diff --git a/notebooks/lectures/Model_Validation/copy.txt b/notebooks/lectures/Model_Validation/copy.txt
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+Given data with many different dimensions, deciding which ones to include is a non-trivial task. Including too many increases the risk of overfitting and multicollinearity while too few means your model might miss out on important relationships. The next important step, once you have selected your model, is to determine whether or not it is well-founded. This lecture provides a brief overview of the methods and criteria behind model selection and validation.
diff --git a/notebooks/lectures/Model_Validation/notebook.ipynb b/notebooks/lectures/Model_Validation/notebook.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Model Selection and Validation\n",
+    "By Chris Fenaroli, Max Margenot, and Delaney Granizo-Mackenzie\n",
+    "\n",
+    "Part of the Quantopian Lecture Series:\n",
+    "\n",
+    "* [www.quantopian.com/lectures](https://www.quantopian.com/lectures)\n",
+    "* [github.com/quantopian/research_public](https://github.com/quantopian/research_public)\n",
+    "\n",
+    "Notebook released under the Creative Commons Attribution 4.0 License.\n",
+    "\n",
+    "---\n",
+    "Linear regression is a technique that models the relationship between a set of independent variables $X_1,\\ldots, X_k$ and a dependent outcome variable $Y$. Simple linear regression with two variables allows us to determine which linear model of the form $Y = \\beta_0 + \\beta_1 X$ best explains the data, while a multiple linear regression allows for the dependent variable to be a linear function of multiple independent variables $X_1,\\ldots, X_k$: \n",
+    "\n",
+    "$$ Y = \\beta_0 + \\beta_1 X_1 + \\ldots + \\beta_k X_k + \\epsilon_i $$\n",
+    "\n",
+    "More background information on regressions can be found in the [simple linear regression](https://www.quantopian.com/lectures#Linear-Regression) and [multiple linear regression](https://www.quantopian.com/lectures#Multiple-Linear-Regression) lectures.\n",
+    "\n",
+    "In many cases, choosing which explanatory variables to include in a model is not trivial. If you include too many variables $X_1,\\ldots, X_k$ you risk overfitting and multicollinearity (correaltion of explanatory variables) which would invalidate your regression results. With too few variables you risk excluding predictive interactions. **Model selection** is the process of determining which combination of explanatory variables maximizes explanatory power and minimizes complexity. \n",
+    "\n",
+    "Once we have chosen our model fitted the data using an estimation method like OLS, it would be useful to quantify just how \"good\" our end-result model is. But what exactly makes a model \"good\"? Is it how well it fits the data? It's simplicity/complexity? Or is it how well it performs when applied to out-of-sample data? **Model validation** is the process of determining how well-founded a model is and whether it is a satisfactory explanation for the given data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Import libraries\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from statsmodels import regression\n",
+    "import statsmodels.api as sm\n",
+    "import statsmodels.stats.diagnostic as smd\n",
+    "import scipy.stats as stats\n",
+    "import matplotlib.pyplot as plt\n",
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Model Selection\n",
+    "\n",
+    "When presented with many possible explanatory variables, choosing which to include in a regression model can be a difficult task. Luckily, there exists a variety of strategies and criteria that simplify model selection. \n",
+    "\n",
+    "As an example, let's attempt to model the US unemployment rate with US inflation rate, gold prices, USD vs. EUR exchange rate, and two ETFs that track the Russel-2000 and NASDAQ-100 indices as potential explanatory variables. All of these macro indicators are available as free [Quantopian Data Feeds](https://www.quantopian.com/data).\n",
+    "\n",
+    "Let's begin by pulling the above as Blaze expressions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "from quantopian.interactive.data.quandl import fred_unrate as unemployment_bz\n",
+    "from quantopian.interactive.data.quandl import rateinf_inflation_usa as inflation_bz\n",
+    "from quantopian.interactive.data.quandl import bundesbank_bbk01_wt5511 as gold_bz\n",
+    "from quantopian.interactive.data.quandl import currfx_usdeur as fx_bz\n",
+    "\n",
+    "import blaze as bz\n",
+    "from odo import odo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's migrate the data into Pandas series using `asof_date` as our index keeping in mind that:\n",
+    "\n",
+    "* The specific inflation and unemployment datasets we are using have one month intervals, so time index intervals cannot be anything smaller than monthly\n",
+    "* The unemployment data is released at the start of the month after the relevant month and inflation rate data is released ~3 weeks after so we must shift both back a month from the asof_date to prevent look-ahead bias\n",
+    "* Gold prices must be shifted back one day from asof_date to prevent look-ahead bias\n",
+    "* Equity pricing data only goes back to 2002, so we can only consider data from 2002 on\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# Start date dictated by QQQ and IWM (tickers of index-tracking ETFs)\n",
+    "start = '2002-01-01'\n",
+    "end = '2017-01-01'\n",
+    "\n",
+    "# Sample period will be 2002-2012, saving 2012-2017 for model validation\n",
+    "s = '2002-01-01'\n",
+    "e = '2012-01-01'\n",
+    "\n",
+    "index = pd.date_range(start=start, end = end, freq= 'MS')\n",
+    "\n",
+    "# Migrating Blaze expressions into Pandas DataFrames and setting index\n",
+    "unemployment = odo(unemployment_bz, pd.DataFrame).set_index(['asof_date'])\n",
+    "inflation = odo(inflation_bz, pd.DataFrame)\n",
+    "inflation = inflation.set_index(inflation['asof_date'] + pd.Timedelta('1 days'))\n",
+    "gold = odo(gold_bz[gold_bz.asof_date >= start], pd.DataFrame).set_index(['asof_date'])\n",
+    "fx = odo(fx_bz, pd.DataFrame).set_index(['asof_date'])\n",
+    "qqq = get_pricing('QQQ', start_date=start, end_date=end, fields = 'price')\n",
+    "iwm = get_pricing('IWM', start_date=start, end_date=end, fields = 'price')\n",
+    "\n",
+    "# Adjusting data along points mentioned above\n",
+    "unemployment = unemployment.shift().loc[index].ffill()['value'][1:];\n",
+    "inflation = inflation.shift().loc[index].ffill()['value'][1:];\n",
+    "gold = gold['value'].sort_index().asof(index).ffill()[1:];\n",
+    "fx = fx['rate'].sort_index().asof(index).ffill()[1:];\n",
+    "qqq = qqq.asof(index).ffill()[1:];\n",
+    "iwm = iwm.asof(index).ffill()[1:];"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we have data for on independent variable and 5 predictor variables. If we were to include every one of these preditors, our model would look like:\n",
+    "\n",
+    "$$ Y_{unrate} = \\beta_0 + \\beta_1 X_{inflation} + \\beta_2 X_{gold} + \\beta_3 X_{FX} + \\beta_4 X_{qqq} + \\beta_5 X_{iwm} + \\epsilon_i $$\n",
+    "\n",
+    "The next step is to figure out which predictors to include in our model. We could include every single predictor we have, but we would not be sure if every predictor was significant. Adding many insignificant predictors causes a few different issues. If there are more predictors there is a larger chance that the predictors themselves are correalted with each other which would lead to regression model instability and invalidate our results. Furthermore, including many regressors hurts the predictive power of a model. A solution might be to include as few variables as possible, but we would likely exclude some explanatory effects this way. Let's look at some ways to find this balance and determine which variables to include."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Selection Criteria\n",
+    "\n",
+    "There exist a number of metrics we can use to asses the relative and absolute strength of a specfic model. The ones we will focus on are $R^2$, adjusted $R^2$, AIC, and BIC."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Primer on Sum of Squares ($SS_{err}$, $SS_{reg}$, and $SS_{total}$)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The residual sum of squares ($RSS$ or $SS_{err}$) measures the variation in the difference between modelled values and observed values (residuals). The explained sum of squares ($SSR$ or $SS_{reg}$) measures the variation of model values. Finally, the total sum of squares ($SST$ or $SS_{tot}$) measures the variation in the observed data.\n",
+    "\n",
+    "\n",
+    "$$SS_{err} = \\sum_{i=1}^{n}{(Y_i - \\hat{Y_i})^2}\\:\\:\\:\\:\\:\\:\\:SS_{reg} = \\sum_{i=1}^{n}{(\\hat{Y_i} - \\bar{Y_i})^2}\\:\\:\\:\\:\\:\\:\\:SS_{tot} = \\sum_{i=1}^{n}{(Y_i - \\bar{Y_i})^2}$$\n",
+    "\n",
+    "Where $Y_i$ are observed sample values, $\\hat{Y_i}$ are the model values and $\\bar{Y}$ is the sample mean.\n",
+    "\n",
+    "In cases of simple linear regression, the following is generally true:\n",
+    "\n",
+    "$$ SS_{tot} = SS_{reg} + SS_{err} $$\n",
+    "\n",
+    "For more intuition on their relationship, look at the graph below. Change the value of $p$ to choose which point to see squared error, regression, and total values for."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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8fvdPljBw4ECKFClCmzZtMn0/0dHRvPTSS7Ru3Zpt27bh7u5OSEhImhG/Bz2XCQkJabZn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\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f1b64012490>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Change p to choose which point to see sum of squares visualization at\n",
+    "p = 6\n",
+    "\n",
+    "np.random.seed(0)\n",
+    "X = np.linspace(1,5,9)\n",
+    "Y = X + np.random.normal(0,1,9)\n",
+    "\n",
+    "params = regression.linear_model.OLS(Y, sm.add_constant(X)).fit().params\n",
+    "model = params[0] + X * params[1]\n",
+    "\n",
+    "err = Y-model\n",
+    "reg = model - np.mean(Y)\n",
+    "tot = Y-[np.mean(Y)]*9\n",
+    "\n",
+    "plt.scatter(X,Y, color = 'black', label = 'data');\n",
+    "plt.plot(X,[np.mean(Y)]*9, color = 'black', alpha = 0.3, label = 'Y_mean')\n",
+    "\n",
+    "x_p= X[p]\n",
+    "\n",
+    "plt.annotate('p = %i'%p, xy=(x_p+0.1,Y[p]+0.1))\n",
+    "\n",
+    "plt.fill_between([x_p,x_p+err[p]], model[p], Y[p], \n",
+    "                 facecolor='blue', alpha = 0.4, label = 'S_err');\n",
+    "plt.fill_between([x_p+reg[p],x_p], np.mean(Y), model[p], \n",
+    "                 facecolor='yellow', alpha = 0.4, label = 'S_reg');\n",
+    "plt.fill_between([x_p-tot[p],x_p], np.mean(Y), Y[p], \n",
+    "                 facecolor='red', alpha = 0.4, label = 'S_tot');\n",
+    "                                                                     \n",
+    "plt.plot(X,model, linestyle = '--', c = 'r', label = 'model');\n",
+    "plt.xlabel('X');\n",
+    "plt.ylabel('Y');\n",
+    "plt.title('Single point RS, SR, and ST of simple linear regression', \n",
+    "          fontsize = 15);\n",
+    "plt.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$SS_{err}$, $SS_{reg}$, and $SS_{total}$ are the sums of the areas of the above squares for every point in the sample."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Coefficient of Determination ($R^2$)\n",
+    "\n",
+    "The coefficient of determination, or $R^2$, is a metric that tells us the proportion of in-sample variance 'explained' by a certain model. For example, an $R^2$ of 0.9 tells us that the magnitude of the model residual variance is about 90% of that of the sample data. The formula for $R^2$ is:\n",
+    "\n",
+    "$$R^2 = \\frac{SS_{reg}}{SS_{tot}} = \\frac{\\sum_{i=1}^{n} (\\hat{Y_i} - \\bar{Y_i})^2}{\\sum_{i=1}^{n} (Y_i - \\bar{Y})^2}$$\n",
+    "\n",
+    "\n",
+    "One major drawback of $R^2$ in model selection is that as the number of explanatory variables increases, $R^2$ will always also increase or stay the same, even if the incremental variables are not adding much predictive insight. To illustrate this, let's find the $R^2$ of five unemployment models, each model having one more predictor than the next. We will do this by defining a function that takes predictor variables $X_1,\\ldots, X_k$ and an independent variable $Y$, runs a regression, and calculates $R^2$ using the `model.rsquared` attribute."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "------ R Squared Values ------\n",
+      "1 predictor: 0.124499233139\n",
+      "2 predictors: 0.459882090695\n",
+      "3 predictors: 0.700574522302\n",
+      "4 predictors: 0.734219660452\n",
+      "5 predictors: 0.875758340212\n"
+     ]
+    }
+   ],
+   "source": [
+    "def rsquared(X,Y):\n",
+    "    X = sm.add_constant(X)\n",
+    "    model = regression.linear_model.OLS(Y, X).fit()\n",
+    "    return model.rsquared\n",
+    "\n",
+    "# Defining variables, making sure to keep data\n",
+    "# within the sample period [:e]\n",
+    "Y = unemployment[:e]\n",
+    "X = [qqq[:e], inflation[:e], iwm[:e], fx[:e], gold[:e]]\n",
+    "X_str = ['qqq', 'inflation', 'iwm', 'fx', 'gold']\n",
+    "\n",
+    "print '------ R Squared Values ------'\n",
+    "print '1 predictor:', rsquared(np.column_stack(X[:1]), Y)\n",
+    "print '2 predictors:', rsquared(np.column_stack(X[:2]), Y)\n",
+    "print '3 predictors:', rsquared(np.column_stack(X[:3]), Y)\n",
+    "print '4 predictors:', rsquared(np.column_stack(X[:4]), Y)\n",
+    "print '5 predictors:', rsquared(np.column_stack(X[:5]), Y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we can see, increasing the number of predictors inflates the $R^2$ output. If we only went by the $R^2$ prediction criteria, the best model would always be the one with the most predictor variables. This invalidates $R^2$ as a model selection criteria for models with different amounts of predictor variables."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###  $\\bar{R}^2$ (Adjusted $R^2$)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because regular $R^2$ becomes inflated as more variables are added we cannot use $R^2$ alone for model selection. To account for this effect, there exists an alternate version of $R^2$ which includes a penalty for adding more variables. The formula is below, where $p$ is the number of predictor variables, and $n$ is the sample size:\n",
+    "\n",
+    "$$ \\bar{R}^2 = 1-(1-R^2)\\frac{n-1}{n-p-1} $$\n",
+    "\n",
+    "Let's repeat the experiment above, this time using $\\bar{R}^2$, to see if it still inflates as more predictors are added."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "------ Adj R Squared Values ------\n",
+      "1 predictor: 0.117079735115\n",
+      "2 predictors: 0.45064930592\n",
+      "3 predictors: 0.692830759948\n",
+      "4 predictors: 0.724975126903\n",
+      "5 predictors: 0.870309144607\n"
+     ]
+    }
+   ],
+   "source": [
+    "def rsquared_adj(X,Y):\n",
+    "    X = sm.add_constant(X)\n",
+    "    model = regression.linear_model.OLS(Y, X).fit()\n",
+    "    return model.rsquared_adj\n",
+    "\n",
+    "print '------ Adj R Squared Values ------'\n",
+    "print '1 predictor:', rsquared_adj(np.column_stack(X[:1]), Y)\n",
+    "print '2 predictors:', rsquared_adj(np.column_stack(X[:2]), Y)\n",
+    "print '3 predictors:', rsquared_adj(np.column_stack(X[:3]), Y)\n",
+    "print '4 predictors:', rsquared_adj(np.column_stack(X[:4]), Y)\n",
+    "print '5 predictors:', rsquared_adj(np.column_stack(X[:5]), Y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The $\\bar{R}^2$ are similar to the $R^2$ values, however we can see that the values are slightly smaller due to the predictor penalty. $\\bar{R}^2$ should always be used when comparing models with different amounts of predictors to avoid the predictor inflation effect of $R^2$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Akaike and Bayesian Information Criterion (AIC and BIC)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$R^2$ and $\\bar{R}^2$ provide absolute measures on the quality on a model, meaning they can be calculated on any single regression model. They tell you how \"good\" a model is on its own on a scale of 0 to 1. \n",
+    "\n",
+    "AIC and BIC provide relative measures of quality and are calculated with an underlying selection pool of models. Instead  of yeidling a metric with an absolute scale like $R^2$ and $\\bar{R}^2$, they return values for every model in the selection pool and determining model quality requires looking at all of the values and comparing them.\n",
+    "\n",
+    "AIC is calculated along the following formula: \n",
+    "\n",
+    "$$ AIC = 2p + 2Log(\\hat{L}) $$\n",
+    "\n",
+    "BIC is calculated similarly:\n",
+    "\n",
+    "$$ BIC = p \\cdot ln(n) -2Log(\\hat{L}) $$\n",
+    "\n",
+    "Where $\\hat{L}$ is defined as the maximumized value of the likelihood function. The likelihood function is the probability  our model in question is in fact correct given the data we observed. For more information on likelihood functions refer to the [Wikipedia](https://en.wikipedia.org/wiki/Likelihood_function) page on the subject.\n",
+    "\n",
+    "\n",
+    "Let's use AIC and BIC to compare 5 simple linear regression models for unemployment. We will compute them using the `model.aic` and `model.bic` attributes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                         AIC values:     BIC values:  \n",
+      "Y = b0 + b1*qqq          481.904294922   487.479278408\n",
+      "Y = b0 + b1*inflation    464.408261441   469.983244926\n",
+      "Y = b0 + b1*iwm          497.835819526   503.410803012\n",
+      "Y = b0 + b1*fx           488.599333286   494.174316772\n",
+      "Y = b0 + b1*gold         398.65240035    404.227383836\n"
+     ]
+    }
+   ],
+   "source": [
+    "def aic(X,Y):\n",
+    "    X = sm.add_constant(X)\n",
+    "    model = regression.linear_model.OLS(Y, X).fit()\n",
+    "    return model.aic\n",
+    "\n",
+    "def bic(X,Y):\n",
+    "    X = sm.add_constant(X)\n",
+    "    model = regression.linear_model.OLS(Y, X).fit()\n",
+    "    return model.bic\n",
+    "\n",
+    "AICs = pd.Series([aic(X[_],Y) for _ in range(5)])\n",
+    "BICs = pd.Series([bic(X[_],Y) for _ in range(5)])\n",
+    "\n",
+    "print \"%-24s %-15s %-13s\" % ('', 'AIC values:', 'BIC values:')\n",
+    "print \"%-24s %-15s %-13s\" % ('Y = b0 + b1*qqq', AICs[0], BICs[0])\n",
+    "print \"%-24s %-15s %-13s\" % ('Y = b0 + b1*inflation', AICs[1], BICs[1])\n",
+    "print \"%-24s %-15s %-13s\" % ('Y = b0 + b1*iwm', AICs[2], BICs[2])\n",
+    "print \"%-24s %-15s %-13s\" % ('Y = b0 + b1*fx', AICs[3], BICs[3])\n",
+    "print \"%-24s %-15s %-13s\" % ('Y = b0 + b1*gold', AICs[4], BICs[4])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's plot the AICs and BICs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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6Z0OHDsW2bdsAAJcuXcLWrVsxf/58iEQiAMChQ4cQHR2N3bt3QyqV4urVq1i7di0OHjwI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TAAAE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\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f1b5eba7f10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "AICs.plot(title = 'Model AICs and BICs', label = 'AIC');\n",
+    "BICs.plot(label = 'BIC');\n",
+    "plt.xticks(range(5), X_str);\n",
+    "plt.xlabel('Model Predictor');\n",
+    "plt.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The \"best\" model according to AIC and BIC is the one with the smallest value. In this case, both the Akaike and Bayesian information criterion point to the same conclusion; they are both at their minimum with the model $Y = b_0 + b_1 \\cdot gold$ meaning gold is the best single predictor for the model on this sample.\n",
+    "\n",
+    "### AIC vs BIC\n",
+    "\n",
+    "In this test the two criteria pointed to the same model as the best, but this is not always the case as the two criteria have a few key differences. \n",
+    "\n",
+    "* The BIC imposes a harsher penalty on additional regressors than the AIC does and will usually select \"smaller\" models with less parameters\n",
+    "* AIC is more suitable for selecting models that serve as the best predictors\n",
+    "* BIC is best used to select a model that best explains the behavior in-sample"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selection Methods\n",
+    "\n",
+    "Now that we have a handful of criteria to quantify model quality we need to have a method to cycle through and search for the possible models.\n",
+    "\n",
+    "The first idea that might come to mind is to simply pool all possible models together and test them all. While this might work for a situation in which there are a small number of potential regressors, when dealing with big data sets and many possible predictors it becomes a more difficult issue. The number of predictor combinations begins to rise quickly as more possible predictors are added making cycling through every combination not efficient.\n",
+    "\n",
+    "### Step AIC/BIC\n",
+    "\n",
+    "If the number of possible predictors large, the best for selecting the \"best\" is likely iterating through a stepwise linear regression. Broadly, the method builds a model by selecting regressors one at a time, at each step choosing the one that minimizes the model's AIC or BIC. The process ends when adding another variable can no longer decrease the AIC/BIC or when the algorithm exhausts the predictor set and there are no more potential predictors to add. Note that stepwise regressions need not only step forward; the process can be conducted in reverse, starting with all variables included and subtracting off those yielding the *highest* AIC/BIC, or in tandem with steps alterating forward and backwards.\n",
+    "\n",
+    "Let's use a step-forward AIC algorithm to select a set of unemployment predictors to use in our model. Some print statements have been added to show the iteration process but can and should be deleted if you would like to use the function elsewhere."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def forward_aic(response, data):\n",
+    "    # This function will work with pandas dataframes and series\n",
+    "    \n",
+    "    # Initialize some variables\n",
+    "    explanatory = list(data.columns)\n",
+    "    selected = pd.Series(np.ones(data.shape[0]), name=\"Intercept\")\n",
+    "    current_score, best_new_score = np.inf, np.inf\n",
+    "    step = 1\n",
+    "    \n",
+    "    # Loop while we haven't found a better model\n",
+    "    while current_score == best_new_score and len(explanatory) != 0:\n",
+    "        \n",
+    "        \n",
+    "        scores_with_elements = []\n",
+    "        count = 0\n",
+    "        \n",
+    "        # For each explanatory variable\n",
+    "        for element in explanatory:\n",
+    "            # Make a set of explanatory variables including our current best and the new one\n",
+    "            tmp = pd.concat([selected, data[element]], axis=1)\n",
+    "            # Test the set\n",
+    "            result = regression.linear_model.OLS(response, tmp).fit()\n",
+    "            score = result.aic\n",
+    "            scores_with_elements.append((score, element, count))\n",
+    "            count += 1\n",
+    "        \n",
+    "        # Sort the scoring list\n",
+    "        scores_with_elements.sort(reverse = True)\n",
+    "        # Get the best new variable\n",
+    "        best_new_score, best_element, index = scores_with_elements.pop()\n",
+    "        print '--- Step', step, ' ---'\n",
+    "        step += 1\n",
+    "        print 'Current Best AIC:', current_score\n",
+    "        print 'Best New AIC:', best_new_score\n",
+    "        print 'Variable to Add:', best_element    \n",
+    "        \n",
+    "        if current_score > best_new_score:\n",
+    "            # If it's better than the best add it to the set\n",
+    "            explanatory.pop(index)\n",
+    "            selected = pd.concat([selected, data[best_element]],axis=1)\n",
+    "            current_score = best_new_score\n",
+    "            print 'Chosen Model Predictors:', selected.columns.values[1:], '\\n'\n",
+    "            \n",
+    "        else:\n",
+    "            print 'Best new AIC did not beat current best. The new variable to add is rejected and the algorithm is finished.\\n\\n'\n",
+    "\n",
+    "    # Return the final model\n",
+    "    return selected"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Step 1  ---\n",
+      "Current Best AIC: inf\n",
+      "Best New AIC: 398.65240035\n",
+      "Variable to Add: gold\n",
+      "Chosen Model Predictors: ['gold'] \n",
+      "\n",
+      "--- Step 2  ---\n",
+      "Current Best AIC: 398.65240035\n",
+      "Best New AIC: 307.741249417\n",
+      "Variable to Add: iwm\n",
+      "Chosen Model Predictors: ['gold' 'iwm'] \n",
+      "\n",
+      "--- Step 3  ---\n",
+      "Current Best AIC: 307.741249417\n",
+      "Best New AIC: 258.210706106\n",
+      "Variable to Add: inflation\n",
+      "Chosen Model Predictors: ['gold' 'iwm' 'inflation'] \n",
+      "\n",
+      "--- Step 4  ---\n",
+      "Current Best AIC: 258.210706106\n",
+      "Best New AIC: 253.653929589\n",
+      "Variable to Add: qqq\n",
+      "Chosen Model Predictors: ['gold' 'iwm' 'inflation' 'qqq'] \n",
+      "\n",
+      "--- Step 5  ---\n",
+      "Current Best AIC: 253.653929589\n",
+      "Best New AIC: 255.596196199\n",
+      "Variable to Add: fx\n",
+      "Best new AIC did not beat current best. The new variable to add is rejected and the algorithm is finished.\n",
+      "\n",
+      "\n",
+      "Selected Predictors: ['gold' 'iwm' 'inflation' 'qqq']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Reformatting the data to work with the forward_aic function\n",
+    "Y_series = pd.Series(Y).reset_index(drop=True)\n",
+    "data_df = pd.DataFrame(np.column_stack((X[0],X[1],X[2],X[3],X[4])), columns = X_str)\n",
+    "\n",
+    "predictors = forward_aic(Y_series, data_df)\n",
+    "print 'Selected Predictors:', predictors.columns.values[1:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `forward_aic` algorithm selected gold prices, the IWM index, inflation rate, and the QQQ index as the most appropriate combination of predictors. The model is now:\n",
+    "\n",
+    "$$ Y_{unrate} = \\beta_0 + \\beta_1 X_{gold} + \\beta_2 X_{iwm} + \\beta_3 X_{inflation} + \\beta_4 X_{qqq} + \\epsilon_i $$\n",
+    "\n",
+    "Let's fit this model using OLS to determine the beta coefficients and graph both the model's prediction for unemployment and its actual values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intercept    7.180962\n",
+      "gold         0.003986\n",
+      "iwm         -0.093050\n",
+      "inflation   -0.386373\n",
+      "qqq          0.067445\n",
+      "dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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ERDxH/okyPl2+h3YhAcy4YOAZxyxmE7dfOZSIDsG893Uif/zPaiLDQuq8T7BfJX0HlNOh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\n8WLVv0OH1IjRmDFqfUluWraEXr1g504VEOUkM5M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AxhWM70jt2mBTyFOs7Oxg9Gg1TS8h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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f1b5c136a10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model = regression.linear_model.OLS(Y.reset_index(drop=True), predictors).fit()\n",
+    "theta = model.params\n",
+    "print theta\n",
+    "predictions = (model.params[0] + model.params[1]*gold[s:e] + model.params[2]*iwm[s:e]\n",
+    "            + model.params[3]*inflation[s:e] + model.params[4]*qqq[s:e])\n",
+    "\n",
+    "predictions.plot(label = 'model', linestyle = '--', c = 'r');\n",
+    "Y.plot(label = 'unemployment');\n",
+    "plt.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At first glance the regression model seems decent, but further evaluation is required to determine it's validity."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Model Validation\n",
+    "\n",
+    "Now that we used model selection criteria and methods to find the most valuable combination of regressors, we now must determine whether this best model is acceptable. Because \"best\" is a relative term, it is possible that the best model does not explain the independent variable in a satisfactory way. As a result, more work needs to be done to ensure that our model is well-founded.\n",
+    "\n",
+    "### $R^2$ and $\\bar{R}^2$\n",
+    "\n",
+    "As well as being criteria model selection, $R^2$ and $\\bar{R}^2$ can also be used as model validation criteria. The intuition, formulas, and weaknesses detailed in the selection section still hold. However, in terms of validation they are additionally limited as they cannot  determine the accuracy of the form of the relationship, a vital part of a well-founded model. \n",
+    "\n",
+    "To illustrate this weakness we can run a linear regression on two datasets with very different forms. Both regressions have a similar $R^2$ value but only the second accurately represents the form of the data it is modeling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R^2 of First Model: 0.671416768972\n",
+      "R^2 of Second Model: 0.67216727851\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Umhq5JwPEWMjBZ9myZSotLZXNZtPChQuVn5/f6Zjnn39eO3bs0KuvvhrWIAEAAKws0F6e\nC5a0+StPaw85FypP89c9LSGh554sECMhBZ9t27apvLxcxcXF2r9/vxYtWqTi4mKfY/bv369//etf\nSkyMQZcRAAAAEwm0l8fhSNLN38453z3t+TfPh5u9e7suT/M+/w3laYhzIQWfrVu3atq0aZKk4cOH\nq76+XqdOnVKq13Lo8uXL9dBDD+m3v/1tZEYKAABgUXV1iUpsPKH0I3uVfuRzDaj5SPp/KgN3T0tK\nkkaM6NwaeuRIytOAAEIKPrW1tRo7dqzncr9+/VRbW+sJPhs2bNCkSZN08cUXR2aUAAAAVuB2q+nL\nA3K9/k8lfPGVso7tU87pQ7pt52fqfby68/He3dO8Q86ll9I9DQhSRF4xbrfb8/WJEyf0zjvvaM2a\nNTp8+LDPbV1xuVyRGA5ihPkzL+bO3Jg/82LuzO1C82c7e1aOigr1PnBAvb/6qu3v8nL1/uorOU6f\n1uSO3zBwoA7kFao6PUf1gy/WoKmXqSU3Vy0ZGZ3L006ckEpLI/584g2vv/gTUvDJyspSbW2t53J1\ndbUyMzMlSR9++KGOHj2q22+/XU1NTfr666/19NNP69FHH+3yfgsLC0MZDgzA5XIxfybF3Jkb82de\nzJ25eebvxInAJ/c8d873mxwOaeRIfdV7iI5ljtfxi0erbuBonRnSW9//8WgNlTQ0Js8m/vD6M69w\nAmtIwaeoqEgvvPCC5syZo927d8vpdColJUWSNGPGDM2YMUOSdOjQIT322GPdCj0AAACG5HZLhw75\nhJsRH3/cdl1lZefj+/WTJkzQuREj9bnNqar0kWrNy9G1P5gsR0qySt8p8zQykCSns6wHnwwQv0IK\nPuPHj9eYMWM0d+5cJSQkaMmSJdqwYYP69OnjaXoAAABgKs3N57undTy5Z0ODz6EXSVJOjjRz5vmu\nae37cDIzJZtNmzoEnNP/X1untgu2qAYQNSHv8VmwYIHP5by8vE7HDB48WOvWrQv1IQAAACIvmPK0\n9pN7djj3zSenTmn81Vd7DvOch+f/bVR6+n7NnDlEdXW+p/Rov+xwJOnmm3MFoGfRDgQAAFiPn/I0\nz98XKE/zOe/N6NFt3dP8nNyztcM+A3/n4UlPb/u6XXr62Ug+QwBBIvgAAADz8i5P8y5N81OeJqmt\nPG3GDN8VnNGjpQEDwjq5p7/VnblzL6akDTAQgg8AADC+ujr/e28u0D3NuzTNc3LPb5oxhaq9pG3H\njpOqqCjTzJlD5HAkKT39bKfVHUraAGMh+AAAAGNwu6WKCv/7b44c6Xx8RoY0caJvadro0W2rOn7K\n0yKhvaStuTlRVVU5KimhYQFgFgQfAADQs9rL0zruvdmzRzp1yvdYm+1897TRo6W8vIiVp4WChgWA\neRF8AABAdIRSntahe1okytNC4enS5rWCE6ikDYA5EHwAAEDovMvTOq7gXKg8zbt72qhRAbunxYq/\nLm3eJW2VlRVyOilpA8yE4AMAALrW3Czt2+d/BcdfedqQIefL07xXcDIzYzP+C/C3utNVSVt29gkV\nFlLaBpgJwQcAAJznrzzt88+lL7+8cHma9wpOjMrTQsU5eID4QPABACDetJenea/adKc8rWN76Ch2\nT4uGQPt2OAcPEB8IPgAAWFVTk//uaXv3+i9Pu/RS6Vvf8g04Bi1PC0WgfTucgweIDwQfAADM7vjx\n8ys33gEnUHlae0to7/PfjBwpJSfHZvxREMy+Hc7BA8QHgg8AAGbgdktff+2/e5r3ckW7AQOkSZPO\nB5v2sGOy8rRQBbNvh9UdID4QfAAAMJKO5WntAaer8rSOKzgDBsRk+D2NfTsAuovgAwBADCScPClt\n3eq/e1prq+/BvXu3rdh4B5tRo6QRI0zVPS0a2LcDoLsIPgAAREtra8DuaeP8laf1799WnubdHnr0\n6LZz4sRBeVpX2LcDIBwhB59ly5aptLRUNptNCxcuVH5+vue2Dz/8UCtWrFBCQoKGDh2qpUuXRmSw\nAAAYUlNT28k9/ZWnnT7te+w35WknJk9W344touOkPC1U7NsBEI6Qgs+2bdtUXl6u4uJi7d+/X4sW\nLVJxcbHn9scff1zr1q2T0+nUz372M/3jH//QtddeG7FBAwAQE+3d0zo2FwimPO2b7mllLpcKCwtj\n8zwMjn07AKIhpOCzdetWTZs2TZI0fPhw1dfX69SpU0pNTZUkrV+/XmlpaZKkjIwM1dXVRWi4AABE\nWWtr5+5p7V9XV3c+fsAAqaioc8ChPC1k7NsBEA0hBZ/a2lqNHTvWc7lfv36qra31BJ/20FNdXa0t\nW7bo5z//eQSGCgBABAVbnjZ0qHTllb57b/LyKE8LQzArOxL7dgCEJyLNDdxud6frjh49qnvvvVdP\nPPGE+vbt2637cblckRgOYoT5My/mztyYvwtLqK9X7wMH1Purr87/OXBAjsOHZetQntbqcOhMTo7O\nXHqp758hQ+Tu3bvznZeXt/0JUbzP3fvvV+vYscu+uZSosrK/6vrrs1RbW61jx84fl5HxmVyuWklS\ndnbbH0natevTnh1wB/E+f2bH/MWfkIJPVlaWamtrPZerq6uVmZnpudzQ0KAf/ehHeuihhzRp0qRu\n3y+1zublolbdtJg7c2P+vhFOeZrX+W/sOTlKsdvVEw2i423u/K3u7NpVqT59cjzHOBxSYWGOxo7t\neOxUORxJMRx9Z/E2f1bD/JlXOIE1pOBTVFSkF154QXPmzNHu3bvldDqV4nUegaefflp33323ioqK\nQh4YAACddCxPa//7QuVpV13lu/+G8rSoClS+Rkc2ALEWUvAZP368xowZo7lz5yohIUFLlizRhg0b\n1KdPH1199dXauHGjDh48qDfffFM2m03f/e53NXv27EiPHQBgVceOdTrvTZfd07z33rR3T/NXnoao\nCtSYgI5sAGIt5D0+CxYs8Lmcl5fn+Xrnzp2hjwgAEB9aW6WDB/2Xp9XUdD4+M7NzeVr7yT3t9p4f\nP4I6oSgd2QDEWkSaGwAAENCZM23laR3Pf7N3r9TY6Htsx/K00aPPh5z+/WMzfkSkfI2ObABijeAD\nAIiMY8c6773Zs0c6cKBzeVpysm95WnvIGTGC8rQY8xdyIlG+xuoOgFgj+AAAus+7PK3jCk6g8rSr\nr+5cnnbJJZSnGZS/kEP5GgArIPgAADprL0/z1z2tY3ma3d5WnvZv/+a7gkN5mqEFc/JQfwFHonwN\ngLkQfAAgnnmXp3kHnO6Up7X/TXmaoQWzP+fmm3P9hpxAAYfVHQBmQvABAKvz1z2t/W9/5WlZWW3d\n07wbC4waRfc0Ewh3f47kfxWHgAPACgg+AGAVZ85IX3zRefWmu+Vp7X9nZMRm/Oi2YFZxgtmfI7GK\nA8C6CD4AYDZHj3pCzeC///38yT4PHJDcbt9jk5N999xQnmY64a7isD8HANoQfADAiNrL0zqWpn3+\nuVRb6zlsYPsXWVnSNdf4Bhy6p5lKtFZx2J8DAG0IPgAQS42NgbunnTnje6zdLg0bJk2c6Ak3e9xu\njbrlFsrTTKQ94OzYcVIVFWVdNhsIdxWHgAMAbQg+ANATvMrTfPbgBCpP824s4F2e5nD4HHrK5SL0\nGECg1ZoLlak1Nyeqqiqny2YDrOIAQGQQfAAgUlpbpfJy/93TvMrTPLKypGuv9Q03o0ZRnmZwwey5\niUSzAVZxACAyCD4AEKyO5WntAeeLLwJ3T5s40XcFJy+PlRoD8RdmJIW95yYSzQYIOQAQGQQfAAjk\n6NHzocZ7BeerrzqXp6WkdG4sMGqUlJtL97QYCKb0LFCYafs6vD03FypTq6yskNNJwAGAnkLwARDf\nzp3z7Z7W3fK0vLzzAWf0aCk7m/K0KAtlH43UdenZhfbX+LsumD03FypTy84+ocJCgg4A9BSCD4D4\n0Nh4/uSe3qs3X3zRre5plKeFL5hysmjtowl2f024e25YxQEA4wg5+CxbtkylpaWy2WxauHCh8vPz\nPbdt2bJFK1asUEJCgq699lrdd999ERksAHSpttZ/c4FA5WmXXdb5BJ9xXJ4WzKqK1P3QEmw5WbT2\n0QS7v4Y9NwBgHSEFn23btqm8vFzFxcXav3+/Fi1apOLiYs/tS5cu1Zo1a5SVlaU77rhDM2bM0PDh\nwyM2aABx7ty5zt3T2r8+erTz8U6ndN11nQOOCcrTugoi3ueCkYILIuGuqrR9HZ1yskjto+lO6ZkU\nOMwQcADAOkIKPlu3btW0adMkScOHD1d9fb1OnTql1NRUff3110pPT5fT6ZQkXXfddfrwww8JPkAc\nCXd1oP3Yk9XnNPjUF7p6QL0Sy/bp3GefqWHbp0o9dEC9zjb5PGarza7TAy9R8ncmKuGyy3Q2d4S2\nHk/XoT55Shmc0vnx/s+t9PQvQx5bOOEimPvoKoh4nwum7ZjuB5FIlIgFc2ww5WSR2kfTEas1ABC/\nQgo+tbW1Gjt2rOdyv379VFtbq9TUVNXW1irDqwY+IyNDX3/9dfgjhSGF+6lzMMdG4j6sMrZIPl7H\ns8fHoqSpbn+60o/sUb/Kz/X1mg/Vp2K/phw8qD5Hv5LNqzwtQVJKUoqOXzxGdQNHqyU3Uyezh6k8\n+QadyMxVa6JDTmfb/f75nTJVqe3xTkZw5SKYYyNxH9EMIpEoEYtWORlhBgAQaRFpbuDuWDffzdtg\nfuF+6hzMsT39ptXIY4vk43U8e3y0SppsrefUp+ZLpR/Zo/Qjn6t/9b+k5yo0Y8du9W443ul36/RF\nTlWOuFZ1A0erITtL/3Znkd7elaZjKRM95WkOR7kkqakpx+exvP/u7vXROrYny7uk4IJIJErEolVO\nRpgBAERaSMEnKytLtV5tXqurq5WZmem5raamxnNbVVWVsrKyunW/LpcrlOEghnbsOKnm5rY3a+Xl\n5aqsrJAkz3WSVFlZoezsEz7Htl8fzLGRuA+rjC3Sj9c+d5G430sGVMm2c4cu+tKtzNoyZR7dr4F1\nu9Wv5pASW5rlzW2363TGQB24uFA1/Yerpv9wNQ3rpWOZg1V5ZoLnuIyMz5TQv7/K3dU65rWCnJHx\nmSTp2DH5XOdy1aq2trrb1wdzHz39eCNH9lNZ2T41NCQrLa1R+fn95HKdUFbWWZWV7VNSUrJOnvxM\n+fn9JOmCx3bn+l27Tig7u237kyTt2vWpZzz+rg/mWHTG/3vmxvyZG/MXf0IKPkVFRXrhhRc0Z84c\n7d69W06nUykpKZKkwYMH69SpUzp8+LCysrL0/vvv6/nnn+/W/RYWFoYyHMRQRUWZqqpyVF5erpyc\nHDmd7Z86n/8E3uk8q8LCXM+x3tcHc2wk7sMqY4vk43nPXVD3O6SvTif+Re6yfUqv/FzpVXvUv+ZT\npf7nIV3RYaXXnZIid/4YfX3RJarNyNXZEZdq3NxrlHTZaKXKpi+8yuVm+S2tmyqHI0ljx3YsrZsa\n1LGRuI+efjyHI0mTJsmvSZPa/uMuLLze57pAxwZzPaKvbe74f8+smD9zY/7MK5zAanOHWIv2m9/8\nRh9//LESEhK0ZMkSffbZZ+rTp4+mTZumf/3rX3ruueckSTNnztRdd93V5f3xC2hO5/f4VGjcuGzT\n7Gsx+9giu8fn/Nx1OrbPGc0c7ZZ97x7tfecjJX15QP1rypRR/ZVsfrqnuZ1O2UaP9u2cNnq0NHiw\n4bunmRX/dpoXc2duzJ+5MX/mFc7chRx8Io1fQHNj/szL5XKpcPTozif33LMn8Mk9hw8/H2y8W0T3\n6xebJxHHeO2ZF3NnbsyfuTF/5hXO3EWkuQEAk6ip6XRyz7E7d0qVlZ1P7pmaKo0Z0zng5OZKDkds\nxg8AABAigg9gNefOSV995XtSz/av/ZSn2fv3bzu5Z3u4af87O1uy2Xp+/AAAAFFA8AHM6vTpzuVp\nn3/edl2T78k9lZAgDRsmFRV1Kk/buX8/y/0AAMDyCD6AkbndUm2t76pN+9fl5f7L08aO9V25GT26\nbU8O5WkAACCOEXwAIwhUnvb5574nfGk3cKB0/fWd999QngYAAOAXwQfoSadPS3v3dg44gcrThg+X\nrr66Ldzk5Z0POenpsRk/AACASRF8gEhzu/12T/OUp3WUlibl559fvWkPOLm5UlJSz48fAADAggg+\nQKjay9MZ58VhAAAgAElEQVT87b/xV5528cXSlCm+pWntJ/ekPA0AACCqCD5AV7zL0zp2T2tu9j02\nIaFtpeaaa3z33+TlUZ4GAAAQQwQfQDpfnhaoe1pH7eVpo0d37p5GeRoAAIDhEHwQXzqWp3n/ffx4\n5+M7lqe1/015GgAAgKkQfGBNp061laJ1XMHZt89/97TcXOnaazu3h+7bNzbjBwAAQEQRfGBeoXZP\n67h6Q3kaAACA5RF8YHwtLYG7pwUqT7vhBt/OaZSnAQAAxDWCD4zj1Cn/3dP27QvcPe26684HHMrT\nAAAAEADBBz3L7Zaqq33DTfvXBw92Pj4tTSoo6Lz3hvI0AAAABIHgg+joWJ7mHXL8lacNGiRNndq5\nPG3QIMrTAAAAELaQgk9LS4seffRRHT58WAkJCVq2bJmys7N9jvnzn/+sV155RQkJCZowYYIefPDB\niAwYBrRvn/qVlEh/+tP5cHOhk3u2l6d5r+BcdFFsxg4AAIC4EFLw2bRpk/r27avnnntOH3zwgZ5/\n/nmtWLHCc/uZM2f03HPPadOmTUpJSdGcOXN00003afjw4REbOAyislIaNUrDWlvPX9enT+fytNGj\npWHDKE8DAABATIQUfLZu3ap///d/lyRNnjxZCxcu9Lm9d+/e2rhxo1JSUiRJ6enpqqurC3OoMCSn\nU/rNb3Tw0CENmTGD8jQAAAAYUkjBp7a2VhkZGZIkm80mu92ulpYW9ep1/u7S0tIkSXv37tXhw4c1\nbty4CAwXhmO3Sz/7mWpcLg0pLIz1aAAAAAC/ugw+b731lt5++23ZvvkE3+12a+fOnT7HtHqXOXn5\n6quv9Itf/ELPP/+8EhISuhyMy+XqzphhUMyfeTF35sb8mRdzZ27Mn7kxf/Gny+Aze/ZszZ492+e6\nxx57TLW1tcrLy1NLS0vbHfXyvasjR45o/vz5evbZZ5WXl9etwRSyYmBaLpeL+TMp5s7cmD/zYu7M\njfkzN+bPvMIJrPZQvqmoqEglJSWSpL/97W+aMGFCp2MWLVqkxx9/XKNGjQp5cAAAAAAQCSHt8fn2\nt7+tDz74QLfffrscDoeefvppSdJLL72kCRMmqG/fvtq+fbt++9vfyu12y2az6e6779aUKVMiOngA\nAAAA6I6Qgo/dbteyZcs6Xf/jH//Y8/Unn3wS+qgAAAAAIIJCKnUDAAAAADMh+AAAAACwPIIPAAAA\nAMsj+AAAAACwPIIPAAAAAMsj+AAAAACwPIIPAAAAAMsj+AAAAACwPIIPAAAAAMsj+AAAAACwPIIP\nAAAAAMsj+AAAAACwPIIPAAAAAMsj+AAAAACwPIIPAAAAAMsj+AAAAACwvF6hfFNLS4seffRRHT58\nWAkJCVq2bJmys7P9HrtgwQI5HA4tW7YsrIECAAAAQKhCWvHZtGmT+vbtqzfeeEM/+clP9Pzzz/s9\n7oMPPlBFRUVYAwQAAACAcIUUfLZu3app06ZJkiZPnqzt27d3Oqa5uVkvvvii7r333vBGCAAAAABh\nCin41NbWKiMjQ5Jks9lkt9vV0tLic8xLL72kO+64Q6mpqeGPEgAAAADC0OUen7feektvv/22bDab\nJMntdmvnzp0+x7S2tvpcLi8v1969e/XAAw/oo48+6vZgXC5Xt4+F8TB/5sXcmRvzZ17Mnbkxf+bG\n/MUfm9vtdgf7TY899phmzZqloqIitbS0aOrUqfr73//uuX3t2rX64x//qOTkZJ08eVLHjx/XPffc\no3vuuSeigwcAAACA7gipq1tRUZFKSkpUVFSkv/3tb5owYYLP7XfeeafuvPNOSdLHH3+sDRs2EHoA\nAAAAxExIe3y+/e1vq6WlRbfffrt+//vf66GHHpLUtq+ntLQ0ogMEAAAAgHCFVOoGAAAAAGYS0ooP\nAAAAAJgJwQcAAACA5RF8AAAAAFheSF3dIuXcuXNatGiRDh48qNbWVv3yl7/UFVdcoT179uiJJ56Q\n3W5XXl6eHn/88VgOExewbNkylZaWymazaeHChcrPz4/1kNCFZ555Rtu3b9e5c+f04x//WPn5+Xr4\n4YfldruVmZmpZ555RomJibEeJi6gqalJs2bN0v3336+JEycyfyaxceNGvfzyy+rVq5d++tOfKi8v\nj7kzidOnT+uRRx7RiRMndPbsWd1///3Kzc1l/gxuz549mj9/vu666y59//vf15EjR/zO2caNG7Vu\n3TolJCRo9uzZuvXWW2M9dKjz/FVWVmrhwoVqaWlRYmKinn32WfXv3z+o+Yvpis8777yj3r176403\n3tBTTz2lZcuWSZJ+/etf6z/+4z/0xhtvqL6+Xv/85z9jOUwEsG3bNpWXl6u4uFhPPfWUli5dGush\noQsfffSRysrKVFxcrP/+7//Wr3/9a61cuVJ33HGHXnvtNQ0ZMkTr16+P9TDRhVWrVik9PV2StHLl\nSs2bN4/5M7i6ujr97ne/U3FxsVavXq2//vWvzJ2JbNiwQcOGDdO6deu0cuVKLV26lH87Da6xsVHL\nly9XUVGR5zp/r7nGxkatWrVKa9eu1bp167R27VrV19fHcOSQAs/fnDlz9Oqrr2rq1Kl65ZVXgp6/\nmAafm266SY899pgkKSMjw/NJSkVFhcaMGSNJuuGGG7Rly5ZYDhMBbN26VdOmTZMkDR8+XPX19Tp1\n6lSMR4ULueqqq7Ry5UpJ0kUXXaTTp09r27ZtuuGGGyRJU6ZM4fVmcF9++aUOHDig6667Tm63W9u2\nbdOUKVMkMX9GtmXLFhUVFSk5OVkDBgzQk08+qY8//pi5M4mMjAwdP35cknTixAllZGTwb6fBORwO\nrV69WgMGDPBc5+81V1paqssvv1ypqalyOBy64oortH379lgNG9/wN3+PP/64ZsyYIantNVlXVxf0\n/MU0+PTq1UsOh0OStHbtWn33u9/V8ePHPZ9kSm1PrKamJlZDxAXU1tYqIyPDc7lfv36qra2N4YjQ\nFbvdruTkZEnS22+/reuvv16NjY2e8oz+/fvzejO4Z555Ro8++qjnMvNnDocOHVJjY6Puvfde3XHH\nHdq6davOnDnD3JnEt771LR05ckTTp0/XD37wAz3yyCO89gzObrcrKSnJ57qOc1ZdXa2jR4/6vJfh\nfacx+Ju/5ORk2e12tba26o033tCsWbM6vRftav56bI/PW2+9pbfffls2m01ut1s2m03z589XUVGR\nXn/9dX322Wd68cUXdfTo0Z4aEiKMU0KZx1/+8hetX79eL7/8sqZPn+65njk0tj/96U+66qqrNGjQ\nIL+3M3/G5Xa7PeVuhw4d0g9+8AOf+WLujG3jxo0aOHCgXnrpJe3du1eLFi3yuZ35M59Ac8ZcGltr\na6sefvhhTZo0SRMnTtSmTZt8bu9q/nos+MyePVuzZ8/udP1bb72l999/X6tWrVJCQoLPcrIkVVVV\nKSsrq6eGiSBkZWX5rPBUV1crMzMzhiNCd/zzn//USy+9pJdffllpaWlKTU1Vc3OzkpKSeL0Z3N//\n/ndVVFTovffeU1VVlRITE5WSksL8mcCAAQM0fvx42e12XXLJJUpNTVWvXr2YO5PYvn27rrnmGklS\nXl6eqqqqlJyczPyZTMf/75xOp7KysnxWCKqqqjR+/PgYjhIX8thjj2no0KG67777JCno+YtpqdvX\nX3+tP/zhD3rhhRc8S4+9evXSsGHDPPV57733nucfGxhLUVGRNm/eLEnavXu3nE6nUlJSYjwqXEhD\nQ4OeffZZvfjii+rTp48kadKkSZ553Lx5M683A1uxYoXeeust/eEPf9Ctt96q+++/X5MmTVJJSYkk\n5s/IioqK9NFHH8ntduv48eM6ffo0c2ciOTk52rFjh6S2ssWUlBRNnjyZ+TMZf//fXX755dq1a5ca\nGhp06tQpffLJJyosLIzxSOHPxo0blZSUpAceeMBzXUFBQVDzZ3PHcE1vxYoV+vOf/6yLL77YU/62\nZs0alZeXa8mSJXK73SooKNAjjzwSqyGiC7/5zW/08ccfKyEhQUuWLFFeXl6sh4QLePPNN/XCCy/o\n0ksv9bzmli9frkWLFqm5uVmDBg3SsmXLlJCQEOuhogsvvPCCsrOzdfXVV+uXv/wl82cCb775pt56\n6y3ZbDbdd999Gjt2LHNnEqdPn9bChQt19OhRnTt3Tj//+c81dOhQPfLII8yfQZWWlmrx4sU6duyY\nEhIS1LdvX7388st69NFHO83Ze++9p//5n/+R3W7XvHnz9J3vfCfWw497/uavtbVVDodDqampstls\nys3N1ZIlS4Kav5gGHwAAAADoCTEtdQMAAACAnkDwAQAAAGB5BB8AAAAAlkfwAQAAAGB5BB8AAAAA\nlkfwAQAAAGB5BB8AAAAAlkfwAQAAAGB5BB8AAAAAlkfwAQAAAGB5BB8AAAAAlkfwAQAAAGB5BB8A\nAAAAlkfwAQAAAGB5BB8AAAAAlkfwAQAAAGB5BB8AAAAAlkfwAQAAAGB5BB8AAAAAlkfwAQAAAGB5\nQQefPXv26MYbb9Trr7/uc/0///lPjRo1ynN548aNuvXWW3Xbbbfp7bffDn+kAAAAABCiXsEc3NjY\nqOXLl6uoqMjn+ubmZr300kvKysryHLdq1SqtX79evXr10q233qrp06froosuitzIAQAAAKCbglrx\ncTgcWr16tQYMGOBz/Ysvvqh58+YpMTFRklRaWqrLL79cqampcjgcuuKKK7R9+/bIjRoAAAAAghBU\n8LHb7UpKSvK57sCBAyorK9P06dM919XW1iojI8NzOSMjQzU1NWEOFQAAAABCE1Spmz/Lly/XkiVL\nJElut9vvMYGu9+ZyucIdCgAAAACLKywsDOn7wgo+VVVVOnDggBYsWCC3262amhrNmzdPP/3pT/V/\n//d/PseNHz++y/sL9Ukg9lwuF/NnUsyduTF/5sXcmRvzZ27Mn3mFs1gSVvBxOp3avHmz5/INN9yg\nV199VU1NTVq8eLEaGhpks9n0ySefaNGiReE8FAAAAACELKjgU1paqsWLF+vYsWNKSEhQcXGxXnvt\nNfXt21eSZLPZJLU1QXjooYf0wx/+UHa7XfPnz1daWlrkRw8AAAAA3RBU8CkoKNC7774b8Pa//vWv\nnq+nT5/u0/AAAAAAAGIl6BOYAgAAAIDZEHwAAAAAWB7BBwAAAIDlEXwAAAAAWB7BBwAAAIDlEXwA\nAAAAWB7BBwAAAIDlEXwAAAAAWF5QJzAFAAAAJKmpqVklJQdVV5eo9PSzmjlziByOpFgPCwiIFR8A\nAAAEraTkoKqqctXUlKOqqlyVlByM9ZCACyL4AAAAIGh1dYkXvAwYDcEHAAAAQUtPP3vBy4DREHwA\nAAAQtJkzh8jpLJPDUS6ns0wzZw6J9ZCACwq6ucGePXs0f/583XXXXfr+97+vyspKLVy4UC0tLUpM\nTNSzzz6r/v37a+PGjVq3bp0SEhI0e/Zs3XrrrdEYPwAAAGLA4UjSzTfnxnoYQLcFteLT2Nio5cuX\nq6ioyHPdypUrNWfOHL366quaOnWqXnnlFTU2NmrVqlVau3at1q1bp7Vr16q+vj7igwcAAACA7ggq\n+DgcDq1evVoDBgzwXPf4449rxowZkqSMjAzV1dWptLRUl19+uVJTU+VwOHTFFVdo+/btkR05AAAA\nTKGpqVnvvFOmtWvL9c47ZWpqao71kBCHggo+drtdSUm+/dmTk5Nlt9vV2tqqN954Q7NmzVJtba0y\nMjI8x2RkZKimpiYyIwYAAICp0PoaRhCRE5i2trbq4Ycf1qRJkzRx4kRt2rTJ53a3292t+3G5XJEY\nDmKE+TMv5s7cmD/zYu7Mjfnrvh07Tqq5+Xy768rKCmVnn4jhiJi/eBSR4PPYY49p6NChuu+++yRJ\nWVlZPis8VVVVGj9+fJf3U1hYGInhIAZcLhfzZ1LMnbkxf+bF3Jkb8xeciooyVVXleC47nWdVWBi7\nxgjMn3mFE1jDbme9ceNGJSUl6YEHHvBcV1BQoF27dqmhoUGnTp3SJ598wi8XAABAnKL1NYwgqBWf\n0tJSLV68WMeOHVNCQoKKi4vV2toqh8OhefPmyWazKTc3V0uWLNFDDz2kH/7wh7Lb7Zo/f77S0tKi\n9RwAAABgYLS+hhEEFXwKCgr07rvvduvY6dOna/r06SENCgAAAAAiKexSNwAAAAAwOoIPAAAAAMsj\n+AAAAACwPIIPAAAAAMsj+AAAAACwvIicwBQAAMSfpqZmlZQcVF1dotLTz2rmzCFyOJJiPSwA8IsV\nHwAAEJKSkoOqqspVU1OOqqpyVVJyMNZDAoCAWPEBAAAhqatLvOBlWAMre7AKVnwAAEBI0tPPXvAy\nrIGVPVgFwQcAAIRk5swhcjrL5HCUy+ks08yZQ2I9JEQBK3uwCkrdAABASByOJN18c26sh4EoS08/\nq6oq38uAGbHiAwAAgIBY2YNVsOIDAACAgFjZg1UQfAAAABAxdIGDUQVd6rZnzx7deOONev311yVJ\nR44c0bx583THHXfowQcf1NmzbXWfGzdu1K233qrbbrtNb7/9dmRHDQAAAB9NTc16550yrV1brnfe\nKVNTU3NMxkEXOBhVUMGnsbFRy5cvV1FRkee6lStXat68eXrttdc0ZMgQrV+/Xo2NjVq1apXWrl2r\ndevWae3ataqvr4/44AEAANDGKIGDLnAwqqCCj8Ph0OrVqzVgwADPdR9//LGmTJkiSZoyZYq2bNmi\n0tJSXX755UpNTZXD4dAVV1yh7du3R3bkAAAA8DBK4Aj3/E5GWbmC9QQVfOx2u5KSfGs0GxsblZjY\n9sLq37+/qqurdfToUWVkZHiOycjIUE1NTQSGCwAAYD2ReLNvlBPKhtsFzigrV7CeiDY3cLvdQV3f\nkcvliuRw0MOYP/Ni7syN+TMv5s7cIjl/779frWPHLvvmUqLKyv6q66/PCuo+srLOqqxsnxoakpWW\n1qj8/H5yuU5EbIzByM5u+yNJu3Z9GtT37thxUs3N51erKisrlJ0d+efB6y/+hB18UlNT1dzcrKSk\nJFVVVcnpdCorK8tnhaeqqkrjx4/v8r4KCwvDHQ5ixOVyMX8mxdyZG/NnXsyduYUzf/66nu3aVak+\nfXI8xzgcUmFhzgXuxb9Jk0IakqFUVJSpqur8c3c6z6qwMLLttHn9mVc4gTXsE5hOmjRJmzdvliRt\n3rxZ11xzjS6//HLt2rVLDQ0NOnXqlD755BN+uQAAgOm1l6Rt2nQy5JI0f6VcRilTMwJOmIpoCWrF\np7S0VIsXL9axY8eUkJCg4uJivfzyy3r00Uf1hz/8QYMGDdItt9yihIQEPfTQQ/rhD38ou92u+fPn\nKy0tLVrPAQCAsHDeEXRXe2hpbk5UVVWOSkrKgj65p78mBHPnXqySkjKf38F4FfYJU1tapL17pdJS\n6ZJLpGuuidzgYGpBBZ+CggK9++67na5fs2ZNp+umT5+u6dOnhz4yAAB6SPubWUmqqlJIb2YRHyLR\nOS09/ayqqnwvh/1m/wIsHexPnJB27pR27GgLOjt2SLt2SU1Nbbfn5EhffRXTIcI4ItrcAAAAMzJK\nG2AYn7/QEqyZM4f06OqOJYK92y2Vl/sGnNJS6cAB3+OSkqQxY6Rx46SCAulb34rNeGFIBB8AQNyL\nxJtZxIf20FJZWSGnM7TQEs3VHX9MF+wbG6Xdu30Dzs6dbas73jIzpWnTzoecggJp1Cgp0eDPDzFD\n8AEAxL2e/gQe5tUeWrKzT0S801i0GDrYV1X5BpwdO9r255w7d/4Yu10aObJt9aY94IwbJw0cKNls\nfu/W0uV9CBnBBwAQ93r6E3igJxki2Le0SF98cT7gtIcc70QmSX36SBMn+gacsWOllJSgHs4S5X2I\nOIIPAACAhfV4sG9vOOC9krNrl3TmjO9xOTnSTTedDzgFBdLQoW0rPGEyXXkfegTBBwAAwKAMXbLV\n3nCgY6laVw0Hxo2TLr9c6tcvakMzdHkfYobgAwAA4oKhQ0QAhinZOnOmreGAd8AJ1HDgxht9V3Hy\n8nq84YAhyvtgOAQfAAAQFwwTIoIQk5Kt6uq2YNOdhgMzZ54POV00HOhJ7NuDPwQfAAAQF8y47yOq\nJVvtDQe8S9VKS6UjR3yPS0uTJkzwDTghNBwAYo3gAwAA4oIZ931ErGSrvt63m1qghgNDhkSt4QAQ\nawQfAAAQF8y47yPoki23W/rqq84hJ1DDAe+AU1AQ1YYDQKwRfAAAgKEE04QgmGMtt+8jmIYD06b5\nBpxRo3q84UC4zNicAsZC8AEAwMLM+GYxmCYEZmxYEJL2hgPeqzh79vg2HLDZ2jqozZzp2zraIA0H\nwhU3c42oCTv4nD59Wo888ohOnDihs2fP6v7771dubq4efvhhud1uZWZm6plnnlGiyT5VAADACsz4\nZjGYJgRmbFhwQd4NB7xDTqCGA94BJ4iGA0YJxMGMw3JzjR4XdvDZsGGDhg0bpgcffFDV1dW68847\nNW7cON1xxx2aMWOGVqxYofXr12vu3LmRGC8AIIqM8mYoWqz+/Pwx45vFYJoQmLFhgUd9fVtpmvdK\nTqCGA9/9rm+p2rBhYTUcMEogDmYcpp5rGELYwScjI0N79+6VJJ04cUIZGRnatm2bnnzySUnSlClT\ntGbNGoIPAJiAUd4MRYvVn58/kXiz2NOBMZgmBKZoWOB2S+Xlvi2jS0ulL7/0PS4pSbrssvMto6PY\ncMAogTiYcZhirmFoYQefb33rW9qwYYOmT5+ukydPavXq1br33ns9pW39+/dXTU1N2AMFAESfUd4M\nRYvVn58/kXiz2NOBMZgmBIZrWNCx4UD7n44NBwYMaGs44N1VrQcbDhhl9SSYcRhurmE6YQefjRs3\nauDAgXrppZe0d+9eLVq0yOd2t9sd7kMAAHqIUd4MRYvVn58/kXizGI+BsVu8Gw60l6r5azgwcmRb\nwwHvkHPxxTFtOGCU1ROjjAPxIezgs337dl1zzTWSpLy8PFVVVSk5OVnNzc1KSkpSVVWVsrKyunVf\nLpcr3OEghpg/82LuzC2S85eVdVZlZfvU0JCstLRG5ef3k8t1outvNAmjPT+zvPZqa6t17Nj5yxkZ\nn8nlqo3dgHpaS4t6f/21kvfuVcq+fUret0+X790rHT3qc9i5lBQ1jh2r0yNHqnHEiLa/c3Pl7t3b\n9/4qK9v+xFh2dtsfSdq169O4G4dZXn+InLCDT05Ojnbs2KEbb7xRhw4dUkpKiiZMmKCSkhLddNNN\n2rx5sycYdaWwsDDc4SBGXC4X82dSzJ25RWP+Jk2K6N0ZjlGen5lee2PHdtzjM9W6TSG62XCg2en0\nbTgwbpwShg5Vmt2utBgNHd1nptcffIUTWMMOPrfddpsWLlyoefPm6dy5c/rVr36loUOH6pFHHtGb\nb76pQYMG6ZZbbgn3YQAAQIxYcm9Fe8MB75bR/hoOJCZKY8b4to2+/HJ9euAAb5wNIh67NSI0YQef\nlJQU/dd//Ven69esWRPuXQMAAISvveFAx5BzoYYD7SEnUMOBAwd6ZuzoUjx2a0Rowg4+AAAAhlFV\n5dtNzUQNBxAamm+guwg+AABYRFyV/LS0SPv2+e7FKS2VjhzxPS4tTZowwfe8OGPHSqmpsRk3Ii4e\nuzUiNAQfAAAswrIlP+0NB7wDzqefdmo4oCFDfBsOFBRIw4ZJdntsxo0eQUtsdBfBBwAAizB9yY93\nwwHvUrVuNhxQRkZIDxtXK2UWZMnmG4gKgg8AABZhqpKfM2ekzz7zLVXbuVOqq/M9bsAAaepU31Wc\nUaOkpMgFE38rZW2rCIQhwEoIPgAAWIRhS36qqzt3VPv8c/8NB6ZP9w05gwZFveGAv5Uyy5YNAnGM\n4AMgrlDSAiuLecnPuXPSF1/4hpwdOzo3HEhNbWs40B5uCgqk/PyYNRzwt1Jm+rJBAJ0QfADEFT7F\nRbgIz9/obsOBSy5pazjg3TbaYA0H/K2Utf1bcf4YQ5cNAugWgg+AuMKnuAhX3IVnt1s6eLDzKk7H\nhgNJSdJll/mWqRUUhNxwoCf5WykzbNkggJARfADEFVNt/oYhWTo8d2w40P6nq4YD48a1NRxItM7P\nIhJlg6wOAsZC8AEQV/gUF+HyF55N+Qa3veGA90pOdxoOjBsnXXxx1BsOWEHcrQ4CBkfwARBXYr75\nG6YXeD+IQd/g+ms4UFoqVVb6HpeaKv3bv/kGnLFjY9ZwwAosvToImBDBBwAswoyrDmYcs7/wbJg3\nuCdPtjUc8N6Ls2uX1Njoe5x3w4H2kBODhgNmnP9gUFoLGAvBBwAswtCrDgGYccz+9PgbXLdbKi/v\nXKrWseFAYqI0ZoxvwDFQwwGrzH8glNYCxhKR4LNx40a9/PLL6tWrl376058qLy9PDz/8sNxutzIz\nM/XMM88o0UIbHgHAiAyz6hAEM47Zn6i+wW1qknbv7lyqFqjhgHfAGTWqrdua37uN/WpLNOffCM+P\n0lrAWMIOPnV1dfrd736nP/3pTzp16pR++9vfqqSkRPPmzdP06dO1YsUKrV+/XnPnzo3EeAEAAZix\nrMaMY/YnYm9wa2p8w82OHdKePVJLy/ljbDZpxIi2hgPeIWfQoKAaDhhhtSWa82+E5wfAWMIOPlu2\nbFFRUZGSk5OVnJysJ598UlOnTtWTTz4pSZoyZYrWrFlD8AEizAifZsJYzFhWY8YxR8S5c+p94IC0\nb5/vSo6/hgNXXeUbcPLzI9JwwAirbdGcfyM8PwDGEnbwOXTokBobG3Xvvffq5MmTuv/++3XmzBlP\naVv//v1VU1MT9kAB+OLTTHRkxrKaQGO2VLAP0HBgjL+GA7Nm+Yac4cOj1nAg0GpLT/7so/k7a5XV\nRACRE3bwcbvdnnK3Q4cO6Qc/+IHcbrfP7d3lcrnCHQ5iiPnrWTt2nFRz8/lPMCsrK5SdfSKk+2Lu\nzM2K8/f++9U6duyyby4lqqzsr7r++qyYjqlLbreSjhxR8hdfKOWLL5T8xRdK3rdPvSsqfA5r7dVL\nZ6WBmusAAB79SURBVIYN0+mRI9U4YoRO5+WpccQInevb1/f+6uulTz6J2nCzss6qrGyfGhqSlZbW\nqPz8fnK5TpjzZ+9HoOcXSVZ87cUT5i/+hB18BgwYoPHjx8tut+uSSy5RamqqevXqpebmZiUlJamq\nqkpZWd37B7OwsDDc4SBGXC4X89fDKirKVFWV47nsdJ5VYWHwn5wyd+Zm1fnbtatcffqc//12OKTC\nwpwLfEcP627Dgf79OzUcsI8apZSkJH1ugLmbNKnzdYb/2QfB3/OLlI6vPUutUsYBq/7bGQ/CCaxh\nB5+ioiItXLhQP/rRj1RXV6fTp0/r6quvVklJiW666SZt3rxZ11xzTbgPA6CDuN0bgbhgqDKlmhrf\ngHOhhgM33uh7AtAgGw4YgaF+9h0YOVxQfgwYX9jBx+l0asaMGZozZ45sNpuWLFmisWPH6pe//KXe\nfPNNDRo0SLfcckskxgrAixn3c4TLyG96EFkxCfbnzrU1G/BewSktlQ4f9j2uveGAd8AZOzYiDQeM\nIFo/+0i8fo0cLmimABhfRM7jM2fOHM2ZM8fnujVr1kTirgHAw8hvehBZUQ/27Q0HvFdyPv1U6tBw\n4PSAi3W0YKpOj8zTsFuuUeJVV0rDhkWt4YARROtnH4nXr5HDhZFXygC0iUjwAYCeYOQ3PTAot1s6\neND3vDilpdL+/b7HJSZKl13ms4rz54o0VTRe5TlkT0qZbs4laIcqEq9fI4cLyo8B4yP4ADANI7/p\ngQF4NxzwDjn+Gg7ccINvqdqoUVKSb9lVzdpyn8sE7fBE4vVr5HARj+XHgNkQfACYhpHf9KCHVVf7\n7sMJ1HAgNzfkhgME7ciKxOuXcAEgHAQfAKbBm5441LHhQPvflZW+x7U3HPA++Wd+flgNB3o6aFul\neUeg58Hr1xys8nsI+EPwAQAYQzcbDig7W5o1yzfkDB8e8YYDPf1G3SrNO6zyPOIV8wcrI/gAAHqW\n2y19/XXnttFlZb7HtTcc8A44BQVte3RMrP0T9R07TqqioszzibpVmndY5XnEK+YPVkbwAQBET1OT\n9NlnnUvVAjUc8A45o0d3ajhgNv7Khto/UW9uTlRVVY7nE3Wr7CmyyvOIV8wfrIzgAwCIjJoa33BT\nWip9/nnnhgMjRoTccMBs/JUNBfpEPZg9RUbeh0ETEnNj/mBlBB8AQHDaGw54h5wdOzo1HDjrSNHJ\n4fnqe+2VSrjiiraAM3aslJYWo4H3PH8hJ9An6sHsKTLyPgyaGJgb8wcrI/gAAAI7ebKtwYB3wOmi\n4cDHZ536Mm2m6jPbGg44ncZ5U97T/IWc9k/UKysr5HSG9ok6+zAAIHgEHwA+jFxCY2Sm/7m1Nxzo\nWKoWQsOBz9eWq6kpx3M5nt+U+ysbav9EPTv7hAoLQwuE7MMAgOARfAD4MHIJjZEF+rkZMhC1Nxzo\nGHKOH/c9LiPjfMOB9j+XXdZlw4FovSkP9LM05M/4G9EqG2IfBgAEj+ADwAclNKEJ9HOLeZBsbzjQ\n/mfHDv8NB3JzpWnTfFdyBg/2NBzwhIvSyi7DRaA35eEGlEA/y5j/jGOAfRgAELyIBZ+mpibNmjVL\n999/vyZOnKiHH35YbrdbmZmZeuaZZ5SYyJsnwAwooQlNoJ9bjwVJ74YD3is5hw/7HpeaKl15pW+Z\nWn5+lw0HggkXgd6UhxtQAv0s/V1v5FUgIBL4HQeCF7Hgs2rVKqWnp0uSVq5cqXnz5mn69OlasWKF\n1q9fr7lz50bqoQBEkb9P6/kPtmuBVjnCDZJ+f/bNTZ6GA0P+8hfp0KHADQe+8x3fkJObK9ntQT+/\nSAS4cO8j0M/S3/XxuAqE+MLvOBC8iASfL7/8UgcOHNB1110nt9utbdu26cknn5QkTZkyRWvWrCH4\nACbh79P6d94p4z/YLgRa5QhrL4bbrfdf/UiOT+p0WUWp+lfs0Nl7/yVHZbnnkEzJt+FA+59x43wa\nDoQrEiuB4d5HoJ+lv+uLi31ba1OyCauhLBkIXkSCzzPPPKMlS5boj3/8oySpsbHRU9rWv39/1dTU\nROJhAMSIGf+DNcoqVbf3YgRoODCjQ8OBM6np5xsOjBunzxITddn3vtdlw4FwRWIzfbj3Eehn6e96\nSjZhdfyOA8ELO/j86U9/0lVXXaVBgwb5vd3tdof7EABizIz/wUarDCQigaq2tvPJPwM0HDg0eqIO\nZxbpaHaBjl4yTml5jbr530d4Dmt0uaIeeqTIbKbvyQ35dD2D1fE7DgTP5g4zmTz44IOqqKiQ3W5X\nVVWVZ6Xnf//3f5WUlKRt27bptdde08qVKy94Py6XK5xhAN3S3HxWW7YcV0NDstLSGjV5cj8lJRl/\n9SLWzPhz27TppJqbh3ouJyUd0KxZfcK+3/ffr9ax/7+9ew+Osjz7OP7bHMmJwwKJBpBCIgdjiBDp\nECMV0EFtAYeOMEjB6T/1nYI4aIdTKLE6HAbolGGGUqQNLbRQIGEqjGMTRjv0xUIhsykRpFRDfQNh\nZENCAEMiicm+fyxZsjlv9vQ8m+9nxoFskt07Xm7c397XfT03H3N9bLVe1LRpiR1/cVOToq9eVezn\nnyvmiy+cf37+uaLa7II39eun+kcfVf2jj6puzBjVjxmj+tRUNcfGmvLfPQAA/pSZmdmr7/N6x2fb\ntm2uv+/YsUPDhw9XSUmJCgsLNWfOHBUVFWnq1Kk9uq/e/hAIPpvNZor6HT1apoSEKUq4//q3spKz\nKj2tXVZWABbjQxUVZbLbH1xEMympsdcXi2ztwoVyJSQ8uN/oaCkzc6T09dfOAQOtd3LOn5fq6tzv\noIOBA+EpKYoPD1dnc9W6+ndvluce2qN25kb9zI36mZc3myV+uY7PG2+8oZUrV+rw4cNKTk7W3Llz\n/fEwgMfMeFYFveOvNpCBAxpUe+mKBl91DhsYduMf0vrLUlmZ+xdGRkrjxz8IOC1/+nDgAAAA6Dmf\nBp/XX3/d9fc9e/b48q4BnzDjWRX0jk/Ok9y75zx70+oszpzSUlnaDByQ1fpg4EBLyBk/PiBnb3qi\ns3NJRhkAAQBAIPhlxwcwKg6DolMtAwdat6pdvNhu4IAlNVV69tkHAeeJJ6Rhw5zDCAyqs0EPXAcE\nANCXEHzQpwRyqhQMqqnJ2ZbWZmy0rl1z/7rYWCkz0z3gpKdL8Z2dxAmslt2ac+e+VkVFWZe7NZ21\neNL6CQDoSwg+MD0ztuuYcc2mVFsrffpp9wMHhg1zDhxofRYnJUUKDw/OunugZbemoSFSdvvILndr\nOmvxpPUTANCXEHxgemZs1zHjmg3N4ZCuXm3fqnb5svNzLSIipMcecw84GRnSkCHBW3svebJb01mL\nJ62fAIC+hOADQ/JkR8SM7TpmXLNhNDQ4z960vvhnaanU0cCBadPcQ8748c750yHAk92azlo8af0E\nAPQlBB8Ykic7ImZs1/HXmo3cQtertfVw4IBSUpxT1VrO4mRkOK+XY+CBA95q2a356qsKJSWxWwMA\nQHcIPjAkX7TxGJm/1mzkFrou19Z64EDrkNPRwIEnn3TfxTHQwIFAatmtGT78tk8uzAoAQKgj+MCQ\nfNHGY2T+WrORW+ha1hLxTa2s184r6R8fS3+tcIaczgYOfP/77hcANfjAAQAAYFwEHxiSGXdxjMBQ\nbX8Oh1RR4drBee6v/1Bc2WUNuFEmS9uBA+PHu7epmXTgAAAAMC6CDwzJjLs4RhC0wNgycKDttXFu\n3nR9yTBJDQkDdX3cFNWljtUjc55W5JOZITVwoK8x8pkyAADaIvgAISQggbGjgQP//rfU2GZ3KTXV\nOXCg1XmcqOHD9XAIDxzoa4x8pgwAgLYIPghZvBvtJU8GDkya5H4Wp48OHOhrjHymDACAtgg+CFmB\nfjfa1EGrttY5YKB1wPn00/YDB5KT+9TAAVPXNAAMdaYshPHfIQD4BsEHISvQ70abou2n9cCB+yEn\n7exZ6epV5+datB440Hp0dB8bOEB47hpDSALDFL9bAMAEfBJ8tmzZopKSEjU1Nem1115Tenq6VqxY\nIYfDoaFDh2rLli2KjKQFAoEV6HejDdf204OBA5IU0b+/NG3ag2lqTzzBwIH7CM9dYwhJYBjudwsA\nmJTXwefMmTMqKyvTwYMHdevWLc2dO1dTpkzRokWL9Pzzz2vbtm06cuSIFixY4Iv1Aj0W6HejvQ1a\nXr3b33rgQEvQ6WzgwPTpbjs5pXa7Mp980qO1+vVn8cH3+0qfD88wBFoKAcA3vA4+kydP1oQJEyRJ\n/fv3V11dnYqLi/Xuu+9KkqZPn649e/YQfBBwgX432tug1aN3+5uapMuXH+zg9HTgQEaGc+BAQkL7\nB66s9GidPvtZ/Pj9vmK28IzQREshAPiG18EnLCxMMTExkqSCggJNmzZNn3zyiau1bfDgwbpx44a3\nDwMYnrdBq+27+7XXG6TTp7sfODBsmOEGDni7cxHonY/OdpjMFp4RmmgpBADf8Nlwg48++khHjhxR\nXl6eZs6c6brd0frAdDdsNpuvloMgoH695HAosrJS/f/3rOIv39VD9n/r4cqLGlzzf24DB5rCwlWd\n9Igs3xurb8aNU/2YMap/9FF9O2iQ+/19/bUzKLXS0NCoU6dqVFsbo/j4ej311CBFRT0IE76uXVVV\npdtRIqv1omy2qoB9v6dOnKjUzZuP3f8oUmVlH2vatES/PV5Xhg93/iNJFy6c79H38NwzL2pnbtTP\n3Khf3+OT4HPy5Ent3r1beXl5io+PV1xcnBoaGhQVFSW73a7ExJ69gMjMzPTFchAENpuN+vVEQ4Pz\n7E3bVrWbNzWh9ZfFD5Dje8/IMvEJlTQ/rC/7z1TNQ+PVHBmtpCTP276OHi1TQsIUV6dbZeWD+/BH\n7R5/vO0OyrMendHx9vs9deFCuRISRro+jo6WMjNHdvEdxsFzz7yonblRP3OjfublTWD1OvjU1tZq\n69at+sMf/qCE+6+qsrKyVFRUpNmzZ6uoqEhTp0719mEA86mubj9R7eLF7gcOZGQoasQIyWKRJJ3f\nW6579x68CO9N21egW8e8bc0JdGsPZ2sAAAh9XgefDz/8ULdu3dLy5cvlcDhksVi0efNmrV27VocO\nHVJycrLmzp3ri7XCJPw5kcso077cNDdLZWXuIefcufYDB2JinAMHWoYNZGRIEyZ0PHCgFV+8KOeF\nfdc4WwMAQOjzOvjMnz9f8+fPb3f7nj17vL1rmJQ/J3IFfdpXba10/nz3AweSk50DB1pf/DM1tVcD\nB3zxopwX9l3j8DgAAKHPZ8MNgBb+bKsKWMuWwyFVVLhfF6e01Lmz03pgR0SE82KfrcdGZ2RIQ4f6\nbCm+eFHOC3sAANDXEXzgc/5sq/LLfXcxcMDNoEHSM8+4h5zHHnOehAcAAIChEXzgc560VXl6Zsfr\nlq2WgQOtA05HAwdSUpwDB1oCzsSJzhnD9wcOAAAAwFwIPvA5T9qqPD2z0+P77mjgQGmps32ttZgY\nZ6hpvYvTg4EDAAAAMBeCD4LKJ2d27t51Dhxo3ap2/rzz9tZaDxxoGTrQy4EDAAAAMBeCD4LKozM7\nDodzRHTbszidDRxoHXB8PHAAAAAA5kLwQVB1emanZeBA22vjMHAAAAAAvUDwQVBFR0fppanWB8Hm\nf+7/2dHAgdTUBwMHWoLOiBGGHjhgyAuuAgAA9EEEHwROc7N0+XL7VrWOBg5MmuTeqpaebsqBA0G/\n4CoMiUAMAEDgEXzgH54MHHjxxQcBJ8QGDgTsgqswFQIxAACBR/CB9yor1f+TT6TCwgdjo7/4ov3A\ngXHj3ANOHxg44M+LucK8CMQAAAQewQfeuX5dGj1aj9bXP7ht4EDnwIHWZ3H66MABry+4ipBEIAYA\nIPAIPvDOkCHSW2/pWnW1hr34ojPoGHzgQCB5cjFX9B0EYgAAAo/gA+9EREjr1+u6zaZhmZnBXg1g\nCgRiAAACz6/BZ9OmTSotLZXFYlFOTo7S09P9+XAwOCZZhR5qCgAAzCLMX3dcXFys8vJyHTx4UOvX\nr9eGDRv89VAwiZZJVvfujZTdnqrCwivBXlKfdu9eg44eLdMHH3yto0fLdO9eg8f3QU0BAIBZ+C34\nnD59Ws8995wkKSUlRXfu3NHdtqOM0acwycpYWkJLQ8OoXocWagoAAMzCb8GnqqpKVqvV9fGgQYNU\nVVXlr4eDCbSdXMUkq+DyRWihpgAAwCwCNtzA0fqaLp2w2WwBWAn8pbv6JSY2qqzsC9XWxig+vl7p\n6YNks90O0OrQVlVVpW7edP69vLxcVutF2WyevTlBTY2B353mRe3MjfqZG/Xre/wWfBITE912eCor\nKzW0m4tVZjIVzLRsNluP6peVFYDFoEcef9w5mODcuQo98cRwvfDCs70aTBDsmvb1AQs9fe7BeKid\nuVE/c6N+5uVNYPVbq1t2draKiookSZ999pmSkpIUGxvrr4cD4KGWkcqzZiXopZdSTRsWGLAAAAB6\nwm87PhMnTlRaWpoWLFig8PBw5ebm+uuhAPRhDFgAAAA94dczPm+99ZY/7x7wq77eQmUWAwc2ym53\n/xgAAKCtgA03AMympYVKkux2qbCwTC+9lBrkVYUebwPmCy88osLCMrfvBwAAaIvgA3SCFqrA8DZg\ntpxVAgAA6IrfhhsAZsc1agKDgAkAAAKB4AN04oUXHlFSUpmio8uVlFRGC5WfEDABAEAg0OoGdIIW\nqsDgjA4AAAgEgg+AoCJgAgCAQCD4AGJ0NQAAQKjjjA+gB5PF7t0bKbs9VYWFV4K9JAAAAPgQwQcQ\nk8UAAABCHa1u8EpLi9i5c1+roqLMtC1iAwc2ym53/7gztMUBAACYDzs+8EpLi1hDwyhTt4h5Mrqa\ntjgAAADzYccHXgmVFjFPJouFys8MAADQl7DjA6/0xYtP9sWfGQAAwOy8Cj5NTU1avXq1Fi5cqAUL\nFqikpESSdOnSJS1YsEALFy7UO++845OFwphaWsSior7stkUsVHjSFgcAAABj8KrV7ejRo+rXr58O\nHDigsrIyrVmzRvn5+dq4caPWrVuntLQ0/exnP9PJkyc1depUX60ZBtLSIjZ8+G1lZvaNi1BywU0A\nAADz8WrHZ86cOVqzZo0kyWq16vbt22psbFRFRYXS0tIkSTNmzNCpU6e8XykAAAAA9JJXOz4RERGK\niHDexd69ezV79mzV1NRo4MCBrq+xWq26ceOGd6sEAAAAAC/0OPjk5+eroKBAFotFDodDFotFy5Yt\nU3Z2tvbv36+LFy9q165dqq6u7vVibDZbr78XwUf9zIvamRv1My9qZ27Uz9yoX9/T4+Azb948zZs3\nr93t+fn5OnHihHbu3Knw8HBZrVbV1NS4Pm+325WYmNijx8jMzOzpcmAwNpuN+pkUtTM36mde1M7c\nqJ+5UT/z8iawenXG5+rVqzp06JB27NihyEjntUwiIiI0evRo14S348ePM9gAAAAAQFB5dcanoKBA\nt2/f1k9+8hNX+9uePXuUk5Oj3NxcORwOZWRkKCsry1frBQAAAACPeRV83nzzTb355pvtbk9JSdH+\n/fu9uWsAAAAA8BmvWt0AAAAAwAwIPgAAAABCHsEHAAAAQMgj+AAAAAAIeQQfAAAAACGP4AMAAAAg\n5BF8AAAAAIQ8gg8AAACAkEfwAQAAABDyCD4AAAAAQh7BBwAAAEDII/gAAAAACHkEHwAAAAAhj+AD\nAAAAIOT5JPhUVVXpu9/9roqLiyVJly5d0oIFC7Rw4UK98847vngIAAAAAOg1nwSfrVu3asSIEa6P\nN27cqHXr1unAgQO6c+eOTp486YuHAQAAAIBe8Tr4/POf/1RCQoLGjBkjSWpsbNS1a9eUlpYmSZox\nY4ZOnTrl7cMAAAAAQK95FXwaGxv1m9/8RsuXL3fdVlNTowEDBrg+tlqtunHjhjcPAwAAAABeiejp\nF+bn56ugoEAWi0UOh0MWi0VPP/20XnnlFcXHx7t9rcPh6NVibDZbr74PxkD9zIvamRv1My9qZ27U\nz9yoX99jcfQ2pUh65ZVX5HA45HA4dOXKFQ0ePFi//OUvtWTJEv3tb3+TJL3//vv6/PPPtXLlSp8t\nGgAAAAA80eMdn478+c9/dv19zZo1+uEPf6hx48Zp1KhRKikp0aRJk3T8+HEtXrzY64UCAAAAQG95\nFXw6k5OTo9zcXDkcDmVkZCgrK8sfDwMAAAAAPeJVqxsAAAAAmIFPruMDAAAAAEZG8AEAAAAQ8gg+\nAAAAAEKeX4Yb9FRTU5PWrl2rK1euqLm5WStXrtSkSZN06dIl/eIXv1BYWJjGjh2rt99+O5jLRBc2\nbdqk0tJSWSwW5eTkKD09PdhLQje2bNmikpISNTU16bXXXlN6erpWrFghh8OhoUOHasuWLYqMjAz2\nMtGFe/fuadasWVq6dKmmTJlC/Uzi2LFjysvLU0REhN544w2NHTuW2plEXV2dVq1apdu3b6uxsVFL\nly5Vamoq9TO4S5cuadmyZfrxj3+sH/3oR7p+/XqHNTt27Jj27dun8PBwzZs3Ty+//HKwlw61r99X\nX32lnJwcffvtt4qMjNTWrVs1ePBgj+oX1B2fo0ePql+/fjpw4IDWr1+vTZs2SZI2btyodevW6cCB\nA7pz545OnjwZzGWiE8XFxSovL9fBgwe1fv16bdiwIdhLQjfOnDmjsrIyHTx4UL/97W+1ceNGbd++\nXYsWLdKf/vQnPfLIIzpy5Eiwl4lu7Ny5UwMHDpQkbd++XYsXL6Z+Bnfr1i39+te/1sGDB/Xee+/p\n448/pnYm8pe//EWjR4/Wvn37tH37dm3YsIHfnQZXX1+vzZs3Kzs723VbR8+5+vp67dy5U3v37tW+\nffu0d+9e3blzJ4grh9R5/ebPn68//vGPevbZZ/X73//e4/oFNfjMmTNHa9askSRZrVbXOykVFRVK\nS0uTJM2YMUOnTp0K5jLRidOnT+u5556TJKWkpOjOnTu6e/dukFeFrkyePFnbt2+XJPXv3191dXUq\nLi7WjBkzJEnTp0/n+WZw//3vf/Xll1/qmWeekcPhUHFxsaZPny6J+hnZqVOnlJ2drZiYGA0ZMkTv\nvvuuzp49S+1Mwmq1qqamRpJ0+/ZtWa1WfncaXHR0tN577z0NGTLEdVtHz7nS0lJNmDBBcXFxio6O\n1qRJk1RSUhKsZeO+jur39ttv6/nnn5fkfE7eunXL4/oFNfhEREQoOjpakrR3717Nnj1bNTU1rncy\nJecPduPGjWAtEV2oqqqS1Wp1fTxo0CBVVVUFcUXoTlhYmGJiYiRJBQUFmjZtmurr613tGYMHD+b5\nZnBbtmzR6tWrXR9TP3O4du2a6uvr9dOf/lSLFi3S6dOn9c0331A7k3jxxRd1/fp1zZw5U6+++qpW\nrVrFc8/gwsLCFBUV5XZb25pVVlaqurra7bUMrzuNoaP6xcTEKCwsTM3NzTpw4IBmzZrV7rVod/UL\n2Bmf/Px8FRQUyGKxyOFwyGKxaNmyZcrOztb+/ft18eJF7dq1S9XV1YFaEnyMS0KZx0cffaQjR44o\nLy9PM2fOdN1ODY3t/fff1+TJk5WcnNzh56mfcTkcDle727Vr1/Tqq6+61YvaGduxY8f00EMPaffu\n3frPf/6jtWvXun2e+plPZzWjlsbW3NysFStWKCsrS1OmTNEHH3zg9vnu6hew4DNv3jzNmzev3e35\n+fk6ceKEdu7cqfDwcLftZEmy2+1KTEwM1DLhgcTERLcdnsrKSg0dOjSIK0JPnDx5Urt371ZeXp7i\n4+MVFxenhoYGRUVF8XwzuL///e+qqKjQ8ePHZbfbFRkZqdjYWOpnAkOGDNHEiRMVFhamESNGKC4u\nThEREdTOJEpKSjR16lRJ0tixY2W32xUTE0P9TKbt/++SkpKUmJjotkNgt9s1ceLEIK4SXVmzZo1G\njRqlJUuWSJLH9Qtqq9vVq1d16NAh7dixw7X1GBERodGjR7v6844fP+76ZQNjyc7OVlFRkSTps88+\nU1JSkmJjY4O8KnSltrZWW7du1a5du5SQkCBJysrKctWxqKiI55uBbdu2Tfn5+Tp06JBefvllLV26\nVFlZWSosLJRE/YwsOztbZ86ckcPhUE1Njerq6qidiYwcOVLnzp2T5GxbjI2N1VNPPUX9TKaj/99N\nmDBBFy5cUG1tre7evat//etfyszMDPJK0ZFjx44pKipKr7/+uuu2jIwMj+pncQRxT2/btm368MMP\n9fDDD7va3/bs2aPy8nLl5ubK4XAoIyNDq1atCtYS0Y1f/epXOnv2rMLDw5Wbm6uxY8cGe0nowuHD\nh7Vjxw595zvfcT3nNm/erLVr16qhoUHJycnatGmTwsPDg71UdGPHjh0aPny4nn76aa1cuZL6mcDh\nw4eVn58vi8WiJUuW6PHHH6d2JlFXV6ecnBxVV1erqalJy5cv16hRo7Rq1SrqZ1ClpaX6+c9/rps3\nbyo8PFwDBgxQXl6eVq9e3a5mx48f1+9+9zuFhYVp8eLF+sEPfhDs5fd5HdWvublZ0dHRiouLk8Vi\nUWpqqnJzcz2qX1CDDwAAAAAEQlBb3QAAAAAgEAg+AAAAAEIewQcAAABAyCP4AAAAAAh5BB8AAAAA\nIY/gAwAAACDkEXwAAAAAhLz/B8joTYiGW3WQAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f1b5d4566d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.random.seed(13)\n",
+    "X = range(100)\n",
+    "Y1 = [x**5 for x in X]\n",
+    "Y2 = [x + 20*np.random.normal(0,1) for x in X]\n",
+    "\n",
+    "model1 = regression.linear_model.OLS(Y1, sm.add_constant(X)).fit()\n",
+    "model2 = regression.linear_model.OLS(Y2, sm.add_constant(X)).fit()\n",
+    "\n",
+    "print 'R^2 of First Model:', model1.rsquared\n",
+    "print 'R^2 of Second Model:', model2.rsquared\n",
+    "\n",
+    "line1 = [model1.params[0] + model1.params[1]*x for x in X]\n",
+    "line2 = [model2.params[0] + model2.params[1]*x for x in X]\n",
+    "\n",
+    "fig, axes = plt.subplots(nrows = 2, ncols = 1)\n",
+    "\n",
+    "axes[0].plot(X, line1, c = 'r');\n",
+    "axes[0].scatter(X, Y1, alpha = 0.4);\n",
+    "axes[1].plot(X, line2, c = 'r');\n",
+    "axes[1].scatter(X, Y2, alpha = 0.4);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Clearly the second model best represents the data it was meant to model, but it has practically the same $R^2$ as the first. This illustrates the limitations of $R^2$ when it comes to determining model fit and predictive value."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Although it will not provide a complete picture, let's find the $R^2$ and $\\bar{R}^2$ of our unemployment model:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unemployment Model R^2: 0.875698551729\n",
+      "Unemployment Model Adjusted R^2: 0.871375023094\n"
+     ]
+    }
+   ],
+   "source": [
+    "print 'Unemployment Model R^2:', model.rsquared\n",
+    "print 'Unemployment Model Adjusted R^2:', model.rsquared_adj"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Residual Analysis\n",
+    "\n",
+    "Residual analysis can help check that the basic assumptions of the linear model are satisfied. A large portion of model validation has to do with studying the residuals of the regression in question. To learn more about this aspect of model validation refer to the [lecture on residual analysis](https://www.quantopian.com/lectures/residual-analysis).\n",
+    "\n",
+    "Note that our toy model is conducted on the point values of these datasets. Ideally, we would conduct it on a stationary and normal representation of the data (such as first-order differences) in order to satisfy the assumptions of linear regression. While our toy model works for demonstrating model selection and validation, looking at the residuals will make these violations glaringly obvious and should always be done when validating a model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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RAKDcTEUDyqreppfU2/YCQLUyYgOUVb1NL6m37QWAaiXYAGVVb9NL0rY37R4dAEBlCDYA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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f1b589e8350>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "residuals = model.resid;\n",
+    "plt.plot(index[:len(residuals)], [0]*len(residuals), c='r')\n",
+    "plt.scatter(index[:len(residuals)], residuals);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This residual plot uncovers serial correlation and changing variance, both violations of the assumptions of linear regression which invalidate our model. To learn how to detect and adjust for these symptoms refer to the [lecture on residual analysis](https://www.quantopian.com/lectures/residual-analysis)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cross-Validation\n",
+    "\n",
+    "Cross-validation is a technique used to determine how well a model will predict values outside of the sample used to select or fit it. In the case of our unemployment model, we used data from 2002 through 2012 to select our parameters. Now to ensure the accuracy of our model parameters (stored in `predictors`) we will employ a cross-validation technique known as *forward chaining* on the 2002-2012 data. We will leave the 2012-2017 data untouched for further validation methods later on in this lecture.\n",
+    "\n",
+    "Forward chaining cross-validation works by splitting the data up into $k$ equal sized partitions, and conducting an \"out-of-sample test\" for each partition. During each of these out-of-sample tests we will choose a training set and a testing set and use the training set to fit the model and the testing set to asses its performance. For time series data the training set must come before the testing set, and in the case of forward chaining the training set consists of all the data before the testing set. Forward chaining iterates through all possible testing sets and asses model performance in each.\n",
+    "\n",
+    "In the case of our unemployment model, we will partition our 2002-2012 data into 10 yearly blocks. The first 3 iterations of a forward chaining test would be constructed as follows:\n",
+    "\n",
+    "\n",
+    "$$\n",
+    "\\\n",
+    " \\text{Iteration } 1:  \\overbrace{\n",
+    "    \\underbrace{\\textit{2002}}_\\text{Training} +\n",
+    "    \\underbrace{\\textit{2003}}_\\text{Testing}\n",
+    "   }^\\text{First Trail-Test Pair}\n",
+    " \\\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    " \\\n",
+    " \\text{Iteration } 2:  \\overbrace{\n",
+    "    \\underbrace{\\textit{2002 & 2003}}_\\text{Training} +\n",
+    "    \\underbrace{\\textit{2004}}_\\text{Testing}\n",
+    "   }^\\text{Second Trail-Test Pair}\n",
+    " \\\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    " \\\n",
+    " \\text{Iteration } 3:  \\overbrace{\n",
+    "    \\underbrace{\\textit{2002 & 2003 & 2004}}_\\text{Training} +\n",
+    "    \\underbrace{\\textit{2005}}_\\text{Testing}\n",
+    "   }^\\text{Third Trail-Test Pair}\n",
+    " \\\n",
+    "$$\n",
+    "\n",
+    "<center>\n",
+    ".  .  .\n",
+    "</center>\n",
+    "\n",
+    "\n",
+    "The end result is a single performance statistic, usually the  mean squared error (MSE) or adjusted $R^2$ of the model in the testing set averaged from across all of the iterations. Let's implement a forward chaining model-validation test on our unemployment model using adjusted MSE as our model prediction quality metric."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSE in 2003 : 0.0565831690527\n",
+      "MSE in 2004 : 0.653180823804\n",
+      "MSE in 2005 : 0.20938626572\n",
+      "MSE in 2006 : 0.56114441747\n",
+      "MSE in 2007 : 0.021279349198\n",
+      "MSE in 2008 : 2.0220023476\n",
+      "MSE in 2009 : 13.4768461212\n",
+      "MSE in 2010 : 3.51297688997\n",
+      "MSE in 2011 : 2.4009454556\n",
+      "\n",
+      "\n",
+      "Average MSE across Iterations: 2.54603831551\n",
+      "Average MSE Excluding 2009: 1.1796873398\n"
+     ]
+    }
+   ],
+   "source": [
+    "Y = unemployment[:e]\n",
+    "X = [qqq[:e], inflation[:e], iwm[:e], fx[:e], gold[:e]]\n",
+    "\n",
+    "# Our step AIC algorithm selected all predictors except for fx_rate\n",
+    "predictors = pd.DataFrame(data = [qqq[:e], inflation[:e], iwm[:e], gold[:e]], \n",
+    "                          index = ['qqq', 'inflation', 'iwm', 'gold']).T\n",
+    "\n",
+    "# Setting partition dates to the first day of every year 2002-2012\n",
+    "cutoff_dates = pd.date_range(start = '2002-01-01', end = '2012-01-01', freq = 'AS')\n",
+    "n = len(cutoff_dates)\n",
+    "\n",
+    "MSEs = []\n",
+    "\n",
+    "for i in range(1,n-1):\n",
+    "    \n",
+    "    # Defining training and testing sets for each iteration, using yearly cutoff dates\n",
+    "    training_data = predictors.loc[cutoff_dates[0]:cutoff_dates[i]]\n",
+    "    testing_data = predictors.loc[cutoff_dates[i]:cutoff_dates[i+1]]\n",
+    "    \n",
+    "    # Fitting model within the training set\n",
+    "    fitted_theta = regression.linear_model.OLS(Y[cutoff_dates[0]:cutoff_dates[i]],\n",
+    "                                               sm.add_constant(training_data)).fit().params\n",
+    "    \n",
+    "    # Testing performance within the testing set\n",
+    "    testing_model = (fitted_theta[0] + fitted_theta[1] * testing_data['qqq'] \n",
+    "                     + fitted_theta[2] * testing_data['inflation']\n",
+    "                     + fitted_theta[3] * testing_data['iwm']\n",
+    "                     + fitted_theta[4] * testing_data['gold'])\n",
+    "    \n",
+    "    # Caluclate Mean Squared Error for the model runnning on the testing set\n",
+    "    errors = Y[cutoff_dates[i]:cutoff_dates[i+1]]-testing_model\n",
+    "    df = len(testing_model) - len(predictors.columns) - 1\n",
+    "    MSE = np.sum([error**2 for error in errors])/df\n",
+    "    MSEs.append(MSE)\n",
+    "    \n",
+    "    print 'MSE in', cutoff_dates[i].year,':', MSE\n",
+    "    \n",
+    "print '\\n\\nAverage MSE across Iterations:', np.mean(MSEs)\n",
+    "print 'Average MSE Excluding 2009:', np.mean(MSEs[:6]+MSEs[7:])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since unemployment usually is between 3 and 10 percent, a 2.5 MSE is relatively large. However, we can also see that the a couple outliers around the 2009 recession led to this higher error. Excluding 2009, our average MSE would be 1.18, a more reasonable value."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Out-of-Sample Validation\n",
+    "\n",
+    "After conducting the forward chaining test we can be confident in the performance of our model within the time period of 2002-2012. So far in this lecture, all of the testing and development of this model has been done within this 10-year period. Working extensively within a single time period can lead to overfitting."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Overfitting\n",
+    "\n",
+    "A model is overfit when it is trained so much that it models the random noise of the data instead of just the underlying relationship. Conducting out-of-sample tests and using cross-validation helps avoid overfitting. It is easy to have a model that looks perfect in-sample, but has little predictive value. To demonstrate the dangers of overfitting, look at the two models below. They both model the same data, but  `simple_model` is just a simple linear regression while  `complicated_model` includes high-order values of X. The result is that the second more complicated model looks better in-sample, but the first and more simple one explains the relationship better."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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61CuBAFwi05T27699JPLGjdKpU7XHtWolDRxobdJPTrZWXPz8eVFOZ65yckbJ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\nAAAAAJdEmAEAAADgkggzAAAAAFzS/wMJGIC0sYvqaQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f1b63dd3750>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.random.seed(1)\n",
+    "X = np.linspace(1,15,10)\n",
+    "Y = [2*x + 2*np.random.normal(0,1) for x in X]\n",
+    "\n",
+    "X2 = X**2\n",
+    "X3 = X**3\n",
+    "X4 = X**4\n",
+    "\n",
+    "X_is = X[:len(X)/2],X2[:len(X)/2],X3[:len(X)/2],X4[:len(X)/2]\n",
+    "\n",
+    "simple = regression.linear_model.OLS(Y[:len(X)/2], sm.add_constant(X[:len(X)/2])).fit().params\n",
+    "complicated = regression.linear_model.OLS(Y[:len(X)/2], sm.add_constant(np.column_stack(X_is))).fit().params\n",
+    "\n",
+    "simple_model = simple[0] + simple[1] * X\n",
+    "complicated_model = (complicated[0] + complicated[1] * X \n",
+    "                     + complicated[2] * X2 + complicated[3] * X3 \n",
+    "                     + complicated[4] * X4)                                   \n",
+    "                                            \n",
+    "fig, axes = plt.subplots(nrows = 2, ncols = 1)\n",
+    "\n",
+    "axes[0].plot(X[:len(X)/2], simple_model[:len(X)/2], c = 'r');\n",
+    "axes[0].plot(X, simple_model, c = 'r', linestyle='--');\n",
+    "axes[0].scatter(X, Y, alpha = 0.8);\n",
+    "axes[1].plot(X[:len(X)/2], complicated_model[:len(X)/2], c = 'r');\n",
+    "axes[1].plot(X, complicated_model, c = 'r', linestyle='--');\n",
+    "axes[1].scatter(X, Y, alpha = 0.8);\n",
+    "plt.ylim(0,35);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For more information on overfitting, refer to the [Dangers of Overfitting Lecture](https://www.quantopian.com/lectures/the-dangers-of-overfitting).  \n",
+    "\n",
+    "To see if our unemployment model is overfit let's conduct an out-of-sample validation test. We will use the 2002-2012 data to fit the model and then the 2012-2017 data to test it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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gOhUh3kBkqIEifGtzxkzL3r2yzcw07/zuBT0v/nEtfL1VeG1DHoorGq00OBM1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\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f1b63242ed0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Y = pd.Series(unemployment)\n",
+    "X_train = pd.DataFrame([qqq[:e], inflation[:e], iwm[:e], fx[:e],\n",
+    "                        gold[:e]], columns = Y[:e].index, index = X_str).T\n",
+    "\n",
+    "X_test = pd.DataFrame([qqq[e:], inflation[e:], iwm[e:], fx[e:],\n",
+    "                       gold[e:]], columns = Y[e:].index, index = X_str).T\n",
+    "\n",
+    "\n",
+    "thetas = regression.linear_model.OLS(Y.loc[:e], sm.add_constant(X_train)).fit().params\n",
+    "model_insample = (thetas[0] + thetas[1] * X_train['qqq'] + thetas[2] * X_train['inflation']\n",
+    "                     + thetas[3] * X_train['iwm'] + thetas[5] * X_train['gold'])\n",
+    "\n",
+    "model_outsample = (thetas[0] + thetas[1] * X_test['qqq'] + thetas[2] * X_test['inflation']\n",
+    "                     + thetas[3] * X_test['iwm'] + thetas[5] * X_test['gold'])\n",
+    "\n",
+    "model_insample.plot(c = 'r');\n",
+    "model_outsample.plot(c = 'r', linestyle = '--');\n",
+    "Y.plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Out-of-sample our fitted model performs nowhere near as well as it does in-sample. Lets employ forward-chaining again, this time with 6 month partitions instead of yearly ones, on this 2012-2017 validation data to confirm the validity of our predictor selections."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSE in 2012 : 0.23988877334\n",
+      "MSE in 2013 : 0.405434958749\n",
+      "MSE in 2013 : 0.0945991177613\n",
+      "MSE in 2014 : 0.779186505254\n",
+      "MSE in 2014 : 0.10545739628\n",
+      "MSE in 2015 : 0.0494698316077\n",
+      "MSE in 2015 : 0.509364605593\n",
+      "MSE in 2016 : 0.520218562335\n",
+      "MSE in 2016 : 0.226247940777\n",
+      "\n",
+      "\n",
+      "Average MSE across iterations: 0.325540854633\n"
+     ]
+    }
+   ],
+   "source": [
+    "Y = unemployment[e:]\n",
+    "X = [qqq[e:], inflation[e:], iwm[e:], fx[e:], gold[e:]]\n",
+    "\n",
+    "# Our step AIC algorithm selected all predictors except for fx_rate\n",
+    "predictors = pd.DataFrame(data = [qqq[e:], inflation[e:], iwm[e:], gold[e:]], \n",
+    "                          index = ['qqq', 'inflation', 'iwm', 'gold']).T\n",
+    "\n",
+    "# Setting partition dates to the first day of every year 2002-2012\n",
+    "cutoff_dates = pd.date_range(start = '2012-01-01', end = '2017-01-01', freq = '6MS')\n",
+    "n = len(cutoff_dates)\n",
+    "\n",
+    "MSEs = []\n",
+    "\n",
+    "for i in range(1,n-1):\n",
+    "    \n",
+    "    # Defining training and testing sets for each iteration, using yearly cutoff dates\n",
+    "    training_data = predictors.loc[cutoff_dates[0]:cutoff_dates[i]]\n",
+    "    testing_data = predictors.loc[cutoff_dates[i]:cutoff_dates[i+1]]\n",
+    "    \n",
+    "    # Fitting model within the training set\n",
+    "    thetas = regression.linear_model.OLS(Y[cutoff_dates[0]:cutoff_dates[i]], \n",
+    "                                               sm.add_constant(training_data)).fit().params\n",
+    "    \n",
+    "    # Testing performance within the testing set\n",
+    "    testing_model = (thetas[0] + thetas[1] * testing_data['qqq'] \n",
+    "                     + thetas[2] * testing_data['inflation']\n",
+    "                     + thetas[3] * testing_data['iwm'] \n",
+    "                     + thetas[4] * testing_data['gold'])\n",
+    "    \n",
+    "    # Caluclate Mean Squared Error for the model runnning on the testing set\n",
+    "    errors = Y[cutoff_dates[i]:cutoff_dates[i+1]]-testing_model\n",
+    "    df = len(testing_model) - len(predictors.columns) - 1\n",
+    "    MSE = np.sum([error**2 for error in errors])/df\n",
+    "    MSEs.append(MSE)\n",
+    "    \n",
+    "    print 'MSE in', cutoff_dates[i].year,':', MSE\n",
+    "    \n",
+    "print '\\n\\nAverage MSE across iterations:', np.mean(MSEs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This MSE is still low, meaning that the regressor selections we made using the `forward_aic` algorithm were well-founded, but the beta coefficients for each have a lot of variability. Therefore, the parameters from a certain training period will not hold for very long and might need to be recalculated on a rolling basis. For more information on detecting and adjusting for this, refer to the Quantopian lecture on [Regression Model Instability](https://www.quantopian.com/lectures/regression-model-instability)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*This presentation is for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation for any security; nor does it constitute an offer to provide investment advisory or other services by Quantopian, Inc. (\"Quantopian\"). Nothing contained herein constitutes investment advice or offers any opinion with respect to the suitability of any security, and any views expressed herein should not be taken as advice to buy, sell, or hold any security or as an endorsement of any security or company.  In preparing the information contained herein, Quantopian, Inc. has not taken into account the investment needs, objectives, and financial circumstances of any particular investor. Any views expressed and data illustrated herein were prepared based upon information, believed to be reliable, available to Quantopian, Inc. at the time of publication. Quantopian makes no guarantees as to their accuracy or completeness. All information is subject to change and may quickly become unreliable for various reasons, including changes in market conditions or economic circumstances.*"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
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+   "codemirror_mode": {
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+ "nbformat_minor": 0
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+  content: "\e143";
+}
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+  content: "\e144";
+}
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+  content: "\e145";
+}
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+  content: "\e146";
+}
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+  content: "\e148";
+}
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+  content: "\e149";
+}
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+  content: "\e150";
+}
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+  content: "\e151";
+}
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+  content: "\e152";
+}
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+  content: "\e153";
+}
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+  content: "\e154";
+}
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+  content: "\e155";
+}
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+}
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+  content: "\e157";
+}
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+  content: "\e158";
+}
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+  content: "\e159";
+}
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+  content: "\e160";
+}
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+  content: "\e161";
+}
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+  content: "\e162";
+}
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+  content: "\e163";
+}
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+  content: "\e164";
+}
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+  content: "\e165";
+}
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+  content: "\e166";
+}
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+}
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+}
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+  content: "\e169";
+}
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+  content: "\e170";
+}
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+  content: "\e171";
+}
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+  content: "\e172";
+}
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+  content: "\e173";
+}
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+  content: "\e174";
+}
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+  content: "\e175";
+}
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+  content: "\e176";
+}
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+  content: "\e177";
+}
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+  content: "\e178";
+}
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+  content: "\e179";
+}
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+}
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+  content: "\e181";
+}
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+  content: "\e182";
+}
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+}
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+  content: "\e184";
+}
