EGU_ensemble_QBOi_CEN_Exp1_day.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7vHRw5uWYBpO"
   },
   "source": [
    "# QBOi Tigramite notebook.\n",
    "\n",
    "First attempt at a shared jupyter notebook.\n",
    "- Install Tigramite and dependencies\n",
    "- Load data from Dropbox\n",
    "- Run Causal Effect Network analysis on QBOi data\n",
    "\n",
    "All models are loaded, but currently some have errors in the v_flux timeseries.\n",
    "\n",
    "Warning: Runtime type must be Python 2 (Python 3 not supported by Tigramite)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Xa1VlIdSY54z"
   },
   "source": [
    "## Install dependencies and Tigramite\n",
    "Use pip install to load cython and ace, then use pip install git+... to load tigramite directly from the github repo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 271
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 30014,
     "status": "ok",
     "timestamp": 1554312198792,
     "user": {
      "displayName": "Nicholas Tyrrell",
      "photoUrl": "https://lh4.googleusercontent.com/-gi3usNz72WA/AAAAAAAAAAI/AAAAAAAABbE/EZOgb1iXIH8/s64/photo.jpg",
      "userId": "00640113987881425946"
     },
     "user_tz": -180
    },
    "id": "89fnUuxcY4vk",
    "outputId": "cff18cc8-d5d7-4913-9d24-9bb8425b180b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: netcdf4 in /usr/local/lib/python2.7/dist-packages (1.5.0.1)\n",
      "Requirement already satisfied: numpy>=1.7 in /usr/local/lib/python2.7/dist-packages (from netcdf4) (1.14.6)\n",
      "Requirement already satisfied: cftime in /usr/local/lib/python2.7/dist-packages (from netcdf4) (1.0.3.4)\n",
      "Collecting git+https://github.com/jakobrunge/tigramite\n",
      "  Cloning https://github.com/jakobrunge/tigramite to /tmp/pip-req-build-t4zeoy\n",
      "Requirement already satisfied (use --upgrade to upgrade): tigramite==4.0.0b0 from git+https://github.com/jakobrunge/tigramite in /usr/local/lib/python2.7/dist-packages\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python2.7/dist-packages (from tigramite==4.0.0b0) (1.14.6)\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python2.7/dist-packages (from tigramite==4.0.0b0) (1.1.0)\n",
      "Requirement already satisfied: six in /usr/local/lib/python2.7/dist-packages (from tigramite==4.0.0b0) (1.11.0)\n",
      "Building wheels for collected packages: tigramite\n",
      "  Building wheel for tigramite (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /tmp/pip-ephem-wheel-cache-RClxZ9/wheels/b9/5d/5c/bd2c137dc0858cbf840aefa514061af5f6af7e863a2696b0de\n",
      "Successfully built tigramite\n"
     ]
    }
   ],
   "source": [
    "# Install tigramite dependencies\n",
    "!pip install -q cython\n",
    "!pip install -q ace\n",
    "\n",
    "# Install other python modules\n",
    "!pip install netcdf4\n",
    "!pip install -U -q PyDrive\n",
    "\n",
    "# Install Tigramite from github\n",
    "!pip install git+https://github.com/jakobrunge/tigramite\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "UJ3IkoUbWNSZ"
   },
   "outputs": [],
   "source": [
    "# Imports required python packages\n",
    "import numpy\n",
    "import numpy.ma as ma\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "import sklearn\n",
    "\n",
    "import tigramite\n",
    "from tigramite import data_processing as pp\n",
    "from tigramite import plotting as tp\n",
    "from tigramite.pcmci import PCMCI\n",
    "from tigramite.independence_tests import ParCorr, GPDC, CMIknn, CMIsymb\n",
    "from tigramite.models import LinearMediation, Prediction\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import netCDF4\n",
    "from scipy import signal\n",
    "\n",
    "# Import packages for PyDrive\n",
    "from pydrive.auth import GoogleAuth\n",
    "from pydrive.drive import GoogleDrive\n",
    "from google.colab import auth\n",
    "from oauth2client.client import GoogleCredentials\n",
    "\n",
    "# 1. Authenticate and create the PyDrive client to save to google drive\n",
    "auth.authenticate_user()\n",
    "gauth = GoogleAuth()\n",
    "gauth.credentials = GoogleCredentials.get_application_default() \n",
    "drive = GoogleDrive(gauth)\n",
    "\n",
    "\n",
    "%matplotlib inline     \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "o6YGfNFTSCEG"
   },
   "source": [
    "## Load data from Dropbox\n",
    "\n",
    "I'm not sure if this is the best way to load data, but it's a start. Data is in csv format, the folder is not currently shared, I can give permissions if requested.\n",
    "\n",
    "Unfortunately the file names aren't informative (i.e. no model, actor names etc.)\n",
    "\n",
    "Each model has one csv file which contains 8 timeseries, one for each actor, plus a date column.\n",
    "\n",
    "Note: Once analysis for the project is finalised, it might be good to move data to Figshare, so it has a stable address and DOI."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IESLq6BtxNr6"
   },
   "source": [
    "Read data in to Pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "50wuci8XIDmt"
   },
   "outputs": [],
   "source": [
    "CAM = \"https://www.dropbox.com/s/uznzrobn6p3cqt2/CAM_QBOiExp1_r1i1p1_day.csv?dl=1\"\n",
    "CCCmaCMAM = \"https://www.dropbox.com/s/j53knlzk9b9wj7o/CCCmaCMAM_QBOiExp1_r1i1p1_day.csv?dl=1\"\n",
    "ISAC_CNRECHAM5sh = \"https://www.dropbox.com/s/oxb209j62exepgu/ISAC-CNRECHAM5sh_QBOiExp1_r1i1p1_day.csv?dl=1\"\n",
    "MIROCMIROC_AGCM = \"https://www.dropbox.com/s/ivm7zl0lszqa5nk/MIROCMIROC-AGCM_QBOiExp1_r1i1p1_day.csv?dl=1\"\n",
    "MIROCMIROC_ESM = \"https://www.dropbox.com/s/r3tohmgo4so2m5r/MIROCMIROC-ESM_QBOiExp1_r1i1p1_day.csv?dl=1\"\n",
    "#MOHCUMGA7 = \"https://www.dropbox.com/s/vnq57vebl3izq4y/MOHCUMGA7_QBOiExp1_r2i1p1_day.csv?dl=1\"\n",
    "# MRIMRI_ESM2 = \"\"\n",
    "WACCM = \"https://www.dropbox.com/s/u9i7xy33rx18fn0/WACCM_QBOiExp1_r1i1p1_day.csv?dl=1\"\n",
    "# YonseiHadGEM2_A = \"\"\n",
    "# YonseiHadGEM2_AC = \"\"\n",
    "\n",
    "cam = pd.read_csv(CAM,index_col=0)\n",
    "cccmacmam  = pd.read_csv(CCCmaCMAM,index_col=0)\n",
    "isac_cnrecham5sh = pd.read_csv(ISAC_CNRECHAM5sh,index_col=0)\n",
    "mirocmiroc_agcm = pd.read_csv(MIROCMIROC_AGCM,index_col=0)\n",
    "mirocmiroc_esm = pd.read_csv(MIROCMIROC_ESM,index_col=0)\n",
    "#mohcumga7 = pd.read_csv(MOHCUMGA7,index_col=0)\n",
    "#mrimri_esm2 = pd.read_csv(MRIMRI_ESM2,index_col=0)\n",
    "waccm = pd.read_csv(WACCM,index_col=0)\n",
    "#yonseihadgem2_a = pd.read_csv(YonseiHadGEM2_A,index_col=0)\n",
    "#yonseihadgem2_ac = pd.read_csv(YonseiHadGEM2_AC,index_col=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pqDLYlNwjOSt"
   },
   "source": [
    "### Remove seasonal cycle and detrend, fix sign, time means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "c5x2zervWNUF"
   },
   "outputs": [],
   "source": [
    "\n",
    "def rm_seascyc(df):\n",
    "  \"\"\"\n",
    "  Remove seasonal cycle and detrend data from a pandas dataframe, \n",
    "  then return the anomaly dataframe \n",
    "  and a Tigramite dataframe\n",
    "  \"\"\"\n",
    "  \n",
    "  # Remove time of day from date\n",
    "  df['date'] = pd.to_datetime(df['date'])\n",
    "  df['date'] = df['date'].apply( lambda df : pd.datetime(year=df.year, \n",
    "                                                         month=df.month, \n",
    "                                                         day=df.day))\n",
    "\n",
    "  # set date column as index\n",
    "  df.set_index('date', inplace=True)\n",
    "\n",
    "  # Create new column of day of year\n",
    "  df['DoY'] = df.index.dayofyear\n",
    "  #df['mon'] = df.index.month\n",
    "\n",
    "  # Get seasonal cycle, (transform keeps array the same size)\n",
    "  seascyc = df.groupby('DoY').transform('mean')\n",
    "\n",
    "  # Remove seasonal cycle, create anomaly actors dataframe\n",
    "  anomact = df - seascyc\n",
    "  anomact.drop('DoY',axis=1,inplace=True)\n",
    "\n",
    "  #detrend data\n",
    "  for colname, col in anomact.iteritems():\n",
    "      #print(colname)\n",
    "      anomact[colname] = signal.detrend(anomact[colname],axis=0)  \n",
    "\n",
    "  #print(anomact.head(n=3))\n",
    "\n",
    "  # Put data into tigramite dataframe\n",
    "\n",
    "  var_names=anomact.keys()\n",
    "  anomact_tdf = pp.DataFrame(anomact.as_matrix(columns=var_names[1::]))\n",
    "  \n",
    "  return anomact, seascyc\n",
    "\n",
    "# Note: this doesn't work with NaN values\n",
    "cam_anom, cam_seascyc = rm_seascyc(cam)\n",
    "cccmacmam_anom, cccmacmam_seascyc = rm_seascyc(cccmacmam)\n",
    "isac_cnrecham5sh_anom, isac_cnrecham5sh_seascyc = rm_seascyc(isac_cnrecham5sh)\n",
    "mirocmiroc_agcm_anom, mirocmiroc_agcm_seascyc = rm_seascyc(mirocmiroc_agcm)\n",
    "mirocmiroc_esm_anom, mirocmiroc_esm_seascyc = rm_seascyc(mirocmiroc_esm)\n",
    "# mohcumga7_anom, mohcumga7_seascyc = rm_seascyc(mohcumga7)\n",
    "# mrimri_esm2_anom, mrimri_esm2_seascyc = rm_seascyc(mrimri_esm2)\n",
    "# waccm_anom, waccm_seascyc = rm_seascyc(waccm)\n",
    "# yonseihadgem2_a_anom, yonseihadgem2_a_seascyc = rm_seascyc(yonseihadgem2_a)\n",
    "# yonseihadgem2_ac_anom, yonseihadgem2_ac_seascyc = rm_seascyc(yonseihadgem2_ac)\n",
    "\n",
    "# === reverse sign of NAO, PoV, v_flux ===\n",
    "\n",
    "\n",
    "for rev_var in ['PoV']:\n",
    "#  cam_anom[rev_var] = cam_anom[rev_var]*(-1)\n",
    "#  cccmacmam_anom[rev_var] = cccmacmam_anom[rev_var]*(-1)\n",
    "  isac_cnrecham5sh_anom[rev_var] = isac_cnrecham5sh_anom[rev_var]*(-1)\n",
    "#  mirocmiroc_agcm_anom[rev_var] = mirocmiroc_agcm_anom[rev_var]*(-1)\n",
    "  mirocmiroc_esm_anom[rev_var] = mirocmiroc_esm_anom[rev_var]*(-1)\n",
    "\n",
    "for rev_var in ['NAO']:\n",
    "  cam_anom[rev_var] = cam_anom[rev_var]*(-1)\n",
    "  cccmacmam_anom[rev_var] = cccmacmam_anom[rev_var]*(-1)\n",
    "#  isac_cnrecham5sh_anom[rev_var] = isac_cnrecham5sh_anom[rev_var]*(-1)\n",
    "#  mirocmiroc_agcm_anom[rev_var] = mirocmiroc_agcm_anom[rev_var]*(-1)\n",
    "#  mirocmiroc_esm_anom[rev_var] = mirocmiroc_esm_anom[rev_var]*(-1)\n",
    "\n",
    "for rev_var in ['v_flux']:\n",
    "  cam_anom[rev_var] = cam_anom[rev_var]*(-1)\n",
    "#  cccmacmam_anom[rev_var] = cccmacmam_anom[rev_var]*(-1)\n",
    "  isac_cnrecham5sh_anom[rev_var] = isac_cnrecham5sh_anom[rev_var]*(-1)\n",
    "#  mirocmiroc_agcm_anom[rev_var] = mirocmiroc_agcm_anom[rev_var]*(-1)\n",
    "  mirocmiroc_esm_anom[rev_var] = mirocmiroc_esm_anom[rev_var]*(-1)\n",
    "\n",
    "\n",
    "# === define weekly mean ===\n",
    "\n",
    "def weekmean(df):\n",
    "  n=0\n",
    "  for colname, col in df.iteritems():\n",
    "    #print(colname)\n",
    "    #anomact[colname] = signal.detrend(anomact[colname],axis=0)  \n",
    "    new_series = df[colname].resample('W').mean()\n",
    "    new_series.name = colname\n",
    "    if n==0:\n",
    "      newdf = pd.DataFrame(new_series)\n",
    "      n=1\n",
    "    else:\n",
    "      newdf[colname] = new_series\n",
    "    \n",
    "  return newdf\n",
    "      \n",
    "cam_week = weekmean(cam_anom)\n",
    "cccmacmam_week = weekmean(cccmacmam_anom)\n",
    "isac_cnrecham5sh_week = weekmean(isac_cnrecham5sh_anom)\n",
    "mirocmiroc_agcm_week = weekmean(mirocmiroc_agcm_anom)\n",
    "mirocmiroc_esm_week = weekmean(mirocmiroc_esm_anom)\n",
    "#mohcumga7_week = weekmean(mohcumga7_anom)\n",
    "\n",
    "cam_abs_week = weekmean(cam)\n",
    "cccmacmam_abs_week = weekmean(cccmacmam)\n",
    "isac_cnrecham5sh_abs_week = weekmean(isac_cnrecham5sh)\n",
    "mirocmiroc_agcm_abs_week = weekmean(mirocmiroc_agcm)\n",
    "mirocmiroc_esm_abs_week = weekmean(mirocmiroc_esm)\n",
    "#mohcumga7_abs_week = weekmean(mohcumga7)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sT51F2pd7gNr"
   },
   "source": [
    "### Create masks for QBOE and QBOW months. Include mask for months ONDJFM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "cellView": "code",
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "YJmmnRHDWNUJ"
   },
   "outputs": [],
   "source": [
    "# Mask data for QBOW, and create QBOW and QBOE dataframes\n",
    "\n",
    "def qbomask(df_abs, df, u_lim=2):\n",
    "  \"\"\"\n",
    "  Take a dataframe (df) with all timeseriees, put QBO into seperate df,\n",
    "  make masks with the QBO data.\n",
    "  Uses a threshold of +/- 5 m/s for u-wind, unless otherwise specified.\n",
    "  Input: dataframe of anomalies\n",
    "  Output: dataframe w/o QBO, df of QBO, mask for QBOE, mask for QBOW, \n",
    "          mask for no qbo, i.e. mask of months ONDJFM\n",
    "  \"\"\"\n",
    "  \n",
    "  # Put QBO timeseries in a seperate df and create QBOE/W df\n",
    "  df_qbo = df_abs[['QBO']].copy() # Use abs values to calc QBO\n",
    "  df_noqbo = df.drop('QBO',axis=1)\n",
    "  \n",
    "  # Add month series\n",
    "  df_noqbo['mon'] = df_noqbo.index.month\n",
    "  \n",
    "  # Set up masked arrays\n",
    "  mask_qbow = np.zeros(df_qbo.shape).astype('bool')\n",
    "  mask_qboe = np.zeros(df_qbo.shape).astype('bool')\n",
    "  mask_mon = np.zeros(df_qbo.shape).astype('bool')\n",
    "  \n",
    "  # Create QBOW Mask for given u_lim\n",
    "  mask_qbow = np.ma.masked_less(df_qbo.as_matrix(columns=['QBO']),u_lim).mask\n",
    "  # Make mask same size as full df\n",
    "  df_size = df_noqbo.shape[1]\n",
    "  mask_qbow2 = np.tile(mask_qbow,df_size)\n",
    "  # Repeat for QBOE\n",
    "  mask_qboe = np.ma.masked_greater(df_qbo.as_matrix(columns=['QBO']),-u_lim).mask\n",
    "  mask_qboe2 = np.tile(mask_qboe,df_size)\n",
    "\n",
    "  # Create Mask for Months Oct-Mar (i.e. mask out all other months)\n",
    "  mask_mon[:,0] = (\n",
    "                 #(df_noqbo['mon'] == 1) | (df_noqbo['mon'] == 2) |\n",
    "                 #(df_noqbo['mon'] == 3) | \n",
