scatterplots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "16e8acca",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.lines as mlines\n",
    "from statistics import linear_regression\n",
    "from scipy import stats\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e8f7357",
   "metadata": {},
   "source": [
    "Create a function to strip out all the relevant model simulations for a difference array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "47052e8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_experiment_diff(all_runs, generation, experiment_name, control_experiment='piControl'):\n",
    "    # Filter runs for the specific experiment and control experiment\n",
    "    experiment_runs = all_runs.loc[(all_runs['experiment'] == experiment_name) & (all_runs['generation'] == generation)]\n",
    "    experiment_runs.set_index('model', inplace=True)\n",
    "    \n",
    "    control_runs = all_runs.loc[(all_runs['experiment'] == control_experiment) & (all_runs['generation'] == generation)]\n",
    "    control_runs.set_index('model', inplace=True)\n",
    "    \n",
    "    # Filter control runs to only include models present in the experiment runs\n",
    "    experiment_control_runs = control_runs[control_runs.index.isin(experiment_runs.index)]\n",
    "    \n",
    "    # Drop the 'experiment' and 'years' columns\n",
    "    experiment_control_runs.drop(['generation','experiment', 'years'], axis=1, inplace=True)\n",
    "    experiment_runs.drop(['generation','experiment', 'years'], axis=1, inplace=True)\n",
    "    \n",
    "    # Create a difference DataFrame\n",
    "    diff = experiment_runs - experiment_control_runs\n",
    "    diff['experiment'] = experiment_name\n",
    "    diff['generation'] = generation\n",
    "    diff['run'] = list(range(0, np.size(experiment_runs.index)))\n",
    "    diff.reset_index(inplace=True)\n",
    "    diff.set_index('run', inplace=True)\n",
    "    \n",
    "    return diff"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d67dcced",
   "metadata": {},
   "source": [
    "### Load in the tidy numbers file from summary_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a4c07164",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>generation</th>\n",
       "      <th>model</th>\n",
       "      <th>experiment</th>\n",
       "      <th>years</th>\n",
       "      <th>GMTemp</th>\n",
       "      <th>ATL3 mean</th>\n",
       "      <th>ATL3 stddev</th>\n",
       "      <th>Nino34 stddev</th>\n",
       "      <th>AtlNino mean</th>\n",
       "      <th>AtlNino stddev</th>\n",
       "      <th>...</th>\n",
       "      <th>NEB rain djf stddev</th>\n",
       "      <th>r(atl3.nino34)</th>\n",
       "      <th>r(atl3.amo)</th>\n",
       "      <th>ATL3 Period</th>\n",
       "      <th>ENSO Period</th>\n",
       "      <th>AMM Period</th>\n",
       "      <th>ATL3-sst-interann-stdev</th>\n",
       "      <th>ATL3-pr-interann-stdev</th>\n",
       "      <th>ATL3-sst-interjja-stdev</th>\n",
       "      <th>ATL3-pr-interjja-stdev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PMIP3</td>\n",
       "      <td>CNRM-CM5</td>\n",
       "      <td>midHolocene-cal-adj</td>\n",
       "      <td>200</td>\n",
       "      <td>13.44340</td>\n",
       "      <td>27.1278</td>\n",
       "      <td>0.404581</td>\n",
       "      <td>0.738694</td>\n",
       "      <td>27.1278</td>\n",
       "      <td>0.406142</td>\n",
       "      <td>...</td>\n",
       "      <td>0.474636</td>\n",
       "      <td>0.258286</td>\n",
       "      <td>-0.073615</td>\n",
       "      <td>2.94118</td>\n",
       "      <td>3.92157</td>\n",
       "      <td>4.76190</td>\n",
       "      <td>0.209912</td>\n",
       "      <td>0.593376</td>\n",
       "      <td>0.333129</td>\n",
       "      <td>1.079960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>PMIP3</td>\n",
       "      <td>CNRM-CM5</td>\n",
       "      <td>piControl</td>\n",
       "      <td>850</td>\n",
       "      <td>13.20040</td>\n",
       "      <td>27.2500</td>\n",
       "      <td>0.369616</td>\n",
       "      <td>0.810513</td>\n",
       "      <td>27.2500</td>\n",
       "      <td>0.370870</td>\n",
       "      <td>...</td>\n",
       "      <td>0.499597</td>\n",
       "      <td>0.197199</td>\n",
       "      <td>-0.091163</td>\n",
       "      <td>3.63248</td>\n",
       "      <td>2.81457</td>\n",
       "      <td>6.85484</td>\n",
       "      <td>0.206020</td>\n",
       "      <td>0.592720</td>\n",
       "      <td>0.288996</td>\n",
       "      <td>1.194510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>PMIP3</td>\n",
       "      <td>COSMOS-ASO</td>\n",
       "      <td>lgm-cal-adj</td>\n",
       "      <td>600</td>\n",
       "      <td>6.97033</td>\n",
       "      <td>23.3069</td>\n",
       "      <td>0.772255</td>\n",
       "      <td>1.784770</td>\n",
       "      <td>23.3069</td>\n",
       "      <td>0.772358</td>\n",
       "      <td>...</td>\n",
       "      <td>0.477931</td>\n",
       "      <td>0.416506</td>\n",
       "      <td>0.009899</td>\n",
       "      <td>4.51128</td>\n",
       "      <td>4.95868</td>\n",
       "      <td>6.25000</td>\n",
       "      <td>0.532273</td>\n",
       "      <td>0.619322</td>\n",
       "      <td>0.550877</td>\n",
       "      <td>1.530570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>PMIP3</td>\n",
       "      <td>COSMOS-ASO</td>\n",
       "      <td>piControl</td>\n",
       "      <td>400</td>\n",
       "      <td>12.44070</td>\n",
       "      <td>27.3068</td>\n",
       "      <td>0.562300</td>\n",
       "      <td>1.780830</td>\n",
       "      <td>27.3068</td>\n",
       "      <td>0.570502</td>\n",
       "      <td>...</td>\n",
       "      <td>0.478433</td>\n",
       "      <td>0.395212</td>\n",
       "      <td>-0.104874</td>\n",
       "      <td>3.60360</td>\n",
       "      <td>3.60360</td>\n",
       "      <td>7.01754</td>\n",
       "      <td>0.359882</td>\n",
       "      <td>0.881988</td>\n",
       "      <td>0.525629</td>\n",
       "      <td>1.492910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>PMIP3</td>\n",
       "      <td>CSIRO-Mk3-6-0</td>\n",
       "      <td>abrupt4xCO2</td>\n",
       "      <td>150</td>\n",
       "      <td>18.44520</td>\n",
       "      <td>28.0317</td>\n",
       "      <td>0.347694</td>\n",
       "      <td>0.958433</td>\n",
       "      <td>29.2015</td>\n",
       "      <td>0.347693</td>\n",
       "      <td>...</td>\n",
       "      <td>0.267330</td>\n",
       "      <td>0.080685</td>\n",
       "      <td>0.018165</td>\n",
       "      <td>3.10000</td>\n",
       "      <td>6.20000</td>\n",
       "      <td>1.63158</td>\n",
       "      <td>0.234013</td>\n",
       "      <td>0.253396</td>\n",
       "      <td>0.333338</td>\n",
       "      <td>0.080647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>PMIP3</td>\n",
       "      <td>CSIRO-Mk3-6-0</td>\n",
       "      <td>midHolocene-cal-adj</td>\n",
       "      <td>100</td>\n",
       "      <td>12.81170</td>\n",
       "      <td>23.6707</td>\n",
       "      <td>0.446059</td>\n",
       "      <td>0.805980</td>\n",
       "      <td>23.6707</td>\n",
       "      <td>0.450611</td>\n",
       "      <td>...</td>\n",
       "      <td>0.305654</td>\n",
       "      <td>-0.159986</td>\n",
       "      <td>-0.056988</td>\n",
       "      <td>1.75439</td>\n",
       "      <td>3.84615</td>\n",
       "      <td>7.69231</td>\n",
       "      <td>0.281829</td>\n",
       "      <td>0.245367</td>\n",
       "      <td>0.409170</td>\n",
       "      <td>0.125021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>PMIP3</td>\n",
       "      <td>CSIRO-Mk3-6-0</td>\n",
       "      <td>piControl</td>\n",
       "      <td>500</td>\n",
       "      <td>12.73370</td>\n",
       "      <td>23.9278</td>\n",
       "      <td>0.414529</td>\n",
       "      <td>0.680119</td>\n",
       "      <td>23.9278</td>\n",
       "      <td>0.419222</td>\n",
       "      <td>...</td>\n",
       "      <td>0.377981</td>\n",
       "      <td>-0.023831</td>\n",
       "      <td>0.018407</td>\n",
       "      <td>2.45098</td>\n",
       "      <td>7.81250</td>\n",
       "      <td>5.20833</td>\n",
       "      <td>0.285929</td>\n",
       "      <td>0.241426</td>\n",
       "      <td>0.436068</td>\n",
       "      <td>0.151375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>PMIP3</td>\n",
       "      <td>CSIRO-Mk3L-1-2</td>\n",
       "      <td>midHolocene-cal-adj</td>\n",
       "      <td>500</td>\n",
       "      <td>14.09290</td>\n",
       "      <td>26.6729</td>\n",
       "      <td>0.264040</td>\n",
       "      <td>0.441060</td>\n",
       "      <td>26.6729</td>\n",
       "      <td>0.264435</td>\n",
       "      <td>...</td>\n",
       "      <td>0.487207</td>\n",
       "      <td>0.145686</td>\n",
       "      <td>-0.099377</td>\n",
       "      <td>7.81250</td>\n",
       "      <td>7.81250</td>\n",
       "      <td>5.20833</td>\n",
       "      <td>0.213068</td>\n",
       "      <td>0.321289</td>\n",
       "      <td>0.360917</td>\n",
       "      <td>0.401921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>PMIP3</td>\n",
       "      <td>CSIRO-Mk3L-1-2</td>\n",
       "      <td>piControl</td>\n",
       "      <td>1000</td>\n",
       "      <td>14.04120</td>\n",
       "      <td>26.8395</td>\n",
       "      <td>0.283316</td>\n",
       "      <td>0.534729</td>\n",
       "      <td>26.8395</td>\n",
       "      <td>0.283810</td>\n",
       "      <td>...</td>\n",
       "      <td>0.715183</td>\n",
       "      <td>0.226107</td>\n",
       "      <td>-0.192573</td>\n",
       "      <td>7.93651</td>\n",
       "      <td>7.51880</td>\n",
       "      <td>5.37634</td>\n",
       "      <td>0.227940</td>\n",
       "      <td>0.307086</td>\n",
       "      <td>0.375083</td>\n",
       "      <td>0.358038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>PMIP3</td>\n",
       "      <td>FGOALS-g2</td>\n",
       "      <td>abrupt4xCO2</td>\n",
       "      <td>258</td>\n",
       "      <td>18.82780</td>\n",
       "      <td>30.9886</td>\n",
       "      <td>0.228013</td>\n",
       "      <td>0.541508</td>\n",
       "      <td>31.6808</td>\n",
       "      <td>0.228014</td>\n",
       "      <td>...</td>\n",
       "      <td>0.295914</td>\n",
       "      <td>0.409993</td>\n",
       "      <td>0.102987</td>\n",
       "      <td>4.42857</td>\n",
       "      <td>3.44444</td>\n",
       "      <td>5.16667</td>\n",
       "      <td>0.168199</td>\n",
       "      <td>0.458985</td>\n",
       "      <td>0.260991</td>\n",
       "      <td>1.071820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   generation           model           experiment  years    GMTemp  \\\n",
       "10      PMIP3        CNRM-CM5  midHolocene-cal-adj    200  13.44340   \n",
       "11      PMIP3        CNRM-CM5            piControl    850  13.20040   \n",
       "12      PMIP3      COSMOS-ASO          lgm-cal-adj    600   6.97033   \n",
       "13      PMIP3      COSMOS-ASO            piControl    400  12.44070   \n",
       "14      PMIP3   CSIRO-Mk3-6-0          abrupt4xCO2    150  18.44520   \n",
       "15      PMIP3   CSIRO-Mk3-6-0  midHolocene-cal-adj    100  12.81170   \n",
       "16      PMIP3   CSIRO-Mk3-6-0            piControl    500  12.73370   \n",
       "17      PMIP3  CSIRO-Mk3L-1-2  midHolocene-cal-adj    500  14.09290   \n",
       "18      PMIP3  CSIRO-Mk3L-1-2            piControl   1000  14.04120   \n",
       "19      PMIP3       FGOALS-g2          abrupt4xCO2    258  18.82780   \n",
       "\n",
       "    ATL3 mean  ATL3 stddev  Nino34 stddev  AtlNino mean  AtlNino stddev  ...  \\\n",
       "10    27.1278     0.404581       0.738694       27.1278        0.406142  ...   \n",
       "11    27.2500     0.369616       0.810513       27.2500        0.370870  ...   \n",
       "12    23.3069     0.772255       1.784770       23.3069        0.772358  ...   \n",
       "13    27.3068     0.562300       1.780830       27.3068        0.570502  ...   \n",
       "14    28.0317     0.347694       0.958433       29.2015        0.347693  ...   \n",
       "15    23.6707     0.446059       0.805980       23.6707        0.450611  ...   \n",
       "16    23.9278     0.414529       0.680119       23.9278        0.419222  ...   \n",
       "17    26.6729     0.264040       0.441060       26.6729        0.264435  ...   \n",
       "18    26.8395     0.283316       0.534729       26.8395        0.283810  ...   \n",
       "19    30.9886     0.228013       0.541508       31.6808        0.228014  ...   \n",
       "\n",
       "    NEB rain djf stddev  r(atl3.nino34)  r(atl3.amo)  ATL3 Period  \\\n",
       "10             0.474636        0.258286    -0.073615      2.94118   \n",
       "11             0.499597        0.197199    -0.091163      3.63248   \n",
       "12             0.477931        0.416506     0.009899      4.51128   \n",
       "13             0.478433        0.395212    -0.104874      3.60360   \n",
       "14             0.267330        0.080685     0.018165      3.10000   \n",
       "15             0.305654       -0.159986    -0.056988      1.75439   \n",
       "16             0.377981       -0.023831     0.018407      2.45098   \n",
       "17             0.487207        0.145686    -0.099377      7.81250   \n",
       "18             0.715183        0.226107    -0.192573      7.93651   \n",
       "19             0.295914        0.409993     0.102987      4.42857   \n",
       "\n",
       "    ENSO Period  AMM Period  ATL3-sst-interann-stdev  ATL3-pr-interann-stdev  \\\n",
       "10      3.92157     4.76190                 0.209912                0.593376   \n",
       "11      2.81457     6.85484                 0.206020                0.592720   \n",
       "12      4.95868     6.25000                 0.532273                0.619322   \n",
       "13      3.60360     7.01754                 0.359882                0.881988   \n",
       "14      6.20000     1.63158                 0.234013                0.253396   \n",
       "15      3.84615     7.69231                 0.281829                0.245367   \n",
       "16      7.81250     5.20833                 0.285929                0.241426   \n",
       "17      7.81250     5.20833                 0.213068                0.321289   \n",
       "18      7.51880     5.37634                 0.227940                0.307086   \n",
       "19      3.44444     5.16667                 0.168199                0.458985   \n",
       "\n",
       "    ATL3-sst-interjja-stdev  ATL3-pr-interjja-stdev  \n",
       "10                 0.333129                1.079960  \n",
       "11                 0.288996                1.194510  \n",
       "12                 0.550877                1.530570  \n",
       "13                 0.525629                1.492910  \n",
       "14                 0.333338                0.080647  \n",
       "15                 0.409170                0.125021  \n",
       "16                 0.436068                0.151375  \n",
       "17                 0.360917                0.401921  \n",
       "18                 0.375083                0.358038  \n",
       "19                 0.260991                1.071820  \n",
       "\n",
