From 73d3c5486425da036267d977cafaebc41a08bf8c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sebastian=20Sp=C3=A4th?= <sebastian.spaeth@uni-hamburg.de>
Date: Thu, 2 Dec 2021 09:05:26 +0100
Subject: [PATCH] Add weighted Correlation algorithm
Fix some ambiguous Bundesland abbreviation and add an algo for
calculating weighted correlations.
---
.gitignore | 3 +
Analyse.ipynb | 2379 ++--------------------------------------
Uebersterblichkeit.png | Bin 57750 -> 58972 bytes
wcorr/__init__.py | 1 +
wcorr/wcorr.py | 69 ++
5 files changed, 170 insertions(+), 2282 deletions(-)
create mode 100644 .gitignore
create mode 100644 wcorr/__init__.py
create mode 100644 wcorr/wcorr.py
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..1f059ab
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,3 @@
+**/__pycache__/
+/.ipynb_checkpoints/
+
diff --git a/Analyse.ipynb b/Analyse.ipynb
index 4824447..864f9ce 100644
--- a/Analyse.ipynb
+++ b/Analyse.ipynb
@@ -10,7 +10,25 @@
"%matplotlib nbagg\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
- "import numpy as np"
+ "import numpy as np\n",
+ "from wcorr import WeightedCorr\n",
+ "laender_short={\"Schleswig-Holstein\":\"SH\",\n",
+ "\"Hamburg\":\"HH\",\n",
+ "\"Niedersachsen\":\"NI\",\n",
+ "\"Bremen\":\"HB\",\n",
+ "\"Nordrhein-Westfalen\":\"NRW\",\n",
+ "\"Hessen\":\"HE\",\n",
+ "\"Rheinland-Pfalz\":\"RP\",\n",
+ "\"Baden-Württemberg\":\"BW\",\n",
+ "\"Bayern\":\"BY\",\n",
+ "\"Saarland\":\"SAAR\",\n",
+ "\"Berlin\":\"BE\",\n",
+ "\"Brandenburg\":\"BB\",\n",
+ "\"Mecklenburg-Vorpommern\":\"MV\",\n",
+ "\"Sachsen\":\"SA\",\n",
+ "\"Sachsen-Anhalt\":\"S-AN\",\n",
+ "\"Thüringen\":\"TH\",\n",
+ "}"
]
},
{
@@ -88,71 +106,19 @@
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>2021</td>\n",
- " <td>Hamburg</td>\n",
- " <td>451</td>\n",
- " <td>437</td>\n",
- " <td>462</td>\n",
- " <td>396</td>\n",
- " <td>416</td>\n",
- " <td>363</td>\n",
- " <td>405</td>\n",
- " <td>346</td>\n",
- " <td>...</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>2021</td>\n",
- " <td>Niedersachsen</td>\n",
- " <td>2164</td>\n",
- " <td>2092</td>\n",
- " <td>2166</td>\n",
- " <td>2164</td>\n",
- " <td>2179</td>\n",
- " <td>2133</td>\n",
- " <td>2061</td>\n",
- " <td>1945</td>\n",