    "                 (df_noqbo['mon'] == 4) | (df_noqbo['mon'] == 5) |\n",
    "                 (df_noqbo['mon'] == 6) | (df_noqbo['mon'] == 7) |\n",
    "                 (df_noqbo['mon'] == 8) | (df_noqbo['mon'] == 9) |\n",
    "                 (df_noqbo['mon'] == 10) | (df_noqbo['mon'] == 11) |\n",
    "                 (df_noqbo['mon'] == 12)\n",
    "                 ).as_matrix()\n",
    "  \n",
    "\n",
    "  mask_mon2 = np.tile(mask_mon,df_size) #.reshape(mask_qboe2.shape)\n",
    "\n",
    "  # Add month mask to QBO masks\n",
    "  mask_qboe_mon = mask_qboe2 + mask_mon2\n",
    "  mask_qbow_mon = mask_qbow2 + mask_mon2\n",
    "\n",
    "  # Use QBO masks on data\n",
    "  #  df_qboe.mask(mask_qboe2,inplace=True)\n",
    "  #  df_qbow.mask(mask_qbow2,inplace=True)\n",
    "  \n",
    "  return(df_noqbo, df_qbo, mask_qboe_mon, mask_qbow_mon, mask_mon2)\n",
    "\n",
    "cam_noqbo, cam_qbo, cam_qboe_mask, cam_qbow_mask, cam_noqbo_mask = qbomask(cam_abs_week,cam_week)\n",
    "cccmacmam_noqbo, cccmacmam_qbo, cccmacmam_qboe_mask, cccmacmam_qbow_mask, cccmacmam_noqbo_mask = qbomask(cccmacmam_abs_week,cccmacmam_week)\n",
    "isac_cnrecham5sh_noqbo, isac_cnrecham5sh_qbo, isac_cnrecham5sh_qboe_mask, isac_cnrecham5sh_qbow_mask, isac_cnrecham5sh_noqbo_mask = qbomask(isac_cnrecham5sh_abs_week,isac_cnrecham5sh_week)\n",
    "mirocmiroc_agcm_noqbo, mirocmiroc_agcm_qbo, mirocmiroc_agcm_qboe_mask, mirocmiroc_agcm_qbow_mask, mirocmiroc_agcm_noqbo_mask = qbomask(mirocmiroc_agcm_abs_week,mirocmiroc_agcm_week)\n",
    "mirocmiroc_esm_noqbo, mirocmiroc_esm_qbo, mirocmiroc_esm_qboe_mask, mirocmiroc_esm_qbow_mask, mirocmiroc_esm_noqbo_mask = qbomask(mirocmiroc_esm_abs_week,mirocmiroc_esm_week)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0mXH-wJxdGRq"
   },
   "source": [
    "## Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pdl9onThdxRx"
   },
   "source": [
    "### Plot all models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "H2bzSrVeWshQ"
   },
   "source": [
    "#### Function for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1649
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 44747,
     "status": "ok",
     "timestamp": 1554282112248,
     "user": {
      "displayName": "Nicholas Tyrrell",
      "photoUrl": "https://lh4.googleusercontent.com/-gi3usNz72WA/AAAAAAAAAAI/AAAAAAAABbE/EZOgb1iXIH8/s64/photo.jpg",
      "userId": "00640113987881425946"
     },
     "user_tz": -180
    },
    "id": "UfRhha7sWNUX",
    "outputId": "f925d7c7-90eb-489b-b594-4f9c08e2bb9c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------------------\n",
      "CAM\n",
      "['PoV', 'v_flux']\n",
      "All years\n",
      "-----------------------------------------------------\n",
      "CCCMA\n",
      "['PoV', 'v_flux']\n",
      "All years\n",
      "-----------------------------------------------------\n",
      "ISAC\n",
      "['PoV', 'v_flux']\n",
      "All years\n",
      "-----------------------------------------------------\n",
      "MIROC ESM\n",
      "['PoV', 'v_flux']\n",
      "All years\n",
      "-----------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nprint(\"MOHC\")\\nplot_model(mohcumga7_noqbo,mohcumga7_noqbo_mask,mohcumga7_qboe_mask,mohcumga7_qbow_mask,\\n           var_select=var_select,save_plot=save_plot,save_name=\"mohcumga7\",title=\"mohcumga7\")\\nprint(\"-----------------------------------------------------\")\\nprint(\"MRI\")\\nplot_model(mrimri_esm2_noqbo,mrimri_esm2_noqbo_mask,mrimri_esm2_qboe_mask,mrimri_esm2_qbow_mask,\\n           var_select=var_select,save_plot=save_plot,save_name=\"mrimri_esm2\",title=\"mrimri_esm2\")\\nprint(\"-----------------------------------------------------\")\\nprint(\"WACCM\")\\nplot_model(waccm_noqbo,waccm_noqbo_mask,waccm_qboe_mask,waccm_qbow_mask,\\n           var_select=var_select,save_plot=save_plot,save_name=\"waccm\",title=\"waccm\")\\nprint(\"-----------------------------------------------------\")\\nprint(\"YONSEI A\")\\nplot_model(yonseihadgem2_a_noqbo,yonseihadgem2_a_noqbo_mask,yonseihadgem2_a_qboe_mask,yonseihadgem2_a_qbow_mask,\\n           var_select=var_select,save_plot=save_plot,save_name=\"yonseihadgem2_a\",title=\"yonseihadgem2_a\")\\nprint(\"-----------------------------------------------------\")\\nprint(\"YONSEI AC\")\\nplot_model(yonseihadgem2_ac_noqbo,yonseihadgem2_ac_noqbo_mask,yonseihadgem2_ac_qboe_mask,yonseihadgem2_ac_qbow_mask,\\n           var_select=var_select,save_plot=save_plot,save_name=\"yonseihadgem2_ac\",title=\"yonseihadgem2_ac\")\\n'"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2lu9Fn144rJAJoNSCAkDrqHezeYohjlY3QieWxBijgvw/TGcdAqg0MxTTmJLz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\nILj/DyiilZ6mp58ZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YkLSBEDr7GKFzjTinaO6YyjMHVZ4+VOgo8zRkzjeXJ+XhwvYNAH3FJVkXpcMqzRxWqTZZ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\n",
      "text/plain": [
       "<Figure size 720x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LosZMbZzpYqdd6pIkbXD1qTvZGq+lefMnF53VXoxtp3PmBRwOFyXvUh+lMZ2G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\nU5IkScn9f1iXdY9ytV9sAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Fg7uSmoOB7eloUkaRA2izyoIK1ZrQmWMKZ/Jpko+Lg6qMnKrK8B5VhndzoYguR+jsYoTO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\nAQAAkLv/Dzavm77aMCFFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def run_and_plot(dataframe, cond_ind_test, fig_ax, var_select):\n",
    "    \"\"\"\n",
    "    small function from example notebook to calc pcmci and plot masked links\n",
    "    \n",
    "    \"\"\"\n",
    "    pcmci = PCMCI(dataframe=dataframe, cond_ind_test=cond_ind_test)\n",
    "    results = pcmci.run_pcmci(tau_min=0,tau_max=2,pc_alpha=0.4)\n",
    "    link_matrix = pcmci.return_significant_parents(pq_matrix=results['p_matrix'],\n",
    "            val_matrix=results['val_matrix'], alpha_level=0.4)['link_matrix']\n",
    "    tp.plot_graph(fig_ax = fig_ax,  val_matrix=results['val_matrix'],\n",
    "                  link_matrix=link_matrix, var_names=var_select,\n",
    "    )\n",
    "    return\n",
    "\n",
    "def plot_model(df_noqbo,mon_mask,qboe_mask,qbow_mask,var_select,mask_type=None,\n",
    "               save_plot=False,save_name=None,save_qbo=False,title=None):\n",
    "               #var_names=['AO','BK-tas','EA-tas','PoV','Sib-SLP','Ural-SLP',\n",
    "               #           'v_flux']):\n",
    "  \"\"\"\n",
    "  Function to plot three link matrices, for all years, qboe and qbow, for selected\n",
    "  variables\n",
    "  Input:\n",
    "  df_noqbo  = pandas dataframe of timeseries (without qbo)\n",
    "  mon_mask  = masked array of months ONDJFM\n",
    "  qboe_mask = masked array of QBOE, same dimensions as data\n",
    "  qbow_mask = masked array of QBOW, same dimensions as data\n",
    "  var_names = list of variables for link matrix\n",
    "  mask_type = masking mode, 'y','x','z','yz','xy','xz','xyz', if None,'y' used\n",
    "  save_name = name to save plot, if None then not saved\n",
    "  \n",
    "  \"\"\"\n",
    "  print(var_select)\n",
    "  df_names=df_noqbo[var_select]\n",
    "  n_cols = len(var_select)\n",
    "\n",
    "  tig_df_all = pp.DataFrame(df_names.as_matrix(columns=var_select),\n",
    "                             mask=mon_mask[:,0:n_cols])\n",
    "  tig_df_qboe = pp.DataFrame(df_names.as_matrix(columns=var_select),\n",
    "                             mask=qboe_mask[:,0:n_cols])\n",
    "  tig_df_qbow = pp.DataFrame(df_names.as_matrix(columns=var_select),\n",
    "                             mask=qbow_mask[:,0:n_cols])\n",
    "  \n",
    "  # Causal graph all QBO years\n",
    "  print(\"All years\")\n",
    "  fig  = plt.figure(figsize=(10,5)); ax=fig.add_subplot(111); plt.title(\"All, {}\".format(title))\n",
    "  run_and_plot(tig_df_all, ParCorr(significance='analytic', mask_type=mask_type), (fig, ax), var_select=var_select)\n",
    "  # nt 21/02/2019 Removed use_mask=True, because update to code causes this to crash, see\n",
    "  # https://github.com/jakobrunge/tigramite/commit/27d5f3789093164bec86d74ce04cb3a6ec0e7f1c#diff-e773bbc3398ad209273b9db59e379c17\n",
    "  # old code below\n",
    "  # run_and_plot(tig_df_all, ParCorr(significance='analytic',use_mask=True, mask_type=mask_type), (fig, ax), var_select=var_select)\n",
    "\n",
    "  current_var = \"_\".join(var_select)\n",
    "  if save_plot:\n",
    "    fig.savefig('{}_all_day_{}.png'.format(save_name,current_var), bbox_inches='tight')\n",
    "    figfile = drive.CreateFile({'title':'{}_all_day_{}.png'.format(save_name,current_var),\n",
    "                                'parents':[{u'id':'13BVOh-AHmhzpvS30-eSIvLvhKkYC_01R'}]})\n",
    "    figfile.SetContentFile('{}_all_day_{}.png'.format(save_name,current_var))\n",
    "    figfile.Upload()\n",
    "\n",
    "  \"\"\"\n",
    "  # # Causal graph, QBOE\n",
    "  print(\"QBOE years\")\n",
    "  fig  = plt.figure(figsize=(10,5)); ax=fig.add_subplot(111);  plt.title(\"QBOE, {}\".format(title))\n",
    "  run_and_plot(tig_df_qboe, ParCorr(significance='analytic',use_mask=True, mask_type=mask_type), (fig, ax), var_select=var_select)\n",
    "\n",
    "  if save_plot and save_qbo:\n",
    "    fig.savefig('{}_qboe_day_{}.png'.format(save_name,current_var), bbox_inches='tight')\n",
    "    figfile = drive.CreateFile({'title':'{}_qboe_day_{}.png'.format(save_name,current_var),\n",
    "                                'parents':[{u'id':'13BVOh-AHmhzpvS30-eSIvLvhKkYC_01R'}]})\n",
    "    figfile.SetContentFile('{}_qboe_day_{}.png'.format(save_name,current_var))\n",
    "    figfile.Upload()  \n",
    "  \n",
    "  # # Causal graph, QBOW\n",
    "  print(\"QBOW years\")\n",
    "  fig  = plt.figure(figsize=(10,5)); ax=fig.add_subplot(111);  plt.title(\"QBOW, {}\".format(title))\n",
    "  run_and_plot(tig_df_qbow, ParCorr(significance='analytic',use_mask=True, mask_type=mask_type), (fig, ax), var_select=var_select)\n",
    "  \n",
    "  if save_plot and save_qbo:\n",
    "    fig.savefig('{}_qbow_day_{}.png'.format(save_name,current_var), bbox_inches='tight')\n",
    "    figfile = drive.CreateFile({'title':'{}_qbow_day_{}.png'.format(save_name,current_var),\n",
    "                                'parents':[{u'id':'13BVOh-AHmhzpvS30-eSIvLvhKkYC_01R'}]})\n",
    "    figfile.SetContentFile('{}_qbow_day_{}.png'.format(save_name,current_var))\n",
    "    figfile.Upload()\n",
    "  \"\"\"\n",
    "  return\n",
    "\n",
    "# All available variable names:\n",
    "# var_names=['NAO','BK-tas','EA-tas','PoV','Sib-SLP','Ural-SLP','v_flux']\n",
    "\n",
    "save_plot=False #True # set to True/False to save the plots to drive folder\n",
    "\n",
    "# var_select=['NAO','PoV','v_flux']\n",
    "\n",
    "var_select=['NAO','BK-SIC','EA-tas','PoV','Sib-SLP','Ural-SLP','v_flux']\n",
    "var_select=['PoV','v_flux'] #,'PoV','v_flux']\n",
    "#var_select=['PoV','v_flux']\n",
    "\n",
    "\n",
    "print(\"-----------------------------------------------------\")\n",
    "print(\"CAM\")\n",
    "plot_model(cam_noqbo,cam_noqbo_mask,cam_qboe_mask,cam_qbow_mask,\n",
    "           var_select=var_select,save_plot=save_plot,save_name=\"cam\", title=\"cam\")\n",
    "print(\"-----------------------------------------------------\")\n",
    "print(\"CCCMA\")\n",
    "plot_model(cccmacmam_noqbo,cccmacmam_noqbo_mask,cccmacmam_qboe_mask,cccmacmam_qbow_mask,\n",
    "           var_select=var_select,save_plot=save_plot,save_name=\"cccmacmam\",title=\"cccmacmam\")\n",
    "print(\"-----------------------------------------------------\")\n",
    "print(\"ISAC\")\n",
    "plot_model(isac_cnrecham5sh_noqbo,isac_cnrecham5sh_noqbo_mask,isac_cnrecham5sh_qboe_mask,isac_cnrecham5sh_qbow_mask,\n",
    "           var_select=var_select,save_plot=save_plot,save_name=\"isac_cnrecham5sh\",title=\"isac_cnrecham5sh\")\n",
    "#print(\"-----------------------------------------------------\")\n",
    "#print(\"MIROC AGCM\")\n",
    "#plot_model(mirocmiroc_agcm_noqbo,mirocmiroc_agcm_noqbo_mask,mirocmiroc_agcm_qboe_mask,mirocmiroc_agcm_qbow_mask,\n",
    "#          var_select=var_select,save_plot=save_plot,save_name=\"mirocmiroc_agcm\",title=\"mirocmiroc_agcm\")\n",
    "print(\"-----------------------------------------------------\")\n",
    "print(\"MIROC ESM\")\n",
    "plot_model(mirocmiroc_esm_noqbo,mirocmiroc_esm_noqbo_mask,mirocmiroc_esm_qboe_mask,mirocmiroc_esm_qbow_mask,\n",
    "           var_select=var_select,save_plot=save_plot,save_name=\"mirocmiroc_esm\",title=\"mirocmiroc_esm\")\n",
    "print(\"-----------------------------------------------------\")\n",
    "\"\"\"\n",
    "print(\"MOHC\")\n",
    "plot_model(mohcumga7_noqbo,mohcumga7_noqbo_mask,mohcumga7_qboe_mask,mohcumga7_qbow_mask,\n",
    "           var_select=var_select,save_plot=save_plot,save_name=\"mohcumga7\",title=\"mohcumga7\")\n",
    "print(\"-----------------------------------------------------\")\n",
    "print(\"MRI\")\n",
    "plot_model(mrimri_esm2_noqbo,mrimri_esm2_noqbo_mask,mrimri_esm2_qboe_mask,mrimri_esm2_qbow_mask,\n",
    "           var_select=var_select,save_plot=save_plot,save_name=\"mrimri_esm2\",title=\"mrimri_esm2\")\n",
    "print(\"-----------------------------------------------------\")\n",
    "print(\"WACCM\")\n",
    "plot_model(waccm_noqbo,waccm_noqbo_mask,waccm_qboe_mask,waccm_qbow_mask,\n",
    "           var_select=var_select,save_plot=save_plot,save_name=\"waccm\",title=\"waccm\")\n",
    "print(\"-----------------------------------------------------\")\n",
    "print(\"YONSEI A\")\n",
    "plot_model(yonseihadgem2_a_noqbo,yonseihadgem2_a_noqbo_mask,yonseihadgem2_a_qboe_mask,yonseihadgem2_a_qbow_mask,\n",
    "           var_select=var_select,save_plot=save_plot,save_name=\"yonseihadgem2_a\",title=\"yonseihadgem2_a\")\n",
    "print(\"-----------------------------------------------------\")\n",
