diff --git a/01_AutoRegressiveIntegratedMovingAverage/01_AutoRegressiveIntegratedMovingAverage.ipynb b/01_AutoRegressiveIntegratedMovingAverage/01_AutoRegressiveIntegratedMovingAverage.ipynb index 9e4f5bb..8ed0fc8 100644 --- a/01_AutoRegressiveIntegratedMovingAverage/01_AutoRegressiveIntegratedMovingAverage.ipynb +++ b/01_AutoRegressiveIntegratedMovingAverage/01_AutoRegressiveIntegratedMovingAverage.ipynb @@ -1570,7 +1570,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/04_GrangerCausality/04_GrangerCausality.ipynb b/04_GrangerCausality/04_GrangerCausality.ipynb index e38a8c2..288e9f2 100644 --- a/04_GrangerCausality/04_GrangerCausality.ipynb +++ b/04_GrangerCausality/04_GrangerCausality.ipynb @@ -75,9 +75,18 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/prince.javier/opt/anaconda3/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n", + " import pandas.util.testing as tm\n" + ] + } + ], "source": [ "import pandas as pd\n", "import numpy as np\n", @@ -101,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -239,13 +248,13 @@ "[7116 rows x 4 columns]" ] }, - "execution_count": 74, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "data = pd.read_csv('data/Ipo_dataset.csv', index_col='Time');\n", + "data = pd.read_csv('../data/Ipo_dataset.csv', index_col='Time');\n", "data = data.dropna()\n", "data" ] @@ -909,7 +918,7 @@ } ], "source": [ - "data = pd.read_csv('data/La Mesa_dataset.csv', index_col='Time');\n", + "data = pd.read_csv('../data/La Mesa_dataset.csv', index_col='Time');\n", "data = data.dropna()\n", "data = data.drop(['Rain', 'ONI'], axis=1)\n", "# data['Rain'] = data['Rain'] - data['Rain'].shift(1)\n", @@ -1113,7 +1122,7 @@ } ], "source": [ - "jena_data = pd.read_csv('data/jena_climate_2009_2016.csv')\n", + "jena_data = pd.read_csv('../data/jena_climate_2009_2016.csv')\n", "jena_data['Date Time'] = pd.to_datetime(jena_data['Date Time'])\n", "jena_data = jena_data.set_index('Date Time')\n", "jena_data.head(3)" diff --git a/05_SimplexandSmapProjections/05_Empirical Dynamic Modelling (Simplex and SMap_Projections).ipynb b/05_SimplexandSmapProjections/05_Empirical Dynamic Modelling (Simplex and SMap_Projections).ipynb index de026dd..dfad64b 100644 --- a/05_SimplexandSmapProjections/05_Empirical Dynamic Modelling (Simplex and SMap_Projections).ipynb +++ b/05_SimplexandSmapProjections/05_Empirical Dynamic Modelling (Simplex and SMap_Projections).ipynb @@ -32,7 +32,7 @@ "id": "7k0gJqWkg6Le" }, "source": [ - "# Empirical Dynamic Modelling\n", + "## Empirical Dynamic Modelling\n", "\n", "Natural systems are often complex and dynamic (i.e. nonlinear), making them difficult to understand using linear statistical approaches. Linear approaches are fundamentally based on *correlation*, and thus, they are illposed for dynamical systems, where correlation can occur without causation, and causation may also occur in the absence of correlation.\n", "\n", @@ -302,7 +302,7 @@ "id": "SjJWEJzakQqN" }, "source": [ - "# Simplex Projection\n", + "## Simplex Projection\n", "\n", "\n", "- basic idea of simplex forecasting is that even for a chaotic time series, future values may be predicted from the behaviour of similar past values (not possible with random noise)\n", @@ -1347,7 +1347,7 @@ ], "source": [ "# Read Data and Normalize / Plot\n", - "smap_df = pd.read_csv(filepath_or_buffer='./data/ESM2_Data_noise.csv')\n", + "smap_df = pd.read_csv(filepath_or_buffer='../data/ESM2_Data_noise.csv')\n", "smap_df['R'] = (smap_df['R'] - np.mean(smap_df['R']))/np.std(smap_df['R'])\n", "smap_df['L'] = (smap_df['L'] - np.mean(smap_df['L']))/np.std(smap_df['L'])\n", "smap_df['T'] = pd.Series(np.arange(1,10001))\n", @@ -1583,7 +1583,7 @@ "id": "TtYgmhZsyhxb" }, "source": [ - "# Univariate, Multivariate and Multiview Forecasting of a Resource-Consumer-Predator Model" + "## Univariate, Multivariate and Multiview Forecasting of a Resource-Consumer-Predator Model" ] }, { @@ -1719,7 +1719,7 @@ } ], "source": [ - "uni_df = pd.read_csv(filepath_or_buffer='./data/5 specie data set.csv', index_col=False)\n", + "uni_df = pd.read_csv(filepath_or_buffer='../data/5 specie data set.csv', index_col=False)\n", "uni_df['Time'] = pd.Series(np.arange(2000))\n", "uni_df = uni_df[['Time', 'R', 'C1', 'C2','P1', 'P2']]\n", "uni_df.head()" @@ -2170,7 +2170,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Application to the Jenna Climate Dataset\n", + "## Application to the Jenna Climate Dataset\n", "\n", "We will now try to apply the Simplex Projection and SMap Projection to the Jenna Climate Dataset for 1 variable prediction" ] @@ -2203,9 +2203,9 @@ "outputs": [], "source": [ "# read training data and test data\n", - "train_df = pd.read_csv(filepath_or_buffer='./data/train_series.csv')\n", - "test_df = pd.read_csv(filepath_or_buffer='./data/test_series.csv')\n", - "val_df = pd.read_csv(filepath_or_buffer='./data/val_series.csv')\n", + "train_df = pd.read_csv(filepath_or_buffer='../data/train_series.csv')\n", + "test_df = pd.read_csv(filepath_or_buffer='../data/test_series.csv')\n", + "val_df = pd.read_csv(filepath_or_buffer='../data/val_series.csv')\n", "\n" ] }, @@ -2786,7 +2786,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/06_ConvergentCrossMappingandSugiharaCausality/ccm_sugihara.ipynb b/06_ConvergentCrossMappingandSugiharaCausality/ccm_sugihara.ipynb index 7602dcb..e3baceb 100644 --- a/06_ConvergentCrossMappingandSugiharaCausality/ccm_sugihara.ipynb +++ b/06_ConvergentCrossMappingandSugiharaCausality/ccm_sugihara.