Compare_Spectral_Vs_DEC_VoxConverse_OracleVAD.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DT5hNAsu0X-u"
   },
   "source": [
    "# **LOAD LIBRARIES**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 16400,
     "status": "ok",
     "timestamp": 1620721876651,
     "user": {
      "displayName": "Shashi Kant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiHv2woo7B1czb0XKsLBEOsgMWsuYITOryCdNNv7A=s64",
      "userId": "09783338171816410510"
     },
     "user_tz": -330
    },
    "id": "ouVW09h_0Usm",
    "outputId": "4a2ba42a-7e44-45cb-85b8-aca4958b4227"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[K     |████████████████████████████████| 1.9MB 29.6MB/s \n",
      "\u001b[K     |████████████████████████████████| 358kB 32.1MB/s \n",
      "\u001b[K     |████████████████████████████████| 1.2MB 47.8MB/s \n",
      "\u001b[K     |████████████████████████████████| 645kB 44.3MB/s \n",
      "\u001b[K     |████████████████████████████████| 102kB 13.3MB/s \n",
      "\u001b[K     |████████████████████████████████| 552kB 45.2MB/s \n",
      "\u001b[K     |████████████████████████████████| 51kB 7.6MB/s \n",
      "\u001b[K     |████████████████████████████████| 51kB 8.5MB/s \n",
      "\u001b[K     |████████████████████████████████| 61kB 9.5MB/s \n",
      "\u001b[K     |████████████████████████████████| 133kB 42.0MB/s \n",
      "\u001b[?25h"
     ]
    }
   ],
   "source": [
    "!pip install torchaudio -q --no-deps\n",
    "!pip install speechbrain -q\n",
    "!pip install spectralcluster -q\n",
    "!pip install pyannote.metrics -q\n",
    "!pip install gdown -q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 20521,
     "status": "ok",
     "timestamp": 1620721880792,
     "user": {
      "displayName": "Shashi Kant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiHv2woo7B1czb0XKsLBEOsgMWsuYITOryCdNNv7A=s64",
      "userId": "09783338171816410510"
     },
     "user_tz": -330
    },
    "id": "Qe3sDhQYbwDi",
    "outputId": "56336477-7bb9-4bce-c9ee-8381ecebd5e4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1XltkNas1LTOck62GIDYT0WcPDPGZEo7P\n",
      "To: /content/utils.py\n",
      "100% 16.8k/16.8k [00:00<00:00, 14.8MB/s]\n",
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1FWIRF2Kq6JB0fFHwjkZmVfEuGq0-SSC5\n",
      "To: /content/DEC.py\n",
      "100% 10.1k/10.1k [00:00<00:00, 16.5MB/s]\n",
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1hbepzjAxHTQS5QoAlKNP9zK0nMxdjauJ\n",
      "To: /content/optimumSpeaker.py\n",
      "100% 7.08k/7.08k [00:00<00:00, 6.12MB/s]\n",
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1RQkezkiXa5DVFHE7g4UKE2v8vcmgJFpm\n",
      "To: /content/baselineMethods.py\n",
      "100% 4.39k/4.39k [00:00<00:00, 7.30MB/s]\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/shashikg/speaker_diarization_ee698.git\n",
    "!cp -r speaker_diarization_ee698/core/. core"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 20514,
     "status": "ok",
     "timestamp": 1620721880793,
     "user": {
      "displayName": "Shashi Kant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiHv2woo7B1czb0XKsLBEOsgMWsuYITOryCdNNv7A=s64",
      "userId": "09783338171816410510"
     },
     "user_tz": -330
    },
    "id": "0iqaDi7jb5qQ",
    "outputId": "df4b8336-5370-4258-e6db-dfd60a2bcab6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "executionInfo": {
     "elapsed": 28189,
     "status": "ok",
     "timestamp": 1620721888470,
     "user": {
      "displayName": "Shashi Kant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiHv2woo7B1czb0XKsLBEOsgMWsuYITOryCdNNv7A=s64",
      "userId": "09783338171816410510"
     },
     "user_tz": -330
    },
    "id": "FGTt1sJS0bLP"
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function, division\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import torchaudio\n",
    "import os\n",
    "import torch\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from speechbrain.pretrained import SpeakerRecognition\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from sklearn.cluster import KMeans, SpectralClustering\n",
    "from sklearn import decomposition\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "from core.utils import DiarizationDataSet, get_metrics, plot_annot\n",
    "import core.baselineMethods as baselineMethods\n",
    "from core.DEC import diarizationDEC\n",
    "\n",
    "import shutil\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4Dv0fC5mhhao"
   },
   "source": [
    "# Download dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 143549,
     "status": "ok",
     "timestamp": 1620722032030,
     "user": {
      "displayName": "Shashi Kant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiHv2woo7B1czb0XKsLBEOsgMWsuYITOryCdNNv7A=s64",
      "userId": "09783338171816410510"
     },
     "user_tz": -330
    },
    "id": "OwMmmaqYhnn0",
    "outputId": "97180540-fd7e-4fc0-9268-3edb2fe51e9a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading audio dataset...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1-Ft9RKdEv6xcR3XHlVAgMNuf0jfy08aY\n",
      "To: /content/tmp.zip\n",
      "1.99GB [00:28, 69.9MB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Download and Extraction Complete\n",
      "Downloading precomputed VADs...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=18oXqn9Zyt5tJpoEwKKztpTag-AJMQ2Sz\n",
      "To: /content/tmp.zip\n",
      "100%|██████████| 128k/128k [00:00<00:00, 6.50MB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Download and Extraction Complete\n",
      "Precomputed X-vectors exists!\n",
      "Will use precomputed features...\n",
      "\n",
      "Downloading precomputed features...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1-2-AZnabTtHxLmw2DBwj4PJDGwlvFa8J\n",
      "To: /content/tmp.zip\n",
      "69.4MB [00:01, 56.1MB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Download and Extraction Complete\n",
      "\n",
      "Data size: 50\n"
     ]
    }
   ],
   "source": [
    "audio_dataset = DiarizationDataSet(dataset_name='voxconverse',\n",
    "                                   window_len=1500, \n",
    "                                   window_step=750,\n",
    "                                   split='test',\n",
    "                                   use_oracle_vad=True)\n",
    "\n",
    "print(\"\\nData size:\", len(audio_dataset))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OiFZ_66hh6Nv"
   },
   "source": [
    "# Spectral - EigenGap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 115,
     "referenced_widgets": [
      "f15813e5769b45ba9427e5075c7fadd5",
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      "12d018b65998438fb2ab3b149efbc299",