- " <td>...</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
" </tbody>\n",
"</table>\n",
- "<p>3 rows × 55 columns</p>\n",
+ "<p>1 rows × 55 columns</p>\n",
"</div>"
],
"text/plain": [
- " Jahr Bundesland 1 2 3 4 5 6 7 8 \\\n",
- "0 2021 Schleswig-Holstein 803 794 849 826 777 797 819 677 \n",
- "1 2021 Hamburg 451 437 462 396 416 363 405 346 \n",
- "2 2021 Niedersachsen 2164 2092 2166 2164 2179 2133 2061 1945 \n",
+ " Jahr Bundesland 1 2 3 4 5 6 7 8 ... 44 \\\n",
+ "0 2021 Schleswig-Holstein 803 794 849 826 777 797 819 677 ... NaN \n",
"\n",
- " ... 44 45 46 47 48 49 50 51 52 53 \n",
- "0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
- "1 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
- "2 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
+ " 45 46 47 48 49 50 51 52 53 \n",
+ "0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"\n",
- "[3 rows x 55 columns]"
+ "[1 rows x 55 columns]"
]
},
"execution_count": 2,
@@ -164,7 +130,7 @@
"#Datasource https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Bevoelkerung/Sterbefaelle-Lebenserwartung/Tabellen/sonderauswertung-sterbefaelle.html?nn=209016\n",
"# 22.11.2021\n",
"df = pd.read_csv('sterbefaelle.csv')\n",
- "df.head(3)\n",
+ "df.head(1)\n",
"#df.columns\n",
"#df = df.set_index('Bundesland')"
]
@@ -484,153 +450,9 @@
" <td>4205.75</td>\n",
" <td>4054.75</td>\n",
" </tr>\n",
- " <tr>\n",
- " <th>Rheinland-Pfalz</th>\n",
- " <td>977.00</td>\n",
- " <td>989.25</td>\n",
- " <td>969.25</td>\n",
- " <td>969.75</td>\n",
- " <td>1028.00</td>\n",
- " <td>1010.25</td>\n",
- " <td>1061.50</td>\n",
- " <td>1094.00</td>\n",
- " <td>1102.50</td>\n",
- " <td>1087.00</td>\n",
- " <td>...</td>\n",
- " <td>868.00</td>\n",
- " <td>843.00</td>\n",
- " <td>905.75</td>\n",
- " <td>898.50</td>\n",
- " <td>927.25</td>\n",
- " <td>903.00</td>\n",
- " <td>953.25</td>\n",
- " <td>920.50</td>\n",
- " <td>939.75</td>\n",
- " <td>944.25</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Saarland</th>\n",
- " <td>268.25</td>\n",
- " <td>275.00</td>\n",
- " <td>269.25</td>\n",
- " <td>273.75</td>\n",
- " <td>283.25</td>\n",
- " <td>294.50</td>\n",
- " <td>283.25</td>\n",
- " <td>286.50</td>\n",
- " <td>315.75</td>\n",
- " <td>315.75</td>\n",
- " <td>...</td>\n",
