{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulated toy data example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from bhad.utils import mvt2mixture\n",
    "from bhad.model import BHAD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Draw from a two-component multivariate Student-t mixture distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: Anomaly class corresponds to the minority mixture component"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 30)\n"
     ]
    }
   ],
   "source": [
    "seed = 42  \n",
    "outlier_prob_true = .01         # probab. for outlier ; should be consistent with contamination rate in your model\n",
    "k = 30                          # feature dimension \n",
    "N = 5*10**4                     # sample size\n",
    "\n",
    "# Specify first and second moments for each component  \n",
    "bvt = mvt2mixture(thetas = {'mean1' : np.full(k,-1), 'mean2' : np.full(k,.5), \n",
    "                                'Sigma1' : np.eye(k)*.4, 'Sigma2' : np.eye(k)*.1, \n",
    "                                'nu1': 3.*k, 'nu2': 3.*k}, seed = seed, gaussian = False)\n",
    "\n",
    "# Get latent draws and observations:\n",
    "#------------------------------------\n",
    "y_true, dataset = bvt.draw(n_samples = N, k = k, p = outlier_prob_true)\n",
    "\n",
    "print(dataset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01024"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_true.mean()    # probab. latent class 1 (=anomaly)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize your data:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reduce dimension for visualization only:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(dataset)\n",
    "\n",
    "X_reduce = TruncatedSVD(n_components=3).fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot all the ground truth points together with the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,7))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.set_xlabel(r'$c_{1}$') ; ax.set_ylabel(r'$c_{2}$') ;ax.set_zlabel(r'$c_{3}$')\n",
    "\n",
    "# Plot the compressed inliers data points\n",
    "ax.scatter(X_reduce[y_true == 0, 0], X_reduce[y_true == 0, 1], zs=X_reduce[y_true == 0, 2], s=4, lw=0, label = \"inliers\")\n",
    "\n",
    "# outliers\n",
    "ax.scatter(X_reduce[y_true == 1, 0], X_reduce[y_true == 1, 1], zs=X_reduce[y_true == 1, 2], \n",
    "           lw=1, s=6, marker=\"x\", c=\"red\", alpha=1, label = \"outliers\")\n",
    "ax.legend()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Partition your dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(33500, 30)\n",
      "(16500, 30)\n",
      "(array([0, 1]), array([33147,   353]))\n",
      "(array([0, 1]), array([16341,   159]))\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(dataset, y_true, test_size=0.33, random_state=42)\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "\n",
    "print(np.unique(y_train, return_counts=True))\n",
    "print(np.unique(y_test, return_counts=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use another candidate anomaly detector for comparison:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 18 candidates, totalling 54 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       "#sk-container-id-1.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       "#sk-container-id-1.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3, estimator=IsolationForest(),\n",
       "             param_grid={&#x27;bootstrap&#x27;: [True, False], &#x27;contamination&#x27;: [0.01],\n",
       "                         &#x27;max_features&#x27;: [10, 20, 30],\n",
       "                         &#x27;n_estimators&#x27;: [50, 100, 200]},\n",
       "             return_train_score=True,\n",
       "             scoring=make_scorer(f1_score, response_method=&#x27;predict&#x27;, average=macro),\n",
       "             verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('estimator',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=estimator,-estimator%20object\">\n",
       "            estimator\n",
       "            <span class=\"param-doc-description\">estimator: estimator object<br><br>This is assumed to implement the scikit-learn estimator interface.<br>Either estimator needs to provide a ``score`` function,<br>or ``scoring`` must be passed.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">IsolationForest()</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('param_grid',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=param_grid,-dict%20or%20list%20of%20dictionaries\">\n",
       "            param_grid\n",
       "            <span class=\"param-doc-description\">param_grid: dict or list of dictionaries<br><br>Dictionary with parameters names (`str`) as keys and lists of<br>parameter settings to try as values, or a list of such<br>dictionaries, in which case the grids spanned by each dictionary<br>in the list are explored. This enables searching over any sequence<br>of parameter settings.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">{&#x27;bootstrap&#x27;: [True, False], &#x27;contamination&#x27;: [0.01], &#x27;max_features&#x27;: [10, 20, ...], &#x27;n_estimators&#x27;: [50, 100, ...]}</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('scoring',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=scoring,-str%2C%20callable%2C%20list%2C%20tuple%20or%20dict%2C%20default%3DNone\">\n",
       "            scoring\n",
       "            <span class=\"param-doc-description\">scoring: str, callable, list, tuple or dict, default=None<br><br>Strategy to evaluate the performance of the cross-validated model on<br>the test set.<br><br>If `scoring` represents a single score, one can use:<br><br>- a single string (see :ref:`scoring_string_names`);<br>- a callable (see :ref:`scoring_callable`) that returns a single value;<br>- `None`, the `estimator`'s<br>  :ref:`default evaluation criterion <scoring_api_overview>` is used.<br><br>If `scoring` represents multiple scores, one can use:<br><br>- a list or tuple of unique strings;<br>- a callable returning a dictionary where the keys are the metric<br>  names and the values are the metric scores;<br>- a dictionary with metric names as keys and callables as values.<br><br>See :ref:`multimetric_grid_search` for an example.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">make_scorer(f...average=macro)</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Number of jobs to run in parallel.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.<br><br>.. versionchanged:: v0.20<br>   `n_jobs` default changed from 1 to None</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('refit',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=refit,-bool%2C%20str%2C%20or%20callable%2C%20default%3DTrue\">\n",
       "            refit\n",
       "            <span class=\"param-doc-description\">refit: bool, str, or callable, default=True<br><br>Refit an estimator using the best found parameters on the whole<br>dataset.<br><br>For multiple metric evaluation, this needs to be a `str` denoting the<br>scorer that would be used to find the best parameters for refitting<br>the estimator at the end.<br><br>Where there are considerations other than maximum score in<br>choosing a best estimator, ``refit`` can be set to a function which<br>returns the selected ``best_index_`` given ``cv_results_``. In that<br>case, the ``best_estimator_`` and ``best_params_`` will be set<br>according to the returned ``best_index_`` while the ``best_score_``<br>attribute will not be available.<br><br>The refitted estimator is made available at the ``best_estimator_``<br>attribute and permits using ``predict`` directly on this<br>``GridSearchCV`` instance.<br><br>Also for multiple metric evaluation, the attributes ``best_index_``,<br>``best_score_`` and ``best_params_`` will only be available if<br>``refit`` is set and all of them will be determined w.r.t this specific<br>scorer.<br><br>See ``scoring`` parameter to know more about multiple metric<br>evaluation.<br><br>See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`<br>to see how to design a custom selection strategy using a callable<br>via `refit`.<br><br>See :ref:`this example<br><sphx_glr_auto_examples_model_selection_plot_grid_search_refit_callable.py>`<br>for an example of how to use ``refit=callable`` to balance model<br>complexity and cross-validated score.<br><br>.. versionchanged:: 0.20<br>    Support for callable added.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('cv',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=cv,-int%2C%20cross-validation%20generator%20or%20an%20iterable%2C%20default%3DNone\">\n",
       "            cv\n",
       "            <span class=\"param-doc-description\">cv: int, cross-validation generator or an iterable, default=None<br><br>Determines the cross-validation splitting strategy.<br>Possible inputs for cv are:<br><br>- None, to use the default 5-fold cross validation,<br>- integer, to specify the number of folds in a `(Stratified)KFold`,<br>- :term:`CV splitter`,<br>- An iterable yielding (train, test) splits as arrays of indices.<br><br>For integer/None inputs, if the estimator is a classifier and ``y`` is<br>either binary or multiclass, :class:`StratifiedKFold` is used. In all<br>other cases, :class:`KFold` is used. These splitters are instantiated<br>with `shuffle=False` so the splits will be the same across calls.<br><br>Refer :ref:`User Guide <cross_validation>` for the various<br>cross-validation strategies that can be used here.<br><br>.. versionchanged:: 0.22<br>    ``cv`` default value if None changed from 3-fold to 5-fold.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">3</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=verbose,-int\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\">verbose: int<br><br>Controls the verbosity: the higher, the more messages.<br><br>- >1 : the computation time for each fold and parameter candidate is<br>  displayed;<br>- >2 : the score is also displayed;<br>- >3 : the fold and candidate parameter indexes are also displayed<br>  together with the starting time of the computation.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('pre_dispatch',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=pre_dispatch,-int%2C%20or%20str%2C%20default%3D%272%2An_jobs%27\">\n",
       "            pre_dispatch\n",
       "            <span class=\"param-doc-description\">pre_dispatch: int, or str, default='2*n_jobs'<br><br>Controls the number of jobs that get dispatched during parallel<br>execution. Reducing this number can be useful to avoid an<br>explosion of memory consumption when more jobs get dispatched<br>than CPUs can process. This parameter can be:<br><br>- None, in which case all the jobs are immediately created and spawned. Use<br>  this for lightweight and fast-running jobs, to avoid delays due to on-demand<br>  spawning of the jobs<br>- An int, giving the exact number of total jobs that are spawned<br>- A str, giving an expression as a function of n_jobs, as in '2*n_jobs'</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;2*n_jobs&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('error_score',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=error_score,-%27raise%27%20or%20numeric%2C%20default%3Dnp.nan\">\n",
       "            error_score\n",
       "            <span class=\"param-doc-description\">error_score: 'raise' or numeric, default=np.nan<br><br>Value to assign to the score if an error occurs in estimator fitting.<br>If set to 'raise', the error is raised. If a numeric value is given,<br>FitFailedWarning is raised. This parameter does not affect the refit<br>step, which will always raise the error.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">nan</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('return_train_score',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=return_train_score,-bool%2C%20default%3DFalse\">\n",
       "            return_train_score\n",
       "            <span class=\"param-doc-description\">return_train_score: bool, default=False<br><br>If ``False``, the ``cv_results_`` attribute will not include training<br>scores.<br>Computing training scores is used to get insights on how different<br>parameter settings impact the overfitting/underfitting trade-off.<br>However computing the scores on the training set can be computationally<br>expensive and is not strictly required to select the parameters that<br>yield the best generalization performance.<br><br>.. versionadded:: 0.19<br><br>.. versionchanged:: 0.21<br>    Default value was changed from ``True`` to ``False``</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: IsolationForest</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\"><pre>IsolationForest(bootstrap=True, contamination=0.01, max_features=20,\n",
       "                n_estimators=50)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>IsolationForest</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.IsolationForest.html\">?<span>Documentation for IsolationForest</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_estimators',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.IsolationForest.html#:~:text=n_estimators,-int%2C%20default%3D100\">\n",
       "            n_estimators\n",
       "            <span class=\"param-doc-description\">n_estimators: int, default=100<br><br>The number of base estimators in the ensemble.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">50</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_samples',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.IsolationForest.html#:~:text=max_samples,-%22auto%22%2C%20int%20or%20float%2C%20default%3D%22auto%22\">\n",
       "            max_samples\n",
       "            <span class=\"param-doc-description\">max_samples: \"auto\", int or float, default=\"auto\"<br><br>The number of samples to draw from X to train each base estimator.<br><br>- If int, then draw `max_samples` samples.<br>- If float, then draw `max_samples * X.shape[0]` samples.<br>- If \"auto\", then `max_samples=min(256, n_samples)`.<br><br>If max_samples is larger than the number of samples provided,<br>all samples will be used for all trees (no sampling).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('contamination',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.IsolationForest.html#:~:text=contamination,-%27auto%27%20or%20float%2C%20default%3D%27auto%27\">\n",
       "            contamination\n",
       "            <span class=\"param-doc-description\">contamination: 'auto' or float, default='auto'<br><br>The amount of contamination of the data set, i.e. the proportion<br>of outliers in the data set. Used when fitting to define the threshold<br>on the scores of the samples.<br><br>- If 'auto', the threshold is determined as in the<br>  original paper.<br>- If float, the contamination should be in the range (0, 0.5].<br><br>.. versionchanged:: 0.22<br>   The default value of ``contamination`` changed from 0.1<br>   to ``'auto'``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.01</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_features',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.IsolationForest.html#:~:text=max_features,-int%20or%20float%2C%20default%3D1.0\">\n",
       "            max_features\n",
       "            <span class=\"param-doc-description\">max_features: int or float, default=1.0<br><br>The number of features to draw from X to train each base estimator.<br><br>- If int, then draw `max_features` features.<br>- If float, then draw `max(1, int(max_features * n_features_in_))` features.<br><br>Note: using a float number less than 1.0 or integer less than number of<br>features will enable feature subsampling and leads to a longer runtime.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">20</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('bootstrap',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.IsolationForest.html#:~:text=bootstrap,-bool%2C%20default%3DFalse\">\n",
       "            bootstrap\n",
       "            <span class=\"param-doc-description\">bootstrap: bool, default=False<br><br>If True, individual trees are fit on random subsets of the training<br>data sampled with replacement. If False, sampling without replacement<br>is performed.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.IsolationForest.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to run in parallel for :meth:`fit`. ``None`` means 1<br>unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using<br>all processors. See :term:`Glossary <n_jobs>` for more details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.IsolationForest.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls the pseudo-randomness of the selection of the feature<br>and split values for each branching step and each tree in the forest.<br><br>Pass an int for reproducible results across multiple function calls.<br>See :term:`Glossary <random_state>`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.IsolationForest.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Controls the verbosity of the tree building process.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.IsolationForest.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
       "            warm_start\n",
       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just fit a whole<br>new forest. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.21</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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      ],
      "text/plain": [
       "GridSearchCV(cv=3, estimator=IsolationForest(),\n",
       "             param_grid={'bootstrap': [True, False], 'contamination': [0.01],\n",
       "                         'max_features': [10, 20, 30],\n",
       "                         'n_estimators': [50, 100, 200]},\n",
       "             return_train_score=True,\n",
       "             scoring=make_scorer(f1_score, response_method='predict', average=macro),\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.metrics import make_scorer, f1_score\n",
    "from sklearn import model_selection\n",
    "\n",
    "# Tune hyperparameters for fair comparison\n",
    "param_grid = {\n",
    "    'contamination': [outlier_prob_true], \n",
    "    'max_features': [10, 20, 30], \n",
    "    'bootstrap': [True, False], \n",
    "    'n_estimators': [50, 100, 200]\n",
    "}\n",
    "\n",
    "f1sc = make_scorer(f1_score, average='macro')\n",
    "\n",
    "grid_search = model_selection.GridSearchCV(\n",
    "    IsolationForest(), \n",
    "    param_grid,\n",
    "    scoring=f1sc, \n",
    "    refit=True,\n",
    "    cv=3, \n",
    "    return_train_score=True,\n",
    "    verbose=1\n",
    ")\n",
    "\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters: {'bootstrap': True, 'contamination': 0.01, 'max_features': 20, 'n_estimators': 50}\n",
      "(array([-1,  1]), array([  335, 33165]))\n",
      "(array([-1,  1]), array([  149, 16351]))\n"
     ]
    }
   ],
   "source": [
    "# Best model and parameters\n",
    "best_iso_forest = grid_search.best_estimator_\n",
    "best_params = grid_search.best_params_\n",
    "\n",
    "print(\"Best Parameters:\", best_params)\n",
    "\n",
    "# Predict anomalies\n",
    "y_pred_train = best_iso_forest.predict(X_train)\n",
    "y_pred_test = best_iso_forest.predict(X_test)\n",
    "# -1 indicates anomalies, 1 indicates normal points\n",
    "\n",
    "print(np.unique(y_pred_train, return_counts=True))\n",
    "print(np.unique(y_pred_test, return_counts=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      Inlier       1.00      1.00      1.00     16341\n",
      "     Outlier       0.89      0.83      0.86       159\n",
      "\n",
      "    accuracy                           1.00     16500\n",
      "   macro avg       0.94      0.91      0.93     16500\n",
      "weighted avg       1.00      1.00      1.00     16500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# Make comparable to y_true encoding in DGP above\n",
    "y_pred_test[y_pred_test == 1] = 0\n",
    "y_pred_test[y_pred_test == -1] = 1\n",
    "\n",
    "print(classification_report(y_test, y_pred_test, target_names=['Inlier', 'Outlier']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model training and prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-- Bayesian Histogram-based Anomaly Detector (BHAD) --\n",
      "\n",
      "Discretizing continuous features...\n",
      "Setting maximum number of bins 184.\n",
      "Input shape: (33500, 30)\n",
      "Used 30 numeric feature(s) and 0 categorical feature(s).\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var0 using 49 bins\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var1 using 61 bins\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var2 using 42 bins\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var3 using 52 bins\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var4 using 46 bins\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var5 using 51 bins\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var6 using 51 bins\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var7 using 51 bins\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var8 using 55 bins\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var9 using 57 bins\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var10 using 45 bins\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var11 using 45 bins\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var12 using 53 bins\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var13 using 42 bins\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var14 using 60 bins\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var15 using 53 bins\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var16 using 54 bins\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var17 using 47 bins\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var18 using 49 bins\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var19 using 51 bins\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var20 using 50 bins\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var21 using 35 bins\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var22 using 55 bins\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var23 using 47 bins\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var24 using 40 bins\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var25 using 55 bins\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var26 using 42 bins\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var27 using 43 bins\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var28 using 44 bins\n",
      "Determining optimal number of bins for numeric features\n",
      "Feature var29 using 47 bins\n",
      "Fit BHAD on discretized data.\n",
      "Input shape: (33500, 30)\n",
      "One-hot encoding categorical features.\n",
      "Matrix dimension after one-hot encoding: (33500, 1172)\n",
      "Finished training.\n",
      "\n",
      "Score input data.\n",
      "Apply fitted one-hot encoder.\n",
      "\n",
      "Score input data.\n",
      "Apply fitted one-hot encoder.\n",
      "Training predictions: (array([-1,  1]), array([  337, 33163]))\n"
     ]
    }
   ],
   "source": [
    "model = BHAD(\n",
    "    contamination=outlier_prob_true,                  # set 1% contamination as in DGP (oracle knowledge ;)\n",
    "    nbins=None,                       # step only needed if continous features are present\n",
    "    verbose=True\n",
    ")\n",
    "\n",
    "y_pred_train = model.fit_predict(X_train)\n",
    "scores_train = model.decision_function(X_train)\n",
    "\n",
    "print(\"Training predictions:\", np.unique(y_pred_train, return_counts=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([-1,  1]), array([  337, 33163]))\n"
     ]
    }
   ],
   "source": [
    "print(np.unique(y_pred_train, return_counts=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Score input data.\n",
      "Apply fitted one-hot encoder.\n",
      "\n",
      "Score input data.\n",
      "Apply fitted one-hot encoder.\n",
      "(array([-1,  1]), array([  149, 16351]))\n"
     ]
    }
   ],
   "source": [
    "# Get scores first, then derive predictions (avoids processing X_test twice)\n",
    "scores_test = model.decision_function(X_test)\n",
    "y_pred_test = model.predict(X_test)\n",
    "\n",
    "print(np.unique(y_pred_test, return_counts=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot score distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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p06b6a9XtpWaFqYVRe/furYMQAABAnQtA7du3l9WrV+uV3z/99FMZOHCgPl5YWMjDEQEAQN0MQGodMLXkRXBwsISFhUl4eLijNah79+6uriMAAID1T4K+++67pV+/fnL06FG9/pfd7bffLsOGDXNl/QAAANwjAClq4LPaKlKzwQAAAOpkACopKZHk5GTJzMzU437Ky8srnT9w4ICr6gcAAOAeAeiBBx6QjRs3yujRo6Vly5aOJTIAAADqbAD6z3/+I2vXrpW+ffu6vkYAAADuOAusefPmcvXVV7u+NgAAAO4agJ577jk9Fb7iemAAAAB1ugtMrfu1f/9+8ff3188CatCgQZWlMgAAAOpUABo6dKjrawIAAODOASgpKcn1NQEAAHDnMUDKyZMn5Y033pCEhAQ5ceKEo+vr8OHDrqwfAACAe7QA7dixQyIjI8XX11fy8vJk3LhxelbYqlWr5NChQ/L222+7vqYAAABWtgBNmTJFxowZIz/88IN4e3s7jt9xxx3y5ZdfuqpuAAAA7hOAvv32Wxk/fnyV44GBgVJQUOCKegEAALhXAGrYsKGcOnWqyvF9+/bJtdde64p6AQAAuFcAGjJkiDz77LNy4cIFva/WAlNjf6ZPny7Dhw93dR0BAACsD0DqQYinT5/WrT1nz56ViIgIad++vTRt2lReeOEF19YQAADAHWaBqdlfGRkZ8vXXX8v27dt1GLrlllv0zDAAAIA6F4DKy8tl+fLlesq7mgKvur/atWsnAQEBYrPZ9D4AAECd6QJTAUeN/3nggQf0Aw+7du0qN910kxw8eFBPix82bFj11RQAAMCKFiDV8qOe85OZmSkDBgyodG79+vV6jTD1EMSYmBhX1Q8AAMDaFqD3339fZsyYUSX8KH/7298kPj5e3nvvPVfWDwAAwNoApJbAGDRo0EXPDx48WA+KBgAAqDMBSC166u/vf9Hz6twvv/ziinoBAAC4RwAqKyuT+vUvPmzI09NTfvvtN1fUCwAAwD0GQatZYGq2l1oKw5nS0lJX1QsAAMA9AlBsbOxlyzADDAAA1KkAtGzZsuqrCQAAgDuvBQYAAFCbEYAAAIBxCEAAAMA4BCAAAGAcAhAAADCOWwSgRYsWSXBwsHh7e0tYWJhs2bLlkuXT0tKkU6dOurxakX7dunUXLTthwgTx8PCQlJSUaqg5AACojSwPQKmpqTJlyhRJSkqSnJwcCQkJkaioKCksLHRaftOmTTJq1CgZO3as5Obm6hXo1bZz584qZT/66CPZvHmztGrVqgbeCQAAqC0sD0Dz58+XcePGSVxcnHTu3FmWLFkijRs3ljfffNNp+VdeeUUvyDpt2jS58cYb5bnnnpNbbrlFFi5cWKnc4cOHZdKkSXp1+gYNGtTQuwEAALWBpQHo/Pnzkp2dLZGRkf+rUL16ej8rK8vpa9TxiuUV1WJUsXx5ebmMHj1ah6SbbrrpsvVQS3icOnWq0gYAAOouSwNQUVGRXmD19yvMq/2CggKnr1HHL1d+zpw5etHWRx999IrqMXv2bPH19XVsQUFBf+r9AACA2sHyLjBXUy1Kqpts+fLlevDzlUhISJDi4mLHlp+fX+31BAAAtWQtMFfz8/MTT09POXbsWKXjaj8gIMDpa9TxS5X/6quv9ADqNm3aOM6rVqapU6fqmWB5eXlVrqlWt7/YCvcAAHMEx6+1ugowoQXIy8tLevToIZmZmZXG76j98PBwp69RxyuWVzIyMhzl1difHTt2yLZt2xybmgWmxgN9+umn1fyOAABAbWBpC5CipsDHxsZKz549JTQ0VLfSlJSU6FlhSkxMjAQGBupxOsrkyZMlIiJC5s2bJ9HR0bJy5UrZunWrLF26VJ+/5ppr9FaRmgWmWog6duxowTsEAADuxvIANGLECDl+/LgkJibqgczdunWT9PR0x0DnQ4cO6Zlhdn369JEVK1bIzJkzZcaMGdKhQwdZvXq1dOnSxcJ3AQAAahMPm81ms7oS7kZNg1ezwdSAaB8fH6urAwCoISaMAcpLjpa66o/8/q5zs8AAAAAuhwAEAACMQwACAADGIQABAADjEIAAAIBxCEAAAMA4BCAAAGAcyx+ECAAA3PNZR3l1+JlBtAABAADjEIAAAIBxCEAAAMA4BCAAAGAcAhAAADAOAQgAABiHAAQAAIxDAAIAAMYhAAEAAOMQgAAAgHEIQAAAwDgEIAAAYBwCEAAAMA4BCAAAGIcABAAAjEMAAgAAxiEAAQAA4xCAAACAcQhAAADAOAQgAABgHAIQAAAwDgEIAAAYhwAEAACMQwACAADGIQABAADjEIAAAIBxCEAAAMA4BCAAAGAcAhAAADAOAQgAABiHAAQAAIxDAAIAAMYhAAEAAOMQgAAAgHEIQAAAwDgEIAAAYBwCEAAAMA4BCAAAGIcABAAAjEMAAgAAxiEAAQAA4xCAAACAcQhAAADAOAQgAABgHAIQAAAwDgEIAAAYhwAEAACMQwACAADGIQABAADjEIAAAIBxCEAAAMA4BCAAAGAcAhAAADBOfasrAABAdQqOX8sHjCpoAQIAAMZxiwC0aNEiCQ4OFm9vbwkLC5MtW7ZcsnxaWpp06tRJl+/atausW7fOce7ChQsyffp0fbxJkybSqlUriYmJkSNHjtTAOwEAALWB5QEoNTVVpkyZIklJSZKTkyMhISESFRUlhYWFTstv2rRJRo0aJWPHjpXc3FwZOnSo3nbu3KnPnzlzRl9n1qxZ+s9Vq1bJ3r17ZciQITX8zgAAgLvysNlsNisroFp8evXqJQsXLtT75eXlEhQUJJMmTZL4+Pgq5UeMGCElJSWyZs0ax7HevXtLt27dZMmSJU7/jm+//VZCQ0Pl4MGD0qZNm8vW6dSpU+Lr6yvFxcXi4+Pzl94fAMBajAH68/KSo6U2+SO/vy1tATp//rxkZ2dLZGTk/ypUr57ez8rKcvoadbxieUW1GF2svKI+CA8PD2nWrJnT86WlpfpDq7gBAIC6y9IAVFRUJGVlZeLv71/puNovKChw+hp1/I+UP3funB4TpLrNLpYGZ8+erROjfVMtUAAAoO6yfAxQdVIDou+9915RvXyLFy++aLmEhATdSmTf8vPza7SeAADAoOcA+fn5iaenpxw7dqzScbUfEBDg9DXq+JWUt4cfNe5n/fr1l+wLbNiwod4AAIAZLG0B8vLykh49ekhmZqbjmBoErfbDw8OdvkYdr1heycjIqFTeHn5++OEH+fzzz+Waa66pxncBAABqG8ufBK2mwMfGxkrPnj31TK2UlBQ9yysuLk6fV8/wCQwM1ON0lMmTJ0tERITMmzdPoqOjZeXKlbJ161ZZunSpI/zcfffdegq8mimmxhjZxwddffXVOnQBAACzWR6A1LT248ePS2Jiog4qajp7enq6Y6DzoUOH9Mwwuz59+siKFStk5syZMmPGDOnQoYOsXr1aunTpos8fPnxYPv74Y/21ulZFX3zxhdx22201+v4AAID7sfw5QO6I5wABQN3Bc4D+vDyeAwQAAFB31Olp8AAAAM4QgAAAgHEIQAAAwDgEIAAAYBwCEAAAMA4BCAAAGIcABAAAjEMAAgAAxiEAAQAA4xCAAACAcQhAAADAOAQgAABgHAIQAAAwDgEIAAAYhwAEAACMQwACAADGIQABAADjEIAAAIBxCEAAAMA4BCAAAGAcAhAAADAOAQgAABiHAAQAAIxDAAIAAMYhAAEAAOMQgAAAgHEIQAAAwDj1ra4AAAB/VHD8Wj40/CW0AAEAAOMQgAAAgHEIQAAAwDgEIAAAYBwCEAAAMA6zwAAAwF+ebZeXHC21CS1AAADAOAQgAABgHAIQAAAwDgEIAAAYhwAEAACMQwACAADGIQABAADjEIAAAIBxCEAAAMA4BCAAAGAcAhAAADAOAQgAABiHAAQAAIzDavAAgFq16jjgCrQAAQAA4xCAAACAcQhAAADAOAQgAABgHAIQAAAwDgEIAAAYhwAEAACMQwACAADGIQABAADjEIAAAIBxWAoDAFAtWN4C7owWIAAAYBwCEAAAMI5bdIEtWrRIXnzxRSkoKJCQkBBZsGCBhIaGXrR8WlqazJo1S/Ly8qRDhw4yZ84cueOOOxznbTabJCUlyT//+U85efKk9O3bVxYvXqzLAgD+PLq1UFdY3gKUmpoqU6ZM0YElJydHB6CoqCgpLCx0Wn7Tpk0yatQoGTt2rOTm5srQoUP1tnPnTkeZuXPnyquvvipLliyRb775Rpo0aaKvee7cuRp8ZwAAwF152FRziYXCwsKkV69esnDhQr1fXl4uQUFBMmnSJImPj69SfsSIEVJSUiJr1qxxHOvdu7d069ZNBx71dlq1aiVTp06VJ554Qp8vLi4Wf39/Wb58uYwcOfKydTp16pT4+vrq1/n4+Lj0/QJAbUYLEFwhLzlaqsMf+f1taRfY+fPnJTs7WxISEhzH6tWrJ5GRkZKVleX0Neq4ajGqSLXurF69Wn/9008/6a40dQ079WGooKVeeyUBCDDdH/kl90d+kFXXdav72nUdoQYmsjQAFRUVSVlZmW6dqUjt79mzx+lrVLhxVl4dt5+3H7tYmd8rLS3Vm51KjvYkCffRJenTP1R+5zNR1XZt/E+bx9Nq1XWr+9oALq+6fr/ar3slnVtuMQjaarNnz5ZnnnmmynHVFYfayzfF6hoAAKz4+fzrr7/q3h+3DUB+fn7i6ekpx44dq3Rc7QcEBDh9jTp+qfL2P9Wxli1bViqjxgk5o7rgKnarqXFIJ06ckGuuuUY8PDwcqVIFovz8fMYFuQnuiXvivrgf7on74Z5UD9Xyo8KPGgt8OZYGIC8vL+nRo4dkZmbqmVz28KH2J06c6PQ14eHh+vxjjz3mOJaRkaGPK+3atdMhSJWxBx71P5qaDfbQQw85vWbDhg31VlGzZs2cllWDqhgY7V64J+6J++J+uCfuh3viepdr+XGbLjDV8hIbGys9e/bUz/5JSUnRs7zi4uL0+ZiYGAkMDNTdVMrkyZMlIiJC5s2bJ9HR0bJy5UrZunWrLF26VJ9XLTYqHD3//PP6uT8qEKlnBqk0aA9ZAADAbJYHIDWt/fjx45KYmKgHKatWm/T0dMcg5kOHDumZYXZ9+vSRFStWyMyZM2XGjBk65KgZYF26dHGUefLJJ3WIevDBB/WDEPv166ev6e3tbcl7BAAA7sXy5wDVFmqWmGqFUuOFft9dBmtwT9wT98X9cE/cD/fEegQgAABgHMuXwgAAAKhpBCAAAGAcAhAAADAOAQgAABiHAHSF1q5dqxdUbdSokTRv3rzKM4XUdH31XKLGjRtLixYtZNq0afLbb79Vxz3D72ZSqEcnqOc/bdu2rdJns2PHDrn11lv14w/UU7znzp3LZ1dN8vLyZOzYsfq5W+p75Prrr5ekpCS94DH3xFqLFi2S4OBg/X2gfoZt2bLF4hqZQ80c7tWrlzRt2lT/XlC/N/bu3VupzLlz5+SRRx7RKw9cddVVMnz48CqrHaB6EICuwIcffiijR4/WD2fcvn27fP311/KPf/zDcV4t6KrCj/phv2nTJnnrrbdk+fLl+tlGqF7qmU/OHnmunv49cOBAadu2rWRnZ8uLL74oTz/9tOOBmXAttXixeor766+/Lrt27ZKXX35ZlixZop/VxT2xTmpqqn7YrAqjOTk5EhISIlFRUVJYWGhhrcyxceNGHW42b96sVyy4cOGC/rmknlNn9/jjj8snn3wiaWlpuvyRI0fkrrvusrTexlDPAcLFXbhwwRYYGGh74403Llpm3bp1tnr16tkKCgocxxYvXmzz8fGxlZaW8vFWE/W5d+rUybZr1y71LCtbbm6u49xrr71ma968eaXPf/r06baOHTtyP2rI3Llzbe3ateOeWCg0NNT2yCOPOPbLyspsrVq1ss2ePdvKahmrsLBQ/6zauHGj3j958qStQYMGtrS0NEeZ3bt36zJZWVkW1tQMtABdhvpX0+HDh/XTqLt3764XWB08eLDs3LnTUSYrK0u6du3qeHq1ov6VpVoh1L+G4XqqiXjcuHHyzjvv6G7H31P3pH///nq9uYr3RDU///LLL9ySGlBcXCxXX30198QiqkVatX5GRkY6jqmfY2pffX/Amu8Jxf59oe6PahWqeI86deokbdq04R7VAALQZRw4cED/qbpP1PIba9as0WOAbrvtNr1ivKKW8KgYfhT7vjoH11IPLx8zZoxMmDBBryHnDPfEWj/++KMsWLBAxo8f7zjGPalZRUVFunve2c8mfi7VPNVFrNap7Nu3r2PpJnUf1D/Sfr/4NveoZhgbgOLj4/XA2Utt9nENylNPPaUHp6nV65ctW6bPqz5b1Pw9Ub9Yf/31V70sCdzjnlSkWkwHDRok99xzj26lAyB6LJDqOVALeMM9WL4YqlWmTp2qWxEu5brrrpOjR4/qrzt37uw4rtYCU+fUzC8lICCgyswK+yh+dQ6uvSfr16/XzcO/X5NNtQbdd999ehC6+tx/P5OCe1J998RODeAcMGCAXrT49wPOuSc1y8/PTzw9PZ1+H/BzqWZNnDhR9x58+eWX0rp1a8dxdR9UV6VatLtiKxD3qIZYPQjJ3RUXF9saNmxYaRD0+fPnbS1atLC9/vrrlQZBHzt2zFFGnVODoM+dO2dJveuygwcP2r777jvH9umnn+pBgx988IEtPz+/0iBoda/sEhISGARdjX7++Wdbhw4dbCNHjrT99ttvVc5zT6wZBD1x4sRKg6DVpA4GQdeM8vJyPQhdDTzft29flfP2QdDqZ5fdnj17GARdQwhAV2Dy5Mn6h4b6Rav+5xw7dqwOQCdOnNDn1Q/7Ll262AYOHGjbtm2bLT093XbttdfqX7iofj/99FOVWWDqB4u/v79t9OjRtp07d9pWrlxpa9y4sSO0wvXhp3379rbbb79df3306FHHxj2xjvr/Xv0Dbvny5bbvv//e9uCDD9qaNWtWacYqqs9DDz1k8/X1tW3YsKHS98SZM2ccZSZMmGBr06aNbf369batW7fawsPD9YbqRwC6AqoVYerUqTr0NG3a1BYZGal/qVaUl5dnGzx4sK1Ro0Y2Pz8/XV5NoYc1AUjZvn27rV+/fvoXgAqwycnJ3I5qsmzZMn0PnG3cE2stWLBA/4L18vLSLUKbN2+2uEbmuNj3hPp+sTt79qzt4Ycf1i3W6h9pw4YNq/QPB1QfD/WfmupuAwAAcAfGzgIDAADmIgABAADjEIAAAIBxCEAAAMA4BCAAAGAcAhAAADAOAQgAABiHAAQAAIxDAALg1o4fPy4PPfSQtGnTRi+AqxaQjIqKkq+//trqqgGoxYxdDR5A7TB8+HC9YvZbb72lV55XK2VnZmbK//3f/1XL36f+Li8vr2q5NgD3QQsQALd18uRJ+eqrr2TOnDkyYMAAadu2rYSGhkpCQoIMGTLEUWb8+PHi7+8v3t7e0qVLF1mzZo3jGh9++KHcdNNNuvUoODhY5s2bV+nvUMeee+45iYmJER8fH3nwwQf18f/+979y6623SqNGjSQoKEgeffRRKSkpcbzutddekw4dOui/U/3dd999d419LgD+OgIQALd11VVX6W316tVSWlpa5Xx5ebkMHjxYd4e9++678v3330tycrJ4enrq89nZ2XLvvffKyJEj5bvvvpOnn35aZs2aJcuXL690nZdeeklCQkIkNzdXn9+/f78MGjRItz7t2LFDUlNTdSCaOHGiLr9161YdiJ599lnZu3evpKenS//+/WvoUwHgCiyGCsCtqRaccePGydmzZ+WWW26RiIgIHWhuvvlm+eyzz3QA2r17t9xwww1VXnvffffpMUSqnN2TTz4pa9eulV27djlagLp37y4fffSRo8wDDzygQ9Trr7/uOKYCkPq7VSvQunXrJC4uTn7++Wdp2rRptX8GAFyPFiAAbk21whw5ckQ+/vhj3SqzYcMGHYRUK862bdukdevWTsOPooJR3759Kx1T+z/88IOUlZU5jvXs2bNSme3bt+vr21ug1KYGXqsWp59++kn+/ve/6+44NSZp9OjR8t5778mZM2eq6RMAUB0IQADcnhpno0KH6p7atGmTjBkzRpKSkvT4HFdo0qRJpf3Tp0/rcUUqYNk3FYpUcLr++ut1q09OTo68//770rJlS0lMTNRdaGo8EoDagQAEoNbp3Lmz7opS3WCqG2rfvn1Oy914441VpsurfdViZB8n5IxqYVLjidq3b19ls88Qq1+/vkRGRsrcuXP1OKG8vDxZv369i98pgOrCNHgAbktNdb/nnnvk/vvv12FHtbyoAcgqdNx55516TI4afKy6yebPn68Dyp49e8TDw0N3l02dOlV69eqlZ3mNGDFCsrKyZOHChXoG16VMnz5devfurQc9q/FAqoVIBaKMjAz9ejXL7MCBA/rvbt68uR4TpLrHOnbsWGOfDYC/hgAEwG2psTdhYWHy8ssv65lZFy5c0FPS1aDoGTNmOAZJP/HEEzJq1CjdKqRCkJoJZm/J+fe//627qFQIUt1VauaW6kK7FBW2Nm7cKE899ZSeCm+z2XTXlwpRSrNmzWTVqlV6Vtm5c+f0dHjVHaam2wOoHZgFBgAAjMMYIAAAYBwCEAAAMA4BCAAAGIcABAAAjEMAAgAAxiEAAQAA4xCAAACAcQhAAADAOAQgAABgHAIQAAAwDgEIAAAYhwAEAADENP8Pi8OrVS3Y9hoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(scores_train, density=True, bins=40) \n",
    "plt.ylabel('Density')\n",
    "plt.xlabel('Scores');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(scores_test, density=True, bins=40) \n",
    "plt.ylabel('Density')\n",
    "plt.xlabel('Scores');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      Inlier       1.00      1.00      1.00     33147\n",
      "     Outlier       0.97      0.93      0.95       353\n",
      "\n",
      "    accuracy                           1.00     33500\n",
      "   macro avg       0.98      0.96      0.97     33500\n",
      "weighted avg       1.00      1.00      1.00     33500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# Make comparable to y_true encoding\n",
    "y_pred_train[y_pred_train == 1] = 0\n",
    "y_pred_train[y_pred_train == -1] = 1\n",
    "\n",
    "print(classification_report(y_train, y_pred_train, target_names=['Inlier', 'Outlier']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      Inlier       1.00      1.00      1.00     16341\n",
      "     Outlier       0.98      0.92      0.95       159\n",
      "\n",
      "    accuracy                           1.00     16500\n",
      "   macro avg       0.99      0.96      0.97     16500\n",
      "weighted avg       1.00      1.00      1.00     16500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Make comparable to y_true encoding\n",
    "y_pred_test[y_pred_test == 1] = 0\n",
    "y_pred_test[y_pred_test == -1] = 1\n",
    "\n",
    "print(classification_report(y_test, y_pred_test, target_names=['Inlier', 'Outlier']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model explanation:\n",
    "\n",
    "Retrieve local model explanations. Here: Specify all numeric and categorical columns explicitly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cols = list(X_train.select_dtypes(include=['float', 'int']).columns) \n",
    "cat_cols = list(X_train.select_dtypes(include=['object', 'category']).columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-- Bayesian Histogram-based Anomaly Detector (BHAD) --\n",
      "\n",
      "Discretizing continuous features...\n",
      "Setting maximum number of bins 184.\n",
      "Input shape: (33500, 30)\n",
      "Used 30 numeric feature(s) and 0 categorical feature(s).\n",
      "Binned continous features into 50 bins.\n",
      "Fit BHAD on discretized data.\n",
      "Input shape: (33500, 30)\n",
      "One-hot encoding categorical features.\n",
      "Matrix dimension after one-hot encoding: (33500, 1205)\n",
      "Finished training.\n",
      "Score input data.\n"
     ]
    }
   ],
   "source": [
    "model = BHAD(\n",
    "    contamination = 0.01, \n",
    "    num_features = num_cols, cat_features = cat_cols,                 \n",
    "    nbins=50,                      \n",
    "    verbose=True\n",
    ")\n",
    "\n",
    "y_pred_train = model.fit_predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- BHAD Model Explainer ---\n",
      "\n",
      "Using fitted BHAD and discretizer.\n",
      "Marginal distributions estimated using train set of shape (33500, 30)\n"
     ]
    }
   ],
   "source": [
    "from bhad import explainer\n",
    "\n",
    "local_expl = explainer.Explainer(bhad_obj=model, discretize_obj=model._discretizer).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create local explanations for 33500 observations.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b417ed7d2b804c78baed17cba7ffc491",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/33500 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0                      var0 (Cumul.perc.: 0.964): 6.34\n",
       "1    var5 (Cumul.perc.: 0.016): -6.17\\nvar18 (Cumul...\n",
       "2    var17 (Cumul.perc.: 0.998): 9.28\\nvar22 (Cumul...\n",
       "3    var28 (Cumul.perc.: 0.987): 7.12\\nvar12 (Cumul...\n",
       "4    var23 (Cumul.perc.: 0.053): -4.69\\nvar1 (Cumul...\n",
       "Name: explanation, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = local_expl.get_explanation(append = False)\n",
    "\n",
    "df_train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "global_feat_imp = local_expl.global_feat_imp         # based on X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.barh(global_feat_imp.index, global_feat_imp.values.flatten())\n",
    "plt.xlabel(\"Feature importances\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Score input data.\n",
      "Apply fitted one-hot encoder.\n"
     ]
    }
   ],
   "source": [
    "y_pred_test = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create local explanations for 16500 observations.\n",
