMwLcQ/f+NV8W+l0rehboxMQ8AxxqLj7NlNmifB2Bu5wAULEiJ/O8s34+1xQ6cIt+bnQNgMRVoGYAGCgBpBw8PDGEErpsBUE/gRibg+MTbJRIgoF4fgD77ub4Y0FZ0kFqAsedHySJpU4CBOiVAdKHqSiZgXQyoxADsX8LRfYsQDe1iDECdEqAaYkBH9XoABhmDwIA4CShiGsS5pLeGqDpQUaAzr9snUlmNLSIBWr28sNm4DNQCa5syAPS8ajAMsekCgHOOW0P9f8918N0vuCx6TGIupo1JzQHwfawRBmAJg5akAJHzRXfGU4A4r8cDQKcA4wCOrC7AY/T4t5OALdqIiUmAyElVXIAjBmBTP3lUywAYXCjUg82IAYhPvCoDkOkD2Hga+P3XAn/yRqNCo2kPgJUABegTScFKz5ABqBIDqnSqF7tuVERqGQCyyD92YBFd18HlawELUA8DQOU8aTGgigSoDg/ABBkAIJ4FMFLnAKTJf4BCHgDdPqEOBosgMQCXF/qcslC3T2IraYFnVADoOt/1SXOOn+tHzNfLnxUskgTazwA0IwGSGADstoMBGCsMQGeGPQDDPgDNd17091YiQA+v9OA7BQoAKwGymApoASBMczvr9U+A1DEAYliRP9Yv0jUMAB+XYAAKTgLuug5WFwxnAdz7QeCZe4DjXwQe+Gju52g9ABW6PjYGVA8pASiLAXApA1DPHACxUBUyoAsaBoBKgK7cH3TerzwQFAAbOyO5k6tCGgSW0i2ig8BSGQCql1+upQCofQ5AhgcAiH0AiRSgtAhQoJAHQHe8pk43ljwAlylpQw1JgFQGgEqAqLnRZOHWMANwK0n/ee1zj0jenErpRXVjYpOAfckDsMSG7Th/U/ZPZQBmrQBIUx8UPV4pA8AP4PBqDz45/rmXN9vFSoAspgG6yD7y3PAGB3Y36v0c1QMA5CcBaRgA3+SClCgA8rtfo7IeAOpfOH1P7ufUPQk4wQDYGFAA8gyALA+APAisvjkAAHA4lAH1hx52FJMjjfoUBuBj+6kROKNoFRcTdyFd5tLpxQtQ0zkANZiA62cA5HQlFWIWwNjjQQdbFPtZDEABD4BuQWaWAjQZE7D6/coMAPl9TRiAhicB0/jP73zuEaz04kKsXQyAZp9oJAWIY40pEiDTAuD4l4C7P9CMtExlAGa5AKAsGWVTizZAFQbgyOoCOJEA5SoXrATIYioQBYDTAQ5cG99ftwxIqnAVCRCgLwC2NdtgciEtIQGiXb2uq0qAMj6TXlhP35/7OfoUoBpNwG2gkFuArUH8m2UxAHS6bBX9rRQDGjEAJAlIkQGJIWA914kKBdkInKHBF/tcWgKQgDACp0mA6MWvW9cgsHo9AHkSIOHhGPu+4mnIYgDMF+Zaz45JCtDKZVORAEnM0ZCcU40YgOYKgLHn4yuPBmzz2mIHN161X2IAtubQAzBSGIBlDNLlZRQXHgskpx/8x8C9f1X7ds0XA0AaeEsH49tVJEA4gMOrCxID4OUV4J4Bq9sC2AJg1iAkQMuHg/8E6i4AJA+AAQPgjYFBkoWYhAnYJYPAgBwGQCoA8qP29ClA9ZmArQcgwBaRFJjGgNbGALgyAwDIUaCc82gI2BX7F+GELMSVJAr0VCYDEL5XHlUsOvqpEqDw/s5iGM27lHysIFQGoKoEiP4mOgZAkgBJjEZGAVDEA6BLAUr1AIQMwML+ID+9iAa4JJIMQBUJkK4AqEeac8+JjUhK+ZrnHEbHdaTjsvWDwBoo4NQUoCVmyACcvh+Rbv1MftOp+IbROQBqAdDMfjw10GOEFgAVTMBn+AEcWZU9ALnSZU+jkGghbAEwS+BcLgDoAVB3FGhWChCQLABSChBmQnmqdG3hScDM3ANAtX2bJ3O/t9oZAM0gMK4zVM8ZqAQobQowoAyTqqkAUD0AgMwArG+PsBsOzKJd/2N0GJgRA7CY/hwg7ujnxYCK57V8EFiWCXjsc5kBMPYAZJ9PtHMAdMcr5/EiYDVMt5lCDKjMABSUADXoAVD1/wCwstBWCdCEGAAlBcjYBEx/1yaYJXrsz7oEiK476mIAeGhwJwv5XAbATgK2mDh2N+K8/OXDwNKh+LHaGYCCHgCi/9/g8cKEG3kAlG5S0UFgCQmQIQMA5HZkvJpjQNUOJefVu66zACoBWsuUANU0B8CjHoBgYXM4ZRjYSY3+HyjBAKRFgAoIHfxoG9D9bWLBLLrldRQAnloAVIwBzTEBd6kHYFBCApSbAmQoARpcipsBK5eHn2PuNSgLtXnQT0sBmrIESNX/A3I6V7tiQCfjAfDGXrlBYKOmCwCFAaDnGZNCci9hmMIAlCwAxtzBRawmUoDyGQA7Cdhi0qAJQMuH5AOg7lkAtHNv4gEgnXQxWQ+A2Ym4TAwoWah0HEfSjdPFZPKFygkxRwakWzwMK3Tt095v3iFJgDJiQOUUoApzAEZ5DEB8gqeL+0oMQFoEaLQhOcPA1MjMRuYA1MMAMCYbtgViBkD1ABiagPPmAGgHgWn+JmkGgGAAmpcAqcW+JFcsOgegIRPw9nCMO55YBxDs488+Evw2eysFqP6FtuNtw2Hx7xdIgAoyAE3sV3QScIIBmDUJUL0MwDnsB4cT+L9cWgAUGARmY0AtJgIqV1k+HBQBAhNnAJRpwKQAOYd4YqTRHAD1QlbQA9BxGPaVZQByCoC07nzZrr3ugmGnASsSoCwGwKlnDoCuUy0zAPF+Qg2+tOt/ZHUh6minzgLw/Xj/zmMAsjr642H8PmKxnFcwGKD2GNDQVNxzHTBN4lGHDgKTJEBriedGqMgAaGNA1SnAQKG40bJQGZZtmjYlMQDTkwB9/fiFqCnx2ucdiX5HSQLUKgZAVwDUL33pjuQJtMumg8CoP6eJ/UplAOZGAkQajUUKHW8UNVPP8mCtcmS1B04aDbneRUkCZBkAi0mAMgBLh6bgATBlAOICwOiEV2IOAF38dVzZA7Bl6gEA8guAlE5/Wa20jjK2DIAiAcrwAHQ7NAWo5hjQVb0JmM4AoF1/x2E4ui/498n1HT0rJFHFeQwAnQasJAGN+snnNWACruwBCPdlnfwHiE3cI4+DD0wZAAdg4fuVGQSmZQCUIWDAREzA6rakxoCWSU8DaikAbtXIf4CgqBOsTus9AA0stNUCYAkDDI0YAFrYNRwDmigAZkwCVAcDQOJ/z/ID6DgM+5e6UqMhnwGwKUAWk4YkAZqkB0BIgLJMwIQBkAoAA6pYPSn6o9zXeRID4FTwADyg11trPoei7KK9blPxrIBKgDIHgUlzAOqbBAzIBcA5SQJEGACS/Q/EnoDtoYdLO7pOJNnf8mJAqQ5e7ejrEnM6S+nPN0SyAKgnBlSXAATIvx839QAA8TkozwSsHQSmOYb7ORKghmJA1W1JHQRmZAJupgC45ZH4OvOa58QFAGMskgHNowSo58kFwAIbwzcZdEmPzUY8AFQCNOMpQHV4ABQD8OHVHhhj4OR74/M0CZgxdg1j7H8wxjYYY5cYYx9kjF2b/0qAMXYtY+xPGGNPMsZ2GGPfYoy9lTGW0dKxKIVEATApD0BRBiCm5liZGFAglwWgnbSOy7DccyHWFpumMaBA0Fldfzz96eRzFrvVE2h0ixFbAMid0KwYUGkOgPobeGNj86ZuDsCh5fgCcIGkAEkMwAE5ySfXBzAuYBbrZjAA6hAwIOiMiyKgtjkA9XgA0hgAlxQAPj2H5BYA4Tkodw6AYQyoOgSMfgbQWAGQagL2fZnlmdIk4HNbAzxwKpB3vvCKNVy2JhetYhhY6yVATTAA463EfR3P4LiTUoCakACF28Cc4FoteVlmWQJUtgAgU4CxP5Z+lvUA7GUJEGNsGcDnALwQwM8D+DkAzwPw+bxFfPj4ZwC8DsD/BeANAP4bgH8H4I8rbblFElkFQN0SoAopQGeJBwC8hAcAyO2AeYoHgDEWxUdmDgLTUaIZA8GofnipG2tgyy7atRIgOw0YW7tmEiBpDgDtVm+cAP7rtwHveinQP695pQyJAQiLio7r4OBysK9TCZDwACx13YAqJpCSgHQFQCEGoGABAMQyoJoYgLrmAKRLgOL7JQYgKwYUMGcATGNAqQRoNSwAaAxoU3MAlO3rD8eBdEwd/mZkAtYtfMt35p/Z2MVvfuqh6N+vJfIfAVGct34OQAO/34KXHNDnjgsWAE2agDtLgfueLkjzFrJ7DWkFQJHvVY0ADYtcLjUA8jwAe2MSsAk38U8AXA/gBZzzRwCAMXY3gIcB/DMA78h47XciKBZ+gHP+qfC+zzPGDgH494yxZc55uSuTRRJqCpDbCWQ5g0vTnwSc4gFgRjGgmufkdDRHkgcgWFSsLXZxaXdsPghM4PR9wIt+WP90nzIALoBR4vOLQNdhtR4AmQHIlgClMAAPfQK4dCK4/fDfAi/92czPk2NA4/c8tNLDxe0Rzm8NwTkH58HCCACuPLCYMLZKDIAuCpTub7kMQIaply4i6PN6K0HxXaIA4Jwnis+65gCYSID83QIMgGtaAJRhAIQJ2NxsXBbqtvgc2B35WBoo3eUJmYA557jjyXW89yuP42/uOSVt33c9/7LE85fDAmDo+RiO/dRCb6KYEAOgKwA6flEGoMEY0G54LqKNhr3CAPzN/wY89kXgTb8DXP2K9OfVwgDQAmA/joQDINkMpgCZbNkbAdwmFv8AwDk/zhi7FcCbkF0AiCuaEgmDdQTsQzIGwqI81BQgIDgImigAyAG1NWJYBXJSgOLPlz0ABgemTq+ZIwFSGQAgzo/f3A26aroUEn0BcK/R5yxKDEBJE3DNk4VnBcID4DCZaVGROgeAFowGchgRA6qm1RxeXcCjZ/sYej42B2Psjrzo9zmm6P8B2ROgZwDIhaQQA5DlAdAwACVMwLrCs4oHgHOeawKmEiCe9jfpYMgA6CcBGzIAE5kEnNyW/nCMpaFSAJh0bit4AAZjDx+/+xTe+5XHcffT8gR3hwE/9vKrtQzAKkkC2h6O0ctLtpoEdKxHAx6ARS8pAeqaSICa9gAIBkBICPeaBOjSSeBrvx/cvu13gR//o/Tn1uIBIBIgfgAvFd6vIh6gPSIBMikAXgzgI5r77wPwEzmv/QwCpuDXGWP/K4AnAdwM4BcB/D7nPFkyW5SHKgECAiZg/YlwsM2oPjqK7ODv/NxxLIwexH/4/ufFj6cwAGO4WEfczTNjAHQSoBwPgKYAEBIgz+dBV62nWUiKAmBxf7Co8seZw8CSDEB4f40MgPUAxClAKwsdfeEWInUOAN2HDBZvaQvVI8QIfGFriI2d+L3oDIDoPuIJ0A4DK8IA0EWwuiBMy8wXBYA3CBZCTnrxpEKV/wAVpyvnDAEDZA+HdA7JigEFjD0AWgmQlgEIFwHdlfj7dAtIAEpCJ7HqD8Y4ov7eRgxAuQKAc46f+oPbcNdT69L9+5e6+Ombr8HPvepZuPrgsva1y2RGx9ZgjAPLLVj8TIgBWPSTRXbXz0+sk1OAGowBFZPG91oMKD0PCBY377nuguyZKskAnMGBYAYAAFbke9sjEiATfu4QAF37+AKAg5r7I3DOdwG8Nvyc+wBsAvgsgI8B+FeFtjQEY+x28V+Z1880RAHgLsQXLckIvJ58TVmQHXwEF5+451SwuBDygxQPQN9Zw5jHixDGDbSiJUzAVJsvuoqrUhJQyolWXFh7a8CRFwS3zz+a2kGVGYDqQ6i0JmDrAYiiW7P0/0DGHAB6YTW46KWZVaVZAP1B6gwAAcoKaE3ARRiALAkQ/bdUANDXFDMC02FoAlU8ADpfhQrKAKSyGjqIwiZH466dBJw1CGyVyFyc5j0AuvNGf+DJCUCmn68dBJZ/vj2xviMt/p9/dBVv+7Ebcdsvfy9++QdflLr4B+ImC6DMMJgmJuQBWPKT/UwjBmDYIAPAOWEARAGwxyRAdBspM6eDOE4W1uRmRyEPgMwAHFnVmIBzU4BsDCgYY4sA/hLA5QjMw68H8EsAfgrA7zT52XMJUQAsHw7MPkBzUaDkABijg12xWBCdOloAcB4xAJvOPowJ8cTKTAIGck1w4qIuDMBA4AEQSE0CEtR6pwccvUH8AcDZB/VPJwuipkzAA8sARLGCWfp/QGYApM4uPSEXKAAW1AKARoFuDXFiXT8DQODAcjcqDLXDwCQGoEEJEFDYB6AmAAH1zVZINQFLBUCBGFBxkS0hAUoUNcN+bLoVCUD0M4BKZtos6JjD/nCcZHyMYkDD/ZzRAWb5C0zKav3QjVfib//N6/AzN1+rZ0wVLJPnZHqtJgktA1D/ti1p7Iw9IwagQRMwbZSJRDDHRaS+3hMFAPlOcguAcN2xsFr+eN18BgDQ5wvYxmJ0zmed+P1Y3vdGJWbO3i4ALkLf6U9jBij+EYDvBvAGzvn7OOdf4pz/BoIUoH/OGPv2IhsLAJzzV4j/ir52puF78QJfyH+A5qJAyQl0BDdeLOgKgOFW1I26xNYwJrsdMzkwdc/J6WaKhQrtKNLuVOosgDGhS4++OL4/ZSAYZRokCVBJrbR+EvB8FwCDsRfJR7IiQAElBUhiAGgBkH+RHaQyAPIwMGkGgIYBYIxFLMCpjd3kMDApBaiICVhNAUqRAPUypgfnQCsBquABSDNWU9DjlaX9TTpEHoDiEqBE0a3T/wNKDGhTHgC9BCjBAPijzPkkAOL9nBaBBgtfOq/i6L6ksT0LEgPQliQgrQeg/t9vWcMA9LhBAdCkB4BeJwUDQJOA9kIKEP1Ohpvp137fj8+DC2uKab84AyDiyo+saCRAeb/TDEmA7kPgA1BxA4B0cXSAGwFc5Jw/qtz/9fD/LzL4fAsT7KwDCC8ey6TrvzwBBoC78WJBFADjnfg5xJy8gTWMES+UnYZiQEUnjXYU95HusXYasO/Hn+X2gKMviR9L8QHQBTtlAIbj8H7OgwhKw4WTNQEnQX+r1VwJUEoKEO3IFJEAuSoDQCRAWwOpq3/VgSQDAMQ+gOHYx/m+8tnSHIAmGABaABSTAOniZ70qHgDyft0UCRC9n4m/qbuc711wDBkATYc9wQDoEoCA8pKCAtAyAAMvyQAA+ftx2QKAyCP3LRVLMKEFersZgAYkQBoGYCEvBYhz+bete7t0DAAQyw33GgMApLMAoz6iNVBvrdzcjsFW1FwRceVH1oQJOC4A8hmAAvNdpgiTAuCvAbyKMXa9uIMxdh2CiM+/znntMwAOMsaeq9z/HeH/cxwdFsbQGYCB5mYB+FQCpCkAgJgF2KEFwKpUABh5ALQSIDMGgEpCZAZA855SN3YRuPyG+N8pSUB08bBAB4GJhfzn3gq88wbg//u5zO3VvZ/AvJuA6UJiLVcCROcAlJMAZaXVSAxAfyjp+tUpwLr7E0bgIgyAZAJWGQDqASByGbr4K5gEpGcAmpUASQzAKFwY5cl/gHhxzv3MYlu3/Yn70hiACUwC1jIAOgkQkG8EFvt5ZxGR5MOIASAFwGKx7iWVAPXbXAA0UMCtaAqAJQyyfTPjQbDPRtvVJANAzgWumWm+FVDP12kFgDQ3pKQHQJkBAATRzwDgFmEA9kgMqEkB8B4AjwP4CGPsTYyxNyJIBXoKwB+IJzHGnsUYGzPG/iN57XsRGH8/wRj7ecbY/8QY+yUAvwHgdgC31vNnWKQXAE0xAFQC1IHn86B7pSsASOFxIcEAlJ0EbO4BEKCLR60HYKwsxvYdAxbDqcWn7wu6NepLaAHQUTwAg80gtgwAHvyY0cndzgFIQpoC3MspAFJNwOYSoCypisQA9IfRgn7fYidVnpQ5DbiIByBTAkTnAJCuf5ZxOAeDUc0eAJoCZDAHwBHbmyf/AYwX59o5AOp9fbLAkBiAScSAGkqAgHz5hvgenA6RSOU3XC4Rxk0dbJcH2QTc4gKggQJuBfoCILOBoxbyTTIAUgEQLmZN0qSmDfW36qcVADQ1bDWQOhXY7wEoBuD9WFvsRNd11iHexVwT8N6IAc0tAMKozu8B8C0AfwbgzwEcB/A9nHN6VmIAXPqenPPHAbwKwF0A3grgEwgGi/0hgL/HOZ/vlU2dMGEAavUAyAwAEHYMF/bFz4kYgLjwuOivKgVAyRQgQw8A7Qivkm6WVgLkKXIMxmIfwPZ5befBSzUBc+CBj8qLLoMFmJ0EnIQkAcphABhj0SJSlgCZpwANMjrVNAb09KVdnNkMLrDHNPp/AfrYyfWMAqDSHICcGFCgHg9AhWLUyAQcHa8cjihy8qYAA8Z6X20KUIIBIBKgSTMAWglQGgNgKENwe8YmaUBhAAoWAMuSBGi+PACrGgZgmQ2yi2a1kG/SA9AhEsWoAJghCdBQExts6A2K31uZAkwaPh3XwTBMMGR5jcs94gEw4iY4508CeHPOcx6HZrAX5/x+AD9ZZuMsCmBHMwQMmIwHgBQAK/RiLS5apDi5wFcBMIy5gw7zDT0AJRgAX3gA4oWGxADoCgBJLxke+EdfDDwRElWn7wXWjmo/B1BjQH3g794vv/9oG1jchyzoLhZWAkQkQDkeACBYRI59LvspCswBkBeqsvZ832IXrsPg+RwPnLoE8XPpZgAIUHNwIgmoiFa0m2HoHRlIgAoWAFoPQOMpQMH9SxiARXreogVABgOgGwSmFgUSA0BNwCI9hU/WBDz0gFEZCZDwM3WNB6UBigcgp+BWoQ4CawXE3+wuxN9ZA7/fCpJNqSUMghCHtNpeZQAmJgESBcAekACpv1WqBEgzOdzpAtg1L6woA4ADUsOn4zgYo4MevGIMwB6XAFnsBUgMAFn0T8ADMAoLgOE4XwJ0zg+6k174GiMGQOsByJkD4CUZgDWpO6V7T00mO/UBaIzAaQxAd+skcPzL8pPVk70GusW+LQDMGQAgNgLLDIC5B4AuVFUJkOOwSBNKi0hdApCAJAGqxABkeQCoCZgUCr0KcwA0BcCoLglQjgdgFeT4LloAZCyizBiAFA8A/ZwGJskC+u0rJQHyPUCcW90umZNQLAWoMAPQa7EJuEuK9Jp/Pz4eYpkFxzJluJcw0BadEVQmr1ETsIYBMImTnTbU63+qBIh6AMJGm9jvTX9viQHYL8196bgsWuvkDjCdFQmQxR7BpCVA5IAa8+CkPxh7SgFwKfG557xgQSIOJLc0A5A3CTg46bqSB4DMAchlAMKTJU0C0kSBpk0CvvLJjyJKJBAwKAD0JuA5TwGiHgBDBgAoPwgsr1NNjcACuhkAAtkMQIFJwG43fk6iACAXP8kDQE3AxQav6+YA1MUALKR5AMLfboWRYsXEA2DIAOgkNolo07QUIIBIaRoaBKZZLAYpQJrfLosBkDqQ3YIegPISoHbGgIb7A03Bqfn383bj7vO6E19zl9kge3p2YsJzzftVGgMgAge419hMi9pgKgFSPQBAIelb8N6yBIjOfXEdhlEomnH8+ZkEbLEXsJ0iAVrcj0iZVacESMMADAwYgLOeYACCXa+0ByB3EnBw0qWxkKt5JmDdYuxyklSrKQCkScBRAgbHs57SBGQZSDC0JmDrAYhu58WAAnHyU3oKUPZFdpCzUKW6UIG0BCAg2GYhckhDYgAAIABJREFUPzuVYAAKTAIG4sW9ui+lRWZWmQScIgFKzDIwRJFBYCsgx6KJB0DS56f/vjoGI1HUiAVGZ1E+nwGxEbgh6YQuZjXVA5DJACgdyAISoI2d8hIgaRBYayRA4TVGYgBqLgB2NqLbF52YgV9CTgGgHsd1L8bzTMBA+2VA6vFMC3SKoZICBJTwAMgmYMkDQAoAlte4tBIgi4kijQFwXGApTLLZbtgDMEozAZMUID84MMWB5JaVAI3SCwDOedQ1d9NSgLQMgEaOsbAKHLwuuH32wQSVKE7ujMWLxZew49jffyz5/rqLuAJdB9CmAJnHgAJA12mWATikYQCuTJkBICCGgZ3eVGIBvQIpQEDcDU+LAVW75ZIHoGABoEkBAsqzAHQ/TpsD4IYFu6Sn7q1pnyuBFj0FGYCExE4sMFYujyeqRxtovpAug9QYUNrdFDBlAIp6AMICwGH5qVsqZAagLQWASEPqxlORa/79xttxAbDuxgXAMgbZ5++mU4DyTMBA+5OAEgzAaf3zhOIAUDwAKMUAnMP+pAcgNAE7eb+T2GanmzyHtAi2AJgVpHkAgFgGVCsDQCRAEQOgSoCSDMA6FAYA9TMA9BrapSlA1AOgmwOQpscWMiBvCJx/RHqJWAx1HBbJF97sEu3/2rH4tkEOu50EnESRGFAgZgDKpgANvXif1E2spbSwwLEMBgCICwTP51FyULCRlAEw0IqaMADS82kBUEwClLZwKRsFmpWuJCCKt2VGPQAmEiAa0VnMAyAVNKPdeCGxelniuYUXFAWh8/uUSgFKFADmWmjRHFlb7MJxii1eaApQv20SIKfTWP69TxiAjc6R6HaQAlSgAJiUBGhPMQDKPruVwgDU4QEIi/+LfBVjdKTY547LMIwkQIYpQC2W/wC2AJgdiAKguyIf6EA8C2DUr8/041EJkPAApEiAQgaA99YwDp8rXtNpwANAT7iUAVjsulH+uJYBUGNABSQjsCwDGhOvQdd10MEYP+J+NXjQ6QIv/dn4ySYSIJsClECRGFAg1pEPS84BGIyyF6o6CdAVGR4AQJYInaTDwMoyAOPdWC5Ap4mqhtkqJuBRvQWA0SAwV2MCLhwDmn5O0RU10t+TlgAUbWDDEiDNd7s9TPEAZEmA1HSpEgxA0SnAALBMfFD91kiA6DyEZjwc3m7cfd7u7I9CLopLgEbaeTOlMTZhAFoeBapu33BT30zTeQAK7PcAonPkdhjbRP1ekgcgVwIkInhtAWAxCYgCgMp/BJqIAtXOAUgxAYfSI58Ykj1egAEomAJET7gdRWogFpDahApdDCgQzwIAEj6AmAFw0HUZXufcjSMs/Luf9/3A/qvjJxuYMHUShaE1AUe31xbyT6hRCpBfjgEY5KTVqCbgwys9yQCuAzUJn6LDwNThc3nQJQGNB3HiS5YEqIZJwIBep26CUYFBYDIDUJ8HQJsCRO+TZgDoGIBmJCQCOsP/VloKUKYEiOzj0iCw7O32fB75o4pOAQaClCzhA2jFJGDfjyftOm4s4ao7BYgUAANnBUMnOO6WsZszCEzzu9Y5HmmU5gEgv23bk4B0139dEpDOA1DUtB8eH6Mw2OTIGvUAONFax+Xj7EJN7F+OLQAsmoY3AnZDClKV/wDNRIFKDAD1ACgMwHgYDejwFuLtEAdSp7QEKP2kRRd+HYXCFjIgvQcgxZApJQHJUaDiswQD8GNU/vPtPy0vXkwKAMsAJCCnAGUvtIGYAZAMq35JD4Cb/LzDCgOQp/8PnkOSgCQGgO5z+e+jnQUgzQBQC4CM2QE5oClAdMGeKWnIgBEDEBZvxWNADT0AujkARRiApk3Amu3bLmUCJt9BAQaAsm1lCgAgTurqD1sgAaIeswYZAE5SgIadVYzc4FheYoPsFDddUV7nvpXGANDr216TAAF6GZB2DgAp2E2YlfB8LNYnR1b0JuDU7YoesxIgi0mBdvV1DMBSEwwAjQHNmARMPm+8mCwAXKMCQGcCTpcz0C66ygCsEQbAVxfbaZnJh54dR8ilMgAMC94W/p5zBwBgx90HPP8H5Ex2Owm4FIrOAaC/eXTxLSABkuIqu/km4KwEIAFpFkAaA2CSFy1NAw4LyrQpwIBSABSTANHvYYmku9QhAdJ5K4DYsyObgOvzAOgWYxLrljUDAJhADGiyeeEN+0hECgPmDAD1AHAvcyEkR4CWSy9ZaRUDQLZB8gDUK3vhu7EHYNhZwShiAAbZ07N1TaE69y2JASDngj0lATJkAAa6ScCUGTRXG4zQQddl0jHgkjkAwXMNJHi2ALBoHGkJQAJNzAKQUoDS5gBsSp836h0grynCAGiek8EAeAYMAKCJqUvLZHdc4PIXBrc3nozZFsgMwBVP/w0WWPC93H/oe4MuS1ezYMuA1gQ87wxA2JXsuQ4WOvkMQJf85lHHt4gEaJwtVTmimICzZgAImDEAJh4A0g0XBaU0BCwrBajoHID4e1ipowAoMAhshRWMATWdA6DpsHupDIBOAkQ66XVqtUPQxeKB5WDx0BunNA6ypBtpKUBA5kJIjgCtxgBsD71kk2XSUAuApga5kcXnqLOKsRscd0sYZp+/dcdkUwwAjUGlC9O2pwDpCiLdLABtAWDYsQdCuVhwbIzg4vDKAhhJ8Ok6TiQNApD9O1kJkMXEkFcANOwBkCYBdxbixfNgU5Ic6QoABzw48LKg9QCkdzN1XTQBOgxsS5UBZU1lvZz4AM48EG8aYQCOPvah6P47D/794IaVAFWG6CSadP8BefpzVQZA6wFISIDyGYArTTwAJiZgXUGZVQDUNAmYDmAr6wHIk1YBdA5AwRhQ0zkAZNvFqUGK3pU8ABkMANDIACVajIghXNJQNOnJWQuQlDkAQOZCqMoQMAGa1LWdEiU7MUgFgNscg0MiKGkB4DAOb5DB/GoZgBq/MykGlHoA9pAESLd9ugJAMKGdxfh3NjwvqI+P0EmkvbkOk6Y8Zx9/ggFo7xRgwBYAs4GsCFCgIQ+ALgY0vJCK6lthAAaaAgCAwYFZzAPgSSZgtQDImAWQVQBIRuB745eEi/Or2WmsnfkmAOC4fxTHF8PkoIISINGhXCKm0nk3AQtToon+H5Az5qOOaqE5AET7rikAVnquJGG50oABWOy6kXn45EYaA1BWApRRALg9gIXbWrgAiL8HGu+om1Vh9n4mg8CC+4vHgBafAyCOMamg2Xomvq14AN532xN49DzZrgZkQLRA2S8KADoUjRaAWZ1bum2OygBkFAA78WP7yxYA5Did+iwAP8UDUPOil5Hu87i7FhUAAMCzzvs6D0CtEiATBqAdEiDOOR45s4VdtWgsKgGiTTfD84L6OWO4iUYPjQEN3i/ld+KceADaOwQMsAXAbKBIAdBoCpCmACAFx6C7P/Ga4L3yqDnN4xmLGbpA6Th6DwAAbA2UAzgtBhQAjpIoUGIEFh27H/S/FN33Ie+7EK11SkqA6ETNeZ4DwDmPPACrBglAgFIAiI5qoTkA2Vp1xpgUBXrMgAEAYrPw2c1BvLguzADQjr5GAtRVFsuMxfugwf5HMUyRANUzCEyfL98pHQNqOAeATu4OC4DoPs6BE3fE70cSvC70h/iPH7kXZ7dThsvVBCpRigsAcq6jDK9xDGjXeCEkMQAFpwALULZIm7Y2SUhm6C4Z5FZv3KYzjBkAr7sCj3TbedZxp3usVgkQ9bWlzAHI2o8miP/31sfxfe/4It747ltk6ZixBChkAKgMuYgHgBwzQ97BkZU8BiDle5OKznZLgNpdnliYYcoegGgOgKjcpQIg3radDikAeHwgcW8Ihowun1YCVN0DkGQAUmJAATkJ6Jt/DDz8KeDgdfhVuDjuXoYfHH0xevhD/nfiJrHY6WkWbBkQcp+lnguIBu8cS4C2h150nV5bMJQAOVQCJBiAcnMA0syqh1Z6OLEeLMyMC4D9S7j3RLBQOL0xwLWHl9N9J2mQGAADDwAQFADDrUoSoGUi68jMNc+AWQqQTgJUnwdgRCZ3i22ICoCLx4GNp4LbV98kfdcX+kP4HBjxAo2LEqDf7YFIAkTOS8uH4m0sMwkYyFwIXdqpWQI07SSghAdAWRDW1KFlxIg/7u6DTxfbWQWAzgNQ534lmYDTUoDaUQB88r6AffvW6S2curSLq8R51UQC5PtR2qDUMHAMNftAYrgpjQAFQg8AXTKnNRrUGRwthi0AZgFU1pPnAahLAhQeLD5n8EMiKWYARBIQjy9WAHZJAeAR8mk8HiPzUkNPiN2VcKBZ+hyAkZQClO4BSBYAGYbMlSPAweuAi48j+rs2nsKbHQQ8Wnjd/rr/AjzFj+KlYlGhy23PgFiMSAzAHBcA/YIRoIAqAdJ5AMwZgLSF6quuP4R7Tmzg+stWcMU+g/hOAAeXyb4n2CexzzldwDEgZKX9aUv+v/q4gDACF40BHVEPQHUGYJTDrAAxYyeZgI3mABhQ84glQF3Hic4NkSzoeMzi4dmvk18XduaNNcAlQRmKmAEg5zqa6GZsAi7iAagQA3rvXwF3vR8vdN8MILgGtIoBoB4AINhPaioAXMoA9FQGoKgHoMbvTDIB0xSgghIgzoOquUGcuBhv64DKgHTfhyoBooUUTSKUfm/D4V0Igk3UeS/BIDATBkApvlsMKwGaBRRiANbr+cwoLis+IBISIAC4+ER0c5sUALSSHo9yTkD0gBLVfUYBQBcorrKoWl3MoKfpe+rkGD/234Ab3gQcvTHVmPj+8fcEbyUWFaadIARyF7HtvY4TdUPnOQZ0U4oANTuZ0qJv7Pthx5MOBRtm0v9yXKW+6PgPf/+F+NNfuBkf+GevlqZNZ4G+V3SsiC6uSQIQoJcASXMANItlceGvMAeAMgBlPQClTMBuz8wbYZwCFJr2XRYVG9Gi+ziZ4aEUAKNx8JyxaQ54SVCPwv40BkDAJIYQkNNvgBwPQEkGgHPgY/8WeOTT+L5TfxjdPfUo0DQPAJBfwHEOPPUN4PyjuR/jhkX4Jl9C1+3A7xh6v5qeAyAYAKYUP0ViQG95J/Dr1wFff09926Vg7Pl45lK8n6dOcRdQ5wDoZgAAivQt53sl3/swxQMgpQClvZ/KvrUYlgGYBeQVAAv7ghMA9+qTAIU7/1gqABQJEACsxwVA390H4BwAlQHIo+bISby3CuB0sFhP6UpQI52qNd4nmYBVD0CGCRgArrkJuOZPg9ucw++fx4/+2l/gWnYGNx+4hDe+7mZ86MMHwm0Q0yedYAE22s5dgMkDzILJwp7P55oBoElNq8YSIGUOQOICwjPpfxOzatd18Lrna2IiM0C73lF3fVwwLSJXArSMBMR9/ji4OBlelMT34DpM2vbSHgATCZArJgGHx6KJARgw1vqKY6njsKjYGPs8OJcIBqCzCFz9Svl1YdFDGx4nL2zi2H7UCnoOEGzlapoHIGvhlhgEVsIDUGQOgDcEdoPm0so49plNfRgYXYw5HYUpyilOHvoE8N9/NmgG/eJdwL5jqU91R8ECdBNL6LgMnBQALLMA0Ax4q9UEHH52V5EpugUkQLe+K/htb/td4OZ/Ut+2ETxzaVc6r1D2UfoNVy4Puv/DzeD8J85tA80UYKCgB0CONlfjnjumKUDSPtfuAsAyALMAWgAsaUzAjMUsQF0m4FD/JhUAIw0DsB5LgLacmJqjF1IvjwHwNAwAkEqBywxAxhyAIhIgFYxhvHgId/Pn4GP+q/Hh1Z/C+CU/ASD4PGngkKEJc6wULkLKkjlJcsZBO4hrhqZEWvSNtQUAMi96eXMAyoIOFYuK5cIMAO0sGgwCU19TwAgsFuw915F8FaU9AAbSqm40CThc9JpEgALGWl+x7V03ZtjGng+cfSiWFVz7qsTvIYz4tHHxufueNtu2Aoi3j0VmWkkCVMoEXEACtFNSAkT2qw6P32P6DECGByCv0/7U18LnDeLbOnCOTngsbvEldF0HnCy4MwsA3WN1zigQrLY6ZbyIBEhEnA40xUpNoPIfQD4HS7/h/qvi21QGJM0AKOsBkH2NR1QGIOEBSHk/KwGymChEAbCwL50uF9Tx9oV60g/8eGKegFYCJBY4ThfbiE9CHi0AchkA8jhdEKTIgMZS2oiaAhQfkJeKmIA18BIde9p5JicwsSjL0oIimV4kFp9WAhSAmguzIM0B8H39iTrjomfSqS4DrQSoMANAPQDb8v+BbAkQUMgILLZxoetI05XLMgAmzIpYlC+LRa8pA2DY2RVa/g4psH0O+Bn6fyAuwun57vMPnAKveRiY2D7XYZHvQopEpQ0eYxOwxvyagtISILKQ7SB+j3YVABoPQBZogaVLnYmetwuHB++1iWV0HAafSPVSCwBvpD8P1WoCDo/3BANgmAI0Hsbbk+U5qQgRqCBA5YfSvryPFABUBjTUDAEDijE+5LcYcTc5B8BlSgGQ8r3tIQmQLQBmAcLYq4sAFRAMgDconAaihWcoARJYPiTl2UsMQF4MWXTgMvlEllYAmDIA6sUpKwZU+znxgsZ1WLLzLCAWMTmTWNX5BWKRZCVAAYwHgREJUMAA6AqA9It/XgxoWUgSoLIeAJ2pXIoB1UiApGnA5j4AcTwvdGQGoB4PQIoJ2GXoYYQeE+cSAwMwoHS48weBUQYAgGwAvk5TAAgTMEkBOnXhEu47eSnx3CoQ566u40TnKlkC1LQJOHidw+To11yQa4rr0wJg2ilAqgegQEeYFlhZBQDpPm/xpaBYJsehmzbJOY2Nq1MClMYAmKYA0fNFhu+uKhIMgCQBIttHZVhpDABtEhqeF4LPiY+LETo4pJiAu6oJ2MQDYCVAFo1iPIglADr9vwDtHNXhA9B6AET0paYAWDokLaw8TiVAeZV5+LjblaPMUgsA2knPGgSmHMBVGABiKgQUE1OXaLAzip2Rst2iQznPMaC0SDONAZULMb+4BGiUPQisLGQPgBcwcWIRZzIDAKguASpSAIQX4YWOKy2W65gEnDoHwGFKBGi9HoAoBYjImhh8sCduCT9vDTj2ssTrhASInu868PCRu06YbZ8hROPAdVlkvK5uAi4wByBkAPYtdcGKpL6QxazL4/Pq9rBNDECnWCqMxACcTn/eblwEbmI52LeJF8dJm1qfVgDUZQL2/fialmAADCVA9HzhDWqdnUDxtKkESGIAyG9Siwcg/h5Yp5sIgAjmABSVALU7BtQWAHsdeRGg0WO0AKjBBxAuymkutlYCRLZNkrIQas7L0yCKE4DTlTsZKR0w2n3PHgRWwQOAJNPQVdNnBHTRjbr3kxgAJ3q/uWYApBhQUwkQkWL5aQxAhgTIQKteBgtd5Vjxx4jSiUySboAUCRA1AWs65r2SEiBPFACOsm9X8wD0XCd1cdlxnOIRoIBxZ1cwAB2HRVKxF7EnwcQ58Vmv0ZrDxd88UgqAv/67k6UlUTpEEiXCAJSKAVV1yAVjQAtHgJKFIiOf3a4Y0IIeAGMGgBQAfCm45pDjNJUBSCvG65IA0YZWlgQoswBQzhcNyYBUCdDQKygBqtkD4GgW7h3HwZAbvJ+kJGh3zo4tAPY68hKABGgUaB2zACIGIMcDEG3bQbkAICdiYw+A05ELgJTFzFjpzFMYDwIz6MiqA8cYi5NFRGwgAONhYNYEnMTWoLgEqOuYMAAZEiAaA5oSV1kGCQlQ0SnAgL6bL8WAZswBAIxNwJzz2ATccaQ43XFFCVBWUeWqDIDuXKKDYYd7FHkAnKg58GrnvvgJz/4u/es8YQKWC4DTlwb42mPnta8pA2oCXg49AKkm4EwGIEsCpO+Ejj0/Ot4KJQABkg/F8SkDMG0JkOoBKCAJocenmjtPMZAZgI7LwMgxl84ApDSDmigAEiZgwwJAPV80JANKeACoBIj+Tmkm4Fo8APHj3Enu/x3XcBIwNXFbCZBFoyhTANTCAGgkQOokYOnzD0kLK0YZgNwUICEB6pgxABmTgDuug6WwE5tIARIHNHONKned1yBatPsaCRCQaQQeSZ4CJ1ooeT6vtcu4l1AqBlQyY3P9hd7QBEyTeyRsPgN84e3AkxnpIApkE7Anb0MpBkAdBMaSnT6glAmYUvCqB6DqILCsAqDjMnnBa2wCzjd3ej6PFAxdN2YAXu3cHz9JYwAG4n1CYgBCn8JH7jppto0GEN+R67CYAWApHgDjAsCMAaDFdjUGYAwGP/GeU0FCAkQXvuam0CIegK7LwCgD4KUsmtOuBXVJgOixnskAZHye2rBqgAHwfZ6dAhT9TgxYuyK+X5IAVfcAcML4cA0DEAwCMygo9tAkYFsA7HVIBYCBCRio7gEgg5XoBTFaONFJfGTbqLSCFgB+3ok4kgB1FA9ACgPg0YV0UmogOsnJFKBihkxPIzWKp4tSBkCj29a9H1lYdR0mGSXnVQZUJgY0MQisoATIKAb0M/8J+MLbgPf/dDxsJweJOQBlGADHjYtgVQLUW9FP6yzhAZALANkDUDoGlMSKpqHjMFnzXkYClNLhliaEh3MAXHj4DufB4M7FA8GQPw1EsU8bHstu8H6fuPcUdkf1dLrFOaDrOtE08BUQnwj9LTNNwHQRYuYBKB0BCiT2qx6C95q+ByBjEFgRBmDrTKCp10HyAAQSIGchLgA6noEJmC4UJ80AZO1HiQKgfgbg3NYg4XOTU4BEUlo3mAMgIEmAqnsAqBKBaTr3nVKTgK0EaDYx2Awy7s89nBvv2ChMGYA6PQDSwAxDD0CCAYgPMD83BUhIgAw9AHQhrVlsiIXk1kA1ARcrANQUICBe3MgxoGQRkyHBkBYoRAIEzK8ReLOEB6CbSAEqJwHqOAxO2pTf848E/9+5AFx4zGi75DkAvnKBLtApUif70gIg6/n0NTlQWRDqAag6CCyTAXCcOAIUKFAA5Gu7ZWlgIAG6kR3HmuiwX/faYHCfBuLYpJLHV14TnOc2d8f4wkMZHeICGPmxRyE2AYuZCGGBJ4pFUwbAURkA/fdTeggYkDivdcMCYGvqKUAZJuC8Tju9vvijaNBZApQBQMAAOD1aAKQsmmkzaJFMlGuEAVDSwTqmEqDmGYCn15ONPK0EyOkG+n4RsVrzHIDxkDZkkvt/ggFI9QBYCdDs43NvBX7rJcC7Xwmcumt622FqAq7TA6AMzBDIjQEliwq3QwoAUwbA7ciL8zQPgJeU5lCINJndkS8v1L1iiSyqBwCIu8/aQWBAZrGYMAGThdJoPJ8FQDkJEImsLJECNDSQqkj73oVHjbYrUwJkygAAZK6ESAHalu9PPL+MBIgkIbmyB6BsDOjA4Ht1XYZVWgAYx4Dmd7jl+SAMrssU+c/rU98+mgNAQg9efV28aPvwnfXIgMQ5xXUYXIdhqevG34f4LsQ50NgEbOYBoDMA9heZAQAk9quVkB3ZbpUEyC0YC6l8v2kyIOIBuMSX4ToOnMX4WOx6BilAtABoxASc5QHIkgCpJuD6GQA1AQhQGl40BRAAVsMJ7NIcgBQGoIAHYDSiBUCyIcMYg8+KmoCtBGg2IWVr15CrXxbGHoCGGACaAqSbBEw+nx7UjsQAGObzOh2gQ+cApE0Czo4bpMPAJB9AlJlsdtDSjqLoFAspkMwAmEmAKKMQSIDoQnZOPQCDMgUANaxyvdbXwAOQOQOAXrwFG5CDTBOw6RwAQGYAOI8vft00BqD4HABJAtR1K3sAJFNxhgSo6zBZ817KA6C/0I8UyV7XYUYG4OC1yRjQF1+xjAPLwed+7sEz2Nip1rnlnEsSICDI4l9RpyKLRYVpDKjbMfIASAxARQnQ/l7wfbVrEJjKABSIAQXSo0BJAbCFJXRcBocwV10/rQAg35lUANTFAJD372R5ALIkQKoJuH4GQNX/A2oMaPh9iN9OyICGm/F3SH4DiTU0TL8CgDHxIrKUAV4eMyggrQRoDtDZawVAjSZgciDp5wCsAFAW3lkMQN4JT5IAkYVSSjdiJDEAyV08NQlInPBVvWQKdAyA6G6OpTkAmuFNGoyV7ZYkQHPKAIgFxFLX1bI5OlRNARLdb2MGwLQA6CoegLKdIrEo9obhhY/L96swZKAoKAW/0HEqewDoMZmfAlQxBjSNAVAaAz3m4SbnoeCxpSPAZS9Mffu0OQA/dOOVAIKO5SfvPWW2rWmfoWEuDy54cFl4v2AAxL6S6QEg34HhIDDJA1CUAVDOa/vCl/enngJUwQNgygBQDwBfRtdx0F2Ij7nUAkCSAB0gn1tT0TTKYgBM5wA0zwCcWE+ek+gsluhcLX67VeIDEDIgIw9AXgFAGzL687HMAJhMArYMwGzCYCDVRFDGA1BVAuRRCRAxAXs+fJ8HOlXVCKwwAG43PjByGQDJBJw/CVgy02oZAFIACB8A54UlQLoUoCgGVDcJGMiWACnbTRdK8+4BMI0ABTQpQEUHgRlo1eUCwNADoEqAyjIAdH/qn9PfT1HKBBxfgBc6jiSrKsMASLMVMk3AjswAGMeAGngAFAbg2bsPYIkF+8HWsdfoDdQhRhoTMLwRfvRlcSxhVRmQ7rx1pBv/LVz8vmJxkiXdUItLExNwFQ+Asl8dIAwAr3t41GNfAH7vO4Ev/2b+cxMMQIFceLXASosCVTwAHZeh03GxzYNjuuenpQClSYBqYgBoSEaCAaCTgDM+LxEDOgUGQGyf+O1WNUZg8Rt0FuXipoAHgKYROikMgO8YpEjZScBzgNYxAEzuIqjoLsfVaGUGgJqA5QtFdKFXL9wZDADP6nj4PsB98SLFBJzCAGjMuRR0MRlJgPxx/DmmKUCaicPaGFBDCVCWCXheU4DE72M6BRhAciCbbsFjJAHKmAFAFzxlJUBetuY0FXRBT7uSTZmAO640UK+MB4C+XyMxoAYLXPX4ev72HdG/N694debb6yRA8Ed4xbUHcdWB4Fpw2/HzOHOpfDNIngQefEeHu/E+4nXC7yIyARt6ABxVApTvAagqAVrthQWTz+tvXnzlt4HT9wKff1v+tTdrEFhuLnxxCdBxEe20AAAgAElEQVQmX44iZreRVwCkSYAmwQCUHQQ2GQ+AXgIUbrOUBBT+JkIGqa47CngAPCL5clIYAGk+gE0BmmO0hgEIu/lLB7J3NsZiH0DVGFBPLwECMpKAFg9Ii4AOOcD8rMpcOoErKUAp8Yu6eE4K6gGIJEAlurGqZAeIF5+ck45e15ABUDXKc14AeD7HTkgFF2IAHBMGQL/Pcc6libX6DRvJJ/n+GUkGkIakB6DY5OkItKDsmxQAxZsVA2XBTgtpr5QEyKwACCRAJVKADDwAajrY9f07o3+vH31V5tsLSZ+aAuI4DN/9gsCUyDnw+PnyiXByCEAoASIMwKijMADjAZDWXc8cBJbGAFSRAMl/975uvF39upOAdjeC//ujggWAay59AZLd7lQTcMwAiBjQnutgJ2QAFlILACJbaSIFiDIAiTkA5HvISuFrOAaUcx4NAaPnGDkGVJUAXRY/FkmAwt9APV8U8AD4RAKUVgAYeQCsBGgOUGK4TiMQDECW/EdA+AB2LqZfOEzg6yVAQEoS0OJ+wO1InaBOlzIAWQWA0skyYACyBoEBcjc5MpmOi3djJQ+AKzMAAFn09Aw9AKpGWU2zmTNQA/BKr4gESNGrF5AAjbx4WFTqQlV3vBskAS10laF5EgNQUgJkwgD0qs4BcBRWpYQEyGS2AsQcgBImYIMLPT2GFjHENf17AQAn+GH0V67NfHsh6aOhB6KTTuNpxxWO07GGUTzoxvvp0AkXcdG+wg2HEZkNAquVAXDjBVztRmApntMwQQ5IMiF5C+0EA5DtARhxF7voBRIg18EOguvIAk+LASXf2RJh75tgAFQJEG04ZDIAzcaArm+PomnR1xyMtzE6X3AyyDGSAB2N32DrbKASSGMACjA+dA5AKgNgkp5kJUBzAKkTPaUCYLgdV/kmBYDwAfhjOTe3KFJSgICUJKCQeRAHteswSWOXLQGiZjYlBjS1AJCpfhWSB0DoXulizNAErPUA6BbtxilA6SbgwRyagKUEoAIMQDcRA2o+CGxo0qnWLaLPGxQAdTEAlFHqkyi8Ok3A1APQVUzAJSRAKqOQhoQJuCEPwHN270OHB8+7zb8BeYfXMGIAZAkQIDcZRiVnJKjbJ3ws+934HDd0w99ROgemLMgSDMBkPQCr3fgL7dc9DIz+bXldfNUEbDAxOoJ6fclhALawBCCQbnacWALUwVi/T6Z5AGqbA0B+E1UC5BgyIYk5APUyAFT+85zL4u59dL5QFQBAUgKUFgEKFCr46DwiKlGm4EYmYKX4bjFsAVAWBhNpG4epAVigriSgTAZAUwCEhQeVVtBJwJkFgKd0cAxMwDIDoEkBkkzAggEoPpRJlwLUVQ2oQCkJkGoCblUM6Hg4keF3NKK1iAcgoVfXFgD6i8FQ6XxrUbIA6DgMYq2Y8ACUlQBJDECKXKYEW5nlAajMAGQUAIwxrLJmPAC0cKH6/694L84takRn31NMwIByzFco1GUJYLCj7CMMwC4TDIDB4s1TmFMDBmCjCgOgnA9WXCoBqrsAIMdN3mI5ywOQ5z1Tv6ccD8AmD36fjhMUATucNgk154xJzgFQGQDHifeJKTIANAHo+sviYz1qJEqFbIoEKLMAyD8vxA9TD4D+fOwbNBpkD4AtAGYTkgl4Sh4AqQA4lP48AakAqOADkDwA8sJMKwFSGIBex5Fzdv2ME5Cv0GkG3S+dlpZC7wGg3djyDABdDIx1DIDpJGCH1bawqBXbF4DfuhH4jecDZx9q9KO2SkwBBqpJgNT0Gy10i2gDIzBjLDIWJ1KACpmAUxgAddpndH+1OQB1eACMmJUQK2EB4MFJ/5tUFJwDcP1WXAB81b8hN9o0kgAhuaBImM5LQmYug+9ojRRDO0yVAMHQiGg6CCz4e1yHYbmXYYDXQdmvVjqEAajbAyAxAEUKANecAdAZrLfPJb873ycMQLCvdt3geNkB+Z10DZOmCwBpErDmmmYyUbphDwBlAK6XGIDwe1b3Y0BhAM7Kaga1CWJwXhDgRAJEUwql55gwCnYS8BxgLzIAyzUNA5NSgNIYABIDupxdAHAv4wKhdnAM0pd0nXkKOgcg6jKXSGTRpQDRz4sWPfSkZCgB6riOwgC0pAB47AvA1jPBEJYHPtroR5WVAPXUIqwkA1DIA1BwFkBiDkAtDEBKt9ztxhci0wJgJBdCk/IAAIgm3+5gMTOaU4LJHIBwEb+CHRzrPwAAeNw/ipM4kvs3jXQSIA0DMKzA1Om8S5QN6SM891GGMlUCVMIDEEqA9i12wEy/dwGlsbHsxJ9RPwNA/rbcGTIlPQC675X78jUXCLvPwe+2Gf4+ogGxy+jUes1xJ+5zOnJRX5sJOIMBAOLFcSEJUN0MQHwufdah5ajREK0jVAUAEMzDEN9X/0z6DAD6GiC/ACDfQzetAKBrg1QTsJ0EPPuQaPVpMQCki79UkAGoMgsgZQ4AkOMBIBIg2QOQ1YlRIrUMGAA57i+5i+/TeQDGVT0AYQpQhy4+hQTITIM9TjAAmmJi2qAXlUF+8o3AOz79LfzTP/2mdMLPA5UAmU4BBpQ5AH4xBsBooaq7mF941MhYL1iFxCTgMoPAACUFKCMxRxQNJU3AVT0AphIgAFhmpAAwhQE1L84LN7An4CAocL7mvwhA/t+UFgMKKJOnq5iANRKgaAowgG3xfZgwAOI7YE7Q+TbxAIQSoMIJQECiKF6mDEDdw8DocVPJA5AlPU15X1UGRBOAIglQsD/sMrL/6phfIV3prRTzJphilJECBJCBcu1gAK46uBSdH6PzRZqcRsiAts7I16EFNQXIQLITgq5D3G5KQ4a8H0/73mwM6Bygk6PvmwQKewDqYgCyYkA1EiANA+CY5vNKJ/Cu4gEoyQCQAuCJC9tY3x6WigHVegAcjQm4sxBciIHMfUVlAFo5CZh+T7TzkoFHzmzhXZ99GJ+6/zT+/LYnjD+Kdg7XCsWAKpOAdRfUVAmQrH3XQlfE7W4ku4MaSBKgsgwA7RZuURNwhlxGFKGG3o0sD0C5QWDxcZzHAIhF7zbTLFo0OHNpFyc3yW+ckwK0zOJ9+DwCpjLvbxISICkGNPycutK6dBKgZRKJuik05UVMwGKRl7PwHXt+tFAvrP8HEue1JafBFCBJAlQ0BchwQZj2vSYKADIDIJIAhV1slucBCO/rrhTqVBuDLtZ1BUCnjASoZgYgLAAYA67cvyQ3SNRto7+dkAENt2QZpDqAtIAHgEqAOr00CZBB41Kdwt1itLs8aTMMzKiNY1omYJoClPAAhAcunda37xiA+OLYcx2wjuGJOBEDasIAZHsA6AXuzifXcdOvfQb/8prH8W/EnYYHrTwHIMMEzFhwkh9uyoaljPdrrQmYnpANk6TOb8W/04V+TseOYLNkDKjsw+CFJEBGaTVpRdz5R4GVI5nbls4AlJQAUUlZlmFWnK9KzAFIMABlPACmDIA3xiKCfaRvwAA8eX4b3/uOL6Dn7+A+8RWmaNxFgd1D/NsPw/NXvgcgNAFzsu2e8ADUIwEaaRiARR7/Xpt++AdKUYQ5EiCxYMnxAGxKMwAKLgs4T3S4pQKg9hSgof62DqoHQHrMcJIyBS24AXkKsGAAwv1hwBaFOiiFAQjv6ykFQF5RYwp6rOtY7UgClPE9NDwITDDCR9cW0es4YYNkFDcSdSZgQF5f0ACGKh4A8pt3UhgAObzEIAXIegBmFG2IAaVG3qIegCoSIJoCpMSARhf6F/5w8N8L3gDc8KPwfR5d4HodB64xFatKgPLnAOi0+RQrCx38yLcfi/8Gj+O+J+MT+98+tI67n15P36boc5KFhiQHkKYBh4uzjA6sOgm0lYPA6KI1o5ihoAubIlImSQJUhAGgHdkqEiATD8C+q+PbBj4A4QEYjn1wiXUqOQmYIksCJFiD0baRVEk1Q1M5WjkGIH5NZgFACpo+zy8Abjt+HiOPy+ehlIVd1IBAvF8NeXAeaoMESHc+WfTj88WGrgBIlSGEf6OrKwCS51spArQoAzDeRbzSDbDYFAPgK4xeYQ9ACQaAvkZlAHaTDIC45gydDAbA92IGu7c8XQlQ1kRptXCpkQHY3B1FyVNXhTMAJI8UoMSApxQAdAZLBQ8AI997mgfAJ8deKgNgJUBzAHoRnmsGIMUE3FsGfvrPgZ95P7CwmkgBYaYFgHoCd7sACz8zxXsxyokBBYB3/fRL8cF/8Rr8/KufhUMrPSyQruCdJ7fxD373K3jkTPYCV5cClCoHMNBgq+lFvTYWAPRiYcgAUPlHESaDdg6LxIB2HWVBVqQA8OTOtxb0N7ziJfFtkwKAyIq8UVkGIGWhb8IAgBudryQGQJkDUCbr3tgETGRlfeR/J1pzbo4JmDIAA8EA5PxNY50EKDIB1yQBIq8VxX/PixdxG2MhAaIMQE4XUixYcqQQIgEIAPYX9QBoGmByAVCjB0BdHOemAJX1AJBjc/9V8W11FgCRAAkGQPx2UgGgNn7oOaS3WmxAmSkkE7COARAFwDC9KdAgA0D9YFcdCL47cW6IJUApQ7VoEtCFx+LbFTwA9PFuL4UBoO+XVnzbScBzALdLdN1TYgD65+LbRjGg1ANQhQGIT5ypk4AVSAWA68DpmHoANJFa4mSWxgBopDkqGGN4+bUH8ZY3vQRf+5Xvxb/4rmvivwFdeD7HQ89kL3C1KUA6CRAQd2CH/dSTrZoCIksLWlIAjItLgIbj+O8qEmdKZQllY0BHHtdT6nWlAF1xY3zbZBoweU9vVHz2RLBhaXGfGR6AXrFZAFEHDjoPQIMmYNJxFAuqLIiFOYcDjvB3T5FQiC5/lxEGAF3pfdKgZwCSEqAqUr2RrqHgxQvF9XF4/itiAi7DABQtADTylgXyHdfKAKh/b6U5AIYMwP742iCZ7gGpALiE5f+fvTcPui276sN++wz3ftN7/d7rUd3qbkkgoUYCDLLD4IzE8UAccIokjp24kgo4yHEqqbKriFOxsctJJS5jEiopUsGqSjlgbFMWYHCIFDDBgcJIOEjQGrolNLRaPb7X/cZvuvdM+eOcffZae97n3Pv169ffqurq+747nHPPPWeftdZvWBBC/XYVQwC0Y0QLglJHALZhA+pBAFzbbBsTHdggAvAiEQC/XUMA1k2Ltu3c3XQ6C+B1HwIQrwGgz7sKABDqcud0ATqfBHzvhxDKWuuNKgDuvNL/P1/w7r4rzkIDUNmTAz0ByEnCI2ILALlIlv4CgML5ZUBwKF/z9Q+pC35MCkLDgSwuQNZJwADpznbO86XW3IuYC1BM4hxB7ZgdkxAAte8pHdKpNqCF7ss+cQ5AVAHw0FOqEZA4DbidqgGYRAGiBUBYCKwjIcZshcRYxxxXoNfJDHHYhilA9HxqJTIYmAPAKEDjte7/TpLCVItQATCHAkTWLdlQqFXieL0Z1swkEXCcBuA2GwI2bwowACzJMT7epAuQ3nVN0gAUPIn0UW3ocb30hHrscQE67HYZ+lhlJOnWEQBKn9Q1AJsWAevIh4w8YCdrWye2hQDIAoAgpGvdwpkm0wcPq8enhK670AoAVlj5z8OMnA+LZQwCcE4BemtHIBHdesgC4MIjcX7Z5Y5KBDakAaiRs02vHIkqTWBLzQY0WgMgq/kAAmCj5gSDLIBSGBhKum0uQAtdgCpjEU7A6H6XmS4CDiQWLz8N/E/vA37iTwQXullBb8CRGgB6HNM0AOq3n04B2oYNKB3gc0l1CF//Ys9R9gS9wbVVuvNUv2MOqk8UBQhRTkB6IcSclSZpACKoVQCjAB124WNCP7cRwzniSOxkgU3pfuuuYM+5Ynze0kGeWxzJ4OYFQ0NhQADWXY5b6+HcmS0C3jACYFnPFkKdP4fbRAASurpJk4Dpdg4eVu/TKUBMA7DLG0C5uuY6HSVhFKD9NKpKbMhGhW0GABCmktnWiQ0iAMwC9JIsANTa0E9Ld9BpKAWIhlcD4D+uVAOwWNibD1meox6MAKIQgHMK0D0cbyQCsLqjumUXHvW/loZEAWYhAJwCdEAcWlwUoEpLAOikPeG7MPVJwIAqABwaAJufdjDIwrbqFsM+B3jBlkKDUiVYsstmAdiHgenzCxYp1IKnfxq4/SLwpV8FXvyE/7VzYgICUE1EACh3eLIIeMYgsGXpsAGl1/tiH7j/a/vH9Qlw52XvvkmIGwA6pgGYKQIWub+ISEQAdApQPrcAiNUAUAoQdnoagCcqQi9r4UcAlAtQOgIgz1thSSg2pdVpLOtJNhTZx9hRiTRDAAJe5KMGIFAAEA1AsgjYkiiWpMg63qQLkEEBSkUAIsW2+lwYKTo1NAAUAdhj9xtaALQGAqC5d8UiEykh1ynbFGBAKyQt29w2AmCjALECoImjANEwNADTRMALhw1okWcjDVCE6HfAOQXong7ZVXsjEADZ/Qd6BCA2pA7g9GawW+kMigB0OUvOYhCAngKk3iM63yRgTcQFRCAANJGeUAAMCEDoZm6dA1A4uoG0O+soAHRbUTpULEgBosl4woCu5KAJR3UchTbQfU/hSEsb0EwAu65k3BLMBlTnkcqIEQHHDAIrd4H7v0b9OyAEpje4bsLsiX6blgJgse9HAVMLAM0GdJMaAC8tj6BKR91OWJzbWhAAR2e3soiApYYprAEYns/NDvKmtDoVEwH3v6UYjschdlUizfbB0pFtm35qLaCSJsqFtiR7HAGYTwEqumo8HQ83KQKerQGIFNs2mkPX/pBwnlzXdFAcAaDnQpOrxLvzFQDlHk8UN4Xg1gEEIIQkWQuADSIATATcr08U9e6npVs0gMA0BCBwrmTkXFk6NABFJkaGQOdCkPQp3HdxnBcAc0JW1pHWehsN2mm88Lb49+1e6v/ftcDq1rRtaxoAKtB0aQBWWgcwWgNg8wGWyVLXWG/2HAGIPMUbWgD020kpAMY5AJR+YrMBBeIoQLlI4xbTRWebBal+o4igAU1FAA5X/W+/vywgYihuQxiDwFI0ABUvVK3BxHV7CgEAgkJgSgHqpk4CzjKzCPDRfwBOAYrRAGguQHNpLqsm4rgCvADAbtBylFGA5O3M6QI02IAKOgegv9ZDRc2IAFg6yJuiAPEGQDb46/fH46jbUYgYEwEH0C0rAhDSAMwXAYu2Gmd33L0IgGe/aJKfLznnnA6eWrkpQHWurtF2pYuAKQKwJRcgiZBPRgAszIaNIgD9OnT//gK7i35dpOtjTwFyJNPLAz4QUcYcDUDXH4OmE1gu7etxngmFALQBC159+3dhnBcAc4JW1huekBeM26QAuJhQAGxiFkDLO2gHyzAFSLcBzYmaPloELBfJwDTg2uKnHQymAZAFQDwFSG7HKdyNoAAxEXCWKAKm5982KWn6eR5BA6LFX8pEY5nwpPD/Aa77qBIHgennqTV0d40rFAEIFQB0kNREBABILwBYARrhAkQ1AHm2WQpQpAbgqFuG/fkJBWg0JHDNAbBQgFaRFKDxeUuitg0KUJmLPvkZ1r8j7Chb3JAImCVNkRQgNghsvg0omgr7yz5R2qgLkP59kzQAeTzXXkcAqO88FQJTDUC3xxpOdbQGYAtzANpWfQebBSigFQA2DYDlPkXvk3WLv/DTv4M///c+kazzOK0avHbYb1MKgAG+Pq5rbeaDTqfRaUDFrim6TdAA5F3/HSoUTp1SkQllBeycBHxOAXprBK2sLYnoVmMqAkA7GbdfmrZt0nWvkePCBApQRgqArEssAAI3wDowCMwaTAMQiwCo55ULkEY/kRFDAdISgGWKCJgu4NssAPQbxWqLCMCQlKRYgAK9xassntJdgBLnAJR7aRQgogEwuowpoVuBpiAAUSLg/jjkmUCRzxcBV9EIABkEFoEAMApQSAMwDgKziICDWoMBAaDCyaGjaNjOTgx9ECAvhnZUIh2ibtjWzKAGYAYCoFtcAkC9GhGAjc4B0JOuIAKgzwGgv59PA6AjAKQAYAgA0QBglw/MKxwTuwGLC1AOBGxsk4J26l2uYbTomOAC9JFPv4yf/eSL+MWnX8ZHPuXXPulBHYDeTgsAsj6u6sbOAJBBcxnA5P8DSRoAWgC4aIpFno0FgFO7eE4BeosERQAcgtStxVQNwOV3qMc3vjxt2xoCsE9EwK4Or84BLikC4NMAWClA9LhbEIDhJpxnIp460tgQgHgb0FED4LIBjeBg6yLgJH9xuoBvlQKkFwBhBGCKBmBVN2M3PkUALEN24upEBCDKBlT38L70hOr0BBEABXELec6JLN0uToe/bXA4e36aBkAWQRQBmD0HIFYDgJ3g+UKvmdGi0zkHoP+s0iYCDmxnPYqAzUnAm7IBNcwLiCXqEXZRNV1/HEMiYCsC4PdDv0ULgFQNgK2gbNZj4X5SNZOmR1tjrgYgj6TasCFaGgWIIgADBeikW6BGwRpAbUERAF0DQNeQ4dqV97dN2ICyAsCBABQBKllAA/DFa6qouXEcKMS0sDkAARYKkI9Os68hADr/H9AQu7gCQLc2p1FkYpw67mQuyL+LjF93d2GcFwBzIkBF2WpMRQAuv1M9vvHctG1rGgAuAnZQgDxzADKvC9AUBEAVANFhsQENJR+2gWPOpD0CAdBFxUniwuasKEDaQr8OFwBTEIBDQkk4SEQAANWVrVriApQVqtM+ywZUHl/Rn4tZDlwZrqsbX/beaDgFSE5rTez+A2bHP4gApA0CW2sFgBBiLHKnzQGItQElSW+3E9YAUApQ59cAyHNvyQaBSQQgrtgXFt60U/eTGAalkKEhfRJ3tKrDCICtAxlpA1pkIklwD8BJAdpbqM852pQOQP++cwaBxU4CLpZuCtBwvh6izwUoUtb4im7dBUjuH7AZChDdXpQIONYGVBUWL1xXz6ciXy86CoCFzwbUoABpQmDbHJSEAWtjASDc53+vARgQgNgp3HdxnBcAc6L0d6K3GqwAmIgAXJ+KAHAKENcA2G+AXhtQrwsQXQDiNAAyaSgnFgCxImAbAlC4uoERImDuA54oAq7PigKUrgHgCEBcgnRK3pOckEAVYmwOQL5Qi3JMARCiAJV7ynlHCoHbGrj5Fed+0eQ3Gzm66TeKbg4FyEbZ0EIW8rQjJ4vcuXMA4ilAOxGJOUEAAhqA2uICFGMD2rbduKYUbIL5UAAUVKszRwTMEUCdAgQMiXQQAbCgpkERcL+mX9wtkwT3AOznU7Ni94XjTdGA9IQ/lCzrGoA8UgOgU4Co68whoQANGoDbXX890jU7z0vVLdaTaXrM5LUsE9xNiICrCAQg5CZlLexWo+EJ7eK77vuuePGmOh5vv6zWMmYDWjVaMas1gnQnoOVFc0MJGoACYQSgpBQgF3V5bDjd3fQf4LwAmBdUXHPmFKChAFhcsENfrmAUoOembZssUFWnFQCuScAN76yWpbo4cq8GwGYD6kcAZJI5GQGI1gCYCMCCOYK4RMB23nytcYBpBzoonqUL+DbRKEMEHNYArElhEysCrihlzJcwOkIWZBUtALJS3fRi5gAUjsJj9NcmSfWVd6nH17/k3C86WyBrpyMATa4XAJ4pwEC6CHi4jmmyLo/pFDpHtAhYtwFNoQBJBKBrrRbH1agBIAhAxCAwxs3PM5KobXYSsNFQYBQgiQA0EQiApWsaiQAkTwEGHBSgCnvkvrCxYWDJLkDa/SM2ITREwBYKUFONa+2IAJD1v8wznKC/tkXtoQDJa1cmuBuhAJFr3IkAhChAjkbBcA/46g2KACQWABQBuBxLAdIaJToCsCENwGgnbHtNJkbrYNG1dmchuZ27fAowcF4AzIs3igLUdXwKcEos9tRitgENQA19DoC907PSEoCiiEQArBoAKr42C68RAfBxjY03zUUAMvb//v2UAkQWJ4cIkyY7ZZ44CZghAG9+DUClFYypMSIALbmJ5GUQAdDPU/vOEQRABrUC9QiBOQIw7EOqAxCAdaZ19XREQI+JImC6vwU9pomx0jRAztBEwCG0gXbc5Y0ZgPVmb5sDEIMAVOy6zAyu9sYoQIYGgAxF6/rf72itU4As5zEbnhR2AaqaFsfrfg1OdgAC7Ihms8bBUv0eG7MC1RGPkGBWpwAJoY5FCgLAKEDDMDCy9t0Zfh96LhR5huOhAMh8FKBSQwA2UQBEIQCB88jVKKhPsa5bvHJbbSPF3Q3QNAA+F6AUCpCtESpEPyQRCJ4rUhvUCPc1wFyAAIe+7JwC9NaINwoBOL6uTrIUC1AZUgdwcgM4uZn+fuYCVGB/oVXtltA7gEURiwDYNAD+414REXB0TLAB5S5AchCYiwKUJgLOM8FtQEOJM9MAhBO8yaEjAFvSAFDEqIy1ciUhO3FzKEBBFyCaVLMCwC0Eph0uhQCk3yjWmdbV2+AcgLbtxuNPXTk2pQHw24Cq8+kYyyDawM6tjnyuJYmSyTmfBCwRAE8BoBcvY6JmUoCqORQgfYAhQddkInm0iqEA2TQA7uLoDrUATXUAApxi0b3F3YAA2ByRIqg2ugZgeUHdd45kAaAsQA/Rr+8cARA47hwIQOXRAGyCAkQbklNdgGiRknPU/aWbJ2z0UTICMLgAXdwp2DnHXYA0G9AQBciFgkaIq5u2Q4G+CG49CECRqUnA/Rs9FLxzCtA9HvTCOksEYKoAWMZcGpDmArQosjFZiikAyjxDlmcjZJ+lagCCCEA7bic6rCLgBARAzgFw2SVGzAHgPuDZ4GLU/7sKdVhoInAXuwDVbYc2gkISnTA6QlGAiA1oHkEBagIFQFOrz6NFXaQVKP3MXJ7bExCAU6EjAKECIJ4CxI/BFjQAEQjASbdAgzziGiQFAEyHHvZaiQCwQWD9te4rNCgFqMyFSqaHRghD/TYlAs4yRoeSFJNkClCEBoBagN43BQFwUID2t6IB0NafJA3AsD85L+CswRCARd9Jlh1niQCwGQCSAkQLZkUByusTPizUJgLeJAWIXuOuOQBBFyDyGXR+UH3K6D9AWgGwrlu8OqAHj13mxZcGwHYAACAASURBVAmnAGmDPqcgAECUuHpd1ShE/x28BUAuRtpg/2LLb0UR57s8zguAOUGhtbMUAU+1AJUxtwBoOAWozGkBEDcITL4XAHL4KEAWG7DSXwBMcgHa1CRgHcKUEeECVGkc4N7PPoval7NzAUrXAOj7HpMkGbSLxFAUIGIDGkEBChYers7ahbepf/sQgKHDlaFFJs/7CQiA7AjL6II2oPEIgD61W8YcDUA0pWtIeg8HznsQAaAUIIYAmGuK3AerDajnnOTi/MxIIJ3Wv4nBRcDCcEQCYhGANAqQ5P8DEyxAgSgK0OZcgKbagIp+gjZAOu0JLkCAos6ubvdrrDYDAOANoLIQ/Dql67JuJQxsWARM16kYEbCNAkT2cfeyelyfMgoPkCYCfuXWKeRlTR2AAJsLkMdTP7kAcP/eq5XKIxpP555OAu5f7EHgzguAezyKN6oAIAO8Ljya/n5pWQhsBAEo8mwUN7pEwJUlqZCDe/wIQIACZCsAhht29BRgYExsu3wBOZAl5OhBO3b50KrnA5PSXIBqjQIEqGMVtAE9q0nAUxAAvQCIoJDM1QDI375pO3RjAbDgyVtn7kdwDoA+BViGEGoi8K2vOimBssNFeehTEABJLZDBhg7ZIuL8k0GPAaMAjc5K0+cAlLlA5ivMh4LyeEh4gxoAsi/rlt6YLQjA8FnLoQDoRDauQT4KUK2fi1qiNtciVQYrNLLM0EMAA5eeiTcDNqA67QUwC4CTDVKAZCHarBkFaGPDwJILgGG7tACKQgC0OQCAJgS+yihAt20UoCzDCb1O6XEaf1uh1pFtzQGYagPKCgANAbiuIwDx5/0LzAGI7xt3AdIpQNq5udjnyGaoAPAUfKuVuo46LwUoRgNwTgF6awQTAZ+hBmCjCMAEITDVAHQFFmRqrZMCZKFWSL/dHJ4FL0QBsiS7o2XfBA1AR26uQQQgNAegTqMAyeShzNUAM5mEBilAzRlRgAwNQIQLkLbvwe+CBNtIRyhaRqcmNlIEALAu3sE5ACzZ0W6sIw2oc15X8tyfWwActhw1qEMFQIJlMS3ilxYXoEkUoOG4eou5rhvPJ5nwhooNWmSvAxoA5QIkzwd13P0iYI0CZKFqlLGFuicanVLIHJH6fT1cNWHuts05JcswTpr1IgAzKEBZqQrNZs3c4Y42pQHQv2+sBoAWADGddtuUbjp46vAqRwAkBYiJgAWOQe5VdN2XGoDFvrISltQyR3MiKaIQAFpIBuYA7F5Sj+uVgQDErOkyqAOQrwBYN9okYFtCfUB+kxkagPVa3TNbT+JuFgA6Ja0FZEPzHAG4x+ONmgMwWwOwOQRAUoBkkhYzCVi+Vnbf8lgEwOoCZLEBHZICuhgHQ35OQZOCCRoAWgC4EABXAdCa9qWSXhAUAZ8FAtB10+YAGAhARAEQ6xrjCHncSkovoyJgwHrTkwVsngnG5x2j8ojrIpyAZEd9SYveCTagd1r+nkq3BdWDdgEd59/49DY0ALIA8BVz1TGA/rOPBupECgWoDrgAqTkAAwJAzgXftU6RQGYDSrbBROcTg+5DmWlzACgCEOJuu2gTIxXCrQGYZAM6JrN7jGK3nUFg+hyASBcghgBEcO19FCCgtwI9vTX+845VBJxpFCALAkDvCwylmYmYsEnAESLgEAIQ0ACkFL5SAAyYFCCmAah0G1BbAUB+kzkagDVBADL3NZDnmd9tzIdY3IVxXgDMieKNKgBmIgAHD6lFYcowsIZTgHoNwEABqht0VmqFTQPQX2jRGgDrHADe7W7bbmyeJFGAGrMACDl62FyACsYHJu/PS7XAuyhA4wAzdVlGaQDaRnUdgO2di7ZkI6IA0Pc95mbBu67TEQDK92ZzAADrTU/um7NTbePuymBCYLsOYFMUoNsNRwAq3RZUjyxT69VEBCCfMwegiSgAKOVl6KiGig16njShAkC6AEkRMC32fRSgVjsXLW4ti1itjicqHVFkIuD+9z1cxVCAHEmIgwu9MQSg3Gci+60gAFNdgKgLUjICMFxr+jTgAAJQ5oJTgOjaMR4zsoawqbUzdQAxImDaDLFpSZwaAAsCkHDeuyxAAYsLEKOzWc5NisrY5gAABFlx5xnVSm2n8yTuRSZQdR4EIIRY3GVxXgDMCSZGPcMC4DbVAEwoAIRQNKBbL6SLjrRJwFQE3Hb2m/baktC1IkIE3FouKA/1ig3tSaIA9ReyIElBKFG1zQGgiaNBX5DdnsAcAFpERCUWOgqyrXPRlmwkugAB6RqAKTagUoxdQOsgBShAtgFYfMd8BUAEAiApQMIz4CYibtb85rLOAggAoK6bBA0APQ7yum3azlrk+z8zogBgokqpAfBfg/QajZ8D0D8nGAIwhQKkqBrRYn1PcBcwXgAcUxFwRoqQWBEw4C4AZmsAyGA8ub16xQaBHa3fZBoAKwJACoCja0wDIBEAulb1LkDk+EukhFDdGG3Fo9NIDpdWiUYRSQHKF2w/16fHuHaH3wtSRMCcAsTXLXr/7F2APDagAHDf29XjvQfsG4wo+CpCAfIl7kVIBOwTLd+FcV4AzAmGALwBGoC9+yd1DwGoAqBretFiSjAEoMCiEFy8Y1kMbNxq2bEroucADBeeBwFoLEl5VMjPSdEAWFyAnAgAoAoAx4RFuT1KPQlRq/od0RLzbZ2LtmQjQgOgH4dUCpDTj98T0o1jkUgBCnaqfTfWKzEIwGY0AHoBsAohAIAqWBJcgGwUICCdBqREwGEHIICIgAPFIn2eIQBWEbCmAShiCwDNkcpC1ZDXfYoY0tyOhiiuLDagMpGW61RQBEwRANkJ3aALUNep82mxp45ps8bBQv3W20MAIl2AmAaAFEKuQnZc68jgMJ0CZBkEZswBgAUBqE8hqW7MSjjzn79JUccgACEKkCzs9tgadf222fRJKXwlBWi3zHF5j69jO8YcgECj5Pd/H/C2bwK+8d/v/2+LCBegqopL3ItcjNbBAExhcYiydJfF3T+r+G6ONwIBaGo1iGQK/1+GrgO48q7492oagCJTLkAAsKoaBv8CmquL1ACIHOiAHL7k1qYBcBde3LIvsnPcKrcBUSxR5gJV04WTD822E9A0APqiKBMwpwbAFC9TO8u27ewOKnpivq1BYBtCAGKmRq5n2oCOmgxoN5AQBcgyAZeFTwS8dwXYuQSc3gSuOwqAUlKA5iEAr6/5zeVUHwxmC5lshOYA0ALAMggM6Itfcsl7o+u6MLUK0FxvIm1AyTVWhwaBaQgAEwF7EhiDjqZTNfJiIxQgfQ6IPB6dyEdr4jGRLhZ9I8FWlLt4yFEagMSkpTrBmMyW+2TbHfZKdb5szQUodg6ADQGQz9sSNUoJlSJdfRowQQBHDYBG3zzpqGHFcH7bZgDY9mtOsEnAU12Ahv0s91gRceP2bQCX+Usjz/um7fDSUAA8dnl3NLuQYWgAigCl5sH3AD/wa/6N6oidMO+hVAPgQwDy0CCwxkIdu4vjHAGYE5QCcFYIwNFVoBsutlkFwDvU41QdAEnKG2SMAgSEEYDlKALuL8zC6wJkGQQSiQBEzwHQ4N5YON86B4DcAAz6glzs61MrH7EeEQBTBAx4/PP1xHxbLkA2x5HqOChY06lUMd1jY/pqYkgUpWRUmyJMARroL3EIgEa7EULRgA5fZYOCZGwKAXh9zbPvlT4YzBYyEXCcf+NnOdAXel6mIAA10eV40Rwmek23AQ2JgKumhUCLUvTfXRSLKF2DQQGiCcJwDm1CBGw0L+SU7cU+pIPPOFBrFNtuQgNAKECpGgAdESPX136uzrHNuQBtUAMAuDvtcq2jeot9rQCwaAAYBUgXAUsEgBYAdA2J2a/YiEIAAhQg+dsuOAJw+7aJ+sY0dQDg6p3T8ZrWHYAA3QVItwGd2K+mxV9n38+6ojmAO3EvdRcgvQBlGoC7v79+XgDMCWZHuaWuqx7MAWgC/1/GnGFgw0m/7nIAIp0CJPUCgwagQOueDmuzAfVoAGqL1iAYtb0ASNMAWChAuog44MXuEwEDHnqBflOsT3tUY9PhutkGUABTA5AoAp5BATIRgAAFKGRX6UMAAC4Evv4l4+kiE8jEfA3AaysNAUBMARA3DdilAaDdzZRZADb0zxrM9lKKgAPXILkmgi5AbachL8soa1ODAkQTkWE7dM1I1Ueo/XNQgJYXxqbl4UpDL6yDiEIaAJ603JqFAGjdbFYAqO9zvLVBYCEXII8GAHAjCHI7tDhf7AGLwWnm8FVNA2BOAjYoQJWlAKAaAMt5NTmqmS5Abav2t9xluc6dI7MAiKW+veRxAAJsk4A3IKqNKKxqQgESgUFglY9q+CajAJ0XAHPijZgDwByAZiAAbBhYKgLQn/TSxYe6AAH2acDM130UAffvz0SHqnYtxJYOgAcBqCchANzzuRz5vCEEwHQBctqAAsFZAFYRsGuyMHuj5dzbxvloQwCAoA7AmAQcQwEK+fEHYkQADA2A+2ZQN+04oTKKArSwTN8NCIGFEFgWOZYzEYBrK3W9rbscqxg2Z6RtMXcBmq8BCE5XHl9IEYD+mIQ66pULAbBpABqtAChIARBJASpyAdtUXZr4TXFJ0t9XZtl4PMTiAHsD32pMpGWX0ioCdtmAOjQAJ2qiMeVgR4XuikUKgAL1eB0dvuEuQBYNAOAuIOT6qV+bkgZ0eHVE+DqIEbGik4AL1yAwtoZQBGCDBQCzAZ3gAsTev8+Ow9GRee+KRQAOCRXMhjYxF6CqdRezKRFxXBuCAIjCrwHgLkA6AnBOAXrrBEMAzqoAIAjAxRkFwKUnMA6GmYgAyBtumWfmhasFRQVKDQEAgLpyFACUqmCdBMyT0pp16yILAG3q48i7jxQg5pka3MUoO4YLkL8AsM0vWPg0BTJsNIBtFABOBMBfABgagEQEYFFMcAGyagA0FyCt++fyv+c7FnDXoFoalxC4zDgFKHEOQNN2uEoKgGPsxN2AWQHgngXgpABpGoDYiC7mLDagvu10XccKkRgKED/uCzXd2LMdug4sdBHwkAAsfOL/yGCFBip1vS0PsD9oqsYEyicCttEmAbcGYBABX9wpDU52eKdpN3tPSypX434fb8sFaLYGwEUBGrajJ3GyAKhPgDu9G19d7KMbUikvAjBSgMh66ZoDMJcCRIsM1yTgwoOG6kgnueceH6vf/MIwNyJ2DkBoLeAuQDoFaGJHPfdQdoag+YfwJO5Flo2NTwABG9BzCtC9HQwBOCMR8O2ZQ8BkFEvg4mP94xtfSZs8OHRNKloATHABasnIbQrBsbDZgHomAXMYPZYCRLadQAGSyQntjAohSFdRpwARuFejADWO+QUUUXAjAB7+5ibDhQB4KEBt2xnJVUyCNFsEPGEOAC1c42xAAwXAzeetH7EsMq0TndYpunNa4RQLtN3AC8cyrgBYxFGA1gTB24QGwDYDxP5CUwNQRVJzgNgCwIUAuLdjWBhbEgp2nU4UAtN9KOj9ZLFPEmkNAWhrk+7n6kIGbEAnzQDQNTFaUrm/7H+T7SEAnkS566ZrACxzYQBwIfDx6wCAulTrur52n1gpQHQN6a/JF24c4/opOQdnzwFIRAD046AjneQ4nJ70zz14YTlSxmJFwCE0MMvE2LzpKUCWOUCpwRAAeyHakntb5kMAMoG1b53ZBGJxhnFeAMyJvFQn15khADOHgNGQOoDVbeD4evz7DARABKkqfLJrf4FTBKBxUYBaywLgQwAYjD4BAUiiAJmuPQC1BPRRgHgBUDvmF1D++xuPALgoQO4CwJYMJWsAZrkAxduArmO46j4RMMAH5qxMETDQowtzEICesy3wfNcnI1/tHopLOmnB4phFAWgIALH6sWoAui6MALHj6rEOsnCqm0hqDoDwILCmU0PAgAEBiNEA6BQgmkCaFKAUfQTbP6opqmmX+MKYSB+vm14v5RsGxgoAh/3lEOu6xUnVXyOTpgB7KEBoKuwv+s88WtWTtREsDBGwrwAgv4NtEjBgT7RbYj9pIAAPGy+vClUAUP1WkQscs0Fgdhegq3dO8Z1/6//FR595jezDXAoQFQHHuABp59BaRwBMuvPbL+/G2VTTj23szQUaEn1dbwoBSNUAeCiZeSb8CADZX99E4bslzguAuTFO13wjRMAzEAAAuPIO9TiFBjRcRFWCBkDeRBdFNsLMDAGwdbHJtgA4NAAaAqBP04wJpwtQnA2ovh3n+xkFiCdNrPunwcgyokXAwHbOR7odUrz5EABbsp86B8DbNXaE/A24C5B/EFjU7IEQArC8qB47jsuyyLgIOBEBkB3bv1h9ED9Z/yH81eo/jhvEw1zL4goACskbGoB6BXzoXwP+5juBZ3/R+Xm24t++YVUAHEZMAja0JZ1fA1C1rWG/Kosan9i4NkTApl3jJihArAlQk99neYC9hVorj6uG74OOzDX8O45BNQBDMn5n7hRggwLEETaJXLRd2rAoZ6RQgGzNI8BawPFtWIaAyaBOQEOsC4WsUQSgyDIcw2IUoh2z3/3qLaybVptjsSERcFa43XN8ImB94CE5DlK/9Pjlvehm2fixxBjDta7LtTdqEnBMRGgAKAKQhzQArABwuwBdO27xrv/6F/F1f/kj+Gu/8Jm0fT6jOC8A5oaE185MBDwUACLnY7CnBHMCShACDxdR3SVQgIYFYkkSio7AsnUUAjBcmEIoFMBAALRuXUw4XICawXvfFS4EQGkIdATA7QLk0i4sYihAts78NhApup29K+qxpwMcQoNcwTQAkyhA6XMAoqgqDAGwiIDpOHpXATBTAyA527/dfR3+Sv2f4NnuiQkFQJwLkG8OAD7/UeClT/bH8RM/4fy86KFudLDS4KvuKwB01CMKAWDia2ID6knaTRtQs4NM0ZGpswCYpkjrEtO5KkerWpvi6hEi2jQAwNgdn2UBClgQAG4tuU/2eyM0oBQKkKsACGkA6P3ApQEgsS4oBYgOcRR8ErAVATgYERhOYduQDajLAQjQfqsQBUgVMssBRaMIQNXETQdfRSC7vACQv6HgNK6UiNAAtEQDkHkaMkWW+V2AyL8bUY6F70bQry3EeQEwN0YE4Iw0ALIAOHh4+gUhgw0DSygARhcgRQHiBYDFBahWCICMVqiFuHFqAByLuFyQKrcLUPQkYKMAiPDehyo2dK2BTJTWBgJAEkaNglE5tAuMWuVKLGzc/G1oUigCQMeuexEAc+GL0wBsZg7AwicC1hZvZn/p2qYOjRsbXqobqxMByA0uekpQ28ZxtzYoAnYl7IUucP/0z6g3XXvW/XmxxRyZm3AnQgSs8/YrTwEgBcOGDajsYHquc3MSsNkBjKLqBYINAqS0usUB9hbqux2taj8FKDQIDBiPz6whYIDZKdYodvtkv483MQxscgFANQCeDq6+DUMDYFKA1rla16NcgLSi6XQQSHN/+Q0hAK4ZAIAh2GZhUIAsCMCVvWTtS4whgLznrapGnct5aR3gFRUxGgByHuSluwDIM80FSC8otCGpMqag2GcRUXslhHhcCPFhIcQtIcRtIcTPCiGeiN2IEOIpIcQ/FEK8JoQ4EUJ8TgjxX07f7bsozhIBqE6Bkxv947n8f4AXANefi39fKylAOcq8d8Dhk4DdXd/SgQA4NQAuVX1hP+7TKEC6DWiE9z4iEABjEJibAtQ4tAveycK2/R9fvIUCgCEA96vHPg2AJTE9EwqQnMvANAB6AaBpAKIQAC3hscVy8Ar3UYA0N5qUuD21AIgUAXMXIGoDSq7d09vA5/9v9aYbX3HqCqJ/SxsCENmZB4CG3s60BEom1y4RcPwgMDsFqJxokcr2UQ4CzATvEi8vaAhAw2ljBgUoMAeA7PdtRgGawFnWO8UOChCwLQTAYwNqc5ADNCcwyz6xhpCWQFsQgFVuRwCKSBcgKwIw2wVIIgCeAiDr5/hYt2dQgAgCAIIARN4rZcSsBXLN6RGAYb+m0n/09zoKq440t/LS3ZApchGYBKyOY0UozlOaWGcRwb0SQuwB+H8AvBfAfwTgzwB4N4BfFUJYMHDj/b8fwMcBLAF8P4DvAvAjAGa2r++SYNM1N8Bx9AWzAH10/udNGQbWtiN8XCMfT+xYFyB60XcUAXBSgIZFXGQA7bTLjoSHAjTNBnTB9tHnWe/WAAj7ez0UIENoOH5WRAFgndC7jQKALHb7pADwIACbEQGnd37UHIB4CtDakfjyHQvYgAJRBcBSTEcAaNImIx0B8GgA2BwAOwXo4Llf1orvDnjtc9bPixJXA6MGoBU5TgfqhI+bb2gAPNxcWUgYIuBRAzBvEnCUW1cgRgQgzzitbrHPNABHax0B8CTFtjkAAEEACAVoCgLgEwHXGgKwiWFgRsLfuadau5pHnlkgAJIpQKcUAdDW7lNKAZKom1Y0qQLAXcCmRNN2aOU2XAJgYKDSOgbKeQuA/rWPX96Lm1NDIqoAKNU12cn9mjoFGAgjPuAIQBGkAHnmSJDjSMXCdysCEHNU/yyAdwH4uq7rvgAAQoinAfwegB8A8D+63iiEyAD8BIBf6bru3yZP/erkPb7bQlfHLzycu7mxSQcgoOdxLy/2N95YChCDuIoxKQhRgFaNWQAgBgFoHR2AsfDyiYCn2IDusETHl6xK3rCuNRhFwHpS4aEAuUTAC512Yd0RSxdsK4PAKAWIFgCJGoCoAkDzXk+McQ6APnHXSwFKRABc8LosANZ3+oJZOw9NF6A0BMBKAWoi6BUeFyoaqwgb0Etf+gXzjdc+Bzz6zea+Mdjf5wLUF0xNeQE4SZvQC/gRAEnxWWgD2OR36ro+cbKhhv5JwJICFLdm+EKuAWUutC7xAQ6WGgUoGgFwUYD63/j2bBGwmyqyFQTANfgssyS6rqmsFg0H/zyfCNjU3Z1mRASsTXHvkOG4W2JPrAgCwPUdJwMFqPYNmEqIv/+xL+E/HL5Xky/9ndZ80d8rdBqZPqyMUoBEBSGAt13aiWtQkaDrVEgE3H8BhxtTSkRMWO6oCNhDASqMScC6KF19fk1MCd60CACA7wbwMZn8A0DXdV8G8BsAvifw3n8VwFPwFAlv+qAQ27ZpQMwBaAMFgBDA5Sf7x7dfihOOkhO8Qj5exMwFSKMAdV2nXIAYBUhdmK3LBWi0Y9NuTrRzQbqEjEozBQHQKUA+EXBnRwDG4UK+QWAaB5shFw4KkHsOgA0B2IILEKMAxWoALAhAHYaKZ9uADseQUT6yYgMUICKucxWY1AnIMiW5FwHPQABOzJtYHAIQKwK2IyHymF7CHVx88dfNN159xvp50RSgQQPQEF/1WGoO4J8DMCIAhgsQpe7YjyFD57LMSilgFqlTKUCjpkgrAJYH2NMTaS8CYJmdAkRoALZLAdrIMDBbs8OVLLs0AD73G8CPABRLbvUL4CSzzwGQ59ZIA6osBUC55xABTy+WPvP8NbVvXSBxlsdCP4Y6sqNRgB65uNM3MhKRL1pMLx3rOl1zOlcDMCUiXIA60skvQhoAeApIcj7R101pYp1FxOzV+wB82vL3zwD4+sB7/8Xh/ztCiI8JISohxFUhxP8shPBgU2+ioAjAtoXADAHYAAUIIDqAzjm4iAVZKOqOUIDoJGBtIajJkCtGASIXZuOyPRs5gFofw+JLDPCb9WQb0EQKkK4BkF37ttMSGEoB0iYBVw7kgouAHYnFWbkAuRAAS5I7PjWRAhQ9PMoRsggrjDkAHgoQmwQcQABc9B9AIQCAtTgyNQBvBAUoUgRMrmt5Xv6x/LeQdcP1+u4/rN7oEAJHFQBdN1KAmoU6fr5zxSgAOl8B4EAA2GwD+/VVMwqT5gI0rFuxtEFfqPVEpwBdMBNpTyHLRcDkdRbtwmwEwDsHgFOANqMBsHXsE2bIABE2oB4RMGBYgZ5mqrDWKUAAlBB4tAGlRdPBiAD4ROwp0ZE8pMoCa0vuogBp807yAt1g/bxEhccv99+5ZPenNApQGYUASBHwHApQWANAv3/h0QCUeebXalANwD0iAr4C4Ibl79cBXLb8nYbMUn8awC8B+DcA/E30WoC/F7mPLIQQvy3/m/L+jUdpT0S3EsPocQCbQQCAdB0AhbicGgDe6XElAB0RyTQhBEDvALBZAOq4cwRgmgvQIhLWVJOANRcg1/sXbgqGC7lgaETKJOBtuADRQmM/EgGYKAKeiwCMFCBDA7ChOQA+e71gAZCPQjoAyXMA7BSgbSEApgbgu7PfVC/+l/6i6g66EIAYDcD6EEB/DbSkAPAjAJ5JwNpvK5G8hUYJi5lubEyltthIMtrgZBGwiwK0n0gBOkMNAC0ky12jAKDIxfFGCgBLs8NlmekUAc+wAQUMHcCxcFGANATASgFSCECzKREwWfvXIlQASARALwC03xVAOxQLC1R4++X+b2UMRZWEa8YIDbZGbAQBiDiu5DwoF/5BYGsfVYsO2XsTUIC2PapMfuu/23XdDw2P/6kQIgfwN4QQT3VdZ79rvFkiUli3kWAIwMwhYDKuUCvQ58KvZxVuMS4AFLbTu5FO66+cUoAcN4exANBOVUfhRW++0QiAtuDTm7krseq6zuMCxBfFHemQxFxYdATAIQKeOgl4G2gUPU5s4q27AFhZ9jlOAzBTBJzF2IDqcwDC/FRFAZqJAIgZCIClAIiaA7CIHQRmPw5FLvAQbuBbs2HJvvQE8Pi3Ag+8G3jlUz2CuD7i5zn069/xW5LjRAuAlEFgfgpQ/9qlpr1gw80c56VJAbLYgMYU6oGQ28kzwREAbRDY0aoJUIBiuO8mAnDfJBcgss4s9o3r64DQio7mUoC6zkEBSrSQnmMDChhWoMeEzMAoQBIBkAVAW/Xbc1CAqg3NARDkN1mJEAVoeF5vIum/K4AmWyDHMZaiwtuv9GsJbRBsTARM/i5GBGBGAWBBvvQQ5DwoAxoAvwsQpQDdGwjADdg7/S5kgMbrw/9/Wfv7Lw3/NxVjgei67gPyv9T3biWoEHAbtAsamxYBA+nDwDSf7TE8igAAIABJREFU2xgXIGcHkFT1nWsRdy0ATgSA3qwn2IAWOyzpdtECGk+hQZMB9v7SIwJ2zC+IEgFbJwFv2QWo3FMd5W0gALXqhooJ/s+FFQEoo12ArN2ptlXnWnQBcNt82tAAJNqAnpo3sWQNgFcEbD8ORSbwx/OPIRPDufr+7+11RA8+NbyiA177vHffnDdCMgOgI8cvdkAX4C8AJFpQOmxAATfaYFCAbDagDEmYVgDI7Zd5ZhEBay5A0QiAywZ0EAHPnQMQcAEy5hfMCSd9Y4YGIGQDaivOPQgAXftHBIDOAlgfqQKg2AWyfJwDsCkNAM1DVgisLS4XIMu8k2ooJpZYEwQgVQQcbwMKdBDtBgqACA0AzWuEzwVInwRsaADUvzkCMHGGwZYjpgD4DHodgB5fD+CzEe/1xZZ9M88gWCd6yxqA2wMFqNgxhEiTI5UCpHHcogoAV2JFNQBOBGDoGhkaACq+Vgs2pQVEFwC6DWjEosYTdk0E7HIRYhQgNwLgogA5u7w2BGArLkCUKrUgbjeJGoAIEbB831TxlDxuhdA1ADNcgGqNF+uKCArQHA3AZgaBudcq+VlFJhidLc8y/Fs5of+8/3v7/z/0XvW3q6YOgA8Cc3iSkOPULZSI2jega117KEDGHACpAdApQGHBP19T7CJgPhApnQIkB5UBFhFwcBCYxwY0IAKm59I0FyDezWaFSbM2JxjPCZvZATBTA5DoAgQYBcAhRQC0QWAAQQCAHnmT6NvQWbfPAZh+rARZ+09jRcBdwylTzN1pf/is/rVUA8BFwBHmDhEUIKk7yml6uOU5AIIm8p5tmTagvgLg3hAB/wKAbxNCvEv+QQjxDgB/cHjOFx8BsALwR7S//9Hh//9f1F7ezXFWCEDXKQTgwiPTp+Lpcd/jwCDuwfUYBEDXAJgUIEMD4EQA1AXSpdqAsuOukhnaxSuiNQAaApCHKUCxCABLKkq3CxD/PLsI2Dlo5Q1AAP7cP/gM6mLofKW6ACWIxVxCsVDIG7GJAHhcgJgI2JKo6sI4V1AXIKcIeI4LUH9NXCD0inQNQBgB0HUQl1cv4puz3gzuzoV3AQ+/v3/iQVIAXDMZnVEIwOqWeryM0wDonfY4FyBdBEwQAMf1xYrzIrNyijnql97XMtYtjQJkJNIMAdUnAQ/fXeTcqcqmARjQpEWeuXUvvpDXRL7oKZ1agc3nF8ykADmpPqkagIANaB0qADgF6Ah+BOBEHwYmi7uhITQWAN1mKEBZo/KQ4y6QOLvWQ93eFbwAGBGAVBFwhNGC/LsxxHFqRGgARMsbA67IDRtQ9yTgdWu/j99NEbNXHwLwHICfF0J8jxDiuwH8PICvAvhx+SIhxJNCiFoIIbn+6LrudQD/A4APCiH+eyHEHxJC/CUAPwTg/6DWom/aOCsEYHVHJY2b4v8D/YV139v7xzeew2jX4wrqAoTC7gJUeRAAyu+jGgDXguekANkRAF9n3hmaDSgX8TpoAeyG7SkAaGc2y5V7kWcOgFME/AZrAI6P1T5/7rUVbjbE3cIxjGf6HIChALAVccfXgQ9/H/Arf915vsr36R1fHwWInrfWBXttCuOsEaEBWGoDqWLjtGrGBP3BCyqxSLcB9Q0C639L/Rh87bVfGh8//+h3qSYEKwDMYWBRImB6nJYb0ADoImCJKDERML/WY2xAy1ynAFXq7479ign6PUs6CVhkQLnHxLRH60Y7jx0UoOE1n37xFj766ZeZ6YJuA3pxt5hEtRvXMXlu6RqATSIATppoqgZgxiAwwHABOoRdAyCEQJEJTgGqjtQxW/T2oSc2CtAMETAvACI1AIC7ABgKlaO2P4ZLUeNtF/v3pWpfmAtQQATMmjdzEIAYDQBzznJrYXoNAEUA3Ba8qzeBCDi4V13XHQH4TgCfB/CTAH4KwJcBfGfXdRT7F+in++qf+dcB/CCAfw/A/wXgzwH4YfQDxt78EQmrzw42A2CDBQCghMD1CXD4qv+15EKpOjoHIJIC5NIAOBEAKQLWOrJs/oI67rT7Fo0A6DagEd08V8ceCPCBJQ3IQwHiGoCIAsAGjW+YAvTizRP81hfUObhGiRNBEkoHCmCjQ6RwRa3Q6T/7X4BPfxj49R8BXv5d6/vljZh1kTIdAdBcgEKJajQCECgAylxDABwDxSxBRZsPHCQWAMWyTyiBKBcgHQX5mlc+Oj5+7m1/VD1x+R1eJ6CgtgLgx2lHISi+brpOJWs66h7CC9LKigDocwDCxX6pU4CazVCADPvi9XA8FgeAEDhYaIk0owA5aAj5Aq8drvAnfuw38MG/+wl88kVyjLVBYJP4/4BqSknhN7u+Vtij7kXbQgBcdBlXARBKCIMiYFIAZCVOWvV5heEIJ5QLEACc3lL30JIjAJxbPr1YYgVAm4IAkPNozSlAXdfhTk1mgnT9a5dFxP2JRJwIuN8OR283NAnYcVwzl3Wu/jq9ANA/z0EBulsLgKij2nXd8wC+N/Ca59AXAfrfO/SDwO7NYWAOKsrGY5sFgK4D8AmMG50CJAuASAoQuRA4AmC5MNsG0hrQSwEiCXAzCQHgC36M8Lb2iI29CEK5D+B1gwLkQhR4YuFCAGwUoM05Ur108wR/6m9/DH+1WkE2qVZdgdOcahoOgd1LxnttiamTykRfM7zPepN4/mPq8eFV6/vljTiJAhSyAbXA4tYIiYCNOQDxyRe1bXyQFgAxXWch+qRjfWgUoDTkcaCoHq4+g8uHvwcAeLp9J27uPqGey3LggfcArzwN3PyK4QSUKgIWhELltQH1UoB4UqzmAGgIAHMBctiA6t7lNhvQDVKASkoBGo6jkUjHTALOCnzx6uG4tjz90hG+RX5MW2NVNzgdUK9J/H+AIADD9aAllJtFAFwocQwCQM6NJAQgQAFaXkDd2dFboC8YTxryGXS9khqADYuAc1IAHDYJBQD93nKtEzmQl7h+tMZJW4zrP+pToNyNosvSWEWggXYK0IxJwAENQNd1aq6J/npLdJkbRabrzuoeoQCdhy/Oag7ANhyAZIzDwBDWARguQMPE1W0gAK4ODuAsvJhgb9Ik4EVUNy9aA2BMAx4SI40CxBEAWgCQBTZpEvBmzsWXb53gT33oY3j++jFLWisUUQjAVA1ART3RaTQ18PLvqH87rrlFMZyXIp4CFExUN4UAFAoBqMUiSc9DRZuX90vIUyUKAQDUehWFAJBj8OmfGR/+4+bbzcTcQwOKmq9AjpOgCICvANAHDvpcgIbP0ecvFBHuPca1abUBnUcBMtYtWaANNJEyz8ZzMloEnC/YWlxRhKSpcIe4SU0qANpWoa8OCtCyyMb1cWsi4E1rABrN7ECPvfsx9jqXF7TfTkODi4xTgI5eU48NETD/faZGTvb/sAn0eMn3+ys/9wn8Nz/3qf56pUJlIfDVGyfcUWj4LVInAcvXCOFu0C1HCpCG3k6NgAZg3bRJaEPrKyDJtUgpQG9mEfB5+OKNQAAubmgKsIwUJyBtDoBc8PJMjDdBrwbAgQB0oQmPkRqAjdiARvAaa4/bEE8GtARGUoDaiiEPLvHyIgZitY6zn38uvnLrFH/6Qx/HV17vbwYXSrX9ngJEit+V3QnIqgEI3Ci6rhu7SQZ0+trneCfeUQDYEYCQCxDxv7ct2Mwbe64IuN92k3hj477t5Xh+xBcAw3471qq2Vcd+RPW6DvjUh8fX/J/Nt5vn9UOeAiBmqBtBSvJdSgGaOgjMMQlYHwRGJwHHUICMQWCN+rtjv2KCFh8LQRLr5cH4dzlV9zgoAlYUIFoANFqBxC1AJ1AsatMrnh2begUhxOhgdLTelgZgw5OAKSJsQwDyAnjP4Gvy7j/MEJ/S4gjHKEBHJgJwOhYAgcIkMopWrYl3QgUAWQ//2edexk99/Hn8xG8+ZyA7L9w4xgr0t+23kSoCrgi106U5WQ5zc7iD2wwKUIDytapbXmwE0IYu9xUA6vNPu3ME4N6PyOmas+M2pQBtGgF4h3ocmgWgIQA0UZIJQ6wLkCgIAmClAFFrLh0BsM8BMCz7YoLZgC6jPL1pwp4Zi74naXc4AUWJgF02azQBkI5OMxGAV2+f4k9/6GP48mv9Pj52aRdPPUgoJyhwTMRvI2dZiykIAP0NjYXzRW0AuOOas2oADJeSs6AA2TQA2ZiI1qFBPVrovu3y+osaBAaQAsBOAbJeqy99clwXPt6+Fy/jflZoAyCzAGA4AcW5ABEKEEMAPDagc+cA5EttEJifApRnon99ZiZqqX7oetBt74NczwtSAAx0msNVbXDtWYwagGJMLgHeYT46XbF5EpMQAH0GAMDX5WE/JA3oaDVXAxAwitAjSgMwAQEAgD/5U8AHfwP4rh/W6Ju6HizDCUiz6ugaeXIPVdOO5yY/f6cfq6JV+3+rii8AJCr5Y7/6BXTaxPOvXj/RCoB+G1NFwL6EWK5pGxMBBzQAp1WTti3hKdTIv08b0xHqbovzAmBuOMSoG4+zEAEDEQiAulAqQgECVNIUSwES1AbU1olxQbiAk3rFqDnRFCCuAYihAPnchsrCk1Qs7MPAKoZcpCIA5IYlk8+ZxegPfvhpfIkk///gP/02LIdFsu36aYhHDAFwiIAnaAAqX8dYLwActIDSdhMxKEBuEbDdBtSS8NgiYQ5ALRIRgBMdAej3M0oDABAEqrbax1L0biyCNPoPYOmWe2YBBOcrAEwDkO/eNz722oD6EABdAzDOAfCJgF16H23it4UCwKhEkxAAUgAI2lmnCEC/Bh6vG96F1H/Hxo4A0MTl5377K/OHgNkKYkuBPSIAszUArjkAqRqAwCRgzRbaGnkBPPJ+QAiOBhuOcMJDATpgBVrV+akqscEKgDq+AJBr5Y3jNTrN3emFG8dYdSYCwEXAEfNdaoUAuEJqj1K68t4IFACrqk2zHM0KtN3wO3tmcKzae8AF6DwCUVAXoDepBmDnPjVYLFQAUASgy9mJ7aIjuChAWeGB0vS/RU4Cpol0OQUByJdRXQ2vBsCHANACgNxAXTeRKBGwvGHlS5WYzihG13WL3/hCf6O6vFfi7//Zb8PjV/bGG/AaBQCBwy6CAjQJAfC4xhgFgAMBGH4T0wbUjQAEbUBjEYByVyExgTkAyQWA1rVdplKAWAFq/marRt0Ix5v7q58e//ZPmm8BYPHMv/SkSpau8QLAZQLAN6yOU76nCgBfUpEyCVjNAfDYgAbmAIz7buGQR7l1eYLSSPZB1iNKARqEwHXbYQ2PEFH+OysZGvtNT94/Pv61z72M3/iiSkYv7k6gWDCrSAsFaNgPiQCs6naSQFr/PABRw50muwBp94NQhAwh2BwAigAs9kb+P6BTtKYVAHXTYgfqON2sHIP3ZFgKgAVqZHII19Aw6DUAfgQgZQ6ADwGQzZdiGy5AljxjVbcaNdC/JpdFpmYBeAaBnRAR8KQZG2cQd+devZnirBGA5X38Jr6pkELgw1e9DiGuOQCABwFwUYCyEAXIJwK2F140MdETc2fIG4vIgLxgvMYpLkBeOkCpOedYXpcsApadsWKpzscZxegXiHPId3zNA3jifllUyAKg/90OO3LuJyAAoRsF54yTY1udAK9qw8eTEIDSLwLelA2oEAoFcFGAhk50NQMBuLgzRQNgL0BlcARguMmR9eB19Mm5MTVXOgEByglIbiaRApTvRLoA6QVA59YAjEm8bxCYaxKwnIyc2xAAmw3ohAKAbHsPFm49FAUIAE5bhxc5dU7Ly9HlBwAuH6hzNu9afOjXvjT+exICYKMAWcTJGxsGRpMteg90IgAOBFlDcP7x776Ev/DTv4MvXTs0P89FASLho50WmcAJFc9qLkCnayLS3sAcgFWtFwAFWs81RNdDmQTvUgoa0wBQEfCgAZgoAvYXAJsWAdOC3Tz/VnWT5DjUDwMbPtMYBKbWnXME4K0QZ4EAtC2fAryNYDqAr3j2hVOA6IXs1ADEIAC2jscEDQAX7MVSgGQC3Se0JaMFxLgAmd7Pzvc7KECzRMC1cv0Yz8cZNqDPvqKSsafeRugsw41xNSx+t7vtaACcnPGXn+5H1rMPC2gAhHYTyXLVnTdEwBtCAAAlBLbZgOYZlsPNtkLaje2WTgEazpVkChBgOFEBjmMwJPOtKMYbn6EBAICHqA5ACYFjpn+OhVKxg6xcju5G/kFgPgqQhgAMn2OIgH3XqtxOKx2pJAJgswGdSQFqXAWASQECgGOSXLAimCav+YKtxTlZbws0oF93kgaA6kjGAsAssPc3ZQVa27URczQAdbXGD374afzsJ1/Ej/6T3zO3k4gAGDaguccFqNzHccVttdW+TyuU1nWLK0KtOYfY9YuvyX1UomMXMkJjETto2w4vOBAAuk5GIQAxFKBNTwIOaD56EXC8BqAfBuZCANSxO27s9/G7Ke7OvXozxVkMAju5rk7cbRUATAfgEQIzBCBnHTTJ3auajiW0roQuo7CeTQPQeGA5hwaALsbRCIBc8HNzuqGrq+HTAHjpAC4KkD4J1PpZjsSCIQDDcemayV2kZ15WN5D3PkIcbTQE4HY7DQFwipnl80wQTZYonf5D9kkP2YkzKED0/8kiYIoABFA4igBo04qXJAldx41iGYO6AF3cLcbrqWk7b7d8jBAFqLZQgGQBQFAPa7LMrEAVDShqEJjUAAzHTRbB1kJjCD8FyD4HYOlBAFz0FGMqdWYWGrMpQOR77jIKkCrAWSLd0G6xqwAoGaKTk/U2B9/HSS5ANlcs5isvCwC1r8dznIDod2MIQEwBYNcAVNVqpOG8dHP4PjEiYBKyeMszYTjblLngImBaNC32xxkAgP/8jY110+Ip8Xy/qS7Hc90jfvG1RgFa5Bn+3W+6Mv7t89cbXDtcYV23Vg0Ac7xLcHfzIgCDC1ApPAyAlIjQAEi0oUUOBKjDDAHQfydyLnIR8N2Zat+de/VmCpaIbqkAuP2SerxpC1AZsVagxhwAO8+N3vRjXICsXEwvBciBALiSR180GgIQ0XX3aQAKnw0oowARmoRjgnGaBmCxkYL0mZdVMv/Uo6QAGG7AcsLhLVYA2DUAtqJlsgbgpU+YL3Zcc/LGpDo7QiUBjgJAdr+dHtXrFARgSNy6xvgddlgBMB0BoBQgIJIGFKAAsSJIDgIbXtcUpACwFaOBAqDIhOGYNYYsIAfkRB5/vw2o2tedMuM+6g4XoAU7HwquAXDZgOozKSwUIO81HxGMAtQ6EAA6DIwWAFS0qjVNTikCUKr9/sDjBNnDVBcgCwJQmBQgWrgcznECchUAWhI2nhcRCEBD5s+M9KSQDagWcnu2NaPIM24DSkPTANQboABVJ4d4h+jZAl/oHsUaJQ5Xns+iFCBUONgp8Gc+oCYdP3u9wW9+8XUAsCIAKZOAve5uJJQL0BZEwJbjekooQI0IFxplTjUADgE+uAbg3AXoXg02B2BLFKBtCoBlxA4D0+YA2ChAAO8kujqAeUiM5aUA2ecA1J7E3BmygCgkAhAxCdgzB6CIRQDWdhtQrgFIcAEqlhuZSyEpQBd3Cjx6n3mcJW89BgGglBL5vYIaADp5NYQAOK65YryJDOdVTgZuyfNOdwEazlmnR3USBcjtBLTISAHQJSIAZBLwhZ0ieRBPCgVI1wB0hO5oRRscTkDBrl9Tq87ocNzktRtLATpYFtxHXZ8DoLsADedDjAuQMZPCQimYawPKipkurAE4rMmxdCEAmYYAkIbL93zjQ2xK732TKEAWEXBmahPk/AJgmGEwNZwIQP/3tu3wJ3/8N/GB//aX8Vtfvh6lAehIsj+iEwwBiKEAaRQxEoYLEA3NBYhpWCZOAu6uPoNc9PvzbPcEgEDRRQqcEjUu7BS4UqptH7ZL/He/2OuurHMAErQvUfNAoBoPha7fmhohDUClKEBtBNKQZwLVsG4b84tIznI8XKO+mQdvdJwXAHPjLBCAbVqAyqAIwM04DUDvAmTagAI8kXAhANksBMCe6FIYP3oQmLwJSAQggnbjsxtd5B5aAZsDYKcAcReggAi469jkz7nn49U7p3jtsP+8977tolq42nZc3KRzzU064t5CJwF4YiMTmKAGwHa+HF8Hrg+ixR3lEuMeBKbNAaA3EBcFKJSoxoqAAW8BQGkoq0QEQFKADpZ995pN4G4iuquOORTjZ+h0vbYdz9OWoAfWZPnSk0qDQmYByPPWedOn+pEdjgDEioD3lwWaCARgLAiHxC5mDsDY4R0pQKZ7WckGiqUXAPR7sgKAUoBIIn3ECgCShNCmSV7yApystxcXAn/tu98HIYBvevwS3nn/BGMJGyImBLm++n3hCMCcAsAlAu4/87Mv38bHv3wdt09r/NwnX/QgAOpxSz5zpMrUXEcRCnne2CbPF5nmAkSj3MMJEwH7qSoxkV9TJgnPtH0B4NVd0DkAoi8A6H3pFIvxfhAWAfuRr6iJ4HBoAOaIgIMagGbUirUR2/FrACgFaECh79LuP4BEAup5mJHl/UXUrM8IAdhSAbCnLOKoJ7cRDAHQKEAlSUZI58mtAVAXm7AteD4NQAQCoA9lsUbXqSRy6IbEdPO81m8Z7Yr4RMAUAaCfR2hSQmCRZ1g3rX1fmGPFkhcAE85HSv/5+reZ9B8A4/CqGzW5sUVoAPYXOW6dVOE5ADYEgNJ/nvgO4PMfGXbG/h3l+0bRJysAJAJg1wBYZwAAMxAAfj0V5Ca0SkQAJAVIcraTKUBUPGlx+1pVmgaAfOeOFA/WxDzLgQfeDbzyNHDz+Z4WtjwYk9CYGQAjBSiXeqI4DcD+otASKF0DoImAh8QnRAFq2m6UcIyFvcUGlM7+CCVCtqDFx27nogCp7d6uyLFkImCtAKioCJgkcG2Df+cPvB1/5H0PY7fM3dQsX7jmYuTL/toaOun7zAVoUyJgEwG4QyxyT9Z11CRg2sG1IwCOOQAkFAXIhgD4KED7G6cAldc+Mz5+pnsSAD8uRjAKUN2jQtXN8W8UveAagHQRcJQWCGr9XWzDBtQxCVhuq4ugABV5prRbLg1AVkKmQHerABg4RwA2ExtwXvHGHaIB2FYBUDiESnp4NQBhChB9fV6qG5JIdQFydLp91BxrtDVG27wECpDPBYhqCAwEwFUAeNyL5L+tibPuWMF+x/Tz8VkmAKYOQKTIGhCAGzVJKBwFgA0BCAlWmQhYJlYvflK94MnvUI8dBUAvyNMoQOOTvEM5foWxAIhAAEJWvB4EQJDC4zShAOi6brQBlZzt9ALATwEyhqGxAkB9Z2cRR52AXvv8sF+KWmUNenyW0xCAg50CLch1Y7gAaRSgAQHgFCC/XqW0IQADpWCTFKBla6HWQEukKwHI79u4CgA+CKwsTcT1wk4Z1yix7rR9P3WK3Q5BLmhjKDkCGgAqMF7VrVsETNYCXgA0vWWmRAAGW+hQKAqQeb8pc4EaBdadpalgFAAUwZpWACxfVwjAszEIQKFTgEq2LgiyXtgoQEz8Hlh/oiaCg1KAtmAD6pgDIOlGXcR2coIAiLbmJg/ydyPo290qAAbOC4DNhPRedyQjs+MsNABZpro4lsRgDNKVrx2TgAGNAuSA/vLcz82LFwG7EICIAqA2uz0xzjveScCZp4BwUIBcImBAFRTWBE/3rKafP+F8pA5AT1EEgMDizVAAnDRQ23MhAOR77ZEOpi9JWtNhVPJYUP7/k39QPfagHGWWqQIgi6AAhTrVjAIUaQMKmMeGJGwpBcDRWlk3ygJgmcDBBRCmAOmTgAm1K4gAAFYhsNyvoAUoMEsDAAiVaBlzADQRsEQA6ByAgGWtmgNgJhQbpQDRAoAMAtujIuCqUWsgEwFrLkCkEVNYCoBZwUTA5HrQri/XfSE5GAXItAE9Jo46fQHg0ADkdgQAAI6rxkCEQ6EoYnYRMAA7DWixj1PmAhRwxQtF12HvRn/NXesu4houAQigLpoLkE4B+hfe83b18czhyUQAJlE7LSHvv8zCeWOTgG0agGYUHHcRBV9BXYAAfl6OdNzSdA+7C+Pu3bM3UxTzhy95g2oADh7ezjYAlRz4xKMtpQDxQWCLRA0AhaSzbg4FaIYNKOugm7QAV1LlnQTsKyASRcD086wLrI4AlPNEwJIClAngPQ+bMwAAoM7647SqW3Sy0+3QAMikOs8Edigy4knsKIWizLO+wyILgMUF4JFvUC/26ByKXDg0AMPjtu457tq+uhEAkvAU00XANGFjA50CoTsAAVugAOkuQGt7l9f5+1EE4GqvAxiT74ghYFIDIM953+RYej3sDV3mcZqqoQFwIACBOQBWS1qbCJhQgEI2t7agg9UWzAVInUc7BGE9rVqVoLoQgIwPAitCmqvknXbY4spkzeIUo8+ISYrGRQGyIQCNhwJEHtdaAbCqialCXNI5ukRZKUD9eXEMC5Wo3GNFi2+ORVTc+iqKql9rnmmfHP+cQgG6sDQLgMcu9WvdYw9eVu+bIgKOpgDZXIBmUIAS5gB0Wfg3L/JMKwAsLlxZGb6f3AVxrgHYRMjux9ZsQIcCYP/B6EVpUowFQOwkYLcNKKMAOWwdZ4mAs7zv6rYVK7xoYm5bkI2w8D1Ln4hX/t2DAPCkwkMBihABA/APe9I9q4vpNqCrusEXh2mY73hgH7sLyklV25FCqa7DkFC+GkQAylzwblHdwkWNZbSLIgNuvQAcDRM0H/19/ffMiv78cMwBAPrfZdF5KEBAf/5kSzRtp4ZFhRCAYifoFe0tABgC4NAbWOK2NgRM39fNuACp63aRcw0APXed/vwPfp16fO1zjO7lLgCmIgBqHy4MmogKOXYBxxyATs1gGBEAf1FqpwCZnVr6OdUkBICskY39mO+UlErT9NfBChwBaHUKkPo9y5JrAGYHOzfIeVVwih27L2yDAjT8nSEAlU4BciAA2nlytG6IrXIcAiDXeBsCIO9BJ90C0J9e7OOkUuh+NXcOwCufHh9KByAgJAImg8DEQAEiv+ti9wA/9f3fio9+5hXks+21AAAgAElEQVR87wP7wD8cnhjW3ahJ9fJ5fV13hBD9vaKgTcGNTQK2zQFoCFIciQB0jt+KUIDOEYC3SsgCoFlvZmGl0VTA0bX+8bboPzIWERSgVqcAuTQAYREwtaXLgjaglgWgMKlX9Iatu/NYg9JkhhtXDAWo8SAN3vc75gBwUTG/LOUxs3Isdc9qx4C0mPjC1cMxCWL0H207LemStLKjXB1bz/2x85pn0TxpI+miAuDHvqX//6i7cRc5ZZ75NQCAmm0Q052S2wrRfwCvCJgWLScJCAAtAC7uDiLgVBtQNgcghADkHNkh565TA3DpHcwJKG4I2C31OEkDoJ6T/HiFAPBzsWo7Liq0aQACFKAx2bHagIZ1Q76g32XZ2ClAO8Rk4bRq7FQ2gwJE1pWtUoCoCFinAKn7QvTEaltQdKO0aQB0CpBrEBgxn9AoQEcMAQgXAF3Xjb+dTQQsiwKDApQv+jkNRAPQIUPbSV3HhN/nVSIAbmMLAHUsRgrQmou73/HAPj74r3wNHrxEKaGDBiCFAhSJAAB90VhuwwbUpgGo6tE6NYZq1GsA/BSgLi+VNqSIyEPeoDgvADYRM7quwTi8ilGkui0BsAy5iLeV24WAugB1ORaFQwMQ4QJEIWnRhTQAlk6pRXuRjABYhr5EuQBtcQ5AkghYRwBmDAJzOgBp26EFQFMSSokFBaC8etYt8mkA9POF8v8f+0D//5H/7EYAFllLFnZ7B1Cez1ECNdkZC1mAAgEEQJ1zJ22BroujjIQoQKuY5CokAtb1OlQQSJJRZ2KeZcCD7+kf33we62NV/KQgADJxqtvOeXwYBWjQl4xCSl0E3LSK/gNEuwDRa85nAyrITAHf8DJXsOufagBKOwLQU4BsBQB3AZIJZpmL8NyV1HC6AEmKXQW07eY0AE4XoFQKkJtqc7SqwQYrBoLdb6wi4P67G05Aw/7TScAAQQGmIACvfmp8KB2AAOBOrA3oMAjMaXdscd4rmeNdfAEQosUsi1yjAG1vDkBdqetHRGynzH0aAFNMHCp23si4e/fszRSlnY++kTgLC1AZDoEqC+YCxDUAzAY00QUosy14Pg0A4EAAEgeBWYa+FBGwJp8DwC+jwtcNdFCAGHLh0ACsm9ZMhvQCZsYgMKcDkLadNqcFAC1oTB3AyP82EIA4asciF8CLFAEYCoAI2t1ORrYRQACoh37QBSiqAPCIgEkis+qK6KToNuHyTqcA2QvQcX9q7TiQ12QxGgAAeFDpABoyEThOA9DPeKDOWq5ioyL0MvmbjZ25RqcAdVpH0RQB2xL32kZfdCSQpY+qFwiKKJb1sC4sDhjVjCEAdWMvgjUNgDy3dorcm/hOCuaKpdmAjtupNHvoORoA1xyA/u9HKx8CQBI2IcYiTnefO143SQhAyHZaFgUn+jCwYd080Y5H7dCwRMVAAVp3Ob7YPTr+OdkFqHKIu5nxRn/PzTJV+Ia0L9TcIWSNuSyyLc0BMI9FXZk6QO/HZRmna1kQOFoAnFOA7vXYKgJwBg5AMgLdQQDGHAAKezopQI298g8jAJEUIIcGIMoGtDYLgDLQFdT/rm+HVvxGUlHsYCSDkmPMO0maC5Bvf/QCZsYgsGdecTgAadvpyA2+LuIQgLLINGrUcE7ceRX4yH8FfOrD6j20G5p1wEuDBej+Q8DFx/rHY/HnRgB2c4eLBEMAhgKgCiAAXUcQgFQKkLsAWKOMLgAYAjC1AAhRgPTjQF+zJAWAL8mlOoCrn1ObjrIBHRCAgD1nvw9qAqtcVxoHAuCiAOWB7axtFCAhVELJCgCJ1M2jAJXNcMw1q9klEwFTCtBKWREyCtBi/D2XZRbkQidHiAI07M8it98XkoOJgKkLUP+daTd97SsAAG4EQOJotVZ/iygAmEuU5X5TjAiAJgJeBAqA1DkA66NxUOIXu8dYh/rIOwnYQgFy2R07Zu/INSiMABChewwFSGwDATCPa0MRgCK8HcMFSJ4vXTd+fke2eT4H4F6PbSIAx6+rx3sPbPaz9YhCALgGwEkBcmkAyIVfUATA5gIUogBRBGC4AcoFORNA1qyAT/4U8NV/bv8ugNUGNIYC5HMBKnxUFyHUoko65lVkQWEkebqL0cRBYF3XjRSg+3ZLvO0+7YZV0wJA/W51Qc6ZlYkArB0agPF7/NoPAx//34Cf+T7gsz/PnwNw+fgr6jg99oH++AGk+DvhPswkdjJyrGyTgAFFAWJOVZZzLWUKMBAtAl6jiHZGYRqAnYkagKAImBbr+TQEgDgBZa9FIABsEFh8ASB/syJTCMAozrNRgISNAhTSADg6vLIhQRI15Vw0gQJENUAUASBhUIBogir3Q5sEfDqcW8siD1IhkkPeI/Kl5rNP/eLXDAGIOkdd4ZwD0P/WR7FzAIDx99OR59UJuc4jRMC1zSWKhLSENilA/XV4uikE4OozkFThzxIBMBBPAVqKwQXINuEZcDrvjchXggg4lBQvimz05gewQRGwjQKk1mMRrQGwIADks1syUOycAnSvB0u6NjwMjBUAVzb72Xo46CksfC5ADqi3IjdqOnGSiYBtCIDGZzViLLy68bUyMS/yDPitvw38/H8G/J1/Ezi8Zv8+FhvQPBNjUh9DAXLZdgKOZGB0WyIuQJ45AF6hlT4JeOIgsGt3Vrh+1H/Wex+5ACG0bpbWWRw3kVNbSZ7odl2nEIA841aJ8nu8onir+IX/Arj1IvuOD9xWzhYj/QfQfnvu5y9jJ3PcQKaIgFNmAABpCECkM8rtU9MFiBbdUdSTYqcfcARYzw+fBiCL0QAAbBZA8bpCAJZRCICcBEz9+f0UoEWRjdeI2wbUJQL2o31WChDAee5DeO16AyG3naFF0Qzn2lIvAHQRMC0AhnPKmATsQgCmDZpiIa+JhVYQs8JkvaU5AKYL0AlzAdI1ANr9Y9AECa3xdHpKrokIxz3q+OSdA6BTgIbi7ni9IQSArKPPtrwAiHUBUhQguwbFNXtnNKlI0ACEaDHLUtcAbGgSsOW4Ug1AFoEAlLnAurN8JrkPnVOA3kpRTOu6RgUrAO7f7GfrQRMbFwXIMwcg5AKkV/1UcGNHAByDXGSwjkR/MxoLgEwAz/9m/1yzAl77nP5u9Zzl80JwvncOQGgo0IgA0AIgLAIGLEmeDwFIQKM+6xoAZt2OuhGsc4oA8ESXJlS9CNiiAbj5vHrD6U3g534ANfHmvnKDFAiPfbN67OhG0aAagM42BwCwFgC0kFU77BA8uoJ2b3UXIHKjWCdoAGwUIOawEvM5QqibukWzQdGInTJjr8l31HfyIgCXnhzXxMX1z49/jtIAjDaggWsI6hzqKUD9cagcCVTdtlgyEbCp97EVNWsXxUOuR0SnVPjE+oGQ1/8+yLm85NchQwDq1ipmp9+7y9QgsOU2NAASHSo5VUm/vlwT4pMjIAI2EQDP/WNEAPhxWNECIBEBsLkAOUXAwxpiioBNallUUAcgIgAGJrgAOe1d7Wuu16aaROwkYMDmAjTD/jygAWiJvi2LoBqZCICJvjXinAL01onSTEQ3FsfX1eOtFwARFCA6CbjzzQFojcfGhUAW5QxTNABmR0J2ZIpMADeeU8+f3DDfD1htQAGVxMdpADTOPul0r23CKEoBktQlj62oVzyrIwATXYCefcXjAKRtR5TquK9yciPWKEB6V93QANRrPuQOAJ77dXzzCz85/vPi9afVc49+i3rMkA5XAaDO1daJAPTn2CoFAdA7nrbIMjXEKagBiKUAbUAEDHjtfg0KUEURgEgNAHECWhy+gD2YloF8o2YBQKdph0TABZkxMSIAXcOoYbWBAFhEwJZCgxXmdP8tHPLFBhCAA9DzjCMAZZ6Na8OKTgIG1DlFrtNGFOPk6J1taADkuaFfD9r15RoQmRyMAmROAqbJdN12aJkg2q4B0BtPqxW9H6RSgGwuQC4KUH8tSQrQKGJ3UNiC8apCSp/REIBD7yAw6gJUDy5A8poXfJ3NC0AM+0fWsLFZFqIA0RkjZykCDmkAaAFQhn/zItMGgcnPbOj9Rj1/jgDc63GvIAAxFCCGAOQsWXIlI2oYlF4AqIs6t04Cph27AAIwJGhNQxAAVgDcNN8PWG1AAXWzdy1qvjkARah7ORZa3ViA+OxLaeJh7I/emS+mIQDPUAegt10wX2ChSgHAaeZGACqN82lMjbz9AkaL2/vUTetf/uqP4xvFF7HEGvs3B+Tmyrs4Bc7iSKHHDhGRuQsACwJguzlVDl6sL5aOAoAkMqsEETClAFlFwLGJp4WCNu6P3qWjNqCL/Sh/fgDMCeidoi/y3HMAhnNvcTB2qek1VQVdgDLiAmTvcleGDaiFAmTp3FsHgQFEBKw+c7QunYQA9NvZF6QA0ChAAMZp2kwEDCgkk35nkqQsC70AmKkBaBt13enXg7Zf26EAkXWnlQgA/05t49MA9MdCLwCqU6oBSKUAmee3vF5MCtCAAAwFwP6ywCLPlIg9hQLUdSMCcKe4gtdxH3v6aF27rYZpASBqHCyIBqDcU5qr8QuZznvRImCHGYgtjAJgAgXoH33yRXzf3/nn+J0XbqnCxXLed3UaBagXAVs0AGRdbwWxAT1HAO7x2CoCcIYagBgKkKYBoBC6C+odKUBGAaBeb9UA+FwcAKsrgUwWHs5u8QTHhQBYbEAB1dVYO27mPheg4FAgixBTdvYzAaaTADShp6EB0OcATNMAyAIgE8B7HrYUAGQ7ghwnVgBoGgDO+RQcyahbTv956o8D3/6fAwByNPjR8sfwgezzSqRH+f9AFNVpmZEhOyJAAQrZgKaKgAF3ATBVAzBQgPJMYH+Y0kzPjejkSnZP10eGgNrUAPBBYDETegEAF5UN4YOiH/QVnANAKC9xGgBlMbsIFAB122EhKKWgNLYTnAQcoAB57XoDoRAAci4vLAXAQAMyRMC1mYTQY2GKgGciAOx60ClAvMDeXAFA1rpyD6ObmpwErFFd2jodAVivZyAAFhcg2bwxBoENv60sAHbLHMuSdJZTNBo3nx9RtJd3vmb88+5wrrSd6TY0BkG9d0TT33t8bmcW61mlfXHP7JDPywi7AOX8ek1EAOqmxV/+R5/Grzx7FT/yS58j16sNAVB/y2JEwMYcgGE/XRSgmIGkb1CcFwCbCEad2VIBUO7Hdx6nRrILkK4B8LsAGYmVUFw6KwLgE3EB1sJLdiYfF1f5a50UIFcB4Ifz+RyABMoOwG/sg81iPdIZzEvS7wKkTwKmv2EcArCqG3zxWr8f73xgn3GNbdvJyHE6EeSc1BJd3fWBi4A7XgBcegL4138IeOQbAADvyl7Bj5b/q3qe0n+ASA2A2n5DEwCbC1CIn+ryxvaFLACalebVTjQAE1yALu4Uo0h7FgUInbFe8TkAnAKExcF4bgcRgP0Hx4cPBAuAAQEgwumQBqBtOyL4F2PzwcrNRX99lVYRcKgACFGATBEwEHF8tBg1ACEEQBYAtUsEzM8t9b5ssxoAF08cMK6vQqcuTQ35e4pBzyB/gyEBO658CIBdA6Dfd2paAMQgAMy8wVIAZHEagN1FjmWRcxeg2CKS0H9eXKgC4Mq+2n8nDYh8x5Ey6aJ2AV4EAPDrX1a1A02zhIkApGkAjtYNDoeC8JVbp07bVwDoGLodhwBYXYAaewFwTgG612PG8KVgyAJg2/QfINkFSKcAcRcgkwJkSwAkZzeHJXmZggAM23pCvMpfG1UAUBGwvwDwIQDeQWAAT9IHIV1Nxcv6y23uOTJ0BCAiMdbj9149HBMWqwBY2w7lSR4Ltw2o7vpgaABuflW9+L7H+6Tse/93rEX/+Q8JQtvSEYAIDcCSagCEnwJkUF/0mIMAAPzYsEFgKRSg/vtI+o++r9EFgKfQNylApPBZKAQgyHM/eGh8+AD6AsB6I6xX6ga6o869MpSYt9q5NXKoyTYYBaiLoADZbEBdImCbDahW4CaELHI4AmAicXKNPa0a7lIzIgDqO1Onko0jAGtPQUyvr+Fcl82fjdiAys/XJiEfrxIKgIFS0t93iFaEIQCaFbIlQoPACtcgsMU+2rYbr7fdMseyyDQEK7JYIgLgr5TvGh9f3lfrxKFDCEz96kfKJKUA6eFBAIDECe+eWJaaDWgiBYhOhb5zWqvi14KsMKQootAosoy7AI0aAFIAkOL7nAJ0r8dE55VgNBVw2t88t07/ATQKkDkkCIA2CThnyamNAtSQTp3tQhgRAFgWqBQNgMalfzs0BODUpQGwc9vLAJ832gXIigCYFKCaipe18CIKBgKQbkn7TMgBSNsOKwDogBtDA+B2AVo3rYkAAMCD78GHH/zzfNsiB972jfxvEbS7JekgNZmfApQkAk5FAAAudDXmAISTorppx5v4xR1HAdBEJgyeacCycC+kFe74vACK3XgNAEEA7hf9d7dSqyxDwABtQJeVm8/91+VvVtsG9EC6AJki4DxAAaq1c1jtIKFqdMqNSEbqNOBqdAGiCIBZAOwUigJErQZtCMA4EwFb0AD4KECFWWDLYzeLAiTXarlOEx1G1bTGMe98DSRq00jWCV4AhJPB2kURkx/hcgFa7I8zGoC+ANgp9QIgkgZELEC/XLxzfHxlX23TNQxs1fSTgwFgkTX9PVdu11oAWBAAndrpiDQXIM0GNJECRO1V75xW6v02DYBPLG4JYxCYxQWoPkcA3kKxLQSAdq3PAgEoZ84BsFCAmBDUciGMCMBcDcDQBZY37Me6V/hrkzUAfj6vz/4tI3ME7BoAGwXITCLGl/sGk+n7n5fqWEVSgKgD0FM2AbC2nZwk34egxYzfBcgYsHaLIACyAADwT/e/Cx9t/oB67uGvN5Nux1RKGguiAaCDWUIUoKWNApVqAwqwjjZLdGtK0yijaBF3CIR/H0UAUgeBAf4CYLSNzPjz5R6QZUrouikKkGxwAG4NgA0B0M4t2Rmv6S2NFgBNpw0CG6Z+s0LDPH58ErBFBAwAnUnf87okWUKaCuwLagNqowARmlFmJto0CVmRAmCn3LQGIJ4CBKjzaSMi4EJHACrDTx/QEzvtmiZNANppbumaGWED6hwUN4Tkf5sagH22zzs6BQiIFwJLClBW4vnssfHPV/bUd7yzsn/W7dNqTGaXqMLrnLxHNmugNZF9HzJIGxTJLkCJk4ApGnS0bhTSYTumjhk3rshzhw2o5sAl4xwBuNeDXiibRADO0gEI4Au5q5AZbhx9d0mwjrWtAAjx/ppBnV9YbUBTNABDATAsSo+2UyhAZgHQf6aZgPhcgPr3ewoACwVI3khsPFIvzcOGYEgnoEhBehwCQAqAhTpOh0jQAFCKBBUBLy8Cu5fUc22Hv1R9P77UPtL/4ff9B+b+RBTdS+IC1DgLgCmDwGIpQI4CYAICwGcAaO4u8rM2QAEa9TqyCBr5wH3RIIvdYIJroQBZj6sDAaBFdWXRAOgDmKwIALnZr5vWagOaBzUAAQoQ2c5iDgVoeP0Fjw0owGcB1MJGASLf2YsAbJICpBcAJjIh0eFZcwAMCpCiYel++gDQeTUApENL7j1tZb8fuKIODQKTGgCdAlTusX3eLfsilqI2Ub/R6hC4/uX+8YPvxUmjrpsYBODwtB4LgBJ12O6YrrvDb0vXdd9aFlxjSSz0OQDJCAA/dp0g2gotWKEYUWiUug1oY9Lv6Dp0Pgn4Xo9ySwjAWRcAMRSg4WLpu/+CTYylnVPZ1QzBfkoD0Jid9tCFqVGA2rYbfa8fMRCAGAqQ6QIE2JN4nwYACMwRWNg0AJJ6YR4jL7VAnwMAqPMx4lzsum4sAC7tlXjkooP3SrZDEYCjzk0BMjQA5PevqzVw+8X+H6T7D/TJ001cwB9b/w3c/v6PAd/6QXN/YhAA4iJRB1yAghoAH+fZFS4KEEUAIjUAzALUSQHaBAKgCfbl88M5G+0CtHtlnDj8wEABsh5Xelx2lH0hTcytCIBGAYpCAKyDwPzCXTcFiA4X6j+XFS2pFKDWJgK2UIBYAUCTEHMSMEUAlmXGaZSbRACMAoCKkzeJALgLgKO1JbHzIcgMAXAUABHdYO4C5NYAMKok0FOAKo0CpCMAMb/R1WcwahgeeT9bc+8/UPvvGgZ257TGGv2xKFGH1zmL/bKB7DqCXrMxNqClmIEA6JawI11ME303LReCR1CAcp0C1JoIAH3eNh/ibonzAmATUUwbvhQMNgTsLDQA8XMAKo3+A9gRAL0LrIfszBZozBswW8AttAytAJBJyQ5WuL+9zl/rKgACFCDA3s3zaQAAMhXUdsOzHOfagwB4F1gbAiAX7ohz8eqdFW4c97/pex+5wAo613aKhTrupw1UApAwB2Bx8upIncB9j7P3yZvYCgsUD73b9KIGojQAC8QgALEuQHNFwC4EIG4QmG0ImL6vkyhADhHwssh6brs27TVaA5BlwN4DAAIUICcCENAA6BSg3OICpGkAWAFgcwGybIdPAnZQgIZzqIykQthipAAFbUDJNmhRaxkEdtoSCpAhAp6rAYilAHENwLpOt0jVP0tpAJQTkw0BQIQLEMALgM6BCLsi6AI0rHk2ChC15txd9DagyRSgV8mk9Iffx87XS4wC5CsAhntwV/m1HYC18RLbhNAbQr5YFvksCpBeEDawIwCruuVagxgRcC44UmPTAJDfcVFYcpe7JM4LgE2EhYqykTjLGQBAHAVoWFR1/j/Q30zl/XQsAAKJVSsUAmB0FUMUIE0DILvoj4tr5mtXt+w3vQgKUAgBsFOABhchKwJAO7AxImCfC5AFARgpQOFz8bMx9B9tO7QAWNWtStw8GoBS0wDsHb2oXqghAE7eNY2I4XsUAaCuDFYKUNIcgLkIgEYBipgDwClAdg1AdHfVQkFTn9Efh0WR9cdGanMkBShWAwCMNKAruI0MrR0KP6VTgKkGgFLwbNefRgHSJwEDFhcg6iqyYN/HuR3XpFeGANTG81HHh4RsMhyENAAkmZCdWwBWK8JVS/RZm54ETOfEGHMAzMJkOQWpotF1pgiYuADZOtydr4FE9pFRTZpEBKDlSJQe8m8mBWifFS07VhegmAJAOQDh4fePa0mZC1wgSKELAThcVWMyW6D2F3aAFQHgIuA4G9BkF6AZImCANIC0ouq0avjvH1Fo5JnQqIY2CpD6Hc8RgHs9toYAnDUFyJ0YjNFKClBhLHhCCNLp6S9AmrAuLQtkO/IPG0tyG1DnOxAAwwJUBhUcju/z24AC9gKgCVGAfDaiC1M46xUBF54F1kZhKolTg20SMYlnX6YCYE8BQG3faAFQtapT6UEAlpoGYO/EXQA4edc0IiYBl0J9DqNL2ChAVeDmFLox2sKJAKRPAuYUILvAbK4IuG27MRFdFrlhAQoof/4okesgBM5Fh0s4nI4AWJLpdc0TL/nZ1s7csL+lsBQAbN5AYBAY3X+LBoBSQFLtLuV6EnIBojTLiloRyuuTJI2nTAOwRRGwIdC3UYCoQ9yEAqBtMFJdRhGwonXonG/1HvQuYjqKSI5FQagmXCeSiAB4kGCbCxBFAPZsIuAYlOYVNQMAj3yDstzOMxws1We5CoDbhAKUt7oI2EYBmoEABNgANJZFprkApXXR9aFw44RlCwJQJFKNyixjMzZsIuDq3Ab0LRTlWVCAzrgACLgA9TMAzAVPib3SEQAvBch2YZZ8MZJJ9JP6EDAZNiFwwAYUsHc1wgiAx0ZUowB1XafmANhEwLTL60UANBEwEEQBmAD4kTgEoFxSBKBRiUp1zG5avJMv2Pc4OH1ZffYlOwVokWduSlKE9S6lfNS0W2qjAIXG1G9SBKwjAFEUIAcCMFcDQK5z4xiwAqAv8mSS03Z9weANzQnIXgCQopy4JjENgNUGlKNEedYbEjSOBKpqO7sNaCbG3NBuN0ptHmkBYCIN3nkdgZCv3w/MAaAUoFUgCTklgtCdMhs1GXSfJ0ciBcg1IyY6bE4tZDsnp5Y1QH5HW/PIoQHgNLGIOQABFyB5zqyg3b8We6YGoNTFpQEEoG0VAnDwMLD/ABPx7y/UZ91xDAK7Q0TAeVdpGgAbBcivAfAVvmuyzoWSYioCbkRhp4F64khDAFTHvmPrQk8BSkMacmMQmEkBoo2IcxHwvR4sGXkzIwARHvLDolp3Oe+IDaGLvYIuQMPivBCNyZdvUxCAE0IBIgXA/V+rHtsKgBgNgKWLTl2AbMJdeTOwJmXaHAAuKPaLgI1jZEUA0guATADvftikHNi2Uy7U5zMKEMASXcMGlJwvF05eUu9xIADem0QUAkAoIEkuQCEb0CkUIBMBqLocHbIJLkAzbUBLu3UrTcyWpVYAlFwEDABNiMtNnYDErSQXoDJEzWlb47XLIkPloFDUTWsVAQOqqLFtp0qgAIXQBF+MLkCRk4ABnQIkNQDqnKcagGUxdMFHMeRMDUAsBUhqAGbMSOg/xzKtlSRrp6kFgGMOgI0m5t2t1nF+yL8NRWGHDCtB7leLA4YA9BSgHA0bZBcoAG49D6yH6+fh9wPgzZMDghQ6KUCkAADAKXmRCECsCDjFBYhqANgQx8jQNSGuAYGrWqcARWoArCJg4sDFRMB3b5p99+7ZmykippJOirMuAIRQycHajwDUyK2Qp+z0xLoAdeSGXTfaIkVvUhGTgOVN9AlaADz6zeqxTQg8VQNAEoPcI/6yUiUYBeNQmylgu4l49kWfBAzEFXLo+Y9feq1P8t714AFLLowg21ksHRoAgCWUa40+Qb/bfSuKADzJNlWNdChP1ydCA1CS2RKsY2NJUPgcgAAFaK4IeDjn5E0ipiPqcgESQiEr0yhA6ntRJGKRZ+OMCvoelpiHrC4pAgAHAsA0ANQFyJ9M6xQgoF9fbBoA6Q5mEwH323LrGrjI008B4lS9mRSgrLRSUKgGYGWjAJFO+UljsWh2uKEkh68gtiIApkNcUjSW4o1cx6uV6QQm5PUfQABoAshmRcRQgAJ20LQoXGfkflXuMZ767iI3CyhQjWgAACAASURBVNgQAsDoP1oBUGQ4WJICwEaRQj8ki9FZ6MDMRRwCEEtDZIMhgwWAogAxA4fI0L+v67iuqjZZA1Bk+u9k6m8YAnBOAbrHowg7kkwKWgDsnoEIGFDJTdAFyNQAABYKUID315LFua7W/MkUG9DqZLyJPik1AFkBPPw+9ZogBUgtbovCTwEKawAUVcKgNmkUoJCX9MInAtYnAQPRBekXrh6O++bl/2vbWSwpAtBwtxKS6OpOLfR8uW892LSW+8DuZbYpec54OyeWKdDGS6gNqFMEXA3fI9Cdmi0CNhEA2cGd4wIEEIeV2M6qg+pHj4GpAejPWe6bH9geowDdnqwBsNuAmshiz6Em25B0RTmwyNHpGy17LQUN/Y7svLDagJLrNFUErA8Cs3T/AU4BOqUFwJiE0AJAGwQGbK4AsJwbYzAb0KEAsDjEJYWVAhQoAEYEwNLYcBQAS4YSzbcBZR75EgEQOVAstTkAOXbKXJtkHVgXXiUFwIAArEgBsE8KgEPHHIA7pzXW9DyiTbJYDUDAMluGLA6KTCBzabuGWBb5yM2fUgDoCEDlRADaZLehPBNcfyMblwSxoTM4zhGAez2yTC1620AAlhcRM5Z8I7HwFABdpyhAyK03dEoB6rouCPtJDQAA1JXW8QhOAqbdiBWqpoVAq1yALj0xWhEC+P/Ze9NY27LtPOibq9n7dPfe6m7V6/w623p+NjLYBsmOE7AT/AMleUYO4YcVQEKOEkuAkFAQnW0RRYoFSPALBCgCEbAUKUqwQyKDgm0picBgYmxsp3t5z35tvap6Vbc5Z3er48dac80x5hyzW3vfqlN1z5BKde+5++y19tprzTnH/LrFFCBpYZXqAgQIg6JNAerc3Uzfex3sRYp0/iyYzt+Q8gAwTwLwfBwzAa/PSAPQ2BQgGQFYVUaoWaLDC82E0rzwcYffSXexvJXgvEUn9ibiAhR1qDgaAXBdgGYE4IggMIBbLCaVRwS8t1GQg/uZ6Y5m1Ao0iQJEcwDkJOAYN18vQFaOi0rHft+3u6sRPOnzMKSBbAqINqAhql6k9DleaQRA4P8DnAK07yURsLnnN4wCpBEAfyBSVoU0MQLCdnQD0AoUIPIc7/fuOBdEADw2oGsPSuSrmA0oRY1+b/3t4x8+/E8CSjkagGwXIKsBGIbBEgGTBmAnv9f13qYA0QYgEgQmIQAJIuCUHfF1TTUA+Taatgbg4AlYG12AKNMgBQGwNQBCCB+zAb29LkD5rdVdyVWfj4uxZyECfjcsQHWFKEDUV1uwAQVcuzfbC96ugfD7us5CAGINgKW96PoBr+IR1nqif/FTLGE2iACUK7YQZXzeBS5AQRcTJsK84ammwnuFk4Cna6ZKM7knBtN944lZOH/iJQHupdWZ46xqsvPW9ny3kizobMhX3y+v4R2UetC1BMDj75lJzFsJScDV4GsABApQqgi4XKc7UqzCOQCHQSMAyylAAI6kANEGwLJCFShAVY7VZQoFKMkFKMzNrwgFSMoBmBsADwIwpxvH7EZ9OQBToxHTLYRqvJaDEQEnIADbCAKwaWlIo00BOjYHwHWImos22C3PAQDyHZIAcDRYL8zJd3A4WHMHKAIgUYAsF6DpduL3SEoScEQETO6J/+7Ffxvf9yM/Dnz6hwGAawCW5ABoClC5Al75dmfDZT3RLtt+8CYBP9k1XEuyjTUA5Jo0uSLg9AZgVZoGoLUF1AlluwAdiCWujQBI7mChqkrFaVOSBoA036Km7JbUHQJwqtKL0VNRgNq9Efi8G/x/Xfqh7xt3AKIctykJ2C7b7o1xq6UGoAggAFEKkI0ADIb+AwAvfpLTS3YBDYA12HNHjzA1IIoA2INizRdgTE8QEwH7NAD0WiQsjgEeDnPvLLIX0JrjFIXFO1/JGgBbAK7vl4+qt8zrLQEwYD5jOgVITgJmCMAQpgBFHSo0ApBK/wHGRYYUktZqCpDWAMQXYxoBWFeFo9XIRgCOoADFgrNYkQbgZR8FSGsAVMnOK6YBkClABdrB3UE1FCB5d3cWAUeRhjAFKIjURarteqzRoNLWtYIFKMARgB1d1Ai7kNvW/Ps8LuvFcErIVKgEdGiuStAAsHlhiQaAUoBcBOCwd1HAVASg9roApVCAqEuUPxEeAB6re8A/9ePA/Q8DALYH87sXMwUoEQHYPwXe+eL454efAcramWuVUjMN6DoQBMb47NQqW8wBcBGAWGjmfMptwsbOVGMOwITgLUAA7ByAA6UAUQ1A21kUoPieeFV4GjXyfe3J8epbjADcNQCnKv1gnIoC9G5bgOoKpIQyV43BowGw7N7iNqDmgevbXAoQd7vp+gEfL4gA+KVP8QYghABYcO8qtOiGjQCEd34cF6FqZT7PYRN3kggmAVvpmIBFAfLfj9fEGu4q1gB0BCkBpXp1XgqQjf7oa/oxGtRmNQDDYLzogwNngvNWSQb2hk4iUg5AahJwKv1Hl742EgKADARg0gDcP3cbYX2+jkWsr3wIgJ2FQBd5R2sAHs9pvaz0dVnfsxC4HA2AoQBJIuAoAhAIN6MUIEbxODEFqOsH3KMZAEIKMMAX0luJAqQXI6rEjizGzhwE4JQi4JANqGlcdZ3OBtQ8C4eDuwlQzA1AWANQMRFwHgIgIVG06D1jN+hOErDjYhVolB592fz5lc8476/HhKtIA3C9b/0i4BgCIFCAwhoAEjIYKeoCxJ7pxLJzIfY+BKDp+biQaAMq2rXSfBey4XRnA/o81KkRgHfbAUhXzfnprDpKqYhTgPZtxxYl0usHKgJ2GoCYDSiHI5u+5xagNgIQ0gBYDcCxGoAqtiuiUYDmxu80MlWwGZHOP5ECRK3h7q1jCMCBHWd2e3IoQLINKE0CZgjAAysDwLfjapeF/khVD1SUFUMAIsc9LEAAALcB6FpgmATyGRoATQGyBcD0fA+T7iZayTkAJNl5iQagWuGpGu+NaA7AmovQqzKCwAm6GddFpZ1+f9p59ED9BgGI2Y3GEIDlFKCm73EZsQAFOAVo0wk8ZD1mlivsqK3rjACcSgOQ2gBMScB0Y+hoEbDrAtQIDUCOBkCH6/lQIl/FDBzoPWM3mK4GoOQIVgilad17RaIx6gbgZt+KY8PTXTNTEQEkUIAkEXCeC1BSA1CqGZlpFjDVbQ0AzcQIi4BTbUAlDQB5X9Jw3ImAn4fSD0bfHg+vAu9hAxCwkKQIQAIF6GBRgKSFFW0A+tbWAASSHO1znRAAhwJ0RjUAEgWIL2x1VRFng5gGgH7WoBXo4YbHyYs2oHQXyZMETHerEpOp6a5QOgIwNQDU7YnZgBIXIAsB0HamHAGQLUCBGAUo/hlLogFgIrBADsCqEsLHhoFQgBYiAM1mnCCIaLtR43nEKBG7ppvP777wPfEduIQGoKzNAoghAFQDUFphT1MQWI4GAMDjYnz+XsETOO6qw2AaozPeALC8AWExfRA2FlZVKSMAvUYAPBQgbdkbQRrY+MVsQFt2HuP55ecAXEVCwABOAdqICIBGBGuu6Ti1BkA3xNXZaIJBS3i+eA7AgmNLImDyHTS2gxyAAoEGgFA9anR46XI859wk4LgLkH8eoLvUZ1MQWJdKARIS7KU088spDbjtB7HxeurkAMRcgCJBYCki4BQKUDmgUOO1bRcgALYL0K4nY3owByCFAmS5AOn3owgA1QDc2YA+B8V4tSdAAVgD8C6KgEMUoM5uAITdasvtIUYBAqMAWbtS+ng+a66iMumW7Q5N1/MMgBc/OQ5ienEsUoCmSbf0U4BEe8DpZ0pBtDSL7WDO/MrDJuokEQzR0YMO5asmBtPRdMjLZATAogA1fhtQJwhs+mwhClATu190FYVZaHgQgMrrAuQXAa+lyandw6gEFzYAwNgckXNtJwF8jBLhSwHWtSwN2BX7uy5ArtCzzNEAAHgbo7f/WjWo2mv+j81mRkNsznssVEukAJVyENiMAHhFwKEcAA8FSLIBjTX9gWr7wWQAAAEEwHy+GwkBIGOmo+kATk8BkhpiiQLEcgBOJAImx+kEBKDMQABMA5BnA+q9P/RbkKRpuynckuswUoBKEcESi1I7p+shoafcCpS/X9v12Bw6PjZuYzkAAgKQIPDu+mHeNEtCAArzPmyxnVh2DgBDAMi9tGt61CoTASgKbtdqo2+wGoA7BOA5qARbwqzaEg3Au5UBAIQpQDTqGqV4Y9t2b9EgMDI4dw4CMB1PGsCBcfWtByStAZgagJvqRcMr1jQguwHoe3MMS/AV29XQg5m0+2//vsiL1Ne53aIlO3VREbAvCZg2MHU8JAvgEwKNjRfLQgBm3nnbc/pGwAZU75DNFKDqHLgkNq2Qd3a9NX/3xyAAUw5AE3CoWJICrItdG94AdCpNA0AdgCQK0DphAnZKN22NbAO6Kq0GYKYA5dFc3lYEgbt+k/8jCwHzU4BCDThgvrO1s4NqUYBg6DEUUdRNTdcPDk1CEhsD4LzyXiMAaX7oUrVdbzIAAK8GgFKAWANgawDKFaOYPLMgMGmRKAWBPZMcAEof5Q2AQo8C03FiOQCqnRuAdWYQmJRIbZfJmeCfe3ewKUCyi5V8YIoAjGPSQWj47gXSgLUzENMAtAF7V8CDAMTv+5wUYABYkUV5s0gDYFOAEhGARA3AQRIBsxRuKgK+vcvs23tm77dKdF5JrvdKBMwWjzf838gN3kaCwIBxdzhmAwrWAHhEwL4GAGDi62H/FK+ocVHx+Oyj5jXaCnT3aKQdzAd0YVRd0STgaeCX+P/j70d2SsnE2ZNGS5pEvCKrvgP0LhdtYNi96E8C1hPC5ar0fo7xOL35LiwEoO0HtBWZKLwagDH8pS4G0wC88C3eDAAgYaKICO9LogHYezUAHAEICoCB5RQgYLw25J7rijQKEMsAOPNrAIAFTkBkke+kITMEYFyQljkaAADfHMjC/sZqADwWoEDERhe8UdRowdpGADqPCLi06X5+tEFCGgBEKUBJVCxSIwWIIgAyBYiOrxvi8uPYgBY2AqAbgBNpAA4BBICORdNC1daGZVcnUIAoAjBRgPR9U4I8ByICQGxA0eGFCwkByBQBC5s3gLlv7HuL2YDWJdZ1yXfjQxQgOiZNi3LJyIBu7NgIgN5YOPg49qlBYAki4CgTwD404eWzzZuEarreGQd9OQCjBiAvCbgulYUAuCLgHQsCu3MB+uCXxUc/ut4rDQCjAFmNjK0BEFxabLEXEybFNACO7aiesAMPJUEA6ifGGeHJOW0AJgSgO/AFMeOVcgSAUXiERZVBAORHiEXAhzQAAAayay47Cnney5NinOwCNE0Iyfx/chy6EGlqslt58LsAAcBHyidmd8cSANu/E905Id+9VCUTAYddgPSEIWcAHIMAWA0AQbmSEQCSAmyHgAELPdY1BajdzXxwngNgawDG19cxaptVbw4PzF9u3uD/6AkBA+IaAGlhvq4LdELip27W5yAwC+0LOQ7p5qEqFNeGRGxAsxGAfuAIQIII+LqVRMBmzNyTe3o+93m8HcbGfkn1nRkTpOfhmbgACQtz0oTpzSO9kGfCTlEDIFOA1noxqIokPjjd1ZcoQOPPx89u3xO6AVhVBcpCYV0V6IQka/nA7uaVtMhmFKAdbwA0BVSk2FRnMnIiuQAlbEDsie4jpQFQZJHeZDYA9u4/AH5dLRegekkSsNSokfURpRzdUYCehwqJZ5fUrXQBIhSgoRQXqyEKkLigCzUAKQiApl61e6yf/t7846fnHzOv8TkBUVjZQgBWgV1B+jM/AhDhA5PrPJCdVtkG1AOxMgSDagAScwCmCSDO/3ePwxo9FUcA9PX8eBnLAAhborKqww1AQQZ6GswyCjemgV5TgEIWdadsACgCMC2UYu49MQoQ1wAk7q5Sisn0+ZwdYyEHoIzYc9r1JkUArgMNgLXjXUc0OFKjuCotbu6sAYggAAG0QTfcDtop2oAeQQHqe44A+ETAFdUAkOdDEAFrChBralmA2UIUQLgvWIkUIGIOkXlt6PuM71/z/wPom/Hz3z+vUKiEBsDKAXjRRgASdv8Bfr/4KIv6vnAagGmherEar82ZHQQWpABFNADTd84oQBYvXm8AiQiAb5wTEIAU8bsdChkt8n0zD/+EsgXAAGTbTkgi4DQNgOwCRDI4JgpQXSrXVOIW1V0DcKqq0njXyXUrXIAsCpCVBCwtluzAF4dXbBcZxAdbBDxrAFIQgC3WTw0CcH1Bdpd9TkDCIKrreA1ATARMKEB7c52lhsK7w9sK1nhAkkNO0/XzdxO1ABUs+OiiYlfIDUAj8LQ/XoQbgDwK0PSZ2x2ndulTJRqAnb2LpAf67oC+N9kDcQpQJDHZLtYAPOEIQGG+sxAKkEMB2qXurgqN/r4JNAC1EASWsIP8RhegAAU0AGWEAiS5r6zrEq2w0+dSgPg15HoD/plmBMBuRiMIQIpAWpfOvrhKsgElOQAtSCN7GJ8B5gLUO79zkgYgRokrSmPOcCoNgITW0rljWnxdriqsq9KiAMVzAB6c1ygUuUcSQsAAvqj3zQVz0rR1T+gG7Xz6ftZVKbvLSEWvxzRnUxcvWQTMF8ZPp40F0WbTN85FcgB8CEB0I9Cu3kPfTCi70QECCIBtAxrabJxqHAuUQSY0U4GKi6cNp9u8+w/cNQCnKyYCPrELEN3BftYVogCxJODKYwPKod4Y90+RwbnzUYCkAVwXsV+9ePrF+cc3FwkIAF1AOw0ApQAdhwA4QWCA121JjJMnSAtrJhgC4MkB8OyO3+RYgLbucVij1wNS4q2dAwAA3xJwAALkXSxvsSA4IQiITSJ2A2AWTtHsgWeEAAyFWWSEFkXP1AUImGlbBxum15+7XM90CHp/piAAr3fk8wc1AJYIOII0HAQK0IgACDkAdhKwbfkbcByaBcQhBGCiUKXaIdqlD3mJuAiYNd5NR5rgPbf2LFfzgpCidadpAGhj6Hke9IaEbgBYQOQSDUDYBUjv4p6vSpYiCyBJA7CuClyuKkITO3N/Rygpj8IuTZX1UYDmBsBGAFJzAAIIQBIF6IQIQIoGQHRas4voDXMRgM1eQgDkgLVd03nzQXw1u4bp9xRcgHbt+JrbLAAG7hqA09XJEYBJBHz2QhIX8WTFdgZtBCBuAxrSAIgLOmEXxxwvQwMA4OrpP57/vLkkCIC3ASDfU+lHAKRBrY8gADEXE991lnIAikLNx2l8GoDKowHwIADUAvQqCwHgImBgGtz1QvfgdwECgI+oGAUowwUo0nQXVATc+xCAxgrQEZrNo0TAfhegvqQNgH9RRBGAKAUoWQTsNqB7OzhK35ekWWAIQGSXu+8HvN4nUoDObBeg8PMnUoA8LirjeQ5pFKBObgAcBICJgBvnNTkIgD5Gig1oUaj5+941HUOy2HNa1NjNGoATIwBCQrRT9LwArEqKDJ+IAkQX8ZOu6HJK1GUIgDR/lJwCtK4LXKxL7z3iq1gQGEBcgEhzOQzD3ACcEQSgPSIH4GBT+MDRXdsF6On09z2E6+OzO67czSU6F5xKBOyz1EwpOwUYALpBvq5uEFiaBgAgjZN+P7J+2UznfJtDwIC7BuB09awQgHeT/gNYi8f8HACbAtTEKB1kEHcbgBQKkBmo7z/9/Hjcocb+4jXzmnNCAaJBJz4OPeI7+DMC4Bv0Y7siDAEwDYCEAND3SxIBJzhSMQvQHA1AKWgA2t7sWHoQAL3I+vBAFoHHNgDCbhQtrQFohwIOM4YsUKQAHX5StAE4FgEQ7AwRFkYyEbBEAVokAib330HQAFAXINIsxKg5tA5dj7eYCHiZC5CEAIgUIKcBaKbXjpO8DhZyRMCBBF+NuDn3YulqDYKJ3YHSn+9KxYPAAOCMWvDShTZZ2AxlPd8LZ14EYGEYWEpDrBdSrYsAJN+jtEQbUPM96oX7xUQBytEAVKrDuipHBGBGiVIpQHEXoErQAOzbfmYtnq90AyAjWGJJGgChAQjlAMwUIIli4/tehQR2hnz5KECZImAeqlWkJZxPRUXAOjix8Wgr9m2fbQM6i7pnBEBoAFqDSt7mut1n936qUwaBHTZm8f1uNwCrwOegyvyEHICD7QIkUYCokIvagA4DEQEHdgDIgmzdjDuKXx4eoiY7Tn4EIGQDGqYAxVyAooJAcr+oiA0o/XmaCDgeBEYng7gGQBABW40eS7ydFhYzfYK4kHxoGBeB+6ECLl91DpW1UxRpdDQFqEHlLlZLIwKOolSHBMqDrxwXIEIBKhMpQDtKAXK/q3XCBOyUQAHiLkCkASDNQhVx56G1b3tscYbNMC0aTqgBYBSgiWLhQwCafrBCwAIUIA8C4FKAXBvQaPifp/QxGQXIYwMKmB3jXdOb577dswVITxa9J0cAGjcgzqnKogCdNAdAU4C4kw8wCmrXVYGShjuJGgCeBLyuRgRgPTUAQwIVBEjLAdD3V9OZnImtlQEASA1sag7AhACIFCDzfm4DEKIApSMA9HP7qG9RLaBdHV1rVFmUOtoAfOjBeL6dh1q1b7rZBWhQpZtqLVRlIwCCDehWawDuKEDPSZ0yB2D7HmUAAGEKEEUAhkpOrQ25AAkPviID8UAHvD4RlhPCWr40vMq5+RQB8DUA1oBfR2DNWA5A5ePt6yKLKtWERcAAsJom8SQRcEIQ2HWWBiAsAt43vbvQhRD9Pgx4tR8XgV8dXkEH97My69CYC1AEAVCkAXC+Q7JzKu2c8ZM6VQ7AE3khgxNSgFInSokCRK9D0ZvGj1KAIu5YtPR1fUs7AdlBYAEXoBgC0Ajc63VVoh3cHdS267m/u7W7G2o2loqAcxAAjTCmiIAB0wDsm45z7WkDoMz5cRegxAVmqCgFKIYAzDag1oZBbrGx2s0BqGcEQGsAqAg4lgTcYlUVuKwVKjX+Xl8sQAC8GgBXN2NnAAAYN0mE+0o+MNUAaBGw4AK0Nu9nU4C0JiDLBaisADV9lxlJwJIhRLAsunEOakRFwK/dP5vfw7y3ufYMAUig/wBmvHA1AOa42+4OAXi+6pQ5AO+VAxAQpgClaACswJe4CNijAaCDXzAIzB2ofm94jZ/b2XE2oNJkHnUBIp9VtAElDUAREQGP56MRAJ8IOC8I7JppACIDnyA2ths9toCbdpQ1/Wt+7fUbWGO85l8dXhGv6yFBVGdeEKbd6QbggMrlZHsagKgNqG/H01cBBIA2r6kIgKTXyKUAfe3RFr3gAkR/92wg50kEqTkaAN2MvIWJBnR4yhcux2gAhB3FkAYgiAB4KEDDMARsQF0NALcOXkABQprblKb07FpLBEzGs05V5PWnRgBSGgD9fLmLxGUIgCACLvguPgBcrCusygJlZg7Auirw4srcz11iA8ByAHxzgdBg0gZAU4AAoKDUsiAFyB1HuJnB+J5hBCDgAuTTdgBO/kquCDiJF28ZjuTcMxvyOV+9JzUAHg1AIuqjv+eDpk7p54jagE45AFJW0m2quwbgVMUWXadsAF467r1yK0QB6lIoQDQJuMc+Qq9QvgGvT+TlCQjAl4MIgM8GNBAEJix0oi5ABf39CAWILF4lETBgGoomBQFQyhmk7WIIwDpAsXKO44qA923HXUssBGCGiB8bm9avDA/lBiBnokhEAFqU7oJMLwCGHvuD+XynRwAsETBpplSVpwG4Wldig5iTBPyf/OLfx+/72V/Cz/06EWNPCNSeNQDkviGfOUcDoO/Vb/p0AEe4AEniS58GoOl74+4CuM+6p6mhn8+57sJOOh0zDgJt0FdGBDxd89VVkIqgF/RNNxiqytCxZ92PAJxCA5AiAn6WFCBXAzAjAHU5aQDSEYCRAlTiXm1+J7kBSMgBqASKDKUAXdS0ASDHDYqA6dw1joNS8vNVUAMQQgAC4xxtOmFvlsn3/TEi4BZlXgPQUArQen4P8940CKxDPbkAqQQLUEAQAdsp3OTf7kTAz0sl8K6Ta/NeUoCoODDiApSSBDw9uErJOySq8iAALPkxFATmIgBfGl7lfMwUG9AMF6C+H2YBlz8HIEYBIouqdqEI2GcDCpjr4qMA7TIoQMJxOKRvU4AmW0kbAXhkgtrGBiAc8HSsBmCmAA2VG1BDJtrmYD7fM7cBJfecIuefQgGS6D+AoYcBYA23VL/4W68DAH7ta+TeP+gGgGgAeipIXaYB0PcqDwMjDcDOTwGKawDchdeqKjjXNxkBcCka+vd0OXQ0RtVo2XkAeRSgWQOgKUAeC1BdNAyMUVWIA1erqAbA1wCcggLko4qYBht9d4IGQKIAcSEvMCIA69pGAMIagGpyAbpfmfNqVRodRI9hSqWGQo6v3/kQgGpBDoAgApZsQH0uQIccETDgbC5Rl7oUEbC4yWIXQwDKLOtYagMqUoA6jgDUmQiAUuPnbakImGgWB1Wgn5bWdxSg56XqePhScr2nFKBAorGTAxCnAFERnZSIV1AKkE8DEKQAuQjA7w2voaQ7aGdkB9JnA5pBAaILEq8GICYCJuFk9f6x+b3IJOK1AbUHL02N8lGA9hkUoJgLUNNzzvJE7XDoE4++NL/ERwHiGoAYBShMu1MdpQB5EAAALWkA1rWwWDgGAajW5rsJIQCeibPvhxmqv+dp1HIoQJofuwV5boQgsNVAU2llDUBM6DprAEARAOICpRGAcu168wfCuQCOhNWMAkTuGZ0D0PVWAxBAAEhTw7MG4hSgmuUJZDQAMwVouocD/H+AP3s9PQ+yYdPhWVKAqCg+QgECgO6AqizmsfLoHABBBMw0AFVKDgBtHlqsygL3avM7TWIDoO/NOoDYSKnwkgZgPFV63RIRgGkclBqAi1UJPe26QWBTg6wkClA6AgB45idS2QgAXWsMeRQgqgHQFCAeBEY1AB2qTA0AMM77hjo1jO+pz5ncW3ci4OelEtJXk4shAO8yBag+B7Q4M6IBkBxw7J1hZxfYKuoCBK8GIC0HQNeXh4dctFeUpglYYgNqDWp0l9DnAsT5wMJCkgtMJwAAIABJREFU6crYlJ7tza6oz0taX79+IAsipmGwEYAcClBGDkAliICpCxBgNAA6gXVuACgF6BVxscoTI2MiYDeVUjrvJqQBANAQlOTkCABgro2FABR1XANwc2jnoCg/ApDeAOjdsRtQ9GRc0LEFbyfz0csINYfWPpUCZPH/AQTDucafue4rPheVth+CImCfCxDjdyfYgNL7VXIO81Xb9yjR4VxN90bAAQjgC8bO0wD4EYATiIDp/OajANFrPC0U9XkcbwOqcwA4jQfQDYCdBBzWAKzQYl0XuEcQgEbyxhdK35u+cRvgDaZ+xjaCCxAAlBQRT6YAaQ2Aa7WplMLVavz813v+fnpjoVoJoWehcU6gl+rj+UwIDgxNe7YiYEqveu3+eG0aXw5AQxGA9AagLgvLWrSZ79GBNqZ3CMBzUgnpq8n1XiIASpldnYONAJgJo0WJlUQB8iQB+x56RSBPjgBQDUBCEvBU3xhewA5rdydd04CYBsBvAxrSANDFhx8BiNABSANwcTB8bH9DIZxPCAHQ32GzBQQP5eucIDAJAbApQCtOden6YV4gGgoQRQA8GoCsHICA29EwMA2Ak+XAKEBmcREVAeciAABvAEjTWZAxw7cr+jiSAgykawCGYZj5sbM1J+AgAHWpmDDdRwFKdwEiDQANA9MiYGHBG2s0JAqQVwPQWi5AQRGwed8mRAGSbECp81cOAtANPAQsRgEiC8ZWUQqQaQDo4mTtRQAWagCSXIDcnWz9bC2iAAk6JNkFqEpEAOwk4BJXpAE4JDYAc1CcZx4AUihA5t9L0jgxW2y7JA0ARfDIMTUN6MZCAPQm0EpsAEIi4On56RsnBdtLAcpGALjeMA8BIDkA5zXO61KkBgKWC1BCBoCuslA8P6E7GAoQRQDuGoDnpE5pA/peNgAA4Y/7EYBmSEkC7owVpOehL3y2ZwzyTUcAvjSM3vLOwlxTbvZPzOASsgFNRQAivv3S74+/uALOR3TnYm8aAH8OAFnkpSAA+roMnQglZ9mARhEAmwJ0LVN5pgagGUp8Ay/KGoA2Y6cohACwzAoJATD3VNcQDcCpRcCAhQCY8ywTEIBYCBhgZW90/kXdvu2NDSGjAHENwLoqvV7vnC+fpgEQw8D6znDWhQagZhQg6T5x3VccG1CdR9EPWCmyoZBoA0rvYacxLwQEIPbMe6rtB0P/AeIIALWVpFQVIqpuKQXIqwEILDBDtYACBJj79GgRsH7uGY9//H4vJxvQUlEEQNIAmPPTLkBXlblHUhsAfW+GNitqwWVKygEAgIIgAF2boAFQxfyd+jJ39PhOx/xhGGYK0Hq9EAEg52Fc6k7kAsQQgCrLOnZLKECXqwpXZxXfrZ/m/74fXb6MDWiaBgAA1wAA4xyrEQCKTN1RgJ6T+qDYgAJmwg8mAfs0AHkUINYA0MULQwBCImC5AXDOjQqBdxPnfqENaIoGQNr1cerehwAAF83bKCbIOiYCZufjSwIGoqL0LAqQcBwnCZhRgJ6ySb4uixGFmFyAvja8jB5FVAMQHTxDzxz5bg+hHAAALWkA4i5ASyhAmuIyABujQSlXVAQsT5yxDAAgnQJEFx2UAtTtdRDY+LssBAzw2oCmawAIxUc3AAEHIMBelLufSf+Maov8NqBhBIDZNJL7JHgvCiJgpdS82ItZpLLP0vW4ZCnAOQgApQAZEXAaAnACEbCPK87GdU0BmvILluQASBQgmgRMRcApScAlpw+tqwIXBeGGS8JYofR9GKQACboZnwagqs1nGijq4Rx4ul+qc2iSP88zMe+pEYADmY+3TWcQWqkBCNqAuhsvqwi9Kx8B4I46IZc0uyjScb4qce+sEhGAQ9ejQI9SJ4SHzEasqkrF3ZO6Zl4fDeR+u0MAnpcKiWdzS2sAVMEFrO9WafjPpgD1nAIUEwHTAcf3INAdD+WlAGUgAP1IrXEW5pITUMAGlKcb8smcawB8QWDxdERNAyrQ42U8Cb6f2AAENAwxW1qWBJyFAHiSgC0bUMfNZ/PN+bn46vAKAPm6ZImAQwgAnUCGUkgCNtera1wuKz+pE2kAAGBj0J6yjrsAxVKAgfQGgIrjtoQC9PajR+x3V3YD4LEBjWkAghSgSANAd9xDQWB04bUqC9Hto+0HrKkI2ELLSo/egIeN2RQga/K3zjsnuXREANJCwACTAwBYYlXyndHFyek1ACkUIHKN5zCwEyEA+tm1wrwAkgScQQFaqQ5KKVyW5nd2iQ3AfB8GRcAuMkQbgIsVQTLIfNiHRMB6zCL3sm+RTW2etRMQpYCuz45HAIwI2GMDKugTgnUEBWhDxrmLVYl7Z7VIDdw1ndn9B7IoQFVRMJSNUoB6or+RaNK3qe4agFNViI+cWxoBOH8xzH9/VqV3dfqGTW62NZe0QKM/2yVQgKjoiU1IqTagHgqQ48ogpQEHbEDpQqexBh+6SCgEZyPADgILIwAA8KoaF2G+BsBurMY39p9/FAGYJoCyUHFbNiG91k0Cpn731y7ka1mAAu51BazI+KgNaOCZY3qVsAtQ1xANgCgCnq5fUWcJxeaiDcAN0XtQCpBnh+sJ1QB4KEAriR4mFEUAzi7MOT16ND4PfgRgmQZAL3Ye49JMijMC4A8BA8b7Uj9a0qJC31+0MV7XMgLQOAiAP/ODPqsMAbDHEwEBGM8nTIWQqu2GxQhA46EAUX7yyRGAnCAwYB4/6C7xIOiSgiUlaHtdgBJyAKgIeEIPzov8BmB2AQqKgF0EYHdI0ACkuACR+e9gb7pMJWUBPCENwPmZsNhPyQEg50FFwNJ3y9CJXArQUGahRlpgvSoL1GWB+2eVOC6wEDAgiwI0ugBZ7zndo9SZ604E/LxUtcbsnnNMDsAwmAbgvaD/APzhpwsB2wVIGPSKQs0LEgrF+SlA5qFTA3UBSrUBTdQAMArQJAQO2IAqZT6HTUHoWPy7LwgsIgIGmBD4oXpnej8fBUjgF4cQgIgtrZ4IrtaVaM/KqnWP4+YA+BGAdVVYDkBTAxDJATgqCdiCkJ1jkfuuj2kA9DOwhP8PeBGAamW+o1NRgEI7ZVQc94Of/RbzO9trPNocLA2ARwScowGYz0Vht5qePxEBkDnvutmQEQC3AXAQAJIDUAcQAJ8NKKcAhUTA5jvS55NDAWr6nouAM1yAWIorRQCIFuLkQWC5FKDWDQPLQUgAkOdZmU0xi8YDjLvpKwcBEDbR6CJN04dKgpD1iQjA7AIUQgDoZlDYBpRRgIINwDRmVfImAh0TLoUG4ClBFs/OcxsAPwIA+MZ1ou3KtAHNdQHSDcDFhHxcra0GYNocGh2AqD1wBgWI2YBivD+nc2YIwF0D8JwUTV89BgE43JiF3W1oAOjiMSEHADADPR1kfK8tfcEnyTagfDL/0vDadLwEClBoAQ26mxdyAfIs2KklYAYCkCQC1kLZEAIQEaXTBiBawu4bX3S6NqAcAVBOBgAgN0a8AYjZgAaSgJkGQEoCphQgmgQcyAEIeWOHiiEARt9TEe7tzuMC9CTFBSiRAkSh8XsXaxzU+F2eDzv8td/8+nyfrutEBCCyyKWhZLv1+J1j+/Y4AbMQMBcBAEwTL2sAtPjSnM+6Lj02oD0XAVsoji8IjD63DsXDY14Q80OXquvyRMB0Ic3sKj0NwDPNAag8VBE6Ls8iYGvTIKf0WFeuZs677AKkcwByEIDxtWcqvwHQC/qwC5CLmtEGgIqA6+QGwEUA9h765JUQBkYpoOfnkgYgEwGIuN5lIbuAs9ZYkgNwOVGr7p1VaAdyzBkB6IwFKJAnAi4tF6B2PxpuwEIA7kTAz1HN7jlHIADvtQAY4A8/3QmkGoBBpgABRiBKYUYfzYQGnygfBSgoAia7qOoMb06hQ2kaAL8IGDAPr72oStMAUF5xHAF4FY/k87bOBfAhADYFiHyHFj9+GIZ5Aojy/wFIqZOOC5ClAXAG/McuAiDtAjIea1QDEGhyrAkk5ALUt7QBCFCAlvD/AUcgratOEAG/dWPO7eUreYJKtQGlCZkXqwpqPS7sz9Uef+n/Ng3aqrQ1AHIOQKoNKAAc1mQs27zFKUCeBa9G0UQEQKAA+TQATTeEbUA9TU3roVQAsLj05rpWnk2DULV9jyu1zAZ0P8giYD8CcMIcgPoC8HHfmQh4agDs8MCc0g09HecsK89CjZ/VTQKWNAAl+mH8rjQCcKbM72z6OPW274c5oyOEVlZMTzZ+bpYDQJKA6xVtADzfT9+ZppOgoM6my1QSBegpmZvvnZ+PekNamQhAbBPiGBegUQOQTwHS13XUALjuV8wCFMi0AbXGGjL/dHcIwHNaeoFwDAWINQDvcgiYLh8FyILlfEFNeqcnZTFHEQBFIek+EZojE8Jb9YehaVjOIHMmaQDIwljo/vUifokLUGxHBABDAB7OCIB8ncT3k7yxdVF6jCVK3zXGDvIyCQFISQLmOQCOCJgiAAghABlQcQgBsGzkQi5APUELnGMOg7l+iylA8g53vaYUIHmCe+OJ+VwPr9zUayCdWrFhwsMS1dQAXGKH3/qqWZCv68KiABEb0AUiYABozkgDcP1GVAMAAGVgMX0QuNd1qdDThQxzAQpQgDw5APRaOo2+hwK0WoAANHYOQFQEbBYezImENAD7JArQkS5AoYZY0gAkalXE0s8obSyYC1CLy9VIZ0xyAYJxStKLwJUy3+NNG28A2gQ7aCAhB8BDAfImAXuoq4eZwlcwWmeMAnS1rvw5MlIJCEDMAtenT/CW5TiY2jC2nTEeuZwbgIonhBMEoFIUAcgRAVsuQAQV4yLg273Evt1n936rU1CAWApwGAH4jS8/wr/yF34Vf/H/+N3lx5PKRwGyuvIYBYiW70GoGAJANQCJnTnZnXyj+vD85zACMGkAQj76MN7G9k5nEgIQCBKbiyIAERGwuMMSQgACAtmne2vwj1XrXifHBagozX2zfyqIgMcGoFclvj6Mje3RQWCJGoBDxAVoCCEAXTNDuydBAEitEhCAN69JA3BPbgCSKUAE9r9YV1DTTvM5ePO0rko/BSilsRXOpTl/xfzDzZtRFyAgrAEwFCBzPqNupzSpn1oEbCcB2yJgjwagZS5AeRSgHA1A1w+4YiLgmAaAGC1QGsLeNAC7PoUCtFADoBc7obCoUqAAUeTCQ3nz1twAkPcl30GFbt7xXVd2DoA7xrVdP+/g6maBOkXddCkNQKBBJCVqADw5AGerGt2ETHgbNAGRBeA13JAoQAwBOBMagCAFKIIAiMgu0QMuQgDSGoCN4K40agAoX3/87DtHA5DRAJRWDgDRxVAEIEpjfY/rrgFYWP/lr3wef/A/+xX8vj//v+P//fK0oKQIQK7Lga5ECtCjzQF/8n/4Nfytf/QW/uz/8jtsQFlaf/5v/D384M/+Er50TW5ayve0nFW8u9UZDQB1AVKDBwEIUYBe/jbgI98DFBX+9uWPzD92dmSiNqB+ClDIBcifBJwQCiQ0AL5rWgswcjgJ2O+RT8XZ0RAwQEYAJOGpXugert0U1UkEvFm/Nnsy09AvXVki4JDV6UIKkHOfstCjEzcAxH7Pt8P11tPx2t8/q/hCjr5PsgaATI51OU/yK9XNNoqA4AJUyxqAKAJArP/6i4fmH27ezNQAuA14JzQAwHgtOj2tEQRgrfwIALMB9bgAuRQgjw3o9Jz63FCkajpbBBxBAGjzTRsA8p3tCeeZNbUe96KsStHESBSgRLG6WBICQLnWaOedbjcJ2H1uDqQB0BSgejDjwHVCAxDUiJCSLKGZCHjF0Zp5seoLavMiAMTFixSnAI3H5Q1AnYkA0AZAIwDpFKCo6xzA1xpDmYwYcZrjeF3vn9VeBGCxBsAWARPElDYbt50ClC57vitWjzYNvvDWOODOnrpz+mo/TgqCsDRaiQ3Af/zXfgdvTIuDphvwzZs9PrZUpIgREvxv/tYXMAzAr31th4/rf6CODw4C4KEACQsVLwWoNoN4MSywAS0K4Cd+Cdg/wf/1c/8AwOiy4gzIURtQSQQ8aQCcJOD4zs9KgH2dWl+NfN/D9awB8CcLCy4LIQQjkEtBPaDvLUYAhMl8dQXgG8DmbXzyV38Kf676BgoM+O7fvZy575uLD2P6qKfVAARyAFpUaAIi4KENuACxELDAjmeoxAZAYb2iScBuAz8MA96cnnHf7j+QTq2w/bHpJH+BPZ7ALKBwLSMASzUA/QVBAK7fSHQBmnbTre8uJBRfVyWapsIZjIVx62gA/Jkf9FhBCpBSYxPQt5YNKNX+DEk7gC4CkKEBoFx10gDsOpoDQBGAIzUAnbE7TKcA8RwA4EgRsK6iwKBKqGFcyOmd9HVdooyIgPdNj26633WzoMiYcd3GF25UI5IaBKbngi1p+CkCMDYAxZjT7dMA0DGpclFEe9xkFKCdiwA4FKCY3TGjAOkkYGF+oqec7QJEx+8yGTGiWScXayICFnIA9k3PNj6CG41WOTag5NlrKQJwyylAdw3AwqLx6jOfz150LWkAtnEK0P/226/jr/76V9nPHm0afOxF8eVJdb1vZ9DiUUM7W9kFyBcEBsgdvu9BYBQghgAkioCBsQk4f4EttIMIQIINKGAmfUcD0FEEwPO5UvmuV68Bb19PCMCQlCyclAQc2B2nFKB8DcB4nKosUBYKXT+YwVkv5PoGH/v8z+FP6Ld+w/z69uKj7ucgFdx1tSuYBMwRgGEYF1rz9RV2KAGh6Tg2BRiQF7jVmlMihAXR420z3zuhBqAqCxQK6IcMBGBdsYX9BXZ4gvHvq4poAFTBJny6oM3RAAyXFgKQoAHQz3DnuHD5qTnrqkDXaARg/LwjBSiEAMhNTZACBJgGgNmAckGxB7Rh1fQDXshwAaIUoC1DACgFqBBff7QGoJGRIaekRWIiUiWWJAIGxoVru50QAEMBimkAxudtfH2pN57IOPe0ydMAhNDKldBg6hyAulRWloVxslJeBIBSgFwEIEgBOmgXIPPeIwWIjIcxrZOw8UKPKVI7c12AyGc/ZLgAbW2UExCCwEwOABsXMhCAuiy4CxDZZOveRwhA0tkppb5FKfWXlVKPlVJPlFJ/RSn18fhvOu/z7ymlBqXU384/1dtVFLbbtUIDYC9IUosiAOeuCPidmwP+g7/6W87PqV/4kqIPzuOOPAh0wLdcgFJCq3SlJAEXzAaUDuBp3LwgN78+NwPXbAM6TSqqEFEGPVD1A3/vLkH8xRcCgcFrEgKvVYP7uHEDh/S5SBCrkNBrTsAfBHZt7/7ESsgBAMz3PDc4n/0j4fcpKnztW/7w/FdZAxBIX7WrdMVoc3V0Aind4wk7lICAXqWEHsVKoriU6+iOqEb4AODVe4JVHykasuQr1gCsStYA3CssK1S9o7W6MraL4A1vVANAF+6Xr5o/OxoAecHrowA1tr6E1LoqzM5crxEAm+truwDFKUDivajHJQ8CkEpbaLseVyD3WUYOwK6PNwD+ILAFtNGUDADAQwGydEOp1XdGh2PtTA+F2cU/XxkEK+YCdGhdDQAd554mIABNCCEiVQm745oCZNP6xvt3PF+2IUaLbVy5ScC2lTGleUouQPfPat5YxZgEogg4PJbtc6idgEM3Tm0AbpjOacoBOKvQElH8nAPQdlYQWI4LkF8DQH9+20XA0dlfKXUB4JcA7AH8awAGAH8OwC8rpb57GIab0O+T9/k0gP8IbD/w/VuUhzkvniPe60kVcQH6mV/4bbw1CQOrQs2T46PNkQ0Agdget3RSIQM+WSipsvYGSEkNgJf3RwZnLwUoEZprGDVHON7ZC8D164QCpMNU5MWVveteTvB5igagFtIfxbJ0AL6GQhRZhRCAQBAY9YBOsgGVUjgxfqebQ2f46//snwE++zlg+w5++R+8hf/il/4xehT4iT/wrfjR7/kYcP8jePSFBsDfHU9LgoqngV4p/7WdqyjG8+n2ggaABIFNOzVsISm4lADvJgKwklEUUm8+jQuAda3KArumD06UIQrQ7//EBf7RF6fTpRoAq+lZ6gJUXBEE4PoNjvD5XJIWUIBGDQAXAbsUICsHwIMANLEdXr1pQBoATvdIW7TkU4DMMZhdJTmPbc9pJXMdjQAkNsQxClCODWjnp2oOUxNWq3Z2fVk5OQDubv6+7VANJaCAUjeH5DhPmgJ9P6AIjEFRhGgqen/pe3e2qnQaAIoApIiAx7mr74f53nURAHMMiQLkiIBj41w0CMyPACgVbpbmWmgDSjc5aA5A50EAarWMAuRqADwUoA8AAvAnAXwawL84DMP/PAzDzwP4HIBPAPhTGcf6rwD8TwD+XvZZ3sJiuzB6ojsJAuCnAP3ib30dv/AbXwMwcrf/9D/3rfO/PdoecEztyID8zoFMkI2sAQh1y1KYkrcTLqgGYIENKKmoO4+mAW0fjSJtPZB6oD+ftVmKC1BRqHkBG9wptcLA/CJgYYBl1Bzr+wg0o3SXJBsBKCkCMH7PbNH58DPAx78frz/4bvzG8G34/4ZP48lL3wV8+LuBy1fiYjGS8BpNKAbM54xoAABrQcZ2KImNo6MBoAueUzYA43mvAzv3WQ2Att4N3Gs3DAHgFKAf+Xbz55euVuZzW7uBWRoAci7FvYeYk9IdEXAeAhByiho51OO10EFKjR0EFrABpfqeENIwfqjpHmI2oAnuX1aNNqDj/dsVdZQ6SsfXrcevfkt2sE8aBLaoARjvY2YdnEMBCjQA/fR5auYCVEYRgH3bz+NCObgIwAE1c5SRirkALbQBpRkAwNjc6fu3GBJEwJMTWshm81JyAaJzgEMBimidpCCwaA7A+HlXqeO6RTdObRilfIV7ZxXn60/rmF1zjAi44O9J5lhKDfogUIA+B+D/HIbh8/oHwzB8EcDfAfCjKQdRSv04gO8F8O8vOcnbWGfMB/0ZIACqBM4ezD/+5vUe/yGh/vzUH/lOfPbDZufsWASA+hI/7jwNgIUA+CqHAuRFAFKTgEnpCbdQkHdudAPQNzxxWbAABTzCW6QhAIBpDoILJSsMzNdQsGZED7AtscazB9VAEBgd/NM0ANNxipoF/+hBX9qdoU3PmlzHmDuS/tk6deCsPQ0AWdwYCpCMAFDxn3PvHk5AAaov3KAdy01JpgCZz/RqpAEINRK6tmx3jFOAvv+j5/jXf/BT+Bf+iQ/hX/rejxIKEF8MVFkaAGL9V68MokkpQKsrcYeWHmsYxh1OXaGd11VVGLhf24B2fcQGVKYARRd4egwkY1WVQZEyx+xxOQWBdVVcaE4X9Ded/PxuvAjAkSLgZApQLAcggwLU+b87nbpao513fFOSgPdtNy+0jQaApofXzDZXqlQXIDaP9KM7lEbcRQRgun8LLwLgatfoAjkoArZyAM7qYjw/iiAvQABWkXHdZ1HqLarhGqpkOh0VAc85AOvaQgDGa78/wga0LC0EwEMB+iCIgL8LwM8LP/9tAH889stKqRcB/OcA/t1hGN5O6v7eB0UfXCMC9vjn55RuAC5eZou6n/6F38Y3p1TQH/rMQ/zxf/pj+DufN3ShU2oAtgMZDA5yEvAQWJTTnR5d3gef7O6XFAHo8qE5vXvnHYxtJ6A5Tj3eALQMAUj3f963fQYC8I4/WKwSmpFQAxMIAmMagJwkYOs4ocUrywEggXGxgLQ54TV14NTnFKIAaQSAUkloA9DTHdwAArDUZUupcZd799g573E3txGbqDwEIN4AUOTn3KIAFe0GP/1Hv3P8y2GDkekJZzcwZ4HrCP8uH47j282bJpgvwHdn1Im+x7pwtRwSBagVKEDrgAjYFwTGrWw9ImCAjVViYnek2n7AlUYA6jD9B7BEwJ38nOifFzbl4r2kALEcgAwEIGB3rEOXKnSz7eO6LlBFqB3j4m9aaKMH+t5CACqGmImnlahXqixheNMZG1tHA0AQgBwNwJ40VA4CsPJrAO6dTfM4XfzGxrk6XwSsLZ+TLEABlwKUeL9sbaMDjM/LINj27tt+sQagsjUAhAJEG4MPQg7ASwDeEX7+NoAU35n/FMA/BPDfp5+Wv5RS/4/+7xTvt7QYBUjfnKFgopQaBt4ATPXXf/Pr+Ou/+XUAI5z1sz/23VBK4YULc8M+2hxHAaIagA3IBOlBAIoALUeiAHm5cAwB8FGA0h5MPSB74VjbCYjuoAsleu/DRgBCOz+KnZdYBAH4UPHYC4+KVo+SNZ6uQBAY0wDkIADWcXSjd2hdz3MGSZfyjkhqwmuw9OcMUIDmBoAhACR/oj/Mx3SQI6YBWG6z6/DcrURlaYJ7M0ME7AiyhdraITl0d58+554QMIAjXvEcAGtXUjsB9S2weWs6cZn/D/Bmgx4rTAEqnQbARQBsEbAs2KcNTBVqAJgIOJ8C1HbdnAOQ1gBQBEBGTzatml/LxpRjRcDJDQAZs6eF9WIb0IDZQafG46zQzsFPYxJwRANAcgAAjAtOMoYcULOGWaommQLEd8e3ViI3LSpiLwcfAuBqAEIuO2Wh5uPoz6Q3gebxn2kAclyAXA2AuCGkEYBUZNemACVqAG5snRPGgMDzNfl8vhyARKYBMF7TwyAjAJQalNzwvEf1TM9OKfUHAPyrAH5ySE1FeZ8U3eWeH+jAoiup9k/MZDI1AE92DX7q5w3152f+6HfhQw/GB/DBOW0ATicC9jYApCtXAZ5qThIwfehKukvHKEAJXnowC3MvLYc2AJu3CQKQIgL2uAAluD8E4UuCALw2hYHFzsVJAo4hALYLkM3/jJVHK8FdPfhn5EnAivw57JLis7Lzlv6c3X7cxdNlTSCA3wVITa8VJ6dTiIABd6fbylOIuQClIgA0JMsuPfmvq1F8zCZ6uuinzl8rvwg4JwdgVRXA1avuiwIIQIo9p70wX5UmCEz1LTCM4khuA8rvY+9xevkeNr/oUoDqBRQg1W5RqvG4XULWRD2Jx4FAAzAhAM5YfCwCkEoBomOSmAOQQwHyawCMk09rEIAEF6B903NdQujuAAAgAElEQVQKR9ew4+yHKtoAtEsoQN3A6LY2BeisLme6SolODhQVEIBY0JamAV3vO9aEzCYQ9PuKNgBhFyBxYycX2aWOgxkuQDQIjCIfFyR0cc4BaG0KUIYNaEEC2wC2TjoQx6EPggj4Hcg7/T5kgNZ/DeAvAPiKUuoFpdQLGGlH5fT38Kwm1DAM36f/y/3dU9aZSAEKBBOllOAA9KtfeBtvT9SfH/7MQ/yx7zU+6gwBOJICRAel3UAeBOYCNEHqQ4Fa2OXXldcAJCAAiZ15O+8ee451RihAm7cw0xw8zYyPrsJzAPwNgP79oBuIkAYslUgtCCEAAToapQDRQdJbc6NhNwD+XR+fn7+oZWC/Jye8eos2b1QUTW1AYy5A04RwLl2LU4iAAT8CQMS7vbWg1ghAVSi8cB5+BkSbWKs0PD5zgunuPm0AGALAd6SrVHtbCA0AzQLQ5ckAcI/lSei1g8BqS5zXd6MLkPIjAJJIczxOjALkioAp3S21AShIwzUkIACAyaHxBVaZBsAap4/VALAcgDwNwGlcgPh3104IQKkGXE6HTNYADDYCYImAoxSgSIM4FdeY9IymciYgAJJnPSu6uThtOoZEwIDZ6b/eNzwIcgkFSNIAREXAuQgARXDL9AZAEAEDwOXZCt0waYo0AtD0qBSlAGUEgZX+IDAmAv4AIAC/jVEHYNd3AvidyO9+FsCfxtgo6P9+EMD3T3/+yeQzvWV1LlGAKn/6alIJDkBv35hB6Yc+8yqDc6/W1bwAfXwsAnBIRwBCIWCAnATstwE1r2UIQLeAAhRFAEgD8PQb5P3lPpQuQOig1g2D+Brf7wepAOcvYo/x8z1U/n5adCQKIQCBILBsG1APVSq0o+dL9I1qAHKhYp/wXqAAcQSAUhTG196XrsXJKEBhBABwEZE3r00KcMiOEEgLWbqxrQe9FCA/zcNHy5FKT9qz9Z/UACRqAHwJvY4IuCwswV+DxskBSEMAmM97yAYUw0ynYYu9yPWZ34b49/ertLRpvQF13cobMTczBejECAB9HkLnyly2NAXInGuqqHP8fdoA8LmALpYvq/F6K6WwKmINgEUBognHAPaoGZ1EKmoTK94fU4UoQJIIuIk1AEcgALumZ5uFV4soQIILUGBcH4bhOBEwquTguM1B3twa04DHv/fT2mLXdosRANcGlCAAZFn9QUAAfgHA908+/gAApdQnMS7kfyHyuz8s/PcbAH5r+vNfzj7jW1LcBlRKAj4WARgbAErtoTv+wDjQ6Z3BY21AOQVIFpDOtnqonJ03WlkuQEoRNwYfApDWmesJt06hAD39uvlzigjYEwQWQgD07wd3ApXCNzE2Jq/AjwA4UevD4OXmjwcPBIFluwD5RMB+UZ8v+j0EFVMv60UIQCsjALIGwFyzYmpsxWYodcczVvZCd2qmmDUiuYb7tpuf/Rj9B+DXeO9xWDEIwPS9LaAA0ds9tsClzZxSykMB8iMApafZoPeNTSlYCTuojAJUVMzJCrBC+6gNaGoQGDDfb0wMmbhoqVqCAEQyAHTp+ccXWHU9NQAuAkBpL0soQPR5CCBiZYwClCMC9lOA6CJMNwAAog3Aoe1da0gbAdhnIACpQWD9wHepHQpQgZZyyzthY0/QAOw9Gy66LkkWwOuPzdpkHvPKHApQGAGw57sYOiEWodV1KDI0AFQEbD7zmAY8HntoJwqQ7QKUoQEYbUBlDcCBhPN9EGxA/1sAvwvg55VSP6qU+hxGV6AvY6T4AACUUp9QSrVKqZ/WPxuG4Vfs/wA8AvB4+vtXTvlh3s2iOys61ju06EoqoQF4hzUA7kLvwdQUHG8DShYfqDFoz24hCKxFGdzxyKIAwfi0V+iMmJRpABIbgGmgKb0iYIIAXBMEIMkGVBYBBzUAKTagAN6aGHZX2PIJltTKnjyZME44/7I2182DAMwWcKHqWmCYPrtHBAy4O3r7Vt6lrQM73osmCp/WwYKQAb8LkA6DmeFwWs8MAeAUIICjKG9dm/N/eJXQAEQoQE3Xz9d3pjrRxWYiBUgpNS+GU0XA83d5mdcAcHEu1QD4F17URhEA0LdcBCygfaVn1147lwC+HAC6m94459ykIgALGgD97PkpQCV73VwndQEKIQACBUiyzk6pgAiYftdXtAFQ5NpHcgDmY0zHaYcCPYo4AhDQotCyU+GZBsChAJXzQhVAAgIQFwEDwNXajG2vPzFjmkgBymoA4knAUSqdVFNzOi6yVYYLkCsCBkZ0V28M9CQJeKkNaFUqRvWhmyb7gSDe73cK0JT0+wcxOvn8RYxhXl8E8AeHYbgmL1UAypT3/CAUTQKeEYAA7SKphAbgMdnZl3jA+mf7lg8sucV/V6Etp89CEQBGAQpw3zMbgH7ySC/REYvLYxAAz7EYAvC6+bOnAfDt5nVkARJyAaLCTJvfTesNUGrS6+JrnGYklAKsS1PSPDagSSFglFfvsQEFJAQgQQPQ+X8n2wUI4M9c7yIAjccFSE8CMgLwjETApUsBohMncwC6n4cASA3Axs4AAPjufiIFCEBawB05j/kz5lKAvPacAQqQjQB07aQBmMYTQe/D0o2pBqD3HwcA5wxPCzX2nC5AAGIpwLr0/POkkccfvbg9O7UGgAW4Bc6VUYB0DoDfNCBYgRyAA1nEn5dkzIlSgDqXAjSNqYeJkhkVAS8MAmMaAAsBqEvLXlJEAIQgMEYBcmlhNA346wQBmE0g6HWNagAoBUi7APnH9VhzIhZZawDp98sNQW0uCAXoijQAp7ABLQtbA0BFwO+fJOCkldUwDF8C8Mcir/ldzFGPwdf9UMoxb3s9ExvQTArQ+DPz4D7eNs6AklpbS/B0KM5Rd1vLBlR35RENQI4NKIBuug1r1ZldxSU2oDkuQNcJGgDyPj4b0BQEAOA+5na9MbxgnpzrbwAvf6vzGqcBCOyKmV86Aw5PHUG6RgCOSQEG/LvXgO0CJGsAbHtURu1IpgC5fFQATpDM+P4yArCaJgHxejwrEfCMAMg6ijeemM+ShAAEkBWAc2PnnTEfBYhw0iWe98hz7xOCwCw9x1WmCJhZjlINgJ8CJIko277HuvAjAJWXAhTxeWcUoPH60u+BIU6BqjtyjwUQEVoagdaLVbt003tyBOCabFAQAwOn2HPpIgCpnG4AVuK51QCQ3dbLiiw0VaQBaAQKUKcbgPH1WTkAgY0gex4JaQCUUnO42XheaQjAPrLIplRPSgGadU90DomNc6WgAQhsQPj0YMGa5rduynnQJgkxLdTGY7F676yatUGzCLi1EsIzKEC1rQEg670PFAJwV3LRiftkQWBMBDy6AD2KUIBeOJEV6NZCDw5qamYOggh4KIMPci4FqFfa9qw3u24LkoBnCpBvkDjz7LQnBYHlawCqAN+d1jf6OALgiKxSEAA9kJN7sSMc1CQL0ADVKNUFaM0QgAAFaMlEwWh3tAGI5QAQChDeAwrQjAAIGwkwAmAgTQOwzkAA5p0xnwsQCz9zG4AykdrmWP9lIgA+ag6lANkNuI0ADN0BTTcYCpCIAMSPIyMAdKHmUoAOIfE/PefWNFwqtKtOSm/0dCgx2CnTMLS3sA3oAsSYmieEGoCYC9DSHACrAdiT3dYLsutfRxqAQ9dzCkfXOAhAPAl4IQIQyAEAzHw4/iWmAZhEwBH6JB3rvy5qAGgDEBGil5W5pnMScEADsAQBmBrqjiyyU4Tj+jtblZzeeu+sNg3fbAPaWQhAugi4LKyNBlL7Xka8b2PdNQALqyjUPKCZHACPI0lqCTag70wBX4WSQ5senCgMzG4A9tqhVQgCa1BFXIAyRMAwXX6F1izSmAg4jmr0/QA9d/ttQB9g3mrfEcFtCgVoAQLAd7v9g9c3+gfmLxSZIEXtBQ/tYFFzPANX5TYAlNd6LALg6BJI+RAAn67C/nvyROFFAFwNgM8FKEgBShU9xsrnAlTL15CnAIdDwIC4BmCzFxYdC4LAAORrAPS51efAyroOgR3vOsUGVEQAiHi4nRYEmusrIQAerUGIagTAEtQ2zuvSKUAUAchrAAA4n2lMPZ1EwDYifCoEYHWVQQHSImA/YhisAAVo35v3PK/kBqAVljn7RgoCy0QAkl2AOCoUygEAEBdqSwgAeU9prr1ayQjArA2gz3kAlbOPO2sAAgGPh0BKsbemRbpeGwBpTaPe6KACYGCc67ppZ15Nje/OFgFn2IBWpWL0M1p7mgMQQIZuQ93us7vlpQfhmf/s241MLcEG9PFk2fXgvBbhrxfOzYB4TBaArR/YaSvQvpkHYDVNGC3KYKCHRAEKJeL1c5hLbxbKmTagbcqufFFOTYBVmUnA6QiA/Pu0+n7A64NHnEzKSQJu/d7YcwkhWdQDOk0DQO6poAuQhSAt0ACE7B295dMAkPtnRgA8OQBGBBzRACRaNIrlIAARClBGCBiQogGgFCAN+58Bktj/EHY+MhoAfwPQ98P872zSt2lAC4LADgGq2NgAmO+xm54TgwAIImAPBejARJ6BIDCAaADk9wrVmlCAyjP/9aBFTSh6a1E8kAXkSYPAhsEgAKHdf8BCAFynmDwXIP8mBKVbnBXm+aENwKEXGoC24xQgYgO6HyYEICICTncB4unQoRwAABgU/Y7SXIBiCAClAIkIwHf9GPDSp4GP/TPAp3/Y+1nMcdfsXOj8ZH+3MYcisaZ5p2cNQLxp1N/ZhdVY3Tur5nlAEQSgXogAVIXyIgC7SYAvJsvfsrprAI4oPQjvTo0AlKtZDKZpPS8K9B+A6wKOyQKwNQDMCvRwAwzD/OC0KIMDXi4FyCAAnXHOyHQBoovyIOxGnYB0eRCAqpB3NfjOTzr0K1XT93hzoOJkHwJg7SymIAB08TY1pCwFOFcEbOcAeHavAX8SMHWRoS4r7u8sQQBycgCoBmC8JvdDFKCiyhKJOeXNAZCFkUwEnG0DGqMATcdUyjQ1jc8FyKcB4Lx8u7wLEtsJSGrI5+PIGoAQNWdlIQBt26BAPyftSpN8XcjPaZQCFEEAkilAvWkAVKAhokXFvYNFkRwK8xlPGgS2e2TGA5JgLtazogBZY92OJCGvyaK/JgFPtEmgx2cuQL1LAbqO2IAmuwBZ6dAhDQAA9LEmja4tUl2AyObGW4ReODcAL30K+Df/LvATf5NrGX1lIwBBZFe2hA7W9Dx15FqkOAHdzAgAn9vun9UmIXyyG983lgg4QwNQFpYLEKndoBuA27+8vv1neItLP7xGA3AsAjA1ABcvA0ph13TzYPFAEAADdhrw6ShAm4Ecr9kyvugSEXDowR+URgA6M+lm5gBQx47QrjwTAuvyaQCooI8hAGkuQD4KA622G0YRsK7rVA1AAgIgNKRPKQKQFALmbzSWJAED/nwE/juJOyfsmaM5AOb6HCQNQFFC736HXYCmxdkx/H8gkARMrmEjNwBJCABxWIlqACg8rhf4J9YA8LRe2gC8wl8YdAGSG/CQW9RoA0oQgOZgdv8BGQFICAILJgED83hVJdL+2Dn35tqXiQ0Apfb0BX8uqYjUDQKj55ypAbh+w/w5hgAo5SQlL28A/BSgHaFbFGTTqGIIgPuWTg5AuwP0wnAaL2IagLb334e0KH3TdgGSKUDmO+raRA1AJAjMt9nDxjyVsVttIwA2XfawAf7h/wps3j7KBahX5lrE7pm26+djXa5cBEA3fMVgRMCLbUBtFyBSuymF+64B+ICXpgBtm8m/ngWBZSIAfQ9sJwqQRf8BZAtQALh/IhGwTQG6Hqw0YDK4tqjYoGaXpAEIugBRBGC2Ac1EAFIdZDIagJWHrpKqAQgJXuf36gZ8E/fnmHIvAuC4APntOc0vubkUNwwBSBjwOn+jEXQBYvC43ADY1yS64JLKmwRM7tdByAFQal5MJImAj+H/A4kaAHMNdQNw76xKcvaKUYCo9oPB47qxyaAAadTL19Ta58ApQBYCkJgD4A0CExCAjiIAXcMbAGGS9yUON1EKkEvVCFHcfLXqzH1bnOe5AAF8wQ9w2kQwCCwXAaAGBTEEADCL9WmRWJXF3Gzl5QD4KUC7jnwvngZg38kUoNZj49hoG9CIBoDdH0EXIDJ29xYCsJLE5eY7ag97998jOQChJGC7xDEvpSwEwNEg/Y0/A/zcvwz8jz+20AVo/C4pnS1GAdoE8hVGG9Dx2AUGoO9HCpCiFKAMBKC0gsBI7SbK2W13AALuGoCjSu/C9MM0GFRHNAC7RyZwKdEBCLBcgI7SAPDJ6rojxzvc8AVVFAHIowANEyxdqw6tFgz1eQ/mUQiAVwMgw/m02QhqAJi7iKcB6Hv0KPAWJiqEBwEoCzWnsB66IciLNR/A5cdzClCCZWwqAmDnALQ60dflQfoQgEMseEkqXxJwLAcAEBqAgAbg1A3AjAC4FKBhGOYGIGX3H4g3AFuGAJDPSSlAOoQvEAQGmAVzSARMm7t1iAK0RAMQoIqtq4LtzHVNgzXb5XOvZ0GeLZ8IWGz0JRvQROcvWmcEASiSNQDkMwYQgJNqAKg+SUp1tkuPF2Tu0OeTZwPqdwHaEgoQfR3ldu9FDYAlAib3fKfGY8Q1AKlUUD8CIDb35Ps7iA2A/pma58aoC1AKApBTFdEI9h3bEGy6Hvj83xz/8rVfR7s3LldJi+K+AzBeW9rMxu4ZanRwueKfa0wC5u5gLgKQrgGoQxqASZh+21OAgbsG4Kg6o1agbTc+jNqSLZcCJAiAqauPlAEw/pzkAJzQBvRpb1OAiKgyagMqUIACr6cPeTu5duTagHZ92m4MswKdf0F+8I9NAma7ga2HAjS910wD2nyT03tIrebJs4snAQNWQzrucOWLgAMIQEgDMF0vaSGvkRXXLYIs7E6aBKwpQNYEMk2eehdIbgCmhUHMGi9WXg2Aew2fbNv5WqTw/4F4DsCNJyFz3uEfejNmMQqQJAIejxUSuXoRAEoBUkVQWO2j04QCmEYEgCyO2yZKAaLH6gQKUF0qKIkeIdmABrjQvjrrzX1bXyQiAGSMbRUfvzpiI+ksMI/RAFAE4CoDASDPor4X8kTAcgMwDAM2FAEgwv8S5v13vfvd7RvLBpRkX3SFDgKLuQClUYCUUiw8L6YBoE1a00gUILIpMd2XXGgrBYG5Y1tZKPn4KWVtvLD5/XDDNrLUzZvk3BLGddIwcgQg0gDQMW5tNwAVW7A3TYNhGJ0H50oMHAV0EJgHAZjuyTsE4ANeFGbaNd34MArWi0klhIC9QxGA8xQE4AgNgAV3PmnJ8ZoTIACB11PXg5nzyChA8UGK7cZkawBk0ZMd4T6fY6ILEGsgPIslvUh4gwqBb94QX2t2zlMRADey/SlFAFLgX4Fvqos2er7wF2kQ1It72yaxWQIV+xAAcv8cJBcgYL5uWgTsaCK6xiySjkUA7J30UqAATQuDN56SELAEC1AAWEdsQLcHz+4YywKYFv40CEygAOnnoh/gTbj27tLTneP1vSDvuPJy84mo0KYAlRYC0B542I/nWakEXYMeU7xjnSgCzqcAnQ3jde8HhSLRaYpSgDprg6RTqQhArgaAIAD3IhoAgDQA5rlcL2kAPCLgfduz1FX6uopQOw6de48dOhsBMPe81lTEEIBUChBg7oum4zagFythEUkay6YR5nQ9zpHxOMazvxTQ3qt1JTe2KWXZL9MNm5cOX2UvVeS+SVoUk++RCtzjDQC5rlZjc7WqZiooAOwmZGW1MAm4Kv0NwLbXGoDb7QAE3DUAR9UZs0HUVqDThH2CBuDxNo4AnEIDMAyDgwDcUA3AwdUAhCBPV/QZtsOiXb627culAKU685yCApRkOWqdh88TXC8y3hyIG4pHB7Ci1JkkBMDlx98c5QIUEgHLGgBp8ZSkAUjOAfBpAMz1aeccgDAFyHEBOlUKMAAUBffAr/wUICYATkgBBhI0AHsPP5ZlAUxoh24EqnOx+fZRc2h5rf8oBWjtdwCyj9OlUoDqkiEAva0B8Dp+6QaA2oD672EAIp0mxfnLrvNhvG83OINK9A2nO/utlQZMw5NOmgS8GAGgFKDJOvsEOQCbQ+c6+UxVMQQgRQNgKEDaVnVz6LwNLmAF0kUWe/WMmg1RBKAoKQIgNQDTBgEZ+2JJwNJYv5j+Yx3bRgAeHr7CX7olCEASBcjclwOZ+2O6ETq32TkARaHYvX/Yj9e1WkgBqooCPQqj3SO1nW1Ab//y+vaf4S0uuguztdOA2+MbAK4BkBfBZaHmOO+lDYDUWW9BEYAtG4TjLkDurlyoBgJZt41EAYoPVKF0UFYZNqA+ClCXqDdgQWCeiWSmACHuBMS480lJwG4ydbYNaOtvNFJcgKTv3qsBCLi7eKtyUQ4ADmIFBChAaFGXyt0tPVUKsC5KAyr9FCCaAvzq/dNQgLaN+d4ZAkA/14wATIshgf4D+MW5tPw2oCQHIOJ449tND1KALASgbxoTAgb4EYDpnuQ2oEbHIpaYAxAX/tulG4AbpKE9AJ97WsWfYxqedHZKETBDAJZRgJYhAPImxM2+xYF5+Ztnvpx2dvtBYS80YvvGcgEiDcBAm4zAorNl1tPhOa6arY97KwfA/T1F7qu2CWgAPAhAqgg4afz3lY0AkM//asMRgHqT2QB0Mv03igBEEpYH0lgdputaL6QAzRsGAgqgkYE7CtAHvOgujJMF0ORqACIUII8ImP7b44UiYJv+AwBbUBegGzZZtAhrAKjbAxDnc1Pf415PFswGNBcBCBwvxwbUk+rZJuoNUoLA9GKGUYCeehqASWh1SM4BcPnx2TagAQRgFUIAAhSgFaGQpO7sekugOY3nPdnIFTW03WfjoQDVaHHvrHbh8FMiAABf8IoIwEQBerIAAQgE8QCJCIBeBGkkwENH4QiAfF+HXYCm35/MDnxFLXYZN7/1U4DWdYGOQP1d1/BJ3vOsS9amTRQBcNNuK0vwmVIXMwKQfo/RuadRfHykDYGLAByhAdANQLmSx1G79EK2O8wCc6Nj6kfnvJTyiIC3jYUACA1Ai0L0j3dyAAgFaCjNmBKyAo2KxEnpOanpB2yn8ymUvEHCGgDJBlRvTBCNV0wEXJeF0xiIuSepZSEAZWF0Dh9qv8aPTRCApHHdo/+LNgD7MLVKCQgAdwFKRwD0Z5XSgHVjeYcAfMCLNwAWBajdGleNlGINwDgxMgqQxwYUMOjA9b5N5p3Ssuk/ALCxKUAOAhAe8OhgE+VzMwrQMg1AlyjMzWsA5B3IJRqAUA4AALyZkQacnAQspOSeFgEQKHD6cAkIAH3d+OcFgTHeJODxvKlHug8BWKsW9yRHpHcDAWAagBMhALEcgFQKkEf4nHJfexuAs/vAD/5bwL0PA9//k+Lv6vJrAPyN4qosGLWj7xqsld9Hfn4f3QCQ99aLKi+9Q7ABdfI6YtU1WGO8V29UegPA9DcWBYhykx1DBqUAjbjmagBoCnAKd5yFgblZAKkIiY8CdLNvOQ+bNArl5KjXoRQXjk4SMHW+IhsqISvQNmBHa9dqRph6bCdtwXldihx8VZEG4GBRgPrOLJDJeEzpMb751h7vj6MAcQSAHtduANZ70wBI6IRT5PtWdNc+0gBQowM7B2B8L3Ndr7dTgNnCHAD9fUtOQLqxTPqs73Hd/jO8xSUjAJ5golhtJRegOAUIAB6Q5mAJCmBnAAA2AmBpAIYquqvPGoDIa5kGoLEQgKJKmmyaVD6m5AKUoAGgu8dLcgB8oUBGBEzOy4cAUBFwEgLgugBRnmTSBLAgCbjvh3kxL+VF+GgSoYWdt4SJaDxv10fapmFRqP+FM+F7PITdcLKLIQABClBmCBiQ0gAkUoDag3nWkxAAT8I1tQG1v8sf+bPAv/P3ge/4w+LvSsdhCEAg9Xtd8yTgvk2jAJWliwDERcCuDShvbhM2gPZP5z9uVfo9RilAdgNAd7adIDDAbLjkIADNFtg/Hv8cCwHTRZ/NOQ1YTr4OlsfwYHuwFvHk8xQzAlCKeoND2zNRKG0AFDnvmxACEKCi2UWzM/SGm+1VP587WYh2tiMcM2Uwu/Be211SNuKbhAD7SjBf0M/iR3veAJzt3pr/nOsCRL/vmG6EWR1LCAC5ro+ux7H9mCRgAKIQuLlLAn4+StYAUFHiBskl2oDmUYCAZQ2AhAA4DUDHKUCxm5sO9NHdXLJIG/Rx5gYg7aFMtgEVEYAEG1AyWaXnAKRQgCwbUMCPAEzXsesH9E2KBsBNpqY2oL5wGH6CqUnAJCmaTIwiAlDJ13VREJgvfbvXDQBx1LC+A8qVfnElfI/PkgIk5gAILkBLRMCdgOgdfBQgqhO5MSiA/W+kkjQAS9I/rfK5cDUhEXBpiYDbNkkErEWakg2o914sXASgynUBItSTTQYCQDefmBMOwBbFkiXzogaACYATGwC6o6obAAHxipbHBejm0HErT/K6Ykr17VB4EAB/DkCR2AC0GS5A+pk5dEYD4Av4Yw2AbQPKQsDSXYAAyRv/xAhAVeACO7yCd9hLzw+G3ZAmAqYIQDoFiFkdC4guva6Pr0d0d86LUOVo1JBY1dwAuMfRTcH7wQXoiDvgrkQEgO6o5WQBUArQ+UgBemfKASgUcC+wWHvhSCcgCQFwKEA9pwBdxChAZKCPioBp9HlnUYAShTlNsg2oJAKWxXcrO9xkqlTHoSwKENIRAADom73p3jNcgLQNqFKuVZpYyUnAhDoR4fLzhGVzXfZLNABeBGA675JSgPh30Kp6HgBfWAvfD2sAToAAvPxt4/+LCrj/UQBhBKAqFF4MNP60nCROqzQCUJeKT8LUnvRwEw0BAxI1AEscnZzjyCL6IAXICgIb+kQEoDA7tMDojKaP6acAuSLgbAoQCUnaLWwA9tY0ThfF4m7wkgYg1wIUsChA4/O48qB/wfK6ALV8ES9qAEqvBqDxaACK2owpmwAFqAsgUXbNlJF+wDCdjyRUBYCyJmOWrQGgGzJkU4KLgOX3dSlAp9MAAON3+1C5m73cWIsAACAASURBVFfZDQClABE6VKxh9FodT1WQ93p8oxuA6RnI4P8DBAEYqlnSpEvfkyvP93Cb6q4BOKJoENh8c/psCWOlG4DqbN5507v5D87roI0mpQc9XpAFsD2YB+ulyxXevjm4IuCMHACATzxRLlxBd+ym42h+apl2izJefmgwrs/Ha0wXi54ddBbhTpOAE12AUjzB9QLqgBrb6j7O2ydeBIC+X+/ZCeK/4Daj1/vx+l6tquA9ZX4vgAB4dvNiXH6/BmCJDahPAzAhAFRQZy1WKVXigXQJKSonUcdy6wf+jfH/H/ke4Gp0wxE1AFMD8MrVOu07gsWtDmgAHNtBmwJ0iDc9VUJj67UBzSgf0hByi7KDwPrMIDB9jzQp/G4hB6DyNLfeOtAGIL3JZN+3hQBQZxw5aVZrAJYiAAkOQIDYAKxp4xKxdTS/K1OANofO0gCQhSNDAPhx2q5H1w9oqbaMNGIlQfFvAlkAdLwKps/D3ENdP6CbUm59IVwF+Yy9QwEiawqKACSMnQ4F6IQuQMCI7H5SuZtXV803MSb7qrSxgNyXHAGI2YB6UM6pStIAPNlsAFwYClAG/x8g44WAABzeRwjAHQXoiGJBYPrm9FESYrWb+JWEoqJ382O7gA+ORAAoBUhTDjasAdg6OQA5FKDobi5BAEwD0Dj/FipKO6ljUJ5NA/LRAip5oZrqAuTTENCiC6ibekpJvX5DFOjRHYUuiQIk5QCM75vM/wwiADIF6BBZ/KVpAFJtQOlEJOQAMAoQ/w7o4uHBSvh+Noa7yhJsl9bly8A//zPAd35u/pFNATq0/ez+lcr/B+I2oLoBcGhfNgWIhoAlUIB8GgCGAi1EAHyLaUa9sO6vslAsWXzo2qwgMB1uxu5F3zMesQFNQwCMBmBXLEMAdjYFiHDbT4cAkHDCRQiAKwJO1gAEcgC4BsC8rhjGz9YKImD9fPgoQOWKIACBNOAlNqC0fBSgsqKueAEEgGoAps9I3Xjssp/9+yfWAKzKAp8SGoByaPEA4/VNGgsolYtpACIuQD6dkz4P2gDcTE2LHhtyG4CABkDfV3ci4A940Qd4hp8YArCgAViPUfC7ppsX5g8CAmCAawCObgCmRcfWcQEyD1cTsQEF8kTAzAVID3hUBJxQqbx8AOkNwJEuQHRx4gsCo+97s54WmUPHKWFTUerM0CSIgNnu+JQDMGkAkvj/AEJJwD7rydhOvm+RFGscxFLKPHNSEjBFAOzcATJ435cagBvSAEy6nFOXvSB6izoALW0AAhQgZ2fMpgBR2lMCBShJA3ASBCBdK6KYpigVAeBNDXN4EYTsAEQb0JrRlnIbgLQUYIDrz3a9hQCQhkBGAJY0AEciANOzuagB8IiANwEXoFkDMBTO86CRNp8LUEk28VIRgJgNqNRE+kTAFaEAdQ4FSEZ+9bUMPWtXFi/+dBSgaTFdFvgEpQC9+Kn5jw/Vo+j5zUWanoJci5gLkFfnpE+ZNADXG+0C1OkDxc+LVBnQALR3NqDPR9HdO2MDSnfUEkXAzc4MXmdjA0DFvCELUMBCAJa4AB2EBoAFgd1YLkBxCtAqqwEw5z/YGoBEChCdbKO7xzadw7MryPm8S1yA4lxpuoDarklIkqADoNd8SAoC42jUvu3m3a9k+JfB7/w+VMqEZ9HBOcblZ9qKlnK7F9iAAm72Rt+NTRTAvlsbhTmQndL7tfD90CbsFAiAUPaCaIkDEBDOAej6YR6fnJ0xhwJ0I/8b/ZWERe67pwFwnz8a+DP0raUBCCcBj8fqLZqRjwLk7j7XnnvbVz1pAPZFhgsQnXsGvgg5PAsE4OkCDUAlaAAijapYDIU0Y9Cm6bwaANVTFyCrAZj+3lLk5GC+h3pNKECpIuDIfCg1kT4KUEmuWy4CEHrW7DH/mVCACjJvffwH5j8+VOMGZ9KuOEVyMihADAEQRMC0sbre2hqAvAZgduSzEACaO3PXAHzAi+3CzBQgTzBRqPZPzJ8nBCDVAWj8d6IB2CzQAAgIwAYWfeRZ5gCUkgYgDwFo2a58LgVIFgFXHqrKkhwAHx+YLkp3a7LIFHQATAQc2Jk3v8BtQKkDULIDRCRvQEr2zEEA6HVNWnRJNSMA0/PmoQzYCMCeLJTuSeM/RQBogu0Ji4bm7ZsObyxtAAILK/p8uwiATQGiImCPDWhC2FUsmTSlfKFa+llSSn7+CmtDgTUAHrTMtjZlacNLKUAJCEC/M5SrfbmMArS1EAB6XztBYADRAGTkAByLAMwUINf1KlrzZtCKWUJv9i3XPzANwPh9dIINqP47270dyPe9oghAiAKUvukk3UO+BoAuVAcbAaC6QjJv7RMaABv1Pc4FyKUArUujARjOXgBe/Y75Ja9OzkBpImDzvJZ1hgtQJAiMXtebKQdgqQZgNg2wEICB0A/vkoA/4HUeywFIFQHvSANw9gAA8Igs5EMZAIDlAnSkDajWAOxRo9fy9sPGSQKO7Xjk2ICq0oXSc21AeShLJgXIFw7ksSFckgPgFQGTn+/O0hEAHyzOyqKj0QFyEQIgLJ60qM+nAZAW8l4R8BIXIICE72kEQLaRsxerlDpxVUkIwLOnAAFgKApFABZTgKx7jaaZOgE5NOzrsLEoQHIDkKIBWCToTjzOnNBbFGKQEp3Mh86yAfU+61zYzNKGkyhArXPOKRqAnmz+HMp0ChBtqtwGgBownMoGVG9IqPRmmDUAAgUo2QZ0774fRspH69EAqF5rANwkYD0+SQJOAFgRBCCcBJxuAyrNSWdeCpCAiOvyIgDj+Btqtk/rAuQiAFfFDq9NVJ/uxU8zu1iNAOTagFI0JHa/bBraALjXtqbNxBSwNm8OZFKAZlvXQUIApuPdIQAf7AomAQMZCMBj8qYjAvAORQDOIyJg0iAcawP6yj19LIWDmh5yxwWoimsAqA1orAEgu/y9npQybUDbxF15ANwKtFx5/X/rI12AfDuYtOjP97QBuHYbAHodh1wEoN3i6d58h8kNQAQB0PeBDwGQJiSvBoAtGjMcFGYK0NRwMwSAiIB7uwEwz+9VHdAArO/7r/EJiqIoLANgIQXIRgA2oYAcush3bEDjQWBpGoBldnhlRAPga/RpeujQNVixJGD5mtrWpocuFwEYj6GUms8riQJEEIBDBgWoKIyd67bj57fvjQuJOD4dowG4fCWZlim7AB0hAhYaAFEDMAzEBShAAfI1AGfvNQJg7lG3AfDkAHRxBOC0ScDuOudjw9fNjx58Erh6df57ngaAJDrTBiBGAdobK16RekrE3Zr7Xy20AZ2D3ax7qCfrldWdC9AHu+Qk4AU2oDvSAKy1BiAdAThWA0ARgJcvzUOyBVlYWTkAXmHcVHkUIGvHYxgIhzuxAWBJwDEKEG0A/IusolBG7U8RgGnRXhZK3oGcijcQHhEwmUQO57QBeMN5LRtQGC82JQl4mx8CBlgIgEABmiZ0ujvDEQD3+qw84mou7sxYNOpnrm9GWgO5NqryU4C2vfl+LithctEagGe4+w+YXdp92y3WAChlLPbsBoAF5ORQgHwagISEa2YDujgITG7ATcq0/L6FhSimUIDoAq2zKEA5NqD0vVIoQFQE3FR5WRPahnrT8Wd5N93XPj/47Aaga00znEr/AUQKEM8BSKUAuZkewMj55g2ADpE079sKNqCGAiSPgeu1GTc3ARFw6jwAyPeqLwegrjmCxQ/qIgDDMBgNQGDuOy0FyEUAPtKZBuDw4FMiApDmAkQpQLLVqVSz1bHnuq5W5v4p0aNAj1JN40pqUzvV/IzbGoA7CtDzU0wDIAWBLaIASRqAcAOwrsp5MFmiAaAi4Mt1OdME5iyAw2ZBDkAOBchKAqYTUyoFKJGWA4BTgCI7u/pzShqAuPdz3BOcJY+eE3FdDgUoMQjsen+sBkCgAJHF6zCMnyUmAGUaALJLSv8cazBZ2btRtAEIBIFtO4IAlNbk0myNJeYzEgDrok0UpwDJ2hRf6WttT5TbQwAatylA2UFgHgTgJCJgGWnQjaJvZ14RG8XOoQAliIBTKUCCBgAwz30KBWgg/vNNJV9vX+kNqE3Pv1ONbJ1J/H/ALHaGHkhpUm7eBCbv+mQBMJCQA5DpAmSNPze2Dah+7sl3ISIATRgBWJ+Z5+46YANq7sP4WFULr/HZgFKuOt14A8Ctjie2QdsP0I9HDgXoOBGwqwH4cP+1+Ue7+5/kDQAyEIClFKCpWXNojlPRBqBGawTAwOIgsIPTANxRgJ6bEjUAjAKU2AAwEfCoAXgnQwQMGB3AsQjAWV3OHvFzGnBz42gAsmxAI6+lXr9D13AKRyoFKCOUhbkARRqAGepjFKApITRynCpBA8B2Ni8oAiCIgMk1VQF/fvMiapG5Yw3AMg2AXwTcD2YxGNUAePIVQgmvwaqtyYglSVIXIIsaQ6gT53YDwCxAn3EDwChA5nq/cpVHO1oRLQEtSmO4sL/3amWa7MbWAMRzAHwUILqpsFgEHNEA+CB2igB07SENAbBsQJsUEbAHAZhTX1OCwAgCkKMBAMwC8qbjC55tKgIAGKQ1VEsEwABvkI6xAdXXtnIRABrmNy8crbnKXjjuO20DKo+BZ2fmvg9pAPQ9mTJWSTkAPgoQXaimaAAOiWgbzX65WMV1fMESEIAPNaYB2F59Ajh/CVDjZ5w1AJk2oLQZioqApzHHGeOmWhMKUIneCICBbA1A7aEAdXcIwPNTa0kD4EsmDZUgAmYUoIgNKAA8mJqEx9sGvWdS9tWWDJDndTkvDm+G6bh9yxYGzZAQBJajAQghAInqfB7KkoEARDp/PWCxHIAhDQGgg53PLpE2Lmp9z+y6xhCAOeiq8moYxl+a7kcLAUgOAouIjaUJfWkScKxx8JYtvKc+0pVfBEwbgIvSWgixELB3hwJ06Hq88WQcM+6tKy+U7SsfBWhLKUDSokMv9A/XPAjMQwFKQQAo7WjpTiNdeLesUYxQgMh33rd2EFgKAtCnCdILHwLgjhm+UuR6d57r7Su9w3/d8vPbdboBSGhcUmhASyxAAb5IFIPAjqUAdbILEEUABoECpHMABj8CoG+HoAZgug+lxb1d0j3ke75rQntRDgJANQBuA+Bt+sAtgI/a/SfHHs9pnCMeNl+df7S5+uQ4L006gIfqEepSpSWbk++vqOp5Pg/dL23Xz9fBR62izUSlOgsBWJgDYImAO5URgHoL6vaf4S0uCrFuT4UALKAAAaZJGAbg6S6R2znV1grQuJrcAW56MoCTJmV0AYppADIoQPZOGqMApS2CFtuAeixAdUmT+TzwRxGAOAXIoS5p2PT6G+OXSWrNEAANi0d2iSvSAOyWIACyBd98TgzSH+8jyu2VdnxSNABZu8b2bpRFASoFHQcA3JCF0wrW5HJDMgDeJQQAAF6fGoCH9/NFxz4EYBNCAABDAzpsxv/mN5QpKSkagE2IdpRY9PmRKUA+BID6qB8Sg8Bosz6w59W7oVAKu8/kvH3PPKvD8RSgrbUI0dS2tWeHObsBOAUCoClAuS5AfW+urbVI2+w9OQBMAyCJgE1GgFSqOp8XyyENQIyKRktsAHwIwJrco7ZVq0D9TKXb0TH/KP4/OfZ4TtOYdfgKAODRcIltfX866NgAvKye4sJGWX1l6dsMzdT/+zEHIABsLVGhMyFgwIIkYK0BsBEA8z5LAxDfzbr9Z3iLa1UW85roOBtQVwSckwMwvoZkAWTSgKgL0Hld4t40UGxAGwBzjk2SBiAdAaA7dkO/TANAFwgS35IVFQH7UnT1e038X7qoMhqA8OdKswG1GoB70wTb7vh9Ab4QKfQgGTl/apF5FAXI02iICECbjgAcOndhZ78mWlbgmZ0DIAm5Ab5z6uy0MQTg3dEAAJi5vA8z6T+Audb2RHkTW4xrBKCxNQDLEYAN0xUtW2z4jmNcgOR7pCQagL61g8B8ImBLA5AkAnZtQAEZNfSVRgD2Q40ycxGiw8DsXcjDtCjxIwDUOvMZIgCSBoBsDMVEnQA4B94ag1wRsKQBEJKA9TjlaQBQrXAxBUndBDQAhgIU39WWmlWfBmAV0gAIOQCpqds0HOsoC1BybABjU7K/xr123DT53eFD5pyIDuC18imSyqIAzxTJQMO4Id+TE3ZI3ms+fdgIwDINgE0ju6MAPUellJq7+HnSrY/NAdA2oONgVijMC/JQ0Qbg0TZPCOxoAKbj7TwNQIoGYJWhAaAUINW3XhvHUDU5GoAcBGBa5NMFSJuoAahTbEDthQYZMG0dAF2IqF7D4pGFYm0Wd9e7I2xAPY2G1ADsIwt5po0QEoQLT8CTt+huVLPjk2ZRG062tVh92npclYB3WQPgLgRyHIB0zRSgrp8F2YBFARIbgAkBaDacApSQA+DTAOgE1UKdRgOgj9MRwaNvgi0tV7EUBMC2AaX3pRftFGxAgTwNQNGM1/saZ0lUElq6cTyAj5G6IUijAOVqAHIaAEoBEhCAFA1AgIK4sUXAeuHPNACVcxyTA+AZA8t1HgKQ0gBkUIBoDkBhN2iCBoBSY0KLzhcvVvj0K+Mz/X2feNH7uqSyjRfe/sL81y8OHzLzMblfPlSSdU6oej7/+8wNaNHvyUudJA17hQ6VohqAXBcgrQHgv0f//n6gAB2JA93VWV1ic+iICNjajUwpIQlY7+I/OK+TeHMPSFZAbhaAPnc1TdaOCNg6xxZVdNeDLjR8Ox26CrK4HBEAuoBLow8wBCBqA/oi8OkfBr7wy8BnPxd8qUQB0p7ksUWqL0mYFguTKQkCAIw6gIefcc4FAIo+EQHQA/XQY7s192OyBiCKAJAdvRkBCO9I1REKUPbAWVnP3MChXUnI3fcDrpsC89rJFtttnn0KsC5poZbrAAS4YWD6u4klZDInINr41MuDwDQCcLmqohaJvpJE9CkoEU0PHXobAZA3FGqLAtSmjCeFQD+BWRDqRiz0+YsJcbkZzuLURav0uGo7kehdSe+4m00BIpbEWQ2AQAGiOQBNQvNBn0sy1h3aHm0/YIggALINaNgFiCIAm0OHvh/EOViPJ3XC9yYJ1v1UFXPdlNMAuBqAVMvdolD4S3/qB/CbX3mE3//tR25qMArQnjUAvze8hgdCA/BawRFtb3WcAWAQAP/9whDHBASgdBCAPESkKBQKJSEA6dTn21B3DcCRpb2Yt6fKAZiTgMeB78UE+g9gIwB5DYA+97OqhFJq3h32UYBalNHF7w9/x6t49d4aA4A/9NlXg68tKALQNXxXKpECRBfSSbvHf+KvAE+/Djz4aPBlmgLUdMM8mc8IQKQJ8iUJ02L5BUUYAVhJDUAUATCL4/3O0DtOhgCwYB+tASCLNGEQXHmoUfo7zB44Q7qbcmX4muRYN4fWEhAGEIBnLgJ2P+8iBIA2AK1pACjCJ1rkUarPzZvj/4va+52XSRqAcXK9WKc18FJJCEDDnhf5+aPWgVgSBNYNVibFMhEwMDYT3s2SYSAIwEUSlYSWXuDvbQTg/2fvzYOuyc76sN/p7ru827fPzDczmpE0izQaLSONFCRL2AShKEKAwAGF1UFRFYaQik1SDuDgcnCVq2JiVxwnJE6cosoUZceJiSrgSmKrKhASC0gMwgIDktA+kmb71ne/9/aSP7pPn+ec85zu09t9+873PlUqvd+du/Tt233Oec5vq6UANRUBEwRgr2UOQDGO0HwPLwTAkXcir6+Ekhg4ETDnAlTmADiuzXCmNcrHq4QdLyV62xoBcDVoZDEqsioEoNAANMjcuG9vhm95Q4MmzlWhoQG49fnyn19Mr+MphgJ0v/BtAOhvPvHTABg6Rv6Yye68pQFoRgEC8vnavIZ0BGD8QWDnDUDHkoNwfwjAHk5XSTlpX/QQAAN6GFjTLIBTI0BDCoRcFKAsmNTu6t2/N8cnfvq9yLL6QUmbsLPY4gD6FE0K9YLSg6B28Q/YYUTTSPjnADiShGlpImAOASBFz2MoUZK6hFpyPa4WLRqAGgSALo4W3ghAtQagsXjK5KMK8vpwqizbyLk+OHXwh2Udr1ME3A8FaGY0ALKOFjXwOKX6yO/t4P8DfgiARB2cu3EexWkA6hymAJ1CYSEAThGw3mxYyBx7gNU5AEDeTDgB0OURROGvf4i5l5iUltx8ciEAbhFwQw2A3IiYXdTnt7qKGArQhL9GnUVtiMn5Vgs+gRUmmIDYR1tBYHwOQIYAmQggMuM4opk2Ph4vYmu8zDJ1jfj8btw15EZoqihAdS5Aa9p1DqN8bk7jfMy9SRGA62pcZ9KAa8ugAMlrJk4zxEnKNlPUdWzHtemgIQCpjgA0pAAB+W+6Svh7DzgXAd8TNSsbgIJ32wUBmO4BQaiJeH0sQM3nNaUAyWZD7khI0Z5GAaI6Bc+bZRIGXru5FAFASxtQbcJuOJFWFZfm660BcPjd04pTY0eTDJguDUCAFEGZlOxJAQIQL5TDixcFKMvUjpMPAlBMrLorhX2OXOdFTmTNKUDGPadpSCLiyqI+K28AeAoHALUTDqxVBCzr/g4aAED/DU7qBLka1SdjHtPLFdBFHyvHlJYOQIB+78n7xIsCRHdQU6UByETopBTSe3mVpn6CdE0EbGsAgBqhK9FbHGXNNQAlBSjTx0hJbZn3oQHIMjUONREAA/UuQF4NAI/eUM53KmkXTA5AghDLJNWsselvkgnjfiiuEUrP4axAdcqpTxCYvwaAXqOBhQCQBqBAPvsI3WtVJGPG1ADIsTzZIQ0APBsABwUIcN9Px3U0x+K9ZE1EtyAwIB8HTRoZ/fcmUIDGf4Qjr62JMaB1EQGzFqB+F+bFPihAxXepowA15cvVFXUBgqUB8EUAPHbsWhRNpJWcT18XIG1R4eEClIuA3QiAnGh8RI3qRep6jE/z63EaBpV+0WWlMcoFoYcGgKMAUci/PCSqASCLAPm6xgNnRRIwwqkScicUAVjpO6cuCtBkp9muZ4saigIki+6OsbQDbrffIQAG3BoOWTrlqAMCwIjovShAmotKUiIAWcUkTzcNEoMC5GcDqs5x5EGRAgCQFOAjbHklytKalyJg0wVIIgA9UIBObqt7own/HzBcgGQOgD1eVJaTAqRem8rvw2kAMpWxUX4uoQRlJsW0GE/pdXvEhIHpyG39eMVdQz4UoMoGgEMA1rnrLOeeeFFSgG5nu7iLXaXZmSv91JXstt/7mggAozMz67jO6ACwEICogw0okI8/LvQN2AwR8PiPcOQ1N9OAw4merFlXWaYoQKUFKAkB86QAXWopAk7TrAwxsylAZABfEXvAnhsASgESWWzsAHgmAZs76T2VuZuXZZl3ErDJBeaqUgRsIgDFAk9rAOp2LsjiNS0Eh06I1CyGb2oWt6OnB3rZ52jqOC9KBNzw95uYDYBhA1pSgEwEwIMCNDD/H+ApQK0QAEcDUGvJyQVQVVCAaOPLIQA0PbV/DQBpmB2N4oRQgMKMUIAq9DKR6QJkNuZcORAAPeeiwgloqWwRD7N5iwagsAE1nUgyqQHoQQTclv8PGC5A+VgybYoAxDwFiArbS+91OW8YNqCAvuinjUdmzi/FOEevW64B8GlEtbdtogEg11WIRG8imTHZVwTce8mNl+Nb5Vz15SxvEuX5WcwVenol80UA9A1An2vGSwRsagBEc6YBrSgMLASAhsudNwD3QM25NOBCyGv6uLO1OlEDVvG62xQB2GouAr7bwAaU3lByQNrlKECkRMPY7LqiCIAwcwB8k4CHogAZglW64KnTAISFU0D+2nob0CgQuUORXNSbGoDiWGZNEICINgA5AuDvAMTvvtHikj1XNZC0iyJRJrx2ogCZCMCEiICz0h5z/3Sl+6fTSSdeqKZ8YP4/YCMAYSC8xf+0OD0G4BHKxQV+OULAgHoNgNdk7FE6LcdGAFwcWz3xMy1FwFmFY5aGNqQmAuCzk85TgCqzAEwEoOF1r1yATBGwdAHycC+qawDaWoAC/VOAyFh3slLHXe7ilwiAHgSWf5Z6TPtcc34JbQTgmKEAWchtTXGbGj4i7QiJfrw1CMB6GwCJACimwxez69oxLcItHBXriEupJwKQmAiATTM1i6Kczk0HywWoudkIrSgQVvNNaaVr02N0qPEf4ciLDrKlEHj7Sv7/x7esNFerNAcgaQHaAgGgFKAGCICZAQBQCpDDijBsP6lzFVHIsyUFyBLT9lQm3cFK7q0pOan7BYEFuRcrTQMmJQf3mWjAXSS741lBSdudeQ52PgiAlgTMIQDVDYCkACWpQlY6U4CsHAB7J/lwUYEAUAHwwPx/wKZqXNudeln/mmXagMqSCxinJz+328+hAkXVaQCOfOB4j9I/x9YAuO6/CW0AkHghAJreIMm0XVc3BYgXAXP2pWwtCAKAreY5AA4RcEkB8kEAkroGoKUFKOCgADVtAPhNCIoAlA0AqwEIrM/SPtdCAPLP0BAAJgtg1dB0whwH55PAfY8HAdLiuCdmA7CSDYAoz4dvEFjvxWTofCktGoBEzQUvZ3nw5sXklt/7pjqCy20ymXXikzxu5AD0oQEwXYCos9w5AnAPlIYAyItTBk0li3odAJMBoGsA/BZrWxMVztVEA3DKNQAcBYiU6JsCRCB7ZImRBNyiARiIArRKskYIAKAGZKcNKDeRyIl2sQ8sFY1MHksjDQBBAGbIJ1OfYDkAhgOHfxAYRTu4BScVBnP+7o0nsbokYIZydHAauzUAawwBA+yFWpsMAKCKAlRYcro8+VkKkJ8GgLuu+0gBBvKgRXmPKQ1APQUoshqA/HqoGrdMx6Glzw6vELloFNAW0hNvCpBCALpQgAChaDBQu9692IB2ogCR811sJkRhUJ5rvxwAngJEF3wljYexAVUIADUbqOB+F02i7gJUjQD4uQDpz3EKVYuSfvIREm2OLhGAaJ5ff9CDF53OT0MUM/d8qaAALclc8DJyZsM8O9VQL2dZScD11rG1WSfFe5WHjsTQALRwAQqElcK9OhcB31vF+F8ZdgAAIABJREFUUoBo0uxJDezFpgA3FwELIUohcFsEQFKA9oodYhcFqH8NAEUAVq00AJoN6IAUoKZIg3KgcVCAuIWGpgN4gfx3KQKmOxf+IuB50QD4awDIothLA1CIgJsgAMX3X/pQLlylhdJwFCC74Tg4XRkIALlnjteXAQDYC7U2AmCgXgPgdB1hKUBVLkDV2hbKme6CAAAkcbMBBWjqQgAqmmWzqdGQhqrrMTR2n8E7h7FFEIBcBNyOAgQASaC+c69BYBSFbIoAaDag6vyUyEUHFyBtV15uTjhyAIAGFCCpAaAiYAYB0MdtHxcg/TlO/n9RSfEbhhYFSLqyqXMxKgSgoACtGAQAgIVqs2VQgLmsGbMoJcw55oR6A6DbgPajAVhoGoDx5wCcNwAda06603JXYuuKesJJDey1IBSgGUMB8rQBpc+9e7Isuc51RXdStgwEQHMBIhX03ADQnTmRJZYLgE95+Xa3qCoNQOCRcMolCdNacciFZgWq7CinLALgLwKei/y62p17/n7a7pt/EnCdLR2nAdCahsYUIOq8ZTcAmhg7UQiAkwJ0RClAw6YAAzYF6L7ddg0AdQDRGoBiQc6GgAGNKUCcOJdWXwgA/SwuCMw1wU4nIeJMUihiTESRdF7RAJhNjRcFCFALB2oDyiBcbBkIQPMgMCLGJguY+iCwBhqATggApQCp+0uOCc0pQFwOANRvkK5yyq2RA2B+FuWRW3q24pjpvcKKgFPPBtHxHKc+o6i0sCfNKUAOBKAo+t/PRANASjUAxcZOnOLl7KJ6AqWUuSrRKZwubROtNghA9yAwxgXoXAR8b5WmATApQEBDBEBPAQb8k4ABRRdaJRkrXGI/niIAU5kDUCSIOhqAvilA9MYMsrgVBSgZjAJkagCauT/InR+3BoBBLuhO25EaMOXgrrsA1VGA1EQhEYDGKcCAXw4A6wJUhwBwFKCGv5+GAJzoCBJxAQLUxF3dAJAMgLOgAF3oAQEozmeWZTguPfkdv3tDClCdCLhPBKCkAJUagHr7xWkUlDu/26jXseTv5Q4Cq9xVlYtpqgGoQUjK0kTA81pbYbPo5hOlAElaQi82oF0QAE0ErH6HWZMGIHbZgNJwN9NGmkEAHC5A1lxWugBRBKBGBOwzDxhjWl0+RlpcV5FIFLMAIA2AAwE4CxegouLZJdzFrnZMyyRpjgAYug9ujjHr2CsIjGgAhOkC1HyjgssBoAjAeRDYPVBbmgiyTQNgi4BvExtQ3yRgALhIrUA9dQCcCHgWhZhGAU5cLkAtuuXKCgwEoEUSsDdk37CqNQAe7g+R1AB4JgED+q4zGTDltTalA1cDBGBLagC8XYB8EABOA1A9IU25BiCuT3h1lqYBWHgiACtNsOWmAK3fBahPClAeUJg/5kYAmMV+VQPQQAPQmQJU/HaJ1ADUNJdAfj7lLviWUNdw1bhl0sQaU4CoDSgNuqta5C5NF6C2GgBgRRsASQHysgGt2SiSCEA0V+52vhW6KEA2LcdZCU9DpDu+wnQb0nIAZLOhnk8RShHVIwDHdTagLUTAdRQgiQBEiHWthGwAyJh35i5ARS0uvLb8W57jRZziJdAGwAMBSPWFuaYBcLgAaWPOpI0GoAUCEAaWC9AyLRq3QLQyclh3nTcAHYvVAGyTBuC4jgJERcA6AhCIBoJNGFagnjoAjgIE5J/rpABNBkQA0liflDzRhqbiXN+yNABJM6RBPodCxrRibiKhO22EAiSpWY0QAE4D4GvNqLkAeYiAV7YGgNsF0SgScc8agNWJHQTGBErtW0nA4xEBt6UATRkKEN0Zc2sAmjUAtRoAzQWoHwoQbwPK33+zKCjdX/wRAEKnsWxAPShAZNGioYaVCICeA9CFAhSDoQD1ggAUC7bdB0rRqXc5KECNEAAPEXAQGW5DrAbApgBNo8CmABW72jt1CEDDIDBzrnDqM4rKioYuQqqOPU3VeXQgAGu1njQQgNXF15R/Lwka3BwB0ClAPi5A9DdyjnOGBmDaVQPA2IBKBGATBMDAeQPQueggfNIKAbBFwHeL3fuLW5NGXSTVC9zxzALQRMBT9V12ZpGTAhT2TQEiN2aAljagDUVZvjU1KEBao9Fg58edA0ChZEkBohoAHQEIA4EZHbhqXYAIBajUALTJAfBJAvZDAGo1AI0bABMB0CcQSslwU4AoAkA1AGsQARsLtfYUIPJbJLIB8PDk5yhAXTQAZHfWW3Be81mlBsBj4TWLwvK33Rb1KBb9HEDmAHj6vMuxi4xZ2vVWtcg1bEAbU4BqEIDOQWDLIxVW1pT+AxT0qOK8xrYGYBmn9Vo1DxFwVQPAagCKv2dRwIiAJQJAcwA4ETAZr1oEgdUiAIFEAIgLEG2GyLhON0/W2wDo91NyWSEAitqZGRoAHxFwRQ6AiwJUoDTTMHAvvjUEIEWkmWk0X9OEgdA4/wCwSAvt0QbQf4DzBqBzzcwkYKBZA1BhA9o0DKgNAnDKuAABOU98gQnSTB/ckkwg7DkHQAvoSNvagKqBYSgEYBlnje1G5W5EkmbarpUsudAQAqrZow0A4aMLIbA7ixomAauF3LypDWhTBCBOtf8PBP9bcK48dU1DZZlJwIaPdMQgALYL0FkiAKYIuD8bUC86DosAVASB1dhc6p/ZbayQTXZcUsXqG8UpQQC0qgoCC/RrxCsIDCAiYDVmaRQgTxHwUbbltZCkpenPRH7NJAg9gsA8GwBNANyiARBCLRIZBADQF69sOXIAnAhAutIQZKUBoC5AiToOc34J7STgQ8YG1EeLQstEQmupcYUGYIJYLXq1EDCXC9A6bUD1cSq7/Fj5dycXIPqbB5G2xnI5R9U6nQFGDkBsUICaNwCTUCA2EIDTggJ03gDcI7XF2oA2cAEyRMCnq6TclW/C/wdyxECWtwaADKT0Rst3iYVFA4oRNXdpqStyYwaIrSRAn2J30nso3UO+WRIwAFzdUefv5tHC+u9yYaMd8w6PAAAoGoAGCMCEEQH3mgRMEQA9Cdi1kBdClBNiLzkAZhCYqQFgbBkrRcBSAxDNK6kwfZVFAepRA+CVkMk2AL4IAKcBIIK8jg2A/O14G1CHCxDDzQVQ2SxrNLE09acAMTagXIIxW0QEfIh5440Let38+t53ALML+K2r/1a5KPFDACp4+JoAuKEDkKzSopM2APW+7mXFPAWIXtdhZFCNmBwA2mgsSwQgdNqAaggAowFg81sqynxOnQhYhptpScAr2gAo1HMxEg0ArqoGgIqAb+KCeo5XA1Cc72ACkLkCqBcBO3VOgJEEnOpaulZBYPY4syg0J5uQAgycNwCda16LANypfgNDBHyXLNybWIACwEWCGPhmAZySG8rUAAC2E9AKYf/+tpoLUEsEgNtJ76G6ugBd21W/ya0jm5YlGwptgphuA9O9/G9DNLU3jzAVDRCAyLYB9bZmjD1EwNShQSYBy6amYiEvzyufA9CGa1y8ZsUFgen0jizLcLiIkYLsFNPXSARg+1pz3nOLopPF3iyqXRy4itMAnPjsxjekANVrAMhndqQAKRcgmwLkFAFPAiQZ898q7hXbBtSTAmSGUBnP9xEBJ5nACWaNdw3p3PN/z98L/NSX8cv3/fvlY52DwLoiAAArkubGDGfR+5IsOOl1HU5oA2C6AAXW52gUIIcN6HwSQA7v9S5AHmYQxphWpwGQv1EkUpzKZscHAThDDYC4+kT595LYgMaIcDPj5zO2ZDNdXDs+OQBeCABhLkxMEXBrDYD+eaeJRADGLwAGzhuAzqXBsD1QgNpagAItNQAOEbDcJT7J9GOIEfZvb0VuzNBqAHwRAGYnvYfSHT2auwBd2VHn7+ah/ZtITrrVTOwWTkCHLwGEJ7s3b4oA2C5A3jagSTsKkBT2Vu2CTAgPmP4/0AI+FUJNRrEpAjZdgFKcrJLydyzFk/I1yQo4LZr2NfD/AX2Ca7v7D5jUivy+9rLkDEI71KeKAlSrAegPAQhNDUBMd14dFCAmoCd/QUUQmEYBSv1F6eViOstFmrDRBGcVGoAjzAGIFi5AxtwTBNoOqTsIzDMHoBcEQFKA1FgyZTRAznLkAMhF+TQMEJhi45okYPn3NAps+8fiGhFClNcuqwFoGAjZ1AUoI/PeclWcg5jXAIwCAdi6jMmuYj6sjHG9pAEdvlTeJ86STV9xDupcgOJECaUrN7coAiCMILAWFCBWBJyei4DvqWIRgNmeuti8RcACmO62tgAFetAAkAWCXCQew1D6I+yf36ZBcwYFKPDbQZQDcp/8f8AWrDbVAFwlji43DjkKUGZ9DgAluotPNLGgrQFo7gLkbQMaNxUBFy5AXgiASQHqYAMKKKqTKQK2koAzHJySHUIhd3CL70pdu9bA/wd027reGgCJAKw8NACAveNfRQGq0QB40Y48S14nSYHc+FBzZpOQbwAq7pXQEgF7UjzowqHYudTHjAoKUIEAHGHLOgaf0uYeQ38D9I0AtG0AjAYbOtVUs7jkqkYDsD0L7d9A0wBIEXD+WEyMHKoQgPK9oVuOytLzW/oXAQvSmMSrYt5wIQBnJgIma4Mrj7ON3aJsAAohcJbU06LlNRlKKls1BejYd4wjv/XECgJrkwTMIADnIuB7q6jXcqkBEEKhALU2oAUFaHYBCAINAbi01RQBaE4B4nIAAIIAcBqA3hsAdfOFSJEmzTtzuZDuMwUYsAWrTTUAdRQguYCy3otmARAh8N58YiAAdRQgxgWoFQLAL56mLAJQrQEAUKkBaHV9ye+5OmEoQLqO4+BU/ffETHKlIWBrSAEG8kb/O9/6EOaTAD/4rle3fh9WA+CTkAnYO/6VNqB1GgDqyd0PBQgoqDnk/nMhkW4EoCIHwLABjb2DwKgHvd0AcDkJZRWN/WG2Zb3OpyZhUJ4fuZCmGzqdXYAoXaONCxBANAA0B6Ce012Ww4lMNpnbE6MBSFaaHiPOdARAXyyHNi2MjHNVCICPGxUtU+BdS/Ojyc7L4vvQBkDLATirJGDSAFx93BHwmJ+nl9FACCx/8+K3qbMB9aI5AsZGo4EAtKAAhUFQhu7JksnAm9IA9Gzncu8Vtc48pRfn1uV8MXFyO6dwuLjEEgEoQlbuEurOpaYi4O3+KEClBsCkAGXDagAixEiTpepMPW/MkkvfMwJAB9Q4yRrnAOgiYE4D4Ngt17IAXgSuPp4/bGkA6hAAtZO7VXii+2sA6kXAYSAwCQVWSVYuOhdtNAA12QG1FVEEoDoHYJ8gAGkwARIQBGC9IWCy/s73vQ3LOO00gXNJwMdLDwoQYO/4T6qSgGs0ANKSLwo6h/KZdKOlBwUod+dohgCYKJF3Q0opJMXCMzJ0Q2zFy/KaOypQ1jZj1zwKcLRUVpEaAuDlAlQlAiYIQNcGILaTgIGmDYCaC45LBCAy8gYcOQDF5hylkMwmbgpQ/t5h+Vlpmmnasq4IQJ0GgCIAq5ICxCMAi65jZ9uim0JXHkMYCISB0O7TpYkAAPl89sAb3e9rUIC4jQ1a3snjQQCIAMhSWwPQkgJkjjPy3+cUoHukZhoCYDQAQL6LujrhX5xlSgMw1y1AgeYNwN4sKoVLbRAAngLEiYD7RgDU50ZIkcbtk4CbemnXlUkBapoDQDUAVRQgC7nQsgDUTtzeLDJyAGoQgDAqF3MXcJS/dY8IAECTPVONplE1GcnzWuYAdBEBA2pHLDYQgCDSfdmTVKMAlVxbudDQLEDXowGQ1XXS4NAYb0tOiwLklwTMagCKz6x05PAsczHtQwESQiARTREAHdWg9ryVSB8dnxLlRa6O2UEBIhagh5lsAJr//nIhKdFnukPanQJU7NSKoH0zLM95lpTNRt2CTitmEyJO0vJ129PQ+A1MDYBOAbIoUhYFiDQA1AnIoCp5i8TL5xgIQG0DoK7VuEYD0HnzpG295hvz30QEwFPfBkB9TzWu5+dNtwKtEQJbFKBq1yivrBNZxbUSIjFcgPrRAMh/r/V36FDnCEDHop38CRWomFagHKd2eQhkxWuKDIDbWgPQjAIUBAIXtya4fbzS3ISqypkDMM9vCJsCNEADIPJOWsZzZxoFyO8SlQuRvtEJKwm4gwtQMxGwowGYN9QAAMD2FeDuES6JI2xPQ3+usYcNKFBM6IuchpC77OSPV9nFUg2Aye2euqgLVSUblDTOmwBy3KYL0JJQgDKTAqSFgK0PAeij+CRgT36stuAXGsXALC00i9MAFDtyXTMA8s/SqTm+drGZYD67EgHQkb6VB4oFQF9AlgiATYWwiuh6pAagDX2xbABiSQFS9DvhQp29RcAFArBzv7cWyypzdz4IWd2Qs+gYVNzjFuebfoYrByC2G6QpGwRGKUDqOI8XsbZxsmpoA2peR3U5ALoGoBoBkPf6JBS9OuDV1pXHgJ/4g3wj88KDxTEEOF2pRt2JAFSVKQKmYauMI9OxTwqwrGACJEvsRBkemIaAPKUtbECjUJSUH1lSE3DuAnSPFOsCBPg5AbEpwIQC1NAGFFBNQzsNgPouuyUFiMsB6P/ilmLMCAnSpAsC0LMGoCoJ2GPH7vJOtQagRADM96KQ+5FqACwRcJ0LEABs5bsvF3GEC9MG5ye2J1+u5E7jItZ3aGdVCEDxmiwrFnZxRwSApgGTxVXuAqT/hhQBsHzK1xwC1mfxQWDEkadKkEsbgMl2pf2pzsu3F7hyTOmaAmx/lp7QW0UvSjkEoNIGlBcB14ZzaQLU/Fyb1xtbSz0DAGhHAZKLI0UByv9/XoUm+SAA8VI1w20tQAGjAch3sDUKUK0NqJ0DcGzqWiwNgDsJeKkhAGGlCJhSJU0r0KY2oOZvO69ZqAaROq54VYz3Wg6AnQTs1HwMWXvXy8V/fgy8u5uuAahDAHQb0OsX5iWz4TMvHlhPP/Id44Dy2n/4wgTf+NhF6/EmFXIUoOycAnRP1dzlaODTACz0EDAAnWxAARUGdrJK9IbEUadkAJ4bScAATwEaAt5KixvJbgCaaQD6Rifod42T5knAkzAoqVw3DQpQlqn3s3aRHGFgu6YI2GfnorgWA5HhgZmfNgSAMfn6NQCanWdFozg1nGSWGgLQRgNAjk821iIEgtCgAGWaCLg8f2mc29OdkQagj+I1ABTh86QA1YSfmbvytJaxos/0gQCY+g3fgK6UQwAqKEB2EFgxntRdixr9hBMBOyhACz0FGPATk5olTShOVzmSJhfUsyqKiY8G4IgKgFs6AAE2AgDT171BDkAxBlm6looGwEwCtihAFRoASic5MsLAvF2i5KGbIuBaCpD6TknMIQA2BWgMi06F7Or5Li/5pgGniWJFFOdgZxbhqev5BunnXjrEnWN9DvMWAQPq907NwNEWCEBgmw2sNkwEvBlHOeKahEG5EKSLaWyTBsDlBHSqZwAA6GQDCui6gX0PGpC8eYTQd2bcLkADUIBAEACRIGujAViTDWhTFyBA6QBuHC2REU//pMpJQqMAURcgQwTsgQCkc3UtXp869ChcaQiAe4BUGoBEX8h7aACA/LyuGnJq7TdkEIBiAtEXkToCICKDPnCGGoCuNQvVZNQcAdjm/2YqDEQJEJgcd+/P8ywTbfAV52YsAuBpA9qEAsQiAHYjZpVGAeogAjYW03LTZ+4SAAN+CMABWaT1hgDk44me7NrcBtSitWlOTNVJwJZGwsMGFLAbgLihC5AwEm3rGgCKAKgGoFoDMAbe+dRCAAoXIN8GwLH59+yr1et/7zk9XNVbBAyoa99qANppAACBZaY+c3UuAr73Su6cnzSmAOkpwIBCAAKhnHiaFKUN+egA5ISxNQk1zqibAhT2brUJKMg+1wDQG7OZBqBvFyATzk8aIgAAcK1wAlrGKQ7JYEUnEYtqQC0oyYC5N4saIwCrqYI674+OvY4ZgD8CMFG7PhTS98kByF+X9uACRI5vlYud5bnRONmpngMQRMYCZZM1ACwFyJMfS21AK0LAZMlr30QAjprsxnmUiTZo9pwVk2xzBEB3NpL3Zi0FiLEBjQzUgq2lagAOsu4aACCn06iU2yoEgGoAHHNEHyFggL6wKhawFJ1oJgIuKEDmNaZpAGI+B4BxAarTAFBb7edu6xsn1AWo9hqRb01+3zquehgyFCAHArAYIQJgmjvcxY7SW1VRgFJ+Uf72V6v11Ce/rK+n2oiA8waAd5jyLWkCQoXAmyYC3oyjHHnNDR4mgOYUoJnUAOQ3wMWtSStBDxUO3/FBAEgDQEuGRVkUoGwYBEA2ACESZA2TgLNMJfT23ZxoC1UrCdjvs646sgAqYeTJvKSFUW/6XVME7IEALCYXyr+vhQ0agLg+CRjQkSO6uK6akMwGQNvZbUUBYkSrxaCui1bTigaAIADBpLwnN6VYCtDCc3KkFCDTEYgpee2bGgA9Bbh/DQDdUa9qwDMOOWwSBCaFlbUUIHsxPTWubbYoBUiKgDu4AAH5WK4agI4IALUA7YIA0PFJUoDa2oAW72VxvsMKBCBT6KT5ebkGwLhOyDVCF52/+fkb2tN8tSi06PVajwCocSlJOATAFgGPqQGQ5g4qo0Ag2S42tWjAnFkO/d+zj6rf4ncrGoB6EbAMfoz1a79FDoDUflAaUFyKgM/+t/CpzTjKkZeyYqMNgOECxFUFAtCG/w8oDQB9r6o6KSFj/cYpEQAmCGwQDUCxYzcxEQAPCpDOyx/OBnSVNk8CBkwrUDWh6ZkCzHFLHcDhi5DWOlYQmIcL0GmkEICrwZHXMQNwhvCYRXcbKb++6jqZEn3AKtb93dvZgM7tx4qdQZOTTY8xnNAFylI1WzvXKoWwYyzpww0QBGClrpXKRcfUXwMAqOu1EgFogWBan1OpAaigAHHjRkWzPNGQhrRsNGrvcW3xaVOAfGxAJQWozXVPqT70uq70mffRAFAKUNsMAIClANUFO2lFx6CguQg4MUTAtgbAjQC84zWXy0X1Jz53Q6Nvxg1dgAB9gV6XAxBO1HkrbbGpu1lBecwy1RSPYdd5SswdYiO3I5Pz2ekdvZmh5aDlPHplu3TU+9RzdzQEphHt0IkANF9vlWMthwCMoBnzqc04ypGXsmKjNqDNRcCnq6RckLfh/wNmA1Av+JQaALNz3p6GEMIOAhskBwCqAQiRNrYBbRrO1aQmhliVJp+Gnufh6i4JAyNC4ForOTnxJsuyWdydRZhpGoD6gesoVDvZl5o0AI4dJ7PohE4pTr4IwNJAAFrF2UdMA1AsGDQnp1RHACLaAKxO1L26YQ5AsuQioGwAFgrhq0QUafCXTwNgBLnJoghA1xRgwEBvUl0rUjnJsghAhQuQcZ/HvqYCga0B8AoCI/qvgyxvvtrol2gSPUV8e0UA+qIAcQ1ArQtQMYcFUR7kBEYEbNKwyHcKis/nbEBnE0YDQMa5+STEOwoU4MX9BT7/sho7m7oAAfomT91OdUDOW8pqAApaKR03q3Qfa6qpce1rGhgt3NJBA9IoQOp+FULgbQUKcLRMNDcgmnZeaXQAqOsxXfWkAcg3RWXJZOBzBOAeKrkLs4wJR1xrAO4wr4IhAr6ocfbbWIACugi4TgOQplk5MJqiMSEEdmcRjqEvrHIRcP87o1K0N0HshAFd1WY3xrd0ClA7BOCagwKU1CEXu1QHkA+YeQ5AMwTgMNgr/76Ew4pnGuW5Q0InHtoANNIAdBUBcw1ASQEyEIAFQQCm5PwdvgigOI4N4//LkotiMwisVhxn2oDWlJcGoAcEIKzIAWhMAapoYvUkYDWO1+7kaSLgBhQgsil0F/m5b3PdUz793eMeG4DeRMAGwgYdMXSKpI3X0PexRMBWDgAV+csGwKYATUMGATDGufc8ocaBT3xO0YB0ClALDUBdc0yoZaUrHqMBOLMQMEfR+8VEdsWeRwNQMfdrOoCvqDXVyaotAlB8lghbob2S+rXKbArQOQJwD9WcCzbZJhQgpwuQTgHqagEK6A1AHQXoNKads33j7M2itbkApYFCAJpy82qpNB1KG9BaugBd3SEIwBFPAWKbKm3HJJ+QZ1GgIwAeOxcHQok6L2S2j7KzvBEASgFqpwHQKUB9NQCSAsRrAHamIQI64e9/Xf294Q2AXFhJvvR23cQ4bYYAKA1AhQtQDxoAE4Hz1oqwQWAVScDkXqa2grVNPmMDGhnHzNapWsDcyXb9PospunFDN3yqKUAeQWAaAtClAahBAHw1AOR9bBGwmQSs/nsoG4AVQwGaMBoAY5yjDcA/Jw0A3XTy3RCT9+Y0CurnjoAiAMVvxASBaQ3NCBaddOxeJIm2sRPs2fOZVfR6NOY2lxD4yKSEVZX8vbNUmVy0oP8A6n7VRcBFA3AeBHbvFIXzSivQ6a662DxFwF0tQAHg4hYVAVdTgFwZALJ25xFDAYoGaQBKBECYLkAeDUDafDfGt7SdQcOFpJ0GgFCAyGKGnRCoE1Dhyy2EwLyIMF9g4rVzcTdTDcBO2qABKF2ARCUS4xIBV10nJlTsy+12FqsBkBQg3gVobz7Rr6+D59XfrwAKUJZl5WJ2uw4ab5ADAFANgCEC7h0BUNdJYgSBVe54cuOGZxAY1XLVU4Ds3XSzuWWLIgDZjnUMvkXH7XYUIAcHX+7Qbl32Cxt0FacBoNaldVk1kv5C3sfOAaCfERsUoPy78kFgHAKgf9c3P3yxNMT47c/fLLnnqxabTh94Y06l+uCbPChVlAIk50MtCCzXAFjf54zL1L/I4wuEZwNQkQH05ocvlnPuJ7+i7h/vtHNAv/ZXhaaiBf0HUPcr5wJ0TgG6h4rugJaThxCKBuSZBEx37KkFWZNqggBQ21IOAcgpQAYCkA1DAUqFOm6RkIHOI4K+jTWnb000SLMdAkApQDepCDitob04OJOzogFYZn4LrNuZWtDtJE0QgOJYo1llozHVNAAr9nGzNA1AnA2jAZA5AJYLUH6Me3PDQlBDADYrA0AWTeJcJoqyVosAPPgWNTk+/Gzt58hG27S5POrZBcjUAMQvUiZiAAAgAElEQVS+jSI3blQsZIUQ5WedNGkATAEqdE44l5ScfwhBALCDKBCaDbNvUfT5rtYAVJx7JrtAqzRVC7Qu/H9Av79imQNAEXNPBCByUYCiyhyAwKIA0RwAJgnY0FSFgcC7H8/HgoNFjD/4Wo7a0+vQd9PpJz/wFH77L38L/vb3vrX+yfS4Yo4CpKylZY0BATCtiKUL0CQM/DQAFdac80mINz6cG1p8+eYxXj7IN6h01NFTAwB0bgDKMZBxARrDb+FTm3GUIy8Kw7JOQCe3ShcXrQwR8F2yY3+pJQLQJAfgpMY+a3c+4SlAA1zcGZmwBR3oPChAOi+4ZwqQsZvXxnGIioBvOShAbOOihYGRBgAKAUhTB8WA1I1Y7e7O4/2KZxpVQqTVO4BOG9CKiXFiUKs6IwAVFCCKABwuknL3zmoAXgkIAKEAnTTZGbv4KuBHfh34wf8FePrP1n6OmwLUcw6A4eAkKQVC1DTgDREAQL0fPW+1mx0cAmA4XLFVbAqtkNMs2wYY0rlnX6MAddAAnNxSj9MxqE2ZORvQEYDaHACWAlSRBGxoAKSfvkTEqOiYTQJmxjpOB1C7eeOo6xfnfo0emQ+zdJU7EDFBYL7Bi+sqc74sHYoiswFoTgECgGcfVYFgEgU4amMDCqgGoIUFKKDm/y9leZN8NL9eOgKdIwD3UJlezGVJBCBZAivGf11qAEQITLZ1BKAXF6AaDUAtAhBaQWArhIMMNFS0F9CBzkMErO3KDykCTlLdBchz0r60NYF8KqUA6eJlDgHgG4CpRAAQ4bgOQgewHysq1yy+W/NsUiUCUL1woruNhy01AIvONqAVOQDk/W6TBmzXpADtkwZgwzUAiRF4VuuOAeQowJPvK91WqsotAu43CTiyKEB+Cb0BN6nXUFnke556htkBqE0CXrkQgEIDsC92AYjWCwaNAkQopLMuGgCqWaM0xDal0XM65ABoFCCSbWHlAKw0WlNUIABpYUtpceZrEACA1wH4itFbF/lOQZbkDUcNAlCJ+qyp6NidIwAkl0Kbz5pTgABTCJw3ACdLed+J+p13up6QtqodNQB/ffWD+JeP/3v49Wf+C2TFknoMzZhPbcZRjrx0BKCBFaikAM0vAELgttYAtLwow6BMEK7TANBmhdUAcBSgwTQApAGgFCAvDUDzVEbfCgNRsl+WxB4Q8B/4g0CUOoCbWhBYjQh4hwyYR6oBmGT5dbLIJpr3t6sOFzFuI9cBTJYNGoAWCMB+HxqAVhQg5hiLgZ0OxrfIIslGAAgFaFMRAPJd6QZAH4txWnL3y1zgWh7tHcsM6JLUi9oJlrMP9kQAlk3QKC4J2HAUsirLyvlgv7gv22qXXCLgThoAOlfROaxNaQ1AkQTMmWZwlWVsA6DZPppJwIYNaBip77qI03oNAIMkPnZtBw9ezB//5Jfv4GSZGAYOAyyjyHU1QZJv1jEuQGMTAZthhGVImS8FyJEELIsGgkkhsLwevMYb1h643Tglx4sXcBWffO2P4vmdN5T/bQy/hU9txlGOvLa0OHYyoNU5AUkKUJkCTChALW1AASUgrtUA1FGAZjYFaIWwd6EtAO3G1BoAUX+Jxhovv/9LWg7wsekC1OA8SCeg20fLkrYTNxEBkx2TSZEEvMRE23F31cEiLoXA4eJOzvH1KV8EQLMBbaEBSBqIO13FJQEzOQAUAbhgaQBeOQgAoG8A1FKAGpY8p1kGjYbWNwKgh7ipHIC6MSjgNg5qEQD7PWvRKMYGVAhRvo6lAK1OyoXtfmEB2nYXmW7c+LsA1VGAhmoACg2ALwKgmUGo99FsH6eGk4+hAYhIoNZilRg5AIwGgNnsEELg3Y/n48EySfEvvnRrUOvp/DjUd4pEkfDMugCp7zOGRadpmy3v12kU5GGDMl3dCwGwF+YPXdoqm7Hf/+pdLOO0pIR5jXEtqIGu0kIK07TZxsFIajOOcuRFB1tqrVmJAKQpsCgEmUYKMNDeBhRQ9KGD01hbZJqlIQAMfLg7j7DABElGLvQ1UIBC2QAEfi43tXaaHUt+X1sD0KABKITAcZphv9i1r+WRRlOlIzksEmqzDFEmG4AIB4v6BuDwNC6tBkWW6tqTqvJGAHgb0KpBMLImiq4aAA4BsHMAKMpmuQAlhHq2vZkiYLoIoN+1j914WubOvCwNAfChHTX8nKUnBUiwE331dcw14Sw1jxal05DFiwshAWBkAOxqz29asyGCwAZrABpSgKgglNzfOrJlNPGp7gIkKUDys+wkYHIuSNiYWd/4pBoPPvG5G61cgBoV+Y0miIsGoBifonk5L45aBEwRAPm4pAEdvsTrIh1NH61nCxrQIk7xR8/v+2edALw5QEcNAJCPTXQOG8Nv4VObcZQjL00DsKQUICVYsRqA5QHK0KF5/rw+bEAB3UFov2KHWNMATO1LIacSCQ0FGCoHQOM8lgtPv3MQt3DmaVLlbl6StXIBAkwr0Px39mom5IB59FLeNJJJcYmJtuB21dEixh0Qa0eXKxUtCr/XagAoAhCzj5tlJUYWE0UYiHa/IasBsHMA6D22Zy4eZImwvCc3rWhzfvd4OARgYgR0yaIIQK3zkEe5NAB1mxBBpDcfKRjBp/VZ9nu2SQLOXyfHDGaBSzIApAVoHxSgfe8GoEYDMFQDEEsKUIsGgMwF0kjh0vYk/33MrAGqAZi4GwBLA1DRIL7ncV0H0CYHoFGR4wqR6hQg0gyNLghMc3dL7YZd0oDiE34jqoYCBOg0oN/50q3yN93xsR3mFvsdbUABIEkyI8vmPAfgnik6oLEuQEDurECLhoDNdAQgECh5/G3qomYF6tYBaBQgRw4AAK0BWGXRMBe3UJ8fpUUD4CEABuBvDdiy5C7gMkk1tCFsYNt3jTgB3SyEwLqVnOO4ZQOQxvnCgQikl5kfBehwoRAAAH4NgDb51iAAZBHiiwDoQkm6s9vy2vJ0AaKLVUsDIGvnmpcQdox1FggA3eXWBJp9uAAZfPq4JQUo9djl496zNtDHYampaIPMLie5/+5kw1CAqkXAa9QAMC5AUaiCsCpzAJgk8izLSh1VualiiYDJ76AhAElpSwkwGoCKjY77L8zx5P35GPpHz++XFpTy+/Re5LgixLl7EUUAiqK0k1mV89Oaio7r1J3HQgAAXgdQQwECdCHw//MnKpytNl3Z9Z4tGwAtpNBEAEbQjPnUZhzlyEsLAvOlABkZAIAawC9uTRB02Mn2tQI99RABA8BxZiAAg9iAMjehbwMwMAIwLSdz3QWoya7dVYIAyB0sTQTsOu4dwzlBQwAijXPvqoPTuKQaAPBrAKgTUw1H0ukC5NsAEApQ64GTbQAKSzbHubUoQLI2VAAMGBoArQEYRgMA5LtfsmQDIESNFaVnhUbz5ksBCozd/jSop1S2owDZScD0+FgEgGQA3E4lAtCWAkTdtDL2cavOmAIE6HkVzmIagMNFXL6mHFNNITZtAKakAViZFCBDP1Cz0SHdgLIM+OyLh+Xjg7gAaRSgQrsgg8BoAzA2BIBcdzQTZGoiAACvA6ixAQWApx+8UF4//+8Xb5aPeyEA3Hu2pADpIYXp6OhYPrUZRznymmtBYJ4uQEYKMKAm7C78f8CwAq1oAE7IsfI5ADYCMJQGQHDwvDcCMFwSMKBTgNoKjmkWwI0jSQEiImDXcZsDJkUAPClAh4sYt7sgAA0oQEtPNx8zB8DiijYtNgnYRgBouRGAzeT/A/pvcWdACpBTA1BQgHamUatgK7Mi43O8bUCNa9anAZi0oQAxImBAZQGsahCA2z0iALQqLSHX2gAY9Jyi5H3eVARMc1QUAkA1AFUIQKrnAEz8EQAA+MYn+I2BYVyAiAgYSb6uiKsbgLElAbOW0HVWoHTecSzMp1GAt7wqDwSj663WGoC2QWCGBmA5tDPUALUZRzny0kTAThegagTgdJWUotwu/H9AzxC4W+EEVJcELGlIGgUI4eA7HmV5awCG9WSeEApQ29RhqgGQFCD6XtziAwCwS52AXtaEqgtEtQ1AlmWFBqALAlC9M+ZatFc1irYNaH4uWg+clRSgKgSAmfQ3GQGgNqAnw1GAIkfarbLk66fhoI1GHuaU/11HzQkigwLk4fTBIQCNgsASsvCUIuAaDYBCAPptACrRF3FGGgCyuJv5NAAMCkltlMtNFZcGQISYUoe+WHcBmob+GgAAeOdjV6xrpDaQrm1RCpBIsFjFauwnGoAx24BSPVDZnNRZgSb1GgBA1wHI8msAhtMAvGJFwEKIR4QQvyyEuCuE2BdCfEwI8ajH694hhPh7QohPCyGOhRBfEUL8AyHEa7sf+njKnQTsiQDML2pUnS4WoPnr1aBbpQGoDQKbcxSgaKABrycEYADudkTg/LZ0o2u7tAGwKUDOBYCFAOgi4MMaF6DjZR4iI7nGADwRAJo66U8BojWN3OfHsgHtigCwDUB+H7maqxwBYO61DbUABUwNAEEAes8BIDvzCYMAdNAw0aILcLphUUeZiYwGIPNBAFgb0CYiYF8KENEAlDag7a5710K/GgEIlL1ybQPQUQxPF9VaA5AfX2UOAINC3jokDQCrASAuQEGknQcqSs2PwUQAmDGE1N58gmeKnWdZzo2brkWuqwgJVks7AwAYoQsQuYc0ChDXABy8YL+BBwUIUE5AtNrnALRjXExCHZ3URcBn/1v4VO1RCiG2AfwagKcA/DCAPwfgSQC/LoTYqXotgO8D8EYA/xWAbwXw0wCeBfA7QohHOhz3qGrLhQBUagB0EXBfFqCAIQKuogARkc6czQGQCIA6nizoB9o3S/SkARgCAZCDWpbpA24zG1A1EUoY20sEbIaBJVQEHNUGgcnPaq4BaCACdiIA7kWImQTsy+12Fjd5MzkAtJwUoE1GAMhvQdG/bR+BXIOilDWJZKVp1sySz+dzyAKLjld1O/MmApC1RABqd+ZDnk4TEdpgZtodEg3AflcKkGOhXysIlWNrlQh4utd6d7QsBwXICwFgKEA3j9T4d4XVACyNBkB3HJIUoGkU5PNYAwoQYNOABskAAPQcACRYnR6p/zbhRcBV4+26SqMALRhDiL3r6slcA1CTBCyrNQLAagC6BYEBOQr6ShUB/wiAxwB8V5Zl/2uWZb8C4EMAXg3gR2te+3NZlr0ny7L/Nsuy38iy7B8C+ACAy8X7viJqNnFoAKa76iKucgGaX+jNAhTQEYSqMLDaHACGAtRWMFNXrAagBQWoSTiXb9FBjZ6zJgjAVYIA3CgoQKsmNqBADpk2RADkddXcBagBAuBYbEx8EQDOLq5phRM7NE5SgCpFwK8sDQBdBNAxpa8deVkmNx/Q740+HIDMz6GbK3XXSRg2bwC4Jrx2Ig+M3Wfm+OgGBQADAeiaBOygAFUhAABpACoQgK70H8CwAbU1ADmti9FJAPoYxFCAWBcgqgGwGoCkbDjKxxtQgADg3WYDMAQaDlgi4NiBAFi5BmdcdAPicGHQrQBg70H1ZJq8LsvDBhQA7tub4dEr29pjfjagnAagZRCYYYU8NjTGp3yO8kMAfjvLss/JB7Is+yKATwD4zqoXZln2MvPYlwG8DODhZoc63qIwLJ0EIYQaRGtEwHShTik8beoy4ZvfrrIB1XIA7Btjp3QBUgNO1rJbris2uMfzs7y49B2KTuZ0EdIEtt+bKfvUmxwC4NsAUATAQwMgEYDmFCA6EPsHgdGq1ABE+sLOl9vtLCHsNODimhJCsOfXSQF6pSAABP3j7u8uxWkAKOe3r8+jv9txgwYgmupjqFcDwGoAWoqAKT3AFAIzOQBtG1/Xos8bAUiMDaI0UZtTWzrdpVVFLgoQbxygFeMCRClApbVyEKrmP1kpVCMItc253AUo0T+fHh9nJGDU2x69pCH+g1E9tByABMnyRP03Vw7ACBadugiYSYXfvqoW3CwCwGc/cPV2gwa0bg1ApOnYMiMJ+JWTA/BGAP+KefwPATzd9AOFEG8AcD+AP2762uL1vyv/1+b1Q5RTBAzoDQDd6Tg1NQDqwr/UEQG4wlhOcnVakwMwCQPMJ4FFARqiujQAlEs/SBAYGVgpDaEJ2iCEwNWdfOCWv0lSlwQMFIvR4nMOX2rsAlQiAF1EwFzKLinXIsQ3B+CY84tuU+ZxkkWfucM6nwRFiJAjB2BDi54/uvHc1468LE4DQFOAd3rSHNDf7bQBBchEAHx2+TgEoHZn3rSgLI+vYoGrJQHnDUDbcSsIBHvP1O4Gy51QkwJ0ehdlQGUvCICLAkTFuY4GIGYaAA4BIP9dswHlKEAlAlB8/vYV4Ml/M59r3vzh2q8zi0J8w2uVucc6KEATqwGgCEBPY2dPpduAMscmhKIB7T9vvwFB0erYBs8+qutT/BqA/nIAXCGFwDh+C5/yOcorALgVwy3kVB7vEkJEAP475AjALzR57Zhry0UBApQTULIEloTHZ4iANQSgYwNweXsqk8LL1FmuaGaBK0RjdzZZCwWIFQF73pjJwKmM1EeenrOm8K+kAd0+XiImzjdAxUQSRvmuCVBoAMwcgDoEIL+uTjFDEhS/Y1MKUG0OgEuI6NcAHHFc0TZlpgGT68dEhvbmxX97BWsAaPWNAHAaAC0FuKeGQ9MANEAAJutCALRUXb4BiK0GIEcA0skOYkTFZ7e/7ufMb+6iBpXlogD16QAEuF2ACEKxMOdM5vmShnjjiBEBA2peSpaqEWMoQEuTAgQAP/A/AT/5ReCtP+DzjfCeJxRFcAjTCQCWCHhTEICpQwOgHdveQ/n/Lw+AxYH+Bp4UIMAWAnuNOazZSHcXoFesCLjn+nkA7wbwQ1mWeaxC7Mqy7O3yf/0eWvuaG1ZjWrmEwIYI+LbWAHSjAIWBwJXiPaTlJFe6CNjtlPJ76RMAgDgL8IXosU7H5iozuCd/sA0CMCwFSEMAGjYAcscqy/KUVp0CVHHc0jnh6GVgpSYCnyTg22TCjGcFpH98y/FsUnT3rQ4BcCw2qiYkPTGypwbAQgDIJGo0WHuFwxXvAnSf/diG1Mxx/vrOAZhwGgAtBbifz6Of06QBCA0RcN01DLS0ATUdaJjXWVkAxTyQztUOZhcuObfYr0cAXA2Aoif10wDwFKBpFULCPF8hAGo+u6whAMXvkLpdgGgQmDY2CVGGcfrUe4gOYDCqB80BEDFSTQOgNjrGFgTmGte1cekC0QGYKIBHErCs1z+wp41rXqhjr0nAepNP7/Mx/BY+5XOUt8Hv9LuQAbaEEH8DwJ8H8NEsyz7u+7pNKKcNKFDRAOg5ABoFqKMNKKB2m28dLZ0iKxkEFgj3Bbs7i/Dx9B34/uXP4IPL/wx3Jg+wz+tagtuha6EBGCQILKK/b/vMgWvECejm0cJPBAwoHUCWAgdqwMxzAGpcgIgGJJ076Ghc9YAAVC3SphoC4L+wqyxLA0ApQJ4IgAj6WficUXFN1zQMet+Roo22bGSPSAOw3ZPomC7Km7gAmQiA8BB4drcB5RtZzQo0TcvNn2SqOPZdxi2+ARgLAsBTpHRuvsMKtEIDcHFrov82IUEANA0AXZAm5Vzh2rTwqTdcv4BXXc7Hmkev1hkhtiy6eYEUWUwbAIIAkGurj+TtruVKAtZ+qyohsKcNKJCP6W99RDXRWxMfEfAwOQA0pDAKBIKhxOE9l88o/YfIdQBmPQ3gj3w+RAjxMwB+CsB/kGXZL/kf3mYUdVw4qWwAyM6rpAAFEyCa92oDCqjd5jjNsH8Ss85CslnZmoROa8/cCUjgt9L8Enh6oM62GwIwdBAYvwvZFAGgkPWtw6VGXapcAFAh8J3nyj+XmJSTmutYKAIgti7nLXuW5NBr1a5XAwQgCgQCoXPOgRoEgIiAj7nAmDZVoQGYGOfnggsB2LqS+6RvaHHnvO8MAEC/XuWi6phM+H0hAJoGoAkFyHSu8rB45NDDehEwtQFVY3jkagAWimOfzNTipUuDZi78AuETYObQAAxJASK6IpObz5bRAGRZVhooaPQf+jmmBoCcV7pZ4kLKfCoIBH7hh/81fPwPX8CffXYgLxMtCThG6tAA6AjAGGxA1XV3wCUBA0YDYAiBmaavqt7/9AP4zc/fxDQM8OQDu7XPZ9cULSlApgZA/habQv8B/BqAXwXwt4QQj2VZ9gUAEEK8BsB7kPv6V5YQ4i8A+OsAfibLsp9vf6jjrSAQmIYBlklqawDqEID5RUCIXm1AAd13/sbRgn1PuaNWxReVYWCyJgPxDANugvbWAFAEYAAKEFkYtHUBAszfZKm5g1QOGrQBuKs3AEAOtV6Y8+eKiubCHZJMfXK7ugFI/JOAhRCYRaHV/FY1YxMnAtChgTM1AHQStRAA2QAY190GC4ABHsnrOwMAsHe/AAMB6M0GtJ0GIJoYCIAHBchsEvPPaSIC9qAAEYpNTBCALuYF5vg9i9wbOmUFhDJDa10aAK8GQM8BOFoqG88rZgNAXY1oAzChDQDZaOi4W/7663t4/fW9Tu9RWVS/hASINyUIjP9dtWO78JD6e99AABpQgADgz/2p1+D6xTkevbKjIezO6tMG1Jllsxm7/4AfBeh/APAlAL8ihPhOIcSHAPwKgOcA/PfySUKIVwshYiHEXyWPfR+A/xLAPwXwa0KId5H/NXYQGnPJXRhvCpBEAIpFmEQAApFbRnata2SAvOkQAssJtaoBMI+ltU1jTQVsZz6OIDC6W90XAnDzcKGLgKvea8fRAGT5+alyApKNZRQIRGYDUFWxfw4AYE8+ZdCOo4bRABg2fhUuQHszBwVogwXAgAsB6N+5S9cA5BMfRXL6cgEKnRqAGgpQiwaA1wB4cukBXQQcOBAAct+tZqoB6LJoMD3/vRa366IABQFrOUqv06XTBUinIWopwLsOBMDKAVDnZp8iACNYLFeWhgAkyOi5mPA5AKNoAFyhkE4EwNAANKAAAfk9+4E3PYinH/LUcHDvybEPfN7KgQBM6+h3I6rab55l2ZEQ4r0A/jaAX0LuSfh/AviJLMsOyVMFgBB6U/GB4vEPFP+j9RsA/vXWRz6ymk9C7J/GbhcgQA2uSQwsi1M3u4AX90/x2RdzNfyDF7d64Y/pybO8EFhOqFUOISYCMJTrgZncWTzo9drYdyHdsugigFLn27oAAXlTFntTgIju4u5Xyz8lAlAlBJYuQJd3phDctegqDYqtXzyZE2qdCIr+92POLq5NVTQApgvQrosCtMEhYICjAehZAAyYGoACAVgMgABQ+t3S32XDFAH7NACsDWjdeOfguNNNA60BOOURgC7jqrngrw0BA9bXAAD5fZjGFTagHhqAaGqkADsE//ECpY1pEGrj0v4JbQBGvkgzXIA2BQFw3ZfafKCJgKsQgAEcB1kRcNsgMF4DMNQm6RDlNUpnWfYVAN9d85wvoTQsLx/7CICPtDu0zSq5i16JAEj3lYUuAP7YJ79W8qe//Rlyc3QoCpFyVqC0Y3VZgAIqDVjWYBQgtjNvngQ8hAiYW8wKgcaN2lVNBKz/JtUuQMSV5vhm+eeyuH0PF7wQOMuyEgG4sj11o1FcNUQAzEVI3WREdzz1AJUO15cZ5OPlAnQPIAADNACsBkCzAe0/CKyJBsCc6MXEowFg7udpRZq19TkOEbCWBEzuu+VkIAqQFwJQvCZL8l0NidYN0gBMgBU0WqFGAXLagOoUoFsuC1D5GQDKxT/AIAAOTvoYK9QRAOHIZVlQ7/kRcM87IwANgsBaFddU9GADmucA5Nfe6K8tUptzpCMvPwpQsftDGoBsdgH/+HcVrePDb3+kl+O5tltNAVp4ZAAAdrz2YBSgSQcEQKMA9X9Jc01FG6TBpAD5i4B55yWJAOw7EID907hcnF3embgF6Vw1RgD0a6g2qCkQ4BhC/SIAtAHwdAF6JWoAeg4BAxwagCEQAKcGoIE9J4DAY5evFQVISwJ2uABRigvRACwne+T5fWoAGlCAAF0IfNqzDSigxg/NBchHA6BTgOg8ZmsA+PlD1wBsEgWIjF0igUiqEQDhI/xeQ3khAJMtQFrgmiLghhSgxsVqAPqxAd1EEfDmHOnISy6i4zTTg1+2GNoFsQC9mWzhCy/nAWHPPnoJT9zvoWT3qKuG5aRZegZAhQbAFAEPdHFbyZ2Ad2ceD+4CZH/nNjt2GgWoiQiYagBIlQiAowG4baZmtkYAWlCAaiZYIQT7nTtNYpUUIM8cgA1HALiFzSAIwBloALySs2UZE33ggQBw117thoIzCZhQgBwIwGJCbUA7uAAZv3ltCBjgRC4GowABzhwALwpQONVQU6cGgJYRBLZ/0pPb2DqK/D4TCwGgDUB+7qZhteZqXeVCIaz5QAqBD17QG9ChKUAdmAZm0SlllWREBDzya4vU5hzpyIv6Cp/SHQ1u15UgAJ8mG7Effkc/u/+Asdt8ZCMAdDdtqwIytihAQ9mAshoAv0VEPLQLUBtuMFPb06hsFG8dLf1zALavAMI+F4ssP2cuETDNALhsUYDuMK8g1SAHALAnVJ/rhJssOlnZVSIADhtQIfSJ5lwD4FVaA5AM6ALkaAgb+fPDrwHgbECbUYCIDSgVAce8BmAR7ZHnnyUCwDQA0dx21WpbJT+fJgGrY3aLgPUGgGrZrloaAM5EItSQSTrndckBWEuRsStEgiDlGwA22OwMy4sCBCgaUJbkAZeyGiQBt6oeNQBCiPK+PY170rGtuTbnSEdedBCmu+uY7qjJqEQAVArw79/IitcH+Pa39MP/B/QBkksDPl35UYDW1QBYyZ2ANQAkptE883gXLq2ruIVq28+RO1c3Dhf+ScBByFJT6jQA3RAA/xwAwKYA+fBR2eClugVX5RtWIAAuCpDxvE1OAQZcDcAAFCByPpMyCbh/BMB1X9RqkYyJPjCvDe49WyEAQR4eB2g2oPR3cLkAnUb9iIDNHAAvgSvdXOEagD7D8OT40dgG1I0AWBQgBwLgXJCOfZdWiHLTZ4IEYcIjsrJ5GguiERaZMCfNDd0AACAASURBVGZZ59slBG5oA9q4HFSxtiU3KOiab/TXFqnNOdKRF4VhNR2AEMoJiKMAxfnE9ME3PagvSjrWha2o7E45DQB11GiUAzAQzzDkhKbkZv2n/+p5vOVn/xn+wv/4e1ay8Uqj0vR/fH1pAACFzBycxtqOaa14edemAdW5AFHRnI0A1LkANUQAGoqAAb6Z7DR4mknAlEfrogABeqO54RQgXgPQ/27nZG0aAAcCUHf/mRqAtjagXrvp0lOfIgAuCpBCAE4jZV3YZxKwVyIspwHIsmEaAJrSW4zdegPgogCRxWCkawAsCpBjYRcGgp0TxrJgrqzivEUVCMCydJ4Zz/dh08hdCACgC4EN4Xfv1aMGAFCNO0WXzhGAe7ColaY1oMnB9PhWPgASCtA+tgH0S/8BcnhKDpJ1FKDqHAD95hiM38bdmOSxX/jnX8TRMsGvfurr+MKNI+1pVEw7BALQlwYA0LUZL+0rYVftAoDRAUgEwCUCpuFyV3amvCWtqxojAM1sQAH+vHbLAXBYAzLv60YANrwBWJsNKKUA2RqAvj4zbE0BMj7fJwisrSZFXmcNRcDH4VAUoJYagOWh+rvXBkDeX1n5/n4uQEYOgLmhoX2GG0HmxqKuQWBrqeI3ihBjUoMAjGnRyW7s+DYAQ1OA2OukfaMhx8HjZQODghHVeK6aDS/qvexMA05XwPJIQwD2s208cmUL73ztFfRdkgZ0+3hp0Wc0DUCDHIDhGgD3AJ5lGT79/EH58O9/Veeve4tpWxY3gXRFAADgpQM1qJs+9VYxTkDLQgNwuHAhAGowvbwzBSbbarA7rnMB8k8CBjgXIA8NgM9OUZMyOcuVFKCIfZ4m2t/AWhcFiDasZhLwNAp6uw9d90U9BcgYT9q6APlQc8qgKz4JmNoUlxoAEWARbJcPd9Eumc13aw3AEAJgQB8/CloPHS+oDbBWiakByP99Yc5Qe1gTifwzOL7/6HMAANIApAgzci7IOKcoQOP5Pry2yyECBoB9BwIwCAWI04q0bzTkfU51LOci4HuwKOxqW4EaO69ECHaAbXz47Y/0Ev5llkQAskzfDQZ0zlqzHICButuKJOCv3TnBAVnkfuq5u9rT4oE1ANx3du1M1pUe0KZ+k9r327W56XUUIE0DsD3N6WhyYl9DEnBdsRqAXhEAdxKwdl1ffDj//yuPtU6FHEutiwJEBbNlDkBxj+70+Hmu+7mWAmSOJz5BYG0pQKFNAaLX8TJhXIDmlxCn6vN6RQC8XIAYDYDWAFxqfTxWaWFpsgHw0QCo85mFk9LNjo6h6jN4DYD5WbLGtGPuLEkBEgmi1EZkkzQr574xfZ9OFCCS4sz6RHetio3GNsWNT2P6Lepqc4505KWJgCuzAG4hJQjAIbbx3W9/1SDHpPvO6w2ArwjYtAEdjGtYYQP6mRcOtIf/5XMGAkB22Lx27BoWJ9BrK9qzAmyKaoUAFBSgA4cImFK/Lu8U55c2ABkvqgbQOQnYZyHfvw2oiQCQMB1yfqdhoC+aPvg3gbd/BPiuv9v+s0dSQgjrHt2e9d/UsBqAYlOhT8TBqQFoGATmgwBwn+W1MC8RAL4B0GyhJQVo65LhXjYCF6DBEABy7gtqoZYDYM6X5XPVJsRxEpbIuiUABio3kLjzsREagEBqAGJMKAJQaADGlgIsy4sCpCEAVARcfM8hLECBwTQAtMakx6irzTnSkRcdhG0KENlNObmNmzeV7dXrHn0YD1/qyW7NKC0LwHACog1AVQ7ALAq0SXCtGoBiAfdpowH4o6/va4MfpQC13Zmvqn41APxCpF4EbDcAizoEwNQAADYdzVVNk4Cj5ouQ/kXAbgSANhZmU4sHnwG+4+8Aj76r/WePqMzJdnsAy0NdA6AnAfeJOAQOV5F6ChBx5wH8KEBtNSmcCJjmAMgGIF4Cq+Ke27rc28aFKfptngNQzAVDNQCRTQGidr8+LkC3F+p8spsolQjAZlOAJkgwh40AaA3AiBad3CaOdXzb19Q1yImAhxAAA4NpAGidU4DuwdIbAGNHwxBf3rhxo/znt7zticGOyQyeonXiiQAIITQdQJedqsqqsOcyG4BlkuLTLygUhe6k1VIDWhTnBd5aA8DB1z7vx9hTBpPCUcihAZAUoFkUqN/Y1wmoKQJgLEJ8dvJZruhAGoCoqgF4hZXVAPRkyUmLns8kzVMwpRtX34gDt8vmdZ/TRa6PCNh4zzAQfo2+RJqoDSgVAcsNCpqyO7+kuZd1oS7OWzTf60UAGAoQGS+cOQAEUblJgnDZTZRKDcCGIgDFdRUixUxQR6R8nFsk43SemTLNlXV8QQDsXs//5ihAQ1ExK5CiNsWth8b0W9TV5hzpyKtaA6AG05M7L2NxpAbab37LgA2ARgHSEQDfBgAAdgikP9hOQ4U459PP71v/6VOEBjS4BqBPBIDZvYoCUZ/iaCIAQYSdWb6oqQsCu7IzVe/v2wDE3YLAvDQATGPVTQNgeL0HPAWoT7vdMZZFARoiB4CczzjN9BTgnjUHne05Aa8m1vwc7yY/sF2AIq0BKBa4xgKbupf1SgHysgGlGoAVe3y9FR0/ikW9nwZAjUE3T9RzWAoQ2wBUUIA2wgUo/04TxJiBNgA2AjCmhmbquyiWWQCnd4Hlcf63bPoGowD1iwCwuqFzBODeq0oEgAymn/7SV7CX5TDwSkwx39rGUKWFgRkIwOmS2oBWXwZ0x3Swi9sxgC/ipLT9pBP0p76qhMBaoNbakoD7owB5NRNmDkA4K9NsOQpQnKS4e5IPppplXlMEQAReuzFtXID6twElDUAw0URklRSgV1iZk23fC3LA1gAcD5ACLItbHPtRcygCUD/Jm+/pvdnBioAZChBN397SEYA+g8BMRICtM0MA8kU9HS9qcwBEiBvH6jlXzBRgoBJB5ug+Y6LMOIvkAJQNQDgrx7WxagC4Y2EpbpwQWN5DQ1iAAr1rAPj08PH8FnW1OUc68tIRAFMDoChAX/rqV7EnTooXXcSQ1QcFCNAdU9atAfj8S0ely8g3ve6+kg/sQgC6uGm4iqOztEUAuN0rr3M6v6RPctG0pGadrBJdaAjg7smq1Phqn2kI0p0lEQCPnVOgJQLQOwWINADGrg5tDO+1BqDK5rdtmRqA4wFSgGXxu2wNqDlAOwTAd1deLqazFCh29XURcHEjGgtsql1aPwKwRg2AZgOaL/CmPghAOQbpGQDXWApQMxcgL6eks65iToxEqjQAZJNjMdIGwBzXJ6HgXQ45IXAycANQkRfRpni9w3kOwD1XW54IQHxwE3vI4a5oe9gG4FqFCPjEUwQM6FkAg4VcOHZwKNf/bY9cwuseyMNzPvfyIQ5O88GCLn7HTgGaRaG1APWa/INA1wGEM60xM7MAqAD4srMBqEIAiuvFY+cUYJKAfXIAehcB0wbACLALKAJwb1GAdgbOAUjSdJAUYFncLtswCIAw/u15LWqL6ZX12tLn3tAAUApQl3HVRABaB4FpCMVQFKAmNqDL8vW0AeApQByFND8P3OJ4TJQZZ5E5cUduGpJNjrFSgLyRtL3r6m+JAAxOAeo3B+BcBHxeAPQdhVNXEjCAa+IutkQ+mInZBQxZVypsQE+WavBoggAMttPguDGpBejrr+/hmVfljkpZBvzB13IaEA05G+Lm67MBAGwdgDf8T2lA0VRrJEwdAA0Bu7JNBjifBiDLVFjdxI+iZi46WucAdMmZqGgAzhGAfisyNABHA2oAWvNsG2sAzJ3LhhQgoFzAaEFgDgRgpWmX2o9b5r1XR+kE4JEDMBAFKJYUIJ8GoBjDoqk2f/ENQEMEYEQLZmeR87aDQgVNU4CTcboAeWfC7BEEYG0UoH4RAG5sGhMaU1ebc6QjL8q7pItrAJoL0CNCWYAOTQHanoblZGBpABpQgOiCqQtXtbIc0NwfkwbgDQ9ewDOPKEtVGQhGubQDAAAODUD782A6AXnTlmgDEM6wO1PnzEQAbh11QACObqjdyiuPex1afzkAfSEAJgXoHkIAyG8RBmKQxY5FAaIIQN8uQKwGwAc1I+OaTw6AhQA0pAAB5WKaXscuDUDSk3uZnQPg0YDR8dZsAIIImO62Ph6rIpsCFIVBeQ05cwASRQGSIWCArm0rq6kGYBMWaeS6mon8N0pJIztaDYCJALiO7QLRAOw/n9PnsuI7DZECDKwlB+AcAbgHS9MAmAjAZLucgPQGYFgEQAhRDpZVOQB1O4S6BmAoChBzDEGIzxQUoJ1piIcvbeGZR1TTJHUAciKdhB5uOi2qTw0AwCAAvudUQwBmlQgAmwEA+DUANz6j/r7vdV6H1s4FqGcK0KSKAkQQgAGCscZU9LfYnoSD3BP0mjURgL6Th1sjAKGumWn6Oa0QAKYBWDpcgFY9mRfYFKCOGoCty/2msDIUIEAdZ60NqBcFqKIBYG1AN0EDYI9Tp5n6njoFaDzfpx0C8HVNRL9WDUAHCtC5Deh5AdAX0ZYGQIhy4aX5+Q5MAQKUYGr/NNYGDE0DUIMAPFnw7gHgtdd2ej7Copib8HAl8OJ+3ri8/voegkDgdQ/slRPe7381bwBWBZd2CP4/0K8LEGAjAN4LjR2KAOgUoEMjDVhDAKgLkJZJoScql/UyaQCuvd7r0MxBz+c79Z4DQJOAjR1fqnO5uP3KRgC0BmCADABAv/6TNNVcgNZiA9pUA9BKBOyrAbApQBFHATI0AHFfOQATkwLU0QWoT/oPwLoAAepe99EASArQ3jzix4iqHIBNtQFlvtNxqn638YqAPRtpqgHYf97InhkoCIxDFnoOAhsTHauuNudIR16UArQwXYAAzQlIvWhYChCg75bQRWETF6DvfOtD+GsfeiP+7g8+qzUDvRZzYz53Vx3v66/nzdIkDPCmh/Lz9vW7p3hp/1QhAAPRk4bXAPgiACQLINJFwBYC4Nox80EAXm6DAPSkAegyeIZR7pYEWPfb+59+AI9c2cJrr+3g/U/bqcqvpKLnfogMAECHvldphqMFRQD6/UyeKuZxz0yLzYpw5rWjaIsX21CACpcbjxwALcCwA7I6IXQawBcBMDQAqxMgPi2PrdeizdcXfgPSnmxW1QBkGesCxKYAF8+xqjIJeAOWPsym2EFMG4BxBoF5i4Bnu2oT9OB5LfhtOApQ3xoAZmzqomNbc72ysfA1Ft11OeE4jdygugYEgO423zhc4PrFnCZxUuzYBaJ+8plFIX743a8Z7BgBsDfhc3fUgPDUddV4vOVVl/A7X84n00999W65kxYORE/iBrAutn1mFoD3TuMudQGaalx2SwR87EAAprv54JrGwLHDBlSjAD3ldWi2C1D9+Wm9sKuqb/054JO/BHzTf6w9/MCFOf6vv/TNCAQGocSMqej12jcdRxZdcCaJngPQtw1oawTgXT8OfPyvAO/4qBelxfwcfwoQmUYZBKDUKBkagDh5yfnZTWseBTgqfoPmNqCx3pzML9nP71Kv+Ub19yd/EZjtAe//6+XCnM0BSBMA+XlLwkk5p7L0H6Ay4ZVb7G/ELi3jbHR3pe4tTQMwou9jNiOVzdbeg8BiHzh4QW8ABhMBBwAE5LWFIOpEd2NFwOF46Fh1dd4A9FSVScAA4tlF+2SvAQGgi02KAMhj3BqII9y4hD1IfPmOOl7aAJg6gLigAA0lUOYW+11cO9qLgA0EQKMAeSIAko529HIFAvDZ/P+ne3pYS0W10QBwjU/nnaxnvi//H1NDUcTGVjoCMBAFyNAADBoE1rYBePP35P/zLBNB9M8BqNYAWAhANAcmWwYC0O26n0/CsgFoHgSWDOcABAAPvgX44N8C/ve/lP/7t34eEAKz8H0AHAgAoYPEZOY0x86yqhAAc3MiCsYx59UV09TcWqrj1lyARoQAeIuAgVwIfOMzOXImnYCA4WxAgby5kNdXx8/hNh0H00kOUOO5aja8KpOAARyHzGJ/YBEwAFzT0oAV/1LuqAxhEdiqhNAGegD44i11vE9dV+fqrdQJ6Kt3SgRgiBAwYAANQGsRMOFMTrY0MauZBnzrWO2mXN4xBjk5wZ/cLuH4sk73c0EWkNN/PCfKNknAHEowFI3rXqp1UIBCSwMwYBCYcS0FYphmrj0CUN0AyA2KUgNQ7LD3GWBI55/OCEDfDQAAfMOP5E2ArN/8r/Gjq18EkGEZp8jMcYhoBZYZaQCcFCB/F6CNoP8ATgrQjcLQY2NyACoRACIEvvMV9bdH+nzr0rRB3bQG7ObEiH6LutqcIx15zaKgXCtZScAADgRjq7YGCpArC0BSgLwEY2uqVOjH8sXb+fE+eHGuCTcfvbKNS8W/P/XcnXKHrQstp6p6dwEyKEDei96rjwNP/Bu5q9Qz32+4AOkiYIkA7M4imwMrJ/hkkXN/ad34rPrbUwAMMAhACxvQKHAkRp5Xo6IQ9FAIwMTMARg0CKzlwrxh9RIExuQArOIsb7QNkS0NMOyKXtJFf6sgsKEbACBvAr71b5b//J7Tj+Gno38EINN2swFodJAl1NjvpAA1EAGPyTGnsphF8Gk2xWcLa+zR2oA2mQuoFajWAAwkAgaMfJBuYxV3385GRMeqq3MKUE8lRO63fbpKbRtQAHewi4