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+  content: "\e185";
+}
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+}
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+}
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+}
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+  content: "\e189";
+}
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+}
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+  content: "\e191";
+}
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+  content: "\e192";
+}
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+  content: "\e193";
+}
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+  content: "\e194";
+}
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+  content: "\e195";
+}
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+  content: "\e197";
+}
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+  content: "\e198";
+}
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+  content: "\e199";
+}
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+  content: "\e200";
+}
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+}
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+  content: "\e202";
+}
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+}
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+}
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+  content: "\e205";
+}
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+  content: "\e206";
+}
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+  content: "\e209";
+}
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+}
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+  content: "\e211";
+}
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+}
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+}
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+  content: "\e214";
+}
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+  content: "\e215";
+}
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+  content: "\e216";
+}
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+  content: "\26fa";
+}
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+  content: "\e218";
+}
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+  content: "\e219";
+}
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+  content: "\f8ff";
+}
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+  content: "\e221";
+}
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+  content: "\231b";
+}
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+  content: "\e223";
+}
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+  content: "\e224";
+}
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+  content: "\e225";
+}
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+  content: "\e226";
+}
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+  content: "\e227";
+}
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+  content: "\e227";
+}
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+  content: "\e227";
+}
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+  content: "\00a5";
+}
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+  content: "\00a5";
+}
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+}
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+  content: "\20bd";
+}
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+}
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+  content: "\e231";
+}
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+  content: "\e232";
+}
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+  content: "\e233";
+}
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+  content: "\e234";
+}
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+  content: "\e235";
+}
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+  content: "\e236";
+}
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+  content: "\e237";
+}
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+}
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+  content: "\e239";
+}
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+  content: "\e240";
+}
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+  content: "\e241";
+}
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+  content: "\e242";
+}
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+}
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+  content: "\e244";
+}
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+  content: "\e245";
+}
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+  content: "\e246";
+}
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+  content: "\e247";
+}
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+  content: "\e248";
+}
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+}
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+}
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+}
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+}
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+}
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+}
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+  content: "\e255";
+}
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+  content: "\e256";
+}
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+  content: "\e257";
+}
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+  content: "\e258";
+}
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+  content: "\e259";
+}
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+  content: "\e260";
+}
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+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+*:before,
+*:after {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+html {
+  font-size: 10px;
+  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
+}
+body {
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #000;
+  background-color: #fff;
+}
+input,
+button,
+select,
+textarea {
+  font-family: inherit;
+  font-size: inherit;
+  line-height: inherit;
+}
+a {
+  color: #337ab7;
+  text-decoration: none;
+}
+a:hover,
+a:focus {
+  color: #23527c;
+  text-decoration: underline;
+}
+a:focus {
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+figure {
+  margin: 0;
+}
+img {
+  vertical-align: middle;
+}
+.img-responsive,
+.thumbnail > img,
+.thumbnail a > img,
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+  display: block;
+  max-width: 100%;
+  height: auto;
+}
+.img-rounded {
+  border-radius: 3px;
+}
+.img-thumbnail {
+  padding: 4px;
+  line-height: 1.42857143;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  -webkit-transition: all 0.2s ease-in-out;
+  -o-transition: all 0.2s ease-in-out;
+  transition: all 0.2s ease-in-out;
+  display: inline-block;
+  max-width: 100%;
+  height: auto;
+}
+.img-circle {
+  border-radius: 50%;
+}
+hr {
+  margin-top: 18px;
+  margin-bottom: 18px;
+  border: 0;
+  border-top: 1px solid #eeeeee;
+}
+.sr-only {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  margin: -1px;
+  padding: 0;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  border: 0;
+}
+.sr-only-focusable:active,
+.sr-only-focusable:focus {
+  position: static;
+  width: auto;
+  height: auto;
+  margin: 0;
+  overflow: visible;
+  clip: auto;
+}
+[role="button"] {
+  cursor: pointer;
+}
+h1,
+h2,
+h3,
+h4,
+h5,
+h6,
+.h1,
+.h2,
+.h3,
+.h4,
+.h5,
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+  font-family: inherit;
+  font-weight: 500;
+  line-height: 1.1;
+  color: inherit;
+}
+h1 small,
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+.h3 .small,
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+.h5 .small,
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+  font-weight: normal;
+  line-height: 1;
+  color: #777777;
+}
+h1,
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+h2,
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+h3,
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+  margin-top: 18px;
+  margin-bottom: 9px;
+}
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+.h1 small,
+h2 small,
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+h3 small,
+.h3 small,
+h1 .small,
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+h2 .small,
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+}
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+h6,
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+  margin-top: 9px;
+  margin-bottom: 9px;
+}
+h4 small,
+.h4 small,
+h5 small,
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+h6 small,
+.h6 small,
+h4 .small,
+.h4 .small,
+h5 .small,
+.h5 .small,
+h6 .small,
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+  font-size: 75%;
+}
+h1,
+.h1 {
+  font-size: 33px;
+}
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+  font-size: 27px;
+}
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+  font-size: 23px;
+}
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+  font-size: 17px;
+}
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+  font-size: 13px;
+}
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+  font-size: 12px;
+}
+p {
+  margin: 0 0 9px;
+}
+.lead {
+  margin-bottom: 18px;
+  font-size: 14px;
+  font-weight: 300;
+  line-height: 1.4;
+}
+@media (min-width: 768px) {
+  .lead {
+    font-size: 19.5px;
+  }
+}
+small,
+.small {
+  font-size: 92%;
+}
+mark,
+.mark {
+  background-color: #fcf8e3;
+  padding: .2em;
+}
+.text-left {
+  text-align: left;
+}
+.text-right {
+  text-align: right;
+}
+.text-center {
+  text-align: center;
+}
+.text-justify {
+  text-align: justify;
+}
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+  white-space: nowrap;
+}
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+  text-transform: lowercase;
+}
+.text-uppercase {
+  text-transform: uppercase;
+}
+.text-capitalize {
+  text-transform: capitalize;
+}
+.text-muted {
+  color: #777777;
+}
+.text-primary {
+  color: #337ab7;
+}
+a.text-primary:hover,
+a.text-primary:focus {
+  color: #286090;
+}
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+  color: #3c763d;
+}
+a.text-success:hover,
+a.text-success:focus {
+  color: #2b542c;
+}
+.text-info {
+  color: #31708f;
+}
+a.text-info:hover,
+a.text-info:focus {
+  color: #245269;
+}
+.text-warning {
+  color: #8a6d3b;
+}
+a.text-warning:hover,
+a.text-warning:focus {
+  color: #66512c;
+}
+.text-danger {
+  color: #a94442;
+}
+a.text-danger:hover,
+a.text-danger:focus {
+  color: #843534;
+}
+.bg-primary {
+  color: #fff;
+  background-color: #337ab7;
+}
+a.bg-primary:hover,
+a.bg-primary:focus {
+  background-color: #286090;
+}
+.bg-success {
+  background-color: #dff0d8;
+}
+a.bg-success:hover,
+a.bg-success:focus {
+  background-color: #c1e2b3;
+}
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+  background-color: #d9edf7;
+}
+a.bg-info:hover,
+a.bg-info:focus {
+  background-color: #afd9ee;
+}
+.bg-warning {
+  background-color: #fcf8e3;
+}
+a.bg-warning:hover,
+a.bg-warning:focus {
+  background-color: #f7ecb5;
+}
+.bg-danger {
+  background-color: #f2dede;
+}
+a.bg-danger:hover,
+a.bg-danger:focus {
+  background-color: #e4b9b9;
+}
+.page-header {
+  padding-bottom: 8px;
+  margin: 36px 0 18px;
+  border-bottom: 1px solid #eeeeee;
+}
+ul,
+ol {
+  margin-top: 0;
+  margin-bottom: 9px;
+}
+ul ul,
+ol ul,
+ul ol,
+ol ol {
+  margin-bottom: 0;
+}
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+  padding-left: 0;
+  list-style: none;
+}
+.list-inline {
+  padding-left: 0;
+  list-style: none;
+  margin-left: -5px;
+}
+.list-inline > li {
+  display: inline-block;
+  padding-left: 5px;
+  padding-right: 5px;
+}
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+  margin-top: 0;
+  margin-bottom: 18px;
+}
+dt,
+dd {
+  line-height: 1.42857143;
+}
+dt {
+  font-weight: bold;
+}
+dd {
+  margin-left: 0;
+}
+@media (min-width: 541px) {
+  .dl-horizontal dt {
+    float: left;
+    width: 160px;
+    clear: left;
+    text-align: right;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+  }
+  .dl-horizontal dd {
+    margin-left: 180px;
+  }
+}
+abbr[title],
+abbr[data-original-title] {
+  cursor: help;
+  border-bottom: 1px dotted #777777;
+}
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+  font-size: 90%;
+  text-transform: uppercase;
+}
+blockquote {
+  padding: 9px 18px;
+  margin: 0 0 18px;
+  font-size: inherit;
+  border-left: 5px solid #eeeeee;
+}
+blockquote p:last-child,
+blockquote ul:last-child,
+blockquote ol:last-child {
+  margin-bottom: 0;
+}
+blockquote footer,
+blockquote small,
+blockquote .small {
+  display: block;
+  font-size: 80%;
+  line-height: 1.42857143;
+  color: #777777;
+}
+blockquote footer:before,
+blockquote small:before,
+blockquote .small:before {
+  content: '\2014 \00A0';
+}
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+blockquote.pull-right {
+  padding-right: 15px;
+  padding-left: 0;
+  border-right: 5px solid #eeeeee;
+  border-left: 0;
+  text-align: right;
+}
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+blockquote.pull-right footer:before,
+.blockquote-reverse small:before,
+blockquote.pull-right small:before,
+.blockquote-reverse .small:before,
+blockquote.pull-right .small:before {
+  content: '';
+}
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+blockquote.pull-right footer:after,
+.blockquote-reverse small:after,
+blockquote.pull-right small:after,
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+blockquote.pull-right .small:after {
+  content: '\00A0 \2014';
+}
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+  margin-bottom: 18px;
+  font-style: normal;
+  line-height: 1.42857143;
+}
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+pre,
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+  font-family: monospace;
+}
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+  font-size: 90%;
+  color: #c7254e;
+  background-color: #f9f2f4;
+  border-radius: 2px;
+}
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+  font-size: 90%;
+  color: #888;
+  background-color: transparent;
+  border-radius: 1px;
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
+}
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+  padding: 0;
+  font-size: 100%;
+  font-weight: bold;
+  box-shadow: none;
+}
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+  padding: 8.5px;
+  margin: 0 0 9px;
+  font-size: 12px;
+  line-height: 1.42857143;
+  word-break: break-all;
+  word-wrap: break-word;
+  color: #333333;
+  background-color: #f5f5f5;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+}
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+  padding: 0;
+  font-size: inherit;
+  color: inherit;
+  white-space: pre-wrap;
+  background-color: transparent;
+  border-radius: 0;
+}
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+  max-height: 340px;
+  overflow-y: scroll;
+}
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+  margin-right: auto;
+  margin-left: auto;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+@media (min-width: 768px) {
+  .container {
+    width: 768px;
+  }
+}
+@media (min-width: 992px) {
+  .container {
+    width: 940px;
+  }
+}
+@media (min-width: 1200px) {
+  .container {
+    width: 1140px;
+  }
+}
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+  margin-right: auto;
+  margin-left: auto;
+  padding-left: 0px;
+  padding-right: 0px;
+}
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+}
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+  position: relative;
+  min-height: 1px;
+  padding-left: 0px;
+  padding-right: 0px;
+}
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+  float: left;
+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+  width: 16.66666667%;
+}
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+  width: 8.33333333%;
+}
+.col-xs-pull-12 {
+  right: 100%;
+}
+.col-xs-pull-11 {
+  right: 91.66666667%;
+}
+.col-xs-pull-10 {
+  right: 83.33333333%;
+}
+.col-xs-pull-9 {
+  right: 75%;
+}
+.col-xs-pull-8 {
+  right: 66.66666667%;
+}
+.col-xs-pull-7 {
+  right: 58.33333333%;
+}
+.col-xs-pull-6 {
+  right: 50%;
+}
+.col-xs-pull-5 {
+  right: 41.66666667%;
+}
+.col-xs-pull-4 {
+  right: 33.33333333%;
+}
+.col-xs-pull-3 {
+  right: 25%;
+}
+.col-xs-pull-2 {
+  right: 16.66666667%;
+}
+.col-xs-pull-1 {
+  right: 8.33333333%;
+}
+.col-xs-pull-0 {
+  right: auto;
+}
+.col-xs-push-12 {
+  left: 100%;
+}
+.col-xs-push-11 {
+  left: 91.66666667%;
+}
+.col-xs-push-10 {
+  left: 83.33333333%;
+}
+.col-xs-push-9 {
+  left: 75%;
+}
+.col-xs-push-8 {
+  left: 66.66666667%;
+}
+.col-xs-push-7 {
+  left: 58.33333333%;
+}
+.col-xs-push-6 {
+  left: 50%;
+}
+.col-xs-push-5 {
+  left: 41.66666667%;
+}
+.col-xs-push-4 {
+  left: 33.33333333%;
+}
+.col-xs-push-3 {
+  left: 25%;
+}
+.col-xs-push-2 {
+  left: 16.66666667%;
+}
+.col-xs-push-1 {
+  left: 8.33333333%;
+}
+.col-xs-push-0 {
+  left: auto;
+}
+.col-xs-offset-12 {
+  margin-left: 100%;
+}
+.col-xs-offset-11 {
+  margin-left: 91.66666667%;
+}
+.col-xs-offset-10 {
+  margin-left: 83.33333333%;
+}
+.col-xs-offset-9 {
+  margin-left: 75%;
+}
+.col-xs-offset-8 {
+  margin-left: 66.66666667%;
+}
+.col-xs-offset-7 {
+  margin-left: 58.33333333%;
+}
+.col-xs-offset-6 {
+  margin-left: 50%;
+}
+.col-xs-offset-5 {
+  margin-left: 41.66666667%;
+}
+.col-xs-offset-4 {
+  margin-left: 33.33333333%;
+}
+.col-xs-offset-3 {
+  margin-left: 25%;
+}
+.col-xs-offset-2 {
+  margin-left: 16.66666667%;
+}
+.col-xs-offset-1 {
+  margin-left: 8.33333333%;
+}
+.col-xs-offset-0 {
+  margin-left: 0%;
+}
+@media (min-width: 768px) {
+  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
+    float: left;
+  }
+  .col-sm-12 {
+    width: 100%;
+  }
+  .col-sm-11 {
+    width: 91.66666667%;
+  }
+  .col-sm-10 {
+    width: 83.33333333%;
+  }
+  .col-sm-9 {
+    width: 75%;
+  }
+  .col-sm-8 {
+    width: 66.66666667%;
+  }
+  .col-sm-7 {
+    width: 58.33333333%;
+  }
+  .col-sm-6 {
+    width: 50%;
+  }
+  .col-sm-5 {
+    width: 41.66666667%;
+  }
+  .col-sm-4 {
+    width: 33.33333333%;
+  }
+  .col-sm-3 {
+    width: 25%;
+  }
+  .col-sm-2 {
+    width: 16.66666667%;
+  }
+  .col-sm-1 {
+    width: 8.33333333%;
+  }
+  .col-sm-pull-12 {
+    right: 100%;
+  }
+  .col-sm-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-sm-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-sm-pull-9 {
+    right: 75%;
+  }
+  .col-sm-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-sm-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-sm-pull-6 {
+    right: 50%;
+  }
+  .col-sm-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-sm-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-sm-pull-3 {
+    right: 25%;
+  }
+  .col-sm-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-sm-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-sm-pull-0 {
+    right: auto;
+  }
+  .col-sm-push-12 {
+    left: 100%;
+  }
+  .col-sm-push-11 {
+    left: 91.66666667%;
+  }
+  .col-sm-push-10 {
+    left: 83.33333333%;
+  }
+  .col-sm-push-9 {
+    left: 75%;
+  }
+  .col-sm-push-8 {
+    left: 66.66666667%;
+  }
+  .col-sm-push-7 {
+    left: 58.33333333%;
+  }
+  .col-sm-push-6 {
+    left: 50%;
+  }
+  .col-sm-push-5 {
+    left: 41.66666667%;
+  }
+  .col-sm-push-4 {
+    left: 33.33333333%;
+  }
+  .col-sm-push-3 {
+    left: 25%;
+  }
+  .col-sm-push-2 {
+    left: 16.66666667%;
+  }
+  .col-sm-push-1 {
+    left: 8.33333333%;
+  }
+  .col-sm-push-0 {
+    left: auto;
+  }
+  .col-sm-offset-12 {
+    margin-left: 100%;
+  }
+  .col-sm-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-sm-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-sm-offset-9 {
+    margin-left: 75%;
+  }
+  .col-sm-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-sm-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-sm-offset-6 {
+    margin-left: 50%;
+  }
+  .col-sm-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-sm-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-sm-offset-3 {
+    margin-left: 25%;
+  }
+  .col-sm-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-sm-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-sm-offset-0 {
+    margin-left: 0%;
+  }
+}
+@media (min-width: 992px) {
+  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
+    float: left;
+  }
+  .col-md-12 {
+    width: 100%;
+  }
+  .col-md-11 {
+    width: 91.66666667%;
+  }
+  .col-md-10 {
+    width: 83.33333333%;
+  }
+  .col-md-9 {
+    width: 75%;
+  }
+  .col-md-8 {
+    width: 66.66666667%;
+  }
+  .col-md-7 {
+    width: 58.33333333%;
+  }
+  .col-md-6 {
+    width: 50%;
+  }
+  .col-md-5 {
+    width: 41.66666667%;
+  }
+  .col-md-4 {
+    width: 33.33333333%;
+  }
+  .col-md-3 {
+    width: 25%;
+  }
+  .col-md-2 {
+    width: 16.66666667%;
+  }
+  .col-md-1 {
+    width: 8.33333333%;
+  }
+  .col-md-pull-12 {
+    right: 100%;
+  }
+  .col-md-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-md-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-md-pull-9 {
+    right: 75%;
+  }
+  .col-md-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-md-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-md-pull-6 {
+    right: 50%;
+  }
+  .col-md-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-md-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-md-pull-3 {
+    right: 25%;
+  }
+  .col-md-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-md-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-md-pull-0 {
+    right: auto;
+  }
+  .col-md-push-12 {
+    left: 100%;
+  }
+  .col-md-push-11 {
+    left: 91.66666667%;
+  }
+  .col-md-push-10 {
+    left: 83.33333333%;
+  }
+  .col-md-push-9 {
+    left: 75%;
+  }
+  .col-md-push-8 {
+    left: 66.66666667%;
+  }
+  .col-md-push-7 {
+    left: 58.33333333%;
+  }
+  .col-md-push-6 {
+    left: 50%;
+  }
+  .col-md-push-5 {
+    left: 41.66666667%;
+  }
+  .col-md-push-4 {
+    left: 33.33333333%;
+  }
+  .col-md-push-3 {
+    left: 25%;
+  }
+  .col-md-push-2 {
+    left: 16.66666667%;
+  }
+  .col-md-push-1 {
+    left: 8.33333333%;
+  }
+  .col-md-push-0 {
+    left: auto;
+  }
+  .col-md-offset-12 {
+    margin-left: 100%;
+  }
+  .col-md-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-md-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-md-offset-9 {
+    margin-left: 75%;
+  }
+  .col-md-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-md-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-md-offset-6 {
+    margin-left: 50%;
+  }
+  .col-md-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-md-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-md-offset-3 {
+    margin-left: 25%;
+  }
+  .col-md-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-md-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-md-offset-0 {
+    margin-left: 0%;
+  }
+}
+@media (min-width: 1200px) {
+  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
+    float: left;
+  }
+  .col-lg-12 {
+    width: 100%;
+  }
+  .col-lg-11 {
+    width: 91.66666667%;
+  }
+  .col-lg-10 {
+    width: 83.33333333%;
+  }
+  .col-lg-9 {
+    width: 75%;
+  }
+  .col-lg-8 {
+    width: 66.66666667%;
+  }
+  .col-lg-7 {
+    width: 58.33333333%;
+  }
+  .col-lg-6 {
+    width: 50%;
+  }
+  .col-lg-5 {
+    width: 41.66666667%;
+  }
+  .col-lg-4 {
+    width: 33.33333333%;
+  }
+  .col-lg-3 {
+    width: 25%;
+  }
+  .col-lg-2 {
+    width: 16.66666667%;
+  }
+  .col-lg-1 {
+    width: 8.33333333%;
+  }
+  .col-lg-pull-12 {
+    right: 100%;
+  }
+  .col-lg-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-lg-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-lg-pull-9 {
+    right: 75%;
+  }
+  .col-lg-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-lg-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-lg-pull-6 {
+    right: 50%;
+  }
+  .col-lg-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-lg-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-lg-pull-3 {
+    right: 25%;
+  }
+  .col-lg-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-lg-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-lg-pull-0 {
+    right: auto;
+  }
+  .col-lg-push-12 {
+    left: 100%;
+  }
+  .col-lg-push-11 {
+    left: 91.66666667%;
+  }
+  .col-lg-push-10 {
+    left: 83.33333333%;
+  }
+  .col-lg-push-9 {
+    left: 75%;
+  }
+  .col-lg-push-8 {
+    left: 66.66666667%;
+  }
+  .col-lg-push-7 {
+    left: 58.33333333%;
+  }
+  .col-lg-push-6 {
+    left: 50%;
+  }
+  .col-lg-push-5 {
+    left: 41.66666667%;
+  }
+  .col-lg-push-4 {
+    left: 33.33333333%;
+  }
+  .col-lg-push-3 {
+    left: 25%;
+  }
+  .col-lg-push-2 {
+    left: 16.66666667%;
+  }
+  .col-lg-push-1 {
+    left: 8.33333333%;
+  }
+  .col-lg-push-0 {
+    left: auto;
+  }
+  .col-lg-offset-12 {
+    margin-left: 100%;
+  }
+  .col-lg-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-lg-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-lg-offset-9 {
+    margin-left: 75%;
+  }
+  .col-lg-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-lg-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-lg-offset-6 {
+    margin-left: 50%;
+  }
+  .col-lg-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-lg-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-lg-offset-3 {
+    margin-left: 25%;
+  }
+  .col-lg-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-lg-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-lg-offset-0 {
+    margin-left: 0%;
+  }
+}
+table {
+  background-color: transparent;
+}
+caption {
+  padding-top: 8px;
+  padding-bottom: 8px;
+  color: #777777;
+  text-align: left;
+}
+th {
+  text-align: left;
+}
+.table {
+  width: 100%;
+  max-width: 100%;
+  margin-bottom: 18px;
+}
+.table > thead > tr > th,
+.table > tbody > tr > th,
+.table > tfoot > tr > th,
+.table > thead > tr > td,
+.table > tbody > tr > td,
+.table > tfoot > tr > td {
+  padding: 8px;
+  line-height: 1.42857143;
+  vertical-align: top;
+  border-top: 1px solid #ddd;
+}
+.table > thead > tr > th {
+  vertical-align: bottom;
+  border-bottom: 2px solid #ddd;
+}
+.table > caption + thead > tr:first-child > th,
+.table > colgroup + thead > tr:first-child > th,
+.table > thead:first-child > tr:first-child > th,
+.table > caption + thead > tr:first-child > td,
+.table > colgroup + thead > tr:first-child > td,
+.table > thead:first-child > tr:first-child > td {
+  border-top: 0;
+}
+.table > tbody + tbody {
+  border-top: 2px solid #ddd;
+}
+.table .table {
+  background-color: #fff;
+}
+.table-condensed > thead > tr > th,
+.table-condensed > tbody > tr > th,
+.table-condensed > tfoot > tr > th,
+.table-condensed > thead > tr > td,
+.table-condensed > tbody > tr > td,
+.table-condensed > tfoot > tr > td {
+  padding: 5px;
+}
+.table-bordered {
+  border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > tbody > tr > th,
+.table-bordered > tfoot > tr > th,
+.table-bordered > thead > tr > td,
+.table-bordered > tbody > tr > td,
+.table-bordered > tfoot > tr > td {
+  border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > thead > tr > td {
+  border-bottom-width: 2px;
+}
+.table-striped > tbody > tr:nth-of-type(odd) {
+  background-color: #f9f9f9;
+}
+.table-hover > tbody > tr:hover {
+  background-color: #f5f5f5;
+}
+table col[class*="col-"] {
+  position: static;
+  float: none;
+  display: table-column;
+}
+table td[class*="col-"],
+table th[class*="col-"] {
+  position: static;
+  float: none;
+  display: table-cell;
+}
+.table > thead > tr > td.active,
+.table > tbody > tr > td.active,
+.table > tfoot > tr > td.active,
+.table > thead > tr > th.active,
+.table > tbody > tr > th.active,
+.table > tfoot > tr > th.active,
+.table > thead > tr.active > td,
+.table > tbody > tr.active > td,
+.table > tfoot > tr.active > td,
+.table > thead > tr.active > th,
+.table > tbody > tr.active > th,
+.table > tfoot > tr.active > th {
+  background-color: #f5f5f5;
+}
+.table-hover > tbody > tr > td.active:hover,
+.table-hover > tbody > tr > th.active:hover,
+.table-hover > tbody > tr.active:hover > td,
+.table-hover > tbody > tr:hover > .active,
+.table-hover > tbody > tr.active:hover > th {
+  background-color: #e8e8e8;
+}
+.table > thead > tr > td.success,
+.table > tbody > tr > td.success,
+.table > tfoot > tr > td.success,
+.table > thead > tr > th.success,
+.table > tbody > tr > th.success,
+.table > tfoot > tr > th.success,
+.table > thead > tr.success > td,
+.table > tbody > tr.success > td,
+.table > tfoot > tr.success > td,
+.table > thead > tr.success > th,
+.table > tbody > tr.success > th,
+.table > tfoot > tr.success > th {
+  background-color: #dff0d8;
+}
+.table-hover > tbody > tr > td.success:hover,
+.table-hover > tbody > tr > th.success:hover,
+.table-hover > tbody > tr.success:hover > td,
+.table-hover > tbody > tr:hover > .success,
+.table-hover > tbody > tr.success:hover > th {
+  background-color: #d0e9c6;
+}
+.table > thead > tr > td.info,
+.table > tbody > tr > td.info,
+.table > tfoot > tr > td.info,
+.table > thead > tr > th.info,
+.table > tbody > tr > th.info,
+.table > tfoot > tr > th.info,
+.table > thead > tr.info > td,
+.table > tbody > tr.info > td,
+.table > tfoot > tr.info > td,
+.table > thead > tr.info > th,
+.table > tbody > tr.info > th,
+.table > tfoot > tr.info > th {
+  background-color: #d9edf7;
+}
+.table-hover > tbody > tr > td.info:hover,
+.table-hover > tbody > tr > th.info:hover,
+.table-hover > tbody > tr.info:hover > td,
+.table-hover > tbody > tr:hover > .info,
+.table-hover > tbody > tr.info:hover > th {
+  background-color: #c4e3f3;
+}
+.table > thead > tr > td.warning,
+.table > tbody > tr > td.warning,
+.table > tfoot > tr > td.warning,
+.table > thead > tr > th.warning,
+.table > tbody > tr > th.warning,
+.table > tfoot > tr > th.warning,
+.table > thead > tr.warning > td,
+.table > tbody > tr.warning > td,
+.table > tfoot > tr.warning > td,
+.table > thead > tr.warning > th,
+.table > tbody > tr.warning > th,
+.table > tfoot > tr.warning > th {
+  background-color: #fcf8e3;
+}
+.table-hover > tbody > tr > td.warning:hover,
+.table-hover > tbody > tr > th.warning:hover,
+.table-hover > tbody > tr.warning:hover > td,
+.table-hover > tbody > tr:hover > .warning,
+.table-hover > tbody > tr.warning:hover > th {
+  background-color: #faf2cc;
+}
+.table > thead > tr > td.danger,
+.table > tbody > tr > td.danger,
+.table > tfoot > tr > td.danger,
+.table > thead > tr > th.danger,
+.table > tbody > tr > th.danger,
+.table > tfoot > tr > th.danger,
+.table > thead > tr.danger > td,
+.table > tbody > tr.danger > td,
+.table > tfoot > tr.danger > td,
+.table > thead > tr.danger > th,
+.table > tbody > tr.danger > th,
+.table > tfoot > tr.danger > th {
+  background-color: #f2dede;
+}
+.table-hover > tbody > tr > td.danger:hover,
+.table-hover > tbody > tr > th.danger:hover,
+.table-hover > tbody > tr.danger:hover > td,
+.table-hover > tbody > tr:hover > .danger,
+.table-hover > tbody > tr.danger:hover > th {
+  background-color: #ebcccc;
+}
+.table-responsive {
+  overflow-x: auto;
+  min-height: 0.01%;
+}
+@media screen and (max-width: 767px) {
+  .table-responsive {
+    width: 100%;
+    margin-bottom: 13.5px;
+    overflow-y: hidden;
+    -ms-overflow-style: -ms-autohiding-scrollbar;
+    border: 1px solid #ddd;
+  }
+  .table-responsive > .table {
+    margin-bottom: 0;
+  }
+  .table-responsive > .table > thead > tr > th,
+  .table-responsive > .table > tbody > tr > th,
+  .table-responsive > .table > tfoot > tr > th,
+  .table-responsive > .table > thead > tr > td,
+  .table-responsive > .table > tbody > tr > td,
+  .table-responsive > .table > tfoot > tr > td {
+    white-space: nowrap;
+  }
+  .table-responsive > .table-bordered {
+    border: 0;
+  }
+  .table-responsive > .table-bordered > thead > tr > th:first-child,
+  .table-responsive > .table-bordered > tbody > tr > th:first-child,
+  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+  .table-responsive > .table-bordered > thead > tr > td:first-child,
+  .table-responsive > .table-bordered > tbody > tr > td:first-child,
+  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+    border-left: 0;
+  }
+  .table-responsive > .table-bordered > thead > tr > th:last-child,
+  .table-responsive > .table-bordered > tbody > tr > th:last-child,
+  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+  .table-responsive > .table-bordered > thead > tr > td:last-child,
+  .table-responsive > .table-bordered > tbody > tr > td:last-child,
+  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+    border-right: 0;
+  }
+  .table-responsive > .table-bordered > tbody > tr:last-child > th,
+  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
+  .table-responsive > .table-bordered > tbody > tr:last-child > td,
+  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
+    border-bottom: 0;
+  }
+}
+fieldset {
+  padding: 0;
+  margin: 0;
+  border: 0;
+  min-width: 0;
+}
+legend {
+  display: block;
+  width: 100%;
+  padding: 0;
+  margin-bottom: 18px;
+  font-size: 19.5px;
+  line-height: inherit;
+  color: #333333;
+  border: 0;
+  border-bottom: 1px solid #e5e5e5;
+}
+label {
+  display: inline-block;
+  max-width: 100%;
+  margin-bottom: 5px;
+  font-weight: bold;
+}
+input[type="search"] {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+input[type="radio"],
+input[type="checkbox"] {
+  margin: 4px 0 0;
+  margin-top: 1px \9;
+  line-height: normal;
+}
+input[type="file"] {
+  display: block;
+}
+input[type="range"] {
+  display: block;
+  width: 100%;
+}
+select[multiple],
+select[size] {
+  height: auto;
+}
+input[type="file"]:focus,
+input[type="radio"]:focus,
+input[type="checkbox"]:focus {
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+output {
+  display: block;
+  padding-top: 7px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+}
+.form-control {
+  display: block;
+  width: 100%;
+  height: 32px;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+  background-color: #fff;
+  background-image: none;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+}
+.form-control:focus {
+  border-color: #66afe9;
+  outline: 0;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.form-control::-moz-placeholder {
+  color: #999;
+  opacity: 1;
+}
+.form-control:-ms-input-placeholder {
+  color: #999;
+}
+.form-control::-webkit-input-placeholder {
+  color: #999;
+}
+.form-control::-ms-expand {
+  border: 0;
+  background-color: transparent;
+}
+.form-control[disabled],
+.form-control[readonly],
+fieldset[disabled] .form-control {
+  background-color: #eeeeee;
+  opacity: 1;
+}
+.form-control[disabled],
+fieldset[disabled] .form-control {
+  cursor: not-allowed;
+}
+textarea.form-control {
+  height: auto;
+}
+input[type="search"] {
+  -webkit-appearance: none;
+}
+@media screen and (-webkit-min-device-pixel-ratio: 0) {
+  input[type="date"].form-control,
+  input[type="time"].form-control,
+  input[type="datetime-local"].form-control,
+  input[type="month"].form-control {
+    line-height: 32px;
+  }
+  input[type="date"].input-sm,
+  input[type="time"].input-sm,
+  input[type="datetime-local"].input-sm,
+  input[type="month"].input-sm,
+  .input-group-sm input[type="date"],
+  .input-group-sm input[type="time"],
+  .input-group-sm input[type="datetime-local"],
+  .input-group-sm input[type="month"] {
+    line-height: 30px;
+  }
+  input[type="date"].input-lg,
+  input[type="time"].input-lg,
+  input[type="datetime-local"].input-lg,
+  input[type="month"].input-lg,
+  .input-group-lg input[type="date"],
+  .input-group-lg input[type="time"],
+  .input-group-lg input[type="datetime-local"],
+  .input-group-lg input[type="month"] {
+    line-height: 45px;
+  }
+}
+.form-group {
+  margin-bottom: 15px;
+}
+.radio,
+.checkbox {
+  position: relative;
+  display: block;
+  margin-top: 10px;
+  margin-bottom: 10px;
+}
+.radio label,
+.checkbox label {
+  min-height: 18px;
+  padding-left: 20px;
+  margin-bottom: 0;
+  font-weight: normal;
+  cursor: pointer;
+}
+.radio input[type="radio"],
+.radio-inline input[type="radio"],
+.checkbox input[type="checkbox"],
+.checkbox-inline input[type="checkbox"] {
+  position: absolute;
+  margin-left: -20px;
+  margin-top: 4px \9;
+}
+.radio + .radio,
+.checkbox + .checkbox {
+  margin-top: -5px;
+}
+.radio-inline,
+.checkbox-inline {
+  position: relative;
+  display: inline-block;
+  padding-left: 20px;
+  margin-bottom: 0;
+  vertical-align: middle;
+  font-weight: normal;
+  cursor: pointer;
+}
+.radio-inline + .radio-inline,
+.checkbox-inline + .checkbox-inline {
+  margin-top: 0;
+  margin-left: 10px;
+}
+input[type="radio"][disabled],
+input[type="checkbox"][disabled],
+input[type="radio"].disabled,
+input[type="checkbox"].disabled,
+fieldset[disabled] input[type="radio"],
+fieldset[disabled] input[type="checkbox"] {
+  cursor: not-allowed;
+}
+.radio-inline.disabled,
+.checkbox-inline.disabled,
+fieldset[disabled] .radio-inline,
+fieldset[disabled] .checkbox-inline {
+  cursor: not-allowed;
+}
+.radio.disabled label,
+.checkbox.disabled label,
+fieldset[disabled] .radio label,
+fieldset[disabled] .checkbox label {
+  cursor: not-allowed;
+}
+.form-control-static {
+  padding-top: 7px;
+  padding-bottom: 7px;
+  margin-bottom: 0;
+  min-height: 31px;
+}
+.form-control-static.input-lg,
+.form-control-static.input-sm {
+  padding-left: 0;
+  padding-right: 0;
+}
+.input-sm {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+select.input-sm {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.input-sm,
+select[multiple].input-sm {
+  height: auto;
+}
+.form-group-sm .form-control {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.form-group-sm select.form-control {
+  height: 30px;
+  line-height: 30px;
+}
+.form-group-sm textarea.form-control,
+.form-group-sm select[multiple].form-control {
+  height: auto;
+}
+.form-group-sm .form-control-static {
+  height: 30px;
+  min-height: 30px;
+  padding: 6px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+}
+.input-lg {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+select.input-lg {
+  height: 45px;
+  line-height: 45px;
+}
+textarea.input-lg,
+select[multiple].input-lg {
+  height: auto;
+}
+.form-group-lg .form-control {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+.form-group-lg select.form-control {
+  height: 45px;
+  line-height: 45px;
+}
+.form-group-lg textarea.form-control,
+.form-group-lg select[multiple].form-control {
+  height: auto;
+}
+.form-group-lg .form-control-static {
+  height: 45px;
+  min-height: 35px;
+  padding: 11px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+}
+.has-feedback {
+  position: relative;
+}
+.has-feedback .form-control {
+  padding-right: 40px;
+}
+.form-control-feedback {
+  position: absolute;
+  top: 0;
+  right: 0;
+  z-index: 2;
+  display: block;
+  width: 32px;
+  height: 32px;
+  line-height: 32px;
+  text-align: center;
+  pointer-events: none;
+}
+.input-lg + .form-control-feedback,
+.input-group-lg + .form-control-feedback,
+.form-group-lg .form-control + .form-control-feedback {
+  width: 45px;
+  height: 45px;
+  line-height: 45px;
+}
+.input-sm + .form-control-feedback,
+.input-group-sm + .form-control-feedback,
+.form-group-sm .form-control + .form-control-feedback {
+  width: 30px;
+  height: 30px;
+  line-height: 30px;
+}
+.has-success .help-block,
+.has-success .control-label,
+.has-success .radio,
+.has-success .checkbox,
+.has-success .radio-inline,
+.has-success .checkbox-inline,
+.has-success.radio label,
+.has-success.checkbox label,
+.has-success.radio-inline label,
+.has-success.checkbox-inline label {
+  color: #3c763d;
+}
+.has-success .form-control {
+  border-color: #3c763d;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-success .form-control:focus {
+  border-color: #2b542c;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+}
+.has-success .input-group-addon {
+  color: #3c763d;
+  border-color: #3c763d;
+  background-color: #dff0d8;
+}
+.has-success .form-control-feedback {
+  color: #3c763d;
+}
+.has-warning .help-block,
+.has-warning .control-label,
+.has-warning .radio,
+.has-warning .checkbox,
