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dkohlsdorf/convnet.ipynb

Created August 26, 2019 14:28
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convnet.ipynb
{
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"metadata": {
"colab": {
"name": "ConvNet.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": [
"EtogDK8E-9Ey"
],
"include_colab_link": true
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"kernelspec": {
"name": "python3",
"display_name": "Python 3"
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"accelerator": "GPU"
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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
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"source": [
"<a href=\"https://colab.research.google.com/gist/dkohlsdorf/685145bae73e99278a570d4f603b0a96/convnet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IxMYaian98iP",
"colab_type": "text"
},
"source": [
"# Building A Small Convnet on the Whistle Examples\n",
"\n",
"first we:\n",
"+ install a library so we can read google sheets\n",
"+ import a lot of libraries including pandas, tensorflow and numeric libraries\n",
"+ we also connect to google drive\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "DrTITQfgx6dh",
"colab_type": "code",
"colab": {}
},
"source": [
"!pip install --upgrade --quiet gspread"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "afSEvzwKwo2y",
"colab_type": "code",
"colab": {}
},
"source": [
"import os\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import gspread\n",
"\n",
"import pandas as pd\n",
"import tensorflow as tf\n",
"\n",
"from numpy.fft import fft, ifft\n",
"from scipy.io import wavfile\n",
"from scipy.signal import resample\n",
"\n",
"from tensorflow.keras.layers import *\n",
"from tensorflow.keras.models import *\n",
"from tensorflow.keras.losses import *\n",
"from tensorflow.keras.utils import to_categorical\n",
"\n",
"from google.colab import drive, auth\n",
"from oauth2client.client import GoogleCredentials"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "PckUFNnTwu0-",
"colab_type": "code",
"outputId": "d928ffdd-adf7-4481-c687-6c5979f5ac47",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 122
}
},
"source": [
"drive.mount('/content/drive')\n",
"auth.authenticate_user()\n",
"gc = gspread.authorize(GoogleCredentials.get_application_default())"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
"\n",
"Enter your authorization code:\n",
"··········\n",
"Mounted at /content/drive\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "439nFnzI-plX",
"colab_type": "text"
},
"source": [
"## Labels and files\n",
"\n",
"We load the filenames with the associated labels and the start and stop from the google sheet"
]
},
{
"cell_type": "code",
"metadata": {
"id": "-5wlxTFcyhoy",
"colab_type": "code",
"outputId": "cfb79fdd-824d-4d96-e080-e8a74078273b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"sheet = gc.open_by_url('https://docs.google.com/spreadsheets/d/19oVcaDAX84iaccsf8dGIgS_NwnzKdIn58X_cD2-CgAA/edit?usp=drive_web&ouid=100484477148727513689')\n",
"sheet = sheet.sheet1\n",
"rows = sheet.get_all_values()\n",
"df = pd.DataFrame.from_records(rows)\n",
"\n",
"new_header = df.iloc[0] \n",
"df = df[1:] \n",
"df.columns = new_header \n",
"\n",
"df = df[['filename', 'label', 'start', 'stop', 'importance']]\n",
"df"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" <td>SCA</td>\n",
" <td>0</td>\n",
" <td>1.012567708</td>\n",
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" <tr>\n",
" <th>2362</th>\n",
" <td>sca--warps-01-06---239.wav</td>\n",
" <td>SCA</td>\n",
" <td>0</td>\n",
" <td>1.012567708</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2363</th>\n",
" <td>sca--warps-01-06---240.wav</td>\n",
" <td>SCA</td>\n",
" <td>0</td>\n",
" <td>1.012567708</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2364</th>\n",
" <td>sca--warps-01-06---241.wav</td>\n",
" <td>SCA</td>\n",
" <td>0</td>\n",
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" <td>2</td>\n",
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" <tr>\n",
" <th>2365</th>\n",
" <td>sca--warps-01-06---242.wav</td>\n",
" <td>SCA</td>\n",
" <td>0</td>\n",
" <td>1.012567708</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2366</th>\n",
" <td>sca--warps-01-06---243.wav</td>\n",
" <td>SCA</td>\n",
" <td>0</td>\n",
" <td>1.012567708</td>\n",
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" <tr>\n",
" <th>2367</th>\n",
" <td>sca--warps-01-06---244.wav</td>\n",
" <td>SCA</td>\n",
" <td>0</td>\n",
" <td>1.012567708</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2368</th>\n",
" <td>sca--warps-01-06---245.wav</td>\n",
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" <td>1.009661458</td>\n",
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"<p>2368 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
"0 filename ... importance\n",
"1 den_00_chat1-2019-06-23T123304-192k_13848228.wav ... 5\n",
"2 den_00_chat1-2019-06-23T123304-192k_18339907.wav ... 5\n",
"3 den_00_chat1-2019-06-23T123304-192k_22832010.wav ... 5\n",
"4 monotone-synth-mimic-june2019-box3to1.wav ... 5\n",
"5 monotone-synth-mimic-june2019-box3to1.wav ... 5\n",
"6 monotone-synth-mimic-june2019-box3to1.wav ... 5\n",
"7 monotone-synth-mimic-june2019-box3to1.wav ... 5\n",
"8 monotone-synth-mimic-june2019-box3to1.wav ... 5\n",
"9 monotone-synth-mimic-june2019-box3to1.wav ... 5\n",
"10 monotone-synth-mimic-june2019-box3to1.wav ... 5\n",
"11 monotone-synth-mimic-june2019-box3to1.wav ... 5\n",
"12 monotone-synth-mimic-june2019-box3to1.wav ... 5\n",
"13 monotone-synth-mimic-june2019-box3to1.wav ... 5\n",
"14 monotone-synth-mimic-june2019-box3to1.wav ... 5\n",
"15 monotone-synth-mimic-june2019-box3to1.wav ... 5\n",
"16 monotone-synth-mimic-june2019-box3to1.wav ... 5\n",
"17 monotone-synth-mimic-june2019-box3to1.wav ... 5\n",
"18 monotone-synth-mimic-june2019-box3to1.wav ... 5\n",
"19 monotone-synth-mimic-june2019-box3to1.wav ... 5\n",
"20 monotone-synth-mimic-june2019-box3to1.wav ... 5\n",
"21 monotone-synth-mimic-june2019-box3to1.wav ... 5\n",
"22 monotone-synth-mimic-june2019-box3to1.wav ... 5\n",
"23 monotone-synth-mimic-june2019-box3to1.wav ... 5\n",
"24 monotone-synth-mimic-june2019-box3to1.wav ... 5\n",
"25 monotone-synth-mimic-june2019-box3to1.wav ... 5\n",
"26 monotone-synth-mimic-june2019-box3to1.wav ... 5\n",
"27 monotone-synth-mimic-june2019-box3to1.wav ... 5\n",
"28 monotone-synth-mimic-june2019-box3to1.wav ... 5\n",
"29 monotone-synth-mimic-june2019-box3to1.wav ... 5\n",
"30 monotone-synth-mimic-june2019-box3to1.wav ... 5\n",
"... ... ... ...\n",
"2339 sca--warps-01-06---216.wav ... 2\n",
"2340 sca--warps-01-06---217.wav ... 2\n",
"2341 sca--warps-01-06---218.wav ... 2\n",
"2342 sca--warps-01-06---219.wav ... 2\n",
"2343 sca--warps-01-06---220.wav ... 2\n",
"2344 sca--warps-01-06---221.wav ... 2\n",
"2345 sca--warps-01-06---222.wav ... 2\n",
"2346 sca--warps-01-06---223.wav ... 2\n",
"2347 sca--warps-01-06---224.wav ... 2\n",
"2348 sca--warps-01-06---225.wav ... 2\n",
"2349 sca--warps-01-06---226.wav ... 2\n",
"2350 sca--warps-01-06---227.wav ... 2\n",
"2351 sca--warps-01-06---228.wav ... 2\n",
"2352 sca--warps-01-06---229.wav ... 2\n",
"2353 sca--warps-01-06---230.wav ... 2\n",
"2354 sca--warps-01-06---231.wav ... 2\n",
"2355 sca--warps-01-06---232.wav ... 2\n",
"2356 sca--warps-01-06---233.wav ... 2\n",
"2357 sca--warps-01-06---234.wav ... 2\n",
"2358 sca--warps-01-06---235.wav ... 2\n",
"2359 sca--warps-01-06---236.wav ... 2\n",
"2360 sca--warps-01-06---237.wav ... 2\n",
"2361 sca--warps-01-06---238.wav ... 2\n",
"2362 sca--warps-01-06---239.wav ... 2\n",
"2363 sca--warps-01-06---240.wav ... 2\n",
"2364 sca--warps-01-06---241.wav ... 2\n",
"2365 sca--warps-01-06---242.wav ... 2\n",
"2366 sca--warps-01-06---243.wav ... 2\n",
"2367 sca--warps-01-06---244.wav ... 2\n",
"2368 sca--warps-01-06---245.wav ... 2\n",
"\n",
"[2368 rows x 5 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "YeIo2J3KDiFu",
"colab_type": "code",
"outputId": "8e403d5a-f0ab-470a-baa0-1617dd538630",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"sheet2 = gc.open_by_url('https://docs.google.com/spreadsheets/d/1YyqNSsHcHPrtJr6Ac0jjG9kED6gA7gTsjDVjEoZsZpQ')\n",
"sheet2 = sheet2.sheet1\n",
"rows2 = sheet2.get_all_values()\n",
"df2 = pd.DataFrame.from_records(rows2)\n",
"\n",
"new_header2 = df2.iloc[0] \n",
"df2 = df2[1:] \n",
"df2.columns = new_header2\n",
"\n",
"df2 = df2[['filename', 'label', 'start', 'stop', 'importance']]\n",
"df2"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" <td>fm-synth-whistle-grass-5.wav</td>\n",
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" <th>11</th>\n",
" <td>fm-synth-whistle-grass-6.wav</td>\n",
" <td>GRA</td>\n",
" <td>0</td>\n",
" <td>2.102609375</td>\n",
" <td>5</td>\n",
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" <tr>\n",
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" <td>fm-synth-whistle-rope-1.wav</td>\n",
" <td>ROP</td>\n",
" <td>0</td>\n",
" <td>2.057630208</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>fm-synth-whistle-rope-2.wav</td>\n",
" <td>ROP</td>\n",
" <td>0</td>\n",
" <td>2.034307292</td>\n",
" <td>5</td>\n",
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" <tr>\n",
" <th>14</th>\n",
" <td>fm-synth-whistle-rope-3.wav</td>\n",
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" <td>5</td>\n",
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" <tr>\n",
" <th>15</th>\n",
" <td>fm-synth-whistle-rope-4.wav</td>\n",
" <td>ROP</td>\n",
" <td>0</td>\n",
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" <td>5</td>\n",
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" <th>16</th>\n",
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" <tr>\n",
" <th>18</th>\n",
" <td>fm-synth-whistle-sargassum-2.wav</td>\n",
" <td>SAR</td>\n",
" <td>0</td>\n",
" <td>2.0629375</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>fm-synth-whistle-sargassum-3.wav</td>\n",
" <td>SAR</td>\n",
" <td>0</td>\n",
" <td>1.966989583</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>fm-synth-whistle-sargassum-4.wav</td>\n",
" <td>SAR</td>\n",
" <td>0</td>\n",
" <td>2.016354167</td>\n",
" <td>5</td>\n",
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" <th>21</th>\n",
" <td>fm-synth-whistle-sargassum-5.wav</td>\n",
" <td>SAR</td>\n",
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" <th>22</th>\n",
" <td>fm-synth-whistle-scarf-1.wav</td>\n",
" <td>SCA</td>\n",
" <td>0</td>\n",
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" <td>5</td>\n",
