{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MHKiT Loads Module\n",
    "The following example will help familiarize you with some of the functions in the [MHKiT loads module](https://mhkit-software.github.io/MHKiT/mhkit-python/api.loads.html) that you can use to assist you in your loads analysis. \n",
    "\n",
    "Start by importing the necessary python packages and MHKiT module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "from mhkit import utils\n",
    "from mhkit import loads \n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Loads Data \n",
    "\n",
    "This example data comes from a land based wind turbine, since there is limited availablality of loads data for MHK devices. The example uses a subset of data from a database of 331 files, each containing 10 minutes of data sampled at 50Hz. \n",
    "\n",
    "As a start, lets look at the data for one of these files to figure out what formatting we need to apply. We utilize pandas to read in the csv and peek into what the data looks like. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Time</th>\n",
       "      <th>uWind_80m</th>\n",
       "      <th>WD_ModActive</th>\n",
       "      <th>WD_Nacelle</th>\n",
       "      <th>WD_NacelleMod</th>\n",
       "      <th>LSSDW_Tq</th>\n",
       "      <th>LSSDW_My</th>\n",
       "      <th>LSSDW_Mz</th>\n",
       "      <th>TTTq</th>\n",
       "      <th>TT_ForeAft</th>\n",
       "      <th>TT_SideSide</th>\n",
       "      <th>TB_ForeAft</th>\n",
       "      <th>TB_SideSide</th>\n",
       "      <th>BL3_FlapMom</th>\n",
       "      <th>BL3_EdgeMom</th>\n",
       "      <th>BL1_FlapMom</th>\n",
       "      <th>BL1_EdgeMom</th>\n",
       "      <th>ActivePower</th>\n",
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       "      <td>3.221099</td>\n",
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       "      <td>157.279582</td>\n",
       "      <td>-39.717967</td>\n",
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       "      <td>-7.970862</td>\n",
       "      <td>-67.860011</td>\n",
       "      <td>-922.971018</td>\n",
       "      <td>-22.485796</td>\n",
       "      <td>-292.252115</td>\n",
       "      <td>876.299461</td>\n",
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       "      <td>-166.018111</td>\n",
       "      <td>35.495810</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>-310.213400</td>\n",
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       "      Timestamp  Time  uWind_80m  WD_ModActive  WD_Nacelle  WD_NacelleMod  \\\n",
       "0  42795.061586  0.00   3.226754           1.0  157.302829     157.279582   \n",
       "1  42795.061586  0.02   3.221099           1.0  157.302829     157.279582   \n",
       "2  42795.061586  0.04   3.223492           1.0  157.302829     157.279582   \n",
       "3  42795.061586  0.06   3.223274           1.0  157.302829     157.279582   \n",
       "4  42795.061587  0.08   3.223927           1.0  157.302829     157.279582   \n",
       "\n",
       "    LSSDW_Tq    LSSDW_My   LSSDW_Mz       TTTq  TT_ForeAft  TT_SideSide  \\\n",
       "0 -41.380694 -234.487436  -6.207381 -70.130726 -936.247028   -12.605151   \n",
       "1 -38.614459 -233.715870  -8.886200 -66.916338 -942.675906   -24.350452   \n",
       "2 -39.717967 -234.341966  -7.970862 -67.860011 -922.971018   -22.485796   \n",
       "3 -41.415143 -235.645598 -10.451794 -72.371951 -939.515265   -33.030892   \n",
       "4 -38.614459 -234.755991  -8.648988 -76.530014 -924.771486   -29.228398   \n",
       "\n",
       "   TB_ForeAft  TB_SideSide  BL3_FlapMom  BL3_EdgeMom  BL1_FlapMom  \\\n",
       "0 -330.410779  1024.816867   470.774738  -165.541786    33.427748   \n",
       "1 -315.445562   873.214212   469.244736  -163.588005    32.697822   \n",
       "2 -292.252115   876.299461   468.736474  -166.018111    35.495810   \n",
       "3 -274.812769   763.833828   467.373452  -164.645639    37.952455   \n",
       "4 -310.213400   704.537757   466.318754  -161.233863    37.430668   \n",
       "\n",
       "   BL1_EdgeMom  ActivePower  yawoffset  \n",
       "0   -59.452360    -5.279680   0.023247  \n",
       "1   -62.300637    -5.671178   0.023247  \n",
       "2   -61.733604    -5.551847   0.023247  \n",
       "3   -64.390050    -4.626557   0.023247  \n",
       "4   -65.974766    -4.708621   0.023247  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loads_data_file = './data/loads/data_loads_example.csv'\n",
    "\n",
    "# Import csv data file\n",
    "raw_loads_data = pd.read_csv(loads_data_file)\n",
    "raw_loads_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Format Loads Data with datetime index\n",
    "\n",
    "To use MHKiT it is important to index your DataFrame by datetime. The example loads data has two references to time, but neither are in the right format. The `'Timestamp'` column is what will give us the datetime index that we need, but we first need to convert it from microsoft excel format to pd.Datetime. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "                               uWind_80m  WD_ModActive  WD_Nacelle  \\\n",
       "Timestamp                                                            \n",
       "2017-03-01 01:28:40.999987200   3.226754           1.0  157.302829   \n",
       "2017-03-01 01:28:41.020032000   3.221099           1.0  157.302829   \n",
       "2017-03-01 01:28:41.039990400   3.223492           1.0  157.302829   \n",
       "2017-03-01 01:28:41.060035200   3.223274           1.0  157.302829   \n",
       "2017-03-01 01:28:41.079993600   3.223927           1.0  157.302829   \n",
       "\n",
       "                               WD_NacelleMod   LSSDW_Tq    LSSDW_My  \\\n",
       "Timestamp                                                             \n",
       "2017-03-01 01:28:40.999987200     157.279582 -41.380694 -234.487436   \n",
       "2017-03-01 01:28:41.020032000     157.279582 -38.614459 -233.715870   \n",
       "2017-03-01 01:28:41.039990400     157.279582 -39.717967 -234.341966   \n",
       "2017-03-01 01:28:41.060035200     157.279582 -41.415143 -235.645598   \n",
       "2017-03-01 01:28:41.079993600     157.279582 -38.614459 -234.755991   \n",
       "\n",
       "                                LSSDW_Mz       TTTq  TT_ForeAft  TT_SideSide  \\\n",
       "Timestamp                                                                      \n",
       "2017-03-01 01:28:40.999987200  -6.207381 -70.130726 -936.247028   -12.605151   \n",
       "2017-03-01 01:28:41.020032000  -8.886200 -66.916338 -942.675906   -24.350452   \n",
       "2017-03-01 01:28:41.039990400  -7.970862 -67.860011 -922.971018   -22.485796   \n",
       "2017-03-01 01:28:41.060035200 -10.451794 -72.371951 -939.515265   -33.030892   \n",
       "2017-03-01 01:28:41.079993600  -8.648988 -76.530014 -924.771486   -29.228398   \n",
       "\n",
       "                               TB_ForeAft  TB_SideSide  BL3_FlapMom  \\\n",
       "Timestamp                                                             \n",
       "2017-03-01 01:28:40.999987200 -330.410779  1024.816867   470.774738   \n",
       "2017-03-01 01:28:41.020032000 -315.445562   873.214212   469.244736   \n",
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       "2017-03-01 01:28:41.079993600 -310.213400   704.537757   466.318754   \n",
       "\n",
       "                               BL3_EdgeMom  BL1_FlapMom  BL1_EdgeMom  \\\n",
       "Timestamp                                                              \n",
       "2017-03-01 01:28:40.999987200  -165.541786    33.427748   -59.452360   \n",
       "2017-03-01 01:28:41.020032000  -163.588005    32.697822   -62.300637   \n",
       "2017-03-01 01:28:41.039990400  -166.018111    35.495810   -61.733604   \n",
       "2017-03-01 01:28:41.060035200  -164.645639    37.952455   -64.390050   \n",
       "2017-03-01 01:28:41.079993600  -161.233863    37.430668   -65.974766   \n",
       "\n",
       "                               ActivePower  yawoffset  \n",
       "Timestamp                                              \n",
       "2017-03-01 01:28:40.999987200    -5.279680   0.023247  \n",
       "2017-03-01 01:28:41.020032000    -5.671178   0.023247  \n",
       "2017-03-01 01:28:41.039990400    -5.551847   0.023247  \n",
       "2017-03-01 01:28:41.060035200    -4.626557   0.023247  \n",
       "2017-03-01 01:28:41.079993600    -4.708621   0.023247  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the datetime conversion from the utils module\n",
    "datetime = utils.excel_to_datetime(raw_loads_data['Timestamp'])\n",
    "\n",
    "# Replace the 'Timestamp' column with our newly formatted datetime\n",
    "raw_loads_data['Timestamp'] = datetime \n",
    "\n",
    "# Set this as our index for our DataFrame\n",
    "loads_data = raw_loads_data.set_index('Timestamp')\n",
    "\n",
    "# Remove the 'time' column since it will not be used\n",
    "loads_data.drop(columns='Time',inplace=True)\n",
    "loads_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loads Analysis\n",
    "Now that we have our loads data in the correct format for MHKiT, we do some analysis.\n",
    "\n",
    "### Damage Equivalent Loads\n",
    "Lets say that we wanted to investigate fatigue. We can do this by calculating short-term damage equivalent loads (DELs). In this instance, we calculate the DELs on the tower base moment `'TB_ForeAft'`, and on blade 1 root flap moment `'BL1_FlapMom'`. Our tower is steel while our blade is composite so they will have different material slopes. We will run the function `damage_equivalent_load` on each data signal. \n",
    "\n",
    "We call our function and apply the default inputs of using at least 100 bins for the load ranges and we let data_length=600 seconds so that we get an equivalent 1Hz DEL for our 10 minute file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEL TB_ForeAft: 3912.6390862773\n",
      "DEL BL1_FlapMom: 1435.8222478714554\n"
     ]
    }
   ],
   "source": [
    "# Calculate the damage equivalent load for blade 1 root momement and tower base moment\n",
    "DEL_tower = loads.general.damage_equivalent_load(loads_data['TB_ForeAft'],4,\n",
    "                                         bin_num=100,data_length=600)\n",
    "DEL_blade = loads.general.damage_equivalent_load(loads_data['BL1_FlapMom'],10,\n",
    "                                         bin_num=100,data_length=600)\n",
    "print('DEL TB_ForeAft: '+ str(DEL_tower))\n",
    "print('DEL BL1_FlapMom: '+ str(DEL_blade))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate Statistics\n",
    "\n",
    "Another important part of loads analysis is looking at statistics. Here, we use another function to help us calculate the mean, max, min, and std for this 10 minute file. Per standards, a valid statistical window has to be consecutive in time with the correct number of datapoints. If this 10 minute file did meet this criteria, then no stats would be generated and a warning message would appear. \n",
    "\n",
    "NOTE: Sometimes individual files may contain enough data for multiple statistical windows. This function can still handle this scenario as long as the correct inputs are specified.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
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       "    }\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>uWind_80m</th>\n",
       "      <th>WD_ModActive</th>\n",
       "      <th>WD_Nacelle</th>\n",
       "      <th>WD_NacelleMod</th>\n",
       "      <th>LSSDW_Tq</th>\n",
       "      <th>LSSDW_My</th>\n",
       "      <th>LSSDW_Mz</th>\n",
       "      <th>TTTq</th>\n",
       "      <th>TT_ForeAft</th>\n",
       "      <th>TT_SideSide</th>\n",
       "      <th>TB_ForeAft</th>\n",
       "      <th>TB_SideSide</th>\n",
       "      <th>BL3_FlapMom</th>\n",
       "      <th>BL3_EdgeMom</th>\n",
       "      <th>BL1_FlapMom</th>\n",
       "      <th>BL1_EdgeMom</th>\n",
       "      <th>ActivePower</th>\n",
       "      <th>yawoffset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-03-01 01:28:41</th>\n",
       "      <td>7.773325</td>\n",
       "      <td>1.0</td>\n",
       "      <td>178.612256</td>\n",
       "      <td>178.602595</td>\n",
       "      <td>127.244191</td>\n",
       "      <td>-252.23813</td>\n",
       "      <td>3.50322</td>\n",
       "      <td>7.032573</td>\n",
       "      <td>-846.663367</td>\n",
       "      <td>271.446574</td>\n",
       "      <td>3785.034515</td>\n",
       "      <td>7.199176</td>\n",
       "      <td>-494.858287</td>\n",
       "      <td>266.790368</td>\n",
       "      <td>-452.652744</td>\n",
       "      <td>21.259999</td>\n",
       "      <td>234.578289</td>\n",
       "      <td>0.009661</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     uWind_80m  WD_ModActive  WD_Nacelle  WD_NacelleMod  \\\n",
       "2017-03-01 01:28:41   7.773325           1.0  178.612256     178.602595   \n",
       "\n",
       "                       LSSDW_Tq   LSSDW_My  LSSDW_Mz      TTTq  TT_ForeAft  \\\n",
       "2017-03-01 01:28:41  127.244191 -252.23813   3.50322  7.032573 -846.663367   \n",
       "\n",
       "                     TT_SideSide   TB_ForeAft  TB_SideSide  BL3_FlapMom  \\\n",
       "2017-03-01 01:28:41   271.446574  3785.034515     7.199176  -494.858287   \n",
       "\n",
       "                     BL3_EdgeMom  BL1_FlapMom  BL1_EdgeMom  ActivePower  \\\n",
       "2017-03-01 01:28:41   266.790368  -452.652744    21.259999   234.578289   \n",
       "\n",
       "                     yawoffset  \n",
       "2017-03-01 01:28:41   0.009661  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the means, maxs, mins, and stdevs for all data signals in the loads data file\n",
    "means,maxs,mins,stdevs = utils.get_statistics(loads_data,50,period=600)\n",
    "\n",
    "# Display the results, indexed by the first timestamp of the corresponding statistical window\n",
    "means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, it would be nice to start visualizing some of this data. In order to do this, we need to calculate the stats and DELs for all the files in our database. In this case, it would be done through a loop that imports each file and applies all the functions we just saw. At the end of the loop, we store the result by appending to a list (this is computationally more efficient than concatenating dataframes). Finally, we can convert our lists to dataframes so that its easier to play with the data. To speed things up, this was already done so we just need to import the resulting dataframes.  \n",