+.has-warning .radio-inline,
+.has-warning .checkbox-inline,
+.has-warning.radio label,
+.has-warning.checkbox label,
+.has-warning.radio-inline label,
+.has-warning.checkbox-inline label {
+  color: #8a6d3b;
+}
+.has-warning .form-control {
+  border-color: #8a6d3b;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-warning .form-control:focus {
+  border-color: #66512c;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+}
+.has-warning .input-group-addon {
+  color: #8a6d3b;
+  border-color: #8a6d3b;
+  background-color: #fcf8e3;
+}
+.has-warning .form-control-feedback {
+  color: #8a6d3b;
+}
+.has-error .help-block,
+.has-error .control-label,
+.has-error .radio,
+.has-error .checkbox,
+.has-error .radio-inline,
+.has-error .checkbox-inline,
+.has-error.radio label,
+.has-error.checkbox label,
+.has-error.radio-inline label,
+.has-error.checkbox-inline label {
+  color: #a94442;
+}
+.has-error .form-control {
+  border-color: #a94442;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-error .form-control:focus {
+  border-color: #843534;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+}
+.has-error .input-group-addon {
+  color: #a94442;
+  border-color: #a94442;
+  background-color: #f2dede;
+}
+.has-error .form-control-feedback {
+  color: #a94442;
+}
+.has-feedback label ~ .form-control-feedback {
+  top: 23px;
+}
+.has-feedback label.sr-only ~ .form-control-feedback {
+  top: 0;
+}
+.help-block {
+  display: block;
+  margin-top: 5px;
+  margin-bottom: 10px;
+  color: #404040;
+}
+@media (min-width: 768px) {
+  .form-inline .form-group {
+    display: inline-block;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .form-inline .form-control-static {
+    display: inline-block;
+  }
+  .form-inline .input-group {
+    display: inline-table;
+    vertical-align: middle;
+  }
+  .form-inline .input-group .input-group-addon,
+  .form-inline .input-group .input-group-btn,
+  .form-inline .input-group .form-control {
+    width: auto;
+  }
+  .form-inline .input-group > .form-control {
+    width: 100%;
+  }
+  .form-inline .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .radio,
+  .form-inline .checkbox {
+    display: inline-block;
+    margin-top: 0;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .radio label,
+  .form-inline .checkbox label {
+    padding-left: 0;
+  }
+  .form-inline .radio input[type="radio"],
+  .form-inline .checkbox input[type="checkbox"] {
+    position: relative;
+    margin-left: 0;
+  }
+  .form-inline .has-feedback .form-control-feedback {
+    top: 0;
+  }
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox,
+.form-horizontal .radio-inline,
+.form-horizontal .checkbox-inline {
+  margin-top: 0;
+  margin-bottom: 0;
+  padding-top: 7px;
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox {
+  min-height: 25px;
+}
+.form-horizontal .form-group {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+@media (min-width: 768px) {
+  .form-horizontal .control-label {
+    text-align: right;
+    margin-bottom: 0;
+    padding-top: 7px;
+  }
+}
+.form-horizontal .has-feedback .form-control-feedback {
+  right: 0px;
+}
+@media (min-width: 768px) {
+  .form-horizontal .form-group-lg .control-label {
+    padding-top: 11px;
+    font-size: 17px;
+  }
+}
+@media (min-width: 768px) {
+  .form-horizontal .form-group-sm .control-label {
+    padding-top: 6px;
+    font-size: 12px;
+  }
+}
+.btn {
+  display: inline-block;
+  margin-bottom: 0;
+  font-weight: normal;
+  text-align: center;
+  vertical-align: middle;
+  touch-action: manipulation;
+  cursor: pointer;
+  background-image: none;
+  border: 1px solid transparent;
+  white-space: nowrap;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  border-radius: 2px;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+.btn:focus,
+.btn:active:focus,
+.btn.active:focus,
+.btn.focus,
+.btn:active.focus,
+.btn.active.focus {
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+.btn:hover,
+.btn:focus,
+.btn.focus {
+  color: #333;
+  text-decoration: none;
+}
+.btn:active,
+.btn.active {
+  outline: 0;
+  background-image: none;
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn.disabled,
+.btn[disabled],
+fieldset[disabled] .btn {
+  cursor: not-allowed;
+  opacity: 0.65;
+  filter: alpha(opacity=65);
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+a.btn.disabled,
+fieldset[disabled] a.btn {
+  pointer-events: none;
+}
+.btn-default {
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+.btn-default:focus,
+.btn-default.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+.btn-default:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.btn-default:active:hover,
+.btn-default.active:hover,
+.open > .dropdown-toggle.btn-default:hover,
+.btn-default:active:focus,
+.btn-default.active:focus,
+.open > .dropdown-toggle.btn-default:focus,
+.btn-default:active.focus,
+.btn-default.active.focus,
+.open > .dropdown-toggle.btn-default.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+  background-image: none;
+}
+.btn-default.disabled:hover,
+.btn-default[disabled]:hover,
+fieldset[disabled] .btn-default:hover,
+.btn-default.disabled:focus,
+.btn-default[disabled]:focus,
+fieldset[disabled] .btn-default:focus,
+.btn-default.disabled.focus,
+.btn-default[disabled].focus,
+fieldset[disabled] .btn-default.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+.btn-default .badge {
+  color: #fff;
+  background-color: #333;
+}
+.btn-primary {
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #2e6da4;
+}
+.btn-primary:focus,
+.btn-primary.focus {
+  color: #fff;
+  background-color: #286090;
+  border-color: #122b40;
+}
+.btn-primary:hover {
+  color: #fff;
+  background-color: #286090;
+  border-color: #204d74;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+  color: #fff;
+  background-color: #286090;
+  border-color: #204d74;
+}
+.btn-primary:active:hover,
+.btn-primary.active:hover,
+.open > .dropdown-toggle.btn-primary:hover,
+.btn-primary:active:focus,
+.btn-primary.active:focus,
+.open > .dropdown-toggle.btn-primary:focus,
+.btn-primary:active.focus,
+.btn-primary.active.focus,
+.open > .dropdown-toggle.btn-primary.focus {
+  color: #fff;
+  background-color: #204d74;
+  border-color: #122b40;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+  background-image: none;
+}
+.btn-primary.disabled:hover,
+.btn-primary[disabled]:hover,
+fieldset[disabled] .btn-primary:hover,
+.btn-primary.disabled:focus,
+.btn-primary[disabled]:focus,
+fieldset[disabled] .btn-primary:focus,
+.btn-primary.disabled.focus,
+.btn-primary[disabled].focus,
+fieldset[disabled] .btn-primary.focus {
+  background-color: #337ab7;
+  border-color: #2e6da4;
+}
+.btn-primary .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.btn-success {
+  color: #fff;
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.btn-success:focus,
+.btn-success.focus {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #255625;
+}
+.btn-success:hover {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.btn-success:active:hover,
+.btn-success.active:hover,
+.open > .dropdown-toggle.btn-success:hover,
+.btn-success:active:focus,
+.btn-success.active:focus,
+.open > .dropdown-toggle.btn-success:focus,
+.btn-success:active.focus,
+.btn-success.active.focus,
+.open > .dropdown-toggle.btn-success.focus {
+  color: #fff;
+  background-color: #398439;
+  border-color: #255625;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+  background-image: none;
+}
+.btn-success.disabled:hover,
+.btn-success[disabled]:hover,
+fieldset[disabled] .btn-success:hover,
+.btn-success.disabled:focus,
+.btn-success[disabled]:focus,
+fieldset[disabled] .btn-success:focus,
+.btn-success.disabled.focus,
+.btn-success[disabled].focus,
+fieldset[disabled] .btn-success.focus {
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.btn-success .badge {
+  color: #5cb85c;
+  background-color: #fff;
+}
+.btn-info {
+  color: #fff;
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.btn-info:focus,
+.btn-info.focus {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #1b6d85;
+}
+.btn-info:hover {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.btn-info:active:hover,
+.btn-info.active:hover,
+.open > .dropdown-toggle.btn-info:hover,
+.btn-info:active:focus,
+.btn-info.active:focus,
+.open > .dropdown-toggle.btn-info:focus,
+.btn-info:active.focus,
+.btn-info.active.focus,
+.open > .dropdown-toggle.btn-info.focus {
+  color: #fff;
+  background-color: #269abc;
+  border-color: #1b6d85;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+  background-image: none;
+}
+.btn-info.disabled:hover,
+.btn-info[disabled]:hover,
+fieldset[disabled] .btn-info:hover,
+.btn-info.disabled:focus,
+.btn-info[disabled]:focus,
+fieldset[disabled] .btn-info:focus,
+.btn-info.disabled.focus,
+.btn-info[disabled].focus,
+fieldset[disabled] .btn-info.focus {
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.btn-info .badge {
+  color: #5bc0de;
+  background-color: #fff;
+}
+.btn-warning {
+  color: #fff;
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.btn-warning:focus,
+.btn-warning.focus {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #985f0d;
+}
+.btn-warning:hover {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.btn-warning:active:hover,
+.btn-warning.active:hover,
+.open > .dropdown-toggle.btn-warning:hover,
+.btn-warning:active:focus,
+.btn-warning.active:focus,
+.open > .dropdown-toggle.btn-warning:focus,
+.btn-warning:active.focus,
+.btn-warning.active.focus,
+.open > .dropdown-toggle.btn-warning.focus {
+  color: #fff;
+  background-color: #d58512;
+  border-color: #985f0d;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+  background-image: none;
+}
+.btn-warning.disabled:hover,
+.btn-warning[disabled]:hover,
+fieldset[disabled] .btn-warning:hover,
+.btn-warning.disabled:focus,
+.btn-warning[disabled]:focus,
+fieldset[disabled] .btn-warning:focus,
+.btn-warning.disabled.focus,
+.btn-warning[disabled].focus,
+fieldset[disabled] .btn-warning.focus {
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.btn-warning .badge {
+  color: #f0ad4e;
+  background-color: #fff;
+}
+.btn-danger {
+  color: #fff;
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.btn-danger:focus,
+.btn-danger.focus {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #761c19;
+}
+.btn-danger:hover {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.btn-danger:active:hover,
+.btn-danger.active:hover,
+.open > .dropdown-toggle.btn-danger:hover,
+.btn-danger:active:focus,
+.btn-danger.active:focus,
+.open > .dropdown-toggle.btn-danger:focus,
+.btn-danger:active.focus,
+.btn-danger.active.focus,
+.open > .dropdown-toggle.btn-danger.focus {
+  color: #fff;
+  background-color: #ac2925;
+  border-color: #761c19;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+  background-image: none;
+}
+.btn-danger.disabled:hover,
+.btn-danger[disabled]:hover,
+fieldset[disabled] .btn-danger:hover,
+.btn-danger.disabled:focus,
+.btn-danger[disabled]:focus,
+fieldset[disabled] .btn-danger:focus,
+.btn-danger.disabled.focus,
+.btn-danger[disabled].focus,
+fieldset[disabled] .btn-danger.focus {
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.btn-danger .badge {
+  color: #d9534f;
+  background-color: #fff;
+}
+.btn-link {
+  color: #337ab7;
+  font-weight: normal;
+  border-radius: 0;
+}
+.btn-link,
+.btn-link:active,
+.btn-link.active,
+.btn-link[disabled],
+fieldset[disabled] .btn-link {
+  background-color: transparent;
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+.btn-link,
+.btn-link:hover,
+.btn-link:focus,
+.btn-link:active {
+  border-color: transparent;
+}
+.btn-link:hover,
+.btn-link:focus {
+  color: #23527c;
+  text-decoration: underline;
+  background-color: transparent;
+}
+.btn-link[disabled]:hover,
+fieldset[disabled] .btn-link:hover,
+.btn-link[disabled]:focus,
+fieldset[disabled] .btn-link:focus {
+  color: #777777;
+  text-decoration: none;
+}
+.btn-lg,
+.btn-group-lg > .btn {
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+.btn-sm,
+.btn-group-sm > .btn {
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.btn-xs,
+.btn-group-xs > .btn {
+  padding: 1px 5px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.btn-block {
+  display: block;
+  width: 100%;
+}
+.btn-block + .btn-block {
+  margin-top: 5px;
+}
+input[type="submit"].btn-block,
+input[type="reset"].btn-block,
+input[type="button"].btn-block {
+  width: 100%;
+}
+.fade {
+  opacity: 0;
+  -webkit-transition: opacity 0.15s linear;
+  -o-transition: opacity 0.15s linear;
+  transition: opacity 0.15s linear;
+}
+.fade.in {
+  opacity: 1;
+}
+.collapse {
+  display: none;
+}
+.collapse.in {
+  display: block;
+}
+tr.collapse.in {
+  display: table-row;
+}
+tbody.collapse.in {
+  display: table-row-group;
+}
+.collapsing {
+  position: relative;
+  height: 0;
+  overflow: hidden;
+  -webkit-transition-property: height, visibility;
+  transition-property: height, visibility;
+  -webkit-transition-duration: 0.35s;
+  transition-duration: 0.35s;
+  -webkit-transition-timing-function: ease;
+  transition-timing-function: ease;
+}
+.caret {
+  display: inline-block;
+  width: 0;
+  height: 0;
+  margin-left: 2px;
+  vertical-align: middle;
+  border-top: 4px dashed;
+  border-top: 4px solid \9;
+  border-right: 4px solid transparent;
+  border-left: 4px solid transparent;
+}
+.dropup,
+.dropdown {
+  position: relative;
+}
+.dropdown-toggle:focus {
+  outline: 0;
+}
+.dropdown-menu {
+  position: absolute;
+  top: 100%;
+  left: 0;
+  z-index: 1000;
+  display: none;
+  float: left;
+  min-width: 160px;
+  padding: 5px 0;
+  margin: 2px 0 0;
+  list-style: none;
+  font-size: 13px;
+  text-align: left;
+  background-color: #fff;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.15);
+  border-radius: 2px;
+  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+  background-clip: padding-box;
+}
+.dropdown-menu.pull-right {
+  right: 0;
+  left: auto;
+}
+.dropdown-menu .divider {
+  height: 1px;
+  margin: 8px 0;
+  overflow: hidden;
+  background-color: #e5e5e5;
+}
+.dropdown-menu > li > a {
+  display: block;
+  padding: 3px 20px;
+  clear: both;
+  font-weight: normal;
+  line-height: 1.42857143;
+  color: #333333;
+  white-space: nowrap;
+}
+.dropdown-menu > li > a:hover,
+.dropdown-menu > li > a:focus {
+  text-decoration: none;
+  color: #262626;
+  background-color: #f5f5f5;
+}
+.dropdown-menu > .active > a,
+.dropdown-menu > .active > a:hover,
+.dropdown-menu > .active > a:focus {
+  color: #fff;
+  text-decoration: none;
+  outline: 0;
+  background-color: #337ab7;
+}
+.dropdown-menu > .disabled > a,
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  color: #777777;
+}
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  text-decoration: none;
+  background-color: transparent;
+  background-image: none;
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  cursor: not-allowed;
+}
+.open > .dropdown-menu {
+  display: block;
+}
+.open > a {
+  outline: 0;
+}
+.dropdown-menu-right {
+  left: auto;
+  right: 0;
+}
+.dropdown-menu-left {
+  left: 0;
+  right: auto;
+}
+.dropdown-header {
+  display: block;
+  padding: 3px 20px;
+  font-size: 12px;
+  line-height: 1.42857143;
+  color: #777777;
+  white-space: nowrap;
+}
+.dropdown-backdrop {
+  position: fixed;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  top: 0;
+  z-index: 990;
+}
+.pull-right > .dropdown-menu {
+  right: 0;
+  left: auto;
+}
+.dropup .caret,
+.navbar-fixed-bottom .dropdown .caret {
+  border-top: 0;
+  border-bottom: 4px dashed;
+  border-bottom: 4px solid \9;
+  content: "";
+}
+.dropup .dropdown-menu,
+.navbar-fixed-bottom .dropdown .dropdown-menu {
+  top: auto;
+  bottom: 100%;
+  margin-bottom: 2px;
+}
+@media (min-width: 541px) {
+  .navbar-right .dropdown-menu {
+    left: auto;
+    right: 0;
+  }
+  .navbar-right .dropdown-menu-left {
+    left: 0;
+    right: auto;
+  }
+}
+.btn-group,
+.btn-group-vertical {
+  position: relative;
+  display: inline-block;
+  vertical-align: middle;
+}
+.btn-group > .btn,
+.btn-group-vertical > .btn {
+  position: relative;
+  float: left;
+}
+.btn-group > .btn:hover,
+.btn-group-vertical > .btn:hover,
+.btn-group > .btn:focus,
+.btn-group-vertical > .btn:focus,
+.btn-group > .btn:active,
+.btn-group-vertical > .btn:active,
+.btn-group > .btn.active,
+.btn-group-vertical > .btn.active {
+  z-index: 2;
+}
+.btn-group .btn + .btn,
+.btn-group .btn + .btn-group,
+.btn-group .btn-group + .btn,
+.btn-group .btn-group + .btn-group {
+  margin-left: -1px;
+}
+.btn-toolbar {
+  margin-left: -5px;
+}
+.btn-toolbar .btn,
+.btn-toolbar .btn-group,
+.btn-toolbar .input-group {
+  float: left;
+}
+.btn-toolbar > .btn,
+.btn-toolbar > .btn-group,
+.btn-toolbar > .input-group {
+  margin-left: 5px;
+}
+.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
+  border-radius: 0;
+}
+.btn-group > .btn:first-child {
+  margin-left: 0;
+}
+.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.btn-group > .btn:last-child:not(:first-child),
+.btn-group > .dropdown-toggle:not(:first-child) {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group > .btn-group {
+  float: left;
+}
+.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
+  border-radius: 0;
+}
+.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group .dropdown-toggle:active,
+.btn-group.open .dropdown-toggle {
+  outline: 0;
+}
+.btn-group > .btn + .dropdown-toggle {
+  padding-left: 8px;
+  padding-right: 8px;
+}
+.btn-group > .btn-lg + .dropdown-toggle {
+  padding-left: 12px;
+  padding-right: 12px;
+}
+.btn-group.open .dropdown-toggle {
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn-group.open .dropdown-toggle.btn-link {
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+.btn .caret {
+  margin-left: 0;
+}
+.btn-lg .caret {
+  border-width: 5px 5px 0;
+  border-bottom-width: 0;
+}
+.dropup .btn-lg .caret {
+  border-width: 0 5px 5px;
+}
+.btn-group-vertical > .btn,
+.btn-group-vertical > .btn-group,
+.btn-group-vertical > .btn-group > .btn {
+  display: block;
+  float: none;
+  width: 100%;
+  max-width: 100%;
+}
+.btn-group-vertical > .btn-group > .btn {
+  float: none;
+}
+.btn-group-vertical > .btn + .btn,
+.btn-group-vertical > .btn + .btn-group,
+.btn-group-vertical > .btn-group + .btn,
+.btn-group-vertical > .btn-group + .btn-group {
+  margin-top: -1px;
+  margin-left: 0;
+}
+.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
+  border-radius: 0;
+}
+.btn-group-vertical > .btn:first-child:not(:last-child) {
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn:last-child:not(:first-child) {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+  border-bottom-right-radius: 2px;
+  border-bottom-left-radius: 2px;
+}
+.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
+  border-radius: 0;
+}
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group-justified {
+  display: table;
+  width: 100%;
+  table-layout: fixed;
+  border-collapse: separate;
+}
+.btn-group-justified > .btn,
+.btn-group-justified > .btn-group {
+  float: none;
+  display: table-cell;
+  width: 1%;
+}
+.btn-group-justified > .btn-group .btn {
+  width: 100%;
+}
+.btn-group-justified > .btn-group .dropdown-menu {
+  left: auto;
+}
+[data-toggle="buttons"] > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn input[type="checkbox"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
+  position: absolute;
+  clip: rect(0, 0, 0, 0);
+  pointer-events: none;
+}
+.input-group {
+  position: relative;
+  display: table;
+  border-collapse: separate;
+}
+.input-group[class*="col-"] {
+  float: none;
+  padding-left: 0;
+  padding-right: 0;
+}
+.input-group .form-control {
+  position: relative;
+  z-index: 2;
+  float: left;
+  width: 100%;
+  margin-bottom: 0;
+}
+.input-group .form-control:focus {
+  z-index: 3;
+}
+.input-group-lg > .form-control,
+.input-group-lg > .input-group-addon,
+.input-group-lg > .input-group-btn > .btn {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+select.input-group-lg > .form-control,
+select.input-group-lg > .input-group-addon,
+select.input-group-lg > .input-group-btn > .btn {
+  height: 45px;
+  line-height: 45px;
+}
+textarea.input-group-lg > .form-control,
+textarea.input-group-lg > .input-group-addon,
+textarea.input-group-lg > .input-group-btn > .btn,
+select[multiple].input-group-lg > .form-control,
+select[multiple].input-group-lg > .input-group-addon,
+select[multiple].input-group-lg > .input-group-btn > .btn {
+  height: auto;
+}
+.input-group-sm > .form-control,
+.input-group-sm > .input-group-addon,
+.input-group-sm > .input-group-btn > .btn {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+select.input-group-sm > .form-control,
+select.input-group-sm > .input-group-addon,
+select.input-group-sm > .input-group-btn > .btn {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.input-group-sm > .form-control,
+textarea.input-group-sm > .input-group-addon,
+textarea.input-group-sm > .input-group-btn > .btn,
+select[multiple].input-group-sm > .form-control,
+select[multiple].input-group-sm > .input-group-addon,
+select[multiple].input-group-sm > .input-group-btn > .btn {
+  height: auto;
+}
+.input-group-addon,
+.input-group-btn,
+.input-group .form-control {
+  display: table-cell;
+}
+.input-group-addon:not(:first-child):not(:last-child),
+.input-group-btn:not(:first-child):not(:last-child),
+.input-group .form-control:not(:first-child):not(:last-child) {
+  border-radius: 0;
+}
+.input-group-addon,
+.input-group-btn {
+  width: 1%;
+  white-space: nowrap;
+  vertical-align: middle;
+}
+.input-group-addon {
+  padding: 6px 12px;
+  font-size: 13px;
+  font-weight: normal;
+  line-height: 1;
+  color: #555555;
+  text-align: center;
+  background-color: #eeeeee;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+}
+.input-group-addon.input-sm {
+  padding: 5px 10px;
+  font-size: 12px;
+  border-radius: 1px;
+}
+.input-group-addon.input-lg {
+  padding: 10px 16px;
+  font-size: 17px;
+  border-radius: 3px;
+}
+.input-group-addon input[type="radio"],
+.input-group-addon input[type="checkbox"] {
+  margin-top: 0;
+}
+.input-group .form-control:first-child,
+.input-group-addon:first-child,
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group > .btn,
+.input-group-btn:first-child > .dropdown-toggle,
+.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
+.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.input-group-addon:first-child {
+  border-right: 0;
+}
+.input-group .form-control:last-child,
+.input-group-addon:last-child,
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group > .btn,
+.input-group-btn:last-child > .dropdown-toggle,
+.input-group-btn:first-child > .btn:not(:first-child),
+.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.input-group-addon:last-child {
+  border-left: 0;
+}
+.input-group-btn {
+  position: relative;
+  font-size: 0;
+  white-space: nowrap;
+}
+.input-group-btn > .btn {
+  position: relative;
+}
+.input-group-btn > .btn + .btn {
+  margin-left: -1px;
+}
+.input-group-btn > .btn:hover,
+.input-group-btn > .btn:focus,
+.input-group-btn > .btn:active {
+  z-index: 2;
+}
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group {
+  margin-right: -1px;
+}
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group {
+  z-index: 2;
+  margin-left: -1px;
+}
+.nav {
+  margin-bottom: 0;
+  padding-left: 0;
+  list-style: none;
+}
+.nav > li {
+  position: relative;
+  display: block;
+}
+.nav > li > a {
+  position: relative;
+  display: block;
+  padding: 10px 15px;
+}
+.nav > li > a:hover,
+.nav > li > a:focus {
+  text-decoration: none;
+  background-color: #eeeeee;
+}
+.nav > li.disabled > a {
+  color: #777777;
+}
+.nav > li.disabled > a:hover,
+.nav > li.disabled > a:focus {
+  color: #777777;
+  text-decoration: none;
+  background-color: transparent;
+  cursor: not-allowed;
+}
+.nav .open > a,
+.nav .open > a:hover,
+.nav .open > a:focus {
+  background-color: #eeeeee;
+  border-color: #337ab7;
+}
+.nav .nav-divider {
+  height: 1px;
+  margin: 8px 0;
+  overflow: hidden;
+  background-color: #e5e5e5;
+}
+.nav > li > a > img {
+  max-width: none;
+}
+.nav-tabs {
+  border-bottom: 1px solid #ddd;
+}
+.nav-tabs > li {
+  float: left;
+  margin-bottom: -1px;
+}
+.nav-tabs > li > a {
+  margin-right: 2px;
+  line-height: 1.42857143;
+  border: 1px solid transparent;
+  border-radius: 2px 2px 0 0;
+}
+.nav-tabs > li > a:hover {
+  border-color: #eeeeee #eeeeee #ddd;
+}
+.nav-tabs > li.active > a,
+.nav-tabs > li.active > a:hover,
+.nav-tabs > li.active > a:focus {
+  color: #555555;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-bottom-color: transparent;
+  cursor: default;
+}
+.nav-tabs.nav-justified {
+  width: 100%;
+  border-bottom: 0;
+}
+.nav-tabs.nav-justified > li {
+  float: none;
+}
+.nav-tabs.nav-justified > li > a {
+  text-align: center;
+  margin-bottom: 5px;
+}
+.nav-tabs.nav-justified > .dropdown .dropdown-menu {
+  top: auto;
+  left: auto;
+}
+@media (min-width: 768px) {
+  .nav-tabs.nav-justified > li {
+    display: table-cell;
+    width: 1%;
+  }
+  .nav-tabs.nav-justified > li > a {
+    margin-bottom: 0;
+  }
+}
+.nav-tabs.nav-justified > li > a {
+  margin-right: 0;
+  border-radius: 2px;
+}
+.nav-tabs.nav-justified > .active > a,
+.nav-tabs.nav-justified > .active > a:hover,
+.nav-tabs.nav-justified > .active > a:focus {
+  border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+  .nav-tabs.nav-justified > li > a {
+    border-bottom: 1px solid #ddd;
+    border-radius: 2px 2px 0 0;
+  }
+  .nav-tabs.nav-justified > .active > a,
+  .nav-tabs.nav-justified > .active > a:hover,
+  .nav-tabs.nav-justified > .active > a:focus {
+    border-bottom-color: #fff;
+  }
+}
+.nav-pills > li {
+  float: left;
+}
+.nav-pills > li > a {
+  border-radius: 2px;
+}
+.nav-pills > li + li {
+  margin-left: 2px;
+}
+.nav-pills > li.active > a,
+.nav-pills > li.active > a:hover,
+.nav-pills > li.active > a:focus {
+  color: #fff;
+  background-color: #337ab7;
+}
+.nav-stacked > li {
+  float: none;
+}
+.nav-stacked > li + li {
+  margin-top: 2px;
+  margin-left: 0;
+}
+.nav-justified {
+  width: 100%;
+}
+.nav-justified > li {
+  float: none;
+}
+.nav-justified > li > a {
+  text-align: center;
+  margin-bottom: 5px;
+}
+.nav-justified > .dropdown .dropdown-menu {
+  top: auto;
+  left: auto;
+}
+@media (min-width: 768px) {
+  .nav-justified > li {
+    display: table-cell;
+    width: 1%;
+  }
+  .nav-justified > li > a {
+    margin-bottom: 0;
+  }
+}
+.nav-tabs-justified {
+  border-bottom: 0;
+}
+.nav-tabs-justified > li > a {
+  margin-right: 0;
+  border-radius: 2px;
+}
+.nav-tabs-justified > .active > a,
+.nav-tabs-justified > .active > a:hover,
+.nav-tabs-justified > .active > a:focus {
+  border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+  .nav-tabs-justified > li > a {
+    border-bottom: 1px solid #ddd;
+    border-radius: 2px 2px 0 0;
+  }
+  .nav-tabs-justified > .active > a,
+  .nav-tabs-justified > .active > a:hover,
+  .nav-tabs-justified > .active > a:focus {
+    border-bottom-color: #fff;
+  }
+}
+.tab-content > .tab-pane {
+  display: none;
+}
+.tab-content > .active {
+  display: block;
+}
+.nav-tabs .dropdown-menu {
+  margin-top: -1px;
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.navbar {
+  position: relative;
+  min-height: 30px;
+  margin-bottom: 18px;
+  border: 1px solid transparent;
+}
+@media (min-width: 541px) {
+  .navbar {
+    border-radius: 2px;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-header {
+    float: left;
+  }
+}
+.navbar-collapse {
+  overflow-x: visible;
+  padding-right: 0px;
+  padding-left: 0px;
+  border-top: 1px solid transparent;
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
+  -webkit-overflow-scrolling: touch;
+}
+.navbar-collapse.in {
+  overflow-y: auto;
+}
+@media (min-width: 541px) {
+  .navbar-collapse {
+    width: auto;
+    border-top: 0;
+    box-shadow: none;
+  }
+  .navbar-collapse.collapse {
+    display: block !important;
+    height: auto !important;
+    padding-bottom: 0;
+    overflow: visible !important;
+  }
+  .navbar-collapse.in {
+    overflow-y: visible;
+  }
+  .navbar-fixed-top .navbar-collapse,
+  .navbar-static-top .navbar-collapse,
+  .navbar-fixed-bottom .navbar-collapse {
+    padding-left: 0;
+    padding-right: 0;
+  }
+}
+.navbar-fixed-top .navbar-collapse,
+.navbar-fixed-bottom .navbar-collapse {
+  max-height: 340px;
+}
+@media (max-device-width: 540px) and (orientation: landscape) {
+  .navbar-fixed-top .navbar-collapse,
+  .navbar-fixed-bottom .navbar-collapse {
+    max-height: 200px;
+  }
+}
+.container > .navbar-header,
+.container-fluid > .navbar-header,
+.container > .navbar-collapse,
+.container-fluid > .navbar-collapse {
+  margin-right: 0px;
+  margin-left: 0px;
+}
+@media (min-width: 541px) {
+  .container > .navbar-header,
+  .container-fluid > .navbar-header,
+  .container > .navbar-collapse,
+  .container-fluid > .navbar-collapse {
+    margin-right: 0;
+    margin-left: 0;
+  }
+}
+.navbar-static-top {
+  z-index: 1000;
+  border-width: 0 0 1px;
+}
+@media (min-width: 541px) {
+  .navbar-static-top {
+    border-radius: 0;
+  }
+}
+.navbar-fixed-top,
+.navbar-fixed-bottom {
+  position: fixed;
+  right: 0;
+  left: 0;
+  z-index: 1030;
+}
+@media (min-width: 541px) {
+  .navbar-fixed-top,
+  .navbar-fixed-bottom {
+    border-radius: 0;
+  }
+}
+.navbar-fixed-top {
+  top: 0;
+  border-width: 0 0 1px;
+}
+.navbar-fixed-bottom {
+  bottom: 0;
+  margin-bottom: 0;
+  border-width: 1px 0 0;
+}
+.navbar-brand {
+  float: left;
+  padding: 6px 0px;
+  font-size: 17px;
+  line-height: 18px;
+  height: 30px;
+}
+.navbar-brand:hover,
+.navbar-brand:focus {
+  text-decoration: none;
+}
+.navbar-brand > img {
+  display: block;
+}
+@media (min-width: 541px) {
+  .navbar > .container .navbar-brand,
+  .navbar > .container-fluid .navbar-brand {
+    margin-left: 0px;
+  }
+}
+.navbar-toggle {
+  position: relative;
+  float: right;
+  margin-right: 0px;
+  padding: 9px 10px;
+  margin-top: -2px;
+  margin-bottom: -2px;
+  background-color: transparent;
+  background-image: none;
+  border: 1px solid transparent;
+  border-radius: 2px;
+}
+.navbar-toggle:focus {
+  outline: 0;
+}
+.navbar-toggle .icon-bar {
+  display: block;
+  width: 22px;
+  height: 2px;
+  border-radius: 1px;
+}
+.navbar-toggle .icon-bar + .icon-bar {
+  margin-top: 4px;
+}
+@media (min-width: 541px) {
+  .navbar-toggle {
+    display: none;
+  }
+}
+.navbar-nav {
+  margin: 3px 0px;
+}
+.navbar-nav > li > a {
+  padding-top: 10px;
+  padding-bottom: 10px;
+  line-height: 18px;
+}
+@media (max-width: 540px) {
+  .navbar-nav .open .dropdown-menu {
+    position: static;
+    float: none;
+    width: auto;
+    margin-top: 0;
+    background-color: transparent;
+    border: 0;
+    box-shadow: none;
+  }
+  .navbar-nav .open .dropdown-menu > li > a,
+  .navbar-nav .open .dropdown-menu .dropdown-header {
+    padding: 5px 15px 5px 25px;
+  }
+  .navbar-nav .open .dropdown-menu > li > a {
+    line-height: 18px;
+  }
+  .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-nav .open .dropdown-menu > li > a:focus {
+    background-image: none;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-nav {
+    float: left;
+    margin: 0;
+  }
+  .navbar-nav > li {
+    float: left;
+  }
+  .navbar-nav > li > a {
+    padding-top: 6px;
+    padding-bottom: 6px;
+  }
+}
+.navbar-form {
+  margin-left: 0px;
+  margin-right: 0px;
+  padding: 10px 0px;
+  border-top: 1px solid transparent;
+  border-bottom: 1px solid transparent;
+  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+  margin-top: -1px;
+  margin-bottom: -1px;
+}
+@media (min-width: 768px) {
+  .navbar-form .form-group {
+    display: inline-block;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .navbar-form .form-control-static {
+    display: inline-block;
+  }
+  .navbar-form .input-group {
+    display: inline-table;
+    vertical-align: middle;
+  }
+  .navbar-form .input-group .input-group-addon,
+  .navbar-form .input-group .input-group-btn,
+  .navbar-form .input-group .form-control {
+    width: auto;
+  }
+  .navbar-form .input-group > .form-control {
+    width: 100%;
+  }
+  .navbar-form .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .radio,
+  .navbar-form .checkbox {
+    display: inline-block;
+    margin-top: 0;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .radio label,
+  .navbar-form .checkbox label {
+    padding-left: 0;
+  }
+  .navbar-form .radio input[type="radio"],
+  .navbar-form .checkbox input[type="checkbox"] {
+    position: relative;
+    margin-left: 0;
+  }
+  .navbar-form .has-feedback .form-control-feedback {
+    top: 0;
+  }
+}
+@media (max-width: 540px) {
+  .navbar-form .form-group {
+    margin-bottom: 5px;
+  }
+  .navbar-form .form-group:last-child {
+    margin-bottom: 0;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-form {
+    width: auto;
+    border: 0;
+    margin-left: 0;
+    margin-right: 0;
+    padding-top: 0;
+    padding-bottom: 0;
+    -webkit-box-shadow: none;
+    box-shadow: none;
+  }
+}
+.navbar-nav > li > .dropdown-menu {
+  margin-top: 0;
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
+  margin-bottom: 0;
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.navbar-btn {
+  margin-top: -1px;
+  margin-bottom: -1px;
+}
+.navbar-btn.btn-sm {
+  margin-top: 0px;
+  margin-bottom: 0px;
+}
+.navbar-btn.btn-xs {
+  margin-top: 4px;
+  margin-bottom: 4px;
+}
+.navbar-text {
+  margin-top: 6px;
+  margin-bottom: 6px;
+}
+@media (min-width: 541px) {
+  .navbar-text {
+    float: left;
+    margin-left: 0px;
+    margin-right: 0px;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-left {
+    float: left !important;
+    float: left;
+  }
+  .navbar-right {
+    float: right !important;
+    float: right;
+    margin-right: 0px;
+  }
+  .navbar-right ~ .navbar-right {
+    margin-right: 0;
+  }
+}
+.navbar-default {
+  background-color: #f8f8f8;
+  border-color: #e7e7e7;
+}
+.navbar-default .navbar-brand {
+  color: #777;
+}
+.navbar-default .navbar-brand:hover,
+.navbar-default .navbar-brand:focus {
+  color: #5e5e5e;
+  background-color: transparent;
+}
+.navbar-default .navbar-text {
+  color: #777;
+}
+.navbar-default .navbar-nav > li > a {
+  color: #777;
+}
+.navbar-default .navbar-nav > li > a:hover,
+.navbar-default .navbar-nav > li > a:focus {
+  color: #333;
+  background-color: transparent;
+}
+.navbar-default .navbar-nav > .active > a,
+.navbar-default .navbar-nav > .active > a:hover,
+.navbar-default .navbar-nav > .active > a:focus {
+  color: #555;
+  background-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .disabled > a,
+.navbar-default .navbar-nav > .disabled > a:hover,
+.navbar-default .navbar-nav > .disabled > a:focus {
+  color: #ccc;
+  background-color: transparent;
+}
+.navbar-default .navbar-toggle {
+  border-color: #ddd;
+}
+.navbar-default .navbar-toggle:hover,
+.navbar-default .navbar-toggle:focus {
+  background-color: #ddd;
+}
+.navbar-default .navbar-toggle .icon-bar {
+  background-color: #888;
+}
+.navbar-default .navbar-collapse,
+.navbar-default .navbar-form {
+  border-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .open > a,
+.navbar-default .navbar-nav > .open > a:hover,
+.navbar-default .navbar-nav > .open > a:focus {
+  background-color: #e7e7e7;
+  color: #555;
+}
+@media (max-width: 540px) {
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
+    color: #777;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
+    color: #333;
+    background-color: transparent;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #555;
+    background-color: #e7e7e7;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+    color: #ccc;
+    background-color: transparent;
+  }
+}
+.navbar-default .navbar-link {
+  color: #777;
+}
+.navbar-default .navbar-link:hover {
+  color: #333;
+}
+.navbar-default .btn-link {
+  color: #777;
+}
+.navbar-default .btn-link:hover,
+.navbar-default .btn-link:focus {
+  color: #333;
+}
+.navbar-default .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-default .btn-link:hover,
+.navbar-default .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-default .btn-link:focus {
+  color: #ccc;
+}
+.navbar-inverse {
+  background-color: #222;
+  border-color: #080808;
+}
+.navbar-inverse .navbar-brand {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-brand:hover,
+.navbar-inverse .navbar-brand:focus {
+  color: #fff;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-text {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a:hover,
+.navbar-inverse .navbar-nav > li > a:focus {
+  color: #fff;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-nav > .active > a,
+.navbar-inverse .navbar-nav > .active > a:hover,
+.navbar-inverse .navbar-nav > .active > a:focus {
+  color: #fff;
+  background-color: #080808;
+}
+.navbar-inverse .navbar-nav > .disabled > a,
+.navbar-inverse .navbar-nav > .disabled > a:hover,
+.navbar-inverse .navbar-nav > .disabled > a:focus {
+  color: #444;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-toggle {
+  border-color: #333;
+}
+.navbar-inverse .navbar-toggle:hover,
+.navbar-inverse .navbar-toggle:focus {
+  background-color: #333;
+}
+.navbar-inverse .navbar-toggle .icon-bar {
+  background-color: #fff;
+}
+.navbar-inverse .navbar-collapse,
+.navbar-inverse .navbar-form {
+  border-color: #101010;
+}
+.navbar-inverse .navbar-nav > .open > a,
+.navbar-inverse .navbar-nav > .open > a:hover,
+.navbar-inverse .navbar-nav > .open > a:focus {
+  background-color: #080808;
+  color: #fff;
+}
+@media (max-width: 540px) {
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
+    border-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
+    background-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
+    color: #9d9d9d;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
+    color: #fff;
+    background-color: transparent;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #fff;
+    background-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+    color: #444;
+    background-color: transparent;
+  }
+}
+.navbar-inverse .navbar-link {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-link:hover {
+  color: #fff;
+}
+.navbar-inverse .btn-link {
+  color: #9d9d9d;
+}
+.navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link:focus {
+  color: #fff;
+}
+.navbar-inverse .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-inverse .btn-link:focus {
+  color: #444;
+}
+.breadcrumb {
+  padding: 8px 15px;
+  margin-bottom: 18px;
+  list-style: none;
+  background-color: #f5f5f5;
+  border-radius: 2px;
+}
+.breadcrumb > li {
+  display: inline-block;
+}
+.breadcrumb > li + li:before {
+  content: "/\00a0";
+  padding: 0 5px;
+  color: #5e5e5e;
+}
+.breadcrumb > .active {
+  color: #777777;
+}
+.pagination {
+  display: inline-block;
+  padding-left: 0;
+  margin: 18px 0;
+  border-radius: 2px;
+}
+.pagination > li {
+  display: inline;
+}
+.pagination > li > a,
+.pagination > li > span {
+  position: relative;
+  float: left;
+  padding: 6px 12px;
+  line-height: 1.42857143;
+  text-decoration: none;
+  color: #337ab7;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  margin-left: -1px;
+}
+.pagination > li:first-child > a,
+.pagination > li:first-child > span {
+  margin-left: 0;
+  border-bottom-left-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.pagination > li:last-child > a,
+.pagination > li:last-child > span {
+  border-bottom-right-radius: 2px;
+  border-top-right-radius: 2px;
+}
+.pagination > li > a:hover,
+.pagination > li > span:hover,
+.pagination > li > a:focus,
+.pagination > li > span:focus {
+  z-index: 2;
+  color: #23527c;
+  background-color: #eeeeee;
+  border-color: #ddd;
+}
+.pagination > .active > a,
+.pagination > .active > span,
+.pagination > .active > a:hover,
+.pagination > .active > span:hover,