    "print(\"YONSEI AC\")\n",
    "plot_model(yonseihadgem2_ac_noqbo,yonseihadgem2_ac_noqbo_mask,yonseihadgem2_ac_qboe_mask,yonseihadgem2_ac_qbow_mask,\n",
    "           var_select=var_select,save_plot=save_plot,save_name=\"yonseihadgem2_ac\",title=\"yonseihadgem2_ac\")\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "V1pzrqvvvsoL"
   },
   "source": [
    "### Ensemble mean plots\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fA9Uxf1QBqx2"
   },
   "source": [
    "The code  below calculates the link matrices for all models, then combines them. You can change the models used by changing the \"mods\" list. Change the actors used by changing the var_select list. Rerun the two cells below to see the changes.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "UgLcBFHfeFxj"
   },
   "outputs": [],
   "source": [
    "# Function for ensemble plots\n",
    "\n",
    "\n",
    "def run_and_plot_prepare_ensemble(dataframe, cond_ind_test, var_select, tau_max,\n",
    "                                  tau_min, pc_alpha=0.1):\n",
    "    \"\"\"\n",
    "    small function from example notebook to calc pcmci and plot masked links\n",
    "    \n",
    "    \"\"\"\n",
    "    pcmci = PCMCI(dataframe=dataframe, cond_ind_test=cond_ind_test) #, var_names=var_select)\n",
    "    results = pcmci.run_pcmci(tau_min=tau_min,tau_max=tau_max,pc_alpha=pc_alpha)\n",
    "    link_matrix = pcmci.return_significant_parents(pq_matrix=results['p_matrix'],\n",
    "            val_matrix=results['val_matrix'], alpha_level=pc_alpha)['link_matrix']\n",
    "# #     tp.plot_graph(fig_ax = fig_ax,  val_matrix=results['val_matrix'],\n",
    "# #                   link_matrix=link_matrix, var_names=var_select,\n",
    "#     )\n",
    "    return results, link_matrix\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 803
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 5591,
     "status": "ok",
     "timestamp": 1554297935269,
     "user": {
      "displayName": "Nicholas Tyrrell",
      "photoUrl": "https://lh4.googleusercontent.com/-gi3usNz72WA/AAAAAAAAAAI/AAAAAAAABbE/EZOgb1iXIH8/s64/photo.jpg",
      "userId": "00640113987881425946"
     },
     "user_tz": -180
    },
    "id": "RmR1FjLqeTmj",
    "outputId": "fddbd967-3c17-4596-d2b5-ee10f6f0450b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current variables: v_flux_Sib-SLP_EA-tas_PoV\n",
      "0\n",
      "results all\n",
      "results qboe\n",
      "1\n",
      "results all\n",
      "results qboe\n",
      "2\n",
      "results all\n",
      "results qboe\n",
      "3\n",
      "results all\n",
      "results qboe\n",
      "ALL YEARS\n",
      "QBOW YEARS\n",
      "QBOE YEARS\n",
      "ALL YEARS\n"
     ]
    },
    {
     "data": {
      "image/png": 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LGd8odhbqTPl8xzkDwY8G8fxyT63nzAKW57X1O8UO4PO4lwAHomqIAqwCPBBP\nplpKG97Y5PJrYSv53Bl403ZpVdR/RiLpmLmXVYheCFFiMr0uxBgSNhObApfRdxLOYNwG/DqkeT8t\nT1RjQyYa2R94ADVqaHexMxD85UDP7+zqbkWttSzr1HpOT4dhP/EI1DnrqzS26wOOTseTqe2AOfTF\nejdwuDUaKoB0zMwdpXkxMCXv8izgxy6/VnN1aYWoJhnpFGIMCJuJiWEzcTnwNoNPOJ8Etghp3qMl\n4YxsgdoklJ9wzkKt9RuM3elL4t4sZ8JpyZ9ib0GN0A3I53G/zIobiQ5FTSMLi8uv9br82s2ADvwa\nldTn7A68nY6Z16djZltVAhSiBknSKcQoFjYTjrCZOBpV/ugc+k7B6c/rwG4hzTszpHnfKmuAY0Am\nGpmCSuDceZc+Ao50BoKZQd7KXs/z0aK9SueRAm37DvbJPo/7BuAaW9Pv48nUgFP09cbl11Iuv/Z7\n1DKV6+krPdWAqgLwcTpm/jodM/N/foQQQyTT60KMUmEzsRkqadhxkE/5GDgfuCekeeUfNpCJRjzA\nC6y8cSiO2qk+mHWcdHZ1O4DPgXarabu21pZXShZoET0dxjxgU1tT0dOJCoknUy7gKfpGSJPA1j6P\nW84lLyIdMzcDLmXlGYUvUP++bnH5td6VniiEGJCMdAoxyoTNRGvYTFyBOkloMAnnAuA0YOOQ5r1b\nEk4lE400ojZa5Secy4EDBptwWjajL+FMoDZxVUL+FHvR04kK8XncadTUeq6AvQd4MJ5MeUsTXu1x\n+bV5Lr+2J2pk+7+2S2sA/wbC6Zi5cxVCE2LMk6RTiFHCmko/BvgAOJuBp9K7UesR1wtp3utCmjdd\n7hjHmCsoXGboRGcg+MIQ72U/0/upttaWwU7Jj1R+0glDmGIH8HncX6NKPaWspvWB2+PJlJQH6ofL\nrz0BTAVORdVmzdkCeDYdM+9Jx8w1qhKcEGOUJJ1CjALWVPpzwM3AYDYu3AlsaJ0iVK/lj4rKRCNn\nAqcXuHShMxAsdBLRQOxJ5xPDi2pYhnw6USE+jzvCisXm9wSuq8ejMofC5dd6XH7tH6hE/WJUMfmc\ng4H30zHzjHTMHMxaayHqnqzpFKKKwmaiFbgQOIPBbRJ6Hzg9pHmfKWdcY1kmGtkPdX55fkJ1C6oe\n55De9Dq7uj3AQvp2rre3tbZ8OeJAB6nI6URtje36V4WfUVw8mboItVM750Kfx/3bkUVYP6xjMy9G\nlVqyex04xeXX6n7jnhD9kZFOIarANpVuMPip9J8CUyXhLC4TjUyjcGmkF4CThppwWnalL+F8u5IJ\npyV/F7uDlY9zHKwLUEdl5lzALdAEAAAgAElEQVQYT6ZOGua96o7Lr5kuv3YM6mfiQ9ul6cDr6Zh5\nWTpmTqhOdEKMfpJ0ClFhVoH33FT6YI6uvAPQQ5r30pDmzT+lRlis0kiPoOpZ2n0MHOgMBJcN89b2\nUkmVnFrPeYqVTyca0rrOHKs4/EnWPXP+Fk+mhnW/euXya8+iNqj9lr6/mwbgXOC9dMyU0lRCFCDT\n60JUSNhMuICfo6Y3XQN0BzWV/qOQ5n22rIHVgFKVRspnlUr6DAhYTTu1tbY8P+xAh6nA6USLUacT\nDSuRjidTHtR/fKZZTSlgF5/HXald+TUjHTN11BGaO+dduh840+XXPq94UEKMUjLSKUQFhM1EEJgL\nXMTACedi4CdAUBLOgVmlkW5n5YQzjRrhHFbCadHpSziTQNlrcxaRv4t9AoM8nagQn8edRO3sn281\nuYFH48nUesO9Z71y+TUDNd3+PdTa35wDURuNzpSNRkIoknQKUUZhM+EKm4nfoDYaTBuoPyp52jCk\neS+TEkiDdjmF1zie4AwERzoqaZ9an93W2lKtv5NCpxONaArX53F3onbl5xKlVYAn48nUYJZ8CBuX\nX8u6/Np/gA2BG22XJgBXAnPTMXPLqgQnxCgiSacQZWKNbr6GWvc1UE3E94BdQ5r3qJDm/aLswdWI\nTDTyE9TO/3y/HWZppHz2UkmPl+B+w9LYrs8H3s1r3renwxhRySOfx22gktdcKaB1gEfiyZRshhkG\nl1/72uXXTgB2QW0SzNkSeC0dM69Ix0xPdaITovok6RSixMJmoilsJi5AjW5OHaB7N2oqfapMpQ9N\nJho5GfhzgUu3ohL9Eens6nYDO9maqrGJyC5/ij0AbDTSm/o87leAI+g7c3w6cJd1hKYYBpdfm4Na\n7nEBK240Ohu10ei7VQpNiKqSpFOIEgqbiamo0c0LGXh082lgU5lKH7pMNHI48PcCl15EnThUih2S\nOwPN1uf/bWtt+V8J7jkSjxZo27kUN/Z53A+ijlLN2Qv4uxSPHz6XX1vm8msXoY4ttZc5mwI8kI6Z\nN6dj5qTqRCdEdUjSKUQJWKObFwJhVt7Qkm8x8ANg95DmnV/m0GpOJhrZG1XoPT8hep+RlUbKV61T\niIoJ03eUZc4upbq5z+P+O/B7W9P3KcGIcb1z+bUPge8AxwFf2y4dA7yTjpm7ViUwIapASiYJMULW\n6OZNDJxsAswGTgpp3mhZg6pRmWhkR+BJYFzepfnADGcgWLL1sJ1d3R8Bud3c32lrbXm6VPcerp4O\n4ylgd1vT18Cqje16Sd7IrZHNf7HiCUg/sBJSMULpmDkZuAo4Ku/SFcD5Lr+2dOVnCVE7ZKRTiGGy\nRjd/y+BHN08F9pCEc3gy0ciWqF3c+QlnJ/CdEiec69GXcKZQ0/ajQf6631WATUp1c6t4/CmsOLJ7\nbTyZ2r9Ur1HPXH5tocuvHQ0cDiRsl84B3kjHzMFUuBBizJKkU4hhsHamh4HfMPDazVmotZv/CGle\nmVoYhkw0sjFqhDN/528c2N0ZCH5S4pfc0/b5M22tLaWash+pOQXadi7lC/g87jRwKPCG1dQA3BFP\nprYv5evUM5dfuwu11vNJW/PGqB3u56dj5kDvKUKMSZJ0CjEE1pnpp6M2C20+QPckatRoTxndHL5M\nNLI2KnGfnHdpMbCXMxDMLyVUCvb6nFUrlVTA66iKB3YlW9eZ4/O4F6Nqn35qNY0HHosnU1uV+rXq\nlcuvfYH6OfsRsMRqbgT+ADyfjpnrVis2IcpF1nQKMUhhM+FDrXcbTLmTWai1m2Z5o6ptmWhkddTU\n9jp5l5ahEs6Sl5nq7OoehyqY7raa1m1rbfm0n6dUVE+H8QQrjsQuRK3r7C3ylGGLJ1PrAy8Bfqvp\nG2A3n8f9Zqlfq55ZR2neDIRszd2oafd/uvya/KIWNUFGOoUYhLCZmAG8zcAJZxI4GTW6KQnnCGSi\nkcmo5D0/4cwAh5Yj4bRsRV/C+dFoSjgt+V/3ZGDTcryQz+P+CNiNvlOLJgGz4snUQKP8oghd14/W\ndf0sXdf/mGuzjtLc/rJrr7//Vxf/OZdgtgD/AB467eQTdtR1/c+6rl+t67o773636Lp+uq7rhU7l\nEmJUkaRTiH6EzYQzbCZ+BTwHrDlA96dQazf/KWs3RyYTjXiAx1h5k0wWOM4ZCOYXSi+lGbbP55Tx\ndYZrToG2ncv1Yj6Pex5qx/w3uSbg6XgyVbINTHXmHtQO9hU2xLn8WvrG2+469I3IvDuBD22X9j3v\njB88MPveW98A5gFa3v0yqFFRWQcqRj1JOoUoImwmVkclkr+j/38r3ajRzZkyujlymWhkHPAgasQx\n32nOQPC2ModgTzpHy651uzdQ61ntdi7nC/o87reAPYBFVtMqqMRTL+fr1igH6qSiq/IvGIaR+d8X\nXy4ApgHX5NrXWL3NuyD29e3bbz39J6edcNzivOd8zzCMG1nx51aIUUmSTiEKCJuJmUAEGKhw89vA\nFjK6WRqZaMQF3EXhzTE/dwaCfyvn63d2dTuB7WxNoy7pbGzXe4AX8pp36ukwyvp+7vO4w6i1pLmk\nZzXgmXgytV7xZ4kC/gD0APvout5inxbXdf1EILjpjN133nTG7ve/89/3DwE63oy8yzvvfcA6AW39\nnbffZu4p3z/uF7bnXKTr+ilArOJfiRBDJBuJRN3TdX0a8CfgsFtmvZpCncryU4De3l5uuOKPTNtm\nBtO33yn/qVcD54U0rxR0LoFMNNII/Ac4ssDlS5yB4C8KtJdUZ1f35qj/bAB8CazR1toy6t4kezqM\nnwL/l9c8tbFdjxTqX0rxZGoGqtRPbm3h/4CdfB73Z+V+7Vqj67oDmGEYRv5/ItB1fUvg/XdfnDUO\nuBZV2xOA8FuRntC04NnAtbLJSIwlMtIpBLwDvHzGr/4QQI0g/TR34ZlH72ezLVea5U0AB4Q075mS\ncJaGNcJ5K4UTzuuA8ysUygpT66Mx4bTMKdBW8tJJhfg87hdR5ZRyZX7WBJ6NJ1P5aw3FAAzDyBZK\nOK1rbxiGkXL5tbjLrx2BOsVoEUBoWrARNf1+azpmTqhcxEKMjCSdou4ZhpHZcPNpG2+6Reh5bOsI\nuxIL+WbhQuZ//CGffPDfXPNLwNSQ5n2wGrHWokw00oyaUj+swOXbgNOdgWClkr/Rvp4z5y361lfm\n7FypF/d53HNQlRxyRfMDqMRzjUrFUG9cfu12YEv6RuJB/SfttXTM3Kg6UQkxNJJ0iroWNhPua26+\n54Hm5nGHPvv4QxNT3Yt5a+5LALR6J3PI8aeweWgb1t1wkyxq2n1n2SxUOtamofuAAwpcfgg43hkI\nlrz+ZD/sSedLFXzdIelnXaezUjH4PO5ZwIHAcqtpHdQaz9UrFUO9cfm1j4FtUfWCczYCwumYWWiW\nQIhRRdZ0iroVNhPrAfdjq3GYzWYx3o2w4WZT7V07gaNDmveZCodY0zLRiBt4AFWOJ9/9wBHOQHB5\ngWtl0dnVrQG5k6O6gUltrS09lXr9oerpMH4C/DmveYvGdv2tSsZhnct+L30le94HdvZ53F9VMo56\nk46ZJwB/ZcXSS9cC57r82mg5tlWIFchIp6hLYTOxB+rs9BWKajscjvyE8wkgKAlnaWWikQnAoxRO\nOO8EDq9kwmmxj3K+MpoTTkuh4vgVWddp5/O4HwKOQNWLBDXyNjueTK1S6Vjqicuv/QvYBvjY1nwa\n8EI6ZgaqE5UQ/ZOkU9QV6+z0H6PO057UT9ce1IaifUKaV0ZsSigTjbSidj/vXODyzcAxzkAwXdGg\nlLGynjPnbaArr23nKsSBz+O+FzgGyC2F2Ax1cpGvGvHUC5dfiwDTUUtUckLAm+mYuVd1ohKiOEk6\nRd0Im4nxqJI8l9L/z/5nwPYhzXtpSPNWcj1hzctEI17U0ZbbFbh8A/B9ZyBYrRHGMZV0NrbrGeD5\nvOYdK7mu087ncd8BHI86NQpgKvCcrPEsL5df6wIOAX5M32izD3gsHTN/l46ZVfl5EKIQSTpFXQib\niSmoX9DHDND1bmBaSPO+Vv6o6ksmGlkFeBo1EpPvOuAUZyCYKXCt7Dq7ur30LbXIAHOrEccwzMl7\n3IpK9qrC53HfDJxka9oUeDGeTK1TpZDqgsuvZV1+7XLUSHeH7dKvgCfTMXPVqgQmRB5JOkXNC5uJ\n7YHXUdNQxWSAs4DDQ5o3f8pSjFAmGlkNtQZxWoHLfwF+VOFd6vm2RR1PCPBWW2tL/jGTo1WhdZ07\nVjwKG5/H/S/gWPpG3dZBJZ6bFn+WKAWXX3sR2AKwr0HfDTXdvn11ohKijySdoqaFzcTJqF/Mq/XT\nLQ7sEdK8V8lRlqWXiUbaUSNyhZKOS4BzK1iHs5gxNbVu8w6QzGvr7z9XFeHzuG8BDqKvjufqwPPx\nZGrr6kVVH1x+bQGwB6rEW84awJx0zDy9OlEJoUjSKWpS2Ew0hc3EtcA/AFc/Xd8Bpsvu9PLIRCNr\nAs8BGxa4/Fvg/FGQcMIYTTqtdZ1v5jVXPemEb3e170XfWe1e4Ol4MvWd6kVVH1x+LePya79GnRyV\nsJobgavTMfOadMxsLP5sIcpHkk5Rc8JmYlXUZpUfDtD1HtSGITkzugwy0cjaqHW06xW4/EtnIHjh\naEg4O7u6m7GdRMUoLgpfxOt5jzfo6TBaqxJJHp/H/SyqjNNCq6kFeDSeTB1Uvajqh8uvPYZa0mL/\nGfkR8Eg6Zo6KnxFRXyTpFDUlbCa2QL3B9reuLYtaYH9YSPOOlbV7Y0omGtkMdWLOWgUu/9gZCF5c\n2Yj6NR1otj7/uK21pbOawQxDftIJal3fqODzuF8HdgC+sJqagLvjydTxVQuqjrj8WhT1fni3rXlP\n4OV0zFy7OlGJeiVJp6gZYTNxBGpqdM1+uiWB74Y07x9k/WZ5ZKKRXVB/D4XO4T7dGQheXuGQBjIm\np9ZtCiWdhSoEVI3P434f9X3OFTJvAG6MJ1NnVy+q+uHya0tQBfzt6zw3BuamY2ah8mVClIUknWLM\nC5uJhrCZ+CNwOzC+n64fAVuHNO/DlYms/mSikaNQhd8n5l3Kokoi/bXyUQ1orCednwDf5LWNinWd\ndj6Pez5qxPMdW/MV8WTqongy5Sj8LFEqLr/Wa63zPA7InfblB55Jx8yjqheZqCeSdIoxLWwmmoFb\ngJ8P0PUJYKuQ5n2//FHVn0w04shEI+cBt7Lyxq0e4HhnIHh95SPrX2dXdwNgLyUz5pLOxnY9y8qj\nnaMu6QTwedydwE7Ay7bmXwNXxZMp+X1UAS6/djPwHfrW2TYDt6Zj5oXpmCnJvygr+UcuxqywmWgF\nHgOOHKDr/wH7hjRv/miQKIFMNOIErgL+VODyYmBfZyD4n8pGNWjro3ZVA3wNfFjFWEYiP+lcu6fD\nmFyVSAbg87i/QZX0ecLWfDrw73gy1V+lCVEiLr/2ArA18IGt+QJU8jmuOlGJeiBJpxiTwmaiHbUz\netd+ui0Fjgpp3p+FNG9VTrqpdZloZDxqg0Kh+n+dwE7OQPDJykY1JPbTe95oa20Zq+t8C63r3LLi\nUQySz+PuBr4L3GVrPga4N55M9bdERpSIy699gjoU4Wlb85HAs+mY2V9dYyGGTZJOMeaEzcRGwCvA\n5v10+x+qHNLtlYmq/mSikcmoX1gHFrhsANs6A8H8GpKjTdD2+dtVi2LkRv1monw+j3s5cBRgX3ax\nH/BkPJlapTpR1ReXX/sGVUv177bmbVAbjOQEKVFyknSKMSVsJmag6ihq/XR7HQiFNO9oT3jGLKsG\n58uokZJ8LwHbOQPB+RUNanjsI52RqkUxciYQy2sbles67XwedwY4FbUEJmcH4NV4MqVXJ6r64vJr\naVRN43NRG/4AAqiSSjOrFpioSZJ0ijEjbCYOAmbTtwavkMeBXUKad0Floqo/mWhkS+BVYIMCl+8D\ndncGgvHKRjVsNTHSOZY2E+XzedxZn8f9M+A8W/O6qMRztyqFVVdcfi3r8mtXoJY8dFvNHuBROTpT\nlJIknWJMCJuJ01EnCDX30+1fqBqcUvC9TDLRyF6oYy1XLXD5KuAwZyC4pLJRDU9nV7cfaLceLkWV\n1BrL8pPOKT0dRltVIhkGn8f9Z+AQIPfzMwl4Ip5MnVy9qOqLy689jKrm8LnV1IA6OvPydMyUfEGM\nmPwQiVEtbCYcVg3Oq4H+ynlcBJwU0rzpykRWfzLRyInAw6ijDPP9BDjbGQiOpQ1b9lHOeW2tLT1V\ni6Q0Cq3rHBOjnTk+j/te1Ok5X1pNjcA/4snUpfFkylm9yOqHy69FUMfC2n+ezgFukDPbxUhJ0ilG\nrbCZaAL+Tf81OHuBU0Oa9wI5Yag8rBqcFwL/BPJ/8S8HjnQGgpeNhnPUh6hW1nPmjPmkE749NnMr\nVlzu8GPgvngyNaE6UdUXl1/7ElVP9QFb8/HAXVJSSYyEJJ1iVAqbCQ/wCHBsP92WAAeGNO8/KhNV\n/clEI27gZlQNv3xdwJ7OQPCOykZVMvakc8yu58xpbNc7gI685jGXdAL4PO7PURuK7KeH7Q+8EE+m\nplQnqvri8msp4FDgJlvzgcAj6ZjpqUpQYsyTpFOMOmEzsTpq3eDu/XRbCOwa0rwPVSaq+pOJRgKo\nE3qOLnD5c2B7ZyA4p6JBlZZ9er0WRjqhwGaing5jTJ4y4/O4F6OSnMtszVOB1+LJ1JhMpscal1/r\nAU4ErrA17wbMTsdMX3WiEmOZJJ1iVAmbifVRpXim9dNtPqoG56sVCaoOZaKRnVEJTKG/h3eAbZyB\n4H8rGlQJdXZ1jwM2sjW9U6zvGJOfdK4GrFGNQErB53FnfB73T4BTUMepAqwOPB9Ppg6qXmT1w+XX\nelHLG35la94KeD4dM9sLP0uIwiTpFKNG2ExsiBrhXKufbm8B24Y0r1GRoOqMtX7zDFRpqkIFumcB\nOzoDwS8qG1nJbUzf+tRP21pbFlUzmBIac0XiB8PncV8PzARyR9mOR51e9PN4MjUmR3LHEquk0h9Y\n8eSxTYAX0zFz3SqFJcYgSTrFqBA2E5uiEs7V++n2FLBTSPN2Viaq+pKJRsahyk5dxcobhgAuBfZ2\nBoJdFQ2sPGpqPadNTWwmKsTncT+NOozgE1vzH4F/xZOppupEVV9cfu2vqONKc1Uq1kYlnptVLyox\nlkjSKaoubCaCwLMUrv2YczOwX0jzJisTVX3JRCNTUGfZH1/g8hLgKGcg+FNnIDjWywrl1OJ6Thrb\n9RgQzWvub6nKmOLzuD9AHdP4gq35eGBWPJmaXJWg6ozLr92KWmu71GpqA55Lx8xtqheVGCsk6RRV\nFTYTW6ISzv7OWv4j8L2Q5l1emajqSyYamQG8QeFp2CjqSMtaO8O+Vkc6YeX1qRtWJYoy8XncX6M2\nGf7b1rwj8Ho8mdqyOlHVF6uI/EwgNwjgBZ5Ox8z+Nn8KIUmnqJ6wmdgaeJr+j7U8L6R5z5canKVn\nrd/8AcVHmZ8FpjsDwZpKyjq7uh3UyPGXRXyQ93itng5jfFUiKROfx70M+D5wvq15LeDleDJ1qqzz\nLD+XX3sO2AVVSQTAjSqnJBu8RFGSdIqqCJuJ7VGbUlr76XZOSPP+uUIh1ZVMNNIM/B24DnXqS76/\nAHs4A8GvKxpYZQTo+7n7BvhfFWMph/yk0wGsX41Aysk6s/2PwMH0jbg1AX8D/h1PpgqdnCVKyOXX\n3kDVU80dm9kE3J2OmSdULyoxmknSKSoubCZ2BJ4E+iswfHpI8/6lQiHVlUw00o4axSx0pvUy4Dhn\nIHhODa3fzLfC1Hpba0utjaLnJ51QY1Psdj6P+z7UZql5tuZjgVfjyZRenajqh8uvvQ/MAD6ymhpQ\nR2aeW72oxGglSaeoqLCZ2BV4gsLndwNkgVNCmvevlYuqfmSikW1QO5y3LXD5c2CGMxC8ubJRVVxN\nbiKyKVROrGaTTgCfx/0haoORfZ3npqh1nodVJ6r64fJrUdSIp32pymXpmPmTKoUkRilJOkXFhM3E\nHsCjqBp7hWSBE0Ka9/rKRVUfrPWbP6J4WarngS2dgWChkju1ppY3EdHYri8EYnnNNT/i5/O4U6h1\nniejRuwBJgB3xpOpK6WsUnm5/NoC1BrPF23Nf07HzDOrFJIYhSTpFBURNhP7oM5RHlekSy9wbEjz\n3lSxoOpEJhqZDNwPXINac5XvGuA7zkDwq4oGVj21PtIJK0+x1/RIZ461zvOfqJH8T22XzgSeiydT\na1Ynsvrg8mvfoHa120taXZmOmT+sUkhilJGkU5Rd2Ex8F5X0FBtpyABHhjTvrZWLqj5kopGdUKN5\n3y1weTlwgjMQPMMZCKYrG1l1dHZ1e1AFrUEdq/heFcMpp5WSzp4Oo27e730e91vAlsCDtuZtgDfj\nydQe1YmqPrj8WjewD/CKrfnadMw8sUohiVGkbt6ERHWEzcQhwD2Aq0iXNHBoSPPeVbmoal8mGmnM\nRCO/BZ4BphTo0oE6zvLGykZWdfYj+z5ra21ZVrTn2Ja/rtPNGD6DfTh8Hvc3qCLmP6PvBJ1VgCfi\nydQF8WRKfv+VicuvJYG9gLCt+fp0zDyuSiGJUUL+0YmyCZuJg4A7KFySB9RI28EhzXt/5aKqfZlo\nREPtTv8Nhf+NPwpMdQaCcysa2OhgTzo/Kdpr7KurHezFWNPt/wfsBuSOz3UAFwKPxZOp/g6lECPg\n8mtdwJ7AW1aTA7gxHTOPrF5Uotok6RRlETYTuwO3U/gMb1AL/b8b0rwPVy6q2peJRg5CrVOcUeDy\ncuBsYD9nIJi/0aReSNJZh3we93Oo40Dn2Jr3BN6KJ1NyfGOZuPxaAnV6VK6cVQNwczpmHlK9qEQ1\nSdIpSi5sJrYFHqD4Gs4lwL4hzftE5aKqXbquT9too41mf/nmszcA9wKTctd6e3v59eV/49FnX+wA\ntnEGglc6A8Gsruub67p+ka7rV+i67qta8JVnTzo/rloU5Tcf9Z8Mu7pNOgF8HncnKgG6xNY8BXgh\nnkydH0+mis3IiBFw+bWFwHfoWz/tBG5Px8xC68xFjZOkU5RU2ExsDjyGWkNWSDewV0jzzq5cVLXt\n1XtvzBx30D6bj2tuXukUkLsenc3aa7bP+dPf/v1jZyD4lu3S+6gpxi8pXlGgFq1n+7xmRzob2/UM\n8GFec10nnQA+j7vH53H/AtgfdRoVqOU/fwCejydT6xZ9shg2l1/7CrXEIfcz2Yg6uWjv6kUlqkGS\nTlEyYTOxHvAUtpG2PClgZkjzPle5qGpX7uz0iZ6Wue5xzf7867F4YvHjz71836XX3zL360SXvUwQ\nhmGkgZOANwzD6KhUzKNAvUyvQ52WTRoMn8f9MLAFYF/XvC0QiSdTJ8vZ7aXn8mudwK70/btzAfel\nY6ZUE6gjknSKkgibiTWA2cBqRbqkgQNCmvfFItfFEGSiER+qKsB1b/73g3HzjE+4+7GnSXaneG7u\nmwCv+X3e4C13338w6gSoubqu75qbStd1/QDUL9mNdF3XqvV1VFJnV3cTYK/T+Fm1YqmQ/KSzvafD\n6O/o2bri87g/Q619/g19u9tbgH8AD8WTqbZqxVarXH7tC1TiOd9qagYeTMfMXasWlKgoRzZba8cO\ni0oLm4lVUCfabFSkSy9wWEjz3lu5qGpXJhrZAbiVFRMoALLZLA/Ofu7WA3bf+fv5tTd1Xd8SeN8w\njFSFQh1VOru6N6CvlNAXba0thUpJ1YyeDuNo4Ja85lBju14Pp04Niq7rRwFT1lp77Q1uv/u+7bGN\nBl95+WVLovM/u+OVl1960jCMO6sXZe1Jx8y1Uaej5d7DUsBMl197ofizRC2QhdNiRMJmYiLwOMUT\nToCTJeEcuUw00gL8HjgLVX4kX6fD4Tju4JPOmlXo+YZhvFHO+MaAetlElFNsB7sknX3eNAzjNl3X\nL0dNt1+COr2IH55+xviFX3/9/bvuuH3TeDL1hM/j7qpqpDXE5dc+s0Y3nwPaUXsAHkvHzD1cfu2V\n/p8txjKZXhfDFjYT44GHgOn9dPtxSPP+q0Ih1axMNLIz8A6q5FGhhPMJIOgMBAsmnAKor/WcsHKB\neJB1nSswDOMDXdePBHp8HvcSn8d9FrAH8MVnn37KM0/P5oyzzwmh1nruVN1oa4vLr32MmmrP1U+d\nADyRjpmbVS8qUW6SdIphCZsJF3AX0N8b8e9DmvfyCoVUkzLRiCcTjVyLKva+ToEuaeDHwD51dHb6\ncNVV0tnYri8GPs9rlqTTRtf144CJwGRd1z26ru/j87hnPfP07Om//+0FC5qamgi/NhcgADwbT6b+\nHE+mmqsadA1x+TUDtas9Vzd4IirxrIt15vVI1nSKIQubiQbgZuCofrpdA5wZ0rzyAzZMmWhkD+B6\noNgb8AfAMc5AsN6nzQels6v7YWBf6+ERba0tNb9Or6fDmIWqkZjz38Z2fdNqxTOa6bruAGYYhvHt\nusJ4MnUEcB0rVuSYBxzr87gjFQ6xZqVjZhB4AchtdHsPmGEVlxc1REY6xZCEzYQDuJr+E85bgLMk\n4RyeTDQyKRON3AA8SeGEMwNcDEyThHNI6mqk05I/xb5eT4ch5YAKMAwja084AXwe9x3AZoB92cpm\nQDieTP0snkwVO3FNDIHLr0WAA1EzNwAbo3a111MN4bogSacYqt8Bp/Vz/SHghJDm7a1QPDUlE43s\nC/wXWKnQu+UdYGtnIPhLZyC4tHKRjW2dXd0NrLg8oV6Szvl5j5sBOW98CHwe9+fATNQGo9y/ORdq\n09GceDIlSxZKwOXXngaOtzXtgDoyU/KUGiJ/mWLQwmbiHOCX/XR5Fjg8pHnT/fQRBWSikcmZaOQW\n4GHUbs58PagThEIyujks7aiECyDe1tpSL9N2+Ws6AdaoeBRjnM/j7vV53Fejzm+3//ubAbwTT6Z+\nG0+mZFRuhFx+7TbgPFvTIcAV6Zgpo/M1QpJOMShhM3EQcFl/XYDvhjSvjL4NUSYaOQS1hunoIl3e\nALZ0BoK/dQaC+edpi1QqJdkAACAASURBVMGpx6l1gC8KtNV0fdJy8nncH6AOVbiIvoLyLlSB+Xfi\nyZQUOR+5S4GrbI/PRG2WFDVAkk4xoLCZCKHWaRb73+Z7qPPUk5WLauzLRCOrZaKRu4G7gVULdFkO\n/ALYxhkIvlPR4GpPvSadhUY6JekcAZ/HnfZ53BegSsW9Zru0PvB0PJn6TzyZWulYWjE4Lr+WBc5F\nnbiW8+d0zOxvH4EYIyTpFP0KmwkNtU5zfJEu84E9Qpp3YcWCGuOsM9OPQa3dPKRIt1eBqc5A8BJn\nINhTuehqVr0mnR0F2mR6vQR8HvfbwHbAj4BFtkvHAh/Ek6kT48mU/I4dBpdfy6C+j/aNXTelY+Zu\nVQpJlIiUTBJFWacNvYjarVnIAmD7kOatp1/iI5KJRoKo3f87FOmyFLVu9kpnIJgp0kcMUWdX953A\nYdbD77e1ttxUqN/yeEcj0IqqFzgBcKL+c96Q93lDGdpz10AdC5j76O7n8fImX3u/b+I9HcZXgH3k\n7abGdv37/T1HDE08mWoH/gIcmnfpBeAHPo/7vcpHNfalY6YX9TtoY6spCexg7XYXY5AknaKgsJlo\nRG1qmVmky1Jgp5Dmfa3IdWGTiUa8qJ3/P6T4DMPzwEnOQPCjigVWI5bHOxxACypZbLX92QpMTDR4\nLsw6GqYAtPSmHmvOppfYr9s+LzaiP1r10n9SmiKT3pVs1qe6ZwHHxzQ2XVWg/2JUke4FTb72xZX9\nMmpDPJnaB/grqph8Thr4E3Cxz+NeUpXAxrB0zFwTeIW+EfovgW1dfi1avajEcEnSKVZi1eK8hv5L\nIx0a0rz39HNdAJloxIkqf3QxxUvVdAM/A65zBoJ1XWrKSh5bUb9gch9tFE4Q7QnmRPpGCVeSaPCQ\ndahcf1JmEQ3I+94AUqiZjK+sP/v7PDHQaGs9iSdTLaiNRT9mxZ/Jj4Ef+jzu2VUJbAyzjsZ8EfXv\nHNTBGNu7/Fq8elGJ4ZCkU6wkbCbOQk0VFfPzkOb9U6XiGasy0cjWqOS9v7PpHwdOcwaC8ysSVBVZ\nU9dtqERyCismlvYPdylfNwsknK3Wgyze3kVFd8SJYemhLwkdKEmNNfna62KNcjyZ2hz4B7B13qVb\ngXN9HrccWzsE6Zi5C/AE0GQ1vQTs7vJrMno8hkjSKVYQNhP7AQ9SfKf6DcDJctpQcZloZDXgj0B/\n6+Y+Bc4GHnEGgmP+e7k83jGR4klk7mM1qrB5MYODLqcaIHFke/H2SpGFKlvIisnol8BnqJHAT4D5\nTb72ZdULr3SsjUSnoArJt9ouJVD1KP/l87jrenZjKNIx8wjgdlvTfcBh1sYjMQZI0im+FTYT0/h/\n9s47vI0q68PvqIwdO5ZjBUdGAYc+ggQCCaGbTiD0jkOvywLLLmUpH7t0WMpSFtglsAssHdOWTrJ0\nYrqTkISmAQLESwYrDpNYjp141L4/rhzLsmzLiSXZ0n2fZx5Lc++Mjm1J85tz7jlHLHwv7WXKO8AB\nsvh7aiKLFzgQmazX0RUGSmYVItR+23DpKGSZRjGwWXzrzUM5MmcG9kMYO0G7MM8ei0TLoyuXAi2I\njOOWpMeJ+1Yi1uNFgegRJ5x+tmdM5eKykaUtf73+qnc69wPRw6afeo6qOoObbDTum1uu/fOHnfvv\n+Pv9m779fv0+Vig04rILzpux7567L4+PRX7zh0t2+HzhF+c1vDtresK5IghhPgLh8e3cSnt53P/z\nWKwSEGs6lWHh440B/0MI0M6tU5AuUt3eYB/HDknM1vb1gTuB45KGGhBezw+yb9XwJNTceBHda0bf\n7qys/mOu7JEMDCk6JQA0NC4fC3xK7+VU/MAuU6orCqWTy4CILF6wJyIrfUIf054DLraPm9iYFaMG\nQHwtpQfQAF/Sz43IfXm1KD0FYm9isdvjoK10UlhxPBg/z5tV5aVT18YATdP+gajLGNV1/bGE/XZE\ni83/AVfquv6nhDEnQkj+EXhc13Uj6bjbdV2/YG3sSZewoe9LvHf4mm97m/3YmFrSQHeRWo6oF+uJ\nb4mPPXQK19yzjO6CNFGYBoby+lKztX0acC/iM5XI88Bl7rISWQkkDULNjXcAFybsOtlZWf1Yb/Ml\nQwdHrg2Q5J6GxuUjEZnqvQnOZcBBUnD2JLJ4wQaIDhrJHoxEvgZ+bx838e3sWNU7lmkUITyWqcRl\neR+HZoooIsy6JGH7Oem5AbSurZgIt7RNSngaWFtDdV0/D0DTtBs1TXPquh6K749omtaM8HC/l3RM\nSNO03wBzEwVnwnFra85AWFMgfo2fMxoZ6XR7fxrISSzTcCJKLyUL0lSPx9BHYtc6sl58S14rCdBm\nmcYPpBali3O9ntRdVjLTbG0fD1yJKIDeuT7xKOBQs7X9HuAGd1mJ/K7tmz8iau8eGn/+r1Bzo99Z\nWd2QQ5skaSA9nQVOQ+NyO/ACcEgvUzqAvadUV3yUPauGPpHFC4oRd9p/pvfEl1bgauDv9nETs7Yk\nIe61HENqYbkx2fNattFdPKbamjItBJpa2i5HrLEFuKOqvHStWuppmnYxIuS+IXAjsLeu669pmlaE\nqD5gIt4Lc4D5uq6bmqYdDhyGaGX6MrCjruvPxs93QPy4J3Vd/9da/4L9EDb0MroXLwe40uHVbsjU\na1qmYUN4RvsTpx7Ai2glmWnCwGK6ROh3wEJgger2LsvC63fDbG3fGPG+TL5hNRE3MDPcZSWy7W0v\nhJobXYhSSp01PA1ge2dl9S+5s0rSH1J0FjgNjcuTwxTJHD+luuKpPsYLivi6zVOAa+i7neAjwOX2\ncRObMmVL3Gu5KT2FpY/Mei1jiASQ3jyTnVtwKIQ6m1ra7kQkbQFcVlVeeuu6nlPTNAXYTdf1+hRj\nk4FvdF1vTzG2u67rs9f19QdK2NBb6L7O+H6HV/tttu1IhWUadoSQ3zRp2yz+MxvrhZcA84EFCT+/\nV93ejCf5mK3tOwF3IHq6J/I9cAnwkrusJOefo6FIqLlxM8S62FHxXR8Dezkrq/MiES0fkaKzgGlo\nXP5bYEYfU66aUl1xfbbsGcpEFi9QgCMRHq6+YqLzgN/Zx038eLBeO+659CJKL20PbIsQlpuQOa9l\nBJFhr8c3f/znYuAX1e0dNslkTS1tTwLT409PrSovfSSX9uSCsKF/RZdHCOA1h1c7OFf2pEv8vV9J\nT0HauXky+PJtwBd0F6NfqG5v22C/kNnariC6Gd2MiEYkMhu42F1WMmewXzcfCDU3TkWUnuv8LnwQ\nOCvew10yxJCis0BpaFy+C/A+va/rfQw4RZZGgsjiBfsiwmB91ds0gSuAB9a1faVlGlXx15pMl9Cs\nWpdz9sEKugRl4s9FqtubF6G9ppa2t4G940+nVZWXzsqlPbkgbOhvAPsl7Jrv8Grb5cqewcIyjTLE\nzVcqQTqOwb8piyHC8sleUWMwvPpma3sRcD5i2U5ytOJx4Ap3Wcn/1vV18o0UGe3nOyur/54reyS9\nI0VnAdLQuNyD8Mh5e5kyG5g6pbqioEMUkcULdkCIzb37moYoAH2lfdzEXwf6GpZpVNJdXG5P7wld\na0uULq9lssBsHgoh8EzS1NL2BV1VBSZXlZfOy6U9uSBs6A/RvW7sModXq+xtfj4QT3waR+qQ/SYM\nbsvTZXQXofMB/9pGBMzW9vUQ68HPoXtC1mqEuLrFXVYiC87GCTU3KoglTSfFd0WAfZ2V1e/lzChJ\nSqToLDDiPdXfBPbsZcr3wE5TqisGLKDyhcjiBVsCNyDC6X3xNHCVfdzEb9M5r2UaFfQUmOP6PGhg\ntNC717JgbyCaWtqWIkK0ABtUlZcuyaU9uSBs6NchMqYTGeHwasOiVuxgE0902gixVGViws/B/Dxa\nwFckeUVVt3dFuicwW9s14Fa6srQ7CSBabT7kLispiA5P/RFqbhyBiN5Nie/6FZFY9FPOjJL0QIrO\nAqOhcfmtiMXpqWgBdphSXZGWiMo3IosXVCMShE6h77Dcf4Er7OMm9uoxs0yjHJhEd4G5ySCZ+jMi\n6zZZYC7Nd6/lQGlqaXMgLv6d1YLUqvLSYbMedbAIG/rZwH1Juzd1eLUfcmHPUCV+Y7gNQoR2CtHx\ndJU2Ggx+Aj5C9BL/APiqv4Qls7V9L4SHM3lJxHfA9cBTUnxCqLlxLKJ6ROdypIXALs7K6kFfhytZ\nO6ToLCAaGpcfhShQ3huHTqmueCVb9gwVIosXVCLWY55L3xeXT4D/s4+b+F7iTss0RiIuBokCc4tB\nMu8XxJdo5zZXdXvXutZkodHU0laF+BsCmFXlpaNzaU+uCBv6wYhavIns5vBqH+bCnuFEPEzvo7sQ\n3RYYrPfSCrqL0AbV7e3hgY631DwJkcyYvATne4T4fLLQxWeouXEXRL3czjJczyFaZUqxMwSQorNA\naGhc7kOUluit/MgNU6orksNveU1k8QIXokDzxfRdluUrhCh9JVJWCWJd2J7AbgiBuSW996ofCM2I\n/1GiwDT6PkTSF00tbdsgQpsA/qry0i1zaU+uCBv6LkCywDzY4dVey4U9w52EihLJ4fnNWffvAgvx\n+e8UoR+pbu+a5U5ma3sJ4nvrj/RMNpLiEwg1N54BPJCw68/Oyuobc2WPpAspOguAhsblZYgWl71d\ncN8ADpxSXbFOWdfDhcjiBaMQXs0LEZ1NeuOnGFwVHTn6ExTbHgihuSeDk+hj0t2DOQf4WYbHB5em\nlrb9EO9vgPerykv3zKE5OSNs6OOBL5N2n+Dwak/mwp58JR712JruQnQbem8gkS5f0yVCPwB+Wukc\nVQ78HiFAU4nPG4AnClV8hpob7wF+l7DrMGdl9cu5skcikKIzz2loXK4gEl6O6WVKIzB5SnVF1jty\nZJvI4gVVCKF5DlCWak4MQLH/GlNHzIo5ixUUZXf6LgKfDi2IjjSJAvMnKTAzT1NL24mI8l8Az1SV\nl/bVrjRvCRv6Boje8Imc6/BqfdXplQwC8eL3myJE6HbATvGteB1OaxAXoBHFPn+VfeTeKMoFdBVJ\n72QRQnw+XmjiM9Tc6ETccO4Z39UK7OSsrP46Z0ZJpOjMdxoalyfXL0vEAnadUl2R10WHI4sXbIJI\nnjoNKEoejyk2YnYnONRQzKG2o9jWpZvPSnoKzB+y0dlE0pOmlraLgdviT++pKi/9fS7tyRVhQx+J\nuOgmcoXDq92Uar4ks1imoSIE6G4JW19Rl/5ojUFDWHHGwjZ1x4jiGInSLcpfkOIz1Ny4HuI7uLMq\nwffADs7KatnbPkf0Vhhckgc0NC7fHVFuozfOy2fBGVm8YBvgckRv4zXZ6AkiU/y0rSmD52Rg7SM7\n114lrsP8VgrMIcWYhMdLc2ZF7mlD1C5MrPmYyVapkj6IN174NL7dHl8jugXdRehmAzhlmQJ7O2Mh\nnJEQMYhGFXssojjsEcVOVHFsGlNs/wauNFvbO8Vn3ldxcFZWLws1Nx6GSNQqQfxNHwo1Nx4pE4ty\ng/R05ikNjcu9iALwvbWJe3BKdcWZWTQpa0QWL9gV+D/gIEgUmU5idjVRZA4UC5HB/h7wLvCp6vau\nGgSTJRmiqaXtYUQJLIDfVJWX/iuH5uSUsKGbQEXCriHTf13Sk3hnsl3pEqHb0f2mYUBEsRG2OYgo\nTiKK40cU5XoKRHyGmhuPAZ5J2PV7Z2X1Pbmyp5CRojMPaWhcriJE0S69TJkL7DaluiJvCkPHe6NP\nA/4vpth2i9mdYHcSc6yTyAzRJTLfAz6WInN40dTSNhM4IP70sKry0oJNJAgb+g907+td5/Bq03ub\nLxlaxJOUdqRLhO4MlK7NuWJARHEQURzLo4r9nxHFfpPbNbJl8KwdeoSaG+9CJF6BcCDs4qysnptD\nkwoSGV7PT/5K74LTBI7OF8EZWbzAEbM7fxNzFl+GzVE9CCLzM4Rgfw8hMtsHyVRJbpDh9S6SRYUM\nrw8jVLd3JfB2fMMyDQciOz4xJF/V6wkSUABHLIwjFq4ALouiXNq+vP0Lhdjf7bFIner25mOLzUsR\nf6NJiHrMT4eaGyc5K6uDuTWrsJCezjyjoXH58cATvQzHgGlTqiv+m0WTBh3LNOxEI7sRjVyGYtsH\nu2Ntu4WEESLzPYTQ/Fh1e2XnijyiqaXtO7rWxvmqykv1XNqTS8KG/h6wR8Kujx1erbebU8kwI74u\ndBO6i1DfQM8Tgxgon0PsaQVmAl/mS6WNUHPjZohlZ53VS54Gpsv1ndlDis48oqFx+ZaIZJbeasJd\nNaW64vosmjRoWKZRBuxHLFoLykEoytrUvQsjkn7eQ4jMj6TIzG+aWtoMYP340+qq8tLkskEFQ9jQ\nXwQOS9j1tcOrjc+VPZLMY5lGJSLqtTswFZgw0HPEwFBgFkKAvjWQ3vFDkVBzYy3wVMKu3zgrqwt2\nrXe2kaIzT2hoXF6EyISc2MuU1xBtLodNZrVlGuOAQ4jFDgH2QlGc/R3TjVgsgqLMoStc/mE8RCUp\nEJpa2lbQFUYeXVVeaubSnlwSNvRHgJMTdi1xeLV1rUErGUZYprEhsD9i/fu+gGuAp4gAH9MlQucP\nx2odoebGfwJnxZ+uRpRR+iKHJhUMUnTmCQ2Ny29DtHNMxQ/A9lOqK4Z0bbJ4EeUdgEPi28DuymOx\nGMS+QbG9ihCaH+bp2iRJmjS1tFl09WAeUVVemhdrmdeGsKHfDZyfsKvN4dX6av8qyWPiPeV3QgjQ\nAxDZ8QNlKfBfhAB9U3V7h0WTkVBzYwnCSdN5jfkGmOKsrJaRrwwjRWce0NC4PLHVXzKrgZ2mVFcs\n6GU8p8TD5lMRIvNAoHJAJ4hGIBLWUbgbR9HjqtsrF4VLAGhqaXMislRBrGe2V5WXFuwXXtjQrwOu\nTNrtdHi1gikWLukdyzTWR3wXHxCD/ZXu5bXSIYZYIz8rvjWobu+Qba0cam7cCrHcqnOp1r+dldWn\n59CkgkCKzmFOQ+Py9YCFdK1bS+bsKdUV/8yiSf1imcZGdHkz96TLE9U/sRhEwyhhq02JhJ8iYt3g\nGDdxcUYMlQxrmlrayoHO9Wcrq8pLU7Y+LRTChp7YnamT9Rxe7ddc2CMZusSjTlNicHAMZbpCbBOl\n36N6YCKcITOBV1S3d8hF2kLNjacC/07YdbKzsvqxXqZLBgEpOocx8b7qLwKH9jLlBeCoKdUVOf0n\nx7/AdkSIzIMZeNgcwhZKuAMlEpqjxKJ3Ac/ax03sGHxrJflCU0vb+oge1QBLq8pLe2uUUBCEDf1M\nIDlhYlOHV/shF/ZIhg8dplEZUZwXQOwUWywy1saALykh4C3gWeAl1e0dEmurQ82NCvAIcFJ8Vxsw\n2VlZXbBVLjKNFJ3DmIbG5b8FZvQybADbTKmuyIkXwzINFyJUczCiM9DA+gpHIyhxoUkktEoRZaBm\n2MdNnDf41krykaaWts2A7+JPf6oqL924r/n5TtjQj0Zc9BOZ5PBqn+fCHsnwxAy2bWmPha+zxSKH\nOWIhpy0WYYBe0DCi1uizwIuq25tTT3uouXEkomHKFvFdC4GdnJXVshFIBpDF4Ycp8fJId/Qx5ZRs\nC85BCpsLoRmNoMD3wL3Aw/ZxE4dcaEYy5EksqyUTBHoWhwcYlXUrJMMat6v0G+AYs7V9dIji04lF\nz7LHwps7omHssRBpeEEdiAz6/YH7LNN4hy4BmvVEJGdl9cpQc+OxiMSiImAb4Hbg3GzbUghIT+cw\nJI3ySLdNqa64JBu2WKaxAXAcMB2YPKCDu4XNLRTxXowCrwL/AN6yj5s47MpxSIYGTS1tOwMfxZ82\nVJWX7pBLe3JN2NB3QHxvJHKEw6u9mAt7JPmB2dquIHrEn0EsdqyNaIk9GsIeC2OPhQfiBY0gqo48\nC7ygur3NGTG4F0LNjecgnBydHOOsrH4umzYUAtLTOTz5C70Lzs+BP2XyxS3TGA0cjRCau8MAvle6\nh80TD1wGPADcZ5eJQZLBIdHTKduZdiVVJSJbYUrWCXdZSQz4APjAbG3/QxT7cVG7/cwQ7EAsKtpt\nRq10BKgdUTt0X+BeyzTeo0uAZqOF7X3A3ohrG8CDoebGec7KarnmeRCRns5hRkPj8qmIumipWAVM\nmlJd4R/s17VMYySim8l0RFgkvRuW1GHzRD5BeDVlYpBkUGlqaTsEeDn+9PWq8tKDcmlPrgkbugdo\nStp9gcOr3ZULeyT5jdnaPgE4A9GQwE0shiMWSleAJhJFNPfoFKCBDJgLQKi5cRSiTWbn+u/PgF2d\nldWyrNggIT2dw4iGxuWViEy73rhgMAWnZRpFiMLB0xHrNEekdWAsCuFQctg8kaWINmSP2MdNlEkM\nkkxRmvBYejohVaOEgi4jJckc7rKSL4ELzdb2y4HDUJQzwoq6X9imKgMUoDaEB3Jv4B+WacxGCND/\nqG5v8k3UOuGsrF4Ram48DvgQkZOwA3AFcN1gvk4hI0XnEKD1l8VFX9XdM+VXfd7WofaVY2PRSJFi\ns3c4S0YuGa1N+mJ87fkN/pDLAh4Eqno5zQv0LIcyYOLljfZCCM2jSDf8FosKb2aoAyJWqi+RDkR5\np8eAN+zjJobW1VaJpB9keL07qbw19qxbISko3GUlHcAzwDNma/s44DQU5bSwolaHbSpdAjSEPRZK\nR4DuGd/+niRAfxkMe52V1Q2h5sYrgZvju64KNTfOdFZWN3TOiYWDRbR+PoVQYGuiobEQKwKlA5tz\nCU7PF5Rt16A4XDJylwIZXs8dyuzrzzyg+es5p642A9NWr1jWq8ehuKIy6Dn2su/K9z25t0SddSqP\nZJmGgqijOR2RFJRePcN4IpAtvFokBKWe9T5CaD5nHzcxVfasRJIRmlrazgfujj+dUVVeWtDZqGFD\ntyGSNRK53uHVrsqFPZLCxWxttwP7AGcCh9NZ6WRgAjSRznWlzwLPq26v0c/8Pgk1N9oRIf3d4rt0\nFNskh+3rPehoOpVo+zSiq3qPEthGBLGVzKKo6mHFPXVW3D4JUnTmhPevOXW3pV98entwyaIdSOPv\nXzR2cza/5U1sRSWphmPAflOqK94eqB2WaUwAjgdq6VrD0jexmEgACq0Wns3Un6VvgUeBJ+zjJv40\nULskksGgqaXtMrq8FbdXlZf+MZf2DAXChp78gf2Lw6tlNPFQIukLs7V9PURU7ViEB9MGrKsA/RDh\nXX16bZOQQs2NmwALgJEASvSXgN36aOANJhyjPkNd/2Jl9AEfrI0d+YYMr2eRX+bNLpo748rrlumf\n/yHSsaoonWMUh0r1Bff3JjhBlEdKW3BaprEJQmROZyCdgTpLG4U7Uq3RBPgVqEOIzQb7uInybkaS\na2Sdzp5E6B5Sl+F1SU5xl5UsA+4H7jdb26uAI4HjUJSasKIqiSF4ezSEo38BqiA8lLsBd1qmMROR\nC/Gq6vauTtcuZ2X1D6Hmxj8istqJ2db3RG1jsEUHqGHDK3YgsvKtWKDlLsp3vUop3qCgw+5SdGaJ\nb199ZOQXj9/x1Iofvz54IMeNOeZiRmyUWhuGmv/X4moP3ET1fn2ewzKNKsRd5PGIMHp6REIooU6h\nmbJcpgW8ghCas+zjJlppn1siyTwykagnUnRKhizuspImRK3Me83Wdi/CA3ocirJrWFEJ21Q6YjHs\ncQ9oGgLUjuiKdzCwwjKNpxHXq49Vt7dPx0h05dcj7eGGg6OKRszuFfuck1E63kJhgCkJsXARlnEp\nK97ZKlY2ebpSOn7lwE6QP0jRmQV+mTe7aG0E54hNJjLmsPNTjkVXt/HDDceWlxQXPzq69O6j15+0\ne7e7J8s0KhB3jNMRiUG2tF40Gu4SmtHk5V9iBlAPPIkocyQ7BUmGKjKRqCfJH2opOiVDEndZiQHc\nA9xjtrZvgKifeRyKslNEUYkMXICOAs6Ob4ss03gUeEx1e39Mnhhb/XNRtO2X95RY6WRbeB4RmxuU\nYlBKiDq3xRaaR9QxAVvkO5TYAL5aQr8eTOvcp2L28qML1eOZnhCRrBNzZ1x53UAFp+JQ2fC8u1Hs\nqe8LjIevJLzsZ9SiooPn3PvnawAs01At0zjCMo0XEfX4HkAs1u77/xyNoFjt2NqWi81qTxacHYgu\nQWcAVfZxE/e0j5v4Tyk4JUMcGV7viRSdkmGHu6zkZ3dZyd/cZSU7AxsBlwANKIoQn45S2hzlrLKX\nEFKc6WTtbApcC/xgmcZsyzTOtEyjq1JLy4fXxZRRkyNFOxKzj8IW6qrsF7NX8/789Zg6/Vq2nvo3\nrru7HgDfvvdx0kUv9f/KoV8PpuWDa9L+5fMM6elMA03TTgX+neb0Cl3X13T+eO/qU2o+Xvj1hc+t\n2ASAE0YuYayj/xsczzF/pLh6yzXPP3hrJu7KMWw1cTLBObNo++wV9r9qBuHVq5hTd99FK76fv1GJ\ne8w+QGVaVkaja9ZoJnUG6iQIvIYoxTTLPm5iqhp/EslQRobXeyJFp2RY4y4rWQzcBtxmtrZvjFg6\ndiyKMinRAzqAOqA18e0eyzReVGLmXHt45R9ixR5QbESKdsW++j2U8E/EHBsRjUb501/uZvXqDi4/\n7xC22mQtfolQ84WxX2e9VojJRVJ0DownELUm+yLRo6I0f/nZbZ+vLHIqxIihsNByMdbRd0vZEZtu\nS+Xh3cPqz/77fnbf/yC22HQTlj9/Owfe+DBlG25OzK5y0K4Hq4jkoL7pv5ZmAHgJITTfkWs0JcOc\nREElO4oIpOiU5A3uspIfgVuAW8zW9s2AQ4EDUZTdw4rqDNtUlFgUR9TCGbWwkTI3oZNioDamuGvD\nxVNBiQcIFSeR4j0g3I4CmMtX8Ku5nKl71jD9yH2xh78auOGxcBHWL7cDO1Fg5ZSk6BwYX+q6/ly6\nk2dff+YBS/730w4/hDdknGMVLVEHfquUvUcso0hJ/T5TnEUirG7ruha0LDf5dalovBD57CUOuvkx\n7CUuYkoaRSTitTTXZJ73nLEIITJfAD61j5uYciGnRDIMSbzCyKVEwAsz33KsbGtn2fLlXHjWKZAg\nOjVNswH/BF7VwwoqdwAAIABJREFUdf3FhP2nAhqwBHhA1/W0M4AlkmzhLiv5HrgDuMNsbS9DLC07\nMKbYDgzZi8eGbEXYiMQFaKi3cn8CJeleTFHB4SQWjdBhCV/MiBFijedaE16xQ8x8Y/94Hc+CQYrO\nDNL89ZxTF3aMJIaC5mwjGHXwcUcFX1sj2a4odbTac+wlFG/oW/P8+Uf/xQuPPQjAC489yAvA9fZy\nDj/oAABmvfUuTz3/Ivq33xMKh/BWVbHHrjvzm5OOo7zYsaaWZigc5smXZvHSW7NpNJoioVA4YrMp\nP3dYoSeAv+i6XpCLmiV5jRSdSRy4z+5tqtPpuuXeBzt3JV5dzwbe7OXQlYBKT0+pRDLkcJeVtAIv\napp2MeC94sqr9z3ksMOnRHEcaNkdu1i2mH1Z0xIOPfRQtpu4NY/MuKv/kyoKf/rLrbz8uviIvDTz\nTV6a+SaHT92Cmy/du8f0y299hxff+Ja3Hj+eDapc3ca2mfYv1nOP4J2n/3CqpmnzgK8QS9rGJ97U\naZp2KcKT+ydd1/+ytn+PoYT8Is4Qrb8sLmo3l05b2OHCQRSfupLxqhCaCy1XymPUqo2pPPS8bvt2\n2mNfjjz5TAD223sPbr/xGnaYtC0Az7zwMpdceR3RSISLz/8t11x2ETtN2obHnn6Osy+4FKxVnXdz\nqy+56e7GW+5/lCVNS19pX7X6rFA4fGaHFXoXuBLRB10iyTek6EwiFotF7n3kKU48ck1eox1A0zQP\nsD6wHUll1XRdf1jX9RsRF8bds2iuRLKuPAUof7n+2i3dZSU3u8tKdgcqUZTjnnjq6YZYLMZ+0w5h\ntW0EkWTvZgqOP/oo/nzx2QBM2W4id1x3KSccln656x5E26f5v2poAX4HbAL8uXNI07Rq4CqgASE8\n8wLp6RwYxZqmjepjfHXnXcqXT/5tytfNbWWtsTK2crZSpMQosofZwL6KnyMjaAqrVDm6L5m0mn5k\n8V9PY+xZt+J0V+FUomy68ThaVmwHwGYbb8TUvfdYM//nJQaTJm7NvTdfTZlqh0iIw2smE1yxnNff\n+4jX3v3w6UP2qXkIqH+j/pMm4Ks58z4/NOElH9M07XtgB03