python_scripts/FlywireAnalysis/v630-Proximity-Model.ipynb

{
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  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set_context(\"talk\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dev_lim = 10_000 # used only 10,000 pre and 10,000 post neurons for fast developement\n",
    "# dev_lim = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# >=5 syn threshold was applied to the following data already\n",
    "ver = 630\n",
    "\n",
    "fdir = \"data/synapse_coordinates.csv\"\n",
    "# load id as integers and allowing NaN\n",
    "syn_xyz = pd.read_csv(fdir, dtype={'pre_root_id': pd.Int64Dtype(), 'post_root_id': pd.Int64Dtype(), \n",
    "                                   'x': np.int32, 'y': np.int32, 'z': np.int32})\n",
    "\n",
    "fdir = \"data/connections.csv\"\n",
    "syn_table = pd.read_csv(fdir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check if # of syn. matches\n",
    "syn_table.syn_count.sum(), len(syn_xyz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/lq/1wn16kmd2k1g0rjr_hy10kvr0000gn/T/ipykernel_47118/3673498734.py:6: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "  syn_xyz.fillna(method='ffill', inplace=True)\n"
     ]
    }
   ],
   "source": [
    "# according to the description: also for compactness, \n",
    "# empty pre/post id columns should be inferred by \n",
    "# looking up in the table at the last non-empty \n",
    "# value respectively.\n",
    "\n",
    "syn_xyz.fillna(method='ffill', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert id to long int (faster than pd.Int64)\n",
    "syn_xyz.pre_root_id = syn_xyz.pre_root_id.astype(np.int64)\n",
    "syn_xyz.post_root_id = syn_xyz.post_root_id.astype(np.int64)"
   ]
  },
  {
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   "execution_count": 7,
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       "      <th>post_root_id</th>\n",
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       "          pre_root_id        post_root_id       x       y      z\n",
       "0  720575940630786105  720575940610739662  366592  246144  74360\n",
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     "execution_count": 7,
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   "source": [
    "# x, y, z in nanometers\n",
    "syn_xyz.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "# check number of neurons\n",
    "len(np.unique(syn_table[['pre_root_id', 'post_root_id']])), len(np.unique(syn_xyz[['pre_root_id', 'post_root_id']]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Proximity Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1. Compute Average Pre-Synaptic and Post-Synaptic Centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_centers = syn_xyz.groupby('pre_root_id')[['x', 'y', 'z']].mean()\n",
    "post_centers = syn_xyz.groupby('post_root_id')[['x', 'y', 'z']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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       "                                x              y              z\n",
       "pre_root_id                                                    \n",
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    "pre_centers.head()"
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       "                                x              y              z\n",
       "post_root_id                                                   \n",
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    "post_centers.head()"
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  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "del syn_xyz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Compute Pairwise Distance between Pre-Synaptic and Post-Synaptic Centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.spatial import distance_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdist = pd.DataFrame(\n",
    "        distance_matrix(pre_centers.loc[pre_centers.index[:dev_lim]], \n",
    "                                        post_centers.loc[post_centers.index[:dev_lim]],\n",
    "                        threshold= 124891 * 124891 * 3 # ~ 180GB data\n",
    "                        ),\n",
    "        columns=post_centers.index[:dev_lim],\n",
    "        index=pre_centers.index[:dev_lim]\n",
    "        )"
   ]
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       "      <td>35695.876853</td>\n",
       "      <td>50226.234416</td>\n",
       "      <td>36746.014865</td>\n",
       "      <td>35555.259020</td>\n",
       "      <td>38060.983739</td>\n",
       "      <td>...</td>\n",
       "      <td>114910.306491</td>\n",
       "      <td>189217.417907</td>\n",
       "      <td>96882.279240</td>\n",
       "      <td>491007.664810</td>\n",
       "      <td>87780.512731</td>\n",
       "      <td>287283.902800</td>\n",
       "      <td>221547.914775</td>\n",
       "      <td>444090.378411</td>\n",
       "      <td>417469.341939</td>\n",
       "      <td>459303.157809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720575940599333574</th>\n",
       "      <td>84780.656100</td>\n",
       "      <td>112820.034138</td>\n",
       "      <td>73853.438196</td>\n",
       "      <td>37691.457823</td>\n",
       "      <td>28369.814552</td>\n",
       "      <td>37460.811198</td>\n",
       "      <td>68900.760963</td>\n",
       "      <td>48086.875832</td>\n",
       "      <td>49938.138145</td>\n",
       "      <td>53369.586276</td>\n",
       "      <td>...</td>\n",
       "      <td>129994.516457</td>\n",
       "      <td>188141.980373</td>\n",
       "      <td>116121.742145</td>\n",
       "      <td>499528.500665</td>\n",
       "      <td>88997.718688</td>\n",
       "      <td>291613.181756</td>\n",
       "      <td>220264.509310</td>\n",
       "      <td>455116.791694</td>\n",
       "      <td>427823.371644</td>\n",
       "      <td>467105.625131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720575940611287928</th>\n",
       "      <td>398273.563717</td>\n",
       "      <td>487830.515461</td>\n",
       "      <td>464249.365964</td>\n",
       "      <td>398955.886739</td>\n",
       "      <td>433115.435818</td>\n",
       "      <td>408183.451589</td>\n",
       "      <td>378173.157289</td>\n",
       "      <td>398813.766013</td>\n",
       "      <td>396375.815190</td>\n",
       "      <td>394323.124948</td>\n",
       "      <td>...</td>\n",
       "      <td>380440.402742</td>\n",
       "      <td>277140.768618</td>\n",
       "      <td>407126.487930</td>\n",
       "      <td>105079.634110</td>\n",
       "      <td>460508.533311</td>\n",
       "      <td>297682.585860</td>\n",
       "      <td>295222.091901</td>\n",
       "      <td>117191.249285</td>\n",
       "      <td>102008.853011</td>\n",
       "      <td>69910.935352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720575940611287978</th>\n",
       "      <td>262221.266455</td>\n",
       "      <td>358388.985774</td>\n",
       "      <td>323128.105454</td>\n",
       "      <td>248274.248551</td>\n",
       "      <td>261140.699825</td>\n",
       "      <td>267499.477560</td>\n",
       "      <td>253846.147590</td>\n",
       "      <td>264636.446972</td>\n",
       "      <td>262548.277308</td>\n",
       "      <td>262412.271071</td>\n",
       "      <td>...</td>\n",
       "      <td>204083.692660</td>\n",
       "      <td>71640.234001</td>\n",
       "      <td>245852.952024</td>\n",
       "      <td>293602.724396</td>\n",
       "      <td>269972.814397</td>\n",
       "      <td>87798.848244</td>\n",
       "      <td>51080.693070</td>\n",
       "      <td>249982.468496</td>\n",
       "      <td>222399.423657</td>\n",
       "      <td>305947.403705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720575940611288689</th>\n",
       "      <td>143137.478149</td>\n",
       "      <td>74583.774701</td>\n",
       "      <td>73661.663387</td>\n",
       "      <td>129572.582466</td>\n",
       "      <td>114630.277679</td>\n",
       "      <td>123412.630934</td>\n",
       "      <td>152348.994037</td>\n",
       "      <td>131248.502575</td>\n",
       "      <td>133017.289923</td>\n",
       "      <td>135335.701758</td>\n",
       "      <td>...</td>\n",
       "      <td>201752.812420</td>\n",
       "      <td>304154.908039</td>\n",
       "      <td>167267.739869</td>\n",
       "      <td>613705.598776</td>\n",
       "      <td>116549.623125</td>\n",
       "      <td>391661.854965</td>\n",
       "      <td>329775.643121</td>\n",
       "      <td>565025.579192</td>\n",
       "      <td>538985.424632</td>\n",
       "      <td>579397.482035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720575940611288690</th>\n",
       "      <td>308282.663364</td>\n",
       "      <td>423596.979396</td>\n",
       "      <td>395661.128073</td>\n",
       "      <td>315436.261555</td>\n",
       "      <td>349178.663155</td>\n",
       "      <td>344295.080101</td>\n",
       "      <td>316626.412187</td>\n",
       "      <td>335948.291577</td>\n",
       "      <td>331814.185533</td>\n",
       "      <td>330543.304793</td>\n",
       "      <td>...</td>\n",
       "      <td>237667.554509</td>\n",
       "      <td>193236.847879</td>\n",
       "      <td>278349.982852</td>\n",
       "      <td>257747.224912</td>\n",
       "      <td>331150.595066</td>\n",
       "      <td>104474.663543</td>\n",
       "      <td>172511.823303</td>\n",
       "      <td>182476.877289</td>\n",
       "      <td>165924.611626</td>\n",
       "      <td>303410.461514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720575940611290005</th>\n",
       "      <td>116775.333341</td>\n",
       "      <td>69782.369649</td>\n",
       "      <td>87323.058615</td>\n",
       "      <td>127100.123589</td>\n",
       "      <td>145430.326179</td>\n",
       "      <td>130647.717652</td>\n",
       "      <td>139824.394630</td>\n",
       "      <td>129763.167078</td>\n",
       "      <td>128991.635730</td>\n",
       "      <td>129596.074512</td>\n",
       "      <td>...</td>\n",
       "      <td>187397.680013</td>\n",
       "      <td>313472.958506</td>\n",
       "      <td>143912.021496</td>\n",
       "      <td>599837.671779</td>\n",
       "      <td>129009.037410</td>\n",
       "      <td>391774.707849</td>\n",
       "      <td>340284.282756</td>\n",
       "      <td>546641.343471</td>\n",
       "      <td>522461.343796</td>\n",
       "      <td>565981.160933</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 10000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "post_root_id        720575940596125868  720575940597856265  \\\n",
       "pre_root_id                                                  \n",
       "720575940596125868        17881.272761       113657.994238   \n",
       "720575940597856265        98780.904168        28436.401963   \n",
       "720575940597944841        71628.113486        78526.079501   \n",
       "720575940598267657        60087.360899       109625.910318   \n",
       "720575940599333574        84780.656100       112820.034138   \n",
       "...                                ...                 ...   \n",
       "720575940611287928       398273.563717       487830.515461   \n",
       "720575940611287978       262221.266455       358388.985774   \n",
       "720575940611288689       143137.478149        74583.774701   \n",
       "720575940611288690       308282.663364       423596.979396   \n",
       "720575940611290005       116775.333341        69782.369649   \n",
       "\n",
       "post_root_id        720575940597944841  720575940598267657  \\\n",
       "pre_root_id                                                  \n",
       "720575940596125868       101992.486804        60386.950681   \n",
       "720575940597856265        15865.413618        84556.478404   \n",
       "720575940597944841        43513.274554        38289.843683   \n",
       "720575940598267657        75145.456501        13101.434646   \n",
       "720575940599333574        73853.438196        37691.457823   \n",
       "...                                ...                 ...   \n",
       "720575940611287928       464249.365964       398955.886739   \n",
       "720575940611287978       323128.105454       248274.248551   \n",
       "720575940611288689        73661.663387       129572.582466   \n",
       "720575940611288690       395661.128073       315436.261555   \n",
       "720575940611290005        87323.058615       127100.123589   \n",
       "\n",
       "post_root_id        720575940599333574  720575940599457990  \\\n",
       "pre_root_id                                                  \n",
