{ "metadata": { "name": "", "signature": "sha256:2be34651646d672442140d1fc4158c3fe65f4deb31b9a4c3a74a2136aad3fcdc" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Sea-level rise \n", "================" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# We need a few libraries\n", "import io\n", "\n", "import numpy as np\n", "import pandas\n", "import requests\n", "\n", "import matplotlib.pyplot as plt\n", "import matplotlib.style\n", "matplotlib.style.use('ggplot')\n", "%matplotlib inline\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 48 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's start with downloading the data from the different tidal guages. We use the 6 main Dutch stations.\n", "\n", "- http://www.psmsl.org/data/obtaining/stations/23.php Den Helder\n", "- http://www.psmsl.org/data/obtaining/stations/32.php IJmuiden\n", "- http://www.psmsl.org/data/obtaining/stations/20.php Vlissingen\n", "- http://www.psmsl.org/data/obtaining/stations/25.php Harlingen\n", "- http://www.psmsl.org/data/obtaining/stations/22.php Hoek van Holland\n", "- http://www.psmsl.org/data/obtaining/stations/24.php Delfzijl" ] }, { "cell_type": "code", "collapsed": false, "input": [ "stations = [\n", " ['Vlissingen', 20, lambda x: x - (6976-46)],\n", " ['Hoek van Holland', 22, lambda x:x - (6994 - 121)],\n", " ['Den Helder', 23, lambda x: x - (6988-42)],\n", " ['Delfzijl', 24, lambda x: x - (6978-155)],\n", " ['Harlingen', 25, lambda x: x - (7036-122)],\n", " ['IJmuiden', 32, lambda x: x - (7033-83)],\n", "]\n", "\n", "# you could use the monthly data\n", "monthly_url = 'http://www.psmsl.org/data/obtaining/rlr.monthly.data/%d.rlrdata'\n", "# and you can use the annual dat\n", "annual_url = 'http://www.psmsl.org/data/obtaining/rlr.annual.data/%d.rlrdata'\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 49 }, { "cell_type": "code", "collapsed": false, "input": [ "# Let's download them both\n", "monthly_dfs = []\n", "annual_dfs = []\n", "\n", "for station, id, to_local in stations:\n", " f = io.BytesIO(requests.get(monthly_url % (id,)).content)\n", " df = pandas.read_csv(f, sep=';', names=('year', 'waterlevel', 'code', 'another code'))\n", " # add a column with the station name\n", " df['station'] = station\n", " # convert to local coordinate system\n", " df['nap'] = df['waterlevel'].apply(to_local)\n", " # ignore the part before 1890\n", " df = df[df.year >= 1890]\n", " monthly_dfs.append(df)\n", " \n", " f = io.BytesIO(requests.get(annual_url % (id,)).content)\n", " df = pandas.read_csv(f, sep=';', names=('year', 'waterlevel', 'code', 'another code'))\n", " df['station'] = station\n", " df['nap'] = df['waterlevel'].apply(to_local)\n", " df = df[df.year >= 1890]\n", " annual_dfs.append(df) " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 50 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have downloaded the stations, we can create a new station with the mean. You can use many other techniques, but the mean works just fine here." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# compute the monthly mean\n", "monthly_df = pandas.concat(monthly_dfs)\n", "mean = monthly_df.groupby('year').mean().reset_index()\n", "mean['station'] = 'mean'\n", "monthly_df = pandas.concat([monthly_df, mean[['year', 'waterlevel', 'nap', 'station']]])\n", "monthly_df = monthly_df.reset_index()\n", "# add the year number \n", "monthly_df['year_floor'] = np.floor(monthly_df['year'])\n", "\n", "annual_df = pandas.concat(annual_dfs)\n", "mean = annual_df.groupby('year').mean().reset_index()\n", "mean['station'] = 'mean'\n", "annual_df = pandas.concat([annual_df, mean[['year', 'waterlevel', 'nap', 'station']]])\n", "annual_df = annual_df.reset_index()\n", "\n", "# save the files to json\n", "# add the annual mean also to the monthly means\n", "monthly_df = monthly_df.merge(annual_df[['year', 'nap']], left_on='year_floor', right_on='year', suffixes=['', '_annual'])\n", "monthly_df[['year', 'nap', 'nap_annual', 'station']].to_json('monthly.json', orient='records')\n", "annual_df[['year', 'nap', 'station']].to_json('annual.json', orient='records')\n", "\n", "df = annual_df" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 51 }, { "cell_type": "code", "collapsed": false, "input": [ "# in memory file\n", "grouped = df.groupby(['station'])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 52 }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "fig, ax = plt.subplots(figsize=(10,7))\n", "for station, df in grouped:\n", " annual_jitter = np.random.uniform(-0.5, 0.5, size=len(df))\n", " monthly_jitter = np.random.uniform(-0.5*1/12.0, 0.5*1/12.0, size=len(df))\n", " if station == 'mean':\n", " pch = '-'\n", " alpha = 1.0\n", " ax.plot(df['year'] + annual_jitter, df['nap']/10 , pch, alpha=alpha, label=station)\n", " else:\n", " pch = '.'\n", " alpha = 0.5\n", " ax.plot(df['year'] + annual_jitter, df['nap']/10 , pch, alpha=alpha, label=station)\n", " ax.set_ylabel('waterlevel [cm NAP]')\n", " ax.set_xlabel('year')\n", "legend = ax.legend(loc='best')\n", "legend.legendPatch.set_alpha(0.5)\n", "legend.legendPatch.set_facecolor('white')\n", "\n", "fig.savefig('stations.png')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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IyJ0clt/4ctO5dlQVarnStn3rtQnbUDJKgX4QUGSOIAiCeOXpJ1o1bpGtccDvAK6U3aFY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8E6w3v0zHQaS9NAEEJN37ttsPxM3ds37/NbX3qtrwjyg7TuGid+oHQmt0gu98yuPJ6dtvFKSEhI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\n63opmWtkvpK28u5OD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"text": [ "" ] } ], "prompt_number": 53 }, { "cell_type": "code", "collapsed": false, "input": [ "import statsmodels.api as sm\n", "df = grouped.get_group('mean')\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 54 }, { "cell_type": "code", "collapsed": false, "input": [ "y = df['waterlevel']\n", "X = df['year']\n", "X = sm.add_constant(X)\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 55 }, { "cell_type": "code", "collapsed": false, "input": [ "model = sm.OLS(y, X)\n", "results = model.fit()\n", "from IPython.display import display\n", "display(results.summary())" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: waterlevel R-squared: 0.828
Model: OLS Adj. R-squared: 0.827
Method: Least Squares F-statistic: 589.0
Date: Sun, 16 Nov 2014 Prob (F-statistic): 1.60e-48
Time: 23:49:03 Log-Likelihood: -598.81
No. Observations: 124 AIC: 1202.
Df Residuals: 122 BIC: 1207.
Df Model: 1
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [95.0% Conf. Int.]
const 3219.4017 149.443 21.543 0.000 2923.564 3515.239
year 1.8581 0.077 24.269 0.000 1.707 2.010
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Omnibus: 3.101 Durbin-Watson: 1.544
Prob(Omnibus): 0.212 Jarque-Bera (JB): 2.704
Skew: -0.357 Prob(JB): 0.259
Kurtosis: 3.120 Cond. No. 1.06e+05
" ], "metadata": {}, "output_type": "display_data", "text": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: waterlevel R-squared: 0.828\n", "Model: OLS Adj. R-squared: 0.827\n", "Method: Least Squares F-statistic: 589.0\n", "Date: Sun, 16 Nov 2014 Prob (F-statistic): 1.60e-48\n", "Time: 23:49:03 Log-Likelihood: -598.81\n", "No. Observations: 124 AIC: 1202.\n", "Df Residuals: 122 BIC: 1207.\n", "Df Model: 1 \n", "==============================================================================\n", " coef std err t P>|t| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const 3219.4017 149.443 21.543 0.000 2923.564 3515.239\n", "year 1.8581 0.077 24.269 0.000 1.707 2.010\n", "==============================================================================\n", "Omnibus: 3.101 Durbin-Watson: 1.544\n", "Prob(Omnibus): 0.212 Jarque-Bera (JB): 2.704\n", "Skew: -0.357 Prob(JB): 0.259\n", "Kurtosis: 3.120 Cond. No. 1.06e+05\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] The condition number is large, 1.06e+05. This might indicate that there are\n", "strong multicollinearity or other numerical problems.\n", "\"\"\"" ] } ], "prompt_number": 56 }, { "cell_type": "code", "collapsed": false, "input": [ "def fit(startyear):\n", " df_selected = df[df['year'] > startyear]\n", " y = df_selected['waterlevel']\n", " X = df_selected['year']\n", " X = sm.add_constant(X)\n", " model = sm.OLS(y, X)\n", " results = model.fit()\n", " display(results.summary())" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 57 }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.html.widgets import interactive" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 58 }, { "cell_type": "code", "collapsed": false, "input": [ "interactive(fit, startyear=(1890, 1980, 10))" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: waterlevel R-squared: 0.681
