btc_DNN.ipynb

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   "source": "# https://colab.research.google.com/github/https-deeplearning-ai/tensorflow-1-public/blob/adding_C4/C4/W4/ungraded_labs/C4_W4_Lab_3_DNN_only.ipynb#scrollTo=13XrorC5wQoE\n",
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    "cell_id": "00001-770dd50b-63e9-4cb2-820c-d2f2e5e7f9dc",
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   "source": "import tensorflow as tf\nprint(tf.__version__)\n\nimport os\nimport random as rn\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\nos.environ['PYTHONHASHSEED'] = '0'\nos.environ['CUDA_VISIBLE_DEVICES'] = ''\n\nnp.random.seed(42)\nrn.seed(42)\ntf.random.set_seed(42)",
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    {
     "name": "stderr",
     "text": "2021-09-26 20:14:48.920678: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n2021-09-26 20:14:48.920723: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n2.4.1\n",
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   "execution_count": null
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  {
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    "cell_id": "00002-214b5772-88e4-4755-95bc-8caee69195b7",
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   "source": "\ndef plot_series(time, series, format=\"-\", start=0, end=None):\n    plt.plot(time[start:end], series[start:end], format)\n    plt.xlabel(\"Time\")\n    plt.ylabel(\"Value\")\n    plt.grid(True)\n\n# https://stackoverflow.com/questions/30399534/shift-elements-in-a-numpy-array/30401705#30401705\ndef shift2(arr,num):\n    arr=np.roll(arr,num)\n    if num<0:\n         np.put(arr,range(len(arr)+num,len(arr)),np.nan)\n    elif num > 0:\n         np.put(arr,range(num),np.nan)\n    return arr",
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   "execution_count": null
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    "execution_start": 1632687299974,
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   "source": "df_cm = pd.read_csv('btc_cm_metrics_final.csv')\n\ndf_cm = df_cm[df_cm['PriceUSD'].notna()]\ndf_cm = df_cm.reset_index(drop=True)\n#df_cm.index = pd.to_datetime(df_cm.date)\ndf_cm = df_cm.drop('date', 1)\ndf_cm",
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         "CapMVRVCur": 1.933233183973,
         "CapMVRVFF": 1.491091132977,
         "CapMrktCurUSD": 762806301935.1322,
         "CapMrktFFUSD": 588347914997.61,
         "CapRealUSD": 394575423316.2143,
         "DiffLast": 18997641161758.95,
         "DiffMean": 18656674724896.094,
         "FeeByteMeanNtv": 9.8085e-8,
         "FeeMeanNtv": 0.00005977,
         "FeeMeanUSD": 2.422268270009246,
         "FeeMedNtv": 0.00001494,
         "FeeMedUSD": 0.6054657512788713,
         "FeeTotNtv": 14.70251214,
         "FeeTotUSD": 595841.2020436295,
         "FlowInExNtv": 23739.2913868,
         "FlowInExUSD": 962070140.1831928,
         "FlowOutExNtv": 21273.99024835,
         "FlowOutExUSD": 862160139.786922,
         "FlowTfrFromExCnt": 26196,
         "HashRate": 114073685.15409195,
         "HashRate30d": 133255521.96927428,
         "IssContNtv": 768.75,
         "IssContPctAnn": 1.4907427455,
         "IssContPctDay": 0.0040842267,
         "IssContUSD": 31154738.707873646,
         "IssTotNtv": 768.75,
         "IssTotUSD": 31154738.707873646,
         "NDF": 0.352977104338,
         "NVTAdj": 52.473381658036,
         "NVTAdj90": 88.04062895512332,
         "NVTAdjFF": 40.472403823959,
         "NVTAdjFF90": 67.90520784820401,
         "PriceBTC": 1,
         "PriceUSD": 40526.4893760958,
         "ROI1yr": 287.61374069824063,
         "ROI30d": -17.90160673589265,
         "RevAllTimeUSD": 32638810334.315823,
         "RevHashNtv": 7.9e-11,
         "RevHashRateNtv": 0.000006867951,
         "RevHashRateUSD": 0.278333954645,
         "RevHashUSD": 0.000003221458,
         "RevNtv": 783.45251214,
         "RevUSD": 31750579.90991728,
         "SER": 0.081566052336,
         "SplyAct10yr": 16447940.3265743,
         "SplyAct180d": 5069573.368674,
         "SplyAct1d": 342454.67501099,
         "SplyAct1yr": 8610483.03227089,
         "SplyAct2yr": 10245825.48316679,
         "SplyAct30d": 1319942.99815126,
         "SplyAct3yr": 12190680.97491803,
         "SplyAct4yr": 13726347.85797881,
         "SplyAct5yr": 14500721.82196948,
         "SplyAct7d": 620420.88876763,
         "SplyAct90d": 2972295.34578551,
         "SplyActEver": 17047582.39716395,
         "SplyActPct1yr": 45.7459053819,
         "SplyAdrBal1in100K": 11063331.27982503,
         "SplyAdrBal1in100M": 18457965.456229,
         "SplyAdrBal1in10B": 18811906.99766092,
         "SplyAdrBal1in10K": 6643880.67589399,
         "SplyAdrBal1in10M": 17416583.05690223,
         "SplyAdrBal1in1B": 18749178.7244238,
         "SplyAdrBal1in1K": 2212703.00981773,
         "SplyAdrBal1in1M": 15524113.34033281,
         "SplyAdrBalNtv0.001": 18815766.37968149,
         "SplyAdrBalNtv0.01": 18779007.75330865,
         "SplyAdrBalNtv0.1": 18587469.94912087,
         "SplyAdrBalNtv1": 17829012.58725902,
         "SplyAdrBalNtv10": 16132654.77060863,
         "SplyAdrBalNtv100": 11878975.92356234,
         "SplyAdrBalNtv100K": 577931.78662216,
         "SplyAdrBalNtv10K": 2689661.1471615,
         "SplyAdrBalNtv1K": 7950368.37712285,
         "SplyAdrBalNtv1M": 0,
         "SplyAdrBalUSD1": 18819744.30297061,
         "SplyAdrBalUSD10": 18818865.23215254,
         "SplyAdrBalUSD100": 18809402.82470044,
         "SplyAdrBalUSD100K": 17206787.21924716,
         "SplyAdrBalUSD10K": 18387527.16407186,
         "SplyAdrBalUSD10M": 10665415.16735838,
         "SplyAdrBalUSD1K": 18733404.69614489,
         "SplyAdrBalUSD1M": 15230033.56639925,
         "SplyAdrTop100": 2858032.2107158,
         "SplyAdrTop10Pct": 18639016.02827849,
         "SplyAdrTop1Pct": 17235472.91206804,
         "SplyCur": 18822412.54247566,
         "SplyExpFut10yr": 20620654.6875,
         "SplyFF": 14517613.64123096,
         "SplyMiner0HopAllNtv": 1797729.01730632,
         "SplyMiner0HopAllUSD": 72855645920.96371,
         "SplyMiner1HopAllNtv": 2688687.2920547,
         "SplyMiner1HopAllUSD": 108963056977.0986,
         "TxCnt": 245996,
         "TxCntSec": 2.847175925926,
         "TxTfrCnt": 699377,
         "TxTfrValAdjNtv": 358704.01235315,
         "TxTfrValAdjUSD": 14537014345.79287,
         "TxTfrValMeanNtv": 1.612642522142,
         "TxTfrValMeanUSD": 65354.7400410281,
         "TxTfrValMedNtv": 0.00378359,
         "TxTfrValMedUSD": 153.3356199385023,
         "VelCur1yr": 13.302867923965,
         "VtyDayRet180d": 0.0408601886727646,
         "VtyDayRet30d": 0.037208743725278,
         "_deepnote_index_column": 4083
        }
       ]
      },
      "text/plain": "      AdrActCnt  AdrBal1in100KCnt  AdrBal1in100MCnt  AdrBal1in10BCnt  \\\n0         860.0           49287.0           52093.0          52190.0   \n1         929.0           49297.0           52208.0          52311.0   \n2         936.0           49367.0           52344.0          52456.0   \n3         784.0           49382.0           52397.0          52521.0   \n4         594.0           49504.0           52577.0          52715.0   \n...         ...               ...               ...              ...   \n4079   943981.0            9336.0         2246927.0       15899233.0   \n4080   819880.0            9338.0         2247669.0       15911798.0   \n4081   714326.0            9336.0         2248051.0       15932651.0   \n4082  1014971.0            9323.0         2249147.0       15916017.0   \n4083   844883.0            9330.0         2250262.0       15922863.0   \n\n      AdrBal1in10KCnt  AdrBal1in10MCnt  AdrBal1in1BCnt  AdrBal1in1KCnt  \\\n0               199.0          51245.0         52190.0            54.0   \n1               207.0          51276.0         52311.0            54.0   \n2               213.0          51353.0         52456.0            54.0   \n3               220.0          51380.0         52521.0            54.0   \n4               222.0          51508.0         52715.0            57.0   \n...               ...              ...             ...             ...   \n4079           1065.0         471724.0       6797743.0            44.0   \n4080           1063.0         471889.0       6802337.0            44.0   \n4081           1063.0         472008.0       6804772.0            44.0   \n4082           1063.0         471974.0       6807912.0            44.0   \n4083           1063.0         472333.0       6811336.0            44.0   \n\n      AdrBal1in1MCnt   AdrBalCnt  ...  TxTfrCnt  TxTfrValAdjNtv  \\\n0            51064.0     52190.0  ...     396.0    17180.550000   \n1            51085.0     52311.0  ...     512.0    40243.730000   \n2            51157.0     52456.0  ...     738.0    16063.230000   \n3            51179.0     52521.0  ...     377.0    20834.150000   \n4            51304.0     52715.0  ...     344.0    33049.030000   \n...              ...         ...  ...       ...             ...   \n4079         99326.0  38417526.0  ...  784062.0   263865.756580   \n4080         99364.0  38416290.0  ...  670401.0   166864.001496   \n4081         99370.0  38437441.0  ...  589280.0    94508.875166   \n4082         99365.0  38355678.0  ...  