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" <th>23</th>\n",
" <td>fm-synth-whistle-scarf-2.wav</td>\n",
" <td>SCA</td>\n",
" <td>0</td>\n",
" <td>2.051916667</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>fm-synth-whistle-scarf-3.wav</td>\n",
" <td>SCA</td>\n",
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" <td>5</td>\n",
" </tr>\n",
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" <th>25</th>\n",
" <td>fm-synth-whistle-scarf-4.wav</td>\n",
" <td>SCA</td>\n",
" <td>0</td>\n",
" <td>2.040947917</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>fm-synth-whistle-scarf-5.wav</td>\n",
" <td>SCA</td>\n",
" <td>0</td>\n",
" <td>2.153932292</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>fm-synth-whistle-scarf-6.wav</td>\n",
" <td>SCA</td>\n",
" <td>0</td>\n",
" <td>1.977036458</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>whistles-2019--synth-den--den-space_click_0.wav</td>\n",
" <td>DEN</td>\n",
" <td>0</td>\n",
" <td>2.141333333</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>whistles-2019--synth-den--den-space_click_1.wav</td>\n",
" <td>DEN</td>\n",
" <td>0</td>\n",
" <td>2.141333333</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>whistles-2019--synth-den--den-space_click_2.wav</td>\n",
" <td>DEN</td>\n",
" <td>0</td>\n",
" <td>2.141333333</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
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" <td>...</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>273</th>\n",
" <td>whistles-2019--synth-sca--sar-space_mimic_0.wav</td>\n",
" <td>SCA</td>\n",
" <td>0</td>\n",
" <td>2.340203125</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>274</th>\n",
" <td>whistles-2019--synth-sca--sar-space_mimic_1.wav</td>\n",
" <td>SCA</td>\n",
" <td>0</td>\n",
" <td>2.340203125</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>275</th>\n",
" <td>whistles-2019--synth-sca--sar-space_mimic_2.wav</td>\n",
" <td>SCA</td>\n",
" <td>0</td>\n",
" <td>2.340203125</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>276</th>\n",
" <td>whistles-2019--synth-sca--sar-space_mimic_3.wav</td>\n",
" <td>SCA</td>\n",
" <td>0</td>\n",
" <td>2.340203125</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>277</th>\n",
" <td>whistles-2019--synth-sca--sar-space_mimic_4.wav</td>\n",
" <td>SCA</td>\n",
" <td>0</td>\n",
" <td>2.340203125</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>278</th>\n",
" <td>whistles-2019--synth-den--den-space_burst.wav</td>\n",
" <td>DEN</td>\n",
" <td>0</td>\n",
" <td>2.140864583</td>\n",
" <td>2</td>\n",
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" <tr>\n",
" <th>279</th>\n",
" <td>whistles-2019--synth-gra--gra-space_burst.wav</td>\n",
" <td>GRA</td>\n",
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" <th>280</th>\n",
" <td>whistles-2019--synth-rop--rop-space_burst.wav</td>\n",
" <td>ROP</td>\n",
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" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>281</th>\n",
" <td>whistles-2019--synth-sca--sca-space_burst.wav</td>\n",
" <td>SCA</td>\n",
" <td>0</td>\n",
" <td>2.348526042</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>282</th>\n",
" <td>whistles-2019--synth-sar--sar-space_burst.wav</td>\n",
" <td>SAR</td>\n",
" <td>0</td>\n",
" <td>2.061723958</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>283</th>\n",
" <td>whistles-2019--synth-sar--sca-space_burst.wav</td>\n",
" <td>SAR</td>\n",
" <td>0</td>\n",
" <td>2.10525</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>284</th>\n",
" <td>whistles-2019--synth-den--gra-space_burst.wav</td>\n",
" <td>DEN</td>\n",
" <td>0</td>\n",
" <td>2.328833333</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>285</th>\n",
" <td>whistles-2019--synth-den--rop-space_burst.wav</td>\n",
" <td>DEN</td>\n",
" <td>0</td>\n",
" <td>2.358515625</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>286</th>\n",
" <td>whistles-2019--synth-den--sar-space_burst.wav</td>\n",
" <td>DEN</td>\n",
" <td>0</td>\n",
" <td>2.427765625</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>287</th>\n",
" <td>whistles-2019--synth-den--sca-space_burst.wav</td>\n",
" <td>DEN</td>\n",
" <td>0</td>\n",
" <td>2.461401042</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>288</th>\n",
" <td>whistles-2019--synth-gra--den-space_burst.wav</td>\n",
" <td>GRA</td>\n",
" <td>0</td>\n",
" <td>1.91728125</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>289</th>\n",
" <td>whistles-2019--synth-gra--rop-space_burst.wav</td>\n",
" <td>GRA</td>\n",
" <td>0</td>\n",
" <td>2.144880208</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>290</th>\n",
" <td>whistles-2019--synth-gra--sar-space_burst.wav</td>\n",
" <td>GRA</td>\n",
" <td>0</td>\n",
" <td>2.212098958</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>291</th>\n",
" <td>whistles-2019--synth-gra--sca-space_burst.wav</td>\n",
" <td>GRA</td>\n",
" <td>0</td>\n",
" <td>2.241776042</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>292</th>\n",
" <td>whistles-2019--synth-rop--den-space_burst.wav</td>\n",
" <td>ROP</td>\n",
" <td>0</td>\n",
" <td>1.893541667</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>293</th>\n",
" <td>whistles-2019--synth-rop--gra-space_burst.wav</td>\n",
" <td>ROP</td>\n",
" <td>0</td>\n",
" <td>2.081510417</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>294</th>\n",
" <td>whistles-2019--synth-rop--sar-space_burst.wav</td>\n",
" <td>ROP</td>\n",
" <td>0</td>\n",
" <td>2.180442708</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>295</th>\n",
" <td>whistles-2019--synth-rop--sca-space_burst.wav</td>\n",
" <td>ROP</td>\n",
" <td>0</td>\n",
" <td>2.19825</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>296</th>\n",
" <td>whistles-2019--synth-sar--den-space_burst.wav</td>\n",
" <td>SAR</td>\n",
" <td>0</td>\n",
" <td>1.786692708</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>297</th>\n",
" <td>whistles-2019--synth-sar--gra-space_burst.wav</td>\n",
" <td>SAR</td>\n",
" <td>0</td>\n",
" <td>1.974661458</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>298</th>\n",
" <td>whistles-2019--synth-sar--rop-space_burst.wav</td>\n",
" <td>SAR</td>\n",
" <td>0</td>\n",
" <td>1.992473958</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>299</th>\n",
" <td>whistles-2019--synth-sca--den-space_burst.wav</td>\n",
" <td>SCA</td>\n",
" <td>0</td>\n",
" <td>2.068427083</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>300</th>\n",
" <td>whistles-2019--synth-sca--gra-space_burst.wav</td>\n",
" <td>SCA</td>\n",
" <td>0</td>\n",
" <td>2.255161458</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>301</th>\n",
" <td>whistles-2019--synth-sca--rop-space_burst.wav</td>\n",
" <td>SCA</td>\n",
" <td>0</td>\n",
" <td>2.262338542</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>302</th>\n",
" <td>whistles-2019--synth-sca--sar-space_burst.wav</td>\n",
" <td>SCA</td>\n",
" <td>0</td>\n",
" <td>2.348526042</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>302 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
"0 filename ... importance\n",
"1 fm-synth-whistle-denise-1.wav ... 5\n",
"2 fm-synth-whistle-denise-2.wav ... 5\n",
"3 fm-synth-whistle-denise-3.wav ... 5\n",
"4 fm-synth-whistle-denise-4.wav ... 5\n",
"5 fm-synth-whistle-denise-5.wav ... 5\n",
"6 fm-synth-whistle-grass-1.wav ... 5\n",
"7 fm-synth-whistle-grass-2.wav ... 5\n",
"8 fm-synth-whistle-grass-3.wav ... 5\n",
"9 fm-synth-whistle-grass-4.wav ... 5\n",
"10 fm-synth-whistle-grass-5.wav ... 5\n",
"11 fm-synth-whistle-grass-6.wav ... 5\n",
"12 fm-synth-whistle-rope-1.wav ... 5\n",
"13 fm-synth-whistle-rope-2.wav ... 5\n",
"14 fm-synth-whistle-rope-3.wav ... 5\n",
"15 fm-synth-whistle-rope-4.wav ... 5\n",
"16 fm-synth-whistle-rope-5.wav ... 5\n",
"17 fm-synth-whistle-sargassum-1.wav ... 5\n",
"18 fm-synth-whistle-sargassum-2.wav ... 5\n",
"19 fm-synth-whistle-sargassum-3.wav ... 5\n",
"20 fm-synth-whistle-sargassum-4.wav ... 5\n",
"21 fm-synth-whistle-sargassum-5.wav ... 5\n",
"22 fm-synth-whistle-scarf-1.wav ... 5\n",
"23 fm-synth-whistle-scarf-2.wav ... 5\n",
"24 fm-synth-whistle-scarf-3.wav ... 5\n",
"25 fm-synth-whistle-scarf-4.wav ... 5\n",
"26 fm-synth-whistle-scarf-5.wav ... 5\n",
"27 fm-synth-whistle-scarf-6.wav ... 5\n",
"28 whistles-2019--synth-den--den-space_click_0.wav ... 4\n",
"29 whistles-2019--synth-den--den-space_click_1.wav ... 4\n",
"30 whistles-2019--synth-den--den-space_click_2.wav ... 4\n",
".. ... ... ...\n",
"273 whistles-2019--synth-sca--sar-space_mimic_0.wav ... 3\n",
"274 whistles-2019--synth-sca--sar-space_mimic_1.wav ... 3\n",
"275 whistles-2019--synth-sca--sar-space_mimic_2.wav ... 3\n",
"276 whistles-2019--synth-sca--sar-space_mimic_3.wav ... 3\n",
"277 whistles-2019--synth-sca--sar-space_mimic_4.wav ... 3\n",
"278 whistles-2019--synth-den--den-space_burst.wav ... 2\n",
"279 whistles-2019--synth-gra--gra-space_burst.wav ... 2\n",
"280 whistles-2019--synth-rop--rop-space_burst.wav ... 2\n",
"281 whistles-2019--synth-sca--sca-space_burst.wav ... 2\n",
"282 whistles-2019--synth-sar--sar-space_burst.wav ... 2\n",
"283 whistles-2019--synth-sar--sca-space_burst.wav ... 1\n",
"284 whistles-2019--synth-den--gra-space_burst.wav ... 1\n",
"285 whistles-2019--synth-den--rop-space_burst.wav ... 1\n",
"286 whistles-2019--synth-den--sar-space_burst.wav ... 1\n",
"287 whistles-2019--synth-den--sca-space_burst.wav ... 1\n",
"288 whistles-2019--synth-gra--den-space_burst.wav ... 1\n",
"289 whistles-2019--synth-gra--rop-space_burst.wav ... 1\n",
"290 whistles-2019--synth-gra--sar-space_burst.wav ... 1\n",
"291 whistles-2019--synth-gra--sca-space_burst.wav ... 1\n",
"292 whistles-2019--synth-rop--den-space_burst.wav ... 1\n",
"293 whistles-2019--synth-rop--gra-space_burst.wav ... 1\n",
"294 whistles-2019--synth-rop--sar-space_burst.wav ... 1\n",
"295 whistles-2019--synth-rop--sca-space_burst.wav ... 1\n",
"296 whistles-2019--synth-sar--den-space_burst.wav ... 1\n",
"297 whistles-2019--synth-sar--gra-space_burst.wav ... 1\n",
"298 whistles-2019--synth-sar--rop-space_burst.wav ... 1\n",
"299 whistles-2019--synth-sca--den-space_burst.wav ... 1\n",
"300 whistles-2019--synth-sca--gra-space_burst.wav ... 1\n",
"301 whistles-2019--synth-sca--rop-space_burst.wav ... 1\n",
"302 whistles-2019--synth-sca--sar-space_burst.wav ... 1\n",
"\n",
"[302 rows x 5 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EtogDK8E-9Ey",
"colab_type": "text"
},
"source": [
"## Modeling\n",
"\n",