      "Using custom thresholds.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "51049cb40baf4e4dafa8935913883963",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/16500 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_test = local_expl.get_explanation(thresholds = [.1]*X_test.shape[1])  # make threshold more conservative (only more extreme values are considered as outliers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Obs. 0:\n",
      " var26 (Cumul.perc.: 0.002): -8.44\n",
      "var4 (Cumul.perc.: 0.021): -5.5\n",
      "var29 (Cumul.perc.: 0.028): -5.66\n",
      "var8 (Cumul.perc.: 0.025): -4.79\n",
      "var25 (Cumul.perc.: 0.952): 5.58\n",
      "\n",
      "Obs. 10:\n",
      " var26 (Cumul.perc.: 0.999): 10.05\n",
      "var16 (Cumul.perc.: 0.966): 5.64\n",
      "var5 (Cumul.perc.: 0.953): 5.65\n",
      "\n",
      "Obs. 20:\n",
      " var12 (Cumul.perc.: 0.997): 7.92\n",
      "var18 (Cumul.perc.: 0.964): 6.13\n",
      "\n",
      "Obs. 30:\n",
      " var9 (Cumul.perc.: 0.031): -4.28\n",
      "var29 (Cumul.perc.: 0.044): -4.96\n",
      "var15 (Cumul.perc.: 0.97): 5.77\n",
      "var22 (Cumul.perc.: 0.048): -4.37\n",
      "\n",
      "Obs. 40:\n",
      " var29 (Cumul.perc.: 0.014): -6.62\n",
      "var3 (Cumul.perc.: 0.016): -5.43\n",
      "\n",
      "Obs. 50:\n",
      " var17 (Cumul.perc.: 0.967): 5.87\n",
      "var5 (Cumul.perc.: 0.049): -4.64\n",
      "\n",
      "Obs. 60:\n",
      " var8 (Cumul.perc.: 0.017): -5.34\n",
      "var20 (Cumul.perc.: 0.035): -4.42\n",
      "var16 (Cumul.perc.: 0.96): 5.45\n",
      "\n",
      "Obs. 70:\n",
      " var25 (Cumul.perc.: 0.988): 7.5\n",
      "var15 (Cumul.perc.: 0.981): 6.45\n",
      "\n",
      "Obs. 80:\n",
      " var21 (Cumul.perc.: 0.979): 7.25\n",
      "var13 (Cumul.perc.: 0.036): -5.41\n",
      "var22 (Cumul.perc.: 0.959): 5.61\n",
      "\n",
      "Obs. 90:\n",
      " var19 (Cumul.perc.: 0.01): -6.76\n",
      "var15 (Cumul.perc.: 0.989): 7.07\n",
      "\n",
      "Obs. 100:\n",
      " None\n",
      "\n",
      "Obs. 110:\n",
      " var4 (Cumul.perc.: 0.97): 6.07\n",
      "var5 (Cumul.perc.: 0.03): -5.31\n",
      "var17 (Cumul.perc.: 0.956): 5.46\n",
      "\n",
      "Obs. 120:\n",
      " var20 (Cumul.perc.: 0.999): 8.89\n",
      "var14 (Cumul.perc.: 0.003): -8.42\n",
      "var0 (Cumul.perc.: 0.994): 8.78\n",
      "var19 (Cumul.perc.: 0.963): 5.99\n",
      "\n",
      "Obs. 130:\n",
      " var20 (Cumul.perc.: 0.028): -4.69\n",
      "var24 (Cumul.perc.: 0.973): 6.56\n",
      "\n",
      "Obs. 140:\n",
      " var5 (Cumul.perc.: 0.015): -6.32\n",
      "var25 (Cumul.perc.: 0.022): -5.85\n",
      "var28 (Cumul.perc.: 0.024): -5.47\n",
      "\n",
      "Obs. 150:\n",
      " var20 (Cumul.perc.: 0.026): -4.81\n",
      "var18 (Cumul.perc.: 0.972): 6.51\n",
      "var28 (Cumul.perc.: 0.048): -4.48\n",
      "\n",
      "Obs. 160:\n",
      " var12 (Cumul.perc.: 0.027): -4.78\n",
      "var16 (Cumul.perc.: 0.047): -4.28\n",
      "\n",
      "Obs. 170:\n",
      " var9 (Cumul.perc.: 0.003): -6.9\n",
      "var26 (Cumul.perc.: 0.022): -5.62\n",
      "\n",
      "Obs. 180:\n",
      " var17 (Cumul.perc.: 0.025): -5.39\n",
      "var25 (Cumul.perc.: 0.956): 5.73\n",
      "\n",
      "Obs. 190:\n",
      " var28 (Cumul.perc.: 0.003): -7.87\n",
      "var3 (Cumul.perc.: 0.004): -6.85\n",
      "var6 (Cumul.perc.: 0.041): -5.37\n",
      "\n",
      "Obs. 200:\n",
      " var27 (Cumul.perc.: 0.975): 6.61\n",
      "var9 (Cumul.perc.: 0.964): 5.13\n",
      "\n",
      "Obs. 210:\n",
      " var21 (Cumul.perc.: 0.968): 6.63\n",
      "var2 (Cumul.perc.: 0.022): -6.07\n",
      "var8 (Cumul.perc.: 0.025): -4.81\n",
      "var17 (Cumul.perc.: 0.044): -4.55\n",
      "\n",
      "Obs. 220:\n",
      " var25 (Cumul.perc.: 0.979): 6.81\n",
      "var21 (Cumul.perc.: 0.031): -5.82\n",
      "var26 (Cumul.perc.: 0.977): 6.44\n",
      "\n",
      "Obs. 230:\n",
      " var7 (Cumul.perc.: 0.015): -5.89\n",
      "var6 (Cumul.perc.: 0.032): -5.75\n",
      "var22 (Cumul.perc.: 0.035): -4.79\n",
      "\n",
      "Obs. 240:\n",
      " var28 (Cumul.perc.: 0.986): 7.09\n",
      "\n",
      "Obs. 250:\n",
      " var29 (Cumul.perc.: 0.986): 7.5\n",
      "var1 (Cumul.perc.: 0.966): 5.58\n",
      "\n",
      "Obs. 260:\n",
      " var7 (Cumul.perc.: 0.009): -6.45\n",
      "var22 (Cumul.perc.: 0.028): -5.13\n",
      "var4 (Cumul.perc.: 0.97): 6.06\n",
      "\n",
      "Obs. 270:\n",
      " var26 (Cumul.perc.: 0.032): -5.08\n",
      "var9 (Cumul.perc.: 0.046): -3.84\n",
      "var7 (Cumul.perc.: 0.959): 5.55\n",
      "\n",
      "Obs. 280:\n",
      " var27 (Cumul.perc.: 0.008): -6.99\n",
      "var2 (Cumul.perc.: 0.024): -5.95\n",
      "var18 (Cumul.perc.: 0.959): 5.94\n",
      "\n",
      "Obs. 290:\n",
      " var19 (Cumul.perc.: 0.992): 7.95\n",
      "var11 (Cumul.perc.: 0.986): 7.49\n",
      "var10 (Cumul.perc.: 0.964): 5.45\n",
      "var18 (Cumul.perc.: 0.037): -5.07\n",
      "var9 (Cumul.perc.: 0.948): 4.66\n",
      "\n",
      "Obs. 300:\n",
      " var7 (Cumul.perc.: 0.025): -5.25\n",
      "\n",
      "Obs. 310:\n",
      " var27 (Cumul.perc.: 0.99): 7.8\n",
      "var4 (Cumul.perc.: 0.022): -5.47\n",
      "var15 (Cumul.perc.: 0.03): -5.02\n",
      "var20 (Cumul.perc.: 0.045): -4.1\n",
      "var11 (Cumul.perc.: 0.956): 5.91\n",
      "\n",
      "Obs. 320:\n",
      " var1 (Cumul.perc.: 0.01): -6.27\n",
      "var24 (Cumul.perc.: 0.031): -5.38\n",
      "var20 (Cumul.perc.: 0.957): 5.13\n",
      "var3 (Cumul.perc.: 0.038): -4.35\n",
      "var5 (Cumul.perc.: 0.953): 5.62\n",
      "\n",
      "Obs. 330:\n",
      " var28 (Cumul.perc.: 0.985): 6.98\n",
      "var17 (Cumul.perc.: 0.966): 5.84\n",
      "var9 (Cumul.perc.: 0.039): -4.04\n",
      "var15 (Cumul.perc.: 0.958): 5.33\n",
      "var12 (Cumul.perc.: 0.948): 4.81\n",
      "\n",
      "Obs. 340:\n",
      " var23 (Cumul.perc.: 0.987): 7.52\n",
      "var14 (Cumul.perc.: 0.015): -6.39\n",
      "var26 (Cumul.perc.: 0.022): -5.59\n",
      "var10 (Cumul.perc.: 0.957): 5.21\n",
      "\n",
      "Obs. 350:\n",
      " var25 (Cumul.perc.: 0.985): 7.24\n",
      "var5 (Cumul.perc.: 0.967): 6.1\n",
      "\n",
      "Obs. 360:\n",
      " var13 (Cumul.perc.: 0.987): 7.78\n",
      "var6 (Cumul.perc.: 0.972): 6.91\n",
      "var10 (Cumul.perc.: 0.962): 5.38\n",
      "\n",
      "Obs. 370:\n",
      " var28 (Cumul.perc.: 0.994): 8.2\n",
      "var24 (Cumul.perc.: 0.993): 8.18\n",
      "var6 (Cumul.perc.: 0.981): 7.5\n",
      "var3 (Cumul.perc.: 0.969): 5.53\n",
      "var11 (Cumul.perc.: 0.978): 6.86\n",
      "\n",
      "Obs. 380:\n",
      " var8 (Cumul.perc.: 0.977): 5.8\n",
      "var3 (Cumul.perc.: 0.03): -4.66\n",
      "var1 (Cumul.perc.: 0.024): -5.15\n",
      "\n",
      "Obs. 390:\n",
      " var6 (Cumul.perc.: 0.006): -8.15\n",
      "var29 (Cumul.perc.: 0.008): -7.34\n",
      "var24 (Cumul.perc.: 0.028): -5.53\n",
      "var23 (Cumul.perc.: 0.028): -5.7\n",
      "var28 (Cumul.perc.: 0.033): -5.01\n",
      "\n",
      "Obs. 400:\n",
      " var10 (Cumul.perc.: 0.98): 6.15\n",
      "var11 (Cumul.perc.: 0.018): -6.27\n",
      "var29 (Cumul.perc.: 0.956): 5.94\n",
      "\n",
      "Obs. 410:\n",
      " var8 (Cumul.perc.: 0.01): -6.03\n",
      "var9 (Cumul.perc.: 0.99): 6.43\n",
      "var29 (Cumul.perc.: 0.973): 6.72\n",
      "var24 (Cumul.perc.: 0.046): -4.81\n",
      "\n",
      "Obs. 420:\n",
      " var14 (Cumul.perc.: 0.006): -7.59\n",
      "var2 (Cumul.perc.: 0.983): 7.47\n",
      "var24 (Cumul.perc.: 0.988): 7.51\n",
      "var0 (Cumul.perc.: 0.971): 6.64\n",
      "var22 (Cumul.perc.: 0.958): 5.59\n",
      "\n",
      "Obs. 430:\n",
      " var3 (Cumul.perc.: 0.985): 6.37\n",
      "var22 (Cumul.perc.: 0.028): -5.13\n",
      "var9 (Cumul.perc.: 0.957): 4.9\n",
      "var16 (Cumul.perc.: 0.05): -4.2\n",
      "\n",
      "Obs. 440:\n",
      " var10 (Cumul.perc.: 0.963): 5.42\n",
      "var28 (Cumul.perc.: 0.964): 5.79\n",
      "\n",
      "Obs. 450:\n",
      " var27 (Cumul.perc.: 0.017): -6.08\n",
      "var13 (Cumul.perc.: 0.022): -6.24\n",
      "var7 (Cumul.perc.: 0.048): -4.37\n",
      "\n",
      "Obs. 460:\n",
      " var13 (Cumul.perc.: 0.045): -5.11\n",
      "\n",
      "Obs. 470:\n",
      " var2 (Cumul.perc.: 0.979): 7.19\n",
      "var24 (Cumul.perc.: 0.031): -5.39\n",
      "\n",
      "Obs. 480:\n",
      " var24 (Cumul.perc.: 0.994): 8.26\n",
      "var11 (Cumul.perc.: 0.041): -5.06\n",
      "\n",
      "Obs. 490:\n",
      " var8 (Cumul.perc.: 0.003): -7.23\n",
      "var18 (Cumul.perc.: 0.997): 9.16\n",
      "var16 (Cumul.perc.: 0.988): 6.9\n",
      "var22 (Cumul.perc.: 0.954): 5.44\n",
      "\n",
      "Obs. 500:\n",
      " var15 (Cumul.perc.: 0.998): 8.64\n",
      "var1 (Cumul.perc.: 0.051): -4.14\n",
      "\n",
      "Obs. 510:\n",
      " var18 (Cumul.perc.: 0.02): -5.94\n",
      "var5 (Cumul.perc.: 0.971): 6.29\n",
      "var1 (Cumul.perc.: 0.037): -4.55\n",
      "\n",
      "Obs. 520:\n",
      " var24 (Cumul.perc.: 1.0): 11.33\n",
      "var18 (Cumul.perc.: 0.003): -8.09\n",
      "var2 (Cumul.perc.: 0.994): 8.9\n",
      "var22 (Cumul.perc.: 0.014): -5.97\n",
      "var14 (Cumul.perc.: 0.017): -6.25\n",
      "\n",
      "Obs. 530:\n",
      " var4 (Cumul.perc.: 0.994): 8.1\n",
      "var15 (Cumul.perc.: 0.987): 6.85\n",
      "var14 (Cumul.perc.: 0.974): 6.56\n",
      "var24 (Cumul.perc.: 0.043): -4.91\n",
      "\n",
      "Obs. 540:\n",
      " var17 (Cumul.perc.: 0.014): -6.12\n",
      "var12 (Cumul.perc.: 0.022): -5.03\n",
      "var25 (Cumul.perc.: 0.029): -5.51\n",
      "\n",
      "Obs. 550:\n",
      " var7 (Cumul.perc.: 0.993): 7.71\n",
      "var27 (Cumul.perc.: 0.029): -5.35\n",
      "var25 (Cumul.perc.: 0.977): 6.69\n",
      "\n",
      "Obs. 560:\n",
      " var14 (Cumul.perc.: 0.982): 7.09\n",
      "var6 (Cumul.perc.: 0.969): 6.8\n",
      "var21 (Cumul.perc.: 0.976): 7.13\n",
      "var17 (Cumul.perc.: 0.032): -5.02\n",
      "var24 (Cumul.perc.: 0.044): -4.86\n",
      "\n",
      "Obs. 570:\n",
      " var16 (Cumul.perc.: 0.05): -4.2\n",
      "\n",
      "Obs. 580:\n",
      " var14 (Cumul.perc.: 0.964): 6.11\n",
      "\n",
      "Obs. 590:\n",
      " var29 (Cumul.perc.: 0.001): -10.35\n",
      "var2 (Cumul.perc.: 0.007): -7.51\n",
      "var10 (Cumul.perc.: 0.987): 6.69\n",
      "var9 (Cumul.perc.: 0.037): -4.08\n",
      "\n",
      "Obs. 600:\n",
      " var13 (Cumul.perc.: 0.991): 8.24\n",
      "\n",
      "Obs. 610:\n",
      " var8 (Cumul.perc.: 0.022): -4.97\n",
      "var2 (Cumul.perc.: 0.972): 6.77\n",
      "var23 (Cumul.perc.: 0.033): -5.45\n",
      "var18 (Cumul.perc.: 0.036): -5.1\n",
      "var17 (Cumul.perc.: 0.041): -4.64\n",
      "\n",
      "Obs. 620:\n",
      " var4 (Cumul.perc.: 0.003): -7.96\n",
      "var24 (Cumul.perc.: 0.022): -5.88\n",
      "var9 (Cumul.perc.: 0.98): 5.78\n",
      "var0 (Cumul.perc.: 0.034): -5.52\n",
      "\n",
      "Obs. 630:\n",
      " var28 (Cumul.perc.: 0.993): 7.88\n",
      "var29 (Cumul.perc.: 0.974): 6.73\n",
      "var19 (Cumul.perc.: 0.041): -4.83\n",
      "var17 (Cumul.perc.: 0.969): 5.97\n",
      "\n",
      "Obs. 640:\n",
      " var12 (Cumul.perc.: 0.004): -7.28\n",
      "var6 (Cumul.perc.: 0.991): 8.46\n",
      "var10 (Cumul.perc.: 0.011): -5.88\n",
      "var8 (Cumul.perc.: 0.969): 5.46\n",
      "var1 (Cumul.perc.: 0.045): -4.28\n",
      "\n",
      "Obs. 650:\n",
      " var10 (Cumul.perc.: 0.027): -4.95\n",
      "var5 (Cumul.perc.: 0.975): 6.52\n",
      "var21 (Cumul.perc.: 0.049): -5.1\n",
      "var11 (Cumul.perc.: 0.049): -4.76\n",
      "\n",
      "Obs. 660:\n",
      " var6 (Cumul.perc.: 0.01): -7.42\n",
      "var29 (Cumul.perc.: 0.985): 7.4\n",
      "\n",
      "Obs. 670:\n",
      " var7 (Cumul.perc.: 0.019): -5.62\n",
      "var6 (Cumul.perc.: 0.964): 6.55\n",
      "\n",
      "Obs. 680:\n",
      " var20 (Cumul.perc.: 0.008): -6.21\n",
      "\n",
      "Obs. 690:\n",
      " var28 (Cumul.perc.: 0.991): 7.6\n",
      "var21 (Cumul.perc.: 0.986): 7.8\n",
      "\n",
      "Obs. 700:\n",
      " var0 (Cumul.perc.: 0.014): -6.72\n",
      "var3 (Cumul.perc.: 0.031): -4.61\n",
      "var19 (Cumul.perc.: 0.038): -4.97\n",
      "var28 (Cumul.perc.: 0.961): 5.72\n",
      "\n",
      "Obs. 710:\n",
      " var18 (Cumul.perc.: 0.024): -5.7\n",
      "var1 (Cumul.perc.: 0.97): 5.75\n",
      "\n",
      "Obs. 720:\n",
      " var11 (Cumul.perc.: 0.995): 8.56\n",
      "var15 (Cumul.perc.: 0.986): 6.75\n",
      "var23 (Cumul.perc.: 0.985): 7.31\n",
      "var8 (Cumul.perc.: 0.041): -4.22\n",
      "var9 (Cumul.perc.: 0.963): 5.1\n",
      "\n",
      "Obs. 730:\n",
      " var9 (Cumul.perc.: 0.037): -4.08\n",
      "\n",
      "Obs. 740:\n",
      " var29 (Cumul.perc.: 0.989): 7.89\n",
      "var10 (Cumul.perc.: 0.989): 6.88\n",
      "var25 (Cumul.perc.: 0.991): 7.82\n",
      "var12 (Cumul.perc.: 0.966): 5.37\n",
      "\n",
      "Obs. 750:\n",
      " var9 (Cumul.perc.: 0.99): 6.44\n",
      "\n",
      "Obs. 760:\n",
      " var26 (Cumul.perc.: 0.995): 8.21\n",
      "var9 (Cumul.perc.: 0.973): 5.47\n",
      "var16 (Cumul.perc.: 0.966): 5.66\n",
      "var17 (Cumul.perc.: 0.034): -4.95\n",
      "\n",
      "Obs. 770:\n",
      " var25 (Cumul.perc.: 0.036): -5.17\n",
      "var23 (Cumul.perc.: 0.957): 5.9\n",
      "\n",
      "Obs. 780:\n",
      " var10 (Cumul.perc.: 0.996): 7.8\n",
      "var2 (Cumul.perc.: 0.969): 6.64\n",
      "var16 (Cumul.perc.: 0.975): 6.02\n",
      "\n",
      "Obs. 790:\n",
      " var16 (Cumul.perc.: 0.034): -4.68\n",
      "\n",
      "Obs. 800:\n",
      " var5 (Cumul.perc.: 0.993): 8.19\n",
      "var8 (Cumul.perc.: 0.011): -5.85\n",
      "var25 (Cumul.perc.: 0.955): 5.69\n",
      "\n",
      "Obs. 810:\n",
      " var10 (Cumul.perc.: 0.012): -5.8\n",
      "var0 (Cumul.perc.: 0.021): -6.2\n",
      "var11 (Cumul.perc.: 0.026): -5.68\n",
      "var28 (Cumul.perc.: 0.03): -5.17\n",
      "var3 (Cumul.perc.: 0.026): -4.81\n",
      "\n",
      "Obs. 820:\n",
      " var22 (Cumul.perc.: 0.014): -6.02\n",
      "var4 (Cumul.perc.: 0.988): 7.13\n",
      "var14 (Cumul.perc.: 0.987): 7.49\n",
      "var18 (Cumul.perc.: 0.021): -5.85\n",
      "var11 (Cumul.perc.: 0.017): -6.35\n",
      "\n",
      "Obs. 830:\n",
      " var3 (Cumul.perc.: 0.992): 7.03\n",
      "var19 (Cumul.perc.: 0.975): 6.56\n",
      "var23 (Cumul.perc.: 0.025): -5.86\n",
      "\n",
      "Obs. 840:\n",
      " var27 (Cumul.perc.: 0.977): 6.72\n",
      "var3 (Cumul.perc.: 0.027): -4.78\n",
      "var16 (Cumul.perc.: 0.033): -4.74\n",
      "var20 (Cumul.perc.: 0.032): -4.52\n",
      "var17 (Cumul.perc.: 0.049): -4.41\n",
      "\n",
      "Obs. 850:\n",
      " var17 (Cumul.perc.: 0.003): -8.09\n",
      "var1 (Cumul.perc.: 0.013): -5.96\n",
      "var8 (Cumul.perc.: 0.963): 5.23\n",
      "var26 (Cumul.perc.: 0.954): 5.56\n",
      "\n",
      "Obs. 860:\n",
      " var20 (Cumul.perc.: 0.998): 8.73\n",
      "var24 (Cumul.perc.: 0.031): -5.39\n",
      "var13 (Cumul.perc.: 0.967): 6.48\n",
      "var4 (Cumul.perc.: 0.972): 6.15\n",
      "var11 (Cumul.perc.: 0.046): -4.85\n",
      "\n",
      "Obs. 870:\n",
      " var21 (Cumul.perc.: 0.025): -6.17\n",
      "\n",
      "Obs. 880:\n",
      " var24 (Cumul.perc.: 0.009): -7.0\n",
      "var3 (Cumul.perc.: 0.016): -5.42\n",
      "var22 (Cumul.perc.: 0.023): -5.37\n",
      "var19 (Cumul.perc.: 0.036): -5.04\n",
      "var11 (Cumul.perc.: 0.042): -4.99\n",
      "\n",
      "Obs. 890:\n",
      " var4 (Cumul.perc.: 0.987): 7.05\n",
      "var20 (Cumul.perc.: 0.987): 6.53\n",
      "var9 (Cumul.perc.: 0.015): -5.14\n",
      "var27 (Cumul.perc.: 0.979): 6.78\n",
      "var0 (Cumul.perc.: 0.975): 6.89\n",
      "\n",
      "Obs. 900:\n",
      " var27 (Cumul.perc.: 0.96): 5.93\n",
      "\n",
      "Obs. 910:\n",
      " var29 (Cumul.perc.: 0.993): 8.4\n",
      "var25 (Cumul.perc.: 0.972): 6.48\n",
      "var8 (Cumul.perc.: 0.039): -4.29\n",
      "var27 (Cumul.perc.: 0.039): -4.96\n",
      "\n",
      "Obs. 920:\n",
      " var18 (Cumul.perc.: 0.996): 8.84\n",
      "var17 (Cumul.perc.: 0.01): -6.58\n",
      "var23 (Cumul.perc.: 0.017): -6.33\n",
      "var7 (Cumul.perc.: 0.043): -4.52\n",
      "\n",
      "Obs. 930:\n",
      " var26 (Cumul.perc.: 0.991): 7.65\n",
      "var7 (Cumul.perc.: 0.971): 6.04\n",
      "var10 (Cumul.perc.: 0.045): -4.21\n",
      "\n",
      "Obs. 940:\n",
      " var16 (Cumul.perc.: 0.997): 8.29\n",
      "var26 (Cumul.perc.: 0.995): 8.19\n",
      "var21 (Cumul.perc.: 0.014): -6.98\n",
      "var3 (Cumul.perc.: 0.017): -5.36\n",
      "var15 (Cumul.perc.: 0.955): 5.22\n",
      "\n",
      "Obs. 950:\n",
      " var13 (Cumul.perc.: 0.016): -6.59\n",
      "var16 (Cumul.perc.: 0.989): 7.01\n",
      "var17 (Cumul.perc.: 0.985): 6.9\n",
      "var23 (Cumul.perc.: 0.973): 6.52\n",
      "var25 (Cumul.perc.: 0.039): -5.06\n",
      "\n",
      "Obs. 960:\n",
      " var8 (Cumul.perc.: 0.989): 6.56\n",
      "var1 (Cumul.perc.: 0.018): -5.51\n",
      "var4 (Cumul.perc.: 0.967): 5.94\n",
      "\n",
      "Obs. 970:\n",
      " var5 (Cumul.perc.: 0.994): 8.27\n",
      "var26 (Cumul.perc.: 0.009): -6.72\n",
      "var11 (Cumul.perc.: 0.972): 6.52\n",
      "var23 (Cumul.perc.: 0.05): -4.79\n",
      "var15 (Cumul.perc.: 0.05): -4.34\n",
      "\n",
      "Obs. 980:\n",
      " None\n",
      "\n",
      "Obs. 990:\n",
      " var27 (Cumul.perc.: 0.973): 6.49\n",
      "var28 (Cumul.perc.: 0.98): 6.6\n",
      "var0 (Cumul.perc.: 0.964): 6.33\n",
      "\n",
      "Obs. 1000:\n",
      " var7 (Cumul.perc.: 0.987): 7.04\n",
      "var14 (Cumul.perc.: 0.971): 6.44\n",
      "var12 (Cumul.perc.: 0.952): 4.92\n",
      "\n",
      "Obs. 1010:\n",
      " var29 (Cumul.perc.: 0.005): -7.96\n",
      "var11 (Cumul.perc.: 0.99): 7.84\n",
      "var9 (Cumul.perc.: 0.04): -3.99\n",
      "\n",
      "Obs. 1020:\n",
      " var19 (Cumul.perc.: 0.032): -5.22\n",
      "\n",
      "Obs. 1030:\n",
      " var23 (Cumul.perc.: 0.008): -7.44\n",
      "var24 (Cumul.perc.: 0.017): -6.25\n",
      "var3 (Cumul.perc.: 0.045): -4.12\n",
      "\n",
      "Obs. 1040:\n",
      " var9 (Cumul.perc.: 0.005): -6.2\n",
      "var23 (Cumul.perc.: 0.013): -6.76\n",
      "var10 (Cumul.perc.: 0.976): 5.97\n",
      "\n",
      "Obs. 1050:\n",
      " var25 (Cumul.perc.: 0.986): 7.3\n",
      "var16 (Cumul.perc.: 0.951): 5.18\n",
      "\n",
      "Obs. 1060:\n",
      " var22 (Cumul.perc.: 0.997): 8.48\n",
      "var15 (Cumul.perc.: 0.008): -6.72\n",
      "var19 (Cumul.perc.: 0.986): 7.23\n",
      "var17 (Cumul.perc.: 0.021): -5.61\n",
      "var12 (Cumul.perc.: 0.03): -4.63\n",
      "\n",
      "Obs. 1070:\n",
      " var20 (Cumul.perc.: 0.992): 7.15\n",
      "var27 (Cumul.perc.: 0.989): 7.6\n",
      "var6 (Cumul.perc.: 0.044): -5.26\n",
      "\n",
      "Obs. 1080:\n",
      " var13 (Cumul.perc.: 0.019): -6.41\n",
      "var14 (Cumul.perc.: 0.971): 6.41\n",
      "\n",
      "Obs. 1090:\n",
      " var6 (Cumul.perc.: 0.009): -7.51\n",
      "var25 (Cumul.perc.: 0.009): -6.94\n",
      "var7 (Cumul.perc.: 0.013): -6.14\n",
      "var27 (Cumul.perc.: 0.972): 6.43\n",
      "var29 (Cumul.perc.: 0.02): -6.12\n",
      "\n",
      "Obs. 1100:\n",
      " var8 (Cumul.perc.: 0.992): 6.92\n",
      "\n",
      "Obs. 1110:\n",
      " var14 (Cumul.perc.: 0.005): -7.87\n",
      "var27 (Cumul.perc.: 0.009): -6.83\n",
      "var28 (Cumul.perc.: 0.033): -5.02\n",
      "var4 (Cumul.perc.: 0.037): -4.74\n",
      "\n",
      "Obs. 1120:\n",
      " var29 (Cumul.perc.: 0.048): -4.82\n",
      "\n",
      "Obs. 1130:\n",
      " var16 (Cumul.perc.: 0.005): -7.08\n",
      "var18 (Cumul.perc.: 0.987): 7.49\n",
      "var17 (Cumul.perc.: 0.985): 6.9\n",
      "var8 (Cumul.perc.: 0.966): 5.34\n",
      "\n",
      "Obs. 1140:\n",
      " var1 (Cumul.perc.: 0.979): 6.15\n",
      "var2 (Cumul.perc.: 0.96): 6.26\n",
      "\n",
      "Obs. 1150:\n",
      " var11 (Cumul.perc.: 0.037): -5.21\n",
      "\n",
      "Obs. 1160:\n",
      " var27 (Cumul.perc.: 0.026): -5.54\n",
      "var26 (Cumul.perc.: 0.955): 5.59\n",
      "\n",
      "Obs. 1170:\n",
      " var15 (Cumul.perc.: 0.984): 6.65\n",
      "var16 (Cumul.perc.: 0.044): -4.38\n",
      "\n",
      "Obs. 1180:\n",
      " var21 (Cumul.perc.: 0.983): 7.55\n",
      "var8 (Cumul.perc.: 0.035): -4.42\n",
      "var3 (Cumul.perc.: 0.045): -4.12\n",
      "\n",
      "Obs. 1190:\n",
      " var23 (Cumul.perc.: 0.041): -5.11\n",
      "var20 (Cumul.perc.: 0.964): 5.35\n",
      "var12 (Cumul.perc.: 0.045): -4.1\n",
      "var17 (Cumul.perc.: 0.049): -4.4\n",
      "\n",
      "Obs. 1200:\n",
      " var4 (Cumul.perc.: 1.0): 12.74\n",
      "var14 (Cumul.perc.: 0.998): 9.62\n",
      "var11 (Cumul.perc.: 0.005): -7.95\n",
      "var18 (Cumul.perc.: 0.011): -6.7\n",
      "var24 (Cumul.perc.: 0.026): -5.64\n",
      "\n",
      "Obs. 1210:\n",
      " var17 (Cumul.perc.: 0.015): -6.04\n",
      "var19 (Cumul.perc.: 0.973): 6.45\n",
      "var25 (Cumul.perc.: 0.017): -6.19\n",
      "var13 (Cumul.perc.: 0.965): 6.38\n",
      "var23 (Cumul.perc.: 0.039): -5.2\n",
      "\n",
      "Obs. 1220:\n",
      " var22 (Cumul.perc.: 0.984): 6.69\n",
      "\n",
      "Obs. 1230:\n",
      " var21 (Cumul.perc.: 0.05): -5.08\n",
      "\n",
      "Obs. 1240:\n",
      " var28 (Cumul.perc.: 0.996): 8.55\n",
      "var4 (Cumul.perc.: 0.975): 6.28\n",
      "var24 (Cumul.perc.: 0.971): 6.48\n",
      "var22 (Cumul.perc.: 0.951): 5.36\n",
      "\n",
      "Obs. 1250:\n",
      " var9 (Cumul.perc.: 0.007): -5.97\n",
      "var11 (Cumul.perc.: 0.97): 6.42\n",
      "\n",
      "Obs. 1260:\n",
      " var13 (Cumul.perc.: 0.999): 10.75\n",
      "var24 (Cumul.perc.: 0.991): 7.78\n",
      "var25 (Cumul.perc.: 0.015): -6.29\n",
      "var3 (Cumul.perc.: 0.022): -4.99\n",
      "\n",
      "Obs. 1270:\n",
      " var23 (Cumul.perc.: 0.997): 9.28\n",
      "var12 (Cumul.perc.: 0.02): -5.11\n",
      "var3 (Cumul.perc.: 0.968): 5.49\n",
      "\n",
      "Obs. 1280:\n",
      " var23 (Cumul.perc.: 0.999): 10.1\n",
      "var11 (Cumul.perc.: 0.01): -7.03\n",
      "var26 (Cumul.perc.: 0.022): -5.59\n",
      "var3 (Cumul.perc.: 0.959): 5.17\n",
      "var8 (Cumul.perc.: 0.952): 4.9\n",
      "\n",
      "Obs. 1290:\n",
      " var19 (Cumul.perc.: 0.979): 6.82\n",
      "\n",
      "Obs. 1300:\n",
      " var21 (Cumul.perc.: 0.996): 9.33\n",
      "var12 (Cumul.perc.: 0.995): 7.39\n",
      "var24 (Cumul.perc.: 0.968): 6.3\n",
      "var27 (Cumul.perc.: 0.037): -5.02\n",
      "\n",
      "Obs. 1310:\n",
      " var28 (Cumul.perc.: 0.959): 5.62\n",
      "var3 (Cumul.perc.: 0.948): 4.87\n",
      "\n",
      "Obs. 1320:\n",
      " var7 (Cumul.perc.: 0.004): -7.4\n",
      "var20 (Cumul.perc.: 0.981): 6.09\n",
      "var24 (Cumul.perc.: 0.028): -5.51\n",
      "var3 (Cumul.perc.: 0.967): 5.46\n",
      "\n",
      "Obs. 1330:\n",
      " None\n",
      "\n",
      "Obs. 1340:\n",
      " var24 (Cumul.perc.: 0.014): -6.49\n",
      "var4 (Cumul.perc.: 0.02): -5.57\n",
      "var0 (Cumul.perc.: 0.027): -5.8\n",
      "var16 (Cumul.perc.: 0.037): -4.58\n",
      "var17 (Cumul.perc.: 0.047): -4.44\n",
      "\n",
      "Obs. 1350:\n",
      " var24 (Cumul.perc.: 0.991): 7.87\n",
      "\n",
      "Obs. 1360:\n",
      " var21 (Cumul.perc.: 0.011): -7.3\n",
      "var14 (Cumul.perc.: 0.046): -4.81\n",
      "\n",
      "Obs. 1370:\n",
      " var13 (Cumul.perc.: 0.011): -7.15\n",
      "var11 (Cumul.perc.: 0.042): -5.0\n",
      "var5 (Cumul.perc.: 0.95): 5.56\n",
      "\n",
      "Obs. 1380:\n",
      " var21 (Cumul.perc.: 0.005): -8.41\n",
      "var12 (Cumul.perc.: 0.994): 7.34\n",
      "var22 (Cumul.perc.: 0.012): -6.21\n",
      "var28 (Cumul.perc.: 0.012): -6.37\n",
      "var24 (Cumul.perc.: 0.983): 7.14\n",
      "\n",
      "Obs. 1390:\n",
      " var16 (Cumul.perc.: 0.005): -7.12\n",
      "var26 (Cumul.perc.: 0.024): -5.48\n",
      "var25 (Cumul.perc.: 0.049): -4.7\n",
      "\n",
      "Obs. 1400:\n",
      " var27 (Cumul.perc.: 0.97): 6.34\n",
      "var12 (Cumul.perc.: 0.954): 5.0\n",
      "\n",
      "Obs. 1410:\n",
      " var22 (Cumul.perc.: 0.986): 6.89\n",
      "var20 (Cumul.perc.: 0.042): -4.18\n",
      "var29 (Cumul.perc.: 0.959): 6.06\n",
      "\n",
      "Obs. 1420:\n",
      " var8 (Cumul.perc.: 0.991): 6.78\n",
      "var16 (Cumul.perc.: 0.016): -5.63\n",
      "var26 (Cumul.perc.: 0.025): -5.42\n",
      "var12 (Cumul.perc.: 0.017): -5.34\n",
      "var0 (Cumul.perc.: 0.031): -5.67\n",
      "\n",
      "Obs. 1430:\n",
      " var8 (Cumul.perc.: 0.991): 6.83\n",
      "var1 (Cumul.perc.: 0.015): -5.73\n",
      "var6 (Cumul.perc.: 0.02): -6.47\n",
      "var10 (Cumul.perc.: 0.975): 5.91\n",
      "\n",
      "Obs. 1440:\n",
      " var21 (Cumul.perc.: 0.996): 9.18\n",
      "var14 (Cumul.perc.: 0.965): 6.16\n",
      "var7 (Cumul.perc.: 0.966): 5.86\n",
      "\n",
      "Obs. 1450:\n",
      " var29 (Cumul.perc.: 0.018): -6.28\n",
      "var15 (Cumul.perc.: 0.969): 5.73\n",
      "var17 (Cumul.perc.: 0.049): -4.38\n",
      "var3 (Cumul.perc.: 0.044): -4.15\n",
      "\n",
      "Obs. 1460:\n",
      " None\n",
      "\n",
      "Obs. 1470:\n",
      " var11 (Cumul.perc.: 0.001): -10.35\n",
      "var4 (Cumul.perc.: 0.99): 7.32\n",
      "var24 (Cumul.perc.: 0.012): -6.71\n",
      "var2 (Cumul.perc.: 0.968): 6.59\n",
      "var7 (Cumul.perc.: 0.026): -5.2\n",
      "\n",
      "Obs. 1480:\n",
      " var16 (Cumul.perc.: 0.977): 6.08\n",
      "var7 (Cumul.perc.: 0.042): -4.57\n",
      "\n",
      "Obs. 1490:\n",
      " var27 (Cumul.perc.: 0.041): -4.87\n",
      "var12 (Cumul.perc.: 0.955): 5.03\n",
      "var9 (Cumul.perc.: 0.971): 5.39\n",
      "\n",
      "Obs. 1500:\n",
      " var27 (Cumul.perc.: 0.013): -6.42\n",
      "var9 (Cumul.perc.: 0.013): -5.3\n",
      "var2 (Cumul.perc.: 0.045): -5.05\n",
      "\n",
      "Obs. 1510:\n",
      " var27 (Cumul.perc.: 0.961): 5.95\n",
      "\n",
      "Obs. 1520:\n",
      " var10 (Cumul.perc.: 0.008): -6.38\n",
      "var12 (Cumul.perc.: 0.025): -4.84\n",
      "var9 (Cumul.perc.: 0.036): -4.13\n",
      "var23 (Cumul.perc.: 0.95): 5.65\n",
      "\n",
      "Obs. 1530:\n",
      " var8 (Cumul.perc.: 0.997): 7.8\n",
      "var15 (Cumul.perc.: 0.989): 7.09\n",
      "var14 (Cumul.perc.: 0.979): 6.89\n",
      "var18 (Cumul.perc.: 0.019): -6.01\n",
      "var7 (Cumul.perc.: 0.043): -4.53\n",
      "\n",
      "Obs. 1540:\n",
      " var0 (Cumul.perc.: 0.994): 8.7\n",
      "var29 (Cumul.perc.: 0.977): 6.9\n",
      "var9 (Cumul.perc.: 0.964): 5.13\n",
      "var11 (Cumul.perc.: 0.957): 5.93\n",
      "\n",
      "Obs. 1550:\n",
      " var28 (Cumul.perc.: 0.009): -6.72\n",
      "var4 (Cumul.perc.: 0.974): 6.22\n",
      "var29 (Cumul.perc.: 0.975): 6.82\n",
      "var25 (Cumul.perc.: 0.041): -4.99\n",
      "\n",
      "Obs. 1560:\n",
      " var6 (Cumul.perc.: 0.98): 7.39\n",
      "\n",
      "Obs. 1570:\n",
      " var12 (Cumul.perc.: 0.029): -4.65\n",
      "\n",
      "Obs. 1580:\n",
      " var5 (Cumul.perc.: 0.987): 7.37\n",
      "\n",
      "Obs. 1590:\n",
      " var28 (Cumul.perc.: 0.982): 6.71\n",
      "\n",
      "Obs. 1600:\n",
      " var25 (Cumul.perc.: 0.999): 10.59\n",
      "var4 (Cumul.perc.: 0.004): -7.45\n",
      "var6 (Cumul.perc.: 0.957): 6.26\n",
      "var3 (Cumul.perc.: 0.035): -4.46\n",
      "\n",
      "Obs. 1610:\n",
      " var16 (Cumul.perc.: 0.023): -5.19\n",
      "var8 (Cumul.perc.: 0.025): -4.79\n",
      "var11 (Cumul.perc.: 0.976): 6.76\n",
      "\n",
      "Obs. 1620:\n",
      " var10 (Cumul.perc.: 0.045): -4.22\n",
      "\n",
      "Obs. 1630:\n",
      " var12 (Cumul.perc.: 0.004): -7.22\n",
      "var10 (Cumul.perc.: 0.015): -5.6\n",
      "\n",
      "Obs. 1640:\n",
      " var9 (Cumul.perc.: 0.003): -6.6\n",
      "var27 (Cumul.perc.: 0.028): -5.42\n",
      "var19 (Cumul.perc.: 0.041): -4.84\n",
      "var16 (Cumul.perc.: 0.023): -5.16\n",
      "var4 (Cumul.perc.: 0.965): 5.83\n",
      "\n",
      "Obs. 1650:\n",
      " var19 (Cumul.perc.: 0.017): -6.11\n",
      "var12 (Cumul.perc.: 0.983): 6.13\n",
      "var13 (Cumul.perc.: 0.032): -5.62\n",
      "var28 (Cumul.perc.: 0.96): 5.66\n",
      "var26 (Cumul.perc.: 0.955): 5.59\n",
      "\n",
      "Obs. 1660:\n",
      " var15 (Cumul.perc.: 0.997): 8.31\n",
      "var6 (Cumul.perc.: 0.026): -6.11\n",
      "var25 (Cumul.perc.: 0.015): -6.28\n",
      "var9 (Cumul.perc.: 0.986): 6.14\n",
      "var26 (Cumul.perc.: 0.966): 5.91\n",
      "\n",
      "Obs. 1670:\n",
      " var14 (Cumul.perc.: 0.035): -5.22\n",
      "var12 (Cumul.perc.: 0.034): -4.45\n",
      "\n",
      "Obs. 1680:\n",
      " var11 (Cumul.perc.: 0.01): -7.04\n",
      "var2 (Cumul.perc.: 0.033): -5.53\n",
      "var4 (Cumul.perc.: 0.961): 5.68\n",
      "\n",
      "Obs. 1690:\n",
      " var1 (Cumul.perc.: 0.032): -4.75\n",
      "var16 (Cumul.perc.: 0.969): 5.73\n",
      "\n",
      "Obs. 1700:\n",
      " var7 (Cumul.perc.: 0.026): -5.18\n",
      "var5 (Cumul.perc.: 0.96): 5.87\n",
      "\n",
      "Obs. 1710:\n",
      " var25 (Cumul.perc.: 0.965): 6.12\n",
      "var15 (Cumul.perc.: 0.043): -4.53\n",
      "\n",
      "Obs. 1720:\n",
      " None\n",
      "\n",
      "Obs. 1730:\n",
      " var27 (Cumul.perc.: 0.046): -4.68\n",
      "\n",
      "Obs. 1740:\n",
      " None\n",
      "\n",
      "Obs. 1750:\n",
      " var9 (Cumul.perc.: 0.001): -7.78\n",
      "var21 (Cumul.perc.: 0.962): 6.37\n",
      "var26 (Cumul.perc.: 0.031): -5.13\n",
      "var12 (Cumul.perc.: 0.949): 4.83\n",
      "\n",
      "Obs. 1760:\n",
      " var11 (Cumul.perc.: 0.97): 6.4\n",
      "\n",
      "Obs. 1770:\n",
      " var6 (Cumul.perc.: 0.004): -8.65\n",
      "var2 (Cumul.perc.: 0.995): 9.11\n",
      "var26 (Cumul.perc.: 0.993): 7.83\n",
      "var20 (Cumul.perc.: 0.99): 6.85\n",
      "var27 (Cumul.perc.: 0.977): 6.68\n",
      "\n",
      "Obs. 1780:\n",
      " var18 (Cumul.perc.: 0.991): 7.88\n",
      "var19 (Cumul.perc.: 0.016): -6.12\n",
      "var11 (Cumul.perc.: 0.966): 6.26\n",
      "\n",
      "Obs. 1790:\n",
      " var20 (Cumul.perc.: 0.993): 7.21\n",
      "var23 (Cumul.perc.: 0.026): -5.79\n",
      "\n",
      "Obs. 1800:\n",
      " var8 (Cumul.perc.: 0.004): -7.2\n",
      "var6 (Cumul.perc.: 0.01): -7.38\n",
      "var25 (Cumul.perc.: 0.974): 6.56\n",
      "\n",
      "Obs. 1810:\n",
      " var28 (Cumul.perc.: 0.978): 6.46\n",
      "var6 (Cumul.perc.: 0.953): 6.16\n",
      "\n",
      "Obs. 1820:\n",
      " var24 (Cumul.perc.: 0.98): 6.96\n",
      "var23 (Cumul.perc.: 0.974): 6.56\n",
      "\n",
      "Obs. 1830:\n",
      " var8 (Cumul.perc.: 0.993): 7.03\n",
      "var4 (Cumul.perc.: 0.03): -5.06\n",
      "var15 (Cumul.perc.: 0.021): -5.49\n",
      "\n",
      "Obs. 1840:\n",
      " var21 (Cumul.perc.: 0.012): -7.21\n",
      "var6 (Cumul.perc.: 0.044): -5.26\n",
      "var18 (Cumul.perc.: 0.039): -5.02\n",
      "\n",
      "Obs. 1850:\n",
      " var8 (Cumul.perc.: 0.036): -4.38\n",
      "\n",
      "Obs. 1860:\n",
      " var19 (Cumul.perc.: 0.037): -4.99\n",
      "var15 (Cumul.perc.: 0.96): 5.4\n",
      "var18 (Cumul.perc.: 0.046): -4.77\n",
      "var23 (Cumul.perc.: 0.951): 5.67\n",
      "\n",
      "Obs. 1870:\n",
      " None\n",
      "\n",
      "Obs. 1880:\n",
      " var25 (Cumul.perc.: 0.998): 9.64\n",
      "var16 (Cumul.perc.: 0.033): -4.72\n",
      "var10 (Cumul.perc.: 0.04): -4.37\n",
      "\n",
      "Obs. 1890:\n",
      " var21 (Cumul.perc.: 0.022): -6.38\n",
      "var29 (Cumul.perc.: 0.984): 7.34\n",
      "var27 (Cumul.perc.: 0.975): 6.61\n",
      "\n",
      "Obs. 1900:\n",
      " var0 (Cumul.perc.: 0.991): 8.17\n",
      "var17 (Cumul.perc.: 0.012): -6.28\n",
      "var8 (Cumul.perc.: 0.035): -4.4\n",
      "var27 (Cumul.perc.: 0.958): 5.86\n",
      "\n",
      "Obs. 1910:\n",
      " None\n",
      "\n",
      "Obs. 1920:\n",
      " var15 (Cumul.perc.: 0.002): -8.7\n",
      "var13 (Cumul.perc.: 0.997): 9.71\n",
      "var1 (Cumul.perc.: 0.996): 7.89\n",
      "var4 (Cumul.perc.: 0.01): -6.32\n",
      "var5 (Cumul.perc.: 0.959): 5.82\n",
      "\n",
      "Obs. 1930:\n",
      " var28 (Cumul.perc.: 0.003): -7.86\n",
      "var17 (Cumul.perc.: 0.01): -6.46\n",
      "var26 (Cumul.perc.: 0.987): 7.18\n",
      "var15 (Cumul.perc.: 0.983): 6.58\n",
      "var2 (Cumul.perc.: 0.977): 7.05\n",
      "\n",
      "Obs. 1940:\n",
      " var27 (Cumul.perc.: 0.026): -5.49\n",
      "var18 (Cumul.perc.: 0.021): -5.85\n",
      "\n",
      "Obs. 1950:\n",
      " var11 (Cumul.perc.: 0.995): 8.59\n",
      "var23 (Cumul.perc.: 0.007): -7.51\n",
      "var0 (Cumul.perc.: 0.026): -5.86\n",
      "var25 (Cumul.perc.: 0.043): -4.93\n",
      "var4 (Cumul.perc.: 0.04): -4.62\n",
      "\n",
      "Obs. 1960:\n",
      " var26 (Cumul.perc.: 0.005): -7.35\n",
      "var25 (Cumul.perc.: 0.013): -6.5\n",
      "\n",
      "Obs. 1970:\n",
      " var27 (Cumul.perc.: 0.012): -6.47\n",
      "var14 (Cumul.perc.: 0.021): -5.94\n",
      "var29 (Cumul.perc.: 0.032): -5.46\n",
      "\n",
      "Obs. 1980:\n",
      " var14 (Cumul.perc.: 0.993): 8.22\n",
      "var15 (Cumul.perc.: 0.991): 7.28\n",
      "var10 (Cumul.perc.: 0.023): -5.12\n",
      "var9 (Cumul.perc.: 0.041): -3.98\n",
      "\n",
      "Obs. 1990:\n",
      " var20 (Cumul.perc.: 0.976): 5.85\n",
      "\n",
      "Obs. 2000:\n",
      " var5 (Cumul.perc.: 0.015): -6.24\n",
      "var10 (Cumul.perc.: 0.977): 6.03\n",
      "var11 (Cumul.perc.: 0.979): 6.93\n",
      "var17 (Cumul.perc.: 0.963): 5.7\n",
      "\n",
      "Obs. 2010:\n",
      " var19 (Cumul.perc.: 0.006): -7.51\n",
      "var26 (Cumul.perc.: 0.98): 6.61\n",
      "var16 (Cumul.perc.: 0.979): 6.2\n",
      "\n",
      "Obs. 2020:\n",
      " var29 (Cumul.perc.: 0.03): -5.59\n",
      "var16 (Cumul.perc.: 0.037): -4.59\n",
      "var15 (Cumul.perc.: 0.969): 5.75\n",
      "\n",
      "Obs. 2030:\n",
      " var28 (Cumul.perc.: 0.011): -6.48\n",
      "var4 (Cumul.perc.: 0.051): -4.3\n",
      "var1 (Cumul.perc.: 0.953): 5.13\n",
      "\n",
      "Obs. 2040:\n",
      " var18 (Cumul.perc.: 0.013): -6.48\n",
      "var4 (Cumul.perc.: 0.974): 6.23\n",
      "var9 (Cumul.perc.: 0.025): -4.59\n",
      "\n",
      "Obs. 2050:\n",
      " var27 (Cumul.perc.: 0.993): 8.16\n",
      "var16 (Cumul.perc.: 0.021): -5.3\n",
      "\n",
      "Obs. 2060:\n",
      " var15 (Cumul.perc.: 0.008): -6.61\n",
      "var17 (Cumul.perc.: 0.975): 6.25\n",
      "var27 (Cumul.perc.: 0.952): 5.66\n",
      "var23 (Cumul.perc.: 0.952): 5.72\n",
      "\n",
      "Obs. 2070:\n",
      " var15 (Cumul.perc.: 0.005): -7.26\n",
      "var16 (Cumul.perc.: 0.99): 7.07\n",
      "\n",
      "Obs. 2080:\n",
      " var4 (Cumul.perc.: 0.031): -4.99\n",
      "var14 (Cumul.perc.: 0.031): -5.42\n",
      "var9 (Cumul.perc.: 0.98): 5.76\n",
      "\n",
      "Obs. 2090:\n",
      " None\n",
      "\n",
      "Obs. 2100:\n",
      " var0 (Cumul.perc.: 0.012): -6.98\n",
      "var27 (Cumul.perc.: 0.017): -6.06\n",
      "\n",
      "Obs. 2110:\n",
      " None\n",
      "\n",
      "Obs. 2120:\n",
      " var25 (Cumul.perc.: 0.027): -5.6\n",
      "\n",
      "Obs. 2130:\n",
      " var20 (Cumul.perc.: 0.99): 6.88\n",
      "var29 (Cumul.perc.: 0.019): -6.25\n",
      "var28 (Cumul.perc.: 0.973): 6.17\n",
      "var12 (Cumul.perc.: 0.972): 5.63\n",
      "\n",
      "Obs. 2140:\n",
      " var22 (Cumul.perc.: 0.024): -5.3\n",
      "var21 (Cumul.perc.: 0.04): -5.44\n",
      "var11 (Cumul.perc.: 0.031): -5.46\n",
      "var28 (Cumul.perc.: 0.036): -4.91\n",
      "\n",
      "Obs. 2150:\n",
      " var6 (Cumul.perc.: 0.0): -12.94\n",
      "var22 (Cumul.perc.: 0.993): 7.71\n",
      "var2 (Cumul.perc.: 0.992): 8.45\n",
      "var19 (Cumul.perc.: 0.972): 6.41\n",
      "var5 (Cumul.perc.: 0.018): -6.03\n",
      "\n",
      "Obs. 2160:\n",
      " var3 (Cumul.perc.: 0.988): 6.63\n",
      "var7 (Cumul.perc.: 0.016): -5.86\n",
      "var0 (Cumul.perc.: 0.955): 5.99\n",
      "\n",
      "Obs. 2170:\n",
      " var4 (Cumul.perc.: 0.004): -7.73\n",
      "var17 (Cumul.perc.: 0.976): 6.27\n",
      "var24 (Cumul.perc.: 0.954): 5.79\n",
      "var8 (Cumul.perc.: 0.964): 5.26\n",
      "var12 (Cumul.perc.: 0.958): 5.11\n",
      "\n",
      "Obs. 2180:\n",
      " var7 (Cumul.perc.: 0.962): 5.69\n",
      "\n",
      "Obs. 2190:\n",
      " var12 (Cumul.perc.: 0.009): -6.12\n",
      "var26 (Cumul.perc.: 0.015): -6.16\n",
      "var10 (Cumul.perc.: 0.024): -5.07\n",
      "\n",
      "Obs. 2200:\n",
      " None\n",
      "\n",
      "Obs. 2210:\n",
      " var25 (Cumul.perc.: 0.983): 7.11\n",
      "var27 (Cumul.perc.: 0.04): -4.89\n",
      "\n",
      "Obs. 2220:\n",
      " var27 (Cumul.perc.: 0.97): 6.33\n",
      "var28 (Cumul.perc.: 0.974): 6.23\n",
      "var22 (Cumul.perc.: 0.973): 6.09\n",
      "\n",
      "Obs. 2230:\n",
      " var15 (Cumul.perc.: 0.03): -5.03\n",
      "var17 (Cumul.perc.: 0.038): -4.75\n",
      "\n",
      "Obs. 2240:\n",
      " var23 (Cumul.perc.: 0.007): -7.64\n",
      "var26 (Cumul.perc.: 0.954): 5.57\n",
      "\n",
      "Obs. 2250:\n",
      " var8 (Cumul.perc.: 0.002): -7.93\n",
      "var5 (Cumul.perc.: 0.018): -6.05\n",
      "var23 (Cumul.perc.: 0.026): -5.78\n",
      "\n",
      "Obs. 2260:\n",
      " var26 (Cumul.perc.: 1.0): 10.52\n",
      "var19 (Cumul.perc.: 0.034): -5.15\n",
      "var17 (Cumul.perc.: 0.957): 5.49\n",
      "var27 (Cumul.perc.: 0.963): 6.01\n",
      "var21 (Cumul.perc.: 0.042): -5.36\n",
      "\n",
      "Obs. 2270:\n",
      " var23 (Cumul.perc.: 0.009): -7.21\n",
      "var18 (Cumul.perc.: 0.969): 6.34\n",
      "var8 (Cumul.perc.: 0.035): -4.4\n",
      "var16 (Cumul.perc.: 0.969): 5.74\n",
      "var20 (Cumul.perc.: 0.962): 5.28\n",
      "\n",
      "Obs. 2280:\n",
      " var5 (Cumul.perc.: 0.979): 6.8\n",
      "var27 (Cumul.perc.: 0.975): 6.6\n",
      "var7 (Cumul.perc.: 0.045): -4.45\n",
      "\n",
      "Obs. 2290:\n",
      " var21 (Cumul.perc.: 0.995): 9.02\n",
      "var16 (Cumul.perc.: 0.007): -6.48\n",
      "var23 (Cumul.perc.: 0.976): 6.71\n",
      "var8 (Cumul.perc.: 0.987): 6.42\n",
      "var4 (Cumul.perc.: 0.024): -5.33\n",
      "\n",
      "Obs. 2300:\n",
      " var12 (Cumul.perc.: 0.02): -5.1\n",