       "[10 rows x 32 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_runs = pd.read_csv(\"summary_data/tidy_numbers.csv\")\n",
    "# Jump through some hopes to remove suspect data...\n",
    "numerical=all_runs.iloc[:,3:]\n",
    "numerical.mask(abs(numerical) > 1e6, np.nan,inplace=True)\n",
    "#numerical.mask(abs(numerical) < 1e-6, np.nan,inplace=True)\n",
    "all_runs.iloc[:,3:]=numerical\n",
    "all_runs[10:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d05dd451",
   "metadata": {},
   "source": [
    "Select the lgm runs (we'll take the calendar adjusted ones).\n",
    "\n",
    "Then duplicate this and populate with the piControl equivalent numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f5033fc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>GMTemp</th>\n",
       "      <th>ATL3 mean</th>\n",
       "      <th>ATL3 stddev</th>\n",
       "      <th>Nino34 stddev</th>\n",
       "      <th>AtlNino mean</th>\n",
       "      <th>AtlNino stddev</th>\n",
       "      <th>AtlNino AnnCycAmp</th>\n",
       "      <th>AMM mean</th>\n",
       "      <th>AMM stddev</th>\n",
       "      <th>...</th>\n",
       "      <th>r(atl3.amo)</th>\n",
       "      <th>ATL3 Period</th>\n",
       "      <th>ENSO Period</th>\n",
       "      <th>AMM Period</th>\n",
       "      <th>ATL3-sst-interann-stdev</th>\n",
       "      <th>ATL3-pr-interann-stdev</th>\n",
       "      <th>ATL3-sst-interjja-stdev</th>\n",
       "      <th>ATL3-pr-interjja-stdev</th>\n",
       "      <th>experiment</th>\n",
       "      <th>generation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>run</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AWI-ESM-1-1-LR</td>\n",
       "      <td>-3.71394</td>\n",
       "      <td>-1.5402</td>\n",
       "      <td>0.045615</td>\n",
       "      <td>0.146279</td>\n",
       "      <td>-1.5402</td>\n",
       "      <td>0.045074</td>\n",
       "      <td>0.06988</td>\n",
       "      <td>-0.229227</td>\n",
       "      <td>0.049754</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.113669</td>\n",
       "      <td>0.20270</td>\n",
       "      <td>-1.31362</td>\n",
       "      <td>0.23810</td>\n",
       "      <td>0.023975</td>\n",
       "      <td>0.005319</td>\n",
       "      <td>0.097718</td>\n",
       "      <td>0.268470</td>\n",
       "      <td>lgm-cal-adj</td>\n",
       "      <td>PMIP4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>INM-CM4-8</td>\n",
       "      <td>-3.72303</td>\n",
       "      <td>-1.2889</td>\n",
       "      <td>0.145785</td>\n",
       "      <td>0.076214</td>\n",
       "      <td>-1.2889</td>\n",
       "      <td>0.140282</td>\n",
       "      <td>-0.68872</td>\n",
       "      <td>-3.210517</td>\n",
       "      <td>0.317259</td>\n",
       "      <td>...</td>\n",
       "      <td>0.253801</td>\n",
       "      <td>-4.59478</td>\n",
       "      <td>0.52968</td>\n",
       "      <td>1.98263</td>\n",
       "      <td>0.084050</td>\n",
       "      <td>0.577534</td>\n",
       "      <td>0.175200</td>\n",
       "      <td>1.975299</td>\n",
       "      <td>lgm-cal-adj</td>\n",
       "      <td>PMIP4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MIROC-ES2L</td>\n",
       "      <td>-4.04850</td>\n",
       "      <td>-2.5732</td>\n",
       "      <td>0.149163</td>\n",
       "      <td>-0.156850</td>\n",
       "      <td>-2.5732</td>\n",
       "      <td>0.149556</td>\n",
       "      <td>0.57702</td>\n",
       "      <td>-0.019281</td>\n",
       "      <td>0.074201</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.00000</td>\n",
       "      <td>-0.50654</td>\n",
       "      <td>-2.94444</td>\n",
       "      <td>0.174272</td>\n",
       "      <td>0.136910</td>\n",
       "      <td>0.312965</td>\n",
       "      <td>0.679370</td>\n",
       "      <td>lgm-cal-adj</td>\n",
       "      <td>PMIP4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UofT-CCSM-4</td>\n",
       "      <td>-5.27000</td>\n",
       "      <td>-2.4261</td>\n",
       "      <td>0.091440</td>\n",
       "      <td>-0.137060</td>\n",
       "      <td>-2.4261</td>\n",
       "      <td>0.091297</td>\n",
       "      <td>0.58700</td>\n",
       "      <td>0.522140</td>\n",
       "      <td>0.015283</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.61919</td>\n",
       "      <td>-0.89955</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.074780</td>\n",
       "      <td>0.089172</td>\n",
       "      <td>0.215456</td>\n",
       "      <td>0.482600</td>\n",
       "      <td>lgm-cal-adj</td>\n",
       "      <td>PMIP4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CCSM4</td>\n",
       "      <td>-4.91608</td>\n",
       "      <td>-2.1456</td>\n",
       "      <td>0.052002</td>\n",
       "      <td>-0.177133</td>\n",
       "      <td>-2.1456</td>\n",
       "      <td>0.051852</td>\n",
       "      <td>0.04609</td>\n",
       "      <td>-0.118035</td>\n",
       "      <td>0.020961</td>\n",
       "      <td>...</td>\n",
       "      <td>0.045641</td>\n",
       "      <td>0.57162</td>\n",
       "      <td>0.11836</td>\n",
       "      <td>-1.29429</td>\n",
       "      <td>0.023364</td>\n",
       "      <td>0.063091</td>\n",
       "      <td>0.101323</td>\n",
       "      <td>0.350140</td>\n",
       "      <td>lgm-cal-adj</td>\n",
       "      <td>PMIP3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CNRM-CM5</td>\n",
       "      <td>-2.63480</td>\n",
       "      <td>-1.4687</td>\n",
       "      <td>-0.003960</td>\n",
       "      <td>0.020978</td>\n",
       "      <td>-1.4687</td>\n",
       "      <td>-0.004890</td>\n",
       "      <td>0.40581</td>\n",
       "      <td>0.050328</td>\n",
       "      <td>-0.013339</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004531</td>\n",
       "      <td>-2.41297</td>\n",
       "      <td>0.12661</td>\n",
       "      <td>-1.29928</td>\n",
       "      <td>-0.013341</td>\n",
       "      <td>-0.049764</td>\n",
       "      <td>-0.008784</td>\n",
       "      <td>-0.157190</td>\n",
       "      <td>lgm-cal-adj</td>\n",
       "      <td>PMIP3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>COSMOS-ASO</td>\n",
       "      <td>-5.47037</td>\n",
       "      <td>-3.9999</td>\n",
       "      <td>0.209955</td>\n",
       "      <td>0.003940</td>\n",
       "      <td>-3.9999</td>\n",
       "      <td>0.201856</td>\n",
       "      <td>0.22525</td>\n",
       "      <td>0.260643</td>\n",
       "      <td>-0.036029</td>\n",
       "      <td>...</td>\n",
       "      <td>0.114773</td>\n",
       "      <td>0.90768</td>\n",
       "      <td>1.35508</td>\n",
       "      <td>-0.76754</td>\n",
       "      <td>0.172391</td>\n",
       "      <td>-0.262666</td>\n",
       "      <td>0.025248</td>\n",
       "      <td>0.037660</td>\n",
       "      <td>lgm-cal-adj</td>\n",
       "      <td>PMIP3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FGOALS-g2</td>\n",
       "      <td>-4.58484</td>\n",
       "      <td>-2.8489</td>\n",
       "      <td>0.043447</td>\n",
       "      <td>-0.348958</td>\n",
       "      <td>-2.8489</td>\n",
       "      <td>0.042782</td>\n",
       "      <td>0.45131</td>\n",
       "      <td>-0.128027</td>\n",
       "      <td>0.078076</td>\n",
       "      <td>...</td>\n",
       "      <td>0.042913</td>\n",
       "      <td>1.42045</td>\n",
       "      <td>-0.34078</td>\n",
       "      <td>-1.76086</td>\n",
       "      <td>0.043412</td>\n",
       "      <td>0.034186</td>\n",
       "      <td>0.081789</td>\n",
       "      <td>0.195030</td>\n",
       "      <td>lgm-cal-adj</td>\n",
       "      <td>PMIP3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GISS-E2-R</td>\n",
       "      <td>-4.80725</td>\n",
       "      <td>-1.8912</td>\n",
       "      <td>0.027831</td>\n",
       "      <td>-0.162594</td>\n",
       "      <td>-1.8912</td>\n",
       "      <td>0.027849</td>\n",
       "      <td>0.03201</td>\n",
       "      <td>-0.626854</td>\n",
       "      <td>0.074722</td>\n",
       "      <td>...</td>\n",
       "      <td>0.102716</td>\n",
       "      <td>3.93291</td>\n",
       "      <td>-1.63174</td>\n",
       "      <td>-1.93015</td>\n",
       "      <td>0.019337</td>\n",
       "      <td>0.098620</td>\n",
       "      <td>0.051354</td>\n",
       "      <td>0.353490</td>\n",
       "      <td>lgm-cal-adj</td>\n",
       "      <td>PMIP3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>IPSL-CM5A-LR</td>\n",
       "      <td>-4.59955</td>\n",
       "      <td>-3.5842</td>\n",
       "      <td>0.088374</td>\n",
       "      <td>0.222348</td>\n",
       "      <td>-3.5842</td>\n",
       "      <td>0.088400</td>\n",
       "      <td>0.41375</td>\n",
       "      <td>-0.356891</td>\n",
       "      <td>0.051615</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.124031</td>\n",
       "      <td>-3.54167</td>\n",
       "      <td>-0.60607</td>\n",
       "      <td>-2.13451</td>\n",
       "      <td>0.072432</td>\n",
       "      <td>0.061988</td>\n",
       "      <td>0.058449</td>\n",
       "      <td>-0.101261</td>\n",
       "      <td>lgm-cal-adj</td>\n",
       "      <td>PMIP3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MIROC-ESM</td>\n",
       "      <td>-5.00889</td>\n",
       "      <td>-2.1300</td>\n",
       "      <td>0.033166</td>\n",
       "      <td>0.078147</td>\n",
       "      <td>-2.1300</td>\n",
       "      <td>0.020758</td>\n",
       "      <td>0.23454</td>\n",
       "      <td>-0.373266</td>\n",
       "      <td>0.056652</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.80342</td>\n",
       "      <td>-1.88979</td>\n",
       "      <td>2.80164</td>\n",
       "      <td>0.013258</td>\n",
       "      <td>0.009769</td>\n",
       "      <td>0.001200</td>\n",
       "      <td>-0.195160</td>\n",
       "      <td>lgm-cal-adj</td>\n",
       "      <td>PMIP3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MPI-ESM-P</td>\n",
       "      <td>-4.41558</td>\n",
       "      <td>-2.0742</td>\n",
       "      <td>0.024309</td>\n",
       "      <td>0.032227</td>\n",
       "      <td>-2.0742</td>\n",
       "      <td>0.024599</td>\n",
       "      <td>-0.21409</td>\n",
       "      <td>-0.663112</td>\n",
       "      <td>0.056199</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.03127</td>\n",
       "      <td>-1.50512</td>\n",
       "      <td>-1.38052</td>\n",
       "      <td>0.008834</td>\n",
       "      <td>-0.157909</td>\n",
       "      <td>0.005205</td>\n",
       "      <td>-0.324090</td>\n",
       "      <td>lgm-cal-adj</td>\n",
       "      <td>PMIP3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MRI-CGCM3</td>\n",
       "      <td>-4.68431</td>\n",
       "      <td>-3.1551</td>\n",
       "      <td>0.053328</td>\n",
       "      <td>-0.024906</td>\n",
       "      <td>-3.1551</td>\n",
       "      <td>0.053074</td>\n",
       "      <td>0.98978</td>\n",
       "      <td>0.382973</td>\n",
       "      <td>-0.018635</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.65217</td>\n",
       "      <td>0.96970</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.040899</td>\n",
       "      <td>-0.224070</td>\n",
       "      <td>0.091899</td>\n",
       "      <td>-0.545599</td>\n",
       "      <td>lgm-cal-adj</td>\n",
       "      <td>PMIP3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              model   GMTemp  ATL3 mean  ATL3 stddev  Nino34 stddev  \\\n",
       "run                                                                   \n",
       "0    AWI-ESM-1-1-LR -3.71394    -1.5402     0.045615       0.146279   \n",
       "1         INM-CM4-8 -3.72303    -1.2889     0.145785       0.076214   \n",
       "2        MIROC-ES2L -4.04850    -2.5732     0.149163      -0.156850   \n",
       "3       UofT-CCSM-4 -5.27000    -2.4261     0.091440      -0.137060   \n",
       "0             CCSM4 -4.91608    -2.1456     0.052002      -0.177133   \n",
       "1          CNRM-CM5 -2.63480    -1.4687    -0.003960       0.020978   \n",
       "2        COSMOS-ASO -5.47037    -3.9999     0.209955       0.003940   \n",
       "3         FGOALS-g2 -4.58484    -2.8489     0.043447      -0.348958   \n",
       "4         GISS-E2-R -4.80725    -1.8912     0.027831      -0.162594   \n",
       "5      IPSL-CM5A-LR -4.59955    -3.5842     0.088374       0.222348   \n",
       "6         MIROC-ESM -5.00889    -2.1300     0.033166       0.078147   \n",
       "7         MPI-ESM-P -4.41558    -2.0742     0.024309       0.032227   \n",
       "8         MRI-CGCM3 -4.68431    -3.1551     0.053328      -0.024906   \n",
       "\n",
       "     AtlNino mean  AtlNino stddev  AtlNino AnnCycAmp  AMM mean  AMM stddev  \\\n",
       "run                                                                          \n",
       "0         -1.5402        0.045074            0.06988 -0.229227    0.049754   \n",
       "1         -1.2889        0.140282           -0.68872 -3.210517    0.317259   \n",
       "2         -2.5732        0.149556            0.57702 -0.019281    0.074201   \n",
       "3         -2.4261        0.091297            0.58700  0.522140    0.015283   \n",
       "0         -2.1456        0.051852            0.04609 -0.118035    0.020961   \n",
       "1         -1.4687       -0.004890            0.40581  0.050328   -0.013339   \n",
       "2         -3.9999        0.201856            0.22525  0.260643   -0.036029   \n",
       "3         -2.8489        0.042782            0.45131 -0.128027    0.078076   \n",
       "4         -1.8912        0.027849            0.03201 -0.626854    0.074722   \n",
       "5         -3.5842        0.088400            0.41375 -0.356891    0.051615   \n",
       "6         -2.1300        0.020758            0.23454 -0.373266    0.056652   \n",
       "7         -2.0742        0.024599           -0.21409 -0.663112    0.056199   \n",
       "8         -3.1551        0.053074            0.98978  0.382973   -0.018635   \n",
       "\n",
       "     ...  r(atl3.amo)  ATL3 Period  ENSO Period  AMM Period  \\\n",
       "run  ...                                                      \n",
       "0    ...    -0.113669      0.20270     -1.31362     0.23810   \n",
       "1    ...     0.253801     -4.59478      0.52968     1.98263   \n",
       "2    ...          NaN     -1.00000     -0.50654    -2.94444   \n",
       "3    ...          NaN     -0.61919     -0.89955     0.00000   \n",
       "0    ...     0.045641      0.57162      0.11836    -1.29429   \n",
       "1    ...    -0.004531     -2.41297      0.12661    -1.29928   \n",
       "2    ...     0.114773      0.90768      1.35508    -0.76754   \n",
       "3    ...     0.042913      1.42045     -0.34078    -1.76086   \n",
       "4    ...     0.102716      3.93291     -1.63174    -1.93015   \n",
       "5    ...    -0.124031     -3.54167     -0.60607    -2.13451   \n",
       "6    ...          NaN      3.80342     -1.88979     2.80164   \n",
       "7    ...          NaN     -3.03127     -1.50512    -1.38052   \n",
       "8    ...          NaN      0.65217      0.96970     0.00000   \n",
       "\n",
       "     ATL3-sst-interann-stdev  ATL3-pr-interann-stdev  ATL3-sst-interjja-stdev  \\\n",
       "run                                                                             \n",
       "0                   0.023975                0.005319                 0.097718   \n",