      "bcfd0fa80fde4a41ae1ac5d32b4b462e",
      "b9a6309bc4fa43d2886133a1798ea688",
      "8812a74d461c41788881f47a0478689c",
      "3c728c80538e4e3c9eb6275db1dea8bb",
      "702f410396d1416bb529d419373ff0e0",
      "a23f02ca146b4b0aa7239bf7f76b1e0d"
     ]
    },
    "executionInfo": {
     "elapsed": 161224,
     "status": "ok",
     "timestamp": 1620722049713,
     "user": {
      "displayName": "Shashi Kant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiHv2woo7B1czb0XKsLBEOsgMWsuYITOryCdNNv7A=s64",
      "userId": "09783338171816410510"
     },
     "user_tz": -330
    },
    "id": "x5JJ_CkFiEcI",
    "outputId": "58889805-919b-48ef-8fbd-a6931a4dd337"
   },
   "outputs": [
    {
     "data": {
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       "version_major": 2,
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       "HBox(children=(FloatProgress(value=0.0, max=50.0), HTML(value='')))"
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     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4d2d2e13a217404dbc36dd5969758a2b",
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       "HBox(children=(FloatProgress(value=0.0, max=50.0), HTML(value='')))"
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     },
     "metadata": {
      "tags": []
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "hypothesis_dir_spectral = baselineMethods.diarizationEigenGapNumSpkrs(audio_dataset, hypothesis_dir=\"./rttm_output_spectral/\")\n",
    "metric_spectral = get_metrics(audio_dataset.label_dir, hypothesis_dir_spectral)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WVWdrk5kv4vp"
   },
   "source": [
    "# DEC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 166,
     "referenced_widgets": [
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    },
    "executionInfo": {
     "elapsed": 210006,
     "status": "ok",
     "timestamp": 1620722113153,
     "user": {
      "displayName": "Shashi Kant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiHv2woo7B1czb0XKsLBEOsgMWsuYITOryCdNNv7A=s64",
      "userId": "09783338171816410510"
     },
     "user_tz": -330
    },
    "id": "D3myQoh-lv-U",
    "outputId": "26cb5dc1-1272-4d6c-c5c3-86f2caf95b1f"
   },
   "outputs": [
    {
     "data": {
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       "version_major": 2,
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     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading pre-trained weights for auto encoder...\n",
      "Downloading Complete!\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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     "metadata": {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "hypothesis_dir_dec = diarizationDEC(audio_dataset, hypothesis_dir=\"./rttm_output_dec/\")\n",
    "metric_dec = get_metrics(audio_dataset.label_dir, hypothesis_dir_dec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2TgnLCbohjn6"
   },
   "source": [
    "# Compare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "executionInfo": {
     "elapsed": 2053,
     "status": "ok",
     "timestamp": 1620722131059,
     "user": {
      "displayName": "Shashi Kant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiHv2woo7B1czb0XKsLBEOsgMWsuYITOryCdNNv7A=s64",
      "userId": "09783338171816410510"
     },
     "user_tz": -330
    },
    "id": "PTlgV9PWmJ-k"
   },
   "outputs": [],
   "source": [
    "df_dec = metric_dec.report()\n",
    "df_spectral = metric_spectral.report()\n",
    "\n",
    "df_spectral = df_spectral[[df_dec.keys()[0]]]\n",
    "df_dec = df_dec[[df_dec.keys()[0]]]\n",
    "\n",
    "df_both = df_dec\n",
    "df_both[\"DER (Spectral)\"] = df_spectral[[df_dec.keys()[0]]]\n",
    "df_both.columns = [\"DER (DEC)\", \"DER (Spectral)\"]\n",
    "\n",
    "df_both[\"DER Difference\"] = df_both[\"DER (Spectral)\"]-df_both[\"DER (DEC)\"]\n",
    "\n",
    "df_both = df_both[:-1]\n",
    "df_both = df_both.sort_values(by=['DER Difference'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 111
    },
    "executionInfo": {
     "elapsed": 1769,
     "status": "ok",
     "timestamp": 1620722131061,
     "user": {
      "displayName": "Shashi Kant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiHv2woo7B1czb0XKsLBEOsgMWsuYITOryCdNNv7A=s64",
      "userId": "09783338171816410510"
     },
     "user_tz": -330
    },
    "id": "zEIp5MM9uR_A",
    "outputId": "9efcc349-7e3f-436d-dcf5-8cb6874ad79e"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DER (DEC)</th>\n",
       "      <th>DER (Spectral)</th>\n",
       "      <th>DER Difference</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TOTAL</th>\n",
       "      <td>11.695932</td>\n",
       "      <td>17.979152</td>\n",
       "      <td>6.28322</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "       DER (DEC)  DER (Spectral)  DER Difference\n",
       "item                                            \n",
       "TOTAL  11.695932       17.979152         6.28322"
      ]
     },
     "execution_count": 15,
     "metadata": {
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   ],
   "source": [
    "df_dec[-1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 1283,
     "status": "ok",
     "timestamp": 1620722134536,
     "user": {
      "displayName": "Shashi Kant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiHv2woo7B1czb0XKsLBEOsgMWsuYITOryCdNNv7A=s64",
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    "id": "vjQP9QqUvuOZ",
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    {
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DER (DEC)</th>\n",
       "      <th>DER (Spectral)</th>\n",
       "      <th>DER Difference</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ehpau</th>\n",
       "      <td>29.702110</td>\n",
       "      <td>10.777824</td>\n",
       "      <td>-18.924286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>qjgpl</th>\n",
       "      <td>57.762641</td>\n",
       "      <td>40.362988</td>\n",
       "      <td>-17.399653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>akthc</th>\n",
       "      <td>40.628986</td>\n",
       "      <td>28.736140</td>\n",
       "      <td>-11.892847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cqaec</th>\n",
       "      <td>37.296847</td>\n",
       "      <td>27.160742</td>\n",
       "      <td>-10.136105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pilgb</th>\n",
       "      <td>14.029515</td>\n",
       "      <td>7.609876</td>\n",
       "      <td>-6.419639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xmfzh</th>\n",
       "      <td>22.951305</td>\n",
       "      <td>19.608307</td>\n",