      "var11 (Cumul.perc.: 0.019): -6.19\n",
      "var21 (Cumul.perc.: 0.045): -5.25\n",
      "\n",
      "Obs. 2310:\n",
      " var12 (Cumul.perc.: 0.012): -5.73\n",
      "var13 (Cumul.perc.: 0.974): 6.8\n",
      "var22 (Cumul.perc.: 0.961): 5.67\n",
      "\n",
      "Obs. 2320:\n",
      " var6 (Cumul.perc.: 0.022): -6.32\n",
      "var5 (Cumul.perc.: 0.045): -4.76\n",
      "\n",
      "Obs. 2330:\n",
      " var23 (Cumul.perc.: 0.006): -7.86\n",
      "var15 (Cumul.perc.: 0.012): -6.21\n",
      "var22 (Cumul.perc.: 0.985): 6.77\n",
      "var3 (Cumul.perc.: 0.046): -4.09\n",
      "var11 (Cumul.perc.: 0.954): 5.82\n",
      "\n",
      "Obs. 2340:\n",
      " var0 (Cumul.perc.: 0.011): -7.09\n",
      "var28 (Cumul.perc.: 0.023): -5.57\n",
      "var18 (Cumul.perc.: 0.981): 6.98\n",
      "var24 (Cumul.perc.: 0.048): -4.73\n",
      "var8 (Cumul.perc.: 0.049): -3.98\n",
      "\n",
      "Obs. 2350:\n",
      " var26 (Cumul.perc.: 0.006): -7.17\n",
      "var20 (Cumul.perc.: 0.966): 5.42\n",
      "\n",
      "Obs. 2360:\n",
      " None\n",
      "\n",
      "Obs. 2370:\n",
      " var3 (Cumul.perc.: 0.985): 6.4\n",
      "var7 (Cumul.perc.: 0.984): 6.82\n",
      "var4 (Cumul.perc.: 0.973): 6.19\n",
      "var28 (Cumul.perc.: 0.954): 5.45\n",
      "\n",
      "Obs. 2380:\n",
      " var28 (Cumul.perc.: 0.95): 5.35\n",
      "var23 (Cumul.perc.: 0.951): 5.69\n",
      "\n",
      "Obs. 2390:\n",
      " var17 (Cumul.perc.: 0.007): -7.08\n",
      "var9 (Cumul.perc.: 0.977): 5.64\n",
      "var24 (Cumul.perc.: 0.051): -4.65\n",
      "var5 (Cumul.perc.: 0.957): 5.75\n",
      "\n",
      "Obs. 2400:\n",
      " var7 (Cumul.perc.: 0.036): -4.76\n",
      "var16 (Cumul.perc.: 0.04): -4.47\n",
      "\n",
      "Obs. 2410:\n",
      " var19 (Cumul.perc.: 0.001): -10.9\n",
      "var20 (Cumul.perc.: 0.996): 7.84\n",
      "var4 (Cumul.perc.: 0.011): -6.27\n",
      "var11 (Cumul.perc.: 0.009): -7.23\n",
      "var15 (Cumul.perc.: 0.014): -6.05\n",
      "\n",
      "Obs. 2420:\n",
      " var4 (Cumul.perc.: 0.983): 6.78\n",
      "\n",
      "Obs. 2430:\n",
      " var19 (Cumul.perc.: 0.015): -6.26\n",
      "\n",
      "Obs. 2440:\n",
      " var19 (Cumul.perc.: 0.005): -7.71\n",
      "var17 (Cumul.perc.: 0.989): 7.21\n",
      "var29 (Cumul.perc.: 0.026): -5.78\n",
      "var21 (Cumul.perc.: 0.963): 6.44\n",
      "\n",
      "Obs. 2450:\n",
      " var10 (Cumul.perc.: 0.98): 6.19\n",
      "var15 (Cumul.perc.: 0.024): -5.34\n",
      "\n",
      "Obs. 2460:\n",
      " var18 (Cumul.perc.: 0.024): -5.71\n",
      "var0 (Cumul.perc.: 0.969): 6.57\n",
      "var25 (Cumul.perc.: 0.958): 5.79\n",
      "\n",
      "Obs. 2470:\n",
      " var14 (Cumul.perc.: 0.009): -7.02\n",
      "var26 (Cumul.perc.: 0.026): -5.4\n",
      "var27 (Cumul.perc.: 0.963): 6.01\n",
      "var22 (Cumul.perc.: 0.967): 5.86\n",
      "\n",
      "Obs. 2480:\n",
      " var6 (Cumul.perc.: 0.007): -7.89\n",
      "var1 (Cumul.perc.: 0.009): -6.35\n",
      "var23 (Cumul.perc.: 0.023): -5.95\n",
      "var5 (Cumul.perc.: 0.956): 5.74\n",
      "\n",
      "Obs. 2490:\n",
      " var3 (Cumul.perc.: 0.995): 7.72\n",
      "var29 (Cumul.perc.: 0.979): 7.02\n",
      "\n",
      "Obs. 2500:\n",
      " var4 (Cumul.perc.: 0.012): -6.21\n",
      "var29 (Cumul.perc.: 0.031): -5.5\n",
      "var8 (Cumul.perc.: 0.97): 5.5\n",
      "\n",
      "Obs. 2510:\n",
      " var25 (Cumul.perc.: 0.988): 7.5\n",
      "var18 (Cumul.perc.: 0.022): -5.81\n",
      "var15 (Cumul.perc.: 0.046): -4.45\n",
      "\n",
      "Obs. 2520:\n",
      " var19 (Cumul.perc.: 0.992): 7.9\n",
      "var7 (Cumul.perc.: 0.008): -6.55\n",
      "var4 (Cumul.perc.: 0.034): -4.87\n",
      "var17 (Cumul.perc.: 0.04): -4.7\n",
      "var15 (Cumul.perc.: 0.043): -4.53\n",
      "\n",
      "Obs. 2530:\n",
      " var12 (Cumul.perc.: 0.977): 5.86\n",
      "\n",
      "Obs. 2540:\n",
      " var8 (Cumul.perc.: 0.981): 5.98\n",
      "var15 (Cumul.perc.: 0.977): 6.2\n",
      "var29 (Cumul.perc.: 0.961): 6.12\n",
      "\n",
      "Obs. 2550:\n",
      " var8 (Cumul.perc.: 0.017): -5.34\n",
      "var16 (Cumul.perc.: 0.963): 5.55\n",
      "\n",
      "Obs. 2560:\n",
      " var15 (Cumul.perc.: 0.033): -4.87\n",
      "var11 (Cumul.perc.: 0.041): -5.04\n",
      "var9 (Cumul.perc.: 0.953): 4.81\n",
      "\n",
      "Obs. 2570:\n",
      " var8 (Cumul.perc.: 0.998): 8.28\n",
      "var28 (Cumul.perc.: 0.986): 7.03\n",
      "var10 (Cumul.perc.: 0.986): 6.58\n",
      "\n",
      "Obs. 2580:\n",
      " var4 (Cumul.perc.: 0.968): 5.98\n",
      "var1 (Cumul.perc.: 0.037): -4.56\n",
      "\n",
      "Obs. 2590:\n",
      " var14 (Cumul.perc.: 0.003): -8.27\n",
      "\n",
      "Obs. 2600:\n",
      " var12 (Cumul.perc.: 0.988): 6.56\n",
      "\n",
      "Obs. 2610:\n",
      " var13 (Cumul.perc.: 0.027): -5.92\n",
      "var22 (Cumul.perc.: 0.046): -4.42\n",
      "\n",
      "Obs. 2620:\n",
      " var5 (Cumul.perc.: 0.03): -5.3\n",
      "\n",
      "Obs. 2630:\n",
      " var7 (Cumul.perc.: 0.035): -4.8\n",
      "\n",
      "Obs. 2640:\n",
      " var8 (Cumul.perc.: 0.979): 5.89\n",
      "var6 (Cumul.perc.: 0.03): -5.83\n",
      "var26 (Cumul.perc.: 0.043): -4.71\n",
      "\n",
      "Obs. 2650:\n",
      " var10 (Cumul.perc.: 0.008): -6.31\n",
      "var28 (Cumul.perc.: 0.966): 5.89\n",
      "var23 (Cumul.perc.: 0.957): 5.88\n",
      "\n",
      "Obs. 2660:\n",
      " var15 (Cumul.perc.: 0.987): 6.82\n",
      "var12 (Cumul.perc.: 0.047): -4.06\n",
      "var8 (Cumul.perc.: 0.049): -3.97\n",
      "\n",
      "Obs. 2670:\n",
      " var13 (Cumul.perc.: 0.001): -10.08\n",
      "var4 (Cumul.perc.: 0.993): 7.86\n",
      "var3 (Cumul.perc.: 0.993): 7.3\n",
      "var14 (Cumul.perc.: 0.972): 6.48\n",
      "var2 (Cumul.perc.: 0.952): 5.98\n",
      "\n",
      "Obs. 2680:\n",
      " var25 (Cumul.perc.: 0.987): 7.44\n",
      "var14 (Cumul.perc.: 0.983): 7.16\n",
      "var6 (Cumul.perc.: 0.036): -5.56\n",
      "var4 (Cumul.perc.: 0.048): -4.38\n",
      "\n",
      "Obs. 2690:\n",
      " var0 (Cumul.perc.: 0.998): 10.02\n",
      "var19 (Cumul.perc.: 0.979): 6.79\n",
      "var22 (Cumul.perc.: 0.968): 5.9\n",
      "var14 (Cumul.perc.: 0.04): -5.02\n",
      "var2 (Cumul.perc.: 0.96): 6.24\n",
      "\n",
      "Obs. 2700:\n",
      " var29 (Cumul.perc.: 0.992): 8.24\n",
      "var1 (Cumul.perc.: 0.984): 6.49\n",
      "var9 (Cumul.perc.: 0.975): 5.55\n",
      "var20 (Cumul.perc.: 0.032): -4.51\n",
      "\n",
      "Obs. 2710:\n",
      " var25 (Cumul.perc.: 0.003): -8.43\n",
      "var16 (Cumul.perc.: 0.986): 6.73\n",
      "var27 (Cumul.perc.: 0.952): 5.66\n",
      "var15 (Cumul.perc.: 0.962): 5.47\n",
      "\n",
      "Obs. 2720:\n",
      " var3 (Cumul.perc.: 0.95): 4.92\n",
      "\n",
      "Obs. 2730:\n",
      " var26 (Cumul.perc.: 0.019): -5.83\n",
      "var29 (Cumul.perc.: 0.965): 6.32\n",
      "var14 (Cumul.perc.: 0.964): 6.12\n",
      "\n",
      "Obs. 2740:\n",
      " var1 (Cumul.perc.: 0.015): -5.72\n",
      "var13 (Cumul.perc.: 0.041): -5.24\n",
      "var9 (Cumul.perc.: 0.947): 4.64\n",
      "var7 (Cumul.perc.: 0.966): 5.85\n",
      "\n",
      "Obs. 2750:\n",
      " var0 (Cumul.perc.: 0.019): -6.3\n",
      "\n",
      "Obs. 2760:\n",
      " var10 (Cumul.perc.: 0.006): -6.63\n",
      "var23 (Cumul.perc.: 0.97): 6.38\n",
      "\n",
      "Obs. 2770:\n",
      " None\n",
      "\n",
      "Obs. 2780:\n",
      " var0 (Cumul.perc.: 0.007): -7.68\n",
      "var17 (Cumul.perc.: 0.016): -5.9\n",
      "var27 (Cumul.perc.: 0.013): -6.41\n",
      "var3 (Cumul.perc.: 0.02): -5.16\n",
      "var9 (Cumul.perc.: 0.038): -4.08\n",
      "\n",
      "Obs. 2790:\n",
      " None\n",
      "\n",
      "Obs. 2800:\n",
      " var5 (Cumul.perc.: 0.005): -7.84\n",
      "var13 (Cumul.perc.: 0.952): 5.94\n",
      "\n",
      "Obs. 2810:\n",
      " var2 (Cumul.perc.: 0.011): -7.08\n",
      "var27 (Cumul.perc.: 0.953): 5.69\n",
      "var25 (Cumul.perc.: 0.028): -5.52\n",
      "var12 (Cumul.perc.: 0.964): 5.3\n",
      "\n",
      "Obs. 2820:\n",
      " var6 (Cumul.perc.: 0.998): 10.11\n",
      "var22 (Cumul.perc.: 0.005): -7.28\n",
      "var17 (Cumul.perc.: 0.993): 7.79\n",
      "var16 (Cumul.perc.: 0.05): -4.19\n",
      "\n",
      "Obs. 2830:\n",
      " var0 (Cumul.perc.: 0.022): -6.1\n",
      "var14 (Cumul.perc.: 0.043): -4.92\n",
      "var12 (Cumul.perc.: 0.039): -4.25\n",
      "\n",
      "Obs. 2840:\n",
      " var15 (Cumul.perc.: 0.981): 6.45\n",
      "var8 (Cumul.perc.: 0.033): -4.46\n",
      "\n",
      "Obs. 2850:\n",
      " var8 (Cumul.perc.: 0.99): 6.71\n",
      "var27 (Cumul.perc.: 0.041): -4.86\n",
      "var9 (Cumul.perc.: 0.962): 5.06\n",
      "\n",
      "Obs. 2860:\n",
      " var6 (Cumul.perc.: 0.967): 6.7\n",
      "var4 (Cumul.perc.: 0.038): -4.68\n",
      "\n",
      "Obs. 2870:\n",
      " var10 (Cumul.perc.: 0.999): 9.06\n",
      "var27 (Cumul.perc.: 0.002): -8.98\n",
      "var26 (Cumul.perc.: 0.002): -8.3\n",
      "var8 (Cumul.perc.: 0.998): 8.25\n",
      "var6 (Cumul.perc.: 0.997): 9.89\n",
      "\n",
      "Obs. 2880:\n",
      " var0 (Cumul.perc.: 0.022): -6.13\n",
      "var16 (Cumul.perc.: 0.025): -5.07\n",
      "\n",
      "Obs. 2890:\n",
      " var8 (Cumul.perc.: 0.98): 5.94\n",
      "var7 (Cumul.perc.: 0.027): -5.13\n",
      "\n",
      "Obs. 2900:\n",
      " var12 (Cumul.perc.: 0.953): 4.96\n",
      "\n",
      "Obs. 2910:\n",
      " var16 (Cumul.perc.: 0.954): 5.27\n",
      "\n",
      "Obs. 2920:\n",
      " var13 (Cumul.perc.: 0.026): -5.96\n",
      "var11 (Cumul.perc.: 0.971): 6.44\n",
      "var25 (Cumul.perc.: 0.965): 6.09\n",
      "var24 (Cumul.perc.: 0.962): 6.07\n",
      "var2 (Cumul.perc.: 0.964): 6.42\n",
      "\n",
      "Obs. 2930:\n",
      " var20 (Cumul.perc.: 0.03): -4.59\n",
      "\n",
      "Obs. 2940:\n",
      " var22 (Cumul.perc.: 1.0): 11.84\n",
      "var16 (Cumul.perc.: 0.013): -5.9\n",
      "var9 (Cumul.perc.: 0.034): -4.21\n",
      "var10 (Cumul.perc.: 0.956): 5.15\n",
      "\n",
      "Obs. 2950:\n",
      " var25 (Cumul.perc.: 0.991): 7.84\n",
      "var13 (Cumul.perc.: 0.014): -6.8\n",
      "var14 (Cumul.perc.: 0.988): 7.58\n",
      "var12 (Cumul.perc.: 0.988): 6.5\n",
      "var22 (Cumul.perc.: 0.021): -5.49\n",
      "\n",
      "Obs. 2960:\n",
      " var5 (Cumul.perc.: 0.967): 6.12\n",
      "var8 (Cumul.perc.: 0.971): 5.51\n",
      "var29 (Cumul.perc.: 0.039): -5.14\n",
      "var18 (Cumul.perc.: 0.039): -5.01\n",
      "var3 (Cumul.perc.: 0.95): 4.91\n",
      "\n",
      "Obs. 2970:\n",
      " var26 (Cumul.perc.: 0.993): 7.82\n",
      "var13 (Cumul.perc.: 0.014): -6.8\n",
      "var23 (Cumul.perc.: 0.023): -5.99\n",
      "var21 (Cumul.perc.: 0.961): 6.35\n",
      "var2 (Cumul.perc.: 0.966): 6.49\n",
      "\n",
      "Obs. 2980:\n",
      " var23 (Cumul.perc.: 0.95): 5.65\n",
      "\n",
      "Obs. 2990:\n",
      " var10 (Cumul.perc.: 0.031): -4.74\n",
      "var17 (Cumul.perc.: 0.037): -4.79\n",
      "\n",
      "Obs. 3000:\n",
      " var24 (Cumul.perc.: 0.995): 8.58\n",
      "var27 (Cumul.perc.: 0.992): 8.09\n",
      "var1 (Cumul.perc.: 0.986): 6.67\n",
      "var6 (Cumul.perc.: 0.043): -5.31\n",
      "var14 (Cumul.perc.: 0.965): 6.15\n",
      "\n",
      "Obs. 3010:\n",
      " var27 (Cumul.perc.: 0.973): 6.48\n",
      "var28 (Cumul.perc.: 0.035): -4.94\n",
      "var6 (Cumul.perc.: 0.044): -5.24\n",
      "\n",
      "Obs. 3020:\n",
      " var19 (Cumul.perc.: 0.004): -8.16\n",
      "var2 (Cumul.perc.: 0.986): 7.76\n",
      "\n",
      "Obs. 3030:\n",
      " var15 (Cumul.perc.: 0.038): -4.73\n",
      "var3 (Cumul.perc.: 0.031): -4.59\n",
      "var19 (Cumul.perc.: 0.035): -5.07\n",
      "\n",
      "Obs. 3040:\n",
      " var9 (Cumul.perc.: 0.003): -6.72\n",
      "var18 (Cumul.perc.: 0.989): 7.63\n",
      "var20 (Cumul.perc.: 0.954): 5.05\n",
      "var16 (Cumul.perc.: 0.95): 5.13\n",
      "\n",
      "Obs. 3050:\n",
      " var6 (Cumul.perc.: 0.032): -5.76\n",
      "var23 (Cumul.perc.: 0.044): -4.98\n",
      "\n",
      "Obs. 3060:\n",
      " var21 (Cumul.perc.: 0.992): 8.48\n",
      "\n",
      "Obs. 3070:\n",
      " var17 (Cumul.perc.: 0.992): 7.68\n",
      "var27 (Cumul.perc.: 0.967): 6.18\n",
      "var8 (Cumul.perc.: 0.959): 5.1\n",
      "\n",
      "Obs. 3080:\n",
      " var3 (Cumul.perc.: 0.01): -5.93\n",
      "var21 (Cumul.perc.: 0.022): -6.39\n",
      "var4 (Cumul.perc.: 0.021): -5.51\n",
      "var13 (Cumul.perc.: 0.964): 6.35\n",
      "var27 (Cumul.perc.: 0.961): 5.95\n",
      "\n",
      "Obs. 3090:\n",
      " var20 (Cumul.perc.: 0.014): -5.53\n",
      "var15 (Cumul.perc.: 0.975): 6.08\n",
      "var0 (Cumul.perc.: 0.976): 6.96\n",
      "var17 (Cumul.perc.: 0.97): 6.03\n",
      "var9 (Cumul.perc.: 0.958): 4.93\n",
      "\n",
      "Obs. 3100:\n",
      " var8 (Cumul.perc.: 0.008): -6.22\n",
      "var20 (Cumul.perc.: 0.013): -5.54\n",
      "var3 (Cumul.perc.: 0.99): 6.81\n",
      "var2 (Cumul.perc.: 0.03): -5.68\n",
      "var4 (Cumul.perc.: 0.957): 5.53\n",
      "\n",
      "Obs. 3110:\n",
      " var2 (Cumul.perc.: 0.033): -5.56\n",
      "var12 (Cumul.perc.: 0.044): -4.14\n",
      "var9 (Cumul.perc.: 0.05): -3.75\n",
      "\n",
      "Obs. 3120:\n",
      " None\n",
      "\n",
      "Obs. 3130:\n",
      " var16 (Cumul.perc.: 0.995): 7.86\n",
      "var11 (Cumul.perc.: 0.013): -6.71\n",
      "\n",
      "Obs. 3140:\n",
      " var18 (Cumul.perc.: 0.0): -13.58\n",
      "var9 (Cumul.perc.: 0.0): -9.96\n",
      "var1 (Cumul.perc.: 0.0): -14.81\n",
      "var12 (Cumul.perc.: 0.001): -9.35\n",
      "var7 (Cumul.perc.: 0.003): -7.76\n",
      "\n",
      "Obs. 3150:\n",
      " var28 (Cumul.perc.: 0.005): -7.3\n",
      "var29 (Cumul.perc.: 0.046): -4.89\n",
      "\n",
      "Obs. 3160:\n",
      " var23 (Cumul.perc.: 0.01): -7.08\n",
      "var19 (Cumul.perc.: 0.015): -6.24\n",
      "var25 (Cumul.perc.: 0.017): -6.15\n",
      "\n",
      "Obs. 3170:\n",
      " var18 (Cumul.perc.: 0.993): 8.33\n",
      "var0 (Cumul.perc.: 0.985): 7.57\n",
      "var29 (Cumul.perc.: 0.967): 6.39\n",
      "var24 (Cumul.perc.: 0.96): 6.02\n",
      "var22 (Cumul.perc.: 0.953): 5.42\n",
      "\n",
      "Obs. 3180:\n",
      " var2 (Cumul.perc.: 0.979): 7.16\n",
      "var22 (Cumul.perc.: 0.97): 5.97\n",
      "var23 (Cumul.perc.: 0.952): 5.72\n",
      "\n",
      "Obs. 3190:\n",
      " var3 (Cumul.perc.: 0.005): -6.68\n",
      "var0 (Cumul.perc.: 0.017): -6.47\n",
      "\n",
      "Obs. 3200:\n",
      " var17 (Cumul.perc.: 0.966): 5.82\n",
      "\n",
      "Obs. 3210:\n",
      " var8 (Cumul.perc.: 0.045): -4.11\n",
      "\n",
      "Obs. 3220:\n",
      " var19 (Cumul.perc.: 0.023): -5.66\n",
      "var3 (Cumul.perc.: 0.974): 5.73\n",
      "\n",
      "Obs. 3230:\n",
      " var18 (Cumul.perc.: 0.984): 7.21\n",
      "\n",
      "Obs. 3240:\n",
      " None\n",
      "\n",
      "Obs. 3250:\n",
      " var22 (Cumul.perc.: 0.965): 5.8\n",
      "\n",
      "Obs. 3260:\n",
      " var26 (Cumul.perc.: 0.999): 10.1\n",
      "var11 (Cumul.perc.: 0.999): 10.7\n",
      "var0 (Cumul.perc.: 0.013): -6.83\n",
      "var25 (Cumul.perc.: 0.026): -5.65\n",
      "var14 (Cumul.perc.: 0.043): -4.92\n",
      "\n",
      "Obs. 3270:\n",
      " var11 (Cumul.perc.: 0.965): 6.22\n",
      "var4 (Cumul.perc.: 0.047): -4.38\n",
      "\n",
      "Obs. 3280:\n",
      " var29 (Cumul.perc.: 0.003): -8.57\n",
      "var25 (Cumul.perc.: 0.015): -6.34\n",
      "var22 (Cumul.perc.: 0.034): -4.82\n",
      "var4 (Cumul.perc.: 0.045): -4.43\n",
      "var28 (Cumul.perc.: 0.047): -4.52\n",
      "\n",
      "Obs. 3290:\n",
      " var2 (Cumul.perc.: 0.008): -7.37\n",
      "var19 (Cumul.perc.: 0.962): 5.96\n",
      "\n",
      "Obs. 3300:\n",
      " var28 (Cumul.perc.: 0.981): 6.68\n",
      "\n",
      "Obs. 3310:\n",
      " var0 (Cumul.perc.: 0.008): -7.42\n",
      "var4 (Cumul.perc.: 0.048): -4.37\n",
      "\n",
      "Obs. 3320:\n",
      " None\n",
      "\n",
      "Obs. 3330:\n",
      " var28 (Cumul.perc.: 0.979): 6.54\n",
      "var19 (Cumul.perc.: 0.044): -4.73\n",
      "\n",
      "Obs. 3340:\n",
      " var29 (Cumul.perc.: 0.004): -8.17\n",
      "var6 (Cumul.perc.: 0.013): -7.04\n",
      "var3 (Cumul.perc.: 0.013): -5.61\n",
      "var8 (Cumul.perc.: 0.957): 5.06\n",
      "var18 (Cumul.perc.: 0.035): -5.18\n",
      "\n",
      "Obs. 3350:\n",
      " var7 (Cumul.perc.: 0.011): -6.32\n",
      "\n",
      "Obs. 3360:\n",
      " var12 (Cumul.perc.: 0.998): 8.68\n",
      "var24 (Cumul.perc.: 0.985): 7.29\n",
      "var19 (Cumul.perc.: 0.014): -6.29\n",
      "var15 (Cumul.perc.: 0.96): 5.4\n",
      "\n",
      "Obs. 3370:\n",
      " var13 (Cumul.perc.: 1.0): 15.57\n",
      "var25 (Cumul.perc.: 0.0): -10.86\n",
      "var21 (Cumul.perc.: 0.0): -11.28\n",
      "var17 (Cumul.perc.: 0.997): 8.7\n",
      "var28 (Cumul.perc.: 0.005): -7.55\n",
      "\n",
      "Obs. 3380:\n",
      " var27 (Cumul.perc.: 0.005): -7.55\n",
      "var6 (Cumul.perc.: 0.983): 7.67\n",
      "var18 (Cumul.perc.: 0.973): 6.53\n",
      "var16 (Cumul.perc.: 0.047): -4.27\n",
      "var3 (Cumul.perc.: 0.967): 5.45\n",
      "\n",
      "Obs. 3390:\n",
      " var20 (Cumul.perc.: 0.965): 5.4\n",
      "\n",
      "Obs. 3400:\n",
      " var8 (Cumul.perc.: 0.006): -6.68\n",
      "\n",
      "Obs. 3410:\n",
      " var17 (Cumul.perc.: 0.019): -5.76\n",
      "\n",
      "Obs. 3420:\n",
      " var0 (Cumul.perc.: 0.991): 8.15\n",
      "var5 (Cumul.perc.: 0.015): -6.29\n",
      "var2 (Cumul.perc.: 0.951): 5.93\n",
      "\n",
      "Obs. 3430:\n",
      " var17 (Cumul.perc.: 0.008): -6.81\n",
      "var25 (Cumul.perc.: 0.984): 7.18\n",
      "var26 (Cumul.perc.: 0.974): 6.29\n",
      "var9 (Cumul.perc.: 0.043): -3.91\n",
      "\n",
      "Obs. 3440:\n",
      " var5 (Cumul.perc.: 0.001): -10.14\n",
      "var0 (Cumul.perc.: 0.001): -10.13\n",
      "var3 (Cumul.perc.: 0.013): -5.67\n",
      "var8 (Cumul.perc.: 0.021): -5.0\n",
      "var22 (Cumul.perc.: 0.985): 6.81\n",
      "\n",
      "Obs. 3450:\n",
      " var23 (Cumul.perc.: 0.995): 8.63\n",
      "var25 (Cumul.perc.: 0.962): 5.97\n",
      "\n",
      "Obs. 3460:\n",
      " var2 (Cumul.perc.: 0.987): 7.8\n",
      "var21 (Cumul.perc.: 0.016): -6.85\n",
      "var22 (Cumul.perc.: 0.024): -5.33\n",
      "var7 (Cumul.perc.: 0.97): 6.01\n",
      "var11 (Cumul.perc.: 0.034): -5.34\n",
      "\n",
      "Obs. 3470:\n",
      " var2 (Cumul.perc.: 0.012): -6.87\n",
      "var18 (Cumul.perc.: 0.034): -5.2\n",
      "\n",
      "Obs. 3480:\n",
      " var11 (Cumul.perc.: 0.976): 6.71\n",
      "\n",
      "Obs. 3490:\n",
      " var20 (Cumul.perc.: 0.97): 5.57\n",
      "var13 (Cumul.perc.: 0.959): 6.16\n",
      "var18 (Cumul.perc.: 0.963): 6.11\n",
      "\n",
      "Obs. 3500:\n",
      " var0 (Cumul.perc.: 0.995): 9.01\n",
      "var21 (Cumul.perc.: 0.959): 6.26\n",
      "\n",
      "Obs. 3510:\n",
      " var21 (Cumul.perc.: 0.002): -9.41\n",
      "var4 (Cumul.perc.: 0.998): 9.13\n",
      "var22 (Cumul.perc.: 0.023): -5.36\n",
      "var18 (Cumul.perc.: 0.046): -4.78\n",
      "\n",
      "Obs. 3520:\n",
      " var9 (Cumul.perc.: 0.98): 5.8\n",
      "var6 (Cumul.perc.: 0.963): 6.5\n",
      "var25 (Cumul.perc.: 0.039): -5.07\n",
      "\n",
      "Obs. 3530:\n",
      " var5 (Cumul.perc.: 0.989): 7.52\n",
      "var4 (Cumul.perc.: 0.023): -5.37\n",
      "var29 (Cumul.perc.: 0.03): -5.56\n",
      "var20 (Cumul.perc.: 0.952): 5.0\n",
      "\n",
      "Obs. 3540:\n",
      " None\n",
      "\n",
      "Obs. 3550:\n",
      " var0 (Cumul.perc.: 0.978): 7.07\n",
      "var21 (Cumul.perc.: 0.033): -5.73\n",
      "var18 (Cumul.perc.: 0.025): -5.66\n",
      "var3 (Cumul.perc.: 0.033): -4.55\n",
      "\n",
      "Obs. 3560:\n",
      " None\n",
      "\n",
      "Obs. 3570:\n",
      " var9 (Cumul.perc.: 0.002): -7.24\n",
      "var13 (Cumul.perc.: 0.008): -7.51\n",
      "var4 (Cumul.perc.: 0.989): 7.26\n",
      "var7 (Cumul.perc.: 0.009): -6.49\n",
      "var22 (Cumul.perc.: 0.981): 6.5\n",
      "\n",
      "Obs. 3580:\n",
      " var3 (Cumul.perc.: 0.98): 6.03\n",
      "var1 (Cumul.perc.: 0.974): 5.9\n",
      "\n",
      "Obs. 3590:\n",
      " var0 (Cumul.perc.: 0.99): 8.07\n",
      "var22 (Cumul.perc.: 0.025): -5.29\n",
      "var7 (Cumul.perc.: 0.048): -4.36\n",
      "\n",
      "Obs. 3600:\n",
      " var29 (Cumul.perc.: 0.984): 7.35\n",
      "\n",
      "Obs. 3610:\n",
      " var13 (Cumul.perc.: 0.013): -6.94\n",
      "var14 (Cumul.perc.: 0.987): 7.5\n",
      "\n",
      "Obs. 3620:\n",
      " var11 (Cumul.perc.: 0.965): 6.2\n",
      "var18 (Cumul.perc.: 0.037): -5.08\n",
      "\n",
      "Obs. 3630:\n",
      " var14 (Cumul.perc.: 0.982): 7.06\n",
      "var5 (Cumul.perc.: 0.024): -5.6\n",
      "var21 (Cumul.perc.: 0.95): 5.97\n",
      "var4 (Cumul.perc.: 0.035): -4.79\n",
      "var26 (Cumul.perc.: 0.956): 5.6\n",
      "\n",
      "Obs. 3640:\n",
      " var1 (Cumul.perc.: 0.018): -5.48\n",
      "\n",
      "Obs. 3650:\n",
      " var24 (Cumul.perc.: 0.987): 7.46\n",
      "var1 (Cumul.perc.: 0.982): 6.36\n",
      "var9 (Cumul.perc.: 0.048): -3.8\n",
      "\n",
      "Obs. 3660:\n",
      " var12 (Cumul.perc.: 0.013): -5.69\n",
      "var7 (Cumul.perc.: 0.988): 7.13\n",
      "var11 (Cumul.perc.: 0.972): 6.52\n",
      "var4 (Cumul.perc.: 0.037): -4.75\n",
      "\n",
      "Obs. 3670:\n",
      " var25 (Cumul.perc.: 0.997): 9.5\n",
      "var26 (Cumul.perc.: 0.016): -6.03\n",
      "var28 (Cumul.perc.: 0.033): -5.02\n",
      "\n",
      "Obs. 3680:\n",
      " var16 (Cumul.perc.: 0.043): -4.39\n",
      "\n",
      "Obs. 3690:\n",
      " var1 (Cumul.perc.: 0.991): 7.1\n",
      "var12 (Cumul.perc.: 0.987): 6.44\n",
      "var24 (Cumul.perc.: 0.97): 6.45\n",
      "\n",
      "Obs. 3700:\n",
      " var12 (Cumul.perc.: 0.001): -9.55\n",
      "var13 (Cumul.perc.: 0.999): 10.67\n",
      "var9 (Cumul.perc.: 0.009): -5.74\n",
      "var10 (Cumul.perc.: 0.013): -5.71\n",
      "var18 (Cumul.perc.: 0.01): -6.82\n",
      "\n",
      "Obs. 3710:\n",
      " var28 (Cumul.perc.: 0.008): -6.83\n",
      "var20 (Cumul.perc.: 0.011): -5.75\n",
      "var5 (Cumul.perc.: 0.014): -6.37\n",
      "var14 (Cumul.perc.: 0.978): 6.84\n",
      "\n",
      "Obs. 3720:\n",
      " var24 (Cumul.perc.: 0.001): -9.33\n",
      "var10 (Cumul.perc.: 0.989): 6.85\n",
      "var2 (Cumul.perc.: 0.989): 7.98\n",
      "var28 (Cumul.perc.: 0.973): 6.16\n",
      "\n",
      "Obs. 3730:\n",
      " None\n",
      "\n",
      "Obs. 3740:\n",
      " var6 (Cumul.perc.: 0.009): -7.61\n",
      "\n",
      "Obs. 3750:\n",
      " var21 (Cumul.perc.: 0.012): -7.19\n",
      "var28 (Cumul.perc.: 0.986): 7.05\n",
      "\n",
      "Obs. 3760:\n",
      " var10 (Cumul.perc.: 0.01): -6.06\n",
      "var29 (Cumul.perc.: 0.969): 6.48\n",
      "var9 (Cumul.perc.: 0.956): 4.87\n",
      "\n",
      "Obs. 3770:\n",
      " var0 (Cumul.perc.: 0.995): 9.02\n",
      "var9 (Cumul.perc.: 0.039): -4.04\n",
      "\n",
      "Obs. 3780:\n",
      " var21 (Cumul.perc.: 0.994): 8.88\n",
      "var3 (Cumul.perc.: 0.996): 7.9\n",
      "var5 (Cumul.perc.: 0.014): -6.41\n",
      "var18 (Cumul.perc.: 0.977): 6.75\n",
      "var29 (Cumul.perc.: 0.967): 6.42\n",
      "\n",
      "Obs. 3790:\n",
      " None\n",
      "\n",
      "Obs. 3800:\n",
      " var0 (Cumul.perc.: 0.995): 9.13\n",
      "var16 (Cumul.perc.: 0.991): 7.25\n",
      "var23 (Cumul.perc.: 0.993): 8.19\n",
      "var9 (Cumul.perc.: 0.961): 5.02\n",
      "\n",
      "Obs. 3810:\n",
      " None\n",
      "\n",
      "Obs. 3820:\n",
      " var28 (Cumul.perc.: 0.986): 7.07\n",
      "var2 (Cumul.perc.: 0.022): -6.1\n",
      "var25 (Cumul.perc.: 0.952): 5.59\n",
      "\n",
      "Obs. 3830:\n",
      " var23 (Cumul.perc.: 0.003): -8.72\n",
      "var1 (Cumul.perc.: 0.994): 7.63\n",
      "var2 (Cumul.perc.: 0.968): 6.59\n",
      "var16 (Cumul.perc.: 0.023): -5.16\n",
      "var9 (Cumul.perc.: 0.03): -4.36\n",
      "\n",
      "Obs. 3840:\n",
      " var17 (Cumul.perc.: 0.012): -6.28\n",
      "var7 (Cumul.perc.: 0.985): 6.87\n",
      "var21 (Cumul.perc.: 0.028): -5.97\n",
      "\n",
      "Obs. 3850:\n",
      " var24 (Cumul.perc.: 0.015): -6.41\n",
      "var14 (Cumul.perc.: 0.976): 6.66\n",
      "\n",
      "Obs. 3860:\n",
      " var19 (Cumul.perc.: 0.016): -6.14\n",
      "var23 (Cumul.perc.: 0.949): 5.64\n",
      "var11 (Cumul.perc.: 0.041): -5.04\n",
      "\n",
      "Obs. 3870:\n",
      " var23 (Cumul.perc.: 0.044): -4.97\n",
      "var21 (Cumul.perc.: 0.041): -5.39\n",
      "\n",
      "Obs. 3880:\n",
      " var5 (Cumul.perc.: 0.021): -5.84\n",
      "var29 (Cumul.perc.: 0.023): -5.9\n",
      "var14 (Cumul.perc.: 0.958): 5.87\n",
      "var25 (Cumul.perc.: 0.959): 5.85\n",
      "\n",
      "Obs. 3890:\n",
      " var2 (Cumul.perc.: 0.034): -5.52\n",
      "var8 (Cumul.perc.: 0.957): 5.06\n",
      "var3 (Cumul.perc.: 0.952): 4.96\n",
      "\n",
      "Obs. 3900:\n",
      " var14 (Cumul.perc.: 0.997): 9.06\n",
      "var8 (Cumul.perc.: 0.994): 7.16\n",
      "var4 (Cumul.perc.: 0.991): 7.45\n",
      "var15 (Cumul.perc.: 0.979): 6.28\n",
      "var5 (Cumul.perc.: 0.018): -6.01\n",
      "\n",
      "Obs. 3910:\n",
      " var29 (Cumul.perc.: 0.991): 8.03\n",
      "var20 (Cumul.perc.: 0.974): 5.72\n",
      "var1 (Cumul.perc.: 0.05): -4.17\n",
      "\n",
      "Obs. 3920:\n",
      " var21 (Cumul.perc.: 0.029): -5.93\n",
      "\n",
      "Obs. 3930:\n",
      " None\n",
      "\n",
      "Obs. 3940:\n",
      " var15 (Cumul.perc.: 0.038): -4.71\n",
      "\n",
      "Obs. 3950:\n",
      " var13 (Cumul.perc.: 0.032): -5.6\n",
      "var11 (Cumul.perc.: 0.968): 6.31\n",
      "var17 (Cumul.perc.: 0.967): 5.88\n",
      "var1 (Cumul.perc.: 0.048): -4.22\n",
      "\n",
      "Obs. 3960:\n",
      " None\n",
      "\n",
      "Obs. 3970:\n",
      " var26 (Cumul.perc.: 0.004): -7.66\n",
      "var20 (Cumul.perc.: 0.989): 6.79\n",
      "var4 (Cumul.perc.: 0.958): 5.54\n",
      "var24 (Cumul.perc.: 0.96): 6.0\n",
      "\n",
      "Obs. 3980:\n",
      " var4 (Cumul.perc.: 0.02): -5.57\n",
      "var9 (Cumul.perc.: 0.976): 5.58\n",
      "\n",
      "Obs. 3990:\n",
      " var6 (Cumul.perc.: 0.998): 10.26\n",
      "var23 (Cumul.perc.: 0.003): -8.42\n",
      "var5 (Cumul.perc.: 0.99): 7.69\n",
      "var21 (Cumul.perc.: 0.035): -5.65\n",
      "var13 (Cumul.perc.: 0.047): -5.02\n",
      "\n",
      "Obs. 4000:\n",
      " var8 (Cumul.perc.: 0.039): -4.28\n",
      "var25 (Cumul.perc.: 0.957): 5.76\n",
      "\n",
      "Obs. 4010:\n",
      " var26 (Cumul.perc.: 0.025): -5.41\n",
      "var27 (Cumul.perc.: 0.024): -5.61\n",
      "var5 (Cumul.perc.: 0.025): -5.56\n",
      "\n",
      "Obs. 4020:\n",
      " var0 (Cumul.perc.: 0.0): -12.31\n",
      "var1 (Cumul.perc.: 0.003): -8.09\n",
      "var25 (Cumul.perc.: 0.005): -7.58\n",
      "var24 (Cumul.perc.: 0.023): -5.83\n",
      "var17 (Cumul.perc.: 0.021): -5.56\n",
      "\n",
      "Obs. 4030:\n",
      " var4 (Cumul.perc.: 0.979): 6.48\n",
      "\n",
      "Obs. 4040:\n",
      " var0 (Cumul.perc.: 0.998): 10.39\n",
      "var15 (Cumul.perc.: 0.031): -5.0\n",
      "var18 (Cumul.perc.: 0.037): -5.08\n",
      "\n",
      "Obs. 4050:\n",
      " var0 (Cumul.perc.: 0.015): -6.67\n",
      "var16 (Cumul.perc.: 0.03): -4.85\n",
      "var27 (Cumul.perc.: 0.951): 5.62\n",
      "var9 (Cumul.perc.: 0.046): -3.84\n",
      "var29 (Cumul.perc.: 0.048): -4.82\n",
      "\n",
      "Obs. 4060:\n",
      " None\n",
      "\n",
      "Obs. 4070:\n",
      " None\n",
      "\n",
      "Obs. 4080:\n",
      " var11 (Cumul.perc.: 0.995): 8.54\n",
      "\n",
      "Obs. 4090:\n",
      " var6 (Cumul.perc.: 0.025): -6.14\n",
      "var10 (Cumul.perc.: 0.983): 6.39\n",
      "var4 (Cumul.perc.: 0.018): -5.67\n",
      "var7 (Cumul.perc.: 0.045): -4.47\n",
      "\n",
      "Obs. 4100:\n",
      " var11 (Cumul.perc.: 0.994): 8.33\n",
      "var0 (Cumul.perc.: 0.991): 8.17\n",
      "var1 (Cumul.perc.: 0.033): -4.74\n",
      "\n",
      "Obs. 4110:\n",
      " var7 (Cumul.perc.: 0.967): 5.88\n",
      "var1 (Cumul.perc.: 0.052): -4.13\n",
      "\n",
      "Obs. 4120:\n",
      " var15 (Cumul.perc.: 0.01): -6.41\n",
      "var12 (Cumul.perc.: 0.015): -5.47\n",
      "var27 (Cumul.perc.: 0.965): 6.09\n",
      "\n",
      "Obs. 4130:\n",
      " var2 (Cumul.perc.: 0.007): -7.55\n",
      "\n",
      "Obs. 4140:\n",
      " var25 (Cumul.perc.: 0.016): -6.22\n",
      "var20 (Cumul.perc.: 0.967): 5.46\n",
      "var11 (Cumul.perc.: 0.034): -5.36\n",
      "\n",
      "Obs. 4150:\n",
      " var9 (Cumul.perc.: 0.98): 5.77\n",
      "var19 (Cumul.perc.: 0.965): 6.09\n",
      "\n",
      "Obs. 4160:\n",
      " var28 (Cumul.perc.: 0.028): -5.27\n",
      "var3 (Cumul.perc.: 0.981): 6.08\n",
      "var26 (Cumul.perc.: 0.05): -4.48\n",
      "\n",
      "Obs. 4170:\n",
      " var24 (Cumul.perc.: 0.005): -7.65\n",
      "var8 (Cumul.perc.: 0.989): 6.54\n",
      "var20 (Cumul.perc.: 0.988): 6.7\n",
      "var25 (Cumul.perc.: 0.022): -5.85\n",
      "var15 (Cumul.perc.: 0.035): -4.79\n",
      "\n",
      "Obs. 4180:\n",
      " var14 (Cumul.perc.: 0.969): 6.34\n",
      "var5 (Cumul.perc.: 0.959): 5.83\n",
      "\n",
      "Obs. 4190:\n",
      " var11 (Cumul.perc.: 0.002): -8.95\n",
      "var0 (Cumul.perc.: 0.037): -5.42\n",
      "var5 (Cumul.perc.: 0.038): -5.02\n",
      "var26 (Cumul.perc.: 0.959): 5.7\n",
      "\n",
      "Obs. 4200:\n",
      " var10 (Cumul.perc.: 0.997): 8.36\n",
      "var8 (Cumul.perc.: 0.997): 7.92\n",
      "var21 (Cumul.perc.: 0.962): 6.39\n",
      "var18 (Cumul.perc.: 0.047): -4.74\n",
      "\n",
      "Obs. 4210:\n",
      " var26 (Cumul.perc.: 0.002): -8.34\n",
      "var1 (Cumul.perc.: 0.007): -6.66\n",
      "var19 (Cumul.perc.: 0.979): 6.81\n",
      "var29 (Cumul.perc.: 0.029): -5.63\n",
      "var2 (Cumul.perc.: 0.024): -5.95\n",
      "\n",
      "Obs. 4220:\n",
      " var15 (Cumul.perc.: 0.001): -9.91\n",
      "var6 (Cumul.perc.: 0.022): -6.31\n",
      "var19 (Cumul.perc.: 0.981): 6.95\n",
      "var5 (Cumul.perc.: 0.968): 6.17\n",
      "\n",
      "Obs. 4230:\n",
      " var8 (Cumul.perc.: 0.977): 5.76\n",
      "var26 (Cumul.perc.: 0.017): -5.93\n",
      "var6 (Cumul.perc.: 0.036): -5.56\n",
      "var1 (Cumul.perc.: 0.023): -5.22\n",
      "\n",
      "Obs. 4240:\n",
      " var5 (Cumul.perc.: 0.006): -7.4\n",
      "var0 (Cumul.perc.: 0.027): -5.8\n",
      "var9 (Cumul.perc.: 0.953): 4.8\n",
      "var16 (Cumul.perc.: 0.043): -4.42\n",
      "\n",
      "Obs. 4250:\n",
      " var28 (Cumul.perc.: 0.999): 10.85\n",
      "var2 (Cumul.perc.: 0.001): -9.84\n",
      "var20 (Cumul.perc.: 0.006): -6.74\n",
      "var11 (Cumul.perc.: 0.004): -8.2\n",
      "var24 (Cumul.perc.: 0.993): 8.17\n",
      "\n",
      "Obs. 4260:\n",
      " var6 (Cumul.perc.: 0.964): 6.56\n",
      "var25 (Cumul.perc.: 0.036): -5.17\n",
      "var10 (Cumul.perc.: 0.041): -4.36\n",
      "var18 (Cumul.perc.: 0.952): 5.7\n",
      "\n",
      "Obs. 4270:\n",
      " var0 (Cumul.perc.: 0.986): 7.68\n",
      "var10 (Cumul.perc.: 0.982): 6.29\n",
      "\n",
      "Obs. 4280:\n",
      " var4 (Cumul.perc.: 0.987): 7.04\n",
      "var11 (Cumul.perc.: 0.013): -6.72\n",
      "var7 (Cumul.perc.: 0.024): -5.3\n",
      "var3 (Cumul.perc.: 0.976): 5.86\n",
      "var1 (Cumul.perc.: 0.963): 5.46\n",
      "\n",
      "Obs. 4290:\n",
      " var6 (Cumul.perc.: 0.009): -7.48\n",
      "var23 (Cumul.perc.: 0.984): 7.25\n",
      "var10 (Cumul.perc.: 0.038): -4.46\n",
      "var4 (Cumul.perc.: 0.035): -4.78\n",
      "var11 (Cumul.perc.: 0.045): -4.89\n",
      "\n",
      "Obs. 4300:\n",
      " var15 (Cumul.perc.: 0.0): -11.2\n",
      "var12 (Cumul.perc.: 0.014): -5.57\n",
      "var4 (Cumul.perc.: 0.03): -5.06\n",
      "var26 (Cumul.perc.: 0.032): -5.11\n",
      "var9 (Cumul.perc.: 0.961): 5.02\n",
      "\n",
      "Obs. 4310:\n",
      " var2 (Cumul.perc.: 0.018): -6.37\n",
      "var3 (Cumul.perc.: 0.98): 6.01\n",
      "\n",
      "Obs. 4320:\n",
      " var4 (Cumul.perc.: 0.027): -5.21\n",
      "\n",
      "Obs. 4330:\n",
      " var20 (Cumul.perc.: 0.974): 5.72\n",
      "var17 (Cumul.perc.: 0.041): -4.66\n",
      "\n",
      "Obs. 4340:\n",
      " var25 (Cumul.perc.: 0.026): -5.61\n",
      "var19 (Cumul.perc.: 0.026): -5.52\n",
      "var21 (Cumul.perc.: 0.953): 6.07\n",
      "\n",
      "Obs. 4350:\n",
      " var25 (Cumul.perc.: 0.998): 9.86\n",
      "var17 (Cumul.perc.: 0.983): 6.7\n",
      "var18 (Cumul.perc.: 0.026): -5.6\n",
      "var16 (Cumul.perc.: 0.038): -4.55\n",
      "\n",
      "Obs. 4360:\n",
      " var25 (Cumul.perc.: 0.006): -7.32\n",
      "var20 (Cumul.perc.: 0.984): 6.33\n",
      "var0 (Cumul.perc.: 0.984): 7.49\n",
      "var14 (Cumul.perc.: 0.02): -6.01\n",
      "var29 (Cumul.perc.: 0.022): -5.96\n",
      "\n",
      "Obs. 4370:\n",
      " var29 (Cumul.perc.: 0.01): -7.01\n",
      "var28 (Cumul.perc.: 0.034): -4.99\n",
      "var7 (Cumul.perc.: 0.98): 6.58\n",
      "\n",
      "Obs. 4380:\n",
      " var22 (Cumul.perc.: 0.032): -4.92\n",
      "\n",
      "Obs. 4390:\n",
      " var8 (Cumul.perc.: 0.988): 6.48\n",
      "\n",
      "Obs. 4400:\n",
      " var14 (Cumul.perc.: 0.997): 9.07\n",
      "var10 (Cumul.perc.: 0.031): -4.75\n",
      "var29 (Cumul.perc.: 0.034): -5.36\n",
      "var0 (Cumul.perc.: 0.95): 5.82\n",
      "\n",
      "Obs. 4410:\n",
      " var4 (Cumul.perc.: 0.994): 7.97\n",
      "var18 (Cumul.perc.: 0.026): -5.59\n",
      "var12 (Cumul.perc.: 0.042): -4.2\n",
      "\n",
      "Obs. 4420:\n",
      " var12 (Cumul.perc.: 0.006): -6.69\n",
      "var14 (Cumul.perc.: 0.006): -7.55\n",
      "var6 (Cumul.perc.: 0.016): -6.73\n",
      "\n",
      "Obs. 4430:\n",
      " var5 (Cumul.perc.: 0.997): 9.05\n",
      "var26 (Cumul.perc.: 0.005): -7.35\n",
      "var28 (Cumul.perc.: 0.985): 6.97\n",
      "var17 (Cumul.perc.: 0.978): 6.41\n",
      "var9 (Cumul.perc.: 0.025): -4.57\n",
      "\n",
      "Obs. 4440:\n",
      " var8 (Cumul.perc.: 0.005): -6.72\n",
      "var20 (Cumul.perc.: 0.007): -6.48\n",
      "var27 (Cumul.perc.: 0.983): 7.07\n",
      "var7 (Cumul.perc.: 0.955): 5.45\n",
      "\n",
      "Obs. 4450:\n",
      " var13 (Cumul.perc.: 1.0): 12.09\n",
      "var19 (Cumul.perc.: 0.976): 6.63\n",
      "\n",
      "Obs. 4460:\n",
      " var10 (Cumul.perc.: 0.012): -5.85\n",
      "var21 (Cumul.perc.: 0.03): -5.86\n",
      "\n",
      "Obs. 4470:\n",
      " var25 (Cumul.perc.: 0.983): 7.02\n",
      "var3 (Cumul.perc.: 0.955): 5.06\n",
      "\n",
      "Obs. 4480:\n",
      " var23 (Cumul.perc.: 0.028): -5.72\n",
      "var14 (Cumul.perc.: 0.968): 6.27\n",
      "\n",
      "Obs. 4490:\n",
      " var2 (Cumul.perc.: 0.026): -5.86\n",
      "\n",
      "Obs. 4500:\n",
      " var10 (Cumul.perc.: 0.979): 6.11\n",
      "var29 (Cumul.perc.: 0.026): -5.74\n",
      "var19 (Cumul.perc.: 0.967): 6.19\n",
      "\n",
      "Obs. 4510:\n",
      " var10 (Cumul.perc.: 0.004): -7.24\n",
      "var26 (Cumul.perc.: 0.993): 7.83\n",
      "var13 (Cumul.perc.: 0.988): 7.89\n",
      "var18 (Cumul.perc.: 0.015): -6.33\n",
      "var2 (Cumul.perc.: 0.978): 7.12\n",
      "\n",
      "Obs. 4520:\n",
      " var18 (Cumul.perc.: 0.952): 5.72\n",
      "\n",
      "Obs. 4530:\n",
      " var2 (Cumul.perc.: 0.995): 8.91\n",
      "var26 (Cumul.perc.: 0.98): 6.6\n",
      "\n",
      "Obs. 4540:\n",
      " var17 (Cumul.perc.: 0.999): 10.04\n",
      "var3 (Cumul.perc.: 0.998): 8.47\n",
      "var9 (Cumul.perc.: 0.004): -6.46\n",
      "var20 (Cumul.perc.: 0.978): 5.93\n",
      "var16 (Cumul.perc.: 0.035): -4.65\n",
      "\n",
      "Obs. 4550:\n",
      " var13 (Cumul.perc.: 0.984): 7.5\n",
      "\n",
      "Obs. 4560:\n",
      " var15 (Cumul.perc.: 0.959): 5.38\n",
      "\n",
      "Obs. 4570:\n",
      " var14 (Cumul.perc.: 0.989): 7.62\n",
      "var6 (Cumul.perc.: 0.953): 6.15\n",
      "\n",
      "Obs. 4580:\n",
      " var26 (Cumul.perc.: 0.995): 8.17\n",
      "var16 (Cumul.perc.: 0.98): 6.25\n",
      "var19 (Cumul.perc.: 0.96): 5.89\n",
      "\n",
      "Obs. 4590:\n",
      " var29 (Cumul.perc.: 0.004): -8.32\n",
      "var7 (Cumul.perc.: 0.02): -5.55\n",
      "var21 (Cumul.perc.: 0.956): 6.16\n",
      "var6 (Cumul.perc.: 0.952): 6.08\n",
      "\n",
      "Obs. 4600:\n",
      " var14 (Cumul.perc.: 0.994): 8.46\n",
      "var5 (Cumul.perc.: 0.02): -5.89\n",
      "\n",
      "Obs. 4610:\n",
      " var10 (Cumul.perc.: 0.996): 7.95\n",
      "var27 (Cumul.perc.: 0.976): 6.64\n",
      "var12 (Cumul.perc.: 0.947): 4.79\n",
      "var7 (Cumul.perc.: 0.954): 5.42\n",
      "\n",
      "Obs. 4620:\n",
      " var25 (Cumul.perc.: 0.047): -4.77\n",
      "var8 (Cumul.perc.: 0.952): 4.91\n",
      "\n",
      "Obs. 4630:\n",
      " var20 (Cumul.perc.: 0.019): -5.19\n",
      "\n",
      "Obs. 4640:\n",
      " var26 (Cumul.perc.: 0.013): -6.31\n",
      "var19 (Cumul.perc.: 0.961): 5.94\n",
      "\n",
      "Obs. 4650:\n",
      " None\n",
      "\n",
      "Obs. 4660:\n",
      " var18 (Cumul.perc.: 0.021): -5.84\n",
      "var14 (Cumul.perc.: 0.045): -4.85\n",
      "var28 (Cumul.perc.: 0.956): 5.52\n",
      "\n",
      "Obs. 4670:\n",
      " var6 (Cumul.perc.: 0.973): 6.98\n",
      "var7 (Cumul.perc.: 0.975): 6.28\n",
      "var10 (Cumul.perc.: 0.032): -4.69\n",
      "var12 (Cumul.perc.: 0.033): -4.47\n",
      "\n",
      "Obs. 4680:\n",
      " var4 (Cumul.perc.: 0.998): 9.19\n",
      "var19 (Cumul.perc.: 0.005): -7.81\n",
      "var14 (Cumul.perc.: 0.963): 6.07\n",
      "\n",
      "Obs. 4690:\n",
      " var11 (Cumul.perc.: 0.998): 10.02\n",
      "var12 (Cumul.perc.: 0.959): 5.14\n",
      "\n",
      "Obs. 4700:\n",
      " var22 (Cumul.perc.: 0.997): 8.5\n",
      "var27 (Cumul.perc.: 0.018): -5.96\n",
      "var24 (Cumul.perc.: 0.018): -6.16\n",
      "var15 (Cumul.perc.: 0.968): 5.71\n",
      "var3 (Cumul.perc.: 0.96): 5.2\n",
      "\n",
      "Obs. 4710:\n",
      " var21 (Cumul.perc.: 0.995): 8.94\n",
      "var9 (Cumul.perc.: 0.022): -4.73\n",
      "\n",
      "Obs. 4720:\n",
      " var7 (Cumul.perc.: 0.973): 6.13\n",
      "\n",
      "Obs. 4730:\n",
      " var20 (Cumul.perc.: 0.964): 5.37\n",