       "720575940596125868       122957.042205        84137.828029   \n",
       "720575940597856265        93883.626558        66490.881945   \n",
       "720575940597944841        57904.434489        22141.172762   \n",
       "720575940598267657        52726.390821        35695.876853   \n",
       "720575940599333574        28369.814552        37460.811198   \n",
       "...                                ...                 ...   \n",
       "720575940611287928       433115.435818       408183.451589   \n",
       "720575940611287978       261140.699825       267499.477560   \n",
       "720575940611288689       114630.277679       123412.630934   \n",
       "720575940611288690       349178.663155       344295.080101   \n",
       "720575940611290005       145430.326179       130647.717652   \n",
       "\n",
       "post_root_id        720575940599459782  720575940599704006  \\\n",
       "pre_root_id                                                  \n",
       "720575940596125868        54559.170873        70025.856561   \n",
       "720575940597856265        89906.373659        70043.957014   \n",
       "720575940597944841        53407.767683        28368.088699   \n",
       "720575940598267657        50226.234416        36746.014865   \n",
       "720575940599333574        68900.760963        48086.875832   \n",
       "...                                ...                 ...   \n",
       "720575940611287928       378173.157289       398813.766013   \n",
       "720575940611287978       253846.147590       264636.446972   \n",
       "720575940611288689       152348.994037       131248.502575   \n",
       "720575940611288690       316626.412187       335948.291577   \n",
       "720575940611290005       139824.394630       129763.167078   \n",
       "\n",
       "post_root_id        720575940599729862  720575940599733446  ...  \\\n",
       "pre_root_id                                                 ...   \n",
       "720575940596125868        64435.515084        62175.718777  ...   \n",
       "720575940597856265        72293.282892        73759.263225  ...   \n",
       "720575940597944841        30838.350175        33904.777571  ...   \n",
       "720575940598267657        35555.259020        38060.983739  ...   \n",
       "720575940599333574        49938.138145        53369.586276  ...   \n",
       "...                                ...                 ...  ...   \n",
       "720575940611287928       396375.815190       394323.124948  ...   \n",
       "720575940611287978       262548.277308       262412.271071  ...   \n",
       "720575940611288689       133017.289923       135335.701758  ...   \n",
       "720575940611288690       331814.185533       330543.304793  ...   \n",
       "720575940611290005       128991.635730       129596.074512  ...   \n",
       "\n",
       "post_root_id        720575940611351514  720575940611352014  \\\n",
       "pre_root_id                                                  \n",
       "720575940596125868       112517.688448       224658.228650   \n",
       "720575940597856265       182577.156516       267368.195902   \n",
       "720575940597944841       144902.162267       217184.453952   \n",
       "720575940598267657       114910.306491       189217.417907   \n",
       "720575940599333574       129994.516457       188141.980373   \n",
       "...                                ...                 ...   \n",
       "720575940611287928       380440.402742       277140.768618   \n",
       "720575940611287978       204083.692660        71640.234001   \n",
       "720575940611288689       201752.812420       304154.908039   \n",
       "720575940611288690       237667.554509       193236.847879   \n",
       "720575940611290005       187397.680013       313472.958506   \n",
       "\n",
       "post_root_id        720575940611352234  720575940611352418  \\\n",
       "pre_root_id                                                  \n",
       "720575940596125868        77655.385648       488583.603815   \n",
       "720575940597856265       148632.060698       557337.340023   \n",
       "720575940597944841       119948.628419       513568.606361   \n",
       "720575940598267657        96882.279240       491007.664810   \n",
       "720575940599333574       116121.742145       499528.500665   \n",
       "...                                ...                 ...   \n",
       "720575940611287928       407126.487930       105079.634110   \n",
       "720575940611287978       245852.952024       293602.724396   \n",
       "720575940611288689       167267.739869       613705.598776   \n",
       "720575940611288690       278349.982852       257747.224912   \n",
       "720575940611290005       143912.021496       599837.671779   \n",
       "\n",
       "post_root_id        720575940611352469  720575940611352538  \\\n",
       "pre_root_id                                                  \n",
       "720575940596125868       120119.806715       304682.984403   \n",
       "720575940597856265       129319.403238       368494.769706   \n",
       "720575940597944841       105336.317907       320186.373073   \n",
       "720575940598267657        87780.512731       287283.902800   \n",
       "720575940599333574        88997.718688       291613.181756   \n",
       "...                                ...                 ...   \n",
       "720575940611287928       460508.533311       297682.585860   \n",
       "720575940611287978       269972.814397        87798.848244   \n",
       "720575940611288689       116549.623125       391661.854965   \n",
       "720575940611288690       331150.595066       104474.663543   \n",
       "720575940611290005       129009.037410       391774.707849   \n",
       "\n",
       "post_root_id        720575940611353457  720575940611353554  \\\n",
       "pre_root_id                                                  \n",
       "720575940596125868       256192.222411       435562.475153   \n",
       "720575940597856265       302230.121662       512768.086477   \n",
       "720575940597944841       252155.258229       468952.296945   \n",
       "720575940598267657       221547.914775       444090.378411   \n",
       "720575940599333574       220264.509310       455116.791694   \n",
       "...                                ...                 ...   \n",
       "720575940611287928       295222.091901       117191.249285   \n",
       "720575940611287978        51080.693070       249982.468496   \n",
       "720575940611288689       329775.643121       565025.579192   \n",
       "720575940611288690       172511.823303       182476.877289   \n",
       "720575940611290005       340284.282756       546641.343471   \n",
       "\n",
       "post_root_id        720575940611353806  720575940611353954  \n",
       "pre_root_id                                                 \n",
       "720575940596125868       411522.762545       456896.771419  \n",
       "720575940597856265       486708.449949       515417.956110  \n",
       "720575940597944841       442337.060954       476004.456148  \n",
       "720575940598267657       417469.341939       459303.157809  \n",
       "720575940599333574       427823.371644       467105.625131  \n",
       "...                                ...                 ...  \n",
       "720575940611287928       102008.853011        69910.935352  \n",
       "720575940611287978       222399.423657       305947.403705  \n",
       "720575940611288689       538985.424632       579397.482035  \n",
       "720575940611288690       165924.611626       303410.461514  \n",
       "720575940611290005       522461.343796       565981.160933  \n",
       "\n",
       "[10000 rows x 10000 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdist.reset_index(inplace=True)\n",
    "pdist.reset_index(drop = True, inplace=True)\n",
    "pdist = pdist.melt(id_vars=['pre_root_id'], var_name='post_root_id', value_name='dist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove autapses since they are not in our dataset\n",
    "mask_autapses = (pdist.pre_root_id == pdist.post_root_id)\n",
    "pdist = pdist[~mask_autapses]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pre_root_id</th>\n",
       "      <th>post_root_id</th>\n",
       "      <th>dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>720575940597856265</td>\n",
       "      <td>720575940596125868</td>\n",
       "      <td>98780.904168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>720575940597944841</td>\n",
       "      <td>720575940596125868</td>\n",
       "      <td>71628.113486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>720575940598267657</td>\n",
       "      <td>720575940596125868</td>\n",
       "      <td>60087.360899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>720575940599333574</td>\n",
       "      <td>720575940596125868</td>\n",
       "      <td>84780.656100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>720575940599457990</td>\n",
       "      <td>720575940596125868</td>\n",
       "      <td>57368.897847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999995</th>\n",
       "      <td>720575940611287928</td>\n",
       "      <td>720575940611353954</td>\n",
       "      <td>69910.935352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999996</th>\n",
       "      <td>720575940611287978</td>\n",
       "      <td>720575940611353954</td>\n",
       "      <td>305947.403705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999997</th>\n",
       "      <td>720575940611288689</td>\n",
       "      <td>720575940611353954</td>\n",
       "      <td>579397.482035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999998</th>\n",
       "      <td>720575940611288690</td>\n",
       "      <td>720575940611353954</td>\n",
       "      <td>303410.461514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999999</th>\n",
       "      <td>720575940611290005</td>\n",
       "      <td>720575940611353954</td>\n",
       "      <td>565981.160933</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99990434 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 pre_root_id        post_root_id           dist\n",
       "1         720575940597856265  720575940596125868   98780.904168\n",
       "2         720575940597944841  720575940596125868   71628.113486\n",
       "3         720575940598267657  720575940596125868   60087.360899\n",
       "4         720575940599333574  720575940596125868   84780.656100\n",
       "5         720575940599457990  720575940596125868   57368.897847\n",
       "...                      ...                 ...            ...\n",
       "99999995  720575940611287928  720575940611353954   69910.935352\n",
       "99999996  720575940611287978  720575940611353954  305947.403705\n",
       "99999997  720575940611288689  720575940611353954  579397.482035\n",
       "99999998  720575940611288690  720575940611353954  303410.461514\n",
       "99999999  720575940611290005  720575940611353954  565981.160933\n",
       "\n",
       "[99990434 rows x 3 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Compute Pairwise Connections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dev_mask = syn_table.pre_root_id.isin(pre_centers.index[:dev_lim]) & syn_table.post_root_id.isin(post_centers.index[:dev_lim])\n",
    "pconn = syn_table[dev_mask].groupby(['pre_root_id', 'post_root_id']).sum()[['syn_count']]\n",
    "pconn['connected'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "del syn_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>syn_count</th>\n",
       "      <th>connected</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pre_root_id</th>\n",
       "      <th>post_root_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th rowspan=\"2\" valign=\"top\">720575940596125868</th>\n",
       "      <th>720575940608552405</th>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>720575940611348834</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">720575940597856265</th>\n",
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       "    <tr>\n",
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       "      <td>8</td>\n",
       "      <td>1</td>\n",
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       "      <th>720575940610239790</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
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       "      <th>720575940611285454</th>\n",
       "      <th>720575940610964946</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>720575940611285858</th>\n",
       "      <th>720575940608810435</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720575940611287978</th>\n",