fNB9eAANDF5o1DSgHKB486AfA6KxERkKmdH5ll9frruvBYCIFnXnUJv/HZl7FPdr6HQgCEEJiEQssb6IYAGBQg38ZFCOCHfjkXx0Uz7JKU5AMHBcja/QdsIfCUhH21EAAD9q6jzy5Io52i8/KutSAAhsuNhgAMTAEaqgGwGw3P+7IGAVilKbA8Um47WzkCoI0nHTcvKO2nVRDYOhoAAHjnnweQAf/HTwIAfiz6J0ghsFi9X1+YEwrQaaoed2sA3AhAI076mIr5TgtM8JkXD/DuJ65p1KkxfSfz/qy8X/ccDcCQFKCG+SBVdY4AnFdZcjf9dGk3ADdSxj5zLTagarF5q6AAJanacRkNBQhAZiAAMfJ/mw0AAC0QTFYXL+26Mjv9LgjA5e2Jxqxp/F5FsM6ugwK0jNOyIbiyzQxw1CHH1AG0EAADDMzugwC0sA49r/paiwZAowBlJf8c6N961Lyvh+LYNlq40KKhWYWwnp6fVZKyC+wkpTkA3b7Tq6/mTfy13VkLDcAaGwAAeOePAh/4ufKfPx79KqJf+2s6HTFWG0G0AbjWRgOwsRQgu5FeYILPbBoCUCcClnUWFKABNABjEmTX1eYc6QaU3E0/ZURNLy63rMfWLQKWFKDTBhkA66xE6Df9qgCo3nDdbpTe+oh97oZCAAB70dHls6IwwKUtNfC0Xfi6XIDuHDtSgGVpCIDhBPQypQA1QADMxbzHhGRqAM4bgH5qPTkAOsf9ePFKRAAaUPNk3fwTAAo1BAoKkJEBAJgagG7f6S9/6xvwI3/6tfhvfuBtfjS6dYqAuXrXj+EfXf3x8p/b/+LngX/yF1UoGaEAnfggAJUagA1FGpnvJBEAwBABj8h5xhzXa0XAstaFAIT9UYC4BO9Nmsc250g3oEoEgBEBf305tx5bdw7AjYIW0iQEbJ1lIgBJcXlyCMBbXsUhAMM1AOYg1sUFCNDdLNo2ExoCQBZg1AKURwAqsgBe/nT+/+EMuPwa72PpQwPg7bt+XpWl2YBOhsoBMJOA8zFFCE8KSoNalwbAbjQ8r0faKN/4E/J++XGuUh4BiHukAD16dRs/821P452PXfV7wVkiAEV94tq/jZ9d/TvqgU/+IvCPP5Lv/hMR8FGifu9WGgAzKXlEc15lcQhANsFnXzhAlmWjRQDM+7PaBtQlAh5SA9AfBYhbB0w3KAdgPFfNK6DkhX66SixXg6+cMAjAbKBQLe2YwtIt5mbhHnCypCFg47kE0sBEAEJEgcDj99kC6mu7Mzx8ST+nXRflVWUJVjtO2NTNou2CJgxESfHQGoCjOgSANE904l+dAne+XBzgE/pAWVNCCG3h6fOdzjUAwxQ9j3078sgKAlHS2KgGYHsAW2FbnDvMBGtSjbwphVefVH+TBqBEAJLUygAAiDsQzsD9qlID0HMOgKNmUYC/n3wA/9Hyx9Tmzx//KvAPPqxllByTBsBJAarQAGwsBYhpak4xxdEywVdvn4xWA9BIBLx7v7IAXx6ox4fKAQD0a6Uj0sCtA8aExtTVeK6aV0DJ3fQ0g+Vq8KUjYyE22R72IiclaUByYXjaIAV4nZUZFKAkC/H4fbvOheFbDR0AB8f1VeZitasNIZ3IujQT0gmIugDdPlJ/sztmLgTg5ueArLhu7/N3AJJFJyG/JOBzDcAQ9cAFdW3J4L8hSu6YJ8QFaHvWP+JgNvbrogB5c3l3rik096ZqAOQ9ECcZu8NORcBrz6gYAQIgx4uPpX8GX3zf38vzEQDgi78BfOxHyucdkPAr1tQAqNEAbKoImNcAAMBnXzzYGASg8tiCUM+3KR9flw1oxwaAFQGfIwD3ZOlhYOrmzLIMnz80LrQ10H9kSbrJ8TLB8TLWKEBjEgGnlgYgZOk/sp4xdABDTqLmIr2r3oAuzLsct6QBUQ2AMwVYlqsB0ATALRoAcv37LJ5MqPS8Aein3vvUA/h33/MafOTdr8H7n75e/4KWRSkuJwUCsDPAeLIuDUBrpEEIhQIcvphnaYCcnyRlNQBJjzkAjcupARBrm5vowvDGQ+8Ffuhjyhjj6OXyvx3E+bnZ4yyNZXFcbkcQ2OY0AJwGIB/PP/PigZagPKYGwDyW2vuVC5sccnNU0wD03wCci4Dv0aK76QuyyL59vMLdZIJFRi62NTgAyTKzACgFaFQagMDUAIR46sGKBsDQAQy5gDRv6q7NBhVnd4H/dwsh8CJOyx0hPQXYwwZUFrUAbSAAlkUn51YIwIgmsU2uaRTgP/2ON+JnP/TGQRcGcvJbJSmOi/FuCNvR1vacHT+nkasYvV8KFEDuBK6cCEB+vwYC68+/MLMLJEVpfrER9a9L0fFiESfAa94DfOR/A3bu0563v8p/hyu7FXxtFgGQOQAmBWg8c15lMU2NXEN85gUDARjRorNREjAAXHjIfmxDbEA52vEmzWObc6QbUHoasLo5X9w/BSBwB8QKdA0ZALKuUSego6WOAIypATAQgBghnqpAAN708EXQeXNIBIALrepSV3uiAF2Y20JgTQPAIQDbDhvQlzsiAGTg81mkWWKxEU1i51Vf0gLvaJGUDo5DaA5aB3Q1/ZwuVKNrT6i/Cx3AhCIArAagSDA/i+vepQFYE/0H0MeLhZwvH3wL8NF/Blx6tPxvt+KcGuRMAQZqNACvIARAFAjACwcazXhM36mxtmvdCICmAegYBHZuA3pesmgDQBfZL+yfAgDuZETMukYEgAZP3TpaaM3JqBoARgT8FGMBKmtnFuHJ+1WDMNTOIPfenV2AehABA3wWwG3qAlSnATimFKDCAlQEuQi4YU27agA2iDt5XqoJ3j9RmpP1IADDTFtU2Aw01BQxQmB5nO4cgKz55/RVdKxdnQDLw/zvdTYAhJpDBa24+jjw0Y8Dr/82LB56J34leQ8A4MqOQwAMVNqANvKlH1Mxi9NLe/l894WXj3C83AwKUO2C+MK6GwCqARggCOy8Abg3S9cAqJvzJdkAgDQAa0QAzDTg05FqAMwGYGs+w4M1IkaqAxhWA9AvAvDoFZW+W7mzVVO0AdgvhMC1LkCzC4B03ZALkyTORcAAcPm1ZdhYk6INsM8gaE4MmzRwnpfiuFM/+yEQANMjf0i7X4oCNILyr5EGoKAARaGiAGWMBkBSgNYuAAb0RdDxDfX3GhsAev9TPjuAfFH4/f8Qn/ng/4yXkB9T5TjZIAdgTLvllcVQgO6/nK8blkmKz76oXHPGRGsyN8tqzzeHAKwtB6BjEFhgbgyKs7mfW9aG3AmbUToFSA1oL+7n9psaArBGEbClASDHNipPZKMBePKBy7WWgjQReEgovW8NwJsevoifeN+T+M63PoTv/YZHWr8PDQOTFCCKANDAsbKEUFZ/sgG4/SUgKV7Xgv4DAB96JudyfuCN1720JeaO/3kDsFnF3QNDIABmsz0kxE6/U6Pr8cpjys7wxufs11OkrRj7SwTgTChA5Hcigtv1IgBqjFgy4ZlATlmV1VwD4LABHdOcV1XMIvjBq2q+u3OskLcxIQD9UICGdAGiGoB+RcCbRP8BgAHP8r1XWgMQmxqAs6MAUcvJW0cLrSMfKwUozQRe92B9k/S2R9SEtTeABaEsKwm4h13In3hfc6GtWbtzhgJU2IBe3Jq4m6Kty8DxTdUA3OgmAAaAj37ja/Fdb3sYl7f9BtXzHIDNLu4e2B4AUQzXmBgdaQ1Ag3s8mgGXXg3c/mKOpKWJthjITu9AAMDsYrm4kTagQyIazqKLy6OzQQA0DYCjAbh1qBqAag0A5wKUX4uTMKd2SZ3K5iAA9jj68H1XACz0p41s19lKha+lADEi4EGDwPrMATC/63h+B5/akDthM2oeuRAASQE6GxEwpQCZCMCYGgAQEXCdBaispx+6gI++57V488MX8UPvevVgh9Z3DkBfRZueg4VOAXKmZgJqoo9Pcg6wJgB+qvXxXNmZegdBWW4RG7Z7cq8XR4NbBwIw5IKZvnfjRkPSgJIFcPc57b1EKbJVmxoyCMykOK2l6C7oWSEAWgOQsM+5eaQWu1erEAAh7MVc8W8hhJ5RsinjDNPUPPqA/fuM7fv0ggAM6gI0XA7Apm1inSMAPZZLA6AoQGRBO1tjDgART904WuJhTQMwngs2IzdjjBBvqLAApfVXv+PpoQ6prL5dgPqqPQMBOFkmZYNXuRO/RZ2A7igBMADc1x2Z8Ck7CGwc5/S8/IpbuA6TA7C+RpGK+xvf41efBP7k4/nfNz6HSZhv8gRIIRZ5NgBdYCdnigBQDcBN9fdZuwAZpVGAqkTAQL6YSxUthn7HWRSW5hdmLsBoi2kAHr52GdPwJc0BaGyLzsYi4PkFYLqrhOjAwDkA/TUArcMDf/QCJgAAHZtJREFUR1KbdbQjLyqo5RCA52aPqydff9Pajuvy9qR0t7h5uBhtDoAgA16CEK97wK8BWEf17QLUV1EK0MEirncAkmVmAbz8afXvlhSgpmWe07FNZOdVXawGYAAa3rqCwPL3Vp/V+Ho0hMDyvS7gSD0+VxzuVToSEXCqQgTX2wAQDUDSkQIE2Is5rQEIyN/jmfMqi1mcRtMtPH7/rvbY2ChNraide0Zg4bqSgDsiDZueZbNZRzvy0ilA+YAWJyluHOYIwOcvvAv47l8APvz3gUf/1NqOKwqDUgx6a8Q5APTGTESkCVzPusaKAFAXoIPTuD4DQJZmBXqztC7EhYeB2XoaL3OwPBcBb1ZxO9dDIACWBmBAu1i6GG9MzaENwI3PltfzRUEaAHLfxYm0AT1jETCtsWkAyHhWSQECGAqQuhbprv/YFszO4han0Qyvf0BvAMa2cdI4CAywaUDrygHoHAS22UYW5xSgHmvGUIBuHC4hXfIeuDgH3vw9Z3FouLo7w+3jla0BGJENKIXmxJAuAC3KXKyORgNAXYBOWyIAL/6hgl9bOgC1qXMNwGYXqwEYAAEwF8hDcubpBN6YkmZkAUSz/L0ugVAbCvetLMtIENhZIACOcf+scgBWvAbgxpHneAbYizmDAiRrbAtmZ3FN2mQLr79+AcDXy4fG9n0aU4AAWwg8qAiYUoA6BoGdU4DOSxYXBCbpPwDwwF61p/2QJeHTZZLi5QMlrBoTAjCbKY5nGI1n9x+ww3rGggDsGUnAtRkAsuhE/9xvq7+vra8BsDUA58PRJtW6NAAWz3bABY9mA9r0c3bvV9qum58rGwgOAUhIdsKZjCUjQACmobpW3AhAPlftzqJ66o65mNt4ChDzG4UzvP66gQCMbNxsRe00EYAhKUCaBqBjENg5Bei8ZG1NbAqQ1gBcaB6u1FdRK9Cv3T4p/x6TBuDSzlb5987WVsUz119jdQHSKUAr3KY7Zr4UoK+QBmBNAmAgP4f0NI5tJ+u8qmttOQCWDeiALkC0AWiKNAgBXCsStA+ex06Wj/2XGA0ADU8bMr/EWSNoACgC4MoBkBqA2t1/oBIBmGoNwIaMMyYFKJwCQWBp48b2faIwaD6uWwjAmlyAutqAGmPgbGTNWF1t1tGOvLggsBfJbvsDNam2QxYdQL96Z5wNgCA3fTByCtCZwPZMUQTg4DTGLRIO440AHDyv/l4jAgCYlIvz4WiTitUADJAEbDYaQ1KAdBvQFvc4oQE9mDwHALjAIADxaBGAS/zjA1SdBuB0leCoMKyo5f8D1RoA2gBsiguQuQiO8vXDw5e2tI2fMW6c0LHcC6GwRMDr0gD06wI0pD5piBrflbPBRW1Apa/xi3dHQgEiAyjdbRkTBajPzrzvshGAcdw6O1OdAqQhADsV53DbsdO3Rg0AoE8O5zagm1V8DkD/44m5Ez8sBYjYgLZpSCUCAOD6Mm8AOA1ATFxvRqMBmO4Nu/NqFKXicDkA1AK01gEIqHEBCtm/R13mb1Q0AEIIvI4Igcf4fei47kcBMhGANbkA9ZwDsGmbWJt1tCMvTQOwZDQAF86yAbDpR2EgxrXo0hL6xoUATE0bUM+wq6ErCES5G5QjAC1cgGRtXwV2rvV9iJVFkZUx7mSdl7u4JngICpDttDHcvUe1Pq241cRC9/6iAeA0ADoCMBIK0BrpP4B+v3MIALUA9aMA+dmAbsw4Y26CRWr9kAuB8xrj95k2Pd8XTBegAUXAYY8UoA03stisox15cTagGgXoLDUAzAC6NQm9U1vXUnTHY2QUIPNGH4sGAFA0IBsBaNgArJn+Axi+6xs2eN7rxSEAO2vRAKxLBNyNAnRt8RUABgIgNQDJCClAa6T/APUUIJoCXBsCBlS7AE14OtCoy6IAqXNArUDHOG42pgDtPgCA3AcbkgRsU4DG91tU1WYd7chrTlJ1TwtI86UCAQgDwe7Cr6u4xeCY+P8A9JtxZAiAlQMwIuREIgCHJAcgEMCFqhyF2UVoAy6wVgGwrHMNwOYWdw8MYSts7pAPKZql12Crnfkrj0HeV1dPvgyARwBWZ04BOnsEoK4BoI5m17w0AG4XoGdelbszXdya4NVXtxse6RlVBQLwuutKCDxGBIA2z17HF05yFy3676GqRw3ApouAx7XK2vDiRMAvFA3AfbuzM9015pqPrenILtYRawAsCtCIEACZBrxM0vJ6u7w9RVB1jEGQ7/id3FaPnQECoGkARjiRnZe7OA/sIRYjlg3ogAvmsCsFaDIHLj0K3PkyLp1+BQKp0QDku+zJGF2A1twARGGAMBBI0ozNAbjZmAJEnyPyMa6oj7z7NXj8vl08cf/uIDS1QcrUAExUA/Dso5fx8KUtfO3OCb7pdfet+cDqa9oUAQByIfDhi/nf60oC7mwDutkagA25EzajKAXoZJXidJXgTuHKcpb0H4DfQZmPTTykdebjujTHmgQM6GFg8nqrdACStXVZbwDWLAAGWkDF5zWaMjUA2wM4AAHrFdpREXNrNOPa64A7X8YkXeAh3MRFaQMaRMA0p27EKUEAzoQCFCBHKlQjsm4KEJCjAMfLhLUBvdkkBAyoRJCjMMA3P3U/NqocLkBAvtn48f/wz+DF/VM8dt8uxla5/uwIe7PIv8G9+iTw/KfydcD84nAHNyUI0KQbGmSihOcuQPdwTULla75YJVrg1v1nKAAGcjqIOdGMKgUY0Hc8RkYBGrUGgElfrcwAkGXu+J1FA6BBxeM5p+dVX+Z4MgT/H7B32YbcMf+Bdz6K+/Zm+N53PIL79lpu2lxTOoDHgudxSRQagPmlPCsAwCo5YxEwYI+xa0YAAEUP4SlAav686qUBGC+FtFVZFCD9HOzMolEu/gHgL77vSTz76CX8lW9/g/+LvumngDd8B/DB/3zYZvQNH8qpetffAjz+3k5vZaOTI1tT1dQr4C4ZTwkhsDUJcbRMcLpKNAeg62fcAASBwOWdqdaUjFsDMC4KkOk8cmaTNlO7TANwucoCVNbWFfX3dBe48HCPR+VX5xqAzS1zYT6EBShg32tDUoD+9JP34f/7T76lmznCVWUF+rj4ukIAyAKbUoDOzIktiIBU5YacRQPw/7d350FylOcdx7/P7K1dIWkloRskIYSQLCQQGFKAEcIGIRMJDIQjOA5X2RR2AU4MwaYcwmXHUMbGODZ2iFMxiqGMSQAnNveRIlAxhyAITIwRN9gcQiAQYld680f37Pb2zK56tTPTb3f/PlVTO+rplt7VvD3TT7/P877lOoCNm3vZ3LtlwJSW0RqAYa8DkIcAYIgRAN8duOtEDtx1mKlJE+fCcdfVp0FRY2fAlx4Nno9wEpT4uasRgIIrX1R/2NOfjw3ppwBB5VzKXq0BALEaAL8+wOPpKV6NALRXGQFIcscs+oU/YdcRfxhuDwUA2RUfARhVJRCthcppQOvbT0Y8M1pkBGB3e5EOCy9kI3c1o0XAqX2WeDACMG1ssOL7hk09nLn6sQGpQG+OpAag2joHWVOxDkD61xC5YVaT77vKEYBsfYdlq7UZ0BcA9G7hD+/6kwIEMCFWCOx1AKAagMS6qgYASUYAIl/4E+fVsEXJDXvBGPFGRQ1AnT5PKkbffP+SjawFsGfpd/3bBx0BSCsFKPZ+pRAAnLt8Xt95f+fTf+Cs6x/rC47KIwCdrU3JRqub/L2BtF0qUoA60mmHDKpydNLzz6aYbLU2A8rLjG/6aEvfFKCQ7iJgZfFhVP9qAPydBSie7uDTCEDVFKDh1gBMaPwUoKB1ALIsfk501qkIuJELgdVE16RgVV1gbumV/u3t0RGAlNcBAC9GAPabPZ4ffXZJ37n/qydf55wb1tC7ZWtfANCdJP0HYiMAOQgAhlgHQPxQMQtQxm5iZau1GVC+q765d2AKUNo1AFA5jOp3DYBfH+Dxi1Of1gGoNt9/oiHzyR/rfz7zgBq2KDmlAGVXRQpQvYqAs3aXzQwmzKncHrnAjs4C1JTWZ0n8AjOFAABg6W478oOT9uoL7H75xGucfcMaNm7uBRIWAEP+agDiv0OGagCKotpUyFmSrdZmQPSi+sW3P+h77kMNgFKAtl88svdqBKBKClCiaUB3WwFHXQPHrYYZH69Dy7atMzJ6Ua8iUqmPilmA6jUNaBbn2o6sCNwnUgPQG00BKvAsQGWH7D6J752wV9/n6i+feK3vtXjt2qAG3EDKwWeJGVjk99AIgHe0ErAM0N7S/1/6wltBANDaXGJMR/opLfEP0mhbveBxEXBlDYA//3dVi4CTpACVmmDR8bD7EXVoVTLH7zODSTu0ceTiqczozsgKnQJUqQGo2whAvAbAn+B7UNVS6qIjAJEUoPSKgNOvAYha/rHJXHX8nsT/OxKNZoLXI8jbLfo7tagGwDeNnKGsHnJylvijvco0ZpN2aBv5zBI1EF8N2OsRAN9qAErZqgFI/KWZsn1nj+eh80c47aKkoqIGoE4jOJmcaaNaClCkBqA3MgtQqtOAljW3e3GB+ek9ptC7dTHn3LCG8iBJtVXsq8pbDQCE34NhKrFGALwT/wzM2kQW2WptBrRX+RL0If8fKi8KvSsC9vgOTvzE9mkWoGojAIlSgDyhi/9satQ0oBWrbWYhAKiaAhStAYgUAac2C1Dk/Ur57n/UqsXTuPyYRX2zNO43u3voA8ryVgMAA1NhVQPgnSbLYHpiRE7OEn9ERwDKfJgCFGBCl+dFwD7XAHi9DsDA0ZLWplLd7saKlMXPgXr1ucqVgP059wY1fhcchtF/oT+wBqB/BMCLWYA8CgAAjl4ynb12HseGTT0smj4m2UF5qwGA2CiNRgB8UyoZJaNvtCoTo5MRfl1l5UC1vPpJo/0IALxPAerasf95546D75eCypWA/bkIiacAjets0V11qbvGzQKUwRSglg56uqbTuvGl/m2Ri2w/pgGNfP57FgAAzJrQObwDPB5B3m7RUQ2tA+Cl5qZS3wJ2WSsCzslZ4o9qd9V9mAEIgjt0bc0lNoed1bsUoCmLYen58M5LsOdJabdmAJ9HAEa1Ng24C5FoDQCREYqnrtRrFqdGrwRcKz3jZg8MACI1AFuUAlR7eawBaNIIgO+aS0Z5zepM3JyIyMlZ4o9qIwCTx/gxAmBmjO9s5dUNQVGRdyMAZrD0b9JuRVXRi46mknl1h93M6Gpr5t0Pg3mzs1IALNnWsBoAj9fgGErPuDnw0n39GzqqFwH7kQI0dvD9ssLjWeS224ARAD+uI2Sg6A0KFQEXXLWL6h09SQGCgWlA3tUAeCyaAuTT3f+yaB1AlgqAJbsaVgOQ0RGArd39hcCbrX3AHVwVAdfBgBGAnHy3qQbAe1lezDJbrc0An1OAAMZHCoG9WwfAY9ET26f8/7JoHUCiNQBERih+J75eNQBZTQFifP9UoO+XRvc9X/vqBv59zat9f1YNQI3ksQagSSMAvhswApCVz6ZQTs4Sf7RVDQD8OXGnje0vJEq8xLpUpAD5JjoVqEYApBHi03PWayXg+Lnn4/lX1YT+EYD3S130vPshV9z2DDc++jIuMjnQzOEWu9ZK7kYAchgARH+PFn+uI6Rf84AUoIx8NoVycpb4oz2WAza6rZnOOuXGbo9TD5jF82+9z+IZY9lpvFZeTaopMt2XlyMA7dERAL8WUZN8atQsQNF/xsdzbzBNY6by1NadmV96gf/umcuFl9/Lpp4tfa9PHdPOBUfMZ/GMlPLv8xYARPPlm3LyGagRAO9Fb0hkZnQy5M+VaU7EZ9bZ0aP0H4DZE7tYfdp+aTcjk1qaghmUmkr+neTRFCCNAEgjVNQA1GkEwMxoLhm9W12mhthbmkuc+NFX2aP0HA9uXUAPwcV/V1szZyzdhVMPmJVuHVbeAgDVAEgKohf9WSsCVgBQY/GFwHxK/5GRaQ0DAB/vQkaLgDULkDRC9IvPrPoiiLXS3BQEAFmaZ7ulqcQ7jOb+rYuAYCTj+I/vxDmfnMvE0R5czOWuBiCPswBpJWDfZXkEIFutzYD4HR0FAPnRHRZQj/UwxWbfWd1AMBPLwmkJV84UGYHoF9+oliZKdQyMy/UGPgbfg2lpKrFy0VQADpo7kV+d9QkuO2qhHxf/AGNmBD+bWvufZ1ke1wEYMz342do1YB0J8UdzhgOAnJwl/ojPrKMAID++tmJ3/uXBFzjtwFlpN6XCqsVT2Wn8KKaMaWesZgGSBoh+8XXUKf+/bIf2ZjZu7mVMh3/B91C+e/xiLvvMworVur2w/1lgJZixL4zqTrs1I1fKYRHwQedBSwfM+SS0qmbPR9HZ0NoyNEIJCgBqrnIEwJO7PTJihy6YzKELJqfdjKrMjL12ysEwvmRGdASgXvn/ZX916G780wPrOPPgOdve2SPlRfq8NHoyHHZp2q2onWjBrOWkBqB7FhxxZdqtkCFM3qGdJ195l+7O1kzVKEHCAMDMZgBXAp8CDLgTONs592KCY9uBi4GTgLHAGuA859z929tonykFSESKILqAVb1mACo7esl0jl4yva7/hmTcxHnQ0Q2b3oaZ+6fdGimIc5fPY9yoVlbsMaWuaZD1sM1PbTMbBdwNbAY+BzjgEuAeM9vDOff+Nv6Ka4FPA18BngPOBG4zsz9xzq0ZSeN9VJkCpBEAEcmfaApQvVYBFkmsdRR88WHY8BJMWZR2a6Qg5k4azeXHZrO/JbltczowG9jNOfcsgJk9AfwO+Dzw7cEONLNFwInAKc65n4Tb7gPWAhcBK0fUeg9pBEBEimBAEbCvaS5SLJ3jg4eIbFOShKWVwEPli38A59w64AFgVYJje4AbIsf2AtcDh5lZ7m6Pd8QCAG9mfBARqaFo8ZtGAEREsiVJALAAeLLK9rXA/ATHrnPOfVDl2FZg2BVdZvZI+THcYxshOgLQ3dlKWx3nxhYRScuUMR19KY9zJ41OuTUiIjIcScZtu4H1Vba/DWxr2pGhji2/nitNJWNCVxtvbtzM7AmdaTdHRKQuxnS08NNT92XtKxs4Zu8czCMvIlIgmUvcdM4tKT/fe++9XZptGcy3jlnITY++wukHzk67KSIidbPPzG72mZm7+zgiIrmXJABYT/U7/YPd3Y8fu/Mgx0L/SECuLJs3iWXzJqXdDBERERGRCklqANYS5PLHzQeeSnDsrHAq0fixHwHPVh4iIiIiIiL1kiQAuAXYz8z68lnMbCawf/jaUG4FWoBjI8c2A8cBtzvnNg+zvSIiIiIiMgJJAoAfA88DN5vZKjNbCdwMvARcU97JzHY2s14z+3p5m3PuMYIpQL9jZqeZ2SEEU4DOAv62dr+GiIiIiIgksc0AIFzpdxnwf8BPgdXAOmCZc25jZFcDmqr8nScDPyFYPfg/gBnAcufcoyNuvYiIiIiIDEuiWYCccy8CR29jn+cJgoD49k3Al8OHiIiIiIikKEkKkIiIiIiI5IQCABERERGRAlEAICIiIiJSIAoAREREREQKRAGAiIiIiEiBKAAQERERESkQBQAiIiIiIgWiAEBEREREpEAUAIiIiIiIFIgCABERERGRAlEAICIiIiJSIAoAREREREQKRAGAiIiIiEiBKAAQERERESkQc86l3YbtZmZvAC+k2ITdw59Pp9gG8YP6gpSpL0iZ+oJEqT9IWSP7ws7OuYnxjZkOANJmZo8AOOeWpN0WSZf6gpSpL0iZ+oJEqT9ImQ99QSlAIiIiIiIFogBARERERKRAlAIkIiIiIlIgGgEQERERESkQBQAiIiIiIgWiAEBEREREpEAUAIiIiIiIFIgCABERERGRAlEAICIiIiJSIAoAREREREQKRAGAiIiIiEiBKAAQERERESkQBQAiIiIiIgWiAGA7mNkMM7vRzDaY2btmdpOZ7ZR2u6R+zOwYM/uFmb1gZpvM7Bkz+4aZjY7tN87M/tHM3jSz983sTjNbmFa7pTHM7Ndm5szskth29YcCMLMVZna/mW0MvxMeNrNlkdfVDwrAzPY3s9vN7I9m9p6ZPWpmp8T2aTezy83stfC75EEz+0RabZaRM7PpZva98L38IPwumFllv0TvvZmVzOx8M3vezD40s8fN7Ohat1sBwDCZ2SjgbmAe8Dngs8CuwD1m1plm26Su/hrYAnwVWA78ADgDuMPMSgBmZsCt4etfAo4GWgj6xvQ0Gi31Z2YnAIuqbFd/KAAz+zxwM/AIcBRwLPBzYFT4uvpBAZjZHsCdBO/t6cBngN8A15rZGZFdrw1f/zpwBPAacJuZLW5si6WG5gB/BqwH/muI/ZK+9xcDFwJXA4cDDwE/N7MVNW21c06PYTyAswguBOdEts0CeoEvp90+Per2vk+ssu0vAAcsC/+8KvzzwZF9xgBvA1el/TvoUZd+MQ54HTghfO8vibym/pDzBzAT2AScPcQ+6gcFeACXAR8BXbHtDwIPhs8XhX3h5MjrzcAzwC1p/w56bPd7X4o8Py18j2fG9kn03gM7ApuBv4sdfxfwRC3brRGA4VsJPOSce7a8wTm3DniA4INecsg590aVzb8Jf04Lf64EXnXO3RM5bgPB3T/1jXz6e+BJ59zPqrym/pB/pwBbgR8OsY/6QTG0Aj0EAWHUBvqzLVaG+9xQftE51wtcDxxmZm0NaKfUmHNua4Ldkr73hxH0petix18HLDSzWSNvcUABwPAtAJ6ssn0tML/BbZF0HRT+fDr8OVTf2MnMuhrSKmkIMzuAYBTozEF2UX/IvwOA3wLHm9nvzazXzJ41s2ifUD8ohn8Of15lZlPNbKyZnQ4cAlwZvrYAWOec+yB27FqCi745DWmppCHpe7+AYATg2Sr7QQ2vMxUADF83QZ5X3NsE6QBSAGY2DbgIuNM593C4eai+AeofuWFmrcA1wBXOuWcG2U39If+mEtSAXQ58EzgUuAO42szOCvdRPygA59yTwFKCUZ1XCN7z7wNfcM5dH+62rb7QXedmSnqSvvfdwDsuzPsZYr8Ra67VXyRSFOEdu5sJ6j5OTrk5ko5zgQ7g0rQbIqkqAaOBv3TO3RRuuzucAeR8M7sqrYZJY5nZrsAvCO7UfoEgFWgV8EMz+9A5tzrN9onEKQAYvvVUv2MzWHQnOWJmHQS5u7OBg5xzL0deHqpvlF+XjAun/P0aQbFXWyxvt83MxgLvof5QBG8RjADcEdt+O8GsP1NQPyiKywhyvI9wzvWE2+4ys/HAd83sZwTv9c5Vji33hbervCb5kPS9Xw+MNTOLjQLUvI8oBWj41hLkaMXNB55qcFukgcysBbgR2BtY4Zz739guQ/WNF51zG+vcRGmM2UA7QVHW+sgDguli1wMLUX8ogrXbeH0r6gdFsRB4PHLxX/Y/wHiC2V3WArPC6cSj5hPMIBTP+5b8SPrerwXagF2q7Ac1vM5UADB8twD7mdns8oZwuHf/8DXJoXCu/9XAMuBI59xDVXa7BZhmZgdFjtsB+FPUN/JkDXBwlQcEQcHBBB/m6g/592/hz8Ni25cDLzvnXkf9oCheBxaH9UFR+wIfEty5vZVgnYBjyy+aWTNwHHC7c25zg9oqjZf0vf81wUjSn8eOP4lgxrl1tWqQUoCG78fAF4GbzewCgnldLwZeIigKlHz6PsGJeynwvpntF3nt5TAV6BaCOZ+vM7OvENwJPh8w4FsNbq/UiXPuHeDe+PZgvSdecM7dG/5Z/SH//hO4B7jGzCYAzxF8ThxKf32Q+kExXE2wANytZvYPBDUAKwnWCLnSOfcR8JiZ3QB8JxxRXkewoOQsKi/4JEPM7Jjw6ZLw5+Fm9gbwhnPuPudcovfeOfdHM/s2QQ3Re8CjBEHCMoL+VLs2VxYay7aEOcBXAp8i+BC/i2AhmOfTbJfUj5k9T/X8PQgW7Lgw3K8buAI4kiBN5EGCBeIeb0AzJUVm5oBLnXMXRLapP+RceDf/G8AxBLn+vwW+6Zz718g+6gcFYGaHA+cRpHy1A78HfgRc45zbEu5TnjzgRGAs8DhwXvnGgWRT+PlfzX3OuaXhPoneezNrIrhJcDowmWCxsIucczfWtM0KAEREREREikM1ACIiIiIiBaIAQERERESkQBQAiIiIiIgUiAIAEREREZECUQAgIiIiIlIgCgBERERERApEAYCIiIiISIEoABARERERKRAFACIiIiIiBfL/gIkXolEswScAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 921.6x633.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# lets take an example run - 47th, i.e. 46 in pythonic counting\n",
+    "run_no = 46\n",
+    "# check the score\n",
+    "print(scores[run_no])\n",
+    "# show parameters\n",
+    "print(parameters[run_no])\n",
+    "# get the timeseries and plot them\n",
+    "ts = results[run_no]\n",
+    "print(ts[0].shape)\n",
+    "plt.plot(ts[0])\n",
+    "# but it's random data, so...\n",
+    "print(extras)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# cleaning - run after you check the results!\n",
+    "rmtree(output_dir)"
+   ]
+  }
+ ],
+ "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": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/3-saving-resuming-optimisation.ipynb b/examples/3-saving-resuming-optimisation.ipynb
new file mode 100644
index 0000000..635e391
--- /dev/null
+++ b/examples/3-saving-resuming-optimisation.ipynb
@@ -0,0 +1,501 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# GPSO optimisation example with saving the state and resuming\n",
+    "\n",
+    "Yes, you can optimise for $X$ and then realise: oh, I need more! You can even realise that after few days, so yes, you can save the state of the optimiser and resume from saved state on the disk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "import os\n",
+    "import numpy as np\n",
+    "from shutil import rmtree\n",
+    "import logging\n",
+    "from gpflow.utilities import print_summary\n",
+    "\n",
+    "from gpso.param_space import ParameterSpace\n",
+    "from gpso.optimisation import GPSOptimiser\n",
+    "from gpso.utils import set_logger, make_dirs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "again, objective function and parameter space are without change"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define objective function - compare with paper Fig. 2\n",
+    "def obj(point, offset=0.0, rotate=True):\n",
+    "    x, y = point\n",
+    "    if rotate:\n",
+    "        ct = np.cos(np.pi / 4)\n",
+    "        st = np.sin(np.pi / 4)\n",
+    "        xn = ct * x + st * y\n",
+    "        yn = ct * y - st * x\n",
+    "        x = xn\n",
+    "        y = yn\n",
+    "    return (\n",
+    "        3 * (1 - x) ** 2.0 * np.exp(-(x ** 2) - (y + 1) ** 2)\n",
+    "        - 10 * (x / 5.0 - x ** 3 - y ** 5) * np.exp(-(x ** 2) - y ** 2)\n",
+    "        - 1 / 3 * np.exp(-((x + 1) ** 2) - y ** 2)\n",
+    "        - offset\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# bounds of the parameters we will optimise\n",
+    "x_bounds = [-3, 5]\n",
+    "y_bounds = [-3, 3]\n",
+    "# number of points per dimension for plotting\n",
+    "N_POINTS = 120"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "space = ParameterSpace(\n",
+    "    parameter_names=[\"x\", \"y\"], parameter_bounds=[x_bounds, y_bounds]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### First optimisation\n",
+    "Let's be naive and optimise only for 25 evaluations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "opt = GPSOptimiser(\n",
+    "    parameter_space=space,\n",
+    "    exploration_method=\"tree\",\n",
+    "    exploration_depth=5,\n",
+    "    update_cycle=1,\n",
+    "    budget=25,\n",
+    "    stopping_condition=\"evaluations\",\n",
+    "    gp_lik_sigma=1.0e-3,\n",
+    "    n_workers=4,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2020-03-20 20:13:04] INFO: Starting 2-dimensional optimisation with budget of 25 objective function evaluations...\n",
+      "[2020-03-20 20:13:04] INFO: Sampling 2 vertices per dimension within L1 ball of 0.25 of the domain size radius in normalised coordinates using 4 worker(s)...\n",
+      "[2020-03-20 20:13:04] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:13:07] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:13:07] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:13:07] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:13:07] INFO: After 1th iteration: \n",
+      "\t number of obj. func. evaluations: 6 \n",
+      "\t highest score: 2.019346080880167 \n",
+      "\t highest UCB: 0.546413730058452\n",
+      "[2020-03-20 20:13:07] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:13:07] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:13:07] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:13:08] INFO: After 2th iteration: \n",
+      "\t number of obj. func. evaluations: 7 \n",
+      "\t highest score: 2.019346080880167 \n",
+      "\t highest UCB: 3.88313824695841\n",
+      "[2020-03-20 20:13:08] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:13:08] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:13:08] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:13:08] INFO: After 3th iteration: \n",
+      "\t number of obj. func. evaluations: 9 \n",
+      "\t highest score: 2.019346080880167 \n",
+      "\t highest UCB: 3.5729359051552176\n",
+      "[2020-03-20 20:13:08] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:13:08] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:13:08] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:13:09] INFO: After 4th iteration: \n",
+      "\t number of obj. func. evaluations: 12 \n",
+      "\t highest score: 2.019346080880167 \n",
+      "\t highest UCB: 3.259899162869712\n",
+      "[2020-03-20 20:13:09] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:13:09] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:13:09] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:13:09] INFO: After 5th iteration: \n",
+      "\t number of obj. func. evaluations: 15 \n",
+      "\t highest score: 6.822127355673395 \n",
+      "\t highest UCB: 7.162332354240296\n",
+      "[2020-03-20 20:13:09] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:13:10] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:13:10] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:13:10] INFO: After 6th iteration: \n",
+      "\t number of obj. func. evaluations: 19 \n",
+      "\t highest score: 6.822127355673395 \n",
+      "\t highest UCB: 6.965065385265492\n",
+      "[2020-03-20 20:13:10] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:13:10] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:13:10] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:13:11] INFO: After 7th iteration: \n",
+      "\t number of obj. func. evaluations: 23 \n",
+      "\t highest score: 6.822127355673395 \n",
+      "\t highest UCB: 7.586308159952344\n",
+      "[2020-03-20 20:13:11] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:13:11] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:13:11] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:13:12] INFO: After 8th iteration: \n",
+      "\t number of obj. func. evaluations: 27 \n",
+      "\t highest score: 6.822127355673395 \n",
+      "\t highest UCB: 7.071776374810146\n",
+      "[2020-03-20 20:13:12] INFO: Done. Highest evaluated score: 6.822127355673395\n"
+     ]
+    }
+   ],
+   "source": [
+    "# log_level INFO: reasonable amount of information on what is happening\n",
+    "# log_level DEBUG: a lot of information on what is happening\n",
+    "set_logger(log_level=logging.INFO)\n",
+    "# run vanilla, with default initialisation and just 1 repetition of objective function (since it's deterministic...)\n",
+    "best_point_v1 = opt.run(obj)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GPPoint(normed_coord=array([0.27777778, 0.72222222]), score_mu=6.822127355673395, score_sigma=0.0, score_ucb=0.0, label=<PointLabels.evaluated: 1>)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead>\n",
+       "<tr><th>name                   </th><th>class    </th><th>transform       </th><th>prior  </th><th>trainable  </th><th>shape  </th><th>dtype  </th><th>value                 </th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "<tr><td>GPR.mean_function.c    </td><td>Parameter</td><td>                </td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>0.3111087091292495    </td></tr>\n",
+       "<tr><td>GPR.kernel.variance    </td><td>Parameter</td><td>Softplus        </td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>3.3853761677721703    </td></tr>\n",
+       "<tr><td>GPR.kernel.lengthscales</td><td>Parameter</td><td>Softplus        </td><td>       </td><td>True       </td><td>(1,)   </td><td>float64</td><td>[0.10490081]          </td></tr>\n",
+       "<tr><td>GPR.likelihood.variance</td><td>Parameter</td><td>Softplus + Shift</td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>1.0483154985737434e-06</td></tr>\n",
+       "</tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(best_point_v1)\n",
+    "print_summary(opt.gp_surr.gpr_model, fmt=\"notebook\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So we can see we are not there yet...\n",
+    "Let's resume"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2020-03-20 20:13:12] INFO: Resuming optimisation for with additional budget of 25\n",
+      "[2020-03-20 20:13:12] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:13:12] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:13:12] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:13:13] INFO: After 9th iteration: \n",
+      "\t number of obj. func. evaluations: 31 \n",
+      "\t highest score: 6.822127355673395 \n",
+      "\t highest UCB: 6.375786411526237\n",
+      "[2020-03-20 20:13:13] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:13:13] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:13:13] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:13:14] INFO: After 10th iteration: \n",
+      "\t number of obj. func. evaluations: 36 \n",
+      "\t highest score: 7.880218798440746 \n",
+      "\t highest UCB: 7.62971639089788\n",
+      "[2020-03-20 20:13:14] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:13:14] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:13:14] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:13:15] INFO: After 11th iteration: \n",
+      "\t number of obj. func. evaluations: 40 \n",
+      "\t highest score: 8.04056301525883 \n",
+      "\t highest UCB: 8.036435700243205\n",
+      "[2020-03-20 20:13:15] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:13:15] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:13:15] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:13:16] INFO: After 12th iteration: \n",
+      "\t number of obj. func. evaluations: 45 \n",
+      "\t highest score: 8.04056301525883 \n",
+      "\t highest UCB: 8.105553226532628\n",
+      "[2020-03-20 20:13:16] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:13:16] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:13:16] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:13:16] INFO: After 13th iteration: \n",
+      "\t number of obj. func. evaluations: 52 \n",
+      "\t highest score: 8.102201204076508 \n",
+      "\t highest UCB: 8.074071892420118\n",
+      "[2020-03-20 20:13:16] INFO: Done. Highest evaluated score: 8.102201204076508\n"
+     ]
+    }
+   ],
+   "source": [
+    "# let's just resume for additional 25 evaluations\n",
+    "best_point_v2 = opt.resume_run(additional_budget=25)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GPPoint(normed_coord=array([0.23662551, 0.68518519]), score_mu=8.102201204076508, score_sigma=0.0, score_ucb=0.0, label=<PointLabels.evaluated: 1>)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead>\n",
+       "<tr><th>name                   </th><th>class    </th><th>transform       </th><th>prior  </th><th>trainable  </th><th>shape  </th><th>dtype  </th><th>value                 </th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "<tr><td>GPR.mean_function.c    </td><td>Parameter</td><td>                </td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>0.19149976301992186   </td></tr>\n",
+       "<tr><td>GPR.kernel.variance    </td><td>Parameter</td><td>Softplus        </td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>3.163835951715155     </td></tr>\n",
+       "<tr><td>GPR.kernel.lengthscales</td><td>Parameter</td><td>Softplus        </td><td>       </td><td>True       </td><td>(1,)   </td><td>float64</td><td>[0.12871433]          </td></tr>\n",
+       "<tr><td>GPR.likelihood.variance</td><td>Parameter</td><td>Softplus + Shift</td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>1.0482618379448408e-06</td></tr>\n",
+       "</tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(best_point_v2)\n",
+    "print_summary(opt.gp_surr.gpr_model, fmt=\"notebook\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So now we done! We have the same result as optimising 50 straight (makes sense...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save and resume\n",