      "\n",
      "Obs. 4740:\n",
      " var28 (Cumul.perc.: 0.005): -7.43\n",
      "var22 (Cumul.perc.: 0.952): 5.4\n",
      "var6 (Cumul.perc.: 0.046): -5.15\n",
      "\n",
      "Obs. 4750:\n",
      " var26 (Cumul.perc.: 0.991): 7.57\n",
      "var14 (Cumul.perc.: 0.017): -6.25\n",
      "var10 (Cumul.perc.: 0.985): 6.51\n",
      "var23 (Cumul.perc.: 0.047): -4.89\n",
      "var17 (Cumul.perc.: 0.972): 6.12\n",
      "\n",
      "Obs. 4760:\n",
      " var20 (Cumul.perc.: 0.973): 5.7\n",
      "var17 (Cumul.perc.: 0.035): -4.92\n",
      "\n",
      "Obs. 4770:\n",
      " None\n",
      "\n",
      "Obs. 4780:\n",
      " var15 (Cumul.perc.: 0.011): -6.28\n",
      "var17 (Cumul.perc.: 0.023): -5.47\n",
      "\n",
      "Obs. 4790:\n",
      " var11 (Cumul.perc.: 0.031): -5.51\n",
      "var22 (Cumul.perc.: 0.956): 5.51\n",
      "\n",
      "Obs. 4800:\n",
      " var14 (Cumul.perc.: 0.997): 8.9\n",
      "var5 (Cumul.perc.: 0.008): -7.11\n",
      "var7 (Cumul.perc.: 0.964): 5.75\n",
      "\n",
      "Obs. 4810:\n",
      " var14 (Cumul.perc.: 0.999): 10.26\n",
      "var7 (Cumul.perc.: 0.989): 7.22\n",
      "var26 (Cumul.perc.: 0.977): 6.46\n",
      "var18 (Cumul.perc.: 0.049): -4.68\n",
      "\n",
      "Obs. 4820:\n",
      " var2 (Cumul.perc.: 0.003): -8.97\n",
      "var16 (Cumul.perc.: 0.99): 7.16\n",
      "\n",
      "Obs. 4830:\n",
      " var27 (Cumul.perc.: 0.007): -7.23\n",
      "var5 (Cumul.perc.: 0.039): -4.97\n",
      "\n",
      "Obs. 4840:\n",
      " var20 (Cumul.perc.: 0.993): 7.24\n",
      "var4 (Cumul.perc.: 0.992): 7.6\n",
      "var26 (Cumul.perc.: 0.98): 6.61\n",
      "var5 (Cumul.perc.: 0.034): -5.14\n",
      "\n",
      "Obs. 4850:\n",
      " var25 (Cumul.perc.: 0.012): -6.59\n",
      "var0 (Cumul.perc.: 0.986): 7.69\n",
      "var13 (Cumul.perc.: 0.03): -5.69\n",
      "var19 (Cumul.perc.: 0.028): -5.42\n",
      "\n",
      "Obs. 4860:\n",
      " var1 (Cumul.perc.: 0.008): -6.61\n",
      "var5 (Cumul.perc.: 0.987): 7.34\n",
      "var24 (Cumul.perc.: 0.027): -5.58\n",
      "var14 (Cumul.perc.: 0.02): -6.0\n",
      "var27 (Cumul.perc.: 0.956): 5.79\n",
      "\n",
      "Obs. 4870:\n",
      " var26 (Cumul.perc.: 0.981): 6.65\n",
      "var17 (Cumul.perc.: 0.985): 6.88\n",
      "\n",
      "Obs. 4880:\n",
      " var0 (Cumul.perc.: 0.997): 9.57\n",
      "var19 (Cumul.perc.: 0.972): 6.41\n",
      "var24 (Cumul.perc.: 0.953): 5.74\n",
      "var29 (Cumul.perc.: 0.038): -5.21\n",
      "\n",
      "Obs. 4890:\n",
      " var29 (Cumul.perc.: 0.998): 9.72\n",
      "var0 (Cumul.perc.: 0.997): 9.74\n",
      "var28 (Cumul.perc.: 0.997): 9.09\n",
      "var22 (Cumul.perc.: 0.98): 6.42\n",
      "var25 (Cumul.perc.: 0.963): 6.01\n",
      "\n",
      "Obs. 4900:\n",
      " var16 (Cumul.perc.: 0.997): 8.44\n",
      "var24 (Cumul.perc.: 0.989): 7.62\n",
      "var2 (Cumul.perc.: 0.046): -5.03\n",
      "var8 (Cumul.perc.: 0.954): 4.97\n",
      "\n",
      "Obs. 4910:\n",
      " var10 (Cumul.perc.: 0.013): -5.73\n",
      "var7 (Cumul.perc.: 0.98): 6.51\n",
      "var5 (Cumul.perc.: 0.043): -4.83\n",
      "\n",
      "Obs. 4920:\n",
      " var1 (Cumul.perc.: 0.989): 6.89\n",
      "var22 (Cumul.perc.: 0.974): 6.14\n",
      "var24 (Cumul.perc.: 0.976): 6.72\n",
      "var29 (Cumul.perc.: 0.036): -5.3\n",
      "\n",
      "Obs. 4930:\n",
      " var23 (Cumul.perc.: 0.002): -8.73\n",
      "var5 (Cumul.perc.: 0.008): -7.14\n",
      "var2 (Cumul.perc.: 0.983): 7.44\n",
      "var26 (Cumul.perc.: 0.962): 5.81\n",
      "\n",
      "Obs. 4940:\n",
      " var1 (Cumul.perc.: 0.993): 7.36\n",
      "var26 (Cumul.perc.: 0.013): -6.32\n",
      "var28 (Cumul.perc.: 0.018): -5.92\n",
      "var0 (Cumul.perc.: 0.022): -6.13\n",
      "var2 (Cumul.perc.: 0.035): -5.45\n",
      "\n",
      "Obs. 4950:\n",
      " None\n",
      "\n",
      "Obs. 4960:\n",
      " var29 (Cumul.perc.: 0.013): -6.69\n",
      "var17 (Cumul.perc.: 0.023): -5.46\n",
      "var3 (Cumul.perc.: 0.981): 6.1\n",
      "var8 (Cumul.perc.: 0.96): 5.13\n",
      "\n",
      "Obs. 4970:\n",
      " None\n",
      "\n",
      "Obs. 4980:\n",
      " None\n",
      "\n",
      "Obs. 4990:\n",
      " var8 (Cumul.perc.: 0.983): 6.12\n",
      "var4 (Cumul.perc.: 0.049): -4.34\n",
      "var15 (Cumul.perc.: 0.963): 5.5\n",
      "\n",
      "Obs. 5000:\n",
      " var23 (Cumul.perc.: 0.002): -9.01\n",
      "var26 (Cumul.perc.: 0.014): -6.19\n",
      "var5 (Cumul.perc.: 0.978): 6.7\n",
      "\n",
      "Obs. 5010:\n",
      " var2 (Cumul.perc.: 0.971): 6.72\n",
      "var24 (Cumul.perc.: 0.044): -4.87\n",
      "\n",
      "Obs. 5020:\n",
      " var15 (Cumul.perc.: 0.023): -5.36\n",
      "\n",
      "Obs. 5030:\n",
      " var23 (Cumul.perc.: 0.981): 7.04\n",
      "var24 (Cumul.perc.: 0.03): -5.43\n",
      "var18 (Cumul.perc.: 0.037): -5.1\n",
      "\n",
      "Obs. 5040:\n",
      " var24 (Cumul.perc.: 0.012): -6.7\n",
      "var12 (Cumul.perc.: 0.025): -4.87\n",
      "\n",
      "Obs. 5050:\n",
      " var13 (Cumul.perc.: 0.996): 9.04\n",
      "var2 (Cumul.perc.: 0.01): -7.15\n",
      "var18 (Cumul.perc.: 0.031): -5.3\n",
      "var28 (Cumul.perc.: 0.035): -4.94\n",
      "var16 (Cumul.perc.: 0.027): -4.97\n",
      "\n",
      "Obs. 5060:\n",
      " var6 (Cumul.perc.: 0.023): -6.29\n",
      "var15 (Cumul.perc.: 0.973): 5.99\n",
      "\n",
      "Obs. 5070:\n",
      " var17 (Cumul.perc.: 0.007): -6.99\n",
      "var0 (Cumul.perc.: 0.027): -5.82\n",
      "var8 (Cumul.perc.: 0.031): -4.54\n",
      "\n",
      "Obs. 5080:\n",
      " var13 (Cumul.perc.: 0.978): 7.02\n",
      "var18 (Cumul.perc.: 0.024): -5.67\n",
      "var19 (Cumul.perc.: 0.955): 5.72\n",
      "\n",
      "Obs. 5090:\n",
      " var11 (Cumul.perc.: 0.02): -6.06\n",
      "var6 (Cumul.perc.: 0.028): -5.92\n",
      "var2 (Cumul.perc.: 0.954): 6.05\n",
      "\n",
      "Obs. 5100:\n",
      " var0 (Cumul.perc.: 0.009): -7.32\n",
      "var21 (Cumul.perc.: 0.011): -7.3\n",
      "var6 (Cumul.perc.: 0.989): 8.14\n",
      "var17 (Cumul.perc.: 0.011): -6.39\n",
      "var25 (Cumul.perc.: 0.046): -4.79\n",
      "\n",
      "Obs. 5110:\n",
      " var12 (Cumul.perc.: 0.013): -5.63\n",
      "\n",
      "Obs. 5120:\n",
      " var22 (Cumul.perc.: 0.973): 6.11\n",
      "var23 (Cumul.perc.: 0.947): 5.59\n",
      "\n",
      "Obs. 5130:\n",
      " var23 (Cumul.perc.: 0.04): -5.14\n",
      "var20 (Cumul.perc.: 0.963): 5.31\n",
      "var18 (Cumul.perc.: 0.956): 5.85\n",
      "var6 (Cumul.perc.: 0.045): -5.2\n",
      "var26 (Cumul.perc.: 0.959): 5.69\n",
      "\n",
      "Obs. 5140:\n",
      " var5 (Cumul.perc.: 0.995): 8.37\n",
      "\n",
      "Obs. 5150:\n",
      " var25 (Cumul.perc.: 0.018): -6.09\n",
      "\n",
      "Obs. 5160:\n",
      " var0 (Cumul.perc.: 0.97): 6.63\n",
      "var23 (Cumul.perc.: 0.964): 6.13\n",
      "var21 (Cumul.perc.: 0.95): 5.96\n",
      "var7 (Cumul.perc.: 0.032): -4.91\n",
      "var16 (Cumul.perc.: 0.95): 5.14\n",
      "\n",
      "Obs. 5170:\n",
      " var9 (Cumul.perc.: 0.009): -5.74\n",
      "var22 (Cumul.perc.: 0.987): 6.96\n",
      "var2 (Cumul.perc.: 0.031): -5.62\n",
      "var4 (Cumul.perc.: 0.953): 5.39\n",
      "\n",
      "Obs. 5180:\n",
      " None\n",
      "\n",
      "Obs. 5190:\n",
      " var3 (Cumul.perc.: 0.996): 7.94\n",
      "var28 (Cumul.perc.: 0.029): -5.21\n",
      "var0 (Cumul.perc.: 0.973): 6.77\n",
      "var16 (Cumul.perc.: 0.019): -5.38\n",
      "\n",
      "Obs. 5200:\n",
      " var25 (Cumul.perc.: 0.979): 6.77\n",
      "var23 (Cumul.perc.: 0.954): 5.79\n",
      "\n",
      "Obs. 5210:\n",
      " var8 (Cumul.perc.: 0.009): -6.07\n",
      "\n",
      "Obs. 5220:\n",
      " var11 (Cumul.perc.: 0.996): 9.13\n",
      "var15 (Cumul.perc.: 0.995): 7.9\n",
      "\n",
      "Obs. 5230:\n",
      " var21 (Cumul.perc.: 0.0): -11.14\n",
      "var20 (Cumul.perc.: 0.969): 5.52\n",
      "\n",
      "Obs. 5240:\n",
      " var18 (Cumul.perc.: 0.958): 5.91\n",
      "var11 (Cumul.perc.: 0.043): -4.95\n",
      "\n",
      "Obs. 5250:\n",
      " var20 (Cumul.perc.: 0.0): -14.13\n",
      "var5 (Cumul.perc.: 0.001): -11.71\n",
      "var13 (Cumul.perc.: 0.0): -12.69\n",
      "var2 (Cumul.perc.: 0.001): -10.11\n",
      "var15 (Cumul.perc.: 0.003): -8.1\n",
      "\n",
      "Obs. 5260:\n",
      " var25 (Cumul.perc.: 0.974): 6.55\n",
      "var15 (Cumul.perc.: 0.037): -4.76\n",
      "\n",
      "Obs. 5270:\n",
      " var23 (Cumul.perc.: 0.99): 7.81\n",
      "var12 (Cumul.perc.: 0.027): -4.74\n",
      "\n",
      "Obs. 5280:\n",
      " var14 (Cumul.perc.: 0.022): -5.92\n",
      "\n",
      "Obs. 5290:\n",
      " var9 (Cumul.perc.: 0.006): -6.0\n",
      "var23 (Cumul.perc.: 0.98): 7.01\n",
      "\n",
      "Obs. 5300:\n",
      " var9 (Cumul.perc.: 0.004): -6.53\n",
      "\n",
      "Obs. 5310:\n",
      " None\n",
      "\n",
      "Obs. 5320:\n",
      " var6 (Cumul.perc.: 0.002): -9.76\n",
      "var4 (Cumul.perc.: 0.031): -4.98\n",
      "var13 (Cumul.perc.: 0.962): 6.27\n",
      "\n",
      "Obs. 5330:\n",
      " var15 (Cumul.perc.: 0.002): -8.56\n",
      "var3 (Cumul.perc.: 0.007): -6.31\n",
      "var28 (Cumul.perc.: 0.018): -5.92\n",
      "var21 (Cumul.perc.: 0.023): -6.33\n",
      "var0 (Cumul.perc.: 0.024): -5.99\n",
      "\n",
      "Obs. 5340:\n",
      " var21 (Cumul.perc.: 1.0): 14.42\n",
      "var1 (Cumul.perc.: 0.999): 9.84\n",
      "var16 (Cumul.perc.: 0.011): -6.05\n",
      "var3 (Cumul.perc.: 0.977): 5.88\n",
      "var13 (Cumul.perc.: 0.038): -5.37\n",
      "\n",
      "Obs. 5350:\n",
      " var4 (Cumul.perc.: 0.969): 6.03\n",
      "var19 (Cumul.perc.: 0.961): 5.92\n",
      "\n",
      "Obs. 5360:\n",
      " var8 (Cumul.perc.: 0.987): 6.4\n",
      "var3 (Cumul.perc.: 0.974): 5.74\n",
      "var1 (Cumul.perc.: 0.02): -5.42\n",
      "\n",
      "Obs. 5370:\n",
      " var14 (Cumul.perc.: 0.029): -5.48\n",
      "var15 (Cumul.perc.: 0.031): -5.01\n",
      "var9 (Cumul.perc.: 0.026): -4.51\n",
      "\n",
      "Obs. 5380:\n",
      " var4 (Cumul.perc.: 0.015): -5.88\n",
      "var14 (Cumul.perc.: 0.014): -6.51\n",
      "var12 (Cumul.perc.: 0.977): 5.85\n",
      "\n",
      "Obs. 5390:\n",
      " var27 (Cumul.perc.: 0.008): -6.92\n",
      "var21 (Cumul.perc.: 0.973): 6.89\n",
      "var3 (Cumul.perc.: 0.023): -4.93\n",
      "var9 (Cumul.perc.: 0.026): -4.53\n",
      "var11 (Cumul.perc.: 0.037): -5.22\n",
      "\n",
      "Obs. 5400:\n",
      " var1 (Cumul.perc.: 0.015): -5.73\n",
      "var21 (Cumul.perc.: 0.017): -6.75\n",
      "var9 (Cumul.perc.: 0.986): 6.12\n",
      "\n",
      "Obs. 5410:\n",
      " var7 (Cumul.perc.: 0.009): -6.47\n",
      "var10 (Cumul.perc.: 0.98): 6.2\n",
      "var14 (Cumul.perc.: 0.026): -5.69\n",
      "var22 (Cumul.perc.: 0.033): -4.86\n",
      "var8 (Cumul.perc.: 0.969): 5.45\n",
      "\n",
      "Obs. 5420:\n",
      " var17 (Cumul.perc.: 0.993): 7.75\n",
      "var19 (Cumul.perc.: 0.011): -6.7\n",
      "var7 (Cumul.perc.: 0.011): -6.23\n",
      "var25 (Cumul.perc.: 0.982): 6.98\n",
      "var18 (Cumul.perc.: 0.975): 6.6\n",
      "\n",
      "Obs. 5430:\n",
      " var3 (Cumul.perc.: 0.028): -4.74\n",
      "var29 (Cumul.perc.: 0.048): -4.82\n",
      "var18 (Cumul.perc.: 0.036): -5.13\n",
      "\n",
      "Obs. 5440:\n",
      " var6 (Cumul.perc.: 0.999): 11.32\n",
      "var24 (Cumul.perc.: 0.997): 9.13\n",
      "var10 (Cumul.perc.: 0.012): -5.84\n",
      "var2 (Cumul.perc.: 0.019): -6.32\n",
      "var17 (Cumul.perc.: 0.045): -4.52\n",
      "\n",
      "Obs. 5450:\n",
      " var10 (Cumul.perc.: 0.995): 7.62\n",
      "var21 (Cumul.perc.: 0.034): -5.72\n",
      "var1 (Cumul.perc.: 0.026): -5.0\n",
      "var25 (Cumul.perc.: 0.961): 5.92\n",
      "\n",
      "Obs. 5460:\n",
      " var21 (Cumul.perc.: 0.994): 8.84\n",
      "var19 (Cumul.perc.: 0.974): 6.52\n",
      "var14 (Cumul.perc.: 0.969): 6.34\n",
      "var10 (Cumul.perc.: 0.973): 5.82\n",
      "var9 (Cumul.perc.: 0.039): -4.05\n",
      "\n",
      "Obs. 5470:\n",
      " var24 (Cumul.perc.: 0.98): 6.98\n",
      "var12 (Cumul.perc.: 0.981): 6.04\n",
      "var23 (Cumul.perc.: 0.05): -4.78\n",
      "\n",
      "Obs. 5480:\n",
      " var14 (Cumul.perc.: 0.99): 7.8\n",
      "var6 (Cumul.perc.: 0.018): -6.65\n",
      "var11 (Cumul.perc.: 0.028): -5.6\n",
      "var5 (Cumul.perc.: 0.961): 5.89\n",
      "\n",
      "Obs. 5490:\n",
      " var22 (Cumul.perc.: 0.974): 6.16\n",
      "var24 (Cumul.perc.: 0.031): -5.36\n",
      "var3 (Cumul.perc.: 0.041): -4.23\n",
      "\n",
      "Obs. 5500:\n",
      " var1 (Cumul.perc.: 0.999): 9.37\n",
      "var20 (Cumul.perc.: 0.01): -5.89\n",
      "var26 (Cumul.perc.: 0.981): 6.66\n",
      "var5 (Cumul.perc.: 0.027): -5.45\n",
      "var10 (Cumul.perc.: 0.046): -4.19\n",
      "\n",
      "Obs. 5510:\n",
      " var29 (Cumul.perc.: 0.001): -9.2\n",
      "var8 (Cumul.perc.: 0.956): 5.02\n",
      "var7 (Cumul.perc.: 0.042): -4.57\n",
      "\n",
      "Obs. 5520:\n",
      " var6 (Cumul.perc.: 0.991): 8.44\n",
      "var25 (Cumul.perc.: 0.97): 6.39\n",
      "var24 (Cumul.perc.: 0.959): 5.97\n",
      "\n",
      "Obs. 5530:\n",
      " var17 (Cumul.perc.: 0.013): -6.21\n",
      "var24 (Cumul.perc.: 0.987): 7.49\n",
      "\n",
      "Obs. 5540:\n",
      " var20 (Cumul.perc.: 0.979): 5.98\n",
      "var25 (Cumul.perc.: 0.029): -5.49\n",
      "\n",
      "Obs. 5550:\n",
      " var10 (Cumul.perc.: 0.997): 8.19\n",
      "var29 (Cumul.perc.: 0.013): -6.69\n",
      "var15 (Cumul.perc.: 0.97): 5.77\n",
      "var7 (Cumul.perc.: 0.039): -4.67\n",
      "\n",
      "Obs. 5560:\n",
      " var25 (Cumul.perc.: 0.983): 7.09\n",
      "var13 (Cumul.perc.: 0.971): 6.66\n",
      "var14 (Cumul.perc.: 0.96): 5.95\n",
      "\n",
      "Obs. 5570:\n",
      " var21 (Cumul.perc.: 0.993): 8.59\n",
      "var2 (Cumul.perc.: 0.015): -6.68\n",
      "var16 (Cumul.perc.: 0.981): 6.32\n",
      "var12 (Cumul.perc.: 0.96): 5.18\n",
      "\n",
      "Obs. 5580:\n",
      " var0 (Cumul.perc.: 0.991): 8.18\n",
      "var29 (Cumul.perc.: 0.023): -5.93\n",
      "var10 (Cumul.perc.: 0.032): -4.7\n",
      "\n",
      "Obs. 5590:\n",
      " var18 (Cumul.perc.: 0.952): 5.72\n",
      "\n",
      "Obs. 5600:\n",
      " var23 (Cumul.perc.: 0.987): 7.51\n",
      "var17 (Cumul.perc.: 0.979): 6.44\n",
      "\n",
      "Obs. 5610:\n",
      " var27 (Cumul.perc.: 0.999): 9.69\n",
      "var16 (Cumul.perc.: 0.993): 7.58\n",
      "var0 (Cumul.perc.: 0.964): 6.32\n",
      "var15 (Cumul.perc.: 0.045): -4.48\n",
      "\n",
      "Obs. 5620:\n",
      " var25 (Cumul.perc.: 0.044): -4.86\n",
      "\n",
      "Obs. 5630:\n",
      " var26 (Cumul.perc.: 0.029): -5.22\n",
      "var2 (Cumul.perc.: 0.034): -5.47\n",
      "var13 (Cumul.perc.: 0.964): 6.33\n",
      "var4 (Cumul.perc.: 0.04): -4.61\n",
      "var5 (Cumul.perc.: 0.04): -4.94\n",
      "\n",
      "Obs. 5640:\n",
      " var10 (Cumul.perc.: 0.003): -7.61\n",
      "var8 (Cumul.perc.: 0.012): -5.73\n",
      "\n",
      "Obs. 5650:\n",
      " None\n",
      "\n",
      "Obs. 5660:\n",
      " var14 (Cumul.perc.: 0.955): 5.8\n",
      "var5 (Cumul.perc.: 0.03): -5.3\n",
      "\n",
      "Obs. 5670:\n",
      " var18 (Cumul.perc.: 0.007): -7.27\n",
      "var11 (Cumul.perc.: 0.026): -5.72\n",
      "\n",
      "Obs. 5680:\n",
      " var18 (Cumul.perc.: 0.043): -4.86\n",
      "\n",
      "Obs. 5690:\n",
      " var10 (Cumul.perc.: 0.037): -4.52\n",
      "var23 (Cumul.perc.: 0.955): 5.83\n",
      "\n",
      "Obs. 5700:\n",
      " var7 (Cumul.perc.: 0.991): 7.34\n",
      "var24 (Cumul.perc.: 0.023): -5.77\n",
      "var28 (Cumul.perc.: 0.034): -4.99\n",
      "var3 (Cumul.perc.: 0.032): -4.56\n",
      "\n",
      "Obs. 5710:\n",
      " var6 (Cumul.perc.: 0.964): 6.56\n",
      "\n",
      "Obs. 5720:\n",
      " var9 (Cumul.perc.: 0.005): -6.16\n",
      "var10 (Cumul.perc.: 0.989): 6.91\n",
      "var25 (Cumul.perc.: 0.977): 6.7\n",
      "var29 (Cumul.perc.: 0.971): 6.59\n",
      "\n",
      "Obs. 5730:\n",
      " var29 (Cumul.perc.: 0.0): -11.22\n",
      "var2 (Cumul.perc.: 0.003): -8.74\n",
      "var3 (Cumul.perc.: 0.004): -7.01\n",
      "var15 (Cumul.perc.: 0.011): -6.39\n",
      "var12 (Cumul.perc.: 0.009): -6.17\n",
      "\n",
      "Obs. 5740:\n",
      " var5 (Cumul.perc.: 0.998): 9.51\n",
      "var12 (Cumul.perc.: 0.045): -4.1\n",
      "var1 (Cumul.perc.: 0.046): -4.27\n",
      "\n",
      "Obs. 5750:\n",
      " var6 (Cumul.perc.: 0.994): 9.04\n",
      "var8 (Cumul.perc.: 0.992): 6.89\n",
      "\n",
      "Obs. 5760:\n",
      " var9 (Cumul.perc.: 0.025): -4.59\n",
      "var17 (Cumul.perc.: 0.046): -4.5\n",
      "\n",
      "Obs. 5770:\n",
      " var14 (Cumul.perc.: 0.981): 7.05\n",
      "var26 (Cumul.perc.: 0.017): -5.93\n",
      "var28 (Cumul.perc.: 0.024): -5.5\n",
      "var17 (Cumul.perc.: 0.03): -5.12\n",
      "var16 (Cumul.perc.: 0.033): -4.72\n",
      "\n",
      "Obs. 5780:\n",
      " var19 (Cumul.perc.: 0.025): -5.57\n",
      "var28 (Cumul.perc.: 0.971): 6.11\n",
      "var12 (Cumul.perc.: 0.983): 6.21\n",
      "var16 (Cumul.perc.: 0.978): 6.15\n",
      "var18 (Cumul.perc.: 0.955): 5.82\n",
      "\n",
      "Obs. 5790:\n",
      " var23 (Cumul.perc.: 0.011): -6.94\n",
      "var20 (Cumul.perc.: 0.011): -5.77\n",
      "var4 (Cumul.perc.: 0.009): -6.52\n",
      "var17 (Cumul.perc.: 0.018): -5.81\n",
      "var28 (Cumul.perc.: 0.012): -6.39\n",
      "\n",
      "Obs. 5800:\n",
      " None\n",
      "\n",
      "Obs. 5810:\n",
      " var11 (Cumul.perc.: 0.019): -6.14\n",
      "\n",
      "Obs. 5820:\n",
      " var5 (Cumul.perc.: 0.01): -6.82\n",
      "var7 (Cumul.perc.: 0.992): 7.53\n",
      "var22 (Cumul.perc.: 0.984): 6.69\n",
      "var2 (Cumul.perc.: 0.968): 6.58\n",
      "var15 (Cumul.perc.: 0.959): 5.38\n",
      "\n",
      "Obs. 5830:\n",
      " var28 (Cumul.perc.: 0.982): 6.78\n",
      "var14 (Cumul.perc.: 0.958): 5.89\n",
      "\n",
      "Obs. 5840:\n",
      " var4 (Cumul.perc.: 0.003): -7.74\n",
      "var15 (Cumul.perc.: 0.018): -5.72\n",
      "var27 (Cumul.perc.: 0.021): -5.76\n",
      "var7 (Cumul.perc.: 0.025): -5.21\n",
      "var25 (Cumul.perc.: 0.952): 5.59\n",
      "\n",
      "Obs. 5850:\n",
      " var6 (Cumul.perc.: 0.99): 8.36\n",
      "\n",
      "Obs. 5860:\n",
      " var25 (Cumul.perc.: 0.958): 5.79\n",
      "var28 (Cumul.perc.: 0.965): 5.86\n",
      "\n",
      "Obs. 5870:\n",
      " var25 (Cumul.perc.: 0.002): -8.79\n",
      "var28 (Cumul.perc.: 0.96): 5.65\n",
      "var7 (Cumul.perc.: 0.042): -4.55\n",
      "\n",
      "Obs. 5880:\n",
      " var25 (Cumul.perc.: 0.989): 7.59\n",
      "var20 (Cumul.perc.: 0.958): 5.17\n",
      "var3 (Cumul.perc.: 0.955): 5.06\n",
      "\n",
      "Obs. 5890:\n",
      " var5 (Cumul.perc.: 0.99): 7.68\n",
      "var13 (Cumul.perc.: 0.014): -6.75\n",
      "\n",
      "Obs. 5900:\n",
      " None\n",
      "\n",
      "Obs. 5910:\n",
      " var16 (Cumul.perc.: 0.997): 8.2\n",
      "var13 (Cumul.perc.: 0.993): 8.56\n",
      "var17 (Cumul.perc.: 0.013): -6.18\n",
      "var18 (Cumul.perc.: 0.967): 6.26\n",
      "var19 (Cumul.perc.: 0.96): 5.91\n",
      "\n",
      "Obs. 5920:\n",
      " var0 (Cumul.perc.: 0.017): -6.52\n",
      "\n",
      "Obs. 5930:\n",
      " None\n",
      "\n",
      "Obs. 5940:\n",
      " None\n",
      "\n",
      "Obs. 5950:\n",
      " var23 (Cumul.perc.: 0.003): -8.66\n",
      "var4 (Cumul.perc.: 0.987): 7.03\n",
      "var3 (Cumul.perc.: 0.956): 5.08\n",
      "\n",
      "Obs. 5960:\n",
      " var7 (Cumul.perc.: 0.011): -6.24\n",
      "var18 (Cumul.perc.: 0.967): 6.26\n",
      "var3 (Cumul.perc.: 0.979): 5.95\n",
      "var8 (Cumul.perc.: 0.042): -4.21\n",
      "\n",
      "Obs. 5970:\n",
      " var15 (Cumul.perc.: 0.019): -5.62\n",
      "\n",
      "Obs. 5980:\n",
      " var22 (Cumul.perc.: 0.002): -8.29\n",
      "var13 (Cumul.perc.: 0.989): 8.0\n",
      "var27 (Cumul.perc.: 0.022): -5.74\n",
      "var7 (Cumul.perc.: 0.975): 6.26\n",
      "\n",
      "Obs. 5990:\n",
      " None\n",
      "\n",
      "Obs. 6000:\n",
      " var25 (Cumul.perc.: 0.993): 8.19\n",
      "\n",
      "Obs. 6010:\n",
      " var16 (Cumul.perc.: 0.014): -5.8\n",
      "var19 (Cumul.perc.: 0.019): -5.91\n",
      "var29 (Cumul.perc.: 0.028): -5.65\n",
      "\n",
      "Obs. 6020:\n",
      " var23 (Cumul.perc.: 0.963): 6.07\n",
      "\n",
      "Obs. 6030:\n",
      " None\n",
      "\n",
      "Obs. 6040:\n",
      " var7 (Cumul.perc.: 0.974): 6.23\n",
      "\n",
      "Obs. 6050:\n",
      " var8 (Cumul.perc.: 0.997): 7.75\n",
      "var28 (Cumul.perc.: 0.01): -6.62\n",
      "var20 (Cumul.perc.: 0.989): 6.78\n",
      "var0 (Cumul.perc.: 0.015): -6.64\n",
      "var23 (Cumul.perc.: 0.034): -5.4\n",
      "\n",
      "Obs. 6060:\n",
      " var6 (Cumul.perc.: 0.995): 9.12\n",
      "var14 (Cumul.perc.: 0.986): 7.43\n",
      "var27 (Cumul.perc.: 0.024): -5.63\n",
      "var11 (Cumul.perc.: 0.952): 5.76\n",
      "\n",
      "Obs. 6070:\n",
      " var20 (Cumul.perc.: 0.994): 7.4\n",
      "var24 (Cumul.perc.: 0.042): -4.94\n",
      "var9 (Cumul.perc.: 0.036): -4.14\n",
      "\n",
      "Obs. 6080:\n",
      " var14 (Cumul.perc.: 0.985): 7.37\n",
      "var4 (Cumul.perc.: 0.022): -5.44\n",
      "\n",
      "Obs. 6090:\n",
      " var26 (Cumul.perc.: 0.002): -8.24\n",
      "\n",
      "Obs. 6100:\n",
      " var18 (Cumul.perc.: 0.975): 6.65\n",
      "var3 (Cumul.perc.: 0.033): -4.52\n",
      "var11 (Cumul.perc.: 0.976): 6.73\n",
      "var21 (Cumul.perc.: 0.049): -5.12\n",
      "var26 (Cumul.perc.: 0.955): 5.58\n",
      "\n",
      "Obs. 6110:\n",
      " var27 (Cumul.perc.: 0.016): -6.18\n",
      "var16 (Cumul.perc.: 0.982): 6.42\n",
      "var6 (Cumul.perc.: 0.979): 7.37\n",
      "var8 (Cumul.perc.: 0.024): -4.89\n",
      "var14 (Cumul.perc.: 0.962): 6.03\n",
      "\n",
      "Obs. 6120:\n",
      " var24 (Cumul.perc.: 0.016): -6.37\n",
      "var23 (Cumul.perc.: 0.032): -5.51\n",
      "\n",
      "Obs. 6130:\n",
      " var23 (Cumul.perc.: 0.97): 6.36\n",
      "var0 (Cumul.perc.: 0.952): 5.9\n",
      "var6 (Cumul.perc.: 0.044): -5.24\n",
      "\n",
      "Obs. 6140:\n",
      " var2 (Cumul.perc.: 0.016): -6.53\n",
      "var13 (Cumul.perc.: 0.98): 7.21\n",
      "var27 (Cumul.perc.: 0.973): 6.47\n",
      "var1 (Cumul.perc.: 0.967): 5.64\n",
      "var9 (Cumul.perc.: 0.049): -3.77\n",
      "\n",
      "Obs. 6150:\n",
      " var8 (Cumul.perc.: 0.019): -5.13\n",
      "var12 (Cumul.perc.: 0.978): 5.91\n",
      "\n",
      "Obs. 6160:\n",
      " var26 (Cumul.perc.: 0.048): -4.53\n",
      "var3 (Cumul.perc.: 0.048): -4.03\n",
      "\n",
      "Obs. 6170:\n",
      " var10 (Cumul.perc.: 0.035): -4.59\n",
      "var19 (Cumul.perc.: 0.971): 6.33\n",
      "\n",
      "Obs. 6180:\n",
      " var25 (Cumul.perc.: 0.021): -5.9\n",
      "var16 (Cumul.perc.: 0.961): 5.47\n",
      "var4 (Cumul.perc.: 0.957): 5.52\n",
      "var27 (Cumul.perc.: 0.955): 5.75\n",
      "var26 (Cumul.perc.: 0.049): -4.51\n",
      "\n",
      "Obs. 6190:\n",
      " var22 (Cumul.perc.: 0.995): 8.16\n",
      "var8 (Cumul.perc.: 0.989): 6.58\n",
      "var12 (Cumul.perc.: 0.011): -5.87\n",
      "var24 (Cumul.perc.: 0.967): 6.28\n",
      "var11 (Cumul.perc.: 0.969): 6.37\n",
      "\n",
      "Obs. 6200:\n",
      " var18 (Cumul.perc.: 0.003): -8.09\n",
      "var13 (Cumul.perc.: 0.008): -7.56\n",
      "var16 (Cumul.perc.: 0.007): -6.51\n",
      "var2 (Cumul.perc.: 0.99): 8.19\n",
      "var27 (Cumul.perc.: 0.042): -4.81\n",
      "\n",
      "Obs. 6210:\n",
      " var26 (Cumul.perc.: 0.972): 6.19\n",
      "\n",
      "Obs. 6220:\n",
      " var5 (Cumul.perc.: 0.012): -6.57\n",
      "\n",
      "Obs. 6230:\n",
      " var19 (Cumul.perc.: 0.018): -5.98\n",
      "var29 (Cumul.perc.: 0.02): -6.12\n",
      "var21 (Cumul.perc.: 0.957): 6.21\n",
      "var16 (Cumul.perc.: 0.952): 5.21\n",
      "\n",
      "Obs. 6240:\n",
      " var4 (Cumul.perc.: 0.983): 6.76\n",
      "var23 (Cumul.perc.: 0.026): -5.79\n",
      "var19 (Cumul.perc.: 0.04): -4.91\n",
      "var20 (Cumul.perc.: 0.967): 5.47\n",
      "\n",
      "Obs. 6250:\n",
      " var14 (Cumul.perc.: 0.007): -7.43\n",
      "var10 (Cumul.perc.: 0.967): 5.56\n",
      "\n",
      "Obs. 6260:\n",
      " var20 (Cumul.perc.: 0.983): 6.23\n",
      "var23 (Cumul.perc.: 0.026): -5.81\n",
      "\n",
      "Obs. 6270:\n",
      " var4 (Cumul.perc.: 0.999): 10.84\n",
      "var9 (Cumul.perc.: 0.008): -5.83\n",
      "var8 (Cumul.perc.: 0.027): -4.72\n",
      "\n",
      "Obs. 6280:\n",
      " var28 (Cumul.perc.: 0.992): 7.75\n",
      "var0 (Cumul.perc.: 0.97): 6.6\n",
      "var10 (Cumul.perc.: 0.045): -4.24\n",
      "var12 (Cumul.perc.: 0.032): -4.53\n",
      "\n",
      "Obs. 6290:\n",
      " var10 (Cumul.perc.: 0.003): -7.27\n",
      "var22 (Cumul.perc.: 0.994): 7.9\n",
      "\n",
      "Obs. 6300:\n",
      " var4 (Cumul.perc.: 0.009): -6.46\n",
      "var3 (Cumul.perc.: 0.009): -6.1\n",
      "var21 (Cumul.perc.: 0.019): -6.55\n",
      "var29 (Cumul.perc.: 0.957): 5.97\n",
      "var9 (Cumul.perc.: 0.966): 5.21\n",
      "\n",
      "Obs. 6310:\n",
      " var22 (Cumul.perc.: 0.025): -5.26\n",
      "var2 (Cumul.perc.: 0.978): 7.11\n",
      "var14 (Cumul.perc.: 0.03): -5.47\n",
      "var17 (Cumul.perc.: 0.968): 5.91\n",
      "var8 (Cumul.perc.: 0.957): 5.05\n",
      "\n",
      "Obs. 6320:\n",
      " var9 (Cumul.perc.: 0.995): 7.22\n",
      "var28 (Cumul.perc.: 0.03): -5.14\n",
      "\n",
      "Obs. 6330:\n",
      " var11 (Cumul.perc.: 0.994): 8.38\n",
      "var19 (Cumul.perc.: 0.963): 6.01\n",
      "\n",
      "Obs. 6340:\n",
      " var21 (Cumul.perc.: 0.983): 7.55\n",
      "var6 (Cumul.perc.: 0.036): -5.57\n",
      "var13 (Cumul.perc.: 0.038): -5.34\n",
      "var14 (Cumul.perc.: 0.956): 5.81\n",
      "\n",
      "Obs. 6350:\n",
      " var20 (Cumul.perc.: 0.973): 5.69\n",
      "var7 (Cumul.perc.: 0.981): 6.62\n",
      "var24 (Cumul.perc.: 0.955): 5.83\n",
      "var11 (Cumul.perc.: 0.048): -4.79\n",
      "\n",
      "Obs. 6360:\n",
      " var1 (Cumul.perc.: 0.008): -6.55\n",
      "var17 (Cumul.perc.: 0.016): -5.88\n",
      "var3 (Cumul.perc.: 0.033): -4.53\n",
      "\n",
      "Obs. 6370:\n",
      " var19 (Cumul.perc.: 0.004): -8.11\n",
      "\n",
      "Obs. 6380:\n",
      " var8 (Cumul.perc.: 0.011): -5.92\n",
      "var10 (Cumul.perc.: 0.011): -5.93\n",
      "var17 (Cumul.perc.: 0.048): -4.41\n",
      "\n",
      "Obs. 6390:\n",
      " var27 (Cumul.perc.: 0.972): 6.42\n",
      "var4 (Cumul.perc.: 0.035): -4.82\n",
      "\n",
      "Obs. 6400:\n",
      " var18 (Cumul.perc.: 0.003): -8.4\n",
      "var10 (Cumul.perc.: 0.996): 8.11\n",
      "var19 (Cumul.perc.: 0.989): 7.59\n",
      "var5 (Cumul.perc.: 0.973): 6.37\n",
      "\n",
      "Obs. 6410:\n",
      " var2 (Cumul.perc.: 0.998): 10.05\n",
      "var10 (Cumul.perc.: 0.005): -6.78\n",
      "var4 (Cumul.perc.: 0.025): -5.27\n",
      "var24 (Cumul.perc.: 0.971): 6.5\n",
      "var16 (Cumul.perc.: 0.037): -4.59\n",
      "\n",
      "Obs. 6420:\n",
      " var5 (Cumul.perc.: 0.015): -6.33\n",
      "var14 (Cumul.perc.: 0.046): -4.83\n",
      "\n",
      "Obs. 6430:\n",
      " var19 (Cumul.perc.: 0.987): 7.33\n",
      "var18 (Cumul.perc.: 0.019): -6.04\n",
      "\n",
      "Obs. 6440:\n",
      " var22 (Cumul.perc.: 0.994): 7.79\n",
      "var18 (Cumul.perc.: 0.993): 8.31\n",
      "var17 (Cumul.perc.: 0.015): -6.0\n",
      "var16 (Cumul.perc.: 0.014): -5.79\n",
      "var4 (Cumul.perc.: 0.04): -4.63\n",
      "\n",
      "Obs. 6450:\n",
      " var25 (Cumul.perc.: 0.988): 7.52\n",
      "var7 (Cumul.perc.: 0.023): -5.37\n",
      "var23 (Cumul.perc.: 0.976): 6.7\n",
      "var22 (Cumul.perc.: 0.027): -5.19\n",
      "var12 (Cumul.perc.: 0.951): 4.89\n",
      "\n",
      "Obs. 6460:\n",
      " var18 (Cumul.perc.: 0.997): 9.25\n",
      "var21 (Cumul.perc.: 0.991): 8.41\n",
      "var29 (Cumul.perc.: 0.993): 8.33\n",
      "var12 (Cumul.perc.: 0.981): 6.04\n",
      "var20 (Cumul.perc.: 0.958): 5.14\n",
      "\n",
      "Obs. 6470:\n",
      " var21 (Cumul.perc.: 0.99): 8.18\n",
      "var22 (Cumul.perc.: 0.032): -4.94\n",
      "var13 (Cumul.perc.: 0.046): -5.06\n",
      "var5 (Cumul.perc.: 0.976): 6.57\n",
      "var0 (Cumul.perc.: 0.046): -5.04\n",
      "\n",
      "Obs. 6480:\n",
      " var7 (Cumul.perc.: 0.964): 5.76\n",
      "\n",
      "Obs. 6490:\n",
      " var28 (Cumul.perc.: 0.997): 8.98\n",
      "var4 (Cumul.perc.: 0.991): 7.51\n",
      "var18 (Cumul.perc.: 0.989): 7.65\n",
      "var12 (Cumul.perc.: 0.975): 5.75\n",
      "var1 (Cumul.perc.: 0.962): 5.45\n",
      "\n",
      "Obs. 6500:\n",
      " var25 (Cumul.perc.: 0.003): -8.28\n",
      "var19 (Cumul.perc.: 0.978): 6.72\n",
      "\n",
      "Obs. 6510:\n",
      " var1 (Cumul.perc.: 0.017): -5.61\n",
      "var10 (Cumul.perc.: 0.976): 5.94\n",
      "var24 (Cumul.perc.: 0.041): -4.98\n",
      "var18 (Cumul.perc.: 0.96): 5.99\n",
      "\n",
      "Obs. 6520:\n",
      " None\n",
      "\n",
      "Obs. 6530:\n",
      " var5 (Cumul.perc.: 0.005): -7.59\n",
      "var29 (Cumul.perc.: 0.039): -5.14\n",
      "var22 (Cumul.perc.: 0.048): -4.36\n",
      "\n",
      "Obs. 6540:\n",
      " var4 (Cumul.perc.: 0.02): -5.59\n",
      "var20 (Cumul.perc.: 0.039): -4.29\n",
      "\n",
      "Obs. 6550:\n",
      " var7 (Cumul.perc.: 0.007): -6.8\n",
      "var17 (Cumul.perc.: 0.023): -5.5\n",
      "\n",
      "Obs. 6560:\n",
      " var20 (Cumul.perc.: 0.991): 7.02\n",
      "var12 (Cumul.perc.: 0.02): -5.12\n",
      "var19 (Cumul.perc.: 0.965): 6.07\n",
      "\n",
      "Obs. 6570:\n",
      " var7 (Cumul.perc.: 0.003): -7.82\n",
      "var20 (Cumul.perc.: 0.992): 7.09\n",
      "var24 (Cumul.perc.: 0.016): -6.31\n",
      "var21 (Cumul.perc.: 0.954): 6.1\n",
      "var11 (Cumul.perc.: 0.045): -4.88\n",
      "\n",
      "Obs. 6580:\n",
      " var11 (Cumul.perc.: 0.006): -7.67\n",
      "var5 (Cumul.perc.: 0.981): 6.92\n",
      "\n",
      "Obs. 6590:\n",
      " var26 (Cumul.perc.: 0.044): -4.65\n",
      "\n",
      "Obs. 6600:\n",
      " var22 (Cumul.perc.: 0.012): -6.1\n",
      "var15 (Cumul.perc.: 0.975): 6.09\n",
      "var29 (Cumul.perc.: 0.028): -5.66\n",
      "\n",
      "Obs. 6610:\n",
      " var14 (Cumul.perc.: 0.988): 7.6\n",
      "var5 (Cumul.perc.: 0.963): 5.96\n",
      "var15 (Cumul.perc.: 0.041): -4.62\n",
      "\n",
      "Obs. 6620:\n",
      " var25 (Cumul.perc.: 0.999): 10.86\n",
      "var14 (Cumul.perc.: 0.021): -5.99\n",
      "var3 (Cumul.perc.: 0.043): -4.19\n",
      "\n",
      "Obs. 6630:\n",
      " var0 (Cumul.perc.: 0.029): -5.73\n",
      "var28 (Cumul.perc.: 0.026): -5.34\n",
      "var20 (Cumul.perc.: 0.041): -4.24\n",
      "var15 (Cumul.perc.: 0.963): 5.51\n",
      "\n",
      "Obs. 6640:\n",
      " var0 (Cumul.perc.: 0.01): -7.23\n",
      "var11 (Cumul.perc.: 0.975): 6.65\n",
      "\n",
      "Obs. 6650:\n",
      " var10 (Cumul.perc.: 0.005): -6.9\n",
      "\n",
      "Obs. 6660:\n",
      " var23 (Cumul.perc.: 0.002): -9.33\n",
      "var4 (Cumul.perc.: 0.985): 6.89\n",
      "var12 (Cumul.perc.: 0.983): 6.18\n",
      "var29 (Cumul.perc.: 0.967): 6.38\n",
      "var13 (Cumul.perc.: 0.048): -5.01\n",
      "\n",
      "Obs. 6670:\n",
      " var27 (Cumul.perc.: 0.01): -6.77\n",
      "var21 (Cumul.perc.: 0.017): -6.75\n",
      "var13 (Cumul.perc.: 0.975): 6.88\n",
      "var0 (Cumul.perc.: 0.02): -6.23\n",
      "var23 (Cumul.perc.: 0.025): -5.88\n",
      "\n",
      "Obs. 6680:\n",
      " var24 (Cumul.perc.: 0.998): 9.59\n",
      "var20 (Cumul.perc.: 0.017): -5.3\n",
      "var6 (Cumul.perc.: 0.972): 6.9\n",
      "var10 (Cumul.perc.: 0.035): -4.58\n",
      "\n",
      "Obs. 6690:\n",
      " var21 (Cumul.perc.: 0.979): 7.26\n",
      "var2 (Cumul.perc.: 0.949): 5.86\n",
      "var3 (Cumul.perc.: 0.041): -4.24\n",
      "\n",
      "Obs. 6700:\n",
      " var20 (Cumul.perc.: 0.985): 6.42\n",
      "var19 (Cumul.perc.: 0.014): -6.32\n",
      "\n",
      "Obs. 6710:\n",
      " var9 (Cumul.perc.: 0.038): -4.06\n",
      "\n",
      "Obs. 6720:\n",
      " var17 (Cumul.perc.: 0.982): 6.64\n",
      "var22 (Cumul.perc.: 0.954): 5.45\n",
      "\n",
      "Obs. 6730:\n",
      " var8 (Cumul.perc.: 0.998): 8.18\n",
      "\n",
      "Obs. 6740:\n",
      " var25 (Cumul.perc.: 0.973): 6.54\n",
      "\n",
      "Obs. 6750:\n",
      " None\n",
      "\n",
      "Obs. 6760:\n",
      " var24 (Cumul.perc.: 0.992): 7.99\n",
      "var8 (Cumul.perc.: 0.971): 5.54\n",
      "\n",
      "Obs. 6770:\n",
      " var11 (Cumul.perc.: 0.007): -7.45\n",
      "var2 (Cumul.perc.: 0.023): -6.05\n",
      "var1 (Cumul.perc.: 0.967): 5.61\n",
      "\n",
      "Obs. 6780:\n",
      " var18 (Cumul.perc.: 0.984): 7.24\n",
      "var27 (Cumul.perc.: 0.975): 6.59\n",
      "var25 (Cumul.perc.: 0.036): -5.17\n",
      "var22 (Cumul.perc.: 0.966): 5.84\n",
      "var12 (Cumul.perc.: 0.043): -4.17\n",
      "\n",
      "Obs. 6790:\n",
      " var7 (Cumul.perc.: 0.021): -5.48\n",
      "var4 (Cumul.perc.: 0.043): -4.52\n",
      "var15 (Cumul.perc.: 0.047): -4.41\n",
      "\n",
      "Obs. 6800:\n",
      " var15 (Cumul.perc.: 0.021): -5.51\n",
      "var19 (Cumul.perc.: 0.958): 5.84\n",
      "\n",
      "Obs. 6810:\n",
      " var5 (Cumul.perc.: 0.013): -6.55\n",
      "var29 (Cumul.perc.: 0.026): -5.78\n",
      "\n",
      "Obs. 6820:\n",
      " var12 (Cumul.perc.: 0.002): -8.28\n",
      "var25 (Cumul.perc.: 0.032): -5.35\n",
      "var26 (Cumul.perc.: 0.036): -4.95\n",
      "var11 (Cumul.perc.: 0.04): -5.11\n",
      "\n",
      "Obs. 6830:\n",
      " var11 (Cumul.perc.: 0.972): 6.52\n",
      "\n",
      "Obs. 6840:\n",
      " None\n",
      "\n",
      "Obs. 6850:\n",
      " var1 (Cumul.perc.: 0.993): 7.49\n",
      "var4 (Cumul.perc.: 0.021): -5.51\n",
      "var0 (Cumul.perc.: 0.036): -5.45\n",
      "\n",
      "Obs. 6860:\n",
      " var22 (Cumul.perc.: 0.972): 6.08\n",
      "\n",
      "Obs. 6870:\n",
      " var6 (Cumul.perc.: 0.961): 6.45\n",
      "var15 (Cumul.perc.: 0.047): -4.42\n",
      "\n",
      "Obs. 6880:\n",
      " var9 (Cumul.perc.: 0.004): -6.43\n",
      "var11 (Cumul.perc.: 0.029): -5.57\n",
      "\n",
      "Obs. 6890:\n",
      " var11 (Cumul.perc.: 0.014): -6.62\n",
      "var18 (Cumul.perc.: 0.013): -6.55\n",
      "var9 (Cumul.perc.: 0.041): -3.97\n",
      "\n",
      "Obs. 6900:\n",
      " var7 (Cumul.perc.: 0.007): -6.87\n",
      "\n",
      "Obs. 6910:\n",
      " var22 (Cumul.perc.: 0.042): -4.54\n",
      "var18 (Cumul.perc.: 0.952): 5.72\n",
      "\n",
      "Obs. 6920:\n",
      " var2 (Cumul.perc.: 0.964): 6.41\n",
      "var29 (Cumul.perc.: 0.04): -5.12\n",
      "\n",
      "Obs. 6930:\n",
      " var21 (Cumul.perc.: 0.034): -5.72\n",
      "var14 (Cumul.perc.: 0.032): -5.37\n",
      "var22 (Cumul.perc.: 0.963): 5.73\n",
      "\n",
      "Obs. 6940:\n",
      " var11 (Cumul.perc.: 0.999): 11.52\n",
      "var19 (Cumul.perc.: 0.954): 5.67\n",
      "var0 (Cumul.perc.: 0.956): 6.03\n",
      "\n",
      "Obs. 6950:\n",
      " var13 (Cumul.perc.: 0.993): 8.59\n",
      "var6 (Cumul.perc.: 0.993): 8.89\n",
      "var17 (Cumul.perc.: 0.978): 6.36\n",
      "var19 (Cumul.perc.: 0.984): 7.1\n",
      "var8 (Cumul.perc.: 0.967): 5.38\n",
      "\n",
      "Obs. 6960:\n",
      " var23 (Cumul.perc.: 0.004): -8.39\n",
      "var22 (Cumul.perc.: 0.993): 7.66\n",
      "var21 (Cumul.perc.: 0.008): -7.62\n",
      "var27 (Cumul.perc.: 0.028): -5.4\n",
      "var2 (Cumul.perc.: 0.021): -6.14\n",
      "\n",
      "Obs. 6970:\n",
      " None\n",
      "\n",
      "Obs. 6980:\n",
      " None\n",
      "\n",
      "Obs. 6990:\n",
      " var0 (Cumul.perc.: 0.037): -5.41\n",
      "\n",
      "Obs. 7000:\n",
      " var6 (Cumul.perc.: 0.008): -7.81\n",
      "var18 (Cumul.perc.: 0.991): 7.92\n",
      "var1 (Cumul.perc.: 0.046): -4.26\n",
      "\n",
      "Obs. 7010:\n",
      " var1 (Cumul.perc.: 0.977): 6.07\n",
      "var2 (Cumul.perc.: 0.04): -5.24\n",
      "var17 (Cumul.perc.: 0.045): -4.51\n",
      "var28 (Cumul.perc.: 0.037): -4.85\n",
      "\n",
      "Obs. 7020:\n",
      " var17 (Cumul.perc.: 0.995): 8.36\n",
      "var28 (Cumul.perc.: 0.971): 6.09\n",
      "var18 (Cumul.perc.: 0.962): 6.03\n",
      "\n",
      "Obs. 7030:\n",
      " var11 (Cumul.perc.: 0.034): -5.32\n",
      "\n",
      "Obs. 7040:\n",
      " var16 (Cumul.perc.: 0.006): -6.78\n",
      "var18 (Cumul.perc.: 0.018): -6.1\n",
      "\n",
      "Obs. 7050:\n",
      " var13 (Cumul.perc.: 0.008): -7.56\n",
      "var7 (Cumul.perc.: 0.991): 7.41\n",
      "\n",
      "Obs. 7060:\n",
      " var22 (Cumul.perc.: 0.951): 5.36\n",
      "\n",
      "Obs. 7070:\n",
      " var15 (Cumul.perc.: 0.972): 5.87\n",
      "var21 (Cumul.perc.: 0.042): -5.35\n",
      "var5 (Cumul.perc.: 0.043): -4.83\n",
      "var9 (Cumul.perc.: 0.95): 4.71\n",
      "var28 (Cumul.perc.: 0.048): -4.48\n",
      "\n",
      "Obs. 7080:\n",
      " var26 (Cumul.perc.: 0.998): 9.06\n",
      "var4 (Cumul.perc.: 0.96): 5.64\n",
      "\n",
      "Obs. 7090:\n",
      " var12 (Cumul.perc.: 0.997): 7.9\n",
      "var4 (Cumul.perc.: 0.004): -7.44\n",
      "var7 (Cumul.perc.: 0.005): -7.25\n",
      "var0 (Cumul.perc.: 0.035): -5.5\n",
      "var20 (Cumul.perc.: 0.039): -4.3\n",
      "\n",
      "Obs. 7100:\n",
      " var12 (Cumul.perc.: 0.963): 5.28\n",
      "\n",
      "Obs. 7110:\n",
      " var10 (Cumul.perc.: 0.026): -4.99\n",
      "var25 (Cumul.perc.: 0.022): -5.81\n",
      "var28 (Cumul.perc.: 0.981): 6.62\n",
      "var3 (Cumul.perc.: 0.045): -4.13\n",
      "\n",
      "Obs. 7120:\n",
      " var19 (Cumul.perc.: 0.007): -7.18\n",
      "var5 (Cumul.perc.: 0.035): -5.1\n",
      "\n",
      "Obs. 7130:\n",
      " var29 (Cumul.perc.: 0.98): 7.06\n",
      "var6 (Cumul.perc.: 0.022): -6.32\n",
      "var27 (Cumul.perc.: 0.97): 6.36\n",
      "var7 (Cumul.perc.: 0.019): -5.63\n",
      "var4 (Cumul.perc.: 0.973): 6.15\n",
      "\n",
      "Obs. 7140:\n",
      " var18 (Cumul.perc.: 0.0): -11.86\n",
      "var8 (Cumul.perc.: 0.0): -11.65\n",
      "var25 (Cumul.perc.: 0.0): -11.47\n",
      "var15 (Cumul.perc.: 0.001): -9.77\n",
      "var10 (Cumul.perc.: 0.001): -9.7\n",
