Repository: pastas/pastas

Issue:
[Development] Groundwater Signatures in Pastas
**Describe the proposed feature**
I propose to add a `signatures` module to the stats sub-package of Pastas. Groundwater signatures can be used to objectively describe the characteristics of groundwater time series (see references below), and can be applied in all phases of the groundwater modelling process. However, the signatures can also be applied without any modelling to characterise groundwater systems.

**Expected usage**
Each signature should be accessible through an individual method:

`ps.stats.signatures.parde_seasonality`

A summary of all signatures for a time series should be available:

`ps.stats.signatures.summary`

**Additional context**

- Heudorfer, B., Haaf, E., Stahl, K., Barthel, R., 2019. [Index-based characterization and quantification of groundwater dynamics.](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2018WR024418) Water Resources Research.
- Haaf, E., Giese, M., Heudorfer, B., Stahl, K., Barthel, R., 2020. [Physiographic and Climatic Controls on Regional Groundwater Dynamics.](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2019WR026545) Water Resources Research.
- Giese, M., Haaf, E., Heudorfer, B., Barthel, R., 2020. [Comparative hydrogeology – reference analysis of groundwater dynamics from neighbouringobservation wells](https://www.tandfonline.com/doi/full/10.1080/02626667.2020.1762888) Hydrological Sciences Journal.
- McMillan, H.K., 2021. [A review of hydrologic signatures and their applications](https://wires.onlinelibrary.wiley.com/doi/10.1002/wat2.1499). WIREs Water.

Discussion:

Patch:
diff --git a/examples/notebooks/21_signatures.ipynb b/examples/notebooks/21_signatures.ipynb
new file mode 100644
index 00000000..0197a455
--- /dev/null
+++ b/examples/notebooks/21_signatures.ipynb
@@ -0,0 +1,439 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "32a23110-a3a7-4d3f-b76d-72f1c49bc2b2",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Groundwater signatures\n",
+    "\n",
+    "*R.A. Collenteur, University of Graz, 2022*\n",
+    "\n",
+    "In this notebook we introduce the **groundwater signatures** module available in Pastas. The signatures methods can be accessed through the `signatures` module in the `pastas.stats` sub-package. This module is partly based on the work from [Heudorfer et al. (2019)](#References) and others (see [References](#References)).\n",
+    "\n",
+    "Groundwater signatures characterize different aspects of a groundwater time series, and can be subdivided in different categories: shape, distribution, and structure. Groundwater signatures are also referred to as 'indices' or 'quantitative' metrics. In Pastas, 'signatures' is adopted to avoid any confusion with time indices and goodness-of-fit metrics. \n",
+    "\n",
+    "The signatures can be used to *objectively* characterize different groundwater systems, for example distinquishing between fast and slow groundwater systems. The use of signatures is common in other parts of hydrology (e.g., rainfall-runoff modeling) and can be applied in all phases of modeling (see, for example [McMillan, 2020](#References) for an overview). \n",
+    "\n",
+    "<div class=\"alert alert-warning\">\n",
+    "<b>Note:</b>\n",
+    "The `signatures` module is still work in progress and needs further verification. Please report any issues and bugs on the Pastas GitHub repository!\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b5b691ee-189b-452b-951e-17b3f25b8571",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python version: 3.8.2 (default, Mar 25 2020, 11:22:43) \n",
+      "[Clang 4.0.1 (tags/RELEASE_401/final)]\n",
+      "Numpy version: 1.21.2\n",
+      "Scipy version: 1.7.3\n",
+      "Pandas version: 1.3.5\n",
+      "Pastas version: 0.20.0b\n",
+      "Matplotlib version: 3.5.0\n",
+      "numba version: 0.53.1\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pastas as ps\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.gridspec import GridSpec\n",
+    "\n",
+    "ps.show_versions(numba=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2185d603-de47-4ba4-bfc4-065cd3a802ad",
+   "metadata": {},
+   "source": [
+    "## 1. Load two time series with different characteristics\n",
+    "\n",
+    "To illustrate the use of groundwater signatures we load two time series of hydraulic heads with visually different characteristics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1bc0e9ca-75a3-4490-8ac5-1fdfea0de670",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Date'>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head1 = pd.read_csv(\"data_notebook_20/head_threshold.csv\", index_col=0, parse_dates=True).squeeze()\n",
+    "head2 = pd.read_csv(\"data_wagna/head_wagna.csv\", index_col=0, parse_dates=True, skiprows=2).squeeze()\n",
+    "head2 = head2.resample(\"D\").mean().loc[\"2012\":]\n",
+    "\n",
+    "fig, [ax1, ax2] = plt.subplots(2,1, figsize=(10,4), sharex = True);\n",
+    "\n",
+    "head1.plot(ax=ax1)\n",
+    "head2.plot(ax=ax2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb273bff-f8dd-45fe-93b8-d9994cec2ad6",
+   "metadata": {},
+   "source": [
+    "## 2. Compute signatures\n",
+    "\n",
+    "To compute all available signatures at once, we can use the `stats` method from the `signatures` module. This is shown below. Alternatively, each signature can be computed with a separate method (e.g., `ps.stats.signatures.baseflow_index`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "414c850b-10d3-4c87-b642-63b165393359",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>cv_period_mean</th>\n",
+       "      <td>0.015684</td>\n",
+       "      <td>0.001481</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cv_date_min</th>\n",
+       "      <td>0.119795</td>\n",
+       "      <td>0.352998</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cv_fall_rate</th>\n",
+       "      <td>-0.874055</td>\n",
+       "      <td>-0.689338</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cv_rise_rate</th>\n",
+       "      <td>1.453789</td>\n",
+       "      <td>1.416925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>parde_seasonality</th>\n",
+       "      <td>0.662627</td>\n",
+       "      <td>0.389114</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>avg_seasonal_fluctuation</th>\n",
+       "      <td>0.839583</td>\n",
+       "      <td>0.873115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>magnitude</th>\n",
+       "      <td>0.072510</td>\n",
+       "      <td>0.007240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>interannual_variation</th>\n",
+       "      <td>0.550667</td>\n",
+       "      <td>1.403426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>low_pulse_count</th>\n",
+       "      <td>29.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>high_pulse_count</th>\n",
+       "      <td>33.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>low_pulse_duration</th>\n",
+       "      <td>15.678571</td>\n",
+       "      <td>286.375000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>high_pulse_duration</th>\n",
+       "      <td>15.848485</td>\n",
+       "      <td>53.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>amplitude_range</th>\n",
+       "      <td>1.336000</td>\n",
+       "      <td>1.899606</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bimodality_coefficient</th>\n",
+       "      <td>0.701896</td>\n",
+       "      <td>0.426865</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean_annual_maximum</th>\n",
+       "      <td>19.713750</td>\n",
+       "      <td>263.811451</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rise_rate</th>\n",
+       "      <td>0.030969</td>\n",
+       "      <td>0.014569</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fall_rate</th>\n",
+       "      <td>-0.016077</td>\n",
+       "      <td>-0.006244</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>reversals_avg</th>\n",
+       "      <td>124.000000</td>\n",
+       "      <td>32.875000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>reversals_cv</th>\n",
+       "      <td>0.184202</td>\n",
+       "      <td>0.251066</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>colwell_contingency</th>\n",
+       "      <td>0.374443</td>\n",
+       "      <td>0.285729</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>colwell_constancy</th>\n",
+       "      <td>-0.035437</td>\n",
+       "      <td>-0.036287</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>recession_constant</th>\n",
+       "      <td>-13.188954</td>\n",
+       "      <td>308.128403</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>recovery_constant</th>\n",
+       "      <td>-0.681043</td>\n",
+       "      <td>296.426463</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>duration_curve_slope</th>\n",
+       "      <td>0.311783</td>\n",
+       "      <td>0.131626</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>duration_curve_range</th>\n",
+       "      <td>0.592216</td>\n",
+       "      <td>0.550804</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>baseflow_index</th>\n",
+       "      <td>1.102733</td>\n",
+       "      <td>1.208890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>richards_pathlength</th>\n",
+       "      <td>1.000356</td>\n",
+       "      <td>1.000033</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>richards_baker_index</th>\n",
+       "      <td>0.021849</td>\n",
+       "      <td>0.011404</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>baseflow_stability</th>\n",
+       "      <td>0.278629</td>\n",
+       "      <td>0.345635</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   0           1\n",
+       "cv_period_mean              0.015684    0.001481\n",
+       "cv_date_min                 0.119795    0.352998\n",
+       "cv_fall_rate               -0.874055   -0.689338\n",
+       "cv_rise_rate                1.453789    1.416925\n",
+       "parde_seasonality           0.662627    0.389114\n",
+       "avg_seasonal_fluctuation    0.839583    0.873115\n",
+       "magnitude                   0.072510    0.007240\n",
+       "interannual_variation       0.550667    1.403426\n",
+       "low_pulse_count            29.000000    8.000000\n",
+       "high_pulse_count           33.000000   11.000000\n",
+       "low_pulse_duration         15.678571  286.375000\n",
+       "high_pulse_duration        15.848485   53.000000\n",
+       "amplitude_range             1.336000    1.899606\n",
+       "bimodality_coefficient      0.701896    0.426865\n",
+       "mean_annual_maximum        19.713750  263.811451\n",
+       "rise_rate                   0.030969    0.014569\n",
+       "fall_rate                  -0.016077   -0.006244\n",
+       "reversals_avg             124.000000   32.875000\n",
+       "reversals_cv                0.184202    0.251066\n",
+       "colwell_contingency         0.374443    0.285729\n",
+       "colwell_constancy          -0.035437   -0.036287\n",
+       "recession_constant        -13.188954  308.128403\n",
+       "recovery_constant          -0.681043  296.426463\n",
+       "duration_curve_slope        0.311783    0.131626\n",
+       "duration_curve_range        0.592216    0.550804\n",
+       "baseflow_index              1.102733    1.208890\n",
+       "richards_pathlength         1.000356    1.000033\n",
+       "richards_baker_index        0.021849    0.011404\n",
+       "baseflow_stability          0.278629    0.345635"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sigs1 = ps.stats.signatures.summary(head1)\n",
+    "sigs2 = ps.stats.signatures.summary(head2)\n",
+    "\n",
+    "# Create a dataframe for easy comparison and plotting\n",
+    "df = pd.concat([sigs1, sigs2], axis=1)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f4da6fe-3215-4145-b6b2-7f7f9ddb6649",
+   "metadata": {},
+   "source": [
+    "## 3. Plot the results\n",
+    "\n",