776709.0   431154.750752   \n4083         99366.0  38369405.0  ...  699377.0   358704.012353   \n\n      TxTfrValAdjUSD  TxTfrValMeanNtv  TxTfrValMeanUSD  TxTfrValMedNtv  \\\n0       1.474778e+03        48.783737         4.187596        1.850000   \n1       3.251693e+03        86.951152         7.025653        2.625000   \n2       1.200497e+03        42.528835         3.178423        1.375000   \n3       1.649916e+03        71.822334         5.687816       18.500000   \n4       1.932369e+03       112.714622         6.590397        1.010000   \n...              ...              ...              ...             ...   \n4079    1.245810e+10         0.799209     37733.696252        0.003150   \n4080    8.043515e+09         0.610503     29428.696581        0.002580   \n4081    4.462565e+09         0.460092     21724.829251        0.002390   \n4082    1.847736e+10         1.667030     71441.404401        0.003521   \n4083    1.453701e+10         1.612643     65354.740041        0.003784   \n\n      TxTfrValMedUSD  VelCur1yr  VtyDayRet180d  VtyDayRet30d  \n0           0.158804   1.820565            NaN           NaN  \n1           0.212100   1.828863            NaN           NaN  \n2           0.102762   1.831412            NaN           NaN  \n3           1.465068   1.833749            NaN           NaN  \n4           0.059054   1.840245            NaN           NaN  \n...              ...        ...            ...           ...  \n4079      148.725340  13.201068       0.040265      0.033389  \n4080      124.366355  13.203984       0.040105      0.032926  \n4081      112.847903  13.206157       0.040132      0.031576  \n4082      150.888252  13.203028       0.040680      0.036079  \n4083      153.335620  13.302868       0.040860      0.037209  \n\n[4084 rows x 139 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        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>AdrActCnt</th>\n      <th>AdrBal1in100KCnt</th>\n      <th>AdrBal1in100MCnt</th>\n      <th>AdrBal1in10BCnt</th>\n      <th>AdrBal1in10KCnt</th>\n      <th>AdrBal1in10MCnt</th>\n      <th>AdrBal1in1BCnt</th>\n      <th>AdrBal1in1KCnt</th>\n      <th>AdrBal1in1MCnt</th>\n      <th>AdrBalCnt</th>\n      <th>...</th>\n      <th>TxTfrCnt</th>\n      <th>TxTfrValAdjNtv</th>\n      <th>TxTfrValAdjUSD</th>\n      <th>TxTfrValMeanNtv</th>\n      <th>TxTfrValMeanUSD</th>\n      <th>TxTfrValMedNtv</th>\n      <th>TxTfrValMedUSD</th>\n      <th>VelCur1yr</th>\n      <th>VtyDayRet180d</th>\n      <th>VtyDayRet30d</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>860.0</td>\n      <td>49287.0</td>\n      <td>52093.0</td>\n      <td>52190.0</td>\n      <td>199.0</td>\n      <td>51245.0</td>\n      <td>52190.0</td>\n      <td>54.0</td>\n      <td>51064.0</td>\n      <td>52190.0</td>\n      <td>...</td>\n      <td>396.0</td>\n      <td>17180.550000</td>\n      <td>1.474778e+03</td>\n      <td>48.783737</td>\n      <td>4.187596</td>\n      <td>1.850000</td>\n      <td>0.158804</td>\n      <td>1.820565</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>929.0</td>\n      <td>49297.0</td>\n      <td>52208.0</td>\n      <td>52311.0</td>\n      <td>207.0</td>\n      <td>51276.0</td>\n      <td>52311.0</td>\n      <td>54.0</td>\n      <td>51085.0</td>\n      <td>52311.0</td>\n      <td>...</td>\n      <td>512.0</td>\n      <td>40243.730000</td>\n      <td>3.251693e+03</td>\n      <td>86.951152</td>\n      <td>7.025653</td>\n      <td>2.625000</td>\n      <td>0.212100</td>\n      <td>1.828863</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>936.0</td>\n      <td>49367.0</td>\n      <td>52344.0</td>\n      <td>52456.0</td>\n      <td>213.0</td>\n      <td>51353.0</td>\n      <td>52456.0</td>\n      <td>54.0</td>\n      <td>51157.0</td>\n      <td>52456.0</td>\n      <td>...</td>\n      <td>738.0</td>\n      <td>16063.230000</td>\n      <td>1.200497e+03</td>\n      <td>42.528835</td>\n      <td>3.178423</td>\n      <td>1.375000</td>\n      <td>0.102762</td>\n      <td>1.831412</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>784.0</td>\n      <td>49382.0</td>\n      <td>52397.0</td>\n      <td>52521.0</td>\n      <td>220.0</td>\n      <td>51380.0</td>\n      <td>52521.0</td>\n      <td>54.0</td>\n      <td>51179.0</td>\n      <td>52521.0</td>\n      <td>...</td>\n      <td>377.0</td>\n      <td>20834.150000</td>\n      <td>1.649916e+03</td>\n      <td>71.822334</td>\n      <td>5.687816</td>\n      <td>18.500000</td>\n      <td>1.465068</td>\n      <td>1.833749</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>594.0</td>\n      <td>49504.0</td>\n      <td>52577.0</td>\n      <td>52715.0</td>\n      <td>222.0</td>\n      <td>51508.0</td>\n      <td>52715.0</td>\n      <td>57.0</td>\n      <td>51304.0</td>\n      <td>52715.0</td>\n      <td>...</td>\n      <td>344.0</td>\n      <td>33049.030000</td>\n      <td>1.932369e+03</td>\n      <td>112.714622</td>\n      <td>6.590397</td>\n      <td>1.010000</td>\n      <td>0.059054</td>\n      <td>1.840245</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4079</th>\n      <td>943981.0</td>\n      <td>9336.0</td>\n      <td>2246927.0</td>\n      <td>15899233.0</td>\n      <td>1065.0</td>\n      <td>471724.0</td>\n      <td>6797743.0</td>\n      <td>44.0</td>\n      <td>99326.0</td>\n      <td>38417526.0</td>\n      <td>...</td>\n      <td>784062.0</td>\n      <td>263865.756580</td>\n      <td>1.245810e+10</td>\n      <td>0.799209</td>\n      <td>37733.696252</td>\n      <td>0.003150</td>\n      <td>148.725340</td>\n      <td>13.201068</td>\n      <td>0.040265</td>\n      <td>0.033389</td>\n    </tr>\n    <tr>\n      <th>4080</th>\n      <td>819880.0</td>\n      <td>9338.0</td>\n      <td>2247669.0</td>\n      <td>15911798.0</td>\n      <td>1063.0</td>\n      <td>471889.0</td>\n      <td>6802337.0</td>\n      <td>44.0</td>\n      <td>99364.0</td>\n      <td>38416290.0</td>\n      <td>...</td>\n      <td>670401.0</td>\n      <td>166864.001496</td>\n      <td>8.043515e+09</td>\n      <td>0.610503</td>\n      <td>29428.696581</td>\n      <td>0.002580</td>\n      <td>124.366355</td>\n      <td>13.203984</td>\n      <td>0.040105</td>\n      <td>0.032926</td>\n    </tr>\n    <tr>\n      <th>4081</th>\n      <td>714326.0</td>\n      <td>9336.0</td>\n      <td>2248051.0</td>\n      <td>15932651.0</td>\n      <td>1063.0</td>\n      <td>472008.0</td>\n      <td>6804772.0</td>\n      <td>44.0</td>\n      <td>99370.0</td>\n      <td>38437441.0</td>\n      <td>...</td>\n      <td>589280.0</td>\n      <td>94508.875166</td>\n      <td>4.462565e+09</td>\n      <td>0.460092</td>\n      <td>21724.829251</td>\n      <td>0.002390</td>\n      <td>112.847903</td>\n      <td>13.206157</td>\n      <td>0.040132</td>\n      <td>0.031576</td>\n    </tr>\n    <tr>\n      <th>4082</th>\n      <td>1014971.0</td>\n      <td>9323.0</td>\n      <td>2249147.0</td>\n      <td>15916017.0</td>\n      <td>1063.0</td>\n      <td>471974.0</td>\n      <td>6807912.0</td>\n      <td>44.0</td>\n      <td>99365.0</td>\n      <td>38355678.0</td>\n      <td>...</td>\n      <td>776709.0</td>\n      <td>431154.750752</td>\n      <td>1.847736e+10</td>\n      <td>1.667030</td>\n      <td>71441.404401</td>\n      <td>0.003521</td>\n      <td>150.888252</td>\n      <td>13.203028</td>\n      <td>0.040680</td>\n      <td>0.036079</td>\n    </tr>\n    <tr>\n      <th>4083</th>\n      <td>844883.0</td>\n      <td>9330.0</td>\n      <td>2250262.0</td>\n      <td>15922863.0</td>\n      <td>1063.0</td>\n      <td>472333.0</td>\n      <td>6811336.0</td>\n      <td>44.0</td>\n      <td>99366.0</td>\n      <td>38369405.0</td>\n      <td>...</td>\n      <td>699377.0</td>\n      <td>358704.012353</td>\n      <td>1.453701e+10</td>\n      <td>1.612643</td>\n      <td>65354.740041</td>\n      <td>0.003784</td>\n      <td>153.335620</td>\n      <td>13.302868</td>\n      <td>0.040860</td>\n      <td>0.037209</td>\n    </tr>\n  </tbody>\n</table>\n<p>4084 rows × 139 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00004-d79a0d0b-4e76-44ac-a6ad-eef218ed716e",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "56eb99ad",
    "execution_start": 1632687301217,
    "execution_millis": 1458200,
    "deepnote_cell_type": "code"
   },
   "source": "time_index_split = int(np.round((len(df_cm)*.90)))                 # index of where to split train/test, 90%\ntime_index_split",
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 5,
     "data": {
      "text/plain": "3676"
     },
     "metadata": {}
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "42681e9",
    "execution_start": 1632687301280,
    "execution_millis": 650,
    "cell_id": "00003-9beba994-7b8f-4ee8-8148-b80d3fc44363",
    "deepnote_cell_type": "code"
   },
   "source": "\nprice_series = np.array(df_cm['PriceUSD'])\ntime_index_series = np.array(df_cm.index)\n\nplt.figure(figsize=(10, 6))\nplot_series(time_index_series, price_series)\nplt.axvline(x=time_index_split, color='r', label='axvline - full height')\n\n",
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 6,
     "data": {
      "text/plain": "<matplotlib.lines.Line2D at 0x7f18ca847e50>"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 720x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 624,
       "height": 371
      }