      "\n",
      "Obs. 7150:\n",
      " var11 (Cumul.perc.: 0.001): -9.75\n",
      "var7 (Cumul.perc.: 0.969): 5.96\n",
      "var15 (Cumul.perc.: 0.033): -4.9\n",
      "var13 (Cumul.perc.: 0.047): -5.02\n",
      "var21 (Cumul.perc.: 0.048): -5.14\n",
      "\n",
      "Obs. 7160:\n",
      " var14 (Cumul.perc.: 1.0): 11.23\n",
      "var8 (Cumul.perc.: 0.019): -5.18\n",
      "var18 (Cumul.perc.: 0.967): 6.25\n",
      "var7 (Cumul.perc.: 0.98): 6.55\n",
      "var3 (Cumul.perc.: 0.975): 5.8\n",
      "\n",
      "Obs. 7170:\n",
      " var27 (Cumul.perc.: 0.98): 6.86\n",
      "var11 (Cumul.perc.: 0.981): 7.0\n",
      "\n",
      "Obs. 7180:\n",
      " var25 (Cumul.perc.: 0.986): 7.34\n",
      "var19 (Cumul.perc.: 0.018): -6.0\n",
      "\n",
      "Obs. 7190:\n",
      " var13 (Cumul.perc.: 0.016): -6.63\n",
      "var0 (Cumul.perc.: 0.981): 7.29\n",
      "var6 (Cumul.perc.: 0.972): 6.92\n",
      "var10 (Cumul.perc.: 0.047): -4.17\n",
      "var3 (Cumul.perc.: 0.963): 5.29\n",
      "\n",
      "Obs. 7200:\n",
      " var14 (Cumul.perc.: 0.957): 5.86\n",
      "\n",
      "Obs. 7210:\n",
      " var0 (Cumul.perc.: 0.998): 10.25\n",
      "var11 (Cumul.perc.: 0.983): 7.18\n",
      "var25 (Cumul.perc.: 0.017): -6.15\n",
      "var29 (Cumul.perc.: 0.032): -5.46\n",
      "var2 (Cumul.perc.: 0.028): -5.77\n",
      "\n",
      "Obs. 7220:\n",
      " var7 (Cumul.perc.: 0.993): 7.65\n",
      "\n",
      "Obs. 7230:\n",
      " var0 (Cumul.perc.: 0.011): -7.04\n",
      "var4 (Cumul.perc.: 0.029): -5.1\n",
      "var21 (Cumul.perc.: 0.044): -5.3\n",
      "\n",
      "Obs. 7240:\n",
      " var29 (Cumul.perc.: 0.032): -5.47\n",
      "var0 (Cumul.perc.: 0.949): 5.8\n",
      "\n",
      "Obs. 7250:\n",
      " var29 (Cumul.perc.: 0.003): -8.48\n",
      "var21 (Cumul.perc.: 0.007): -7.89\n",
      "var22 (Cumul.perc.: 0.034): -4.83\n",
      "var5 (Cumul.perc.: 0.967): 6.11\n",
      "var13 (Cumul.perc.: 0.97): 6.59\n",
      "\n",
      "Obs. 7260:\n",
      " var18 (Cumul.perc.: 0.009): -7.04\n",
      "var0 (Cumul.perc.: 0.975): 6.89\n",
      "\n",
      "Obs. 7270:\n",
      " var14 (Cumul.perc.: 0.021): -5.99\n",
      "var4 (Cumul.perc.: 0.035): -4.78\n",
      "\n",
      "Obs. 7280:\n",
      " var12 (Cumul.perc.: 0.992): 6.88\n",
      "var2 (Cumul.perc.: 0.031): -5.61\n",
      "var3 (Cumul.perc.: 0.973): 5.7\n",
      "\n",
      "Obs. 7290:\n",
      " var7 (Cumul.perc.: 0.003): -8.11\n",
      "var10 (Cumul.perc.: 0.993): 7.27\n",
      "\n",
      "Obs. 7300:\n",
      " var25 (Cumul.perc.: 0.976): 6.64\n",
      "var16 (Cumul.perc.: 0.032): -4.77\n",
      "var4 (Cumul.perc.: 0.052): -4.26\n",
      "\n",
      "Obs. 7310:\n",
      " var12 (Cumul.perc.: 0.009): -6.12\n",
      "var6 (Cumul.perc.: 0.967): 6.71\n",
      "var8 (Cumul.perc.: 0.969): 5.46\n",
      "\n",
      "Obs. 7320:\n",
      " var6 (Cumul.perc.: 0.014): -6.92\n",
      "var14 (Cumul.perc.: 0.021): -5.97\n",
      "var3 (Cumul.perc.: 0.03): -4.64\n",
      "var8 (Cumul.perc.: 0.023): -4.93\n",
      "\n",
      "Obs. 7330:\n",
      " var23 (Cumul.perc.: 0.986): 7.42\n",
      "var26 (Cumul.perc.: 0.03): -5.17\n",
      "var12 (Cumul.perc.: 0.027): -4.78\n",
      "var10 (Cumul.perc.: 0.036): -4.56\n",
      "var2 (Cumul.perc.: 0.043): -5.14\n",
      "\n",
      "Obs. 7340:\n",
      " var0 (Cumul.perc.: 0.001): -9.77\n",
      "var19 (Cumul.perc.: 0.018): -5.99\n",
      "var13 (Cumul.perc.: 0.026): -5.93\n",
      "var20 (Cumul.perc.: 0.983): 6.23\n",
      "var25 (Cumul.perc.: 0.045): -4.84\n",
      "\n",
      "Obs. 7350:\n",
      " var15 (Cumul.perc.: 0.008): -6.68\n",
      "var10 (Cumul.perc.: 0.015): -5.63\n",
      "var12 (Cumul.perc.: 0.021): -5.06\n",
      "var9 (Cumul.perc.: 0.047): -3.83\n",
      "\n",
      "Obs. 7360:\n",
      " var6 (Cumul.perc.: 0.991): 8.55\n",
      "var26 (Cumul.perc.: 0.983): 6.77\n",
      "var28 (Cumul.perc.: 0.982): 6.75\n",
      "var13 (Cumul.perc.: 0.034): -5.49\n",
      "var19 (Cumul.perc.: 0.033): -5.15\n",
      "\n",
      "Obs. 7370:\n",
      " var2 (Cumul.perc.: 0.996): 9.13\n",
      "\n",
      "Obs. 7380:\n",
      " var28 (Cumul.perc.: 0.018): -5.91\n",
      "\n",
      "Obs. 7390:\n",
      " var24 (Cumul.perc.: 0.966): 6.22\n",
      "var7 (Cumul.perc.: 0.973): 6.15\n",
      "\n",
      "Obs. 7400:\n",
      " var6 (Cumul.perc.: 0.956): 6.24\n",
      "var10 (Cumul.perc.: 0.046): -4.19\n",
      "\n",
      "Obs. 7410:\n",
      " var27 (Cumul.perc.: 0.03): -5.32\n",
      "\n",
      "Obs. 7420:\n",
      " None\n",
      "\n",
      "Obs. 7430:\n",
      " var15 (Cumul.perc.: 0.012): -6.2\n",
      "var5 (Cumul.perc.: 0.025): -5.59\n",
      "var19 (Cumul.perc.: 0.962): 5.98\n",
      "var17 (Cumul.perc.: 0.046): -4.47\n",
      "\n",
      "Obs. 7440:\n",
      " var13 (Cumul.perc.: 0.014): -6.77\n",
      "var4 (Cumul.perc.: 0.982): 6.65\n",
      "var17 (Cumul.perc.: 0.974): 6.18\n",
      "\n",
      "Obs. 7450:\n",
      " var17 (Cumul.perc.: 0.992): 7.56\n",
      "var26 (Cumul.perc.: 0.009): -6.71\n",
      "var23 (Cumul.perc.: 0.98): 6.95\n",
      "\n",
      "Obs. 7460:\n",
      " var21 (Cumul.perc.: 0.98): 7.34\n",
      "var19 (Cumul.perc.: 0.972): 6.43\n",
      "var25 (Cumul.perc.: 0.033): -5.31\n",
      "var10 (Cumul.perc.: 0.038): -4.47\n",
      "var2 (Cumul.perc.: 0.956): 6.1\n",
      "\n",
      "Obs. 7470:\n",
      " var9 (Cumul.perc.: 0.002): -7.15\n",
      "var1 (Cumul.perc.: 0.966): 5.58\n",
      "var7 (Cumul.perc.: 0.037): -4.73\n",
      "\n",
      "Obs. 7480:\n",
      " var5 (Cumul.perc.: 0.004): -8.09\n",
      "var13 (Cumul.perc.: 0.992): 8.31\n",
      "var24 (Cumul.perc.: 0.986): 7.34\n",
      "var6 (Cumul.perc.: 0.973): 6.99\n",
      "var15 (Cumul.perc.: 0.983): 6.53\n",
      "\n",
      "Obs. 7490:\n",
      " var20 (Cumul.perc.: 0.004): -7.15\n",
      "var13 (Cumul.perc.: 0.007): -7.72\n",
      "var24 (Cumul.perc.: 0.992): 8.02\n",
      "var2 (Cumul.perc.: 0.008): -7.35\n",
      "var15 (Cumul.perc.: 0.028): -5.1\n",
      "\n",
      "Obs. 7500:\n",
      " var20 (Cumul.perc.: 0.01): -5.88\n",
      "var10 (Cumul.perc.: 0.975): 5.93\n",
      "var19 (Cumul.perc.: 0.953): 5.64\n",
      "\n",
      "Obs. 7510:\n",
      " var21 (Cumul.perc.: 0.998): 10.0\n",
      "var19 (Cumul.perc.: 0.013): -6.44\n",
      "var6 (Cumul.perc.: 0.953): 6.13\n",
      "var5 (Cumul.perc.: 0.044): -4.8\n",
      "\n",
      "Obs. 7520:\n",
      " var28 (Cumul.perc.: 0.009): -6.64\n",
      "var0 (Cumul.perc.: 0.008): -7.44\n",
      "var29 (Cumul.perc.: 0.044): -4.97\n",
      "\n",
      "Obs. 7530:\n",
      " var0 (Cumul.perc.: 0.043): -5.13\n",
      "var22 (Cumul.perc.: 0.951): 5.36\n",
      "\n",
      "Obs. 7540:\n",
      " var24 (Cumul.perc.: 0.997): 9.11\n",
      "var15 (Cumul.perc.: 0.986): 6.79\n",
      "var25 (Cumul.perc.: 0.971): 6.4\n",
      "var12 (Cumul.perc.: 0.962): 5.23\n",
      "\n",
      "Obs. 7550:\n",
      " var27 (Cumul.perc.: 0.009): -6.85\n",
      "var3 (Cumul.perc.: 0.957): 5.1\n",
      "\n",
      "Obs. 7560:\n",
      " var24 (Cumul.perc.: 0.972): 6.52\n",
      "\n",
      "Obs. 7570:\n",
      " None\n",
      "\n",
      "Obs. 7580:\n",
      " var2 (Cumul.perc.: 0.986): 7.73\n",
      "var6 (Cumul.perc.: 0.039): -5.43\n",
      "var26 (Cumul.perc.: 0.036): -4.95\n",
      "var28 (Cumul.perc.: 0.047): -4.51\n",
      "\n",
      "Obs. 7590:\n",
      " var23 (Cumul.perc.: 0.04): -5.13\n",
      "var9 (Cumul.perc.: 0.031): -4.28\n",
      "var18 (Cumul.perc.: 0.96): 5.97\n",
      "var26 (Cumul.perc.: 0.954): 5.57\n",
      "\n",
      "Obs. 7600:\n",
      " var9 (Cumul.perc.: 0.038): -4.06\n",
      "var28 (Cumul.perc.: 0.971): 6.08\n",
      "var11 (Cumul.perc.: 0.954): 5.84\n",
      "\n",
      "Obs. 7610:\n",
      " var26 (Cumul.perc.: 0.007): -7.06\n",
      "var28 (Cumul.perc.: 0.021): -5.71\n",
      "var6 (Cumul.perc.: 0.039): -5.46\n",
      "\n",
      "Obs. 7620:\n",
      " var27 (Cumul.perc.: 0.999): 10.28\n",
      "var26 (Cumul.perc.: 0.996): 8.4\n",
      "var17 (Cumul.perc.: 0.992): 7.56\n",
      "var6 (Cumul.perc.: 0.026): -6.09\n",
      "var7 (Cumul.perc.: 0.981): 6.62\n",
      "\n",
      "Obs. 7630:\n",
      " var2 (Cumul.perc.: 0.002): -9.3\n",
      "var14 (Cumul.perc.: 0.979): 6.86\n",
      "var4 (Cumul.perc.: 0.954): 5.42\n",
      "\n",
      "Obs. 7640:\n",
      " var8 (Cumul.perc.: 0.981): 6.0\n",
      "var17 (Cumul.perc.: 0.029): -5.16\n",
      "\n",
      "Obs. 7650:\n",
      " var27 (Cumul.perc.: 0.989): 7.61\n",
      "var9 (Cumul.perc.: 0.985): 6.06\n",
      "var0 (Cumul.perc.: 0.952): 5.89\n",
      "\n",
      "Obs. 7660:\n",
      " var19 (Cumul.perc.: 0.999): 10.31\n",
      "var16 (Cumul.perc.: 0.997): 8.4\n",
      "var12 (Cumul.perc.: 0.005): -6.94\n",
      "var21 (Cumul.perc.: 0.023): -6.3\n",
      "var22 (Cumul.perc.: 0.034): -4.83\n",
      "\n",
      "Obs. 7670:\n",
      " var11 (Cumul.perc.: 0.004): -8.25\n",
      "var5 (Cumul.perc.: 0.019): -5.97\n",
      "var8 (Cumul.perc.: 0.04): -4.24\n",
      "var16 (Cumul.perc.: 0.953): 5.24\n",
      "\n",
      "Obs. 7680:\n",
      " var12 (Cumul.perc.: 0.011): -5.89\n",
      "var17 (Cumul.perc.: 0.975): 6.22\n",
      "var20 (Cumul.perc.: 0.041): -4.23\n",
      "\n",
      "Obs. 7690:\n",
      " var6 (Cumul.perc.: 0.008): -7.8\n",
      "var4 (Cumul.perc.: 0.977): 6.36\n",
      "var5 (Cumul.perc.: 0.027): -5.46\n",
      "var25 (Cumul.perc.: 0.037): -5.14\n",
      "var29 (Cumul.perc.: 0.035): -5.33\n",
      "\n",
      "Obs. 7700:\n",
      " var13 (Cumul.perc.: 0.987): 7.71\n",
      "var24 (Cumul.perc.: 0.986): 7.36\n",
      "var23 (Cumul.perc.: 0.049): -4.82\n",
      "var0 (Cumul.perc.: 0.044): -5.11\n",
      "var19 (Cumul.perc.: 0.956): 5.74\n",
      "\n",
      "Obs. 7710:\n",
      " var24 (Cumul.perc.: 0.013): -6.59\n",
      "var23 (Cumul.perc.: 0.97): 6.37\n",
      "var19 (Cumul.perc.: 0.038): -4.97\n",
      "var17 (Cumul.perc.: 0.962): 5.67\n",
      "var28 (Cumul.perc.: 0.954): 5.46\n",
      "\n",
      "Obs. 7720:\n",
      " var14 (Cumul.perc.: 0.986): 7.4\n",
      "var27 (Cumul.perc.: 0.038): -4.98\n",
      "var28 (Cumul.perc.: 0.95): 5.34\n",
      "\n",
      "Obs. 7730:\n",
      " var25 (Cumul.perc.: 0.004): -8.1\n",
      "var27 (Cumul.perc.: 0.981): 6.94\n",
      "var17 (Cumul.perc.: 0.949): 5.26\n",
      "\n",
      "Obs. 7740:\n",
      " var2 (Cumul.perc.: 0.006): -7.95\n",
      "var13 (Cumul.perc.: 0.978): 7.04\n",
      "var3 (Cumul.perc.: 0.952): 4.96\n",
      "\n",
      "Obs. 7750:\n",
      " var29 (Cumul.perc.: 0.997): 9.24\n",
      "var16 (Cumul.perc.: 0.969): 5.73\n",
      "var1 (Cumul.perc.: 0.963): 5.47\n",
      "\n",
      "Obs. 7760:\n",
      " var20 (Cumul.perc.: 0.994): 7.46\n",
      "var22 (Cumul.perc.: 0.034): -4.82\n",
      "\n",
      "Obs. 7770:\n",
      " var20 (Cumul.perc.: 0.965): 5.4\n",
      "\n",
      "Obs. 7780:\n",
      " var20 (Cumul.perc.: 1.0): 10.35\n",
      "var2 (Cumul.perc.: 0.968): 6.58\n",
      "var29 (Cumul.perc.: 0.971): 6.56\n",
      "\n",
      "Obs. 7790:\n",
      " var6 (Cumul.perc.: 0.991): 8.49\n",
      "var23 (Cumul.perc.: 0.973): 6.54\n",
      "var8 (Cumul.perc.: 0.962): 5.2\n",
      "var18 (Cumul.perc.: 0.033): -5.25\n",
      "\n",
      "Obs. 7800:\n",
      " var7 (Cumul.perc.: 0.998): 8.94\n",
      "var10 (Cumul.perc.: 0.972): 5.78\n",
      "\n",
      "Obs. 7810:\n",
      " var2 (Cumul.perc.: 0.027): -5.82\n",
      "var16 (Cumul.perc.: 0.953): 5.25\n",
      "\n",
      "Obs. 7820:\n",
      " var11 (Cumul.perc.: 0.991): 7.97\n",
      "var27 (Cumul.perc.: 0.033): -5.19\n",
      "var13 (Cumul.perc.: 0.983): 7.37\n",
      "var22 (Cumul.perc.: 0.031): -4.98\n",
      "var10 (Cumul.perc.: 0.045): -4.22\n",
      "\n",
      "Obs. 7830:\n",
      " var10 (Cumul.perc.: 0.997): 8.36\n",
      "var27 (Cumul.perc.: 0.997): 8.91\n",
      "var20 (Cumul.perc.: 0.01): -5.91\n",
      "var0 (Cumul.perc.: 0.962): 6.24\n",
      "var13 (Cumul.perc.: 0.963): 6.29\n",
      "\n",
      "Obs. 7840:\n",
      " var11 (Cumul.perc.: 0.002): -9.25\n",
      "var8 (Cumul.perc.: 0.992): 6.93\n",
      "var19 (Cumul.perc.: 0.021): -5.82\n",
      "var16 (Cumul.perc.: 0.959): 5.43\n",
      "\n",
      "Obs. 7850:\n",
      " var26 (Cumul.perc.: 0.0): -10.93\n",
      "var7 (Cumul.perc.: 0.002): -8.6\n",
      "var5 (Cumul.perc.: 0.993): 8.19\n",
      "var20 (Cumul.perc.: 0.967): 5.45\n",
      "var17 (Cumul.perc.: 0.045): -4.53\n",
      "\n",
      "Obs. 7860:\n",
      " var25 (Cumul.perc.: 0.019): -6.03\n",
      "var23 (Cumul.perc.: 0.031): -5.55\n",
      "\n",
      "Obs. 7870:\n",
      " var3 (Cumul.perc.: 0.003): -7.31\n",
      "var15 (Cumul.perc.: 0.006): -7.01\n",
      "\n",
      "Obs. 7880:\n",
      " var29 (Cumul.perc.: 0.0): -13.5\n",
      "var16 (Cumul.perc.: 0.016): -5.6\n",
      "var2 (Cumul.perc.: 0.977): 7.07\n",
      "var27 (Cumul.perc.: 0.047): -4.67\n",
      "\n",
      "Obs. 7890:\n",
      " var0 (Cumul.perc.: 0.045): -5.07\n",
      "var9 (Cumul.perc.: 0.034): -4.19\n",
      "\n",
      "Obs. 7900:\n",
      " var19 (Cumul.perc.: 0.991): 7.85\n",
      "var12 (Cumul.perc.: 0.024): -4.88\n",
      "var17 (Cumul.perc.: 0.023): -5.47\n",
      "var13 (Cumul.perc.: 0.959): 6.15\n",
      "var1 (Cumul.perc.: 0.05): -4.16\n",
      "\n",
      "Obs. 7910:\n",
      " var13 (Cumul.perc.: 0.005): -8.03\n",
      "var22 (Cumul.perc.: 0.01): -6.47\n",
      "var26 (Cumul.perc.: 0.029): -5.21\n",
      "var8 (Cumul.perc.: 0.041): -4.21\n",
      "var11 (Cumul.perc.: 0.039): -5.15\n",
      "\n",
      "Obs. 7920:\n",
      " var18 (Cumul.perc.: 0.99): 7.79\n",
      "var5 (Cumul.perc.: 0.981): 6.92\n",
      "\n",
      "Obs. 7930:\n",
      " var2 (Cumul.perc.: 0.001): -10.79\n",
      "var4 (Cumul.perc.: 0.999): 9.58\n",
      "var9 (Cumul.perc.: 0.006): -6.02\n",
      "var10 (Cumul.perc.: 0.017): -5.47\n",
      "\n",
      "Obs. 7940:\n",
      " var8 (Cumul.perc.: 0.016): -5.4\n",
      "var17 (Cumul.perc.: 0.026): -5.31\n",
      "var5 (Cumul.perc.: 0.976): 6.58\n",
      "var25 (Cumul.perc.: 0.963): 6.02\n",
      "\n",
      "Obs. 7950:\n",
      " var21 (Cumul.perc.: 0.982): 7.54\n",
      "var0 (Cumul.perc.: 0.041): -5.24\n",
      "var28 (Cumul.perc.: 0.961): 5.71\n",
      "\n",
      "Obs. 7960:\n",
      " var6 (Cumul.perc.: 0.026): -6.09\n",
      "var4 (Cumul.perc.: 0.972): 6.13\n",
      "var24 (Cumul.perc.: 0.05): -4.7\n",
      "var28 (Cumul.perc.: 0.044): -4.61\n",
      "var19 (Cumul.perc.: 0.043): -4.77\n",
      "\n",
      "Obs. 7970:\n",
      " var21 (Cumul.perc.: 0.005): -8.4\n",
      "var7 (Cumul.perc.: 0.025): -5.22\n",
      "var6 (Cumul.perc.: 0.034): -5.69\n",
      "var8 (Cumul.perc.: 0.976): 5.72\n",
      "var22 (Cumul.perc.: 0.956): 5.52\n",
      "\n",
      "Obs. 7980:\n",
      " var23 (Cumul.perc.: 0.953): 5.75\n",
      "\n",
      "Obs. 7990:\n",
      " var18 (Cumul.perc.: 0.01): -6.82\n",
      "var28 (Cumul.perc.: 0.011): -6.48\n",
      "var23 (Cumul.perc.: 0.961): 6.02\n",
      "var6 (Cumul.perc.: 0.028): -5.97\n",
      "\n",
      "Obs. 8000:\n",
      " var0 (Cumul.perc.: 0.043): -5.15\n",
      "var20 (Cumul.perc.: 0.952): 4.99\n",
      "\n",
      "Obs. 8010:\n",
      " None\n",
      "\n",
      "Obs. 8020:\n",
      " var27 (Cumul.perc.: 0.015): -6.27\n",
      "var7 (Cumul.perc.: 0.985): 6.88\n",
      "var13 (Cumul.perc.: 0.034): -5.53\n",
      "\n",
      "Obs. 8030:\n",
      " var22 (Cumul.perc.: 0.999): 10.47\n",
      "var6 (Cumul.perc.: 0.018): -6.62\n",
      "var2 (Cumul.perc.: 0.969): 6.62\n",
      "var13 (Cumul.perc.: 0.968): 6.5\n",
      "var10 (Cumul.perc.: 0.957): 5.19\n",
      "\n",
      "Obs. 8040:\n",
      " var7 (Cumul.perc.: 0.988): 7.13\n",
      "var21 (Cumul.perc.: 0.974): 6.94\n",
      "\n",
      "Obs. 8050:\n",
      " var21 (Cumul.perc.: 0.036): -5.58\n",
      "\n",
      "Obs. 8060:\n",
      " var1 (Cumul.perc.: 0.993): 7.45\n",
      "var28 (Cumul.perc.: 0.03): -5.14\n",
      "var15 (Cumul.perc.: 0.025): -5.27\n",
      "var25 (Cumul.perc.: 0.969): 6.3\n",
      "\n",
      "Obs. 8070:\n",
      " var18 (Cumul.perc.: 0.963): 6.08\n",
      "\n",
      "Obs. 8080:\n",
      " var23 (Cumul.perc.: 0.009): -7.19\n",
      "var11 (Cumul.perc.: 0.988): 7.62\n",
      "var10 (Cumul.perc.: 0.97): 5.66\n",
      "var4 (Cumul.perc.: 0.956): 5.49\n",
      "var17 (Cumul.perc.: 0.035): -4.91\n",
      "\n",
      "Obs. 8090:\n",
      " var12 (Cumul.perc.: 1.0): 16.02\n",
      "var3 (Cumul.perc.: 0.0): -11.68\n",
      "var4 (Cumul.perc.: 0.001): -9.24\n",
      "var7 (Cumul.perc.: 0.001): -9.28\n",
      "var1 (Cumul.perc.: 0.003): -7.83\n",
      "\n",
      "Obs. 8100:\n",
      " var21 (Cumul.perc.: 0.993): 8.64\n",
      "\n",
      "Obs. 8110:\n",
      " var10 (Cumul.perc.: 0.982): 6.3\n",
      "var23 (Cumul.perc.: 0.967): 6.21\n",
      "var8 (Cumul.perc.: 0.041): -4.22\n",
      "var21 (Cumul.perc.: 0.957): 6.19\n",
      "var26 (Cumul.perc.: 0.035): -4.99\n",
      "\n",
      "Obs. 8120:\n",
      " var18 (Cumul.perc.: 0.988): 7.53\n",
      "var14 (Cumul.perc.: 0.981): 7.01\n",
      "var0 (Cumul.perc.: 0.023): -6.03\n",
      "\n",
      "Obs. 8130:\n",
      " var2 (Cumul.perc.: 0.989): 8.01\n",
      "var12 (Cumul.perc.: 0.042): -4.19\n",
      "\n",
      "Obs. 8140:\n",
      " var5 (Cumul.perc.: 0.97): 6.22\n",
      "\n",
      "Obs. 8150:\n",
      " var22 (Cumul.perc.: 0.01): -6.46\n",
      "var17 (Cumul.perc.: 0.984): 6.76\n",
      "var15 (Cumul.perc.: 0.033): -4.91\n",
      "var11 (Cumul.perc.: 0.964): 6.17\n",
      "var14 (Cumul.perc.: 0.05): -4.7\n",
      "\n",
      "Obs. 8160:\n",
      " var16 (Cumul.perc.: 0.981): 6.36\n",
      "var28 (Cumul.perc.: 0.951): 5.38\n",
      "var3 (Cumul.perc.: 0.95): 4.91\n",
      "\n",
      "Obs. 8170:\n",
      " var9 (Cumul.perc.: 0.013): -5.28\n",
      "var6 (Cumul.perc.: 0.978): 7.31\n",
      "var27 (Cumul.perc.: 0.029): -5.35\n",
      "\n",
      "Obs. 8180:\n",
      " var6 (Cumul.perc.: 0.019): -6.52\n",
      "var1 (Cumul.perc.: 0.045): -4.3\n",
      "\n",
      "Obs. 8190:\n",
      " var20 (Cumul.perc.: 0.006): -6.72\n",
      "var28 (Cumul.perc.: 0.031): -5.1\n",
      "var25 (Cumul.perc.: 0.971): 6.4\n",
      "\n",
      "Obs. 8200:\n",
      " var21 (Cumul.perc.: 0.007): -7.99\n",
      "var3 (Cumul.perc.: 0.008): -6.24\n",
      "\n",
      "Obs. 8210:\n",
      " var6 (Cumul.perc.: 0.015): -6.86\n",
      "var11 (Cumul.perc.: 0.023): -5.83\n",
      "\n",
      "Obs. 8220:\n",
      " None\n",
      "\n",
      "Obs. 8230:\n",
      " var20 (Cumul.perc.: 0.011): -5.8\n",
      "var15 (Cumul.perc.: 0.99): 7.11\n",
      "var23 (Cumul.perc.: 0.972): 6.49\n",
      "\n",
      "Obs. 8240:\n",
      " var8 (Cumul.perc.: 0.98): 5.95\n",
      "var27 (Cumul.perc.: 0.02): -5.84\n",
      "\n",
      "Obs. 8250:\n",
      " var25 (Cumul.perc.: 0.01): -6.85\n",
      "var8 (Cumul.perc.: 0.983): 6.09\n",
      "var9 (Cumul.perc.: 0.976): 5.57\n",
      "var13 (Cumul.perc.: 0.964): 6.37\n",
      "var27 (Cumul.perc.: 0.96): 5.92\n",
      "\n",
      "Obs. 8260:\n",
      " None\n",
      "\n",
      "Obs. 8270:\n",
      " var6 (Cumul.perc.: 0.023): -6.29\n",
      "var17 (Cumul.perc.: 0.974): 6.19\n",
      "\n",
      "Obs. 8280:\n",
      " var21 (Cumul.perc.: 0.983): 7.61\n",
      "var13 (Cumul.perc.: 0.029): -5.8\n",
      "\n",
      "Obs. 8290:\n",
      " var2 (Cumul.perc.: 0.014): -6.75\n",
      "var26 (Cumul.perc.: 0.026): -5.35\n",
      "var23 (Cumul.perc.: 0.96): 5.98\n",
      "var8 (Cumul.perc.: 0.971): 5.5\n",
      "var11 (Cumul.perc.: 0.047): -4.83\n",
      "\n",
      "Obs. 8300:\n",
      " var29 (Cumul.perc.: 0.99): 7.93\n",
      "var21 (Cumul.perc.: 0.979): 7.27\n",
      "var22 (Cumul.perc.: 0.983): 6.67\n",
      "var18 (Cumul.perc.: 0.968): 6.3\n",
      "\n",
      "Obs. 8310:\n",
      " var19 (Cumul.perc.: 0.035): -5.09\n",
      "var10 (Cumul.perc.: 0.039): -4.45\n",
      "var8 (Cumul.perc.: 0.955): 4.98\n",
      "\n",
      "Obs. 8320:\n",
      " None\n",
      "\n",
      "Obs. 8330:\n",
      " var22 (Cumul.perc.: 0.982): 6.57\n",
      "var19 (Cumul.perc.: 0.98): 6.87\n",
      "var21 (Cumul.perc.: 0.965): 6.54\n",
      "var6 (Cumul.perc.: 0.96): 6.39\n",
      "var0 (Cumul.perc.: 0.956): 6.03\n",
      "\n",
      "Obs. 8340:\n",
      " None\n",
      "\n",
      "Obs. 8350:\n",
      " None\n",
      "\n",
      "Obs. 8360:\n",
      " var4 (Cumul.perc.: 0.991): 7.58\n",
      "var19 (Cumul.perc.: 0.032): -5.24\n",
      "\n",
      "Obs. 8370:\n",
      " var19 (Cumul.perc.: 0.006): -7.42\n",
      "var29 (Cumul.perc.: 0.983): 7.27\n",
      "var23 (Cumul.perc.: 0.973): 6.53\n",
      "var24 (Cumul.perc.: 0.958): 5.94\n",
      "var20 (Cumul.perc.: 0.048): -4.02\n",
      "\n",
      "Obs. 8380:\n",
      " var14 (Cumul.perc.: 0.017): -6.21\n",
      "var19 (Cumul.perc.: 0.965): 6.07\n",
      "\n",
      "Obs. 8390:\n",
      " var12 (Cumul.perc.: 0.01): -5.99\n",
      "var29 (Cumul.perc.: 0.011): -6.86\n",
      "var18 (Cumul.perc.: 0.018): -6.11\n",
      "var28 (Cumul.perc.: 0.03): -5.16\n",
      "var10 (Cumul.perc.: 0.034): -4.64\n",
      "\n",
      "Obs. 8400:\n",
      " var11 (Cumul.perc.: 0.966): 6.24\n",
      "\n",
      "Obs. 8410:\n",
      " var0 (Cumul.perc.: 0.031): -5.66\n",
      "var3 (Cumul.perc.: 0.02): -5.12\n",
      "var25 (Cumul.perc.: 0.031): -5.41\n",
      "var7 (Cumul.perc.: 0.966): 5.82\n",
      "\n",
      "Obs. 8420:\n",
      " var19 (Cumul.perc.: 0.002): -9.48\n",
      "var20 (Cumul.perc.: 0.985): 6.38\n",
      "var28 (Cumul.perc.: 0.992): 7.76\n",
      "var16 (Cumul.perc.: 0.983): 6.45\n",
      "var29 (Cumul.perc.: 0.985): 7.46\n",
      "\n",
      "Obs. 8430:\n",
      " var10 (Cumul.perc.: 0.013): -5.77\n",
      "var6 (Cumul.perc.: 0.981): 7.5\n",
      "var8 (Cumul.perc.: 0.965): 5.32\n",
      "var29 (Cumul.perc.: 0.035): -5.32\n",
      "\n",
      "Obs. 8440:\n",
      " var26 (Cumul.perc.: 0.953): 5.53\n",
      "var7 (Cumul.perc.: 0.043): -4.54\n",
      "\n",
      "Obs. 8450:\n",
      " var10 (Cumul.perc.: 0.964): 5.44\n",
      "var15 (Cumul.perc.: 0.965): 5.59\n",
      "\n",
      "Obs. 8460:\n",
      " var21 (Cumul.perc.: 0.032): -5.8\n",
      "var0 (Cumul.perc.: 0.972): 6.71\n",
      "\n",
      "Obs. 8470:\n",
      " None\n",
      "\n",
      "Obs. 8480:\n",
      " var12 (Cumul.perc.: 0.011): -5.91\n",
      "var13 (Cumul.perc.: 0.982): 7.33\n",
      "var29 (Cumul.perc.: 0.019): -6.19\n",
      "var17 (Cumul.perc.: 0.038): -4.75\n",
      "\n",
      "Obs. 8490:\n",
      " var2 (Cumul.perc.: 0.99): 8.14\n",
      "var24 (Cumul.perc.: 0.027): -5.6\n",
      "var5 (Cumul.perc.: 0.025): -5.57\n",
      "var14 (Cumul.perc.: 0.044): -4.88\n",
      "var17 (Cumul.perc.: 0.955): 5.42\n",
      "\n",
      "Obs. 8500:\n",
      " var10 (Cumul.perc.: 0.998): 8.91\n",
      "var20 (Cumul.perc.: 0.994): 7.34\n",
      "var17 (Cumul.perc.: 0.985): 6.85\n",
      "\n",
      "Obs. 8510:\n",
      " var22 (Cumul.perc.: 0.011): -6.25\n",
      "var15 (Cumul.perc.: 0.955): 5.25\n",
      "\n",
      "Obs. 8520:\n",
      " var4 (Cumul.perc.: 0.99): 7.4\n",
      "\n",
      "Obs. 8530:\n",
      " var26 (Cumul.perc.: 0.009): -6.75\n",
      "var2 (Cumul.perc.: 0.033): -5.54\n",
      "var14 (Cumul.perc.: 0.047): -4.79\n",
      "var29 (Cumul.perc.: 0.044): -4.95\n",
      "\n",
      "Obs. 8540:\n",
      " var25 (Cumul.perc.: 0.007): -7.26\n",
      "var8 (Cumul.perc.: 0.987): 6.41\n",
      "var29 (Cumul.perc.: 0.032): -5.46\n",
      "var26 (Cumul.perc.: 0.039): -4.85\n",
      "var20 (Cumul.perc.: 0.966): 5.44\n",
      "\n",
      "Obs. 8550:\n",
      " var29 (Cumul.perc.: 0.015): -6.51\n",
      "var24 (Cumul.perc.: 0.018): -6.14\n",
      "var19 (Cumul.perc.: 0.018): -5.97\n",
      "\n",
      "Obs. 8560:\n",
      " var5 (Cumul.perc.: 0.026): -5.51\n",
      "\n",
      "Obs. 8570:\n",
      " var12 (Cumul.perc.: 0.985): 6.28\n",
      "var21 (Cumul.perc.: 0.95): 5.98\n",
      "\n",
      "Obs. 8580:\n",
      " var18 (Cumul.perc.: 0.969): 6.31\n",
      "var12 (Cumul.perc.: 0.037): -4.34\n",
      "var1 (Cumul.perc.: 0.048): -4.22\n",
      "\n",
      "Obs. 8590:\n",
      " var17 (Cumul.perc.: 0.008): -6.76\n",
      "var21 (Cumul.perc.: 0.009): -7.48\n",
      "var4 (Cumul.perc.: 0.978): 6.41\n",
      "var13 (Cumul.perc.: 0.98): 7.2\n",
      "var24 (Cumul.perc.: 0.966): 6.23\n",
      "\n",
      "Obs. 8600:\n",
      " var0 (Cumul.perc.: 0.03): -5.68\n",
      "var26 (Cumul.perc.: 0.033): -5.04\n",
      "\n",
      "Obs. 8610:\n",
      " var14 (Cumul.perc.: 0.014): -6.48\n",
      "var0 (Cumul.perc.: 0.021): -6.18\n",
      "var11 (Cumul.perc.: 0.97): 6.43\n",
      "\n",
      "Obs. 8620:\n",
      " var17 (Cumul.perc.: 0.02): -5.65\n",
      "\n",
      "Obs. 8630:\n",
      " var17 (Cumul.perc.: 0.965): 5.79\n",
      "var29 (Cumul.perc.: 0.96): 6.11\n",
      "\n",
      "Obs. 8640:\n",
      " var21 (Cumul.perc.: 0.965): 6.52\n",
      "var23 (Cumul.perc.: 0.034): -5.41\n",
      "var18 (Cumul.perc.: 0.965): 6.18\n",
      "\n",
      "Obs. 8650:\n",
      " var5 (Cumul.perc.: 0.011): -6.65\n",
      "var24 (Cumul.perc.: 0.979): 6.89\n",
      "var8 (Cumul.perc.: 0.957): 5.05\n",
      "\n",
      "Obs. 8660:\n",
      " var19 (Cumul.perc.: 1.0): 13.36\n",
      "var10 (Cumul.perc.: 0.001): -10.53\n",
      "var1 (Cumul.perc.: 0.999): 10.16\n",
      "var18 (Cumul.perc.: 0.001): -10.08\n",
      "var12 (Cumul.perc.: 0.004): -7.12\n",
      "\n",
      "Obs. 8670:\n",
      " var8 (Cumul.perc.: 0.99): 6.7\n",
      "var16 (Cumul.perc.: 0.98): 6.25\n",
      "var0 (Cumul.perc.: 0.043): -5.15\n",
      "\n",
      "Obs. 8680:\n",
      " var2 (Cumul.perc.: 0.988): 7.91\n",
      "var29 (Cumul.perc.: 0.966): 6.37\n",
      "var3 (Cumul.perc.: 0.023): -4.95\n",
      "var14 (Cumul.perc.: 0.043): -4.9\n",
      "\n",
      "Obs. 8690:\n",
      " var6 (Cumul.perc.: 0.967): 6.67\n",
      "\n",
      "Obs. 8700:\n",
      " var6 (Cumul.perc.: 0.026): -6.11\n",
      "var29 (Cumul.perc.: 0.964): 6.28\n",
      "var28 (Cumul.perc.: 0.049): -4.42\n",
      "\n",
      "Obs. 8710:\n",
      " var10 (Cumul.perc.: 0.999): 9.39\n",
      "var2 (Cumul.perc.: 0.98): 7.26\n",
      "var11 (Cumul.perc.: 0.958): 5.97\n",
      "\n",
      "Obs. 8720:\n",
      " var22 (Cumul.perc.: 0.03): -4.99\n",
      "var29 (Cumul.perc.: 0.041): -5.1\n",
      "\n",
      "Obs. 8730:\n",
      " None\n",
      "\n",
      "Obs. 8740:\n",
      " var5 (Cumul.perc.: 0.019): -5.99\n",
      "var12 (Cumul.perc.: 0.976): 5.81\n",
      "var17 (Cumul.perc.: 0.975): 6.22\n",
      "\n",
      "Obs. 8750:\n",
      " var6 (Cumul.perc.: 0.992): 8.67\n",
      "var23 (Cumul.perc.: 0.016): -6.37\n",
      "var24 (Cumul.perc.: 0.027): -5.55\n",
      "var28 (Cumul.perc.: 0.982): 6.74\n",
      "var0 (Cumul.perc.: 0.023): -6.02\n",
      "\n",
      "Obs. 8760:\n",
      " var15 (Cumul.perc.: 0.034): -4.83\n",
      "var22 (Cumul.perc.: 0.954): 5.46\n",
      "var9 (Cumul.perc.: 0.025): -4.6\n",
      "var12 (Cumul.perc.: 0.03): -4.6\n",
      "\n",
      "Obs. 8770:\n",
      " var11 (Cumul.perc.: 0.997): 9.18\n",
      "var10 (Cumul.perc.: 0.994): 7.54\n",
      "var18 (Cumul.perc.: 0.993): 8.18\n",
      "var2 (Cumul.perc.: 0.971): 6.75\n",
      "var12 (Cumul.perc.: 0.952): 4.96\n",
      "\n",
      "Obs. 8780:\n",
      " var18 (Cumul.perc.: 0.973): 6.54\n",
      "\n",
      "Obs. 8790:\n",
      " var25 (Cumul.perc.: 0.981): 6.95\n",
      "var2 (Cumul.perc.: 0.026): -5.85\n",
      "var1 (Cumul.perc.: 0.971): 5.77\n",
      "\n",
      "Obs. 8800:\n",
      " var0 (Cumul.perc.: 1.0): 12.38\n",
      "var28 (Cumul.perc.: 0.989): 7.39\n",
      "var16 (Cumul.perc.: 0.981): 6.31\n",
      "var9 (Cumul.perc.: 0.012): -5.32\n",
      "var4 (Cumul.perc.: 0.97): 6.06\n",
      "\n",
      "Obs. 8810:\n",
      " var8 (Cumul.perc.: 0.997): 7.84\n",
      "var24 (Cumul.perc.: 0.982): 7.1\n",
      "\n",
      "Obs. 8820:\n",
      " var3 (Cumul.perc.: 0.977): 5.87\n",
      "var16 (Cumul.perc.: 0.031): -4.82\n",
      "var15 (Cumul.perc.: 0.967): 5.64\n",
      "\n",
      "Obs. 8830:\n",
      " var8 (Cumul.perc.: 0.963): 5.22\n",
      "var21 (Cumul.perc.: 0.05): -5.06\n",
      "var12 (Cumul.perc.: 0.954): 5.0\n",
      "\n",
      "Obs. 8840:\n",
      " var19 (Cumul.perc.: 0.991): 7.88\n",
      "\n",
      "Obs. 8850:\n",
      " var10 (Cumul.perc.: 0.037): -4.52\n",
      "var12 (Cumul.perc.: 0.952): 4.95\n",
      "\n",
      "Obs. 8860:\n",
      " var10 (Cumul.perc.: 0.969): 5.65\n",
      "\n",
      "Obs. 8870:\n",
      " var17 (Cumul.perc.: 0.005): -7.44\n",
      "var6 (Cumul.perc.: 0.953): 6.14\n",
      "var10 (Cumul.perc.: 0.038): -4.45\n",
      "\n",
      "Obs. 8880:\n",
      " var11 (Cumul.perc.: 0.975): 6.67\n",
      "\n",
      "Obs. 8890:\n",
      " var12 (Cumul.perc.: 0.025): -4.87\n",
      "var29 (Cumul.perc.: 0.032): -5.45\n",
      "var28 (Cumul.perc.: 0.022): -5.61\n",
      "var18 (Cumul.perc.: 0.046): -4.77\n",
      "var11 (Cumul.perc.: 0.048): -4.8\n",
      "\n",
      "Obs. 8900:\n",
      " var17 (Cumul.perc.: 0.994): 8.05\n",
      "var29 (Cumul.perc.: 0.012): -6.83\n",
      "var27 (Cumul.perc.: 0.967): 6.2\n",
      "var6 (Cumul.perc.: 0.033): -5.73\n",
      "var0 (Cumul.perc.: 0.04): -5.3\n",
      "\n",
      "Obs. 8910:\n",
      " var18 (Cumul.perc.: 0.993): 8.2\n",
      "var2 (Cumul.perc.: 0.01): -7.2\n",
      "var13 (Cumul.perc.: 0.014): -6.8\n",
      "var16 (Cumul.perc.: 0.95): 5.13\n",
      "\n",
      "Obs. 8920:\n",
      " var20 (Cumul.perc.: 0.987): 6.57\n",
      "var26 (Cumul.perc.: 0.015): -6.13\n",
      "var6 (Cumul.perc.: 0.97): 6.82\n",
      "var24 (Cumul.perc.: 0.958): 5.93\n",
      "var22 (Cumul.perc.: 0.961): 5.68\n",
      "\n",
      "Obs. 8930:\n",
      " var18 (Cumul.perc.: 0.984): 7.21\n",
      "var13 (Cumul.perc.: 0.98): 7.19\n",
      "var12 (Cumul.perc.: 0.032): -4.5\n",
      "var28 (Cumul.perc.: 0.046): -4.53\n",
      "\n",
      "Obs. 8940:\n",
      " var14 (Cumul.perc.: 0.993): 8.11\n",
      "var23 (Cumul.perc.: 0.965): 6.16\n",
      "var20 (Cumul.perc.: 0.048): -4.02\n",
      "\n",
      "Obs. 8950:\n",
      " var24 (Cumul.perc.: 0.007): -7.42\n",
      "var0 (Cumul.perc.: 0.022): -6.11\n",
      "var11 (Cumul.perc.: 0.042): -4.99\n",
      "\n",
      "Obs. 8960:\n",
      " var3 (Cumul.perc.: 0.987): 6.5\n",
      "var27 (Cumul.perc.: 0.043): -4.8\n",
      "var22 (Cumul.perc.: 0.97): 5.97\n",
      "var26 (Cumul.perc.: 0.04): -4.8\n",
      "\n",
      "Obs. 8970:\n",
      " var0 (Cumul.perc.: 0.977): 7.01\n",
      "var17 (Cumul.perc.: 0.981): 6.61\n",
      "\n",
      "Obs. 8980:\n",
      " var28 (Cumul.perc.: 0.001): -9.92\n",
      "var13 (Cumul.perc.: 0.047): -5.04\n",
      "var2 (Cumul.perc.: 0.953): 6.0\n",
      "var6 (Cumul.perc.: 0.046): -5.16\n",
      "var11 (Cumul.perc.: 0.037): -5.21\n",
      "\n",
      "Obs. 8990:\n",
      " var15 (Cumul.perc.: 0.997): 8.52\n",
      "var28 (Cumul.perc.: 0.986): 7.04\n",
      "var17 (Cumul.perc.: 0.957): 5.51\n",
      "var4 (Cumul.perc.: 0.965): 5.81\n",
      "\n",
      "Obs. 9000:\n",
      " var29 (Cumul.perc.: 0.999): 10.5\n",
      "var22 (Cumul.perc.: 0.026): -5.21\n",
      "var8 (Cumul.perc.: 0.043): -4.18\n",
      "var4 (Cumul.perc.: 0.044): -4.47\n",
      "\n",
      "Obs. 9010:\n",
      " var23 (Cumul.perc.: 0.989): 7.7\n",
      "var15 (Cumul.perc.: 0.98): 6.35\n",
      "var21 (Cumul.perc.: 0.967): 6.61\n",
      "var18 (Cumul.perc.: 0.975): 6.61\n",
      "\n",
      "Obs. 9020:\n",
      " var24 (Cumul.perc.: 1.0): 10.78\n",
      "var22 (Cumul.perc.: 0.999): 9.74\n",
      "var1 (Cumul.perc.: 0.996): 8.04\n",
      "var4 (Cumul.perc.: 0.012): -6.2\n",
      "\n",
      "Obs. 9030:\n",
      " var8 (Cumul.perc.: 0.005): -6.79\n",
      "\n",
      "Obs. 9040:\n",
      " var17 (Cumul.perc.: 0.014): -6.1\n",
      "var3 (Cumul.perc.: 0.021): -5.05\n",
      "var15 (Cumul.perc.: 0.048): -4.37\n",
      "var1 (Cumul.perc.: 0.045): -4.27\n",
      "\n",
      "Obs. 9050:\n",
      " var6 (Cumul.perc.: 0.003): -9.13\n",
      "var20 (Cumul.perc.: 0.98): 6.02\n",
      "\n",
      "Obs. 9060:\n",
      " var12 (Cumul.perc.: 0.008): -6.26\n",
      "var26 (Cumul.perc.: 0.045): -4.61\n",
      "\n",
      "Obs. 9070:\n",
      " var10 (Cumul.perc.: 0.007): -6.54\n",
      "var15 (Cumul.perc.: 0.983): 6.57\n",
      "var4 (Cumul.perc.: 0.963): 5.73\n",
      "var1 (Cumul.perc.: 0.967): 5.61\n",
      "\n",
      "Obs. 9080:\n",
      " var5 (Cumul.perc.: 0.962): 5.93\n",
      "var3 (Cumul.perc.: 0.965): 5.37\n",
      "\n",
      "Obs. 9090:\n",
      " var22 (Cumul.perc.: 0.01): -6.45\n",
      "\n",
      "Obs. 9100:\n",
      " var15 (Cumul.perc.: 0.988): 6.89\n",
      "var17 (Cumul.perc.: 0.013): -6.19\n",
      "\n",
      "Obs. 9110:\n",
      " var17 (Cumul.perc.: 0.993): 7.78\n",
      "var5 (Cumul.perc.: 0.013): -6.42\n",
      "var18 (Cumul.perc.: 0.982): 7.05\n",
      "var11 (Cumul.perc.: 0.98): 6.97\n",
      "\n",
      "Obs. 9120:\n",
      " var26 (Cumul.perc.: 0.031): -5.15\n",
      "var12 (Cumul.perc.: 0.966): 5.39\n",
      "\n",
      "Obs. 9130:\n",
      " var9 (Cumul.perc.: 0.004): -6.43\n",
      "var18 (Cumul.perc.: 0.981): 7.01\n",
      "var15 (Cumul.perc.: 0.973): 6.0\n",
      "var0 (Cumul.perc.: 0.045): -5.09\n",
      "\n",
      "Obs. 9140:\n",
      " var10 (Cumul.perc.: 0.007): -6.55\n",
      "var15 (Cumul.perc.: 0.988): 6.95\n",
      "var19 (Cumul.perc.: 0.016): -6.15\n",
      "var18 (Cumul.perc.: 0.967): 6.27\n",
      "\n",
      "Obs. 9150:\n",
      " var2 (Cumul.perc.: 0.002): -9.03\n",
      "var28 (Cumul.perc.: 0.025): -5.44\n",
      "var1 (Cumul.perc.: 0.966): 5.58\n",
      "\n",
      "Obs. 9160:\n",
      " None\n",
      "\n",
      "Obs. 9170:\n",
      " var4 (Cumul.perc.: 0.996): 8.41\n",
      "var25 (Cumul.perc.: 0.009): -6.92\n",
      "var21 (Cumul.perc.: 0.95): 5.95\n",
      "var14 (Cumul.perc.: 0.041): -4.97\n",
      "var26 (Cumul.perc.: 0.955): 5.59\n",
      "\n",
      "Obs. 9180:\n",
      " var21 (Cumul.perc.: 0.004): -8.54\n",
      "var15 (Cumul.perc.: 0.006): -6.95\n",
      "var23 (Cumul.perc.: 0.989): 7.69\n",
      "var17 (Cumul.perc.: 0.969): 5.95\n",
      "var19 (Cumul.perc.: 0.957): 5.78\n",
      "\n",
      "Obs. 9190:\n",
      " var27 (Cumul.perc.: 0.0): -10.29\n",
      "var28 (Cumul.perc.: 0.004): -7.82\n",
      "var6 (Cumul.perc.: 0.971): 6.88\n",
      "\n",
      "Obs. 9200:\n",
      " var4 (Cumul.perc.: 0.018): -5.65\n",
      "\n",
      "Obs. 9210:\n",
      " var28 (Cumul.perc.: 0.0): -16.16\n",
      "var11 (Cumul.perc.: 1.0): 15.37\n",
      "var15 (Cumul.perc.: 0.0): -12.07\n",
      "var18 (Cumul.perc.: 1.0): 14.19\n",
      "var0 (Cumul.perc.: 0.001): -10.69\n",
      "\n",
      "Obs. 9220:\n",
      " var21 (Cumul.perc.: 0.021): -6.45\n",
      "var18 (Cumul.perc.: 0.979): 6.85\n",
      "\n",
      "Obs. 9230:\n",
      " var2 (Cumul.perc.: 0.953): 6.0\n",
      "\n",
      "Obs. 9240:\n",
      " var24 (Cumul.perc.: 0.011): -6.73\n",
      "var9 (Cumul.perc.: 0.974): 5.51\n",
      "var17 (Cumul.perc.: 0.025): -5.37\n",
      "\n",
      "Obs. 9250:\n",
      " var23 (Cumul.perc.: 0.991): 7.91\n",
      "var1 (Cumul.perc.: 0.983): 6.41\n",
      "var22 (Cumul.perc.: 0.968): 5.91\n",
      "\n",
      "Obs. 9260:\n",
      " var23 (Cumul.perc.: 0.009): -7.27\n",
      "var13 (Cumul.perc.: 0.044): -5.13\n",
      "var9 (Cumul.perc.: 0.03): -4.34\n",
      "var8 (Cumul.perc.: 0.043): -4.16\n",
      "var5 (Cumul.perc.: 0.957): 5.75\n",
      "\n",
      "Obs. 9270:\n",
      " None\n",
      "\n",
      "Obs. 9280:\n",
      " None\n",
      "\n",
      "Obs. 9290:\n",
      " var11 (Cumul.perc.: 0.015): -6.49\n",
      "var21 (Cumul.perc.: 0.976): 7.12\n",
      "var29 (Cumul.perc.: 0.037): -5.23\n",
      "\n",
      "Obs. 9300:\n",
      " var7 (Cumul.perc.: 0.011): -6.25\n",
      "var4 (Cumul.perc.: 0.967): 5.92\n",
      "var9 (Cumul.perc.: 0.044): -3.89\n",
      "\n",
      "Obs. 9310:\n",
      " var18 (Cumul.perc.: 0.02): -5.93\n",
      "var4 (Cumul.perc.: 0.035): -4.79\n",
      "\n",
      "Obs. 9320:\n",
      " var22 (Cumul.perc.: 0.011): -6.25\n",
      "var20 (Cumul.perc.: 0.012): -5.73\n",
      "var29 (Cumul.perc.: 0.014): -6.64\n",
      "var23 (Cumul.perc.: 0.978): 6.82\n",
      "var19 (Cumul.perc.: 0.021): -5.83\n",
      "\n",
      "Obs. 9330:\n",
      " var18 (Cumul.perc.: 0.962): 6.07\n",
      "\n",
      "Obs. 9340:\n",
      " var17 (Cumul.perc.: 0.045): -4.52\n",
      "\n",
      "Obs. 9350:\n",
      " var6 (Cumul.perc.: 0.008): -7.83\n",
      "var20 (Cumul.perc.: 0.991): 6.98\n",
      "var13 (Cumul.perc.: 0.96): 6.21\n",
      "var22 (Cumul.perc.: 0.958): 5.59\n",
      "var17 (Cumul.perc.: 0.034): -4.92\n",
      "\n",
      "Obs. 9360:\n",
      " var6 (Cumul.perc.: 0.998): 10.2\n",
      "var24 (Cumul.perc.: 0.997): 8.97\n",
      "var16 (Cumul.perc.: 0.009): -6.21\n",
      "var7 (Cumul.perc.: 0.011): -6.23\n",
      "var8 (Cumul.perc.: 0.986): 6.38\n",
      "\n",
      "Obs. 9370:\n",
      " var28 (Cumul.perc.: 0.999): 10.23\n",
      "var29 (Cumul.perc.: 0.02): -6.14\n",
      "\n",
      "Obs. 9380:\n",
      " var5 (Cumul.perc.: 0.015): -6.32\n",
      "var25 (Cumul.perc.: 0.015): -6.31\n",
      "var0 (Cumul.perc.: 0.027): -5.78\n",
      "\n",
      "Obs. 9390:\n",
      " var6 (Cumul.perc.: 0.024): -6.22\n",
      "\n",
      "Obs. 9400:\n",
      " var13 (Cumul.perc.: 0.004): -8.38\n",
      "var19 (Cumul.perc.: 0.989): 7.56\n",
      "var0 (Cumul.perc.: 0.045): -5.08\n",
      "var11 (Cumul.perc.: 0.04): -5.11\n",
      "\n",
      "Obs. 9410:\n",
      " var16 (Cumul.perc.: 0.001): -11.42\n",
      "var22 (Cumul.perc.: 0.999): 10.6\n",
      "var0 (Cumul.perc.: 0.998): 10.4\n",
      "var6 (Cumul.perc.: 0.003): -8.91\n",
      "var5 (Cumul.perc.: 0.003): -8.54\n",
      "\n",
      "Obs. 9420:\n",
      " var14 (Cumul.perc.: 0.991): 7.89\n",
      "var6 (Cumul.perc.: 0.985): 7.76\n",
      "var22 (Cumul.perc.: 0.969): 5.94\n",
      "\n",
      "Obs. 9430:\n",