+.pagination > .active > a:focus,
+.pagination > .active > span:focus {
+  z-index: 3;
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+  cursor: default;
+}
+.pagination > .disabled > span,
+.pagination > .disabled > span:hover,
+.pagination > .disabled > span:focus,
+.pagination > .disabled > a,
+.pagination > .disabled > a:hover,
+.pagination > .disabled > a:focus {
+  color: #777777;
+  background-color: #fff;
+  border-color: #ddd;
+  cursor: not-allowed;
+}
+.pagination-lg > li > a,
+.pagination-lg > li > span {
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+}
+.pagination-lg > li:first-child > a,
+.pagination-lg > li:first-child > span {
+  border-bottom-left-radius: 3px;
+  border-top-left-radius: 3px;
+}
+.pagination-lg > li:last-child > a,
+.pagination-lg > li:last-child > span {
+  border-bottom-right-radius: 3px;
+  border-top-right-radius: 3px;
+}
+.pagination-sm > li > a,
+.pagination-sm > li > span {
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+}
+.pagination-sm > li:first-child > a,
+.pagination-sm > li:first-child > span {
+  border-bottom-left-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.pagination-sm > li:last-child > a,
+.pagination-sm > li:last-child > span {
+  border-bottom-right-radius: 1px;
+  border-top-right-radius: 1px;
+}
+.pager {
+  padding-left: 0;
+  margin: 18px 0;
+  list-style: none;
+  text-align: center;
+}
+.pager li {
+  display: inline;
+}
+.pager li > a,
+.pager li > span {
+  display: inline-block;
+  padding: 5px 14px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 15px;
+}
+.pager li > a:hover,
+.pager li > a:focus {
+  text-decoration: none;
+  background-color: #eeeeee;
+}
+.pager .next > a,
+.pager .next > span {
+  float: right;
+}
+.pager .previous > a,
+.pager .previous > span {
+  float: left;
+}
+.pager .disabled > a,
+.pager .disabled > a:hover,
+.pager .disabled > a:focus,
+.pager .disabled > span {
+  color: #777777;
+  background-color: #fff;
+  cursor: not-allowed;
+}
+.label {
+  display: inline;
+  padding: .2em .6em .3em;
+  font-size: 75%;
+  font-weight: bold;
+  line-height: 1;
+  color: #fff;
+  text-align: center;
+  white-space: nowrap;
+  vertical-align: baseline;
+  border-radius: .25em;
+}
+a.label:hover,
+a.label:focus {
+  color: #fff;
+  text-decoration: none;
+  cursor: pointer;
+}
+.label:empty {
+  display: none;
+}
+.btn .label {
+  position: relative;
+  top: -1px;
+}
+.label-default {
+  background-color: #777777;
+}
+.label-default[href]:hover,
+.label-default[href]:focus {
+  background-color: #5e5e5e;
+}
+.label-primary {
+  background-color: #337ab7;
+}
+.label-primary[href]:hover,
+.label-primary[href]:focus {
+  background-color: #286090;
+}
+.label-success {
+  background-color: #5cb85c;
+}
+.label-success[href]:hover,
+.label-success[href]:focus {
+  background-color: #449d44;
+}
+.label-info {
+  background-color: #5bc0de;
+}
+.label-info[href]:hover,
+.label-info[href]:focus {
+  background-color: #31b0d5;
+}
+.label-warning {
+  background-color: #f0ad4e;
+}
+.label-warning[href]:hover,
+.label-warning[href]:focus {
+  background-color: #ec971f;
+}
+.label-danger {
+  background-color: #d9534f;
+}
+.label-danger[href]:hover,
+.label-danger[href]:focus {
+  background-color: #c9302c;
+}
+.badge {
+  display: inline-block;
+  min-width: 10px;
+  padding: 3px 7px;
+  font-size: 12px;
+  font-weight: bold;
+  color: #fff;
+  line-height: 1;
+  vertical-align: middle;
+  white-space: nowrap;
+  text-align: center;
+  background-color: #777777;
+  border-radius: 10px;
+}
+.badge:empty {
+  display: none;
+}
+.btn .badge {
+  position: relative;
+  top: -1px;
+}
+.btn-xs .badge,
+.btn-group-xs > .btn .badge {
+  top: 0;
+  padding: 1px 5px;
+}
+a.badge:hover,
+a.badge:focus {
+  color: #fff;
+  text-decoration: none;
+  cursor: pointer;
+}
+.list-group-item.active > .badge,
+.nav-pills > .active > a > .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.list-group-item > .badge {
+  float: right;
+}
+.list-group-item > .badge + .badge {
+  margin-right: 5px;
+}
+.nav-pills > li > a > .badge {
+  margin-left: 3px;
+}
+.jumbotron {
+  padding-top: 30px;
+  padding-bottom: 30px;
+  margin-bottom: 30px;
+  color: inherit;
+  background-color: #eeeeee;
+}
+.jumbotron h1,
+.jumbotron .h1 {
+  color: inherit;
+}
+.jumbotron p {
+  margin-bottom: 15px;
+  font-size: 20px;
+  font-weight: 200;
+}
+.jumbotron > hr {
+  border-top-color: #d5d5d5;
+}
+.container .jumbotron,
+.container-fluid .jumbotron {
+  border-radius: 3px;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.jumbotron .container {
+  max-width: 100%;
+}
+@media screen and (min-width: 768px) {
+  .jumbotron {
+    padding-top: 48px;
+    padding-bottom: 48px;
+  }
+  .container .jumbotron,
+  .container-fluid .jumbotron {
+    padding-left: 60px;
+    padding-right: 60px;
+  }
+  .jumbotron h1,
+  .jumbotron .h1 {
+    font-size: 59px;
+  }
+}
+.thumbnail {
+  display: block;
+  padding: 4px;
+  margin-bottom: 18px;
+  line-height: 1.42857143;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  -webkit-transition: border 0.2s ease-in-out;
+  -o-transition: border 0.2s ease-in-out;
+  transition: border 0.2s ease-in-out;
+}
+.thumbnail > img,
+.thumbnail a > img {
+  margin-left: auto;
+  margin-right: auto;
+}
+a.thumbnail:hover,
+a.thumbnail:focus,
+a.thumbnail.active {
+  border-color: #337ab7;
+}
+.thumbnail .caption {
+  padding: 9px;
+  color: #000;
+}
+.alert {
+  padding: 15px;
+  margin-bottom: 18px;
+  border: 1px solid transparent;
+  border-radius: 2px;
+}
+.alert h4 {
+  margin-top: 0;
+  color: inherit;
+}
+.alert .alert-link {
+  font-weight: bold;
+}
+.alert > p,
+.alert > ul {
+  margin-bottom: 0;
+}
+.alert > p + p {
+  margin-top: 5px;
+}
+.alert-dismissable,
+.alert-dismissible {
+  padding-right: 35px;
+}
+.alert-dismissable .close,
+.alert-dismissible .close {
+  position: relative;
+  top: -2px;
+  right: -21px;
+  color: inherit;
+}
+.alert-success {
+  background-color: #dff0d8;
+  border-color: #d6e9c6;
+  color: #3c763d;
+}
+.alert-success hr {
+  border-top-color: #c9e2b3;
+}
+.alert-success .alert-link {
+  color: #2b542c;
+}
+.alert-info {
+  background-color: #d9edf7;
+  border-color: #bce8f1;
+  color: #31708f;
+}
+.alert-info hr {
+  border-top-color: #a6e1ec;
+}
+.alert-info .alert-link {
+  color: #245269;
+}
+.alert-warning {
+  background-color: #fcf8e3;
+  border-color: #faebcc;
+  color: #8a6d3b;
+}
+.alert-warning hr {
+  border-top-color: #f7e1b5;
+}
+.alert-warning .alert-link {
+  color: #66512c;
+}
+.alert-danger {
+  background-color: #f2dede;
+  border-color: #ebccd1;
+  color: #a94442;
+}
+.alert-danger hr {
+  border-top-color: #e4b9c0;
+}
+.alert-danger .alert-link {
+  color: #843534;
+}
+@-webkit-keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+@keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+.progress {
+  overflow: hidden;
+  height: 18px;
+  margin-bottom: 18px;
+  background-color: #f5f5f5;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+}
+.progress-bar {
+  float: left;
+  width: 0%;
+  height: 100%;
+  font-size: 12px;
+  line-height: 18px;
+  color: #fff;
+  text-align: center;
+  background-color: #337ab7;
+  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+  -webkit-transition: width 0.6s ease;
+  -o-transition: width 0.6s ease;
+  transition: width 0.6s ease;
+}
+.progress-striped .progress-bar,
+.progress-bar-striped {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-size: 40px 40px;
+}
+.progress.active .progress-bar,
+.progress-bar.active {
+  -webkit-animation: progress-bar-stripes 2s linear infinite;
+  -o-animation: progress-bar-stripes 2s linear infinite;
+  animation: progress-bar-stripes 2s linear infinite;
+}
+.progress-bar-success {
+  background-color: #5cb85c;
+}
+.progress-striped .progress-bar-success {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-info {
+  background-color: #5bc0de;
+}
+.progress-striped .progress-bar-info {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-warning {
+  background-color: #f0ad4e;
+}
+.progress-striped .progress-bar-warning {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-danger {
+  background-color: #d9534f;
+}
+.progress-striped .progress-bar-danger {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.media {
+  margin-top: 15px;
+}
+.media:first-child {
+  margin-top: 0;
+}
+.media,
+.media-body {
+  zoom: 1;
+  overflow: hidden;
+}
+.media-body {
+  width: 10000px;
+}
+.media-object {
+  display: block;
+}
+.media-object.img-thumbnail {
+  max-width: none;
+}
+.media-right,
+.media > .pull-right {
+  padding-left: 10px;
+}
+.media-left,
+.media > .pull-left {
+  padding-right: 10px;
+}
+.media-left,
+.media-right,
+.media-body {
+  display: table-cell;
+  vertical-align: top;
+}
+.media-middle {
+  vertical-align: middle;
+}
+.media-bottom {
+  vertical-align: bottom;
+}
+.media-heading {
+  margin-top: 0;
+  margin-bottom: 5px;
+}
+.media-list {
+  padding-left: 0;
+  list-style: none;
+}
+.list-group {
+  margin-bottom: 20px;
+  padding-left: 0;
+}
+.list-group-item {
+  position: relative;
+  display: block;
+  padding: 10px 15px;
+  margin-bottom: -1px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+}
+.list-group-item:first-child {
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.list-group-item:last-child {
+  margin-bottom: 0;
+  border-bottom-right-radius: 2px;
+  border-bottom-left-radius: 2px;
+}
+a.list-group-item,
+button.list-group-item {
+  color: #555;
+}
+a.list-group-item .list-group-item-heading,
+button.list-group-item .list-group-item-heading {
+  color: #333;
+}
+a.list-group-item:hover,
+button.list-group-item:hover,
+a.list-group-item:focus,
+button.list-group-item:focus {
+  text-decoration: none;
+  color: #555;
+  background-color: #f5f5f5;
+}
+button.list-group-item {
+  width: 100%;
+  text-align: left;
+}
+.list-group-item.disabled,
+.list-group-item.disabled:hover,
+.list-group-item.disabled:focus {
+  background-color: #eeeeee;
+  color: #777777;
+  cursor: not-allowed;
+}
+.list-group-item.disabled .list-group-item-heading,
+.list-group-item.disabled:hover .list-group-item-heading,
+.list-group-item.disabled:focus .list-group-item-heading {
+  color: inherit;
+}
+.list-group-item.disabled .list-group-item-text,
+.list-group-item.disabled:hover .list-group-item-text,
+.list-group-item.disabled:focus .list-group-item-text {
+  color: #777777;
+}
+.list-group-item.active,
+.list-group-item.active:hover,
+.list-group-item.active:focus {
+  z-index: 2;
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+}
+.list-group-item.active .list-group-item-heading,
+.list-group-item.active:hover .list-group-item-heading,
+.list-group-item.active:focus .list-group-item-heading,
+.list-group-item.active .list-group-item-heading > small,
+.list-group-item.active:hover .list-group-item-heading > small,
+.list-group-item.active:focus .list-group-item-heading > small,
+.list-group-item.active .list-group-item-heading > .small,
+.list-group-item.active:hover .list-group-item-heading > .small,
+.list-group-item.active:focus .list-group-item-heading > .small {
+  color: inherit;
+}
+.list-group-item.active .list-group-item-text,
+.list-group-item.active:hover .list-group-item-text,
+.list-group-item.active:focus .list-group-item-text {
+  color: #c7ddef;
+}
+.list-group-item-success {
+  color: #3c763d;
+  background-color: #dff0d8;
+}
+a.list-group-item-success,
+button.list-group-item-success {
+  color: #3c763d;
+}
+a.list-group-item-success .list-group-item-heading,
+button.list-group-item-success .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-success:hover,
+button.list-group-item-success:hover,
+a.list-group-item-success:focus,
+button.list-group-item-success:focus {
+  color: #3c763d;
+  background-color: #d0e9c6;
+}
+a.list-group-item-success.active,
+button.list-group-item-success.active,
+a.list-group-item-success.active:hover,
+button.list-group-item-success.active:hover,
+a.list-group-item-success.active:focus,
+button.list-group-item-success.active:focus {
+  color: #fff;
+  background-color: #3c763d;
+  border-color: #3c763d;
+}
+.list-group-item-info {
+  color: #31708f;
+  background-color: #d9edf7;
+}
+a.list-group-item-info,
+button.list-group-item-info {
+  color: #31708f;
+}
+a.list-group-item-info .list-group-item-heading,
+button.list-group-item-info .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-info:hover,
+button.list-group-item-info:hover,
+a.list-group-item-info:focus,
+button.list-group-item-info:focus {
+  color: #31708f;
+  background-color: #c4e3f3;
+}
+a.list-group-item-info.active,
+button.list-group-item-info.active,
+a.list-group-item-info.active:hover,
+button.list-group-item-info.active:hover,
+a.list-group-item-info.active:focus,
+button.list-group-item-info.active:focus {
+  color: #fff;
+  background-color: #31708f;
+  border-color: #31708f;
+}
+.list-group-item-warning {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+}
+a.list-group-item-warning,
+button.list-group-item-warning {
+  color: #8a6d3b;
+}
+a.list-group-item-warning .list-group-item-heading,
+button.list-group-item-warning .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-warning:hover,
+button.list-group-item-warning:hover,
+a.list-group-item-warning:focus,
+button.list-group-item-warning:focus {
+  color: #8a6d3b;
+  background-color: #faf2cc;
+}
+a.list-group-item-warning.active,
+button.list-group-item-warning.active,
+a.list-group-item-warning.active:hover,
+button.list-group-item-warning.active:hover,
+a.list-group-item-warning.active:focus,
+button.list-group-item-warning.active:focus {
+  color: #fff;
+  background-color: #8a6d3b;
+  border-color: #8a6d3b;
+}
+.list-group-item-danger {
+  color: #a94442;
+  background-color: #f2dede;
+}
+a.list-group-item-danger,
+button.list-group-item-danger {
+  color: #a94442;
+}
+a.list-group-item-danger .list-group-item-heading,
+button.list-group-item-danger .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-danger:hover,
+button.list-group-item-danger:hover,
+a.list-group-item-danger:focus,
+button.list-group-item-danger:focus {
+  color: #a94442;
+  background-color: #ebcccc;
+}
+a.list-group-item-danger.active,
+button.list-group-item-danger.active,
+a.list-group-item-danger.active:hover,
+button.list-group-item-danger.active:hover,
+a.list-group-item-danger.active:focus,
+button.list-group-item-danger.active:focus {
+  color: #fff;
+  background-color: #a94442;
+  border-color: #a94442;
+}
+.list-group-item-heading {
+  margin-top: 0;
+  margin-bottom: 5px;
+}
+.list-group-item-text {
+  margin-bottom: 0;
+  line-height: 1.3;
+}
+.panel {
+  margin-bottom: 18px;
+  background-color: #fff;
+  border: 1px solid transparent;
+  border-radius: 2px;
+  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.panel-body {
+  padding: 15px;
+}
+.panel-heading {
+  padding: 10px 15px;
+  border-bottom: 1px solid transparent;
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel-heading > .dropdown .dropdown-toggle {
+  color: inherit;
+}
+.panel-title {
+  margin-top: 0;
+  margin-bottom: 0;
+  font-size: 15px;
+  color: inherit;
+}
+.panel-title > a,
+.panel-title > small,
+.panel-title > .small,
+.panel-title > small > a,
+.panel-title > .small > a {
+  color: inherit;
+}
+.panel-footer {
+  padding: 10px 15px;
+  background-color: #f5f5f5;
+  border-top: 1px solid #ddd;
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .list-group,
+.panel > .panel-collapse > .list-group {
+  margin-bottom: 0;
+}
+.panel > .list-group .list-group-item,
+.panel > .panel-collapse > .list-group .list-group-item {
+  border-width: 1px 0;
+  border-radius: 0;
+}
+.panel > .list-group:first-child .list-group-item:first-child,
+.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
+  border-top: 0;
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel > .list-group:last-child .list-group-item:last-child,
+.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
+  border-bottom: 0;
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.panel-heading + .list-group .list-group-item:first-child {
+  border-top-width: 0;
+}
+.list-group + .panel-footer {
+  border-top-width: 0;
+}
+.panel > .table,
+.panel > .table-responsive > .table,
+.panel > .panel-collapse > .table {
+  margin-bottom: 0;
+}
+.panel > .table caption,
+.panel > .table-responsive > .table caption,
+.panel > .panel-collapse > .table caption {
+  padding-left: 15px;
+  padding-right: 15px;
+}
+.panel > .table:first-child,
+.panel > .table-responsive:first-child > .table:first-child {
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
+  border-top-left-radius: 1px;
+  border-top-right-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
+  border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
+  border-top-right-radius: 1px;
+}
+.panel > .table:last-child,
+.panel > .table-responsive:last-child > .table:last-child {
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
+  border-bottom-left-radius: 1px;
+  border-bottom-right-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
+  border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
+  border-bottom-right-radius: 1px;
+}
+.panel > .panel-body + .table,
+.panel > .panel-body + .table-responsive,
+.panel > .table + .panel-body,
+.panel > .table-responsive + .panel-body {
+  border-top: 1px solid #ddd;
+}
+.panel > .table > tbody:first-child > tr:first-child th,
+.panel > .table > tbody:first-child > tr:first-child td {
+  border-top: 0;
+}
+.panel > .table-bordered,
+.panel > .table-responsive > .table-bordered {
+  border: 0;
+}
+.panel > .table-bordered > thead > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
+.panel > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-bordered > thead > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
+.panel > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-bordered > tfoot > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+  border-left: 0;
+}
+.panel > .table-bordered > thead > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
+.panel > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-bordered > thead > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
+.panel > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-bordered > tfoot > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+  border-right: 0;
+}
+.panel > .table-bordered > thead > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
+.panel > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-bordered > thead > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
+.panel > .table-bordered > tbody > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
+  border-bottom: 0;
+}
+.panel > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-bordered > tfoot > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
+  border-bottom: 0;
+}
+.panel > .table-responsive {
+  border: 0;
+  margin-bottom: 0;
+}
+.panel-group {
+  margin-bottom: 18px;
+}
+.panel-group .panel {
+  margin-bottom: 0;
+  border-radius: 2px;
+}
+.panel-group .panel + .panel {
+  margin-top: 5px;
+}
+.panel-group .panel-heading {
+  border-bottom: 0;
+}
+.panel-group .panel-heading + .panel-collapse > .panel-body,
+.panel-group .panel-heading + .panel-collapse > .list-group {
+  border-top: 1px solid #ddd;
+}
+.panel-group .panel-footer {
+  border-top: 0;
+}
+.panel-group .panel-footer + .panel-collapse .panel-body {
+  border-bottom: 1px solid #ddd;
+}
+.panel-default {
+  border-color: #ddd;
+}
+.panel-default > .panel-heading {
+  color: #333333;
+  background-color: #f5f5f5;
+  border-color: #ddd;
+}
+.panel-default > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #ddd;
+}
+.panel-default > .panel-heading .badge {
+  color: #f5f5f5;
+  background-color: #333333;
+}
+.panel-default > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #ddd;
+}
+.panel-primary {
+  border-color: #337ab7;
+}
+.panel-primary > .panel-heading {
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+}
+.panel-primary > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #337ab7;
+}
+.panel-primary > .panel-heading .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.panel-primary > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #337ab7;
+}
+.panel-success {
+  border-color: #d6e9c6;
+}
+.panel-success > .panel-heading {
+  color: #3c763d;
+  background-color: #dff0d8;
+  border-color: #d6e9c6;
+}
+.panel-success > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #d6e9c6;
+}
+.panel-success > .panel-heading .badge {
+  color: #dff0d8;
+  background-color: #3c763d;
+}
+.panel-success > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #d6e9c6;
+}
+.panel-info {
+  border-color: #bce8f1;
+}
+.panel-info > .panel-heading {
+  color: #31708f;
+  background-color: #d9edf7;
+  border-color: #bce8f1;
+}
+.panel-info > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #bce8f1;
+}
+.panel-info > .panel-heading .badge {
+  color: #d9edf7;
+  background-color: #31708f;
+}
+.panel-info > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #bce8f1;
+}
+.panel-warning {
+  border-color: #faebcc;
+}
+.panel-warning > .panel-heading {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+  border-color: #faebcc;
+}
+.panel-warning > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #faebcc;
+}
+.panel-warning > .panel-heading .badge {
+  color: #fcf8e3;
+  background-color: #8a6d3b;
+}
+.panel-warning > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #faebcc;
+}
+.panel-danger {
+  border-color: #ebccd1;
+}
+.panel-danger > .panel-heading {
+  color: #a94442;
+  background-color: #f2dede;
+  border-color: #ebccd1;
+}
+.panel-danger > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #ebccd1;
+}
+.panel-danger > .panel-heading .badge {
+  color: #f2dede;
+  background-color: #a94442;
+}
+.panel-danger > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #ebccd1;
+}
+.embed-responsive {
+  position: relative;
+  display: block;
+  height: 0;
+  padding: 0;
+  overflow: hidden;
+}
+.embed-responsive .embed-responsive-item,
+.embed-responsive iframe,
+.embed-responsive embed,
+.embed-responsive object,
+.embed-responsive video {
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  height: 100%;
+  width: 100%;
+  border: 0;
+}
+.embed-responsive-16by9 {
+  padding-bottom: 56.25%;
+}
+.embed-responsive-4by3 {
+  padding-bottom: 75%;
+}
+.well {
+  min-height: 20px;
+  padding: 19px;
+  margin-bottom: 20px;
+  background-color: #f5f5f5;
+  border: 1px solid #e3e3e3;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.well blockquote {
+  border-color: #ddd;
+  border-color: rgba(0, 0, 0, 0.15);
+}
+.well-lg {
+  padding: 24px;
+  border-radius: 3px;
+}
+.well-sm {
+  padding: 9px;
+  border-radius: 1px;
+}
+.close {
+  float: right;
+  font-size: 19.5px;
+  font-weight: bold;
+  line-height: 1;
+  color: #000;
+  text-shadow: 0 1px 0 #fff;
+  opacity: 0.2;
+  filter: alpha(opacity=20);
+}
+.close:hover,
+.close:focus {
+  color: #000;
+  text-decoration: none;
+  cursor: pointer;
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+}
+button.close {
+  padding: 0;
+  cursor: pointer;
+  background: transparent;
+  border: 0;
+  -webkit-appearance: none;
+}
+.modal-open {
+  overflow: hidden;
+}
+.modal {
+  display: none;
+  overflow: hidden;
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1050;
+  -webkit-overflow-scrolling: touch;
+  outline: 0;
+}
+.modal.fade .modal-dialog {
+  -webkit-transform: translate(0, -25%);
+  -ms-transform: translate(0, -25%);
+  -o-transform: translate(0, -25%);
+  transform: translate(0, -25%);
+  -webkit-transition: -webkit-transform 0.3s ease-out;
+  -moz-transition: -moz-transform 0.3s ease-out;
+  -o-transition: -o-transform 0.3s ease-out;
+  transition: transform 0.3s ease-out;
+}
+.modal.in .modal-dialog {
+  -webkit-transform: translate(0, 0);
+  -ms-transform: translate(0, 0);
+  -o-transform: translate(0, 0);
+  transform: translate(0, 0);
+}
+.modal-open .modal {
+  overflow-x: hidden;
+  overflow-y: auto;
+}
+.modal-dialog {
+  position: relative;
+  width: auto;
+  margin: 10px;
+}
+.modal-content {
+  position: relative;
+  background-color: #fff;
+  border: 1px solid #999;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 3px;
+  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+  background-clip: padding-box;
+  outline: 0;
+}
+.modal-backdrop {
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1040;
+  background-color: #000;
+}
+.modal-backdrop.fade {
+  opacity: 0;
+  filter: alpha(opacity=0);
+}
+.modal-backdrop.in {
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+}
+.modal-header {
+  padding: 15px;
+  border-bottom: 1px solid #e5e5e5;
+}
+.modal-header .close {
+  margin-top: -2px;
+}
+.modal-title {
+  margin: 0;
+  line-height: 1.42857143;
+}
+.modal-body {
+  position: relative;
+  padding: 15px;
+}
+.modal-footer {
+  padding: 15px;
+  text-align: right;
+  border-top: 1px solid #e5e5e5;
+}
+.modal-footer .btn + .btn {
+  margin-left: 5px;
+  margin-bottom: 0;
+}
+.modal-footer .btn-group .btn + .btn {
+  margin-left: -1px;
+}
+.modal-footer .btn-block + .btn-block {
+  margin-left: 0;
+}
+.modal-scrollbar-measure {
+  position: absolute;
+  top: -9999px;
+  width: 50px;
+  height: 50px;
+  overflow: scroll;
+}
+@media (min-width: 768px) {
+  .modal-dialog {
+    width: 600px;
+    margin: 30px auto;
+  }
+  .modal-content {
+    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+  }
+  .modal-sm {
+    width: 300px;
+  }
+}
+@media (min-width: 992px) {
+  .modal-lg {
+    width: 900px;
+  }
+}
+.tooltip {
+  position: absolute;
+  z-index: 1070;
+  display: block;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-style: normal;
+  font-weight: normal;
+  letter-spacing: normal;
+  line-break: auto;
+  line-height: 1.42857143;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  white-space: normal;
+  word-break: normal;
+  word-spacing: normal;
+  word-wrap: normal;
+  font-size: 12px;
+  opacity: 0;
+  filter: alpha(opacity=0);
+}
+.tooltip.in {
+  opacity: 0.9;
+  filter: alpha(opacity=90);
+}
+.tooltip.top {
+  margin-top: -3px;
+  padding: 5px 0;
+}
+.tooltip.right {
+  margin-left: 3px;
+  padding: 0 5px;
+}
+.tooltip.bottom {
+  margin-top: 3px;
+  padding: 5px 0;
+}
+.tooltip.left {
+  margin-left: -3px;
+  padding: 0 5px;
+}
+.tooltip-inner {
+  max-width: 200px;
+  padding: 3px 8px;
+  color: #fff;
+  text-align: center;
+  background-color: #000;
+  border-radius: 2px;
+}
+.tooltip-arrow {
+  position: absolute;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+.tooltip.top .tooltip-arrow {
+  bottom: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.top-left .tooltip-arrow {
+  bottom: 0;
+  right: 5px;
+  margin-bottom: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.top-right .tooltip-arrow {
+  bottom: 0;
+  left: 5px;
+  margin-bottom: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.right .tooltip-arrow {
+  top: 50%;
+  left: 0;
+  margin-top: -5px;
+  border-width: 5px 5px 5px 0;
+  border-right-color: #000;
+}
+.tooltip.left .tooltip-arrow {
+  top: 50%;
+  right: 0;
+  margin-top: -5px;
+  border-width: 5px 0 5px 5px;
+  border-left-color: #000;
+}
+.tooltip.bottom .tooltip-arrow {
+  top: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.tooltip.bottom-left .tooltip-arrow {
+  top: 0;
+  right: 5px;
+  margin-top: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.tooltip.bottom-right .tooltip-arrow {
+  top: 0;
+  left: 5px;
+  margin-top: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.popover {
+  position: absolute;
+  top: 0;
+  left: 0;
+  z-index: 1060;
+  display: none;
+  max-width: 276px;
+  padding: 1px;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-style: normal;
+  font-weight: normal;
+  letter-spacing: normal;
+  line-break: auto;
+  line-height: 1.42857143;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  white-space: normal;
+  word-break: normal;
+  word-spacing: normal;
+  word-wrap: normal;
+  font-size: 13px;
+  background-color: #fff;
+  background-clip: padding-box;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 3px;
+  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+}
+.popover.top {
+  margin-top: -10px;
+}
+.popover.right {
+  margin-left: 10px;
+}
+.popover.bottom {
+  margin-top: 10px;
+}
+.popover.left {
+  margin-left: -10px;
+}
+.popover-title {
+  margin: 0;
+  padding: 8px 14px;
+  font-size: 13px;
+  background-color: #f7f7f7;
+  border-bottom: 1px solid #ebebeb;
+  border-radius: 2px 2px 0 0;
+}
+.popover-content {
+  padding: 9px 14px;
+}
+.popover > .arrow,
+.popover > .arrow:after {
+  position: absolute;
+  display: block;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+.popover > .arrow {
+  border-width: 11px;
+}
+.popover > .arrow:after {
+  border-width: 10px;
+  content: "";
+}
+.popover.top > .arrow {
+  left: 50%;
+  margin-left: -11px;
+  border-bottom-width: 0;
+  border-top-color: #999999;
+  border-top-color: rgba(0, 0, 0, 0.25);
+  bottom: -11px;
+}
+.popover.top > .arrow:after {
+  content: " ";
+  bottom: 1px;
+  margin-left: -10px;
+  border-bottom-width: 0;
+  border-top-color: #fff;
+}
+.popover.right > .arrow {
+  top: 50%;
+  left: -11px;
+  margin-top: -11px;
+  border-left-width: 0;
+  border-right-color: #999999;
+  border-right-color: rgba(0, 0, 0, 0.25);
+}
+.popover.right > .arrow:after {
+  content: " ";
+  left: 1px;
+  bottom: -10px;
+  border-left-width: 0;
+  border-right-color: #fff;
+}
+.popover.bottom > .arrow {
+  left: 50%;
+  margin-left: -11px;
+  border-top-width: 0;
+  border-bottom-color: #999999;
+  border-bottom-color: rgba(0, 0, 0, 0.25);
+  top: -11px;
+}
+.popover.bottom > .arrow:after {
+  content: " ";
+  top: 1px;
+  margin-left: -10px;
+  border-top-width: 0;
+  border-bottom-color: #fff;
+}
+.popover.left > .arrow {
+  top: 50%;
+  right: -11px;
+  margin-top: -11px;
+  border-right-width: 0;
+  border-left-color: #999999;
+  border-left-color: rgba(0, 0, 0, 0.25);
+}
+.popover.left > .arrow:after {
+  content: " ";
+  right: 1px;
+  border-right-width: 0;
+  border-left-color: #fff;
+  bottom: -10px;
+}
+.carousel {
+  position: relative;
+}
+.carousel-inner {
+  position: relative;
+  overflow: hidden;
+  width: 100%;
+}
+.carousel-inner > .item {
+  display: none;
+  position: relative;
+  -webkit-transition: 0.6s ease-in-out left;
+  -o-transition: 0.6s ease-in-out left;
+  transition: 0.6s ease-in-out left;
+}
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+  line-height: 1;
+}
+@media all and (transform-3d), (-webkit-transform-3d) {
+  .carousel-inner > .item {
+    -webkit-transition: -webkit-transform 0.6s ease-in-out;
+    -moz-transition: -moz-transform 0.6s ease-in-out;
+    -o-transition: -o-transform 0.6s ease-in-out;
+    transition: transform 0.6s ease-in-out;
+    -webkit-backface-visibility: hidden;
+    -moz-backface-visibility: hidden;
+    backface-visibility: hidden;
+    -webkit-perspective: 1000px;
+    -moz-perspective: 1000px;
+    perspective: 1000px;
+  }
+  .carousel-inner > .item.next,
+  .carousel-inner > .item.active.right {
+    -webkit-transform: translate3d(100%, 0, 0);
+    transform: translate3d(100%, 0, 0);
+    left: 0;
+  }
+  .carousel-inner > .item.prev,
+  .carousel-inner > .item.active.left {
+    -webkit-transform: translate3d(-100%, 0, 0);
+    transform: translate3d(-100%, 0, 0);
+    left: 0;
+  }
+  .carousel-inner > .item.next.left,
+  .carousel-inner > .item.prev.right,
+  .carousel-inner > .item.active {
+    -webkit-transform: translate3d(0, 0, 0);
+    transform: translate3d(0, 0, 0);
+    left: 0;
+  }
+}
+.carousel-inner > .active,
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  display: block;
+}
+.carousel-inner > .active {
+  left: 0;
+}
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  position: absolute;
+  top: 0;
+  width: 100%;
+}
+.carousel-inner > .next {
+  left: 100%;
+}
+.carousel-inner > .prev {
+  left: -100%;
+}
+.carousel-inner > .next.left,
+.carousel-inner > .prev.right {
+  left: 0;
+}
+.carousel-inner > .active.left {
+  left: -100%;
+}
+.carousel-inner > .active.right {
+  left: 100%;
+}
+.carousel-control {
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  width: 15%;
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+  font-size: 20px;
+  color: #fff;
+  text-align: center;
+  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+  background-color: rgba(0, 0, 0, 0);
+}
+.carousel-control.left {
+  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
+}
+.carousel-control.right {
+  left: auto;
+  right: 0;
+  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
+}
+.carousel-control:hover,
+.carousel-control:focus {
+  outline: 0;
+  color: #fff;
+  text-decoration: none;
+  opacity: 0.9;
+  filter: alpha(opacity=90);
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-left,
+.carousel-control .glyphicon-chevron-right {
+  position: absolute;
+  top: 50%;
+  margin-top: -10px;
+  z-index: 5;
+  display: inline-block;
+}
+.carousel-control .icon-prev,
+.carousel-control .glyphicon-chevron-left {
+  left: 50%;
+  margin-left: -10px;
+}
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-right {
+  right: 50%;
+  margin-right: -10px;
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next {
+  width: 20px;
+  height: 20px;
+  line-height: 1;
+  font-family: serif;
+}
+.carousel-control .icon-prev:before {
+  content: '\2039';
+}
+.carousel-control .icon-next:before {
+  content: '\203a';
+}
+.carousel-indicators {
+  position: absolute;
+  bottom: 10px;
+  left: 50%;
+  z-index: 15;
+  width: 60%;
+  margin-left: -30%;
+  padding-left: 0;
+  list-style: none;
+  text-align: center;
+}
+.carousel-indicators li {
+  display: inline-block;
+  width: 10px;
+  height: 10px;
+  margin: 1px;
+  text-indent: -999px;
+  border: 1px solid #fff;
+  border-radius: 10px;
+  cursor: pointer;
+  background-color: #000 \9;
+  background-color: rgba(0, 0, 0, 0);
+}
+.carousel-indicators .active {
+  margin: 0;
+  width: 12px;
+  height: 12px;
+  background-color: #fff;
+}
+.carousel-caption {
+  position: absolute;
+  left: 15%;
+  right: 15%;
+  bottom: 20px;
+  z-index: 10;
+  padding-top: 20px;
+  padding-bottom: 20px;
+  color: #fff;
+  text-align: center;
+  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+}
+.carousel-caption .btn {
+  text-shadow: none;
+}
+@media screen and (min-width: 768px) {
+  .carousel-control .glyphicon-chevron-left,
+  .carousel-control .glyphicon-chevron-right,
+  .carousel-control .icon-prev,
+  .carousel-control .icon-next {
+    width: 30px;
+    height: 30px;
+    margin-top: -10px;
+    font-size: 30px;
+  }
+  .carousel-control .glyphicon-chevron-left,
+  .carousel-control .icon-prev {
+    margin-left: -10px;
+  }
+  .carousel-control .glyphicon-chevron-right,
+  .carousel-control .icon-next {
+    margin-right: -10px;
+  }
+  .carousel-caption {
+    left: 20%;
+    right: 20%;
+    padding-bottom: 30px;
+  }
+  .carousel-indicators {
+    bottom: 20px;
+  }
+}
+.clearfix:before,
+.clearfix:after,
+.dl-horizontal dd:before,
+.dl-horizontal dd:after,
+.container:before,
+.container:after,
+.container-fluid:before,
+.container-fluid:after,
+.row:before,
+.row:after,
+.form-horizontal .form-group:before,
+.form-horizontal .form-group:after,
+.btn-toolbar:before,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:before,
+.btn-group-vertical > .btn-group:after,
+.nav:before,
+.nav:after,
+.navbar:before,
+.navbar:after,
+.navbar-header:before,
+.navbar-header:after,
+.navbar-collapse:before,
+.navbar-collapse:after,
+.pager:before,
+.pager:after,
+.panel-body:before,
+.panel-body:after,
+.modal-header:before,
+.modal-header:after,
+.modal-footer:before,
+.modal-footer:after,
+.item_buttons:before,
+.item_buttons:after {
+  content: " ";
+  display: table;
+}
+.clearfix:after,
+.dl-horizontal dd:after,
+.container:after,
+.container-fluid:after,
+.row:after,
+.form-horizontal .form-group:after,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:after,
+.nav:after,
+.navbar:after,
+.navbar-header:after,
+.navbar-collapse:after,
+.pager:after,
+.panel-body:after,
+.modal-header:after,
+.modal-footer:after,
+.item_buttons:after {
+  clear: both;
+}
+.center-block {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.pull-right {
+  float: right !important;
+}
+.pull-left {
+  float: left !important;
+}
+.hide {
+  display: none !important;
+}
+.show {
+  display: block !important;
+}
+.invisible {
+  visibility: hidden;
+}
+.text-hide {
+  font: 0/0 a;
+  color: transparent;
+  text-shadow: none;
+  background-color: transparent;
+  border: 0;
+}
+.hidden {
+  display: none !important;
+}
+.affix {
+  position: fixed;
+}
+@-ms-viewport {
+  width: device-width;
+}
+.visible-xs,
+.visible-sm,
+.visible-md,
+.visible-lg {
+  display: none !important;
+}
+.visible-xs-block,
+.visible-xs-inline,
+.visible-xs-inline-block,
+.visible-sm-block,
+.visible-sm-inline,
+.visible-sm-inline-block,
+.visible-md-block,
+.visible-md-inline,
+.visible-md-inline-block,
+.visible-lg-block,
+.visible-lg-inline,
+.visible-lg-inline-block {
+  display: none !important;
+}
+@media (max-width: 767px) {
+  .visible-xs {
+    display: block !important;
+  }
+  table.visible-xs {
+    display: table !important;
+  }
+  tr.visible-xs {
+    display: table-row !important;
+  }
+  th.visible-xs,
+  td.visible-xs {
+    display: table-cell !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-block {
+    display: block !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-inline {
+    display: inline !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm {
+    display: block !important;
+  }
+  table.visible-sm {
+    display: table !important;
+  }
+  tr.visible-sm {
+    display: table-row !important;
+  }
+  th.visible-sm,
+  td.visible-sm {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-block {
+    display: block !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md {
+    display: block !important;
+  }
+  table.visible-md {
+    display: table !important;
+  }
+  tr.visible-md {
+    display: table-row !important;
+  }
+  th.visible-md,
+  td.visible-md {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-block {
+    display: block !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg {
+    display: block !important;
+  }
+  table.visible-lg {
+    display: table !important;
+  }
+  tr.visible-lg {
+    display: table-row !important;
+  }
+  th.visible-lg,
+  td.visible-lg {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-block {
+    display: block !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (max-width: 767px) {
+  .hidden-xs {
+    display: none !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .hidden-sm {
+    display: none !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .hidden-md {
+    display: none !important;
+  }
+}
+@media (min-width: 1200px) {
+  .hidden-lg {
+    display: none !important;
+  }
+}
+.visible-print {
+  display: none !important;
+}
+@media print {
+  .visible-print {
+    display: block !important;
+  }
+  table.visible-print {
+    display: table !important;
+  }
+  tr.visible-print {
+    display: table-row !important;
+  }
+  th.visible-print,
+  td.visible-print {
+    display: table-cell !important;
+  }
+}
+.visible-print-block {
+  display: none !important;
+}
+@media print {
+  .visible-print-block {
+    display: block !important;
+  }
+}
+.visible-print-inline {
+  display: none !important;
+}
+@media print {
+  .visible-print-inline {
+    display: inline !important;
+  }
+}
+.visible-print-inline-block {
+  display: none !important;
+}
+@media print {
+  .visible-print-inline-block {
+    display: inline-block !important;
+  }
+}
+@media print {
+  .hidden-print {
+    display: none !important;
+  }
+}
+/*!
+*
+* Font Awesome
+*
+*/
+/*!