+    "Depending on the signature, different ranges of parameters can be expected. We therefore split the signatures in two range for visualization purposes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a9200ad7-2d7b-4091-8c85-ee04bbfdf3f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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DT21rciSEhNkotsViqZYUFBRwww038Pvvv7NkyRK++OILliwpsfmzpRxU+jNNmwchYWxudhxsX2rqdyyuYB1si6UmoQqTX4Hvr4VWQ+CKXyG+WaFd8iLqQscTYMEYKMgP0kQtFouleGbMmEH79u1p27YtERERnH/++fz444/BnlaNptKfadpcaNKF3fV7m8cbDmnEbakkrrZKt1gsAcTjgT8fgGmvQ7cz4Iy3ISyy+H17XwjLfoHV/0DHUVU7T4vFUmN45OfFLElLd81eQUEBPVrW56FTupW4z6ZNm2jZ8mCH8sTERKZPrx2OndufJ0DX5vHcPqJVqftU6jNVhc3zoMspZNTpAKERsG4KdDrRhVlbbATbYqkJ5OfAd1cb53rQdXDWByU71wAdRkFMI5j3WdXN0WKxWMqB6TBdmJpQJFidqdRnumcd7N8NzXrjCY2A5n1hvc3DdgsbwbZYqjvZ6TDmIkidAMc+AsNugbIOnKHh0PNcmPme1cS2WCwlUlqkuTKUR685MTGRDRs2HHi8ceNGmjdv7uo8goXbn6eXjIyMUp+v1GeaNs/cN+8DK9MhaQhMeRVysyAixs8ZVyEF+YQUVD9JRxvBtliqMxlb4aPRZtnu9Ldg+K1lO9deel8IBblWE9tisVQrBgwYwMqVK0lNTSU3N5cvv/ySU089NdjTqtFU6jNNmwsh4ZDgXBS0GgKefNg0O/ATdpOUZxg44waTRlmNsA62xVJd2bEK3j8Odq6BC8ZA7wsq9vqmPczNqolYLJZqRFhYGK+99hrHH388Xbp04dxzz6Vbt8BEfg8XKvWZbp4HCV0Pphu2HARIzUsTWfYrUTk7YFv1UqKxKSIWS3Vk42z4/BxA4PKfoUW/ytnpfRGMvddoYjfp4uoULRaLpbKMHj2a0aNHB3satYoKfaaqJkWk62kHt0XXM9HsdTWo4UzmNti22Px//VRo2j248/HBRrAtlurGyr/g45MhItZoXFfWuQbocY7VxLZYLBZLYXavhew9Jv/al1ZDYOPMmiPxmjoBAI+EwbrJQZ5MYayDbbFUJ+Z9Dp+fBw3bw1V/QcN2/tmr08hqYlssFoulME4HR5r3Lrw9aQjkZsKWBVU+pUqxJhmi6rKj0WDTibIYNZVgYR1si6U6oAoTX4Afroc2R8AVv0Fcgju2e18ImVuNJrbFYrFYLJvnGd3rJl0Lb281xNyvn1blU6owqrAmBVofwZ563SFzC+xaE+xZHcA62BZLsPEUwO/3wLhHoPvZcOHXEFm6zFWFsJrYFovFYvElba5xrov2U4hvDvWSYH0NyMPenQp710PbEeyp5xR0VqMCTetgWyzBJC8bvrkSZrwNQ26EM9+FsAh3x/BqYi//zWhiWywWi+XwRRXS5h+af+0laWi1S7coljUp5r7tCLJiWkJ0AzPvaoJ1sC2WYJG9Fz47G5b8AKMeh+OfgJAA/SStJrbFYrFYwKRR5Owt2cFuNQSydsDOVVU7r4qSmgJxzU3NkohzYVB9Ch2tg32YMXHldpbuLAj2NCzpm+HD0SbP7cx3YehNgR3PamJbLJZqxJVXXkmTJk3o3r36yKrVZCr0eW6eZ+6LFjh6SRpq7quzXJ/HYyLYbY862Hyt1RCTNpK+Obhzc7AO9mHGvd8u5KU52azbuS/YUzlsic7aCO+PMjJJF31l0jeqgt4XQdoco4ltsVgsQeTyyy9n7NixwZ5GraFCn2faXAiNhMYl9EZo2N7U7VTnQseti2D/Lmhz1MFt3guDapI/bh3sw4i0Pfu5LvN17pWPuW3MPPILqldb0cOCjbPoO+deyN8Pl/8K7Y6uurGtJrbFYqkmHHnkkTRo0CDY06g1VOjzTJtnGsqUVO8jAq0GVxtHtVhSvfnXPg52056mf0Q1ycO2nRwPI2av3sw5oSlESR7fbDiSt1KacOPRHYI9rcMHjwe+u4b8sBjCrxoLDdpW7fi+mtjHPASh9udvsRz2/H4vbFnomrnognxo0QdOfNo1mzUKlz9PwKT3Df+PO7ZUYfN8E3ApjaShsOwXk24R38ydsd1kTTI06mhUT7yEhkHigGqT2mIj2IcR25ZMJEryUITn63/PS3+vZMHGPcGe1uHD2omwaw2pbS6seufai9XEtlgslsMXTz7kpJecf+3lgB529XBWC5Gfa5xo3/QQL0nDYNsS2L+76udVBBvCOoyI3jiJAkJY3/o8uqz9gtF1lnHrmBh+vekIoiNCgz292s/sjyCqHjsaDQ3YEMu2pLMxo5TUH19N7I6jAjYPi8VSQ3A50rw/I4O4OBd1/GsagYrcZ2S4Y6cgz9yXpCDipWlPCK9j0i26n+XO2G6xaRbkZUHbEYc+lzQEUFg/HTqdUNUzK4SNYB8m7MnKpfP+OWyL68b6VmdB3VY8GfctqdszePp3W/QWcPbtNMttvS7AE+qyzrXDpj37OeetqTw+bT+L0/YWv5PVxLZYLJbDl4Jcp8Cxc+n7hYZBywHVs9BxTQpICLQefuhzLfqZDpXVQK7POtiHCfNWrqenrKGg9ZFoSDgc/R9idy3iuS6pfDx1HSkrtgd7irWb+V+YA1u/ywJi3uNR7vhqHh6PEh0mXPnRTNL27C9+Z6uJbbFYgswFF1zAkCFDWL58OYmJibz//vvBnlKNptyfZ0GuyekODS/baKuhjlrHHlfn6jdrkqFZb4iud+hz4dHQvG+16OhoHezDhO2LxxEmHpr0dNICepwDTbpx5p4P6dIkiru+ns/ufbnBnWRtRdWkhyQOhCYlyCL5yXuT1jBtzS4eOrUbt/ePIiungCs/mkl6dt6hO1tNbIvFEmS++OILNm/eTF5eHhs3buSqq64K9pRqNOX6PFWNg11W/rUXb7rFhhluTtU/cjJNikhx6SFekoYaKcLc4MoRWwf7MCF6wyRyiCSi9WCzISQUjn2YkN1reL/nUnZn5XL/9wvR6t4atSayfirsXAn9Lg+I+SVp6Tz3x3JO6NaUc/ol0jIuhDcv7seqbZnc8Nkc8oqTY7Sa2BaLxXJ4kZ8D6ik7/9pLi/5G2rU6FTqum2IKNdsWU+DoJWmo2WfjrKqbVzFYB/swIDuvgI5Zc0iL7wXhUQef6HAcJA2j+dyXuXtkS35ftIXv5mwK3kRrK7M/hsh46Ha666az8wq4dcxc6sdE8OSZPRCno9XwDo146sweTFy5g/u/K+bCyWpiWywWy+FFXpa5b9a7fPtHxJh9q4muNGDSQ0IjoeWgkvdpORCQoMv1WQf7MGDxipV0lI0UtD6y8BMicOwjsG8bV4X9xsDWDXjop8Vs2JUVnInWRvbvhiU/GIc2oo7r5p8Zu4wVWzN57pxeNKhTuHjynP4tufmYDnw9eyOv/rOq8AvrNIIOxxtN7IJ81+dlsdQ6VOHnW+k99z6T8pWdHuwZWSwVIy/LnPfLKnD0JWmIWe3Myw7cvCpCagq0GmRyrUsiqq5Jgwxy5N062IcBOxb8BUBCr+MPfbLlAOh8MiFTXuXFk1sAcMdX8ynw2FQRV1jwFeRnB6S4ccKK7Xw4eS2XD23NUR0bF7vPbcd24My+LXjhrxV8N2dj4SetJrbFUn5SnoXZHxKVvRV+vgWe7wjfXmMiap6a1xW3uqcDVvf5FaUmzFdz90FIeMWajLUaavK20+YEbmLlJXO7KbosLf/aS9JQ2DDTaGYHCetgHwZEbZxIBnWIa9Ov+B2OeQjy9tFi4Rs8fGo3ZqzdxbsT11TtJGsjqiY9pHkfaNbLVdO79+Vy59fz6dAklntPLDkaISI8fWZPhrZryD3fLmDKqh0Hn+wwCmIaGk1si8VSMou+g+QnodeFTBv8Plz9D/S+AFb8Af87DV7uBeOfhN1rgz3TchEVFcXOnTurrVOoquzcuZOoqKiyd64GREVFsXfv3mr7eQKox8POPRlEaQVXqFs5dVvVoTuitz16mxFl75s0FPL3m66VQcI2mqnl5OcX0D5zDuvr9aNbSAnNZBp3hD4Xw8z3OOvGfzGue1P+++dyjujQiG7N61bthGsTm2bDtsVw8kuumlVV7vtuIbuzcvnwigFEhZfeJCgizBQ9nvPWFP716Wy+vX4oHRPiICwCepwLs943mtgxDVydp8VSK9g4G3643nS2O+UlmDQVEvuZ2/FPwrJfzUVqyrOQ8gy0PsKsDnU9LSBpYW6QmJjIxo0b2b7dfXnW7OxsVxzjqKgoEhMTXZhR4ElMTGT+/PlkZmYGbAy/P9eCPKI2ziCxSQWP8zENoHGXaiF7R2oKRNYtnwqKbyfKlgMCOq2SsA52LWfNykV0lO3sTPpX6TuOuA8WfIUkP8WTZ7zG7HW7uW3MPH66cXiZDpylBGZ/ZDph9TjbVbPfzN7I2MVbuO/EzuW+AKobHc4Hlw/gjDemcMWHM/n+30NpEh9lHIHpbxpN7IHXuDpPi6XGs3cjfHkBxCbAeZ+yK0dI3VvAoNwC0/02PNr8vnucbfad/4UpHP7hevjtLlPY3PtiEwV0CpCrA+Hh4bRp0yYgtpOTk+nTp5wqFbWE8PBwMjMz6d+/f8DG8PtznT8Gpt0H11fCUW412JwjPAVGgSxYrEmBNkeUbw6xTaBhBxN5H3ZL4OdWDDZFpJazff6fADTtXUz+tS/xzWHQdbDgK+pnLOe5c3qZ4rk/llfBLGsh2enmgNT9TIh0r23w+p1ZPPzTYga3bcDVR7St0GsT68fw4eUD2J2Vy5Ufz2RfTj406wkJVhPbYjmEnEz44nzI2w8XjmHRnnCOeyGFR6Zm0+2hsRzz32Ru/HwObySvYvzybWyTRugRd8JNc+CKsca5XvwDfHgCvNoXJjxnnHBLwBCRliIyXkSWishiEbnF2d5ARP4SkZXOfX2f19wnIqtEZLmIlHGirMGkzYWwaGjUseKvTRoKOemwdbH78yovu1JhzzpoU4o8X1GShpjIe5BqJFxxsEXkAxHZJiKL3LBncY+ojRPZRkOatOlR9s7DbzXVt38/wlEdG3PpkCTen5TKZN+8XUv5WPSNqdjud4VrJvMLPNz21TxCQoT/ntub0JCKR8S6t6jL6xf2ZUlaOjd9MZf8Ao+JYltNbIvlIB4PfP8v41Cc/SFTM5pw/jvTiAoP5dqekdx0dAfaNo5l3oY9PDt2OVd8OJOBT46j/+N/c8kHM3hycT1+aHU/Ky6ZTf6pb0Bcc/jncXixO3xyBiz8pvqoMtQu8oE7VLULMBi4QUS6AvcC41S1AzDOeYzz3PlAN+AE4A0RqZ1LtpvnmYBKRQocvRxItwhimog3/7o0/euiJA2D7L2wbUlg5lQGbqWIfAS8BvzPJXsWF1BPAe0y57Cy7lCalGd5Mro+HHE7/PUgpE7kvhOHMnnVDu74aj5/3HokdWPK0VrVYpj9MSR0hxZ9XTP5ZvJqZq/bzcvn96ZFvVIkispgZOcmPHpadx74YREP/7yYx449G/nr/0wUe9Rjrs3XYqmx/PMoLPsFTniGP3O7c+MXM0hqEMMnVw1i2dxpjBhxMAq4d38eyzans2RzOkud+48mryXXafAUEdqAjk3vZViHTE4o+IeuW34h8turTDCj+1kmhaRF32qVQlJTUdXNwGbn/xkishRoAZwGjHB2+xhIBu5xtn+pqjlAqoisAgYC1SDh2EU8BabYr++llXt9vZZQt6VJtxhURrppoFiTAnHNKhaB970waNo9MPMqBVccbFWdICKt3bBlcY+05bNoQQb5SUeWvbOXgdfC9Lfh74eIvnocL57XmzPfmML//biIVy44vPLqKk3aPBMtOPE5106a8zbs4aVxKzmtd3NO693Cb3sXD05iw+4s3k5ZQ6sGMVzr1cQ+5qHKRTgsltrCvM9h0ovQ/0q+CTuJez6bQ48Wdfnw8gHUrxPBsiK7140OZ1Dbhgxq2/DAtrwCD2u27zvgcC/dnM43a8J4e98IhCMZErKEy0ImMXL2p0TM+oCM+Pbk97iQeoMvqtr3WotxfJI+wHQgwXG+UdXNItLE2a0FMM3nZRudbUVtXQtcC5CQkEBycvIh42VmZha73S38sR+zbz0D87JYujeSrSXYKMt+l8i21FuVwtTx4yt1XvPr81EPQ1eMY1eDPixLSSm/fVUGRzYkfeb3LMnqULmxS7NfBvZMWovZvuBPWgAJvUaV/0Xh0TDyfvjxBlj6Mz27nsotx3Tgv3+t4JguTVxx7mo9cz6GsCjoeY4r5rJy87ltzDwS4iJ59DT3rsLvOb4zm3bv58nfltF3xIn0X/6r0cTuWIHvi8VSm1g3FX66Gdocxftx1/HYNwsY3r4Rb1/SjzqR5T9dhoeG0KlpHJ2axnF6H3PMVFW2Z+SweHM6Szd34ee043g9bTPddv/D2XtS6Df5UfInPU5u7CkwYkSA3uDhgYjEAt8Ct6pqupTsEBb3xCFae6r6DvAOQP/+/XVEMX+f5ORkitvuFn7Zn/cFzIQuR19IlybFy7qWab/Oavg1hRE9W0HDdhWegl/z37IQUvbSdMh5NO1dvI0S7e86mqjUiTQ56ii/Al6VmX+VOdjluQJ0i+p8JVmV9uutGscabca69ZtYvyGt/Pa1GQNiWiI/38PMLXXoKiG0rxfCvd/MI3/zchpGl566X1M+n0DYDynIZujcL9jRaDDLphevv1lR+x8tzmHtjnzuHhDF3OmTy9y/IvZPTVBW1A/hkgnxzImOJ+Ovl1iSFlHqa6rz52/tW/uVtR+1fwt959xFfmRjHtp/OV//vor+CaFc1jaLmVMn+W3fiwBdga7NgeZ1yCk4mfkZo5m6cwM9N49h1L4f+eHTN6mX2KXSY5RGdf383UJEwjHO9Weq+p2zeauINHOi182Abc72jUBLn5cnAmnUNjbPM4pWjfyI4iYNNffrp1XKwfaLNV796wrkX3tpNQQWfg271lT9vFXVlRvQGlhUnn379eungWT8+PHWfl6OZj3URP/578WVs7/0V9WH4lVnfqCqqmt3ZGqX//tdL3hnqhYUeEp9aY34fAJlf84n5nNbO8UV+38u3qJJ9/yiT/62pNyvqej8d2Xm6MjnxuunD52vnkcaqe7b6ar9imLtW/tVbn//XtXXBqrnqVb6/Be/atI9v+i9387X/GKOdYGc/76M3brtwVa65LFBWpBfEJAxquXnXwGAWVqyHyKYWrCXimx/DrjX+f+9wLPO/7sB84FIoA2wBggtyb6W4r9U68/1veNU3z/eP/sFBapPJ6n+8O9KTcGv+X96tuorpfuNJdrfutSck+d8UvnxS7Ff2vfRyvTVUnavnEo02eS3qkD+tS+dToSWgyH5acjNIqlhHR48uStTVu/kg8mp7k62NjH7Y2jU6WD3Kz/YlpHNPd8uoGuzeG4/rhLSSuWkfp0IPrxiAD+HjEQ8uWTOHhOwsSyWakdBPnxzJbpzFS83epBX5ynXj2jHk2f0qJRSjz/ExNZjesL5dMlfyqyxH1Xp2LWEYcAlwNEiMs+5jQaeBo4TkZXAcc5jVHUx8BWwBBgL3KCqBcGZeoAoyIfNC6BZb//shISYaPC6Kq7/zM+FtZMrph7iS+NOEN2g6ueNezJ9X2CqbjuJyEYRucoNu5bKs33Bn3hUSOh1bOUMiMCxD0PmFtOIBDhvQEuO7ZLAs38sZ/mWDPcmW1vYugQ2zjCV2n4WN6oq93yzgH05+bx8fm8iwwKrHJXUsA53X34OSzWJzSkfkJ1Xu84xFkuJ/PkArPqLD+reyEurmnL/6M7cc0JnSsnbDSgxnY8jNbQ1LWY+Tfb+Cra1PsxR1UmqKqraU1V7O7ffVHWnqh6jqh2c+10+r3lCVdupaidV/T2Y8w8IO1aYluHNXRApaDUEdq2GjK3+2yovm2ZD3r7KpYeAORcnDYV1ZadXuo0rDraqXqCqzVQ1XFUTVfV9N+xaKk/khokspg1d2iZV3kjSEOh4Ikx6GbJ2ISI8fVYP4qPCuHXMPHLyg+CE7d1EeO6eqh+3PMz5GEIjoNcFfpv6dPp6xi/fzn0ndqZDgnuNakqjb6v60PtCOuSv4Nn/fY/Hc0itj8VSu5j1AUx/k5+iT+eJLQN59uyeXHtkFedpFiEkJIzskY/Qgq3M+vqZoM7FUgtIm2vuy9NevCyCoYedmgKI6eBYWVoNgd2pkL7ZtWmVB5siUhvJyaRF5iJS4/oTHurnn/iYByE3Ayb+F4BGsZE8c1ZPlm5O54W/Vrgw2Qqw6m94fSB959wDudUsspOXDfO/hC6nQJ2GZe9fCqu2ZfLEr0s4smNjLh3S2p35lZMux12FR8Jokvo9T/5mG89YajFrktFf72R6WH/uTj+XNy7qx7n9W5b9uiqgy/DTWRQ9gB6r32X7ttpXc2epQjbPg4hYaNjef1vNeplukOunlb2vW6xJMRcH0fXL3LVEDhRoTnFlSuXFOti1kKxVEwmjgLyK6F+XREJXE5Gd8S7s2QDAMV0SuGBgK96ZsIZpa3b6P0Y5SJ/yAZ7PzmWLJ57o7C2Q8nSVjFtulv4E2Xug72V+mcnN93DrmLlEh4fy3Nk9CaniHFBiGyMdR3FR1FQ+nLSKj6esrdrxLZaqYMcqCsZcyhpacFPuDXxw5SBO6N402LMqRP3TnyWWLJaPeTDYU7HUZNLmQtOeEOJCmmFYBCT2rzpHNSfTpF1WNj3ES9Oe5iKjivOwrYNdC9m58C9yNIxm3Ue4Y3DEfeY++akDmx44qQtJDWK446v5pGfnuTNOEdbvzOLdlNV89dz1xP95G5Pyu3KO52m+zB+BZ8prpjNVdWH2R1C/DbT2YxkLeHncChZtSuepM3uQEB/lztwqiPS+iLj8ndzcegOP/LyYv5ZUYb6dxRJosnaR88nZ7M1RbpZ7ee/aoxnarlGwZ3UILTr1ZX7jUxm04ztWLa1GxzpLzaEg32hIu5F/7SVpqLGZne6ezZJYPxU8+dB2hH92QsMgcYDpRFmFWAe7FhKxfiJztSO92jZzx2C9ljDwGpj/hSnkA+pEhvHieb3Zkp7Nwz8tdmUYVWXF1gxeGbeS0S9P5Jjn/qL+37dy7r7PWZxwCk2v/4l//nMKn8dczC6NJff7G80BJNjsWGkKKPpeaiqtK8mM1F28kbyac/snckJ3l/52laHDKIhpyA31ptOjRV1u+mIO8zfsCd58LBa3KMhj7/8uQvZu4L7we3j5utPomVgv2LMqkfbnP0WuhLPrx/u8cnMWS/nZvgzys93Jv/bSagiox0SWA82aZAiNdEWVi6RhsG0J7N/tv61yYh3s2sa+nSRkrWBVbMU6j5XJEXdARByMe/TApj6t6nPDyPZ8N2cTvy2sXPGAqjJ/wx6eGbuMY/6bwqgXJ/DCXytoEJbN+Gavc3boBBhxP92u+4SOzRsQHhrCxb0b8qTnCiK2LcAz7Q233mHlmfMxhIRB78q3OU7PzuO2MfNoWT+GB0/p5uLkKkFYBPQ4l7CVv/P+ue1pHBfJVR/PZMOuapb3brFUBFU2fX4DdbdM4b9RN/LgDVfRvklssGdVKvGNWrC8/dUMzJ7M7Am/Bns6lprG5nnm3s0IduIAkNCqSbdYkwItB5oO0/6SNARQWD/df1vlxDrYtYy81ckA5LbyL1XhEGIawPBbYMXvhX5YNx3dnl6Jdbn/+4VsTc8ul6kCjzJtzU4e/mkxw57+h9Nen8w7E9bQvF40j53enZk3deHTkIdJ3DsbTn8TRtxTSPauSUwIw0+7mr8K+lIw7nHYFURd7vwcmPe50Q2PS6i0mYd/WsyW9GxePK83sW5eGFWW3hdCQS6N1v7Mh5cPJK9AufzDGezNCkw6kMUSaBZ++zQtVo/h6+hzufbG+2lRz4WTdhXQ4+z72CYNiU15iNy8arBiZ6k5pM01gbEGLirjRMZCs56BL3TctwO2LvQ/PcRLi35G5asK5fqsg13L2L3obzI0mhZdh7lvfND1ENsU/n4InOXK8NAQXjyvNzl5Hu76ZkGJy5g5+QWMX76Ne79dwMAn/ub8d6bx+Yz1dG1el+fP6cXsB47l06sHcUnrDBp/eRLsXgcXfWMcvWI4o28ikzreS06BkP7NTQfmU+Us+xWydkLfyytt4pcFaXw3ZxM3jmxPvyQ/KqXdpFlPSOgB8z6nfZNY3rmkHxt27efaT2aRZ+X7LDWMv374H10XPsP0qGGceMvrNIyNDPaUyk14VCw7Bt5DZ88qpv74VrCnY6lJpM0zyh9+pC4WS6uhsGmWCTAFitQJ5t4tBzs8Gpr3rVKJQetg1zIiNkxkmqcL/ds2DoDxGBh5H2yYDst/O7C5beNY/nNSFyas2M7/pq47sD0rN5/fF27mli/n0v+xv7niw5n8PD+Noe0b8fqFfZn7f8fx3mX9ObtfIvViImD1ePjwRPPiK8dCu5ElTkVEuOOco3k34hLi0yayf/bn7r/f8jDnY6jbstS5lsbmvfv5z/eL6N2yHjce7YKMkpv0vhDS5sC2pQxq25DnzunJ9NRdfLAwx+aDWmoEqsr/fviNwXPvZmNke3rd9CWxURHBnlaF6XL81awJ70DHRS+wZ+/eYE/HUhMoyHMKHHu7bztpiMntTpvnvm0va5IhMt7/DpS+JA01Uf3cfe7ZLAXrYNcm9qyn3v4NrIjpG7gITe+LoWEHk4vtU2B40aBWjOzUmCd/W8pf6/L41yez6PvYX1z/2RwmrNjOiT2a8sHl/Zn9f8fx6gV9OKlns8I54vM+h8/ONs7q1X9D0+5lTiU+KpwjL7yXOZ4OFPx+L5q5PRDvuGR2pZqDQN9LKyWB5PEod349n7wCDy+e19t/zXK36XGOyS2fZy5eTuvdgjtHdWTq5gLeSlkT5MlZLKXj8SjPfTuJkXNuxhMeQ4vrvyeqTnywp1UpJCSU0BOfoBk7mT3myWBPx1IT2L4MCnLczb/2cqDhTABVOVJToPVwowDiFklDjSrJxlnu2SyFanZGt/iDJ1D5176EhpnmM9uXGVURBxHhmbN7UicyjM+W5jJ/w17O69+Sz68ZxMz/HMuzZ/fi6M4JRIUXcURVIfkZ+OF682O68neo26Lc0+nXphFL+z9ORP4+1n9xm1vvsnzM/QQkpNLFjR9MTmXyqp08eHJX2jSq4/LkXCC2MXQ4HhaMOXAxdcPI9gxqGsqzfyzjbyvfZ6mm5BV4uPOLGYxccDtNQ/cSf/nXhNWvHk1kKktS3+NZHD+cQZs+Zu26tcGejqW6c6CDYwAc7DqNTKAtUIWOu9eam1vpIV5aDgSkyuT6rINdi8hYOo7tWpdWnfoFdqAup0CL/kYXO2//gc1N4qL45rohPDg4iin3Hs0jp3VnaLtGhJUUmS3Ig59uhOQnodeFcOHXEFW3wtM5/+Tj+SnuPJI2/cyW2VVUaV+QB3M/NZJ2Fbgg8LJsSzrPjl3OcV0TOG9ANT7x974QMrfC6n8AcyF1ZY9Iujevyy1fzmXF1owgT9BiKUxOgXLNxzMZtuxRBoSsIOysd5DEAB8Tq4hmZz1HJLms/eaBYE/FUt1Jm2dSLOq3CYz9pCGwYRp4PO7bXpNi7v1tMFOUqLrQtEeVNcqxDnZtQZXw9ZOY4unGwLb+teouExE47hFI3wQz3in0VNvGsbStF1p2B8LsdPj8XOOkHnUPnP6GkYerBKEhwtDLnySV5sivt5G3vwqcvhV/GMezEp0bcwuUW7+cR3x0OE+f2QORKu7WWBEcTWzmfXZgU2So8M6l/YiJDOPqj2exe19uECdosRxk7/48np+ZTZc1H3BW6CQY+R+k+xnBnpZrNEjqytLEszki/Rfmzq7arnSWGkba3MAUOHppNRSy98L2pe7bTk0xggqNO7lvO2kobJgJ+YE/b1kHu7awfRkxuTtYFNGbxPpVID/Veji0Pw4mvlBx4fb0zfDhaHOVeuprMPL+QjJ8laF5o/psOepZEjxbmfe/u/2yVS7mfAxxzYwDWkG+XZnLsi0ZPHdOz+qvZuBoYrP8N8jadWBzs7rRvH1JP7akZ/Pvz+aQVxCAKIbFUk5y8gv4ZNo6jn9xAu0zpnNP2JfQ/Ww48q5gT811Op77OFkSTd7vD1BgFX0sxZGfC1sXB6bA0UuSk4ftdrqFqlEQaXuU335BsSQNhfz9VdIJ2jrYtQRdkwxATqsjqi4ieuxD5gp20kvlf83WJfDesbA7FS76Cvpe4tp0how8hWkNTqNv2hfMn/6Pa3YPYc8GWPU39Lm4wgUYk1ft4I+1+Vw6JImRnZoEaIIu42his+jbQpv7tqrP02f2YOqanTz685IgTc5yOJNX4OGLGes5+vkU/u+HRQyP3cQrkW+YFLbTXgvMCTrIRNVtwvruNzAwfxYT//g62NOxVEe2Lw1cgaOXekkQ19x92bttS2DfdvfTQ7xURYGmg3WwawnZy8ezztOE9h27Vt2gTXtAz3Nh+luQnlb2/mtS4IMTTBXvFb9D+2Ndn1LPy19id0g9osfexu70AEnxzP3UXGX3qdjFwaSVO7juk9k0qyPcd2KXwMwtEPhoYhflzL6J/OvItnwybR2fTltXzIstFvfJL/Dw1awNHP3fZO77biGN4yL57didPJd5PwXhcXD+5+50f6umdD3tTraENKX59MfZtz+AWsSWmolXPs9NibuiiJgW5uumutuHwgkW0jZADnZsE6dA0zrYlvJQkE/YhslM9nRnQOsGVTv2yP+AekzBY2nMHwOfngXxzY0MX7OeAZlOTHwDso55mo66ln8+etB9vWZPgXGw242E+knlftlXMzdw+YczaFE/mjv7RxEdUXFZv6Dio4ldlLtP6MzITo15+KfFTF29MwiTsxwuFHiU7+Zs5NgXUrj7mwXUi47go0t68X2bH+k66SakcSfm9nnar66qNQEJjyLryAfoyDomffNKsKdjqW6kzYXIutCgbWDHSRoKGWmwZ717NtekQMP2UDfRPZtFSRpiIu+BKND0wTrYtYHN8wjPz2ROWE86JcRV7dj1k6D/Vcbp3L7i0OdVYcJz8P215mr3yrFQL7CqGa2Gn8/axiM5aefH/DR+krvGV42D9I3Q7/Jy7a6q/PfP5dz97QKGtGvI19cNoWF0DfzZFdHE9iU0RHj5gj60blSHf382m/U7s4IwQUttpsCj/DhvE8e9mMLtX80nOiKMdy/tz08Xt2TElEuRGW/D4H/DFb+TExWAJlvVkLZHXUxqVDf6rHqdzdt2BHs6lupE2lxo3ivwKVIH0i1cShMpyDOtzAOVHuIlaZhJb90W2NTGGnimtxzCmvEA5CYOK1u9IxAceSeE14FxjxTeXpAPP98C/zwOPc+Di7+D6HpVMqVWF72OJyScJin3sHJLunuGZ38EdRpDxxPL3DUnv4Bbx8zj1X9Wcf6Alnxw+QDiosLdm0tV4qOJLZ6CQ56OjwrnvUv741G45n+zyMzJL8aIxVIxPB7l1wWbOeGlCdzy5TzCQ0J46+K+/HrTcI4LnYu8fSTsWAnn/g9OeKrSSkQ1EhHqnPo0TWQ38796LNizsVQX8nOcAscA5l97adLVSN+5lW6xaTbkZgYuPcSL2xcGJWAd7FpA3spklniS6Nw+wMtBJVGnEQy7GZb9AhtmmG05mfDF+UZt44g74Yy3q/TkF1KvBfnHPMwQWcxPHz9Pdt6hTmGFydgCK8aadIky3suerFwueW8GP85L467jO/HUmT2qX6fGiuJoYtffPbfYp1s3qsMbF/Vl1fZMbv1yHh6rcGCpJKrK2EVbGP3KRG74fA4KvHZhH36/5QhO6NKYkHEPwxfnQb1W8K8U6HpasKccFJp0PZJlDY/lyO1fsGjZ8mBPx1Id2LYEPHmBzb/2EhICLQe756iuSQEEWgewWR6Y40Z8CxMtDyA1/IxvIW8/IZtmMNnTjYFVnX/ty+B/Q50m8NdDROTsgo9Gm+Ykp7wMx/xfUKr544dew55G/bkq6z1e+9mFK+y5n4IWlKl9vW7nPs58cwrzNuzhlQv6cMPI9tVb67q8OJrYSeu+gn3F51oPa9+I/zupC38v3cp//7InfEvFUFX+XrKVk1+dxHWfziY338PL5/fmj1uP5OSezQnJ3AIfnwKTX4J+V8BVfwU+z7Sa0/LcZwmTArb98B/3a04sNQ9vgWNVRLDBpH7uWAH7XEhTSk0x2t0xAfZlREz+uNsFmkWwDnZNZ/00Qj25zJAe9EiseBdE14iMhaPuhvVTGDDzZtixCi4cU+5c5YAQEkK9894gNiSXjnOfYPyybZW35fHAnP+ZK+uG7Urcbfa63ZzxxhR27cvl06sHcWqv5pUfs7oRFgEnPE1cxmp4a3iJbXIvG9qaCwa25PXxq/lx3qYqnqSlJqKqjF++jdNen8zV/5tFRnY+/z2nF3/ediSn9W5BaIjA6vHme7d5HpzxDpzyEoRHBXvqQadOQjtWt7mYEfv/ZtLE8cGejiXYpM2FqHpQv3XVjJc01Nyvn+afndx9ZgU80OkhXloNgcwtRjI4QFgHu6aTmkI+oWQ3G0xkWJCVKfpdDg3b4wkJgyt+gw7HBXc+AI07oUfcwamhU/nhqw/YlpFdOTupybBnXakXDL8v3MyF704jLiqM764fysA2QVxRCBQ9z2VO32chLBI+Ogkm/veQSmwR4ZFTuzOwTQPu/mYB8zfsCc5caxlrtmfy2j8rSdmYV2vSb1SViSu3c+abU7jiw5ns2pfLs2f1ZNwdR3FWv0TCQkOMck/yM/DJGSYd7Zrx0Ou8YE+9WtHxnIfJCIklOvkhsnNt/cNhzeZ5psFMVa2aNu8DoZH+p4msm2pSWwJd4OglaZgzbuDk+qyDXRFysyD5aZpsnRDsmRygYHUy8zzt6dmuGkRKQ8Phqr+YMfD1wHaQqiDhR95BTv2O3FPwDv/5cmrlnJPZH0N0feh88iFPqSrvTFjNvz+fQ7fm8Xx3/VDaNo51YebVk8y4dvCvCSbvddyj8NnZkLm90D4RYSG8eVFfGsVGcu0ns9iWXskLm8OcjbuzeDtlNSe9MpGj/5vC83+u4MNFuVz9v1nsquEt6qes3sG5b0/lkvdnsHVvNk+e0YN/7hjBuQNaHqxXyNxu5D2TnzSF0tf8A006B3fi1ZDQmPrs6Hcb/T0L+OfnT4M3kc0LaLhjZvDGP9zJzzHN3Koi/9pLWCQk9vffUU1NhtCIgwWIgaZxJ4huUOJKrBtYB7u8rBoHbwyG5KfotPx1yPQj3cAt9u8hZMt8Jnm6Vb3+dUnENKAgrE6wZ1GYsAgiz3iNZrKLoeve5IPJFVwS2rcDlv0KvS44ZEk6v8DD//24iCd/W8bo7s34/JrB1b/9uRtExcPZH8DJL8LaSWbpfm1hScSGsZG8d1l/MrLzueaT2e4Umh4GbMvI5qPJqZz15hSGPzOep35fRnhoCP93clem3XcMF3eJYNLKHYx+eSIzUneVbbCasXxXAee/M5UL353O+l1ZPHZaN8bfNYILB7UiIsznlLRuKrx9hDlxn/IKnPEWRFSzY0s1ot2JN7MlLJFOC55lR6CabJVE9l747W545yjarvko4PrClhLYuthEgasq/9pLqyGm9XhOZuVtrEmBloMgIsa9eZXGgTzswBU6Wge7LPbthO+uhU/PNBHa094gxJMLE54P9sxg7SREPUzxdKdvUv1gz6Z602oQDLiay8L+ZOwfv7Bo097yv3be5+agVaS4cV9OPtf8bxafTlvPdUe149UL+hAVXsMayPiDCPS/Eq4ZZ3LwPz4FUp41S/oOXZrF88K5vZm/YQ/3fbfQFmGVwJ6sXL6YsZ4L353G4CfH8fDPS9iXk89dx3diwl0j+eGGYVw1vA1N60ZxbFI43/17KJHhIZz/zlRe+2dljUgZ2bAriys+nMFTM7JZtW0fD53SlZS7RnLJkNaF09tUYfLLJgUpPNo0pup3Wa1se+4qoeHocY/STjYxecx/q2ZMVdNE7NX+MOMd6H8lc/s8Y9QlLFVPmqPwVNUryK2GGAGAjZVcvdi3E7YsqLr0EC+thpgc7PTNATEfFhCrtQFVWDAGxt4HOelw5F1Gbi48is0zfqD5rA9gyL+rrpCgONYkky2RZDfpQ3xN1VeuQuSYB9Glv/D0vnf59xdd+OHmkcRElPETUDVSgy0HF1qa3rI3mys/msnyrRk8cUZ3LhpU/q6OtY6mPeDaZPjldhj/hIlkn/WeaUkLnNC9KXcc15H//rWCTk3juO6okotEDycyc/L5a/Fm/pyXysJV64jRfXSq5+GF3hEMbhZK08iNkD0NZu2F7D0mSpi9l/a5cSQOeIVfbhrO/d8v4vk/VzA9dRcvnNubxnHVb/Ukv8DD+5NSefHvFYSIcG6ncB65aGTx3UyzdsEP/4YVv5sUpFNfM6sllnLRbOCZrJ34KsM2vsuqDVfTvmUAUwe3LYVf74R1k6BFP/TCr0jOaMHk2fMZHrhRLaWxeZ5JZaxXxeejlgNBQkyhY7uRFX/9Wifttu0IV6dVJgcKNKdA97NK3O2fZVv5Znkug4YWVKgLs3Wwi2P3WvjlNiMzlzjALE8mdD3w9Lqk82i+fQIkP22WLYOErklhhqcLfdvW7rbArhEVT8jJL9D+ywsYtecrHvslgafOLKNl+7rJsHMVHHHHgU1LN6dz5UczSd+fx3uX9WdkpyYBnngNIDIOznwH2hwBv90Fbw4zTrZTEX7j0e1ZvjWDZ8Yuo2NCLEd3rqXf2b0babBzFizY5jjEe2D/ngPOccH+vWTu2UHOvt2E52ZwCvs4QzzglVXfDyx1bl7C65hmDlF1ITKW5mmT4ZW+xI24l1fOuZph7Rry0E+LOfHlibx8fm+GtW9U1e+6ROZv2MO93y1k6eZ0ju2SwKOndWPFvOnFn6Q2zYavLoeMzXDiszDwWhu1rigiNDjjOeI/OZapXz9C+9vfdn+MnExIeRqmvQkRseSf9CI/hx7L21+vZdmWmbSMC+E/qrVDmrSmkTbX5F9X9WcfFQ8J3Y2jWhnWpEBkfNWntjTtCRGxJh2tBAe7wKM8/fsy0jPyCQ+t2OdqHWxfCvJh2hsw/kkICYUTn4MBV5n/+5AT1cgc/Ke8CkNvLuR8VxnpacjOFUzMv7D65F/XBDqPhq6ncevSHxg1cyC/dWjM6B7NSt5/9scQWRe6ng7AhBXb+fdnc4iNDOPr64bStbmNrh1ABPpeCi36wdeXw/9OM9KNR92DhITy3Nm9WLtzHzd/MY/v/z2UDglxwZ6xe2xZZNIaFn1LTy2AhQef0vAYcsNi2V0Qw+acCHZ7YsgJa0fDxo1p2bwpCU2aEhLtONBR9Q4601H1zIkrtPDq1KxfP2Hgru/hj/uQ2R9x/olP0/vGYdz4+Vwufn86N41sz83HdDAKHEEiIzuP//65go+nrqVJXCRvXdyX47s1RURYUXRnVZjxLvxxP8Q1gyv/gMR+wZh2rSC+3QBWNj2JUZu/Zeqc6xjS1yWnRRWW/ABj74eMNPJ7XcxX9a7i9XF72LRnIR2axPL8Ob2ot3elda6DQV62WVUYenNwxk8aaqRsC/IOOWaVyZpko+oRWsUuaWiYCaKWUqD5w9xNrNiayQ29Iyt8TLUOtpe0efDzzSZRv+OJcNLzUDex5P2H32baZv/zOFzweVXN8iCpZklliqc717S2+dcV4sTnCF2TzCuhH3HRt4n0blmP5vWiD90vaxcs+dE4jRExfDFjPQ/8sIgOTWL58IoBNKtbzGsskNDNSKn9diekPGMOXme9R3RcU965pD+nvma0jn/49zDq16nBra1VzXub/BKs/NNEmgddx5ycVvQaegyzt3r4cWkmvyzZyd6MPOpGh3Niz6ac0qs5R7ZpUGkHOKtOSxj9rekqOvY++OQMOnc6iZ8vepQHJ+zjlX9WMS11F6+c34emdateJ/qPxVt46MfFbM3I5pLBSdx5fKeSU9iy081xd/H30PEEOP3NwDeZOAxIOudp9NW/yPrtIfJ7/eD/xdaOVeb3vGY8+U26803rx3hmYTy7szbTL6k+D5/ajWM6NyEkREhOXuXOm7BUjK2LwZNf9VFgL62GwPS3jA+V2L/8r9uz3uRBD/pX4OZWGknDTGrj/t0mvcaHnPwCXvhrBT1a1KV/Ql6FTVsHOzfLSEBNfQNiGsI5H5loZVlX4DENTHvwfx434ugtB1bFbA+yJoWMkHj21e9Mk3jbbKFCxCUgxz1G959v5nTPP9w6ph5fXDPYNLPwZcEYKMjB0+dSnh+7jDeSV3NUx8a8dmEf4mzOe+lExpr0qdZHwK93GJWRM9+hebujefuSflzwzjRu+HwOH185sOa1kPd4YPlvxrHeOBNiGsHIB9ABVzFnu/DmrzOY9/Y6dmTmUCcilFHdmnJKr2YMb9+4sEqGP4hApxOh3dEw9XWY8DzRq4bw3NCbGH7medz3SyonvjyBF87tzcjOVZPCtHnvfh76cTF/LtlK56ZxvHFxX/q2KuXif8tC+Ooyk5J37CMm8maL41whomErVne5kmOWvsVvf/3G6BMOlRctF7lZRut+yit4QiMZm3gbd68bQOZ6OLZLfa47qh397Qpq9WBzkAocvXjl9dZPrZiDvSbF3Fd1/rWXpCGAwvrp0OmEQk99Nm09m/bs55mzepK/aVGFTR/eR7PV/xjpvSmvQp+L4MYZ0O2M8ucvDboe6jSGvx8JaLvNQ1BFU1OY5ulK/zbVJ9+yRtH3Ukgazn8iviA1dTVvFo26qMLsj/E068vNyfm8kbyaCwe14v3L+lvnuiL0ucgUQMY0hE/OhHGP0S8xjifP7MGU1Tt5/JclwZ5h+cnPhbmfwhuDYMxFkLkVRj/P+kun8VLeaYx8fT5nvTmF5I35DGhdnzcu6svs/zuOF8/rzdGdE9xzrn0Ji4QjboebZptj18T/ctrE0xg/ahsJcZFc8dFMnvxtKXkFgZNNK/AoH01O5bgXJjBh5XbuOaEzP980vGTnWtUsJb93rOnedtnPMPxW61y7TNvT/sOekHo0m/YYe7MqoZm+7Dd4fRBMfJ5ZdY5iSOaz3LxmIMf3SOTP247kvcsGWOe6OpE21xxn67YMzvhxCdCgbcV1pdckQ2wCNA6Svn2LfkZ/u4hcX2ZOPq+NX8Ww9g0Z3qFyftbhGcHet9Pk+y34Ehq2h8t/hdaVqHuOjIUj74bf74LV46D9se7PtTh2rkbSNzE+7wQG2gNc5RCBU14m/M2hvNP4K87+uwFD2jWin1fucONM2L6Ud+Ju5pfUzdx7Ymf+dWRbm1tYGZp0Nikjv98FE5+HdVM4++z3WX5EG96dmEqnpvFcOKhVsGdZMjkZJhd/6uuQkQYJ3ck8+W1+yB3Ad7O2MOe7GYjAkLYNuWFke2L3rOLEY6s4hzi+mSkyHXA1/HYXCX/fxG+Jg3gz4Vqem7CGGam7ePWCPrRs4K7G7JK0dO77fiHzN+zhiA6NeOL0HrRqWMoYufvovOwV2PqPiVid+R7ENnZ1ThaDRMWzb+jd9Jl0P99+9x5nXfzv8r1wVyo69h5kxR9sDG/NHTn/x8I93blgaCuuGt6m+HQ6S/BJmx+cAkdfWg01q3seT/kumFVNumvbo4I37/BoaN73kE6U701cw659udx9fOUd/8PLwVaFBV/BH/eZqv4j7jTye+Glp1ioKlvSs1mSls7SzemE7C5ghPfJfpfD1FdNFLvt0VUThUlNBmCypxtX2/zrytOoPXLU3fT55zHOiR3CLV9G8dstRwCQMeU9Qoni7d19eO3CPpzcsxp0yqzJRMTAaa+blJFfboe3hnPv6W+zcltjHvxxEW0b12Fw24bBnmVhMrebnMKZ70L2XjxJw5nT6xHe2dSG8d9vJ6/AKKLcc0JnTu/T/EBOfnLy6uDNueVAczEz7zNCxj3CDRuv5pTOZ3NJ6ihOeiWTZ8/uxQndm/o9TFZuPi//vZL3JqVSLzqcl8/vzam9mhd/AZq91zTqWv47rPyThOy9MOI+c+wNOYx044NAi5H/YsvMd+i38mXWb7uQVk3qlbxzXjaeSS+hE18gV4UX8i7kp5BTufjY9rw9JIl6MTW4XqK2k7cfti2BjscHdx5JQ2Dep7BjRfk6rm5bCvu2BS89xEvSUJjyillRi6jDzswc3p2whhO7N6VXy3qVNnv4ONi715oT++px0KI/nPqKKcYqQn6Bh9Qd+1icls6Szeksce592xLHhsNFo03REmERMPI/8P2/YMn3pWopusaaFHaFJbAvtCVtGtnOZn4x7BZY9B2PZn7EgD0dePCHRfQOS2fQ0u/5jSN475oR9EuyqwSu0et8Ey34+nJCPz+btwffwkk7j+L6T2fz043DXY+wVopdqTD1NZj7KZqfw55Wo/gi4kzeWlWf9OX5NI7by2VDWnN6nxZ0ax5f/VY1QkKg7yXQ9VRIeZZW099ifOQffBB+Hjd+ms3FQ9tz3+jOhZu7VIDk5dt44IdFbNy9n/P6t+S+0Z0Pdb52rzNFmMt/g7WTTaOmmIbQ+STm0Y0+I25w4Y1ayiQ0jMjRT9L0+wv5+qtnaHXjU8Xulrv8D3J+vJO4rPX8UjCYD+pcxRmjBpLcr2WFdH+DjYh8AJwMbFPV7s62BsAYoDWwFjhXVXc7z90HXAUUADer6h9BmLb/bF1sGr0EK//ay4E87Cnlc7BTnfzrqm4wU5SkoTDpBdg4C9oexevjV7M/r4A7RnXyy2ztd7AL8mH6m0Z6T0IKSe/ty8ln2ZYMx5Hey5K0dJZtySAn3+QrRoSG0KlpHMd1SaBbi3i6NotHgXPfmsob41dx3+guZowe5xiJrn+egC6nVlyipiJ4CiB1AlO1LwPaNKx+J/eaRmg4nPoKEe8dyydJYzl13hnEhf5DdHguQ865g+bWuXafxh1N98ff7yFy2sv80mwKp2RewTX/m8U31w8N3ry2LIRJL8Hi71AJZVGjE3kqfRRTVjQgOjyU47s14Yy+iQxr1zCoEnjlJqouHP8E9L2MkLH3cvXqdzmt3l/cPu18zlq3i9cu6EvrClygb8vI5rFflvLz/DTaNa7DmGsHM8i76uDxmCKr5b+b21anIKhRR9OQq9NoI4cVEsre5GT336ulROr3HM368YM4bvvHzF52TaHnMretZfvXt9Fm+z9s8DTjqfjHGHzc2XzVvWnN+I4fykfAa8D/fLbdC4xT1adF5F7n8T0i0hU4H+gGNAf+FpGOqlpATeNAB8cgKYh4adAW6jQxDWf6X1n2/muSzWvqBSlv3EvLgYDAuilsrD+AT6et45x+LWnfJNYvs6442CJyAvAyEAq8p6pPu2HXbzbPh59uhs3zyGk7ijndH2Bueh2WfDmfJWnppO7cd6A2sW50ON2ax3PJ4CS6No+nW/O6tG1cp1iFg2Etwvhw8louHpxkIm4hoXDMg/DF+aYIqv8VgXtPWxZA9h7+zO1s9a/dIrE/DLqOntPf5JqkoZy3fTz5DbvRvGsQnb3aTni0WUVqcyRRP9/CbxH3c932a7htTAwXtKzagmHWTjSO9epx5IXG8EvUGTy9eyTbNzRgWPtGvDCqBcd3a0qdyBoaj2jcES42sn6Nx97HJ9lPM35nf65/9VKuP/M4Tu1VevqTx6N8OXMDT/++lOw8D7cd25HrRrQlUnNhuROlXjHWFH1KiMnDHPWEUTlpaLt2Bh0Rmpz9PBHvHcmGHx4hftDlbN+TweLvnmbg+ndpqsqX8ZfTfPRdPNG5RY0O2qjqBBFpXWTzaXAgq/NjIBm4x9n+parmAKkisgoYCFSwSq8akDbPqBnFtwjuPERMmkh5Ch0L8s3qVs9zAj+vsoiqazoSr5/CSztGg8Atx3bw26zfZwwRCQVeB44DNgIzReQnVQ2KPIDHo2xP38+qz26j7cqPSQ+J5+nQO/hySV9YshGAxPrRdG0Wz2m9W9C1eTxdm8fTvG5UuQ8sZ3UIZ9a2HJ77YzmvXOBcMXY8ARIHGt3fXucbByIQOJI2UzzduNo62O5x9AOw7Bf+s+8Z8GyBAc/bLnJVQY+zoVlvwr++nPdzn+PtFUt4acsFTMhYTMM6ETSIjaBhnUgaxUbQMDaShrERxEWG+e8EeDyw7Bc8k14kJG0O6SH1eDf/PD7OPobmcc24anQLTuvdgoTaIoHpK+s37Q2OSnmW4Xm3887Xo3loxQ3ce9qAYlMBVm7N4P7vFzJz7W4GtWnAMyc0pfXOSfD1Q0aFKX8/RMRB+2NMlLrDcVbHuhoSldiTta3OZPS6H3htanNaJd/ACNnE/DrDCD/pGc7v2iPYUwwkCaq6GUBVN4uIV7eyBTDNZ7+NzraaR9pcE72uDuesVkNN/4i9G0vvJZI2B3Izgp8e4iVpKJ7ZH/HzvrVcNryDK8W8boRkBgKrVHUNgIh8ibkyDIqD/cALr/Kv9FdJCtnGmIKRfFP3X7Rq0Zz/a25SPLo2i6dujH8pHPWjQrj2iLa88s8qrhzeht4t65kv9rEPw0ejYcY7Jrc3EKSmsC2qNVmeRnRpVos64QWbyFg46QX4/BwKQiII7VENrqoPFxq1h6v/Rsfex79mf8AVWX+wf04kWRrJfo0ghwj2E0GaRrKGCHIkEg2LhvAoQiJiCI2oQ3hUNBFRsUTGxBITU4eYOnHExsURFxtPZHSsueANj4KwKBLS/mT/S7cSnZ7KJhJ4K+9KJsYcy4lD2zKmTwu6NKvF3TnDImH4bYT0PB/+eogbFo5hy6KJvLLmCs687DY6NDXvPTuvgNfHr+KtlFX0CN/CT73X0SNzCvLhLECNFFjfS0xgofVwY9dSrWl11hPkvPgrt+e+wa7IZmw+7mN6DTg92NMKJsV5o8Uun4nItcC1AAkJCSQXk+aUmZlZ7Ha3KMl+SEEOR2xbyrrobqz1Y3y35h+bEUF/YMnY99mWcNB5Lmo/ae0YWiNMTgshf7v/4/o7/0aZdemen02f0FR6hceTnLzNb/uifuo3i8jZwAmqerXz+BJgkKreWGQ/3y9ovy+//NKvcYtFPXSZeishnlxmt7me0GY9CS/aPMQFMjMzCY2qwz0T9tO0jnDfwIPR7x4LHiU+fTnTB71Nfnjl8ncyMzOJjT30teLJY/ikC/lOR/JxzJXcOaBy0bWS7LtFTbbfdvXHZHnC2NLhooDYh5r9+QTafsMdM4nZPpfIMJD8bDz5OWh+LuTnIAU5iCeXsIIcQjWXcM0hUnOIIJfQ4s+LJbLI05r3PKews/FQBreIpEvDEEJciv7UpM8/fu8yWix9h4Ts1czxdGBOq6vIjGzGijUr6Jc3h5Mj5pDg2QpAelx7djYcyI5GA9lXp3Wlo2U16fOpbfbrbplMyN717G1/Jp7QwFwUBfrzGTly5GxVLbGTiZMi8otPkeNyYIQTvW4GJKtqJ6fAEVV9ytnvD+BhVS01v6F///46a9asQ7YnJyczYsSISr6rsinR/oYZ8P5xcP7n0Pkk9+1XFE8BPJ1kUj9OfrFk+x+eBDnpcN1E/8cszn4FWbB8BT2/GMCkNjcz/LLHym1fREr8ProRwS7XVaCqvgO8A+YLGrAvYu+fmTBnOSccMyow9jn4Qe+OW8/93y8kp3GXg9JXnerD20cyPGQujPg/v+wfwtpJMCGXP/O6c/zwdowYUbkcoaAdCGqC/REjavb8a7z9Snz+qlCQS1ZWJrv37GVvRjrp6elkZmSQuS+d/fv2kbM/k+z9+8jLzmSrpyH9R53Pk92bEhPhfl51zfr8R8Ap17J32ke0+/sRem+4j0yiiJf9eCIiCGk30qSWdDyB+PhmxANt/ByxZn0+tc1+zT++VYKfgMuAp537H322fy4iL2CKHDsAM4IyQ3/wFjg26x3UaRwgJNQUDa6fVvI+uftg4wwYdF3VzasUVJUnknfyDM0ZHLrcNbtunF02Ar4loIlAmgt2K0f9JDyhqVUy1Ln9E/lwcipP/76Uozs3MZ3amvUyUn3T3oCB15ruRm6xJgWVEKYVdOFKm39tsRhEICySmPhIYuIblplEmZyczIi+peQGHm6EhFB36JUU9D6TeWMeIWf7WvqeeAWRnY6BCCsDaqk5iMgXmILGRiKyEXgI41h/JSJXAeuBcwBUdbGIfIVJZ80HbqiZCiLzjHJHfDXq1ZA0BP55HLJ2FV+TsX4qFOSaBjPVgAkrdzA9dRe57QYTtnFc+RvllIEbOjwzgQ4i0kZEIjCyNz+5YLfaExYawv0ndWHtziw+m77u4BMj/wP5OTDhOXcHTE0hLaYL2aF16NOqnru2LRbLYU1oTD36XvEiOQNvIbLHqda5ttQ4VPUCVW2mquGqmqiq76vqTlU9RlU7OPe7fPZ/QlXbqWonVf09mHOvNGlzjf51dShw9NLKUeDaML3459ekQEj4Qd3sIOLxKM+OXUZi/Wja9jvONMba5k4Jod8OtqrmAzcCfwBLga9UdbG/dmsKIzo2Znj7Rrw8biV79+eZjQ3bQd9LYfZHpmmFG2Snw8ZZTKM7PVrUJSq85oj/WywWi8VicZncfbBjefD1r4vSoh+ERsC6KcU/n5oCLQdVi4v4XxduZnFaOneM6khYm2Fm43p3lBpdUZJX1d9UtaNzJfiEGzZrCiLCfaM7s3d/Hm+MX3XwiaPuMblIycV3zqow66aAFvDD3vYMaGPTQywWi8ViOazZshDUU33yr72ER5mOvcU5qlm7YPOCapEeklfg4b9/Lqdz0zhO7dUC6rUyWuLrJrtiv0a2aqpudGtel7P6JvLh5LVs2JVlNsY3g0H/ggVfmTam/pKagic0khn5HRhguwtaLBaLxXJ4kzbP3Fe3CDZAq8FmfrlZhbenTgC0WuhffzVrA2t3ZnHX8Z0IDRGnUc5Q0yjHT4U9sA62a9w5qhMhIfDsHz4VqMNuhch4GHeo5EuFWZPCprhe5BBB/9b1/bdnsVgsFoul5pI2F2KbmoBedSNpKHjyYNPswttTU0xzqhZ9gzMvh/25Bbz890oGtK7P0Z2bHHyi1RDI3AK7/U/vtQ62SzStG8W1R7Tl5/lpzF2/22yMaQDDb4EVv5cuWVMWmdth22Kmanc6JcRRLybCnUlbLBaLxWKpmWyeZwocqyMtBwFyaJrImhRoPQxC/Wv45y8fTVnLtowc7j6hc+HOwElOHnZJ+eMVwDrYLnLtUe1oFBvJk78t5UADn0HXGQmdvx+p/JJDqmmP/t2e9jZ6bbFYLBbL4U5OJmyvhgWOXqLrQUK3wo7qng2wa3XQ00P2ZuXxZvIqjunchAFFJY8bd4LoBiZNxE+sg+0isZFh3H5cR2au3c0fi033MyLqwFF3w/opsOrvyhlOTaEgIp4ZOa0YaAscLRaLxWI5vNmyENDqV+DoS6shsHEmFOSbx06wkLYjgjYlgLcmrCYjJ587j+906JMH8rD9L3S0DrbLnNs/kY4JsTz9+1Jy8z1mY9/LoH5rE8X2eCpudE0KG+v2w0PIoVdbFovFYrFYDi+8HRyra4oImELH3EzYutA8XpNiVvSbdAnalLamZ/Ph5FRO792CLs3ii9+p1RCTg52+2a+xrIPtMmGhIdw3ukjzmbAI03xm60JY/F3FDO5KhT3rmEZ3WtSLpnm9aPcnbbFYLBaLpeaweR7ENYO4psGeSckkOQ1nvKocqSnQ5sigNsV5ZdxKCjzKbcd2LHkn77zX+5eHbR3sAFBs85nuZ0NCd9M+tCCv/MacJZWvd7VngM2/tlgsFovFkja3+uZfe4lvDvWSYP0UYrI2QObWoOpfp+7Yx5czN3DhwFa0ahhT8o5Ne0JErN952NbBDgAiwv2juxRuPhMSAkf/n1l2mPO/8htbk0J+TAKz9jWyDWYsFovFYjncycmAHSurd/61F0dXuv7u+eZxEPOvX/hrBZFhIdx4dIfSdwwNg8QBfiuJWAc7QHRtHs/ZRZvPdDweWg6GlGcPFV8vDo8HUiewsf4AQBho868tFovFYjm82bwA0OofwQaTz5y1g+ZpY6F+G9MtMQgs2rSXn+encdXwNjSOiyz7BUnDYNsS2L+70mNaBzuA3FG0+YwIHPuwETGf8XbZBrYtgawdTNce1I8Jp32T2IDO12KxWCwWSzVn8zxzX50LHL20GgJAnayNQU0PefaP5dSLCeeaI9uW7wVJQwCF9dMrPaZ1sANIsc1nkoZAh1Ew6cWyr4yc/Otv97SjX1KDwmLoFovFYrFYDj/S5kJ8C4htUva+waZRB4hpZP4fpPSQKat3MGHFdm4Y0Z74qHI2uGnRD0Ij/JLrsw52gCm2+cwxD0L2Xpj8SukvXpNCfv12zNgVw8A2tsDRYrFYLJbDnrS5NSP/GszKfavB5v+tj6zy4VWVZ8cup1ndKC4ZklT+F4ZHQ/O+h3airADWwQ4wsZFh3DHK23xmi9nYtAf0OAemvQkZW4p/YUEerJvMpvoDAaz+tcVisVgshzvZ6bBzVc3Iv/Yy/DZWtbsK6jSs8qH/XLKVeRv2cOuxHYgKD63Yi5OGmouZ3H2VGts62FXAOf28zWeWHWw+M/J+8OTBhOeKf9GmOZCbyTTtTlR4CN1b1K26CVssFovFYql+bHbUOGpC/rWXxP5sbHlqlQ9b4FGe+2M5bRvX4ay+iRU3kDQUPPmwcValxrcOdhVQbPOZBm1Nh8fZH8GuNYe+KDUFEL7f05Y+LesTHmr/VBaLxWKxHNZ4CxxrSopIEPluzkZWbcvkrlGdCKuMD9VyICCVluuzXlsVUWzzmaPuhpBwGP/koS9Yk0xBQg9mbFGrf22xWCwWi8UpcEyE2MbBnkm1JjuvgJf+XknPxLqc0L2S3S6j6pqU3kp2dLQOdhXh23zmdW/zmbimMPg6WPgNbFl4YN+QgmzYMIO0BoPwKFb/2mKxWCwWC6TNq1npIUHis+nr2bRnP/ec0Nk/BbakobBhJuKpQAduB+tgVyHe5jMf+TafGXYLRMXDuMcO7Fd37xLw5DGd7oSGCH1a1QvOhC0Wi8VisVQPsvfCrtXWwS6DjGwTyBzevhHD2jfyz1jSUMjfT1xGMam8ZWAd7CrmkOYz0fVh2K2w8o8Dfe/r714AoRH8tCuJbs3jqRMZFrwJWywWi8ViCT4HChxrkIJIEHhvYiq79uVy1/Gd/DfmNMqpu3dxhV9qHewqptjmM4Oug9im8PfDoEr93QvwtBjA9E3ZVp7PYrFYLJbDEI9H2ZuVx7qd+9id7TH51wDNrINdEjsyc3hv4hpG92hKr5b1/DcY2wQadjCZBRXEhkaDwL+OasfnMzbwxK9L+fq6IUhEjCl4/PV2WDCG2Mw1pHU4lZwVHutgWywWi8VSg1FVMnPy2ZOVx56sPHZn5bJnfx57snIPPN6blcee/Qf/vzsrl7378/DoQTsN4v+iT2QzNqWH0zlGbXfnYnh9/Cqy8z3cMcqF6LWXpCHUXfAteDwQUv64tHWwg0Adp/nMfd8t5I/FWzihezPoeylMeRV+vhVBmU5PAPq3th0cLRaLxWKprkxZtYOfVucyKXPJoY7zfuNU5/t6ykWIjQyjbnQ49euEUy86ghb1oqkXY/5fLyacejERzFiwhI4bVzE5tyXXvzyRlg2iGdW1KaO6JtAvqX7lZOhqGRt2ZfHZtPWc0y+Rdo1j3TPc73KW5zSnu3qoSOKHdbCDxDn9EvlwcipP/76MozsnEBEWDkc/AN9eRX5oFGN3NaNt4xwaxUYGe6oWi8VisVhK4K+lW/luZR4x69ZTLzqcujER1I8Jp1PTOOrFRFAvOpz6MRHUjTH3xnk2jnPd6HAiwsp22prumkezdZuJO+pKno7vwZ9LtvLJ1HW8PymVBnUiOKZzE0Z1a8oRHRpVvGNhLeGlv1eCwC3HdnDXcIt+7GicAaEVc5mtgx0kvM1nrvhwJp9NX8cVw9pAtzNh2hvszI1h+voMTqysdqPFYrFYLJYq4c5RnRgSs41Rx4wM2BixmavNfZv+nN+uFecPbEVmTj4TVmznz8VbGLt4C1/P3kh0eChHdmzEqK5NObpzE+rXiQjYnKoTGzM8fDd3I9cc0ZZmdaODPR3AOthBxbf5zJl9EqkbEw5XjOWv31LYuyHX5l9bLBaLxVLNqRMZRkRoYPOh4zKMg+3bwTE2MozRPZoxukcz8go8TF+ziz+XbOHPxVv5Y/FWQkOEga0bMKpbAsd1TSCxfkxA5xhMvl2ZS2xEGNcf1S7YUzmAdbCDiLf5zEmvTuT15FXcP7oLhEWwYo953jrYFovFYrFY4jJWQb0kiCneLwgPDWF4h0YM79CIR07txsJNe/lz8Vb+XLKFR35ewiM/L6Fb83iTt90tgc5N42pkkWR2XsGBvPY9TrHo+p1ZzN1WwF3Hd6pWEXvrYAcZ3+YzlwxOomWDGFbsLiAhPpKWDarHMofFYrFYLJbgEZexGtoOKte+IkLPxHr0TKzHncd3InXHPv5yItsvjVvBi3+vKFQk2b+Kg3mqSkZOvlFOycozDvP+3IP/z/L5//48R2HFbMvJ9xRrMyFGuGJY6yp9H2VhHexqwB2jOvHzgjSe/WM5r5zfmxW7PQzt2KBGXl1aLBaLxWJxkaxdRGdvqXSDmTaN6nDtke249sh2bM/IYdzSraZIctrBIslWMQV8tn4WekDsRFEFxTjEZguHbOPANj3wWt/H3r127NrPo7OSjcO8P4+CUlRVosNDqRcTTt1oc2vdKIZ60fXMNh91Fe/z9WLCWTZ3OjER1culrV6zOUzxNp955Z9VjOqawK5sZWAbmx5isVgsFsthj7eDo0/+dWVpHBfJ+QMLF0n+sXgLc1ZvIXtXFiKCAN74nggIcvCxs7HQPnDo6xCcf4hAdBi0aR5fSH6wro+SildZJT46vFIqKKtCql9A0jrY1QRv85m7v1kAQP8k62BbLBaLxVIryc+FnAzISXfunVtu5qHbvB0cm/d2dQq+RZLJycmMGHGkq/Z9Mfb7Bsx+dcQ62NUE3+Yz0WHQqWlcsKdksVgsFoulLDbNoenmcTBt2UGnOTezsJNcyGnOhIKcchgWiIyHyFi2NjmShGjbeK4mYR3sasQ5/RL5dNo6Yjz7CK2Gyx0Wi8VisdQmROQE4GUgFHhPVZ+usJH5X9J5+duw3HkcGgmRcT63eIhPhMjYQ7d7/x8RW/hxZByExxxozb00OZkEt960pUqwDnY1Iiw0hG+vH8qkiROCPRWLxWKxWGo1IhIKvA4cB2wEZorIT6q6pEKGjriDadKPwUcdZxzlsOojFWcJHn41rxeRc0RksYh4RKS/W5M6nIkKDyXMRq8tFovFYgk0A4FVqrpGVXOBL4HTKmwlLoHs6ASjUW2da4uDXw42sAg4E7AhV4vFYrFYLDWJFsAGn8cbnW0Wi9/4lSKiqksBq9dssVgsFoulplGc83KIQLOIXAtcC5CQkEBycvIhL8rMzCx2u1tY+zXPvs3BtlgsFovFcjiyEWjp8zgRSCu6k6q+A7wD0L9/fx0xYsQhhowM3aHb3cLar3n2y3SwReRvoGkxT/1HVX8s70C+V4BApogsL21/P2kE7LD2rX1r39q39q19a79W2U9y0dZMoIOItAE2AecDF5b2gtmzZ+8QkXXFPFXTP1drv3L2S/w+lulgq+qx/szIx86BK8BAIyKzVDVgRZfWvrVv7Vv71r61b+1XvX03UdV8EbkR+AMj0/eBqi4u4zWNi9te0z9Xa999+zZFxGKxWCwWy2GJqv4G/BbseVhqH/7K9J0hIhuBIcCvIvKHO9OyWCwWi8VisVhqJv6qiHwPfO/SXNwk0Kko1r61b+1b+9a+tW/tV7396kpN/1ytfZfti+ohijQWi8VisVgsFoulkvjbaMZisVgsFovFYrH4YB1si8VisVgsFovFRayDbbFYLBaLxWKxuIh1sC2uICLdgz0HC4hInQDZjSzPNhfGCcj8azq15fdl/74Wi+VwodY42CISKSIXisj9IvKg9xbseZUXMVzsnbOItBKRgS6PMVxErnD+39jpXuUWb4nIDBH5t4jUc9EuEPi/b1V8/oFERIaKyBJgqfO4l4i84eIQU8u5rVJUwfwRkWgR6eSmTR/bL4vI0EDYdgj072uWiNwgIvXdtu3YD+jfV0RiROT/RORd53EHETnZRftdi9k2wi37jr2hzjHuUu/NRdtXFXkcKiIPuWg/oN+f6oyIfFKebdXVvmMvVESaO+e9ViLSqqbYF5H+IvK9iMwRkQUislBEFrhov0Ext/DyvLbWONjAj8BpQD6wz+fmCiIyTET+EpEVIrJGRFJFZI1b9oE3MHriFziPM4DX3TLuHEzvAe5zNoUDn7plX1WHAxcBLYFZIvK5iBznln0C/Pcl8J//mSKyUkT2iki6iGSISLpb9oEXgeOBnQCqOh840l+jItJURPoB0SLSR0T6OrcRQIy/9n0IyPy9iMgpwDxgrPO4t4j85JZ9YA7wgIisEpHnRMTVjmJV8Ps6H2gOzBSRL0XkeBERF+0H9O8LfAjkYH7DABuBx120/5WI3ONciEeLyKvAU24Zdxym54HhwADn5uZ36BgR+U1EmolZDZkGxLloP9Dfn+pMN98HIhIK9Ksp9kXkJmAr8Bfwq3P7pabYBz7D/P7PAk4BTnbu3WIOsB1YAax0/p/qOPSl/x1UtVbcgEUBtr8MOBFoAjT03ly0P8e5n+uzbb6L9ucBUsT+ggB8TqGYL/omTLRqGXBmDfj7BvrzXwV0CeD8pwdi/sBlwHjMBcd4n9tPbvxdAz1/H1uzgbpV8P1vAFwDjANWBsB+QH5fPvZDgFMd+xuAR4AGNeDvOyvA9usAr2FWbRZhAhUhLtpfiiObG6gbcB6wA1gPDAvQGAH5/lTHm/MdyMAEfdKdWwbmIvKp6m7fZ5xVuOjLBMH+pAD/nd8Cjvd5PAp4ARjsPa6VdKtNrdKniEgPVV0YIPt7VfX3ANkGyHOuTBVMCgfgcdF+rqqqiHjtu5oLKSI9gSuAkzBXqqeo6hwRaY45KX3n5xCB/vsG+vPfqqpLXbRXlA1OioKKSARwM85yvD+o6sfAxyJylqp+66+9UgjI/H3IV9W9VRBUaw90BloDS9wyWgW/L98xRgPfYiJDw4F/gN5+mg/03zdXRKI5+Ptth4lou0UesB+IBqKAVFV18/iwCGgKbHbR5gFEpANwC+bv2gW4RETmqmqWi2ME8vtT7VDVp4CnROQpVb2vzBdUM/s+bAD21mD7D4nIe5igxoHfvKr6fUx06K+q1/nY/VNEnlTV26WMOqTa5GAPBy4XkVTMhyyAqmpPl+yPF5HnMCcy3z/iHJfsv4LpitlERJ4Azgb+zyXbYJY43wbqicg1wJXAey7afw14F7hfVfd7N6pqmog84IL9QP99i/v83Zi3l1kiMgb4gcAcBK4DXgZaYJbH/wRucMk2wC8iciHGcTxw3FDVR12yX9z8/+2SbYBFzvxDHWfjZmCKW8ZF5BngTGA1MAZ4TFX3uGWfAP++RGQ2sAd4H7hXVb3f0ekiMsxf+xT++24C/sDd7+dDmPSfliLyGTAMuNxF+zMxaWoDMKuXb4vI2ap6tj9GReRnzEVBHLBERGZQ+Phwqj/2ffgZuFFV/3ZSN27HvKdupb+sfFTB96faoqr3iUgLIInCx8YJNcE+sAZIFpFfKfzde6GG2L8CE9QI52BQTHEh6OCwS0TuAb50Hp8H7HYCcqVeZNeaTo4iklTcdlVd55L98cWb16PdsO+M0Rk4BuM8jnM74unkbI5y7P+hqn+5aPtWVX2pyLZbVPVll+wH9O/rjBGwz19EPixms6rqlW6NEUhEZCwmCjEbKPBuV9X/umR/mKpOLmubH/ZjgP9gvv9gHLzHfBwBf+1fB3yjqjvcsFeM/Vhgv6oWOI9DgCi3IpAi0lZV3awpqXJEpCFm2VaAaW7+LUSkv6rOKrLtElX1q9hMRI4q7XlVTfHHvs848aqaXmRbB1Vd6ZL9Q74/ItJGVVPdsF+dEZGnMTnoSzh4bFS3Lo6qwH6xxa6q+kgNsb9QVXu4YasE+40wF/DDMceWicCjmPNhK1VdVeJra4uD7UVEmmCW8ABQ1fVBnE65EZFPVPWSsrb5Yf8ZVb2nrG1+2J+jqn2LbJurqn3csO/Y6wUc4TycqKZQyl+bDUp7XlV3+TtGVSAirxSzeS8mN/VHF+wvUtWAScWV8P05ZJsf9s9R1a/L2uaH/TOAf1R1r/O4HjBCVX9wyf404FhVzXQexwJ/qqoryiUi8iTwrDfqLkYN4g5VdWUVR0TaYiLYgzHRpanAbW459SJS3PdkL7BOVfNdsC+YItO2qvqoGBWEpqo6w1/bjv1AH58TgCeBFqp6ghhVlCGq+r5L9ov7/c5WVTeL/aolIrIc6OnWxXpV2/cZJw7juGfWJPtilINeVFXXUvKK2O+jqnMr89paoyIiIqeKyEogFUgB1gJ+50yLyMXO/e3F3fy170OgK5GLUxw40V+jInKBs8zZRkR+8rmNx1EMcAMRuQWT09fEuX0qpjrZX2YDs5z7opXCs12wD4CIJIqREtomIltF5FsRSXTLPuaisjdm7iuBnpiCu6tE5CUX7E8REdejBCIyRETuABoX+W09jCnoc4vichjdzGt8yOtcAziOqmsyaJho9YETk/N/N1VcTvRNaVHV3ZhcWrf4HPgKaIZRm/ga+MJF+29glDHewaTSTMUs6a4QkVGlvbAC9gOmMkSAjs8+fIRZtWnmPF4B3OqvURHpLCJnAXXFKCV5b5fjE+iq5azBpCfUSPsi0l1E5mLqABaLyGwRcSV1qCrsYyLL80RkuQRApg94QUSWichjFZ13bcrBfgwTHflbVfuIyEgOHgz9wVsM6Kak0QFE5D7gfowMWjpmCQIgF3Oy8Nf+9Zhc1rZFvnRxgBvL71MwhTmNAN90gQzAzS/5VcAgVd0HB3JepwKv+mNUVds49t4CflLV35zHJwLH+jXjwnyIcTLOcR5f7GxzS2qtPXC0N1onIm9i8piPA9woDA1UDnwEEIs5Fvn+xtIxefB+4fwdRwMtikT54zHV+W5RXLDCzePrPhHp6635ECMPtb+M11SEUBGJ9EbJxBQMutlISIqkU3wqIje6aH8tcJWqLgZwIrR3Yc4L32F+C/4wSFX7Oo4CqrpbTLGmX1TB8dlLI1X9yjnfoKr5IlJQ1ovKQSeMLFo9CkujZWDUdA4HsjAOXtEiu5triP13gNtVdTyAGAnWdwG3dP0Dbf8El+wUi6qOFJGmwLnAOyISD4xR1TJlQGuTg52nqjtFJEREQlR1vOOE+YWqvu3cu5IvVIz9QFcKf46J5D8F3OuzPcON9AcnB3odB/VnA4Xgk/vr/N9NSYgBWrhS+HcRecxF+41V1TcP+yMRudVF+y0wF4PeKGodoLmqFoiIG0uLbkbTDuDkmKaIyEdu5tP7kIZZoTiVwisSGcBtLo4zS0RewEQ1FbgJF1dAMNHGr0UkzXncDFNs4xafAuPE1Aoopgj6YxftjxeRezFRZcXM/VdxUrRcOBZ19jrXjr0lztLuGnFHOSZQKkMBPT77sE9Mjrp3/oNxQdnBST/7UUSGqKprjadqGD85t5pqv47X+QVQ1WRxV2Us0PYDnuesqluAV5yV+buBBymHzn6tycEWkb+B04GnMVXe2zBOk1s5im0wJ83WFK7kdavK25v32IHCOeRuVQp7x3A1R11EJqnqcBHJoPAX3RvhjPfHvs84t2M0mb93Np0OfKRFCiv9sP8HpnjhU8z7uBg4UlWPd8n+35hlWu+y+AXAFap6jEv2r8KoniRjPvsjMTmXXwAPq+pdLowxHOigqh86DkasW0VMjr27MalSvt9PV4qIRSRcVfPcsFWC/ToY1R/vqsefwBPeFReXxgjHRAwFWOb2+3Gi/d4i3z9V9Q8XbZf2PVFVbeun/THALgpX+jcCLsHo5A7w0/5Fjs2+mAuPs4EHXMzhL64WJMOtv7GYHPVXge6YpfrGwNmq6tcqo4jcrarPimm8c4gz4WKU1RIgROR7TDMV7wrTxRhputNriP2FmO+eYM4dbYDlquqWQk4XzG//bEza65fAt6q6rczX1iIHuw5myTQEU4xSF/hMVV3JAxaR+RgJooX4RC7UvSrvqzE6pYmYpjCDgakuOhinYMTRm2MuPpKApW59CasC5yThreSdUNnCgxJsN8DkzHq7y00AHnEriiSmKOo1TKRfMak1t7gZtRWRZsBAzOczQ1XTynhJRWw/hOks10lVO4rRX/5aVV2R4BKRPzHydndiJN0uA7a7WOTVARMl7EphB94vx64C47+qqn7VDIjRkW5N4Qv8//k5tVqBk9Lybw4eHyZh8qazgRg3CqsksCpDazFdOnc79uthUu+2Adeoqt+rISISxsELtOVuOO8icoqq/iwilxX3vBod/VqNc/FY3MWFK8eWKrBfH9MQ6MC5FROU2V0T7BczXl/gX6r6L5fsTcMEqr6u6Dm11jjYAGKk3Dqo0fqMAUJVNcMl29NVdZAbtkqwvxCjsTpNVXs7B/NHVNWVZWDnAuFoiuSoq+q1ftoNqAqHOPJSJY3j8jJqjUYCqJUqIvOAPpiOl32cbQtcyMH22p+tqv18bYpIiqqWKmNWAfuTMBdQL2JyRa/AHP/cLEQsbXy/FFHEtNJuh7n49pXqciVCKCJnAs9gCogF91egwoHrOXgBmwy8HchVBTcI9PHNZ5y3gO+9qwZiCjNPwBSGvlzZc4/zdy0RdU+H/7DFSb3xEoWps2mgqg/WBPu1EX+Pt25Ra3KwxTRPuRajnNAOk5P6Fibi4AYvO1G8PwlMo5lsVc0WEcQUGy0TkU4u2YYA5ahj8ky9yzNFUcDfq+zPMUU03nG8iEv2jTGTW1VclMCvFYSqWkJ1/pbnAYspLLbvVopRQDuBYjrlAWwWkZMwudNuqqxEq+o4ERFn1eBhEZmIu0ofgaQ/0FUDFxF5FtMdMlDdRt/EKCG84Ty+xNl2tRvGxTQzeZhDLzD9PT74Ht9aUTjCvB6zHO0Gle4WVwbewsMmmKKyf5zHIzEXOX452HKwUU6xuJlCWV0pZpX8JeeC3hUHOFD2ReQlVb21pL+hv3+7QNv3GcdXzS0Ek8a13Q3bjv1Kr37WGgcb0xVsIDAdQFVXOvnGbtEDc1I4msIOjFuNZjaK0c79AfhLRHZjnAy32CNGO3cC8JmIbMMFFQV1VDgChaqeXBXjYFITvEQBZ+GOyoTXYZlV6l7+czomfSNQWqnFdQJ910X7j4tIXeAOTK5oPO4WIWaLac6yUox6xSaM01FTCGgrbWBrAJ1rMPUwvXwe/+OsqrnF+5jvS6FGSP6iVacyVOlucaWhqlcAiMgvmAu0zc7jZrgjM/i8CzZqNFJYgz0EczHsmupYAO17c6ID9TcMtH0vvp9FPvAr8K2L9j/k4OrnSJzVz/K8sNakiHhTOMRpbuLkm81xcQl7GUbsPdcNe2WMdRQmh3ysW+MFOkfdGSNgRZoiMq5oQWBx29zE5RSFQDc6+R04x41c01LGCEgnUMeJuFlVX3TDXgljDMBc7NTDSLfFA8+p6rRAjVlk/LnqR9MlZ4WlNxCQVtoi8jLGgf+hiH1XUghEZA7m+7naedwW0/nSrUZCgU7hO6RpiojMUtX+Ltkv2i1uEiZvtcxuceW0X6hRlHOxuUAD2DzqcEEKd3nOx0hGPq+qy2uI/Vu0SMfl4rZVV/uBxid98UDHSBGZqKpHlPnaWuRgPwvsAS7FqH38G1iiqv9xyf4Y4CYtR+VoJWwH9GDnODB/qKqbEZeiYwSkSFNEojANNcYDIzh45RgP/K6qXfyx7zOOb65lCKbJzyuq6kqaTnE5YW7miYnIt0AvIFBaqQFFRMar6sgA2Q4FnlYXlFTKOV4IRmEl3Wfb5ar6kR82i73QU/eKrD8sZrOq6pUu2T8GEwlag/kNJ2FUdMaX+sLy238a05joOwKQwicBVhkKNCLyGib48QVm/ucDq9T/wtuvVPVcOajkcOAp3NHJtwSYEs5NfgUEqth+f+A/HJoe5lZwdTKmg/Q3mBSrTZjzSZm+QW1ysEMwzUgORNiA99zKWRSRZEx3vJkEJoL0GXCfBqi1u4j8BFyiPt3mXLYfkCJNMR0cb8Won2zioIOdDryrqq/5Y99nHG+ltmCiBKnAo6o6yU+73kYn52JUMrzEY5ZsB/pj32ecgFTxS9XJMD6BWVUZAxyQtnPRQfoHOCZQOcwi8jlG/aQAk6ZQF3hBVZ9zcYyAFXFXBU4usa/MoGvpTEWifF7U3wt8H/uBVhnqiElTa01hJ8GtFERvwaM36jZBVb8vbf9y2mymqpud7+YhaGC07asVTmqb73cjBXPucOVcGyj7InIBcCFm1WSiz1NxQIG/AblA2/cZZzmmqVRRhTdXvnv+rH7WGgc70FRBBOkfjIM6g8IOhlsO/FeYqPJfRey7VWQ3U1UHiFGbGKSqOSIyT1V7u2T/JlX1q2tjGfajVDW7yLZIf50AEemFWdp/lMJFKRnAeA2QVFFNowocpP9iInhfU/j771YKxDznwvIizOrHPcBsF6MoB4q4VbWdmMKbt9xKkXJWiq7iUB1yvyLYYlUsyoWTj/4WRXLI1QV5vqpCTLe7gZgL8ZlqmnPUepzVw0UcbMx0CdBLVUv97gfbvnNR1IZimhxhVtT9qkEKtH2fcSap6nA3bFVy/BIlWGuNgy0iJ2OuLrzLBK5G2Mox/lRVrXQ3wypw4AOqUypGTP4KTLT5aEy1fbiqjnbDvjNGdw6t5HVFB7gKUjgC3egkoDrPYjq/LfZGTMUUzHZT1elu2C/H+Jf5812tghSIxZgLqc+B11Q1RUTma+HCPn/sz8Mp4taDMokHcgJdsP81sAwTcXoUU6exVFVv8dNucZ+7F9c+f2eskzj0AuFRl2wHNMJcXI63m0jgZRivxgQQ/nFsH4WJsn7ghv3qTHGBJJeDSwG1X9Nx0s8u4ND0yCq5eC/NT6hNKiIvAWcCCwO1DFwGUWXvUjJlOdL+OvBlOSci8q2qnuWH/TOc/z7sRCPrAmMra68oYiQSR2AcyN8wrbsnAX452E7UpQUQLSJ9KJzjHeOP7SK0FpFANjqpdKVzOXkTI3/kJauYbYHkFvxo3a2OmkJJiMh9qvpUZe0Db2OKj+YDE5zoTXqpr6gYOaqaK07bbzFF3G4e59qr6jkicpqqfuykvPjdybGsz90txKh8xGC+++9huq7NcHGIrzER5vdwUaXEh59F5N+YTrW+ToJbOv+BlmG8C+ijTtG8GO3mKUCtd7CB/SIy3JtOKEYycn9Nse8ET14FugARmFqGfS5efAXUPuZc1xkjA+qr8Bb01bHa5GBvABYFybkGd092xeGXA18O/Hb0nGKyBEz+MhhVArdyys/GFPHNVdUrRCQBc7Lzl+OByzHFmS/4bM8A7nfBvpdAO8CB1nkW39+WqnocJ6+qcPOzKo5zMCsAlUJVXwFe8dm0TkwzJ7dIEZH7MReCx2GKuH920b53dWWPs1K0BROtdQWnluJDzO/qXcyF2b2q+qdLQwxV1Z5iGhU94qQEuXmCzVfVN120VxTvCqNvIa5rOv8EXoZxI+Zv6yUDc04+HLge+NjJlQazent5DbL/Gqbo9WuMBOClQPsaZL+XWyt5blObHOy7gd9EJIXCEYAXSn5JjSLQDrxf9kXkJowzt5XCV5FuVZHvd5y6fBGJx7QQ9vvk40T2PxaRs1TVTe3MogTaAQ60zvMaEbkZE7UG4+CtcdF+WQT6+18pB14KNzkoDreOP/dicqQXAv/CrOK4cYHp5R0xMpsPAD8BscD/uWj/SlV9WUSOx3wvr8A43G452N76iSwRaQ7sxL0mMBDgCLMGXud/lhglrB9wcRnd5/u/CZguIj9ifqun4e4KQrVFVecBvZzzEuqjHlQT7Ds2V4lIqKoWAB+KyJQaZH+aiHRV1SUu2qwIJZ47apOD/QSQiYn0RgRh/EBH2Ko7t2Aanbimq12EWWIa8byLKQTKxIUDuIhcrKqfYlI4DnGWXLxAC7QDfCtmifxmTC3CSA5GxdzgOkyE9gHMCXQcpuiuqgj076uyDrxrDSVKQ1U9mO++m819fBnnFNxOwLlwFRE3nT7v32808KGqzhdvvos7/OwcH54D5mD+nm5+VgGNMItRhbkdo3l9rVNT0UlVf3HDPiblLQujsuXFjWV07/d/tXPz8qOfdmsMIvIk8Kyq7nEe1wfuUNUHaoJ9zEVpBDBPjNzxZsDNTr2Btj8cuEyMElgOBE4iUoqRYAVK1POuTUWOron+lzKGr0xWNBDmU/TVXVUXBXDsueqSbmQg7Dt518e5VRlcxlitgXhVXeCCrX+p6ttOjvchqOoj/o7hjFNU6qcu5qDpd6MTqWKd52AgIq+p6o0BtB/Q35e/yKE6w2CakMwCHvf3wra4Qh03C++cYscWmKhyL0weZrIb9p2T3mBVneI8jgSiNECSpIHAiS7PBi5V1e7O+WWqLWSr/hR37Cit8K0a2k/CrDxHYLqh1gXeUD+bG1Wx/UNQ92T6Ki3BWpsc7KeBf1zM6StqP6AyWeUY328H3jlot9JiOkCJyCh/PjsReR+jcfsrAUjREZEji9uuLnWKrOlIgHSeReRuVX1WRF6lmCivuifzmAA8CTRX1RNFpCswRFXfd8N+Oca/X1Wf9OP1AZG587H/LOYA/7mz6XznPh0YrqqnVNJuZ8ycn6VwdDYeuEtVu1VuxoeME4JRWVmjqnucIrgW3otkEemmqov9sO9XEXgpdo9W1X+kBLlBf1MsfMaZpar9fZ0pcUGFpgp/v40xaZpFv/+u6XhXV0RkATBAHUlX5zw7y8XfTsDsO8GZj1X1Yn9tVbV9EYlX1XQp3CTuAG6lb4kfEqy1KUXkBuBuEcnBFOy4LdN3A45MFsbwShHxe4lfDm3gceApfObvgnN9CvA85iqyjYj0xsgonerY9/fCZL1ziyAwKTq+J/8ozN9iNkYS0G+cE8Q1HCrD5a8O8EuqequI/EzxJzhXdM6BucCPYuTW3NR59ua1zfLTTll8hMnJ9XZeXYFpOuOXg12SY+HF62D441w7fIKRuTseH5k7P236MkxVh/k8Xigik1V1mIj4c/LqBJyMWVnxddIzML8HV3BSXOb4PN6JyZP28gn+KdL8KSJnAd+5fJF5FEZ6rrgLGDeVCnIdx0kBRKQdPoEKP/B+BwP9+/0M83s9GRPtuwzYHuAxqwufAuOcVRoFrsQPxaOqtK+qBSLSWEQiVDXXDZtVaP9zzPdtNgebxB0YGvcKhMNFJBw4HSPBmici5TrG1BoHW1VLzYX0N0JCgGSyypq3izyMcUqTnXHnOakWruBNpRCROPNQM92y7dgvdIITkZaYqJtb/IjpNvU37spwfeLcP++izeJogHFYfC843HAAzgN+Aeqpaom5Zi7QSFW/EpH7AFQ1X0Tc+DsE2rHwEhCZOx9iRWSQOrrjIjIQU4gIpvNopVDVHzEXZkNUdaoL86ws/uZj347J68wXkWxcCrCo6kPOfVkyj5epfz0FHsLImrYU09V3GC4oRajqz859WTKtJTbLKCcNVfV9EblFjeRsihjBgVqPs0KwADgW8717TFVd++0H2j5GXnSymG7PvsEZt+qPAmJfVU927gNdIFxpCdZa42CXA38jJCkSAJmskpY3vLi1zIGRmdorrtYVHUSMtNcnGEcPEdmBySf056KmNDYC3V20F6Oq97hoDzCd2JxlsmsCtQznjBMoned+zgHlShH5H0UcIRe/n/uctAFvBG8wJsfYL/x0eipCQGXugKuBD8Q0+BHMAf5qEamDH/KCPpwhplnOfoyj1wu4VU0BcFXgV7CiCgIsZeGvTvtfIjIH021XgFtUdYdbkysHw8repVS83//NYhr+pGGkTw8LVHUsJfR9cCN9KcD205xbCIEp2g6ofRG5yjeV0DnfPqAu1U+pHxKsh5OD7a9neYhMlqq6UaVe3PKGFzeXORaJyIVAqJM/fjOmEYBbvAPcrqrjAURkBKaKf6gbxoss9XvzOee7YdvhFxEZraq/uWgTCPwyXDmprM7zW5gDe1vMdzVQy3C3Y+Th2onIZKAxRvvcL0pKzfHiYopOcTJ3D7pkG1WdCfQQo4Ur6igKOHzlwhCjVPVuETkDc/F6DjAeszxdG/A3wFIWlZV5LDqnzc59KxFppapzir6mmvK48928A9NUJB6jbGQJfA8Lf5vcueKIBss+cIyTHnYV0AjT3Mjv1RNxQYL1cHKw/U3nuMlZIj/gVDvLYX4tm1fB8oaXmzD5rTnAF5jl68dctF/H61wDqGqyE11zC9+l/nzgC1Wd7KL9W4D7A5jDv5bALsOVRWUvMH9W1VdE5E1Vvd7VGfmgqnNE5ChMTrAAy9Wd1vKBTs0BQFW9mtQHZO7cRIwyxlk4NQLelSh1qRU4pgsaGBm9L1R1V6BWu0og0Bee1VXm8b9l2KwpRYK71ai27MVIhHo7Dlqqfw+L8cXZcKtANdD2VfVCETkPE/zMAi5wyTfwO9p+ODnY/nIZh+odXl7MtkrjRMA6ULgK2xWVDFXNwjjY/3GWUOqoanYZL6sIa0Tk/ziYc3wxBzs6usEhOcBuXOB4qYJc+EAvw5VFZQ/C32Aqpzu6OJdDEJFzgLGqulhEHgD6isjj/kbwnHzQgCOB71T4I8Z5mY07xW9F+VlElmFSRP7tFP26dnwQ461fBLRV1UdFpBXQVFVnAKjqYLfGKoFq2ahIVd3s9ukP/l6AvMqhKwTFbbNUP+70+X8U5kLeTbndgNp3VuRvAb7FtGO/RIwaT5Y/dt2IvB9ODnalIiQicgFwIUZ54yefp+IoXAXvFyJyNeZLkgjMw+TiTcU9lYxDtBxFpFxajuXkSuARTFGdYJZoSs0LriABvcApZqkWjEOzTl3Q9q6CZbKyqOwJNESMRnjH4pbMXIzA/5+qfi0iwzFKHM9jukYOcsO4cxB+CuhK4QtYt6LNge5UmKiqJ7hk6xBU9V4ReQZId1Ka9mG68bnFG5gOr0djVFYyMCfEAS6OEUz8jpiJyFAOVTH6nwt2y6OTX6njqIgMwaQBNi5yfIjHaJ1bAr964pd9VZ1dZNNkNwtUA20fUwt3gzqdkjHphjMxkpF+I35IsNZ4B7sEx+gA3giYHxGSKZi8uEYUXs7LAPxudOLDLZiTzTRVHSlGn9ZNp6yrGs3IizBtlu/BONquONhqusDdDAcO6HXUhZauVXWBg3EA+mKWmQB6YHK8G4rIdf5GIiX4OrFfV/J152PkicIIbOTdqxhyEvCmqv4oIg+7aP9DjFLDi5gl7Ctw98QX6E6FU0Skh6ouLHvXStMF09HU97zgt4PnMEhV+4rIXDDHCzHd3aoKv1JQRGQ1MA2jNDRBi7RlVj+bIInIJ0A7THDF+1tQXPj8nQumfiIiqsVLGKrqR5U0H4GpNyh6fEjHhRqKmoKU0oQOuKQ62y8itBCCWbFs6o/NqrQPDPT6Gs73+79FfAV/qbQEa413sDno9EYB/TFOkQA9MZrVw/0xrqYb0DrA9SYGRchW1WwRQUQiVXWZiHRy0X6ltRzLQwAj5FV1gbMWuMqrNCCm0cldmDz17/A/EhlQnVgR6YiJ+Cao6QTXEzhVVR+Hyus8q2lK9IyILFDV392abzFsEpG3MVJUzzg5xyEu2o/2Rjic3/TDIjIR43S7wWwR+RPTqfA+MXKVHpdsgzmOXS4BagccSAfPIc+58PaqxDTGxc+nClJQumJWU44AnncCIPNV9Qw/7XrpjwmCBCqVJSA6+XpQku8j53flbSpUtJ10rUV8mtBhfkOJmOLwY8CVHhYBtU9hoYV8TGrnVX7arDL7TuCwO0VWJ4GVLg1RaQnWGu9ge3PYRORL4FpvhMf5wO8s7bUVQYxs2KuYKE8EZvlrn4tFcBtFpB7wA/CXiOzG5Oy6RaW1HMtJQCLk3gscx26aN2/cuYpPxLwnN+isPjJeqrpERPqo6hqXApGB1ol9F3NB8DaAqi5wDgSPu2R/jphunYHqtHgucALwvJpOf80o3FzIX7KdE/9KEbkR2IRJ5fAbx7l7EKN8skZVs8RIDrqZInWii7aKI9AO3ivA90ATEXkCE918wEX7gU5BKcAUPxc442wFtrlkG2ARJqq3uawdK0mgdPK9PCUigUxBrM4EpAldVdnXAAstBNq+k8I4AuNg/4Y5Vk7CxeCAc19hCdYa72D70Nl3+VRVF4npVugWr2GWy7/GnIwuBdr7a1RE2qhqqk8k5GExVbd1KUH3sjKoH1qO5SSgEXKMFJmv5F8B5m/h1gl0uYi8CXzpPD4PWOFEUt1Qswi0TmyMqs4ocjHgZqHKRwSm02K8E+mKwmmC5Cwp5uBCkxgR+URVL8EUCcZg0pgewzgal/lrH0wYWUR+UNV+PtuKdiqsFD6fT0aZO/tHQB08Vf1MRGZjom4CnK6qbna6DHQKSjomfewF4F3n7+s3clBGMg5YIiIz8CliVZdkJLUMnXwXCGgKYjUnIE3oAm1fRM4s7Xl/VzcCbd+HszG6/XNV9QoRSQDeK+M1FaHSEqy1ycFeKiLvYXRbFaNi4eYBHFVdJSKhqloAfCgibuhIf4Np5jFOVb1LPq5FNkXkYlX9VErWdHSrSC3QEfIw9dGQdg44bp5AL8c0D7oV4wBMwqyA5OHITvlJcTqxt7lg18sOMe2VvUvwZ+OusxSoToufi8gpwA7M98dtnW1vo5yLMFH+LMzfwG2micgANXrVblJV7YAbEUAHT0ReBsao6utu2CuGgKagABdg0nT+jWnwMwWTiz3OT7tVIiNZVgqZCwQ6wFKdSZEANKGrAvve7shNMMGrf5zHIzHBDn8d4EDb97JfVT0iki8i8ZiVJdekUtUPCdba5GBfAVyPKRYE82G86aL9LMehmyciz2KcFzd0ngOt0uCdY0Cl4aogQr5dRE5V1Z8AROQ0jFPmCqq6H5PjXZwurRtt36drEZ1Yl7kB0+yns4hswuS5udk5MlCdFk927M1T1UBIehXXKEd97t06EI8ErhORtZgcV1dypLXq2gE/HGD7c4AHHEfve4yz7WYb+4CmoOjBlvKdMUvQt2KKlqP9tJsCICLPaJFOsmJUXdwKtgQ6hSzQAZbqzCFN6HA3ghoQ+95VDRH5BbMCsdl53Azw+0I40PZ9mOWk176LOcZnAjPcMi5+SLBK4FLuqh7HAe6EOXG61ajCazsJc2UUjok81gXeUNVVftrthLnqvxXjDBRCXZB3cyI7N6vqi/7aKmOckzhUJcOVRhhOdPYzoDnGedmAacXu1+fvYz+gMm4ishLj9I4BvlOjuuI6Ypr7hOjBCnO37PbFRN67Y9IJGgNnq6orhaYi8hrwcQAiwF77AW2U4xwfDsFb+OWC/QMrXKVt83OMBA6mXM1QVTdzjL1jNMDo4J4PtFLVDi7a7szBFJRxbqagiMi3mO6xqzCrWxMwF82uaIWLyJyiF5hiCovdKmKdqaoDxOgD93G2zVPV3m7YL2Y8AULVkTgVkctU/7+9Mw+zq6rS9/sloEEQBKVtFQIYMYhDUEBQEQeUbhVQVEBApBGlBQTUnzQqtiC0rSioCN2igkwCyiAiYMsMYZ4kkMjQzkgL4sAoyuT3+2Odmzp169aQOnvfKft9njyVe1N3nZOqe89Ze+1vfcvH5zhWP1G9v1dLdV3sRnxJi2y/pPZ4BnBL/bl+jt92rDWBFRP/fG62PU9hwbon8O+EU9SkBaGhqWArRnMfz8g28+rVhzrVoJbWjfKvJLTP8xRdGppcoBw2TVsRFmVZkHQUoXF9A7G6fjcJV5G2fwFsLGkFYmGYWpOa1cbN9tqSXkkkFvtLuhX4ru1Go6jHk/5oZNJfEgmQ801abPFGogL8GxJWgFvkTK6r+L9ReHivbfvYSqKwQtO4Cg/WpwHPqnSArffkisRiMwmStiX0spdWxzhC0r62T091jIoXAOsQTUK3TvytU6cLEpQvAD+p5IHJkLQ7seX/fEn1pODphINSKnJLyEbhqNzVe0D2Ie7PQ4ekS4GtiHxqAbHbepntyUZt90V84FJJ5xETnk3coy6Z+CV9FZ9K8rQmVU4r6QUJNd7TtmAdmgq2ooFmhyphbWnOTqk3Hk0z7kImaChIlQBM4TzGVDiW8PWfI6ru32O0TVOjSXm1+LfYflnt6wpEpXbzRPE7NhUkrJDfaHt9SQttv7R67nLbr00Rv+1YzyK07zvabjSMoZIXQSS+GxJNGBD6t/m2P9Akfu04yxISrE2rpy4FvpEqyc5dAc5N9XvYAJhr+4WSngucZrvRuOhqe/IjRDL9f4xc7B8kmu2ObBK/dpybgTe3qtbVAuFC2/MSxT8EeCfwC+IadKbt+1PEruLvTDQmZ5GgdHj/XwYc1fT9r+jLWJnYPftE7Z8esv3nJrHbjvN8QkL2auA+Yjdtx259vuqV82Gj9X9TDItb3fYBiXcfssavjrE1I+/t+bbPTBU7d3xJ3yZsmX/KSN+FPYVBMFOMfyzwPMKCdR7hIHfpVHLLoalgA8u2kmsA2/9bXRSbskX1dc/qa2sU+I5Ew1S3aFpNbTlw1BNSk2hSJFHZh9CqP5dwUEipG/1L7e+ziN9LyibWbDZuEG4QwNbE6n0OkQS8smncloRI4cH8ilZlXzGkZbrDZTrxdUIe9d/V452q55Ik8IOSSE/A1sDLCa0xtn+n8MJuhO3DgcMl7WX7iKbxJmBGmyTkTyTyIa+qPQ8Tto7J+ibqVLt7x9ckKIdISilByfL+90hfxvYACvu1WcAKklawfWeT+DVWtv2muoRM0Vzcrc/dcFTyOrOMQle8LSMuS4MUnyrh7Zj0SrradqM5IJnjb2x73QavH5fq2jVtC9ZhSrBvUPj01hPg9hGdS4xHzPNf01aN+oSkKxmdsOak0QXKlV/4eCTQyJ2jaDT4EpFkmISNHrZHNR9KOpSRam0KPsJoG7c3EFaMqbiZ8Dg/yPbVCeO2mM3oaXWPMUWvzimyYVs18+Kq6lkIHrNtVc4JVSKTDNtHKNMo7Yof17ZxIarBSQYLVT+Xd9g+OEW8ScgiQSHz+79Kdr9M7FTcC6xBFBCSjHsGvlVd41tzIt5D9BKldLuYiJRTTfuNg4jBI1fYvr7aLUg15KQb8Sdj1uTf0tP4V0ta123TVVNQXbumbcE6TAn27kSVeW/iwzyfkWpDCpaXtIntKwCqm13Sm+gk5L5ANdLI1W6eZyi6hmdV1ZlcPI2EVjzEguBE4sbW2vn4FrH1lILnewI9lqQjbO/VIP6JwHWSziT+L1uTzmgf4ElJcyotfGvLOakedcA5VTGJ8hmKyWvvJ94/SVDmSYu291X41m5CXGu+mXibOJeNIdBRgnJwSgkK+d///wFsTMhyXq5wYNo+Yfx3A6crfKo3IYoHSeR7U+TKLh6rq9g+jdpuoe1fErsoAxF/KqfQ5/GPJ5Lse8gw5ZYG166h0WDnRtL6wLcJHbOJbb33J9Qwt/y1x/v3I21/OMWxxonfSCMn6WmEv/Bs2x9UuHLMtX1OovOra+FnEls2ByXUoN5B2FgtpOaf20WNYiONfRXjFcQoZwid203Nz2xx7M2IRtBfEhewNYBdbCdtVhlkFB61mxM/n/NsX5Aw9m1knLQoaS3gbo+elPps279OFP9WQh+dvIm12sb9NPD1XBKU3O9/STfY3qCqir/c4et7ne3GMrLaMV5I7KL9lhj089eJX7FEsX8BXANcTlx7klcT+w1JRzBxf9be/Rx/Cc6j8b0pZ3xJPwc+RqZ7d3XtmksYaCzRtWvgK9iSTrW9rcZpRky1irF9IzCv0tKqvTqbQGLxK0k/JqovF7ffSHMm161DNHz9sYQkp6WluotYdSdJsBnRwkN0p//elQVUIv7gymN7EJE0m/AFP7P+XCoNp+2LWosm4gJzu+1HJ3nZUoOkjxJNjcmS6jZyj9I+jbyTUrONeu+GBKUL7//7FY3hlwMnSbqXBJNYO9wXVyEKFNdKStmkvy6wEbHAP1RhmXizRyYUDyMpfdx7EX+q5N49bxr/zsz37mlfuwY+wWZksMwWE35XIhxji8c7jyYJ9lzC+WFP4JhKZvHdliSlCzR9k8+xvZ2k7SEGt1SVpWYnFU1LMHZU9IrVDSJVp/0BikmgFzF6kl0qq5/cnMvIjXQ5osH0DhJpOCXtCZzkyl9U0sqSdrWdUoY1yKwInCfpz8B3gdNt/z5h/KyTFsk/KTX3VmkWCYrGH/c8p7r+pLo+vB34G9ELsiOxU5qiv6cr90ViQfZ49fXvwO8JLfnQ0rCg1vP4dRQuTmvbvrDavVrGI1a4O/V5/NsVQ5POJsO92w0sWAc+wbZ9t2KQyjG239TDU2mUTFbbdacSWs6VgcMJK6hGNm4tJpOg0Fwj91j1wWk1ec2h9mZvQKcR0S1STuLbhWiOWpaa1Q/pxrlORtP3z0tHBQu5yL82OqPRfNA1j2Hb91Va45Jgs9jN5bMKP9btiPHGdyW8Jh2YKM54ZJ2UysgCUERTU9IFINGU/K9K76O+5QT/luz6YPsvkv6RcBb6MyExmlIj1SRxR22Ta8SlJDUPElv0XybsIxuf+6Ag6RI6754ncejqQvwPArsRuxtzgNWIoXebVcdZ1M/xiYLSo4zuKUj22VTNgpXYqV8W+A4wqQXrwCfYsHiQyiOSVsrcWDfhaTQNoBjksR2xJXE9YcuTitwSlAOIkdSrSzqJePP9S8OYADvZvkLSLCeamjYO89qT1C5zeMpgjsEwqbb3AWZIUut9Uy1qU1Y4h4V7gXuILvNkNo+2L1PeSYsfIqQJrZ6Gu0hQuWrRhQVgLgnKAtuH1xvcc6DwOP4McDEsHvRzkO1vJ4q/FXAY+VxKtieaJ/cAPiDpKkKLfVGi+P3Mx2t/n0U0IKaUL+aOvyexsLsWwPbPqoXYQMR3NZJ9PCR90vbnGxxi2hasQ5FgV/wNWCjpAkYPUulKIwANK5CSfkU4BJwK7Gv7LxO/YonJKkGxfYGknxCd8AL2SdRwdDiwPjHVLFujBbHFnNzqR9LZTNyoslX19biGx6lP9ZpB/Kz+0CRmG+cRuytHEf+fDxELqgK0JvJtRzTfnk5U/FNOKsw6adGTTEpN0GPSfrzUC8BcEpRdiGvQ18h7/dmXaG78E4DCa/cqorE+BQeT0aXE9lnAWZX2+i2E1OXfiOriUFP1Z9W5UtJlgxIfeLSShAEgaRnSfp5yx5+MbYhBTtNl2hasw5Rgn1v9yUIXJBY3Abvavq863srAYU40jagLEpTXENWecyW9F/iUpMMTdPI+rpiktJqkr7X/Y8IF1CbAztVCJ6XVz6GNz2xq1FfUTxCfhTMSxt+P2ObbnfjZnE9Cn/MhYA3gI7YXZIq/P+HFPGrSIpHMJ8P2w+P8U6Meky4sAHNJUG6T9GtgVY0eZZ7aCuwuRveZPES4faTicdt/kjRD0gzblyisDZMg6QxgPeDnwBWEDeC1qeL3M7U+IYj39vpEQ/JAxCfkbJ8CllM4Ie1BWn/03PEno2kv2LQtWIfKpq/SAM92baJjwth3EhW7jhKLBPFvcptNXqfnGh6jXYLyPdtJkrDq5jOP8I0+gai8vNP26xrGfRbwJuAQYgt1FKmqahr8Ud3bOPxSJ3wu4/HPsN1Nb9a+o1MjjO1fJYq9sC6zUEwdvblbsqam16JKx9jiCcLy6oxcsq+WBMV2YxlKpY0+DxjTUJrq+iDpBOClwFnEQuHtwHXA/1bH+XLD+BcC7wC+ADyTkIlsaPvVE71uCeJvCPxkkiLUUFIVZVqLuyeIMfQHpdod7kL8GcCu1CxGgaNT5Ti540/h+CkscKdlwTo0CbZiEtahwFNsryVpPeJNmKTLvkretyRGXb+CsJ9LJrFQ+J++vlbBXgW4LNUNtE2C8sPUEpTWm1jSZ4D/s31Mijd2Lf482+NOTkugs8qKwuLr84Sd1eImI9tJmjQ7/axT/vyncPyki8FBo94IY/uFkp5L2PZN2ggzxfhfIhav9UmLC23/W4r4Uzh+195LqUh9zpkLOAdM9O+OJtom8ZcnZJRixKXkJCdqRpS0LLG7tWn11GXAUbYfTxG/0B2qvGM1V25RgxZ/nGM2LQ60LFjvWtLXDpNE5EBCSH8pgO0FiuEJScgtsSAaUK6SdDqxWt0W+Fyi2JBZggI8JOmTwHuBTRVNcMtO8popM1FyXdFUZ5WbY4lG0K8Qjge7kMBfVNJbgLcCz2uT0KxI2kaYyRiOlfr0mXYjzFRw/kmLk9G0x+QCYBtX0xWr6893bf9TgnPLLkGpF3CA5AWcyRJoNZz06kwuJTW+TlzvW65CO1XPfSDhMfoSSbMI2cMmxHXwCmLoUZLdmS7Ev5TYnVmGKML9QdJltj820ev6Jf4UaLqLO20L1mFKsJ+w/YBGWy+nlnFkc/mwfYKkG4A3EjezdyZuuFurlVxXx7tPUsqK43bADkQSf49i8MmXEsafjNxm+E1ZzjGsQtW28oGSLieS7ib8jhhIsBVhadjiIeCjDWMXps60G2GmQlUs+JErb1dJy0la04kmLU6Bpj0mq7o2ury6/qR0Ksjdg3AgYws4ayaMPxmNdkKU2aWEkJvMqz2+uNqVXRo4gbjeHlE93h44kSj6DEL8lWw/WL1HjrV9QFu/QV/G1xQnXdr+zybHcQML1mFKsBdJ2gGYWW3H7010YSdB+V0+qBLqXCNmZ0hauU2Ckuz3b/sewgO19fhO4sJAdbyrbb+q02tTnULG2Cn4W6VF+5mkDwP/RwIbt6qyf7Okk5x2suWS0u8LnGwoVvXnTLcRZopkmbTYVvkdQ0v76+Y2nk+qNlm06nlI9pltKqGYAp0KOINEbpeSJyXNcbjRIOn5xHt0aWBu2+LiksSLi9zxl5H0HKJguH/CuLnjd3vS5RJbsA5Tgr0X8ct7lNApnkdYE6Uit8QiN7klKJORY7hBnX6/830EeBqx8DuY2KnYuWlQSafa3ha4qVU9reNELgeStiAqqH8f51v2S3GcQaSqXL+D+Bk8SFhifmaqjTBTJNekxWQylknYH7hCI/ZimxKuNEnILUEhcwGnC+R2KdmXSPx+SVyL1yBkcEsDN0na2PY1AJI2ovmOTzfjH0TkS1fYvr5aHP2s3+O7S5Mu1cCCdWiaHFtIWpG457WP1m4ad4xQftAauySty4gE5aLEEpTJjp21SUrSp5puBQ0ikp7jmGaa1QVF0neAVxHb7sfavi1F3GFB0n8BxznxqO5a/AuAIzx60uLetjfLcbwcKByBWj75V7vmky/pxbZ/2iD2AtvrtT2X7Pos6WnEIqHuhHBwKh3sFI4/rf9LbYdiPTq4lNj+UMJzfCqxuBRwu+0Uk3z7Hkm3Ef/vO6unZhNDfP5OAivH3PEHHYVj036MNRBINenyC8RifcESv3ZYEmyFTdC3GanIPAC832NN2qcbP6vLx7Az3QR7qjqrfkeZx912g2rxuj1RmTLRuHlK6sXsICLpVuCFQGtUN5B0B2EOcBIxiQ+qSYutLfkGccd4y9fp1uer6QJc0o3A1m0SlDNzLOoVDdzL234wdewq/gzC4vHB2nP/4mkMo1J+d5J3ThI/ybjqfma84kaNB+v9T/0SP/e9tVv3bknnE/bJHycGoO0M/MF2sl1VTdOCdZgkIscAe9i+HBb/QI4lrK1S0GuJxaAzXQlHt3VWucgy7lbSQ4y+iIkRz1TbXrHpMVpUjSpnENPZPkI4Z+wr6Wu2j5jwxcNPrlHdQNZJi0kKEAloKvHKLUE5mbh5P0n8zFaS9GXbSRq5J4s/neS6et2UEmhN36Vky4kODwx9gj3ZLqFiwvG0F3oZ4+e+t3br3v1Mhy3wPrYvI5oQk026VM2ClcgplwW+wxQaj4epgn2l2zxnOz3X8Bg9k1gMAtVKe23bFyo8Y5dpJQKSXmJ7UW/PsL9QWBU1GsTTLSRtRVSu5xAd7MfbvrfaOr/N9mRVlkJGUkmwJC2fo4F7CsdNMQwiuwRF0o7EJL39gBsT7lBkjT+F4093h3Ef24dL2sSJZkIMG7mlpIMmVU2NpGtsbyzpPOBrhLPW6bbnJIq/gMqCtfVzlnTLVD6bw1TBvk7RxX8KsXLeDrhUMdEL2z9pegDndfkYaBTOCbsBqxBJ2GrAUcBmAE2T69w6q9wo/7hbqvf6Yq9U2zclDP9u4Cu259eftP2IpEFp9B1mmvpUv4rYBVwBmC1pHjEJcY8UJ9cNqoT6nHH++UQaVBGBZRXDVN4BHGn78U5NxX0cPxe7EDMhvkazn+8wk/v32Ch+bvliF+SR/yFpJeD/EVaGK5LWonbaFqzDlGCvV31t15y9mvjlDkQiNsDsSfjEXgtg+2dK63N7EqGzehs1nVXC+Lm5kbHjbndNFVwxQXMbRrZkj5N0mu3/SHSIu9uTa0mH2N7P9kWJjlGYPk1v4l8F/gn4IYT9o6RNJ3xFWh6b/Fsa0VSC8g1ivPvNwPxqty6lBjt3/FzcJunXwKoa7W3ckqgt1Q14A0IW+WK34ttuLaofIIa4JUNqZsE6NBKRyWigUSxMAUnX2t6otV0laRliSyXVFuqNttevb80MksQiN1Wn+ctbrgaVROcntl+UKH6nUexT2iYr5KfpNnH757d67maP9t9teo7PI+zbFhd22hdtuUgloWmLuYwzes/njt92rGm/fxQTIs8jhl2NYjL98NLAIEpEct9bU8aXdCydK+RJdlYrjft+1ByEPEUL1mGqYE/GPkBJsPNxmaRPActJejMx2vXshPEfr77eLelthM5qtYTxsyPp1cCajE4wThj3BUvGr4nqQMs27KlAI4cJoOUBugcwp61C9XTSerEWmtH0d/Hb6v1phb/23oQVWBIkHULI9m5lZACJga4k2CmorjsvZrSn/0ENY042LvrLk/z7dI45xqWEkHlMC8eQsXnVon627TuanuMgIekg4HLgqnH6FxpbaSqca57N6HtHy7avUfzc8sUuyCPrsrBZRPP97xLGvxq43/a+S/rCpamCvVQ3AuSmumjvymif2KOd6A2mGHRyObA6Izqrz7ryBe53JJ1IaNMXUEswEloV/YCY6ncBkbi8GbiCmD41bUukStu2MvB54BO1f3rI9p8bnHJhCkyWgLmatJjgOM8ikqw3EZ/f84F9XE3+SxD/DuBl7pE3cqsRqsHrjyIGRb0BOJroSbjOdiOZl0Zs9OYSn9/W9WxLYL7tDzSJXzvOGJcSIKULypbAocBTbK8laT3gINtjqtrDRtWDsgkxJ+Ah4j413/ZZieLvRUhff094X0NC+Y1iSnW7fPGgVE2rueN3ON4M4MKEGvJpW7AuTQl21kEnhcJEVBKOdVMtODrEn3Aq5HTlUZJWdNjzrdLp30uSnRdl9jGuHWeWMw5NkfQ/xKTFhzMeI5sEpSWHqn1dAfi+7c0TxT8feJdHXJeeDpxm+58Txc/tgnIj0ed06ZI6LQwLlVRmW0JzvLLtJFNSJf0c2CjVYnfYkTQXONf2CxLFm/YQt6VJItLvo7QHEkkLmdhMPtUFfFXgg4yVWAyKg8UiYlvs7hzBM/YXnAxswegmzcWHBZ6f6bgF0iXQU2CRpN9TVd+AK20/kDD+I8ACSRcBi6vYCXdwcktQ/lp9fUTSc4E/AWslig0xna/e6PkYca1LRW6XkidsPxA9YUsXko4m3K1an593A41dy2r8lmjgy4KkWYQMcLEDFfD1VAvuLsRvzYJozYC4h1hAJqFJH8HQJNiSZtp+coJvKXrRPGxRfd2z+npi9XVH4qaairOIi9eFjNxAB4lnAbdKuo7RCUaSLdRKQnMwIxW8JINmbG9RfU2ZTBSmiLo0adH2CyTNBl5LfKb/W9L9bhs/3oAfMiJ/yME7gLkZJSjnSHoG8EVGhvMcnTD+iYTV7JlEkrA1aXuGcruULJK0AzBT0tqEhv+qhPH7mWcCM4H7gT8Df0zRnFqTh/2SsBw+l9H3jlT6/BMIaUtrWNj2xPtxm0GIn2qnIAdDIxGRdCfwY8LK7eJcW/GFzijzoJ/WFmeKWL1AUseOacfkqRTxfw68E1iY8r2vykd+PJzAX74wPrmkPx2OsxqRXL8OmEckClfY/nyK+NUxnkJoGQHusP34RN+/hLGzSlCqBr7diZ+RicV+kiqcouy7GrBqFR9Cw5vSx77TcZO5lCgGTu3P6B6cg3PKjvoNSS8irC4/Csy03agJv4vysDFuQSkdhHLHr+L1zKFoIoamgk00iWxJVFKPkXQO8N1cQvrCGJZXbZpX5UgwZUP2KXCOpLfa/lHCmF0jVSI9Ab8FFmVYWB42wb8Vf/nMtCfQyjdp8U7geuA/bX8odXBJrycqsr8mErDVFdapqW6CWSUoxLk/RAxUgajCnUBobhth25J+YHt90koLuuZSYvsRIsHev3K8WH5pSa6r3cPXApsSDeEXEwuwRnRRHnaTpI1tXwMgaSPS7vhnjd/PDkVDU8GuI2lloiN+R9sze30+SwNVpfNYojvdhGbs/akqnJXOanlCm9iqfDWWQHQLSe8EDgH+gUgwkkg4avE3JCQil5FnG7HQQ1SbtGg7+aTFKt4mRJIwG/gZcJntYxLFvxHYwZWFm6QXAqdUSWWK+B0r/Qkr/LmrfP8FHGf7+hTxanGHwqWkn6l+d/OBy22ntIdrxT+bsX1ODwA3AN9oupCpGvDnEotsiM//bYRjSWO3ki7E76lD0UQMVYJdbcNvB7yFqMZ8z/YZvT2r4aeqWOxt+yuSViTeV9maMgaRSsKxpe1k3sJt8c8HHgYWMmLllLQKIukljB1Vn8rHuzABkq4lmqd+WHNpWGT7JQmPsQKRZL8WeC9x81szUewxjhKpXSYyS1COA45qq8LtnHCB024FlnQS4qC7lPQ7kp5NLGAg7BvvTRj7cEI+dEr11HZEI99ywIq2d2oYv6NLRo0Hbd/Xx/GzOxRNl6GRiCi8FhcApwL7ZtpGLXTA9pOS3g58xaOHFyRF0lZEhQ3CDuqcib6/z/h9ruS6YhUnsgzrRFUJez2RYP+IWMReQWyTF7qA7d+2uTQka/aVdAMxnOgq4ve6aZPu+Q7cIOkYRjdB3zjB9y8RuSQoNZekZYH3Vb0+JvSetzaJ3cZbEsbqxKC7lPQtkrYhPMAvJd57R0ja1/bpiQ7xctub1h6fLWm+7U0l/bRp8Mk+54pJhtO2OM4dn/zysGkzNAk2cBOwa2slVMlEDvPg2LgNOldKOpJoMq2bsaeSiHyBqBCcVD21T6X5/sQEL+snbpD0PeAHjL4IfD9R/AslbW77/ETx2nk30fx2k+1dqopNSheFwsRknbQIvMX2HxLGa2d3oj9mbyIJmQ/8d8L4hwGbt0tQiGpqE7aY/Fua00pCJP0DoydFpmLQXUr6mU8DG7aq1gpL2QuBVAn2qpJmu5rcqHD7eVb1b4+N/7Jk5PZebBo/t0PRtBkaiYg6TGrs9FwhD5Iu6fC0nW6a0i3Aerb/Xj2eSSR7A7EFKenYDk871QKwplF/lNCop9Z4X2f7lZWW9g1Ew9ci2y9OEb8wMco0aXGyJrhB0fB3Q4KSk2p37jDgucT01TWA21J8vobBpaSfkbTQ9ktrj2cAN9efaxj/rcBRwC+Iz/5ahK/0pcAHbX81xXEmOH7WIX254/eSYapgz5C0cq2CvQrD9f/ra2y/oQuHeQZhHwbRRDMw2N4lc/ynV+/5tclTAbtB4QP8LWJr/2HgugzHKXTmYds7Zoib1UNW0qm2t9U4A6kSJsBZJShd4GBgY2LE88slvYFwKmmMnc+lpI6ktwEvZvT156Bcx+sjfizpPEZrpJO5Xdn+kcJbfB0iwb691tj41VTHGVQkvQY4kLEzIHo+BG2YKtjvAz5JbMuYsE/6nO0TJ3xhIRmdLrC2k1xgJW0PfAG4hPgAbQp80vZ3U8TPjWKa1a6M/fmkqmB/ANiHqFQtIG7WV9neLEX8tmOtSTTX3JI6dqEzVZNszkmLWZD0HNt3j9folErnLemphARlE2oSlH50FuiEpBtsbyDpZkJz+/fWrlGi+FlcSmrxjwKeRuxuHU1Iyq6zvWuO4/Ubkt4FvIbqvWf7zAQx32j7YoUD1RgSygsnO4+sSoCm8SXdTniP30itL6Xp7l4KhibBBpC0LuHLK+Ai2ymbUAoT0I0LrKTnEDpsAdfavidV7NxIOg24HdiBqOrsSGwB75Mo/kLiZ3NN1c2/DvBZ29s1jFsGzfQJGpm0+BrgrUCySYuKQTNHVLFb44z3sX1XoviH2N5vsueWViRdSDQIfp7Q195L6HpfnSh+bpeSW2y/rPZ1BeD7ORuvhx1Jn7V9QE1e2ErWWr+7VMWZg4iF+1WdzCEkrWL7z2NfuUTHmAk8m9GDYFqa8kbxJV1re6Mm55eLoUqwC70j9wW22gZaYPsvkt5LdB0fntjpIButVXrt57MscF5Cjfr1tjeUtADYyPajSjD9sqatnwVsQDQxCXgZscjZpEn8wtRQ5kmLki4ATmZEYvFeYo7AmxPFH6OzTKGR7qIEJSuSlgf+Rny2diQkcCelqsJ1YQfhWtsbSbqGmCj7J6JHY+0U8fuRqu+lUwKVuv9lFvAuwvWllaA64e7w+4mdn1cRvTWXE1X4sxLF3ws4gNiBa1nIplzcfYEYVf99RhsI9Lz4UzTKhVT8tfr6iKTnEhfYtRLG/zowTzEQY1/g24RFXMcR5H1Iy5P3foWf9D2ktcm6q9JI/wC4QNJ9QOOhBy1tvaTvArvZXlg9fgnw8abxC1Mm66RFYFXb9Ubc4yR9pGlQSbsTDVnPrxqVWzydNNPcWjtAXXH7yEVb5TClu0crfm6XknOq688XGdG+D7XLkO2s/Qs1fgDcT+jnW9rrZJVR298Gvi3pHwlp7ceB3UjXn7EPMDejZKNVvd6g9lxfTBkuCXYhFbkvsE9UzTpvB75m+xiNM72tT/mmwjry04Sl0ArAv6cKbnvr6q8HVlXnlYAfp4oPrNNKrqvjLZK0XsL4hYl5OVFl2kHSJ0g8aRH4Y7Uz1GrU2p5YJDflZOB/COlD3VLzoabbzgC2767+ukcnCQox8KTvUf5Jrx1dSoiekBQcSlgxvha4mqiCfj1R7KWd1ZxoIFAnJB1NzDdo9Xi8m7TNsL8lJk9moUsGC9OiSEQKSZC0HCMXWFNdYN1wjGst/mVEwrgL0eD4B0IyksQKKSeVbdO7bZ/a63OZLpJOIbSb3yF+v+8lxnYncTooTI7yTlqcDRxJbBObGDizd0snmYr2Cmqq+LkkKN1C+Se93kxU9Ea5lNjeLVH8Uwl5wXeqp7YHnmF72xTxl2YkfRM4ol7gSBz/TGLhdStwGSEP+WWCuC0L0BcTo9LPZbSEI5kFaE6DhSaUCnYhFccTF9ivVY+3JyQcqS6w2xENgrvavqdKCL6UKHZWKkeADxNTRgeVXYgFVGtLfj6lQtU1lH/S4sHE6O+6zemhQKpGqi2BL5O4gtoFCUq3yD3p9XHbf5I0Q9IM25dUFf5UzLU9r/b4kiqpL0yTWl/BMsAukn5JJKhJG1Rbu5+SXgT8E/G7m2l7tYahWxKTO6s/T6n+JGU8g4XUx5kOpYJdSIKkm9susB2fW1qR9O+ETr190mXjbfJuoZggOJe46N9h+/FJXlJIhKRVnXHSYierrJT2XLkqqJJWAlYmkwQlNzULttcB/0imSa9dcCk5DjjK9jXV442IBdseKeIvjYzXmNoiYYPqFsSu2KbEZ+lq4PJKm9339LODTalgF1Jxk6SN2y6wySpIkjYmbMReRKyCZxLDNwZl4EyrErhn7TkDPTfDnwqSXk/sUvyaqKCsLmln2/N7eFpDT22bFWnsROGE26y5B3VlqaA6vMAfoBrKUpOgrCBphdQSlwxsWfv7I0A9KTDhjJCCtxMNch9lxKWk8RZ6rcq6LPA+SXdWj9cgJAeFadJFh6y3EDuSh9tu3BjfjqSzGduU+QBwA/CNBDLS3AYL06Yk2IVUbMTIBRZgNnBb6wKcYDvrSOA9wGlEt/D7iKmFA4HtvvjAN+AwYHPbdwBIeiHRELd+T89q+OmWU8FhwFWSRg3qShj//qqyNB84SdK9QLIx2rkkKLlx5gmvtePkcikZaPeWAtjeU9KzgQ0Vcw+us31vwkP8EliV0ZMuf0/4sn8L2Klh/JbBwpeI5kxXcXtOkYgUkpB7O0sjk84WNy5JuirVFmduJD0N+Bgw2/ZuitG3c22f0+NTmxKdGsYGqYmsMDnKOKir8nn+KzCDPD7PWZv4ciPpeGKwz/3V45WBw5xumEhWl5LC4CJpG6Lf4lLiffFaYF/bpyeKP9/2pp2ek/RT28kWwYqJrrNcm3Ir6c22L0h1jCWhVLALSejCdtYjlQZ4gaQvAncDy2c+ZkqOJewLWwuCu4hq/EAk2MANko5hZBDJjozYMRYyo8yTFgGqhDr5tr5iittZtt9EDJpI7vNM/ia+3LyslVwD2L5PUsrx1F8ko0tJYaD5NKHHvxei3wO4EEiSYAOrSprtkcmNs4k+AIDHEh0DANuPUuthqDgE6EmCPaMXBy0UpsFOxPv1w0ST4OrEdKtBYY7tL1INnLH9V6JaMCjsDvwU2JtwErkVyDHwpNCZYwn/9OcCzwPOrp7re2w/SSyQc/ZLtEtQDiehBKULzKiq1kAWDXxul5LC4DKjTRLyJ9Lmhv8PuELSJZIuJSx89612tXIsttvp2X22SEQKA0PltT27pQMeJCRdBWwGXGn7FZLmAKfYfmWPT60wAKjD2PtOz/UrlU/yxkQlqe6is3ei+FklKLmR9D7gk4xUDbcBPmf7xPFfNaW4XXEpKQwukr4EvIzRGulb3Da4qeExngqsQyS7t6eajzHFY4/xyO/asUuCXRgEqiamQ4Gn2F5LMUXwINtb9fbMpoakzYH9iYlZ5xNb/bvYvqSnJzZFKiung4nmsWUoGs6uUtmsHcfoSYu72N6sZye1BGicqau2G1ewKgnKeZUEZWDJoYGXNNEuh1NpvAuDjaR3EfckEYNmzkwQ8422L64t8kbRrcVdSbALhUmQdCNx87m05c07aE12kp5JVPEEXGP7jz0+pSmjmDT3TmChy0Wj66hLkxYHFUk/BHaqNzcNApJWtP1gJQkZwyB4eRcKnZD0WdsH1BZ5rftGqzjTlcWdpO/b7pjk56Y0ORYGhSdsP9DJC3gQkHRRVW08t8Nzg8BvgUUlue4ZWSct5kbSrxjrhYvtVD7wfwMWSsoiQcnIydXu3B8Jj/kWIqFPfm6XksLgIekhOnwmSbQ7afuA6q+7E/1SazKScya9j0h6dVt8bJ9Qfe1Jcg0lwS4MDosk7QDMrCzu9iaqeH2NpFnEGNdnVTe11gphRaJhbVD4N+BHki5jtIYz1aCTwsS8rJVcQ1Q2E7tM5GaD2t9nERrjjlXbaXIutcXroGB7C1isp8+5jZ3bpaQwYNjulsf+D4D7CY/qlvY6WYIt6URgDrAAeLIW/4RUx5guJcEuDAp7ERrmRwkd6nlEVa/f+VfgI0QyfSMjCfaDwH/16Jymw+eAh4nk6Ck9PpelkdyTFrPSodnwq5KuAD6TKH433AhycpWkDW1fnyn+QL9/CgPNarb/OWP8DYB1+3F3tXzACgOB7UeIBHv/qqlp+W52Ik8X24cDh0vay/YRvT6fBqxie/PJv62QidyTFrNSTYhrMYO4KSaroHVBgpKbNwIfkvQbQuLS2qZP1WNSf/9A5VKSKHahMBFXSXqp7YWZ4i8iHHLuzhR/2pQmx8JAIOlkwnf5SaISvBLwZdtf6umJLQET6cT6HUlfAC62fX6vz2VpJeekxdxIuoSRBPgJQm98qO3/TRT/mbWHiyUotpNUyHMz3iTclAO8Bvn9Uxg8JC0kPvPLAGsTI9MfJfHisbq2rAdcx2j5Ys8dxkqCXRgIWp6/knYE1gf2A24cFBeR8XRiA9CEBSxuiFmeuIA9TrHpK0wBSR9r/ZW42bYkUoa8Gn5JV9jeJFf8QaC4lBR6xXiLxhapFo+SXjdO/MtSxG9CkYgUBoVlJS0LvAM40vbjkgZpddi3OrGp0MWGmMJw0XrfzAU2BM4ikuwtiamLScgtQRlguuJSUii0k3L3ZZLj9DyRHo+SYBcGhW8QN4ibgfnV6vjBnp7RktG3OrGJkLSO7dvbEpjF2P5Jt8+pMDjY/iyApPOBV9h+qHp8IHBawkMdxlgJyjYJ4w8kXXQpKRR6gqSNgSOAFxEN+DOBv/TD7mqRiBQGEoUh9kzbT1SPd+5nJ4F+1olNhKRv2t6tTUMLIxKRN/bo1AoDhKTbgXm2H60ePxW42fY6DeP2TIIySEg6Ejg+o0tJodATJN0AvIdYsG8AvA9Y2/anenpilAp2YUCppBZP1J7aB+jbBBs4sNcnMB1s71b99a3AHsAmRPJyOfD1Xp1XYeA4EbhO0pnE+2dr0nxeuyJBGQJyu5QUCj3D9s8lzbT9JHCspL6YkVEq2IWhQNJNrRHqhfRIOpWQ5JxUPbU98Azb2/burAqDRCUzem31cL7tmxLGPh94V02C8nTgtMz+uwNDN1xKCoVeIGk+8CbgaOAeQob5L7bn9fTEKBXswvDQlyvFlpNBh7G0g+bCMbftgnWJpJt7djaFgaPS6+fS7M8GHqs9foywxCxQEunCULMT0dj8YeCjwOrEaPaeUxLswrCgyb+l+7RswobAheMmSRvbvgZA0kbAlT0+p0KhRS4JSqFQ6GNs/0bScsBzWk3V/UKRiBSGAklH2v5wr89j2KgNC1iW0LneWT1eA7jV9kt6eHqFwmJySlAKhUJ/UtlQHgo8xfZaktYDDuoHA4GSYBcGgppbQJ0HiGEzC7p8OksN3RoWUCgUCoXCkiLpRqKJ99JWH5akW/qhgbdIRAqDwgbVn7Orx28Dric640+z/cWendkQUxLoQqFQKPQxT9h+IJx7+4uSYBcGhWcSgyoeBpB0AHA6sClwI1AS7EKhUCgUli4WSdoBmClpbWBvoC9s+mb0+gQKhSnS7hLwOLCG7b9SG9xSKBQKhUJhqWEv4MVEHnAyIR3dp6dnVFEq2IVB4WTgGklnVY+3BE6RtDxwa+9Oq1AoFAqFQo9Yt/qzTPXn7cBWQM812KXJsTAwSFqfmCQo4ArbN/T4lAqFQqFQKPQISXcAHwcWAX9vPd8P/UMlwS4MBJIOB75nuy+0VYVCoVAoFHpLa5hbr8+jEyXBLgwEknYGtgNeCJxJJNulgl0oFAqFwlKKpM2A7YGLqPVj2f5+z06qoiTYhYFC0irEGNT3ALNtr93jUyoUCoVCodADJH0HWAf4KSMSEdt+f+/OKihNjoVB4wXEh2lNSnNjoVAoFApLM/Nsv7TXJ9GJYtNXGAgkHSLpZ8BBRDPD+ra37PFpFQqFQqFQ6B3XSFq31yfRiSIRKQwEkj4EnAE8H3hq63nb83t2UoVCoVAoFHqGpNuAOcCvCA22CIlIz236ikSkMCg8CVwMrAYsADYGrgbe2MNzKhQKhUKh0Dv+udcnMB6lgl0YCCQtBDYErrG9nqR1gM/a3q7Hp1YoFAqFQqEwiqLBLgwKf7P9NwBJT7V9OzC3x+dUKBQKhUKhMIYiESkMCndJegbwA+ACSfcBv+vpGRUKhUKhUCh0oEhECgOHpNcBKwE/tv1Yr8+nUCgUCoVCoU5JsAuFQqFQKBQKhYQUDXahUCgUCoVCoZCQkmAXCoVCoVAoFAoJKQl2oVAoFAqFQqGQkJJgFwqFQqFQKBQKCSkJdqFQKBQKhUKhkJD/DzwsimnbioU8AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 720x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(constrained_layout=True, figsize=(10, 4))\n",
+    "gs = GridSpec(1, 5, figure=fig)\n",
+    "ax1 = fig.add_subplot(gs[0, :4])\n",
+    "ax2 = fig.add_subplot(gs[0, 4])\n",
+    "\n",
+    "low_values = df.where(df < 2).dropna()\n",
+    "low_values.plot(ax=ax1)\n",
+    "ax1.set_xticks(np.arange(len(low_values)),low_values.index, rotation=90);\n",
+    "ax1.grid()\n",
+    "high_values = df.where(df > 2).dropna()\n",
+    "high_values.plot(ax=ax2)\n",
+    "ax2.set_xticks(np.arange(len(high_values)),high_values.index, rotation=90);\n",
+    "ax2.grid()\n",
+    "\n",
+    "ax1.set_title(\"Low value signatures\")\n",
+    "ax2.set_title(\"High value signatures\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2969a81-3965-4870-9ced-97550469cd9e",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## 4. Interpretation of signatures\n",
+    "The different signatures can be used to compare the different systems or characterize a single system. For example, the first head time series has a high bimodality coefficient (>0.7), indicating a bimodel distribution of the data. This makes sense, as this time series is used as an example for the non-linear threshold model (see notebook). Rather than (naively) testing all model structures, this is an example where we can potentially use a groundwater signature to identify a 'best' model structure beforehand.\n",
+    "\n",
+    "Another example. The second time series is observed in a much slower groundwater system than the first. This is for example clearly visible and quantified by the different values for the 'pulse_duration', the 'recession and recovery constants', and the 'slope of the duration curves'. We could use this type of information to determine whether we should use a 'fast' or 'slow' response function (e.g., an Exponential or Gamma function). These are just some examples of how groundwater signatures can be used to improve groundwater modeling, more research on this topic is required. Please contact us if interested!\n",
+    "\n",
+    "A little disclaimer: from the plot above it is actually not that straightforward to compare the indices, because the range in values is large. For example, the rise and fall rate visually show no differences, but their numbers vary by over 200%. Thus, interpretation requires some more work.  \n",
+    "\n",
+    "### References\n",
+    "The following references are helpfull in learning about the groundwater signatures:\n",
+    "\n",
+    "- Heudorfer, B., Haaf, E., Stahl, K., Barthel, R., 2019. [Index-based characterization and quantification of groundwater dynamics.](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2018WR024418) Water Resources Research.\n",
+    "- Haaf, E., Giese, M., Heudorfer, B., Stahl, K., Barthel, R., 2020. [Physiographic and Climatic Controls on Regional Groundwater Dynamics.](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2019WR026545) Water Resources Research.\n",
+    "- Giese, M., Haaf, E., Heudorfer, B., Barthel, R., 2020. [Comparative hydrogeology – reference analysis of groundwater dynamics from neighbouringobservation wells](https://www.tandfonline.com/doi/full/10.1080/02626667.2020.1762888) Hydrological Sciences Journal.\n",
+    "- McMillan, H.K., 2021. [A review of hydrologic signatures and their applications](https://wires.onlinelibrary.wiley.com/doi/10.1002/wat2.1499). WIREs Water."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pastas/stats/__init__.py b/pastas/stats/__init__.py
index 56f152e4..ca7be714 100644
--- a/pastas/stats/__init__.py
+++ b/pastas/stats/__init__.py
@@ -9,6 +9,7 @@
     pastas.stats.tests
     pastas.stats.sgi
     pastas.stats.dutch
+    pastas.stats.signatures
 """
 
 from .core import acf, ccf, mean, std, var
@@ -16,6 +17,8 @@ from .dutch import ghg, glg, gvg, q_ghg, q_glg, q_gvg
 from .metrics import (aic, bic, evp, kge_2012, mae, nse, pearsonr, rmse, rsq,
                       sse)
 from .sgi import sgi
+import pastas.stats.signatures as signatures
+import pastas.stats.metrics as metrics
 from .tests import (diagnostics, durbin_watson, ljung_box, plot_acf,
                     plot_cum_frequency, plot_diagnostics, runs_test,
                     stoffer_toloi)
diff --git a/pastas/stats/signatures.py b/pastas/stats/signatures.py
new file mode 100644
index 00000000..00b370e4
--- /dev/null
+++ b/pastas/stats/signatures.py
@@ -0,0 +1,1380 @@
+"""This module contains methods to compute the groundwater signatures."""
+import pandas as pd
+from pandas import NA, Timedelta, DatetimeIndex, cut
+from numpy import diff, sqrt, log, arange
+import pastas as ps
+from scipy.stats import linregress
+
+__all__ = ["cv_period_mean", "cv_date_min", "cv_fall_rate", "cv_rise_rate",
+           "parde_seasonality", "avg_seasonal_fluctuation", "magnitude",
+           "interannual_variation", "low_pulse_count", "high_pulse_count",
+           "low_pulse_duration", "high_pulse_duration", "amplitude_range",
+           "bimodality_coefficient", "mean_annual_maximum", "rise_rate",
+           "fall_rate", "reversals_avg", "reversals_cv", "colwell_contingency",
+           "colwell_constancy", "recession_constant", "recovery_constant",
+           "duration_curve_slope", "duration_curve_range", "baseflow_index",
+           "richards_pathlength", "richards_baker_index", "baseflow_stability"]
+
+
+def _normalize(series):
+    series = (series - series.min()) / (series.max() - series.min())
+    return series
+
+
+def cv_period_mean(series, freq="M"):
+    """Coefficient of variation of mean head over a period (default monthly).
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+    freq: str, optional
+        frequency to resample the series to by averaging.
+
+    Returns
+    -------
+    cv: float
+        Coefficient of variation of mean head over a period (default monthly).
+
+    Notes
+    -----
+    Coefficient of variation of mean monthly heads [hughes_1989]_.
+
+    References
+    ----------
+    .. [hughes_1989] Hughes, J., & James, B. (1989). A hydrological
+       regionalization of streams in Victoria, Australia, with implications
+       for stream Ecology. Marine and Freshwater Research, 40(3), 303–326.
+
+    """
+    series = series.resample(freq).mean()
+    cv = series.std() / series.mean()
+    return cv
+
+
+def cv_date_min(series):
+    """Coefficient of variation of the date of annual minimum head.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+