- " <td>230.25</td>\n",
- " <td>246.75</td>\n",
- " <td>253.00</td>\n",
- " <td>259.25</td>\n",
- " <td>256.75</td>\n",
- " <td>253.00</td>\n",
- " <td>262.25</td>\n",
- " <td>253.75</td>\n",
- " <td>259.50</td>\n",
- " <td>262.50</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Sachsen</th>\n",
- " <td>1133.75</td>\n",
- " <td>1146.25</td>\n",
- " <td>1128.50</td>\n",
- " <td>1175.75</td>\n",
- " <td>1174.00</td>\n",
- " <td>1215.50</td>\n",
- " <td>1234.75</td>\n",
- " <td>1254.00</td>\n",
- " <td>1286.25</td>\n",
- " <td>1273.50</td>\n",
- " <td>...</td>\n",
- " <td>996.75</td>\n",
- " <td>1004.75</td>\n",
- " <td>1021.50</td>\n",
- " <td>1039.75</td>\n",
- " <td>1027.75</td>\n",
- " <td>1058.25</td>\n",
- " <td>1073.00</td>\n",
- " <td>1096.75</td>\n",
- " <td>1123.75</td>\n",
- " <td>1125.50</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Sachsen-Anhalt</th>\n",
- " <td>692.00</td>\n",
- " <td>677.75</td>\n",
- " <td>653.25</td>\n",
- " <td>664.25</td>\n",
- " <td>686.00</td>\n",
- " <td>690.50</td>\n",
- " <td>744.50</td>\n",
- " <td>731.25</td>\n",
- " <td>768.50</td>\n",
- " <td>776.00</td>\n",
- " <td>...</td>\n",
- " <td>586.75</td>\n",
- " <td>570.00</td>\n",
- " <td>603.50</td>\n",
- " <td>579.50</td>\n",
- " <td>617.25</td>\n",
- " <td>656.00</td>\n",
- " <td>627.50</td>\n",
- " <td>651.75</td>\n",
- " <td>671.25</td>\n",
- " <td>635.75</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Schleswig-Holstein</th>\n",
- " <td>711.75</td>\n",
- " <td>690.00</td>\n",
- " <td>728.00</td>\n",
- " <td>737.00</td>\n",
- " <td>749.25</td>\n",
- " <td>770.25</td>\n",
- " <td>773.00</td>\n",
- " <td>792.25</td>\n",
- " <td>794.75</td>\n",
- " <td>787.75</td>\n",
- " <td>...</td>\n",
- " <td>632.00</td>\n",
- " <td>634.50</td>\n",
- " <td>660.75</td>\n",
- " <td>658.75</td>\n",
- " <td>660.50</td>\n",
- " <td>696.50</td>\n",
- " <td>695.00</td>\n",
- " <td>663.50</td>\n",
- " <td>715.25</td>\n",
- " <td>684.75</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Thüringen</th>\n",
- " <td>609.25</td>\n",
- " <td>600.50</td>\n",
- " <td>603.75</td>\n",
- " <td>629.50</td>\n",
- " <td>667.25</td>\n",
- " <td>634.00</td>\n",
- " <td>640.00</td>\n",
- " <td>661.00</td>\n",
- " <td>686.00</td>\n",
- " <td>697.00</td>\n",