       "1                   0.084050                0.577534                 0.175200   \n",
       "2                   0.174272                0.136910                 0.312965   \n",
       "3                   0.074780                0.089172                 0.215456   \n",
       "0                   0.023364                0.063091                 0.101323   \n",
       "1                  -0.013341               -0.049764                -0.008784   \n",
       "2                   0.172391               -0.262666                 0.025248   \n",
       "3                   0.043412                0.034186                 0.081789   \n",
       "4                   0.019337                0.098620                 0.051354   \n",
       "5                   0.072432                0.061988                 0.058449   \n",
       "6                   0.013258                0.009769                 0.001200   \n",
       "7                   0.008834               -0.157909                 0.005205   \n",
       "8                   0.040899               -0.224070                 0.091899   \n",
       "\n",
       "     ATL3-pr-interjja-stdev   experiment  generation  \n",
       "run                                                   \n",
       "0                  0.268470  lgm-cal-adj       PMIP4  \n",
       "1                  1.975299  lgm-cal-adj       PMIP4  \n",
       "2                  0.679370  lgm-cal-adj       PMIP4  \n",
       "3                  0.482600  lgm-cal-adj       PMIP4  \n",
       "0                  0.350140  lgm-cal-adj       PMIP3  \n",
       "1                 -0.157190  lgm-cal-adj       PMIP3  \n",
       "2                  0.037660  lgm-cal-adj       PMIP3  \n",
       "3                  0.195030  lgm-cal-adj       PMIP3  \n",
       "4                  0.353490  lgm-cal-adj       PMIP3  \n",
       "5                 -0.101261  lgm-cal-adj       PMIP3  \n",
       "6                 -0.195160  lgm-cal-adj       PMIP3  \n",
       "7                 -0.324090  lgm-cal-adj       PMIP3  \n",
       "8                 -0.545599  lgm-cal-adj       PMIP3  \n",
       "\n",
       "[13 rows x 31 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgm4_diff=calculate_experiment_diff(all_runs, \"PMIP4\", \"lgm-cal-adj\", control_experiment='piControl')\n",
    "lgm3_diff=calculate_experiment_diff(all_runs, \"PMIP3\", \"lgm-cal-adj\", control_experiment='piControl')\n",
    "lgm_diff=pd.concat([lgm4_diff,lgm3_diff])\n",
    "lgm_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d4129be6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>GMTemp</th>\n",
       "      <th>ATL3 mean</th>\n",
       "      <th>ATL3 stddev</th>\n",
       "      <th>Nino34 stddev</th>\n",
       "      <th>AtlNino mean</th>\n",
       "      <th>AtlNino stddev</th>\n",
       "      <th>AtlNino AnnCycAmp</th>\n",
       "      <th>AMM mean</th>\n",
       "      <th>AMM stddev</th>\n",
       "      <th>...</th>\n",
       "      <th>r(atl3.amo)</th>\n",
       "      <th>ATL3 Period</th>\n",
       "      <th>ENSO Period</th>\n",
       "      <th>AMM Period</th>\n",
       "      <th>ATL3-sst-interann-stdev</th>\n",
       "      <th>ATL3-pr-interann-stdev</th>\n",
       "      <th>ATL3-sst-interjja-stdev</th>\n",
       "      <th>ATL3-pr-interjja-stdev</th>\n",
       "      <th>experiment</th>\n",
       "      <th>generation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>run</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACCESS-ESM1-5</td>\n",
       "      <td>0.4740</td>\n",
       "      <td>272.1327</td>\n",
       "      <td>-0.115760</td>\n",
       "      <td>-0.114513</td>\n",
       "      <td>272.1327</td>\n",
       "      <td>-0.114982</td>\n",
       "      <td>-1.66067</td>\n",
       "      <td>0.917830</td>\n",
       "      <td>-0.011128</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.14286</td>\n",
       "      <td>0.89184</td>\n",
       "      <td>0.63492</td>\n",
       "      <td>-0.088097</td>\n",
       "      <td>-0.442552</td>\n",
       "      <td>-0.216145</td>\n",
       "      <td>-1.282359</td>\n",
       "      <td>lig127k-cal-adj</td>\n",
       "      <td>PMIP4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AWI-ESM-1-1-LR</td>\n",
       "      <td>-0.0981</td>\n",
       "      <td>-0.7774</td>\n",
       "      <td>-0.025851</td>\n",
       "      <td>-0.147396</td>\n",
       "      <td>-0.7774</td>\n",
       "      <td>-0.026393</td>\n",
       "      <td>-1.87723</td>\n",
       "      <td>0.452882</td>\n",
       "      <td>-0.041555</td>\n",
       "      <td>...</td>\n",
       "      <td>0.089122</td>\n",
       "      <td>0.27778</td>\n",
       "      <td>1.48810</td>\n",
       "      <td>3.80953</td>\n",
       "      <td>-0.007716</td>\n",
       "      <td>-0.044848</td>\n",
       "      <td>-0.038624</td>\n",
       "      <td>-0.348710</td>\n",
       "      <td>lig127k-cal-adj</td>\n",
       "      <td>PMIP4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CESM2</td>\n",
       "      <td>0.0534</td>\n",
       "      <td>-0.3136</td>\n",
       "      <td>-0.026407</td>\n",
       "      <td>-0.227121</td>\n",
       "      <td>-0.3136</td>\n",
       "      <td>-0.026035</td>\n",
       "      <td>-1.70759</td>\n",
       "      <td>0.048310</td>\n",
       "      <td>-0.018903</td>\n",
       "      <td>...</td>\n",
       "      <td>0.046349</td>\n",
       "      <td>1.66299</td>\n",
       "      <td>1.95972</td>\n",
       "      <td>1.97681</td>\n",
       "      <td>0.000167</td>\n",
       "      <td>-0.178651</td>\n",
       "      <td>-0.061881</td>\n",
       "      <td>-0.611060</td>\n",
       "      <td>lig127k-cal-adj</td>\n",
       "      <td>PMIP4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CNRM-CM6-1</td>\n",
       "      <td>0.4924</td>\n",
       "      <td>0.0366</td>\n",
       "      <td>-0.024749</td>\n",
       "      <td>-0.195641</td>\n",
       "      <td>0.0366</td>\n",
       "      <td>-0.024334</td>\n",
       "      <td>-2.15172</td>\n",
       "      <td>0.156544</td>\n",
       "      <td>-0.024103</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.046924</td>\n",
       "      <td>3.05644</td>\n",
       "      <td>-0.58279</td>\n",
       "      <td>-0.66082</td>\n",
       "      <td>-0.015631</td>\n",
       "      <td>-0.130875</td>\n",
       "      <td>-0.066429</td>\n",
       "      <td>-0.771550</td>\n",
       "      <td>lig127k-cal-adj</td>\n",
       "      <td>PMIP4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FGOALS-f3-L</td>\n",
       "      <td>-0.2616</td>\n",
       "      <td>-0.7187</td>\n",
       "      <td>-0.060795</td>\n",
       "      <td>-0.270160</td>\n",
       "      <td>-0.7187</td>\n",
       "      <td>-0.061246</td>\n",
       "      <td>-1.25381</td>\n",
       "      <td>0.302060</td>\n",
       "      <td>-0.082742</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.095079</td>\n",
       "      <td>0.54773</td>\n",
       "      <td>0.72384</td>\n",
       "      <td>0.89555</td>\n",
       "      <td>-0.023382</td>\n",
       "      <td>-0.267709</td>\n",
       "      <td>-0.075353</td>\n",
       "      <td>-0.950410</td>\n",
       "      <td>lig127k-cal-adj</td>\n",
       "      <td>PMIP4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FGOALS-g3</td>\n",
       "      <td>0.4364</td>\n",
       "      <td>-0.5635</td>\n",
       "      <td>0.013314</td>\n",
       "      <td>-0.275393</td>\n",
       "      <td>-0.5562</td>\n",
       "      <td>0.012970</td>\n",
       "      <td>0.84165</td>\n",
       "      <td>0.297784</td>\n",
       "      <td>-0.047087</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.021427</td>\n",
       "      <td>-0.56427</td>\n",
       "      <td>-0.56427</td>\n",
       "      <td>-4.33136</td>\n",
       "      <td>-0.021603</td>\n",
       "      <td>-0.570731</td>\n",
       "      <td>-0.159806</td>\n",
       "      <td>-1.543930</td>\n",
       "      <td>lig127k-cal-adj</td>\n",
       "      <td>PMIP4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GISS-E2-1-G</td>\n",
       "      <td>0.0188</td>\n",
       "      <td>-0.3218</td>\n",
       "      <td>-0.001263</td>\n",
       "      <td>0.003790</td>\n",
       "      <td>-0.3218</td>\n",
       "      <td>-0.001311</td>\n",
       "      <td>-1.26759</td>\n",
       "      <td>0.273985</td>\n",
       "      <td>-0.017904</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011157</td>\n",
       "      <td>-0.61610</td>\n",
       "      <td>-0.61610</td>\n",
       "      <td>-0.31980</td>\n",
       "      <td>0.000755</td>\n",
       "      <td>-0.276773</td>\n",
       "      <td>0.008799</td>\n",
       "      <td>-0.954670</td>\n",
       "      <td>lig127k-cal-adj</td>\n",
       "      <td>PMIP4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>HadGEM3-GC31-LL</td>\n",
       "      <td>0.6255</td>\n",
       "      <td>-0.0910</td>\n",
       "      <td>-0.092976</td>\n",
       "      <td>-0.281339</td>\n",
       "      <td>-0.0910</td>\n",
       "      <td>-0.093126</td>\n",
       "      <td>-1.53251</td>\n",
       "      <td>1.017152</td>\n",
       "      <td>-0.152201</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.32975</td>\n",
       "      <td>2.07208</td>\n",
       "      <td>-1.78197</td>\n",
       "      <td>-0.063614</td>\n",
       "      <td>-0.411658</td>\n",
       "      <td>-0.221479</td>\n",
       "      <td>-1.735541</td>\n",
       "      <td>lig127k-cal-adj</td>\n",
       "      <td>PMIP4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>INM-CM4-8</td>\n",
       "      <td>-0.0185</td>\n",
       "      <td>-0.2458</td>\n",
       "      <td>-0.013364</td>\n",
       "      <td>0.046921</td>\n",
       "      <td>-0.2458</td>\n",
       "      <td>-0.009156</td>\n",
       "      <td>-0.36360</td>\n",
       "      <td>0.267533</td>\n",
       "      <td>0.014061</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.078864</td>\n",
       "      <td>-1.79366</td>\n",
       "      <td>-1.59617</td>\n",
       "      <td>1.98263</td>\n",
       "      <td>-0.003396</td>\n",
       "      <td>0.138384</td>\n",
       "      <td>0.010754</td>\n",
       "      <td>0.062776</td>\n",
       "      <td>lig127k-cal-adj</td>\n",
       "      <td>PMIP4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>IPSL-CM6A-LR</td>\n",
       "      <td>0.0637</td>\n",
       "      <td>-0.1566</td>\n",
       "      <td>-0.002827</td>\n",
       "      <td>-0.168876</td>\n",
       "      <td>-0.1566</td>\n",
       "      <td>-0.002646</td>\n",
       "      <td>-2.78962</td>\n",
       "      <td>0.520958</td>\n",
       "      <td>-0.003803</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.25973</td>\n",
       "      <td>-0.25031</td>\n",
       "      <td>-1.55674</td>\n",
       "      <td>0.036986</td>\n",
       "      <td>-0.017100</td>\n",
       "      <td>-0.010639</td>\n",
       "      <td>-0.307068</td>\n",
       "      <td>lig127k-cal-adj</td>\n",
       "      <td>PMIP4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MIROC-ES2L</td>\n",
       "      <td>-0.2589</td>\n",
       "      <td>-0.9456</td>\n",
       "      <td>-0.159067</td>\n",
       "      <td>-0.642287</td>\n",
       "      <td>-0.9456</td>\n",
       "      <td>-0.154707</td>\n",
       "      <td>-1.27388</td>\n",
       "      <td>0.185840</td>\n",
       "      <td>-0.051413</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.66667</td>\n",
       "      <td>1.02798</td>\n",
       "      <td>0.19842</td>\n",
       "      <td>-0.083143</td>\n",
       "      <td>-0.538000</td>\n",
       "      <td>-0.202124</td>\n",
       "      <td>-1.442416</td>\n",
       "      <td>lig127k-cal-adj</td>\n",
       "      <td>PMIP4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>MPI-ESM1-2-LR</td>\n",
       "      <td>0.0402</td>\n",
       "      <td>-0.6671</td>\n",
       "      <td>-0.062199</td>\n",
       "      <td>-0.196289</td>\n",
       "      <td>-0.6671</td>\n",
       "      <td>-0.062193</td>\n",
       "      <td>-0.56565</td>\n",
       "      <td>0.204338</td>\n",
       "      <td>-0.192786</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.92317</td>\n",
       "      <td>-2.23112</td>\n",
       "      <td>-1.68651</td>\n",
       "      <td>-0.043199</td>\n",
       "      <td>-0.231460</td>\n",
       "      <td>-0.117278</td>\n",
       "      <td>-0.643080</td>\n",
       "      <td>lig127k-cal-adj</td>\n",
       "      <td>PMIP4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NESM3</td>\n",
       "      <td>0.2725</td>\n",
       "      <td>-0.3797</td>\n",
       "      <td>-0.036462</td>\n",
       "      <td>-0.159818</td>\n",
       "      <td>-0.3797</td>\n",
       "      <td>-0.036571</td>\n",
       "      <td>-0.94078</td>\n",
       "      <td>-0.178726</td>\n",
       "      <td>-0.040073</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.52526</td>\n",
       "      <td>0.59523</td>\n",
       "      <td>1.96970</td>\n",
       "      <td>-0.045510</td>\n",
       "      <td>-0.153920</td>\n",
       "      <td>-0.054617</td>\n",
       "      <td>-0.654618</td>\n",
       "      <td>lig127k-cal-adj</td>\n",
       "      <td>PMIP4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NorESM1-F</td>\n",
       "      <td>-0.1196</td>\n",
       "      <td>-0.4402</td>\n",
       "      <td>-0.115810</td>\n",
       "      <td>-0.123622</td>\n",
       "      <td>-0.4402</td>\n",
       "      <td>-0.115672</td>\n",
       "      <td>-1.91746</td>\n",
       "      <td>0.144422</td>\n",
       "      <td>-0.065948</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.87805</td>\n",
       "      <td>-1.42977</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.084219</td>\n",
       "      <td>-0.325993</td>\n",
       "      <td>-0.278253</td>\n",
       "      <td>-0.999910</td>\n",
       "      <td>lig127k-cal-adj</td>\n",
       "      <td>PMIP4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NorESM2-LM</td>\n",
       "      <td>0.0600</td>\n",
       "      <td>-0.1564</td>\n",
       "      <td>-0.001524</td>\n",
       "      <td>-0.026470</td>\n",
       "      <td>-0.1564</td>\n",
       "      <td>-0.000793</td>\n",
       "      <td>-1.91672</td>\n",
       "      <td>0.088030</td>\n",
       "      <td>-0.023334</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.78670</td>\n",
       "      <td>4.16979</td>\n",
       "      <td>0.21568</td>\n",
       "      <td>0.013898</td>\n",
       "      <td>-0.073854</td>\n",
       "      <td>-0.097846</td>\n",
       "      <td>-0.434890</td>\n",
       "      <td>lig127k-cal-adj</td>\n",
       "      <td>PMIP4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               model  GMTemp  ATL3 mean  ATL3 stddev  Nino34 stddev  \\\n",
       "run                                                                   \n",
       "0      ACCESS-ESM1-5  0.4740   272.1327    -0.115760      -0.114513   \n",
       "1     AWI-ESM-1-1-LR -0.0981    -0.7774    -0.025851      -0.147396   \n",
       "2              CESM2  0.0534    -0.3136    -0.026407      -0.227121   \n",
       "3         CNRM-CM6-1  0.4924     0.0366    -0.024749      -0.195641   \n",
       "4        FGOALS-f3-L -0.2616    -0.7187    -0.060795      -0.270160   \n",