TSnVdlxm+knxCis4k/jrj367K0RW8\n/8kcjjhgX15/+/3q2t9o6LoeAK7SNG1PYJSmaXsD83VdNzVNOwgYD4wGbs2h+RLJQHkWuAtR7/Pv\nAO6ykuXAM3VPPv4HoGOD6uppYXvR7uGo/Vglxlal0d5LaI7fUmPUqHK4/X7WH1PM1L32wN7Rylov\ny4yuctE6b3td15/WNO1o4BJN0x7Xdd0ft9sBnKLret5EGKToHBhXx7feuBa4BsD8bsHWCzpEa9YJ\natebeIK6kp9XjWCh5aLKsazHCYJzZtH2zcdoVzyOZ8IkqtQwy4tT5PNEwlx85gmipiZROoItLJrz\nAWFb0bNzvvimCTj/slvuefXw03/3RvyIMDBW07SNdF3/qfM0uq7f3PPkEkleIEVnElde8NtlgAsg\nFovhKivrdjHTdf09AE3TJgOr4/teQ1SykEiGFbquN2ua9hawn6ZpY3RdXwqgadoGwM7Ai1P33vNd\n4N1o4NnmRbE9/7HCNoIiJUKREqVIieAkRqr0iZjdTYgm7CisUy5QaOk2iLad5wJ7ADM0TbsN0ZP+\nMl3Xv1n7kw89pOgcGP9CFEXvjZ/WPGhu8f0QLsGlhBjnWLVmgk9dydurRvO1NZK9RvyKM55QFIop\nhGLxd3ZrKwuuO4Et6+YARdiT3/CxKETDtLW3M+PfT/LWB5/wy9JmItEYwDEJMxP/v9ci7pz8mqbN\nBN4C/qvr+vcD+gtIJMMHKTp7skZkKorC1D12MVNN0nV9bvZMkkgyylPAAQgR98/4vqMR7TIf75yk\nxCxvsRKiLVZER6wr1K4Qo0iJoCpRVJvCapwARBUFR2QZSt8Z8f0TDY2FNQL5PETP+O2BT4Db1+3k\nQw8pOgfGD52egP5431i9fQyFjZyrWBHt/meudqxiUbgUv1XK1kXCC/rp6lF81FHRNSkIFW++xRGH\nHNTz5IqNmKOIc/70F+bNX8iuO27P2ScfR9M7z/LrytXP1flXLAfOSjxE1/W7NU37BvgDXR9ANE37\nEDhH1/Uv0v0jSCTDBCk6eyJLJkkKjRcQvd2Pokt0HgOsIMGDH0MZURI1aLNt0e3gGAqrYw5Wx4Ao\nLA0J0WmFo9jCgUEwL6YmPHkZaEbU2348n8LqnUjRmQE0TbMX25kAImmot8ShhZaLrYtWEgirNIaL\nObLkF9S453PUxho1u+zMHf+4n1WrVvU49ouvv2He/IVsv91E7r3jFmw2GxxxJNFI5IjQrXc2Pv/y\na2ypbe6xTEPp7DGr6/qbwJuapo1AuPGPB04E3tE0bfPEovYSSR4gRWdPpOiUFBS6rrdqmvYqcJim\naRWIphE7I8p/dfgDQRswqYxtJ1sUpX1etd0ktlQlNno1ij19b2c4EiUSSZyvJK6fuw5YD/gGuFbT\ntOfi663zBik6M8OBqyO4vPbVTClqSTlh9uoKlkSKWRZxUmm3qHasZgNHB8U28WacsFsN641287uz\nTuPt9z+g7vnu7bWWGKJu5w6TtxOCM47Nbrcv+vGnjQGOP+bIm4GzLNN4HXgdeF91e1fpur4KmAXM\n0jRtGXAhQoSm0cNLIsk9mqbdC3wN/Krr+lNJYycDe77/2TwpOnuSXCRfik5JIfAkIqR+EELUKSf9\n5ryAPxB8HNgfWK9V2YD+WhcBqNEgAPaIRax9BOHVRdjdwR7znHbxlWOFugvSRiPYuRROYHMuAdA0\nbUfgYoRX9m/AAuA+4IiB/KJDHflFnBnOBtipeAWa2pZym1wk3qQLLBe2FG/0tsDPBL74hO+++465\n8xcCrClKCzDaLULxS35p6nbci6/N4oefGsX8jg6ATb/y6+cffOxJM5954eUVlmm8ZpnGeZZpbBw/\npPPTIut0SoYTMaCV1DfOTyDe11J09iTZ0ykdD5K8Z/udd51ls9lXaltNuGxz31bXj6lan6NOOOXP\nwAkIEZoWI6IGYwJzuu+M2oiu6ukhrRwtuvB+9e3SbvsffyFpJZvq+ULTtCJEq+0AcLmu6zpwE3C4\npmknpGvfcEB+4QyMCfGyBn0RQKyZXLTVeiVjrJb2slSTxqutzF7l5mtrJJs5VtIUKWKBVca2RUF+\nDhfDuy/y3Tsv8Xy7ly3HjgbglVdfZ1TZSLQtNmfythOp8ozhtf++haeyko3GbcicefP5pGEuf77k\nAi696npeem0Wo8rL2WePGoqKVG664x71u0U/Hjh+S+1Au93Ogi++Mu12e3lxkfrTSbXHfDi4fyqJ\nJHPoun4egKZpN2qa5tR1PZQwFtE0DaToTIUMr0sKAn8gWATsDRxx5S1/O+zum68b+dkHsye0t63k\niOknoaTT0S8BV+RbKs35/Lw66ThbBLu7Z0Rzr53HMeOJudxy38f8unwVZSOL+HDO/2hqbmNsVRmx\nWAxsI4KM3K4BEVbfEjha1/XOk92EaG99t6Zpb+u63tTjRYYhUnQOjBPiW18EEV/k95SM9uxitSw7\nNtWkIiXGBLWVeVY5bTEHR48U76dYjDXrOh1KjONKl8CKJbQUVTB3eZS/z7ifaZtW4Dp0f/5y/mnM\n+M9/eeKZ5ykqKmKnKZN45L67qVxvNK+/8TYffzaHfz3yOPvvsycPz7iLfz38OO/M/pCXXp9FOBzB\nW+Vxn3DskfzmlBM3Ki93/WKZxn8RmXOvqW5v78XKJJIcE+80shLh8XRomjY1XtoHTdPOACa++Pyz\nbYcftaaYgxSdgoFdaSWSYYQ/ECwDDkSEpA8E1jh9dt9nf95+/RUA9pg6LeXxtpi1emR0cXHQtmlX\n73VgdORz3KsXEFlRicg/6sK+XguKvWfJpK21Mdx11VRmPD6Xux5uoKTYyV47j+P+G6dx1LnPE47E\nwFYy0zd+ykREWP1lXdef7zxe13VL07SzgfcQIffD1uZvMtRQYrGC6jWfVWZff+a071579HUy+Dcu\nHrUe3sm7M3bK3lTvduBKm90+chBOuwqxBrRTgMqi8ZIhi6ZpCrCbruv1ifubWtruQXT6APhDVXnp\n3Vk3bogRNvQvEYXeO3nJ4dUOz5U9Esm64g8ExwCHIoTmvoh2rekSRtTInAXM2tzxqdfW/uVrix0H\ns9rmgVgUT+QjyiM6kabRxDq6n1opbcdRmTpvIy1Kx0+Tvdclg8buVz4wKzD/w8+CP3+/Q6ZeY/WK\nZfzw9n9Ypi/8bOO9j9w1FgntBExD3OVtu5anHYEoL3EUsMoyjVcRnR1elwJUMtTQdT0G1KcYkuH1\nnoxIet6zNIZEMsTxB4IbIUTmEcCuDOzz3UhcZALv+DyuBNW434KY9fNnpbElO3TERuMNv8PI2P+I\nBEt7CE7skZQJRGnjGPWZ4p7637U/wfBEis7MEhuz9Y4XtzUveSvSkWKl8SBhLxrRMWabnS5ylleG\ngQ/i258s0/Ai1pceCEwlIdQwAEYgapodA7QnCdD2QfkFJJLMIEVnT5JFp/wMS4Y8/kBQAbZG1Jc+\ngoE7VD5H1Ot8EfjS53HFADRNc5KUSPTQXedet/EG3/5npPKtGootw7QchAM2iLVRWlyE6hSyyT46\ndVg9LRRHB+r6F7FOrYyGJ1J0Zpg9rnn4g9YlP94VWPjRpZl6jdFbbHvnHlf/u0cikOr2GsBDwEOW\naTgRd4SdXtAJa/FSJcCx8a3dMo1XECH4marbKz0mkqGGFJ09kZ5OybDBHwhORORRHAlsOoBDo4jo\nx4vAiz6P66de5u0KvJu44/Q/3NvrSa8/7XAO33U7lNJ2bCXrUPDFWXmnMvqAgkzelaIzC0w+5/qr\nPrr1/K1W/Pj1wYN97opNxr+6/bk3XNPfPNXtDSEWJL8HXGaZRjVCgE5DrIMpHeBLlwDHxbe2BAE6\nSwpQyRBBis6eSNEpGdL4A0EvonHJScA2Azi0A3gT4dF8xedxNadxzHxgr+Sdo8pdzusu3vfasohz\n51hb10dm4/XXW/ewunP0q5Tvds3an2B4I0VnFlh/0u4dW5940fQvn7jzqeU/fDVowrNik/GvTjjh\nwunrT9p9wLdcqtvbiMiIu98yjRGIMPyxwCEMXICWIko71AIrLdN4DngQ+LCzG5JEkgOk6EwgbOh2\n6NFyRYpOSc7xB4KliLD5SQgnSLqf1yCileULwCyfx9U6kNeNd+F7L9WY9clzIwiFezRMsY821z6s\n7hz9KmWTpyvFGxRsXWwpOrPEFgefsrLMu/HRc++76tpl+ucXRFa3r/UaT3txScd62nZ/m/zb665e\nG8GZTNwz+QLwgmUaJQjv5zEIAVoywNONBE6Nb99apvEQ8Kjq9v6yrnZKJANEis7uFKfYJ0WnJCf4\nA0E7sCdwMiJpNV1nRxOie94LwLs+j8vqZ/6AserrKhFOmW7YXCvXLqyuODpwjvkb5bteXciCE2TJ\npJzw/jWn7rb0i09vDy5ZtMOAyikpCq6xm342ZusdL97jmoc/yJyFgrgAPRDhAT2YnqG5dIkAMxHe\nz9fioX6JJKM0tbTdCFwRf3plVXnpDbm0J9eEDX09IDnkeIHDq92VC3skhYk/EByP8GieCIxN87BG\nxPKtF4BPfB5X+s3OB4hVX6cgRO0h3Qac0XbH+oESZaC3r45Rn6Guf7Ey+oCMX7OHA9LTmQPignGn\n9689ff9f/fNOXbV86bTVy5tdvc0vrqgMjqgYM7Ny/JSHa/78r/+SpYy3eHb6c8BzlmmU0iVAD2Jg\nAtSOEK0HA0st03gUeEh1e78ZZJMlkkQSPSCpvHyFRqrPrPR0SjKOPxD0ANMRXs3t0jwsiKiU8hhQ\nn0mhmcRvSRacsJqwfYoycqtqrMCpRNunEV3V6zUb24ggtpKZFFU9HC+LJL17caSncwjQ+svioq/q\n7t7+V33+NqH21rGxaFRVbDbLWVK2ZLS27cLxtb+fU7b+uCHjkrdMYyRCeB6LEKJre0H/BOH9fEZ1\ne9dhZbZE0pOmlrYLgDvjT++pKi/9fS7tyTVhQ9cAf9Lukx1e7bFc2CPJb/yB4AhEF52TgP1Jr+Vq\nBFE/8zHgZZ/HldWbIqu+bitgLj2vaeepNbVr0tpj4WARrfO2J7R0G6KhsRBTQbGwOZfgHLOQsklz\nFIdryFyzhxJSdErWibgAPRiRxX4Q4FyL07QjQicPAR/I5CPJYNDU0nY64qYG4NGq8tJTcmlPrgkb\n+raIeoWJHOPwas/lwh5JfuIPBDdFeAtPB9xpHjYPeBSo83lcgUzZ1hdWfV0R8CkwMWnoFeAwtaZW\nXpcGARlel6wT8R7tdUCdZRrrIUpdnMHASl2U0JV89F08+ehh1e1tGlxrJQVGove891BY4SDD65KM\nEE8KOgA4L/5TSeOwn4EngMd8HtdXGTQvXW6ip+BsAs6QgnPwkBmdkkFDdXuXqW7v3YhuEdsD9wID\nbUy7OeLD32iZxqOWaUwaZDMlhUPie688Z1YMHaTolAwq/kBwPX8geCnwHfAqovJJX4JzJfAIoizS\nRj6P6/KhIDit+rqpwIUphk5Wa2rTqfcpSRPp6ZQMOvHw+FxgrmUaf0TUXzsd2GcAp3Ei1gKdZJlG\nPXAX8JLq9oYH215J3iI9nd1JVf5MtsGUDBh/ILgDwqt5HD1rvyYTRRRtfwzRHagtw+YNiHh5pEdS\nDN2h1tS+mW178h0pOiUZJV4D9EngScs0NkaE0E8DNhzAaWri22LLNO4BHlTd3hWDbask75Cezu5I\nT6dkrYknBtUC5yIiWf3RBPwT+KfP41qSSdvWlnh5pIeAqqSh+XSVW5MMIjK8Lskaqtv7o+r2Xg1s\njMhmfJruZW36YxxwG/CzZRp/t0xjiwyYKckfpKezO1J0SgaMPxDc1B8I/hWxBvMh+hecsxEe0Gqf\nx3X1UBWccc5FJMImsgo4Xq2pldnnGUB6OiVZR3V7I8AbwBuWabiBExDJR8mLuHujFBHaOc8yjdcQ\nofe3ZNa7JAnp6eyOFJ2StFiLxKCViPD5vT6P68sMmzcoWPV14xFOjGQuUmtqZQ3pDCE9nZKcorq9\npur23qO6vdsCkxF30gO5wzwIIWC/sEzjrHgfeYkExHrFSPxxUVNL21q3ns0TpOiU9Ik/ECzyB4Jn\nIeq5ppMY9A3wO2Csz+M6dxgJzmLEsq/kepwvkaL9pWTwkKJTMmRQ3d55qtt7BmK955WINUHpMh6x\nfuh/lmn8xTKNdNurSfKUqvLSGDLEnohMJJKkxB8IlvkDwYuBHxDfo5v1MT2C6FS3FzDe53H9w+dx\nDbfmHnfTs6zfL8CZsjxSZpGiUzLkUN3eZtXtvQGxhvMkRCZ8uowG/g/4yTKNhyzT6OvLU5L/SNHZ\nRSpP5+qsWyEZMsRLHl0HLEaEmr19TG8CrgPG+TyuY3we13s+j2vYCTSrvu4M4KwUQ6eoNbXLsm1P\noSFFp2TIorq9lur2Pg5MAXZD3F2n23/XgciS1+P1PrUMmSkZ2iSKzkJf15n8+7c7vFq2+llLhhD+\nQHBDfyD4N4TYvBKo6GP6cEoM6hOrvm574B8phv4qyyNlB5lIJBnyxBOEPgQ+tExjHGIN0VmkJyJs\nCG/piZZp1AE3qm5vzosRS7JGYjJRoXs6K5Oey6LXBYY/EPQBlwEn0vf1P4roNHerz+NakA3bMo1V\nX7ce8Dw964q+jyyPlDWkp1MyrFDd3sWq23sJsAEis/LbNA9VgOmIhKNnLNMYSJtOyfBFejq7GJP0\nfGlOrJBkHX8guL0/EHwe+BpRK7k3wdkB3Ads7vO4TsgjwWlHJA5VJw0ZwHFqTa1sOpIlpOiUDEtU\nt3el6vbeC2wJHIjIYE8HBTgGWGCZxguyzWbeIz2dXUhPZwHhDwQVfyC4jz8QfAtoAI6k90z0VuAW\nRGvKc3we1w/ZsjNLXAvsl7QvBByj1tQGcmBPwSLD65Jhjer2RoGZwEzLNMYj+ueeQnrv7cOBwy3T\neBW4XnV7P8ucpZIcIT2dXUjRWQD4A0EbcBgioXJKP9ObgTuBGT6PKy+7vFn1dYcBf0oxdKFaU/tR\ntu0pdKSnU5I3qG7vV6rbeyai3Md9iDvZdDgY+NQyjZmWaeycMQMluUB6OoGwodvoKTpleD2PiHs2\nDwDmAP+hb8G5GLE2fpzP47opjwXn5sCjKYYeB+7NsjkSpOiU5CHxdZ/nAJsCfyf9YvMHAB9ZpvGm\nZRo1GTNQkk2kp1MwCrAn7ZOezjzBHwjuDLyLiPps18fUrxCJlZvH62vmbXMAq76uFCG+k282FwJn\ny3qcuUGKTkneorq9/1Pd3vMRvd7vJP3uK/sCsy3TeMMyjW0zZqAkG8g6nYLkJCKQns5hjz8QnOAP\nBF8CPgL26GPqJ4iQ+zY+j+txn8eVbhRoWGLV1ynAv4AJSUMtwJFqTa1sipAjpOiU5D2q2/uL6vZe\nhBCffyX9Liz7AfMs03jEMo0NM2agJJPI/uuC5NA6SE/nsMUfCG7sDwQfRXjtDu1j6huIzkG7+Dyu\nl30eV6HUZT0fUa0kmRPVmtpF2TZG0oUUnZKCQXV7A6rbeymwEXATsDKNwxTgZOBbyzRuskyjkIXL\ncGR5wuPRObMi90hPZx7gDwQ9/kDwHkBHhMl7y0b/ANjN53HtP1w7B60tVn3dbsDtKYauV2tqX822\nPZLuSNEpKTjibTavQLTZvI7u3rDeKAYuB763TON8yzTUTNooGTSMhMdjc2ZF7pGezmGMPxAs9weC\nNwCLEAlAzl6mLgQOAnb3eVwfZsu+oYJVX7c+8Cw9q5fMQpRNkuQYKTolBYvq9pqq23s1wvN5Fd29\nYr2xHnA38JVlGkdZptGbp0EyNEhs2ddXX+l8R4rOYYg/EBzhDwQvAX5AlP0p7WXqD8DxwHY+j+v1\nQvJsdmLV1zmBZ4CqpKGfgBPUmtpI1o2S9ECKTknBo7q9K1S393q6xGc6YffNEL3gP7BMY5cMmidZ\nN5qBzotNRVNL24hcGpNDksPrbQ6vJpMphij+QNDhDwTPAr4DbgXcvUxtAs4FtvR5XE8V0JrNVNwG\n7Ja0bzVwlFpTa+bAHkkKpOiUSOKobm8wLj43A2bQJVb6YhdET/jnLNPYPKMGSgZMVXlpBPglYVeh\nhthljc5hQLzW5qGI0kb/pPf36wpE8ffNfB7XDJ/HZWXLxqGIVV93LvD7FEPnqDW187Jtj6R3pOiU\nSJKIJxydiyi38VKahx0FfG2Zxt2WaayXOeska0FiiL1QRWeyp1OG1ocY/kBwC+B1xHfOFr1MWwXc\nDGzi87hu9nlcbdmyb6hi1ddNA+5JMXS/WlP7cJbNkfSDFJ0SSS+obq9fdXsPB3YHPk3jEAeiVMci\nyzQut0yjOKMGStJFik7p6Ryy+APBkf5A8GbgS0SDilSEEdGXTX0e1//5PK501p/nPVZ93TaIdZzJ\nWuYT4A/Zt0jSH1J0SiT9oLq99cDOwHGIBfv94UKUZPrCMo39MmmbJC2k6JSeziFHPJReC/iBy+g9\nI/0pxJrNc30e1y+9zCk4rPo6L/AaMDJp6EfgMLWmNt1OdJIsIkWnRJIGqtsbU93eZ4CtgAuBdBam\nbwa8YZnG05ZpFHLmdK4p6Az2eN/15Bql0tOZQ/yB4DaItpVP0fuN0GxENvrxPo/r+6wZNwyIt7h8\nBdggaagFOEitqZXv7yGKFJ0SyQBQ3d4O1e39G6Kv+19Jr6/7sYDfMo0/WKaRXD9OknkK3dNZgey7\nPiTwB4Kj/IHg3cDn9N62cgmim86ePo9rftaMGyZY9XV24AlgUtJQGJGp/k32rZKkixSdEslaEC+z\ndCmgAY+ncUgZ8DegwTKNHTNqnCSZQi8QL7sR5Rh/IGjzB4JnAN8i1n2nuvaGEElCPp/HVVeItTbT\n5FZEH/lkzlZrat/OtjGSgSFFp0SyDqhu72LV7T0JmAy8k8Yh2wIfW6Zxn2UaFZm1ThKn0D2dsjB8\nDvEHgjsgElseIPX/AmAmMCGeJJROneCCxKqvOwe4KMXQzWpN7UPZtkcycKTolEgGAdXtnQfsCxxD\nd89aKhTgbEC3TOMU2dUo43Rb09nU0lZo33vVKfY1Zd2KAsMfCI7xB4IPICpfTOll2o/AocBBPo/r\n26wZNwyx6usOIHVppGcR3Zokw4BC+/KVSDJGPNnoOcAH3En/xeUrgYeB9yzTGJ9h8wqWqvLSlUAw\n/tSJaGVaSGycYt+PWbeiQPAHgnZ/IPg7RCj9jF6mrUJ0P9vK53G9IkPpfWPV122NKI2UvDb5U+AU\ntaa2kDsxDSuk6JRIBhnV7W1V3d6LECH3j9M4ZHdgvmUaN1um0VtvZcm6UcgZ7JskPf/V4dVacmJJ\nnuMPBDVE1vk9QHkv055DlEC63udxrc6accMUq75ufURppLKkoZ+AQ9Wa2lVZN0qy1kjRKZFkCNXt\nXYDoBXwW/ZdYciBq9X1lmcahmbatACnkdZ3JolN6OQeZeK/0S4EFiNa4qfgG2NfncR3j87gWZ8+6\n4Uu8NNLLwIZJQ7I00jBFik6JJIOobm9UdXsfQITc/53GIeOAlyzTeN4yjd6SDiQDp5Az2JNFZzoN\nDiRp4g8EJyAiGrcARSmmtAIXAxN9HpfMrk6TeGmkx4Htk4bCwNFqTe3X2bdKsq5I0SmRZAHV7W1W\n3d7TEaH0r9I45EiE1/OIzFpWMBSkpzNs6EX0/H2l6BwE/IGg0x8IXgnMo6cw6qQO2MLncd3h87hC\n2bNueGPV1ymIOsiHpxj+rVpT+1aWTZIMElJ0SiRZJN5SczvgUqC9n+mVwH8s03hMlldaZwpSdCI8\n58nVEaToXEf8geB2QANwHanbV/4CHO7zuKb7PC5ZKWDg/B+i81syt6g1tQ9m2xjJ4CFFp0SSZVS3\nN6S6vX8FtgReTOOQE4EvLdM4ILOW5TWFKjqTQ+sg13SuNf5AsMgfCN6AEJwTe5n2b2C8z+N6KXuW\n5Q9Wfd0tZgtLAAAgAElEQVR5wI0php4DrsiyOZJBRopOiSRHqG5vo+r2HgEcgsjE7AsvMNMyjfst\n00jO4pT0T6Fmr6cSndLTuRb4A8EdEaH0P9GzdA/A/4BpPo/rdJ/HtTyrxuUJVn3dicDfUwx9Apws\nSyMNf6TolEhyjOr2vgqMR9T27K9e32+AhZZp9Na3WZKanxMej2tqaSuUgvzJNTojCHEkSRN/IDjC\nHwjeBnwEbNXLtPsRHYVmZc+y/MKqrzsMUbc4mS8QmeqyNFIeIEWnRDIEUN3e9nhtz73o3+u5EaKg\n/J2WaYzItG15QhPQ2V6wnNT9yPORZE9no8OryYSWNPEHgjWIMkgXk/p6+SOwj8/j+q3P4wqmGJek\ngVVftw+pi78vAqaqNbX9lZyTDBOk6JRIhhCq2/s+sA3Cc8INf72TJ599gdff6Flp5Y5/3H/B/f9+\n7KezTjulWws4TdNGa5p2v6ZpqTI/C5Kq8tIYoCfs8uXKliwja3SuBXHv5t3A+8DmKabEgLuBbXwe\n1ztZNS7PsOrrdgJeAtSkoSXAvmpNrUzEyiMcuTZAIpF0R3V7W4HfWqbxYigUfq60ZERpJNKzo+bv\nzjqN5l/NMSUjiq+3TKMEuFZ1ey1gBfAUMCq7lg95dESXKAANISjylrChK8ganQMmXnezDrHkJRXf\nAmf4PK4PsmdVfmLV120DzASSO7EtQwjOn7JulCSjSE+nRDJEUd3eWddeccmGhx10wOM/Lm4kFA53\nG1/042LefOd9Tjj2KAWR1dlgmcZEXdf76/leqCR6OrWcWZE9KgBX0j4pOnvBHwgq/kDwt4jM9FSC\nM4qoHbmtFJzrjlVftxnwBj1vjoPA/mpNrT/7VkkyjRSdEskQZuud9zp96533+qBh3vznwuFw8+wP\nRSv3jg6L2+6ZgaqqfNIwl0/nzKOlJbgN0PDx2zNvAI4ADtQ0LTmRpJBJvIgVguiUmetp4g8E3YiS\nPDOA4hRTvgZ29nlcl/o8LpnQso5Y9XUbAG8BnqShVcDBak3tvOxbJckGSizWX7KsRCIZCqz4eVHl\nxw1znt1nj5oemetf+XU22WgcI4rXXC9fAU5V3V65AD9OU0vbRGB+/OmiqvLSzXJpT6YJG/qxwNNJ\nu3d0eLXPcmHPUMUfCO4GPEnP/t6d/BW40udxdWTPqvzFqq+rBGbTc111CDhUramVFQDyGCk6JZJh\nhGUaCnA8opZdf2s2G4HjVLf3k4wbNgxoamkrAdriT6NASVV5ad4KibChXw7clLS70uHVluXCnqGG\nPxC0I2puXk3qqF8AONnncb2RVcPyGKu+rhx4F9GVLZEoUKvW1D6bfask2USG1yWSYYTq9sZUt/cJ\nYAJiAX5fVAP1lmlcFBerBU1VeWk7sDj+1AZsmkNzskHy0opW4NdcGDLU8AeCGwDvANeS+jr4X2Ci\nFJyDh1VfVwK8Sk/BCXCWFJyFgRSdEskwRHV7lwAHAWfRVX8yFQ7gduBFyzTc2bBtiFNIyUQ9Mtcd\nXq0gQluapm2nadobmqb1iAbsuPMuX7z0zJP6D9/puyfu/2nRdzzxwIzolRecO/uUww84wedxBbJn\ncX5j1depwPPAbimGL1Jrah/KskmSHCFFp0QyTIl7PR9A9ICe28/0Q4F5lmnsmHnLhjSFVKuzkGt0\nLkR0EFqDPxAs9geCf994s80nOOyOEputex3ysdUbfX/0iafttHBew2srzF+LsmlsPmPV1xUD/wEO\nSDF8nVpTe2eWTZLkECk6JZJhjur2/gDsCtzTz9RxwAeWaVxYwOH2gvB0hg3diVhekUjBZK4nlw3z\nB4JbAZ8B511z2z0cdNSxvPfG64lTHnc6nZOOnVqzHTBX13Uji+bmLfGQ+kuIqEwydwPXZNUgSc6R\nolMiyQNUt7dDdXt/DxyNqHPXGw7gDuAFyzQqsmLc0KJQyiZtQc/mH9/nwpBcoGnarsAOwFnvzvv6\n9599OHsesPWq9nYeuPt2Xnn2KbbYcgLz53y6avEPi87xeVwnHbb7lH2AnYEtNU1LFuySAWLV141E\nrOGcmmL4YeBCtaa2IJZ7SLqQ2esSSZ5hmcamiFI5k/uZuhg4VnV7C6aETlNL2wbA/+JPVwDueIvM\nvCJs6NMRZYAS2c3h1T7MhT25wB8IlgAPxGKx6V8vnM/4iT3yV+b+7carb3j3v6+/oet6ew5MzFus\n+joX8DoiApPME8Cpak1tOMWYJM+RolMiyUMs0ygCbgN+18/UEHApcJfq9ub9l0FTS5uCyOLubLvn\nqSovXZpDkzJC2NBvAi5P2j3K4dVacmFPtvEHghsBLwDb9jLlduAKn8dlZc2oAsGqr6sAZiE8zck8\nBPxGramVXdMKFBlel0jykHi4/XzgGPoOtzuBO4H/FEK4Pe7VLIR1ndskPf+pgATn3sAcUgvOpcA0\nn8f1Ryk4Bx+rvm408DapBecMRGkkKTgLGCk6JZI8RnV7nwMmAf21lTsc+MwyjS0zb1XOKUTRuTAn\nVmSReO/0CxD9vEenmPIWovam7HiTAaz6ujGkLvwOcBdwnlpTG82uVZKhhhSdEkmeo7q9i4BdEF2M\n+mIz4BPLNKZl3qqcktdlk8KG7gY2SNqd16LTHwiOAB5FeO3tKabcDBzg87iasmpYgWDV160PvAds\nnWL4FmTSkCSOFJ0SSQGQEG4/FrGmsTdcwKt53sUo3zPYU13481Z0+gPBauAD4MQUw+3AsT6P6/98\nHpcM62YAq75uA+B9IFWU5Drg/6TglHQiRadEUkCobu+ziHD7531MsyESLR6KJyTlG/keXk8OrQMs\nyLoVWcAfCO6BWL85KcXwj8DOPo9LtlfMEFZ93UbAbGDzFMN/Umtqr5aCU5KIFJ0SSYGhur3fI8Lt\nM/qZeirwjmUaYzJuVHb5LuHxJk0tbWrOLMkMyaJzFbAoF4Zkivj6zd8h1mlWppjyJjDF53HlrYc3\n11j1dZshBOfGKYb/qNbU/iXLJkmGAVJ0SiQFiOr2rlbd3nOBs4G+6uXtAjRYpjExO5Zlnqry0jag\nMf7Ujiiknk8ki84vHV4tb0LL/kCwGHgQ0YEruQA+iFJhB/o8rl+zalgBYdXX+RAh9Q1TDJ+v1tTe\nnmWTJMMEKTolkgJGdXv/CewL9HWBrgY+skzjiOxYlRXmJzzur4j+sCFs6HZgQtLuvPH2+QPBsQix\nc1qK4VXACT6P6xKfxyULj2cIq75uIuJ/4E0aigFnqzW1/SUsSgoYKTolkgJHdXvfB6YAX/UxrQRR\ny/PPeZJgNCfh8fY5s2Lw2QTxv0okL0SnPxDcFZhL6hqQjcCuPo8ruQuTZBCx6uv2BeqB5CU3UeA0\ntab2n9m3SjKckKJTIpGgur0/IkLpr/Qz9XrgScs0koXNcCNfRWeqJKJhLzr9geCZiBqQnhTD7wLb\n+zyuvpLjJOuIVV93EjATKEsaigAnqjW1j2TfKslwQ4pOiUQCgOr2BoEjEDUN+6IWmG2ZxtjMW5Ux\n5iY83rappS3V2sDhyP+3d9/xbVX3/8df8rgZTuREEGxUygpwDYQdCEsQyi7QQiGglFJoGd/Ob6Hw\nawu0pQsofEspdBfaQktB7FHKpqSIDWUXfCiErVgEBFZwgm9s6ffHuU5kWVLkIdmy38/HI49I9xzd\n+5HiXH18ZqGk87mqRzFC/AlDPwEuwe6ele8iYP+2luDS6kY2cXjxWMCLx07HroOa//+kBzjKiUSv\nqn5kUouUdIrIKk4o3OuEwqcDxwDdJarugJ1gVKirc8zz91t/0386GdhiFMMZSflJ59sNYbcmJ9S0\nJ9MOcBlwZoHibuDYtpbgyW0twZVVDWwC8eKxeuDXQKGZ6MuAA5xI9PrqRiW1TEmniAzghMJXAHsC\npXZwWRfb4rmgOlGNuPHYxT4utr9sT6aDwK3A5wsUvwXs3tYS/Et1o5pYvHhsKnAD8OUCxQkg4kSi\n91Y3Kql1SjpFpCAnFH4UO8Go1L7tk4CrvVTiq9WJakTldrHXfNLZkzDTsROJctVc0tmeTIexs6P3\nLVD8FLBTW0vwiQJlMkK8eGwW8E/gUwWKXwB2cSLRcbnhgFSWkk4RKcoJhd8CIsA1JaoFgF95qcSP\namxme27iMh6WTcpfKglqLOlsT6a3AB4Gti1QfCewZ1tLcEl1o5pYvHhsNvAQMK9A8b+A3Z1I9I0C\nZSJrpKRTREpyQuHl2MlD319D1e8Bv/NSifrKRzUicls6txkHOxPV9Mz19mQ6AjyIXRc235+BQ9pa\ngsuqG9XE4sVjO2KT/k0KFF8D7O9Eou9XNyoZT5R0isgaOaFw1gmFfwwcASwvUfUk4FovlZhcnciG\nrrW56V3gdf/pJGDLUQxnJOQnnSvpv8/8mNWeTC/Abl05o0Dxj4DjNWGosrx47GBgEYW3Fb0AWOhE\noqUmF4qskZJOESmbEwpfD+xF6R2MDgPu9FKJQgnEWDOeutjzu6RfaAi7Yz5Ra0+mTwauxib+uXqB\nE9tagme1tQSz1Y9s4vDisZOAmxm4sUAWONmJRE9zItFM9SOT8UZJp4gMihMKPwbsxur9ywvZA/iX\nl0qsW52ohmxczGDvSZhJDEyany5Ud6xoT6br2pPpnwMXYscF51oOfKqtJXhp9SObOPw1OH8M/J6B\n+UA3cKQTiV5U/chkvFLSKSKD5oTCBruD0fMlqm2N3bN90+pENSTjZQb79gxsKXxoNAIpR3syPRm4\nCjilQPFSYH5bS/C26kY1sXjx2GTsOqjfLVD8PrCvE4leV9WgZNxT0ikiQ+KEwm9jWzQfKFFtQ+BB\nL5UYq13XuUnn1h2dXfmJW63YtcCxB6seRRnak+mZ2JnoRxYo/i+wS1tL8PHqRjWxePHYesD9FF4H\n9XVgNycSjVc3KpkIlHSKyJA5ofD7wH7ALSWqzQIWeanEPtWJqnytzU0pYLH/tJHCyw7VgvyksxN4\ncTQCKaU9mf449peUPQoUPwLs2tYSfKW6UU0sXjy2O3ZYyY4Fip/CrsE55n52ZHxQ0ikiw+KEwiuA\nw4E/lag2DbjNSyWOqk5Ug1LTXew9CRPAjrHN9XBD2B1TEz/ak+mNsK1rhbYcvRnYu60l+G51o5o4\n/PGbXwLuA1oKVLkL2NOJRLUOqlSMkk4RGTYnFO4BTqDwHs19GoGrvFTi69WJqmy1PploIwYmEWOq\na709md4Eu7D4hgWKfwMc3tYSLLUUlwyDF49Nwk4W+i3QUKDKL4CDnEhU66BKRRX64RMRGTQnFM4C\nZ3qpRBIoNuM1AFzspRKzgLP814y2Wl82qdB4zjEziag9mW7DbqlYaCWD04HztCRS5XjxWBi4Dtil\nQHE3cKITif61ulHJRKWWThEZUU4ofDHwWezi5MV8Dzh7jGybmbu3/FYdnV1jfmH7PPld673AY6MR\nSL72ZHoOtoUzP+HMAie1tQR/qoSzcrx4bGfsL1WFEs63sFtaKuGUqlHSKSIjzgmFrwIOBrpKVDsd\nOGe0E8/W5qYPgJf9pw0U3k5yLMtv6XymIex+OCqR5GhPprfD7nCzTl5RBvhCW0vwkqoHNYF48dgJ\nFE74AeLAXCcSfaJAmUjFKOkUkYpwQuG7sLsXlZoc8h3g3NFOPOnfxT5v1KIYpJ6ECQJb5R0e9a71\n9mR6R2yX+lp5Rb3A0W0twcurH9XE4MVjjheP/Rq4BHAKVPkVsLcTiSarG5mIkk4RqSAnFH4c2/37\neolq3wZ+OsqJZ+7Emz1HLYrB25mBu/mMatLZnkzvCtzLwH3UVwJHtrUEY9WPamLw4rEW7Gf/lULF\nwPFOJPp1JxId89ujyvikpFNEKsoJhV8CIqxeD7OQbwHnjWLiuSjn8Z4dnV21cm8cU5OI2pPpPbFL\n70zPK/KAz7S1BG+oflQTgxeP7Yhd/mv3AsUJYA8nEi21rJlIxdXKjVVEapgTCr8JzAdKLfz9/4Dz\nRynxfIHVwwDWpvBakmNRftL5NvDGaATSnkzvA9wONOUVfYTdR/3W6kc1MXjx2HHYcZofK1D8ELCD\nE4k+WtWgRApQ0ikiVVFm4nkao5B4tjY3ZbCTLvrMr+b1h6InYeqx3eu5HmoIu1WfDd6eTH8SuBWY\nkle0HDiorSV4Z7Vjmgi8eCzoxWN/Bf4MFNrC9ffAXk4k2lHdyEQKU9IpIlXjhMJvYRO6l0tUOw34\nv1Fo8VyU83h+la89FHMY2I1d9a719mT6UOAmBiY9HwIHtLUE/1ntmCYCvzv9SeBzBYpXAl9yItEv\nOZGoV93IRIpT0ikiVVVm4nkq8LMqJ56Lch7XwrjOQuM5q7oTUXsyfSRwLXa3qVydwL5tLcF4NeOZ\nCLx4rM6Lx07D/oIxu0CVDmzr5u+rG5nImo31m6qIjENOKPw2NvH8b4lq3wQuqGLiWWvjOvOTzhXA\n09W6eHsyfRRwFQN3tkth91F/pFqxTBT+7PTbgP+j8I6C/8KuvzmmtkEV6aOkU0RGhZ947kXpxPMU\n4OfVSDxrcFxn/k5EjzeE3aoshdOeTB8EXMHA75ClwCfaWoL/rkYcE4kXj+0PPAvsX6C4F7vL195O\nJPp2VQMTGQTtvS4io8YJhd/2Uon5wH3AZkWqnQwEvFTilCrs1b4IONx/PB+7kPaY05Mw6wIb5R2u\nSuuWvyzSdQz8/ujAtnC+UI04xiPXdbcDzgOONMZ8AHaxd+Dsn19312lrB6exzowgB+w0Z9Vr2t9Y\nsuTbl1x33+Il77YAk4yJLs853xXAI8Crxph/VPXNiBSglk4RGVVOKJzAtni+VKLaN6jOGM9FOY/H\n8rjOQntpV3wSkb/T0K1A/v70S4A9lXAO27Pk/Dt68dgm2F8mTvvapz/B3ttvztLOZbn1r7/ktvj+\ni5e8eyLwHLB+3vl6sVvRqoFJxoSxekMVkQnETzznA6ZEtW9idy+qpFoZ17lHgWMPV/KC7cn0lsAd\nwLS8ohR20lCpXxqkDMaY3r7HXjx2NPAUMBfglSVLufvfL3D03vPArn36P8CCX/7thueArbHbtyby\nznesMebPFF4wXqTqlHSKyJjghMJLsC2e7SWqneulEidUKoYC4zr3qtS1hmm/vOcvNITd9yp1sfZk\nemPgbiCUV9S3LNJ/KnXticR13d0a6ut2+ewn5t2zbPlHV9z/7EvTALpXruRn19yJ09jAfc+Y1075\nzdVf2eqEs65zItGs67q7YxPONLCB67oLcs73I9d1T8KOtRUZdYFsturrCIuIFOWlEq3YMZ5tRapk\ngAVOKFyRLRU7Oru+BvzSf3pDa3PT4aXqV1tPwnycgbsO/aIh7J5Sieu1J9Nh7G43G+cVfQQc2NYS\nXFSJ605EXjy2A3ZFgE2z2SxP/vcNdthsg9wqvwFO2+qEs7YAXjTGLM8/h+u6exhj7q9OxCKDo6RT\nRMYcP/H8F8UnF3nAAU4ofN9IX7ujs2sOdnwcwHvAOn4L6JjQkzDHA5fmHT6wIezeMdLXak+m1wLu\nZ+Awgx7g0LaWoCanjAAvHmsEvgWcxcA1TwHeB453ItEbqxqYyAhT97qIjDlOKNyB7UIutvyLA9zs\npRLbV+DyueM61wK2rMA1hiN/yZxubGI4otqT6SB2DGd+wpkFPq+Ec2R48di2wKPATyiccMaBbZRw\nynigpFNExiQnFH4dm2C9X6TKdOAOL5XYdCSv67dqLso5NH8kzz8c/n7r++QdjjeE3QHdrMPRnkxP\nAW7Bn8SS58ttLcGrRvJ6E5EXjzlePPYj4HFguwJVMsAPgE84keib1YxNpFKUdIrImOWEwv8BDgKK\nJVWzgLu8VCI8wpdelPN4/gifezjmAjPzjt01khdoT6YbgWuAPQsUf7utJajtFYfJ3zf939gF3Qst\nZ/QWdivLHzqRaE9VgxOpICWdIjKmOaHww9gF24t9+W4I3OmlEvnJ2HAsynk8ltbrLLQbzZ0jdfL2\nZLoe+AtwcIHic9pagueP1LUmIi8em+zFYz/FLtg+p0i1S4E5TiSqyUAy7oyVG6mISFFOKHwHcGyJ\nKnOAv3upxNQRuuRYHdeZv1RSB6snPQ1LezIdAH4NRAsU/wb47khcZ6Ly4rFdgaexa80W+u59HdjP\niURPdCLRzqoGJ1IlSjpFpCY4ofCV2J2JitkNuMZLJQpNxhiU1uamLGOsi70nYZqBnfMO39UQdkdq\nCZJzsQuO57sC+HpbS1BLnQyBF481efHYL4AHALdItV8DWzmR6N3Vi0yk+pR0ikjNcELhi7GzfIs5\nCPijl0qMxL1tUc7jvUfgfMP1CaA+79iIdK23J9Nfo/BuTzcDX2xrCY6ZJaNqiRePzcdubfkNoNAW\nrq8AezqR6NecSHRZgXKRcUVJp4jUmu8DpSazHMPI7NOe2+q0T0dnV/5+49VWaDznPcM9aXsyfRBw\nUYGifwLRtpbgyuFeY6Lx4rHpXjz2G+wmB/mL6oNddurnwNYauykTiRaHF5Ga46US9UAMOKJEtTOc\nUPjc4Vyno7PrJaBvSaYDW5ubRnwB9nL0JEwAWIydNNXnyYawu8NwztueTG+D7fbN30/9MWCftpag\nWt8GyYvH9gMuAdYvUqUd+KITiT5cvahExga1dIpIzXFC4V7gc9jWuGLO8VKJo4Z5qVtzHhea0V0t\nm9A/4YRhLpXkb295KwMTzsXAwUo4B8eLxz7mxWNXYoc8FEo4e7HjZrdTwikTlZJOEalJTijcDRyK\nXe+wmMu8VGKnYVzm7zmPD+no7Bpul/1Q5c9ah2GM52xPppuw7229vKIPgIPaWoJLh3ruicaLxyZ5\n8dh3AAMsLFLtOWCeE4me4USiH1UvOpGxRUmniNQsJxReBhwIvFSkymTsdpn5yVW5HgD6lq9Zn+Jr\nK1Za/njOLuChoZzIX4vzSiB/C9Ee4DNtLcH2oZx3IvLisQOwCeW5QFOBKj3YXYXmOpFoqV+ORCYE\nJZ0iUtOcUHgptiUwUaRKK3CLl0oUSgpKam1uWondf7zPIYOPcHh6EsYB9so7fF9D2PWGeMqfAZ8q\ncPzEtpbgfUM854TixWMbe/HYzcDtrB7zm+/fwA7+rkJD/bcSGVeUdIpIzfP3aT+Y4ttlbgf8dYhL\nKY32uM5dGDjuckjjOduT6a8AJxcoOrutJXjZUM45kXjx2FR/v/QXKJy4A6SAL2O705+tWnAiNaDQ\nnq8iIjXHCYWf8lKJY4Dri1Q5DLvG5xmDPPXtQAb7S/rOHZ1d67Q2N70z9EgHbUS2vmxPpg8Eflmg\n6GrsMlRShBePBYDPYJc5KjYrPYtdyuu7TiT6XrViE6klaukUkXHDCYVvoHRSebqfmJattbnpPVaP\nnwxgx5BWU/4koteB/w7mBP7SSNcw8J7/MHCcFn8vzovHtsCu2XodxRPOh7HjNr+shFOkOCWdIjLe\n/BT4a4nyS71UYrdBnrPfLPbBhzQ0PQkzi4ETfu4czNaXJZZGehX4dFtLULOpC/DisaAXj10APEPx\nHamSwLHA7k4k+mTVghOpUUo6RWRccULhLHAixWd3O8CNXiqx4SBOmzuuc/+Ozi5niOEN1icZuH1i\n2V3rJZZG6kRLIxXkxWN1Xjz2eeyKCN+k8DC0HmxX+2ZOJPoXJxJVS7FIGZR0isi446/heRi2K7qQ\nWcDfvVQiWOYpX8Qumg62xXDP4UVYtsPznq+k9IL4q/hLI/2N4ksjvTj88MYXf6/0h4DLgZYi1e7B\nbl95qhOJpqsVm8h4oKRTRMYlJxR+B9sV/mGRKnOAK/0tNUtqbW7KUuVZ7D0JM52B4znvbQi7H5R5\nivOBTxc4flJbS7CsxHWi8OKxHbx47E7sXunzilR7A7vt6n5OJKqEXWQIlHSKyLjlhMLPYXeJKTYG\n8iBsclaOau9O9ElgUt6xYjPz+2lPpo/Fdg3nO7etJfjn4QY2Xnjx2GZePHY18ASFd30C6AZ+DGzu\nRKLXO5Fo2eNpRaS/QDar/z8iMr55qcQ3gQtKVDnRCYUvLXUOfxznu8B0/9CWrc1NL4xQiAP0JMw1\nwIKcQxmgtSHslhyH2Z5Mb4ftIp6cV3QtENVMdfDisfWwy0R9ESjV0n0LcIoTiS4uUUdEyqSkU0TG\nPS+VCACXAMcXqdID7OuEwotKnaejs+tabBcrwHdam5vOG7Egc4NJmCnAUvpvrbioIezm70zUT3sy\nHcLuhLNhXtGjwF5tLcEVIxlnrfHisbWA7wBfZ2Arcq7HgdOdSPTeqgQmMkGoe11Exj1/RvtXgH8V\nqdIAXOelEh9fw6mqNa5zfwbu5V2yaz1n4tCGeUUdwGETOeH04rFpXjz2XexksNMonnC2YydvzVPC\nKTLytCORiEwITijseanE4dhWv9kFqqwFXOOlEns6oXCxvbJvw44PDQC7dnR2reUvHj/S8metA9y4\nhtd8Hzgg71gPsKCtJbhkRKKqMV485gAnAd8D1ilR9U3gLOCvTiTaU43YRCYida+LyITipRJtwCNA\nc5EqFzmhcKH9yQHo6Ox6CLsfOsAxrc1NV4xkfD0J4wDv5MX3cEPY3bXYa9qT6YPpP9Gpz8ltLcGL\nRjK+WuDFY/XAZ4EfMbDlN9e7wDnAb51IVIvki1SYutdFZEJxQuF24EjsxJxCvuGlEguKlEHldyfa\nm4EJcdGu9fZkejaFd2C6Crh4BOMa8/yF3Q8Dngb+QvGE80Pgh8BsJxK9UAmnSHWopVNEJiQvlfgO\ncG6R4g+BuU4obPILOjq7tgKe9Z+mgXVam5u6RyqunoS5lIETnjZuCLuv5tdtT6anYvf93jqv6Hlg\n57aWYNdIxTWWefFYI7Zl89vA5qWqAr8BznEiUe3GJFJlSjpFZELyUok64CaKt1Y+D8xzQuHluQf9\n9TlfBjb2Dx3a2tx080jE1JMwDcASYO2cw082hN0d8uu2J9MBbGve5/KK0sDctpbgf0ciprHMi8em\nAidgJweVmgSWwX5WP3Ai0WK7VIlIhal7XUQmJCcUzgDHAq8VqTIH+K2/3NIq/u5EsZxDC0cwrAj9\nE/E5ZLIAABy7SURBVE4o3rX+FQYmnACfH+8JpxePzfRno78OXETphPNGYCsnEv2CEk6R0aWWThGZ\n0LxUYgfsYupOkSoDFo7P62Jfge1iL7bdZtl6EuZXwFfzDm/eEHbbcw+0J9O7Ypd/yl+B5Jy2luCZ\nw41jrPLisXWxOy19CZi2hup3A99zItFHKx6YiJRFSaeITHheKvE/wO+KFHcDuzih8FO5Bzs6u54H\ntvSfHt3a3HTlcGLoSZg64C1g3ZzDLzSE3S1z67Un063YBeDDeae4GziwrSXYO5w4xiIvHtsE+H/A\ncRT/5QDsclbXA+c5kegTVQhNRAZB3esiIvAHoNjSR5OwC8fPyDt+Vc7jkehi35n+CSfkda23J9ON\nwNUMTDjfAD473hJOLx7b1ovHYoDBrrdZLOFcCfwJuz/6AiWcImOTWjpFRAAvlWjCLhy/ZZEqtwCH\n+rsb0dHZNRs7oQhs0tPa2tyUGur1exLmAmzXca5tG8LuM31P2pPpnwGn5tXpBnZvawmOi0TLi8cC\n2LGt3wEOXEP15cDvgZ87kehblY5NRIZHSaeIiM9LJVzgCYqPF/yWEwr/X9+Tjs6uR4Gd/KcntTY3\nXTKU6/YkTAB4Fdgg5/ArwKYNYTcL0J5MLwCuKfDyE9pagn8cynXHEn/3oEOBbwBFF8L3pYBfAr90\nItFK7AglIhWgbTBFRHxOKGy8VOIE+s9Oz3Wul0o86oTC9/vPr2J10rkQGFLSCWxH/4QT4PqchHNj\noFBieWmtJ5xePPYxbNf5SUDrGqq/DVwAXOJEosOeuCUi1aWWThGRPF4qcTHw9SLFHcB2Tijc0dHZ\nFcZO/glgJ7Gs19rclBjs9XoS5mzgjLzD8xrC7mPtyXQDcD+rt97s8wQQaWsJ1txuOn4X+nzsTP1D\ngfo1vOQl4Dzgb04kOmIL8YtIdamlU0RkoNOwLZjzCpS1Ald5qcS+raFwoqOzaxGwFzbxXIBdN7Js\nftf64XmH3wIe9x9/j4EJ5/vAEbWWcHrxWBD4PHaN0VI7B/V5Ertr1I1OJDquJkmJTERKOkVE8jih\nsOelEkdik561ClSZj22Z/BG2i30v//hCBpl0AtsAbt6xGxrCbrY9md4d+G6B1xzf1hKsmYXOvXhs\nDrZV8xigaQ3Vs8DtwC+Ae5xIVN1xIuOEutdFRIrwUokDgNuwrZj5MkAkVd9ssF3ufb/Ez25tblpc\n7jWKzFqPvFy/7vPA0wwc63lJW0vwpHLPP1r8iUGHYVs19yjjJSnsuNXfO5HoK5WMTURGh5JOEZES\nvFTih8D3ixS/Bmybqm/+G3CQf+zM1uamc8o5d0/C1GO70nMn0Ly2LDB5drJu5hUMXP/TADu0tQS7\nyo2/2gY5MQjs2NRfA1c7keiKSsYmIqNLSaeISAleKlEP3AnsXaTKlan65ttYvbj8863NTVuVc+6e\nhNkXuCvv8Nkv169rgL/kHV8J7NzWEnyyvMirx4vHGoH9gS8An2bNE4O6sSsE/NqJRB9fQ10RGSc0\nplNEpAQnFO71UonPY/daLzS+87PNvel/dtYHVwBTgDkdnV1zWpubni/j9J/LP5AKTFsE3FCg7plj\nKeH0Z6DvgB2nuRCYVcbLXgV+C/xJ62uKTDxq6RQRKYOXSnwKuLlI8bL364L3ZwMB28WezZ4TyqTf\nBV51QuGbCr2gJ2GasGNBVy1En4V/v1K/7krslpi57gX2a2sJZob5NobNi8fWB47GJpvlzEDvmxj0\nG+AOzUIXmbjU0ikiUgYnFL7FSyV+C3y5QPH0qdkVm3YFpgIQIHtKFqYE7FjQgkkn8Cnydj5aUjfz\nXWw3da4UcOxoJpxePDYdu6zT57Ez9wtNrMqniUEi0o+SThGR8p2GTboGtPA52ZWbdWWzKwkEGrOB\nuim91NNA76YlztWva30FTu9yJu1boN7xbS3Bt4cV9RB48VgDsA820TwUO3SgHA8Cl6KJQSKSR0mn\niEiZnFB4uZdKLAQeA5zcsgDgZFc2egF7uDvQSH2mZ7OXfnvq7un/PrlV74oPP5bNZCYF6uq6m9Zv\n63Q/f+b+gYBtMOwlQEfdjJUEApPzLvmHtpZgsZbSEeeP09wG23V+NNBS5ktfwU58usKJRMteLkpE\nJhYlnSIig+CEws94qcS3sIuX9zMpuxLPz0W9QCONXR07vXHDRXGAzb7wA5a9+h+WPnYHwQ03py/h\nzAJLA830BurzE07DwPU7K8Ifp3kUNtksa+Y9dlekq7HJ5iNaxF1E1kRJp4jI4F0MHOD/WaWBHgLZ\nDNlAHdlAHYFpawUamprp6epkcmhdwvMXsMnR3yHjrd69cllgCh/WDei5XgksrNR6nH6L5ubYxdsP\nw85CL8dK4B/AX4F/aB90ERkMJZ0iIoO3OQUWPve72OkOTAKgO+AwpXVDlr3yDN3vJwGodyZT79hG\nTY96lgaCA8+ezXy3rXXGUyMZsBeP1QE7sjrR3GwQL38U26J5tZY6EpGhUtIpIlImL5Wow+4hfj6Q\n3x0OwKSsRzc26fQCjUzdcEs/6XynX70skKybQTZQ1+949wtxll/13Uji8G/8Jrz/cR8OK167aPt8\n7ESgQ4HwIF7+GrZF8wonEn1pOHGIiICSThGRsnipRAD4HXBiqXoNZKjP9tIbqIdAgOlzDyJ575Wr\nWjr7fBBoojvQby4SdZkevNsvpuvV5w5+/boLr5qy7sZHzNx6j0F1YXvxWBN22aXDgIOBGYN4eSdw\nLbZV80EnEh31dUFFZPxQ0ikiUgYnFM56qcT/Ai8BZwAzi9WdlPVYHrDjNJ3NdgLol3R208B7gekD\nXpe+4nQ6n7qXusZJdL3+wsEv//HMH+x4Ufz0NcXmxWMh4BBsorkf5S9vBLAUu+j9TcC9TiT60Rrq\ni4gMiXYkEhEZJC+VmNmd6vh14/SZC+saJw0ozxDgg7rp4M9Q/+83d6GRDHN/fD1Z4K26tQa0cn70\n6I28+8vjaZwaZMuFp/LK7ZfT9e6S7nX2+Mw+c759+QP9rm8nArnYiUyHAHuy5v3Oc70G3Oj/eUi7\nBIlINailU0RkDVzXPQ74c/7xAFmap01l++2359jPHsX229jVhurI4mRX0rdm58y9jiZ104UAvJ/T\nrf7gffdw/lmns/c++3D4m1cyeWYLWx39LaaEWpix8RyWJRZP6nzh0QuAnbfbeqvzl3d7px27364P\nnnbk/usBGwzybTzH6kTzGS1xJCLVpqRTRKR8fwNumr/pOtvNfv+5M1ZmoKPH4cGHVrAo/hBnn3U6\nB+9vNxXKXbNzxp5R3rnyx3yUqSPVsLpbfV5kPjNCIR64716O2n4W237uVJymZgBCs7cm9fIzhDbe\nYqdld1/2TNOUSVtls1n+5+A9dysz1izwMH6iqa0oRWS0KekUESnf88aY6x4+cZsFXd2rJ5bvNi3N\nuUs+ztnn/JQ9Nl+f4PouDfRQl82QCdTREFybaXMP5J3A6i53gIaGBiJbbMjfH0iRcA9iFz/hBGje\noI0dTjobgPuffWmrpR8s49O7bsv0qQUnzfdZCdyLTTRvcSLRjhF99yIiw6CkU0RkEFYkX5/kffDO\ngbnH1m7oYdPJK3huRRO3nvIpdjpkIa2f+ip/uelv3HXPvSxJJKgPBFjv2a+w/yGHse/Bnwag5923\nOLA5za0BuOnh5zh8/ryC17zxgScBWLDn3ELFncBd2ETzNicS7Ry5dysiMnKUdIqIDMIb112448oP\nlg6Yet4YsEMke3ozvHXbnzjjint4sauRT+y3PwsWHo3nedx799386vyfkFyS4HMnfpnJT91M5LBj\nmLf4ch55cTGLlyxl43Vn9TtvalkXi555ic3Wa2Gb2R/vO/wEcIf/51EnEu2p6JsWERkBSjpFRAZh\n2SvPDNib3MsEWNw9mcZAhnCjx1PLm3ixq5HdpnXy/445lLqGRt659XfscvQnOaPrQ66/8nIOmbsp\n222wEWBbMB95cTE3PvAkpy7Yv9+5//7wM/T09rL3Vhu9BnwXuNuJRN/Jj0FEZKxT0ikiUr7J77yf\nXqsnY3cRWpkN8M7KRm7rnMkHvQ0c1JzCqcvyzIomACLT0rx61kFke1baV8evZqfgPF7t7eWVpx5j\nu923BWCvbdtYKziNWx56hv89bB8aG1avfnTVP+7GCWRwO5+/zolc/rfqvl0RkZGjpFNEpHxnnfLQ\nh8BG/Q421fVy2Ix32Xu6HU7ZsdLOWl+30SOb1/E9M/k80MrT8bvZdfJ7BNfbhKZ1Ps6hu23LH29/\ngPuffYm9t98cgGdeeZO3uzLs3PQhMycFtcSRiNQ0JZ0iIuW75Ittk6dMT73yOYAANuFsbVxJ3epJ\n6XRn66gnS0Ng4AkaA3ZnyfeTb/Pybc8AUO9MZs7MjQgAsdv/yW4bz2LyjLW58cGnANh9WppAYD2v\nou9MRKTClHSKiJRv8d6zQ50fLP/P50pVmhTI0EuAniwDEk8vYw9Mrlu9rXmv9xFO8kXcyevy2KtJ\n7vr5qQSnN3Hby7PYoNlho0nd1E+d/vbIvx0RkeqpG+0ARERqyfTZ2zy3pjrrNtpGycRKZ0BZX9d7\na8PAhsvdp6XJEODJ5dN48t0MK3ph5zqbawY322GN1xURGcuUdIqIDML6R5zyeGPz2stK1dluahcA\n8WXN/Y6vzMIjXdNpDGTYcsryAa/bekoX0+t6+PfyaTy1vAknkGHHpmU0zpiV/vihX3t8BN+GiEjV\nqXtdRGQQprRs0O3MbLl9Zee7Rxars82ULuZM7uKhriArswE2m7yC7kwdTyyfRrLH4YiZ7zKtPjPg\ndfUB2GXaMu5Kz6QxkGHu1A+ZUpfFmbHO7VNaNuiu6BsTEakwtXSKiAxS0N3xstztLPMFAnDirA4O\nbk7xhjeJq1OzuLUzREMgy0lrL2Gv6cU3DdptWpoAWVZm69htWhoCAZrbdrqsAm9DRKSqAtmsVuEQ\nERmkwIPHtj2yIvHyTpW+0JTwJo/tdnn7zoBu1iJS09TSKSIyeNnmLeadWudMqWiXd50zpXvGlrt8\nEyWcIjIOKOkUERmCOd++/IHpm253USWvMX2TbS/c8lt/frCS1xARqRYlnSIiQ7TJF3/y/aYNtri1\nEudu2nDLWzc5/uwfVOLcIiKjQUmniMgQzdx6j+4NFnxzYdOGW45o4tm04Za3bnDEKQtnbr2HZqyL\nyLihiUQiIsP0/rP3T3r5T9/74bKXnzo507180lDPUzdpavf0Tbb7xSZf/PFZSjhFZLxR0ikiMkKe\nP+/Y3TtfePSCFUte2YnB3FsDAaasO/ux5i3mnTrn25c/ULkIRURGj5JOEZGRFfjP+V/YP/3fJ4/z\nPnjnwJUfLA0Wq9g4Y1bambHO7c1tO122xamX3IlmqYvIOKakU0SkQlYkX5/0xg0XzV328tNb9y5f\n9rFsNuMEAnVe/dTpb0/fZNtn1//MN57QTkMiMlEo6RQRERGRitPsdRERERGpOCWdIiIiIlJxSjpF\nREREpOKUdIqIiIhIxSnpFBEREZGKaxj6SxNZyEI2g11azv+Tzfjlfccy2EWS85+z+jnZnDq5z1n9\nfNWxTPFrZXNemy1wnmzOOfIf++fJZnPizX8Mea9ZU50if6B4WTYD2d4y6pQ4D/45cutkcmLN9H1G\nWcjkxNyvLNO/HpQ4R857L3Q8t37ftTJZsn49W8V/vOqfq68cspnVZdm+c/V9zNmsX27LbLlfD4q+\nvu+19uNZXZbNOc+q+n0fc5Hy1f/sRcr7Ylj1MZd3nszqjwr/Lfa9df94oF+dvtf0r7P6dbl1cv9J\nM9nAqjqrrpdXZ9U56H+Ovv+Btk6gaF3/n6LoOVbFTP948utif7r5/UvdAaQoZ7svZgN19fT7U19P\nXV09QM7xugF1Ajl16vLPkVOv/3nqqasLEKgLEAgEVj22dQLUBfyyur4yVtXrq1OwXsDWBQqevz7n\nD7DqcUO/srqBdQKBInX7n6fgn4CtUxcIUB+wcdUHAv2e1/l16gN+vbqc8lXP8ev4ZXV95+z/HCAQ\ngDoC9u/AwOcAAT8mW+Z/vqvq+nX6jtF3ntznq68V8L8DAqu+V3KeQ//vm8zAekDOsczq12ZK1Mn0\nks3Yv8n0ku31vwczfWW9kMn4f/uPV9XpHfj6nMeArZtzrmwmQ7bX/+N/D/Y9z/QOLM/09q+TW55Z\n9di/Vm5Zv7rZ1efJZMn0ZnOOZ/1zZcn2Zv23Zcv6Xpf/Glsn68eX9euufpzxb5q92Sy92eJ/2zql\n64G9966pTm8Wfpd9rej9WS2dIiIiIlJxSjpFREREpOKUdIqIiIhIxSnpFBEREZGKU9IpIiIiIhWn