       "      <th>720575940606198473</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">720575940611290005</th>\n",
       "      <th>720575940604003104</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>720575940607245186</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17063 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       syn_count  connected\n",
       "pre_root_id        post_root_id                            \n",
       "720575940596125868 720575940608552405          5          1\n",
       "                   720575940611348834          7          1\n",
       "720575940597856265 720575940605842278         11          1\n",
       "                   720575940609624003          8          1\n",
       "                   720575940610239790         12          1\n",
       "...                                          ...        ...\n",
       "720575940611285454 720575940610964946          6          1\n",
       "720575940611285858 720575940608810435          7          1\n",
       "720575940611287978 720575940606198473          5          1\n",
       "720575940611290005 720575940604003104          5          1\n",
       "                   720575940607245186          7          1\n",
       "\n",
       "[17063 rows x 2 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pconn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Compute Connection Probability as a Function of Distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist_vs_syn = pdist.merge(pconn, how='left', on=['pre_root_id', 'post_root_id']).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_bins = 50\n",
    "dist_vs_syn['dist_bins'] = pd.qcut(dist_vs_syn.dist, q=np.linspace(0,1,n_bins))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/lq/1wn16kmd2k1g0rjr_hy10kvr0000gn/T/ipykernel_47118/3691504039.py:2: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  dist_vs_syn_fn =  dist_vs_syn.groupby('dist_bins').agg({'syn_count': [('mean', np.mean), ('error', error)],\n",
      "/var/folders/lq/1wn16kmd2k1g0rjr_hy10kvr0000gn/T/ipykernel_47118/3691504039.py:2: FutureWarning: The provided callable <function mean at 0x1075f53a0> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"mean\" instead.\n",
      "  dist_vs_syn_fn =  dist_vs_syn.groupby('dist_bins').agg({'syn_count': [('mean', np.mean), ('error', error)],\n"
     ]
    }
   ],
   "source": [
    "error = lambda x: np.std(x) / np.sqrt(len(dist_vs_syn) // n_bins)\n",
    "dist_vs_syn_fn =  dist_vs_syn.groupby('dist_bins').agg({'syn_count': [('mean', np.mean), ('error', error)], \n",
    "                                        'connected': [('mean', np.mean), ('error', error)]})\n",
    "# dist_vs_syn_fn                                        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dist_vs_syn_fn = pd.read_pickle('saved/dist_vs_syn_fn_n=10000.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th colspan=\"2\" halign=\"left\">syn_count</th>\n",
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       "      <th></th>\n",
       "      <th>mean</th>\n",
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       "      <th>dist_bins</th>\n",
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       "      <th>(90940.148, 97990.653]</th>\n",
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       "      <th>(97990.653, 104887.76]</th>\n",
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       "      <th>(104887.76, 111720.359]</th>\n",
       "      <td>0.000862</td>\n",
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       "      <td>5.877860e-06</td>\n",
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       "    <tr>\n",
       "      <th>(111720.359, 118573.935]</th>\n",
       "      <td>0.000632</td>\n",
       "      <td>0.000082</td>\n",
       "      <td>5.831558e-05</td>\n",
       "      <td>5.399897e-06</td>\n",
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       "    <tr>\n",
       "      <th>(118573.935, 125534.082]</th>\n",
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       "      <td>5.488525e-05</td>\n",
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       "      <th>(125534.082, 132642.273]</th>\n",
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       "      <th>(132642.273, 139917.941]</th>\n",
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       "    <tr>\n",
       "      <th>(139917.941, 147381.363]</th>\n",
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       "      <td>3.283314e-05</td>\n",
       "      <td>4.051865e-06</td>\n",
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       "    <tr>\n",
       "      <th>(147381.363, 155070.385]</th>\n",
       "      <td>0.000279</td>\n",
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       "      <td>2.940281e-05</td>\n",
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       "    <tr>\n",
       "      <th>(155070.385, 162993.924]</th>\n",
       "      <td>0.000333</td>\n",
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       "      <td>3.136300e-05</td>\n",
       "      <td>3.960115e-06</td>\n",
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       "    <tr>\n",
       "      <th>(162993.924, 171172.6]</th>\n",
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       "      <th>(171172.6, 179550.353]</th>\n",
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       "      <th>(179550.353, 188154.267]</th>\n",
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       "      <td>1.911183e-05</td>\n",
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       "    <tr>\n",
       "      <th>(188154.267, 196887.26]</th>\n",
       "      <td>0.000094</td>\n",
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       "      <td>1.127108e-05</td>\n",
       "      <td>2.374030e-06</td>\n",
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       "    <tr>\n",
       "      <th>(196887.26, 205662.506]</th>\n",
       "      <td>0.000086</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>1.029098e-05</td>\n",
       "      <td>2.268465e-06</td>\n",
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       "    <tr>\n",
       "      <th>(205662.506, 214421.692]</th>\n",
       "      <td>0.000099</td>\n",
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       "      <td>8.820844e-06</td>\n",
       "      <td>2.100192e-06</td>\n",
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       "    <tr>\n",
       "      <th>(214421.692, 223153.277]</th>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>4.900469e-06</td>\n",
       "      <td>1.565394e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(223153.277, 231947.037]</th>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>2.940281e-06</td>\n",
       "      <td>1.212550e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(231947.037, 240881.382]</th>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>2.940280e-06</td>\n",
       "      <td>1.212550e-06</td>\n",
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       "    <tr>\n",
       "      <th>(240881.382, 250021.637]</th>\n",
       "      <td>0.000057</td>\n",
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       "      <th>(250021.637, 259435.754]</th>\n",
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       "      <td>5.880563e-06</td>\n",
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       "      <th>(259435.754, 269159.895]</th>\n",
       "      <td>0.000023</td>\n",
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       "      <td>2.940281e-06</td>\n",
       "      <td>1.212550e-06</td>\n",
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       "    <tr>\n",
       "      <th>(269159.895, 279219.085]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
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       "    <tr>\n",
       "      <th>(279219.085, 289492.614]</th>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>1.470141e-06</td>\n",
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       "    <tr>\n",
       "      <th>(289492.614, 299936.847]</th>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>3.430328e-06</td>\n",
       "      <td>1.309703e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(299936.847, 310672.687]</th>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>4.900469e-07</td>\n",
       "      <td>4.950221e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(310672.687, 321827.363]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(321827.363, 333394.281]</th>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>1.470141e-06</td>\n",
       "      <td>8.574030e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(333394.281, 345374.738]</th>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>9.800938e-07</td>\n",
       "      <td>7.000668e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(345374.738, 357713.074]</th>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>4.900469e-07</td>\n",
       "      <td>4.950221e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(357713.074, 370303.274]</th>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>4.900467e-07</td>\n",
       "      <td>4.950220e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(370303.274, 383073.173]</th>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>1.960188e-06</td>\n",
       "      <td>9.900434e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(383073.173, 395916.187]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(395916.187, 408812.828]</th>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>4.900469e-07</td>\n",
       "      <td>4.950221e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(408812.828, 421799.976]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(421799.976, 435029.492]</th>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>4.900469e-07</td>\n",
       "      <td>4.950221e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(435029.492, 448553.338]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(448553.338, 462479.273]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(462479.273, 477325.579]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(477325.579, 494024.464]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(494024.464, 514060.602]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(514060.602, 541751.553]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(541751.553, 771157.53]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         syn_count               connected              \n",
       "                              mean     error          mean         error\n",
       "dist_bins                                                               \n",
       "(200.893, 43432.185]      0.076315  0.001399  5.483132e-03  5.221873e-05\n",
       "(43432.185, 56880.468]    0.011611  0.000412  9.516711e-04  2.180432e-05\n",
       "(56880.468, 67049.226]    0.005807  0.000253  5.253303e-04  1.620347e-05\n",
       "(67049.226, 75732.119]    0.003480  0.000186  3.239210e-04  1.272492e-05\n",
       "(75732.119, 83590.052]    0.002724  0.000212  2.278718e-04  1.067337e-05\n",
       "(83590.052, 90940.148]    0.001715  0.000165  1.470141e-04  8.573406e-06\n",
       "(90940.148, 97990.653]    0.001360  0.000180  1.171212e-04  7.652409e-06\n",
       "(97990.653, 104887.76]    0.001063  0.000111  9.310891e-05  6.823092e-06\n",
       "(104887.76, 111720.359]   0.000862  0.000103  6.909661e-05  5.877860e-06\n",
       "(111720.359, 118573.935]  0.000632  0.000082  5.831558e-05  5.399897e-06\n",
       "(118573.935, 125534.082]  0.000611  0.000086  5.488525e-05  5.238679e-06\n",
       "(125534.082, 132642.273]  0.000529  0.000094  4.067389e-05  4.509775e-06\n",
       "(132642.273, 139917.941]  0.000667  0.000112  4.557434e-05  4.773711e-06\n",
       "(139917.941, 147381.363]  0.000387  0.000063  3.283314e-05  4.051865e-06\n",
       "(147381.363, 155070.385]  0.000279  0.000042  2.940281e-05  3.834369e-06\n",
       "(155070.385, 162993.924]  0.000333  0.000058  3.136300e-05  3.960115e-06\n",
       "(162993.924, 171172.6]    0.000293  0.000059  2.499239e-05  3.535121e-06\n",
       "(171172.6, 179550.353]    0.000147  0.000027  1.764169e-05  2.970107e-06\n",
       "(179550.353, 188154.267]  0.000195  0.000040  1.911183e-05  3.091383e-06\n",
       "(188154.267, 196887.26]   0.000094  0.000023  1.127108e-05  2.374030e-06\n",
       "(196887.26, 205662.506]   0.000086  0.000021  1.029098e-05  2.268465e-06\n",
       "(205662.506, 214421.692]  0.000099  0.000029  8.820844e-06  2.100192e-06\n",
       "(214421.692, 223153.277]  0.000055  0.000020  4.900469e-06  1.565394e-06\n",