- " <td>...</td>\n",
- " <td>513.50</td>\n",
- " <td>538.50</td>\n",
- " <td>545.75</td>\n",
- " <td>558.50</td>\n",
- " <td>549.75</td>\n",
- " <td>583.75</td>\n",
- " <td>596.75</td>\n",
- " <td>576.25</td>\n",
- " <td>590.25</td>\n",
- " <td>555.50</td>\n",
- " </tr>\n",
" </tbody>\n",
"</table>\n",
- "<p>16 rows × 52 columns</p>\n",
+ "<p>10 rows × 52 columns</p>\n",
"</div>"
],
"text/plain": [
@@ -646,12 +468,6 @@
"Mecklenburg-Vorpommern 452.00 455.50 439.50 450.00 449.50 429.75 \n",
"Niedersachsen 1973.50 1966.50 1979.75 1996.25 2057.25 2024.00 \n",
"Nordrhein-Westfalen 4191.50 4305.75 4238.75 4357.75 4489.75 4513.75 \n",
- "Rheinland-Pfalz 977.00 989.25 969.25 969.75 1028.00 1010.25 \n",
- "Saarland 268.25 275.00 269.25 273.75 283.25 294.50 \n",
- "Sachsen 1133.75 1146.25 1128.50 1175.75 1174.00 1215.50 \n",
- "Sachsen-Anhalt 692.00 677.75 653.25 664.25 686.00 690.50 \n",
- "Schleswig-Holstein 711.75 690.00 728.00 737.00 749.25 770.25 \n",
- "Thüringen 609.25 600.50 603.75 629.50 667.25 634.00 \n",
"\n",
" 7 8 9 10 ... 43 \\\n",
"Bundesland ... \n",
@@ -665,12 +481,6 @@
"Mecklenburg-Vorpommern 471.75 489.75 455.25 480.25 ... 387.00 \n",
"Niedersachsen 2075.50 2111.25 2223.25 2165.50 ... 1725.75 \n",
"Nordrhein-Westfalen 4549.00 4568.00 4780.25 4786.50 ... 3757.50 \n",
- "Rheinland-Pfalz 1061.50 1094.00 1102.50 1087.00 ... 868.00 \n",
- "Saarland 283.25 286.50 315.75 315.75 ... 230.25 \n",
- "Sachsen 1234.75 1254.00 1286.25 1273.50 ... 996.75 \n",
- "Sachsen-Anhalt 744.50 731.25 768.50 776.00 ... 586.75 \n",
- "Schleswig-Holstein 773.00 792.25 794.75 787.75 ... 632.00 \n",
- "Thüringen 640.00 661.00 686.00 697.00 ... 513.50 \n",
"\n",
" 44 45 46 47 48 49 \\\n",
"Bundesland \n",
@@ -684,12 +494,6 @@
"Mecklenburg-Vorpommern 385.50 395.50 400.75 404.75 405.75 410.25 \n",
"Niedersachsen 1736.50 1774.50 1795.00 1799.00 1840.00 1884.25 \n",
"Nordrhein-Westfalen 3764.50 3841.75 3892.25 3980.00 3967.75 4133.75 \n",
- "Rheinland-Pfalz 843.00 905.75 898.50 927.25 903.00 953.25 \n",
- "Saarland 246.75 253.00 259.25 256.75 253.00 262.25 \n",
- "Sachsen 1004.75 1021.50 1039.75 1027.75 1058.25 1073.00 \n",
- "Sachsen-Anhalt 570.00 603.50 579.50 617.25 656.00 627.50 \n",
- "Schleswig-Holstein 634.50 660.75 658.75 660.50 696.50 695.00 \n",