+    Returns
+    -------
+    cv: float
+        Coefficient of variation of the date of annual minimum head.
+
+    Notes
+    -----
+    Coefficient of variation of the date of annual minimum groundwater head
+    according to [richter_1996]_.
+
+    References
+    ----------
+    .. [richter_1996] Richter, B. D. (1996). A method for assessing hydrologic
+       alteration within ecosystems. Society for Conservation Biology, 10(4),
+       1163–1174.
+
+    """
+    data = series.groupby(series.index.year).idxmin().dropna().values
+    data = DatetimeIndex(data).dayofyear.to_numpy(float)
+    cv = data.std() / data.mean()
+    return cv
+
+
+def parde_seasonality(series, normalize=True):
+    """Parde seasonality according to [parde_1933]_.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+    normalize: bool, optional
+        normalize the time series to values between zero and one.
+
+    Returns
+    -------
+
+    Notes
+    -----
+    Pardé seasonality is the difference between the maximum and minimum
+    Pardé coefficient. A Pardé series consists of 12 Pardé coefficients,
+    corresponding to 12 months. Pardé coefficient for, for example, January is
+    its long‐term monthly mean groundwater head divided by the overall mean
+    groundwater head.
+
+    References
+    ----------
+    .. [parde_1933] Pardé, M. (1933). Fleuves et rivieres.
+
+    """
+    coefficients = parde_coefficients(series=series, normalize=normalize)
+    return coefficients.max() - coefficients.min()
+
+
+def parde_coefficients(series, normalize=True):
+    """Parde coefficients for each month [parde_1933]_.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+    normalize: bool, optional
+        normalize the time series to values between zero and one.
+
+    Returns
+    -------
+
+    Notes
+    -----
+    Pardé seasonality is the difference between the maximum and minimum
+    Pardé coefficient. A Pardé series consists of 12 Pardé coefficients,
+    corresponding to 12 months. Pardé coefficient for, for example, January is
+    its long‐term monthly mean groundwater head divided by the overall mean
+    groundwater head.
+
+    References
+    ----------
+    .. [parde_1933] Pardé, M. (1933). Fleuves et rivieres.
+
+    """
+    if normalize:
+        series = _normalize(series)
+
+    coefficients = series.groupby(series.index.month).mean() / series.mean()
+    coefficients.index.name = "month"
+    return coefficients
+
+
+def _martens(series, normalize=True):
+    """Functions for the Martens average seasonal fluctuation and
+    interanual fluctuation.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+    normalize: bool, optional
+        normalize the time series to values between zero and one.
+
+    Returns
+    -------
+    hl: pandas.Series
+        Lowest heads
+    hw: pandas.Series
+        Largest heads
+    """
+
+    if normalize:
+        series = _normalize(series)
+
+    s = series.resample("M")
+    hl = s.min().groupby(s.min().index.year).nsmallest(3).groupby(
+        level=0).mean()
+    hw = s.max().groupby(s.max().index.year).nlargest(3).groupby(
+        level=0).mean()
+
+    return hl, hw
+
+
+def avg_seasonal_fluctuation(series, normalize=False):
+    """Classification according to [martens_2013]_.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+    normalize: bool, optional
+        normalize the time series to values between zero and one.
+
+    Returns
+    -------
+    float
+
+    Notes
+    -----
+    Mean annual difference between the averaged 3 highest monthly
+    groundwater heads per year and the averaged 3 lowest monthly groundwater
+    heads per year.
+
+    Average seasonal fluctuation (s):
+
+        s = MHW - MLW
+
+    References
+    ----------
+    .. [martens_2013] Martens, K., van Camp, M., van Damme, D., & Walraevens,
+       K. (2013). Groundwater dynamics converted to a groundwater
+       classification as a tool for nature development programs in the
+       dunes. Journal of Hydrology, 499, 236–246.
+
+    """
+
+    hl, hw = _martens(series, normalize=normalize)
+
+    return hw.mean() - hl.mean()
+
+
+def interannual_variation(series, normalize=False):
+    """Interannual variation after [martens_2013]_.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+    normalize: bool, optional
+        normalize the time series to values between zero and one.
+
+    Returns
+    -------
+    float
+
+    Notes
+    -----
+    The average between the range in annually averaged 3 highest monthly
+    groundwater heads and the range in annually averaged 3 lowest monthly
+    groundwater heads.
+
+    Inter-yearly variation of high and low water table (y):
+
+        y = ((max_HW - min_HW) + (max_LW - min_LW)) / 2
+
+    Warning: In this formulating the water table is references to a certain
+    datum and positive, not as depth below the surface!
+
+    References
+    ----------
+    .. [martens_2013] Martens, K., van Camp, M., van Damme, D., & Walraevens,
+       K. (2013). Groundwater dynamics converted to a groundwater
+       classification as a tool for nature development programs in the
+       dunes. Journal of Hydrology, 499, 236–246.
+
+    """
+
+    hl, hw = _martens(series, normalize=normalize)
+
+    return (hw.max() - hw.min()) + (hl.max() - hl.min()) / 2
+
+
+def colwell_components(series, bins=11, freq="M", method="mean",
+                       normalize=True):
+    """Colwell predictability, constant, and contingency [colwell_1974]_.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+    bins: int
+        number of bins to determine the states of the groundwater.
+    freq: str, optional
+        frequency to resample the series to.
+    method: str, optional
+        Method to use for resampling. Only "mean" is allowed now.
+    normalize: bool, optional
+        normalize the time series to values between zero and one.
+
+    Returns
+    -------
+    p, c, m: float, float, float
+        predictability, constancy, contingency
+
+    Notes
+    -----
+    The difference between the sum of entropy for each time step and
+    possible state of the seasonal cycle, and the overall entropy across all
+    states and time steps, divided by the logarithm of the absolute number of
+    possible states. Entropy according to definition in information theory,
+    see reference for details.
+
+    References
+    ----------
+    .. [colwell_1974] Colwell, R. K. (1974). Predictability Constancy and
+       Contingency of periodic phenomena. Ecology, 55(5), 1148–1153.
+
+    """
+    # Prepare data and pivot table
+    if normalize:
+        series = _normalize(series)
+
+    if method == "mean":
+        series = series.resample(freq).mean().dropna()
+    else:
+        raise NotImplementedError
+
+    series.name = "head"
+    binned = cut(series, bins=bins, right=False, include_lowest=True,
+                 labels=range(bins))
+    df = pd.DataFrame(binned)
+    df["time"] = df.index.month
+    df["values"] = 1
+    df = df.pivot_table(columns="head", index="time", aggfunc="sum",
+                        values="values")
+
+    # Count of rows and column items
+    x = df.sum(axis=1)  # Time
+    y = df.sum(axis=0)  # Head
+    z = series.size  # Total number of observations
+
+    hx = -((x / z) * log(x / z)).sum()
+    hy = - ((y / z) * log(y / z)).sum()
+    hxy = - (df / z * log(df / z)).sum().sum()
+
+    # Compute final components
+    p = 1 - (hxy - hy) / log(bins)  # Predictability
+    c = 1 - hx / log(bins)  # Constancy
+    m = (hx + hy - hxy) / log(bins)  # Contingency
+    return p, c, m
+
+
+def colwell_constancy(series, bins=11, freq="M", method="mean",
+                      normalize=True):
+    """Colwells constancy index after [colwell_1974]_.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+    bins: int
+        number of bins to determine the states of the groundwater.
+    freq: str, optional
+        frequency to resample the series to.
+    method: str, optional
+        Method to use for resampling. Only "mean" is allowed now.
+    normalize: bool, optional
+        normalize the time series to values between zero and one.
+
+    Returns
+    -------
+    p, c, m: float, float, float
+        predictability, constancy, contingency
+
+    Notes
+    -----
+    One minus the sum of entropy with respect to state, divided by the
+    logarithm of the absolute number of possible states.
+
+    References
+    ----------
+    .. [colwell_1974] Colwell, R. K. (1974). Predictability Constancy and
+       Contingency of periodic phenomena. Ecology, 55(5), 1148–1153.
+
+    """
+    return \
+        colwell_components(series=series, bins=bins, freq=freq, method=method,
+                           normalize=normalize)[1]
+
+
+def colwell_contingency(series, bins=11, freq="M", method="mean",
+                        normalize=True):
+    """Colwell contingency [colwell_1974]_
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+    bins: int
+        number of bins to determine the states of the groundwater.
+    freq: str, optional
+        frequency to resample the series to.
+    method: str, optional
+        Method to use for resampling. Only "mean" is allowed now.
+    normalize: bool, optional
+        normalize the time series to values between zero and one.
+
+    Returns
+    -------
+    p, c, m: float, float, float
+        predictability, constancy, contingency
+
+    Notes
+    -----
+    The difference between the sum of entropy for each time step and
+    possible state of the seasonal cycle, and the overall entropy across all
+    states and time steps, divided by the logarithm of the absolute number of
+    possible states. Entropy according to definition in information theory,
+    see reference for details.
+
+    References
+    ----------
+    .. [colwell_1974] Colwell, R. K. (1974). Predictability Constancy and
+       Contingency of periodic phenomena. Ecology, 55(5), 1148–1153.
+
+    """
+    return \
+        colwell_components(series=series, bins=bins, freq=freq, method=method,
+                           normalize=normalize)[2]
+
+
+def low_pulse_count(series, quantile=0.2):
+    """Number of times the series drops below a certain threshold.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+    quantile: float, optional
+        Quantile used as a threshold.
+
+    Returns
+    -------
+    int
+        Number of times the series exceeds a certain threshold.
+
+    Notes
+    -----
+    Number of times during which the groundwater head drops below a certain
+    threshold. The threshold is defined as the 20th percentile of
+    nonexceedance [richter_1996]_.
+
+    References
+    ----------
+    .. [richter_1996] Richter, B. D. (1996). A method for assessing hydrologic
+       alteration within ecosystems. Society for Conservation Biology, 10(4),
+       1163–1174.
+
+    """
+    h = series < series.quantile(quantile)
+    return (h.astype(int).diff() > 0).sum()
+
+
+def high_pulse_count(series, quantile=0.8):
+    """Number of times the series exceeds a certain threshold.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+    quantile: float, optional
+        Quantile used as a threshold.
+
+    Returns
+    -------
+    h: int
+        Number of times the series exceeds a certain threshold.
+
+    Notes
+    -----
+    Number of times during which the groundwater head exceeds a certain
+    threshold. The threshold is defined as the 80th percentile of
+    nonexceedance.
+
+    References
+    ----------
+    .. [richter_1996] Richter, B. D. (1996). A method for assessing hydrologic
+       alteration within ecosystems. Society for Conservation Biology, 10(4),
+       1163–1174.
+
+    """
+    h = series > series.quantile(quantile)
+    return (h.astype(int).diff() > 0).sum()
+
+
+def low_pulse_duration(series, quantile=0.2):
+    """Average duration of pulses where the head is below a certain threshold.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+    quantile: float, optional
+        Quantile used as a threshold.
+
+    Returns
+    -------
+
+    Notes
+    -----
+    Average duration of pulses where the groundwater head drops below
+    a certain threshold. The threshold is defined as the 20th percentile of
+    non-exceedance.
+
+    References
+    ----------
+    .. [richter_1996] Richter, B. D. (1996). A method for assessing hydrologic
+       alteration within ecosystems. Society for Conservation Biology, 10(4),
+       1163–1174. https://doi.org/10.1046/j.1523‐1739.1996.10041163.x
+
+    """
+    h = series < series.quantile(quantile)
+    sel = h.astype(int).diff().replace(0.0, NA).shift(-1).dropna().index
+    return (diff(sel.to_numpy()) / Timedelta("1D"))[::2].mean()
+
+
+def high_pulse_duration(series, quantile=0.8):
+    """Average duration of pulses where the head exceeds a certain threshold.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+    quantile: float, optional
+        Quantile used as a threshold.