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Chapter 6: Convergent Cross Mapping / Sugihara Causality\n", + "# Chapter 6: Convergent Cross Mapping\n", "Prepared by: Prince Joseph Erneszer Javier" ] }, @@ -141,7 +141,11 @@ "from scipy.spatial import distance\n", "from scipy.interpolate import make_interp_spline\n", "from tqdm import tqdm # for showing progress bar in for loops\n", - "from scipy.stats import pearsonr" + "from scipy.stats import pearsonr\n", + "\n", + "### Just to remove warnings to prettify the notebook. \n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" ] }, { @@ -187,7 +191,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -745,7 +749,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 17, @@ -811,22 +815,7 @@ "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/prince.javier/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: RuntimeWarning: overflow encountered in double_scalars\n", - " \n", - "/Users/prince.javier/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: RuntimeWarning: invalid value encountered in double_scalars\n", - " \n", - "/Users/prince.javier/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:15: RuntimeWarning: invalid value encountered in true_divide\n", - " from ipykernel import kernelapp as app\n", - "/Users/prince.javier/opt/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:3524: RuntimeWarning: invalid value encountered in subtract\n", - " xm = x.astype(dtype) - xmean\n" - ] - } - ], + "outputs": [], "source": [ "# Checking convergence for various betas\n", "# params\n", @@ -879,14 +868,6 @@ "execution_count": 19, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/prince.javier/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:9: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", - " if __name__ == '__main__':\n" - ] - }, { "data": { "image/png": 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\n", @@ -973,7 +954,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 21, @@ -1303,7 +1284,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -1334,7 +1315,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -1358,7 +1339,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -1442,7 +1423,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -1466,7 +1447,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1541,7 +1522,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -1565,7 +1546,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -1640,7 +1621,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1664,7 +1645,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -1739,7 +1720,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -1763,7 +1744,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -1838,7 +1819,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -1866,7 +1847,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -1951,7 +1932,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -1975,7 +1956,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -2054,7 +2035,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -2077,7 +2058,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -2133,7 +2114,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -2156,7 +2137,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -2212,7 +2193,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -2235,7 +2216,7 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -2382,7 +2363,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/06_ConvergentCrossMappingandSugiharaCausality/using_causal_ccm_package.ipynb b/06_ConvergentCrossMappingandSugiharaCausality/using_causal_ccm_package.ipynb index d5be6e9..4905b3b 100644 --- a/06_ConvergentCrossMappingandSugiharaCausality/using_causal_ccm_package.ipynb +++ b/06_ConvergentCrossMappingandSugiharaCausality/using_causal_ccm_package.ipynb @@ -26,14 +26,18 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "from causal_ccm.causal_ccm import ccm\n", "import pandas as pd\n", "import numpy as np\n", - "import matplotlib.pyplot as plt" + "import matplotlib.pyplot as plt\n", + "\n", + "### Just to remove warnings to prettify the notebook. \n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" ] }, { @@ -45,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -83,7 +87,7 @@ " 0.0\n", " -0.7\n", " 38.225693\n", - " 78.63\n", + " 100.70\n", " \n", " \n", " 1\n", @@ -91,7 +95,7 @@ " 0.0\n", " -0.7\n", " 57.996530\n", - " 78.63\n", + " 100.63\n", " \n", " \n", " 2\n", @@ -99,7 +103,7 @@ " 0.0\n", " -0.7\n", " 49.119213\n", - " 78.61\n", + " 100.56\n", " \n", " \n", " 3\n", @@ -107,7 +111,7 @@ " 0.0\n", " -0.7\n", " 47.034720\n", - " 78.59\n", + " 100.55\n", " \n", " \n", " 4\n", @@ -115,34 +119,34 @@ " 0.0\n", " -0.7\n", " 42.223380\n", - " 78.56\n", + " 100.48\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Time Rain ONI NIA Dam\n", - "0 0 0.0 -0.7 38.225693 78.63\n", - "1 1 0.0 -0.7 57.996530 78.63\n", - "2 2 0.0 -0.7 49.119213 78.61\n", - "3 3 0.0 -0.7 47.034720 78.59\n", - "4 4 0.0 -0.7 42.223380 78.56" + " Time Rain ONI NIA Dam\n", + "0 0 0.0 -0.7 38.225693 100.70\n", + "1 1 0.0 -0.7 57.996530 100.63\n", + "2 2 0.0 -0.7 49.119213 100.56\n", + "3 3 0.0 -0.7 47.034720 100.55\n", + "4 4 0.0 -0.7 42.223380 100.48" ] }, - "execution_count": 23, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df = pd.read_csv('../data/La Mesa_dataset.csv')\n", + "df = pd.read_csv('../data/Ipo_dataset.csv')\n", "df.head()" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -160,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -175,12 +179,12 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -198,16 +202,16 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.3167420091903731, 1.5115109778795218e-165)" + "(0.2775286388886883, 5.428162535446003e-126)" ] }, - "execution_count": 27, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -221,12 +225,12 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -245,28 +249,30 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 25, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/prince.javier/opt/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:3508: PearsonRConstantInputWarning: An input array is constant; the correlation coefficent is not defined.