Model: OLS Adj. R-squared: 0.677
Method: Least Squares F-statistic: 172.6
Date: Sun, 16 Nov 2014 Prob (F-statistic): 8.98e-22
Time: 23:49:06 Log-Likelihood: -403.92
No. Observations: 83 AIC: 811.8
Df Residuals: 81 BIC: 816.7
Df Model: 1
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [95.0% Conf. Int.]
const 3107.2138 287.403 10.811 0.000 2535.372 3679.056
year 1.9146 0.146 13.138 0.000 1.625 2.205
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Omnibus: 3.114 Durbin-Watson: 1.547
Prob(Omnibus): 0.211 Jarque-Bera (JB): 2.643
Skew: -0.434 Prob(JB): 0.267
Kurtosis: 3.101 Cond. No. 1.62e+05
" ], "metadata": {}, "output_type": "display_data", "text": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: waterlevel R-squared: 0.681\n", "Model: OLS Adj. R-squared: 0.677\n", "Method: Least Squares F-statistic: 172.6\n", "Date: Sun, 16 Nov 2014 Prob (F-statistic): 8.98e-22\n", "Time: 23:49:06 Log-Likelihood: -403.92\n", "No. Observations: 83 AIC: 811.8\n", "Df Residuals: 81 BIC: 816.7\n", "Df Model: 1 \n", "==============================================================================\n", " coef std err t P>|t| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const 3107.2138 287.403 10.811 0.000 2535.372 3679.056\n", "year 1.9146 0.146 13.138 0.000 1.625 2.205\n", "==============================================================================\n", "Omnibus: 3.114 Durbin-Watson: 1.547\n", "Prob(Omnibus): 0.211 Jarque-Bera (JB): 2.643\n", "Skew: -0.434 Prob(JB): 0.267\n", "Kurtosis: 3.101 Cond. No. 1.62e+05\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] The condition number is large, 1.62e+05. This might indicate that there are\n", "strong multicollinearity or other numerical problems.\n", "\"\"\"" ] } ], "prompt_number": 59 }, { "cell_type": "code", "collapsed": false, "input": [ "def plot(startyear):\n", " df_selected = df[df['year'] > startyear]\n", " y = df_selected['waterlevel']\n", " X = df_selected['year']\n", " X = sm.add_constant(X)\n", " model = sm.OLS(y, X)\n", " results = model.fit()\n", " fig, ax = plt.subplots()\n", " ax.plot(df['year'], df['waterlevel'], '.', alpha=0.1)\n", " ax.plot(df_selected['year'], results.fittedvalues)\n", " \n", " ax.set_ylim(6400,7300)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 60 }, { "cell_type": "code", "collapsed": false, "input": [ "interactive(plot, startyear=(1860, 1980, 10))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 61 }, { "cell_type": "code", "collapsed": false, "input": [ "from statsmodels.sandbox.regression.predstd import wls_prediction_std\n", "\n", "def plot(startyear):\n", " df_selected = df[df['year'] > startyear]\n", " y = df_selected['waterlevel']\n", " X = df_selected['year']\n", " X = sm.add_constant(X)\n", " model = sm.OLS(y, X)\n", " results = model.fit()\n", " fig, ax = plt.subplots()\n", " ax.plot(df['year'], df['waterlevel'], '.', alpha=0.1)\n", " ax.plot(df_selected['year'], results.fittedvalues)\n", " fit, lower, upper = wls_prediction_std(results)\n", "\n", " plt.fill_between(df_selected['year'], lower, upper, alpha=0.3)\n", " \n", " ax.set_ylim(6400,7300)\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 62 }, { "cell_type": "code", "collapsed": false, "input": [ "import statsmodels.sandbox.regression.predstd" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 63 }, { "cell_type": "code", "collapsed": false, "input": [ "interactive(plot, startyear=(1860, 1980, 10))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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MQa2zHkMDXIe9Ve16HUVRxPxqLWkojhPBXHmNSriQ9BgqcN3lo7z7zSUuH8uR2UrS3M7f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\nBjycLAsK8Ahwt7sfBg6b2bG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"text": [ "" ] } ], "prompt_number": 64 }, { "cell_type": "code", "collapsed": false, "input": [ "%load_ext rpy2.ipython" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "The rpy2.ipython extension is already loaded. To reload it, use:\n", " %reload_ext rpy2.ipython\n" ] } ], "prompt_number": 65 }, { "cell_type": "code", "collapsed": false, "input": [ "%Rpush df\n", "%R library('ggplot2')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 66, "text": [ "\n", "[str, str, str, ..., str, str, str]" ] } ], "prompt_number": 66 }, { "cell_type": "code", "collapsed": false, "input": [ "def test(startyear):\n", " df_selected = df[df['year']>startyear]\n", " %Rpush df_selected\n", " %R print(ggplot(df, aes(year, waterlevel)) + geom_point(alpha=0.3) + stat_smooth(data=df_selected, method='loess'))\n", "interactive(test, startyear=(1860, 1980, 10))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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IkZFbhyXt+5KJbmYGo+YK1ndL6zS3fZJ+kwWcpLOhtoiACIhASgiMHj3a6urqDAFu3Tq3\nLUdayOXLlzuXMSI9YMCAc46uffv2TZbhVs5X6PPFYo7TAs+3r3Isz02tHHvWPkRABERABFJNALHM\nJ74c2LJlywzRpJ8YIc6VKAMhp+8XIa6vr8/bD0ymKyZZ4L2Qsm3bNtc3TZR0rv0WUkep15EFXGrC\nql8EREAEaoAAQVT03XqLFis1KJa4mb2rOYgDd/b06dODi875TF2IL2JeSCFBx4EDB9yq5Idm6FL/\n/v0L2bSs60iAy4pbOxMBERCB6iOwcuVK27hxoxNgLNpBgwa5zyNGjLDVq1c7l/GwYcMaxTksAQQU\nIS20ZLuogw8ChdZRjvUkwOWgrH2IgAiIQJUSOH78uBNfDg/hQ3ARYMrQoUOtX79+7nPU8br79+9v\nTDnpKirgD0OT2B9WOX3UvXr1KmCr8q8iAS4/c+1RBERABKqGAH3AuJ691ZndJxxVeAF0+PBhI89z\n2EIbhg8f7lzW9FMHhzWdPNXalqzrlXlICFtr/OsrCCt+pqpRBERABMpCgOCiVatW2aJFi4wcyZUo\n9PnidiYjFp/Hjh0bSzNwORcb8Ux7guK7fW8n+8dfTbe5S/pm+qNjaWZRlcgCLgqfNhYBERCByhGg\n73Xr1q2uAQjwpZdeah07dix7g3A5e7dzHDsn2IqgK29Vx1Hnm8svsP/6/Ri7Zvp6+8SHdmSitytv\nAkuA4zizqkMEREAEKkDAT8fHrokwxmWLADPshyFAhw4dsgsuuMAN7ylX8wh4YggQhXG/2S7pltqB\nVc/2cQVOnT7Tyn73yihbtLqv3Xfju1Y/aH/GKu7QUjPK8rsEuCyYtRMREAERyE8A8cRVGnSX5l/7\n7C99+/Z1ossS3K3du3d3PzJNH0kwKEyYcP7555ctdePChQuNwCkK0ctTpkxxnwv5AwfElweIOMre\ngx3s35+eYB3an7Hv3zXXunaKp9442kYdEuC4SKoeERABEYhAgOE7RA5jKY4ZM6YxariQqshERcQv\nkciIsc8ixfdgIRq4HAXXsRdf9kcAFZYskcgtFcQXd3pcbV2ytrf94oUG+9DELXbDZWsyfFtqQfl/\nlwCXn7n2KAIiIAKOAIJFEBV9nQgQswr5YTuFIso1KQF5msmXjPh16NDBiXOh9RWzHlY4LnA/Zpck\nG4WIL8dPwFX2g0OUtmQw2lOv19nr7w6wP7v2PRtXFz6KOsp+o2wjAY5CTduIgAiIQAkIxBV01LNn\nT7vsssucexr3MxHK5Sq4nHF7UxgK1FLhmHGXHzlypKVVW/z90NF29sAT9XYoU9X3Pj/fenb7wBNA\nvzL94zwgdOrUqcV6yrVC+c5KuY5I+xEBERCBlBBAEBApZgSi/7eYWYWyD5nxt8WMwc2ur9DvCFxD\nQ0Ohq7t+YoLFii1rt55vP31mgk0be8j+4lNLrE3rD3JG4wWArw/qwsMQ1stQbNvybS8BzkdGy0VA\nBESgDATox8VlTB9wIe7aMjSpbLvA8g1Gckfd8ey3B9uzb4ywz3x0uV37Ifqhcel/UBuWtRdflpAj\nWgIclbS2EwEREIEqI+CDp6rssJo9HCKkixXfEyfb2KMvjrVNO7vZt+5YYAN648bu2WS/2V4AvA5J\nKbKAk3Im1A4REAERqBECJA3xsxVFPWSyWv3kqUnWr+cR++6d86zjebmnKUSAGY/M/hDfXEFrUdtQ\n7HYS4GIJansREAEREIGCCWD5Fiu+b6/sa/85a6zNyGS1uvqiDS3um/mGeSWtSICTdkbUHhEQARGo\nQgKMD37nnXfcUCOsUKKzw5Yz77eyJ1+rtwXL+9m9n1pkowbvC1tFotaXACfqdKgxIiACIlB9BBhq\n9NZbbzWODybPc5cuXXIGnRERTTIOLNZgf+3BI+1dlPP7GRH+3p3zrXvX8iQXKeXZkACXkq7qFgER\nEIEaJ+CTbAQzXLGMyOTsqG/6hv2sTiQSIUKcMcxuiNHTE21S/U779FUrM9v9qSqoSoCr4jTqIERA\nBEQgeQQQWnI7Hz161Hr37t2YnzrbuvUtDybjIDMY272ztsGe+kO9G2I0vWG7X7Uq3qtOgLNDzqOc\nJT+Bcxx1Rdl/MdvwRJm2dtPmNLbbz/KS/RRfzPkrx7a0lxtjGq+TsJMVlINnS/vgOuGegqCkqRT7\nP8nxbtmyxU2swPEzK1OPHj0cB9Jj5iq4pUnPSTnzflt74o8XZ6zfnvbtO9+xwX0ZYtTO/dbSH6xm\nrvF8hfYk4fqvOgEOujnywW9pOf/knLw46mppX3H+TrvpM0lbu/lH4CaVtnb79H6kuUtTwfVHm9PG\nm2ubWXKau7Em8TwgRLQ7bby5n0RtM9dYrokVqBNhzzfbESk0Kbv2tbPH533UunU5bd/53Fzr3PFM\nZpvCzy7Xd3MPPLQh6rFltwIxj1qqToCjgtB2IiACIiACxRNAXBHffCLb3B4Q6F2HR9rPXhxvl47f\naBMGvmybNpx0AVuDBg1qbtNU/iYBTuVpU6NFQAREoHQE8DIwVtd71QrdE1Ylfb5RvUIvvTnEZs4b\nbp+fsdR6d3zHDh36wB3NRApkzUriWN5C2eRaTwKci4qWiYAIiEAJCCAkSel/zHd4uI8XLFjg+mJx\n406cONG8azjfNiwnYIopBdk+bDl5qnUmpWRDJqVkV/urzy7IZLc6muk/blpL2roemrY+9zcJcG4u\nWioCIiACsRIgCQVDbIh3GDt2rPXv3z/W+uOqjAkS/NR9WLIbNmxoUYCxTslwFUUkdx/oYP/25CQ3\ndeB3Pje/MaUkUdPMK0wbmGGp2qxfzpcEOK6rVvWIgAiIQB4CZIHy41uxKpkeL6kCnB1UFEyGkevw\nGK/LK0pZsbGH/UdmCsEPT95s11+6NuPyPlsLwZn19fVOgH3A49lfq+OTBLg6zqOOQgREIMEEskUt\nyYKC5TlkyBDbs2ePde7c2UaMGJGTLNZuMTMavfzWEHt+7gf9vZPqd+XcBwuTzCpvowv8QQJcICit\nJgIiIAJRCSBkI0eOtPXr17vxp2PGjIlaVVm2GzVqlDFW9/jx4zn3Rz8v/b30+4Ytp063tl9lJlJY\nt+38zBSCb1r/Xozvrc0iAa7N866jFoGqIMBQl82bN7t+1YEDBybaWho6dKjxSmqBIxZt165dXQrI\nfO2EOZHOPmFGvvVyLd9/6Dz7t6cmWucOp+27mf7eTpn3Wi4S4Fo++zp2EUg5ARL8EzBEoY/1wgsv\nTPkRVab5uJuXL1/uds5nAsUaGhrOaQwWMRMpRBlmtG5rN/vJ05Ns2pjtdtMVqzL7OKf6mlsgAa65\nU64DFoHqIMCYUy++HNG+fftyJvivjqMt7VEEObKn7O8sY5YiIqSjRDrPfa+//Xb26JLkc+a8k0Oa\nSOlChktxLEkpEuCknAm1QwREIBQBomS56fp+SIapkGJQJTwB5uclMhvLluQb/fr1a1JJ1Ejn9zPp\nmJ98baSbv/d/fPptG9b/YJN6i/3CQwF90RQeGrDc0yTCEuBirwBtLwIiUDECU6dOdeNUufESuasS\njQAPMhdffLGLfOZBxo+5ZciUHxcctuZjJ9rYz56bYIePtnf9vcH5ewniwtWN4COY+SZnaGmf2UFi\nwe9ETxN9HnzxgMbypDyoSYBbOsP6XQREILEEuHGPHj06se1LU8M6duxowXzLiCM5nYOiVujx7N7f\n0R54YrIN7H3I7vnMImvfrulMUARxeTc37wx1iiKKzJ6EkFN4CBswYIANHz7cGHeNJZ/0IgFO+hlS\n+0RABESgzAQQXYQtiviu3tzdfpKJdL5q6ia77pJ1OVtOhitfsLKJqOYBIEyhCwLrGXc53RBMdYjl\nfv755ztxj5ISM8z+41hXAhwHRdUhAiIgAlVCgH5VhiNFsUjnLBlgv3tllH3umqU2ddTOvEQY6oSV\nSsFFXKgLmvUYU43lm53cJO/OEvyDBDjBJ0dNEwEREIFyESC6GavXC2MYAfbBVm8u72dfu/UtG9rv\nULPNxmrF4sVKxWJtzl1MOxBsXli91VQkwNV0NnUsIiACIhCBQDGZrU5kZjJ6+NkJtv/wecZkCsFg\nq+aagvA2V8hB3b17dye8zQl0c3Uk/TcJcNLPkNonAiIgAiUkQP8rQVFkuApb9mUyWz2YCbbqff4x\n++btb54TbBW2PtbHMkZ4cTVXe5EAV/sZ1vGJgAiIQB4CJLBgHC2BUGHLxh1dnfhOH7vNbrxidZOZ\njMLWxfq4l3v16uXGdkfZPo3bSIDTeNbUZhEQAREokgDJNcgiFSWz1Tur+tgvZ46zT1+10i4dv7Wo\nlhBMhfASWFVs4XgY1kSUNclFcpU1a9bYpk2bXODXuHHjnIs713rlWCYBLgdl7UMEREAEEkKgmOQa\nHMJLC4barMzrLz71ro0asi/yUTFul6FDuJvj6OM9cOCAvf3228Z0igSTMT6cCTqC5eDBgy7jF8sQ\n6pUrV1Y0f7gEOHh29FkEREAEqphAMTMZnXm/lf3X78fYyo093DSCF/QMPxWhR4u1i1DGOdcvFn3Q\nmud7tgBnTyIRpd/bH0Mc7xLgOCiqDhEQARFIOIFi+nuPnWhr//70BDt1uo19+3MLrEvH8AFb4EFw\ncQ2XIsAKazpoSfM9u5C4A3c3FjLDm+rq6rJXKet3CXBZcWtnIiACIlB+AvSNZluIhbZiz4EO9sDj\nk21Q38N25zXvWLu2mRkWIhSyVGH14nouRcGVPWnSJJegg3HGF1xwQc7dTJkyxbmfGebEq5JFAlxJ\n+tq3CIiACJSQAC5Zopx93uWwu1q/rZs99OQk+9DELXbDZWvDbu7Wx9LE6o0jyKqlBiDwTMqxZcsW\nl+Qj3/rlaEu+fQeXS4CDNPRZBERABKqEQDH9vSB4e0Vv+/lzxc3hyyxLffv2jbWvt0pOjzsMCXA1\nnU0diwiIgAhkCDAMZ/v27c1agc2B+iDSeZjdd+M7NnLwBzmbm1s/+zf6YulvzdUPm71uLX+XANfy\n2dexi4AIVB0BhtowmUIwIrjQgyTS+bHfj7YVG3vadz+/0Hp1O1jopo3rEWhF/2vY2Y0aK6ihDxLg\nGjrZOlQREIHqJrBu3ToX4UsfZ9ghPsdOtLH/eGaiHT/5QaRzj26WSU8ZjheiSwBUmIkcwu2hutaW\nAFfX+dTRiIAI1CABrN0lS5bYhg0b3NEjgExMX6gIk9P5gcenWL+eh+0vbnw3E+lMasp2oUgShcwQ\nn+BQoFAV1ODKEuAaPOk6ZBEQgeohwExGTKaA29kXljHut6UZh1jf53S+ZNxW++Tla0LndGZYEYFW\nSYks9gzS8C4BTsNZUhtFQASqigDpIBFMLFSsxqglGOnMZAZHj57NTlXI3LmL1vS2X7wwzm7+8Cq7\nbEL4nM7kccblXMi+oh5jNW8nAa7ms6tjEwERSBwB3MVvvvmmESxFGTx4sMtbHLahJ06ccJavT6+I\nFYr7l+W4gzt06NBslbPfHmzPz62zL31isY0ZurfZdXP9SP39+/dXf28uOAUukwAXCEqriYAIiEAc\nBEiK4cWX+nAfM3FAmJJrmBHiiwi3VJh58LevjLbFGeuXOXz79zrS0ibn/I67mUhn9feegybUAglw\nKFxaWQREIA4CTAK/f/9+129IsoZaKliOuJ695Ro2LzICvnPnzkhz+J7IRDj/x7MT7PCxdvadTE7n\nbp1PhkbP+F5eKsUTkAAXz1A1iIAIhCBw/PhxmzdvXmaIyymXF5j8vcX0g4bYdSJWpd904sSJtn79\neifEI0eOLLhdTLm3e/fuSGN89xPp/MRk633+MfvGbW9Z+3ZEOhdesHZJKUlOZ5V4CEiA4+GoWkRA\nBAokQG5iPw0cwUhbt26tKQEGUxQrkskUeEUpm3d2ceI7bcx2u+mK1aEjnRnWhMu51rwVUViH2UYC\nHIaW1hUBESiaQHaGJN3Um0dK0BZWL9ZvlLJ4bW975PlxdmNGeJlUIWzBXT5gwICKzxwUtt1pWF8C\nnIazpDaKQBURIFCIeVgZhoM7c9iwYVV0dPEeCuJbzGxGr2QinZ+dU2d/nol0Hhsh0pnhRUQ6F5rQ\nI96jr/7aJMDVf451hCKQOAIIcKUnQ08clKwG4Z4nQpqI57AlGOn8LSKde4ePdMYzQd98qebvDXtM\n1bi+BLgaz6qOSQSqnADiVM3CQIQ04suY3rDl5KnW9rNMpPPBo+0jRzozzGjgwIFGtLpK6QhIgEvH\nVjWLgAiUgADRw2vXrnUJIBoaGlxkbgl2U7EqET3E1weqhWnIoaPt7MFMpHPXTicjRTqzL9JXEu1c\nzQ84YZiWcl0JcCnpqm4REIFYCTCEac2aNW4YDlbwsmXLqkqAcyXYKBTgzn0d7V//zxSX1eozH12e\nEdBCtzy7XpTo7LNb61NYAhLgsMS0vgiIQEUJEJjkS/CzX5bW90OHDrnANB4swpYN27u62Yw+euFG\nu2b6+rCbu4xWGuMbGlvRG0iAi0aoCkRABMpFgCxSRE3jhsZFOmrUqHLtOtJ+cCVjsSOqQ4YMyTtp\nwb59+9wY3ygPFMs29MzM4zvBPn3VSrtk3LbQ7YSjZjMKjS2WDSTAsWBUJSIgAuUiUF9fb0OHDnUC\nnOSJ35kOcOnSpW5iBD7jXiYDVrAguAzHCuaGDv7e0uc3l19gv35prH3h+iU2vm53S6uf8zviyxjf\nliZuOGdDLYiFgAQ4FoyqRAREoJwESOeY9MLUgEGLNjhVIG3HKt6+fXuTKQTDHNOrCwfZc5kxvl+9\nZaHVDQifpEMJNsLQLs26EuDScFWtIiACNU6AgKZgli+G9fgSnMfXLwvz/tyc4fbHRYPsLz/zVqQx\nvjzAYPmm4UEmDJe0rSsBTtsZU3tFQARSQQD3+PTp052rnHG97du3d+0uJtKZ+LPfvjLK3sukl/zm\nHQsyEyscD82C7FaIb5Ld96EPKqUbSIBTeuLUbBEQgeQTwM3br18/l8cZ4S0m0png6F++2GCbd3a1\nb93xZqSpBMnDTWpJjfFNxrUjAU7GeVArREAEqpzAnj17jGjnKOXU6VYuu9WhTHYr3M6dOpwOXQ3Z\nrZjRiGkFVZJBQAKcjPOgVoiACFQpAYKtNm/eHFl8T2RSSz6UyW7VutWf7H/c+radF3IeX7D67FZV\niji1hyUBTu2pU8NFQASSToBgq9WrVxvDkKKUo8fb2r8+PsXO73zCvnjDYmvb5mwSkkLrU3arQkmV\nfz0JcPmZa48iIAI1QMAHW+H6jVIOHmlv//K7qTa470G785qlmX7bcLXgau7du7ezfoNbMuYYV3iP\nHj3cdJDB3/S5vAQkwOXlrb2JgAjUAIEDBw7Y7t27m4wDDnPYew92sP/vt1Nt3PDddmsmw1XYbtt8\n2a327t1rCxcudO1CoKdOneqEOEzbtG58BCTA8bFUTSIgAjVOgMQbCC8CHLVs39vJ/iUjvpeM22qf\nuHxt6GoQXyKvg2OQfSVk3fLJQXwWLixhlcoQkABXhrv2KgIiUGUEis1sBY6NO7q6GY0+nplQ4WOZ\niRXCFsb2MswoX2rJrl27NqmS9QkQI0gr+7cmK+pLSQiUVIAZfD537lxbvny5O8G33HKLG/z97rvv\n2pw5c1wWlk996lNuOjGeGp944gkXrHDllVfa5MmT3QHPnj3bFi9e7Poq7rjjjrwXVknoqFIREAER\nKIBAsZmt2MXqzd3toScn2S1XrrJLx28tYK9NVykkuxUJOM6cOeMmfkCkN27c6L7jjuae26tXr6aV\n6ltJCYTs1g/Xltdff90lIL/nnntcQvKVK1ca83k+//zzdvfdd9tNN91kP//5z12ljz32mM2YMcNY\nd+bMmS4/6rp169zE2/fff78NHz7cZs2aFa4BWlsEREAESkyAe9qWLVvs5MmTkfe0OJPZ6sHMUCOC\nrbz4njhxwjZt2uREMjuPdPaOyLJFqstCUksOHjzYJk2aZCQJQYwpuKN37NiRXa2+l5hASS3gRYsW\n2c033+ysYFKy4eZYsWKFm5aLjCy8uLC4cInM48Kg1NXV2YYNG9xFzYWCm2TatGn24IMPNsHB9+BF\ng+V8+eWXN1knyheeBulHCeZujVJPJbah7b6PpxL7j7JPWNPufG6zKHWWYxvaTEkbb/6faHPaXI5J\nvLb379/vIoqbY0m7O3fu7F65rss/vNPTfvHCYPvm59ba+BEk2PigT5Z7JYXt2U++DFbcRzFQENQw\nhXqDszCNGDGiyT0vibwLOT7aTR94uQrR7lFLuDMWci+c3BdffNHGjh3rxPOuu+5y4vqb3/zGjY0j\nIo9weEQ0ePEQPMC4OS46XCYULrLssXQ33nij4foJFoIMii3kSuUfCrd4mgoXHk/AxTyJV+J4uTnR\nbs53mgpCRvFWRFrazoMw3UPZ/09Jbz/XCO1OygMP9y+yW7XUHoYhYSXT9uzy0oJB9vzcgfa1W9+1\noX0PZVJVfrAGdRLIFayb/49sC5f7IveqKBm2qAuh4j7XvXt3o53B+ydWddruJdDjmDg35fq/RC+i\nlpIKMCcYtzKWLaK6YMECQzRxM7/xxhtuEmis3T59+jQ50Zx0LgYuLtwwFIQ2+ymTJ8Jg4Z+BXKvF\nFiwyLvy0XXwIMCVt7cbyhXna2u0fGnPdWIu9Bku5PcFC3JzSxhsm3AeColRKTvnqZv9hIp1ZH+bZ\n18mTr9fbgmX97Ju3v2n9eh7N/P7BHr1wcA/00dQ8pPL/HayDZX379nXn0m+Tr835lnMP9ffRbGMm\njf+T/ji5tqMy8XUU+u4n2Sh0/eB6JRVghNe7OLiQuNHyJIhrmoAs3M/z5893yzkInsgRagIDPvKR\njzjxXbt2rU2ZMsXoD/bWcPAA9FkEREAEmiPAQ/yaNWvcA8fQoUOLGvcaR6Tzmfdb2X/OGmvrt3Wz\nv/rsAuvR9QMjg2PAiPBWKIYJ91AEHDEOFr4rr3OQSDo/l1SAr7/+ehdQNW/ePGeZ3nvvvU5sEeOf\n/exnToBvvfVWRw7L+OGHH3ZPeA0NDc4l0q1bNxdB/dBDDzkhv++++9JJWa0WARGoGIH33nvPuSRp\nAG7cyy67rHFqwDCNwkLcvn17o1cuzLZ+XfI6//TpiXbsRFtn+XbueNYtjdB68WV9Po8ePdpZvn57\n3rkvIs7e4xX8TZ/TRaCkAswAb4YO4Q4Imum33XbbOctGjhxpvLjIfT8HLpDbb7/9nHXThVitFQER\nqCSBYLcULly8cMH7USFtYxvEN+gCLmS74DqkliTSuUfX43bPJ9+29jkmVUBUEWJKLoHVpApBoun/\nXNJhSB5Pros91zLW9+Lrt+U937rBdfRZBERABHIRCEbE4rrNdufm2ia47PDhw7Z169aixHfH3o72\nz/95kQ3rf8C+9MlFecWXthLch/HB56AIEyiF5atSPQRKagFXDyYdiQiIQFoJ4MZlRiA8cfSbIm6F\nFqJpeRVTlq3rav/vr0fYxy9ab1dftKHZqrBweWUXIm2Jg1m1apUbchR8qMheV9/TQ0ACnJ5zpZaK\ngAhEJBDWcowj2IqmzlnS3/7Pq/V29/UrbPzwbZFaT3YqxgRjiVOWLl3qAsmKGf4SqSHaKHYCEuDY\nkapCERCBNBPAUqa/l/eohW7cp/5Qb/OW9rfv373C+vfck6kvfG2IL7E0fjgmNfBwQKyMBDg8z6Rt\nUbgvJmktV3tEQAREIGYCBGwxOUEx4nv8ZBuX03np+l72nc/Nt7qBRyO1krl8/UxFQ4YMaawDUQ7b\nj924sT4kioAs4ESdDjVGBESgEgSwKkmu4fMWEPXMy2dpK7RNu/Z1tAefnGwDeh+2v7pjwX8HW3Uo\ndPPG9XCZB/uCSTXJMqKwCcaqVMHy3rlzp7O+eUBQKY6ABLg4ftpaBEQg5QSyXc5YwUyuQCFga9iw\nYQWNxFi2vqc9/Nx4+2hmGsEZF69324f9Q9QzwhYUX19Hpa1eMkuROMnnPuahgPzRKtEJSICjs9OW\nIiACKSdADmVeWMC+eCuY7ywn+Iko6ubKrAVD7cX5w+zPrnvPJtRFyyHfnPg2t+9y/UYSEy++7JMc\n/hLg4uhLgIvjp61FQARSSABXLq7UXNP8EdwUTN7RXB4CMlv9cuY427qri307k1bygkxO5ygF8cXF\nTJarpBbc8XgE/MMK31WKIyABLo6fthYBEUgZASxcci7nS9ZPkBPZqOgDZgKYfK7fXfs72k+emmS9\nzj9q37kzk9O+/Qdz64bFkQbx5ZjI5T9x4kQ3RzEPKfX19WEPVetnEZAAZwHRVxEQgeokQAAR+ZVz\nWb0+DzPDfXyu5eYoLFnb2x55YZx9ZOpGu/bidZmMVc2tnf83xJfkIPlEPteWfnIJ3pmsoZzBUOyr\nnPvLdfzVtEwCXE1nU8ciAlVMAMFBKMNksvI4WrJ6EWaf8YpZ2XA7Mx1qdmF87/Nzh9urCwfb3dct\nsXHD92SvUvD3KOJL5cuWLWucq9xPLqExwQVjT9SKEuBEnQ41RgREIBcBsj/RZ0thtjTmwS2kNGf1\nBrfH3RwsiH22AB893tYefn68HTh8XmZ87wLr3f1YcJPGz0RVI/iIYvYc5n4lxJd0klH6UX1GLOrC\njY5FLwH2ZNP1rkQc6Tpfaq0I1BwBAqKYDIGC4JAPuZDCtKebNm3K6XLO3j447IfJELJdwpt2drV/\n+OXF1qXjSTe+N5/4Ety1fv16Z6EylIm+5uyCBc/c5lHEl7r69+/fWCXzpyc5cKuxofqQk4As4JxY\