{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Music Authorship" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Find this notebook at `EpyNN/epynnlive/author_music/prepare_dataset.ipynb`. \n", "* Regular python code at `EpyNN/epynnlive/author_music/prepare_dataset.py`.\n", "\n", "Run the notebook online with [Google Colab](https://colab.research.google.com/github/Synthaze/EpyNN/blob/main/epynnlive/author_music/prepare_dataset.ipynb).\n", "\n", "**Level: Advanced**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook is part of the series on preparing data for Neural Network regression with EpyNN. \n", "\n", "It deals with a real world problem and therefore will focus on the problem itself, rather than basics that were reviewed along with the preparation of the following dummy dataset: \n", "\n", "* [Boolean dataset](../dummy_boolean/prepare_dataset.ipynb)\n", "* [String dataset](../dummy_string/prepare_dataset.ipynb)\n", "* [Time-series (numerical)](../dummy_time/prepare_dataset.ipynb)\n", "* [Image (numerical)](../dummy_image/prepare_dataset.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Authorship prediction of artistic content" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Detection of fake art is a challenge for a market reportedly costing more than 60 billion USD per year. Indeed, the price of an art piece is directly linked to its author and the more famous one author is, the more falsifications are to be expected. You might like to visit [Deep learning approaches to pattern extraction and recognition in paintings and drawings](https://link.springer.com/article/10.1007/s00521-021-05893-z) for an overview of deep learning methods used to assign one painting to the corresponding author.\n", "\n", "Herein, we are going to work with audio art or music. More specifically, we will prepare one *True* and *False* dataset from audio `.wav` files that may have been produced by one author or another. The goal will be to train a model able to predict, for one piece of music, which of the *True* or *False* authors is playing.\n", "\n", "Note that by one author or another, in the context of this notebook, we do not *necessarily* mean that \"author-specific\" patterns in data are directly related to one author's style. Herein we make things simple, although \"author-specific\" patterns may include style, background noise, specific guitar model, etc..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prepare music clips from .wav files" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Audio files can contain music of any duration. Moreover, we may just have a single audio file for each of the *True* and *False* authors. Among other reasons, we therefore need to make clips to have more than one training example per author." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# EpyNN/epynnlive/author_music/prepare_dataset.ipynb\n", "# Install dependencies\n", "!pip3 install --upgrade-strategy only-if-needed epynn\n", "\n", "# Standard library imports\n", "import tarfile\n", "import random\n", "import glob\n", "import os\n", "\n", "# Related third party imports\n", "import wget\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy.io import wavfile\n", "\n", "# Local application/library specific imports\n", "from epynn.commons.logs import process_logs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note the `tarfile` which is a Python built-in *standard* library and the first choice to deal with `.tar` archives and related.\n", "\n", "Note the import of `wavfile` from `scipy.io`. Scipy is a [*\"Python-based ecosystem of open-source software for mathematics, science, and engineering\"*](https://www.scipy.org/) while the [wavfile](https://docs.scipy.org/doc/scipy/reference/io.html?highlight=wavfile#module-scipy.io.wavfile) module provides easy-to-use read/write functions for audio `.wav` files." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "### Seeding" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "random.seed(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For reproducibility." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Download music from author A and B" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Simple function to download data from the cloud as `.tar` archive. Once uncompressed, it yields a `data/` directory containing subdirectories `data/true` and `data/false` themselves containing audio `.wav` files." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def download_music():\n", " \"\"\"Download some guitar music.\n", " \"\"\"\n", " data_path = os.path.join('.', 'data')\n", "\n", " if not os.path.exists(data_path):\n", "\n", " # Download @url with wget\n", " url = 'https://synthase.s3.us-west-2.amazonaws.com/author_music.tar'\n", " fname = wget.download(url)\n", "\n", " # Extract archive\n", " tar = tarfile.open(fname).extractall('.')\n", " process_logs('Make: '+fname, level=1)\n", "\n", " # Clean-up\n", " os.remove(fname)\n", "\n", " return None" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Retrieve the data as follows." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m\u001b[32mMake: author_music.tar\u001b[0m\n" ] } ], "source": [ "download_music()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Walk through the uncompressed archive directory to see what it actually contains." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('data', ['true', 'false'], [])\n", "('data/true', [], ['STE-001.wav', 'STE-012.wav', 'STE-010.wav', 'STE-004.wav', 'STE-002.wav', 'STE-007.wav', 'STE-011.wav', 'STE-017.wav', 'STE-006.wav', 'STE-020.wav', 'STE-019.wav', 'STE-005.wav', 'STE-018.wav', 'STE-008.wav'])\n", "('data/false', [], ['top_30_songs_acoustic_guitar.wav'])\n", "data/true/STE-001.wav\n" ] } ], "source": [ "for path in os.walk('data'):\n", " print(path)\n", " \n", "wav_test = os.path.join('data', 'true', 'STE-001.wav')\n", "print(wav_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These audio `.wav` files are instrumental guitar music. Credits to:\n", "\n", "* *True* author: Ewen Lescop (ewen.lescop@cnrs.fr) who was my beloved PhD supervisor (first) in addition to be a great NMR scientist and guitar player (both second ex-aequo).\n", "* *False* author: Paul Davids from Youtube, see [TOP 30 songs for ACOUSTIC guitar!](https://www.youtube.com/watch?v=T-U3c1nU3eM) for the original content." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Clip music" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By clipping music we mean make chunks of it. We need a ``TIME`` for one clip, which defaults to ``1`` second. \n", "\n", "Also, we will resample the data, because the ``.wav`` file has sampling rate of 44100 Hz which means 44100 data points per second. This is a lot when considering that we will later retrieve many clips of one second. Indeed, while one ``.wav`` file weights only a few MB, it weights much more when embedded into NumPy arrays. To prevent overloading the RAM, we need to reduce the number of data points per clip. This ``SAMPLING_RATE`` defaults to 10000 Hz and as a consequence we will lose most of the patterns with frequencies is higher than 5000 Hz (See [Nyquist rate](https://en.wikipedia.org/wiki/Nyquist_rate) for more).\n", "\n", "Fortunately, the highest pitch a guitar can play is lower than 5000 Hz (See [Range (music)](https://en.wikipedia.org/wiki/Range_(music)) for more). Although harmonics can go beyond this limit, we can expect to not lose the patterns or signatures for each of the *True* and *False* authors. \n", "\n", "For the sake of rigor, note that when saying resampling - downsampling - we mean *decimation* instead. See [Downsampling (signal processing)](https://en.wikipedia.org/wiki/Downsampling_(signal_processing)) for more details. In brief, a complete scheme for downsampling requires applying a lowpass filter to eliminate high frequencies and then proceeding with the *decimation* process. In the context of this example the impact of not applying a low pass filter is limited, and so we choose to only apply *decimation* for the sake of simplicity. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def clips_music(wav_file, TIME=1, SAMPLING_RATE=10000):\n", " \"\"\"Clip music and proceed with resampling.\n", "\n", " :param wav_file: The filename of .wav file which contains the music.