"The two things we need are a method to create a spectrogram and\n",
"a method to create a small neural network.\n",
"\n",
"The neural network has one convolutional layer. The max pooling is \n",
"at each time stamp pool across the whole spectrogram.\n",
"Then we have a simple dense layer for the classification.\n",
"\n",
"Since we are only interested in the sequence resulting from the convolutional filters\n",
"we also create an application model that outputs right after the pooling.\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "I_n24uUx1ZhS",
"colab_type": "code",
"colab": {}
},
"source": [
"def fwd_spectrogram(audio, win=512, step=256):\n",
" '''\n",
" Compute the spectrogram of audio data\n",
"\n",
" audio: one channel audio\n",
" win: window size for dft sliding window\n",
" step: step size for dft sliding windo\n",
" '''\n",
" spectrogram = []\n",
" hanning = np.hanning(win)\n",
" for i in range(win, len(audio), step):\n",
" dft = fft(audio[i - win: i])\n",
" spectrogram.append(dft)\n",
" return np.array(spectrogram)\n",
"\n",
"def small_model():\n",
" input = Input(shape=input_shape)\n",
" x = Conv2D(25, kernel_size = (10, 10), activation='relu', name='filters', padding='same')(input)\n",
" x = MaxPool2D((1, 256))(x)\n",
" pooled = Reshape((1000, 25))(x)\n",
" x = Flatten()(pooled)\n",
" x = Dropout(0.5)(x)\n",
" x = Dense(output_shape, activation='softmax')(x)\n",
" model = Model(inputs = [input], outputs = [x])\n",
" model.compile(optimizer='adam', loss='categorical_crossentropy')\n",
" model.summary()\n",
" apply = Model(inputs = [input], outputs = [pooled])\n",
" return (model, apply)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "YExMW0fT_efj",
"colab_type": "text"
},
"source": [
"# Loading\n",
"\n",
"We load all files from google drive and convert the files into a power\n",
"spectrogram. We also min/max normalise the spectrogram."
]
},
{
"cell_type": "code",
"metadata": {
"id": "riot83EZyiRo",
"colab_type": "code",
"outputId": "cb872b50-7ba0-4f2e-ab42-7b29a62aa3f2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"x = []\n",
"y = []\n",
"f = []\n",
"imp = []\n",
"labels = {}\n",
"max_label = 0\n",
"\n",
"PATH = '/content/drive/Shared drives/Dolphin recordings/192k-recognizer-variations'\n",
"for _, row in df2.iterrows():\n",
" filename = row['filename']\n",
" label = row['label']\n",
" importance = int(row['importance'])\n",
" start = float(row['start'])\n",
" stop = float(row['stop'])\n",
" if filename.endswith('.wav'):\n",
" name = \"{}/{}\".format(PATH, filename)\n",
" fs, data = wavfile.read(name)\n",
" if len(data.shape) > 1:\n",
" data = data[:, 0]\n",
" start_sample = int(start * fs)\n",
" stop_sample = int(stop * fs)\n",
" spec = np.abs(fwd_spectrogram(data[start_sample: stop_sample]))[:, 0:256]\n",
" \n",
" target_spec = np.zeros((1000, 256, 1))\n",
" target_spec[0:spec.shape[0], :, 0] = spec[0:1000, :]\n",
" spec = target_spec\n",
" hi = np.max(spec)\n",
" lo = np.min(spec)\n",
" spec = (spec - lo) / (hi - lo)\n",
" print(\"Reading 192k: {} {} {} {} :: {} / {}\".format(filename, label, start_sample, stop_sample, len(y), len(df2) + len(df)))\n",
" if label not in labels:\n",
" labels[label] = max_label\n",
" max_label += 1\n",
" y.append(labels[label])\n",
" x.append(spec) \n",
" imp.append(importance)\n",
" f.append(name)\n",
" \n",
" \n",
"PATH = '/content/drive/Shared drives/Dolphin recordings/32k-recognizer-variations'\n",
"for _, row in df.iterrows():\n",
" filename = row['filename']\n",
" label = row['label']\n",
" importance = int(row['importance'])\n",
" start = float(row['start'])\n",
" stop = float(row['stop'])\n",
" if filename.endswith('.wav'):\n",
" name = \"{}/{}\".format(PATH, filename)\n",
" fs, data = wavfile.read(name)\n",
" if len(data.shape) > 1:\n",
" data = data[:, 0]\n",
" start_sample = int(start * fs)\n",
" stop_sample = int(stop * fs)\n",
" spec = np.abs(fwd_spectrogram(data[start_sample: stop_sample]))[:, 0:256]\n",
" \n",
" target_spec = np.zeros((1000, 256, 1))\n",
" target_spec[0:spec.shape[0], :, 0] = spec[0:1000, :]\n",
" spec = target_spec\n",
" hi = np.max(spec)\n",
" lo = np.min(spec)\n",
" spec = (spec - lo) / (hi - lo)\n",
" print(\"Reading 32k: {} {} {} {} :: {} / {}\".format(filename, label, start_sample, stop_sample, len(y), len(df2) + len(df)))\n",
" if label not in labels:\n",
" labels[label] = max_label\n",
" max_label += 1\n",
" y.append(labels[label])\n",
" x.append(spec) \n",
" imp.append(importance)\n",
" f.append(name)\n"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/scipy/io/wavfile.py:273: WavFileWarning: Chunk (non-data) not understood, skipping it.\n",
" WavFileWarning)\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Reading 192k: fm-synth-whistle-denise-1.wav DEN 0 382383 :: 0 / 2670\n",
"Reading 192k: fm-synth-whistle-denise-2.wav DEN 0 419318 :: 1 / 2670\n",
"Reading 192k: fm-synth-whistle-denise-3.wav DEN 0 399854 :: 2 / 2670\n",
"Reading 192k: fm-synth-whistle-denise-4.wav DEN 0 424008 :: 3 / 2670\n",
"Reading 192k: fm-synth-whistle-denise-5.wav DEN 0 446709 :: 4 / 2670\n",
"Reading 192k: fm-synth-whistle-grass-1.wav GRA 0 398391 :: 5 / 2670\n",
"Reading 192k: fm-synth-whistle-grass-2.wav GRA 0 402158 :: 6 / 2670\n",
"Reading 192k: fm-synth-whistle-grass-3.wav GRA 0 399284 :: 7 / 2670\n",
"Reading 192k: fm-synth-whistle-grass-4.wav GRA 0 426048 :: 8 / 2670\n",
"Reading 192k: fm-synth-whistle-grass-5.wav GRA 0 435803 :: 9 / 2670\n",
"Reading 192k: fm-synth-whistle-grass-6.wav GRA 0 403701 :: 10 / 2670\n",
"Reading 192k: fm-synth-whistle-rope-1.wav ROP 0 395064 :: 11 / 2670\n",
"Reading 192k: fm-synth-whistle-rope-2.wav ROP 0 390587 :: 12 / 2670\n",
"Reading 192k: fm-synth-whistle-rope-3.wav ROP 0 399765 :: 13 / 2670\n",
"Reading 192k: fm-synth-whistle-rope-4.wav ROP 0 406583 :: 14 / 2670\n",
"Reading 192k: fm-synth-whistle-rope-5.wav ROP 0 405131 :: 15 / 2670\n",
"Reading 192k: fm-synth-whistle-sargassum-1.wav SAR 0 385598 :: 16 / 2670\n",
"Reading 192k: fm-synth-whistle-sargassum-2.wav SAR 0 396084 :: 17 / 2670\n",
"Reading 192k: fm-synth-whistle-sargassum-3.wav SAR 0 377661 :: 18 / 2670\n",
"Reading 192k: fm-synth-whistle-sargassum-4.wav SAR 0 387140 :: 19 / 2670\n",
"Reading 192k: fm-synth-whistle-sargassum-5.wav SAR 0 377994 :: 20 / 2670\n",
"Reading 192k: fm-synth-whistle-scarf-1.wav SCA 0 410472 :: 21 / 2670\n",
"Reading 192k: fm-synth-whistle-scarf-2.wav SCA 0 393968 :: 22 / 2670\n",
"Reading 192k: fm-synth-whistle-scarf-3.wav SCA 0 404775 :: 23 / 2670\n",
"Reading 192k: fm-synth-whistle-scarf-4.wav SCA 0 391862 :: 24 / 2670\n",
"Reading 192k: fm-synth-whistle-scarf-5.wav SCA 0 413555 :: 25 / 2670\n",
"Reading 192k: fm-synth-whistle-scarf-6.wav SCA 0 379590 :: 26 / 2670\n",
"Reading 192k: whistles-2019--synth-den--den-space_click_0.wav DEN 0 411135 :: 27 / 2670\n",
"Reading 192k: whistles-2019--synth-den--den-space_click_1.wav DEN 0 411135 :: 28 / 2670\n",
"Reading 192k: whistles-2019--synth-den--den-space_click_2.wav DEN 0 411135 :: 29 / 2670\n",
"Reading 192k: whistles-2019--synth-den--den-space_click_3.wav DEN 0 411135 :: 30 / 2670\n",
"Reading 192k: whistles-2019--synth-den--den-space_click_4.wav DEN 0 411135 :: 31 / 2670\n",
"Reading 192k: whistles-2019--synth-den--den-space_mimic_0.wav DEN 0 410046 :: 32 / 2670\n",
"Reading 192k: whistles-2019--synth-den--den-space_mimic_1.wav DEN 0 410046 :: 33 / 2670\n",
"Reading 192k: whistles-2019--synth-den--den-space_mimic_2.wav DEN 0 410046 :: 34 / 2670\n",
"Reading 192k: whistles-2019--synth-den--den-space_mimic_3.wav DEN 0 410046 :: 35 / 2670\n",
"Reading 192k: whistles-2019--synth-den--den-space_mimic_4.wav DEN 0 410046 :: 36 / 2670\n",
"Reading 192k: whistles-2019--synth-gra--gra-space_click_0.wav GRA 0 409599 :: 37 / 2670\n",
"Reading 192k: whistles-2019--synth-gra--gra-space_click_1.wav GRA 0 409599 :: 38 / 2670\n",
"Reading 192k: whistles-2019--synth-gra--gra-space_click_2.wav GRA 0 409599 :: 39 / 2670\n",
"Reading 192k: whistles-2019--synth-gra--gra-space_click_3.wav GRA 0 409599 :: 40 / 2670\n",
"Reading 192k: whistles-2019--synth-gra--gra-space_click_4.wav GRA 0 409599 :: 41 / 2670\n",
"Reading 192k: whistles-2019--synth-gra--gra-space_mimic_0.wav GRA 0 408408 :: 42 / 2670\n",
"Reading 192k: whistles-2019--synth-gra--gra-space_mimic_1.wav GRA 0 408408 :: 43 / 2670\n",
"Reading 192k: whistles-2019--synth-gra--gra-space_mimic_2.wav GRA 0 408408 :: 44 / 2670\n",
"Reading 192k: whistles-2019--synth-gra--gra-space_mimic_3.wav GRA 0 408408 :: 45 / 2670\n",
"Reading 192k: whistles-2019--synth-gra--gra-space_mimic_4.wav GRA 0 408408 :: 46 / 2670\n",
"Reading 192k: whistles-2019--synth-rop--rop-space_click_0.wav ROP 0 403455 :: 47 / 2670\n",
"Reading 192k: whistles-2019--synth-rop--rop-space_click_1.wav ROP 0 403455 :: 48 / 2670\n",
"Reading 192k: whistles-2019--synth-rop--rop-space_click_2.wav ROP 0 403455 :: 49 / 2670\n",
"Reading 192k: whistles-2019--synth-rop--rop-space_click_3.wav ROP 0 403455 :: 50 / 2670\n",
"Reading 192k: whistles-2019--synth-rop--rop-space_click_4.wav ROP 0 403455 :: 51 / 2670\n",
"Reading 192k: whistles-2019--synth-rop--rop-space_mimic_0.wav ROP 0 401856 :: 52 / 2670\n",
"Reading 192k: whistles-2019--synth-rop--rop-space_mimic_1.wav ROP 0 401856 :: 53 / 2670\n",
"Reading 192k: whistles-2019--synth-rop--rop-space_mimic_2.wav ROP 0 401856 :: 54 / 2670\n",
"Reading 192k: whistles-2019--synth-rop--rop-space_mimic_3.wav ROP 0 401856 :: 55 / 2670\n",
"Reading 192k: whistles-2019--synth-rop--rop-space_mimic_4.wav ROP 0 401856 :: 56 / 2670\n",
"Reading 192k: whistles-2019--synth-sar--sar-space_click_0.wav SAR 0 396288 :: 57 / 2670\n",
"Reading 192k: whistles-2019--synth-sar--sar-space_click_1.wav SAR 0 396288 :: 58 / 2670\n",
"Reading 192k: whistles-2019--synth-sar--sar-space_click_2.wav SAR 0 396288 :: 59 / 2670\n",
"Reading 192k: whistles-2019--synth-sar--sar-space_click_3.wav SAR 0 396288 :: 60 / 2670\n",
"Reading 192k: whistles-2019--synth-sar--sar-space_click_4.wav SAR 0 396288 :: 61 / 2670\n",
"Reading 192k: whistles-2019--synth-sar--sar-space_mimic_0.wav SAR 0 395657 :: 62 / 2670\n",
"Reading 192k: whistles-2019--synth-sar--sar-space_mimic_1.wav SAR 0 395657 :: 63 / 2670\n",
"Reading 192k: whistles-2019--synth-sar--sar-space_mimic_2.wav SAR 0 395657 :: 64 / 2670\n",
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],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2GLWF8bT_rvq",
"colab_type": "text"
},
"source": [
"# Training\n",
"\n",
"we prepare the training data, hotcode the labels and train the model.\n",
"Training time is about 1 hour."
]
},
{
"cell_type": "code",