    "\n",
    "As a reference, an example code block is shown of how to create a loop that calculates all the means for each file which are then stored into a dataframe. \n",
    "\n",
    "```python\n",
    "# pre-allocate lists for storage\n",
    "means = []\n",
    "time = []\n",
    "\n",
    "# start loop\n",
    "for f in os.listdir(pathOut):\n",
    "    if f.endswith('.csv'):\n",
    "        # import csv file\n",
    "        raw_loads_data = pd.read_csv(pathOut+'/'+f)\n",
    "        # replace the timestamp column with formatted datetime\n",
    "        datetime = utils.excel_to_datetime(raw_loads_data['Timestamp']) \n",
    "        raw_loads_data['Timestamp'] = datetime \n",
    "        # set this as our index for our dataframe\n",
    "        loads_data = raw_loads_data.set_index('Timestamp')\n",
    "        # remove the \"time\" column as its unnecessary \n",
    "        loads_data.drop(columns='Time',inplace=True)\n",
    "        # get stats\n",
    "        fmean, fmax, fmin, fstd = utils.get_statistics(loads_data,freq=50,period=600)\n",
    "        means.append(fmean.values.tolist())\n",
    "        time.append(fmean.index.values)\n",
    "\n",
    "# convert lists into a dataframe\n",
    "loads_means = pd.DataFrame(np.squeeze(means),columns=loads_data.columns.values,index=time)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "        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>uWind_80m</th>\n",
       "      <th>WD_ModActive</th>\n",
       "      <th>WD_Nacelle</th>\n",
       "      <th>WD_NacelleMod</th>\n",
       "      <th>LSSDW_Tq</th>\n",
       "      <th>LSSDW_My</th>\n",
       "      <th>LSSDW_Mz</th>\n",
       "      <th>TTTq</th>\n",
       "      <th>TT_ForeAft</th>\n",
       "      <th>TT_SideSide</th>\n",
       "      <th>TB_ForeAft</th>\n",
       "      <th>TB_SideSide</th>\n",
       "      <th>BL3_FlapMom</th>\n",
       "      <th>BL3_EdgeMom</th>\n",
       "      <th>BL1_FlapMom</th>\n",
       "      <th>BL1_EdgeMom</th>\n",
       "      <th>ActivePower</th>\n",
       "      <th>yawoffset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.773325</td>\n",
       "      <td>1.0</td>\n",
       "      <td>178.612256</td>\n",
       "      <td>178.602595</td>\n",
       "      <td>127.244191</td>\n",
       "      <td>-252.238130</td>\n",
       "      <td>3.503220</td>\n",
       "      <td>7.032573</td>\n",
       "      <td>-846.663367</td>\n",
       "      <td>271.446574</td>\n",
       "      <td>3785.034515</td>\n",
       "      <td>7.199176</td>\n",
       "      <td>-494.858288</td>\n",
       "      <td>266.790368</td>\n",
       "      <td>-452.652743</td>\n",
       "      <td>21.259999</td>\n",
       "      <td>234.578289</td>\n",
       "      <td>0.009661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.294855</td>\n",
       "      <td>1.0</td>\n",
       "      <td>171.095503</td>\n",
       "      <td>171.104399</td>\n",
       "      <td>6.705063</td>\n",
       "      <td>-242.954279</td>\n",
       "      <td>8.781801</td>\n",
       "      <td>13.574055</td>\n",
       "      <td>-1005.504041</td>\n",
       "      <td>210.432881</td>\n",
       "      <td>504.631955</td>\n",
       "      <td>-63.459058</td>\n",
       "      <td>-123.664013</td>\n",
       "      <td>179.906995</td>\n",
       "      <td>-95.184346</td>\n",
       "      <td>-44.686204</td>\n",
       "      <td>32.156167</td>\n",
       "      <td>-0.008896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.210606</td>\n",
       "      <td>1.0</td>\n",
       "      <td>168.688106</td>\n",
       "      <td>168.680758</td>\n",
       "      <td>51.782698</td>\n",
       "      <td>-224.455118</td>\n",
       "      <td>15.310279</td>\n",
       "      <td>-53.986054</td>\n",
       "      <td>-926.132830</td>\n",
       "      <td>138.439249</td>\n",
       "      <td>1992.287971</td>\n",
       "      <td>-42.414665</td>\n",
       "      <td>-314.992337</td>\n",
       "      <td>228.895991</td>\n",
       "      <td>-270.323145</td>\n",
       "      <td>-5.000843</td>\n",
       "      <td>87.337237</td>\n",
       "      <td>0.007348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.210652</td>\n",
       "      <td>1.0</td>\n",
       "      <td>182.695493</td>\n",
       "      <td>182.688322</td>\n",
       "      <td>386.484523</td>\n",
       "      <td>-218.151791</td>\n",
       "      <td>13.457946</td>\n",
       "      <td>-39.223265</td>\n",
       "      <td>-568.024026</td>\n",
       "      <td>515.128540</td>\n",
       "      <td>7075.518787</td>\n",
       "      <td>649.320139</td>\n",
       "      <td>-689.885628</td>\n",
       "      <td>301.399047</td>\n",
       "      <td>-718.343755</td>\n",
       "      <td>77.781426</td>\n",
       "      <td>692.061262</td>\n",
       "      <td>0.007171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.558234</td>\n",
       "      <td>1.0</td>\n",
       "      <td>182.443087</td>\n",
       "      <td>182.426475</td>\n",
       "      <td>561.122866</td>\n",
       "      <td>-228.167278</td>\n",
       "      <td>-30.095950</td>\n",
       "      <td>-51.686133</td>\n",
       "      <td>-347.327549</td>\n",
       "      <td>621.974202</td>\n",
       "      <td>10992.154570</td>\n",
       "      <td>598.674976</td>\n",
       "      <td>-1089.599789</td>\n",
       "      <td>374.679600</td>\n",
       "      <td>-1151.084828</td>\n",
       "      <td>138.214755</td>\n",
       "      <td>997.975514</td>\n",
       "      <td>0.016613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   uWind_80m  WD_ModActive  WD_Nacelle  WD_NacelleMod    LSSDW_Tq    LSSDW_My  \\\n",
       "0   7.773325           1.0  178.612256     178.602595  127.244191 -252.238130   \n",
       "1   4.294855           1.0  171.095503     171.104399    6.705063 -242.954279   \n",
       "2   5.210606           1.0  168.688106     168.680758   51.782698 -224.455118   \n",
       "3  14.210652           1.0  182.695493     182.688322  386.484523 -218.151791   \n",
       "4  10.558234           1.0  182.443087     182.426475  561.122866 -228.167278   \n",
       "\n",
       "    LSSDW_Mz       TTTq   TT_ForeAft  TT_SideSide    TB_ForeAft  TB_SideSide  \\\n",
       "0   3.503220   7.032573  -846.663367   271.446574   3785.034515     7.199176   \n",
       "1   8.781801  13.574055 -1005.504041   210.432881    504.631955   -63.459058   \n",
       "2  15.310279 -53.986054  -926.132830   138.439249   1992.287971   -42.414665   \n",
       "3  13.457946 -39.223265  -568.024026   515.128540   7075.518787   649.320139   \n",
       "4 -30.095950 -51.686133  -347.327549   621.974202  10992.154570   598.674976   \n",
       "\n",
       "   BL3_FlapMom  BL3_EdgeMom  BL1_FlapMom  BL1_EdgeMom  ActivePower  yawoffset  \n",
       "0  -494.858288   266.790368  -452.652743    21.259999   234.578289   0.009661  \n",
       "1  -123.664013   179.906995   -95.184346   -44.686204    32.156167  -0.008896  \n",
       "2  -314.992337   228.895991  -270.323145    -5.000843    87.337237   0.007348  \n",
       "3  -689.885628   301.399047  -718.343755    77.781426   692.061262   0.007171  \n",
       "4 -1089.599789   374.679600 -1151.084828   138.214755   997.975514   0.016613  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load DataFrames containing load statistics\n",
    "means = pd.read_csv('./data/loads/data_loads_means.csv')\n",
    "maxs = pd.read_csv('./data/loads/data_loads_maxs.csv')\n",
    "mins = pd.read_csv('./data/loads/data_loads_mins.csv')\n",
    "std = pd.read_csv('./data/loads/data_loads_std.csv')\n",
    "\n",
    "means.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Statistics\n",
    "\n",
    "Now that we have the load statistics, lets display the data as scatter plot. Using the `plot_statistics` function, we can quickly create a standard scatter plot showing how load variables trend with wind speed. Using this we can quickly identify expected trends and track down outliers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ckB06dJDZ2dkyJydH5uVp7cjRo0elrf397bffpJ+fnzxx4oQ0m82yX79+ctOmTU7P76wOgF3SSfuuLKhGSGV9S2pdKYXiMuD8EWh9Xcm01tdp6bXEttyGTqdzutzG6tWrmTVrFt27d2fo0KHk5+dz6tQpioqKePDBB+nWrRsTJ07k4MGD9jz79u1LWFgYOp2O7t27c/LkyVqXU00W28ioSt+SWldKobgMCOoEp7ZBxODitFPbtPRaUtlyG3q9nkWLFtGpU8lzzZgxg+bNm/PHH39gsVjw8PBwmqder8dkMlFblEA1MmzWkfd/07m1l/MFzNS6UvVHSmY+zyz8k3cmXq2CThT1y+BnYOljEP2xZjmd2qZ9H/5KvZ/6pptu4qOPPuKjjz5CCMHevXvp0aMHGRkZdivpiy++wGyu30Br5eJrRNisp0eGtCVhZgKPDGlbp8tA1yWX6xgsFbqvuGR0m6CJ0Yrn4PVm2r/DX7kkUXwvv/wyRUVFXH311XTt2pWXX34ZgL/97W988cUXXHPNNRw+fBhvb+/6LYizjqnG9HclBUnMXHZAzlx2QEop5W/8Zk97dfmBco+Jmx5Xq/LVlJnLDsirZ6yqsGwVUVEgSEN1+NuCT/YnXmx0QScqSKL6NHiQRCOkoKCg0n1UkMRlisec8wyOSmG9WA/AerGewVEpuP/nfLnHJMxMuESlK8axn6ymFl5VLJX4GfHlbqsPVOi+QnFpUQLViHju58EMlUMZKocC2D8/9/Pgig+8xNS2Ia+qwF1K8XV0rwIu7V5VKC4XlEBdhsTPiGe9WF/C0lov1l8Si6MuGnJ7mLyvO95uBt5bc7S+ilv9MqnQfYXikqGi+Bop4dPDy90WMSOCiBkRgCZONovrUlBRQ1462tAZNoFb8+Rg5myIIyu/iMV7knjyxo408/UgfkY8CTMTOIomWjYRDp8ebr/m+kCF7isUlx4lUI2U+myMa4NjQz52s5HYgUVA1Rtym8ABLNqTyLcP9mP8f7by3pqj/N+tVxMxIwL3h9wx+ARw0H87XTL7XZJwb8fQ/fgZ8S5b/wrF5YQSqMuciiyt+sCxIV8v1vP+5hHVOr60wN2StxmAXw+llNhvzoY4BkO5lll9jldKmJmgBEqhuASoPqjLnMbWkC58tD87XxyOv6eRsVvcODlrNDtfHE6eycykudtIycrnfE4Ri/Yk4vlki3L7t9R4JYWi5ixbtoxZs2Y1dDGUBaWoW2x9RDZq0kdU7ObTrKZmfh60auLFnlOa4GRnZzO+ZxjXjunMrcstZawoWz/W3IzWPLznVLlLjVTHyqqL61IoqsO9K+9lb8reMuk9mvXgy5u/rNdzR0VFERUVVa/nqApKoBR1Sm0DNOJnxDN4ZrE7zyYEHQYUIid68+Pu01jMkl+fuRrQogRHvL+xhAjZBC4v6iy3Lis/QMPRyqosgKMhA08UVyZdA7vSNbAr0/pOs6fN3jkbIUSt8j158iQjR46kX79+bN26lT59+jBlyhSmT59OSkoK33zzDQcPHmTXrl18/PHH3Hffffj5+bFr1y7Onj3LW2+9xYQJl2ZNKuXiU7gUETMiSoz1Ati4rBkB08JY8cRgWjXxIsTfrdxw76qGudfFYGKFoj6JiYxh2YllpOamApCam8ryuOXERMbUOu/jx4/z9NNPc/jwYQ4fPsy3337L5s2beeedd3jzzTfL7H/mzBk2b97MTz/9xPPPP1/r81cVJVCKeqOuAjRsghM/Ix6DXnA0NY82z/9s/5u/JZ4/Tl8EYOXDf/LBDDcO+m8H4KD/dt6f7sbKh/8skWdtBhNf6sATxZVJsFcwUe2imP/XfADm/zWfqHZRBHkG1TrvypbbKM3YsWPR6XR06dKFc+fO1fr8VUW5+BT1Rk37Zkr393www42DMzTBWSqHMm3BTnx8fJy65ZYPNrErPAeAz2d7c9807XPvcDcmW/dxHGsFzt2E9XFdCkV1iYmMYezSsUS1i2J53HJio2PrJN/KltuoaH9ZFyvlVhElUAqXo3R/j01kPp/tTZvnfwagd7jztWZKhLnPXs/JWaPL7FPbwcQKxaXCZkU9sPoBottH14n11JhQAqVweT6f7V3mc/j0lpUeV54rTs0KoWhMxETGEJ8RXyd9T40NcSnNtfqgd+/ecteuXQ1djBqTnJxMSEhIQxfDZXGctcEWPafqrPqoOqs+9VFnhw4d4qqrrqrTPF2JwsJC3NzcKtzHWR0IIXZLKXuX3lcFSShcGtXfo1BcuSiBUjQaVPScQnFloQRK0WhQ1pRCcWWhBEqhUCgULokSKIVCoVC4JEqgFAqFQuGSKIFSKBSKK5z333+f3Nxcp9s+//xzHnvssUtcIg0lUAqFQuHixM+Ir3ynWlCRQDUkSqAUCoXCxXGcm7K25OTkMHr0aK655hoiIyOZOXMmycnJXH/99Vx//fUA/O9//6Njx4707duXLVu22I9NTU1l/Pjx9OnThz59+rBlyxYsFgtt2rTh4sWL9v06dOhQJ5PK1lqghBCthBC/CSEOCiEOCCGesKYHCCHWCCGOWf9tak0XQogPhRDHhRB/CiF6OuQ12br/MSHE5PLOqVAoFIqa8csvvxASEsIff/zBX3/9xT/+8Q9CQkL47bff+O233zhz5gzTp09ny5YtbN68mYMHD9qPfeKJJ3jyySf5/fffWbRoEQ888AA6nY7o6GiWLFkCwI4dOwgPD6d58+a1LmtdWFAm4GkpZRegHzBVCNEFeB5YJ6XsAKyzfge4Gehg/XsI+A9oggZMB64F+gLTbaKmUCgUVxrxM+JZL9bbF+20fa6tu69bt26sWbOGadOmsWnTJvz9/Uts37FjB0OHDiU4OBg3Nzduv/12+7a1a9fy2GOP0b17d6KiosjMzCQ7O5vbb7+d77//HoAFCxaUOKY21HqyWCnlGeCM9XOWEOIQEApEA0Otu30BrAemWdO/lNokgNuFEE2EEC2t+66RUqYDCCHWACOB72pbRoVCoWhs1Ncqzh07dmTPnj2sWLGCl156ieHDh1f5WIvFwvbt2/HwKLkszXXXXcfx48dJTU0lNjaWl156qU7KWqezmQsh2gA9gB1Ac6t4AZwFbPZeKHDa4bBEa1p56c7O8xCa9UVYWBjJycl1dAWXnvT09IYuQqND1Vn1UXVWfeqjzsxmM4WFhTU6tqbHlSY5OZmAgABuu+02vL29+d///oePjw9paWn4+fnRo0cPnnjiCc6cOYOfnx8//PAD3bp1o7CwkBtuuIH33nuPp59+GoA//viDa665BoCoqCieffZZOnXqhK+vb7nlNZvNVW6z60yghBA+wCLgH1LKTCGEfZuUUgoh6mzadCnlXGAuaLOZN/ZZmht7+RsCVWfVR9VZ9anrOsvIyKh0tm9nhE8Pr9Fxzjhy5AjPPvssOp0Oo9HIf/7zH7Zt20ZUVJS9L2rGjBkMGTKEJk2a0L17d/R6PW5ubnz88cdMnTqV3r17YzKZGDx4MHPmzAHgzjvvpE+fPnz++ecVllWv11e5XutEoIQQRjRx+kZKudiafE4I0VJKecbqwkuxpicBrRwOD7OmJVHsErSlr6+L8ikUCkVjpi7nobzpppu46aabSqT17t2bxx9/3P59ypQpTJkypcyxQUFB9r6m0vTu3ZuCgoI6E1Komyg+AcwDDkkp33XYtAzsq2xPBpY6pN9rjebrB2RYXYGrgBFCiKbW4IgR1jSFQqFQXIHUhQU1ALgH2C+E2GdNexGYBfwghLgfSABus25bAYwCjgO5wBQAKWW6EOI14Hfrfq/aAiYUCoVCceVRF1F8mwFRzuYy4SHW6L2p5eQ1H5hf2zIpFAqFqyKlxLGP/kqiuiu4q5kkFAqF4hLh4eFBWlpatRvqywEpJWlpaWVC1CuiTsPMFQqFQlE+YWFhJCYmkpqa2tBFqRfMZjN6vb7c7R4eHoSFhVU5PyVQLkr8jHi1gqxCcZlhNBqJiLh8n+vk5OQ6Dc1XLj4XpS4nh1RcXqRk5nPv/J2kZOU3dFEUinpFCZRC0ciYsyGOP05f5NMNcQ1dFIWiXlEC5ULU1+SQisuHlMx8Fu1J5JsHrmXRnkRlRSkua5RAuRARMyIYKofaJ4W0fVZ9UXVLY3aRzdkQx/ieYUSG+nNrjzBlRSkua5RAKa44GquLzGY9PTKkLQCPDGmrrCjFZY0SKBclfHp4QxehQahv66Yxu8hs1lMzP20cSTM/D2VFKS5rVJi5i3KluvUcrZuXb+lSL/mXdpHVx3nqgz8TL7Ir4QLzt5Tsk+wdrtb1VFyeKIFSuAyO1s3d83bw8JC2NPOt+qjzqua/5snBgOYiG/H+xjo/T32x8NH+DV0EheKSolx8CpehvgMAXM1F1piDNRSKS4ESqMuQxtjwXYoAgD8TLzJ/Szxtnv/Z/jd/Szx/nL5YZ+eoDo01WEOhuFQoF99lSE36cVIy83lm4Z+8M/HqBnF3VWTd1FUfkSu5yOrbnalQXA4oC+oyo6ZRag39Nu9q1k1946rjmRqj9a1oOOr7flEW1GVGTaLUXOFt3pWsm/rGlYM16juKUnF5Ud/3i7KgLiOq2o9T+q3HVd/mL1dcLVjDRmMeI6a49FyK+0UJlAtTkfnsbFtVGz7Ht56UzHwW7j7NgTMZpGTl11lwgnIVlU9duDPrY35G9aLiOjSG5+dS3C9KoOoYZzfWweQMery6moNnMqqVl7N+IVteb6w4VGZbVRq+0m897649SqumXhw+k8WnG+LKFbXqPjAN3aflyix8tD8nZ40u81cdN2ddL8fSUNMo1WdD3Bga+fJw9eenvPslLaeoTs9zxQtUafGw3dQHkzPs/94+dxvj/72FW/+9lfH/3sKkudvt6ZPmbitjxew7dYGoj7Ywae42Nh9LZcxHm7mQW8Q/FuyzPzC286TlFNk/bz6WyjUzVzH+P1s4mJxhF5Ifd5+2n+fZH//kQm4Ry/9ILrPNseHb+eJwBncMZuc/h5do+Eq/9aw9eI4DZzLJyCti3ub4ct/m52yIo+WXWVV6YJSryDmu3GA2lNvR9ryM+2RrndeLqzfy5dEYnp/y7pevd5+r0/Nc8QL17I9/cjG3iGd//BMovqmfXfin/d+9CRfZd/oi+05fYN/pi+xJuGBP33Oq+AGw3VjXd2rG2cx89iRcZOq3ezBLCGvqydFz2exJuMCnG+Ls5/l69zn7578v2EdGnol9p7Tzju8ZRjNfd0xmyY74dF6J/YuDZzIZ0jEYi4SLeYVYLJLd1jwdcfbgO3vryco3MalPK2YmBONm0NE1xI+dLw7Hy91Q5ribN+ir9MDU1vR3dF+5YqNe0zLVtsGsz+VYGiKK0vF5Sb6Yx3trjpbZXtPf3ua6bhfszY+7T7vU/VMZjcHVWt79cuhcbp2e54oWqIPJGRw8k8n7k7pzMDmTzcdSWbQnkY/u6MHB5Exej+7KgeRM9DowS7BI7V+DXnAgOROjXuBm0NkfgDkb4rg5sgVrD59DAGazhYw8E3f/7knihTx0Apr5ufPj7tP8uPs03zxwLSsOpfPj7tO8Ht2V9JxCPI16zBIOJGcyoVco7645Sm6hGSlh1YFzRAR50eLLLDo29+GnB/8ku8AMUOIhtD34j+73K/HgO7712Bo1KSG/yEz4glzm3dubg8mZvFnKfWg7Dqj0gakLV5Gj+6oh34LLayBrUqa6eCuuz+VY6sLtWF1sz8v6o6lEdQ9h8Z6kMt6Imv72czbE0aqpFydSc2jV1MtpHq768tMYZqx3vF9+8+hi/zxnYsc6Pc8VLVDP/vgnXVr6Ed09lC4t/XhiwT7G9wxj/ZFUurT0Y87GOAK83fByMxDg7Ya7QUegtxseRj3uBh1tgry5rVcrWjX14r01R1m0JxEAd4Meo16H2XqeG37VcccOD3w9DMSn5hDs407rAC8iQ/1p6edG6wAv5myIw92g4+5dnvZzfbU9gcL3z2HUC8ZtMSIBswVu3qDnlVu6MHK9nqEdg9EJQQs/jxKC8vgBP65aXFDiwXd860mYmUDfN9dRaLaw9UQaAGHfZtOhmQ/L/0jmk1+asmhPIjufPszgqBQGR6UAMDgqhUFjUvjrhWNO67QuXUU1bdSdWRSl06pidThrIGszzszV34ovJbZ6