+    "Are we **really** done? Let's try to save the current state of the optimisation and imagine getting back in a few days...\n",
+    "\n",
+    "Side note: saving the optimiser state unfortunately loses callbacks and saver if any. We need to provide new ones for the resuming operation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2020-03-20 20:13:17] WARNING: When saving, all callbacks and saver will be lost!\n",
+      "[2020-03-20 20:13:17] WARNING: output could not be created: [Errno 17] File exists: 'output'\n",
+      "[2020-03-20 20:13:17] INFO: Saved optimiser to output\n"
+     ]
+    }
+   ],
+   "source": [
+    "output_dir = \"output\"\n",
+    "opt.save_state(output_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2020-03-20 20:13:17] INFO: Resuming optimisation for with additional budget of 25\n",
+      "[2020-03-20 20:13:17] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:13:17] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:13:17] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:13:18] INFO: After 14th iteration: \n",
+      "\t number of obj. func. evaluations: 56 \n",
+      "\t highest score: 8.10343046869997 \n",
+      "\t highest UCB: 8.098495608167328\n",
+      "[2020-03-20 20:13:18] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:13:18] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:13:19] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:13:19] INFO: After 15th iteration: \n",
+      "\t number of obj. func. evaluations: 61 \n",
+      "\t highest score: 8.10343046869997 \n",
+      "\t highest UCB: 8.09876340778756\n",
+      "[2020-03-20 20:13:19] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:13:20] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:13:20] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:13:21] INFO: After 16th iteration: \n",
+      "\t number of obj. func. evaluations: 66 \n",
+      "\t highest score: 8.106012429922291 \n",
+      "\t highest UCB: 8.105366980413711\n",
+      "[2020-03-20 20:13:21] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:13:22] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:13:22] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:13:22] INFO: After 17th iteration: \n",
+      "\t number of obj. func. evaluations: 71 \n",
+      "\t highest score: 8.106012429922291 \n",
+      "\t highest UCB: 8.10560815883375\n",
+      "[2020-03-20 20:13:22] INFO: Exploration step: sampling children in the ternary tree...\n",
+      "[2020-03-20 20:13:23] INFO: Selecting step: evaluating best leaves...\n",
+      "[2020-03-20 20:13:23] INFO: Update step: retraining GP model and updating scores...\n",
+      "[2020-03-20 20:13:24] INFO: After 18th iteration: \n",
+      "\t number of obj. func. evaluations: 77 \n",
+      "\t highest score: 8.106141171216883 \n",
+      "\t highest UCB: 8.105864463205666\n",
+      "[2020-03-20 20:13:24] INFO: Done. Highest evaluated score: 8.106141171216883\n"
+     ]
+    }
+   ],
+   "source": [
+    "# we need to provide objective function again, it's hard to save callable like that...\n",
+    "best_point_v3, opt_loaded = GPSOptimiser.resume_from_saved(\n",
+    "    output_dir, additional_budget=25, objective_function=obj\n",
+    ")\n",
+    "# the `resume_from_saved` directly resumes the optimisation and in the end return new best point and optimiser itself,\n",
+    "# so you can save it again"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GPPoint(normed_coord=array([0.23433928, 0.68564243]), score_mu=8.106141171216883, score_sigma=0.0, score_ucb=0.0, label=<PointLabels.evaluated: 1>)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead>\n",
+       "<tr><th>name                   </th><th>class    </th><th>transform       </th><th>prior  </th><th>trainable  </th><th>shape  </th><th>dtype  </th><th>value                 </th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "<tr><td>GPR.mean_function.c    </td><td>Parameter</td><td>                </td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>0.19149976301992186   </td></tr>\n",
+       "<tr><td>GPR.kernel.variance    </td><td>Parameter</td><td>Softplus        </td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>3.163835951715155     </td></tr>\n",
+       "<tr><td>GPR.kernel.lengthscales</td><td>Parameter</td><td>Softplus        </td><td>       </td><td>True       </td><td>(1,)   </td><td>float64</td><td>[0.12871433]          </td></tr>\n",
+       "<tr><td>GPR.likelihood.variance</td><td>Parameter</td><td>Softplus + Shift</td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>1.0482618379448408e-06</td></tr>\n",
+       "</tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(best_point_v3)\n",
+    "print_summary(opt.gp_surr.gpr_model, fmt=\"notebook\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Not much better... Meaning that we couldn't top the highest score by optimising more. At least we see that we optimised the hell out of it!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# cleaning - run after you check the results!\n",
+    "rmtree(output_dir)"
+   ]
+  }
+ ],
+ "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": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/optimisation_example.ipynb b/examples/optimisation_example.ipynb
deleted file mode 100644
index 0c526cb..0000000
--- a/examples/optimisation_example.ipynb
+++ /dev/null
@@ -1,458 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# GPSO optimisation example\n",
-    "\n",
-    "This notebook showcases the basic example of optimisation using the Gaussian Processes surrogate optimisation package. The method is described in Hadida et al. and reading the paper is recommended in order to grasp what is happening.\n",
-    "\n",
-    "**Reference:**\n",
-    "Hadida, J., Sotiropoulos, S. N., Abeysuriya, R. G., Woolrich, M. W., & Jbabdi, S. (2018). Bayesian Optimisation of Large-Scale Biophysical Networks. *NeuroImage*, 174, 219-236."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# imports\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import logging\n",
-    "\n",
-    "from gpso.param_space import ParameterSpace\n",
-    "from gpso.optimisation import GPSOptimiser\n",
-    "from gpso.plotting import plot_parameter_marginal_distributions, plot_conditional_surrogate_distributions, plot_ternary_tree\n",
-    "from gpso.utils import set_logger\n",
-    "from gpflow.utilities import print_summary\n",
-    "\n",
-    "plt.style.use('seaborn-poster')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First we define the objective function we would like to optimise. Note that this example is the same as in the original paper that introduced the GPSO method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# define objective function - compare with paper Fig. 2 \n",
-    "def obj(point, offset=0., rotate=True):\n",
-    "    x, y = point\n",
-    "    if rotate:\n",
-    "        ct = np.cos(np.pi/4)\n",
-    "        st = np.sin(np.pi/4)\n",
-    "        xn = ct*x + st*y\n",
-    "        yn = ct*y - st*x\n",
-    "        x = xn\n",
-    "        y = yn\n",
-    "    return 3*(1-x)**2. * np.exp(-(x**2) - (y+1)**2) - 10*(x/5. - x**3 - y**5)*np.exp(-x**2-y**2) - 1/3*np.exp(-(x+1)**2 - y**2) - offset\n",
-    "\n",
-    "# bounds of the parameters we will optimise\n",
-    "x_bounds = [-3, 5]\n",
-    "y_bounds = [-3, 3]\n",
-    "# number of points per dimension for plotting\n",
-    "N_POINTS = 120"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First we will plot the true objective function with its maximum point so we get ourselves familiar with the problem we want to optimise."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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R/VV/9sLrq9L3JygL/tm0h0EVT74AADRj8AUAoBmDLwAAzV6e+R6umgulut+0XOxFbTnZ3rlW6G18dB2vSpmwL497Nev+Umasn8cidbpaq6oZrvWSDhmw1vVqbujbh8z7aVnn39Vz/S37+k+o270UIyy9V5N0r1V7M1flGv3xzXXs2Tky5e+NJ18AAJox+AIA0IzBFwCAZt8/851kGaGur9Yq+gHG2xd7PUeTuZHX7Y4zzer8vXF+X8uAZdlOb5wZP6QYNdYNx7rj8bJnvLJ9yHGft7e8WftKb1+q8fbyf8Kv3popj/LnI6TvV9j2zd9fAGbw5AsAQDMGXwAAmjH4AgDQ7OMz3/n+rfu+INcyFg9X2/x4mumOV9fXp+0nl5XVCYeM2DPgccZt8/eG+X29V/T2eJen5bTtbzmW9lr+z318cVbtu1387FS1TpWyVnxnPPkCANCMwRcAgGYMvgAANPv4zHdv1TraNH9wNQPeOzNOpjPanTPZdDyVe0GPL5jWJcf9y/pY56u1t3H+X13ebn9Z/3k/F+vlvF2+SmGvZr5n+UaBrtdez5oBa8Y8mnt4Wf4lM7bl8FkM135e72N6N/9sPPkCANCMwRcAgGYMvgAANCPzbZYy4sR6E4fcKNa9xl7Kxf0vmnEOD5frcENmW2VztganZZxrep2wLIcM15ZXXdac9Z8/XyTT/SXb/tYQV891vNre6y99L9aHOtQFT9Z4fzff/f1hjCdfAACaMfgCANCMwRcAgGYvz3xThnn069P+9ubz3YZMMcwHXK2Trdat6vla3aq+nxdnvJrpvrpu2nJP6/U8rvv1XPefP/+WjPcmhbl3fXPX7aLO/3uXzfXcL9ZnWvYXa57Derv3istLWB/vvbB+vBrfnH6fZRZPvgAANGPwBQCgGYMvAADNDs98985k91atm/XX6wbjDLd6PpYRhxl+PcPVjHa83jPecH72+rB9MYdLUm9sX5ZM2Pan61PvZ/28w/y+8hepzvdiy//8+ddle+yrhLb3u56LbH/bnpt9n0CWtY5XM16tA9aM2DLhWGM+d29Fsa83KS+Ow5MvAADNGHwBAGjG4AsAQLOX1/nuTXOah2aws5lztU632ls5ZM7nmEHK9sXezdW61HLdb6jNVF63m3JN2YH0N9aPX1+vta53fX3IkJVd71Dbmub33WS+UgN8k59mvVanm35W2/Xp+w7ey1nPTeqA5fzSXMWpDjjVnFtGa9+X2DfDPb4nwLH7x2vx5AsAQDMGXwAAmjH4AgDQrD3zLdf9WiY6Dklne0N7TqSn8wjrdTnkbNpfV9brb0dpOlo7vmWM4zrT1Gs4ZZaWGYfzS7wOd7zeMtoQnFnmG5btfIZ7/7d+w+l6j+t8fz39xN7kvVovZzu21N3qewtvRj/rVMervZ81A055d/o+QpqLOvZ6tvWErOjDky8AAM0YfAEAaMbgCwBAs0uqfdM62aPtPb+v1fVWM91i3a5meJrDpTpby3TT+1Gxl7BsHnO3Wm1lvl61D1Q//7tcIM14PSQf7/8mta+3WPdby0mr94v2dvblfw6oGarXHMv3B2T1TXtBp8w33LtpLmLNr63uV9ZbTXn1+wWzvaBtezJh7IcnXwAAmjH4AgDQjMEXAIBmsc736Ey4Op+uRbih7rfa61lzJZ3P9ax1ppaDjXM2/4vxLKqWw6U639S7OdSVpjpU7/Ws+x+fT4rNci/n7fY3/XzD7ZgyZMuALROW14fez6o6v6/V+T4tP+ynd3yz6L2Z+lirfG9tl1PGq+tTpusZcLg3U6/nkBlX2b+VRMQfbe9e4IonXwAAmjH4AgDQjMEXAIBm072d986EZ3s/6+Fm6341J9NfV7zXr+Rcsn656f40h5JMOuRwKmeKS1ge53A+B6suj88nSfPlWgYu1/MeDljOfO/j9Xo+MfNdxrljqvu9XJ4y35gvy/cPZN92LYr3Wsr3073l8/3qe5fjpcy53Os51KwvY5T9YgZPvgAANGPwBQCgGYMvAADNDp/PdzYTrucqob+t9l6W0O5szZFlb+PIdnlYRjx+AyfrrzvOBFOEbvP3Wg4X+udabeZ2h97beRkvD851WfL8uJ7xSm4Zckeluab2ataM93obn4/N/xsy4JTx+py58n6f63yL94JmsHrvV889fb8g1YCnDDjl36mut3xvFmvoq8iI8YwnXwAAmjH4AgDQjMEXAIBmh2e+Sbl/ZnE+2N0zYCvclaPJ5jpnqp6P1RXLsp5PYr2Z9fQt0x3XAVtOFjJlnwP1T2f67yyTtWBxez0et/EBLFMOma32br7K/lMGXJ1/2uuwtxf4vm5P6Pk7Ab9lX3ap5Fxvlvlul73G2k53eLzqXNKxpjzM7xvv1ZgBh+9jNIe0ZMKvdXQvZ8WTLwAAzRh8AQBoxuALAECz12e+xV7O8f/lYyYc+reGut5UZ2v9ca1frPYiHh9Pc65qEJfqfD0DPg/Xp1rOamqSckbNYLXXc9x/OJ7NDxwyYF0fc1M5fsxJw+fzeLpBT9L4275vcNb3Inm51SjL+mJ+nep+0/cF0ny93ss51P3a8caFvt77OSwzfy8m8OQLAEAzBl8AAJox+AIA0Gz3zPfV/U81h7HM7zTOtc6aKWp/W12W/WuupDmaZrzay/mhuZzlbqk2cbzstZLjnM3qhkPGmz6/lLlabaX2Hw7Hq84HbHXFljlLbqq1syHzVXa/6K+/ej9or+/Nn7XvttSoy5tZw71m92I19BWp5jzV3abM1ns7j/eX5uvdu66Xul2M8OQLAEAzBl8AAJox+AIA0Gw68311xlvfv+Zay3hZQt/TQ3OmccYrpZi2veZq91BnXO2367WSqV9uyIQnezdrhqnX1+pmiwdI16s6H6/XAY/3N9vb+SG9qy/y+/HtKdc9nfXaScarma59nyDV+Y5/Vqqq8wFXM2HPmENdb3n+333resmEfzaefAEAaMbgCwBAMwZfAACalTPfak5R3n7n/VUPmOpqLePT3EperZmiZrwpV9McrsrrSkMGLPMVW61lmENVpV7HnvFO7n/nOt+UGT+Kc+JazrkInV9aanefa1utN3PIgNO9Zd8/0Pem56rSmxXpRzllurFOt5wp68tr/9iQ4X6W7vl7FU++AAA0Y/AFAKAZgy8AAM1i5rt3xjub6aacpnq8RHOuVdenjNHqdmsZr81fW42Ai7WV3k93uLvyBbb3q/MrF99fzJBTZhvW6wFyXfhW+X4OvcafVz/ks/L3pr2cZX2qsS7O72vbjzf/i38Laj/cKQOeznip68WOePIFAKAZgy8AAM12n1KwKv5XUPF/guLrj/6vn8n/pvTSpPH2s9IUd/rfzmlKQZX+W7jennK8/zRloV2/yc+rKlTDxAs6On6ONMbbx/aS4b+tq+0o05SF1Sn+6v92zP03M/+N/FleXVqkePIFAKAZgy8AAM0YfAEAaGaZ79GlRdWMdzbT3fv4VSmTLGeMxddX7Z2Z2/nEdp7j9dXrFds/huMl3d9JeF6d2z3KYjUD1msndXb2/QTLgMfHt16iurqYsJczvaNb5b5XxIg3w5MvAADNGHwBAGjG4AsAQLPd63xnY47pjLZYl5qmyPPzSbWJ2+Wcaf65feC/vT7VrVYz4L0zzrT/csZrG9T2P3t91HTGm9p37vgdhOp7T601bfpFbW9pGfD49SnTtdaqtn64Oto7kyXjfS/vVterePIFAKAZgy8AAM0YfAEAaFbOfGdr3fZervYi9oxtGxzl7cPyUlOtjYxT4oXcbbpudjJD3jvTTdcjno8s7z7lZbif1vCdg9F3FtL3D2a/T6DX1s5NXx9mv9T199ALOkW6j+L8k0d/vwGv9e4Zr+LJFwCAZgy+AAA0Y/AFAKDZZTbXqL682rvZejVPZsDr+hiu99fPZcJKc7o0B+psTldfH+aEnawznu29bMffuVdz6g0eNi9nvKv0S0732zbzDSeXvk8gW6d7QdffwtzMejy7VpP5vmZ88bMuZsTAkXjyBQCgGYMvAADNGHwBAGi2f2/ncgYa1styrOsNGa9lbudxxpa33y7X64Afg6Vcx5syXM3lfP1Jlrc71O31Hej62Nt6to55597V5brd6ncMQsar95NuX6nzTdc+1f16He52i5t89nq8m75e7x29lsXvI+iTQjnBDb2ibfP6EdDo0+p6FU++AAA0Y/AFAKAZgy8AAM1i5rv7nJfFDarz8WoG6xmcZGySwV0sI06ZXKoDHudyiWeeqfZSMlwJ4q6yfJag7STrb35Gcn6hoa++ulrHXO5FHeZbDndgqjtPdd32nYFixusZ8Z/vn3Su6drYtQ69l0/yev0+gZ6RXekwH/BZ9yfrU815NQKM3weYzBTJjOd8eqab8OQLAEAzBl8AAJox+AIA0Gz3Ol9VrqMM62Pv5WKdpWa8KfO19WF7y6w1A17GvP+u1uWOM97bul1eZf3XdVy7af1z5XxsTtdizFWeX7iY8SaxjjfNz1usC7+kDNh6Pevx/tm+nvlu6b1k18I+zOrdOr53qhmwRbx6OuH9zn7foorMuOa7Z7yKJ18AAJox+AIA0IzBFwCAZuXMN85vWu3lXOyFXO61W637leVfl3Hme7lskyjP8MYZb7U/r9ZeWsYrOd71ur0gZznASX7/yqmezqGqvaClDrmas+ny0RlvWF/u3RzreEMGHOebfsp8i9fWeiVrb+ZQt6uf9eM8znTts7MifVmvReX6Wel8vsUI+tMS1L0z0KMz5J+W2c7iyRcAgGYMvgAANGPwBQCg2XSd7+69n4sZcJrP1+swx5lcqvv9JRmvZr5eByyZsNYla6FsoJnqVYojNeP1umPJeO3z26732tDt8k12YHXNYb7fo2kOVa7rjRnveNnvJ9m+2Ev8+TsC1Wup10Ln6/WfrlC3GzLdh7wX+76AZLz2fQc9evi3IApvV99PnJv6w5DJvheefAEAaMbgCwBAMwZfAACaHd/beXK9bR/qYnX9qE5yWf4lcwt1vLqsGfB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-      "text/plain": "<Figure size 921.6x633.6 with 2 Axes>"
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-     "metadata": {
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-   "source": [
-    "# true objective - reference surface\n",
-    "x = np.linspace(x_bounds[0], x_bounds[1], N_POINTS)\n",
-    "y = np.linspace(y_bounds[0], y_bounds[1], N_POINTS)\n",
-    "x, y = np.meshgrid(x, y)\n",
-    "o = obj([x, y], rotate=True)\n",
-    "plt.imshow(o, vmax=8., vmin=-8., cmap=plt.get_cmap(\"cividis\"), origin='lower')\n",
-    "cbar = plt.colorbar()\n",
-    "i, j = np.unravel_index(o.argmax(), o.shape)\n",
-    "plt.scatter(j, i , marker='d', color='black')\n",
-    "cbar.set_label(\"Objective-True\")\n",
-    "_ = plt.title(\"Reference surface\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now the party begins.\n",
-    "\n",
-    "## The party\n",
-    "\n",
-    "### Parameter Space\n",
-    "First we will initialise parameter space by calling `ParameterSpace` object which takes two arguments: parameter names as a list and parameter bounds as a list of lists or tuples. The order must agree, i.e. first parameter in names will have bounds as first tuple in the bounds argument.\n",
-    "\n",
-    "The `ParameterSpace` object initialises optimisation space, set-up normalisation (GP surrogate works with normalised coordinates between 0-1) and initialises ternary tree for partitioning with root leaf."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "space = ParameterSpace(parameter_names=[\"x\", \"y\"], parameter_bounds=[x_bounds, y_bounds])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Optimiser\n",
-    "\n",
-    "The main guest our party is, obviously, the optimiser itself. Since we want to make him confortable, we offer number of user-defined arguments. Please note, that the defaults are very sensible, if you are not sure, what you are doing, just keep them at their default parameters.\n",
-    "\n",
-    "#### Arguments:\n",
-    "* `parameter_space`: parameter space to explore, which we initialisd in the previous step; the only required parameter\n",
-    "* `exploration_method`: method used for exploration of child leaves intervals; \"tree\" for growing tree up to `exploration_depth` and predicting at the center of each leaf, or \"sample\" for uniform sampling within the leaf's region; in both methods the best sample is recorded and represents the current leaf's score\n",
-    "* `exploration_depth`: depth of the exploration step, for \"tree\" method it is a depth to which the particular leaf is grown, for \"sample\" is this number multiplied by number of parameters (i.e. dimensionality of the optimisation problem) squared and this many samples are taken in each exploration steps\n",
-    "* `budget`: budget for the `stopping_condition`, i.e. how many iterations, evaluations of objective function, or up to which depth is the partition tree grown\n",
-    "* `stopping_condition`: to what stopping condition the `budget` referes to: either \"evaluations\" (algorithm stops after this many objective function evaluations), \"iterations\" (algorithms stops after this many iterations), or \"depth\" (algorithm stops after partition tree is grown to this depth)\n",
-    "* `update_cycle`: how often (in iterations) to update the GPR hyperparameters\n",
-    "* `varsigma`: expected probability that upper confidence bound (UCB) < f; it controls how \"optimistic\" we are during the exploration step; at a point x evaluated using GP, the UCB will be: $\\mathrm{UCB} = \\mu(x) + \\varsigma\\cdot\\sigma(x)$; $\\varsigma = \\mathrm{erfc}^{-1}(p/100)$ which corresponds to the upper bound of a $p$ confidence interval for Gaussian likelihood kernel\n",
-    "* `gp_lik_sigma`: initial standard deviation of Gaussian likelihood function (in normalised units)\n",
-    "* `n_workers`: number of workers to use where applicable (e.g. during initialisation or multiple objective function evaluations)\n",
-    "* possible keyword arguments:\n",
-    "    * `gp_kernel`: covariance kernel for GPR, Matern52 by default\n",
-    "    * `gp_kernel_ARD`: whether to use ARD for lengthscales in the GPR kernel, by default False (usually worse, but for large dimensions might help)\n",
-    "    * `gp_meanf`: mean function for GPR, constant by default\n",
-    "    * `gp_mu`: initial value for constant mean function in GPR, 0.0 by default\n",
-    "    * `gp_length`: initial value for lengthscales in the kernel function in GPR, 0.25 by default\n",
-    "    * `gp_var`: initial value for variance in the kernel function in GPR, 1.0 by default"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "opt = GPSOptimiser(parameter_space=space, exploration_method=\"tree\", exploration_depth=5, update_cycle=1, \n",
-    "                   budget=50, stopping_condition=\"evaluations\", gp_lik_sigma=1.0e-3, n_workers=4)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Run\n",
-    "\n",
-    "Finally run the optimisation! Fortunately, now running the optimisation is easy! Just call the `run` method of the `GPSOptimiser` instance. This method take two arguments:\n",
-    "* `objective_function`: this is obvious, the actual objective function we want to optimise for; must be callable and take unnormalised parameters as an argument and output scalar score (see example objective function in this notebook)\n",
-    "* `init_samples`: specify how to deal with initialisation of the optimisation problem; default is to evaluate two verticies per dimension, equally spaced from the center (i.e. within L1 ball of 0.25 of the domain size radius), or user can specify at which coordinates we should initialise\n",
-    "* `eval_repeats`: number of repetitions for objective evaluation function when it is stochastic and some statistics on the score is necessary; repeats are not counted towards the budget of objective evaluations; multiprocessing is used when n_workers > 1; 1 by default\n",
-    "* `eval_repeats_function`: function for aggregating multiple evaluations (see `eval_repeats`), has to take axis as an argument, good choices are mean, median or max; their nan- version can be used as well, when the stochastic evaluation might expectedly fail; mean by default"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": "[2020-02-12 23:04:26] INFO: Starting 2-dimensional optimisation with budget of 50 objective function evaluations...\n[2020-02-12 23:04:26] INFO: Sampling 2 vertices per dimension within L1 ball of 0.25 of the domain size radius in normalised coordinates using 4 worker(s)...\n[2020-02-12 23:04:26] INFO: Update step: retraining GP model and updating scores...\n[2020-02-12 23:04:29] INFO: Exploration step: sampling children in the ternary tree...\n[2020-02-12 23:04:29] INFO: Selecting step: evaluating best leaves...\n[2020-02-12 23:04:29] INFO: Update step: retraining GP model and updating scores...\n[2020-02-12 23:04:30] INFO: After 1th iteration: \n\t number of obj. func. evaluations: 6 \n\t highest score: 2.019346080880167 \n\t highest UCB: 0.5464070082940224\n[2020-02-12 23:04:30] INFO: Exploration step: sampling children in the ternary tree...\n[2020-02-12 23:04:30] INFO: Selecting step: evaluating best leaves...\n[2020-02-12 23:04:30] INFO: Update step: retraining GP model and updating scores...\n[2020-02-12 23:04:30] INFO: After 2th iteration: \n\t number of obj. func. evaluations: 7 \n\t highest score: 2.019346080880167 \n\t highest UCB: 3.8831167804743725\n[2020-02-12 23:04:30] INFO: Exploration step: sampling children in the ternary tree...\n[2020-02-12 23:04:30] INFO: Selecting step: evaluating best leaves...\n[2020-02-12 23:04:30] INFO: Update step: retraining GP model and updating scores...\n[2020-02-12 23:04:31] INFO: After 3th iteration: \n\t number of obj. func. evaluations: 9 \n\t highest score: 2.019346080880167 \n\t highest UCB: 3.5729634688840584\n[2020-02-12 23:04:31] INFO: Exploration step: sampling children in the ternary tree...\n[2020-02-12 23:04:31] INFO: Selecting step: evaluating best leaves...\n[2020-02-12 23:04:31] INFO: Update step: retraining GP model and updating scores...\n[2020-02-12 23:04:31] INFO: After 4th iteration: \n\t number of obj. func. evaluations: 12 \n\t highest score: 2.019346080880167 \n\t highest UCB: 3.2593252005336995\n[2020-02-12 23:04:31] INFO: Exploration step: sampling children in the ternary tree...\n[2020-02-12 23:04:32] INFO: Selecting step: evaluating best leaves...\n[2020-02-12 23:04:32] INFO: Update step: retraining GP model and updating scores...\n[2020-02-12 23:04:32] INFO: After 5th iteration: \n\t number of obj. func. evaluations: 15 \n\t highest score: 6.822127355673395 \n\t highest UCB: 7.162291047007811\n[2020-02-12 23:04:32] INFO: Exploration step: sampling children in the ternary tree...\n[2020-02-12 23:04:32] INFO: Selecting step: evaluating best leaves...\n[2020-02-12 23:04:32] INFO: Update step: retraining GP model and updating scores...\n[2020-02-12 23:04:33] INFO: After 6th iteration: \n\t number of obj. func. evaluations: 19 \n\t highest score: 6.822127355673395 \n\t highest UCB: 6.960651973659721\n[2020-02-12 23:04:33] INFO: Exploration step: sampling children in the ternary tree...\n[2020-02-12 23:04:33] INFO: Selecting step: evaluating best leaves...\n[2020-02-12 23:04:33] INFO: Update step: retraining GP model and updating scores...\n[2020-02-12 23:04:34] INFO: After 7th iteration: \n\t number of obj. func. evaluations: 23 \n\t highest score: 6.822127355673395 \n\t highest UCB: 7.586285098159925\n[2020-02-12 23:04:34] INFO: Exploration step: sampling children in the ternary tree...\n[2020-02-12 23:04:34] INFO: Selecting step: evaluating best leaves...\n[2020-02-12 23:04:34] INFO: Update step: retraining GP model and updating scores...\n[2020-02-12 23:04:35] INFO: After 8th iteration: \n\t number of obj. func. evaluations: 27 \n\t highest score: 6.822127355673395 \n\t highest UCB: 7.0717997260158905\n[2020-02-12 23:04:35] INFO: Exploration step: sampling children in the ternary tree...\n[2020-02-12 23:04:35] INFO: Selecting step: evaluating best leaves...\n[2020-02-12 23:04:35] INFO: Update step: retraining GP model and updating scores...\n[2020-02-12 23:04:36] INFO: After 9th iteration: \n\t number of obj. func. evaluations: 31 \n\t highest score: 6.822127355673395 \n\t highest UCB: 6.375799694658347\n[2020-02-12 23:04:36] INFO: Exploration step: sampling children in the ternary tree...\n[2020-02-12 23:04:36] INFO: Selecting step: evaluating best leaves...\n[2020-02-12 23:04:36] INFO: Update step: retraining GP model and updating scores...\n[2020-02-12 23:04:37] INFO: After 10th iteration: \n\t number of obj. func. evaluations: 36 \n\t highest score: 7.880218798440746 \n\t highest UCB: 7.629715839329732\n[2020-02-12 23:04:37] INFO: Exploration step: sampling children in the ternary tree...\n[2020-02-12 23:04:37] INFO: Selecting step: evaluating best leaves...\n[2020-02-12 23:04:37] INFO: Update step: retraining GP model and updating scores...\n[2020-02-12 23:04:38] INFO: After 11th iteration: \n\t number of obj. func. evaluations: 40 \n\t highest score: 8.04056301525883 \n\t highest UCB: 8.036437181815858\n[2020-02-12 23:04:38] INFO: Exploration step: sampling children in the ternary tree...\n[2020-02-12 23:04:38] INFO: Selecting step: evaluating best leaves...\n[2020-02-12 23:04:38] INFO: Update step: retraining GP model and updating scores...\n[2020-02-12 23:04:39] INFO: After 12th iteration: \n\t number of obj. func. evaluations: 45 \n\t highest score: 8.04056301525883 \n\t highest UCB: 8.105552860078896\n[2020-02-12 23:04:39] INFO: Exploration step: sampling children in the ternary tree...\n[2020-02-12 23:04:40] INFO: Selecting step: evaluating best leaves...\n[2020-02-12 23:04:40] INFO: Update step: retraining GP model and updating scores...\n[2020-02-12 23:04:40] INFO: After 13th iteration: \n\t number of obj. func. evaluations: 52 \n\t highest score: 8.102201204076508 \n\t highest UCB: 8.074069359067812\n[2020-02-12 23:04:40] INFO: Done. Highest evaluated score: 8.102201204076508\n"
-    }
-   ],
-   "source": [
-    "# log_level INFO: reasonable amount of information on what is happening\n",
-    "# log_level DEBUG: a lot of information on what is happening\n",
-    "set_logger(log_level=logging.INFO)\n",
-    "# run vanilla, with default initialisation and just 1 repetition of objective function (since it's deterministic...)\n",
-    "best_point = opt.run(obj)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": "GPPoint(normed_coord=array([0.23662551, 0.68518519]), score_mu=8.102201204076508, score_sigma=0.0, score_ucb=0.0, label=<PointLabels.evaluated: 1>)\n"
-    },
-    {
-     "data": {
-      "text/html": "<table>\n<thead>\n<tr><th>name                   </th><th>class    </th><th>transform       </th><th>prior  </th><th>trainable  </th><th>shape  </th><th>dtype  </th><th>value                 </th></tr>\n</thead>\n<tbody>\n<tr><td>GPR.mean_function.c    </td><td>Parameter</td><td>                </td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>0.191463341062018     </td></tr>\n<tr><td>GPR.kernel.variance    </td><td>Parameter</td><td>Softplus        </td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>3.1636517343464297    </td></tr>\n<tr><td>GPR.kernel.lengthscale </td><td>Parameter</td><td>Softplus        </td><td>       </td><td>True       </td><td>(1,)   </td><td>float64</td><td>[0.12871221]          </td></tr>\n<tr><td>GPR.likelihood.variance</td><td>Parameter</td><td>Softplus + Shift</td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>1.0001404331383313e-06</td></tr>\n</tbody>\n</table>",
-      "text/plain": "<IPython.core.display.HTML object>"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# WOHOOO, check the best point\n",
-    "print(best_point)\n",
-    "# and print summary of our trained GPR model\n",
-    "print_summary(opt.gp_surr.gpr_model, fmt=\"notebook\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Afterparty\n",
-    "\n",
-    "OK, optimiser told us we are done, let's check."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": "<Figure size 921.6x633.6 with 2 Axes>"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# first, just draw coordinates of the \"best point\" as returned by optimiser to the reference surface\n",
-    "plt.imshow(o, vmax=8., vmin=-8., cmap=plt.get_cmap(\"cividis\"), origin='lower')\n",
-    "cbar = plt.colorbar()\n",
-    "i, j = np.unravel_index(o.argmax(), o.shape)\n",