+ *  Font Awesome 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
+ *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
+ */
+/* FONT PATH
+ * -------------------------- */
+@font-face {
+  font-family: 'FontAwesome';
+  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.2.0');
+  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#fontawesomeregular') format('svg');
+  font-weight: normal;
+  font-style: normal;
+}
+.fa {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+/* makes the font 33% larger relative to the icon container */
+.fa-lg {
+  font-size: 1.33333333em;
+  line-height: 0.75em;
+  vertical-align: -15%;
+}
+.fa-2x {
+  font-size: 2em;
+}
+.fa-3x {
+  font-size: 3em;
+}
+.fa-4x {
+  font-size: 4em;
+}
+.fa-5x {
+  font-size: 5em;
+}
+.fa-fw {
+  width: 1.28571429em;
+  text-align: center;
+}
+.fa-ul {
+  padding-left: 0;
+  margin-left: 2.14285714em;
+  list-style-type: none;
+}
+.fa-ul > li {
+  position: relative;
+}
+.fa-li {
+  position: absolute;
+  left: -2.14285714em;
+  width: 2.14285714em;
+  top: 0.14285714em;
+  text-align: center;
+}
+.fa-li.fa-lg {
+  left: -1.85714286em;
+}
+.fa-border {
+  padding: .2em .25em .15em;
+  border: solid 0.08em #eee;
+  border-radius: .1em;
+}
+.pull-right {
+  float: right;
+}
+.pull-left {
+  float: left;
+}
+.fa.pull-left {
+  margin-right: .3em;
+}
+.fa.pull-right {
+  margin-left: .3em;
+}
+.fa-spin {
+  -webkit-animation: fa-spin 2s infinite linear;
+  animation: fa-spin 2s infinite linear;
+}
+@-webkit-keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+    transform: rotate(0deg);
+  }
+  100% {
+    -webkit-transform: rotate(359deg);
+    transform: rotate(359deg);
+  }
+}
+@keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+    transform: rotate(0deg);
+  }
+  100% {
+    -webkit-transform: rotate(359deg);
+    transform: rotate(359deg);
+  }
+}
+.fa-rotate-90 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
+  -webkit-transform: rotate(90deg);
+  -ms-transform: rotate(90deg);
+  transform: rotate(90deg);
+}
+.fa-rotate-180 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
+  -webkit-transform: rotate(180deg);
+  -ms-transform: rotate(180deg);
+  transform: rotate(180deg);
+}
+.fa-rotate-270 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
+  -webkit-transform: rotate(270deg);
+  -ms-transform: rotate(270deg);
+  transform: rotate(270deg);
+}
+.fa-flip-horizontal {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);
+  -webkit-transform: scale(-1, 1);
+  -ms-transform: scale(-1, 1);
+  transform: scale(-1, 1);
+}
+.fa-flip-vertical {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);
+  -webkit-transform: scale(1, -1);
+  -ms-transform: scale(1, -1);
+  transform: scale(1, -1);
+}
+:root .fa-rotate-90,
+:root .fa-rotate-180,
+:root .fa-rotate-270,
+:root .fa-flip-horizontal,
+:root .fa-flip-vertical {
+  filter: none;
+}
+.fa-stack {
+  position: relative;
+  display: inline-block;
+  width: 2em;
+  height: 2em;
+  line-height: 2em;
+  vertical-align: middle;
+}
+.fa-stack-1x,
+.fa-stack-2x {
+  position: absolute;
+  left: 0;
+  width: 100%;
+  text-align: center;
+}
+.fa-stack-1x {
+  line-height: inherit;
+}
+.fa-stack-2x {
+  font-size: 2em;
+}
+.fa-inverse {
+  color: #fff;
+}
+/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
+   readers do not read off random characters that represent icons */
+.fa-glass:before {
+  content: "\f000";
+}
+.fa-music:before {
+  content: "\f001";
+}
+.fa-search:before {
+  content: "\f002";
+}
+.fa-envelope-o:before {
+  content: "\f003";
+}
+.fa-heart:before {
+  content: "\f004";
+}
+.fa-star:before {
+  content: "\f005";
+}
+.fa-star-o:before {
+  content: "\f006";
+}
+.fa-user:before {
+  content: "\f007";
+}
+.fa-film:before {
+  content: "\f008";
+}
+.fa-th-large:before {
+  content: "\f009";
+}
+.fa-th:before {
+  content: "\f00a";
+}
+.fa-th-list:before {
+  content: "\f00b";
+}
+.fa-check:before {
+  content: "\f00c";
+}
+.fa-remove:before,
+.fa-close:before,
+.fa-times:before {
+  content: "\f00d";
+}
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+  content: "\f00e";
+}
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+  content: "\f010";
+}
+.fa-power-off:before {
+  content: "\f011";
+}
+.fa-signal:before {
+  content: "\f012";
+}
+.fa-gear:before,
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+  content: "\f013";
+}
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+  content: "\f014";
+}
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+  content: "\f015";
+}
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+  content: "\f016";
+}
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+  content: "\f017";
+}
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+  content: "\f018";
+}
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+  content: "\f019";
+}
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+  content: "\f01a";
+}
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+  content: "\f01b";
+}
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+  content: "\f01c";
+}
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+  content: "\f01d";
+}
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+  content: "\f01e";
+}
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+  content: "\f021";
+}
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+  content: "\f022";
+}
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+  content: "\f023";
+}
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+  content: "\f024";
+}
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+  content: "\f025";
+}
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+  content: "\f026";
+}
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+  content: "\f027";
+}
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+  content: "\f028";
+}
+.fa-qrcode:before {
+  content: "\f029";
+}
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+  content: "\f02a";
+}
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+  content: "\f02b";
+}
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+  content: "\f02c";
+}
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+  content: "\f02d";
+}
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+  content: "\f02e";
+}
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+  content: "\f02f";
+}
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+  content: "\f030";
+}
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+  content: "\f031";
+}
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+  content: "\f032";
+}
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+  content: "\f033";
+}
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+}
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+}
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+}
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+}
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+  content: "\f038";
+}
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+  content: "\f039";
+}
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+  content: "\f03a";
+}
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+}
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+  content: "\f03c";
+}
+.fa-video-camera:before {
+  content: "\f03d";
+}
+.fa-photo:before,
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+  content: "\f03e";
+}
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+  content: "\f040";
+}
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+  content: "\f041";
+}
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+  content: "\f042";
+}
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+  content: "\f043";
+}
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+  content: "\f044";
+}
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+  content: "\f045";
+}
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+  content: "\f046";
+}
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+  content: "\f047";
+}
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+  content: "\f048";
+}
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+  content: "\f049";
+}
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+  content: "\f04a";
+}
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+  content: "\f04b";
+}
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+  content: "\f04c";
+}
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+  content: "\f04d";
+}
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+  content: "\f04e";
+}
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+  content: "\f050";
+}
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+  content: "\f051";
+}
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+  content: "\f052";
+}
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+  content: "\f053";
+}
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+  content: "\f054";
+}
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+  content: "\f055";
+}
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+  content: "\f056";
+}
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+  content: "\f057";
+}
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+  content: "\f058";
+}
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+  content: "\f059";
+}
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+  content: "\f05a";
+}
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+  content: "\f05b";
+}
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+  content: "\f05c";
+}
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+  content: "\f05d";
+}
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+  content: "\f05e";
+}
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+  content: "\f060";
+}
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+  content: "\f061";
+}
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+  content: "\f062";
+}
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+  content: "\f063";
+}
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+  content: "\f064";
+}
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+  content: "\f065";
+}
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+  content: "\f066";
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+  content: "\f067";
+}
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+  content: "\f068";
+}
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+  content: "\f069";
+}
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+  content: "\f06a";
+}
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+  content: "\f06b";
+}
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+  content: "\f06c";
+}
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+  content: "\f06d";
+}
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+  content: "\f06e";
+}
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+  content: "\f070";
+}
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+  content: "\f071";
+}
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+  content: "\f072";
+}
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+  content: "\f073";
+}
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+  content: "\f074";
+}
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+  content: "\f075";
+}
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+  content: "\f076";
+}
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+}
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+  content: "\f078";
+}
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+  content: "\f079";
+}
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+  content: "\f07a";
+}
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+  content: "\f07b";
+}
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+  content: "\f07c";
+}
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+  content: "\f07d";
+}
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+  content: "\f07e";
+}
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+  content: "\f080";
+}
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+  content: "\f081";
+}
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+  content: "\f082";
+}
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+  content: "\f083";
+}
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+  content: "\f084";
+}
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+  content: "\f085";
+}
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+  content: "\f086";
+}
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+  content: "\f087";
+}
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+  content: "\f088";
+}
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+  content: "\f089";
+}
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+  content: "\f08a";
+}
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+  content: "\f08b";
+}
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+  content: "\f08c";
+}
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+  content: "\f08d";
+}
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+  content: "\f08e";
+}
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+  content: "\f090";
+}
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+  content: "\f091";
+}
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+  content: "\f092";
+}
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+  content: "\f093";
+}
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+  content: "\f094";
+}
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+  content: "\f095";
+}
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+  content: "\f096";
+}
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+  content: "\f097";
+}
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+  content: "\f098";
+}
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+  content: "\f099";
+}
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+  content: "\f09a";
+}
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+  content: "\f09b";
+}
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+  content: "\f09c";
+}
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+  content: "\f09d";
+}
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+  content: "\f09e";
+}
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+  content: "\f0a0";
+}
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+  content: "\f0a1";
+}
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+  content: "\f0f3";
+}
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+  content: "\f0a3";
+}
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+  content: "\f0a4";
+}
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+  content: "\f0a5";
+}
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+  content: "\f0a6";
+}
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+  content: "\f0a7";
+}
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+  content: "\f0a8";
+}
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+  content: "\f0a9";
+}
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+  content: "\f0aa";
+}
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+  content: "\f0ab";
+}
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+  content: "\f0ac";
+}
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+  content: "\f0ad";
+}
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+  content: "\f0ae";
+}
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+  content: "\f0b0";
+}
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+  content: "\f0b1";
+}
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+  content: "\f0b2";
+}
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+  content: "\f0c0";
+}
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+}
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+  content: "\f0c2";
+}
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+  content: "\f0c3";
+}
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+  content: "\f0c4";
+}
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+  content: "\f0c5";
+}
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+  content: "\f0c6";
+}
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+  content: "\f0c7";
+}
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+  content: "\f0c8";
+}
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+  content: "\f0c9";
+}
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+  content: "\f0ca";
+}
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+  content: "\f0cb";
+}
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+  content: "\f0cc";
+}
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+  content: "\f0cd";
+}
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+  content: "\f0ce";
+}
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+  content: "\f0d0";
+}
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+  content: "\f0d1";
+}
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+  content: "\f0d2";
+}
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+  content: "\f0d3";
+}
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+  content: "\f0d4";
+}
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+  content: "\f0d5";
+}
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+  content: "\f0d6";
+}
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+  content: "\f0d7";
+}
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+  content: "\f0d8";
+}
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+  content: "\f0d9";
+}
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+  content: "\f0da";
+}
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+  content: "\f0db";
+}
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+  content: "\f0dc";
+}
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+  content: "\f0dd";
+}
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+}
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+  content: "\f0e0";
+}
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+  content: "\f0e1";
+}
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+  content: "\f0e3";
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+  content: "\f0e4";
+}
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+  content: "\f0e9";
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+}
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+  content: "\f0eb";
+}
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+}
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+  content: "\f0ed";
+}
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+  content: "\f0ee";
+}
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+}
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+}
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+}
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+  content: "\f0a2";
+}
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+  content: "\f0f4";
+}
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+  content: "\f0f5";
+}
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+}
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+}
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+  content: "\f0f8";
+}
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+  content: "\f0f9";
+}
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+  content: "\f0fa";
+}
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+  content: "\f0fb";
+}
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+  content: "\f0fc";
+}
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+  content: "\f0fd";
+}
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+  content: "\f0fe";
+}
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+  content: "\f100";
+}
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+  content: "\f101";
+}
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+  content: "\f102";
+}
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+  content: "\f103";
+}
+.fa-angle-left:before {
+  content: "\f104";
+}
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+  content: "\f105";
+}
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+  content: "\f106";
+}
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+  content: "\f107";
+}
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+  content: "\f108";
+}
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+  content: "\f109";
+}
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+  content: "\f10a";
+}
+.fa-mobile-phone:before,
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+  content: "\f10b";
+}
+.fa-circle-o:before {
+  content: "\f10c";
+}
+.fa-quote-left:before {
+  content: "\f10d";
+}
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+  content: "\f10e";
+}
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+  content: "\f110";
+}
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+  content: "\f111";
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+  content: "\f112";
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+  content: "\f113";
+}
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+  content: "\f114";
+}
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+  content: "\f115";
+}
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+}
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+  content: "\f119";
+}
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+  content: "\f11a";
+}
+.fa-gamepad:before {
+  content: "\f11b";
+}
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+  content: "\f11c";
+}
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+  content: "\f11d";
+}
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+  content: "\f11e";
+}
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+  content: "\f120";
+}
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+  content: "\f121";
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+  content: "\f122";
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+  content: "\f123";
+}
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+  content: "\f124";
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+  content: "\f127";
+}
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+  content: "\f128";
+}
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+  content: "\f129";
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+  content: "\f12a";
+}
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+  content: "\f12b";
+}
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+  content: "\f12c";
+}
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+  content: "\f12d";
+}
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+  content: "\f12e";
+}
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+  content: "\f130";
+}
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+}
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+  content: "\f132";
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+}
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+}
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+  content: "\f135";
+}
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+  content: "\f137";
+}
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+  content: "\f138";
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+  content: "\f139";
+}
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+  content: "\f13a";
+}
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+}
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+  content: "\f13c";
+}
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+  content: "\f13d";
+}
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+  content: "\f13e";
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+}
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+  content: "\f149";
+}
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+  content: "\f14a";
+}
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+  content: "\f14b";
+}
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+}
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+  content: "\f14e";
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+  content: "\f16a";
+}
+.fa-dropbox:before {
+  content: "\f16b";
+}
+.fa-stack-overflow:before {
+  content: "\f16c";
+}
+.fa-instagram:before {
+  content: "\f16d";
+}
+.fa-flickr:before {
+  content: "\f16e";
+}
+.fa-adn:before {
+  content: "\f170";
+}
+.fa-bitbucket:before {
+  content: "\f171";
+}
+.fa-bitbucket-square:before {
+  content: "\f172";
+}
+.fa-tumblr:before {
+  content: "\f173";
+}
+.fa-tumblr-square:before {
+  content: "\f174";
+}
+.fa-long-arrow-down:before {
+  content: "\f175";
+}
+.fa-long-arrow-up:before {
+  content: "\f176";
+}
+.fa-long-arrow-left:before {
+  content: "\f177";
+}
+.fa-long-arrow-right:before {
+  content: "\f178";
+}
+.fa-apple:before {
+  content: "\f179";
+}
+.fa-windows:before {
+  content: "\f17a";
+}
+.fa-android:before {
+  content: "\f17b";
+}
+.fa-linux:before {
+  content: "\f17c";
+}
+.fa-dribbble:before {
+  content: "\f17d";
+}
+.fa-skype:before {
+  content: "\f17e";
+}
+.fa-foursquare:before {
+  content: "\f180";
+}
+.fa-trello:before {
+  content: "\f181";
+}
+.fa-female:before {
+  content: "\f182";
+}
+.fa-male:before {
+  content: "\f183";
+}
+.fa-gittip:before {
+  content: "\f184";
+}
+.fa-sun-o:before {
+  content: "\f185";
+}
+.fa-moon-o:before {
+  content: "\f186";
+}
+.fa-archive:before {
+  content: "\f187";
+}
+.fa-bug:before {
+  content: "\f188";
+}
+.fa-vk:before {
+  content: "\f189";
+}
+.fa-weibo:before {
+  content: "\f18a";
+}
+.fa-renren:before {
+  content: "\f18b";
+}
+.fa-pagelines:before {
+  content: "\f18c";
+}
+.fa-stack-exchange:before {
+  content: "\f18d";
+}
+.fa-arrow-circle-o-right:before {
+  content: "\f18e";
+}
+.fa-arrow-circle-o-left:before {
+  content: "\f190";
+}
+.fa-toggle-left:before,
+.fa-caret-square-o-left:before {
+  content: "\f191";
+}
+.fa-dot-circle-o:before {
+  content: "\f192";
+}
+.fa-wheelchair:before {
+  content: "\f193";
+}
+.fa-vimeo-square:before {
+  content: "\f194";
+}
+.fa-turkish-lira:before,
+.fa-try:before {
+  content: "\f195";
+}
+.fa-plus-square-o:before {
+  content: "\f196";
+}
+.fa-space-shuttle:before {
+  content: "\f197";
+}
+.fa-slack:before {
+  content: "\f198";
+}
+.fa-envelope-square:before {
+  content: "\f199";
+}
+.fa-wordpress:before {
+  content: "\f19a";
+}
+.fa-openid:before {
+  content: "\f19b";
+}
+.fa-institution:before,
+.fa-bank:before,
+.fa-university:before {
+  content: "\f19c";
+}
+.fa-mortar-board:before,
+.fa-graduation-cap:before {
+  content: "\f19d";
+}
+.fa-yahoo:before {
+  content: "\f19e";
+}
+.fa-google:before {
+  content: "\f1a0";
+}
+.fa-reddit:before {
+  content: "\f1a1";
+}
+.fa-reddit-square:before {
+  content: "\f1a2";
+}
+.fa-stumbleupon-circle:before {
+  content: "\f1a3";
+}
+.fa-stumbleupon:before {
+  content: "\f1a4";
+}
+.fa-delicious:before {
+  content: "\f1a5";
+}
+.fa-digg:before {
+  content: "\f1a6";
+}
+.fa-pied-piper:before {
+  content: "\f1a7";
+}
+.fa-pied-piper-alt:before {
+  content: "\f1a8";
+}
+.fa-drupal:before {
+  content: "\f1a9";
+}
+.fa-joomla:before {
+  content: "\f1aa";
+}
+.fa-language:before {
+  content: "\f1ab";
+}
+.fa-fax:before {
+  content: "\f1ac";
+}
+.fa-building:before {
+  content: "\f1ad";
+}
+.fa-child:before {
+  content: "\f1ae";
+}
+.fa-paw:before {
+  content: "\f1b0";
+}
+.fa-spoon:before {
+  content: "\f1b1";
+}
+.fa-cube:before {
+  content: "\f1b2";
+}
+.fa-cubes:before {
+  content: "\f1b3";
+}
+.fa-behance:before {
+  content: "\f1b4";
+}
+.fa-behance-square:before {
+  content: "\f1b5";
+}
+.fa-steam:before {
+  content: "\f1b6";
+}
+.fa-steam-square:before {
+  content: "\f1b7";
+}
+.fa-recycle:before {
+  content: "\f1b8";
+}
+.fa-automobile:before,
+.fa-car:before {
+  content: "\f1b9";
+}
+.fa-cab:before,
+.fa-taxi:before {
+  content: "\f1ba";
+}
+.fa-tree:before {
+  content: "\f1bb";
+}
+.fa-spotify:before {
+  content: "\f1bc";
+}
+.fa-deviantart:before {
+  content: "\f1bd";
+}
+.fa-soundcloud:before {
+  content: "\f1be";
+}
+.fa-database:before {
+  content: "\f1c0";
+}
+.fa-file-pdf-o:before {
+  content: "\f1c1";
+}
+.fa-file-word-o:before {
+  content: "\f1c2";
+}
+.fa-file-excel-o:before {
+  content: "\f1c3";
+}
+.fa-file-powerpoint-o:before {
+  content: "\f1c4";
+}
+.fa-file-photo-o:before,
+.fa-file-picture-o:before,
+.fa-file-image-o:before {
+  content: "\f1c5";
+}
+.fa-file-zip-o:before,
+.fa-file-archive-o:before {
+  content: "\f1c6";
+}
+.fa-file-sound-o:before,
+.fa-file-audio-o:before {
+  content: "\f1c7";
+}
+.fa-file-movie-o:before,
+.fa-file-video-o:before {
+  content: "\f1c8";
+}
+.fa-file-code-o:before {
+  content: "\f1c9";
+}
+.fa-vine:before {
+  content: "\f1ca";
+}
+.fa-codepen:before {
+  content: "\f1cb";
+}
+.fa-jsfiddle:before {
+  content: "\f1cc";
+}
+.fa-life-bouy:before,
+.fa-life-buoy:before,
+.fa-life-saver:before,
+.fa-support:before,
+.fa-life-ring:before {
+  content: "\f1cd";
+}
+.fa-circle-o-notch:before {
+  content: "\f1ce";
+}
+.fa-ra:before,
+.fa-rebel:before {
+  content: "\f1d0";
+}
+.fa-ge:before,
+.fa-empire:before {
+  content: "\f1d1";
+}
+.fa-git-square:before {
+  content: "\f1d2";
+}
+.fa-git:before {
+  content: "\f1d3";
+}
+.fa-hacker-news:before {
+  content: "\f1d4";
+}
+.fa-tencent-weibo:before {
+  content: "\f1d5";
+}
+.fa-qq:before {
+  content: "\f1d6";
+}
+.fa-wechat:before,
+.fa-weixin:before {
+  content: "\f1d7";
+}
+.fa-send:before,
+.fa-paper-plane:before {
+  content: "\f1d8";
+}
+.fa-send-o:before,
+.fa-paper-plane-o:before {
+  content: "\f1d9";
+}
+.fa-history:before {
+  content: "\f1da";
+}
+.fa-circle-thin:before {
+  content: "\f1db";
+}
+.fa-header:before {
+  content: "\f1dc";
+}
+.fa-paragraph:before {
+  content: "\f1dd";
+}
+.fa-sliders:before {
+  content: "\f1de";
+}
+.fa-share-alt:before {
+  content: "\f1e0";
+}
+.fa-share-alt-square:before {
+  content: "\f1e1";
+}
+.fa-bomb:before {
+  content: "\f1e2";
+}
+.fa-soccer-ball-o:before,
+.fa-futbol-o:before {
+  content: "\f1e3";
+}
+.fa-tty:before {
+  content: "\f1e4";
+}
+.fa-binoculars:before {
+  content: "\f1e5";
+}
+.fa-plug:before {
+  content: "\f1e6";
+}
+.fa-slideshare:before {
+  content: "\f1e7";
+}
+.fa-twitch:before {
+  content: "\f1e8";
+}
+.fa-yelp:before {
+  content: "\f1e9";
+}
+.fa-newspaper-o:before {
+  content: "\f1ea";
+}
+.fa-wifi:before {
+  content: "\f1eb";
+}
+.fa-calculator:before {
+  content: "\f1ec";
+}
+.fa-paypal:before {
+  content: "\f1ed";
+}
+.fa-google-wallet:before {
+  content: "\f1ee";
+}
+.fa-cc-visa:before {
+  content: "\f1f0";
+}
+.fa-cc-mastercard:before {
+  content: "\f1f1";
+}
+.fa-cc-discover:before {
+  content: "\f1f2";
+}
+.fa-cc-amex:before {
+  content: "\f1f3";
+}
+.fa-cc-paypal:before {
+  content: "\f1f4";
+}
+.fa-cc-stripe:before {
+  content: "\f1f5";
+}
+.fa-bell-slash:before {
+  content: "\f1f6";
+}
+.fa-bell-slash-o:before {
+  content: "\f1f7";
+}
+.fa-trash:before {
+  content: "\f1f8";
+}
+.fa-copyright:before {
+  content: "\f1f9";
+}
+.fa-at:before {
+  content: "\f1fa";
+}
+.fa-eyedropper:before {
+  content: "\f1fb";
+}
+.fa-paint-brush:before {
+  content: "\f1fc";
+}
+.fa-birthday-cake:before {
+  content: "\f1fd";
+}
+.fa-area-chart:before {
+  content: "\f1fe";
+}
+.fa-pie-chart:before {
+  content: "\f200";
+}
+.fa-line-chart:before {
+  content: "\f201";
+}
+.fa-lastfm:before {
+  content: "\f202";
+}
+.fa-lastfm-square:before {
+  content: "\f203";
+}
+.fa-toggle-off:before {
+  content: "\f204";
+}
+.fa-toggle-on:before {
+  content: "\f205";
+}
+.fa-bicycle:before {
+  content: "\f206";
+}
+.fa-bus:before {
+  content: "\f207";
+}
+.fa-ioxhost:before {
+  content: "\f208";
+}
+.fa-angellist:before {
+  content: "\f209";
+}
+.fa-cc:before {
+  content: "\f20a";
+}
+.fa-shekel:before,
+.fa-sheqel:before,
+.fa-ils:before {
+  content: "\f20b";
+}
+.fa-meanpath:before {
+  content: "\f20c";
+}
+/*!
+*
+* IPython base
+*
+*/
+.modal.fade .modal-dialog {
+  -webkit-transform: translate(0, 0);
+  -ms-transform: translate(0, 0);
+  -o-transform: translate(0, 0);
+  transform: translate(0, 0);
+}
+code {
+  color: #000;
+}
+pre {
+  font-size: inherit;
+  line-height: inherit;
+}
+label {
+  font-weight: normal;
+}
+/* Make the page background atleast 100% the height of the view port */
+/* Make the page itself atleast 70% the height of the view port */
+.border-box-sizing {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+.corner-all {
+  border-radius: 2px;
+}
+.no-padding {
+  padding: 0px;
+}
+/* Flexible box model classes */
+/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
+/* This file is a compatability layer.  It allows the usage of flexible box 
+model layouts accross multiple browsers, including older browsers.  The newest,
+universal implementation of the flexible box model is used when available (see
+`Modern browsers` comments below).  Browsers that are known to implement this 
+new spec completely include:
+
+    Firefox 28.0+
+    Chrome 29.0+
+    Internet Explorer 11+ 
+    Opera 17.0+
+
+Browsers not listed, including Safari, are supported via the styling under the
+`Old browsers` comments below.
+*/
+.hbox {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+.hbox > * {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+}
+.vbox {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+.vbox > * {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+}
+.hbox.reverse,
+.vbox.reverse,
+.reverse {
+  /* Old browsers */
+  -webkit-box-direction: reverse;
+  -moz-box-direction: reverse;
+  box-direction: reverse;
+  /* Modern browsers */
+  flex-direction: row-reverse;
+}
+.hbox.box-flex0,
+.vbox.box-flex0,
+.box-flex0 {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+  width: auto;
+}
+.hbox.box-flex1,
+.vbox.box-flex1,
+.box-flex1 {
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+.hbox.box-flex,
+.vbox.box-flex,
+.box-flex {
+  /* Old browsers */
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+.hbox.box-flex2,
+.vbox.box-flex2,
+.box-flex2 {
+  /* Old browsers */
+  -webkit-box-flex: 2;
+  -moz-box-flex: 2;
+  box-flex: 2;
+  /* Modern browsers */
+  flex: 2;
+}
+.box-group1 {
+  /*  Deprecated */
+  -webkit-box-flex-group: 1;
+  -moz-box-flex-group: 1;
+  box-flex-group: 1;
+}
+.box-group2 {
+  /* Deprecated */
+  -webkit-box-flex-group: 2;
+  -moz-box-flex-group: 2;
+  box-flex-group: 2;
+}
+.hbox.start,
+.vbox.start,
+.start {
+  /* Old browsers */
+  -webkit-box-pack: start;
+  -moz-box-pack: start;
+  box-pack: start;
+  /* Modern browsers */
+  justify-content: flex-start;
+}
+.hbox.end,
+.vbox.end,
+.end {
+  /* Old browsers */
+  -webkit-box-pack: end;
+  -moz-box-pack: end;
+  box-pack: end;
+  /* Modern browsers */
+  justify-content: flex-end;
+}
+.hbox.center,
+.vbox.center,
+.center {
+  /* Old browsers */
+  -webkit-box-pack: center;
+  -moz-box-pack: center;
+  box-pack: center;
+  /* Modern browsers */
+  justify-content: center;
+}
+.hbox.baseline,
+.vbox.baseline,
+.baseline {
+  /* Old browsers */
+  -webkit-box-pack: baseline;
+  -moz-box-pack: baseline;
+  box-pack: baseline;
+  /* Modern browsers */
+  justify-content: baseline;
+}
+.hbox.stretch,
+.vbox.stretch,
+.stretch {
+  /* Old browsers */
+  -webkit-box-pack: stretch;
+  -moz-box-pack: stretch;
+  box-pack: stretch;
+  /* Modern browsers */
+  justify-content: stretch;
+}
+.hbox.align-start,
+.vbox.align-start,
+.align-start {
+  /* Old browsers */
+  -webkit-box-align: start;
+  -moz-box-align: start;
+  box-align: start;
+  /* Modern browsers */
+  align-items: flex-start;
+}
+.hbox.align-end,
+.vbox.align-end,
+.align-end {
+  /* Old browsers */
+  -webkit-box-align: end;
+  -moz-box-align: end;
+  box-align: end;
+  /* Modern browsers */
+  align-items: flex-end;
+}
+.hbox.align-center,
+.vbox.align-center,
+.align-center {
+  /* Old browsers */
+  -webkit-box-align: center;
+  -moz-box-align: center;
+  box-align: center;
+  /* Modern browsers */
+  align-items: center;
+}
+.hbox.align-baseline,
+.vbox.align-baseline,
+.align-baseline {
+  /* Old browsers */
+  -webkit-box-align: baseline;
+  -moz-box-align: baseline;
+  box-align: baseline;
+  /* Modern browsers */
+  align-items: baseline;
+}
+.hbox.align-stretch,
+.vbox.align-stretch,
+.align-stretch {
+  /* Old browsers */
+  -webkit-box-align: stretch;
+  -moz-box-align: stretch;
+  box-align: stretch;
+  /* Modern browsers */
+  align-items: stretch;
+}
+div.error {
+  margin: 2em;
+  text-align: center;
+}
+div.error > h1 {
+  font-size: 500%;
+  line-height: normal;
+}
+div.error > p {
+  font-size: 200%;
+  line-height: normal;
+}
+div.traceback-wrapper {
+  text-align: left;
+  max-width: 800px;
+  margin: auto;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+body {
+  background-color: #fff;
+  /* This makes sure that the body covers the entire window and needs to
+       be in a different element than the display: box in wrapper below */
+  position: absolute;
+  left: 0px;
+  right: 0px;
+  top: 0px;
+  bottom: 0px;
+  overflow: visible;
+}
+body > #header {
+  /* Initially hidden to prevent FLOUC */
+  display: none;
+  background-color: #fff;
+  /* Display over codemirror */
+  position: relative;
+  z-index: 100;
+}
+body > #header #header-container {
+  padding-bottom: 5px;
+  padding-top: 5px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+body > #header .header-bar {
+  width: 100%;
+  height: 1px;
+  background: #e7e7e7;
+  margin-bottom: -1px;
+}
+@media print {
+  body > #header {
+    display: none !important;
+  }
+}
+#header-spacer {
+  width: 100%;
+  visibility: hidden;
+}
+@media print {
+  #header-spacer {
+    display: none;
+  }
+}
+#ipython_notebook {
+  padding-left: 0px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+}
+@media (max-width: 991px) {
+  #ipython_notebook {
+    margin-left: 10px;
+  }
+}
+[dir="rtl"] #ipython_notebook {
+  float: right !important;
+}
+#noscript {
+  width: auto;
+  padding-top: 16px;
+  padding-bottom: 16px;
+  text-align: center;
+  font-size: 22px;
+  color: red;
+  font-weight: bold;
+}
+#ipython_notebook img {
+  height: 28px;
+}
+#site {
+  width: 100%;
+  display: none;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  overflow: auto;
+}
+@media print {
+  #site {
+    height: auto !important;
+  }
+}
+/* Smaller buttons */
+.ui-button .ui-button-text {
+  padding: 0.2em 0.8em;
+  font-size: 77%;
+}
+input.ui-button {
+  padding: 0.3em 0.9em;
+}
+span#login_widget {
+  float: right;
+}
+span#login_widget > .button,
+#logout {
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+span#login_widget > .button:focus,
+#logout:focus,
+span#login_widget > .button.focus,
+#logout.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+span#login_widget > .button:hover,
+#logout:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+span#login_widget > .button:active:hover,
+#logout:active:hover,
+span#login_widget > .button.active:hover,
+#logout.active:hover,
+.open > .dropdown-togglespan#login_widget > .button:hover,
+.open > .dropdown-toggle#logout:hover,
+span#login_widget > .button:active:focus,
+#logout:active:focus,
+span#login_widget > .button.active:focus,
+#logout.active:focus,
+.open > .dropdown-togglespan#login_widget > .button:focus,
+.open > .dropdown-toggle#logout:focus,
+span#login_widget > .button:active.focus,
+#logout:active.focus,
+span#login_widget > .button.active.focus,
+#logout.active.focus,
+.open > .dropdown-togglespan#login_widget > .button.focus,
+.open > .dropdown-toggle#logout.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+  background-image: none;
+}
+span#login_widget > .button.disabled:hover,
+#logout.disabled:hover,
+span#login_widget > .button[disabled]:hover,
+#logout[disabled]:hover,
+fieldset[disabled] span#login_widget > .button:hover,
+fieldset[disabled] #logout:hover,
+span#login_widget > .button.disabled:focus,
+#logout.disabled:focus,
+span#login_widget > .button[disabled]:focus,
+#logout[disabled]:focus,
+fieldset[disabled] span#login_widget > .button:focus,
+fieldset[disabled] #logout:focus,
+span#login_widget > .button.disabled.focus,
+#logout.disabled.focus,
+span#login_widget > .button[disabled].focus,
+#logout[disabled].focus,
+fieldset[disabled] span#login_widget > .button.focus,
+fieldset[disabled] #logout.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+span#login_widget > .button .badge,
+#logout .badge {
+  color: #fff;
+  background-color: #333;
+}
+.nav-header {
+  text-transform: none;
+}
+#header > span {
+  margin-top: 10px;
+}
+.modal_stretch .modal-dialog {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  min-height: 80vh;
+}
+.modal_stretch .modal-dialog .modal-body {
+  max-height: calc(100vh - 200px);
+  overflow: auto;
+  flex: 1;
+}
+@media (min-width: 768px) {
+  .modal .modal-dialog {
+    width: 700px;
+  }
+}
+@media (min-width: 768px) {
+  select.form-control {
+    margin-left: 12px;
+    margin-right: 12px;
+  }
+}
+/*!
+*
+* IPython auth
+*
+*/
+.center-nav {
+  display: inline-block;
+  margin-bottom: -4px;
+}
+/*!
+*
+* IPython tree view
+*
+*/
+/* We need an invisible input field on top of the sentense*/
+/* "Drag file onto the list ..." */
+.alternate_upload {
+  background-color: none;
+  display: inline;
+}
+.alternate_upload.form {
+  padding: 0;
+  margin: 0;
+}
+.alternate_upload input.fileinput {
+  text-align: center;
+  vertical-align: middle;
+  display: inline;
+  opacity: 0;
+  z-index: 2;
+  width: 12ex;
+  margin-right: -12ex;
+}
+.alternate_upload .btn-upload {
+  height: 22px;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+[dir="rtl"] #tabs li {
+  float: right;
+}
+ul#tabs {
+  margin-bottom: 4px;
+}
+[dir="rtl"] ul#tabs {
+  margin-right: 0px;
+}
+ul#tabs a {
+  padding-top: 6px;
+  padding-bottom: 4px;
+}
+ul.breadcrumb a:focus,
+ul.breadcrumb a:hover {
+  text-decoration: none;
+}
+ul.breadcrumb i.icon-home {
+  font-size: 16px;
+  margin-right: 4px;
+}
+ul.breadcrumb span {
+  color: #5e5e5e;
+}
+.list_toolbar {
+  padding: 4px 0 4px 0;
+  vertical-align: middle;
+}
+.list_toolbar .tree-buttons {
+  padding-top: 1px;
+}
+[dir="rtl"] .list_toolbar .tree-buttons {
+  float: left !important;
+}
+[dir="rtl"] .list_toolbar .pull-right {
+  padding-top: 1px;
+  float: left !important;
+}
+[dir="rtl"] .list_toolbar .pull-left {
+  float: right !important;
+}
+.dynamic-buttons {
+  padding-top: 3px;
+  display: inline-block;
+}
+.list_toolbar [class*="span"] {
+  min-height: 24px;
+}
+.list_header {
+  font-weight: bold;
+  background-color: #EEE;
+}
+.list_placeholder {
+  font-weight: bold;
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+}
+.list_container {
+  margin-top: 4px;
+  margin-bottom: 20px;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+}
+.list_container > div {
+  border-bottom: 1px solid #ddd;
+}
+.list_container > div:hover .list-item {
+  background-color: red;
+}
+.list_container > div:last-child {
+  border: none;
+}
+.list_item:hover .list_item {
+  background-color: #ddd;
+}
+.list_item a {
+  text-decoration: none;
+}
+.list_item:hover {
+  background-color: #fafafa;
+}
+.list_header > div,
+.list_item > div {
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+  line-height: 22px;
+}
+.list_header > div input,
+.list_item > div input {
+  margin-right: 7px;
+  margin-left: 14px;
+  vertical-align: baseline;
+  line-height: 22px;
+  position: relative;
+  top: -1px;
+}
+.list_header > div .item_link,
+.list_item > div .item_link {
+  margin-left: -1px;
+  vertical-align: baseline;
+  line-height: 22px;
+}
+.new-file input[type=checkbox] {
+  visibility: hidden;
+}
+.item_name {
+  line-height: 22px;
+  height: 24px;
+}
+.item_icon {
+  font-size: 14px;
+  color: #5e5e5e;
+  margin-right: 7px;
+  margin-left: 7px;
+  line-height: 22px;
+  vertical-align: baseline;
+}
+.item_buttons {
+  line-height: 1em;
+  margin-left: -5px;
+}
+.item_buttons .btn,
+.item_buttons .btn-group,
+.item_buttons .input-group {
+  float: left;
+}
+.item_buttons > .btn,
+.item_buttons > .btn-group,
+.item_buttons > .input-group {
+  margin-left: 5px;
+}
+.item_buttons .btn {
+  min-width: 13ex;
+}
+.item_buttons .running-indicator {
+  padding-top: 4px;
+  color: #5cb85c;
+}
+.item_buttons .kernel-name {
+  padding-top: 4px;
+  color: #5bc0de;
+  margin-right: 7px;
+  float: left;
+}
+.toolbar_info {
+  height: 24px;
+  line-height: 24px;
+}
+.list_item input:not([type=checkbox]) {
+  padding-top: 3px;
+  padding-bottom: 3px;
+  height: 22px;
+  line-height: 14px;
+  margin: 0px;
+}
+.highlight_text {
+  color: blue;
+}
+#project_name {
+  display: inline-block;
+  padding-left: 7px;
+  margin-left: -2px;
+}
+#project_name > .breadcrumb {
+  padding: 0px;
+  margin-bottom: 0px;
+  background-color: transparent;
+  font-weight: bold;
+}
+#tree-selector {
+  padding-right: 0px;
+}
+[dir="rtl"] #tree-selector a {
+  float: right;
+}
+#button-select-all {
+  min-width: 50px;
+}
+#select-all {
+  margin-left: 7px;
+  margin-right: 2px;
+}
+.menu_icon {
+  margin-right: 2px;
+}
+.tab-content .row {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+.folder_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f114";
+}
+.folder_icon:before.pull-left {
+  margin-right: .3em;
+}
+.folder_icon:before.pull-right {
+  margin-left: .3em;
+}
+.notebook_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f02d";
+  position: relative;
+  top: -1px;
+}
+.notebook_icon:before.pull-left {
+  margin-right: .3em;
+}
+.notebook_icon:before.pull-right {
+  margin-left: .3em;
+}
+.running_notebook_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f02d";
+  position: relative;
+  top: -1px;
+  color: #5cb85c;
+}
+.running_notebook_icon:before.pull-left {
+  margin-right: .3em;
+}
+.running_notebook_icon:before.pull-right {
+  margin-left: .3em;
+}
+.file_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f016";
+  position: relative;
+  top: -2px;
+}
+.file_icon:before.pull-left {
+  margin-right: .3em;
+}
+.file_icon:before.pull-right {
+  margin-left: .3em;
+}
+#notebook_toolbar .pull-right {
+  padding-top: 0px;
+  margin-right: -1px;
+}
+ul#new-menu {
+  left: auto;
+  right: 0;
+}
+[dir="rtl"] #new-menu {
+  text-align: right;
+}
+.kernel-menu-icon {
+  padding-right: 12px;
+  width: 24px;
+  content: "\f096";
+}
+.kernel-menu-icon:before {
+  content: "\f096";
+}
+.kernel-menu-icon-current:before {
+  content: "\f00c";
+}
+#tab_content {
+  padding-top: 20px;
+}
+#running .panel-group .panel {
+  margin-top: 3px;
+  margin-bottom: 1em;
+}
+#running .panel-group .panel .panel-heading {
+  background-color: #EEE;
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+  line-height: 22px;
+}
+#running .panel-group .panel .panel-heading a:focus,
+#running .panel-group .panel .panel-heading a:hover {
+  text-decoration: none;
+}
+#running .panel-group .panel .panel-body {
+  padding: 0px;
+}
+#running .panel-group .panel .panel-body .list_container {
+  margin-top: 0px;
+  margin-bottom: 0px;
+  border: 0px;
+  border-radius: 0px;
+}
+#running .panel-group .panel .panel-body .list_container .list_item {
+  border-bottom: 1px solid #ddd;
+}
+#running .panel-group .panel .panel-body .list_container .list_item:last-child {
+  border-bottom: 0px;
+}
+[dir="rtl"] #running .col-sm-8 {
+  float: right !important;
+}
+.delete-button {
+  display: none;
+}
+.duplicate-button {
+  display: none;
+}
+.rename-button {
+  display: none;
+}
+.shutdown-button {
+  display: none;
+}
+.dynamic-instructions {
+  display: inline-block;
+  padding-top: 4px;
+}
+/*!
+*
+* IPython text editor webapp
+*
+*/
+.selected-keymap i.fa {
+  padding: 0px 5px;
+}
+.selected-keymap i.fa:before {
+  content: "\f00c";
+}
+#mode-menu {
+  overflow: auto;
+  max-height: 20em;
+}
+.edit_app #header {
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+.edit_app #menubar .navbar {
+  /* Use a negative 1 bottom margin, so the border overlaps the border of the
+    header */
+  margin-bottom: -1px;
+}
+.dirty-indicator {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-dirty {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator-dirty.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-dirty.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-clean {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator-clean.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-clean.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-clean:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f00c";
+}
+.dirty-indicator-clean:before.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-clean:before.pull-right {
+  margin-left: .3em;
+}
+#filename {
+  font-size: 16pt;
+  display: table;
+  padding: 0px 5px;
+}
+#current-mode {
+  padding-left: 5px;
+  padding-right: 5px;
+}
+#texteditor-backdrop {
+  padding-top: 20px;
+  padding-bottom: 20px;
+}
+@media not print {
+  #texteditor-backdrop {
+    background-color: #EEE;
+  }
+}
+@media print {
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+    background-color: #fff;
+  }
+}
+@media not print {
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+    background-color: #fff;
+  }
+}
+@media not print {
+  #texteditor-backdrop #texteditor-container {
+    padding: 0px;
+    background-color: #fff;
+    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  }
+}
+/*!
+*
+* IPython notebook
+*
+*/
+/* CSS font colors for translated ANSI colors. */
+.ansibold {
+  font-weight: bold;
+}
+/* use dark versions for foreground, to improve visibility */
+.ansiblack {
+  color: black;
+}
+.ansired {
+  color: darkred;
+}
+.ansigreen {
+  color: darkgreen;
+}
+.ansiyellow {
+  color: #c4a000;
+}
+.ansiblue {
+  color: darkblue;
+}
+.ansipurple {
+  color: darkviolet;
+}
+.ansicyan {
+  color: steelblue;
+}
+.ansigray {
+  color: gray;
+}
+/* and light for background, for the same reason */
+.ansibgblack {
+  background-color: black;
+}
+.ansibgred {
+  background-color: red;
+}
+.ansibggreen {
+  background-color: green;
+}
+.ansibgyellow {
+  background-color: yellow;
+}
+.ansibgblue {
+  background-color: blue;
+}
+.ansibgpurple {
+  background-color: magenta;
+}
+.ansibgcyan {
+  background-color: cyan;
+}
+.ansibggray {
+  background-color: gray;
+}
+div.cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  border-radius: 2px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  border-width: 1px;
+  border-style: solid;
+  border-color: transparent;
+  width: 100%;
+  padding: 5px;
+  /* This acts as a spacer between cells, that is outside the border */
+  margin: 0px;
+  outline: none;
+  border-left-width: 1px;
+  padding-left: 5px;
+  background: linear-gradient(to right, transparent -40px, transparent 1px, transparent 1px, transparent 100%);
+}
+div.cell.jupyter-soft-selected {
+  border-left-color: #90CAF9;
+  border-left-color: #E3F2FD;
+  border-left-width: 1px;
+  padding-left: 5px;
+  border-right-color: #E3F2FD;
+  border-right-width: 1px;
+  background: #E3F2FD;
+}
+@media print {
+  div.cell.jupyter-soft-selected {
+    border-color: transparent;
+  }
+}
+div.cell.selected {
+  border-color: #ababab;
+  border-left-width: 0px;
+  padding-left: 6px;
+  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 5px, transparent 5px, transparent 100%);
+}
+@media print {
+  div.cell.selected {
+    border-color: transparent;
+  }
+}
+div.cell.selected.jupyter-soft-selected {
+  border-left-width: 0;
+  padding-left: 6px;
+  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 7px, #E3F2FD 7px, #E3F2FD 100%);
+}
+.edit_mode div.cell.selected {
+  border-color: #66BB6A;
+  border-left-width: 0px;
+  padding-left: 6px;
+  background: linear-gradient(to right, #66BB6A -40px, #66BB6A 5px, transparent 5px, transparent 100%);
+}
+@media print {
+  .edit_mode div.cell.selected {
+    border-color: transparent;
+  }
+}
+.prompt {
+  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
+  min-width: 14ex;
+  /* This padding is tuned to match the padding on the CodeMirror editor. */
+  padding: 0.4em;
+  margin: 0px;
+  font-family: monospace;
+  text-align: right;
+  /* This has to match that of the the CodeMirror class line-height below */
+  line-height: 1.21429em;
+  /* Don't highlight prompt number selection */
+  -webkit-touch-callout: none;
+  -webkit-user-select: none;
+  -khtml-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+  /* Use default cursor */
+  cursor: default;
+}
+@media (max-width: 540px) {
+  .prompt {
+    text-align: left;
+  }
+}
+div.inner_cell {
+  min-width: 0;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_area {
+  border: 1px solid #cfcfcf;
+  border-radius: 2px;
+  background: #f7f7f7;
+  line-height: 1.21429em;
+}
+/* This is needed so that empty prompt areas can collapse to zero height when there
+   is no content in the output_subarea and the prompt. The main purpose of this is
+   to make sure that empty JavaScript output_subareas have no height. */
+div.prompt:empty {
+  padding-top: 0;
+  padding-bottom: 0;
+}
+div.unrecognized_cell {
+  padding: 5px 5px 5px 0px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+div.unrecognized_cell .inner_cell {
+  border-radius: 2px;
+  padding: 5px;
+  font-weight: bold;
+  color: red;
+  border: 1px solid #cfcfcf;
+  background: #eaeaea;
+}
+div.unrecognized_cell .inner_cell a {
+  color: inherit;
+  text-decoration: none;
+}
+div.unrecognized_cell .inner_cell a:hover {
+  color: inherit;
+  text-decoration: none;
+}
+@media (max-width: 540px) {
+  div.unrecognized_cell > div.prompt {
+    display: none;
+  }
+}
+div.code_cell {
+  /* avoid page breaking on code cells when printing */
+}
+@media print {
+  div.code_cell {
+    page-break-inside: avoid;
+  }
+}
+/* any special styling for code cells that are currently running goes here */
+div.input {
+  page-break-inside: avoid;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.input {
+    /* Old browsers */
+    display: -webkit-box;
+    -webkit-box-orient: vertical;
+    -webkit-box-align: stretch;
+    display: -moz-box;
+    -moz-box-orient: vertical;
+    -moz-box-align: stretch;
+    display: box;
+    box-orient: vertical;
+    box-align: stretch;
+    /* Modern browsers */
+    display: flex;
+    flex-direction: column;
+    align-items: stretch;
+  }
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_prompt {
+  color: #303F9F;
+  border-top: 1px solid transparent;
+}
+div.input_area > div.highlight {
+  margin: 0.4em;
+  border: none;
+  padding: 0px;
+  background-color: transparent;
+}
+div.input_area > div.highlight > pre {
+  margin: 0px;
+  border: none;
+  padding: 0px;
+  background-color: transparent;
+}
+/* The following gets added to the <head> if it is detected that the user has a
+ * monospace font with inconsistent normal/bold/italic height.  See
+ * notebookmain.js.  Such fonts will have keywords vertically offset with
+ * respect to the rest of the text.  The user should select a better font.