\npFNEREREKk5Jp4iIiIhUnJJOEREREak4JZ0iIiIiUnFKOkVERESk4pR0ioiIiEjFKekUERERkYpT\n0ikiIiIiFaekU0REREQqTkmniIiIiFSckk4RERERqTglnSIiIiJScUo6RURERKTilHSKiIiISMUp\n6RQRERGRilPSKSIiIiIVp6RTRERERCpOSaeIiIiIVJySThERERGpOCWdIiIiIlJxSjpFREREpOKU\ndIqIiIhIxSnpFBEREZGKU9IpIiIiIhWnpFNEREREKk5Jp4iIiIhUnJJOEREREak4JZ0iIiIiUnFK\nOkVERESk4gLZbHa0YxARERGRcU4tnSIiIiJScUo6RURERKTilHSKiIiISMUp6RQRERGRilPSKSIi\nIiIVp6RTRERERCpOSaeIiIiIVFxDOZVc170Q2BnIAt8wxjyeU7YPcA7QC9xmjPlxJQIdD9bwOe4F\nnIv9HA1wgjEmMyqBjnGlPsecOucCuxhj5lc5vJqxhp/HjwNXAQ7wpDHmS6MTpYwG13UbgcuADbD3\npC8YYxbn1dkG+KP/9OZq3/vLiTGn7lVAtzHmuKoFuPra5XyWRwGnAhngXmPMmVWMb8x/v9fCd2ct\nfC+NhXv+Gls6XdfdE9jUGLMLcDxwcV6Vi4HDgd2A/VzX3WLEoxwHyvgc/wAcYYzZDZgOHFDlEGtC\nGZ8j/s/gHtWOrZaU8TleAFxgjNkJ6HVdd/1qxyij6rPAB8aY3YGzsV/q+f4AnATsBGzhuu7UKsYH\n5cWI67r7ArOrGVieknH6n9t5wN7ALsA+1foerYXv91r47qyF76Wxcs8vp3t9b+AmAGPMi8BM13WD\nAK7rbgykjDFv+r9Z3ObXl4GKfo6+HYwxb/mPlwJrVTm+WrGmzxHsf56qtRTUqFL/r+uACHCLX/5V\nY8wboxWojIq9gRv9x/dgk45VXNdtAaYZY540xmSMMQuNMcvHUowArutOAr4L/KSKceUrGaf/uW1l\njFlmjMkC71G9+38tfL/XwndnLXwvjYl7fjlJZyv2H7LPUv9YobJ3gHVHJrRxp9TniDEmDeC67rrA\nftj/4DJQyc/Rdd3jgH8Br1U1qtpT6nOcBSwDLnRd9wG/S0gmllU/H37CkXVd18kp3xBIua57meu6\nD7que/IYjBHgdOC3QLrKseVaY5zGmGUArutuhf1sH6l2bL6x+P1eC9+dtfC9NCbu+WWN6cwTGGKZ\n9Dfgs3Jddx3g78BXjDHvVT+kmrTqc3RdNwR8AdgH+NioRVSbAnmPPwZchL1J/sN13YOMMf8YjcCk\nslzXPQE4Ie/wvLzn+ferALARcCiwAnjYdd27jTH/GSsxuq67KTDXGPMD13XnVyKufEP8LPteuylw\nJfBZY8zKCoRXjlr4fq+F785a+F4alXt+OUlngpyMHQgDS4qUfcw/JgOV+hzxm7lvB840xtxV5dhq\nSanP8RPY39jiwCRgtuu6FxpjTqluiDWh1Of4LvC6MeYVANd17wW2BJR0jkPGmEuBS3OPua57Gfbn\n4xl/IkzAGOPlVEkC/+n7gndd9wHsz0hFks4hxngQsL7ruo8AQWCW67rfMsacX4kYhxEnruuuh+36\nPMYY83Sl4iugFr7fa+G7sxa+l8bEPb+c7vW7gCP8QLYHEn1dAcaY14Cg67obuq7bABzs15eBin6O\nvguAC40xd4xGcDWk1M/jdcaYLYwxOwOHYWfgKeEsrNTn2AMs9lteAHbAzgqVieMuYIH/+BDgvtxC\nY8yrwHTXdUP+eLBtqf7PyJpi/IUxZmv/fvAV4B+VTDhLKBmn74/Al40xT1YtKqsWvt9r4buzFr6X\nxsQ9P5DNZtdYyXXdn2JnXWWArwLbAZ3GmBtd190DO/MO4HpjzM8qEeh4UOxzBO4E3gcezql+pTHm\nD1UPsgaU+nnMqbMhcJmWTCpuDf+vN8Eu81IHPIf9QtQSXhOE67r12Ba7TYFu4DhjzJuu634H+Jcx\n5mHXdedhZ8BmgTuMMT8YazHm1J3vlx9XzRjLiRM7cehp4LGcl/3cGHNLleIb89/vtfDdWQvfS2Ph\nnl9W0ikiIiIiMhzakUhEREREKk5Jp4iIiIhU3FCWTBIpyXXdw40x15dZ9zjgF0CLMabbPzYTOzv2\nJGPMZa7rNgEXAnOBj4AUdrzJm/7M0OuMMbeO/DsREREA13WzwEJjTCzn2LXArL4xiq7rHgN8Azt2\ntRE43xhznT+m9mvGmCOqHriMKWrplBHlD5ReOMiXvQd8Muf54cCbOc8vBF4zxmxvjNkVuByIISIi\n1bKYnHu767rTgc1znu8GfA3Yx9+S8mDgp67rutUOVMYutXRKWfy10K4EmoCpwNeBa4A5xpgPXdf9\nGfA8dmmQnVzX/T52odnLgBnY33r/t8iSILdh9yfum+V3JHC3f93pwP7Axn2VjTHXuq5790i/RxGR\nWuKv+3k5sAG2F+ifwNbYNRij2CVyon71m4wx57muux92W9AV2B6lo4G98o8VWKD+TWA913VnGmPe\nBz4N3A/07cf+deCHxpgPAIwx77iuO9cY84G/W5CIWjqlbK3ApcaYvbBby327SL3/wy5X8iNsN8sj\n/mtOxrZYFvJvYAvXdaf7ezo7QIdfNhswxpje3Bf03dhERCawY4EOv2XxEuzQo/Wxy+I4wHHYPbUj\nwFGu687GtkaeaozZE9tjtFaRY4Xcgu2JAjgKuCGnrA279NMquk9LPiWdUq4kcLi/88h5FL8p5ZoL\nLAIwxjwBbFKi7j+wW+otwO7M0ScL1A8hXhGR8W574EEAf6zlCuBxY0wWuwbjI8aYHn/x7weBbYBr\ngd+5rnsG8JQxpqPIsUKuBRb64+5bgJdzynSvljVS0inlOhl42xizO/Bl/1juIq+NBV6Tpf/+rvWu\n627kuu4i/88OOWXXYhPOI4DcSUiLgTbXdSflnth13blDfB8iIuNFLwO/x/u22My//zpAxhjzV2x3\n+rvA313XbSt0zHXd3/v36TP7TmCMeQG7peOJ2FbPXO3ATrkH/PNMG9Y7lHFFSaeUa23gFf/xYdgb\nWBpY199xY2e/LMPqscKPY29kuK67M/C8MeZVY8x8/8+/+05ujHkc2BBoMMa8mXN8GXAz8OO+Y67r\nHg5c4Lpu7g1VRGSieRy7tzeu6x6MHcvZ5ylgF9d1G/xtLOcBT7mu+z1gpb9rTww7tGnAMWPM//j3\n6bPzrnkDdnhV/golFwE/cF13HT+eVmxjwvoj+H6lxinplHL9Bfim67p3AY9ix3j+Cvg79ib0H7/e\ni8D2ruteiL0J7eC67j+Bn2LHeJZyJzbBzHcy4Liu+5zruvcDnwEO87uQREQmqhjQ5Lruv7D3yVW/\niPt7p/8Bu9VmHDsm/3XgDeAe13XvwXa331HkWDHXYseRvph70BjzCHAGcKc/DCuGnTz6wki8URkf\ntA2miIiIiFScWjpFREREpOKUdIqIiIhIxSnpFBEREZGKU9IpIiIiIhWnpFNEREREKk5Jp4iIiIhU\nnJJOEREREam4/w/E/56mn2NjqgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### WORKING QBOE/W MASK\n",
    "### \n",
    "\n",
    "mods = [cam_noqbo,cccmacmam_noqbo,isac_cnrecham5sh_noqbo,\n",
    "        mirocmiroc_esm_noqbo]\n",
    "        #mirocmiroc_agcm_noqbo,\n",
    "        #,mohcumga7_noqbo]\n",
    "        #,mrimri_esm2_noqbo,waccm_noqbo,\n",
    "        #yonseihadgem2_a_noqbo,yonseihadgem2_ac_noqbo]\n",
    "\n",
    "qemask = [cam_qboe_mask, cccmacmam_qboe_mask, isac_cnrecham5sh_qboe_mask, \n",
    "        mirocmiroc_esm_qboe_mask]\n",
    "        # mirocmiroc_agcm_qboe_mask,\n",
    "        #,mohcumga7_qboe_mask]\n",
    "        #,mrimri_esm2_qboe_mask,waccm_qboe_mask,\n",
    "        #yonseihadgem2_a_qboe_mask,yonseihadgem2_ac_qboe_mask]\n",
    "qwmask = [cam_qbow_mask, cccmacmam_qbow_mask, isac_cnrecham5sh_qbow_mask,\n",
    "        mirocmiroc_esm_qbow_mask]\n",
    "        # mirocmiroc_agcm_qbow_mask,\n",
    "        #,mohcumga7_qbow_mask]\n",
    "        #,mrimri_esm2_qbow_mask,waccm_qbow_mask,\n",
    "        #yonseihadgem2_a_qbow_mask,yonseihadgem2_ac_qbow_mask]\n",
    "monmask = [cam_noqbo_mask, cccmacmam_noqbo_mask, isac_cnrecham5sh_noqbo_mask, \n",
    "        mirocmiroc_esm_noqbo_mask]\n",
    "        #mirocmiroc_agcm_noqbo_mask,\n",
    "        #,mohcumga7_noqbo_mask]\n",
    "        #,mrimri_esm2_noqbo_mask,waccm_noqbo_mask,\n",
    "        #yonseihadgem2_a_noqbo_mask,yonseihadgem2_ac_noqbo_mask]\n",
    "\n",
    "# var_select=['EA-tas','BK-tas','Sib-SLP','Ural-SLP'] #,'EA-tas']\n",
    "var_select=['NAO','BK-SIC','EA-tas','PoV','Sib-SLP','Ural-SLP'] # ['BK-tas','EA-tas','PoV','v_flux','Sib-SLP']\n",
    "#var_select=['NAO','PoV','BK-SIC']\n",
    "var_select=['v_flux','Sib-SLP','EA-tas','PoV']\n",
    "\n",
    "current_var = \"_\".join(var_select)\n",
    "print(\"Current variables: {}\".format(current_var))\n",
    "\n",
    "# Change these variables\n",
    "tau_max = 5         # Maximum lag\n",
    "tau_min = 1         # Minimum lag\n",
    "robust_thres = .4   #Only show links that are significant in that fraction of models\n",
    "alpha_level = 0.3\n",
    "\n",
    "n_models = len(mods)\n",
    "n_cols = len(var_select)\n",
    "\n",
    "link_matrices_all = numpy.zeros((n_models, n_cols, n_cols, tau_max + 1))\n",
    "average_vals_all = numpy.zeros((n_models, n_cols, n_cols, tau_max + 1))\n",
    "link_matrices_qe = numpy.zeros((n_models, n_cols, n_cols, tau_max + 1))\n",
    "average_vals_qe = numpy.zeros((n_models, n_cols, n_cols, tau_max + 1))\n",
    "link_matrices_qw = numpy.zeros((n_models, n_cols, n_cols, tau_max + 1))\n",
    "average_vals_qw = numpy.zeros((n_models, n_cols, n_cols, tau_max + 1))\n",
    "\n",
    "for i in range(n_models):\n",
    "    print(i)\n",
    "    # Initialize dataframe object\n",
    "    data = mods[i].as_matrix(columns=var_select)\n",
    "    \n",
    "    df_all = pp.DataFrame(data,mask=monmask[i][:,0:n_cols])\n",
    "    df_qbow = pp.DataFrame(data, mask=qwmask[i][:,0:n_cols])\n",
    "    df_qboe = pp.DataFrame(data, mask=qemask[i][:,0:n_cols])\n",
    "    \n",
    "    print('results all')\n",
    "    results_all, link_matrix_all = run_and_plot_prepare_ensemble(df_all, \n",
    "                                      ParCorr(significance='analytic',mask_type='y'),\n",
    "                                      var_select=var_select, tau_max=tau_max,\n",
    "                                      tau_min=tau_min,pc_alpha=alpha_level)\n",
    "    print('results qboe')\n",
    "    results_qboe, link_matrix_qboe = run_and_plot_prepare_ensemble(df_qboe,\n",
    "                                      ParCorr(significance='analytic',mask_type='y'),\n",
    "                                      var_select=var_select, tau_max=tau_max,\n",
    "                                      tau_min=tau_min,pc_alpha=alpha_level)\n",
    "    results_qbow, link_matrix_qbow = run_and_plot_prepare_ensemble(df_qbow,\n",
    "                                      ParCorr(significance='analytic',mask_type='y'),\n",
    "                                      var_select=var_select, tau_max=tau_max,\n",
    "                                      tau_min=tau_min,pc_alpha=alpha_level)\n",
    "\n",
    "    link_matrices_all[i] = link_matrix_all\n",
    "    average_vals_all[i] = results_all['val_matrix']\n",
    "    link_matrices_qw[i] = link_matrix_qbow\n",
    "    average_vals_qw[i] = results_qbow['val_matrix']\n",
    "    link_matrices_qe[i] = link_matrix_qboe\n",
    "    average_vals_qe[i] = results_qboe['val_matrix']\n",
    "\n",
    "\n",
    "print(\"ALL YEARS\")\n",
    "link_color_matrix_all = average_vals_all.mean(axis=0)\n",
    "link_width_all = link_matrices_all.mean(axis=0)\n",
    "link_matrix_all = link_width_all > robust_thres\n",
    "\n",
    "print(\"QBOW YEARS\")\n",
    "link_color_matrix_qw = average_vals_qw.mean(axis=0)\n",
    "link_width_qw = link_matrices_qw.mean(axis=0)\n",
    "link_matrix_qw = link_width_qw > robust_thres\n",
    "\n",
    "print(\"QBOE YEARS\")\n",
    "link_color_matrix_qe = average_vals_qe.mean(axis=0)\n",
    "link_width_qe = link_matrices_qe.mean(axis=0)\n",
    "link_matrix_qe = link_width_qe > robust_thres\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "## PLOT ENSEMBLE PLOTS\n",
    "\n",
    "# FIGURE PROPERTIES\n",
    "\n",
    "savefig = False #True # False # Save figures in the google drive, True or False\n",
    "\n",
    "vmax_edges=0.7; vmin_edges=-0.7\n",
    "vmax_nodes=0.7; vmin_nodes=0.0\n",
    "edge_ticks=0.2; node_ticks=0.2\n",
    "cmap_nodes='YlOrBr'; cmap_edges='RdBu_r'\n",
    "    \n",
    "\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "ax = plt.subplot(111,frameon=False)\n",
    "plt.title(\"MMM, All, week\")\n",
    "print(\"ALL YEARS\")\n",
    "tp.plot_graph(\n",
    "    val_matrix=link_color_matrix_all,\n",
    "    fig_ax=(fig,ax),\n",
    "    link_matrix=link_matrix_all,\n",
    "    var_names=var_select,\n",
    "    link_colorbar_label='cross-MCI',\n",
    "    node_colorbar_label='auto-MCI',\n",
    "    link_width=link_width_all,\n",
    "    vmax_edges=vmax_edges,\n",
    "    vmin_edges=vmin_edges,\n",
    "    edge_ticks=edge_ticks,\n",
    "    node_ticks=node_ticks,\n",
    "    vmax_nodes=vmax_nodes,\n",
    "    vmin_nodes=vmin_nodes,\n",
    "    cmap_nodes=cmap_nodes,\n",
    "    cmap_edges=cmap_edges,\n",
    "    node_size=30,\n",
    "    node_label_size=20,\n",
    "    arrowhead_size=20,\n",
    "    arrow_linewidth=30,\n",
    "    show_colorbar=True\n",
    "    )\n",
    "\n",
    "if savefig:\n",
    "  fig.savefig('modens_week_{}.png'.format(current_var), bbox_inches='tight')\n",
    "  figfile = drive.CreateFile({'title':'modens_week_{}.png'.format(current_var),\n",
    "                              'parents':[{u'id':'17iJOGBwy-Hq4DQO-nzeTcIOYFGPpD_GB'}]})\n",
    "  figfile.SetContentFile('modens_week_{}.png'.format(current_var))\n",
    "  figfile.Upload()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 515
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 44547,
     "status": "ok",
     "timestamp": 1554203508680,
     "user": {
      "displayName": "Nicholas Tyrrell",
      "photoUrl": "https://lh4.googleusercontent.com/-gi3usNz72WA/AAAAAAAAAAI/AAAAAAAABbE/EZOgb1iXIH8/s64/photo.jpg",
      "userId": "00640113987881425946"
     },
     "user_tz": -180
    },
    "id": "YD7Fq5SQF9Lq",
    "outputId": "228df252-2a0d-41e8-9e9f-913d5924bf1f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QBOW YEARS\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,8))\n",
    "ax = plt.subplot(111,frameon=False)\n",
    "plt.title(\"MMM, QBOW\")\n",
    "print(\"QBOW YEARS\")\n",
    "tp.plot_graph(\n",
    "    val_matrix=link_color_matrix_qw,\n",
    "    fig_ax=(fig,ax),\n",
    "    link_matrix=link_matrix_qw,\n",
    "    var_names=var_select,\n",
    "    link_colorbar_label='cross-MCI',\n",
    "    node_colorbar_label='auto-MCI',\n",
    "    link_width=link_width_qe,\n",
    "    vmax_edges=vmax_edges,\n",
    "    vmin_edges=vmin_edges,\n",
    "    edge_ticks=edge_ticks,\n",
    "    node_ticks=node_ticks,\n",
    "    vmax_nodes=vmax_nodes,\n",
    "    vmin_nodes=vmin_nodes,\n",
    "    cmap_nodes=cmap_nodes,\n",
    "    cmap_edges=cmap_edges\n",
    "    )\n",
    "\n",
    "if savefig:\n",
    "  fig.savefig('modens_week_qbow_{}.png'.format(current_var), bbox_inches='tight')\n",
    "  figfile = drive.CreateFile({'title':'modens_week_qbow_{}.png'.format(current_var),\n",
    "                              'parents':[{u'id':'17iJOGBwy-Hq4DQO-nzeTcIOYFGPpD_GB'}]})\n",
    "  figfile.SetContentFile('modens_week_qbow_{}.png'.format(current_var))\n",
    "  figfile.Upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 515
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 34675,
     "status": "ok",
     "timestamp": 1554122127658,
     "user": {
      "displayName": "Nicholas Tyrrell",
      "photoUrl": "https://lh4.googleusercontent.com/-gi3usNz72WA/AAAAAAAAAAI/AAAAAAAABbE/EZOgb1iXIH8/s64/photo.jpg",
      "userId": "00640113987881425946"
     },
     "user_tz": -180
    },
    "id": "0m_r5Q3fvkvE",
    "outputId": "d2d96f29-a419-425e-b59f-9ffbb1d26798"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QBOE YEARS\n"
     ]
    },
    {
     "data": {
      "image/png": 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OyhNOvnDRPg/svL5on8UbVz7q5AV9XhedDRz6souuGF954gP6xRpfeeKFh77sIp9GBAC7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\nAIDmRCcAAM2JTgAAmhOdAAA09/8B273Nr38noloAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,8))\n",
    "ax = plt.subplot(111,frameon=False)\n",
    "plt.title(\"MMM, QBOE\")\n",
    "print(\"QBOE YEARS\")\n",
    "tp.plot_graph(\n",
    "    val_matrix=link_color_matrix_qe,\n",
    "    fig_ax=(fig,ax),\n",
    "    link_matrix=link_matrix_qe,\n",
    "    var_names=var_select,\n",
    "    link_colorbar_label='cross-MCI',\n",
    "    node_colorbar_label='auto-MCI',\n",
    "    link_width=link_width_qe,\n",
    "    vmax_edges=vmax_edges,\n",
    "    vmin_edges=vmin_edges,\n",
    "    edge_ticks=edge_ticks,\n",
    "    node_ticks=node_ticks,\n",
    "    vmax_nodes=vmax_nodes,\n",
    "    vmin_nodes=vmin_nodes,\n",
    "    cmap_nodes=cmap_nodes,\n",