     },
     "output_type": "display_data"
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "531694b3",
    "execution_start": 1632687301876,
    "execution_millis": 9,
    "cell_id": "00004-97640ce4-fb66-4cbe-b1bf-668a1c57e398",
    "deepnote_cell_type": "code"
   },
   "source": "#time_index_split = 3750                 # index of where to split train/test\ntime_train = time_index_series[:time_index_split]    # \nprice_train = price_series[:time_index_split]\ntime_test = time_index_series[time_index_split:]\nprice_test = price_series[time_index_split:]\n\n# window, batch, shuff, lr, epochs, mae\n# 80, 200, 700, 1e-10, 100, 2658.229\n# 80, 200, 1000, 1e-10, 100, 2249.0\n# 60, 200, 1000, 1e-10, 100, 2196.6887\n# 50, 200, 1000, 1e-10, 100, 1954.4086\n# 40, 200, 1000, 1e-10, 100, 1907.042\n# 30, 200, 1000, 1e-10, 100, 2234.8728\n\n# 40, 200, 1500, 1e-10, 100, 1916.3392\n# 40, 200, 900, 1e-10, 100, 2171.6875\n# 40, 200, 1100, 1e-10, 100, 1952.641\n\n# 40, 250, 1000, 1e-10, 100, 9979.537\n# 40, 100, 1000, 1e-10, 100, 1743.6237\n# 40, 80, 1000, 1e-10, 100, 1566.0966\n# 40, 60, 1000, 1e-10, 100, 1559.5983\n# 40, 30, 1000, 1e-10, 100, 1584.3143\n\n# 40, 50, 1000, 1e-10, 100, 1494.6829 ** 1655.6729, 1798.231, 1509.8649\n# 40, 40, 1000, 1e-10, 100, 1675.0016\n\n# 40, 55, 1000, 1e-10, 100, 1699.6995\n\n# 40, 50, 1000, 2e-10, 100, 1610.3257\n# 40, 50, 1000, 3e-10, 100, 1374.5299 ****\n# 40, 50, 1000, 4e-10, 100, 1714.0444\n\n# 2nd layer:\n# 60, 1428.4601, 1425.0819\n# 70, 1537.3285\n# 40, 1444.577\n# 50, 1696.2174, 1517.4219, 1467.785, 1434.8826\n# 65, 1458.0967\n\n# 50 2nd, 200 epochs, random seed 42 set\n# 40, 50, 1000, 3e-10, 200, 1410.8774 **\n# 40, 50, 2000, 3e-10, 200, 1725.5352\n# 40, 50, 700, 3e-10, 200, 1462.0631\n# 40, 50, 1100, 3e-10, 200, 1496.3527\n# 40, 50, 100, 3e-10, 200, 2101.5652\n# 40, 50, 900, 3e-10, 200, 1507.1869\n\n\nwindow_size = 40 # 40\nbatch_size =  50 # \nshuffle_buffer_size = 1000",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00007-cdbcedba-3bc4-41e6-9a42-69ec396e5e51",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "ee51cc3c",
    "execution_start": 1632687301915,
    "execution_millis": 852,
    "deepnote_cell_type": "code"
   },
   "source": "days_ahead_to_predict = 1\nnaive_pred = price_series[time_index_split-days_ahead_to_predict:-days_ahead_to_predict]\n\nplt.figure(figsize=(10, 6))\nplot_series(time_test, price_test)\nplot_series(time_test, naive_pred)\n\ntf.keras.metrics.mean_absolute_error(naive_pred, price_test).numpy()",
   "outputs": [
    {
     "name": "stderr",
     "text": "2021-09-26 20:15:02.040984: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n2021-09-26 20:15:02.041289: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n2021-09-26 20:15:02.041317: W tensorflow/stream_executor/cuda/cuda_driver.cc:326] failed call to cuInit: UNKNOWN ERROR (303)\n2021-09-26 20:15:02.041408: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (p-465bb6a2-b771-4fe2-b2d2-aed723f7eb7d): /proc/driver/nvidia/version does not exist\n2021-09-26 20:15:02.041830: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n2021-09-26 20:15:02.042015: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
     "output_type": "stream"
    },
    {
     "output_type": "execute_result",
     "execution_count": 8,
     "data": {
      "text/plain": "1030.0814543647814"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 720x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 633,
       "height": 371
      }
     },
     "output_type": "display_data"
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "fabde66f",
    "execution_start": 1632687302781,
    "execution_millis": 219,
    "cell_id": "00005-39154d1a-fa03-4890-8811-a0b23017adc7",
    "deepnote_cell_type": "code"
   },
   "source": "# # https://stackoverflow.com/questions/55429307/how-to-use-windows-created-by-the-dataset-window-method-in-tensorflow-2-0\n\ndef make_windowed_training_set(series, window_size, batch_size, shuffle_buffer, days_ahead_to_predict):\n  dataset = tf.data.Dataset.from_tensor_slices(series)\n  \n  new_window_size = window_size + days_ahead_to_predict\n\n  dataset = dataset.window(new_window_size, shift=1, drop_remainder=True)\n\n  dataset = dataset.flat_map(lambda window: window.batch(new_window_size))\n  dataset = dataset.shuffle(shuffle_buffer).map(lambda window: (window[:-days_ahead_to_predict], window[-1:]))\n  dataset = dataset.batch(batch_size).prefetch(1)\n  \n  return dataset\n\ndataset = make_windowed_training_set(price_train, window_size, batch_size, shuffle_buffer_size, days_ahead_to_predict)",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00009-35ee7547-c16d-4536-88aa-e2a61ffd5a49",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "d2956e68",
    "execution_start": 1632687303021,
    "execution_millis": 387069,
    "deepnote_cell_type": "code"
   },
   "source": "model = tf.keras.models.Sequential([\n    tf.keras.layers.Dense(40, input_shape=[window_size], activation=\"relu\"), \n    tf.keras.layers.Dense(50, activation=\"relu\"),\n    tf.keras.layers.Dense(50, activation=\"relu\"),\n    tf.keras.layers.Dense(1)\n])\n\nlr_schedule = tf.keras.callbacks.LearningRateScheduler(\n    lambda epoch: 1e-12 * 10**(epoch / 20))\noptimizer = tf.keras.optimizers.SGD(learning_rate=1e-12, momentum=0.9)\nmodel.compile(loss=tf.keras.losses.Huber(),\n              optimizer=optimizer,\n              metrics=[\"mae\"])\nhistory = model.fit(dataset, epochs=100, callbacks=[lr_schedule])",
   "outputs": [
    {
     "name": "stderr",
     "text": "2021-09-26 20:15:03.356382: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)\n2021-09-26 20:15:03.377008: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2499995000 Hz\nEpoch 1/100\n73/73 [==============================] - 5s 39ms/step - loss: 667.9312 - mae: 668.3975\nEpoch 2/100\n73/73 [==============================] - 5s 66ms/step - loss: 642.1175 - mae: 642.5866\nEpoch 3/100\n73/73 [==============================] - 7s 77ms/step - loss: 650.7765 - mae: 651.2456\nEpoch 4/100\n73/73 [==============================] - 5s 67ms/step - loss: 640.2461 - mae: 640.7143\nEpoch 5/100\n73/73 [==============================] - 4s 46ms/step - loss: 658.4466 - mae: 658.9140\nEpoch 6/100\n73/73 [==============================] - 6s 70ms/step - loss: 654.2519 - mae: 654.7202\nEpoch 7/100\n73/73 [==============================] - 5s 55ms/step - loss: 661.5541 - mae: 662.0199\nEpoch 8/100\n73/73 [==============================] - 4s 40ms/step - loss: 648.4769 - mae: 648.9442\nEpoch 9/100\n73/73 [==============================] - 6s 71ms/step - loss: 643.5806 - mae: 644.0477\nEpoch 10/100\n73/73 [==============================] - 4s 43ms/step - loss: 650.1255 - mae: 650.5910\nEpoch 11/100\n73/73 [==============================] - 6s 58ms/step - loss: 647.2264 - mae: 647.6927\nEpoch 12/100\n73/73 [==============================] - 5s 43ms/step - loss: 640.1799 - mae: 640.6464\nEpoch 13/100\n73/73 [==============================] - 8s 97ms/step - loss: 648.2859 - mae: 648.7521\nEpoch 14/100\n73/73 [==============================] - 7s 75ms/step - loss: 646.4018 - mae: 646.8665\nEpoch 15/100\n73/73 [==============================] - 7s 76ms/step - loss: 648.3368 - mae: 648.8019\nEpoch 16/100\n73/73 [==============================] - 5s 56ms/step - loss: 648.0678 - mae: 648.5342\nEpoch 17/100\n73/73 [==============================] - 4s 54ms/step - loss: 647.5373 - mae: 648.0063\nEpoch 18/100\n73/73 [==============================] - 8s 99ms/step - loss: 648.7803 - mae: 649.2500\nEpoch 19/100\n73/73 [==============================] - 5s 61ms/step - loss: 649.2242 - mae: 649.6895\nEpoch 20/100\n73/73 [==============================] - 4s 47ms/step - loss: 635.5333 - mae: 636.0022\nEpoch 21/100\n73/73 [==============================] - 8s 90ms/step - loss: 637.4899 - mae: 637.9560\nEpoch 22/100\n73/73 [==============================] - 5s 58ms/step - loss: 633.4135 - mae: 633.8807\nEpoch 23/100\n73/73 [==============================] - 6s 69ms/step - loss: 656.2793 - mae: 656.7467\nEpoch 24/100\n73/73 [==============================] - 6s 66ms/step - loss: 638.0224 - mae: 638.4885\nEpoch 25/100\n73/73 [==============================] - 3s 26ms/step - loss: 641.8758 - mae: 642.3434\nEpoch 26/100\n73/73 [==============================] - 3s 26ms/step - loss: 637.7227 - mae: 638.1873\nEpoch 27/100\n73/73 [==============================] - 3s 39ms/step - loss: 638.5152 - mae: 638.9829\nEpoch 28/100\n73/73 [==============================] - 5s 35ms/step - loss: 620.7292 - mae: 621.1951\nEpoch 29/100\n73/73 [==============================] - 4s 46ms/step - loss: 627.4440 - mae: 627.9112\nEpoch 30/100\n73/73 [==============================] - 3s 34ms/step - loss: 621.4894 - mae: 621.9539\nEpoch 31/100\n73/73 [==============================] - 5s 55ms/step - loss: 623.7023 - mae: 624.1691\nEpoch 32/100\n73/73 [==============================] - 5s 49ms/step - loss: 616.3346 - mae: 616.8017\nEpoch 33/100\n73/73 [==============================] - 4s 50ms/step - loss: 619.9973 - mae: 620.4636\nEpoch 34/100\n73/73 [==============================] - 4s 47ms/step - loss: 605.6837 - mae: 606.1503\nEpoch 35/100\n73/73 [==============================] - 5s 56ms/step - loss: 610.1432 - mae: 610.6086\nEpoch 36/100\n73/73 [==============================] - 4s 42ms/step - loss: 593.4051 - mae: 593.8717\nEpoch 37/100\n73/73 [==============================] - 5s 52ms/step - loss: 584.2279 - mae: 584.6927\nEpoch 38/100\n73/73 [==============================] - 5s 58ms/step - loss: 582.8460 - mae: 583.3107\nEpoch 39/100\n73/73 [==============================] - 4s 42ms/step - loss: 578.8635 - mae: 579.3285\nEpoch 40/100\n73/73 [==============================] - 4s 52ms/step - loss: 570.7054 - mae: 571.1718\nEpoch 41/100\n73/73 [==============================] - 8s 81ms/step - loss: 537.9622 - mae: 538.4274\nEpoch 42/100\n73/73 [==============================] - 5s 50ms/step - loss: 532.7086 - mae: 533.1703\nEpoch 43/100\n73/73 [==============================] - 3s 36ms/step - loss: 519.7102 - mae: 520.1743\nEpoch 44/100\n73/73 [==============================] - 4s 45ms/step - loss: 504.6268 - mae: 505.0909\nEpoch 45/100\n73/73 [==============================] - 3s 35ms/step - loss: 485.2854 - mae: 485.7470\nEpoch 46/100\n73/73 [==============================] - 5s 60ms/step - loss: 460.9784 - mae: 461.4407\nEpoch 47/100\n73/73 [==============================] - 6s 64ms/step - loss: 453.2597 - mae: 453.7200\nEpoch 48/100\n73/73 [==============================] - 6s 70ms/step - loss: 419.7295 - mae: 420.1899\nEpoch 49/100\n73/73 [==============================] - 6s 66ms/step - loss: 389.3953 - mae: 389.8532\nEpoch 50/100\n73/73 [==============================] - 3s 35ms/step - loss: 362.4371 - mae: 362.8922\nEpoch 51/100\n73/73 [==============================] - 4s 44ms/step - loss: 327.4053 - mae: 327.8596\nEpoch 52/100\n73/73 [==============================] - 6s 77ms/step - loss: 279.3048 - mae: 279.7564\nEpoch 53/100\n73/73 [==============================] - 4s 45ms/step - loss: 248.9620 - mae: 249.4107\nEpoch 54/100\n73/73 [==============================] - 6s 73ms/step - loss: 201.7944 - mae: 202.2373\nEpoch 55/100\n73/73 [==============================] - 5s 61ms/step - loss: 169.5310 - mae: 169.9707\nEpoch 56/100\n73/73 [==============================] - 4s 48ms/step - loss: 133.7876 - mae: 134.2150\nEpoch 57/100\n73/73 [==============================] - 3s 39ms/step - loss: 114.1053 - mae: 114.5272\nEpoch 58/100\n73/73 [==============================] - 5s 51ms/step - loss: 108.6730 - mae: 109.0799\nEpoch 59/100\n73/73 [==============================] - 3s 30ms/step - loss: 105.5544 - mae: 105.9589\nEpoch 60/100\n73/73 [==============================] - 6s 68ms/step - loss: 104.6379 - mae: 105.0425\nEpoch 61/100\n73/73 [==============================] - 6s 62ms/step - loss: 105.7001 - mae: 106.1014\nEpoch 62/100\n73/73 [==============================] - 3s 31ms/step - loss: 103.8707 - mae: 104.2732\nEpoch 63/100\n73/73 [==============================] - 3s 39ms/step - loss: 101.7670 - mae: 102.1677\nEpoch 64/100\n73/73 [==============================] - 6s 67ms/step - loss: 106.1233 - mae: 106.5245\nEpoch 65/100\n73/73 [==============================] - 4s 50ms/step - loss: 104.0798 - mae: 104.4798\nEpoch 66/100\n73/73 [==============================] - 6s 65ms/step - loss: 103.5084 - mae: 103.9127\nEpoch 67/100\n73/73 [==============================] - 6s 69ms/step - loss: 101.8610 - mae: 102.2628\nEpoch 68/100\n73/73 [==============================] - 7s 84ms/step - loss: 100.7677 - mae: 101.1684\nEpoch 69/100\n73/73 [==============================] - 5s 65ms/step - loss: 101.9099 - mae: 102.3136\nEpoch 70/100\n73/73 [==============================] - 4s 38ms/step - loss: 99.0328 - mae: 99.4332\nEpoch 71/100\n73/73 [==============================] - 5s 48ms/step - loss: 98.3263 - mae: 98.7267\nEpoch 72/100\n73/73 [==============================] - 2s 19ms/step - loss: 98.5716 - mae: 98.9737\nEpoch 73/100\n73/73 [==============================] - 1s 14ms/step - loss: 98.5474 - mae: 98.9497\nEpoch 74/100\n73/73 [==============================] - 1s 12ms/step - loss: 95.8845 - mae: 96.2859\nEpoch 75/100\n73/73 [==============================] - 1s 14ms/step - loss: 101.4515 - mae: 101.8569\nEpoch 76/100\n73/73 [==============================] - 1s 11ms/step - loss: 101.3835 - mae: 101.7819\nEpoch 77/100\n73/73 [==============================] - 1s 14ms/step - loss: 94.3717 - mae: 94.7743\nEpoch 78/100\n73/73 [==============================] - 1s 15ms/step - loss: 95.0694 - mae: 95.4681\nEpoch 79/100\n73/73 [==============================] - 2s 15ms/step - loss: 95.0557 - mae: 95.4553\nEpoch 80/100\n73/73 [==============================] - 1s 17ms/step - loss: 94.1604 - mae: 94.5600\nEpoch 81/100\n73/73 [==============================] - 1s 14ms/step - loss: 89.7356 - mae: 90.1310\nEpoch 82/100\n73/73 [==============================] - 1s 11ms/step - loss: 90.3032 - mae: 90.7010\nEpoch 83/100\n73/73 [==============================] - 1s 11ms/step - loss: 87.8763 - mae: 88.2769\nEpoch 84/100\n73/73 [==============================] - 1s 11ms/step - loss: 88.6435 - mae: 89.0371\nEpoch 85/100\n73/73 [==============================] - 1s 11ms/step - loss: 83.3757 - mae: 83.7700\nEpoch 86/100\n73/73 [==============================] - 1s 10ms/step - loss: 83.5280 - mae: 83.9213\nEpoch 87/100\n73/73 [==============================] - 1s 14ms/step - loss: 80.0882 - mae: 80.4839\nEpoch 88/100\n73/73 [==============================] - 1s 10ms/step - loss: 80.1391 - mae: 80.5298\nEpoch 89/100\n73/73 [==============================] - 1s 11ms/step - loss: 75.5003 - mae: 75.8922\nEpoch 90/100\n73/73 [==============================] - 1s 11ms/step - loss: 75.8617 - mae: 76.2475\nEpoch 91/100\n73/73 [==============================] - 1s 11ms/step - loss: 74.7211 - mae: 75.1050\nEpoch 92/100\n73/73 [==============================] - 1s 11ms/step - loss: 73.8308 - mae: 74.2170\nEpoch 93/100\n73/73 [==============================] - 1s 11ms/step - loss: 70.2196 - mae: 70.6035\nEpoch 94/100\n73/73 [==============================] - 1s 12ms/step - loss: 68.4947 - mae: 68.8752\nEpoch 95/100\n73/73 [==============================] - 1s 10ms/step - loss: 69.5297 - mae: 69.9294\nEpoch 96/100\n73/73 [==============================] - 1s 14ms/step - loss: 66.8248 - mae: 67.2109\nEpoch 97/100\n73/73 [==============================] - 1s 15ms/step - loss: 66.2192 - mae: 66.6002\nEpoch 98/100\n73/73 [==============================] - 1s 10ms/step - loss: 62.3000 - mae: 62.6769\nEpoch 99/100\n73/73 [==============================] - 1s 12ms/step - loss: 61.8863 - mae: 62.2651\nEpoch 100/100\n73/73 [==============================] - 1s 11ms/step - loss: 65.0888 - mae: 65.4771\n",
     "output_type": "stream"
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00010-cc5019c9-9d49-4c15-87ec-0359e8294b16",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "2592138f",
    "execution_start": 1632687690089,
    "execution_millis": 1309,
    "deepnote_cell_type": "code"
   },
   "source": "plt.semilogx(history.history[\"lr\"], history.history[\"loss\"])\nplt.axis() # [1e-8, 1e-6, 0, 3000]",
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 11,
     "data": {
      "text/plain": "(5.655877543841433e-13,\n 1.5757960424855714e-07,\n 83.50715789794921,\n 1943.3453506469727)"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 381,
       "height": 252
      }
     },
     "output_type": "display_data"
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "c6f70eb2",
    "execution_start": 1632687691392,
    "execution_millis": 220382,
    "cell_id": "00006-95ebb60f-fb05-4b27-9d51-60d8443fc569",
    "deepnote_cell_type": "code"
   },
   "source": "\n\n\n\nmodel = tf.keras.models.Sequential([\n    tf.keras.layers.Dense(40, input_shape=[window_size], activation=\"relu\"), \n    tf.keras.layers.Dense(50, activation=\"relu\"),\n    tf.keras.layers.Dense(50, activation=\"relu\"),\n    tf.keras.layers.Dense(1)\n])\n\nmodel.compile(loss=\"mse\", optimizer=tf.keras.optimizers.SGD(learning_rate=8e-10, momentum=0.9)) # was 3e-10\nmodel.fit(dataset,epochs=200,verbose=0)\n",
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 12,
     "data": {
      "text/plain": "<tensorflow.python.keras.callbacks.History at 0x7f189fe6fad0>"
     },
     "metadata": {}
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00009-8d22fe9d-0440-4aab-9adf-b6e0e6a2bf95",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "3edf7aca",
    "execution_start": 1632687911774,
    "execution_millis": 225707,
    "deepnote_cell_type": "code"
   },
   "source": "forecast=[]\nfor time in range(len(price_series) - window_size):\n  forecast.append(model.predict(price_series[time:time + window_size][np.newaxis]))\n\nforecast = forecast[time_index_split-window_size:]\nresults = np.array(forecast)[:, 0, 0]\n\n\nplt.figure(figsize=(10, 6))\n\nplot_series(time_test, price_test)\nplot_series(time_test, results)\n\ntf.keras.metrics.mean_absolute_error(price_test, results).numpy()\n\n# first: 1272.0206, \n# 1233.9647 - with 2 50 layers, lr: 3e-10\n# 1530.4454 -  bad params, 1 50 layer, lr: 3e-10\n# 1958.0427 - with 2 50 layers, lr: 1e-9\n# 1334.721 - with 2 50 layers, lr: 1e-10\n# 1388.0386 - with 2 50 layers, lr: 1e-11\n# 1236.3889 - with 2 50 layers, lr: 5e-10\n# 1210.836 - with 2 50 layers, lr: 7e-10 \n# - with 2 50 layers, lr: 9e-10\n#  - with 2 50 layers, lr: 8e-10\n#  - with 2 50 layers, lr: 6e-10\n\n# overfitting - low bias, high varaince",
