Timbre Encoder.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Timbre Encoder\n",
    "This is the notebook used to train the Vocal Pitch Modulator.\n",
    "\n",
    "This notebook makes use of the data to train our timbre encoder, which is a VAE that takes in an MFCC, reduces its dimensionality, and attempts to reconstruct the provided MFCC.\n",
    "\n",
    "The first cells are the \"walkthrough\" that we used to verify the data at each stage, while the latter section (\"Experiment\" sections) are the cells run per experiment.\n",
    "\n",
    "## Global variables/Imports\n",
    "Run these cells before running either of the following sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 1\n",
    "\n",
    "import os\n",
    "import csv\n",
    "\n",
    "import scipy.io as sio\n",
    "from scipy.io import wavfile\n",
    "from scipy.io.wavfile import write\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import subplots\n",
    "\n",
    "import time\n",
    "import math\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, mean_squared_error, log_loss\n",
    "\n",
    "from tqdm.notebook import trange, tqdm\n",
    "\n",
    "from IPython.display import HTML\n",
    "import warnings\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "import torch\n",
    "warnings.filterwarnings('ignore')\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch import optim\n",
    "\n",
    "%aimport VPM\n",
    "from VPM import *\n",
    "%aimport Utils\n",
    "from Utils import *\n",
    "%aimport ANN\n",
    "from ANN import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Constants that should not change without the dataset being changed\n",
    "n_pitches = 16\n",
    "n_vowels = 12\n",
    "n_people = 3\n",
    "\n",
    "# These dictionaries are more for reference than anything\n",
    "label_to_vowel = { 0: \"bed\",  1: \"bird\",   2: \"boat\",  3: \"book\", \n",
    "                   4: \"cat\",  5: \"dog\",    6: \"feet\",  7: \"law\",  \n",
    "                   8: \"moo\",  9: \"nut\",   10: \"pig\",  11: \"say\" }\n",
    "\n",
    "vowel_to_label = { \"bed\": 0,  \"bird\": 1,  \"boat\":  2, \"book\":  3,\n",
    "                   \"cat\": 4,  \"dog\":  5,  \"feet\":  6, \"law\":   7,\n",
    "                   \"moo\": 8,  \"nut\":  9,  \"pig\":  10, \"say\":  11}\n",
    "\n",
    "noteidx_to_pitch = {  0: \"A2\",   1: \"Bb2\",  2: \"B2\",   3: \"C3\",\n",
    "                      4: \"Db3\",  5: \"D3\",   6: \"Eb3\",  7: \"E3\", \n",
    "                      8: \"F3\",   9: \"Gb3\", 10: \"G3\",  11: \"Ab3\",\n",
    "                     12: \"A3\",  13: \"Bb3\", 14: \"B3\",  15: \"C4\" }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Constants\n",
    "Used to tune the data generation and ANN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_mfccs = [ 40, 24, 12 ]\n",
    "n_mels = 256\n",
    "n_ffts = 2048"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Generation\n",
    "This is all the code that was explained in the Data Walkthrough. It generates data structures to hold all wav file data, spectrograms, mel spectra and MFCC data for all wav files.\n",
    "\n",
    "For diagram-visualization of the data set, refer to the [readme](https://github.com/zioul123/VocalPitchModulator/blob/master/README.md).\n",
    "\n",
    "For the classifier, MFCC are normalized by row to `[-1, 1]`, as the classifer can learn it better.\n",
    "For the VAE, MFCC are normalized by row to `[0, 1]`, to allow the VAE to output it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# File reference lists\n",
    "data_ref_list = create_data_ref_list(os.path.join(\"Data\", 'dataset_files.csv'),\n",
    "                            n_pitches, n_vowels, n_people)\n",
    "# flat_data_ref_list[flat_ref_idx(vowel, pitch, person)]\n",
    "flat_data_ref_list = flatten_3d_array(data_ref_list, \n",
    "                                      n_vowels, n_pitches, n_people)\n",
    "\n",
    "# File reference list accessors\n",
    "# Returns a flat_ref_idx, given a vowel, pitch, person\n",
    "flat_ref_idx = lambda vowel, pitch, person: flat_3d_array_idx(\n",
    "    vowel, pitch, person, n_vowels, n_pitches, n_people)\n",
    "# Returns vowel, pitch, person, given a flat_ref_idx\n",
    "nd_ref_idx = lambda idx: nd_array_idx(idx, n_vowels, n_pitches, n_people)\n",
    "\n",
    "# wav, spectrogram, mels, mfcc for each file in flat_data_ref_list\n",
    "# wav_data:     (576, ~29400)  (n_wavs, n_samples)\n",
    "# spectrograms: (576, 513, 58) (n_wavs, n_freq_bins, n_windows)\n",
    "# mels:         (576, 128, 58) (n_wavs, n_mels, n_windows)\n",
    "# mfccs:        (576, 20, 58)  (n_wavs, n_mfcc, n_windows)\n",
    "all_wav_data = load_wav_files(os.path.join(\"Data\", \"dataset\"), \n",
    "                              flat_data_ref_list)\n",
    "all_spectrograms = np.array([ stft(waveform, win_length=n_ffts, plot=False) \n",
    "                              for waveform in all_wav_data ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data-Label Structuring\n",
    "This puts together the actual data-label pairs to be fed into the ANN.\n",
    "\n",
    "Generate `data` and `labels` from `all_mfcc` and using `nd_ref_idx`.\n",
    "\n",
    "Note that `labels` is only used used by the classifier, and not TimbreVAE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "n_mfcc = n_mfccs[0]\n",
    "all_mels, all_mfcc = map(np.array, map(list, zip(*\n",
    "                         [ ffts_to_mel(ffts, n_mels = n_mels, n_mfcc = n_mfcc) \n",
    "                           for ffts in all_spectrograms ])))\n",
    "n_files, n_mfcc_dummy, n_windows = all_mfcc.shape\n",
    "\n",
    "# Returns a flat 2d idx, given a wavfile index and a window index\n",
    "flat_data_idx = lambda wav_idx, win_idx: flat_2d_array_idx(\n",
    "    wav_idx, win_idx, n_files, n_windows)\n",
    "\n",
    "# data:   (33408, 20) (n_wavs * n_windows, n_mfcc)\n",
    "data_prenorm = np.array([ all_mfcc[wav_file_idx][:, window_idx] \n",
    "                          for wav_file_idx in range(n_files) \n",
    "                          for window_idx in range(n_windows) ])\n",
    "\n",
    "# Globally normalize to [-1, 1]\n",
    "# log(-n) is nan, so we put things on a log scale from absolute values, and\n",
    "# mutliply the sign back.\n",
    "signs = np.sign(data_prenorm)\n",
    "data_logged = np.log(np.abs(data_prenorm))\n",
    "max_mfcc = np.max(np.abs(data_logged))\n",
    "data = data_logged / max_mfcc\n",
    "# If going to zero-one\n",
    "# data = normalize_rows(data, NormMode.NEG_ONE_ONE_TO_ZERO_ONE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.4232253e+02  2.5124673e+02 -3.5486549e+01  7.2455963e+01\n",
      "  3.2391647e+01  5.8577888e+01 -9.0863409e+00 -2.3206078e+01\n",
      " -3.1755833e+01 -1.1568871e+01  1.5435003e+01 -1.6487875e+01\n",
      " -6.3505516e+00  1.6824434e+01 -3.1595240e+00  7.1181893e-01\n",
      "  2.4520906e+01  1.6444090e+01 -4.0628190e+00 -1.6550110e+01\n",
      "  5.4667397e+00 -1.1811255e+01 -2.3326445e-01 -1.9993465e+01\n",
      " -1.1135010e+01 -8.1160154e+00 -4.9543185e+00  7.8407574e+00\n",
      " -1.8072077e+01  2.9504021e+01  7.4153967e+00 -4.5475411e+00\n",
      "  1.9492166e+00 -1.2899987e+01 -1.4165734e+01 -8.2396507e+00\n",
      " -9.3923483e+00 -7.5823956e+00  1.2699495e+01 -1.1117794e+01]\n",
      "[ 0.50722885  0.5653718   0.36513573  0.4381623   0.3558002   0.41641042\n",
      "  0.22575979  0.32168353  0.35377216  0.25047067  0.2799667   0.2867174\n",
      "  0.18911165  0.28878468  0.11769175 -0.03477609  0.32732162  0.28644538\n",
      "  0.14341645  0.28710285  0.17378059  0.25259194 -0.14891069  0.30643946\n",
      "  0.24656026  0.2142064   0.16371162  0.21067654  0.296103    0.34624776\n",
      "  0.20497036  0.15494701  0.06827994  0.26161236  0.27118787  0.21575308\n",
      "  0.22914837  0.20724872  0.26000986  0.24640197]\n",
      "[-1.4232246e+02  2.5124667e+02 -3.5486557e+01  7.2455956e+01\n",
      "  3.2391640e+01  5.8577885e+01 -9.0863409e+00 -2.3206079e+01\n",
      " -3.1755831e+01 -1.1568871e+01  1.5435001e+01 -1.6487873e+01\n",
      " -6.3505507e+00  1.6824434e+01 -3.1595240e+00  7.1181893e-01\n",
      "  2.4520899e+01  1.6444088e+01 -4.0628185e+00 -1.6550108e+01\n",
      "  5.4667401e+00 -1.1811255e+01 -2.3326446e-01 -1.9993467e+01\n",
      " -1.1135010e+01 -8.1160154e+00 -4.9543185e+00  7.8407574e+00\n",
      " -1.8072073e+01  2.9504017e+01  7.4153957e+00 -4.5475411e+00\n",
      "  1.9492165e+00 -1.2899987e+01 -1.4165733e+01 -8.2396507e+00\n",
      " -9.3923473e+00 -7.5823941e+00  1.2699495e+01 -1.1117794e+01]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To restore:\n",
    "restored_data = signs * np.e ** (data * max_mfcc)\n",
    "# If going to zero-one\n",
    "# restored_data = signs * np.e ** np.abs(\n",
    "#     denormalize_rows(data, DenormMode.ZERO_ONE_TO_NEG_ONE_ONE) * max_mfcc)\n",
    "# Visualize MFCCs\n",
    "plt.figure()\n",
    "plt.plot(data_prenorm[0])\n",
    "plt.figure()\n",
    "plt.plot(data[0])\n",
    "plt.figure()\n",
    "plt.plot(restored_data[0])\n",
    "# Numeric verification\n",
    "print(data_prenorm[0])\n",
    "print(data[0])\n",
    "print(restored_data[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split Data into `train` and `test`, and convert to Torch tensors of the correct types. Run **only one of these cells.**\n",
    "\n",
    "First method (**not-recommended**, simple): Random sampling to train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_train, Y_train: (25056, 20) (25056) \n",
    "# X_val, Y_val:     (8352, 20) (8352)\n",
    "X_train, X_val, Y_train, Y_val = train_test_split(data, labels, stratify=labels, random_state=0)\n",
    "X_train, Y_train, X_val, Y_val = map(torch.tensor, (X_train, Y_train, X_val, Y_val))\n",
    "# Default tensor is float\n",
    "X_train = X_train.float(); X_val = X_val.float()\n",
    "# Used as index, so it is long\n",
    "Y_train = Y_train.long(); Y_val = Y_val.long()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second method **(recommended)**: 1 person from each wav will be the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_train: (22272, 20)\n",
    "# X_val: (11136, 20)\n",
    "X_train = []; X_val = [];\n",
    "for vow_idx in range(n_vowels):\n",
    "    for pit_idx in range(n_pitches):\n",
    "        # Choose the person for this pitch/vowel to be used as test data\n",
    "        test_pid = int(np.random.rand() * 3)\n",
    "        for pid_idx in range(n_people):\n",
    "            wav_idx = flat_ref_idx(vow_idx, pit_idx, pid_idx)\n",
    "            if (pid_idx != test_pid):\n",
    "                for win_idx in range(n_windows):\n",
    "                    X_train.append(data[flat_data_idx(wav_idx, win_idx)])\n",
    "            else:\n",
    "                for win_idx in range(n_windows):\n",
    "                    X_val.append(data[flat_data_idx(wav_idx, win_idx)])\n",
    "X_train, X_val = map(torch.tensor, (X_train, X_val))\n",
    "# Default tensor is float\n",
    "X_train = X_train.float(); X_val = X_val.float()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Timbre-VAE - MFCC -> MFCC\n",
    "This takes MFCC, reduces dimensionality to a `n_timb` latent space, and attempts to recreate the MFCC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_hid = 10; n_timb = 4; lr = 1e-3; n_epochs = 2000; batch_size=22272\n",
    "\n",
    "# Training model \n",
    "model = TimbreVAE(n_mfcc=n_mfcc, n_hid=n_hid, n_timb=n_timb)\n",
    "\n",
    "# Define loss - from pytorch VAE example.\n",
    "def loss_fn(recon_x, x, mu, logvar):\n",
    "    BCE = F.binary_cross_entropy(recon_x, x, reduction='sum')\n",
    "    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())\n",
    "    return BCE + KLD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"GPU Available\" if torch.cuda.is_available() else \"GPU Not available\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Use GPU if possible (will run on CPU otherwise)\n",
    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "\n",
    "# Move inputs to GPU (if possible)\n",
    "X_train = X_train.to(device)\n",
    "X_val = X_val.to(device)\n",
    "\n",
    "# Move the network to GPU (if possible)\n",
    "model.to(device) \n",
    "# Define optimizer \n",
    "# opt = optim.SGD(model.parameters(), lr=lr)\n",
    "opt = optim.Adam(model.parameters(), lr=lr)\n",
    "\n",
    "# Fit the model\n",
    "tic = time.time()\n",
    "train_loss, val_loss = model.train_func(X_train, X_val, model, opt, loss_fn, batch_size=batch_size,\n",
    "                        epochs=n_epochs, print_graph = True)\n",
    "toc = time.time()\n",
    "print('Final loss: {}\\nTime taken: {}'.format(train_loss, toc - tic))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Model's state_dict:\")\n",
    "for param_tensor in model.state_dict():\n",
    "    print(param_tensor, \"\\t\", model.state_dict()[param_tensor].size())\n",
    "model_path = os.path.join(\"model_data\", \"TimbreVAE_Expts_nmels256_nfft2048\", \n",
    "                          \"TimbreVAE_lr{}_n_epochs{}_n_mfcc{}_n_hid{}_n_timb{}_{:.6f}.pt\"\n",
    "                          .format(lr, n_epochs, n_mfcc, n_hid, n_timb, val_loss))\n",
    "torch.save(model.state_dict(), model_path)\n",
    "print(\"Model saved at {}\".format(model_path))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading the saved model, and using the model for prediction example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = TimbreVAE(n_mfcc=n_mfcc, n_hid=n_hid, n_timb=n_timb)\n",
    "model.load_state_dict(torch.load(model_path))\n",
    "model.eval()\n",
    "\n",
    "data_tensor = torch.tensor(data)\n",
    "data_tensor = data_tensor.float();\n",
    "\n",
    "wav_idx = flat_ref_idx(5, 5, 1)\n",
    "data_idx = flat_data_idx(wav_idx, 30)\n",
    "label = data_tensor[data_idx]\n",
    "pred, _, _ = model(data_tensor)\n",
    "\n",
    "# To restore:\n",
    "restored_pred = signs * np.e ** np.abs(\n",
    "    denormalize_rows(pred.detach().numpy(), DenormMode.ZERO_ONE_TO_NEG_ONE_ONE) * max_mfcc)\n",
    "    \n",
    "# Visualize MFCCs\n",
    "fig, ax = plt.subplots(2, 1, figsize=(10, 10))\n",
    "fig.suptitle(\"TimbreVAE lr{} n_epochs{} n_mfcc{} n_hid{} n_timb{} loss{:.6f}\"\n",
    "             .format(lr, n_epochs, n_mfcc, n_hid, n_timb, val_loss))\n",
    "# plt.figure()\n",
    "ax[0].plot(data_prenorm[data_idx])\n",
    "ax[0].plot(restored_pred[data_idx])\n",
    "# fig, ax = plt.subplots(2, 1)\n",
    "# plt.figure()\n",
    "ax[1].plot(data[data_idx])\n",
    "ax[1].plot(pred.detach().numpy()[data_idx])\n",
    "fig.savefig(os.path.join(\"model_data\", \"TimbreVAE_Expts_nmels256_nfft2048\", \n",
    "    \"TimbreVAE_lr{}_n_epochs{}_n_mfcc{}_n_hid{}_n_timb{}_{:.6f}.jpg\"\n",
    "            .format(lr, n_epochs, n_mfcc, n_hid, n_timb, val_loss)))\n",
    "\n",
    "# Numeric verification\n",
    "np.set_printoptions(suppress=True)\n",
    "print(data_prenorm[data_idx])\n",
    "print(restored_pred[data_idx])\n",
    "print(data[data_idx])\n",
    "print(pred.detach().numpy()[data_idx])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment: Choosing `n_hid`, `n_timb`, `n_mfcc`\n",
    "\n",
    "Linear, relu, linear/linear/reparam, linear, relu, linear, sigmoid (normalized data from 0-1), BCE+KLD\n",
    "\n",
    "From running this, we find the results:  \n",
    "For n_mfcc=12: 12, 10, 8  \n",
    "For n_mfcc=24: 24, 10, 4  \n",
    "For n_mfcc=40: 40, 38, 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Loss before/after: 28.45667766702586, 26.955838609015803\n",
      "Validation Loss before/after: 28.434831290409484, 26.95325689206178\n",
      "Final loss: 26.955838609015803\t\tTime taken: 19.300349950790405\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid10_n_timb4_26.953195.pt\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Loss before/after: 28.468539533943964, 26.95561972431753\n",
      "Validation Loss before/after: 28.431932471264368, 26.95299872036638\n",
      "Final loss: 26.95561972431753\t\tTime taken: 20.529834032058716\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid10_n_timb6_26.953049.pt\n"
     ]
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Loss before/after: 28.329626324533045, 26.95562814295977\n",
      "Validation Loss before/after: 28.293746071300287, 26.952979076867816\n",
      "Final loss: 26.95562814295977\t\tTime taken: 20.430832386016846\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid10_n_timb8_26.952987.pt\n"
     ]
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Loss before/after: 28.317472611350574, 26.95610800556753\n",
      "Validation Loss before/after: 28.293959343570403, 26.953349497126435\n",
      "Final loss: 26.95610800556753\t\tTime taken: 21.283608198165894\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid12_n_timb4_26.953338.pt\n"
     ]
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Loss before/after: 28.602797234195403, 26.955673042385058\n",
      "Validation Loss before/after: 28.559957570043103, 26.953038007363507\n",
      "Final loss: 26.955673042385058\t\tTime taken: 19.93850016593933\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid12_n_timb6_26.953139.pt\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Loss before/after: 28.390540813577587, 26.955557987607758\n",
      "Validation Loss before/after: 28.362877155172413, 26.952951014727013\n",
      "Final loss: 26.955557987607758\t\tTime taken: 18.76777744293213\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid12_n_timb8_26.952959.pt\n"
     ]
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    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Loss before/after: 28.405020878232758, 26.95568426724138\n",
      "Validation Loss before/after: 28.385273549748565, 26.953043619791668\n",
      "Final loss: 26.95568426724138\t\tTime taken: 18.847283840179443\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid12_n_timb10_26.953024.pt\n"
     ]
    },
    {
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       "HBox(children=(FloatProgress(value=0.0, description='n_mfcc40,n_hid14,n_timb4', max=2000.0, style=ProgressStyl…"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Loss before/after: 28.071216100933906, 26.955594468390803\n",
      "Validation Loss before/after: 28.04327743354885, 26.95299872036638\n",
      "Final loss: 26.955594468390803\t\tTime taken: 19.411520957946777\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid14_n_timb4_26.953004.pt\n"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "\n",
      "Training Loss before/after: 28.200279498922413, 26.95561972431753\n",
      "Validation Loss before/after: 28.159656743893677, 26.95295662715517\n",
      "Final loss: 26.95561972431753\t\tTime taken: 21.306511402130127\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid14_n_timb6_26.953004.pt\n"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "\n",
      "Training Loss before/after: 28.318516522988507, 26.95564498024425\n",
      "Validation Loss before/after: 28.299299568965516, 26.95304642600575\n",
      "Final loss: 26.95564498024425\t\tTime taken: 19.248207092285156\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid14_n_timb8_26.953038.pt\n"
     ]
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    {
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     "text": [
      "\n",
      "Training Loss before/after: 28.447069190014368, 26.955726360452587\n",
      "Validation Loss before/after: 28.40168709590517, 26.953096937859197\n",
      "Final loss: 26.955726360452587\t\tTime taken: 18.14755630493164\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid14_n_timb10_26.953072.pt\n"
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    {
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     "text": [
      "\n",
      "Training Loss before/after: 28.21374932650862, 26.955535537895116\n",
      "Validation Loss before/after: 28.193073141163794, 26.952973464439655\n",
      "Final loss: 26.955535537895116\t\tTime taken: 17.98383331298828\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid16_n_timb4_26.952948.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 28.187637504489942, 26.955614111889368\n",
      "Validation Loss before/after: 28.14032754130747, 26.952928565014368\n",
      "Final loss: 26.955614111889368\t\tTime taken: 18.562549352645874\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid16_n_timb6_26.952996.pt\n"
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    {
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     "text": [
      "\n",
      "Training Loss before/after: 28.53577642331178, 26.955779678520116\n",
      "Validation Loss before/after: 28.485012010596265, 26.953155868354884\n",
      "Final loss: 26.955779678520116\t\tTime taken: 18.05796504020691\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid16_n_timb8_26.953117.pt\n"
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    {
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     "text": [
      "\n",
      "Training Loss before/after: 28.228178879310345, 26.955577631106323\n",
      "Validation Loss before/after: 28.167284033764368, 26.95299872036638\n",
      "Final loss: 26.955577631106323\t\tTime taken: 18.087127923965454\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid16_n_timb10_26.952987.pt\n"
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    {
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     "text": [
      "\n",
      "Training Loss before/after: 28.80414309446839, 26.955838609015803\n",
      "Validation Loss before/after: 28.735814587823278, 26.953082906788794\n",
      "Final loss: 26.955838609015803\t\tTime taken: 18.120599269866943\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid18_n_timb4_26.953181.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 28.35168036099138, 26.95570110452586\n",
      "Validation Loss before/after: 28.287269329202587, 26.953043619791668\n",
      "Final loss: 26.95570110452586\t\tTime taken: 18.175711393356323\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid18_n_timb6_26.953069.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 28.26044753502155, 26.955521506824713\n",
      "Validation Loss before/after: 28.22252155172414, 26.952903309087645\n",
      "Final loss: 26.955521506824713\t\tTime taken: 18.799524784088135\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid18_n_timb8_26.952892.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 28.59082592492816, 26.955802128232758\n",
      "Validation Loss before/after: 28.54204550556753, 26.953197961566094\n",
      "Final loss: 26.955802128232758\t\tTime taken: 18.207248210906982\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid18_n_timb10_26.953131.pt\n"
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      "Training Loss before/after: 28.474502738864942, 26.955729166666668\n",
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      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid20_n_timb4_26.953058.pt\n"
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      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid20_n_timb6_26.953038.pt\n"
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      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid20_n_timb8_26.952990.pt\n"
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      "Validation Loss before/after: 28.17518352640086, 26.95301275143678\n",
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      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid20_n_timb10_26.953021.pt\n"
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      "Validation Loss before/after: 28.029981591235632, 26.95293417744253\n",
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      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid22_n_timb4_26.952996.pt\n"
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      "Validation Loss before/after: 28.274251302083332, 26.953018363864942\n",
      "Final loss: 26.955720748024426\t\tTime taken: 18.192272186279297\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid22_n_timb6_26.953136.pt\n"
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      "Validation Loss before/after: 28.362966954022987, 26.953155868354884\n",
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      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid22_n_timb8_26.953091.pt\n"
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      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid22_n_timb10_26.953066.pt\n"
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      "Final loss: 26.95569268588362\t\tTime taken: 18.049214124679565\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid24_n_timb4_26.953041.pt\n"
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      "Validation Loss before/after: 27.928800736350574, 26.952900502873565\n",
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      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid24_n_timb6_26.952943.pt\n"
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      "Training Loss before/after: 28.347917227909484, 26.955586049748565\n",
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      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid24_n_timb8_26.952920.pt\n"
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      "Training Loss before/after: 28.077622687679597, 26.95562814295977\n",
      "Validation Loss before/after: 28.02238516971983, 26.952987495510058\n",
      "Final loss: 26.95562814295977\t\tTime taken: 18.140634298324585\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid24_n_timb10_26.953038.pt\n"
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      "Training Loss before/after: 28.452322422772987, 26.95567584859914\n",
      "Validation Loss before/after: 28.38842492816092, 26.953102550287355\n",
      "Final loss: 26.95567584859914\t\tTime taken: 18.255545139312744\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid26_n_timb4_26.953038.pt\n"
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      "Training Loss before/after: 28.17583456806753, 26.955614111889368\n",
      "Validation Loss before/after: 28.13139816810345, 26.9530099452227\n",
      "Final loss: 26.955614111889368\t\tTime taken: 18.366602420806885\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid26_n_timb6_26.953055.pt\n"
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      "Validation Loss before/after: 28.03558279454023, 26.95295943336925\n",
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      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid26_n_timb8_26.952940.pt\n"
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      "Training Loss before/after: 28.36590786637931, 26.955636561602013\n",
      "Validation Loss before/after: 28.309126930675287, 26.95306326329023\n",