-    "plt.scatter(j, i , marker='d', color='black')\n",
-    "ii, jj = best_point.normed_coord\n",
-    "plt.scatter(int(np.around(ii*N_POINTS)), int(np.around(jj*N_POINTS)), marker='o', color='purple')\n",
-    "cbar.set_label(\"Objective-True\")\n",
-    "_ = plt.title(\"Reference surface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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drqM5n/3xNaN9jzNaDepCZqu9qENOqvv1gPENawas/zq+frtAOfPV7WRGqz9rzIS1b/X4Bvx6wy4TzqWsNkPOjpc6Gj8dT74AADRj8gUAoBmTLwAAzdrrfD8toizX9f7wjDfbrzbUUppaU10eN+aM4/V0XxLqal2vrnGr3ykI/7p9jTNgvX+t8828/dUaZ7++r2wn64L1eBu5mvdauc9G1tnXvz2bo/dyruLJFwCAZky+AAA0Y/IFAKBZyHyPVte7OuL0vZqT45UP0Osf6/1Q49Vvf3O8BGfpn88U+tpez5dxxqt1u1fNNfV+pC7X1fXGTFpzTzl+kPm6Vy6b+Wbrfl3Gm+/tPHcbc/H6zsWTLwAAzZh8AQBoxuQLAECzj1/Pd3aGvTqDzY4/+27KtZCync2A/6A595YGiy+XseofyHBmvNigeHy/mum+TKYb1gvWTPnb52N95qv5+Xg8l/Hqa5vtDW3v7xIOGN5P/H6A7h9L95I255Op/iw8+QIA0IzJFwCAZky+AAA0exytrjdt8u131/WWe0MfXLJ1cz0D1hwx/IH0bpZeyzcdMJkBx4x5XIesw+kHMF7+71/R2V9HsH2zbe/nXO9mm8Gm1//NHe+kM1kyXAzw5AsAQDMmXwAAmjH5AgDQbHmd78FbE7f79Iw3y8Zi2QzYLPp6k7pZzYDfmqlqraxeTfY/dT1fsx6w1vFqXXCo1R3V+S4u+s7Wqdq6XxPSzq7rtYqFttm6Xz9e9oSpl8diPPkCANCMyRcAgGZMvgAANPv43s5V6V7Oaw9P38/sHKp7/LBer64HHJb31TpeGU+CyLu8nOF4eYde2p/Y1P2G+wnr98rper3Q2/m/Z5gS4bLq+r+hV7PL400v6HJdr6kjVtmff7Xw+uHUePIFAKAZky8AAM2YfAEAaEbmu1h1/d/Z51czWnv+5FgqXM8EneH4UMerw0mGK8dr3a32an5pjvge1w1rXbCu76v3E37873W+tm/4eH9WtjdyyFhNL+jsesHZut6w364XrNtz1+/txnrBa2XXz+bJFwCAZky+AAA0Y/IFAKAZmW9SNUZz51cz3nbdtZAumJOX7y77XyGTHWe0WsardcF6O3r8qDfz5RL7G8fz//7+bJ3v6o9SMmO16/2a49MZ7eS63ni8u+Hc9chkfxaefAEAaMbkCwBAMyZfAACanT/zNcu9au7zPtiKuXZ93+LtjupE/7N/HDRlc61sbWM154p1u+MXLKwhGzJerfPVXstSF3zfDqfr8+r9ZTNhzaDfm3061rimWWU/Wu6t8plqLuO9aiZsrmczXrcecDg8d/xstpfz4hsig16LJ18AAJox+QIA0IzJFwCAZofPfGNm2X398Q0cK0Hu5zLe2bWV/nxdT/cy3r6M627fkuFeJeMN6/WG7xjI+cVM+D3YOcqHf+do6//mey3ntsP9pI8fh9TZ34XVmerZM1v93Tzbesau1zNPvgAANGPyBQCgGZMvAADNDp/5KpsBm7pfN57qzpgP19p5cq7VXbsYPg5Xk5Nqxir/PA3tk5sz4e85mL13GTuGUOPdVTZzrGa8xbpet17vT6vrRS+efAEAaMbkCwBAMyZfAACaLc98V2eq2Qz4GmohjxayruVyLdeLOY5ntk1ovLoOOMac45xUPw66+6WZbHMm/P0PsjXMLiM2SyOn1XtB63hzM95wvXD8z6rrPXpd8KfV/fLkCwBAMyZfAACaMfkCANBs9zrf2Zlwthd0zCTdBX9Wr+d0rmWCsZv5557N9ZJsf+NQ9xsO2GzeTca7OhP+nttqH+jMWsD/OUHvbe3a0uHyyRrw2b2b099vCOPXPpzVut6jZ7QY48kXAIBmTL4AADRj8gUAoNnuma+TzTWqtYtaB6wZsM29XI5lMuK91y+OdbhzM16X6WZrMR19O8N+k4seLRP+fjmb8SZ7Pev54fDh3rp0Xezk3s2urtc5Wl9z5+yZ8dnrfnnyBQCgGZMvAADNmHwBAGj2cBnp2cyuC9UM+BrqgE2mGxaUlbOvmtPVkrVqzpVdU7Wa8d6KtYzVOnFf+2pyVNl+hQMWZsIytl471gjrWLmMUzNmVa3Jzx6Q7d0ch9+5d3MxU8bW2TJgnnwBAGjG5AsAQDMmXwAAmrXX+e7+/+Ft7+YtPfom9/+KKbEM4AqPXeg+t7rS5lw218rV/cYMWMfLbWeVM2FTKxvLwscZcTYT1v7N30/XjOum3x8Y1Aj/Z6xcDXvIv2X/7EwyGwlnv++wWvXvuumv57Ej0B+HJ18AAJox+QIA0IzJFwCAZssz32zusTqX0PsJOZfJhF0GrL2AXy+X4WYz4Jxwtq1l1P2uTjeX8Yb9yXa6sz8fIcM113N1vi4TvrzGnxcd7xYy5Pe3/zY1yFojnLxXzYSzr0VVfr3eWu/mal3v3uvzkuluHb3ulydfAACaMfkCANCMyRcAgGaP+bV5ph/rwfqZxhxrXMto13eVTO92S2bAmjOFXG0crGVzrZs0V9YM9yr/PAv77fh6f+Pjp/fLNTlkNscMx2fPD3W82c/bf4+Pdby59X21hjh81vReZDu+dr29n8l4a+f/NEfLgHnyBQCgGZMvAADNmHwBAGhWrvOtZrx2v7v+6rrPbCZ8H2e8md69/xk/V/frXg6X8cZez+NtXY83m/Hq8UHx8xHX5zWHh17Ncr4NQt0NbemPn/m8vW56c+MMVDPhUHNc/P5BWJtarr+69/PsjLd8Q+F6yeHIdD8aT74AADRj8gUAoBmTLwAAzdKZb3fGWx4vmZuk13cNtZPjOlldz1V792pQpplwlu3VnKzjzWa8enx4P5K9nVU6R5Nt+367DLzY67nUKzrZJ9rVoNte0e77B+FmJzd7Fi7jrY+vw+euR8Z7bHvX/fLkCwBAMyZfAACaMfkCANDMZr7V/w9eXa/V91fNjWeZTM7VjV61366uzyoDvPR4zSFMDudk+9NqhusyW7c/m/GuzsnSyyVnM2EznGs97TLh758Xfa9cDXq1Lnh6JlyV/rtgcu/mMH7qcDLeH44nXwAAmjH5AgDQrN5e0v1vxuL55SXoqqUo7gT938rufxva4037StfuUGRfz+7SodmlYo4bzy4hqPv1fPf6uA+UiTm+vz/ZsqZqaVKoqwrM/8Y2P3xudC/7v5n9gLXz+d/M+I4nXwAAmjH5AgDQjMkXAIBmIfPNto+stovMLllXLV1Znbu4TDZUe2TbC05vN2n2Ty4VOnruZTPexaVJ7vrfNzNlSZfLHqVJcoD7u2W4Ny//d9XcdpVH/6xjXzz5AgDQjMkXAIBmTL4AADR7VJcIDMeb82dnvNn2hqsz4WqGmz2/KluHu3dt4+JV6oJqXXA8QS+Q2p06OGa849MlIg6tUG2mW86E55qd8Z79+ww4Fp58AQBoxuQLAEAzJl8AAJqleztXM9TqknThfO1FfDH77f3Lkn6TM+C4f24v5+4crXq95gh3uu5M+Pu2tlrO9nqOvZzd8aYuOCwpOL4B/1LUPh12ScCDZbzZJQyrrw/2xZMvAADNmHwBAGjG5AsAQLPY23nx+qm2F3Ay49XM2GfA40y3mmE7MSMd1z7ajNeOn9sfjx//gNWMWgf4tBSrWhcda2m/H6wny2a2Znx2XbB7N10fbVcE7Rx8vd1sxuvOJwPOqb7+VTz5AgDQjMkXAIBmTL4AADSzdb7lTNQc73o1ZzPemwRdyzPindcH9sfX6ojf8gfu/NdL+wHnrpe1d3/g6vFZ38ePGemWXU5X1/vV8xfXBaczymKNefW9KZ+/c8aIY+HJFwCAZky+AAA0Y/IFAKDZY3YOku2HajNksz+b8d51/y13vvZ+tusPX3R/LqiydbauFlNzueTxmuG+QsYrA8jrqee7Om69Ib3e7Drm6b2Z3fWyxw9OcJmsqxl+6fcdFtcFL8+Ei86+Hi91v2NHy9x58gUAoBmTLwAAzZh8AQBoZns72zpd3e+OL67Xm60D1oz3fh9ntm5/yIjN/npdcK73c8j1JFezme5N92+3n7otP9BVM+CLyYCLsr2vswdU13TN/v5k2Lpe3W96P4f83hzv63xz46uQYbpM+2AZZ3fGeLSfH2M8+QIA0IzJFwCAZky+AAA0s72dnfa6XpO5uow37Jfx4vHjOuC4f5z5al2yytadaq1krMPV/dvt53N7wvOl+/X+XNI4zuneV319xscrW3uaNL77vPR3JCb2Ci+/dsm6Xf1dCBlv8vjR0sV/xNTEh8NPnpGe/f5XO1pdr+LJFwCAZky+AAA0Y/IFAKBZOvNN1z0W63qz6wf7THic8T5M5usy4WydsPZ6Lme+phfz8ymZ7n27/fXcvgFf+npLBhwzXqnr1TrgZMaZXq83e3yoFR2Pt7oXuht+1Bv8bc62vaCTfbVd3e5N64Dl8FASroqBvPvsVDPB1Zkrme5n48kXAIBmTL4AADRj8gUAoFm5zreqWtfrej2HDFYzWrP9eOQy4cdjm2RpXa/eT8h8L1t6vtbpxvV4tU53+4LEjHe7HdYnjne0vb7mgPdx5qzbWS6TTa/na/8gOZ77ToJZH7rSS1rz9rhftjWv17FNZuvqgrVPuI6nu/X6IVM2dcHZPD79fQK9nlk/l8y219HrehVPvgAANGPyBQCgGZMvAADNbOabXa+0Op47vpoJx97P222X8f6STPdx325rpvsr7B9nwi4D9L2dx5nvX7J9/9pux7dHMt6Qa22FumKTIVdTGreesZq5nu7lkq9Dd+9vaT1t91kJ+8drResvT9iva0Pr6Xq4yYDTNdXJDLia8QIz8eQLAEAzJl8AAJox+QIA0Gx6nW++V23xeJOphUw4W+d7H2e8mgHHTHjbDDn2hpYMWLe19rGY+d6/tiH3v68SehuxrljGlxvWTP0pOd/1pRno2lq9o2XCse43tz3i8vCwfm6o89V8X29ejx9uht7R7vz0W5Fc65lMGHviyRcAgGZMvgAANGPyBQCg2e69nZXrdVzN0ELvZ5cB63q/JuP99Xi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-      "text/plain": "<Figure size 921.6x633.6 with 2 Axes>"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# interested how the surrogate surface actually looks like?\n",
-    "stk = np.hstack([x.reshape((-1, 1)), y.reshape((-1, 1))])\n",
-    "mean, var = opt.gp_surr.gpr_model.predict_y(opt.param_space.normalise_coords(stk))\n",
-    "mean = mean.numpy().reshape(x.shape)\n",
-    "var = var.numpy().reshape(x.shape)\n",
-    "plt.imshow(mean, vmax=8., vmin=-8., cmap=plt.get_cmap(\"cividis\"), origin='lower')\n",
-    "cbar = plt.colorbar()\n",
-    "i, j = np.unravel_index(o.argmax(), o.shape)\n",
-    "plt.scatter(j, i , marker='d', color='black')\n",
-    "ii, jj = best_point.normed_coord\n",
-    "plt.scatter(int(np.around(ii*N_POINTS)), int(np.around(jj*N_POINTS)), marker='o', color='purple')\n",
-    "cbar.set_label(\"Objective-GP\")\n",
-    "_ = plt.title(\"Surrogate surface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Ly1FhM3INzb9YVRCyopOkTasRDrSWZejItOULF/QZo0B8ee+8Q49UXGbRkxEOCCShPyXSjuBk+R51/23UrhCQ9V7evN9puiCcQTa8hV/XhrofUk9oUFnN6NzeELv9c7THIOMT875QUREWKH83qfJAo5R5Z9+51y4iHqODtrcOcDUr+SCISZMm/UUSHc3K3IJu2NhgIDsvxoyZHAL1FE2RiR3nWmQt29J1itvyE8UxopnyZSO9UBtcyGeAolCVbcs9M/+O3mE3KDbipLYcb04WUEGXUBHDA1Bkddx2LaFBHo9I4vQOZtmIY+TiPgj6e2SIwXGjSHfnkIc7I2NL5tL2T+/7jvpf2mf6zCaEtf/Z79sbO73yr3zfNagRDqQbfxx1Iv/OjdTAts4QrEmT3kcJs1JX5kbNhsQcSfM7tjLq1TAXoja8JTsOaK95rmPLvRR0ynUIdx1YlWwI1rMQoKrNYgZ2XJzK8B3sUS1fB+0iNdRugy0ECWtDa8Q0LkgXO9mu6vKxKDEYX7QsRQr1MRddeKom9xoxtZ4oVrhtMG5iCM136H1fSVXukAdUOaxHgRJnIViD7xso0CjQ3Y6jYQgWz6EfjPsfZZGgcc2HOWnSpL9OSWRvcQyrzE3C8ZwcbAtw2YzVkcnP9hBN5P+WC5F7zlttEsUm65Unm51rlAMCZJfdnGV68u/gN/WwyzXO0BNHX7bkwXfbGT9IncKyoFR8bsf1O0VBEyxT+nAWT/Wj6SYX/vVsnsLiEHGVb8Y6H3RgY6H072m34fXLlnr3ym+3Etj7vUroIRnaB/TXMvz37ZXfW8B7vJ+OI182+hRzB/PmZ0mCaB9AVZ8y+l50tZPsWyypnetO6g3SxcFz6HQUyA/HHuwHd27NwCPKpqWvrEdbLiiuug6qFjFQ+kZZxUDXBl+GJbQYotmjdxe/trpkMBmVOTZPOSNVJOOolmJ5ckOkJPutXV/Lq+iX8ZElXrTdqTT91JZiYhMH30HerbSrufWemlER0bDcXoSoMH9xR4zHj8ZpsggpRpFIP7nff9IUHSdNehsZ0ZGV2xBl9mpGpT6C2KhxER07XONZiE4PyfScqnKP8s7Jpnf2WY089dKQn5WPXBoFIqPsBw1Eald391Z7++e0fGdk6SCbXzd3C0sbV8n3qgsA5ZlYPVeuoEuw12pDjBevg/ki+EY3cQshBK00KK/lA22efwfbE179kOt4f3N5VO4poj2ph6iN4r8LskX6wdd+uO8xadKkv0hJFVZGtBIIvA6cbxtwo1Q02EQjmmsjRqI+5+iRnfU+10KNkDU5SzQRtbpKKWOOOiqvRyhFnbN8bvJZyoYfcq1GqQdlioJAx3+f7h67OnnzoT8wQEn+jtL85/Nv2A0iR7YkyaBltsZCM2U/ar6g521eQ9QlSURJ+781nHA3fPM7tDppW7Yvz+v/7jEutwzr8SsjBNFg7ieiO//5gV1AO+8wadKkP0zYW0EQZA3BmPURRtJ/eCmhkVO9jrZQaDgAyOtjofqvvmbdBf5aj65kKPZ19doB8pxNV/TyOd/woHarWeRwhL4VKcjaPjiWz9Ze/eJaxKIWVBcgOkzdYiF2Fj9ZFwWzfOdbWiQjIoqRCKvgsNhcEYnLx5a4WdObSx90lvV4K7DUXVmna7qCaJ7wfSrpyN3Tq6dFtHBaD1G70h2TaY2F7oxkc5OLSZPeSLLC2srKsmhw4K+ymxC0mxUQH/XcO689Il1BMgTAVve65gWRq6/phP65M/J5OzRn/nEumX0pCJJ9uc0Zv7RCYZWo+WuwuNY6wP0K94yN3E2Ik8+dIdazTtm+r+0WwNjWNjasmMvnpqQcmn0D0Ce7vGMwluCBBbtj6XxlY8gzasfJc2oQzup+bixibCZGsTVmWlkMvPEWu6nX3pF51e/sksgq3fVEWzsTbbTqVydR6exZ7c/bPay13e3bjPawvrJvda+czCMWKd/EGIJ1WoXoyzGfXxg4pkqYoeGYhhgIsWuU7rzftnE0CqgnuviBIjuSpvJ0b+2MwPwSZII1O5Ka9ObP9hKv9sh7jU8+Jb/H3Fn5A5sZ5RKG5SwL+orF6SXTuh79hr2sp+g4adIbKPkNCnrUQzIirF0D9yY+1siWghEnsYs9EM1x0hBq8eW4R/8jOrhHg2TPgh/tu7xwL6iUINw55xrZth3i0nH82tU48CVZ5iAyw3hhRU0NUyIiWgNcK8fDqUTZU8ynQu/FTmpduIfbUPS7NN9DOLFy5MQbSuO/s34DkjV9VNp7RmTLb0S9C98M5Z/d+wpSjpDNlqNjk/sRyLZkSsuBZOs6EW3SpLdRks0MnPJM1OpWIJvuuGf8IDKmzkCUHJKBS3qOuiyGOzhOquctJ+nlU30F5UaUDRfGkrk9MypB8efjxnsWpy3QY6/RReO/YDKOJmi1Tif9EF3NrO0Sg4tDPz6/lUXCnjRnSB0WpVKIRTQca45cIRocrrHOadIjoIBHr2dodpTbfq/vSB/187/1+JD82ELf2L4Coi1TR5s06X2UPp2OZoMnm3U7fKryq/7fhqXwMZTG/GmtW0RUWbgE3WBeBpcIyi1GMrK06gIn7N0z4rylBLWoiY7G6LLXiBbjQvEBfYGviVcbiBZ0xTIjUfEbYZg8LbBA+lXKsolGiXRnzLq7lb2rIJxaQq1jlajWzXBMKVfXFNFGZv+W8gtRA73yfgfRfhcNXyl78X1jJAL020S0SZPeQOmXhAIZqw8f7Ra6ROoHUh2tNEtoeuEtMZwj2SqzvwgnFf+N8+eEUEQWDhcQ7Tu6Wu7oGOojcoi2sC60Wx3yuBawG4ixNh7PKqIVQTToyPVOWgsFCTj2m+7ZjfCAZB/NkVHPBHkLkuGYHLIlq6Ptcu14P28N/jPI8ad0tBD/HpJJHeLH7fdFjJniRLRJk95P6Z9weHQsHwA/xybiyIkA8x5CZHbj8Rc1BAXb8gZ+EY9aKWW55mVca9Hxz/eQzXO2KxYo7zspJuIBiJYW6GoctLuxHsbWwxBK9bsuH+gVpGxdYOjzTR49uIRAqwuv0nvU+isb0TskuyOHCN/7EYluTjeDRGH1Mo9ksKKJrmaswKDvIFBP4viujv0uUmt3X7KKMSuSoW9/mV1QiA6Ii6Ko14YSiernCmOIuj6Jry1+4pVz599RjkJwjNpYIusEZHOpmWh4iRGU22ue7PUzIwjRIUrKb5jum84d11dMKBLKo8b0f1B0aaxTibIeze+fHcTAEcVJjgn24ScYV3OLhVbu09taMzUxSS97M8HETB3rPg6hfM8YdSLiXflm36HR81fcD73n/Riwx7jUIDJFx0mT3kDpP7x/p2WsyEWxsQg02tghlEBtTomDZAUuBYniBhM74yA9xdJfb7hFh6N4eH+FSoMyQYwf2JkzsGUj7GcI5kKT5JiJtlhdQ34z7b+j71fKkgp8lFJtKcGs7mUEw6pfLv4uhqciSLYyaiFMyDqpR0jWM2WDRsYpS9/57xUk+13UG43NM2NNK0KacTuNIZMmvY+Mjqarf3UFL3SCfkLVaFZPR5c/0G6M92CmB8c1llHEqCuEn1GNVmhHjXqWw3xHbwCVUCMSZVNH8djNKGBQbOGXB1olp6OVSm/lfmMUXTL0roMSRbOuCzVC7te+Xo35nqjWyYiI7mzC/1gz3VJt/LDGqOM8kw+5WhyHtlx8pKtYetb/rz7zDjP+M7IIVp+XxhgyEW3SpDdQ+sXmfSxAtKmZn226Z68rsuEvRThZosHIFlnfWThGRwJ090ALw96zbMRX6RVkGy61iNQsUQaaAv3ESloClQUItvORy891PURkll3DdUJSDtGBhrDgKqI5fY7aYG4g2Q0WxkUXInorozfdLzELYjQulI5J+xmivaqXPUOra+b/p7c8tYZfLbv3/r6NE9EmTXoDJZ9h6dhUAUIn39VBMLk+4Bw2L6MiGh+ZRT8EDRCQWygzypVYhzoV5qwHGtTc4sqiv7N7r/pRrlCPwyNAOjO6lJI1A+6K9rAsz3466LWp6KJS/3aiLQYNbwOiJYdk6jOzYW7wUfY48vP39O97hb6jd/2pxZxnz34X3Z7Xwd+ll/hFVvL6CdabcO6aT7ccSzSGkVqElIm38TEGWjaY0b2ZX6/DaYzaIN5gUkbK0pDRJDybXM16qhyGJvszghM7Z+eqWAItfK2IeTwOntUkNz4hjc2n4tf4adwizlUsbIxHHUf/M+r1H8rxfX51PZp3q2Dindflrz+tSqg3ua4x3RHL07b58TJFx0mT3kBJd0VkJzUVTTENlHsB2drMVETB7VssyMbFY7fRZY+NOAM02DblIhpHWaOB5aDiUP5GlHlvhbBHMh+SJdH8Oep/J+uxFuHWauKw7xIZxnZ6vqlHpFKtUidqV0bYfvXmaFA23LcOF1PxP+9O4ohjY4jtxxG6naFecJBTifvIjeLEze+IgB5JR+3ptcO2QaWQKFID+msi2qRJb6Bk00oTEW1GV7uEbB7BhCvCmWrThjNXbYwjjHhboQW62IOGpJH0bMhxOlCOY659RQ/pr0fj3y6Hoa68Ri6R2Px3hTuKE95tx3nI+6XbLpshDNy0ycc4iDD372ePQOeDHNJ69Cqd4IBm5UTHhP+boVjyO7f/XaXed7mS76TpS7htjDNfdLMZgjVp0vso3bAOyqDU7pZljJAth9KgnG761kE0yeBL1blYIfd4imREB1cXF4CzbtkA5O84T0Gv6GgNsu2hQTnRd3Cen1st9Z2C6HrLoO0xtgHWo3Tax7tA/63DyHp8V97PSAtH++p22rrhYf9jSHaCkL8VameznD2RPqweN1qHJlbvmNUdBZ35cqsmTZr0bUofsOEZZELueHKLQrvIxiFcsjkeo9dnAHrFKgMTEdEqixsPkq2edqLR3AcqpCVQSjViaJiQWsRGCzGvhGD1rnsL4khHOwKGW5Sz5wcq47cv77wt47bzD34M5QZX3l71cY1kQLoYiXJ01kVxvtcdcXXh5/Owqt61sX79OxILqIdiIz9pKWHYnsYfaVaoxzx1tEmT3kbpo6OjKVcEWzzR2YA0LJgn2bzw+ONhFi4iC++XBDDX2XRjtpvw1vrNLigWJIfiKPdjHQZVv/D3rI72dz9CxAYM5wE6WfQa3iPljNvl3yHnQ5cjItrFCod3qPXZGNWKiXtyE4IVyS+PwX8PqvOh2HyM30GX3j3tri3tuQ9y1v/Gz7W6Nx87kT+jZ3rtskhGdPhAE2dHk5whd7fWLIciE2wVueM4bG5/ro2KGDaQ8FP36TqOj5A1zbXbCdPnG4klyFJvdBES2uxwXMcgYUXNWimjoP/ORhigKiX4QKTzol82rgB9tnVge4ewf+Zsm6PdtSFSAS80fcDlxHpiLMbBjHu3ve7PypgUF7lmqSe6XQl/OvsOzyaqndwjJ/kr39katrK4Z0iu2eNZe33a9CVmTUM4d/ycNOl9lJB4E4h0K5FyqFHOh17tg90pLSFYOVOR1dtY8/bpHdimAr/1D7h3YuhYgiYr1e2evDNV2zFKXf2qUWQk0nlEskjUIltbVyOmdtozCiYGBQrm3eHy4Hv3+nrsIFDPcDRKu35lS6XvRNSfZS7DJiZnqsHIMGF/j1wyVgoRZOuoBJ5GiHZsRMiq1D43uZg06W1kQrB4k4VQzL7HpTpKAs+i89OnBAfZZ8Fb8DzyFMriYoMGqBtIhlhicXoHDd3yex2jJfV+yOfUk+l7zuQi/z2/fmUP7Wd7MF/RNOr3HfVJcOdKZ1vO6vbH/Xv6iU8vNPqEvPRxhlpL7P/nDWM98mi159i4W3ywwSmimS11cYTBbk0zqHjSpLdRQrrvB6PMSlEshzDVwwp5Cw7ZqM6oS2RWZzNlMogolsnj+Clch8N7SpT8GNh6FqhnndvRoRxIOHznRcfc9jkbtrqV105L5/ozNDqr8RXbqJRTOgtuce6tr50Seu1RBCzd600bfpN6IWOK2LUrwS4LGm3YfkUHt/YAZGbbnW1gd1bwXvsE0ZCnZc1023j7rNvU0SZNehuldivWLEiWxEHNCFdq6x5RuywmCSDnAAAgAElEQVTGH4kUyR6lzhUIx2mR+iySsU6GwGMpV7eK8kywi2T48bobrSL/eB5eH1dk+2SEBN9CNLKIVp+/kk2surOgnOt9/bs0qgMWU/G3BrU4++2BgXo2ABvj1VuILWohXcSGa3LPcczUGuElCxnyoDKiPfZAW6oRMWEgq/IbTL6P45qIkGIMsZ3RTixLhYo8tznBa8dOlrLfV9ZokSalmp4HV1d3gr0wwK4MGplYPioD1y9MES/WXaEzR4oMxGIS2bq6/NTtZM47LRukg+v3hEXPiHrl+YGMLk/CfI1xjO9JmIzihtAegFia3fDdzaR6YKLxfwiq8Lsk9dq58rM3jmm95UA/3B7nU3ScNOkNJOk3RMQ4YVg97jPicLXDulRlw4GwOfEkhkCLSU1OZJC2nCHaiSb9hF7lNN71AdIdO9uye+19Vm+ufp+jQAwGLaWrx5y0Neh0TPUDqaFFOr1vhOpXEPSsbEkmy9cXCopufA3bVqmqoWKltM+IgURUGd58eOCXM+DtVJr3AKqibtlgpARdsSGJhCdNmvTXSRANIViHg5l/Q7Z1jutX6HiGI8i9OT60XNPrfKISGR3yd/UET60S3pbfIlh9/YoRxBoWnr2DLW9UstWbRhJAt2wf1X7SDqmjCWQek+f8vr5nZKUXIqIltFnU7g5N/JrHxZTjtVWbLJjosBF8umAKuJ4QNrhTad4Dm5es3D5sArmVSBmt36eONmnS2ygBtTBXS2i5dHOEZacXfCuc4TiPRBQHyfY66fREx/BpyZ9ZN23dV6hv7XrOfbyV8RVEW4y8fhXRMj23aNqNSbw5vg0gKNjz8DU0RrByaf9v0N6V/zLBZO9cRyv330qRvvj3Ta7VCLeSbpIpm7bwOVolelkoskYS25h9hjoQfg+l2tqMyOhosgzs0Bg3SsY9MnW0SZPeRslz5o2yzNzm6JbH5FBkdsOZ7blYptIoQSNOmqkI5wQ1FrYL+tOrdOZsH9U1Ol4pN5WxnqlSg56P6rDlJh84EPrvYsvbwguIBg49KLdqu3uHZ3UM6+TzZJAMRyxY3vnaLudBjotIXHXdEkABC2PI9NkgGh/ZNvkVcvMO0AvvbOqwoYmJryWnb06aNOkvUlKfguoE4HQeySDHWoKMLGjndSxDI12vWYZDbcDwFUvjd5FNufZzZPvOO/iA6xTiJf8j0fENfB29diN6R/dS6PPQCtEQzO3epaqjaJtt+bYNV1D+FcujfIdQv8uNUetDlgybuhD1IYHwkdKgTkR/iIWRsiDZv5zVDUiG61+0NxLdTXSyup6FgqDvjb998p2yW9HRTbCHyxmylCj/ybq2k8GO8nDPl/vQduX2UuqB8qoR5NkqcFu+KLcSU/d8ou3uePYOyL2CcnMpTyca+mqnIgr5iNYQ5TkMUq+42++cO6Zre48vu1eeNYT5SeTLHZU9oui+AwZttQOt+WmfsRnVJcEuwXlcO6EfMq6zrF7BNUywXzwtP8PefIeHAxcVJaMpGwaZSZMm/XVK/kJP+VZRw3GlkGW1NbiXRPZ3GJhHA48Ctnxk3Eqi3B60lDhEq6Z9J7R1Nl7M8i41R7VirEeyh0syW5HjeOBrMZShl9giGdHBYXv9Y9ttLwOVh8apoAipput++bbspk86C/xsm5+We4GQzwYEND0MdjwuGHF32aSFpYdi1+J5o1t93Kk8NQA+KFebddpi9V4dG3hnSUp76Y0nTZr0W9Qg2t8IcbJlE9Wy+5+g73LM/ynK1Opor4YpjehvfbtJY8qnUtxBE9EmTXoDyQpra9IeOlodt1xKrJyIRK2JvCKe8Srfx+o6XbQ6RuSiJFjzarqSd7JXjw+tObM+gqDXUafOxZn1e+VFr2e5qnYKogf79wStFIf9762OVNSSKG2QpSVjpzusylesjuTa0iv3jEYhTskcNSNAfa91EPslQ7A6oul2UXHq1HGUZyzH7jVWd48NGkjFf/NJkyb9dZJUBuByK0Vxsm1Ub4CxuCBiG8zqnbL26K2X4usJWcohItpLO+9HeSN7BN1v7ZRzRh5p2iUh+g4a4uScxyfLUjwapNI6rAV5iuur0Abt+mdiUY78LJwsU9EsZ873eeawPi23aNm946jsEfk6vcP61pGk8M0tyvjFqsgiLWku+JCLzTlaO5pt1m4vCWDMY+szhGKtJTYbuCQf9XynxcTA1S+enMOuJ2b6iIRYxuvavAn6ilPTfuDR/d+N4n8lrrJyntK5eNRjRs/iKq2o93SiGdHHr37oxzpiEA3eaVBHr/xum3Fuy39BevR1iZjIx7uZaHAB3F0kxmJiHXWiHbSL8UL7Qfub7YMQL7F6OizCoEBoF3Zkuhc9RzvuVLdh0qRJf5HSTeCP47ZCkfTgcpMP4zHMzHMdz+kCmT2TRzFxjrO+Sn8qen90vRKBXJxgg8oXUvElikOk8HRlzV6iSH4t3RmiyW/5LgfZdW/jnCEXDEQGjX2dV8jX4Q0eK0VBMrv+jMgYL0jzsqClaAVSyFlDhX8/PIMYz63kJhBOVnW7OfRBi6y2vk1EmzTpfZQ+qA6hyiU18unDBZb2GFSzwtfc4+0EPqSrlBYxzuhP6Gaga6sClLByHAjuc1XaJowMCFZHa9aR+faUMbqgDaGcuxDseQ+dz3oNpTTf9+SZ33W9S53edG8QDgh2Mwhm712qth+kIVcoD8a52Lh0FD1NzpAmBA6GGG4LI9rPEumDn/+Qfps0adJfp4QZt4uuQYTILOxLvXnUu2Bhq8jJ6nh6deXZfCWgXl1X8hL+SbKB1p4zfXfFt9ezbD4Rez1SEOmg0V3MubewhQGyvdxOGpT7h7u83b28rdPmd0wN8lB1vlD7rXw+x908s8o2z7XO90CgebiQ11F0tUA/YRWFdNh/7UmTJv1JSnf+gWUHZBx9MqsHy1vOMtyWShfgY6gRzVsfS8dnZNHOX/Pl/2myAaHqbOf/BnpnTwcC9Xx2o/wY1tL2zAoXKehvZ1HT+p6/Z+/eZhuok/yOV7i21zf7deJacOcHLdRDMn421OeWdNwdtBVFP+R4XATJoHfp2G8QzWVQRmDCBwXCvPqIfUlo0qRJf4HSjTnAbjhVEO86hzQ5RNtMAcN866G9R7YtlRQGbAkzSAcrHkrthSb5TFlN3SfBsVfoTHcZRblYP5oP1+r5k0Zo1wTJUuxca++1AbJEijxe37lCPX3pGcrYa/7Z+lp9V6j+8+2or1vUko0p3Sb28kzoZCjG+HOovxQrvR0ElJKsWqUd6X5HG/jMbkT0wY3GVk4JP4pkUrVbAGklR6X1RCuhyGQB+UlV7Sslt7JZ2js2Q2nM5mc69xXDif/vO8aB81Xn9Upcu9ZOYi/dzqnHs33zuw9lO0zZGlaEa8c9JNe9McTvIWff+xnLidQO8tEE602U7iQcGAWq52Wy1JMHtAR9Rvezxn/1t7O8WDe3YKaNvav5+iOrGBncJDzbtik2E42PUSfYij21O89PmjTpD1O6Lc5AkYNYHjALAaeaNwKoFao0dURqtFCuZlBPdvhE9bXh5Myo0XMB5MFzZ6FEIzN99Q4d9ALg+5wS2eSJIKLTjFXWaSyi9QCELSoISnWQDNe9GOO57ZmI1hPnRujUiHOhFteOe1pxrhXtWgRq99uuUcuiGHb29HtV95BM9qxmMQsbYGIJYaAgaesDNhDkMmTnz44c4EVa3uSTUiiCZGuaiDZp0tso6V7AbC4NhhPIbTX7DSK/au6L3aDccRPQINCIbXtEKuYanhBdzSDK72w0AfJbSPWeV9Q6z45kj3vIZqtglgzAUWUv5eve3kBmq2Nzjag2eHhdyiOZ/b+nQ/l7GyRzOpbqS0V+A7WWjv7UQyWiGpkaRJN7W53NI1mPBMmASpmDAsS+b26GiOH8N9FdtuRdCSv0sljMntoT0SZNehulhWfckoFWJmOvsRwStU7AQqqjjTYnL8bRp6bxgyRHoklB7pFM/wPSaQ6+K8tsomvXldAhX1618ccJktl2/g0afYcz6lkNn5nRKx3NcW2PWkswJvZY/wdEWhbD2XEt1mgVQotg0j7XYK/fWYJEVbIi2i4KcZ2TauFySzG2BbhMoHvL+dix7/VP+y5yHLZ40qRJf4zavI5GpfKcrudLAHnLoT3uA8ucRTL837tmzzOVZruhMx2tWS5ysi3U0AlNpUEyReM/g2S+7UWOutVx8f8BeYvKE7D4BYNg9qkj/wnx7+fUop9KPjh6JANapYTzLNc8ki2Lngenv0kbLuhjQDJILCUG0ck8SfZgQNHZwEb5z285pZRFYVTFcRTl3Eul7J3YzaQyKaj1Wv98o9zEU/p0y7knOpqoDE+9RDtn571yt5A7TGI8wTRyo3Y02xXXwxXVzSQPWsfIJVDZm/xNoTo7krfijnDyRH0RBo/iRXG7otxNnoWtDksslJZc/Rf5PxFNYzYTrRZFzwjOZ/StiotHhCoRUclWKG6fJTIiJ59DZMTYL6WdbL6/MNT2HCjHel5N0XHSpDdQgqVZHHMlGCfdQZtHLYhNVJq9ptrz0myJ04qOKC93Eey4x4qONaKdphgXZf45koF8uXZ17TNRMVHQ7ZqoPtrknM+yX2l9WreYq2EdMMYqMeCU6q+G69bm/eL+03vwnBheHKjqpiNByvEkhpNYGiSDCCnnS2kQ7cyEL1syBSAZt2tH3W14IEjQqzfmHZJtpo97gsRREArmNsVCG/9e80S0SZPeRgkzDzN6K0Rf/Kfs8Sucvb6+h1JxfSLdeA0R+lZHA7JhA0LcY1FspH9ZFBttAnhGPoTmLDHr2eaCZ0hGRN006ZIymjRTmJrbnSMdcj7C1Qxf1t0s4UgPUrdE8gdw5NpFo2hVOg5w/Ic2FQ0rcg5wICYi1bdSxAGcxYox/h7qqG51tkX0tuKecYaiEowzmo+sh/UWdngzf871+ZaDBBV/8bsg7FAktK6h7SD09c0gG9D8sU1EmzTpbZS+eFZ/8Yz+RYpgOD4atNKj17MaiyK125Z+IReJ3yubcmNBvKIvedqoNBty2I0MbblndMV07y2MdsOJJLn9Fr4X19utsdosv2qOB0PXzR6BMnpU5InNf0fdBwVqdSoN5UKd+hw4Op7XAHFpqCIis+19Z+ToeNa9U9oeX9HRpDxpV0se9WDuhx4Hae6RFckgzX127BJtzpD6CItsKUSRy172iWiTJr2NBNE+mXl8UaFfQDTZtV51MiJFpmKcx15v2oxeJkgWHKK5sKae/gWyHP8K0gz/e2FLpzPqIRmOZ0hGNPLd4ejbXUSJ6mXIOtoQNCicn0/OKpdE/9K67cbq9j+LaLIJigSPo1WKbJJ/kf8UPREIkosiosnTcbRP/XENoo2djYJWnqw+JsjqdLMHt8tKc7/4eUhxmAPWHuHXWKO/VrFUcj0UZLkN+mIi2qRJb6D0iycpZvC/lOkXkCxAV6t1qr6+5P1e4Ai7PIdytLzaCtnTm3p62O+EO51FclyhMyQjOvSyEZJJZuFOfWdLfkb5RaS8ECQDE6yVsmk8X3/Y5/3qXCkXl1WHXEVXg3U6cnv1blgdET2ygJuzxW1dFF2gxxXxZV3ve6TbsM9kV449AlEfOx+5PY8dFkaV5jD+P3ks/hK7gton2kzFXB76XlBe/aToi+Sh8p9gJxomRm2Gt5NqtDasZ7J/NsHsWq53kl8/5sk6oUEjE/6NlqcTzJbkRUZ1Z9g+rv9T0TFLPTuMIAGuBDCEXD3DDTklm74OExabSsgSrmLvrgcVJlyCqLZHid5P3B4/MXIJTSo7b32wz+iqaRg4vLgaRVTcBhPsy4AMJtg/oZ5omAOPDgz4HUl3s65CxOkZgjVp0vsoAcl+mZn8Tzj41icj0KeY7GtkI6KniUSrIGAneuIecI3/Njrb33rkjE4UGyTrbTwxci/0XB7NRhquTSlE2bDhJhnLgC5wkiu1wbGtko/7V5MS+3jW2eyLuhRgFFicaXvZAq0L0IXbgyj+HQYQ09dPzPtWLFSDB86j1AORcdthBKmd0mL0o1KNfyJq5sDDBJaD8F3vVO+pTpRMfpe2/ydNmvSXSHU0mdE7/WKE+Zclcm+Wf2V7pJ6O4ddy/bcRkMyimEclj2RiADH7SdskqCPanZ7qddyvsA+Dp1F+LqaPA/RNfgeno1VGFgbYng6I+8XIMgh3S9RuBsgrYijBILMHQRcg2b6rWV/P2dDyBNFyji2SIayKr297EN1sEySD8eIgOKd/GbsExn5vDnjJ4ubQHl0TKdDK736bOtqkSe+j5NHqk3aZxTK7+dwvTyEyFrDBAssjh2GfM4kO9IZ8G79DdqOJnk52XFf08kjWff0Lui2OI/eHmJMDDeN4owv0tRJGm3uFUcaEiG2B9Q+sKeHi1MWgaK99wM8WRRSgy8LRtjHANaENL6UOKh4t/Nz30CDZ43G087GxdXSP9LXXjmksefGhhZ/G6oixjyN0tS/aZfyDNtHN0DUqAWHz+q8ydbRJk95GyQcMf4ZddDIg2b88q3tyuuSFl1ATOO20Eu8gbZaoOGSz9A6U8/4zb2EkovHG7+76EeBbI9mzAOLef1YvG1lnk+lrIBeQB2jVq3u0X4DogKHW/3DN9gO++4OyOGyBFNhKaTVhSBE6GjvifF79UgItC9CO+yCX5p6jTcH4yGBZrJ3TX4/YWBuhm+kRPksT+A4Jz0l6/4atDQt0upl1YPvFzamXDGeUWq33oV/KycENwqYPSC0O0YiMOR2GEhFD3jDhvFMaZI0DV45BOn4sMOBtRvuFgY4VEs+NRleSyOL/URpz+33hoPYTDMYRXZFRNHqioLx6YKccJA5S1rzJ2jpuVwnqDjAJeyxVE02MHi7642HEVX5cjoSjTjCc+7w2vTmA/rEM7jivswLsVMzOSTAwTZo06a9T0hCqg7rm+AsctZfpyR4rUnZORMolv8x6tEVMp+9zAaCuWF5JUXqQ3TP6bA+wqzQyIH2Xqg07LrhZtC9qt4A41Iui4UM4em0Egfl8KUHWZ4krnP8sK8KqsmSO2sSJXbfJbloh5nwXZiUGkD005nxBXkEtkn44M0b5PgIiymYvLtW7lRqAbBPRJk16AzUJVHsEdNlOQqXOdBbvLBXuyObcnpHkFaf475IPKhZUtS6KizqQpRGSRQqNYt3cM4iw/y719s9Wg8fYzTLKrWKDyH1uz4frz4VIIvxtK/A8EdG6H6nDiWiYSLVCNB6KNu8HkQkYzkGChr05X3KPBkUkSUY7+C5LiTL+kx/jF1YgTESbNOkNlPxMO7Ow3Z3uEknXQXndzGZ8GlbO9wDZlvCc07+TbFuiM+W+gmy98CdRwZwD+JW9tq/U2SsvCYK5Z8R0r+RDz7qrwxsrJvRstbjJcy7rGjJHbWGhxelmPrcJ8pWUYkKuxLLI+pixNGo2N77mdFOfar33nuirNZQmKGDUN70+mog2adIbKAUnr1uHqyxmdBwLtFAYhyQJ0un9yPvnQ3/+W0hz3Ld+vWWgo2XHHTOVoV7jN208rv1Zeubns/pmNGFj+I/oQCgvoSBoGt/T+v/wNg2idXxIPgcikGkhDUZWZ3a/H3MZZxS2uUd9ftKH+Iz5GfPNJLgg9PttJTsW+tKbDV7weluKnQpwLYlTkTvMmZyjiVQfTbDaYY1yrkPu/+SKazvhfFSMDkp+6IVmviIcLhSqNAQjGgUKtAwvPF9DWErzTfwau9h5XtYrcvkY6IWCGFw0zTxV5S6kq5HRPM+ErCvKb8CiYqGe+6S+NhLkKKf9aMG/r0m/7vt4NPaPNOn1O0zRcdKkN1Dy+2gtJcgsTh1OZ6lrOJGwlPEc1lneIpk1qR/lQNz8n0W239mHukdX0PyPlOu+R6TQoO9IHO6V11tj553Z5Nw2ewliSv+CKFbq8o41f9VjQ9qpFVd724f5/3yyX5uXxVvovURg39Pf49O7H1lU/LVJkyb9dRLzvnWQXuW2Z64Ae09TKUzkXlcziUChNP93ZhMZ05U+IWoNS/Z5/8xo3Z7d6HC4ugB6g+gRbYgY6Mw41YSVmVs9umAobaQO3gdpW+0RERMLhacZo7I5ljBGsqNNmqFNV7Hz84Okvz06N9nXUsMZTUSbNOkN1O5h/Yd1hSoFta9c9B5j8XQcLwvXPugsyPhv6XFLiY3J3nOz3nIX7YOWnpnhpe6O1dGvn7MrwL2VEEiGwIKlhNN2ebrSo6PwNNTzCETIQem/ryJbkPTloz4VF0qwaNRHq3LSLpzZ5L9wOXlL5O+6oFDeRLRJk95ADaKdpaLuze4rCIjZnIXr1s9WDlMXmgSOh8WhsSxjLnNhSc0V1OutuNa8GHVoWXTvtBS9V/xBzqRVwnNO2QsCJrd06FRvMEiBdhEdjtd2SVNLPYQ4rtcoYZHDB+aejRdvvbSoLCFYrt9sW+xC5ao9rg1XyGbcPlsuMyoTfslilsloAAO/y+XWTJo06duUGs4V2tnsl3WDLMfREJ8xeWQTH4X4XZbKAmnrlpwYxuMPkuXixWeMbXW6Kxta+LyON4pPQ816nHmEGNmUDetd6O0JS33U6qVceKbzWZ1oLWqBJHqy6YaENtVRH6Dd+KBkqy4XYtdFttAiWpM1zNVl0WoTRK3H6BmKjizivWVQ1seG8tucltHVrXkeN9d/Ijpa0cCmoz4qrTsQtITQmRB1ejI78PQFUVd9JRbdo8SHaVWrV01M3lFHa6Z9RXTwA9dOMKJ6hxg5+t1kzERcSz2AffR+LGqGbvrGDbIzptFbCe4NCT419Wr22Nb/WgIz1F1Ga6Yoe22b7+H3wevt5AryqztsKnX/7bVNZtC7yT1Km+7rsOd1HG5dHuqSpMGd3WTkvLTP4s5pDJk06Y2Uevty+Y0WHtRHtJ2Cch+XW6JHXlxSzqV1K7dxWbAMlAvnKHXbrTFk1A5vDLGiZA/JjvOlQTJsbLA2yBbEpA4u5sNwinnPKzRUwk/7un4nRa/Qhj85Edc6hOUdxBV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-      "text/plain": "<Figure size 921.6x633.6 with 8 Axes>"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# now compare true objective function with a surrogate that was learnt by the GPR\n",
-    "plt.subplot(2, 2, 1)\n",
-    "plt.imshow(mean, vmax=8., vmin=-8., cmap=plt.get_cmap(\"cividis\"), origin='lower')\n",
-    "cbar = plt.colorbar()\n",
-    "cbar.set_label(\"Objective-GP mean\")\n",
-    "plt.subplot(2, 2, 3)\n",
-    "plt.imshow(var, vmax=5., vmin=0., cmap=plt.get_cmap(\"plasma\"), origin='lower')\n",
-    "cbar = plt.colorbar()\n",
-    "cbar.set_label(\"Objective-GP var\")\n",
-    "\n",
-    "plt.subplot(2, 2, 2)\n",
-    "plt.imshow(o, vmax=8., vmin=-8., cmap=plt.get_cmap(\"cividis\"), origin='lower')\n",
-    "cbar = plt.colorbar()\n",
-    "cbar.set_label(\"Objective-True\")\n",
-    "plt.subplot(2, 2, 4)\n",
-    "plt.imshow(np.abs(o - mean), vmax=5., vmin=0, cmap=plt.get_cmap(\"magma\"), origin='lower')\n",
-    "cbar = plt.colorbar()\n",
-    "cbar.set_label(\"abs(True - GP)\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`GPSO` package itself contain some useful plotting methods.\n",
-    "\n",
-    "First is a look at the marginal distribution of the parameters. The method gathers all evaluated points, sorts them according to their score, selects $p$th percentile (50th in this example) and plots distribution of the values, either a kernel density estimate (`plot_type=\"kde\"`) or histogram (`plot_type=\"hist\"`).\n",
-    "\n",
-    "As we see from the plot, `x` around -1 and `y` around 1 lead to high score of the objective function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": "<Figure size 921.6x633.6 with 2 Axes>"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_parameter_marginal_distributions(opt, percentile=0.5, plot_type=\"kde\")\n",
-    "plot_parameter_marginal_distributions(opt, percentile=0.5, plot_type=\"hist\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Another useful look at the data is to visualise the conditional surrogate distribution. This is easy in 2D (i.e. for optimisation of 2 parameters) - we already did it by hand. However, for more-than-2-D problems it becomes more challanges. `GPSO` offers `plot_conditional_surrogate_distributions` to the rescue.\n",
-    "\n",
-    "The resulting plot contains $n^{2}$ ($n$ being the number of optimised parameters) subplots arranged into a square - lower triangle contains conditional surrogate mean of a pair of parameters, upper triangle contains conditional surrogate variance, and the diagonal just states the mean and variance of scores from points evaluated within those parameter ranges."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": "<Figure size 921.6x633.6 with 6 Axes>"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_conditional_surrogate_distributions(opt, mean_limits=(-8, 8), var_limits=(0, 3),\n",
-    "                                         mean_cmap=\"cividis\", var_cmap=\"plasma\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, `GPSO` offers a view of the ternary tree: larger nodes with black edge represent leaves at whose center the objective function was evaluated, while the smaller nodes without an edge represent leaves that were only estimated using the surrogate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": "<Figure size 1440x1080 with 2 Axes>"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# plotting tree requires python-igraph package\n",
-    "plot_ternary_tree(opt.param_space, cmap=\"cividis\", figsize=(20, 15), center_root_node=True, cmap_limits=[-8, 8])"
-   ]
-  }
- ],
- "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": "3.7.6-final"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
\ No newline at end of file
diff --git a/gpso/callbacks.py b/gpso/callbacks.py
index 67edcf7..aed7d36 100644
--- a/gpso/callbacks.py
+++ b/gpso/callbacks.py
@@ -61,15 +61,12 @@ def __init__(
         self.marginal_plot_type = marginal_plot_type
         self.marginal_percentile = marginal_percentile
         self.from_iteration = from_iteration
-        # since this is post-iteration callback, we start at/after first
-        # iteration
-        self.iterations_counter = 1
 
     def run(self, optimiser):
         super().run(optimiser)
-        if self.iterations_counter >= self.from_iteration:
+        if optimiser.iterations >= self.from_iteration:
             filename_pat_w_it = (
-                self.filename_pattern + f"_iter{self.iterations_counter}"
+                self.filename_pattern + f"_iter{optimiser.iterations}"
             )
             plot_conditional_surrogate_distributions(
                 optimiser,
@@ -88,7 +85,6 @@ def run(self, optimiser):
                 cmap_limits=self.gp_mean_limits,
                 fname=filename_pat_w_it + f"_tree{self.plot_ext}",
             )
-        self.iterations_counter += 1
 
 
 class PostUpdateLogging(GPSOCallback):
diff --git a/tests/test_callbacks.py b/tests/test_callbacks.py
index e4f0aab..e0c6c8b 100644
--- a/tests/test_callbacks.py
+++ b/tests/test_callbacks.py
@@ -144,12 +144,12 @@ def test_callback(self):
         rmtree(os.path.join(TEMP_FOLDER, "checkpoints"))
 
         # now test plots after each iteration
-        n_iterations = self.opt_done.callbacks[0].iterations_counter
+        n_iterations = self.opt_done.iterations
         from_iter = self.opt_done.callbacks[0].from_iteration
         extension = self.opt_done.callbacks[0].plot_ext
         all_files = os.listdir(TEMP_FOLDER)
         # assert number of plots
-        self.assertEqual(len(all_files), (n_iterations - from_iter) * 3)
+        self.assertEqual(len(all_files), (n_iterations - from_iter + 1) * 3)
         # assert all are png
         self.assertTrue(all(fname.endswith(extension) for fname in all_files))