ntFAERCApBLJdztnfs9uJCDJEJjhkJnud7O8IMG5nLF/Et23bs7fGNxYPyORzHmU3XrHKrpj0wfhg\ntudhAMsZ69Ovj/vaRwmzTtBa5TvijoAS0BS1MPSH9mJpEzlNnSrpJHD2Kktn+9VqERCBKieApUjS\nByxKxIbZjfIVXL9ktAqKYL51s5fjcg66nU9mhhg99vJoW7Gxl33t1rdtaL+DjZsg8sxOhAjzQEAw\nlN8e97Kf0xcL1RdEGsu3uWFNft2W3hUI1RKhdPwuAU7HeVIrRaCmCWD1jRkzxqVi9OIWBIIQ0keM\nBRpHIaXkvz8z0bp1PmHf+/y8jOv5VJNqcW+zTwpiTzAUAoy4Isb8zmefwIN5zhHNXPOdN6lYX2qK\ngAS4pk63DlYE0ksAyzJXIQgJ8cUqjaMsXNnXfjVrrH3MpZTMPcTIu5z9/oLfsXqDli+fmVShmAke\n/H70Xl0EJMDVdT51NCJQMwSwhBk6hPWZyyoOC+LMmVb2+Gsj7a0VF9g9n1xko4fsy1sFfbD0/9LH\ni+VL8o5chf5k3OfBzFq51tOy2iQgAa7N866jFoFUE8CaJNCKoKk4yt6DHeynz0ywdm3ft+9nXM7n\nd2l58l4SaPDKV5i1CLdzS0Fj+bbX8uonIAGu/nOsIxSBqiJQTKBVLhCL1vS2R2c22OUTt9gnLluT\nEcxcaxW+DMElOjnfGODCa9Ka1U5AAlztZ1jHJwJVQoCgJ6xeBDhXwc27fft29xOJOoJje3Otj8v5\nidfrbcGy/pmsVu9ZQxFZrXz9BFmRYEOJMTwRvTdHQALcHB39JgIikAgCjOmlvzc7Y1Wwcdu2bWsc\nfoQQk6AiX+DWngMd7D+enWBt23zgcu7etXhXNsFWuKQ1Ljd4VvS5OQIS4Obo6DcREIGKEiC4ys/Z\n25ywsV4wEIvPDA/Ktc07q/pkopwb7IqJm+2GGFzOiDzDjYh0VhGBMAQkwGFoaV0REIGyESDAiuFF\nhQRaIYIEPDGpAoWo5GzxPXU6E+WcyWi1cFVf++INi23s0L1FHwsuZ6xeknIsWrTI9fuOGzdOLuii\nydZGBRLg2jjPOkoRSA0BrFeGFmH5hslohej6ft/guFwOfMe+TvYfmShncjn/X3fNyyTYaDnKuSVg\nDDGirxnRJ0sXBTf5mjVr3IQRLW2v30VAAqxrQAREwBEgyAnxyxavcuLB2kXQmuvrba49udo+f2k/\n+83s0faxaRvsmunrrXXufB7NVdvkNyxrrG0f5cyMS8ESV0KQYJ36XJ0EJMDVeV51VCIQisDmzZtt\n5cqVToDr6upc8ohQFRS5MpYu1iMpHIN9ucVUe+JkG/v178fY6s097Ms3vWMjBh4opjq3LUk3sHqD\nKSWJeoYfSTl4ABg6dGjR+1EFtUFAAlwb51lHKQJ5CSB4q1evbnT3rl271gYOHBjLpAF5dxr4AeFi\n2r5sSzKwSuiPTB/4s2fGW//eRzIu57nWqUNxaSp9oBXJNbIjqxHd6dOnN2bFCopz6IZrg5oiIAGu\nqdOtgxWBcwlkCwpr5FrGcoKicA8TeFTsWFfczcxcFGbaQNrQUpn91mB7ds4I++SHVtuVkze3tHqL\nv/tAq+amECT5hublbRGlVsgiIAHOAqKvIlCLBJjib8WKFc4Krq+vb+Ji9TwILiLal8Kk85deemnO\n9fz6+d6xdHE3Y/nG5W5mX4eOtrNfzBxnew50tG/e/qYN7HM4XxMKXo6oktUq3wNJwRVpRRHIQUAC\nnAOKFolArRFgknj6MhHEfLmL/RAf2JCLmUhlxKnQQpAXwksmqziFl/0v39DTHnlhnI2v2233fGKR\ntW/3fqHNyrkegVYcG5HOKiJQKgIS4FKRVb0ikDICWHnNWXpE/WK1UhBpHwXc0mEivIg1AVZhhhW1\nVC+/n86kk3z6D/U2973+dsfVy23KqJ2FbNbsOriacbGrL7dZTPoxBgIS4BggqgoRqAUCuKmZZJ4+\nYIK0musThQdi64UXEc5XGLaDsNOnTJRxoWX73k728+fGZ6zdM/b9zNjeHjGkk2QcMUOMmnsQKbR9\nWk8EWiIgAW6JkH4XARFwBIj2HTlyZIs0cC9j7ZJIoznhpSLEl35lvx6ucJ9MI9+O3v+T2StvD7Hn\n5tTZxy9abx+PYWwvFj3Di+Ryzkddy0tBQAJcCqqqUwRqlAAzEjGkqNBkFFi+XnxBRv9wcwK8e3+H\nTF9vgx0/2db+8jNv2qC+xQdaYdUj/HI51+hFW8HDlgBXEL52LQLlIIArmOE+CEypJgwIk7c5eMzZ\nQ5myv/t1M4dgLy0YZM/8cZhdOWWjXX/Jukyu54wpXGTB4qW/Vy7nIkFq80gEJMCRsGkjEUgHAdzB\nb731lnMJ0+Jhw4Y5N2vQ6izmSBB3LN6okc30+RJ9zfb0KeeKqt68s4ubvSgzOjlj9b6VsXoPFdTk\no0ePun7oXG5lBFczGBWEUSuVkIAEuIRwVbUIVJoALmH6Y32ZP3++E0yEc/DgwTZo0CD/U+h33MdY\n1oW6m/PtgOxSvLLLiVOtXT/vHxcNsusuWZvp692aGb5EvursNc/9vmPHDtcHzS/M0ztkyJDGlRhi\nhNXLchURqCQBCXAl6WvfIlBiAliVCA4WLwkwvBhjGZN+MooAUxdjgv2QpFIcAnP2/jYzgQLJNL6f\nSSXZ+/zjmaFPbTPHUdjeiL72BUuYccv09aq/11PRexIISICTcBbUBhEoEQEEZ8KECS5zVfYu8iXc\nyF4PKxoBw2WLmCG+xVq92fvw33fu6+hmLtq+p4t95qMrbGL9B/P7+t8Lfee4/TzCHCcR3J07d3aW\nb6HHXei+tJ4IRCVQsAAvWLDAvvSlLzW7n3/6p3+ya665ptl19KMIiEB5CTCulRdl06ZNLp8zVnEh\nQ4o2bNhgq1atcpmrsKaJFi5FYeaiF+YNt9feGZQJstpk936yuGxWAwYMcMeJq90fPw8QKiKQJAIF\nC/CoUaPsgQceaLbtY8aMafZ3/SgCIlBZAvT7Tp482bmjCXxqqSDYiBjuayxK+mrDJMtoqX5+X7Cs\nnz3xer0Nwt38+XnWp8exQjZrdh2iqTlWje9tFpN+rDCBggWYsXmXX355k+bihqKvhX9KXDwqIiAC\n1UOAfmKihXE/+xLnWNmNO7rab14ebYePtbfPXb3MxtXt8buJ5Z22YrHjjq71wjk8cuSISx+qe3Vy\nroZIqslMKH//939vTz31lH3ta18zIg4J5vj+97+fnCNTS0RABCITwOLl/5pxw1i+BF7hwo3j5n3w\nSHt76g8j7N3VfW3GxevsIxmXcxxjeoMHS4Qzkc642mu94Ol4++23Xb89ngHmLs433rrWWZX7+FtH\n2eGf//mfW11dnf3t3/6t2/y73/2u/epXv7KVK1dGqU7biIAItEAAMVy6dKlt2bKlhTWL/5kbNq5n\ncj5jPTKEZ/jw4c1mqCpkr0yc8NKCofZ3D1+asazN/voLc+zqaRtjFV8/vpc+YInvB2dl8+bNjUFz\nPExt3bq1kNOldcpAILQFjFvqvffes5kzZ9qjjz7qmsg/6O23324vv/yy0VesIgIiEB8BxtouXrzY\nVcjNk//BKMOHWmpRKYcXLV7b2/7PK6Osa6eT9rVb37YhFxSWTKOlNgd/R3DJ50y0s8pZAtnWbvb3\ns2vqU7kJhBZgnjDJLONvCDSYII1nnnlGLuhynz3tryYI+LG7/mALCZ7y6xb6fuzYMedyLnZ4Edtj\npVMf94l2nUba714ZbVt3d7GbPrzKpo3ZUWiTQq1HYBgu5zhc5KF2nIKVhw4d6oaPEa9DRHipItlT\ngCJxTQwtwBzBP/7jP9qHP/xh55Yi0OGhhx6yhoYG+8QnPpG4A1SDRCDtBHr16uXG8WL5UvgeV6FO\nUkki8r7+YuqmLsT35Ol29uKbE2zJpol21ZTN9qXMsKLz2mUSOsdcvMuZQFA+l6IQvAQf+sPjjgAv\nRXuz6+ShhLHgKskjEEmAP/3pT9v48ePt2WefdcMTbrnlloLGFCbv8NUiEUg+AcTlwgsvdELp56uN\no9VExtK37BNWxFEn3rDV20bY68uusD7ddtlXP/mi1Q87L46qz6mD/mlczi3NS3zOhiEWMKXiwoUL\nnZcPF/dFF12kKQtD8NOqzROIJMAk5Lj22mvt/vvvVzRd83z1qwjEQgAR5hVXwVIlOAfBjKvsymSx\n+u0fr7WtuzrYleNetbFDttqQwWdzMMe1HyxdrFFepbJ6fVu3bdvWyIg+ch5Yck3u4NfXuwiEIRAp\nCppsVz/5yU/cQHeGIRHiriICIpB8AgwvYhjhzp07G4Wl2FafyUQ3vzBvmP3Doxfb0H6H7f++Z759\n/LLWrosq7khkXMAk2GBIVKnFFy7ZAV3Z34tlp+1rm0AkC5iIZ15EZBIJfe+997ow95///Ocuy05t\nI9XRi0AyCRC8RUR1nLMArdvazU0V2KH9afurzy6w/r2O/PfBt2sCgf5lrEn6UxFRhgmFycmMu5lp\nC4ngjaOvuknjmvnCCA8eWghgou+dNqiIQFwEIgmw3zl9SFycuLH4BwnzD+Xr0LsIiED8BBjDywMy\n/aP0kzKBAuIXV2GqwKf+UG/zl/a3T31ojX1o4mY3tjdf/QiYj95mFiVc4Lnm/s3eHgsaa7dbt25O\nfLnflLNgZdfX15dzl9pXDRGIJMAPP/yw/fSnP3WJN+6880575JFHFGVXQxeNDjXZBBAp5v3lAZl+\nS8SrELEr9KiWb+iZsXrH2oDeh+2HfzbXunc90eKm2cObWup75mGegDP6veN2Y7fYWK0gAmUiEEmA\n33jjDfvWt77lArF4qo4zOKTY444jItJb83HUVezxhN2em1U5+sbCtqu59RkmQbvTxtt7fMo99hQL\nEou2a9eujbMcBfnyP4nA+RdTCAZzONNu3LjBZcHt830+erxNJnfzCFu0upfdfvVqm97gpwps6m7O\ntT0PALQbIYYXVnmu/XPt8sCAuzf7d64RzzzXPpK6jDZzLOV0ncfBgvOUtv9Jf9x0VbT0kOfXreR7\nJAH+wQ9+4HJB33fffYnLBY3rLY7CyYurrjjaU0gd3Lx4eIhzWEkh+y12HdpM29PG2wtvtnVXLI/m\ntkdc582b13hzYfw9/anBAkfG4mL9Urj5B123/uaEheyTfGBtNvfgtmh1H/uv34+x+kH77Id3z8lk\ntDqVqTO415Y/Dxs2zF2b/nwH28TW9E2TKILfabtvv6+Z5WyTNiHjXkK703Z9I75pa7O/Vnz+cv+9\nlO/FRMVHEmByQV999dUuFzRP4uSCJgkH44GVirKUp1p1J5EAgrB9+3YnGGQZistlSp8p41AZboNV\nSCGIKvhkz/+fF2AEi+9YmkQKsy0PCX4u4Gx2GzdudELNcvZFwFF2OXS0nf129mhbvbmH3f6x5Tax\n3lu92Wu2/B2Bz2VRIay0Mc7gsJZbozVEoPIEQgswNxvlgq78iVMLkkOAtKwM66EQ+MRsM8UWxJPh\nffy/IVxTp05tIsS+fi/Mhw4dcuLsrUaErrmUg1jtWMm+4KZG2IMu3jeXX+DEd3zdLmf1dupw2q8e\nyzsPKjxctGR9x7KzQCU8pGAh4QZH/FVEoFIEQgswNwPlgq7U6dJ+k0YAgeSG7guWJMLmUxZyo+fl\nhdKv19I7gk7dFL8Pn3yCtIL8Tp0DBw501jdWb5iCZRx0TSNEXnz3H25vv35prMvffPf1S2zs0L0t\nVs0DA+5K+qULccmxHv283o3f4g5iWmHNmjW2bt06VxvjoS+55JKytyGmQ1E1VUAgtABzzMoFXQVn\nXocQCwH/QIoFSkHI/GwzjHtlCkEEFLGZMmVKwftEoIIl+J1JB3ghukwb6K3e4PqFfMZNzXAgindT\nv7F4gD3+2kibPnabffGGxQXlb6YO/xBCn/KwTF9vLlcz+0H0CciqlLuZTFa+8MBAezk3KiJQCQKR\nBFi5oCtxqrTPpBKYNGmSrV271rlwmXnGW5JYWt6KRaQYC1voiAH6dRHWvXv3unGwQXcyy+kL9qIf\nlQsPC77ePQc6uKFF+w52sC/f9K6NGLi/4GpxXwcLwpYtwDyocOzlymAVbE/wM9a5by8ucGW2CtLR\n53ITiCTANHL06NHuVe4Ga38ikDQCiA3RyNkFay9Ysr8Hf8v1GQuVV7Dg3sb9nB1BHFwnzGe83K++\nM9ie+eMIu2LSZie+7dqGyw+NqPkkHwhttnULH6xe7xkI07641x07dmzjSAHmVM5+UIh7f6pPBJoj\nULAAL1iwwJiEobnyT//0T0aeaBUREAGzMWPGOBc0fcBYxsVYW1jSWNG4TL1VXSzjHXs72aMvNtjx\nk23t67e9ZUMu+MCNHrZe+qbpy/V9wD6wCU8AVi+/I8xJKDwEcV5URCAJBAoWYIYXPfDAA67N3FBy\nPc3qwk7CKVUbkkKAftuLL7646ObEPW1gJtjZnntjoD37x0H20Qs32Izp6zNDpz4I+IraWI412E/N\n/YEo41z3iaj70HYiUG0EChZghgpcfvnl7vh5mp81a5bG/Fbb1aDjSRwBLF4s3+DY32IauWVXF/vP\nl8ZnqviTfeuOBZl0kvHlh6ZdWLo+WjspVm8xvLStCJSSQMECHGwEfSeMfVTSjSAVfRaB+AgQaEVf\nr+9bLbbm05kpA2fOG26z3x5in/rwFvv4RZszw6POjgMutn62x9olOtu7oOOoU3WIQDUTiCTA/JPd\neuutLrAimOT9f//v/20zZsyoZl46NhEoOQFEl2E9caW43LC9q/1y5jjr1OGUfefO+TZicOtMhHV8\nh4GlS3Qz/b2yeuPjqpqqn0AkAf7Rj37k0k9m45FFnE1E30WgcAIEVzG8CLdzoYVtGFaD1ZkdZX3q\ndGt79o06+8OiQfbJy1fbhyf7KQO7FFp9i+vJ6m0RkVYQgbwEIgkwafEoPKH7sY3lzmiT94j0gwik\nkACBVuST5r3QgpuabE4MScLyJCuWz0K1Zkt3e3TmWOvZ7bj9IDNlIO9xFiKcsXrLnUYyzmNQXSJQ\naQKRBJh/+r//+7+3p556KnGzIVUaaLXtn5s86RUZ26mI1tKcXR5iSbgRNtCKTFh+PDCWMHW0a3++\nPfmHeluwrJ/dcuUqu3T81tgbzbVA11O2xR37jlShCFQ5gUgCrNmQqvyq+O/DY7gZ478Z34mHY/Lk\nyQVncqoNQsUdJR4kAq18ZqawtWUL4KbdQ+zfX7zEBvY+7CZP6N6lcGu6kH1zDZC2MTjcqJDttI4I\niEBuAqEFmCdtzYaUG2a1LcUlivhSEIvNmzdLgGM6yVivBFrhYYhavCW6c/cxe33pZbZm+zC77SMr\nbNqYs/mOo9Yd3A73Nq5mhhfFNdVisH59FoFaJdA67IHzz0g/E8OQfMF19swzzzTmlfXL9Z5uAtku\nZw0vKf588r/ChAB+/uBia9y6f7T98pXPWtt23exHd8+JXXyZ1Ylhh0zWIPEt9mxpexFoSiC0Bczm\nmg2pKcRq/davXz8XkYulxtR3w4cPb/FQvVsVsfYz7LS4UY2sEGce58PH2tlvXh5tqzb3sDs+ttwm\n1p+dErFYnFjl9EsjvkwKkf0gVmz92l4EROADApEE+KqrrrK33nrLnn32WRcEcssttzgXFf+0mtqr\nui6tMJNuYN29+eabbpo8KJAxbeTIkdUF5L+PBgt21apV7lt9fX2z3h8fIMX/B5+LLW+tuMCJ7/i6\n3c7q7dThdLFVNm6Ph4vMW4xFZrYlhkRddtllsn4bCdXeB4IwmVqTBzImB+EaUYmHQCgB9v2BCHAt\nQIEAADSuSURBVDDBOV/96lddK7jxEph17bXX2he+8IV4WqZaUkeAG3ZwYnj+aUstwFhrXJf0hxZz\nY6Dt/iaDy7W5uhDRZcuWNZ6f5cuXuwxQDM2hPczRS3Qy9bCMQCsC2ootBw63t1//foxt3tnV7r5u\niY0dtrfYKptszw2W6OaNGzc2Hj/txnL3w5uabKAvVU+A/y2MLR+rwHfle4jvtIcS4BtuuMFefvll\nt3f+WX3hZsUYxL/927/1i/RegwS4Jugn9P+spb5pI5oLFy50Y2fZ14UXXhhpaAwiw00G9zmFqGQs\n/3yFB05eiCuFz96yXbJkSePk9AhxXBbDnCX97fHXRtm00dvtC9e9Z+e1jx68lX1c2dHNjO+l24HC\nOeXhRuUDAsz7zHAvAtLq6uoaH1SqlQ8eEP//zDHu27evWg+1IscVSoBfeukldzL+7M/+zB555JHG\nBnMj8jejxoX6UHME6PdlcvoNGza4zEy4ZktZsNR84gosb9zC2fPnFrJ/XGxefFm/pZsMDxncfDlO\nCn3jPkCJbRFkLGBEGXEvZs7ZvQc72K9mjbU9BzraX3zqXasftN/tM44/PDjnim4eP368bdmypYkV\nH8f+wtaBGxyvBPx4wK90gQkCTKE7gb5xvBzVXEgvygOa//9QF2O8ZzuUAPMPy8n41a9+lbMVuCeK\nudnkrFQLU0UA64lXOYoXPb+v7O9+eUvvBJgFbzJYNy0VRHfIkCFuNb9fBBdr0bvhqTNqABNdxa+/\nO8ie/uMIuyyTTAPxbd8uM49gTAXLliC5XO3jePyxxbS70NXwYEU8gU80ghhPmDAhdD1xbpA9Xps2\nVXvh+sCzxMMt10wSHoSqiXkoAfYH/vvf/97+5m/+xlkK3HRwUeCW+dd//Vf79Kc/7VfTuwiUlAAi\niNjhikZM+vfvH2l//iYT7AMupCIvXlgHWLr09SLe/E9gBUednGDX/o726IsNRqTz/bcstGH9DxbS\nnILWwVMFJ5JpYPkntdA2L760kcCwShcmoWEsPPc7ODJKoBZK9lzPtXDM5TrGSAL8la98xXBDL126\n1HBX0f/26KOP2s0331yudms/IuCst2nTphVEAoH0gplrg6g3GYTWT6CA8OIliuqmez9j9b6SmS7w\nuTl1duWUjXbdJeusbZvio6b98fJ/yoMKHoqguPnfk/TO+Qh6JXiYqXTBU3LxxRc79zOu+86dO1e6\nSdp/ygmEFmBuMnTM//CHP3T9wOvWrbOvf/3rzkXxwgsv2PXXX59yJGp+NRHAOiVQi2sWFxoTifAe\nR8EFSbASXS/Flu17O2UmT2iwk6fb2F9+5k0b1PdwsVU2bo+Q9e3bN1XBVDwsca62bt3qurUq7RL3\nMOliUFCap6H3YgmEFmCe8Hny44ZGwM0vfvEL1waeqgmKURGBJBHgBs61SmE4zfrMRCJjx44tqomI\nOlYv9RVbMga0vfTmUHtx/nD72LQNds309damdTxWrw+ywiLnc9oKFicvFRGoVgKhBRgQX/7yl23i\nxIkuIpAb2u233+6GJ82dO7daOem4UkogW3iyv4c5LLw/RDkTAYvr2QdfhakjuO623Z3tFxmr16yV\nfeuOBTagd3xBPViQjOlVUGSQuD6LQLIIRBJg+pEeeOAB10dDQBZW8De/+U0bMWJEso5Oral5AqRS\nxE1MkCB9oMOGDYvEhEAv6oij7/TM+61s1oKh9tKCYc7ivXra+kxQT6RmnbMRDxh4o6IGgJ1ToRaI\ngAiUjEAkAV6zZo39z//5P+373/++3XTTTe5FqLqKCCSNAFYqfYlErmZbrFi0CCvWYr4ALdzMuJsJ\n4oqjbNnVxX6ZsXrbZIKrvv3Z+dav19E4qnV10LdNX2/2NIWx7UAViYAIxEog0nM34osIP/bYY66P\nBpc0QRJz5syJtXGqTATiIpAtvgjy/Pnz3euNN95wIhvcF4FVJF7gFYf4njnTKhPdPNz+n/+aZheO\n2e5cznGJL8eG8DJGU+IbPIv6LALJJhDJAmaQ/Lx581y/L6kpyQhEwnYFTCT7ZKt1Zwlg1WL9UhBj\nAgjpWsHipZ83O+nC2S3Df9q8s0umr3ectW97xr7zufl2Qc/4rF7c6vT1Zj9ghG+lthABESg3gUgC\n3NDQ4OY0ZTzwP/zDP9hFF12kG0C5z5z2VxSBbEsRdzRJFuIYUuQbhtU7c/4we/mtoXbdpWvtI1M3\nWuuYgpFpPw8MGovqaetdBNJHIJIA//jHP3ZTET7zzDP26quv2sc+9jG7+uqr7fLLL8/bl5Y+NGpx\ncwQIRiI7EWMi0+j5IFCJvNGILtYjQhan+Hqr97x2Gav3zozV2yMeq5cgKwKsyLil/OvNXaH6TQSS\nTyCSAF933XXGi0KSgx/84Af2v/7X/3J9wrfddlvyj1otLIoA4ksXhBesMWPGpCYpPe5m3Mu4n0lQ\nwZzFcRas3hfmDbfZmYxW12es3qtitHp52MHqZdILFREQgfQTiCTAjPd9/vnnbebMma7/l3mAH3/8\ncZsxY0b6iegIWiTAcBwvvqxMDuUkzwqD6JK1ihd9vIzhLUUJWr3fvXOe9e1RfKIO2smDAsk0SM+o\nEi8Brg0eKDVeOl6uqq0wApEE+Ec/+pEb2vHP//zPdumll8oVVhjrqlkLSwxXKP2mlKSl5uOmygMC\nL6xdggZ9W0txEoJ9vXFavXI3l+Jsna2TYLtFixY5AeYBZ/LkyanMGHb2iPQpbQQiCTDzAqvULgEs\nMQLxGKKD+JZ63t98pBFVrBdeiCwvhgzxXmxBxOljRQSbK5t3drafPzfG2rV9v+C+XtpNchDaSVL/\nXJYtY3pxN+cbn9xcm/RbYQQYSsm1QyGegch4IspVRKBcBCIJcLkap/0klwBT2kWd/q+lo8JVjABS\nECtcxv7FcnIx8+JzKSxbXOrkj0aAyaTFUJ/sQjarF+aRzWpIZtaiTITzhYVHODNtIdYXhekUh2Wy\nc3kXqNzN2aRL9z176Fb299LtWTWLwAcEJMC6EhJHAGskDis2yoHhtvaTNyD6iGW2AG8lh/ML49xU\ngd+7603rc364mYuyE3vwHYsXa5joZglBlDMXfhs8N8QEcM55mCQyXkUEyklAAlxO2tpX2QhwU8WS\nxkUeZvrBbJdz8DuxW7My+Zt5zbh4XSaP8+aMlUwij3CHxbAtn+gDseXGj6Utd3M4jsWujeufBEJ4\nUYLnudh6tb0IFEpAAlwoKa2XeAJYM7y4me7YsaOxvYz3LTRhBSKIIBLpjThecMEFrp5te7B6GzJ1\nW2MO59at2zTuI8wHxvEylIj+R1K4VnO/I8eImx2xw72exCLxTeJZqY02JfM/ojbY6yhjJMBNnqQa\nFFzIWL1+vCxjfgsVYLYnrzKiyI35/UygNzMXzcyM7f34RevdK46ZixB2gqzS7G6mqwBLHla+Dxt+\nvnBO3nrrLfegwbkgY14Yb4SvR+8iUK0EJMDVemZr7Lh8XmcOmzSNWMJegKPc9J0Vva+T6+tlmFFc\n8/XSJoQ3aUO3wl4u5M5euXKl22zt2rV2ySWXnONCJ0reRxnTp79161ZNWRoWtNavagIS4Ko+vbVz\ncIisD55C3AicQkT5THBTmILV++rbg+3ZOSPsoxduyPT3rrc2rT8Y8xymnuC6tAXXM+5tPqe9BF38\niCxR3f369WtyWP4ByC/M/u6X610EapWABLhWz3yVHTfiRtQyLlHEl+9Ryq79Hd18vcdOtLW//Myb\nNqhvuAjnXPvEPYtbu5oEiD5d/8DDA0UuFz/92wTCIc4kumC6RBUREIGzBCTAZ1noU8oJYF3yilIy\ngbD22ruD7Ok/1NuVUzba9Zesy/TPFmf1Mo6Y9mCBYyW+9957zjWOEJVqDHWUY4+yzciRI13/NQJL\nBHeuZCL0b48fPz5U9QcPHnSpTRF0OFWDtyAUAK1cUwQkwDV1unWwuQjsPdjBWb0Hj7S3r936lg3t\n98E8wbnWLXQZLnGsXj/t4YoVKxojs7EcEazs8cWF1p2E9RBXRDjOQr89QVs+CQtDySqVZS3O41Jd\nIpCPgAQ4HxktP4cAgTS4E7FO0iwewQP74+IB9sRrI+3yCVvshsvWZFJKFm/14m7N7nf2437ZN+NO\nEZtqYRjkWcxnHky8+FIPQ8FURKCaCUiAq/nsxnhsZGtiCkJEGLcgrkU/RjbG3ZStqv2HzrNHZ421\nPZk+36/c/I7VDThQ9L6zrd5ghbhTly9f7haxHhmvVJoSoN8ey9qLcNTuhKa16psIJJeABDi55yZR\nLSNRvU8PiQXHkJK0CvC89/rb714dZdPHbrN7P7nI2rcrbnpCHkgQi+ZElekayYCF5YuFnNSkFJW8\n6AhWu/DCC52rnoeUJE9xWUlO2nf1EJAAV8+5LOmRZI9bzf5e0p3HVDl9vP/50hjbuqtrRnjftZGD\n9xddsx/XW0gaSQSYl0p+AmKUn41+qT4CEuDqO6clOSKsu9GjR7sIVfouR4wYUZL9lKrSt1f0tf/6\n/RibMmqnfeG6uXZe+5AJnHM0jH5eskB5l2mOVbRIBERABPISkADnRaMfsgmQU5lXmsrhY+3ssd+P\ntrVbu9sXblhiY4cWH9jD8CIinKOONU4TP7VVBESgdAQkwKVjq5pjJhB21ppFa3rbf2YCrRqG7bEf\n3j3XOp53uugW0U9J37cfXlR0hapABESgZglIgGv21KfnwBkPykQLp0+fdn2oJH5orpDF6rezR9nS\n9b3scx9fZhNG7G5u9YJ/w+VMHmclhygYmVYUARFohkBJBZgb5ty5c93wC25et9xyixtmsGTJEpsz\nZ46zIq655hqXSYco2yeeeMKlrrvyyitt8uTJrtmzZ8+2xYsXuxvvHXfckXPWlWaOTz9VAYGdO3c6\n8eVQyJREoE6+MbTLNvS0R2c2ZIYV7XdWb5eOp4omgMuZvt5c2Z6KrlwViIAI1CyBzHTipSuvv/66\nG3Zxzz33uP4yZk8hYOWZZ56xL3zhC3bttdfak08+6Rrw2GOP2YwZM4x1Z86c6XL6rlu3zphp5f77\n77fhw4fbrFmzStdY1ZxqAidOtbZfZyKcH352vN1y5Ur7808ssTjEl+FCjOGV+Kb68lDjRSCRBEpq\nAS9atMhuvvlmZwVPnz69MTsQfXlMVYZl44dvYNn4AJ+6ujrbsGGDW2fSpEnOap42bZo9+OCDTSCS\nXYgE/L6Qbxdrpdji6/DvxdZXru1xjfJKe7uzj4E+102bNrmHN4QweyjP6s3n2yPPN1j/3oftr784\nz7p1xuot/jqgv5eczfnm7PWc/Xu5znOx+0nrdQJnXtw/0lTSyjv7/zBNzNNynZRUgBHVF1980caO\nHevE86677nI3tIkTJxoWL0kJbrrpJmftBhMTMMaUJO/79+937mlOPAPzWRYsP/7xj11CCL/s+uuv\nN1zacRX/QBBXfaqneQJ+Rh3OM/2+vjAECiHEexK8Tk6eamWPzepvr7zVy+66fotdeSERzl38ZkW9\ns09m8+EmVK1FUdzlO7N0YaiUj0BLcSJxtiQ4F3nYeksqwESK4lZGyBDVBQsW2JQpU4zJvL/73e+6\nG+rf/d3f2fe+973GLEscABmX6ONDdEmBSMG6zXYDsl2w7Nmzx1nOwWVRPmP5cAPetm1blM0rtg1i\nQWIIz6xiDQm5YyxarhXOH2XXrl1Nrodc1W3Y3tUeeWG8de9y3L5/1xzr0fVEZvtca4ZbBkOfApHr\ntLniHwaIdUhTIRMX/088IKepcG3T7rRZwMyTTJ5rDI40Fe6DwQfhtLSdB2c8rOUan58vHqUQXsX7\n6ZrZC8Lr/8m5ADmh/BMhrBTcBHzGxcdyLB/+ubjxcdGSim79+vVuXfqDy/lU43aqP4kjcOZMK3v2\njTr78W8vtCsnb7L/8emFTnzjaCjiy/heHr4qVfgf2L59e+oeoirFS/sVgTQTKKkFjEuYgCqS+GOm\n33vvvc4S5gb3yCOPuCfCK664wonvjTfeaA8//LCLdm1oaHBBW1hGJLB/6KGHnJDfd999aWad6Lbz\npMtDEtHqPCglsWzd3TnT1zvO5W7+3p3zrE+P+CwKHgZ56Ktkik1m/3nnnXdcXAPW9UUXXZRzovsk\nnhu1SQREIDyBkgowQsvQIVzKWLi+ILa4krB8ufFRmFuUF8t9kgN+u/3228/Z3tej93gI4KXw87By\n4ychfra7P549Ravl/UzMze/fHGoz5w23ay9eZx+dtsFax9g1yzHTx+wDAqO1svitsHx9UCFu7R07\ndhgBiSoiIALVSaCkAuyRBcXXL/Mi67/791zLc23v19d78QTo6/b9Jf7GnxQB3pWZLvAXL4yzk5lh\nRt+6Y4EN6N00EK/Yo+d6Q3yTcI35rhl/TJW0xn0b9C4CIlA6AmUR4NI1XzXHQSD7xp8UF/Tr7w6y\nJ1+vt6umbLTrLlmX8ZjEO/wE8WWMrw+mioNlMXUMHTrUeYDoCiBQCpe4igiIQPUSkACn8NzipiRY\nLd/41LCHRLAckdP79u1zEcCVnod136H29rNnxtm+gx3sa7e+bUP7xR+ti8VLUF9SxJdzRpfLqFGj\nwp4+rS8CIpBSAhLglJ24rVu3usA0BJgpAYcNG1b0ERD9S/97EsqpzIief/jFZJtUv9Puu/Fda9f2\nbKKVuNpHXy/iG9cDTFztUj0iIAK1RUACnLLzTTpPH6izZs0aJyRJ6L+MC2O7zBX5wy8stPPaxtvX\n69vnLV+JryeidxEQgUoRKOk44EodVC3tF+u12krXTsVPoJCLCeJLn6/ENxcdLRMBESg3AQlwuYkX\nub/Ro0c7AaG/sL6+vnHIVpHVVv3msnyr/hTrAEUgdQTkgk7ZKWPIDJMTxBmElTIEoZtLoFXSAq5C\nH4Q2EAERqDoCEuAUnlKfvCSFTS97k3E3S3zLjl07FAERKICAXNAFQKrWVUi+cfjw4UQlt2dCBmbB\nwsIvtvCggvhWU5BasUy0vQiIQHIIyAJOzrkoa0sQ3rffftul+WQaQNJPVlqoVq9e7SbfYEwybRk+\nfHhkJohvEtJLRj4AbSgCIlD1BGQBV/0pzn2AzDhFjm4KM/AkYepFxjj7gghHnQrNz2qUneHL1x31\nHaucqRL9tIlR69F2IiACIgABWcA1eh1kZ4BKwtAcLHH/UICI5soLXsjpIo1jMXN05tsHMxV58cW1\nPXHixHyrarkIiIAItEhAFnCLiKpzBTJoMfE8wktUNeNjK13GjRvn8h8jnqTDjPJQwHSK3bt3j/1Q\nsMa9+FI5HoM4+qljb6gqFAERSA0BWcCpOVXxNpQ+1qlTp8ZbaZG1MQnE+PHjLegeD1MlFnTv3r3D\nbFLwuvDCIme6TAru7WpMglIwEK0oAiJQNAEJcNEIVQGpMZnIgRzLpXD9FkKYfTN7UKlEkaCuSZMm\n2dq1a90+SIKiIgIiIALFEJAAF0NP27q81AsWLLBDhw45YSJTV7lnU6I/u5Ti608zru1SeA12797t\n+PXp06diDzD+GPUuAiJQPgLqAy4f66rcE5Yv4kuhT3TTpk1lPU4sXsQ3asBWWRubY2dbtmwxgruY\nWGP+/PkuIj3HalokAiJQhQRkAVfhSS3nIdFviwj6gCS+x1Hoa92+fbvrcyVYLF9gFQFkce0zjnaH\nrYNhTb7gyifQi75sFREQgeonIAu4+s9xSY8QsRgzZoxznTL8h8/B4oU5uKyQz4gv45MZlsRnPzwp\nuG2PHj1S77IlajtYunXrFvyqzyIgAlVMQBZwFZ/cch0aQ5hyDWPasGGDc60ynKihocHo4yy0+Ghj\nv/7p06ebZOrq1KmTG0blf0/rO8PBCPA6ePCgGw6Wz9JP6/Gp3SIgAvkJSIDzs9EvRRAgkxWpJbGA\nca0uW7YslADjdsbypeBiDma1YkgQrudSRTwXcdihN+UYhg4dGnq7XBvgJcClDSv4qYiACCSbgAQ4\n2ecnta1DeIPu5+DnQg4KSxArF8s3OOYWaxG3N+LMmF+GH6mY6yufN2+e8eBDGTlyZGzCLr4iIAKl\nIaA+4NJwrflasVpxr1IQTQQhbMHSRYSzLd2FCxc6i3ru3LmNghO27mpbn2h0L74cm/ceVNtx6nhE\noJoIyAKuprOZsGMhWcWQIUNcSkksWYbaIKaDBw+ONGwIqzg4zIl+YlyuYccdE9zFA0KUVJcJQ9zY\nHLwCsPWehq5duzb+pg8iIALJJCABTuZ5qWirGAqzd+9eN/QnTOBUrkZjxVJI1sEUiBTqv+iii9zn\nQv8gmERZ065gTuYwmbeY//jNN99045YZNzxlyhSrlqhjBHjChAnGuGJc9iNGjCgUrdYTARGoEAEJ\ncIXAJ3W3CBwuXgpRzNzUCXgqpuAa9eJLPQcOHDDEsFALlPV80BXCgpWHFcuyMFHDuGV90hCs5/Xr\n11fVjEZ9+/Y1XioiIALpICABTsd5KlsrEeBgwdosVoB9jmgvwox9LVR8cauyf5/piu1IdxmlZE/B\nmP09Sp3aRgREQASiEpAARyVXpdtlW5Qku4ij4O7dvHmzq4o+4EIL+ycQK46CkJN3eceOHS6BR11d\nXRzVqg4REAERiERAAhwJW/VuxNAeZv3B8kWMybMcR8EKDtsvSV9mXA8A/hiYc5hXoYWxtcz9i+VN\nspHsiOxC69F6IiACIpBNQAKcTUTfXcKMYoOvisUY7Pcttq6o25NAhKCto0ePuir279/v5iuOWp+2\nEwEREIEgAY0DDtLQ58QQwF1c6T5aAr28+AIG97WKCIiACMRFQAIcF8kQ9RDFizV1/PjxEFvVzqoE\nacXV71sMNdrgh1FRT7UMWSqGibYVARGIj4Bc0PGxLKgm79Yk+T4ZouiPLDbKuKAdp2il7BmCKtV0\n3OBTp061jRs3OmvcZ/aqVHu0XxEQgeoiIAEu8/kkuAnxpSDGjLUthQDjLsXCZlxo0Ior8+Gmfnck\n+mAmJxUREAERiJuABDhuoi3Ulz15QCnEcd26dS7tI00h2cQll1xS8f7UFrDoZxEQARGoOQLqAy7z\nKacfkRzJpFZkiA0JJmbPnm1//OMfXYaoOJrDOFdfsIJJ1K8iAiIgAiKQLAKygCOeD9IaklKR/krG\niSJyCCqpG1uK3qUvkRdpEZlCjnLs2DFbsWKFTZ8+PWKLzm6G29RnnfLT9539VZ9EQAREQASSQEAC\nHOEsbN261ZYuXeq2ZJwoifARPfp3CdgpNMMSfcDBQn7kOAqpGnkIwPoleUQSIorjOC7VIQIiIALV\nREACHOFsMoTIF0QUa9PPypMtqn69XO9YzwRg4TIm4hbXdBwFt/aYMWPiqEp1iIAIiIAIlIiABDgC\nWFzNWMEUpsjDzUshdWLYuWlxWTNZPRZrS65rtxP9EQEREAERqAoCEuAIp7F///5OdOkDRoARZPpw\nEWAvxmGqJSBLRQREQAREoLYISIAjnm9cx8Hxu/QDq4iACIiACIhAoQQ0DKlQUlpPBERABERABGIk\nIAGOEaaqEgEREAEREIFCCUiACyWl9UpGgMkpwkSPl6whqlgEREAEykhAfcBlhK1dnUtg586dNn/+\nfPcDcxAPHz783JW0RAREQASqkIAs4Co8qWk6pOXLl9vp06cNK3jt2rV24sSJNDVfbRUBERCByAQk\nwJHRacM4CCC8vgQ/+2V6FwEREIFqJSABTvGZPXLkiC1cuNAWLFhgTD+YxjJq1CiXBaxVq1bO/Zw9\nW1Qaj0ltFgEREIFCCKgPuBBKCV1nyZIlbkIHmrd48WK74oorUjf3L0lNSMFJFjASm6iIgAiIQK0Q\nkAWc4jNN9i1fmMghLf2np06dcvMVr1mzxrWZPNhKw+nPpN5FQARqhYAEOMVnOph3mnSYfkKIpB/S\nO++8Y+vWrXMvXOgqIiACIlCLBOSCTvFZx3VLLmqiiHv37p2KI8FSD7qamUmKaROZwUlFBERABGqJ\ngAQ45WcbyzdNBXcz0zB6EcZq12QUaTqDaqsIiEBcBCTAcZFUPQUTmDx5sm3atMmN/Q260QuuQCuK\ngAiIQBUQqDoBjsOaat++vZtWMI66yn2NYGEypCfJBa4NDQ2NTSQAi3anjbefejJtAWSwZsx12njT\nbs+88eJJwQfaTBdL2sa5c12n7RrxlwPDGdOQ3rbqBJj+xDgKJy+uuuJoTyF1ILw8PKQlGtofE22m\n7Wnj7YWXPvg0FabOpM1p4811QgR92oSMewntThtvxDdtbfb/h9wDiTcpRykm+FVR0OU4Q9qHCIiA\nCIiACGQRkABnAdFXERABERABESgHAQlwOShrHyIgAiIgAiKQRUACnAVEX0VABERABESgHASqLgir\nHNDStA8CVjZu3GhM3EDe5bSNG04Ta7VVBERABMIQkACHoZXCdcm3vH79etfy7du328UXX2xEwaqI\ngAiIgAhUloBc0JXlX/K979u3r3EfDIc4ePBg43d9EAEREAERqBwBCXDl2Jdlz+SK9oVEBnJBexp6\nFwEREIHKEpALurL8S773uro669ixox09etQuuOCC1Ga2KTko7UAEREAEykxAAlwgcDKrvPfee86F\n27dv3yapFAusomKrEXylIgIiIAIikCwCckEXeD7Wrl1re/fudSn8tm7dagQ0qYiACIiACIhAVAIS\n4ALJZef7zf5eYDVaTQREQAREQAQcAQlwgRfC0KFDGyeNZxgP/akqIiACIiACIhCVgPqACyTXrVs3\nu/zyy10wU9euXRM/5V+uwzp58qQtXbrUDh8+bP369bP6+vpcq2mZCIiACIhAGQjIAg4BmennEOKk\nz7eb75BIyrF79243xRjJOXbt2pVvVS0XAREQAREoMQEJcIkBJ6l6LOBgSdu8wcG267MIiIAIpJ2A\nBLgEZ5CMU0ksgwcPNpJxUBgbzHAqFREQAREQgcoQUB9wjNzPnDlj7777rhuuRD/x5MmT7bzzzotx\nD8VV1bNnT7vssstcPzaudC/GxdWqrUVABERABKIQkAUchVqebbZs2eLEl58PHTpkGzZsyLNm5Rbz\nQEA6Solv5c6B9iwCIiACEJAAx3gdMPVfsCTVFR1soz6LgAiIgAhUhoAEOEbuAwYMMFzPFPpYGTus\nIgIiIAIiIAK5CKgPOBeViMvatWvn5tslujhK3++ePXuc25p6GKOLiBdasLY3b95sWOF9+vSxTp06\nFbqp1hMBERABEagAAQlwCaBHEV+GCC1evNjlmqZJx48ft4suuqjg1i1btszlp2asMmN8CbZCyFVE\nQAREQASSSUAu6IScFwQ3mF+a6QPDFCaK8OXUqVMu25X/rncREAEREIHkEZAAN3NOGFa0atUqW7hw\nYdGzHyGuZJ46cuRIzj3Sd8zQIF/CTiHIECNfsHy7dOniv+pdBERABEQggQTkgm7mpKxevdo2bdrk\n1qB/ln7VoEg2s2mTn7BI582b59zKpLEcP378OZM5sPzCCy90qSJxI/fq1atJHS19GTNmjAsAoy+Y\nPmC5n1sipt9FQAREoLIEJMDN8M+2VpnEIIoA+/zL7IogKcYL55pNibG5uZY308TGn9iWqOv27dub\nUkw2YtEHERABEUgsAbmgmzk1wVSNWJRhrVJfdYcOHfxH9579vcmP+iICIiACIlATBGQBN3OaBw0a\nZMz9iyWMWzdKdDPVk3lq1KhRtnXrVtc3q2kAm4Gun0RABESgRghIgFs40Ygnr2LLkCFDjJeKCIiA\nCIiACEBALmhdByIgAiIgAiJQAQIS4BJCP3bsmMtOdeDAgRLuRVWLgAiIgAikkYBc0CU6a4gvQ498\nco0JEyZEjnAuURNVrQiIgAiIQAUJyAIuEXySbnjxZRfbt28v0Z5UrQiIgAiIQBoJSIBLdNayM1Fl\nfy/RblWtCIiACIhASgjIBV2iE0VqyLFjx9rOnTtdhqrhw4eXaE+qVgREQAREII0EJMAlPGsDBw40\nXioiIAIiIAIikE1ALuhsIvouAiIgAiIgAmUgIAEuA2TtQgREQAREQASyCUiAs4nouwiIgAiIgAiU\ngYAEuAyQtQsREAEREAERyCYgAc4mou8iIAIiIAIiUAYCEuAyQNYuREAEREAERCCbgIYhZRE5c+aM\nrVmzxtq2besmt49jJqSsXeirCIiACIiACJgEOOsiWLVqlUueQeaq/fv326WXXmodO3bMWktfRUAE\nREAERKA4AnJBZ/ELzlz0/vvv26FDh7LW0FcREAEREAERKJ6ALOAshr1797YtW7a4pe3atbPzzz8/\na43yfN28ebOtXr3aWrVqZQ0NDdanT5/y7Fh7EQEREAERKAsBCXAW5hEjRhh5nNu3b2+tW7e28847\nL2uN0n89efKkrVixwv70pz+5nS1dutSuvPLK0u9YexABERABESgbAQlwDtT9+/c3gq+2bduW49fS\nL0J4vfiyt+Dn0u9dexABERABESgHAfUBl4NyyH1gdQ8bNsxthQu6vr4+ZA1aXQREQAREIOkEZAEn\n9AwhuoMHD3ZucPqiVURABERABKqLgAQ4weezEv3PCcahpomACIhAVRGQC7qqTqcORgREQAREIC0E\nJMBpOVNqpwiIgAiIQFURkABX1enUwYiACIiACKSFgAQ4LWdK7RQBERABEagqAiUNwjp9+rTNnTvX\nli9f7jJK3XLLLbZ+/Xp7/PHHm0D80pe+ZEyC8MQTT9iRI0dc0onJkye7dWbPnm2LFy+2bt262R13\n3GEdOnRosq2+iIAIiIAIiEAaCZTUAn799dft2LFjds8991j37t1t5cqVNnz4cPvGN77hXtdff711\n7tzZJb147LHHbMaMGW7dmTNn2tGjR23dunW2du1au//++912s2bNSiNjtVkEREAEREAEziFQUgt4\n0aJFdvPNNzsrePr06Y15lUnxeOLECXv66aftK1/5imvUwYMH3bhXvtTV1dmGDRtcTuZJkyZZmzZt\nbNq0afbggw82OQBmLjp+/Hjjsk6dOhmzGBVbfBrKtM2CRNIOplGEb5oK45xpd9p4c11S8N6kqfh2\np4031wivtGWG4/+Re0raCm3mnpLGgqeUyXTKUYq535ZUgBHVF1980caOHevE86677rIBAwY4JgsW\nLHDLcS1j7fKP5QtCiiua6QD9+twsWBYs8+bNs927dzcuQuQHDhzY+D3qB4Byk6rURAxR2812tL1c\nF14x7QxuC2v+0dPG29+c0iYIXsTSluAF3mljzXUObzx9aXvg4V6StjbDm+uka9eufCxLwZiMWs6q\nXtQamtmOf3DcymR0QlQR3RtvvNFtMWfOHKPvl0LCCSYg8IXPWLKcfH9wp06dOgfq5z//eb+Je9+z\nZ49t3769ybIoX3h6Ihd0HHVF2X/UbbjweGr1zKLWU+7teAjjWuH8pan4h0ZiHdJUevXqZfw/8YCc\npsK1TbvTJsL9+vUzpjmlOy5Nhftg0MOYlrYPGTLEdu3aVTbPVDFe15L6KhFe/0/OBegDqPwyZh2i\nYAHxz4WFyz/Xxo0bjYt20KBBLmiLdegP9tYw31VEQAREQAREIM0ESmoBE2RFQBWuYia2v/feex2r\nHTt2GDMOBQuW8cMPP2xYE8x/S9AWlhER1A899JAT8vvuuy+4iT6LgAiIgAiIQGoJlFSAceMydAiX\nMhauLyNHjjReweKX4WLyfVP0Qdx+++3nbB/cTp9FQAREQAREII0ESuqC9kCC4uuX5Xv34hv8Pcz2\nwe30WQREQAREQASSSqAsApzUg1e7REAEREAERKBSBCTAlSKv/YqACIiACNQ0AQlwTZ9+HbwIiIAI\niEClCEiAK0Ve+xUBERABEahpAhLgmj79OngREAEREIFKEZAAV4q89isCIiACIlDTBCTANX36dfAi\nIAIiIAKVIiABrhR57VcEREAERKCmCUiAa/r06+BFQAREQAQqRUACXCny2q8IiIAIiEBNE5AA1/Tp\n18GLgAiIgAhUioAEuFLktV8REAEREIGaJiABrunTr4MXAREQARGoFAEJcKXIa78iIAIiIAI1TUAC\nXNOnXwcvAiIgAiJQKQJtK7Vj7bdwAqdPn7atW7e6DQYOHGht2rQpfGOtKQIiIAIikEgCEuBEnpam\njVq4cKEdOHDALdy5c6dNmzat6Qr6JgIiIAIikDoCckEn/JSdPHmyUXxp6v79+w2LWEUEREAERCDd\nBCTACT9/7du3t86dOze2skuXLta2rRwXjUD0QQREQARSSkB38hScuClTptj69eutVatWNnTo0BS0\nWE0UAREQARFoiYAEuCVCCfi9Q4cONmbMmAS0RE0QAREQARGIi4Bc0HGRVD0iIAIiIAIiEIKABDgE\nLK0qAiIgAiIgAnERkADHRVL1iIAIiIAIiEAIAhLgELC0qgiIgAiIgAjERUACHBdJ1SMCIiACIiAC\nIQhIgEPA0qoiIAIiIAIiEBcBCXBcJFWPCIiACIiACIQgIAEOAUurioAIiIAIiEBcBCTAcZFUPSIg\nAiIgAiIQgoAEOAQsrSoCIiACIiACcRGQAMdFUvWIgAiIgAiIQAgCEuAQsLSqCIiACIiACMRFQAIc\nF0nVIwIiIAIiIAIhCEiAQ8DSqiIgAiIgAiIQFwEJcFwkVY8IiIAIiIAIhCAgAQ4BS6uKgAiIgAiI\nQFwEWv0pU+KqrFrqWblypT3//PP2jW98o1oOKdHH8dprr9m2bdvs9ttvT3Q7q6Vxv/nNb6xPnz52\n1VVXVcshJfo4/uVf/sU+9rGP2dixYxPdzmpp3F//9V/bt771LevRo0fiD0kWcI5T9P7779vJkydz\n/KJFpSBw5swZO3XqVCmqVp05CMAa5irlIXDixAnjnqJSHgLHjx+3tNiVEuDyXBPaiwiIgAiIgAg0\nISABboJDX0RABERABESgPATUB5yD8+HDh23Hjh02YsSIHL9qUdwEdu/ebbiNBg0aFHfVqi8HgS1b\ntlj79u1dP3COn7UoZgJr1qyxvn37WteuXWOuWdXlIrB06VKrr69313iu35O0TAKcpLOhtoiACIiA\nCNQMAbmga+ZU60BFQAREQASSREACnKSzobaIgAiIgAjUDIG2NXOkgQM9ffq0PfTQQ/bFL37ROnXq\nZAwTePLJJ23v3r1WV1dn11xzjVv73XfftTlz5li7du3sU5/6lOszo7/yiSeesCNHjtiVV15pkydP\nDtSsj7kI7Nmzx37729/afffd534+dOiQzZw50/Wzjxkzxo2R5IfnnnvONm7caOeff77NmDHDevbs\nacuWLbNXXnnFOGe33Xab9evXL9cutCxAYPHixbZ+/Xr75Cc/6ZZu3rzZXnrpJXedX3HFFdbQ0GCc\ng3/7t39r3Are48ePF+9GIoV/yOad676R7x4ze/ZsY/tu3brZHXfcYR06dCh8xzW2Jtfss88+a/v3\n77cBAwbY9ddfb23bts17zWbf5/nOGHjie0aNGuW2rzTCmrOAd+7caQyM37BhQ+NYsXnz5lnv3r3t\ny1/+shGgws2LoCCScdx9991200032c9//nN3rh577DEnDvfcc48TkaNHj1b6HCZ6/wRE/PSnPzVE\n2JdXX33VCelXv/pV98+wZMkSY72DBw+6c8BDzQsvvODOwdNPP+3Owc0332y//vWvfRV6z0Ng1qxZ\n9rvf/c6x86vw8IPA8sDJNQ1nRHn48OH29a9/3b0QZa558fbUCnvP5p3vvpHrHrNu3Tpbu3at3X//\n/e5cUJdKfgIvv/yyjRw50t0jWGvhwoV5r9lc93m25wH+a1/7mrvvLF++PP/OyvRLzQkwNx+eNPv3\n79+IuGPHjk54OWn8zlMVAj1kyBDjt169ehlCS3IOfh88eLB17tzZWcusp5KfAE/+PNgEy+rVq521\n1apVK/cPxU0IMbjxxhvdangcDhw44P5JYI2XggjpY8eOOUs4WJc+NyXAdfmZz3ymcSFJN4jq53on\n8pl3HjIRYK5zvAu7du2y1q1bi3cjtcI/ZPPOd9/IdY8hOnrSpEnWpk0bmzZtmpGBTyU/ATK3eY8j\n9wgsYazZXPeIXPd5eE+dOtXxnjJlSiJ415wAE56e7cYcOnSouylhYfHPgODiikYYEIv58+fbvn37\n3MnmpuULwoArWiU/AS50blLBgquTp31EYMGCBU5suUHx4p8Kiw33Ep9h7Au/