      "Final loss: 26.955636561602013\t\tTime taken: 18.15203857421875\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid26_n_timb10_26.953060.pt\n"
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      "Training Loss before/after: 28.431298266882184, 26.955855446300287\n",
      "Validation Loss before/after: 28.378861350574713, 26.95320076778017\n",
      "Final loss: 26.955855446300287\t\tTime taken: 18.51302671432495\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid28_n_timb4_26.953167.pt\n"
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      "Training Loss before/after: 28.1593087733477, 26.95562253053161\n",
      "Validation Loss before/after: 28.1064453125, 26.952973464439655\n",
      "Final loss: 26.95562253053161\t\tTime taken: 18.55705738067627\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid28_n_timb6_26.953024.pt\n"
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      "Training Loss before/after: 28.172096690912355, 26.95556921246408\n",
      "Validation Loss before/after: 28.107761426903735, 26.95299310793822\n",
      "Final loss: 26.95556921246408\t\tTime taken: 18.245831966400146\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid28_n_timb8_26.952937.pt\n"
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      "Training Loss before/after: 28.25452922952586, 26.9556954920977\n",
      "Validation Loss before/after: 28.192848644037355, 26.953096937859197\n",
      "Final loss: 26.9556954920977\t\tTime taken: 18.178083658218384\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid28_n_timb10_26.953063.pt\n"
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      "Training Loss before/after: 28.291343952047413, 26.955639367816094\n",
      "Validation Loss before/after: 28.24430899784483, 26.952906115301722\n",
      "Final loss: 26.955639367816094\t\tTime taken: 18.19087791442871\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid30_n_timb4_26.952982.pt\n"
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      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid30_n_timb6_26.953002.pt\n"
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      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid30_n_timb8_26.952920.pt\n"
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      "Final loss: 26.95556360003592\t\tTime taken: 18.296106338500977\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid30_n_timb10_26.952951.pt\n"
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      "Validation Loss before/after: 27.971516927083332, 26.953018363864942\n",
      "Final loss: 26.955729166666668\t\tTime taken: 18.23370909690857\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid32_n_timb4_26.953069.pt\n"
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      "Validation Loss before/after: 28.173440867456897, 26.952889278017242\n",
      "Final loss: 26.955636561602013\t\tTime taken: 18.131617307662964\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid32_n_timb6_26.953035.pt\n"
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      "Validation Loss before/after: 27.951138200431036, 26.95288366558908\n",
      "Final loss: 26.955557987607758\t\tTime taken: 18.268142700195312\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid32_n_timb8_26.952945.pt\n"
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      "Final loss: 26.955574824892242\t\tTime taken: 18.133341312408447\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid32_n_timb10_26.952982.pt\n"
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      "Training Loss before/after: 28.43123091774425, 26.9556814610273\n",
      "Validation Loss before/after: 28.368124775502874, 26.9530099452227\n",
      "Final loss: 26.9556814610273\t\tTime taken: 18.87287926673889\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid34_n_timb4_26.952982.pt\n"
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      "Training Loss before/after: 27.946623001975574, 26.95557201867816\n",
      "Validation Loss before/after: 27.90302566002155, 26.95288366558908\n",
      "Final loss: 26.95557201867816\t\tTime taken: 18.44648241996765\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid34_n_timb6_26.952996.pt\n"
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      "Validation Loss before/after: 28.151751638829023, 26.952973464439655\n",
      "Final loss: 26.955605693247126\t\tTime taken: 18.327088594436646\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid34_n_timb8_26.952909.pt\n"
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      "Validation Loss before/after: 28.08166363595546, 26.95293137122845\n",
      "Final loss: 26.955583243534484\t\tTime taken: 18.322729349136353\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid34_n_timb10_26.952954.pt\n"
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      "Training Loss before/after: 28.176008553340516, 26.95557201867816\n",
      "Validation Loss before/after: 28.11693774694684, 26.952869634518677\n",
      "Final loss: 26.95557201867816\t\tTime taken: 18.38816261291504\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid36_n_timb4_26.952929.pt\n"
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      "Training Loss before/after: 28.221281205100574, 26.955636561602013\n",
      "Validation Loss before/after: 28.1658865391523, 26.95293698365661\n",
      "Final loss: 26.955636561602013\t\tTime taken: 18.301319122314453\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid36_n_timb6_26.953021.pt\n"
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      "Training Loss before/after: 28.002977393139368, 26.955557987607758\n",
      "Validation Loss before/after: 27.93567315463362, 26.952894890445403\n",
      "Final loss: 26.955557987607758\t\tTime taken: 18.13182306289673\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid36_n_timb8_26.952901.pt\n"
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      "Training Loss before/after: 28.240492546695403, 26.955656205100574\n",
      "Validation Loss before/after: 28.17708052711925, 26.952976270653735\n",
      "Final loss: 26.955656205100574\t\tTime taken: 18.224880933761597\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid36_n_timb10_26.952937.pt\n"
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      "Training Loss before/after: 28.061986462823278, 26.955723554238507\n",
      "Validation Loss before/after: 28.013629781788794, 26.953077294360632\n",
      "Final loss: 26.955723554238507\t\tTime taken: 18.1978497505188\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid38_n_timb4_26.953030.pt\n"
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      "Training Loss before/after: 28.102317371587645, 26.95564498024425\n",
      "Validation Loss before/after: 28.045920887212645, 26.952864022090516\n",
      "Final loss: 26.95564498024425\t\tTime taken: 18.296937227249146\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid38_n_timb6_26.952979.pt\n"
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      "Training Loss before/after: 27.93482006555316, 26.955543956537355\n",
      "Validation Loss before/after: 27.889148931393677, 26.952925758800287\n",
      "Final loss: 26.955543956537355\t\tTime taken: 18.414739847183228\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid38_n_timb8_26.952884.pt\n"
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      "Training Loss before/after: 28.071948522808906, 26.955647786458332\n",
      "Validation Loss before/after: 28.01665488056753, 26.952970658225574\n",
      "Final loss: 26.955647786458332\t\tTime taken: 18.53981065750122\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc40_n_hid38_n_timb10_26.953018.pt\n"
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      "Training Loss before/after: 17.048021338451868, 15.941331885326868\n",
      "Validation Loss before/after: 17.011693494073278, 15.953394396551724\n",
      "Final loss: 15.941331885326868\t\tTime taken: 18.067667961120605\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc24_n_hid10_n_timb4_15.953354.pt\n"
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      "\n",
      "Training Loss before/after: 17.049341662176722, 15.941448343211206\n",
      "Validation Loss before/after: 17.055520945581897, 15.953589428430316\n",
      "Final loss: 15.941448343211206\t\tTime taken: 18.261723279953003\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc24_n_hid10_n_timb6_15.953501.pt\n"
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      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc24_n_hid10_n_timb8_15.953441.pt\n"
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      "Validation Loss before/after: 17.015721814385778, 15.95343228044181\n",
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      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc24_n_hid12_n_timb6_15.953408.pt\n"
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      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc24_n_hid12_n_timb8_15.953546.pt\n"
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      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc24_n_hid12_n_timb10_15.953507.pt\n"
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      "Final loss: 15.941451149425287\t\tTime taken: 18.20049500465393\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc24_n_hid14_n_timb4_15.953523.pt\n"
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      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc24_n_hid14_n_timb6_15.953361.pt\n"
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      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc24_n_hid14_n_timb8_15.953640.pt\n"
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      "Final loss: 15.941379590966235\t\tTime taken: 19.19064164161682\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc24_n_hid14_n_timb10_15.953465.pt\n"
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      "Final loss: 15.941441327676007\t\tTime taken: 18.16101622581482\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc24_n_hid16_n_timb4_15.953595.pt\n"
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      "Validation Loss before/after: 16.957856276939655, 15.953607668821839\n",
      "Final loss: 15.941490436422415\t\tTime taken: 18.06369113922119\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc24_n_hid16_n_timb6_15.953613.pt\n"
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      "Training Loss before/after: 17.036045819863507, 15.94140625\n",
      "Validation Loss before/after: 17.003387100395116, 15.953526288613507\n",
      "Final loss: 15.94140625\t\tTime taken: 18.141820669174194\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc24_n_hid16_n_timb8_15.953525.pt\n"
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      "Validation Loss before/after: 17.16436276490661, 15.953450520833334\n",
      "Final loss: 15.94140905621408\t\tTime taken: 18.073572158813477\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc24_n_hid16_n_timb10_15.953428.pt\n"
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      "Validation Loss before/after: 16.90826065238865, 15.95342807112069\n",
      "Final loss: 15.94134731950431\t\tTime taken: 18.22636365890503\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc24_n_hid18_n_timb4_15.953394.pt\n"
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      "Training Loss before/after: 17.218212610003594, 15.941569010416666\n",
      "Validation Loss before/after: 17.18192826194325, 15.953602056393677\n",
      "Final loss: 15.941569010416666\t\tTime taken: 18.29123306274414\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc24_n_hid18_n_timb6_15.953634.pt\n"
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      "Training Loss before/after: 17.078286357309626, 15.941326272898706\n",
      "Validation Loss before/after: 17.045238977191094, 15.953397202765805\n",
      "Final loss: 15.941326272898706\t\tTime taken: 18.272611618041992\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc24_n_hid18_n_timb8_15.953413.pt\n"
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      "Training Loss before/after: 17.17175854211566, 15.941402040678879\n",
      "Validation Loss before/after: 17.128036323635058, 15.953456133261493\n",
      "Final loss: 15.941402040678879\t\tTime taken: 18.235727787017822\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc24_n_hid18_n_timb10_15.953476.pt\n"
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      "Training Loss before/after: 17.126679519127155, 15.941500258171695\n",
      "Validation Loss before/after: 17.10570727819684, 15.953472970545977\n",
      "Final loss: 15.941500258171695\t\tTime taken: 18.934784412384033\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc24_n_hid20_n_timb4_15.953571.pt\n"
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      "Training Loss before/after: 16.930042486081177, 15.941323466684626\n",
      "Validation Loss before/after: 16.900285391971984, 15.953321434985632\n",
      "Final loss: 15.941323466684626\t\tTime taken: 18.232239246368408\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc24_n_hid20_n_timb6_15.953385.pt\n"
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      "Training Loss before/after: 17.063504624640803, 15.941444133890085\n",
      "Validation Loss before/after: 17.029382464529455, 15.953576800466953\n",
      "Final loss: 15.941444133890085\t\tTime taken: 18.20495891571045\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc24_n_hid20_n_timb8_15.953563.pt\n"
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      "Training Loss before/after: 17.224066372575432, 15.941504467492816\n",
      "Validation Loss before/after: 17.195123080549568, 15.953529094827585\n",
      "Final loss: 15.941504467492816\t\tTime taken: 18.545262098312378\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc24_n_hid20_n_timb10_15.953486.pt\n"
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       "model_id": "e9c7a11c850b4e258f27c9e5f6c3505d",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='n_mfcc24,n_hid22,n_timb4', max=2000.0, style=ProgressStyl…"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Loss before/after: 17.04717666801365, 15.941411862428161\n",
      "Validation Loss before/after: 16.996812140804597, 15.953538916576868\n",
      "Final loss: 15.941411862428161\t\tTime taken: 18.115490198135376\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc24_n_hid22_n_timb4_15.953523.pt\n"
     ]
    },
    {
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       "model_id": "2cf370814d7c40eab475d244f458671a",
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       "HBox(children=(FloatProgress(value=0.0, description='n_mfcc24,n_hid22,n_timb6', max=2000.0, style=ProgressStyl…"
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     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Loss before/after: 16.947579920977013, 15.94128558279454\n",
      "Validation Loss before/after: 16.932412333872126, 15.953296179058908\n",
      "Final loss: 15.94128558279454\t\tTime taken: 18.359264373779297\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc24_n_hid22_n_timb6_15.953368.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "215982169ab54e98987c2b295193ed30",
       "version_major": 2,
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      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='n_mfcc24,n_hid22,n_timb8', max=2000.0, style=ProgressStyl…"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Loss before/after: 17.088587969198993, 15.94134170707615\n",
      "Validation Loss before/after: 17.051593648976294, 15.953450520833334\n",
      "Final loss: 15.94134170707615\t\tTime taken: 18.206353425979614\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc24_n_hid22_n_timb8_15.953465.pt\n"
     ]
    },
    {
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      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3d43d36eff1b4654b4810cd82060160b",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='n_mfcc24,n_hid22,n_timb10', max=2000.0, style=ProgressSty…"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Loss before/after: 17.327356097341955, 15.941490436422415\n",
      "Validation Loss before/after: 17.296397943606323, 15.953578203573993\n",
      "Final loss: 15.941490436422415\t\tTime taken: 18.223353147506714\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc24_n_hid22_n_timb10_15.953511.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a18ecf6e28d445288febaabec1c870c7",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='n_mfcc12,n_hid10,n_timb4', max=2000.0, style=ProgressStyl…"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Loss before/after: 8.446460241558908, 7.444801067483836\n",
      "Validation Loss before/after: 8.451487574084052, 7.462992350260417\n",
      "Final loss: 7.444801067483836\t\tTime taken: 18.216956853866577\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc12_n_hid10_n_timb4_7.463037.pt\n"
     ]
    },
    {
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       "model_id": "d8bfd2bee440422aa211cac914fff74d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='n_mfcc12,n_hid10,n_timb6', max=2000.0, style=ProgressStyl…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Loss before/after: 8.609753137347342, 7.445134305405891\n",
      "Validation Loss before/after: 8.593942225664511, 7.463326991289511\n",
      "Final loss: 7.445134305405891\t\tTime taken: 18.311349630355835\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc12_n_hid10_n_timb6_7.463325.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e4b3b5b6089547678950d906a928167a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='n_mfcc12,n_hid10,n_timb8', max=2000.0, style=ProgressStyl…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Loss before/after: 8.552070003816452, 7.444711970186781\n",
      "Validation Loss before/after: 8.530677532327585, 7.4628639659662355\n",
      "Final loss: 7.444711970186781\t\tTime taken: 18.26224398612976\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_lr0.001_n_epochs2000_n_mfcc12_n_hid10_n_timb8_7.462854.pt\n"
     ]
    }
   ],
   "source": [
    "n_mels = 256\n",
    "n_ffts = 2048\n",
    "\n",
    "%matplotlib agg\n",
    "%matplotlib agg\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # #\n",
    "# Data that is constant throughout the entire run #\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # #\n",
    "\n",
    "# File reference lists\n",
    "data_ref_list = create_data_ref_list(os.path.join(\"Data\", 'dataset_files.csv'),\n",
    "                            n_pitches, n_vowels, n_people)\n",
    "# flat_data_ref_list[flat_ref_idx(vowel, pitch, person)]\n",
    "flat_data_ref_list = flatten_3d_array(data_ref_list, \n",
    "                                      n_vowels, n_pitches, n_people)\n",
    "# File reference list accessors\n",
    "# Returns a flat_ref_idx, given a vowel, pitch, person\n",
    "flat_ref_idx = lambda vowel, pitch, person: flat_3d_array_idx(\n",
    "    vowel, pitch, person, n_vowels, n_pitches, n_people)\n",
    "# Returns vowel, pitch, person, given a flat_ref_idx\n",
    "nd_ref_idx = lambda idx: nd_array_idx(idx, n_vowels, n_pitches, n_people)\n",
    "\n",
    "# wav, spectrogram, mels, mfcc for each file in flat_data_ref_list\n",
    "# wav_data:     (576, ~29400)  (n_wavs, n_samples)\n",
    "# spectrograms: (576, 513, 58) (n_wavs, n_freq_bins, n_windows)\n",
    "all_wav_data = load_wav_files(os.path.join(\"Data\", \"dataset\"), \n",
    "                              flat_data_ref_list)\n",
    "all_spectrograms = np.array([ stft(waveform, win_length=n_ffts, plot=False) \n",
    "                              for waveform in all_wav_data ])\n",
    "\n",
    "# Define loss - from pytorch VAE example.\n",
    "def loss_fn(recon_x, x, mu, logvar):\n",
    "    BCE = F.binary_cross_entropy(recon_x, x, reduction='sum')\n",
    "    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())\n",
    "    return BCE + KLD\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # \n",
    "# Data that is constant per n_mfcc  #\n",
    "# # # # # # # # # # # # # # # # # # # \n",
    "\n",
    "def create_data_for_mfcc(n_mfcc):\n",
    "    # mels:         (576, 128, 58) (n_wavs, n_mels, n_windows)\n",
    "    # mfccs:        (576, 20, 58)  (n_wavs, n_mfcc, n_windows)\n",
    "    all_mels, all_mfcc = map(np.array, map(list, zip(*\n",
    "                             [ ffts_to_mel(ffts, n_mels = n_mels, n_mfcc = n_mfcc) \n",
    "                               for ffts in all_spectrograms ])))\n",
    "    n_files, n_mfcc_dummy, n_windows = all_mfcc.shape\n",
    "\n",
    "    # Returns a flat 2d idx, given a wavfile index and a window index\n",
    "    flat_data_idx = lambda wav_idx, win_idx: flat_2d_array_idx(\n",
    "        wav_idx, win_idx, n_files, n_windows)\n",
    "\n",
    "    # data:   (33408, 20) (n_wavs * n_windows, n_mfcc)\n",
    "    data_prenorm = np.array([ all_mfcc[wav_file_idx][:, window_idx] \n",
    "                              for wav_file_idx in range(n_files) \n",
    "                              for window_idx in range(n_windows) ])\n",
    "\n",
    "    # Globally normalize to [0, 1]\n",
    "    # log(-n) is nan, so we put things on a log scale from absolute values, and\n",
    "    # mutliply the sign back.\n",
    "    signs = np.sign(data_prenorm)\n",
    "    data_logged = np.log(np.abs(data_prenorm))\n",
    "    max_mfcc = np.max(np.abs(data_logged))\n",
    "    data_negtopos = data_logged / max_mfcc\n",
    "    data = normalize_rows(data_negtopos, NormMode.NEG_ONE_ONE_TO_ZERO_ONE)  \n",
    "    \n",
    "    # X_train: (22272, 20)\n",
    "    # X_val: (11136, 20)\n",
    "    X_train = []; X_val = [];\n",
    "    for vow_idx in range(n_vowels):\n",
    "        for pit_idx in range(n_pitches):\n",
    "            # Choose the person for this pitch/vowel to be used as test data\n",
    "            test_pid = int(np.random.rand() * 3)\n",
    "            for pid_idx in range(n_people):\n",
    "                wav_idx = flat_ref_idx(vow_idx, pit_idx, pid_idx)\n",
    "                if (pid_idx != test_pid):\n",
    "                    for win_idx in range(n_windows):\n",
    "                        X_train.append(data[flat_data_idx(wav_idx, win_idx)])\n",
    "                else:\n",
    "                    for win_idx in range(n_windows):\n",
    "                        X_val.append(data[flat_data_idx(wav_idx, win_idx)])\n",
    "    X_train, X_val = map(torch.tensor, (X_train, X_val))\n",
    "    # Default tensor is float\n",
    "    X_train = X_train.float(); X_val = X_val.float()\n",
    "    \n",
    "    return data, data_prenorm, max_mfcc, signs, flat_data_idx, X_train, X_val\n",
    "\n",
    "# # # # # # # # # # # # # #\n",
    "# Actual experiment loop  #\n",
    "# # # # # # # # # # # # # # \n",
    "\n",
    "# Results: \n",
    "# Indices: [ [ n_mfcc, n_hid, n_timb ], [ ... ], ... ]\n",
    "# Losses: [ loss1, loss2, ... ]\n",
    "indices = []; train_losses = []; val_losses = [];\n",
    "\n",
    "n_mfccs = [ 40, 24, 12 ]\n",
    "n_hid_candidates = [10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38]\n",
    "n_timb_candidates = [4, 6, 8, 10 ] # >= 12 means we might as well have used 12 mfcc\n",
    "f = open(os.path.join(\"model_data\", \"TimbreVAE_Expts_nmels256_nfft2048\", \"Log.txt\"), \"w\")\n",
    "for n_mfcc in n_mfccs:\n",
    "    data, data_prenorm, max_mfcc, signs, flat_data_idx, X_train, X_val = create_data_for_mfcc(n_mfcc)\n",
    "    lr = 1e-3; n_epochs = 2000; batch_size=22272\n",
    "\n",
    "    for n_hid in n_hid_candidates:\n",
    "        for n_timb in n_timb_candidates:\n",
    "            if (n_timb >= n_hid) or (n_timb >= n_mfcc) or (n_hid >= n_mfcc):\n",
    "                continue\n",
    "\n",
    "            # Training model \n",
    "            model = TimbreVAE(n_mfcc=n_mfcc, n_hid=n_hid, n_timb=n_timb)\n",
    "\n",
    "            # Use GPU if possible (will run on CPU otherwise)\n",
    "            device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "\n",
    "            # Move inputs to GPU (if possible)\n",
    "            X_train = X_train.to(device)\n",
    "            X_val = X_val.to(device)\n",
    "\n",
    "            # Move the network to GPU (if possible)\n",
    "            model.to(device) \n",
    "            # Define optimizer \n",
    "            # opt = optim.SGD(model.parameters(), lr=lr)\n",
    "            opt = optim.Adam(model.parameters(), lr=lr)\n",
    "\n",
    "            # Fit the model\n",
    "            tic = time.time()\n",
    "            train_loss, val_loss = model.train_func(X_train, X_val, model, opt, loss_fn, batch_size=batch_size,\n",
    "                                    epochs=n_epochs, print_graph = True, desc=\"n_mfcc{},n_hid{},n_timb{}\".format(n_mfcc, n_hid, n_timb))\n",
    "            toc = time.time()\n",
    "            print('Final loss: {}\\t\\tTime taken: {}'.format(train_loss, toc - tic))\n",
    "            model_path = os.path.join(\"model_data\", \"TimbreVAE_Expts_nmels256_nfft2048\", \n",
    "                                      \"TimbreVAE_lr{}_n_epochs{}_n_mfcc{}_n_hid{}_n_timb{}_{:.6f}.pt\"\n",
    "                                      .format(lr, n_epochs, n_mfcc, n_hid, n_timb, val_loss))\n",
    "            torch.save(model.state_dict(), model_path)\n",
    "            print(\"Model saved at {}\".format(model_path))\n",
    "\n",
    "            indices.append([n_mfcc, n_hid, n_timb]); train_losses.append(train_loss); val_losses.append(val_loss)\n",
    "\n",
    "            # Create the validation printout and graph\n",
    "            data_tensor = torch.tensor(data)\n",
    "            data_tensor = data_tensor.float();\n",
    "            data_tensor = data_tensor.to(device)\n",
    "\n",
    "            wav_idx = flat_ref_idx(5, 5, 1)\n",
    "            data_idx = flat_data_idx(wav_idx, 30)\n",
    "            label = data_tensor[data_idx]\n",
    "            pred, _, _ = model(data_tensor)\n",
    "\n",
    "            # To restore:\n",
    "            restored_pred = signs * np.e ** (\n",
    "                denormalize_rows(pred.cpu().detach().numpy(), DenormMode.ZERO_ONE_TO_NEG_ONE_ONE) * max_mfcc)\n",
    "            # Visualize MFCCs\n",
    "            fig, ax = plt.subplots(2, 1, figsize=(10, 10))\n",
    "            fig.suptitle(\"TimbreVAE lr{} n_epochs{} n_mfcc{} n_hid{} n_timb{} loss{:.6f}\"\n",
    "                         .format(lr, n_epochs, n_mfcc, n_hid, n_timb, val_loss))\n",
    "            # plt.figure()\n",
    "            ax[0].plot(data_prenorm[data_idx])\n",
    "            ax[0].plot(restored_pred[data_idx])\n",
    "            # fig, ax = plt.subplots(2, 1)\n",
    "            # plt.figure()\n",