BBjfM4x/jroKwP4yZds+N6N1tRtom/WUkJ5Du2BvEtJznFpRrugCdNUIz4qo6/5QR65YgbJZT29PvBqAF0Z1Ji2nkJ6t/Vm0J5EXRnXmQHImeYUm0nIKyS8yU2CykFdkJj2nkAKThYT0HCb0CuX0hVwW7U5iaKdglv+ZTHpOIe+cbVHifDet15ORZ6JVU0/iU3M4fSGXg8kZJGUUkJCWQ4cf8zDoBAN+kQT5uFFgsvDdztNEbTZSZJZEb3YD4FS6ZkIvnLIXgPVHU7l5vZ707EIW7Um09121/1F7GB0ffMe3HoC/7ffl89nevPWSNhwuYWYCLz4tGbPJiGl7Drf2CGNmeCoblzXDf4g/oL21b1zWjCUDtM5QW0O/d6hWHkcR/MfA1VV2FTlzXx30385Th5vg/d90+0NambCkZOaTMDOhRGMUPyO+zENU2UNVnhDZGhDHMlWG41tx/Ix4JvQK5cLsJA6eyShzPVV9q3fF5ViqY5G8u+YonkY9P+8/w61bjTTz82Bcj1D7y5StnvPeO1vtBtpmPYUHeHMiNYfwAO8yVpSr9vNcaQPWK+OKFSib9dQlxJ/4GfH8ejiVAG83pi87yPieYfbvnlbrycOop2tLPzyMejwMOgK83QgP8GbRniQm9GyFQSdYui+ZnAIzYzcb8Zufweezvfl8trf9nJ/P9ubOTyR6vWBCz1Y8u/BPbukSiJtBz9gtbtzepzUAgzoE07G5D2M3GwH4fnML+/H/s+Y3doubPW3sFjdu+FXHyK4t2DV4Lx/McLOf86D/dj59w5PC98/x1wvHSohA3xUWABImedn3f2JGIR5Paud7ZEhbDp/NYv6WeDI2aEEkpR+YhJkJ7B26177dUQTHbnGrlqvI0X3VJaMfT8wopFebpiTMTLC7OioTljnWRmjlw1qfok2wqovdrflxCt5uBt5bc7SE0DiWqap9cs38PEiYmcDC3UlEbTby7I9/limbs2AURxGzfXZ069nS6ir0vKr5OBtPV1WL5NfDKZzNzCcr38T5WUn8Y+BqFvx+Gn9Po90bUVM31+6EdA6cyeTAmUwenafnQHImB5Iz2XUy3b6Pq1q05bnOXG0ge3n9oef/db5Oz3PFClRCeg4HzmTa3V3zt8STnlNIWk4h87fE27+n5xQyeJUkPaeQDj/mkZ5TyMj1etJzCjmQnEnarETmb4knt8iMl5tWnTbxcEbn03pGbzAwf0s8HX7I4/t9qQxeJQHsbrRBY1J48SlpzydvS1a5+d03LQfQ+pHaPJlK6xPO99t0k7BbPTZsYhC+oLhj84MZboycoD3IB/23M+//vHj/Z816Cp8ezncLAjg5azRvn2tpP8YmTlB+4+aYbrO2HHHWUD91uAnnZyUBxa4OoMxDED8j3v7A2Oow/LtcuxVmw/YQVRZk4ChE52clMfAXC4v3JPHumqOM7xlGzrtnAMh59wy39ghj5cN/kpKZz9tjNvHOmE129+feoXuJnxFvfyv+x8DVAPb1nA6fzXJ63ps36Mn51xn+euFYGevPmdja0hy31UasHPMp/Vs55lt6PF1VXXIpmfkUmCzE7PW2v4TZ7vWUrAJ0H5/ngxlu9t/uoP923p/uZn/pqIxe4QF0belHzIAIOp/WEzMggq4hfvRuE2A/v6NF66r9PPXpOqst5fWHBj0dVKfnEVLKOs3wUtO7d2+5a9euWuWxXqy3VzRoD2HEjAj7v7btpf8tfewXd+ws0diXxtmx5fHxv9147G+FJdJiBxRWKH4VETugkMS7fErkaStPddH76zFnmCvfsRQ2t1TCzAT8h/jTY32PEnUcPj3cXu8//n7abuFVlJ/j7/Pq8oNAsdBXpTwJMxPsv4et8d14JBX//YX039rb3kg+/M88nvvanXYJzt/pEiZ5lfjtHevWdp7KSJjkRcKdPk7LP1QOtYuV470KVHpfAiQnJ1Mwt8BpQIXtN0jJzOebe3bRY5mp3Hxs31My87nxvY3MzWjNw01OMbJrC7zcDAyOSmHjsmYIQblT37y6/CDh32Y7fVbCp4fzbPMzPPa3Qu6blsPns73tL2G9w5tWyZKI/ngzfyRqL02Ox18T5s/Sxwba75NXxnQpce+ULnNycjIhISGVnq++KF33ropjOWtaZ0KI3VLK3mXSr1SBctYvASUbLccH31m6Y4NRXn6V0TGpIwVzC1z6bam6lBaO8kSwvIbbf4h/CausPBwb0aDnQ/lq2ylu3qCvdnn9h/jTZGiTGv0GXTL6lbDS7OmZ/Tjot71MOcs7Nuj5ULu1WFeUFqijoUedNniO97czumT2o5mvBymZ+Rz0306XzH7MWa+5xAZHpfDLwgAW70li87Trte0Z/Rjx/kZWPzm4zNyCNmH77sFrSQkta0mD82ewOsRGbqbJAVOZ9IuRBsbuH8iE/2xlV8IFgAoFsCEEqqJ2yVVX2ba94IASqDLUl0BV9a239LGfdsuyu8hcmapYcZeKqlgbVRWtxoTjtW5c1qxcq6I0pa39yu7T8OnhuD/kXqFAdcnox43vbSzRf1kZNlE9PtHDHpTjSMIdXkz+tm+JtIq8DOX9xtVtnN8avdGp9b1zlI7nfh5cZRFQFlT1UQJVirpy8VVXkBwbVdtNlJKZz8DZvzH3Tc9aledS4B7uTkFCQUMXo05xdAnZglN2jtJV6iqsb6pjYVfVjevYmK4X6/llYQAjJ6SzalEAT97Q0W7p5PzrTI0Ff+UQMzdv0POPmYV8+8C13PHfHXwww43lg4oYs8lYbtkrcsnZrJexm41OrzN8ejjeT7W0l7+2s7tX1shXtF0JVPWpa4G6YoMkSuOs068iHC2P9WI929psY9kD+yoUp4dezKuDktYNriBOVXn4bOHtVcEmSo6Rk7URJ1tDW/ozFJf97feduxTf/qBkemkLwPFec/z+/uYRZULIE+7woktGP0CzWkoPzg16XgvPBrRgjrXaWKJPN8TZ7+uOSR1LnKfH+h4lyvDEjMIS53j4n3nkP6IFFYzs2sI+swloATf3Tcux14ntc+JdPgAVRp4tfLQ/MQMiCJgWViL9+G0ebFrejIgZEfZITFeJrGsoXHEowaXG5QRKCDFSCHFECHFcCPH8pThnRTeC7aGt6NihcigFCQVO3RyOVNeyqk7j7FgeW5kqo7ZvZ78sDLB/dgxVryqOFmh5ZamOW6+uH+g7+rayf27iVWwxhE8PJyVT+631OkHsAC3wJHZAof3zgeRMjoVbCHo+tMRxtjI6RhPavtuCNLyfasnvt+hZOcSE55MteK/zRd61DmAtHYmZkpnPA76nAPB8sgVSShbvTsLzyRbVikwrMTj0hVZICYfPaNeQX2TmoHVmk/Dp4fz0+ECaeBnZ+c/hQElBqspvYItqdKyv1yPS+OP0RXuEXXXLXx6VlceVRcBV+5wuJS4lUEIIPfAJcDPQBbhDCFHvq6aVvhFsN2349HD72xxQwrqyfa7Pm6i6fS7h08O5uP4iCTMTqtS3ZNvHPdy9xIPaJaMf8+dpgmO7zr1RBntjGz49HM8Bvizek8TaYZqFcvOnV/PLUDPHJxa7ZGxC6VifNvyH+JfY5nhMTamqi3bjsmb2z7brtJ076PlQlg8sIvUqPSv/Oouhnzd7owyM7NqChDu87K61ORviSJjkxbLHB9qtnoBpYQQ+H4beXwtt/niKqYSgRMyIIGJGhL1e7GH+1u+2e2nOhjj+e3U2f93qTsSMNngYdOU22u+uOcrZzHwigry49t3OtAnyxmyxEDGzTZnxPeXV7c5ROvvg0NgBhfYZRo6n5nDdll6s/OssHZr7sGhPEhEzIkrMblA6z6o8D7axPi+uGMxvN2rC2sTLyL/v7mkfn3Ttu53rZHxSZeVRIuDauJRAAX2B41LKOCllIbAAiL7UhbCFOifMTCgR8lu60XcPdy/zJlwRTq2xh32d7us/xJ8l3/qXeMucYnWpbLgRHnhR+7x1pKBLZj97w2lz3Tie68mZhRj6eRM+PbyEVWYTiOtOXof3Uy3ZG2XAc4CvfXyLjYRJXnzeM5clA4rs51k1zRO9TvB1nzwOtzLT9811LLg2n9fbppWoR9u/pTuge6zvUWIfKO5XcbSmnphRaC+rDY9+xSLoKHJdMvpxLNzC49OL3ZePTc/nsVe07yuHaJFdjwxpyy9DzaR30duv03buxf2L2DRS8GxUJhdzi7h7SAofXJXBgt9Ps3yQCe+nWnLv/J3sTkhneniqfbT/9fkH7QOYO53qU+EsBaUbRcfvKZn5/Lj7NAa94PDZLN5YcYi0nEIsFvB9pqV90LBt30W7EzHqBWcy8zmYnMHZjHz0OsF7a46WGN9zPqeI6a1TnS5e+dd4D3b+czgnZ43m/c0j6B3eFACzRXLLR5spMFk4ei7bPtu942Dt2jTwpcWotgN0FZcfLhUkIYSYAIyUUj5g/X4PcK2U8rFS+z0EPAQQFhbWa8eOHTU6n+fxn/HZOwfDxThMTdqS3eMR8tqPtm//YKM2V9jNd+Sy8jvtTfuu3z0IejqoxLEFfhFsf+pdFr+whlcMX/DXa1+XOdehgSeYPaAFn8/2LtERPv3NAhIyTPZ+k9ZDFnBqwyR8F/3BhJ3tsf06n8/2ZvELa7j1/25k0CtjOS5DOfPaJ9w3LYeorgGcvlhIkdkCSNz0esKauHPrvdqDfd+0HDoGe/L5HZ0BOBqqNXABRyN4Y00Cb3c4gNvvcwguSCDFrTXvFUZz3ci72PjcKXq+FsL0VQl0DPYkMaOA7+7uQqC3kfu/P+J05uKrW3rz+inN9eds0N6p8afw6u9VYtv5nCLeWJPASzeGE+itudJO/V8K+R9fRO4N45d/nOTu/3YgraPmAgvaH4Tlf9a+pUf8eWNNAo9OtbDyOy/2JmXTI9SHu373IP3ddP4xQ6uDqft9OD7BjfYLCxn9YVtmrUvgl8MX+HRiR3v+Erjr60N8c/dVBHkbOZqay33fHeH96Ha8suok39x1FV/vPseKQ+mMuiqAJwaX7EdxvG/yisycyyqiua8bnkZdufue/9f5EnXxwcZE9iZlAxCfno/ZLPEw6iiySFo1cedcViFFZsmi+7ry9e5zLP3rPPmmss9wkLeBZfd3s9/D+fn5/BqfW6bcH2xMLHM953OKStTD+Zwi7v7mEN/cdZX996ktzs4x4fMDjOwcwPPDW5coH1Bu/dUn6enpBAQEVL6jwk5N6yw0NNRpkIShTkp1iZFSzgXmghbFV6NIm/0LYc9HEP0xtL4O46ltNF36GE2bam+PpvVv8WbaUfBpwSbmMOvITZyQoQTf/iL+aW4ljv3h+wW0Bv7P4wvcLFqjHThsJWm/3syugUncoNtF9JBYhuFFTutHeXTYf/hVPMUxWpOQYSJKtxXhPgBZ4MVN/W7mYXMSt+37iDG62/mZ/kiLJnDPGL7nh4FdeKDgC/rojjBt8DKidJ6sONQfs7W9HqPbymOGWNqdT2JP23fYFSJ4LXo4Ly89wMOL4jDqBf1v0TPoghexh3Nok7IWn4s/8FzhQ/zjgXt5a+7nvOfxGeuPBvLzkE6sWHsagMMpebQL9ib2SA4v39KF69pf5Lr22lvuwLd+4+bIFvx2JAUvTw9azOhYbvRVyLbi3yolM59nFv5JiL8Hh1Pz7XkDfDbiIuHnvZjcvT2bni4k9kgO454PZc3Bc9zg7svV/9KspleXH+Rwaj4JdzTh8PlCjqbmcTQ1j++NMHaAkYt5ZjyNOt5onwH7gPbwxofa+Jtmvu5c75C/lDCxd2uu7qDl/dnug3Rt6cf+NAsTerXm2/0Z/HLkIt89dB13z9vB06OvdjrO55cjf3FzZAsOx52lbYsmrPzrLJMHd2LWL0d4Z2LJY0L+FVLm2NAmHhw8UzzLRG6R9uPGp+Wz/PGBjP/PVubtTue3Ixfp1MLPPijVkfM5Jvpbr/OaMH/iUnPLlNt2vtLpny0/WKIeQoAJvXJK/D61xdk5mngdY9mBNJYdSCuxb+/wpg0WTdeQUXwuz/6FsPEdOH8EgjrB4GcgsH+d1pmrCVQS0Mrhe5g1re7Z+A50uw1WPFdcwS26wZKHwWIiy9iSAvcIWmTHET7kO4S00I7TsOJR7XjPQMhOISXHzNtHm/GviHN4mbOQAnyH/EyXgXPZd7wVfYd+xzW6vwAwGoz8565E2ll68/CwZzFg5mEZhp/IZeHTiVy3uRmH3f/L8etDWWoewFRDLIebjiAls4Bh17/LUvMAxlz/Jk+JRIowYBxq4m0MUATL6E+UbivPGL5nmukh9onOXHvXUV7XfcrmgxkY9J35KykDIeCvq3Wk9fJnxZ5Ednn/wNnsIuaK17DELiBQjuCL4Ke55cT7/GN4LG+tOoqwVlnc+RxOpMaz4UgK8Wm5LH9sAHM2xDEqsgU//XmG8EAv+3pXjg2ZTYhKN862Nau2F1n4bHJvYj7/nfG9QgnydmfRnkTWfDoY0ERwxPsbyenTgpUeuZzdfY7ZHcJLTPh597wdjGzZgl7hAbwypguvLj/I1/oEAjzcGNcjFClh0Z5EDDpBZn4RQgg+n9KHe+fv5PmRnbjzsx2EB3jxR2KGfSoiG55uev59V08GvfUb43qEEhnqz8iuLRj3yVaWTO1f4pre+PkQeYVmfvozmQXWht8WBZd4Ia9M3TjiuKzJ1WGasBUUmck3aQIlgVs+2gzA6oPnmNSnNa+MKc7L2WwItvTOQW4l5p17+ZYuTueje/mWLvyZeJFdCRfK1IPN9VcXVHQOV5t3TuGE/Qth3av2l3RObYOlj+HZ83EIebDOTuNqLj4DcBQYjiZMvwN3SikPlHdMjcdBzWgC/q1g7CdaBa+fBZvfA2mGe5aw/fu3uLZgG4ctYYSINHxFHgJI0LWmjTwFQgfSQoaxGZ+6TebWnO9oJxOxIMjGEz9Kur8ksNrSm2v1RzFL+NQSxUTxG9NN9/Gt8Q3SpC+Pm/7O75ZO9NEdYbbhU8LEeY7JMNqLJHRI0qQP31luIFq3hS/MI5is/4UwkUaiDOYd0+1MNcTymnkym81d8TQK8ookj7ZOZNy5DxlR8Ja9LJ5uOsxmycy2h5l0eiZTTC9wzKs7k5onc+vp/+PtwvH8yziH7mIBWfnalEZDOgaz4WhqiWsa2bU5aw6lEODtRnp2AWYJPu56dDrB2qeG2N/Sx/17K5n5RdzWuxUPD27LMwv/5PmRnbjjvzsY2jGYXw6c1Way3ptEh2Y+XBsRSG6hieSMfLuoPfHdXpb9mcyHt3fnyR/2sezxgSzcpb272ATp5z+TOZdVefh8E08jOQVFCJ0Oo14wqU9rpKRE4+44HQ5os9+P/nAz0d1DeH9SD55f9Cff/36aSX1b8X+3Xm3Pu/NLK+2C4oheJ1g6dQB3z9vhdIYFoMQMB6WP9XLTM7pbS7zdDTw8uC3XzfoVs6Xss1u6gbfN3PDVnZ242irqI97faB/XtObJwTTz87Cnl1e2K5GGHgfl0nzSD0a9BRGDi9PiN1K07EmMT+yudnbljYNyKQtKSmkSQjwGrAL0wPyKxKlW6N3g2oeLK/jwz9DrPtg1D9oMop95EgjBVcYU8G0OGaeh9/202fW5tr+7H2Y3H7IyC7iz6AtCSUECOiR+5FIodbiJ4oYqXjbnBt0e8qQbucKXLU1v5fmMbwgmAwkEiixmGL7gE9NYlln6s0t2IkycZ4ZpMr9bOnHQfQo6IRmv28jTpkfZZumKAJ61WkwzDF/QXiSx3dwJAeQVaY3X/5Ja8IwhCX9PAxl5WpBAYZEFCVyf8iUX3VoS7OXNhnQz/85tyVbLg8w2fMpxGUpWQfF8e6XFCeCXA9ryzqkOohDaxBODXmd/G393zVGSL+Yxomtzvth6kvPZBfaVim+ObMHKv85yc2QLFu9JYt7k3tw7fydSSruLq+8b6+x5C2DOxjiEEDy5YB9nMwtY82SxlbV4byI7/zncPg2PTbh2xKdxbUQgADvi0jiXlU+RBbBYKDTBvM3aW7yPu4G9py7gZtCRV2h2ak1ti0sjJTOflX+dJeoaba2tJ2/UXJoHkzNKiFN09xB+PZxCboGZib201ZE9DDqiPt7CsscG2AXcZl0ufLS/XRhtFgZowQpZ+SYW/H6a3uFNaebnweTr2lQ4350Nm5UUZO07skXg2VdsdrLuUF258RSXMeePaC/2jrS+DsPFuh275mpRfEgpV0gpO0op20kp36i3E5kLYedciN8I5iJIPQzH12jbTm0DcwEEdQBLIWRavYzDpwNm8AyA/IuIzCRCSMXfoDX8ArBY/WFuOmtDZf3eVpxDIPEhnxCZyk8Zt2JBx+uGz+wutBYinXeNnxDnfidjdVsACCaDn91exIgZf3JpKdL52vgmm9ye4BH9MoyY+N3SifYiieMylD66Izi+V/eQRzguQ+3iBNqqwFJCcMFJPtVN4vHsD+inO0CRqRA9ZlqJ83xiGmvfXyfsl2HH1935u82Rc9l2d9rB5AwW700i6poQ1h9JRQDL/0jmNetKxRdyCxnfMwwPox4hoFNLX7q09MNskfh7Gu3jbb6+vy8C+MC68vFLN7TmyLlshnYKLtPAlo4Em9ArlIPJmQzrHMyiPYm8PfFqMnKL7D+NQBOSriF+FJos7Dt9kT2nLtK7TQA7XxzO4I7BrPj7QPQ6rQbOZRbQ9811ZOQVsfSPZPvyEPfO38nj32l9Pka9tu/yP5L55M6emKUkv8jMnA1xpOUUcjYjv8Qqx45LVNjGCDmzpAB2JVyo1hpBtvz6f7i3RAReQlqOWndIUXOCOmntpCOntmFq0rZOT+NSLr6aUGMX3yf9oPNozXI6fwR0RugSDSc3g94ImcnaG8LJjZo7z6MptOoLR1fCwKdg9xeY89LRSYkJPQacz+4tKdu429LzpRF3oQmHzkFWTAj0SPtxiTKIQF0m7hSis+6WJn0xCjM50oOnTY/arS9bH1Rz0nnasJAwkcoZGcAs050ss5T07a9ye44ZpskEk8FUQyztRRIXpQ9+IhcdFo7LULtQ2bbb0krnBXB9xyDWHztP9DUhBHi7s+V4KvHnc4md2p+xn2ylpb8HCem5eBp1FJgseBj15BaWrTcB3N6nFbPGX82ryw8Suy+Jln4eXNs2kB1xaUQ2d2fZgXTyisoe66YX3NozjFnjNbfbq8sPsiMuDQR0C/XnZFoO2+PS7ft6u2vrfZ1IzcHTqCOvyIK3ux69TjAqsiUr/zpLWFNPro0I5JEhbYn6eAtpOQVsmTbM7hob9NZv6HSQV1j+rBVBPm4UmixYJBSYzAggduoA7vjvDnsfWn2615S7qvqoOquAcvqgLvR8nKaDq98HpebiK03pCl4/C7a8DwOehKD28PMzUJiluQLNRWATEGEAd1/AgiU/kwK9N57DX0Cu/qfWj1HNYpQnYI7bzOjQo7nl8jHiTrGomdFxUXrzqmkyyyxaoMTzhm9pKdJJFsG8XTiBcwQw2zCXd0y3lxAWW1DF0b5v8shmd54wLOZR/TL2hd/HpKND6aM7wkfGj0DKUv1jZfNyJEq31S5oKe7hbA2dwka3ISz7Ixlvdz3ZBc7F/Ilmf/C4MRZx/iipHm1occtLHAy8kVEfbna6v14n2PbCMHuj/uryg3yx7aTTvpnyCPA0kp5XhJ+HAYNeR0s/DxBgMluIP5/LZ5N7M3n+Tn7++0AW7k7i6+0JmC0Wtr043O6iGzj7N9wNOrIKTNzUpTkZ+UXsik/HJMHDqCO/qKxwebnpiQjy5tqIQLsrsiouu5qiGtvqo+qsEpxE8SXXMIpPCZQzSldwxCCI3wTnj3Bvq9bs1ZdtWHrk5/PlmRTwCkTmpvFm0R3co19LK5FKrPk6lrQ6wV6Psm/BPfLz+SI5BZMUGHQSYfRGFuaU2c+Go3CZEBisAlmeoF2U3kw3TWGZpT9b3R7DjJ4Qcd5u8aTib7eyHK2hbZYujDbuJsiihfamSV+72AFsdPs7AsGgwg/s57pOd4AZhi+4qfCtMuWwid5r+r+xPq8dvSsQtKZeRgpMFnILzSUiEH+3dOKulsnMlP9hUdMY5l/sBQJ7Yz5twU58fHyQEvKKTCRdzOf5kZ2YNHc77Zv5cDw12x6kYSMlM58b3t1AdoGJ8vRLLwTLHx/ApP9uJ6/AhBA6bu0Vyv7TGVzdyp8V+8+WCQGvDINOsPX5YQyc/SsS2DJtGADD3l1Pdr6Znq2bMOeeXiCp1yAF1dhWH1Vn1UfNZl6KupjN3JF7V97L3hTn69RURo9CC4k6C6kG5/0z3hYLObqy3X428arImiqNEFabTmJ1BkrSpS8/Wa5jsn419xQ9z3ZLF7vF865pAu8a55Aog+wi0Ed3hI8MH4IQPF70OF8b32Ry0TTeNMyzC8oJ9zsBQbuCbzDowGQBAyaOuE+mXcE3Zcplcxtus3S1p1UkaHYr6MuhMOotJvxisPe/VHScjWa+7uQVmbFISWgTT85lFhDW1JN+bQNLWCM2V9/RlCyKzM7v+ZgBEbwypgtPfLeXpX8k0y7Ym+wCE+cyy0YGehr1GPSCApOFzc9dzy0fbSalkgjCZr7u7PznDQCM/mATx1OzkcA9/cJ5+Zb6taJUY1t9GqzOnI0v6jbh0pejBtS1QLlUFN+lpDwhCvYM5u6r7mZfyj7+SvvL6bGeek88DB5k5adjEsWSstdNR0VxJ6XFqUd+Pl0LChE276HjRieBCaUR9v20DIJEFpN1q7FI+Nz4lt0tCPC2cS5mBNNMD9nFY5ulK3l4gJSkB1/L6axWDGnXkn8efZiXjZ/zU2F/kmQQwloSW4BaH50WeOGM9iKJA4YuUKhNtrruUAq/Z2lBHI50aenHidRs2gR68emGOF46f4SYXw38++6exRaEeQS8/n+cnDXaHu32/MhOPPfDbk5nmPjkzh5M/XYv/dsFsurAOeJTs1kydSB3fradU7tyOZCcyYd3dOd8VgFfbjtJh2Y+5YoTaEuxO0btnUjNoXd4U5Y/NpCBb/3G+J6h/N+tV9tDzkd0bM76I6kgoHWAl1OBso0d2pVwgZSsAto8/3OZfeZtjrdHEtblWCNFI6Scvh2g0YhUXXLFClTXwK50DezKtL7T7Gmzd84m35TPshPLuKH1DSRlJ+Hv5s/JrJMYdUbC/cI5nXWaPHMeeeY8zYyxYRtIUw1OGQzEGY3EJp2p7qEVosmgpiZ5uOFFoT2I42bdDs426UN8ujZOK0ScByRHzmXzri6Kfyb8H8d9H6VdbiLPd07FJ74Qi0Vyne5AmT6o3uFNuTqsiT2M+5UxXTjzZjhdsw+SFNCbxXuSWPK3/rz733mcKjH+Gg6eyQS0qD8Po55U9zboT2/n0w1Nii2IU9u0N0iKo92eXfgnh87lYZHwxIJ99hB1Pw8DOQVmmvm5M6FnK2L3JdkHDW8/kYYQkJlvYuc/h/Pu6qMs+P00fp4GCoosjOupLVfRoZkP/7uvT4kxRrsSLtD3TS3U/dg5bQqihbuT6NLSj32nLlJgspQIhQdNZP59V09GvL+xpOA64DjOqlqWUyN+u76kfDUOTvyG3Sne7nq4Z0nxdletx43vaOJkG/4SMVj7vuI51yjfJeaKdfHd8dMd5VpIpWnl04qk7CR0Oh0WacHXzZeW3i2J8Itg5cmVtDHDKZ3E1mOlB8w2E8hWv04USEiJdJJu6+e6t2WzcvuzvjyTQnGgdMm+MgkUSR1GUTwDgQBSpR8BZPNU0d/s/UGb3J5AIhlc+CGg9SE9a/ieMJHKURlWpSg+vdBC123H2/qS4jy7MbXdeUbHv8HMnPFOgyqa+boz/KpmWP5cyGu+i/lbdgxP3X8vC5cs5OGL71E05J88c7gDB5Iz+fedPbln/k4A3A2CApNkZNfm7Eq4wPnswjJ5d2zmw9msfLLzTJQXXze0YxAbjp3nw9u78/cF+4jqHkKgt3sJ4fjj9EWnYd+OQjQysgVeRkOVZnawDZ6t9iDZ8t6uh79SaePV6Fx8tRGQr8bBiV+h9/3a0JB1M7Xxje2GaSJVxXpskDqb2RReStEiiW2Yi+D1ZjDd+dADV0K5+OqI7s26Y5ImejfvzbS+05i4fCLncs5xS7tbmNJ1CmOXjiXAI4CTmSc5k3MGicRisaATOjIKMnh70Ns8s/EZWvm04mT2aV6+9hVe2/EaAK1823Ay66R2ImemkZToKC0rxez18KBbhDZhZrDJRJjJVEKoHLcXi5U1a+v/jMJi7ZkqdhUGo1ktHxg/ZqpcwmpzHzzJx02Y2OT2BCHiPMkyCE/yeaLoMZJbjbYPHv1B3MXLt3ShE/Ch9Y/9C0n5+Q0C8+NJ84hgXfA9vHisP5jQBg6bkjh+OJSZJk2cbOVxJCWrgHWHUril1yTc2lzNmz+/QeBnrzFJhjDLMpGj+yI4lnKRDs18+O1Iql0MfdyNSFnEmoPnmNi7FV5uBnILTSzZm8S4HqHsT8ygX7tAtp9II4k8MvKKaB3gSUJ6HgYdTOjViv2JGfyRmIGUsDUujQ7NfVj+RzLbX9DWObJNseQoHKVnmAC4tUcYP+9P5lxmQZWmB7INnq32INnL7e16/0JY87I2pAPANwSuugUO/6SlNWkNY/8DfiHVc3Od+E0Tp1ve1b7b/t01X/vXlevRNr7IcYYGB0/ClcYVK1AxkTHEHo8lKSuJqHZRJGYlkl2UTXS7aIK9grkx/EYWHVuEXuiRSPRCj0ma0As9nZt2ZmPSRqLaRbHg0AIAfNx87Hmfzj5d4lyjI0az8uRKLNKCuzBQQPlv9ABIyT2Z2eQKWO7jzaDCIvKEoEtBIeu8vYhNOsM8fz92ebgTWeBgOXg0gfyL2JSgjG3s0K/VSSTRXn+OeJ+etDfH4Wf0hAxw0+vArO21K+ECn780kWn6X3ETJtjrDr0mw6i3Yf9CzGtnEpvfm3ub6Am6eJwJp14nPCSa/n//HymZL9Pr/Y2M7NqCFbsTAVmmPEE+bqz4+yBufG+jNrDWrwvnA2+k74eb8XbTI3WQm5yJl5uek2k5HDqTiUWChwEu5BTa63DB7yXr2/ZdAqfSc8guMHNT1+b8elgTcm93A8dTsjlgdTMCfLfztK2K7JXkTDgqmkNux4s3VPSr2qnxXHfljN7n/JEqnbdeqY7Fs38hrHga8i5q370Coedk+P0z7c+jqWbp6AzFlk21BERaB9U7MHy6ZkWBa9fj4Ge0a3Zm3V2BXLECFewVzNj2Y9l1bhcPrH6AMF9tks7Y47FM6zsNKSUh3iFcF3Idy08sp8iizT5QZCnicPphDqcftucV4RfBtE3TcNO5UWgpxCxLjvP5Ob64Y7xAamOYmlugY14um7y8EAJGZeeyytsTE2AEorOyuL9lc8Zka6HoSQYjJ4xG/CwWrm9dvPTAYXd3vvL30ywp317aQGIHJbBQHLZR2purkybaZe3U3lQ7joT4TTRPPQQCPnT7mA/5WNvR3Q8KMsFi0mbf2P8jGD3Z4jWCiXmr8Yj+lKilZvqnL+KptG/5+4v/tLvzSotHaUpbEwt3JxHg7UZLfw+QcDw1m4ggb5Iu5pEjzbgbdERHBrLmWAbpOWXdegBe1gi7hPQcmvu6k1eYi6dRj9miWa4ZeaYyLjsvox5fDwPnsgqc9inZqIuJTGucR32+XZe2Zoxe2mwrFhPo3aHNAMg843RIBr4twFQIE/9Xecf+/oWwchrkZ4HRF4wekJsKWz7QzukVBDkp0GaQ5uayCdMjm6ohIEJz69ksJ9C+2948XNlKsdWX4yTWVXDhXq5csQIFxVZUuybtiMuI4383/Y+YVTFEtYti3el1xEbHIqXkVOYpTmWdYmDoQGb0nwFoARU/HPmBdk3a8Wr/V3lwzYMMCBnAyviVWKzv9l4GL0wWE4WWQvzd/MkszLTbEZ9ELyTAI4BhPw7TBuCG9sB08Qggic7OJbbzUKLC+jAlI5OxiUsZlJvLLz7eNDWbCTZrAtgnv4Dn0i8yO6CJ9uidWVnCAgCtP8yGEHptMtzSXDylCU95FFgtDduxeReQeRcYmPm51nh9MYalgEloM2BMNcSyrFBrhJv5uhPs4063MH8W70lifC8tEu75RX+yZG8SuxPSnc55F+BlJD4tF7NFciA5Uxv862btBzsYSp5pLMvQzmHQaTNCTOgVxu/x6fyZlAHWhWwji9byH2Ms7Q8l8aix/FkwcovM5FpnpnDZGbXr+u3aZvWkHtZmS3Hz1iyXHXO1Fx2jN4z5EOLWw/4foPMtmlCsnw1b3oMB/4Chm+DDnqAzQ7a176Qil9nGd8DNC3LPQ1EWTPpSS1/8sCZMhdYxZjYBsVk21RGQdtcXW0ul+6Dqox7rmm4TrlhBKs0VLVA2K2rZiWWMbT+WTgGdiGoXxQOrHyC6fTRBntpCcvNHzic1N5WxS8cytftUgr2CiW4XzYLDC3h9wOt0DOjIpkmbSM1NZV3COvIt+XRq2gmTxcSJjBMAZBSWXLNnwvIJ9GjWg5aeLTmTd4Z1GUfs/qWFvt6QfRQOH+Uvs46oltexNG0felMBSQYDeTod