       "      <td>-3.342997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kbkon</th>\n",
       "      <td>3.126518</td>\n",
       "      <td>2.750121</td>\n",
       "      <td>-0.376396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vmaiq</th>\n",
       "      <td>17.741048</td>\n",
       "      <td>17.629521</td>\n",
       "      <td>-0.111527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>falxo</th>\n",
       "      <td>57.071026</td>\n",
       "      <td>57.071026</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oxxwk</th>\n",
       "      <td>27.314046</td>\n",
       "      <td>27.314046</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jsdmu</th>\n",
       "      <td>6.291195</td>\n",
       "      <td>6.291195</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cmhsm</th>\n",
       "      <td>1.541770</td>\n",
       "      <td>1.541770</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ufpel</th>\n",
       "      <td>32.086302</td>\n",
       "      <td>32.086302</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pnook</th>\n",
       "      <td>32.378289</td>\n",
       "      <td>32.378289</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ysgbf</th>\n",
       "      <td>6.113070</td>\n",
       "      <td>6.113070</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>txcok</th>\n",
       "      <td>2.541708</td>\n",
       "      <td>2.911319</td>\n",
       "      <td>0.369611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jnivh</th>\n",
       "      <td>1.751784</td>\n",
       "      <td>2.233757</td>\n",
       "      <td>0.481972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>irvat</th>\n",
       "      <td>2.816792</td>\n",
       "      <td>3.376522</td>\n",
       "      <td>0.559731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dhorc</th>\n",
       "      <td>3.266616</td>\n",
       "      <td>3.897335</td>\n",
       "      <td>0.630719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zyffh</th>\n",
       "      <td>3.554732</td>\n",
       "      <td>4.228615</td>\n",
       "      <td>0.673883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>qygfk</th>\n",
       "      <td>3.751242</td>\n",
       "      <td>4.464007</td>\n",
       "      <td>0.712764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blwmj</th>\n",
       "      <td>2.092484</td>\n",
       "      <td>2.942492</td>\n",
       "      <td>0.850008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uatlu</th>\n",
       "      <td>7.821791</td>\n",
       "      <td>8.783666</td>\n",
       "      <td>0.961875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bravd</th>\n",
       "      <td>9.360491</td>\n",
       "      <td>10.553428</td>\n",
       "      <td>1.192937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>djngn</th>\n",
       "      <td>2.663632</td>\n",
       "      <td>4.490650</td>\n",
       "      <td>1.827018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ahnss</th>\n",
       "      <td>3.141424</td>\n",
       "      <td>5.063378</td>\n",
       "      <td>1.921954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fsaal</th>\n",
       "      <td>4.906745</td>\n",
       "      <td>6.961614</td>\n",
       "      <td>2.054869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uvnmy</th>\n",
       "      <td>3.243494</td>\n",
       "      <td>5.449195</td>\n",
       "      <td>2.205700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hgdez</th>\n",
       "      <td>8.788644</td>\n",
       "      <td>11.891982</td>\n",
       "      <td>3.103338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imbqf</th>\n",
       "      <td>3.857455</td>\n",
       "      <td>9.959515</td>\n",
       "      <td>6.102060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eapdk</th>\n",
       "      <td>8.738537</td>\n",
       "      <td>14.842697</td>\n",
       "      <td>6.104160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>esrit</th>\n",
       "      <td>4.911400</td>\n",
       "      <td>13.249001</td>\n",
       "      <td>8.337601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>luvfz</th>\n",
       "      <td>12.660266</td>\n",
       "      <td>21.098455</td>\n",
       "      <td>8.438190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sduml</th>\n",
       "      <td>9.227857</td>\n",
       "      <td>18.605568</td>\n",
       "      <td>9.377711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kckqn</th>\n",
       "      <td>2.478940</td>\n",
       "      <td>12.594045</td>\n",
       "      <td>10.115105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gwtwd</th>\n",
       "      <td>20.379597</td>\n",
       "      <td>31.601423</td>\n",
       "      <td>11.221827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gzvkx</th>\n",
       "      <td>22.554047</td>\n",
       "      <td>36.651584</td>\n",
       "      <td>14.097536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ccokr</th>\n",
       "      <td>7.537370</td>\n",
       "      <td>23.555865</td>\n",
       "      <td>16.018495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aufkn</th>\n",
       "      <td>4.820945</td>\n",
       "      <td>22.572606</td>\n",
       "      <td>17.751661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tguxv</th>\n",
       "      <td>25.086446</td>\n",
       "      <td>43.579509</td>\n",
       "      <td>18.493063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sosnj</th>\n",
       "      <td>10.932560</td>\n",
       "      <td>32.380456</td>\n",
       "      <td>21.447896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>goyli</th>\n",
       "      <td>3.803749</td>\n",
       "      <td>25.630617</td>\n",
       "      <td>21.826868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gofnj</th>\n",
       "      <td>3.192511</td>\n",
       "      <td>27.812450</td>\n",
       "      <td>24.619939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ycxxe</th>\n",
       "      <td>3.818112</td>\n",
       "      <td>32.359939</td>\n",
       "      <td>28.541827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ngyrk</th>\n",
       "      <td>6.360381</td>\n",
       "      <td>40.355660</td>\n",
       "      <td>33.995280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mevkw</th>\n",
       "      <td>8.512949</td>\n",
       "      <td>49.883041</td>\n",
       "      <td>41.370092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wmori</th>\n",
       "      <td>4.894444</td>\n",
       "      <td>47.410986</td>\n",
       "      <td>42.516542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>szsyz</th>\n",
       "      <td>19.026258</td>\n",