       "(223153.277, 231947.037]  0.000027  0.000014  2.940281e-06  1.212550e-06\n",
       "(231947.037, 240881.382]  0.000026  0.000012  2.940280e-06  1.212550e-06\n",
       "(240881.382, 250021.637]  0.000057  0.000017  6.860657e-06  1.852197e-06\n",
       "(250021.637, 259435.754]  0.000053  0.000020  5.880563e-06  1.714802e-06\n",
       "(259435.754, 269159.895]  0.000023  0.000010  2.940281e-06  1.212550e-06\n",
       "(269159.895, 279219.085]  0.000000  0.000000  0.000000e+00  0.000000e+00\n",
       "(279219.085, 289492.614]  0.000010  0.000006  1.470141e-06  8.574030e-07\n",
       "(289492.614, 299936.847]  0.000023  0.000009  3.430328e-06  1.309703e-06\n",
       "(299936.847, 310672.687]  0.000002  0.000002  4.900469e-07  4.950221e-07\n",
       "(310672.687, 321827.363]  0.000000  0.000000  0.000000e+00  0.000000e+00\n",
       "(321827.363, 333394.281]  0.000012  0.000007  1.470141e-06  8.574030e-07\n",
       "(333394.281, 345374.738]  0.000007  0.000005  9.800938e-07  7.000668e-07\n",
       "(345374.738, 357713.074]  0.000004  0.000004  4.900469e-07  4.950221e-07\n",
       "(357713.074, 370303.274]  0.000002  0.000002  4.900467e-07  4.950220e-07\n",
       "(370303.274, 383073.173]  0.000012  0.000006  1.960188e-06  9.900434e-07\n",
       "(383073.173, 395916.187]  0.000000  0.000000  0.000000e+00  0.000000e+00\n",
       "(395916.187, 408812.828]  0.000003  0.000003  4.900469e-07  4.950221e-07\n",
       "(408812.828, 421799.976]  0.000000  0.000000  0.000000e+00  0.000000e+00\n",
       "(421799.976, 435029.492]  0.000003  0.000003  4.900469e-07  4.950221e-07\n",
       "(435029.492, 448553.338]  0.000000  0.000000  0.000000e+00  0.000000e+00\n",
       "(448553.338, 462479.273]  0.000000  0.000000  0.000000e+00  0.000000e+00\n",
       "(462479.273, 477325.579]  0.000000  0.000000  0.000000e+00  0.000000e+00\n",
       "(477325.579, 494024.464]  0.000000  0.000000  0.000000e+00  0.000000e+00\n",
       "(494024.464, 514060.602]  0.000000  0.000000  0.000000e+00  0.000000e+00\n",
       "(514060.602, 541751.553]  0.000000  0.000000  0.000000e+00  0.000000e+00\n",
       "(541751.553, 771157.53]   0.000000  0.000000  0.000000e+00  0.000000e+00"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_vs_syn_fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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wu9atW5f6N5GUlGRhGgAAADRWHlcQiouL0/XXX6+QkBAFBASoR48emjt3rmw2myV92u12PfXUU+rfv78CAwMVEBCgTp06ady4cXrvvffczuRJgoODHceZmZkWJgEAAABqh2matdoeDZthGKWW22aGEAAAAKzgdkHoyJEjNRjDNcuXL1d0dLQ+/PBD+fv7q2fPnjpy5IgWL16sfv366cSJE3Xa5y+//KKLL75YDz74oHbv3q127drpoosuUl5entavX6/Vq1dX5+t6DOflD9z5/wAAAABoSPLz8+Xl5XHv3qGWOb9Ix1LbAAAAsILbo5SuXbtq9OjRevfdd1VUVFSTmSq0a9cuzZw5U5K0cuVKJSQkaPfu3frtt9/Ut29fxcfHa+rUqXXWZ2ZmpkaMGKEjR47oT3/6k9LT07V3717t2rVLKSkpOnr0qP785z9X5yt7jNDQUMfxqVOneBsSAAAAjdapU6eUnp6u1q1bWx0F9Ux4eLjjODk52cIkAAAAaKzcXti6uLhYn376qT799FOFhYVpypQpmjJlijp16lSD8X63aNEiFRcX64477tC0adMc59u1a6c1a9aoR48eWr9+vfbs2aNevXrVep+zZs1SSkqKpk+fruXLl5fru2PHjurYsaOb39aztG3b1nGcnZ2tvLw8+fv7W5gIAAAAcN+ePXv0ww8/lDqXk5OjV155pdJ7TNPUqVOn9Pbbb6u4uFhRUVG1nBKexvlFOpaMAwAAgBXcLght3rxZsbGxevfdd5WamqonnnhCTz75pP7whz9o2rRpuuGGG+Tt7V0jIbOzs7Vx40ZJKlW4KdGtWzeNGDFCmzdv1tq1a10qCFWnz9TUVK1Zs0a+vr5asGCBu1+rwXB+081ut8tut1MQAgAAgMd65513tHDhwlLnTp8+rf/3//7fee81TVOGYej++++vrXjwUM5LbWdkZFiYBAAAAI2V2wWhESNGaMSIETp+/Lhefvllvfjii/rll1+0ceNGffLJJwoPD9fkyZN19913KyIiolohv//+e+Xl5cnPz0/9+/evsM2QIUO0efNmffPNN7Xe50cffaSCggINHTpUQUFBevnll/XRRx/p+PHjatu2rf7whz/oj3/8o5o0cfvX61Gc33Sz2Wyy2WwKCgqyMBEAAADgvtatW5ea7X/06FF5eXmpQ4cOld7j5eWlli1b6pJLLtG0adM0ZMiQuogKD+JcELLZbBYmAQAAQGNV7YpFmzZtNGvWLM2aNUvbt2/XypUrtW7dOqWkpDhmDV199dWKiYlxe9bQgQMHJJ1dhs3Hx6fCNl26dJEk7d+/v9b7/O677yRJERERGjp0qL7++utS11977TUtXbpUH374odq3b+9SHk/mvDlqyQwhAAAAwFP95S9/0V/+8hfHZy8vL4WEhOjw4cMWpoKnc36RjjETAAAArFCjU1gGDx6swYMH67nnntMrr7yi//znP/rpp58cew2VzBqaOnVqlfbXOXHihCSdc9ZJybWTJ0/Wep8l6z2/8cYbKiws1Ny5c3XvvfeqdevWiouL0/Tp0/Xjjz9q/Pjx+uqrr2QYRqXPWLlypWJjY13KHB8f71K7ulb2TTcGNwAAAGhI5s2bp+bNm1sdAx6u7LipoKCg0pcTAQAAgNpQK2uatWrVSn/+85919dVXa/r06fr8888lyTFr6O9//7smTJigJ5980qXl5HJzcyVJvr6+lbbx8/OTdHazV1dUp8/s7GxJUkFBgWJiYkqtLz569Gi98847uvzyy/XNN9/o448/1rXXXlvpM1JSUrR7926XMtdXzgOb/Px8l4tyAAAAgCeYN2+e1RHQADiPm0pWVmjVqpWFiQAAANDY1HhBKC8vT2vXrtWqVau0fft2x/mIiAjdfPPN2rx5s3766Se9+eab+vjjj7V161b17t37nH36+/tLOltsONdzJSkgIMClnNXps+ReSZo1a1a5+6KionTVVVcpLi7uvAWhtm3bKioqyqXM8fHxLhe86pLzwEb6fQYVAAAAAOCssnuvnjlzhoIQAAAA6lSNFYT27t2rVatWafXq1Tp16pRM05SXl5dGjx6t6dOn69prr3UsnbZ161bNnDlTe/bs0YMPPqiNGzees+/AwEBJvy/zVpGSayVtz6c6fZZ89vHxcewzVFbPnj0VFxd33nXGY2JiFBMT41Lmvn371svZRP7+/goICHAUq9LS0ixOBAAAALinZPZ/cHCw7r333lLnquqxxx6rsVzwfM4v0pmmqdTUVHXo0MHCRAAAAGhsqlUQys3N1ZtvvqnY2Fh98803ks7+YVuyV9C0adMq3Cvoqquu0qeffqoOHTpox44d533OhRdeKElKSEiodJ3lQ4cOlWpbm3326NFD0tnl5ry8vCrsv2QWUVFRkUt5PF1QUJCSkpIkSenp6RanAQAAANwzf/58GYah7t27OwpCJeeqioIQnLVu3VpNmjRRYWGhJCkpKUmXX365xakAAADQmLhdEPrTn/6k1157TadPn5ZpmpKk4cOHa/r06brpppvUpMm5uw4NDVV4eLijiHAuUVFR8vX1VV5ennbs2KFBgwaVa/PFF19IkgYMGOBS/ur0WdLWZrMpLS1NYWFh5e4tKSa1b9/epTyezrkglJmZaXEaAAAAwD1Dhw6VYRilXmwrOQdUh2EYatOmjWNFheTkZIsTAQAAoLFxuyD073//W9LZ5dPuvPNOTZ8+3eXZOSUGDhzo0vJizZs316hRo7RhwwbFxsaWK94cPHhQW7ZskSSNHz/epWdXp88hQ4aobdu2SklJ0X//+1899NBDpa4nJyfrk08+kSRFR0e7lMfTOS9/cK5l+AAAAID6bOvWrS6dA9wRHBzsGAOnpqZanAYAAACNTcXrnbmgf//+eumll5SUlKSlS5dWuRgkSW+88Ybi4uJcajt37lwZhqHVq1crNjbWMSspJSVFkyZNUnFxscaOHavevXuXum/w4MHq1KmTli1bVmN9ent7a8GCBZKkJ554Qps2bXJcy8zM1B133KHc3Fx169bN5QKVp3OeJXXy5EkLkwAAAABA/eQ8bmKGEAAAAOqa2wWhb775Rnfeeadjr5za1q9fPy1dulSSFBMTo4iICEVFRSkyMlK7du1S9+7dtWrVqnL3JSYm6ujRozp16lSN9SlJU6dO1dSpU5Wdna0//OEP6tatmy6//HJ16NBBn332mcLDw7V+/foK9yZqiMLDwx3HZ86ccayLDQAAAAA4KzQ01HGckpJiYRIAAAA0Rm4XhCZPnqz777/f5fazZ8/WlClT3H2cJGnmzJnatGmTRo8eLZvNpn379ikiIkIPP/ywdu7cqeDg4DrtMzY2Vm+++aauuuoqHT9+XD/99JMuuOAC/fWvf9UPP/ygSy65pDpf16M4F4RsNpvsdruFaQAAAACg/nEuCGVkZFiYBAAAAI2R23sIvfzyywoPD3fMsDmftWvXKiEhQS+++KK7j5R0dk+equzLc+TIkRrv09nEiRM1ceJEt+5tSJwHNna7XXa7XS1btrQwEQAAAFB1CxcurLG+HnvssRrrCw2D896rvEQHAACAuuZ2QaiqSvbnQcPkPLCx2Wyy2WwWpgEAAADcM3/+fBmGUSN9URBCWWXHTQAAAEBdqrOCUGZmppo2bVpXj0MdK/umG2+7AQAAwBMNHTq0xgpCQFnOKyvYbDYVFhaqSZM6G5YDAACgkav1vzyzsrL0n//8R3a7XZdeemltPw4WcS4I5eTk6PTp0xamAQAAANyzdetWqyOgAavoRTqW2gYAAEBd8XK14YIFC+Tt7e34kaS0tLRS5yr6CQoK0uzZs2UYhsaPH19rXwTWch7YSFJKSopFSQAAAICGIS4uTtdff71CQkIUEBCgHj16aO7cudVaaszdPgsKCvT000+rd+/eatasmYKCgjRixAitX7++Ss9PTk5W69atZRiGDMNwac/XhqRsQejMmTMWpgEAAEBj43JBSDq7D1DJj2EYpT6f68fHx0eTJ0/WnDlzaut7wGLNmjWTr6+v43NaWpqFaQAAAADPtnz5ckVHR+vDDz+Uv7+/evbsqSNHjmjx4sXq16+fTpw4UWd95ubmasSIEZo9e7b27t2rrl27KigoSHFxcRo3blyVxnn33nuvsrKyqpy9oXBeMq64uFipqakWpgEAAEBj4/KScXfddZeuuuoqSWcLQyNGjFBQUJDWrVtX6T1eXl5q2bKlLrzwQgUEBFQ7LOovwzDUunVrpaenS6IgBAAAALhr165dmjlzpiRp5cqVmjp1qgzDUHJyssaMGaNdu3Zp6tSp5xyL1WSfDz74oLZv367IyEh9/PHH6t69uyTp/fff18SJE7VkyRINGjRIN9xwwzkzvPXWW3rvvfc0duxYvfvuuy5nb0hat26tJk2aqLCwUJKUlJSkvn37WpwKAAAAjYXLBaGIiAhFREQ4Pnfs2FFhYWEaNmxYrQSD52nTpo2jIJSZmWlxGgAAAKDqFi5cKEkKDg7WvffeW+pcVT322GNu3bdo0SIVFxfrjjvu0LRp0xzn27VrpzVr1qhHjx5av3699uzZo169etVqn2lpaVqxYoUk6cUXX3QUgyRpzJgxmj17thYtWqT58+efsyB04sQJzZgxQxdccIEWLVrUaAtChmEoKCjIMW5iqW0AAADUJZcLQmU1trWecX4hISGKj4+XJB0/ftziNAAAAEDVzZ8/X4ZhqHv37o6CUMm5qnKnIJSdna2NGzdKUqnCTYlu3bppxIgR2rx5s9auXetSQag6fb7//vvKz89X165dNXz48HL3xsTEaNGiRdq9e7cOHTqkLl26VJjhr3/9q9LS0vT++++refPm583ckIWEhDgKQiwZBwAAgLrkdkEIKMt5PWx31jQHAAAArDZ06FAZhqGOHTuWO1cXvv/+e+Xl5cnPz0/9+/evsM2QIUO0efNmffPNN7XeZ8nnIUOGVHhf+/btFRkZqcOHD+ubb76psCD06aef6pVXXtG4ceN0ww03NPqXC8PCwrR3715JZ5eMAwAAAOqKSwWhhIQESZKPj4/atm1b6lxVOQ+s0LCEh4c7jrOysmSaZp0NnAEAAICasHXrVpfO1ZYDBw5IOjtu8vHxqbBNSdFl//79td5nyb1du3attP8uXbro8OHDFeax2WyKiYlRy5Yt9a9//culvA2d84t0LBkHAACAuuRSQSgyMlKS1KNHD8ebTCXnqsIwDMfmmWh4nAtCNptNOTk5atq0qYWJAAAAAM9SMtM+KCio0jYl106ePFnrfVY3z8MPP6wjR47o+eefV7t27VzKW5GVK1cqNjbWpbYly1jXVyEhIY7jjIwMC5MAAACgsXGpIGSaZqn/LXvsKnfugedwftPNbrfLbrdTEAIAAACqIDc3V5Lk6+tbaRs/Pz9JUk5OTq33WZ17v/76az333HO68sorNX36dJeyViYlJUW7d++uVh/1hfO4yWazWZgEAAAAjY1LBaHDhw9LUqnlBUrOASWc33QrKQgBAAAADc3evXu1c+dOpaenSzr7H/gvv/xyXXzxxdXu29/fX5KUn59faZu8vDxJUkBAQK336e69+fn5uvvuu+Xl5aXY2Fh5eXm5lLUybdu2VVRUlEtt4+PjXS6WWcF53ERBCAAAAHXJpYJQRESES+fQuJUd2DC4AQAAQEPywQcf6KGHHtK+ffsqvH7RRRfp8ccf15gxY9x+RmBgoKTfl2qrSMm1kra12ae79y5ZskT79u3TnDlzdOmll7qU81xiYmIUExPjUtu+ffvW69lEZV+kKyoqkre3t4WJAAAA0FhU7zUtwEnZgU12draFaQAAAICas3DhQt14443au3evTNOUt7e3QkNDFRoaKm9vb5mmqb179+qmm27S/Pnz3X7OhRdeKElKSEhQQUFBhW0OHTpUqm1t9lny+ddff620/4ru3bVrlyRp1apVCg8PL/XTr18/R7t+/fopPDxczzzzjEvfpSEo+yIdKysAAACgrlAQQo1xHtiYpulYQgMAAADwZBs3btT8+fNlmqaGDh2qTz/9VNnZ2UpJSVFKSoqys7P16aef6qqrrpJpmlq0aJE++eQTt54VFRUlX19f5eXlaceOHRW2+eKLLyRJAwYMqPU+r7zySknS9u3bK7wvKSnJsZx4SVtnx48fV1paWqmfzMxMx/XMzEylpaU1qpfJyu692pi+OwAAAKzl0pJxCQkJNfbAjh071lhfqF9at24tb29vFRUVSTq78SsAAADg6ZYuXSpJmjBhgt544w0ZhlHquq+vr0aOHKno6GjdeuutWrt2rZYuXapRo0ZV+VnNmzfXqFGjtGHDBsXGxmrQoEGlrh88eFBbtmyRJI0fP77W+7zxxhv1pz/9SQcPHlRcXJyGDx9e6vrKlSslSZdddpm6du3qOP/uu+9WmufIkSOKjIyUdHZv2k6dOrn0PRoK5xfpiouLlZqaqrZt21qYCAAAAI2FSzOEIiMja+Snc+fOtf19YCHDMNS6dWvH57S0NOvCAAAAADVk586dMgxDS5cuLVcMcmYYhp599llJ0nfffef28+bOnSvDMLR69WrFxsbKNE1JZ1+4mjRpkoqLizV27Fj17t271H2DBw9Wp06dtGzZshrrMywszLF3z5QpU7R//37HtQ0bNuipp56SJM2bN8/t79vYlLxIVyIxMdHCNAAAAGhMXCoImaZZIz/FxcW1/X1gsTZt2jiOMzIyLEwCAAAA1Iz8/Hy1bt1a7du3P2/bDh06KDAwsNK9elzRr18/x6ykmJgYRUREKCoqSpGRkdq1a5e6d++uVatWlbsvMTFRR48e1alTp2qsT0l66qmnNGDAAB0+fFgXX3yx+vTpo65du2rMmDHKy8vTrFmzdOONN7r9fRsbLy+vUuOm5ORkC9MAAACgMXFpybiSNaGB83Ee2DivDQ4AAAB4qs6dO2v//v3Kz8+Xr6/vOdvm5eUpOztbPXr0qNYzZ86cqUsvvVTPPvusvv32W6WnpysiIkLjx4/XQw89pObNm9dZnwEBAdq6dauWLVumV199VQcOHJCvr6+GDRumP//5zxo3bly1vmtjFBIS4thzlZUVAAAAUFdcKghFRETUdg40EM4bpJ48edLCJAAAAEDN+OMf/6iHH35Yr7zyiu6+++5ztl29erUKCgr0xz/+sdrPjY6OVnR0tMvtjxw5UuN9lvD19dXs2bM1e/bsKt9bVqdOnRxL1jVWoaGh2rt3ryQpKSnJ4jQAAABoLFxaMg5wVXh4uOP4xIkTFiYBAAAAasasWbM0ePBgzZgxQ//73/8qbffKK69oxowZGjJkiGbNmlWHCeFpwsLCHMcsGQcAAIC64tIMIcBVzgUhm83m0rIaAAAAQH2xcOHCCs8PGTJEP/30kyZPnqx58+bpqquuUvv27WUYhhITE7Vt2zYlJCSoVatWGjJkiJ544gk99thjdZweniIkJMRxzN6rAAAAqCsuFYQSEhIkST4+Pmrbtm2pc1XVsWNHt+6DZ3BeMs5ms8lut1MQAgAAgMeYP3++DMOo9LppmkpISNDq1avLnZekrKwsPfnkk5JEQQiVci4I2Ww2C5MAAACgMXGpIBQZGSlJ6tGjh2Od45JzVWEYhgoLC6t8HzyH88DGbrfLbrerdevW1gUCAAAAqmDo0KHnLAgBNaHsuAkAAACoCy4VhErednPe+NOdTUAb+8ahjUHZN9142w0AAACeZOvWrVZHQCNQdmWF4uJieXmxxS8AAABql0sFocOHD0s6u2Rc2XOAs7JvulEQAgAAAIDSyr5IZ7fb1bx5cwsTAQAAoDFwqSAUERHh0jnAeWBTVFSkzMxMC9MAAAAAQP1T0Yt0FIQAAABQ25iTjhrVpk2bUmuup6SkWJgGAAAAAOof5yXjiouLGTcBAACgTrg0Q8hVRUVFOnHihCQpKChI3t7eNdk9PIC3t7datmyprKwsSVJqaqrFiQAAAICakZeXp3Xr1mn79u1KTEyUzWardJ9UwzD02Wef1XFCeIrWrVvL29tbRUVFkqTExET16dPH2lAAAABo8KpdELLZbFqxYoXeeOMN7dmzR4WFhWc7btJEvXr10q233qqYmBimvzcibdq0cRSEMjIyLE4DAAAAVN9XX32lW265RcnJyTJN0zErvqQg5DxL3vk6UBEvLy8FBQU5xkvMEAIAAEBdqFZB6IcfftBNN92khISEcm/GFRQUaNeuXdq9e7eee+45rV+/Xpdddlm1wsIzBAcH67fffpMk9hACAACAxzt27Jiuu+46ZWVlqVevXrrmmmv01FNPqXnz5po5c6ZSU1O1ZcsW/fbbbwoODtb06dNZLQHnFRISQkEIAAAAdcrtglBycrJGjhypEydOyNfXV+PHj9eIESPUvn17SVJSUpLi4uL09ttv6+jRo7r66qu1Z88etWvXrsbCo35y3iC1ZAlBAAAAwFMtXbpUWVlZGj16tD744AMZhuEoCC1cuNDR7oUXXtCMGTP0448/6r333rMwMTxBWFiY9u3bJ+ns+BkAAACobW4XhBYtWqQTJ04oIiJCH3/8sXr06FGuzeTJk/Xoo4/qmmuuUUJCghYtWqQXXnihWoFR/4WHhzuOKQgBAADA03366acyDEMLFiw451Jw99xzj06ePKm5c+dq1apVmjp1ah2mhKcJDQ11HDNDCAAAAHXBy90bP/roIxmGoVWrVlVYDCrRvXt3rVq1SqZp6sMPP3T3cfAgzgWh06dPq7i42MI0AAAAQPUkJCTIy8tLUVFRpc7n5+eXa3vffffJMAy9/PLLdZQOnsp5ZYX09HQLkwAAAKCxcLsglJaWpoCAAI0cOfK8bUeOHKmmTZs61kdGw+Y8sLHb7bLb7RamAQAAAKrHNE21aNFCXl6/D5+aNWum06dPl9tLtVWrVmrVqpV++eWXuo4JD+M8Q8hms1mYBAAAAI2F2wWhkJCQKm2U6uXlVapQ4K64uDhdf/31CgkJUUBAgHr06KG5c+dW6w9od/rs1KmTDMM4509ubq7bmTyZ8//PNpuNghAAAAA8Wvv27ZWVlaW8vDzHuQ4dOqioqEjx8fGl2trtdp06dYq/gXFeZV+kAwAAAGqb2wWh6OhoZWdna9euXedtu3PnTmVnZys6Otrdx0mSli9frujoaH344Yfy9/dXz549deTIES1evFj9+vVza7+a6vZ5ySWXaNCgQRX+OL9B2JgwQwgAAAANSbdu3SRJR44ccZy74oorJEkrVqwo1Xbp0qUyTVOdOnWqq3jwUGVfpGOpbQAAANQ2tysWjz76qJo1a6apU6fq+PHjlbY7ceKEpk2bppYtW+qRRx5x93HatWuXZs6cKUlauXKlEhIStHv3bv3222/q27ev4uPjq7xpa030uXz5cm3fvr3CH19fX3e+qscrO7Bh+QMAAAB4stGjR8s0TW3YsMFx7u6775Zpmnr++ed17bXX6pFHHtH111+vefPmyTAMTZo0ycLE8ARll4zLycmxMA0AAAAaA5cKQgkJCeV+fH199Z///EeHDx9Wz549NW/ePG3dulUHDx7Ur7/+qq1bt2revHmOGTerVq2qVoFk0aJFKi4u1u23365p06bJMAxJUrt27bRmzRp5eXlp/fr12rNnj6V9onRBqKCgwK2ZWwAAAEB9MWbMGA0bNkwJCQmOc4MHD9bf/vY3maapjRs36u9//7s++ugjmaapIUOGaM6cORYmhicou7ICL9IBAACgtjVxpVFkZOR52yxevFiLFy+u9Pqtt94qwzBUWFjoerr/X3Z2tjZu3ChJmjZtWrnr3bp104gRI7R582atXbtWvXr1sqRPnBUcHFzqc0pKikVJAAAAgOq74IILFBcXV+78kiVLdPXVV+uNN97QsWPH1KpVK11zzTW644471KSJS0MtNGLOBaHi4mKlpqaWmjUEAAAA1DSXRimmadbIw9zt5/vvv1deXp78/PzUv3//CtsMGTJEmzdv1jfffFOnfa5YsULPPPOMcnJyFB4eriFDhui2225TixYtXMrREPn5+alZs2aON9woCAEAAKChGjlypEaOHGl1DHigwMBAeXt7q6ioSJKUmJjIi4gAAACoVS4VhA4fPlzbOc7pwIEDkqSOHTvKx8enwjZdunSRJO3fv79O+3zzzTdLfX799dc1d+5cvf7667r66qtdytIQBQUFOQpCGRkZFqcBAAAAgPrFy8tLQUFBjvFScnKyxYkAAADQ0LlUEIqIiKjtHOdUsgdNUFBQpW1Krp08ebJO+hw4cKAeeeQRDRkyRB07dlR+fr62b9+uxx57TN9//73GjBmjL7/8UlFRUefMsXLlSsXGxrqUOT4+3qV29UFwcLCOHTsmiYIQAAAAGpYzZ85