3hYL/8lIJVWvZ7mPD7HJ2sStlQdWlIezHqKKEYAwF4CbL6J3DObt/8VgyaWTSOLkrNFk7PyW1BVvEnEhieNpIeTKsXy3s7/dndbM153ebQJY+thAoHiskm1KI71O0LmFL1dfWMOj5u951/Pv/JQRzi1+ccxkLkNCg/k0vQcn03KLl9zo2oIuIX6s/OssE923c2/u9yXGfc02zAUTLLf0p0NzHwa2Dy4xqWzp8VM1oirurpoEAdT07Xr/QoJ//T+4GFd8LigOFFjxLOSma/2luenFAQbH18DWj7R9e98Pe7/WBOjwT3DDTO378JchMxHuXgS/vFhclvJcZrY0nRt4NtHceK2vgxzrZMRCr03ObBMQiwn8w6onIPcs0QIlds23CpUoDpCoTT0qLjlXtECBZkXFZ8QTExnj9LuNYK9gotpFMf+v+UzrO42lJ5Zyx1V30DGgY4l9JnSawK5zu+jdvDe7zu3CXe9OgbkAHTrGdRjH1uSt9GzekzUn1xDiHcKx9GMlztO+SXviM+LtbsK9egt7U6wrBgso0ms2UavgrgTl+TG7RQhRqUcIMmkmQ9eCQroWFDLNuxOknYCiPGb7GouNKuEGslCbskmatIbCUgh6DzDnVbneJCBs/VwFWbDlA9wBKYQ2Lmv/QixrX2Nrxxdpf/skvv34M55J+xeYsFswKVkF+ByNhU9i4PwR7tO14nzeGOafKbZeDiRn8q7bIm38VkY7AGIzO3JON4UZJ/5LuvF9BNDMz52RXbWQ81t7hjK+Zxj37FtoH/dlm34pVKTyjtun+OoMfHOuL0fPZZew3gy6Wsb7VzaOxdGVVjoI4PQOzW2Wetg6xVYhBHcu6U6rSv9O6dlRjq4iY+BMgnrcUlwecxHc+qlmpZw/qvl/71msiYy5QLM8dv+vON+YVSX7cHrHaNcBxZaNoyCVZ/EEdYKiHM1qlxZY+AD0nwreQZpVJc1wzV3adGOxUyHjlBY8MeK16gmIY0i5M5SV0ii4YsPMa4LNivpsxGc8uOZBYqNj7VaW4z5RsVFIJFJKck25dGjSgbiMOObcMIdnNj7DvBHzuHPFnZgt5jL9VQBGnREfow8XCsqGlRp1Rpq4NSHfks+8tnfw4MG5xPZ/k6D2I+HUNlK/jubGVqGYyw1fT6W6llKNMHoz3edlLpxLtIeoJ8lAfEUeWYYgWltOl9t3sahpDAcCRvDKmC6MeG8DKy9Gc+TB49wxbw/92gaw6sA5bu3ejH8dGUF3vufmyBZ4uxvIKTBRsPcH/mY9n5AWFoS9xNa4i7xi+II8PAgVqXaxThGBNB//lr2hKhEWXtMw59KTEAd10r7v+xZMucWBAR4B0Od+zXU2/BVIOWydC/IJrY+v70Ow41NoeTUc/cXqTnveeUj0imdh9xeasIDmipv4ubbvV7fCgCdIvur+4vDf+I3wxRh4+bxmEX3SDwpzIOoD+Hq8NnFyj7s1C8rorR0T3l+zmF5O0fbvcbf2fep2ra5+eRGM7vD4norD3219UHkXoMs4iPsV8tKt94wPGAzg09Ilxic1utB8F0BNdVSKSylQoPU9LTuxjOj20TzX57ly9/nhyA8AtG3Slt7NtXrfdW4XfVr0QUpJnimPrclb6d6kO8/2f5aXt7xMC+8WHEg7QNfAriw5vgQjRgpk2dkJIgMj6d6su1aOJl14Lm6//YG+1z2bvcay4hRsMjEyJ5fn0i8WJwojyCKtQbIGgdQ5OqPW6A59Hr6/F46ucDi/Tpt4d/S79kbowl9rKVj4EME+buizzyABk9QzxzSGf5lvsx9aehqk3uFNicn8NyNyf0ZfOkZSgAU9+s43w2ltHFVObg7ullz0yBKzvfcOb8rCgcnlj5OBioVrRhPNMnI89scpmnUg9HDDdFgzHXyagcUMvaZoLjMpIe2olqdtIbh1r8Gmf1nrUQ/ezSD7LPiFadbvja/Bz09rkZvezbR9Oo2CPV9oFs6ot7XyBHciedyS4obDXASvBcHk5dp5bKJhMYO7DzSPLO6DGvV2yT6oiZ+X6oN6vvga9W5a+aobxecbAt3v1ITZhVxtSqCqjxKoUlxqgUrNTeXlLS/z+sDXy1hPjvtM2zgNBEzrM42YVTEEewbb+6OqgrD+17ZJWww6A5GBkSw6tshJsDb0aNaDL2/WOptnr3yA9FM7WOEBnw77hOc2PsfA7BzWuEtWJZ61uwJLYIvSqxUOI7vcfKBQm3WBbrfDiXVQlKe5dkATpgH/gM3vgpsfePjAU4cAWPz5vxh38lWEd3Poficp274h2Kz1nUmgCD1GzJjQk2QII8SUhBsmTNYZ33OlGznCk63mrkTrt2GbqXCz+2AGFW3RGuDQXhC3QXMn+beCwlwt3dY42lYLzU4pFiP/MMg5rzXu0gxBHSGgHRxbrfWT+LYEU55mGUBJt9TbHbQAAOsvi94NQnpA4u8lg1Z8QyDrjNbAny+OEsWnhdbw+zSHG1/VxO2rcVrEW36G1g8U0kOzisZ9qpVz3auatfPxtZB2lOQHD5S0oJY8rL08OM7mb1tRGqoXxVdTS8dVZ3OwogSq+iiBKsWlFqiaYLOovI3emKXZbn3N3jmb5ceX09K3JT+O+ZHZO2ez69wu4i7G0dqvNScunnAqSI4EewYzMmKk3ZpLzU1l7OJRNCss4ITO4nSmitJ4WyxsT0isk2vVMmxW3CAbvbU3e5sAGn216C27o816fb4hkH0WkxQgLfzHHM0dunUIAQFkIYVDuLzDqUSrflp/wwfXaOc0esOdC7QO8B53ayKYm1Z8gEcTzZ0V0K6kCLTsAdnnwMMfUg9p+xVkaWcL6ggGDzizTyt3cCcIaA/HfoHQ3mWFpgSlgk/8W2njfs7sK07zCtZCrfVumhUTOR4OLIH2N2iWjMELLAXaUjCHf9bE86tbwa+l1pfz4hktMu79btqppu6AN1vCjAxY9zpseZ/zo/5bsg+qKtbgFY4SqOqjBKoUjUGgbBaVzX331pC3kFIybtk4nu/yPC/sfcHpcR56D/LN+fi5+ZFZmIkePeZyFkaEYktq9s7ZpOenszJ+JWE+YZzLO8eYtmP4NW4lFOVyS1YWz+lbMLttJD+n7adFQS4/DPsEDsTCns+1RtIZBi/NtWR2Pv7IjnczzXX018JKasah8fZpDt3v0gQFNIEwuIPencUFfRmX5yQv28BjodM63G34hkBWMmXEwRlG72LLDqDTaK3PpzzBCeoEV0XBprfLz9MvTItsK03HmyF5j2bh6PTF9egbAqE9NVefu59mbVqKwK8V+IfC6e0QOQHO7oe0Y5rIXUzQrltn1MSm/2PwxwLNMup9P+z5Cu5ZpIlRx5soOvYrRscoPiVElaIEqvoogSpFYxAoG7N3zgZgWt9pzN45m1UnV5Gal1pmv2DPYACn2+oS2yzlCzs/TMddX2lv0j4ttHY984zWiFpMTo+0N/yO1pJtW8QQSNis9WU4Wgo2DJ6aO8xZfuisnx3uS1tfmZWyy5JUP0S+BH6tINNhQLGtT87gqYmI3q1ked19rdZVKdrdACfWap8jJ5QUaFteA54oFmHQgiFGvV3cL1TimtCstdTDmihlnNbcbWP/DWtnart0HKmFU98wE4I7wsL7rdaq0CIArWKkGtvqo+qs+qi5+BoxMZEx3LjwRr4+9HWF+5UWJh06vN28KTAXUFiB9RIZGInJOlNF7+a9iT0eS25Rrn3gsCOeek8sWGjr35aO106Fa6cCDsuQBLYqc0xpehSY+DI5uVSqhPj10OpaOH+ckuJh/VxCnADPAJjwmea2MrprFkSJLEv2m0lhQEhH4ZRWa8INzPmVlrsMjuJk9IIibaZ3TAWARSuv0QeKrP1qBTnadDz5tihL63W16gMZiZrr8GBscZ6+IXDNJE2YHMXJK1ATJ9Bcbzqj5rYzemsBCft/gB73wLZPoCBbe2GI+lDrg7KV8aoxmsW7doYmqjpDsegpFI0cJVCXkGCvYDo07UDcxThu73w7U7pOIWpJFC18WtjHPumEjnHtx/FH6h/EZcTR2qc1STlJtPJpxcH0gxXm7zg7u+NUTI5oo5SktlwI8PqA1ytdpNHT4Em4Xzg/jvnRnjb7wzYIz6aaMNjcRqtfLnatJe/Twp4dAzC6joOja6x9UA4MekprWPVumkBYZIVCI2RZwUVaisOsa4Ot4Qfw8NMi5EBzwZ3cqH32CtCi8mxcNxW2fQzr/w8iJ2puOItJC+E2F2muyz1fatF9j++B2L9p4mMxaYNiARY/ok37U5gL3W7Ttrfqp0X8SZMWAejuC7GPavV9s2aNlxhsqlx3issM5eKrRypq+BeOWUingE48t/Y5ViatrFJ+nnpPQn1DOX7xuD3NTe9WoVVVUwI9AtEJXZXcjPYownWvce/xr9nr4VZ2n/wCvjxzrjhB71EsQt7NNSMk27rdqxnkpmhRao5CYKXclYc9m2pRdDoD3PWjFukm9NZJCC3l5kfpueVtU0LZBjHbdysVjt+sC6RU/NKAb4iWV1GeZhlln9XKFzFIG/uUZbVA/UK0sHEonn7IcbBuLcVHuauqj6qz6qNcfI0IZ4siDvthGKl5qUxYXrKx0aGzR+y56d0wWbSZ0wE+GvYRj6x9hOHhw5ncZTK3/XQbEokOXZXFyWY5hXiFkJybjFFnZMHoBSw5voSV8StJy08rsX9afhpN3ZuiEzpu7XArHnoPdp0rfhFIy0oi1WoJ7U3ZS7cvugEQ7OXL3flmpiWf1Br0/n9ndtp2VplO2JcIcaRHfj5fZphA516cGDEYUg5ojfKi+0tcRZkQbBtG3+IQb6GHH60zgTiGTeeml1M7pSwy2zGO4mTrQwLNavRoCg9vhB/v04IboFhQbNjGBfm00MLYs85WTWyUFaRQAMqCqjfKs54EglERo9iUtInPRnzG/avup4l7E05nn9aW9pDS3mfkafBkdMRolhxf4nTGiZpyQ+sb2Hl2J7HRsQDctPAmzJgZ134cAEuOadPEhPqEcjr7NAvHaBPbDv9xeKVh76WJNMFpD29u1DfBI+UI09KtIuHRhNnX3IhA8NyRHdp4nMxkiH0EENo4qYhB8PV47m0RVIWFGx3LpbMmOQiPZ0DxjAWO6N0dXINWS0pntAaHOOTpFQQ3vaH1q23+V7G15h8GuRfAw1cTIBd1tSlroPqoOqs+KoqvFK4qULaIPRu2RRFNFhMpuSncGH4jaxLWMCB4AJtTN2O2mDHqjCVmPK8qNuuoMnTo0Ol0rJmwhnn759nLNS52HHEZcQBOAypAc+OdzDjJhYILdA7ozL+H/5uRi0biafCkpU9LugR04af4n1g1fhWPrn2UhMwE8koHQ5RDD+9WfHkuXetLcffTBp9ar+fekBD2upc19INNWr9Mqr5qc+dpYnYe9AatX8hWX7Y1tBz7ygY9C236w7IntAlUO95UclBqXQxSvcSoxrb6qDqrPsrF10iIiYxh7NKxzBsxj/tX38+g0EEcST/CwjELWXJ8CblFuXQL6sa21G1kFToJWUYTlAW3LOD2n25HInHTuxHqHUp8ZnwJUaqqoFmwYLFYuP6H6+1p5UUUCgQtvVvSo1kPfo7/uYQ1eDj9MMN+HKaVwSRJzEqknX87isxFJfKujGDPYG5qcxNCCJjgMG2UwwwDXYUHER7NWZuXRGxiMsFNO/BCizaszjtGVNsoPAweJVyofb/qSZ6TaZsSDQbAorng3Hy0fqD8i8VBEIU52iDdyAmw/3ttnJNfCNxQzUlKGwnlWfiOs5I0Viq7tsv52i83lEDVE7bZz5eeWEpUuyge//VxOgd0pmNAR7t4fTbiM+5acRcTOkxgev/pTFw+0R59ZxuUO3nlZPQ6vbaulLmQF5sN5JmL8WQICyAwCD0mzPgYfci2hUGXQ1P3pmycpEWiTVw+kWMXjpXrOpRIknOSSY4vHUZejG06JoPOUGJRRkf83fztFtbqhNVkF2UzvNVwNiVv4tqW17I8brnd1WjHYabpGOsEvUbPQIa1AsiB7AMALDy2EL3QExMZQ7BXMKm5qZgRGIWRVRNW2dNGLh7JkI4T4P7pJafX8Q3RXIFZZ7UlHiTaDN5BnWD8vHoTJldoIJ31j87eORshRKNv4Cu6tqpsV7gOysVXj9hmP3978NtMXTeV72/53r48x+yds/nu8HdOBUIgaNekXYllN5zR3SeCfVnxdPcOY19uYomlPXzdfMk15dpXAr6UvDXoLV7a8hI6oSPfnM9/b/wvU9dNxdvo7XSGdseGrbKQ9/Lo0awHXQO7km/KZ9mJZdzS9hZmDpjJ9C3T7a7H8uZOvNQ4Dth2TBNClDsBcU0prz4jAyM5lXWK2OhYnt7wdKWC41i+S1n+mnDHT3eUGHJhIzIoku9Gf2d/LmOjY+0vMeOWjSuzOoFy8ZVPefdV1yZdWRC9oNr5KRdfA2Czop7d+GyZtaNiImNYd2od3Zt0J9AvECjZT3U25yy2lwfblEd6CWahuf4A9mXH464zciAnEW+jN4WWQgQCndCRUZhB18CuHEw7iETaLRnbOlXxGfF4G7wxYyajIAO90NdZIMa0TdMw6o0Maz2MX07+wpLjS7QZxaWFNn5tSMhMYHjr4fx+7nduaH0DXkYv+7GJWdWfE1AgCPEOsVtjEsnyE8uZ1HkSP8X/RFS7KJcRJyh2/zpafk4tSWpvbdmshXvC7rE3tjYx6d6sO/P/mk/XwK7kmfLo06IPz/V5TrM6F42knb+2/lbp8lWn/A1B92bdMUkTvZv3ts/asuvcLno06wGUXdtt/l/zXe4ecXXKs0Jzc3IrOKr6KAuqnqlo9vPU3FSilkTxv5H/4/7V9/P24Ld5ZO0j9n6q7w9/T5F1FoWrAq6iTcIuVvp6Y8DAiIgRbE3eyku9pzFt8/P83+C3eW6T87dXgzDgrndHIhFCYLFYyDPnEe4bTkJWQq2ub3TEaBIyE5y+seqFnpFtRvJz/M8Mbz2cdafW4WnwxGwx46Z3w13vXia8HTTB+eGWH7h/9f30ad6HdafXEeYTRmJ2Ijp0WLAQ4RfBwLCB5BTmEHsiFm+jN2Pbjy3RwJospnKDPsrjUrmpSk97VZ71UVtrxWYtzOk3h24R3UpYC1JKu4X/yNpH+HHMj3QK6FSuBVLaoqpK+atDXbkOU3NTiY6NRiKZf9N87l99PwLB0rFL7c9gVdZ2awgLytXdpzbKs0LnXDuHyIjIauenLKgGItgrmDk3zil3240hN5bbT3Uk/QgnMk6QXZjN6wNe56Hz4wEwYWJFvLau0jNbXiBYCl7f8TqdAzrTJaALp7NOsy91HxZp0RqhDmPZcHpDmUG3CVkJeOo97bNK2DDqjPi7+XM+XxvUOqzVMH49/avTayiv78kojLRv2p6nez/N6oTVrDu1DsAe2VdoKSS7KBtPvSc779bWaLI9nBLJxJ8mArDutHacQBDkEcT5/PMIBInZiUS3i+bOFXdyc8TNXMy/yN5zxeOxqovNRVhf/RDlNTy/n/2ds7lny7U+6sLaCvYM5seTP9ItolsZa8Fm4XcK6ETs8Vim9Z2GSZpo79+elLwUewNe2tK1lSuqXVS1rKeKXLjBnsHcfdXdte4bCvYKJrp9NLvO7eKB1Q8Q6hNKnxZ9SgiQzYp6YPUDRLePdhnrqbH0j5VnhQa4B9TpeWolUEKIt4ExQCFwApgipbxo3fYCcD9gBv4upVxlTR8JfADogc+klLOs6RHAAiAQ2A3cI6Ws+ykSXIyJbSbyyPZHeHvw2yw4vIDXB7wOaDfA/JHz7W8qZmkmS29gfJ6JGSM+sS+GN3v1VPJCe3LW3ZMnez1JzKoYYqNj+Xjvxyw9sRS9Ts/U7lOxWCwsOb6kTMRfaXECKLIU2cUJKFecbNgiCm3WDUCRLOJQ+iGG/TiswmPzzHlORUUgmHPDHB5e+zDeBm9OZ5+mnX87zuefRyIpshTZBzv/HOdcJKvD3pS97E3ZS2Rg2be/8hpVb6M3OY4zodeAIxe0ZdLLi37s0axHld1R5TVu+aZ8fon/hTvS7igjJjGRMcRnxNvvnah2USRnJ2ORFm5qcxMPrH6AEeEjWHNqTYnjatrA29yJNvebrYy7zu2ia2BXlp1YVieuw5jIGJYeX0rbJm2Jz4gnJjLG6T7lbWsoXN196oizl5TCC3XbZNfKxSeEGAH8KqU0CSFmA0gppwkhugDfAX2BEGAtYOuAOQrcCCQCvwN3SCkPCiF+ABZLKRcIIeYAf0gp/1NZGVzdxVcZycnJfJX4VYWr9NpW8R0RPoLVcT8RmykITj1GanAHxvlBqybtnLpkjDoj0e2jmX7ddFJzU7nhxxvQ6/RM6jyJKV2nMHKx5gbzd/NnWOthLDq2CIBREaNYEb8Cf3d/8oryKLQUVhol6Bj2bnO//XX+r0oDHjz0HrTxb0Pv5r3ZfmZ7ldbAAmrdZ+Yopo4EewYT5hvmXJB0buRYyj6APZr1YP/5/ZiczPxu0BnwNfqSY8rBZDYxrsM4ZvSfwbil44i7GEewVzCDwgbZZ+pIy0tzOr2UXuj5bvR35bqjoHy3S2x0LB9u/5B1Z9dVuhK07T6UUpJblMu53HMcTj/s1BUbGRiJv7t/hYt3OitjVGyU3eUGEL002v7dcXxebV2HVVn9uiIaKkiiPtyn9UXpOq7rcVA6ZztXFSnlaint00pvB8Ksn6OBBVLKAillPHAcTaz6AsellHFW62gBEC00+3UYYFuf4AtgbG3K1piIiYyhW1C3ct/kbNun9phKVMcJzO89HqZfYH7v8UR1mkD3Zt25+6q72T95P79O/BU/Nz9GRYxCCMHU7tos5cFe2pgjk8VEdLto5v81nxta34BAUGAuYET4CPToMeqMbDi9geZezSk0F1JobZBLi1NkYCT+bv727zZRGd5qOCczTxLdLpqugV25uc3N9n383fxp36S9PcgjzCeMYa2HcTj9MEuOL+Fc7jna+LWpUp05itOwVsMw6oz25UPa+LXBTVd2PkBHyuubSs1LLSNOkYGRCHAqTqBZXzZxspXBU++pDYxGx4WCCxSaC7FgYdGxRXT7ohvHLx5Hr9OTa8plavepRLeL5nD6YafiZNAZMEszt/10GxkFGVz/w/V0+6Ib9668t8R+jm4XoIS1NbHNxArvMSh5H8ZExrD21Fqe6PkEWUVZTOgwgf2T99v/7r7qbno078GcG+dUyz0W7BXM2PZjCfUN5fafbmfYj8PIKswiszCT63+4nq8Pfc13h7/jUNohlsctr5V1U9lz5arERMaw7MSyOqmD+qa+67jOgiSEEMuB76WUXwshPga2Sym/tm6bB9hmRB0ppXzAmn4PcC0ww7p/e2t6K2CllNJpb5sQ4iHgIYCwsLBeO3bsqJNraAjS09MJCKi63zatII2Htj7E7F6zeX7P88y9bi4SyUNbH2Ju/7kEugfy/sH3WZm0klsLdMw4cwpTk7Zk93iExFb9mLxpMgObD2RX2i4GNhuIp94TiWRN8hpGhIwgz5zH7rTdfND3A17a+xInsk5gFEb7Mh4CwU2hN+Gp96SjX0dm/TXLbj3p0eNh8KClZ0u6Ne3GxDYTmbxpMqFeoZikicTcRHseoAVvfDnoSx7Z9ghZRVmMbT2WC4UX+O3sb3jpvcg1Vx4RpEfPiNARrEleYy9jeVR1xo2K0KHjptCbOJp5FEthFgn5KdwUdjNHMo4Qlx3ndH+DzkAr71Z09OvI2uS1uOvdyTXl0tq7NQadgU/6fcKcI3P49cyvNHFrQlpBGoOaD7L3D65KWsXQFkPJKMrgma7PEOAewJwjcxAIjmQe4cDFA2XO29anLakFqcy9bi4B7gHVvs8A5hyZw5rkNQxqPohN5zbZ76+0gjQe3vawPe/qklaQxt0b73b6khDgHkCuKZd8J7PZd23SlXf7vFsmvb6oSZ3VFba6HxEygoc7PdwgZagJNa2z0NDQmgVJCCHWAi2cbPqnlHKpdZ9/Aibgm2qXrAZIKecCc0Fz8TX2sQrVKX8IIYxNHcsLe19gbIex9oiZsaljWZG6gml9p+G55ywhZnh8yOuI9iMxntpG06WP0bRpU8a0G8Pi44sZHTGazcmb7dFcKVtSeLzf4/bILr2fntSCVLoEdOFg+kE6NOlAXEYcQ8OGsi55HavGr2Le/nk0dW/KhYIL6NAxtsNYzuactfdn3HH1HQidoEuzLmxK3GS/BomkjV8bkrOTmbF/BhlFGQAsPrXYvk+uOZf2TdrTr2U/lsctJ6MgAx06Wvu1Jik7CS+DFxmFGZgxlzsbfBu/NpzMPGkP06+qOPkafckqvSSIFQuWkucTVDgbvQULhZZCnu/3PE9veJq+LfuyJXkLACdzTgJw05qbAGjn344TGScY3mo465LX8e2ob7lzxZ2MajuKjYkbS7jufj33K7HRsdy2/Dan543PjufuLneXiKiq7nPyeJPHSdmSwnMDniNwf6D9/vpq51cl7r3qEkIIHf/qyNH0o1iw2Aeqz9g6g+Vxy7mh9Q3sTdnLt6O/tVtnNlfXpX7WG6ptsdX94/0ed5kAjqpSl3VWqYtPSnmDlDLSyZ9NnO4DbgHuksXmWBLguOJdmDWtvPQ0oIkQwlAqXVGKe1fey9eHviazMJOvDn5Fty+60e2LbuxL2Wd3C6w7v5dvBrxJUKcx2ppEEYO1yVg3vsNjPR6jpXdLNiZttLt/bJGGts+Ond/fj/mezgGdSchM4M6r7sTf3R9k8diYtwa/hZ+bH1cHX81jPR5jzo1z6BTQyZ5HVNsoNidtZlDYIPs1BHgEkJ6fTlS7KOIz4ukc0Jm7r7obPzc/7ulyD6MiRgFw8uJJlp1YRq/gXoDW2CdlJTGw2UAyCjVR8zH62PcvzblcbfkOszTb3YoVYdunPHFypKl7U3QIAqsQxd45oDMbEjcQ1S6KnWd3IhAYdUbu6XKP/bo7B3Smf2h/Ogd0ZmPiRpDYXXVP9366XNfd4LDBuOnc+HXir3YXr1FnpJlXs1q7XRzvi7p2OzlOirzw2EK6fdGNRccW4Wv05Znez5BTlGMfB2i711zZ1VXXONb9lUyt+qCsEXnPAVFSSkd/zDJgkhDC3Rqd1wHYiRYU0UEIESGEcAMmAcuswvYbYJtbZjKwtDZlu1zpGtjV3t9Uuj/ALgqZWQS1H1nywNbXwfkjBHsF882ob7g66OpK+7xs21/r/xpmaWZQyCDWnV5XfJ52UfQL6ceWO7bw1aivSjxMJfrN2kWxMXEjzb2a08yzGYXmQqLbR/O37n/jmuBrSMzSQsa7BXUjul00W5K3MDpiNCZMBHgEsDt1tyaMaGJjW2LEIAzc1OYme94GYUAgtL40YaTAVED7Ju3tLkWgQqEqr19KL/T46z0xSNBbj79QcIGb8yWZer09z1Y+rUr0ywmEVidSm/Pw60NfU2Qpskch7jyzk5jIGDo17URSdhIxkTG81v81JJJBYYNYGb/SLkTlCYStj/HjvR/b/xVCsOCWBXXauDm+uNTFoNY+LfrQOaAz4zuMt/eZ6oSO61tfX2FfmuLKorbjoD4G3IE11jj97VLKR6SUB6xReQfRXH9TpdR6tYUQjwGr0MLM50spbQ70acACIcTrwF5gXi3LdllSURiqlFILm71QpC0hHjG4+MBT27Q55qh4bJaz7Z0DO3NH5zt4ZuMzRLePZkrXKZzJOVPhG61jHrZw3tcHvo6Ukpe3vExMZAxBnkHMHzmf2Ttns/TEUubcOIfZO2cT1S6KKV2n8Pu53zmZeZLREaNZn7gevdAjEGxJ3YJRZySqXRRSSq4JvoYW3poX+lTmKQ6kHaCNfxuOpB1h9qDZfHv4W2KPx4IsFiFnofG2wcQGoQUl2FyCZmkmV5oYG9ST31L3kGbVuJ89sK8d5a5352zuWQw67ZEyCAOeRk9+HPOjFpkmIDk72R7tNHH5RHs9dQroROfAzgR5BhHkGcSkzpOIPR5LS++WJYJcHC3bp9Y/VSKYY/HxxSw+vhiBYHzH8dVuzKsyQLQuw7JtYeCJWYkMDhvMivgVuOnc7Ndb03FWVb0WReOgVgJlC2ooZ9sbwBtO0lcAK5ykx6FF+SkqoLJpWubcOAdaLISlj2luPet4KZY+BsNfqfF5HRunIM+gCgXOWZkd9y99rLPGKMgziAWjF3DXirvYmLSRWzvcipSS7498j5AQXWDhb79+xNiwED676iEejP/efpx9nkPMjF8+vkx5nAVLCATbkrfRvkl7LhZc5ELeBTyNnvboRbM0M3X4v9i3+kHyspPRCZ19lgKzxUwzr2YkZydjsHqp2/i34bqQ6+zWz9LjS+kc0JmYyBhSc1PtY41sFpGzsUmlw7cdf4N5++fZxzzZZs6wSAsSaW/kq0NVBohW9mJTHRwH025K3IS3wZub295sv97aDKRtLINdFZWjZpJohFT6dmmbhXvFc8VrFg1/pVazc9dl4+Qsb3e9O7f9pHX4Ow5ajQyMpK1/W2IiY5BScuT0JmT6SaYOfo2g9iOJ+u1pHjg4l+gW/eyNmG2ewwEhA5jef7o9rxlbZ7DsxDIs0oJFWtAJHRZpoUOTDlwdfDVbk7cya9As7vz5TkZGjGRD4gaMwogZM3r0pOamci73HHqdnhtb31g8iDVhDefzzjMoVHOBBnoEci73nN3SsDXGgF1AbWONnDXAtslbSw/eLW3NOFrSN4bfyM/xP3NL21tq5AqrzQDRmlospQfTlhbWmlpsjWmwq6JilEA1Qqr0dumwZEVdUx8ulEGhg9h2ZpvTyC3HQYrzz57n/LWvaQEgQEz/l4lfe5aY+D/s+9j62cYuHcvfuv/N3kitPbWWqHZRLDu+DDNmhoYNZUPiBmYNmkVTj6asSViDRVpAwMakjYzrMI4pXafw8paXaendkgdWP8DY9mORUpJnyqNbUDcEwi42sce1OQELzAWM6zCujPVT+qXC7pIt1QB3DexKYlZimTFRe1P2cu/Ke/ny5i/LWNLuendCvEN4uvfTNar/2kygWlOLxSbctoGepc9V05ciNRnsZYSUslH/9erVSzZmkpKSanRcSk6KfHj1wzI1N7WOS1TMPSvukZGfR5b5u/776+WsHbPK3X7Pinuqfa6UnBTZ/9v+MiUnxf59wHcDyl7fjCYy6fTJkmmmQilnNCmT56wds+SsHbPsn2fvnC1TclLklJVT5PAfhsvrvr1Ozt45u8T+/b/tL6dvmV6mbh3r21bWg+cP2sto23447XC5v4stf8dzllcXPb/sKV/Z/EqJupi+ZXqJY52Vozyqcp9VJz9nx1X625VzbH3cxzW9Fkdq+mxeydS0zoBd0kn73uACU9u/K1WgLgWODbxj2owtM2T/b/vLVza/ImftmFWiQbIJQW3PV24+H18rU39fXDItboOUH19bZteKGilnDWN1Gsuqik3p8lQ1/+lbpsueX/aUKTkp9jp31tBWtRxVvc9qcl224yr97S4xNb0WG678bLoqSqCUQF0yKnoznrVjlpy+Zbrd4rBZJzV9W3U8X4VvvX/+KIve6aKJkqlQ+/e9blL++aPTPGvbSFVU1vq0YG1W1HMbnnNqPVW3HFW9z2p6XXVhsdQ1tf2NXPnZdFXqWqBUH5SiXCry5dv6VAaGDmR53HL7zBK18fVXtW8t68IFmlYxAKS+Zqyuz6ARW/5j2o1h0bFFjI4YzdpTa2nl08rpzO89mvWos/6V2vb7uNLyFfX9GynqHyVQCjvlBT/ohb5MxKCtQVp6fGmJmSVqGylVFUHJaz+apoMfrFJ+jbmRmtp9KluTt7IhcQPjOoxDSkn3Zt1dNnzaFZevUDRulEAp7JQXjWVb+K30m7GtQWrh3aLO3pwbs6DUNbZoRNvAZimlS4dPq99OUdfUaqojxeWFbTqd1FwtvNnWAL4+4HWnU+rbGqSp3ac2ymUNGgPO5klUUwAprhSUQCnslNcAdgroVOHElWpiy0tHY1orSKGoLUqgFCVQDaBrU9eTtioUrozqg1KUwBWjsRQlUcEIiisFJVCKMqgG0LVRwQiKKwUlUIoyqAZQoVC4AqoPSqFQKBQuiRIohUKhULgkSqAUCoVC4ZIogVIoFAqFS6IESqFQKBQuiRIohUKhULgkSqAUCoVC4ZIogVLUPfsXwif9YGZT7d/9Cxu6RAqFohGiBuoq6pb9C2HdqxD9MbS+Dk5tg6WPadvKWVRQoVAonKEsKEXdsvEdTZwiBoPeqP0b/bGWXlWUBaZQKFAWlKKuOX9Es5wcaX2dll4VlAWmUCisKAtKUbcEddJExZFT27T0qlAXFphCobgsUAKlqFsGP6NZPPEbwVyk/bv0MS29KtTWAlMoFJcNysWnqFtsbrgVz2miEtQJhr9SdfeczQKLGFycVh0LTKFQXDYogVLUPd0m1Ly/yGaBdZsIh3+C80dB6KH3lLoto0KhcHmUQClci24T4PQO2PIBWEya5XTVGNj/A7S6tmrCt3+h1mdls+AGP6MCLBSKRogSKFdDNa4QvwnuWVzSzdd2sOY