       "      <td>79.835886</td>\n",
       "      <td>60.809628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>plbbw</th>\n",
       "      <td>3.822692</td>\n",
       "      <td>80.940762</td>\n",
       "      <td>77.118070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bkwns</th>\n",
       "      <td>5.778474</td>\n",
       "      <td>82.957158</td>\n",
       "      <td>77.178683</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       DER (DEC)  DER (Spectral)  DER Difference\n",
       "item                                            \n",
       "ehpau  29.702110       10.777824      -18.924286\n",
       "qjgpl  57.762641       40.362988      -17.399653\n",
       "akthc  40.628986       28.736140      -11.892847\n",
       "cqaec  37.296847       27.160742      -10.136105\n",
       "pilgb  14.029515        7.609876       -6.419639\n",
       "xmfzh  22.951305       19.608307       -3.342997\n",
       "kbkon   3.126518        2.750121       -0.376396\n",
       "vmaiq  17.741048       17.629521       -0.111527\n",
       "falxo  57.071026       57.071026        0.000000\n",
       "oxxwk  27.314046       27.314046        0.000000\n",
       "jsdmu   6.291195        6.291195        0.000000\n",
       "cmhsm   1.541770        1.541770        0.000000\n",
       "ufpel  32.086302       32.086302        0.000000\n",
       "pnook  32.378289       32.378289        0.000000\n",
       "ysgbf   6.113070        6.113070        0.000000\n",
       "txcok   2.541708        2.911319        0.369611\n",
       "jnivh   1.751784        2.233757        0.481972\n",
       "irvat   2.816792        3.376522        0.559731\n",
       "dhorc   3.266616        3.897335        0.630719\n",
       "zyffh   3.554732        4.228615        0.673883\n",
       "qygfk   3.751242        4.464007        0.712764\n",
       "blwmj   2.092484        2.942492        0.850008\n",
       "uatlu   7.821791        8.783666        0.961875\n",
       "bravd   9.360491       10.553428        1.192937\n",
       "djngn   2.663632        4.490650        1.827018\n",
       "ahnss   3.141424        5.063378        1.921954\n",
       "fsaal   4.906745        6.961614        2.054869\n",
       "uvnmy   3.243494        5.449195        2.205700\n",
       "hgdez   8.788644       11.891982        3.103338\n",
       "imbqf   3.857455        9.959515        6.102060\n",
       "eapdk   8.738537       14.842697        6.104160\n",
       "esrit   4.911400       13.249001        8.337601\n",
       "luvfz  12.660266       21.098455        8.438190\n",
       "sduml   9.227857       18.605568        9.377711\n",
       "kckqn   2.478940       12.594045       10.115105\n",
       "gwtwd  20.379597       31.601423       11.221827\n",
       "gzvkx  22.554047       36.651584       14.097536\n",
       "ccokr   7.537370       23.555865       16.018495\n",
       "aufkn   4.820945       22.572606       17.751661\n",
       "tguxv  25.086446       43.579509       18.493063\n",
       "sosnj  10.932560       32.380456       21.447896\n",
       "goyli   3.803749       25.630617       21.826868\n",
       "gofnj   3.192511       27.812450       24.619939\n",
       "ycxxe   3.818112       32.359939       28.541827\n",
       "ngyrk   6.360381       40.355660       33.995280\n",
       "mevkw   8.512949       49.883041       41.370092\n",
       "wmori   4.894444       47.410986       42.516542\n",
       "szsyz  19.026258       79.835886       60.809628\n",
       "plbbw   3.822692       80.940762       77.118070\n",
       "bkwns   5.778474       82.957158       77.178683"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_both"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1269,
     "status": "ok",
     "timestamp": 1620722140601,
     "user": {
      "displayName": "Shashi Kant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiHv2woo7B1czb0XKsLBEOsgMWsuYITOryCdNNv7A=s64",
      "userId": "09783338171816410510"
     },
     "user_tz": -330
    },
    "id": "cNkpz4-XpHDs",
    "outputId": "54919b7f-6a29-4afb-8ee3-4b755c93c73f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better DER for 35 examples.\n",
      "Poor DER for 8 examples.\n",
      "Same DER for 7 examples.\n"
     ]
    }
   ],
   "source": [
    "print(\"Better DER for\", np.sum(df_both['DER Difference'].values>0), \"examples.\")\n",
    "print(\"Poor DER for\", np.sum(df_both['DER Difference'].values<0), \"examples.\")\n",
    "print(\"Same DER for\", np.sum(df_both['DER Difference'].values==0), \"examples.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "executionInfo": {
     "elapsed": 889,
     "status": "ok",
     "timestamp": 1620722143234,
     "user": {
      "displayName": "Shashi Kant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiHv2woo7B1czb0XKsLBEOsgMWsuYITOryCdNNv7A=s64",
      "userId": "09783338171816410510"
     },
     "user_tz": -330
    },
    "id": "vnXwVIAwqoSC"
   },
   "outputs": [],
   "source": [
    "filenames = [\"hgdez\"] + list(df_both.index[[-1, -2, -3, 2, 1, 0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 8992,
     "status": "ok",
     "timestamp": 1620722154938,
     "user": {
      "displayName": "Shashi Kant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiHv2woo7B1czb0XKsLBEOsgMWsuYITOryCdNNv7A=s64",
      "userId": "09783338171816410510"
     },
     "user_tz": -330
    },
    "id": "9hAGvSouLxTe",
    "outputId": "ccbdbe66-aad5-4dd7-c971-4db4b7d4dbc5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "DEC\n",
      "---------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "Spectral\n",
      "---------------------------\n",
      "\n"
     ]
    },
    {
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AT1XVk4F3A+8ZUfkkreGS7Jrk4iR3JTmj/QPmiN7Q9SRvS3ITcFKS9ZMck+SG9nFMkvXbfFbrDdrt2dn++XNskrPbbV2Q5DGdtM9O8l9J7kjyYSDt/B2AjwFPTXJ378+zNr+PJvlqkl8A/3v8kP5umZJ8q519SZvPfp10b07ysyQ3JnlV/1tZkiRJC8GCCpom2bA9ub4kyeVJ9ktyTZL3tb0dvzvRMKskf9+eTK8DPBM4s130SeBFAFV1TVVdCtw/tAoNyYDb7c7OKhsCNej6DMsg220xG2K7HQCcOriaDNeA2+2JwL+30+cBLxx0fSQtPG3v9C8CJwMPpznG/nEnySPb+dsChwBvB3YHdgZ+D/h94B2z2OT+wOHAZsBVwD+05diC5k+xdwBbAD8G/hCgqq4EDgW+U1UbVdWmnfz+pM1jY2DK4ftV9fR28vfafE7v1HETYAnNH0/HJtlsFnWSJEnSIrFGD62awJ7ADVW1F0CSTWh6Vt1RVTsleQVwDLB3b4Uk76c5eX4VsDlwe1Xd2y6+nuakeKh2P+zcMeCwPmZ5+PmHP3dsiuUDbbckrwfeBKxHE7Tpu33+Za8x+txmZ73o7LFp0gx6f3tJkqcDPwTeWFXXzb9aq/r1u5eN0ed22+Bde4xNk2bgn9Mk2wKP5oFAYF+dut8OY/S53Q44/cqxadIMst0uAV4MfJAmALJxks2r6pZ+VE7SorE7zbnhh6qqgC8k+W5n+f3AYVX1G4AkLwf+oqp+1r4+HDgOeOcMt/fFqvpuu+5neGC0yvOBK6rqzHbZMcCbZ5Dfl6rqP9rpXyeZYTFWcQ/w7vZY+tUkdwOPB86fS2aSJElauObV03TFkm3GVizZpvr4GJtmk5cBz05yZJKnVdUd7fxTO89P7aR/J7BJVR3anvyvrQbablV1bFU9Bngbs+thsqYbZLt9GdiuHS79dZpegYvFMD6n+wNnVtV9/SnyGmGQ7fYW4BlJLgaeAawAFlPbSeqPrYAV444p3T/0bq6qX49Lf23n9bXtvJm6qTP9S2CjTr4rt9uWZyZ/LPbjz8dbOn8+jS+XJEmS1iLz6mm6ZMV1Y8BYX0oyA1X1wyS70vRAOCLJN3qLusk6098Ddkvy8Kq6FbgF2DTJuu0J8dY0wYNFbYjtdhrw0f7XYDQG2W7jevh9nOZ6sIvCkPa3/YHXD6YGozHg/e0Gmp6mJNkIeElVrZU30ZI0pRuBJUnSCZxuQzM8Hla/BM8NNEP1r2hfP6qdB/AL4KG9hEkeOctybNNZN93XE5RjsvmrlIFm6L0kSZI0IwtqeH6SrYBbq+qUNBf+713cfz/gve3zdzqrfA04Fzg7yXOq6q4k5wH70gT4Xgl8aWgVaLVD6ceGtb1BtluS7avqR+16ewE/YgDaofRjg8h7MgNut9+tqhvb9fYBrhxEHdqh9GODyHsyg/6cJnkCzfXvunn0VTuUfmxQ+U9kwPvbFm3e9wN/C3xiGHWStOB8h6YX+p8n+SjN9/rvA8smSX8q8I4k36MJWL4LOKVddgnwpCQ7A//F7I6pZwMfTvJi4CyaP8m6Ac+fAlsnWa+qfjtFPt8HXpzk4zS9Vw9u1+3m8z9orqcqSZIkrWJBBU2BnYD3J7mf5ppTr6O56clmSS4FfkNzc5iVquqMJBsDZyV5Ps0Q8tOSHAFcDJwIkOQpNDc/2Ax4QZLDq+pJQ6rXoA2s3Wh+WD2rzfc2mkDNYjHIdntDkn2Ae4FbgYOGUJ9hGWS7QdPL9LRFeMmNQbbbHsB7khTwLRZZL11J/VFVv20DlR8H3gOcA3yF5vgzkSOAhwGXtq/PaOf1es+/G/g34Fc0f9i8dobl+HmSlwIfAk4CPg38RyfJv9P0br0pyf1VtcUkWR0NPIUmOHop8BngWZ3lY8AnkzyE5sZWP5tJ+SRJkrR2yEKPOyS5BlhaVT8fdVkWEtttbmy3ubHd5sZ2kzRqSS4APlZVJ426LJIkSdIwzetGUJIkSVo8kjwjySOTrJvklcCTaS4HIkmSJK1VFtrw/NVU1XajLsNCZLvNje02N7bb3Nhukkbg8cDngA2B/wb27VyDW5IkSVprLPjh+ZIkSZIkSZLUTw7PlyRJkiRJkqSOWQ3P32KLLWq77bYbUFEkSZIkSZIkaTguvPDCn1fVlhMtm1XQdLvttmP58uX9KZUkSZIkSZIkjUiSaydb5vB8SZIkSZIkSeowaCpJkiRJkiRJHQZNJUmSJEmSJKnDoKkkSZIkSZIkdRg0lSRJkiRJkqQOg6aSJEmSJEmS1GHQVJIkSZIkSZI6DJpKkiRJkiRJUodBU0mSJEmSJEnqMGgqSZIkSZIkSR0GTSVJkiRJkiSpw6CpJEmSJEmSJHUYNJUkSZIkSZKkDoOmkiRJkiRJktRh0FSSJEmSJEmSOgyaSpIkSZIkSVKHQVNJkiRJkiRJ6jBoKkmSJEmSJEkdBk0lSZIkSZIkqcOgqSRJkiRJkiR1GDSVJEmSJEmSpA6DppIkSZIkSZLUYdBUkiRJkiRJkjoMmkqSJEmSJElSh0FTSZIkSZIkSeowaCpJkiRJkiRJHQZNJUmSJEmSJKnDoKkkSZIkSZIkdRg0lSRJkiRJkqQOg6aSJEmSJEmS1GHQVJIkSZIkSZI6DJpKkiRJkiRJUodBU0mSJEmSJEnqMGgqSZIkSZIkSR0GTSVJkiRJkiSpw6CpJEmSJEmSJHUYNJUkSZIkSZKkDoOmkiRJkiRJktRh0FSSJEmSJEmSOgyaSpIkSZIkSVLHrIKm9/30p4Mqx0p3fuAoTjjvqoFvZyonnHcVn73yM6vMe8PnvjjvfF/3ie/OO48Tzrtq0vbpzp8oTW9eL4+55jOd4/7xvFmvM0yHf+jbE86frE0uPOkNU2d43ljzGKaT9gDghE98fMarLPvzd8x5c3d+4Cju/MBRM0rXfe6nOz9wFMtPvaRv+R3+oW+v8pmYiwuvfk8zcd7e7fPYyjIuP/WSB/aL9nkQ7dIvH/6HTw8s7177XnbGh6dNe9kZH17tfemtN/X6x02T8yHTbrt/jlttn5rq+D/Z/rdKvccfY6Y57tz55/vMpKBzMtd9ZaF+N0xmPvVZfuolqxzPZrrt133iu31vx+m23fvuuPMDR7F8+fKV83vT3Xm914M4jzvhvKu47IwPr3yM9w8f/uyE60z1uuf4M/91ldfLly9f+Rhv9fnHMdHxZ6J1u/MnW37WWWdNOL/r8M98a9o0PXN5L2azznzOLSZz80teOut1er8fTjjvqmm/a8f/1ujtW7Pxb2OvAJr9brr1V3zkc9yz7OoJ01x2xoe5Z9nVs9o2wD3Lrp7VOdFMjjHjz4On+60w2T48V5N95sangcGeT3X3v4nOf2d6Tjx+nd7zZN+hg/gszcb4co3693i/3fmBo/jFe05dbf591xzJCeedvsq8qc7ZRv0+rWmafbv7HTjd+fb45ceNe5467epxiunO/2dShm5ZZprfTH5XzCa/2Rm/zw7Syt+7Ezqu798Fa6JZBU3vH0LQ9K6jjubEZT8e+HamcuKyH3PaD1Y9+f7uFQ+dd74XX3vbvPM4cdmPJ22f7vyJ0vTm9fKYaz7TumDF7NcZonNu+cWE8ydrk92u/eepM/zm4c1jmK79JgAnXrvNjFfZ/oufnPPm7jrqaO466ugZpes+99NdRx3Nhadd1rf8zrnlF6t8JuZit0d/vpn45tnt8+Ery3jhaZc9sF+0z4Nol3455be/M7C8e+17+ZnHTpv28jOPXe196a039fonTJPzRdNuu39OWG2fmur4P9n+t0q9xx9jpjnu3PXFi2dY1tmb876yQL8bJjWP+lx42mWrHM9muu2Lr72t7+043bZ73x13HXU0F130wOeoN92d13s9iPO4E5f9mMvPPHblY7wv37z5hOtM9XqlW69Z5eVFF1208jHe6vNPYKLjz0TrdudPtvymm26auIwd5/zwV9Om6ZnLezGbdeZzbjGZ355//qzX6f1+OHHZj6f9rh3/W6O3b83GzVd+D2j2u+nW3/znv8N937p2wjSXn3ks933r2lltG+C+b107q3OimRxjxp8HT/dbYbJ9eK4m+8yNTwODPZ/q7n8Tnf/O9Jx4/Dq958m+QwfxWZqN8eUa9e/xfrvrqKNZ557fXW3+OtudwYnLNl1l3lTnbKN+n9Y0zb7d/Q6c7rgwfvkJ456nTrt6nGK68/+ZlKFblpnmN5Pj32zym53x++wgrfy9O6ET+v5dsCZyeL4kSZIkSZIkdRg0lSRJkiRJkqQOg6aSJEmSJEmS1GHQVJIkSZIkSZI61p3tCiuWzPzGM/Ox+2HnDmU7U9nnX/bqvHpDX8rUr3rNJJ9hpul6FXDcC0+Z1TpDtfOWU9Zp/LLzA4xl+nxnkqafxgJ8bcbvz+eZ/+d3NusP6ljRt31r5y1XeTmXz+b5hwMsbV503v9VyjhuvxjWMXTWXv3xoRx3T91vhxmlG1+Wma23dJ7L+2t8HebSvqvUe6JjzKTHna0Ht6/NcV9Z6N8N4/WjPivXn+W2+9qO02x7/HfH8ccfv1qaieYN+niy2jHhCcfM+Zzmz5ZMXIf5mi7PyZZPX5ZtB96+szm3WBPN5Pg3t++ZcemfcMy06//xkz46bZpfv3vZrLbdM+NjwSyOMbP57hrF56ZnkOdT4/OeaFvz2f5EbdqP8/R5GdJ54KiN/6xt8K7meab7/cjfpzXW0kmmp0s73fzV563+3szl/H6qdWaaX7/TzcY/DO3zusrv3UkM4rtgTTLroKkkSZIkwWACCAYlJEnSmsCgqSRJkqQ5WbLiur7mt2LJNrPO0yCrJEkahFkHTft9YjRe76Tn/MOfO9DtTKXX1fmsF539wLyLz513mXY/rD95wMTts9qw8nFpJurCPZd8pnPcC0/htV86cFbrDNNJk7wPk7btGDBWk2fYGx47VZp+G0uzvVnsUys+PvfPb+9zOd363R8tg/gRBfRt3zppnvt54+3AcqB9P9p94bVfOvCBIXOd+TD4Y+ic9eH4NJnuMeWA06+cMm1v6OL5hz93tfWmHzq5fIplS6dZ3k/NEJZue051/J9qeM0q9e4eY6Y77pww+6DDjM1xX1mo3w2TmU99eseH3voz3XZvX+lnO0637d53R+8YfMghhwCrDsXqzevO7/fxZPznZPyx5IMT1GOm5z3HH3/8hHWYr26eE+U92fKJ5nd9Yhb76lyH783m3GJNNNXxb/xvjV4bTff91HXqfjtwwOlX8sEZfL91hwOPT9M7vm/wrj1mvO1unjM9FszkGDP+PHgmvzmm21dno/fZmCrP7udnUN9x3aD9ROe/Mz0nHp9n10RtOp/z9L4Yt48s1qH6k33WZnrONvL3aQ3zwL7dO8ee7nx7/PLusO/x602cdvwxavbn95OVsVeWmeQ3k98Vs8lvtgb3u211vd+7E2nq2M/vglF57WtfO+kybwQlSZIkSZIkSR0GTSVJkiRJkiSpw6CpJEmSJEmSJHUYNJUkSZIkSZKkjlndCOpBj3jEoMqx0sZveiMH7/qYgW9nKgfv8Rg2fOSfrDLv95/0y3nnu8u2m807j4P3mLxtussmStebN1UeM8lnWv9zyezXGaLnbb7hhPMnq+uF2/4Fu02V4TMOm3+hZmvbZwBw8LYzvxD5j/74lcz1ndn4TW+cVbqZpp9tGXbbaqe+5fe8zTdkq50eCcxxPwcuvPol7PZo4Bl7NTOecRi7Pb4p42777wSPPGzlfBhMu/TLgev9bGB599p3x31fP23aHfd9PQdvseqxqrfe1Ou/Zpqcd5122/3zmtX2qamO/5Ptf6vUe4ufr7pwmuPOxn+8ywzKOTdz3lcW6HfDpOZRn932X/VYNtNt77LtZrD+enPe7kSm23bvu2PjN72RXXd94HPUm+7O673O9pv2tYzQfE523HHyY8ALtrxlwnWmer3Sw7db5eX4Ok29bOJjz2R5TNZuPY985CMn3XbP8x73kGnT9Mzl+20268zn3GIy6+2++6zX6f5+2PhhU3/Xjv+tMd2+NZEtd3gK0Ox3v/Ok359y/Vu2+Bm/88SnsOMOq6fZcd/Xs86W285q2wDrPH1bdtt+5r8rZnKMmfHnpV22y8abz3j7MzHV5258mgK+cGYAACAASURBVEGeT3X3v4m2M5dtd8+PD/z1xN+hg/gszcb47/a5nhuvqTZ+0xu577c3rjb/vmteysF73L7KvKnO2Ub9Pq1pmn27e3yZ7nM82XfoRN+lq6ddPU4x3fn/TMowvizzyWOu+c3O+H12kFb+3p3Qa2Z07F7oUjXzO34vXbq0li8f1t2HJUmSJEmSJGkwklxYVUsnWubwfEmSJEmSJEnqMGgqSZIkSZIkSR0GTSVJkiRJkiSpw6CpJEmSJEmSJHUYNJUkSZIkSZKkDoOmkiRJkiRJktRh0FSSJEmSJEmSOgyaSpIkSZIkSVKHQVNJkiRJkiRJ6jBoKkmSJEmSJEkdBk0lSZIkSZIkqcOgqSRJkiRJkiR1GDSVJEmSJEmSpA6DppIkSZIkSZLUYdBUkiRJkiRJkjoMmkqSJEmSJElSh0FTSZIkSZIkSeowaCpJkiRJkiRJHQZNJUmSJEmSJKnDoKkkSZIkSZIkdRg0lSRJkiRJkqQOg6aSJEmSJEmS1GHQVJIkSZIkSZI6DJpKkiRJkiRJUodBU0mSJEmSJEnqMGgqSZIkSZIkSR0GTSVJkiRJkiSpw6CpJEmSJEmSJHUYNJUkSZIkSZKkDoOmkiRJkiRJktRh0FSSJEmSJEmSOgyaSpIkSZIkSVKHQVNJkiRJkiRJ6jBoKklaAI6b5/KpXXbGh1c+d6dnst5M0k3pvLH5LZ+hE867ihPOu6oveS0W9yy7epVnNW5+yUtHXYQFZfny5RNOj9qdHzhqKOtobnrH47XluNzdt+a6n61tbTaR4X1vze+8as21WOslaVAMmkqSFoAT5rl8apefeezK5+70TNabSbopffPw+S2foROX/ZgTl/24L3ktFvd969pVntX47fnnj7oIC8pFF1004fSo3XXU0UNZR3PTOx6vLcfl7r411/1sbWuziQzve2t+51VrrsVaL0mDYtBUkiRJkiRJkjoMmkqSJEmSJElSh0FTSZIkSZIkSeowaCpJkiRJkiRJHeuOugCSJM3M0oHmfup+O0w4PRRjGe72tNKv371s1EXQInD88cePughagHY/7NxRF2GoVizZZt55rG1tNpHhfW8N9rxLkhYCg6aSJEnSItSPIJUkSdLayqCpJEmStAgtWXHdrNIbZJUkSXqAQVNJ0gKxfIpl8x9CdsDpV64clt+dHoqxmmKZQ/cHaYN37eEQfc3bIYccAjhMX7Nz/uHPXauGm/eC+PMJzq9tbTaR4X1vTXXetVB5yQFJs+ONoCRJkiRJkiSpw6CpJEmSJEmSJHUYNJUkSZIkSZKkDoOmkiRJkiRJktThjaAkSQvAa+a5fGo77vv6VZ7HT0+33rw847D5LZ+hg/d4TF/yWUzWefq2qzyrsd7uu4+6CAvKrrvuOuH0qG38pjcOZR3NTe+YvLYcm7v71lz3s7WtzSYyvO+t+Z1XrbkWa70kDUqqprhj7zhLly6t5csX4130JEmSJEmSJK1NklxYVUsnWubwfEmSJEmSJEnqMGgqSZIkSZIkSR0GTSVJkiRJkiSpw6CpJEmSJEmSJHUYNJUkSZIkSZKkDoOmkiRJkiRJktRh0FSSJEmSJEmSOgyaSpIkSZIkSVKHQVNJkiRJkiRJ6jBoKkmSJEmSJEkdBk0lSZIkSZIkqcOgqSRJkiRJkiR1GDSVJEmSJEmSpA6DppIkSZIkSZLUYdBUkiRJkiRJkjoMmkqSJEmSJElSh0FTSZIkSZIkSeowaCpJkiRJkiRJHQZNJUmSJEmSJKnDoKkkSZIkSZIkdRg0lSRJkiRJkqQOg6aSJEmSJEmS1GHQVJIkSZIkSZI6DJpKkiRJkiRJUodBU0mSJEmSJEnqMGgqSZIkSZIkSR0GTSVJkiRJkiSpw6CpJEmSJEmSJHUYNJUkSZIkSZKkDoOmkiRJkiRJktRh0FSSJEmSJEmSOgyaSpIkSZIkSVKHQVNJkiRJkiRJ6jBoKkmSJEmSJEkdBk0lSZIkSZIkqcOgqSRJkiRJkiR1GDSVJEmSJEmSpA6DppIkSZIkSZLUYdBUkiRJkiRJkjoMmkqSJEmSJElSR6pq5omTm4FrB1ccSVppC+Dnoy6EpLWCxxtJw+LxRtKweLyRZmbbqtpyogWzCppK0rAkWV5VS0ddDkmLn8cbScPi8UbSsHi8kebP4fmSJEmSJEmS1GHQVJIkSZIkSZI6DJpKWlMdP+oCSFpreLyRNCwebyQNi8cbaZ68pqkkSZIkSZIkddjTVJIkSZIkSZI6DJpKGrok2yQ5L8l/JrkiyV+28x+e5OtJftQ+b9bOT5IPJbkqyaVJdh1tDSQtNEnWSXJxkq+0rx+d5IL2uHJ6kvXa+eu3r69ql283ynJLWliSbJrkzCT/leTKJE/1/EbSICR5Y/tb6vIkpybZwPMbqb8MmkoahXuBN1fVE4HdgdcneSLwN8A3qmp74Bvta4DnAdu3j0OAjw6/yJIWuL8Eruy8PhI4uqoeC9wGHNzOPxi4rZ1/dJtOkmbqg8DXquoJwO/RHHc8v5HUV0mWAG8AllbVjsA6wP54fiP1lUFTSUNXVTdW1UXt9F00PyiWAC8EPtkm+yTwonb6hcCnqnE+sGmS3x1ysSUtUEm2BvYCPt6+DvBM4Mw2yfjjTe84dCbwR216SZpSkk2ApwMnAlTVb6vqdjy/kTQY6wIPSbIu8FDgRjy/kfrKoKmkkWqHhuwCXAA8oqpubBfdBDyinV4CXNdZ7fp2niTNxDHAXwP3t683B26vqnvb191jysrjTbv8jja9JE3n0cDNwEnt5UA+nmRDPL+R1GdVtQL4J+AnNMHSO4AL8fxG6iuDppJGJslGwOeBv6qqO7vLqqqAGknBJC0aSfYGflZVF466LJIWvXWBXYGPVtUuwC94YCg+4PmNpP5or438Qpo/a7YCNgT2HGmhpEXIoKmkkUjyYJqA6Weq6gvt7J/2hqW1zz9r568AtumsvnU7T5Km84fAPkmuAU6jGbb2QZphsOu2abrHlJXHm3b5JsAtwyywpAXreuD6qrqgfX0mTRDV8xtJ/fYs4Oqqurmq7gG+QHPO4/mN1EcGTSUNXXv9nBOBK6vqqM6is4BXttOvBL7Umf+K9i6zuwN3dIa5SdKkqupvq2rrqtqO5gYJ/15VLwfOA/Ztk40/3vSOQ/u26e0VJmlaVXUTcF2Sx7ez/gj4Tzy/kdR/PwF2T/LQ9rdV73jj+Y3UR/FzImnYkvwv4P8Cl/HANQb/jua6pp8DHgVcC7ysqm5tTwQ+TDPk5JfAq6pq+dALLmlBS7IH8Jaq2jvJ/6Dpefpw4GLgwKr6TZINgE/TXGv5VmD/qvrvUZVZ0sKSZGeam86tB/w38Cqajiqe30jqqySHA/sB99Kcy7ya5tqlnt9IfWLQVJIkSZIkSZI6HJ4vSZIkSZIkSR0GTSVJkiRJkiSpw6CpJEmSJEmSJHUYNJUkSZIkSZKkDoOmkiRJkiRJktRh0FSSJEkDlWTzJN9vHzclWdFO353kI6MunyRJkjReqmrUZZAkSdJaIskYcHdV/dOoyyJJkiRNxp6mkiRJGokkeyT5Sjs9luSTSf5vkmuTvDjJ+5JcluRrSR7cptstyTeTXJjk3CS/O9paSJIkaTEyaCpJkqQ1xWOAZwL7AKcA51XVTsCvgL3awOk/A/tW1W7AJ4B/GFVhJUmStHitO+oCSJIkSa1zquqeJJcB6wBfa+dfBmwHPB7YEfh6Eto0N46gnJIkSVrkDJpKkiRpTfEbgKq6P8k99cDF9++nOW8NcEVVPXVUBZQkSdLaweH5kiRJWih+AGyZ5KkASR6c5EkjLpMkSZIWIYOmkiRJWhCq6rfAvsCRSS4Bvg/8wWhLJUmSpMUoD4x6kiRJkiRJkiTZ01SSJEmSJEmSOgyaSpIkSZIkSVKHQVNJkiRJkiRJ6jBoKkmSJEmSJEkdBk0lSZIkSZIkqcOgqSRJkiRJkiR1GDSVJEmSJEmSpA6DppIkSZIkSZLUYdBUkiRJkiRJkjoMmkqSJEmSJElSh0FTSZIkSZIkSeowaCpJkiRJkiRJHQZNJUmSJEmSJKnDoKkkSZIkSZIkdRg0lSRJkiRJkqQOg6aSJEmSJEmS1GHQVJIkSZIkSZI6DJpKkiRJkiRJUodBU0mSJEmSJEnqMGgqSZIkSZIkSR0GTSVJkiRJkiSpw6CpJEmSJEmSJHUYNJUkSZIkSZKkDoOmkiRJkiRJktRh0FSSJEmSJEmSOgyaSpIkSZIkSVKHQVNJkiRJkiRJ6jBoKkmSJEmSJEkdBk0lSZIkSZIkqWPRBk2TnJxk3wnmPzrJBUmuSnJ6kvXa+eu3r69ql2837DKvCebQbm9K8p9JLk3yjSTbDr/Uozfbdussf0mSSrJ0eKVdc8yl3ZK8rN3nrkjy2eGWeM0wh8/ptu3n89Iky5JsPfxSS5IkSZK0cCzaoOkUjgSOrqrHArcBB7fzDwZua+cf3abTAyZrt4uBpVX1ZOBM4H0jKt+aarJ2I8nGwF8CF4yobGuyCdstyfbA3wJ/WFVPAv5qdEVcI022v/0T8Kn2c/pu4D0jKp8kSZIkSQvCggqaJtkwydlJLklyeZL9klyT5H1JLkvy3SSPnWC9v297Zq0DPJMmuAfwSeBF7fQL29e0y/8oSQZdp2EYZLtV1XlV9ct2/vnAounBNuD9DeDvaYJcvx54ZYZowO32GuDYqroNoKp+Now6DcOA2+2JwL+30+fRHO8kSZIkSdIk1p3Pyscff/wYcFh/igLA4YcccsjYFMv3BG6oqr0AkmxCE3S6o6p2SvIK4Bhg794KSd4PbAy8CtgcuL2q7m0XXw8saaeXANcBVNW9Se5o0/+8T3VbacWSbcboc7stWXHd2BTLB9luXQcD58yzLhMbyxh9bjPGamyaNANrtyS7AttU1dlJ3tq/aq3quBeeMkaf2+21XzpwbJo0g9zfHtem/w9gHWCsqr7Wj4p1nbrfDmP0ud0OOP3KsWnSDLLdLgFeDHwQ+GNg4ySbV9Ut/aicJEmSJEmLzYLqaQpcBjw7yZFJnlZVd7TzT+08P7WT/p3AJlV1aFXVMAu6hhl4uyU5EFgKvL9fhV4DDKTdkjwIOAp48yAKvQYY5P62LrA9sAdwAHBCkk37V/SRGmS7vQV4RpKLgWcAK4D7+lh2SZIkSZIWlXn1NB22qvph20Pv+cARSb7RW9RN1pn+HrBbkodX1a3ALcCmSdZte2NtTRM8oH3eBrg+ybrAJm36BW/A7UaSZwFvB55RVb8ZZF2GaYDttjGwI7CsvQLEI4GzkuxTVcsHW6vBG/D+dj1wQVXdA1yd5Ic0QdTvDbBKQzHIdquqG2h6mpJkI+AlVXX7YGskSZIkSdLClYXUATPJVsCtVfXrJHsDrwZ2Bj5WVe9tezvuV1UvSHIy8BUgwJuA51TVXUnOAD5fVacl+RhwaVV9JMnrgZ2q6tAk+wMvrqqXjaKe/TbgdtuF5hqKe1bVj0ZRv0EZZLuN284y4C2LIWAKA9/f9gQOqKpXJtmC5kZkOy+GYeYDbrct2rzvT/IPwH1V9a5R1FOSJEmSpIVgQfU0BXYC3p/kfuAe4HU0AbvNklwK/IZmyO5KVXVGmruUn5Xk+cDbgNOSHEETcDmxTXoi8OkkVwG3AvsPo0JDMsh2ez+wEXBG22vyJ1W1zxDqNAyDbLfFbJDtdi7wnCT/STO8/K2LIWDaGmS77QG8J0kB3wJeP4T6SJIkSZK0YC2onqYTSXINsLSq+n7DpsXMdpsb221ubLe5sd0kSZIkSRqNhXYjKEmSJEmSJEkaqAXf01SSJEmSJEmS+smeppIkSZIkSZLUYdBUkiRJkiRJkjoMmkqSJGk1Sa5J8qwRl2EsySl9zvNRSe5Osk4/85UkSdLiYtBUkiRJI5dkjyTXD3o7VfWTqtqoqu4b9LYkSZK0cC3aoGmSk5PsO8H8Rye5IMlVSU5Psl47/+lJLkpy70TrrS3m0G6HJrksyfeTfDvJE4df6tGbQ7sdlOTmtt2+n+TVwy/16M2h3R6V5LwkFye5NMnzh19qSZIkSZK02C3aoOkUjgSOrqrHArcBB7fzfwIcBHx2ROVa003Wbp+tqp2qamfgfcBRoyrgGmqydgM4vap2bh8fH03x1liTtds7gM9V1S7A/sBHRlQ+SVpb7Nz+SXVH+yfWBkkuT/KCXoIkD07y8yS7JNkuSSU5JMkNSW5M8pZO2vWTHNMuu6GdXj/JhsA5wFbt0Pm7k2zVrrZekk8luSvJFUmWdvLbKsnn2z8ir07yhs6y30+yPMmdSX6a5Kh2fq+M67avD0ry323+Vyd5+YDbVJIkSQvAggqaJtkwydlJLmlP2Pdrr7f1vra343eTPHaC9f6+7dG2DvBM4Mx20SeBFwFU1TVVdSlw/9AqNCQDbrc7O6tsCNSg6zMsg2y3xWzA7VbAw9rpTYAbBl0fSVrLvQzYE3g08GSaP5g/BRzYSfN84Maqurgz738D2wPPAd6WB66N+nZgd2Bn4PeA3wfeUVW/AJ4H3NAOnd+oqnrH+H2A04BNgbOADwMkeRDwZeASYAnwR8BfJXluu94HgQ9W1cOAxwCfG1+5Nlj7IeB5VbUx8AfA92fZRpIkSVqE1h11AWZpT5qT6b0AkmxC0yPtjqraKckrgGOAvXsrJHk/sDHwKmBz4PaqurddfD3NSfZQ7X7YuWPAYX3M8vDzD3/u2BTLB9puSV4PvAlYjybY1Xf7/MteY/S5zc560dlj06QZ9P72kiRPB34IvLGqrpt/tcZbOkaf2w2Wj02TZpDtNgb8a5K/oAnSj/QGJZK0FvhQL3iZ5Ms0wc53A+9M8rD2z9M/BT49br3D20DoZUlOAg4A/g14OfAXVfWzNs/DgeOAd05Rhm9X1Vfb9J8G/qqd/xRgy6p6d/v6v5OcQDMS4VzgHuCxSbaoqp8D50+S//3Ajkl+UlU3AjdO3yySJEla7ObV03TFkm3GVizZpvr4GJtmk5cBz05yZJKnVdUd7fxTO89P7aR/J7BJVR1aVYumB+QcDLTdqurYqnoM8Daa4dOLxSDb7cvAdlX1ZODrNL0pF4tBttsBwMlVtTVNz6ZPtz2NJEmDcVNn+pdArwfof9D8+bcpTQ/Rz4xbr/tH4LVAb6j9Vu3riZbNtAwbtEPrt6UZzn977wH8HfCINu3BwOOA/0ryvSR7M04b2N0POBS4sR0p8YRpyiNJkqS1wLx6mi5Zcd0YTc+voaiqHybZlSZYckSSb/QWdZN1pr8H7Jbk4VV1K3ALsGmSddtebFsDK4ZR9lEaYrudBny0/zUYjUG2W1Xd0lnv4zTXg10UBry/HUzTk5Wq+k6SDYAtgJ8NrkaSpAl8Eng1zbnkd6pq/HnBNsB/tdOP4oHLqdxAE+y8YoJls/2D+zrg6qrafqKFVfUj4ID2z7UXA2cm2XyCdOcC5yZ5CHAEcALwtFmWRZIkSYvMghqen+aGALdW1Sltb4LeHcf3A97bPn+ns8rXaIZnnZ3kOVV1V5LzgH1pAnyvBL40tAq02qH0Y8Pa3iDbLcn27Y8SgL2AHzEA7VD6sUHkPZkBt9vvtkMAoblW25WDqcXyMRZRu9HcsO2PgJOT7ABsANw86DpJklbzLzQ343sEE//x984kr6G5FuqreOAaqKcC70jyPZog6buAU9plPwU2T7JJZ5TCVL4L3JXkbTTXJf0tsAPwkKr6XpIDgXOr6ub2+wjGXbs+ySNorrH6b8CvgLvHp5EkSdLaaUEFTYGdgPcnuZ/mOlWvo7lZzGZJLgV+QzN8d6WqOiPJxsBZSZ5PM4T8tCRHABcDJwIkeQrwRWAz4AVJDq+qJw2pXoM2sHYD/ry9ucM9NHc5f+UwKjQkg2y3NyTZB7gXuJXmxhqLxSDb7c3ACUneSPNj+6C1/NIbkjQSVfWrJJ+nOZ5/YYIk3wSuorkU1D9V1b+284+guaHfpe3rM9p5VNV/JTmV5tqk6wBPnKYM97VD7j8AXA2sD/yABy4VtCdwVJKH0lwGYP+23N1sHkRzXfZP0XyvfJ/me0uSJElruSz0eEOSa4Cl7QX+NUO229zYbnNju0nS4pPkXcDjqurAzrztaAKYD+7c0E+SJElacBZaT1NJkiSNWJKH01xn+k9HXRZJkiRpEBZ8T1NJkiQNT3ut0mOAT1fVoeOWbYc9TSVJ+v/t3XuwXVV9B/DfUsQHTaUBpBrQUEppNbSaZKZgO5XRzrQMDmilFVumVqPwh6+iTqevGZJ2qKMjSUWok4TIUHkEjVIdO/gYm1CtEzr3Bm3QSAURMQOCoCaiQ7Ss/nH