\n", " :type wav_file: str\n", "\n", " :param SAMPLING_RATE: Sampling rate (Hz), default to 10000.\n", " :type SAMPLING_RATE: int\n", "\n", " :param TIME: Sampling time (s), defaults to 1.\n", " :type TIME: int\n", "\n", " :return: Clipped and re-sampled music.\n", " :rtype: list[:class:`numpy.ndarray`]\n", " \"\"\"\n", " # Number of features describing a sample\n", " N_FEATURES = int(SAMPLING_RATE * TIME)\n", "\n", " # Retrieve original sampling rate (Hz) and data\n", " wav_sampling_rate, wav_data = wavfile.read(wav_file)\n", "\n", " # 16-bits wav files - Pass all positive and norm. [0, 1]\n", " # wav_data = (wav_data + 32768.0) / (32768.0 * 2)\n", " wav_data = wav_data.astype('int64')\n", " wav_data = (wav_data + np.abs(np.min(wav_data)))\n", " wav_data = wav_data / np.max(wav_data)\n", "\n", " # Digitize in 4-bits signal\n", " n_bins = 16\n", " bins = [i / (n_bins - 1) for i in range(n_bins)]\n", " wav_data = np.digitize(wav_data, bins, right=True)\n", "\n", " # Compute step for re-sampling\n", " sampling_step = int(wav_sampling_rate / SAMPLING_RATE)\n", "\n", " # Re-sampling to avoid memory allocation errors\n", " wav_resampled = wav_data[::sampling_step]\n", "\n", " # Total duration (s) of original data\n", " wav_time = wav_data.shape[0] / wav_sampling_rate\n", "\n", " # Number of clips to slice from original data\n", " N_CLIPS = int(wav_time / TIME)\n", "\n", " # Make clips from data\n", " clips = [wav_resampled[i * N_FEATURES:(i+1) * N_FEATURES] for i in range(N_CLIPS)]\n", "\n", " return clips" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition to clipping and resampling, we have done the following signal normalization within \\[0, 1\\]: Audio files 16-bits encoded are lists of integers ranging from -32768 , +32767 for a total of 2^16 bins. We need to normalize within \\[0, 1\\] for one technical reason: such high values are likely to generate floating point errors when passed through exponential functions, among others. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "inf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: RuntimeWarning: overflow encountered in exp\n", " \"\"\"Entry point for launching an IPython kernel.\n" ] } ], "source": [ "print(np.exp(32767))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Normalization was achieved on a per-file basis, because we assume that sound level extrema may be different from one recording to the other or from one author to the other. Therefore, the minimum and maximum sound level from one file to the NumPy array will be equal to zero and one, respectively, after normalization. In addition to preventing floating point errors, this helps to make further network training faster because the network does not need to perform such normalization by itself.\n", "\n", "The last thing we have done is signal re-digitalization using a 4-bits encoder over 16 bins. This is because we make the choice to later one-hot encode these data. When doing the latter, we lose information over signal amplitude and we focus on patterns in signal.\n", "\n", "Note that:\n", "\n", "* One-hot encoding does reasonably apply on digitized data only. More general, it does apply on data encoded over a given, limited number of semantic units (including bins, characters, words and so on).\n", "* In the context of this notebook, signal normalization is not necessary if applying one-hot encoding. It may still be done, but mostly for convenience.\n", "\n", "See [One-hot encoding of string features](https://epynn.net/epynnlive/dummy_string/prepare_dataset.html#One-hot-encoding-of-string-features) for details about the process. Note that while one-hot encoding is mandatory when dealing with string input data, it can also be done with digitized numerical data as is the case here.\n", "\n", "We can now see how it looks with different settings for the same clip length of 0.1s:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(2, 2, sharey=True)\n", "\n", "clips = clips_music(wav_test, TIME=0.1, SAMPLING_RATE=44100)\n", "ax[0, 0].plot(clips[5])\n", "ax[0, 0].set_title('44100 Hz')\n", "\n", "clips = clips_music(wav_test, TIME=0.1, SAMPLING_RATE=11025)\n", "ax[0, 1].plot(clips[5])\n", "ax[0, 1].set_title('11025 Hz')\n", "\n", "clips = clips_music(wav_test, TIME=0.1, SAMPLING_RATE=900)\n", "ax[1, 0].plot(clips[5])\n", "ax[1, 0].set_title('900 Hz')\n", "\n", "clips = clips_music(wav_test, TIME=0.1, SAMPLING_RATE=100)\n", "ax[1, 1].plot(clips[5])\n", "ax[1, 1].set_title('100 Hz')\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Along with decreasing the sampling rate, we observe that patterns corresponding to higher frequencies are eliminated." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Prepare dataset\n", "\n", "Below is a function we use to prepare the labeled dataset." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def prepare_dataset(N_SAMPLES=100):\n", " \"\"\"Prepare a dataset of clipped music as NumPy arrays.\n", "\n", " :param N_SAMPLES: Number of clip samples to retrieve, defaults to 100.\n", " :type N_SAMPLES: int\n", "\n", " :return: Set of sample features.\n", " :rtype: tuple[:class:`numpy.ndarray`]\n", "\n", " :return: Set of single-digit sample label.\n", " :rtype: tuple[:class:`numpy.ndarray`]\n", " \"\"\"\n", " # Initialize X and Y datasets\n", " X_features = []\n", " Y_label = []\n", "\n", " wav_paths = os.path.join('data', '*', '*wav')\n", "\n", " wav_files = glob.glob(wav_paths)\n", "\n", " # Iterate over WAV_FILES\n", " for wav_file in wav_files:\n", "\n", " # Retrieve clips\n", " clips = clips_music(wav_file)\n", "\n", " # Iterate over clips\n", " for features in clips:\n", "\n", " # Clip is positive if played by true author (0) else negative (1)\n", " label = 0 if 'true' in wav_file else 1\n", "\n", " # Append sample features to X_features\n", " X_features.append(features)\n", "\n", " # Append sample label to Y_label\n", " Y_label.append(label)\n", "\n", " # Prepare X-Y pairwise dataset\n", " dataset = list(zip(X_features, Y_label))\n", "\n", " # Shuffle dataset\n", " random.shuffle(dataset)\n", "\n", " # Truncate dataset to N_SAMPLES\n", " dataset = dataset[:N_SAMPLES]\n", "\n", " # Separate X-Y pairs\n", " X_features, Y_label = zip(*dataset)\n", "\n", " return X_features, Y_label" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's check the function." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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4f98kDn18JuAyOmhGaMaBl2KF4nHgVaZQeJnS2K5bWFjokSkBHj4PPLlAYBrmgcdkEqcYJo1SyQWaSIM0GlqhcKSxQmlsJgqlJm81cAuLSiBTAjy0iCn+NpQNOjNCfjlKxnAtvxigUJJz8eXYyJNOA2ehdKsBfxHT7sS0sCgnsiXAIzgUU5Gkt0IJcuCk4Bm43BOjKDIWskeP23GYc2tdyYFXcCcmT6PamjDn9S2FYlEOWKcrPjImwKX/gXtmjSrvBuSC2rNCiRBuKg2WMRbKV9yZHz4n7Ifjv40plDQC3E2jqcqacF6xaGthkRbWHNVHpgS43G6pOHDZCoWC1z1TaYUgLSoolCIL5yOug6k38nAzQkMKJdVhVs1DoeSsFYpFGWH7kY9YAU5EDxDRZiKaK1zrSUQvE9ES97tHZbPpQKYqAlYohnHodnP6G3n0duAqnpyxcNpxHYzL6CYVBx7TIjz/afhknka1qQzLgVuUE1YD92GigT8E4Fzp2s0ApjLGRgOY6v6vOKIpFLM4Qm7ZJO47Sqvn91hAAw9TKHEC0qNQiqIVipkZIRf6jak48OahULgVSqPlwC3KALuW4iNWgDPGXgcgu5a5EMDD7u+HAVxU3mwF8dTMtbjxsQ/wm3/6jgi+/9xcLN281/v/m1eXolhkmL1mJ56btc67/q9lW/Hff5vv/1+6FS/N2+j958J34cY9+OFf5+FAg164+RSKf83hwIMdShSQO/Y14Df/XILFm/Z4XnjyCrto0630PNzUBZuwbMte/PEd39PNoaYC7nh5seco4kCD858PDjw9eYbQ0FT0nmtoKuK25+d73oSe+WAtbn12LhZt3INbnv4Qd70a7wxi6oJN+PlLi7x6UVFGHK8u2ozXF2/BEzPWYNqizbj3tWVgjGH7vgbc9epSzF23Cz/4yzzsPqh3BvGvZVvx8vxN3u+bn/oQK7buw879DfivZ+fgL7PXx+ZZhydnrsX9b65Q3ntq5lr8/o3luOvVpXj+Q/M0FmzYje8/Nxe7DqRzcMEYw+0vLsKFd72FXfsb8fC/VmL1tv3efV6nSbFq2z7c/NSHeGvp1tiwq7ftx0NvrcADb65QerqS8cHqHfjhX+cFnJhwPPbuavzgL/NwoCH6nPr563fjB3+ZFzh3f/u+Bvx66hKt4cCGXQfwu9eXR66RzV23C0+/vxa/eGkR7nxlSWxZTHCw0Xn3uL+BSqEm5XN9GWMb3N8bAfTVBSSi6wFcDwBDhgxJldisNTtDL+HDbwdddP1q6hIcP6wHPnX/uwCAC48eCAD42uOzsHmP7ySBe95Z+dOPAQhq3A++tRLnHt4vEC+T+G75GYYwhSIKqpuf/hAvztuE219aDAC4fNIQj0JRmRHGQXTK8PFfv4n9DQVcdeJQAMDD/1qJO6cuQW2e8OUzRuPX/1yCu6ctQ5+udbjyhKFaM8LH31uNO6cuQZExnHdEf9z/5gq8unAz/vnN03HT/80GY8CWPYfwD3fgu/7UEajN68f+6x52PBN9evJw9OzUzj86QFHGTz/4XujaR8f1we0vLsJL8zfhrleXYn9DAWeM64NTx9Qr07vid9MBOG36wJsr8cqCTRjeuxNG1HfGH99ZjSdnrsUnjhqgzW8UvvnEbKdMpwwP3fuGe4/jgglmafz8pcV4ZcEmnD6uDz46tk/iPO3c34jfuAPpt5/6EP+YtxH3dF2Gd75zJgC/TnkfN8XL8zfh8ffWYP2ug5g8qndk2Mt/9w7W7Tzg/f/kxMGR4b/33DzMWbcLHz9qAI4dEmRcb3l6DgDg40f1x3FDe2rj+PlLizB14WYcMbCbd+27z8zBC3M34shB3XC6oi6/8MhMzF67C2cf3hdDe3VSxnvBr98M/L/+1BHo0C4fWZ443Pvactw5dQm6dqhV9p1yoeRFTOZIOK30YYzdxxibyBibWF+vfgHjYOozQTXaicJbhTB/XQykGTAZVAjAImMh2kXUrHdEuBFT7