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 13,
     "data": {
      "text/plain": "1168.2238"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 720x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 633,
       "height": 371
      }
     },
     "output_type": "display_data"
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00008-47202147-1214-4848-9367-b1be47b5f476",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "9cb7ced6",
    "execution_start": 1632688137475,
    "execution_millis": 22926,
    "deepnote_cell_type": "code"
   },
   "source": "fore=[]\n\nserN = price_series[time_index_split-window_size:time_index_split] # last N(windows size) values from before the split\n\nnum_after_split = len(price_series) - time_index_split\n\nfor i in range(num_after_split):\n    pred = model.predict(serN[np.newaxis])\n    serN = np.append(serN[1:], [pred[0][0]])\n    fore.append(pred)\n    \nprint(fore)",
   "outputs": [
    {
     "name": "stdout",
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array([[154879.75]], dtype=float32), array([[155948.6]], dtype=float32), array([[157024.67]], dtype=float32), array([[158108.]], dtype=float32), array([[159198.67]], dtype=float32), array([[160296.69]], dtype=float32), array([[161402.12]], dtype=float32), array([[162515.08]], dtype=float32), array([[163635.56]], dtype=float32), array([[164763.66]], dtype=float32), array([[165899.44]], dtype=float32), array([[167042.95]], dtype=float32), array([[168194.28]], dtype=float32), array([[169353.5]], dtype=float32), array([[170520.7]], dtype=float32), array([[171695.92]], dtype=float32), array([[172879.28]], dtype=float32), array([[174070.8]], dtype=float32), array([[175270.56]], dtype=float32), array([[176478.67]], dtype=float32), array([[177695.16]], dtype=float32), array([[178920.17]], dtype=float32), array([[180153.67]], dtype=float32), array([[181395.8]], dtype=float32), array([[182646.6]], dtype=float32), array([[183906.16]], dtype=float32), array([[185174.55]], dtype=float32), array([[186451.78]], dtype=float32), array([[187737.95]], dtype=float32)]\n",
     "output_type": "stream"
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00010-eddb8db6-d92f-4caf-bfa1-15ffcb497046",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "afbc1cc8",
    "execution_start": 1632688160324,
    "execution_millis": 438,
    "deepnote_cell_type": "code"
   },
   "source": "# Attempt to use only new predicted values in the 40 day time window, instead of actual values.\n\nresults = np.array(fore)[:, 0, 0]\n\nplt.figure(figsize=(10, 6))\n\ndays_to_show = 281\n\nplot_series(time_test[:days_to_show], price_test[:days_to_show])\nplot_series(time_test[:days_to_show], results[:days_to_show])\n\ntf.keras.metrics.mean_absolute_error(price_test, results).numpy()",
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 15,
     "data": {
      "text/plain": "30579.895"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 720x432 with 1 Axes>",
      "image/png": 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fMLyWwBlj4oEzgKcArLWN1tpy4FLgWfe0Z4HL3MeXAs9Zx1IgwRiTBpwPLLLWllpry4BFwFz3WJy1dqm11gLPtbmXiIiI3xVU1jNzSDIJUaF8uCXAEpnW5G3VM3Da9w5K3qy15FXUtdt92mpESgz94yNYqfXgvMKbW2kNAYqAZ4wxk4BVwH1AqrW2dTRnPpDqPh4A7GtzfY5bdqTynHbKD2OMuQunVY/U1FQyMzO7/KZ6m+rqatWHj6iufUd17Ruq56PbW1zLxD7BjEmwvL8xl49Sygjqwmgfn9e1bWHUtsdIy/+A7EFXsTv4DPjkE/ZWtvDMxkb2VnnwWDgjPeSIccUENbI1Oz9gfk8C6XfamwlcCDAV+Ja1dpkx5s982V0KgLXWGmOsF2NofZ0ngCcApk+fbmfPnu3tlwwYmZmZqD58Q3XtO6pr31A9H1lTi4fKhe8xZVQGSdFhfPH2ZiaedAp9YsKP+V4+reuWZvjv3ZD/AZx5P4Nn389gY1iwcT+/XLSG+Mgwbj+tP/VNHq6bMZBx/eM7vNV/9q9hQ055wPyeBNLvtDcTuBwgx1q7zH3+Ok4CV2CMSbPW7ne7QVun5uQCA9tcn+6W5QKzDynPdMvT2zlfRETE74qqGrAWUuMjSI52krb8ivouJXA+09wI/7kDtsyDOT+D079/4NBzX2STnhjFG984hcTosE7drl9cOO9X1GOt7XCsnHSN18bAWWvzgX3GmFFu0RxgMzAPaJ1Jegvwlvt4HnCzOxv1ZKDC7WpdCJxnjEl0Jy+cByx0j1UaY052Z5/e3OZeIiIifpXvLiHSLy7iwFppeeV1/gwJgBaPxRk6foimenj1Zid5O/+3ByVv9U0trMwuY87olE4nbwD94iNpaPZQURegM3B7MG+2wAF8C3jBGBMG7AJuw0kaXzXG3AFkA9e4584HLgSygFr3XKy1pcaYXwIr3PN+Ya1tHRF5D/AvIBJ4z/0SERHxuwJ3nbR+8RH0jXVb4PywI4PHYymtbaS4uoHnl2bzyop9PHz1JC6d3GbYeGMtvHIj7PwILvp/cNKdB91jVXYZjc0eThmefEyv3S/OSVz3V9STENX5xE+OzqsJnLV2LTC9nUNz2jnXAvd2cJ+ngafbKV8JjD++KEVERLpf2xa4xKgwQoIM+yu+TOCstfxryR7OGZPKwKTDt6M6XnuKa/jte1tYuqv0QAtYaLAhOMiwaHPBlwlcQzU5j11C/4rV8JW/EjTt5sPutTirmJAgw4whx5jAxTsJXH5lPWPS4o7vDXWjP76/jbjIUO48fai/Q+kyb7fAiYiInJDyK+sJCw4iKToMYwypcRHkt0ng9pXW8X9vb2blnjIevXHqEe7UNb99bwuf7Sjm4olpjE2LIyEqjOkZiTy0YBvLd5c449IaKin756X0K1/Hd5ruYUbTmdzUzr2W7Cxh0sAEYsKPLW04kMBV9JzFfCtqm3g0cyctHovHWu46Y5i/Q+oSJXAiIiJeUFBRT0pc+IHB+/0TIg4aA7cprwKA9zbuJ6eslsSoMN5el8eG3Ar6J0Ry71nDu/zaJdUNfLilkFtPyeAnF4896NiMjETeXpdHbl4eKW9dT0zJRv4Qez+FcWfw0IKtnDculZTYL/c3raxvYn1OOd/sQjwpseEY07MSuMzthbR4LJMGJvCb+VsZ1jeGOWNSj35hD6O9UEVERLwgv7L+wBgwcAb0tx0DtymvkuAggzGGf3yyi1ueXs79b2zgtZU5/GHhNtbuK+/ya89bl0ezx3LltPTDjs0YkkwSlcS8cjlBRZv5dsv3uen2b/HryyfQ0OThTx/sAJzJDk99vpt7X1iNx8KsYX2OOY7Q4CD6xIT3qATuwy2FJEeH8cpdJxMbEcLH2wJzn1q1wImIiHSjm55cxpRBCRRUNjC2/5fjvtLiI1i46cslNTblVTC8bwzDU2L499Jsggz85fopnDWqL2f+IZOHF27j+TtndimG11flMH5AXLvjzkZE1fBaxK+Iqirka43fZ/DMSw6MwZszJoVPtxcB8MXOEn75zmbSEyO5dvpApmckHnavzugXF+GVyRuV9U389cMdfLjVScDe/84ZhAQfuV2qucVD5rZCzhvXj4jQYEalxrItv6rbY/MFtcCJiEivUVhVz+Is/+05mlNWy+dZxfz1oyz2lNQc3AIXF0Fjs4eyWmdCwaa8Ssb1j+PuM4eRFB3Gw1dP4pJJ/YmNCOWe2cP4PKuYJTuP/b1sza9kU14lV009vPWN8n0EPXsRA0wxNzf8kE88E7njtCEHDk8bnEhOWR0FlfUs3VVCcJBhwXfO4KGrJhJ6lOSoI/3iI7zSAvfB5gL++dluwoKD2FVUw7LdR9+ya2V2GZX1zcwZnQLAqH6xbM2van9ZlR5OCZyIiPQaf3x/O7c9s4IWj3/+IK9w9/0c1jcaazkogWu7FlxRVQOFVU4L3YT0eFb95ByuaJNw3XTyYFJiw3l2yZ5jjuHzHU7Sd8GEtIMPFGfB03OhuogFkx9jqWcsF0xIO2gG7PSMJMBZNmTZ7hLGD4g/5okLh/JWC1zrPV+4cyaRocG8t9HZpTOrsIqymkbASei/9dIaiqoaAFi0uYCw4CBOH9kXgNH9Yqmqbz5odnCgUAInIiK9xpKdJTS2eCis8s8f5OW7y4gND+GVr8/iK5P6c+aovgeO9Yt3Nn7Pr6g/MIGhdRuqQ3cpiAgNZsqgBHYW1RxzDKuyy0hPjCS1TfLI/vXwzFxorodb32b8rPNJjQvnntkHz8AcmxZHeEgQn2cVs25fBScPSTrm1z9Uv/gIKuqaqGtsOe57tVVY2UBseAjJMeHMHtWXhZsKyC6p4eK/fs7D728DYOmuUt5el8ffPtpBWU0jr67YxzljUw4kpSNTYwECshtVY+BERKRXyCmrZW9pLQC5ZXWkuQmTLy3fXcL0jET6xITz1+unHHQszV1SY39lPZXuumxtx8gdKiM5mo+3FeE5htZEay2r95Zx8tA267XtXQYvXA3hMXDzW9BnBMOBZT8+57Drw0KCmDQwgTdW59DY4mHm0G5I4OK+XAtuSJ/o475fq8IqZ5YvwNzx/XhvYz63PrOC+iYP2SXO70HrrN+Xlu+jqr6Z6sZm7psz8sA9Rvdz6n9bQRVnud2qgUItcCIi0it8sbPkwONcP2xZVVzdwM6img4Xu+0TE+4s5ltex+a8StITI4mPDO3wfoOSo2hs9hxT92NueR0FlQ1MG+xOONj5Efz7MojuA7cvgD4jjnqP6YMTqW/yEGS+7FI9HgcS14ru/ZkUVDYcWO7k7NEphAUHsbu4huiw4AOJW155HRGhTqrzxppcvjKxP6P6xR64R3xUKP3iItiWX0VueR3zdzcGzHg4JXAiItIrfLGrhNgIp2Mpr9z3Xagr3fFvM4a0P1szOMhZzHfprhI+zypmwoD4I94vI9lprdpT0vlu1NV7ywGYOigRtrwNL14LSUOd5C1hUKfu0Zr8je0fR1xExwlmZ6W2JnDuz+Tl5Xs5++FMGpqPr0u1sKqeVLcFLjYilLnj+zF1UALXnjSI3PI6rLXkldeTkRzNDTMHERxkuO+cwxPY1okMP3x9Ha9uayIvQMbDKYETEZGAZ61l6c4STh/Rh/jIUL9sGr9sdynhIUFMGJDQ4Tn94iNYvbecyNBgvn/eqCPeb3CyM7mgtTuwM1ZnlxEZGsyYgnecjenTJsOt70BM57sHpw1OJMjArKHHtm1WR9ITI+kbG84/Pt1JdkkNv3xnM7uKa9hZeOzj+1pZa50WuDbj/P583WReu/sUBiVF0tDsoaSmkbzyOtLiI3jgwtEs/M4ZDOsbc9i9RveLZcv+ShZnOS24rRMgejqNgRMRkYCWV17Hun3l5FXU841hfdhdXOuXBG5xVjHTBicSFtJx28jkgQlU1zfz9G0nMSDhyGP00uIjCQsOIruklrQjnNrisVz26GLS4iPYU1LDjxI/Jnje4zB0Nlz7gjP27RgkRIXx8l2zGJl6bNd1JDwkmD9cNZFbn1nBJX9bTG2T0/K2o7DqiGMAj6SyrpnGZg8pseEHyowxBBvo79ZrXnkd+yvqmDIogfCQYIantP9+WicyRIcFU9PYQrm7zEtPpxY4EREJWHnldZz20Ed844XVGAOnD+/DgIRIn4+B21tSy/aCas4+ykD4n1w0hgXfOf2oyRs4Xa7pSZFkH6ULdfnuUjbkVvD+5nzOL36OWysfh9EXww2vHnPy1mrGkCQSosK6dG17Zo9K4dZTMqioa+JbZ48gJMgc18zPAneW8UEzbV0DEp26zSqspqy26UBC15EZQ5IYkBDJ/17kbDlWVqsWOBEREa/aU1KDx8KDXxnLuWNTSU+MYkBCBMt2lxz94m70wZYCAM4de+Q9NQ9dLuRoMpKj2VNSC+2sydtq3ro8IkOD+HjiIvptep3KUVcTd/XjENyz/sQ/cOFoTh3eh9mj+rJg4362FxxHAudO7GjbAteqNTlemV0GfLn+XkcGJkWx+P6zKayq58dvQnmAJHBqgRMRkYDVukDr6SP6kp7ojBnrnxBJVX0zlfW+6wr7YEsBI1JiGJzcfctkgDMOLrukpsOZkY3NHt7fkMPTSc/Rb9NTMOPrxF37RI9L3sDpSj13bCqhwUGMTI1le0F1l+9VWOn83NtrgYuPDCUqLPjApJLOLieTEOm0OJapC1VERMS7DrTExH3ZEtPaZbbfRzNRK+qaWL67lHOO0vrWFRnJ0dQ2tlDZQaPQF1tz+G3zH5hVMR/O/BFc8BAE9fw/7SNTY9lbWkttY3Onzn9x2V4eWbSd6gbn/NYu1LY/91bGGAYkRB5IEDvTXQ3OGngRwYHThdrzf8oiIiIdKKhsIDI0mNg22z21HcTuC59sL6LZYzlnTPcvBDvInYlaWOs5/GBdGQPfvYFzglfTfP5DcNaP4Ri7aP2ldeJAVuHRW+Gq6pv45Tub+fOHO5j9h0xW7Ck9sAtDVFj7LY2tvwPGtN9K15GYMKNJDCIiIt5WWNVAalz4QWPLWltcfDWRYZm7/tzkge2v/3Y8WteCW7inie+9svZAiyOV++GZCxlYt4Un+v6EkFl3d/tre1PrYrqdmcjwzvr91DW18MvLxhMcBH/5cMdBuzC0p3UiQ9+Y8CPOCj5UTKgJmBa4ntdJLiIi0kkFlfUHVuNv1TfW2fHAVy1wO4uqGZkaS3BQ97d+pSdGOuO5ClpYWZDLhPR4bhvVDP++AltXyl0t95Mx6IJuf11vG5QURXhIUKcmMryyYh8jUmK4aeYg8ivqePyTXQxOijpiy1prEp/Wye7TVk4CpxY4ERERryqqajisJSY4yNAvPsJnLXBZhTUM69u9kxdahQYH8e63T+eR2ZH0i4ugbPsX8NR50FxHxbX/5eOmsQx0J28EkuAgw/CUmKNOZNheUMXafeVcM30gxhi+Mqk/LR7LruKaA7swtKd15umAo8xAPVRMmGahioiIeJWzGn99uy0x/RMifdICV1HbRHF1Q7sr/HeXIX2iSYwI4obkbXwj+zsQEQe3L2RPmLMt1MCkwEvgAEalxrI1v/KI57y1NpeQIMPlUwccuKZ1Qd6UI7bAuTOSOzkDtVV0qAmYnRiUwImISECqbmimtrGl3bXA+saEU+KDP8Q7i50WpI5W+e8uKQWZ3Jv/U3Z7+lFx/TuQPIx9pc4WW+mJx5ak9BRj+8dRUNlwYCmY9uwtrSM9MZI+Mc7P2BjDVyb2B9pfA67VwCSnTo61bmJCDZX1zTS3tDNppIdRAiciIgGpsKrjtcASo0N90pLSOovSmy1wfPEoY7c8QnXKNK5t/Clry533m1PmtDAGagvc+AHxAGzKq+jwnJLqBpKiD94R4tLJ/QkPCWJ0v4634UqLj+SZW0/iqukDjymmmDBnHGNFXc8fB6cETkREAlJ7a8C1SooKo6KuiRZP+wvgdpedRdWEBQd5pxXM0wILHoCFP6aozyyCvvofqk0Ua/eWA7CvrJbEqFBiwgNzPmLrPqib8jruRi2taSQ55uCfb0afaNY9eB6nDk8+4v3PGp1yzHUTE+okcIEwkUEJnIiIBKTWrrdDZ6GCsyG7x0Kll1tSdhbWMKRPNCHB3fzntKkOXrsFlj4GM+9m07gfEBsTy/C+MazLKQecFrj