3uVn33PPY+9zzuDd8PjOZe87aa++9zj77/PY639xzLnAEEJoCAAAAABSeMukBAAAAAAAsJo2+0/T444/PK1euHNFQAAAAAADGY3p6+vs55xM6LWsUmq5cuTKmpqaGMyoAAAAAgAlJKd3XbZmP5wMAAAAAFISmAAAAAAAFoSkAAAAAQEFoCgAAAABQEJoCAAAAABSEpgAAAAAABaEpAAAAAEBBaAoAAAAAUBCaAgAAAAAUhKYAAAAAAAWhKQAAAABAQWgKAAAAAFAQmgIAAAAAFISmAAAAAAAFoSkAAAAAQEFoCgAAAABQEJoCAAAAABSEpgAAAAAABaEpAAAAAEBBaAoAAAAAUBCaAgAAAAAUhKYAAAAAAAWhKQAAAABAQWgKAAAAAFAQmgIAAAAAFISmAAAAAAAFoSkAAAAAQEFoCgAAAABQEJoCAAAAABSEpgAAAAAABaEpAAAAAEBBaAoAAAAAUBCaAgAAAAAUhKYAAAAAAAWhKQAAAABAQWgKAAAAAFAQmgIAAAAAFISmAAAAAAAFoSkAAAAAQEFoCgAAAABQEJoCAAAAABSEpgAAAAAABaEpAAAAAEBBaAoAAAAAUBCaAgAAAAAUhKYAAAAAAAWhKQAAAABAQWgKAAAAAFAQmgIAAAAAFISmAAAAAAAFoSkAAAAAQEFoCgAAAABQEJoCAAAAABSEpgAAAAAABaEpAAAAAEBBaAoAAAAAUBCaAgAAAAAUhKYAAAAAAAWhKQAAAABAQWgKAAAAAFAQmgIAAAAAFISmAAAAAAAFoSkAAAAAQEFoCgAAAABQaBSa/t/3vjeqccw6cMXGuHHfDSPfz1J1474b4sZ9N8TbP3rLnPatO+/ueb/V1qm9U79u26hj8z/tHGi9cdlw5ZfmtfU6NtPXvr37xnaun/k3CTvXN3qOdr317wfe1YErNsaBKzb27VP+HLYDV2yMqZu+OrTtbbjyS7VfE53MrLe5aNlc/NwcsXN9HHjreRFR7/hN0rUffuu8mjIs5fHd+7Graq93uG91PGdvj0qn/fTb3+aOfbbuvLn2XrfuvLt7DVlAfWmde7P3h3wOXnX5RxqvsxivDa2aMnXTVzteG3oZ9PGU+2ypu+9RHMP2fbfXxS07PhdTU1MxNTU129btduv+oHW1m6077469H7tqti6015K9H7uq67yn0+12W3Z8bl5b++PqpFOfVlvTZU1suOE/a/XbuvPuxq/7A1dsrP38LWReMUxlfXv4NX9ca52rLv9IXPe+f5hzXg2i7vrt/Vrn7KDH8L/e1vx62KvOdJoP9ZojdXrN9NOvbtR9XXzjXe9uvO9Bdbp2DnI9bZ8jt19Dm5wHo5pTlmOavvc9Q9/+5M2d223deXPj97yt18RiqX2T9viX/yK6zYXrazLnrtNvsRj9eJu83xhU/1qwecFzmqWgUWj6xBhC04MbN8X2u24c+X6Wqu133Rjb77ox/vtrz5rTvm3XPT3vt9o6tXfq120btdy+f7D1xuTWRx6b19br2Ky574PdN3bbhpl/k3DbhkbP0Wm3XDfwrg5u3BQHN27q26f8OWwHN26K6e17h7a9Wx95rPZropOZ9bYWLVuLn1sjbtsQB2+5IyLqHb9JumX5vfNqyrCUx/fOHVfXXu9w3+p4zt4elU776be/rR37bNt1bO29btt1T/casoD60jr3Zu8P+Ry8/tBzmq+0CK8NrZoyvX1vx2tDTwM+nnKfLbX3PYJj2L7veXXx0W/Hnj17Ys+ePbNN3W637g88f+hi26574s4dV8/WhfZacueOq7vOezrdnufRb89ran9cnXTq02pruqyJW//3p7X6bdt1T+PX/cGNm2o/fwuZVwxTWd8O7d5da53rDz0njp6+ac55NYi667f3a52zgx7DO79zTON1etWZTvOhnnOkDq+ZfvrVjbqvi2XbRx8UtHS6dg5yPW2fI7dfQ5ucB6OaU5ZjWnPKx4e+/cmbO7fbtuvYxu95W6+JxVL7Ju3pL70zus2F62sy567Tb7EY/XibvN8YVP9asHXBc5qlwMfzAQAAAAAKQlMAAAAAgILQFAAAAACgIDQFAAAAACgITQEAAAAACkc1XWH/ipNHMY55zvu3c8eyn6XszMs+O9DyfusN2rflDRGx+fzrG683Ni8+odGx2Z0iYn3qvc1+y0eo7nP08Vj467fu+qOsE0M7t158wuzNQc7zw9b2XFoei3HVz8au/s2IWOhxqOem1/7GArfQ+3gvXPv26+yvvc/lzY9lrxoyUH05qeP5NrRz8E3XNH6Mi/7aEM1eA8N4PLPr97guDXuf83TZd6vtjSsOt23ZsqXjJjq1j7qedKolg86L3rii+2Prp9d6gy7r7wUjP751tr9Y/752rTr3pmvm3F3otanu+p36DVSX176neS2oUWd61YLSQl4zLZ3Wr7PNc2P886lhXk/3rzh53jV0kDn60I9BMabdGyJGP9+apLURcfmclqY1ddHO6cdoxf4Ti3vDOF/qbmOpnZujGu8A7zcaqlsLFno9WOwah6YAAMDSMMo394spOFhMYwEAjgxCUwAAOEKt2H9/7b5Ng8cm2x6V1phX7L9fcAoADFXj0HTUk6PWZOdTr/r3ke5nqSq/tmD3hj+Yvd3xY+XF8rJPe3u7clv9kTuKewAAC1tJREFU+nay+fzr45JPXtR4vXG59rLPNjs26yNife68sdbHZrstH6Vq33Wfo/3XDP76Ld+Q9OvTr9+gWtsf1rl17QLP88Ovk6nqZ+ePLpRv4hbDm8uOqroyyHHop702ve7mfbXW6/5Rx6ku7QvVev6mYu5z2Wt/a7v0mV9jupk9Pp1qyELqy9aT55xvQz8HO9TRfhbjtaH9461NHtOgj6fcZ2v9TtelYe6zl/Z9t18Py49cXXzxxfPayvZy2TDrSac5TllLWvWi27W9pduYtmzZMucxtNrq6LVek2VNfLjm+bKQj+3V2f7+a/p2mYhadW7Aa1O71rnXb/32fuU1bqC6PEAt6FVnOs2De82NO71m+qlTN+psc/+GfxzbfKrTtXOQ6+m8OXLbc9Fkjj6yOeWcMf1djG6+NSntc7t614dSWVMX7Zx+rOrOl4exjWHsa5zGMd7mc/Hm+tWCmce5kDnNYnHJJZd0XeYPQQEAAAAAFISmAAAAAAAFoSkAAAAAQEFoCgAAAABQaPSHoJ5y4omjGsesZe+8NC48ffT7WaouPP1PIyJi96GfzGlfd/apPe93a+uk1a9u/3l+e8Vg643JOccdM6+t12OdfsHbYk23hS+7bDiDGsTLLot1Uf85+uarXx+DPjPL3nlp7T51+g46hjXPO2No2zvnuGPieWf88sDrz5wzby5a3jz358teGcu+vyciRndMhuXVj54Sd7zoJ/07DqB8ba264C211zvct9MxHoUez2WtdQ5bd/YPa+915vh0qSMLqC/LXv2SufeHfA5edPRDzVdahNeGNReeMfvzwYcPNlt5wMdT7rOl03VpmPvspX3f866Hy1fG6pXL5zStXr264+3W/XTasUMd47qzT41Vqw7Xj/ZasuqCt8S643vPe3rOaZavnNfU/rg66dSn1dZ0WRPn/Noza/Vbd/apsewXm732l73z0li3ut7cYiHzimEq69vRZ55Za52Ljn4oDq15Xaw+ZXn/zj3Uva51O2e/+chgx3DV8x9rvE6vOtP4fUOH10w//epGXQcvfG3jfQ+q07VzkOtp+xy5/Rra5LU0qjllOabpe18Ta04ZyW4maO7cbmautqa63ew98qCv2yPN419eFU9/6VkL3ErTOfco3wsM0+jH2+T9xqD614I3L3hOsxSknOv/Vd61a9fmqaml8NfKAAAAAAC6SylN55zXdlrm4/kAAAAAAAWhKQAAAABAQWgKAAAAAFAQmgIAAAAAFISmAAAAAAAFoSkAAAAAQEFoCgAAAABQEJoCAAAAABSEpgAAAAAABaEpAAAAAEBBaAoAAAAAUBCaAgAAAAAUhKYAAAAAAAWhKQAAAABAQWgKAAAAAFAQmgIAAAAAFISmAAAAAAAFoSkAAAAAQEFoCgAAAABQEJoCAAAAABSEpgAAAAAABaEpAAAAAEBBaAoAAAAAUBCaAgAAAAAUhKYAAAAAAAWhKQAAAABAQWgKAAAAAFAQmgIAAAAAFISmAAAAAAAFoSkAAAAAQEFoCgAAAABQEJoCAAAAABSEpgAAAAAABaEpAAAAAEBBaAoAAAAAUBCaAgAAAAAUhKYAAAAAAAWhKQAAAABAQWgKAAAAAFAQmgIAAAAAFISmAAAAAAAFoSkAAAAAQEFoCgAAAABQEJoCAAAAABSEpgAAAAAABaEpAAAAAEBBaAoAAAAAUBCaAgAAAAAUhKYAAAAAAAWhKQAAAABAQWgKAAAAAFAQmgIAAAAAFISmAAAAAAAFoSkAAAAAQEFoCgAAAABQEJoCAAAAABSEpgAAAAAABaEpAAAAAEBBaAoAAAAAUBCaAgAAAAAUhKYAAAAAAAWhKQAAAABAQWgKAAAAAFAQmgIAAAAAFFLOuX7nlB6OiPtGNxyAWcdHxPcnPQjgSUG9AcZFvQHGRb2Bel6Qcz6h04JGoSnAuKSUpnLOayc9DuDIp94A46LeAOOi3sDC+Xg+AAAAAEBBaAoAAAAAUBCaAovVlkkPAHjSUG+AcVFvgHFRb2CBfKcpAAAAAEDBb5oCAAAAABSEpsDYpZROTintTCl9PaX0tZTSO6r25Smlz6eUvln9/KWqPaWUrkwp3Z1S+p+U0urJPgJgqUkpPTWldEdK6dPV/VNSSrdXdeXmlNLRVfvTq/t3V8tXTnLcwNKSUjo2pbQjpfSNlNK+lNJZ5jfAKKSULq3eS92ZUroppfQM8xsYLqEpMAk/j4h35ZxfGBFnRsRbUkovjIi/jogv5JxPi4gvVPcjIs6JiNOqfxdHxIfGP2RgiXtHROwr7r83IjblnH81In4QEeuq9nUR8YOqfVPVD6CuD0TEZ3LOvx4RvxUzdcf8BhiqlNKKiHh7RKzNOa+KiKdGxIVhfgNDJTQFxi7n/EDOeU91+2DMvKFYERHnR8R1VbfrIuJV1e3zI+Jf84zdEXFsSum5Yx42sESllE6KiHMj4prqfoqIl0fEjqpLe71p1aEdEfGKqj9ATymlZ0fE70XEtoiInPOhnPMPw/wGGI2jIuKZKaWjIuJZEfFAmN/AUAlNgYmqPhrykoi4PSJOzDk/UC16MCJOrG6viIj7i9W+W7UB1PHPEfFXEfFEdf+4iPhhzvnn1f2ypszWm2r5j6r+AP2cEhEPR8S11deBXJNSOibMb4Ahyznvj4j3R8R3YiYs/VFETIf5DQyV0BSYmJTSL0TExyPiL3POB8plOeccEXkiAwOOGCmlV0bEQznn6UmPBTjiHRURqyPiQznnl0TEY3H4o/gRYX4DDEf13cjnx8x/1jwvIo6JiD+c6KDgCCQ0BSYipfS0mAlMb8g5f6Jq/l7rY2nVz4eq9v0RcXKx+klVG0A/vxMR56WUvh0R22PmY2sfiJmPwR5V9Slrymy9qZY/OyIeGeeAgSXruxHx3Zzz7dX9HTEToprfAMP2+xFxb8754ZzzzyLiEzEz5zG/gSESmgJjV31/zraI2Jdz3lgs+lREvL66/fqI+GTR/ufVX5k9MyJ+VHzMDaCrnPPf5JxPyjmvjJk/kPAfOec/i4idEXFB1a293rTq0AVVf78VBvSVc34wIu5PKZ1eNb0iIr4e5jfA8H0nIs5MKT2rem/VqjfmNzBEyesEGLeU0u9GxBcjYm8c/o7Bv42Z7zX9aEQ8PyLui4g/yTk/Wk0EroqZj5z8JCLekHOeGvvAgSUtpXR2RLw75/zKlNKvxMxvni6PiDsi4qKc8+MppWdExEdi5ruWH42IC3PO35rUmIGlJaX04pj5o3NHR8S3IuINMfOLKuY3wFCllDZExGsj4ucxM5d5U8x8d6n5DQyJ0BQAAAAAoODj+QAAAAAABaEpAAAAAEBBaAoAAAAAUBCaAgAAAAAUhKYAAAAAAAWhKQAAI5VSOi6l9JXq34Mppf3V7R+nlP5l0uMDAIB2Kec86TEAAPAkkVJaHxE/zjm/f9JjAQCAbvymKQAAE5FSOjul9Onq9vqU0nUppS+mlO5LKf1RSul9KaW9KaXPpJSeVvVbk1K6LaU0nVL6bErpuZN9FAAAHImEpgAALBanRsTLI+K8iLg+InbmnM+IiJ9GxLlVcPrBiLgg57wmIj4cEZdParAAABy5jpr0AAAAoHJrzvlnKaW9EfHUiPhM1b43IlZGxOkRsSoiPp9SiqrPAxMYJwAARzihKQAAi8XjERE55ydSSj/Lh798/4mYmbemiPhazvmsSQ0QAIAnBx/PBwBgqbgrIk5IKZ0VEZFSelpK6UUTHhMAAEcgoSkAAEtCzvlQRFwQEe9NKX01Ir4SES+d7KgAADgSpcOfegIAAAAAwG+aAgAAAAAUhKYAAAAAAAWhKQAAAABAQWgKAAAAAFAQmgIAAAAAFISmAAAAAAAFoSkAAAAAQEFoCgAAAABQ+H8skW4sGWkmMAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "DEC\n",
      "---------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "Spectral\n",