       "5          FGOALS-g3  0.4364    -0.5635     0.013314      -0.275393   \n",
       "6        GISS-E2-1-G  0.0188    -0.3218    -0.001263       0.003790   \n",
       "7    HadGEM3-GC31-LL  0.6255    -0.0910    -0.092976      -0.281339   \n",
       "8          INM-CM4-8 -0.0185    -0.2458    -0.013364       0.046921   \n",
       "9       IPSL-CM6A-LR  0.0637    -0.1566    -0.002827      -0.168876   \n",
       "10        MIROC-ES2L -0.2589    -0.9456    -0.159067      -0.642287   \n",
       "11     MPI-ESM1-2-LR  0.0402    -0.6671    -0.062199      -0.196289   \n",
       "12             NESM3  0.2725    -0.3797    -0.036462      -0.159818   \n",
       "13         NorESM1-F -0.1196    -0.4402    -0.115810      -0.123622   \n",
       "14        NorESM2-LM  0.0600    -0.1564    -0.001524      -0.026470   \n",
       "\n",
       "     AtlNino mean  AtlNino stddev  AtlNino AnnCycAmp  AMM mean  AMM stddev  \\\n",
       "run                                                                          \n",
       "0        272.1327       -0.114982           -1.66067  0.917830   -0.011128   \n",
       "1         -0.7774       -0.026393           -1.87723  0.452882   -0.041555   \n",
       "2         -0.3136       -0.026035           -1.70759  0.048310   -0.018903   \n",
       "3          0.0366       -0.024334           -2.15172  0.156544   -0.024103   \n",
       "4         -0.7187       -0.061246           -1.25381  0.302060   -0.082742   \n",
       "5         -0.5562        0.012970            0.84165  0.297784   -0.047087   \n",
       "6         -0.3218       -0.001311           -1.26759  0.273985   -0.017904   \n",
       "7         -0.0910       -0.093126           -1.53251  1.017152   -0.152201   \n",
       "8         -0.2458       -0.009156           -0.36360  0.267533    0.014061   \n",
       "9         -0.1566       -0.002646           -2.78962  0.520958   -0.003803   \n",
       "10        -0.9456       -0.154707           -1.27388  0.185840   -0.051413   \n",
       "11        -0.6671       -0.062193           -0.56565  0.204338   -0.192786   \n",
       "12        -0.3797       -0.036571           -0.94078 -0.178726   -0.040073   \n",
       "13        -0.4402       -0.115672           -1.91746  0.144422   -0.065948   \n",
       "14        -0.1564       -0.000793           -1.91672  0.088030   -0.023334   \n",
       "\n",
       "     ...  r(atl3.amo)  ATL3 Period  ENSO Period  AMM Period  \\\n",
       "run  ...                                                      \n",
       "0    ...          NaN     -5.14286      0.89184     0.63492   \n",
       "1    ...     0.089122      0.27778      1.48810     3.80953   \n",
       "2    ...     0.046349      1.66299      1.95972     1.97681   \n",
       "3    ...    -0.046924      3.05644     -0.58279    -0.66082   \n",
       "4    ...    -0.095079      0.54773      0.72384     0.89555   \n",
       "5    ...    -0.021427     -0.56427     -0.56427    -4.33136   \n",
       "6    ...    -0.011157     -0.61610     -0.61610    -0.31980   \n",
       "7    ...          NaN     -2.32975      2.07208    -1.78197   \n",
       "8    ...    -0.078864     -1.79366     -1.59617     1.98263   \n",
       "9    ...          NaN     -0.25973     -0.25031    -1.55674   \n",
       "10   ...          NaN      1.66667      1.02798     0.19842   \n",
       "11   ...          NaN      0.92317     -2.23112    -1.68651   \n",
       "12   ...          NaN      2.52526      0.59523     1.96970   \n",
       "13   ...          NaN     -0.87805     -1.42977     0.00000   \n",
       "14   ...          NaN      1.78670      4.16979     0.21568   \n",
       "\n",
       "     ATL3-sst-interann-stdev  ATL3-pr-interann-stdev  ATL3-sst-interjja-stdev  \\\n",
       "run                                                                             \n",
       "0                  -0.088097               -0.442552                -0.216145   \n",
       "1                  -0.007716               -0.044848                -0.038624   \n",
       "2                   0.000167               -0.178651                -0.061881   \n",
       "3                  -0.015631               -0.130875                -0.066429   \n",
       "4                  -0.023382               -0.267709                -0.075353   \n",
       "5                  -0.021603               -0.570731                -0.159806   \n",
       "6                   0.000755               -0.276773                 0.008799   \n",
       "7                  -0.063614               -0.411658                -0.221479   \n",
       "8                  -0.003396                0.138384                 0.010754   \n",
       "9                   0.036986               -0.017100                -0.010639   \n",
       "10                 -0.083143               -0.538000                -0.202124   \n",
       "11                 -0.043199               -0.231460                -0.117278   \n",
       "12                 -0.045510               -0.153920                -0.054617   \n",
       "13                 -0.084219               -0.325993                -0.278253   \n",
       "14                  0.013898               -0.073854                -0.097846   \n",
       "\n",
       "     ATL3-pr-interjja-stdev       experiment  generation  \n",
       "run                                                       \n",
       "0                 -1.282359  lig127k-cal-adj       PMIP4  \n",
       "1                 -0.348710  lig127k-cal-adj       PMIP4  \n",
       "2                 -0.611060  lig127k-cal-adj       PMIP4  \n",
       "3                 -0.771550  lig127k-cal-adj       PMIP4  \n",
       "4                 -0.950410  lig127k-cal-adj       PMIP4  \n",
       "5                 -1.543930  lig127k-cal-adj       PMIP4  \n",
       "6                 -0.954670  lig127k-cal-adj       PMIP4  \n",
       "7                 -1.735541  lig127k-cal-adj       PMIP4  \n",
       "8                  0.062776  lig127k-cal-adj       PMIP4  \n",
       "9                 -0.307068  lig127k-cal-adj       PMIP4  \n",
       "10                -1.442416  lig127k-cal-adj       PMIP4  \n",
       "11                -0.643080  lig127k-cal-adj       PMIP4  \n",
       "12                -0.654618  lig127k-cal-adj       PMIP4  \n",
       "13                -0.999910  lig127k-cal-adj       PMIP4  \n",
       "14                -0.434890  lig127k-cal-adj       PMIP4  \n",
       "\n",
       "[15 rows x 31 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lig127k_diff=calculate_experiment_diff(all_runs, \"PMIP4\", \"lig127k-cal-adj\", control_experiment='piControl')\n",
    "lig127k_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5447ffe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "midHolocene4_diff=calculate_experiment_diff(all_runs, \"PMIP4\", \"midHolocene-cal-adj\", control_experiment='piControl')\n",
    "midHolocene3_diff=calculate_experiment_diff(all_runs, \"PMIP3\", \"midHolocene-cal-adj\", control_experiment='piControl')\n",
    "midHolocene_diff=pd.concat([midHolocene4_diff,midHolocene3_diff])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "38234adf",
   "metadata": {},
   "outputs": [],
   "source": [
    "abrupt4xCO24_diff=calculate_experiment_diff(all_runs, \"PMIP4\", \"abrupt4xCO2\", control_experiment='piControl')\n",
    "abrupt4xCO23_diff=calculate_experiment_diff(all_runs, \"PMIP3\", \"abrupt4xCO2\", control_experiment='piControl')\n",
    "abrupt4xCO2_diff=pd.concat([abrupt4xCO24_diff,abrupt4xCO23_diff])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7e101303",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>GMTemp</th>\n",
       "      <th>ATL3 mean</th>\n",
       "      <th>ATL3 stddev</th>\n",
       "      <th>Nino34 stddev</th>\n",
       "      <th>AtlNino mean</th>\n",
       "      <th>AtlNino stddev</th>\n",
       "      <th>AtlNino AnnCycAmp</th>\n",
       "      <th>AMM mean</th>\n",
       "      <th>AMM stddev</th>\n",
       "      <th>...</th>\n",
       "      <th>r(atl3.amo)</th>\n",
       "      <th>ATL3 Period</th>\n",
       "      <th>ENSO Period</th>\n",
       "      <th>AMM Period</th>\n",
       "      <th>ATL3-sst-interann-stdev</th>\n",
       "      <th>ATL3-pr-interann-stdev</th>\n",
       "      <th>ATL3-sst-interjja-stdev</th>\n",
       "      <th>ATL3-pr-interjja-stdev</th>\n",
       "      <th>experiment</th>\n",
       "      <th>generation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>run</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CESM2</td>\n",
       "      <td>4.9495</td>\n",
       "      <td>3.7814</td>\n",
       "      <td>0.027295</td>\n",
       "      <td>0.045710</td>\n",
       "      <td>3.7814</td>\n",
       "      <td>0.080397</td>\n",
       "      <td>0.02713</td>\n",
       "      <td>-0.262000</td>\n",
       "      <td>-0.006603</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.311281</td>\n",
       "      <td>0.04970</td>\n",
       "      <td>-0.12774</td>\n",
       "      <td>3.51967</td>\n",
       "      <td>0.033475</td>\n",
       "      <td>0.130459</td>\n",
       "      <td>0.045068</td>\n",
       "      <td>0.451628</td>\n",
       "      <td>midPliocene-eoi400</td>\n",
       "      <td>PMIP4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EC-Earth3-LR</td>\n",
       "      <td>4.8483</td>\n",
       "      <td>3.0072</td>\n",
       "      <td>-0.119469</td>\n",
       "      <td>-0.330729</td>\n",
       "      <td>3.0072</td>\n",
       "      <td>-0.118955</td>\n",
       "      <td>-0.68993</td>\n",
       "      <td>0.303007</td>\n",
       "      <td>-0.017350</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.102393</td>\n",
       "      <td>0.04374</td>\n",
       "      <td>4.55169</td>\n",
       "      <td>3.59027</td>\n",
       "      <td>-0.058580</td>\n",
       "      <td>-0.063367</td>\n",
       "      <td>-0.246324</td>\n",
       "      <td>-0.189470</td>\n",
       "      <td>midPliocene-eoi400</td>\n",
       "      <td>PMIP4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GISS-E2-1-G</td>\n",
       "      <td>2.1005</td>\n",
       "      <td>0.8174</td>\n",
       "      <td>0.002945</td>\n",
       "      <td>0.025390</td>\n",
       "      <td>0.8174</td>\n",
       "      <td>0.003913</td>\n",
       "      <td>0.49958</td>\n",
       "      <td>0.096270</td>\n",
       "      <td>0.017700</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004862</td>\n",
       "      <td>0.44210</td>\n",
       "      <td>0.44210</td>\n",
       "      <td>0.02803</td>\n",
       "      <td>0.011735</td>\n",
       "      <td>-0.181787</td>\n",
       "      <td>-0.015496</td>\n",
       "      <td>-0.618100</td>\n",
       "      <td>midPliocene-eoi400</td>\n",
       "      <td>PMIP4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HadGEM3-GC31-LL</td>\n",
       "      <td>5.1708</td>\n",
       "      <td>3.6976</td>\n",
       "      <td>-0.104310</td>\n",
       "      <td>-0.220912</td>\n",
       "      <td>3.6976</td>\n",
       "      <td>-0.103401</td>\n",
       "      <td>0.12031</td>\n",
       "      <td>0.382978</td>\n",
       "      <td>-0.094991</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.095371</td>\n",
       "      <td>-0.79366</td>\n",
       "      <td>1.66667</td>\n",
       "      <td>-0.55556</td>\n",
       "      <td>-0.053272</td>\n",
       "      <td>0.116040</td>\n",
       "      <td>-0.173637</td>\n",
       "      <td>-1.066720</td>\n",
       "      <td>midPliocene-eoi400</td>\n",
       "      <td>PMIP4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IPSL-CM6A-LR</td>\n",
       "      <td>3.2650</td>\n",
       "      <td>1.9066</td>\n",
       "      <td>-0.038457</td>\n",
       "      <td>0.024732</td>\n",
       "      <td>1.9066</td>\n",
       "      <td>-0.038487</td>\n",
       "      <td>0.04036</td>\n",
       "      <td>0.351457</td>\n",
       "      <td>-0.029249</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.16971</td>\n",
       "      <td>0.25679</td>\n",
       "      <td>-2.45211</td>\n",
       "      <td>-0.029063</td>\n",
       "      <td>0.013717</td>\n",
       "      <td>-0.096269</td>\n",
       "      <td>-0.371895</td>\n",
       "      <td>midPliocene-eoi400</td>\n",
       "      <td>PMIP4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NorESM1-F</td>\n",
       "      <td>1.7280</td>\n",
       "      <td>1.0939</td>\n",
       "      <td>-0.002019</td>\n",
       "      <td>-0.182541</td>\n",
       "      <td>1.0939</td>\n",
       "      <td>-0.001929</td>\n",
       "      <td>0.04088</td>\n",
       "      <td>0.096867</td>\n",
       "      <td>-0.010939</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.26481</td>\n",
       "      <td>1.18256</td>\n",
       "      <td>2.52936</td>\n",
       "      <td>-0.003727</td>\n",
       "      <td>0.075700</td>\n",
       "      <td>0.017585</td>\n",
       "      <td>0.135360</td>\n",
       "      <td>midPliocene-eoi400</td>\n",
       "      <td>PMIP4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>UofT-CCSM-4</td>\n",
       "      <td>4.1854</td>\n",
       "      <td>2.3427</td>\n",
       "      <td>-0.004327</td>\n",
       "      <td>-0.253232</td>\n",
       "      <td>2.3427</td>\n",
       "      <td>-0.002041</td>\n",
       "      <td>0.22817</td>\n",
       "      <td>0.227509</td>\n",
       "      <td>-0.040739</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.88235</td>\n",
       "      <td>0.41407</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.016820</td>\n",
       "      <td>0.066127</td>\n",
       "      <td>0.046724</td>\n",
       "      <td>0.126690</td>\n",
       "      <td>midPliocene-eoi400</td>\n",
       "      <td>PMIP4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               model  GMTemp  ATL3 mean  ATL3 stddev  Nino34 stddev  \\\n",
       "run                                                                   \n",
       "0              CESM2  4.9495     3.7814     0.027295       0.045710   \n",
       "1       EC-Earth3-LR  4.8483     3.0072    -0.119469      -0.330729   \n",
       "2        GISS-E2-1-G  2.1005     0.8174     0.002945       0.025390   \n",
       "3    HadGEM3-GC31-LL  5.1708     3.6976    -0.104310      -0.220912   \n",
       "4       IPSL-CM6A-LR  3.2650     1.9066    -0.038457       0.024732   \n",
       "5          NorESM1-F  1.7280     1.0939    -0.002019      -0.182541   \n",
       "6        UofT-CCSM-4  4.1854     2.3427    -0.004327      -0.253232   \n",
       "\n",
       "     AtlNino mean  AtlNino stddev  AtlNino AnnCycAmp  AMM mean  AMM stddev  \\\n",
       "run                                                                          \n",
       "0          3.7814        0.080397            0.02713 -0.262000   -0.006603   \n",
       "1          3.0072       -0.118955           -0.68993  0.303007   -0.017350   \n",
       "2          0.8174        0.003913            0.49958  0.096270    0.017700   \n",
       "3          3.6976       -0.103401            0.12031  0.382978   -0.094991   \n",
       "4          1.9066       -0.038487            0.04036  0.351457   -0.029249   \n",
       "5          1.0939       -0.001929            0.04088  0.096867   -0.010939   \n",
       "6          2.3427       -0.002041            0.22817  0.227509   -0.040739   \n",
       "\n",
       "     ...  r(atl3.amo)  ATL3 Period  ENSO Period  AMM Period  \\\n",