\n", - " warnings.warn(PearsonRConstantInputWarning())\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "X->Y r 0.3 p value 0.0\n", - "Y->X r 0.1 p value 0.0\n" + "X->Y r 0.31 p value 0.0\n", + "Y->X r 0.02 p value 0.0775\n" ] }, { "data": { - "image/png": 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hRTcYjGYQRr2QFmH86ZPDvLXtOD9dnM135w+nvK4poIyx4iobRoMgPT6wxAgXma2pwN59+23Fy4MvFLMgO52mFgebjp4Memyg7D5exfMbjnLJtMGMywgtESAxxsy80Wms3l3s80uurrGFj/aXcP6kzIAjHfoy+i8QLCarXkiLIN7bVcRjq3NYNm0Qt5wzmgmDVCm+3cf99x0rrGoIKjHCRWqcGuNL2PMrGhAieCsaYO6IFCwmQ7e4GA6X1vKjV7dx4R8/IybKyK3nZnfpfEsnZpB7st7nwt9H+09ga3ZEVNRCb6JFN1hMFm3pRghf5Vdy6xs7mDksmd9+YwpCCMZnxmMQypLzR3EQHSPcMRoEA+MtPt0LBRX1ZCZYAyr/2BGr2cicESmsOxi+xbT88npu/+dXnPv7T1m7/wQ3nzWaT352Vqc2QsFy7oSBCAGrdnt3MbzzVSED4i3MHh6ZroWeRidHBIs5WidHRACFlQ1c/+IW0uIsrPzOzNYEhJgoE6PS49hT6N/SLa6yMT7EHPuBib47SBSU+y/p6IuF2ek89O4+CisbAi7G44mSahtPf3yI1zbnIYTgu/NHcNOiUQHXmvBHeryF2cNSWLW7mP9b3NlqrrE188mBUpbPGRr0HUVfRVu6waIt3V6nrrGF617Ygq3JznPXzu4kIBMHJbCn0LelK6WkMITECBf+OkgUVNQHVNLRG10NHZNS8sePDrLg0bX848s8vjlrCJ/+bBH3XjghbILrYumkDPYX13DUQ9nHNftKaGpxcNFU7VpwoUU3WEzR2qfbi9gdkp+8tp2c4mqe/tYMsgd2DmafNDiRoiqbzxbbVQ0qMSIU9wKoBIniKpvHBaSmFgdF1bYuWbpjBsSRkWANycXgcEju/e9ufvfhARaPH8jHty3i11+fHJJ/ORCWTFINID0lSrzzVRGDEq1MH5LcaV9/RYtusGhLt1d59cs81uw7wS8vnshCLxlUE52Lab5cDIWVwdXR7UhmopX6Jjs1jS2d9hVVNSBlYCUdvSGE4MwxaXx2sKxTHzBftNgd3P6vr3h5Yx4/WDiSp5dPZ2hq1/y2/hicFM2UrMROft2q+mbWHSzlgimZAacX9we06AaLOVrH6fYSDofk+c+OMm1IElefPtzrca4CNr5cDMXV6m6lK5Yu4LHwTX6577brgbIgO51qWwtfFfhfFARlYf/4H9t5a9txbjs3mzuXjuux2NMlEzPYkV/ZmnACsHpvMc12yQURmBDRm2jRDRaTRYtuL/HpwVKOlNXx3fnDfR6XGG1maEoMe3yEjYWaAuzCdavuya/blRhdd84YnYYQgfl1bc12bnhpC+/vLubeCyfw43PG9JjgApzvcjG4Wbvv7CxiSEo0U7PC31H3VEaLbrCYrFp0u4iUkmMng++19fyGXAbEWzh/kv9FmUmDE9jtw9ItqlSJEQPiQ19IA88dJPIr6jEaRGvmWqgkx0YxbUgSK9cd5sf/2M6q3cXYmjtnqdU2tnDNc1/y6YFSHrl0MtedMaJLrxsKI9PjyB4Y15qdVl7XxIZDZVwweVCPiv+pgBbdYDFZdbueLrJ6TzELH/uEtftPBDzm0Ila1h0o5TunDQso9nXioESOnayn2tbscX9RlY0BISRGuBiQoCIAPCVIFFQ0MCjJGpbsq99fPo2vT8/is4Ol3PjyVmb+6kP+77XtfLi3BFuznar6Zr79101sOVbBH66Y1qlrbk+ydGIGXx4t52RtI6v3FGN3SC7UCRGd0HG6wWKy6oW0LvK+8xb04ff2ceaYtIDE6YXPc4kyGrhqbmCiMtHp191bWM1pI1M77S+qagjZtQCqkHdqbJRH90J+eT1ZSeFZvBqRFstvLp3Mr5ZN5IsjJ3l3ZxGr9hTznx2FxFtMJESbKa1p5JlvzeC8iRlhec1QWTIpg6c+PsSafSW8s7OQEWmxrf8HTRva0g0Ws1UnR3SBZruDtftPMDw1hkMnavnH5ny/Y6oamnlzWwEXTxsUcIzpxNZ0YM8uhuIqW5dDqAYmeE6QKKho6LI/tyMmo4Ezx6TzyDemsPkXi3nxe3P42uRMrGYDf7t2Vq8LLsCEzASGpsTw6qY8vjh8kgunZGrXgge0pRss2tLtEptzy6m2tfDoZVN5fsNR/vDhAZZNG+Sz6+sbm/Opb7K3tgUPhPR4CwMTLOz1EDamOkbYOGtccP29OpKZaKWwg6Vra7Zzoqaxy5ELvjAbDSzITm9NoIgUhBAsnZTBs+uOAOhaC16IOEtXCLFUCJEjhDgkhLjTw/5bhRB7hRA7hRAfCSGGue2zCyF2OH/e7pYJ6oW0LrFm7wmiTAYWZKdxzwUTOFnXxJ/WHvZ6vN0heeGLXOYMT2HS4OBWwScNSvS4mFbV0ExDs71L7gXwnAp8vNJZ0jHMlu6pwhKnxT16QBxjPSSuaCJMdIUQRmAFcD4wAbhKCDGhw2HbgVlSyinAv4BH3fY1SCmnOX8u7pZJmqxgbwJH99Y67YtIKflofwnzR6USE2ViclYil04fzHMbjpJfXu9xzJp9JRRUNPgNE/PExMGJHDpR26kubah1dDuSmWClvK6pXUSB6zq609KNZKYPSWLmsGSumTdcuxa8EFGiC8wBDkkpj0gpm4DXgGXuB0gp10opXZ/QjUBWj86wtSOwdjEEy6ETtRw7Wd/aHgXg9iVjEcBjq3M8jnl+w1EGJ0VzrtuYQJk4KAGHhH3F7V0MrR0jkrpu6QKcqG57LxRUhF68vC9gMAjevGke3zmtZ1rfnIpEmugOBtxXVgqc27xxHfC+23OrEGKLEGKjEOISb4OEEDc4j9tSWhpkbrvZaR1pF0PQfLhPtdE5Z1ybgA5Kiub7Z47k7a8K2ZHfvu3LvqJqNh4p5zunDwsp/MrljuiYDtzVxAgXrvHuWVj5FfVEGQ0MCLIwuqb/EGmi6+l+xGNJeiHEt4FZwGNum4dKKWcBy4E/CCFGeRorpXxWSjlLSjkrPT3IxYhWS1eLbrCs2VvC5MGJnVJvb3SWGnzonb3tCsj8fUMuVrOBK2cPCen1BiVaSY4xs6dDBENxlQ2DgPQuVttqTZBw8+sWVDQwODla1xrQeCXSRLcAcP+EZQGFHQ8SQiwGfgFcLKVsvbeTUhY6H48AnwDTwz5Dk9PS1ZXGgqKstpHt+ZUsHt/ZTRBnMXHbedlsOVbRGsNbXtfEf3Yc5+vTs0iKiQrpNYUQTPSwmFZYaWNgQteTF1z1F9yz0grK68nqQqEbTd8n0kR3MzBGCDFCCBEFXAm0i0IQQkwHVqIE94Tb9mQhhMX5exowH9gb9hlqn25IfLz/BFLC4gmew7QunzWEsQPjeeT9/TS22PnHl3k0tjhCWkBzZ+LgBA4U19LU0lapq7i6IeRCN+7EW83EWUztEiQKKrpWvFzT94ko0ZVStgA3A6uBfcAbUso9QogHhRCuaITHgDjgnx1Cw8YDW4QQXwFrgUeklOEXXe3TDYk1e0sYlGhlQqbnDCWjQXD3BePJK6/n+Q25vPTFMc4YneaxXm4wTBqUSJPdwcETbT28iqpsXfbnuhiYYGkNG6trbOFkXVO/DRfTBEbEJUdIKd8D3uuw7T633xd7Gfc5MLl7Z4f26YaArdnO+oNlXDYzy2cY0cLsdBZmp/Poqv04JDx0yaQuv7YrDXXP8WomDkpUiRGVNs4a27XECBeZidGtlq4rRldbuhpfRJSle0pgclpIWnQD5ovDJ2lotrcLFfPGLy4YD8Cw1BjO7mLGGMDw1Fhio4yttXWrG1rCkhjhwj0V2BWj25Xi5Zq+T8RZuhGPS3R1pbGA+XBfCbFRRk4b6b8bbPbAeH53+VQyE8MTAWAwuBbTVNhYoStGN0ytazITrZyoacTukK0xutrS1fhCi26waEs3KBwOyUf7SliQnY7FZAxozNenhzffZcKgBF7fnI/dIVsjDcKxkAYqQcLukJTVNpJfXo/VbCAtLrRoC03/QLsXgsWsRTcYdhdWUVLd6DFUrKeYNDiRhmY7R8tqw5YY4cLVTbioytYauaDTXzW+0KIbLNrSDYo1+05gEHS5oldXmDTY1TOtmqKqBgyCsGWMZbh1kMivqNf+XI1ftOgGS6vo6jjdQFizt4SZw5JJie29W+5R6XFEmQzsPl7l7BgRnq4O4C66DTpGVxMQWnSDpXUhTWek+eN4ZQN7i6p71bUAqv7s+Ix4dh9Xlm5XC924kxITRZTRwIETtVQ1NOsYXY1ftOgGi85IC5iPXAVuell0QZV53FNYRVFl+BIjQEVHDEiwsDW3AtCRCxr/aNENFiGchcy1peuPNftOMCItllHpsb09FSYOSqDa1sLRk3VkJITXGs1MtJJTojLe+mtJR03gaNENBZNFW7p+qLE188XhMhaPHxARq/mTnD3TpIRBYXQvQFvhG0AXu9H4RYtuKJiitU/XD+sPltFslxHhWgAYmxHf2m49XDG6LlzuijiLiaQY773eNBrQohsa2tL1y5q9JSRGm5k1LLm3pwKA1WxkzIA4IHwxui5clm5WcnREWPWayEaLbiiYo3Wcrh++zC1n/ujUsIVmhQNXW/ZwpQC7cJ1PL6JpAiFyPhGnEiaLFl0fVNQ1UVDRwOTBSb09lXYsmTiQaUOSwt5KJyNRnU+Hi2kCQddeCAXdht0nrk4Nk4Nsmd7dnDcxg/OcLcLDyeAkZeEOS9GWrsY/WnRDwWTVVcZ8sMvZk8yVftvXyUi08vx3ZzNnuP8qahqNFt1QMFnBVuX/uH7KnuPVDEmJDrm32alIuIqia/o+2qcbCmbtXvDFruNVEeda0GgiBS26oaB9ul6pqm8mr7y+NVJAo9G0R4tuKJisOk7XC5G6iKbRRApadEPBZO0XGWnrDpTy3x3HgxrjWkTToqvReEYvpIVCP8lI+9Mnh9hXVMOFUwa1ptD6Y9fxKgYnRZPci/VzNZpIRlu6oWCOVlXGpOztmXQr+eUNVDU0s9fZ1DEQdutFNI3GJ1p0Q8FVU9fe1Lvz6EZa7A6Kna3FNxwuC2