"metadata": {
"id": "KhFjXnCpz5Qo",
"colab_type": "code",
"colab": {}
},
"source": [
"input_shape = (1000, 256, 1)\n",
"output_shape = len(labels)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "PjOj8KT_64DA",
"colab_type": "code",
"outputId": "bd747842-5ab1-4861-808d-66c1ae07c821",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
}
},
"source": [
"x = np.array(x)\n",
"y = to_categorical(y)\n",
"print(\"Input: \", x.shape)\n",
"print(\"Output: \", y.shape)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Input: (2670, 1000, 256, 1)\n",
"Output: (2670, 5)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "cjmjGzUU5uqi",
"colab_type": "code",
"outputId": "86332aa5-75fb-4a30-bbf1-1bfb783e916a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"(model, apply) = small_model()\n",
"model.fit(x, y, epochs=250, batch_size = 75, shuffle=True)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"WARNING: Logging before flag parsing goes to stderr.\n",
"W0801 23:15:37.243471 140215882626944 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Call initializer instance with the dtype argument instead of passing it to the constructor\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Model: \"model\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"input_1 (InputLayer) [(None, 1000, 256, 1)] 0 \n",
"_________________________________________________________________\n",
"filters (Conv2D) (None, 1000, 256, 25) 2525 \n",
"_________________________________________________________________\n",
"max_pooling2d (MaxPooling2D) (None, 1000, 1, 25) 0 \n",
"_________________________________________________________________\n",
"reshape (Reshape) (None, 1000, 25) 0 \n",
"_________________________________________________________________\n",
"flatten (Flatten) (None, 25000) 0 \n",
"_________________________________________________________________\n",
"dropout (Dropout) (None, 25000) 0 \n",
"_________________________________________________________________\n",
"dense (Dense) (None, 5) 125005 \n",
"=================================================================\n",
"Total params: 127,530\n",
"Trainable params: 127,530\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"Epoch 1/250\n",
"2670/2670 [==============================] - 21s 8ms/sample - loss: 1.3938\n",
"Epoch 2/250\n",
"2670/2670 [==============================] - 12s 5ms/sample - loss: 0.9813\n",
"Epoch 3/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.6938\n",
"Epoch 4/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.5712\n",
"Epoch 5/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.4509\n",
"Epoch 6/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.4159\n",
"Epoch 7/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.3564\n",
"Epoch 8/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.3128\n",
"Epoch 9/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.2777\n",
"Epoch 10/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.2440\n",
"Epoch 11/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.2329\n",
"Epoch 12/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.2106\n",
"Epoch 13/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.1971\n",
"Epoch 14/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.2016\n",
"Epoch 15/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.1869\n",
"Epoch 16/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.1669\n",
"Epoch 17/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.1581\n",
"Epoch 18/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.1413\n",
"Epoch 19/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.1241\n",
"Epoch 20/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.1281\n",
"Epoch 21/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.1213\n",
"Epoch 22/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.1036\n",
"Epoch 23/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.1048\n",
"Epoch 24/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0958\n",
"Epoch 25/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0912\n",
"Epoch 26/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0861\n",
"Epoch 27/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.1000\n",
"Epoch 28/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0904\n",
"Epoch 29/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0781\n",
"Epoch 30/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0747\n",
"Epoch 31/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0719\n",
"Epoch 32/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0674\n",
"Epoch 33/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0589\n",
"Epoch 34/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0610\n",
"Epoch 35/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0604\n",
"Epoch 36/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0735\n",
"Epoch 37/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0549\n",
"Epoch 38/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0517\n",
"Epoch 39/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0531\n",
"Epoch 40/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0444\n",
"Epoch 41/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0564\n",
"Epoch 42/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0501\n",
"Epoch 43/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0384\n",
"Epoch 44/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0380\n",
"Epoch 45/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0370\n",
"Epoch 46/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0478\n",
"Epoch 47/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0381\n",
"Epoch 48/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0469\n",
"Epoch 49/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0396\n",
"Epoch 50/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0350\n",
"Epoch 51/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0281\n",
"Epoch 52/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0323\n",
"Epoch 53/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0292\n",
"Epoch 54/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0279\n",
"Epoch 55/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0310\n",
"Epoch 56/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0304\n",
"Epoch 57/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0302\n",
"Epoch 58/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0258\n",
"Epoch 59/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0230\n",
"Epoch 60/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0212\n",
"Epoch 61/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0188\n",
"Epoch 62/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0181\n",
"Epoch 63/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0182\n",
"Epoch 64/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0195\n",
"Epoch 65/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0202\n",
"Epoch 66/250\n",
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"Epoch 67/250\n",
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"Epoch 68/250\n",
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"Epoch 69/250\n",
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"Epoch 70/250\n",
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"Epoch 71/250\n",
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"Epoch 72/250\n",
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"Epoch 73/250\n",
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"Epoch 74/250\n",
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"Epoch 75/250\n",
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"Epoch 76/250\n",
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"Epoch 77/250\n",
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"Epoch 78/250\n",
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"Epoch 79/250\n",
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"Epoch 80/250\n",
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"Epoch 81/250\n",
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"Epoch 82/250\n",
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"Epoch 83/250\n",
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"Epoch 85/250\n",
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"Epoch 86/250\n",
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"Epoch 87/250\n",
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"Epoch 88/250\n",
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"Epoch 89/250\n",
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"Epoch 90/250\n",
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"Epoch 91/250\n",
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"Epoch 92/250\n",
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"Epoch 93/250\n",
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"Epoch 94/250\n",
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"Epoch 95/250\n",
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"Epoch 96/250\n",
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"Epoch 97/250\n",
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"Epoch 98/250\n",
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"Epoch 99/250\n",
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"Epoch 100/250\n",
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"Epoch 101/250\n",
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"Epoch 148/250\n",
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"Epoch 156/250\n",
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"Epoch 159/250\n",
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"Epoch 160/250\n",
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"Epoch 162/250\n",
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"Epoch 163/250\n",
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"Epoch 164/250\n",
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"Epoch 165/250\n",
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"Epoch 166/250\n",
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"Epoch 170/250\n",
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"Epoch 176/250\n",
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"Epoch 177/250\n",
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"Epoch 179/250\n",
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"Epoch 180/250\n",
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"Epoch 181/250\n",
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"Epoch 182/250\n",