       "run  ...                                                      \n",
       "0    ...    -0.311281      0.04970     -0.12774     3.51967   \n",
       "1    ...    -0.102393      0.04374      4.55169     3.59027   \n",
       "2    ...     0.004862      0.44210      0.44210     0.02803   \n",
       "3    ...    -0.095371     -0.79366      1.66667    -0.55556   \n",
       "4    ...          NaN     -1.16971      0.25679    -2.45211   \n",
       "5    ...          NaN      2.26481      1.18256     2.52936   \n",
       "6    ...          NaN     -0.88235      0.41407     0.00000   \n",
       "\n",
       "     ATL3-sst-interann-stdev  ATL3-pr-interann-stdev  ATL3-sst-interjja-stdev  \\\n",
       "run                                                                             \n",
       "0                   0.033475                0.130459                 0.045068   \n",
       "1                  -0.058580               -0.063367                -0.246324   \n",
       "2                   0.011735               -0.181787                -0.015496   \n",
       "3                  -0.053272                0.116040                -0.173637   \n",
       "4                  -0.029063                0.013717                -0.096269   \n",
       "5                  -0.003727                0.075700                 0.017585   \n",
       "6                   0.016820                0.066127                 0.046724   \n",
       "\n",
       "     ATL3-pr-interjja-stdev          experiment  generation  \n",
       "run                                                          \n",
       "0                  0.451628  midPliocene-eoi400       PMIP4  \n",
       "1                 -0.189470  midPliocene-eoi400       PMIP4  \n",
       "2                 -0.618100  midPliocene-eoi400       PMIP4  \n",
       "3                 -1.066720  midPliocene-eoi400       PMIP4  \n",
       "4                 -0.371895  midPliocene-eoi400       PMIP4  \n",
       "5                  0.135360  midPliocene-eoi400       PMIP4  \n",
       "6                  0.126690  midPliocene-eoi400       PMIP4  \n",
       "\n",
       "[7 rows x 31 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "midPliocene_diff=calculate_experiment_diff(all_runs, \"PMIP4\", \"midPliocene-eoi400\", control_experiment='piControl')\n",
    "midPliocene_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a8e5d771",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_diffs=pd.concat([lgm_diff,midHolocene_diff,lig127k_diff,abrupt4xCO2_diff,midPliocene_diff])\n",
    "not_abrup4xCO2_diffs=pd.concat([lgm_diff,midHolocene_diff,lig127k_diff,midPliocene_diff])\n",
    "orb_diffs=pd.concat([midHolocene_diff,lig127k_diff])\n",
    "pmip4_diffs=pd.concat([lgm4_diff,midHolocene4_diff,abrupt4xCO24_diff,lig127k_diff,midPliocene_diff])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b6cdc927",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add some colors to allow for convenient plotting...\n",
    "all_diffs['color'] = all_diffs['experiment']\n",
    "all_diffs['color'] = all_diffs['color'].map({'lgm-cal-adj':'tab:blue',\n",
    "                             'midHolocene-cal-adj':'tab:olive',\n",
    "                             'lig127k-cal-adj':'tab:green',\n",
    "                             'abrupt4xCO2':'tab:red',\n",
    "                             'midPliocene-eoi400':'tab:pink'},\n",
    "                             na_action=None)\n",
    "all_diffs['marker'] = all_diffs['generation']\n",
    "all_diffs['marker'] = all_diffs['generation'].map({'PMIP4':'o',\n",
    "                             'PMIP3':'.'},na_action=None)\n",
    "\n",
    "pmip4_diffs['color'] = pmip4_diffs['experiment']\n",
    "pmip4_diffs['color'] = pmip4_diffs['color'].map({'lgm-cal-adj':'tab:blue',\n",
    "                             'midHolocene-cal-adj':'tab:olive',\n",
    "                             'lig127k-cal-adj':'tab:green',\n",
    "                             'abrupt4xCO2':'tab:red',\n",
    "                             'midPliocene-eoi400':'tab:pink'},\n",
    "                             na_action=None)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7250d8f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig=all_diffs.plot.scatter(x='AtlNino mean',y='AtlNino stddev',c='color',marker=\".\").get_figure()\n",
    "ax=fig.gca()\n",
    "pmip4_diffs.plot.scatter(x='AtlNino mean',y='AtlNino stddev',c='color',marker='o',ax=ax)\n",
    "plt.axhline(0,color='black')\n",
    "plt.axvline(0,color='black')\n",
    "plt.xlim(-6, 6)\n",
    "plt.xlabel('Change in mean SST in the ATL3 region ($\\degree C$)')\n",
    "plt.ylabel('Change in SST standard deviation (ATL3, $\\degree C$)')\n",
    "# Create custom legend handles\n",
    "legend1 = mlines.Line2D([], [], color='tab:blue',marker='o',linestyle='None', markersize=4,label='lgm')\n",
    "legend2 = mlines.Line2D([], [], color='tab:olive',marker='o', linestyle='None', markersize=4,label='midHolocene')\n",
    "legend3 = mlines.Line2D([], [], color='tab:green',marker='o', linestyle='None', markersize=4,label='lig127k')\n",
    "legend4 = mlines.Line2D([], [], color='tab:red',marker='o', linestyle='None', markersize=4,label='abrupt4xCO2')\n",
    "legend5 = mlines.Line2D([], [], color='tab:pink',marker='o', linestyle='None', markersize=4,label='midPliocene')\n",
    "legend6 = mlines.Line2D([], [], color='black', marker='.', linestyle='None', markersize=4, label='PMIP3')\n",
    "legend7 = mlines.Line2D([], [], color='black', marker='o', linestyle='None', markersize=4, label='PMIP4')\n",
    "ax.legend(handles=[legend1, legend2, legend3,legend4,legend5,legend6,legend7],labelcolor='linecolor')\n",
    "fig.savefig('plots/AtlNino_w_mean.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c5f5ebf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig=all_diffs.plot.scatter(x='AMM AnnCycAmp',y='AMM stddev',c='color',marker='.').get_figure()\n",
    "ax=fig.gca()\n",
    "pmip4_diffs.plot.scatter(x='AMM AnnCycAmp',y='AMM stddev',c='color',marker='o',ax=fig.gca())\n",
    "plt.axhline(0,color='black')\n",
    "plt.axvline(0,color='black')\n",
    "plt.xlim(-1.5, 1.5)\n",
    "plt.xlabel('Change in amplitude of the AMM index annual cycle ($\\degree C$)')\n",
    "plt.ylabel('Change in AMM standard deviation ($\\degree C$)')\n",
    "# Create custom legend handles\n",
    "legend1 = mlines.Line2D([], [], color='tab:blue',marker='o',linestyle='None', markersize=4,label='lgm')\n",
    "legend2 = mlines.Line2D([], [], color='tab:olive',marker='o', linestyle='None', markersize=4,label='midHolocene')\n",
    "legend3 = mlines.Line2D([], [], color='tab:green',marker='o', linestyle='None', markersize=4,label='lig127k')\n",
    "legend4 = mlines.Line2D([], [], color='tab:red',marker='o', linestyle='None', markersize=4,label='abrupt4xCO2')\n",
    "legend5 = mlines.Line2D([], [], color='tab:pink',marker='o', linestyle='None', markersize=4,label='midPliocene')\n",
    "legend6 = mlines.Line2D([], [], color='black', marker='.', linestyle='None', markersize=4, label='PMIP3')\n",
    "legend7 = mlines.Line2D([], [], color='black', marker='o', linestyle='None', markersize=4, label='PMIP4')\n",
    "ax.legend(handles=[legend1, legend2, legend3,legend4,legend5,legend6,legend7],labelcolor='linecolor')\n",
    "fig.savefig('plots/AMM_w_AnnCycAmp.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a6445513",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig=all_diffs.plot.scatter(x='AMM mean',y='AMM stddev',c='color',marker='.').get_figure()\n",
    "ax=fig.gca()\n",
    "pmip4_diffs.plot.scatter(x='AMM mean',y='AMM stddev',c='color',marker='o',ax=fig.gca())\n",
    "plt.axhline(0,color='black')\n",
    "plt.axvline(0,color='black')\n",
    "plt.xlabel('Change in mean AMM index ($\\degree C$)')\n",
    "plt.ylabel('Change in AMM standard deviation ($\\degree C$)')\n",
    "# Create custom legend handles\n",
    "legend1 = mlines.Line2D([], [], color='tab:blue',marker='o',linestyle='None', markersize=4,label='lgm')\n",
    "legend2 = mlines.Line2D([], [], color='tab:olive',marker='o', linestyle='None', markersize=4,label='midHolocene')\n",
    "legend3 = mlines.Line2D([], [], color='tab:green',marker='o', linestyle='None', markersize=4,label='lig127k')\n",
    "legend4 = mlines.Line2D([], [], color='tab:red',marker='o', linestyle='None', markersize=4,label='abrupt4xCO2')\n",
    "legend5 = mlines.Line2D([], [], color='tab:pink',marker='o', linestyle='None', markersize=4,label='midPliocene')\n",
    "legend6 = mlines.Line2D([], [], color='black', marker='.', linestyle='None', markersize=4, label='PMIP3')\n",
    "legend7 = mlines.Line2D([], [], color='black', marker='o', linestyle='None', markersize=4, label='PMIP4')\n",
    "ax.legend(handles=[legend1, legend2, legend3,legend4,legend5,legend6,legend7],labelcolor='linecolor')\n",
    "fig.savefig('plots/AMM_w_mean.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c7f4d5a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JCRlJ+4h7K9snms6dX2bHzjuJjX0drdbtgj+3I9idHL300ktMnz6dF154AY1GdhgWoiXwG3MTbp06cXrZ1xhTU3Hr1Bn/u+/CtX17Z4cmhM1KSpP4OzE6S6WkJKlBn+vt3cX6vaJo0bn44e0Vaz3meiYpqajIxcXFu8q9BsMxgoKqzgzV6/tUS460Wm8uveSHas/esnXY322VHMPbu0uVoSg/v3jAQokhqVpyVFKagqoa0ev/XrpDo9Hh69sTQ8kxAIqLE/Hzi7cmRueqqMilvDydAwencfDQP6zHVdWEVlu14Pzc35Gba7D1fi+vDhQV7aek5ASZmT+e04YKWCgrO4mXV8P3YtudHFVUVHD77bdLYiREC+MR1x2PuNedHYYQ9ebpEUOx4RBVEyQFT8+GrZXVKOe9tSoKyjnJxN+9sFXXB6x0fjJXM0VR8PSMushVKgq19PjW1BOsqufFd7aVyh43AI3G/QJPq7y/a5c38PXtdd7jtOe9Pvd3VPX3oaoW2ra9g4h2E6s9w909vNbnO5Ldmc3EiRNZtmyZI2MRQgghHCY6ehKc88Z+dogtOvpJ5wV1EV5eHSks3FXlWGHBbvva8uxIUfEBzOYS67HKImpNlaGwszw9I1EU1zPXVLJYjBQVJeDlVZlQent34fTp7Vgsxmr3u7kG4uYWSmlpCp6eUVW+PDwi6hy3j093DIYj1drw9IxCo3G14TdgP7t7jsxmM7NmzWLlypX07NmzWkH27Nmz6x2cEEIIYa/g4BH0iJtL8vEPKClJwtPzzGy1oIarN6qviHYT2b7jVk6cWEBQ0HBy8zaRe96QWl2Fho4mKfl99ic+S0z0pMrC5sPTCQu9qdqQGoBW60m7tndx9Oib6HR63NzDSTmxELO5lPCw287EN55Tpz4nYf9koiIfwcXFh4KC3fj69sLLq/L3e/jwdLQu3gQGDMViqaCwcB8mUwHt29dtW7HIyIfZvv1mDh56hbbht6PVemAwHCMv/3diO79q1+/CVnYnR/v27aNPnz4AJCQkVDknhZtCCCGaguDgEQ1afO1oen0funaZSVLyHJKSP6BNm8uIinqc48f/bXNbWq0HfXov5vDh6fy1fUyVqfy16dDhOVQs7E98xjqVv0/vxeh0egB0On/69PmSo0ffZMfOu1AULT7eXfHzq6xTaht+O1qNBydSFnH06Cy0Wg+8vWOJiLinznH7eHehX9+vOHbsXXbsvANQ8fBoT0jwDTb/DuylqKpatwFOAUBhYSF6vZ6CggJ8fWte6l0I0XQZDAa8vSuLYIuLi/Hyqnm9F+E8ZWVlJCcnEx0djbt77TUuQpzvQn92bHn/bpCNZxVFYeTIkfVpWgghhBDCKeq18ez48ePJzc2tdk42nhVCCCFEc1WvjWdvu+022XhWCCGEEC2KbDwrhBBCCHEOu5OjsxvPCiGEEEK0JLLxrBBCCCHEOWTjWSGaIFVVwWxGcanXhFIhhBB2kI1nhWhiSnbu4tSkSZhzc/G94QbC35wpSVIDyfn4E/KTk/C+/HL0Y8fKArZCCEA2nhWiyUl79lnMeXmgqhT+9794DRyA3803OzusFinn//4PT62Wov/9CooGv7FjnB2SEKIJkI1nhWhiTHm5cHZRVY0GU071tcSEg6hq5e9ao6Hkz63Ojka0APf+ei9vbXsLgBH/GcEXiV84OaKazd09l1t+vMXZYTRZzX7j2blz5/L222+Tnp5O9+7dmTNnDoMHD77ofX/88QdXXHEFcXFx7N69u+EDFaKO/O+6i7yPPwFA4+GB73XXOjmiFuxsz7eq4t6tm3NjES3OVzd+hYeLh033LNy7kI2nNnIo7xA6jY7Nd22ucv5Q3iE+3vcxO7N2crr8NOHe4dzW+TbGdRtnvWbu7rnM2zOvWtseLh5su3ubfT9MK9OsN55dtmwZTz31FHPnzuWyyy5jwYIFXHfddSQmJtK+ffta7ysoKGDChAkMGzaMzMzMRolViLoKfuYZPPv1w5iWjs+VQ9G1bevskFosv9Gj0Rw9ivcVQ/AfN+7iNwhhgzbubWy+x2gxMjxyOL2CevHdke+qnU/MTcTf3Z+Zg2cS6hXK7qzdTN8yHY2i4a6udwFwT/d7uC32tir3PbDyAboHdrfvB2mF7E6O1q9f78g47DJ79mzuv/9+HnjgAQDmzJnDypUrmTdvHjNnzqz1vocffpi77roLrVbL999/30jRiubIlJ9PyZ/b0IWH4dGzZ6M8U1EUfK66qlGe1dqFvvxP2Xi2hVtzYg3z9szjeMFxovRRPNrrUa6OvLpRnj3iPyMY120c47uNByCpIIlXN7/K/pz9tPNpxwuXvsBDqx9izpVzGNZ+GACP934cgO+Pfl9jm2M6Va2Li/CJYE/2HtamrLUmR546Tzx1ntZrDuUd4ljBMf458J+1xnqq6BQPrX6IgWED+ceAf6BRWnc9cbP96SsqKtixYwfDhw+vcnz48OFs3ry5lrvg008/5dixY7zyyit1ek55eTmFhYVVvkTrYMzMJOnGkaQ+9RTHb7ud3DNDXUKI5mHNiTVM2TCFI/lHqLBUcCT/CFM2TGHNiTWNHotFtTB53WTcte4svWEprwx8hQ93feiQtouNxejd9LWeX35kOVG+UfQL6Vfj+SP5R5jwvwkMjxzOPwf+s9UnRtCMk6OcnBzMZnO17UtCQkLIyMio8Z4jR47wwgsvsGTJElzqODV65syZ6PV661dERES9YxfNQ8H3P2DOz7e+zpk/34nRCCFsNW/PPBQUVFQAVFQUFObvafy/y5vTNnOq6BRvDH6D2Dax9A3py5N9nqx3u7uzdrPy+Epu6VxzcXWFuYKfk36u1uN07v33rryXCd0m8FS/p+odT0vRbJOjs86vb1JVtcaaJ7PZzF133cVrr71G586d69z+tGnTKCgosH6dPHmy3jGL5kHj5VU5mwlAUdB42lZYKYRwruMFx62J0VkqKskFyU6JJcQrhECPQOuxHoE96tXm0fyjTF4/mUd6PsKg8EE1XrPmxBpKjCWM6jCq2rl0QzoPrX6Ih3o8xD1x99Qrlpam2SZHgYGBaLXaar1EWVlZNW6GW1RUxPbt23niiSdwcXHBxcWF6dOns2fPHlxcXFi3bl2Nz3Fzc8PX17fKl2gd/G4ei2d8PACKmxthM2Y4OSIhhC2i9FEoVP2wrKAQrY9u9FjO9lo5yrHTx7h/1f3c3OlmHu71cK3XrTiygiHthlRJys5q496GuMA4fj3+K8UVxQ6LrSVotsmRq6sr/fr1Y/Xq1VWOr169mkGDqmfQvr6+7Nu3j927d1u/HnnkEWJjY9m9ezf9+/dvrNBbNGNqKplvzeL43Xdz6qmnKP7jD2eHZDeNhwftP/+Mjht/o/PWLXjXYYkIIUTT8WivR6skJWeH2B7t9WijxxKtjybDkEFOaY71WEJuwgXuqN3R/KPct/I+RncYzaS+tW/VdaroFNsytjG209gaz7tp3fj3Vf/GVevKw2sexmA02BVPS9Ss9ySYOnUq48ePJz4+noEDB7Jw4UJSUlJ45JFHgMohsdTUVD7//HM0Gg1xcXFV7g8ODsbd3b3acWGfssREjo8bj1peDmYzaDUU/bqSoMmTCHy08f8xcgRFUdAFBzs7DCGEHa6OvJr3hr7H/D3zSS5IJlofzaO9HmVY5LBGj2Vg2EDa+bTjpd9fYkq/KZSYSvhwZ2VB9rk9SunF6RRUFJBuSMesmjmYdxCA9j7t8dR5cjT/KPevup+B4QOZ0H2CNdnSKJpqSwd8d/Q7gjyCuLzt5bXG5anzZO6wuTy65lEeXfMo86+eX2WmW2vVIMmRRqNh6NChvP322/TrV3N1vCPcfvvt5ObmMn36dNLT04mLi+OXX34hMjISgPT0dFJSUhrs+aKqjNdnoJaV/b26s7ny/7M/+BDfkaNwbSfr9QghGtfVkVc32tT9C9FqtLx/1fu8uvlV7vz5Ttr5tOPpfk/zxLoncNO6Wa/79+5/8+OxH62vb/3pVgA+GfEJl4RewqoTq8gry+PnpJ/5Oeln63XhXuGsvGWl9bVFtfDD0R8Y3XE0Wo32grF56jyZd/U8Hl79MI+ueZR5V89r9QmSoqqqevHLbLN48WJOnDjBqlWr+KMZD6vUpLCwEL1eT0FBgdQfncOUn8+RgTUXBKIohEx7gTYTJjRuUELUwGAw4O3tDUBxcbGsc9QElZWVkZycTHR0NO7u7s4Op8HsytrFhP9N4JcxvxDhKzOhHeFCf3Zsef+2u+eosLCw1sbvuecegDqvJSRagLO9RTVRQL3QeSGEaAXWnliLh86DSN9IUgpTeGvbW/QJ7iOJURNkd3Lk7+/PN998w82yW7gAXAICcO/ejbLEA39Pfz/LouIzdKhT4hJCiKbCYDIwe8dsMgwZ+Ln7MSBsAM/GP+vssEQN7E6OVFVl3rx5zJo1C0VRiI+PZ/z48TLrqxUL+cc/SLnnXlSzubIgW6MBi4U2996La1SUs8MTQginGtVhVI3rDYmmp15T+ffs2cOll17K0KFDOXToEFdccQVTpkxxVGyimfHs25fo5f9BP3oUrtHReF5yCW1nv0vwc/LJSAghRPNRr9lqS5cu5ZprrrG+3rdvHzfddBPt2rXj6aefrndwovlx69SJ8DfecHYYQgghhN3s7jkKCAiots9Yjx49+OCDD5gve1AJIYQQopmyOznq1asXH3/8cbXjHTt2lP3HhBBCCNFs2T2sNmPGDK688kpSU1N57LHH6NmzJ6WlpbzxxhtERzf+vjVCCCGEEI5gd3I0YMAAtm7dyuTJkxk6dChn15J0d3fn22+/dViAQgghhBCNqV4F2b169WLDhg1kZWWxY8cOLBYL/fv3JzCw+u6/QgghRGtWcSqVY1dfTfR3K3Dv2tXZ4YgLsCk5SklJoX379tWOBwcHc91111U7npqaStu2sp+WEEII0RQcvWoYbSZOoM3EiTWerzhxguQxY0GrJfavbTa3X7hyFflffknZgQOoFguu7drhM2I4be6+G62fHwCWsjJyFy6i8JdfMKamovHywvPSSwl68gncOnWytpX/zTcU/PAj5UeOAODevRvBU6bg0bOn7T+4jWwqyL7kkkt48MEH2bat9l9YQUEBixYtIi4ujhUrVtQ7QCGEEKK1UlUV1WRqnGcZjaQ+/Qwe8fZtGJ/13hxSp07FvUcPIhYuJObHHwl+/jnKDx6i4MfKzXQtFRWk3Hsfp1esIGjyJDr8+j8iFi5AtZhJvv0OSnfvtrZXsu0vfG+4nsjPFhP19VfowsJJuf8BjJmZjvhxL8imnqMDBw7wxhtvcO2116LT6YiPjyc8PBx3d3fy8/NJTExk//79xMfH8/bbb9fYmySEEEI0lsJVq8j5v7lUJCfjGh1N4OOP4Tt8eIM9r3jTJnLmzaf8yBEUjQaP3r0J+ceLuJ4z6lKelETGa9MpS0xE1z6C0H++jFf/SwEw/LmNlIkTiVi0iOw5cyg7fJj2ixZR8P33mIuKiPi/f1vbyXjjDcoPHCTyi88BODF+grXnpeCnn1A0GvzuvIOgyZNRFIUT4ydgTEsjc+abZM58E4CuBw9Y28t+/31cY6LxGjCQ0l27rcdVVSXlvvtQtC5ELFqIoiiYCwtJGn0T+lGjCJ7yFKV795K7YAEhL06rssm4a7u2eF92GebCQgDyPvuM0t27K4cWu3QBQNe2Le0++IDjt91O2ksvEfPTTyiKQtt33q7yuw17fTpFK1di2LIFv5tuqu9/qguyqeeoTZs2vPPOO6SlpTFv3jw6d+5MTk4OR850ed19993s2LGDP/74QxIjIYQQTlW4ahWpkyZTfvgwakUF5YcPkzppMoWrVjXYMy0lpbS5ZyLR335D+8WfgkbDqSeerLL5dtbb79Dm3nuJ/m4Fnn36cOqxxzDl51dpJ+uddwiaOoUOP/8Xt9jOdX5+wfffg4uWqGXLCHnpH+R99jmnz0ySavfhB7iEhhI46Uk6bdpIp00brfcZtm6l8NeVhL78crU2FUUh/M03Kd23j/wvvgAg49VXcQkIIOiJxyuf+9N/0Xh64n/nnTXGpT2zUX3hf3/Ga9Aga2JkfYZGQ5uJE6k4eozygwdrbMNSWoZqMqHV6+v8+7CXXQXZ7u7ujB07lrFjxzo6HiGEEMIhcv5vLijK35thqyooCjlz5zZY75HviKrthv1rBkcGXUb50aNoPL0A8L/7Lut1oa+8QvGm3ylYvpyABx6w3hc06Um8L7vM5ue7hIURMm0aiqLgFhNN+eHD5H32Of633YbWzw9Fo0Hr5YVLUJD1HlN+PmnTXqTtrLfQenvX2K4uJISw114l7bnnMeXkUrR+A9ErlqPodABUnDiOLiLC+ro2FceP43mml+x8bh1irNfUVLCePftdXEJC8Bo0qE6/i/qo12w1IYQQoqmqSE7+OzE6S1WpSEpuuGempJD