      "---------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "DEC\n",
      "---------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "Spectral\n",
      "---------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "DEC\n",
      "---------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "Spectral\n",
      "---------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "DEC\n",
      "---------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "Spectral\n",
      "---------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "DEC\n",
      "---------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "Spectral\n",
      "---------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "DEC\n",
      "---------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "Spectral\n",
      "---------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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SM6rqrklenuSYqrogo2dOnjI0PSXJvYftxyT53eU+mRW0bONWVY+uqkuSPCvJn1XV+Vk7lvPn7fUZTfd7T1V9qarOWPazAYB5DAHkMzIKF69N8rwkZybZ0WyI45PMJDk3o0fanDNsS2vtn5K8OsnHkvxzFvhiprE6rsrouuI1Gc3aeFCSz3RNPpHR3a2XV9V8z54/KcnNGYWjb0/yl2P7Nyd5e1VdW1W/vNj6AADYM9Rqv5myqi5MsnG4wGaRjNvOMW4A7Emq6qwkb2mtvXXatQAAwEpabXeaAgCwTKrqcVV1n6paV1XPT/LwjB4/AwAAe5RV9UVQc2mtbZh2DauRcds5xg2ANe7BSf46yd2TfCPJUa21y6ZbEgAArLxVPz0fAAAAAGCSTM8HAAAAAOgsaXr+gQce2DZs2LBMpQAAAAAArIyzzz77qtbaQXPtW1JoumHDhszMzEymKgAAAACAKamqi3a0z/R8AAAAAICO0BQAAAAAoCM0BQAAAADoCE0BAAAAADpCUwAAAACAjtAUAAAAAKAjNAUAAAAA6AhNAQAAAAA6QlMAAAAAgI7QFAAAAACgIzQFAAAAAOgITQEAAAAAOkJTAAAAAICO0BQAAAAAoCM0BQAAAADoCE0BAAAAADpCUwAAAACAjtAUAAAAAKAjNAUAAAAA6AhNAQAAAAA6QlMAAAAAgI7QFAAAAACgIzQFAAAAAOgITQEAAAAAOkJTAAAAAICO0BQAAAAAoCM0BQAAAADoCE0BAAAAADpLCk1vveKKJMn1J5y43fbx9Um68pnPWnLb2XoWquv6E07crs34+ny1zC6P1zdXH3OtL2XMZttPapyXet5L7bv/e9xc4zUzM3P7+szMzHbr4xbaP58zzjjjB/qapB3VtqOx+Myb3jzxGgAAAADYdUsKTW8bQtMbTjxpu+3j65N08+c+t+S2s/UsVNcNJ560XZvx9flqmV0er2+uPuZaX8qYzbaf1Dgv9byX2nf/97i5xuucc865ff2cc87Zbn3cQvvnc/nll/9AX5O0o9p2NBbn77P3xGsAAAAAYNeZng8AAAAA0BGaAgAAAAB0hKYAAAAAAB2hKQAAAABAZ92kOtq2/rBJdbVLfY+3XcyxSzmm3ze7PFf75RqPSfa7M2O1K/3Pt33Lli3zri/UfrF29jgAAAAA9hxLDk2XMxyFtWrO/25edezKFwIAAADAgpYcmq7ftlVwCku0ftvWH9zorlcAAACA3dLEpufPGQpNwLb1hy2679m2fag737Gz7WbbjK/PV0v/Wn37ufqYZMg8iXFe6nnvTN876m9H47Vp06Ykd0yfn10ft9D++WzZsmW740zVBwAAAGAuvggKAAAAAKAjNAUAAAAA6AhNAQAAAAA6QlMAAAAAgM6SvgjqTgcfnCTZ75iXbLd9fH2S9j7iiCW3na1nobqWeh59LbPL4/XN1ceujtekx3c537+Fxn6u8Tr88MNvX++X57LQ/vnc5z73mVhfc9lRfzsai4fcdHP2OXLxP98AAAAArIxqrS268caNG9vMzMwylgMAAAAAsPyq6uzW2sa59pmeDwAAAADQEZoCAAAAAHSEpgAAAAAAHaEpAAAAAEBHaAoAAAAA0BGaAgAAAAB0hKYAAAAAAB2hKQAAAABAR2gKAAAAANARmgIAAAAAdISmAAAAAAAdoSkAAAAAQEdoCgAAAADQEZoCAAAAAHSEpgAAAAAAHaEpAAAAAEBHaAoAAAAA0BGaAgAAAAB0hKYAAAAAAB2hKQAAAABAR2g6AdefcGKuP+HE25dX4vVWg7nGZGZmJjMzM4tquzOufOazFuxrpcdv/PVmx2ChOlbL+wwAAACw1lRrbdGNN27c2OYKvPZ029YfliRZv21rtq0/LOu3bV3211vu15iE2Tr7erds2ZIk2bRp04Jtd/Y1k/nfi5Uev/HXmx2Dpxz3h/PWsVreZwAAAIDVqKrObq1tnGufO00BAAAAADpCUwAAAACAjtAUAAAAAKAjNAUAAAAA6KybdgEwa/ZLnFair0m+1q7YXeoAAAAA4A5C02UgCLuDsQAAAABgtRGaLoP127Yua/+rKYhcv23rqqoXAAAAAISm7DYmGbAu1NdyB9u9na1D2AwAAAAwHb4ICgAAAACgIzQFAAAAAOgITQEAAAAAOkJTAAAAAICO0BQAAAAAoLNu2gWsBfsd85I5l1fi9XZns3X29R5++OGLbrsz9j7iiOzzmCPn7Wulx2/89WbHYKE6Vsv7DAAAALDWVGtt0Y03btzYZmZmlrEcAAAAAIDlV1Vnt9Y2zrXP9HwAAAAAgI7QFAAAAACgIzQFAAAAAOgITQEAAAAAOkJTAAAAAICO0BQAAAAAoCM0BQAAAADoCE0BAAAAADpCUwAAAACAjtAUAAAAAKAjNAUAAAAA6AhNAQAAAAA6QlMAAAAAgI7QFAAAAACgIzQFAAAAAOgITQEAAAAAOkJTAAAAAICO0BQAAAAAoCM0BQAAAADoCE0BAAAAADpCU5iQ6084MdefcOK0ywAAAABgFwlNYUJuOPGk3HDiSdMuAwAAAIBdJDQFAAAAAOgITQEAAAAAOkJTAAAAAICO0BQAAAAAoCM0BQAAAADoCE0BAAAAADpCUwAAAACAjtAUAAAAAKAjNAUAAAAA6AhNAQAAAAA6QlMAAAAAgI7QFAAAAACgs27aBcBasd8xL5l2CQAAAABMgNAUJuQeLz1m2iUAAAAAMAGm5wMAAAAAdISmAAAAAAAdoSkAAAAAQEdoCgAAAADQEZoCAAAAAHSEpgAAAAAAHaEpAAAAAEBHaAoAAAAA0BGaAgAAAAB0hKYAAAAAAB2hKQAAAABAR2gKAAAAANARmgIAAAAAdISmAAAAAAAdoSkAAAAAQEdoCgAAAADQEZoCAAAAAHSEpgAAAAAAHaEpAAAAAEBHaAoAAAAA0BGaAgAAAAB0hKYAAAAAAB2hKQAAAABAR2gKAAAAANARmgIAAAAAdISmAAAAAAAdoSkAAAAAQEdoCgAAAADQqdba4htXXZnkouUrB+B2Bya5atpFAHsEnzfASvF5A6wUnzewOPdrrR00144lhaYAK6WqZlprG6ddB7D2+bwBVorPG2Cl+LyBXWd6PgAAAABAR2gKAAAAANARmgK7qy3TLgDYY/i8AVaKzxtgpfi8gV3kmaYAAAAAAB13mgIAAAAAdISmwIqrqsOq6pNV9dWqOr+qXjxsv1dVfbSq/nn4+57D9qqqN1bVBVV1blUdPt0zAFabqtqrqr5YVWcO6/evqrOGz5V3V9Xew/Z9hvULhv0bplk3sLpU1QFVdXpVfb2qvlZVR7q+AZZDVb1k+H+pr1TVaVV1F9c3MFlCU2Aabkny0tbajyU5IslvVNWPJfndJB9vrT0oyceH9ST5+SQPGv5sSvKnK18ysMq9OMnXuvXXJjmptfbAJNckOXrYfnSSa4btJw3tABbrDUk+3Fr7t0kekdHnjusbYKKqan2S30qysbX20CR7JXlOXN/ARAlNgRXXWrustXbOsHxDRv9DsT7J05O8fWj29iS/OCw/Pck72sjnkhxQVT+0wmUDq1RVHZrkKUn+YlivJD+T5PShyfjnzezn0OlJnjC0B5hXVe2f5LFJTkmS1trNrbVr4/oGWB7rkty1qtYluVuSy+L6BiZKaApM1TA15FFJzkpycGvtsmHX5UkOHpbXJ9naHXbJsA1gMU5O8t+T3Das3zvJta21W4b1/jPl9s+bYf91Q3uAhdw/yZVJ3jo8DuQvqurucX0DTFhrbVuSP05ycUZh6XVJzo7rG5gooSkwNVW1b5L3Jvnt1tr1/b7WWkvSplIYsGZU1VOTfKu1dva0awHWvHVJDk/yp621RyX5Tu6Yip/E9Q0wGcOzkZ+e0S9rDkly9yRPmmpRsAYJTYGpqKo7ZxSY/mVr7X3D5itmp6UNf39r2L4tyWHd4YcO2wAW8u+SPK2qLkzyroymrb0ho2mw64Y2/WfK7Z83w/79k3x7JQsGVq1LklzSWjtrWD89oxDV9Q0waU9M8s3W2pWtte8neV9G1zyub2CChKbAihuen3NKkq+11k7sdp2R5PnD8vOT/E23/deGb5k9Isl13TQ3gB1qrf1ea+3Q1tqGjL4g4ROttecm+WSSo4Zm4583s59DRw3t3RUGLKi1dnmSrVX14GHTE5J8Na5vgMm7OMkRVXW34f+tZj9vXN/ABJX/ToCVVlU/leTTSc7LHc8YfEVGzzX96yT3TXJRkl9urV09XAi8OaMpJ99N8sLW2syKFw6salX1+CQva609tap+OKM7T++V5ItJntdau6mq7pLknRk9a/nqJM9prX1jWjUDq0tVPTKjL53bO8k3krwwoxtVXN8AE1VVxyV5dpJbMrqWeVFGzy51fQMTIjQFAAAAAOiYng8AAAAA0BGaAgAAAAB0hKYAAAAAAB2hKQAAAABAR2gKAAAAANARmgIAsKyq6t5V9aXhz+VVtW1YvrGq/te06wMAgHHVWpt2DQAA7CGqanOSG1trfzztWgAAYEfcaQoAwFRU1eOr6sxheXNVvb2qPl1VF1XVM6rqdVV1XlV9uKruPLT78ar6u6o6u6o+UlU/NN2zAABgLRKaAgCwu3hAkp9J8rQkpyb5ZGvtYUm+l+QpQ3D6piRHtdZ+PMn/TvJH0yoWAIC1a920CwAAgMGHWmvfr6rzkuyV5MPD9vOSbEjy4CQPTfLRqsrQ5rIp1AkAwBonNAUAYHdxU5K01m6rqu+3Ox6+f1tG162V5PzW2pHTKhAAgD2D6fkAAKwW/y/JQVV1ZJJU1Z2r6iFTrgkAgDVIaAoAwKrQWrs5yVFJXltVX07ypSSPmW5VAACsRXXHrCcAAAAAANxpCgAAAADQEZoCAAAAAHSEpgAAAAAAHaEpAAAAAEBHaAoAAAAA0BGaAgAAAAB0hKYAAAAAAB2hKQAAAABAR2gKAAAAANARmgIAAAAAdISmAAAAAAAdoSkAAAAAQEdoCgAAAAD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      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name in filenames:\n",
    "    print(\"\\n---------------------------\\nDEC\\n---------------------------\\n\")\n",
    "    plot_annot(name=name, collar=0.25, skip_overlap=True, groundtruth_path=audio_dataset.label_dir, hypothesis_path=hypothesis_dir_dec)\n",
    "    plt.show()\n",
    "\n",
    "    print(\"\\n---------------------------\\nSpectral\\n---------------------------\\n\")\n",
    "    plot_annot(name=name, collar=0.25, skip_overlap=True, groundtruth_path=audio_dataset.label_dir, hypothesis_path=hypothesis_dir_spectral)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "M6diVRDO1wt8"
   },
   "outputs": [],
   "source": []
  }
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