      " var20 (Cumul.perc.: 0.047): -4.04\n",
      "\n",
      "Obs. 9440:\n",
      " var28 (Cumul.perc.: 0.996): 8.56\n",
      "var12 (Cumul.perc.: 0.017): -5.32\n",
      "var3 (Cumul.perc.: 0.969): 5.52\n",
      "var17 (Cumul.perc.: 0.966): 5.82\n",
      "\n",
      "Obs. 9450:\n",
      " var14 (Cumul.perc.: 0.004): -8.03\n",
      "var29 (Cumul.perc.: 0.976): 6.86\n",
      "var25 (Cumul.perc.: 0.033): -5.33\n",
      "\n",
      "Obs. 9460:\n",
      " var1 (Cumul.perc.: 0.996): 7.91\n",
      "var2 (Cumul.perc.: 0.99): 8.17\n",
      "var29 (Cumul.perc.: 0.981): 7.14\n",
      "var26 (Cumul.perc.: 0.985): 6.92\n",
      "var0 (Cumul.perc.: 0.04): -5.29\n",
      "\n",
      "Obs. 9470:\n",
      " var27 (Cumul.perc.: 0.958): 5.85\n",
      "var24 (Cumul.perc.: 0.96): 6.0\n",
      "\n",
      "Obs. 9480:\n",
      " None\n",
      "\n",
      "Obs. 9490:\n",
      " var0 (Cumul.perc.: 0.975): 6.9\n",
      "var11 (Cumul.perc.: 0.974): 6.64\n",
      "\n",
      "Obs. 9500:\n",
      " var26 (Cumul.perc.: 0.02): -5.74\n",
      "\n",
      "Obs. 9510:\n",
      " var10 (Cumul.perc.: 0.994): 7.5\n",
      "var3 (Cumul.perc.: 0.01): -6.0\n",
      "var6 (Cumul.perc.: 0.037): -5.54\n",
      "var16 (Cumul.perc.: 0.968): 5.71\n",
      "var19 (Cumul.perc.: 0.967): 6.18\n",
      "\n",
      "Obs. 9520:\n",
      " var28 (Cumul.perc.: 0.026): -5.34\n",
      "\n",
      "Obs. 9530:\n",
      " var7 (Cumul.perc.: 0.995): 7.96\n",
      "var23 (Cumul.perc.: 0.011): -6.97\n",
      "\n",
      "Obs. 9540:\n",
      " var27 (Cumul.perc.: 0.995): 8.47\n",
      "var17 (Cumul.perc.: 0.013): -6.15\n",
      "var22 (Cumul.perc.: 0.986): 6.84\n",
      "var10 (Cumul.perc.: 0.015): -5.62\n",
      "var8 (Cumul.perc.: 0.024): -4.88\n",
      "\n",
      "Obs. 9550:\n",
      " var15 (Cumul.perc.: 0.994): 7.76\n",
      "var13 (Cumul.perc.: 0.959): 6.18\n",
      "var5 (Cumul.perc.: 0.034): -5.14\n",
      "\n",
      "Obs. 9560:\n",
      " var6 (Cumul.perc.: 0.018): -6.65\n",
      "var15 (Cumul.perc.: 0.979): 6.28\n",
      "\n",
      "Obs. 9570:\n",
      " var11 (Cumul.perc.: 0.045): -4.91\n",
      "\n",
      "Obs. 9580:\n",
      " var22 (Cumul.perc.: 0.005): -7.38\n",
      "var4 (Cumul.perc.: 0.011): -6.23\n",
      "var5 (Cumul.perc.: 0.97): 6.24\n",
      "\n",
      "Obs. 9590:\n",
      " None\n",
      "\n",
      "Obs. 9600:\n",
      " var13 (Cumul.perc.: 0.026): -5.98\n",
      "var4 (Cumul.perc.: 0.953): 5.4\n",
      "var20 (Cumul.perc.: 0.954): 5.04\n",
      "\n",
      "Obs. 9610:\n",
      " var10 (Cumul.perc.: 0.974): 5.84\n",
      "\n",
      "Obs. 9620:\n",
      " var20 (Cumul.perc.: 0.992): 7.1\n",
      "var27 (Cumul.perc.: 0.014): -6.35\n",
      "var25 (Cumul.perc.: 0.03): -5.44\n",
      "var28 (Cumul.perc.: 0.037): -4.86\n",
      "\n",
      "Obs. 9630:\n",
      " None\n",
      "\n",
      "Obs. 9640:\n",
      " var12 (Cumul.perc.: 0.987): 6.45\n",
      "\n",
      "Obs. 9650:\n",
      " var29 (Cumul.perc.: 0.018): -6.27\n",
      "var19 (Cumul.perc.: 0.022): -5.76\n",
      "var15 (Cumul.perc.: 0.98): 6.37\n",
      "\n",
      "Obs. 9660:\n",
      " var26 (Cumul.perc.: 0.015): -6.16\n",
      "var14 (Cumul.perc.: 0.027): -5.61\n",
      "var25 (Cumul.perc.: 0.039): -5.04\n",
      "var22 (Cumul.perc.: 0.952): 5.39\n",
      "\n",
      "Obs. 9670:\n",
      " var20 (Cumul.perc.: 0.984): 6.28\n",
      "\n",
      "Obs. 9680:\n",
      " var21 (Cumul.perc.: 0.99): 8.19\n",
      "var0 (Cumul.perc.: 0.979): 7.17\n",
      "var25 (Cumul.perc.: 0.968): 6.23\n",
      "var14 (Cumul.perc.: 0.037): -5.14\n",
      "\n",
      "Obs. 9690:\n",
      " var10 (Cumul.perc.: 0.996): 8.02\n",
      "var25 (Cumul.perc.: 0.993): 8.26\n",
      "var24 (Cumul.perc.: 0.008): -7.1\n",
      "\n",
      "Obs. 9700:\n",
      " var6 (Cumul.perc.: 0.987): 8.04\n",
      "var25 (Cumul.perc.: 0.973): 6.49\n",
      "\n",
      "Obs. 9710:\n",
      " var2 (Cumul.perc.: 0.986): 7.73\n",
      "var14 (Cumul.perc.: 0.011): -6.81\n",
      "var18 (Cumul.perc.: 0.954): 5.77\n",
      "var1 (Cumul.perc.: 0.047): -4.24\n",
      "\n",
      "Obs. 9720:\n",
      " var3 (Cumul.perc.: 0.997): 8.13\n",
      "var21 (Cumul.perc.: 0.01): -7.42\n",
      "var20 (Cumul.perc.: 0.033): -4.51\n",
      "\n",
      "Obs. 9730:\n",
      " var19 (Cumul.perc.: 0.009): -6.86\n",
      "var26 (Cumul.perc.: 0.994): 8.07\n",
      "\n",
      "Obs. 9740:\n",
      " var13 (Cumul.perc.: 0.022): -6.22\n",
      "var8 (Cumul.perc.: 0.96): 5.14\n",
      "var3 (Cumul.perc.: 0.951): 4.95\n",
      "\n",
      "Obs. 9750:\n",
      " var12 (Cumul.perc.: 0.998): 8.54\n",
      "var10 (Cumul.perc.: 0.995): 7.6\n",
      "var26 (Cumul.perc.: 0.979): 6.55\n",
      "\n",
      "Obs. 9760:\n",
      " var8 (Cumul.perc.: 0.989): 6.57\n",
      "var1 (Cumul.perc.: 0.02): -5.37\n",
      "\n",
      "Obs. 9770:\n",
      " var15 (Cumul.perc.: 0.001): -9.04\n",
      "var14 (Cumul.perc.: 0.005): -7.78\n",
      "var4 (Cumul.perc.: 0.014): -5.97\n",
      "var1 (Cumul.perc.: 0.012): -6.09\n",
      "var11 (Cumul.perc.: 0.991): 7.94\n",
      "\n",
      "Obs. 9780:\n",
      " var6 (Cumul.perc.: 0.021): -6.42\n",
      "var2 (Cumul.perc.: 0.959): 6.23\n",
      "\n",
      "Obs. 9790:\n",
      " var10 (Cumul.perc.: 0.032): -4.69\n",
      "var19 (Cumul.perc.: 0.967): 6.17\n",
      "var18 (Cumul.perc.: 0.035): -5.15\n",
      "\n",
      "Obs. 9800:\n",
      " var24 (Cumul.perc.: 0.015): -6.4\n",
      "var16 (Cumul.perc.: 0.988): 6.9\n",
      "\n",
      "Obs. 9810:\n",
      " var29 (Cumul.perc.: 0.032): -5.47\n",
      "var24 (Cumul.perc.: 0.029): -5.48\n",
      "var14 (Cumul.perc.: 0.964): 6.1\n",
      "var7 (Cumul.perc.: 0.967): 5.87\n",
      "var1 (Cumul.perc.: 0.046): -4.25\n",
      "\n",
      "Obs. 9820:\n",
      " var0 (Cumul.perc.: 0.048): -4.98\n",
      "var14 (Cumul.perc.: 0.043): -4.91\n",
      "\n",
      "Obs. 9830:\n",
      " var2 (Cumul.perc.: 0.996): 9.3\n",
      "var5 (Cumul.perc.: 0.011): -6.72\n",
      "var28 (Cumul.perc.: 0.017): -5.95\n",
      "var29 (Cumul.perc.: 0.985): 7.45\n",
      "var13 (Cumul.perc.: 0.978): 7.03\n",
      "\n",
      "Obs. 9840:\n",
      " var25 (Cumul.perc.: 0.993): 8.24\n",
      "var24 (Cumul.perc.: 0.01): -6.92\n",
      "var6 (Cumul.perc.: 0.041): -5.37\n",
      "\n",
      "Obs. 9850:\n",
      " var6 (Cumul.perc.: 0.987): 7.92\n",
      "var8 (Cumul.perc.: 0.975): 5.67\n",
      "var9 (Cumul.perc.: 0.032): -4.26\n",
      "var2 (Cumul.perc.: 0.042): -5.19\n",
      "var3 (Cumul.perc.: 0.04): -4.28\n",
      "\n",
      "Obs. 9860:\n",
      " var21 (Cumul.perc.: 0.981): 7.43\n",
      "var2 (Cumul.perc.: 0.964): 6.42\n",
      "\n",
      "Obs. 9870:\n",
      " var2 (Cumul.perc.: 0.993): 8.61\n",
      "\n",
      "Obs. 9880:\n",
      " var1 (Cumul.perc.: 1.0): 11.52\n",
      "var20 (Cumul.perc.: 1.0): 10.49\n",
      "var10 (Cumul.perc.: 0.001): -10.02\n",
      "var9 (Cumul.perc.: 0.001): -7.74\n",
      "var15 (Cumul.perc.: 0.002): -8.62\n",
      "\n",
      "Obs. 9890:\n",
      " var2 (Cumul.perc.: 0.003): -8.39\n",
      "var21 (Cumul.perc.: 0.014): -6.98\n",
      "var16 (Cumul.perc.: 0.989): 6.98\n",
      "var6 (Cumul.perc.: 0.98): 7.41\n",
      "var26 (Cumul.perc.: 0.954): 5.54\n",
      "\n",
      "Obs. 9900:\n",
      " var11 (Cumul.perc.: 0.963): 6.15\n",
      "\n",
      "Obs. 9910:\n",
      " var16 (Cumul.perc.: 0.955): 5.31\n",
      "\n",
      "Obs. 9920:\n",
      " var14 (Cumul.perc.: 0.026): -5.65\n",
      "var23 (Cumul.perc.: 0.964): 6.13\n",
      "var20 (Cumul.perc.: 0.047): -4.05\n",
      "\n",
      "Obs. 9930:\n",
      " var21 (Cumul.perc.: 0.007): -7.98\n",
      "var6 (Cumul.perc.: 0.016): -6.74\n",
      "\n",
      "Obs. 9940:\n",
      " None\n",
      "\n",
      "Obs. 9950:\n",
      " None\n",
      "\n",
      "Obs. 9960:\n",
      " var29 (Cumul.perc.: 0.011): -6.94\n",
      "var22 (Cumul.perc.: 0.02): -5.56\n",
      "\n",
      "Obs. 9970:\n",
      " var14 (Cumul.perc.: 0.984): 7.28\n",
      "var18 (Cumul.perc.: 0.039): -5.01\n",
      "var9 (Cumul.perc.: 0.949): 4.7\n",
      "\n",
      "Obs. 9980:\n",
      " var19 (Cumul.perc.: 0.987): 7.37\n",
      "var17 (Cumul.perc.: 0.017): -5.85\n",
      "var29 (Cumul.perc.: 0.016): -6.44\n",
      "var24 (Cumul.perc.: 0.04): -5.01\n",
      "var27 (Cumul.perc.: 0.039): -4.95\n",
      "\n",
      "Obs. 9990:\n",
      " var7 (Cumul.perc.: 0.012): -6.19\n",
      "var2 (Cumul.perc.: 0.962): 6.35\n",
      "var1 (Cumul.perc.: 0.038): -4.51\n",
      "\n",
      "Obs. 10000:\n",
      " var6 (Cumul.perc.: 0.019): -6.48\n",
      "var4 (Cumul.perc.: 0.977): 6.35\n",
      "var15 (Cumul.perc.: 0.975): 6.06\n",
      "var17 (Cumul.perc.: 0.021): -5.58\n",
      "var3 (Cumul.perc.: 0.951): 4.93\n",
      "\n",
      "Obs. 10010:\n",
      " var17 (Cumul.perc.: 0.971): 6.07\n",
      "var9 (Cumul.perc.: 0.954): 4.82\n",
      "\n",
      "Obs. 10020:\n",
      " var5 (Cumul.perc.: 0.999): 11.0\n",
      "var16 (Cumul.perc.: 0.98): 6.29\n",
      "var29 (Cumul.perc.: 0.989): 7.87\n",
      "var10 (Cumul.perc.: 0.043): -4.3\n",
      "\n",
      "Obs. 10030:\n",
      " var18 (Cumul.perc.: 0.984): 7.22\n",
      "var19 (Cumul.perc.: 0.958): 5.82\n",
      "\n",
      "Obs. 10040:\n",
      " var13 (Cumul.perc.: 0.023): -6.11\n",
      "var19 (Cumul.perc.: 0.955): 5.71\n",
      "\n",
      "Obs. 10050:\n",
      " var11 (Cumul.perc.: 0.012): -6.77\n",
      "var20 (Cumul.perc.: 0.959): 5.18\n",
      "var16 (Cumul.perc.: 0.955): 5.28\n",
      "\n",
      "Obs. 10060:\n",
      " var18 (Cumul.perc.: 0.952): 5.71\n",
      "var28 (Cumul.perc.: 0.041): -4.7\n",
      "\n",
      "Obs. 10070:\n",
      " var14 (Cumul.perc.: 0.006): -7.61\n",
      "var20 (Cumul.perc.: 0.987): 6.52\n",
      "var12 (Cumul.perc.: 0.971): 5.57\n",
      "var19 (Cumul.perc.: 0.028): -5.42\n",
      "\n",
      "Obs. 10080:\n",
      " None\n",
      "\n",
      "Obs. 10090:\n",
      " var21 (Cumul.perc.: 0.999): 11.07\n",
      "var0 (Cumul.perc.: 0.977): 7.0\n",
      "var26 (Cumul.perc.: 0.981): 6.64\n",
      "var25 (Cumul.perc.: 0.971): 6.42\n",
      "var13 (Cumul.perc.: 0.04): -5.28\n",
      "\n",
      "Obs. 10100:\n",
      " var13 (Cumul.perc.: 0.006): -7.98\n",
      "\n",
      "Obs. 10110:\n",
      " var3 (Cumul.perc.: 0.998): 8.42\n",
      "var17 (Cumul.perc.: 0.998): 9.05\n",
      "var23 (Cumul.perc.: 0.993): 8.18\n",
      "var1 (Cumul.perc.: 0.988): 6.78\n",
      "\n",
      "Obs. 10120:\n",
      " var28 (Cumul.perc.: 0.997): 8.88\n",
      "var4 (Cumul.perc.: 0.972): 6.12\n",
      "var1 (Cumul.perc.: 0.047): -4.23\n",
      "\n",
      "Obs. 10130:\n",
      " var17 (Cumul.perc.: 0.01): -6.47\n",
      "var20 (Cumul.perc.: 0.04): -4.26\n",
      "var2 (Cumul.perc.: 0.045): -5.05\n",
      "\n",
      "Obs. 10140:\n",
      " var10 (Cumul.perc.: 0.008): -6.33\n",
      "var3 (Cumul.perc.: 0.026): -4.83\n",
      "\n",
      "Obs. 10150:\n",
      " var9 (Cumul.perc.: 0.032): -4.27\n",
      "var12 (Cumul.perc.: 0.048): -4.03\n",
      "var17 (Cumul.perc.: 0.952): 5.34\n",
      "\n",
      "Obs. 10160:\n",
      " var12 (Cumul.perc.: 1.0): 10.16\n",
      "var6 (Cumul.perc.: 0.009): -7.61\n",
      "var24 (Cumul.perc.: 0.983): 7.13\n",
      "var26 (Cumul.perc.: 0.974): 6.28\n",
      "var11 (Cumul.perc.: 0.965): 6.22\n",
      "\n",
      "Obs. 10170:\n",
      " var2 (Cumul.perc.: 0.006): -7.84\n",
      "var19 (Cumul.perc.: 0.014): -6.28\n",
      "var10 (Cumul.perc.: 0.048): -4.14\n",
      "\n",
      "Obs. 10180:\n",
      " var5 (Cumul.perc.: 0.007): -7.35\n",
      "var17 (Cumul.perc.: 0.963): 5.69\n",
      "\n",
      "Obs. 10190:\n",
      " var19 (Cumul.perc.: 0.037): -5.01\n",
      "\n",
      "Obs. 10200:\n",
      " var25 (Cumul.perc.: 0.971): 6.42\n",
      "var19 (Cumul.perc.: 0.958): 5.81\n",
      "var26 (Cumul.perc.: 0.039): -4.83\n",
      "var23 (Cumul.perc.: 0.95): 5.66\n",
      "\n",
      "Obs. 10210:\n",
      " var24 (Cumul.perc.: 0.023): -5.81\n",
      "var13 (Cumul.perc.: 0.023): -6.15\n",
      "var17 (Cumul.perc.: 0.022): -5.53\n",
      "var26 (Cumul.perc.: 0.959): 5.7\n",
      "\n",
      "Obs. 10220:\n",
      " var0 (Cumul.perc.: 0.981): 7.26\n",
      "var28 (Cumul.perc.: 0.978): 6.44\n",
      "var25 (Cumul.perc.: 0.981): 6.92\n",
      "\n",
      "Obs. 10230:\n",
      " var24 (Cumul.perc.: 0.023): -5.79\n",
      "var8 (Cumul.perc.: 0.035): -4.39\n",
      "var27 (Cumul.perc.: 0.04): -4.9\n",
      "\n",
      "Obs. 10240:\n",
      " var16 (Cumul.perc.: 0.995): 7.94\n",
      "var29 (Cumul.perc.: 0.047): -4.88\n",
      "\n",
      "Obs. 10250:\n",
      " None\n",
      "\n",
      "Obs. 10260:\n",
      " var28 (Cumul.perc.: 0.997): 8.98\n",
      "\n",
      "Obs. 10270:\n",
      " var23 (Cumul.perc.: 0.005): -8.2\n",
      "var14 (Cumul.perc.: 0.029): -5.48\n",
      "var4 (Cumul.perc.: 0.051): -4.27\n",
      "\n",
      "Obs. 10280:\n",
      " var6 (Cumul.perc.: 0.005): -8.45\n",
      "\n",
      "Obs. 10290:\n",
      " var26 (Cumul.perc.: 0.027): -5.33\n",
      "var1 (Cumul.perc.: 0.956): 5.23\n",
      "\n",
      "Obs. 10300:\n",
      " var2 (Cumul.perc.: 0.995): 9.11\n",
      "var5 (Cumul.perc.: 0.025): -5.56\n",
      "var0 (Cumul.perc.: 0.974): 6.84\n",
      "\n",
      "Obs. 10310:\n",
      " var12 (Cumul.perc.: 0.993): 7.18\n",
      "var13 (Cumul.perc.: 0.009): -7.38\n",
      "var21 (Cumul.perc.: 0.966): 6.55\n",
      "var3 (Cumul.perc.: 0.979): 5.95\n",
      "var18 (Cumul.perc.: 0.043): -4.85\n",
      "\n",
      "Obs. 10320:\n",
      " var27 (Cumul.perc.: 0.972): 6.41\n",
      "\n",
      "Obs. 10330:\n",
      " var26 (Cumul.perc.: 0.0): -10.16\n",
      "var23 (Cumul.perc.: 0.979): 6.85\n",
      "var22 (Cumul.perc.: 0.023): -5.4\n",
      "var20 (Cumul.perc.: 0.016): -5.36\n",
      "\n",
      "Obs. 10340:\n",
      " var21 (Cumul.perc.: 0.98): 7.34\n",
      "var15 (Cumul.perc.: 0.981): 6.4\n",
      "var25 (Cumul.perc.: 0.966): 6.15\n",
      "var17 (Cumul.perc.: 0.038): -4.77\n",
      "\n",
      "Obs. 10350:\n",
      " var24 (Cumul.perc.: 0.037): -5.1\n",
      "var14 (Cumul.perc.: 0.964): 6.1\n",
      "var12 (Cumul.perc.: 0.034): -4.43\n",
      "var15 (Cumul.perc.: 0.042): -4.57\n",
      "var5 (Cumul.perc.: 0.955): 5.69\n",
      "\n",
      "Obs. 10360:\n",
      " var25 (Cumul.perc.: 0.962): 5.98\n",
      "var7 (Cumul.perc.: 0.96): 5.59\n",
      "\n",
      "Obs. 10370:\n",
      " var22 (Cumul.perc.: 0.01): -6.37\n",
      "var3 (Cumul.perc.: 0.976): 5.85\n",
      "\n",
      "Obs. 10380:\n",
      " var9 (Cumul.perc.: 0.004): -6.5\n",
      "var0 (Cumul.perc.: 0.032): -5.6\n",
      "var29 (Cumul.perc.: 0.019): -6.22\n",
      "var28 (Cumul.perc.: 0.042): -4.69\n",
      "\n",
      "Obs. 10390:\n",
      " var17 (Cumul.perc.: 0.001): -9.51\n",
      "var2 (Cumul.perc.: 0.025): -5.94\n",
      "var19 (Cumul.perc.: 0.032): -5.25\n",
      "var5 (Cumul.perc.: 0.968): 6.15\n",
      "var28 (Cumul.perc.: 0.959): 5.62\n",
      "\n",
      "Obs. 10400:\n",
      " var29 (Cumul.perc.: 0.007): -7.51\n",
      "var11 (Cumul.perc.: 0.008): -7.29\n",
      "var5 (Cumul.perc.: 0.987): 7.33\n",
      "var26 (Cumul.perc.: 0.033): -5.05\n",
      "var6 (Cumul.perc.: 0.045): -5.2\n",
      "\n",
      "Obs. 10410:\n",
      " None\n",
      "\n",
      "Obs. 10420:\n",
      " var1 (Cumul.perc.: 0.996): 7.9\n",
      "\n",
      "Obs. 10430:\n",
      " var7 (Cumul.perc.: 0.992): 7.55\n",
      "var8 (Cumul.perc.: 0.017): -5.31\n",
      "var20 (Cumul.perc.: 0.025): -4.84\n",
      "var14 (Cumul.perc.: 0.951): 5.69\n",
      "\n",
      "Obs. 10440:\n",
      " var10 (Cumul.perc.: 0.995): 7.74\n",
      "var14 (Cumul.perc.: 0.026): -5.68\n",
      "var20 (Cumul.perc.: 0.959): 5.18\n",
      "\n",
      "Obs. 10450:\n",
      " var1 (Cumul.perc.: 0.999): 9.71\n",
      "var5 (Cumul.perc.: 0.012): -6.61\n",
      "var13 (Cumul.perc.: 0.975): 6.85\n",
      "var2 (Cumul.perc.: 0.044): -5.08\n",
      "\n",
      "Obs. 10460:\n",
      " var29 (Cumul.perc.: 0.01): -7.06\n",
      "var28 (Cumul.perc.: 0.98): 6.59\n",
      "\n",
      "Obs. 10470:\n",
      " var11 (Cumul.perc.: 0.041): -5.07\n",
      "\n",
      "Obs. 10480:\n",
      " var21 (Cumul.perc.: 0.998): 10.48\n",
      "var0 (Cumul.perc.: 0.037): -5.42\n",
      "var7 (Cumul.perc.: 0.956): 5.48\n",
      "\n",
      "Obs. 10490:\n",
      " var10 (Cumul.perc.: 0.967): 5.57\n",
      "\n",
      "Obs. 10500:\n",
      " var6 (Cumul.perc.: 0.018): -6.6\n",
      "var15 (Cumul.perc.: 0.027): -5.19\n",
      "var19 (Cumul.perc.: 0.97): 6.28\n",
      "var11 (Cumul.perc.: 0.049): -4.77\n",
      "\n",
      "Obs. 10510:\n",
      " var19 (Cumul.perc.: 0.013): -6.45\n",
      "\n",
      "Obs. 10520:\n",
      " var23 (Cumul.perc.: 0.994): 8.34\n",
      "var27 (Cumul.perc.: 0.004): -7.91\n",
      "var26 (Cumul.perc.: 0.014): -6.22\n",
      "var9 (Cumul.perc.: 0.03): -4.34\n",
      "var20 (Cumul.perc.: 0.967): 5.48\n",
      "\n",
      "Obs. 10530:\n",
      " var2 (Cumul.perc.: 0.967): 6.52\n",
      "\n",
      "Obs. 10540:\n",
      " None\n",
      "\n",
      "Obs. 10550:\n",
      " var21 (Cumul.perc.: 0.012): -7.23\n",
      "var6 (Cumul.perc.: 0.01): -7.44\n",
      "var14 (Cumul.perc.: 0.017): -6.3\n",
      "var22 (Cumul.perc.: 0.974): 6.17\n",
      "var5 (Cumul.perc.: 0.049): -4.64\n",
      "\n",
      "Obs. 10560:\n",
      " var8 (Cumul.perc.: 0.004): -7.12\n",
      "var20 (Cumul.perc.: 0.031): -4.58\n",
      "var15 (Cumul.perc.: 0.959): 5.36\n",
      "var23 (Cumul.perc.: 0.955): 5.8\n",
      "\n",
      "Obs. 10570:\n",
      " var24 (Cumul.perc.: 0.99): 7.7\n",
      "var17 (Cumul.perc.: 0.015): -5.97\n",
      "var10 (Cumul.perc.: 0.026): -5.0\n",
      "var27 (Cumul.perc.: 0.033): -5.19\n",
      "var4 (Cumul.perc.: 0.031): -5.01\n",
      "\n",
      "Obs. 10580:\n",
      " var24 (Cumul.perc.: 0.015): -6.44\n",
      "var25 (Cumul.perc.: 0.972): 6.44\n",
      "var0 (Cumul.perc.: 0.962): 6.25\n",
      "var2 (Cumul.perc.: 0.95): 5.91\n",
      "var9 (Cumul.perc.: 0.037): -4.1\n",
      "\n",
      "Obs. 10590:\n",
      " var26 (Cumul.perc.: 0.962): 5.79\n",
      "var29 (Cumul.perc.: 0.955): 5.89\n",
      "\n",
      "Obs. 10600:\n",
      " var9 (Cumul.perc.: 0.982): 5.92\n",
      "var24 (Cumul.perc.: 0.029): -5.5\n",
      "var3 (Cumul.perc.: 0.039): -4.32\n",
      "\n",
      "Obs. 10610:\n",
      " var26 (Cumul.perc.: 0.007): -7.02\n",
      "var16 (Cumul.perc.: 0.97): 5.78\n",
      "var22 (Cumul.perc.: 0.974): 6.13\n",
      "\n",
      "Obs. 10620:\n",
      " var19 (Cumul.perc.: 0.028): -5.39\n",
      "var8 (Cumul.perc.: 0.048): -4.02\n",
      "\n",
      "Obs. 10630:\n",
      " var27 (Cumul.perc.: 0.026): -5.5\n",
      "var2 (Cumul.perc.: 0.96): 6.27\n",
      "\n",
      "Obs. 10640:\n",
      " var26 (Cumul.perc.: 0.002): -8.24\n",
      "var3 (Cumul.perc.: 0.983): 6.25\n",
      "var24 (Cumul.perc.: 0.954): 5.78\n",
      "var10 (Cumul.perc.: 0.036): -4.55\n",
      "var28 (Cumul.perc.: 0.961): 5.72\n",
      "\n",
      "Obs. 10650:\n",
      " None\n",
      "\n",
      "Obs. 10660:\n",
      " var18 (Cumul.perc.: 0.03): -5.36\n",
      "\n",
      "Obs. 10670:\n",
      " var26 (Cumul.perc.: 0.965): 5.88\n",
      "var1 (Cumul.perc.: 0.034): -4.68\n",
      "\n",
      "Obs. 10680:\n",
      " var27 (Cumul.perc.: 0.002): -8.4\n",
      "var6 (Cumul.perc.: 0.986): 7.9\n",
      "var25 (Cumul.perc.: 0.965): 6.13\n",
      "var2 (Cumul.perc.: 0.959): 6.24\n",
      "var7 (Cumul.perc.: 0.044): -4.49\n",
      "\n",
      "Obs. 10690:\n",
      " var4 (Cumul.perc.: 0.997): 8.88\n",
      "var3 (Cumul.perc.: 0.982): 6.13\n",
      "var5 (Cumul.perc.: 0.042): -4.85\n",
      "\n",
      "Obs. 10700:\n",
      " var25 (Cumul.perc.: 0.967): 6.22\n",
      "\n",
      "Obs. 10710:\n",
      " var24 (Cumul.perc.: 0.991): 7.89\n",
      "\n",
      "Obs. 10720:\n",
      " var6 (Cumul.perc.: 0.012): -7.18\n",
      "var5 (Cumul.perc.: 0.01): -6.93\n",
      "var27 (Cumul.perc.: 0.042): -4.81\n",
      "var22 (Cumul.perc.: 0.961): 5.67\n",
      "var2 (Cumul.perc.: 0.956): 6.1\n",
      "\n",
      "Obs. 10730:\n",
      " None\n",
      "\n",
      "Obs. 10740:\n",
      " var4 (Cumul.perc.: 0.017): -5.73\n",
      "var2 (Cumul.perc.: 0.023): -6.05\n",
      "var23 (Cumul.perc.: 0.968): 6.27\n",
      "\n",
      "Obs. 10750:\n",
      " var6 (Cumul.perc.: 0.022): -6.36\n",
      "var14 (Cumul.perc.: 0.022): -5.9\n",
      "var15 (Cumul.perc.: 0.957): 5.3\n",
      "var5 (Cumul.perc.: 0.048): -4.67\n",
      "var12 (Cumul.perc.: 0.965): 5.35\n",
      "\n",
      "Obs. 10760:\n",
      " var3 (Cumul.perc.: 0.993): 7.19\n",
      "var4 (Cumul.perc.: 0.977): 6.38\n",
      "var18 (Cumul.perc.: 0.965): 6.19\n",
      "\n",
      "Obs. 10770:\n",
      " var10 (Cumul.perc.: 0.012): -5.84\n",
      "var17 (Cumul.perc.: 0.975): 6.23\n",
      "var9 (Cumul.perc.: 0.045): -3.87\n",
      "var28 (Cumul.perc.: 0.954): 5.47\n",
      "\n",
      "Obs. 10780:\n",
      " var13 (Cumul.perc.: 0.031): -5.65\n",
      "var1 (Cumul.perc.: 0.036): -4.6\n",
      "\n",
      "Obs. 10790:\n",
      " var1 (Cumul.perc.: 0.992): 7.27\n",
      "var16 (Cumul.perc.: 0.018): -5.46\n",
      "var17 (Cumul.perc.: 0.966): 5.82\n",
      "var22 (Cumul.perc.: 0.953): 5.43\n",
      "var3 (Cumul.perc.: 0.039): -4.31\n",
      "\n",
      "Obs. 10800:\n",
      " None\n",
      "\n",
      "Obs. 10810:\n",
      " var8 (Cumul.perc.: 0.988): 6.48\n",
      "var25 (Cumul.perc.: 0.024): -5.73\n",
      "var0 (Cumul.perc.: 0.028): -5.78\n",
      "var23 (Cumul.perc.: 0.041): -5.08\n",
      "var9 (Cumul.perc.: 0.948): 4.67\n",
      "\n",
      "Obs. 10820:\n",
      " var26 (Cumul.perc.: 0.011): -6.54\n",
      "var5 (Cumul.perc.: 0.98): 6.85\n",
      "\n",
      "Obs. 10830:\n",
      " var18 (Cumul.perc.: 0.984): 7.21\n",
      "var21 (Cumul.perc.: 0.958): 6.24\n",
      "\n",
      "Obs. 10840:\n",
      " var18 (Cumul.perc.: 0.988): 7.6\n",
      "var20 (Cumul.perc.: 0.982): 6.2\n",
      "var7 (Cumul.perc.: 0.029): -5.03\n",
      "\n",
      "Obs. 10850:\n",
      " var1 (Cumul.perc.: 0.047): -4.24\n",
      "\n",
      "Obs. 10860:\n",
      " var20 (Cumul.perc.: 0.015): -5.43\n",
      "var27 (Cumul.perc.: 0.982): 7.0\n",
      "var7 (Cumul.perc.: 0.984): 6.86\n",
      "var25 (Cumul.perc.: 0.956): 5.72\n",
      "var15 (Cumul.perc.: 0.965): 5.6\n",
      "\n",
      "Obs. 10870:\n",
      " var3 (Cumul.perc.: 0.994): 7.37\n",
      "var28 (Cumul.perc.: 0.974): 6.25\n",
      "var0 (Cumul.perc.: 0.974): 6.8\n",
      "var29 (Cumul.perc.: 0.972): 6.67\n",
      "var8 (Cumul.perc.: 0.045): -4.1\n",
      "\n",
      "Obs. 10880:\n",
      " var1 (Cumul.perc.: 0.008): -6.61\n",
      "var24 (Cumul.perc.: 0.033): -5.27\n",
      "\n",
      "Obs. 10890:\n",
      " None\n",
      "\n",
      "Obs. 10900:\n",
      " var4 (Cumul.perc.: 0.975): 6.24\n",
      "\n",
      "Obs. 10910:\n",
      " var8 (Cumul.perc.: 0.028): -4.67\n",
      "var21 (Cumul.perc.: 0.952): 6.04\n",
      "\n",
      "Obs. 10920:\n",
      " var4 (Cumul.perc.: 0.988): 7.11\n",
      "var15 (Cumul.perc.: 0.983): 6.56\n",
      "var12 (Cumul.perc.: 0.98): 6.01\n",
      "var10 (Cumul.perc.: 0.956): 5.15\n",
      "\n",
      "Obs. 10930:\n",
      " var18 (Cumul.perc.: 0.045): -4.81\n",
      "\n",
      "Obs. 10940:\n",
      " var19 (Cumul.perc.: 0.039): -4.92\n",
      "var5 (Cumul.perc.: 0.967): 6.12\n",
      "var15 (Cumul.perc.: 0.964): 5.53\n",
      "\n",
      "Obs. 10950:\n",
      " var29 (Cumul.perc.: 0.973): 6.7\n",
      "\n",
      "Obs. 10960:\n",
      " var6 (Cumul.perc.: 0.996): 9.54\n",
      "var19 (Cumul.perc.: 0.987): 7.31\n",
      "var21 (Cumul.perc.: 0.981): 7.41\n",
      "var17 (Cumul.perc.: 0.025): -5.39\n",
      "\n",
      "Obs. 10970:\n",
      " var11 (Cumul.perc.: 0.972): 6.52\n",
      "\n",
      "Obs. 10980:\n",
      " var18 (Cumul.perc.: 0.012): -6.59\n",
      "var9 (Cumul.perc.: 0.048): -3.79\n",
      "\n",
      "Obs. 10990:\n",
      " var19 (Cumul.perc.: 0.014): -6.31\n",
      "var21 (Cumul.perc.: 0.035): -5.64\n",
      "var5 (Cumul.perc.: 0.976): 6.58\n",
      "\n",
      "Obs. 11000:\n",
      " var14 (Cumul.perc.: 0.997): 8.9\n",
      "var5 (Cumul.perc.: 0.967): 6.13\n",
      "\n",
      "Obs. 11010:\n",
      " var8 (Cumul.perc.: 0.008): -6.3\n",
      "var29 (Cumul.perc.: 0.989): 7.78\n",
      "var18 (Cumul.perc.: 0.977): 6.73\n",
      "var3 (Cumul.perc.: 0.979): 5.99\n",
      "\n",
      "Obs. 11020:\n",
      " var4 (Cumul.perc.: 0.995): 8.24\n",
      "var15 (Cumul.perc.: 0.967): 5.67\n",
      "\n",
      "Obs. 11030:\n",
      " var25 (Cumul.perc.: 0.008): -7.09\n",
      "var8 (Cumul.perc.: 0.984): 6.13\n",
      "\n",
      "Obs. 11040:\n",
      " var12 (Cumul.perc.: 0.988): 6.54\n",
      "var22 (Cumul.perc.: 0.977): 6.3\n",
      "var10 (Cumul.perc.: 0.956): 5.17\n",
      "\n",
      "Obs. 11050:\n",
      " var0 (Cumul.perc.: 0.998): 10.19\n",
      "var23 (Cumul.perc.: 0.998): 9.6\n",
      "var17 (Cumul.perc.: 0.99): 7.33\n",
      "var15 (Cumul.perc.: 0.987): 6.84\n",
      "var28 (Cumul.perc.: 0.975): 6.27\n",
      "\n",
      "Obs. 11060:\n",
      " var12 (Cumul.perc.: 0.025): -4.83\n",
      "var9 (Cumul.perc.: 0.046): -3.83\n",
      "\n",
      "Obs. 11070:\n",
      " var25 (Cumul.perc.: 0.021): -5.87\n",
      "var17 (Cumul.perc.: 0.963): 5.69\n",
      "var24 (Cumul.perc.: 0.96): 5.99\n",
      "var16 (Cumul.perc.: 0.045): -4.31\n",
      "\n",
      "Obs. 11080:\n",
      " var21 (Cumul.perc.: 0.988): 7.96\n",
      "\n",
      "Obs. 11090:\n",
      " var11 (Cumul.perc.: 0.997): 9.39\n",
      "var28 (Cumul.perc.: 0.975): 6.26\n",
      "var21 (Cumul.perc.: 0.05): -5.07\n",
      "\n",
      "Obs. 11100:\n",
      " var21 (Cumul.perc.: 0.983): 7.6\n",
      "var8 (Cumul.perc.: 0.96): 5.14\n",
      "\n",
      "Obs. 11110:\n",
      " var19 (Cumul.perc.: 0.994): 8.25\n",
      "var25 (Cumul.perc.: 0.972): 6.46\n",
      "var21 (Cumul.perc.: 0.025): -6.18\n",
      "var29 (Cumul.perc.: 0.026): -5.77\n",
      "var11 (Cumul.perc.: 0.976): 6.75\n",
      "\n",
      "Obs. 11120:\n",
      " var23 (Cumul.perc.: 0.967): 6.22\n",
      "var26 (Cumul.perc.: 0.031): -5.14\n",
      "var20 (Cumul.perc.: 0.044): -4.11\n",
      "\n",
      "Obs. 11130:\n",
      " var6 (Cumul.perc.: 0.007): -7.92\n",
      "var24 (Cumul.perc.: 0.009): -7.02\n",
      "var27 (Cumul.perc.: 0.989): 7.67\n",
      "var5 (Cumul.perc.: 0.989): 7.64\n",
      "var14 (Cumul.perc.: 0.013): -6.55\n",
      "\n",
      "Obs. 11140:\n",
      " None\n",
      "\n",
      "Obs. 11150:\n",
      " var22 (Cumul.perc.: 0.987): 6.98\n",
      "var17 (Cumul.perc.: 0.016): -5.93\n",
      "\n",
      "Obs. 11160:\n",
      " var25 (Cumul.perc.: 0.993): 8.25\n",
      "var3 (Cumul.perc.: 0.973): 5.72\n",
      "\n",
      "Obs. 11170:\n",
      " var19 (Cumul.perc.: 0.995): 8.56\n",
      "var14 (Cumul.perc.: 0.969): 6.34\n",
      "\n",
      "Obs. 11180:\n",
      " None\n",
      "\n",
      "Obs. 11190:\n",
      " var18 (Cumul.perc.: 0.994): 8.47\n",
      "var6 (Cumul.perc.: 0.026): -6.08\n",
      "var7 (Cumul.perc.: 0.017): -5.74\n",
      "var21 (Cumul.perc.: 0.043): -5.3\n",
      "var19 (Cumul.perc.: 0.967): 6.17\n",
      "\n",
      "Obs. 11200:\n",
      " var10 (Cumul.perc.: 0.981): 6.23\n",
      "var23 (Cumul.perc.: 0.024): -5.91\n",
      "var29 (Cumul.perc.: 0.021): -6.02\n",
      "var11 (Cumul.perc.: 0.956): 5.88\n",
      "\n",
      "Obs. 11210:\n",
      " var13 (Cumul.perc.: 0.995): 8.77\n",
      "var19 (Cumul.perc.: 0.956): 5.75\n",
      "var20 (Cumul.perc.: 0.966): 5.44\n",
      "var18 (Cumul.perc.: 0.047): -4.76\n",
      "\n",
      "Obs. 11220:\n",
      " var27 (Cumul.perc.: 0.974): 6.55\n",
      "\n",
      "Obs. 11230:\n",
      " var11 (Cumul.perc.: 0.998): 10.17\n",
      "var8 (Cumul.perc.: 0.998): 8.41\n",
      "var20 (Cumul.perc.: 0.027): -4.74\n",
      "var1 (Cumul.perc.: 0.028): -4.9\n",
      "var4 (Cumul.perc.: 0.044): -4.47\n",
      "\n",
      "Obs. 11240:\n",
      " var5 (Cumul.perc.: 0.006): -7.4\n",
      "var25 (Cumul.perc.: 0.97): 6.37\n",
      "var15 (Cumul.perc.: 0.045): -4.48\n",
      "\n",
      "Obs. 11250:\n",
      " var10 (Cumul.perc.: 0.025): -5.02\n",
      "var1 (Cumul.perc.: 0.032): -4.75\n",
      "var25 (Cumul.perc.: 0.045): -4.85\n",
      "\n",
      "Obs. 11260:\n",
      " var19 (Cumul.perc.: 0.959): 5.84\n",
      "\n",
      "Obs. 11270:\n",
      " var5 (Cumul.perc.: 0.97): 6.24\n",
      "var8 (Cumul.perc.: 0.039): -4.27\n",
      "\n",
      "Obs. 11280:\n",
      " var13 (Cumul.perc.: 0.011): -7.09\n",
      "var19 (Cumul.perc.: 0.042): -4.82\n",
      "var24 (Cumul.perc.: 0.047): -4.77\n",
      "\n",
      "Obs. 11290:\n",
      " var23 (Cumul.perc.: 0.994): 8.39\n",
      "var28 (Cumul.perc.: 0.959): 5.63\n",
      "\n",
      "Obs. 11300:\n",
      " var11 (Cumul.perc.: 0.024): -5.82\n",
      "var14 (Cumul.perc.: 0.038): -5.11\n",
      "var3 (Cumul.perc.: 0.05): -3.97\n",
      "\n",
      "Obs. 11310:\n",
      " var21 (Cumul.perc.: 0.042): -5.35\n",
      "\n",
      "Obs. 11320:\n",
      " var17 (Cumul.perc.: 0.969): 5.97\n",
      "\n",
      "Obs. 11330:\n",
      " var8 (Cumul.perc.: 0.012): -5.71\n",
      "var20 (Cumul.perc.: 0.027): -4.76\n",
      "var29 (Cumul.perc.: 0.972): 6.63\n",
      "var26 (Cumul.perc.: 0.961): 5.76\n",
      "var10 (Cumul.perc.: 0.048): -4.13\n",
      "\n",
      "Obs. 11340:\n",
      " var21 (Cumul.perc.: 0.968): 6.65\n",
      "var7 (Cumul.perc.: 0.028): -5.1\n",
      "\n",
      "Obs. 11350:\n",
      " var13 (Cumul.perc.: 0.979): 7.11\n",
      "var4 (Cumul.perc.: 0.018): -5.71\n",
      "var17 (Cumul.perc.: 0.962): 5.66\n",
      "var25 (Cumul.perc.: 0.036): -5.17\n",
      "var5 (Cumul.perc.: 0.038): -5.02\n",
      "\n",
      "Obs. 11360:\n",
      " var27 (Cumul.perc.: 0.978): 6.76\n",
      "var24 (Cumul.perc.: 0.953): 5.72\n",
      "\n",
      "Obs. 11370:\n",
      " var29 (Cumul.perc.: 0.009): -7.17\n",
      "var10 (Cumul.perc.: 0.994): 7.42\n",
      "var20 (Cumul.perc.: 0.968): 5.5\n",
      "\n",
      "Obs. 11380:\n",
      " var21 (Cumul.perc.: 0.043): -5.32\n",
      "\n",
      "Obs. 11390:\n",
      " var7 (Cumul.perc.: 0.991): 7.35\n",
      "var21 (Cumul.perc.: 0.036): -5.59\n",
      "var28 (Cumul.perc.: 0.021): -5.7\n",
      "\n",
      "Obs. 11400:\n",
      " var6 (Cumul.perc.: 0.974): 7.0\n",
      "var28 (Cumul.perc.: 0.981): 6.7\n",
      "\n",
      "Obs. 11410:\n",
      " var14 (Cumul.perc.: 0.025): -5.74\n",
      "\n",
      "Obs. 11420:\n",
      " var20 (Cumul.perc.: 0.969): 5.52\n",
      "\n",
      "Obs. 11430:\n",
      " var23 (Cumul.perc.: 0.017): -6.35\n",
      "var12 (Cumul.perc.: 0.97): 5.51\n",
      "var14 (Cumul.perc.: 0.962): 6.01\n",
      "var0 (Cumul.perc.: 0.047): -5.01\n",
      "var21 (Cumul.perc.: 0.05): -5.07\n",
      "\n",
      "Obs. 11440:\n",
      " var7 (Cumul.perc.: 0.004): -7.35\n",
      "var1 (Cumul.perc.: 0.989): 6.93\n",
      "var5 (Cumul.perc.: 0.963): 5.96\n",
      "var25 (Cumul.perc.: 0.03): -5.45\n",
      "\n",
      "Obs. 11450:\n",
      " var5 (Cumul.perc.: 0.998): 9.84\n",
      "var3 (Cumul.perc.: 0.995): 7.72\n",
      "var4 (Cumul.perc.: 0.013): -6.08\n",
      "var26 (Cumul.perc.: 0.017): -5.96\n",
      "var17 (Cumul.perc.: 0.985): 6.91\n",
      "\n",
      "Obs. 11460:\n",
      " var16 (Cumul.perc.: 0.009): -6.22\n",
      "var13 (Cumul.perc.: 0.987): 7.76\n",
      "var4 (Cumul.perc.: 0.016): -5.84\n",
      "var8 (Cumul.perc.: 0.987): 6.38\n",
      "var26 (Cumul.perc.: 0.021): -5.7\n",
      "\n",
      "Obs. 11470:\n",
      " var27 (Cumul.perc.: 0.002): -8.68\n",
      "var21 (Cumul.perc.: 0.978): 7.19\n",
      "\n",
      "Obs. 11480:\n",
      " var6 (Cumul.perc.: 0.973): 6.99\n",
      "var13 (Cumul.perc.: 0.974): 6.82\n",
      "var26 (Cumul.perc.: 0.044): -4.65\n",
      "\n",
      "Obs. 11490:\n",
      " var1 (Cumul.perc.: 0.979): 6.17\n",
      "var4 (Cumul.perc.: 0.04): -4.61\n",
      "\n",
      "Obs. 11500:\n",
      " None\n",
      "\n",
      "Obs. 11510:\n",
      " var15 (Cumul.perc.: 0.999): 9.29\n",
      "var17 (Cumul.perc.: 0.976): 6.29\n",
      "var16 (Cumul.perc.: 0.039): -4.54\n",
      "\n",
      "Obs. 11520:\n",
      " var19 (Cumul.perc.: 0.986): 7.31\n",
      "var20 (Cumul.perc.: 0.028): -4.68\n",
      "var25 (Cumul.perc.: 0.027): -5.59\n",
      "var15 (Cumul.perc.: 0.034): -4.84\n",
      "var23 (Cumul.perc.: 0.045): -4.94\n",
      "\n",
      "Obs. 11530:\n",
      " var16 (Cumul.perc.: 0.998): 8.59\n",
      "var14 (Cumul.perc.: 0.007): -7.44\n",
      "var5 (Cumul.perc.: 0.965): 6.05\n",
      "var17 (Cumul.perc.: 0.96): 5.6\n",
      "\n",
      "Obs. 11540:\n",
      " var13 (Cumul.perc.: 0.986): 7.69\n",
      "var16 (Cumul.perc.: 0.019): -5.37\n",
      "var11 (Cumul.perc.: 0.956): 5.9\n",
      "var8 (Cumul.perc.: 0.952): 4.92\n",
      "\n",
      "Obs. 11550:\n",
      " var22 (Cumul.perc.: 0.009): -6.68\n",
      "var16 (Cumul.perc.: 0.01): -6.15\n",
      "var23 (Cumul.perc.: 0.97): 6.39\n",
      "var15 (Cumul.perc.: 0.959): 5.36\n",
      "var17 (Cumul.perc.: 0.955): 5.41\n",
      "\n",
      "Obs. 11560:\n",
      " var18 (Cumul.perc.: 0.963): 6.09\n",
      "var8 (Cumul.perc.: 0.047): -4.04\n",
      "\n",
      "Obs. 11570:\n",
      " var17 (Cumul.perc.: 0.011): -6.39\n",
      "\n",
      "Obs. 11580:\n",
      " None\n",
      "\n",
      "Obs. 11590:\n",
      " var23 (Cumul.perc.: 0.01): -7.11\n",
      "var20 (Cumul.perc.: 0.986): 6.5\n",
      "var25 (Cumul.perc.: 0.021): -5.88\n",
      "var21 (Cumul.perc.: 0.968): 6.64\n",
      "var27 (Cumul.perc.: 0.957): 5.83\n",
      "\n",
      "Obs. 11600:\n",
      " var28 (Cumul.perc.: 0.997): 9.02\n",
      "var25 (Cumul.perc.: 0.961): 5.95\n",
      "var22 (Cumul.perc.: 0.957): 5.53\n",
      "var5 (Cumul.perc.: 0.038): -5.01\n",
      "var7 (Cumul.perc.: 0.965): 5.79\n",
      "\n",
      "Obs. 11610:\n",
      " var6 (Cumul.perc.: 0.027): -6.01\n",
      "var20 (Cumul.perc.: 0.964): 5.34\n",
      "\n",
      "Obs. 11620:\n",
      " var25 (Cumul.perc.: 0.957): 5.75\n",
      "var26 (Cumul.perc.: 0.036): -4.94\n",
      "var15 (Cumul.perc.: 0.041): -4.59\n",
      "\n",
      "Obs. 11630:\n",
      " None\n",
      "\n",
      "Obs. 11640:\n",
      " var10 (Cumul.perc.: 0.991): 7.11\n",
      "var3 (Cumul.perc.: 0.031): -4.62\n",
      "\n",
      "Obs. 11650:\n",
      " var1 (Cumul.perc.: 0.961): 5.4\n",
      "\n",
      "Obs. 11660:\n",
      " var26 (Cumul.perc.: 0.993): 7.88\n",
      "\n",
      "Obs. 11670:\n",
      " var23 (Cumul.perc.: 0.02): -6.14\n",
      "var16 (Cumul.perc.: 0.012): -5.92\n",
      "var5 (Cumul.perc.: 0.987): 7.4\n",
      "var21 (Cumul.perc.: 0.968): 6.64\n",
      "var8 (Cumul.perc.: 0.044): -4.14\n",
      "\n",
      "Obs. 11680:\n",
      " var15 (Cumul.perc.: 0.97): 5.8\n",
      "var7 (Cumul.perc.: 0.049): -4.32\n",
      "\n",
      "Obs. 11690:\n",
      " var23 (Cumul.perc.: 0.982): 7.13\n",
      "var19 (Cumul.perc.: 0.039): -4.91\n",
      "\n",
      "Obs. 11700:\n",
      " var1 (Cumul.perc.: 0.997): 8.34\n",
      "var7 (Cumul.perc.: 0.023): -5.36\n",
      "var17 (Cumul.perc.: 0.962): 5.68\n",
      "var24 (Cumul.perc.: 0.958): 5.94\n",
      "var18 (Cumul.perc.: 0.041): -4.94\n",
      "\n",
      "Obs. 11710:\n",
      " None\n",
      "\n",
      "Obs. 11720:\n",
      " var20 (Cumul.perc.: 0.981): 6.08\n",
      "\n",
      "Obs. 11730:\n",
      " var8 (Cumul.perc.: 0.009): -6.13\n",
      "\n",
      "Obs. 11740:\n",
      " var14 (Cumul.perc.: 0.038): -5.1\n",
      "var3 (Cumul.perc.: 0.042): -4.23\n",
      "\n",
      "Obs. 11750:\n",
      " var5 (Cumul.perc.: 0.967): 6.1\n",
      "\n",
      "Obs. 11760:\n",
      " var24 (Cumul.perc.: 1.0): 10.93\n",
      "var13 (Cumul.perc.: 0.998): 9.78\n",
      "var1 (Cumul.perc.: 0.976): 5.96\n",
      "\n",
      "Obs. 11770:\n",
      " None\n",
      "\n",
      "Obs. 11780:\n",
      " var11 (Cumul.perc.: 0.007): -7.5\n",
      "var24 (Cumul.perc.: 0.034): -5.23\n",
      "var26 (Cumul.perc.: 0.049): -4.51\n",
      "\n",
      "Obs. 11790:\n",
      " var22 (Cumul.perc.: 0.032): -4.9\n",
      "var5 (Cumul.perc.: 0.953): 5.64\n",
      "\n",
      "Obs. 11800:\n",
      " var19 (Cumul.perc.: 0.032): -5.25\n",
      "var21 (Cumul.perc.: 0.952): 6.04\n",
      "\n",
      "Obs. 11810:\n",
      " var29 (Cumul.perc.: 0.004): -8.13\n",
      "var21 (Cumul.perc.: 0.038): -5.51\n",
      "var19 (Cumul.perc.: 0.967): 6.18\n",
      "\n",
      "Obs. 11820:\n",
      " var3 (Cumul.perc.: 0.997): 8.27\n",
      "var27 (Cumul.perc.: 0.019): -5.9\n",
      "var9 (Cumul.perc.: 0.023): -4.69\n",
      "var6 (Cumul.perc.: 0.977): 7.23\n",
      "var12 (Cumul.perc.: 0.983): 6.2\n",
      "\n",
      "Obs. 11830:\n",
      " var25 (Cumul.perc.: 0.993): 8.11\n",
      "\n",
      "Obs. 11840:\n",
      " var10 (Cumul.perc.: 0.999): 8.99\n",
      "var4 (Cumul.perc.: 0.005): -7.31\n",
      "var11 (Cumul.perc.: 0.991): 7.92\n",
      "var0 (Cumul.perc.: 0.025): -5.94\n",
      "var23 (Cumul.perc.: 0.034): -5.43\n",
      "\n",
      "Obs. 11850:\n",
      " var16 (Cumul.perc.: 0.998): 9.03\n",
      "var9 (Cumul.perc.: 0.949): 4.69\n",
      "\n",
      "Obs. 11860:\n",
      " var18 (Cumul.perc.: 0.002): -8.62\n",
      "var29 (Cumul.perc.: 0.012): -6.79\n",
      "var19 (Cumul.perc.: 0.964): 6.03\n",
      "\n",
      "Obs. 11870:\n",
      " var4 (Cumul.perc.: 0.97): 6.06\n",
      "\n",
      "Obs. 11880:\n",
      " var14 (Cumul.perc.: 0.047): -4.8\n",
      "\n",
      "Obs. 11890:\n",
      " var25 (Cumul.perc.: 0.008): -6.99\n",
      "var19 (Cumul.perc.: 0.959): 5.87\n",
      "\n",
      "Obs. 11900:\n",
      " var22 (Cumul.perc.: 0.977): 6.29\n",
      "var20 (Cumul.perc.: 0.023): -4.98\n",
      "var5 (Cumul.perc.: 0.048): -4.67\n",
      "var11 (Cumul.perc.: 0.955): 5.86\n",
      "\n",
      "Obs. 11910:\n",
      " var12 (Cumul.perc.: 0.014): -5.51\n",
      "var21 (Cumul.perc.: 0.984): 7.68\n",
      "var26 (Cumul.perc.: 0.986): 7.03\n",