- "Thüringen 538.50 545.75 558.50 549.75 583.75 596.75 \n",
"\n",
" 50 51 52 \n",
"Bundesland \n",
@@ -703,14 +507,8 @@
"Mecklenburg-Vorpommern 441.50 434.25 410.50 \n",
"Niedersachsen 1866.00 1910.00 1847.00 \n",
"Nordrhein-Westfalen 4066.00 4205.75 4054.75 \n",
- "Rheinland-Pfalz 920.50 939.75 944.25 \n",
- "Saarland 253.75 259.50 262.50 \n",
- "Sachsen 1096.75 1123.75 1125.50 \n",
- "Sachsen-Anhalt 651.75 671.25 635.75 \n",
- "Schleswig-Holstein 663.50 715.25 684.75 \n",
- "Thüringen 576.25 590.25 555.50 \n",
- "\n",
- "[16 rows x 52 columns]"
+ "\n",
+ "[10 rows x 52 columns]"
]
},
"execution_count": 3,
@@ -723,7 +521,7 @@
"sterb_norm = df[df.Jahr<2020].groupby(['Bundesland']).mean()\n",
"#Jahr does not make sense after aggregation\n",
"sterb_norm = sterb_norm.drop('Jahr', axis=1)\n",
- "sterb_norm.head(20)\n"
+ "sterb_norm.head(10)\n"
]
},
{
@@ -731,6 +529,53 @@
"execution_count": 4,
"id": "ab1f10bc",
"metadata": {},
+ "outputs": [],
+ "source": [
+ "sterb_21 = df[df.Jahr==2021].groupby(['Bundesland']).mean()\n",
+ "uebersterb = sterb_21 / sterb_norm\n",
+ "uebersterb = uebersterb.mean(axis=1)\n",
+ "uebersterb.name = \"Übersterblichkeit\"\n",
+ "# Next line plots:\n",
+ "# uebersterb.sort_values().plot(style='.')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "9d77bb4a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Datasource Rbert Koch Institut\n",
+ "# https://github.com/robert-koch-institut/COVID-19-Impfungen_in_Deutschland\n",
+ "df_impf = pd.read_csv('Impfquoten.csv')\n",
+ "df_impf = df_impf[df_impf.Bundesland!=\"Deutschland\"].drop('Datum', axis=1) # Drop column 'Datum', row \"Deutschland\"\n",
+ "df_impf = df_impf.set_index('Bundesland')\n",
+ "# next line plots\n",
+ "# df_impf.sort_values(by='Impfquote').plot(style='.')\n",
+ "\n",
+ "# Merge both dataset into a combined one\n",
+ "df_total = pd.merge(df_impf,uebersterb, left_index=True, right_index=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "426b3884",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Unweighted correlation is\n",
+ "correlation = df_total.corr(method=\"pearson\")\n",
+ "# Weighted correlation is\n",
+ "#TODO: get population numbers WeightedCorr()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "8e99105a",