9/geU7tmDOT/fusyNKT0d1w4dAfDs3dt6veLigntcd8qPJVVpxz0uzq7ne/TqhaIof7/u3ZvcTxejms0oWm2N92S8/DL6G2/A85JLLti277XXUrR6DbkLFxL66iu4nbumoUplIloPZ39XNbWT+9FHFPz8C5Gff4bGza1ez6kLSY6EEEK0SK7RlT0nVRIkRcE1JqbBnnny0cfQhYYS9vp0XIKDwWIhaeQoVKPxwjeelxBoPDyqntdoqid6DirUNmz9k6J168n95NPKA6oKFgsHuscRNv01/G6+GQBLaSll+/eDVkvF8RNV2nCNiqJ0xw5Uo/GCvUeuUVFUHD1W47mzSatrZGSV47kff0LOgoW0/+QT3GNj7f0xbWL39iFCCCFEUxb4+GPWoTTAOsQW+PhjDfI8U34+FceOEfjoI3gNHIhbhw7WQuRzle7ZY/1eNZko25+IW8yFd5ZwaeOPKTu7yrGyA9Vrc85tG6B09x5cIyOtvUaKTodqtlS5Jurrr4j+boX1K2jSk2i8vIj+bgU+V19tvS7zrbdAoyFi4QLyvvwSw9at1nP6G2/AUlJC/ldf1Rj/2d+D7/XXY9iyhbLz6opUi4W8zz7DtWMH3M6pR8r9+GNy5s2j/aKFePSwrzfNHpIcCSGEaJF8hw+n7Qfv4xbbGcXVFbfYzrT98AN8r7mmQZ6n1evR+vmR/803VJw4gWHrVjLffKvadXlLl1K4enXlrLXpr2MpKEB/pnemNp79B1CWkMDp77+n4vhxsj/40Lr+z7lM6elkznyT8qRkCv77M3lLltBm/HjreV3btpRs344xM9NaBO7WoQPunTtbv1yCQ0Cjwb1zZ2vxc9GGDRQsX0H427PwvuwyAh58gLQXpmEuKAAqh/MCHrifzLdmkfn225Ts2oUxNRXDli2cmvxUZaE40Oaeibj37MHJRx+j8NdfMaalUbpvH6cmTaI8KYnwGTOsw4K5H31E9pz3CfvXv9C1bYspOxtTdjYWg8H2/zg2kmE1IYQQLZbv8OENOnX/XIpGQ9vZ75LxrzdIGjkK1+hoQv7xIikTqi64GDz1aXI/+ojyxAPo2ren3dz/w8Xf/4Jtew++nMBHHyXrnXdRy8vxGzsW/ejRlcOG59DfNBpLeRnHb7sNtFra3H03frffZj0fNOlJ0l95lWPXDEetqKgylb82prw80l/6J4FPPIFH9+6V7Tz+OIY/NpP+6qu0e++9yp/rmWdw796d/CVLOf31MlRVxTUiAp8Rw9GfmXqvcXMjcvFichYuJOu99zCmpaP19MSzf3+ivv4K985/z8zLX/pV5dpLkydXiSfw8ccJevKJi8ZdH4qqnj+IWTf33HMP9913H0OGDHF0TE1aYWEher2egoICfM9MTRRCNB8GgwHvMzNyiouL8fLycnJE4nxlZWUkJycTHR2Nu7u7s8NpNk6Mn4Bb1y6Evviis0Nxmgv92bHl/dvuYbWioiKGDx9Op06deOONN0hNTbW3KSGEEEKIJsPu5Gj58uWkpqbyxBNP8O233xIVFcV1113Hf/7zH4wXq8oXQgghhGii6lWQHRAQwOTJk9m1axfbtm2jY8eOjB8/nvDwcKZMmWJdOVsIIYQQDSvyi89b9ZCaIzlktlp6ejqrVq1i1apVaLVarr/+evbv30+3bt1470yhlhBCCCFEc2B3cmQ0Glm+fDk33ngjkZGRfPvtt0yZMoX09HQ+++wzVq1axRdffMH06dMdGa8QQgghRIOyeyp/WFgYFouFO++8k23bttH7nOXQzxoxYgR+fn71CE8IIYQQonHZnRxNnjyZp59+Gk9PzyrHVVXl5MmTtG/fHn9/f5KTG24PGyGEEEIIR7N7WO3VV1+luLi42vG8vDyioy+8DLoQQgghRFNld3JU29qRxcXFsmiXEEIIIZotm4fVpk6dCoCiKLz88stVhtXMZjN//vlnjfVHQgghhKiq7NhpchbtI/yVgWg8an5LNmzP5PR/j9H21UF1bvfUC5sIGN8Vj+6BmPLKyJj1F8GT+uAa7u2o0Fs0m5OjXbt2AZU9R/v27cPV1dV6ztXVlV69evHMM884LkIhhBCihXKL9CXsH/1R3LV1vsewPZP8//y9p5rGR4dblB79ddG4tKk+cqP1cyPsH/3ReOocEnNrYHNytH79egDuvfde3n//fdlfTAghhLCT4qJB6+N68QvPv89NS+gz8aCCMbuE098dJeez/YRM7ouiUapeq1HsekZrZvdstU8//dSRcQghhBAOV5qQQ8GaE5hySnEJ9EB/dSQecYEN9rysBXvRhXqiaBQMO7JQXBR8r4nEs08wp384Rum+HDTeOvxGd8Ajtk2Nw2qG7ZkUrj6BpcSIWyd/3KJq6IRQsCY8Wl9XfIe1J2/ZIUy5peiCqs4ir2lYrTzpNKd/ScaYbkDj6YJX3xB8h0ehaCsTK9WiUrTxFIa/MjCfLkfr7YpX/1B8r2oPgLmgnNM/J1F2+DSKBlwjffEb2cHac5X3zSEsZWbconwp2nQKzCoePYPwGxmDoq0sd1ZNFgpWnaBkdxZqqQmXUC/010bh3sHP4f9dbGVTcjR16lRef/11vLy8rLVHtZk9e3a9AhNCCCHqozQhh9wvD1hfmzJKyP3yAAHjujZoglSyMwufIe0IfqI3pXuyOf39UcoSc3HvHoDPlREU/55K/rJDuL1wabV7y1MKyV9+GN8RUXh0D6DscD6Fq1Mu+kxFd2Z+lbnmyVLnMheUk/Ppfjz7hdDmtlhM2SXkrzgCLhr010QCULDyOIZtGfjdGINblC/mogpMWaUAWCrMZC/ah2uUL8GP9ASNQuG6FHI+TajsuXKpjKX82Gm0PjqCHuyJKbeUvKUH0YV74X1pGAB5/zmMOb+cgDu7oPV1pXR/bmUbT/VDF+hRp991Q7EpOdq1a5d1U9mztUc1URSl1nNCCCFEYyhYc6L6QQUK16Y0aHKkC/PCd1hlD4vLlREU/XYSjafOmhT4DmuPYWs6xgxDtXuL/0jDrZM/vkMjKtsK8qTiRCFlh/NrfZ6poJyijafQ6l1xqUNSUbw1Ha2fG36jO6AoCrpgT8yFFRT87zi+w9qjGs0U/5GK/6iOePULqfw5Ajxwi9IDULonGxTwv7mT9f2+zS2dSXttC+VJBbh39gdA4+GC3+iOKJrKZ7h3aUP50dN4XxqGKbeU0j3ZhE27FK2vGwA+Q9pRdjifku2Z6K+NqsuvusHYlBydrTc6/3shhBCiqTHllFY/eKZGpyHpQr2s3ysaBY2nrsoxjXdlYbSl2IjiVrUQ25RVgkf3gCrHXNv7VkuO1DIzqS//ASqoRgu6tt4EjOtm7bW5EGNWCa7tfat0ZLhG+qJWmDEXlmMpMoJJxa2jX433V6QWY8otJe2VzVVjMlkw5ZUClcmRLsSzSv2T1sfVmhBWpBaDChnvbD+vDRWNp90VPw7j/AiEEEKIBuAS6IEp47xESKFaTY6jna3bqeKcY9akpJb1Auv0DDctIU/2AQU0Pq5oXOs+240LPlb5e4iu1vtVdG19CLg9ttqps4kfANrz2lHO+ZFVQAPBT/apNtp0fsLoDDbXHNWV1BwJIYRwJv3VkZU1RwqVb8Zn/v/skFdT5BLsScXJoirHzn8NgEKdhtBqogvxpDQhB1VVrYlJxYlCFDctWl9X8NKh6DSUHz2Ny6Wh1e9v603J3srCco27fX0sunAvsFT2nrlF6+1qoyHZXHNUF1JzJIQQwtk84gIJGNeVwrUpGLNL0AV54jusfYPWG9WX92XhZM/bQ9FvJ3HvFkD5kdOUHaq93siuZwwIo/j3VE7/eAzvgeGYskspXHMC78vbVg6DaRR8rmjH6f8lg1apLMg2GDFlluB1SSievYMp3phK7ueJ+F4TiVbvhvl0GaUJuXhf0Q4XvdtFY9AFeeLZO4i8bw7hd0MMunBvLAYjZcdOowv1wqNLG4f+zLayu+ZICCGEaOo84gKbdDJ0Prf2vviP7UThmhMUrknBraMfvldFULju4jPW6kqrdyPw3u6c/iWZzG07K6fyx/89TR/A56r2lbPQVp/AXFSB1scVr/6VBeUaVy1BD/ek4H/J5H6ZiKXcjNbXDfeOfmhsGBLzv7UzhetOcvrnJMyFFWg8XXBt7+v0xAhAUWvbJE3UqLCwEL1eT0FBgSyAKUQzZDAY8PauXOuluLgYLy+vi9whGltZWRnJyclER0fLXp3CJhf6s2PL+7escySEEEIIcQ5Z50gIIYQQ4hyyzpEQTmBMT8d46hRuXbuh9ZZhHSGEaEocss7R2bIl6TES4uIKV68m9akpYDbjEhRE1LKv0YWHOzssIYQQZ1x8Kc0L+Pjjj4mLi8Pd3R13d3fi4uL46KOPHBWbEC1S9uz3wGIBwJSXR/7SpU6OSAghxLns7jn65z//yXvvvceTTz7JwIEDAdiyZQtTpkzh+PHjzJgxw2FBCtGiaM75TKKqoNTrM4oQQggHszs5mjdvHosWLeLOO++0Hhs1ahQ9e/bkySeflORIiFqEvPA8px5/ArWiAl1YGP7jxzk7JCGEEOewOzkym83Ex8dXO96vXz9MJlO9ghKiJfMePJiOv23AlJ6Oa4cOaNwuvpqsqD9VVbEUGzGXVDg7FCFEE2d3cjRu3DjmzZtXbT2jhQsXcvfdd9c7MCFaMhd/f1z8/Z0dRqtRnlRA/o/HMGUYKKmoYad20WAqUosp2ZGJ2WDEtZ0PXvEhaDxkz3PRtNm98ayiKHz00UesWrWKAQMGALB161ZOnjzJhAkTHBulEELYqSLdQPbH+8BSfTMA0+kykBWyG0zx5jRO/3iscuqPCqV7sinaeIrgR3vh0kZWvhZNl02VoLt27bJ+7du3j379+hEUFMSxY8c4duwYQUFB9O3bl/379zdUvEIIYZOijScrC99r2CipeFtG4wfUSphOl3H6p2OVLyxYf/+W4orKhKmFuueee1AUBUVR0Ol0xMTE8Mwzz2AwGDh+/DiKouDi4kJqamqV+9LT03FxcUFRFI4fPw5gvX737t1VXp/98vf3Z8iQIfz222/WdubNm0fPnj3x9fXF19eXgQMH8r///a+xfvwWQzaeFUK0aMaUoso35xpUnCxq3GBakdJ9uTWfUKHsUB6WcrNNm5Q2J9deey2ffvopRqORTZs28cADD2AwGHj++ecBCA8P5/PPP2fatGnWez777DPatm1LSsrFN5hds2YN3bt3JysrixdffJHrr7+ehIQEoqOjadeuHW+++SYdO3a0tjt69Gh27dpF9+7dG+YHboHqPYc4MTGRX3/9lR9//NH69dNPPzkiNiGEqDeNryvUsj6t1se1cYNpRVSj+QInAXMtGWsD+PHHH5kyZQo//vhjozzPzc2N0NBQIiIiuOuuu7j77rv5/vvvrecnTpzIp59+WuWexYsXM3HixDq1HxAQQGhoKD179mTBggWUlJSwatUqAEaOHMn1119P586d6dy5M//617/w9vZm69atDvv5WgO7q+KSkpIYM2YM+/btQ1GUaqtkm80X+IshhBCNxLt/GHnJhTWe8+oX0sjRtB7unfwpXHWixnMuoZ4ojVSU/eOPPzJ69Gi0Wi1z5szhhx9+YNSoUY3y7LM8PDys+5JC5bI38+fP5/fff+fyyy/n999/Jy8vj5EjR/L666/b1LanpydAlfbPMpvNfPvttxgMBut6hKJu7O45mjx5MtHR0WRmZuLp6cn+/fvZuHEj8fHxbNiwwYEhCiGE/Tx6BeF9edvKF+f1ILl38Gv0eFoLXTtvPHoEVj2oVH753RDTaNtNrV+/Hq1Wi9lsRqvVNvr707Zt21i6dCnDhg2zHtPpdIwbN45PPvkEgE8++YRx48ah0+lsattgMDBt2jS0Wi1XXHGF9fi+ffvw9vbGzc2NRx55hO+++45u3bo55gdqJexO3bds2cK6desICgpCo9Gg0Wi4/PLLmTlzJpMmTWLXrl2OjFMIIeyiKAp+N8bg1T+UskP56MpL4T1nR9XyKYpCmztiKY7wwbAto3Iqf3sffK+MwC1K32hxXHnllcyZM8eaIA0dOrTBn/nf//4Xb29vTCYTRqOR0aNH8+GHH1JSUmK95v7772fgwIG88cYbfPvtt2zZsqXOawQOGjQIjUZDSUkJYWFhLF68mB49eljPx8bGsnv3bk6fPs3y5cuZOHEiv/32myRINqjXIpDe3t4ABAYGkpaWRmxsLJGRkRw6dMhhAQohhCPogjzRBXmiMRicHUqroWg1+Axph8+Qdk6LYdSoUfzwww9s2LCBoUOHNsqQ2pVXXsm8efPQ6XSEh4dbe4TOzkIDiIuLo0uXLtx555107dqVuLg466y0i1m2bBndunXDz8+PgICAauddXV2tBdnx8fH89ddfvP/++yxYsKDeP1trYXdyFBcXx969e4mJiaF///7MmjULV1dXFi5cSExMjCNjFEIIIew2atSoRq0z8vLysiYnF3Lffffx2GOPMW/ePJvaj4iIoEOHDnW+XlVVysvLbXpGa2d3cvTSSy9hOPMJbMaMGdx4440MHjyYgIAAli1b5rAAhRBCiJbowQcf5NZbb8XPz89hbb744otcd911REREUFRUxNdff82GDRv49ddfHfaM1sDu5GjEiBHW72NiYkhMTCQvLw9/f/9GK7QTQgghmisXFxcCAwMvfqENMjMzGT9+POnp6ej1enr27Mmvv/7KNddc49DntHSKenYOvqiTwsJC9Ho9BQUF+Pr6OjscIYSNDAaDtV6yuLgYL9k+pMkpKysjOTmZ6Oho3N1lmxFRdxf6s2PL+7fNe6u9/vrreHl5VdlnrSbnb0jbUObOncvbb79Neno63bt3Z86cOQwePLjGa1esWMG8efPYvXs35eXldO/enVdffbVKL5gQQgghWjebkqNdu3ZZF5q60FT9xhpWW7ZsGU899RRz587lsssuY8GCBVx33XUkJibSvn37atdv3LiRa665hjfeeAM/Pz8+/fRTRo4cyZ9//kmfPn0aJWYhhBBCNG3Nelitf//+9O3bt0qlf9euXbnpppuYOXNmndro3r07t99+Oy+//HKdrpdhtebPlJND0erVWEpK8BwwAA/Zb6hVkWG1pk+G1YS9nDKsdpbRaGT48OEsWLCAzp0729NEvVVUVLBjxw5eeOGFKseHDx/O5s2b69SGxWKhqKiINm3a1HpNeXl5lSmQhYU1b0MgmofT331P+j//CWYzKApYLPhcey1t356FYuPqtEIIIVomu7YP0el0JCQkOHVWWk5ODmazmZCQqnsjhYSEkJGRUac23n33XQwGA7fddlut18ycORO9Xm/9ioiIqFfcwnnKk5JIf/FFMJlAVcFSufFl0cqV5H66uNHjKdmxg1NPTuLYtddx8pFHMdQxqRdCCNGw7N5bbcKECXz88ceOjMUu5ydoqqrWKWn76quvePXVV1m2bBnBwcG1Xjdt2jQKCgqsXydPnqx3zMI5Cr77DjQ1/JFXVfK//rpxY/nvz5wYN56ideuoOH6c4o0bSbnv/kaPo7FYSksp+OEHCn76CYssRieEaOLsXueooqKCjz76iNWrVxMfH19t3L6hZ6sFBgai1Wqr9RJlZWVV600637Jly7j//vv59ttvufrqqy94rZubG25ubvWOVzifKSe31nPmvLzqxwrKqUg3oPXWoWvr7bCeUrWigozXX6/svTKbKw+e6cXKfPMtfG8cida75dTBqEYjJ8aNp2z/fgA8ln5F5JdfoGi1To5MCCFqZndylJCQQN++fQE4fPhwlXONMdzm6upKv379WL16NWPGjLEeX716NaNHj671vq+++or77ruPr776ihtuuKHB4xRNh3tcdwq+/776CY0G93OKslWThfwfjlKyPRPOTFdwCfUkYFw3dIEe9Y6jNCEBS0FBjefUsjJK/tqGz5VX1vs5TUXZ/v3WxAigdNcuyg8dwl02wRRCNFF2J0fr1693ZBx2mTp1KuPHjyc+Pp6BAweycOFCUlJSeOSRR4DKIbHU1FQ+//xzoDIxmjBhAu+//z4DBgyw9jp5eHig1zfeLtHCOfSjR5O7cBGmnJy/e2wUBVSVoMcfs15XsOpElcQIwJRZQs5H+wh9Nh5Fa/dodOUjaxraO/d8C+tR0Z6/NYKiVD8mhBBNiN3J0VmJiYmkpKRQUVFhPaYoCiNHjqxv0xd1++23k5uby/Tp00lPTycuLo5ffvmFyMhIANLT00lJSbFev2DBAkwmE48//jiPP/649fjEiRNZvHhxg8crnEvr7U3kkiVkTJ+OYdMmUFV07dsT8tyzeA0aBIBqtGDYklYlMao8AebT5ZQdyMMjrn7L/bvHxaENCKgcyjtvJQ2Nlxeel1xSr/Ybk6qqlB3OpywhF9Vswb1LGzy6B6Jo/+49do2KIvjZZ8maPRs0GkKefRZdeLgToxZCiAuze52jpKQkxowZw759+1AUhbPNnB1SM5/9ZN7CyDpHLYO5sBBLWRkuQUFVhoFNp8vJeHNbzTcpoL82Gp8r2tX7+UXr13PqiSfPBGMGrRYsFsLffhv9jc1juFe1qOR9c4jS3dl/T+2wgGuMnqB741B0VXvIVKMRFAXFpd6fyepF1jlq+przOkf33HMPn332GVC5d1pERARjx47ltddeIzs7m+joaLRaLSdOnKBt27bW+9LT04mIiMBsNpOcnExUVBTHjx8nOjqaXbt20bt3b+vrs/z8/OjRowevv/46V1xxRbVYZs6cyYsvvsjkyZOZM2dOg//sTYGj1jmye3xg8uTJREdHk5mZiaenJ/v372fjxo3Ex8ezYcMGe5sVol5Us4WSvdnkf3eE0/9NouJkUY3XaX190QUHV6uP03rpUFxr+WuhgkugY/6h9rnySqJXrMDvllvw6NsX/ehRRH3zTbNJjABKE3IqEyMAy5kvoCK5gOItadWuV3Q6pydGovVZsWIFvXr1wsPDg169erFixYoGf+a1115Leno6SUlJzJgxg7lz5/LMM89Yz4eHh7Pok08pLjNiOdOx8Nlnn1VJli5kzZo1pKen89tvv+Hr68v1119PcnJylWv++usvFi5cSM+ePR33g7UididHW7ZsYfr06QQFBaHRaNBoNFx++eXMnDmTSZMmOTJGIerEUm4ia/5e8pYexPBXJsWb08j6v90UrDpe5zYUnQbvgeFw/pwCBbT+brh3qX3BUFu5x3Ym7LVXiVq6hPA33sCjR5zD2j5X7uLFHLliKEmjR1O6b5/D2i3Zk1399wSgQsmuLIc9Rwh7rVixgptvvpl9+/ZRVlbGvn37uPnmmxs8QXJzcyM0NJSIiAjuuusu7r77br4/ZzLIdWNv59NPF5OUY+BgeiGFpUYWL17MxIkT69R+QEAAoaGh9OzZkwULFlBSUsKqVaus54uLi7n77rtZtGgR/v7+jv7xWgW7kyOz2Wztmg4MDCQtrfKTYmRkJIcOHXJMdELYoGj9SYynzvQUWdTKL6Bo3UnKT9R9ZXPf4ZF4XRJa5Y1fF+pF0P096l2M3dgM27aR9eZbmDIzKT9ylJOPPIp6ZtmA+lIrzNVrs86wVLTMYXXRvLz22mtVyj7OroM3ffr0Ro3Dw8MDo9FIUVnl3qRXXH0dRQWn2bltCyaLynf/W0teXp5dtbqenp4A1n1PAR5//HFuuOGGiy5VI2pndx93XFwce/fuJSYmhv79+zNr1ixcXV1ZuHAhMTExjoxRiDox7Miq+c1aAyW7s3CLrFuNmKLV4D+2E75XR2JML0bj44ouzMupK8Lbq+LEib9fWCyYc3NRS0tRHFBn497Jn/Kjp6v/zjXgEeu4HjYh7HX48GHOL6tVVbVRP8Bv27aNpUuXMmzYMPINlROXXHQ6bhhzG98vW0LfSwfy/bIvuenWO9DZuIWRwWBg2rRpaLVaa83R119/zc6dO/nrr78c/rO0JnZ/DH7ppZewnPkEOmPGDE6cOMHgwYP55Zdf+OCDDxwWoBB1pdbWW6GCWm57T4bW1xX32Da4hjtuAcjG5jVwEIqHR2XBt6LgOXAAGgcVIHtdGorW373q0JoGNO4ueA+pf9G6EPXVuXPnan93FUUhNja2QZ/73//+F29vb9zd3Rk4cCBDhgzhww8/pML8d6/tTXeMY/XPP5CTlcmqn39g7B3j6tz+oEGD8Pb2xsfHh59++onFixfTo0cPTp48yeTJk/nyyy+bXSF7U2N3z9GIESOs38fExJCYmEheXh7+/v7N9o1ENG9uHfSUHcircRq+W4yfM0JyOtd2bYn+9hsKvv8ejV5Pm7vvdljbGncXgh/rRdG6k5TszUY1q3h0a4PvsEhc/GRVeeF8r7zyCjfffLN1aO3s/7/yyisN+twrr7ySefPmodPpCA8Pt/YIuZ7Mtl7TqUs3ojt04vknHiCmY2d69uhB1vG69WgtW7aMbt264efnR0BAgPX4jh07yMrKol+/ftZjZrOZjRs38u9//5vy8nK0LWwdtYZid3J07733Mm7cOK666iprMnSh3e2FaGi+V0dSdjgfzOrfCZICLsGeePYKcmpszuTWsSPB58yUcSSttyt+ozrgN6pDg7QvRH2MHTuW5cuXM336dA4dOkRsbCyvvPJKlV0VGoKXlxcdO3asdtzf27XK65tuv5t//eMZXpo5mzZertR1GkNERAQdOlT/Ozds2DD2nTfp4t5776VLly48//zzkhjZwO7kKDc3lxtuuIGAgADuuOMOxo0bR58+fRwZmxA2cQ33JvjR3hSuPk7Z0dMoOi1efYPxHda+2po7QojWYezYsYwdO9bZYQDg41bZg6Q5M7gy9q6JXDdqLF0jQ3HX1T9x8fHxIS6u6qxXLy8vAgICqh0XF2Z3cvTjjz9y+vRpvvnmG5YuXcqcOXOIjY1l3Lhx3HXXXURFRTkwTCHqxrWtN4H3yD8CQoimKzrQm5jAykkeHq4BaKQUpcmxe4Xs8506dYqvvvqKTz75hCNHjmAymRzRbJMjK2QL0bzJCtlNX3NeIVs4l9NXyD6X0Whk+/bt/Pnnnxw/fpyQkBBHNCtEq2ApLcWYno56zv6EQgghnKdeydH69et58MEHCQkJYeLEidZphSdPnnRUfEK0WJaSEtJfeZXD/Qdw9MqrOHz5YHIWLHTYIo1CCCHsY3fNUbt27cjNzWXEiBEsWLCAkSNHSvenEDY4NWUKhk2/w5lkyFJYSPZ776EajQQ98biToxNCiNbL7uTo5Zdf5tZbb5V9W4SwQ9nBgxh+21jjudyPPybgvnvRnNkWQAghROOye1jtoYceksRICDuV7d9f6zm1tLTqth9CtFIOmi8kWhFH/Zmxu+dICGE/7Tmr2tZ4vs2FzwvbGbNKKE8qoMRS5uxQxEWcXVG6pKQEDw8PJ0cjmpOSkhIAm/epO58kR0I4gfdll+ESFIQpN9dacwSAVovXoEHoQoKdF1wLo1pU8pcfoWRHJgAlFaVOjkhcjFarxc/Pj6ysyjWjPT09ZVsqcUGqqlJSUkJWVhZ+fn71Xg1ckiMhnEDR6Wg3bx4nH3wQc34+aDVgtuAWE0P4G/9ydngtSvHvqdbE6HymvDKQdY6apNDQUABrgiREXfj5+Vn/7NSHJEdCOIlHXHc6rl9H0dq1mNLTcYuNxeuyy1A0stWJIxVvTqv1nGFXFvoIGcJsihRFISwsjODgYIxGo7PDEc2ATqdz2P5xkhwJ4UQad3f0N9zg7DBaNHNR7YtrmgvLGzESYQ+tVisbpopGZ1NyNHXq1DpfO3v2bJuDEUIIR9OFeWFMLYYaJrG4hnk3fkBCiCbPpuRo165ddbpOCueEEE2F71Xtyf08scZznr2DGjkaIURzYFNytH79+oaKQwghGoRHtwD8b4+l4JdkLOcNsWncpbJACFGd/MsghGjxvPoE49kzCFNeKaWmMnjP2REJIZoyqTkSQrQKilZBF+RJhUFWXRZCXJjUHAkhhBBCnENqjoQQQgghzlHvmqPExERSUlKoqPi70FFRFEaOHFnfpoUQQgghGp3dyVFSUhJjxoxh3759KIpi3Qn37JCa2Wx2TIRCCCGEEI3I7n0KJk+eTHR0NJmZmXh6erJ//342btxIfHw8GzZscGCIQgjRcFRVpTylkOItaZTuz0E1WS5+kxCiRbO752jLli2sW7eOoKAgNBoNGo2Gyy+/nJkzZzJp0qQ6F28LIYSzWMpMZH+5j4rkAusxjbeOwHu649rOx4mRCSGcye6eI7PZjLd35dL7gYGBpKVVbu4YGRnJoUOHHBOdEEI0oPz/JlFxvKDKMYvBSM6n+6UHSTQJFotKQakRs0WWoGhMdvccxcXFsXfvXmJiYujfvz+zZs3C1dWVhQsXEhMT48gYhRCNoOC/P2P4/Xfcu3bBf9w4lFaw2WfZvmw8XTyqHlQrE6TSA7l49pDtRYRzWCwqH/2exPzfksgzVODj7sI9g6KYNKwTOq3d/RqijuxOjl566SUMBgMAM2bM4MYbb2Tw4MEEBASwbNkyhwUohGh4BT/9l7RnnwWtloIffsCUk0vw03Vf9NXZckpz+PbwtyTmJBLgEcDYTmPpGdTz4jfW1jmkgLmgopaTQjS899Yc5sN1R62vi8pM/HvdUTILy5h1Sy8nRtY62JQc7d27l7i4ODQaDSNGjLAej4mJITExkby8PPz9/WURSCGameJNG0GjgTOzTIs3bGg2ydHR/KNM/HUixcZiLKoFraJl+ZHlvHDpC9zd9e4L3qu4aaGm0QoVdGFeDROwEBdRWGZkwW9J1Y6rwLfbTzFpWCfa+Xs2fmCtiE19c3369CEnJweoTIhyc3OrnG/Tpo0kRkI0Q+6xsXBmOQ40Gty6dnVuQDaY8ecMa2IEYFYrE7xZf80iqyTrgvd6X962+kEFdO28cYvROzxWIeriUEYRFeaauzVVYM/JghrPCcexKTny8/MjOTkZgOPHj2OxSMGiEC1Bm4kTaXPvPbjGxOB7ww2EvvQPZ4dUJ/ll+ezI3GFNjM6lqirrUtZd8H6fIe3wvSaysgcJQAGP7gEE3hsnH/SE0/h7ul74vJeukSJpvWwaVrv55pu54oorCAsLQ1EU4uPj0dZStJmUVL1LUAjRNCkuLoQ89xwhzz3n7FBsYrKYLni+wnzhuiFFo+A7rD0+Q9piyi9H46VDK288wsk6BnvTo62exLRCzOrf474aBUJ83ekfHeDE6FoHm5KjhQsXMnbsWI4ePcqkSZN48MEH8fGRtUCEEM4R6BFIB30HkgqSUM8rHlJRubzt5XVqR9Fp0QVLDYdoOj68sw93LtpKekEZLhoFk0XF113HognxaDXSq9nQbJ6tdu211wKwY8cOJk+eLMmRgxRv3Ihhy1bcu3bBd+RI6dIXog4UReG5S5/jsTWPoaJiUS0oKKio3Nr5VmL8ZFkR0TxFBXqx/pmhrNyfwbGsYtr5e3JDzzC83Oq9JaqoA0VVVVlZygaFhYXo9XoKCgrw9fV1TJurVpE6aTJotWA2E/T0VAIffNAhbQvRGuzL3sfHCR+zN3svQR5B3BZ7G2M6jUGjVC+rNBgM1gVsi4uL8fKSWWlCtAa2vH/XKwVdu3Yta9euJSsrq1px9ieffFKfpluVolWrq0yjLvzlf5IcCWGDHkE9mHPlHGeHIYRoIexOjl577TWmT59OfHy8tUBb2Mc1MvLvF1otbtFRTotFiJbMbDGTXZLt7DCEEE2c3cnR/PnzWbx4MePHj3dkPK1SwIMPUHHqJIZNv+PevRshL73k7JCEaFEO5B5gzo457MzaSUlJifV4cYUMqwkhqrM7OaqoqGDQoEGOjKXV0ri70/att5wdhhAt0v/t/j/m75lf47kpG6aw5KYl0vMthKjC7t3rHnjgAZYuXerIWIQQwqF2Z+2uNTEC2JO9h51ZOx36zB0n8nngs+089uUODmUUObRtIUTjsLvnqKysjIULF7JmzRp69uyJTld14bTZs2fXOzghhKiPn479hFbRWrcUOZ9G0ZCQk0C/kH4OeV5WURl3f7SVClPlBJUtSbn88cJVeLrK9GshmhO7/8bu3buX3r17A5CQkFDlnHRRCyGagiJjERdarcSiWmjj3sZhzzuUUUSZ8e+Zu/klRk7mlRIb+vd6cCazhbUHsyg3Wbi6a7AkTkI0QXb/rVy/fr0j4xBCCIeLD4nnf8n/q/W8l4sXw9oPc9jzYkN9cNdprD1Heg8dEW08rOdVVeWRL3ew5kDlhrhdQn34/vHLcNfVvA2TEMI57K45EkKIpu7GmBtp79O+xsUgAd4Z+g6eOsdtGxLs487SBwdwddcQrosLY9nDA6v0DJ3KL7UmRgAHM4rYmpTrsOcLIRzDpp6jqVOn8vrrr+Pl5cXUqVMveK3UHAkhnM1T58nn133OBzs/4JfkXyg3l9POux2JJAI4rNboXH3b+7NwQnyN57zdXNAoYDlnpE/vIRvdCtHU2JQc7dq1C6PRaP2+NlJzJIRoKgI8Anjtstd47bLXUFWVkpISvPF2Siz+Xq68flMcr/ywH7NF5aEhMfSO8HNKLEKI2sneajZqiL3VhGgMxtRUCleuwlJWitfAgXj07t0qP8g0hb3VyoxmLKoqxdhCNKJG21sNIDExkZSUFCoqKqzHFEVh5MiR9W1aCOEgeV98SeYbb4CigKKQ88GH+FxzDW1nv4uik2GdxiYF2EI0bXYnR0lJSYwZM4Z9+/ahKIp1uuzZT6Jmc83rigghGldZYiKZ//pX5YtzOoqL1qwh7/PPCbj/fidFJoQQTZPds9UmT55MdHQ0mZmZeHp6sn//fjZu3Eh8fDwbNmxwYIhCiPo4/d33oK2hp0JVyV/2TaPHI4QQTZ3dPUdbtmxh3bp1BAUFodFo0Gg0XH755cycOZNJkyZdsGBbCNF4zPn5VXqMqp0TQghRhd09R2az2VrUGBgYSFpaGgCRkZEcOnTIMdEJIerNo2ePmpMjjQaPPr0bPR4hhGjq7E6O4uLi2Lt3LwD9+/dn1qxZ/PHHH0yfPp2YmBiHBSiEqB/9mDG4BAdXHVo7UxsY+MijTopKCCGaLruTo5deegmLpXKJ/BkzZnDixAkGDx7ML7/8wgcffOCwAIVwJvPp05hyci64P1dTp/XxIWrpEryHDgVN5V95ty5daP/RIjz79nFucKIak6mMhP1T+P2Py9m+4zaKiqQnXojG5tB1jvLy8vD392/Ra6fIOketQ9nhw2ROf52S7dsBcIuNJeTFF/Hqf6mTI6sfS2kpqtGIthX/2W0K6xzVprw8mz+33YDRWHVLka5d3iQ8/FYnRSVEy2DL+7ddPUdGo5Err7ySw4cPVznepk2bFp0YidbBmJnFibvHUbJzp/VY+eHDpNx/P6X79zsxsvrTeHi06sSoqTtyZAZGY1614wcO/oOy8gwnRCRE62RXcqTT6UhISGgSidDcuXOJjo7G3d2dfv36sWnTpgte/9tvv9GvXz/c3d2JiYlh/vz5jRSpaC7yv/4Ki8EAZ4aNgcqCZlUl96OPnReYaNHM5nKysv8H1NSZr5KV+UtjhyREq2V3zdGECRP4+GPnvlEsW7aMp556