+ * See: https://github.com/ipython/ipython/issues/1503
+ *
+ * .CodeMirror span {
+ *      vertical-align: bottom;
+ * }
+ */
+.CodeMirror {
+  line-height: 1.21429em;
+  /* Changed from 1em to our global default */
+  font-size: 14px;
+  height: auto;
+  /* Changed to auto to autogrow */
+  background: none;
+  /* Changed from white to allow our bg to show through */
+}
+.CodeMirror-scroll {
+  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
+  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
+  overflow-y: hidden;
+  overflow-x: auto;
+}
+.CodeMirror-lines {
+  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
+  /* we have set a different line-height and want this to scale with that. */
+  padding: 0.4em;
+}
+.CodeMirror-linenumber {
+  padding: 0 8px 0 4px;
+}
+.CodeMirror-gutters {
+  border-bottom-left-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.CodeMirror pre {
+  /* In CM3 this went to 4px from 0 in CM2. We need the 0 value because of how we size */
+  /* .CodeMirror-lines */
+  padding: 0;
+  border: 0;
+  border-radius: 0;
+}
+/*
+
+Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
+Adapted from GitHub theme
+
+*/
+.highlight-base {
+  color: #000;
+}
+.highlight-variable {
+  color: #000;
+}
+.highlight-variable-2 {
+  color: #1a1a1a;
+}
+.highlight-variable-3 {
+  color: #333333;
+}
+.highlight-string {
+  color: #BA2121;
+}
+.highlight-comment {
+  color: #408080;
+  font-style: italic;
+}
+.highlight-number {
+  color: #080;
+}
+.highlight-atom {
+  color: #88F;
+}
+.highlight-keyword {
+  color: #008000;
+  font-weight: bold;
+}
+.highlight-builtin {
+  color: #008000;
+}
+.highlight-error {
+  color: #f00;
+}
+.highlight-operator {
+  color: #AA22FF;
+  font-weight: bold;
+}
+.highlight-meta {
+  color: #AA22FF;
+}
+/* previously not defined, copying from default codemirror */
+.highlight-def {
+  color: #00f;
+}
+.highlight-string-2 {
+  color: #f50;
+}
+.highlight-qualifier {
+  color: #555;
+}
+.highlight-bracket {
+  color: #997;
+}
+.highlight-tag {
+  color: #170;
+}
+.highlight-attribute {
+  color: #00c;
+}
+.highlight-header {
+  color: blue;
+}
+.highlight-quote {
+  color: #090;
+}
+.highlight-link {
+  color: #00c;
+}
+/* apply the same style to codemirror */
+.cm-s-ipython span.cm-keyword {
+  color: #008000;
+  font-weight: bold;
+}
+.cm-s-ipython span.cm-atom {
+  color: #88F;
+}
+.cm-s-ipython span.cm-number {
+  color: #080;
+}
+.cm-s-ipython span.cm-def {
+  color: #00f;
+}
+.cm-s-ipython span.cm-variable {
+  color: #000;
+}
+.cm-s-ipython span.cm-operator {
+  color: #AA22FF;
+  font-weight: bold;
+}
+.cm-s-ipython span.cm-variable-2 {
+  color: #1a1a1a;
+}
+.cm-s-ipython span.cm-variable-3 {
+  color: #333333;
+}
+.cm-s-ipython span.cm-comment {
+  color: #408080;
+  font-style: italic;
+}
+.cm-s-ipython span.cm-string {
+  color: #BA2121;
+}
+.cm-s-ipython span.cm-string-2 {
+  color: #f50;
+}
+.cm-s-ipython span.cm-meta {
+  color: #AA22FF;
+}
+.cm-s-ipython span.cm-qualifier {
+  color: #555;
+}
+.cm-s-ipython span.cm-builtin {
+  color: #008000;
+}
+.cm-s-ipython span.cm-bracket {
+  color: #997;
+}
+.cm-s-ipython span.cm-tag {
+  color: #170;
+}
+.cm-s-ipython span.cm-attribute {
+  color: #00c;
+}
+.cm-s-ipython span.cm-header {
+  color: blue;
+}
+.cm-s-ipython span.cm-quote {
+  color: #090;
+}
+.cm-s-ipython span.cm-link {
+  color: #00c;
+}
+.cm-s-ipython span.cm-error {
+  color: #f00;
+}
+.cm-s-ipython span.cm-tab {
+  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
+  background-position: right;
+  background-repeat: no-repeat;
+}
+div.output_wrapper {
+  /* this position must be relative to enable descendents to be absolute within it */
+  position: relative;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  z-index: 1;
+}
+/* class for the output area when it should be height-limited */
+div.output_scroll {
+  /* ideally, this would be max-height, but FF barfs all over that */
+  height: 24em;
+  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
+  width: 100%;
+  overflow: auto;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+  display: block;
+}
+/* output div while it is collapsed */
+div.output_collapsed {
+  margin: 0px;
+  padding: 0px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+div.out_prompt_overlay {
+  height: 100%;
+  padding: 0px 0.4em;
+  position: absolute;
+  border-radius: 2px;
+}
+div.out_prompt_overlay:hover {
+  /* use inner shadow to get border that is computed the same on WebKit/FF */
+  -webkit-box-shadow: inset 0 0 1px #000;
+  box-shadow: inset 0 0 1px #000;
+  background: rgba(240, 240, 240, 0.5);
+}
+div.output_prompt {
+  color: #D84315;
+}
+/* This class is the outer container of all output sections. */
+div.output_area {
+  padding: 0px;
+  page-break-inside: avoid;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+div.output_area .MathJax_Display {
+  text-align: left !important;
+}
+div.output_area .rendered_html table {
+  margin-left: 0;
+  margin-right: 0;
+}
+div.output_area .rendered_html img {
+  margin-left: 0;
+  margin-right: 0;
+}
+div.output_area img,
+div.output_area svg {
+  max-width: 100%;
+  height: auto;
+}
+div.output_area img.unconfined,
+div.output_area svg.unconfined {
+  max-width: none;
+}
+/* This is needed to protect the pre formating from global settings such
+   as that of bootstrap */
+.output {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.output_area {
+    /* Old browsers */
+    display: -webkit-box;
+    -webkit-box-orient: vertical;
+    -webkit-box-align: stretch;
+    display: -moz-box;
+    -moz-box-orient: vertical;
+    -moz-box-align: stretch;
+    display: box;
+    box-orient: vertical;
+    box-align: stretch;
+    /* Modern browsers */
+    display: flex;
+    flex-direction: column;
+    align-items: stretch;
+  }
+}
+div.output_area pre {
+  margin: 0;
+  padding: 0;
+  border: 0;
+  vertical-align: baseline;
+  color: black;
+  background-color: transparent;
+  border-radius: 0;
+}
+/* This class is for the output subarea inside the output_area and after
+   the prompt div. */
+div.output_subarea {
+  overflow-x: auto;
+  padding: 0.4em;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+  max-width: calc(100% - 14ex);
+}
+div.output_scroll div.output_subarea {
+  overflow-x: visible;
+}
+/* The rest of the output_* classes are for special styling of the different
+   output types */
+/* all text output has this class: */
+div.output_text {
+  text-align: left;
+  color: #000;
+  /* This has to match that of the the CodeMirror class line-height below */
+  line-height: 1.21429em;
+}
+/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
+div.output_stderr {
+  background: #fdd;
+  /* very light red background for stderr */
+}
+div.output_latex {
+  text-align: left;
+}
+/* Empty output_javascript divs should have no height */
+div.output_javascript:empty {
+  padding: 0;
+}
+.js-error {
+  color: darkred;
+}
+/* raw_input styles */
+div.raw_input_container {
+  line-height: 1.21429em;
+  padding-top: 5px;
+}
+pre.raw_input_prompt {
+  /* nothing needed here. */
+}
+input.raw_input {
+  font-family: monospace;
+  font-size: inherit;
+  color: inherit;
+  width: auto;
+  /* make sure input baseline aligns with prompt */
+  vertical-align: baseline;
+  /* padding + margin = 0.5em between prompt and cursor */
+  padding: 0em 0.25em;
+  margin: 0em 0.25em;
+}
+input.raw_input:focus {
+  box-shadow: none;
+}
+p.p-space {
+  margin-bottom: 10px;
+}
+div.output_unrecognized {
+  padding: 5px;
+  font-weight: bold;
+  color: red;
+}
+div.output_unrecognized a {
+  color: inherit;
+  text-decoration: none;
+}
+div.output_unrecognized a:hover {
+  color: inherit;
+  text-decoration: none;
+}
+.rendered_html {
+  color: #000;
+  /* any extras will just be numbers: */
+}
+.rendered_html em {
+  font-style: italic;
+}
+.rendered_html strong {
+  font-weight: bold;
+}
+.rendered_html u {
+  text-decoration: underline;
+}
+.rendered_html :link {
+  text-decoration: underline;
+}
+.rendered_html :visited {
+  text-decoration: underline;
+}
+.rendered_html h1 {
+  font-size: 185.7%;
+  margin: 1.08em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h2 {
+  font-size: 157.1%;
+  margin: 1.27em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h3 {
+  font-size: 128.6%;
+  margin: 1.55em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h4 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h5 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h6 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h1:first-child {
+  margin-top: 0.538em;
+}
+.rendered_html h2:first-child {
+  margin-top: 0.636em;
+}
+.rendered_html h3:first-child {
+  margin-top: 0.777em;
+}
+.rendered_html h4:first-child {
+  margin-top: 1em;
+}
+.rendered_html h5:first-child {
+  margin-top: 1em;
+}
+.rendered_html h6:first-child {
+  margin-top: 1em;
+}
+.rendered_html ul {
+  list-style: disc;
+  margin: 0em 2em;
+  padding-left: 0px;
+}
+.rendered_html ul ul {
+  list-style: square;
+  margin: 0em 2em;
+}
+.rendered_html ul ul ul {
+  list-style: circle;
+  margin: 0em 2em;
+}
+.rendered_html ol {
+  list-style: decimal;
+  margin: 0em 2em;
+  padding-left: 0px;
+}
+.rendered_html ol ol {
+  list-style: upper-alpha;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol {
+  list-style: lower-alpha;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol ol {
+  list-style: lower-roman;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol ol ol {
+  list-style: decimal;
+  margin: 0em 2em;
+}
+.rendered_html * + ul {
+  margin-top: 1em;
+}
+.rendered_html * + ol {
+  margin-top: 1em;
+}
+.rendered_html hr {
+  color: black;
+  background-color: black;
+}
+.rendered_html pre {
+  margin: 1em 2em;
+}
+.rendered_html pre,
+.rendered_html code {
+  border: 0;
+  background-color: #fff;
+  color: #000;
+  font-size: 100%;
+  padding: 0px;
+}
+.rendered_html blockquote {
+  margin: 1em 2em;
+}
+.rendered_html table {
+  margin-left: auto;
+  margin-right: auto;
+  border: 1px solid black;
+  border-collapse: collapse;
+}
+.rendered_html tr,
+.rendered_html th,
+.rendered_html td {
+  border: 1px solid black;
+  border-collapse: collapse;
+  margin: 1em 2em;
+}
+.rendered_html td,
+.rendered_html th {
+  text-align: left;
+  vertical-align: middle;
+  padding: 4px;
+}
+.rendered_html th {
+  font-weight: bold;
+}
+.rendered_html * + table {
+  margin-top: 1em;
+}
+.rendered_html p {
+  text-align: left;
+}
+.rendered_html * + p {
+  margin-top: 1em;
+}
+.rendered_html img {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.rendered_html * + img {
+  margin-top: 1em;
+}
+.rendered_html img,
+.rendered_html svg {
+  max-width: 100%;
+  height: auto;
+}
+.rendered_html img.unconfined,
+.rendered_html svg.unconfined {
+  max-width: none;
+}
+div.text_cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.text_cell > div.prompt {
+    display: none;
+  }
+}
+div.text_cell_render {
+  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
+  outline: none;
+  resize: none;
+  width: inherit;
+  border-style: none;
+  padding: 0.5em 0.5em 0.5em 0.4em;
+  color: #000;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+a.anchor-link:link {
+  text-decoration: none;
+  padding: 0px 20px;
+  visibility: hidden;
+}
+h1:hover .anchor-link,
+h2:hover .anchor-link,
+h3:hover .anchor-link,
+h4:hover .anchor-link,
+h5:hover .anchor-link,
+h6:hover .anchor-link {
+  visibility: visible;
+}
+.text_cell.rendered .input_area {
+  display: none;
+}
+.text_cell.rendered .rendered_html {
+  overflow-x: auto;
+  overflow-y: hidden;
+}
+.text_cell.unrendered .text_cell_render {
+  display: none;
+}
+.cm-header-1,
+.cm-header-2,
+.cm-header-3,
+.cm-header-4,
+.cm-header-5,
+.cm-header-6 {
+  font-weight: bold;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+}
+.cm-header-1 {
+  font-size: 185.7%;
+}
+.cm-header-2 {
+  font-size: 157.1%;
+}
+.cm-header-3 {
+  font-size: 128.6%;
+}
+.cm-header-4 {
+  font-size: 110%;
+}
+.cm-header-5 {
+  font-size: 100%;
+  font-style: italic;
+}
+.cm-header-6 {
+  font-size: 100%;
+  font-style: italic;
+}
+/*!
+*
+* IPython notebook webapp
+*
+*/
+@media (max-width: 767px) {
+  .notebook_app {
+    padding-left: 0px;
+    padding-right: 0px;
+  }
+}
+#ipython-main-app {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  height: 100%;
+}
+div#notebook_panel {
+  margin: 0px;
+  padding: 0px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  height: 100%;
+}
+div#notebook {
+  font-size: 14px;
+  line-height: 20px;
+  overflow-y: hidden;
+  overflow-x: auto;
+  width: 100%;
+  /* This spaces the page away from the edge of the notebook area */
+  padding-top: 20px;
+  margin: 0px;
+  outline: none;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  min-height: 100%;
+}
+@media not print {
+  #notebook-container {
+    padding: 15px;
+    background-color: #fff;
+    min-height: 0;
+    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  }
+}
+@media print {
+  #notebook-container {
+    width: 100%;
+  }
+}
+div.ui-widget-content {
+  border: 1px solid #ababab;
+  outline: none;
+}
+pre.dialog {
+  background-color: #f7f7f7;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  padding: 0.4em;
+  padding-left: 2em;
+}
+p.dialog {
+  padding: 0.2em;
+}
+/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
+   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
+ */
+pre,
+code,
+kbd,
+samp {
+  white-space: pre-wrap;
+}
+#fonttest {
+  font-family: monospace;
+}
+p {
+  margin-bottom: 0;
+}
+.end_space {
+  min-height: 100px;
+  transition: height .2s ease;
+}
+.notebook_app > #header {
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+@media not print {
+  .notebook_app {
+    background-color: #EEE;
+  }
+}
+kbd {
+  border-style: solid;
+  border-width: 1px;
+  box-shadow: none;
+  margin: 2px;
+  padding-left: 2px;
+  padding-right: 2px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+}
+/* CSS for the cell toolbar */
+.celltoolbar {
+  border: thin solid #CFCFCF;
+  border-bottom: none;
+  background: #EEE;
+  border-radius: 2px 2px 0px 0px;
+  width: 100%;
+  height: 29px;
+  padding-right: 4px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+  /* Old browsers */
+  -webkit-box-pack: end;
+  -moz-box-pack: end;
+  box-pack: end;
+  /* Modern browsers */
+  justify-content: flex-end;
+  display: -webkit-flex;
+}
+@media print {
+  .celltoolbar {
+    display: none;
+  }
+}
+.ctb_hideshow {
+  display: none;
+  vertical-align: bottom;
+}
+/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
+   Cell toolbars are only shown when the ctb_global_show class is also set.
+*/
+.ctb_global_show .ctb_show.ctb_hideshow {
+  display: block;
+}
+.ctb_global_show .ctb_show + .input_area,
+.ctb_global_show .ctb_show + div.text_cell_input,
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+  border-top-right-radius: 0px;
+  border-top-left-radius: 0px;
+}
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+  border: 1px solid #cfcfcf;
+}
+.celltoolbar {
+  font-size: 87%;
+  padding-top: 3px;
+}
+.celltoolbar select {
+  display: block;
+  width: 100%;
+  height: 32px;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+  background-color: #fff;
+  background-image: none;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+  width: inherit;
+  font-size: inherit;
+  height: 22px;
+  padding: 0px;
+  display: inline-block;
+}
+.celltoolbar select:focus {
+  border-color: #66afe9;
+  outline: 0;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.celltoolbar select::-moz-placeholder {
+  color: #999;
+  opacity: 1;
+}
+.celltoolbar select:-ms-input-placeholder {
+  color: #999;
+}
+.celltoolbar select::-webkit-input-placeholder {
+  color: #999;
+}
+.celltoolbar select::-ms-expand {
+  border: 0;
+  background-color: transparent;
+}
+.celltoolbar select[disabled],
+.celltoolbar select[readonly],
+fieldset[disabled] .celltoolbar select {
+  background-color: #eeeeee;
+  opacity: 1;
+}
+.celltoolbar select[disabled],
+fieldset[disabled] .celltoolbar select {
+  cursor: not-allowed;
+}
+textarea.celltoolbar select {
+  height: auto;
+}
+select.celltoolbar select {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.celltoolbar select,
+select[multiple].celltoolbar select {
+  height: auto;
+}
+.celltoolbar label {
+  margin-left: 5px;
+  margin-right: 5px;
+}
+.completions {
+  position: absolute;
+  z-index: 110;
+  overflow: hidden;
+  border: 1px solid #ababab;
+  border-radius: 2px;
+  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
+  box-shadow: 0px 6px 10px -1px #adadad;
+  line-height: 1;
+}
+.completions select {
+  background: white;
+  outline: none;
+  border: none;
+  padding: 0px;
+  margin: 0px;
+  overflow: auto;
+  font-family: monospace;
+  font-size: 110%;
+  color: #000;
+  width: auto;
+}
+.completions select option.context {
+  color: #286090;
+}
+#kernel_logo_widget {
+  float: right !important;
+  float: right;
+}
+#kernel_logo_widget .current_kernel_logo {
+  display: none;
+  margin-top: -1px;
+  margin-bottom: -1px;
+  width: 32px;
+  height: 32px;
+}
+#menubar {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  margin-top: 1px;
+}
+#menubar .navbar {
+  border-top: 1px;
+  border-radius: 0px 0px 2px 2px;
+  margin-bottom: 0px;
+}
+#menubar .navbar-toggle {
+  float: left;
+  padding-top: 7px;
+  padding-bottom: 7px;
+  border: none;
+}
+#menubar .navbar-collapse {
+  clear: left;
+}
+.nav-wrapper {
+  border-bottom: 1px solid #e7e7e7;
+}
+i.menu-icon {
+  padding-top: 4px;
+}
+ul#help_menu li a {
+  overflow: hidden;
+  padding-right: 2.2em;
+}
+ul#help_menu li a i {
+  margin-right: -1.2em;
+}
+.dropdown-submenu {
+  position: relative;
+}
+.dropdown-submenu > .dropdown-menu {
+  top: 0;
+  left: 100%;
+  margin-top: -6px;
+  margin-left: -1px;
+}
+.dropdown-submenu:hover > .dropdown-menu {
+  display: block;
+}
+.dropdown-submenu > a:after {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  display: block;
+  content: "\f0da";
+  float: right;
+  color: #333333;
+  margin-top: 2px;
+  margin-right: -10px;
+}
+.dropdown-submenu > a:after.pull-left {
+  margin-right: .3em;
+}
+.dropdown-submenu > a:after.pull-right {
+  margin-left: .3em;
+}
+.dropdown-submenu:hover > a:after {
+  color: #262626;
+}
+.dropdown-submenu.pull-left {
+  float: none;
+}
+.dropdown-submenu.pull-left > .dropdown-menu {
+  left: -100%;
+  margin-left: 10px;
+}
+#notification_area {
+  float: right !important;
+  float: right;
+  z-index: 10;
+}
+.indicator_area {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+}
+#kernel_indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+  border-left: 1px solid;
+}
+#kernel_indicator .kernel_indicator_name {
+  padding-left: 5px;
+  padding-right: 5px;
+}
+#modal_indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+}
+#readonly-indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+  margin-top: 2px;
+  margin-bottom: 0px;
+  margin-left: 0px;
+  margin-right: 0px;
+  display: none;
+}
+.modal_indicator:before {
+  width: 1.28571429em;
+  text-align: center;
+}
+.edit_mode .modal_indicator:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f040";
+}
+.edit_mode .modal_indicator:before.pull-left {
+  margin-right: .3em;
+}
+.edit_mode .modal_indicator:before.pull-right {
+  margin-left: .3em;
+}
+.command_mode .modal_indicator:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: ' ';
+}
+.command_mode .modal_indicator:before.pull-left {
+  margin-right: .3em;
+}
+.command_mode .modal_indicator:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_idle_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f10c";
+}
+.kernel_idle_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_idle_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_busy_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f111";
+}
+.kernel_busy_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_busy_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_dead_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f1e2";
+}
+.kernel_dead_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_dead_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_disconnected_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f127";
+}
+.kernel_disconnected_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_disconnected_icon:before.pull-right {
+  margin-left: .3em;
+}
+.notification_widget {
+  color: #777;
+  z-index: 10;
+  background: rgba(240, 240, 240, 0.5);
+  margin-right: 4px;
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+.notification_widget:focus,
+.notification_widget.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+.notification_widget:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.notification_widget:active:hover,
+.notification_widget.active:hover,
+.open > .dropdown-toggle.notification_widget:hover,
+.notification_widget:active:focus,
+.notification_widget.active:focus,
+.open > .dropdown-toggle.notification_widget:focus,
+.notification_widget:active.focus,
+.notification_widget.active.focus,
+.open > .dropdown-toggle.notification_widget.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+  background-image: none;
+}
+.notification_widget.disabled:hover,
+.notification_widget[disabled]:hover,
+fieldset[disabled] .notification_widget:hover,
+.notification_widget.disabled:focus,
+.notification_widget[disabled]:focus,
+fieldset[disabled] .notification_widget:focus,
+.notification_widget.disabled.focus,
+.notification_widget[disabled].focus,
+fieldset[disabled] .notification_widget.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+.notification_widget .badge {
+  color: #fff;
+  background-color: #333;
+}
+.notification_widget.warning {
+  color: #fff;
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.notification_widget.warning:focus,
+.notification_widget.warning.focus {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #985f0d;
+}
+.notification_widget.warning:hover {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.notification_widget.warning:active:hover,
+.notification_widget.warning.active:hover,
+.open > .dropdown-toggle.notification_widget.warning:hover,
+.notification_widget.warning:active:focus,
+.notification_widget.warning.active:focus,
+.open > .dropdown-toggle.notification_widget.warning:focus,
+.notification_widget.warning:active.focus,
+.notification_widget.warning.active.focus,
+.open > .dropdown-toggle.notification_widget.warning.focus {
+  color: #fff;
+  background-color: #d58512;
+  border-color: #985f0d;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+  background-image: none;
+}
+.notification_widget.warning.disabled:hover,
+.notification_widget.warning[disabled]:hover,
+fieldset[disabled] .notification_widget.warning:hover,
+.notification_widget.warning.disabled:focus,
+.notification_widget.warning[disabled]:focus,
+fieldset[disabled] .notification_widget.warning:focus,
+.notification_widget.warning.disabled.focus,
+.notification_widget.warning[disabled].focus,
+fieldset[disabled] .notification_widget.warning.focus {
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.notification_widget.warning .badge {
+  color: #f0ad4e;
+  background-color: #fff;
+}
+.notification_widget.success {
+  color: #fff;
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.notification_widget.success:focus,
+.notification_widget.success.focus {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #255625;
+}
+.notification_widget.success:hover {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.notification_widget.success:active:hover,
+.notification_widget.success.active:hover,
+.open > .dropdown-toggle.notification_widget.success:hover,
+.notification_widget.success:active:focus,
+.notification_widget.success.active:focus,
+.open > .dropdown-toggle.notification_widget.success:focus,
+.notification_widget.success:active.focus,
+.notification_widget.success.active.focus,
+.open > .dropdown-toggle.notification_widget.success.focus {
+  color: #fff;
+  background-color: #398439;
+  border-color: #255625;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+  background-image: none;
+}
+.notification_widget.success.disabled:hover,
+.notification_widget.success[disabled]:hover,
+fieldset[disabled] .notification_widget.success:hover,
+.notification_widget.success.disabled:focus,
+.notification_widget.success[disabled]:focus,
+fieldset[disabled] .notification_widget.success:focus,
+.notification_widget.success.disabled.focus,
+.notification_widget.success[disabled].focus,
+fieldset[disabled] .notification_widget.success.focus {
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.notification_widget.success .badge {
+  color: #5cb85c;
+  background-color: #fff;
+}
+.notification_widget.info {
+  color: #fff;
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.notification_widget.info:focus,
+.notification_widget.info.focus {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #1b6d85;
+}
+.notification_widget.info:hover {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.notification_widget.info:active:hover,
+.notification_widget.info.active:hover,
+.open > .dropdown-toggle.notification_widget.info:hover,
+.notification_widget.info:active:focus,
+.notification_widget.info.active:focus,
+.open > .dropdown-toggle.notification_widget.info:focus,
+.notification_widget.info:active.focus,
+.notification_widget.info.active.focus,
+.open > .dropdown-toggle.notification_widget.info.focus {
+  color: #fff;
+  background-color: #269abc;
+  border-color: #1b6d85;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+  background-image: none;
+}
+.notification_widget.info.disabled:hover,
+.notification_widget.info[disabled]:hover,
+fieldset[disabled] .notification_widget.info:hover,
+.notification_widget.info.disabled:focus,
+.notification_widget.info[disabled]:focus,
+fieldset[disabled] .notification_widget.info:focus,
+.notification_widget.info.disabled.focus,
+.notification_widget.info[disabled].focus,
+fieldset[disabled] .notification_widget.info.focus {
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.notification_widget.info .badge {
+  color: #5bc0de;
+  background-color: #fff;
+}
+.notification_widget.danger {
+  color: #fff;
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.notification_widget.danger:focus,
+.notification_widget.danger.focus {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #761c19;
+}
+.notification_widget.danger:hover {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.notification_widget.danger:active:hover,
+.notification_widget.danger.active:hover,
+.open > .dropdown-toggle.notification_widget.danger:hover,
+.notification_widget.danger:active:focus,
+.notification_widget.danger.active:focus,
+.open > .dropdown-toggle.notification_widget.danger:focus,
+.notification_widget.danger:active.focus,
+.notification_widget.danger.active.focus,
+.open > .dropdown-toggle.notification_widget.danger.focus {
+  color: #fff;
+  background-color: #ac2925;
+  border-color: #761c19;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+  background-image: none;
+}
+.notification_widget.danger.disabled:hover,
+.notification_widget.danger[disabled]:hover,
+fieldset[disabled] .notification_widget.danger:hover,
+.notification_widget.danger.disabled:focus,
+.notification_widget.danger[disabled]:focus,
+fieldset[disabled] .notification_widget.danger:focus,
+.notification_widget.danger.disabled.focus,
+.notification_widget.danger[disabled].focus,
+fieldset[disabled] .notification_widget.danger.focus {
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.notification_widget.danger .badge {
+  color: #d9534f;
+  background-color: #fff;
+}
+div#pager {
+  background-color: #fff;
+  font-size: 14px;
+  line-height: 20px;
+  overflow: hidden;
+  display: none;
+  position: fixed;
+  bottom: 0px;
+  width: 100%;
+  max-height: 50%;
+  padding-top: 8px;
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  /* Display over codemirror */
+  z-index: 100;
+  /* Hack which prevents jquery ui resizable from changing top. */
+  top: auto !important;
+}
+div#pager pre {
+  line-height: 1.21429em;
+  color: #000;
+  background-color: #f7f7f7;
+  padding: 0.4em;
+}
+div#pager #pager-button-area {
+  position: absolute;
+  top: 8px;
+  right: 20px;
+}
+div#pager #pager-contents {
+  position: relative;
+  overflow: auto;
+  width: 100%;
+  height: 100%;
+}
+div#pager #pager-contents #pager-container {
+  position: relative;
+  padding: 15px 0px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+div#pager .ui-resizable-handle {
+  top: 0px;
+  height: 8px;
+  background: #f7f7f7;
+  border-top: 1px solid #cfcfcf;
+  border-bottom: 1px solid #cfcfcf;
+  /* This injects handle bars (a short, wide = symbol) for 
+        the resize handle. */
+}
+div#pager .ui-resizable-handle::after {
+  content: '';
+  top: 2px;
+  left: 50%;
+  height: 3px;
+  width: 30px;
+  margin-left: -15px;
+  position: absolute;
+  border-top: 1px solid #cfcfcf;
+}
+.quickhelp {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+  line-height: 1.8em;
+}
+.shortcut_key {
+  display: inline-block;
+  width: 21ex;
+  text-align: right;
+  font-family: monospace;
+}
+.shortcut_descr {
+  display: inline-block;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+span.save_widget {
+  margin-top: 6px;
+}
+span.save_widget span.filename {
+  height: 1em;
+  line-height: 1em;
+  padding: 3px;
+  margin-left: 16px;
+  border: none;
+  font-size: 146.5%;
+  border-radius: 2px;
+}
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+<body>
+  <div tabindex="-1" id="notebook" class="border-box-sizing">
+    <div class="container" id="notebook-container">
+
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Model-Selection-and-Validation">Model Selection and Validation<a class="anchor-link" href="#Model-Selection-and-Validation">&#194;&#182;</a></h1><p>By Chris Fenaroli, Max Margenot, and Delaney Granizo-Mackenzie</p>
+<p>Part of the Quantopian Lecture Series:</p>
+<ul>
+<li><a href="https://www.quantopian.com/lectures">www.quantopian.com/lectures</a></li>
+<li><a href="https://github.com/quantopian/research_public">github.com/quantopian/research_public</a></li>
+</ul>
+<p>Notebook released under the Creative Commons Attribution 4.0 License.</p>
+<hr>
+<p>Linear regression is a technique that models the relationship between a set of independent variables $X_1,\ldots, X_k$ and a dependent outcome variable $Y$. Simple linear regression with two variables allows us to determine which linear model of the form $Y = \beta_0 + \beta_1 X$ best explains the data, while a multiple linear regression allows for the dependent variable to be a linear function of multiple independent variables $X_1,\ldots, X_k$:</p>
+$$ Y = \beta_0 + \beta_1 X_1 + \ldots + \beta_k X_k + \epsilon_i $$<p>More background information on regressions can be found in the <a href="https://www.quantopian.com/lectures#Linear-Regression">simple linear regression</a> and <a href="https://www.quantopian.com/lectures#Multiple-Linear-Regression">multiple linear regression</a> lectures.</p>
+<p>In many cases, choosing which explanatory variables to include in a model is not trivial. If you include too many variables $X_1,\ldots, X_k$ you risk overfitting and multicollinearity (correaltion of explanatory variables) which would invalidate your regression results. With too few variables you risk excluding predictive interactions. <strong>Model selection</strong> is the process of determining which combination of explanatory variables maximizes explanatory power and minimizes complexity.</p>
+<p>Once we have chosen our model fitted the data using an estimation method like OLS, it would be useful to quantify just how "good" our end-result model is. But what exactly makes a model "good"? Is it how well it fits the data? It's simplicity/complexity? Or is it how well it performs when applied to out-of-sample data? <strong>Model validation</strong> is the process of determining how well-founded a model is and whether it is a satisfactory explanation for the given data.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[33]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span class="c"># Import libraries</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">statsmodels</span> <span class="kn">import</span> <span class="n">regression</span>
+<span class="kn">import</span> <span class="nn">statsmodels.api</span> <span class="kn">as</span> <span class="nn">sm</span>
+<span class="kn">import</span> <span class="nn">statsmodels.stats.diagnostic</span> <span class="kn">as</span> <span class="nn">smd</span>
+<span class="kn">import</span> <span class="nn">scipy.stats</span> <span class="kn">as</span> <span class="nn">stats</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">math</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Model-Selection">Model Selection<a class="anchor-link" href="#Model-Selection">&#194;&#182;</a></h1><p>When presented with many possible explanatory variables, choosing which to include in a regression model can be a difficult task. Luckily, there exists a variety of strategies and criteria that simplify model selection.</p>
+<p>As an example, let's attempt to model the US unemployment rate with US inflation rate, gold prices, USD vs. EUR exchange rate, and two ETFs that track the Russel-2000 and NASDAQ-100 indices as potential explanatory variables. All of these macro indicators are available as free <a href="https://www.quantopian.com/data">Quantopian Data Feeds</a>.</p>
+<p>Let's begin by pulling the above as Blaze expressions.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[34]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span class="kn">from</span> <span class="nn">quantopian.interactive.data.quandl</span> <span class="kn">import</span> <span class="n">fred_unrate</span> <span class="k">as</span> <span class="n">unemployment_bz</span>
+<span class="kn">from</span> <span class="nn">quantopian.interactive.data.quandl</span> <span class="kn">import</span> <span class="n">rateinf_inflation_usa</span> <span class="k">as</span> <span class="n">inflation_bz</span>
+<span class="kn">from</span> <span class="nn">quantopian.interactive.data.quandl</span> <span class="kn">import</span> <span class="n">bundesbank_bbk01_wt5511</span> <span class="k">as</span> <span class="n">gold_bz</span>
+<span class="kn">from</span> <span class="nn">quantopian.interactive.data.quandl</span> <span class="kn">import</span> <span class="n">currfx_usdeur</span> <span class="k">as</span> <span class="n">fx_bz</span>
+
+<span class="kn">import</span> <span class="nn">blaze</span> <span class="kn">as</span> <span class="nn">bz</span>
+<span class="kn">from</span> <span class="nn">odo</span> <span class="kn">import</span> <span class="n">odo</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Now let's migrate the data into Pandas series using <code>asof_date</code> as our index keeping in mind that:</p>
+<ul>
+<li>The specific inflation and unemployment datasets we are using have one month intervals, so time index intervals cannot be anything smaller than monthly</li>
+<li>The unemployment data is released at the start of the month after the relevant month and inflation rate data is released ~3 weeks after so we must shift both back a month from the asof_date to prevent look-ahead bias</li>
+<li>Gold prices must be shifted back one day from asof_date to prevent look-ahead bias</li>
+<li>Equity pricing data only goes back to 2002, so we can only consider data from 2002 on</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[35]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span class="c"># Start date dictated by QQQ and IWM (tickers of index-tracking ETFs)</span>
+<span class="n">start</span> <span class="o">=</span> <span class="s">&#39;2002-01-01&#39;</span>
+<span class="n">end</span> <span class="o">=</span> <span class="s">&#39;2017-01-01&#39;</span>
+
+<span class="c"># Sample period will be 2002-2012, saving 2012-2017 for model validation</span>
+<span class="n">s</span> <span class="o">=</span> <span class="s">&#39;2002-01-01&#39;</span>
+<span class="n">e</span> <span class="o">=</span> <span class="s">&#39;2012-01-01&#39;</span>
+
+<span class="n">index</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">date_range</span><span class="p">(</span><span class="n">start</span><span class="o">=</span><span class="n">start</span><span class="p">,</span> <span class="n">end</span> <span class="o">=</span> <span class="n">end</span><span class="p">,</span> <span class="n">freq</span><span class="o">=</span> <span class="s">&#39;MS&#39;</span><span class="p">)</span>
+
+<span class="c"># Migrating Blaze expressions into Pandas DataFrames and setting index</span>
+<span class="n">unemployment</span> <span class="o">=</span> <span class="n">odo</span><span class="p">(</span><span class="n">unemployment_bz</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span><span class="o">.</span><span class="n">set_index</span><span class="p">([</span><span class="s">&#39;asof_date&#39;</span><span class="p">])</span>
+<span class="n">inflation</span> <span class="o">=</span> <span class="n">odo</span><span class="p">(</span><span class="n">inflation_bz</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span>
+<span class="n">inflation</span> <span class="o">=</span> <span class="n">inflation</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="n">inflation</span><span class="p">[</span><span class="s">&#39;asof_date&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timedelta</span><span class="p">(</span><span class="s">&#39;1 days&#39;</span><span class="p">))</span>
+<span class="n">gold</span> <span class="o">=</span> <span class="n">odo</span><span class="p">(</span><span class="n">gold_bz</span><span class="p">[</span><span class="n">gold_bz</span><span class="o">.</span><span class="n">asof_date</span> <span class="o">&gt;=</span> <span class="n">start</span><span class="p">],</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span><span class="o">.</span><span class="n">set_index</span><span class="p">([</span><span class="s">&#39;asof_date&#39;</span><span class="p">])</span>
+<span class="n">fx</span> <span class="o">=</span> <span class="n">odo</span><span class="p">(</span><span class="n">fx_bz</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span><span class="o">.</span><span class="n">set_index</span><span class="p">([</span><span class="s">&#39;asof_date&#39;</span><span class="p">])</span>
+<span class="n">qqq</span> <span class="o">=</span> <span class="n">get_pricing</span><span class="p">(</span><span class="s">&#39;QQQ&#39;</span><span class="p">,</span> <span class="n">start_date</span><span class="o">=</span><span class="n">start</span><span class="p">,</span> <span class="n">end_date</span><span class="o">=</span><span class="n">end</span><span class="p">,</span> <span class="n">fields</span> <span class="o">=</span> <span class="s">&#39;price&#39;</span><span class="p">)</span>
+<span class="n">iwm</span> <span class="o">=</span> <span class="n">get_pricing</span><span class="p">(</span><span class="s">&#39;IWM&#39;</span><span class="p">,</span> <span class="n">start_date</span><span class="o">=</span><span class="n">start</span><span class="p">,</span> <span class="n">end_date</span><span class="o">=</span><span class="n">end</span><span class="p">,</span> <span class="n">fields</span> <span class="o">=</span> <span class="s">&#39;price&#39;</span><span class="p">)</span>
+
+<span class="c"># Adjusting data along points mentioned above</span>
+<span class="n">unemployment</span> <span class="o">=</span> <span class="n">unemployment</span><span class="o">.</span><span class="n">shift</span><span class="p">()</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">index</span><span class="p">]</span><span class="o">.</span><span class="n">ffill</span><span class="p">()[</span><span class="s">&#39;value&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">:];</span>
+<span class="n">inflation</span> <span class="o">=</span> <span class="n">inflation</span><span class="o">.</span><span class="n">shift</span><span class="p">()</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">index</span><span class="p">]</span><span class="o">.</span><span class="n">ffill</span><span class="p">()[</span><span class="s">&#39;value&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">:];</span>
+<span class="n">gold</span> <span class="o">=</span> <span class="n">gold</span><span class="p">[</span><span class="s">&#39;value&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">sort_index</span><span class="p">()</span><span class="o">.</span><span class="n">asof</span><span class="p">(</span><span class="n">index</span><span class="p">)</span><span class="o">.</span><span class="n">ffill</span><span class="p">()[</span><span class="mi">1</span><span class="p">:];</span>
+<span class="n">fx</span> <span class="o">=</span> <span class="n">fx</span><span class="p">[</span><span class="s">&#39;rate&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">sort_index</span><span class="p">()</span><span class="o">.</span><span class="n">asof</span><span class="p">(</span><span class="n">index</span><span class="p">)</span><span class="o">.</span><span class="n">ffill</span><span class="p">()[</span><span class="mi">1</span><span class="p">:];</span>
+<span class="n">qqq</span> <span class="o">=</span> <span class="n">qqq</span><span class="o">.</span><span class="n">asof</span><span class="p">(</span><span class="n">index</span><span class="p">)</span><span class="o">.</span><span class="n">ffill</span><span class="p">()[</span><span class="mi">1</span><span class="p">:];</span>
+<span class="n">iwm</span> <span class="o">=</span> <span class="n">iwm</span><span class="o">.</span><span class="n">asof</span><span class="p">(</span><span class="n">index</span><span class="p">)</span><span class="o">.</span><span class="n">ffill</span><span class="p">()[</span><span class="mi">1</span><span class="p">:];</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Now we have data for on independent variable and 5 predictor variables. If we were to include every one of these preditors, our model would look like:</p>
+$$ Y_{unrate} = \beta_0 + \beta_1 X_{inflation} + \beta_2 X_{gold} + \beta_3 X_{FX} + \beta_4 X_{qqq} + \beta_5 X_{iwm} + \epsilon_i $$<p>The next step is to figure out which predictors to include in our model. We could include every single predictor we have, but we would not be sure if every predictor was significant. Adding many insignificant predictors causes a few different issues. If there are more predictors there is a larger chance that the predictors themselves are correalted with each other which would lead to regression model instability and invalidate our results. Furthermore, including many regressors hurts the predictive power of a model. A solution might be to include as few variables as possible, but we would likely exclude some explanatory effects this way. Let's look at some ways to find this balance and determine which variables to include.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Model-Selection-Criteria">Model Selection Criteria<a class="anchor-link" href="#Model-Selection-Criteria">&#194;&#182;</a></h2><p>There exist a number of metrics we can use to asses the relative and absolute strength of a specfic model. The ones we will focus on are $R^2$, adjusted $R^2$, AIC, and BIC.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Primer-on-Sum-of-Squares-($SS_{err}$,-$SS_{reg}$,-and-$SS_{total}$)">Primer on Sum of Squares ($SS_{err}$, $SS_{reg}$, and $SS_{total}$)<a class="anchor-link" href="#Primer-on-Sum-of-Squares-($SS_{err}$,-$SS_{reg}$,-and-$SS_{total}$)">&#194;&#182;</a></h3>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>The residual sum of squares ($RSS$ or $SS_{err}$) measures the variation in the difference between modelled values and observed values (residuals). The explained sum of squares ($SSR$ or $SS_{reg}$) measures the variation of model values. Finally, the total sum of squares ($SST$ or $SS_{tot}$) measures the variation in the observed data.</p>
+$$SS_{err} = \sum_{i=1}^{n}{(Y_i - \hat{Y_i})^2}\:\:\:\:\:\:\:SS_{reg} = \sum_{i=1}^{n}{(\hat{Y_i} - \bar{Y_i})^2}\:\:\:\:\:\:\:SS_{tot} = \sum_{i=1}^{n}{(Y_i - \bar{Y_i})^2}$$<p>Where $Y_i$ are observed sample values, $\hat{Y_i}$ are the model values and $\bar{Y}$ is the sample mean.</p>
+<p>In cases of simple linear regression, the following is generally true:</p>
+$$ SS_{tot} = SS_{reg} + SS_{err} $$<p>For more intuition on their relationship, look at the graph below. Change the value of $p$ to choose which point to see squared error, regression, and total values for.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[36]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span class="c"># Change p to choose which point to see sum of squares visualization at</span>
+<span class="n">p</span> <span class="o">=</span> <span class="mi">6</span>
+
+<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">9</span><span class="p">)</span>
+<span class="n">Y</span> <span class="o">=</span> <span class="n">X</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">9</span><span class="p">)</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="n">regression</span><span class="o">.</span><span class="n">linear_model</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">sm</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">X</span><span class="p">))</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span><span class="o">.</span><span class="n">params</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">X</span> <span class="o">*</span> <span class="n">params</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+<span class="n">err</span> <span class="o">=</span> <span class="n">Y</span><span class="o">-</span><span class="n">model</span>
+<span class="n">reg</span> <span class="o">=</span> <span class="n">model</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span>
+<span class="n">tot</span> <span class="o">=</span> <span class="n">Y</span><span class="o">-</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">Y</span><span class="p">)]</span><span class="o">*</span><span class="mi">9</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">Y</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s">&#39;black&#39;</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s">&#39;data&#39;</span><span class="p">);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X</span><span class="p">,[</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">Y</span><span class="p">)]</span><span class="o">*</span><span class="mi">9</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s">&#39;black&#39;</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.3</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s">&#39;Y_mean&#39;</span><span class="p">)</span>
+
+<span class="n">x_p</span><span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">p</span><span class="p">]</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="s">&#39;p = </span><span class="si">%i</span><span class="s">&#39;</span><span class="o">%</span><span class="k">p</span>, xy=(x_p+0.1,Y[p]+0.1))
+
+<span class="n">plt</span><span class="o">.</span><span class="n">fill_between</span><span class="p">([</span><span class="n">x_p</span><span class="p">,</span><span class="n">x_p</span><span class="o">+</span><span class="n">err</span><span class="p">[</span><span class="n">p</span><span class="p">]],</span> <span class="n">model</span><span class="p">[</span><span class="n">p</span><span class="p">],</span> <span class="n">Y</span><span class="p">[</span><span class="n">p</span><span class="p">],</span> 
+                 <span class="n">facecolor</span><span class="o">=</span><span class="s">&#39;blue&#39;</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.4</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s">&#39;S_err&#39;</span><span class="p">);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">fill_between</span><span class="p">([</span><span class="n">x_p</span><span class="o">+</span><span class="n">reg</span><span class="p">[</span><span class="n">p</span><span class="p">],</span><span class="n">x_p</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">Y</span><span class="p">),</span> <span class="n">model</span><span class="p">[</span><span class="n">p</span><span class="p">],</span> 
+                 <span class="n">facecolor</span><span class="o">=</span><span class="s">&#39;yellow&#39;</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.4</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s">&#39;S_reg&#39;</span><span class="p">);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">fill_between</span><span class="p">([</span><span class="n">x_p</span><span class="o">-</span><span class="n">tot</span><span class="p">[</span><span class="n">p</span><span class="p">],</span><span class="n">x_p</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">Y</span><span class="p">),</span> <span class="n">Y</span><span class="p">[</span><span class="n">p</span><span class="p">],</span> 
+                 <span class="n">facecolor</span><span class="o">=</span><span class="s">&#39;red&#39;</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.4</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s">&#39;S_tot&#39;</span><span class="p">);</span>
+                                                                     
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">model</span><span class="p">,</span> <span class="n">linestyle</span> <span class="o">=</span> <span class="s">&#39;--&#39;</span><span class="p">,</span> <span class="n">c</span> <span class="o">=</span> <span class="s">&#39;r&#39;</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s">&#39;model&#39;</span><span class="p">);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s">&#39;X&#39;</span><span class="p">);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s">&#39;Y&#39;</span><span class="p">);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s">&#39;Single point RS, SR, and ST of simple linear regression&#39;</span><span class="p">,</span> 
+          <span class="n">fontsize</span> <span class="o">=</span> <span class="mi">15</span><span class="p">);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">();</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
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+<div class="prompt"></div>
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>$SS_{err}$, $SS_{reg}$, and $SS_{total}$ are the sums of the areas of the above squares for every point in the sample.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Coefficient-of-Determination-($R^2$)">Coefficient of Determination ($R^2$)<a class="anchor-link" href="#Coefficient-of-Determination-($R^2$)">&#194;&#182;</a></h3><p>The coefficient of determination, or $R^2$, is a metric that tells us the proportion of in-sample variance 'explained' by a certain model. For example, an $R^2$ of 0.9 tells us that the magnitude of the model residual variance is about 90% of that of the sample data. The formula for $R^2$ is:</p>
+$$R^2 = \frac{SS_{reg}}{SS_{tot}} = \frac{\sum_{i=1}^{n} (\hat{Y_i} - \bar{Y_i})^2}{\sum_{i=1}^{n} (Y_i - \bar{Y})^2}$$<p>One major drawback of $R^2$ in model selection is that as the number of explanatory variables increases, $R^2$ will always also increase or stay the same, even if the incremental variables are not adding much predictive insight. To illustrate this, let's find the $R^2$ of five unemployment models, each model having one more predictor than the next. We will do this by defining a function that takes predictor variables $X_1,\ldots, X_k$ and an independent variable $Y$, runs a regression, and calculates $R^2$ using the <code>model.rsquared</code> attribute.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[37]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span class="k">def</span> <span class="nf">rsquared</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">Y</span><span class="p">):</span>
+    <span class="n">X</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+    <span class="n">model</span> <span class="o">=</span> <span class="n">regression</span><span class="o">.</span><span class="n">linear_model</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
+    <span class="k">return</span> <span class="n">model</span><span class="o">.</span><span class="n">rsquared</span>
+
+<span class="c"># Defining variables, making sure to keep data</span>
+<span class="c"># within the sample period [:e]</span>
+<span class="n">Y</span> <span class="o">=</span> <span class="n">unemployment</span><span class="p">[:</span><span class="n">e</span><span class="p">]</span>
+<span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">qqq</span><span class="p">[:</span><span class="n">e</span><span class="p">],</span> <span class="n">inflation</span><span class="p">[:</span><span class="n">e</span><span class="p">],</span> <span class="n">iwm</span><span class="p">[:</span><span class="n">e</span><span class="p">],</span> <span class="n">fx</span><span class="p">[:</span><span class="n">e</span><span class="p">],</span> <span class="n">gold</span><span class="p">[:</span><span class="n">e</span><span class="p">]]</span>
+<span class="n">X_str</span> <span class="o">=</span> <span class="p">[</span><span class="s">&#39;qqq&#39;</span><span class="p">,</span> <span class="s">&#39;inflation&#39;</span><span class="p">,</span> <span class="s">&#39;iwm&#39;</span><span class="p">,</span> <span class="s">&#39;fx&#39;</span><span class="p">,</span> <span class="s">&#39;gold&#39;</span><span class="p">]</span>
+
+<span class="k">print</span> <span class="s">&#39;------ R Squared Values ------&#39;</span>
+<span class="k">print</span> <span class="s">&#39;1 predictor:&#39;</span><span class="p">,</span> <span class="n">rsquared</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">1</span><span class="p">]),</span> <span class="n">Y</span><span class="p">)</span>
+<span class="k">print</span> <span class="s">&#39;2 predictors:&#39;</span><span class="p">,</span> <span class="n">rsquared</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">2</span><span class="p">]),</span> <span class="n">Y</span><span class="p">)</span>
+<span class="k">print</span> <span class="s">&#39;3 predictors:&#39;</span><span class="p">,</span> <span class="n">rsquared</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">3</span><span class="p">]),</span> <span class="n">Y</span><span class="p">)</span>
+<span class="k">print</span> <span class="s">&#39;4 predictors:&#39;</span><span class="p">,</span> <span class="n">rsquared</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">4</span><span class="p">]),</span> <span class="n">Y</span><span class="p">)</span>
+<span class="k">print</span> <span class="s">&#39;5 predictors:&#39;</span><span class="p">,</span> <span class="n">rsquared</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">5</span><span class="p">]),</span> <span class="n">Y</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>------ R Squared Values ------
+1 predictor: 0.124499233139
+2 predictors: 0.459882090695
+3 predictors: 0.700574522302
+4 predictors: 0.734219660452
+5 predictors: 0.875758340212
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>As we can see, increasing the number of predictors inflates the $R^2$ output. If we only went by the $R^2$ prediction criteria, the best model would always be the one with the most predictor variables. This invalidates $R^2$ as a model selection criteria for models with different amounts of predictor variables.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="$\bar{R}^2$-(Adjusted-$R^2$)">$\bar{R}^2$ (Adjusted $R^2$)<a class="anchor-link" href="#$\bar{R}^2$-(Adjusted-$R^2$)">&#194;&#182;</a></h3>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Because regular $R^2$ becomes inflated as more variables are added we cannot use $R^2$ alone for model selection. To account for this effect, there exists an alternate version of $R^2$ which includes a penalty for adding more variables. The formula is below, where $p$ is the number of predictor variables, and $n$ is the sample size:</p>
+$$ \bar{R}^2 = 1-(1-R^2)\frac{n-1}{n-p-1} $$<p>Let's repeat the experiment above, this time using $\bar{R}^2$, to see if it still inflates as more predictors are added.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[38]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span class="k">def</span> <span class="nf">rsquared_adj</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">Y</span><span class="p">):</span>
+    <span class="n">X</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+    <span class="n">model</span> <span class="o">=</span> <span class="n">regression</span><span class="o">.</span><span class="n">linear_model</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
+    <span class="k">return</span> <span class="n">model</span><span class="o">.</span><span class="n">rsquared_adj</span>
+
+<span class="k">print</span> <span class="s">&#39;------ Adj R Squared Values ------&#39;</span>
+<span class="k">print</span> <span class="s">&#39;1 predictor:&#39;</span><span class="p">,</span> <span class="n">rsquared_adj</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">1</span><span class="p">]),</span> <span class="n">Y</span><span class="p">)</span>
+<span class="k">print</span> <span class="s">&#39;2 predictors:&#39;</span><span class="p">,</span> <span class="n">rsquared_adj</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">2</span><span class="p">]),</span> <span class="n">Y</span><span class="p">)</span>
+<span class="k">print</span> <span class="s">&#39;3 predictors:&#39;</span><span class="p">,</span> <span class="n">rsquared_adj</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">3</span><span class="p">]),</span> <span class="n">Y</span><span class="p">)</span>
+<span class="k">print</span> <span class="s">&#39;4 predictors:&#39;</span><span class="p">,</span> <span class="n">rsquared_adj</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">4</span><span class="p">]),</span> <span class="n">Y</span><span class="p">)</span>
+<span class="k">print</span> <span class="s">&#39;5 predictors:&#39;</span><span class="p">,</span> <span class="n">rsquared_adj</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">5</span><span class="p">]),</span> <span class="n">Y</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>------ Adj R Squared Values ------
+1 predictor: 0.117079735115
+2 predictors: 0.45064930592
+3 predictors: 0.692830759948
+4 predictors: 0.724975126903
+5 predictors: 0.870309144607
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>The $\bar{R}^2$ are similar to the $R^2$ values, however we can see that the values are slightly smaller due to the predictor penalty. $\bar{R}^2$ should always be used when comparing models with different amounts of predictors to avoid the predictor inflation effect of $R^2$.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Akaike-and-Bayesian-Information-Criterion-(AIC-and-BIC)">Akaike and Bayesian Information Criterion (AIC and BIC)<a class="anchor-link" href="#Akaike-and-Bayesian-Information-Criterion-(AIC-and-BIC)">&#194;&#182;</a></h3>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>$R^2$ and $\bar{R}^2$ provide absolute measures on the quality on a model, meaning they can be calculated on any single regression model. They tell you how "good" a model is on its own on a scale of 0 to 1.</p>