    "            ax[1].plot(data[data_idx])\n",
    "            ax[1].plot(pred.cpu().detach().numpy()[data_idx])\n",
    "            fig.savefig(os.path.join(\"model_data\", \"TimbreVAE_Expts_nmels256_nfft2048\", \n",
    "                \"TimbreVAE_lr{}_n_epochs{}_n_mfcc{}_n_hid{}_n_timb{}_{:.6f}.jpg\"\n",
    "                        .format(lr, n_epochs, n_mfcc, n_hid, n_timb, val_loss)))\n",
    "\n",
    "            # Numeric verification\n",
    "            np.set_printoptions(suppress=True)\n",
    "            f.write(\"TimbreVAE lr{} n_epochs{} n_mfcc{} n_hid{} n_timb{} loss{:.6f}\"\n",
    "                    .format(lr, n_epochs, n_mfcc, n_hid, n_timb, val_loss))\n",
    "\n",
    "            f.write(str(data_prenorm[data_idx]))\n",
    "            f.write(str(restored_pred[data_idx]))\n",
    "            f.write(str(data[data_idx]))\n",
    "            f.write(str(pred.cpu().detach().numpy()[data_idx]))\n",
    "f.close()\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "1 [40, 10, 6] 26.95304923221983\n",
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      "3 [40, 12, 4] 26.953338272270116\n",
      "4 [40, 12, 6] 26.953139031070403\n",
      "5 [40, 12, 8] 26.95295943336925\n",
      "6 [40, 12, 10] 26.953023976293103\n",
      "7 [40, 14, 4] 26.95300433279454\n",
      "8 [40, 14, 6] 26.95300433279454\n",
      "9 [40, 14, 8] 26.953038007363507\n",
      "10 [40, 14, 10] 26.95307168193247\n",
      "11 [40, 16, 4] 26.952948208512932\n",
      "12 [40, 16, 6] 26.9529959141523\n",
      "13 [40, 16, 8] 26.953116581357758\n",
      "14 [40, 16, 10] 26.952987495510058\n",
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      "19 [40, 20, 4] 26.953057650862068\n",
      "20 [40, 20, 6] 26.953038007363507\n",
      "21 [40, 20, 8] 26.95299030172414\n",
      "22 [40, 20, 10] 26.953021170079023\n",
      "23 [40, 22, 4] 26.9529959141523\n",
      "24 [40, 22, 6] 26.953136224856323\n",
      "25 [40, 22, 8] 26.953091325431036\n",
      "26 [40, 22, 10] 26.95306606950431\n",
      "27 [40, 24, 4] 26.953040813577587\n",
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      "34 [40, 26, 10] 26.95306045707615\n",
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      "38 [40, 28, 10] 26.95306326329023\n",
      "39 [40, 30, 4] 26.952981883081897\n",
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      "55 [40, 38, 4] 26.953029588721265\n",
      "56 [40, 38, 6] 26.952979076867816\n",
      "57 [40, 38, 8] 26.95288366558908\n",
      "58 [40, 38, 10] 26.953018363864942\n",
      "59 [24, 10, 4] 15.953353706447558\n",
      "60 [24, 10, 6] 15.953501032686782\n",
      "61 [24, 10, 8] 15.953440699084052\n",
      "62 [24, 12, 4] 15.953487001616379\n",
      "63 [24, 12, 6] 15.953408427622126\n",
      "64 [24, 12, 8] 15.95354593211207\n",
      "65 [24, 12, 10] 15.953506645114942\n",
      "66 [24, 14, 4] 15.953523482399426\n",
      "67 [24, 14, 6] 15.953360721982758\n",
      "68 [24, 14, 8] 15.953639940283765\n",
      "69 [24, 14, 10] 15.953464551903735\n",
      "70 [24, 16, 4] 15.953595040858477\n",
      "71 [24, 16, 6] 15.95361328125\n",
      "72 [24, 16, 8] 15.953524885506466\n",
      "73 [24, 16, 10] 15.95342807112069\n",
      "74 [24, 18, 4] 15.953394396551724\n",
      "75 [24, 18, 6] 15.953634327855603\n",
      "76 [24, 18, 8] 15.953412636943247\n",
      "77 [24, 18, 10] 15.953475776760058\n",
      "78 [24, 20, 4] 15.953571188038794\n",
      "79 [24, 20, 6] 15.953384574802442\n",
      "80 [24, 20, 8] 15.953562769396552\n",
      "81 [24, 20, 10] 15.953485598509339\n",
      "82 [24, 22, 4] 15.953523482399426\n",
      "83 [24, 22, 6] 15.95336773751796\n",
      "84 [24, 22, 8] 15.953464551903735\n",
      "85 [24, 22, 10] 15.953510854436063\n",
      "86 [12, 10, 4] 7.463036548132184\n",
      "87 [12, 10, 6] 7.463324886628951\n",
      "88 [12, 10, 8] 7.462854144216954\n",
      "59\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(val_losses)):\n",
    "    print(i, indices[i], val_losses[i])\n",
    "# 0-58: n_mfcc = 40\n",
    "# 59-85: 24\n",
    "print(np.argmin(val_losses[59:86])+59)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment: Changing normalization method to see if it produces better data\n",
    "\n",
    "Linear, relu, linear/linear/reparam, linear, relu, linear, **tanh** (normalized data from **-1 to 1**), **MSE**\n",
    "\n",
    "\n",
    "From running this, we used the configuration:   \n",
    "For n_mfcc=12: 12, 10, 8  7.197920877978891e-07  \n",
    "For n_mfcc=24: 24, 10, 4  4.151019792275867e-06  \n",
    "For n_mfcc=40: 40, 38, 8  2.7174930604731387e-06  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "612e6a38abd64fa4a33792acddbf76d4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='n_mfcc12,n_hid10,n_timb8', max=2000.0, style=ProgressStyl…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Loss before/after: 2.5233323418203443e-05, 3.25163214831431e-07\n",
      "Validation Loss before/after: 4.9614979104063975e-05, 7.197920877978891e-07\n",
      "Final loss: 3.25163214831431e-07\t\tTime taken: 17.43793511390686\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_tanh_lr0.001_n_epochs2000_n_mfcc12_n_hid10_n_timb8_0.000001.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "39d0827ad13348dfb5729983ad3c7c1f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='n_mfcc24,n_hid10,n_timb4', max=2000.0, style=ProgressStyl…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Loss before/after: 1.885824911724562e-05, 2.046878919294425e-06\n",
      "Validation Loss before/after: 3.693698659196667e-05, 4.151019792275867e-06\n",
      "Final loss: 2.046878919294425e-06\t\tTime taken: 16.16489577293396\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_tanh_lr0.001_n_epochs2000_n_mfcc24_n_hid10_n_timb4_0.000004.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0fe4892bec4945748c7cbdf81f70a76a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='n_mfcc40,n_hid38,n_timb8', max=2000.0, style=ProgressStyl…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Loss before/after: 1.1766253135584551e-05, 1.176477185097234e-06\n",
      "Validation Loss before/after: 2.2506145974514127e-05, 2.7174930604731387e-06\n",
      "Final loss: 1.176477185097234e-06\t\tTime taken: 16.75874662399292\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_tanh_lr0.001_n_epochs2000_n_mfcc40_n_hid38_n_timb8_0.000003.pt\n"
     ]
    }
   ],
   "source": [
    "n_mels = 256\n",
    "n_ffts = 2048\n",
    "\n",
    "%matplotlib agg\n",
    "%matplotlib agg\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # #\n",
    "# Data that is constant throughout the entire run #\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # #\n",
    "\n",
    "# File reference lists\n",
    "data_ref_list = create_data_ref_list(os.path.join(\"Data\", 'dataset_files.csv'),\n",
    "                            n_pitches, n_vowels, n_people)\n",
    "# flat_data_ref_list[flat_ref_idx(vowel, pitch, person)]\n",
    "flat_data_ref_list = flatten_3d_array(data_ref_list, \n",
    "                                      n_vowels, n_pitches, n_people)\n",
    "# File reference list accessors\n",
    "# Returns a flat_ref_idx, given a vowel, pitch, person\n",
    "flat_ref_idx = lambda vowel, pitch, person: flat_3d_array_idx(\n",
    "    vowel, pitch, person, n_vowels, n_pitches, n_people)\n",
    "# Returns vowel, pitch, person, given a flat_ref_idx\n",
    "nd_ref_idx = lambda idx: nd_array_idx(idx, n_vowels, n_pitches, n_people)\n",
    "\n",
    "# wav, spectrogram, mels, mfcc for each file in flat_data_ref_list\n",
    "# wav_data:     (576, ~29400)  (n_wavs, n_samples)\n",
    "# spectrograms: (576, 513, 58) (n_wavs, n_freq_bins, n_windows)\n",
    "all_wav_data = load_wav_files(os.path.join(\"Data\", \"dataset\"), \n",
    "                              flat_data_ref_list)\n",
    "all_spectrograms = np.array([ stft(waveform, win_length=n_ffts, plot=False) \n",
    "                              for waveform in all_wav_data ])\n",
    "\n",
    "# Define loss \n",
    "loss_fn = nn.MSELoss()\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # \n",
    "# Data that is constant per n_mfcc  #\n",
    "# # # # # # # # # # # # # # # # # # # \n",
    "\n",
    "def create_data_for_mfcc(n_mfcc):\n",
    "    # mels:         (576, 128, 58) (n_wavs, n_mels, n_windows)\n",
    "    # mfccs:        (576, 20, 58)  (n_wavs, n_mfcc, n_windows)\n",
    "    all_mels, all_mfcc = map(np.array, map(list, zip(*\n",
    "                             [ ffts_to_mel(ffts, n_mels = n_mels, n_mfcc = n_mfcc) \n",
    "                               for ffts in all_spectrograms ])))\n",
    "    n_files, n_mfcc_dummy, n_windows = all_mfcc.shape\n",
    "\n",
    "    # Returns a flat 2d idx, given a wavfile index and a window index\n",
    "    flat_data_idx = lambda wav_idx, win_idx: flat_2d_array_idx(\n",
    "        wav_idx, win_idx, n_files, n_windows)\n",
    "\n",
    "    # data:   (33408, 20) (n_wavs * n_windows, n_mfcc)\n",
    "    data_prenorm = np.array([ all_mfcc[wav_file_idx][:, window_idx] \n",
    "                              for wav_file_idx in range(n_files) \n",
    "                              for window_idx in range(n_windows) ])\n",
    "\n",
    "    # Globally normalize to [-1, 1]\n",
    "    # log(-n) is nan, so we put things on a log scale from absolute values, and\n",
    "    # mutliply the sign back.\n",
    "    signs = np.sign(data_prenorm)\n",
    "    data_logged = np.log(np.abs(data_prenorm))\n",
    "    max_mfcc = np.max(np.abs(data_logged))\n",
    "    data = data_logged / max_mfcc\n",
    "\n",
    "    # X_train: (22272, 20)\n",
    "    # X_val: (11136, 20)\n",
    "    X_train = []; X_val = [];\n",
    "    for vow_idx in range(n_vowels):\n",
    "        for pit_idx in range(n_pitches):\n",
    "            # Choose the person for this pitch/vowel to be used as test data\n",
    "            test_pid = int(np.random.rand() * 3)\n",
    "            for pid_idx in range(n_people):\n",
    "                wav_idx = flat_ref_idx(vow_idx, pit_idx, pid_idx)\n",
    "                if (pid_idx != test_pid):\n",
    "                    for win_idx in range(n_windows):\n",
    "                        X_train.append(data[flat_data_idx(wav_idx, win_idx)])\n",
    "                else:\n",
    "                    for win_idx in range(n_windows):\n",
    "                        X_val.append(data[flat_data_idx(wav_idx, win_idx)])\n",
    "    X_train, X_val = map(torch.tensor, (X_train, X_val))\n",
    "    # Default tensor is float\n",
    "    X_train = X_train.float(); X_val = X_val.float()\n",
    "    \n",
    "    return data, data_prenorm, max_mfcc, signs, flat_data_idx, X_train, X_val\n",
    "\n",
    "# # # # # # # # # # # # # #\n",
    "# Actual experiment loop  #\n",
    "# # # # # # # # # # # # # # \n",
    "\n",
    "# Results: \n",
    "# Indices: [ [ n_mfcc, n_hid, n_timb ], [ ... ], ... ]\n",
    "# Losses: [ loss1, loss2, ... ]\n",
    "indices = []; train_losses = []; val_losses = [];\n",
    "\n",
    "cands = [ [12, 10, 8], [24, 10, 4  ], [40, 38, 8] ]\n",
    "\n",
    "f = open(os.path.join(\"model_data\", \"TimbreVAE_Expts_nmels256_nfft2048\", \"_tanhLog.txt\"), \"w\")\n",
    "for cand in cands:\n",
    "    n_mfcc, n_hid, n_timb = cand\n",
    "    data, data_prenorm, max_mfcc, signs, flat_data_idx, X_train, X_val = create_data_for_mfcc(n_mfcc)\n",
    "    lr = 1e-3; n_epochs = 2000; batch_size=22272\n",
    "\n",
    "    # Training model \n",
    "    model = TimbreVAE(n_mfcc=n_mfcc, n_hid=n_hid, n_timb=n_timb)\n",
    "\n",
    "    # Use GPU if possible (will run on CPU otherwise)\n",
    "    device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "\n",
    "    # Move inputs to GPU (if possible)\n",
    "    X_train = X_train.to(device)\n",
    "    X_val = X_val.to(device)\n",
    "\n",
    "    # Move the network to GPU (if possible)\n",
    "    model.to(device) \n",
    "    # Define optimizer \n",
    "    # opt = optim.SGD(model.parameters(), lr=lr)\n",
    "    opt = optim.Adam(model.parameters(), lr=lr)\n",
    "\n",
    "    # Fit the model\n",
    "    tic = time.time()\n",
    "    train_loss, val_loss = model.train_func(X_train, X_val, model, opt, loss_fn, batch_size=batch_size,\n",
    "                            epochs=n_epochs, print_graph = True, desc=\"n_mfcc{},n_hid{},n_timb{}\".format(n_mfcc, n_hid, n_timb))\n",
    "    toc = time.time()\n",
    "    print('Final loss: {}\\t\\tTime taken: {}'.format(train_loss, toc - tic))\n",
    "    model_path = os.path.join(\"model_data\", \"TimbreVAE_Expts_nmels256_nfft2048\", \n",
    "                              \"TimbreVAE_tanh_lr{}_n_epochs{}_n_mfcc{}_n_hid{}_n_timb{}_{:.6f}.pt\"\n",
    "                              .format(lr, n_epochs, n_mfcc, n_hid, n_timb, val_loss))\n",
    "    torch.save(model.state_dict(), model_path)\n",
    "    print(\"Model saved at {}\".format(model_path))\n",
    "\n",
    "    indices.append([n_mfcc, n_hid, n_timb]); train_losses.append(train_loss); val_losses.append(val_loss)\n",
    "\n",
    "    # Create the validation printout and graph\n",
    "    data_tensor = torch.tensor(data)\n",
    "    data_tensor = data_tensor.float();\n",
    "    data_tensor = data_tensor.to(device)\n",
    "\n",
    "    wav_idx = flat_ref_idx(5, 5, 1)\n",
    "    data_idx = flat_data_idx(wav_idx, 30)\n",
    "    label = data_tensor[data_idx]\n",
    "    pred, _, _ = model(data_tensor)\n",
    "\n",
    "    # To restore:\n",
    "    restored_pred = signs * np.e ** (pred.cpu().detach().numpy() * max_mfcc)\n",
    "\n",
    "    # Visualize MFCCs\n",
    "    fig, ax = plt.subplots(2, 1, figsize=(10, 10))\n",
    "    fig.suptitle(\"TimbreVAE tanh lr{} n_epochs{} n_mfcc{} n_hid{} n_timb{} loss{:.6f}\"\n",
    "                 .format(lr, n_epochs, n_mfcc, n_hid, n_timb, val_loss))\n",
    "    # plt.figure()\n",
    "    ax[0].plot(data_prenorm[data_idx])\n",
    "    ax[0].plot(restored_pred[data_idx])\n",
    "    # fig, ax = plt.subplots(2, 1)\n",
    "    # plt.figure()\n",
    "    ax[1].plot(data[data_idx])\n",
    "    ax[1].plot(pred.cpu().detach().numpy()[data_idx])\n",
    "    fig.savefig(os.path.join(\"model_data\", \"TimbreVAE_Expts_nmels256_nfft2048\", \n",
    "        \"TimbreVAE_tanh_lr{}_n_epochs{}_n_mfcc{}_n_hid{}_n_timb{}_{:.6f}.jpg\"\n",
    "                .format(lr, n_epochs, n_mfcc, n_hid, n_timb, val_loss)))\n",
    "\n",
    "    # Numeric verification\n",
    "    np.set_printoptions(suppress=True)\n",
    "    f.write(\"TimbreVAE_tanh lr{} n_epochs{} n_mfcc{} n_hid{} n_timb{} loss{:.6f}\"\n",
    "            .format(lr, n_epochs, n_mfcc, n_hid, n_timb, val_loss))\n",
    "\n",
    "    f.write(str(data_prenorm[data_idx]))\n",
    "    f.write(str(restored_pred[data_idx]))\n",
    "    f.write(str(data[data_idx]))\n",
    "    f.write(str(pred.cpu().detach().numpy()[data_idx]))\n",
    "f.close()\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment: Choosing the best n_timb, n_hid, n_mfcc\n",
    "Since it gave rather promising results to compress n_mfcc, we wanted to see whether different configurations could get more optimal results. We repeat the first experiment with the same NN configuration.\n",
    " \n",
    "Best results:  \n",
    "[40, 36, 10] 1.2298838011973977e-06\n",
    "[24, 22, 10] 9.564522268443273e-07\n",
    "[12, 10, 6] 1.2616396763886529e-06"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "model_id": "26362717196c4602824afe46db363eef",
       "version_major": 2,
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      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='n_mfcc40,n_hid6,n_timb4', max=2000.0, style=ProgressStyle…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Loss before/after: 1.7212469655292474e-05, 1.0238961723429718e-06\n",
      "Validation Loss before/after: 3.408971135558068e-05, 2.0342843202959703e-06\n",
      "Final loss: 1.0238961723429718e-06\t\tTime taken: 17.025894165039062\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid6_n_timb4_2.0343646067248164e-06.pt\n"
     ]
    },
    {
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      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='n_mfcc40,n_hid8,n_timb4', max=2000.0, style=ProgressStyle…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Loss before/after: 2.1013459351299138e-05, 9.72251676092588e-07\n",
      "Validation Loss before/after: 4.1804506561194346e-05, 1.934369197704069e-06\n",
      "Final loss: 9.72251676092588e-07\t\tTime taken: 16.25984811782837\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid8_n_timb4_1.934226187502687e-06.pt\n"
     ]
    },
    {
     "data": {
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       "model_id": "cdd111806e3547ed9050a7b8125de100",
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       "version_minor": 0
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      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='n_mfcc40,n_hid8,n_timb6', max=2000.0, style=ProgressStyle…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Loss before/after: 1.239999406168173e-05, 9.066690376360269e-07\n",
      "Validation Loss before/after: 2.42582474160811e-05, 1.7995729670463793e-06\n",
      "Final loss: 9.066690376360269e-07\t\tTime taken: 16.35883617401123\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid8_n_timb6_1.8002413515665237e-06.pt\n"
     ]
    },
    {
     "data": {
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       "model_id": "03a73218438e4f6a9c22f63a3cb7d835",
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       "version_minor": 0
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      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='n_mfcc40,n_hid10,n_timb4', max=2000.0, style=ProgressStyl…"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Loss before/after: 1.2921549422645021e-05, 8.43345960361305e-07\n",
      "Validation Loss before/after: 2.5455714149893016e-05, 1.6831429260408226e-06\n",
      "Final loss: 8.43345960361305e-07\t\tTime taken: 16.569201946258545\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid10_n_timb4_1.6831603214337395e-06.pt\n"
     ]
    },
    {
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      "Training Loss before/after: 1.5487808776997972e-05, 8.583774197386343e-07\n",
      "Validation Loss before/after: 3.054494509237936e-05, 1.718680041135642e-06\n",
      "Final loss: 8.583774197386343e-07\t\tTime taken: 15.852180242538452\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid10_n_timb6_1.71878675518065e-06.pt\n"
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      "Training Loss before/after: 1.569481261459918e-05, 8.473586928013755e-07\n",
      "Validation Loss before/after: 3.0948954430976136e-05, 1.6890408005565405e-06\n",
      "Final loss: 8.473586928013755e-07\t\tTime taken: 17.052359342575073\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid10_n_timb8_1.6892657698207032e-06.pt\n"
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      "Training Loss before/after: 1.6092730889460822e-05, 8.857764180578377e-07\n",
      "Validation Loss before/after: 3.155279133854241e-05, 1.76698069125628e-06\n",
      "Final loss: 8.857764180578377e-07\t\tTime taken: 15.351408958435059\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid12_n_timb4_1.7669684810285595e-06.pt\n"
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      "Training Loss before/after: 1.7616686400497098e-05, 8.834324724940134e-07\n",
      "Validation Loss before/after: 3.477515406564049e-05, 1.7566140406583061e-06\n",
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      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid12_n_timb6_1.7566469915468118e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.637990072626492e-05, 8.756441871057554e-07\n",
      "Validation Loss before/after: 3.2009995788678354e-05, 1.7324183872183679e-06\n",
      "Final loss: 8.756441871057554e-07\t\tTime taken: 14.944334745407104\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid12_n_timb8_1.732456690535463e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.3032715350132564e-05, 8.073267921666218e-07\n",
      "Validation Loss before/after: 2.5665446387967845e-05, 1.6140415675766853e-06\n",
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      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid12_n_timb10_1.6141016151349264e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.5088625990882001e-05, 8.874872716775313e-07\n",
      "Validation Loss before/after: 2.9597375220779714e-05, 1.7592223460154458e-06\n",
      "Final loss: 8.874872716775313e-07\t\tTime taken: 14.978884935379028\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid14_n_timb4_1.7591796938501212e-06.pt\n"
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      "Training Loss before/after: 1.9743443124167535e-05, 8.919905040188339e-07\n",
      "Validation Loss before/after: 3.8297105601978025e-05, 1.76060377438178e-06\n",
      "Final loss: 8.919905040188339e-07\t\tTime taken: 15.052025079727173\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid14_n_timb6_1.7604502265866118e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.7386755435031722e-05, 9.121572841012358e-07\n",
      "Validation Loss before/after: 3.420866640477345e-05, 1.8058608997955747e-06\n",
      "Final loss: 9.121572841012358e-07\t\tTime taken: 14.970778226852417\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid14_n_timb8_1.8059002066930305e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.4219635123408388e-05, 9.439203516184085e-07\n",
      "Validation Loss before/after: 2.7595074504785153e-05, 1.8769533616950972e-06\n",
      "Final loss: 9.439203516184085e-07\t\tTime taken: 14.919651746749878\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid14_n_timb10_1.876165049321864e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.5692512408412737e-05, 8.480382003371829e-07\n",
      "Validation Loss before/after: 3.065061273759809e-05, 1.6848603865645568e-06\n",
      "Final loss: 8.480382003371829e-07\t\tTime taken: 15.853214263916016\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid16_n_timb4_1.6849242811808469e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.6948261737138376e-05, 8.781264594959459e-07\n",
      "Validation Loss before/after: 3.3239989231030144e-05, 1.741484899459214e-06\n",
      "Final loss: 8.781264594959459e-07\t\tTime taken: 14.960427284240723\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid16_n_timb6_1.7415019603253438e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.5795837028016303e-05, 7.73473935811941e-07\n",
      "Validation Loss before/after: 3.068521886464508e-05, 1.5582704325153053e-06\n",
      "Final loss: 7.73473935811941e-07\t\tTime taken: 16.043341398239136\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid16_n_timb8_1.5582930130734184e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.3816126236884759e-05, 8.126371540129185e-07\n",
      "Validation Loss before/after: 2.6959275569895218e-05, 1.6095950375257552e-06\n",
      "Final loss: 8.126371540129185e-07\t\tTime taken: 15.07455849647522\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid16_n_timb10_1.6096226359856711e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.8949554858450917e-05, 8.44996933686151e-07\n",
      "Validation Loss before/after: 3.6704396807599345e-05, 1.6809860645825494e-06\n",
      "Final loss: 8.44996933686151e-07\t\tTime taken: 15.019963264465332\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid18_n_timb4_1.6810161719933666e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.5275611745557567e-05, 7.721359122962016e-07\n",
      "Validation Loss before/after: 2.9800550733832107e-05, 1.5576774837855977e-06\n",
      "Final loss: 7.721359122962016e-07\t\tTime taken: 15.427778720855713\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid18_n_timb6_1.5576249630800609e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.465005200655981e-05, 8.290674371494986e-07\n",
      "Validation Loss before/after: 2.83907237195078e-05, 1.6471068642434032e-06\n",
      "Final loss: 8.290674371494986e-07\t\tTime taken: 15.081303358078003\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid18_n_timb8_1.6470479875289161e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.394926522184035e-05, 7.760948695552846e-07\n",
      "Validation Loss before/after: 2.7166205811603315e-05, 1.551400255893582e-06\n",
      "Final loss: 7.760948695552846e-07\t\tTime taken: 15.119373083114624\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid18_n_timb10_1.551378511652436e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.6762864315646818e-05, 8.451870285327835e-07\n",
      "Validation Loss before/after: 3.285250551570421e-05, 1.6852994529723094e-06\n",
      "Final loss: 8.451870285327835e-07\t\tTime taken: 15.222776889801025\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid20_n_timb4_1.6852128105345128e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.2734500776933527e-05, 7.471664983982584e-07\n",
      "Validation Loss before/after: 2.472359023388775e-05, 1.5060158430373874e-06\n",
      "Final loss: 7.471664983982584e-07\t\tTime taken: 15.02014446258545\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid20_n_timb6_1.506013668613273e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.2824726665670844e-05, 6.970311779309406e-07\n",
      "Validation Loss before/after: 2.492866050666091e-05, 1.3976176229209221e-06\n",
      "Final loss: 6.970311779309406e-07\t\tTime taken: 15.411784648895264\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid20_n_timb8_1.397611768702152e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.1154171480829345e-05, 7.663051940329458e-07\n",
      "Validation Loss before/after: 2.16926664820519e-05, 1.5472270342275158e-06\n",
      "Final loss: 7.663051940329458e-07\t\tTime taken: 15.16538953781128\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid20_n_timb10_1.5471912398613218e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.3955096692788875e-05, 8.280200337798431e-07\n",
      "Validation Loss before/after: 2.7255674331695184e-05, 1.6596270310317818e-06\n",
      "Final loss: 8.280200337798431e-07\t\tTime taken: 14.962506771087646\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid22_n_timb4_1.6596310453532242e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.251381345564264e-05, 7.578487750196337e-07\n",
      "Validation Loss before/after: 2.4395625524479768e-05, 1.5306443741395214e-06\n",
      "Final loss: 7.578487750196337e-07\t\tTime taken: 15.055171012878418\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid22_n_timb6_1.5306657838538802e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.2285573857611624e-05, 6.859767820750331e-07\n",
      "Validation Loss before/after: 2.391654034626895e-05, 1.383007165504855e-06\n",