o9+7dSk9PlySFhobqsssuU8uWLS1OBk8TEhLiGC+xsgIAAABqm0csbJ2bmytJ8vX1rbSNn5+fJCknJ6dO+nz99ddLfW7atKmuv/56RUdHa/Dgwdq9e7cefPBBbdq06Zw5UlJStHv3bpcyexLnta9Lim8AAACAJ/vhhx80d+5cbdy4UcXFxaWueXl56ZprrtHChQt12WWXWZQQniYsLEz79u2TJCUlJVmcBgAAAA2dRxSE/P39JUn5+fmVtsnLy5MkBQQEWNZnSdvHH39co0eP1pYtW3Tq1Cm1bt260vZt27Y97yyiEvHx8S4XvKwWFhbmOD5+/LiFSQAAAIDqe+mllzR9+nQVFhZWuDdqUVGRPvzwQ33yySdasWKFJk+ebEFKeBrnF+lYMg4AAAC1rUYKQvn5+frhhx+UmJgom81W4QCpxB133FHl/gMDAyWde6ZJybWStlb0WWLgwIGSpOLiYh06dEh9+/attG1MTIxiYmJc6rdv374eM5soPDzccZyVlSXTNGUYhoWJAAAAAPfs2LFDU6dOVXFxsbp166a//e1vGjFihNq3by/DMJSYmKgtW7Zo6dKl+uWXXzRt2jRdcskl6t+/v9XRUc+FhIQ4jllqGwAAALWtWgWhvLw8PfLII4qNjZXNZjtve8Mw3CoIXXjhhZKkhIQEFRQUyMfHp1ybQ4cOlWprRZ8lnJehKywsrNK9DYXzm242m035+fmOJfgAAAAAT/Lkk0+quLhYV111lT766CPHagMlOnfurM6dO+v222/Xtddeq7i4OD355JN65513LEoMT+FcEHJlTA0AAABUh5e7NxYWFmrUqFH6xz/+oezsbIWEhDhmgbRv315+fn4yTVOmaapZs2bq2LGjLrjgAreeFRUVJV9fX+Xl5WnHjh0Vtvniiy8kSQMGDLCszxI///yz47h9+/ZVurehcB7Y2O12BjcAAADwWNu3b5dhGHrhhRfKFYOc+fn56fnnn3fcA5wPBSEAAADUJbcLQi+++KI+//xztWvXTjt37lRqaqqkszNDEhISlJ2drbi4OA0cOFCFhYVavHixDh8+7NazmjdvrlGjRkmSYmNjy10/ePCgtmzZIkkaP368ZX2WeOqppyRJF110kTp06FClexuKsgMbu91uYRoAAADAfTabTS1btlT37t3P27ZHjx5q1aoVf//CJWVXVjjX8usAAABAdbldEFqzZo0Mw9Djjz+uqKio8h17eWnYsGHatm2bBg8erMmTJ1dr/5u5c+fKMAytXr1asbGxjj+UU1JSNGnSJBUXF2vs2LHq3bt3qfsGDx6sTp06admyZTXW5zPPPKPly5fr+PHjpc4fP35cMTExWrt2rSRpwYIFbn9fT1d2hhADYgAAAHiqjh07Kjc3V8XFxedtW1RUpNzcXLdXR0DjUnbclJOTY2EaAAAANHRuF4RKlkUrO3umqKio1Gdvb28tXbpUBQUFeuaZZ9x9nPr166elS5dKkmJiYhQREaGoqChFRkZq165d6t69u1atWlXuvsTERB09elSnTp2q0T5nzJihkJAQde7cWVdccYV69eql8PBwxcbGysvLS0uWLKnyzKKGJDg42HGcm5tb4e8fAAAA8AQ33XST8vPz9e6775637bvvvqu8vDyNGzeu9oPB45VdWYFl4wAAAFCb3C4InTlzRi1btlTTpk0d5/z8/JSdnV2u7SWXXKIWLVo49uRx18yZM7Vp0yaNHj1aNptN+/btU0REhB5++GHt3LmzVBGiNvu89dZbNWPGDF1xxRXKy8vTjz/+qEOHDqlz586aOnWqdu3apdmzZ1fru3o654GNdHbWFQAAAOCJHnnkEXXr1k3Tpk3T1q1bK233+eefKyYmRj169NBDDz1UdwHhsZyXjCsuLmbcBAAAgFrVxN0bQ0NDlZ6eXupcUFCQUlNTlZ6eXuoPW9M0lZ+fr4yMDPeT/v+io6MVHR3tcvsjR47UeJ9XXnmlrrzySpfbN0bNmjWTn5+f8vLyJMmxxxQAAABQn73yyisVnp8+fboWLFig6OhoDRo0SCNGjFD79u1lGIYSExMVFxen7du3q1WrVoqJidH69et1xx131HF6eJrAwEB5e3s7VtpISkpSr169LE4FAACAhsrtglCHDh2UnJys1NRUhYeHSzo7Eyg1NVUbN24sNfjZunWr8vLyys0aQcMWFBTkeMMtLS3N4jQAAADA+d11110yDKPS66Zp6ssvv9SXX35Z4bWsrCzdf//9MgyDghDOy8vLS4GBgcrMzJQkJScnW5wIAAAADZnbBaFhw4Zpx44d+vLLLx3rY990003atGmTHnjgAQUEBKhPnz768ccfHQOiESNG1Fhw1H/BwcGOglBNzA4DAAAAalvHjh3PWRACalpoaKijIMSScQAAAKhNbheExowZo6eeekqvvfaaoyA0ZcoU/fvf/9bPP/+sW2+91dHWNE01b95c8+bNq35ieAznGWElAxwAAACgPnNlyWmgJoWFhWnfvn2SpMTERIvTAAAAoCHzcvfGAQMG6PDhw1q+fLnjnI+Pjz777DNNmjRJfn5+Mk1TkjR48GBt3bpVPXr0qH5ieIywsDDH8YkTJyxMAgAAAAD1k/OLdMwQAgAAQG1ye4aQYRiKiIgodz4kJESvvfaaCgsLlZGRoZYtW6pZs2bVCgnPVLK3lERBCAAAAAAq4lwQSk9PtzAJAAAAGjq3C0Ln7bhJE7Vt27a2uocHCA0NdRzbbDYVFhaqSZNa+ycHAAAA1Im9e/dq586dSk9Pl2EYCgkJUb9+/XTRRRdZHQ0eqOy4CQAAAKgt/Nd51BrnN93sdrtycnLUokULCxMBAAAA7vvkk080e/Zs/fzzzxVev/TSS/XUU0/pD3/4Qx0ngydzHjdREAIAAEBtcnsPoRKmaWrdunWaMGGCIiMj1axZMzVr1kyRkZGaMGGC1q1bp+Li4prICg9TtiDE4AYAAACe6rnnntO1116rn3/+WaZpysvLS6GhoQoNDZW3t7dM09SePXs0evRoPf/881bHhQcpO24q2YsXAAAAqGnVKgglJCRowIABmjhxotavX6+jR48qJydHOTk5Onr0qNavX6+JEyfqyiuv1NGjR2sqMzxE2Tfd7Ha7hWkAAAAA9/z444+aOXOmTNNU//799dFHHyk7O1spKSlKSUnRmTNn9NFHH2nAgAEyTVMzZ87Unj17rI4ND1F2ybjc3FwL0wAAAKAhc3vJuKysLA0bNkwJCQkyTVMDBw7UiBEj1L59e0lSUlKS4uLi9OWXX2rnzp0aPny4vv/+e7Vq1arGwqN+cy4I5eTkKDs728I0AAAAgHuWLl2q4uJi3XDDDVq/fr28vb1LXffz89M111yjq6++WjfffLM2bNigf/zjH3rppZcsSgxPUvZFOpvNpoCAAAsTAQAAoKFyuyD0+OOP6+jRowoKCtKbb76p6OjoCtvFxcVpwoQJOnr0qJ544gktWbLE7bDwLM4DG9M0lZKSol69elmYCAAAAKi6bdu2yTAM/fOf/yxXDHLm7e2tZcuWacOGDYqLi6vDhPBkzuOm4uJipaamKjg42MJEAAAAaKjcXjLunXfekWEYWrFiRaXFIEkaPny4VqxY4dhrCI1Hy5Yt1aTJ7zXHlJQUC9MAAAAA7klLS1OrVq3UqVOn87aNjIxU69atlZaWVvvB0CAEBQXJy+v3oXlSUpKFaQAAANCQuV0QSkxMlK+vr26++ebztr3pppvk5+fHH7aNjGEYCgoKcnxmUAwAAABPFBAQILvdrsLCwvO2LSwslN1uZ8kvuMzLy6vUuIlxMwAAAGqL2wWhwMBA+fv7l3qTqTLe3t7y9/dXYGCgu4+Dh3Ie2KSnp1uYBAAAAHBPz549VVBQoLfffvu8bdeuXav8/Hz17NmzDpKhoXBeNo6VFQAAAFBb3C4IDRw4UKdPn9aBAwfO2/bAgQPKysrS4MGD3X0cPJTzwCYzM9PCJAAAAIB7JkyYINM0de+992rz5s2Vttu8ebPuvfdeGYahiRMn1mFCeLqwsDDHcWJiooVJAAAA0JA1OX+Tis2ZM0cffPCB7r33Xn344Yfy8/OrsF1+fr7uvfde+fj4aM6cOW4HhWdyHtgcP37cwiQAAACAe+655x69+OKL2rt3r0aNGqUBAwZo5MiRat++vQzD0LFjx/TZZ5/p66+/lmmauuSSS3TPPfdYHRseJDQ01HGcnJxsYRIAAAA0ZG4XhC6//HK99dZbuvPOO9WnTx/Nnj1bw4cPV/v27SWdXfc4Li5OzzzzjFJSUvT2228rKiqqxoLDM4SHhzuOMzIyLEwCAAAAuMfPz0+ffPKJbr75Zu3YsUNfffWVvv7661JtTNOUJF1xxRVat26dfH19rYgKD+W8sgJLbQMAAKC2uFQQ8vb2Puf106dP6+677z5nm7Fjx8owDJc2YkXD4fymm91ul2maMgzDwkQAAABA1bVr105fffWV3n77bb355pvauXOn4z/ch4aG6vLLL9ett96qcePGubTPKuDMuSBks9ksTAIAAICGzKWCUMnbbkBVlR3Y5ObmKiAgwMJEAAAAgHu8vLw0ceJE9gdCjXN+kY6CEAAAAGqLSwWhuLi42s6BBsq5IGS322Wz2SgIAQAAwKN4eXnJy8tLv/zyi7p27Wp1HDRAZcdNrKwAAACA2uBSQWjYsGG1nQMNVNkZQna73cI0AAAAQNUFBATIx8eHYhBqTdlxU15envz9/S1MBAAAgIaIxa1RqyqaIQQAAAB4kg4dOqigoMDqGGjAGDcBAACgLtRoQaioqEgZGRnKzMxUUVFRTXYND+U8sCkuLnZsvAsAAAB4iuuuu065ubnatm2b1VHQQDnvIVRUVKTU1FQL0wAAAKChqnZByG63a+nSperXr5+aNm2q8PBwhYWFqWnTpurfv7+WLVvGMmGNWFBQkLy8fv9nlpycbGEaAAAAoOoeeughhYSE6J577lFKSorVcdAAlR03JSUlWZgGAAAADZVLewhVZv/+/brhhht06NAhmaZZ6lpBQYF27typXbt26YUXXtCGDRt04YUXVissPI+Xl5dat26tEydOSBJvugEAAMDjxMfH6/HHH9df//pXXXTRRbr99ts1aNAghYaGytvbu9L7hg4dWocp4cm8vLwUFBSkzMxMSbxIBwAAgNrhdkHozJkz+sMf/qBjx46pSZMmuvnmm3X11VerQ4cOkqTExERt3rxZ69at08GDBzVq1Cj99NNPat68eY2Fh2do06aNoyDEknEAAADwNFdddZUMw3B8fv755/X888+f8x7DMFRYWFit58bFxenZZ5/Vt99+q+zsbEVERGjChAmaM2eOmjVrVqd9FhQUaNmyZXr11Vf166+/ys/PT3369NGf/vQn3XzzzRXe88MPP+iNN97Qd999p99++00ZGRkqLCxU27ZtNXDgQN13330aOHCgW9+jIQoJCaEgBAAAgFrl9pJxy5Yt07Fjx9SuXTvt2LFDb7zxhqZMmaJRo0Zp1KhRmjJlitasWaPvvvtO7dq1U0JCgv75z3/WZHZ4COd9hEoGOAAAAIAnMU2zSj/FxcXVet7y5csVHR2tDz/8UP7+/urZs6eOHDmixYsXq1+/fo4Xruqiz9zcXI0YMUKzZ8/W3r171bVrVwUFBSkuLk7jxo3TnDlzKrzv3Xff1ZIlS7R161bl5uaqe/fuioyMVGpqql5//XUNGjRIixYtqvL3aKjCwsIcx8eOHbMwCQAAABoqtwtC7777rgzD0MqVK9WnT59K2/Xu3VuxsbEyTVPr169393HwYM4bpFIQAgAAgKcpLi5268ddu3bt0syZMyVJK1euVEJCgnbv3q3ffvtNffv2VXx8vKZOnVpnfT744IPavn27IiMjtXfvXv3444/69ddf9d5778nPz09LlizRhg0byt03ZMgQrV27VhkZGUpJSdGuXbsUHx+vlJQU3XfffZKkxx57TN98802VvktD5fwiHXtVAQAAoDa4XRAqWSbguuuuO2/b0aNHy9/fX7/++qu7j4MHCw8PdxxnZGRYmAQAAACo/xYtWqTi4mLdfvvtmjZtmmO5unbt2mnNmjXy8vLS+vXrtWfPnlrvMy0tTStWrJAkvfjii+revbvj2pgxYzR79mxJ0vz588s9Mzo6WuPHj1dQUFCp861bt9by5ct10UUXSZLeeecdl79HQ+b8Ih1LbQMAAKA2uF0QKigokK+vr0ttDcOQr6+vCgoK3H0cPJjzm25nzpyxMAkAAABQv2VnZ2vjxo2SpGnTppW73q1bN40YMUKStHbt2lrv8/3331d+fr66du2q4cOHl7s3JiZGkrR7924dOnTIpTzS2TFijx49JEl2u93l+xoy53FTdna2hUkAAADQULldEOrQoYPOnDmjvXv3nrftzz//rNOnT6tDhw7uPg4ezHlgY7fbKQwCAADAo505c0bbtm3T2rVrtXbtWm3btk2nT5+ukb6///575eXlyc/PT/3796+wzZAhQyTJ5aXWqtNnyeeS62W1b99ekZGRVcojnd2XaNeuXZKkyy+/3OX7GrKy4yYAAACgpjVx98bo6Gj9+uuvuvfee/XJJ5/I39+/wna5ubm69957ZRiGRo4c6XZQeC7ngY3NZpPdblerVq0sTAQAAABU3Q8//KC5c+dq48aN5fYI8vLy0jXXXKOFCxfqsssuc/sZBw4ckCR17NhRPj4+Fbbp0qWLJGn//v213mfJvV27dq20/y5duujw4cMu5cnKytLPP/+s+fPn6+jRoxo4cKBuu+228963cuVKxcbGnredJMXHx7vUrr4pO24yTdOxtB8AAABQE9wuCP3tb3/Tyy+/rO3bt6t3796aPXu2oqOj1b59e0lSYmKiPvvsMz399NM6ePCg/Pz89MADD9RYcHiOsm+62Ww2CkIAAADwKC+99JKmT5+uwsJCmaZZ7npRUZE+/PBDffLJJ1qxYoUmT57s1nNOnDghSeX23XFWcu3kyZO13mdN5ElMTNQFF1xQ6lxgYKAef/xx3X///WrS5PzD0pSUFO3evfu87TyZ8x5CNputSsu0AwAAAK5wuyDUuXNn/e9//9Ptt9+ugwcPVrgWtSSZpikfHx/973//U+fOnd0OCs9V0QwhAAAAwFPs2LFDU6dOVXFxsbp166a//e1vGjFihNq3by/DMJSYmKgtW7Zo6dKl+uWXXzRt2jRdcskllS7Pdi65ubmSdM5CgJ+fnyQpJyen1vusiTx+fn4aNGiQJCk9PV1Hjx7VyZMn9eabb2rw4MEaOnToeb9D27ZtFRUVdd520tkZQq7+buqTsi/SZWdnn7MQBwAAAFSV2wUhSZo4caK6du2qRx55RJ9++mm5N+VKlk14/PHH1adPn+o8Ch7MeWBTWFiozMzMcy45AQAAANQnTz75pIqLi3XVVVfpo48+KrdcdufOndW5c2fdfvvtuvbaaxUXF6cnn3xS77zzTpWfVdJ3fn5+pW3y8vIkSQEBAbXeZ03kCQkJ0fbt2x2fT58+raeeekpPPPGERo4cqW3btmnAgAHn/A4xMTGKiYk5Z5sSffv29cjZRM7jpqKiIqWlpVEQAgAAQI2qVkFIkqKiovTxxx8rKytLu3fvVnp6uqSz092joqJYGgwKDg4u9Tk5OdmiJAAAAEDVbd++XYZh6IUXXqh071Tp7EyY559/XhdddFGpAkhVBAYGSvp9qbaKlFwraVubfdZGnpYtW2rx4sXKzMzUypUr9dhjj2nTpk0u3duQBQUFycvLy7E/VWJionr27GlxKgAAADQkXu7eOHnyZE2ePFmHDx+WJLVq1UrDhw/XLbfcoltuuUXDhw+vlWJQXFycrr/+eoWEhCggIEA9evTQ3LlzZbPZLO9zwoQJMgxDhmFo/vz5budpaHx8fNSiRQvH55SUFAvTAAAAAFVjs9nUsmVLde/e/bxte/TooVatWrm9TPKFF14oSUpISFBBQUGFbQ4dOlSqbW32WfL5119/rbT/quYpccMNN0iSdu7cWaX7Gipvb+9SM4KSkpIsTAMAAICGyO2C0CuvvKLXX39dnTp1qsE457Z8+XJFR0frww8/lL+/v3r27KkjR45o8eLF6tev3znfWqvtPt999129/fbbVX5+Y9GmTRvHccksMgAAAMATdOzYUbm5uY6ZG+dSVFSk3NxcXXDBBW49KyoqSr6+vsrLy9OOHTsqbPPFF19I0nmXWauJPq+88kpJqnTGU1JSkuMlwZK2riopTrnye20snJeN40U6AAAA1DS3C0KhoaFq2rSpDMOoyTyV2rVrl2bOnClJWrlypRISErR792799ttv6tu3r+Lj4zV16lRL+szKytJ9992nCy64QH379q3qV2sUnAc2GRkZFiYBAAAAquamm25Sfn6+3n333fO2fffdd5WXl6dx48a59azmzZtr1KhRkqTY2Nhy1w8ePKgtW7ZIksaPH1/rfd54443y8fHRwYMHFRcXV+7elStXSpIuu+yyKu8Tum7dOse9OCs0NNRxfOzYMQuTAAAAoCFyuyDUv39/ZWVl1dk09kWLFqm4uFi33367pk2b5ihEtWvXTmvWrJGXl5fWr1+vPXv21HmfDzzwgJKTk/Xcc8+pefPm7n/JBsx5YJOZmWlhEgAAAKBqHnnkEXXr1k3Tpk3T1q1bK233+eefKyYmRj169NBDDz3k9vPmzp0rwzC0evVqxcbGyjRNSWdnjEyaNEnFxcUaO3asevfuXeq+wYMHq1OnTlq2bFmN9RkWFqaYmBhJ0pQpU7R//37HtQ0bNuipp56SJM2bN6/cM++88059/fXX5WYAHT9+XA888IBeffVVSdL9999flV9Pg+Y8bmKGEAAAAGqa2wWhv/zlL5Iq/sO/pmVnZ2vjxo2SpGnTppW73q1bN40YMUKStHbt2jrtMy4uTi+++KJuvvlmjRkzxqVnN0bh4eGOY2YIAQAAwJOsX79e06dPV1FRkaKjozV06FDNnz9fq1at0n/+8x/Nnz9fw4YN0/Dhw1VcXKyYmBitX79er7zySrkfV/Tr109Lly6VJMXExCgiIkJRUVGKjIzUrl271L17d61atarcfYmJiTp69KhOnTpVY31K0lNPPaUBAwbo8OHDuvjii9WnTx917dpVY8aMUV5enmbNmqUbb7yx3H2vvPKKBg4cqJYtW6p3794aMGCAevToobCwMD377LNq0qSJnn76acZRTpxXVkhLS7MwCQAAABqiJu7eOHz4cC1btkz333+/Tp8+rTlz5igqKqomszl8//33ysvLk5+fn/r3719hmyFDhmjz5s365ptv6qzPnJwcTZ06VS1atNDy5ctd+zKNlPObbllZWRYmAQAAAKrmrrvucqwmYJqmvvzyS3355Zfl2pmmqaysrEpnvBiGoTvuuMOlZ86cOVOXXnqpnn32WX377bdKT09XRESExo8fr4ceesitlQnc7TMgIEBbt27VsmXL9Oqrr+rAgQPy9fXVsGHD9Oc//7nS5fFWr16tuLg4fffdd0pOTtapU6fUtGlTXXLJJRo2bJhiYmJ00UUXVfl7NGTOBSGbzWZhEgAAADREbheEOnfuLEny8fHRunXrtG7dOgUEBKhNmzby9vau8B7DMHTo0KEqP+vAgQOSzm7m6uPjU2GbLl26SFKpJQxqu8+5c+fq0KFDeu6559SuXTuXnttYlR3YFBcXy8vL7QlqAAAAQJ3p2LFjne2d6iw6OlrR0dEutz9y5EiN91nC19dXs2fP1uzZs12+5//+7//0f//3f1V+VmPm/CIdBSEAAADUNLcLQhUNNux2u+x2e6X3uDuIOnHihCQpKCio0jYl106ePFknfe7cuVPLli3TFVdcoXvuucelZ1Zk5cqVFW7sWpH4+Hi3n2M154KQ3W5XTk6OmjVrZmEiAAAAwDWuFFqAmsAMIQAAANQmtwtCL730Uk3mOKfc3FxJZ99Kq4yfn5+ks8u41XafBQUFmjJligzDUGxsbLVmuqSkpGj37t1u3+8pyg5s7HY7BSEAAAAAcFL2RbqCgoJKV7QAAAAAqsrtgtCdd95ZkznOyd/fX5KUn59faZu8vDxJZ9e3ru0+lyxZoj179ujBBx9Ur169XHpeZdq2bevy3kvx8fEuF7zqm+DgYMex3W6XzWYrNdgBAAAAgMau7JJx2dnZCgwMtDARAAAAGhK3C0J1qeQP4JJl3ipScs3VP5bd7fPXX3/V4sWL1blzZ82bN8+lZ51LTEyMYmJiXGrbt29fj51N5Fz8ycvL06lTp6wLAwAAAAD1kPO4qaioSGlpaRSEAAAAUGPcX+usDl144YWSpISEBBUUFFTY5tChQ6Xa1lafP//8s/Ly8pSamqrIyEiFh4eX+vnqq68kSc8884zCw8PVr18/l/I0dGVnAyUnJ1uUBAAAAADqp6CgoFJ77yYlJVmYBgAAAA1NtQtCpmlq3bp1mjBhgiIjI9WsWTM1a9ZMkZGRmjBhgtatW6fi4uJqPSMqKkq+vr7Ky8vTjh07KmzzxRdfSJIGDBhQJ33a7XalpaWV+ykpLtlsNqWlpSkjI8OlPA1dQEBAqaX3KAgBAAAAQGne3t4KCgpyfKYgBAAAgJpUrYJQQkKCBgwYoIkTJ2r9+vU6evSocnJylJOTo6NHj2r9+vWaOHGirrzySh09etTt5zRv3lyjRo2SJMXGxpa7fvDgQW3ZskWSNH78+Frtc+zYsTJNs9KfYcOGSZLmzZsn0zR15MgR179oA9emTRvHcVpamoVJAAAAAKB+ct5HiIIQAAAAapLbBaGsrCwNGzZM3333nUzT1IABA/Too4/qhRde0AsvvKBHH31UAwcOlGma2rlzp4YPH66srCy3g86dO1eGYWj16tWKjY2VaZqSpJSUFE2aNEnFxcUaO3asevfuXeq+wYMHq1OnTlq2bFmN9Qn3BAcHO46ZOQUAAAAA5TkXhI4dO2ZhEgAAADQ0Tdy98fHHH9fRo0cVFBSkN998U9HR0RW2i4uL04QJE3T06FE98cQTWrJkiVvP69evn5YuXar7779fMTExWrx4sYKDg7Vv3z7l5eWpe/fuWrVqVbn7EhMTdfToUZ06darG+oR7nAc2mZmZFiYBAAAAgPrJef/VlJQUC5MAAACgoXF7htA777wjwzC0YsWKSotBkjR8+HCtWLHCsddQdcycOVObNm3S6NGjZbPZtG/fPkVEROjhhx/Wzp07S81AsbJPVCw8PNxxzJJxAAAAAFCe84t06enpFiYBAABAQ+P2DKHExET5+vrq5ptvPm/bm266SX5+fjWy/nF0dPQ5C1BlubKHT1X7PJetW7fWSD8NkfPA5uTJkxYmAQAAAID6yXmGUHZ2toVJAAAA0NC4XRAKDAxUTk6OvLzOP8nI29tb/v7+CggIcPdxaACcBzY2m02macowDAsTAQAAAED9UnbcBAAAANQUtwtCAwcO1DvvvKMDBw7owgsvPGfbAwcOKCsrS1dffbW7j0MD4Dywsdvtys/Pl5+fn4WJAAAAANcdOnRIb731lvbs2aMTJ06ooKCg0raGYeizzz6rw3RoKMqOmwAAAICa4nZBaM6cOfrggw9077336sMPP6z0P+zn5+fr3nvvlY+Pj+bMmeN2UHi+sgMbu91OQQgAAAAeYcGCBVq8eLGKi4tlmuZ52zMTHu5yXmrbZrOpsLBQTZq4PXQHAAAAHNz+q/Lyyy/XW2+9pTvvvFN9+vTR7NmzNXz4cLVv316SlJSUpLi4OD3zzDNKSUnR22+/raioqBoLDs9TdukDu92uwMBACxMBAAAA5/faa69pwYIFkqR27dpp1KhRateuHf+RHrWi7LgpOztbrVu3ti4QAAAAGgy3RzDe3t6O49OnT+vuu+8+Z/uxY8dWeN4wDBUWFrobAx7EeWCTk5Oj06dPOwqIAAAAQH31/PPPS5LGjBmjt956S76+vhYnQkPmPG4qKipSRkYGBSEAAADUCC93bzRNs8Z+0Dg4D2wkKSUlxaIkAAAAgOt+/vlnGYahf//73xSDUOvatGlTasnBhIQEC9MAAACgIXF7hlBcXFxN5kAj0Lx5c/n4+Dg2301OTrY4EQAAAHB+hmGoZcuWateundVR0Ah4e3srKChIx48fl8S4CQAAADXH7YLQsGHDajIHGgHDMNSmTRulpqZKkuN/AQAAgPqsR48e+uGHH5SXlyc/Pz+r46ARCAkJcRSEkpKSLE4DAACAhsLtJeMAdwQHBzuO09PTLUwCAAAAuObuu+9WQUGB1q5da3UUNBJhYWGO48TERAuTAAAAoCGhIIQ65byPUEZGhoVJAAAAANdMnTpVY8aM0YwZM/T5559bHQeNgPO4iSXjAAAAUFPcXjIOcEd4eLjjOC0tzcIkAAAAgGsWLlyo3r1764svvtDw4cM1aNAgXXHFFWrRosU573vsscfqKCEaGueCEOMmAAAA1BQKQqhToaGhjuPMzEwLkwAAAACumT9/vgzDkCSZpqnt27fryy+/PO99FITgLueCUHZ2toVJAAAA0JBQEEKdch7Y2O12C5MAAAAArhk6dKijIATUBecX6Ww2m4VJAAAA0JBQEEKdci4I2Ww2FRUVydvb28JEAAAAwLlt3brV6ghoZHiRDgAAALXBy+oAaFzKDmwY3AAAAABAaWVfpCssLLQwDQAAABoKCkKoU2UHNhSEAAAAAKC0skvGsWwcAAAAagJLxqFOlZ0hlJ2drbCwMAsTAQAAAK7Lz8/Xpk2btHPnTqWnp8swDIWEhKhfv34aOXKkfH19rY6IBsB53FRUVKSMjAy1atXKwkQAAABoCGqsIGSapo4fPy673a6OHTvWVLdoYJwHNqZpKjU1VV26dLEwEQAAAOCa2NhYzZ07V5mZmRVeDw4O1uLFizV16tQ6ToaGpk2bNjIMQ6ZpSpKOHTumrl27WpwKAAAAnq7aS8bt3r1bN998s1q1aqWwsDB17ty51PWTJ08qJiZG06dPV35+fnUfBw/XunVreXt7Oz6npKRYmAYAAABwzYMPPqh77rlHGRkZMk1T7dq1U//+/dW/f3+1a9dOpmkqIyND06dP15w5c6yOCw/n7e2twMBAx+ekpCQL0wAAAKChqFZBaPXq1RowYIDeffddZWdnyzRNxxtMJQIDA3X48GGtWrVKmzZtqlZYeD4vL69SAxsKQgAAAKjvtm3bpqefflqmaWrcuHHat2+fjh07pq+//lpff/21jh07pvj4eI0fP16maerpp5/WF198YXVseDjnfYQoCAEAAKAmuF0Qio+P19SpU1VQUKAZM2Zo586dCg4OrrDtHXfcIdM09d5777kdFA2H87+TtLQ0C5MAAAAA5/f8889LkqZMmaK1a9eqR48e5dp0795db731lqZMmSLTNPXcc8/VdUw0MM4FoWPHjlmYBAAAAA2F2wWhpUuXKj8/X/fdd5+WLVumqKioUkuBORsxYoQk6euvv3b3cWhAnAc2GRkZFiYBAAAAzu+rr76Sl5eXHn/88fO2Xbx4sQzD0JdfflkHydCQOe+/mpycbGESAAAANBRuF4S2bNkiwzD04IMPnrdtu3bt1LRpUyUkJLj7ODQgzgWh1NRUC5MAAAAA55eZmalWrVqV+ju2MmFhYWrdurUyMzPrIBkaMud/b+np6RYmAQAAQEPhdkEoOTlZzZo1U4cOHVxqHxAQoJycHHcfhwaEGUIAAADwJC1atNCZM2eUm5t73rY5OTk6c+aMmjdvXgfJ0JA5zxA6c+aMhUkAAADQULhdEPLz81N+fr5M0zxv25ycHJ06dUqtWrVy93FoQJwHNtnZ2RYmAQAAAM6vV69eKioq0n//+9/ztv3vf/+rwsJC9e7duw6SoSFzHjfZ7XYLkwAAAKChcLsg1KlTJxUUFOjgwYPnbfvRRx+pqKhIF110kbuPQwNSdmDjSlERAAAAsMptt90m0zQ1a9Ysvfjii5W2+89//qNZs2bJMAzdfvvtdZgQDZHzygo2m83CJAAAAGgomrh74zXXXKM9e/bon//8p55//vlK2x0/flyzZ8+WYRi67rrr3H0cGhDngpDNZlNubq4CAgIsTAQAAABU7q677tLq1au1bds2TZs2TQsXLtTw4cPVvn17GYahY8eOKS4uTklJSTJNU1dddZXuvPNOq2PDw5UdNxUVFcnb29vCRAAAAPB0bheE/vrXv+rf//63VqxYodDQUN1///2lrufk5Oidd97RI488oqNHjyo4OFjTp0+vdmB4vrIzhOx2OwUhAAAA1FteXl567733NHnyZK1fv17Hjh3T6tWrS7UpmfU+btw4vfjiizIMw4qoaEDKjptsNptatmxpYSIAAAB4OrcLQmFhYXr99dc1fvx4LVy4UE8++aSKi4slSRdffLF+++03xx5Dfn5+WrNmDX+8QlL5N91sNpvatGljYSIAAADg3Fq2bKm3335b3333nd544w3t3LlT6enpks4u7XX55Zfr1ltvVb9+/SxOiobCedxUWFiozMxMxtQAAACoFrcLQpJ0/fXX6/PPP9ef/vQn7dy503E+Pj7ecXzZZZdpxYoVDIzg4DywKSoqUkZGhjp27GhhIgAAAMA1/fr1Y2yDOtGmTRsZhuGYfZaQkKDOnTtbnAoAAACerFoFIUnq37+/duzYoT179mj79u1KTk5WUVGRwsPDNWjQIF1++eU1kRMNSNnZQElJSerbt69FaQAAAACg/mnSpIkCAwN14sQJSVJycrLFiQAAAODpql0QKtGrVy/16tWrprpDA9akSRO1bt1ap06dkiSlpKRYGwgAAAAA6qGQkBBHQejYsWMWpwEAAICnq7GCEFAVbdq0cRSE0tLSrA0DAAAA/P9eeeUVSVKrVq104403ljpXVXfccUeN5ULjFBYWpv3790uiIAQAAIDqq5WCUFFRkV544QVt2rRJ3t7euu666zRlypTaeBQ8VGhoqA4dOiRJjs14AQAAAKvdddddMgxD3bt3dxSESs5VhWEYFIRQbc77r7JkHAAAAKrL7YLQSy+9pLvvvlvjxo3TW2+9VerapEmTtG7dOkmSaZp67733tGnTJr3xxhvVS4sGIywszHHMDCEAAADUFx07dpRhGGrXrl25c0Bdcy4I8SIdAAAAqsvtgtAnn3wiSbrttttKnd+6davefvttSdKgQYMUEBCgzz77TGvXrtWkSZMcb9mhcQsNDXUcM7ABAABAfXHkyBGXzgF1wXnclJ2dbWESAAAANARe7t74ww8/SDpb9HFWsr721KlT9cUXX+jTTz/VggULZJqmXn75ZbeDloiLi9P111+vkJAQBQQEqEePHpo7d65sNlud9Wm32/XMM8/olltu0UUXXaTg4GD5+PgoODhYV111lV544QUVFBS4nacxcH7T7fTp0xYmAQAAAID6yXncVJ0xLwAAACBVoyCUmZkpPz8/BQcHlzq/efNmGYahGTNmOM7dd999kqSdO3e6+zhJ0vLlyxUdHa0PP/xQ/v7+6tmzp44cOaLFixerX79+OnHiRJ30mZ6err/97W966623lJSUpJCQEPXq1UuGYWjbtm269957NXDgQJ06dapa37chY2ADAAAAT5GQkKCkpCSX2ycnJyshIaEWE6GxYNwEAACAmuR2Qej06dPy9/cvdS4lJUWJiYkKDQ3VxRdf7DgfGBioli1bKiMjw+2gu3bt0syZMyVJK1euVEJCgnbv3q3ffvtNffv2VXx8vKZOnVonfbZs2VL//Oc/tW/fPmVlZSk+Pl67du1Senq63nnnHTVr1kw7d+7UQw895Pb3beicC4l2u12FhYUWpgEAAAAq16lTJ/Xv39/l9oMGDVLnzp1rMREaC+eCkN1uV3FxsYVpAAAA4OncLgi1atVKWVlZstvtjnPbtm2TJA0cOLDCe8oWkKpi0aJFKi4u1u23365p06Y5NnVt166d1qxZIy8vL61fv1579uyp9T6DgoI0Y8YM9ezZs9R5wzA0duxYPfbYY5Kk9evXu/19G7qyb7rxthsAAADqM9M0a7U9UBHnPYQYNwEAAKC63C4IXXLJJZKkt956y3HulVdekWEYGjZsWKm2WVlZOn36tMLDw916VnZ2tjZu3ChJmjZtWrnr3bp104gRIyRJa9eutazPEiWFIudiGUor+6YbvysAAAA0FLm5uWrSpInVMdAAOI+bCgsLlZmZaWEaAAAAeDq3C0KTJk2SaZq67777dM899+imm27Sxo0b5evrq4kTJ5Zq+/XXX0s6W2Rxx/fff6+8vDz5+flVulTDkCFDJEnffPONZX2W2L59uyTp8ssvr9J9jYnzwCY/P18nT560MA0AAABQM5KTk5WRkaE2bdpYHQUNQNl/R8eOHbMoCQAAABoCt19bmzJlit5++21t3rxZsbGxMk1ThmFo8eLF5WYCrV27tsKZQ646cOCAJKljx47y8fGpsE2XLl0kSfv377ekz/z8fB07dkyvvvqqnn32WTVv3lxLlixxKUtj5FwQkqTExERddNFFFqUBAAAAfvf5559r69atpc5lZ2dr4cKFld5jmqZOnTqljz76SKZp6oorrqjllGgMmjRpoqCgIJ04cULS2YIjAAAA4C63C0Le3t7auHGj1qxZo6+++kqtW7fWtddeq0GDBpVql5+fr5SUFA0dOlSjR49261klf/wGBQVV2qbkmqszTWqqz5EjR+qzzz4rde6WW27RvHnzyu0xVJGVK1cqNjbWlciKj493qZ0n8PPzU7NmzRxrYKekpFicCAAAADgrLi5OCxYscOwxKp3dv2XBggXnvdc0Tfn7++uhhx6qzYhoREJCQhzj14SEBIvTAAAAwJNVa2FrLy8v3XbbbbrtttsqbePr66uPPvqoOo9Rbm6uo6/K+Pn5SZJycnLqtM9LL71Uubm5stvtOnLkiE6ePKlNmzapR48eeuyxx+Tlde5V+VJSUrR7926XMjc0wcHBjoJQamqqxWkAAACAszp16lRqdYNt27bJx8dHAwYMqPQeLy8vtWzZUpdcconuvPNOde3atS6iohEIDQ11rFrBknEAAACoDo/Y6dTf31/S2dlGlcnLy5MkBQQE1Gmf//jHP0p9fv/99/WnP/1JCxYs0PHjx7V8+fJz5mjbtq2ioqJcyhwfH+9ywcsThISE6OjRo5KktLQ0i9MAAAAAZ91555268847HZ+9vLwUFBSkuLg4C1OhsXJebpsl4wAAAFAdNVYQ2rt3r3bu3Kn09HRJZ99iuvzyy3XxxRdXu+/AwEBJvy/zVpGSayVtrehTksaMGaPQ0FANGDBAL7zwgv72t7+pY8eOlbaPiYlRTEyMS3337du3Qc0mCgsLcxwzQwgAAAD11UsvveTyi2dATQsNDXUc8yIdAAAAquPc65m54IMPPtCll16qXr16afLkyZozZ47mzJmjyZMnq1evXrr00kv1/vvvV+sZF154oaSz6yUXFBRU2ObQoUOl2lrRZ4krr7xSwcHBKioq0vfff1+lexsTBjYAAADwBHfeeacmTpxY58+Ni4vT9ddfr5CQEAUEBKhHjx6aO3euY9nluuyzoKBATz/9tHr37q1mzZopKChII0aM0Pr16yu95+DBg3rqqac0evRotW/fXr6+vmrVqpX69++vv//978rOznb7ezQmzjOE+J0BAACgOqpVEFq4cKFuvPFG7d27V6ZpytvbW6GhoQoNDZW3t7dM09TevXt10003af78+W4/JyoqSr6+vsrLy9OOHTsqbPPFF19I0jnX9a7tPp0VFhZKkoqLi6t8b2PhPLA5deqUdUEAAACAc8jIyNC//vUvrVmz5rxtX3vtNf3rX/9SZmZmtZ65fPlyRUdH68MPP5S/v7969uypI0eOaPHixerXr985Vzqo6T5zc3M1YsQIzZ49W3v37lXXrl0dS+iNGzdOc+bMKXdPUVGRLrzwQj344IPauHGjiouL1bt3b7Vs2VLfffedHnroIfXp08exhDQq5zxuqk4xEAAAAHC7ILRx40bNnz9fpmlq6NCh+vTTT5Wdna2UlBSlpKQoOztbn376qa666iqZpqlFixbpk08+cetZzZs316hRoyRJsbGx5a4fPHhQW7ZskSSNHz/esj5LfPbZZ44Cx2WXXValexsTBjYAAADwBKtXr9Zf//pX/frrr+dt++OPP+qvf/2rXn/9dbeft2vXLs2cOVOStHLlSiUkJGj37t367bff1LdvX8XHx2vq1Kl11ueDDz6o7du3KzIyUnv37tWPP/6oX3/9Ve+99578/Py0ZMkSbdiwodQ9pmmqZcuWmj17tuLj45WSkqLvvvtOx44d09dff63IyEgdOnRIt9xyS5V/P42N88oKjJsAAABQHW4XhJYuXSpJmjBhguLi4jRy5Ej5+vo6rvv6+mrkyJH67LPPNGHCBJmm6bjHHXPnzpVhGFq9erViY2NlmqYkKSUlRZMmTVJxcbHGjh2r3r17l7pv8ODB6tSpk5YtW1ZjfT777LN67bXXdObMmVLni4qK9M4772jSpEmSpJtuukmdOnVy+zs3dM4FIbvdzmwqAAAA1EslS2CPGzfuvG1vv/12maap9957z+3nLVq0SMXFxbr99ts1bdo0GYYhSWrXrp3WrFkjLy8vrV+/Xnv27Kn1PtPS0rRixQpJ0osvvqju3bs7ro0ZM0azZ8+WpHIrQnh7e+vw4cNasmSJevToUeralVdeqVdffVWS9O233+qHH35w+Xs0RoybAAAAUFPcLgjt3LlThmFo6dKljsFERQzD0LPPPitJ+u6779x9nPr16+coKMXExCgiIkJRUVGKjIzUrl271L17d61atarcfYmJiTp69GiFS5K52+dPP/2k//u//1Pr1q3VrVs3XXnllYqKilJQUJBuvvlmZWRkaNiwYXrppZfc/r6NQdmBTW5uroVpAAAAgIodOnRIhmGoW7du523bvXt3GYbh2I+0qrKzs7Vx40ZJ0rRp08pd79atm0aMGCFJWrt2ba33+f777ys/P19du3bV8OHDy90bExMjSdq9e3ep72wYhoKCgirNNHDgQLVq1UqStH//fpe+R2NVdmWFnJwcC9MAAADAk7ldEMrPz1fr1q3Vvn3787bt0KGDAgMDVVBQ4O7jJEkzZ87Upk2bNHr0aNlsNu3bt08RERF6+OGHtXPnTgUHB9dJnzNmzNDDDz+sQYMGKTc3Vz/88IPi4+MVGBiom2++WWvXrlVcXJxjgIOKlR3YsPwBAAAA6qOMjAy1bt1aPj4+523r6+ur1q1bKy0tza1nff/998rLy5Ofn5/69+9fYZshQ4ZIkr755pta77Pkc8n1stq3b6/IyMgq5ZHOrq5QMj5s2rSpy/c1Rs5LxhUWFiojI8PCNAAAAPBkTdy9sXPnztq/f7/y8/NLLRVXkby8PGVnZ5dbKsAd0dHRio6Odrn9kSNHarzPqKgoRUVFudweFXMuCOXm5iorK6vUOQAAAKA+aNGihU6dOqXc3Fz5+/ufs21ubq5Onz6tli1buvWsAwcOSJI6duxYaQGqS5cuklyfWVOdPkvu7dq1a6X9d+nSRYcPH67STJ/33ntPdrtdTZo00YABA87bfuXKlRXu/VqR+Ph4l3N4gjZt2pT6fOzYMZYmBwAAgFvcLgj98Y9/1MMPP6xXXnlFd9999znbrl69WgUFBfrjH//o7uPQAJWdfZWcnHzOgSYAAABghYsvvlhffPGFPvjgA40fP/6cbTds2KCioiK3X4Y7ceKEJJ1zubWSaydPnqz1Pmsjz+nTp/XAAw9IkqZMmeLSSg8pKSnavXu3S/03NE2aNFFgYKDj95uUlGRxIgAAAHgqtwtCs2bN0kcffaQZM2bIx8dHd955Z4XtXnnlFc2YMUNDhgzRrFmz3A6KhqdZs2by8/NTXl6epLMFIQAAAKC+GTNmjD7//HM98MADGjhwoNq1a1dhu6SkJD3wwAMyDENjx45161kl+2qeaxUGPz8/SXJ5L5nq9FnTeYqKijRp0iQdPnxYnTp10pIlS857jyS1bdvW5VUa4uPjG9w+OyEhIY6C0LFjxyxOAwAAAE/ldkHoySef1NChQ/XTTz9p8uTJmjdvnq666iq1b99ehmEoMTFR27ZtU0JCglq1aqUhQ4boiSeeqLCvxx57zO0vAM9lGIbatGnjKASlpqZanAgAAAAob/r06Vq2bJmOHTumPn366KGHHtL111+viIgISdLRo0e1YcMG/f3vf1dmZqY6dOige++9161nlSxJl5+fX2mbkheqAgICar3PmsxjmqamTZumjz76SIGBgdqwYYPL+67GxMQoJibGpbZ9+/ZtcLOJwsLCHMv3JSQkWJwGAAAAnsrtgtD8+fNlGIaks3/YJyQkaPXq1aXamKYpScrKytKTTz5ZaV8UhBqvkJAQCkIAAACo15o2bap3331X11xzjTIzM/XAAw84ljxzZpqmgoOD9f7776tZs2ZuPSswMFDS70u1VaTkWknb2uyzJvPMmDFD//3vf9WiRQt98sknuuSSS84fHpJK77/KygoAAABwl9sFoaFDhzoKQoC7wsLCHMcUhAAAAFBfRUVFaffu3XrooYf01ltvqaCgoNR1X19f3XrrrXr88cfVvn17t59z4YUXSjo7C6SgoEA+Pj7l2hw6dKhU29rs88ILL9SXX36pX3/9tdL+XcnzwAMP6LnnnlPTpk310UcfqV+/fi5lx1nOBaH09HQLkwAAAMCTuV0Q2rp1aw3GQGMVGhrqOKYgBAAAgPqsQ4cOWr16tVauXKmdO3c6/n5t27atLr/8cpeXcDuXqKgo+fr6Ki8vTzt27NCgQYPKtfniiy8kSQMGDKj1Pq+88kq99NJL2r59e4V9JyUl6fDhw462FXn00Uf17LPPyt/fX++//74GDx7sUm78znncdObMGQuTAAAAwJN5WR0AjZvzm27Hjx+3MAkAAADgmqZNm2ro0KGaOHGiJk6cqCFDhtRIMUiSmjdvrlGjRkmSYmNjy10/ePCgtmzZIkkaP358rfd54403ysfHRwcPHlRcXFy5e1euXClJuuyyy9S1a9dy15944gk9/vjj8vX11bp16xQdHe1SZpTmPG6y2WwWJgEAAIAnoyAESzkPbOx2u4VJAAAAgPph7ty5MgxDq1evVmxsrGNv1pSUFE2aNEnFxcUaO3asevfuXeq+wYMHq1OnTlq2bFmN9RkWFqaYmBhJ0pQpU7R//37HtQ0bNuipp56SJM2bN6/cM//1r3/pkUceUZMmTfTGG2/o2muvdf+X0sgxbgIAAEBNoCAES5V9061kYAoAAADUN3a7Xf/617903XXX6ZJLLlGXLl1KXc/KytLrr7+uNWvWVOs5/fr109KlSyVJMTExioiIUFRUlCIjI7Vr1y51795dq1atKndfYmKijh49qlOnTtVYn5L01FNPacCAATp8+LAuvvhi9enTR127dtWYMWOUl5enWbNm6cYbbyx1T3JysmbOnClJatGihZ599lkNHjy4wp///ve/1fhtNQ6MmwAAAFAT3N5DqEReXp7WrVun7du3KzEx8Zx/nBqGoc8++6y6j0QDUvZNt4KCAvn6+lqYCAAAACjvhx9+0I033qjExETHeMcwjFJtWrZsqccff1y//PKLOnfurCuuuMLt582cOVOXXnqpnn32WX377bdKT09XRESExo8fr4ceekjNmzevsz4DAgK0detWLVu2TK+++qoOHDggX19fDRs2TH/+8581bty4cvfk5+c7fk8nT57Ul19+WWmukSNHVvm7NDbOewjZbDbl5ubW2DKFAAAAaDyqVRD66quvdMsttyg5OVmmaToGRBUNkJyvAyXKvulmt9spCAEAAKBeOX78uK677jqlpKSob9++mjRpkhYuXKgzZ86UamcYhqZMmaIHHnhA7777brUKQpIUHR1dpT13jhw5UuN9lvD19dXs2bM1e/Zsl9p36tSJWSw1yHncVFhY6CjmAQAAAFXh9pJxx44d03XXXaekpCRdeumlmj17tkzTVLNmzfToo4/q7rvvVmRkpEzTVJs2bfToo4/qscceq8nsaACcBzY5OTnlBtUAAACA1ZYtW6aUlBRFR0fr22+/1f3331/p7IzrrrtOkhQXF1eXEdHAtWnTptTnY8eOWZQEAAAAnsztGUJLly5VVlaWRo8erQ8++ECGYeipp55S8+bNtXDhQke7F154QTNmzNCPP/6o9957r0ZCo+FwLgiZpqmUlBRdcMEFFiYCAAAAStuwYYNjvOPlde536rp37y4fHx/9+uuvdZQOjYGPj48CAwN18uRJSVJSUpLFiQAAAOCJ3J4h9Omnn8owDC1YsOCcS8Hdc889WrBggT744INKNylF49WqVSv5+Pg4PicnJ1uYBgAAACjv0KFD8vX1VZ8+fVxq37JlS50+fbp2Q6HRcX6ZjhlCAAAAcIfbBaGEhAR5eXkpKiqq1Pn8/Pxybe+77z4ZhqGXX37Z3cehgTIMQ0FBQY7PFIQAAABQ3xQXF6tJkyYu7YlaXFysM2fOqFmzZnWQDI1JaGio4zghIcHCJAAAAPBUbheETNNUixYtSi2Z0KxZM50+fbrc5qGtWrVSq1at9Msvv7ifFA1WcHCw4zgtLc3CJAAAAEB5bdu2ld1uV3p6+nnbfvPNN8rPz1enTp1qPxgaFeeCUEpKioVJAAAA4KncLgi1b99eWVlZysvLc5zr0KGDioqKFB8fX6qt3W7XqVOnZLfb3U+KBouBDQAAAOqzoUOHSpL+97//nbNdcXGx5s6dK8MwFB0dXRfR0Ig4LxmXmppqYRIAAAB4KrcLQt26dZMkHTlyxHHuiiuukCStWLGiVNulS5fKNE3ekkOFKAgBAACgPpsxY4Yk6fHHH9fWrVsrbHPs2DGNHTtWcXFx8vb21r333luHCdEYOBeEzpw5Y2ESAAAAeCq3C0KjR4+WaZrasGGD49zdd98t0zT1/PPP69prr9Ujjzyi66+/XvPmzZNhGJo0aVKNhEbD4jywycjIsDAJAAAAUF6fPn306KOP6vTp0xo5cqSuvPJKnT59WtLZ/VKHDh2qLl266MMPP5QkPfnkk+rcubOVkdEAOY+bWH0DAAAA7mji7o1jxozR22+/XWozy8GDB+tvf/ubnn76aW3cuFGffPKJYz+hoUOHas6cOdVPjAbHeWBjs9ksTAIAAABUbMGCBQoMDNSjjz6qHTt2OM6vWLHCMeYJCAjQ3//+d/35z3+2KiYaMOeVFRg3AQAAwB1uF4QuuOACxcXFlTu/ZMkSXX311XrjjTd07NgxtWrVStdcc43uuOMONWni9uPQgPGmGwAAADzBzJkzdfvtt+u1117T9u3blZycrKKiIoWHh2vQoEG6/fbbFRYWZnVMNFBlX6T7/9q77/ioqvz/4++ZkEYvASSU0EtokkgzsJiABd0FLIio9Gr5KZZVlhXRFXcXKyvrKlEERUQRUcCGlNCUjoIC0nuHUNPL+f3Bd8aETCqZ3JnJ6/l4zOORufeccz93Tozz4XPvucYY2Ww2CyMCAACAt3FLhaZHjx7q0aOHO4aGD7o6scnIyJCfn5+FEQEAAACuVatWTY899pjzuUJASbn6QrqUlBQFBQVZGBEAAAC8TZGfIQQUl6sTm6SkJAujAQAAAADPk3XJuLS0NJ06dcrCaAAAAOCNKAjBclffIcR62AAAAACQXbVq1bK9P3z4sEWRAAAAwFtd85Jxly5d0tdff62tW7cqPj5eaWlpuba12WyaNm3atR4SPiZrQSgzM1MnTpxg7XUAAAB4HHIfWMnf31+VK1fW+fPnJUnHjh2zNiAAAAB4nWsqCM2YMUOPP/64Ll++7NxmjMnRzmazOR94SVKEq1WtWtX5OyJdSWzatm1rcVQAAADAH8h94AmqV6/uLAgdPHjQ2mAAAADgdYpcEFq0aJGGDRsmY4yCgoLUuXNnhYaGqkyZa77pCKWMn5+fqlSpovj4eElc6QYAAADPQu4DT1GzZk3t3r1bknTo0CGLowEAAIC3KXIG88orr8gYo86dO2v+/PkKCQkpzrhQylSvXt1ZEDpx4oTF0QAAAAB/IPeBp8i63Pbx48ctjAQAAADeyF7Ujps2bZLNZtOMGTNIiHDNatSo4fyZO4QAAADgSch94CmyFoROnjxpYSQAAADwRkUuCKWnp6t8+fJq0qRJccaDUiprQYgr3QAAAOBJyH3gKbLmTRcvXrQwEgAAAHijIheEGjVqpJSUFGVkZBRnPCilsl7pdvr0aQsjAQAAALIj94GnyJo3JSYmWhgJAAAAvFGRC0IPPvig0tLS9N133xVnPCilsiY2ly5dsjASAAAAIDtyH3iKrHlTQkKChZEAAADAGxW5IDRmzBi1b99eDz/8sHbv3l2cMaEUyroWO4kNAAAAPAm5DzxF1iXjEhMTZYyxMBoAAAB4mzJF7Th79mwNGDBAzz//vNq2bat77rlHHTt2VIUKFfLsN3DgwKIeEj7s6qUPjDGy2WwWRgQAAABcQe4DT3H1HUKpqakKDAy0MCIAAAB4kyIXhAYPHuz8B3tjjGbNmqVZs2bl2cdms5EUwaWrE5uUlBQFBQVZGBEAAABwBbkPPEXWvCktLU2nT59WnTp1LIwIAAAA3qTIBaF69epxBweKzdV3CCUmJlIQAgAAgEcg94GnyLrUtiQdOXKEghAAAAAKrMgFoQMHDhRjGAUXFxen119/XevWrdPly5cVFhamvn37auzYsSpXrlyJjHn58mUtXLhQixYt0vr163XgwAFlZmaqdu3auummm/TEE0+oVatW13qqpcrVV7qdOXNGVatWtTAiAAAA4Aqrch/gav7+/qpcubLOnz8vSTp8+LA6depkbVAAAADwGnarAyiMKVOmqHv37vrmm28UFBSkFi1a6MCBA5o4caLat2+v+Pj4EhnzoYce0v33368PP/xQBw4cUJMmTdSkSRMdPXpUH3zwgSIiIjR9+vTiOOVSw9WVbgAAAACA7GrUqOH8+eDBgxZGAgAAAG9T5IJQYmJiccaRr02bNmnMmDGSpKlTp+rQoUPavHmz9u3bp8jISO3YsUMjRowosTHvuOMOffPNNzp//ry2bNmiX3/9VcePH9f999+vtLQ0jRgxQr/99tu1nHKpEhAQoIoVKzrfHz9+3MJoAAAAAMAzZV1d4fDhwxZGAgAAAG9T5IJQrVq19NBDD+nnn38uznhy9dJLLykzM1MDBgzQyJEjnWt4h4aGavbs2bLb7Zo3b562bt3q9jEnT56sr7/+WrfffrsCAgKc26tUqaIZM2aoZcuWysjI0Pvvv18MZ156ZL1LiIIQAAAAAOSU9Q4h8iYAAAAURpGfIXTp0iXFxsYqNjZWkZGRGjVqlPr376+yZcsWZ3ySrjyz5/vvv5ckjRw5Msf+Jk2aKCYmRkuWLNHnn3+uNm3auHXMatWq5Tquv7+/unfvrm3btmnnzp35xoE/1KhRQ/v27ZMkHTt2zOJoAAAAgOz27t2rOXPmaOvWrYqPj1daWlqubW02m5YuXVqC0aG0yHqH0IkTJyyMBAAAAN