    "    cmap_edges=cmap_edges\n",
    "    )\n",
    "\n",
    "if savefig:\n",
    "  fig.savefig('modens_week_qboe_{}.png'.format(current_var), bbox_inches='tight')\n",
    "  figfile = drive.CreateFile({'title':'modens_week_qboe_{}.png'.format(current_var),\n",
    "                              'parents':[{u'id':'17iJOGBwy-Hq4DQO-nzeTcIOYFGPpD_GB'}]})\n",
    "  figfile.SetContentFile('modens_week_qboe_{}.png'.format(current_var))\n",
    "  figfile.Upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 500
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1484,
     "status": "ok",
     "timestamp": 1553676970869,
     "user": {
      "displayName": "Nicholas Tyrrell",
      "photoUrl": "https://lh4.googleusercontent.com/-gi3usNz72WA/AAAAAAAAAAI/AAAAAAAABbE/EZOgb1iXIH8/s64/photo.jpg",
      "userId": "00640113987881425946"
     },
     "user_tz": -120
    },
    "id": "XP3eHwWTaVhQ",
    "outputId": "ce467893-19a8-4081-9b1f-cd4b3498d3ac"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QBOE - QBOW\n"
     ]
    },
    {
     "data": {
      "image/png": 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FzU7buwMHwt1dczoTfQfO6Aj/pCKdcn+UriOis4FVBUnmsanNZMJrbdBax/gShJjwJDkV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\n",
      "text/plain": [
       "<Figure size 720x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## PLOT ENSEMBLE PLOTS for difference between QBOE-QBOW\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "ax = plt.subplot(111,frameon=False)\n",
    "\n",
    "link_color_matrix_diff = link_color_matrix_qe - link_color_matrix_qw\n",
    "link_matrix_diff = link_matrix_qe ^ link_matrix_qw\n",
    "link_width_diff = np.abs(link_width_qe - link_width_qw)\n",
    "\n",
    "fig = plt.figure(1)\n",
    "ax = plt.subplot(111,frameon=False)\n",
    "print(\"QBOE - QBOW\")\n",
    "tp.plot_graph(\n",
    "    val_matrix=link_color_matrix_diff,\n",
    "    fig_ax=(fig,ax),\n",
    "    link_matrix=link_matrix_diff,\n",
    "    var_names=var_select,\n",
    "    link_colorbar_label='cross-MCI',\n",
    "    node_colorbar_label='auto-MCI',\n",
    "    vmax_edges=0.3,\n",
    "    vmin_edges=-0.3,\n",
    "    edge_ticks=0.1,\n",
    "    node_ticks=0.1,\n",
    "    vmax_nodes=0.3,\n",
    "    vmin_nodes=-0.3,\n",
    "    cmap_nodes='BrBG_r',\n",
    "    cmap_edges='RdBu_r'\n",
    "    )\n",
    "if savefig:\n",
    "  fig.savefig('modens_week_qbodif_{}.png'.format(current_var), bbox_inches='tight')\n",
    "  figfile = drive.CreateFile({'title':'modens_week_qbodif_{}.png'.format(current_var),\n",
    "                              'parents':[{u'id':'17iJOGBwy-Hq4DQO-nzeTcIOYFGPpD_GB'}]})\n",
    "  figfile.SetContentFile('modens_week_qbodif_{}.png'.format(current_var))\n",
    "  figfile.Upload()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 336
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 38837,
     "status": "ok",
     "timestamp": 1553670933051,
     "user": {
      "displayName": "Nicholas Tyrrell",
      "photoUrl": "https://lh4.googleusercontent.com/-gi3usNz72WA/AAAAAAAAAAI/AAAAAAAABbE/EZOgb1iXIH8/s64/photo.jpg",
      "userId": "00640113987881425946"
     },
     "user_tz": -120
    },
    "id": "kwi_CVZpiTyQ",
    "outputId": "06455a3a-0899-44c2-b5a5-9e90aa34bacd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ALL YEARS\n",
      "QBOW YEARS\n",
      "QBOE YEARS\n",
      "QBO DIFF: QBOE - QBOW\n"
     ]
    },
    {
     "data": {
      "image/png": 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Azgd+kKux2Q3l+fm+J3BOa/o+ePhRje/3OeQgnn70MbbfbVcAjj71VH7/swsA+Pfvncyd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\n3wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "####### NOTE - new version of Tigramite is doing something screwy to the figures, individual figs work - Nick 03/2019\n",
    "## PLOT ENSEMBLE PLOTS\n",
    "\n",
    "# FIGURE PROPERTIES\n",
    "\n",
    "savefig = False # Save figures in the google drive, True or False\n",
    "\n",
    "vmax_edges=0.7; vmin_edges=-0.7\n",
    "vmax_nodes=0.7; vmin_nodes=0.0\n",
    "edge_ticks=0.2; node_ticks=0.2\n",
    "cmap_nodes='YlOrBr'; cmap_edges='RdBu_r'\n",
    "    \n",
    "\n",
    "fig = plt.figure(1)\n",
    "ax = plt.subplot(111,frameon=False)\n",
    "plt.title(\"MMM, All\")\n",
    "print(\"ALL YEARS\")\n",
    "tp.plot_graph(\n",
    "    val_matrix=link_color_matrix_all,\n",
    "    fig_ax=(fig,ax),\n",
    "    link_matrix=link_matrix_all,\n",
    "    var_names=var_select,\n",
    "    link_colorbar_label='cross-MCI',\n",
    "    node_colorbar_label='auto-MCI',\n",
    "    link_width=link_width_all,\n",
    "    vmax_edges=vmax_edges,\n",
    "    vmin_edges=vmin_edges,\n",
    "    edge_ticks=edge_ticks,\n",
    "    node_ticks=node_ticks,\n",
    "    vmax_nodes=vmax_nodes,\n",
    "    vmin_nodes=vmin_nodes,\n",
    "    cmap_nodes=cmap_nodes,\n",
    "    cmap_edges=cmap_edges\n",
    "    )\n",
    "\n",
    "if savefig:\n",
    "  fig.savefig('ens_week_all_{}.png'.format(current_var), bbox_inches='tight')\n",
    "  figfile = drive.CreateFile({'title':'ens_week_all_{}.png'.format(current_var),\n",
    "                              'parents':[{u'id':'13BVOh-AHmhzpvS30-eSIvLvhKkYC_01R'}]})\n",
    "  figfile.SetContentFile('ens_week_all_{}.png'.format(current_var))\n",
    "  figfile.Upload()\n",
    "\n",
    "\n",
    "fig = plt.figure(1)\n",
    "ax = plt.subplot(111,frameon=False)\n",
    "plt.title(\"MMM, QBOW\")\n",
    "print(\"QBOW YEARS\")\n",
    "tp.plot_graph(\n",
    "    val_matrix=link_color_matrix_qw,\n",
    "    fig_ax=(fig,ax),\n",
    "    link_matrix=link_matrix_qw,\n",
    "    var_names=var_select,\n",
    "    link_colorbar_label='cross-MCI',\n",
    "    node_colorbar_label='auto-MCI',\n",
    "    link_width=link_width_qw,\n",
    "    vmax_edges=vmax_edges,\n",
    "    vmin_edges=vmin_edges,\n",
    "    edge_ticks=edge_ticks,\n",
    "    node_ticks=node_ticks,\n",
    "    vmax_nodes=vmax_nodes,\n",
    "    vmin_nodes=vmin_nodes,\n",
    "    cmap_nodes=cmap_nodes,\n",
    "    cmap_edges=cmap_edges\n",
    "    )\n",
    "\n",
    "if savefig:\n",
    "  fig.savefig('ens_week_qbo_w_{}.png'.format(current_var), bbox_inches='tight')\n",
    "  figfile = drive.CreateFile({'title':'ens_week_qbo_w_{}.png'.format(current_var),\n",
    "                              'parents':[{u'id':'13BVOh-AHmhzpvS30-eSIvLvhKkYC_01R'}]})\n",
    "  figfile.SetContentFile('ens_week_qbo_w_{}.png'.format(current_var))\n",
    "  figfile.Upload()\n",
    "\n",
    "\n",
    "fig = plt.figure(1)\n",
    "ax = plt.subplot(111,frameon=False)\n",
    "plt.title(\"MMM, QBOE\")\n",
    "print(\"QBOE YEARS\")\n",
    "tp.plot_graph(\n",
    "    val_matrix=link_color_matrix_qe,\n",
    "    fig_ax=(fig,ax),\n",
    "    link_matrix=link_matrix_qe,\n",
    "    var_names=var_select,\n",
    "    link_colorbar_label='cross-MCI',\n",
    "    node_colorbar_label='auto-MCI',\n",
    "    link_width=link_width_qe,\n",
    "    vmax_edges=vmax_edges,\n",
    "    vmin_edges=vmin_edges,\n",
    "    edge_ticks=edge_ticks,\n",
    "    node_ticks=node_ticks,\n",
    "    vmax_nodes=vmax_nodes,\n",
    "    vmin_nodes=vmin_nodes,\n",
    "    cmap_nodes=cmap_nodes,\n",
    "    cmap_edges=cmap_edges\n",
    "    )\n",
    "\n",
    "if savefig:\n",
    "  fig.savefig('ens_week_qbo_e_{}.png'.format(current_var), bbox_inches='tight')\n",
    "  figfile = drive.CreateFile({'title':'ens_week_qbo_e_{}.png'.format(current_var),\n",
    "                              'parents':[{u'id':'13BVOh-AHmhzpvS30-eSIvLvhKkYC_01R'}]})\n",
    "  figfile.SetContentFile('ens_week_qbo_e_{}.png'.format(current_var))\n",
    "  figfile.Upload()\n",
    "\n",
    "## PLOT ENSEMBLE PLOTS for difference between QBOE-QBOW\n",
    "fig = plt.figure(1)\n",
    "ax = plt.subplot(111,frameon=False)\n",
    "\n",
    "link_color_matrix_diff = link_color_matrix_qe - link_color_matrix_qw\n",
    "link_matrix_diff = link_matrix_qe ^ link_matrix_qw\n",
    "link_width_diff = np.abs(link_width_qe - link_width_qw)\n",
    "\n",
    "fig = plt.figure(1)\n",
    "ax = plt.subplot(111,frameon=False)\n",
    "plt.title(\"MMM, QBOE - QBOW years\")\n",
    "print(\"QBO DIFF: QBOE - QBOW\")\n",
    "tp.plot_graph(\n",
    "    val_matrix=link_color_matrix_diff,\n",
    "    fig_ax=(fig,ax),\n",
    "    link_matrix=link_matrix_diff,\n",
    "    var_names=var_select,\n",
    "    link_colorbar_label='cross-MCI',\n",
    "    node_colorbar_label='auto-MCI',\n",
    "    vmax_edges=0.3,\n",
    "    vmin_edges=-0.3,\n",
    "    edge_ticks=0.1,\n",
    "    node_ticks=0.1,\n",
    "    vmax_nodes=0.3,\n",
    "    vmin_nodes=-0.3,\n",
    "    cmap_nodes='BrBG_r',\n",
    "    cmap_edges='RdBu_r'\n",
    "    )\n",
    "\n",
    "if savefig:\n",
    "  fig.savefig('ens_week_qbo_diff_{}.png'.format(current_var), bbox_inches='tight')\n",
    "  figfile = drive.CreateFile({'title':'ens_week_qbo_diff_{}.png'.format(current_var),\n",
    "                              'parents':[{u'id':'13BVOh-AHmhzpvS30-eSIvLvhKkYC_01R'}]})\n",
    "  figfile.SetContentFile('ens_week_qbo_diff_{}.png'.format(current_var))\n",
    "  figfile.Upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 37922,
     "status": "ok",
     "timestamp": 1553670933052,
     "user": {
      "displayName": "Nicholas Tyrrell",
      "photoUrl": "https://lh4.googleusercontent.com/-gi3usNz72WA/AAAAAAAAAAI/AAAAAAAABbE/EZOgb1iXIH8/s64/photo.jpg",
      "userId": "00640113987881425946"
     },
     "user_tz": -120
    },
    "id": "LxFNaSrYw5H3",
    "outputId": "76fb3edd-9867-4cd9-89ba-74e19dc07e7f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 0.          0.68852305 -0.1661944   0.01891326  0.02932729]\n",
      "  [ 0.00759075  0.00363316  0.04238395  0.01858568 -0.01005559]]\n",
      "\n",
      " [[ 0.00759075  0.02790492  0.01113722  0.01358088 -0.02807837]\n",
      "  [ 0.          0.47283311 -0.03542194  0.00993412 -0.01269796]]]\n",
      "[[[ 0.00000000e+00  7.16976657e-01 -2.29792284e-01  8.73189554e-02\n",
      "    2.05361906e-02]\n",
      "  [ 2.31646169e-02 -1.46979620e-02  3.18540941e-02  1.71427711e-02\n",
      "   -2.75231808e-02]]\n",
      "\n",
      " [[ 2.31646169e-02 -1.15785799e-02  2.39527268e-02  4.38028859e-04\n",
      "   -2.53993909e-02]\n",
      "  [ 0.00000000e+00  4.37102120e-01  5.94253803e-02 -1.05964620e-02\n",
      "    2.73757615e-02]]]\n"
     ]
    }
   ],
   "source": [
    "print(link_color_matrix_qe)\n",
    "print(link_color_matrix_qw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kibNjJRt2y0y"
   },
   "source": [
    "In the code below I attempted to plot three plots on one figure. Passed the fig and ax to plotting function, but doesn't behave how I'd expect - Nick"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "frzlVNDwhj14"
   },
   "outputs": [],
   "source": [
    "## PLOT ENSEMBLE PLOTS\n",
    "fig = plt.figure(figsize=(20,5))\n",
    "\n",
    "#fig = plt.gcf()\n",
    "ax1 = fig.add_subplot(1,3,1, frameon=False) # should have 1 row, with 3 columns\n",
    "#ax1 = plt.gca()\n",
    "#print(\"ALL YEARS\")\n",
    "tp.plot_graph(\n",
    "    val_matrix=link_color_matrix_all,\n",
    "    fig_ax=(fig,ax1),\n",
    "    link_matrix=link_matrix_all,\n",
    "    var_names=var_select,\n",
    "    link_colorbar_label='cross-MCI',\n",
    "    node_colorbar_label='auto-MCI',\n",
    "    link_width=link_width_all,\n",
    "    show_colorbar=False\n",
    "    )\n",
    "\n",
    "\n",
    "#fig = plt.gcf()\n",
    "ax2 = fig.add_subplot(1,3,2, frameon=False)\n",
    "ax2 = plt.gca()\n",
    "#print(\"QBOW YEARS\")\n",
    "tp.plot_graph(\n",
    "    val_matrix=link_color_matrix_qw,\n",
    "    fig_ax=(fig,ax2),\n",
    "    link_matrix=link_matrix_qw,\n",
    "    var_names=var_select,\n",
    "    link_colorbar_label='cross-MCI',\n",
    "    node_colorbar_label='auto-MCI',\n",
    "    link_width=link_width_qw,\n",
    "    show_colorbar=False\n",
    "    )\n",
    "\n",
    "#fig = plt.gcf()\n",
    "ax3 = fig.add_subplot(1,3,3, frameon=False)\n",
    "ax3 = plt.gca()\n",
    "#print(\"QBOE YEARS\")\n",
    "\n",
    "out = tp.plot_graph(\n",
    "    val_matrix=link_color_matrix_qe,\n",
    "    fig_ax=(fig,ax3),\n",
    "    link_matrix=link_matrix_qe,\n",
    "    var_names=var_select,\n",
    "    link_colorbar_label='cross-MCI',\n",
    "    node_colorbar_label='auto-MCI',\n",
    "    link_width=link_width_qe,\n",
    "    show_colorbar=True\n",
    "    )\n",
    "\n",
    "\n",
    "#print(type(out))\n",
    "#print(type(fig))\n",
    "#fig.savefig('ens_qbo_e.png', bbox_inches='tight')\n",
    "#figfile = drive.CreateFile({'title':'ens_qbo_e.png',\n",
    "#                            'parents':[{u'id':'13BVOh-AHmhzpvS30-eSIvLvhKkYC_01R'}]})\n",
    "#figfile.SetContentFile('ens_qbo_e.png')\n",
    "#figfile.Upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "STLfeVQNzzP-"
   },
   "outputs": [],
   "source": [
    "#Example to save a figure to file\n",
    "#it will be save in the personal downloads so still need to upload it back to the drive\n",
    "#import matplotlib.pyplot as plt\n",
    "#from google.colab import files\n",
    "#fig = plt.figure()\n",
    "#ax = fig.add_axes([1,1,1,1])\n",
    "#plt.plot([1,2])\n",
    "#plt.savefig('abc.png', bbox_inches='tight')\n",
    "#files.download(\"abc.png\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "LROxHGRCThn9"
   },
   "outputs": [],
   "source": []
  }
 ],
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     "timestamp": 1529484299523
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    {
     "file_id": "1zhFfbDq9bVYiW1VSCVt-89L26xNbXW1i",
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     "file_id": "1EJcmh27MZ0waMfu6AEmw-HCyALuYZyNn",
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