uiMKSuPmzGG/a7GwgeZg43FwDe/f8j9z2kXOb+83vY3FLx7W9x640FFj0bGg02B1yW39DF7rrFQxJ6DTYEymNI+PJzoKYnXR0vB3kNOn0hLBYiWVPtc36g7D5h5g4pCo9C34rDT0PsUx3YDT1JxFBufsYjWW9zbky7ePYeajOIWUY5z6w81Of1WNeMoJ9IK8E1E1B8A3O/N5ctSGKa20ek89AT/84b2zAsD8bPQM0UWjkO1OKnOb1i7j4PKlls+ZFB3TjpfRJRlIRewqsGIQ+yIpv07rbUAU1j2mAo70Va/pdsLp13UlTeSOdeaLz9JEDUQxykGvNyNCWauuYTKg5hOKaiWF6q0AvwvAK5xf18D4LnyZKc0pKv44DNyh1AJ2eC1sNATX4QoISbHYwIVDcKfjRvnuBCU08oJsw1ddg8Js5ukeeXZMn1OtTBsqvV7i9FFVhah5uWpAi9iKQMch2phvdT8VHLci2rHuPrgtwvi/onYPRfBZ02gO24jCdKkmyqduABE9BiAtwGMJaK1RHQdgJ8COIuIlgCY4v6vXCZNNfAUtSU/wjuEb53i31Naqig08KCnnWBvYArtSZUPHfiCpHiOiqlg1L2g4iKjiQZump585K2pYCgUWWgaa2p1wwWEyj6/FFTCIXNaCkU18JfDtI7np5JmelFljjvfh5dVtYNZh6gF9Lh0SgGf1VbajWDsIiZj7HLNrTPLnBctTDnwclAoBUnIqTVwMc1obTGs0Qef9fORXgNPSmmENHDhvG5dPoIauFl6ngaeUBsRhS+RUz7T6X1BKGM5BXglhFpaDTzYh8LXSs1PRQV4SRp4OH+mJrtJ+kI5+g0/QrnSNF4mdmKabi9PtzMz+EyToMHp4pctU2TOOUpD0PHeSbXoIAcefFYXlceBayiUYhSFEuDAk+U16XOM+fRH0qNoxTYq57tTid1/qTlw6SiHcqFQhX0CcpnF/Mdy4O6jpmtMgL8xLkk1laOp/XfKCnDj7eXFBJ2BQw7VJK3Ei/Wv45/DG3nCZ5yo8lUMdESj7ArnmYTL6ll7aJ5t9Djw4HVxoUeuN94RDwoauLkViiTAjZ7iC8MskL6pAOVlLCjKUgoqsXkk7camNP3cBI2afQLlhFxmMf9xXqY8CkWIw/TcoWpr4L5hQMlRRSIbAtwwnFjvplqE3Fi8E6nOPVGt+CstJiI0cNUClCofyrwKC3PBQST20cAzeg08TDvwFyDIgZulJ7dBEqcbUXUahUpx4JXQStMKXxWFUg6UctRCUgqQI4k2zZNQ+ZLVIQ0XXVYrFKuBV1YDl9VCjwNX3FZRKIyFjfaiOXC14DV5AXgnrKsJNltSSkPnlcjRfIPP8I4ocuBpX1bTvlxkzJsuqywPoiCWsdR3p5hAuKRBeg68MhRKKRy4+aCu18BNOfCAgqSxrOLwlJNEi5jGQbUQLbsqiYwIcLNwwelYOgpF1OCc77CWLGtA8kuk8rQjxxH+HZ9X3lnb1wZ3icnP6joNp19kZVK0WdWdl55G4/fLnmw6WSyGZ0Cmwo6XUTUzSgpxel4JCiWNVyVAvTu4HOD5SWNxY+pkWy6zyrG3DlyBCQr9MKUowvd+lUCAl6FS01i/pEqnorGXCaaLmCq+Wh+Wa9PB67KWqjL1k4VwtBVKsMPqtG4TYcM7a/vaYLOZ2oH7FjbBtPiu+CIL207nFZWf1A6cQ7fBSEZTsRiq/8QUSrF0O/Ak2mEalMcKpXz5KsVjk/lREDKFItIh0YOA1xcUZwjp6jKfQpCWo0pFy65KIhMCXOUdRwWxsuI4Sx0fLAs5lQd6+SwUuW9Ec+Bq7cmkf/G81dXIGnhSCiV4PSdQKDoOPF16wWMJknD1cliTF7BYZIEylvryBPnZlsSBV0gDL4EDTzrAqv6bmxGGN8rp2sc/utm8/crDgTvflkJBEjNC/3dcJ/Q2tUjXG4UpuPgN+FM4mYMMmSJGTAt1/LjJtK3Ro1AkDVzqmyEXb97qvX9OiAoO92wiKGODuOkZcjsSGgthKsdkit5YLPo0kWIhNCkC1g5lpFB4FaelUJL2G1OUctywaVnkdhQ3aMW9s6qt9PwZXfskNUMFyrP5xi5iiqjAIqa/K1Ge0gWFdEDbKQa/+e+wBq5fJddNf00amscV5sBdCoXPVDQzAl2ZORzeOHhNla+kGr+/lV6KR9NGhWK6rfSFgAZuNhhFIYm1QxLETfvjoLNkAkoT6KXsxDR9JqSBi8pOnAbO7cAVW+l16av8z8ahHAvD1g5cgDkHbt4ZeMeRQ/kaHEL3lUKdhZ0aqxZZVHlU8etR4HHJVihxHLjMbeqEtEprLUWAh61Q1IOlDJEDjwsrhwnuxDTKphaV4sBL3fEYNfAXAv0rWfylDCzGi8wlceDh/MXlOd1WeuOgWlg7cAGmHHhwi210Z/BW2jWanopC4b8DC5GKOMTpWlhYir+TvWw6K5S4R/3zU8IDEOBrNo7mq58xRF3jiBy8pLC6qXpTIbwJx2SK3lRggh1/GTjwiDNtSos32B5JEaAKQ/WUftDh1EQqDtzwGTmcmMe4+lBZoXibjzTtwwVptTlwL64KS/C0Dh2qCmMzQqGunnx/LeoinA4Uigz3TFuGmauCzoZ4h3plwSb8fc4GPDlzrXdPvZXe1/T2NxRwy9NzPM87Koid4+F/rcTgnh3x2qIt6FgXPkD+nmnLcN4R/TB14WYs3bwXa7Y7XlfiFjHvfX055q3fjTddzyq+Bs7NCNVadlFBoagGlreXb8Pcdbtw0TEDA9f/+M4qvLHE9wTz29eW48KjBwYWMdftPICnZ67Fl88YhTXbD0AFFYXy8Nur0LmuFqePrce89btxxQmOc5Bte/3z3m95+kPsds8RV22lv2faMnTrUIsrThiCB95cASLH6QQA3PvaMnSqq8HFxw3Cbc/Px6ThPfHwv1Z6zz43az2emLEWp46px56DjbhsUtg5ydce/wCXHj8EJ43shTlrd+FP767CzecehlcXbcbyLXtx09ljsXzLXsxbvxsA8MBbK8DAML5/V/xj7kYs3bIXD396Erp2qMX/+8dCXH3SMIwf0DWUjtjejYJ9vuOByHNdi3umLUOPjrX41EnDAAB3vboUCzbsxpmH9cGijXtxycRB+OvsDRjQvT06tqvxvPjsPdSE/3xiNrq0r8Vh/btgWO9OOH6Y3tEC4DgFaV+bR+e6GpwyujfG9O0CwPHyc/eryzznD3dOXYxzj+iHh/61Et071uLDtTu9OO6cugQXHj0AI+o7e9eenLkWHWrzeHXRZmzafchri1D6767Bmu0HsL+hyau3R95eiYUbdrt104ifvrAQN501Bu3cGeyqbfvwS4UHHi7rF2zYjd++tgy7DzSiQ7s8zh7fD28t3YqGQhE3nD4KY/t1CT27cddBPP7eavTs1M6JizGs2LoPt7+0CDecPhKHD+gWWY9JkQkBbkqhiFOwe19bHhm2qcjwv/9YqIjDfzluePT94D2FHThj/qLh9n0NkcLbedYXLOt3HfTSGNCtfSDcjn0N+N9/LMSj01dh7Y6goDtyYDe8smCTEGc4nTcFt1hhDjwY1p9tqCiUcNy3ukJCFuD3TFsW8NKywH15/HQYvvjHmfhw7S6cP6E/Zq/ZGY7czac8cDQ0FXHHK4txxyuOZyMuwGes2uGFeXGeUCfF8MDG2/uKE4bgR887bva4AP/JC869Iwd2w6PTV+PR6cF2fMR1Xfdn133YpceHPdA8O2s98rkcThrZCz9/eRGmLdqCcw7vh6/9eRYA4MtnjMY/FwaPzn/wrZWB/1fdPx0/+PjhePy9Ndh9sBF3X3lcKB2xbtoLnmM+WLMTz3+4wfv/i5eduuIC/JevLEZjgXlhfvvaslDcHE8IigsQ793n7ml+XHU1OSz68XlOPDPWenUGOP3pnF++ro3nT9NX478uGO/9/6bk9UiHRZv2YNGmPQCAPQebcNeVx+LW5+Z5929/aRH2HGzCsF4dvcH31YWb8cwH60Jx8X7zwpwNgcHi73N8V4z5HOEXlxwdevbGxz/Auyu245KJg9y4HCXjbx9uwKUxXovSIBsUSgUcOuimNlHTTk/QCWSAePCSiP84dpAmXfUUTXfo1X7JT+BtFx2B0X07B67J526H4pZ8Ysqr7Pw/UbSZVxzEsF3qHN1A5vz3HfI15CgO3BTahVAWPpLWBKZp66LmSgQvp2x1xKf8uv6xv6Hg5VvnI1JkC0wtUhhjRpSNLl9JIO7aTUrjlMMChHspErGXe+YxWNdQWbvIOKTxdbnfTVvcIcrT4c69y4mMCHCzcEm4q3T9JKyBF5maZpBN/fzwavO2EHWhyUGeKGSbzQQBrIK8aBa1UBnk/JNVkvhC8GlqIL7QYq+eAzeF7h1Lu5XeNG2doAkv0gXXBHiZdf0DiK93sb0aDI/5NZWjUflKA9N3l8P07P8oqMqqqlK9AHe+S7E8Ejei8XhqcuUXt9kQ4IaLmEle2DTbhT3LFNkKRZGuzFOL4dUdTK35ytdzFKaU4vqZ740+aGGjer6UTSKiQOZOj4uMee1XZMIZM0y/aFWOBSfGkOosE1ONUZeuf5pl8D//zcvcrkb96ulmdLq0Gww9JZluddf122rBlC6NQrEYcRKlaKmmqRNPA4+qs5joebM7sy4nHtWu5lKRCQFuvJEngcQx7dAi5PcjR0EOXIReA1cLKFOzuZxCAw/ZgUtoKrLA1nJ5UNDvDk2ogQvCqrYmbEZlatudZNqty6M8UJo7RTZLW1c1UWZy3MwxR0C7iAX2uHoXbzcI/ThKczcdwMqugScMXx4NnBm1t66teT2WQwMvMn8wqbUUSjSS1HdjU/LGkc/nyBGBgSl3JsqmfhyMscDmBT9uHsD5UoUBnLqQZ2JxL3yhWPQ31VD0hpooG+NYn5sihSJq4MKmBnFTj+4F0pVdBV3RZTNCcXCJFnSlCXr+vOoQML7RqCaf0/KhRGSsgRPJrvWS51eGrt9WC7kyaKmi0AxB6MS6MPxyZJ1psulF7ylLfjxtWAM3tANPoDE2FNQLRFGQFzFzOUKxqJ5NyZttOAqMKRdHuBDlZeAUj9yJckShRd04mdNU8DWS2nwuvFApdDaeD6aItzZCawSCLwQPG7J4cb8bC0XtFLaxaHrsVYQGXgyuNTREOKQQBbqpbXajZhFLtvQJbsd3ylyTI+Q1fKjqaAYZvkaXM6ZQTO3Ydf02LZJz4KWnWSxGtKNBW/vnoqenUPjAWigyrw+0WQ7cFEkW3XSryJHxS46O80QuB66iUDQceFE98svnrOgO18/lwgNa3E7MpqK/El7nCVa1RhpFoUTZ1Tvp+HXKBYE4qMqcdJwWawK9FiUJ8EKQyhAhFtN02qyj4KI2qvAy53OEmghJZerYoC6fC7SX7jmVpyUdyq2Bm65fcZSLQjE7eiGaAy9l922DcCYPfy/asBVK8rNQ4pBmF5yngXMBniPl5hcg2gpFNbJ7Z3h45z0ENXKOHJF3RKYYZxQKRZ+24YtnKkHt0Cvub0W8uoU3J99BztlPx6dQGPyZp7jtXUZTkaXywhTIj8SBN0SYtgVPsQxHqOp+OgXAd2/H/4c58NoyUShye+hNG/WDpYzm1sDLAYaINQ8hQ3oO3PmOHARiKBTvUDwhnqhBOy0yIcDTnEYYh4Y0GrhHoThwGotBNZ/SreaLnJgI2erEOxlOEvbOImbw2bhyNxaKHiUjWoeono9yFiBTKKJGLZfJo1CE7DsLvm54IU8yTEz5VEf7BvLGoNXA5bYPLHYqBleVUGvQ0j/B0yxFRaGpWERTsRipgReK8aco8vyG2iPCtNGkTnMUT5MlRVKRVY5t7IWo8mrWRVR5iKyzmOh5HysKeWmzHLhpsZNp4GnMCINCNrUGHiHA5QUUOaiKAzfhTD3e1LMOEQV1vDAXn+WI8meoNiMMhtctVhqd/c3C+RUhb6UX21v07ynnS9U2qsFY138iz7suOC9zFAfeVCga24HL7aGrC4dCi+/vNTn9zCCcB6NgiVGOeE0pozg78FL8oDYIDqJ5OuUeHIGMCHDTlekkg3cqM0Lp2+fAw2F1dEMcPydz4DJS2YELWkA7xeKi+DvKDlw2fQscKiR1dpFC8dYOEKRQTHdiqqbhqpMhg/eDMwTR6kimP+KOIVYNxjorJl7PviMBadAqMtTkSWtSVmDxduBcwMvtoauLgnA4WBSlUZMn44U2YxO7hBxKOTTwomaWK+cnjgMvFJk2+7ryexRKk7+IydOxGngMkthtplnE9DqX+5XzNPBwuroXQdSGo9LQDTBEFBrQ5BMUZTQJZoRcC1A5lmBM+A29Vs0hCm1Zm+bCpSAMcKIDaE4nKPNbCFqhqDQX3a5S8b64fiBaHYkauMzdqygU1cKezoqpSaJQ5LOrG4sMNbmc9mUWT2LU9RIeZRSlJaKxWPSonCgtMJ8jYyFTinYahXKc3udQKOr8BY6c1lEoRf4OMq29vq78HoXinTjp132b5cArsYiZhgPnbxRPJ08UEEoidI2l4045eP9NooHH8cGOUAieJa6jUIKaeTA+mQuOOoxf3Eov28/7edJp4FK6ipeIx6V74eWZkThgBwS4ZK2iplAUHLhOA5eelz2oF2I4cNVBXjJ4fsPnwqvDi0pDlCVRTYx1jJdO0fys9eQceMIHlHFEzO4MjtsV30G9AI/OaENAA3cFeFulUExnYZWmUDxB5P7P58hZmFNp4JrGirN+iTNhyudUOzGD3zJEKwS1FUo4fVLEJ9NC8jkfgbACB+4JW+Zbl0QtNBWKQSsUFR0lHoGrgjhwAMF6PySdHyIutKoplOQcuOrsal5mR1BG7cR0vnXdXmeFErmIyRexI6xMojYYiUiy3yKpPE56Rr4OOuVANfPU5aGpWNRSobEUCj9ATnj3WpwGTkRfJ6J5RDSXiB4jovbxTyWHqW1opTVw2Ywwlwtrehy6qWhculyj1Am3qK30uvI3CsKyNsIOXDa9k+OTp9+BbeJSfsWt9PwOg7CRJ4IDl4Wjatofz4HrN/JEaeCqAVatgUdb0Hi+GoU64mWuyVOkoDTdSi/Xi07gNRV8Ci1qC3/cwOLHl0CwJhTCwfWYRI96EM8fkRFojxiapanAtJST/lnnm/ePJsFPa4viwIloIIAbAUxkjB0BIA/gsnJlLJCWYbiqWaHAp1B0pwvqRtu4dHlUUV62dQJch4KCA1cJapnPjxXgwoustUIpBjVwVZ7C+VUPBiJ4EF3R5cEoaIUinh8iWaEo2kf1EuuoMN8xsKuBF8Jlzkdw4E7etbfc+8G2jHsusIgdoYGbcuDJDhszDuqG1/e/JHFoNfCIPus/73w3FZmy70U9y8H7h7MLunIaeKkOHWoAdCCiRgAdAYRdZZQBlTgL5faXFifOxw//Oh8Hm4qBs1DeWb4d2/c1hMLqNKw4Ab7nUBNueHQmutTVKu87ZoTBa3F88Nx1u/GzFx2HBfwF/viv38Rh/bvil5cd7dXba4u34LXFvkcd+f2RX/6n3l+Lr00Zg5+9uBCzJOcMPOzX/zwLa3bsdyP0B+M3Fm/VDlIvzN0Y+K/SGu94eTFqcoQ/Tl8VukcEvLN8O5Zt2edd+9Ff53u/DzX5Gvj3npsb8Hrz0nzfKQSHig77nuAsQEShyPDcrHWeMwtxkHp5/ma8vngLjhvaI1LTvc11NvHqoi24/g8z0LmuBrdeMB4/e2kRThtT77WXKYUyb/1uPD/beTWjDlTK5/TWMV//8yxcfdJQPPLOKuxQ9HcZv5q6BJ3rajBb8Lhjgj+8vQqnj63HGeP6phbgjOkpyL/N2YC1Ow5gYI8O2jC/mroED721ErPW7MTI+k7KMLPX7sIX/zjTsyo6e3w/bNlzyHs397ieoRZu3IOGJmfdw3QtLwlSC3DG2Doiuh3AagAHALzEGHtJDkdE1wO4HgCGDBmSKi3jRcxyrIBEYN3OA7jxsQ9w20VHAPDNGxdv2hsId8LwnhjSs6MyjkON8dqL6PmjXU0ORwzoivdX73TSVFAoKpvx/t3aY9u+Bm8q99bSbV58vCzrdh7Agg27ldoEQ/gl6Nc1yJC9vngLbjxjNO56dRl6d26Howd3x0dG98aMlTu88osecxiAXp3rsGzLPuw80Gg8pZwwqDsONRUDnokeEtydiejesRZXTBqCu6ctw5Y9vru1jbsPer9FDvyJmWuBmf7z+yVnAOcd0Q8dFBz41r2H0K9rexxqKuBQU9FzvNFYYPjq47O8cKK2+vh7jpefs8f3xeEDumJEfScsFwYZFd5evg17DjbhxBG98Kfpq/HkzLW47cLDAQCnjqnHy8KAo9MKl2/Ziw/cAXZ0ny6BgU3E+Uf2x8j6zjh+WA+M69cVa3bsx7RFzoD+zAfrsHjTHsxbvxuDenTAkQO7Yc66Xdp8