i7enkfv9ssv+//bO\n55WaKIzjZ2djwYaFjbLwB4j0dlOKFRs/lorIQlndEkuJFSErC6GIbCykbJWyosRKlsqfodfnqXPf\nMc283LnzvjPufE+595q58+tzzn2ec55zzvf8Mmfq92KoKM+0wDBAz8/PxpvKJBUgQnmnp6fu5eVF\nvD20Kt7DvKPsBs4gysYEy7fK9tfQ6ZrCPlNWHx4eXKlUii2zUXY+yDsvtvuPN/n6+ev2G+fn59Yq\nbm9vd+gSE6oYGhpyhJlvb29tDh8/LPRzgxKVfG5sbKxbLv/qweg7v7+/t/51arQ+PE3/+t7enhsf\nHzeDRTiPFrRPtObCztzv03s8gYmJCSvHRHioEDU1Nbne3t7KAcj23d3dWetAvCtYEn2gwhO2GzgO\numGIvAVtDE7X86Zsa57w18jpqrq8vHRzc3NWYafP3DPk6L/ZCM+bPMqL7S5cCzgqixsaGiwz2UeY\njgzC+D8+PrrR0VEzTGQsmc1+WmEYLQYMhVvTUefXts8E6LvBEDGoCqdLqJ+QM0YKZ0HtleTDpbAm\n/Mx7MALx+az6L47Azc2NDXQbHh52r6+vxpVKp+8+IYxHS1i84wh+f3uU3cCeRNkYulUYb0KiP5g8\nUIonQJ8tkbP5+XmrRPLNaspsmHdbW1v8xf7THrWAP0APDg5arYrwBiL1rMqDs8UpHBwcWP8vrTIS\n/ZSHh4fWF8nAFVoTStURwNAQZmbkJ4aJfrCzszMLJx0fH9vJGBRHS4K+sd3dXRu16/uIq7uavk0I\nmsgC5burq8tGmXd3d7uLiwur0OA0ZmdnLdwv3rWVlzi7EWVjCIPiVJiRQZjazxKo7Q7q92hsBiOZ\nd3Z27CHpz2XGynfLLCtSMUCRCimVIiqkWScpYQVygLAELdxgitrGflrEZKJScgJxbMNn5EfHICH+\nlJIRgCF9vjjhYML54jSCSbyDNJJ9jivbUdujtiW7anGPqqbM5om3HHBxy6yeXAREQAREIEMCalJk\nCF+XFgEREAERKC4BOeDi5r2eXAREQAREIEMCcsAZwtelRUAEREAEiktADri4ea8nr2MCzI1kzi9i\nJz4tLS25k5MTm861urpq6mJMxVhbW6vIsjLPsr+/30ZKIx6xtbVlh6NvPDU1ZQIp6HdLEMVT1bsI\nJCcgB5ycnY4UgdwSYHoX4jHM9yUxSnR/f9/19fW5o6Mjx3QvdJ9ZWAQlLNTeSMzDRoXs7e3NnO/C\nwoItUsI8bI7p6elxGxsbEkQxWnoRgdoIyAHXxk9Hi0BuCaC8xLxH0vWH5nNnZ6e1ellYhIUZOjo6\nbNv09LQ5Y77HCknlctnmZyOWgnoQWtEkpistLy/nYv6k3ZBeROCHE5AQxw/PQN2+CMQRQGgAgQ2U\nrliGDYEZEosxrK+vu+3t7cqhiBqQcLYsWYhABDKhCNO8v7/bPpSElERABNIjIAecHkudSQRyRYDW\nK04YxSv0c1dWVuz+UMEiFI1zJrFaEo6W9bDHxsYsRI0WOmFs1JqQACWFRTxso15EQAQSE1AIOjE6\nHSgC+SdAGHpzc9MxcKq1tdVuGElPJFZZ4QvnSr8vg61wxKSBgQELN9M3jFIWqm9KIiAC6ROQA06f\nqc4oArkhgFYuIWgffubGGGTFIiL08bLAOa3fxcVFWxRjcnLStLnRjL66urJVk7RObW6yUzdSZwQk\nRVlnGarHEYEgAUYv42Sfnp5cc3NzcFdlKlF4iUemGKEbHVyL+dOB+kcERCAVAuoDTgWjTiIC+SPA\ntCGmHLHsY9j5crdhx+ufIG673693ERCBdAjIAafDUWcRgdwRaGlpcSMjI25mZiZ396YbEgERcE4h\naJUCERABERABEciAgAZhZQBdlxQBERABERABOWCVAREQAREQARHIgIAccAbQdUkREAEREAERkANW\nGRABERABERCBDAjIAWcAXZcUAREQAREQATlglQEREAEREAERyIDAbx1KK+R0mt/9AAAAAElFTkSu\nQmCC\n" } ], "prompt_number": 67 }, { "cell_type": "code", "collapsed": false, "input": [ "def plot(startyear):\n", " df_selected = df[(df['year'] >= startyear) & (df['year'] < (startyear + 18.6*2))]\n", " \n", " y = df_selected['waterlevel']\n", " X = np.c_[df_selected['year']-1970, (df_selected['year']-1970)**2]\n", " X = sm.add_constant(X)\n", " model = sm.OLS(y, X)\n", " results = model.fit()\n", " fig, ax = plt.subplots()\n", " ax.plot(df['year'], df['waterlevel'], '.', alpha=0.3)\n", " ax.plot(df_selected['year'], results.fittedvalues, linewidth=5)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 68 }, { "cell_type": "code", "collapsed": false, "input": [ "interactive(plot, startyear=(1860, 1980, 5))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 69 }, { "cell_type": "code", "collapsed": false, "input": [ "def plot(startyear, n_periods):\n", "\n", " df_selected = df[(df['year'] >= startyear) & (df['year'] < (startyear + 18.6*n_periods))]\n", " \n", " y = df_selected['waterlevel']\n", " X = np.c_[\n", " df_selected['year']-1970, \n", " np.cos(2*np.pi*(df_selected['year']-1970)/18.613),\n", " np.sin(2*np.pi*(df_selected['year']-1970)/18.613)\n", "\n", " ]\n", " X = sm.add_constant(X)\n", " model = sm.OLS(y, X)\n", " results = model.fit()\n", " # e^{i\\theta} = \\cos\\theta + i\\sin\\theta\n", " phase = np.angle(complex(results.params['x2'], results.params['x3']))\n", " ampl = np.sqrt(results.params['x2']**2 + results.params['x3']**2)\n", " \n", " # plot it\n", " fig, ax = plt.subplots()\n", " ax.set_title(\"Phase (rad): %.1f, amplitude: %.1f\\nA(cos): %.1f, B(sin): %.1f\" % (phase, ampl, results.params['x2'], results.params['x3']))\n", " ax.plot(df['year'], df['waterlevel'], '.', alpha=0.3)\n", " ax.plot(df_selected['year'], results.fittedvalues, linewidth=5)\n", " " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 70 }, { "cell_type": "code", "collapsed": false, "input": [ "interactive(plot, startyear=(1860, 1980, 5), n_periods=(2,5))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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RFGXQMUDWg1QiuVT8dGCbtfY24HhgGfAx4Dlr7Xki8ivgAmBKyN8APB4p3wRM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\n9nezYvM+drT17Os/fkQt/7F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"text": [ "" ] } ], "prompt_number": 71 }, { "cell_type": "code", "collapsed": false, "input": [ "df = annual_df[annual_df['station']=='mean']\n", "hkv = pandas.read_csv('full_output_station_gemiddeld.csv', sep=';')\n", "df = df.merge(hkv[['year', 'U2sin', 'U2cos']], on='year', suffixes=('', '_hkv'))\n", "df = df.reset_index()\n", "y = df['nap']\n", "X = np.c_[\n", " df['year']-1970, \n", " np.cos(2*np.pi*(df['year']-1970)/18.613),\n", " np.sin(2*np.pi*(df['year']-1970)/18.613),\n", " df['U2cos'], \n", " df['U2sin']\n", "]\n", "X = sm.add_constant(X)\n", "model = sm.OLS(y, X)\n", "results = model.fit()\n", "linear = results.params['const'] + results.params['x1'] * (df['year']-1970)\n", "nodal = results.params['x2'] * np.cos(2*np.pi*(df['year']-1970)/18.613) + results.params['x3'] * np.sin(2*np.pi*(df['year']-1970)/18.613)\n", "wind = results.params['x4'] * df['U2cos'] + results.params['x5'] * df['U2sin']\n", "df['linear'] = linear\n", "df['nodal'] = nodal\n", "df['wind'] = wind\n", "df['residual'] = results.resid\n", "df['loess'] = sm.nonparametric.lowess(df['residual'], df['year'], frac=60.0/len(df))[:,1]\n", "df['residual_loess'] = df['residual'] - df['loess']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 72 }, { "cell_type": "code", "collapsed": false, "input": [ "df[['year', 'nap', 'station', 'linear', 'nodal', 'residual', 'wind', 'loess', 'residual_loess']].to_json('fit.json', orient='records')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 73 }, { "cell_type": "code", "collapsed": false, "input": [ "y = df['nap']\n", "X = np.c_[\n", " np.ones(shape=(len(df),))\n", "]\n", "# compute semipartial correlations\n", "rsquares = []\n", "terms = [df['year']-1970, \n", " np.c_[np.cos(2*np.pi*(df['year']-1970)/18.613), np.sin(2*np.pi*(df['year']-1970)/18.613)],\n", " np.c_[df['U2cos'], df['U2sin']]]\n", "names = ['linear', 'nodal', 'wind']\n", "rsquare = 0\n", "for name, term in zip(names, terms):\n", " X = np.c_[X, term]\n", " model = sm.OLS(y, X)\n", " results = model.fit()\n", " rsquares.append((name, results.rsquared - rsquare))\n", " rsquare = results.rsquared\n", "rsquares.append(('loess', 1- np.var(df['residual_loess'])/np.var(df['nap'])- rsquare))\n", "rsquare = results.rsquared\n", "rsquares.append(('residual', np.var(df['residual_loess'])/np.var(df['nap'])))\n", "\n", "\n", "import json\n", "json.dump(dict(rsquares), open('rsquares.json', 'w'))\n", "rsquares" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 74, "text": [ "[('linear', 0.82769308510876782),\n", " ('nodal', 0.019216129938318116),\n", " ('wind', 0.043763073036397149),\n", " ('loess', 0.0036103727985814515),\n", " ('residual', 0.10571733911793549)]" ] } ], "prompt_number": 74 }, { "cell_type": "code", "collapsed": false, "input": [ "a = results.summary(yname='waterlevel', xname=['constant', 'linear', 'nodal cos', 'nodal sin', 'wind cos', 'wind sin'])\n", "#print(a.as_latex())\n", "a" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: waterlevel R-squared: 0.891
Model: OLS Adj. R-squared: 0.886
Method: Least Squares F-statistic: 190.6
Date: Sun, 16 Nov 2014 Prob (F-statistic): 1.71e-54
Time: 23:49:24 Log-Likelihood: -566.06
No. Observations: 123 AIC: 1144.
Df Residuals: 117 BIC: 1161.
Df Model: 5
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [95.0% Conf. Int.]
constant -44.6608 6.507 -6.863 0.000 -57.548 -31.774
linear 1.7577 0.067 26.278 0.000 1.625 1.890
nodal cos 3.7984 3.233 1.175 0.242 -2.604 10.201
nodal sin -11.8578 3.138 -3.778 0.000 -18.073 -5.642
wind cos 1.2649 0.189 6.694 0.000 0.891 1.639
wind sin -0.4672 0.266 -1.753 0.082 -0.995 0.060
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Omnibus: 0.139 Durbin-Watson: 1.435
Prob(Omnibus): 0.933 Jarque-Bera (JB): 0.311
Skew: 0.013 Prob(JB): 0.856
Kurtosis: 2.755 Cond. No. 122.