      "var18 (Cumul.perc.: 0.042): -4.89\n",
      "\n",
      "Obs. 11920:\n",
      " var11 (Cumul.perc.: 0.996): 8.87\n",
      "var19 (Cumul.perc.: 0.985): 7.18\n",
      "var3 (Cumul.perc.: 0.975): 5.78\n",
      "var16 (Cumul.perc.: 0.025): -5.08\n",
      "var10 (Cumul.perc.: 0.031): -4.75\n",
      "\n",
      "Obs. 11930:\n",
      " var6 (Cumul.perc.: 0.993): 8.76\n",
      "var21 (Cumul.perc.: 0.048): -5.14\n",
      "var2 (Cumul.perc.: 0.039): -5.28\n",
      "var15 (Cumul.perc.: 0.967): 5.66\n",
      "\n",
      "Obs. 11940:\n",
      " var9 (Cumul.perc.: 0.995): 7.17\n",
      "var17 (Cumul.perc.: 0.009): -6.69\n",
      "var11 (Cumul.perc.: 0.982): 7.08\n",
      "var19 (Cumul.perc.: 0.967): 6.16\n",
      "var18 (Cumul.perc.: 0.037): -5.08\n",
      "\n",
      "Obs. 11950:\n",
      " var11 (Cumul.perc.: 0.993): 8.31\n",
      "var20 (Cumul.perc.: 0.974): 5.74\n",
      "var19 (Cumul.perc.: 0.963): 6.01\n",
      "\n",
      "Obs. 11960:\n",
      " var24 (Cumul.perc.: 0.041): -4.98\n",
      "\n",
      "Obs. 11970:\n",
      " var25 (Cumul.perc.: 0.983): 7.1\n",
      "\n",
      "Obs. 11980:\n",
      " var17 (Cumul.perc.: 0.014): -6.07\n",
      "var25 (Cumul.perc.: 0.05): -4.68\n",
      "\n",
      "Obs. 11990:\n",
      " var23 (Cumul.perc.: 0.019): -6.22\n",
      "var27 (Cumul.perc.: 0.968): 6.24\n",
      "var20 (Cumul.perc.: 0.957): 5.12\n",
      "var7 (Cumul.perc.: 0.957): 5.49\n",
      "\n",
      "Obs. 12000:\n",
      " var19 (Cumul.perc.: 0.989): 7.53\n",
      "var27 (Cumul.perc.: 0.023): -5.64\n",
      "\n",
      "Obs. 12010:\n",
      " var16 (Cumul.perc.: 0.031): -4.8\n",
      "var25 (Cumul.perc.: 0.963): 6.03\n",
      "\n",
      "Obs. 12020:\n",
      " var5 (Cumul.perc.: 0.987): 7.39\n",
      "var4 (Cumul.perc.: 0.979): 6.48\n",
      "var7 (Cumul.perc.: 0.97): 6.01\n",
      "\n",
      "Obs. 12030:\n",
      " var19 (Cumul.perc.: 0.964): 6.04\n",
      "\n",
      "Obs. 12040:\n",
      " var24 (Cumul.perc.: 0.041): -4.95\n",
      "var13 (Cumul.perc.: 0.044): -5.12\n",
      "var3 (Cumul.perc.: 0.966): 5.4\n",
      "\n",
      "Obs. 12050:\n",
      " var23 (Cumul.perc.: 0.978): 6.8\n",
      "var28 (Cumul.perc.: 0.979): 6.5\n",
      "var29 (Cumul.perc.: 0.027): -5.73\n",
      "\n",
      "Obs. 12060:\n",
      " var25 (Cumul.perc.: 0.008): -7.1\n",
      "var23 (Cumul.perc.: 0.028): -5.7\n",
      "var6 (Cumul.perc.: 0.043): -5.28\n",
      "var1 (Cumul.perc.: 0.032): -4.76\n",
      "var9 (Cumul.perc.: 0.957): 4.9\n",
      "\n",
      "Obs. 12070:\n",
      " var8 (Cumul.perc.: 0.971): 5.52\n",
      "var10 (Cumul.perc.: 0.041): -4.36\n",
      "\n",
      "Obs. 12080:\n",
      " var22 (Cumul.perc.: 0.996): 8.41\n",
      "var24 (Cumul.perc.: 0.997): 8.88\n",
      "var14 (Cumul.perc.: 0.033): -5.32\n",
      "var7 (Cumul.perc.: 0.023): -5.33\n",
      "var10 (Cumul.perc.: 0.04): -4.4\n",
      "\n",
      "Obs. 12090:\n",
      " var26 (Cumul.perc.: 0.985): 6.97\n",
      "var5 (Cumul.perc.: 0.022): -5.77\n",
      "var16 (Cumul.perc.: 0.049): -4.24\n",
      "\n",
      "Obs. 12100:\n",
      " var18 (Cumul.perc.: 0.001): -9.75\n",
      "var2 (Cumul.perc.: 0.012): -6.96\n",
      "var13 (Cumul.perc.: 0.981): 7.24\n",
      "var3 (Cumul.perc.: 0.024): -4.89\n",
      "\n",
      "Obs. 12110:\n",
      " var29 (Cumul.perc.: 0.993): 8.39\n",
      "var28 (Cumul.perc.: 0.973): 6.17\n",
      "var22 (Cumul.perc.: 0.962): 5.7\n",
      "\n",
      "Obs. 12120:\n",
      " var23 (Cumul.perc.: 0.048): -4.87\n",
      "var18 (Cumul.perc.: 0.952): 5.71\n",
      "\n",
      "Obs. 12130:\n",
      " var8 (Cumul.perc.: 0.984): 6.15\n",
      "var13 (Cumul.perc.: 0.955): 6.05\n",
      "var20 (Cumul.perc.: 0.966): 5.45\n",
      "var11 (Cumul.perc.: 0.952): 5.76\n",
      "\n",
      "Obs. 12140:\n",
      " var6 (Cumul.perc.: 0.994): 9.06\n",
      "var3 (Cumul.perc.: 0.989): 6.7\n",
      "var18 (Cumul.perc.: 0.984): 7.26\n",
      "var27 (Cumul.perc.: 0.965): 6.1\n",
      "\n",
      "Obs. 12150:\n",
      " None\n",
      "\n",
      "Obs. 12160:\n",
      " var19 (Cumul.perc.: 0.04): -4.9\n",
      "var17 (Cumul.perc.: 0.974): 6.19\n",
      "var20 (Cumul.perc.: 0.048): -4.03\n",
      "\n",
      "Obs. 12170:\n",
      " var17 (Cumul.perc.: 0.959): 5.57\n",
      "\n",
      "Obs. 12180:\n",
      " var15 (Cumul.perc.: 0.995): 7.88\n",
      "var21 (Cumul.perc.: 0.008): -7.63\n",
      "var22 (Cumul.perc.: 0.035): -4.77\n",
      "\n",
      "Obs. 12190:\n",
      " var12 (Cumul.perc.: 0.011): -5.86\n",
      "var7 (Cumul.perc.: 0.009): -6.47\n",
      "var26 (Cumul.perc.: 0.015): -6.15\n",
      "var2 (Cumul.perc.: 0.015): -6.69\n",
      "var23 (Cumul.perc.: 0.984): 7.22\n",
      "\n",
      "Obs. 12200:\n",
      " var26 (Cumul.perc.: 0.995): 8.21\n",
      "var29 (Cumul.perc.: 0.02): -6.12\n",
      "var2 (Cumul.perc.: 0.041): -5.22\n",
      "\n",
      "Obs. 12210:\n",
      " var5 (Cumul.perc.: 0.027): -5.48\n",
      "var29 (Cumul.perc.: 0.039): -5.15\n",
      "\n",
      "Obs. 12220:\n",
      " var21 (Cumul.perc.: 0.965): 6.51\n",
      "\n",
      "Obs. 12230:\n",
      " var29 (Cumul.perc.: 0.003): -8.5\n",
      "var21 (Cumul.perc.: 0.966): 6.56\n",
      "var6 (Cumul.perc.: 0.962): 6.48\n",
      "var0 (Cumul.perc.: 0.044): -5.12\n",
      "\n",
      "Obs. 12240:\n",
      " var7 (Cumul.perc.: 0.979): 6.49\n",
      "var23 (Cumul.perc.: 0.037): -5.3\n",
      "\n",
      "Obs. 12250:\n",
      " var9 (Cumul.perc.: 1.0): 12.16\n",
      "var15 (Cumul.perc.: 0.984): 6.62\n",
      "var2 (Cumul.perc.: 0.97): 6.69\n",
      "var23 (Cumul.perc.: 0.04): -5.13\n",
      "var4 (Cumul.perc.: 0.032): -4.92\n",
      "\n",
      "Obs. 12260:\n",
      " var28 (Cumul.perc.: 0.042): -4.68\n",
      "\n",
      "Obs. 12270:\n",
      " var11 (Cumul.perc.: 0.998): 9.62\n",
      "var26 (Cumul.perc.: 0.966): 5.93\n",
      "\n",
      "Obs. 12280:\n",
      " var5 (Cumul.perc.: 0.999): 10.16\n",
      "var22 (Cumul.perc.: 0.009): -6.6\n",
      "var16 (Cumul.perc.: 0.013): -5.84\n",
      "var13 (Cumul.perc.: 0.046): -5.06\n",
      "\n",
      "Obs. 12290:\n",
      " var7 (Cumul.perc.: 0.978): 6.41\n",
      "\n",
      "Obs. 12300:\n",
      " var13 (Cumul.perc.: 0.994): 8.65\n",
      "var29 (Cumul.perc.: 0.008): -7.29\n",
      "var12 (Cumul.perc.: 0.015): -5.51\n",
      "var21 (Cumul.perc.: 0.032): -5.78\n",
      "var9 (Cumul.perc.: 0.96): 5.01\n",
      "\n",
      "Obs. 12310:\n",
      " var11 (Cumul.perc.: 0.985): 7.32\n",
      "var26 (Cumul.perc.: 0.978): 6.48\n",
      "var4 (Cumul.perc.: 0.036): -4.77\n",
      "\n",
      "Obs. 12320:\n",
      " var5 (Cumul.perc.: 0.98): 6.82\n",
      "\n",
      "Obs. 12330:\n",
      " var7 (Cumul.perc.: 0.024): -5.3\n",
      "var0 (Cumul.perc.: 0.954): 5.98\n",
      "\n",
      "Obs. 12340:\n",
      " var16 (Cumul.perc.: 0.019): -5.39\n",
      "var23 (Cumul.perc.: 0.037): -5.29\n",
      "var25 (Cumul.perc.: 0.957): 5.74\n",
      "\n",
      "Obs. 12350:\n",
      " var25 (Cumul.perc.: 0.02): -5.95\n",
      "var1 (Cumul.perc.: 0.039): -4.49\n",
      "\n",
      "Obs. 12360:\n",
      " var13 (Cumul.perc.: 0.019): -6.43\n",
      "var7 (Cumul.perc.: 0.977): 6.38\n",
      "\n",
      "Obs. 12370:\n",
      " var27 (Cumul.perc.: 0.014): -6.31\n",
      "var5 (Cumul.perc.: 0.031): -5.25\n",
      "\n",
      "Obs. 12380:\n",
      " var17 (Cumul.perc.: 0.027): -5.28\n",
      "var24 (Cumul.perc.: 0.964): 6.15\n",
      "var22 (Cumul.perc.: 0.968): 5.89\n",
      "\n",
      "Obs. 12390:\n",
      " var29 (Cumul.perc.: 0.034): -5.39\n",
      "\n",
      "Obs. 12400:\n",
      " var7 (Cumul.perc.: 0.016): -5.81\n",
      "var23 (Cumul.perc.: 0.026): -5.78\n",
      "\n",
      "Obs. 12410:\n",
      " var28 (Cumul.perc.: 0.983): 6.79\n",
      "var3 (Cumul.perc.: 0.954): 5.02\n",
      "\n",
      "Obs. 12420:\n",
      " var0 (Cumul.perc.: 0.993): 8.51\n",
      "var10 (Cumul.perc.: 0.026): -4.97\n",
      "var5 (Cumul.perc.: 0.973): 6.41\n",
      "var21 (Cumul.perc.: 0.95): 5.97\n",
      "var17 (Cumul.perc.: 0.04): -4.66\n",
      "\n",
      "Obs. 12430:\n",
      " var10 (Cumul.perc.: 0.993): 7.38\n",
      "var24 (Cumul.perc.: 0.012): -6.68\n",
      "\n",
      "Obs. 12440:\n",
      " var14 (Cumul.perc.: 0.96): 5.96\n",
      "var27 (Cumul.perc.: 0.046): -4.68\n",
      "\n",
      "Obs. 12450:\n",
      " var24 (Cumul.perc.: 0.018): -6.18\n",
      "var4 (Cumul.perc.: 0.966): 5.84\n",
      "\n",
      "Obs. 12460:\n",
      " var2 (Cumul.perc.: 0.013): -6.8\n",
      "var20 (Cumul.perc.: 0.017): -5.29\n",
      "var28 (Cumul.perc.: 0.983): 6.84\n",
      "var24 (Cumul.perc.: 0.973): 6.57\n",
      "\n",
      "Obs. 12470:\n",
      " var15 (Cumul.perc.: 0.973): 5.98\n",
      "var20 (Cumul.perc.: 0.974): 5.73\n",
      "\n",
      "Obs. 12480:\n",
      " var3 (Cumul.perc.: 0.968): 5.49\n",
      "var1 (Cumul.perc.: 0.953): 5.13\n",
      "\n",
      "Obs. 12490:\n",
      " var3 (Cumul.perc.: 0.031): -4.6\n",
      "var16 (Cumul.perc.: 0.952): 5.19\n",
      "\n",
      "Obs. 12500:\n",
      " var7 (Cumul.perc.: 0.013): -6.06\n",
      "\n",
      "Obs. 12510:\n",
      " var21 (Cumul.perc.: 0.024): -6.22\n",
      "var6 (Cumul.perc.: 0.042): -5.32\n",
      "var5 (Cumul.perc.: 0.953): 5.64\n",
      "\n",
      "Obs. 12520:\n",
      " None\n",
      "\n",
      "Obs. 12530:\n",
      " var19 (Cumul.perc.: 0.023): -5.71\n",
      "var10 (Cumul.perc.: 0.979): 6.13\n",
      "var25 (Cumul.perc.: 0.975): 6.61\n",
      "var0 (Cumul.perc.: 0.966): 6.43\n",
      "var22 (Cumul.perc.: 0.036): -4.75\n",
      "\n",
      "Obs. 12540:\n",
      " var5 (Cumul.perc.: 0.999): 10.55\n",
      "var21 (Cumul.perc.: 0.02): -6.49\n",
      "var26 (Cumul.perc.: 0.022): -5.58\n",
      "var15 (Cumul.perc.: 0.037): -4.75\n",
      "\n",
      "Obs. 12550:\n",
      " var27 (Cumul.perc.: 0.97): 6.33\n",
      "\n",
      "Obs. 12560:\n",
      " var20 (Cumul.perc.: 0.017): -5.28\n",
      "var0 (Cumul.perc.: 0.019): -6.31\n",
      "var16 (Cumul.perc.: 0.02): -5.37\n",
      "var5 (Cumul.perc.: 0.045): -4.76\n",
      "var18 (Cumul.perc.: 0.963): 6.11\n",
      "\n",
      "Obs. 12570:\n",
      " var20 (Cumul.perc.: 0.974): 5.75\n",
      "var17 (Cumul.perc.: 0.03): -5.1\n",
      "var8 (Cumul.perc.: 0.973): 5.63\n",
      "\n",
      "Obs. 12580:\n",
      " var9 (Cumul.perc.: 0.99): 6.52\n",
      "var21 (Cumul.perc.: 0.037): -5.56\n",
      "var26 (Cumul.perc.: 0.974): 6.29\n",
      "var23 (Cumul.perc.: 0.957): 5.89\n",
      "\n",
      "Obs. 12590:\n",
      " var13 (Cumul.perc.: 0.04): -5.29\n",
      "var10 (Cumul.perc.: 0.035): -4.6\n",
      "\n",
      "Obs. 12600:\n",
      " var13 (Cumul.perc.: 0.042): -5.21\n",
      "\n",
      "Obs. 12610:\n",
      " var20 (Cumul.perc.: 0.986): 6.49\n",
      "var14 (Cumul.perc.: 0.015): -6.42\n",
      "var2 (Cumul.perc.: 0.98): 7.25\n",
      "var21 (Cumul.perc.: 0.038): -5.51\n",
      "\n",
      "Obs. 12620:\n",
      " var12 (Cumul.perc.: 0.964): 5.31\n",
      "var7 (Cumul.perc.: 0.963): 5.72\n",
      "\n",
      "Obs. 12630:\n",
      " var20 (Cumul.perc.: 0.009): -6.03\n",
      "var24 (Cumul.perc.: 0.977): 6.77\n",
      "var7 (Cumul.perc.: 0.967): 5.89\n",
      "var25 (Cumul.perc.: 0.971): 6.4\n",
      "\n",
      "Obs. 12640:\n",
      " var11 (Cumul.perc.: 1.0): 15.79\n",
      "var4 (Cumul.perc.: 1.0): 16.06\n",
      "var22 (Cumul.perc.: 0.0): -12.12\n",
      "var13 (Cumul.perc.: 1.0): 12.81\n",
      "var19 (Cumul.perc.: 0.001): -11.64\n",
      "\n",
      "Obs. 12650:\n",
      " var14 (Cumul.perc.: 0.005): -7.86\n",
      "var8 (Cumul.perc.: 0.033): -4.49\n",
      "\n",
      "Obs. 12660:\n",
      " var4 (Cumul.perc.: 0.004): -7.4\n",
      "var27 (Cumul.perc.: 0.008): -7.0\n",
      "var7 (Cumul.perc.: 0.043): -4.51\n",
      "\n",
      "Obs. 12670:\n",
      " var22 (Cumul.perc.: 0.014): -5.97\n",
      "\n",
      "Obs. 12680:\n",
      " var10 (Cumul.perc.: 0.991): 7.07\n",
      "var0 (Cumul.perc.: 0.978): 7.05\n",
      "var17 (Cumul.perc.: 0.951): 5.29\n",
      "\n",
      "Obs. 12690:\n",
      " var26 (Cumul.perc.: 0.99): 7.45\n",
      "var5 (Cumul.perc.: 0.029): -5.36\n",
      "var8 (Cumul.perc.: 0.038): -4.31\n",
      "var17 (Cumul.perc.: 0.961): 5.65\n",
      "var0 (Cumul.perc.: 0.046): -5.06\n",
      "\n",
      "Obs. 12700:\n",
      " var9 (Cumul.perc.: 0.007): -5.85\n",
      "var4 (Cumul.perc.: 0.991): 7.57\n",
      "\n",
      "Obs. 12710:\n",
      " var26 (Cumul.perc.: 0.017): -5.95\n",
      "var15 (Cumul.perc.: 0.023): -5.38\n",
      "var19 (Cumul.perc.: 0.965): 6.08\n",
      "var22 (Cumul.perc.: 0.972): 6.08\n",
      "var7 (Cumul.perc.: 0.963): 5.73\n",
      "\n",
      "Obs. 12720:\n",
      " var27 (Cumul.perc.: 0.026): -5.52\n",
      "var6 (Cumul.perc.: 0.958): 6.3\n",
      "var19 (Cumul.perc.: 0.955): 5.73\n",
      "var29 (Cumul.perc.: 0.042): -5.04\n",
      "var12 (Cumul.perc.: 0.029): -4.67\n",
      "\n",
      "Obs. 12730:\n",
      " var16 (Cumul.perc.: 0.991): 7.22\n",
      "var23 (Cumul.perc.: 0.011): -7.01\n",
      "var3 (Cumul.perc.: 0.012): -5.77\n",
      "var29 (Cumul.perc.: 0.031): -5.5\n",
      "var6 (Cumul.perc.: 0.035): -5.62\n",
      "\n",
      "Obs. 12740:\n",
      " var14 (Cumul.perc.: 0.998): 9.53\n",
      "var21 (Cumul.perc.: 0.99): 8.23\n",
      "var9 (Cumul.perc.: 0.949): 4.7\n",
      "\n",
      "Obs. 12750:\n",
      " var27 (Cumul.perc.: 0.993): 8.17\n",
      "var0 (Cumul.perc.: 0.965): 6.39\n",
      "var18 (Cumul.perc.: 0.969): 6.31\n",
      "\n",
      "Obs. 12760:\n",
      " var18 (Cumul.perc.: 0.013): -6.46\n",
      "var15 (Cumul.perc.: 0.022): -5.44\n",
      "\n",
      "Obs. 12770:\n",
      " var10 (Cumul.perc.: 0.026): -4.99\n",
      "var0 (Cumul.perc.: 0.973): 6.75\n",
      "\n",
      "Obs. 12780:\n",
      " var13 (Cumul.perc.: 0.981): 7.22\n",
      "var28 (Cumul.perc.: 0.02): -5.77\n",
      "var23 (Cumul.perc.: 0.044): -4.97\n",
      "\n",
      "Obs. 12790:\n",
      " var28 (Cumul.perc.: 0.951): 5.38\n",
      "\n",
      "Obs. 12800:\n",
      " var4 (Cumul.perc.: 0.994): 8.09\n",
      "var23 (Cumul.perc.: 0.968): 6.26\n",
      "var26 (Cumul.perc.: 0.962): 5.82\n",
      "var16 (Cumul.perc.: 0.956): 5.32\n",
      "\n",
      "Obs. 12810:\n",
      " var9 (Cumul.perc.: 0.008): -5.77\n",
      "var2 (Cumul.perc.: 0.013): -6.79\n",
      "var21 (Cumul.perc.: 0.983): 7.62\n",
      "var15 (Cumul.perc.: 0.019): -5.61\n",
      "var17 (Cumul.perc.: 0.019): -5.73\n",
      "\n",
      "Obs. 12820:\n",
      " var20 (Cumul.perc.: 0.986): 6.46\n",
      "var4 (Cumul.perc.: 0.98): 6.57\n",
      "\n",
      "Obs. 12830:\n",
      " var26 (Cumul.perc.: 0.992): 7.69\n",
      "var18 (Cumul.perc.: 0.029): -5.45\n",
      "\n",
      "Obs. 12840:\n",
      " var21 (Cumul.perc.: 0.991): 8.37\n",
      "var17 (Cumul.perc.: 0.016): -5.91\n",
      "\n",
      "Obs. 12850:\n",
      " var22 (Cumul.perc.: 0.028): -5.09\n",
      "var2 (Cumul.perc.: 0.966): 6.51\n",
      "\n",
      "Obs. 12860:\n",
      " var2 (Cumul.perc.: 0.0): -12.5\n",
      "var21 (Cumul.perc.: 0.988): 7.97\n",
      "var15 (Cumul.perc.: 0.023): -5.37\n",
      "var14 (Cumul.perc.: 0.957): 5.86\n",
      "var9 (Cumul.perc.: 0.033): -4.23\n",
      "\n",
      "Obs. 12870:\n",
      " var7 (Cumul.perc.: 0.023): -5.35\n",
      "var26 (Cumul.perc.: 0.049): -4.5\n",
      "var28 (Cumul.perc.: 0.95): 5.35\n",
      "\n",
      "Obs. 12880:\n",
      " var7 (Cumul.perc.: 0.986): 6.94\n",
      "var19 (Cumul.perc.: 0.017): -6.1\n",
      "var28 (Cumul.perc.: 0.958): 5.59\n",
      "var3 (Cumul.perc.: 0.048): -4.04\n",
      "\n",
      "Obs. 12890:\n",
      " var21 (Cumul.perc.: 0.965): 6.54\n",
      "var25 (Cumul.perc.: 0.964): 6.07\n",
      "\n",
      "Obs. 12900:\n",
      " var4 (Cumul.perc.: 0.984): 6.83\n",
      "var18 (Cumul.perc.: 0.963): 6.11\n",
      "\n",
      "Obs. 12910:\n",
      " var5 (Cumul.perc.: 0.993): 8.03\n",
      "var4 (Cumul.perc.: 0.01): -6.41\n",
      "var10 (Cumul.perc.: 0.988): 6.7\n",
      "var18 (Cumul.perc.: 0.043): -4.88\n",
      "\n",
      "Obs. 12920:\n",
      " var22 (Cumul.perc.: 0.006): -7.04\n",
      "var29 (Cumul.perc.: 0.011): -6.97\n",
      "var18 (Cumul.perc.: 0.028): -5.47\n",
      "var25 (Cumul.perc.: 0.033): -5.31\n",
      "\n",
      "Obs. 12930:\n",
      " var0 (Cumul.perc.: 0.011): -7.11\n",
      "var8 (Cumul.perc.: 0.958): 5.06\n",
      "\n",
      "Obs. 12940:\n",
      " var10 (Cumul.perc.: 0.009): -6.12\n",
      "var3 (Cumul.perc.: 0.968): 5.49\n",
      "var11 (Cumul.perc.: 0.98): 6.97\n",
      "\n",
      "Obs. 12950:\n",
      " var2 (Cumul.perc.: 0.028): -5.75\n",
      "var14 (Cumul.perc.: 0.958): 5.89\n",
      "var1 (Cumul.perc.: 0.969): 5.69\n",
      "var7 (Cumul.perc.: 0.966): 5.85\n",
      "\n",
      "Obs. 12960:\n",
      " var10 (Cumul.perc.: 0.048): -4.12\n",
      "\n",
      "Obs. 12970:\n",
      " var23 (Cumul.perc.: 0.021): -6.1\n",
      "\n",
      "Obs. 12980:\n",
      " var0 (Cumul.perc.: 0.976): 6.94\n",
      "\n",
      "Obs. 12990:\n",
      " var19 (Cumul.perc.: 0.994): 8.4\n",
      "var0 (Cumul.perc.: 0.039): -5.33\n",
      "var1 (Cumul.perc.: 0.961): 5.4\n",
      "\n",
      "Obs. 13000:\n",
      " var5 (Cumul.perc.: 0.999): 10.1\n",
      "var11 (Cumul.perc.: 0.006): -7.8\n",
      "var9 (Cumul.perc.: 0.007): -5.89\n",
      "var23 (Cumul.perc.: 0.979): 6.88\n",
      "\n",
      "Obs. 13010:\n",
      " var27 (Cumul.perc.: 0.003): -7.94\n",
      "var26 (Cumul.perc.: 0.012): -6.37\n",
      "var12 (Cumul.perc.: 0.019): -5.19\n",
      "var25 (Cumul.perc.: 0.972): 6.48\n",
      "var11 (Cumul.perc.: 0.029): -5.55\n",
      "\n",
      "Obs. 13020:\n",
      " var10 (Cumul.perc.: 0.005): -6.79\n",
      "var24 (Cumul.perc.: 0.994): 8.34\n",
      "var18 (Cumul.perc.: 0.96): 5.99\n",
      "var16 (Cumul.perc.: 0.959): 5.4\n",
      "\n",
      "Obs. 13030:\n",
      " var24 (Cumul.perc.: 0.012): -6.71\n",
      "var0 (Cumul.perc.: 0.014): -6.72\n",
      "var3 (Cumul.perc.: 0.013): -5.6\n",
      "var27 (Cumul.perc.: 0.038): -4.98\n",
      "var12 (Cumul.perc.: 0.044): -4.14\n",
      "\n",
      "Obs. 13040:\n",
      " var2 (Cumul.perc.: 0.011): -7.0\n",
      "var7 (Cumul.perc.: 0.018): -5.68\n",
      "var19 (Cumul.perc.: 0.953): 5.63\n",
      "var28 (Cumul.perc.: 0.959): 5.61\n",
      "\n",
      "Obs. 13050:\n",
      " var22 (Cumul.perc.: 0.99): 7.26\n",
      "var0 (Cumul.perc.: 0.018): -6.4\n",
      "var17 (Cumul.perc.: 0.032): -5.01\n",
      "\n",
      "Obs. 13060:\n",
      " var12 (Cumul.perc.: 0.997): 8.07\n",
      "var2 (Cumul.perc.: 0.974): 6.89\n",
      "var21 (Cumul.perc.: 0.959): 6.26\n",
      "var19 (Cumul.perc.: 0.97): 6.28\n",
      "\n",
      "Obs. 13070:\n",
      " var27 (Cumul.perc.: 0.957): 5.82\n",
      "\n",
      "Obs. 13080:\n",
      " var13 (Cumul.perc.: 0.992): 8.42\n",
      "var19 (Cumul.perc.: 0.959): 5.86\n",
      "var12 (Cumul.perc.: 0.043): -4.15\n",
      "\n",
      "Obs. 13090:\n",
      " var13 (Cumul.perc.: 0.998): 9.82\n",
      "var17 (Cumul.perc.: 0.012): -6.31\n",
      "\n",
      "Obs. 13100:\n",
      " var25 (Cumul.perc.: 0.008): -7.07\n",
      "var0 (Cumul.perc.: 0.972): 6.71\n",
      "var27 (Cumul.perc.: 0.04): -4.92\n",
      "\n",
      "Obs. 13110:\n",
      " var7 (Cumul.perc.: 0.998): 9.37\n",
      "var9 (Cumul.perc.: 0.997): 7.68\n",
      "var20 (Cumul.perc.: 0.013): -5.6\n",
      "var13 (Cumul.perc.: 0.969): 6.54\n",
      "\n",
      "Obs. 13120:\n",
      " var19 (Cumul.perc.: 0.016): -6.11\n",
      "var21 (Cumul.perc.: 0.966): 6.58\n",
      "var20 (Cumul.perc.: 0.978): 5.95\n",
      "var22 (Cumul.perc.: 0.028): -5.1\n",
      "var15 (Cumul.perc.: 0.965): 5.58\n",
      "\n",
      "Obs. 13130:\n",
      " var13 (Cumul.perc.: 0.957): 6.1\n",
      "var5 (Cumul.perc.: 0.042): -4.87\n",
      "\n",
      "Obs. 13140:\n",
      " var2 (Cumul.perc.: 0.978): 7.11\n",
      "var7 (Cumul.perc.: 0.975): 6.28\n",
      "var22 (Cumul.perc.: 0.037): -4.7\n",
      "var8 (Cumul.perc.: 0.963): 5.26\n",
      "var26 (Cumul.perc.: 0.956): 5.62\n",
      "\n",
      "Obs. 13150:\n",
      " var12 (Cumul.perc.: 0.027): -4.77\n",
      "var6 (Cumul.perc.: 0.959): 6.36\n",
      "var26 (Cumul.perc.: 0.034): -5.0\n",
      "var20 (Cumul.perc.: 0.038): -4.32\n",
      "\n",
      "Obs. 13160:\n",
      " var13 (Cumul.perc.: 0.028): -5.84\n",
      "var27 (Cumul.perc.: 0.967): 6.2\n",
      "var1 (Cumul.perc.: 0.026): -5.03\n",
      "var29 (Cumul.perc.: 0.953): 5.85\n",
      "\n",
      "Obs. 13170:\n",
      " var25 (Cumul.perc.: 0.01): -6.83\n",
      "var6 (Cumul.perc.: 0.031): -5.8\n",
      "var13 (Cumul.perc.: 0.954): 5.98\n",
      "var9 (Cumul.perc.: 0.966): 5.22\n",
      "var28 (Cumul.perc.: 0.036): -4.9\n",
      "\n",
      "Obs. 13180:\n",
      " var26 (Cumul.perc.: 0.994): 8.04\n",
      "var27 (Cumul.perc.: 0.984): 7.15\n",
      "var23 (Cumul.perc.: 0.021): -6.06\n",
      "var0 (Cumul.perc.: 0.972): 6.7\n",
      "\n",
      "Obs. 13190:\n",
      " var19 (Cumul.perc.: 0.985): 7.2\n",
      "\n",
      "Obs. 13200:\n",
      " var23 (Cumul.perc.: 0.96): 5.99\n",
      "\n",
      "Obs. 13210:\n",
      " var0 (Cumul.perc.: 0.957): 6.07\n",
      "\n",
      "Obs. 13220:\n",
      " var16 (Cumul.perc.: 0.001): -9.61\n",
      "var22 (Cumul.perc.: 0.017): -5.73\n",
      "var18 (Cumul.perc.: 0.026): -5.59\n",
      "var7 (Cumul.perc.: 0.038): -4.68\n",
      "\n",
      "Obs. 13230:\n",
      " var20 (Cumul.perc.: 0.974): 5.72\n",
      "\n",
      "Obs. 13240:\n",
      " var4 (Cumul.perc.: 0.976): 6.33\n",
      "var7 (Cumul.perc.: 0.042): -4.55\n",
      "\n",
      "Obs. 13250:\n",
      " var8 (Cumul.perc.: 1.0): 10.49\n",
      "var6 (Cumul.perc.: 0.0): -15.54\n",
      "var12 (Cumul.perc.: 0.001): -10.06\n",
      "var23 (Cumul.perc.: 0.001): -10.32\n",
      "var15 (Cumul.perc.: 1.0): 10.56\n",
      "\n",
      "Obs. 13260:\n",
      " var21 (Cumul.perc.: 0.003): -8.8\n",
      "var7 (Cumul.perc.: 0.996): 8.18\n",
      "\n",
      "Obs. 13270:\n",
      " var15 (Cumul.perc.: 0.995): 7.84\n",
      "var26 (Cumul.perc.: 0.977): 6.43\n",
      "\n",
      "Obs. 13280:\n",
      " var7 (Cumul.perc.: 0.005): -7.24\n",
      "var23 (Cumul.perc.: 0.98): 6.96\n",
      "var18 (Cumul.perc.: 0.971): 6.46\n",
      "var13 (Cumul.perc.: 0.03): -5.69\n",
      "var15 (Cumul.perc.: 0.967): 5.65\n",
      "\n",
      "Obs. 13290:\n",
      " var6 (Cumul.perc.: 0.972): 6.92\n",
      "var15 (Cumul.perc.: 0.956): 5.26\n",
      "var12 (Cumul.perc.: 0.041): -4.2\n",
      "\n",
      "Obs. 13300:\n",
      " var28 (Cumul.perc.: 0.013): -6.26\n",
      "var26 (Cumul.perc.: 0.981): 6.66\n",
      "var14 (Cumul.perc.: 0.036): -5.17\n",
      "var15 (Cumul.perc.: 0.971): 5.82\n",
      "\n",
      "Obs. 13310:\n",
      " var19 (Cumul.perc.: 0.982): 6.99\n",
      "var29 (Cumul.perc.: 0.021): -6.08\n",
      "var14 (Cumul.perc.: 0.97): 6.36\n",
      "\n",
      "Obs. 13320:\n",
      " var20 (Cumul.perc.: 0.988): 6.68\n",
      "var10 (Cumul.perc.: 0.973): 5.82\n",
      "\n",
      "Obs. 13330:\n",
      " var9 (Cumul.perc.: 0.004): -6.51\n",
      "var2 (Cumul.perc.: 0.017): -6.45\n",
      "var4 (Cumul.perc.: 0.967): 5.91\n",
      "\n",
      "Obs. 13340:\n",
      " var0 (Cumul.perc.: 0.981): 7.31\n",
      "var6 (Cumul.perc.: 0.979): 7.32\n",
      "var25 (Cumul.perc.: 0.031): -5.39\n",
      "var28 (Cumul.perc.: 0.951): 5.37\n",
      "\n",
      "Obs. 13350:\n",
      " var3 (Cumul.perc.: 0.987): 6.52\n",
      "var16 (Cumul.perc.: 0.011): -6.01\n",
      "var12 (Cumul.perc.: 0.028): -4.73\n",
      "var27 (Cumul.perc.: 0.963): 6.04\n",
      "\n",
      "Obs. 13360:\n",
      " var26 (Cumul.perc.: 0.004): -7.65\n",
      "\n",
      "Obs. 13370:\n",
      " var13 (Cumul.perc.: 0.014): -6.76\n",
      "\n",
      "Obs. 13380:\n",
      " var2 (Cumul.perc.: 0.0): -11.91\n",
      "var4 (Cumul.perc.: 0.989): 7.29\n",
      "var9 (Cumul.perc.: 0.028): -4.43\n",
      "\n",
      "Obs. 13390:\n",
      " var29 (Cumul.perc.: 0.023): -5.94\n",
      "var15 (Cumul.perc.: 0.964): 5.54\n",
      "\n",
      "Obs. 13400:\n",
      " var27 (Cumul.perc.: 0.988): 7.57\n",
      "var26 (Cumul.perc.: 0.982): 6.73\n",
      "\n",
      "Obs. 13410:\n",
      " var1 (Cumul.perc.: 0.024): -5.13\n",
      "\n",
      "Obs. 13420:\n",
      " var1 (Cumul.perc.: 0.98): 6.2\n",
      "var19 (Cumul.perc.: 0.029): -5.35\n",
      "var12 (Cumul.perc.: 0.968): 5.45\n",
      "var17 (Cumul.perc.: 0.956): 5.46\n",
      "\n",
      "Obs. 13430:\n",
      " var12 (Cumul.perc.: 0.015): -5.46\n",
      "var24 (Cumul.perc.: 0.013): -6.55\n",
      "var5 (Cumul.perc.: 0.965): 6.04\n",
      "\n",
      "Obs. 13440:\n",
      " var15 (Cumul.perc.: 0.03): -5.02\n",
      "var28 (Cumul.perc.: 0.047): -4.51\n",
      "\n",
      "Obs. 13450:\n",
      " var11 (Cumul.perc.: 0.008): -7.31\n",
      "var2 (Cumul.perc.: 0.02): -6.25\n",
      "var19 (Cumul.perc.: 0.954): 5.69\n",
      "var8 (Cumul.perc.: 0.043): -4.16\n",
      "\n",
      "Obs. 13460:\n",
      " var28 (Cumul.perc.: 0.996): 8.52\n",
      "var18 (Cumul.perc.: 0.007): -7.28\n",
      "var16 (Cumul.perc.: 0.024): -5.15\n",
      "var19 (Cumul.perc.: 0.969): 6.24\n",
      "var12 (Cumul.perc.: 0.033): -4.45\n",
      "\n",
      "Obs. 13470:\n",
      " var12 (Cumul.perc.: 0.005): -6.95\n",
      "var5 (Cumul.perc.: 0.961): 5.88\n",
      "var25 (Cumul.perc.: 0.032): -5.33\n",
      "var9 (Cumul.perc.: 0.027): -4.5\n",
      "\n",
      "Obs. 13480:\n",
      " var8 (Cumul.perc.: 0.007): -6.36\n",
      "var4 (Cumul.perc.: 0.971): 6.08\n",
      "\n",
      "Obs. 13490:\n",
      " var12 (Cumul.perc.: 0.009): -6.19\n",
      "var23 (Cumul.perc.: 0.969): 6.31\n",
      "var5 (Cumul.perc.: 0.952): 5.62\n",
      "\n",
      "Obs. 13500:\n",
      " var29 (Cumul.perc.: 0.033): -5.44\n",
      "var22 (Cumul.perc.: 0.033): -4.86\n",
      "var0 (Cumul.perc.: 0.039): -5.32\n",
      "\n",
      "Obs. 13510:\n",
      " var19 (Cumul.perc.: 0.989): 7.52\n",
      "var4 (Cumul.perc.: 0.975): 6.26\n",
      "\n",
      "Obs. 13520:\n",
      " var29 (Cumul.perc.: 0.015): -6.52\n",
      "var12 (Cumul.perc.: 0.957): 5.08\n",
      "\n",
      "Obs. 13530:\n",
      " var29 (Cumul.perc.: 0.996): 9.08\n",
      "var22 (Cumul.perc.: 0.985): 6.77\n",
      "var27 (Cumul.perc.: 0.024): -5.6\n",
      "\n",
      "Obs. 13540:\n",
      " var16 (Cumul.perc.: 0.007): -6.45\n",
      "var1 (Cumul.perc.: 0.017): -5.6\n",
      "\n",
      "Obs. 13550:\n",
      " var19 (Cumul.perc.: 0.043): -4.76\n",
      "\n",
      "Obs. 13560:\n",
      " var13 (Cumul.perc.: 0.974): 6.82\n",
      "var27 (Cumul.perc.: 0.97): 6.32\n",
      "var21 (Cumul.perc.: 0.037): -5.55\n",
      "var3 (Cumul.perc.: 0.027): -4.77\n",
      "\n",
      "Obs. 13570:\n",
      " var25 (Cumul.perc.: 0.025): -5.67\n",
      "var1 (Cumul.perc.: 0.033): -4.74\n",
      "var21 (Cumul.perc.: 0.041): -5.38\n",
      "\n",
      "Obs. 13580:\n",
      " var21 (Cumul.perc.: 0.014): -7.02\n",
      "var24 (Cumul.perc.: 0.98): 6.96\n",
      "var16 (Cumul.perc.: 0.949): 5.11\n",
      "\n",
      "Obs. 13590:\n",
      " var7 (Cumul.perc.: 0.01): -6.38\n",
      "var25 (Cumul.perc.: 0.037): -5.12\n",
      "\n",
      "Obs. 13600:\n",
      " var25 (Cumul.perc.: 0.984): 7.18\n",
      "\n",
      "Obs. 13610:\n",
      " var25 (Cumul.perc.: 0.983): 7.1\n",
      "\n",
      "Obs. 13620:\n",
      " var23 (Cumul.perc.: 0.988): 7.62\n",
      "var20 (Cumul.perc.: 0.981): 6.1\n",
      "var24 (Cumul.perc.: 0.035): -5.19\n",
      "var9 (Cumul.perc.: 0.044): -3.88\n",
      "\n",
      "Obs. 13630:\n",
      " var13 (Cumul.perc.: 0.017): -6.55\n",
      "var6 (Cumul.perc.: 0.039): -5.47\n",
      "var14 (Cumul.perc.: 0.963): 6.08\n",
      "var7 (Cumul.perc.: 0.957): 5.51\n",
      "\n",
      "Obs. 13640:\n",
      " var12 (Cumul.perc.: 0.996): 7.67\n",
      "var24 (Cumul.perc.: 0.006): -7.5\n",
      "var15 (Cumul.perc.: 0.958): 5.34\n",
      "\n",
      "Obs. 13650:\n",
      " var25 (Cumul.perc.: 0.016): -6.25\n",
      "var8 (Cumul.perc.: 0.968): 5.43\n",
      "\n",
      "Obs. 13660:\n",
      " var17 (Cumul.perc.: 0.048): -4.43\n",
      "\n",
      "Obs. 13670:\n",
      " var27 (Cumul.perc.: 0.005): -7.6\n",
      "var15 (Cumul.perc.: 0.994): 7.7\n",
      "var28 (Cumul.perc.: 0.035): -4.93\n",
      "\n",
      "Obs. 13680:\n",
      " var10 (Cumul.perc.: 0.985): 6.53\n",
      "var24 (Cumul.perc.: 0.042): -4.94\n",
      "\n",
      "Obs. 13690:\n",
      " var25 (Cumul.perc.: 0.022): -5.82\n",
      "var8 (Cumul.perc.: 0.96): 5.13\n",
      "\n",
      "Obs. 13700:\n",
      " var9 (Cumul.perc.: 0.011): -5.49\n",
      "var20 (Cumul.perc.: 0.975): 5.76\n",
      "\n",
      "Obs. 13710:\n",
      " var20 (Cumul.perc.: 0.013): -5.56\n",
      "var5 (Cumul.perc.: 0.029): -5.35\n",
      "\n",
      "Obs. 13720:\n",
      " var2 (Cumul.perc.: 0.977): 7.05\n",
      "\n",
      "Obs. 13730:\n",
      " var27 (Cumul.perc.: 0.023): -5.65\n",
      "var1 (Cumul.perc.: 0.049): -4.18\n",
      "\n",
      "Obs. 13740:\n",
      " var19 (Cumul.perc.: 0.007): -7.3\n",
      "var6 (Cumul.perc.: 0.005): -8.45\n",
      "var12 (Cumul.perc.: 0.016): -5.38\n",
      "var20 (Cumul.perc.: 0.036): -4.37\n",
      "\n",
      "Obs. 13750:\n",
      " var20 (Cumul.perc.: 0.996): 7.69\n",
      "var19 (Cumul.perc.: 0.013): -6.43\n",
      "var6 (Cumul.perc.: 0.009): -7.49\n",
      "var12 (Cumul.perc.: 0.978): 5.89\n",
      "var14 (Cumul.perc.: 0.04): -5.0\n",
      "\n",
      "Obs. 13760:\n",
      " var26 (Cumul.perc.: 0.002): -8.22\n",
      "var23 (Cumul.perc.: 0.972): 6.45\n",
      "var1 (Cumul.perc.: 0.973): 5.86\n",
      "var3 (Cumul.perc.: 0.955): 5.04\n",
      "\n",
      "Obs. 13770:\n",
      " var24 (Cumul.perc.: 0.002): -8.7\n",
      "var20 (Cumul.perc.: 0.997): 8.18\n",
      "var26 (Cumul.perc.: 0.995): 8.26\n",
      "var23 (Cumul.perc.: 0.019): -6.2\n",
      "var5 (Cumul.perc.: 0.017): -6.1\n",
      "\n",
      "Obs. 13780:\n",
      " var3 (Cumul.perc.: 0.03): -4.65\n",
      "var1 (Cumul.perc.: 0.959): 5.33\n",
      "\n",
      "Obs. 13790:\n",
      " var19 (Cumul.perc.: 0.007): -7.31\n",
      "var24 (Cumul.perc.: 0.989): 7.58\n",
      "var8 (Cumul.perc.: 0.017): -5.35\n",
      "var14 (Cumul.perc.: 0.022): -5.92\n",
      "var12 (Cumul.perc.: 0.95): 4.86\n",
      "\n",
      "Obs. 13800:\n",
      " var8 (Cumul.perc.: 0.96): 5.14\n",
      "\n",
      "Obs. 13810:\n",
      " var19 (Cumul.perc.: 0.998): 9.38\n",
      "var4 (Cumul.perc.: 0.016): -5.82\n",
      "var23 (Cumul.perc.: 0.015): -6.54\n",
      "var17 (Cumul.perc.: 0.02): -5.65\n",
      "var24 (Cumul.perc.: 0.044): -4.87\n",
      "\n",
      "Obs. 13820:\n",
      " var29 (Cumul.perc.: 0.997): 9.3\n",
      "var26 (Cumul.perc.: 0.986): 7.03\n",
      "var5 (Cumul.perc.: 0.977): 6.62\n",
      "var0 (Cumul.perc.: 0.953): 5.94\n",
      "\n",
      "Obs. 13830:\n",
      " var1 (Cumul.perc.: 0.027): -4.98\n",
      "\n",
      "Obs. 13840:\n",
      " var11 (Cumul.perc.: 0.012): -6.8\n",
      "var14 (Cumul.perc.: 0.973): 6.53\n",
      "var10 (Cumul.perc.: 0.031): -4.75\n",
      "var20 (Cumul.perc.: 0.967): 5.45\n",
      "var28 (Cumul.perc.: 0.041): -4.71\n",
      "\n",
      "Obs. 13850:\n",
      " var3 (Cumul.perc.: 0.013): -5.58\n",
      "var10 (Cumul.perc.: 0.015): -5.61\n",
      "var0 (Cumul.perc.: 0.022): -6.1\n",
      "var29 (Cumul.perc.: 0.027): -5.69\n",
      "var11 (Cumul.perc.: 0.025): -5.74\n",
      "\n",
      "Obs. 13860:\n",
      " var18 (Cumul.perc.: 0.012): -6.64\n",
      "\n",
      "Obs. 13870:\n",
      " var0 (Cumul.perc.: 0.967): 6.47\n",
      "var2 (Cumul.perc.: 0.953): 6.02\n",
      "var26 (Cumul.perc.: 0.96): 5.74\n",
      "\n",
      "Obs. 13880:\n",
      " var3 (Cumul.perc.: 0.984): 6.29\n",
      "var17 (Cumul.perc.: 0.973): 6.15\n",
      "var9 (Cumul.perc.: 0.034): -4.21\n",
      "var16 (Cumul.perc.: 0.044): -4.36\n",
      "\n",
      "Obs. 13890:\n",
      " var23 (Cumul.perc.: 0.96): 5.97\n",
      "var4 (Cumul.perc.: 0.955): 5.45\n",
      "var9 (Cumul.perc.: 0.024): -4.62\n",
      "\n",
      "Obs. 13900:\n",
      " var23 (Cumul.perc.: 0.994): 8.49\n",
      "var0 (Cumul.perc.: 0.034): -5.55\n",
      "var3 (Cumul.perc.: 0.976): 5.82\n",
      "var16 (Cumul.perc.: 0.034): -4.7\n",
      "\n",
      "Obs. 13910:\n",
      " var1 (Cumul.perc.: 0.961): 5.38\n",
      "\n",
      "Obs. 13920:\n",
      " var25 (Cumul.perc.: 0.019): -6.05\n",
      "var7 (Cumul.perc.: 0.968): 5.93\n",
      "var22 (Cumul.perc.: 0.963): 5.75\n",
      "var9 (Cumul.perc.: 0.964): 5.12\n",
      "var3 (Cumul.perc.: 0.037): -4.39\n",
      "\n",
      "Obs. 13930:\n",
      " var13 (Cumul.perc.: 0.998): 9.79\n",
      "var27 (Cumul.perc.: 0.967): 6.18\n",
      "\n",
      "Obs. 13940:\n",
      " var21 (Cumul.perc.: 0.006): -8.02\n",
      "var27 (Cumul.perc.: 0.987): 7.5\n",
      "var13 (Cumul.perc.: 0.972): 6.7\n",
      "\n",
      "Obs. 13950:\n",
      " var20 (Cumul.perc.: 0.989): 6.83\n",
      "var18 (Cumul.perc.: 0.984): 7.23\n",
      "var24 (Cumul.perc.: 0.969): 6.39\n",
      "var2 (Cumul.perc.: 0.956): 6.12\n",
      "\n",
      "Obs. 13960:\n",
      " var26 (Cumul.perc.: 0.984): 6.86\n",
      "var7 (Cumul.perc.: 0.973): 6.15\n",
      "\n",
      "Obs. 13970:\n",
      " var4 (Cumul.perc.: 0.013): -6.07\n",
      "var14 (Cumul.perc.: 0.961): 6.0\n",
      "var9 (Cumul.perc.: 0.959): 4.95\n",
      "var3 (Cumul.perc.: 0.049): -4.01\n",
      "\n",
      "Obs. 13980:\n",
      " var24 (Cumul.perc.: 0.016): -6.34\n",
      "var5 (Cumul.perc.: 0.965): 6.03\n",
      "var26 (Cumul.perc.: 0.038): -4.87\n",
      "\n",
      "Obs. 13990:\n",
      " var16 (Cumul.perc.: 0.05): -4.2\n",
      "\n",
      "Obs. 14000:\n",
      " var23 (Cumul.perc.: 0.991): 7.9\n",
      "var4 (Cumul.perc.: 0.992): 7.73\n",
      "var11 (Cumul.perc.: 0.014): -6.58\n",
      "\n",
      "Obs. 14010:\n",
      " var7 (Cumul.perc.: 0.021): -5.5\n",
      "var11 (Cumul.perc.: 0.97): 6.41\n",
      "\n",
      "Obs. 14020:\n",
      " var23 (Cumul.perc.: 0.019): -6.18\n",
      "\n",
      "Obs. 14030:\n",
      " var9 (Cumul.perc.: 0.003): -6.73\n",
      "var26 (Cumul.perc.: 0.015): -6.08\n",
      "var15 (Cumul.perc.: 0.033): -4.9\n",
      "var16 (Cumul.perc.: 0.035): -4.67\n",
      "var10 (Cumul.perc.: 0.968): 5.59\n",
      "\n",
      "Obs. 14040:\n",
      " var17 (Cumul.perc.: 0.013): -6.18\n",
      "var29 (Cumul.perc.: 0.957): 5.98\n",
      "\n",
      "Obs. 14050:\n",
      " var28 (Cumul.perc.: 0.002): -8.77\n",
      "var15 (Cumul.perc.: 0.994): 7.71\n",
      "\n",
      "Obs. 14060:\n",
      " None\n",
      "\n",
      "Obs. 14070:\n",
      " var29 (Cumul.perc.: 0.047): -4.85\n",
      "var16 (Cumul.perc.: 0.045): -4.34\n",
      "\n",
      "Obs. 14080:\n",
      " var17 (Cumul.perc.: 0.007): -7.06\n",
      "var19 (Cumul.perc.: 0.972): 6.43\n",
      "var20 (Cumul.perc.: 0.981): 6.1\n",
      "var29 (Cumul.perc.: 0.023): -5.93\n",
      "var24 (Cumul.perc.: 0.044): -4.88\n",
      "\n",
      "Obs. 14090:\n",
      " var14 (Cumul.perc.: 0.03): -5.45\n",
      "var23 (Cumul.perc.: 0.021): -6.07\n",
      "\n",
      "Obs. 14100:\n",
      " var11 (Cumul.perc.: 0.003): -8.59\n",
      "var25 (Cumul.perc.: 0.002): -8.6\n",
      "var24 (Cumul.perc.: 0.027): -5.59\n",
      "var20 (Cumul.perc.: 0.979): 5.97\n",
      "var17 (Cumul.perc.: 0.037): -4.81\n",
      "\n",
      "Obs. 14110:\n",
      " var29 (Cumul.perc.: 0.969): 6.5\n",
      "var16 (Cumul.perc.: 0.961): 5.47\n",
      "\n",
      "Obs. 14120:\n",
      " var25 (Cumul.perc.: 0.018): -6.08\n",
      "var13 (Cumul.perc.: 0.978): 7.02\n",
      "var15 (Cumul.perc.: 0.046): -4.43\n",
      "var16 (Cumul.perc.: 0.958): 5.38\n",
      "\n",
      "Obs. 14130:\n",
      " var29 (Cumul.perc.: 0.005): -7.91\n",
      "var27 (Cumul.perc.: 0.028): -5.43\n",
      "var7 (Cumul.perc.: 0.022): -5.39\n",
      "var15 (Cumul.perc.: 0.956): 5.27\n",
      "var9 (Cumul.perc.: 0.953): 4.8\n",
      "\n",
      "Obs. 14140:\n",
      " var13 (Cumul.perc.: 0.018): -6.47\n",
      "var12 (Cumul.perc.: 0.97): 5.53\n",
      "var1 (Cumul.perc.: 0.031): -4.78\n",
      "var19 (Cumul.perc.: 0.969): 6.27\n",
      "var7 (Cumul.perc.: 0.04): -4.62\n",
      "\n",
      "Obs. 14150:\n",
      " var11 (Cumul.perc.: 0.016): -6.44\n",
      "var24 (Cumul.perc.: 0.965): 6.17\n",
      "var8 (Cumul.perc.: 0.043): -4.15\n",
      "\n",
      "Obs. 14160:\n",
      " var9 (Cumul.perc.: 0.992): 6.7\n",
      "var16 (Cumul.perc.: 0.969): 5.75\n",
      "var18 (Cumul.perc.: 0.964): 6.15\n",
      "\n",
      "Obs. 14170:\n",
      " var26 (Cumul.perc.: 0.026): -5.37\n",
      "var29 (Cumul.perc.: 0.038): -5.19\n",
      "\n",
      "Obs. 14180:\n",
      " var0 (Cumul.perc.: 0.033): -5.56\n",
      "var3 (Cumul.perc.: 0.028): -4.74\n",
      "var6 (Cumul.perc.: 0.958): 6.31\n",
      "var4 (Cumul.perc.: 0.05): -4.31\n",
      "\n",
      "Obs. 14190:\n",
      " var25 (Cumul.perc.: 0.04): -5.02\n",
      "\n",
      "Obs. 14200:\n",
      " var8 (Cumul.perc.: 0.01): -5.94\n",
      "var21 (Cumul.perc.: 0.986): 7.81\n",
      "\n",
      "Obs. 14210:\n",
      " var27 (Cumul.perc.: 0.991): 7.92\n",
      "var1 (Cumul.perc.: 0.012): -6.08\n",
      "var3 (Cumul.perc.: 0.028): -4.73\n",
      "var20 (Cumul.perc.: 0.043): -4.16\n",
      "\n",
      "Obs. 14220:\n",
      " var28 (Cumul.perc.: 0.976): 6.32\n",
      "var20 (Cumul.perc.: 0.037): -4.35\n",
      "\n",
      "Obs. 14230:\n",
      " var22 (Cumul.perc.: 0.976): 6.24\n",
      "var1 (Cumul.perc.: 0.03): -4.83\n",
      "var9 (Cumul.perc.: 0.046): -3.84\n",
      "var4 (Cumul.perc.: 0.952): 5.37\n",
      "\n",
      "Obs. 14240:\n",
      " var20 (Cumul.perc.: 0.006): -6.69\n",
      "var16 (Cumul.perc.: 0.965): 5.61\n",
      "\n",
      "Obs. 14250:\n",
      " var0 (Cumul.perc.: 0.974): 6.85\n",
      "var15 (Cumul.perc.: 0.029): -5.07\n",
      "var13 (Cumul.perc.: 0.039): -5.32\n",
      "var9 (Cumul.perc.: 0.949): 4.69\n",
      "\n",
      "Obs. 14260:\n",
      " var12 (Cumul.perc.: 0.984): 6.24\n",