hMVUMzx07WMzlLi65G4w0tuqFgcqZ79mG/blGVDbtDWfIbDgUmunsKXUkRWnQ1Gm9EnOgKIZYKIXKEEIeEEHd62H+rEGKvEGKnEOIjIcQwt33XCCEOOn+u6bZJ9oPuEfkV9QBkD4xjc245jS12v2N2uzLRBvWPTDSNJhQiSnSFEEZgBXA+MAG4SggxocNh24FZUsopwL+AR51jU4D7gbnAHOB+IUT31BU0Oy3dPhyrW1ChrPjLZw3B1uxge16l3zG7jlczKNFKalx4C8poNH2JiBJdlFgeklIekVI2Aa8By9wPkFKulVLWO59uBLKcvy8BPpRSlkspK4APgaXdMstWS7dvi64QcOmMLAwCPg/AxbD7eJV2LWg0fog00R0M5Ls9L3Bu88Z1wPshjg2d1jbsfVh0y+vJTLCSEhvFlKwkNhw+6fP4alszR8vqdOSCRuOHSBNdT8GgHuOyhBDfBmYBj4Uw9gYhxBYhxJbS0tLgZ9nahr0Pi25FQ2tR7vmjU/kqv5Laxhavx+85rsLKJunIBY3GJ5EmugXAELfnWUBhx4OEEIuBXwAXSykbgxkLIKV8Vko5S0o5Kz09PfhZ9gdLt6K+tcni/FFptDgkXx71bu3u1ploGk1ARJrobgbGCCFGCCGigCuBt90PEEJMB1aiBPeE267VwHlCiGTnAtp5zm3hx9y3RbepxUFRtY0sZ1HuGcOSsZgMbDjkQ3QLq8hMtJKmF9E0Gp9EVBqwlLJFCHEzSiyNwHNSyj1CiAeBLVLKt1HuhDjgn84mgHlSyoullOVCiF+hhBvgQSllebdMtI9bukVVDUjZ1k7cajYya3iyz3jdXXoRTaMJiIgSXQAp5XvAex223ef2+2IfY58Dnuu+2TlpFd2+GafrChcb4tZocd6oNB5bnUNZbWMna7a2sYWjZXVcMq171i01mr5EpLkXTg1aF9L6ZkZafrmKyHNZugDzR6cB8IWHKIY9x6uQsv+059FouoIW3VDoB5au0SDITLS2bps8OJF4q4nPPdRhaOuJpt0LGo0/tOiGQutCWt+0dAsq6slMtGIytr09jAbBaSNTPS6m7T5excAECwPirZ32aTSa9mjRDYU+bqbTSy4AACAASURBVOnmVzS0cy24mD8qlbzy+lb3gwvdE02jCRwtuqFgMILB3GejF1SMbkyn7S6/rruLobaxhSNlddq1oNEEiBbdUDFZ+2RGmq3ZTkl1Y7vIBRejB8QxIN7SzsWwr6gaKXVShEYTKFp0Q6WPtmEvrFR+ak/uBSEE80al8vnhk0hn14xdBToTTaMJBi26odJH27C7YnQ9iS7AvNFplNU2cqCkFlCLaAPiLQxI0ItoGk0gaNENFZOlT4quq3j5kJTO7gWAeaNSgbZuEjoTTaMJDi26oWKK7pM+3YKKBkwGwUAvlmtWcgzDUmP4/HAZ9U0tHC6t1aKr0QSBFt1Q6aOWbkFFA4OSon22XJ83Ko1NR8rZWVCFQy+iaTRBoUU3VMzRfTJON7+8niEpnv25LuaPTqWmsYV/fJkHaNHVaIJBi26omCx9MiOtoKKBrCTP/lwXp49Uft13dhaRFmdhYIIu56jRBIoW3VAxWfucpWtrtlNW2+jX0k2NszA+MwG7QzJ5cALOEpsajSYAtOiGisna56qMFVS4qov5tnRBpQSDdi1oNMGiRTdU+qClm+8nRtedM7NVm6OpQ5K6dU4aTV/DZxFzIYR/k8cNt9bofR+ztc/5dFuLl3uJ0XVnwZg0Xr1+Lqc5/bsajSYw/HWOqMVLR10vGLswl1OLPmjpFpTXE2U0kB5AnzMhBPOcBXA0Gk3g+BPd7xGc6PYf+mAacEFFA4OTozH4iNHVaDRdw6foSin/3kPzOPUwWcHRAvYWMEZcq7mQcG+7rtFouoegF9KEEBOEEN8RQtwthMhwbhsthIgP//QiGJPzFrwPWbuqeHlQbnyNRhMkAZtoQog4VKfdy4Bm59hVQDHwayAPuL0b5hiZmJ0WYYsNLHG9O5cwUNfYQnldk7Z0NZpuJhhL9/fAPOAcIB5wd/y9BywN47winz5m6R6vDDxyQaPRhE4wzshLgZ9IKdcKITpGKRwDhoVvWqcAJqdF2EcqjXlqu67RaMJPMJZuNNC5FawiHrB3fTqnEH3M0vVXvFyj0YSHYER3M3C1l32XAZ93fTqnEK0+3b4Rq1tQUY/FFFiMrkajCZ1gRPce4FIhxBrgelT87teEEC8B3wTuD8eEhBBLhRA5QohDQog7PexfIITYJoRoEUJc1mGfXQixw/nzdjjm45VWS7dvZKXll6u267p4jUbTvQQsulLKz1CLaBbgadRC2gPASGCxlHJzVyfj9BWvAM4HJgBXCSEmdDgsD7gWeNXDKRqklNOcPxd3dT4+MTk7K/QV90Kl57brGo0mvAQV1S+l3ACcKYSIBpKByjDXW5gDHJJSHgEQQrwGLAP2us0h17nPEcbXDR6X6PaRhbSCigam6eI1Gk23E5ClK4SwCiEahRCXAEgpG6SUhd1Q4GYwkO/2vMC5LVCsQogtQoiNrrl2G33I0q2xNVNZ36wtXY2mBwhIdKWUNuAE0NK908GTQzGY2g9DpZSzgOXAH4QQozy+iBA3OMV5S2lpaSjzVFXGIHDRrcyHjX8GGXmlLFqri2nR1Wi6nWAW0lYCtwghzN01GZRlO8TteRZQGOhgKWWh8/EI8Akw3ctxz0opZ0kpZ6Wnp4c202At3V3/hFV3QOn+0F6vG9HhYhpNzxGMTzcJmATkCiE+Akpob4VKKeUdXZzPZmCMEGIEcBy4EmW1+kUIkQzUSykbhRBpwHzg0S7OxzutohtgyJitSj0e+RQGjO+eOYWITozQaHqOYET3G4BLYc70sF8CXRJdKWWLEOJmYDWqNu9zUso9QogHgS1SyreFELOBf6MW8i4SQjwgpZwIjAdWOhfYDMAjUsq9Xl6q67QupAUYMtZYrR6Pfgqn3dg9cwqRgooGYqKMpMRG9fZUNJo+T8CiK6Uc0Z0TcXud91C1HNy33ef2+2aU26HjuM+Byd0+QRdGMyCCsHSdopv7WcSVg3SVdNQxuhpN9xNM9MIHQohF3TyfUwchVFZaoMkRLku3sRqKdnTfvEJAl3TUaHqOYKIXZtOf2vEEgskSnKU7YKL6/cgn3TalUCioqGeI9udqND1CMNELbwPdG/t6qmGKDs6nmzICBk5Sft0Ioaq+mRpbi7Z0NZoeIhjH4mrgMSFEJsrn2jF6weWP7T8Ea+laE2HEQtj8VyXW5t63LvMrdOSCRtOTBCO6LzsfL3X+dETS39wP5ujA43Qbq8GSACMXwsYVkL8JRi7qztkFRDBt1zUaTdcJRnR7JHrhlMJkCUx0HQ5orAFrAgybBwaT8uuOXNTNE/RPgbZ0NZoeJZiQsWPdOZFTkkDbsDfVAFJZupZ4GDxTJUlEAAUVDcRZTCRGd2eioUajcRFUsKgQwoRKkjgDSAHKgfXAW1LK7q7LEHmYrNBU5/84V4yuNUE9jlgI6x+HhkqI7t3KXjpGV6PpWQKOXhBCDAC2AP8ALkDV0b0AeA3YLIQIsYjBKUyglq4rRtfiFN2Ri0A6VKJEN2N3SGpszV73q+Ll2p+r0fQUwVi6vwdSgbnuBcudablvOvd/J7zTi3DMAYpuR0s3azaYY1To2PgLu2VqUko+3n+Ch9/dx5GyOoalxjB5cCJTs5KYkpXIpMGJxEQZKaio5/RRqd0yB41G05lgRPdrwM0dO0RIKTcLIe4C/hjWmZ0KBG3pJjrHRcHQ0wP36371Gux+C5a/rjLh/HCwpIYH39nL+oNljEyP5SfnjCGnuIbteZW8s7MIUKcZmRZLXZNdRy5oND1IMKJrAWq87KsB+l+1FJM1sDjdjpYuqNCxD++D6iJIyPQxtgpW3QUN5VD0FQya5vXQyvom/rDmIC9tPEZslJF7L5zA1acPw2xs8yKV1jSy63glOwuq2FlQhZQwd0SK/2vQaDRhIRjR3QjcIYT4WErZunokhIhFVRfbGO7JRTwma2DtehpVWccnPyvhxxePxmAQajEN4Og6mHqF97Gf/UEJLgIOrPIoui12B69syuOJNQeobmhm+dyh3HruWI9Vw9LjLZw9biBnjxsYyBVqNJowE4zo3oYqDJ4vhPgAlZE2AFiC6viwKNyTi3gCjdN1Wrp/2ljKObOrmTQ4ETKmQHSy8ut6E93qQtj4DEz+JlTkKtFd1L5Bco2tmctXbmRfUTXzR6dy74UTGJeR4Pl8Go2m1wmmG/AOYDTwLJAOnIsS3T8DY6SUX3XLDCMZczTYG1Xygy8aq2nBRCNmPjtUprYZDDD8TOXX9dbC55PfgKMFzr4HspdC4XbljnDj0VU55BRXs2L5DF6+bq4WXI0mwgkmZGwqMEdKeaeU8hwp5QTn493AHCHElO6bZoRisqhHux+/rq2aGmIAwQaX6ILy61YXQPmRzmNO7IftL8Ps6yF5uBJdgIMftB6yJbeclzYe49p5I7hgSqaOtdVoTgGCqTL2BDDXy77Zzv39C5MzddZPpbGmukqqHNFEm41szi2nscWudoxYpB49lXr86EGIioMFP1PPB06ExCHKxQA0tti5861dDE6K5rbzsrt+LX2FqoLAK79pNKAWq7c8p1L1e4BgRHcGsMHLvi/w0gSyT+OydP1EMNRVl1NDNJfNzMLW7GDbsUq1I3UUJAzuXOoxbyPkvAvzfwKxzhhaIZS1e+QTaG7gT2sPc+hELQ9/fRKxlsjpQtGrOOywcgH863u9PRPNqcLRdfDMfHjnp7D67h55yWBE1wjEetkXS38MGXOVZvSzmNZUV0mNjOGaecMwGtxcDMIZxXB0XZtfWEr44F6Iy4DTbmp/ouyl0FzP8e2r+dMnh7hk2iAWjR0Q5os6hSneBfUnIec92N+/qoxqgqS5Ad6/E164CIxRMOkbsO1F9VnsZoIR3c3ADV723YBKEe5ftFq6vkXXYavGZoxlVHocU7IS2XC4g1+3oQKKd6rn+9+Fgi/hrLsgqsN33PAzkOZYdq19nTiLiXsvnBDGi+kDHHPeiCUNg/fvgKb63p2PJjI5vlXdEW16BubcADd+Bhc/rdZO/veTbndPBSO6vwTOEUJsEkL8UAhxqRDiR0KITcBZwL3dMsNIprUNu2/RNTVVY4hORAjBGaPT+Cq/kmpXPYTWeN1PVcPKjx6AtGyY9u3OJzJbyU+ew5T6jdx7wXhS4yxhvJg+QO4GSBkJlzwDVXmw/ne9PSNNJGFvhrW/hr+eqwpVfec/8LXHICpG/Vz0pFrU/vS33TqNYELG1gHnAQ5Uyu+/gCeBFuBcKeX6bplhJNPaht276EopsdjrsMQlAzBvVBoOCZuOlKsDEjKVyB75FLa/BGUH4Jz7PXYLLqxs4C8lYxkkyvn6oPKwX84pjcMBeZ+resXD58OUK2HDk1B2sLdnpokEyg7BXxcrQZ38Tbjpcxh1VvtjRi5Sxs6Gp5SrqpsIxtJFSvmJlPJ0IB4YAiRIKef3S8GFgCzdgvI64mggLkGl2s4YloTVbGgfOjZiIeR9AZ88AkPmwrgLOp1HSsl9/93Nxw6VkSYOfNDpmH5N6T7lphl2hnp+3q9UUaH3bvceB63pHzjs8MZ3oPIYXP4SXLrSe0nV834FMSnw9o/VnWc3EJToupBS1kspj0sp+7fTzOxfdA8VlGAQkqQUFYVgMRmZPTylc7xucz3UFsO5D3osavPermLW7DvBtefOVUXQD7wf1ks55cl1+nOHz1ePcQPgnHtVtMeet3ptWpoIYMcrcGIvXPgHmHCx72NjUuD836pEpE1/7pbphCS6GicBWLp5hSqDLD2trdzwGaPTOHiilpJq57jhZ6gWPmMvgKGndTpHVX0z97+9h8mDE/nu/OGQfb5aDKg9EbZLOeU5tkHFMScNbds263uQORVW3d1WdEjTv2iqg48fhqw5MGFZYGMmXqoihdY+rNLvw4wW3a7QKrre43SPl5QAYHX6dAHmj04D4HNXFEN0Mlz7HlyywuM5/vDRASrqm/jNpZMxGQ2QvUTtOLC6ixfQR5BSie6w+e23G4xwwRNQW6JcN5r+x+dPqzvI8x4KqCwqoI674HcgDCp+N8zuqYgTXSHEUiFEjhDikBDiTg/7FwghtgkhWoQQl3XYd40Q4qDz55pun2zrQpr3EJMTpU5r1NJWE2FCZgJJMWY+O3iy7cChc5X4duBkbSP/+DKPr08frArlAGRMVkkVzuy0fs/JQ1BXqhbROpI1E2Zeq24Vi3f3+NQ0vUhNiVpMHX+x+nwFQ2IWLP4lHP4Ydr4e1mlFlOgKIYzACuB8YAJwlRCiYzBqHnAt8GqHsSnA/ahU5TnA/UKIzioWTvxkpDW1OKipdEYZWBNbtxsMgnmjUvn8cBnSz7fo8xtyaWxxcOPCUW0bhVDW7uG1gZWW7Ou42h4NP8Pz/nPuUwsn797mvziRpu/wya9VXZTFvwxt/KzrlFti1Z1QWxq2aUWU6KLE8pCU8oiUsgnVf62dI0ZKmSul3IkKXXNnCfChlLJcSlkBfAgs7dbZtmakebZ0D5fWEutaa7S0r/41f3QaRVU2jpR5b2xZY2vmhS9yWTIhg9ED4trvzD4fmut6pM9axHNsg8rgSxnpeX9MilqgzN8IX/2jZ+em6R1O7FcZZrOvV+n2oWAwwMVPQWMtrL4rbFOLNNEdDOS7PS9wbuvusaFh9G3p5hTXEC+comvtILqjnH5d9yiGDryyKY8aWws/PMvDm2bEAhUS1RddDLkbPFde84SU6vhh83z77KYuV+F4H96rCpxo+jYf3gdR8bDg5107z4DxqoZ16piw+XYjTXQ9fWoCvdKAxwohbhBCbBFCbCkt7cJtg8GghNeLT3d/cQ2Jwrmvg6U7LDWGwUnRbfV1O2BrtvPX9Uc5c0waU7I8xBSarSqY+8CqvhOHKiWsexz+/jX453cDu66KXKgpbAsV84bBAOc/qmozbHgqLNPVRChHPoWDq2HBbW0Fo7rCgtth0R2BL8T5IdJEtwCVdOEiCygM91gp5bNSyllSylnp6V3sHO+jT1pOcTVDY1tUOJjLFeFECMH80al8cfgkdkdncfnn1gLKahu5aZGPW6PspVCVr2IQT3XszfC/W+DjXymromiHCovzh6veQsfIBU8MmgaTLoMvVnQqBu+Xxlp1y6qJbBwO+OAeSBwKc37Q27PxSKSJ7mZgjBBihBAiCrgSeDvAsauB84QQyc4FtPOc27oXH23Yc4prGBzdrKxcD9+S80enUW1rYffx9re7LXYHz647zLQhSZw+0sc3tSt0LCfERAl7ixKg+l5OKW6sgVevUD64M2+HG9aqv9mXz/ofe+xziEmF9HGBvdbZ96huHJ8GEULmcMBrV8Ezp8OOV/0fr+k9dr2hikedc19b8lKEEVGiK6VsAW5GieU+4A0p5R4hxINCiIsBhBCzhRAFwDeBlUKIPc6x5cCvUMK9GXjQua178dInraqhmcIqGwOiGjv5c13Mc/p121UdA97ZWUR+eQM/Omu0724Q8RkwaHroft3DH6saotteCG18OKguhOfOV5ljFz2lssgs8TBtOez5t/8EkNzP/Ptz3UkZAbOvg20vQemBwMZs+Zsq+Zc0FP5zE3z5l8DGaXqW5gb46FeQOU2VaoxQIkp0AaSU70kps6WUo6SUDzu33SelfNv5+2YpZZaUMlZKmSqlnOg29jkp5Wjnz/M9MmGTZ0v3QImqQp9isnXy57pIj7cwLiO+XUqwwyF55pPDZA+M45xxAdTKzT4fCraEFtKyz3kTcfjj4MeGg+LdqghJxVH41hsw0y20evb1YG/y/YVQVaDy6QNxLbiz4GdqEfKjB/wfW5ELH94Po86GH25Uf+/3blddmgNBSvXl8cE9qsnovv+pFNO6sr7ji48UNj6j2l+d95Dy4UcouuVAV/HShn1/sRLdBOrbxeh2ZN6oNF7edAxbsx2r2cjH+0+QU1LDE1dMVa3a/ZG9RMUjHlgFM74T+LztLarYN0J1qmiq61y/N1SkhNL9SliiYsAc2/7RZFVC/8Y1yqr93iqV8OFO2hgldJufg/k/9Vh1jWOfq8dgRTc2TXXlWPsQ5H8JQ+Z4Ps7hgP/erDLbLv6j8stf8RK8dQOsuV/9zc6627uVXbwL3vu5qn5mMCm3hjsmqwrCTx2tMqASs4K7jr6IlKEtWFXmwWdPqC/FEWeGf15hRItuV/Fi6eYUVxNvMRFlrwXLcK/DzxiTynMbjrL1WAXzRqWy4pNDZCVHc9GUQYG9fuZUVXx597+CE928L9RK/szvwtbnVdhV9nmBj3fHJbK5n0HuevVYf9LHAAFIGDgJlr8BiV4i+2Z/X/lSc971nDef+xlYElX/uGA5/YfKZ/zh/fDd9zx/0Lf8TV3PxX9sE0SjGb7xV/Xlse5RJbxLHm4/vr4cPn5I/V2jk1Wd1unfgYZKtfBZVeD8cf6e8z58+qiKCe3P2KrhL2fD5MtUmFagNNXDa8vV70se7p65hREtul3Fy0JaTnEN2RnxCFuNV58uwJwRqZicLXwMQrA9r5JfXTJJ1VgIBCFgyhXqQ1tdpOrzBsK+/6kvjHPuUwkDhz8OTnSlVNWbDn6gBLve6SJJHAJjlqjssMQsVT2tqU7521p/r1ctUube6PNvQ/YStQr95V88i+6xz2HY6coSDZaoWPXBfvdWVcNibIc8mvIjKtZz9GIlmO4YjHDRH1Xj0I0roKkWLnT2Zd3ynBLcxhrVlWDRnW3p3bGp6mfQtPbn+99P4KvXVB3lcIQ4napsfxlOHoRPfgPRKTDXW6MaN6SE//5IuaqWvxF6IkQPokW3q5isnbqISinJKa7hwqmDIKfKq08XIM5iYuqQJDYcKmPX8SrS4ix8c2aQt5lTrlDFmXf9E+bf4v94h0OJ7ujFKltr2Pzg/br731Fv9oQsGHOuEtnhZ6hWOeFqBW8wqkWvNfdDyV4Y6JYRXlOiPqAzrg79/DOuho1/gjW/VNfgEu9Wt4JJLe55uh6DAZY+osR7/e+UZV9xDEp2qcSVpb9tP19fzL0Jtv4dtj7X1v05UJrq1JdtbTHUFKuFx9pi9fepLVGlDGeF0Kjz5W9AfKa6/p7wjzrs8OVKyJoNsenw/s/VQrG/Uowb/qBKd55zf+h3aj2MFt2u4iFOt7jaRrWthXED42Cnb0sXVOjYHz8+iJRw5/njsJqDtNxSR8HgWbDzjcBEt3C7SigYf796Pups+OAXUFVAozWd8vJyampqsNvt3s9RlwRL34SEQW2iVNwAxWGOZU0+F5ZOgOMVUL6vbXtTPSx5Q6X/7tvnfbw/Fj6rfM+7tinLFdSX6LifwIwUKKxWP94Y9E1YtgRslTDCpGo8mGOgnPbz9ccF/wNHE+zdG/iXVksj1J1wW5CLVz9xoyHBqLbbCe6coGKmRzmtzK82gdVLwW/AaDQSHx9PSkoKFksX2kcdWK0WLc+5X8Wfv7gM3rweYv/juZARwIEPYM0DqhTjGT8N/bV7GC26XcVk7ZSR5lpEG59qAOnwaekCzB+VylMfHSTBauJbc4f6PNYrU66A938GJXv8+zj3va2sOFec7+hz4INf0HhoPXkJs0hOTmb48OGYzWbPIWsOO5Q0qVvApCGd94ebijglagPHqHkDVOZDgwkypnTNspZStUiyN8OAsUr4SnMgarSq5RDouZvrVXZiKK4OANtgKD8MSRnq7iOQeZfmgGOQ+uIzmtXfxmBWcxBC+ZYrj0HqULDE+T+ni5piqLGrBWBbFSSmq8XHTlOQNDc3U11dTV5eHkOHDg1deDc9o+6axl+sFk2Xvw5/Ow/+cSV8b7VKx3Wn7JAS5YxJsGxF+O6ueoDIjas4VTBZOlm6OU7RHesyEPxYutOHJjMg3sINC0YSbzWHNo9Jl6oPnb8ydFIq0R2xoM3XmD4O4jMpr6ohOTmZtLQ0oqKivMcIN1arLxNvLU/CTWy6ej33JI6mWnVr39UPmxBKtBzNymqszAOE+jIJ5tzmmNAFF1QUh8nSwXL1QUO5KrSUMEiJtCVeRVcYTW3ztiYCIvhaE7YqdT3Jw9V5qwo8FoEXQhAVFUVaWhrJycmUlzv/P/bm4MLhSvaoOOg517dFqcSkwLffVEbNy9+AquPt5/faVerYK19Vi5qnEFp0u4o5ulOVsZziGjISrCQIzxXGOhJlMrDhzrP50VmjQ59HbJry0e78p7JEvXFir1okGn9R2zYhYNTZ1EQNICE+3v9rNVQoiyoqCOupK7jCzVyxrfYWtXgZrte3xKv/UU2R8pEmDlYLfT2JEBA7QN01NXmvPAeo/291kRJGDzWYWzEY1bXZKgMXQXuTstqtiaqId/Jw9WVQkeuzbnRCQgI1NTWw+W/w6Eh4/47AXg9UrWNTNMzoUAI7eRh8619K8F+5TEV/OBwqZK/8CFz+YvtOIacIWnS7igdLd39xDWMz4tusAz+WLoDZaPCdfRYIUy5Xvlpf5R73/Q8QqjWQO6POxm6Mxkyz79dw2NV1RSf17C1dbJqqjdpYo6xcCK/oJzhD9CwJym3SG0QngzAqa9cXdSeUZZ4w2P//wJroFFLvgtkOl1Xs8uMaTJAySr1O+RFlxXrATAv2ykIVDRIVqxbFjnzi//XqTqq1iKlXeHarZE6BK19RXZ1fW64WVQ+sUouY3uonRzhadLuKKVq9oZ2WRLPdweETtYzLiFe34aBiSXuCsV9T5ex2vuH9mH3/U33Y4ge23z5StaMWLkHzhq0KkD4XV7qF6CQlAHWlTtEV4b2tNEcrN0vy8N7zDxqM6svFVuW9BZS9SUUoWBMD89O6EnMCdTE0VCnftHvdAlOUEl5HixJe9zsp6YDqIkTZAfVFsOxP8ONtKuHjvz/uFNnTia3Pq7uWuTd6P2bkQvj6n1Vxo8+fUlEns68P7HoiEC26XcVkAWSrBZBbVkeT3eG0dF1Wg39LNyyYo1U8697/qtX9jpw8DCW727sWXMSmqlvqRh8r9eDmWghT9lqgCAPEpKn5NVQ6/blhfvuao7vmlw0HsWmAUF8unqgpUl/wCQGWijY63UC2Sv/HOlrUF5onX31UjAoHbK5Xi3NSqsprpTkqRM2apELMpn9LHbtshUr+WPNL769nb1buiJGLOi+UdWTyZSrJZMqV8LXHT6mFs45o0e0qrd0jVIKEK3JhbDtLt4dEF9RtWlON5xbt+99Rj+Mu9DzWbFX+RG8+YUeLslyik3vnTe9KHHA095w/uacxRinRqz/Z+f/QXK8WE2PT21pFBYI1Ub0//bV2slWj7mK83JlFJymxt1WpiI+TB5WlmzIKUoa3/8IaehqcdhNs/iscXe/5fHv/q9xhc28K7DpmXguXrgzu2iMQLbpB4HBIth6raL+xtU+aekPnFNdgNAhGpccF5dMNG8POUB+MrzxEMez7nzNteJjnsa5Gm95uCV2uhZ6KWuiIMarNrRFMCNSpRmu0hlsqtZRqBV8YO7uG/OH6m/lzMdgq1V2M2YfbJjZd/TTXq8f0cd7f32ffC8kj4O2bPS8ObvqzCssbc2okNYQLLbpB8OIXuVz25895ZdOxto2tbdjbLN3hqTEqwaGxWn1IfL2Jw43BAJO/CYfWqNV+F9WFULDZs2vBhdGibtm9iW5DhRK+nryejsRnqIWunnZvOHniiSd44oknuvdFomKd0RqlbVEHjdXq1j8+oy1WOVBMzv+ZLxeDww62GmfUgo+7GCHUl/rAySrN25c7xuVmqMiFjx5sv69gq3o/zr0xoiuCdQf962q7yBWzh7IoO51f/Hs3f/rkkNrY2obdaemWVDMuw/nNb6tWVkBP34pPuQKkHXa/2bZt/7vqcbyHGgYuhFC37Z78unaXa6GHoxY6Yo5Wlnq4/bkBsHLlSu666y7uuusunn02gALrXSEuXS2a2aqU8FYXqi9FD0kKAWFNVNZpS5Pn/Y01gMNnRbxWhPBc9c0Tw+erDg6b/qxqdLjY9Ixyu01bHth5+hBadIMgOsrIs1fPYtm0QTy6KoffvLcP6eZeqG1sIb+8QflzQYlXT/pzXQycw7UyXAAAH/ZJREFUoEoluidK7Hsb0sZCerbvsZYE9WHvuHruspKs3dvVPlI5cuQIt99+O3/84x958sknue222zh69Gj3vaA1Sd1V1JWqYkItNmfKdYgfWX8uBluVuivrDrfN4vvVItx/f6QWeKuLVI3h6d9WccT9DJ0GHCRmo4EnLp9GgtXMynVHGFBSzHUALbbWwuWtouuydHuDKVeowtllh9TCV+6GwPLTLW5fGCa3/nENFc5QomjP4/owDoeDa6+9lssuu4zvf//7AGzYsIFrr72WtWvXYuiO22MhlFVbXags1Ki4wKxQb5it6q7MVqmsaHekQ4muKyEi3ETFwrKn4YWLVAW2qBjlzpgTQBWxPogW3RAwGAQPLptIUoyZD9bu4zoLNDXWk1OuRHdcO0u3h2J0OzLpMlWacOfrKmtH2n37c12YLM7QsRq1UAIqtKepFuIGntKhOqFiMBhYt25du20vvvhi979wTKqqgyAdgSVC+MOaqCqP2Vvauwea6tT7oyui7o8RC1Rs7cY/qS+Qseer1kn9EO1eCBEhBLedN5bl89Xt+pOrdrE9r4KYKCNDkp0LTb1p6SZkwoiFSnT3va3q0mZO9T9OCGXtNtaoDzu0uRZ8pZxqwo/BpFwK8ZnhSQRxuRgaO7gYGioBQ/ff6i9+QNVbbqrxnQzRx9Gi20WWzRoJwJGiMt7YUsCYgfFtbXYafdfS7XamXKEC2Q9+oKzcQC0lS4ISXFeCRUOlujXth66FXic2XUUshANztLqLaXCLYpDS6VqI7/7EEEscXPmyCiUbsaB7XyuC0aLbVZzRCzedkUWU0cD0IW4xrL1p6YISWld4VyCuBRcWt7qy9ibvWUoar1x99dUMGDCAujo/xWvc2Lp1K0II/va3v3k95pe//CVCCHJzc4OflBDKhdBY05Z40Vyvkk2607XgTuZUWHB7v3RTudCi21WcOepTBlr47I6z+PnSsWq7lOrN3ZuWriUOJn5d3dJ5a77oCYNJxYm6Um6h30UtVFZWkpSURGpqqqqe1QGHw8Fll12GEILrr29fB2DLli28/PLL3HnnncTGdo4n/v3vf48QgldffbXd9pkzZ3LJJZdwzz33UFvrpwaGB84991yEEAgheOopL/3WrElcd9svEUYTQgiuu845995ae+iHaNHtKq3JEY0MSLASE+VcoGhdnOhF0QXVZfaGT4O/dbTEO9NOT6qiPu4FUPoBSUlJ3HLLLZSXl/P000932n/LLbfw5ptvcuGFF7Jy5cp2++6++24SEhK46SbP6a3btm0DlMh25K677qK4uNi7aPpg27ZtmEzq/bdz506Px2zavpvnX38bo1G9H2ZNHqMWtgKNu9V0GS26XaU1OaJD6bzeqLvgCXN0aM0OXYsqLbZ+61r46U9/SkJCAr/73e/aWZ4PP/wwK1as4LTTTuP1119vFTCAAwcOsGbNGi6//HKioz37wLdu3UpcXBxjxozptG/OnDmMGzeOlStX+m6X1IHDhw9TXl7OnDlzGDhwoEfRdTgc/Ojmm0lPS2XWVNW/bdak7H77/+0ttOh2FTdLtx29UXchnETFqmB56LdRC8nJyfz4xz/m5MmTrFixAoDnn3+ee+65h7Fjx/LOO+8QE9M+quC5555DSskVV1zR6Xx33HEHQgj2799PbW0tRqOx1R3w8ssvtx535ZVXkpeXx5o1awKe65YtWwBlPU+fPp09e/bgcDjaHbNy5Uq2bt3Ko7/+FYdz8zGbzUwZP6bn/LkaQMfpdh2jSflAO7Zh7+lauuFGCIhOVGmjp3hVp65w66238uSTT/L4448zYsQIbrjhBjIzM1m1ahWpqZ3vINasWYPRaOS0007rtG/mzJlcc801vPDCC8ybN49zzz23dd/ChQtbf58/fz4AH374IUuWLAlonu6iGx8fz6pVqzh8+HCrNV1WVsYvfvELTj/9dBaecx5l5TcxY/I4LHFJPd8lo58TcaIrhFgKPAkYgb9KKR/psN8CvAjMBE4CV0gpc4UQw4F9QI7z0I1Syp4JBjRZO4vuqW7pgort7eekpKRw880388gjj3DFFVeQkJDA+++/z/DhwzsdW1dXx44dOxg/frzHBbTLL7+cyspKXnjhBa655hpuuMFzRtbs2bMBOiVk+MIlurNmzWp97Z07d7aK7p133klVVRUrVqxg67bt6tgpE7SV2wtElHtBCGEEVgDnAxOAq4QQEzocdh1QIaUcDTwB/NZt32Ep5TTnT89FX5ssHixdZwB6b/t0u4IQ/Tq0x8WFF7bVH37llVeYOtVzksnx48ex2+1kZmZ6PZdrEW3GjBlej0lMTMRqtZKXlxfQ/KSUbN++nZiYGMaNG9d6bpdfd9OmTTz33HP84Ac/YPr06WzduhWAWVMnan9uLxBplu4c4JCU8giAEOI1YBmw1+2YZcAvnb//C3hadLm5WBcxRXcuEN0XLF03HvjfHvYW+ukq0ctMGJTA/Rf5aT8fJIWFhXzrW99qfb537952IuzOyZOq/m1ysncf+LZt2zCbzUyePNnn66akpFBSUhLQHA8cOEBVVRXz5s3DaDQycuRIkpKS2Llzp1o8+9GPSE1N5aGHHgLcrOJzlrWtSWh6jIiydIHBQL7b8wLnNo/HSClbgCrA5VwbIYTYLoT4VAhxZndPthWPlm6ERC9oQqayspKlS5dy7NgxHnzwQWJjY3n88ce9Jjy4ohVsNs8dGlpaWti1axcTJkzAYvHtJ29oaPAa/dARd9eCi2nTprFr1y6effZZtm7dym9+8xtSUlTjx23btmGxWJg0eUpA59eEl0izdD1ZrB17R3s7pggYKqU8KYSYCfxHCDFRStnJPBNC3ADcADB0aBj8luZozz5dYey1YtvhJtwWZKRjs9lYtmwZu3bt4r777uPee++lurqaxx9/nGeeeYbbb7+905gBAwYAbRZvR/bu3YvNZvPpWgAV2lVZWcmIEYEVhHFfRHMxffp0Pv30U+6++25mz57NddddB8DRo0c5efIkc+bMwWw2tztPVlYWt956K7feemvrtl27djF79my2bdvGhAkdPX2aUIg0S7cAGOL2PAso9HaMEMIEJALlUspGKeVJACnlVuAw4LF4rJTyWSnlLCnlrPT0dE+HBIcnS9dWpWJdtU/0lMNut7N8+XLWrVvHDTfcwAMPPADAz3/+c2JiYnjssceor+/c+DMzM5P09HRycnI67QPYsWMHoATRFzk5OUgpmTZtWkDz9SS6M2bMQEpJZWUlTz/9NP/f3v1HR1GeCxz/Pmx+QUyAKFBONVdQRH5ZfoQAconRXhSQU2hBxdNzxQrFq3gK+Kv88BKsFZVyxIKoaMEiKiIiXm1viyB66bFHlIQIAoIBgsVWJISAIBAgz/1jZpfdZBM3yWR3E57POXsy+8477zy447PvvjPzjn8ELjCeG9Qr9hswYACffPJJSNnkyZMZP368JVwPxVvS/QToJCIdRCQJGAO8XanO28BYd3k0sF5VVUTauCfiEJGOQCdgT1SiTmhe9TrdUzGed8HU2cSJE1m9ejUjR47kmWeeCZS3adOGu+++m2+++YbnnnuuynYiQk5ODiUlJRQVFVVZ7+8Bp6fXfFx89NFHAFx77bXfG2tFRQWFhYWkpqbSpcu5J+oOGzaM1atXs379erKzz90CHm4owq9y0n3rrbfYvHlz4EvHeCOukq47RnsPsAbn8q/XVXWbiPxGRH7iVlsMXCgiRcC9wFS3PAfYIiKf4pxg+y9VLY1K4AnJVe9IOxnDuXRNneXl5bFo0SIGDRrE8uXLQ+42A3jggQdo0aIFc+bM4cSJE1W2HzVqFABr1qypss7fE50xYwYPPPAAs2bNYuXKlVXqvfvuu/h8PkaMqOHRSq4dO3Zw7NgxevbsGTKZekZGBiNHjiQ3Nzekfk093f79+wfubDt16hT3338/M2fODHs9sqkHVT2vX3369NF6e/VW1WcGhpYtGeq8GpHt27fHOoSYevbZZxXQ7t276+HDh6utd9999ymg8+bNq7Lu1KlT2q5dO83Ozg677YIFC/SKK67Q5ORkBXT69Okh68vKyjQlJUVHjBgRdvu8vDwFdO/evaqqunTpUgX0V7/6VUT/xoyMDG3evLmeOXOmyrqTJ09qUlKSrlmzRh9//HHt3LmzlpeXR9Suqh0/lQGbNEzOiXnSi/XLk6S78heq83uHlj0zUPWVW+rfdhTZ/zTemD17tgJaUFBQ623nz5+vgG7YsCHs+spJ12v9+vXTe+65R9PS0vSdd96p1bZ2/ISqLunG1fBCo5WQEmZM94iN6Z6npkyZQmZmJjNnzqzVdidOnOCxxx5j1KhRDBoUvSsegw0YMICFCxcyYMCAaq9HNvVjSdcLCSnVjOla0j0fpaSksGzZMrKysmo1iXlxcTETJkxg7ty5DRhdzfxjw08++WTMYmjq4u063capck9X3QnMrad73srJySEnp3aPpOnSpQuzZs1qmIAi9Morr3DnnXfSrdv5dV12NFnS9UJipQlv/BOYW0/XeMx/NUKrVt7NmVBRUcHBgwf54x//yNatW1mxYoVnbZuqLOl6ISHFec5UxVnnCQ2nmta8CyZ+5ObmVrkMrL42bNjAddddR+fOnVm1alWNc0eY+rOk6wX/fLNnTjq3/Z60eRdM45Gbm1tlwnPTcOxEmhcS3IlJ/DONBXq6dnOEMSaUJV0vBPd0wXq6xphqWdL1QqLb0/UnXf8E5jama4ypxJKuF6yna4yJkCVdLyRU7una1QvGmPAs6XrB39M9HdTTlWaQdEHsYjLGxCVLul7wP2cquKdrE5gbY8KwpOuFxEpJ1+bSNcZUw5KuF8L1dG081xgThiVdL/iTbvCYrl25YIwJw5KuF6r0dG0uXWNMeJZ0vRAY03Wnd7SerjGmGpZ0vRDo6boTmduYrjGmGpZ0veBLAsTp6apaT7cJmjdvHvPmzYt1GKYJsKkdvSBy7pE9p79zJjC3nm6TsWjRIqZNmwZAamoqEyZMiHFEpjGzpOuVhGSnp2vzLjQpe/bs4f7772fBggVUVFRw3333MXjwYDp06BDr0EwjZUnXK4nNnTFdm0u3yaioqOD2229n9OjR/PKXvwTgww8/5Pbbb+f999+nWTMbnTO1Z0nXK9bTbXKaNWvGhg0bQspeeumlGEVjmgr7qvZKQnPnOl2bS9cYU4O4S7oiMkREdopIkYhMDbM+WURWuOs3isilQeumueU7ReSGaMZNQrJzR5r1dI0xNYirpCsiPmAhMBToCtwqIl0rVRsHHFbVy4F5wBPutl2BMUA3YAjwjNtedCS4j2G3uXTPa7fddhtt27bl+PHjEdXPz89HRFi8eHG1dWbNmoWIUFxc7FGUJpbiKukC2UCRqu5R1XLgNWBEpTojgKXu8hvAj0VE3PLXVPWUqu4Fitz2oiPRTbrW0230HnnkEUSE8ePHV1snPz+ftLQ02rZtyxdffAHApk2bePnll5k6dSqpqalVtnnyyScREV599dVAWZ8+fRg5ciQPPfQQx44dq1O8gwcPRkQQEebPn19tvXHjxgXqjRs3rk77MvUXb0n3h8A/gt7vd8vC1lHVM8AR4MIIt204IT1dsQnMG7FJkybRunVrli1bxv79+6usLyoqYtiwYagqf/7zn+nUqRMA06dPJz09nbvuuitsuwUFBYCTaINNmzaNr7/+usaEWZOCggISEpxz4lu2bAlbZ+PGjbz44ov4fM6Pv6ysrDrty9RfvCXdcLN+a4R1ItnWaUBkgohsEpFNBw8erGWI1UhIOTemm5wOdjlRo5Wens6UKVMoLy9n7ty5IesOHDjADTfcQGlpKW+88QZ9+/YFYNeuXaxbt46bb76Z5s2bh203Pz+fCy64IJCk/bKzs7nyyitZtGgRZ8+erVWsu3fvprS0lOzsbNq1axc26VZUVDBx4kTatGkTSLaWdGMn3jLDfuCSoPcXA/+sro6IJAAtgdIItwVAVZ9X1SxVzWrTpo03kQf3dG08t9Hz93ZfeOEFSkpKAPj2228ZOnQoe/bsYfHixQwZMiRQf8mSJagqt9xyS5W2fv3rXyMifP755xw7dgyfzxf4mf/yyy8DMGbMGL788kvWrVtXqzg3bdoEOL3nXr16sW3bNioqKkLqLFq0iPz8fObMmcPu3btJTEzkqquuqtV+jHfiLel+AnQSkQ4ikoRzYuztSnXeBsa6y6OB9aqqbvkY9+qGDkAn4OMoxe2O6Z6yeReaiPT0dCZPnsx3333H73//e8rLy/npT3/K5s2beeKJJ7jttttC6q9btw6fz0f//v2rtNWnTx/GjnUO2auvvpq8vLzA65prrgFg4MCBAKxdu7ZWcQYn3d69e/Pdd9+xe/fuwPqSkhJmzJjBgAEDuOaaaygpKaFHjx4kJyfXaj/GO3F1c4SqnhGRe4A1gA9YoqrbROQ3wCZVfRtYDCwTkSKcHu4Yd9ttIvI6sB04A0xU1dr9VquPhJRzd6Q1xZ7uX6bC11tjHUXNftADhj7uWXOTJ0/mqaee4umnn2b79u289957TJo0iQcffDCk3vHjxyksLKRLly5hT6DdfPPNlJWVsXTpUsaOHRt27gb/MEXlmzG+jz/pZmVlBfa9ZcuWwBDG1KlTOXLkCAsXLiQ/Pz9Q18ROvPV0UdX/VdUrVPUyVX3ULZvpJlxU9aSq3qSql6tqtqruCdr2UXe7zqr6l6gGHrgj7Yj1dJsIf2+3rKyMN998k1tuuSXsTGNfffUVZ8+epX379tW25T+J1rt377DrW7ZsSUpKCl9++WXE8akqmzdvpkWLFlx55ZWBtv3juhs3bmTJkiXceeed9OrVy5JunIirnm6j5r8j7eQRaNM51tF4z8MeZGMyZswY8vLyuPjii3nppZeQME94PnToEACtW7eutp2CggISExPp0aNHtXUyMjI4cOBAxLHt2rWLI0eOcPXVV+Pz+ejYsSOtWrViy5YtgZNnF154Ib/97W+B0F6xiR1Lul5JcMfIjpdYT7cJ2bZtGwD9+vUjKSkpbB3/1QonT54Mu/7MmTNs3bqVrl271jiWeuLEiWqvfAgnXBLt2bMnW7du5fnnnyc/P58XXniBjIwMwEn8ycnJdO/ePeJ9GO/F3fBCo5Xo/s9y+njTHNM9T0Xyk7xt27bAuR5vZdu3b+fkyZPVDi2Ac1lXWVlZoK1IBJ9E8+vVqxd79uxh+vTp9O3bN3ATxN69ezl06BA/+tGPSExMDNRfuXIlycnJ7Nu3L1A2adIkLrvsslr1uk3kLOl6JSGoB2M93SYjkp/k7du3p02bNuzcuTPs+sLCQsBJiNXZuXMnqkrPnj1rHVtw0u3duzeqSllZGU8//XRgOKS6L4/Ro0fTo0ePwBDE3LlzWb58OX/9619p165dxLGYyNnwglcSgn4WWk+3yfAnq8p3kQUTEXJycli1ahVFRUVcfvnlIev9PeD09OqPi48++giAa6+9NqK4KioqKCwsJDU1lS5dugTKhw0bxurVq2nVqhXZ2efugq/uy0NEmD17NjfeeCOXXXYZjz76KOvXr69yA4fxjiVdr4T0dG0C86Zg3759lJSU0LFjxxpPkgGMGjWKVatWsWbNmipJ15+wZ8yYwWeffUZqairdunXjpptuCtR599138fl8jBhReaqR8Hbs2MGxY8cYOHBgyGTqGRkZjBw5skr9moZJrr/+evr27ctDDz3EO++8E7h8zTQMG17wiv+JwGA93SaiNpdYjRo1inbt2oWd5DwnJ4cFCxaQmprKggULePjhhwNDDgBHjhzhrbfeYvjw4VxyySVVtq8ptpp64MEKCgpo3rw5XbtWnrQP1q9fz6effoqq2pBCNKjqef3q06ePeqLoPdW8dOdV/Hdv2oyy7du3xzqERm327NkKaEFBQa22mz9/vgK6YcOGsOvz8vIU0L1793oQZajCwkJt2bKlLl68WH/2s5/p9ddfX+e27PgJhXNDV5WcYz1dr1hP97w3ZcoUMjMzmTlzZsTbnDhxgscee4xRo0YxaNCgBoyuqn379jFs2DDuvfde7rjjDh5++GHWrl3LBx98ENU4zjeWdL0SnHTt6oXzUkpKCsuWLSMrKyviScyLi4uZMGFCldnMGlppaSlDhgxh+PDhgS+J7t27c9NNNwUeN28ahp1I84r1dA3O+G1OTk7E9bt06cKsWbMaLqBqZGRksGPHjirlK1asiHos5xtLul5J9CddgaS0mIZimpbc3FwAWrVqFdtAjCcs6XrF39NNTrMJzI2ncnNzA4nXNH6WHbwSSLo2tGCMqZ4lXa/4k66N5xpjamBJ1yvW0zXGRMCSrleaNQNfkvV0jTE1sqTrpYSURt/TdW6kMaZ27LiJnCVdL6X/EFpfGuso6szn83H69OlYh2EaodOnT+Pz+WIdRqNgl4x5adya0JskGpm0tDSOHj3KRRddFOtQTCNz9OhR0tLs+vRIWE/XSyktQ6d4bGQyMjI4fPgwJSUllJeX209GUyNVpby8nJKSEg4fPhx4LJCpmfV0TUBycjKZmZmUlpZSXFzM2bPRe4K9aZx8Ph9paWlkZmbW+Pw3c44lXRMiOTmZ9u3b1/g4cWNM3dnwgjHGRJElXWOMiSJLusYYE0Vxk3RFJENE1orIF+7fsE8CFJGxbp0vRGRsUPkHIrJTRArdV9voRW+MMZGJm6QLTAXeU9VOwHvu+xAikgHkAf2AbCCvUnL+uar2dF/fRCNoY4ypjXhKuiOApe7yUqDqc6ThBmCtqpaq6mFgLTAkSvEZY0y9xVPSbaeq/wJw/4YbHvgh8I+g9/vdMr8X3aGF/xYRabhQjTGmbqJ6na6IrAN+EGbVjEibCFPmv23q56r6lYikAauA/wReqiaOCcAEgMzMzAh3bYwx9RfVpKuq/1HdOhE5ICLtVfVfItIeCDcmux/IDXp/MfCB2/ZX7t9vReRVnDHfsElXVZ8Hnnf3e1BE9lWqchFQEsm/KYospsjFY1wWU+TiMa66xPRv4QolXu6vF5HfAYdU9XERmQpkqOqDlepkAPlAb7eoAOgDHAVaqWqJiCQCy4F1qvpcHWPZpKpZdf23NASLKXLxGJfFFLl4jMvLmOJpTPdxYLCIfAEMdt8jIlki8gcAVS0FHgE+cV+/ccuSgTUisgUoBL4CXoj+P8EYY2oWN3MvqOoh4MdhyjcB44PeLwGWVKpzHKfHa4wxcS2eerrx5PlYBxCGxRS5eIzLYopcPMblWUxxM6ZrjDHnA+vpGmNMFFnSDSIiQ9z5G4rcKygaen9LROQbEfksqCzsHBTimO/GtkVEegdtE3Y+ijrGdImIvC8iO0Rkm4hMinVcIpIiIh+LyKduTA+75R1EZKPb/goRSXLLk933Re76S4PamuaW7xSRG+oaU1B7PhHZLCJ/ioeYRKRYRLa6Nwltcstieky57bUSkTdE5HP32BoQ42Oqs5ybp6VQRI6KyOSoxKSq9nKGWHzAbqAjkAR8CnRt4H3m4Fz+9llQ2Rxgqrs8FXjCXR4G/AXnBpH+wEa3PAPY4/5t7S63rkdM7YHe7nIasAvoGsu43LYvcJcTgY3uvl4HxrjlzwF3uct3A8+5y2OAFe5yV/dzTQY6uJ+3r56f4b3Aq8Cf3PcxjQkoBi6qVBbTY8ptcykw3l1OAlrFQ1xuuz7ga5zrahs8pgZLKI3tBQwA1gS9nwZMi8J+LyU06e4E2rvL7YGd7vIi4NbK9YBbgUVB5SH1PIjvf3Au4YuLuIAWONdn98O5WD2h8ucHrAEGuMsJbj2p/JkG16tjLBfjTM50HfAndx+xjqmYqkk3pp8dkA7sxT2HFC9xBbVzPfBhtGKy4YVzvm9eh2ipbg6K6uJrsLjdn8C9cHqWMY3L/RlfiHOn4lqcHmGZqp4J035g3+76I8CFXscEPAU8CFS47y+Mg5gUeFdE8sW53R1if0x1BA7izI2yWUT+ICKpcRCX3xicG6qIRkyWdM+paV6HeFBdfA0St4hcgDOHxWRVPRrruFT1rKr2xOldZgNdami/wWMSkeHAN6qaH1wcy5hcA1W1NzAUmCgiOTXUjVZMCTjDaM+qai/gOGGmbo1BXLhj7j8BVn5fVa9isqR7zn7gkqD3FwP/jEEcB8SZewIJnYOiuvg8j1ucW6lXAa+o6pvxEheAqpbhzLfRH2glIv4bfILbD+zbXd8SKPU4poHAT0SkGHgNZ4jhqRjHhKr+0/37DbAa5wsq1p/dfmC/qm5037+Bk4RjHRc4X04FqnrAfd/wMdV3PKSpvHC+jffgnMzwn0jrFoX9XkromO7vCB3In+Mu30joQP7HbnkGznhZa/e1F2feirrGIzgTBT1VqTxmcQFtcObWAGgO/A0YjtM7CT5pdbe7PJHQk1avu8vdCD1ptYd6nkhz283l3Im0mMUEpAJpQct/x5lvOqbHlNvm34DO7vIsN6Z4iOs14BfRPM4bNKE0thfOGcpdOOOFM6Kwv+XAv4DTON+Y43DG+d4DvnD/Zrh1BVjoxrYVyApq5w6gyH39op4x/TvOzyP/PBaF7n+XmMUFXAVsdmP6DJjplncEPnbbXwkku+Up7vsid33HoLZmuLHuBIZ69Dnmci7pxiwmd9+fuq9t/mM41seU215PYJP7Gb7lJqhYH+stgENAy6CyBo/J7kgzxpgosjFdY4yJIku6xhgTRZZ0jTEmiizpGmNMFFnSNcaYKLKka8z3EJFZIhJvD0o0jZQlXWOMiSJLusYYE0WWdI0xJoos6RpjTBRZ0jXGmCiypGuMMVFkSdcYY6LIkq4xxkSRJV1jjImihO+vYowBkkRkdJjy/1PVg1GPxjRalnSNiUwa4R9eeC3OM9uMiYg9OcIYY6LIxnSNMSaKLOkaY0wUWdI1xpgosqRrjDFRZEnXGGOiyJKuMcZEkSVdY4yJIku6xhgTRZZ0jTEmiv4fXyVBpoe9qWAAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", "text/plain": [ "