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 75, "text": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: waterlevel R-squared: 0.891\n", "Model: OLS Adj. R-squared: 0.886\n", "Method: Least Squares F-statistic: 190.6\n", "Date: Sun, 16 Nov 2014 Prob (F-statistic): 1.71e-54\n", "Time: 23:49:24 Log-Likelihood: -566.06\n", "No. Observations: 123 AIC: 1144.\n", "Df Residuals: 117 BIC: 1161.\n", "Df Model: 5 \n", "==============================================================================\n", " coef std err t P>|t| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "constant -44.6608 6.507 -6.863 0.000 -57.548 -31.774\n", "linear 1.7577 0.067 26.278 0.000 1.625 1.890\n", "nodal cos 3.7984 3.233 1.175 0.242 -2.604 10.201\n", "nodal sin -11.8578 3.138 -3.778 0.000 -18.073 -5.642\n", "wind cos 1.2649 0.189 6.694 0.000 0.891 1.639\n", "wind sin -0.4672 0.266 -1.753 0.082 -0.995 0.060\n", "==============================================================================\n", "Omnibus: 0.139 Durbin-Watson: 1.435\n", "Prob(Omnibus): 0.933 Jarque-Bera (JB): 0.311\n", "Skew: 0.013 Prob(JB): 0.856\n", "Kurtosis: 2.755 Cond. No. 122.\n", "==============================================================================\n", "\"\"\"" ] } ], "prompt_number": 75 }, { "cell_type": "code", "collapsed": false, "input": [ "def plot(degree):\n", " fitted = pandas.DataFrame(dict(fit=results.resid, year=df['year']))\n", " %Rpush fitted\n", " %Rpush degree\n", " %R l = predict(loess(fitted, degree=degree, control=loess.control(surface='direct')), se=TRUE, newdata=data.frame(year=seq(1890,2100)))\n", " l = %Rget l\n", " import pandas.rpy.common as com\n", " l = com.load_data('l')\n", " plt.fill_between(np.arange(1890, 2101), l['fit'] - l['se.fit']*1.96, l['fit'] + l['se.fit']*1.96, alpha=0.5)\n", " plt.plot(np.arange(1890, 2101), l['fit'])\n", " plt.ylim(-200,400)\n", " plt.title('Loess model with degree %d' % degree)\n", " plt.ylabel(\"sea surface height residual ['mm']\")\n", "interactive(plot, degree=(1,2))\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 76 }, { "cell_type": "code", "collapsed": false, "input": [ "# The broken linear model\n", "y = df['nap']\n", "X = np.c_[\n", " df['year']-1970, \n", " np.cos(2*np.pi*(df['year']-1970)/18.613),\n", " np.sin(2*np.pi*(df['year']-1970)/18.613),\n", " df['U2cos'], \n", " df['U2sin'],\n", " (df['year']-1990 > 0)*(df['year']-1990)\n", "]\n", "X = sm.add_constant(X)\n", "model = sm.OLS(y, X)\n", "fit = model.fit()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 77 }, { "cell_type": "code", "collapsed": false, "input": [ "%Rpush df \n", "%R fit <- lm(nap ~ year + I((year > 1990)*(year-1990)) + I(cos((2*pi*year-1970)/18.613)) + I(sin((2*pi*year-1970)/18.613) + U2cos + U2sin), data=df)\n", "%R years <- seq(1890, 2100)\n", "%R pred <- predict(fit, interval=\"confidence\", newdata=data.frame(year=years, U2cos=rep(mean(df$U2cos), length(years)), U2sin=rep(mean(df$U2sin), length(years))))\n", "pred = %Rget pred\n", "%R print(summary(fit))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "text": [ "\n", "Call:\n", "lm(formula = nap ~ year + I((year > 1990) * (year - 1990)) + \n", " I(cos((2 * pi * year - 1970)/18.613)) + I(sin((2 * pi * year - \n", " 1970)/18.613) + U2cos + U2sin), data = df)\n", "\n", "Residuals:\n", " Min 1Q Median 3Q Max \n", "-66.682 -19.736 2.081 19.674 63.102 \n", "\n", "Coefficients:\n", " Estimate Std. Error\n", "(Intercept) -3.382e+03 1.867e+02\n", "year 1.688e+00 9.715e-02\n", "I((year > 1990) * (year - 1990)) 8.555e-01 6.372e-01\n", "I(cos((2 * pi * year - 1970)/18.613)) 5.595e+00 3.709e+00\n", "I(sin((2 * pi * year - 1970)/18.613) + U2cos + U2sin) 7.036e-01 1.751e-01\n", " t value Pr(>|t|) \n", "(Intercept) -18.112 < 2e-16 ***\n", "year 17.378 < 2e-16 ***\n", "I((year > 1990) * (year - 1990)) 1.343 0.181947 \n", "I(cos((2 * pi * year - 1970)/18.613)) 1.508 0.134162 \n", "I(sin((2 * pi * year - 1970)/18.613) + U2cos + U2sin) 4.018 0.000104 ***\n", "---\n", "Signif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n", "\n", "Residual standard error: 28.58 on 118 degrees of freedom\n", "Multiple R-squared: 0.8528,\tAdjusted R-squared: 0.8478 \n", "F-statistic: 170.9 on 4 and 118 DF, p-value: < 2.2e-16\n", "\n" ] } ], "prompt_number": 78 }, { "cell_type": "code", "collapsed": false, "input": [ "def compute(split):\n", " %Rpush split\n", " %Rpush df \n", " %R fit <- lm(nap ~ year + I((year > split)*(year-split)) + I(cos((2*pi*year-1970)/18.613)) + I(sin((2*pi*year-1970)/18.613) + U2cos + U2sin), data=df)\n", " %R years <- seq(1890, 2100)\n", " %R pred <- predict(fit, interval=\"confidence\", newdata=data.frame(year=years, U2cos=rep(mean(df$U2cos), length(years)), U2sin=rep(mean(df$U2sin), length(years))))\n", " pred = %Rget pred\n", " %R print(summary(fit))\n", "interactive(compute, split=(1980, 2000))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "text": [ "\n", "Call:\n", "lm(formula = nap ~ year + I((year > split) * (year - split)) + \n", " I(cos((2 * pi * year - 1970)/18.613)) + I(sin((2 * pi * year - \n", " 1970)/18.613) + U2cos + U2sin), data = df)\n", "\n", "Residuals:\n", " Min 1Q Median 3Q Max \n", "-66.682 -19.736 2.081 19.674 63.102 \n", "\n", "Coefficients:\n", " Estimate Std. Error\n", "(Intercept) -3.382e+03 1.867e+02\n", "year 1.688e+00 9.715e-02\n", "I((year > split) * (year - split)) 8.555e-01 6.372e-01\n", "I(cos((2 * pi * year - 1970)/18.613)) 5.595e+00 3.709e+00\n", "I(sin((2 * pi * year - 1970)/18.613) + U2cos + U2sin) 7.036e-01 1.751e-01\n", " t value Pr(>|t|) \n", "(Intercept) -18.112 < 2e-16 ***\n", "year 17.378 < 2e-16 ***\n", "I((year > split) * (year - split)) 1.343 0.181947 \n", "I(cos((2 * pi * year - 1970)/18.613)) 1.508 0.134162 \n", "I(sin((2 * pi * year - 1970)/18.613) + U2cos + U2sin) 4.018 0.000104 ***\n", "---\n", "Signif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n", "\n", "Residual standard error: 28.58 on 118 degrees of freedom\n", "Multiple R-squared: 0.8528,\tAdjusted R-squared: 0.8478 \n", "F-statistic: 170.9 on 4 and 118 DF, p-value: < 2.2e-16\n", "\n" ] } ], "prompt_number": 79 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.fill_between(np.arange(1890, 2101), np.array(pred)[:,1], np.array(pred)[:,2], alpha=0.5)\n", "plt.plot(np.arange(1890, 2101), np.array(pred)[:,0])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 80, "text": [ "[]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 80 }, { "cell_type": "code", "collapsed": false, "input": [ "# The linear model\n", "%Rpush df \n", "%R fit <- lm(nap ~ year + I(cos((2*pi*year-1970)/18.613)) + I(sin((2*pi*year-1970)/18.613)) + U2cos + U2sin, data=df)\n", "%R years <- seq(1890, 2100)\n", "# Padd with means\n", "%R U2cos = df$U2cos\n", "%R U2cos[years>2012] = mean(df$U2cos)\n", "%R U2sin = df$U2cos\n", "%R U2sin[years>2012] = mean(df$U2sin)\n", "%R pred <- predict(fit, interval=\"confidence\", newdata=data.frame(year=years, U2cos=U2cos, U2sin=U2sin))\n", "pred = %Rget pred\n", "%R print(summary(fit))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "text": [ "\n", "Call:\n", "lm(formula = nap ~ year + I(cos((2 * pi * year - 1970)/18.613)) + \n", " I(sin((2 * pi * year - 1970)/18.613)) + U2cos + U2sin, data = df)\n", "\n", "Residuals:\n", " Min 1Q Median 3Q Max \n", "-59.919 -17.607 1.216 15.672 59.747 \n", "\n", "Coefficients:\n", " Estimate Std. Error t value Pr(>|t|)\n", "(Intercept) -3.507e+03 1.289e+02 -27.207 < 2e-16\n", "year 1.758e+00 6.689e-02 26.278 < 2e-16\n", "I(cos((2 * pi * year - 1970)/18.613)) 3.427e+00 3.233e+00 1.060 0.29120\n", "I(sin((2 * pi * year - 1970)/18.613)) -1.197e+01 3.139e+00 -3.814 0.00022\n", "U2cos 1.265e+00 1.890e-01 6.694 7.94e-10\n", "U2sin -4.672e-01 2.665e-01 -1.753 0.08214\n", " \n", "(Intercept) ***\n", "year ***\n", "I(cos((2 * pi * year - 1970)/18.613)) \n", "I(sin((2 * pi * year - 1970)/18.613)) ***\n", "U2cos ***\n", "U2sin . \n", "---\n", "Signif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n", "\n", "Residual standard error: 24.73 on 117 degrees of freedom\n", "Multiple R-squared: 0.8907,\tAdjusted R-squared: 0.886 \n", "F-statistic: 190.6 on 5 and 117 DF, p-value: < 2.2e-16\n", "\n" ] } ], "prompt_number": 81 }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "\n", "fitted = pandas.DataFrame(dict(fit=results.resid, year=df['year']))\n", "degree = 1\n", "%Rpush fitted\n", "%Rpush degree\n", "%R l = predict(loess(fitted, degree=degree, control=loess.control(surface='direct')), se=TRUE, newdata=data.frame(year=seq(1890,2100)))\n", "l = %Rget l\n", "import pandas.rpy.common as com\n", "l = com.load_data('l')\n", "ci_loess = pandas.DataFrame(data=dict(year=np.arange(1890, 2101), se=l['se.fit'], fit=l['fit'], lwr=l['fit'] - l['se.fit']*1.96, upr=l['fit'] + l['se.fit']*1.96))\n", "\n", "# compute angle\n", "beta = (ci_loess[ci_loess['year'] == 2100].fit.item() - ci_loess[ci_loess['year'] == 2013].fit.item())/(2100-2013)\n", "# compute std error (1.96 -> z 5%, 2 two-tailed)\n", "se = ((ci_loess[ci_loess['year'] == 2100].upr.item() - ci_loess[ci_loess['year'] == 2100].lwr.item()))/1.96/2.0/(2100-2013)\n", "beta, se" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 124, "text": [ "(0.27802282630813135, 0.23436251419913748)" ] } ], "prompt_number": 124 }, { "cell_type": "code", "collapsed": false, "input": [ "fitted = pandas.DataFrame(dict(fit=results.resid, year=df['year']))\n", "degree = 2\n", "%Rpush fitted\n", "%Rpush degree\n", "%R fit <- loess(fitted, degree=degree, control=loess.control(surface='direct'))\n", "%R l <- predict(fit, se=TRUE, newdata=data.frame(year=seq(1890,2100)))\n", "\n", "import pandas.rpy.common as com\n", "l = com.load_data('l')\n", "fit = %Rget fit\n", "\n", "ci_loess2 = pandas.DataFrame(data=dict(year=np.arange(1890, 2101), fit=l['fit'], lwr=l['fit'] - l['se.fit']*1.96, upr=l['fit'] + l['se.fit']*1.96))\n", "#ci_loess = ci_loess.set_index('year')\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 114 }, { "cell_type": "code", "collapsed": false, "input": [ "ci_linear = com.load_data(\"pred\")\n", "ci_linear['year'] = np.arange(1890, 2101)\n", "#ci_linear= ci_linear.set_index('year')\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 84 }, { "cell_type": "code", "collapsed": false, "input": [ "ci = pandas.merge(ci_loess, ci_loess2, on='year', suffixes=('_loess', '_loess2'))\n", "ci = pandas.merge(ci_linear, ci, on='year', suffixes=('_linear', ''))\n", "#ci['fit'].name('fit_linear')\n", "#ci['lwr'].rename('lwr_linear')\n", "#ci['upr'].rename('upr_linear')\n", "ci.rename(columns={'fit':'fit_linear', 'lwr': 'lwr_linear', 'upr': 'upr_linear'}, inplace=True)\n", "ci.to_csv('ci.csv')\n", "\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 85 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.fill_between(np.arange(1890,2101), ci_linear['lwr'] + ci_loess['lwr'], ci_linear['upr'] + ci_loess['upr'])\n", "plt.plot(np.arange(1890,2101), ci_linear['fit'] + ci_loess['fit'])\n", "plt.plot(np.arange(1890,2101), ci_linear['fit'], color='black')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 86, "text": [ "[]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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SWyl2V7E1dRLPFF1EvTltwH3Prd2ANeJnRc6pncuaN76NISWLxOK5w/Ex+nTzohxSjKM7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\n3HhKDi6vB3tKNgBvL/4mv4640bibeWL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"text": [ "" ] } ], "prompt_number": 86 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.fill_between(np.arange(1890,2101), ci_linear['lwr'] , ci_linear['upr'] )\n", "plt.plot(np.arange(1890,2101), ci_linear['fit'])\n", "plt.fill_between(np.arange(1890,2101), ci_loess['lwr'] , ci_loess['upr'] )\n", "plt.plot(np.arange(1890,2101), ci_loess['fit'])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 40, "text": [ "[]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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8cYPeqE5nOMnO1hBbGgLpnNz0MRk5iOcVRdGBn6qq+jOgRFXVvm9xO1AywWkQ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\nUkBkgXA0RU80/cHbPZzin19v4l92NnP0QB09zgD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"text": [ "" ] } ], "prompt_number": 40 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 40 }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "df = annual_df[annual_df['station']=='mean']\n", "hkv = pandas.read_csv('full_output_station_gemiddeld.csv', sep=';')\n", "df = df.merge(hkv[['year', 'U2sin', 'U2cos']], on='year', suffixes=('', '_hkv'))\n", "df = df.reset_index()\n", "y = df['nap']\n", "plt.subplots(figsize=(10,6))\n", "for split in np.arange(1990, 2000):\n", " X = np.c_[\n", " df['year']-1970, \n", " (df['year']-split)*(df['year']>split),\n", " np.cos(2*np.pi*(df['year']-1970)/18.613),\n", " np.sin(2*np.pi*(df['year']-1970)/18.613),\n", " df['U2cos'], \n", " df['U2sin']\n", " ]\n", " X = sm.add_constant(X)\n", " model = sm.OLS(y, X)\n", " results = model.fit()\n", " plt.plot(df['year'], results.fittedvalues, label=str(split), alpha=0.3)\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Bd69+DVrougC4fQj+TuP7Ifn00UDs9rt3sZ3jAxZDS/aDyrXXa2QcneJk93o1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\nZaKxxght4vQJql6Id3/4tx3VcXUpTzTiceqRY1sGZwCZvl6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"text": [ "" ] } ], "prompt_number": 41 }, { "cell_type": "code", "collapsed": false, "input": [ "# Based on the records of Amsterdam/Den Helder\n", "df = pandas.read_csv('/Users/baart_f/src/oetpython/applications/sealevel//sealevel/static/data/extra/9000.csv')\n", "\n", "# Create a plot\n", "fig, ax1 = plt.subplots(1,1, figsize=(10,6))\n", "# compute a trend\n", "def trend(index):\n", " # Select based on index\n", " df_selected = df.ix[index]\n", " # Create a linear model\n", " y = df_selected['waterlevel']\n", " X = np.c_[\n", " df_selected['year.month'],\n", " np.cos(2*np.pi*(df_selected['year.month']-1970)/18.613),\n", " np.sin(2*np.pi*(df_selected['year.month']-1970)/18.613)\n", " ]\n", " X = statsmodels.regression.linear_model.add_constant(X)\n", " model= statsmodels.regression.linear_model.OLS(y, X)\n", " # fit the model\n", " result = model.fit()\n", " # ignore if we have missings\n", " if np.isnan(df_selected['year.month']).any():\n", " return np.nan\n", " # create a plot\n", " ax1.plot(df_selected['year.month'], result.fittedvalues, 'k-', alpha=0.2, linewidth=3)\n", " # return the trend\n", " return result.params['x1']\n", "df['waterlevel'][df['waterlevel'] == -99999] = np.nan\n", "ax1.plot(df['year.month'], df['waterlevel'], '.')\n", "ax1.set_xlabel('tijd [jaar]')\n", "ax1.set_ylabel('zeespiegelniveau [m boven NAP]')\n", "df = df[df['year.month']<1890]\n", "betas = pandas.rolling_apply(np.array(df.index, dtype='int'),20, trend)\n", "fig.savefig('amsterdam.pdf')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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dxpgWy7JeAlxpjLmnmgMUkZ2p4qUwypBKpQCYm5tjdHSUyclJEokE/qufwvzA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5mPlddrgC81HP8WVRAPl+0xe8v+QTYxdtB86bSXYuwabQMeiogPUf1H6GfKfnOvPa3iB+ZfO74W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\nSZJUY/8PQ9q0y5hMjtcAAAAASUVORK5CYII=\n", "text": [ "" ] } ], "prompt_number": 42 }, { "cell_type": "code", "collapsed": false, "input": [ "# standard error of beta_1 and confidence range \n", "np.std(betas[~np.isnan(betas)]), 1.96 * 2 * np.std(betas[~np.isnan(betas)]) " ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 43, "text": [ "(0.17157195469765205, 0.67256206241479599)" ] } ], "prompt_number": 43 }, { "cell_type": "code", "collapsed": false, "input": [ "# the same for the whole of amsterdam\n", "resp = requests.get('http://www.psmsl.org/data/longrecords/amsterdam.sea.level')\n", "f = io.BytesIO(resp.content)\n", "df = pandas.read_fwf(f, widths=(10,20), names=('year.month', 'waterlevel'), skiprows=1)\n", "df['waterlevel']/=10.0\n", "# Create a plot\n", "fig, ax1 = plt.subplots(1,1, figsize=(10,6))\n", "ax1.plot(df['year.month'], df['waterlevel'], '.')\n", "ax1.set_xlabel('tijd [jaar]')\n", "ax1.set_ylabel('zeespiegelniveau [m boven NAP]')\n", "\n", "betas = pandas.rolling_apply(np.array(df.index, dtype='int'),20, trend)\n", "np.std(betas[~np.isnan(betas)]), 1.96 * 2 * np.std(betas[~np.isnan(betas)]) " ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 44, "text": [ "(0.17411785038363373, 0.68254197350384416)" ] }, { "metadata": {}, "output_type": "display_data", "png": 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BsVgEOo2AS6USoVCIoaEhpqameO6551hcXLRbeCz1YavX6xw8eJDp6Wly3/w7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pVoNIpOp5P1XkJdK5VKxGIxXnvtNTQaDW63m3g8LgOB4Y0IATGC7HQ6aLVanE4nbre7p6wn4Umy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\nZW5ujlKpRK1Wk7mFq6urpFIpq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