2rKwuVBSgQnHZUCdBEkKIp4UQUggRZP0uhBAfCiGOCyH+FEL0dNh3shDimPVvskN6LyHEfusxHwohRF2UrVFha1xHvQUvpWj/rnv1yhsHVJtACRUFqFBcNtRaoIQQrYARwCmH5JuBDta/h4D/WPcNAKYD1wJ9gelCiKbWY/4DPOhw3Mjalq3RoRpXjdqEqqsoQIXisqEuLKj3gOcA6ZAWDXwpNbYDTYQQLYGbgDVSynQp5QVgDTDSus1PSrldSimBL4GxdVC2xoVqXDVKh6qvew2+uhVSD8Mn/fA8/nP5x9Z2HJZCoXAZatUHJYSIBpKklH+U8siFAqcdvida0ypKT3SSXt55H0KzzAgLCyM5ObkWV9GwpKen2z8HN2lLxt6fKAy51p7mlrwD/yZtSW3E11htAvvj2fNxfJY9ieHCCdDpyb7mAbJ6/Q23s3vw++1FLgB57UeXOdSz2/34Ln6Ui0Nep7BFT9zO7qHJhpfI6vMP8q6kOiyF432mqBqqzqpPXddZpQIlhFgLtHCy6Z/Ai2juvUuKlHIuMBegd+/eMiQk5FIXoU6xl3/YCwStm16yg3/zdBj+Co39GqtNyIMw+EFtLr5Rb+EbMRhfgLBwzgNBO2bTdPCDzo9r2pSgjbOLA01GzKRptwk0Lbv3FcUVdw/VAarOqk9d1lmlAiWlvMFZuhCiGxAB2KynMGCPEKIvkAS0ctg9zJqWBAwtlb7emh7mZP8ri9oOcr0cceL2LGzRs2K3Z23GYSkUCpehxi4+KeV+oJntuxDiJNBbSnleCLEMeEwIsQAtICJDSnlGCLEKeNMhMGIE8IKUMl0IkSmE6AfsAO4FPqpp2Ro1qnEtiZOZJdzO7lF9SgrFFUB9zcW3AogDjgP/Bf4GIKVMB14Dfrf+vWpNw7rPZ9ZjTgAr66lsisaEk7n9mmx4qepz+ykUikZLnQ3UlVK2cfgsganl7DcfmO8kfRcQWVflUVwmOHF7ZvX5B02VlalQXPaomSQUrk8pt2decvIVH/CgUFwJqOU2FAqFQuGSKIFSKBQKhUuiBEqhUCgULokSKIVCoVC4JEqgFAqFQuGSKIFSKBQKhUuiBEqhUCgULokaB+VC3LvyXvam7C2T3qNZD768+csGKJFCoVA0HEqgXIiugV3pGtiVaX2n2dNm75zNlbi4sEKhUCgXnwsRExnDshPLSM1NBSA1N5XlccuJiYxp4JIpFArFpUcJlAsR7BVMVLso5v+lTVU4/6/5RLWLIsgzqIFLplAoFJceJVAuhs2KOpR2SFlPCoXiikYJlIths6IeWP2Asp4UCsUVjQqScEFiImOIz4hX1pNCobiiUQLlggR7BTPnxjkNXQyFQqFoUJRAKa441HgzhaJxoARKccWhxpspFI0DFSShuOJQ480UisaBEijFFYcab6ZQNA6UQCmuSNR4M4XC9VECpbgiUePNFArXRwVJKK5Y1HgzhcK1UQKluGJR480UCtdGCZSiTlFjjBQKRV2hBKoR0RgafzXGSKFQ1BVKoBoRjaHxj4mMYezSscRExhDsFWwfYxQbHdvQRVMoFI0MFcXXiGgMA0zVGCOFQlFXKAuqEeHY+E/rO63cxr+hXYE2KyqqXZSynhQKRY1RAtXIqErj39CuQMcxRtHto6tlPdVWXBtanBUKRd2hBKqRUZXG3xX6gWo6xqi0uNoEZ2/KXrp90c2+X3mCU1/irIRPobj01LoPSgjxuBDisBDigBDiLYf0F4QQx4UQR4QQNzmkj7SmHRdCPO+QHiGE2GFN/14I4Vbbsl2uxETG0C2oW7mNvyv0A9nGGFX3nKX72dr6t8VN78Zvt/3G/sn72T95P+NajyMyKLJKx9dVP13XwK7cfdXd9jLsn7yfu6+6u9xyKBSK2lMrgRJCXA9EA9dIKbsC71jTuwCTgK7ASODfQgi9EEIPfALcDHQB7rDuCzAbeE9K2R64ANxfm7JdzlSl8W+sc80FewXjrndn2I/D6PZFNxYdW0ShuZDrf7iee1feS2puKmvPrL3k4twYAlQUisuN2lpQjwKzpJQFAFLKFGt6NLBASlkgpYwHjgN9rX/HpZRxUspCYAEQLTT/yzBgofX4L4CxtSzbFU1jnmtucNhg3HRu/HDLD/i7+zOhwwQ6B3QmMiiS23+6nayiLK7/4Xq6fdHN/nfvynvtx9eHOLuCVapQXGnUtg+qIzBICPEGkA88I6X8HQgFtjvsl2hNAzhdKv1aIBC4KKU0Odm/DEKIh4CHAMLCwkhOTq7lZTQc6enp9Zb3qOBRHEo5xKigUY2qjsa3HM+yE8u4b+V9jAwdyfUB17Pw2ELubXMvCwoWMCxoGNN6FPcxzTkyB4EocY3DWwwn5pcYRoSMoPBCIckXan/9o4JH8dDWh7jO7zqWHl/K3OvmNpp6rc/77HJF1Vn1qes6q1SghBBrgRZONv3TenwA0A/oA/wghGhbpyV0gpRyLjAXoHfv3jIkJKS+T1mv1Ff5Qwjh84jP6yXv+iSEEKLORLE1eSuP93ucefvn0TmgM7P+mkVUuyhWxa/C2MRoDwD59dyvxEbHlrBmHm/yOClbUni83+N1ZuWEEMLY1LG8sPcFxnYYS2RE4+p/auzPSUOg6qz61GWdVerik1LeIKWMdPK3FM3SWSw1dgIWIAhIAlo5ZBNmTSsvPQ1oIoQwlEpXXKFM7T6VnKIce1/P6wNep1tQNx7r8Rg3htxYqautpkEalVFZgIpCoag7atsHFQtcDyCE6Ai4AeeBZcAkIYS7ECIC6ADsBH4HOlgj9tzQAimWSSkl8BswwZrvZGBpLcumaMSU7kPrFNDJLjgT20xssACQ+hI+hUJRltr2Qc0H5gsh/gIKgclWsTkghPgBOAiYgKlSSjOAEOIxYBWgB+ZLKQ9Y85oGLBBCvA7sBebVsmyKRk55Y6kC3QNrPBBYoVA0HmolUNZIvLvL2fYG8IaT9BXACifpcWhRfgoFUPF6TWqxQYXi8kfNJKFolKjFBhWKyx81m7lCoVAoXBIlUAqFQqFwSZRAKRQKhcIlUQKlUCgUCpdECZRCoVAoXBIlUAqFQqFwSYQ2rrbxIoRIBRIauhy1IAht9g1F1VF1Vn1UnVUfVWfVp6Z1Fi6lDC6d2OgFqrEjhNglpezd0OVoTKg6qz6qzqqPqrPqU9d1plx8CoVCoXBJlEApFAqFwiVRAtXwzG3oAjRCVJ1VH1Vn1UfVWfWp0zpTfVAKhUKhcEmUBaVQKBQKl0QJlEKhUChcEiVQDYgQ4qQQYr8QYp8QYldDl8cVEULMF0KkWBfFtKUFCCHWCCGOWf9t2pBldDXKqbMZQogk6722TwgxqiHL6GoIIVoJIX4TQhwUQhwQQjxhTVf3WjlUUGd1dq+pPqgGRAhxEugtpVSDActBCDEYyAa+lFJGWtPeAtKllLOEEM8DTaWU0xqynK5EOXU2A8iWUr7TkGVzVYQQLYGWUso9QghfYDcwFrgPda85pYI6u406uteUBaVwaaSUG4H0UsnRwBfWz1+gPRQKK+XUmaICpJRnpJR7rJ+zgENAKOpeK5cK6qzOUALVsEhgtRBitxDioYYuTCOiuZTyjPXzWaB5QxamEfGYEOJPqwtQuarKQQjRBugB7EDda1WiVJ1BHd1rSqAaloFSyp7AzcBUq2tGUQ2k5qNWfurK+Q/QDugOnAH+1aClcVGEED7AIuAfUspMx23qXnOOkzqrs3tNCVQDIqVMsv6bAiwB+jZsiRoN56z+b5sfPKWBy+PySCnPSSnNUkoL8F/UvVYGIYQRraH9Rkq52Jqs7rUKcFZndXmvKYFqIIQQ3taORYQQ3sAI4K+Kj1JYWQZMtn6eDCxtwLI0CmyNrJVxqHutBEIIAcwDDkkp33XYpO61ciivzuryXlNRfA2EEKItmtUEYAC+lVK+0YBFckmEEN8BQ9Gm8T8HTAdigR+A1mhLrdwmpVRBAVbKqbOhaC4XCZwEHnboW7niEUIMBDYB+wGLNflFtD4Vda85oYI6u4M6uteUQCkUCoXCJVEuPoVCoVC4JEqgFAqFQuGSKIFSKBQKhUuiBEqhUCgULokSKIVCoVC4JEqgFAqFQuGSKIFSKBQKhUvy/5Vnkq8fgYNGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Tower Base Moment Load Statistics'}>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loads.graphics.plot_statistics(means['uWind_80m'],\n",
    "                      means['BL1_FlapMom'],\n",
    "                      maxs['BL1_FlapMom'],\n",
    "                      mins['BL1_FlapMom'],\n",
    "                      y_stdev=std['BL1_FlapMom'],\n",
    "                      xlabel='Wind Speed [m/s]',\n",
    "                      ylabel='Blade Flap Moment [kNm]',\n",
    "                      title = 'Blade Flap Moment Load Statistics')\n",
    "\n",
    "loads.graphics.plot_statistics(means['uWind_80m'],\n",
    "                      means['TB_ForeAft'],\n",
    "                      maxs['TB_ForeAft'],\n",
    "                      mins['TB_ForeAft'],\n",
    "                      y_stdev=std['TB_ForeAft'],\n",
    "                      xlabel='Wind Speed [m/s]',\n",
    "                      ylabel='Tower Base Moment [kNm]',\n",
    "                      title = 'Tower Base Moment Load Statistics')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another common step is to bin the statistical data. This can easily be done with the bin_stats function from the loads module shown below. A warning message will show if there are any bins that were not filled. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: some bins may be empty!\n",
      "Warning: some bins may be empty!\n",
      "Warning: some bins may be empty!\n"
     ]
    },
    {
     "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>uWind_80m</th>\n",
       "      <th>WD_ModActive</th>\n",
       "      <th>WD_Nacelle</th>\n",
       "      <th>WD_NacelleMod</th>\n",
       "      <th>LSSDW_Tq</th>\n",
       "      <th>LSSDW_My</th>\n",
       "      <th>LSSDW_Mz</th>\n",
       "      <th>TTTq</th>\n",
       "      <th>TT_ForeAft</th>\n",
       "      <th>TT_SideSide</th>\n",
       "      <th>TB_ForeAft</th>\n",
       "      <th>TB_SideSide</th>\n",
       "      <th>BL3_FlapMom</th>\n",
       "      <th>BL3_EdgeMom</th>\n",
       "      <th>BL1_FlapMom</th>\n",
       "      <th>BL1_EdgeMom</th>\n",
       "      <th>ActivePower</th>\n",
       "      <th>yawoffset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.582788</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>179.116583</td>\n",
       "      <td>179.215605</td>\n",
       "      <td>29.890756</td>\n",
       "      <td>-252.235192</td>\n",
       "      <td>-2.931479</td>\n",
       "      <td>-26.271654</td>\n",
       "      <td>-932.953738</td>\n",
       "      <td>126.807011</td>\n",
       "      <td>994.638671</td>\n",
       "      <td>-102.050137</td>\n",
       "      <td>-206.591291</td>\n",
       "      <td>129.390923</td>\n",
       "      <td>-238.085263</td>\n",
       "      <td>-14.874877</td>\n",
       "      <td>21.370858</td>\n",
       "      <td>-0.099022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.498995</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>178.968041</td>\n",
       "      <td>178.963826</td>\n",
       "      <td>77.559257</td>\n",
       "      <td>-248.081262</td>\n",
       "      <td>-3.389182</td>\n",
       "      <td>-51.890684</td>\n",
       "      <td>-856.012087</td>\n",
       "      <td>154.470046</td>\n",
       "      <td>2235.672810</td>\n",
       "      <td>55.864708</td>\n",
       "      <td>-327.707803</td>\n",
       "      <td>162.318746</td>\n",
       "      <td>-354.474414</td>\n",
       "      <td>-7.655706</td>\n",
       "      <td>76.033527</td>\n",
       "      <td>0.004216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.525786</td>\n",
       "      <td>0.980392</td>\n",
       "      <td>180.016225</td>\n",
       "      <td>179.996997</td>\n",
       "      <td>171.459516</td>\n",
       "      <td>-230.697510</td>\n",
       "      <td>-2.588070</td>\n",
       "      <td>-50.333653</td>\n",
       "      <td>-761.047719</td>\n",
       "      <td>213.700239</td>\n",
       "      <td>3822.643018</td>\n",
       "      <td>137.424215</td>\n",
       "      <td>-495.457116</td>\n",
       "      <td>191.503291</td>\n",
       "      <td>-599.658658</td>\n",
       "      <td>1436.266991</td>\n",
       "      <td>167.928433</td>\n",
       "      <td>0.019228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.520641</td>\n",
       "      <td>0.970588</td>\n",
       "      <td>180.835469</td>\n",
       "      <td>180.788600</td>\n",
       "      <td>219.138550</td>\n",
       "      <td>-237.979417</td>\n",
       "      <td>-5.688640</td>\n",
       "      <td>-42.827109</td>\n",
       "      <td>-647.310249</td>\n",
       "      <td>302.591017</td>\n",
       "      <td>6122.425105</td>\n",
       "      <td>192.772384</td>\n",
       "      <td>-708.321569</td>\n",
       "      <td>237.072176</td>\n",
       "      <td>-777.545681</td>\n",
       "      <td>599.754365</td>\n",
       "      <td>317.629187</td>\n",
       "      <td>0.046869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.534458</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>181.373937</td>\n",
       "      <td>181.474371</td>\n",
       "      <td>285.072650</td>\n",
       "      <td>-240.838000</td>\n",
       "      <td>-8.602511</td>\n",
       "      <td>-56.222891</td>\n",
       "      <td>-571.093365</td>\n",
       "      <td>376.755167</td>\n",
       "      <td>7551.899048</td>\n",
       "      <td>296.723677</td>\n",
       "      <td>-861.049383</td>\n",
       "      <td>252.077699</td>\n",
       "      <td>-874.336546</td>\n",
       "      <td>88.957621</td>\n",
       "      <td>454.388684</td>\n",
       "      <td>-0.100434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.483451</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>180.006753</td>\n",
       "      <td>180.002341</td>\n",
       "      <td>405.504595</td>\n",
       "      <td>-233.796744</td>\n",
       "      <td>-7.596248</td>\n",
       "      <td>-32.233513</td>\n",
       "      <td>-430.681550</td>\n",
       "      <td>502.070942</td>\n",
       "      <td>9938.523640</td>\n",
       "      <td>335.667789</td>\n",
       "      <td>-1073.556266</td>\n",
       "      <td>313.335072</td>\n",
       "      <td>-1167.966930</td>\n",
       "      <td>1302.506123</td>\n",
       "      <td>679.886822</td>\n",
       "      <td>0.004413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.635533</td>\n",
       "      <td>0.880000</td>\n",
       "      <td>178.894596</td>\n",
       "      <td>178.885289</td>\n",
       "      <td>581.709245</td>\n",
       "      <td>-218.693601</td>\n",
       "      <td>-8.801577</td>\n",
       "      <td>-72.657764</td>\n",
       "      <td>-330.525678</td>\n",
       "      <td>668.243228</td>\n",
       "      <td>11477.966048</td>\n",
       "      <td>588.384954</td>\n",
       "      <td>-1209.842191</td>\n",
       "      <td>342.343342</td>\n",
       "      <td>-1526.574097</td>\n",
       "      <td>4686.436750</td>\n",
       "      <td>942.635185</td>\n",
       "      <td>0.009307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10.552790</td>\n",
       "      <td>0.892857</td>\n",
       "      <td>179.473587</td>\n",
       "      <td>179.487138</td>\n",
       "      <td>653.097969</td>\n",
       "      <td>-224.998487</td>\n",
       "      <td>-5.756869</td>\n",
       "      <td>-62.714160</td>\n",
       "      <td>-283.620699</td>\n",
       "      <td>750.186980</td>\n",
       "      <td>12002.102796</td>\n",
       "      <td>740.834502</td>\n",
       "      <td>-1234.723513</td>\n",
       "      <td>354.710593</td>\n",
       "      <td>-1380.119025</td>\n",
       "      <td>2065.459821</td>\n",
       "      <td>1101.930764</td>\n",
       "      <td>-0.013551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11.431282</td>\n",
       "      <td>0.814807</td>\n",
       "      <td>179.157452</td>\n",
       "      <td>179.173400</td>\n",
       "      <td>738.418861</td>\n",
       "      <td>-209.685631</td>\n",
       "      <td>-1.320960</td>\n",
       "      <td>-22.419344</td>\n",
       "      <td>-295.116952</td>\n",
       "      <td>874.826602</td>\n",
       "      <td>12237.843754</td>\n",
       "      <td>668.581523</td>\n",
       "      <td>-1248.802900</td>\n",
       "      <td>362.942457</td>\n",
       "      <td>-1291.630102</td>\n",
       "      <td>173.010056</td>\n",
       "      <td>1251.222715</td>\n",
       "      <td>-0.015947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12.447532</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>178.668483</td>\n",
       "      <td>178.658239</td>\n",
       "      <td>791.696059</td>\n",
       "      <td>-188.741923</td>\n",
       "      <td>-2.588168</td>\n",
       "      <td>-29.769987</td>\n",
       "      <td>-291.021580</td>\n",
       "      <td>902.150404</td>\n",
       "      <td>11648.909089</td>\n",
       "      <td>743.454123</td>\n",
       "      <td>-1173.606189</td>\n",
       "      <td>328.218911</td>\n",
       "      <td>-1262.103186</td>\n",
       "      <td>738.232546</td>\n",
       "      <td>1316.246001</td>\n",
       "      <td>0.010243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>13.406650</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>180.438968</td>\n",
       "      <td>180.452124</td>\n",
       "      <td>817.388823</td>\n",
       "      <td>-198.989294</td>\n",
       "      <td>-7.197755</td>\n",
       "      <td>-24.899471</td>\n",
       "      <td>-331.564307</td>\n",
       "      <td>992.711011</td>\n",
       "      <td>10733.428547</td>\n",
       "      <td>791.711119</td>\n",
       "      <td>-1074.877395</td>\n",
       "      <td>300.227189</td>\n",
       "      <td>-1114.404530</td>\n",
       "      <td>121.228715</td>\n",
       "      <td>1384.312354</td>\n",
       "      <td>-0.013155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14.348479</td>\n",
       "      <td>0.542676</td>\n",
       "      <td>179.354862</td>\n",
       "      <td>179.346215</td>\n",
       "      <td>875.198293</td>\n",
       "      <td>-59.267478</td>\n",
       "      <td>-1.437998</td>\n",
       "      <td>4.153358</td>\n",
       "      <td>-374.601981</td>\n",