0AJzA0CouIpSM5Cg25nbcAldc3Uhy9OF7skaEBh/ztmSdERPqfK+o6/nj4JTAiYhIQGptgWtvNmJrt1upl2cU7iqqZlhKN89ArSmGZy+BLe/A+b91dlcwwQBMHpjAun3lWGvJKa09MNYrUI0bEM/GDrpQPR5LWW3jYV2o3tTahVpWoxY4ERERryiobCAqLLjdbrJE94++N8fBNTZ7yC6t7d7xbyU74alzIX89XPMszLrnoMOTByVQUtPI9oJqcsrqAnIJkbbG949nX2kdFe10WVbWO13gh3ahetOXXahqgRMREelWn24vYnFWMYVVDaTEhrfblZYU5bbAeTGByy6pocVju28G6r4VTvJWVw43z4Oxlx52ynlj+xESZPjbx1k0tngCdgZqq3EHxsEd3gpXXO387NrrQvWW1ha4QNhOKzBHPoqIyAnrV+9uZm9pLSmxEfSLb38tsMRoZzCTN1tStrjbQHVLC9yWt+E/d0JsGtz0H0ge1u5pfWPDOW9cKm+vywMgPUBnoLZqTeA25lVwyvA+Bx1rTb6TY3yXwEUEQ0hQYGynpRY4EREJGNZasktqqW/ysLe0tsPtlFrHTXlzNuHrq3JIjQs/sK9nly19HF75KqSOhzsWdZi8tbp+xqADjwO9CzU5Jpz+8RGs2FN22LHSGmeSii/HwBljSIgK0yxUERGR7lRY1UBDs4dzxqQAkNZBC1xkaDBhIUFeGwO3p7iGT7cXccOMwYR2dQaqpwUW/BgW/AhGXwS3vA0xfY962anD+nR5odqe6CuT+vPhloIDCxO3au1C7ePDMXAAiVGhAbGdlhI4EREJGNklzh/5m04ezLO3z+CO04a0e54xhqSoMK+NgXt+aTYhQYbrZxzbQrEHNFTByzfC0kdhxtfhmucgrHOtaUFBhu+eM5KLJqYRERrctdfvQW49NYMgY3hm8Z6Dylt/dolRvmuBa329QOhC1Rg4EREJGHvdVprBydEM6XPk5TsSo73zh7i+qYXXVuVw/vh+R9yPs0Ple+Gl66FwC1z4MMz42jHf4oqp6VwxNf3YX7sHSouP5OKJabyyYi/3nTOC+Ehn/GJJdQOxESGEhfi2rSk+KvSw1sCeSC1wIiISMPaW1BBkYEDC0bsOk6JDvdICtzq7jIq6Jq7qSgK1bwX882wnibvxtS4lb73RnacPpaaxhWcW7z5QVlLT6PPuU3BmvRZWNWCtd3fxOF5eTeCMMXuMMRuMMWuNMSvdsiRjzCJjzA73e6JbbowxfzHGZBlj1htjpra5zy3u+TuMMbe0KZ/m3j/Lvbb7l2UWEZEeY29pLWnxkZ1qlUmMCuvychCrsku57okvqG9qOezYBnfngEkDE47tputfg39dBGHRcOcHMHxOl2LrjcYPiOeiiWk8lrmTPcU1gNOF6ssJDK0mpMdTWtPILjeOnsoXLXBnWWsnW2unu8/vBz601o4APnSfA1wAjHC/7gL+Dk7CBzwIzARmAA+2Jn3uOV9rc91c778dERHxl+zSWgYnd26sWFJ0WJd3Yli4qYClu0rZsv/wfTo35lUyICGy88mFxwMf/RreuBPSp8OdH0HfUV2Kqzf72cVjCQsO4qdvbcRaS0kH22h526nDnOVMlmQV+/y1j4U/ulAvBZ51Hz8LXNam/DnrWAokGGPSgPOBRdbaUmttGbAImOsei7PWLrVOO+dzbe4lIiK90N6SWgZ1cu2zBHdD++YWzzG/zjZ3jbf2NlrfmFtxYP2yo2qshddvg09/D5Nvgq/+F6KPvAn7iSo1LoL/OW8kn+0o5vOsYkpqGn26BlyrwclRDEiIZHFWic9f+1h4O4GzwPvGmFXGmLvcslRr7X73cT6Q6j4eAOxrc22OW3ak8px2ykVEpBeqbmimpKaRQZ1tgYsKxVqo6MKG9q0J3OZDWuCq6pvYXVzDhAHxR79J5X7414Ww+S049xdw6d8gxPcJSSC5bsYgosOCeXf9fp/vg9rKGMOsYcl8sauE2sZmbn56OQs27j/6hT7m7Vmop1lrc40xKcAiY8zWtgettdYY4/VRgm7yeBdAamoqmZmZ3n7JgFFdXa368BHVte+orn3D1/W8t9IZj1adv4fMzJyjnA35ec0ALMxcTP+Yo7dX5FR5SI02NLZAfmU9AEu35pCZ+WVLzLZSJwZbmk1mZm6H94qt3MH4jb8lpLmGzeMfoKRpEnzyyVFj6MiJ9Ds9Ngn+u3ofLR4o27+PzMx8n712az0nNTVTUdfENX/5gI3FLXhqyogo3uazODrDqwmctTbX/V5ojHkTZwxbgTEmzVq73+0GLXRPzwXaLqiT7pblArMPKc90y9PbOb+9OJ4AngCYPn26nT17dnunnZAyMzNRffiG6tp3VNe+4et6XrAxH5as4oLTTmJC+tFbwIJ3FPH4+uUkDh7DA+9s5jdXTOCsUSntnlvT0Mxdv1jE3bOHceqwZPhwKcP6RpNbXsfpZ5xJcJAzRy7rs13AFq6fezp9YzuYIbnmBfjsfyEmFa5/iwn9JnT1LR9wIv1OVyTkct/LawGYOXkssyf7rnOttZ7HVNbzxPoP2VjsJOz1IbHMnn2Kz+LoDK91oRpjoo0xsa2PgfOAjcA8oHUm6S3AW+7jecDN7mzUk4EKt6t1IXCeMSbRnbxwHrDQPVZpjDnZnX16c5t7iYhIL7O31JkV2Nku1NYFYP/+yU72V9Tz8dbCDs8tqKynscXDgo372VbgdJ9eMTWd+iYPu4urD5y3Ka+SfnER7SdvLU0w/wfw1j0waCbclQndkLydaGaPSiHETZiTo32/jAg44/FGpsYwICGSiyamsaek560L580xcKnA58aYdcBy4F1r7QLgd8C5xpgdwDnuc4D5wC4gC/gncA+AtbYU+CWwwv36hVuGe86T7jU7gfe8+H5ERMSPtuyvIjEq9MBCr0fTOn5qfY6z7Me6feUdnltY5ey7ub2gmgUb84mLCDnQWtc6kaGusYW1+8oZP6CdCQzVRfDcpbD8CZj1TbjpTU1W6KL4yFBmDXPqzh9j4Fr98+bpvHr3LMb1j6O4uoHqhma/xdIer3WhWmt3AZPaKS8BDlv8xp1Jem8H93oaeLqd8pXA+OMOVkREepzi6ga+/+o6fvaVsfSJDue9jfu5fErnF89tuwXTiJQYtuyvorHZ0+4acq0JHMCSnSXMyEhiRGoMYcFBbMipYGdRDc8s3k1VfTO3H7p9V+5qeOUmqC2BK56EiVcf+5uVg1w6eQDLd5fSP6ELO110k8HJzk4fGe737JIaxvXvxOQVH9FWWiIi0iN9uKWAT7YX8bO3NjJndCr1TR5unDmo09dHhgUTERpEYlQY35ozgm+/tIat+ZVMTE847NxCd9LCoKQo9pbWMqpfLKHBQYzsF8PTi3fjsXDhhH7cesoQTspI/PLCtS/C299xxrvd8T6kHdZuIV1w5dQBnDMmhQQf74PantZ1B7NLapXAiYiIHM2Snc7sz8VZJazfV8GkgQmM78zyHW1cOTWdqYMSmTooAXC6UdtL4IqqGggLCeKqaen8cdF2RvaLBWD64CR2FFTzm8sncOW0Nq1/LU2w8H9h+T9gyBlw1b/UZdqNjDE9InmDL1vidvewnRmUwImISI9jrWXJzhIumpjGjoIqthdUH1PrW6tfXz7hwP36xISxLqeCr7ZzXmFVA31jwrls8gD+uybXmYkK3H/BaL49Z8TBY7GqC+G12yD7czj5XmeNt2D9Oe2tYsJD6BMTTnaJEjgREZGDvL8pn9V7y/nuuSMIDwlmZ1E1RVUNnDGiD7efOoR/fLKTr0zs3+X7G2OYmJ7Q4USGwqp6UuLCGZQcxUf/M/tAeURoMBGhwV+euGcxvH471FfA5U/ApGu7HJMEjiF9onrcTFR/bKUlIiJykGe/2MPjn+zklqeXU1HXdKD79JRhfZg2OJEnbp5OZFjwUe5yZJPSE8gqqm53NmFhZQMpHa3rBmAtfP4nePYrzmb0X/tQydsJZHBydI9rgVMCJyIifre7qIahfaJZlV3GZY8u5o3VuaQnRjKwk/uedsaUQQlYCyv3lB52rLCqgZTYDmY81pXDyzfABw/CmIud9d1Sx3VbXNLzZSRHUVDZQG1jz1lKRAmciIj4VV1jC3kV9Vw2ZQAvfu1kahubWbuvnFlDu3dSwIwhSUSFBbNoc8FB5fVNLVTUNbXfApe3Fv5xBux4H+Y+BFc/CxGd3Mheeo3BB5YS6TndqErgRETEr/a4XVND+kRzUkYS7377dG6eNZjbTh1ylCuPTURoMLNH9WXR5gI8ni+34S5y14BLiWuTwFkLK5+Bp84DTzPc9h6cfDcY060xSWAY0sdJ4LbmV/o5ki91OoEzxnRfO7aIiIirdXmG1j+SfWLC+cWl4xnbv/tbus4b24/CqgbW5ZQfKCs8kMC5XaiNNfDm3fDOdyDjVPj6ZzBwRrfHIoFjTFocAxIieX1Vjr9DOeCoCZwx5hRjzGZgq/t8kjHmMa9HJiIivdYn24u47NHF5JXXHZbAedNZo1IIDjK836YbtajKWcQ3JTYcCrfCP+fA+ldg9o/hxte1vpsQHGS4YeYgFmeVsKuo+ugX+EBnWuAeAc4HSgCsteuAM7wZlIiI9F6f7yjma8+tZO2+cuZv2M+uohpS48KJDvf+ylbxUaGcPDSJhRvzaXG7UZ0WOMug3a/DE7Ohpgi++gbM/hEEHd/MV+k9rp6eTkiQ4cVle/0dCtDJLlRr7b5Dilq8EIuIiPRytY3NfP3fKxnaJ5qM5CgytxWxu7jaJ61vra6als6u4hrue3kNTS0eyktL+Fvo34hd9D2nq/Qbi2HY2T6LRwJDSmwE54/rx2urcqhv8n8a1Jn/7uwzxpwCWGNMKHAfsMW7YYmISG9UVNVATWMLd54+lG35lTy7JJvw0CAuPo5Feo/V5VPSKaxs4LfvbaV/9Wa+XvQr4oMLYM7P4NTvqNVNOnTJ5P68u2E/m/IqmTY48egXeFFnWuDuBu4FBgC5wGT3uYiIyDGpqnfW0YqNCOHMkSk0tnioqm9mqA9b4AC+fvoQXpuwnB/kfpu6xiYeiHsITv++kjc5otHuHrlZhVV+jqQTLXDW2mLgRh/EIiIivVxlfRPgJHDTBicSFRZMbWOLT7tQqS6EN+/mpJ0fsjXpLK7Zfz0nJQ313etLwEpPjCIiNIjtBf6fyHDUBM4Y8wxgDy231t7ulYhERKTXam2Bi4sIJTwkmFOGJfPBlkKG9PVRArfzY3jjLmiohIsfYeSUW7lp0XYmDIj3zetLQAsOMgzrG8OOwgBI4IB32jyOAC4H8rwTjoiI9GZtEziAq6cPpKCygUHduGVWu1qa4OPfwOePQN9RcPNbkDqWIOCHc0d797WlVxmREsPy3Ydvx+ZrnelC/U/b58aYl4DPvRaRiIj0WlVtulABzh/Xj/PH9fPui5budlrdcpbD1Ftg7u8gTGvTS9eMSI3lv2vzqKpvItb9j4g/dGXRnRFASncHIiIivV9rC1xMhPfXfMNaWPcSzP8hmCC46mkYf6X3X1d6tREpMQBkFVYzZZD/ZqJ2ZgxcFc4YOON+zwd+5OW4RESkF6qqbyIyNJjQYC9vxV1bCu98Fzb/FwafBpc/DgkDvfuackIYkerMRN3R0xM4a22sLwIREZHer6q++UD3qdfs+sTZy7SmEM75OZzybS0PIt1mYGIkYSFBZPl5IkOH/4qMMVOPdKG1dnX3hyMiIr2ZVxO45gb46Jew5G+QPByu/wD6T/HOa8kJKyQ4iKF9otlR4N+14I70r+j/HeGYBbTPiIiIHJNKbw38LtwKb9wJ+Rtg+u1w3q8gzLeLA8uJY2RqLKv3lvk1hg4TOGvtWb4MREREer9ub4GzFpb/Exb9FMJi4PqXYdQF3Xd/kXaMHxDHruJqmlo83h/P2YFO/SsyxowHxuKsAweAtfY5bwUlIiK9U1V9EwMSIrvpZgXw1r2QtQiGnwuXPgqxqd1zb5EjuOuMYdx1xjC/xtCZWagPArNxErj5wAU468ApgRMRkWPSbS1w296Dt74JjdVw4cNw0p1gzPHfVyRAdOZf0VXAJGCNtfY2Y0wq8Lx3wxIRkd7ouBO4hmp4/yew6hlInQBXPgkp2klBTjyd+VdUb631GGOajTFxQCGgxXREROSYNLV4qGtq6fokhr3L4M2vQ9keOOVbcPZPISS8W2MUCRRHWkbkUeAlYLkxJgH4J7AKqAa+8El0IiLSa1S7uzAccwtccyNk/hYW/wni0+HWdyHj1O4PUCSAHOlf0XbgD0B/oAYnmTsXiLPWrvdBbCIi0otUHUjgjqEFrmATvPF1KNgAU74K5/8GIuK8FKFI4DjSMiJ/Bv5sjBkMXAc8DUQCLxlj6qy1O3wUo4iI9AKVh2xkf0SeFvjib/DRryAiHq57CUZf6OUIRQJHZ7bSygYeAh4yxkzBSeR+BmhfEhER6bSqznahlu2BN78Be5fA6IvhK3+G6D7eD1AkgHRmGZEQnKVDrgPmAJnAz70alYiI9DpVbgtcXEddqNbC6udg4Y/BBMFlj8Ok67Q8iEg7jjSJ4VzgeuBCYDnwMnCXtbbGR7GJiEgvcsQWuKoCePvbsH0BDDkDLn0MErTggUhHjrT/wwPAEmCMtfYSa+2LSt5ERLxrf0UdNz25jOW7S/0dSrerOjAG7pAWuM1vwWMnw65MmPs7+OpbSt5EjqLDBM5ae7a19klr7XHt1mqMCTbGrDHGvOM+H2KMWWaMyTLGvGKMCXPLw93nWe7xjDb3eMAt32aMOb9N+Vy3LMsYc//xxCki0hO8u34/n2cVc8vTy1mSVezvcLrVYS1wdeXODNNXb4aEQfD1T+Hkb0CQf/aWFAkkvvhXch+wpc3zh4BHrLXDgTLgDrf8DqDMLX/EPQ9jzFic8XfjgLnAY25SGAw8ijM+byxwvXuuiEjAWpxVTHpiJIOSorjzuZUHWq16g6qGZiJCg5zNv3d+BH8/FTa8BmfeD3d+AH1H+TtEkYDh1QTOGJMOXAQ86T43wNnA6+4pzwKXuY8vdZ/jHp/jnn8p8LK1tsFauxvIAma4X1nW2l3W2kacMXqXevP9iIh4U2Ozh2W7Szl7dAoPXDia2sYWNuRW+DusblNV30RqeDO8/R349+UQGgl3LoKzHoDgLu7OIHKC6oYdhY/oT8APgVj3eTJQbq1tdp/nAAPcxwOAfQDW2mZjTIV7/gBgaZt7tr1m3yHlM9sLwhhzF3AXQGpqKpmZmV1+Q71NdXW16sNHVNe+E6h1va20hdrGFuLr86nKLgLgv5+spnFfmJ8ja9+x1nPkzhW83Pw4dlUJ+wZezp6M6/HsqIIdnb/HiSpQf6cDTSDVs9cSOGPMxUChtXaVMWa2t16nM6y1TwBPAEyfPt3Onu3XcHqUzMxMVB++obr2nUCt61XvbyPIZPG1S88kLiKUh9Z8RE14ArNnT/V3aO3qdD03VMMHDzK75klyg/tjblnIoEEzGeT1CHuPQP2dDjSBVM/ebIE7FbjEGHMhEAHEAX8GEowxIW4rXDqQ656fCwwEcty15+KBkjblrdpe01G5iEjA+TyrmEkDEw6skzYpPYH1ueX+Dep47f6MutfvJqIml3mRlzEv6XaeGtRuZ4mIHAOvjYGz1j5grU231mbgTEL4yFp7I/AxcJV72i3AW+7jee5z3OMfWWutW36dO0t1CDACZ126FcAId1ZrmPsa87z1fkREvKmmoZl1+8o5bfiXOw5MSI9nX2kdpTWN7V7T1OJhVXYPXW6ksQbm/xCevZji2haubvgp95VdQ3hUjL8jE+kV/DFX+0fA94wxWThj3J5yy58Ckt3y7wH3A1hrNwGvApuBBcC91toWtwXvm8BCnFmur7rniogEnKzCajwWxvWPP1A2cYDzuKOJDC+v2MeVf/+CPcU9bInO7CXODNPl/6B43G2cV/drokecTpCB5Ohwf0cn0it4exIDANbaTJwtuLDW7sKZQXroOfXA1R1c/2vg1+2Uzwfmd2OoIiJ+kVVYDcDwlC9bqManOwnc+n3lnDmy72HXLN1ZAsCmvEoy+kT7IMqjaKxxNp9f+ndIHAy3vssz2/rQGLSLR66dTGFVPSmxEf6OUqRX0GqJIiI9QFZRNSFBhsHJUQfK4iJCGdonmvXttMBZa1m+x+k+3Zpf6bM4O7QrEx6bBUsfg5PugLsXQ8ZpLNiYz8whSSRFhzG6XxxJ0T1zRq1IoPFJC5yIiBxZVmE1GX2inUVu2xg/IJ5V2YdviLOnpJaiqgYAtuyv8kmM7aorg/d/Amueh6RhcOu7kHEaADsKqthZVMOtp2T4Lz6RXkoJnIhID7CzsJpR/WIPKx/SJ5q31+fR0NxCeEjwgfIV7l6po/vFsq3APy1wfYqWwKN3QU0xnPZdOPNHzuK8rrfX7wfgvHH9/BKfSG+mLlQRET9rbPaQXVp70Pi3VoOTo7AW9pXWHVS+fE8piVGhXDwxjX2ldb7dcqsqH165ifGbHoKYVLjrYzjn5wclb/VNLbywNJuzRvUlNU7j3kS6mxI4ERE/21NSQ4vHdpjAAewtPXim6Yo9pUzPSGJ0vzgAthf4oBvVWlj9b3h0Bmx/n51Db4avfQRpkwAorWnklN9+yCsr9vKf1TmU1DRy1xnDvB+XyAlIXagiIn7WOgN1WN/DE7hBSc7s0uyS2gNlhZX1ZJfU8tWTBzM6zel23bK/immDk7wXZMlOeOe7sPsTGHwqfOUv7NuYw7A2e5iu2FNKXkU997+xgcSoMCamx3PyUC/GJHICUwInIuJnOwqqMab9BK5PTBhRYcEHJXCtCd/YtDgGJEQSGx7CtnwvtcA11cPiP8Fnf4SQcLj4EZh6KwQF4WxB/aV1+8oJCTJMGpjAquwyfnHpOIwx3olL5ASnBE5ExM+yiqoZkBBJZFjwYceMMQxKimJv6ZcJXF5FPQADEiMxxjCqX6x3lhLZ+RG8+30o3QXjr4TzfwOxHU9IWJ9Twah+sfzrtpNYnFXMeWM1eUHEWzQGTkTEz7IKq9sd/9ZqcHIU2SVfjoHLK3cmNPSLdyYHjB8Qz7qcCna0GQdXUdfE/f9Zz/LdXdhqq6oAXr8D/n258/yrb8JVTx8xefN4LOtzypk0MIHYiFDmjk8jKEitbyLeogRORMSPWjyWXUXVDG+n+7TV4ORo9pXV4fFYwEng+sSEH1hW5BuzhxEXEcrX/72KqvomWjyW+15ew8sr9nHDP5fy0vK9nQvG0wLL/wl/mw5b5sHsB+AbX8Cws4966Z6SGirrm5mUHn/Uc0Xk+KkLVUTEj3LL6mho9hyxBW5QUhSNzR7yK+vpnxBJbnkdAxK+XJojNS6Cv90whRufXMaljy5mUFIUmduKuP+C0SzZWcIDb2wgLT6C2aNSOg4kb40zSSFvDQw9Cy76f5Dc+Rmk63Oc3SImpid0+hoR6Tq1wImI+FFWkdPtebQuVPhyJur+CieRa+vkocn89foppMZGsGRnCdfPGMTXzxjKkzdPJyM5il+9u4XmFs/hN68pgbe/A/88Gyrz4MqnnC7TY0jeANbllBMZGsyII7wPEek+aoETEfGj9jaxP9RgdymRfaW1nDw0ibzyunY3t79wQhoXTkijxWMJMs4EiLAQwwMXjuHr/17Fi8v3cvOsDOfkliZY8RRk/gYaqmHG1+GsByCia12g6/aVM35AHCHBahcQ8QX9SxMR8aOswmr6xISRENXxJu/9EyIICTJkl9ZQUddEbWMLafEd724QHGQOWr7jvLGpzBqazJ8/2IG1FnZ+DI+fBgt+BP2nwjeWwAW/63Ly1tTiYVNepbpPRXxILXAiIn6UVVjd7vpvbYUEBzEgMZLdxTXkujNQBxzShXokxhjOGZvKvt1baHrxRsJ2vAuJGXDdizDqQjjOtdq2F1TR0Oxh0sCE47qPiHSeWuBERPzEWsuOoywh0mpSegLLd5eRV+6sAXfoGLgjqq/g9Oy/8WHYDwjZ/THM+RncswxGX3TcyRvAun0VboyagSriK0rgRET8pKiqgar65k4lcKeN6ENxdQMfbysEOpnANTfCsn/AX6YwcseTvOuZyepLFsHp34fQ7ttgfn1OOQlRoQxKiuq2e4rIkSmBExHxk85MYGh12vA+ALy9No+wkCCSozseM4e1sOm/8NhMeO+HkDqOfVe/x/ea7iHXk9gdoR9kXU4FE9MTtG2WiA9pDJyIiJ9kFTkJ3IiU2KOe2z8hkqF9o9lVVMPg5KiOdznYuwze/wnkLIe+Y+CG12DEucTVNQPvU1TV0OV4V2WX8eRnu9iyv5I37jkVgLrGFrYXVHHOmCOsMSci3U4JnIiIn2QVVhMTHkJqXHinzj99eB92FdXQP76d7tOSnfDBg7DlbYjpB1/5C0y+EYKdj/m4yBDCgoMoqu5aAvfhlgLueHYlseEhVDU08+6G/QwENuVV0OKxmoEq4mPqQhUR8ZNt+VUMT4npdNfjaSOctd8OGv9WkQvvfA8enQFZH8FZ/wvfXg3TbjmQvIEzE7VPTFiXWuCq6pv4yX83Mio1lmX/O4cRKTHMW5sLON2noAkMIr6mFjgRET+w1rJ5fyVfmdS/09ecPDSJiNAghvaNdnZN+OyPsPpZZ8zb1JvhzPshNrXD6/vGhlNc3XjMsT68cBv5lfU8euNUosJCuHRyfx5+fzslgyNZVVpKWnwEKXHdNylCRI5OLXAiIj7i8VhW7y0DIKesjqr6Zsb1j+v09bERoXzwtVF8veYf8OfJsOoZmHS90+J28SNHTN7ASeCOtQWurrGF55ft5bqTBjJ1kDMB4pJJAwB4bG0D8zfkc+7YI7+uiHQ/tcCJiPjIf9fm8r1X1/Ha3bMocVvCxqZ1MoGrLoTP/0T6yqecbbAmXw9n/MBZkLeT+saGs9Zds81aC3DU7tt9ZbW0eCwnD00+UDYoOYrJAxNYu6+cCyf046cXj+10DCLSPZTAiYj4yNvr8gD4aGshocFBBBkY3e8oCVx1ESz+k7NvaUuD0+J2xv9A0tBjfv2+MeGU1jTQ4rFc+fclTBuceNTka29JLQADD1nj7ccXjuGFD1by8HVTCNX+pyI+pwRORMQHKuqa+DyrGIBPthW5y4LEEBkW3P4FNSWw5M+w/J/QXA8TroEzfwjJw7ocQ5/YcDzWmf26dl85W/ZXcu9Zw0lqs6acx2NZuCmfkOAgzh2byr4yJ4E7dJHeGUOSqB0RpuRNxE+UwImI+MCizQU0tVjmjuvHgk355JTVMntUO2un1ZbCkr86Oyg01cKEq+DMH0GfEccdQ98YZ7mSD7cWANDQ7OHFZdl88+wReDyWRVsK+MuHO9iUV0lcRAjrHjyPvaW1RIUFH3nhYBHxOSVwIiI+MH/DfgYkRPKtOcNZsCmfykMnMNSWwhePOolbYzWMu9xJ3FJGd1sMfWOdBG7R5gKMgemDE3nui2ySosN58vNd7CqqYVBSFBeM78d7G/PJq6hnX2ktg5KitMuCSA+jBE5ExMuq6pv4bEcRt56Swdi0OFJiwymsamBs/zioK4elj8HSv0NDJYy9DGbfDyljuj2O1gRu7b5yRqbEcu9Zw7n1mRX8+M0NTBgQz1+vn8IF4/uxdl85723MZ0teJXtLaxmcHN3tsYjI8VECJyLiZdsLqmhqscwalowxhjNH9mXBqu1M2/0EvP4PaKiAMV9x1nHrN95rcfRxu1CthckDEzhzZF9+/pWxjEyNPRAbwGh3Zuzm/ZXsK63jdHcBYRHpOZTAiYh42c7CGgCG942F+kr+N/YdfhX7OOFLKmHURU6LW9pEr8cRHR5CVFgwtY0tTBrobD5/66lDDjsvJjyEQUlRfL6jmLqmlsMmMIiI/ymBExHxsqyiahJCGknf9Hf44q8k1JXByAucxK3/ZJ/G0jc2nOySWiYPTDjieaP7xfLBFmeygxI4kZ5HCZyIiDc11jBix1Nkhr5C0EeVMOI8J3EbMM0v4fSNCaewsoGRqTFHPG9MWhzvb3YSuEPXgBMR/1MCJyLiDY21sPIp+PxPXF1bzOaok0i44XeQPt2vYZ0+oi/DU2IIOcr6bWPa7BCRnhjp7bBE5Bh5LYEzxkQAnwLh7uu8bq190BgzBHgZSAZWAV+11jYaY8KB54BpQAlwrbV2j3uvB4A7gBbg29bahW75XODPQDDwpLX2d956PyIindJUByufhs//BDWFtAyZzTXbZnPaqRcxNn2kv6PjvnM6t55c6xZf/eIiiAjtYLFhEfEbby6h3QCcba2dBEwG5hpjTgYeAh6x1g4HynASM9zvZW75I+55GGPGAtcB44C5wGPGmGBjTDDwKHABMBa43j1XRMT3muph6ePOJvMLf+ys33bbAnac/29WeUYyLOXIXZY9TXpi5IHJDCLS83itBc46OyVXu09D3S8LnA3c4JY/C/wc+DtwqfsY4HXgb8aZ034p8LK1tgHYbYzJAma452VZa3cBGGNeds/d7K33JCJymOYGWP0cfPb/oGo/DD4NrnoKMk4DYOf6/QAM6xtYa6kFBRluPSWD/gnqPhXpibw6Bs5tJVsFDMdpLdsJlFtrm91TcoAB7uMBwD4Aa22zMaYCp5t1ALC0zW3bXrPvkPKZXngbIiKHa26ENf92ErfKXBg0C654AoaccdBpWYXVGAND+wRWCxzA/5w/yt8hiEgHvJrAWWtbgMnGmATgTaD79oQ5BsaYu4C7AFJTU8nMzPRHGD1SdXW16sNHVNe+4826Np4m+uV/xODs14hoKKIibjR7Jv4fZYmTINsD2Qe/7heb6kmOMCxb8plX4vEn/U77juraNwKpnn0yC9VaW26M+RiYBSQYY0LcVrh0INc9LRcYCOQYY0KAeJzJDK3lrdpe01H5oa//BPAEwPTp0+3s2bO74231CpmZmag+fEN17TteqeuWJlj3Enz6ByjfCwOmw1mPEz9sDpOOsE/o79d9xvhB4cyePaPDcwKVfqd9R3XtG4FUz16bxGCM6eu2vGGMiQTOBbYAHwNXuafdArzlPp7nPsc9/pE7jm4ecJ0xJtydwToCWA6sAEYYY4YYY8JwJjrM89b7EZETVEszrHkB/jYd5n0LopLhxtfhzg9g+DnQQfLm8Vh+v2Arm/dXMmVgoo+DFpHezpstcGnAs+44uCDgVWvtO8aYzcDLxphfAWuAp9zznwL+7U5SKMVJyLDWbjLGvIozOaEZuNftmsUY801gIc4yIk9bazd58f2IyImkpRk2vAaf/h5Kd0HaJLj+FRh5fodJGziJ2yc7ivjnp7tYsrOE62cM5J6zhvkwcBE5EXhzFup6YEo75bv4chZp2/J64OoO7vVr4NftlM8H5h93sCIirVqaYeN/nMStJAtSJ8B1L8KoC4+YuLV6/NOd/H7BNvrGhvN/l4zj5lmDD2wSLyLSXbQTg4gIuInb6/DJ76F0J6SOh2v+DaMvhqDOjzZZsDGfyQMTePXrswgL8eZSmyJyIlMCJyIntpZm2PCqMzmhdJfT4nbt8zDqomNK3AAqapvYkFvBfXNGKHkTEa9SAiciJ6aWZlj/ipO4le2GfhPg2hecrtJjTNxaLd1dgrVwyrA+3RysiMjBlMCJyImlpclN3B52E7eJxzTG7Ui+2FlCZGgwkwcmdE+sIiIdUAInIieGliZY9zJ89jCU7XFmlV73Eoy64LgTt1ZLdhZz0pAkdZ+KiNcpgROR3q25wU3c/h+UZ0PaZLj+ZRg5t9sSN4DCqnq2F1RzxdT0bruniEhHlMCJSO/UUA2r/gVf/M3ZZD5tMlzw+6Ou49YVmdsK+etHWQCcqvFvIuIDSuBEpHepLSVj90uw7FaoK4OM0+HSR2HY2d2euAG8sz6Pb764hv7xEfzqsvFMSI/v9tcQETmUEjgR6R0q8+CLR2HlM2Q01TiTEk77Hgw8yWsvWVBZz0/+u5FJAxN4Teu+iYgPKYETkcBWshMW/8kZ5+ZpgQlXsSLsVE66+JajXtpVjyzazpKdxZTVNlHf1MIfr5mk5E1EfEoJnIgEHmthz+dOi9v2BRAcBlNvhlO+BYkZ1GRmeu2lWzyWpz/fTXR4CDERIfz2igkM6xvjtdcTEWmPEjgRCRzNjbDpDWdiQv4GiEqGM34AJ90Jsak+CWF7QRVVDc384rJxXD5FM05FxD+UwIlIz1dbCiufguVPQnU+9B0NX/kLTLwGQiN9GsrKPaUATB+c5NPXFRFpSwmciPRM1kLuKljxlNPq1lwPw+bAZY86370wo7QzVmaXkRIbTnqibxNHEZG2lMCJSM/SUA0bXnNa3PI3QFgMTL4RZnwNUsb4OzpW7iljekYixk8JpIgIKIETkZ6iYBOsfBrWvQKNVZA6AS5+BCZcDeGx/o4OgPyKenLL67j9tCH+DkVETnBK4ETEf+rKYON/YM0LkLcagsNh/BUw/XZIP8lv3aQdWZndOv4t0c+RiMiJTgmciPiWpwV2fewkbVvfhZYGSBkH5/8GJl0PUT13csDSXSVEhAYxtn+cv0MRkROcEjgR8Y2ibc5iu+tehqo8iEyEabfC5BsgbVKPa207VHVDM2+tyeO8sf0IDdaivSLiX0rgRMR7yvY4XaQb34CCjWCCYPi5cMHvYORcCAn3d4Sd9vrKfVQ1NGv8m4j0CErgRKR7Ve6HTW86iVvuSqds4Ey44Pcw9jKfLbjbWaU1jazcU8q5Y1M7nFnq8Vj+tWQPUwYlMHlggm8DFBFphxI4ETl+NSWw5S2npW3P54CFfhPhnP+DcZdD4mB/R9ihH7+xgQWb8pk7rh9/uHoisRGhlNU0cuXjS7hiygC+efYI3lyTy56SWr533ih/hysiAiiBE5Guqq90JiFs/I8zKcHTDMkjYPb9MO4K6DvS3xEe1YacChZsymdGRhKLthRwwz+X8fo3ZvHEZ7vYVVTDw+9vZ2dRDfPW5TFlUAIXjO/n75BFRAAlcCJyLBprYcdCJ2nb/r4zgzR+EMz6Jky4ClLH9/jJCG39cdE2EqJCefLW6SzJKubu51fzs/9u4u31eVw0IY0Wj+XNNbnMGJLE07eepMkLItJjKIETkSNrqoesD5ztrLYtgKYaiEmF6bfB+KsgfXpAJW0Ajc0e/vHJTj7eVsSP5o4mLiKUuePTuGHmIF5ctpcgA989dyQDkyJ5f1MB54xJJTIs2N9hi4gcoARORA7X3Ai7Mp2Wtm3zoaESIpOczePHXwGDT4WgwExo6ptauOKxJWzeX8mFE/px26kZB4795KIxbMytYOqgRIanxADwlUn9/RSpiEjHlMCJiKOlGfZ86kxE2PI21JdDRDyMuQTGXw5DzoTgUH9Hedw+21HM5v2VPHTlBK49adBBx6LCQnjr3lP9FJmISOcpgRM5kXlaIHuJ0z26eR7UFjubx4++yJmIMOxsCAnzd5Td6qOthUSHBXP5lPR2j2uTehEJBErgRE5EJTth7Yuw7iWozIWQSBg110naRpwLoZH+jtArrLV8vLWQ00f0JSxEExJEJHApgRM5UTRUw+a3YO0LkL0YMDB8Dpz7C2dXhPAYf0fodZv3V5JfWc/Zo1P8HYqIyHFRAifSm1kLe5fC2udh03+hsRqShsLZP3U2jo8f4O8IferjrYUAzB7d18+RiIgcHyVwIr1RZZ7TPbrmBSjdCaHRzo4IU26EQbMCbtmP7vLh1kImpseTEhvh71BERI6LEjiR3qK5Aba9B2ueh50fgvXAoFPg9O/D2EtPiC7SI6lrbGF9TgV3nznU36GIiBw3JXAigW7/eidp2/Aq1JVBbH847bsw+UZIHubv6HqMDbkVtHgsUwYm+jsUEZHjpgROJBDVlsL6V52xbfkbIDjMWfpjyk0w9KyAXWTXm9buKwNg8qAE/wYiItINvJbAGWMGAs8BqYAFnrDW/tkYkwS8AmQAe4BrrLVlxll86c/AhUAtcKu1drV7r1uAn7i3/pW19lm3fBrwLyASmA/cZ6213npPIn7laYGdH8GafztdpS2NkDYJLnwYxl8JUUn+jrBHW7O3nPTESPrEhPs7FBGR4+bNFrhm4PvW2tXGmFhglTFmEXAr8KG19nfGmPuB+4EfARcAI9yvmcDfgZluwvcgMB0nEVxljJlnrS1zz/kasAwngZsLvOfF9yTie8VZztIf616Cqv3OllbT73AmJPSb4O/oAsbafeVMG6zuUxHpHbyWwFlr9wP73cdVxpgtwADgUmC2e9qzQCZOAncp8JzbgrbUGJNgjElzz11krS0FcJPAucaYTCDOWrvULX8OuAwlcNIbNFQ5y36seR72LQUTBMPPhQt+76zZ1st2R/C2gsp69lfUM3lggr9DERHpFj4ZA2eMyQCm4LSUpbrJHUA+ThcrOMndvjaX5bhlRyrPaae8vde/C7gLIDU1lczMzK6/mV6murpa9eEjR61ra4mv2Ey//A9IKVxCsKee2sgB7B96CwWps2kMT4JCoHCJr0L2qrJ6D1EhhvCQ7l/S5NC6XlXQ7Dwo3k1m5t5uf70TlT4/fEd17RuBVM9eT+CMMTHAf4DvWGsr2+4zaK21xhivj1mz1j4BPAEwffp0O3v2bG+/ZMDIzMxE9eEbHdZ16S5nQsL6l6BsD4TFwuRrYMpXiUo/iWHG0NvmkrZ4LCf/9kOmD07k7zdNO1CeX1HPrqJqThne57juf2hdf/HeFkKDd3PTxbOJCNUEj+6izw/fUV37RiDVs1cTOGNMKE7y9oK19g23uMAYk2at3e92kRa65bnAwDaXp7tluXzZ5dpanumWp7dzvkjPV1cOm96EdS87XaQYGHIGnHk/jL0EwqL9HWGXWGs7tRn8upxyiqoaeG9jPlv2VzIyNZZnFu/mkUXbqWls4bW7Z3FSRhLWWhZszOfvn+wkIzmaW07J6PQ4tpyyWt7bkM+7G/azdl85MzKSlLyJSK/hzVmoBngK2GKt/WObQ/OAW4Dfud/falP+TWPMyziTGCrcJG8h8BtjTOun9nnAA9baUmNMpTHmZJyu2ZuBv3rr/YgcL+Nphm0LnMkI296DlgboMwrO+TlMuCbgt7Wy1nLXv1dR39TCYzdOJTYitMNzP9lWhDEQHRbCHxdtJyI0mLfX5XH26BQ251Xyy3c288KdM/n2S2v4eFsRQ/tE8/G2Quaty+OfN0/n3LGpHd7b47G8tq2Rdxd8DMC4/nH84PxRXD09vcNrREQCjTdb4E4FvgpsMMasdct+jJO4vWqMuQPIBq5xj83HWUIkC2cZkdsA3ETtl8AK97xftE5oAO7hy2VE3kMTGKSn8XicFrZN/2XWmpehqQKikmH6bTDpOkibHJDbWnk8ltqmFmLCv/wIydxWxKLNBQDc9OQynrt9JvFR7SdxmduLmDwwgdOG9+GvH2UBcP8Fo/n6GUN5c00u33t1Hef88ROKqxv52cVjuXnWYBpbPFz0l8/546LtzBmdQlDQ4fXW2OzhnhdW88HuJq6Zns49s4eT0ScwWzNFRI7Em7NQPwc6+ss0p53zLXBvB/d6Gni6nfKVwPjjCFOk+7VJ2tgyz1n6IySC8sSppJzzbRh+DgR33DrVk9U3tXDz08tZnV1Gs8dy/rhU/ue8UQztG8NDC7YyKCmKH184mm+9tIa/fLSDn1489rB7lNY0sj6nnPvmjODWUzL4YmcJV0xN54aZgwC4bPIAnl2yh635VTzx1WnMGeO0toUEB/HNs4bz/dfWsWhLAbHhIeSW13Hl1PQDydxzX+zhgy0FXD86jN9cObFT3bkiIoFIOzGIdIeWJti3DDbPOyhpY/g5zibyI89n8xerSBk129+RHpeXlu9l+e5Sbj0lg/CQIF5YtpeFmz4lLT6C/RX1/OX6Kcwdn8bJQ/fy+Y7idu/x2Y4irIXZo1JIiArj9W+cctDxoCDDc7fPpKqhifTEqIOOXTq5P3/9aAfffWUttY0tALy2MoeHrppIYlQof/0oi9NH9OH8jDolbyLSqymBE+mq8r2Q9SFkfQC7P4WGysOSNsJj/R1lt6lvauHxT3Yyc0gSP79kHAB3uV2ei7OKmTo4kYsnpAEwa1gyv1+wjZLqBpLb7Hzg8Vj+uyaXpOgwJg6I7/C14qNC2+1+DQkO4kdzR/PgvE3cN2cEidFh/OqdzZz3yCeM6hdLZX0TP75wDAXbVnfzuxcR6VmUwIl0VlMdZC+GrI+cpK14m1Mel+4kbMPnwLCze1XSBlBV30R2SS2LNhdQUNnAI9dOPnAsOSacO08fyp2nDz3omllDkwFYuquUiyY6SZ21ll++u5mPtxXxo7mj2x3D1hkXTEjjAjdRBJg9qi9/fH87r67cx7XTBzImLY6CbV26tYhIwFACJ9KR5gbIW+MkbXsWO9+b6yE4HDJOhWm3OK1tfUYG5ESEo8kqrObBeRtZvruUphZnucaZQ5IOJGdHMmFAPDHhISzZWXwggXt68R6eWbyH207N4O4zhx7lDp2XEhvB766cyHfPHUlStHaoEJETgxI4kVZ15ZC7yhnLlr0EclY4CRtA39Ew7TanlW3wqRAWdcRb9Qa/mb+F9TkV3H7aEKYMTCAiNJgZQ5I6NbYsJDiIGUOS+GJXCQC7i2v4/YKtnDMmhZ9eNNYr49NS4yK6/Z4iIj2VEjg5MbU0Q9FWyF0J+1Y4yVprl6gJgn4TnQ3jB58Cg2ZB9NFbnXqTrMJqPtpayHfOGcF3zhnZpXvMGprMR1sLWZVdxu8XbCUsJIhfXz6hy12nIiLyJSVw0vt5WqB4h9Md2vqVvwGa65zjUcmQfhJMvNr53n8qRMT5N2Y/e2bxbsJCgrjp5MFdvsep7nZYV/7d2bv1oSsnqJVMRKSbKIGT3sXjcfYWbZus7V8HTTXO8dBoSJsE02+H/pNhwDRIGtorx7B1VWlNI/9ZncMVUwbQp80M0mM1tn8cL945k6LqBhKiwjhjxPHtbyoiIl9SAieBy1pn8/dDk7WGSud4SITTFTrlJug/xfnqMwKCtB/mkTz2cRaNzR7uOG3Icd/reDelFxGR9imBk8BRVwY5q5zxajkrnAkH9eXOseAwSB0PE67+MlnrOxqC9Svens15lTz1+W4KKusZkBDJt+YMJz0xir0ltTz3RTZXTUtnRGrvWg5FRKQ30V836blqSpylO1qX8SjYCFhnkkHfMTD2Eme8Wv8pkDIWQrSERGeU1jRy+79WUNPYzNC+Mfx3bS5vrs3lwvH9yK+sJygIvnfuKH+HKSIiR6AETnqOlmZnCY/tC5yFcgs3O+UhkTBwBpz1Yxh0spOw9bLFcn3F47F8/9W1lNY08sY9pzB+QDx55XX89aMs3t+UT0lNI9+eM4J+8ZpsICLSkymBE/+qKYGdH8L2hZC1COorICjUWSh3wlUw+DQnYVPrWofqGlv49fzNlNY0Ehseyv9dOo6I0GBqGpqxQEy488/cWssv3nF2QvjFpeMY725l1T8hkt9eMYFfXzaePSU1DE6O9uO7ERGRzlACJ75lrbOEx46FsP19Zx0264HovjD6Yhg5F4adpRa2Y/Dh1gKeX7qXwclRZJfUMrhPFHecNoQrHlvC3tJarpg6gAkD4lm+p5Q3Vudyx2lD+Go7y4MEBRmG9o3xwzsQEZFjpQROvK+hGnZ/4rSy7VgEVXlOef8pcMYPYeR5kDYFgoL8G2eA+mRbEfGRoXz4vTO5+/lVPPbxTnLL6thWUMW5Y1N5bVUOLyzbC8DXzxzK/XNHe2UnBBER8R0lcOIdjTXOWLZNbzpJW3M9hMc5rWsjznf2EI1N9XeUAc9ayyfbizhtRB9CgoO4/4LRnP+nz3hh2V6umpbOw1dPorqhmar6JsKCg0g+jnXdRESk51ACJ92nsRZ2vO8kbdsXOjsdxPSDabfC6IucLamCQ/0dZa+yZX8VhVUNnDmyLwDDU2K58/QhLNyYz08vGgs4Y+Bax8GJiEjvoE91OT5Ndc6M0U1vwrYFzo4H0SnO4rnjLndmjWrhXK/5ZHsRALPdBA7ggQvG8MPzRxOsPUdFRHotJXBy7JobIOtDN2mbD43VENUHJl3rJG2DT1XS5gPWWj7eVsiYtDhSDtljVMmbiEjvpgROOqehGrKXwKY3YOu7znZVkYkw/konacs4Xbse+Ii1lic/281zS/ewr7SOb88Z4e+QRETEx/QXV9rn8cC+pbDlHaeLtHg7YCEi3tkBYdzlMORMjWnrBo3NHgoq6xmYFHVQeX1TCy8v38u8dXmkxkUwrn8c543rx4vL9vKvJXs4ZVgy984ezhVT0/0UuYiI+IsSOPmSxwP718K292D9K1Ce7ewxmnG609KWPg0yztCiut3IWst9L69h4aZ8HrpyIldOTefTHUXM37CfD7YUUlrTyNi0OLbmV7FgUz4Pv78dgDtOG8JPLhqj5UBERE5QSuBOcOH1RbDhdXedtvehOh8wMOR0OOt/YfSFWlTXi15flcN7G/MZkBDJD15fz58+2EFueR2xESGcNSqFG2YO4uShyQAUVtXz3oZ8goMMN84cpORNROQEpgTuRNTcADs/gqWPMWv3p05ZWCwMn+PshDDiPIhO9m+MvUROWS07i2roGxPO57lNPPXUMoKMYVjfGOqbW3hrTS4zhyTx7O0z+N83N7K7uJofzh3FBePTCAs5eGHjlNgIbjklwz9vREREehQlcL2Zp8XZEL5omzPpoGyPs43V3qXQVAux/dk15KsMPfdOSB2vSQjdaF9pLf/z2jqW7S49qHxwci3RYSEs211CVFgIY9Li+OO1k4kIDeb/XTPJT9GKiEig0V/sQOZpccaple2B2lJn94OKHCjdBWW7oWg7NFZ9eX5wGPQd5azRNmwODJ/D3s8WM7T/ZH+9g17rkUXbWZ9TwQ/OH8X0wYmU1DSSk7WZr102W12fIiJy3JTA+Zm1lqW7SpmUGkJUSzVgvzxYud+ZVBAcColDoKbISdYaqqB8L+z6GOrKDr6hCYL4gZA0xFmXbeBM6DcBIhIguo9mjfpAXnkd89blcfOsDO49a/iB8sySbUreRESkWyiB87X6CijYBHsWY3dnUpGXxaSGMqJMQ+fvERzmLJw7cq6zaG7SEGf3g9BIiEnVLFE/+9eSPVjg9tMy/B2KiIj0UkrgutlHG/YSF9LM9AGRUFMI5fugYBO2YAPkb8CU7Tlwbk74CJbXDSchOY2NFWGUtURxzvj+zBySxJ7iWsLi+jB43CywHqflLboPJA11ErUuyCqs5hfvbObiCWlcc9LA7nnDckDrxvIvLdvLRRPSSE+MOvpFIiIiXaAErhs1t3jIePNihnqyDyr3YNjj6cdmO4jNnhlstoNZ7xlGdVMCt56awQMXjGZMRT0/+s96/rW6mOC1hhaPJcjAjz1NTBucyPwtfWls9tBis1i5p4yGZg9/u2EK4/rHdxhPfVMLLy3fy6rsMsKCg5i/cT/1TR6+2FnMyH6xTB6Y4OUaOXEs3VXCz+dtYmt+Falx4Xx7zvCjXyQiItJFSuC6UUhwEPFzvsf/+3ANnuBwiOrLZ4XhFIYPZu704SREhdInIpRvD0pgeEoMseEhB8ZE9U+I5LnbZ/DexnyW7y7llGHJvLE6l1+9uwWAsJAgIkOD8XgsEwfGs6uohhufXMYPzh/FprxKEiJDuXBCGh9uKeTlFXsJDwmiqr6ZkppG0hMjaW6xnDGiLz+cO4pbnl7BvS+sZv63T/dndfU4zS0e9pTUkLmtiBeX76W0ppFThiUzrG8M4SFB7CyqIbukhjNG9mXu+H54PLA1v5L3NuazaHMB6YmR/PGaSVw8sf9hS4CIiIh0JyVw3Sz51Fs5d/BlXPfEUpJCwrjj4iFcM30g0eFHr2pjDBdOSOPCCWkAnDMmlddX5+DxWC6cmEZcxJcTEPaW1HL9P5fyv29uJDY8hNqmFh7L3AnA7FF9iY0IxWMtN80czKxhB6/p9rcbpnD5Y0t4flk24zSmnvLaRh79OIvnl+6lrqkFgGmDE5kyMJEvdhbz3sZ8rIXUuHD6xUfypw928KcPdhy4PiU2nG+dPZx7Zg8nMizYX29DREROIErgvGBiegJfPDCH6LBgQoK73hITFGS4Znr7Y9UGJUcx/77T2Vday+h+sZTWNvLhlkLG949nQnrH3aoAUwYlclJGIm+uyWXsFHvEc7uqsdnD/A37ySmrJTYilNgI51dtwcZ81uWUc8aIvtwwcxBTBiV65fU7Y3dxDc99sYfXV+VQ3dDMZZMHcPqIPkxMd1pIW1lraWj2EBHqJGf7SmtZsaeUqLAQ0hMjGZsWR1CQMmEREfEdJXBeEh/p/eU64iNDiR/gJGspsRFcP2NQp6+9fEo6P35zA9mVEd0Wj8djWbq7hE+2F/HfNbkUVB4+s7ZPTDhTByUwf8N+3liTy5M3T+es0SkHnVNV38RrK3OICQ8hLjKEfaV1FFU3UNfYQmhwEDERIWzIKWd9TgX94p1N3i+bMoBZQ5MPWqbD47Fs3l/Jkp3FZJfUctHENE4eksy+slr++dkuXlq+jyADc8encc/sYYxJi2v3fRljDiRvAAOTog7beF5ERMSXvJbAGWOeBi4GCq21492yJOAVIAPYA1xjrS0zzl/dPwMXArXArdba1e41twA/cW/7K2vts275NOBfQCQwH7jPWuud5qRe6KIJafx83iaW5DUzt6Ke3PJapg1OOqZ7VNY3kV9R74yx81i+8/JaPtpaSGiwYdawPjx0ZQYnD02mpqGZqvpm6ptbGN43hpDgICrrm7jhn0u554XV/GjuKEprmzh5SBIzhybzzRfX8Mn2ooNeKyLUGQPY2OyhprGFIX2iOWt0CkVVDby3MZ9XV+YQFxFCYnQYcRGhRIYFs72givLapgPXv7BsL2HBQTS2eAgOMtw0cxD3nj2clNjuS2JFRER8wZstcP8C/gY816bsfuBDa+3vjDH3u89/BFwAjHC/ZgJ/B2a6Cd+DwHScFW5XGWPmWWvL3HO+BizDSeDmAu958f30KvFRoZw9OoWPt+Zzxu8/prHFw91nDuOes4bxxc4SMrcVsTq7jHPGpvCts0dQVd/M4qxiPttRzOb9leSU1VJV3wxAWHAQcZEhlNc28dOLx3LtSQOJaTPmLyI0mOSY8INePy4ilGduncFVjy/h529vBuAvwPgBcWzMreRXl43nzJF9qahrYmBiFPFRX7ZoNrV4CG3TNV3f1ML8DftZvbeMqvpmKuuaqG5o5pwxqZw6PJlThvUhPjKUd9bvZ8v+Sob1jeGUYclk9In2Yg2LiIh4j9cSOGvtp8aYjEOKLwVmu4+fBTJxErhLgefcFrSlxpgEY0yae+4ia20pgDFmETDXGJMJxFlrl7rlzwGXoQTumNx08mA+2JLPVdMGAPD4Jzv5x6c7sRZiwkMY3S+WRz/eyXNfZB9I1hKiQpkyMIGTMhJJT4wkJTaCLfsr2VZQxdfPGHbYhIkj6RsbzsLvnEFueR0pseH86YMdPL14N7eeksFNJw8GoL0RgKGHjCuMCA3miqnpXDE1/Yivd9W0Ix8XEREJFMabvY5uAvdOmy7UcmttgvvYAGXW2gRjzDvA76y1n7vHPsRJ7GYDEdbaX7nlPwXqcBK/31lrz3HLTwd+ZK29uIM47gLuAkhNTZ328ssve+X9BqKqqmpiY50B+0vymsmr9jAuOZgRiUGEBBm2lLSQua+JgbFBjOsTzOC4IIK8uB1URYMlLoxeueVUdXU1MTExRz9Rjpvq2jdUz76juvaNnljPZ5111ipr7fRDy/02icFaa40xPhmzZq19AngCYPr06Xb27Nm+eNmAkJmZSWt9zG7n+GzgG74Lp1drW9fiXapr31A9+47q2jcCqZ59vdpogds1ivu90C3P5eDesnS37Ejl6e2Ui4iIiPR6vk7g5gG3uI9vAd5qU36zcZwMVFhr9wMLgfOMMYnGmETgPGChe6zSGHOy2xV7c5t7iYiIiPRq3lxG5CWcHrg+xpgcnNmkvwNeNcbcAWQD17inz8dZQiQLZxmR2wCstaXGmF8CK9zzftE6oQG4hy+XEXkPTWAQERGRE4Q3Z6Fe38GhOe2ca4F7O7jP08DT7ZSvBMYfT4wiIiIigUg7bouIiIgEGCVwIiIiIgFGCZyIiIhIgFECJyIiIhJglMCJiIiIBBglcCIiIiIBRgmciIiISIBRAiciIiISYJTAiYiIiAQY42yCcOIwxhThbOMljj5Asb+DOEGorn1Hde0bqmffUV37Rk+s58HW2r6HFp5wCZwczBiz0lo73d9xnAhU176juvYN1bPvqK59I5DqWV2oIiIiIgFGCZyIiIhIgFECJ0/4O4ATiOrad1TXvqF69h3VtW8ETD1rDJyIiIhIgFELnIiIiEiAUQLXCxljIowxy40x64wxm4wx/+eWf2aMWet+5Rlj/uuWG2PMX4wxWcaY9caYqW3udYsxZof7dYuf3lKP1IV6nm2MqWhz7Gdt7jXXGLPN/Rnc76e31GMdoa7nGGNWu/X5uTFmuFsebox5xa3PZcaYjDb3esAt32aMOd9Pb6lH6kI932qMKWrzO31nm3vps+MIjlDXZ7t1vdEY86wxJsQt1+d0F3ShngPnc9paq69e9gUYIMZ9HAosA04+5Jz/ADe7jy8E3nOvOxlY5pYnAbvc74nu40R/v7+e8tWFep4NvNPOfYKBncBQIAxYB4z19/vrSV8d1TWwHRjjlt8D/KvN48fdx9cBr7iPx7r1Gw4Mces92N/vr6d8daGebwX+1s599NnRtbo+BdgHjHTLfwHc4T7W57Rv6jlgPqfVAtcLWUe1+zTU/Tow2NEYEwecDfzXLboUeM69bimQYIxJA84HFllrS621ZcAiYK6P3kaP14V67sgMIMtau8ta2wi8jPMzEdcR6toCcW55PJDnPr4UeNZ9/Dowxxhj3PKXrbUN1trdQBZO/QtdqueO6LPjKDqo6xag0Vq73S1fBFzpPtbndBd0oZ470uM+p5XA9VLGmGBjzFqgEOcf97I2hy8DPrTWVrrPB+D8b6RVjlvWUbm4jrGeAWa5TfnvGWPGuWWq507ooK7vBOYbY3KArwK/c08/UKfW2magAkhGdX1Ux1jPAFe6XXqvG2MGumWq5044tK6B5UCIMaZ1IdmrgKPVqer6KI6xniFAPqeVwPVS1toWa+1kIB2YYYwZ3+bw9cBLfgmslznGel6NsyXKJOCvHL1lTtrooK6/C1xorU0HngH+6McQe4VjrOe3gQxr7UScP4zPtnNL6cChdQ2Mw+nyf8QYsxyowmktkuNwjPUcMJ/TSuB6OWttOfAxbpO6MaYPzi/wu21Oy+Xg/32ku2UdlcshOlPP1trK1qZ8a+18INQ9T/V8DNrU9QXApDatnq/gjG2BNnXqDk6OB0pQXXdaZ+rZWltirW1wy58EprmPVc/HoO3nh7X2C2vt6dbaGcCnOOMPQZ/Tx60z9RxIn9NK4HohY0xfY0yC+zgSOBfY6h6+CmeAZn2bS+YBN7uznE4GKqy1+4GFwHnGmERjTCJwnlsmHHs9G2P6ueOwMMbMwPn3VwKsAEYYY4YYY8Jw/mc4z2dvJAB0UNdbgHhjzEj3tNYycOqvdTbeVcBH1lrrll9nnFmqQ4ARON0pwrHXszsGq9UlfFn/+uw4io4+P4wxKW5ZOPAj4HH3En1Od8Gx1nMgfU6H+PPFxWvSgGeNMcE4v3yvWmvfcY9dx8HjVwDm48xwygJqgdsArLWlxphf4vziAvzCWlvq7eADyLHW81XAN4wxzUAdcJ2bVDQbY76J86EbDDxtrd3kk3cQONqta2PM14D/GGM8QBlwu3v+U8C/jTFZQCnOzwNr7SZjzKvAZqAZuNdaqy6qLx1rPX/bGHMJTl2W4sxK1WdH53RU138wxlzslv3dWvuRe74+p7vmWOs5YD6ntRODiIiISIBRF6qIiIhIgFECJyIiIhJglMCJiIiIBBglcCIiIiIBRgmciIiISIBRAicicghjTLIxZq37lW+MyXUfVxtjHvN3fCIiWkZEROQIjDE/B6qttQ/7OxYRkVZqgRMR6SRjzGxjzDvu458bY541xnxmjMk2xlxhjPm9MWaDMWaBMSbUPW+aMeYTY8wqY8zCQ3YvEBHpEiVwIiJdNww4G2cbqeeBj621E3BWcL/ITeL+ClxlrZ0GPA382l/Bikjvoa20RES67j1rbZMxZgPO9joL3PINQAYwChgPLHK3VwwG9vshThHpZZTAiYh0XQOAtdZjjGmyXw4q9uB8vhpgk7V2lr8CFJHeSV2oIiLesw3oa4yZBWCMCTXGjPNzTCLSCyiBExHxEmttI3AV8JAxZh2wFjjFr0GJSK+gZUREREREAoxa4EREREQCjBI4ERERkQCjBE5EREQkwCiBExEREQkwSuBEREREAowSOBEREZEAowROREREJMAogRMREREJMP8fGELkVuzA6jwAAAAASUVORK5CYII=\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 624,
       "height": 371
      }
     },
     "output_type": "display_data"
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00009-ccbe00c0-7390-4534-8ae5-60ddd762f361",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "100100d9",
    "execution_start": 1632688160765,
    "execution_millis": 132,
    "deepnote_cell_type": "code"
   },
   "source": "l = [1,2,3,4,5]\nl[2]",
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 16,
     "data": {
      "text/plain": "3"
     },
     "metadata": {}
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": "<a style='text-decoration:none;line-height:16px;display:flex;color:#5B5B62;padding:10px;justify-content:end;' href='https://deepnote.com?utm_source=created-in-deepnote-cell&projectId=465bb6a2-b771-4fe2-b2d2-aed723f7eb7d' target=\"_blank\">\n<img alt='Created in deepnote.com' style='display:inline;max-height:16px;margin:0px;margin-right:7.5px;' src='data:image/svg+xml;base64,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' > </img>\nCreated in <span style='font-weight:600;margin-left:4px;'>Deepnote</span></a>",
   "metadata": {
    "tags": [],
    "created_in_deepnote_cell": true,
    "deepnote_cell_type": "markdown"
   }
  }
 ],
 "nbformat": 4,
 "nbformat_minor": 2,
 "metadata": {
  "orig_nbformat": 2,
  "deepnote": {
   "is_reactive": false
  },
  "deepnote_notebook_id": "338b02c1-cb72-4ae0-bb77-896af0655617",
  "deepnote_execution_queue": []
 }
}