      "Final loss: 6.859767820750331e-07\t\tTime taken: 15.167950868606567\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid22_n_timb8_1.3829976314914296e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.5072947389435494e-05, 6.608577510716672e-07\n",
      "Validation Loss before/after: 2.911414993905473e-05, 1.3349058097174883e-06\n",
      "Final loss: 6.608577510716672e-07\t\tTime taken: 15.714601993560791\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid22_n_timb10_1.3349148419407335e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.2815133775530875e-05, 8.191983115152809e-07\n",
      "Validation Loss before/after: 2.480684726060122e-05, 1.6433875954271047e-06\n",
      "Final loss: 8.191983115152809e-07\t\tTime taken: 14.931365251541138\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid24_n_timb4_1.6433676910832867e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.4812221226079026e-05, 7.938048012832971e-07\n",
      "Validation Loss before/after: 2.890780043584862e-05, 1.5928976347498682e-06\n",
      "Final loss: 7.938048012832971e-07\t\tTime taken: 15.092027187347412\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid24_n_timb6_1.592824707910333e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.4063617182445937e-05, 7.10675396539014e-07\n",
      "Validation Loss before/after: 2.7092446669422347e-05, 1.425883798303361e-06\n",
      "Final loss: 7.10675396539014e-07\t\tTime taken: 16.07848072052002\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid24_n_timb8_1.4258779440845909e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.6191028240511473e-05, 6.687381985897049e-07\n",
      "Validation Loss before/after: 3.1369299381628806e-05, 1.357456009524564e-06\n",
      "Final loss: 6.687381985897049e-07\t\tTime taken: 16.017914056777954\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid24_n_timb10_1.3573649346068415e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.7469430385136057e-05, 8.294483795280343e-07\n",
      "Validation Loss before/after: 3.389093821504335e-05, 1.6612693902918662e-06\n",
      "Final loss: 8.294483795280343e-07\t\tTime taken: 16.33107328414917\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid26_n_timb4_1.6612993304392901e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.2220349162817001e-05, 7.415776430019017e-07\n",
      "Validation Loss before/after: 2.3611079922866548e-05, 1.4957002077744095e-06\n",
      "Final loss: 7.415776430019017e-07\t\tTime taken: 17.890846252441406\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid26_n_timb6_1.4956891683904432e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.3171240216356585e-05, 6.887194835687934e-07\n",
      "Validation Loss before/after: 2.541590813847109e-05, 1.3982354938962508e-06\n",
      "Final loss: 6.887194835687934e-07\t\tTime taken: 16.811344861984253\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid26_n_timb8_1.3982569872423064e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.2132375308408819e-05, 6.121846471897101e-07\n",
      "Validation Loss before/after: 2.3322032721734594e-05, 1.2488636802400237e-06\n",
      "Final loss: 6.121846471897101e-07\t\tTime taken: 15.662812232971191\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid26_n_timb10_1.2488397615747632e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.2138858437538147e-05, 8.206665495828051e-07\n",
      "Validation Loss before/after: 2.3391678522544347e-05, 1.6447788923336514e-06\n",
      "Final loss: 8.206665495828051e-07\t\tTime taken: 14.907917737960815\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid28_n_timb4_1.6447621659943085e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.3860591537394058e-05, 7.401549005773903e-07\n",
      "Validation Loss before/after: 2.6650121970765892e-05, 1.4897036478565685e-06\n",
      "Final loss: 7.401549005773903e-07\t\tTime taken: 15.003881454467773\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid28_n_timb6_1.489704316910142e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.4436217131970942e-05, 6.771316847512391e-07\n",
      "Validation Loss before/after: 2.7902638432623325e-05, 1.3667004063843225e-06\n",
      "Final loss: 6.771316847512391e-07\t\tTime taken: 15.087710618972778\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid28_n_timb8_1.3666833455181928e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.3175439196585238e-05, 5.981786378616489e-07\n",
      "Validation Loss before/after: 2.5416772555688332e-05, 1.2401452431209043e-06\n",
      "Final loss: 5.981786378616489e-07\t\tTime taken: 15.331694841384888\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid28_n_timb10_1.2401317784177332e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.2458611183382314e-05, 8.194630894670798e-07\n",
      "Validation Loss before/after: 2.3884738891802985e-05, 1.647211905654477e-06\n",
      "Final loss: 8.194630894670798e-07\t\tTime taken: 14.834216594696045\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid30_n_timb4_1.647235322529557e-06.pt\n"
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      "Training Loss before/after: 1.2900852919395628e-05, 7.382186595350504e-07\n",
      "Validation Loss before/after: 2.4896187322406932e-05, 1.4934981851999102e-06\n",
      "Final loss: 7.382186595350504e-07\t\tTime taken: 15.173084497451782\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid30_n_timb6_1.493491494664173e-06.pt\n"
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      "Training Loss before/after: 1.1927414760421747e-05, 6.617952205759912e-07\n",
      "Validation Loss before/after: 2.3022433207637962e-05, 1.3578432242803533e-06\n",
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      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid30_n_timb8_1.3578558526665572e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.2025750916579674e-05, 6.509200892619918e-07\n",
      "Validation Loss before/after: 2.3215313324297982e-05, 1.3304791840103765e-06\n",
      "Final loss: 6.509200892619918e-07\t\tTime taken: 15.50155234336853\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid30_n_timb10_1.3304832819635155e-06.pt\n"
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      "Training Loss before/after: 1.3055429718960291e-05, 8.137619167020352e-07\n",
      "Validation Loss before/after: 2.510014696624772e-05, 1.6400983607953135e-06\n",
      "Final loss: 8.137619167020352e-07\t\tTime taken: 14.798396825790405\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid32_n_timb4_1.6400709295987912e-06.pt\n"
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      "Training Loss before/after: 1.2845765724349982e-05, 7.363121913767409e-07\n",
      "Validation Loss before/after: 2.471873558115685e-05, 1.4950411900042974e-06\n",
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      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid32_n_timb6_1.4950669485668855e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.3992697503631827e-05, 6.781059103862693e-07\n",
      "Validation Loss before/after: 2.6954479793878808e-05, 1.3791472281746824e-06\n",
      "Final loss: 6.781059103862693e-07\t\tTime taken: 15.63428258895874\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid32_n_timb8_1.3791545041322966e-06.pt\n"
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      "Training Loss before/after: 1.5639357440087986e-05, 6.123527887159551e-07\n",
      "Validation Loss before/after: 3.0208435230727854e-05, 1.2527044659116487e-06\n",
      "Final loss: 6.123527887159551e-07\t\tTime taken: 15.143940687179565\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid32_n_timb10_1.2527059712821897e-06.pt\n"
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      "Training Loss before/after: 1.1646374792460052e-05, 8.22648537163248e-07\n",
      "Validation Loss before/after: 2.248165326128746e-05, 1.6448096687980425e-06\n",
      "Final loss: 8.22648537163248e-07\t\tTime taken: 14.919665813446045\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid34_n_timb4_1.6447906007711914e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.4180475417738673e-05, 7.365194307212001e-07\n",
      "Validation Loss before/after: 2.707030099613228e-05, 1.4852316937698372e-06\n",
      "Final loss: 7.365194307212001e-07\t\tTime taken: 15.099111318588257\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid34_n_timb6_1.485217978171576e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.630501489786581e-05, 6.682342758011501e-07\n",
      "Validation Loss before/after: 3.124834520035776e-05, 1.360822938002603e-06\n",
      "Final loss: 6.682342758011501e-07\t\tTime taken: 15.232502222061157\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid34_n_timb8_1.3608371553910446e-06.pt\n"
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      "Training Loss before/after: 1.4794764280233575e-05, 6.171818501476584e-07\n",
      "Validation Loss before/after: 2.8406896082491712e-05, 1.262135695981868e-06\n",
      "Final loss: 6.171818501476584e-07\t\tTime taken: 15.697319746017456\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid34_n_timb10_1.2621157080063531e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.3175348205299213e-05, 8.246240014713472e-07\n",
      "Validation Loss before/after: 2.525239947369729e-05, 1.650357795558099e-06\n",
      "Final loss: 8.246240014713472e-07\t\tTime taken: 15.300380945205688\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid36_n_timb4_1.6503442472232313e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.6037909977737516e-05, 7.385899006367672e-07\n",
      "Validation Loss before/after: 3.0771475927583104e-05, 1.5051081046012455e-06\n",
      "Final loss: 7.385899006367672e-07\t\tTime taken: 15.236122846603394\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid36_n_timb6_1.505093887212804e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.554935769145859e-05, 6.601997786977639e-07\n",
      "Validation Loss before/after: 2.993247739371212e-05, 1.3487633309680325e-06\n",
      "Final loss: 6.601997786977639e-07\t\tTime taken: 15.169500589370728\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid36_n_timb8_1.3487493644746813e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.4767197934889246e-05, 5.956026979711378e-07\n",
      "Validation Loss before/after: 2.821866187384759e-05, 1.2298256771681809e-06\n",
      "Final loss: 5.956026979711378e-07\t\tTime taken: 15.272466897964478\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid36_n_timb10_1.2298838011973977e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.824892195605341e-05, 8.224510827273045e-07\n",
      "Validation Loss before/after: 3.511990399110591e-05, 1.6500125639140606e-06\n",
      "Final loss: 8.224510827273045e-07\t\tTime taken: 14.948230743408203\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid38_n_timb4_1.6499806166059156e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.691566678513398e-05, 7.457138158263231e-07\n",
      "Validation Loss before/after: 3.231378625436076e-05, 1.5147297640448843e-06\n",
      "Final loss: 7.457138158263231e-07\t\tTime taken: 15.100581407546997\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid38_n_timb6_1.514732774785966e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.4743831907880717e-05, 6.708918820435032e-07\n",
      "Validation Loss before/after: 2.8151234654688288e-05, 1.3688884624654707e-06\n",
      "Final loss: 6.708918820435032e-07\t\tTime taken: 15.514424324035645\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid38_n_timb8_1.368890051467708e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.3537794597789474e-05, 6.09416103501665e-07\n",
      "Validation Loss before/after: 2.581769889541741e-05, 1.2532470683599341e-06\n",
      "Final loss: 6.09416103501665e-07\t\tTime taken: 15.248666763305664\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc40_n_hid38_n_timb10_1.2532248223286079e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.2214715731726296e-05, 7.858575320395845e-07\n",
      "Validation Loss before/after: 2.402479053828223e-05, 1.5658195312509593e-06\n",
      "Final loss: 7.858575320395845e-07\t\tTime taken: 15.679295301437378\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid6_n_timb4_1.5660324575507948e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.2058958721657595e-05, 7.59728899193478e-07\n",
      "Validation Loss before/after: 2.38431371405892e-05, 1.5246782561592844e-06\n",
      "Final loss: 7.59728899193478e-07\t\tTime taken: 15.226672410964966\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid8_n_timb4_1.5246887937530704e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.2862347548121008e-05, 7.671767199444103e-07\n",
      "Validation Loss before/after: 2.5063755804266053e-05, 1.5444526363207006e-06\n",
      "Final loss: 7.671767199444103e-07\t\tTime taken: 15.238271236419678\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid8_n_timb6_1.5445051570262374e-06.pt\n"
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      "\n",
      "Training Loss before/after: 2.293133219682622e-05, 9.134519027663802e-07\n",
      "Validation Loss before/after: 4.4825080054244773e-05, 1.801664093991035e-06\n",
      "Final loss: 9.134519027663802e-07\t\tTime taken: 15.549293518066406\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid10_n_timb4_1.8008152322693804e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.9173317149966612e-05, 8.175270156881333e-07\n",
      "Validation Loss before/after: 3.780705865001541e-05, 1.6133628127261483e-06\n",
      "Final loss: 8.175270156881333e-07\t\tTime taken: 15.609441995620728\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid10_n_timb6_1.6134114863736362e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.9216873875722802e-05, 9.1375306050625e-07\n",
      "Validation Loss before/after: 3.7600995501738856e-05, 1.7970596672967076e-06\n",
      "Final loss: 9.1375306050625e-07\t\tTime taken: 15.53292441368103\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid10_n_timb8_1.796535296558306e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.5746257481989504e-05, 7.667853236037852e-07\n",
      "Validation Loss before/after: 3.0954668148495687e-05, 1.5586022830878694e-06\n",
      "Final loss: 7.667853236037852e-07\t\tTime taken: 15.685682773590088\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid12_n_timb4_1.5584403721230297e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.4009425181081926e-05, 7.419304851303412e-07\n",
      "Validation Loss before/after: 2.7320690281774807e-05, 1.4925531470270336e-06\n",
      "Final loss: 7.419304851303412e-07\t\tTime taken: 15.429837465286255\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid12_n_timb6_1.4925064805402667e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.6270944013677794e-05, 6.277429026246456e-07\n",
      "Validation Loss before/after: 3.1816144206229296e-05, 1.270571625229485e-06\n",
      "Final loss: 6.277429026246456e-07\t\tTime taken: 15.363823175430298\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid12_n_timb8_1.270587431620164e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.6031619536037417e-05, 6.674565428375512e-07\n",
      "Validation Loss before/after: 3.154303386122331e-05, 1.3431932927034366e-06\n",
      "Final loss: 6.674565428375512e-07\t\tTime taken: 15.454081773757935\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid12_n_timb10_1.343155073018038e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.4456240567325175e-05, 6.846860104679376e-07\n",
      "Validation Loss before/after: 2.790635301806461e-05, 1.3931220010640206e-06\n",
      "Final loss: 6.846860104679376e-07\t\tTime taken: 15.427834272384644\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid14_n_timb4_1.3931148087381032e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.7818252170651122e-05, 7.114969943275397e-07\n",
      "Validation Loss before/after: 3.4617099525599646e-05, 1.4316854963678568e-06\n",
      "Final loss: 7.114969943275397e-07\t\tTime taken: 15.365506649017334\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid14_n_timb6_1.4314655450054969e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.2125955070315421e-05, 5.985155481518643e-07\n",
      "Validation Loss before/after: 2.365046041621559e-05, 1.2218012995000972e-06\n",
      "Final loss: 5.985155481518643e-07\t\tTime taken: 15.684554100036621\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid14_n_timb8_1.2215812645060407e-06.pt\n"
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      "\n",
      "Training Loss before/after: 1.6611865614594398e-05, 8.216227944030416e-07\n",
      "Validation Loss before/after: 3.231010645970531e-05, 1.6417330259393002e-06\n",
      "Final loss: 8.216227944030416e-07\t\tTime taken: 15.453720569610596\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid14_n_timb10_1.6416903737739755e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.7967698667412518e-05, 7.117728952950016e-07\n",
      "Validation Loss before/after: 3.513522799415835e-05, 1.4311331926427524e-06\n",
      "Final loss: 7.117728952950016e-07\t\tTime taken: 15.46042537689209\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid16_n_timb4_1.431142726656178e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.6702393915547035e-05, 6.316277622004393e-07\n",
      "Validation Loss before/after: 3.245428482862725e-05, 1.2820923096104256e-06\n",
      "Final loss: 6.316277622004393e-07\t\tTime taken: 15.40246868133545\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid16_n_timb6_1.282098080197499e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.8260162056091875e-05, 6.2227188428896e-07\n",
      "Validation Loss before/after: 3.567314974364193e-05, 1.2617344311010307e-06\n",
      "Final loss: 6.2227188428896e-07\t\tTime taken: 15.375788450241089\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid16_n_timb8_1.2615583863794461e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.8773288680133463e-05, 7.463913998331061e-07\n",
      "Validation Loss before/after: 3.6562881271900805e-05, 1.503656927399851e-06\n",
      "Final loss: 7.463913998331061e-07\t\tTime taken: 15.416579246520996\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid16_n_timb10_1.5036005596362654e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 2.03624755765001e-05, 7.395804344526567e-07\n",
      "Validation Loss before/after: 3.963819940456714e-05, 1.501020020002435e-06\n",
      "Final loss: 7.395804344526567e-07\t\tTime taken: 15.494130373001099\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid18_n_timb4_1.5013913447358485e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.5843778730894643e-05, 6.631919953586726e-07\n",
      "Validation Loss before/after: 3.086727369448234e-05, 1.3631956528700975e-06\n",
      "Final loss: 6.631919953586726e-07\t\tTime taken: 15.477551460266113\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid18_n_timb6_1.3629924278470805e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.6302532709107316e-05, 6.506004489171479e-07\n",
      "Validation Loss before/after: 3.14856825888842e-05, 1.3086221237056728e-06\n",
      "Final loss: 6.506004489171479e-07\t\tTime taken: 15.428365468978882\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid18_n_timb8_1.3086769860987177e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.9503347896810236e-05, 5.747898630316144e-07\n",
      "Validation Loss before/after: 3.827820350041335e-05, 1.1557417094771718e-06\n",
      "Final loss: 5.747898630316144e-07\t\tTime taken: 16.170546531677246\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid18_n_timb10_1.1558036805644374e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.771734684877012e-05, 7.035140143493298e-07\n",
      "Validation Loss before/after: 3.4521240205771625e-05, 1.4271299105844107e-06\n",
      "Final loss: 7.035140143493298e-07\t\tTime taken: 15.368208646774292\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid20_n_timb4_1.4269425755837696e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.737363930877255e-05, 6.163249597831191e-07\n",
      "Validation Loss before/after: 3.356862567022614e-05, 1.2554059369789287e-06\n",
      "Final loss: 6.163249597831191e-07\t\tTime taken: 15.43495488166809\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid20_n_timb6_1.2551576344713826e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.712143287362381e-05, 4.9818327065675e-07\n",
      "Validation Loss before/after: 3.339876367271632e-05, 1.030704294245733e-06\n",
      "Final loss: 4.9818327065675e-07\t\tTime taken: 15.506272077560425\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid20_n_timb8_1.0307108175180766e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.6372161114524152e-05, 5.680012273141879e-07\n",
      "Validation Loss before/after: 3.1848887688127056e-05, 1.1665233405542443e-06\n",
      "Final loss: 5.680012273141879e-07\t\tTime taken: 15.475748538970947\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid20_n_timb10_1.1665704251994946e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.5056637201415396e-05, 6.650501662121294e-07\n",
      "Validation Loss before/after: 2.91060249524555e-05, 1.3497336259133172e-06\n",
      "Final loss: 6.650501662121294e-07\t\tTime taken: 15.361950635910034\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid22_n_timb4_1.3498496230766606e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 2.117817766493422e-05, 5.695474519388985e-07\n",
      "Validation Loss before/after: 4.094919382498182e-05, 1.185119768267433e-06\n",
      "Final loss: 5.695474519388985e-07\t\tTime taken: 15.325632810592651\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid22_n_timb6_1.185113412258483e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.8025173045609188e-05, 6.422861619724025e-07\n",
      "Validation Loss before/after: 3.490943847031429e-05, 1.3058781677364619e-06\n",
      "Final loss: 6.422861619724025e-07\t\tTime taken: 15.492443323135376\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid22_n_timb8_1.3057563163543484e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 1.4466027483001523e-05, 4.738087708333871e-07\n",
      "Validation Loss before/after: 2.775635120683703e-05, 9.565407091794514e-07\n",
      "Final loss: 4.738087708333871e-07\t\tTime taken: 16.027374029159546\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc24_n_hid22_n_timb10_9.564522268443273e-07.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 2.499789952289784e-05, 8.971348569471518e-07\n",
      "Validation Loss before/after: 4.9663498869230006e-05, 1.8402408867346487e-06\n",
      "Final loss: 8.971348569471518e-07\t\tTime taken: 15.466912508010864\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc12_n_hid6_n_timb4_1.8399774468899973e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 2.427396749884918e-05, 9.800334382085023e-07\n",
      "Validation Loss before/after: 4.770670985353404e-05, 1.9643424632798495e-06\n",
      "Final loss: 9.800334382085023e-07\t\tTime taken: 15.363779067993164\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc12_n_hid8_n_timb4_1.964656918459497e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 3.001928843300918e-05, 6.812198947975947e-07\n",
      "Validation Loss before/after: 5.932773152987162e-05, 1.3646994344087253e-06\n",
      "Final loss: 6.812198947975947e-07\t\tTime taken: 15.450337171554565\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc12_n_hid8_n_timb6_1.3656472324275936e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 2.8615330356633525e-05, 7.464804675901074e-07\n",
      "Validation Loss before/after: 5.6439122163701335e-05, 1.5533165925921036e-06\n",
      "Final loss: 7.464804675901074e-07\t\tTime taken: 15.438395977020264\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc12_n_hid10_n_timb4_1.552475926776727e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 2.2110716578946717e-05, 5.685282324510359e-07\n",
      "Validation Loss before/after: 4.399712358055443e-05, 1.2615124725779497e-06\n",
      "Final loss: 5.685282324510359e-07\t\tTime taken: 15.454068183898926\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc12_n_hid10_n_timb6_1.2616396763886529e-06.pt\n"
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     "text": [
      "\n",
      "Training Loss before/after: 2.5404684095718396e-05, 7.469904536766739e-07\n",
      "Validation Loss before/after: 5.0231075749315065e-05, 1.5488892978314183e-06\n",
      "Final loss: 7.469904536766739e-07\t\tTime taken: 15.391481399536133\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_expttanh_lr0.001_n_epochs2000_n_mfcc12_n_hid10_n_timb8_1.5484948907497114e-06.pt\n"
     ]
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   ],
   "source": [
    "n_mels = 256\n",
    "n_ffts = 2048\n",
    "\n",
    "%matplotlib agg\n",
    "%matplotlib agg\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # #\n",
    "# Data that is constant throughout the entire run #\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # #\n",
    "\n",
    "# File reference lists\n",
    "data_ref_list = create_data_ref_list(os.path.join(\"Data\", 'dataset_files.csv'),\n",
    "                            n_pitches, n_vowels, n_people)\n",
    "# flat_data_ref_list[flat_ref_idx(vowel, pitch, person)]\n",
    "flat_data_ref_list = flatten_3d_array(data_ref_list, \n",
    "                                      n_vowels, n_pitches, n_people)\n",
    "# File reference list accessors\n",
    "# Returns a flat_ref_idx, given a vowel, pitch, person\n",
    "flat_ref_idx = lambda vowel, pitch, person: flat_3d_array_idx(\n",
    "    vowel, pitch, person, n_vowels, n_pitches, n_people)\n",
    "# Returns vowel, pitch, person, given a flat_ref_idx\n",
    "nd_ref_idx = lambda idx: nd_array_idx(idx, n_vowels, n_pitches, n_people)\n",
    "\n",
    "# wav, spectrogram, mels, mfcc for each file in flat_data_ref_list\n",
    "# wav_data:     (576, ~29400)  (n_wavs, n_samples)\n",
    "# spectrograms: (576, 513, 58) (n_wavs, n_freq_bins, n_windows)\n",
    "all_wav_data = load_wav_files(os.path.join(\"Data\", \"dataset\"), \n",
    "                              flat_data_ref_list)\n",
    "all_spectrograms = np.array([ stft(waveform, win_length=n_ffts, plot=False) \n",
    "                              for waveform in all_wav_data ])\n",
    "\n",
    "# Define loss \n",
    "loss_fn = nn.MSELoss()\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # \n",
    "# Data that is constant per n_mfcc  #\n",
    "# # # # # # # # # # # # # # # # # # # \n",
    "\n",
    "def create_data_for_mfcc(n_mfcc):\n",
    "    # mels:         (576, 128, 58) (n_wavs, n_mels, n_windows)\n",
    "    # mfccs:        (576, 20, 58)  (n_wavs, n_mfcc, n_windows)\n",
    "    all_mels, all_mfcc = map(np.array, map(list, zip(*\n",
    "                             [ ffts_to_mel(ffts, n_mels = n_mels, n_mfcc = n_mfcc) \n",