6myAWh9957T++//77WrVunjRs3atOmTXryySf1wAMPaMSIEWrXrl2xBfnzzz8rJSVFgYGB6tChg8s2Xbt21ZIlS7R27VrLxnRITk6WJLcUx3xZ1ivdjh49amEkAAAAQHYvvviiJk6cqMzMTBlj8m3vWH0AKG5ZC0KXLl2yMBIAAAB4myIXhIYNG6Zhw4bpt99+09SpUzVr1iydP39eU6dO1dSpU3XDDTdo5MiRxXLX0K5duyRJ9erVk7+/v8s2jRo1kqQC35XjjjElKSkpSfPnz5d0paCEgsua2Jw6dcrCSAAAAIA/zJo1Sy+++KKkK8tL33rrrQoNDVWZMkVOp4Aiy3ohXUJCgoWRAAAAwNtccwbTqlUrTZkyRa+99prmzJmj2NhY/fjjj9qwYYM2btxYLHcNxcfHS5KqVq2aaxvHvnPnzlk2piQ999xzOnnypKpXr66hQ4fm237q1KmKjY0t0Ng7duwocBzeKGtB6MKFCxZGAgAAAPzh7bffliT16tVLc+bMyfYcUaCkZc2bKAgBAACgMIrtkrbAwEANGDBAAwYM0M6dOxUbG6uZM2fqzJkzzruGivqsIccSbHklXoGBgZKu3KFj1Ziffvqp3njjDUlXltSrWLFivn2OHz+uzZs3F2h8X0diAwAAAE/022+/yWaz6X//+x/FIFgua96UmJgoYwxLFAIAAKBA3LLGQbNmzfT666/rmWeeUb9+/bRy5UpJcj5r6Omnn9ZDDz2ksWPHFqhoEhQUJElKTU3NtU1KSookKTg4uEAxFveYixcv1qBBgyRJL7/8snr37l2gOGrVqqWIiIgCtd2xY0eBi1Pe6OrEBgAAAPAENptNFStWVGhoqNWhADkupEtLS6NQCQAAgAJxS0Fo9erVeu+99zR37lznnTjlypXTLbfcopUrV+rs2bOaNGmSPvnkEy1btkwNGzbMc7wqVapI+mOZN1cc+xxt81OcY65cuVJ9+vRRamqqxo4dq3HjxhUoBkkaNWqURo0aVaC2kZGRPn030dUFofT0dNZlBwAAgOWaN2+uX375RSkpKc5VBACrZH2GUFpamk6fPq3atWtbGBEAAAC8hb24BoqPj9fkyZPVsmVLdevWTTNnzlRSUpJatmypKVOm6NixY/riiy909OhRffDBB6pdu7YOHz6sZ599Nt+xmzZtKkk6dOiQ0tLSXLbZu3dvtrYlNeaaNWt0xx13KDExUY899pj+9a9/Fej4yOnqK924SwgAAACeYPjw4UpLS9Pnn39udSiAQkJCsr0/cuSIRZEAAADA21xzQWjFihV64IEHVKdOHT311FPasWOHAgICdP/992vVqlXaunWrHnnkEVWoUEHSlWf2DB48WHFxcbLZbFqxYkW+x4iIiFBAQIBSUlK0fv16l21WrVolSercuXOB4i6OMTdt2qSePXvq8uXLGjFihCZPnlygY8O1rIlNSkqKzp8/b10wAAAAwP8ZMWKEevXqpccee8y5HDZgFX9/f1WqVMn5noIQAAAACqrI63G99tprev/997V7925JkjFGjRo10siRIzV06FBVq1Ytz/6NGjXSddddp+PHj+d7rPLly+vWW2/VwoULFRsbq6ioqGz7d+/erWXLlkmS7rnnngLFf61j/vrrr7rlllt04cIFDRgwQO+++y4P8rxGWe8Qkq4kNvXq1bMoGgAAAOCKf/zjH2rbtq1WrVql6OhoRUVFqWPHjs6L3nLz/PPPl1CEKG1q1qypCxcuSJIOHjxocTQAAADwFkUuCD3zzDNXBihTRn/+85/10EMP6eabby7UGPXr15e/v3+B2o4fP15ff/21Zs6cqaioKI0YMUI2m03Hjx9X//79lZmZqT59+qht27bZ+nXp0kVHjhzRmDFjNGbMmGIZc/fu3br55psVHx+ve++9V9OnT5fdXmyr75VaZcuWVXBwsJKSkiRJx44dszgiAAAAQHrhhRecF38ZY7R69Wr9+OOP+fajIAR3qV69unbt2iXpyjLoAAAAQEEUuSAUGhqqESNGaMSIEQoNDS3SGKtXry5w2/bt2+uNN97Qk08+qVGjRmnixIkKCQnR9u3blZKSombNmum9997L0e/IkSM6ePCgy+XHijrmo48+qpMnT0q6cjVWt27dXMZcq1Yt1hkvpJCQEB0+fFiSCnT3GAAAAOBuf/rTn1gNAB4l6+oKXEgHAACAgipyQejQoUMlflfMmDFj1Lp1a73++utat26dTp06pbCwMN1zzz3629/+pvLly5fImCkpKc6f161bl+vYYWFhhY6ntKtRo4azIHT06FGLowEAAACk5cuXWx0CkE3WgpDjYkUAAAAgP0UuCFm1RFr37t3VvXv3Arc/cOBAsY9JQug+NWrUcP5MQQgAAAAAcsqaN128eNHCSAAAAOBNilzVSU1N1aFDh3TixIkc+y5fvqynn35abdu2VUREhMaPH+98LgyQF650AwAAAIC8Zc2bEhISLIwEAAAA3qTIdwi9//77+n//7/9p0KBB+uCDD7Ltu+OOO7R69WoZYyRJW7Zs0apVqxQXF8fa28hT1sQmPj7ewkgAAACAnFJTU7V48WJt3LhRp06dks1mU/Xq1dW+fXv16NFDAQEBVoeIUiBr3pSYmGhhJAAAAPAmRS4ILVq0SJJ0//33Z9u+YMECrVq1Sna7Xffff7+Cg4P10UcfadWqVZo5c6YGDhx4bRHDp5HYAAAAwFPFxsZq/PjxOnPmjMv9ISEhmjhxokaMGFHCkaG0ybpkHHcIAQAAoKCKvGTcjh07JEmRkZHZtn/yySey2Wx69tlnNXPmTMXGxmry5MkyxuiTTz65tmjh81j6AAAAAJ7o2Wef1UMPPaTTp0/LGKPQ0FB16NBBHTp0UGhoqIwxOn36tEaPHq2xY8daHS583NV5U1pamoXRAAAAwFsUuSB0+vRplS1bVlWqVMm2PS4uTpI0fPhw57YBAwZIurJ0HJCXq+8Qciw7CAAAAFhlxYoVevXVV2WM0d13363t27fr8OHDWrNmjdasWaPDhw9rx44duueee2SM0auvvqpVq1ZZHTZ8WNa8KS0tLde71gAAAICsilwQSkhIkN2evfuBAwd0+vRp1a1bVw0aNHBuL1eunCpXrswzYZCvq690S0pKsjAaAAAAQHr77bclScOGDdPnn3+u5s2b52jTrFkzzZkzR8OGDZMxRv/9739LOkyUIiEhIdneHzlyxKJIAAAA4E2KXBCqWrWqLl++rPPnzzu3LVu2TJJ044035mifnp6u8uXLF/VwKCWyFoSSkpJ06dIlC6MBAAAApJ9++kl2u10vv/xyvm0nTpwom82mH3/8sQQiQ2kVEBCgypUrO98fPnzYumAAAADgNYpcEIqIiJAkTZs2TZKUmZmpadOmyWazKTo6Olvb06dP6/Lly7ruuuuuIVSUBlkLQpJ07NgxiyIBAAAArjhz5owqVaqkGjVq5Nu2Zs2aqly5Mkt4we2y5k4HDhywLhAAAAB4jSIXhAYPHixjjMaOHauePXuqQ4cOWrNmjcqXL6++fftma+tYP7tFixbXFi18XoUKFRQQEOB8z9IHAAAAsFqFChV06dIlJScn59vWcZc7qyPA3bIWhA4dOmRhJAAAAPAWRS4I3XvvvRoyZIgyMjK0aNEibd68WUFBQXr33Xez3bouSZ999pnLO4eAq9lsNlWrVs35/vjx4xZGAwAAAEht2rRRRkaGPvjgg3zbfvDBB0pPT1fbtm1LIDKUZlnvWGNlBQAAABREmWvpPG3aNA0dOlQ//fSTKleurB49eqhBgwbZ2qSmpqpSpUoaOHCgbr/99msKFqVD9erVnYWgo0ePWhwNAAAASrsHHnhAcXFxeuqppxQYGKhhw4a5bPf+++/rqaeeks1m04ABA0o4SpQ2We8QOnnypIWRAAAAwFtcU0FIkqKiohQVFZXr/oCAAMXGxl7rYVCKZL3SjSXjAAAAYLXBgwdr5syZWrFihUaOHKl//OMfio6OVu3atWWz2XT48GHFxcXp6NGjMsbopptu0qBBg6wOGz4ua0Ho4sWLFkYCAAAAb3HNBSGguGVNbE6cOGFhJAAAAIBkt9s1f/58DR06VPPmzdPhw4c1c+bMbG2MMZKku+++W9OmTZPNZrMiVJQiWS+kS0xMtDASAAAAeAsKQvA4WQtCZ86csTASAAAA4IqKFStq7ty52rBhgz799FNt3LhRp06dknTlH+ZvuOEG3XfffWrfvr3FkaK0yJo3JSQkWBgJAAAAvAUFIXgcEhsAAAB4qvbt25dY0ScuLk6vv/661q1bp8uXLyssLEx9+/bV2LFjVa5cuRIdMy0tTZMnT9bHH3+sPXv2KDAwUNdff70effRR3XXXXS77JCUl6YcfftCGDRu0ceNGbdy4UWfPnpUkXbp0SeXLly/SOeAK8iYAAAAUFgUheJysiQ1LHwAAAKA0mjJlih5//HEZY1SnTh3VrVtX27dv18SJE/XFF19o9erVqlq1aomMmZycrJtvvlmrV6+Wn5+fWrZsqYSEBMXFxSkuLk7PPvus/v3vf+fot3PnTvXp06eoHwHycXXelJ6erjJlSPEBAACQO7vVAQBX40o3AAAAeJLU1FRt3bpVv//+e75tf//9d23dulVpaWlFPt6mTZs0ZswYSdLUqVN16NAhbd68Wfv27VNkZKR27NihESNGlNiYzz77rFavXq0GDRpo27Zt2rJli/bs2aP58+crMDBQkyZN0sKFC3P08/f3V4cOHfTII49o+vTp+vbbbwsVM/KW9RlCqampLLcNAACAfFEQgse5+kq3a0mmAQAAgGv12WefqV27dpo8eXK+bV9++WW1a9dOc+fOLfLxXnrpJWVmZmrAgAEaOXKkbDabJCk0NFSzZ8+W3W7XvHnztHXrVrePefLkSb377ruSpGnTpqlZs2bOfb169dIzzzwjSXrhhRdyHLNly5Zat26d/vvf/2rw4MFq0aJFoT4H5C0kJCTb+8OHD1sUCQAAALwFBSF4nKsLQpcvX7YwGgAAAJR2X3zxhSRpwIAB+bYdNmyYjDFFLghdvnxZ33//vSRp5MiROfY3adJEMTExkqTPP//c7WMuWLBAqampaty4saKjo3P0HTVqlCRp8+bN2rt3b4HiQfEICAhQpUqVnO+PHDliYTQAAADwBhSE4HGyFoQyMzN1/PhxC6MBAABAaffbb79Jktq2bZtv28jISEnSr7/+WqRj/fzzz0pJSVFgYKA6dOjgsk3Xrl0lSWvXrnX7mI73jv1Xq127tho0aFCoeFB8si4bd+DAAesCAQAAgFegIASPU6VKFfn5+TnfHz161MJoAAAAUNodO3ZMlSpVUvny5fNtW6FCBVWuXLnIFzXt2rVLklSvXj35+/u7bNOoUSNJ0s6dO90+pqNv48aNcx2/sPGg+GS9mO7QoUMWRgIAAABvUMbqAICr2e12Va1aVadPn5ZEQQgAAADWCggIUFJSUoHaGmOUlJSUa+ElP/Hx8ZKkqlWr5trGse/cuXNuH9Md8RTF1KlTFRsbW6C2O3bscFscniZrQejYsWMWRgIAAABvQEEIHql69eoUhAAAAOARGjRooK1bt2rNmjXq3Llznm1/+uknpaSk5HlHTV6Sk5MlXSlC5SYwMFCSClykupYx3RFPURw/flybN2922/jeKuuScfv27bMwEgAAAHgDCkLwSDVq1ND27dslSYcPH7Y4GgAAAJRmN998s7Zs2aKxY8dq6dKlKlPGdRqVnp6uv/3tb7LZbLrllluKdKygoCBJUmpqaq5tUlJSJEnBwcFuH9Md8RRFrVq1FBERUaC2O3bscGtxylOcOXNGP/zwg/P9xo0bFRMTozlz5igkJMTCyAAAAOCpKAjBI2Vd+uDEiRMWRgIAAIDS7rHHHtN///tfrV69Wj169NCbb76pdu3aZWuzefNmPfHEE1q9erWCgoL0+OOPF+lYVapUkfTHUm2uOPY52rpzTHfEUxSjRo3SqFGjCtQ2MjKyVNxNdO+99+a4eG7lypXq16+fli5dalFUAAAA8GR2qwMAXMl6RdupU6csjAQAAAClXZ06dTR16lRJ0qpVq3TDDTeodu3auvHGGxUVFaXQ0FC1b99eq1atks1mU2xsrOrVq1ekYzVt2lSSdOjQIaWlpblss3fv3mxt3Tmm4/2ePXtyHb+w8eDabd++XXFxccrMzMy2PSMjQ8uWLXOutgAAAABkRUEIHinrHUIJCQkWRgIAAABIAwYM0MKFCxUWFiZjjI4fP661a9dqzZo1OnHihIwxatiwob755hs9+OCDRT5ORESEAgIClJKSovXr17tss2rVKknK93lGxTFmp06dJEmrV6922e/o0aPav39/trZwv7wKdAXZDwAAgNKJghA8EgUhAAAAeJrbb79du3fv1g8//KAXXnhBo0eP1ujRo/Xiiy9q8eLF2rVrl2677bZrOkb58uV16623SpJiY2Nz7N+9e7eWLVsmSbrnnnvcPmbv3r3l7++v3bt3Ky4uLkdfx51T7dq1U+PGjQsUD65dfp81cwEAAABXKAjBI2UtCCUmJloYCQAAAPAHPz8/9ejRQ88//7z+97//6X//+5/Gjx+v7t27y24vnvRq/PjxstlsmjlzpmJjY2WMkSQdP35c/fv3V2Zmpvr06aO2bdtm69elSxfVr19fkydPLrYxa9as6Xx2z7Bhw7Rz507nvoULF+qVV16RJE2YMKFYzh0FEx4erujoaPn5+eXY17lzZ4WHh1sQFQAAADwdBSF4pKvvELp6bWwAAADAV7Vv315vvPGGJGnUqFEKCwtTRESEGjRooE2bNqlZs2Z67733cvQ7cuSIDh48qPPnzxfbmJL0yiuvqHPnztq/f79atmyp66+/Xo0bN1avXr2UkpKip556Sr1793bZNyIiQiEhIQoJCVFERIRze/369Z3bc+uLvM2ZM0fdunXLsT0mJsaCaAAAAOANKAjBI119hxDLxgEAAKA0GTNmjBYvXqyePXsqISFB27dvV1hYmMaNG6eNGzcqJCSkxMYMDg7W8uXLNWnSJIWHh2vXrl06c+aMunXrprlz5+q1117L9Zjx8fE6e/aszp49q3Pnzjm3O7adPXtWFy5cKPS5QAoJCdHSpUu1bds2NW3a1Ll90aJFSk9PtzAyAAAAeKoyVgcAuJK1IJSenq41a9bolltusTAiAAAAoGR1795d3bt3L3D7AwcOFPuYDgEBAXrmmWf0zDPPFKpfQWLCtQkPD9fYsWM1dOhQSdLWrVu1devWbHdkAQAAABJ3CMFDOdY0d7j11lsVExOjM2fOWBQRAAAAAHimfv36KTg4WJKUmpqq6dOnWxwRAAAAPBEFIXikBx54IMe2lStXql+/fhZEAwAAAACeq2zZsrr99tud77///nslJiZaGBEAAAA8EQUheJzt27crLi4ux/aMjAwtW7ZM27dvtyAqAAAAAPBcjzzyiPPnvXv3avny5dYFAwAAAI9EQQgeZ8+ePde0HwAAAABKm27duqlmzZqSrizBzbJxAAAAuJrXFYTi4uL05z//WdWrV1dwcLCaN2+u8ePHKyEhoUTH3LhxoyZPnqwHH3xQzZo1k91ul81m03//+98ix4ErGjdufE37AQAAAKC0sdvtuv/++53vV6xYwTNYAQAAkI1XFYSmTJmi7t2765tvvlFQUJBatGihAwcOaOLEiWrfvr3i4+NLbMzhw4friSee0KxZs7Rr1y4ZY6719PB/wsPDFR0dLT8/v2zbbTaboqOjFR4eblFkAAAAAOC5HnroIefPp0+f1rx58yyMBgAAAJ7GawpCmzZt0pgxYyRJU6dO1aFDh7R582bt27dPkZGR2rFjh0aMGFFiYzZs2FD9+vXTq6++qri4OHXq1OlaTg9XmTNnjrp165ZtW3BwsCZPnmxNQAAAAADg4Zo0aaLWrVs733/yySdcvAgAAAAnrykIvfTSS8rMzNSAAQM0cuRI2Ww2SVJoaKhmz54tu92uefPmaevWrSUy5rx58/Tpp5/q6aef1k033aTAwMDiOVFIkkJCQrR06VK9+OKLzm0pKSnatm2bhVEBAAAAgGcbPny48+cNGzZo3759FkYDAAAAT+IVBaHLly/r+++/lySNHDkyx/4mTZooJiZGkvT5559bNiaK39/+9jdVrFhRkpSRkaHZs2crPT3d4qgAAAAAwDMNGDBA/v7+kqTExER9+OGHFkcEAAAAT+EVBaGff/5ZKSkpCgwMVIcOHVy26dq1qyRp7dq1lo2J4ufv76/bb7/d+X7Dhg3atWuXhREBAAAAgOeqUqWKoqOjne+//PJLLqoDAACAJC8pCDkKAPXq1XNe6XS1Ro0aSZJ27txp2ZhwjyeffNL584kTJzR//nwLowEAAAAAz/bII484f96xY4c2btxoYTQAAADwFGWsDqAg4uPjJUlVq1bNtY1j37lz5ywbsyimTp2q2NjYArXdsWOH2+LwZO3bt1fjxo21Z88eSdKCBQv02GOPqVy5chZHBgAAAACep2fPnqpcubLOnz+vjIwMTZs2TZ06dbI6LAAAAFjMKwpCycnJkqSAgIBc2wQGBkqSkpKSLBuzKI4fP67Nmze7bXxfMWDAAE2YMEGStGXLFm3evNm5pB8AAAAA4A/+/v66++67NW3aNEnS999/r8TERJUtW9biyAAAAGAlrygIBQUFSZJSU1NzbZOSkiJJCg4OtmzMoqhVq5YiIiIK1HbHjh1uLU55skcffVQTJ05UWlqakpKS9PHHH1MQAgAAAIBcPPLII86C0JEjR/Tdd9/p7rvvtjgqAAAAWMkrCkJVqlSR9Mcyb6449jnaWjFmUYwaNUqjRo0qUNvIyMhSezdR1apV9ac//UlLly6VJMXFxenkyZOqWbOmxZEBAAAAgOe5/vrr1bBhQ+3bt0+SNGPGDApCAAAApZzd6gAKomnTppKkQ4cOKS0tzWWbvXv3ZmtrxZhwr6wPRt2zZ4+WLVtmYTQAAAAA4LlsNpuGDBnifL9q1SqdPn3awogAAABgNa8oCEVERCggIEApKSlav369yzarVq2SJHXu3NmyMeFevXv3VkhIiCTJGKOPP/5YmZmZFkcFAAAAAJ5p2LBhstlskqQLFy5o1qxZFkcEAAAAK3lFQah8+fK69dZbJUmxsbE59u/evdt5t8g999xj2ZhwL7vdnm0u1q1b57yLCwAAAACQXa1atXTjjTc633/66acyxlgYEQAAAKzkFQUhSRo/frxsNptmzpyp2NhY55fY48ePq3///srMzFSfPn3Utm3bbP26dOmi+vXra/LkycU2JqzzxBNPOH8+e/as5syZY2E0AAAAAODZsj6z9ueff9auXbssjAYAAABW8pqCUPv27fXGG29IuvKFNiwsTBEREWrQoIE2bdqkZs2a6b333svR78iRIzp48KDOnz9fbGNK0iuvvKKQkBDn68cff5QkjR07Ntv2w4cPF9MnAOnK85yyFui+/PJLJScnWxgRAAAAAHiuu+++W8HBwZKk1NTUXHNcAAAA+D6vKQhJ0pgxY7R48WL17NlTCQkJ2r59u8LCwjRu3Dht3LjR+XyZkhgzMTFRZ8+edb7S09MlSQkJCdm2Z2RkXNM5I6fhw4c7f/7111+1adMmC6MBAAAAAM9VtmxZ9erVy/l+/vz5zvwVAAAApUsZqwMorO7du6t79+4Fbn/gwIFiH1OSXnjhBb3wwguF6oPiMXToUP31r39VcnKyUlNTNWPGDEVFRVkdFgAAAAB4pIceekifffaZJGnv3r368ccf1a1bN4ujAgAAQEnzqjuEAOnKFW633Xab8/2SJUsUHx9vYUQAAAAA4Lm6du2q6667TpJkjNHzzz+v7du3WxwVAAAAShoFIXilMWPGOH8+cOCAvv/+e+uCAQAAAAAPZrfbdc899zjfr1y5Ui1btlRMTIzOnDljYWQAAAAoSRSE4JX+9Kc/qXbt2s73H330kYwxFkYEAAAAAJ5rw4YNObatXLlS/fr1syAaAAAAWIGCELySzWbTwIEDne/Xrl1boOdFAQAAAEBps337dq1bty7H9oyMDC1btozl4wAAAEoJCkLwWo8++qjs9iu/whcuXNDMmTMtjggAAAAAPM+ePXuuaT8AAAB8AwUheK3Q0FB17tzZ+X7evHlKS0uzMCIAAAAA8DyNGze+pv0AAADwDRSE4NUefvhh58/btm3T+vXrLYwGAAAAADxPeHi4oqOj5efnl2NfzZo11aJFCwuiAgAAQEmjIASvds8996hChQqSpPT0dE2bNs3iiAAAAADA88yZM0fdunXLsT0+Pl7Lly8v+YAAAABQ4igIwasFBATorrvucr7/4YcfdOnSJQsjAgAAAADPExISoqVLl2rbtm16//33FRgYKElKS0vTmDFjlJKSYnGEAAAAcDcKQvB6jz/+uPPno0ePasKECdq+fbuFEQEAAACAZwoPD9ewYcM0btw457atW7dq0qRJFkYFAACAkkBBCF6vXbt2atiwofP9m2++qZYtWyomJkZnzpyxMDIAAAAA8Ex///vf1axZM+f7N954Q/v27bMwIgAAALgbBSH4rJUrV6pfv35WhwEAAAAAHsfPz08zZsyQ3X7lnwUuXLigRx55RMYYiyMDAACAu1AQgtfbvn27yyvZMjIytGzZMpaPAwAAAAAXOnXqpAcffND5/ocfftCcOXMsjAgAAADuREEIXm/Pnj3XtB8AAAAASqu3335bISEhkqTMzEw988wzunTpksVRAQAAwB0oCMHrNW7c+Jr2AwAAAEBpVb58eb311lvO94cOHdKzzz5rYUQAAABwFwpC8Hrh4eGKjo6Wn59fjn2BgYEKCgqyICoAAAAA8A79+/dXt27dnO8/+OADbdiwwcKIAAAA4A4UhOAT5syZky2BcUhJSdHw4cOVnJxsQVQAAAAA4B0++ugj58V0KSkpGjJkiL766iueyQoAAOBDKAjBJ4SEhGjp0qXatm2bvvjiC7Vt29a5Ly4uTs8//7yMMRZGCAAAAACeq169evrb3/7mfL9t2zbdeeedatmypWJiYnTmzBkLowMAAEBxoCAEnxIeHq677rpL33zzjWrWrOncPmXKFC1cuNDCyAAAAADAs/39739XcHBwju0rV65Uv379LIgIAAAAxYmCEHxS7dq1NWPGDPn7+0uSkpOT9fDDD+vw4cMWRwYAAAAAnmnnzp1KSkrKsT0jI0PLli1j+TgAAAAvR0EIPuu2227TM88843x/9OhRDRw4UKmpqRZGBQAAAACeac+ePde0HwAAAJ6NghB82ksvvaSYmBjn++XLl+uhhx7SggULuLoNAAAAALJo3LhxnvsXLlzIs1kBAAC8GAUh+DSbzaa5c+eqdu3azm0ffPCBevfuzcNRAQAAACCL8PBwRUdHy8/Pz+X+999/Xw8++KAyMjJKODIAAAAUBwpC8HlVqlTRnDlzZLPZcuzj4agAAAAA8Ic5c+aoW7du2bbZ7X/808Enn3yi7t27a+PGjay8AAAA4GUoCKFUqFy5ssulDXg4KgAAAAD8ISQkREuXLtW2bds0f/58bdu2TV9//bVCQkKcbVasWKH27duz8gIAAICXoSCEUiG/h5+OHTtWp06dcr7fvn07V7sBAAAAKLXCw8PVq1cvhYeHq2fPnlq2bJkaNGjgsi0rLwAAAHgHCkIoFQrycNTmzZtr8ODBuvHGG9WyZUuudgMAAACA/9O6dWtNnz7d5T5WXgAAAPAOFIRQKuT3cFRJOnfunD788EOtWbMm23audgMAAAAA6cKFC3nu37VrVwlFAgAAgKKgIIRSw9XDUevXr69WrVrJZrPl2o+r3QAAAAAg/5UX/vWvf+ny5cslFA0AAAAKi4IQSg1XD0fdu3evZs2apfHjx6thw4Z59s/vOUQAAAAA4MvyW3lh/fr1atWqlX7//XeeywoAAOCBylgdAFDSwsPDFR4e7nzfpk0btWrVSm3atNE999yTa7/4+PiSCA8AAAAAPNacOXPUr18/LVu2zLktKChIycnJkqSDBw+qZcuWyszMdO6Pjo7WnDlzFBISUuLxAgAA4A/cIQRIstvtuvvuu3XTTTflerXb6NGjNW7cOP36669c6QYAAACgVHK18sLx48d18803O9tkLQZJOZ/Lyt1DAAAA1qAgBGTx+eef53jOkENKSor+9a9/qU2bNurdu7datmypmJgYnTlzpoSjBAAAAABrhYeHq1evXgoPD1flypX1/fffa8iQIS7bOp7LOnXqVEVHR6tly5bkVAAAABagIARkcfXVbosWLdIDDzyggIAAl+1XrFihvn37ZtvG1W4AAAAAShu73a4+ffrk2Wb06NFavnx5tm3cPQQAAFByeIYQ4ELW5wx169ZNTZs21YQJE3K0y8zM1PLly9WqVSv96U9/0tq1a/Xzzz8791+9Vvb27du1Z88eNW7cONtzjAAAAADA2zVu3LjQfRx3D02ZMkVffPGFVqxY4dzHs4cAAACKF3cIAfkIDAzU9ddfn2ebbdu26Z133slWDJKu3EF077336syZM4qJicl1aQSuggMAAADg7cLDwxUdHZ3juaw2m01lyuR9Pepjjz2WrRgkcfcQAABAcfO6glBcXJz+/Oc/q3r16goODlbz5s01fvx4JSQklPiYaWlpevXVV9W2bVuVK1dOVatWVUxMjObNm1fkWOCZinKlm3TlDqK4uDiFhYW5XBrhzjvvzLNQ5E4kUwAAAJ7NV3Kfffv2aejQoapTp