+<p>AIC and BIC provide relative measures of quality and are calculated with an underlying selection pool of models. Instead  of yeidling a metric with an absolute scale like $R^2$ and $\bar{R}^2$, they return values for every model in the selection pool and determining model quality requires looking at all of the values and comparing them.</p>
+<p>AIC is calculated along the following formula:</p>
+$$ AIC = 2p + 2Log(\hat{L}) $$<p>BIC is calculated similarly:</p>
+$$ BIC = p \cdot ln(n) -2Log(\hat{L}) $$<p>Where $\hat{L}$ is defined as the maximumized value of the likelihood function. The likelihood function is the probability  our model in question is in fact correct given the data we observed. For more information on likelihood functions refer to the <a href="https://en.wikipedia.org/wiki/Likelihood_function">Wikipedia</a> page on the subject.</p>
+<p>Let's use AIC and BIC to compare 5 simple linear regression models for unemployment. We will compute them using the <code>model.aic</code> and <code>model.bic</code> attributes.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[39]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span class="k">def</span> <span class="nf">aic</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">Y</span><span class="p">):</span>
+    <span class="n">X</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+    <span class="n">model</span> <span class="o">=</span> <span class="n">regression</span><span class="o">.</span><span class="n">linear_model</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
+    <span class="k">return</span> <span class="n">model</span><span class="o">.</span><span class="n">aic</span>
+
+<span class="k">def</span> <span class="nf">bic</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">Y</span><span class="p">):</span>
+    <span class="n">X</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+    <span class="n">model</span> <span class="o">=</span> <span class="n">regression</span><span class="o">.</span><span class="n">linear_model</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
+    <span class="k">return</span> <span class="n">model</span><span class="o">.</span><span class="n">bic</span>
+
+<span class="n">AICs</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="n">aic</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">_</span><span class="p">],</span><span class="n">Y</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">)])</span>
+<span class="n">BICs</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="n">bic</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">_</span><span class="p">],</span><span class="n">Y</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">)])</span>
+
+<span class="k">print</span> <span class="s">&quot;</span><span class="si">%-24s</span><span class="s"> </span><span class="si">%-15s</span><span class="s"> </span><span class="si">%-13s</span><span class="s">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="s">&#39;&#39;</span><span class="p">,</span> <span class="s">&#39;AIC values:&#39;</span><span class="p">,</span> <span class="s">&#39;BIC values:&#39;</span><span class="p">)</span>
+<span class="k">print</span> <span class="s">&quot;</span><span class="si">%-24s</span><span class="s"> </span><span class="si">%-15s</span><span class="s"> </span><span class="si">%-13s</span><span class="s">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="s">&#39;Y = b0 + b1*qqq&#39;</span><span class="p">,</span> <span class="n">AICs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">BICs</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+<span class="k">print</span> <span class="s">&quot;</span><span class="si">%-24s</span><span class="s"> </span><span class="si">%-15s</span><span class="s"> </span><span class="si">%-13s</span><span class="s">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="s">&#39;Y = b0 + b1*inflation&#39;</span><span class="p">,</span> <span class="n">AICs</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">BICs</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+<span class="k">print</span> <span class="s">&quot;</span><span class="si">%-24s</span><span class="s"> </span><span class="si">%-15s</span><span class="s"> </span><span class="si">%-13s</span><span class="s">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="s">&#39;Y = b0 + b1*iwm&#39;</span><span class="p">,</span> <span class="n">AICs</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">BICs</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
+<span class="k">print</span> <span class="s">&quot;</span><span class="si">%-24s</span><span class="s"> </span><span class="si">%-15s</span><span class="s"> </span><span class="si">%-13s</span><span class="s">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="s">&#39;Y = b0 + b1*fx&#39;</span><span class="p">,</span> <span class="n">AICs</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">BICs</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
+<span class="k">print</span> <span class="s">&quot;</span><span class="si">%-24s</span><span class="s"> </span><span class="si">%-15s</span><span class="s"> </span><span class="si">%-13s</span><span class="s">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="s">&#39;Y = b0 + b1*gold&#39;</span><span class="p">,</span> <span class="n">AICs</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span> <span class="n">BICs</span><span class="p">[</span><span class="mi">4</span><span class="p">])</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>                         AIC values:     BIC values:  
+Y = b0 + b1*qqq          481.904294922   487.479278408
+Y = b0 + b1*inflation    464.408261441   469.983244926
+Y = b0 + b1*iwm          497.835819526   503.410803012
+Y = b0 + b1*fx           488.599333286   494.174316772
+Y = b0 + b1*gold         398.65240035    404.227383836
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Now let's plot the AICs and BICs.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[40]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span class="n">AICs</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">title</span> <span class="o">=</span> <span class="s">&#39;Model AICs and BICs&#39;</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s">&#39;AIC&#39;</span><span class="p">);</span>
+<span class="n">BICs</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">label</span> <span class="o">=</span> <span class="s">&#39;BIC&#39;</span><span class="p">);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">),</span> <span class="n">X_str</span><span class="p">);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s">&#39;Model Predictor&#39;</span><span class="p">);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">();</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>The "best" model according to AIC and BIC is the one with the smallest value. In this case, both the Akaike and Bayesian information criterion point to the same conclusion; they are both at their minimum with the model $Y = b_0 + b_1 \cdot gold$ meaning gold is the best single predictor for the model on this sample.</p>
+<h3 id="AIC-vs-BIC">AIC vs BIC<a class="anchor-link" href="#AIC-vs-BIC">&#194;&#182;</a></h3><p>In this test the two criteria pointed to the same model as the best, but this is not always the case as the two criteria have a few key differences.</p>
+<ul>
+<li>The BIC imposes a harsher penalty on additional regressors than the AIC does and will usually select "smaller" models with less parameters</li>
+<li>AIC is more suitable for selecting models that serve as the best predictors</li>
+<li>BIC is best used to select a model that best explains the behavior in-sample</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Selection-Methods">Selection Methods<a class="anchor-link" href="#Selection-Methods">&#194;&#182;</a></h2><p>Now that we have a handful of criteria to quantify model quality we need to have a method to cycle through and search for the possible models.</p>
+<p>The first idea that might come to mind is to simply pool all possible models together and test them all. While this might work for a situation in which there are a small number of potential regressors, when dealing with big data sets and many possible predictors it becomes a more difficult issue. The number of predictor combinations begins to rise quickly as more possible predictors are added making cycling through every combination not efficient.</p>
+<h3 id="Step-AIC/BIC">Step AIC/BIC<a class="anchor-link" href="#Step-AIC/BIC">&#194;&#182;</a></h3><p>If the number of possible predictors large, the best for selecting the "best" is likely iterating through a stepwise linear regression. Broadly, the method builds a model by selecting regressors one at a time, at each step choosing the one that minimizes the model's AIC or BIC. The process ends when adding another variable can no longer decrease the AIC/BIC or when the algorithm exhausts the predictor set and there are no more potential predictors to add. Note that stepwise regressions need not only step forward; the process can be conducted in reverse, starting with all variables included and subtracting off those yielding the <em>highest</em> AIC/BIC, or in tandem with steps alterating forward and backwards.</p>
+<p>Let's use a step-forward AIC algorithm to select a set of unemployment predictors to use in our model. Some print statements have been added to show the iteration process but can and should be deleted if you would like to use the function elsewhere.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[41]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span class="k">def</span> <span class="nf">forward_aic</span><span class="p">(</span><span class="n">response</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+    <span class="c"># This function will work with pandas dataframes and series</span>
+    
+    <span class="c"># Initialize some variables</span>
+    <span class="n">explanatory</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
+    <span class="n">selected</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">name</span><span class="o">=</span><span class="s">&quot;Intercept&quot;</span><span class="p">)</span>
+    <span class="n">current_score</span><span class="p">,</span> <span class="n">best_new_score</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span>
+    <span class="n">step</span> <span class="o">=</span> <span class="mi">1</span>
+    
+    <span class="c"># Loop while we haven&#39;t found a better model</span>
+    <span class="k">while</span> <span class="n">current_score</span> <span class="o">==</span> <span class="n">best_new_score</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">explanatory</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+        
+        
+        <span class="n">scores_with_elements</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">count</span> <span class="o">=</span> <span class="mi">0</span>
+        
+        <span class="c"># For each explanatory variable</span>
+        <span class="k">for</span> <span class="n">element</span> <span class="ow">in</span> <span class="n">explanatory</span><span class="p">:</span>
+            <span class="c"># Make a set of explanatory variables including our current best and the new one</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">selected</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">element</span><span class="p">]],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+            <span class="c"># Test the set</span>
+            <span class="n">result</span> <span class="o">=</span> <span class="n">regression</span><span class="o">.</span><span class="n">linear_model</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">response</span><span class="p">,</span> <span class="n">tmp</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
+            <span class="n">score</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">aic</span>
+            <span class="n">scores_with_elements</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">score</span><span class="p">,</span> <span class="n">element</span><span class="p">,</span> <span class="n">count</span><span class="p">))</span>
+            <span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
+        
+        <span class="c"># Sort the scoring list</span>
+        <span class="n">scores_with_elements</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">reverse</span> <span class="o">=</span> <span class="bp">True</span><span class="p">)</span>
+        <span class="c"># Get the best new variable</span>
+        <span class="n">best_new_score</span><span class="p">,</span> <span class="n">best_element</span><span class="p">,</span> <span class="n">index</span> <span class="o">=</span> <span class="n">scores_with_elements</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span>
+        <span class="k">print</span> <span class="s">&#39;--- Step&#39;</span><span class="p">,</span> <span class="n">step</span><span class="p">,</span> <span class="s">&#39; ---&#39;</span>
+        <span class="n">step</span> <span class="o">+=</span> <span class="mi">1</span>
+        <span class="k">print</span> <span class="s">&#39;Current Best AIC:&#39;</span><span class="p">,</span> <span class="n">current_score</span>
+        <span class="k">print</span> <span class="s">&#39;Best New AIC:&#39;</span><span class="p">,</span> <span class="n">best_new_score</span>
+        <span class="k">print</span> <span class="s">&#39;Variable to Add:&#39;</span><span class="p">,</span> <span class="n">best_element</span>    
+        
+        <span class="k">if</span> <span class="n">current_score</span> <span class="o">&gt;</span> <span class="n">best_new_score</span><span class="p">:</span>
+            <span class="c"># If it&#39;s better than the best add it to the set</span>
+            <span class="n">explanatory</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="n">index</span><span class="p">)</span>
+            <span class="n">selected</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">selected</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">best_element</span><span class="p">]],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+            <span class="n">current_score</span> <span class="o">=</span> <span class="n">best_new_score</span>
+            <span class="k">print</span> <span class="s">&#39;Chosen Model Predictors:&#39;</span><span class="p">,</span> <span class="n">selected</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">1</span><span class="p">:],</span> <span class="s">&#39;</span><span class="se">\n</span><span class="s">&#39;</span>
+            
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">print</span> <span class="s">&#39;Best new AIC did not beat current best. The new variable to add is rejected and the algorithm is finished.</span><span class="se">\n\n</span><span class="s">&#39;</span>
+
+    <span class="c"># Return the final model</span>
+    <span class="k">return</span> <span class="n">selected</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[42]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span class="c"># Reformatting the data to work with the forward_aic function</span>
+<span class="n">Y_series</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
+<span class="n">data_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">((</span><span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">X</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">X</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span><span class="n">X</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span><span class="n">X</span><span class="p">[</span><span class="mi">4</span><span class="p">])),</span> <span class="n">columns</span> <span class="o">=</span> <span class="n">X_str</span><span class="p">)</span>
+
+<span class="n">predictors</span> <span class="o">=</span> <span class="n">forward_aic</span><span class="p">(</span><span class="n">Y_series</span><span class="p">,</span> <span class="n">data_df</span><span class="p">)</span>
+<span class="k">print</span> <span class="s">&#39;Selected Predictors:&#39;</span><span class="p">,</span> <span class="n">predictors</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>--- Step 1  ---
+Current Best AIC: inf
+Best New AIC: 398.65240035
+Variable to Add: gold
+Chosen Model Predictors: [&#39;gold&#39;] 
+
+--- Step 2  ---
+Current Best AIC: 398.65240035
+Best New AIC: 307.741249417
+Variable to Add: iwm
+Chosen Model Predictors: [&#39;gold&#39; &#39;iwm&#39;] 
+
+--- Step 3  ---
+Current Best AIC: 307.741249417
+Best New AIC: 258.210706106
+Variable to Add: inflation
+Chosen Model Predictors: [&#39;gold&#39; &#39;iwm&#39; &#39;inflation&#39;] 
+
+--- Step 4  ---
+Current Best AIC: 258.210706106
+Best New AIC: 253.653929589
+Variable to Add: qqq
+Chosen Model Predictors: [&#39;gold&#39; &#39;iwm&#39; &#39;inflation&#39; &#39;qqq&#39;] 
+
+--- Step 5  ---
+Current Best AIC: 253.653929589
+Best New AIC: 255.596196199
+Variable to Add: fx
+Best new AIC did not beat current best. The new variable to add is rejected and the algorithm is finished.
+
+
+Selected Predictors: [&#39;gold&#39; &#39;iwm&#39; &#39;inflation&#39; &#39;qqq&#39;]
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>The <code>forward_aic</code> algorithm selected gold prices, the IWM index, inflation rate, and the QQQ index as the most appropriate combination of predictors. The model is now:</p>
+$$ Y_{unrate} = \beta_0 + \beta_1 X_{gold} + \beta_2 X_{iwm} + \beta_3 X_{inflation} + \beta_4 X_{qqq} + \epsilon_i $$<p>Let's fit this model using OLS to determine the beta coefficients and graph both the model's prediction for unemployment and its actual values.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[43]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span class="n">model</span> <span class="o">=</span> <span class="n">regression</span><span class="o">.</span><span class="n">linear_model</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">Y</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="bp">True</span><span class="p">),</span> <span class="n">predictors</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
+<span class="n">theta</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">params</span>
+<span class="k">print</span> <span class="n">theta</span>
+<span class="n">predictions</span> <span class="o">=</span> <span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">model</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="n">gold</span><span class="p">[</span><span class="n">s</span><span class="p">:</span><span class="n">e</span><span class="p">]</span> <span class="o">+</span> <span class="n">model</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">*</span><span class="n">iwm</span><span class="p">[</span><span class="n">s</span><span class="p">:</span><span class="n">e</span><span class="p">]</span>
+            <span class="o">+</span> <span class="n">model</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">*</span><span class="n">inflation</span><span class="p">[</span><span class="n">s</span><span class="p">:</span><span class="n">e</span><span class="p">]</span> <span class="o">+</span> <span class="n">model</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span><span class="o">*</span><span class="n">qqq</span><span class="p">[</span><span class="n">s</span><span class="p">:</span><span class="n">e</span><span class="p">])</span>
+
+<span class="n">predictions</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">label</span> <span class="o">=</span> <span class="s">&#39;model&#39;</span><span class="p">,</span> <span class="n">linestyle</span> <span class="o">=</span> <span class="s">&#39;--&#39;</span><span class="p">,</span> <span class="n">c</span> <span class="o">=</span> <span class="s">&#39;r&#39;</span><span class="p">);</span>
+<span class="n">Y</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">label</span> <span class="o">=</span> <span class="s">&#39;unemployment&#39;</span><span class="p">);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">();</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Intercept    7.180962
+gold         0.003986
+iwm         -0.093050
+inflation   -0.386373
+qqq          0.067445
+dtype: float64
+</pre>
+</div>
+</div>
+
+<div class="output_area">
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+
+
+
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>At first glance the regression model seems decent, but further evaluation is required to determine it's validity.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Model-Validation">Model Validation<a class="anchor-link" href="#Model-Validation">&#194;&#182;</a></h1><p>Now that we used model selection criteria and methods to find the most valuable combination of regressors, we now must determine whether this best model is acceptable. Because "best" is a relative term, it is possible that the best model does not explain the independent variable in a satisfactory way. As a result, more work needs to be done to ensure that our model is well-founded.</p>
+<h3 id="$R^2$-and-$\bar{R}^2$">$R^2$ and $\bar{R}^2$<a class="anchor-link" href="#$R^2$-and-$\bar{R}^2$">&#194;&#182;</a></h3><p>As well as being criteria model selection, $R^2$ and $\bar{R}^2$ can also be used as model validation criteria. The intuition, formulas, and weaknesses detailed in the selection section still hold. However, in terms of validation they are additionally limited as they cannot  determine the accuracy of the form of the relationship, a vital part of a well-founded model.</p>
+<p>To illustrate this weakness we can run a linear regression on two datasets with very different forms. Both regressions have a similar $R^2$ value but only the second accurately represents the form of the data it is modeling.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[44]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">13</span><span class="p">)</span>
+<span class="n">X</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
+<span class="n">Y1</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">**</span><span class="mi">5</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">X</span><span class="p">]</span>
+<span class="n">Y2</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="o">+</span> <span class="mi">20</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">X</span><span class="p">]</span>
+
+<span class="n">model1</span> <span class="o">=</span> <span class="n">regression</span><span class="o">.</span><span class="n">linear_model</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">Y1</span><span class="p">,</span> <span class="n">sm</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">X</span><span class="p">))</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
+<span class="n">model2</span> <span class="o">=</span> <span class="n">regression</span><span class="o">.</span><span class="n">linear_model</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">Y2</span><span class="p">,</span> <span class="n">sm</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">X</span><span class="p">))</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
+
+<span class="k">print</span> <span class="s">&#39;R^2 of First Model:&#39;</span><span class="p">,</span> <span class="n">model1</span><span class="o">.</span><span class="n">rsquared</span>
+<span class="k">print</span> <span class="s">&#39;R^2 of Second Model:&#39;</span><span class="p">,</span> <span class="n">model2</span><span class="o">.</span><span class="n">rsquared</span>
+
+<span class="n">line1</span> <span class="o">=</span> <span class="p">[</span><span class="n">model1</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">model1</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">X</span><span class="p">]</span>
+<span class="n">line2</span> <span class="o">=</span> <span class="p">[</span><span class="n">model2</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">model2</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">X</span><span class="p">]</span>
+
+<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span> <span class="n">ncols</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
+
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">line1</span><span class="p">,</span> <span class="n">c</span> <span class="o">=</span> <span class="s">&#39;r&#39;</span><span class="p">);</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y1</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.4</span><span class="p">);</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">line2</span><span class="p">,</span> <span class="n">c</span> <span class="o">=</span> <span class="s">&#39;r&#39;</span><span class="p">);</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y2</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.4</span><span class="p">);</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>R^2 of First Model: 0.671416768972
+R^2 of Second Model: 0.67216727851
+</pre>
+</div>
+</div>
+
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Clearly the second model best represents the data it was meant to model, but it has practically the same $R^2$ as the first. This illustrates the limitations of $R^2$ when it comes to determining model fit and predictive value.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Although it will not provide a complete picture, let's find the $R^2$ and $\bar{R}^2$ of our unemployment model:</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[45]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span class="k">print</span> <span class="s">&#39;Unemployment Model R^2:&#39;</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">rsquared</span>
+<span class="k">print</span> <span class="s">&#39;Unemployment Model Adjusted R^2:&#39;</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">rsquared_adj</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Unemployment Model R^2: 0.875698551729
+Unemployment Model Adjusted R^2: 0.871375023094
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Residual-Analysis">Residual Analysis<a class="anchor-link" href="#Residual-Analysis">&#194;&#182;</a></h3><p>Residual analysis can help check that the basic assumptions of the linear model are satisfied. A large portion of model validation has to do with studying the residuals of the regression in question. To learn more about this aspect of model validation refer to the <a href="https://www.quantopian.com/lectures/residual-analysis">lecture on residual analysis</a>.</p>
+<p>Note that our toy model is conducted on the point values of these datasets. Ideally, we would conduct it on a stationary and normal representation of the data (such as first-order differences) in order to satisfy the assumptions of linear regression. While our toy model works for demonstrating model selection and validation, looking at the residuals will make these violations glaringly obvious and should always be done when validating a model.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[61]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span class="n">residuals</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">resid</span><span class="p">;</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">index</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">residuals</span><span class="p">)],</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">residuals</span><span class="p">),</span> <span class="n">c</span><span class="o">=</span><span class="s">&#39;r&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">index</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">residuals</span><span class="p">)],</span> <span class="n">residuals</span><span class="p">);</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>This residual plot uncovers serial correlation and changing variance, both violations of the assumptions of linear regression which invalidate our model. To learn how to detect and adjust for these symptoms refer to the <a href="https://www.quantopian.com/lectures/residual-analysis">lecture on residual analysis</a>.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Cross-Validation">Cross-Validation<a class="anchor-link" href="#Cross-Validation">&#194;&#182;</a></h2><p>Cross-validation is a technique used to determine how well a model will predict values outside of the sample used to select or fit it. In the case of our unemployment model, we used data from 2002 through 2012 to select our parameters. Now to ensure the accuracy of our model parameters (stored in <code>predictors</code>) we will employ a cross-validation technique known as <em>forward chaining</em> on the 2002-2012 data. We will leave the 2012-2017 data untouched for further validation methods later on in this lecture.</p>
+<p>Forward chaining cross-validation works by splitting the data up into $k$ equal sized partitions, and conducting an "out-of-sample test" for each partition. During each of these out-of-sample tests we will choose a training set and a testing set and use the training set to fit the model and the testing set to asses its performance. For time series data the training set must come before the testing set, and in the case of forward chaining the training set consists of all the data before the testing set. Forward chaining iterates through all possible testing sets and asses model performance in each.</p>
+<p>In the case of our unemployment model, we will partition our 2002-2012 data into 10 yearly blocks. The first 3 iterations of a forward chaining test would be constructed as follows:</p>
+$$
+\
+ \text{Iteration } 1:  \overbrace{
+    \underbrace{\textit{2002}}_\text{Training} +
+    \underbrace{\textit{2003}}_\text{Testing}
+   }^\text{First Trail-Test Pair}
+ \
+$$$$
+ \
+ \text{Iteration } 2:  \overbrace{
+    \underbrace{\textit{2002 &amp; 2003}}_\text{Training} +
+    \underbrace{\textit{2004}}_\text{Testing}
+   }^\text{Second Trail-Test Pair}
+ \
+$$$$
+ \
+ \text{Iteration } 3:  \overbrace{
+    \underbrace{\textit{2002 &amp; 2003 &amp; 2004}}_\text{Training} +
+    \underbrace{\textit{2005}}_\text{Testing}
+   }^\text{Third Trail-Test Pair}
+ \
+$$<center>
+.  .  .
+</center><p>The end result is a single performance statistic, usually the  mean squared error (MSE) or adjusted $R^2$ of the model in the testing set averaged from across all of the iterations. Let's implement a forward chaining model-validation test on our unemployment model using adjusted MSE as our model prediction quality metric.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[28]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span class="n">Y</span> <span class="o">=</span> <span class="n">unemployment</span><span class="p">[:</span><span class="n">e</span><span class="p">]</span>
+<span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">qqq</span><span class="p">[:</span><span class="n">e</span><span class="p">],</span> <span class="n">inflation</span><span class="p">[:</span><span class="n">e</span><span class="p">],</span> <span class="n">iwm</span><span class="p">[:</span><span class="n">e</span><span class="p">],</span> <span class="n">fx</span><span class="p">[:</span><span class="n">e</span><span class="p">],</span> <span class="n">gold</span><span class="p">[:</span><span class="n">e</span><span class="p">]]</span>
+
+<span class="c"># Our step AIC algorithm selected all predictors except for fx_rate</span>
+<span class="n">predictors</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">qqq</span><span class="p">[:</span><span class="n">e</span><span class="p">],</span> <span class="n">inflation</span><span class="p">[:</span><span class="n">e</span><span class="p">],</span> <span class="n">iwm</span><span class="p">[:</span><span class="n">e</span><span class="p">],</span> <span class="n">gold</span><span class="p">[:</span><span class="n">e</span><span class="p">]],</span> 
+                          <span class="n">index</span> <span class="o">=</span> <span class="p">[</span><span class="s">&#39;qqq&#39;</span><span class="p">,</span> <span class="s">&#39;inflation&#39;</span><span class="p">,</span> <span class="s">&#39;iwm&#39;</span><span class="p">,</span> <span class="s">&#39;gold&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">T</span>
+
+<span class="c"># Setting partition dates to the first day of every year 2002-2012</span>
+<span class="n">cutoff_dates</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">date_range</span><span class="p">(</span><span class="n">start</span> <span class="o">=</span> <span class="s">&#39;2002-01-01&#39;</span><span class="p">,</span> <span class="n">end</span> <span class="o">=</span> <span class="s">&#39;2012-01-01&#39;</span><span class="p">,</span> <span class="n">freq</span> <span class="o">=</span> <span class="s">&#39;AS&#39;</span><span class="p">)</span>
+<span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">cutoff_dates</span><span class="p">)</span>
+
+<span class="n">MSEs</span> <span class="o">=</span> <span class="p">[]</span>
+
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
+    
+    <span class="c"># Defining training and testing sets for each iteration, using yearly cutoff dates</span>
+    <span class="n">training_data</span> <span class="o">=</span> <span class="n">predictors</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">cutoff_dates</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span><span class="n">cutoff_dates</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span>
+    <span class="n">testing_data</span> <span class="o">=</span> <span class="n">predictors</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">cutoff_dates</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span><span class="n">cutoff_dates</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]]</span>
+    
+    <span class="c"># Fitting model within the training set</span>
+    <span class="n">fitted_theta</span> <span class="o">=</span> <span class="n">regression</span><span class="o">.</span><span class="n">linear_model</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">Y</span><span class="p">[</span><span class="n">cutoff_dates</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span><span class="n">cutoff_dates</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span>
+                                               <span class="n">sm</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">training_data</span><span class="p">))</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span><span class="o">.</span><span class="n">params</span>
+    
+    <span class="c"># Testing performance within the testing set</span>
+    <span class="n">testing_model</span> <span class="o">=</span> <span class="p">(</span><span class="n">fitted_theta</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">fitted_theta</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">testing_data</span><span class="p">[</span><span class="s">&#39;qqq&#39;</span><span class="p">]</span> 
+                     <span class="o">+</span> <span class="n">fitted_theta</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">testing_data</span><span class="p">[</span><span class="s">&#39;inflation&#39;</span><span class="p">]</span>
+                     <span class="o">+</span> <span class="n">fitted_theta</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">*</span> <span class="n">testing_data</span><span class="p">[</span><span class="s">&#39;iwm&#39;</span><span class="p">]</span>
+                     <span class="o">+</span> <span class="n">fitted_theta</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span> <span class="o">*</span> <span class="n">testing_data</span><span class="p">[</span><span class="s">&#39;gold&#39;</span><span class="p">])</span>
+    
+    <span class="c"># Caluclate Mean Squared Error for the model runnning on the testing set</span>
+    <span class="n">errors</span> <span class="o">=</span> <span class="n">Y</span><span class="p">[</span><span class="n">cutoff_dates</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span><span class="n">cutoff_dates</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]]</span><span class="o">-</span><span class="n">testing_model</span>
+    <span class="n">df</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">testing_model</span><span class="p">)</span> <span class="o">-</span> <span class="nb">len</span><span class="p">(</span><span class="n">predictors</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
+    <span class="n">MSE</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">([</span><span class="n">error</span><span class="o">**</span><span class="mi">2</span> <span class="k">for</span> <span class="n">error</span> <span class="ow">in</span> <span class="n">errors</span><span class="p">])</span><span class="o">/</span><span class="n">df</span>
+    <span class="n">MSEs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">MSE</span><span class="p">)</span>
+    
+    <span class="k">print</span> <span class="s">&#39;MSE in&#39;</span><span class="p">,</span> <span class="n">cutoff_dates</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">year</span><span class="p">,</span><span class="s">&#39;:&#39;</span><span class="p">,</span> <span class="n">MSE</span>
+    
+<span class="k">print</span> <span class="s">&#39;</span><span class="se">\n\n</span><span class="s">Average MSE across Iterations:&#39;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">MSEs</span><span class="p">)</span>
+<span class="k">print</span> <span class="s">&#39;Average MSE Excluding 2009:&#39;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">MSEs</span><span class="p">[:</span><span class="mi">6</span><span class="p">]</span><span class="o">+</span><span class="n">MSEs</span><span class="p">[</span><span class="mi">7</span><span class="p">:])</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>MSE in 2003 : 0.0565831690527
+MSE in 2004 : 0.653180823804
+MSE in 2005 : 0.20938626572
+MSE in 2006 : 0.56114441747
+MSE in 2007 : 0.021279349198
+MSE in 2008 : 2.0220023476
+MSE in 2009 : 13.4768461212
+MSE in 2010 : 3.51297688997
+MSE in 2011 : 2.4009454556
+
+
+Average MSE across Iterations: 2.54603831551
+Average MSE Excluding 2009: 1.1796873398
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Since unemployment usually is between 3 and 10 percent, a 2.5 MSE is relatively large. However, we can also see that the a couple outliers around the 2009 recession led to this higher error. Excluding 2009, our average MSE would be 1.18, a more reasonable value.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Out-of-Sample-Validation">Out-of-Sample Validation<a class="anchor-link" href="#Out-of-Sample-Validation">&#194;&#182;</a></h2><p>After conducting the forward chaining test we can be confident in the performance of our model within the time period of 2002-2012. So far in this lecture, all of the testing and development of this model has been done within this 10-year period. Working extensively within a single time period can lead to overfitting.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Overfitting">Overfitting<a class="anchor-link" href="#Overfitting">&#194;&#182;</a></h3><p>A model is overfit when it is trained so much that it models the random noise of the data instead of just the underlying relationship. Conducting out-of-sample tests and using cross-validation helps avoid overfitting. It is easy to have a model that looks perfect in-sample, but has little predictive value. To demonstrate the dangers of overfitting, look at the two models below. They both model the same data, but  <code>simple_model</code> is just a simple linear regression while  <code>complicated_model</code> includes high-order values of X. The result is that the second more complicated model looks better in-sample, but the first and more simple one explains the relationship better.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[29]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">15</span><span class="p">,</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="o">*</span><span class="n">x</span> <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">X</span><span class="p">]</span>
+
+<span class="n">X2</span> <span class="o">=</span> <span class="n">X</span><span class="o">**</span><span class="mi">2</span>
+<span class="n">X3</span> <span class="o">=</span> <span class="n">X</span><span class="o">**</span><span class="mi">3</span>
+<span class="n">X4</span> <span class="o">=</span> <span class="n">X</span><span class="o">**</span><span class="mi">4</span>
+
+<span class="n">X_is</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">],</span><span class="n">X2</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">],</span><span class="n">X3</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">],</span><span class="n">X4</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">]</span>
+
+<span class="n">simple</span> <span class="o">=</span> <span class="n">regression</span><span class="o">.</span><span class="n">linear_model</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">Y</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">],</span> <span class="n">sm</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">]))</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span><span class="o">.</span><span class="n">params</span>
+<span class="n">complicated</span> <span class="o">=</span> <span class="n">regression</span><span class="o">.</span><span class="n">linear_model</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">Y</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">],</span> <span class="n">sm</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">(</span><span class="n">X_is</span><span class="p">)))</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span><span class="o">.</span><span class="n">params</span>
+
+<span class="n">simple_model</span> <span class="o">=</span> <span class="n">simple</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">simple</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">X</span>
+<span class="n">complicated_model</span> <span class="o">=</span> <span class="p">(</span><span class="n">complicated</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">complicated</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">X</span> 
+                     <span class="o">+</span> <span class="n">complicated</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">X2</span> <span class="o">+</span> <span class="n">complicated</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">*</span> <span class="n">X3</span> 
+                     <span class="o">+</span> <span class="n">complicated</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span> <span class="o">*</span> <span class="n">X4</span><span class="p">)</span>                                   
+                                            
+<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span> <span class="n">ncols</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
+
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">],</span> <span class="n">simple_model</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">],</span> <span class="n">c</span> <span class="o">=</span> <span class="s">&#39;r&#39;</span><span class="p">);</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">simple_model</span><span class="p">,</span> <span class="n">c</span> <span class="o">=</span> <span class="s">&#39;r&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s">&#39;--&#39;</span><span class="p">);</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.8</span><span class="p">);</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">],</span> <span class="n">complicated_model</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">],</span> <span class="n">c</span> <span class="o">=</span> <span class="s">&#39;r&#39;</span><span class="p">);</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">complicated_model</span><span class="p">,</span> <span class="n">c</span> <span class="o">=</span> <span class="s">&#39;r&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s">&#39;--&#39;</span><span class="p">);</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.8</span><span class="p">);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">35</span><span class="p">);</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
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+<div class="output_area">
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+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>For more information on overfitting, refer to the <a href="https://www.quantopian.com/lectures/the-dangers-of-overfitting">Dangers of Overfitting Lecture</a>.</p>
+<p>To see if our unemployment model is overfit let's conduct an out-of-sample validation test. We will use the 2002-2012 data to fit the model and then the 2012-2017 data to test it.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[30]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span class="n">Y</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">unemployment</span><span class="p">)</span>
+<span class="n">X_train</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([</span><span class="n">qqq</span><span class="p">[:</span><span class="n">e</span><span class="p">],</span> <span class="n">inflation</span><span class="p">[:</span><span class="n">e</span><span class="p">],</span> <span class="n">iwm</span><span class="p">[:</span><span class="n">e</span><span class="p">],</span> <span class="n">fx</span><span class="p">[:</span><span class="n">e</span><span class="p">],</span>
+                        <span class="n">gold</span><span class="p">[:</span><span class="n">e</span><span class="p">]],</span> <span class="n">columns</span> <span class="o">=</span> <span class="n">Y</span><span class="p">[:</span><span class="n">e</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">=</span> <span class="n">X_str</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
+
+<span class="n">X_test</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([</span><span class="n">qqq</span><span class="p">[</span><span class="n">e</span><span class="p">:],</span> <span class="n">inflation</span><span class="p">[</span><span class="n">e</span><span class="p">:],</span> <span class="n">iwm</span><span class="p">[</span><span class="n">e</span><span class="p">:],</span> <span class="n">fx</span><span class="p">[</span><span class="n">e</span><span class="p">:],</span>
+                       <span class="n">gold</span><span class="p">[</span><span class="n">e</span><span class="p">:]],</span> <span class="n">columns</span> <span class="o">=</span> <span class="n">Y</span><span class="p">[</span><span class="n">e</span><span class="p">:]</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">=</span> <span class="n">X_str</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
+
+
+<span class="n">thetas</span> <span class="o">=</span> <span class="n">regression</span><span class="o">.</span><span class="n">linear_model</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">Y</span><span class="o">.</span><span class="n">loc</span><span class="p">[:</span><span class="n">e</span><span class="p">],</span> <span class="n">sm</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">X_train</span><span class="p">))</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span><span class="o">.</span><span class="n">params</span>
+<span class="n">model_insample</span> <span class="o">=</span> <span class="p">(</span><span class="n">thetas</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">thetas</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">X_train</span><span class="p">[</span><span class="s">&#39;qqq&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">thetas</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">X_train</span><span class="p">[</span><span class="s">&#39;inflation&#39;</span><span class="p">]</span>
+                     <span class="o">+</span> <span class="n">thetas</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">*</span> <span class="n">X_train</span><span class="p">[</span><span class="s">&#39;iwm&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">thetas</span><span class="p">[</span><span class="mi">5</span><span class="p">]</span> <span class="o">*</span> <span class="n">X_train</span><span class="p">[</span><span class="s">&#39;gold&#39;</span><span class="p">])</span>
+
+<span class="n">model_outsample</span> <span class="o">=</span> <span class="p">(</span><span class="n">thetas</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">thetas</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">X_test</span><span class="p">[</span><span class="s">&#39;qqq&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">thetas</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">X_test</span><span class="p">[</span><span class="s">&#39;inflation&#39;</span><span class="p">]</span>
+                     <span class="o">+</span> <span class="n">thetas</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">*</span> <span class="n">X_test</span><span class="p">[</span><span class="s">&#39;iwm&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">thetas</span><span class="p">[</span><span class="mi">5</span><span class="p">]</span> <span class="o">*</span> <span class="n">X_test</span><span class="p">[</span><span class="s">&#39;gold&#39;</span><span class="p">])</span>
+
+<span class="n">model_insample</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">c</span> <span class="o">=</span> <span class="s">&#39;r&#39;</span><span class="p">);</span>
+<span class="n">model_outsample</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">c</span> <span class="o">=</span> <span class="s">&#39;r&#39;</span><span class="p">,</span> <span class="n">linestyle</span> <span class="o">=</span> <span class="s">&#39;--&#39;</span><span class="p">);</span>
+<span class="n">Y</span><span class="o">.</span><span class="n">plot</span><span class="p">();</span>
+</pre></div>
+
+</div>
+</div>
+</div>
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+<p>Out-of-sample our fitted model performs nowhere near as well as it does in-sample. Lets employ forward-chaining again, this time with 6 month partitions instead of yearly ones, on this 2012-2017 validation data to confirm the validity of our predictor selections.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[31]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span class="n">Y</span> <span class="o">=</span> <span class="n">unemployment</span><span class="p">[</span><span class="n">e</span><span class="p">:]</span>
+<span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">qqq</span><span class="p">[</span><span class="n">e</span><span class="p">:],</span> <span class="n">inflation</span><span class="p">[</span><span class="n">e</span><span class="p">:],</span> <span class="n">iwm</span><span class="p">[</span><span class="n">e</span><span class="p">:],</span> <span class="n">fx</span><span class="p">[</span><span class="n">e</span><span class="p">:],</span> <span class="n">gold</span><span class="p">[</span><span class="n">e</span><span class="p">:]]</span>
+
+<span class="c"># Our step AIC algorithm selected all predictors except for fx_rate</span>
+<span class="n">predictors</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">qqq</span><span class="p">[</span><span class="n">e</span><span class="p">:],</span> <span class="n">inflation</span><span class="p">[</span><span class="n">e</span><span class="p">:],</span> <span class="n">iwm</span><span class="p">[</span><span class="n">e</span><span class="p">:],</span> <span class="n">gold</span><span class="p">[</span><span class="n">e</span><span class="p">:]],</span> 
+                          <span class="n">index</span> <span class="o">=</span> <span class="p">[</span><span class="s">&#39;qqq&#39;</span><span class="p">,</span> <span class="s">&#39;inflation&#39;</span><span class="p">,</span> <span class="s">&#39;iwm&#39;</span><span class="p">,</span> <span class="s">&#39;gold&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">T</span>
+
+<span class="c"># Setting partition dates to the first day of every year 2002-2012</span>
+<span class="n">cutoff_dates</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">date_range</span><span class="p">(</span><span class="n">start</span> <span class="o">=</span> <span class="s">&#39;2012-01-01&#39;</span><span class="p">,</span> <span class="n">end</span> <span class="o">=</span> <span class="s">&#39;2017-01-01&#39;</span><span class="p">,</span> <span class="n">freq</span> <span class="o">=</span> <span class="s">&#39;6MS&#39;</span><span class="p">)</span>
+<span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">cutoff_dates</span><span class="p">)</span>
+
+<span class="n">MSEs</span> <span class="o">=</span> <span class="p">[]</span>
+
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
+    
+    <span class="c"># Defining training and testing sets for each iteration, using yearly cutoff dates</span>
+    <span class="n">training_data</span> <span class="o">=</span> <span class="n">predictors</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">cutoff_dates</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span><span class="n">cutoff_dates</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span>
+    <span class="n">testing_data</span> <span class="o">=</span> <span class="n">predictors</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">cutoff_dates</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span><span class="n">cutoff_dates</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]]</span>
+    
+    <span class="c"># Fitting model within the training set</span>
+    <span class="n">thetas</span> <span class="o">=</span> <span class="n">regression</span><span class="o">.</span><span class="n">linear_model</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">Y</span><span class="p">[</span><span class="n">cutoff_dates</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span><span class="n">cutoff_dates</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span> 
+                                               <span class="n">sm</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">training_data</span><span class="p">))</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span><span class="o">.</span><span class="n">params</span>
+    
+    <span class="c"># Testing performance within the testing set</span>
+    <span class="n">testing_model</span> <span class="o">=</span> <span class="p">(</span><span class="n">thetas</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">thetas</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">testing_data</span><span class="p">[</span><span class="s">&#39;qqq&#39;</span><span class="p">]</span> 
+                     <span class="o">+</span> <span class="n">thetas</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">testing_data</span><span class="p">[</span><span class="s">&#39;inflation&#39;</span><span class="p">]</span>
+                     <span class="o">+</span> <span class="n">thetas</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">*</span> <span class="n">testing_data</span><span class="p">[</span><span class="s">&#39;iwm&#39;</span><span class="p">]</span> 
+                     <span class="o">+</span> <span class="n">thetas</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span> <span class="o">*</span> <span class="n">testing_data</span><span class="p">[</span><span class="s">&#39;gold&#39;</span><span class="p">])</span>
+    
+    <span class="c"># Caluclate Mean Squared Error for the model runnning on the testing set</span>
+    <span class="n">errors</span> <span class="o">=</span> <span class="n">Y</span><span class="p">[</span><span class="n">cutoff_dates</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span><span class="n">cutoff_dates</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]]</span><span class="o">-</span><span class="n">testing_model</span>
+    <span class="n">df</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">testing_model</span><span class="p">)</span> <span class="o">-</span> <span class="nb">len</span><span class="p">(</span><span class="n">predictors</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
+    <span class="n">MSE</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">([</span><span class="n">error</span><span class="o">**</span><span class="mi">2</span> <span class="k">for</span> <span class="n">error</span> <span class="ow">in</span> <span class="n">errors</span><span class="p">])</span><span class="o">/</span><span class="n">df</span>
+    <span class="n">MSEs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">MSE</span><span class="p">)</span>
+    
+    <span class="k">print</span> <span class="s">&#39;MSE in&#39;</span><span class="p">,</span> <span class="n">cutoff_dates</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">year</span><span class="p">,</span><span class="s">&#39;:&#39;</span><span class="p">,</span> <span class="n">MSE</span>
+    
+<span class="k">print</span> <span class="s">&#39;</span><span class="se">\n\n</span><span class="s">Average MSE across iterations:&#39;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">MSEs</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>MSE in 2012 : 0.23988877334
+MSE in 2013 : 0.405434958749
+MSE in 2013 : 0.0945991177613
+MSE in 2014 : 0.779186505254
+MSE in 2014 : 0.10545739628
+MSE in 2015 : 0.0494698316077
+MSE in 2015 : 0.509364605593
+MSE in 2016 : 0.520218562335
+MSE in 2016 : 0.226247940777
+
+
+Average MSE across iterations: 0.325540854633
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>This MSE is still low, meaning that the regressor selections we made using the <code>forward_aic</code> algorithm were well-founded, but the beta coefficients for each have a lot of variability. Therefore, the parameters from a certain training period will not hold for very long and might need to be recalculated on a rolling basis. For more information on detecting and adjusting for this, refer to the Quantopian lecture on <a href="https://www.quantopian.com/lectures/regression-model-instability">Regression Model Instability</a>.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><em>This presentation is for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation for any security; nor does it constitute an offer to provide investment advisory or other services by Quantopian, Inc. ("Quantopian"). Nothing contained herein constitutes investment advice or offers any opinion with respect to the suitability of any security, and any views expressed herein should not be taken as advice to buy, sell, or hold any security or as an endorsement of any security or company.  In preparing the information contained herein, Quantopian, Inc. has not taken into account the investment needs, objectives, and financial circumstances of any particular investor. Any views expressed and data illustrated herein were prepared based upon information, believed to be reliable, available to Quantopian, Inc. at the time of publication. Quantopian makes no guarantees as to their accuracy or completeness. All information is subject to change and may quickly become unreliable for various reasons, including changes in market conditions or economic circumstances.</em></p>
+
+</div>
+</div>
+</div>
+    </div>
+  </div>
+</body>