       "      <td>907.552054</td>\n",
       "      <td>9896.827494</td>\n",
       "      <td>677.680004</td>\n",
       "      <td>-971.840066</td>\n",
       "      <td>285.582399</td>\n",
       "      <td>-1036.798595</td>\n",
       "      <td>102.258425</td>\n",
       "      <td>1350.838415</td>\n",
       "      <td>0.008648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15.579021</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>178.013520</td>\n",
       "      <td>178.001359</td>\n",
       "      <td>880.603457</td>\n",
       "      <td>-164.536103</td>\n",
       "      <td>-1.851375</td>\n",
       "      <td>-26.848873</td>\n",
       "      <td>-330.960964</td>\n",
       "      <td>1059.024803</td>\n",
       "      <td>9234.857465</td>\n",
       "      <td>926.368719</td>\n",
       "      <td>-899.553762</td>\n",
       "      <td>227.524012</td>\n",
       "      <td>-923.851217</td>\n",
       "      <td>74.708350</td>\n",
       "      <td>1491.638595</td>\n",
       "      <td>0.012160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>16.400735</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>178.314956</td>\n",
       "      <td>178.311169</td>\n",
       "      <td>866.227371</td>\n",
       "      <td>-185.673240</td>\n",
       "      <td>-12.999453</td>\n",
       "      <td>-78.397677</td>\n",
       "      <td>-287.228505</td>\n",
       "      <td>1071.363839</td>\n",
       "      <td>8983.182063</td>\n",
       "      <td>984.403585</td>\n",
       "      <td>-860.376373</td>\n",
       "      <td>200.755492</td>\n",
       "      <td>-864.637118</td>\n",
       "      <td>53.896186</td>\n",
       "      <td>1499.435045</td>\n",
       "      <td>0.003787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>17.602406</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>179.171230</td>\n",
       "      <td>179.157532</td>\n",
       "      <td>886.425220</td>\n",
       "      <td>-137.146930</td>\n",
       "      <td>-8.231045</td>\n",
       "      <td>-3.156350</td>\n",
       "      <td>-383.416299</td>\n",
       "      <td>1063.362197</td>\n",
       "      <td>8563.213120</td>\n",
       "      <td>873.449181</td>\n",
       "      <td>-762.282362</td>\n",
       "      <td>225.211156</td>\n",
       "      <td>-810.106716</td>\n",
       "      <td>48.225543</td>\n",
       "      <td>1476.244726</td>\n",
       "      <td>0.013698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>18.982808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>179.199942</td>\n",
       "      <td>179.421965</td>\n",
       "      <td>863.882390</td>\n",
       "      <td>-158.281553</td>\n",
       "      <td>-23.757701</td>\n",
       "      <td>-74.334173</td>\n",
       "      <td>-270.882688</td>\n",
       "      <td>1056.399578</td>\n",
       "      <td>8206.587839</td>\n",
       "      <td>1349.955119</td>\n",
       "      <td>-699.125685</td>\n",
       "      <td>205.630765</td>\n",
       "      <td>-703.231687</td>\n",
       "      <td>14.362069</td>\n",
       "      <td>1514.168487</td>\n",
       "      <td>-0.222023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>19.519921</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>178.361870</td>\n",
       "      <td>178.356581</td>\n",
       "      <td>881.739431</td>\n",
       "      <td>-169.696939</td>\n",
       "      <td>1.018761</td>\n",
       "      <td>-61.347061</td>\n",
       "      <td>-284.548611</td>\n",
       "      <td>1113.928290</td>\n",
       "      <td>7807.792946</td>\n",
       "      <td>981.873207</td>\n",
       "      <td>-695.129807</td>\n",
       "      <td>151.699529</td>\n",
       "      <td>-709.755338</td>\n",
       "      <td>20.152316</td>\n",
       "      <td>1514.549099</td>\n",
       "      <td>0.005289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>22.062534</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>178.524018</td>\n",
       "      <td>178.507149</td>\n",
       "      <td>854.648338</td>\n",
       "      <td>-142.928299</td>\n",
       "      <td>-43.759922</td>\n",
       "      <td>-111.305482</td>\n",
       "      <td>-262.456906</td>\n",
       "      <td>1067.274340</td>\n",
       "      <td>7566.500039</td>\n",
       "      <td>1369.827084</td>\n",
       "      <td>-572.629544</td>\n",
       "      <td>186.356598</td>\n",
       "      <td>-575.251752</td>\n",
       "      <td>4.025634</td>\n",
       "      <td>1500.616164</td>\n",
       "      <td>0.016870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>24.759699</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>179.912515</td>\n",
       "      <td>179.904301</td>\n",
       "      <td>334.771556</td>\n",
       "      <td>-206.441986</td>\n",
       "      <td>-11.694631</td>\n",
       "      <td>-32.524912</td>\n",
       "      <td>-679.532690</td>\n",
       "      <td>492.819279</td>\n",
       "      <td>3419.356234</td>\n",
       "      <td>752.087159</td>\n",
       "      <td>-226.330646</td>\n",
       "      <td>137.392161</td>\n",
       "      <td>-187.876792</td>\n",
       "      <td>-33.225278</td>\n",
       "      <td>615.054202</td>\n",
       "      <td>0.008214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    uWind_80m  WD_ModActive  WD_Nacelle  WD_NacelleMod    LSSDW_Tq  \\\n",
       "0    3.582788      1.000000  179.116583     179.215605   29.890756   \n",
       "1    4.498995      1.000000  178.968041     178.963826   77.559257   \n",
       "2    5.525786      0.980392  180.016225     179.996997  171.459516   \n",
       "3    6.520641      0.970588  180.835469     180.788600  219.138550   \n",
       "4    7.534458      1.000000  181.373937     181.474371  285.072650   \n",
       "5    8.483451      0.966667  180.006753     180.002341  405.504595   \n",
       "6    9.635533      0.880000  178.894596     178.885289  581.709245   \n",
       "7   10.552790      0.892857  179.473587     179.487138  653.097969   \n",
       "8   11.431282      0.814807  179.157452     179.173400  738.418861   \n",
       "9   12.447532      0.888889  178.668483     178.658239  791.696059   \n",
       "10  13.406650      0.833333  180.438968     180.452124  817.388823   \n",
       "11  14.348479      0.542676  179.354862     179.346215  875.198293   \n",
       "12  15.579021      0.833333  178.013520     178.001359  880.603457   \n",
       "13  16.400735      1.000000  178.314956     178.311169  866.227371   \n",
       "14  17.602406      1.000000  179.171230     179.157532  886.425220   \n",
       "15  18.982808      1.000000  179.199942     179.421965  863.882390   \n",
       "16  19.519921      1.000000  178.361870     178.356581  881.739431   \n",
       "17        NaN           NaN         NaN            NaN         NaN   \n",
       "18        NaN           NaN         NaN            NaN         NaN   \n",
       "19  22.062534      1.000000  178.524018     178.507149  854.648338   \n",
       "20        NaN           NaN         NaN            NaN         NaN   \n",
       "21  24.759699      1.000000  179.912515     179.904301  334.771556   \n",
       "\n",
       "      LSSDW_My   LSSDW_Mz        TTTq  TT_ForeAft  TT_SideSide    TB_ForeAft  \\\n",
       "0  -252.235192  -2.931479  -26.271654 -932.953738   126.807011    994.638671   \n",
       "1  -248.081262  -3.389182  -51.890684 -856.012087   154.470046   2235.672810   \n",
       "2  -230.697510  -2.588070  -50.333653 -761.047719   213.700239   3822.643018   \n",
       "3  -237.979417  -5.688640  -42.827109 -647.310249   302.591017   6122.425105   \n",
       "4  -240.838000  -8.602511  -56.222891 -571.093365   376.755167   7551.899048   \n",
       "5  -233.796744  -7.596248  -32.233513 -430.681550   502.070942   9938.523640   \n",
       "6  -218.693601  -8.801577  -72.657764 -330.525678   668.243228  11477.966048   \n",
       "7  -224.998487  -5.756869  -62.714160 -283.620699   750.186980  12002.102796   \n",
       "8  -209.685631  -1.320960  -22.419344 -295.116952   874.826602  12237.843754   \n",
       "9  -188.741923  -2.588168  -29.769987 -291.021580   902.150404  11648.909089   \n",
       "10 -198.989294  -7.197755  -24.899471 -331.564307   992.711011  10733.428547   \n",
       "11  -59.267478  -1.437998    4.153358 -374.601981   907.552054   9896.827494   \n",
       "12 -164.536103  -1.851375  -26.848873 -330.960964  1059.024803   9234.857465   \n",
       "13 -185.673240 -12.999453  -78.397677 -287.228505  1071.363839   8983.182063   \n",
       "14 -137.146930  -8.231045   -3.156350 -383.416299  1063.362197   8563.213120   \n",
       "15 -158.281553 -23.757701  -74.334173 -270.882688  1056.399578   8206.587839   \n",
       "16 -169.696939   1.018761  -61.347061 -284.548611  1113.928290   7807.792946   \n",
       "17         NaN        NaN         NaN         NaN          NaN           NaN   \n",
       "18         NaN        NaN         NaN         NaN          NaN           NaN   \n",
       "19 -142.928299 -43.759922 -111.305482 -262.456906  1067.274340   7566.500039   \n",
       "20         NaN        NaN         NaN         NaN          NaN           NaN   \n",
       "21 -206.441986 -11.694631  -32.524912 -679.532690   492.819279   3419.356234   \n",
       "\n",
       "    TB_SideSide  BL3_FlapMom  BL3_EdgeMom  BL1_FlapMom  BL1_EdgeMom  \\\n",
       "0   -102.050137  -206.591291   129.390923  -238.085263   -14.874877   \n",
       "1     55.864708  -327.707803   162.318746  -354.474414    -7.655706   \n",
       "2    137.424215  -495.457116   191.503291  -599.658658  1436.266991   \n",
       "3    192.772384  -708.321569   237.072176  -777.545681   599.754365   \n",
       "4    296.723677  -861.049383   252.077699  -874.336546    88.957621   \n",
       "5    335.667789 -1073.556266   313.335072 -1167.966930  1302.506123   \n",
       "6    588.384954 -1209.842191   342.343342 -1526.574097  4686.436750   \n",
       "7    740.834502 -1234.723513   354.710593 -1380.119025  2065.459821   \n",
       "8    668.581523 -1248.802900   362.942457 -1291.630102   173.010056   \n",
       "9    743.454123 -1173.606189   328.218911 -1262.103186   738.232546   \n",
       "10   791.711119 -1074.877395   300.227189 -1114.404530   121.228715   \n",
       "11   677.680004  -971.840066   285.582399 -1036.798595   102.258425   \n",
       "12   926.368719  -899.553762   227.524012  -923.851217    74.708350   \n",
       "13   984.403585  -860.376373   200.755492  -864.637118    53.896186   \n",
       "14   873.449181  -762.282362   225.211156  -810.106716    48.225543   \n",
       "15  1349.955119  -699.125685   205.630765  -703.231687    14.362069   \n",
       "16   981.873207  -695.129807   151.699529  -709.755338    20.152316   \n",
       "17          NaN          NaN          NaN          NaN          NaN   \n",
       "18          NaN          NaN          NaN          NaN          NaN   \n",
       "19  1369.827084  -572.629544   186.356598  -575.251752     4.025634   \n",
       "20          NaN          NaN          NaN          NaN          NaN   \n",
       "21   752.087159  -226.330646   137.392161  -187.876792   -33.225278   \n",
       "\n",
       "    ActivePower  yawoffset  \n",
       "0     21.370858  -0.099022  \n",
       "1     76.033527   0.004216  \n",
       "2    167.928433   0.019228  \n",
       "3    317.629187   0.046869  \n",
       "4    454.388684  -0.100434  \n",
       "5    679.886822   0.004413  \n",
       "6    942.635185   0.009307  \n",
       "7   1101.930764  -0.013551  \n",
       "8   1251.222715  -0.015947  \n",
       "9   1316.246001   0.010243  \n",
       "10  1384.312354  -0.013155  \n",
       "11  1350.838415   0.008648  \n",
       "12  1491.638595   0.012160  \n",
       "13  1499.435045   0.003787  \n",
       "14  1476.244726   0.013698  \n",
       "15  1514.168487  -0.222023  \n",
       "16  1514.549099   0.005289  \n",
       "17          NaN        NaN  \n",
       "18          NaN        NaN  \n",
       "19  1500.616164   0.016870  \n",
       "20          NaN        NaN  \n",
       "21   615.054202   0.008214  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create array containing wind speeds to use as bin edges\n",
    "bin_edges = np.arange(3,26,1)\n",
    "bin_against = means['uWind_80m']\n",
    "\n",
    "# Apply function for means, maxs, and mins \n",
    "[bin_means, bin_means_std] = loads.general.bin_statistics(means,bin_against,bin_edges)\n",
    "[bin_maxs, bin_maxs_std] = loads.general.bin_statistics(maxs,bin_against,bin_edges)\n",
    "[bin_mins, bin_mins_std] = loads.general.bin_statistics(mins,bin_against,bin_edges)\n",
    "\n",