" ] @@ -280,7 +286,7 @@ "source": [ "# checking convergence\n", "# Looking at \"convergence\"\n", - "L_range = range(5, len(X), 200) # L values to test\n", + "L_range = range(5, len(X), 1000) # L values to test\n", "tau = 1\n", "E = 2\n", "\n", @@ -291,16 +297,16 @@ " Xhat_My.append(ccm_XY.causality()[0]) \n", " Yhat_Mx.append(ccm_YX.causality()[0]) \n", " \n", + "print('X->Y r', np.round(Xhat_My[-1], 2), 'p value', np.round(ccm_XY.causality()[1], 4))\n", + "print('Y->X r', np.round(Yhat_Mx[-1], 2), 'p value', np.round(ccm_YX.causality()[1], 4)) \n", + " \n", "# plot convergence as L->inf. Convergence is necessary to conclude causality\n", "plt.figure(figsize=(5,5))\n", "plt.plot(L_range, Xhat_My, label='$\\hat{X}(t)|M_y$')\n", "plt.plot(L_range, Yhat_Mx, label='$\\hat{Y}(t)|M_x$')\n", - "plt.xlabel('L', size=15)\n", - "plt.ylabel('correl', size=15)\n", - "plt.legend(prop={'size': 20}) \n", - "\n", - "print('X->Y r', np.round(Xhat_My[-1], 2), 'p value', np.round(ccm_XY.causality()[1], 4))\n", - "print('Y->X r', np.round(Yhat_Mx[-1], 2), 'p value', np.round(ccm_YX.causality()[1], 4))" + "plt.xlabel('L', size=12)\n", + "plt.ylabel('correl', size=12)\n", + "plt.legend(prop={'size': 12}) " ] }, { @@ -314,7 +320,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "From the charts above, we find stronger convergent cross mapping for the effect of rain $X$ on dam levels $Y$ and weaker CCM for the effect of dam levels $Y$ on rain $X$. Note that for both cases, p-values are 0.0 which means the causalities are significant. We can then say that rain drives dam levels more strongly than dam levels drive rain." + "From the charts above, we find significant convergent cross mapping for the effect of rain $X$ on dam levels $Y$. Note that for $X -> Y$ cases, p-value is 0.0 which means the causality is significant while for $Y -> X$, p value is > 0.05. We can then say that rain drives dam levels but dam levels do not drive rain." ] }, { diff --git a/07_CrosscorrelationsFourierTransformandWaveletTransform/07_CrosscorrelationsFourierTransformandWaveletTransform.ipynb b/07_CrosscorrelationsFourierTransformandWaveletTransform/07_CrosscorrelationsFourierTransformandWaveletTransform.ipynb index b834e6e..1586776 100644 --- a/07_CrosscorrelationsFourierTransformandWaveletTransform/07_CrosscorrelationsFourierTransformandWaveletTransform.ipynb +++ b/07_CrosscorrelationsFourierTransformandWaveletTransform/07_CrosscorrelationsFourierTransformandWaveletTransform.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Cross-Correlations, Fourier Transform, and Wavelet Transform\n", + "# Chapter 7: Cross-Correlations, Fourier Transform, and Wavelet Transform\n", "prepared by Gilbert Chua" ] }, @@ -1934,7 +1934,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Wavelet Transform" + "## Wavelet Transform" ] }, { @@ -2502,7 +2502,7 @@ } }, "source": [ - "# Summary\n", + "## Summary\n", "\n", "\n", "To summarize, using these techniques can extract valuable information about time series which are not readily available.\n", @@ -2561,7 +2561,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.7.4" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/08_WinningestMethods/lightgbm_jena_forecasting.ipynb b/08_WinningestMethods/lightgbm_jena_forecasting.ipynb index 8bdd54e..7c0aac5 100644 --- a/08_WinningestMethods/lightgbm_jena_forecasting.ipynb +++ b/08_WinningestMethods/lightgbm_jena_forecasting.ipynb @@ -6,9 +6,7 @@ "id": "FJogEfYBOfUm" }, "source": [ - "# Chapter 8: Winningest Methods in Time Series Forecasting\n", - "\n", - "## Temperature Forecasting (Supplementary Notebook)\n", + "# Temperature Forecasting (Supplementary Notebook)\n", "\n", "Similar to what was done in previous sections, this notebook applies the methodology used in the M5 Forecasting notebook to the Jena Climate dataset. Specifically, we will be forecasting the temperature variable." ] @@ -987,7 +985,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.7.4" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/08_WinningestMethods/lightgbm_m5_forecasting.ipynb b/08_WinningestMethods/lightgbm_m5_forecasting.ipynb index 2e307c2..06794a8 100644 --- a/08_WinningestMethods/lightgbm_m5_forecasting.ipynb +++ b/08_WinningestMethods/lightgbm_m5_forecasting.ipynb @@ -441,7 +441,6 @@ ], "source": [ "df_sales.rename(columns = dict(zip(df_sales.columns[6:], date_list)), inplace = True)\n", - "\n", "df_sales" ] }, @@ -2603,7 +2602,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.7.4" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/_config.yml b/_config.yml index 73bb4d1..c3e9e8c 100644 --- a/_config.yml +++ b/_config.yml @@ -30,3 +30,10 @@ repository: html: use_issues_button: true use_repository_button: true + +parse: + myst_enable_extensions: + # don't forget to list any other extensions you want enabled, + # including those that are enabled by default! + - amsmath + - dollarmath diff --git a/_toc.yml b/_toc.yml index 57eb86b..12b4f02 100644 --- a/_toc.yml +++ b/_toc.yml @@ -2,7 +2,7 @@ # Learn more at https://jupyterbook.org/customize/toc.html # - file: Preface/Preface - numbered: true + numbered: false - file: 00_Introduction/00_Introduction - file: 01_AutoRegressiveIntegratedMovingAverage/01_AutoRegressiveIntegratedMovingAverage - file: 02_LinearForecastingTrendandMomentumForecasting/02_LinearTrendandMomentumForecasting