    "                               for ffts in all_spectrograms ])))\n",
    "    n_files, n_mfcc_dummy, n_windows = all_mfcc.shape\n",
    "\n",
    "    # Returns a flat 2d idx, given a wavfile index and a window index\n",
    "    flat_data_idx = lambda wav_idx, win_idx: flat_2d_array_idx(\n",
    "        wav_idx, win_idx, n_files, n_windows)\n",
    "\n",
    "    # data:   (33408, 20) (n_wavs * n_windows, n_mfcc)\n",
    "    data_prenorm = np.array([ all_mfcc[wav_file_idx][:, window_idx] \n",
    "                              for wav_file_idx in range(n_files) \n",
    "                              for window_idx in range(n_windows) ])\n",
    "\n",
    "    # Globally normalize to [-1, 1]\n",
    "    # log(-n) is nan, so we put things on a log scale from absolute values, and\n",
    "    # mutliply the sign back.\n",
    "    signs = np.sign(data_prenorm)\n",
    "    data_logged = np.log(np.abs(data_prenorm))\n",
    "    max_mfcc = np.max(np.abs(data_logged))\n",
    "    data = data_logged / max_mfcc\n",
    "    \n",
    "    # X_train: (22272, 20)\n",
    "    # X_val: (11136, 20)\n",
    "    X_train = []; X_val = [];\n",
    "    for vow_idx in range(n_vowels):\n",
    "        for pit_idx in range(n_pitches):\n",
    "            # Choose the person for this pitch/vowel to be used as test data\n",
    "            test_pid = int(np.random.rand() * 3)\n",
    "            for pid_idx in range(n_people):\n",
    "                wav_idx = flat_ref_idx(vow_idx, pit_idx, pid_idx)\n",
    "                if (pid_idx != test_pid):\n",
    "                    for win_idx in range(n_windows):\n",
    "                        X_train.append(data[flat_data_idx(wav_idx, win_idx)])\n",
    "                else:\n",
    "                    for win_idx in range(n_windows):\n",
    "                        X_val.append(data[flat_data_idx(wav_idx, win_idx)])\n",
    "    X_train, X_val = map(torch.tensor, (X_train, X_val))\n",
    "    # Default tensor is float\n",
    "    X_train = X_train.float(); X_val = X_val.float()\n",
    "    \n",
    "    return data, data_prenorm, max_mfcc, signs, flat_data_idx, X_train, X_val\n",
    "\n",
    "# # # # # # # # # # # # # #\n",
    "# Actual experiment loop  #\n",
    "# # # # # # # # # # # # # # \n",
    "\n",
    "# Results: \n",
    "# Indices: [ [ n_mfcc, n_hid, n_timb ], [ ... ], ... ]\n",
    "# Losses: [ loss1, loss2, ... ]\n",
    "indices = []; train_losses = []; val_losses = [];\n",
    "\n",
    "n_mfccs = [ 40, 24, 12 ]\n",
    "n_hid_candidates = [6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38]\n",
    "n_timb_candidates = [4, 6, 8, 10 ] # >= 12 means we might as well have used 12 mfcc\n",
    "f = open(os.path.join(\"model_data\", \"TimbreVAE_Expts_nmels256_nfft2048\", \"expt_tanhLog.txt\"), \"w\")\n",
    "\n",
    "for n_mfcc in n_mfccs:\n",
    "    data, data_prenorm, max_mfcc, signs, flat_data_idx, X_train, X_val = create_data_for_mfcc(n_mfcc)\n",
    "    lr = 1e-3; n_epochs = 2000; batch_size=22272\n",
    "\n",
    "    for n_hid in n_hid_candidates:\n",
    "        for n_timb in n_timb_candidates:\n",
    "            if (n_timb >= n_hid) or (n_timb >= n_mfcc) or (n_hid >= n_mfcc):\n",
    "                continue\n",
    "\n",
    "            # Training model \n",
    "            model = TimbreVAE(n_mfcc=n_mfcc, n_hid=n_hid, n_timb=n_timb)\n",
    "\n",
    "            # Use GPU if possible (will run on CPU otherwise)\n",
    "            device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "\n",
    "            # Move inputs to GPU (if possible)\n",
    "            X_train = X_train.to(device)\n",
    "            X_val = X_val.to(device)\n",
    "\n",
    "            # Move the network to GPU (if possible)\n",
    "            model.to(device) \n",
    "            # Define optimizer \n",
    "            # opt = optim.SGD(model.parameters(), lr=lr)\n",
    "            opt = optim.Adam(model.parameters(), lr=lr)\n",
    "\n",
    "            # Fit the model\n",
    "            tic = time.time()\n",
    "            train_loss, val_loss = model.train_func(X_train, X_val, model, opt, loss_fn, batch_size=batch_size,\n",
    "                                    epochs=n_epochs, print_graph = True, desc=\"n_mfcc{},n_hid{},n_timb{}\".format(n_mfcc, n_hid, n_timb))\n",
    "            toc = time.time()\n",
    "            print('Final loss: {}\\t\\tTime taken: {}'.format(train_loss, toc - tic))\n",
    "            model_path = os.path.join(\"model_data\", \"TimbreVAE_Expts_nmels256_nfft2048\", \n",
    "                                      \"TimbreVAE_expttanh_lr{}_n_epochs{}_n_mfcc{}_n_hid{}_n_timb{}_{}.pt\"\n",
    "                                      .format(lr, n_epochs, n_mfcc, n_hid, n_timb, val_loss))\n",
    "            torch.save(model.state_dict(), model_path)\n",
    "            print(\"Model saved at {}\".format(model_path))\n",
    "\n",
    "            indices.append([n_mfcc, n_hid, n_timb]); train_losses.append(train_loss); val_losses.append(val_loss)\n",
    "\n",
    "            # Create the validation printout and graph\n",
    "            data_tensor = torch.tensor(data)\n",
    "            data_tensor = data_tensor.float();\n",
    "            data_tensor = data_tensor.to(device)\n",
    "\n",
    "            wav_idx = flat_ref_idx(5, 5, 1)\n",
    "            data_idx = flat_data_idx(wav_idx, 30)\n",
    "            label = data_tensor[data_idx]\n",
    "            pred, _, _ = model(data_tensor)\n",
    "\n",
    "            # To restore:\n",
    "            restored_pred = signs * np.e ** (pred.cpu().detach().numpy() * max_mfcc)\n",
    "\n",
    "            # Visualize MFCCs\n",
    "            fig, ax = plt.subplots(2, 1, figsize=(10, 10))\n",
    "            fig.suptitle(\"TimbreVAE expttanh lr{} n_epochs{} n_mfcc{} n_hid{} n_timb{} loss{}\"\n",
    "                         .format(lr, n_epochs, n_mfcc, n_hid, n_timb, val_loss))\n",
    "            # plt.figure()\n",
    "            ax[0].plot(data_prenorm[data_idx])\n",
    "            ax[0].plot(restored_pred[data_idx])\n",
    "            # fig, ax = plt.subplots(2, 1)\n",
    "            # plt.figure()\n",
    "            ax[1].plot(data[data_idx])\n",
    "            ax[1].plot(pred.cpu().detach().numpy()[data_idx])\n",
    "            fig.savefig(os.path.join(\"model_data\", \"TimbreVAE_Expts_nmels256_nfft2048\", \n",
    "                \"TimbreVAE_expttanh_lr{}_n_epochs{}_n_mfcc{}_n_hid{}_n_timb{}_{}.jpg\"\n",
    "                        .format(lr, n_epochs, n_mfcc, n_hid, n_timb, val_loss)))\n",
    "\n",
    "            # Numeric verification\n",
    "            np.set_printoptions(suppress=True)\n",
    "            f.write(\"TimbreVAE_expttanh lr{} n_epochs{} n_mfcc{} n_hid{} n_timb{} loss{}\\n\"\n",
    "                    .format(lr, n_epochs, n_mfcc, n_hid, n_timb, val_loss))\n",
    "\n",
    "            f.write(str(data_prenorm[data_idx]))\n",
    "            f.write(\"\\n\")\n",
    "            f.write(str(restored_pred[data_idx]))\n",
    "            f.write(\"\\n\")\n",
    "            f.write(str(data[data_idx]))\n",
    "            f.write(\"\\n\")\n",
    "            f.write(str(pred.cpu().detach().numpy()[data_idx]))\n",
    "            f.write(\"\\n\")\n",
    "f.close()\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 [40, 6, 4] 2.0343646067248164e-06\n",
      "1 [40, 8, 4] 1.934226187502687e-06\n",
      "2 [40, 8, 6] 1.8002413515665237e-06\n",
      "3 [40, 10, 4] 1.6831603214337395e-06\n",
      "4 [40, 10, 6] 1.71878675518065e-06\n",
      "5 [40, 10, 8] 1.6892657698207032e-06\n",
      "6 [40, 12, 4] 1.7669684810285595e-06\n",
      "7 [40, 12, 6] 1.7566469915468118e-06\n",
      "8 [40, 12, 8] 1.732456690535463e-06\n",
      "9 [40, 12, 10] 1.6141016151349264e-06\n",
      "10 [40, 14, 4] 1.7591796938501212e-06\n",
      "11 [40, 14, 6] 1.7604502265866118e-06\n",
      "12 [40, 14, 8] 1.8059002066930305e-06\n",
      "13 [40, 14, 10] 1.876165049321864e-06\n",
      "14 [40, 16, 4] 1.6849242811808469e-06\n",
      "15 [40, 16, 6] 1.7415019603253438e-06\n",
      "16 [40, 16, 8] 1.5582930130734184e-06\n",
      "17 [40, 16, 10] 1.6096226359856711e-06\n",
      "18 [40, 18, 4] 1.6810161719933666e-06\n",
      "19 [40, 18, 6] 1.5576249630800609e-06\n",
      "20 [40, 18, 8] 1.6470479875289161e-06\n",
      "21 [40, 18, 10] 1.551378511652436e-06\n",
      "22 [40, 20, 4] 1.6852128105345128e-06\n",
      "23 [40, 20, 6] 1.506013668613273e-06\n",
      "24 [40, 20, 8] 1.397611768702152e-06\n",
      "25 [40, 20, 10] 1.5471912398613218e-06\n",
      "26 [40, 22, 4] 1.6596310453532242e-06\n",
      "27 [40, 22, 6] 1.5306657838538802e-06\n",
      "28 [40, 22, 8] 1.3829976314914296e-06\n",
      "29 [40, 22, 10] 1.3349148419407335e-06\n",
      "30 [40, 24, 4] 1.6433676910832867e-06\n",
      "31 [40, 24, 6] 1.592824707910333e-06\n",
      "32 [40, 24, 8] 1.4258779440845909e-06\n",
      "33 [40, 24, 10] 1.3573649346068415e-06\n",
      "34 [40, 26, 4] 1.6612993304392901e-06\n",
      "35 [40, 26, 6] 1.4956891683904432e-06\n",
      "36 [40, 26, 8] 1.3982569872423064e-06\n",
      "37 [40, 26, 10] 1.2488397615747632e-06\n",
      "38 [40, 28, 4] 1.6447621659943085e-06\n",
      "39 [40, 28, 6] 1.489704316910142e-06\n",
      "40 [40, 28, 8] 1.3666833455181928e-06\n",
      "41 [40, 28, 10] 1.2401317784177332e-06\n",
      "42 [40, 30, 4] 1.647235322529557e-06\n",
      "43 [40, 30, 6] 1.493491494664173e-06\n",
      "44 [40, 30, 8] 1.3578558526665572e-06\n",
      "45 [40, 30, 10] 1.3304832819635155e-06\n",
      "46 [40, 32, 4] 1.6400709295987912e-06\n",
      "47 [40, 32, 6] 1.4950669485668855e-06\n",
      "48 [40, 32, 8] 1.3791545041322966e-06\n",
      "49 [40, 32, 10] 1.2527059712821897e-06\n",
      "50 [40, 34, 4] 1.6447906007711914e-06\n",
      "51 [40, 34, 6] 1.485217978171576e-06\n",
      "52 [40, 34, 8] 1.3608371553910446e-06\n",
      "53 [40, 34, 10] 1.2621157080063531e-06\n",
      "54 [40, 36, 4] 1.6503442472232313e-06\n",
      "55 [40, 36, 6] 1.505093887212804e-06\n",
      "56 [40, 36, 8] 1.3487493644746813e-06\n",
      "57 [40, 36, 10] 1.2298838011973977e-06\n",
      "58 [40, 38, 4] 1.6499806166059156e-06\n",
      "59 [40, 38, 6] 1.514732774785966e-06\n",
      "60 [40, 38, 8] 1.368890051467708e-06\n",
      "61 [40, 38, 10] 1.2532248223286079e-06\n",
      "62 [24, 6, 4] 1.5660324575507948e-06\n",
      "63 [24, 8, 4] 1.5246887937530704e-06\n",
      "64 [24, 8, 6] 1.5445051570262374e-06\n",
      "65 [24, 10, 4] 1.8008152322693804e-06\n",
      "66 [24, 10, 6] 1.6134114863736362e-06\n",
      "67 [24, 10, 8] 1.796535296558306e-06\n",
      "68 [24, 12, 4] 1.5584403721230297e-06\n",
      "69 [24, 12, 6] 1.4925064805402667e-06\n",
      "70 [24, 12, 8] 1.270587431620164e-06\n",
      "71 [24, 12, 10] 1.343155073018038e-06\n",
      "72 [24, 14, 4] 1.3931148087381032e-06\n",
      "73 [24, 14, 6] 1.4314655450054969e-06\n",
      "74 [24, 14, 8] 1.2215812645060407e-06\n",
      "75 [24, 14, 10] 1.6416903737739755e-06\n",
      "76 [24, 16, 4] 1.431142726656178e-06\n",
      "77 [24, 16, 6] 1.282098080197499e-06\n",
      "78 [24, 16, 8] 1.2615583863794461e-06\n",
      "79 [24, 16, 10] 1.5036005596362654e-06\n",
      "80 [24, 18, 4] 1.5013913447358485e-06\n",
      "81 [24, 18, 6] 1.3629924278470805e-06\n",
      "82 [24, 18, 8] 1.3086769860987177e-06\n",
      "83 [24, 18, 10] 1.1558036805644374e-06\n",
      "84 [24, 20, 4] 1.4269425755837696e-06\n",
      "85 [24, 20, 6] 1.2551576344713826e-06\n",
      "86 [24, 20, 8] 1.0307108175180766e-06\n",
      "87 [24, 20, 10] 1.1665704251994946e-06\n",
      "88 [24, 22, 4] 1.3498496230766606e-06\n",
      "89 [24, 22, 6] 1.185113412258483e-06\n",
      "90 [24, 22, 8] 1.3057563163543484e-06\n",
      "91 [24, 22, 10] 9.564522268443273e-07\n",
      "92 [12, 6, 4] 1.8399774468899973e-06\n",
      "93 [12, 8, 4] 1.964656918459497e-06\n",
      "94 [12, 8, 6] 1.3656472324275936e-06\n",
      "95 [12, 10, 4] 1.552475926776727e-06\n",
      "96 [12, 10, 6] 1.2616396763886529e-06\n",
      "97 [12, 10, 8] 1.5484948907497114e-06\n",
      "57\n",
      "91\n",
      "96\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(val_losses)):\n",
    "    print(i, indices[i], val_losses[i])\n",
    "# 0-58: n_mfcc = 40\n",
    "# 59-85: 24\n",
    "print(np.argmin(val_losses[0:62]))\n",
    "print(np.argmin(val_losses[62:92])+62)\n",
    "print(np.argmin(val_losses[92:98])+92)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Timbre Encoder Creation\n",
    "This cell simply creates the timbre encoder using the configurations:  \n",
    "[40, 36, 10] 1.2298838011973977e-06\n",
    "[24, 22, 10] 9.564522268443273e-07\n",
    "[12, 10, 6] 1.2616396763886529e-06\n",
    "\n",
    "Validation losses at 10000 epochs:  \n",
    "[12, 10, 8] 2.2488103272264888e-07  \n",
    "[12, 10, 6] 1.1263185732023812e-06  \n",
    "[24, 22, 10] 1.407030537649561e-06  \n",
    "[40, 38, 10] 2.308088473857905e-06  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5d990502986745719cfcf8f0a4b73df6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='n_mfcc12,n_hid10,n_timb6', max=10000.0, style=ProgressSty…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Loss before/after: 2.2289745948523626e-05, 2.3981357671068873e-07\n",
      "Validation Loss before/after: 4.329839614273488e-05, 5.897074996697149e-07\n",
      "Final loss: 2.3981357671068873e-07\t\tTime taken: 82.21688151359558\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_FinalCreationtanh_lr0.001_n_epochs10000_n_mfcc12_n_hid10_n_timb6_5.897228460860621e-07.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2cb4f13d28a848809bd405064de63b7f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='n_mfcc24,n_hid22,n_timb10', max=10000.0, style=ProgressSt…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Loss before/after: 1.4465551116857035e-05, 3.3667076903598746e-07\n",
      "Validation Loss before/after: 2.76900346166101e-05, 7.274374466164616e-07\n",
      "Final loss: 3.3667076903598746e-07\t\tTime taken: 74.98930859565735\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_FinalCreationtanh_lr0.001_n_epochs10000_n_mfcc24_n_hid22_n_timb10_7.274207202771186e-07.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9a20026f91d544b5917729a1347867d9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='n_mfcc40,n_hid36,n_timb10', max=10000.0, style=ProgressSt…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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Qp6p575OHHMg6Byz7GNz1fHIVvz0Q7wKrcEFlHC6PqehVqSJ+u99aW+gVgr2FOwbAXV8+8Lu2LMcFw8nGmGfNnoZcd1FzHu3vKuCVgPOiUk3dUKiz3+cuuLu9HX7D/FdgM+6xhn+wGW2t/QF+uaBPxh0AY3D1L0q8316Fu2guNMZk4OpqQeWNxhqcMeZY4E+4RzuJ1toE3J1JZY+J/weM8O4AqxO4raYHbKsYa+2vvfFv4OpGdbbWxgPP+i27poMmHXfg+uuCqwOzvypb1jO4kt7e1to43COk2lYTqKvA9CzFPTquMAjYFlDq6D/tYQGP+g9jz+Oj/ZlXQ9gMFOPqL1ccE3HWWv9HOx0D0t8Ft7/TgSSzd4tw/32+GVfnbL9YazOstddba1Nxd/ZPm9p147XXtjSuO6dIXCOpXbgnGYHb2n8/NOf9VBtVbe/NQJcqSkMwxgzBnffXWmu/8Rs1AnfDvMzLHx/H5TkZAU/KGksm7pFm4PXhF8a1Dv8QdwxOCJxBNetaX2pTFWN/VXX+1aSmY7o6e+XnXpDYBu/cttY+Ya0dhnsE3AfX2ArvqcC5uJvhD3FP4PCbz9G4QLnaYMtL80TcU6ULrXuKGzhNZfPqjatKM8M7ZisKszK8UuWajpEq85CA6SoNprxH5V8DH1trA6vKVJuH1GGdA9P1vnfzUV8GAc95T0PycHFBRbC6yPtf1XG/Gdduwz/miLLWpllrb7B7quM8VIs8GgDvieDx1LKaSlMHs1caY/p7Rfn34+qllFcx7bPAncar92mMiTeu7pi/Sbh6PRd6nzGuHuoluPqkg/3+bgGuqCrjb2CxuMw6EwgzxvyZfe8SAbDW/g9X//UDY8ww41rZxxpjbqimJOtTXP3BMcaYcO/vcGNMP7/lZ1lri4wxI3B1XCtk4ho2VNVf7ufevC/30vIroL+3zP21DWhjjIn3GxaLq6ifZ4zpC/y60l82jleA67xjNBG4h6of60zD3Ynealy3IxV32xV1yaqdl7cto3CNqUKNMVH1eWx6jwe/Bv5pjIkzxoQYY3qavevstffSH+6dW/1w9c4341rNPuyl6zBcPcmKHk1eAP5qjOltnMNMLeoCG2Mu9rtJ24XLKMu9ceHe9gjBnSNRfoHV68DZxnXh0hqXd7zvV3L8CnCPcXW/+uIauLzsjZtGHfZTM/EC8AcvPzDGmF7GVbeajbtIPGKMae1ts6MBjDEDca2Bb7HWfhIwvy9wgUFF3vhnXJuHwdXkxw3GW+b7uMeYrYwx/fGrFmbc49nJuNKisdZan//va1jX+lJZ3lVXlZ5/8Eu3SFG4EusQb9+Ge7+bRvXHdHXeAK4xrmuvSNyTsFnW2g3eNWOkt5x8XB3mcuO6wrrCGBPvBWI53vL9XQW8F/A0pzLPeOt5tnVVuypT2byW4G52Ko7Zcbh9MhjYXNMxQs15SEUVghMIKB01rnT6K+B7a+0dlaS3pjzkQNe5YvnRwMVUki/VkG9WxEMVXQ1Gmr3b6cwBxhnXr3U0Lmb6GX6pUzwDuNs7xvrhYq2K6/6zwINePoQxpp0x5twq1g2qz6MrjAF+8JZdo6YOZl/FrUAGEIV71FUpr17Mo8Bbxj1+XoKrA+jvO1wJZ5q1do437DzcAf2KVxKUYa3NwN0ZheLql4KrMxvYt1m1/ZrVwVe4C8gq3COeIvZ9xO3vIlym9jZu/ZYAw3GltvvwToBTcfUh03Hb91H2HMQ3AvcbY3JxF653/H5bgNczgnGPDI4ImPdOXIOX3+MeR90OnGX3NLSrNa+e0JvAOm9ZqbjGYpfjHjk8761zk7DWfol7lDUVt5824hpmAL/0+XeXN20J7lgbi2upfC2uKkpJbeaFy/AKcS1Cr/Q+31PPqzQWVydqGS54nMzeVVhm4Uo8duCOgYv8SiQvwwU86cAHuBa2U7xx/8IdQ1/jLmwTgehapOdwYJYxJg9XinabX/2w53Hb4DLc4+9CvDq61tqluKoxr+PqGcaydyPBv+AekW3EPR78u7f962M/NTlr7bu4/fMG7jz5ENdyuBxXd70XrjX7FtwFB9z52g7XULAif1vqza84IG/MBkq9z03lZtwj+AzcNeIlv3FH4fKgU4HdfutzrDe+ynWtL1XkXXVV3fk3BncOPAMc631+3ktLtcd0DevxDa69ynu4G6Ge7KlHH+ctYxfuPNiJ60GlIj0bvGvxDbg8C9irAGmfR+TGvVjiC+9zV1yJ6WAgw29fXVHTvKxrU+F/zGYBPu97OTUcI7XIQyrW8cdKgqnzcXnXNQHxQhdv3lXmIXVZZz/n4c7RqZWMqzLf9BSy5zH/CvZuOHctLo/fgivN7sHeDSEvw5Xi78RV4fw/v6cej+Py8K+9uOInXIPGqlSZR/upVTWVChVd5IjIQcwYczUwzlp7TFOnRUREZH80dcmsiIiIiMgBUzAr0oJUUlUm8BGsNAKzdwte/79nmzptDcns+xrQfR6jBgvvkXhl6/JFzb8WkcakagYiIiIiErRUMisiIiIiQUvBrIiIiIgELQWzIiIiIhK0FMyKiIiISNBSMCsiIiIiQUvBrIiIiIgELQWzIiIiIhK0FMyKiIiISNBSMCsiIiIiQUvBrIiIiIgELQWzIiIiIhK0FMyKiIiISNBSMCsiIiIiQUvBrIiIiIgELQWzIiIiIhK0FMyKiIiISNBSMCsiIiIiQUvBrIiIiIgELQWzIiIiIhK0FMyKiIiISNBSMCsiIiIiQUvBrIiIiIgELQWzIiIiIhK0FMyKiIiISNBSMCsiIiIiQUvBrIiIiIgELQWzIiIiIhK0FMyKiIiISNBSMCsiIiIiQUvBrIiIiIgELQWzIiIiIhK0FMyKiIiISNBSMCsiIiIiQUvBrIiIiIgELQWzIiIiIhK0FMyKiIiISNBSMCsiIiIiQUvBrIiIiIgELQWzIiIiIhK0FMyKiIiISNBSMCsiIiIiQUvBrIiIiIgELQWzIiIiIhK0FMyKiIiISNBSMCsiIiIiQUvBrIiIiIgELQWzIiIiIhK0FMyKiIiISNBSMCsiIiIiQUvBrIiIiIgELQWzIiIiIhK0FMyKiIiISNBSMCsiIiIiQUvBrIiIiIgELQWzIiIiIhK0FMyKiIiISNBSMCsiIiIiQSusqRMQbNq2bWu7devW1MkQERERqdG8efN2WGvbNXU6GpKC2f3UrVs35s6d29TJEBEREamRMWZjU6ehoamagYiIiIgELQWzIiIiIhK0FMyKiIiISNBSMCsiIiIiQUvBrIiIiIgELQWzIiIiIhK0FMyKiIiISNBSMCsiIiIiQUvB7MGitAi2LW3qVIiIiIjUKwWzB4v5r8BzoyB/R1OnRERERKTeKJg9WGStBV8ZbF3Y1CkRERERqTcKZg8WOWnu/9ZFTZsOERERkXqkYPZgkZPu/m/9uWnTISIiIlKPFMweLBTMioiISAukYPZgUF4KuRkQ3hp2rYei7KZOkYiIiEi9UDB7MMjbBljoeYL7nrG4SZMjIiIiUl8UzB4Mcra6/4eMdv9V1UBERERaCAWzB4OKngw6DIbYDgpmRUREpMVQMHswqGj8FZcKHQapey4RERFpMRTMHgxy0iAsGqITXTC7YyWUFDR1qkRERETqTMHswSAn3ZXKGgMph4H1wbalTZ0qERERkTpTMHswqAhmwZXMAmSo3qyIiIgEPwWzB4OcdDJD2jJm4ixKWqdCdJIagYmIiEiL0KKCWWNMZ2PMVGPMcmPMUmPMbd7wJGPMFGPMau9/ot9v7jTGrDHGrDTGnNZ0qW8gPh/kprO2OI4Zq3ewOD0HOhymYFZERERahBYVzAJlwO+ttf2AI4CbjDH9gTuAb6y1vYFvvO944y4FBgCnA08bY0KbJOUNJT8TfGWklScBMH/jLlfVYPtyKCtp4sSJiIiI1E2LCmattVuttfO9z7nAcqAjcC4wyZtsEnCe9/lc4C1rbbG1dj2wBhjRuKluYF4fsxtLEwCYuzHLBbPlJZC5oilTJiIiIlJnLSqY9WeM6QYMAWYBydbareACXqC9N1lHYLPfz7Z4wwLnNd4YM9cYMzczM7Mhk13/vD5mVxfFATBv425symFunKoaiIiISJBrkcGsMSYGeA/4jbU2p7pJKxlm9xlg7X+ttcOttcPbtWtXX8lsHF4wuyw/htYRoezIK2YTKRARAxl6eYKIiIgEtxYXzBpjwnGB7OvW2ve9wduMMR288R2A7d7wLUBnv593AtIbK62NIicNGxrBpqJoTuqXDMC8Tdmuv1mVzIqIiEiQa1HBrDHGABOB5dbaf/mN+hi4yvt8FfCR3/BLjTGRxpjuQG9gdmOlt1HkpFPWOgVLCMf2bktsZBhzN+5yPRpkLAZfeVOnUEREROSAtahgFjgaGAOcaIxZ6P2dATwCnGKMWQ2c4n3HWrsUeAdYBnwJ3GStbVnRXU46hVGuRDY1IZrBXRL29GhQWgA71zZxAkVEREQOXFhTJ6A+WWtnUnk9WICTqvjNg8CDDZaoppaTRnar/gAkx0UxvGsSj32zitykAcSCq2rQrk+TJlFERETkQLW0klnxZy3kpLMzpA0AyXGRDOuaiLWwoKAdhEbC1oVNnEgRERGRA6dgtiUryILyYraSROuIUGKjwhncJYEQA3M35ULyAPVoICIiIkFNwWxL5r0wYXNZAsnxUQDERIbRNyWOeZu8erNbf3YluCIiIiJBSMFsS+b1MbumKJ6UuKhfBg/vlsiCTbspTz4UirJh98amSqGIiIhInSiYbcm8ktmVBXEk+wWzw7omUlBSzoaI3m7AVlU1EBERkeCkYLYly0nHmlBW5EXtE8wC/JCbDCZUL08QERGRoKVgtiXLSccXk0JxuSElLvKXwR0TokmOi2TOlgJo11fBrIiIiAQtBbMtWU4axdEpAKTE7ymZNcYwvGsS8ypenqAeDURERCRIKZhtyXLSyY1sD0B7v2oGAEO7JpK2u5DsxP6Qtw1yM5oihSIiIiJ1omC2pfJemLArtC3AXr0ZAAz36s0u9XV1A1TVQERERIKQgtmWqigbSvPJoA3GQLvYyL1G90+NIyo8hOk5rhqCejQQERGRYKRgtqXy+phNK0+kbUwk4aF77+rw0BAGdUrgxy0l0KaXXmsrIiIiQUnBbEvlBbPrS+JJjousdJJhXRNZmp5DWftDVTIrIiIiQUnBbEtV8cKEwth96stWGN4tkXKfJS26D2RvgoKsxkyhiIiISJ0pmG2pctIBw4rcVnu9MMHf0C6uEdjCMq8RmLroEhERkSCjYLalyknDtm5PZqGtMphNaBVBr/YxTMlKdgPUo4GIiIgEGQWzLVVOOqWtvRcmVBHMAgzrksiMNB82vpPqzYqIiEjQUTDbUuWkkx/lSlyT46sJZrslkl1YSn7SAJXMioiISNBRMNtS5aSzO6wdUEPJrPfyhPVhPWHnGijOa5TkiYiIiNQHBbMtUXEuFGeTadoAVNk1F0CPtq1JbBXOnOIugIVtSxopkSIiIiJ1p2C2JcrZCkC6TSIyLIT46PAqJzXGMKxrIl/saO8GqKqBiIiIBBEFsy1RrnthwsbSBFLiozDGVDv5sK5JzMmKxNeqnYJZERERCSoKZlsi7+1fawrjquyWy5+rN2vIiuurHg1EREQkqCiYbYm8t38tz4+pVTB7WKd4wkMNq0J6QuZyKC1q6BSKiIiI1AsFsy1RTjq2VRs25/pIqabxV4Wo8FAGpMbzY0Eq+Mpg+7JGSKSIiIhI3SmYbYly0imP6UBxma9WJbPgqhp8vsN7E5heaysiIiJBQsFsS5STRlG09/aval6Y4G9410TWlrWlLCJWjcBEREQkaCiYbYly0skJd11t7U/JLBi2tzpEwayIiIgEDQWzLU1pERTsZGeIe2FCdW//8tc+LorOSdEstd1h21IoL2vIVIqIiIjUCwWzLY3Xx+xWXDDbvhYNwCoM65LId3mpUFYEO1Y1SPJERERE6pOC2ZbG62N2c1kCSa0jiAwLrfVPh3VL4seCju6LqhqIiIhIEGhRwawx5kVjzHZjzBK/YUnGmCnGmNXe/0S/cXcaY9YYY1YaY05rmlTXMy+YXVccT/vY2pfKgiuZXWdTKQuNUo8GIiIiEhRaVDALvAjfei4AACAASURBVAycHjDsDuAba21v4BvvO8aY/sClwADvN08bY2pfjNlceS9MWFkYU+ueDCockhJLq8gI0iJ7qWRWREREgkKLCmattd8BWQGDzwUmeZ8nAef5DX/LWltsrV0PrAFGNEpCG1JOOkTFsyEnpNaNvyqEhhiGdElgUVkX91pbn6+BEikiIiJSP1pUMFuFZGvtVgDvf3tveEdgs990W7xh+zDGjDfGzDXGzM3MzGzQxNZZTjo2NpWd+cW0389gFlwXXTPzO0JJLuxa3wAJFBEREak/B0MwWxVTyTBb2YTW2v9aa4dba4e3a9eugZNVRzlpFLdKwdrad8vlb1jXRJb4ursvqmogIiIizdzBEMxuM8Z0APD+b/eGbwE6+03XCUhv5LTVv5x08iLda2lT4vevARjA4M4JrKET5SZMwayIiIg0ewdDMPsxcJX3+SrgI7/hlxpjIo0x3YHewOwmSF/9KSuBvO3sCm0L1P7tX/5io8LpkZLE5rBu6tFAREREmr0WFcwaY94EfgQOMcZsMcZcBzwCnGKMWQ2c4n3HWrsUeAdYBnwJ3GStLW+alNeTvAzAss24FyYcSDALMLxrInNLOmO3/gy20poXIiIiIs1CWFMnoD5Zay+rYtRJVUz/IPBgw6WokXl9zKaVJxEeakhqFXFAsxnWNZF5c7pyUcFU19VXfKf6TKWIiIhIvWlRJbMHPa+P2Q2l8bSPjSIkpLI2bjUb1jWRpb5u7stWVTUQERGR5kvBbEvilcyuLIjb7xcm+OuUGM2O1r3xEaJGYCIiItKsKZhtSXLSISKGDXkhJMftf08GFYwxDOzegY0mVcGsiIiINGsKZluSnDSIS2VbTvEBN/6qMLRLIgvLulKermBWREREmi8Fsy1JTjplMR3ILyk/oBcm+BveLYklvm6E5qVDXjN/65mIiIgctBTMtiQ56RREVbwwoW7BbP8OcawO6eG+ZKh0VkRERJonBbMtRXkZ5GaQHeZet9s+tm7BbERYCCGpg9wX9WggIiIizZSC2ZYifzvYcjKNe/tXXUtmAfp178wm256ytIV1npeIiIhIQ1Aw21J43XKl20SAOvVmUGF410SW+LopmBUREZFmS8FsS+G9MGFjaQKxUWG0iqj7y92GdnHBbFTuRijKrvP8REREROqbgtmWwiuZXVscV+eeDCokto4gK66f+5KxuF7mKSIiIlKfFMy2FDlpEBbF2rzIeqkvW6FV1yEA+NJV1UBERESaHwWzLUVOer29MMFf3569yLCJ5G2YX2/zFBEREakvCmZbipx0bGwqmXnF9dL4q8JQrxGY1ZvAREREpBlSMNtS5KRR3CqFcp+ttzqzAD3btWZtaA9i89ZBSUG9zbc2cotKG3V5IiIiEnwUzLYEPh/kbCUnoj1AvVYzMMZQ2u4wQvDB9mX1Nt+aTPphA4Pu+5r35m1ptGWKiIhI8FEw2xIU7ABfKTtD6u+FCf7ieg4DIH/DvHqdb1VWb8vlwc+XEx4awh8n/8yXSzIaZbkiIiISfBTMtgReH7MZtAHqt2QW4JDe/dhlY9i9dk69zrcyJWU+fvvOQmIiw/j6t6MY1DmBW99cwIzVmQ2+bJFgsHhLNmsz85o6GSIizYaC2ZbA62N2S1kCoSGGtjH11wAMYFCXRJbaboRsb/i+Zp/4ZjVL0nJ4+IJD6dqmNS9fPYIe7Voz/pV5zNuY1eDLF2nO5m3M4sJnfmD0YzOYOHM9Pp9t6iSJiDQ5BbMtgRfMriuOo11MJKEhpl5nHxUeyrbWfWmbvxbKSup13v7mbdzF09PWcPGwTpw2IAWA+FbhvHLdCJLjIrn6pTksS89psOWLNGebdhYw/pV5pCZEMapPW/766TKuemk223OKmjppIiJNSsFsS5CTDiFhrC2IJrme68v+IuUwwimlNGN5g8w+v7iM372zkNSEaP58dv+9xrWPjeK1cSOJiQxj7IuzWKdHrHKQyS4s5dpJcyjzWSZefTjPjx3Og+cPZM6GLE5/fAZTlm1r6iSKiDQZBbMtQU46xKaSkVtCcmz9VjGo0Lb34QCkr/ipQeb/wGfL2ZRVwD8vHkRsVDhsmQevXuD+A50SW/HauJFYC1e+MIu03YUNkg6R5qa03MfNb8xnw458Xjy3DT0/vgDzxq+4orePT285lg7xUVz/ylzu/mAxhSXlTZ1cEZFGp2C2JchJg7hUMrKL6r0ngwp9Bwwmz0aR1wA9Gny7Yhtvzt7E+FE9GNmjDRRkwTtjYe038OJpMPt5sJae7WKYdO0IcovLuPKFWWTmFtd7WkSaE2stf/l4KTNW7+CVI9IZ9sV5kLkSNv4ATx9Jr1XP88GEEUw4rgdvzN7EmU/OYEladlMnW0SkUSmYbQly0imL6UBOUVm992RQITm+FWtDuxO1Y0m9zndnXjG3T15M35RYfndKH9dn7gcTIH87jPkAehwPn/8B3r8eSvIZ2DGel64+nIzsIsa+OJvsAr1Y4YDlboPsNPCpNK+5mjhzPZNnreW9ru9z1PzfQ9s+cMMMuGkW9DoJ/ncvES+eyJ2H5vP6dSMpKC7n/Ke/57npa9U4TEQOGmFNnQCpI2shJ538LicD1OvbvwLtju9P712fYcvLMKF1P3Sstdz1wWJyCkt59boRRIaFwox/weqv4Yx/QM8TofvxMOOfMPVByFgMl7zK8G59eG7MMMZNmss1L8/m1etG0jpSh/J+WTfNVeOw5WBCIbYDxHeEuI7e/05+3ztB63Zg6rdhoVRvyrJtvPr5NKbEPU3XbavhyJvhpL9AWISb4NLXYfmn8Pkf4YWTOerwcXxxw53c9fkGHv5iBdNXZfLPSwbRIT66aVdERKSBKQIIdoW7oKyQXWHtgPrvY9ZfWMfBtNr1HlvXL6VDr0F1nt9789P4auk27hzdl34d4mDD9/DtAzDgAjh8nJsoJASO+yN0Gg7vXQfPnwDnPMmogRfwxGWDufH1+Ux4dR4Trx7ugmGpWf4OeH8CtOkFR9zgSmdz0iB7C2xdCCs+g/KAKhyhERCXuifIje+0J9CtCICjEhTw1pMladl89tYzfB71HK1CIuDSN6HvGftO2O8s6D7KnTez/0viik95evTfePeQIdz7yVJOf2wGj1xwKKMP7dD4KyEi0kgUzAY7r1uu7ca9MCElvmEagAEkHzIClkDa8p/qHMxuzirg3o+XMqJ7EuOO7QF5mTD5WkjsBmc/vm9Q1PMEmDAD3r0aJl8Dm2dz+in387eLBvGHd3/m1jcX8NTlQwkLVc2ZalkLH92MLczimY6PsGFDD2KjjiAuNpzYdmHERYcTGxlKksklqXw7ccXbiSneRmTBVkJyvKB34w/uuLMB1RPCW7ugNqELHPcn6DyiadYxyGXszGb5xAk8FvIFpclDMZdOctu0KlFxcMbf4LBfwSe3Yd4ZwyWHnMkR19zLLZ9t59evz+dXwzvz57P76wmGNLmVGbmszczj1P7Jyq+l3ihnC3ZeMJtWngg0bMls975DKbbhFG5aUKf5+HyWP7z7MwD/vHgQofjg/XFQtBuunOwuzpWJ7whXfwZT/gyznoG0eVx08cvknt2f+z5Zxu3vLeIfFw0ipJ772W1RZj8Pq75g4YA7+Nu8CNrGZFJQUkZBla3gI4EuQBdiIsOIjQojLiqc+HaGThF5dArdSarJor3dQdvyTBJKt9Nuy2KiXrsAc80XkHJoI65c8Cvctprc/17Kxb417Dz0etqc+9CeagU16TQMxk+Fn56GqQ/TZf103j/hHv7daxRPTd/ArPU7efzSIQzqnNCwKyESIK+4jE9+TuftOZtZuHk3AMO6JvLEZUPomKBqMFJ3CmaDnfcq2/WlCbSKKCamAUteQsMj2BDRg9hdS+s0n4kz1zNrfRZ/v+gwOie1gmmPujqcZz9Rc/ATFgGjH3Glfh/fAs8dyzUXTiT3lD78a8oqYiPDuPecARg97t5XxhL4+h5s79P40+Yj6ZsSwhe3HYsxhtJyH3lFZeQUlZJbVEZOYSk5Ad9zf/leSk5hGasKY5hbGEVuUXtyisoo9xocdWAnH0bfR5tJ5xN2/deQ1KOJVzw4+JZ+CJNvpL3PsujYpzns5Cv2fyah4XD0bdD/XPjs94R+dQd/SB3KaRfez4QpJVz4zA/89pQ+3HBcz3p/uUpjm70+i+9WZRIWaggPDSHc+x8WGkJEwOewkBDCw0IIDzGEh4UQFuLGR/h9rphHWGgIrSJCCVepYZ1Ya5m3cRdvz9nMp4u2UlhaTp/kGO45sx9x0eHc/8kyznh8Bv+4eBCn9E9u6uRKkFMwG+xy0sGEsL4whpQ40+BBXH5Sf7pnfEVuYQmx0bUsMfKzIiOHv3+1klP7J3PRsE4uiJ32MBx2KQwdW/sZDbwAkgfCO2Pg1fO55YS7yD3mDJ6fuZG46HB+f+oh+522Fq2kwNU5jk7g+4H3sWrxOv558aBfjpfw0BASW0eQ2Hr/9ym4C1dhaTm5RWUs25rDze/6+G/BPYS/cDYxv/4GYlPqc21alrJi+Pr/CJn9HCt9PVk96gkuPvmYus0zsRtcMRmWvAdf3sGhn53L1MNv4M6sM/n7VyuZvjKTf186OGhLxd6es4m7PliCz1psHTptMPgIp5xwygijnAjvvwUiW8cRG5tAu/hWpMRH0T42iuS4KJLjIr3/UbRpHaEnQQF25BXz/vwtvD1nM2sz82kdEcK1/SwXpe6kW/GPmLULoSSfoy//JxO+zuf6V+ZyzdHduGN0X7V7kANmbF1yghbAGHM68DgQCrxgrX2kuumHDx9u586d2yhpq5UPb4K133Jhq4lEhIbw5vgjGnRxqz97gt5z/o/Z505jxJAh+/Xb4rJyznvqBzJzi/jqN6NoY3fBs8dAdJJ7PBrRev8TVJwHn/4GFr+L7X0q94fdxksLsrnrjL6MH9Vz/+fXUn36W5j7Ioz5gMu/bcW6zHy+u/0EIsIapvQpM7eYJ155kzu23052dCeSbvofUbFJDbKsoJa13tUBT1/AC2Wj2TL0T/zl/MH1e1NauAum/AXmT8LGd+aHvncz4ackjIGHzj+Uswel1t+yGpi1lsf+t5pXvpnHHSlzuKDDTsJ8pfjKS6GsxP0vL8GWl0J5Kba8FFNeAr4yKC/B+Mowvor/pYQE1vuuRBGR5BNFro0i30aRRzT5Nop8oiggGl9EDCGRMYRFxRLeOo7o1vFExyQQG59AQkISSQlJxMQnYCJjXb3ykJZX4lvus3y3OpO3Z21ixYol9GctpyRs5ajozbTPX44p8vo+Do10T992bwLro+TKD3lobggv/7CBgR3j+M9lQ+nW9gCuA1ItY8w8a+3wpk5HQzqoS2aNMaHAU8ApwBZgjjHmY2vtsqZN2X6oeGHCziJGdG/4YCG1/0iYA9tWzoL9DGb/PWU1y7fmMPGq4bSJDoVXroOSfLjq0wMLZAEiY+CC56HLEZgv7uDPscuJ6nM3D32+gtiocC4bUU3DmYPF8k9cIHvUrSyJGsYPa2dy5+i+DRbIArSLjeTeX1/FB5NDOWfpbax87Cxir/+YbiltG2yZQWfZx/DRzZRZy82lv6Ow52gmnjuo/p+uRCfCOU/AoEsxn/yGo2fdwKw+53JL1sXc8uYCpq7czn3nDHBv3mvGSst9PPX6ZDqtepXZ0T8RvrsEQnpAWDShoeEQGk5oaASEtXK9b3jDCAnf+3toBISEVTGNN9z63I1ySR5RxblEleSRWJRLSWEupYU5+IpyoXgXoaX5hJXnE1VQCAVAVvXrkB2aiG/kjSQed6PLu4KZtaRvWs2cH75l15rZ9ChZxSOh60mI8F43XhQO8QOg2wWQOsT9te/ntveONTDpLCJeO5d7x37EkT2HcfvkRZz15EweuuBQzgmiGyxpHg7qYBYYAayx1q4DMMa8BZwLBFEwm45tdwjbNxTRPq7hejKo0LrTIMoJwZe+cL9+N2dDFs99t5bLRnTmpH7J8M1fYeNMOO9ZaN+3bokyxnXl1WEI5t2ruD3tVpJTf81dH1haR4Yd3BljdpqrW5w6BE78P/777lJiIsO4bGTDB/mhIYaLLhnDkik+Bnx/G9OfuYTlF0xi9KDODb7sZq2s2GvE+CxF7Qdz3vZx+Np2YfLlQxq2dXfXo9wLF2Y+RusZ/2Bi+DS+GXgjExb4mLMhi8d+NYRhXRMbbvkHqqyEokUfsPnLx/hNyTJKIqIJGzoGRoyve96xH0KAKO9vHz4flOZDcR7FBdnsyspid/YucrN3kZe7m6L8bIrzc2i3/UeO/uFBimb/h/Bjbib0iAkQFd9o63DArIXcrZC+gLLN89i5ZjZRmYtI9WVzLlBGKAVJfWjd/QLoVBG49oewKq5JbXu5xrwvnwWTzua0sR8z8LZjufXNBdz65gJ+XLuDP581gOgIVTuQ2jnYg9mOwGa/71uAkYETGWPGA+MBunRpZiV9OekUdTmO0nLboC9M+EV4FJlR3UnMXkG5z9aqEUlecRm/e2chnRNbcc+Z/WH1/2DGP2DIGBh8Wf2lrdMwmPAd5v3ruXrN43RPOImb3i4lJvIoTux7EDYw8JXD++OhrAQunMiW3DI+W7yVa4/uRlwjlsINPOUqdkUWcsK3f2Ly5Bu5b+ND3HnGgAYtGa6tikYqyXFRrjFiQ9u1Ad69BtLnUzRsPGcsO4WcMMMHVx3eOPskLBKO/xMMvADz6W85ec3DLOwynBuzx3LJc0XcdHxPbj6xd7PYN+RmwNyXKJ/7IlH52wm3Kcwf8CeGnn0jRDezHhlCQiAyFiJjiYzrQEoKVFZLPCO7iEfemczwTS9w8rQHKfvhCcKOuAGOuBFaNaNqONZCxiJY+QWkL3B/edsAMBiyfJ1YGz6M1j0PZ+Dhx9Gu5zDiwvfz+tOmJ1zzGbx8Nkw6m45jP+Kt8UfwrymreGbaWuZt3MVTlw+ld3JsA6xgM1FS4M7JEAXtdXVQ15k1xlwMnGatHed9HwOMsNbeUtVvmlWd2aIceKQzGSPv5ojpA3jmiqGN0jn6xoljid40nR0TFtM/tYputPz8afIi3p23mXcmHMnwxAJ49lj3xqnrv4HwBmiA4vPBd3/HTnuYjaFdmFB8G/ddex5H9GhT/8tqzr77u+tM/7xnYPDl3P/JMl75cQPf3X4CqU3Q8Kds6qOETX+I/5adyRcdbuSpK4Y1STrAdQ83Zfk2nvjfKrpum0KMKSK5Wz9OOeYoDut7SMO8/GH5J66OO1B69pNcPrMdP2/J5q3xRzC0SxOUiFoLC193PVwU5zEl6TJu2XIi3VPa8I+LBzGwYxOUGFoLW+bArOdg2UfgK+XHkKG8XHYql15+DSf0bRkNCacs28ZrH3zMpUXvMDp0NjaiNebwce4tbzHtmy5huRmw6B34+U3YvgyLITumB/NLuzE9rxMrTA9S+47gwpF9OKpnm/pp/Ja1HiadDcU5MOZD6DiU6asy+d3bC8kvKeP+cwZy8fBOLaOHmvIyd2Owfhrla6Zhtsyi5IqPiOp5dIMuVnVmW74tgP8zz05AehOlZf95fczuCHFBWnJ8I5TMAnHdh5G4+SO+W7WS/qmHVzvt10szeHvuZm48vifDO8fCy5dCeQlcMqlhAllwpSTH/wnTaThd3hvH++V3c/fLW4i+/raDp4/NzbNh6sMw8CIYdBnZBaW8NWcTZw9KbbIAMuz426FwJ+NnP0fe9njOfKKAf/9qMMcf0ngXb5/P8uXSDJ74ZjWZGVv4T+sXODJinhuZBrztGvyUxnclJqU3pk0P17VYxV9cx/0vRSkr2dM3cuoQ7EUvcfuUbOZsSOPJy4Y0TSALLmAfciX0OR3z1V2cuugVlrV+m8XZPfjp2R6s63cMp51+FpFJjfA0qrTI9bww+znY+jNExpFxyBiuXz6YrSEdeWn84RzaKQgex9fSKf2TObLnNfzjq6P4908z+X3IJ5z6/ZOYWf+FYVfD0be6N+41htJC99a/n9+Ctd+A9VGUPJSvOv6Bhzf1I2NHNH1TYvnVqM78ZnDHA+7xpEpJ3V2Vg0lnwSvnwZgPOK7PML647Vh+8/ZCbn9vET+s3cED5x/aoF1PNghrIXMlrJ8O66ZhN8zAFOcCsNJ2Y0b5qfTbHoLaKtfdwV4yGwasAk7CXcrmAJdba6vsSLVZlcyu+QZeu4ApR0zi+mnhfH/HiY3S1Y7d8D3m5TN4rtMjTBj36yqn25FXzGn//o7kuCg+vOloIr79M/zwJFw4EQ69qMHTCUD2FkreHENExnxe4SyOGP8kfVKb0eO8hlCU7XqJALhhJkTF8/S0Nfzty5V8dusxDEhtwqDA54MPxsPid/l39C08sftIbjmhF7ed3KdB+z0t91k+X7yVJ79dzapteVyauIJ7fU8RWZaHOfUB6H0yRdvWsuDneWxas4Sk4jR6hWXSxWwj1FeyZ0ahEZDQ1S/A7b7nc0IX17jF366N7q116fNh5K/hlPt4Yvom/jVlFb8/pQ+3nNS7wdZ5v22YCSs+p2zTbOzWnwm3br1LWyUT3nUEdBwOnQ6H1MEH3mAz0O7NMHcizJsEhVnQri+MuJ4vQ4/n1vdX0ykxmknXjGicKiBNZOHm3dz5/mKKMlbyQJuvOSr/f5iQUHejccxvq3/724GyFjbPgoVvwNIPoTgbG9eJjG7n8HTWCF5bG0F4aAjnD+7I5SO7cFin+IYvGd29ydWhLdwFV74PnQ+n3Gd5auoaHvvfKrq2ac1/Lh/StPlXbWRvgXXTvQB2OuRlALAtLJWpxf2YUT6AldGDGTmwD6MHdmBkj6QG79NYJbMtnLW2zBhzM/AVrmuuF6sLZJsdr2R2U2kCxuTTPrbhG4ABmA6HARCSsajKaay13PHeYnKLy3jz0sFErPnSBbLDr2u8QBYgvhMR474i9+M7GLtoIgufX0va1W/QsWsL7cjfWtcNV3YaXPsVRMVTXFbOy99v4JhebZv+QhASAuc+DYW7+M3ap0jqlcxfvoV5m3bx+KVDaBtTv8dwWbmPTxe5IHZtZj7920XwzYAv6Ln2VddA5cJPIbk/AFFJPTiy3ymM8Fm+XprB72asY+GmLHpH5XJtfx9ndCwirmAzZK1zj0Y3fg8leXsWZkIhoTMkegFu63auNNYCl7wK/c/h45/T+deUVVwwpCM3n9irXte1zrodA92OcReFshLmzJrO9G+/oFfuCo5bt4DE5Z+46Uyo22adDnd/HYdDm16173LKWtgwA2b/15UIAhxyhmvQ1X0UL8xcz4OfL2dol0ReGDu8/ksCm5nBnRP4+OajeXFmKtf9ryNdQ87midSp9Jn/Kmb+KzDoUjjmd66OaV3t2giL3nbVCLLWQXgrfP3OYW78aTyyoh3zZ+eQ0CqcW07oypgju9Guka4pgAvar/ncBbSvng9Xvkdol5HcelJvRnZP4ta3FnD+Uz9wz1n9GHNE1+ZT7aBwF6yf8UvpKzvXAFAUkciCsEF8XHYOM8oH4IvszOkjOnD1oSkM7ZIY9C8taW4O6pLZA9GsSmanPQrTHuLuAd/y1cpdzL3n5EZbdPajh/JjXjJD/vhppa/QfXvOJv703mLuObMf4waGwnPHuo7cr/0a9rehQD3ZOvM14v/3O4qIgsveIOmQOnZM3xwteB0+uhFOvAdG/RGAd+Zu5vbJi3jl2hGM6tOuiRPoKcl3jxS3LmTq8Ge54ftWJLQK5z+XD+XwbnUvOS8r9/HhwnSemrqG9TvyOSQ5lrsOh1GL78BsWwojb4CT76vxWJy3MYvnv1vPV8syCA8J4ZzBqVx/bA8OSYl1QVl+phfcegHuL5/Xudczpw6Bi16CpO7M27iLy57/icGdEnh13Iig6CA+u7CUBz5dxrvztnB4u3IePaKUHsXLYctcSJvn6jmCa5HfcZhfgDts3wZNJfkukJr9PGxf5roMGzrW3eAmdsXnszzw2XJe/H49pw9I4bFLBxMV3vy3UX3anFXA3R8u4btVmZyYWsY/UqeStOJNVzVr4EUw6g/Qbj9fCFOc6+ofL3zT9SAD0O1Yigf+ivcKh/Hsj9vYlFVA1zatGHdMdy4c1olWEU1YzpWd5urQ5m1zL/7oeiQAWfkl/P6dhUxdmclpA5L524WDiG/VBN3JlRbCpp/2BK9bfwbrwxfeirS4oXxb0p+3dvZgha8TXdvEcPrADowemNI4pdtVOBhKZhXM7qdmFcx+fCus/IKr277OjrxiPr3l2EZb9K5JV5C3dhaLL57BGQGNzjbtLGD0499xWKcEXr9mCCEvne7uVidMb/JXm65YPIfoyVeSYnZhLppIxMCzmzQ99WrHGnhuFHQcCmM/gpBQrLWc+u/vCA0xv7y6ttkoyIKXzoDsLaw7622u/aqEzbsKuf20Qxg/qscBpbW03McH89P4z9Q1bMoqoH+HOG49sRenFnxKyJR7XIvzc5+GPqfu13w37sznxZnreWfuFgpLyxnVpx3XH9udY3q1rTqdRdkQGQfGsDmrgPOe+p6YqDA+uPFokoKstHHqyu3c+d5itucWMeG4ntx2Um+iQg3sWAVpc12jrS1zXZBqfe5HST294Ha468Vhwatum6QcCiMmuCc0Xr35otJyfvfOQj5fnME1R3fjnjP7H7QlV9ZaPv45nfs/WcbuwlJ+MzKeGyI+J3z+S1BaAP3PcTeq1b3621cO679zJbDLP3G/S+oBgy5nZ6/zeXlpOa/+tJHdBaUM7pzAhFE9OHVASvPZ5jlbXR3anK1w5WTXrRyuzvvEmet59MsVJMdF8eTljVDn3FrXaGvdVBe8bpoF5cUQEkZR8lAWRw7hvV09eW9bCqWEcUhyLKcPTOH0gSn0TYltFnmuglnZR7MKZl+7CPIzGV30AKnxUUy8uvrGWPWp7Lt/E/btvfx98Jf88bwjfxle7rP86rkfWZmRy5e/HUXHH++FWc/+8pi1OZg6fxmJH17JYSHrYfSjhIwc39RJqruyMpRe2gAAIABJREFUEph4CuzeCDd8D/EdAZi6YjvXvDyHf10yiAuGdmriRFYiJx0mngalBeRd+Sl//LaAL5ZkcEr/ZP5x8SDio2tX8lJS5mPyvC08PW0NW3YVcmjHeG49qTcndwnBfHwLrPoCep3senaoQ2vx3QUlvD5rEy//sIHM3GL6psRy/bE9OHtQapXdWeUUlXLRMz+QkV3EBzcdTc92wdlZfk5RKQ9+upy3526mV/sY/n7RYQwJDCSK89yFP22uC263zHElbCFh0O8cV5WgyxF79Raxu6CE61+Zy5wNu9yTnGNbaBWg/bS7oISHP1/B23M30zkpmkdP78hRmW/DrP9CSa6rmjHqD64UvELmKvj5DdcjQU4aRMbDwPNh0OWsiezHCzM38P6CNErLfZzSL5nxo3owrGtiswi49pGb4Upos9PgindcNRjPgk27uOXNBWRkF/HH0w7h+mN71P9rha11DeKmPeKOY8AmD2BXytHMKO3PS2mpLNxWBsChHeM5fWAKowem0KMZnt8KZmUfzSqYffooSOzG0DXXcPrAFB46v5o79fr2/+zdd3yV5f3/8deVvUMWK2EESJAlKCEoyFIUHFVx4sTROqq1tVu7ft+2Vu2wrXXXgXvVXVdRNrJRZAcIKwQyGDkZZJ7r98d9gABhJznnPnk/H488ktz3GZ94DHnnyuf+XOunwssT+E3in/jDvXftO/zk9PU8/Nlq/n71QCZELoa3bnQufDn/iLsEt7rnpi2n69R7ODd0MQz/IZzz/9y9zeT/fu30JF/9KvS5aN/ha56Zx4bSlt269qSVroPnx0F4DPaWz3hhWS1/+mQVndpF8eR1g484IqqmvoG3FhXw5LR1FJZVM7BLO350Thaje6dh1k+F9+90etrO/b2zGthMr3FNfQMfflPIs7M2sKaonA4JkUwa1p3rcrsd8KfP+gYvN09eyNz1O3jp1lyG9XT/Dmgz8kr45TvfUuSp5nsje3Dv2OzDtwNY61wQExYFcYe2uGzZWcVNLyxgy849PHL1QC46tQ1vcHIY8/J3cP97y8gvqeTSQZ35zdjOpCyfDPOecFpZep4DPcfAivec1g8TCr3OgYHXYHufz/wtVfx7Zj5fri4mMiyEKwZncOtZmQEZug5RXgQvXexcHHbtm5A5ct+psj113Pfut3yybDuje6fxtysHktIcPffWwvqp2OkPYgoW0hCfQWH/2/mgNod382rJL6nEGBjcNWnfCmxGUmBfoKgwK4cIqDD7UDca+l9Bz9ln8+Nzs7mnNa+MrtwBf+nBg/XXce/v/kVUeCgrCsu49PE5nNu3A4+PT8I8MwpSs+DmzyAssP6saq3lvv98Q7+lf+SGsC+cfrRLnzj8jjWBzDfVgpxb4KK/7zu8rKCM7zw2m/svOIXbAn32S+HXzvD0xAy4+RMWlxjufm0JOypr+d13+nJtbtcDVo+q6xp4Y8FmnpqRz3ZPNYO7JfHDc7IYkZWKaaiFL38Pcx9zro6//Dno2L9FyrbWMnNtKc/OymfW2lJiIkK5KqcLtwzPpEtyNL/5YDmvzNvMw5cP4OohAbbhyknwVNfx4CereH3BFnqmxfKXKwce9597l28t4+bJC6mpa+DfN+YwtK3NgT4ONfUNPDFtPU9MX0dsZBj3X9CHK/snYBY9D189BlWl0L6fswnNgKuoj0nj0+Xb+fesfL4tKCM5NoIbz+zGDWd0a57A15oqSpwV2l0b4do3oMfofaestbwyfzN/+O9KkmLCeeiyU+ncLprK2nqqahqc97X1VNU2NPq8gcqag97X1lNVXU+f6sXcVPsGg1jDVpvC4/WX8nbDKOoIIzTEMDQzmfP7d2Rcv460b41NipqJwqwcImDCbG0l/Kkzu4fdz6Cp/f3yw3LPn/vwv/LudLz5FQZ2acclj81hV1Utn9+dS9LrFzq/Td8xq2VGyzSD2novk56bz+kFL/Kz0Neh+wi4+pXA213oSCpK4MlhzsU235sGEftXCH7w+tdMW13MV/ed3ao7fp2wDTPhlcuh0yC48X121oXzoze/YWZeCRNOS+eBCf0xGF6dv4mnZ+ZTUl5Dbvdkfjg2i2E9U5ywW7IG3rkVti9ztjg+748tN8/4ICsLPTw7O5+PlhbS4LWc1jWJxZt2cfuoHtx3fp9WqaG1zfSt0m73VPPdET348blHWKVtZNqaYu56dQlJMRFMvnlIcO/y1IzWFZdz/7vLWbBxJ2f0SOaBCQPomRjijH9KyqSytoE3F27hudkb2Lp7D5mpsdx6ViZXDM5w98V0laXw4sWwcz1MfM1ZeW5kRWEZP3jta/JLK4/6UFHhIcRGhBETGeq8Dw8hl2VcUf4KvaqXUxbenrnpN7Gu86VERUcTHRFKYnQ4w3qmuq7XfS+FWTlEwITZ0nXw2GDyRzzC2VM6MvnmIa06fB6g9pWJbMn7hs/P/i+7Kmv596wNvHDzEMasfRAWPQ/XvAm9x7dqTcdrd1Utlz3xFWdUTuUB8wQmpRdc97YzYinQWQuvXeXMMrxtGnTot+/Ulp1VjP7rdG49K5P7L3BRkFr5Ibw9yfnT6TWv4zVhPDZtHX//Io/MlFg81XWUVtQyrGcK95yTtX9XN2th8Qvw2f1OoL/kceh9vl++hCJPNZO/2sir8zYxIiuNf11zWvP38wWQ8uo6Hvx0Na/N30yP1Fj+cuWpDO52+IkUby7czP3vLad3h3heuHlIk9NQ5PC8Xsubi7bw4CerqK7zcteYXlw+OJ3X5m/mlXmb8FTXk9Mtie+N7MHYPh0C56Kuk1W5w2k5KF3rBNqsA6f3VNbU88WqIsJCQvYH1YhQYiPDiI0IJSYyjOjw0P3/Pax1JhJMfwg2z3U2RBnxY2ebdTf+he4IFGblEAETZvNnwEsXM3fEi1wzJZzPfjSCUzoefWvZZjX9YbzTH+SCmNdYs8ty3dCu/LGnb2Vs2D1w3h9at54TtKG0kksfn8PY6DX8tf5hTGScE2iPdLVwIJj3JHz2Szj/L3DQRWz/99EKXp67yW9b156UxZPhox/CgCthwjMQEsKcdaX85K2lZHWI455zsg4c31W5Az78Aaz5GHqMgQlPQbz/tz1t8FpCDIF5cU0LmL22lF+88y2FZXu4dXgmPzmvN9ER+1cDrbX844u1/PPLtYzISuXJ6we7b0enAFJcXs3vP1rJf7/dBjjX1I3v15HvjnAu6gpKVTudQFuyxrk+4DinkuyzYaazQ+LmryC+sxNiT78x6ELsXm0hzOpfErfybZiwpb4dUElHf6xudBpICJbYXavpnnI6v8oNhRfugS5nwDm/bf16TlBmaixPXT+YG5+vJyLjr/xpz/9hnj8frn4Jep7t7/Katu1bZ4vU7PMh93sHnCqrquPNhVv8unXtSRl8E1TtcPpeY1Jg/EMM75XK3PvOPjQY5k+H9+5w/gx53gNwxvcD5kK+oFkRO0ZnZaXy+b0jeejTVTw7ewNfri7mz1ecypDuydQ1eLn/3WW8vbiAKwZn8OBlA1p816Ng1z4+iseuPZ0rBhfz9ebdTDgtne6pzbQzW6CKSYYbP4SXL4U3r3Om5BzPX/82zHJWYjfNhvhOzkLA6Tf6bfa5NB+FWbfybAVgY10iEWF7jnmEUbPy7QQ2KGwTF102kej3L3f+Ubji+UO39QxwZ/ZM4YEJA/j5f76l3emP8/Mdv8a8eiVc/JhzUUUgqa10Vr+jk50/px8U8F6Zv4mq2ga+5+YRR2f92Flxnfc4xKTCqJ8dGGTra2HqH5wJDqlZzpXOnQb6r14BIC4yjD9eOoAL+nfi5+98y1VPz+WmYd3JL6lkRl4J95yTxb1js9rManVrGN27fau3mPlVTLIzR/vlCfDm9XDVi3DKhUe+z8bZTojdOAviOsL5f4bTJynEBhGFWbfyFEJ0MgXl0DEhyj8/HOI7YWPT+EWPGiKWPQDFq5wdW3wzTt3mqpwu5JdU8uSM9aSPf4rrY34N79/hjBYa+dNDQqPffHaf0zd24/sQe+AV4DX1DUz+aiMjslLp27mV206akzHOxVtVO2DaH50fYENudc6VrnXC/LalMPhmGPenAy58E/8b1iuVz380koc/W80LczYSGmJ46LIBTMwNzItBxWWik+CG950pLm/dCFdOhj5NbICzcQ5Mf9AXYjvA+Iedv/woxAYdhVm38hRCQjrbPdX+aTEAMAbTaSARqz9wdpgZ+bNDmvLd5ufjerOhtILffr6F9OsfZ0z8H50wVbYFLnwEQv38LbPifVjyIgz/0QEjavb64OtCSspreOSqIFilDAmBSx5z5sR+/BMn0FZ7nD7hsMhDZupKYImNDOP3l/Tn0tPSsdYe8aIwkeMW3Q5ueM+ZgPL2Tc5fBPte4pzb9JUTYjfM9IXYh3wh1oVtV3JMFGbdyrMVEjpTVFjNqRl+HCXV8VRY94Uz1mr0ff6ro5mEhBj+fvUgrnp6Lne/uYJ37vwzpyRmwKy/Qvk2uOIFiPTTsPHdW+Cje6Dz6XD2rw857fVanpmVT59OCZzVy/3D+QGnXeXKyc4KzNs3OccyR8KEpyFBA/bdoMW3G5W2KyoRrn8XXr0C3r7ZuVZj/VRnSkFsexj3IOTcrBDbBqgD3608hdiEzhR5qukQ78crMPtc5ISLy5+FEBfPMWwkJiKMZ28cQlxUGLe+uJji3J/BRf9wQvvkC6GiuPWL8jbAu7c57694rsme5Bl5JawrruC2kZnB1ZMYEQPXvAGnXATn/gFu+EBBVkQcUQlw/TvQJRe++B0Ur3QuBv3hUjjz+wqybYTCrBvVVUNVKTXRHamu89Ix0Y/9P+mDYdJHATEKqTl1TIziuUlD2FlZy20vLaZ64I0w8XUozYNnxzp9m61p5l+dMTIX/g2Sm76w6+mZ6+mUGBWcW4JGt4OJr8LwewJmWoGIBIjIeCfQXvUS/PBbGHa3+ujbGP1UcKNyZ67grjBnr3MNHW8Z/dMT+cfEQSwt2M1P316KN2sc3PRfZ5rAc+fC5nmtU8jmeTDjIRhwFQyc2ORNvi3Yzbz8ndwyPFMjj0Sk7YmIdXpmFWLbJP3UcyPfjNniEOdKdr+uzAa5cf068svxp/Dfb7fxjy/XOivR353ijMV68WJY+UHzPqG1zha1m+bCkpdhyu+cXrB2XZ1V2cN4ZmY+8ZFhTMx1wc5lIiIizUgXgLmRL8wWepOACjrEK8y2pNtG9mB9SQWPfrmWHqmxXHpaD7h1Crw+Ed6aBOMfhDPuPL4Hralw9hnfsQ52+N6XrnU+rinbf7uQcEjrDRf/y+kNa8KWnVV8smwb3x3Rg/god833FREROVkKs25U7oTZTTWJQAXtE4JzC75AYYzhj5cOYPPOKn7+n2/JSIomp3sKTPoQ3vmuMyqqrMC5OKlxP2dDHeza5Ausjd/W73sN90nsAik94dQrIaUXpGQ5nyd2Oeo4sOdmbyDEGG4e3r35v3gREZEApzDrRp5CiExkS1UoSTHhRIUHxxSBQBYRFsJT1w9mwhNfcfvLi3n/ruF0SY5xLjj47D6Y+xjszHcuztobWndtBG/9/geJTnaCao/RTlBN6eW8Jfc44T6v3VW1vLVoCxcP7EynRF21KyIibY/CrBvtnTHrqdbFX62oXUwEz03KYcITX3HL5IW88/1hJESFw/kPQ7suMOW3EBrhBNQO/ZyLEfYG1pReztD/Zvbq/M3O1rUjXbx1rYiIyElQmHUjT6ETZnfXKMy2sh5pcTx5/enc+NwC7np1CS/cNISw0BAY9gPIuQXColttdFRNfQMvzHG2ru3TycVb14qIiJwETTNwI1+Y9etWtm3YsJ6pPDChP7PWlvL7/67cfyIitlVnoH7wdSGlFTXcPrJnqz2niIhIoNHKrNs01EH5dhriO1NaUUMHjeXyi6uHdCW/pJKnZ+bTIzWWm4Znturz7926tm+nBIb3SmnV5xYREQkkWpl1m4oiwFIe0R5roYMmGfjNL8afwnl9O/D7/65k2prW3eJ2el6xb+vaHsG1da2IiMhxUph1G9+M2R3GuZhIbQb+ExJi+MfEQfTplMAPXvuaNdvLW+25n56RT6fEKC48tVOrPaeIiEggUph1G89WALbhhFldAOZfMRFhPDdpCLGRodwyeSGLN+1s8edcumU38zdo61oRERFQmHUf38rs5rokQFvZBoKOiVE8e+MQqusauPzJuVz19Fxm5JVgrW2R53tmlrauFRER2Uth1m08hRAew5Y9EYSHGpJjIvxdkQADMhKZ9Ysx/OaivmzZWcWk5xdw0b9m8/G322jwNl+o3bKzik+XbePaoV21da2IiAgKs+6zb8OEGtrHRxESoot/AkVMRBi3npXJjJ+N4c+Xn8qe2gbuem0J5z4ygzcXbqa23nvSz7F/69rWnZ4gIiISqBRm3abRjFlNMghMEWEhXDWkC1N+PIrHrz2d6IhQfvHOMkb+eRrPzsqnqrb+6A/ShN1Vtby5cAsXD+qs9hIRERGfoAizxpgrjTErjDFeY0zOQefuM8asM8asMcaMa3R8sDFmme/co8Yt8408hZCQ7myYoEAT0EJDDBee2on//uAsXrwll24pMfzx41UMf2gq//xiLburao/r8V6dv5k9dQ3cpq1rRURE9gmKMAssBy4DZjY+aIzpC0wE+gHjgSeMMaG+008CtwFZvrfxrVbtifI2QPk2SOhMsa/NQAKfMYZR2Wm8efuZvHPnMAZ3S+LvX+Qx/KGpPPDxSoo81Ud9jOo6Z+vakdlpnNJRW9eKiIjsFRQ7gFlrVwFNDY+/BHjDWlsDbDDGrANyjTEbgQRr7Vzf/V4CLgU+bbWiT0RlCXjrqY7pSEVNvVZmXWhwtySenTSE1ds9PDl9Pc/N3sCLX23i8sHp3D6yJ91TY5u83wffbPVtXatVWRERkcaCZWX2cNKBLY0+L/AdS/d9fPDxJhljbjPGLDLGLCopKWmRQo+Jb8bsztBUQBsmuNkpHRP458TTmP7TMVyZk8E7S7Zy9t+mc/drS1hRWHbAbb1eyzMzna1rh/XU1rUiIiKNuSbMGmO+MMYsb+LtkiPdrYlj9gjHm2StfcZam2OtzUlLSzve0puPb8ZsMU6g0YYJ7tc1JYYHJgxg9s/H8L2RPZi+poQLH53NTS8sYMEGZwOGaWuKWV9Sqa1rRUREmuCaNgNr7dgTuFsB0HiyfAZQ6Due0cTxwOYLswUNSUCFphkEkfYJUdx3fh++P6oXL8/byPNzNnLV03PJ6ZZEZW0DnbV1rYiISJNcszJ7gj4EJhpjIo0xmTgXei2w1m4Dyo0xZ/imGNwIfODPQo+JZyuERrC5OhrQ7l/BKDEmnLvPzmLOL87md9/pS+HuPaza5uGWs7R1rYiISFNcszJ7JMaYCcC/gDTgY2PMN9bacdbaFcaYt4CVQD1wl7W2wXe3O4HJQDTOhV+BffEX7JsxW+SpIT4qjJiIoHj5pAnREaHcPDyT64Z2Y8nmXQzpnuzvkkRERAJSUKQha+17wHuHOfcA8EATxxcB/Vu4tOblmzFb5KlRv2wbEREWwhk9dNGXiIjI4ejvlm7i28p2u6dakwxEREREUJh1D2sbtRlUa2VWREREBIVZ96jaAQ21eOM7U1xeQ8dETTIQERERUZh1C9+GCZ6I9jR4rVZmRURERFCYdQ/fjNlS4+z+pTArIiIiojDrHr6V2a3eJEBb2YqIiIiAwqx7eAohJIwttbGANkwQERERAYVZ9/AUQnwnisrrCDGQEhvh74pERERE/E5h1i32zpgtqyYtPpIwbW0qIiIiojDrGr4Zs9owQURERGQ/hVk32LdhQro2TBARERFpRGHWDap3Q12Vb/evGoVZERERER+FWTfwzZitje1I2Z46TTIQERER8VGYdQNfmN2hDRNEREREDqAw6wa+DRO2kwxAh4RIf1YjIiIiEjAUZt3AUwgmhC118YB2/xIRERHZS2HWDTyFENeB7RUNAHRQz6yIiIgIoDDrDntnzJbVEBMRSnxkmL8rEhEREQkICrNu4AuzReXOjFljjL8rEhEREQkICrNusHfDhLJqXfwlIiIi0ojCbKCrKYeaMm1lKyIiItIEhdlA59kGgI3vTLGnRhd/iYiIiDSiMBvofDNmyyPbU9vgpUO8wqyIiIjIXgqzgc63+1eRTQHQVrYiIiIijSjMBjpfmN3akAhoK1sRERGRxhRmA51nK8Smsa3SAlqZFREREWlMYTbQ7Z0x66kGIC1Oo7lERERE9lKYDXR7Z8x6qkmNiyAiTC+ZiIiIyF5KRoHOs9W3lW21+mVFREREDqIwG8jq9sCenRDfiSJPjcKsiIiIyEEUZgOZb5LB3jYDhVkRERGRAwVFmDXG/MUYs9oY860x5j1jTLtG5+4zxqwzxqwxxoxrdHywMWaZ79yjxhjjn+qPwBdma2M7sqOyVlvZioiIiBwkKMIsMAXob609FcgD7gMwxvQFJgL9gPHAE8aYUN99ngRuA7J8b+Nbu+ij8oXZHSGpAHRM1CQDERERkcaCIsxaa/9nra33fToPyPB9fAnwhrW2xlq7AVgH5BpjOgEJ1tq51loLvARc2uqFH41vK9vtNgmA9lqZFRERETlAUITZg9wCfOr7OB3Y0uhcge9Yuu/jg483yRhzmzFmkTFmUUlJSTOXewSeQohqR2GVs5isNgMRERGRA4X5u4BjZYz5AujYxKlfWWs/8N3mV0A98OreuzVxe3uE402y1j4DPAOQk5Nz2Ns1O9+M2e2+DRMUZkVEREQO5Jowa60de6TzxphJwEXAOb7WAXBWXLs0ulkGUOg7ntHE8cDimzFb5KkmIiyEdjHh/q5IREREJKAERZuBMWY88AvgYmttVaNTHwITjTGRxphMnAu9FlhrtwHlxpgzfFMMbgQ+aPXCj6bRVrYdEiIJxIELIiIiIv7kmpXZo3gMiASm+ALfPGvtHdbaFcaYt4CVOO0Hd1lrG3z3uROYDETj9Nh+esij+lN9LVQWO20G26rVYiAiIiLShKAIs9baXkc49wDwQBPHFwH9W7Kuk1K+zXnvW5ntn57o33pEREREAlBQtBkEJd+MWZvQme0ercyKiIiINEVhNlD5ZsxWRHagus6rrWxFREREmqAwG6h8K7NFJAPQIVFhVkRERORgCrOBylMIEfEU7nHGcanNQERERORQCrOBqtGMWVCYFREREWmKwmygajRjFqB9QqSfCxIREREJPAqzgarRVrbtYsKJCg/1d0UiIiIiAUdhNhA11EPFdkjozPayGrUYiIiIiByGwmwgqigC64WEzhSXV9NeYVZERESkSQqzgcg3louEdLaXVdNR/bIiIiIiTVKYDUS+DRPq4zpSWqE2AxEREZHDUZgNRL6V2dLQVLxWGyaIiIiIHI7CbCDybIWwaLbXRAPQIV5hVkRERKQpCrOBqHybM8nAUwNAR63MioiIiDRJYTYQHbRhQgf1zIqIiIg0SWE2EHm27tswISzEkBIb4e+KRERERAKSwmyg8XrBs23fymz7+EhCQoy/qxIREREJSAqzgaaqFLx1+8KsJhmIiIiIHJ7CbKDxzZjdv2GCwqyIiIjI4SjMBpp9u391pthTo4u/RERERI5AYTbQ+MJsZVQHymvqFWZFREREjkBhNtBUFENoBNvrYwHomBjp54JEREREApfCbKA5+1fw8w0UeWoBzZgVERERORKF2UAUGUdRuTZMEBERETkahdkAtb3Mt5WtwqyIiIjIYSnMBqgiTzXxkWHERob5uxQRERGRgKUwG6C2l2nDBBEREZGjUZgNUEXl1XRI0CQDERERkSNRmA1QRWXVuvhLRERE5CgUZgOQ12spLq/RxV8iIiIiRxEUYdYY8wdjzLfGmG+MMf8zxnRudO4+Y8w6Y8waY8y4RscHG2OW+c49aowx/qn+UKWVNdR7LR3VMysiIiJyREERZoG/WGtPtdYOAv4L/BbAGNMXmAj0A8YDTxhjQn33eRK4DcjyvY1v9aoPo9jjjOVqH68wKyIiInIkQRFmrbWeRp/GAtb38SXAG9baGmvtBmAdkGuM6QQkWGvnWmst8BJwaasWfQTby5wNE7QyKyIiInJkQTPE1BjzAHAjUAaM8R1OB+Y1ulmB71id7+ODjx/usW/DWcWla9euzVf0YWz3+MKsemZFREREjsg1K7PGmC+MMcubeLsEwFr7K2ttF+BV4O69d2vioewRjjfJWvuMtTbHWpuTlpZ2sl/KURV7qgkxkBoX0eLPJSIiIuJmrlmZtdaOPcabvgZ8DPwOZ8W1S6NzGUCh73hGE8cDwnZPNalxkYSFuuZ3DRERERG/CIq0ZIzJavTpxcBq38cfAhONMZHGmEycC70WWGu3AeXGmDN8UwxuBD5o1aKPYLunRv2yIiIiIsfANSuzR/GQMaY34AU2AXcAWGtXGGPeAlYC9cBd1toG333uBCYD0cCnvreAUFRWTdeUGH+XISIiIhLwgiLMWmsvP8K5B4AHmji+COjfknWdqKLyaoZkJvm7DBEREZGAFxRtBsGkuq6B3VV1mmQgIiIicgwUZgNMkW8sVweFWREREZGjUpgNMNowQUREROTYKcwGmKJyZytbrcyKiIiIHJ3CbIApKlObgYiIiMixUpgNMFERofTtlEBCVFAMmhARERFpUUpMAeaGM7pxwxnd/F2GiIiIiCtoZVZEREREXEthVkRERERcS2FWRERERFxLYVZEREREXEthVkRERERcS2FWRERERFxLYVZEREREXEthVkRERERcS2FWRERERFzLWGv9XYOrGGNKgE0t/DSpQGkLP4e0DL127qTXzZ30urmTXrfW1c1am+bvIlqSwmwAMsYsstbm+LsOOX567dxJr5s76XVzJ71u0tzUZiAiIiIirqUwKyIiIiKupTAbmJ7xdwFywvTauZNeN3fS6+ZOet2kWalnVkRERERcSyuzIiIiIuJaCrMiIiIi4lpZL9NQAAAgAElEQVQKswHGGDPeGLPGGLPOGPNLf9cjx8YYs9EYs8wY840xZpG/65HDM8Y8b4wpNsYsb3Qs2RgzxRiz1vc+yZ81yqEO87r9P2PMVt/33TfGmAv8WaMcyhjTxRgzzRizyhizwhjzQ99xfc9Js1GYDSDGmFDgceB8oC9wjTGmr3+rkuMwxlo7SPMTA95kYPxBx34JfGmtzQK+9H0ugWUyh75uAH/3fd8NstZ+0so1ydHVAz+x1vYBzgDu8v1c0/ecNBuF2cCSC6yz1uZba2uBN4BL/FyTSFCx1s4Edh50+BLgRd/HLwKXtmpRclSHed0kwFlrt1lrl/g+LgdWAenoe06akcJsYEkHtjT6vMB3TAKfBf5njFlsjLnN38XIcetgrd0Gzg9foL2f65Fjd7cx5ltfG4L+VB3AjDHdgdOA+eh7TpqRwmxgMU0c0+w0dxhurT0dp0XkLmPMSH8XJNIGPAn0BAYB24C/+bccORxjTBzwDvAja63H3/VIcFGYDSwFQJdGn2cAhX6qRY6DtbbQ974YeA+nZUTco8gY0wnA977Yz/XIMbDWFllrG6y1XuDf6PsuIBljwnGC7KvW2nd9h/U9J81GYTawLASyjDGZxpgIYCLwoZ9rkqMwxsQaY+L3fgycByw/8r0kwHwITPJ9PAn4wI+1yDHaG4Z8JqDvu4BjjDHAc8Aqa+0jjU7pe06ajXYACzC+0TL/AEKB5621D/i5JDkKY0wPnNVYgDDgNb1ugcsY8zowGkgFioDfAe8DbwFdgc3AldZaXWwUQA7zuo3GaTGwwEbg9r19mBIYjDFnAbOAZYDXd/h+nL5Zfc9Js1CYFRERERHXUpuBiIiIiLiWwqyIiIiIuJbCrIiIiIi4lsKsiIiIiLiWwqyIiIiIuJbCrIiIiIi4lsKsiIiIiLiWwqyIiIiIuJbCrIiIiIi4lsKsiIiIiLiWwqyIiIiIuJbCrIiIiIi4lsKsiIiIiLiWwqyIiIiIuJbCrIiIiIi4lsKsiIiIiLiWwqyIiIiIuJbCrIiIiIi4lsKsiIiIiLiWwqyIiIiIuJbCrIiIiIi4lsKsiIiIiLiWwqyIiIiIuJbCrIiIiIi4lsKsiIiIiLiWwqyIiIiIuJbCrIiIiIi4lsKsiIiIiLiWwqyIiIiIuJbCrIiIiIi4lsKsiIiIiLiWwqyIiIiIuJbCrIiIiIi4Vpi/C3Cb1NRU2717d3+XISIiInJUixcvLrXWpvm7jpakMHucunfvzqJFi/xdhoiIiMhRGWM2+buGlqY2AxERERFxLYVZEREREXEthVkRERERca2gDrPGmPHGmDXGmHXGmF8e5jajjTHfGGNWGGNmtHaNIiIiInLigvYCMGNMKPA4cC5QACw0xnxorV3Z6DbtgCeA8dbazcaY9v6pVkRERERORDCvzOYC66y1+dbaWuAN4JKDbnMt8K61djOAtba4lWsUERERkZMQzGE2HdjS6PMC37HGsoEkY8x0Y8xiY8yNTT2QMeY2Y8wiY8yikpKSFipXRERERI5XMIdZ08Qxe9DnYcBg4EJgHPAbY0z2IXey9hlrbY61NictreXnDtc3eFv8OURERESCQTCH2QKgS6PPM4DCJm7zmbW20lpbCswEBrZSfU36z+ICJjzxFTsqavxZhoiIiIgrBHOYXQhkGWMyjTERwETgw4Nu8wEwwhgTZoyJAYYCq1q5zgOkxEaQV1TOVU/PZVvZHn+WIiIiIhLwgjbMWmvrgbuBz3EC6lvW2hXGmDuMMXf4brMK+Az4FlgAPGutXe6vmgHGnNKel27JpchTwxVPzmVjaaU/yxEREREJaMbag9tI5UhycnLsokWLWvx5lhWUMemFBYQYw8u35tKnU0KLP6eIiIgEF2PMYmttjr/raElBuzLrdgMyEnnr9jMICzFc/fRcFm/a5e+SRERERAKOwmwA69U+nrfvOJOk2Aiuf3Y+s9eW+rskERERkYCiMBvguiTH8PYdZ9ItJYZbJi/ks+Xb/V2SiIiISMBQmHWB9vFRvHHbGfRLT+D7ry7mP4sL/F2SiIiISEBQmA00ddXgPXTThHYxEbxy61DO7JnCT99eygtzNvihOBEREZHAojAbaOY/BQ93h5cnwLQHYd0XsGc3ALGRYTw3aQjn9e3A/320kke/XIumUYiIiEhbFubvAuQg6adD/8tgywKY8TDODrwG0k6BLkOIysjlifNy+EVkJx6ZkkfZnjp+fWEfjGlq914RERGR4KY5s8eptebMAlDtga2LoWChE24LFkK1s0pro9qxLqIPH+5IJz5rGLdefQWh0YmtU5eIiIi4QluYM6swe5xaNcwezOuFHWt9wXYBdstCKFmNweLFQPs+hHQZCl1yISMXUnqCVmxFRETaLIVZOYRfw2xT9uzm088/Zs2iLzg7bhMDbB6mptw5F50MGUOccNslF9IHQ0Ssf+sVERGRVtMWwqx6Zt0uuh3nX3od5ekjufTdbzm9SyIvXJRIfMmSfSu4rP3cuW1COnx/HkRpa1wREREJDgqzQeKqIV2Iiwrjh298zdXvWV68ZSJpgyc5J6t2wor34OMfQ/406HuJf4sVERERaSYazRVELhjQiWcnDSG/tIKrnp7L1t17nBMxyXD6JIhMhLz/+bdIERERkWakMBtkRmWn8cqtQymtqOHKJ79ifUmFcyI0DHqdDWv/1+SmDCIiIiJupDAbhHK6J/PGbWdQU+/lqqfmsnxrmXMiaxxUFsP2pf4tUERERKSZKMwGqX6dE3nrjjOJDAvhmn/PY9HGnZB1LmDUaiAiIiJBQ2E2iPVMi+PtO4eRFhfJ9c/NZ3qB1xnPtXe6gYiIiIjLKcwGufR20bx1x5n0SI3j9pcXsydzLGxdAhUl/i5NRERE5KQpzLYBqXGR/OHS/tTUe1kYngNYWDfF32WJiIiInDSF2TZiYEYiidHhfFSUBnEdIU+tBiIiIuJ+CrNtRFhoCGf1SmXmulJs1rmwfio01Pm7LBEREZGTojDbhozKTqPIU8PW9iOgxgOb5/m7JBEREZGTojDbhozMTgNgyp4+EBKuqQYiIiLiegqzbUjHxCh6d4jni/wq6D5c82ZFRETE9RRm25hRvdNYuGEXtT3GQuka2LXR3yWJiIiInDCF2TZmVHYatQ1eFkcMcQ5odVZERERcTGG2jcnpnkR0eCifbYuD5B6wVmFWRERE3Ethto2JDAvlzJ4pzMgrgaxxsHEW1Fb5uywRERGRE6Iw2waNyk5j444qijqOhPpq2DDT3yWJiIiInBCF2TZo74iuL6p6QXisRnSJiIiIaynMtkHdU2LomhzDtHVl0HOMcxGYtf4uS0REROS4Kcy2QcYYRman8tX6HdT3PBc8BVC80t9liYiIiBw3hdk2alR2e6pqG/g6au+ILrUaiIiIiPsozLZRZ/ZMITzU8GVBKHQ8VSO6RERExJUUZtuouMgwBndLckZ0ZY+DLfOhaqe/yxIRERE5LkEdZo0x440xa4wx64wxv2zi/GhjTJkx5hvf22/9Uae/jMpuz6ptHnZ2HgXWC+un+rskERERkeMStGHWGBMKPA6cD/QFrjHG9G3iprOstYN8b79v1SL9bJRvRNfUiq4Qk6K+WREREXGdoA2zQC6wzlqbb62tBd4ALvFzTQGlT6d40uIjmbF2J/QaC+u+AG+Dv8sSEREROWbBHGbTgS2NPi/wHTvYmcaYpcaYT40x/Zp6IGPMbcaYRcaYRSUlJS1Rq18YYxiZlcastSV4e50He3bC1sX+LktERETkmAVzmDVNHDt4Z4AlQDdr7UDgX8D7TT2QtfYZa22OtTYnLS2tmcv0r1G909hdVceKmCFgQtVqICIiIq4SzGG2AOjS6PMMoLDxDay1Hmtthe/jT4BwY0xq65XofyN6pWIMTN1YC12GamtbERERcZVgDrMLgSxjTKYxJgKYCHzY+AbGmI7GGOP7OBfnv8eOVq/Uj5JiIzg1ox0z8ooh+zzYvgw8hUe/o4iIiEgACNowa62tB+4GPgdWAW9Za1cYY+4wxtzhu9kVwHJjzFLgUWCitfbgVoSgNyorlW+27Ka8yznOAW2gICIiIi4RtGEWnNYBa222tbantfYB37GnrLVP+T5+zFrbz1o70Fp7hrX2K/9W7B+jeqfhtTBzdyokdoE8hVkRERFxh6AOs3JsBma0IyEqjBlrSyDrPMifDvU1/i5LRERE5KgUZoWw0BDOykplZl4pNutcqKuEjbP9XZaIiIjIUSnMCuDsBrbdU83amMEQFqW+2QBRWVPPh0sLqanXZhYiIiJNUZgVAEb6tradsaECuo9w5s22vWvhAobXa3nv6wLO/tt07nn9a95ZvNXfJYmIiAQkhVkBoFNiNNkd4piRVwLZ42DXBtixzt9ltUnfbNnN5U99xb1vLqVDQhRJMeHM39CmJsaJiIgcM4VZ2WdUdhoLNuxkT3ffiC7tBtaqij3V/OStpVz6+BwKdu3hr1cO5P3vD2dYr1Tm5++kDU6NExEROSqFWdlnVHZ7ahu8zNsZB2mnqG+2lVTXNfDE9HWM+et0PlpayB2jejLtp6O5YnAGISGGoZnJbPdUU7Brj79LFRERCThh/i5AAkdO9ySiwkOYkVfCmKzzYN6TUFMOkfH+Li0oWWv538oiHvh4FZt3VnFu3w786oI+dE+NPeB2QzNTAJi/YSddkmP8UaqIiEjA0sqs7BMVHsqZPVL2981662D9NH+XFZTWbC/n+ufmc/vLi4kMC+HlW3P59405hwRZgKz2cbSLCWd+vvpmRUREDqaVWTnAyOw0pn20ks0xZ9E1MhHWfg59L/Z3WUFjd1Utj0zJ45V5m4iPCuf/Lu7HdUO7EhZ6+N8rQ0IMQ7ons2DjzlasVERExB0UZuUAo/aO6MrfzQ29zoa1U8DrhRAt4p+M+gYvry3YzCNT8vDsqeP6M7px79hskmIjjun+QzOTmbKyiO1l1XRMjGrhakVERNxDCUUOkJkaS5fkaGasKYGscVBRBNuX+rssV5u9tpQLHp3Fbz9YQd9OCXzywxH8/pL+xxxkAXIzkwG0OisiInIQhVk5gDGGkVlpzF1fSm3m2YCBPE01OBGbdlTyvZcWcf1z89lT18DTNwzm1e8O5ZSOCcf9WH07JRAXGcYCzZsVERE5gMKsHGJUdhqVtQ0sLg2D9NOdvlk5ZhU19Tz06WrOfWQmc9aV8vPxvZly7yjG9euIMeaEHjMsNITB3ZJYsEErsyIiIo0pzMohhvVKJSzEMHOtr9Vg6xKoKPF3WQHP67X8Z3EBY/46nadmrOc7Azsz7aej+f7oXkSFh5704+dmJpNXVMHOytpmqFZERCQ4KMzKIeIiwxjcLcnpm80+D7Cwboq/ywponuo6LnvyK3769lLS20Xz/l3D+dtVA+mQ0HwXaw3d2zer1VkREZF9FGalSaN6p7Fym4fiuN4Q10Fb2x7Fp8u28c2W3TwwoT/v3jmMQV3aNftzDMhIJDIsRGFWRESkEYVZadLeEV2z1u6ErHNh/VRoqPNzVYFr6upiOiVGcW1uV0JCTqwv9mgiw0I5rWs7FmzURWAiIiJ7KcxKk/p0TCA1LtLZDSxrHNR4YMt8f5cVkGrqG5i9tpQxp7Q/4Qu8jtXQzBRWFnrwVOsXCxEREVCYlcMICTGMzE5l1toSGjJHQ0i4Wg0OY+GGXVTWNnB27/Yt/lxDM5PxWli8cVeLP5eIiIgbKMzKYY3KTmNXVR3LS73QbRis1bzZpkxdXUxEWAjDeqW0+HOd1jWJsBDDfPXNioiIAAqzcgRn9UrFGJxWg+xxULIadm3yd1kBZ9qaYs7skUJMRMvvDh0dEcqpGYnaPEFERMRHYVYOKyUuklPTE/f3zYJWZw+yobSSDaWVnH1Ky7cY7JWbmcK3BWXsqW1otecUEREJVAqzckQjs9P4evMuyqK7QlKm+mYPMnV1MUCrhtmhPZKp91q+3qy+WREREYVZOaJR2Wl4LczJ3+G0GmycBbVV/i4rYExdXUSv9nF0SY5ptecc3C2JEIP6ZkVERFCYlaMY1KUd8VFhzm5gWedBfTVsmOnvsgJCRU09CzbsbNVVWYCEqHD6dk5gvvpmRUREFGblyMJCQzirVyoz15Zguw2H8FhYq1YDgNlrS6hrsIzp3R52bYRHT4fXr4GFz8HuzS363LndU/h6825q6tU3KyIibZvCrBzVqOw0tpVVs3ZnHfQYDXn/A2v9XZbfTV1dTHxUGDndk2DpG7AzH7Yvh49/DP8YAI/lwue/gvXToL6mWZ87NzOZmnovywrKmvVxRURE3KblZwmJ6430bW07M6+E7OzzYM3HULwSOvTzc2X+4/Vapq0pYWR2GuGhIbDiPWcW700fQ2kerJ0C66bAgmdg7mPOinbmSMgaC73OhaRuJ/X8uZnJgNM3m9M9uTm+JBEREVdSmJWj6twumqz2cczIK+G7V57nHMz7vE2H2RWFHkrKa5xdv4pXOTN4L/grGANpvZ23YXdDTYVz0dzecJv3qfMAqdlOqM0aC92GQ1jkcT1/cmwE2R3iWLBhJ3eNaYEvUERExCUUZuWYjMpO46V5m9gTlUN0xwFOOBvxY3+X5TdTVxdjDIzunQYLXwITAn0uPvSGkXHQ+3znzVooXeuE2rVTYOG/Yd7jEB7jrNr2GgtZ50JS92OqITczmfeWbKW+wUtYqDqGRESkbVKYlWMyqncaz87ewLwNOxiTNQ5m/x327ILoJH+X5hdT1xQzMKMdKbERvhaD4RDf4ch3MgbSsp23M++C2krYMGt/uM37zLldSpYTanv5Vm3Do5p8uNzMFF6Zt5mV2zycmtGumb9CERERd9ByjhyTId2TiQoPcUZ0ZY8D2wDrvvR3WX5RWlHDtwW7nZFcxSudHtl+E47/gSJiofd4uPBv8MOlcPdiGP8QtOvqTER45TL4c6YzIaF8+yF3z/X1yi7QvFkREWnDFGblmESFh3JGjxRm5pVA+mCITm6zW9tOX1OCtb5dv1a8d/gWg+NhDKT2gjPuhBvehV9shGvfhtOud35pmPqHQ+7SMTGKbikx2jxBRETaNIVZOWYjs9LIL61ky+4a50/ga6eAt+3NOZ22upj28ZH06xTvhNnuIyAurXmfJCIGss+DC/4COTc7o792bTzkZkMzk1m4cSder0aliYhI26QwK8dsVG8nsM3I87Ua7NkJWxf7uarWVdfgZWZeCWN6t8cULYcd606sxeB4DP8RmFCY9bdDTuVmprC7qo61xRUtW4OIiEiACuowa4wZb4xZY4xZZ4z55RFuN8QY02CMuaI163ObHqmxZCRFO2G259nOn9fz2tZuYIs27qK8pp4x+1oMQqHPd1r2SRM6weBJ8M1rsGvTAaeG7ps3q61tRUSkbQraMGuMCQUeB84H+gLXGGP6HuZ2DwNtK5WdAGMMI7PTmLt+B7UR7aDL0Da3te20NcWEhxrO6pXihNnMkRCb2vJPPPxHzi8Psx854HBGUjSdEqPUNysiIm1W0IZZIBdYZ63Nt9bWAm8AlzRxux8A7wDFrVmcW43KTqOipp4lm3dB1nmwfRl4Cv1dVqv5clURQzNTiNu5AnZtaPkWg70S0+H0G+HrV2H3ln2HjTHkZiazYMNOrLYYFhGRNiiYw2w6sKXR5wW+Y/sYY9KBCcBTR3ogY8xtxphFxphFJSUlzV6omwzrmUJYiHGmGmSPcw62kakGm3dUsb6ksnVbDBo7617n/UGrs0MzUygpr2HjjqrWq0VEJNDoF/o2K5jDrGni2MH/p/8D+IW19oiX5Ftrn7HW5lhrc9LSmvmqdZeJjwrn9G5JTt9s+76QkAF5bSPMTl1dBMDZvdOcMNtjNMQkt14BiRlw+g2w5GUoK9h3ODdz77xZ9c2KSBu1YRY83A2KV/u7EvGDYA6zBUCXRp9nAAf/PTwHeMMYsxG4AnjCGHNp65TnXqOy01hR6KGkotYZH5U/Hfbs9ndZLW7qmhIyU2PJrM2D3Ztar8WgsbN8WwjP/vu+Qz3TYkmJjWB+fhD3zVoLcx/XDyoRadrKD6C6DL74f/6uRPwgmMPsQiDLGJNpjIkAJgIfNr6BtTbTWtvdWtsd+A/wfWvt+61fqruMynZWp2etLYFB10NDLbx2FdQE73ioqtp65uXv2L9RQkg49Lmo9Qtp1wVOuw6WvARlW4H9fbNBfRHYlgXw+f0w9zF/VyIigSh/OoRGQt6nsOkrf1cjrSxow6y1th64G2dKwSrgLWvtCmPMHcaYO/xbnbv17ZRAalyE02qQMRiueB4KFsEb10DdnmZ5jtKKGuau3xEwFzXNWbeD2nqvr8Xgfeg5BqKT/FPMWT8G64U5/9h3KDczma2791CwK0j7Zhc87bzfPNe/dYhI4CkrgB1rYdTPIb4zTPmt+mfbmKANswDW2k+stdnW2p7W2gd8x56y1h5ywZe19iZr7X9av0r3CQkxjMxKY9baUmfnqb4Xw6VPOj1Lb02C+toTetzqugY+WlrILZMXMvRPX3LNv+cxbU1gDJmYurqYuMgwciM2Qtlm/7QY7JXUDQZdC4tfBM82YH/f7MKNQbg669nm/AkxOsnZpKIiMP6fEJEAkT/ded/7fBhzPxQshFUfHvEuElyCOsxKyxmZncbOylqWF5Y5BwZeDRc94sydffd70FB/TI/j9Vrm5e/g5/9ZypA/fsEPXv+alYUevjsik/R20Tw5fX0LfhXHxlrL9DXFnNUrlfDV7zstBr0v8G9RI34C3vp9q7OndEwgISqMBcHYarD4BWfb5PEPO59rdVZEGsufDrFpzkXJg66FtD7wxf9BQ52/K5NWojArJ2REVirGwIw1jUaV5dwC4/4EK9+HD+8Gr/ew919XXM6fP1vNiD9PY+Iz8/j4222c168jr353KHN+eTb3nd+H743IZOHGXX5fbVy1rZxtZdX7Wwx6nQPR7fxaE0ndYeA1sHgylG8nNMQwpHsQ9s3W18Ci550xcP0mQFi0+uFEZD9rnTDbYzQYAyGhMPb/wc71zr+P0iYozMoJSYmLZEB6otM329iZd8GYX8HS1+GTnx7Qt1RaUcMLczZw8WOzGfvITJ6asZ5e7eP458RBLPz1WP521UCG90olNMSZqnb1kK4kx0b4fXV2b6vD2MTN4Cnwb4tBYyN/4qw8zPkn4LQa5JdUUlxe7efCmtGK96GyBHJvg7AIyMhRmHWZ8uo6yvZohUxaSPFK59+IHmP2H8seB92Gw4yHoabcf7VJqwnzdwHiXiOz0nhyxnrK9tSRGB3e6MTPoLYS5vyD+rBoPu34fd77ppAZeSU0eC39Oifw6wv7cPGgzrSPjzrs40dHhHLTsO48MiWPVds89OmU0Apf1aGmri5mQHoiyRs+htAIpy8rECT3gFOvdlYuh/9of9/shl1ceGonPxfXTBY8DSlZ+39QdRsGM/8C1R6I8s//D3J87n1zKaUVNbx/13B/lyLBaP00532PUfuPGQPn/h6ePQe+egzG3Oef2qTVaGVWTtio3mk0eC1frSs94LjXwtzMHzAneQJh8x5j3du/YdU2D98b0YP/3TuSj+8ZwXdH9DhikN1r0pndiY0I5akZ/lmd3VVZy9ebdzGmd6qvxWAsRCX6pZYmjfypMxrtq0fpn55ITERo8GyeULAIti52VmVDfP9UdT3TmeSwZYF/a5NjYq1lyeZdfLNlNxtLK/1djgSj/OnOL7yJGQcez8iBvpfAV/+C8iK/lCatR2FWTthpXdoRHxW2r9WgcR/sNc/O5/bSq1jY7nzuDX+HOSNW8MvzTyG7Q/xxPUdiTDjXDu3KR0sL2eyH7Vpn5JXgtfCd5C1QXhg4LQZ7pfSEAVfBwucI31PK4G5JwdM3O/9piIiHQdfsP5YxxNlGeLNaDdygyFPDzkpnuskny7f5uRoJOvW1sGmO0y/blHN+Bw01TruBBDWFWTlhYaEhDO+Zyv9WFvGdfzl9sE/PzCerw94+2HEMuedV6HcZIV/8BhY+e0LP890RPQgLCeGZWa2/Ojt1dTGpcRH0Kp7iDOTOHt/qNRzVyJ85/2B/9Si53ZNZU1TO7qoTG48WMMqLnM0pTrsOIhv9AhQZB50Gwqa2OdHAWsvstaV++cXuRKza5gEgLjKMT5YpzEozK1gIdVXO3O+mpPSEwTc5F4KVrmvNyqSVKczKSRnfvyM7K2uxWH5zUV/m3XcOk2/O5ZJB6URHhDpXll72DGSfDx//BL557bifo0NCFJedns5biwooKa9pga+iafUNXqavKWZ0Vipm5QeQdW5g9mmm9oL+V8DC5xjW0Yu1sHDjLn9XdXIWTwZvHQz53qHnug2DrYugLogudDsKr9fy2fLtXPDobK5/bj6/+3C5v0s6Jit9YfaW4d1ZvtXjmhAuLpE/DUwIdD/r8LcZ9QsIj4Yv/6/16pJWpzArJ+WSQZ1Z9Oux/PcHI7j1rEzS4iMPvVFoOFw52flT0Ad3Ob2nx+n2UT2pa/Dy/JwNJ1vyMVuyeTee6nouSyuAiu2B12LQ2MifQd0eBha8QkRoiLv7ZutrnYvaeo11gvrBup7p9AkXLmn92lqZ12v5dNk2Lnh0Fne8spg9tfX0T09g2VaPv0s7Jiu3eeiaHMOVOV0A+Firs9Kc8qdD+uAjX8cQ1x6G/cDZRGHLwlYrTVqXwqycFGMMqXFNBNiDhUfBxNcgIxfeuRXyPj+u58lMjeWC/p14Ze4mPNWtM+Zn6upiwkIMORXTISwqMFsM9krLhv6XE7boOUakG3dvnrDqQ+eXh6GH2XW665nO+yAe0eX1Wj7+dhvn/3MWd766hNp6L3+/eiBf/HgUE07LoLSihmJP4K9Mryr00KdTPF2SYxiYkahWA2k+1WXOBaI9Rh/9tmfeDbHttc1tEFOYldYTEQvXvQUdB8CbN0D+jOO6+52je1JeU88r8za1UIEHmra6mNxuiUTkfQRZ51M1rSIAACAASURBVDn9moFs1M+hrorbwj9heaGHippj24Ut4Mx/GpJ7Qs9zmj4fmwJppwTlTmANXstHSwsZ/8+Z3PXaEuq8Xv5x9SCm+EJsWGgI/To7rS4rCgN7dbaqtp4NOyrp28lZNbtgQCeWbS1Tq4E0j42znckmPQ7TL9tYZByM/qVz4WjeZy1fm7Q6hVlpXVGJcP27TmP+69fA5vnHfNf+6YmMyErl+dkbqa5raMEi/z975x3e1Hm28d+R5L33nkwvwAMIBAMhZABNCJBBZtP2y2rSdKVtutuv60vaJk3S7KYzafYOkIQEDAECttlgm+GFh7yHvId0vj9ey7Zs4SlZsnx+19XLzTlH57wIYT3nOfdz31DR1MGZ6hZuDq2A1mr7lhgYCZoHSZtIr34LL4OOI6XTUDdbeRTKs2HJXQN2XOaIXibsuQzW/RxMFXqDzAfHK7nqL3v51qtHMcjwxNZF7PzuKq5LjegPEgFI7C9mm2213DFRUNWCLENCmBjgW58ivI8VVwPL0tmjn5