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"Epoch 183/250\n",
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"Epoch 190/250\n",
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"Epoch 191/250\n",
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"Epoch 192/250\n",
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"Epoch 194/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 9.1261e-04\n",
"Epoch 195/250\n",
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"Epoch 202/250\n",
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"Epoch 203/250\n",
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"Epoch 204/250\n",
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"Epoch 205/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0038\n",
"Epoch 206/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0019\n",
"Epoch 207/250\n",
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"Epoch 208/250\n",
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"Epoch 209/250\n",
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"Epoch 210/250\n",
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"Epoch 211/250\n",
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"Epoch 212/250\n",
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"Epoch 213/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0017\n",
"Epoch 214/250\n",
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"Epoch 215/250\n",
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"Epoch 216/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0015\n",
"Epoch 217/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 8.9381e-04\n",
"Epoch 218/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0022\n",
"Epoch 219/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0017\n",
"Epoch 220/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0025\n",
"Epoch 221/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0015\n",
"Epoch 222/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0018\n",
"Epoch 223/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0023\n",
"Epoch 224/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0021\n",
"Epoch 225/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0015\n",
"Epoch 226/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0016\n",
"Epoch 227/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0013\n",
"Epoch 228/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0028\n",
"Epoch 229/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0025\n",
"Epoch 230/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0015\n",
"Epoch 231/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0010\n",
"Epoch 232/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 9.0654e-04\n",
"Epoch 233/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 9.3202e-04\n",
"Epoch 234/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0027\n",
"Epoch 235/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0079\n",
"Epoch 236/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0026\n",
"Epoch 237/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0017\n",
"Epoch 238/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0011\n",
"Epoch 239/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0013\n",
"Epoch 240/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0012\n",
"Epoch 241/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 8.1411e-04\n",
"Epoch 242/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 6.7982e-04\n",
"Epoch 243/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 8.0448e-04\n",
"Epoch 244/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0058\n",
"Epoch 245/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0074\n",
"Epoch 246/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0157\n",
"Epoch 247/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0045\n",
"Epoch 248/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0232\n",
"Epoch 249/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0108\n",
"Epoch 250/250\n",
"2670/2670 [==============================] - 13s 5ms/sample - loss: 0.0113\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<tensorflow.python.keras.callbacks.History at 0x7f8641b50080>"
]
},
"metadata": {
"tags": []
},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "bj2JGvGn6wIZ",
"colab_type": "code",
"colab": {}
},
"source": [
"apply.save('{}/filters_tensorflow_keras_model.h5'.format(PATH))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "nJYdpjx0_5lt",
"colab_type": "text"
},
"source": [
"# Evaluation\n",
"\n",
"First we plot the filters that we learned"
]
},
{
"cell_type": "code",
"metadata": {
"id": "HI9rI65Vd7vF",
"colab_type": "code",
"outputId": "bce25578-e251-4f3a-f21e-2d6fd2a87fbc",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 224
}
},
"source": [
"PATH = '/content/drive/Shared drives/Dolphin recordings/32k-recognizer-variations'\n",
"apply = load_model('{}/filters_tensorflow_keras_model.h5'.format(PATH))"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"WARNING: Logging before flag parsing goes to stderr.\n",
"W0805 09:44:33.333708 139636339169152 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:97: calling GlorotUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Call initializer instance with the dtype argument instead of passing it to the constructor\n",
"W0805 09:44:33.335524 139636339169152 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Call initializer instance with the dtype argument instead of passing it to the constructor\n",
"W0805 09:44:33.342465 139636339169152 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:97: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Call initializer instance with the dtype argument instead of passing it to the constructor\n",
"W0805 09:44:36.634721 139636339169152 hdf5_format.py:221] No training configuration found in save file: the model was *not* compiled. Compile it manually.\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "uoyHy7e77i5_",
"colab_type": "code",
"outputId": "201b74e8-d79e-45e3-8faf-ad67ebb96105",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 286
}
},
"source": [
"layer = apply.get_layer('filters')\n",
"weights = layer.get_weights()[0].reshape(10, 10, 25)\n",
"for i in range(0, 25):\n",
" plt.subplot(5, 5, i + 1)\n",
" plt.imshow(weights[:, :, i].T, cmap='gray')\n",
"plt.savefig('{}/filters.png'.format(PATH))\n",
"plt.show()\n",
"\n",
"for i in range(0, 25):\n",
" np.savetxt(\"{}/filters_{}.csv\".format(PATH, i), weights[:, :, i].reshape((10, 10)), fmt='%.18e', delimiter=',', newline='\\n')"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"W0802 11:52:14.593917 140646699751296 hdf5_format.py:221] No training configuration found in save file: the model was *not* compiled. Compile it manually.\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
"image/png": 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PRj+TgwZ4e8I7BZe/3rt3r2pzbtVdu3apdl1dXbTNuVPlIPqnf/qnQrvbV1JrMm7cOFXW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\nTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 25 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "yjrIZAX17zOM",
"colab_type": "code",
"colab": {}
},
"source": [
"train = []\n",
"test = []\n",
"for i in range(0, len(x)):\n",
" instance = apply.predict(x[i].reshape(1, 1000, 256, 1)).reshape(1000, 25)\n",
" label = y[i] \n",
" if np.random.uniform() > 0.4:\n",
" train.append((instance, label))\n",
" else:\n",
" test.append((instance, label))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Dv9emzLmsPgS",
"colab_type": "code",
"colab": {}
},
"source": [
"%load_ext Cython"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "SqCAk8ELoKEy",
"colab_type": "code",
"colab": {}
},
"source": [
"%%cython\n",
"import numpy as np\n",
"\n",
"def euc(x,y):\n",
" return np.sum(np.square(x - y))\n",
"\n",
"def dtw(x, y, band):\n",
" '''\n",
" Compute the dynamic time warping distance\n",
" \n",
" x: sequence n x d\n",
" y: sequence m x d\n",
" band: the sakoe chiba band\n",
" '''\n",
" assert x.shape[1] == y.shape[1]\n",
" cdef int n = x.shape[0]\n",
" cdef int m = y.shape[0]\n",
" cdef int w = max(band, abs(n-m) + 2)\n",
"\n",
" dp = np.ones((n + 1, m + 1)) * float('inf')\n",
" cdef int i, j\n",
"\n",
" dp[0,0] = 0.0 \n",
" for i in range(1, n + 1):\n",
" for j in range(max(i - w, 0), min(i + w, m + 1)):\n",
" dp[i, j] = euc(x[i - 1], y[j - 1]) + np.min([\n",
" dp[i - 1, j], \n",
" dp[i - 1, j - 1], \n",
" dp[i, j - 1]]\n",
" )\n",
" return dp[n, m]"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "E1DmA1A4ALJ9",
"colab_type": "code",
"outputId": "f64d2c9b-7785-4c3e-dc09-8dc03ea746a2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 527
}
},
"source": [
"with open('{}/results.csv'.format(PATH), 'w') as fp:\n",
" confusion = np.zeros((5,5,5))\n",
" n_done = 0\n",
" for (i, (instance, truth)) in enumerate(test):\n",
" min_dist = float('inf')\n",
" min_label = 0\n",
" truth = np.argmax(truth)\n",
" for (example, label) in train:\n",
" label = np.argmax(label)\n",
" dist = np.sum(np.square(example - instance)) \n",
" if dist < min_dist:\n",
" min_dist = dist\n",
" min_label = label\n",
" fp.write(\"{}, {}, {}, {}\\n\".format(f[i], truth, min_label, imp[i]))\n",
" n_done += 1\n",
" confusion[imp[i] - 1,truth, min_label] += 1 \n",
"print(confusion)\n"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"W0802 12:02:20.801635 140646699751296 hdf5_format.py:221] No training configuration found in save file: the model was *not* compiled. Compile it manually.\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"[[[ 10. 0. 0. 0. 1.]\n",
" [ 0. 9. 0. 0. 0.]\n",
" [ 0. 0. 0. 0. 0.]\n",
" [ 0. 0. 0. 0. 0.]\n",
" [ 0. 0. 0. 0. 0.]]\n",
"\n",
" [[ 3. 0. 0. 0. 2.]\n",
" [ 0. 0. 0. 0. 0.]\n",
" [ 0. 0. 0. 0. 0.]\n",
" [ 0. 0. 0. 0. 0.]\n",
" [ 0. 0. 0. 0. 0.]]\n",
"\n",
" [[149. 0. 8. 2. 13.]\n",
" [ 2. 122. 1. 3. 7.]\n",
" [ 19. 3. 138. 3. 10.]\n",
" [ 2. 3. 2. 117. 7.]\n",
" [ 3. 0. 1. 5. 112.]]\n",
"\n",
" [[ 15. 0. 0. 0. 0.]\n",
" [ 0. 15. 0. 0. 0.]\n",
" [ 0. 1. 10. 0. 0.]\n",
" [ 0. 0. 0. 9. 0.]\n",
" [ 1. 1. 3. 8. 52.]]\n",
"\n",
" [[ 2. 1. 0. 1. 0.]\n",
" [ 3. 63. 0. 3. 6.]\n",
" [ 8. 0. 31. 2. 7.]\n",
" [ 3. 3. 0. 42. 11.]\n",
" [ 2. 1. 0. 1. 0.]]]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "gnJhbSrBN5h9",
"colab_type": "code",
"colab": {}
},
"source": [
"total = np.sum(confusion, axis=0)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "nzt-t-v4YlnG",
"colab_type": "code",