c29AIs4e31c5QALA9aeOwH2vLwcA3PR/szHre2eHzuKZPKoXhvTshOdnr8fYfl2wdseBQLtyOLud1XWxcOMeLNy4x4tPF4Zj0vCeyvpqaCrihbkbMbZvFyzatMd7Z48Y2DUUdu+hJnziqAHKtEpFKRRKDwAXAhgOYACATkR0lRyOMXYfY2wiY2xifX19urQMw5XDhtQIghWKjM51Nfjz509Cl/ZqDTrOCkXGS187FU/fMNn7n8uFuTTP16VQ/j997kQs/vF5+NXlxwTCylYIfHOJDGcaGszrSSODHb4gaDrXnjwMz35pMr5x9lg8dv2J6FwX1g2KReZNI3XmlBMGdQsNfsN6dcJfv3xKKKwKT37hZHzr3HHKF8nLd8RAL3LgRw7shnuuOk7Lgz7zpZPxwffOxg8+frg2bvE/Y8C4fl3w+dNGYnDPjnhGaNffXz0Rp48Nvx9nj+8HADjozhoamnzq6ZzxfQNhefP/WmrzomvZcuUJQ9CrcztlWX539UR8+9xx6NGpHZ74wsm47aIj8NCnJ+G6U4Z7YfjA9z//diT++hWz9hBx1YlD8ca3Pqq8d4XgY3Tn/kYn39L7fP81x+Mn/34k5vzwHDz5xZNRl/DIZhlNBYZJw3viD5+ZFJlnEV3a1+Ast97zOcJzX56seiyAL54+EndcenRsuDQoZRFzCoAVjLEtjLFGAE8DOLk82QrCfBGzEqmHwZNRZSsuq0m5d7nsqq303gl4wjUuKGXeTRZG4kYDOU7d9nj/2aLWRErVZgzBTRLqc0fCpmw5AvKGOwT5o7qdjkC0CZjYPnyg1KXNaRBxQJUFhzw4itSaWM58Xm3CxzcRiTM3XufyQM7rVo6Ht2WNwoKJQzcZEq/z+NMeOFUTwbGr4ow7zliXD/PDrBhq8/o6AcL7OWrzucC7FbbFV8RRAfNBjlJiXg3gRCLqSA7HcSaABeXJVhCVcKlWCngyqs4Y17mTWr+oOq1MKfleYPzye8JHFuDSokyju7lERlOhGLYsyYfT9U5aCwld9QvJk9IdJ5ujcJ5zhvbJYrqRp/1FDKJi+/Dy1mri8l5koV6iTiMEggOLWE6dcOPb+EXLGX9jiHoRU46HL2JGLZ7qBbt/3V/7UQaNRZT9u3q3rRRGmovrXrVi0cz/Z6O7oBxVHrm+xIVeZ0AMhldp/pVYvORILcAZY9MBPAngfQBz3LjuK1O+AjAd8avHoKg1ICC+cyfWwBXaqE4DFwcwLlRkodsuH+RzCwW9gwmZ7lHZgesO6lHVg2gzr9PA80QhjYXI/CxlEwEepYE3qDRwTdpcMxfzFrWRBwgOBqL25pxDEk6Ha+AHhYVXPmOSF8o9DVwz0NbmwwvgHPrr/g3/1MqUGnheXUZAfVyGyp1g8L9OAzfblV0oFlEbs7gYmsEKWjdfmBTDKPt0BQV4SVYojLHvA/h+mfKiRYvTwN1vVQeK1cATHmMrtz0pFjH5Cy0W35/eS1NAxYFUOoEmzxZUZ6E0aSgU1Ush+ntsKjLk4bStmO+ckkIx18B5slF21tEcuF9mXoe6uGoVFEo4rWAdimHFx2q0FIoz4IoUis7Di69YqKmufI6QKybVwP3fxQjFxQTRFEr4WkgDl/Koy0WBGbpUK7h1kkQDz/tlEAU5bxPVelIlXKlxZGQnplkFlOLDLgmippJxeU1KoYQ58PA1Pl0Uiy9O80SEFjEjziSRT+2TNfDGgsCBm2jgEBxVuBsc5EGBVBQKORqaSTfgmlxaDlzVPrrBQ1fHIuSpvDgYiH1FZ4PNNxGJGrjv4UUeyJ1vmfJpdCkF1ZSfw4RC4d2kEhSKKn35fTZNN/I4WQGNhSJq8uGzhUTIHHdNzufAZUEOqPtPVF8sFdkQ4IbhqkWhyFvqRahmiOKLWaoAz+co5BNT5cG+Nq8WLrIQjjrs/2BjNIUS5XFbJYxE70V8g4McZ47Ctsi8DlRWPzJ4GB1vDUTTWKqq0AkdFQcuQxZCOiqoNpfzNHoRXAM/aKCB6zRk3udq8jm9oNZIAlG4lUqhRDmyUC56hxYxzdJtEhSLKDiDmr5OgHDb1uT8XbSyIAeCA634TKVQ6kaeqqClUSgcquRUnaE2T16HSm6FEo5fbwceXsSUO6BqN6XuXO5DUmcMb6XXe9xWv5Dhw7pkDTyXA/KIj0sH3wolHYWigk6D4sItip+XB8cobV5l7eJZoQR8ejp+NUO+URlTzmD4s87gXzqFUpoGrqlLAwrFFKYeiBpd/6RR5ZHbSywDpw3FMKp9Hi1yEbOaqMRZKKXAPxMlnJ5agEdPsaIgx6faSs+tOYw48AQUitwZRWHbvjYXOGNDTkfVZKIZYZOrvcv5cfhu+Zoye0pwocYHLtVZ3nEvt/xM3AFPUYOFbGmjC6vjwFVWKJy7DaVVZCErpfa1Oe/ZUikUTkuUYkaYJP2077Nz5ozqWOBgu/IdklHlkeu5Jk8h6kzsHyoNvJKLmJkQ4KbFr54deJhz5lD1BfEwpEragZtw4EpLEh2F0qTnwNvX5gOn3JmYEQYXMZ1propCkQWmqUMP53m4+cl5+ZQRp4HLz8RpUFGLVHG29GIaqhfds0IRBXhRfchSochCVkrta/O+AI+iULSC1f/doKHLTFGT11t8lFOA6zhwuV0bCsXIOgEUAlzwWuS/Y35bVJkEyIgANzYjrLIGrmDBVZ1BbOCkOzFJaiGVHXiUcwNZGKosSXQCTdbAxc5cV5MLvChmAjx40ltjgSmol3BcSThX2YxQdZJg3AKX/EycwEpC12g18FxO6cG9jluhBCgUjQZeYCErpbqanPesinbhMLnOlY+0R3pEUU1yPwfSC8OCRgOX25VTKFHlkfuxeGZ6jWCFEoUKHIHiISMC3Cxc1axQ+LeSAw9fE033ymOFEgzjUygKAR6jgTcW9EdvylYo4iJi+9p8wClDSGtWcppMsEJhrgael54La6JJFD5vwTPHKZSwBh5nYpZUA4/S4GQKRcuBa7R4LnQOBTbyFJXxNBWLISul9rV579kokzkTaoP33bQUSpSgM7FCSQIVFx3SwA0oFPmWYy0U5L5N/YhWAplYxEyylV62K64E/ONRw/dU0/3aEjRw2fIip5hqq7bSc8haT9iSRL9iL1MoYtna1+QDrtpCvLWiHsSjWwtFBkbh/JBiI4+J9YmfrvPN41CdZBi3S6+9NKiUshU6tIiptSlXX1dZofAT8FRp5SlopdS+Ju89G7VtXCdcxaLzoqQV4JEnIZaRQgHUXLTcrkXm5ClqYJHzlc+R11benoMKctxxyIYGbhiueht5wmZ7HMrNPUIDJ+XATbbSR1Eooa30CksSHQcuazFiVHW1ORSZXx6zrfT+4NdYcBZP2xlQKMmsUIIUiurl1B1jyyEfklTKCxqmUDS7OnUCvIZTKEEOXEVHNBWKISulutqc92ypW+n9a8qgsYgSlCZb6ZNApYGrDr+K20qvWoOSZ0uVtPOOQzY0cMP6qfJhhMYUinipEhSKaiMPR9iMUNLeNacRAgoNXJyaS9YRRlvpwQIaOFh4QFFRKEkUPplC0WmqUZA1tVKsCOQBW29Tru7k7WocjjZgB14oaq1QZCulupqc92wU36u/Hq2QJEEUB25iB54EJhq4k6forfTyLVIoGM2pgWdCgKsWd1Soth24Kr247fWlbqVX2YH7W+lVHHg0hRJphRLSwIOaHeAvrpltpQ96O1FRKM4UNf5kQx14UH8jk9paIwohDbwEjjO0kUd3smHEiYc1OQruxNRQKIUiQ07iuWvzOe/ZmggKJYl1Smozwoh6LKcdOBDuu4BaA3fqRB+P3I9VtuyWA4+DYf1wp7mVFuOvuYfcqwS4SnCJlxorYAf+8NursPNAY8iTDaBYxJQE7fz1uzzvMTKenLk2lDYHt0/+6QsLlemoXopnPljn/W5oKoIorIGrNrTw9yWXo9i32tfAw5ssOJ6bFe04ipeNa7mmCoQKK7ftD/yP2sijkotcYIiUwPMfbsDoPp1DYXm55C36hwQNvBQ7cP+aOo44RK1lyOl8+U/vY8OusLMGU8h9F/DbVUTUEbsqqMxcLQceg/H9u+KUUb1x7uH98O/HDNSGk6nNi48bhFNG9cbXpozGOYf7h98fN7QHenQMOlyYNKyn93vKYX0xoJveP/O7K7cDUA8UYls+cO1EfP60EZIG7mTy86eNwFnj+2LScD/d+i51mDSsZ2DDAX/0/108AVMO64s+XdoHKKURvR2XT8/NWu/RM1+bMtq7361DLQ7r7zs36Nu1vXcgPQBMXbgZAPCR0b1x3NAeOPfwfrjw6KD3kJ9/8ih85YxRknWDk4kVW/fhI6N7Y7jkempEfWd8ZHRvRQ0BQ3p2xLFDuuPowd1x+aTB+OjYenzz7DGYPKoXrj5pqJYDv+msMcr4RMj272P6dYl07gAg1NZXnzQUp4zqjc+fOgIAcOyQHiHvLWJexg/oik8cNQCPfvYEnDyyFyYM6oZBPTrgjHF9Qmn17lwX+P+d88fhoqMHoK4mh4uOHoiTR/bC6WPr0bNTO0w5rA/6dm0f0sABeB5e7rriWJxzuOPhh2NQjw746Nh6XHHCkMCz+VwOJwzvhYlDe4TylcQ6hbfHw5+ZhLMlpxI6XHzcII96ufHM0ThzXB+ce3g/7z4R8P2Pj0f3jrUY0bsTnv9wA2YK3pzSYvKoXvjWuWNxyqjeOGpQt9D9vLSV/sYzR6O35PTimpOG4kyhLY8f1hPHD+vhOaH42JH9A+FH9+mM/t3a4zvnj8P5R/bDySPV70E5kAkNvG/X9vjjZ08AAMxesxNPC5qciCILWmZ/5/zD0LOT0xjFIsOI7/wdAPDbq45DfZc63P/mCs//4B8/ewKO+P6LaCgUcdrYelwycRCuf2SmnEQQCgkucpNnjOuLM8b1xZtL3vCu8Sn1meP64pbzHOF97i9fx8KNezCuXxc8ct0J+PHz8/H7N1cgR742dcnEwbhk4mCvLBw3nT0GX/7TB97/Ad3a42tTfOGSyxF+e9WxOO1n0wA4nOjvrp4IADjs1n/ggMth//6aiZ6GMn35toCW+rEJ/dG+Nh+gA7h1xEkjeuGR604I1UO3DrV45LoTcM4dr2PRpj2BeyeN6IX/vXhCoJ4A4MtnOAPP3+ZsCITnWvoXThuJCQO74YrfTwcAXHj0gJA2LXPf7fI5/PryY/HR26eF8sjx7JcnY9bqnbj+kZnoUleDU8fU49QxvnecIb064tHPnogLfv0G5q7bjTPG9cGNZ/qDZG0+53k+mjzKf1lfXbgZ/3QHSI4TRwQHgutPHen9PnJQN/zpcyeG8leTp8B6S9+udfiKm/7HJvTHxyb0x8KNu3HuL51+1r42jwc/7XiZ+dwfZnjP1uQJRw3ujie/eDI+8Zs38eHaXV6c2i32iutc3p02ph7De3XSuknjGNSjA27/5FHef3HwG3bz39w4CZ+ePByfnjwcUxdswnUPz4iMMwqThvfEuyu2o1endnj0s0593nD6KDyrkBu1+aDVzk1njcHHJ/THWXe87l374YVH4MG3VmDqws0gAIf174onvuD7rrl28nAcO7QHPvGbtwAA91x1LEb16ZI6/0mQCQ1cRLQ3bxbo6GJYcZbEL4tRyfdNeC2VBh5HoXAb5FwgPQp+54L/ZYgvlUxBKBediJT3xTyIvJ5c9ryXH/8at0+Om32q7sctSsuURbAdKXRd1SVELT5uiivurouC306xQQP5i7sWH0/8a6pbINTVg9xPklEo6vdKh6SceakebHQWSLr2CNOU4XBxfUiMu5pWKZkT4JEnv0kSVddh+W8xphyRZx7oWELEV416ETMcThRI/OCogCDlHC/x/Pl5ioPqMKhQ+orBS45fvC6Xnddj8IwNRwOPWzdWlSHpaXZiOwYHHf1Ap/N8o4K4OSMKpPgVBdVLn2bBy4RjjTvyVv4dXhxXx8uvi/1MpxjpkHTNs1ROWXeMhLI9FAJclXxc/wgoQFXkxLMnwCMqUrbv1R9d6X7n1K9kjqKPI+UwtUIJauDhE91CGjgfYAz6QXgHZLTGpNKeaqUzKky2sqsOiVJB1VxJ+7eokeUCAikXusYhmkvGCc3aPBm1N3ntExvUTVdh9ZBCOzMR+rpBSpyhib/l0DplgZdZtBYiRd+NQlINvNTDn3g55fpXtkc+bAeuPA4jpg1qEvS3ciKDAlxfOSEHspqXhWvEYkxEvjZJCltkFVT7QeLswAtFX8v30w5quD7Fk/zlUD2hE+C+8wNJUzHogJwvj8ti3IBigngNPPxMcECK7uY6ZwrhOIPfcVDFmUY7M3lGd0iWXgOXB2l1vDycOCAmpVCSlrhUDzZJNHDuFk2E+jyjGAGuqedKI3MCPMnBQbqg/OAcmVbhT6tMhUJxkLkZoQi+sUNFX8iC26QfyMnFmX2Jhwb5uxaT7zw01cDjOHkT6Dhw3kbKWY/meRWSc+Bm+dcJjKQweUZ/SJZ6ZqXaYagCf0QcIHQKgQ5JKZRSOeQkHLjKtFJnzhmFfKCeLQeuhU7TAMICXHvCmnc/eN33KRjfCKRID9DYugrXPApFiJ4/4y/KmQsKE0/dAdpB8fKF7Vrju4XqkCgVVHIxqQAXNTIx/7wvxMUXp9FFORoQoes3OqgGhaj+q4PJM7oyBqb2OTUNAkSdhcI1cFGA+/dNqqKUNY80qNFSKAoBng8fL6Cqi7g2CDiothSKHpGH5xtu3fIXMYNxiRp4/BGRpDnMShFWkUeVFhMS3GXTwNUaEy+iyutIHFTHtKqQdteeCFEjE+PzNtrEJFGKBivCbxZTDVx19ka1NXC1YJHrTFeHsoMMQFpzMOmj8UECKFUAeqcEhigU1ZqEGYWSTAO3AlyLqMoxPX5SpixkkAmFAvNFTBFqDjz4naT9TbjMwPtG4nX1VNPkBTLVwNWHWiXbKxu0Jgpfj6tzE+3apMz+WkVsUADV5cB1ZTTlwOMoFL0SEJ+3UtY80oCX09yMMHhNlXx8H1PXc6WRPQGewIGsDt4iZohCcb5zZEChkDq9ODvwgokVSoIOYKKBk/blc77DHHh8t1Ad02qSPyD5IUU1mgU03Ysqw+SFSkahmLWPitZIZUZoUNcmZ6zUamYyqv/y9WB/9X+blCbpJKxUDpnXu1z/qvZQHbGrfIdj0hTbSOWculLIngBP4EBWB1/j1XfaOC2AQAnswFV5EKekwWeTdPjwAkz4Ye3Lp+XADQSeoQBXa+BGjyrzIzZ/lBVKUpiUuRwbedIIJyMKRdNpApphBIWiS6IcGnhSDrxUDTaZBh52qZYm+WAftRq4FtEcuNlBUXEUc47M3CQVFZLIdLoYqYEn6vDxnU9vRuh8hzq6gZZoOs1VyaukFIqOA+eDSFIBoU7DRBC534ZxlosDNxpctIuQ6g0mcp3FuVTTmQ6aVH3SEpfOgecC3/L14LXwIWJp1m2qSZuIyJwAj+TADY9qjROUOaLYVWcijUcew8ZX8ZGeXXaCDpTUBEplkSJP+UymgKYvWdk1cFGAl1EDN7H0SGxGqKJQ0gjwEraWixuUdPb0QIQVioL3zweEucngYpJTH6VSKDpXZ7r2CJ1lX6FBthLInACPqtyCoWbHY9D1PSMrFJAyPSWFEmsZEvxOogCYrKBHlRMwm2qG4nRrMc1WetXMRYTsLDrIgQvXIxYxkx4pzMsc9ZyXjGH7qF7qcgiHJBOYvMaM0JQ28Ok99SBqpoEnK3PJi5jeWfCSAFdRWgoOXJU8k75llGMWmAYlCXAi6k5ETxLRQiJaQEQnlStjaWDKgcdq4Ln4TpSEQlHFpLIGSUOhmHHguumx813J841VUSWnUNTCI29oB24CkzKr6IQolGtaXUo8Wg5cChe3lV5UilV9NwpJs29C4UUh2WFWZlvpWypKPU72TgD/YIxdTETtAHQsQ55Sw9gKJUbTJaJYTYmgMSOMOUzKC6eYhiY9a8PJR7z2EGdhEDp/OwkfHBM0DYUil0lnPeFp4DG29ybgZY56LqECnmrTjjqeYIpJ5IvukKUwB65+3pulaWgTo6wkFIilWnHoOHBVe5jagSdt+2ohtQAnom4ATgVwLQAwxhoANJQnW+lgLsCjmyFHet+BYhyq5EynUpFb6fkPg+LIyalS11sYcAFeOSYtzqzSBDrrCa5Rdawt/Vh7IyrAcNDiKJ8GHmyfju3Myxvciammopz/ukHe+VZ5swEM66KEGVcUVC7SAP1sSmkVpND2M6SAl6SBDwewBcCDRHQUgJkAvsoY2ycGIqLrAVwPAEOGDCkhOR9fnzIGxw7tjn/M3Yhx/bvi1mfnBu5PHtUL5x3RX/O0j6hO26WuBtedMhwj6jth9fb9uPe15YEwBODZL03GC3M24N7X/XuqBUgVByh6/ZCpE0/oRXSkOy49Cocai4ntwIPX3TwrOvZXzhiFrXsP4ZghPQLXvzZlNPY3FDBpeE9cdvxgfOmjo/SZhC8A+natwymj6rFjfwOuOnFo5DM3njkKOw80YFy/Lli4cQ86CwJLnB0c1q8rphzWF1edOAQbdh1Ex3a+kLls0mDMXb8LX3UdH3x9yhjsOdiIXQca8d7K7Tjn8H7Y19CEIT2dSSNv73+L8PiUlOIS+0KfLnWRcUfh/CP6YdPugzhtTD0+WL0Tl08arAz37XPH4bD+QUcCJwzvhVNG9cbgnh2VVFRNjnDxcYO0Qu+w/l1x5rg+uO6U4fhw3S6s33kgcL9DbR6fPG4QGgtFjOrTGZ3ravDa4i3o2K7Gc8wRJb5/fNER6NYh6B1LzMuUw/piUI8OqO9SJz+Kb50zDn94eyVeWbDZC3vNyUPRpX0t3l6+DR8/KigDenRsh4uOHoB8Loen3l8bSOuG00fizMMcxyJ1NTlcOnEwxgl1eeoYx8mL6CxFxudPG6Ed6CqFUgR4DYBjAXyFMTadiO4EcDOAW8VAjLH7ANwHABMnTiyLu8qvui7DPjK6Hv9aujV0/xNHDcClx8cPFrr3ME/OtOrWC8Z717gAP2t8X7w8fxNAwNGDHbdgogBXWmdIl7597riAZYFstyqfjaLCvx0zCAAwf/3uwPUk/F2U5/ZvnD1W+YzYgX/6HxOUYUTw7JwwvBd+fslR0YFddO/YDr+45GjlPVEo9uzcDr+/ZqIyXMd2NYE4viq4mVPnM9jeyjBe2MhgHsTB5qazxuCySekUmPOO7I/zjoxXSL54+sjQtbH9unjerETwMnzmlOH4zvmHaePs3bkO9197PADg5FFh12BEhJ99Mtiu104eDgAY/+pS/OzFRZG0g2owF+vt9k9OQPeO7UJhAHiek7hXnxvPHIUJg7oDgNKzUT5H+OVlx2Dmqh2CAHfew2+dOy5QJtFjFOAcp/v/Lo7uv7ecp6/HSqGUufNaAGsZY9Pd/0/CEehVhXrXVHIaIy5ODs+sSnNfpcnIV8LnbQe/VZsndAixHwmmfzoOvJzwZxXlis//XW3TLXmtIgla2sJYudslKo1SkISGSnNKZHPZb5cLqQU4Y2wjgDVExFW1MwHML0uuEkBttmf2rC5YVJtygal7iU2OwgzZnWqsUEz6lskipg48bCVdQBmwQQnja76Xr5SytDD5LSgLlRy8S48jyfpMmrWJap4cWAmUuvrzFQCPuhYoywF8uvQsJYPKYsK0U2q30kf0vLgDjVTnLchh5TA6O/A0JlpJXkhellIP0DdJo1x2smJ5q3nmhJh2GqHXUjXwSo6BZTHvNPRGVCgy4/QCR79WuQ+VGyUJcMbYLABqErJKUJrNGbaJ3nQqKr1oCsXs6M9gBnUaeBLLCDkuE/gaeAUFOP8uG4UiaOBV1p50h6CZoKXJCX82UcnB2/kuZeHLZFcyD5HGPr+5dlCWCy2sWyVHtTnwOC0sjQPakB24YvebDqb2vCpUlQMvk6BQ2YFXC0naJfSs1cDTxWGQwaRl0W1uyiIyL8CVZnslcuBRo36cdqw6t0IWXvK0kD+SlwSEkQCX/yfRwD0rlCpw4OXSwBWnEVYLpQxCLU2A+zOjyuXLc7hRsRQceAOrYX/QHbGbRWQ791C/GMYnAmpKH/W8f8tcA5ejM17ENGidUo7C5GGro4GXNz6g+RYxW4MGjhLKYopqNU/iQ8ZyYRPerCLzAlzVZsaLmBqxEvW46nS2wH0DAS4vnIS30ifQwEviwJ2wWeXAq32AUFKPPCJaqpyoZBVWq31KOafdcuDNjCSn74UDmscp39OFMHOGoOatZWsUswUcKa7YJ3zwspTrzA4lOIVSJh28ORVZkr4TPdvSNHAXLZ0DN0snWXqi1VU1nS9UAtkX4IoSmLZJ1GmEcelpLViUG3kkDlyTQXkrfRorlCSCggetpAZe/o08zffChc6qSfFsS0MlB5aqUSi5ZH0s67SJiMwLcPUipimFYh6nHLdOozRZVNUtGspb6tNRKLGPhNKr5GaGclMozfnyUczsKwotVWhUcjyslnablAqs6Iyzysh8SZKcf20azsSMMAkHLkOngYeplPi45DAt1YywXMuYzSkHfYuaNBp4yxTglV3EbJkUSksdTNMg8wJcuZXe8FktDRIRQRwHbtKJdB2oHBt5koA/WpWt9GV6Z5qTSy5lI08Lld8V5sArF7eIpH0i6wuXIlqBAFdo4CXvxDRYxNSEUVuhBK/ptq7L2r2JplCKlsN3yNVWsEOXQju0NMiLzMmebZk10Bo0cE5bmnp6aqkLymnQKgW4OQcerQknvefcV6UThE7jTeNarZSuyDt8JXejlVsDb054ZUlR6y1VgFcS1Soyf+eSOstuDci8AFd1EtN+IwtbPrWK6nhxHLjJwo2eAw8K7g618YfDy4NVkk7Mw1aWA+ff2RdgpWw/b6mz9mpo4JWWq7U1jhiLc5bdGlG6L6pmhtJNkkbDffSzJ2DBBt8Bgiz8fnnZ0Zi6YDP6d2sfevZ3V0/E1r2HsGLrvlC6v796Il5bvAVb9hzCqaPrQ8/ybvVvxwxEQ6GIkfWdA/c9Ltr9cfiArjj/yH645qRhynIEnpWKr3sdb7voCAzsHiwXczXwirpU47xxGeP8/Kkj0Ltz2ENLpeGvx6bQwFuoBK9ktkpRDO6+8ljsPdRkFPaBa4/H4++uxqAeHYzjv+H0kejSvjY+YAtH5gW4SoPQcceTR/XGZMGriPzoBRMG4IIJA5TPnjXecbf0kxcWhNKdMr4vprj3VeCCcsphffGxCWHPKjKv3qNTO9x95XHa+ESYviKfinBjVlEzwhIsN3S4JcKDTGWRfjBqqTOQSg4s/D005aZFnG/ggYhjZH1nfPdj0d6UZIgeeLKMzFMoqv5nKpBKWYxK8j5yZ8t66xPnO01Hr7bXk6RoqYKrFKQ7C6UCGSkDKpkt/h4WihVMpI0j8wJcpdmZT92Sd9+8xFObgAtwrfWJp6kkzk5ZFoqqcSJba5Lj6cwIW2YFVDJfnJpjKRQTCzNkXoArNXBDgVTKYlSSZ3n/jbP/TtPRy/ECVmUrfaswJHRgFzHNUFMChWJhhlYgwM05cBlphJ9sKWKCQsxiYSkUSjnev+pw4BVLoupoXTsxKxd3voSZpYUZsi/AFT3Q1MdjtTZkcApFJyh5nGk6eln8DlaQQvHMLiuWQvWRpspb6vbtSo4r/Mwfq4FXDtkX4IoOaKyBp9qQkfgRwVwv2v47lQaePDshVPQ88DKfRtgSkKbftNTyV5YD59RgxZJo82gFAtzcDlxGmr6bxuyKUyixVigpVPDyaODZMiNsbrSmrfSVzBXv7wXLoVQMrVOAG1Io1TqUqOiaUemOsSyFKyyHXKjoVvoKbORpbqQa+FuaAK+CTK21FErFkXkBrnovTDXKNFNhEy85MngHjju+trk6eiWFS5lPk20RqBb1lnXkLYVScWRegJdihZJm7a6URcz4jTzVyU810ZrOQuFoFXbgVaC2rBlh5ZF5Aa48C8XQ40a1FqO8U/802SrNDjx5fqqJ1kmhpJi5tUEVnFOZVoBXDpkX4OqNPC3LjJD3X92L39xb6SuJuNMbs4jWtJGnkuDGBHYNs3LIvABXCUXzjTzJ00vzInpWKDEceJozI1q8XGiFOzHTlKSlD7SVgLc4byV4xVCyACeiPBF9QETPlyND5YCp09JUO+rSmBHGcOD+Yk/ro1Bao+aZpg+09HaqBEo5jdDCDOXQwL8KYEEZ4ikbTN+vamlSPoWii9P5TreVvmVLhtakeXNYDdwMbdlTTrVQkgAnokEAPgbg9+XJTnlgKtTSHQua/Bm+mKPTwPmMoRwvuensAwDauWErKVp4kVk1DI+rhNZ0FkolwWcqpkdbWCRHqQ4dfgngWwC66AIQ0fUArgeAIUOGlJicGrecNw6ThvfEY++uRp8uYW86+rwlTysNJfDgtcfj2Q/WoV9Xdd4unzQEq7btx5fPGJU8cjjeRfp0qcPq7Qdww0dHGj/3k38/Eve9vjzg5KLcaE1yi7zByBx3X3ks3liyFX26VN+DUHOjS10Nvj5lDD42oV9zZ6XVIrUAJ6ILAGxmjM0kotN14Rhj9wG4DwAmTpxYETXs86c5QuuYIT0SPVct57Qj6jvjprPHau+3r83jB584PHG8HGm9i/Tp2h7/dUEyTyZtGZ738wScwPlH9k/kXaY1gYjw1SmjmzsbrRqlUCiTAXyCiFYCeBzAGUT0x7Lkqkqo1lZ6i9axG8+e7WHR0pBagDPGbmGMDWKMDQNwGYB/MsauKlvOqoBWcaZFC0dLX2RNAv/YXyvALVoGMm8HXgpSLWK26RpLj9Yg8vj6sBXgFi0FZfFKzxibBmBaOeKqJqwGbpEEOY9CaeaMWFi4aNP6ZLXMCC1aB/KWQrFoYWjTAtxuyLBIAt72dhHToqWgbQvwVBp4BTLSBtAalNactUKxaGFo4wI8zTNWgidBa6ouS6FYtDS0bQGe4hmrgbddcCuUVqGBu0VIc4CaRctBmxbgafjstngwfznQGs5C8SgUK/QsWgjatADn8juJTLaLmMnQmk4jTLOVvsWiCi7VLCqPNi7Ao08JVD9Tqdy0crQCmZe3duAWLQxtXIA730m0aquBJ0Nrqi67ld6ipaFtC3D3O4kGbgV4OrQGkWc9zFi0NLRpAc5fw2QaeGXyYtHywdu+VVihWLQKtG0B7nKZneryxs9wS4TOdWU5RqbVI59inaGloq7G6Sc1tiwWLQRtWgp161iL/zxnbKID9w8f0BWXThyMS44fXMGctR6cd2Q/LN60B5dMzH59XXTMQCzbshdfSuk5qSXh5vPGoWuHGnxsQtt0NtFaQNU05J84cSKbMWNG1dKzsLCwaA0gopmMsYny9TZNoVhYWFhkGVaAW1hYWGQUVoBbWFhYZBRWgFtYWFhkFFaAW1hYWGQUVoBbWFhYZBRWgFtYWFhkFFaAW1hYWGQUVd3IQ0RbAKxK+XhvAFvLmJ0swJa5bcCWuW2glDIPZYzVyxerKsBLARHNUO1Eas2wZW4bsGVuG6hEmS2FYmFhYZFRWAFuYWFhkVFkSYDf19wZaAbYMrcN2DK3DZS9zJnhwC0sLCwsgsiSBm5hYWFhIcAKcAsLC4uMIhMCnIjOJaJFRLSUiG5u7vyUA0Q0mIheJaL5RDSPiL7qXu9JRC8T0RL3u4d7nYjoV24dfEhExzZvCdKDiPJE9AERPe/+H05E092y/ZmI2rnX69z/S937w5o14ylBRN2J6EkiWkhEC4jopNbezkT0dbdfzyWix4iofWtrZyJ6gIg2E9Fc4VridiWia9zwS4jomiR5aPECnIjyAO4CcB6A8QAuJ6LxzZursqAJwDcYY+MBnAjgS265bgYwlTE2GsBU9z/glH+0+7kewD3Vz3LZ8FUAC4T//wvgDsbYKAA7AFznXr8OwA73+h1uuCziTgD/YIyNA3AUnLK32nYmooEAbgQwkTF2BIA8gMvQ+tr5IQDnStcStSsR9QTwfQAnAJgE4Ptc6BuBMdaiPwBOAvCi8P8WALc0d74qUM7nAJwFYBGA/u61/gAWub/vBXC5EN4Ll6UPgEFuxz4DwPMACM7utBq5vQG8COAk93eNG46auwwJy9sNwAo53625nQEMBLAGQE+33Z4HcE5rbGcAwwDMTduuAC4HcK9wPRAu7tPiNXD4nYFjrXut1cCdMh4DYDqAvoyxDe6tjQD6ur9bSz38EsC3ABTd/70A7GSMNbn/xXJ5ZXbv73LDZwnDAWwB8KBLG/2eiDqhFbczY2wdgNsBrAawAU67zUTrbmeOpO1aUntnQYC3ahBRZwBPAfgaY2y3eI85Q3KrsfMkogsAbGaMzWzuvFQRNQCOBXAPY+wYAPvgT6sBtMp27gHgQjiD1wAAnRCmGlo9qtGuWRDg6wAMFv4Pcq9lHkRUC0d4P8oYe9q9vImI+rv3+wPY7F5vDfUwGcAniGglgMfh0Ch3AuhORDVuGLFcXpnd+90AbKtmhsuAtQDWMsamu/+fhCPQW3M7TwGwgjG2hTHWCOBpOG3fmtuZI2m7ltTeWRDg7wEY7a5gt4OzGPKXZs5TySAiAnA/gAWMsV8It/4CgK9EXwOHG+fXr3ZXs08EsEuYqmUCjLFbGGODGGPD4LTjPxljVwJ4FcDFbjC5zLwuLnbDZ0pTZYxtBLCGiMa6l84EMB+tuJ3hUCcnElFHt5/zMrfadhaQtF1fBHA2EfVwZy5nu9fM0NyLAIYLBecDWAxgGYDvNnd+ylSmU+BMrz4EMMv9nA+H+5sKYAmAVwD0dMMTHGucZQDmwFnhb/ZylFD+0wE87/4eAeBdAEsBPAGgzr3e3v2/1L0/ornznbKsRwOY4bb1swB6tPZ2BvBDAAsBzAXwCIC61tbOAB6Dw/E3wplpXZemXQF8xi37UgCfTpIHu5XewsLCIqPIAoViYWFhYaGAFeAWFhYWGYUV4BYWFhYZhRXgFhYWFhmFFeAWFhYWGYUV4BYWFhYZhRXgFhYWFhnF/wd/Xpz73P8CoAAAAABJRU5ErkJggg==\n", 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2Tixg4MnHpV0lUb7HYxp4Rht745mxDrwA1GsUSl/VwItj4N7/tLpqNVEoeTrTIli7KQol7/mT2yYy8HC6vrns7AycM/w0+6YrU3bQNgplH0Wf1MDrSAoSUXaZx8BLTtUPTFZdtRE08CzH5D1/cttKpdCB8zKT5rRkvTezSChpO0t5P9kkG4VSMOpInYhAfNWtJ+ZqGXhCWRl122qiULitTWlHTJGTgcs21iAKRc3Ao2UmyT+1ZOBpy45r4JaB1xTVxDXXEn2Sgddpb1muMDQ5BUWhpHzd5yiCgfMY9jz1pTEzdoxp35xvWfLbWcnx2sQYC65J0r2X9f7ibU9zrdLe93ENPLo9OwO3DtyIumWFwg1d1MJDRaAaW+r2XBfIwPnxaYspIgqlX3MpdRmxTqBA5+Xtm+/+iA1i+oMILhMZeK00cPVxomNP2zHFGHiV4xaWgSegntitiAgDryPtuDoGXj/tEFF2XTSV/CiUaiUU1+wUdPunQZyBe84gGwPPHooYZ+36Y/Jr4KFdRIA/rwouY0GZyQy8WAlF/D0vA69aA7dx4GbUE7sVUa9RKNXcUPXUDhGNw8DVemoWBx4LRUxRf5IsYNo3LUQ7HKIgjt5lLBcDTyOLiIOYqv3FTthUd4SpxwYxrQZeU9QTuxVRrxp4NTdUPbVDRBCF4jhVx4Hz41Nr4BmWC5XvVf69X1N6CSU2Hd9VO6/oPvkHWtOiHHHgYRw9Y+E5TWTgwvlJY0YYB662W3S+JulDrDcpDpyxbOvF2CiUBBSVWqpo1GsUSjVOWGxTWgfXE6i4DA4RnEIZeLZX7jyDicEgZnP4yCWtFKhqX9L9Va5EbTTtXQQDJ1AQRx9l4OmjUNI4vkDucpnSbvGaqM4RU2yTO1nV6Ug6Rywl868VEh04Ed1ERO8S0avCb+1E9DARve3/H1ZbMz3YKJRsqIqBV3r3xtSh7DJBA6+O8fDj0zavGg2cf28p5Y9CUZWb5hj9vvnOn2gDUbiWjMvCMpPelssZSU8ooaRh4PryxG2mMEIe7plkWx7tvUikuZtuBnCG9NtlAB5hjE0D8Ij/vebgJ7N2C0rmg2u4KXoT1bzSRXT9Ouo4XZeh5DgoOYRqFTV+fNrm8Qc0zcxN+Zzx89mSRQNXXL+ktwVeL7fRPJU+tSlRGyQNPJRQWHBOE+3MSHqCSVdgyvtR/Ekle/BLFnlbNiz81VQi5T4yxHYUtTxvFiTeTYyxJwHI6TU+AOAW//MtAD5YrFlx7O6qYNXmXQCAjZ1dyn227urCotVb8eqabcrUZks7OrFo9VZs2x1Pd5WEd7fvwbZd8eO6yi4WrQ4zd6zftju2T8Vl2LxTbTMAdO4tY48mk0+54uK1tduwt6zevqlzr9cmhW3i8//m+h2ppZCusotlm3aG9u2JZ3RxXYbFa7dj0eqt2NQZzz6zbVc3Fq3eimUbd8a2cbsXr90eXNM0qLgMSzfuDNZCeXtD+jZxrNu2Gzv8dGcbtu3x2sIY9nRXsGj11uBvo6JN/AHd2NmFxWu3Gx/Y5Rt34tU127Bo9VYsXrsda7d694U4iJlkuqr/TbqO4rVa0tGJLbtM9103OveWlW0FgO17urHeP0cce8sVLOkIMyFVGAsGMfd0u0HWLJNT3rKzC28J2ZQ2C8/zyk27sKe7grc27MCi1Vvxln+NWdAxqMMExQ5j8brotfGeoe0xu0wRO01+bDu/bhx7uitYIT4bQrajFZt3KZ9z7pdUafaqRbY0GiFGM8Z4PqP1AEYXZI8W3773NfzOT4v0yhp1qqNP3/wi