    "bin_means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets make some more plots with the binned data. Here we use the binned data and corresponding standard deviations as inputs to the plot_bin_statistics function.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lTfbQTCOch+Mn8JJ0f9po8/b0M0reR1Yb93u/XOmnvv7665k8eTKTJ09GKcWWLVto3749KSkphIWF4eLiwowZM8jNza30WETx8juLvff7Xq5tVtNIkKfOsu9MKscS09FGoRofD1ea1PSjX6taNK1pJMZmtfyo4eNRsO58o5BqeLi58OJNzek7cTVj+zUtskR8OZcr6Qb4etC1URBdGwUVPJ6bpzmacK4g7j2nU9kWk8yv209ddFwFLN99Bh8PV3o1C6Vvi5r0bBpKde+qSZQuLorImn5E1vRjSOf6vL5oN4u2naS6txsebq4ciT/H5mNG9b2Lgqa1/OkYHsCVDYyq97o1vKskTiFshSTw/I5qCx83qs2XjDWSdxX0Qn/ppZd4/PHHadOmDXl5eTRs2JBff/2VkSNHcvvttzNz5kz69euHr69vpcdiBlutjtRaE52QztbjSfxzMJ7TZzNRwNxNJ5i76QQuCsKDfWlZx5/brwijaS0/mtfyJyzAGxeXokvR+evO55eaC687X9Ye5GUt6bq6KCJCqhERUo0bW9cu2J6akc3+M6l8sGw/W44l4+/tRtt6NahT3YtX+7cqU0zWXhgo9mwG8zbHEBHsi4ebC5/e3Z6+E1fz51M9OZ6UTlR0ElFHE5m7KYaZa48CRpv8leGBdGgQQIfwAEutx4W/h7SnC0cjCRyMZJ3fYc1K48BLu5zopEmTLqoWB4iMjGT79u0F98ePN6r3e/bsSc+ePQu2f/bZZ1aJ1Sy2Uh2ZnJ7F1uPJbD2ezJZjyWyLSSbZUgXu5qJwd1UE+XoQGepHoK8H4we1sWqp2Sx+Xu7UC/Bh18mzNKtlVOm/OaAVfSeuZmSvxmVKctZeGKi4LzzfrjvKSze34JpIox09JzePvadT2RidSNTRJDYcSWDRtpMAVPN0Kxi+1iE8gBPJMsGMcCzOncCLmont1eoXfpeZ2OxWcaWt7Nw89p5KZcvxJLYeS2bj4TiOp+wCQCloEurH9S1q0a5+DeoH+TDyu80FpcAP7mxL34mrOZuRXeYEXlnrzleUNWsGrKm0X3jcXF1oVbc6repWZ+hVDdFaE5N0nk1Hk9gYncimo0lMXLG/oJnDy92FORuPl2vYnbAdsWczeHrudibc0cap/47OncALz8QmHEp+u/UHS/dxTZMQth5LZsvxZHaeSCEzx+jQFVzNk6bBngzu0pD29WrQOqz6RR2jXl+02yaTmzXZYs0AlP8Lj1KKeoE+1Av0YWD7ugCknM/mmZ+28c/BeLLzNOcycxj53WZ+fLRrsU0eVcFWm5DswZRVh9l2PNmhPovl4dwJXDikNQfiOHM2E1cXmBMVw5yoGDzdjJLavV0a0L5+DdrVq0HdGt4cPXqU8PDwIo9jq8nNmmy1ZsCaMrNzWX8kkSY1/XBRUN3HnT/3xjHkq/V8NqQ9wdXMmUTGVpqQ7E1+/4jvH+7MvV+vd+raFEngwmGkpGfzwfJ9zFx7FAXU8PEgMrQaoX5evH9HWzzcyjYfty0mN2t3FnMGlzYTfP1AR4Z8tZ71hxO48eM1fDy4/UW99oVty/97tqpb3eFqxMpKEriwe3l5mp82HWf87/tIOpeFh6sLzWpVw9vDjY8HG72Xk89nOcS3dGt2FnOWLwOX1qQ0fG4JYExKk5Gdyz1freOJ3pE8fm3kRb3Whe3JL30vH9MdgBE9Iug7cbXTlsKdOoFP3jqZz7d9Xuzjj7V9jJHtRlZhRLbP1trtth1P5uWFu9h2PJmO4QHUjQwm0NfTptqtbTVRWrvnuK26XE3KucwcXpy/k4l/HGDd4QQ+Htyemv7OlwjsRX7pO9TyN7KFz7eZnDqBj2w3siBBD/19KADT+xUz/3klWLhwIdu3b+fFF1+ssnNWlK202yWey+L9pXuZvfE4wdU8+eiutgxsV5c7pqxl/taTBfvZQru1syRKe+Tr6caHd7alW6MgXl6wixs/XsOHd7UrmO5V2Jb82pRp/xy5aLsj9UspC6dO4AD3/3Y/W2K3FNxvPcOY1rR9aHtm3jCzUs/dv39/rr/++ko9h6PJzdP8sOEYE5buIy0zh4euasiT10UW9B63xXZrYduUUtzRoR7t6tVg9A9beGDaBkb0aMRTfZtU2TrmMslM6eR/voXB6RN4y6CWtAxqyd7EvYBRAh+/YXyFV3EqzXKiu3fvZv369Xz++ec8+OCD+Pv7ExUVxenTp3nvvfcYNEjWJC9s09FEXpq/i92nztI1IojXBrSkSU0/s8MSVmZWk0NkTT/mj7qK13/dxZRVh9hwJIFPh1xRJVO0VnRtd+GcHD6Bj98wviA5FyUrN4tdCbvwdPXERblwz+J72JWwi1ZBrQqq1S/VLLAZ4zqNK/HcJS0nOnDgwIv2P3XqFH///Td79+6lf//+ksAtYlMzePe3vfy8+QS1q3vx2ZD23NS6tqyD7qDMbHLw9nDlndva0LVRMM//vIMbP17D+4Pa0LdlrUo5n9aaLceSiU3NJDK0GvM2l29td+GcHD6Bl8TD1YMg7yCSMpLwdPXkdPppgr2DcXet+AIOJS0neqmBAwfi4uJCixYtOHPmzH8edzbZuXnMXHuUicv3k5GTy8iejRjVqzG+npX/trXVjmeiavRvW4c2daszetZmhn+7iaFXhfPsDc3wdCvbDHxFOZ6YzrrDCaw9nMD6w4mcSD4PwKG4c/RqFiKlcFFqDp/AS1NSjkuPo8/cPtT3q8+JcyeYP2A+wd7BFT53ScuJXm5/nT/3o5O4tA1w7aEEXl24i31nUuneJIRXb2lBREi1KotHOp6J8GBf5j3WjXeW7GX6P9FERSfx2ZD2NAgq2+JCJ5LPs+6QkbDXHU4gJslI2IG+HrSrV53Ec1nUreHFyZQMlu+OxdPNhYeubkgdWV1NlMDhE3hphPiEEOQdxL6kfQxpPsQqyVuUTX4b4IfL9nMuK5dF204SFuDNF/ddSZ8WNaW6XJjC082VV/u3pGujIJ75aRs3ffI379zWmlva1im249nplAzWHo5n3SFjTfNjiekA1PBxp3PDQB6+uiFdGgXRJNSPNxfvITyoGrtOphDg40FkLT9+WH+MgZP+4ZdRV8kSqeKyJIFb1PKpRUZOBsNaDTM7FKcTezaDuLQsgn09mL3xOO6uiid7R/JYz0ZlXjREiMpwfctatKzjz+OztvD4rC38eyiBk5bVzT5avp8uEUFGtfihBKITjITt7+VG54ggHugWTteIIJrV8vvP3OuXTjKz0fJ7fFomN32yhg/uaEvv5jWr7oUKuyIJ3MLD1YMmAU2sVvou7XKid999938eB0hLS7NKHLZOa82zP+8ArTmTmkm9QB+6NgwsV/W1tFuLyhQW4MOPj3ZlwrJ9TF11GAAPN8WsDceZteE4fl5udG4YyL1dGtAlIojmtf1LnNmtuGGPR+LPMer7zTw0I4rh3SN45vqmVTakTdgPp07gRc3Elj8OHGQmtsq29XgyL8/fyfYTKXi5udA4yIdvH+pM34mreboc42Gl3VpUNndXF567oTlH4s7x595YfNzdaBtWjTrVvfnwrnZWm4q1YbAvP4/sxluL9/DF6sNsjE7k07vbExbgY5XjC8fg1Am88ExsourEJKXz/tJ9LNh6Ei93F65qHER2Th5KKaefGlHYvtizGaw/kkjrutXxcHPh07uN+fYTzmVadfiXl7srbwxsReeIQJ6dt4ObPvmbD+5oy3UtpEpdGBw2gWutnbLjky33Xk/NyGbyykN8/fcRFDC6V2P+ORTPPwcTCvaxhalPhbicS1c3q+wvnTe3qUOrOtUZ9cNmHp4ZxcNXN2Rsv2ZlXl1POB6HTOBeXl4kJCQQFBTkVElca01CQgJeXrY1CURObh6zNx7no+X7STiXxa3t6/LM9U2pU8Obp2kKyNSnwn6YsU58/pC2t5fs4au/jxB11BjSJlXqzs0hE3hYWBgxMTHExcWZHUqJcnJycHOz3p/By8uLsLAwqx2vIrTWrNwfx9uL93AgNo1O4YFMH9qcNmE1zA5NiHKz1nz7Ze106eXuyusDWtElIohxc7dz48drmHBH20qbJU7YvlJnDqXUNOBmIFZr3cqy7VXgESA/Uz6vtV5ieew54CEgF3hCa73Usr0f8DHgCnyltX7Xsr0hMBsIAjYB92mts8rzotzd3WnYsGF5nlrloqOjCQ8PNzsMq9t7+ixvLd7DmgPxhAf5MOXeK7m+pYznFiJfeTtd3ti6Ni3r+DP6hy0M/3YTw65qyLM3SJW6MyrLX/wboF8R2z/SWrez3PKTdwtgMNDS8pzJSilXpZQrMAm4AWgB3G3ZF2C85ViNgSSM5C/sTGxqBs/OM0oH22NSeOnmFiwb04N+rWpJ8hbCShoE+TL3sa482C2caf8c4Y4p/3LcMmGMcB6lLoFrrVcrpcJLufsAYLbWOhM4opQ6CHSyPHZQa30YQCk1GxiglNoDXAsMsewzA3gVuHiMlyiXoqrqCivv+OjCM1H5ebrz1ZrDfL7qENm5eTzYrSFP9G5MDR+PioQuRIU48twA+bPEdYkI5Jm527nxkzW8P6gtV9SvwdNztzPhjjayKIqDs0bj62il1P1AFPCU1joJqAusK7RPjGUbwPFLtnfGqDZP1lrnFLH/fyilhgPDAerWrVvkwiD2IiEhoeSdKujWSA9ujWwJwJMLjgDw8YCLmxjKcw2PJ6aRlpHD6JnrOJKYSdy5HLo39GN4l5qEVfckOfYkyaU8VkZGRrnjqIiquP6ieJV9/Qu/94tT3vecWe/ZSzWrBlNva8hry48z4rtNNA7y5ExaNu8v2sqobsW3j8t731zWuP4VTeCfA28A2vLzA6DS5yLVWn8BfAHQoUMHbe9tyFUZv5fXKaucM/ZsBknpu3F3dWHD8XM0r+3HZ/e0pHNEkKlxlYe9v3/snb1efzPfs5cKBxa2aMTLC3YyZ2MM10QGs/xgCs/c0u6ypXBbiN3RxJ7NKHUNSEWvf4V6PWitz2itc7XWecCXXKgmPwHUK7RrmGVbcdsTgBpKKbdLtgsb9cmfB0BpcvI03ZsE0zUiqNzJWwhRcZ5urvh6uNO7WSjj+jUrGJsuqtaUVYfZdjy5Sq59hRK4Uqp2obu3AvmTfy8EBiulPC29yyOBDcBGIFIp1VAp5YHR0W2hNmYf+QsYZHn+A8CCisQmKs+ZlPPM2Xic3DyIrFmNCYPa8vOWE8SmZpgdmhBOK/ZsBvM2x/DOba1pVbc6I3pEMG9zjHwuq1D+3+D7hztXybUvdQJXSs0C1gJNlVIxSqmHgPeUUjuUUtuBXsAYAK31LuBHYDfwOzDKUlLPAUYDS4E9wI+WfQHGAf9n6fAWBHxtlVcorO7xWVvJztXUD/TG38v9opmohBDmyJ8hLtTfqLaVz2XVy/8btKpbvUqufVl6od9dxOZik6zW+i3grSK2LwGWFLH9MBeq4IWNWn84gQ3RiQAcSzzPscTzMv2pEDYgf4a4af8cuWi7fC6rRn7pe/mY7gCM6BFB34mrebRHRKWNBnDImdhE5Yg9m8GoH7YQEeLLglFX8fCMKECmPxXCFuTPECfMcbkakMpamEkSuCiV7Nw8Rv2wmXOZOfzwSGf8vNzNDkkIIWyGGTUgksBFqYz/bS8bo5P4eHA7mtT0s8oxHXmSDSGEczGjBkQSuCjR4u2n+OrvIzzYLZwB7YqdX6fMyjsXtBBCiAoOIxOO72BsGmPnbuOK+jV4/sbmZocjhBDCQhK4KNa5zBxGfLcJL3dXJt1zhax2JIQQNkSq0EWRtNaMm7edw3FpfPdQZ2pX9zY7JCFMJ/02hC2RBC6K9M2/0fy6/RRj+zWlW+Ngs8MRwiZIvw1hS6ROVPxHVHQiby3eQ58WNXmsRyOzwxFCCFEEKYHbqMpaw7skcamZjPphM3UDvJlwR1uUUlY/hxBCiIqTBG6jClfV3TV1LVD5M57l5Obx+KzNpJzP5puhnajuLZO1CCGErZIELgpMWLafdYcT+eCOtjSv7W92OEIIIS5D2sAFAEt3nWbKqkPc07k+t18ZZnY4QgghSiAJXHAk/hxP/7iNtmHVefmWypl0XwghhHVJFbqTS8/K4bHvNuHmqph875V4urmaHVL5/fUOrHq3+Md7PAu9nqu6eIQQohJJAndiWmte+GUn+86kMmNoJ+rWsPPJWno9dyFBT7/J+Dl0cfH7CyGEHZME7sS+W3+MX7ac4P/6NKF7k5BSPcdpZqKS0rwQwsZJAndSW48n8/qiXfRqGsLoXo1L/TynmYnKmqV5+TIghKgEksCdTFZOHofi0nh0ZhQ1/b346K52uLjIZC2VSqr2hRCVQBK4kzmZcp6zGTmkZeawcPTV1PDxMDskIYQQ5SDDyJxI7NkMYlMzAfBydyXU39PkiIQQQpSXJHAn8vqvu9Eagqt5MLhjfaauOmx2SEIIIcpJEriTOByXxuIdp/BwVTQI8mVEjwjmbY4hNjXD7NCEEEKUgyRwJzH82yi0hsah1XBzUYT6e3Fb+zAphVelHXPh5GY4+jdM6mLcF0KIcpJObE5g0baTHIw9B8DuU6nAhbHbHRoEmBaXU9kxF1a8DoER4Olv9EpfMNp4rPUgc2MTQtglSeAO7mTyeV74ZQft69fgp0e7cs9X64HKX5pUXGLVeLj6f7BuKmSfh4bdYcBnsGRs2RK4jCkXQlhIAndguXmaMXO2kpunmXhXO9xcpcWkzPKrvbPTjWrv7k8Xn3CzzkHiYUg4BImHIOGw5echOBcLv465sO/kbsZx4veVLR4ZUy6EsJAE7sC+WH2Y9UcSeX9QGxoE+Zodjv0pstp7FJw9AYGNLiTn/KSdevLi51eraezXpC/sXwodH4Z9v0FmKnj4wIrXjP1m9Ie2g6H5LeDpV/WvUwhhlySBO6idJ1L4cPk+bmxdi0Gyvnf5rH4fOj0CaydDSgzMHwUpx2D5yxf28QkyknRED+NnkOUWGHFxMs7/MuDpZyT2Xs/BLyOg7hVwegfMfwx+/T9ofjO0GQwRPcFVPp5CiOLJfwgHdD4rlydmbyHI15O3b22NUjJVaqnlZMLhVbBnAcTthWUvgnIBdx+o3xlq3AVrJsDDKyAoArxL2Qkwv9p94eNGdfySsdDndWO71nB8A2yfDTt/hh0/gW8otL4D2twJtduC/A2FEJeQBO6A3ly8myPx5/j+oc4yVWppZKXDwT9gz0KjqjvzLHj4GdXmXUfCoVXg4gq3fwVHVsPeXyHsyrKfp/UgiJpu/F643Vop48tB/c7Q7104sAy2zYYNX8C6SRDSDNrcZSTz6pbalLK0zQshHJIkcAfzx+4zfL/+GMO7R9CtcbDZ4diujBTYv4yQTbPgxL+Qcx68A6FFf2g+wKgS37PoQrW3p7+RvBeMht4vl3z88nLzNNrCm98C6Ymwez5sm2O0l694HcKvhqDGcHCFDEkTwslJAncgsakZjJu3nRa1/XmqrxMs+Vmc4kqn5xJg3xKjpH14JeRm4ekdAu3vgeb9ocFVF7c7F1Xt3fvlqkuSPoHQYZhxSzwC2380qtmj14CLuxETqvxD0oQQdk0SuIPQWvPMT9tJy8xhzt3t8HRzNTskc1zac7zTcFj8f0aHtPgDoHOhRn1je/P+xOSGEN4wovjjFVftXdUCG0LPcdBjLLweCO3vhy0zID0eZt8D175U9iFpQgi7JgncQcxce5RV++N4fUBLGofayVCkypiUZPUEuGUi/PoUJB+FuUMBDVlpxkQqzftf3CksOrp8sZtFKQhuCq0GQuxuY+ja4ZXG8DSvGnA+qfQd64QQdk0SuAPYfyaVt5bsoVfTEO7r0sDscEqvMiYlid8H67+ApMPg7gu9XoAmN8AX11Ru23VV6v600ebt6Qf+daHPG7BgJJxPhE/aG198Oj4Eru5mRyqEqESSwO1cZk4uT8zagp+nG+8NauvcQ8ayzoGbF+z/zahC96sDPZ4xOp8FNzUvrqJqGl6tfuH3stY0XNo2fz4JbvkYQprC0hfg93Gw8Uvo+yY06SdD0IRwUJLA7dyEpfvYezqVaQ92IMTP0+xwzHM+GX6405hn3CfIGLet86qm53hJCtc0WEtxbfP3LzCGwi17EWYNhoY94Pq3oFbr/x5D5lUXwq5JArdjfx+I58s1R7ivSwOubVbT7HDMcy4evr0VYvfAHd9AXo55PcfNphQ07QeNexsJfuXbMOUaaH+v0dHNr9D7ROZVF8KuSQK3U0nnsnjqp600CvHl+Rubmx2Oec6egpkDjA5rd8+CyD7GdlvoOW4mV3foPBza3GF07Fs/1Zjl7Zox0HU0uHubHaEQooIkgdshrTXP/7KDxHNZfP1AR7w9nHTIWFK0kbzPxcO984xJTsTFvAOMKvQOw+CPV+DPNyHqG7juVWh1O7hYaYU6qY4XospJArdDP0XF8NvO0zx3QzNa1a1e8hMcUdx+I3lnp8P9C8s3takzCWoEd30H0X/D0ufh54dh/edw/dvGQi0VnZZVquOFqHKSwO1MdPw5Xl20i64RQTxyzWUmIHFkp7Ybbd7KBYYugZotzY6o6lS0R3v41fDISthumZ512vVGhz//MPANlmlZhbAjksDtSHZuHk/O2Yq7qwsf3NkWFxcnHB50fAN8P8hYbOT+BRDcuHLOY+2hX9ZijR7tLi7Q7m5j3veJbYz1yRMOQPY5CG0p07IKYSckgduRT1ccYNvxZCYNuYI6NZywE9LhVTDrbqgWCg8sNKZErSyVMfTL1nj4GpO/PLEVvroOzp6Aj9tAp0dlWlYh7IAkcDuQlZPH/thUNhxJZNCVYdzUprbZIVW9fb/Dj/cbE7TcPx/8apkdkWMIbmr04A9uYlSjB9SHvz8wmidWT4DOI8CzmtlRCiGKYKUuqKIynUg+z7nMXKp5uvFqfydq7823cx7MuQdqtjDavCV5W0/+tKwZyeDuZUzB6lcbaraGP9+Aj9vCv58ZE+QIIWyKJHAbF3s2g7jUTAA0kJ6VY25AVW3zTJj7EIR1Mnqb+wSaHZFjaT3ImOgm8TAc+9do++77JoxYDQ+vMGZwW/YCfNwONnwJOZlmRyyEsJAEbuM++fMAGgjx8+TODvWYuuqw2SFVnXWfGzOqNepljPP28jc7IsfUehDUuQIaXA2j1l3ovBbWwWiueHCx0XSx5Gn4tANs/hZyneyLpBA2SNrAbVjs2QzmbToBQKifJyN6RNB34moe7RFBqJ9XqY/z0fL9fLziwEXbwp+9MEb3yd6RjOnTxDpBW4PWRvvrX29Cs5th0DRwc+J53s0WfrXRdHHoT/jrLVg4Gv7+EHo+D61uAxcnnUhICJOVOoErpaYBNwOxWutWlm2BwBwgHIgG7tRaJyljSayPgRuBdOBBrfVmy3MeAF60HPZNrfUMy/YrgW8Ab2AJ8KTWWlfw9dm1KasOU83T+BP5ergS6u/Fbe3DmLrqMC/d3KLUxxnTp4ltJehL7Zh78UQiNerDgaXQZjAMmASu8j3TdEoZ86s3uhb2/w5/vmVMBrPmA+j1PDS/RVY9E6KKlaUK/Rug3yXbngVWaK0jgRWW+wA3AJGW23DgcyhI+K8AnYFOwCtKqQDLcz4HHin0vEvP5XTWH0kgLi2T89m5bIhOIvzZxUz75wjbjiebHZr17JgLK143qmjrdYWAcCN5N+wBAz+X5G1rlIKmN8Cjq42FY3Qu/HgfTO1urIK2/Sfjy9jRv40vYzvmmh2xEA6r1P8dtdarlVLhl2weAPS0/D4DWAmMs2yfaSlBr1NK1VBK1bbsu1xrnQiglFoO9FNKrQT8tdbrLNtnAgOB38rzohxF14gg9p9JpXXd6ri7ujDn0a5mh2R9qycYE4f89S4k7IdzcdDyVojda715ukXRKjJZjYuL8Xdq3h92/AQr3zGWc3X1NHqx+9WGa5+XWd2EqEQVLd7U1Fqfsvx+Gshfq7AucLzQfjGWbZfbHlPE9iIppYZjlOypW7cu0dHR5X8FJktISChye3ZuHj9FHaNrAz9SzmeRm41dv87iNIjfx1Fdm7CEw7idiyOp/ShSWj1Ig+86c7SMr/eH6B+YdXSWcSc/98+4sA723Q3uZkj4kIueU9z1dwoN7zZul1Oav0H1LnDLXMJ+ugGVl4NrcjR55+KJj95DXsfnCFzxNif9OhT5VKe+/iaTa28ua1x/q9VPaq21UqpK2qy11l8AXwB06NBBh4eHV8VpK01R8f++8xQpGbkM7dGUKSsPFbuf3QtuSnjiGjh3CnxDCBjwNgFHVhvby/h6nw9/nud5HoCh3xgJY/qDUSU+zyGvqxkyEmDcMZjaHZfUE4SuGgs+IZAeT3h1BQENinyaXH/zyLU3V0Wvf0XrKM9Yqsax/Iy1bD8B1Cu0X5hl2+W2hxWx3WnN2XicWv5edI8MMTuUynXVE8bYYxc3qNEQjqw2ql27P212ZKKsgpvCqS3gXwfqdIB75kFQBKCNCWG+v9OYUS8v1+xIhXAIFU3gC4EHLL8/ACwotP1+ZegCpFiq2pcCfZVSAZbOa32BpZbHziqlulh6sN9f6FhO53RKBqv2xzHoyjBcHX3Bktg9kJcNyhVObDCSee+Xpc3UHhWe1Q0Nbh6QehpueA+6PwOntsGsu4xkvvp9SD1jdsRC2LWyDCObhdEJLVgpFYPRm/xd4Eel1EPAUeBOy+5LMIaQHcQYRjYUQGudqJR6A9ho2e/1/A5twEguDCP7DSfuwDZvcwx5Gu7oEFbyzlWtqI5PhZVlla6ja+HfT+HKoRBvGadekTWkC8U2PX+bLawg5izyv3QtfNwYEnjpl7EeY2HfbxD1Nfz5Jqx8l5B6vaDH49CwuwxDE6KMytILvbjeLr2L2FcDo4o5zjRgWhHbo4BWpY3HUWmt+THqOF0iAmkQ5Gt2OP9VeJWu6TcZP8uTdDPTYP4IY8x33zfgh8FWjW3o70ONEPtNv9wzhLW1HgRRlmt+6fvC1d1YwrRFf0g4BFHT8Nr8LczsD0GNjS9y7YbIdLlClJIMsrUx648kcjQhnSd7R5odSuX64xVIOgoP/gqefmZHU6kmb53M59s+L/bxx9o+xsh2I6swIhsQ1Aiuf4uYRvfT4NxWo1S+7AVjToDgSDizs/jnSk2KEIAkcJvzY9Rx/DzduKGVAy8ZeuhP2PgVdBllTNPp4Ea2G1mQoKVm4GLazRPa3mXcTu+EqGmwfY7xoKu70Yu9ehj0fsloX5f+EUIUkJkybMjZjGyW7DjFLe3q4O3hoPNLZ6QY/4iDmxj/lIXIV6sV3PwhPLXXWDLWPwxST0LMRljyjNG7/bexsHUWxGwy3ktCODEpgduQX7edIiM7j7s61Ct5Z3v127NGz+SHloO7t9nRCFvk6QdpsfDCGfjqOkhPgMBGELfP+H3+iAv7VqtpfBkMamz8DG4CwY2her2LF1nZMdeY9S9+nzHcrfvTUpIXdk8SuA2ZE3WcpjX9aBNWveSd7dHexbDtB2NIUdiVZkdzWdJubbLgpnB8nZHMPf3g7h+MOQKWPAN3fQfx+42RC/EHjN93/WIZvmbh5mUk9aDGkJcDx9bBtS9Ai1vhzA6Z4lU4BEngVlTUsp2FXW7Zzn2nU9l2PJmXbm6BcsThNOcSYNGTULM1dB9rdjQlknZrE/31DsTtgRm3XNhWeDjgjrn/7cSmtVE6L0js+yHhIBxZBeeTjH1+HWPc8s17CM7sguteqbzXIkQlkgRuRYWX7bxr6lqAUi9A8mPUcdxdFbe2L3YKePulNSweA+eT4b75xgQfQhQnfzhgWaq9lQLfYOPWoNuF7SXNW/DPRDi21hiH3rA7hHWUteeF3ZAEbgOycvL4ZcsJ+rSoSaCvAya3nfNg9wLo/YrRUUmI0mg9qOJV3L2eM957N75nJOh8+5cbXypbD4LDq4yZ4VaNBzdvqN/FktB7QO22Fy9pa82JjISoIEngNmDFnjMknsviDkfsvJZ6GhY/ZZRsuj1hdjTCGeVP8TrgM6jf1ShxL3kKrnv1wheE88lw9F+jnf3IaljxmrHdszqEX3WhhN5j3IXagfwZ50KaS6c4YQpJ4DbgxygHXbhEa+OfXE4mDJxycUlGOJaKrC1e2fIT65KxF6rjLx1P7l0Dmt1o3MDoBR+95kJC37fE2O4TDIENIfGwMczNJ0jWPRemkf+oJotLy2bV/jhG9mzseAuXbPkWDiyDfuONoT3CcRWeYtcWlbU6vlootLrduAEkH7+QzHfONXq2pydAkjus+9yobl/xGjTpB57VKuc1CHEJSeAmW7o/2XYXLqmIpKPw+3MQfg10Gm52NEJUTI160P4e47bjR3hsHcy5BzJTjV7vCZYS+rv1ILQlhHWAep2MpqPARuAic2YJ65MEbqK8PM2Svcm2u3BJeeXlwYJRgIIBk+Sfl3AswU0hPQ78ahu3oYthz2L4fZyxGEvMRtj5M2yyDDv0qmEk9LCOxq3ulUaVfT6ZZEaUkyRwE22ITuTk2SyeucHBOq9t/NJoP+z/KQQ0MDsaIawrv1Ocpx94+hvV6kufu7hTXF6eMRY9ZqPlFgUr3wW08XhwUyOZKwUHV8DAz43OcsfWSnu6KDVJ4Cb6ceNxfD1c6NfSgRYuiT8Iy1+ByL7Q/r7L72vLHZ+EKE5J656DUesU2sy4XWH5HGSchZObLyT0XfMg+7zx2LcDLj7HvIeMqnl5/4vLkARukrMZ2SzZeYq+kdUdZ+GS3Bxjnmo3T7jlE6N0cTm23vFJiEsV9aUzbo+RcOc9dPkvnV7+ENHTuIExSuP1QOj/Gax4AzJTjC8E+Y7+Y5TaG1xlVMHL2gHiEpLATbJo20kysvO4sVmA2aFYz7+fGKWL278GfweqVRAinzW/dK58F3QeLChmTv34/RD9N6DB1cNoO29wlVHVXq8zeDhQvxlRLpLATfJjVAzNavnRNMTL7FCs48wu+OttaDHgwtAbIUTxej0HwZGw4vWLJ5kpvO75+WRjIZajf0P0P/D3R7BmAri4QZ32loR+tZHQvfylQ5yTkQRuAodbuCQnC3551OhZe9NHJVedCyEMJU0y410DmvYzbmAMWzu23qheP/oPrJ1kzOeuXIwlVM8nGTMeXvkAxO2VDnEOThK4CQovXHI27qTZ4VTc6vfg9A4YPAt8g8yORgj7UpZJZjz9IPI64waQlQ4xG4zS+drPIDcb/nrTaJcfNM0o2S8Z61gJXGoZCkgCr2KXLlxyNs7siMppx1yjR212ulG9V7/bhWkohRBVw8PnQse4NRNg3DE4vd3YDka1fPw+MyO0rh1zi25yAKdM4jLDRhXLX7jkTnteuCT/Q1QjHFy9jPmhU44b24UQ5ghuCqe2GJ3c6rQ3th1ba2x3FKsnGMm7YXdwdTd+DvjM2O6EpARexeZEHad2dS+uqcqFS6y9BOLqCXDj+0a7d24G3PaD8WFytKo6IexJUauu5XeIcxTx+4zXVpij1TKUgSTwKnQq5Tyr98cxqlcVL1xSeOjL9JuMn0MXl/948fuMHufnk4x5nhv3NtrenPRDJIRNKM2qa/YuuKnxxaTw2u6OVstQBpLAq9C8TTHGwiVX2nH1efwBUK4Qu8dYB9nH0mnNiT9EQtiMsq66Zm+coZahDCSBV5G8PM2PUTF0jQiifpCP2eGUz7F1MGswuHkZSya6uBoTURxZ7dQfIiFEFXGGWoYykAReRdYfSeRYYjpj+kSaHUr57F4A8x6B6mFw71w4sfnyc0ELIURlcPRahjKQBF5Ffow6jp+XGze0Kt0Uox8t38/HKw5ctC382Qvt1k/2jmRMnyZWjbFYayfD0ueN1ZPunm2M9Q6MgCjLcokVaU8XQghRLpLAq8DZjGyW7DjFoCvD8HIv3cIlY/o0qboEXZy8PFj2IqybBM1uhtu/kgUVhBDCRkgCrwILt54kMyePuzraUee17AxjmNju+dB5BFz/ttHmLYQQwiZIAq8CP0Udp1ktP1rXrV7yzrYgPRFmDzF6ePZ9C7qOkvnNhRDCxkgCr2R7T59lW0wKL9vLwiVJ0fDdIEg+CoOmQ6vbzI5IFDJ562Q+3/Z5sY8/1vYxRrYrZnlKIYRDkQReyX7cGIO7q2Jg+7pmh1KyE5vhh7sgNwvuXwANupkdkbjEyHYjCxL00N+HAjC933QzQxJCmETmQq9EeVrzy5aYgoVLbNr+ZfDNzcYY74eWSfIWQggbJyXwSpSUnk1SerbtL1yy6Rv49f+gVisY8iP41TI7IlEFpDpeCPsmCbwSxaVmVv3CJWWhNfz1Fqx+HxpfB3fMMGZYE05BquOFsG+SwCtJWmY2Keezuf2KulW7cMnlFF7De1Jn8A4wepq3vw9u/shYUUwIIYRdkAReSY4mpAOQlpFjciQW+Wt4B0aAuw/kZhrJu8VA6P+pDBMTQgg7Iwm8Emw+mkhaZi6+Hq4s23OGp1MzCPXzMjeo1ROMFXz+eB3O7DR6mnd7Eg4sk+QtbIq0zQtROpLArSwuNZMHpm9EAY1CqtEhPJCpqw7z0s0tzA0sfh/U7QBxeyAnE+75yVhTd+2n5sYlxCWkbV6I0pEEbkVpmTnc+/U6UjNyiAythreHKyN6RNB34moe7RFhbik8uCn8+j/ISoPgZtC4t7EMqIlreEtJSwghyk8SuJVk5eTx2Heb2H86jT4tQjl73mj7DvX34rb2YeaXwlv0h1XjwasG+ATaxBreUtISQojykwRuBXl5mmfmbmPNgXjCg3xYvju24LH8JUA7NAgwKzzIOAvbZoFPMGSdg2P/yhrepXD/b/ezJXZLwf3WM1oD0D60PTNvmGlWWEIIAUgCt4q3l+xhwdaTPHN9U0b1agzAXVPXAjDn0a5mhmb4bSykxMDQ342e6CBreJdCy6CWtAxqyY74HbgqV2bcMIPxG8bbx5z2QgiHJwm8gr5cfZiv/j7Cg93CGdmzkdnh/NfOn43Sd49xUL+z2dHYlbub3c2tC24lKy8LDxcPZu6ayaJDi5g/cL7ZoQkhhMyFXhG/bInhrSV7uKl1bV6yxdXGUk4YHdfqXgndnzE7Gruy7tQ6hi8fTlZeFl6uXri5uPF+1Pvkkce6U+vIzcs1O0QhhJOTBF5Oq/bH8cxP2+kaEcSHd7W1ndnW8uXlwfwRkJsDt30ps6yVUmpWKq/++yqPLHsEdxd3Pun1Cdl52TTwa4CPmw8h3iE8t+Y5bl94O8uil5Gn88wOWQjhpKQKvRy2xyTz2HebiKzpx9T7r8TTzdXskP5r7WdGT/P+n0KQFav2/3oHVr178bZXq1/4vcez0Os5652vCq2OWc3ra18n7nwcQ1sNZWTbkXi5eRHkHcT+5P0MaT6Epzs8zfKjy5m8dTJPrXqKpgFNGdVuFD3r9bS9GhghhENz+gT+0fL9fLziQLGPP9k7kjF9mhTcPxJ/jqHTNxLo68GMoR3x97LBku2p7UZntWY3G/OcW1Ov5+w2QRcnJTOF9za+x8JDC2lcozETe02kVXCrgsdr+dQiIyeDYa2G4aJcuD78eq6rfx1LjixhyrYpPPHXE7QKasXo9qPpVqebJHIhRJWwSgJXSkUDqUAukKO17qCUCgTmAOFANHCn1jpJGf/dPgZuBNKBB7XWmy3HeQB40XLYN7XWM6wR3+WM6dOkIEGX1HM8NjWD+6etRwMzh3Ui1N/k6VGLkn0efn4EfILglk9kmtQSrI1fy9T1U0nJTGFE2xE80voRPFwvXrvdw9WDJgFNCPYOLtjm6uLKLY1u4YaGN7Do0CKmbJvCiD9G0D60PY+3f5yOtTpW9UsRQjgZa5bAe2mt4wvdfxZYobV+Vyn1rOX+OOAGINJy6wx8DnS2JPxXgA6ABjYppRZqrZOsGGO5pWZkM3T6RuJTs5g1vAsRIZW87GZRVdWFFVdVvfwViNsL9/4MvkGVF5+dS8xI5O31b7M0einNA5szpc8UmgU2K/Nx3FzcuDXyVm6OuJmfD/zMF9u/YNjSYXSu1ZnR7UfTLrSd9YMXQggqtwp9ANDT8vsMYCVGAh8AzNRaa2CdUqqGUqq2Zd/lWutEAKXUcqAfMKsSYyyVzJxcRny3iX2nU/nqgQ60q1ej8k9auKp6+k3Gz5LGbh/4AzZMhc6PGVOliv/QWvN79O+8s/4d0rLTuC/8PsZcMwZ3l4o1hbi7unNXs7sY0HgAP+3/ia92fMV9v93H1XWvZnS70YzfOF4mhRFCWJW1ErgGlimlNDBVa/0FUFNrfcry+GmgpuX3usDxQs+NsWwrbrup8vI0T/24jX8OJvDBHW3p2TTU7JCKdi4e5j8GIc3hulfMjsYmxaXH8ca6N/jr+F+0Dm7N691exy3FrcLJuzAvNy/ua3Eft0fezux9s5m+czqDFw8mrFoYNzW8iTPpZwBjyliZFEYIURHWSuBXa61PKKVCgeVKqb2FH9Raa0tytwql1HBgOEDdunWJjo62ynEzMjIACo6nteazf0/z645EHu1ckyuDckp9rkuPVZyEhIQSj1XLcqzTxR1La0L/HIP3+SRO9p5E9okz5T+WSUp7vcpzPK01f575ky8PfUl2XjZDI4YyIGwArimupbr+5Y3t2mrX0qVDFxadWMS8Y/NYfGQx1dyqEewZzLo961hwYAGTOk4q92u25jWz9vUvrZKuv1lxOYPSvPdF5bHG9bdKAtdan7D8jFVK/QJ0As4opWprrU9ZqsjzJwg/AdQr9PQwy7YTXKhyz9++spjzfQF8AdChQwcdHh5ujZeBl5dRYZB/vCmrDjFvRyLDrmrIszc3L1Np6dJjXU6J+3h5XX6/Td/A8b+g71vUvaJvxY5lEq+91onr0vnLb1l1S8HvV4RewWvdXiO8+sXnKOmcFY2tReMWjMgcwYg/RrAzfidpOWk8uuFRgn2C+Tf9Xzr6daRNSBu83MrWKdJa18wax6rIynKXO6c1X6P4L7mu5qro9a9wAldK+QIuWutUy+99gdeBhcADwLuWnwssT1kIjFZKzcboxJZiSfJLgbeVUvmrfvQFTBuvNG9TDO/+tpdb2tbhxZvKlryrVPxB+P05aNgDusjSm/nzl+9J2EP8+XjiM+LJyMngitAr+Or6r3BR5sxdVN2zOp/0+oQ+c/tQx7cOsedjCfAM4IvtXzBFT8HdxZ02IW3oWKsjHWuWL6GbSVaWE6LqWaMEXhP4xZLg3IAftNa/K6U2Aj8qpR4CjgJ3WvZfgjGE7CDGMLKhAFrrRKXUG8BGy36v53doq2p/7Ytl7LztXNU4iAl3tMHF1mZZy5ebbQwZc/WAgZ+Di0ysN6zVMPrP749Gcy77HO1D2nMw5SDv9XjPtOSdL8QnhCDvIE6eO8mQ5kMY23EsqVmpbIndQtTpKDae3nhRQm8d3NpI6LU60jakbUFCl1XShBBghQSutT4MtC1iewLwn67Qlt7no4o51jRgWkVjKq+snDwOxKby2LebaFbLjyn32ugsa/lWjYeTm+GOGVD9Mv39HHj2tMJSMlP4asdXnMs+B0ADvwa0DG5Jq5BWF43hNlPhSWEA/Dz86B7Wne5h3QH+k9C/3PElU7dPvSihB3kFcXezuzmQZExAJB3ihHBOTj8TW2HHk9JJy8zFz9ONb4Z2ws8WZ1nLd3QtrPkA2t0DLQdefl8HnD2tsOy8bH7a9xOTt00mNSuVWxrdwuLDi/F192XR4UXMHzDf7BALFDUpTGGlSej586/7uPkQ6hNK7LlYm3udQojKJwncYv/pVOLTsnBVxuRlGqt1mre+jBT4ZTjUqA83jDc7GlP9c+If3tv4HodTDtO5dmfGdhxLk4AmrDu1jn1J+xjSfIjNlL7Lo7iEPmnLJPYl7SP6bDTDlw/npoY32fXrFEKUnSRwi1kbj+Hr4Up4sC+dGwYxddVhXrq5hdlhFW3JWEiJgaG/g6dflZ66Ir2NrelIyhEmRE1gdcxq6vvV55Nen1y0oMilVdWOIj+hNw9sTp+5fQj2CuZQyiFclAsn0k5Qt5rpUycIIaqIJHAg9mwGP28+QZOafni4uTCiRwR9J67m0R4RhPrZWE/gnfNg+2zoMQ7qd67y05vd2zglM4Up26Ywe+9svNy8eOrKpxjSfEip5i93JPkd4hLOJ9CrXi+iTkdx1693Mf6a8VxV9yqzwxNCVAHptgxMWXWY268Iw8PNuByh/l7c1j6MqasOmxzZJVJi4NcxULcDdH/G7GiqVE5eDnP2zuHmX27m+z3fMzByIL/e+isPtnrwP8nbWdTyqYWvuy8vd32Z2TfPJtQnlMf+eIwp26bIOuVCOAEpgWOs7x119MKaKeHPGnOOd2gQUNxTqp7W8MsIyM2B274AVxvuYGdla0+u5b2N73Ew+SAda3VkXMdxNA1sanZYpruolsEbvr/xe15b+xqTtk5iZ/xO3r7mbfw9/M0OUwhRSSSBA3Mf6waUvJyoKXbMNYaKZacb9694AIIamRtTFTl69igToiaw8vhKwqqFMbHnRK6tf60MlyqGt5s371z9Dm1D2vLehvcY/OtgPur5kXzZEcJBSQK3ZTvmworXwa82JB6Cel3g8Epje+tBpT6MrXQ8K0pxk5KE+oSSmJGIh4sH/7vif9zb4l48XT1NidGeKKW4u9ndNA9szlMrn+LeJffycteXuaXRLSU/WQhhVySB27LVE6DXC7DwcWO2tcE/QOwuoxd6GRK42R3PLid/6tO9iXvRWnNTo5sYv2E8semx3BZ5G4+3f9xhO6JVpnah7ZhzyxyeWfUMz//9PNvjtjO241jcnajpRQhHJwnclsXvg+Uvg86Fmq3ANwjqdzW2O4hhrYYxcMFA6lSrw9GzR9kUuwlX5crU66bSrW43s8Oza8HewXzZ90smbprIjN0z2JO4hw96fEBN35olP1kIYfOkF7qtit1rzCiTcx5qtgaPasb2Y2sh2HHaNEN8Qrix4Y0cSDpAVm4W19S9hiHNhkjythI3Fzee7vg0E3pMYH/Sfu789U42nt5Y8hOFEGW3Yy5M6gKvBRg/d8yt1NNJArdFZ3bBNzeBuy94+EJeNug8OLIaFoyG7k+bHaHVaK2JTY8lV+dSx7cO2+O3M6y1Y02+YguuD7+e2TfNxt/Dn0eWPcKMXTMwliUQQlhFfp+lG9+DF2ONnyter9QkLgnc1pzaBt/cbLR5P/IX9HkDEg/DsX+Ntu/eL5ep/dvW/bD3B/48/ie+7r6cPHeS/o36S5t3JYmoEcGsm2Zxbf1rmRA1gadWPVWw8IsQooJWT4ABn0HD7sYw34bdjfurJ1TaKaUN3Jac2ATf3gqe/vDAQgiMgODGEGXpcDZ0sbnxWVnU6SgmbJzAtfWuJT49nujUaIeb+tTWVPOoxgc9PmDGrhl8tPkjVsesJjM3s+BxWZpUiHKK32f0USqskvssOX0C/2j5fj5eceCibfkTuQA82TuSMX2aVP6xjm+A724H7wB4YBEENCjlK7BPZ86d4elVTxPmF8ZbV7/F438+7tBTn9oSpRQPtnqQFkEtGLliJG7KjQb+DQjwCpClSYUor+CmRh+lht0vbKvkPktOn8DH9GlS6gRd4rHc5jHG693id3B7FihiWc+ja+H7QVAt1Eje1cOsEo+tysrN4v9W/R/nc87z9fVfUy2/g56oUp1qd2JGvxncs+QeDqUcIjQrlCMpRxxqaVJbngNBOJjuTxt9lAZ8ZpS8j6017vd+udJO6fQJ3KoKr7s9/SbjZwnV3l6nNsKfT4B/XaPa3L9OJQdpvvc2vsf2uO182PNDGtVwjlnlbFXL4Jbc1fQufjnwC7HnY7lj0R20D21PgKe50wgXN8FPWav2bXkOBOFg8vsmLRlrVJsHN630PkuSwM106C9C/xgNgeFw/0Lwc/zxub8c+IU5++YwtNVQ+jToY3Y4Ani49cPM2TeHhv4NOZZ6jHWn1nHXr3cxrtM4OtbqaEpMhSf4AWymal9K9OKyWg+q0k7GksDNcuAPmD2EHP8GeDy4GHwdv+13V8Iu3lz3Jp1rd+aJ9k+YHY6wyF+a9FjqMYY0G0Kb0DZ8FPURw5YOo3f93jzV4Snq+dWr0pgebPkg/ef3x0W5kJ6Tzr1L7mVv4l4m9JhAdl427i7mzCgnJXphSySBm2Hfb/Dj/RDSjNM9P6W+EyTvpIwkxvw1hiDvIN7r/h5uLvLWsyW1fGqRkZPBsNbDCPYOpmdYT2bunslXO75i9fzV3NviXoa3Hl7p/RXSs9NZcGgBP+z5gfScdFxwIdA7kONnj5OZm8njfz6Ot5s3V9a8ks61OtOpdieaBjTF1cW1UuMSwhbJf9GqtnshzB0KtdrAfT+TdybF7IgqXU5eDs+sfoaE8wnMvGEmgV6BZockLnHR0qSAl5sXw9sMZ2DjgXy8+WOm75zOwoMLeeKKJxjQaIDVE2ZMagyz9s7ilwO/kJqdSuvg1rzQ+QXe3fAuod6hnDh3gvn953Pk7BHWn1rPhtMb+GDTBwD4e/jTsVZHOtXqROfanYmoHmF6VbsQVUESeFXaOQ/mPQJ1r4R754JXdcDxE/inWz5l/an1vN7tdVoGtzQ7HFEGoT6hvHX1WwxpNoTxG8fzyr+vMGvvLMZ2HFvh9nGtNTuSd/DRnx+xMmYlLrjQp0Ef7mlxD21D2gLw5Y4v2Ze0jyHNh9AooBGNAhpxXYPrAIhLj2PD6Q1sOL2B9afWs+LYCgCCvILoVLtTQQn9hTUvsCWu4h3ihLA1ksCryrY5MH+EsSToPT+Cp5/ZEVWJ5UeXM23nNO5scie3Rt5qdjiinFoGt2RGvxksjV7Kh5s+ZNjSYfRp0If/u/L/CPMr27DHzNxMlhxewvd7vmdf0j5qeNbgoVYPcVfTu/6z0EpB1X4RE/yE+IRwU8RN3BRhjPiISY1h4+mNrD+9nvWn1vPbkd8A8HXzpVH1RuTpPPw8/Pj+pu9tokOcEBUlCbwqbPkeFoyC8KthyBxjfnMncCj5EC/+/SJtQtowrtM4s8MRFaSUol/DfvSs15MZu2bw9c6vWXl8Jfe3uJ9H2jyCr/vl39ex6bHM2TeHufvnkpiRSOMajRndZDQPdHwALzevIp9zadX+5YT5hRHmF8atkbeiteZIyhHWn17Pmpg1rDmxpmC/b3d/61Bj3YXzkgReGXbMhZObITsdPmgKqachopexnreHj9nRVYnUrFT+99f/8HLz4sMeH+Lh6mF2SMJKvNy8eLTtowxsPJBPtnzC1zu/Zv7B+fi6+3Is9VjBfvlV1U0CmtC4RmOWRS8jV+fSo14P7m1+L51qdeLo0aPFJu+KUEoRUSOCiBoR3N3sbsZvGM+Sw0vI0Tm8t/E9utXpJrP+CbsnCdza8lekCYyArHOQdATcvKHNnWVK3pN/uZvPz+407uQvOWP5hwjwmH8rRt46y4qBW0+ezuOFv1/geOpxvur7law/7aBq+tbkravfYnDTwYzfOJ5tcdsI9AwkxCcEX3dfBjcbzHsb32N/0n5OpJ1gcLPBDGk2hHr+VTskDYx152ftnUVkjUjSs9P59+S/LDq0iFsa3VLlsQhhLZLArW31BOj/KSx8ApKjoemN0PFhWPoCtBtS6sOMvHUW+dNB2Nt40693fM1fx/9iXMdxdKjVwexwRCVrHdKab2/4ljn75vD2+rdJzEzETbmxOXYzLrgwut1o7ml+j6lT5uaPdT+QfIA7m97J4ZTDvPD3CwCSxIXdkgRubfH7IGqakbx9guGOGaBUpa5IY0v+OfEPn275lBsb3sg9ze8xO5xyK2rGrdaFa0Bkxq2LKKUY3Gwwh1IOsejgIjxcPWgX2o661eryaNtHzQ4PuNAhbnib4fi6+/L4isd58Z8XUUpxc8TNZocnRJlJAremM7vBxQ12L4AaDcA/DNw84MjqSl2RxlbEpMYwdvVYIgMieaXrK3bdy7fwjFui9Ia3Hs5P+36igX8DNsdu5uWulbeQQ1ld2iHu096fMnrFaF74+wUUqqA3uxD2QhK4tWybDYv+B25expKgnn6ANpJ3Ja9IYwvO55xnzMoxaDQTe07Ex905OutVNVuvGcivqs4fu23LHcW83bz59NpPGf3naJ7/+3lclAs3NLzB7LCEKDVJ4BWVnQG/jYXNM6DB1TDoa4j+GxY+bvRCXzK20lekMZvWmjfWvsG+xH1M6j3JlE5KzsIeagYuN3bb1vi4+/DZtZ8xcsVInl3zLApjqJwQ9kASeEUkHoYfH4DT2+Hq/4NeL4Crm5GsoywdzkpYTrSqWGt5xqKO1WZmGwBq+9bmmrBrrBCtsGdlGbttC3zcfZjcezKP/fEYz655FhT0C5ckLmyfJPDy2vMrzB9pdFC7ew40te0PvDWXZyx8rNSsVA4lH6KWby161utp5aiFqJjSNjn4uPvw+XWfG0l8tVESvz78+qoOV4gykQReVrnZsOI1+PdTqNPe6GUe0MDsqEo0rNUwBi4YiJ+HHymZKdy/5H52xO+gQ60OPPnnk7i6uOLm4oabciv43VW54u7ijquy3Lds93T15Ls93xHoGUh8Rjw1fWpyNussD7V+yOyXKcRFytLk4OPuw+TrjJL4uNXjcFEusma9sGmSwMvi7EmYOwyOrTXGdl//Nrh5Fjx80bf9oiZfMbGDUbB3MPX96rMzYSderl6cSDtBDc8anM06S2JGIjl5OeTqXHLycgpu+fdzdS7Zednk5OVcdMxT6adwVa60r9meAK8Au6kyFaI4vu6+fH7d54xYPoKxq8byfo/3CxZPEcLWSAIvrcMrYe5DkH0ebv+6yE5phb/tD/3GmMBk+oNRVRllkXLycnh97evsTNiJQtHQvyEn008yf8D8Mifd3LxccnUup8+dpv/8/kTWiGTNiTUyr7RwGAVJ/I8RPLPqGSb0mEDvBr3NDkuI/5AE/tc7sOrd4h/vPg5c3eGvtyCkKdw50/hpJ87nnOeZVc+wKmYVI9qO4Of9P7M/eX+5h/i4urjiiiv1/esXzGxl68OFhCirah7VmHLdFB7941GeXvU0E3pOoHd9SeLCtriUvIuD6/UcvJpi3Bpcbdzy7z9zGE5ugr/ehNZ3wCN/2lXyTs5I5uFlD7M6ZjUvdXmJUe1GUcu3Fr7uvlYZ4lPLx3rHEsLW5CfxFkEteHrl0/x57E+zQxLiIpLAi3N8I0ztbkzEcvNHcNsXdrUM6Mm0k9z3233sTdjLhz0/5M6mdwLWHeJjb8OFhCgrPw8/pvSZQvOg5jy16ilWHl9pdkhCFJAq9HyFlwCd0ATOxUP1MHhomdHbvDQKVccXLDvyavULj/d41ijxV7L9Sft5bPljnM89zxd9v+DKmldW+jnLw9ZnFRMCLiTxR5c9ypiVY5jYcyI96vUwOywhJIEDF5YArdEA0mIh7Qy4e8M1/1f65A1GcrYkaLNWENt4eiNP/vkk3u7ezOg3g8iAyCo9f1nYw6xiQgD4e/gzte9Uhi8bzuN/Po5GFzxWkUmRhKgIqUIHYwnQ698x1u4+nwB9XjcmZ1k3xezIymT50eWMWD6CEJ8QvrvhO5tO3kLYG38Pf6b2mUqAVwAuuNC4RmM61OzAjgd2cG/ze2kV3MrsEIWTkQQOxlKfkX3AOxBqtoarnoQG3exqCdDZe2fz1MqnaB7UnJk3zKR2tdpmhySEw6nuWZ1p108DBQeTD5KUkURcehyLDi+SzpyiykkCB2Opz+PrIDACvCxt1sfW2sUSoFprPtn8CW+tf4se9XrwZd8vqe5ZveQnCiHKpVGNRgyKHISbcuNQyiGeW/Mc/Rv1l86cospJAgfo/rSx5GdGMui8C0uAdn/a7MguKycvh1f+fYUvd3zJ7ZG381HPj/B28zY7LCEc3oi2I9BoqrlVY/3p9eTl5aG1LvmJQliRdGKDC7Oq2dESoJdO0DKy7chyLUwihCi7/HXPE84n0LhGY77f+z2p2am82u1V3F3czQ5POAlJ4PlscAnQ4iRnJDPqz1HsiNvBS11eKhjjLYSoOvnrnn/R5wvmHpjL5K2TSTifwAc9P8DXveQ5I4oaRlmYDKMUJZEEXtRUqiaM3S6tk2kneXT5o5xMO8mHPT+UhRaEMEn+REYhPiE81vYxavrU5PW1rzP096FMvm5yiW3iF62dYIVhp/f/dj9bYrf8Z7sMb3NcksALjd22NZd+IPPHm7oqV76+/mubnaBFCGd0W+RtBHsH8/Sqp7l3yb1MuW4K4dXDq+z8LYNa0jKoJeM6jSvYNn7DeGlac2DSic2GtQxqyb3N76VDzQ40DWiKn7sf3m7e3NjwRkneQtig7mHd+brv16Rnp3Pfb/exLW5blZ17WKthLDi0gPEbxvPbkd9keJsTsLkErpTqp5Tap5Q6qJR61ux4zDS05VDmH5xPTGoM+5P2E+AVgJuLG//X4f/MDk0IUYzWIa357sbv8PPw4+GlD1fJ/OlpWWnMOzCPjJwMvtvzHTvidzBt5zQZ3ubgbKoKXSnlCkwC+gAxwEal1EKt9W5zI6s6p8+dZt2pdaw7tY4NpzaQlp1GWnYafu5+dKndBU83T/lA2hmZ89351Pevz7c3fMuoFaN48q8neaHzC5XS2TQ9O51Ze2cxfdd0UjJT6FqnK9vjtnNLxC08svwR5g+Yb/VzCtthUwkc6AQc1FofBlBKzQYGAA6bwFOzU1l+dDnrT61n3al1HD17FIBAr0A61+pMi6AWTNw8kXp+9Vh6dKl8IO2QzPnunIK8g5h2/TSeWvUUb6x7g9j0WEa1G2WVNumMnAx+3PcjX+/8msSMRK6qexWj242mVXArxm8Yz8PLHmZA4wHyZd/B2VoCrwscL3Q/BuhsUizlUlzHs/yeoOdzzrMldgvrTq1j/an17EnYg0bj4+ZDh1oduLPJnXSu3ZnIgEhclNHC8e2eb9mXtI8hzYfIB1IIO+Lj7sMn137CG2vfYOr2qZxJP8PLXV8u91jxrNws5h2Yx1fbvyL2fCyda3dmdLvRtAttV7DPsFbDOJJyRNq+nYCtJfBSUUoNB4YD1K1bl+joaHMDKqSeez3q1a3H4bTDALzZ9k0+2PMBcRlxDJk/hD1n95Cjc3BTbjT1b8rA0IF0qdOFJn5NcHOx/DnOwrGzxwqOGeAaQLprOn2q96nwa83IyACwyjWz5rGsrbSxJSQkVEE0ts+s90VJ198R3mMAQ+sMxSPLgzkH53A88TjjWozD2/XCrIklHSsnL4cVZ1Yw5+gc4jLjaOHfgv81+R+ta7SG9P8+79nIZ0k7k0YaacXGJO99c1nj+ttaAj8B1Ct0P8yy7SJa6y+ALwA6dOigw8PDqyS40vhf6P8YuGAggV6BJGcmM+TfIaTnpAPQLLAZ9zS/hy51unBF6BX4uPsQHR1NSfH77fWjmU8z2jcpw9KmxfDa6wVQ4jmr+ljWVpbYbDH+qmbm++Jy+znKewzgxYYv0nR/U95c9yav7XmNSb0nEeQddNlj5eTlsPjwYqZsm0JMWgxtgtvwZvc36Vq7q1Wq4m3xujqTil5/W0vgG4FIpVRDjMQ9GBhibkhlE+ITQtc6XVkavRQPFw/q+9entm9t3rz6TQK9As0OT4gC0rmu6t3R5A6CvYIZu3os9/12H1Oum0J9//r/2S9P5/H7kd/5fNvnRJ+Npnlgcyb1nsQ1da+Rcd2igE0lcK11jlJqNLAUcAWmaa13mRxWmZzNOsvWM1sBCPcP58z5M3zZ90tJ3sLm2GrnOkf/YtGrfi++uv4rRq8YTf/5/cnVuQWP5b9OL1cvMnIzaFyjMRN7TuTa+tdK4hb/YVMJHEBrvQRYYnYc5aG15tV/XyUhI4EAzwAOpRySjmfCKVgz6drqFwtrahvSlm9v+JbBiweTkZNBw+oNAXBzcWNv4l48XD1446o36Bvet6AzqxCXsrkEbs/mHpjL8qPLGXPlGFYcXUH02WjpCSqcgjMkXWsLrx7OjH4zuPPXOzmYfBCAOr518Hbz5uf+P1PTt6bJEQpbJ1/trORA0gHGbxhPtzrdeLDlgwULHUjpWwhRnKaBTbmzyZ14u3rTwL8BPev1ZFCTQZK8RalICdwKzuecZ+zqsVRzr8ZbV79lc1Vejt6mKIQ9G95mOD/t/wlfN18WH1kskzWJUpMEbgXvb3yfg8kHmXrdVJsscTtL9aZ8URH2KMQnhCDvIJmsSZSZJPAKWhq9lJ/2/8SwVsPoVreb2eE4NWf5oiIcTy2fWmTkZEifGVEmtlXXa2dOpJ3gtX9fo01wG0a3H212OEIIOyV9ZkR5SAIvp+y8bMatHodGM777+HLPbSyEEEKUh1Shl9PkrZPZFreN97u/T5hfmNnhCCGEcDKSwMth3al1fL3ja26PvJ1+DfuZHU6Vko5iQghhG5w+gReVkAq7NCElnE/guTXP0bB6Q8Z1GlcVIdoU6SgmhBC2wekTeOGENPT3oQBM7ze9yH3zdB4v/vMiZzPPMrXPVLzdvIvcTwjhmKQGStgSp0/gZfHt7m/5+8TfvNj5RZoENDE7HCFEFZMaKGFLpBd6Ke2K38XEzRPpXb83dza90+xwhBBCODlJ4KWQlpXGM6ufIdg7mNe6vSbL+gkhhDCdVKGXQGvNG+ve4GTaSab3m051z+pmhySEEEJIAi/JgkMLWHJkCaPbjaZ9aHuzwxFCOAjpECcqShL4ZRxJOcLb69+mY62OPNz6YbPDEUI4EOkQJypK2sCLkZmbyTOrnsHT1ZN3rn4HVxdXs0MSQgghCkgJvBgfRn3IvqR9TOo9iZq+Nc0ORwghhLiIJPAi/HXsL37Y+wP3Nr+X7mHdTYlB2seEEEJcjiRw4P7f7mdL7JaC+1FnogDYEb+jTMexZtKV9jEhhBCXIwkcaBnUkpZBLdmTsId9SfvI03n0adAHf0//Mh1Hkq4QQoiqIp3YgGGthrHw0EJiUmNIy07jiSueYGXMSoa1GmZ2aEIIIUSRJIEDIT4hXB9+PbHnYwnyCiImNYb+jfoT7B1sdmhCCCFEkSSBWzzW9jFclAvBXsEsOrxISt9CCCFsmiRwixCfEIK8gziYclBK30IIIWyedGIrpJZPLTJyMqT0LYQQwuZJCbwQD1cPmgQ0kdK3EEIImyclcCGEEHbh0jk78rUPbc/MG2aaEJG5JIELIYSwC/lzdozrNK5g2/gN41FKmRiVeaQKXQghhF3In7MjLj0OgLj0OKceNSQJXAghhF0I8Qmhf6P+TNs5DYBpO6c59aghSeBCCCHsRn4pfE/CHqcufYMkcCGEEHYkvxT+8LKHnbr0DdKJTQghhJ0Z1moYR1KOOHXpGySBCyGEsDMhPiFM6TPF7DBMJ1XoQgghhB2SBC6EEELYIaevQp+8dTKfb/v8om2tZ7Qu+P2xto8xst3Iqg5LCCGEuCynT+Aj242UBC2EEMLuSBW6EEIIYYckgQshhBB2SBK4EEIIYYckgQshhBB2SBK4EEIIYYckgQshhBB2SBK4EEIIYYckgQshhBB2SBK4EEIIYYckgQshhBB2qEJTqSqlXgUeAeIsm57XWi+xPPYc8BCQCzyhtV5q2d4P+BhwBb7SWr9r2d4QmA0EAZuA+7TWWRWJTwghhKgK9/92P1tit/xne/vQ9sy8YWalnNMac6F/pLWeUHiDUqoFMBhoCdQB/lBKNbE8PAnoA8QAG5VSC7XWu4HxlmPNVkpNwUj+F68yIoQQQtiglkEtaRnUknGdxhVsG79hPEqpSjtnZVWhDwBma60ztdZHgINAJ8vtoNb6sKV0PRsYoIxXeC0w1/L8GcDASopNCCGEsKphrYax8NBC4tKNCum49DgWHV7EsFbDKu2c1iiBj1ZK3Q9EAU9prZOAusC6QvvEWLYBHL9ke2eMavNkrXVOEfv/h1JqODAcoG7dukRHR1vhZZgjISHB7BCcmlx/c8n1N49ce+vrGdKTif9O5JHGj/DlwS/pGdyTtDNppJH2n32tcf1LTOBKqT+AWkU89AJGFfcbgLb8/ACovK8bFlrrL4AvADp06KDDw8Mr+5SVyt7jt3dy/c0l1988cu2t63+h/2PggoHc2/5eVsWvYv6A+QR7Bxe7f0Wvf4kJXGt9XWkOpJT6EvjVcvcEUK/Qw2GWbRSzPQGooZRys5TCC+8vhBBC2LwQnxD6N+rPw8seZkDjAZdN3tZQoTZwpVTtQndvBXZafl8IDFZKeVp6l0cCG4CNQKRSqqFSygOjo9tCrbUG/gIGWZ7/ALCgIrEJIYQQVW1Yq2G0Dm5dqW3f+SraBv6eUqodRhV6NPAogNZ6l1LqR2A3kAOM0lrnAiilRgNLMYaRTdNa77IcaxwwWyn1JrAF+LqCsQkhhBBVKsQnhCl9plTJuSqUwLXW913msbeAt4rYvgRYUsT2wxi91IUQQghRApmJTQghhLBDksCFEEIIOyQJXAghhLBDksCFEEIIOyQJXAghhLBDksCFEEIIOyQJXAghhLBDksCFEEIIO6SMWUztl1IqDjhqdhwVEAzEmx2EE5Prby65/uaRa2+uslz/BlrrkEs32n0Ct3dKqSitdQez43BWcv3NJdffPHLtzWWN6y9V6EIIIYQdkgQuhBBC2CFJ4Ob7wuwAnJxcf3PJ9TePXHtzVfj6Sxu4EEIIYYekBC6EEELYIUngQgghhB2SBG4ipVS0UmqHUmqrUirK7HgcnVJqmlIqVim1s9C2QKXUcqXUAcvPADNjdGTFXP9XlVInLJ+BrUqpG82M0VEppeoppf5SSu1WSu1SSj1p2S7v/0p2mWtf4fe+tIGbSCkVDXTQWstkClVAKdUdSANmaq1bWba9ByRqrd9VSj0LBGitx5kZp6Mq5vq/CqRprSeYGZujU0rVBmprrTcrpfyATcBA4EHk/V+pLnPt76SC730pgQunobVeDSResnkAMMPy+wyMD5aoBMVcf1EFtNantNabLb+nAnuAusj7v9Jd5tpXmCRwc2lgmVJqk1JquNnBOKmaWutTlt9PAzXNDMZJjVZKbbdUsUsVbiVTSoUD7YH1yPu/Sl1y7aGC731J4Oa6Wmt9BXADMMpSxShMoo32JGlTqlqfA42AdsAp4ANTo3FwSqlqwDzgf1rrs4Ufk/d/5Sri2lf4vS8J3ERa6xOWn7HAL0AncyNySmcsbVT5bVWxJsfjVLTWZ7TWuVrrPOBL5DNQaZRS7hgJ5Hut9c+WzfL+rwJFXXtrvPclgZtEKeVr6dCAUsoX6AvsvPyzRCVYCDxg+f0BYIGJsTid/ORhcSvyGagUSikFfA3s0Vp/WOghef9XsuKuvTXe+9IL3SRKqQiMUjeAG/CD1votE0NyeEqpWUBPjGX8zgCvAPOBH4H6GMvS3qm1lo5WlaCY698TowpRA9HAo4XaZIWVKKWuBtYAO4A8y+bnMdpi5f1fiS5z7e+mgu99SeBCCCGEHZIqdCGEEMIOSQIXQggh7JAkcCGEEMIOSQIXQggh7JAkcCGEEMIOSQIXQggh7JAkcCGEEMIO/T975myWnABDWAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Binned Statistics'}>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Specify center of each wind speed bin, and signal name for analysis\n",
    "bin_centers = np.arange(3.5,25.5,step=1) \n",
    "signal_name = 'TB_ForeAft'          \n",
    "\n",
    "# Specify inputs to be used in plotting\n",
    "bin_mean = bin_means[signal_name]\n",
    "bin_max = bin_maxs[signal_name]\n",
    "bin_min = bin_mins[signal_name]\n",
    "bin_mean_std = bin_means_std[signal_name]\n",
    "bin_max_std = bin_maxs_std[signal_name]\n",
    "bin_min_std = bin_mins_std[signal_name]\n",
    "\n",
    "# Plot binned statistics\n",
    "loads.graphics.plot_bin_statistics(bin_centers,bin_mean,bin_max,bin_min,\n",
    "                          bin_mean_std,bin_max_std,bin_min_std,\n",
    "                          xlabel='Wind Speed [m/s]',\n",
    "                          ylabel=signal_name,\n",
    "                          title='Binned Statistics')\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}