"outputId": "f24b4e65-4277-4f77-ddab-ac8b13fd27ae",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"def plot_confusion_matrix(confusion, labels, importance, acc,\n",
" cmap=plt.cm.Blues): \n",
" title = 'Importance: {} || Accuracy: {}%'.format(importance, int(acc * 100))\n",
" cm = confusion\n",
" classes = [\"\" for i in range(5)]\n",
" for label, i in labels.items():\n",
" classes[i] = label \n",
" fig, ax = plt.subplots()\n",
" im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
" ax.figure.colorbar(im, ax=ax)\n",
" # We want to show all ticks...\n",
" ax.set(xticks=np.arange(cm.shape[1]),\n",
" yticks=np.arange(cm.shape[0]),\n",
" xticklabels=classes, yticklabels=classes,\n",
" title=title,\n",
" ylabel='True label',\n",
" xlabel='Predicted label')\n",
"\n",
" # Rotate the tick labels and set their alignment.\n",
" plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
" rotation_mode=\"anchor\")\n",
"\n",
" # Loop over data dimensions and create text annotations.\n",
" fmt = 'd'\n",
" thresh = cm.max() / 2.\n",
" for i in range(cm.shape[0]):\n",
" for j in range(cm.shape[1]):\n",
" ax.text(j, i, format(int(cm[i, j]), fmt),\n",
" ha=\"center\", va=\"center\",\n",
" color=\"white\" if cm[i, j] > thresh else \"black\")\n",
" fig.tight_layout()\n",
" return ax\n",
"\n",
"for i in range(0, 5):\n",
" accuracy = np.sum(confusion[i, :, :] * np.eye(5)) / np.sum(confusion[i, :, :])\n",
" plot_confusion_matrix(confusion[i, :, :], labels, i + 1, accuracy)\n",
" plt.savefig('{}/confusion_imp_{}.png'.format(PATH, i + 1))\n",
" plt.show()\n",
"\n",
"accuracy = np.sum(total * np.eye(5)) / np.sum(total)\n",
"plot_confusion_matrix(total, labels, \"all\", accuracy)\n",
"plt.savefig('{}/confusion_imp_{}.png'.format(PATH, \"all\"))\n",
"plt.show()\n"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
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G55xLVIIXI4YBi8xsmZl9AtwODGlNTF6ic84lK7k2upeAL0jagFB13R+Y3fxLGueJzjmX\nuCT60ZnZDEm3AXMIE2w9TpzHuaU80TnnEpdUh2EzuxC4cF2P44nOOZe88roxwhOdcy5Zkvymfudc\n5Su3e1090TnnEueJzjlX+corz3mic84lTD7wpnOuwgkos5qrJzrnXNJ8chznXBtQZnnOE51zLnnl\nVqIrrxbDMnH/1PsYUN2f6u37ccnF40odTqNOO3wXZl9zHLV/OI7TDx9Y6nCalIbPMg0xQnrilKB9\ne+W1FEtBE52kLST9TdILkmolPSrpSElDJb0jaa6k/8XB9Rq+9k5JjxUyvsbU19dz1hmnMXnKvTw+\nbz6TJtzCM/PnFzuMZu249WZ8/aCd2Pu7E9n9tL9xyO696bPlJqUOay1p+CzTECOkJ84MKb+lWAqW\n6BTKrncC/zGzPmY2iDDee8+4y8NmNhDYFRghaa+s13YFBgGbSOpTqBgbM2vmTPr27cc2ffrQsWNH\nRh1zLHdPmVzMEHLavtdmzHr2VVZ8/Cn1q4yHn6rjiL36ljqstaThs0xDjJCeODOSmhwnKYUs0e0H\nrDSzazMrzGyxmf0+eyczWwHMBaqyVh8FTAEm0MrJMFrr5Zfr6Nmz1+rnVVU9qaurK2YIOT29+A32\n2qkHm3XpROf1O3BwTW96dutS6rDWkobPMg0xQnriBMJ4dGVWoivkxYhqwjhSzYrDJW8L/Cdr9Wjg\nZ8BrwN+BXxUiwLR6dslb/HZSLVN+cQQffvwJT7ywjPpVq0odlnNAGGG43DoMFy0aSVdLekLSrLhq\nb0lPAHXAVDN7Ne63BSHxPWJmzwGfSNqpiWOOlTRb0uxly5clEmePHlUsXbpk9fO6uqVUVVU184rS\nuOH++ex15gQO+MHfefv9j1lQ93apQ1pLGj7LNMQI6Ykzo9xKdIVMdE8Du2WemNlphKGQu8dVD5vZ\nLoSS38mSMpcOvwJsCiyS9CLQm1DCW4uZjTezGjOr6d6te2O7tFjN4MEsXLiAFxctYuXKlUyaOIHh\nI0Ymcuwkdd+kMwC9um/E4UP6MnHasyWOaG1p+CzTECOkJ86McmujK2TV9V/ArySdamZ/iOs2aLiT\nmS2SNA74ISGhjQYONrNHASRtAzwInF/AWFfr0KEDl19xFYcNP4j6+npOHHMSO1ZXF+PULXLL+Yey\n2cad+eTTes66ZhrvfLCy1CGtJQ2fZRpihPTECaxuoysnMrPCHVzaErgc2ANYBnwAXEtoezvHzEbE\n/ToDC4G9gOlAT8sKTNIc4FQzm9HUuQYNqrHpM1o1b0ZR+byurtzstUcNtbWzE0tNG1b1tx1OvTb3\njkDtBfvVNjXdYZIKemeEmb1C01dNp2Xtt4LPrrqu1fBgZrs1XOecK1/ldmeE3wLmnEtcmeU5T3TO\nuYTJS3TOuQoX+tF5onPOVbgyK9B5onPOJc+rrs65ylaG/eg80TnnEhXmjCivTOeJzjmXOL8Y4Zyr\neF6ic85VNm+jc85VOvl0h865tqB9mbXRldcwoM65ipDUwJuSukq6LU6i9YykPVsTT5MlOkkbN/dC\nM3u3NSd0zlU2JXuv6xXAfWZ2tKSONDKmZT6aq7o+DRihW0xG5rkBW7XmhM65ypdEzVXSJsA+wBgA\nM1sJtGqE2SYTnZn1amqbaz0f1NK1BS3oR9dNUvaIuePNbHx8vA1hwN7rJO0C1AJnmtkHLY4nn50k\nHSvpvPi4p6RBLT2Rc65tEPHKax7/gOWZeV/iMj7rUB0I8878wcx2JYxQfm5rYsqZ6CRdBewLnBBX\nfUgYDt055xrVTvktOSwFlmZNoXAbWRNutSiePPYZYmbfBD4CMLM3gY6tOZlzrg3IcwawXBcs4hSo\nSyT1j6v2B+a3JqR8+tF9Iqkd4QIEkj4H+GzJzrlGiUT70X0H+Gu84voC8PXWHCSfRHc18Hegu6SL\nCPOuXtSakznn2oakepeY2VxgnWcJy5nozOxGSbXAsLhqlJk9ta4nds5VrrTeAtYe+IRQffW7KZxz\nTcr3rodiyueq6/nALUAPoCfwN0k/KnRgzrn0aifltRRLPiW6rwG7mtmHAJJ+CTwO/LqQgTnn0quY\nSSwf+SS6Vxrs1yGuc865tYhkbgFLUnM39V9OaJN7E3ha0tT4/EBgVnHCc86lTh595IqtuRJd5srq\n08A9WesfK1w4zrlKUGZ5rtmb+v9czECcc5Uh4Q7DicjZRiepL/BLYEegU2a9mW1XwLiccylWblXX\nfPrEXQ9cR0jUhwC3AhMLGJNzLuWU51Is+SS6DcxsKoCZPW9mPyYkPOecW4tUfv3o8kl0H8eb+p+X\n9C1JhwFdChxXSd0/9T4GVPenevt+XHLxuFKH06g0xAjpiDMNMUJ64oQw8GY+S9HiyWOf7wIbAmcA\newGnACflc3BJ9ZLmSnpK0hRJXbO2VUv6l6RnJS2QdIFixV7SGEnL4mvnSzql5W+tderr6znrjNOY\nPOVeHp83n0kTbuGZ+a0aGaZg0hAjpCPONMQI6YkzI6nJcZKSM9GZ2Qwze8/MXjKzE8xspJlNz/P4\nK8xsoJntROiPdxqApM7AXcA4M+sP7AIMAb6d9dqJZjYQGAr8StIW+b+t1ps1cyZ9+/Zjmz596Nix\nI6OOOZa7p0wuxqnzloYYIR1xpiFGSE+cEEYXLreqa3Mdhu8gjkHXGDM7qoXnehQYEB9/FZhuZvfH\nY30o6XRgGmFYqOzzvC7peWBr4LUWnrPFXn65jp49P5suo6qqJzNnzmjmFcWXhhghHXGmIUZIT5wA\nlOFN/c11L7kqqZNIak8YHTTTN6+aMNHFamb2vKSNGk6zKKkP0AdY2MhxxwJjAXpt5ZOSOVcu2pdZ\npmuuw/A/Ezh+Z0lzgSrgGeCBFrz2GElfBD4GvhmHcG8Y43hgPMCgQTVNlj5bokePKpYuXbL6eV3d\nUqqqqpI4dGLSECOkI840xAjpiRNi15EyS3SFHltuRWxn25rw/k+L6+cDa8wkFktu72dNjD0xtu/t\nYWZ3FDjO1WoGD2bhwgW8uGgRK1euZNLECQwfMbJYp89LGmKEdMSZhhghPXFmJDQ5TmLyHXhzncQ2\nuDOAOyVdA/wVOE/SMDN7MF6cuBK4uBjxNKdDhw5cfsVVHDb8IOrr6zlxzEnsWF1d6rDWkIYYIR1x\npiFGSE+cGWV2Bxgyy6/GJ2l9M/u4RQeX3jezjbKeTwFuNbObJO0M/B7YkjCC8U3Az8zMJI0Baszs\n9HzPNWhQjU2fMTv3js65Ney1Rw21tbMTS02f33YnO+6yv+e172Ujt681s3WeEyKXfO513Z1wEWET\nYKs4Y/Y3zOw7uV6bneTi88OyHj9J6DrS2OuuJ9x65pxLofZlNuFCPuFcCYwA3gAwsycIE1o759xa\nwsCbKelHl6WdmS1ucBWlvkDxOOcqQJkV6PJKdEti9dVif7jvAM8VNiznXJqVWe+SvBLdqYTq61aE\nOxMejOucc24tktI38KaZvQ4cW4RYnHMVoszyXF5XXf+PRu55NbOxBYnIOZdqmYsR5SSfquuDWY87\nAUcCS5rY1znn0tdGZ2ZrDJsu6SbgkYJF5JxLN6Xopv5mbAMUZWw451z6pGoC6wxJb/FZG107wgCa\n5xYyKOdcuqUq0cWhzXcB6uKqVZbvzbHOuTYrVcM0xaT2DzOrj4snOedcs6Rwr2s+S37HU3tJj0u6\nu7Ux5XOquZJ2be0JnHNtT8L3up5JGLi39fE0tUFSplq7KzArztY1J2bWOetyUudc5cpcjEhi4E1J\nPYHhwJ/WJabm2uhmArsB5TuMqXOuLCXYRPc74Aes41zSzSU6QZi0Zl1O4JxrW4Ra0o+um6TsEXPH\nx7lgkDQCeN3MaiUNXZeYmkt03SV9r6mNZnbZupzYOVehWjYfxPJmRhjeCxgp6VDCXVkbS7rZzI5v\naUjNJbr2wEbEkp1zzuUriXtdzexHwI8AYonunNYkOWg+0b1iZj9rzUGdc21XmO6w1FGsKWcbnXPO\ntVTSo5eY2TRgWmtf31yi27+1B3XOtV0C2pdZManJRGdmbxYzEOdchVD53QJWlAmsnXNtS3mlOU90\nzrmEpXWEYeeca5FUDdPknHMtJ2+jc85VNpHOCaydc65FvETnnKts8osRzrkK51VX51ybUG5V13JL\nvGXh/qn3MaC6P9Xb9+OSi8eVOpxGpSFGSEecaYgR0hMnxBv781iKpeCJTtL5kp6WNE/SXEl7xPXd\nJH0i6VsN9n9R0pNx/4ckbV3oGLPV19dz1hmnMXnKvTw+bz6TJtzCM/PnFzOEnNIQI6QjzjTECOmJ\nEzL3uiqvpVgKmugk7QmMAHYzswHAMGBJ3DwKeAwY3chL9437TwN+XMgYG5o1cyZ9+/Zjmz596Nix\nI6OOOZa7p0wuZgg5pSFGSEecaYgR0hNnhpTfUiyFLtFtSRhB9GMAM1tuZi/HbaOBs4GqOAFGYx4F\nqgoc4xpefrmOnj17rX5eVdWTurq6Zl5RfGmIEdIRZxpihPTEGSjvf8VS6ER3P9BL0nOSrpH0JQBJ\nvYAtzWwmcCtwTBOvPxi4s6mDSxorabak2cuWL0s6dudcK7WpEp2ZvQ8MAsYCy4CJksYQEtutcbcJ\nrF19/bekOuAQ4JZmjj/ezGrMrKZ7t+6JxNyjRxVLly5Z/byubilVVUUtVOaUhhghHXGmIUZIT5yQ\n6V6ivJZiKfjFCDOrN7NpZnYhcDrwZUJiGyPpReAuYICkbbNeti+wNTAXuKjQMWarGTyYhQsX8OKi\nRaxcuZJJEycwfER5zfiYhhghHXGmIUZIT5xA6DDcLr+lWAraj05Sf2CVmS2IqwYSJ90xs6qs/S4i\nJL/Vc1SY2aeSzgKelPSLYg0E2qFDBy6/4ioOG34Q9fX1nDjmJHasri7GqfOWhhghHXGmIUZIT5wZ\nxWx/y4fMrHAHlwYBvwe6Ap8CC4EngM5mdm7WfgOAiWa2Qyzl1ZjZ8rjt94S5HX/e3LkGDaqx6TNm\nN7eLc64Re+1RQ23t7MQyU/+dBto1tz2Y177Dduhe28x0h4kpaInOzGqBIXnsNw/YIT7u3WDbdwoS\nnHOuYMqtROe3gDnnEuc39TvnKloYSr3UUazJE51zLmHF7QycD090zrlkFbkzcD480TnnEpW5qb+c\neKJzziWuvNKcJzrnXCGUWabzROecS5xfjHDOVTzvXuKcq3ye6JxzlSzMB1Femc4TnXMuWWXYj85n\nAXPOJS6JWcAk9ZL0b0nz4wRbZ7Y2Hi/ROecSpqTmdf0UONvM5kjqAtRKesDMWjz9mSc651zikshz\nZvYK8Ep8/J6kZwiTZXmic86VVgsnp+4mKXvE3PFmNn6tY0q9gV2BGa2JyROdcy55+We65blGGJa0\nEfB34Cwze7c14Xiic84lLqmBNyWtR0hyfzWz21t7HE90zrnEJZHmFK5o/Bl4xswuW5djefcS51yy\n8u1bkjsb7gWcAOwnaW5cDm1NSF6ic84lLok7I8zsERK6mcwTnXMuUT5nhHOubfBE55yrdH5Tv3Ou\n4pXbTf2e6JxzifNE55yraD4enXOu8pXheHSe6JxziSuzPOeJzjlXAGWW6TzROecSpsRu6k+KJzrn\nXKJaOB5dUfhN/Y24f+p9DKjuT/X2/bjk4nGlDqdRaYgR0hFnGmKE9MQJJHVTf2IKnugknR8ntpgX\nRx/YQ9J6ksZJWiBpjqRHJR2S9ZqBkkzSwYWOr6H6+nrOOuM0Jk+5l8fnzWfShFt4Zn6LR24uqDTE\nCOmIMw0xQnrizFCe/4qloIlO0p7ACGA3MxsADAOWAD8HtgR2MrPdgCOALlkvHQ08Ev8vqlkzZ9K3\nbz+26dOHjh07MuqYY7l7yuRih9GsNMQI6YgzDTFCeuLMaKf8lqLFU+Djb0kYKvljADNbDrwNnAJ8\nJ2v9a2Z2K6webG8UMAY4QFKnAse4hpdfrqNnz16rn1dV9aSurq6YIeSUhhghHXGmIUZIT5zA6n50\n+SzFUuhEdz/QS9Jzkq6R9CWgH/BSM2O/DwEWmdnzwDRgeFMHlzRW0mxJs5ctX5Z07M65ViuvRrqC\nJjozex8YBIwFlgETgaE5XjYamBAfT6CZ6quZjTezGjOr6d6t+7oHDPToUcXSpUtWP6+rW0pVVVUi\nx05KGmKEdMSZhhghPXFCTGFtrESHmdWb2TQzuxA4HTgM2ErSxg33ldQe+DLwE0kvAr8HDo6T1xZF\nzeDBLFy4gBcXLWLlypVMmjhQXDEPAAANmElEQVSB4SNGFuv0eUlDjJCOONMQI6Qnzoxya6MraD86\nSf2BVWa2IK4aCDwLPA5cIembZrZSUndCSe8dYJ6ZHZR1jBuAI4EbCxlrRocOHbj8iqs4bPhB1NfX\nc+KYk9ixuroYp85bGmKEdMSZhhghPXFmlNtN/TKzwh1cGkQolXUFPgUWEqqx7wK/AI4CPgI+AH4C\nHAvMMLNrs44xEjjVzA6hGYMG1dj0GbOb28U514i99qihtnZ2Yplpl10H2dSHHstr3y036Viba17X\nJBS0RGdmtYSLC435QVyyTW3kGHcBdyUcmnOugMqrPOe3gDnnEiYlN4F1UjzROeeSV155zhOdcy55\nZZbnPNE555JXZjVXT3TOuaQV94b9fHiic84lKnNnRDnxROecS5wnOudcxfOqq3Ousvl0h865Sudt\ndM65NqHcqq4+OY5zLnFJjUcn6WBJz0paKOnc1sbjic45l7gkxheO41NeDRwC7AiMlrRja+LxROec\nS5ykvJYcdgcWmtkLZraSMOL44a2JxxOdcy5RCQ6lXkWYNTBjaVzXYhVzMWLOnNrlndfT4gQP2Q1Y\nnuDxCiUNcaYhRkhHnIWIceskDzZnTu3UzuupW567d5KUPWLueDMbn2Q8UEGJzsySmR0nkjS7GCOf\nrqs0xJmGGCEdcaYhRjNLauL5OqBX1vOecV2LedXVOVeuZgHbStpGUkfCVAutGm28Ykp0zrnKYmaf\nSjqdMMVCe+AvZvZ0a47lia5pibcTFEga4kxDjJCOONMQY2LM7B/AP9b1OAWdBcw558qBt9E55yqe\nJzrnXMXzRNcISZ8vdQzNUR5dyl3L+edauTzRNSCpJ3C+pDGljqUZgvL/w5S0haReufcsHUk9JW0s\nqZOZmaSy/pso9/jKlX9oa3sfeA7YRdJxpQ6mIUlDgImS+gKblDqepkgaTujzdJ+kcZI2LXVMDUk6\nGLgX+AMwXtImZraqxGGtIY7e8ZP4cycTX7l/yZUbT3SRpF6SupvZ28B1wFPAnpKOL3FoDY0Evgxc\nCFwg6avZG8vhGz8mkEuBMcChwJ7AGaWMqaEY40XAmcAVwNuEzzWzvVwSyT7Ad4FLJY2XtLOkDdJQ\n+iwn/kEBknYFFgNTJR0LDDWzPwNPE3pmf62kAa7pl8DtwHzgYeB0Sb+TdCR89o1fKpK6AEcBC4Al\nZrYYOA/YPvZuLykFGwC/A+aY2b/MbCbwOFAlqZOkzlY+/a7uBh4kfKYfAqOBmyVtDaxXysDSpM0n\nuvhL/TgwGRgIbAB8V9I1wG5AR2A/SV9u5jBFEb/BPwQeAz4wszuBa4CTgW9ImiNpUCljNLP3CJ1a\n5wE/ldQVOBh4Nw61U1IWfAgcD+wr6Vtx0xDgNOBOYJqk0ZKqSxGjpB6Stojx/hdYHzjLzM4i1DSO\nAP4P+IOkQ0sRY9q06TsjYjvSgcCZZnakpLuAI81sf0mDgcOA/Qh/BL0l3WdmHxQ5xh2BTQmJ430z\nWxVHe7hM0irg28DxZjZZ0k+BZcWMLyvO7Qhthu8Ac4A3gVOAfwLvmdnQuJ9KVVqStL6ZfQxgZrMl\nnQDcImkU8B6wPbAxcAywBzCtBDEeBPwgxvVALBGfTfgiO5lQOt4feBn4AqFk73Ixsza5AAcATwAH\nNFg/Dbg363kPYBCwbQliPJTwDT4buAnYPWvbecAHwNFl8FmOjHHeQ6hqzQK6A32BPxKqiRvHfVWi\nGA8CJgEnNli/K/AMocS0Oj5g/RL9vJ8A9m2wfjPCoJMfAPtkrS/JZ5nGpeQBlORNh1LcEmBAfL4N\n8MOs7Q8CD5RBjIuB3oQmhmuA32Rt3x24A1gvPm9Xojj3ioliUNa6a4AnCWOn9QF+Qahq9Szh53ks\n8DqhZHwr8BWgV9w2OL6HH5YoNgEbArcBB2XWxf/bx/8HALVA31L+XqZ1aXNtdJLWI/zSvAG8GBum\nJwL1mX3MbBiwcazKliLG9QnVqDeBThYuMPwY6CNpWLzqNpPQXndDjLlUFyG2JIwqURvjxsy+DTxC\naO9aRCjlLQY+KXZwkjaIbZvTCCW6UcB9QD/gAUmHEC5EjAS+XKJuMOtbaBJ5l3D1F2L7uZnVxyvA\n7wH/AobGuRRcC7TJm/olbQJ8jTD+/NbAz8zspqztnczsI0m9zGxJU8cpUGxfJPwRvkKoNh9JqKYe