44CAwNVt25dDRs2TPv37y/yecBzzZkzJ8dzWW+66SZ9+eWXhR7LcffQmDFj1KlTpzzzprzyG3IfAACA/2O8yFtvvWVsNpuRZOrUqWPatWtnAgMDjSTTokULc/bs2RIbMykpyXTp0sVIMn5+fqZNmzamUaNGRpKRZJ599tlrPd0cIiIijCQTERFR7GMjf9HR0cbPz885x5KM3W43NWvWNE2bNs22vTAvx++f4+Xn52diYmKcx922bZuZP3++2bZtW46YirLv9OnTJjo6Otsxo6OjzenTp4v/QwMAALhGpfU7sK/kPj/99JMpX768kWSqVKliIiMjTeXKlY0kU6FCBbNu3bpCn0d+SuvvjKdxlY+4yqlsNpvx9/cvdB5lt9tN69atzZIlS0xUVJTL/KYguU9R8y0AAABPUZjvv15TENq4caOx2+3GZrOZqVOnmszMTGOMMUePHjWRkZFGkrnrrrtKbMzHHnvMSDINGjQwv//+u3P7/PnznUnVggULini2rpHYWOv06dMmJiYmWzIRExNjDh8+bBYsWFDkglBur4cffthcf/31hU5s8kt6XCVhhSlCFUR+/UmsAABAQZXG78C+kvskJCSYWrVqGUlm6NChJikpyRhzpcA0ZMgQZ2EqMTGxUOeSn9L4O+Mtcsupvv/++2LPpypUqGAqV66c4wI8u91uoqKizKlTp4qcUzlcSzGJnAkAABQXnywI9e7d20gyAwcOzLFv165dxm63G0lmy5Ytbh/zxIkTJiAgwEgyy5Yty9F3/PjxbklASGw8Q25fzHO7g6hGjRrFmtgEBQWZ4ODgHNttNpupVauWqVevnvN3N+u+kJAQ07JlyzzHnj17trnpppuKnPjklzgVR2JVmDYAAMD7lcbvwL6S+7z55ptGkmncuLFJSUnJti8lJcV5l9Fbb71V4PMoiNL4O+NtCnP3ULly5Yq9WOQY29W2SpUquTymzWYzYWFh5r333jNffPGF6dSpU5GKScWVM+X2ORZmf0m3AQAA7uFzBaFLly45rzxbvXq1yzY9evQwksxzzz3n9jFjY2OdiY0rR44ccX5x27NnT4HiKQgSG8+W29Vup0+fNt26dcuR3HjLy263m1atWpnly5ebVatW5ViOISoqymzatMls27bN3HDDDTmKUX5+fqZDhw5mx44dplOnTnnenVSQ5Keki0q+2sbKdlaP6antPfUYJdnHG451Lf2Kq7+7xrJifHgHq38PStt3YF/KfW688UYjyUycONFl34kTJxpJpkuXLgU6j4Iqbb8zviK3fGr16tWW50QFfQUHB+dawKpYsaKJiIjItdhUo0YNM3z4cFO3bl2XdzV17NjRHDt2zCQlJRVLUakk2xjjvfmQL+ZC3pxj0adk+1xLv2vtWxz9i3scq8aHd7D698DnCkIrV640kkxgYKBJTU112ebFF180kkyPHj3cPubQoUONJDNkyJBcx2/QoIGRZD7++OMCxVMQJDbewdUfgNySm6ioKJdXwZUtW9byZKakX7169XJ5d5PdbjfNmzc3//vf/8ybb75pGjZs6DJBatOmjVm5cqVZsWKF6dy5s8ckP57Wxsp2Vo/pqe099Rgl2ccbjnUt/Yqrv7vGsmJ8eAdP+T0obd+BfSX3SU9Pd95ZtGLFCpf9VqxY4YwrPT29QOdSEKXtd8bXuMqnclv2ul27dsWak3jLy9XdTY7/lmrXrp3rM5mqVq1q7r//fvPoo4+asLAwl7lX69atzYIFC8zXX39t2rRp47JNZGSk2bBhg9m8ebNp3769y7m56aabTGZmptfmQ76YC3lzjkWfku1zLf2utW9x9C/ucawaH97BU34PCvP912aMMfJw06ZN0/Dhw9WkSRPt2rXLZZtZs2bpwQcfVN26dXXo0CG3jtm1a1etXr1aL7/8ssaNG+ey780336wlS5Zo/Pjx+sc//lGAs8xfZGSkNm/erIiICG3atKlYxkTJ2r59u/bs2aPGjRsrPDxcZ86cUb9+/bRs2TJnm5iYGL344ovq2rVrsR+/e/fu2rFjh44fP66r/9O32+3KzMws9mN6CpvNpoCAANlsNqWmpro8V5vNJj8/P2VmZub6WZQpU0YhISEKDAzUqVOnlJSUlGOMKlWqqHPnzvL399ePP/6oM2fOZPu8bTabatasqVtvvVWStGjRIp08eTJHm2rVqql9+/ZKT0/Xhg0bdP78+RzxVKxYUeHh4ZKu/H5dvHjRZTxRUVEqU6aMypQpoxUrVuj06dM5jle7dm3de++98vPzk5+fn2bPnq1Dhw7laBcWFqYBAwbIXLmoQDNnztThw4dztKtTp47uuusu5+dpjNFXX32V4/fPcey+ffvKz89Pdrtdn376qcsx69evryFDhmQ7x+nTp+vAgQN5xuk4fmZmpmbPnu1y7Lp166pfv36y2Wyy2WzO7Z988onL9mFhYXrwwQed7x1mzpypgwcPumw/aNCgbNscZsyYkWufBx54INs5GGM0e/ZsHTlyxOU53H///c5zsNvtzp8/+ugjl8fI+ple/Xdh+vTpucaVdR6ynktu85HXcYwxuX4G9evX1+DBgwt8LLvdrvr162vo0KHKzbRp00q0X3H1d9dYVowP7+Dq98DPz0/dunXT0qVLSyyO0vYd2Fdyn71796px48aSpKNHjyo0NDRHv6NHj6pOnTrO9g0bNsz3XAqitP3OlAa55U2fffaZ7r33Xq1cuVIZGRnOfY6/VcYYl/uaNWum7du3FymWKlWq6Ny5c0U/mVLE8T3UVW5Vvnx5hYeHq0yZMtq2bZsuXryY43tg1apV1alTJ0nS2rVrFR8fn+N7ZLly5VSnTh0lJSXp+PHjSktLcxlHQECA7Ha77Ha7kpOTs/1OOJQtW1aNGzeWv7+/du/e7TK3qlGjhu644w4FBATI399fX331lcvv5fXq1VP//v2VkZGhjIwMZWZm6vPPP9exY8dytK1evbpuuukmpaenKz09XT/++GOOc7XZbAoJCVGPHj3k7+8vf39/BQQEaMGCBS7HDAsL08CBA7PlNrl9384aa2ZmpjIyMnKNNTQ0VL1793aO6xh73rx5LtvXqVNH/fr1c753vHLLyerVq+fMsa728ccfu8xP69Spo7vvvtuZdzrOIzMzU19//bVOnDiRo0+tWrV05513OnPfrK9Zs2a5PE79+vU1aNAgZ04mKVtOnFfufPXnZbPZNGPGjFzzJsdxsh5Dkj766KMCHSfr5+0qp3N37uMpeRM5E0qCV+ZM+ZaMPMArr7xiJJmOHTvm2ubbb781kkz58uXdPmZ4eLiRZN55551c+957771Gknn00UfzjOPdd981ERERBXo5nhvDlW6+pzBXwcXExOS6Lzo62uXydFdfoXX13Uo33XST+eqrr7Jtu/rlWGYkt1duV6kVdD8vXrx48eLFy3teJbkUQmm728NXcp/169c7f1+SkpJc9ktMTHS22bhxY57nQN4EYwq3GsPp06fz3FeUfCsmJsZs3bq1yH87+/Tpk+d+x39vvHjx4sWLFy/vf3lqzlRGXiA5OVmSFBAQkGubwMBAScpxtb47xizOeI4fP67NmzfnHzB8Wnh4uPMuD4c5c+bkuAquW7du+uyzzySpSPscVxUtXbo0x91KkhQdHe3yCrquXbtq0qRJ6tixY67n8MMPP2jChAlat25djv4dO3bUxx9/rP79+2vjxo3Z9ttsNl133XWqX7++1qxZk+v4devWlZ+fnw4cOJBrm4CAAKWmpua6HwAAFI89e/bk+O6C4uEruY+jX159Hf1cHfdq5E2QXOdNeeU3knLdV9R8KyQkJNe8Ka+7krp166Yvv/xSMTExue5funRprvs7d+6sadOmad26dRo4cGCun9HIkSMVGxub6/4uXbro4sWL2rp1a65tqlatqszMTJcrFDgEBgYqMzPT5R05AADAc3MmrygIBQUFSVKe/9CbkpIiSQoODnb7mMUZT61atRQREZF/wJJ27NhR4KQP3q+oiU1++xwKW4TKL/Hp0aOHrr/++jz7f/311zn2R0dHO/fnlxxJyrPNf/7zH7Vu3TrXzzQ2NlaZmZkaPXp0rm2mTZsmu92eY1myrCZOnKikpCS9/PLLubbp2bOn0tPTtXjx4lzbdOjQQZK0fv36XNvcdttt8vf318KFC3NtM2DAAGVmZmrWrFl5jhMcHKz4+HitWLEi13atWrVSUFCQEhIStGPHjlzbNW/eXEFBQUpOTtbvv/+ea7vWrVurXLlystlsSkpK0i+//JJnW8ex81rCo2nTps6/w0lJSdq9e3eecTr+DttsNiUnJ+c5dsuWLZ1jS1JiYmKBPgeH/D6PZs2aZWvv6LNz5848j1G2bFnnOSQlJeV5DuHh4QoKCsq2xEBSUlKex8j6mWaNK7dlhYqzT1H75Tf3TZo0yfVYJdmvuPq7aywrxod3yO/3wLEUGIqfr+Q+Wf9OpKamuvy74ejn6rhXI29CflzlN3ntu5Z861ou3suvb345WdOmTTV9+vRcc6KpU6dq9+7d15RX5dema9eu+u6775SZmamePXvqxx9/zNGmXbt2evTRR7Mt/Xu1wYMHKy0tLc88pmfPnpKk7777Ltc2jz32mIKDgzVp0qRc28TGxqpOnTrav3+/HnnkkVzbPfzww0pOTtYHH3yQa5v27dsrICBAly5dyrOw1qJFC2culJycrF9//TXXtl27dlXVqlWVkJCgJUuW5NquS5cuCgwMVFpami5evJhnfpU190hKSsrz+3Z4eLjKli1boFjbtm2rsmXLOvONxMTEPD8HR44p/ZGb5JXPZM3hHJKSkgqcdzpeSUlJeV5I0LZtW2fe5FieOyEhIc/jNG3aNFt+KeWfAzZv3jzbBRDGmALlP1k/A8e8FPdx3JH7eEreRM6EkuC1OZP7blQqPu+9956RZJo0aZJrm48//thIMnXr1nX7mFFRUUaSefnll3Pt26NHDyPJjB8/vkDxFERpWy4D1nG1FIMxeS/HUJD+xTF+fm3yWt7BoTS3sbKd1WN6antPPUZJ9vGGY11Lv+Lq766xrBgf3sFTfg9K23dgX8l9du/e7fy9OXr0qMt+R44ccbbZu3dvgc6lIErb7wysk1feU9ScqCD788uJiiOvKq423poP+WIu5M05Fn1Kts+19LvWvsXRv7jHsWp8eAdP+T0ozPdfrygIrVixwkhXnmGSmprqss2LL75oJJnu3bu7fcwhQ4YYSWbIkCG5jt+gQQMjycycObNA8RQEiQ08RX7JS0mM786iki+3sbKd1WN6antPPUZJ9vGGY11Lv+Lq766xrBgf3sFTfg9K23dgX8l90tLSTEBAgJFkVqxYkW9c6enpBTqXgihtvzMova616FQSbbw1H/LFXMibcyz6lGyfa+l3rX2Lo39xj2PV+PAOnvJ7UJjvvzZj/u8eTw92+fJlVatWTampqVq9erWioqJytLn55pu1ZMkSPffcc3rppZfcOmZsbKxGjRqlJk2auLz18ujRo6pTp44kaffu3cV2e1hkZKQ2b96siIgIbdq0qVjGBHxVfkvmlfY2VrazekxPbe+pxyjJPt5wrGvpV1z93TWWFePDO1j9e1DavgP7Uu5z4403as2aNZo4caL+/ve/5+j78ssv67nnnlOXLl20atWqfM+joErb7wzgDbw1H/LFXMibcyz6lGyfa+l3rX2Lo39xj2PV+PAOVv8eFOr7r9vLU8XkL3/5i5FkBg4cmGPfrl27jN1uN5LML7/84vYxT5w4Yfz9/Y0ks2zZshx9x48fbySZdu3aFTiWguBKNwAAAJQ2pfE7sK/kPq+//rqRZBo3bpzjzqSUlBTTqFEjI8n85z//KfB5FERp/J0BAABA6VWY7792d1Sk3GH8+PGy2WyaOXOmYmNjnQ+vO378uPr376/MzEz16dNHbdu2zdavS5cuql+/viZPnlxsY9asWVOjRo2SJA0bNizbg7oXLlyoV155RZI0YcKEYjt/AAAAAKWDr+Q+o0aN0nXXXac9e/Zo9OjRSk5OlnTlAbyjR4/W3r17FRoaquHDhxfxkwIAAABQGF5TEGrfvr3eeOMNSVcSi7CwMEVERKhBgwbatGmTmjVrpvfeey9HvyNHjujgwYM6f/58sY0pSa+88oo6d+6s/fv3q2XLlrr++uvVuHFj9erVSykpKXrqqafUu3fv4vsAAAAAAJQKvpL7lCtXTnPnzlW5cuX0wQcfKDQ0VDfccINCQ0M1ffp0lS9fXl988YXKli17DZ8WAAAAgILymoKQJI0ZM0aLFy9Wz549lZCQoO3btyssLEzjxo3Txo0bFRISUmJjBgcHa/ny5Zo0aZLCw8O1a9cunTlzRt26ddPcuXP12muvXevpAgAAACilfCX3iYqK0pYtWzRo0CAFBwdr69atCg4O1uDBg7VlyxZ16tSp0OcBAAAAoGhsxrFWADweD0cFAABAacN3YBQWvzMAAAAoTQrz/der7hACAAA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      "text/plain": [
       "<Figure size 2000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(20, 6))\n",
    "for ax, syn_type, pt_name in zip(axs, ['syn_count', 'connected'], ['synapse probability', 'connection probability']):\n",
    "    dist_mids = np.array([itv.mid for itv in dist_vs_syn_fn.index]) / 1_000\n",
    "    ax.plot(dist_mids, dist_vs_syn_fn[syn_type]['mean'], color='k', marker ='.')\n",
    "    ax.fill_between(dist_mids, \n",
    "                        dist_vs_syn_fn[syn_type]['mean'] - dist_vs_syn_fn[syn_type]['error'],\n",
    "                        dist_vs_syn_fn[syn_type]['mean'] + dist_vs_syn_fn[syn_type]['error'], \n",
    "                        color='gray')\n",
    "    ax.set_xlabel('distance (um)')\n",
    "    ax.set_ylabel(pt_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(20, 6))\n",
    "for ax, syn_type, pt_name in zip(axs, ['syn_count', 'connected'], ['synapse probability', 'connection probability']):\n",
    "    dist_mids = np.array([itv.mid for itv in dist_vs_syn_fn.index]) / 1_000\n",
    "    ax.plot(dist_mids, dist_vs_syn_fn[syn_type]['mean'], color='k', marker ='.')\n",
    "    ax.fill_between(dist_mids, \n",
    "                        dist_vs_syn_fn[syn_type]['mean'] - dist_vs_syn_fn[syn_type]['error'],\n",
    "                        dist_vs_syn_fn[syn_type]['mean'] + dist_vs_syn_fn[syn_type]['error'], \n",
    "                        color='gray')\n",
    "    ax.set_xlabel('distance (um)')\n",
    "    ax.set_yscale('log')\n",
    "    ax.set_ylabel(pt_name)\n",
    "# plt.savefig('figures/stats-630/dist-vs-conn.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist_vs_syn['dist_bins'] = pd.cut(dist_vs_syn.dist, [0, 50*1e3, 700*1e3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/lq/1wn16kmd2k1g0rjr_hy10kvr0000gn/T/ipykernel_47118/3691504039.py:2: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  dist_vs_syn_fn =  dist_vs_syn.groupby('dist_bins').agg({'syn_count': [('mean', np.mean), ('error', error)],\n",
      "/var/folders/lq/1wn16kmd2k1g0rjr_hy10kvr0000gn/T/ipykernel_47118/3691504039.py:2: FutureWarning: The provided callable <function mean at 0x1075f53a0> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"mean\" instead.\n",
      "  dist_vs_syn_fn =  dist_vs_syn.groupby('dist_bins').agg({'syn_count': [('mean', np.mean), ('error', error)],\n"
     ]
    }
   ],
   "source": [
    "error = lambda x: np.std(x) / np.sqrt(len(dist_vs_syn) // n_bins)\n",
    "dist_vs_syn_fn =  dist_vs_syn.groupby('dist_bins').agg({'syn_count': [('mean', np.mean), ('error', error)], \n",
    "                                        'connected': [('mean', np.mean), ('error', error)]})\n",
    "# dist_vs_syn_fn                                        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dist_vs_syn_fn = pd.read_pickle('saved/dist_vs_syn_fn_n=10000.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">syn_count</th>\n",
       "      <th colspan=\"2\" halign=\"left\">connected</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>error</th>\n",
       "      <th>mean</th>\n",
       "      <th>error</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dist_bins</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(0.0, 50000.0]</th>\n",
       "      <td>0.057453</td>\n",
       "      <td>0.001196</td>\n",
       "      <td>0.004166</td>\n",
       "      <td>0.000046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(50000.0, 700000.0]</th>\n",
       "      <td>0.000564</td>\n",
       "      <td>0.000085</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    syn_count           connected          \n",
       "                         mean     error      mean     error\n",
       "dist_bins                                                  \n",
       "(0.0, 50000.0]       0.057453  0.001196  0.004166  0.000046\n",
       "(50000.0, 700000.0]  0.000564  0.000085  0.000050  0.000005"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_vs_syn_fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(20, 6))\n",
    "for ax, syn_type, pt_name in zip(axs, ['syn_count', 'connected'], ['synapse probability', 'connection probability']):\n",
    "    dist_mids = np.linspace(0, 7800000, 79) / 1_000\n",
    "    ax.plot(dist_mids, dist_vs_syn_fn[syn_type]['mean'], color='k')\n",
    "    ax.fill_between(dist_mids, \n",
    "                        dist_vs_syn_fn[syn_type]['mean'] - dist_vs_syn_fn[syn_type]['error'],\n",
    "                        dist_vs_syn_fn[syn_type]['mean'] + dist_vs_syn_fn[syn_type]['error'], \n",
    "                        color='gray')\n",
    "    ax.set_xlabel('distance (um)')\n",
    "    ax.set_yscale('log')\n",
    "    ax.set_ylabel(pt_name)\n",
    "# plt.savefig('figures/stats-630/dist-vs-conn.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.16562634e-03, 4.96347436e-05])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_vs_syn_fn[syn_type]['mean'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([      0.,  100000.,  200000.,  300000.,  400000.,  500000.,\n",
       "        600000.,  700000.,  800000.,  900000., 1000000., 1100000.,\n",
       "       1200000., 1300000., 1400000., 1500000., 1600000., 1700000.,\n",
       "       1800000., 1900000., 2000000., 2100000., 2200000., 2300000.,\n",
       "       2400000., 2500000., 2600000., 2700000., 2800000., 2900000.,\n",
       "       3000000., 3100000., 3200000., 3300000., 3400000., 3500000.,\n",
       "       3600000., 3700000., 3800000., 3900000., 4000000., 4100000.,\n",
       "       4200000., 4300000., 4400000., 4500000., 4600000., 4700000.,\n",
       "       4800000., 4900000., 5000000., 5100000., 5200000., 5300000.,\n",
       "       5400000., 5500000., 5600000., 5700000., 5800000., 5900000.,\n",
       "       6000000., 6100000., 6200000., 6300000., 6400000., 6500000.,\n",
       "       6600000., 6700000., 6800000., 6900000., 7000000., 7100000.,\n",
       "       7200000., 7300000., 7400000., 7500000., 7600000., 7700000.,\n",
       "       7800000.])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "gt",
   "language": "python",
   "name": "myenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}