k3CEVZ4OQuHE7GQtodEDAbPvvVmOPWFaYPSjGrMPW4+8Pt74FXKLxyPVQeG/NLv7l6NnWtXbx5uNyKCxQSA4DMnn0iQnXuVVa9nsVY9UPUve3c7bRjekoNDr0ATh4iX30kYpZDlw6qp8cg1MXQG2TeP1bBlY/v4cFXjyIBT92cyiffWcnGRaZFrBFvVyei/d3tvjNrdDIwFt9R/u4sUKQGFue5PYVc+Zc9NLfPsJS1wt0i5UvjPLbj1U7Cqqu2AI69Yt21KUw5SjGrYBu8QuCrH4CrL/xnE9Tkj+lll8T7syjKlxf2FtKrN1htebsLaoj1c8GneLsoZJ09rHYtixKcgJS4kTs1n3C6sMTWqxkfrbVw6i1RyJoZ6NAbBmndprlutldv4N2j5Vzx+B6+/dox1CqJp29J45PvrOSaheFmi9jBJIV7230xm1epw9tVQ4SvW/+29SlhnChvpqxhBnYSrcSxsiY6ewx8mldl66VMHc3lUH9ubHrZwSRcI+Y2sv4A3cpn0JFQilkF2+ETCV99X0TE/nsj1I/uIytJEvetnkVZQ4fVJqM7e/QcKKzjzshKpLba6SExGMyqH+Iud5ChfdXqcgyLcuSfwqVgyd3DdjV39HDJHz7nP1+WiA2+UeATNe2K2V69gXeOlHPF43v57uvHcVareObWND7+9ko2LAhDNUoRayQp3JsLDe1TNgw5EfK1OuaHeSNJA3+mDUapgdKdtRjGDviOUzOomDXOW1zMX/ZiGGNuW7Rw8BnLr0vBZijFrIJt8Y+HO94HQy/861poujDqS65ICGF2sCfPZhUiW2Ey9cvCejp7DFwpHxCarDlXWvwaViUkierIq7hd9TEnz0/NsNyk0fdAzt/FMEfQ3GG7//NlCbUtXXzeJ/8AhNTgwpfTYjq5V2/grcPlrH1sD9974ziuTmqeuy2N7Q9msj5l7EWskaRw0bnOs9PurMEgU1DVQmKYqS9zlL87KRGK1MBSNLR1U63rwstVwxfnamnusN+bG4tSlAUeQRCcOP7XxiyDeeth/xPQNo0tDBVMUIpZBdsTPF9oaLtbRIe2ZeQOg0olce+qWRRUtZB1ptbiy9lVUIOnE4RVfirsuJzdLX4Na+O+9sd4Sx0Yvpwm3YeCj0Rc8NJ7hu1q7+7l7/tLADhS2ojBKDeIXgZttWPq6NuKHr2BN3LLuPyxPTz05nHcnTU8f3s62761gquTx1/EGrF3R4PShnbau/XDilkQUoPjitTAIhi7sndlxtOjl/k8v9rGK5oCZFkUs/GrRad1Ilz+S+huhb1/tODCFGyJUswq2AdhC+DWt0WM6b83jnrHvHFROOE+rjyTZdmIQlmW2VVQw9ciypHa66efxKAPr9hUDjhdQnLZf6GjydbLGZ1Dz4NfrNku+GvZZTS0dbN1cRS6zl7O17aKHTHLxc8L9ik1+PR0FZf/eQ8/fOsEXq4aXrwjg20PruCqpNAJF7FGgr1dCfR0sVtHg6HDX4MxSg12KK4Gk8b4Pt+8JJowH1e2n5wBUoOaPGirGb9edjDB8yH1NhGx3jB1QTwK1kMpZhXsh6jFcMvr0FgC/7luxCLMSa3irpXx5JQ0klNiuan9czWtVDR1sNHpkJiqn3OFxc491ZyYdQ8echv6g8/Zeikjoz0h5AKL7xLxx4Po7jXw4hdFLInz555VswDINUb1Bs4F9wAotT+/2V69ge++fgxnjYqXvprBhw+s4IrEEBP96ITp6QB9D0nh3nYrM8ir1KFWScwOHu7NHB3gTnKEN9tmQuFlZfK0OoK8XAjycuHq5FD2nqulxY511BahKEv8jF89ufOs/gmoNLDrt5NckII9oBSzCvZFXCbc9LJwN3jlhhEnTrcujsbfw5lnsyz3mHlXQQ0aeomv3Q3z1olM72lKdNIydurTkb98WqTl2CvZzwttcuptw3a9d7QCbXMn9182m9gAdwI8nMkt7bt5kSQhNSjdP8ULHp3TlTrauvV8+/I5XJ5goSIWoKsFnl0O799PUrg352pa7XLIL1+rY3aQJ65OarP716eEcbysifJGRWowGfK1LST0STk2pITR3WvotxV0WIqyIGCOGCCeDN5hsOybwkGl8qhFlqZgO5RiVsH+mHMFXP8SlOfAB9+66ICPm7OaO5fHsqugpv9x22TZVVDDTYElqDobpq3EwMjiWH+e6N2Eplsn/FvtkbZ6OPkWLLgJ3HxNdukNMs/uKSQp3JuVcwKRJIn0GD8ODw6DiF4GTaWgq5zihY+M0eN3SZy/ZU/8yU+goQjy3mdRsBq9QeZsdYtlr2EB8rS6/rAEcxilBh/PpAl8C9Pda+B8TUv/+5wW7UeIt4tjD9f1dkPJfrNd2dL6Nh568zjPZJ1n//m6sTl9XPptcPOHnb+cFoOkChdHKWYV7JPEjXD5z8Vd8/4nLnrYV5fF4uGs5rk9k+/ONrf3cLi0kZvcc8HZC2avnfQ5bUmQlwsdgSkcc7sEvvwrdNrhI+kj/4LeTrODXztOaSmua+P+y2b3dzbTY/worW+ntqVLHGTUzdqZRVd2SQPR/u6EeLta7qRnP4Ej/xafy95O0tpFR9rehsAa27rRNnea1csaiQnwICnc22r2ejOBwtpWevRy/5CdSiWxLjmMrDO1tE3XKOvRKM+BnrZhxWxnj577Xj7Ce0crePTjM9z6t0Ms+NWnrPlTFt99/Rj/2F/M4dLG4U8xXH1EDHjxHij8fMr+GAqWRylmFeyXFd8T3dHPfgXnPjN7iI+7E7csjebD45WTjnTcc64WydBDYtMemL8enCxYiNiIJXEB/KFjI3Q2QbaddWf1vZDzEsSthOAEk12yLPP07kLigzy4Kim0f3tGrB/AQHc2dAE4ewrNrZ1gMMjkljRYtivb3iCeUoQkw02vgG80AcUf4OWisbshMONTkgQzTgaDWZ8SxtELTVQ2dUzFshwOc+/zuuRQuhxZalCUBZIKYleYbP799nzytDpeuCOdY7+4gn9/fQkPXTmXWcGe7D9fx68/zGPLswdI/uUnrH/iC378zkley75AXqWO3tQ7wTcGdv4KDNYL4lGwLkoxq2C/SBJsfFp8gb/19YtaMP1PZjwalYoXvphcd3Z3QQ1XuRWg6W6e9hIDI0vj/DnUGUNL1BrRne2yo0fSZ7aDrhyWDO/KZp2tJV+r475Vs0zSsJIjfHDWqDhs1M2qNSKb3Y6GwAprW2ls72FJrIWKWVmGj74rCtpNz4mbrJQbkIqyWBpisLvObN44illQAhQmSr5Wh7NGRXzgQDphRqw/gZ4OLDUoyoKIdBNJ0o6TWv79ZSl3ZcaxZn4Ivu7OrJwbxANr5vDiHRlk/3QtB398Oc/fns49q+IJ8HRm24lKHn7nJOuf/ILk32bxuGErVJ/k8EfPU1zXZhX/cgXrohSzCvaNswdsfUUULa/ebPZReYi3K5vTIngjt3zg8fM40Rtkss7UcIf3UXDxhllrJrtyu8DYHdwd+jXoaITsF228okFkvwA+0WLQbgjP7D5PhK8b16VGmGx30ahZEOFD7mDdbMxyYdfTbjlXi8mQ3eeusdhSndlTb0Pee3DZTyA0RWxLvh5kPZtdcijQtphG/dqYPK2OYC8XAj1dRjwuLtCDhDBvxy28rEy+toW5IZ5o1ANf42qVxLrkUHafqaG928GkBp3NUHHYRGJQ1tDOD98+wcIoX35w1fyLvjTUx5WrkkL5wVXz+c83lnL8l1ey+6HVPLF1EbcsieGA60pOyfGEHP4TV//pUxb++lNu+9shHv24gI9PVVHT0mn9P5/CpFCKWQX7xy8GbvgX1J+Hd+42+yjo7pXx9OgN/H3/xDwDj5U10dreQWr7fpi/ATQjfxFPF8J93Yj0c2N7Q4TQWh54Crpabb0sqDoFJV/Akv8ZZseVXdxATkkjd2XG4aQe/isqPdaPUxXNA/q36GWADGWHpmDho5NT3ECgpwuxARYI29BVwrbviTz5S789sD0kEYKTWNL6OR09eorr7ODvtI98bcuIetnBbEgJ5YgiNRg3siyTr9WREOoNTWVw6p3+fetSQunsMVglUMamlOwDWd9fzHb3GnjgVeFC8NebU3HWjL2ckSSJuEAPNi6K4BfXJPLmN1eQcPvjREp1vLboJBsWhNPY3s0Le4u49+XDZD6y225t8BQESjGrMD2Iy4Sr/wBnd0DWH4btjg/yZH1yGC9/WTqhvPrdBTWsVJ/CuUfnMBIDI0vjAsguaUBe9SPoaBBG4bYm+wXQuEHq7cN2Pb37PAEezty0ONrsSzNi/OnRy5wo79OKRmaAysluhsByShpZEuc3eTsuWYb3HxBRv5ueG1b0k3I9gY3HiJRq7EZqMDBhP7Zidn1/gILiajAealu6qG/rFu/zvsfhra9Bq9DJLon1J8DD2fGG64qyhIVf5GIA/vTpGY6XNfHIlgVE+U/+xlE9ezXMXktqyUv8YV0k2x7M5NSvr+Kte5cB8HrO6FHrCrZDKWYVpg9L7hZepHsfhbwPhu2+b/UsWrp6eeXg+H/p7Cqo4XavI+DiA/GXWWK1dsPSOH8a2ropdEkQ8okDT0F3m+0W1N4AJ96ABTeAu+mj+FMVzew5W8vXV8Th5mzeozQtWujl+v1mndwgIs0uhsAqmjqoaOpgsSX0srl/FxPWV/wvBMwavj95CwCbNAftppg9X2M6YT8a8UGezA/1UqQG48REl2x8IlHyBQAatYqrkkPZXVBDR7f9eRBPmKIsiLkUNC7sKqjmhb1F3H5JTP8NkUVY+2shZ/jiMQBcndRkxPqzNjGED09o6e5VBsTsFaWYVZg+SBJseEzcmb97L1SfNtmdHOFD5pxAXtpXPC4j+armTs5r61nWcxASvgIaZ0uv3KYYdbOHihtg1cPQXidcBGzF0Zeht8Ps4NezWYV4uWi4fVnMRV8e4OlCfKAHR4b6zVYeHTFkYyrI6fOXnXQxW18In/5M3Fgt/h/zx/jFQNRSNjsftBtHg7EOfw1mQ0oYh0sb0TYrUoOxkq8Vg5yJ/tLA78HiL/r3r08Oo71bz56zDiI1aK6AurMQvxptcwfff+M4CWHe/HRDwqgvHRehybBwq4jXbirr37wlLYKGtm7HeT8dEKWYVZheaFzgxv+Ai5cYCBsy9HPf6lnUtXbx1uHyMZ9y95kaMlUncNG3OpzEACAmwJ1gLxcOFTVA9FKhOTvwpG0KP4Mecl6EmBXii2MQhbWtbD+l5Y7lMXi7Oo14GmN4Qv/UccxyMPRCRa61Vj4msksa8HLRjKuYG4ZBD+/dB2on4eYxklwh5Qbi9CV0VZy0iwnsfK0OVycVcYMm7Edj/YI+qYESbztm8rU6wn1c8ak/CsjC+L9koJi9JN4fP3cnx+l490XY9sau5NuvHqOr18DTt6ReNGFuUlz2U/Fz9+/7N2XOCSLAw5l3joz9e0VhalGKWYXph3eYcDho0cKbdwq/0j6WxQewKMqX5/cW0qsf2yOhXQU13OCai+zqC3GrrLRo2yFJEkvi/MkubhAFz6qHoa1WPMaeas5+DE0XYOndw3Y9l1WIi0bF1y6NG/U0GbF+NLb3UFjbJ5eIWgpINrfoyi5uID3Wz8RObNwceFI8Ol7/J/CJGPnYxOswSGou69lLZbPtJ67zKnXMC/Ue159/liI1GDf5Wl2fxCBb+K4uvUcMyOrEe6hRq7gqKZTP86vtMu543BRlgUcQT550Irukgd9tSiY+yNM61/KNEr+fjr8qBlUBJ7WKaxeF83l+Dc3t45/JULA+SjGrMD2JzICvPC6SW3b+on+zJEnct3oWZQ0dYxqA6OrVk3O+ktXkIDmgxMDI0jh/qnSdlDV0QMwyEVSw/4mp784eeh68I2HeBpPNFU0dvHu0gq2Lo0e1dAJIjxGP8fv9Zt18ISQJSvdbfMljpaGtm/M1rZOTGFSdEh2hxI2QcsPox3sG0RK+go3qA5wub5r4dS2ALMvkV+lIHCHG9mKsTwkjt7SRKjsoyO2dzh49RXVtfcXsQQhOGrC3G9SdXZcSRlu3nr3T/dG4LENRFrVBy3gqq4gb0iPZlBpp3Wuu+B64eovAnj42p0bSrTfw0Un7is5WECjFrML0JfU2WHovHHwajr3av/mKhBBmB3vybFbhqI9eDxU1sLj3KK6GdoeUGBhZGh8AwKHierFh1cPQVgOv3watU/RlV1Mgbj4Wf134Bg/ixb1FANy1Mn5Mp5oV5IGvuxO5JUN0s+U5YvrfBuT0+ctOOPmrtwvevQdcfWHD4yPLCwbhmnYjkVIdDWf2Tey6FkLb3ElTe8+Yh78GM+BqoHRnR+NcdSt6g0xiiDuU5wrpUEiyiGYt3tt/3PJZAfi4OU1/p4iafGir4ZkLUcwK8uTXG5Osf013f8j8Ppzf2f+eJkd4MyfYk3ePVFj/+grjRilmFaY3V/4WYjPhw29D+WFAZJTfu2oWBVUto3ot7iqo4VrNIWQ3f4eUGBiZHeSJn7sT2X0DSsReKjrbJfvguUuhaI/1F5H9PKhdIO1Ok811rV28mn2BTakRRPi6jelUkiSRHu03EGsLQjfb0w7aExZc9NjJKW7AWaNiQaTPxE6Q9X9QfQqufRI8Asb8Mpeka+nCmaDSjyZ2XQsx1hhbc8wO9mReiCI1GAvG93mBcwV0t0LUJcK2LWaFSWfWSa3iysQQPsurpqt3+koNDIW7ANjdncDTt6Th7qwZ5RUWYsk94inSzl+AwYAkSWxOiyS3tJHSehu6wSiYRSlmFaY3aicRqOAVIrqMLdUAbFwUTriPK89knb/oS2VZZl9+GWvVR5ASrhHnclBUKonFsf796VQAZHwd7tolOjr/3gi7fmuiP7YoHU1w/DXx6HxIofaP/cV06w3cu9qM/dQIpMf6UVTXRkNbt9gQs1z8vGAbv9mckgYWRfriopnAUEpZNuz/i/DdNZOINiKu3uR5LSdNt8t6f39jwFhkzZ/g8JtRalCtU6QGI5Gn1eHmpCa8pe+mLWqJ+BmXCY0lJlP461PCaOnqZd+5uqlfqIW4kLudQkMY91y7inmh45ewTBgnV1jzU+GSkvcuANelhiNJ8O5RpTtrbzh0MStJ0tWSJJ2RJOm8JEkPm9m/UZKkE5IkHZMkKVeSpBW2WKfCJPEIgK3/hc4meON26O3CSa3irpXx5JQ09j/+HUphbRuzmg/iJnc4tMTAyJI4f0rr2011iaHJcHcWLLoV9v4R/vUVaLbCxO6xV0TXdMjgl66zh38fKGV9chizxjnQkdGvm+3rznqFgl+cTYbA2rp6OVWpY3Gc3/hf3N0m5AXekXDV70c/3gw1Mdfgh46W/M8m9HpLkKfVERPgjqfLxDpnGxaEIsvw8XR/LG5l8rU65oV6oSrPBq8w8O0LF4nNFD8HdWcvnR2Il6uG7dPUKSK3sIqg+lwq/JeydXHU1C9gwU1Ck/z5/0JvN2E+biyfFcA7Ryrswj1EYQCHLWYlSVIDTwPrgETgZkmSEocc9jmwUJblRcDXATuIRlKYEKEpcN0zYgp8+w9AlrlpcRR+7k48m1Vo9iW7C2rYoD6I3s1/4IvAgVkaN0Q3a8TZA657Gja/CFUn4dlLoWC75S5s0IvEr6hLIGyhya7/fFlKS1cv942zKwuwINIHJ7U0EJ4Aojt74UuzkcfW5OiFJvQGeWLDXzt/CQ3FsOlZMXQyATyT16GT3Wk//NqEXm8J8rUt5vWyXz4D+/4y6utnB3sxN8TT8ZKrLEh/jG2YN1w4JLqyRm11cKKw6BrkN+usUXFFYgg786qmneF/Y1s3f3vtTTykLhav2Tz5RL2JoFLD5b8QHe8z2wDYlBrJhYZ2U4mTgs1x2GIWWAKcl2W5SJblbuA1YOPgA2RZbpUHbq88AOVWazqTtEmI9o/8C3Jfwt1Zw9cujWNXQU3/I9DB7Mu7wBXqo6gTNw4bSHJEEsO98XTRDOhmh7LgRrhnrzDjf+1m2PEjMZQ0Wc7tFF8GS01DEjq69fx9XzGr5gaRHDF+namrk5rkCB8ODx0C62iAujOTXPT4yC5pQCUJ/9txcf5z4bu77H6InfiDocSoIHbol+B34VPomfrwgbauXkrq24brZXVa+OyXsOeRMaXOrU8JI6ekgRpFamCWyuZOdJ29pPu1Q/MFcYNoRKUSn6GSL4QDQB8bUsLQdfayv3D6SA1kWeahN4+T1HkEWVLhNseG8wxzrgCPYDj1DgBXJ4fi5qTmHUVqYFc4cjEbAZQN+u/yvm0mSJK0SZKkAmAbojs7DEmS7u6TIeTW1k5zmxNH57KfwdyrRSFWsp87lsXg4azm+T2m3VldZw+eZbtxo3NGSAwA1CqJjFi/ixezIGJTv7ETlt4Hh56Dv60VaVSTIft58AqHhGtMNr+ec4H6tm7uv2z2hE+dHu3HiYrmgQEXo262dGp1sznFDSSEeeM1StiDCR2N8P4DEDgP1vx8Utf383Bmn9tqnPVtcPaTSZ1rIhRUtSDLDO/MHngK9N1CYnLu01HPsyElTEgNTk/Px+LWJr8vtjhVOis2RC01PSBuJTSXiZvHPlbMCcTTRcOOadTxfmlfMZ8X1HCT/3mk8DRhvWcrVGpIuk58frta8HTRcHVyKB8dr3QMD18HwZGLWXPPJIZ1XmVZfleW5fnAdcBvzJ1IluUXZFnOkGU5IygoyMLLVLAoKhVsfkFoJ9+4A9/uam5ZGs2HJ7SUNQx4qu47V8c61UF6XANE3vcMYUmcP+dqWqlvHaHjqnGBdf8HW18VX4zPr4Tjr0/sgnXnoHCXGDYbNGDX3Wvghb1FLI71m7iVFSI8obvXwKmKvs67fzx4hgipwRTR3WvgyIXG8f85dvxI2KNtfl4Mm0ySzojl1Et+cPLNSZ9rvPTH2IYPKmbb6kQwx4KbRGfr9LujnmdOiBdzgj3ZdmL6FF5TifEJU1TbKdC4QdgC0wPM6GZdNGrWJgTzaV41PWMMkrElx8uaeOTjAq6d70GQ7rRILLQ1yVugtxPO7ABgU2oEus5edhfU2HhhCkYcuZgtBwYrxiOBi7ody7K8F5glSVKgtRemYGVcfeDmV0VH6LVb+MbSUFQSvNDnZQqwL6+Uy9VHUSfNDImBkaV9BdfFhuJMmL8e7t0n9Mjv3g3vfXNMj4pNyH4B1M6QfqfJ5vePVVDZ3Mk3J9GVBTPhCZIkpAZTOAR2sqKZrl4DS8ajlz39Hpx4HVb+AMJTLbKOxAg/3uu5BPncp8I9YgrJ1+rwcXMi3GdQUf7l06IAWPkDSLwWzn4KXa2jnmt9ShjZJQ3UtChSg6HkV+mI9nfHuSIbItKHO7AEzRM3DoN0syDe06b2Hr4sHKKXtzN0nT088OoRgr1c+X2qDknWw6zLbL0siFwiBjRPvQ2IwbpgLxfeVjxn7QZHLmZzgDmSJMVJkuQMbAU+GHyAJEmzpT5VuSRJaYAzYN//2hXGRuAc2PISVJ0kNOsHbEmN4I3cMmpbujAYZAxnPsGNLlTJm2290iklJcIXF42KQyNJDQbjEwlf/QhW/hCO/ReeX9Uf8TgqnTrxmuQt4DnwRENvkHl2TyGJYd6snju5Jx1BXi7EBLibhifELAdduYjNnQKMNwYZYy1mW6rho++KIjbz+xZbR1K4Nx/olyHpu6Fgaj1n8yp1JIR5DQzpdDRC9otCwhM4R/zs7YBzo0sgNiwQUoNPFFeDYeRrW1gQ4gRVJwYsuQYjSWZ1syvnBuHhrLbrUApZlnn47RNUNnXy5M2L8KzYB07uELnY1ksTT/ySNwmNe3sDapXEdakRZJ2pGbAGVLApDlvMyrLcCzwAfALkA2/IsnxakqR7JUm6t++wLcApSZKOIZwPbpIVvw3HYe6VYhL11Fv8wOsTuvUG/rG/mJMVzazs2UenS8CAxnKG4KxRkRY9im52KGqN8Fv86gfQ1QIvroGcv5l8WZrl2H+FqfsSUzuuT05XUVTbxjcvm2WRCeX0GBGe0P9PN3qZ+DlF3dmc4gbiAz0I8ho9hhdZFgEfPe2w6XmLehsnRfhwXJ6Fzi1qSqUGeoPMmaoWEsMGDfEdegG6WwaK9ehlQv4xBqnB3BAvZgcrrgZDae8WQ3arPMvA0AvRl5g/MC4TWrQmWndXJzVrEkL45HQ1vXYqNXjl0AW2n6zioSvniScuRbvF72fNGP5dTQXJW8DQ03+juDktgl6DzIfHlXhbe8Bhi1kAWZa3y7I8V5blWbIs/65v23OyLD/X9/8fkWU5SZblRbIsL5Nl2bZ5kAqWZ8V3IWkzAV/+nofiyvjPl6XsOHKeNaqjkLhRiPtnGEvj/cnT6tB1jjP2NW6lkB3EZcK278Mbd4gOnDkMBiExiFwMEWn9m2VZ5und54kL9GBdctgk/hQDZMT4U9/WTUl9nyY6JAlcvKF0v0XOPxIGg0xuaePYLbmOvgxnd8DlvxSPhC1IuI8rRfP54wAAIABJREFUvu7OZHuuERGcLVPT2Sypb6OjR09CWJ+hfVcLHHwG5q0XPsYg/p0lbhTOFl0to55zfUoY2cUN1LZYwE3DQTAO2S2U+5w6LtaxjF0pfpbsNdm8ISWUhrbusT+VmULyKnX870d5rJwbxD0r46G5AurO2ode1kjYIqHJ75MazA/1JiHMm3eOWMGXW2HcOHQxq6CAJMHGv0JIMvfW/Y6A7jIqst/FVerBdeH1tl6dTVgS548sY2ppNVY8g+CWN+GK38CZ7fDcSpFeNZTCXdBQCEvvNdm891wdpyt13LdqFmqVZXwjjXZYuUYdsEotulZTMAR2tqaF5o4eFo9l+KuxFD7+sRjSGfK+WAJJkkgK9+at7ktANoypC2oJhsXY5v5dBJhkPmR6YNImoaEdg9vChpQwDIqrgQn9w1+tJ4UDhvtFPnMBs0SYwhDd7Kq5wbg5qe2u493W1csDrx7B182Jx25ciEolQXFfvHa8HehljUgSJG0WN4qtYvBrS1oEx8ubOV8zuhZcwbooxayC4+PsAVtfQa1x4hWPJ7hBtYc258CBx9EzjNQoP5zU0sQ7NCoVXPogfP1T8Qv+71fDvsdNgwoOPSceKydca/LSp3efJ8zHletSh7nkTZg5wZ54u2o4cmGI32zdWTFRb0Vy+t7DUYe/DAYxQAci3ENlnV+9SeE+7KrzQw5JgZNvWeUaQ8mr1KFRScwJ8RQetweegllrIDLd9MCoS8AzdIxSA0/igzzYrrga9JOv1eHtosK1Kte8XtaIJIkbppJ9JlIgN2c1axKC+eRUFXqD/ajpfv7+KYrr2vjL1kUEevZJCoqywCNIBEHYE8lbxI1i3vsAXLsoHJUE7x5VurO2RilmFWYGfjFww78I11ewUn2SnvkbrVZQ2DtuzmoWRPoOTwIbL5HpcO8Xwj/2s1/BK1tEx6K+EM7vFHZcGuf+w3NKGsgubuDulfE4ayz33qtUEmkxfsOHwMDq3dnskkZCvF2I8ncb+cBDz0LpPmF5ZowftQJJ4d506w3UxF4LFbnQUDT6iyZJvlbH7GBPXDRqOPJvaKsVDgZDUan6/Dp3iuHAEZAkiQ0pYRwqrqduJBu5GUS+toU1Qc1InU0X18saicsUtm+1puEh65PDqG/rHp9m3oq8dbicd45U8OCaOSyf1WckJMuimI1bZX+/o0MSISihP0Ah2MuVzDlBvHe0EoMd3SDMROzsk6KgYEXiMpHWPQJqZ3yX3mbr1diUJXH+nCxvpr27d3IncvWBG/4JX/mLCCp49lIRJ6xygvSvmRz6zO7z+Hs4s3Wx5Yu5jBg/ztW00tTeN1kcngpqF6sOgcmyTE5xA4tj/UceZKspgM9+LTSki2612npAdGYBcjxXiw0n37bq9UB4zCaGeYu0uP1PCN/miw1WJm0CfRec/XjU8643Sg0UVwMMBpkCrY7Vbn03J0PDEoZixm8W4LL5Qbg6qdhuB1KD8zUt/Py9U1wS78+Dl88Z2FGTD63V9qWXHUzyFrhwQOh6EYNgFU0ddqlFnkkoxazCzGLJXfCjEpOhpJnI0jh/eg0yHxyrZNIGHpIEGV+Du3YLHV/h56Jo8QrpP+R0ZTO7z9Ty9UtjcXO2/NCd0W+2X2qgcYHIDPGlYyXKGjqo0nX2e/eaRd8D794DLp5wzRPivbIicYEeuDmpyW30EEXlyTdGd52YBPWtXVTruoRe9viroKuAlQ9d/AWRS0Qa3BikBvNDvYgP9LCLwsvWlDW209atZ4F8BtwDIGAUf2a/WPCJEvrOQbg7a7hsXjAfn7at1KCzR8/9rxzFzVnNE1tTTfXzRVniZ/xqG6xsDBjtHPs+w1cmhuLpolGkBjZGKWYVZh7OHrZegc1ZEufP7GBPHn7nJLe8eIjjZRYw2Q9JFAXtlb+Dtb8y2fVsViGeLhpuXxY7+euYYVGUL2qVZCo1iF4G2uNjmp6fCNl9A2cjDn998WfQHoOvPA6ewVZZx2DUKomEMC/yKnWig1R3FqpOWu16+Vrx3iaGusMXjwkj/5GGdoxSg/OfQWfziOeWJIn1KWEcLFKkBsbhr/CWE6IrO9pN0WDdrMHUimtdShi1LV0cLp3AAKiF+PWHeZypbuGxGxcS4j0k/a4oSxTrvlFmX2tzAmYJZ4M+VwM3ZzXrkkPZfrKKjm4l3tZWKMWsgsIMxN1Zw/YHM/nVNYmcqW5h49P7uf+/RyipG2fC11Cc3WH5A+AzMOBVVNvKtpNabl8Wg4+b5XxVB+PmrCYp3Jvc0iG6Wdlg3m3BAuQUN+Dj5sTcYC/zB1QcgT2PijjXxI1WWYM5ksJ9yNPqMCRsBJUGTllvEMxYZC1s+gyaSoVWdrRCK2mTSOfriwYdCaPU4NPT1ZZY7rQlT9tCoKTDtblo5OGvwcRlQkcD1OSZbF4zPxgXje2kBh8cr+TV7Avcsyqe1fOG3ODpe0QBHr/aFksbO8lboPJIvyZ9c1okrV29fJqnSGJshVLMKijMUJw1Ku68NI49P1jNg2tmsyu/hrWP7eHn752yqL/n83uKcFar+PqlcRY7pznSY/w4XtY0kD8ftQQkldWGwHJKGsiI8RNWQkPR98B79wlHh3WPWuX6FyMp3JvWrl4udLrBrMuFbtZgHaP8PK2OcC8nPLOfgJAUmHv16C+KyBDRoGOQGiSEeRGnSA3I1+q42rcv0S5qlOEvIxfRzXq6aFg1N4gdp7RTPrR0rrqFh98+QXqMHw9dacZnuTwHetrsv5hN2iR+9g2CLY3zJ8LXjXePKvG2tkIpZhUUZjherk5878p57PnharYuieK/2RdY9cfdPL7zLK1dkxsQq2zq4J2j5WxdHDW2hKxJkBHjT1evgdOVfZPyLl4QusAqQ2C1LV0U1bVdXGKQ/QLUFsBXHgM3X4tffySMQ2CnK3WQcoOI9i07aJVr5Wt13OpzXMgZVn5/bJrgfqnB59AxsrxFSA1C+bKofkbHhuZrdax0LRKDleGLxvYi3yihnR3iNwui412t6zK1s7MyrV293PvyYdyd1Tx9SxpOajPlR1GWuAE1FuL2im+UkHv03ZCpVBLXpYaz92wtNS2dNl7czEQpZhUUFABhM/Pb61LY+d2VrJ4XxBOfn2PVo7v514ESunsn1tl78YsiZBnuWhlv4dUOJyN2SHgCCKlBRa6YtLcgxmuYTf5qrYGs/4PZV4ytU2lh5oZ6olFJnK5shnnrQONmlXjbrl4952tauKH9NQicO8xTeESSNolo0DPbRz10fUoYeoPMJzM0QEHX2UN5YwfJ+gJRyDqNYgM3mNhMYQlnMNVyXp4QjLNaxfaTU/OeyrLMw2+foLiujSe3phLq42r+wKIsCE+b8hvACZG8BapPCbcSYFNqJAYZPjimxNvaAqWYVVBQMCE+yJNnbk3n3W8uZ3awJ7/84DRXPL6HD4+Pz0uxvrWLV7MvsHFRBJF+7lZcsSDE25VIPzfTwZboZSJ1qvKYRa+VXdKAq5OKlAif4Ts//7UID7j6D1Z3LzCHi0bN7GBP0Zl18YT56+H0e0L6YEHOVbeyisMEt5+HzO+PLxo6Il1M249BapAY5k1sgPuMlRoUaFtwpoeQ1rzRLbmGErdSDNoNGQL0cnVi5dzAKZMa/PNACR+d0PLQVfNYPjvQ/EGdOijPtX+JgZHE60QX+bSQGswO9mRhpA/vHFGkBrZAKWYVFBTMkhrtx2t3X8I/7lyMm5Oab716lI1P72f/+bGlav3zQAldvQbuW239rqyR9Bg/cksbB+zGjClvpfstep2ckgYWRfkOD3+oOAxHX4ZL7oPAOeZfPAUkhfsMyC1SbhCDQIW7LXqN/MpmvqV5jx7vaEgeZzS0JAmpQeEu6Bj5UbfR1eBA4cyUGuRrdSRJJagN3eMvZi+imwVYlxyGtrmTY+UWcDIZgcOljfxuWz5rE4K5d+Wsix9Ysg9k/fQpZr1CIHaFcDXo+32zKTWCPK2OgqqRQ0EULI9SzCooKFwUSZK4bH4w2x7M5M83LKShrZtb/3aIO/6eLR5jX4SWzh7+eaCEq5NCmX2xaX8rkBHjR21LF+WNHWKDZ5B4BG7BIbCWzh7yKnXDI2wNBtjxIzH0ZS4BawpJCvemrrWLGl2nGAJz9bW41KDz7OcsUhWizvweqDUTWOQmMPRCwbZRDzVKDT6dgVKDfK2OTNdC8R/jLWa9w4TNlRnd7NrEEJzUEjus2PGua+3i/leOEO7rxp9vWGR+WNJIURY4uY/drcEeSN4C9eeh6gQA1ywMR6OSeFfpzk45SjGroKAwKmqVxJb0SD7//ip+uj6B42VNbHhyH9957ShlDe3Djn/54AVaOnv55upRzN0tjDE8Ibd0kG42ehlcODRMNzhRjlxowiDDkrgA0x0nXhfT2Gt/Ba7eFrnWREkKF9c/XakTkcJJ14misXv439VESS99iTpVAKrUWyZ2gvA0Ee07BqlBUrg3MQHubJuBUoN8rY4VLoVimGtQEMmYic0U6Xx602FOHzcnVswOZPvJqskHp5hBb5D59mtHaWzv5tnb0vBxH8WWryhLaNw11h0UtSgJ1/bZ3wnP2QBPF1bPC+a9YxU2DaWYiSjFrIKCwphxdVJz18p49v7wMu5bPYsdp6q4/M97+N8P8/ofAXf26HlpXxGZcwJJiTSjKbUi80K98HLRmIYnxCyHruZhfpsTJbu4HrVKIjV60JBKpw4++6WwnVqw1SLXmQyJ/cVsX/c8+XpheXR2dG/XsSCX7Cex+yRfht428eJDkkR3tigL2keOAh0sNWicQVIDvUHmTLWOxN78sVtyDSV2BXS3QNXxYbvWp4RR0dTBifKRAywmwuM7z7L/fD2/2Zjc77BxUXSVUHdm+kgMjLj7w6w1wqKr74Zgc1oE1bouDhSOTY6lYBmUYlZBQWHc+Lg58aOr55P1g9VsSo3gnweKWfXobv666xz/OlBCXWs39182tV1ZEB3kRdG+w4fAwGIWXTnFjSSHe+PhMujR+t4/ijz59Y8K6ykb4+XqREyA+4BuNma5iJE9aZkAha7df6RW9qYlaYJdWSPjkBpsMEoNZpAxfXFdG8G9VXj2Nkz88btRN2tGanBFYggalcT2U5bteH+eX81fd5/npowoblw8hiQve4+wHYmkzdBcJp7KIEIpvF01yiDYFGP737oKCgrTljAfNx65fgGffGcll8wK4E+fnuUPOwpIj/Fj6Ugxr1YkI8afM9UtNHf0Te/7RoN3BFw4MOlzd/XqOVbeZGrJVXcODj4LqbeJKX07ISnce6CYValFpvy5naN2QUel4jCupbv5W+8G5kVO4LH3YMIWicfnY5QaRPu7s22K7KTsgXytjnTprPiP6Al2Zr1CIHCe2SEwX3dnls8OZPtJrcWkBhfq2/nu68dICvfm1xuTxvaioixwD4TgMR5vT8xfD2qX/gAFVyc1GxaE8/GpKtom6dOtMHaUYlZBQWHSzAnx4sU7Mnjr3mWsTwnlZxsSkGxgSwXC0UCW4ajREF6SRHe29ED/o8CJcqK8me5ew0BYgizDxw8L78/LfznJlVuWpHAfLjS0o+vsK+pTbhDervkfTO7Ee/9Mp8abVwxrmR86yeG+iUgNztfR1D4zpAb5Wh2L1WeRXbwgaP7ETxSXKZ5MmLFn25ASSllDx8CNzyTo7NFz3yuHAXj21nRcncZg1ybL4u8/frVdPNUYN64+MOcKcUPWp8vfkhZBR4+ej0/NnBsvWzMNPzkKCgr2SkasP8/cmk5qtJ/N1rAo2heVBEdKh+hmW6v7s9QnSnbxkLCEs5/A+c9g9cPgGTzCK6ceo242z1ikhC0Uk+2TkRpUnYIz2/jUazNBAYGmUouJkrRJWDLlfzjqoRtSwug1yHx6unry150G5Gt1LHMqRIpcMj4f36HEZgrNdOXRYbuuSAxFrZIs4uP7qw9Oc7pSx2M3LiI6YIze0rUF4t9m/OpJX99mJG+B1ipxw4y4oY72d1fibacQpZhVUFBwKDxdNCSEeZM7tJiFSVt05ZQ0MDvYE38PZ5Eq9smPhfXXkrsndV5rYOJoAKILmnKD8PPUTTCl6Is/g7MXz3etJSHMQpZroQvAP35MUoPkCG+i/N1mjKtBWWUVMYbS8VtyDaVfN7t32C5/D2eWxQdMWmrwRm4Zr+WUcf9ls1ibOA75yXTWyxqZexU4efS7GkiSxKbUCPYX1qFt7rDx4mYGSjGroKDgcGTE+HGsrIlefV8Mb+A8cPOb1BCY3iBzuKRxoCt78BnR6b36/0A9iu2QDQj2ciXIy8XUDzj5ekDu1/eNi7pzcPpdutK/wekGFYlhFrIfM0oNivdC28gT4Eapwf4ZIDVobOsmvO0UKmSInmQx6xEg9KhmdLMgXA1K6tvJ17ZM6PSnK5v5+XunuHR2AN+7Yt74Xly4G/xnge8YBsXsFWcPER2d936/lGNzWgSyDO8dVeJtpwKlmFVQUHA40mP9ae/WD3w5q1R9frMTHwIrqNLR0tXLkjg/0Glhzx9h3gaYfbmFVm15ksK9B2QGAIGzITx1YgEKXzwGGlfyY24DIMFSxSxMTGqQ59hSg3ytjnTVWWRJJSzfJktcpvBb7u0atuvKpBBUEuyYgKtBc0cP9718BD93Z57Ymop6pGCEoeh7xJOCWZeN+7p2R/IWkbRXtAeAmAAP0mP8eOdIuVV8fBVMUYpZBQUFhyMjRmh2h4UnNBRBy8SGMnL69LJL4gKEp6yhF6763aTXak2Swr05V9NKZ8+gwIjk60F7DOrOj/1EjSUiFCLj65xoFF1ooybXIoQkCz3vGKQGKRE+RPq5WTW5yh7I0+rIkM6iD0oCF8/JnzA2E3o7ROTyEAI9XbgkPoBt45QaGAwy33/jOJVNHTx9ayqBnuP0HC7PFVre+NXje509MvtycPHplxqA6M6eq2m1yHCdwsgoxayCgoLDEe7rRpiPq3ndbOnEurPZJQ1E+LoRoTsuCrvl3wL/OAus1nokhfugN8icrR70+Dh5MyDBqXEMgu37ixhAWv4t8rU6fN2dCPV2tdxCjVKDki+gtXaUQ4XUYN/5ugH7NQfkTGUjqepCNLHLLHPC2EsByazfLMC6lDCKats4W9065lM+v7eIz/Kr+emGhP70vXFRlAWSSgQ7THc0LpDwFSj4qL/7/ZWUcJzVKsVzdgpQilkFBQWHJD3Gz9TRIGyhyH6fwBCYLMtkFzeyJMYbdvxQBBBkfs+Cq7UOw4bAALzDRfFw8s2xWZU1V8CxV4SPrncYeZU6EsO8LW+9lrQJZMOYrMPWp4TRo5fZ6cBSg87yE7jTOfnhLyNufhCaclHd7FVJIUgSYx6uO1BYxx8/KWDDgjDuXB47sTUV7RayFzfbuZ9YlOTN0KUTDieAj7sTlycE88HxigH9voJVUIpZBQUFhyQjxg9tcycVTX3TxGoniFw8oSGwkvp26lq7uMlpD2iPw5W/EUMfdk6UnzteLhrTITAQrgb154XcYDQOPCX8My/9Dr16AwVVLZbVyxoJThTOEGOQGiyM9CHC143n9xTy1uFyaluG60CnMz16A0FNfX83lipmAeJWQlk29HQO2xXs5cqSWP8xyTeqdZ08+OpR4gI9eGTLgond2HTqhMwg3gH0skbiVoF7wBCpQSR1rd18cU6Jt7UmSjGroKDgkGT0uQ7klgzSzcYsh+pT0NE0rnPlFDfgTRsZ5/8K0cvFsMc0QKWSSBicBGYk8VpQOY3uOdtaC4f/CQu3gl8MJfVtdPUaLOdkMBij1KB0P7SM3HGVJIkfXj2Ppo4eHnrzOIt/9xnX/nUfj+08y9ELjegN03vgprC2lUWcocM1BHwiLXfi2EzQd0F5ttnd61PCOFfTyrnqi7sa9OgN3P/KEdq79Tx3WzqeE/UaLt0vhv7iV0/s9faI2gkSN8KZHdDdBsCquUH4uTvx9pFyGy/OsVGKWQUFBYdkfqgX7s5qDg+WGkQvA2TRnRoH2SUN/Mj1XdRdTbDuEVF4TROSwr0p0LaYFnhufiK16NTb/alFZjn4NPR2wgohqcjrc4ewSmcWxiU12LgoguyfXM5H31rBQ1fOxVmt4q+7zrHpmQNk/HYn33ntKO8fq6CxbfpZeOVrdaSpztEbscSyn7WYZUKjehHd7NXJoUgSbB8hMviRHQXkljbyf1sWMCdkEl7DRVmgcYOoJRM/hz2SvAV62uHsxwA4a1RcuzCcT/OqB9L4FCyOUswqKCg4JBq1ikVRvuSWDCpmIxeDSiO6QuOgtugYW/kYKf1OCFtg2YVamaRwHzp69BTXDRnsSbkeWrQXH4hrb4DsF4UOMHA2IIosJ7XE7GALTNebIzhBxLaefm9Mh0uSRHKEDw+smcNb9y3nyM+v4MmbU7lsXjB7z9Xx7deOkf7bnWx+Zj9PfX6OUxXNGKZB17a8tJBIqQ732Zda9sSuPhC26KK62RBvVzJi/C5q0bX9pJa/7SvmzuWxXLswfHJrKcoST0o043RAsHeil4FXmImX86a0SLp7DQ7vwGFLlGJWQUHBYcmI8aOgSkdrV6/Y4OwuvszHMQRW09zBXa3P06PxhMt+ZqWVWg+zQ2AAc9eJ1KKLec5mvwDdrZD5/f5NeZU6Zgd74ayx4ldHv9Rg/BZqvu7OXLswnMduWkTuT9fy3v2X8uDlc9DL8NhnZ/nKU/tY8vvPeejN42w7obVfN4SyQwCoJxuWYI64TKFV7W43u3tdchgFVS0U1pre/BTWtvKDN4+TGu3LT9YnTG4NukoRY+sI/rJDUakh8To49yl0Cq36wkgf4oM8eFtxNbAaSjGroKDgsKTH+mOQ4diFQRrZmOVQcQR6xhYzeeHA66xQn6Zu8UMiSWmaMTvYE2eNangx6+wO8zeI1KLeIY/iO3Vw8FmY/xUISerfnK/VWS7G9mIkXgfIkDe61GAkVCqJRVG+fGftXN6//1JyfrqWx25cyLJZAezMq+b+/x4h7Tc7ufH5L3k2q5B8rc5uzO0DG47SLbkK9wFLE7sSDD1QdtDs7nUpoQAmXcT27l7ue/kwLk5qnrk1bfI3M33BAg6llx1M8hbQd0PBdkA8QdiSFkl2cQNlDeZvIhQmh1LMKigoOCyp0b5I0pDwhJjl4svcjHn8MHo6mHX0D5yRowlZc5/1FmpFnNQq5od6DXc0AOFq0NkEhZ+bbs99SWwf1JWta+2ipqXLOsNfgwmeL5wNxuBqMB4CPV3YnBbJUzencvhna3n7vmXct2oWbV29PPJxAeue+IJlf9jFj985wbGy8Q0IWpKalk6S9PnU+yZbJyY5+hIhtbmIbjbMx420aN9+3awsy/z4nZOcq2nlya2phPm4TX4NRVngHigidh2RyAzwiTZxNdi4SMgy3juqdGetgVLMKigoOCzerk7MC/EyHQIzWh2NxaJr/5P4dVfxVtC3cHJyts4ip4CkPkeDYZ3HWZeBm7+p1KC7HQ78FWavhYi0/s35WtHZtXoxC0JqcOFL8TjaCmjUKtJj/HnoqnlsezCTQz+5nEe3LCAtxpePjmu59cWDA9KUKeZMWTWJUin6CCtIDECkiYWnXVQ3C8LVIE+ro6SujZcPlvL+sUq+f8VcVswJnPz1ZVkUs/GrRMy0IyJJQmtetBva6gGI9HPnknh/3j1aYTdPABwJB/0kKSgoKAgyYv04eqFpYJrf3V90/i6MkgTWVIa873G26ZfiOX96a/sSw31oau+hsnmIv6jaSRSOBduhq08jeeRf0F4HK39gcmhen0zBak4GJgu2jNRgrIR4u3Lj4iieuTWdf359MW3deradsE4hPRqNZ79EIxnwm2fFVKy4TCG16TJvwbUuJQyAP35yhv/9KI8184P55urZlrl2bQG0VjmWv6w5kreIyOtBzhybUyMpqmuzaeffUVGKWQUFBYcmPcaP1q5eCqoGaUajlwl7Lv0I3bedP8cgy/y+5xYWx03vhKL+IbAKc1KD66G3A85sFzGc+58QfqTRl5gclq/VEebjip/HFHSog+ZCSLLFpQZjIS3ajznBnryaXTbl1wZQ9XnAesy2UIytOWIzhcfrBfO62QhfNxZG+bLtpJYQb1ceu3EhKpWFLMJy/wFIjquXNRKaAgFzTKQG61JCcdEo8bbWQClmFRQUHJqMvsx4k2jbmOViUr/qhPkXFX8Bp9/lQOjt1KiDSY2a3sVsQqg3KglODR0CA4i6BLwjhdTg2CvCrmvlQ8MOy9PqpkZiYCTpOjGk1Dy1X/ySJHHT4iiOlTWZ3gBNEcFNx6hwirFuxGvUUhGaUbz3oodsTo3A1UnFs7em4+tuoRuY8lzhkrH4f8A3yjLntFckSXRnS/b1O3N4uTpxZVIoH56opLtXibe1JA5dzEqSdLUkSWckSTovSdLDZvbfKknSib7/HZAkaaEt1qmgoGA9Iv3cCPZyIXdYeALmLbr0vbDjR+ATzTM9G0iO8MHNWT01i7USbs5q4oM8yTM3BKZSQcoWKNwFe/8kvHjjVpkc0tmjp7C2bWokBkYSN4mfee9P3TX72JwWibNaxWtT3J3t7O5hXk8BdX6p1r2Qs7v4ex5BN3v7JTFk/3QtKZE+lrmmvgc+eFB4sF7+C8uc095J3gzIJr7Jm9MiaGrvYfeZGtutywFx2GJWkiQ18DSwDkgEbpYkKXHIYcXAKlmWFwC/AV6Y2lUqKChYG0mSyIj1Mw1P8IkA3xjzgQGH/wE1p+le+xtyKzpY0heLO91JMhdrayTlBqHv01VA5kPDUqfOVbeiN8gkhk9hMRs4WzyqtYHUwN/DmSuTQnj3aAWdPSMkpFmYsjNH8ZHakKOsNPw1mLhM0B7v90Idikol4e1qQTeFA09CzWnY8CdwncLPkS0JmtcnlxkIUMicHUigpwvvKlIDi+KwxSywBDgvy3KRLMvdwGvAxsEHyLJ8QJZl4zfcQcCCIdgKCgr2QnqMPxVNHVQNHoCKWS40g4Mni9vqYdfyg2Q1AAAYSElEQVRvIW4VR9xX0KOXWexAxay2uZMGc/GuIcnCJil0Acy9athuo5PBlHZmQQynlWdD09TrV29eEk1zRw+fnB5/eMNEaT4rkun851tx+MtIbKaIDr5YApwlqS+ErEcg4VrhbTyTSN4sQjCaLgDCSWPjonA+L6imqX36RS3bK45czEYAg38DlvdtuxjfAHaY2yFJ0t2SJOVKkpRbW1trwSUqKChMBRkxQn9o4jcbvUxM7dedG9i2+7diwnvdI+T0dXIzYqe3XtZIUrh4XGzWb1aS4I734Pb3hnVlQehl3Z3VxPi7W3uZpiRe17eAqZcaLIsPIMrfbUqlBprKbOplbyLik61/scjFoHa5qN+sxZBl+PDboHGF9X+07rXskaTN4uegJwyb0yLo0ct8dEKJt7UUjlzMmhu9NGvuJknSZYhi9kfm9suy/IIsyxmyLGcEBQVZcIkKCgpTQWK4N65OKlOpQcxy8bNUdMPQnhCT1kvuguAEsksamB/qZbnhFxtz0VhbI57BF004y9PqmB/qZbmJ9rESMAvCFkLee6Mfa2FUKomti6P5sqiekrq2KblmSNMxzrskolZPwVezkytELYGSiw+BWYRjrwht7hW/Bq9Q617LHvGPg4h0E1eDxDBv5oV48c6RchsuzLFw5GK2HBg8LhkJDDMOlCRpAfA3YKMsy/VTtDYFBYUpxEmtYmGkL0cuDCpmA2aDR5AYApNlMfTl7g+rH6ZXb+BIaaPDSAwAfN2difB1u3gxexFkWSZfq5tavexgkjZBeU7/Y9qp5Pr0SFQSvJ5r/e6s3FpDmL6Sev+00Q+2FLGZUHUK2htGP3YitNbAJz+F6OWQ9lXrXGM6kLxF6JPrzgNCx785LYIjF5oonqIbJUfHkYvZHGCOJElxkiQ5A1sBEwduSZKigXeA22VZPmuDNSooKEwRGbF+nK7U0d7d5y0rSUJqUPql6JpcOCCmrN38yNPqaOvWszjOcYpZEB1qszKDEShv7KCls3fq9bJGbCg1CPF2Zc38YN46XE6P3rpWSo0F+wBQRS+x6nVMiMsE5IGnE5bm44ehpx2uecJx077GQtImQDIZBLsuNYIFkT7mNewK48ZhP12yLPcCDwCfAPnAG7Isn5Yk6V5Jku7tO+wXQADwjCRJxyRJyrXRchUUFKxMRow/eoNsmr4TsxyaL8COH4rH2am3A5BdLDpVjuJkYCQp3JviujbaxhHVmjeVMbbm8I+D8FSbuBoAbF0cTW1LF7sKrGul1HJ+P12yhqC5l4x+sKWISAeNm3V0s2c/FTeJmQ+JEIyZjHe4+F1z8q3+gdMQb1c+eGAF6TGOocm3NQ5bzALIsrxdluW5sizPkmX5d33bnpNl+bm+//8/siz7ybK8qO9/GbZdsYKCgrVIixZfGodLzPjNttfDukdBJfxkc0oaiPJ3I9THdaqXaVWSwn2QZcYVBpCv1SFJMC/Uy4orG4WkTVBxGBpLp/zSq+cFEeLtwus51pUaOFdmc1KOZ17UFM5laFwgeumIfrMToqsVtn0PgubDiu9a9tzTlaRNUHcGavJsvRKHxKGLWQUFBQUjPu5OzAn2NA1PCE0Bj2BYeHN/fKssy+SWOJZe1sioQ2BmyKvUERfggbuzxlrLGp1+qcHUD4Jp1CpuSI8i60wN2uYO61ykt4vAlnzOOSfi6TLF73Nspiiw2uosd87dv4PmMrjmSdA4xgDlpEm8DiQVnHpn9GMVxo1SzCooKMwYMmL9OHKhEYOhz9hEpYZvHoRrn+o/prC2jfq2boeTGACE+bji5+7E6YpxdGardCTYavjLiF8MhKfZTGpwY0YUBhnezLXS9HnlMZzkHhoDrZz8ZY64leKnpbqzFYfh0HOQ8Q3R9VUQeAaJZL1Tb5t6WytYBKWYVVBQmDGkx/jT0tnLuZrWgY0eAaAeSDrKKRF6WUcb/gIxRZ0U7sNp7diGwHSdPZQ1dNhOLzuYpE1QeRQaiq13jerT8NY34PXbRfxqH9EB7qyYHcjrOWUDN0IWpLtEBBdobFH8haeCk4dldLPGyFrPEFj7y8mfz9FI3gKNxeJzrGBRlGJWQUFhxmA2PGEIOcUNBHo6Ex/oMVXLmlKSwr05W9U6pun8Am0LYMPhr8EkWVFqUHkMXrsVnl0OBdsg/wPY86jJITctjqKiqYN95y34OL6P9vMHKDaEEBMTZ/Fzj4raCWKWWaYze+ApqD4F6/8Erj6TP5+jkfAVUDmZeM4qWAalmFVQUJgxxAS4E+jpbDoENoTskgYWx/ojmUnCcgQSw73p1hs4V9066rE2i7E1h280RGRYVmpQlgOv3AAvrBKdyVU/gu/lwcJb4Is/mUS9XpkUgp+7E6/lWNjvVpZxrcrliDzXdjcNsZlQdxZaqid+jvpC2PMIJFwjijaF4bj5wey14jNssK7V20xDKWYVFBRmDJIkkR7jZzoENojKpg7KGzsccvjLyIixtkPIq9Th7+FMiLeLtZc1NpI2CfP5+sLJnadkP/x7I7y0VgQyrPkZfPckXPYTEZyx/lHwjYG374IO8Vlx0ajZnBbJzrxq6lq7LPCH6aOhCNfuBk6pEoj0c7PcecdDXKb4OdHurCzDR98FtTOsm4GRteMheQvoKqDskK1X4lAoxayCgsKMIj3GjwsN7dS0dA7bZ9TL/n979x4dZZ3fcfz9nZlcyJ1LuARCCBpuUQFDLK1itbvrvSJ1LdiLW61VV121rp7a3dp1u2dXz9mu3eOp62qrdY9bb8crKkddqdbaSuQiyCUqCpgACQRjGMItycyvfzwTiJBAAhOeZ2Y+r38m82Qm+TI/nsx3fs/39/uekYb1st0qR+QzJCvcrx0N6pujTB1TGJxZ6mlzvdtjKTVwDj5/G/7jInj8Iq8+9lv/BLetgbPv/Ppl8ZxCuPxRaG/2krTEgp0FteV0xhwvrtiShH9MQiKp2Tlipn+v8+jpkFN07Mnsyidh43/DN++BojHJjCz9TL7Q29tXpQZJpWRWRDJKTYWXqK7oZXb2g42tFOREgnFZfZCEQ8bUMYWsO0oy2xWL83HzrmDUy3YrKYdxZwys1MA5bwP/R78FT1wGrRvggvvg1o/gzFshp6D3542r8WZq177oJWtA1ahCaiqG8tTSBlySVqS7hjp2unwKy09Jys87JuGIt6n/sSwCa2+BN3/o7dlcc3XyY0s3OQUw6TzvA1ms/81L5MiUzIpIRjllbBHZkRDLeqmbXbqpldMrhhIOBWQmcpBUlxWzril6xJX5G3fspqMrHrzEvnoeNK8+0Oe+T/E41L8Kj5wDT14Bu5rh4l/ALSth9nchO+/ov+vM26DiLFh054HShvm15Wxo2d1nqcpAdW56nxXxk5lSVpKUn3fMJsyB1s8hunVgz3v9LujYrZa1A3HK5bC7Bb54z+9I0ob+54lIRsmJhJk+rviwZOSr3R18uq2dMyakf3vJ6rIi2vd30dC6p8/HHGhj6/ces4c6UGrQx+xsPOZtTP/wHHjmz2Ffm7eP8PdWQO21kDWArm6hMPzJw96K/+f/Gro6uOS0MRTkRHjqgyQsBNvbRnbrJyyPT/L/Q0N33exAZmfX/w7WPAdzvg+lkwcnrnRUdR5kF6jUIImUzIpIxqmpGMbarTvZ1xk7cKw7uU3nxV/dDi4C67vUYF1TlOxwiJNK+7gM75fisVA+G9YeUjcb64JVz8CvZsNzV0PXfpj3MNy8HE6/6tg7URWPg0sf8PYGfedn5GVHuHRGGYtWN7Fzb+fRn38km5cCsMJNYvIoH9sFA4w6FXJLYNO7/Xv8/nZ49XYYMVktawcqawhMuRjWLYSuDr+jSQtKZkUk48yqGEpnzLGqse3AsaWbWskOh5he7vPl3hNg0ugCIiE74o4G67ZGqRpVQFY4gG8T1fO8/UxbPvU26l/xBDxYCy9eB6EIfPsxuKkOpi/w6kGP17S5MPMv4b1fwsZ3ubJ2PPs64yxceZwLwRqWECNE27DTGJIdPv44j0coBBPO6v/M7Ns/g50NXnlBJCC7XaSSUy73rhpseNvvSNJCAP9KiYgMrtMPNE84WGrwwcZWppcXk5vlc1JxAuREwlSNKjzizGx90y7/L333ZdqlgHn1mg+cDgtv9i7bzv8t3PC/XqIQSvI4XnAfDD8JXrieU4bFmDamiKeXNh7fz2ysY32oksqykcmJ8XhNmANtX0DbUUootiyHuodg1jVewwUZuInnelcOxuv1SwYlsyKScYblZzOxNP/AjgZ7OrpYs2VnRpQYdKsuK+ozmd2+ax872vcHayeDnorKvNX3ny+GgpHwZ8/C9e96G/YP1iKknAK4/N9hdwv2yi1cWTuOtVujrN7cv9bAh4l14rYs5/2Ok4PzOvenbjbWCQtvTbSsvedERJWeItnelYPcgIx9ilMyKyIZaVbFUJY3fEU87ljZ0EZX3FGbxvvLHqq6rIgd7fvZHj18v936RBvbwM7MAsz7NVzzBlz7Fkw6H07EHq1lM+Ebd0P9K1weepucSOjYO4JtW4N17kks/vK5XrZb6VTIG37k/WbffxC2rYaLfq6WtRIYSmZFJCPNqhhG255ONuxop25jK2ZeQ4VMcaRFYN170AZmxrA3JeNh/OwTk8T29Pvfg8o/JG/xD/mrSZ28vHIrezqOYb/QBq9ZwrIg7GTQrWfdbG/76LZugHfuhSmXeLPgIgGhZFZEMlJNYguuZZu+YummVqaOLqIoN8vnqE6c7tnA3haB1TdFGVsyhOK8zHk9+i0U8maFI7nc0nYfHfv38tpHTQP/OY11tGWNZF/eaEYXDWC7sME2YQ5EN8NXG79+3Dl45TavZe1FalkrwaJkVkQy0sQR+QzNy2LJhi/5sKEtrVvY9qYwN4sJw/N6n5ltigbn0ncQFZXB3H8lv3UtPyl8kWeOZSFYYx0fhaYydXRRcNoFA1Se7d0eWje76qlEy9ofef9+kQBRMisiGcnMqKkYyqI1zeztjGXU4q9u1WXFhyWz+zpjbGhpD3aJQRBMuRhmXcP8zpfIaXyX9dt29f+5OzdDdAvv7q0MTolBtxGTvMVdPetmd++AN37g7e9bc41/sYn0QcmsiGSsmophdHTFAaitzJx62W7TyopoaN1DdN/Bzf8/ad5F3AV88VdQnPdTuoZN4v6sh1j4f6v7/7yGJQC831kVvBlws8PrZl//e69JglrWSkDpf6WIZKxZibrZCcPzGFkYoLrFE6Q60ap2XY/Z2fqgtrENouw8Ilc8yrDQbmpW3c3+zn4uBGusoyucx8dufDA/NEyYA+3N8OVn8NlbsPpZr2XtyCl+RybSKyWzIpKxTh1bTE4kxO9VDvc7FF/0tqNBfVOU/Oww5UPz/AortYw5jS9m3sk5LOOT1x7o33Ma69iSPw0LRagaFbB2wXCwbvbT1+HVv/VKD+bc7m9MIkegZFZEMlZuVpgn/2Y2d5w/2e9QfFFamMPIwpyv7WjgLf4qIhQK0KKkgKu8+A6WhGYyZeW9sP3jIz94fzs0r+Ejm8xJpQXkRALYcW7YRCgsg8U/8bqB/fEDalkrgaZkVkQyWk3FUEoLM/eNurqs6ECZQTzugt3GNqDC4TCrZ91L1OXS8ew10Hl4I4oDtiwHF+O/9lQGr162W3fdbGw/1FytlrUSeEpmRUQyWHVZMeu3t7OvM8bmr/bSvr9L9bLH4OI/mMHfdV1P9