\nDxwNGOFqcDtC14hPzGxpkWNc3c4m6WzgkPjlgKT1zOyT+IVyB/BNM6vLfKZFjvNQQsn4UjNbKulS\nYHszGxH7pT0ETCFUDa8Gbjez+qaPWLAYDwZ+D1wO/MfMLo7bOpjZp/HxCUAnQrNKUX/eFaHURcpi\nLcC2hIsK+/JZe9H3CVWZbbP2G0PoQNqpBDEeDMwFTiD0n9qY0P9sATAz7tOJkAh/A/Qp0We5ftbj\nbsD1hC+NdnFdh/j/P4DqEsU4gtDedQRZ1XrgSuDXwAuEJLgeofpdijbYTIxHxecD48//aw32O5nQ\n/rlRKT7LSlhKHkBR3iQMJ1wJvIPQ/vYSsAvhsv2ZhG/1PoRp1WqBnUoQ45eAhcAeDdbvAxxHqP7t\nXAaf5YHxc7yQcIUa4EeE0shRWfuNin+0m5cgxs8D/wYGx+cdgY3iz/hX8Ytj77itVG2bDWPsTOja\nNDr+HpxGuDhxCqG9s+i/k5W0VHz3ktgD/gLgu2b2UFx3ISG5HWpmV8RuGvcTqoUjzeyZEoS6K/B7\nM5uRWSHpYkIJ81rC1curJX3PzGY3fojCyrqb4EZgc2CEpP8S+s2NBY6IVdn/EEorx5nZ6yUI9WNC\ne+BHkjoB5wJ7AysIXao2JCQ/rHRtmw1j/CHwRT5rpxtJ6DqyKTDazJ4qUZwVoaITnaTNCNWnkWb2\nUKadyMwuig28kyXtQui6YcA/i53kstq7+hL6oGXWH0L41j8c+CthUpC/AK8VM76seDKf5eFmNkVh\n8INfAv3MbLqk3xCqgV8FXgX+ZGbPlyJWQqKYSrgNrZpQir+FcGV1FCHB7C1puhW53TBHjBNijEcD\n083stuy+f671Kv5iROwUPI5wW9cb2b84kv4NnG1mcyS1K+G3O5L2J5Q8fhjjWY/w81kp6TzgeeA2\nK3JjeYMYhwMXA3ua2buS7iF0Ep4DvAj8CVhhZkW/utqQpI2AnQmzSE3O+plfS7iafYmZvVXCEJuL\n8TpgmpndUMoO1pWkokt0AGZ2T6yazpRUY2ZvZa4MEqoJn8T9Sj1qxWPAdODYeLVtJoCk0XxWDSxZ\nkoM1PstaSfcRrvz+ltA5+GRgR0Iv/neaPkpxmNn7wKNxAUDSV4AaYFSpkxw0GeMoQvL7RdzHk1wC\nKr5ElxGrglcBmWT3NUKD72Elakdai6QqQsLYj9C3awWhGnOEmZXNrT6ShhHaNLc0s9fiunbAZmZW\ndhNAS9qScFvXKcAx5djelYYY06zNJDpYnewuJvTcPwEYW26/UJI6EwYTOIDQLjfNzBaUNqq1xc/y\nt4QmgbL4omhK/Ez3A541s4WljqcxaYgxzdpUogOQNIIwzNGu1so5Il0g6XBCN5OaMqj6O9ekNpfo\nINwWZGGoHreOJG0U25qcK1ttMtE559qWNndTv3Ou7fFE55yreJ7onHMVzxOdc67ieaKrQJLqJc2V\n9JSkSXFw0dYea6iku+PjkZLObWbfrpK+3Ypz/FTSOfmub7DP9ZKObsG5eksqq76TrvA80VWmFWY2\n0Mx2AlYC38reqKDFP3szu8vMxjWzS1fCZD3OlRVPdJXvYaBfLMk8K+lGwkQ2vSQdKOlRhWkSJ8Wb\nzDOzw/9P0hzCfKLE9WMkXRUfbyHpDklPxGUIYfCEvrE0eUnc7/uSZkmaJ+mirGOdL+k5SY8A/XO9\nCUmnxOM8IenvDUqpwyTNjscbEfdvL+mSrHN/c10/SJdenugqmKQOhMFEn4yrtgWuMbNqwoxSPwaG\nmdluhJnGvhfHRvs/wlSPgwhDRTXmSuAhM9uFcMva04TRV56PpcnvSzownnN3wui5gyTtozD37LFx\n3aGEyWlyud3MBsfzPUO4JzijdzzHcODa+B5OBt4xs8Hx+KdI2iaP87gKVPGjl7RRnSXNjY8fBv5M\nmH9isZk9Ftd/gTDayPQwNB8dCaNobA8sytxfK+lmwqCaDe1HmHeDOKrKO1p7YpkD4/J4fL4RIfF1\nAe7I3J2i/CYh2knSLwjV440IY7ll3BpvQVsg6YX4Hg4EBmS1320Sz/1cHudyFcYTXWVaYWYDs1fE\nZJY9+bYIUzqObrDfGq9bRwJ+bWZ/bHCOs1pxrOsJo7g8IWkMMDRrW8Pbeyye+ztmlp0QkdS7Fed2\nKedV17brMWAvSf0AJG0oaTvgf0BvSX3jfqObeP0/gVPja9srzKz2HqG0ljEVOCmr7a9K0uaEodaP\nkNRZUhdCNTmXLsArCgOSHtdg2yhJ7WLMfYBn47lPjfsjaTtJG+ZxHleBvETXRpnZslgyukVxPlbg\nx2b2nKSxwD2SPiRUfbs0cogzgfGSTibMiXuqmT0qaXrsvnFvbKfbAXg0lijfB46PIyhPJMyA9Tow\nK4+QLwBmAMvi/9kxvQTMJMya9i0LU1X+idB2N0fh5MsIM4K5Nshv6nfOVTyvujrnKp4nOudcxfNE\n55yreJ7onHMVzxOdc67ieaJzzlU8T3TOuYr3/3dSw+ZJmztaAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"image/png": 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JT9JuwDBgZzPbAdiXVY2OI4DHgFFNHPrluP8U4OxixphrxvTp9O8/gC369aNj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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"image/png": 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juwJjgXPjKVxStqR54T08r0qqGbOtpaR3JH0l6WtJ14dZHkmjJa0NP/u5pLHF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\njvTIgJ4AnXOJlSKDncbDE6B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KBo24BTjMzKZKup2gOXkWsAr4t5ktjiJWggQykeBxvJYE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\nAJ1zGev/AbxGby+cCfR8AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"image/png": 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yHWOyqqoqLr7wfB5+5AnemD6T+0bfwzszZ+YyhJSKIUYojjhnvTuTB+65nXse\nncADT73KC888yX/mFF5fs8VwLhPCs65Ka8qVrCY6SfsCA4C9zaw70B+YFxcPAV4Dhtaw6cFx/QnA\nL7IZY3WTKivp1KkzO3fsSOPGjRly8ik8+kjKR+lyqhhihOKI88PZ7/G9Hr3YYostKS8vp9f39+eZ\nJ8blO6yNFMO5TCalN+VKtkt0bQndsKwEMLMlZvZxXDYU+AlQIaldLdu/SuhoL2c+/ngB7dq1X/e+\noqIdCxYsqGOL3CuGGKE44uzcdTemVr7C0i8+Y8WK5bz03Hg+/biwYoTiOJfrKe1/uZLtRPcU0F7S\n+5JulXQQrOsLvq2ZVQL3AifXsv2RwEO17VzSSEmTJU1evGRxpmN3DUCnLrty1g8vYeSpgzn39OPo\n2q07jcrK8h1W0WtQJToz+xroCYwEFgNjJA0nJLZ742qj2fjy9XlJC4CjgHvq2P8oM+tlZr1at2qd\nkZh32KGC+fPnrXu/YMF8KipyWqhMqRhihOKJ84Shw7j3iZe444HxbN28BR06ds53SBsplnMJieYl\nSmvKlazfjDCzKjObYGZXAhcAJxAS23BJcwl9S3WX1CVps4OBnYBpwK+zHWOyXr17M3v2LObOmcOq\nVau4b8xojhlQWCM+FkOMUDxxfhavBj5ZMI9nnxjH0YOH5DmijRXLuQRCg+FG6U25ktV2dJK6AmvN\nbFac1YM46I6ZVSSt92tC8ls3RoWZrZF0MaEvqqtz1RFoeXk5N9x4MwOPOYKqqiqGDT+L3bt1y8Wh\n01YMMULxxHnJyNNY+sXnlJdvxhW/vZ6tm7fId0gbKZZzmZDL+rd0yCxld+ubvnOpJ3AT0IIwdNls\n4E1gCzO7PGm97sAYM9stlvJ6mdmSuOwmYJGZ/U9dx+rZs5dNfH1yXau4EuPjumZG3z69mDJlcsYy\nU9c9etit9z+T1rr9d2s9JdWYEZmQ1RKdmU0B9ktjvenAbvF1h2rLfpSV4JxzWVNoJTp/BMw5l3H+\nUL9zrqSFrtTzHcWGPNE55zIst42B0+GJzjmXWTluDJwOT3TOuYxKPNRfSDzROecyrrDSnCc651w2\nFFim80TnnMs4vxnhnCt53rzEOVf6PNE550pZGA+isDKdJzrnXGZ5OzrnXENQYHnOhzt0zmWakNKb\nUu5J+qekRZLeTpq3jaSnJc2K/7dMtR9PdM65jMvgmBG3E8aOSXY58KyZdQGeje/rVDKXrgasqVqb\n7zBKwsrVxXEey8sK/3u6Ze8L8h1CSivf+09G95fJwanN7EVJHarNPhboF1/fQRgW9bK69lMyic45\nV0CyW0nXxsw+ia8/Bdqk2sCnEbYlAAASPklEQVQTnXMu4+rR8WYrScljIIwys1HpbmxmJinleBCe\n6JxzGVePAt2STRgzYqGktmb2iaS2wKJUGxR+JYdzrrioHtOmGQcMi6+HAQ+n2sATnXMu45Tmv5T7\nke4BXgW6SpovaQRwLXCYpFlA//i+Tn7p6pzLqEyOGWFmQ2tZdGh99uOJzjmXeQX2aIQnOudcxvlD\n/c65kucP9TvnSp4nOudcSfP+6Jxzpc/7o3PONQQFluc80TnnsqDAMp0nOudchqk+D/XnhCc651xG\nZbI/ukzxRFfNeSNH8OQTj9G69XZUTp2e73BqVQxxfvvttww88mBWrVzJmjVVDBx8PJdfcWW+w9rI\n7X+7ifvvvgMJuuzWjd/d8Dc2b9Ik32EB8O5jv+arb1ZStXYta6rWsv9p13HNxYM5+sA9WLW6ijnz\nlzDyyv9j2dcr8h3qhgos02X9oX5JV0iaIWm6pGmS+kjaTNK1sc/3qZJelXRU0jY9JJmk6l0oZ91p\nZwxj7LjHc33YeiuGODfffHPGPvo0L7w6lQmvTOa5Z8YzufK1fIe1gYWffMxd//gr9z/5Eo9MmMza\nqrU89vB9+Q5rA0eOvJHvn3It+592HQDPvvYuPYdcwz4n/45ZHy3iZ2cdnucIN5aph/ozJauJTtK+\nwABgbzPrTuhpYB7wP0BbYA8z2xsYDDRL2nQo8HL8P6f2P+BAWrbcJteHrbdiiFMSTZs2BWD16tWs\nXr06rQFRcq2qag3ffruCNWvWsGLFcrZr0zbfIdXp2dfepSoOG1D51hwq2rTIc0Qba6T0ppzFk+X9\ntyV0rLcSwMyWAEuBs4EfJc1faGb3Aij8JQwBhhO6YimMawi3Saqqqui3X09267gD/Q7uT8/effId\n0gbatN2Bs869iEN67coBe3aiWbOt2b9f/3yHtY6Z8citFzDx35dy1vF9N1p+5rH7Mn7izDxEVoc0\nB8bJ5XdethPdU0B7Se9LulXSQUBn4D9m9mUt2+wHzDGzDwiDXhxT284ljZQ0WdLkJYsXZzp2lwFl\nZWVMeGUK09+dy9Qpk3hn5tupN8qhZUu/4Nnxj/LM6zN4cdpsVixfzrj778l3WOsc+oMb2O/U3zP4\ngls55+QD6Lt3p3XLLh1xBFVVaxn9+KQ8Rlib7Pa8WV9ZTXRm9jXQExgJLAbGsH70ntoMBUbH16Op\n4/LVzEaZWS8z69WqdevvHrDLmuYtWrD/gf149umn8h3KBl596Xna7diBbVq1ZrPNNuOwowfxxuTX\n8x3WOh8vXgbA4i++Ztxz0+ndrQMApw/sw9EH7sHwK27PX3C1EA2vRIeZVZnZBDO7ErgAGAjsKGnr\n6utKKgNOAH4laS5wE3CkpGbV13WFb8nixSxbuhSAFStW8MJzz9Bll655jmpDbSva8+aUSaxYvhwz\n49WXJ9CxS2HEuGWTxjTdcvN1r/vvuyszPviYw/bbjR8P78+JF/+NFd+uznOUNSu0OrqsNi+R1BVY\na2az4qwewHvAG8CNks4xs1WSWhNKesuA6WZ2RNI+7gCOA+7MZqwJPzjjVF566QU+W7KErp125Oe/\nuJJhPxiRi0PXSzHEuXDhJ1xwzllUVVWxdq1x7PEncsRRtdZE5MWee/fm8AGDOf7wvpSXl7HbHnty\n8uln5TssALbbthljrj8bgPKyMsY8MZmnX3mHtx++ks0bl/PoX8OYsZVvzeXC346ua1c5V2gP9css\n5Uhhm75zqSehVNYCWAPMJlzGfglcDRwPfAt8A/wKOAV43cxuS9rHIOA8MzuKOuzds5e9+EplNj5G\ng1MsA1gv+XpVvkNIqcdRl+Y7hJRWvncva5cvylhm2nOvnjb+hfSaEbVt3njKJowCVm9ZLdGZ2RTC\nzYWaXBqnZONr2Mc4wqg/zrkiUVjlOX8ywjmXYVK9BrDOCU90zrnMK6w854nOOZd5BZbnPNE55zKv\nwK5cPdE55zIttw/sp8MTnXMuoxJPRhQST3TOuYzzROecK3l+6eqcK20+3KFzrtR5HZ1zrkEotEvX\nrHfT5JxreDLVH52kIyW9J2m2pMs3NR5PdM65jMtE/8Kxf8pbgKOA3YGhknbflHg80TnnMk5SWlMK\n+wCzzexDM1tF6HH82E2JxxOdcy6jMtiVegVh1MCE+XFevZXMzYg3pk5Z0qxJ2UcZ3GUrYEkG95ct\nxRBnMcQIxRFnNmLcKZM7mzp1yvgtNlOrNFdvImly0vtRZjYqk/FACSU6M8vo6DiSJuei59Pvqhji\nLIYYoTjiLIYYzSxTA88vANonvW8X59WbX7o65wrVJKCLpJ0lNSYMtbBJvY2XTInOOVdazGyNpAsI\nQyyUAf80sxmbsi9PdLXLeD1BlhRDnMUQIxRHnMUQY8aY2ePA4991P1kdBcw55wqB19E550qeJzrn\nXMnzRFcDSdvnO4a6KI0m5a7+/LyWLk901UhqB1whaXi+Y6mDoPD/MCW1kdQ+9Zr5I6mdpK0lNTEz\nk1TQfxOFHl+h8pO2sa+B94E9JZ2W72Cqk7QfMEZSJ6B5vuOpjaRjCG2enpR0raSW+Y6pOklHAk8A\nfwVGSWpuZmvzHNYGYu8dv4o/dxLxFfqXXKHxRBdJai+ptZktBf4FvA3sK+n0PIdW3SDgBOBK4JeS\nTk1eWAjf+DGB/BEYDhwN7AtcmM+Yqosx/hq4CLgRWEo4r4nlhZJIDgQuAf4oaZSk70nashhKn4XE\nTxQgaS/gI2C8pFOAfmb2D2AGoWX2mXkNcEO/BR4EZgIvARdI+rOk42D9N36+SGoGHA/MAuaZ2UfA\nz4FdY+v2vFKwJfBnYKqZPWdmlcAbQIWkJpK2sMJpd/Uo8AzhnC4HhgL/J2knYLN8BlZMGnyii7/U\nbwAPAz2ALYFLJN0K7A00Bg6RdEIdu8mJ+A2+HHgN+MbMHgJuBUYA/yVpqqSe+YzRzL4iNGqdDlwl\nqQVwJPBl7GonryxYDpwOHCzp3LhoP+B84CFggqShkrrlI0ZJO0hqE+N9BdgcuNjMLiZcaQwG/hf4\nq6Sj8xFjsWnQT0bEeqTDgYvM7DhJ44DjzOxQSb2BgcAhhD+CDpKeNLNvchzj7kBLQuL42szWxt4e\nrpe0FvghcLqZPSzpKmBxLuNLinMXQp3hMmAq8DlwNvAs8JWZ9YvrKV+lJUmbm9lKADObLOkM4B5J\nQ4CvgF2BrYGTgT7AhDzEeARwaYzr6Vgi/gnhi2wEoXR8KPAx8H1Cyd6lYmYNcgIOA94EDqs2fwLw\nRNL7HYCeQJc8xHg04Rt8MnAXsE/Ssp8D3wAnFsC5HBTjfIxwqTUJaA10Av5GuEzcOq6rPMV4BHAf\nMKza/L2AdwglpnXxAZvn6ef9JnBwtfnbEDqd/AY4MGl+Xs5lMU55DyAvHzqU4uYB3eP7nYHLkpY/\nAzxdADF+BHQgVDHcCvw+afk+wFhgs/i+UZ7i7BsTRc+kebcCbxH6TusIXE241GqXx/N5CrCIUDK+\nFzgJaB+X9Y6f4bI8xSZgK+B+4IjEvPh/Wfy/OzAF6JTP38tinRpcHZ2kzQi/NJ8Bc2PF9BigKrGO\nmfUHto6XsvmIcXPCZdTnQBMLNxh+AXSU1D/edask1NfdEWPO102ItoReJabEuDGzHwIvE+q75hBK\neR8Bq3MdnKQtY93mBEKJbgjwJNAZeFrSUYQbEYOAE/LUDGZzC1UiXxLu/kKsPzezqngH+CvgOaBf\nHEvB1UODfKhfUnPgTEL/8zsBvzGzu5KWNzGzbyW1N7N5te0nS7HtT/gj/IRw2Xwc4TL1dOBEwAh3\ngxsRmkasNrP5OY5xXT2bpJ8AR8UvByRtZmar4xfKWOAcM1uQOKc5jvNoQsn4j2Y2X9IfgV3NbEBs\nl/YC8Ajh0vAW4EEzq6p9j1mL8UjgJuAG4EUzuy4uKzezNfH1GUATQrVKTn/eJSHfRcpcTUAXwk2F\ng1lfX/QzwqVMl6T1hhMakDbJQ4xHAtOAMwjtp7YmtD+bBVTGdZoQEuHvgY55OpebJ71uBdxO+NJo\nFOeVx/8fB7rlKcYBhPquwSRd1gN/AX4HfEhIgpsRLr/zUQebiPH4+L5H/PmfWW29EYT6z6b5OJel\nMOU9gJx8SDiGcCdwLKH+7T/AnoTb9hcRvtU7EoZVmwLskYcYDwJmA32qzT8QOI1w+fe9AjiXh8fz\neCXhDjXAfxNKI8cnrTck/tFul4cYtweeB3rH942BpvFnfE384jggLstX3Wb1GLcgNG0aGn8Pzifc\nnDibUN+Z89/JUppKvnlJbAH/S+ASM3shzruSkNyONrMbYzONpwiXhYPM7J08hLoXcJOZvZ6YIek6\nQgnzNsLdy1sk/djMJte8i+xKeprgTmA7YICkVwjt5kYCg+Ol7IuE0sppZrYoD6GuJNQHfiupCXA5\ncACwgtCkaitC8sPyV7dZPcbLgP1ZX083iNB0pCUw1MzezlOcJaGkE52kbQiXT4PM7IVEPZGZ/TpW\n8D4saU9C0w0Dns11kkuq7+pEaIOWmH8U4Vv/WODfhEFB/gkszGV8SfEkzuWxZvaIQucHvwU6m9lE\nSb8nXAaeCnwK/N3MPshHrIREMZ7wGFo3Qin+HsKd1SGEBHOApImW43rDFDGOjjGeCEw0s/uT2/65\nTVfyNyNio+BrCY91fZb8iyPpeeAnZjZVUqM8frsj6VBCyeOyGM9mhJ/PKkk/Bz4A7rccV5ZXi/EY\n4DpgXzP7UtJjhEbCU4G5wN+BFWaW87ur1UlqCnyPMIrUw0k/89sId7P/YGZf5DHEumL8FzDBzO7I\nZwPrUlLSJToAM3ssXppWSuplZl8k7gwSLhNWx/Xy3WvFa8BE4JR4t60SQNJQ1l8G5i3JwQbncoqk\nJwl3fv9EaBw8Atid0Ip/We17yQ0z+xp4NU4ASDoJ6AUMyXeSg1pjHEJIflfHdTzJZUDJl+gS4qXg\nzUAi2Z1JqPAdmKd6pI1IqiAkjEMIbbtWEC5jBptZwTzqI6k/oU6zrZktjPMaAduYWcENAC2pLeGx\nrrOBkwuxvqsYYixmDSbRwbpkdx2h5f4ZwMhC+4WStAWhM4HDCPVyE8xsVn6j2lg8l38iVAkUxBdF\nbeI5PQR4z8xm5zuemhRDjMWsQSU6AEkDCN0c7WWbOEakCyQdS2hm0qsALv2dq1WDS3QQHguy0FWP\n+44kNY11Tc4VrAaZ6JxzDUuDe6jfOdfweKJzzpU8T3TOuZLnic45V/I80ZUgSVWSpkl6W9J9sXPR\nTd1XP0mPxteDJF1ex7otJP1wE45xlaSfpju/2jq3SzqxHsfqIKmg2k667PNEV5pWmFkPM9sDWAWc\nm7xQQb1/9mY2zsyurWOVFoTBepwrKJ7oSt9LQOdYknlP0p2EgWzaSzpc0qsKwyTeFx8yT4wO/66k\nqYTxRInzh0u6Ob5uI2mspDfjtB+h84ROsTT5h7jezyRNkjRd0q+T9nWFpPclvQx0TfUhJJ0d9/Om\npAeqlVL7S5oc9zcgrl8m6Q9Jxz7nu55IV7w80ZUwSeWEzkTfirO6ALeaWTfCiFK/APqb2d6EkcZ+\nHPtG+1/CUI89CV1F1eQvwAtmtifhkbUZhN5XPoilyZ9JOjwecx9C77k9JR2oMPbsKXHe0YTBaVJ5\n0Mx6x+O9Q3gmOKFDPMYxwG3xM4wAlplZ77j/syXtnMZxXAkq+d5LGqgtJE2Lr18C/kEYf+IjM3st\nzv8+obeRiaFrPhoTetHYFZiTeL5W0v8ROtWs7hDCuBvEXlWWaeOBZQ6P0xvxfVNC4msGjE08naL0\nBiHaQ9LVhMvjpoS+3BLujY+gzZL0YfwMhwPdk+rvmsdjv5/GsVyJ8URXmlaYWY/kGTGZJQ++LcKQ\njkOrrbfBdt+RgN+Z2d+qHePiTdjX7YReXN6UNBzol7Ss+uM9Fo/9IzNLTohI6rAJx3ZFzi9dG67X\ngL6SOgNI2krSLsC7QAdJneJ6Q2vZ/lngvLhtmcLIal8RSmsJ44Gzkur+KiRtR+hqfbCkLSQ1I1wm\np9IM+EShQ9LTqi0bIqlRjLkj8F489nlxfSTtImmrNI7jSpCX6BooM1scS0b3KI7HCvzCzN6XNBJ4\nTNJywqVvsxp2cREwStIIwpi455nZq5ImxuYbT8R6ut2AV2OJ8mvg9NiD8hjCCFiLgElphPxL4HVg\ncfw/Oab/AJWEUdPOtTBU5d8JdXdTFQ6+mDAimGuA/KF+51zJ80tX51zJ80TnnCt5nuiccyXPE51z\nruR5onPOlTxPdM65kueJzjlX8v4fBphsiod4tqoAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"image/png": 