+
+    Returns
+    -------
+    float
+
+    Notes
+    -----
+    Average duration of pulses where the groundwater head drops exceeds a
+    certain threshold. The threshold is defined as the 80th percentile of
+    nonexceedance.
+
+    References
+    ----------
+    .. [richter_1996] Richter, B. D. (1996). A method for assessing hydrologic
+       alteration within ecosystems. Society for Conservation Biology, 10(4),
+       1163–1174. https://doi.org/10.1046/j.1523‐1739.1996.10041163.x
+
+    """
+    h = series > series.quantile(quantile)
+    sel = h.astype(int).diff().replace(0.0, NA).shift(-1).dropna().index
+    return (diff(sel.to_numpy()) / Timedelta("1D"))[::2].mean()
+
+
+def amplitude_range(series):
+    """Range of unscaled groundwater head.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+
+    Returns
+    -------
+    float
+
+    Notes
+    -----
+    Range of unscaled groundwater head.
+
+    """
+    return series.max() - series.min()
+
+
+def rise_rate(series, normalize=False):
+    """Mean of positive head changes from one day to the next.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+    normalize: bool, optional
+        normalize the time series to values between zero and one.
+
+    Returns
+    -------
+    float
+
+    Notes
+    -----
+    Mean rate of positive changes in head from one day to the next.
+
+    References
+    ----------
+    .. [richter_1996] Richter, B. D. (1996). A method for assessing hydrologic
+       alteration within ecosystems. Society for Conservation Biology, 10(4),
+       1163–1174. https://doi.org/10.1046/j.1523‐1739.1996.10041163.x
+
+    """
+    if normalize:
+        series = _normalize(series)
+
+    difference = series.diff()
+    rises = difference[difference > 0]
+    return rises.mean()
+
+
+def fall_rate(series, normalize=False):
+    """Mean negative head changes from one day to the next.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+    normalize: bool, optional
+        normalize the time series to values between zero and one.
+
+    Returns
+    -------
+    float
+
+    Notes
+    -----
+    Mean rate of negative changes in head from one day to the next,
+    according to [richter_1996]_.
+
+    References
+    ----------
+    .. [richter_1996] Richter, B. D. (1996). A method for assessing hydrologic
+       alteration within ecosystems. Society for Conservation Biology, 10(4),
+       1163–1174. https://doi.org/10.1046/j.1523‐1739.1996.10041163.x
+
+    """
+    if normalize:
+        series = _normalize(series)
+
+    difference = series.diff()
+    falls = difference[difference < 0]
+    return falls.mean()
+
+
+def cv_rise_rate(series, normalize=False):
+    """Coefficient of Variation in rise rate.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+    normalize: bool, optional
+        normalize the time series to values between zero and one.
+
+    Returns
+    -------
+    float
+
+    Notes
+    -----
+    Coefficient of Variation in riserate.
+
+    References
+    ----------
+    .. [richter_1996] Richter, B. D. (1996). A method for assessing hydrologic
+       alteration within ecosystems. Society for Conservation Biology, 10(4),
+       1163–1174. https://doi.org/10.1046/j.1523‐1739.1996.10041163.x
+
+    """
+    if normalize:
+        series = _normalize(series)
+
+    difference = series.diff()
+    rises = difference[difference > 0]
+    return rises.std() / rises.mean()
+
+
+def cv_fall_rate(series, normalize=False):
+    """Coefficient of Variation in fall rate.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+    normalize: bool, optional
+        normalize the time series to values between zero and one.
+
+    Returns
+    -------
+    float
+
+    Notes
+    -----
+    Coefficient of Variation in fall rate.
+
+    References
+    ----------
+    .. [richter_1996] Richter, B. D. (1996). A method for assessing hydrologic
+       alteration within ecosystems. Society for Conservation Biology, 10(4),
+       1163–1174. https://doi.org/10.1046/j.1523‐1739.1996.10041163.x
+
+    """
+    if normalize:
+        series = _normalize(series)
+
+    difference = series.diff()
+    falls = difference[difference < 0]
+    return falls.std() / falls.mean()
+
+
+def magnitude(series):
+    """Difference of peak head to base head, divided by base head.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+
+    Returns
+    -------
+    float
+
+    Notes
+    -----
+    Difference of peak head to base head, divided by base head.
+
+    (h_max - h_min ) / h_min
+
+    References
+    ----------
+    .. [hannah_2000] Hannah, D. M., Smith, B. P. G., Gurnell, A. M.,
+       & McGregor, G. R. (2000). An approach to hydrograph classification.
+       Hydrological Processes, 14(2), 317–338.
+
+    """
+
+    return (series.max() - series.min()) / series.min()
+
+
+def reversals_avg(series):
+    """Average annual number of rises and falls in daily head.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+
+    Returns
+    -------
+    float
+
+    Notes
+    -----
+    Average annual number of rises and falls (i.e., change of sign) in daily
+    head.
+
+    References
+    ----------
+    .. [richter_1996] Richter, B. D. (1996). A method for assessing hydrologic
+       alteration within ecosystems. Society for Conservation Biology, 10(4),
+       1163–1174.
+
+    """
+    reversals = (series.diff() > 0).astype(int).diff().replace(-1, 1)
+    return reversals.resample("A").sum().mean()
+
+
+def reversals_cv(series):
+    """Coefficient of Variation in annual number of rises and falls.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+
+    Returns
+    -------
+    float
+
+    Notes
+    -----
+    Coefficient of Variation in annual number of rises and falls in daily head.
+
+    References
+    ----------
+    .. [richter_1996] Richter, B. D. (1996). A method for assessing hydrologic
+       alteration within ecosystems. Society for Conservation Biology, 10(4),
+       1163–1174.
+
+    """
+    reversals = (series.diff() > 0).astype(int).diff().replace(-1, 1) \
+        .resample("A").sum()
+    return reversals.std() / reversals.mean()
+
+
+def mean_annual_maximum(series, normalize=False):
+    """Mean of annual maximum.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+    normalize: bool, optional
+        normalize the time series to values between zero and one.
+
+    Returns
+    -------
+    float
+
+    Notes
+    -----
+
+    References
+    ----------
+    .. [clausen_2000] Clausen, B., & Biggs, B. J. F. (2000). Flow variables
+       for ecological studies in temperate streams—Groupings based on
+       covariance. Journal of Hydrology, 237(3‐4), 184–197.
+
+    """
+    if normalize:
+        series = _normalize(series)
+
+    return series.resample("A").max().mean()
+
+
+def bimodality_coefficient(series, normalize=True):
+    """Bimodality coefficient after [Ellison_1987]_.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+    normalize: bool, optional
+        normalize the time series to values between zero and one.
+
+    Returns
+    -------
+    float
+
+    Notes
+    -----
+    Squared product moment skewness plus one, divided by product moment
+    kurtosis.
+
+        b = (skew **2 + 1 ) / kurtosis
+
+    Adapted from the R "modes" package
+
+    References
+    ----------
+    .. [Ellison_1987] Ellison, A. M. (1987). Effect of seed dimorphism on the
+       density‐dependent dynamics of experimental populations of atriplex
+       triangularis. American Journal of Botany, 74(8), 1280–1288.
+
+    """
+    if normalize:
+        series = _normalize(series)
+    series = series.dropna()
+    n = series.size
+    # Compute the skew for a finite sample
+    skew = (1 / n) * sum((series - series.mean()) ** 3) / \
+           (((1 / n) * sum((series - series.mean()) ** 2)) ** 1.5)
+    skew *= (sqrt(n * (n - 1))) / (n - 2)
+
+    # Compute the kurtosis for a finite sample
+    kurt = (1 / n) * sum((series - series.mean()) ** 4) / \
+           (((1 / n) * sum((series - series.mean()) ** 2)) ** 2) - 3
+    kurt = ((n - 1) * ((n + 1) * kurt - 3 * (n - 1)) / ((n - 2) * (n - 3))) + 3
+
+    return ((skew ** 2) + 1) / \
+           (kurt + ((3 * ((n - 1) ** 2)) / ((n - 2) * (n - 3))))
+
+
+def recession_constant(series, bins=20, normalize=False):
+    """Recession constant after [kirchner_2009]_.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+    bins: int, optional
+        Number of bins to bin the data to.
+    normalize: bool, optional
+        normalize the time series to values between zero and one.
+
+    Returns
+    -------
+    float
+
+    Notes
+    -----
+    Slope of the linear model fitted to percentile‐wise binned means in a
+    log‐log plot of negative head versus negative head one time step ahead.
+
+    References
+    ----------
+    .. [kirchner_2009] Kirchner, J. W. (2009). Catchments as simple dynamical
+       systems: Catchment characterization, rainfall‐runoff modeling, and doing
+       hydrology backward. Water Resources Research, 45, W02429.
+
+    """
+    if normalize:
+        series = _normalize(series)
+
+    series.name = "diff"
+    df = pd.DataFrame(series.diff().loc[series.diff() < 0], columns=["diff"])
+    df["head"] = series.loc[df.index]
+
+    binned = pd.Series(dtype=float)
+    for g in df.groupby(pd.cut(df["head"], bins=bins)):
+        binned[g[0].mid] = g[1]["diff"].mean()
+
+    binned = binned.dropna()
+    fit = linregress(log(binned.index), log(-binned.values))
+
+    return fit.slope
+
+
+def recovery_constant(series, bins=20, normalize=False):
+    """Recovery constant after [kirchner_2009]_.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+    bins: int, optional
+        Number of bins to bin the data to.
+    normalize: bool, optional
+        normalize the time series to values between zero and one.
+
+    Returns
+    -------
+    float
+
+    Notes
+    -----
+    Slope of the linear model fitted to percentile‐wise binned means in a
+    log‐log plot of positive head versus positive head one time step ahead.
+
+    References
+    ----------
+    .. [kirchner_2009] Kirchner, J. W. (2009). Catchments as simple dynamical
+       systems: Catchment characterization, rainfall‐runoff modeling, and doing
+       hydrology backward. Water Resources Research, 45, W02429.
+
+
+    """
+    if normalize:
+        series = _normalize(series)
+
+    series.name = "diff"
+    df = pd.DataFrame(series.diff().loc[series.diff() > 0], columns=["diff"])
+    df["head"] = series.loc[df.index]
+
+    binned = pd.Series(dtype=float)
+    for g in df.groupby(pd.cut(df["head"], bins=bins)):
+        binned[g[0].mid] = g[1]["diff"].mean()
+
+    binned = binned.dropna()
+    fit = linregress(log(binned.index), log(binned.values))
+
+    return fit.slope
+
+
+def duration_curve_slope(series, l=0.1, u=0.9, normalize=True):
+    """Slope of the duration curve between percentile l and u.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+    l: float
+        lower percentile, a float between 0 and 1, lower than u
+    u: float, optional
+        upper percentile, a float between 0 and 1, higher than l.
+    normalize: bool, optional
+        normalize the time series to values between zero and one.
+
+    Returns
+    -------
+    float
+
+    Notes
+    -----
+    Slope of the duration curve (analogue flow duration curve for streamflow)
+    between percentile l and u.
+
+    References
+    ----------
+    .. [oudin_2010] Oudin, L., Kay, A., Andréassian, V., & Perrin, C. (2010).
+       Are seemingly physically similar catchments truly hydrologically
+       similar? Water Resources Research, 46, W11558.
+
+    """
+    if normalize:
+        series = _normalize(series)
+
+    s = series[(series.quantile(l) > series) & (series < series.quantile(
+        u))].sort_values()
+    s.index = arange(s.size) / s.size
+    return linregress(s.index, s.values).slope
+
+
+def duration_curve_range(series, l=0.1, u=0.9, normalize=True):
+    """Range of the duration curve between the percentile l and u.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+    l: float
+        lower percentile, a float between 0 and 1, lower than u
+    u: float, optional
+        upper percentile, a float between 0 and 1, higher than l.
+    normalize: bool, optional
+        normalize the time series to values between zero and one.
+
+    Returns
+    -------
+    float
+
+    Notes
+    -----
+    Range of the duration curve between the percentile l and u.
+
+    References
+    ----------
+    .. [richards_1990] Richards, R. P. (1990). Measures of Flow Variability
+       and a New Flow‐Based Classification of Great Lakes Tributaries.
+       Journal of Great Lakes Research, 16(1), 53–70.
+
+    """
+    if normalize:
+        series = _normalize(series)
+
+    return series.quantile(u) - series.quantile(l)
+
+
+def richards_pathlength(series, normalize=True):
+    """The path length of the time series, standardized by time series length.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+    normalize: bool, optional
+        normalize the time series to values between zero and one.
+
+    Returns
+    -------
+    float
+
+    Notes
+    -----
+    The path length of the time series, standardized by time series length
+    and the median head.
+
+    References
+    ----------
+    .. [baker_2004] Baker, D. B., Richards, P., Loftus, T. T., & Kramer,
+       J. W. (2004). A new flashiness index: Characteristics and applications
+       to midwestern rivers and streams. Journal of the American Water
+       Resources Association, 40(2), 503–522.
+
+    """
+    if normalize:
+        series = _normalize(series)
+
+    series = series.dropna()
+    dt = diff(series.index.to_numpy()) / Timedelta("1D")
+    dh = series.diff().dropna()
+    # sum(dt) is more fair with irregular time series
+    return sum(sqrt(dh ** 2 + dt ** 2)) / sum(dt)
+
+
+def richards_baker_index(series, normalize=True):
+    """Richards-Baker index according to [baker_2004]_.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+    normalize: bool, optional
+        normalize the time series to values between zero and one.
+
+    Returns
+    -------
+    float
+
+    Notes
+    -----
+    Sum of absolute values of day‐to‐day changes in head divided by the sum
+    of scaled daily head. Equivalent the Richards Pathlength without the
+    time component.
+
+    References
+    ----------
+    .. [baker_2004] Baker, D. B., Richards, P., Loftus, T. T., & Kramer,
+       J. W. (2004). A new flashiness index: Characteristics and applications
+       to midwestern rivers and streams. Journal of the American Water
+       Resources Association, 40(2), 503–522.
+
+    """
+    if normalize:
+        series = _normalize(series)
+
+    return series.diff().dropna().abs().sum() / series.sum()
+
+
+def _baseflow(series, normalize=True):
+    """Baseflow function for the baseflow index and stability
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+    normalize: bool, optional
+        normalize the time series to values between zero and one.
+
+    Returns
+    -------
+    series: pandas.Series
+        Pandas Series of the original for
+    ht: pandas.Series
+        Pandas Series for the base head
+    """
+    if normalize:
+        series = _normalize(series)
+
+    # A/B. Selecting minima hm over 5 day periods
+    hm = series.resample("5D").min()
+
+    # C. define the turning points ht (0.9 * head < adjacent heads)
+    ht = pd.Series(dtype=float)
+    for i, h in enumerate(hm.iloc[1:-1], start=1):
+        if (h < hm.iloc[i - 1]) & (h < hm.iloc[i + 1]):
+            ht[hm.index[i]] = h
+
+    # ensure that index is a DatetimeIndex
+    ht.index = pd.to_datetime(ht.index)
+
+    # D. Interpolate
+    ht = ht.resample("D").interpolate()
+
+    # E. Assign a base head to each day
+    ht[ht > series.resample("D").mean().loc[ht.index]] = \
+        series.resample("D").mean()
+
+    return series, ht
+
+
+def baseflow_index(series, normalize=True):
+    """Baseflow index according to [wmo_2008]_.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+    normalize: bool, optional
+        normalize the time series to values between zero and one.
+
+    Returns
+    -------
+    float
+
+    Notes
+    -----
+    Adapted analogously to its application in streamflow. Here, a baseflow
+    time series is separated from a 5‐day minimum groundwater head in a
+    moving window. BFI equals the total sum of heads of original time series
+    divided by the total sum of heads from the baseflow type of time series.
+
+    References
+    ----------
+    .. [wmo_2008] WMO (2008). Manual on Low‐Flow Estimation and Prediction.
+       Geneva, Switzerland: World Meteorological Organization.
+
+    """
+
+    series, ht = _baseflow(series, normalize=normalize)
+
+    return series.resample("D").mean().sum() / ht.sum()
+
+
+def baseflow_stability(series, normalize=True):
+    """Baseflow stability after [heudorfer_2019]_.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        Pandas Series with DatetimeIndex and head values.
+    normalize: bool, optional
+        normalize the time series to values between zero and one.
+
+    Returns
+    -------
+    float
+
+    Notes
+    -----
+    Originally developed for streamflow, here the Base Flow Index
+    algorithm is analogously adapted to groundwater time series as a filter
+    to separate the slow component (“baseflow”) of the time series. Then,
+    the mean annual baseflow is calculated. Base Flow Stability is the
+    difference of maximum and minimum annual baseflow.
+
+    References
+    ----------
+    .. [heudorfer_2019] Heudorfer, B., Haaf, E., Stahl, K., & Barthel, R.
+       (2019). Index‐based characterization and quantification of groundwater
+       dynamics. Water Resources Research, 55, 5575–5592.
+
+    """
+
+    series, ht = _baseflow(series, normalize=normalize)
+
+    return ht.resample("A").mean().max() - ht.resample("A").mean().min()
+
+
+def hurst_exponent(series):
+    """Hurst exponent according to [wang_2006]_.
+
+    Returns
+    -------
+
+    Notes
+    -----
+    The slope of a linear model fitted to the relationship between the
+    sample size and the logarithmized sample range of k contiguous
+    subsamples from the time series.
+
+    References
+    ----------
+    .. [wang_2006] Wang, X., Smith, K., & Hyndman, R. (2006).
+       Characteristic‐based clustering for time series data. Data Mining and
+       Knowledge Discovery, 13(3), 335–364.
+
+    """
+    return NotImplementedError
+
+
+def autocorr(series, freq="w"):
+    """Lag where the first peak in the autocorrelation function occurs.
+
+    Returns
+    -------
+
+    Notes
+    -----
+    Lag where the first peak in the autocorrelation function occurs.
+
+    References
+    ----------
+    .. [wang_2006] Wang, X., Smith, K., & Hyndman, R. (2006).
+       Characteristic‐based clustering for time series data. Data Mining and
+       Knowledge Discovery, 13(3), 335–364.
+
+    """
+    return NotImplementedError
+
+
+def lyapunov_exponent(series):
+    """The exponential rate of divergence of nearby data points
+    [hilborn_1994]_.
+
+    Returns
+    -------
+
+    Notes
+    -----
+    The exponential rate of divergence of nearby data points when moving
+    away in time from a certain data point in the series. Iteratively
+    estimated for every point in the time series, then averaged.
+
+    References
+    ----------
+    .. [hilborn_1994] Hilborn, R. C. (1994). Chaos and Nonlinear Dynamics: An
+       Introduction for Scientists and Engineers. New York: Oxford University
+       Press.
+
+    """
+    return NotImplementedError
+
+
+def peak_timescale(series):
+    """Area under peak divided by difference of peak head to peak base.
+
+    Returns
+    -------
+
+    Notes
+    -----
+    Area under peak divided by difference of peak head to peak base,
+    averaged over all peaks.
+
+    References
+    ----------
+    .. [gaal_2012] Gaál, L., Szolgay, J., Kohnová, S., Parajka, J., Merz, R.,
+       Viglione, A., & Blöschl, G. (2012). Flood timescales: Understanding
+       the interplay of climate and catchment processes through comparative
+       hydrology. Water Resources Research, 48, W04511.
+
+    """
+    return NotImplementedError
+
+
+def excess_mass(series):
+    """Test statistic of the dip test, after [hartigan_1985]_.
+
+    Returns
+    -------
+
+    Notes
+    -----
+    Test statistic of the dip test; maximum distance between the empirical
+    distribution and the best fitting unimodal distribution. By default the
+    best fitting distribution is the uniform.
+
+    References
+    ----------
+    .. [hartigan_1985] Hartigan, J. A., & Hartigan, P. M. (1985). The dip
+       test of unimodality. The Annals of Statistics, 13(1), 70–84.
+
+    """
+    return NotImplementedError
+
+
+def critical_bandwidth(series):
+    """Test statistic of the Silverman test, after [silverman_1981]_.
+
+    Returns
+    -------
+
+    Notes
+    -----
+    Test statistic of the Silverman test; minimum kernel bandwidth required
+    to create a unimodal distribution estimated by fitting a Kernel Density
+    Estimation.
+
+    References
+    ----------
+    .. [silverman_1981] Silverman, B. W. (1981). Using kernel density
+       estimates to investigate multimodality. Journal of The Royal
+       Statistical Society Series B‐ Statistical Methodology, 43(1), 97–99.
+
+    """
+    return NotImplementedError
+
+
+def peak_base_time(series):
+    """Difference between peak and base head, standardized by duration of peak.
+
+    Returns
+    -------
+
+    Notes
+    -----
+    Difference between peak and base head, standardized by duration of peak.
+
+    References
+    ----------
+    .. [heudorfer_2019] Heudorfer, B., Haaf, E., Stahl, K., & Barthel, R.
+       (2019). Index‐based characterization and quantification of groundwater
+       dynamics. Water Resources Research, 55, 5575–5592.
+       https://doi.org/10.1029/2018WR024418
+
+    """
+    return NotImplementedError
+
+
+def summary(series, signatures=None):
+    """Method to get many signatures for a time series.
+
+    Parameters
+    ----------
+    series: pandas.Series
+        pandas Series with DatetimeIndex
+    signatures: list
+        By default all available signatures are returned.
+
+    Returns
+    -------
+    data: pandas.Series
+        Pandas series with every row a signature
+
+    Examples
+    --------
+    idx = pd.date_range("2000", "2010")
+    head = pd.Series(index=idx, data=np.random.rand(len(idx)), dtype=float)
+    ps.stats.signatures.summary(head)
+
+    """
+    if signatures is None:
+        signatures = __all__
+
+    data = pd.Series(index=signatures, dtype=float)
+
+    for signature in signatures:
+        func = getattr(ps.stats.signatures, signature)
+        data.loc[signature] = func(series)
+
+    return data


Test patch:
diff --git a/tests/test_signatures.py b/tests/test_signatures.py
new file mode 100644
index 00000000..8e92e099
--- /dev/null
+++ b/tests/test_signatures.py
@@ -0,0 +1,43 @@
+import pandas as pd
+import pastas as ps
+import numpy as np
+
+
+def test_summary():
+    """Test all signatures for minimal functioning."""
+    idx = pd.date_range("2000", "2010")
+    head = pd.Series(index=idx, data=np.random.rand(len(idx)), dtype=float)
+    ps.stats.signatures.summary(head)
+
+
+def test_colwell_components():
+    """Example Tree C from the publication."""
+    n = 9
+    x = ["200{}-04-01".format(t) for t in range(0, n)] + \
+        ["200{}-08-02".format(t) for t in range(0, n)] + \
+        ["201{}-08-01".format(t) for t in range(0, n)]
+    y = [1] * n + [2] * n + [3] * n
+    obs = pd.Series(y, index=pd.to_datetime(x))
+    ps.stats.signatures.colwell_components(obs, freq="4M", bins=3)
+    return obs
+
+
+def test_colwell_predictability():
+    """Example Tree C from the publication."""
+    obs = test_colwell_components()
+    p = ps.stats.signatures.colwell_components(obs, freq="4M", bins=3)[0]
+    assert p.round(2) == 1.0
+
+
+def test_colwell_constancy():
+    """Example Tree C from the publication."""
+    obs = test_colwell_components()
+    c = ps.stats.signatures.colwell_components(obs, freq="4M", bins=3)[1]
+    assert c.round(2) == 0.42
+
+
+def test_colwell_contingency():
+    """Example Tree C from the publication."""
+    obs = test_colwell_components()
+    m = ps.stats.signatures.colwell_components(obs, freq="4M", bins=3)[2]
+    assert m.round(2) == 0.58