in/84x/s2rWLwYMHc91115GSklLj9cnJyVx//fUMHjyYXbt28eKLLzJp0iSWL1/eyJGLpqxs376qidFZZjOle/Y0fkCiVVDVClS15sVzFUXBZCpq5IiEaL3sXueooqKCjz76iNWrVxMfH19t3H727Nn1Du5iZs+ezf33388DDzwAwJw5c1i5ciXz5s1j5syZ1a6fP38+7du3Z86cOQB07dqV7du3884773DzzTfb9GyDwYC2poX1RLNX4aunRFHg/FXeFQV3fz8MBoNzAhMOce5/v6b031JVFTSaaAyGJKr3Hllwde3ZpOIVormx5e+P3clRQkICffv2BahWe9QYw20VFRXs2LGDF154ocrx4cOHs3nz5hrv2bJlC8OHD69ybMSIEXz88ccYjUZ0NewxVV5eTnl5ufV1YWEhAOHh4fX9EURzdPgQrFjh7CiEg4SEhDg7BBtc5ewAhGg17E6O1q9f78g4bJaTk4PZbK72j1tISAgZGTUXLmZkZNR4vclkIicnh7CwsGr3zJw5k9dee81xgQshhBCiSbM7OWoqzu+lUlX1gj1XNV1f0/Gzpk2bxtSpU62vCwsLiYiIIC0tTabyt3DGjAyK1q9HNZnxvmwQbrK4aYtgMBisH5IyMzMdOpU/O3sdCfsn1XBGQ2T7+4mJmeywZwkhbFNYWFjnUZ9mmxwFBgai1Wqr9RJlZWXV2lUeGhpa4/UuLi4EBATUeI+bmxtubm7Vjnt5eTWp9VFEA+jQAb8OHZwdhWhAjv57fPz4ejw8dED1wurColV4eb3osGcJIWxjPr+O9ALsnq3mbK6urvTr14/Vq1dXOb569WoGDRpU4z0DBw6sdv2qVauIj4+vsd5ICCFsYbFUADXMdAQslvIajwshmp5mmxwBTJ06lY8++ohPPvmEAwcOMGXKFFJSUnjkkUeAyiGxCRMmWK9/5JFHOHHiBFOnTuXAgQN88sknfPzxxzzzzDPO+hGEEC1IQMAQal6nSEtQ4LDGDkcIYSebh9WSkpKIjo5uEgtA3n777eTm5jJ9+nTS09OJi4vjl19+ITIyEoD09PQqax5FR0fzyy+/MGXKFP7v//6P8PBwPvjgA5un8QshRE1CQkZzKnUJRUWJnO1BUhQtLi6+REbKJr9CNBc2762m1WpJT08nODgYqExQPvjgg2Y2JdZ+sreaEM1bQ++tZjIVk5LyERmZP2CxVBAYeBWR7R/Bw6OtQ58jhLCNLe/fNidHGo2GjIwMa3Lk4+PDnj17iGklM3kkORLCMQrKC9ieuR2dRseloZfi7uLeKM+ta3JkMBwjI/MHTKYi/PwuJSjwajQaqU0Uormy5f272c5WE0I0X4sTFvPBrg8wWowAeOu8mX7ZdK6JvMbJkVU6efIzDh95HQUNKAqnTn2Oj3d3+vT5Ep1OPhQJ0dLZXJCtKEq1eqOmUH8khGge1qas5d0d71oTI4BiYzHP/PYMR/OPOjGySgZDEoePvA6oqJhRVRMARcUHSUpu+G2R6sJotvDt9pMs+O0Yp/JLnB2OEC2OzT1Hqqpyzz33WNf+KSsr45FHHqnWNb1CtlgQQtTgy8Qv0aDBct6Ud4tq4cXfX+SDqz4g1CvUSdFBRuYPVH5uPH9NFDPp6cvp3OkVp38gnPrNbn7ak46iwP+tP8rKKUMI03s4NSYhWhKbe44mTpxIcHAwer0evV7PuHHjCA8Pt74++yWEaLosBgOm/HynPPtk0clqidFZB/IOMPaHsbX2IGVm/cL27beyZ8+DlJQkN0h8JlNRrcmP2VxKzVP1G0+5ycxPe9IBUFUoLDOx9kCWU2MSoqWxuefo008/bYg4hBCN5PTyFaS/8gqYTOhvGk3YG2+gaBpvybMOfh3ILs3GotacIBlMBt7e/jYLrllQ5XhhUQIJCZOoTE60FBsOM2jgBof34vj7XcqpU5/VcEaDXt8HRXHu8nCuWg1+njoKSo2cnU4Tpm+cYnYhWotmvQikEMI2lrIya2IEUPD9Dxj++KNRY5jYfWKtiRFUDq9tSdtCqam0yvHiogP83WtjpqzsFGZzscPjCwwcho9PD6r+81j5fYeYqTXe05gURWH+uH6E+Ljj5qLh4SExXNUl2NlhCdGi2NxzNHbs2Is36uJCaGgo11xzDSNHjrQrMCGE46kVFdbE6CxLseMTjAsZFD6IGZfN4PWtr1NurnlLDRWV81cZ0ev7oiguqKoFUPD26oRW6+3w+DQaHX37fMGxpPdIT1+O2WxAr+9Lh5ip+Pv3d/jz7DEgJoCtL8qK20I0FJuTo7rUE1ksFo4cOcJHH33EM888w/Tp0+0KTghRP+aiIvKXfkXx+vUoLi74XHct+ptvpmD5cgBcY2LwHjKk0eMa3XE0MfoY7vrlrmrnNIqGvsF98dR5Vjnu5dWBPr2/IDVtKToXP6Kjn2iwwmgXFx9iO79MbOeXUVXV6QXYQojGZfMikLb4+eefefTRR6ts4dHcySKQorkwFxRw/PY7qEhJAcuZYSxFwaNfPwIffghLaSnel12GxsErRNvi9S2v883hb1BQUFHRKlp0Gh2Lr1tM94DuDfLMhl4hWwjRNDWZRSAvu+wy4uPjG/IRopGlFqeyLX0bblo3BrcbjI+rj7NDErXI/fTTqokRgKpSun07pjE34dcE9hR8acBL9Anpw4rDK8gty6VfSD8mdJtAlD7K2aEJIVoxu3qOLBYLixcvZsWKFRw/fhxFUYiOjuaWW25h/PjxLboLurX2HKmqytt/vc2XB75EPVMU66Z1Y/qg6Vwfc72ToxM1OXb9DVQkJVU/oSh4Dx1KxLy5jR9UEyA9R0K0Tra8f9s8W01VVUaNGsUDDzxAamoqPXr0oHv37pw4cYJ77rmHMWPG2B24aLpWHFnBFwe+sCZGAOXmcqZtmsax08ecGJmo1YU+99j4mUhVVQoKdpOS8jFpad9iNBbWMzghhGi6bB5WW7x4MRs3bmTt2rVceeWVVc6tW7eOm266ic8//5wJEyY4LEjhfEsPLrXWhZxLURSWH1nOc5c856TILq7cXE6GIYM27m1a1TCgz/Dh5C5aVHVYDUBV8bm67jOdzOZy9iU8Tm7ueio/T1k4dPgV4rp/QFDQ1Q6NWQghmgKbe46++uorXnzxxWqJEcBVV13FCy+8wJIlSxwSnGg6MgwZ1RIjqFyTJtOQ6YSILs6iWpi3ex5Dvh7Cjd/dyOCvB/PiphcprmjcqevOEnDvPejatYNzF3hUFDz69sV31Kg6t5OcPIfc3N/OvKpMtCyWcvYlPEF5uazMLIRoeWxOjvbu3cu1115b6/nrrruOPXv21Cso0fR0adMFTQ0rAyuKQmf/zk6I6OLm7p7L3D1zKTFVbsxpVs38nPwzk9dPdnJkjUPr50f0f74laNKTuPfogUffvoRMm0b7Tz9B4+papzZUVeVU6ldQw3Yfqmo+sw+ZEEK0LDYPq+Xl5RESElLr+ZCQEPKdtGeTaDj397ifvzL+qnJMo2jwdPHk5s7On/V0vhJjCZ/tr74FhEW1sC1jG/tz9tM9sGGmijclWl9fAh95hMBHHrHrflU1YTYX1XhOUTRUlGfXJzwhhGiSbO45MpvNuLjUnlNptVpM563AK5q/QeGDeGvIWwR5BFmPxfrH8um1nxLoEejEyGp2qvgUZeayWs8fyDvQiNE0XxqNDk/PDkD1GaiqasLHJ67xg2phiooOsGPHHfy57QYyMqQnToimwOaeI1VVueeee3Bzc6vxfHl5zdsBiObvuujruCbyGk4UnsBN60Y7n3bODqlWbdzb1FhAflawp+xFVVcx0ZNI2H/+UKQWD492BAePcEpMLYXFYmLX7okYjfmAhf2JT+Pl1Qkfn27ODk2IVs3m5GjixIkXvUZmqrVcLhoXOvh1cHYYFxXoEcjQiKFsPLURs2q2HtcoGgLdAxkYPtCJ0TUvISE3YrFUcOzYO5RXZAIKgYFD6RL7OhpNzR+SHMVgOMrhIzMwGQto3/4BQkJuaNDnNTaT6TRGY+45R1QMhqOSHAnhZA26fUhL1FoXgWyO8svyeXTNo+zP3W/tRQpwD2D+NfPp0qaLs8NrdlTVTHl5JlqtNzqd73nnVNamrOX7o99zuvw0fYP7clfXuwj1Cq3X8/7YPJjy8mwqC8IVLr3kx2qJg6qqpKV/w4kTCyktPYmHR1vaRzxA27Z31bggbVNaBFJVVbb9NZLi4sMogKLRMaD/Kjw82jotJmcxW1QKSo34eejQaFruQsLCeWx5/5bkyEaSHDUvqqqyPXM7h/MPE+oZypB2Q9Bpdc4Oq8V5c9ubLDmwBA0aLFjQKBq8dd58cd0XxPjF2NWm0ZjPxk1Vtx/q1vUdwsKqLjSbnPxvkpLfq3Z/VORjdOjwdLXjTSk5AqioyONEykJMpiLatb0LH5+WP1HgfMdzDNz90Z+kni6lQ5AXSx4YQKje3dlhiRamQVfIFqI5URSFS0Iv4e6udzMscpgkRg3gQO4BlhyoXNvMcnYdJNVCsbGYd7a/Y3e7Li5+eHnFAlpAg6K4otf3qXKN0VjI8RP/V+P9J1IWUlGRZ/fzG4uraxs6dXyBrl3+1SoTI4CZ/ztARkHlBIrjOQbmrDns5IhEayfJkRCiXtafXI9W0VY7blEt/J76O0az0a52FUWhb5/PadfubkJDR9Gv71I8PaOqXFNUtA+LpaLG+1XVRGGhrLnWHBSWmrCcGcRQgcIy+/7MCOEoNhdkCyHEuWqbEVjX8xfi6hpIbOdXaj2vdfG+4P0XOy+ahvsvj+bP5FxUFbQahfEDopwdkmjlJDkSooU5nH+YD3d+yOa0zbhoXLg+5nqe6P0EAR4BDfK8K9pdwfw986sd1ygaBoQNwFVbt9W47eHr0wN39wjKylKpuoq3Bje3YAxqVzbvTSc21IeOwZIoNVVXdwvhf5OHkJBaQJ/2fsQEyX8r4Vw2FWRPnTq1zg3Pnj3broCaOinIFs6kqmYqKnJxcfFBq/Wodj7pdBK3/3w7FeYKLGplsqBVtIR7h/PtyG/x0jVM8fGrm19l+ZHl1lmBWkWLu9adz6//vMG3lyks3MvOXeMxmw0oihZVtaDVuuMV9m/GfV5CucmCRoEF4+O5pltIkyvIFkI0Dlvev23qOdq1a1edrqtp+qxouVSzmYrjx1F0OnQREfLfvwGoqkpq6hKSkz+kwpiDougIDR1N504v4eLiY71u4b6FGM1Ga2IElXvKnSo6xfdHv+furnc3SHwvD3yZ+NB4vjvyHXlleVwSegnju40nwieiQZ53Ll/fnlw2aAPp6d9RUpKEh2ckYaFjeeG7k5jMlZsMqyrM/+0Y13SrfeujhlJaYeb9tUc4ll3MNd1CuC2+4X8nQoj6sSk5Wr9+fUPFIZqpojVryJjxL0wZGQC4depE2L9m4NGzp5Mja1lOpX7J4cOvWl+rqpH09BWUlCTTr+8ya0K6NW1rlUUvz7Ujc0eDJUcaRcONMTdyY8yNDdL+xeh0/rRvf1+VYx6uaVRue6KiKODt5pwqgpe+38d3u1KxqLA6MRNvNxeu7xHmlFiEEHUjs9WE3Up27ODUk5OsiRFA+bFjnJh4D8a0NCdG1rJYLCaSkz+o6QwFBTs4fXqb9Yi3a821GhpF02BDak3Vk1d1IqJN5dBjGy9XXry+q1Pi+ONoLpYzxQtajcK25Ka/vIAQrZ1NH6WmTp3K66+/jpeX10Xrj1pqzZH4W+7HH4OiVI5ZnGWxoFZUkP/V1wQ/XfcatZbAolooM5Xh4eLh0KHFioosjMba3lA1FBbtw9+/PwA3dbyJD3Z+UG2GmFk1c0NMy9p642JC9e6sfXooWUVlBHi54epS/bOg2VyKqrqj1LAUgaP0i/TnfwnpWNTKVaB7Regb7FlCCMewuebIaDRav6+N1Jy0DmX7E8FiqX7CbKbs0KHGD8hJTBYTi/YuYsmBJRRUFBDkEcT9Pe7nri41b19hKxcXnzOFxjUNl1lw1f09C218t/FsTdvKnxl/4qK4oKJiVs1M7DaR/qH9L/oss7mMzMyfyM/fitbFi9CQUfj5xV/0vqZKq1EI01cvXD9r46ZL8Pb2IS7uAwIDhjZIDDNv7oGfp44jmUWMiAvjpt6tb2sQIZob2T7ERjJb7W/H77yL0t27q/YcAWi1+N18M2HTX3NKXI3tlT9e4buj31XrrXms92M82utRhzwjYf8UMjN/Bs5NkBS0Wk8uv2wLLi5/D5mZLWY2pW7i99TfcdO6MSJqBD2DLl4DZjSeZsfOOzAYjlC5IrWCqpqJjHyUjh2eccjP0RScO1vtp/9G4eGhxcXFhyGDd8oHOyFasAabrSbEufzvvpvSmnoQLRb8brut8QNygtTi1BoTI4CP933M+K7ja60DskVs51coKUmiqCiByu00zGi1nvTsuaBKYgSg1WgZGjGUoRFDbXpGUvL7GAxJZ15ZrDnviRPzCA4ajq9vSy2yVzGZiqlcJ6nhhtfqSlVVsrJ+pqj4AG38L6NNm0HODkmIVsfuguyrrrqK116r3jOQn5/PVVddVa+gRPPge8P1BDzyMGj+/mOkuLoSNuN1POJaxx5R+3L21boCdLm5nCOnjzjkOTqdH5fEf0fvXovpEDOFrl3e4vLLNtPGf6BD2gfIyPiBqj1TlRRFS0bmTw57TlPUrt2EBq07skVKyiIS9k8m5cQidu0eT07OOmeHJESrY3fP0YYNG9i3bx+7du1iyZIl1oXUKioq+O233xwWoGi6FEUh+Kmn8L/jDgx/bEZx1eE9ZAhafespONW7Xvhn1bs57nehKBoCAgYTEDDYYW2ey2Ipq/Wc2VzSIM90ti6x0/Hza0dAA9Ub2SM943sAVMyAhsysXwgMlA+cQjSmek3lX7NmDRkZGQwYMIDjx487KCTR3OhCQ/G7eSz6kSObbGJ0OP8wG09tJK3YsUsMXBJ6CUEeQWjO+6ukUTR0C+hGjD7Goc9rSG38L6OmYSVVNdOmzWWNH1AjCAsbS2DglU2q1qhyc92//zt4eLR3WixCtFb1So7CwsL47bff6NmzJ5dccgkbNmxwUFhCOEaGIYO7f76bm3+8mcfXPs61y6/l2d+epdRU6pD2XTQuzLlyDp46TxQUXDSVnbEB7gG8NfgthzyjscTETEGjcaFqgqTB17c3QYHXOCusVie282v4+V2Ci4svIcE3ENn+IWeHJESrY/ew2tlPWm5ubixZsoQZM2Zw7bXX8vzzzzssOCHqQ1VVHlvzGEkFSX8fQ2XViVV4unjy2mWOmU3XM6gnq29ZzS/Jv5BWnEaMXwzDI4fj7uLukPYbi49PN+LjV5Cc/AH5eZvRungRFjqWyMhH0Gh0zg7PqVTVQl7e7xQU7ESn8yM45EbcXAMb5FlubkH067ukQdoWQtSN3VP5NRoNGRkZBAcHW48tX76ciRMnUlpaitlc8xYGzZ1M5W8+tmds596V99Z4zkVxYcPtGxxaE9SU5JTm8PG+j1mbshYFheFRw7kv7j783f2dHZrT2brxrMlUxK7d91BYuNu6sa2iuNC9+3uEBF/XGCELIRygUabyJycnExhY9ZPTzTffTJcuXdi+fbu9zQrhMClFKbWeM6kmMgwZLTI5yivL466f7yKrJMu6z9rniZ+zLmUdX934Fb6uktTb4uixWRQW7gWwLsSpqkb275+Cnz4eN7cgZ4YnhGgAdidHkZGRACQmJpKSkkJFRYX1nL+/fDoVztfep/ZCVhfFhVCv0EaMpvEsObCEzJJMLOrfq5dbVAunik7xzaFveKDHA06MrnmxWEykpy+ncg2kqlTVTGbmT9U2vBVCNH92J0dJSUmMGTOGffv2nVlJt3J07mwtUksdVhPNR7+QfnT278yx08eq7FSvUTSM6jCqRfYaAfx28rcqidFZFixsOrVJkiMbqGoFFkt5jecURXOBPe+EEM2Z3bPVJk+eTHR0NJmZmXh6erJ//342btxIfHy8zFoTTYKiKMwdNpe4wLi/j6EwImoE0/pPc2JkDctN61bjcQUF1ZRPUVFiI0fUfGk0Hnh6xgDVp/qrqglffZ/GD0oI0eDs7jnasmUL69atIygoCI1Gg0aj4fLLL2fmzJlMmjTpghvTitbDZComK+tXKiqy8fHpTps2l6Mo9VpBwiYhXiF8ef2XHM0/SkZJBh30HQjzDmu05zvD9THX17hyt4qFzupBtv01ip495hEU1DKn51ssJrJzVmMxlxAUNBwXFx+721IUhZiYqSQkPHHeGS3e3rENtlmtEMK57E6OzGazdcZHYGAgaWlpxMbGEhkZyaFWtCO7qF1+/p/s2fsgZrOBs/uB+fjE0af3YnS6xq1L6+jfkY7+HRv1mc5yS+dbWHNiDdszt1cuTqlU1hx1cbdwqZcZUDh56otmkxyZTEXk52/F1S0YvW+vC16rqioJCU+QnbMaAI/jc7n0kh+r7T9ni5Dg66D7BxxLmk1p6XEURUdoyGg6dZpW65YjFouJgoLtmM0l6PV9Gv3PuxDNUUmFiZd/2M/243kM7BDAKyO7465zzrY+didHcXFx7N27l5iYGPr378+sWbNwdXVl4cKFxMQ0n1WBRcMwm0vYs/fhc7adqKz5KSo6wMFDr9Aj7gPnBdfCuWndWHjNQn49/isbTm5AURS6aLNoX74VrQKgQafzc26QdVRRkctff91EWXnlyuYxMVOJjnq81uvLKzKtiRFAaelx8vM31zsRDAm5geDg6zGZitBq3dBoah66BMjP30rC/qeoqMgGQFFciIp6nOioJ5vUStxCNDVvrzzEip2nsKiQkleCn6crz1/bxSmx2J0cvfTSSxgMBgBmzJjBjTfeyODBgwkICGDZsmUOC1A0T9nZqzGbi2o4YyYr63+YTEX1Gu4QF6bT6hjZYSQjO4wEoLw8k127J2IwHMHDI4KOHZ5zcoR1k5n5X8rK062vk5P/TVTkY7UmGS5arzNrEf1dgO+IXhuLxYTZXISLi+8FN6gtL89k9577sFj+nr2rqiaSk9/H3S2M8PBb6x2LEC3VgfRCLGeqASwqHMqo6T2kcdidHI0YMcL6fUxMDImJieTl5eHv7y+fjgQVFblU1vtXnzUFFozGAkmOGpGbWwj9L/0fZnMxWq13k/07ajQWkpu7AYulHH//QWi1nnBO7ZRWe+FVx11cfOja5S0OHnwRi2qkffsH0Ov72R2PxWIi+fiHnDr12ZmE3o/I9vcTGflwjUlSWtq3WCzGKjFXUkg5+bHDk6ODGYV8sPYIfxzNxdtNy839Injkihg8Xe3+p10IpxnWJYStSXloNQpmi8qVsc5bQ8yuv0FGo5Hhw4ezYMECOnfubD3epk0bhwUmmjdf357UnBhVfpJ3cwtp3IAEiqI06YQ0I+MHDhycds7UeYWIdhPx8xvI6dNbUBQdXbvMvGhiFxY2hpCQkaiqGW0tM/fq6tDhV0hLW8bZZMdkOs2xpNkYjQV06lR9xmNpaQo1zWwD9cw5x9mfVsDYuZsxWVTMFpWCUiP/XneEzUdz+PqhAbhoG2/igxCO8MDgaHw9XNh54jSXRLfh5r5tnRaLXcmRTqcjISGhyX76FM6n1/fDT38Jpwt2cH6SFB31ZKvfq0tUVVx8iP2JT1O1x0Xl5KnFdOkyk+7dZuHi4ouLi3ed2qvcQLd+vSdlZelVEqPz44qKeqTakJ2nZ3QN1wMoeHpE1yue881edRiT2YL5nMdZVNh+Ip/ViZlc16Nlz8oULY+iKNx+SXtuv6T2BXwbi90fLSZMmMDHH3/syFhEC6IoCr16LSIs7BYUpTIRctUF0rnzq7RrN8HJ0YmmJi3tm1qWeFA4deoL3N3D65wYOUpRUQI1JzqVdURFRQeqHQ8Lu+XM0N/5P4tK+8gHHRrf70dzqiRGZ7loFH4/muPQZwnR2tj90aqiooKPPvqI1atXEx8fX23zxtmzZ9c7ONG8ubj40K3rTGI7v4zJVISra8AFi1lF61VWnoFaw6reoFJ+TkF2Y9K5XrhMoKbzbm5B9On9GQn7p1BWdgoAjcadmOjJhIXe5ND4XF00lJtqHrp21vRnIVoKu5OjhIQE+vbtC8Dhw4ernJPhNnEurdYDrdbD2WGIJszbuwvZ2auoqZDZzS0Ui8V0Zqis8eh9++DhEUlp6UmqDg1r8fbujLdXbM336fsyaOB6Cov2YTYZ8PXt0SC1XmP6tGXJ1hTMatXfmcmiMqpXuMOfJ0RroqiqWnO/sahRYWEher2egoICfH1ld3MhHKG8PIstW685sy5W9d6QgIAr6dVzkUM+eBkMBusCtsXFxdV6vc9VVHyQXbsmYDTmoiguqKoJN7cQ+vZZcqa+yHnyDRXcumALR7OK0SiVH0rNFpVHh3Zw2towQjRltrx/250c5ebmEhAQAMDJkydZtGgRZWVljBw5ksGDB9vTZLMgyZEQDaOwKIEDB6ZRXFzz3m8D+q/Ey6v+q5zbkhwBmM2lZGX9SmlpCp6e0QQHj7jgIpCNqbTCzPe7U9malIuXmws39W7LpdEya1iImjRocrRv3z5GjhzJyZMn6dSpE19//TXXXnstBoMBjUaDwWDgP//5DzfddFN9foYmS5IjIRpWaelJiooS2ZfwWJXjlw36HXf3+s/AsjU5EkK0DLa8f9s8W+25556jR48e/PbbbwwdOpQbb7yR66+/noKCAvLz83n44Yd588037Q5eCNG6eXhEEBw8gsj2D585otCxw3MOSYyEEKIubO45CgwMZN26dfTs2ZPi4mJ8fX3Ztm0b8fHxABw8eJABAwZw+vTphojX6aTnSIjGYzQWoigKSomKpawMl6CgetcdSc+REK2TLe/fNk//yMvLIzQ0FABvb2+8vLyqrIzt7+9PUZHz9kMRQrQcamYR6dOnY9i0CVQVXfv2hDz3LD5XX+3s0IQQLZhdc2PP/+QmU/eFEHV1trP6Yv9umIsNnBg3DlN2Npy5x5iSwqknJxGxaBHel19W7Z6KU6coWLECY2Ym7l26or9pNFqfprtlihCiabIrObrnnntwc6ucrVFWVsYjjzxi7ZouLy+/0K1CiFYsPX05hw6/hqqa6NDhWdpH3FvrtYU//YgpM9OaGFkpCjnz5lZLjgpXrSJ1ytTK6xWFAst35MyfT+SXX+AW7dxp90KI5sXmguyJEycSHByMXq9Hr9czbtw4wsPDra+Dg4OZMKHht4fIz89n/Pjx1ueOHz/+gnVORqOR559/nh49euDl5UV4eDgTJkwgLS2twWMVQkBFRQ6JB17AbDZgsZRz5MgMDIajtV5fmpAAmhr+ibJYKEvYX+WQubiYtOeeB7MZLJbK/1dVzKdPk/7SPx39owghWjibe44+/fTThojDZnfddRenTp3i119/BeChhx5i/Pjx/PTTTzVeX1JSws6dO/nnP/9Jr169yM/P56mnnmLUqFFs3769MUMXolWqMOZz/gKP5RXZta5d5BIQWONxFcDXBYul3LreUPH69ahlZdUvNpsp3bEDY2YWupDgekQvhGhNGnc9fgc5cOAAv/76K1u3bqV///4ALFq0iIEDB3Lo0CFiY6sv66/X61m9enWVYx9++CGXXnopKSkptG/v/F2AhWjJvDxj8PXtTWHhbgA8PWPQ+/ap9Xr9TTeRu2hR9ROKSuHAQvYlTKJXzwUAWAyGCz7bUnLh80IIcS6bh9Wagi1btqDX662JEcCAAQPQ6/Vs3ry5zu0UFBSgKAp+fn61XlNeXk5hYWGVLyGE7RRFS98+X9Il9l/Edp5OfL/lZ3awr5lbTDRhb/wLVQsqKqpSWXtU1ttC8TVmcnLWUFi4FwDPSy+ttR2XoCBcIyIc+8MIIVq0ZtlzlJGRQXBw9S7y4OBgMjIy6tRGWVkZL7zwAnfdddcF1zuYOXMmr732mt2xCiH+ptV60LbtHXW+3v26/mRqKnDfpUEph4pYFWP7swXaGk6f/gtf3564xcSgHzuGghXf/X2zooCqEvzM0yguzfKfOiGEkzSpnqNXX321csG3C3ydrQ+qaRqwqqp1WlbAaDRyxx13YLFYmDt37gWvnTZtGgUFBdavkydP2vfDCSFsptV6YfFRKBliwXCN5ZzECMBSZbf7sNdfJ/jZZ9G1bYvi6op79+4Ev/carsPjcdT+2qeKTvFl4pd8vv9zjhccd0ibqqqSmraMxMRnOZW61GGxCiHs16Q+Tj3xxBPccceFP1VGRUWxd+9eMjMzq53Lzs4mJCTkgvcbjUZuu+02kpOTWbdu3UVXyXRzc7MuWyCEcCyzxUy5uRwPF48aP9jodHoCA4aSk7sRMFc5pyiuKBp3UlO/JjDwKtzcggm4/z4C7r+PvLw/OHT4VZJKpsEW8PKKpUuX1/HT97M71nl75jFv9zzr67e3v8293e9lSr8p9Vrr7dSpzzl8ZDqgJT1jBWazgcj2D9rdnhCi/mzuOTq7j9pZ//rXv6pMoc/NzaVbt252BRMYGEiXLl0u+OXu7s7AgQMpKChg27Zt1nv//PNPCgoKGDRoUK3tn02Mjhw5wpo1awgICLArTiFE/ZQYS3hr21sM/Gog/Zf258bvbuS/Sf+t8drY2OnWfdUUxQXQoCgu+PsPIjFxCgcP/YM/t11PeXnlB6aiogPs3nMfJSXJ1jYMhiPs2jWekpLjdsX7R+ofzN09t7L26cz/AD7d/ymrTqyyq82zcnN/O/NdZfKXm/Nb7RcLIRqFzcnRypUrqyz0+NZbb5GXl2d9bTKZOHTokGOiq0XXrl259tprefDBB9m6dStbt27lwQcf5MYbb6wyU61Lly5899131rhuueUWtm/fzpIlSzCbzWRkZJCRkUFFRUWDxiuE+JuqqkxeP5mlB5dSaioFIKUohWmbpvHdke+qXe/uHs6A/ivp2vUt2ra9k5iYpxg4YC15eZus1xiN+WRlVS7rkXLyYyon/FcdgrNYTJw89bldMf/n8H/QKNX/udSg4dvD39rV5lnePl35+59izZnXQghnsnlY7fzxcGeNjy9ZsoRJkyYxfPhwAEaNGsW///3vKtccOnTI2st16tQpfvzxRwB69+5d5br169czdOjQBo9ZCAF7svewNX1rjef+vfvfjOowCq1GW+W4VutOeNgtEHYLUPnvjk7ni9F4mrNJkM61co/HwsK9qGrVIbhKZoqKEuyKObs0G4tqqXbcgoXskmy72jwrOmoSJlMx+flb8PO7hA4xU+vVnhCi/ppUzZEt2rRpw5dffnnBa85N3KKioqTQUYgmYHfWbjSKpsZkI6ski+zSbEK9Qi/YhqIodO8+h4SEJzGZCgkNHUNI8PVAZU9T5ZBa1fYVtLi5Xbjd2vQM6klCTgLm85IuraKlV1Avu9q0tqF1o0uszIgVoimxOTk6O2vs/GNCCFEXejd9jYkRgEbR4KXzqlM7AW0uZ8jgHaiq0bpSNkC7tndVGXI7S8VM27Z32hXzXV3u4j+H/0O5udwauwYNWo2Wid0n2tWmEKLpsmtYTTaeFULY6+rIq3njzzcoN5dbC5uhshfmqvZX4ePqc4G7q1IUDYpSdTZpUNBwoqMnk5z8IX/3Hmnp1PF52vgPxHCR1bRr0s6nHYuvXcwbf77Bnuw9AHQN6MoLl75AB78ONrcnhGjaFNXGsaZ77619F+1zNZU92BytsLAQvV5PQUHBRZcBEELUbOOpjUxZPwWjxYhW0WJSTcToY/h4xMcEetS8p5qtysrSyM39DUXREhBwJW5uQQAYDAa8vb0BKC4utn6wq6v8snwsqoUAD5ntKkRzYsv7t83JUWsnyZEQjpFXlscvSb+QU5pD98DuDI0Yik6ja/Dn1jc5Eg0r7XQpn/yezOZjufh66LilXzvG9mmLRiPlG6J+bHn/brYF2UKI5q2NexvGdRvn7DBEE5KUXcyYuZspLjPSO2g3l+i3kXuimIXpvbnn6mes610J0dAkORJCCNEkzFp5iOIyE7fHfsuw9pswWxS0GhWzeoI/tq5j4KUr8PSMdnaYohWweRFIjUaDVqu94JeLbPIohBDCBqqqsiYxk3Y+JxjWvnK2oVZTWfWhVVRUczFHjr7pzBBFK2JzFnN2xemabN68mQ8//FDWExJCCGEzRYG+wXsxWzRoNeetU6VYyMlZh8ViQqORD+CiYdn8J2z06NHVjh08eJBp06bx008/cffdd/P66687JDghhBCtg6IoXNMtFE3pha6SD96icdg8rHautLQ0HnzwQXr27InJZGL37t189tlntG/f3lHxCSGEaCWevzaWo0W9qvUaVdISEHCF9BqJRmFXclRQUMDzzz9Px44d2b9/P2vXruWnn34iLi7O0fEJIYRoJSIDvFhw30SyzVcDYFHPTt/XotV60LHD884LTrQqNqfgs2bN4q233iI0NJSvvvqqxmE2IYQQwh6heg9uv3oeGRnfk5b+H4zG0/j796d9xH14eEQ4OzzRSti8CKRGo8HDw4Orr74arVZb63UrVqyod3BNkSwCKUTzJotACtE6NegikBMmTJCNZoUQQgjRYtmcHC1evLgBwhBCCCGEaBrqNVtNCCGEEKKlkeRICCGEEOIckhwJIYQQQpxDkiMhmomSv/7iyNChHOzVm8y335ZteoQQooFIciREM6CqKqcmTcKUmYVaXk7ex59QvGGDs8MSQogWqV7rsK9du5a1a9eSlZWFxVJ1ufdPPvmkXoEJIc5hNGLOP13lkCkjwzmxCCFEC2d3z9Frr73G8OHDWbt2LTk5OeTn51f5EkI4juLqiu8NN1S+0GjQ+PjgfeWVzg1KCCFaKLt7jubPn8/ixYsZP368I+MRQtQi/K038brsMsz5efiMuBZdaGiDP9NiMZKdvZLc3I1oNK4EB1+Pv/9AWQhWCNGi2Z0cVVRUMGjQIEfGIoS4AMXFBb+xYxrteWZzKbt2T6SgYAegRVEgNe0r2obfSWzs65IgCSFaLLuH1R544AGWLl3qyFiEEE1ISspHFBTsOvPKjKqagcoEKS9vo/MCE0KIBmZ3z1FZWRkLFy5kzZo19OzZE51OV+X87Nmz6x2cEMJ50jO+Ayw1nNGSkfkjAQFXNHZIQgjRKOxOjvbu3Uvv3r0BSEhIqHJOutuFaP7M5tJazlgucE4IIZo/u5Oj9evXOzIOIUQTExAwhPT07wDzeWdU2vhf5oyQGlR+/p8UFu7Bzy8evb6vs8MRQjhRvdY5EkK0XFGRj5KVtRKzuYS/EyQtnp5RhIU1XmF4Y0jP+J7ExKeByl7vnj3mExR0tXODEkI4jU3J0dSpU3n99dfx8vJi6tSpF7xWao6EaN48PaO49JLvSEr+gNyc9SgaV0JDRxEd9QRaraezw3OotLRvznynAgrpGcslORKiFbMpOdq1axdGo9H6fW2k5kiIlsHTM5q47u85O4wG5+YWAmip7CFTcHMNcXJEQghnsik5OrfOSGqOhBAtRaeOL1BacpzCogT89P2IiZns7JCEEE4kNUdCiFbPzS2ESy75DlVVpedbCGH/IpBCCNHSSGIkhABJjoQQQgghqpDkSAghhBDiHJIcCSGEEEKco17J0aZNmxg3bhwDBw4kNTUVgC+++ILff//dIcEJIYQQQjQ2u5Oj5cuXM2LECDw8PNi1axfl5eUAFBUV8cYbbzgsQCGEEEKIxmR3cjRjxgzmz5/PokWL0Ol01uODBg1i586dDglOiPMZMzNJvu02DsT1IOWBBzEXFTk7JCGEEC2M3cnRoUOHGDJkSLXjvr6+nD59uj4xCVGrzDdmUrY/EUwmDJs3kzNvvrNDEkII0cLYnRyFhYVx9OjRasd///13YmJi6hWUELUxpqeD+e9d4k0Z6U6MRgghREtkd3L08MMPM3nyZP78808URSEtLY0lS5bwzDPP8NhjjzkyRiGs/G6+ufIbjQZUFd9Ro5wbkBBCiBbH7u1DnnvuOQoKCrjyyispKytjyJAhuLm58cwzz/DEE084MkYhrPxvvw1deDhliYl4XnIJnn37ODskIYQQLYyiqqpanwZKSkpITEzEYrHQrVs3vL29HRVbk1RYWIher6egoABfX19nhyOEsJHBYLD+O1VcXIyXl5eTIxJCNAZb3r/rvfGsp6cn8fHx9W1GCCFEA/lhdypv/HIAjaLwzxu7cX2PMGeHJESTZndyNHXq1BqPK4qCu7s7HTt2ZPTo0bRp08bu4IQQQtTPqfwSpizbjeXMGMGTX+0iPsqfYB935wYmRBNmd3K0a9cudu7cidlsJjY2FlVVOXLkCFqtli5dujB37lyefvppfv/9d7p16+bImIUQQtRRekGZNTECMFtUMgvKJTkS4gLsnq02evRorr76atLS0tixYwc7d+4kNTWVa665hjvvvJPU1FSGDBnClClTHBmvEEIIG8SF62nn74FGAY0C0YGedA5t2bWhQtSX3QXZbdu2ZfXq1dV6hfbv38/w4cNJTU1l586dDB8+nJycHIcE2xRIQbYQzVtrLMjOLirn620paDQKd17anjZers4OSYhGZ8v7t909RwUFBWRlZVU7np2dTWFhIQB+fn5UVFTY+wghhBD1tGp/Bh+uO0KgjxuPXtFBEiMh6sDumqPRo0dz33338e6773LJJZegKArbtm3jmWee4aabbgJg27ZtdO7c2VGxCiGEsMEv+9J5bMlOtBqlstaosIynrpZ/k4W4GLt7jhYsWMCwYcO44447iIyMJCIigjvuuINhw4Yxb948ALp06cJHH33ksGCFEELU3ZrETDRKZRE2wK8JGU6OSIjmwe6eI29vbxYtWsR7771HUlISqqrSoUOHKotA9u7d2xExCiGEsEOHYG/OFpVqFYXOIVKILURd1GsRyLVr17J27VqysrKwWCxVzn3yySf1CkwIIUT9PDg4htT8UjYcyqJ7Wz2vjYpzdkhCNAt2J0evvfYa06dPJz4+nrCwMBRFcWRcQggh6snVRcMbY3s4Owwhmh27k6P58+ezePFixo8f78h4hBBCCCGcyu6C7IqKCgYNGuTIWIQQQgghnM7u5OiBBx5g6dKljoxFCCGEEMLp7B5WKysrY+HChaxZs4aePXui0+mqnJ89e3a9gxNCCCGEaGx2J0d79+61TtVPSEiock6Ks4UQQgjRXNmdHK1fv96RcQghhBBCNAl21xw5W35+PuPHj0ev16PX6xk/fjynT5+u8/0PP/wwiqIwZ86cBotRCNF0mCwmvjr4FeN+GWc9djT/qBMjEkI0VfVaBBIgMTGRlJSUahvMjho1qr5NX9Bdd93FqVOn+PXXXwF46KGHGD9+PD/99NNF7/3+++/5888/CQ8Pb9AYhRBNg6qqvLDpBVYeX4ml/O8Fayf+OpEvb/qSuEBZHFEI8Te7k6OkpCTGjBnDvn37UBQFVa1cpP5svZHZbHZMhDU4cOAAv/76K1u3bqV///4ALFq0iIEDB3Lo0CFiY2NrvTc1NZUnnniClStXcsMNNzRYjEKIpmN39m5WHl9Z7bhZNfPu9nf59NpPnRCVEKKpsntYbfLkyURHR5OZmYmnpyf79+9n48aNxMfHs2HDBgeGWN2WLVvQ6/XWxAhgwIAB6PV6Nm/eXOt9FouF8ePH8+yzz9K9e/c6Pau8vJzCwsIqX0KI5uX31N/RKtpqxy2qhe2Z2yk3lzshKiFEU2V3crRlyxamT59OUFAQGo0GjUbD5ZdfzsyZM5k0aZIjY6wmIyOD4ODgaseDg4PJyKh91+m33noLFxcXm+KbOXOmta5Jr9cTERFhV8xCCOdx0dTeSa5RNGiUZlt+KYRoAHb/i2A2m/H2rtzhOTAwkLS0NAAiIyM5dOiQXW2++uqrKIpywa/t27cDNS8XoKpqrcsI7Nixg/fff5/FixfbtNTAtGnTKCgosH6dPHnSrp9NCOE817S/BrNafahfo2gYGjEUnUZXw11CiNbK7pqjuLg49u7dS0xMDP3792fWrFm4urqycOFCYmJi7GrziSee4I477rjgNVFRUezdu5fMzMxq57KzswkJCanxvk2bNpGVlUX79u2tx8xmM08//TRz5szh+PHjNd7n5uaGm5tb3X8IIUST09G/Iw/3fJgFexdU6SXyc/Xj2fhnnRiZEKIpUtSzldQ2WrlyJQaDgbFjx5KUlMSNN97IwYMHCQgIYNmyZVx11VWOjtXqwIEDdOvWjT///JNLL70UgD///JMBAwZw8ODBGguyc3NzSU9Pr3JsxIgRjB8/nnvvvfeCRdznKiwsRK/XU1BQgK+vb/1/GCFEo/kz/U++3fct7454F4CTOSdpF9DOyVEJIRqDLe/fdidHNcnLy8Pf379RVsi+7rrrSEtLY8GCBUDlVP7IyMgqU/m7dOnCzJkzGTNmTI1tREVF8dRTT/HUU0/V+bmSHAnRvBkMBmtJQHFxMV5eXk6OSAjRGGx5/3ZoFWKbNm0abeuQJUuW0KNHD4YPH87w4cPp2bMnX3zxRZVrDh06REFBQaPEI4QQQoiWoV6LQK5du5a1a9eSlZWFxWKpcu6TTz6pV2AX06ZNG7788ssLXnOxTrHa6oyEEEII0XrZnRy99tprTJ8+nfj4eMLCwmSzWSGEEEK0CHYnR/Pnz2fx4sWMHz/ekfEIIYQQQjiV3TVHFRUVDBo0yJGxCCGEEEI4nd3J0QMPPMDSpUsdGYsQQgghhNPZNKw2depU6/cWi4WFCxeyZs0aevbsiU5XdYXZ2bNnOyZCIYQQQohGZFNytGvXriqve/fuDUBCQkKV41KcLYQQQojmyqbkaP369Q0VhxBCCCFEk2BzzdG6devo1q0bhYWF1c4VFBTQvXt3Nm3a5JDghBBCCCEam83J0Zw5c3jwwQdrXHpbr9fz8MMPS72REEIIIZotm9c52rNnD2+99Vat54cPH84777xTr6CasrOrbtfUcyaEaPoMBoP1+8LCQsxmsxOjEUI0lrPv23XZUtbm5CgzM7PazLQqDbq4kJ2dbWuzzUZRUREAERERTo5ECFFf4eHhzg5BCNHIioqK0Ov1F7zG5uSobdu27Nu3j44dO9Z4fu/evYSFhdnabLMRHh7OyZMn8fHxqfOsvMLCQiIiIjh58uRFdwJurlrDzwit4+eUn7FlaA0/I//f3t2HxZjvfwB/TzUVPcwaoRLC0sPpYYmIXWFTLOHs7qEt5PFsVPKQxXIUe53dOIfDOomThOtY5aG91m42woZVCpVNxcYW5zi1SA9ySg/z+f2xp/k1zVQzNdODPq/rmutyf+/P/b2/n/mUvvOd+55B98iTc1QPIsKLFy+UelGk8uTovffew5YtWzBt2jTo6+vL7KusrERISAhmzJiharddhpaWFiwsLFp1rLGx8Wv7g12vO+QIdI88OcfXQ3fIEegeeXKObdfSilE9lSdHmzdvRlxcHIYPH46AgABYWVlBIBAgNzcX4eHhqKurw6ZNm1QeMGOMMcZYZ6Dy5Khfv35ITk7G8uXLsXHjRumFTQKBAB4eHti3bx/69eun9oEyxhhjjLUHlSdHADBo0CCcPXsWJSUluH//PogIw4YNQ69evdQ9vteCnp4eQkJCoKen19FD0ZjukCPQPfLkHF8P3SFHoHvkyTm2PwEpc08bY4wxxlg3ofKHQDLGGGOMvc54csQYY4wx1gBPjhhjjDHGGuDJEWOMMcZYAzw5UoOSkhLMnz8fIpEIIpEI8+fPR2lpaZPxNTU1WL9+Pezt7WFgYABzc3MsWLAA//nPf2TiXr16hcDAQJiYmMDAwAAzZ87Ev//9bw1no5iqOQJAXFwcPDw8YGJiAoFAgMzMTLmYiRMnQiAQyDy8vLw0k4QSNJVnV68lESE0NBTm5ubo0aMHJk6ciOzsbJmYjq7lvn37MHjwYOjr68PJyQlXr15tNv7y5ctwcnKCvr4+hgwZgv3798vFnD59Gra2ttDT04OtrS2+/vprTQ1fKerO8fDhw3I1EwgEqKqq0mQazVIlx8LCQnh7e8PKygpaWlpYtWqVwriuXEdlcuyMdQRUyzMuLg5TpkxBnz59YGxsDBcXF5w7d04urt1qSazNpk6dSnZ2dpScnEzJyclkZ2dHM2bMaDK+tLSU3NzcKDY2lu7evUspKSk0ZswYcnJykonz8/Oj/v37U2JiIqWnp9OkSZPI0dGRamtrNZ2SHFVzJCI6evQobd26lSIjIwkAZWRkyMW4urrSsmXLqLCwUPooLS3VUBYt01SeXb2WYWFhZGRkRKdPn6asrCyaO3cumZmZUXl5uTSmI2sZExNDQqGQIiMjKScnh4KCgsjAwIAePnyoMP6XX36hnj17UlBQEOXk5FBkZCQJhUI6deqUNCY5OZm0tbXp888/p9zcXPr8889JR0eHrl+/3i45NaaJHKOjo8nY2FimZoWFhe2VkhxVc8zPz6eVK1fSkSNH6K233qKgoCC5mK5eR2Vy7Gx1JFI9z6CgINq+fTulpaXRzz//TBs3biShUEjp6enSmPasJU+O2ignJ4cAyBQnJSWFANDdu3eV7ictLY0ASH9wSktLSSgUUkxMjDTm8ePHpKWlRQkJCepLQAltzTE/P7/ZyZGiX/aOoKk8u3otJRIJmZqaUlhYmLStqqqKRCIR7d+/X9rWkbV0dnYmPz8/mTZra2vasGGDwvhPPvmErK2tZdo+/vhjGjt2rHR7zpw5NHXqVJkYDw8P8vLyUtOoVaOJHKOjo0kkEql9rK2lao4NNfXz19Xr2FBTOXa2OhK1Lc96tra2tHXrVul2e9aS31Zro5SUFIhEIowZM0baNnbsWIhEIiQnJyvdT1lZGQQCAd544w0AwK1bt1BTUwN3d3dpjLm5Oezs7FTqVx3UlWNTjh07BhMTE/zud79DcHAwXrx40eY+W0NTeXb1Wubn56OoqEhm/Hp6enB1dZU7piNqWV1djVu3bsmMDwDc3d2bzCklJUUu3sPDAzdv3kRNTU2zMe1dM0BzOQJARUUFBg0aBAsLC8yYMQMZGRnqT0AJrclRGV29jsrqLHUE1JOnRCLBixcvIBaLpW3tWctWfUI2+39FRUXo27evXHvfvn1RVFSkVB9VVVXYsGEDvL29pV+4V1RUBF1dXblPHe/Xr5/S/aqLOnJsio+PDwYPHgxTU1PcuXMHGzduxO3bt5GYmNimfltDU3l29VrWtzf+WqB+/frh4cOH0u2OquWzZ89QV1encHzN5aQovra2Fs+ePYOZmVmTMe1dM0BzOVpbW+Pw4cOwt7dHeXk59uzZg/Hjx+P27dsYNmyYxvJRpDU5KqOr11EZnamOgHry3LlzJ16+fIk5c+ZI29qzlrxy1ITQ0FCFF7g1fNy8eRPAb98r1xgRKWxvrKamBl5eXpBIJNi3b1+L8cr2q4z2yrE5y5Ytg5ubG+zs7ODl5YVTp07hwoULSE9Pb1O/DXWGPBXparVsvL/xMe1Ry7aMT5n4xu2q9qlp6s5x7NixmDdvHhwdHfHOO+/gxIkTGD58OPbu3avmkStPE895V69jSzpjHYHW53n8+HGEhoYiNjZW7sVce9WSV46aEBAQ0OKdNpaWlvjpp5/w66+/yu17+vRpi1/AW1NTgzlz5iA/Px+XLl2SrhoBgKmpKaqrq1FSUiKz4vDkyROMGzdOxWwUa48cVTVy5EgIhULk5eVh5MiRaumzo/Ps6rU0NTUF8NurNjMzM2n7kydPmn1eNFFLRUxMTKCtrS336rG58ZmamiqM19HRQe/evZuN6Ygv1tZUjo1paWlh9OjRyMvLU8/AVdCaHJXR1evYGh1ZR6BtecbGxmLJkiU4efIk3NzcZPa1Zy155agJJiYmsLa2bvahr68PFxcXlJWVIS0tTXpsamoqysrKmv3DVz8xysvLw4ULF+T+s3JycoJQKJR5S6KwsBB37txR2x9UTefYGtnZ2aipqZH5I9xWHZ1nV69l/VtlDcdfXV2Ny5cvNzt+TdRSEV1dXTg5Ocm9fZeYmNjk+FxcXOTiz58/j1GjRkEoFDYbo+6feWVoKsfGiAiZmZkar5kirclRGV29jq3RkXUEWp/n8ePHsXDhQnz11VeYPn263P52raXaL/HuhqZOnUoODg6UkpJCKSkpZG9vL3drtJWVFcXFxRERUU1NDc2cOZMsLCwoMzNT5tbLV69eSY/x8/MjCwsLunDhAqWnp9PkyZM79PZvVXIkIiouLqaMjAyKj48nABQTE0MZGRnSW0zv379PW7dupRs3blB+fj7Fx8eTtbU1jRgxokNyJNJMnkRdv5ZhYWEkEokoLi6OsrKy6KOPPpK5lb+ja1l/23BUVBTl5OTQqlWryMDAgAoKCoiIaMOGDTR//nxpfP1t7qtXr6acnByKioqSu8392rVrpK2tTWFhYZSbm0thYWGd4hZwdeYYGhpKCQkJ9ODBA8rIyKBFixaRjo4Opaamtnt+RKrnSESUkZFBGRkZ5OTkRN7e3pSRkUHZ2dnS/V29jkQt59jZ6kikep5fffUV6ejoUHh4eJMfB9KeteTJkRoUFxeTj48PGRkZkZGREfn4+FBJSYlMDACKjo4mov+/5VvR44cffpAeU1lZSQEBASQWi6lHjx40Y8YMevToUfsl1oCqORL9dnupohxDQkKIiOjRo0c0YcIEEovFpKurS0OHDqWVK1dScXFx+yXWiCbyJOr6tZRIJBQSEkKmpqakp6dHEyZMoKysLOn+zlDL8PBwGjRoEOnq6tLIkSPp8uXL0n2+vr7k6uoqE5+UlEQjRowgXV1dsrS0pIiICLk+T548SVZWViQUCsna2ppOnz6t6TSape4cV61aRQMHDiRdXV3q06cPubu7U3Jycnuk0iRVc1T0uzdo0CCZmK5ex5Zy7Ix1JFItT1dXV4V5+vr6yvTZXrUUEP3vCj3GGGOMMcbXHDHGGGOMNcSTI8YYY4yxBnhyxBhjjDHWAE+OGGOMMcYa4MkRY4wxxlgDPDlijDHGGGuAJ0eMMcYYYw3w5IgxxhhjrAGeHDHGGGOMNcCTI8aUMHHiRKxataqjh9FqXX387UnV50qZ+MYxLW2rU3FxMfr27YuCggKN9P+6+vDDD7Fr166OHgbrIDw5YgxAUVERAgMDMWTIEOjp6WHAgAHw9PTExYsXO3poahEXF4fPPvtMLX0lJydDW1sbU6dOlduXlJQEgUDQ5GPSpEkAgIULF0IgEMDPz0+ujxUrVkAgEGDhwoVqGa+q1PlcKdtn4/3qnCx98cUX8PT0hKWlpUx7c3Vsrob1tVm4cCFmz57d5HkjIiLg4OAAY2NjGBsbw8XFBd9//71acmpJRUUFvLy8YGZmBi8vL7x8+VIu5tatW/Dy8oK5uTn09fUxdOhQLF68GD///DMAYMuWLfjzn/+M8vLydhkz61x4csS6vYKCAjg5OeHSpUvYsWMHsrKykJCQgEmTJsHf37+jh6cWYrEYRkZGaunr0KFDCAwMxI8//ohHjx7J7Bs3bhwKCwvlHgcOHIBAIMCKFSuksQMGDEBMTAwqKyulbVVVVTh+/DgGDhyolrGqorq6GoB6n6t6LfWpiXMCQGVlJaKiorB06VK5fc3VsWHtdu/eDWNjY5m2PXv2tHhuCwsLhIWF4ebNm7h58yYmT56MWbNmITs7W235NWX37t0wNDTE+fPn0bNnT+zevVtm/8GDBzFmzBiIRCLExcXh3r17iIyMxPPnzxEVFQUAcHBwgKWlJY4dO6bx8bJOSCNfZ8tYFzJt2jTq378/VVRUyO2r/7Z6V1dXCgwMpHXr1lGvXr2oX79+FBISIhP7/fff0/jx40kkEpFYLKbp06fT/fv3ZWJa6qe8vJy8vb2pZ8+eZGpqSrt27SJXV1cKCgqSxkgkEtq+fTsNHjyY9PX1ycHBgU6ePNlsjo37UCYfRSoqKsjIyIju3r1Lc+fOpa1bt7Z4TE5ODhkbG9OmTZukbb6+vjRr1iyyt7enf/7zn9L2Y8eOkb29Pc2aNUvu27gb2r9/P5mbm1NdXZ1Mu6enJy1YsICIWq6Hq6sr+fv70+rVq6l37940YcIEaXvD50rZfvz9/aUxmzZtIolEIhPT+PlvatvX11fum8m3bt1KYrGYqqqqZPJ9//33af78+U0+T6dPnyYTExO5dlXqGB0dTSKRSK69voaq6NWrFx08eLDZmJSUFJo8eTL17t1b7nmo/31sydq1a2n79u1ERLR9+3Zat26ddN/Vq1dJS0uL9u3bp/DY4uJi6b9DQ0PpnXfeUeqc7PXCK0esW3v+/DkSEhLg7+8PAwMDuf1vvPGG9N9HjhyBgYEBUlNTsWPHDmzbtg2JiYnS/S9fvsSaNWtw48YNXLx4EVpaWvj9738PiUQi02dz/axZswbXrl3DmTNnkJiYiKtXryI9PV3m+M2bNyM6OhoRERHIzs7G6tWrMW/ePFy+fFml3FvKR5HY2FhYWVnBysoK8+bNQ3R0NIioyfjS0lLMnj0brq6uCt9WWrRoEaKjo6Xbhw4dwuLFi1sc+x/+8Ac8e/YMP/zwg7StpKQE586dg4+PDwDl6nHkyBHo6Ojg2rVrOHDggMJzqdJPamoqvvzyS/ztb3/DwYMHW8xDkT179sDFxQXLli2TrtSsXbsWdXV1OHPmjDTu2bNn+O6777Bo0aIm+7py5QpGjRol165qHduqrq4OMTExePnyJVxcXJqMu337NiZOnAhHR0dcuXIFCQkJEIvFmDRpEmJjY2V+H5sTEBCAAwcOQCgUIjo6GkFBQdJ9a9asgaurK5YvX67wWLFYLP23s7Mz0tLS8OrVK+USZa+Pjp6dMdaRUlNTCQDFxcU1G+fq6kpvv/22TNvo0aNp/fr1TR7z5MkTAkBZWVlK9VNeXk5CoVBmFai0tJR69uwpXVWoqKggfX19Sk5OluljyZIl9NFHHzU7/sYrFarmQ0Q0btw42r17NxER1dTUkImJCSUmJiqMrauro2nTppGNjQ2VlZXJ7KtfdXj69Cnp6elRfn4+FRQUkL6+Pj19+rTFlSMiopkzZ9LixYul2wcOHCBTU1Oqra1VGN+4Hq6urvTWW2/JxTV+rpTpx8bGRmalaP369WRjY9Nkn6puExEtX76cpk2bJt3evXs3DRkyROa8jc2aNUvmOaqnSh3bsnL0008/kYGBAWlra5NIJKL4+Phm4ydMmEBz5syRafP396dRo0Y1e5widXV1VFhYKPP85OTkEAA6deqUUn3cvn2bAFBBQYHK52ddG68csW6N/vdqWSAQtBjr4OAgs21mZoYnT55Itx88eABvb28MGTIExsbGGDx4MADIXc/RVD+//PILampq4OzsLN0nEolgZWUl3c7JyUFVVRWmTJkCQ0ND6ePo0aN48OCBklkrl09j9+7dQ1paGry8vAAAOjo6mDt3Lg4dOqQw/tNPP0VKSgq++eYbGBsbK4wxMTHB9OnTceTIEURHR2P69OkwMTFRavw+Pj44ffq09FX9sWPH4OXlBW1tbQDK1UPRqkpjyvQzduxYmZ8hFxcX5OXloa6uTqlclLFs2TKcP38ejx8/BgBER0dLL2xvSmVlJfT19WXaVK1jW1hZWSEzMxPXr1/H8uXL4evri5ycHIWxv/76K3788UeZ69IAwMDAQKnfz8a0tLRgamoqc2z9KqyTk5NSffTo0QMA8N///lfl87OuTaejB8BYRxo2bBgEAgFyc3ObvfMGAIRCocy2QCCQeWvF09MTAwYMQGRkJMzNzSGRSGBnZye90LelfpqaqFGDtzvqzxcfH4/+/fvLxOnp6TU7flXzaSwqKgq1tbUy5yUiCIVClJSUoFevXtL22NhY/PWvf0V8fDyGDRvW7DgWL16MgIAAAEB4eLjS4/f09IREIkF8fDxGjx6Nq1evytx6rUw9FL2Vqug8ytRV00aMGAFHR0ccPXoUHh4eyMrKwrffftvsMSYmJigpKZFpU6WObaWrq4s333wTwG8T0Rs3bmDPnj0K38K8desWJBIJHB0d5dqVmcQqo36SY2hoqFT88+fPAQB9+vRRy/lZ18ErR6xbE4vF8PDwQHh4uMLbfUtLS5Xqp7i4GLm5udi8eTPeffdd2NjYyP1RasnQoUMhFAqRlpYmbSsvL0deXp5029bWFnp6enj06BHefPNNmceAAQNUOp8qamtrcfToUezcuROZmZnSx+3btzFo0CCZO3oyMzOxePFihIWFwcPDo8W+p06diurqalRXVysVX69Hjx54//33cezYMRw/fhzDhw+Xrgioox6q9HP9+nW57WHDhklXsVSlq6urcNVp6dKliI6OxqFDh+Dm5tZizUeMGCGzUqNKHTWBiJq8fqd+Yt7w7sWsrCxcuXIF8+bNU8v57ezsAABXr15VuL/huQHgzp07sLCwUHo1k70+eOWIdXv79u3DuHHj4OzsjG3btsHBwQG1tbVITExEREQEcnNzW+yjV69e6N27N/7xj3/AzMwMjx49woYNG1Qah5GREXx9fbFu3TqIxWL07dsXISEh0NLSkq4mGRkZITg4GKtXr4ZEIsHbb7+N8vJyJCcnw9DQEL6+vq16Dlry3XffoaSkBEuWLIFIJJLZ9+GHHyIqKgoBAQF49uwZZs+ejYkTJ2LevHkoKiqSidXW1pZ7Fa6trS19jlWdTPj4+MDT0xPZ2dkyf0DVUQ9V+vnXv/6FNWvW4OOPP0Z6ejr27t2LnTt3qny+epaWlkhNTUVBQQEMDQ0hFouhpaUFHx8fBAcHIzIyEkePHm2xHw8PD2zcuFG6IqRsHZVVVlaGzMxMmTaxWIyBAwfi008/xbRp0zBgwAC8ePECMTExSEpKQkJCgsK+xowZgx49euCTTz7Bpk2b8ODBAwQGBsLPzw/jxo1TekzNcXFxgbu7O1asWIGKigq4uLhAIpHgxo0b2L9/PyIiIqQTKOC3SZS7u7tazs26Fl45Yt3e4MGDkZ6ejkmTJmHt2rWws7PDlClTcPHiRURERCjVh5aWFmJiYnDr1i3Y2dlh9erV+Mtf/qLyWHbt2gUXFxfMmDEDbm5uGD9+PGxsbGSuG/nss8+wZcsWfPHFF7CxsYGHhwe+/fZb6bUwmhAVFQU3Nze5P6gA8MEHHyAzMxPp6emIj4/Hw4cPcfbsWZiZmck9Ro8erbD/+g8KVNXkyZMhFotx7949eHt7S9vVVQ9l+1mwYAEqKyvh7OwMf39/BAYG4o9//KPK56sXHBwMbW1t2Nraok+fPtLrm4yNjfHBBx/A0NCwxbeBAcDe3h6jRo3CiRMnAChfR2UlJSVhxIgRMo8tW7YA+O0aovnz58PKygrvvvsuUlNTkZCQgClTpijsq0+fPjhx4gTS0tLg4OCAlStXws/PT+4zig4fPtyqa5DqnTlzBkFBQdixYwccHR0xduxY7NmzB+7u7rC1tZXGVVVV4euvv8ayZctafS7WdQmINHj/JmOsTV6+fIn+/ftj586dWLJkSUcPh3UCU6ZMgY2NDb788kul4s+ePYvg4GDcuXMHWlpd//VwaGgokpKSkJSUpNHzhIeH45tvvsH58+c1eh7WOfHbaox1IhkZGbh79y6cnZ1RVlaGbdu2AQBmzZrVwSNjHe358+c4f/48Ll26hL///e9KH/fee+8hLy8Pjx8/1uh1ae3l3LlzSn1Cd1sJhULs3btX4+dhnROvHDHWiWRkZGDp0qW4d+8edHV14eTkhF27dsHe3r6jh8Y6mKWlJUpKSvCnP/0JwcHBHT0cxl5rPDlijDHGGGug678BzRhjjDGmRjw5YowxxhhrgCdHjDHGGGMN8OSIMcYYY6wBnhwxxhhjjDXAkyPGGGOMsQZ4csQYY4wx1gBPjhhjjDHGGuDJEWOMMcZYAzw5YowxxhhrgCdHjDHGGGMN/B+mIgRuZYU7IwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig=all_diffs.plot.scatter(x='AtlNino stddev',y='NEB rain jja stddev',c='color',marker='.').get_figure()\n",
    "ax=fig.gca()\n",
    "pmip4_diffs.plot.scatter(x='AtlNino stddev',y='NEB rain jja stddev',c='color',marker='o',ax=fig.gca())\n",
    "plt.axhline(0,color='black')\n",
    "plt.axvline(0,color='black')\n",
    "plt.ylabel('Change in NE Brazil DJF rainfall variability ($\\sigma$, mm/day)')\n",
    "plt.xlabel('Change in AZM variability (ATL3 $\\sigma$, $\\degree C$)')\n",
    "# Create custom legend handles\n",
    "legend1 = mlines.Line2D([], [], color='tab:blue',marker='o',linestyle='None', markersize=4,label='lgm')\n",
    "legend2 = mlines.Line2D([], [], color='tab:olive',marker='o', linestyle='None', markersize=4,label='midHolocene')\n",
    "legend3 = mlines.Line2D([], [], color='tab:green',marker='o', linestyle='None', markersize=4,label='lig127k')\n",
    "legend4 = mlines.Line2D([], [], color='tab:red',marker='o', linestyle='None', markersize=4,label='abrupt4xCO2')\n",
    "legend5 = mlines.Line2D([], [], color='tab:pink',marker='o', linestyle='None', markersize=4,label='midPliocene')\n",
    "legend6 = mlines.Line2D([], [], color='black', marker='.', linestyle='None', markersize=4, label='PMIP3')\n",
    "legend7 = mlines.Line2D([], [], color='black', marker='o', linestyle='None', markersize=4, label='PMIP4')\n",
    "ax.legend(handles=[legend1, legend2, legend3,legend4,legend5,legend6,legend7],labelcolor='linecolor')\n",
    "fig.savefig('plots/NEB-DJF_w_AtlNino_stddev.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "06f0fd00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig=all_diffs.plot.scatter(x='ATL3-sst-interjja-stdev',y='ATL3-pr-interjja-stdev',c='color',marker='.').get_figure()\n",
    "ax=fig.gca()\n",
    "pmip4_diffs.plot.scatter(x='ATL3-sst-interjja-stdev',y='ATL3-pr-interjja-stdev',c='color',marker='o',ax=fig.gca())\n",
    "plt.axhline(0,color='black')\n",
    "plt.axvline(0,color='black')\n",
    "plt.ylabel('Change in Trop. Atl. rainfall variability (ATL3 $\\sigma$, mm/day)')\n",
    "plt.xlabel('Change in AZM variability (ATL3 $\\sigma$, $\\degree C$)')\n",
    "# Create custom legend handles\n",
    "legend1 = mlines.Line2D([], [], color='tab:blue',marker='o',linestyle='None', markersize=4,label='lgm')\n",
    "legend2 = mlines.Line2D([], [], color='tab:olive',marker='o', linestyle='None', markersize=4,label='midHolocene')\n",
    "legend3 = mlines.Line2D([], [], color='tab:green',marker='o', linestyle='None', markersize=4,label='lig127k')\n",
    "legend4 = mlines.Line2D([], [], color='tab:red',marker='o', linestyle='None', markersize=4,label='abrupt4xCO2')\n",
    "legend5 = mlines.Line2D([], [], color='tab:pink',marker='o', linestyle='None', markersize=4,label='midPliocene')\n",
    "legend6 = mlines.Line2D([], [], color='black', marker='.', linestyle='None', markersize=4, label='PMIP3')\n",
    "legend7 = mlines.Line2D([], [], color='black', marker='o', linestyle='None', markersize=4, label='PMIP4')\n",
    "ax.legend(handles=[legend1, legend2, legend3,legend4,legend5,legend6,legend7],labelcolor='linecolor')\n",
    "fig.savefig('plots/Rainfall_vs_SST_variability_changes.png')"
   ]
  }
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