o61sPjHfT+wsQ6HsXhXRbA/NMy4EirOUstaSQlKZkVEMlh1WRGxuOOT5l2sSyz+CnSSFVBlJUOIn3wez4YuhCW/gvVv9f7Axjr2lFQRJT/Yr/NJfwRXvwZDSvyOROSolMyKiGSwnovA1jVFCRlMHhXQy98BN792PHfvmU978SR46bvQ3vL1B8Tj0LiUhvxTAX1oEEkWJbMiIhmsfNgQCnMjrN26k/qmKJUj8hmSHcBFSSngG1NHUlhQyM8L7oR9O+HlGw/u1QrQ8jHs38mHbjIjCnIyulZbJJmUzIqIZDAzY9qYIm9mdmuUaYmZWhm4rHCIb9eM47cbC4ie/Y+w/k344JGDD2j0miW81R7gxV8iKUjJrIhIhqsuK2bd1ihb2vYqyTpO82vLicUdT8TOh6rz4M27Ydta75sNdbj8Ut77skDtgkWSSMmsiEiGqy4roiPmtfVVHefxqRyRz+yJw3hm2Wbilz4IucXw/LXQuRca62gfWUNHzOl1FkkiJbMiIhmueuzBxKpaSdZxW1A7nobWPby/LQSXPQTb18FLN8JXG9mUp8VfIsmmZFZEJMOdVFpAdiTE8PxsLUpKggtOGU3xkCyeXtoIVd+E2TfC2hcAWBGfRHY4xMTSfJ+jFEkfSmZFRDJcVjjEzPISTq8Yipna2B6v3Kww82aO5Y01zbTu7vAaD4w6FSJDeGfXGKpGFZAV1tuvSLLobBIRER65ahb3/+l0v8NIG/Nry+mIxXnxwy0QyYG/eA6ueonVzftUYiCSZGmdzJrZBWb2iZl9ZmZ39fL9KWb2vpntN7M7/IhRRCQIiodkUZib5XcYaWPqmCKml5fw9AcNOOegcDTbh85gR3uHklmRJEvbZNbMwsCDwIXANOBKM5t2yMNagVuAfz7B4YmISJq7srac9dvbWdHQBkB90y4AbX8mkmRpm8wCZwCfOec2OOc6gKeBuT0f4Jzb7pxbCnT6EaCIiKSvS6aXkZcd5pmlDQDUN0UBtMesSJKlczI7FmjscX9z4tiAmdl1ZrbMzJa1tLQc/QkiIpLxCnIiXDq9jFdWNbFrXyf1TVHGFOdSkpftd2giaSWdk9neluS6Xo4dlXPuEefcLOfcrNLS0uMMS0REMsX82nL2dsZ4ZVUT9U1R1cuKDIJ0TmY3A+U97o8DtvoUi4iIZKAZ5SVMHlXIE0u+4POW3aqXFRkE6ZzMLgWqzKzSzLKBBcBCn2MSEZEMYmYsOKOc+qYosbja2IoMhrRNZp1zXcDNwBtAPfCsc26tmd1gZjcAmNloM9sM3A78g5ltNjP9pRERkaSZN3Ms2RHv7VbJrEjyRfwOYDA55xYBiw459useXzfjlR+IiIgMipK8bC4+dQxv1W9jwnC1sRVJtrROZkVERILgx3OruenckwmH1C5YJNmUzIqIiAyyotwsitRhTWRQpG3NrIiIiIikPyWzIiIiIpKylMyKiIiISMpSMisiIiIiKUvJrIiIiIikLCWzIiIiIpKylMyKiIiISMpSMisiIiIiKUvJrIiIiIikLHPO+R1DSjGzFuCLQf41I4Adg/w7ZHBo7FKTxi01adxSk8btxKpwzpX6HcRgUjIbQGa2zDk3y+84ZOA0dqlJ45aaNG6pSeMmyaYyAxERERFJWUpmRURERCRlKZkNpkf8DkCOmcYuNWncUpPGLTVp3CSpVDMrIiIiIilLM7MiIiIikrKUzIqIiIhIylIyGzBmdoGZfWJmn5nZXX7HI/1jZpvMbLWZrTSzZX7HI30zs8fMbLuZrelxbJiZ/c7M1iduh/oZoxyuj3G7x8y2JM67lWZ2kZ8xyuHMrNzM3jazejNba2a3Jo7rnJOkUTIbIGYWBh4ELgSmAVea2TR/o5IBONc5N0P7Jwbe48AFhxy7C1jsnKsCFifuS7A8zuHjBvAvifNuhnNu0QmOSY6uC/i+c24qMBu4KfG+pnNOkkbJbLCcAXzmnNvgnOsAngbm+hyTSFpxzr0LtB5yeC7wm8TXvwEuO6FByVH1MW4ScM65JufcisTXu4B6YCw65ySJlMwGy1igscf9zYljEnwOeNPMlpvZdX4HIwM2yjnXBN6bLzDS53ik/242s48SZQi6VB1gZjYBmAnUoXNOkkjJbLBYL8e0d1pqONM5dzpeichNZna23wGJZICHgJOAGUAT8At/w5G+mFkB8Dxwm3Mu6nc8kl6UzAbLZqC8x/1xwFafYpEBcM5tTdxuB17EKxmR1LHNzMYAJG63+xyP9INzbptzLuaciwP/hs67QDKzLLxE9j+dcy8kDuuck6RRMhssS4EqM6s0s2xgAbDQ55jkKMws38wKu78GzgPWHPlZEjALge8kvv4O8LKPsUg/dSdDCfPQeRc4ZmbAo0C9c+7+Ht/SOSdJow5gAZPYWuaXQBh4zDn3U59DkqMws4l4s7EAEeBJjVtwmdlTwDnACGAb8CPgJeBZYDzQAFzhnNNiowDpY9zOwSsxcMAm4PruOkwJBjM7C/gfYDUQTxz+AV7drM45SQolsyIiIiKSslRmICIiIiIpS8msiIiIiKQsJbMiIiIikrKUzIqIiIhIylIyKyIiIiIpS8msiIiIiKQsJbMiIiIikrL+HxfvVWFkx9plAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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43vuWcqArQnt+AABgDCym6+Nzkrwiyder6qbhvjcnOT9JWmvvS/KyJP+yqvYneSTJNe1YTDsqagAAwBg4bFBrrX0xByPMQse8O8m7l2pQfbFGDQAAGAdH1PXxuDf3bQhqAABAjwS1Lr+jBgAAjAFBrcPURwAAYBwIal2aiQAAAGNAUOvSnh8AABgDglqXihoAADAGBLUOa9QAAIBxIKh1qagBAABjQFDrGn4b1qgBAAB9EtQ6Dkx9nO13HAAAwIlNUOsy9REAABgDglqX9vwAAMAYENS6VNQAAIAxIKh1aM8PAACMA0GtS0UNAAAYA4JalzVqAADAGBDUOmrC1EcAAKB/glrX3NRHv6MGAAD0SFDrMvURAAAYA4Jal2YiAADAGBDUOrTnBwAAxoGg1qWiBgAAjAFBrcsaNQAAYAwIal0qagAAwBgQ1Dr8jhoAADAOBLWuuZw2K6kBAAD9EdS6TH0EAADGgKDWoT0/AAAwDgS1LhU1AABgDAhqXdrzAwAAY0BQ61JRAwAAxoCg1mGNGgAAMA4Eta65b0NQAwAAeiSodfkdNQAAYAwIah2mPgIAAONAUOvSTAQAABgDglqX9vwAAMAYENS6VNQAAIAxIKh1WKMGAACMA0GtS0UNAAAYA4Ja1/DbsEYNAADok6DWcWDq42y/4wAAAE5sglqXqY8AAMAYENS6tOcHAADGgKDWpaIGAACMAUGtQ3t+AABgHAhqXSpqAADAGBDUuqxRAwAAxoCg1lETpj4CAAD9E9S65qY++h01AACgR4Jal6mPAADAGBDUOnR9BAAAxoGgNh9BDQAA6JGgNqoiqAEAAL0S1EaVNWoAAEC/BLVRKmoAAEDPBLURNVGCGgAA0CtBbVQlbVZSAwAA+iOojTL1EQAA6JmgNqLK1EcAAKBfgtooFTUAAKBngtoo7fkBAICeCWqjVNQAAICeCWojrFEDAAD6JqiNmoigBgAA9EpQG+V31AAAgJ4JaiNMfQQAAPomqI3STAQAAOiZoDZKe34AAKBngtooFTUAAKBngtoIa9QAAIC+CWqjVNQAAICeCWqjJqxRAwAA+iWojaiqZLbvUQAAACcyQW2UqY8AAEDPBLVRpj4CAAA9E9RGmPoIAAD0TVAbNRFTHwEAgF4JaiNqotJmJTUAAKA/gtqoiqmPAABArw4b1KrqKVX1haq6taq+UVW/MM8xVVW/VVW3VdVfV9Uzl2e4K0AzEQAAoGdTizhmf5JfbK3dWFWnJrmhqv6itfbNzjE/nuTi4e3vJ/md4f0xRzMRAACgb4etqLXW7mqt3TjcfijJrUk2jBz24iQfagNfTnJGVZ235KNdCZqJAAAAPTuiNWpVdUGSK5N8ZeSpDUnu6DzelseHuVTV66pqS1Vt2bFjx5GNdIVoJgIAAPRt0UGtqtYm+aMkb2itPTj69DwveVzaaa29v7W2qbW2af369Uc20pWimQgAANCzRQW1qprOIKR9uLX2yXkO2ZbkKZ3HG5PcefTD64FmIgAAQM8W0/Wxkvxukltba/9xgcM+neSVw+6Pz0qys7V21xKOc8XUhGYiAABAvxbT9fE5SV6R5OtVddNw35uTnJ8krbX3Jflskp9IcluS3UlevfRDXSEVa9QAAIBeHTaotda+mPnXoHWPaUl+fqkG1aeaKF0fAQCAXh1R18cTwkRMfQQAAHolqI0y9REAAOiZoDbC1EcAAKBvgtqoCRU1AACgX4LaiCrt+QEAgH4JaqMmYuojAADQK0FtlGYiAABAzwS1EZqJAAAAfRPURmkmAgAA9ExQG6GZCAAA0DdBbZRmIgAAQM8EtRE1UaY+AgAAvRLURlVMfQQAAHolqI2aSFpTUQMAAPojqI2oCc1EAACAfglqo/zgNQAA0DNBbYQfvAYAAPomqI3STAQAAOiZoDZKMxEAAKBngtoIzUQAAIC+CWqjNBMBAAB6JqiN0EwEAADom6A2aiKmPgIAAL0S1EaZ+ggAAPRMUBth6iMAANA3QW3UhIoaAADQL0FtRJX2/AAAQL8EtVETMfURAADolaA2oibK1EcAAKBXgtqoiqmPAABArwS1URNJaypqAABAfwS1EZqJAAAAfRPURmkmAgAA9ExQG6GZCAAA0DdBbZRmIgAAQM8EtVGaiQAAAD0T1EbUhGYiAABAvwS1URVr1AAAgF4JaiNqonR9BAAAeiWojZqIqY8AAECvBLVRpj4CAAA9E9RGmPoIAAD0TVAbpaIGAAD0TFAboaIGAAD0TVAbpZkIAADQM0FtlKmPAABAzwS1EaY+AgAAfRPURk0kaUlr0hoAANAPQW1EVQ025DQAAKAngtqouW9EUAMAAHoiqI2oiUFFTUMRAACgL4LaqOHMRy36AQCAvghqo4bfiGYiAABAXwS1EXNTH1XUAACAvghqo+aaPlqjBgAA9ERQG3GgoianAQAAPRHURmkmAgAA9ExQG6WZCAAA0DNBbYRmIgAAQN8EtVGaiQAAAD0T1EZoJgIAAPRNUBs1942Y+ggAAPREUBtl6iMAANAzQW2EqY8AAEDfBLVRc+35VdQAAICeCGojqrTnBwAA+iWojZr7RhTUAACAnghqozQTAQAAeiaojdBMBAAA6JugNkozEQAAoGeC2gjNRAAAgL4JaqM0EwEAAHomqI2YW6Nm6iMAANAXQW3UcOajqY8AAEBfBLVRc81EmooaAADQD0FtxIH2/CpqAABATwS1UX7wGgAA6JmgNsIPXgMAAH0T1EbNfSOmPgIAAD0R1EaZ+ggAAPTssEGtqq6tqnuq6pYFnn9eVe2sqpuGt19d+mGuHFMfAQCAvk0t4pjfS/LuJB86xDHXt9ZetCQj6puKGgAA0LPDVtRaa9cluX8FxjIWVNQAAIC+LdUatWdX1c1V9WdV9YyFDqqq11XVlqrasmPHjiV66yWmmQgAANCzpQhqNyZ5amvt8iS/neRTCx3YWnt/a21Ta23T+vXrl+Ctl4GpjwAAQM+OOqi11h5sre0abn82yXRVnXXUI+uJqY8AAEDfjjqoVdW5VVXD7auH57zvaM/biy9/OfnjTyZRUQMAAPpz2K6PVfUHSZ6X5Kyq2pbk3yaZTpLW2vuSvCzJv6yq/UkeSXJNa+3YTDmf/Wzq3Z9O8i5r1AAAgN4cNqi11l5+mOffnUH7/mPf5GQOJLRjM2oCAADHgaXq+nh8mJhIDROaqY8AAEBfBLWuiYkcKKWZ+ggAAPREUOuamMhcQjtWl9kBAADHPkGta2Ji7mfUVNQAAIDeCGpd3YqaNWoAAEBPBLWuTjMRXR8BAIC+CGpdmokAAABjQFDr6vyOmmYiAABAXwS1ru7URxU1AACgJ4JaV2fqo2YiAABAXwS1Ls1EAACAMSCodXXa85v6CAAA9EVQ6zL1EQAAGAOCWpepjwAAwBgQ1Lq67flV1AAAgJ4Ial0TE6m5bWvUAACAnghqXd1mIgpqAABATwS1Ls1EAACAMSCodWkmAgAAjAFBrasz9VFFDQAA6Iug1qWZCAAAMAYEtS7NRAAAgDEgqHVNTh5Yo2bqIwAA0BdBravT9dHURwAAoC+CWle3mUhTUQMAAPohqHV12/OrqAEAAD0R1Lr84DUAADAGBLUuFTUAAGAMCGpdfvAaAAAYA4Ja1+Rkaq6UpqIGAAD0RFDr6lbUZlTUAACAfghqXRMTKmoAAEDvBLWuTjMRFTUAAKAvglqXZiIAAMAYENS6JiZSSVLN1EcAAKA3glrXxPDrKFMfAQCA/ghqXcOgVgdnQAIAAKw4Qa1rcjLJIKipqAEAAH0R1Lq6Ux81EwEAAHoiqHUdmPqomQgAANAfQa1LMxEAAGAMCGpdmokAAABjQFDrmgtq1VTUAACA3ghqXZqJAAAAY0BQ65prz1+aiQAAAP0R1LrmKmp+Rw0AAOiRoNbVWaOmogYAAPRFUOvSTAQAABgDglpXd+qjZiIAAEBPBLWuuYpaTH0EAAD6I6h1HWjPb+ojAADQH0GtSzMRAABgDAhqXQd+R017fgAAoD+CWld36qNmIgAAQE8EtS5THwEAgDEgqHVpJgIAAIwBQa1LRQ0AABgDglpXJ6ipqAEAAH0R1Lrmpj5GMxEAAKA/glqXqY8AAMAYENRGTUwkMfURAADoj6A2amIiVbMqagAAQG8EtVETE5qJAAAAvRLURk1MDH5HTTMRAACgJ4LaqImJVDQTAQAA+iOojdJMBAAA6JmgNkozEQAAoGeC2qjJyZSKGgAA0CNBbdTERFKzmokAAAC9EdRGaSYCAAD0TFAbNTGRZNbURwAAoDeC2qiJiVQ0EwEAAPojqI2anEypqAEAAD0S1EZNTibRTAQAAOiPoDZqcjLVTH0EAAD6I6iNmquomfoIAAD0RFAbNVyjpqIGAAD0RVAbNZz6qKIGAAD0RVAbNTmZZEYzEQAAoDeC2qipqUEzkZm+BwIAAJyoDhvUquraqrqnqm5Z4Pmqqt+qqtuq6q+r6plLP8wVNDmZNO35AQCA/iymovZ7SV54iOd/PMnFw9vrkvzO0Q+rR5OTqTajmQgAANCbwwa11tp1Se4/xCEvTvKhNvDlJGdU1XlLNcAVNzmZyoxmIgAAQG+WYo3ahiR3dB5vG+57nKp6XVVtqaotO3bsWIK3XgamPgIAAD1biqBW8+ybN+W01t7fWtvUWtu0fv36JXjrZTA39VEzEQAAoCdLEdS2JXlK5/HGJHcuwXn7MQxqKmoAAEBfliKofTrJK4fdH5+VZGdr7a4lOG8/hlMfNRMBAAD6MnW4A6rqD5I8L8lZVbUtyb9NMp0krbX3Jflskp9IcluS3UlevVyDXRFzUx+TtNmWmphvZicAAMDyOWxQa629/DDPtyQ/v2Qj6tvUVCKoAQAAPVqKqY/Hl05FTUMRAACgD4LaqJGpjwAAACtNUBs1OXlg6qOGIgAAQB8EtVGTk6nZ/UmStl9FDQAAWHmC2qju1McZQQ0AAFh5gtqoyclkrqImqAEAAD0Q1EZNTqaaoAYAAPRHUBtljRoAANAzQW3U1NSBiprfUQMAAPogqI3qVtRMfQQAAHogqI2yRg0AAOiZoDaq2/XRGjUAAKAHgtqoycnU7L4kKmoAAEA/BLVRk5OpmUFQ00wEAADog6A2Snt+AACgZ4LaqMnJ1LCUZuojAADQB0Ft1ORkIqgBAAA9EtRGTU0drKiZ+ggAAPRAUBs1OZnKbBIVNQAAoB+C2qhOUNP1EQAA6IOgNkozEQAAoGeC2qju1Edr1AAAgB4IaqN0fQQAAHomqI0y9REAAOiZoDbK1EcAAKBngtooXR8BAICeCWqjpqb8jhoAANArQW2UNWoAAEDPBLVR3a6P1qgBAAA9ENRGdZuJqKgBAAA9ENRGdaY+aiYCAAD0QVAbpT0/AADQM0FtlKmPAABAzwS1Ubo+AgAAPRPURun6CAAA9ExQG+UHrwEAgJ4JaqM6a9R0fQQAAPogqI2yRg0AAOiZoDZqcjKVQUCzRg0AAOiDoDZqcnJwP9FU1AAAgF4IaqOGQa0mTX0EAAD6IaiNmgtqE6Y+AgAA/RDURh0Iak3XRwAAoBeC2qhuRc3URwAAoAeC2qipqSSDipqpjwAAQB8EtVEHuj6qqAEAAP0Q1EZNTydJqrTnBwAA+iGojepMfdRMBAAA6IOgNmouqJU1agAAQD8EtVGmPgIAAD0T1EYdmPo4q6IGAAD0QlAbNVdRm5hN2yeoAQAAK09QG3VgjdpsZvfN9jwYAADgRCSojZqcTKpU1AAAgN4IavOZmspECWoAAEA/BLX5TE+nJmYENQAAoBeC2nymplKxRg0AAOiHoDafqalUqagBAAD9ENTmMz2dCUENAADoiaA2HxU1AACgR4LafKanU9lvjRoAANALQW0+U1OpqKgBAAD9ENTmMz2diewX1AAAgF4IavOZmkoJagAAQE8EtflMTaWaNWoAAEA/BLX5DJuJqKgBAAB9ENTmMzWVibZPUAMAAHohqM1nejrVBhW11oQ1AABgZQlq85maSmVfkqTNCGoAAMDKEtTmMz2dmh0GNdMfAQCAFSaozWe4Ri0R1AAAgJUnqM1nejo1uzeJoAYAAKw8QW0+U1OpYUXNb6kBAAArTVCbz9RUakZFDQAA6IegNp/paSGOYXYAABi0SURBVGvUAACA3ghq85maSs08mkRQAwAAVp6gNp9OMxFr1AAAgJUmqM3HGjUAAKBHgtp8pqczYeojAADQE0FtPtaoAQAAPVpUUKuqF1bVt6rqtqr65Xmef15V7ayqm4a3X136oa6g6enU/kFQs0YNAABYaVOHO6CqJpO8J8k/TLItydeq6tOttW+OHHp9a+1FyzDGlbdqVSZmhxW1vSpqAADAylpMRe3qJLe11m5vre1N8tEkL17eYfVs1apMZFhR26OiBgAArKzFBLUNSe7oPN423Dfq2VV1c1X9WVU9Y74TVdXrqmpLVW3ZsWPHExjuClm9+mBQe0RQAwAAVtZiglrNs290PuCNSZ7aWrs8yW8n+dR8J2qtvb+1tqm1tmn9+vVHNtKV1Kmozeye6XkwAADAiWYxQW1bkqd0Hm9Mcmf3gNbag621XcPtzyaZrqqzlmyUK23VqkyqqAEAAD1ZTFD7WpKLq+rCqlqV5Jokn+4eUFXnVlUNt68enve+pR7siumuURPUAACAFXbYro+ttf1V9a+TfD7JZJJrW2vfqKqfGz7/viQvS/Ivq2p/kkeSXNNaO3bbJZr6CAAA9OiwQS05MJ3xsyP73tfZfneSdy/t0Hq0enUmMpuaUlEDAABW3qJ+8PqEs2pVkmRitaAGAACsPEFtPp2gZuojAACw0gS1+QyD2uQqFTUAAGDlCWrzmauorWqCGgAAsOIEtfkcCGqzpj4CAAArTlCbz+rVSZLJ6VkVNQAAYMUJavOZq6hNqagBAAArT1Cbz4FmIjOZfVhFDQAAWFmC2nzmgtr0/ux/aH/PgwEAAE40gtp8DlTU9mfmIVMfAQCAlSWozWfYTGRqel9mHpxJa63nAQEAACcSQW0+cxW1qb1p+1tmH7VODQAAWDmC2nw6QS1JZh40/REAAFg5gtp8JieTqkxN7EkS69QAAIAVJajNpypZtSqTU4Ogtn+nzo8AAMDKEdQWsnr1gYqaoAYAAKwkQW0hq1ZlamJ3EkENAABYWYLaQlatylQNgppmIgAAwEoS1BayalUm264kKmoAAMDKEtQWsmpVpvJwkmT/A4IaAACwcgS1haxalYn9ezK5djL7vy+oAQAAK0dQW8jq1cnevZlaN5V99+3rezQAAMAJRFBbyKpVyaOPZvrM6ey/X0UNAABYOYLaQk46KdmzR0UNAABYcYLaQk46Kdm9O9PrprPvfkENAABYOYLaQk4+eRDUzpzO/vtMfQQAAFaOoLaQYVCbWjeVfffvS5ttfY8IAAA4QQhqCzn55OSRR7LqvFXJbLL3nr19jwgAADhBCGoLGVbU1jx1TZLk0a2P9jwgAADgRCGoLWTYTGTNU1YnSfZ8d0/PAwIAAE4UgtpCTj45aS2rzxt8RXu2CmoAAMDKENQWcvLJSZLpVXszedqkqY8AAMCKEdQWMgxqc+vUVNQAAICVIqgtZDSoWaMGAACsEEFtISedNLh/5JGsfurq7PnOnrTmt9QAAIDlJ6gtpFNRO+Xpp2Rm50z2fEdVDQAAWH6C2kLmgtrDD2ftFWsHm19/uMcBAQAAJwpBbSGnnz6437kza69Ym5qu7PzLnf2OCQAAOCEIagtZt25wf//9mTx5Mqc967Tc9//eZ50aAACw7AS1hXSCWpKc88/Oye5v7s5DWx7qcVAAAMCJQFBbyMknJ6tWJd//fpLk7H9ydibWTOR7H/hezwMDAACOd4LaQqqSJz3pQEVt6vSprP8n63PXf7oru/56V8+DAwAAjmeC2qGsW3cgqCXJRb92USZPn8wtL70le+7Qqh8AAFgegtqhrFt3YOpjkqzesDqXffqy7Lt3X758/pdzy0tvyf4H9/c4QAAA4HgkqB3KmWcmO3Y8ZtfpP3x6rvzilZk+Zzr3furefPH0L+brP/X13PGbd+S+P7svu2/bndn9sz0NGAAAOB5M9T2AsbZxY3L99Y/bvfaytXnO956TB657IHf97l3Z+cWdue9P7zvwfE1V1vzAmpx88clZvXF1Vp23Kqs3rs6ai9Zk9ZNXZ82FazIxLSMDAADzE9QO5fzzB1Mfd+1K1q593NNnbD4jZ2w+I0myd8fePPI3j2T33+we3P/t7jzyt49k55d2Zv99j50eWVOVNRetydrL1+a0Z5+WdS9Yl5MvOTlVtSIfCwAAGG+C2qGcf/7g/o47kksuOeShq9avyqr1q3L6c05/3HMzj8xk7117s+fbe/Lo9kez+1u7s/vW3Xnoaw9lxx/uyP/M/8xJf+eknPvqc/Pk1z4502dOL8enAQAAjhGC2qHMBbXvfvewQe1QJk+azEkXnZSTLjrpcc/tuWNP7vvMfbnnD+7Jt9/07Wz9d1tz9j89OxtevyGnXnHqE35PAADg2CWoHUo3qC2TNU9Zkw0/tyEbfm5Ddt2yK9vfvT13/5e7871rv5e1V67NmS86M6f/6Ok57dmnZWqt/1wAAHAi8Df/QznvvGRiYlmDWtfaS9fmae97Wi56+0W5+/fvzt2/f3e2/trWZDbJZHLqlafmtOecNrh/9mk56eKTrGsDAIDjkKB2KFNTyYYNKxbU5kw/aTobX78xG1+/Mfsf3J8Hv/RgHrj+gey8fmfu+n/uyvY92wfDO3MqZ/zoGTn9uafnjOeekbV/d21qUnADAIBjnaB2OOefP2gm0pOp06ay7gXrsu4F65Ikbabl4VsfzoNfenAQ4P77A7n3U/cOjj1jKk/6h0/Kuh9fl9N/+PRBxW1CcAMAgGONoHY455+ffPWrfY/igJqsrL10bdZeujZPfu2TkyR7tu3Jzut25vv/9fu5/8/uz44/HPxI98QpE1l7+dqsvWJt1l65NqdeeWpOufSUTKz2G24AADDOBLXDOf/85I/+KJmZSSYn+x7NvNZsXJM1/3RNzvmn56S1loe/8XAe2vJQdv3Vruz6q125+7/cnTvfe2eSwW+4nfz0kw8Et7VXDoLc1GkuBQAAGBf+dn44l1yS7N2b/M3fHFWL/pVSdbDillcN9rXZlkduf+RAcNv1V7ty/+fuz90fvPvA69b8wJqc8vRTsubCNVlzwZoD9yddeFKmTneZAADASvI38MO56qrB/de+dkwEtfnUROXkHzw5J//gyTn7H599YP+jdz16ILg99FcP5ZG/eSQPfOGBzOyaeczrp86Yyuqnrs70WdOZPnM60+umM7VuKtNnHryfPmtwW3X2qkyeNqkbJQAAHAVB7XCe9rTklFOSr3wleeUr+x7Nklp93uqsPm91zvyJMw/sa61l//37s+c7e/LItx/Jnu/syZ5v78mj3300++7fl10378r++/dn3/37kpn5z1urK6vOXpXpswfBbdU5B7enz5k++Nw5qzK9fjoTU9bMAQBAl6B2OJOTyebNyec/n7SWHOeVoqoaVMjOnM6pf+/UBY9rsy0zD81k3337Brd792XfjsFt7z17s/fuvdl3z2D74Vsezt6796btbfO8YTJ91nRWb1id1RtXZ9WTB8Fu1TnDUHfOwccqdQAAnCgEtcV40YuSn//55JZbkssu63s0Y6EmKlOnT2Xq9KmcdNFJhz2+tZaZB2ey955hgLt778Hb9/Zm7/a9eXTbo3nwqw9m3459yXyZblipmy/EzT2eOmMqE6smUqtq/vvp8pMFAACMvWptnr8Rr4BNmza1LVu29PLeR2zHjuQHfiD5sR9LPvWpvkdz3GszLfvufWyY23f3wcfdoLfvnn1p+4/sGq6pemxwWyjULdX99JEfX5OVTA4CcU1WMhHVRACA40xV3dBa2zTfcypqi7F+ffLmNydvetMgqL3kJX2P6LhWk3WgUnY4bbZl//f3HwhuMw/OZHbvbNq+Nrjfe3T3M7tmFnXcQuv1ltwwvGVi8D11tzMxEuwmav7jj/QcE0lquF3D7Tq4vdyPu++70u/9mMcTPb5397uY++8ytz8jx2Xktcnj7h8T+hd6v4XOU53Xd07zmPPNZ4H9C/4DxHKd+wjPcTyd+4i+6z7OvRTXyHKee9yv7eU89wlybfsHUcaNitpi7dmT/MiPJF//evKrv5q89rXJ2Wcf/nWcENpMy+y++YPckYbG2Udnk9lBCM3M8H528B7d7bljutvzHr8U52gZPG7D7eFtRR7PLs+5AeBxuv9YNc9zj/mHqr7+0W70HxAnOvsnDo5/vrEuan/3u5jvfgk94XD8BF42tW4ql31q/JYwqagthTVrkj//8+TVr05+5VeSt741+eEfHrTsn7tdfHFyzjnJSSfleG86wmPVZGVycjJZ0/dIOBLd8HbYoDd7BMcu1+O5MD0XmpPHHDf6mQ48n5Hjk8e8dr4QO995DuwbPU8Osa/73os8ftnOfSTv18e5j/Acx9O5l+QaWc5zu7bH99xLdW2P/D9+NEA87v9/ff5Z0H089+dC8tjt+cbaeTy3Pe+fDaOvTxb+73o0nugpn+DravLY+7u5oHYk1q1L/uRPkm9+M/nAB5K//Mvk4x9Pvv/9xx43NZWcccbgdtppg9uppyann5486UkHb2eemWzYkFx4YXL++YMOk8CK6U7rq+X4p0IAgCdIUHsinv705B3vGGy3ltxzT3Lrrcnttw+2d+5MHnhgcL9zZ/Lgg8l3vzvY/v73B/ej1qxJ/s7feWyF7pJLBvtWr17ZzwcAAPRKUDtaVYPpjueckzzveYt7zczMIKzdd19yxx2DgPc//scg7H31q4Mq3VyJeWIiueiig8HtgguSc889eNuwIVl1+KYbAADAsUNQ68Pk5GAa5bp1g3VtP/Zjj33+kUeSb31rENy6t899Ltm377HHViXnnTeYOvnUpx68726ffvrKfTYAAOCoCWrj6KSTkiuuGNy6ZmYGv+n2ve8NbnfeOajIbd06uN1wQ/LHf5zs3fvY15122iCwbdyYPPnJg2D35CcfvG3cOOhgaY0cAACMBUHtWDI5eXDK40JmZ5O77x6sidu69eD91q3J9u3JTTcNnp+dffy55wLcWWcNGp2sW3fwfq4hyny3tWuFPAAAWEKC2vFmYmIQuM47L/n7f3/+Y/bvHzQ9ufPOwW379sfe7r570Nny/vsHjVAW4+STHx/eFgp28wW97uNTThl8DgAAOEEJaieiqamD0x4PZ9++g4Ft167koYcOf5s77u67k9tue+z+xagahLXFhLqTTx40U1m9enC/0PbUVDI9Pbg/kptKIQAAPRDUOLTp6YNdLY/W7Gzy8MOPD3SLDX7btj12/+7dRz+mw6l6bGibmDh4G3280G2pj1vKc1YdvM193uW6P17e43j7PCv5nXXvD7dvMdted2K+DuAEIaixciYmDlbClsLMTLJnz6B5yqOPDu672937/fuP7LZv3/z7WxsEzpmZwf3hbkd63P79S3O+Izl27qcgRu8Bxt04B8qVeN04j+1Ef92h7hfzD1nzvZ99R/e6M85I3v/+xx83xgQ1jl2Tk4Mpkqec0vdIjm8LBbkjuV+Kc5wo73G8fp7u/eH2LWbb67zuRH/dOI/tRH/doe7nmrkd6v+P872ffUd/rrPOevy+MSeoAYd2qH/pAwBgWWitBwAAMGYENQAAgDEjqAEAAIwZQQ0AAGDMCGoAAABjZlFBrapeWFXfqqrbquqX53m+quq3hs//dVU9c+mHCgAAcGI4bFCrqskk70ny40menuTlVfX0kcN+PMnFw9vrkvzOEo8TAADghLGYitrVSW5rrd3eWtub5KNJXjxyzIuTfKgNfDnJGVV13hKPFQAA4ISwmKC2IckdncfbhvuO9JhU1euqaktVbdmxY8eRjhUAAOCEsJigVvPsa0/gmLTW3t9a29Ra27R+/frFjA8AAOCEs5igti3JUzqPNya58wkcAwAAwCIsJqh9LcnFVXVhVa1Kck2ST48c8+kkrxx2f3xWkp2ttbuWeKwAAAAnhKnDHdBa219V/zrJ55NMJrm2tfaNqvq54fPvS/LZJD+R5LYku5O8evmGDAAAcHw7bFBLktbaZzMIY9197+tstyQ/v7RDAwAAODEt6gevAQAAWDmCGgAAwJgR1AAAAMaMoAYAADBmBDUAAIAxI6gBAACMGUENAABgzAhqAAAAY0ZQAwAAGDPVWuvnjat2JNnay5sf2llJ7u17EBy3XF8sN9cYy8n1xXJyfbGcxvX6emprbf18T/QW1MZVVW1prW3qexwcn1xfLDfXGMvJ9cVycn2xnI7F68vURwAAgDEjqAEAAIwZQe3x3t/3ADiuub5Ybq4xlpPri+Xk+mI5HXPXlzVqAAAAY0ZFDQAAYMwIagAAAGNGUOuoqhdW1beq6raq+uW+x8P4q6qnVNUXqurWqvpGVf3CcP+6qvqLqvrb4f2TOq950/Aa+1ZVvaCz/+9V1deHz/1WVVUfn4nxU1WTVfVXVfWnw8euL5ZMVZ1RVZ+oqv8x/H/Zs11jLJWq+t+Gfz7eUlV/UFVrXF88UVV1bVXdU1W3dPYt2fVUVaur6mPD/V+pqgtW8vONEtSGqmoyyXuS/HiSpyd5eVU9vd9RcQzYn+QXW2uXJHlWkp8fXje/nOS/ttYuTvJfh48zfO6aJM9I8sIk7x1ee0nyO0lel+Ti4e2FK/lBGGu/kOTWzmPXF0vp/07yudbaDyW5PINrzTXGUauqDUn+1ySbWmuXJpnM4PpxffFE/V4e/99+Ka+n1yT5fmvtB5O8M8l/WLZPsgiC2kFXJ7mttXZ7a21vko8meXHPY2LMtdbuaq3dONx+KIO/4GzI4Nr54PCwDyZ5yXD7xUk+2lp7tLX27SS3Jbm6qs5Lclpr7Utt0OHnQ53XcAKrqo1JfjLJf+7sdn2xJKrqtCSbk/xukrTW9rbWHohrjKUzleSkqppKcnKSO+P64glqrV2X5P6R3Ut5PXXP9Ykkz++zeiuoHbQhyR2dx9uG+2BRhuXxK5N8Jck5rbW7kkGYS3L28LCFrrMNw+3R/fCuJP9HktnOPtcXS+WiJDuSfGA4vfY/V9UpcY2xBFpr25P8X0m+m+SuJDtba38e1xdLaymvpwOvaa3tT7IzyZnLNvLDENQOmi8t++0CFqWq1ib5oyRvaK09eKhD59nXDrGfE1hVvSjJPa21Gxb7knn2ub44lKkkz0zyO621K5M8nOG0oQW4xli04VqhFye5MMmTk5xSVf/sUC+ZZ5/riyfqiVxPY3WtCWoHbUvylM7jjRmU5+GQqmo6g5D24dbaJ4e77x6W1jO8v2e4f6HrbNtwe3Q/J7bnJPnpqvpOBtOxf6yqfj+uL5bOtiTbWmtfGT7+RAbBzTXGUvgHSb7dWtvRWtuX5JNJfjiuL5bWUl5PB14znK57eh4/1XLFCGoHfS3JxVV1YVWtymDx4ad7HhNjbjhv+XeT3Npa+4+dpz6d5J8Pt/95kj/p7L9m2FXowgwWsH51WKp/qKqeNTznKzuv4QTVWntTa21ja+2CDP6f9N9aa/8sri+WSGvte0nuqKqnDXc9P8k34xpjaXw3ybOq6uThdfH8DNZyu77+//bu30WqK4zD+PPFiCxImghBELXQSogiwUKbkH/BiAaxkK2WmFhJJI1NCu1ETKPEQlkCIlgmGiwEMZgiWWO2C4uFoKBFECGILm+Ke4RBdsEfs8519vnA4Z77zsyZc+HAnfece+9omIY5ngbb+oLuvDuyFbUPRvXFfVNVz5McAq7QPZXoXFXNjrhb6r9dwAHgTpKZFvsOOA5cTDJJd6LaA1BVs0ku0v0Qeg58VVXz7XNTdE8zmgB+bkVaiONLw/Q1MN0mKeeAg3QTuY4xvZWqupXkEvAH3Xj5EzgDrMbxpTeQ5CfgM2BNknvAMYZ7TvwRuJDkH7qVtH3v4LAWlREmiZIkSZKkBXjpoyRJkiT1jImaJEmSJPWMiZokSZIk9YyJmiRJkiT1jImaJEmSJPWMiZok6b2XZD7JzEA5OsS2Nyb5e1jtSZL0KvwfNUnSOPivqraNuhOSJA2LK2qSpLGV5G6SE0l+b2VTi29Ici3JX227vsU/TnI5ye1WdramViQ5m2Q2ydUkEyM7KEnSsmCiJkkaBxMvXfq4d+C1x1W1AzgNnGyx08D5qvoEmAZOtfgp4HpVbQW2A7Mtvhn4oaq2AP8Cu5f4eCRJy1yqatR9kCTprSR5UlWrF4jfBT6vqrkkK4EHVfVRkkfA2qp61uL3q2pNkofAuqp6OtDGRuDXqtrc9r8FVlbV90t/ZJKk5coVNUnSuKtF6ou9ZyFPB+rzeI+3JGmJmahJksbd3oHtb61+E9jX6vuBG61+DZgCSLIiyYfvqpOSJA1yRlCSNA4mkswM7P9SVS8e0b8qyS26yckvW+wb4FySI8BD4GCLHwbOJJmkWzmbAu4vee8lSXqJ96hJksZWu0ft06p6NOq+SJL0Orz0UZIkSZJ6xhU1SZIkSeoZV9QkSZIkqWdM1CRJkiSpZ0zUJEmSJKlnTNQkSZIkqWdM1CRJkiSpZ/4H8Py8VNlOC3UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Loss before/after: 1.4760775020581553e-05, 5.325409697486495e-07\n",
      "Validation Loss before/after: 2.824025089456432e-05, 1.1545675204552968e-06\n",
      "Final loss: 5.325409697486495e-07\t\tTime taken: 78.31953144073486\n",
      "Model saved at model_data\\TimbreVAE_Expts_nmels256_nfft2048\\TimbreVAE_FinalCreationtanh_lr0.001_n_epochs10000_n_mfcc40_n_hid36_n_timb10_1.1545755490981813e-06.pt\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_mels = 256\n",
    "n_ffts = 2048\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # #\n",
    "# Data that is constant throughout the entire run #\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # #\n",
    "\n",
    "# File reference lists\n",
    "data_ref_list = create_data_ref_list(os.path.join(\"Data\", 'dataset_files.csv'),\n",
    "                            n_pitches, n_vowels, n_people)\n",
    "# flat_data_ref_list[flat_ref_idx(vowel, pitch, person)]\n",
    "flat_data_ref_list = flatten_3d_array(data_ref_list, \n",
    "                                      n_vowels, n_pitches, n_people)\n",
    "# File reference list accessors\n",
    "# Returns a flat_ref_idx, given a vowel, pitch, person\n",
    "flat_ref_idx = lambda vowel, pitch, person: flat_3d_array_idx(\n",
    "    vowel, pitch, person, n_vowels, n_pitches, n_people)\n",
    "# Returns vowel, pitch, person, given a flat_ref_idx\n",
    "nd_ref_idx = lambda idx: nd_array_idx(idx, n_vowels, n_pitches, n_people)\n",
    "\n",
    "# wav, spectrogram, mels, mfcc for each file in flat_data_ref_list\n",
    "# wav_data:     (576, ~29400)  (n_wavs, n_samples)\n",
    "# spectrograms: (576, 513, 58) (n_wavs, n_freq_bins, n_windows)\n",
    "all_wav_data = load_wav_files(os.path.join(\"Data\", \"dataset\"), \n",
    "                              flat_data_ref_list)\n",
    "all_spectrograms = np.array([ stft(waveform, win_length=n_ffts, plot=False) \n",
    "                              for waveform in all_wav_data ])\n",
    "\n",
    "# Define loss \n",
    "loss_fn = nn.MSELoss()\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # \n",
    "# Data that is constant per n_mfcc  #\n",
    "# # # # # # # # # # # # # # # # # # # \n",
    "\n",
    "def create_data_for_mfcc(n_mfcc):\n",
    "    # mels:         (576, 128, 58) (n_wavs, n_mels, n_windows)\n",
    "    # mfccs:        (576, 20, 58)  (n_wavs, n_mfcc, n_windows)\n",
    "    all_mels, all_mfcc = map(np.array, map(list, zip(*\n",
    "                             [ ffts_to_mel(ffts, n_mels = n_mels, n_mfcc = n_mfcc) \n",
    "                               for ffts in all_spectrograms ])))\n",
    "    n_files, n_mfcc_dummy, n_windows = all_mfcc.shape\n",
    "\n",
    "    # Returns a flat 2d idx, given a wavfile index and a window index\n",
    "    flat_data_idx = lambda wav_idx, win_idx: flat_2d_array_idx(\n",
    "        wav_idx, win_idx, n_files, n_windows)\n",
    "\n",
    "    # data:   (33408, 20) (n_wavs * n_windows, n_mfcc)\n",
    "    data_prenorm = np.array([ all_mfcc[wav_file_idx][:, window_idx] \n",
    "                              for wav_file_idx in range(n_files) \n",
    "                              for window_idx in range(n_windows) ])\n",
    "\n",
    "    # Globally normalize to [-1, 1]\n",
    "    # log(-n) is nan, so we put things on a log scale from absolute values, and\n",
    "    # mutliply the sign back.\n",
    "    signs = np.sign(data_prenorm)\n",
    "    data_logged = np.log(np.abs(data_prenorm))\n",
    "    max_mfcc = np.max(np.abs(data_logged))\n",
    "    data = data_logged / max_mfcc\n",
    "    \n",
    "    # X_train: (22272, 20)\n",
    "    # X_val: (11136, 20)\n",
    "    X_train = []; X_val = [];\n",
    "    for vow_idx in range(n_vowels):\n",
    "        for pit_idx in range(n_pitches):\n",
    "            # Choose the person for this pitch/vowel to be used as test data\n",
    "            test_pid = int(np.random.rand() * 3)\n",
    "            for pid_idx in range(n_people):\n",
    "                wav_idx = flat_ref_idx(vow_idx, pit_idx, pid_idx)\n",
    "                if (pid_idx != test_pid):\n",
    "                    for win_idx in range(n_windows):\n",
    "                        X_train.append(data[flat_data_idx(wav_idx, win_idx)])\n",
    "                else:\n",
    "                    for win_idx in range(n_windows):\n",
    "                        X_val.append(data[flat_data_idx(wav_idx, win_idx)])\n",
    "    X_train, X_val = map(torch.tensor, (X_train, X_val))\n",
    "    # Default tensor is float\n",
    "    X_train = X_train.float(); X_val = X_val.float()\n",
    "    \n",
    "    return data, data_prenorm, max_mfcc, signs, flat_data_idx, X_train, X_val\n",
    "\n",
    "# # # # # # # # # # # # # #\n",
    "# Actual experiment loop  #\n",
    "# # # # # # # # # # # # # # \n",
    "\n",
    "# Results: \n",
    "# Indices: [ [ n_mfcc, n_hid, n_timb ], [ ... ], ... ]\n",
    "# Losses: [ loss1, loss2, ... ]\n",
    "indices = []; train_losses = []; val_losses = [];\n",
    "\n",
    "cands = [ [12, 10, 6],  [24, 22, 10], [40, 36, 10] ]\n",
    "\n",
    "f = open(os.path.join(\"model_data\", \"TimbreVAE_Expts_nmels256_nfft2048\", \"FinalCreation.txt\"), \"w\")\n",
    "for cand in cands:\n",
    "    n_mfcc, n_hid, n_timb = cand\n",
    "    data, data_prenorm, max_mfcc, signs, flat_data_idx, X_train, X_val = create_data_for_mfcc(n_mfcc)\n",
    "    lr = 1e-3; n_epochs = 10000; batch_size=22272\n",
    "\n",
    "    # Training model \n",
    "    model = TimbreVAE(n_mfcc=n_mfcc, n_hid=n_hid, n_timb=n_timb)\n",
    "\n",
    "    # Use GPU if possible (will run on CPU otherwise)\n",
    "    device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "\n",
    "    # Move inputs to GPU (if possible)\n",
    "    X_train = X_train.to(device)\n",
    "    X_val = X_val.to(device)\n",
    "\n",
    "    # Move the network to GPU (if possible)\n",
    "    model.to(device) \n",
    "    # Define optimizer \n",
    "    # opt = optim.SGD(model.parameters(), lr=lr)\n",
    "    opt = optim.Adam(model.parameters(), lr=lr)\n",
    "\n",
    "    # Fit the model\n",
    "    tic = time.time()\n",
    "    train_loss, val_loss = model.train_func(X_train, X_val, model, opt, loss_fn, batch_size=batch_size,\n",
    "                            epochs=n_epochs, print_graph = True, desc=\"n_mfcc{},n_hid{},n_timb{}\".format(n_mfcc, n_hid, n_timb))\n",
    "    toc = time.time()\n",
    "    print('Final loss: {}\\t\\tTime taken: {}'.format(train_loss, toc - tic))\n",
    "    model_path = os.path.join(\"model_data\", \"TimbreVAE_Expts_nmels256_nfft2048\", \n",
    "                              \"TimbreVAE_FinalCreationtanh_lr{}_n_epochs{}_n_mfcc{}_n_hid{}_n_timb{}_{}.pt\"\n",
    "                              .format(lr, n_epochs, n_mfcc, n_hid, n_timb, val_loss))\n",
    "    torch.save(model.state_dict(), model_path)\n",
    "    print(\"Model saved at {}\".format(model_path))\n",
    "\n",
    "    indices.append([n_mfcc, n_hid, n_timb]); train_losses.append(train_loss); val_losses.append(val_loss)\n",
    "\n",
    "    # Create the validation printout and graph\n",
    "    data_tensor = torch.tensor(data)\n",
    "    data_tensor = data_tensor.float();\n",
    "    data_tensor = data_tensor.to(device)\n",
    "\n",
    "    wav_idx = flat_ref_idx(5, 5, 1)\n",
    "    data_idx = flat_data_idx(wav_idx, 30)\n",
    "    label = data_tensor[data_idx]\n",
    "    pred, _, _ = model(data_tensor)\n",
    "\n",
    "    # To restore:\n",
    "    restored_pred = signs * np.e ** (pred.cpu().detach().numpy() * max_mfcc)\n",
    "\n",
    "    # Visualize MFCCs\n",
    "    fig, ax = plt.subplots(2, 1, figsize=(10, 10))\n",
    "    fig.suptitle(\"TimbreVAE FinalCreation tanh lr{} n_epochs{} n_mfcc{} n_hid{} n_timb{} loss{}\"\n",
    "                 .format(lr, n_epochs, n_mfcc, n_hid, n_timb, val_loss))\n",
    "    # plt.figure()\n",
    "    ax[0].plot(data_prenorm[data_idx])\n",
    "    ax[0].plot(restored_pred[data_idx])\n",
    "    # fig, ax = plt.subplots(2, 1)\n",
    "    # plt.figure()\n",
    "    ax[1].plot(data[data_idx])\n",
    "    ax[1].plot(pred.cpu().detach().numpy()[data_idx])\n",
    "    fig.savefig(os.path.join(\"model_data\", \"TimbreVAE_Expts_nmels256_nfft2048\", \n",
    "        \"TimbreVAE_FinalCreationtanh_lr{}_n_epochs{}_n_mfcc{}_n_hid{}_n_timb{}_{}.jpg\"\n",
    "                .format(lr, n_epochs, n_mfcc, n_hid, n_timb, val_loss)))\n",
    "\n",
    "    # Numeric verification\n",
    "    np.set_printoptions(suppress=True)\n",
    "    f.write(\"TimbreVAE_FinalCreationtanh lr{} n_epochs{} n_mfcc{} n_hid{} n_timb{} loss{}\"\n",
    "            .format(lr, n_epochs, n_mfcc, n_hid, n_timb, val_loss))\n",
    "\n",
    "    f.write(str(data_prenorm[data_idx]))\n",
    "    f.write(str(restored_pred[data_idx]))\n",
    "    f.write(str(data[data_idx]))\n",
    "    f.write(str(pred.cpu().detach().numpy()[data_idx]))\n",
    "f.close()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "0 [12, 10, 8] 2.2488103272264888e-07\n",
      "1 [12, 10, 6] 1.1263185732023812e-06\n",
      "2 [12, 10, 4] 1.7029106156665017e-06\n",
      "3 [12, 8, 6] 1.1723956237707673e-06\n",
      "4 [12, 8, 4] 2.330470490322887e-06\n",
      "5 [12, 6, 4] 2.383953796688938e-06\n",
      "6 [24, 22, 10] 1.407030537649561e-06\n",
      "7 [40, 38, 10] 2.308088473857905e-06\n"
     ]
    }
   ],
   "source": [
    "print(np.argmin(val_losses[1:8])+1)\n",
    "for i in range(len(val_losses)):\n",
    "    print(i, indices[i], val_losses[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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