/rpyKwDgirNn4TPHTQ62vfNuJ077zycAAEdPbsfvPn9MpvqP+t4jmNA+AE9eenLk9x/++S0s3bgTIwf1Q8eOvfj2HxbjwvdOjuzz3ftfx03PLMMr356HQa3NsbIPvvJBjB3Sir9cfmps241PL8P3H3gDnz9xCi5//4Gx7R+9/lks6diJ90wahtv/4djINpHBnf5fT+KmC+fglJnJl+q/H307yMMIAFfdtxj/dd7hkX3u/OtqfOOORQCAqaMG4s//dGJk++f+dz5e8PMXPvq1EzFl5MDI9pOvfRzbfWez4FunYfjAfol2/e+zywEAbf1KIALWbduDP7/+Lt43K93t11V2ccz3H8WsMYPxx4uPD+xjjOF7f3wd//vsimDfySPa8NjXT4ocLz78Z/74Kfz7uYfio3P2V9Z149PLlDkvxUHMRD1b4cE/9JO/4P8+fwyOmtyuPOael708lau37Map//FEbHtrs4M93V65n755fvC7qi0f+emzeHPDDiz53pko+ZLCZXe+gt/7+TYBoLviBhLKtQ++iRWbvA7ZJKGcc93TWLU5dIwnXfs4ln3/THR07sUJ1zyG/s2lSGrC33z26OBcMKbWwMWfLv7ty+jcW8b5R08EANz0zDK87KdQiwxiyhq4cLqPnDgMT7zVgTN/9BTe/u77g07q67cvxH2L1uGpS0/G/u0DcPV9i4NjfvDAG7j+iSV46V/nRcr9zC3zsWDFFtz8qffgpBmjtOclD6oexGRet6W9WkR0ERHNJ6L5HR0duevZsqsL44f1x9RRA7X7bN3VjaMmtYMI2CYxj63+99ZmJzMD5z3zSgVb5OXe9YVjccyU4crj73/Fe6h27tXnzFsrMR2OLT6zVjFsr/7uyH8R3EF8/OgJ2n3UdXZhUL8m/PaiuQCgzPXHy5o7pV1Z7pZdXWj1B+y2Ks73doEp7lAwfLVdXjnfmDcTl71/pm+HnmHK6PLfymSG5jKv7NGD++HGC+bg5BkjleXKDld1TUrSVPkLj50U+S4OYmbRs5tLYbmmNg/0U5upZuyff/QEPHvZqfjG6TNi255R5JJ808/JKSbrfert6DP89XkzAgmFs/0xQ1qNA7Si8+aouCy4D7jz/vyJUwB4948Ys68cpJTeSkTmLD7v3YbkKy5jGDe0Px786gn4+d/PwXsmDUPZjWZSeuLNjkiZcg7cLapnYWcXhvRvxsHjhsS2VYu8DnwDEY0BAP+/Nq03Y+wGxtgcxtickSNH5qzOO9lDBzTjzIP302qQZZdh3LD+aFJEKPDvA1qaci+ko6yzwjB2SCv2bx+AOZOGpU4OkBZJU5NNcbd820nTRxrLiNfJ0L+lhLlThuPgcYONZU9oH6AcDKu4DANamrS2qcpKtstbSnbIgGaMHdo/VdmR48WBWSY6cG8K+ODWZpx64GhMHN6mtEl3T3Ew37m0t7UEv8kPrSihZDkv4pub6bjQ0cW3nTxjFIa1teCYA+JEw2SKqb4ZowcF93zFZRgzpBX7DxuQed2isiJE8sgJw4JtYsy+emwg+l1cIthl3uBxa7MTdOJeufHFrIiAGfsNQkuTE7BleeISb6t3DMPM/QZh1pjB2rZVGMPJM0ZiRIq3zKzI68DvBXCB//kCAPcUY44e5WDwytHGZ4qTPLRhXE3ZV7EzDQZWXIaSz45K/qI+RQ5mJE2MMM18C0PXSsYyYnVWWDAKX3IcTdl8ZmFJ6+y4s0qKSEhtl399Pbso+C0tdJEPLvNs5GXqZnnKHVX8e3iPcciLV4kSimqcRlWeXGbaKAsZ3PmoOIZp0NFUZqkUTuQpu8yf2JM98ULFjecMDe9bN5GBy/VRxIEzEHm6tomBM8Yijl8VieJI953n9CkY9FTBu7dqE7GdJozwNgDPAphBRKuJ6DMAfgDgfUT0NoDT/O81RcXly4jqH9xwsf+40xFnwhW5xoE3scQ7jcEF19y8ebKJJE1N5m0xseTAkWZg4LxT0jmzICxO0yFW3PTJh9Nej4rQsfBzni1sTtA/hc+cOfN7q1RSx5gnMXDVbEtZUsnLwJtL6Y4zhQYGDlzxCpt3yn2TE0ahVFzmhxXmS3IsPx8iARDj29UaePQ38bQz5tlUcgjdZfU9wMsQj1ORBLGtXtneMfJ1FsF9Vy2QOIjJGPuYZlN8xK2GKLtuwK4B9U1lZuAhY9yRcTTY5Ph4nUC4KlvFZVCtWZQnxDBpYkTo4FUyRnT9jbQzCcVOyZuyrpZIHPIci64zHdivRWubqg1p7KqOgQsSSmQmIAve8AATA1e/1cnfRZYdY+A5NXCR4RkZuOFthztulS8xXSLT9Ss5FJRXdl1/khXQnTHGs+KymA0iARAn8qg18Oj3iITi36tNDiVo4FAz8IrowL3//JzwY0o6XRf+fWtg6NWgYWZiBgw8eHDjN1VZ2EfeHkxlblY7HGPdhpuRs34Agm06tpyDgfOpyRobjBq4NH07EwNPkBO4kzc5O+6sitPAGZpK0ttOzqnj4v3hsihLKjneW4XMSlMz8GYTA88XhSJ2BKY2m8qk4H/cmZjeDs0M3ImwUj61PuvbZtl1FQw8vG9Fh6l6FkwMPHCyDkkaeJyBi364VIq/uZLEwDlrN80NqAg+omg0jAPn7MvIwCvCJA8tO8qjgadl4PEeO7pv/rRaSt3PDeNjzRp4NrlB7JR0i0bJbzsqZ6eTbuQxgl5h4MJnj4G7kU5LZZd8XU33GIesjcpatlG6EOoTNdT8Gjj5/+PbTKfRxOpLTjiVPtTAyVieChXhXuYQ79tkDTz6Xa2Byww8+jxyqYVDqYELbeX1JiXFFu/botEwDjzOwDWs0J9mHVtMSHi9LXKVMc76AQj6vNpR52LghigUnSQgb8+lgScx8Er0eqicHWdQsW2ys0+9fnP8bSfLglbiAxvVwGUGrr7HYt9j91go03HIg1fyaoRGx6lwHF478rFlXobK3xSrgWdfP6dcMWjgbhiFwhhTPl+mwAHGGByHUCpRRNqJr4USHcQM74OwvqCtwkJoSb65lhp4wzjwciWMQgESNPBSD0ehyAxcJ6HkmPttYuC6QTl5u86RmuqU5YR42S5KJQq0PZWz03UcSVqyya7aMXCWzMCLiEKRBkeS7i0O0ema7iMjA/elk6wSijEKxfE0b24Xn1qfh4HrJJRoFIr6fpHNl+P8HaLEKBRZQlEzcDkKJer0VRDHV4pGwzjwdAw8RRRKc8FRKJV4FIruhs8ziGkapOS/tWgS/AaRIgotL6nOdBq4gYG7ogZuTkyQOgpF6FiI1IPVJuiWEuVOIQ0DFxej0jFyswYefeSS3u5Ui19VH4US32Y6jUYGXiJJA/cGNXUdgo4pe1Eo0d/4eYzGgetmYsrOOLqNR4p0lU0aODQMPN6RhoOqSHTgloHDj0IpkVZndv0bIE0USu00cLPWrCon6VUzDQPXhUbyNmddvU/slHQhdaEGru4cyq4bDkLJ2nGORAW8DtEhZk3qINYjnguXsUisrrFTMoQBpopCaZIZuPneUiWAqDYOXOVwzAxcf980OWIcuAsiGDVwXYitahwljJ4SNXDNeFDMgUc7aCJKjELxpBaxbfHn2RHayusx+W8eomo1cFeOA5deX/0Lpo1CiTi7bA488rqtcFRNQsy0WJepHNNvqu0mDbxfs6OcQMS3Z81gkzUKRW4H70x1ceC6a5Nol9CxhLZlSLIg1KOMA+cdcfDGErfTFAaYNQ6ct0lvrxupLzgmdxSKfhDTvGqhSUJxIjMxOQPXERM9uXFjTl98c+STntKshcL3Ez9zBt6teQvjZSgZuCKMkLdDHviUwfezDNw1R6FUAmeVHIWSOdOGZgo2LzeugesGMdWhj8a6UzHwktq2SrRTSzvgJ3ZKpjhw8XpEZjmyqCOrhQYe2paPgUcywrjhGx6QxMD1YYBqDVwaxJQccjIDj08oyB8H7v9XbMurgYuDmGXXY7CmMEITuZHX4xZlsmgUSvx+lDuMuISiYuDxlGpiRElqDdzgRQMSZePAPYZUIjXL5d9DBi5tD/TJbDHRvG7VZ14Ot0mnnfJ7Kx8D1y+QLzM+lW3crrwMvERqx2/SwANHppnCnxTdobfLjTnwTBq4diq9xMAVzIvb2dJkYuD+mISwj0zOZIecqIH7ZSVlnEmzjTsfVdib7HDTZppxHFIwcIOEYpjPINogkqKoBq5+FuT6ZAmFZ3Eyx4FHo31U5ARCW3k9ppmnAbFMs4h8DjSMA09k4P5FdRzvQunYEX8ty/vgq9hkLDIiYdJN0m+R7RUDA/e3tRiYrkPZB/zETqlJq4G7wbkW7RTt0A2eJkV3GO1yogwpNwN3ow94pM2GKJSWyJR2dRSKKWlDs8TEdPoyj/FXDWJWq4GnGcTUva3IiGrgHoM1rYWia2/FjcaBRwhEjrVQZAaujAOX2iWvhaLyNXEGbo4DF0lULdAwDpw7St1aKPzGCBi4Jg48dHb5ZvDJF53HnvO65f1FqGSbJCnHFIXCpYoWYbAnZlswJT699i93SkoNXIoDV2UF53YlTdxJmxlGHs0vOZQx6Wy4rxiNwBjzM/0kRKFUJA3cMNdAh2bJIScNeKs1cBMDN0ShSP8jkIrUhVzKKDlyFIp5LRRteytqBs476TAOXD0QanqDyLsWimpMK3jbEDR52Ter3l72eQ284jsjXaQHv6hBFIpmVLpf4FTS1x0d/JJ0MzeMXgh67NjKaPFyVGWrwMtS7RZb60TRZvFBSOvsKpFOSe34edkqZydrwUmSSVoGLo/m62zTQWy/+CrNQ9PENvPfI8ezaFRIbLvUbhVkJqbt7KX7VSR5eSUU02JWclsipMWgjzcJEkq54iaGEZoWehNNDweUo2RMvx64vj3cMXv3i6vcB/B8AikYuJxCzmsHLwPBCoxhueHnQELJkAs1CxrKgZc0mqv4nceBx3TXiuxUcjJwhTNKu0JedVEo+gHQcM2I+PovSVPidXWKnZI+CoWUbS7LHYtGapDbkcYucWp6dg083HevsBC/y9JJYWU3OqiYJgpFhszEEhm4gs3nd+Be3SoyKDuzaIesf1YcDQPXSiipNfAwpFNMJ+iyaBksIDgmCUVYC8UYBy4xcMXbfrgWiqs8Rral1gw8b0q1HsVf3tmIzr1lbxDTPxF/WbIxklbq/kVehjfOChes2IJtu7sxpH8ztu7qwq3PrwAANPsP12trt+OE6WGCiVWbd+HhxRuCN8m2lhIOHjcEzy/bHMkB+MCr63GBkGVF9eCv2rwL75kU2rZhu5dz8M+LN2Ddtj1oKRHGDOmPddv3oGNHmI/w5meW4axDx2LkIG/h99fXbQ/yba7avBsvrdyCw/1F7rft6sa9fvos7jBeW7Mdo2a2AgA6duzFb15YgWb/QXAcLw3a1+ZND5IhLOnoxFNvdYCIMHH4AJw0YxQWrd6KpRt3Yvb+QwF4N17n3jK6ym4giSzbuBOPv9mBwycMDdp8z8trcNEJB2BI/2ZBQlEPGMdCQIXt9y5ci44dezGoXxMO3X8ImhzC1FGDgnIGSGGEb23YgeeWbsJcPxvSso078egb0fwizSXCBw4bF7mO97+yLvj8xFsdaG12YhOy1m/fjVljw4X6Ky6LaNiys/zryi0A4rMtRcgMfMGKLZg2elDw/bE33sVRk9vxgG+fOg48PH8rNu3EghVb8KHDx4HI3EmHEkrcmbyxfgcWrNiMbbu7ceTEdvzm+ZXBth17yvjtCytx+kH7xY4T2efOroqvgRPWbd2D/3txFT50xLhANlqxaWeQm1KG3EmIxOOZdzYFvz8tZQRyGVCi+Bvq88s24XcvrsRh+w/Dm+u3Bxq4mAlKvH7vvNuJ55dtxrFCsgveiTzy+gYMbG3CERPChC2/em4FzjtqglIDFx04v1a10sAbwoE/8Op6AMD49gHYb7DnoG59bgW+etp0AMCurjKuvv91AMC4of2DjCh/fGUdPnbUBNzzsucURg3qh/HDBgAArn9yScSBX//kEtz6XHjTAsDAfk2RpKUAcOW9r+Hv3rM/WptD1stvtv2GeLa9uHwL/vaI8QCAncLxd720BncJ+QRlfPsPi7FjTxlfPnUaAOB7f3w9sv1f73kNf/jycQCAexetxU8eX4KSQ3jP5HY88sa7+NkTS3DyTC+LyG0vrMSebhfTxnnOYdLwNqzavBs3Pr0MV5w9CwDwhVsXRJLLLv/BWbjy3tcAAPu3e+eJD1K+uHwz3jt1BADgR39+C4CXjWfc0P4oOYTrHluCsUP74/yjJwYMqbnkvU4nhQ3y/d/dsQdfue2l2HlZ/oOzguNEJrN/+wA88VYHvn77Qjz9z6cEtt3td2wiSg5h0aptwfd7Xl6LliYnYGR7ut2gzWOGetfx2SWbIjlE5YX5ZTb57396EwAwerDXAZ9/9ASMH9Y/2D5p+AAMb/O2HTxuMF5dsx03PbMM5x3lpbx7be02fOrmFyNl8uPPOGg/rN+2Bxs7uyLn7yu3vYSFq7fh0PFDMXXUwFQMXDXmtqurgg//9FkAwNghrZEUf/cvWoeHFm/A8k3xlIL9mpzIwJ/HwL3O9tI7F2HSiLaAaJ14zeORY6eMaMNSP+l1d4VFIjVC6c/BGiE92pZd3fjDwvD6uoyhhPib2KtrtuOf73wl+D5t1MDYsywOaF70Ky8/6IbtYbv5vXbLsytwy7MrsPwHZwW/rdq8G396ZX2ggZ924Gi8uNzrwFURQ/u0Bv4vZx6IV749D5+cOxGTRrThg4eNjfR6/CH8xukzcOzUEYGD4q/J/BXs4UtOxInTR+KgsYNjmtnebhf7DW7Fwivn4ZZPHwXAyzh9xIShWHjlPLxx1Rn4p/d5HYY8m4vfbJNHtGFQaxNEuYvbdtn7Z2LhlfPwxDdOitQ7ddRAvPad0/HKt+eh5BD2Cq94e8suDho7GMu+fyaOmzoikrmet+3Zy07BP5x4AA4dPyTaHr/Nv/+il+j45k8dhX5NTuR1VHTeHHu6XRw1uR2XnOZ1Iu8/eL9IedyusUNa8cOPHoZDxg/BXy47JTiH/JwAfAaoflmD6z5+RGR/fvxZh46J2cWPE5nMTRe+B+ceOT52zg4Y2YaFV87Dwivn4Sk/CfXebjfGghZdOQ+D/RyS//V3hwVJsA8aO8RzRAq5o8khLP/BWThmynCtsxze1g9vXHUGrv7gwRg6oAXLf3AW3rjqDDz8TydiyIBmvHHVGfjDPx6H46eNiLBhVa7W6fsNwhtXnYF/PGUa5n/rfRg7pDXScby+LsxbqZvkwsGbo3LgM4S3ADk/684uz/HJuTiXff9MNJeiDtyrJ/wu3jcifnTeYXjokhNw95feC8BzepE4cGEegozzj56Ar/gkhx/Cj731M0dj2IB44nCVrCN+XdrhdSTiNdWx5pn7DQraxsMIP3/iAYF/iDLwcGyuFmgIBt6/pQQgfC1t69cUGenlJ4k/jLJ2JY/oD2qN58WsuAzNTYQh/ZuDcgBPgxzSvzlSvhyOJvau/ZtLse3cZl6OiJaSg7Z+XrmyTl1xGYYNaAERYeiAZqzdtjuyDQiT2A5qbQocIK+3X5MTrJ9dcigib6jAozHa/Tr5cUA8THDIgJbAwXH7K9L5TjOpStyf/z6on/q2lNdVLjmkPN8twjUTQ8Fk6aa1OXpPiejfXFJE9QiTfUqkdU4lhyJly3Xxz4Nbm7FOk8yao0kqS16ozRU04KThgHAmZtyZDOinl310urVKU2cMkYktug6ltbmEJj9PpXccgyt0ZvLcioitLaUwGMFvP7dRjIqJ2hq3IWkZCxVrdplXP8B1+7DD6u9fJ9Ugpjyhqyg0BAOXIcf/VgSHwbeLv4szEr3/ikFOV1yUSr2es2qB94r0Wi2HMMqvUPJNIZYvTw0XGac8pV3u2eUwQTH9mM42GdzJlSI2xQcpZSlDDreKDigrQjqlRZ/kJXN1g4AyA/faTZHckibbTJ2XfK5Ug77yZB9deab8iHIdSYOw8ip28hsNd2CMJQ/Mm2ZiyuGNIroT4jzFDoFJmrBu4g4/j9z5uSzqUE22lhwn5pDFtz5VB6Ve/0VpmlCPOlqnWZhLIq6Fwv9HGHgl+pwWjYZg4DJKjhO5McqSk5RD2+LOTv1wyoOR8mdVBIwYfgbEGZI8iKFyQOJnefRfXNZV3TGEnZZsV6wuxTK7IriTk1ku36YrW15CQGyzV6dmwktJzcB1g4CybWnaLdpmkhfkc9WkWOFR7lB15aXVO1Vr9iSVJTt9/lEXXifCtJiVasIQR1eCAxdNlKMydOeIn0cn4vTSnzfeBu4rxXtOdfpVBDgpa5CKNbssOnFOXAslsEk4XTYKRQF5dmBF6uVk1sidM++ZVYsgRVKjSayYQxfzbIpNTmLgcgYQeQZcEgPnxankl6aSmb3J8Bh4fLaj2A7vsyRlSLPTYgw85gi9c9/c5EQGOeXwQxmq7N7yaokm23qKgadd+7nkJK9PE+tYNE7fZcnhmKbFrOQZoiJEaU4FcaxAXh9b+5bi8Ph2Co5Li5ITjhzw48R7TtVB5WLginPCFAycNz/aGXkIOpZ9fS0UEfLDE8zCLEV7dZGBx5iuYknT1Ay8Eq1bdvJmqUN/IWMyiMDudY5K1ynp5IZCGHglWjZfDyOugeuW9hUdvBNz/DoHnoqBG2xLSg0WLze+GmFJkNmqZuCaZQpMZemuoctYjMjIyCuhiIPEKkQZeNRZ6t4wQgYeMum0Tty7773P/AjxnlM1XyWr5NPAw1BSPns0YOBOvDOyUSgKyCykIjlJvvYvfwBlVtakkBKiE3KijJojdGZ+EL8brkGeZJvIOHQPmLxiYCXiMMwO2iS/6GyTETLw+BhARWqTrPOqHLG33Kw+CkV28ElT0ctuPLs37/SYwMR0thkZeKzcbAxcdAZp9c50GrjqGsaPESNQdGzatJhVs2HykW6wliOqgUfXx04aJxBZa1oSXiqJEkr8nlNr4PFykjoMtQbO6/CeCXH2Zvg2Ee5f6yiUhnTgJYci61/LGjjfR8/A4/pmOgYuSTNMXa/MCOVydNdSdhplVz+TUh6kTKWBJzJwN4MGLkkZoiMW2mxm4E5kkDPUwHUMXN0pAeFDY7LN1HnFBwsVnXwlmvhYzmwv25SEWOemuDSqjkXNwMPzp2PTpsWsTBp4MgMXJRSZgSdp4OH1y8LAxfrEekoOKfXuJAlFdcl0DJxPCuLEQZZQ1Guh2CiUAIGUIfW+cjRIRXAMsrOLscKKJgrF4Mx09Zo0cEA9Ew7gbwbqGPPkwbr4IKeagSdo4BXX2GkZy5YccZBg2sTAhTbLyx3I0HVK3rYwBl1nW+EauCSlyTYlQXbGqnVClFEoquVUhQ5K54xJ+i+iKg08IqFEBzH1GjhnreFxCS8jAUoiy2a8nnAxuyQbRVvD7fEdVNdRXBir4kbDCB3LwNNBXtAqHQOPSiH5GDgF+5rqVUahpBjEiDNwKQpFdqIlqcOqloFXDBp4JaFs0RGLUShOXEsWHzbRwYcaeLYoFPFYk208Nl6FuFThKFd3FOPAVR01PzYNZBav6lzTa+BpGHjU0Ygwa+AVvw71vVMEA2csntRYB2+g0vvMEH/rU71oqTVwdRvCelRRKOLCWCyS0EGUgziSOpZq0ZAOPB53HF9vQAwDU8kN8XRZ8dRoXjmCM5MmCKkGjWSGlGUQQ3bCmRi4IjJHvgF17E2sT9aZ00Sh8P1UUSiqAWOdxJIYhaKRR/i2JNsqBgdebRSKuG/aiIMYA1dcm3h4o3ocI6KBN6nrN0koJgcuyxS6crkd0ThwNXvn96YuDtyEaBSKX4/w1qcqJ4mBq86JfO55JyMz8LgGbpZQi0RDOvAgtrcS7X3Ta+AJDFxa8Y4jdGZRpmlaIS/LK5TMsmUNvOKKg3VurFPqsSiUhLLFNqsGjKMSiyIKRauBGxh4RX2tRdvKLtPGmKscpfggcgepi0IRnW+2OHB1J6ArqyoGHvyP22dKQsGRNODq2ZE/DjythCJGX/HnQbznVOUka+AqBh79reKyYNCSk8CoBs5tih4DxMcyikJDO3BR9wSi8ag8k4e3PZ6KS6XLBtN3KVqO/FnWeuUsHsoIGXGkHlHHEB6LGCPjbQo6LcFJRhcRikehqBxZqigUxaJCOm0+2I/iDLzkUOR3jrJwThwHMcffUoo7WZ54WF6fRJ4da7Kt4rKYPswtk4+Rzyf/yM+NQ3oGbkpyG6nDoUh+VpWDjLVXs+Kgy8IwyWaNhBMwY4V5aTodb9Au/rt4S1XcdFEowfkOHHiUuTLpvwjHiYcRis+ZaiyBKOpYm6UOWi6P1yOCD1oSIcj65TIWxteDt0VB4FLeE1nR0A48rQZeYWadmpel1sDjYYTZNPD0PbAcHVNh8TyNQYIHl8UmHKXSwA0sh0+ZjrRZmqQDxGOtAe+tRXZEXEJRJUbg9ojLGvDjVWxQJ0Vx+8QIGLUG7g3ypdfA5Tea6NuWLGVENfD0DNwrOyohqfYR7dTGgSfcayFTjG8zSSgcuklH0TeVdBo4b1dUAzfXz+0WGbjL5GfR0Ugo8vWN7pem0+VvCZ4GTn4cuHBeHb5feIwc4lw0GtKBx6JQWPwkNUUcihxZoXbgSRp4EBMdi36RyhazgQT7hKdafIWVs2DHnbB6fReVBi4e6zLdpBc9A+fhYmKb+UQYN7HsuBRS4nHgGg28SVrsKowDVzhwxTXmbRK362zjr79y50DCPiJk7Z6fNrGTjzBP0RmkfFjlwXjVIJ5K2lE5cCbYoJdQfKaomkqfRkKRYrzFusXPEQ08UUIRZAeREUv/AUQWZgt+9w8JnrOSPqGyaHtzKZr2zTTJiaPsMjAIGjhjEc1fjk0HbBihEnJkRKUSP0kRDbyiCLlTMvDwBpHr4scBiinjkgauSvCbhpWp5BeZgYt1G+PAVUzUiQ8oiuDRBqqIjExx4BID12ngpVKoJYq/m/JAqhgpIGvg+jjwLBq4KppInOwVuVYJU+JVkDsf1bWJdyzxOQwAl5gSwggNTsoURsihc8aq9GVJx4QMPDwuiYGH596JSR7iPZdmMLSlyck0fR/wrrHLxImC0bVQVBN5xHu9FqjKgRPRJUT0GhG9SkS3EVFrUYaZEM4OjDoMHcsW2bW8jUMcMBRnS8pOEog6C6/e6MxFOWJDtk3bLsEurvnKdugcVaoZiQmLWQUMPOG1XRtrLYw58ONUURM6B6+bienFOGsYeBAZlBAH7k9SSh+Fol7TJsLAmTiZLEOS1aCO6DVVXRvVG4dSA3fDDiApCiVrGCGHTg5hMQeenoGLTi/tzMiSE1/MSrznVFXKZTeXnMh+aXy5OHGHE0TVWiiqpa7rLoyQiMYB+AqAOYyxgwGUAJxXlGEmpGPC4QOoiwOXX3XEh4V/VLFxVbicWK9SA0/NwNVlqwbrVJ0Lr1rNRNXsjYNP2EhyGvJkH35MegYuOPhIm30GKTnZsstiSwKL9XrHchart80UBx7XSDVjGXKHKslpWSB3PtVFoYQaeFIceNYwQg6d/Caa47pRjV2vgfMwQu97Gg1cPPe8ClkDL5E6H6dsekli6mnYOA8bdIiCeQIuCyUz1USeLAQuD6pdjbAJQH8i6gYwAEA8l1UNwC/ka2u3YeTAfrj8rkUAZGcLPLx4A8674Vm8824nJg5vix3/9ze9gNbmEnbs6cbGzr2Rh0XlePnn55ZuwrxZo3HFPa/G6m1yCEs37sSdC1bjw0eOx/VPLI3tI77DliQNfMm7nTjxmscwYqCXeqsk6fKX3rEQrc0lLFq9LZKui5f/o0fexsWnTsPLq7biFD+9mlj+um178IVbFyjP60+fWBJrM8eNTy/DN06fgVY/gYKK5b68ahuO/t6fgwQE3EEv3bgzqHPrrm50dO4N2l5yCK+u2YYv3LoAKzd7KbuapbL/+c5FmDZ6oH8+4lPeAe96LVixGdv3lJW2bd7Zha27urWThGTwzvSllVvwm+dXYuLwAf7vTuR/xWV45PX1+OUzy1KVK9cBAF+49a+YMHwA1mzZrd1HbMuarbtxxd2v4tvnHBT8/n/zV6Gj08uYkxRGqIJpKj2HTn4Tx0fk9bh1Tp/f16HTYxE2GcyKFkgZb1epFEahbOrswq+eW4HH/DyoPLInZqP0o5ehR79dhcvuWoTNO7tiDDx8s/H+X3nvq/jNZ+diw449uOKe1/z21BkDZ4ytAXAtgJUA1gHYxhh7SN6PiC4iovlENL+jo0PenAsHjvESzS7buBMvr96K7XvKGDqgGWOHhA5t3iwvFdhzSzej4jKcemDozN4zuR2Thg/AU29vxMOLN+C5pZvR3taC46aNCPb58BHjMXO/QZgjJCce5zvMjZ17sW77HrywbLNnz35h4tsTpntl3PyX5QCAV9ZsAwCMEWz70klTMWpQPwxva8HnT5wS/H7KzFFob2vBik27sGDFFhw4ZjDmTPTqnz1+KA4eNxhrtu7Gko5OtLc14zSxTb6dP3nsHaz38/rJD/8J00dg//b+WNLRiSUdnZg+eiCIgItOmIIjJgzF7q4yDho7GIftPyxyHE+C/Lafgk2e7AMA75s1Gq3NDjZs34sVm3bhxOkjMaR/M06aMQpjhrRiSUcnnn5nI55dugmrt+zCWYeMgeMQTps1Gu1tLVjS0Ynuiovjp43AsLYWnDN7bFD2719ag2se9PJNHu4nW+YQHSlP8nyMkJgW8B7ql1dtBeClwzpmynBc/cGDAQA/++SROGXmKAxqjXIZPuB77UNv4vYFq/EfD3t5QGfvPyTYzs/FP9y6AM/798JQRTovHXgZLyzfjDsWrMbzyzZFth97wHCM8vNrhu31jvnVcysiDv/Pr7+LhX4bRWd82oFhTk95yrcIlezy3qnDgxy0AALNV8bs/Ydi5n6D0NZSwtfmzcCcicOCZ1THwAf4nbzIWvkbxIFjBuOrfkq/S/y8t9NGDcR3zjkIh08YihmjBwWdxLfufgU/fXwJ3li/IyhbpYEzBnznnIODdg3p3xxx2tx3/MdHZyvtBYC/+MmVifj8BjciGfE2P7d0Mzbs2IPnlnr7zxg9CINa098XWZCbgRPRMAAfADAZwFYAtxPRJxhjt4r7McZuAHADAMyZMyf7e6YCk0e0gcjr+XkPf8unjvJTr3n4yqnT8PuX1mDZxp34+NET8MWTpgbb5k4Zjms+Mhsf+dmzwW//fMZMnH1o6DSu/Uj8Qg5oacL00QMjS3f+x0dmY4LPzgDgQ4ePx/2L1mOtn4iVCPjyyVMjtl182jRc7N+gIj55zCQcO3UETv2PJwAAD1x8fLBt1tjBuO/Lx8eO4Thqcju+cNIBuPGpZYFtZ0hZxD90+Hh86PDx2jJ0uOzMmfjUL1+MyAUyo/jHU6bh6CnDg3PK84p+Yu5EfGLuRADAN25fiNsXrMah44fiuvOPAAB88aSpkWvD8eOPHY4ff+xwvLxqKz543TNgDDhy4jAcPG5IZD/RkZZdhhEDW3Cq4LSAKNs6eeYofP30GcG2Yw8YgWMPGAEZnGF17vUGdhkDjprUjoPGDgm2A/HBy2vP1TuAeB1R/iT6uv0Gt+I3n5sbO0Y876p4ZyA6IPmLC+Zg0mX3AzDPxJRt2b+9P3792bk4+drHg98Y1Ey1tbmEP331hMhvD1x8PA7/t4diIaKAlwybyw7iWii86Lu/dGzwpvT+Q8YESa35dyB8m9gt5In9G6HTj4G8Z4iX9cHrnomcbyIvF+uRE9uVh9/35eNwye9extvvdgZRKPye42+ME4e34d/PPRSX3rHIk+z8e+MXF8ypyzDC0wAsY4x1MMa6AdwF4NhizEoG105NU1VDHTveTJVOmgZ80SjVLEzRNi9szbsp0y7wz4/NCz5YZ7Itb7mA9zosz0gUkXQOVWGaaevWlS9ONFJN4gGimmyWGG1Za5bDVIH44GWWaDGTLbqwZPG86+SJ5JmY+m2hbfGIrCyzJb1jhdDShHVUGBPHfZJPIkmDmN5x6e8rh6JMXY5flyEOnDrk1eUlkpaCGIQxmVrPwgSqc+ArAcwlogHknc1TAbxejFnJ4IM5ppPET7jqwuqmZCehSapX6VRK3JFmv4DV9NR8BL674PUXxLBN/gCnOae6crLYJY8v6Or0OtX4+i9ANC45a0cdqUuxxILs5FUx1vo69PvqnIlog06e0K3tbVrMSv5NFYWVJlIkYqtDxggbr16/bCHSKM0lCgY/FTangZylXg5/lOFNHvKP9QfnVfMmxECHLMto5EU1GvjzAO4A8FcAr/hl3VCQXYngsclpojxMzM20j66spHrTOHkdqgn457Z0acIB85cb6sxyjk8RiQxcmpSUru60DJylY+ApO9NEBi48qNG68rUtLUQbustqp5gYB66oVv5Nnn8AeIw1S+h0ZC6G1oGLGng0y5QJovQi25wGDkUnYrkZGDiRdy+rorbEZ6XWk3iAKqNQGGNXAriyIFsyQWbgJplC7WSlaIaMD7ap3tDJZ3ek1TFwz5ZwQk4xN47IKnQTarzfzPWFDDy9XYkMXFghUozlFyHnHU1VryLdmRwuCsSZZZbLZ7rWOl8i2qDLlKOblBNIKEoGrrYtysDTL/kKIBjoA/QTnURHrFr+QYcgkFAoNsuzQxSfgGTqN5ocR1g2ljPwir9NwcArdc7Aexuh3lsUA093Kkop6q2Ogee/2PxY3YScasutuOabMpmB6yUt/THRyUrxOkXGE48BB+TFxtKv1S07q8IZuIE06MopRRx4Ng3cbJtaUoxo4G725MOmdV4ArzPxFppisWWfTVAtPpWVgYtNSdTAS3ENnJ9/1X3B78esdmVFwzrwkIEnB8oXqoGXkustZZB3YsdWMeBRChy4ekp8teWaJtToflOVk6WNqrXJVXXyAW1Vm8XnMjUDdwhlaS1rVX5UeSAxy6Jzps6kOgZullBU0DPwsKysg5iiDGWa6MSjhDIxcGkxK8/m9O7MccxLAMgQpZ2QgccddEkY3LYM3IAmhyKvKUYGrrihq4pCEaJf0jHwHopC8W8ers0VxsCFxMbBTZninMbKycXA4w+Hqk4ehWIazE5jo1hvb2rgui3ivaRLdaZbmEqXyg9Qr9bn1Rf+zlj6pAvc1iQN3Ks7nEnalGJCERCen0gUSgZioB7ENL/F81PB48D3dselyggDN/iIotCwDrxUSi9TmLRT0z66shKjUHz2louBV6WBRyWUonp+0VkZNfCEB4jf6FmcXOooFFcf3igelp6Bxxc802md0bqKudbaKJRUEor6WDI87XJ1AQMXyqoVA6ccDDwIP4TIwNOfe14nh8vMEUTieXfIuz+UDFy6H7PalRUN68CbYkxY35TCo1BE5m+IA1elXEtCEVEoweBKQfGnos5cVRRKDnuyRqEkDWIWxsClBdXCulIVH9ShhWZTVAPPKKEYbIkxcL998TjwvBq4mYHz1RSzauCiglVdHLhZQilFHDgZNPBoFErJSRdVkxcN68AdiobqqPwevzypolDSDmJSlIHrslmXE5ydDtV01twWfmNlYYMmiJmIgjcPRdlpo1Ay1S06cEOdfHKVap88ceAOdz5MzfD4uZUdU5aH1bRuuF5CSWbgOhnCaJuGgYvnUzodifDGqaIrVAJR1gyEckbZNcsYBnODcrzyFZB+jK+FkhxGGB6rJwX81AdvhDV03kADO3CPgYsyRUYGLp3YtMQ3NklHE0aYNwqlmt6as6YwDrygMEKBbZrePJJuVr49m44aZ70ieBPTxoFnYeBAdAahjmnp6kpbhwq6+yDiwHUaeEIYoQqq1Rjl+rKuuMjfVoEkBk7BWihp39KqjwPPNogpnge+HrhqW/i26mqjoopEwzpwz0lG03OZ9o39FtPA04eXJTnnJsfL1sEf/loOYogI48Brp4G7hnYnRZdUzcANb1IVn8ElDWKmftNSaNxqrVOaSl+YBq7+XbShS5PxPU8YobxFpYFndeCiDGVKJMJjsvNEoYhcIEt0U0wDd1mCBh6eU4dIGx0VnxlsHbgSfJKAKRqE/5Im5C2TBu6ap/CXHAeMhTPlat0Lc8TiwAvTwDkDT45/N9rn25PlLaMpMsKvj3zhnYuSgTvx/ZPr9fbThanxtsjhzdmiUAxhhBq+XE0cuDmMUP08iG3OmrSi5FAk1V1gh9Q2HpPtZnB4fC9RjuHH8m2RoqRi4xq4+dqJZTmkH5spCfeN68ZX7SwaDevAQ0fqBlmi9fsmh7xljUIx6dtBOF/Bg4lJqFUcuCoKJc9iVnnsSZI/AqnDj/pRduR5ZmKqGLhyzYtq4sBNEor6d9GGrIOYJsimyLlYgSoZeIowQl0UkQpKBi4dazoPWddCEe8hHgfOIWf7Aswzg4tEwzrw0JEmnyTVdlmvzRoHbgqni4fz9cxpDhh4wXHgYqSHiYEnaeB57BH1RmWdwlR6vQbOmb+5o1fZKsoUuhl3qrqy1KGCXgNPEweuPtZom7RJpYGbnLAK4oJgyRN59FFE6mO8/6aZmKYkFcpBzNTyjczAVRO89PdjkWhYBy5KGXmYn3yxsq6FYorxjIXz9ZgGLnccRTFwP9LDNc9ATXoA8nZkJYUzCW2Ldi4qWYIflm1GbHQ8QT5elG4idWVoYnRgLLpNZ2m6OPDiJJQIA8+YuDm9Bp5nJqb3n2kGmQH9qoz8+CxroYjwGHh88o73WYyKUt+PRaJhHXiTn98xzUlK8+BmiwN3jSuNBY5Uk2OyVmgKBjErke/VIsLAq5hdlvc0GBm4JO+YGHi2eHx+DUOZQhWFUs1EHtWrN4eumFrFgcvbCmHgQkJrMwPPEQeOuISSZVwr61oosr1aDVyI2OoJBl5tTsxeQ1OJsHDpVry2dps2z2F7WwuAdA9uWmfXXCJs31PGd+9/XVs2j8O99M5Fftk95MD9m+e+Reu0tuUq1y/nusfeCfMS5nLg3jFidqIsUDF4ft14yrXZ44fE9uHPZZYOTX6b8Y6P6573LYqmgc1yWrg9zSVPU+0WOgOdMxFnWd711zXKffhU+jFDvHRoIwb2w8bOvVpZpqXJib09OYpOM6sG3uwQlnTsxNdvX4g7FqwOfh8mpZ1z/LkVurVsVOBN2bSzK/iNH9s+sAU7N++O2D64Va7TS8n4+5dW48ePvIOuimvs4CLHOqTtfPnnOxasxgvLN2PyiLbY8UWiYR34Z4+bEuSZPFTx0ALAv/7NLNz78locOn6ocvtVHzwYL63cgsnD22I3lQ4fPnI8dnVV4DIv7dWIgS2xfU47cBTe2TARXRWGQa1NsTRgSfjZJ44I8lBmwezxQ/G54yejc29Fa1seOA7hW2cdiCUdOwEAA/uVtOf0h383G4do2nvC9JH43PGTcf7REzPV/82zZmHxum34m9ljYttKvm1X+x3quGHx81YNA+ca+CfmTsBZQsq9SX6S7Gom8owY2IJLTpuOwycMjSWa1hUzZ1I7LjphCp5bugmLVm9T7jNlRBu+dPIBOGmGlzP1ri8ci2eXblS2//0H74fzj54Y63hCBh6PQjnr0DE494jk1HyfPGYi7n55beC8Z4wehHOPHI9zDoumPis5YRx4v+Zsg5iAl+d07pThOHHGSADAbZ+biyff2hgkmj56cjuuOffQyPHnHTUBd7+8FvcvWo9lG3dG2izigYuPx9vvdsbqPmf2WGzs3IsBLdHnm5/jF1d4OVK/qkidWCQa1oEfN21EJAmxCgeNHRLkMFThk3Mn4pNzszmTmfsNxnc/dIhxnzFD+uM7Hzg4U7kizjg47qjSoH9LCd88a1buek347PFTUu1nyrnZ3taSy76PHz3BuP2zx0/BNQ++ib1lF0dMGBbbnksDd8JX9HOPHI+rP3hIbPu0UQPRXckfB05EQW5U+ThdKYNbm/EvZx6IlZt24YRrHlPu01Ry8I3TZwbfJwwfgAnD1efwa/OmY+qoQViwYkvk9yCbVWQtFO//UZPacfLMUUjCkRPbcerMUXjEzxh/2ZkzcfKM+HF84lvZZRiQNgpF+Czn4xw/bAA+fvQE3OInFr/ohCkY1hYlM0f5ScBFGUr1hnfgmMFBsmIOh4ApIwfG7gkgvMcYA4a3teADh41L1Z68aFgN3MJCheI0cPUglVxu3IGnriKC2CBm0qxW41ri6esNFxhT25M0szW5/HBn0wJdfAwjfRRK8n58F2UKOcdbXTAikaUMZEg75b6Wa6AEttS8BguLHoQpJ2beVSF1jp8ontYs7/ozsgadVIypLXlCGWVnY8onm2kiVkl04Op9+JopeaJQTBDDR5W2CSsKcjvSwLRbdMZmquKqgnXgFn0KJsaYZVacamAqXi7FprPnJV1ZHX+eSUCmcuTignNmeKNJV350CrrOBm+htCxRKMkQU6Dp6hWjjNKvhJiOgRe1mJwJ1oFb9CmY1kLJFIUSmXWpWVvEQVUaePS4bPsXzsAV09t19WSLtBElBc0+pVADz7oWiglhCjS9dNMlMPD0YYT6/eR1w2sN68At+hSMDDxHFAqg10bVGni+pzarXpoU45y1HJ0Gr06QkU+K0jNhJ7MGnsaEMAWaxrYSRSSUtLeHaT/HocA2q4FbWGSESbMtXgOPxm4D+VlXdgZumiaevRzZuZoid7L4paYUDjyY3VzJsBZKin2cBEfqJWVQT4Yyl2uunbe5J1bQsA7cok+hZ6NQEHkFB/KzrmI18AwdVbBCpNqe6jXwdIOYZZ7TNO1AYor9nCQG7pB2PRkTkpofjitYBm5hkQmFrYWSgoEXGUaY9WEvanZvk8bZBOfMkEQjS/mAmQkHGnjahA4p9kk6RXIUSlokM/DsuV/zwjpwiz4F1TIgQTxwXgeueRAJ8UHManXP1AsqFeTAtaF2hgFAU3b7WPkpBjHFpaHTpiBLpYH7dupWAHCc6GqTaRcKSKo7kG5SllcNrAO36BPgD59p0C0La40sVmQYxIwvZpW6iuhxCSFvJuiy0KdByMAle3pQAxdnYqaXuZL3U61YGLUtn/tLZOA+i+gBAm4duEXfgmkQM4sGrlr7O14u0B1L6FCdBp5nkTDTuteJ9Qb1yRJKURp48sQWMU1h1vXAzXWbGXjWc833TjrMauAWFhnBHxVTGGEWxqVbsD9abpFRKL7DzMPADetep69f/T0px2gSsjDwShYNPEMcONOII7ElfBPK46Uk1a0bV6gFrAO36FNQTzypDQN3nOoy8ogwdUBJyJNCTUZsMS1jHHj6ck1JKzia8sSBp9nH36koBs6RdI11sfW1gHXgFn0KZgaehTnG02TFy62OnUZA5rpMqEZCCaqPMXD9uEG2pNTpGHi54mbKiZm0zom4j6vVwPM6cPP2hmHgRDSUiO4gojeI6HUiOqYowyws8sC0mFWmKBRDthy53OhvqatQ15vDqRSRNFsXRlj1aoSlZAfe5IRSVNaZmGkyyesGMWvNwHtiIk+164H/CMCfGGPnElELgAEF2GRhkRsqDTWPvqxLmRUtV/Vbdc40z/F5dPO0dpgkqTRIszZISZgRmdWpmtoeMHBNqHfeKJSk5vdkHHhuB05EQwCcAOBCAGCMdQHoMh1jYVFrqB5o7hPyJhw2rYWiqysv8rzWF+EndMvZqpNXpy9XlES0E3lKhI2dnuvIuh64yRZKkFBqr4HXt4QyGUAHgF8S0UtE9Asiqm0COAsLDd7jZ1hROxz/gcowtaK1Oczb2b9ZzXMKZeC+j8njVPI4ijYpL6lcAm/HgJZ427PUJzbHxMA5BqTMl9rqp147bP+h2n1mjRkEABgxqJ9yu9wxjxtmFhB4fkvx3jCVW9BcK3NdVR57BIAvM8aeJ6IfAbgMwBXiTkR0EYCLAGDCBHNqLAuLvPjZJ4/E8o07g4S+IgIHkeGBGtivCb/57NHYsqsbJ88cqdxH5cjyzpDkHDGLnv3AxcdjT3cFl97hJc++8NhJ+HCKXJUA8MSlJ2Prru7gu04DP3LiMNzwySMxdEALPnr9s8p9TThpxih85w+LjcdxyWHKiDb8bUr7J49ow00XzjGmTPzKqdNwwvSRyjR7QHhflBzCbZ+bi6MmtxvrvOKsWTjjoP1w5ER1eRzhKoh1LKEAWA1gNWPsef/7HfAceASMsRsA3AAAc+bMyZbW2sIiJQb20yePzhttcOxUc87VIhkWf83PEhIo52qcvf8QHKJJ8C1jxMB+GDEwZKaxOHDBuc07aD9s2L5Hu68Jg1pDF6NzaPzncw4bi7Z+6VwSEeGUmaON+zSVHMyZpHfK/L5ocijReQPAsLYWzDtov8T9AsmunsMIGWPrAawiohn+T6cCWFyIVRYWBSKv1pmEIhkWd+DVLFKVNgRPCala+e0ib6aZNAkduLRV1AJdaVFyalNvGOJY3wwcAL4M4Nd+BMpSAJ+q3iQLi2JRK8dQqAP3IyWqmZRTTTt1Eoqq7CzNjjj+BPuq6oBygEs3RXfwPcnAq3LgjLGXAcwpxhQLi9qgVo6hSILFAgmlGgae/1jdIKaq7GwMPH2S315j4AVMhBLRkxq4nYlp0efREAzcHx2qxpnUloEnJydWIYvj74mp5yKaBJ2/SIQM3DpwC4uqUTsNvLiy3F5m4Lq1UFRlZ6kmr/TSE6i9Bl5oseq6al+FhUXvooip5irUgoFXp4EXN4gpty1NZh0VnJzSS0+A3xfFM3AroVhYFIbA4RQcxFpklAELolCyP5K8WdX4b91yssH3nAw8Wqb6QN1yr7VGNWuwmxCu01JosUpYB25hkRO1kFCqeZ2vhoHLnZF5kah8NvbwGGUiaqeBWwZuYVH3qM0gZm9p4NHvaZZpzYqeiIvOAh6dVLwG7v2v97VQLCwaAhT7UAyKjE7kEkqetb15s6pxRPI6MSYnndcv6czLskZNkQg18GLdYMjACy1WXVftq7Cw6JvgDKsIBsd6mYHrEjqokF9CqTcGXpsoFBsHbmHRADAlPciKQAOvJgqlCuefFAce2ZbTxHpz4DWPA+8B72oduIVFTpiSHmQF18CryjBfhYOUDzXpt/k18FyH1Qxh9vhiy+3JtVCsA7ewyIkiw9CKiEKpBpkYeMFhhL0Ffq6LdrSceVsJxcKijsGfzyLW0mAFTKWvBrKrMQ9i9o0wwtqtkWMHMS0s6h61YOAtNZo1moTYIKbBM/SVQcxar5FjGbiFRR2DP//FaODVD2JWA5lVmzXwvHXkO65WqPUaOXYtFAuLAsAz18ybZc7gkhUi03IImDTcnFPRhA8eNg4A0N7WkvnYDx/ppSEbOVCd+zEPRrTpy8rKLA8ZNwRNDmk7hROme5mPklKVFY1Rg702TmzPf91U6EkGXm1CBwuLusfUUQPx5tVnoF9TuoS5aUGChPLW1e+vqqxrPjIbV5w9Cy8u3+yVmeHh//wJU/Cp904qrH0LvnUahhs6g6x+6fZ/OAbdFVe7/aQZo2pyfZJw9qFjccSEYRilSXqcFz25Fop14Bb7BGrhHMTMK9VKHyWHMKytJdckECIqtH0m5w1kZ5atzaXETO497bw5xg7tX3iZVgO3sGgA1OJB5SX1cHaxTKi3wch6g10LxcKiAVCLwSruuGs1wFYE6ti0uoBdC8XCogHAGVaRTIsv7FTPLLfeVhWsN9i1UCwsGgDBlOkall2PsAzcjJ7MSm8duIVFTtTiAeXx4PUtodSvbfUAuxaKhUUDoBaZ2ir+qlZ17L/rbkJOvYFqMDaig3XgFhY5UQuGxVclrGeduZ5tqwfYMEILiwZALTTwQEKpYydZz28H9QCb1NjCogFgNXALFSwDt7BoANTiAeUaeD37SOvAzbATeSwsGgC1eD4bgYFb/22GnchjYdEAqAUTdf01n+pbA69f2+oBdiKPhUUDoBYMq8K4hFK/TrKOXw7qAnYij4VFA8CpwRPqulxCKbzowmAZuBnUSBo4EZWI6CUiuq8IgywsGgW1jAO3GnjjoifXsymin78YwOsFlGNh0VDYVyWUeratnlD3MzGJaDyAswD8ohhzLCwaB7UZxKz/iTwW6dATV7BaBv5fAC4FoM2XREQXEdF8Iprf0dFRZXUWFvWDWjDwMw7eDyMGtuCCYycWX3iVuPYjs3HwuMG9bUbdg/e9bpGL5GiQO6UaEZ0N4F3G2AIiOkm3H2PsBgA3AMCcOXN6oEkWFj2DWkgJowe3Yv633ld4uUXg3CPH41w/gbKFHvy+YIUuc6ZGNQz8vQDOIaLlAH4L4BQiurUQqywsGgA2GsNCBacHGXhuB84Yu5wxNp4xNgnAeQAeZYx9ojDLLCzqHHUcKGLRi+AdO2P1zcAtLPZpWAZuoULIwGvvwHNr4CIYY48DeLyIsiwsGgXWf1uowDXwupZQLCz2dVgGbqEC9SADtw7cwiInrAO3UCHUwHugrtpXYWHRN2EHMS1UCDTwHtBQrAO3sMgJO6XcQoWAgfdEXT1Qh4VFn4Rl4BYqhIOYloFbWNQtrAZuoQLv2K0GbmFRx3Ds02OhgGMZuIVF/cNq4BYq9OREHuvALSxywkooFirYiTwWFg0AO4hpoYJdC8XCogFgGbiFCmEceA/UVfsqLCz6Jqz/tlDBDmJaWDQALAO3UKEnM/JYB25hkRPcgQ9sLWRRT4s+gpYmz602l2rfwds7z8IiJ2aNHYy/m7M/PvqevpFm7Od/P6dHXvv7Os48ZAwWr9uOL544teZ1UU+MlHLMmTOHzZ8/v8fqs7CwsOgLIKIFjLE58u9WQrGwsLBoUFgHbmFhYdGgsA7cwsLCokFhHbiFhYVFg8I6cAsLC4sGhXXgFhYWFg0K68AtLCwsGhTWgVtYWFg0KHp0Ig8RdQBYkfPwEQA2FmhOI8C2ed+AbfO+gWraPJExNlL+sUcdeDUgovmqmUh9GbbN+wZsm/cN1KLNVkKxsLCwaFBYB25hYWHRoGgkB35DbxvQC7Bt3jdg27xvoPA2N4wGbmFhYWERRSMxcAsLCwsLAdaBW1hYWDQoGsKBE9EZRPQmEb1DRJf1tj1FgIj2J6LHiGgxEb1GRBf7v7cT0cNE9Lb/f5j/OxHRj/1zsIiIjujdFuQHEZWI6CUius//PpmInvfb9jsiavF/7+d/f8ffPqlXDc8JIhpKRHcQ0RtE9DoRHdPXrzMRXeLf168S0W1E1NrXrjMR3URE7xLRq8Jvma8rEV3g7/82EV2QxYa6d+BEVAJwHYD3A5gF4GNENKt3rSoEZQBfY4zNAjAXwJf8dl0G4BHG2DQAj/jfAa/90/y/iwD8tOdNLgwXA3hd+P7/APyQMTYVwBYAn/F//wyALf7vP/T3a0T8CMCfGGMzAcyG1/Y+e52JaByArwCYwxg7GEAJwHnoe9f5ZgBnSL9luq5E1A7gSgBHAzgKwJXc6acCY6yu/wAcA+BB4fvlAC7vbbtq0M57ALwPwJsAxvi/jQHwpv/5egAfE/YP9mukPwDj/Rv7FAD3ASB4s9Oa5OsN4EEAx/ifm/z9qLfbkLG9QwAsk+3uy9cZwDgAqwC0+9ftPgCn98XrDGASgFfzXlcAHwNwvfB7ZL+kv7pn4AhvBo7V/m99Bv4r4+EAngcwmjG2zt+0HsBo/3NfOQ//BeBSAK7/fTiArYyxsv9dbFfQZn/7Nn//RsJkAB0AfunLRr8gojb04evMGFsD4FoAKwGsg3fdFqBvX2eOrNe1quvdCA68T4OIBgK4E8BXGWPbxW3M65L7TJwnEZ0N4F3G2ILetqUH0QTgCAA/ZYwdDmAnwtdqAH3yOg8D8AF4nddYAG2ISw19Hj1xXRvBga8BsL/wfbz/W8ODiJrhOe9fM8bu8n/eQERj/O1jALzr/94XzsN7AZxDRMsB/BaejPIjAEOJqMnfR2xX0GZ/+xAAm3rS4AKwGsBqxtjz/vc74Dn0vnydTwOwjDHWwRjrBnAXvGvfl68zR9brWtX1bgQH/iKAaf4Idgu8wZB7e9mmqkFEBOBGAK8zxv5T2HQvAD4SfQE8bZz//vf+aPZcANuEV7WGAGPscsbYeMbYJHjX8VHG2PkAHgNwrr+b3GZ+Ls71928opsoYWw9gFRHN8H86FcBi9OHrDE86mUtEA/z7nLe5z15nAVmv64MA5hHRMP/NZZ7/Wzr09iBAyoGCMwG8BWAJgG/2tj0Ftek4eK9XiwC87P+dCU/7ewTA2wD+DKDd35/gReMsAfAKvBH+Xm9HFe0/CcB9/ucpAF4A8A6A2wH0839v9b+/42+f0tt252zrYQDm+9f6bgDD+vp1BvAdAG8AeBXArwD062vXGcBt8DT+bnhvWp/Jc10BfNpv+zsAPpXFBjuV3sLCwqJB0QgSioWFhYWFAtaBW1hYWDQorAO3sLCwaFBYB25hYWHRoLAO3MLCwqJBYR24hYWFRYPCOnALCwuLBsX/B/NIjpx0EU2FAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "X_features, Y_label = prepare_dataset(N_SAMPLES=5)\n", "\n", "for features, label in zip(X_features, Y_label):\n", " plt.plot(features[:features.shape[0] // 10])\n", " plt.title('Label: %s data points: %s' % (label, features.shape[0]))\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are ready to proceed with the training of a network based on these data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Live examples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function ``prepare_dataset()`` presented herein is used in the following live examples:\n", "\n", "* Notebook at`EpyNN/epynnlive/author_music/train.ipynb` or following [this link](train.ipynb). \n", "* Regular python code at `EpyNN/epynnlive/author_music/train.py`." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.2" } }, "nbformat": 4, "nbformat_minor": 4 }