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oDQyVdABhKIfrzKwL8BEwLt2OZBmOXiupkZmtz2jjz9+zxsyapsw/DNxjZndK\n2pfweElrQg/GdwI/SXm8pL+ZnZ/psfbt3dcefDLTm8HJabNzk6RDSMsHsM6e1QUwqPrwIQfz8pxZ\nWbvW3LPrl+z03/w9o21/M6L7LDPrn247STsSxqs5l9DJyJ5mtknSgcAVZnZMTe/PpK3r/oSbCC2A\nvSTtB3zNzL6V7r2pSS7OH5/y+hXCoyNVve92QtMz51wBKsn8WrGVpJkp8xNi3Tuw5UbmLKAL4YmM\nt4FVZrYpbrKEcA+gRpk8MPw7YDjwAICZzZV0eEYfwTlX74SONzMuIK6sqURnZuVAb0ktgfuBbaow\nzyTRNTCzRZXuopRvy8Gcc/VDtiv/zWyVpGeAA4GWkkpjqa4toQXVdsezOF6+mqQSSRcBb21X1M65\nopaNcV0l7RZLchU3LI8iPKb2DHBy3GwskLZZUCYlunMJl697EVomPBWXOefcViRlq+PN1sAdsZ6u\nAeFG5iOSXgcmSrqKMFBX2k6CMxnA+n1g9HYG7JyrR7KR58zsZaBPFcvfAfavzb4yuev6B6po82pm\n42tzIOdc/VDLmxE5kcml61MprxsDJwKLq9nWOecKqlE/AGb2hW7TJd1JeHDPOee2pgJq1F+DjkD+\ntih2ziWqoAawriDpIz6vo2tA6EDz0roMyjlX2Aoq0Sk8Jbwfnz+Qt9kybRzrnKu38q2bphoTXWxg\n/8/YFbpzzqUl1aqta05kEs4cSVs9y+Kcc9UppDEjKtqS9QFmxHEbPiXUNZqZ9c1RjM65AlJoNyOm\nA32B/Oi61jlXMPKsiq7GRCcIg9bkKJbtUtJAtNxxh6TDSGvt+k3pN0rY1cP2STqEjAz+1bNJh5DW\nbWf0S79RwsqzfH9RqKCeo9tN0neqW2lmv6mDeJxzhS6zbtJzqqZEVwI0JZbsnHMuU4XU1nWZmeX3\n4JHOubwThjtMOoovSltH55xztVVIJbohOYvCOVc0BJTkV56rPtGZ2Ye5DMQ5VyRUYE3AnHNuW+RX\nmvNE55zLskLtYdg552qlkJ6jc865bSCvo3POFTeR/QGst5cnOudc1nmJzjlX3JR/NyPyrYTpnCtw\nFZeumUw17kdqJ+kZSa9Lek3ShXH5LpKelDQv/r9zupg80Tnnsk5SRlMam4CLzawHcABwnqQehMG5\nnjazrsDTZDBYlye6Sj777DOOPOxADj2gLwf134+rr7oy6ZC2UggxVnhi8uP06tmNnt27cO01Vycd\nzhc0ENzx1b786uQwJMqVx3dn0jkDuGtcf3503N6UJPyMxGUXn8uh+3Vk5JD9tyyb/Mj9nHDEAPZt\n15xX585OMLqaKcOpJma2zMyMy/PJAAAWuUlEQVRmx9efAG8AZcAJwB1xszuAkeniqfNEJ+lHsdj5\nsqQ5kgbG5a0kbZT0jUrbL5T0Stz+WUnt6zrGVI0aNeKBR5/kuRdm8+y0mTz91GRmTH8hlyGkVQgx\nApSXl3PRBefx4MOP8dLLr3PvxLt54/XXkw5ri1P7t2XhyrVb5h9/7X1O/cMMTr9tJo1KSzhhvz0T\njA5GjjqdW/96/xeWdem2D7/9w130G3hwQlGlF9q6KqMJaCVpZso0vsp9Sh0Iwzq8COxhZsviqvfI\nYJzpOk10kg4EhgN9zawXcCSwOK4eBbwAjKnirYfH7acAl9VljJVJomnTpgBs3LiRTRs35t0dpEKI\nEWDG9Ol07tyFjp060bBhQ0adOppHHn4w6bAA2K1ZQw7qvAsPvfzelmXT3vm8effryz5m92aNkght\ni/4HDKJFyy9WP3Xu2p2OnfdOKKLMSZlNwEoz658yTdh6X2oK/B24yMw+Tl0Xh19N20VyXZfoWhM+\nyPoY1EozezeuGwNcDJRJalvN+6cRiqo5VV5ezmEH9qN7xzYcdsSR9B8wMNchpFUIMb777lLatm23\nZb6srC1Lly6t4R258+0hXbjxmXeoapjikgbi2J57MO2djxKIrBgo439p9yTtQEhyd5nZP+Li5ZJa\nx/WtgffT7aeuE90TQDtJb0m6WdJhMbh2QGszmw7cA5xazfuHAg9Ut3NJ4yuKvB+sXJm1oEtKSnh2\n2ixeeXMhL82cwRuvvZq1fWdLIcSYrw7uvAsfrd3Am8vXVLn+e0d35aXFq5m7ZHWOIysetSjR1bAP\nCbgNeKPS0A0PAWPj67FA2suEOk10ZrYG6AeMB1YAkySdRUhs98TNJrL15eszkpYCxwJ317D/CRVF\n3l1btcp2+LRo2ZJBhw7m6aeeyPq+syWfY2zTpowlSxZvmV+6dAllZTkvoG+lV9sWHNKlFfefO5Cf\njuhB//YtuWJ4dwDGHdyeljvuwPVPF8SYUHkpPF6ijKY0DgbOAI6I9ftzJB0HXA0cJWkeoTos7V2u\nOn9g2MzKCXVtUyS9QsjAbYA9JZ0eN2sjqauZzYvzhwOrgLuAK4FqB+nJtpUrVrDDDjvQomVL1q1b\nx5R/PcUF37kkV4fPSCHECNB/wADmz5/HwgULaFNWxr2TJnL7nX9LOixueXYBtzy7AIC+e7XgtP3b\nccUj/2VErz0Z2HFnvjXx5fSVPq56ggZZKEKZ2b+p/uZsrToGrtNEJ6kbsDklgfUmDrpjZmUp211J\nKNVtGaPCzDZJugh4RdJVueoIdPnyZZw3/mzKy8vZvNkYedLJHHPssFwcOmOFECNAaWkp111/I8cP\nO4by8nLGnnU2PXr2TDqsan1v6N68t/oz/nBGHwCmvLWSP01dlFg8l5z3VWZMe55VH37AkP7d+ObF\nP6RFy535xY8v4cMPV/LNsSfTvWcvJtxVbe1OYjKpf8slVVUZm7WdS/2AG4CWhIf/5gNzgSZmdmnK\ndr2ASWa2j6SFQH8zWxnX3QC8b2Y/relYvfv2s389/2LdfJB6ZsdGhdEy0Md1zY5TjjuU1+bOzlpm\n6val3nbzfU9ltO2R++w2y8z6Z+vY1anT32gzmwUclMF2LwP7xNcdKq37Vp0E55yrM/lWoiuMr27n\nXEHJt0b9nuicc1kVulJPOoov8kTnnMuyzB4GziVPdM657MrgYeBc80TnnMuqikb9+cQTnXMu6/Ir\nzXmic87VhTzLdJ7onHNZ5zcjnHNFzx8vcc4VP090zrliFsaDyK9M54nOOZdd/hydc64+yLM854nO\nOZdtGY3ZmlOe6JxzWZdnea54Ep1Q4gMOZ6IQYiwUT337kKRDSKu0JP/HiG9cmt0YMxmcOteKJtE5\n5/JInmU6T3TOuazzjjedc0Uvv9KcJzrnXLblYSVd/teUOucKjjL8l3Y/0p8kvS/p1ZRlu0h6UtK8\n+P/O6fbjic45l1UVY0ZkMmXgdmBopWWXAk+bWVfg6ThfI090zrnsU4ZTGmb2HFB58PoTgDvi6zuA\nken243V0zrmsq+NG/XuY2bL4+j1gj3Rv8ETnnMu6Wjxd0krSzJT5CWY2IdM3m5lJsnTbeaJzzmVd\nLRLdSjPrX8vdL5fU2syWSWoNvJ/uDV5H55zLqor+6LJx17UaDwFj4+uxwIPp3uCJzjmXXbE/ukym\ntLuS7gamAd0kLZE0DrgaOErSPODIOF8jv3R1zmVdtm5FmNmYalYNqc1+PNE557Ivz1pGeKJzzmWZ\nvFG/c6645WFTV78ZUdmSJYsZPnQIA/vuywH9enHLTb9LOqQqnTt+HB3b7cn+fXslHUqNnpj8OL16\ndqNn9y5ce03aOuNE+LmsA1lqGZEtdZ7oJP1I0muSXpY0R9JASTtIujo2yp0taZqkY1Pe01uSSarc\nxq3OlZaUctUvruXF2a/w5JSp/PH3t/DfN17PdRhpnX7GWO5/6J9Jh1Gj8vJyLrrgPB58+DFeevl1\n7p14N2+87udyWxTKuaxQx4+X1FqdJjpJBwLDgb5m1otwK3gx8FOgNfAlM+tLaKvWLOWtY4B/x/9z\nas/Wrendpy8AzZo1Y+9u3Vn27tJch5HWoEMOZeedd0k6jBrNmD6dzp270LFTJxo2bMioU0fzyMNp\nH3nKOT+X2ZfFRv3ZiaeO99+a8OTzegAzWwmsAs4BvpWyfLmZ3QOgMHzQKOAswrMyjes4xmotWrSQ\nV+bOod+AgUmFUNDefXcpbdu22zJfVtaWpUvz70ujEBTUuczic3TZUteJ7gmgnaS3JN0s6TCgC/A/\nM/u4mvccBCwws7eBKcCw6nYuabykmZJmfrByRVYDX7NmDWeOOYWfX/MbmjdvntV9O1f88quSrk4T\nnZmtAfoB44EVwCRgcJq3jQEmxtcTqeHy1cwmmFl/M+u/a6vdtj/gaOPGjZx52ihGjR7DiJEnZm2/\n9U2bNmUsWbJ4y/zSpUsoKytLMKLCVUjnUtS/Eh1mVm5mU8zscuB84HhgL0lbFZMklQBfBv5P0kLg\nBmCopGaVt63DeDn/3HPYu9s+nH/Bt3N12KLUf8AA5s+fx8IFC9iwYQP3TprIsOEjkg6rIBXauaxX\ndXSSuknqmrKoN/AmcBtwvaSGcbvdJI0iNOt42czamVkHM2sP/B3IWbHqhWlTmfS3v/Lcs88waGA/\nBg3sxxOP598dua+ecRpDBh/MvLfepFvnvbjjz7clHdJWSktLue76Gzl+2DH03ncfvjzqFHr07Jl0\nWFvxc5l9+XbXVWZpu3La9p1L/QilspbAJmA+4TL2Y+Aq4CTgM+BT4P+A0cCLZnZryj5GAOea2bHU\noE/f/jZl6ot18TGyqhAGsC6EQZcBNpVvTjqEtArhXB48sD+zZs3M2i/mfn362eRnX8ho29YtGs7a\nhm6aaq1OW0aY2SzCzYWqfC9OqSZXsY+HCN2yOOcKRL59nXsTMOdcVkk+gLVzrj7Irzznic45l315\nluc80Tnnsi/Prlw90Tnnsi23j45kwhOdcy6rKlpG5BNPdM65rPNE55wren7p6pwrbjlusJ8JT3TO\nuazyOjrnXL2Qb5eu+d/i2DlXcLLVH52koZLelDRf0qXbGo8nOudc1mWjf+HYP+VNwLFAD2CMpB7b\nEo8nOudc1knKaEpjf2C+mb1jZhsIPY6fsC3xeKJzzmVVFrtSLyOMGlhhSVxWa0VzM2LOS7NWttyx\ndFEWd9kKWJnF/dWVQoizEGKEwoizLmJsn82dzZ49a3KTHdQqw80bS5qZMj/BzCZkMx4ookRnZtkb\nHQeQNDMXPZ9ur0KIsxBihMKIsxBiNLNsDTy/FGiXMt82Lqs1v3R1zuWrGUBXSR3j+DKj2cbexoum\nROecKy5mtknS+YQhFkqAP5nZa9uyL0901ct6PUEdKYQ4CyFGKIw4CyHGrDGzfwLbPQxfnY4C5pxz\n+cDr6JxzRc8TnXOu6Hmiq4KkPZOOoSbK4JFyV3t+XouXJ7pKJLUFfiTprKRjqYEg//8wJe0hqV36\nLZMjqa2k5pIam5lJyuu/iXyPL1/5SdvaGuAtYD9JpycdTGWSDgImSeoMtEg6nupIGkZ45ulxSVdL\n2jnpmCqTNBR4DLgFmCCphZltTjisL4i9d/xf/LlTEV++f8nlG090kaR2knYzs1XAn4FXgQMlfSXh\n0CobAXwZuBz4saTTUlfmwzd+TCC/As4CjgMOBC5IMqbKYoxXAhcC1wOrCOe1Yn2+JJJDgW8Dv5I0\nQdK+knYshNJnPvETBUjqAywCJksaDQw2s9uA1whPZp+ZaIBf9DPgH8DrwPPA+ZJ+K+lE+PwbPymS\nmgEnAfOAxWa2CPgh0D0+3Z4oBTsCvwVmm9m/zGw68BJQJqmxpCaWP89dPQI8RTina4ExwF8ltQd2\nSDKwQlLvE138pX4JeBDoDewIfFvSzUBfoCFwhKQv17CbnIjf4GuBF4BPzewB4GZgHPA1SbMl9Usy\nRjP7hPBQ68vAFZJaAkOBj2NXO4myYC3wFeBwSd+Iqw4CzgMeAKZIGiOpZxIxSmojaY8Y73+ARsBF\nZnYR4UpjJPAH4BZJxyURY6Gp1y0jYj3S0cCFZnaipIeAE81siKQBwPHAEYQ/gg6SHjezT3McYw9g\nZ0LiWGNmm2NvD7+RtBn4JvAVM3tQ0hXAilzGlxLn3oQ6w9XAbOBD4BzgaeATMxsct1NSpSVJjcxs\nPYCZzZR0BnC3pFHAJ0B3oDlwKjAQmJJAjMcA34txPRlLxBcTvsjGEUrHQ4B3gQMIJXuXjpnVywk4\nCpgLHFVp+RTgsZT5NkA/oGsCMR5H+AafCdwJ7J+y7ofAp8DJeXAuR8Q4HyVcas0AdgM6A78nXCY2\nj9sqoRiPAe4FxlZa3gd4g1Bi2hIf0Cihn/dc4PBKy3chdDr5KXBoyvJEzmUhTokHkMiHDqW4xUCv\nON8R+H7K+qeAJ/MgxkVAB0IVw83AL1PW7w/cD+wQ5xskFOfBMVH0S1l2M/AKoe+0TsBVhEuttgme\nz9HA+4SS8T3AKUC7uG5A/AzfTyg2ATsB9wHHVCyL/5fE/3sBs4DOSf5eFupU7+roJO1A+KX5AFgY\nK6YnAeUV25jZkUDzeCmbRIyNCJdRHwKNLdxguAzoJOnIeNdtOqG+7o4Yc1I3IVoTepWYFePGzL4J\n/JtQ37WAUMpbBGzMdXCSdox1m1MIJbpRwONAF+BJSccSbkSMAL6c0GMwjSxUiXxMuPsLsf7czMrj\nHeBPgH8Bg+NYCq4W6mWjfkktgDMJ/c+3B35iZnemrG9sZp9Jamdmi6vbTx3FNojwR7iMcNl8IuEy\n9SvAyYAR7gY3IDwasdHMluQ4xi31bJIuBo6NXw5I2sHMNsYvlPuBr5vZ0opzmuM4jyOUjH9lZksk\n/QrobmbD43NpzwIPEy4NbwL+YWbl1e+xzmIcCtwAXAc8Z2bXxHWlZrYpvj4DaEyoVsnpz7soJF2k\nzNUEdCXcVDicz+uLLiFcynRN2e4swgOkjROIcSgwBziD8PxUc8LzZ/OA6XGbxoRE+EugU0LnslHK\n61bA7YQvjQZxWWn8/59Az4RiHE6o7xpJymU98DvgF8A7hCS4A+HyO4k62IoYT4rzvePP/8xK240j\n1H82TeJcFsOUeAA5+ZAwjHAn8H5C/dv/gP0It+0vJHyrdyIMqzYL+FICMR4GzAcGVlp+KHA64fJv\n3zw4l0fH83g54Q41wA8IpZGTUrYbFf9od08gxj2BZ4ABcb4h0DT+jH8evzgOieuSqtusHGMTwqNN\nY+LvwXmEmxPnEOo7c/47WUxT0T9eEp+A/zHwbTN7Ni67nJDcjjOz6+NjGk8QLgtHmNkbCYTaB7jB\nzF6sWCDpGkIJ81bC3cubJH3HzGZWvYu6ldKa4C/A7sBwSf8hPDc3HhgZL2WfI5RWTjez9xMIdT2h\nPvAzSY2BS4FDgHWER6p2IiQ/LLm6zcoxfh8YxOf1dCMIj47sDIwxs1cTirMoFHWik7QL4fJphJk9\nW1FPZGZXxgreByXtR3h0w4Cnc53kUuq7OhOeQatYfizhW/8E4C7CoCB/ApbnMr6UeCrO5Qlm9rBC\n5wc/A7qY2VRJvyRcBp4GvAf80czeTiJWQqKYTGiG1pNQir+bcGd1FCHBHCJpquW43jBNjBNjjCcD\nU83svtRn/9y2K/qbEfGh4KsJzbo+SP3FkfQMcLGZzZbUIMFvdyQNIZQ8vh/j2YHw89kg6YfA28B9\nluPK8koxDgOuAQ40s48lPUp4SHg2sBD4I7DOzHJ+d7UySU2BfQmjSD2Y8jO/lXA3+1oz+yjBEGuK\n8c/AFDO7I8kHrItJUZfoAMzs0XhpOl1SfzP7qOLOIOEyYWPcLuleK14ApgKj49226QCSxvD5ZWBi\nSQ6+cC5nSXqccOf314SHg8cBPQhP8a+ufi+5YWZrgGlxAkDSKUB/YFTSSQ6qjXEUIfldFbfxJJcF\nRV+iqxAvBW8EKpLdmYQK3+MTqkfaiqQyQsI4gvBs1zrCZcxIM8ubpj6SjiTUabY2s+VxWQNgFzPL\nuwGgJbUmNOs6Bzg1H+u7CiHGQlZvEh1sSXbXEJ7cPwMYn2+/UJKaEDoTOIpQLzfFzOYlG9XW4rn8\nNaFKIC++KKoTz+kRwJtmNj/peKpSCDEWsnqV6AAkDSd0c9THtnGMSBdIOoHwmEn/PLj0d65a9S7R\nQWgWZKGrHredJDWNdU3O5a16meicc/VLvWvU75yrfzzROeeKnic651zR80TnnCt6nuiKkKRySXMk\nvSrp3ti56Lbua7CkR+LrEZIurWHblpK+uQ3HuELSdzNdXmmb2yWdXItjdZCUV89Ourrnia44rTOz\n3mb2JWAD8I3UlQpq/bM3s4fM7OoaNmlJGKzHubziia74PQ90iSWZNyX9hTCQTTtJR0uapjBM4r2x\nkXnF6PD/lTSbMJ4ocflZkm6Mr/eQdL+kuXE6iNB5QudYmrw2bneJpBmSXpZ0Zcq+fiTpLUn/Brql\n+xCSzon7mSvp75VKqUdKmhn3NzxuXyLp2pRjf317T6QrXJ7oipikUkJnoq/ERV2Bm82sJ2FEqcuA\nI82sL2Gkse/EvtH+QBjqsR+hq6iq/A541sz2IzRZe43Q+8rbsTR5iaSj4zH3J/Se20/SoQpjz46O\ny44jDE6Tzj/MbEA83huENsEVOsRjDANujZ9hHLDazAbE/Z8jqWMGx3FFqOh7L6mnmkiaE18/D9xG\nGH9ikZm9EJcfQOhtZGromo+GhF40ugMLKtrXSvoroVPNyo4gjLtB7FVltbYeWOboOL0U55sSEl8z\n4P6K1inKbBCiL0m6inB53JTQl1uFe2ITtHmS3omf4WigV0r9XYt47LcyOJYrMp7oitM6M+uduiAm\ns9TBt0UY0nFMpe2+8L7tJOAXZvb7Sse4aBv2dTuhF5e5ks4CBqesq9y8x+Kxv2VmqQkRSR224diu\nwPmla/31AnCwpC4AknaStDfwX6CDpM5xuzHVvP9p4Nz43hKFkdU+IZTWKkwGzk6p+yuTtDuhq/WR\nkppIaka4TE6nGbBMoUPS0yutGyWpQYy5E/BmPPa5cXsk7S1ppwyO44qQl+jqKTNbEUtGdyuOxwpc\nZmZvSRoPPCppLeHSt1kVu7gQmCBpHGFM3HPNbJqkqfHxjcdiPd0+wLRYolwDfCX2oDyJMALW+8CM\nDEL+MfAisCL+nxrT/4DphFHTvmFhqMo/EuruZiscfAVhRDBXD3mjfudc0fNLV+dc0fNE55wrep7o\nnHNFzxOdc67oeaJzzhU9T3TOuaLnic45V/T+H7ZJqNM2BrY6AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"image/png": 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Dv5sWstr9pKam0r5tK6pWrcbHn38Zmo6XBp3MOc2rs3bTTlrf+gUAb950GvWq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\nx1Og8UbQ4/EUaLwR9Hg8BZr/B3iLD6IJLiFyAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
}
]
}
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