      "var4 (Cumul.perc.: 0.954): 5.43\n",
      "var29 (Cumul.perc.: 0.955): 5.9\n",
      "var16 (Cumul.perc.: 0.044): -4.36\n",
      "\n",
      "Obs. 14270:\n",
      " var2 (Cumul.perc.: 0.996): 9.13\n",
      "var23 (Cumul.perc.: 0.995): 8.62\n",
      "var26 (Cumul.perc.: 0.034): -5.01\n",
      "var5 (Cumul.perc.: 0.949): 5.51\n",
      "\n",
      "Obs. 14280:\n",
      " var23 (Cumul.perc.: 0.019): -6.18\n",
      "var28 (Cumul.perc.: 0.016): -6.06\n",
      "\n",
      "Obs. 14290:\n",
      " var21 (Cumul.perc.: 0.004): -8.55\n",
      "var26 (Cumul.perc.: 0.986): 7.06\n",
      "var12 (Cumul.perc.: 0.021): -5.06\n",
      "var25 (Cumul.perc.: 0.016): -6.2\n",
      "var0 (Cumul.perc.: 0.049): -4.95\n",
      "\n",
      "Obs. 14300:\n",
      " var23 (Cumul.perc.: 0.017): -6.36\n",
      "var9 (Cumul.perc.: 0.956): 4.87\n",
      "\n",
      "Obs. 14310:\n",
      " var11 (Cumul.perc.: 0.006): -7.78\n",
      "var14 (Cumul.perc.: 0.961): 5.99\n",
      "\n",
      "Obs. 14320:\n",
      " var5 (Cumul.perc.: 0.993): 8.02\n",
      "var8 (Cumul.perc.: 0.992): 6.84\n",
      "var21 (Cumul.perc.: 0.037): -5.56\n",
      "var29 (Cumul.perc.: 0.972): 6.65\n",
      "var17 (Cumul.perc.: 0.963): 5.7\n",
      "\n",
      "Obs. 14330:\n",
      " var1 (Cumul.perc.: 0.996): 7.94\n",
      "var24 (Cumul.perc.: 0.988): 7.56\n",
      "var15 (Cumul.perc.: 0.034): -4.83\n",
      "\n",
      "Obs. 14340:\n",
      " var21 (Cumul.perc.: 0.013): -7.12\n",
      "var23 (Cumul.perc.: 0.019): -6.2\n",
      "var9 (Cumul.perc.: 0.981): 5.87\n",
      "var13 (Cumul.perc.: 0.955): 6.03\n",
      "\n",
      "Obs. 14350:\n",
      " var19 (Cumul.perc.: 0.004): -8.2\n",
      "var12 (Cumul.perc.: 0.993): 7.16\n",
      "var0 (Cumul.perc.: 0.013): -6.84\n",
      "var7 (Cumul.perc.: 0.986): 6.99\n",
      "var26 (Cumul.perc.: 0.983): 6.75\n",
      "\n",
      "Obs. 14360:\n",
      " var25 (Cumul.perc.: 0.004): -7.97\n",
      "var3 (Cumul.perc.: 0.037): -4.39\n",
      "\n",
      "Obs. 14370:\n",
      " var7 (Cumul.perc.: 0.996): 8.28\n",
      "var14 (Cumul.perc.: 0.973): 6.52\n",
      "var11 (Cumul.perc.: 0.018): -6.22\n",
      "\n",
      "Obs. 14380:\n",
      " var18 (Cumul.perc.: 0.005): -7.7\n",
      "var25 (Cumul.perc.: 0.022): -5.8\n",
      "var6 (Cumul.perc.: 0.953): 6.13\n",
      "var8 (Cumul.perc.: 0.961): 5.16\n",
      "\n",
      "Obs. 14390:\n",
      " var14 (Cumul.perc.: 0.979): 6.91\n",
      "var28 (Cumul.perc.: 0.974): 6.22\n",
      "var1 (Cumul.perc.: 0.982): 6.32\n",
      "var7 (Cumul.perc.: 0.979): 6.47\n",
      "var24 (Cumul.perc.: 0.045): -4.84\n",
      "\n",
      "Obs. 14400:\n",
      " var13 (Cumul.perc.: 0.002): -9.36\n",
      "var27 (Cumul.perc.: 0.016): -6.18\n",
      "var25 (Cumul.perc.: 0.98): 6.86\n",
      "var12 (Cumul.perc.: 0.978): 5.9\n",
      "var0 (Cumul.perc.: 0.036): -5.44\n",
      "\n",
      "Obs. 14410:\n",
      " var10 (Cumul.perc.: 0.039): -4.44\n",
      "var3 (Cumul.perc.: 0.964): 5.34\n",
      "\n",
      "Obs. 14420:\n",
      " var19 (Cumul.perc.: 0.027): -5.47\n",
      "var4 (Cumul.perc.: 0.972): 6.12\n",
      "var6 (Cumul.perc.: 0.045): -5.18\n",
      "var7 (Cumul.perc.: 0.956): 5.47\n",
      "\n",
      "Obs. 14430:\n",
      " var23 (Cumul.perc.: 0.009): -7.22\n",
      "var25 (Cumul.perc.: 0.01): -6.79\n",
      "\n",
      "Obs. 14440:\n",
      " var26 (Cumul.perc.: 0.002): -8.7\n",
      "var15 (Cumul.perc.: 0.011): -6.34\n",
      "var1 (Cumul.perc.: 0.982): 6.36\n",
      "var21 (Cumul.perc.: 0.952): 6.04\n",
      "\n",
      "Obs. 14450:\n",
      " var2 (Cumul.perc.: 0.992): 8.46\n",
      "var19 (Cumul.perc.: 0.975): 6.58\n",
      "\n",
      "Obs. 14460:\n",
      " var0 (Cumul.perc.: 0.014): -6.78\n",
      "var27 (Cumul.perc.: 0.03): -5.34\n",
      "var5 (Cumul.perc.: 0.03): -5.33\n",
      "\n",
      "Obs. 14470:\n",
      " var12 (Cumul.perc.: 0.014): -5.57\n",
      "var6 (Cumul.perc.: 0.983): 7.67\n",
      "var11 (Cumul.perc.: 0.022): -5.89\n",
      "\n",
      "Obs. 14480:\n",
      " var23 (Cumul.perc.: 0.029): -5.64\n",
      "var26 (Cumul.perc.: 0.952): 5.49\n",
      "var9 (Cumul.perc.: 0.952): 4.77\n",
      "\n",
      "Obs. 14490:\n",
      " var4 (Cumul.perc.: 0.021): -5.51\n",
      "var5 (Cumul.perc.: 0.963): 5.97\n",
      "\n",
      "Obs. 14500:\n",
      " var19 (Cumul.perc.: 0.013): -6.47\n",
      "var7 (Cumul.perc.: 0.993): 7.61\n",
      "var23 (Cumul.perc.: 0.05): -4.78\n",
      "\n",
      "Obs. 14510:\n",
      " var23 (Cumul.perc.: 0.983): 7.19\n",
      "var24 (Cumul.perc.: 0.953): 5.75\n",
      "\n",
      "Obs. 14520:\n",
      " var28 (Cumul.perc.: 0.992): 7.67\n",
      "var21 (Cumul.perc.: 0.95): 5.97\n",
      "\n",
      "Obs. 14530:\n",
      " var17 (Cumul.perc.: 0.025): -5.36\n",
      "var14 (Cumul.perc.: 0.956): 5.82\n",
      "var21 (Cumul.perc.: 0.955): 6.14\n",
      "\n",
      "Obs. 14540:\n",
      " var4 (Cumul.perc.: 0.994): 7.91\n",
      "\n",
      "Obs. 14550:\n",
      " var0 (Cumul.perc.: 0.032): -5.62\n",
      "\n",
      "Obs. 14560:\n",
      " var23 (Cumul.perc.: 0.953): 5.73\n",
      "var26 (Cumul.perc.: 0.954): 5.56\n",
      "\n",
      "Obs. 14570:\n",
      " var25 (Cumul.perc.: 0.033): -5.32\n",
      "\n",
      "Obs. 14580:\n",
      " var19 (Cumul.perc.: 0.042): -4.79\n",
      "var12 (Cumul.perc.: 0.047): -4.04\n",
      "\n",
      "Obs. 14590:\n",
      " var6 (Cumul.perc.: 0.023): -6.24\n",
      "var23 (Cumul.perc.: 0.034): -5.43\n",
      "var29 (Cumul.perc.: 0.048): -4.82\n",
      "\n",
      "Obs. 14600:\n",
      " var17 (Cumul.perc.: 0.989): 7.28\n",
      "var1 (Cumul.perc.: 0.993): 7.4\n",
      "var23 (Cumul.perc.: 0.969): 6.31\n",
      "var21 (Cumul.perc.: 0.047): -5.18\n",
      "\n",
      "Obs. 14610:\n",
      " var20 (Cumul.perc.: 0.026): -4.79\n",
      "var26 (Cumul.perc.: 0.017): -5.93\n",
      "var18 (Cumul.perc.: 0.03): -5.38\n",
      "var11 (Cumul.perc.: 0.032): -5.43\n",
      "\n",
      "Obs. 14620:\n",
      " var7 (Cumul.perc.: 0.999): 9.57\n",
      "var17 (Cumul.perc.: 0.978): 6.41\n",
      "var27 (Cumul.perc.: 0.955): 5.77\n",
      "\n",
      "Obs. 14630:\n",
      " var17 (Cumul.perc.: 0.986): 6.93\n",
      "\n",
      "Obs. 14640:\n",
      " var16 (Cumul.perc.: 0.984): 6.55\n",
      "var2 (Cumul.perc.: 0.953): 6.0\n",
      "\n",
      "Obs. 14650:\n",
      " var14 (Cumul.perc.: 0.997): 9.01\n",
      "var17 (Cumul.perc.: 0.003): -8.07\n",
      "\n",
      "Obs. 14660:\n",
      " var27 (Cumul.perc.: 0.962): 5.97\n",
      "\n",
      "Obs. 14670:\n",
      " var24 (Cumul.perc.: 0.009): -7.02\n",
      "var20 (Cumul.perc.: 0.989): 6.79\n",
      "var26 (Cumul.perc.: 0.979): 6.57\n",
      "var4 (Cumul.perc.: 0.975): 6.28\n",
      "var0 (Cumul.perc.: 0.039): -5.32\n",
      "\n",
      "Obs. 14680:\n",
      " var22 (Cumul.perc.: 0.014): -5.98\n",
      "var6 (Cumul.perc.: 0.019): -6.52\n",
      "\n",
      "Obs. 14690:\n",
      " var26 (Cumul.perc.: 0.993): 7.89\n",
      "var13 (Cumul.perc.: 0.034): -5.52\n",
      "var28 (Cumul.perc.: 0.969): 5.99\n",
      "\n",
      "Obs. 14700:\n",
      " var17 (Cumul.perc.: 1.0): 10.46\n",
      "var13 (Cumul.perc.: 0.035): -5.45\n",
      "var19 (Cumul.perc.: 0.032): -5.24\n",
      "var9 (Cumul.perc.: 0.034): -4.21\n",
      "\n",
      "Obs. 14710:\n",
      " var3 (Cumul.perc.: 0.961): 5.23\n",
      "\n",
      "Obs. 14720:\n",
      " var12 (Cumul.perc.: 0.999): 9.04\n",
      "var10 (Cumul.perc.: 0.993): 7.36\n",
      "var8 (Cumul.perc.: 0.019): -5.11\n",
      "\n",
      "Obs. 14730:\n",
      " var15 (Cumul.perc.: 0.012): -6.2\n",
      "var24 (Cumul.perc.: 0.988): 7.5\n",
      "var18 (Cumul.perc.: 0.977): 6.75\n",
      "var27 (Cumul.perc.: 0.963): 6.02\n",
      "\n",
      "Obs. 14740:\n",
      " var2 (Cumul.perc.: 0.979): 7.17\n",
      "\n",
      "Obs. 14750:\n",
      " var18 (Cumul.perc.: 0.998): 9.47\n",
      "var6 (Cumul.perc.: 0.955): 6.2\n",
      "var19 (Cumul.perc.: 0.043): -4.77\n",
      "var16 (Cumul.perc.: 0.955): 5.28\n",
      "\n",
      "Obs. 14760:\n",
      " var11 (Cumul.perc.: 0.01): -7.08\n",
      "var22 (Cumul.perc.: 0.021): -5.48\n",
      "\n",
      "Obs. 14770:\n",
      " var1 (Cumul.perc.: 0.009): -6.4\n",
      "var19 (Cumul.perc.: 0.953): 5.65\n",
      "var20 (Cumul.perc.: 0.045): -4.1\n",
      "\n",
      "Obs. 14780:\n",
      " None\n",
      "\n",
      "Obs. 14790:\n",
      " var12 (Cumul.perc.: 0.003): -7.51\n",
      "var15 (Cumul.perc.: 0.005): -7.17\n",
      "var25 (Cumul.perc.: 0.013): -6.49\n",
      "var8 (Cumul.perc.: 0.956): 5.02\n",
      "var7 (Cumul.perc.: 0.046): -4.41\n",
      "\n",
      "Obs. 14800:\n",
      " var18 (Cumul.perc.: 0.019): -6.01\n",
      "var21 (Cumul.perc.: 0.038): -5.5\n",
      "\n",
      "Obs. 14810:\n",
      " var19 (Cumul.perc.: 0.954): 5.68\n",
      "var11 (Cumul.perc.: 0.952): 5.77\n",
      "\n",
      "Obs. 14820:\n",
      " var10 (Cumul.perc.: 0.003): -7.46\n",
      "var0 (Cumul.perc.: 0.962): 6.23\n",
      "var28 (Cumul.perc.: 0.962): 5.74\n",
      "\n",
      "Obs. 14830:\n",
      " var16 (Cumul.perc.: 0.007): -6.54\n",
      "var18 (Cumul.perc.: 0.017): -6.21\n",
      "var5 (Cumul.perc.: 0.979): 6.75\n",
      "var13 (Cumul.perc.: 0.025): -6.0\n",
      "var7 (Cumul.perc.: 0.03): -4.97\n",
      "\n",
      "Obs. 14840:\n",
      " var16 (Cumul.perc.: 0.986): 6.68\n",
      "\n",
      "Obs. 14850:\n",
      " var21 (Cumul.perc.: 0.995): 8.96\n",
      "var5 (Cumul.perc.: 0.041): -4.89\n",
      "\n",
      "Obs. 14860:\n",
      " None\n",
      "\n",
      "Obs. 14870:\n",
      " var21 (Cumul.perc.: 0.996): 9.26\n",
      "var6 (Cumul.perc.: 0.989): 8.24\n",
      "var27 (Cumul.perc.: 0.016): -6.17\n",
      "var16 (Cumul.perc.: 0.976): 6.04\n",
      "\n",
      "Obs. 14880:\n",
      " var13 (Cumul.perc.: 0.997): 9.3\n",
      "var23 (Cumul.perc.: 0.033): -5.48\n",
      "var20 (Cumul.perc.: 0.969): 5.52\n",
      "\n",
      "Obs. 14890:\n",
      " var11 (Cumul.perc.: 0.025): -5.74\n",
      "var21 (Cumul.perc.: 0.949): 5.94\n",
      "\n",
      "Obs. 14900:\n",
      " var18 (Cumul.perc.: 0.021): -5.84\n",
      "var17 (Cumul.perc.: 0.045): -4.51\n",
      "var9 (Cumul.perc.: 0.963): 5.09\n",
      "\n",
      "Obs. 14910:\n",
      " var21 (Cumul.perc.: 0.01): -7.37\n",
      "var15 (Cumul.perc.: 0.024): -5.33\n",
      "var9 (Cumul.perc.: 0.971): 5.38\n",
      "\n",
      "Obs. 14920:\n",
      " None\n",
      "\n",
      "Obs. 14930:\n",
      " var6 (Cumul.perc.: 0.997): 9.97\n",
      "var28 (Cumul.perc.: 0.992): 7.75\n",
      "var21 (Cumul.perc.: 0.95): 5.98\n",
      "\n",
      "Obs. 14940:\n",
      " var26 (Cumul.perc.: 0.0): -20.32\n",
      "var16 (Cumul.perc.: 1.0): 11.07\n",
      "var8 (Cumul.perc.: 0.001): -9.05\n",
      "var3 (Cumul.perc.: 0.001): -8.63\n",
      "var28 (Cumul.perc.: 0.996): 8.72\n",
      "\n",
      "Obs. 14950:\n",
      " var11 (Cumul.perc.: 0.974): 6.6\n",
      "\n",
      "Obs. 14960:\n",
      " var15 (Cumul.perc.: 0.998): 8.81\n",
      "var7 (Cumul.perc.: 0.019): -5.65\n",
      "var28 (Cumul.perc.: 0.964): 5.81\n",
      "var26 (Cumul.perc.: 0.952): 5.49\n",
      "var22 (Cumul.perc.: 0.95): 5.33\n",
      "\n",
      "Obs. 14970:\n",
      " var27 (Cumul.perc.: 0.02): -5.84\n",
      "\n",
      "Obs. 14980:\n",
      " var21 (Cumul.perc.: 0.979): 7.29\n",
      "\n",
      "Obs. 14990:\n",
      " var5 (Cumul.perc.: 0.031): -5.26\n",
      "\n",
      "Obs. 15000:\n",
      " var27 (Cumul.perc.: 0.011): -6.59\n",
      "var24 (Cumul.perc.: 0.022): -5.89\n",
      "var6 (Cumul.perc.: 0.022): -6.32\n",
      "var25 (Cumul.perc.: 0.982): 7.01\n",
      "\n",
      "Obs. 15010:\n",
      " None\n",
      "\n",
      "Obs. 15020:\n",
      " var12 (Cumul.perc.: 0.96): 5.18\n",
      "\n",
      "Obs. 15030:\n",
      " var11 (Cumul.perc.: 0.99): 7.84\n",
      "var8 (Cumul.perc.: 0.988): 6.48\n",
      "var2 (Cumul.perc.: 0.978): 7.1\n",
      "\n",
      "Obs. 15040:\n",
      " var21 (Cumul.perc.: 0.995): 9.1\n",
      "var27 (Cumul.perc.: 0.986): 7.36\n",
      "var17 (Cumul.perc.: 0.047): -4.45\n",
      "\n",
      "Obs. 15050:\n",
      " var5 (Cumul.perc.: 0.004): -8.18\n",
      "var16 (Cumul.perc.: 0.983): 6.52\n",
      "var9 (Cumul.perc.: 0.976): 5.59\n",
      "var18 (Cumul.perc.: 0.967): 6.24\n",
      "var6 (Cumul.perc.: 0.038): -5.49\n",
      "\n",
      "Obs. 15060:\n",
      " None\n",
      "\n",
      "Obs. 15070:\n",
      " var22 (Cumul.perc.: 0.015): -5.93\n",
      "\n",
      "Obs. 15080:\n",
      " var6 (Cumul.perc.: 0.984): 7.68\n",
      "var21 (Cumul.perc.: 0.02): -6.48\n",
      "var27 (Cumul.perc.: 0.038): -5.0\n",
      "\n",
      "Obs. 15090:\n",
      " var13 (Cumul.perc.: 0.008): -7.59\n",
      "var17 (Cumul.perc.: 0.985): 6.82\n",
      "var2 (Cumul.perc.: 0.969): 6.64\n",
      "var22 (Cumul.perc.: 0.032): -4.94\n",
      "\n",
      "Obs. 15100:\n",
      " var6 (Cumul.perc.: 0.991): 8.41\n",
      "var28 (Cumul.perc.: 0.974): 6.21\n",
      "\n",
      "Obs. 15110:\n",
      " var13 (Cumul.perc.: 0.013): -6.81\n",
      "var12 (Cumul.perc.: 0.985): 6.3\n",
      "var17 (Cumul.perc.: 0.045): -4.52\n",
      "\n",
      "Obs. 15120:\n",
      " var23 (Cumul.perc.: 0.001): -9.75\n",
      "var21 (Cumul.perc.: 0.997): 9.54\n",
      "var27 (Cumul.perc.: 0.016): -6.18\n",
      "var19 (Cumul.perc.: 0.032): -5.23\n",
      "var0 (Cumul.perc.: 0.046): -5.04\n",
      "\n",
      "Obs. 15130:\n",
      " var11 (Cumul.perc.: 0.979): 6.9\n",
      "var25 (Cumul.perc.: 0.957): 5.75\n",
      "var4 (Cumul.perc.: 0.042): -4.56\n",
      "\n",
      "Obs. 15140:\n",
      " var25 (Cumul.perc.: 0.032): -5.33\n",
      "\n",
      "Obs. 15150:\n",
      " var8 (Cumul.perc.: 0.995): 7.41\n",
      "var7 (Cumul.perc.: 0.011): -6.3\n",
      "var15 (Cumul.perc.: 0.972): 5.9\n",
      "var10 (Cumul.perc.: 0.973): 5.82\n",
      "var29 (Cumul.perc.: 0.039): -5.16\n",
      "\n",
      "Obs. 15160:\n",
      " var3 (Cumul.perc.: 0.96): 5.21\n",
      "\n",
      "Obs. 15170:\n",
      " var21 (Cumul.perc.: 0.993): 8.7\n",
      "var12 (Cumul.perc.: 0.029): -4.68\n",
      "var27 (Cumul.perc.: 0.966): 6.16\n",
      "\n",
      "Obs. 15180:\n",
      " var16 (Cumul.perc.: 0.987): 6.82\n",
      "var3 (Cumul.perc.: 0.968): 5.48\n",
      "\n",
      "Obs. 15190:\n",
      " var21 (Cumul.perc.: 0.026): -6.13\n",
      "\n",
      "Obs. 15200:\n",
      " var29 (Cumul.perc.: 0.019): -6.24\n",
      "var13 (Cumul.perc.: 0.979): 7.08\n",
      "var27 (Cumul.perc.: 0.98): 6.9\n",
      "var24 (Cumul.perc.: 0.96): 5.98\n",
      "var4 (Cumul.perc.: 0.051): -4.27\n",
      "\n",
      "Obs. 15210:\n",
      " var0 (Cumul.perc.: 0.012): -6.96\n",
      "var29 (Cumul.perc.: 0.985): 7.38\n",
      "var3 (Cumul.perc.: 0.971): 5.62\n",
      "var23 (Cumul.perc.: 0.049): -4.82\n",
      "var18 (Cumul.perc.: 0.964): 6.15\n",
      "\n",
      "Obs. 15220:\n",
      " var18 (Cumul.perc.: 0.987): 7.45\n",
      "var12 (Cumul.perc.: 0.972): 5.61\n",
      "var16 (Cumul.perc.: 0.968): 5.71\n",
      "\n",
      "Obs. 15230:\n",
      " var22 (Cumul.perc.: 0.02): -5.57\n",
      "\n",
      "Obs. 15240:\n",
      " var2 (Cumul.perc.: 0.007): -7.55\n",
      "var13 (Cumul.perc.: 0.978): 7.01\n",
      "var23 (Cumul.perc.: 0.97): 6.36\n",
      "var3 (Cumul.perc.: 0.033): -4.51\n",
      "var16 (Cumul.perc.: 0.966): 5.66\n",
      "\n",
      "Obs. 15250:\n",
      " var10 (Cumul.perc.: 0.958): 5.22\n",
      "var7 (Cumul.perc.: 0.961): 5.62\n",
      "\n",
      "Obs. 15260:\n",
      " var14 (Cumul.perc.: 0.994): 8.3\n",
      "var24 (Cumul.perc.: 0.039): -5.04\n",
      "\n",
      "Obs. 15270:\n",
      " var4 (Cumul.perc.: 0.994): 7.99\n",
      "var14 (Cumul.perc.: 0.009): -7.04\n",
      "var25 (Cumul.perc.: 0.015): -6.3\n",
      "\n",
      "Obs. 15280:\n",
      " var23 (Cumul.perc.: 0.011): -6.98\n",
      "var9 (Cumul.perc.: 0.025): -4.58\n",
      "\n",
      "Obs. 15290:\n",
      " var23 (Cumul.perc.: 0.009): -7.28\n",
      "var19 (Cumul.perc.: 0.024): -5.62\n",
      "var7 (Cumul.perc.: 0.962): 5.66\n",
      "\n",
      "Obs. 15300:\n",
      " var25 (Cumul.perc.: 0.982): 6.96\n",
      "\n",
      "Obs. 15310:\n",
      " var4 (Cumul.perc.: 0.016): -5.81\n",
      "\n",
      "Obs. 15320:\n",
      " var19 (Cumul.perc.: 0.978): 6.74\n",
      "var7 (Cumul.perc.: 0.026): -5.18\n",
      "var0 (Cumul.perc.: 0.97): 6.62\n",
      "\n",
      "Obs. 15330:\n",
      " var17 (Cumul.perc.: 0.013): -6.15\n",
      "var28 (Cumul.perc.: 0.024): -5.5\n",
      "var26 (Cumul.perc.: 0.979): 6.55\n",
      "\n",
      "Obs. 15340:\n",
      " var0 (Cumul.perc.: 0.023): -6.05\n",
      "var4 (Cumul.perc.: 0.046): -4.42\n",
      "\n",
      "Obs. 15350:\n",
      " var17 (Cumul.perc.: 0.99): 7.34\n",
      "var15 (Cumul.perc.: 0.016): -5.83\n",
      "var26 (Cumul.perc.: 0.026): -5.36\n",
      "\n",
      "Obs. 15360:\n",
      " var15 (Cumul.perc.: 0.031): -4.99\n",
      "var24 (Cumul.perc.: 0.04): -4.99\n",
      "var23 (Cumul.perc.: 0.051): -4.74\n",
      "var0 (Cumul.perc.: 0.041): -5.24\n",
      "\n",
      "Obs. 15370:\n",
      " var29 (Cumul.perc.: 0.985): 7.45\n",
      "var28 (Cumul.perc.: 0.957): 5.53\n",
      "\n",
      "Obs. 15380:\n",
      " var13 (Cumul.perc.: 0.007): -7.66\n",
      "var5 (Cumul.perc.: 0.978): 6.66\n",
      "var16 (Cumul.perc.: 0.975): 6.01\n",
      "\n",
      "Obs. 15390:\n",
      " var7 (Cumul.perc.: 0.005): -7.27\n",
      "var24 (Cumul.perc.: 0.033): -5.28\n",
      "\n",
      "Obs. 15400:\n",
      " var5 (Cumul.perc.: 0.971): 6.3\n",
      "\n",
      "Obs. 15410:\n",
      " var26 (Cumul.perc.: 0.019): -5.82\n",
      "var18 (Cumul.perc.: 0.019): -6.04\n",
      "var15 (Cumul.perc.: 0.035): -4.79\n",
      "\n",
      "Obs. 15420:\n",
      " var14 (Cumul.perc.: 0.012): -6.66\n",
      "var27 (Cumul.perc.: 0.976): 6.64\n",
      "var16 (Cumul.perc.: 0.03): -4.86\n",
      "var0 (Cumul.perc.: 0.037): -5.41\n",
      "var2 (Cumul.perc.: 0.954): 6.03\n",
      "\n",
      "Obs. 15430:\n",
      " var19 (Cumul.perc.: 0.997): 8.86\n",
      "var22 (Cumul.perc.: 0.016): -5.8\n",
      "\n",
      "Obs. 15440:\n",
      " var14 (Cumul.perc.: 0.003): -8.69\n",
      "var11 (Cumul.perc.: 0.983): 7.15\n",
      "var1 (Cumul.perc.: 0.985): 6.58\n",
      "var29 (Cumul.perc.: 0.974): 6.74\n",
      "var13 (Cumul.perc.: 0.956): 6.06\n",
      "\n",
      "Obs. 15450:\n",
      " var10 (Cumul.perc.: 0.019): -5.34\n",
      "\n",
      "Obs. 15460:\n",
      " var3 (Cumul.perc.: 0.013): -5.66\n",
      "var23 (Cumul.perc.: 0.967): 6.22\n",
      "var14 (Cumul.perc.: 0.045): -4.85\n",
      "\n",
      "Obs. 15470:\n",
      " var28 (Cumul.perc.: 0.982): 6.77\n",
      "\n",
      "Obs. 15480:\n",
      " var23 (Cumul.perc.: 0.954): 5.79\n",
      "var5 (Cumul.perc.: 0.959): 5.82\n",
      "\n",
      "Obs. 15490:\n",
      " var3 (Cumul.perc.: 0.994): 7.37\n",
      "var4 (Cumul.perc.: 0.005): -7.27\n",
      "var18 (Cumul.perc.: 0.991): 7.92\n",
      "var27 (Cumul.perc.: 0.993): 8.12\n",
      "var12 (Cumul.perc.: 0.985): 6.3\n",
      "\n",
      "Obs. 15500:\n",
      " var16 (Cumul.perc.: 0.009): -6.22\n",
      "var3 (Cumul.perc.: 0.979): 5.95\n",
      "var11 (Cumul.perc.: 0.976): 6.71\n",
      "var1 (Cumul.perc.: 0.958): 5.3\n",
      "\n",
      "Obs. 15510:\n",
      " var17 (Cumul.perc.: 0.966): 5.82\n",
      "var26 (Cumul.perc.: 0.037): -4.9\n",
      "\n",
      "Obs. 15520:\n",
      " var6 (Cumul.perc.: 0.993): 8.8\n",
      "var5 (Cumul.perc.: 0.012): -6.6\n",
      "var22 (Cumul.perc.: 0.959): 5.62\n",
      "\n",
      "Obs. 15530:\n",
      " var24 (Cumul.perc.: 0.016): -6.32\n",
      "var1 (Cumul.perc.: 0.027): -4.95\n",
      "var20 (Cumul.perc.: 0.951): 4.96\n",
      "\n",
      "Obs. 15540:\n",
      " var5 (Cumul.perc.: 0.003): -8.35\n",
      "var20 (Cumul.perc.: 0.99): 6.97\n",
      "var18 (Cumul.perc.: 0.972): 6.5\n",
      "var4 (Cumul.perc.: 0.958): 5.57\n",
      "var6 (Cumul.perc.: 0.045): -5.22\n",
      "\n",
      "Obs. 15550:\n",
      " var6 (Cumul.perc.: 0.015): -6.8\n",
      "\n",
      "Obs. 15560:\n",
      " var5 (Cumul.perc.: 0.987): 7.33\n",
      "\n",
      "Obs. 15570:\n",
      " var17 (Cumul.perc.: 0.964): 5.75\n",
      "\n",
      "Obs. 15580:\n",
      " var16 (Cumul.perc.: 0.002): -8.04\n",
      "var25 (Cumul.perc.: 0.993): 8.25\n",
      "\n",
      "Obs. 15590:\n",
      " var19 (Cumul.perc.: 0.013): -6.37\n",
      "var16 (Cumul.perc.: 0.983): 6.46\n",
      "var17 (Cumul.perc.: 0.027): -5.26\n",
      "var3 (Cumul.perc.: 0.03): -4.67\n",
      "var0 (Cumul.perc.: 0.041): -5.23\n",
      "\n",
      "Obs. 15600:\n",
      " var20 (Cumul.perc.: 0.983): 6.26\n",
      "var27 (Cumul.perc.: 0.017): -6.1\n",
      "var29 (Cumul.perc.: 0.963): 6.25\n",
      "\n",
      "Obs. 15610:\n",
      " var10 (Cumul.perc.: 0.02): -5.27\n",
      "var26 (Cumul.perc.: 0.047): -4.55\n",
      "\n",
      "Obs. 15620:\n",
      " var20 (Cumul.perc.: 0.966): 5.43\n",
      "\n",
      "Obs. 15630:\n",
      " var6 (Cumul.perc.: 0.008): -7.75\n",
      "var24 (Cumul.perc.: 0.006): -7.56\n",
      "var25 (Cumul.perc.: 0.989): 7.58\n",
      "var7 (Cumul.perc.: 0.993): 7.6\n",
      "var13 (Cumul.perc.: 0.023): -6.13\n",
      "\n",
      "Obs. 15640:\n",
      " var29 (Cumul.perc.: 0.03): -5.57\n",
      "var17 (Cumul.perc.: 0.04): -4.67\n",
      "var3 (Cumul.perc.: 0.049): -3.99\n",
      "\n",
      "Obs. 15650:\n",
      " var0 (Cumul.perc.: 0.007): -7.58\n",
      "var9 (Cumul.perc.: 0.007): -5.91\n",
      "var21 (Cumul.perc.: 0.952): 6.02\n",
      "var17 (Cumul.perc.: 0.039): -4.73\n",
      "\n",
      "Obs. 15660:\n",
      " var17 (Cumul.perc.: 0.958): 5.53\n",
      "\n",
      "Obs. 15670:\n",
      " var1 (Cumul.perc.: 0.004): -7.59\n",
      "var12 (Cumul.perc.: 0.993): 7.02\n",
      "var21 (Cumul.perc.: 0.989): 8.1\n",
      "var20 (Cumul.perc.: 0.985): 6.39\n",
      "var14 (Cumul.perc.: 0.025): -5.71\n",
      "\n",
      "Obs. 15680:\n",
      " var8 (Cumul.perc.: 0.016): -5.37\n",
      "var15 (Cumul.perc.: 0.028): -5.1\n",
      "var28 (Cumul.perc.: 0.969): 5.98\n",
      "var11 (Cumul.perc.: 0.037): -5.22\n",
      "\n",
      "Obs. 15690:\n",
      " var19 (Cumul.perc.: 0.005): -7.71\n",
      "var24 (Cumul.perc.: 0.022): -5.9\n",
      "var23 (Cumul.perc.: 0.039): -5.22\n",
      "\n",
      "Obs. 15700:\n",
      " var18 (Cumul.perc.: 0.011): -6.75\n",
      "var14 (Cumul.perc.: 0.978): 6.82\n",
      "var12 (Cumul.perc.: 0.024): -4.91\n",
      "var1 (Cumul.perc.: 0.985): 6.52\n",
      "var3 (Cumul.perc.: 0.024): -4.9\n",
      "\n",
      "Obs. 15710:\n",
      " var7 (Cumul.perc.: 0.003): -7.9\n",
      "var5 (Cumul.perc.: 0.997): 8.95\n",
      "var8 (Cumul.perc.: 0.994): 7.11\n",
      "var16 (Cumul.perc.: 0.955): 5.28\n",
      "\n",
      "Obs. 15720:\n",
      " var5 (Cumul.perc.: 0.015): -6.26\n",
      "var7 (Cumul.perc.: 0.02): -5.53\n",
      "var11 (Cumul.perc.: 0.974): 6.59\n",
      "var27 (Cumul.perc.: 0.956): 5.8\n",
      "\n",
      "Obs. 15730:\n",
      " var29 (Cumul.perc.: 0.964): 6.27\n",
      "\n",
      "Obs. 15740:\n",
      " var0 (Cumul.perc.: 0.006): -7.73\n",
      "var8 (Cumul.perc.: 0.986): 6.31\n",
      "var5 (Cumul.perc.: 0.978): 6.69\n",
      "var16 (Cumul.perc.: 0.033): -4.72\n",
      "\n",
      "Obs. 15750:\n",
      " var26 (Cumul.perc.: 0.009): -6.75\n",
      "var19 (Cumul.perc.: 0.992): 7.9\n",
      "var13 (Cumul.perc.: 0.986): 7.63\n",
      "var24 (Cumul.perc.: 0.971): 6.47\n",
      "var7 (Cumul.perc.: 0.046): -4.44\n",
      "\n",
      "Obs. 15760:\n",
      " var16 (Cumul.perc.: 0.998): 8.55\n",
      "var23 (Cumul.perc.: 0.98): 6.99\n",
      "var5 (Cumul.perc.: 0.027): -5.42\n",
      "\n",
      "Obs. 15770:\n",
      " var27 (Cumul.perc.: 0.98): 6.92\n",
      "var10 (Cumul.perc.: 0.038): -4.46\n",
      "\n",
      "Obs. 15780:\n",
      " var26 (Cumul.perc.: 0.008): -6.89\n",
      "var21 (Cumul.perc.: 0.984): 7.63\n",
      "var23 (Cumul.perc.: 0.978): 6.8\n",
      "var7 (Cumul.perc.: 0.048): -4.36\n",
      "\n",
      "Obs. 15790:\n",
      " var28 (Cumul.perc.: 0.998): 9.29\n",
      "var20 (Cumul.perc.: 0.008): -6.16\n",
      "var15 (Cumul.perc.: 0.988): 6.9\n",
      "var26 (Cumul.perc.: 0.98): 6.57\n",
      "\n",
      "Obs. 15800:\n",
      " var5 (Cumul.perc.: 0.017): -6.09\n",
      "\n",
      "Obs. 15810:\n",
      " var25 (Cumul.perc.: 0.984): 7.17\n",
      "var11 (Cumul.perc.: 0.97): 6.41\n",
      "var27 (Cumul.perc.: 0.965): 6.11\n",
      "var28 (Cumul.perc.: 0.96): 5.69\n",
      "\n",
      "Obs. 15820:\n",
      " var0 (Cumul.perc.: 0.023): -6.07\n",
      "var19 (Cumul.perc.: 0.97): 6.29\n",
      "\n",
      "Obs. 15830:\n",
      " var4 (Cumul.perc.: 0.014): -5.99\n",
      "var3 (Cumul.perc.: 0.989): 6.7\n",
      "\n",
      "Obs. 15840:\n",
      " var3 (Cumul.perc.: 0.999): 9.02\n",
      "var7 (Cumul.perc.: 0.003): -7.75\n",
      "var13 (Cumul.perc.: 0.009): -7.41\n",
      "var20 (Cumul.perc.: 0.044): -4.12\n",
      "\n",
      "Obs. 15850:\n",
      " var11 (Cumul.perc.: 0.965): 6.21\n",
      "var19 (Cumul.perc.: 0.955): 5.72\n",
      "\n",
      "Obs. 15860:\n",
      " var19 (Cumul.perc.: 0.991): 7.77\n",
      "var9 (Cumul.perc.: 0.011): -5.5\n",
      "var25 (Cumul.perc.: 0.016): -6.22\n",
      "var29 (Cumul.perc.: 0.032): -5.49\n",
      "var10 (Cumul.perc.: 0.974): 5.87\n",
      "\n",
      "Obs. 15870:\n",
      " var25 (Cumul.perc.: 0.968): 6.26\n",
      "var24 (Cumul.perc.: 0.96): 6.02\n",
      "var18 (Cumul.perc.: 0.957): 5.89\n",
      "\n",
      "Obs. 15880:\n",
      " var13 (Cumul.perc.: 0.008): -7.53\n",
      "var23 (Cumul.perc.: 0.991): 7.94\n",
      "var20 (Cumul.perc.: 0.034): -4.45\n",
      "var15 (Cumul.perc.: 0.968): 5.68\n",
      "\n",
      "Obs. 15890:\n",
      " None\n",
      "\n",
      "Obs. 15900:\n",
      " var7 (Cumul.perc.: 0.994): 7.79\n",
      "var0 (Cumul.perc.: 0.01): -7.16\n",
      "var28 (Cumul.perc.: 0.014): -6.17\n",
      "var22 (Cumul.perc.: 0.028): -5.13\n",
      "\n",
      "Obs. 15910:\n",
      " var10 (Cumul.perc.: 0.019): -5.38\n",
      "var25 (Cumul.perc.: 0.974): 6.55\n",
      "var29 (Cumul.perc.: 0.964): 6.28\n",
      "var3 (Cumul.perc.: 0.969): 5.55\n",
      "var2 (Cumul.perc.: 0.966): 6.48\n",
      "\n",
      "Obs. 15920:\n",
      " None\n",
      "\n",
      "Obs. 15930:\n",
      " var21 (Cumul.perc.: 0.998): 10.56\n",
      "var23 (Cumul.perc.: 0.988): 7.62\n",
      "var6 (Cumul.perc.: 0.964): 6.57\n",
      "var5 (Cumul.perc.: 0.033): -5.2\n",
      "var17 (Cumul.perc.: 0.953): 5.37\n",
      "\n",
      "Obs. 15940:\n",
      " var29 (Cumul.perc.: 0.988): 7.7\n",
      "var13 (Cumul.perc.: 0.036): -5.44\n",
      "\n",
      "Obs. 15950:\n",
      " var26 (Cumul.perc.: 0.983): 6.74\n",
      "var19 (Cumul.perc.: 0.022): -5.75\n",
      "\n",
      "Obs. 15960:\n",
      " var21 (Cumul.perc.: 0.035): -5.67\n",
      "var18 (Cumul.perc.: 0.026): -5.59\n",
      "var19 (Cumul.perc.: 0.967): 6.15\n",
      "var1 (Cumul.perc.: 0.04): -4.43\n",
      "\n",
      "Obs. 15970:\n",
      " None\n",
      "\n",
      "Obs. 15980:\n",
      " var19 (Cumul.perc.: 1.0): 13.53\n",
      "var27 (Cumul.perc.: 0.008): -6.94\n",
      "var12 (Cumul.perc.: 0.016): -5.41\n",
      "\n",
      "Obs. 15990:\n",
      " var6 (Cumul.perc.: 0.997): 10.06\n",
      "var20 (Cumul.perc.: 0.005): -7.0\n",
      "var0 (Cumul.perc.: 0.994): 8.71\n",
      "var23 (Cumul.perc.: 0.99): 7.75\n",
      "\n",
      "Obs. 16000:\n",
      " var11 (Cumul.perc.: 0.007): -7.48\n",
      "var18 (Cumul.perc.: 0.979): 6.86\n",
      "var19 (Cumul.perc.: 0.032): -5.25\n",
      "\n",
      "Obs. 16010:\n",
      " var28 (Cumul.perc.: 0.01): -6.56\n",
      "var6 (Cumul.perc.: 0.031): -5.81\n",
      "var13 (Cumul.perc.: 0.954): 5.98\n",
      "\n",
      "Obs. 16020:\n",
      " var12 (Cumul.perc.: 0.004): -7.1\n",
      "var23 (Cumul.perc.: 0.994): 8.43\n",
      "var17 (Cumul.perc.: 0.971): 6.09\n",
      "var20 (Cumul.perc.: 0.042): -4.19\n",
      "var15 (Cumul.perc.: 0.956): 5.25\n",
      "\n",
      "Obs. 16030:\n",
      " var4 (Cumul.perc.: 0.999): 10.3\n",
      "var11 (Cumul.perc.: 0.007): -7.45\n",
      "var26 (Cumul.perc.: 0.017): -5.97\n",
      "var2 (Cumul.perc.: 0.025): -5.93\n",
      "\n",
      "Obs. 16040:\n",
      " var15 (Cumul.perc.: 1.0): 10.47\n",
      "var13 (Cumul.perc.: 0.976): 6.9\n",
      "var26 (Cumul.perc.: 0.974): 6.32\n",
      "\n",
      "Obs. 16050:\n",
      " var19 (Cumul.perc.: 0.024): -5.63\n",
      "\n",
      "Obs. 16060:\n",
      " var21 (Cumul.perc.: 0.002): -9.29\n",
      "var0 (Cumul.perc.: 0.995): 9.04\n",
      "var16 (Cumul.perc.: 0.994): 7.72\n",
      "var13 (Cumul.perc.: 0.957): 6.08\n",
      "var1 (Cumul.perc.: 0.965): 5.56\n",
      "\n",
      "Obs. 16070:\n",
      " var18 (Cumul.perc.: 0.002): -8.6\n",
      "var0 (Cumul.perc.: 0.99): 8.07\n",
      "var16 (Cumul.perc.: 0.01): -6.12\n",
      "var12 (Cumul.perc.: 0.964): 5.32\n",
      "\n",
      "Obs. 16080:\n",
      " var24 (Cumul.perc.: 0.989): 7.6\n",
      "var27 (Cumul.perc.: 0.023): -5.66\n",
      "var8 (Cumul.perc.: 0.968): 5.43\n",
      "var29 (Cumul.perc.: 0.953): 5.86\n",
      "var9 (Cumul.perc.: 0.949): 4.68\n",
      "\n",
      "Obs. 16090:\n",
      " var3 (Cumul.perc.: 0.992): 7.03\n",
      "var17 (Cumul.perc.: 0.984): 6.8\n",
      "var11 (Cumul.perc.: 0.017): -6.37\n",
      "var19 (Cumul.perc.: 0.032): -5.24\n",
      "\n",
      "Obs. 16100:\n",
      " var4 (Cumul.perc.: 0.955): 5.45\n",
      "\n",
      "Obs. 16110:\n",
      " None\n",
      "\n",
      "Obs. 16120:\n",
      " var12 (Cumul.perc.: 0.003): -7.38\n",
      "var23 (Cumul.perc.: 0.971): 6.42\n",
      "\n",
      "Obs. 16130:\n",
      " var28 (Cumul.perc.: 0.009): -6.71\n",
      "var13 (Cumul.perc.: 0.983): 7.4\n",
      "var19 (Cumul.perc.: 0.97): 6.29\n",
      "var7 (Cumul.perc.: 0.956): 5.46\n",
      "\n",
      "Obs. 16140:\n",
      " var5 (Cumul.perc.: 0.977): 6.6\n",
      "var8 (Cumul.perc.: 0.966): 5.35\n",
      "\n",
      "Obs. 16150:\n",
      " var20 (Cumul.perc.: 0.017): -5.3\n",
      "var22 (Cumul.perc.: 0.955): 5.49\n",
      "\n",
      "Obs. 16160:\n",
      " var29 (Cumul.perc.: 1.0): 11.28\n",
      "var5 (Cumul.perc.: 0.992): 7.96\n",
      "var1 (Cumul.perc.: 0.989): 6.92\n",
      "var14 (Cumul.perc.: 0.015): -6.38\n",
      "var17 (Cumul.perc.: 0.966): 5.81\n",
      "\n",
      "Obs. 16170:\n",
      " var4 (Cumul.perc.: 0.973): 6.2\n",
      "var9 (Cumul.perc.: 0.978): 5.68\n",
      "\n",
      "Obs. 16180:\n",
      " var27 (Cumul.perc.: 0.033): -5.18\n",
      "\n",
      "Obs. 16190:\n",
      " var12 (Cumul.perc.: 0.99): 6.67\n",
      "var8 (Cumul.perc.: 0.957): 5.05\n",
      "\n",
      "Obs. 16200:\n",
      " None\n",
      "\n",
      "Obs. 16210:\n",
      " var7 (Cumul.perc.: 0.02): -5.59\n",
      "var19 (Cumul.perc.: 0.965): 6.08\n",
      "var0 (Cumul.perc.: 0.96): 6.15\n",
      "\n",
      "Obs. 16220:\n",
      " var1 (Cumul.perc.: 0.991): 7.15\n",
      "var19 (Cumul.perc.: 0.98): 6.87\n",
      "var17 (Cumul.perc.: 0.969): 5.94\n",
      "\n",
      "Obs. 16230:\n",
      " var9 (Cumul.perc.: 0.989): 6.39\n",
      "var27 (Cumul.perc.: 0.968): 6.26\n",
      "var25 (Cumul.perc.: 0.984): 7.17\n",
      "\n",
      "Obs. 16240:\n",
      " var21 (Cumul.perc.: 0.998): 9.99\n",
      "var14 (Cumul.perc.: 0.998): 9.38\n",
      "var26 (Cumul.perc.: 0.008): -6.9\n",
      "var12 (Cumul.perc.: 0.025): -4.83\n",
      "var20 (Cumul.perc.: 0.964): 5.36\n",
      "\n",
      "Obs. 16250:\n",
      " var13 (Cumul.perc.: 0.999): 11.18\n",
      "var6 (Cumul.perc.: 0.991): 8.57\n",
      "var1 (Cumul.perc.: 0.954): 5.19\n",
      "\n",
      "Obs. 16260:\n",
      " var13 (Cumul.perc.: 0.98): 7.2\n",
      "\n",
      "Obs. 16270:\n",
      " var19 (Cumul.perc.: 0.992): 7.94\n",
      "var11 (Cumul.perc.: 0.982): 7.09\n",
      "var7 (Cumul.perc.: 0.026): -5.18\n",
      "var20 (Cumul.perc.: 0.042): -4.2\n",
      "\n",
      "Obs. 16280:\n",
      " var18 (Cumul.perc.: 0.984): 7.25\n",
      "var25 (Cumul.perc.: 0.977): 6.69\n",
      "var12 (Cumul.perc.: 0.949): 4.85\n",
      "\n",
      "Obs. 16290:\n",
      " var23 (Cumul.perc.: 0.997): 9.21\n",
      "var27 (Cumul.perc.: 0.995): 8.6\n",
      "var7 (Cumul.perc.: 0.039): -4.66\n",
      "\n",
      "Obs. 16300:\n",
      " var1 (Cumul.perc.: 0.991): 7.11\n",
      "var5 (Cumul.perc.: 0.026): -5.54\n",
      "var13 (Cumul.perc.: 0.97): 6.58\n",
      "\n",
      "Obs. 16310:\n",
      " var6 (Cumul.perc.: 0.01): -7.43\n",
      "var28 (Cumul.perc.: 0.017): -5.98\n",
      "var25 (Cumul.perc.: 0.96): 5.89\n",
      "var1 (Cumul.perc.: 0.044): -4.32\n",
      "\n",
      "Obs. 16320:\n",
      " var15 (Cumul.perc.: 0.998): 8.64\n",
      "\n",
      "Obs. 16330:\n",
      " var0 (Cumul.perc.: 0.015): -6.65\n",
      "var11 (Cumul.perc.: 0.021): -5.99\n",
      "var8 (Cumul.perc.: 0.049): -3.99\n",
      "\n",
      "Obs. 16340:\n",
      " var20 (Cumul.perc.: 0.001): -8.59\n",
      "var21 (Cumul.perc.: 0.013): -7.1\n",
      "var10 (Cumul.perc.: 0.992): 7.22\n",
      "var22 (Cumul.perc.: 0.029): -5.06\n",
      "var29 (Cumul.perc.: 0.957): 5.96\n",
      "\n",
      "Obs. 16350:\n",
      " var27 (Cumul.perc.: 0.005): -7.66\n",
      "var26 (Cumul.perc.: 0.008): -6.82\n",
      "var13 (Cumul.perc.: 0.986): 7.67\n",
      "\n",
      "Obs. 16360:\n",
      " var4 (Cumul.perc.: 0.0): -11.88\n",
      "var19 (Cumul.perc.: 0.981): 6.92\n",
      "var13 (Cumul.perc.: 0.973): 6.75\n",
      "var29 (Cumul.perc.: 0.021): -6.1\n",
      "\n",
      "Obs. 16370:\n",
      " var12 (Cumul.perc.: 0.022): -5.01\n",
      "var20 (Cumul.perc.: 0.972): 5.65\n",
      "var22 (Cumul.perc.: 0.949): 5.31\n",
      "\n",
      "Obs. 16380:\n",
      " None\n",
      "\n",
      "Obs. 16390:\n",
      " var22 (Cumul.perc.: 0.98): 6.47\n",
      "var0 (Cumul.perc.: 0.956): 6.02\n",
      "\n",
      "Obs. 16400:\n",
      " var10 (Cumul.perc.: 0.966): 5.51\n",
      "\n",
      "Obs. 16410:\n",
      " var4 (Cumul.perc.: 0.009): -6.5\n",
      "var19 (Cumul.perc.: 0.972): 6.4\n",
      "var20 (Cumul.perc.: 0.974): 5.74\n",
      "\n",
      "Obs. 16420:\n",
      " var29 (Cumul.perc.: 0.987): 7.58\n",
      "var2 (Cumul.perc.: 0.969): 6.61\n",
      "\n",
      "Obs. 16430:\n",
      " var9 (Cumul.perc.: 0.999): 8.26\n",
      "var27 (Cumul.perc.: 0.005): -7.51\n",
      "var4 (Cumul.perc.: 0.013): -6.09\n",
      "var21 (Cumul.perc.: 0.981): 7.42\n",
      "var24 (Cumul.perc.: 0.021): -5.91\n",
      "\n",
      "Obs. 16440:\n",
      " var14 (Cumul.perc.: 0.037): -5.15\n",
      "var11 (Cumul.perc.: 0.952): 5.78\n",
      "\n",
      "Obs. 16450:\n",
      " var5 (Cumul.perc.: 0.99): 7.73\n",
      "var18 (Cumul.perc.: 0.026): -5.6\n",
      "var10 (Cumul.perc.: 0.957): 5.18\n",
      "\n",
      "Obs. 16460:\n",
      " var24 (Cumul.perc.: 0.01): -6.82\n",
      "var26 (Cumul.perc.: 0.987): 7.2\n",
      "\n",
      "Obs. 16470:\n",
      " var0 (Cumul.perc.: 0.038): -5.37\n",
      "\n",
      "Obs. 16480:\n",
      " var10 (Cumul.perc.: 0.957): 5.19\n",
      "var25 (Cumul.perc.: 0.033): -5.31\n",
      "var3 (Cumul.perc.: 0.951): 4.95\n",
      "\n",
      "Obs. 16490:\n",
      " var10 (Cumul.perc.: 1.0): 10.56\n",
      "var1 (Cumul.perc.: 0.008): -6.51\n",
      "var5 (Cumul.perc.: 0.98): 6.82\n",
      "var2 (Cumul.perc.: 0.975): 6.97\n",
      "var25 (Cumul.perc.: 0.974): 6.57\n"
     ]
    }
   ],
   "source": [
    "for obs, ex in enumerate(df_test.explanation.values):\n",
    "    if (obs % 10) == 0:\n",
    "        print(f'\\nObs. {obs}:\\n', ex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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    "_isFastLaunch": false,
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   {
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    "memoryGiB": 64,
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   },
   {
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    "_isFastLaunch": false,
    "category": "Memory Optimized",
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    "memoryGiB": 128,
    "name": "ml.r5.4xlarge",
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   },
   {
    "_defaultOrder": 43,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
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    "memoryGiB": 256,
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   },
   {
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    "_isFastLaunch": false,
    "category": "Memory Optimized",
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    "memoryGiB": 384,
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   },
   {
    "_defaultOrder": 45,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
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    "memoryGiB": 512,
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   },
   {
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    "_isFastLaunch": false,
    "category": "Memory Optimized",
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    "hideHardwareSpecs": false,
    "memoryGiB": 768,
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   },
   {
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    "_isFastLaunch": false,
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    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.g5.xlarge",
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   },
   {
    "_defaultOrder": 48,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.g5.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 49,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 64,
    "name": "ml.g5.4xlarge",
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   },
   {
    "_defaultOrder": 50,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 128,
    "name": "ml.g5.8xlarge",
    "vcpuNum": 32
   },
   {
    "_defaultOrder": 51,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 256,
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