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1693,7 +1538,7 @@
{
"data": {
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MgxDY8aMOed6jx49qkOHDplLcXGx3wcIAAC7oQDW4wI4c+ZMn8s2btwowzBUWloqSVq3bp2aNWum+fPna/v27Vq5cqXi4uL05JNPnnW9qampMgyj0uLPAQIAwG4ogPW0ANbkEHDv3r316KOP+lznjTfeUGhoqE6dOlXletkDCACA/VEA62kBnDx5sjp06OBz2ejRo30+BNKtWzdNnjzZ5zpLly6Vy+XSyZMna7QtBAgAAPthftukAB45ckQej0cej0eGYSgtLU0ej0dFRUWSpJSUFI0YMcK8vvdrYB5++GHl5eVpwYIFlb4GJjU1VREREVq2bJkKCwv1/vvv6/LLL9ewYcNqvF0ECAAA+2F+26QArlu3rspz79xutyTJ7XYrOTnZ5zZZWVnq2rWrQkJC1Lp160pfBH3ixAk98cQTuvzyy+VyuRQXF6exY8fqu+++q/F2ESAAAOyH+W2TAnipIkAAANgP85sCaAkBAgDAfpjfFEBLCBAAAPbD/KYAWkKAAACwH+Y3BdASAgQAgP0wvymAlhAgAADsh/lNAbSEAAEAYD/MbwqgJQQIAAD7YX5TAC0hQAAA2A/zmwJoCQECAMB+mN8UQEsIEAAA9sP8pgBaQoAAALAf5jcF0BICBACA/TC/KYCWECAAAOyH+U0BtIQAAQBgP8xvCqAlBAgAAPthflMALSFAAADYD/ObAmgJAQIAwH6Y3xRASwgQAAD2w/ymAFpCgAAAsB/mNwXQEgIEAID9ML8pgJYQIAAA7If5TQG0hAABAGA/zG8KoCUECAAA+2F+UwAtIUAAANgP85sCaAkBAgDAfpjfFEBLCBAAAPbD/KYAWkKAAACwH+Y3BdASAgQAgP0wvymAlhAgAADsh/lNAbSEAAEAYD/MbwqgJQQIAAD7YX5TAC0hQAAglZSVK7vggErKyut6U4AaYX5TAC0hQAD8XXpukRJSMhX/WKYSUjKVnltU15sEVIv5TQG0hAAB8GclZeVm+fMuiSlr2BOISx7zmwJoCQEC4M+yCw74lD/vklNwsK43DTgn5jcF0BICBMCfsQcQdsX8pgBaQoAA+Lv03CIlpqwxyx/nAMIOmN8UQEsIEACc3hOYU3CQPX+wDeY3BdASAgQAgP0wvymAlhAgAADsh/lNAbSEAAEAYD/MbwqgJQQIAAD7YX5TAC0hQAAA2A/zmwJoCQECAMB+mN8UQEsIEAAA9sP8pgBaQoAAXEpKysqVXXCA7+MDqsH8tkkBXL9+vQYNGqTY2FgZhqFVq1ZVe5usrCx169ZNTqdTCQkJmjt3bqXrfPfddxo7dqyaN28up9OppKQkrVmzpsbbRYAAXCrSc4vMP8uWkJLJX+QAzoH5bZMCuHbtWk2dOlUZGRk1KoCFhYUKCwvThAkTlJeXp/nz5ys4OFgrVqwwr3Ps2DFdffXVuummm7Rx40bt379fH330kT799NMabxcBAnAp4G/yAueH+W2TAlhRTQrg5MmTlZSU5HPZqFGjdO2115r/njt3rhITE3X8+PEL3hYCBOBSkF1wwKf8eZecgoN1vWnAJYn5XU8LYJ8+fTR+/Hify1auXKmgoCCz8A0cOFB33XWX7r//fjVt2lRXXHGFZsyYoZMnT9Z4WwgQgEsBewCB88P8rqcFsG3btpoxY4bPZdnZ2TIMQyUlJZKk9u3by+l06r777tPHH3+sZcuWKSYmRtOnTz/reo8ePapDhw6ZS3Fxsd8HCMClIT23SIkpa8zyxzmAwNlRAOtxAZw5c6bPZRs3bpRhGCotLTWvExcX57PH77nnnlPz5s3Put7U1FQZhlFp8ecAAbh0lJSVK6fgIHv+gGpQAOtpAazJIeDrrrtO/fr187nO2rVrZRiGjh07VuV62QMIAID9UQDraQGcPHmyOnTo4HPZ6NGjfT4EMmXKFMXHx+vUqVPmZS+88IJiY2NrvC0ECAAA+2F+26QAHjlyRB6PRx6PR4ZhKC0tTR6PR0VFp89xSUlJ0YgRI8zre78G5uGHH1ZeXp4WLFhQ6WtgvvjiCzVo0EDjxo3T7t27lZmZqaZNm+rPf/5zjbeLAAEAYD/Mb5sUwHXr1lV57p3b7ZYkud1uJScn+9wmKytLXbt2VUhIiFq3bl3lF0Hn5OTommuukdPpVGJiIp8CBgDADzC/bVIAL1UECAAA+2F+UwAtIUAAANgP85sCaAkBAgDAfpjfFEBLCBAAAPbD/KYAWkKAAACwH+Y3BdASAgSgKiVl5couOMBf5AAuUcxvCqAlBAjAmdJzi5SQkqn4xzKVkJLJ3+QFLkHMbwqgJQQIQEUlZeVm+fMuiSlr2BMIXGKY3xRASwgQgIqyCw74lD/vklNwsK43DUAFzG8KoCUECEBF7AEE7IH5TQG0hAABOFN6bpESU9aY5Y9zAIFLD/ObAmgJAQJQlZKycuUUHGTPH3CJYn5TAC0hQAAA2A/zmwJoCQECAMB+mN8UQEsIEAAA9sP8pgBaQoAAALAf5jcF0BICBACA/TC/KYCWECAAAOyH+U0BtIQAAQBgP8xvCqAlBAgAAPthflMALSFAAADYD/ObAmgJAQIAwH6Y3xRASwgQAAD2w/ymAFpCgAAAsB/mNwXQEgIEAID9ML8pgJYQIAAA7If5TQG0hAABAGA/zG8KoCUECAAA+2F+UwAtIUAAANgP85sCaAkBAgDAfpjfFEBLCBAAAPbD/KYAWkKAAACwH+Y3BdASAgRc2krKypVdcEAlZeV1vSkALiHMbwqgJQQIqJm6KGLpuUVKSMlU/GOZSkjJVHpu0UW7bwCXNuY3BdASAgRUry6KWElZuXmf3iUxZQ17AgFIYn5LFEBLCBBwbnVVxLILDvjcp3fJKTj4s94vAHtgflMALSFAsJuLfSi2rooYewABnAvzmwJoCQGCnfjbodj03CIlpqwx75NzAAF4Mb8pgJYQINiFvxaxkrJy5RQcZM8fAB/MbwqgJQQIF8pfDsV6UcQAXEqY3xRASwgQLoS/HYoFgEsN85sCaAkBwvny10OxAHApYX5TAC0hQDhfHIoFgLrH/KYAWkKAcL44FAsAdY/5TQG0hADhQnAoFgDqFvObAmgJAbK/uvgbtd775VAsANQN5jcF0BICZG918WlcAEDdY35TAC0hQLXnYu+J41w8APBfzG+bFMD169dr0KBBio2NlWEYWrVqVbW3ycrKUrdu3eR0OpWQkKC5c+ee9brLli2TYRgaPHjweW0XAaoddbEnrq4/jQsAqDvMb5sUwLVr12rq1KnKyMioUQEsLCxUWFiYJkyYoLy8PM2fP1/BwcFasWJFpevu379fLVq0UJ8+fSiAdaCu9sSxBxAA/Bfz2yYFsKKaFMDJkycrKSnJ57JRo0bp2muv9bns5MmT+uUvf6lXX31VbrebAlgH6nJPHJ/GBQD/xPyupwWwT58+Gj9+vM9lK1euVFBQkI4fP25eNm3aNA0ZMkSSalQAjx49qkOHDplLcXGx3wfIqrreE8encQHA/1AA62kBbNu2rWbMmOFzWXZ2tgzDUElJiSRp48aNatGihQ4cOCCpZgUwNTVVhmFUWvw5QLWBPXEAgIuJAliPC+DMmTN9Ltu4caMMw1BpaakOHz6s1q1ba+3atebP2QNYt9gTBwC4WCiA9bQAVncI2OPxyDAMBQYGmovD4ZDD4VBgYKAKCgpqtC0ECAAA+2F+19MCOHnyZHXo0MHnstGjR5sfAvnhhx+0Y8cOn2Xw4MG6/vrrtWPHDh07dqxG20KAAACwH+a3TQrgkSNH5PF4zD13aWlp8ng8Kio6fa5YSkqKRowYYV7f+zUwDz/8sPLy8rRgwYKzfg2MF58CBgDAPzC/bVIA161bV+WHL9xut6TT5S05OdnnNllZWeratatCQkLUunXrc34RtHcdFEAAAOo/5rdNCuCligABAGA/zG8KoCUECAAA+2F+UwAtIUAAANgP85sCaAkBAgDAfpjfFEBLCBAAAPbD/KYAWkKAAACwH+Y3BdASAgQAgP0wvymAlhAgAADsh/lNAbSEAAEAYD/MbwqgJQQIAAD7YX5TAC0hQAAA2A/zmwJoCQECAMB+mN8UQEsIEAAA9sP8pgBaQoAAALAf5jcF0BICBACA/TC/KYCWECAAAOyH+U0BtIQAAQBgP8xvCqAlBAgAAPthflMALSFAAADYD/ObAmgJAQIAwH6Y3xRASwgQAAD2w/ymAFpCgAAAsB/mNwXQEgIEAID9ML8pgJYQIAAA7If5TQG0hAABAGA/zG8KoCUECAAA+2F+UwAtIUAAANgP85sCaAkBAgDAfpjfFEBLCBAAAPbD/KYAWkKAAACwH+Y3BdASAgQAgP0wvymAlhAgAADsh/lNAbSEAAEAYD/MbwqgJQQIAAD7YX5TAC0hQAAA2A/zmwJoCQECAMB+mN8UQEvqY4BKysqVXXBAJWXldb0pAAD8LOrj/D5fFEAL6luA0nOLlJCSqfjHMpWQkqn03KK63iQAAGpdfZvfF4ICaEF9ClBJWblZ/rxLYsoa9gQCAOqd+jS/LxQF0IL6FKDsggM+5c+75BQcrOtNAwCgVtWn+X2hKIAW1KcAsQcQAOAv6tP8vlAUQAvqW4DSc4uUmLLGLH+cAwgAqI/q2/y+EBRAC+pjgErKypVTcJA9fwCAeqs+zu/zRQG0gAABAGA/zG8KoCUECAAA+2F+UwAtIUAAANgP85sCaAkBAgDAfpjfNimA69ev16BBgxQbGyvDMLRq1apqb5OVlaVu3brJ6XQqISFBc+fO9fn5vHnz1Lt3b0VHRys6Olr9+vXT5s2bz2u7CBAAAPbD/LZJAVy7dq2mTp2qjIyMGhXAwsJChYWFacKECcrLy9P8+fMVHBysFStWmNcZPny45syZI4/Ho127dmnkyJGKiorSl19+WePtIkAAANgP89smBbCimhTAyZMnKykpyeeyUaNG6dprrz3rbU6ePKmIiAgtXry4xttCgAAAsB/mdz0tgH369NH48eN9Llu5cqWCgoJ0/PjxKm9z+PBhuVwurV69usbbQoAAALAf5nc9LYBt27bVjBkzfC7Lzs6WYRgqKSmp8jZjx47V5Zdfrh9++OGs6z169KgOHTpkLsXFxX4fIAAA7IYCWI8L4MyZM30u27hxowzDUGlpaaXrz549Ww0bNtS2bdvOud7U1FQZhlFp8ecAAQBgNxTAeloAz+cQ8LPPPquoqCht2bKl2vtmDyAAAPZHAaynBXDy5Mnq0KGDz2WjR4+u9CGQZ555RpGRkfrXv/51QdtCgAAAsB/mt00K4JEjR+TxeOTxeGQYhtLS0uTxeFRUVCRJSklJ0YgRI8zre78G5uGHH1ZeXp4WLFhQ6WtgZs+erZCQEK1YsUKlpaXmcuTIkRpvFwECAMB+mN82KYDr1q2r8tw7t9stSXK73UpOTva5TVZWlrp27aqQkBC1bt260hdBx8fHV7nO1NTUGm8XAQIAwH6Y3zYpgJcqAgQAgP0wvymAlhAgAADsh/lNAbSEAAEAYD/MbwqgJQQIAAD7YX5TAC35OQNUUlau7IIDKikrr/V1AwDgzyiAFEBLfq4ApecWKSElU/GPZSohJVPpuUW1un4AAPwZBZACaMnPEaCSsnKz/HmXxJQ17AkEAKCWUAApgJb8HAHKLjjgU/68S07BwVq7DwAA/BkFkAJoCXsAAQCwHwogBdCSn/McwMSUNWb54xxAAABqDwWQAmjJz/0p4JyCg+z5AwCgllEAKYCWECAAAOyH+U0BtIQAAQBgP8xvCqAlBAgAAPthflMALSFAAADYD/ObAmgJAQIAwH6Y3xRASwgQAAD2w/ymAFpCgAAAsB/mNwXQEgIEAID9ML8pgJYQIAAA7If5TQG0hAABAGA/zG8KoCUECAAA+2F+UwAtIUAAANgP85sCaElZWZkMw1BxcbEOHTrEwsLCwsLCYoOluLhYhmGorKysrqtEnaEAWuANEAsLCwsLC4v9luLi4rquEnWGAmjBqVOnVFxcrLKysp/ttxN/2bvI463fC5jzuWAAACAASURBVI+3fi883vq91MfHW1ZWpuLiYp06daquq0SdoQBeog4d8q/zE3i89RuPt37j8dZv/vZ4/QUF8BLlby84Hm/9xuOt33i89Zu/PV5/QQG8RPnbC47HW7/xeOs3Hm/95m+P119QAC9RR48eVWpqqo4ePVrXm3JR8HjrNx5v/cbjrd/87fH6CwogAACAn6EAAgAA+BkKIAAAgJ+hAAIAAPgZCqAfWLRokaKioqq9nmEYWrVqlaX7Sk5O1oQJEyytozbU9DGfy48//ijDMBQeHi7DMOTxeKq9zb59+2p83dqSmpqqzp07X7T7u1Dr1q2TYRj67rvv6npT6kR8fLyef/5589+18XrDTy6V956fy9keX03fcxYtWqTQ0FBFRUVp8ODBcrvdNbrfM3OL+oMCeAHcbrfPn5KJiYnRgAEDtG3btvO6/dNPP+1z+apVq2QYtf9fcjEL4H/+8x8dPnz4gm8/d+5cNWjQQCdOnDAvO3LkiIKCgtS7d29JPz1/DzzwgAzD0O7duyX5Pn/l5eX6+uuva3SfZysma9euNf+fSktLfbbpbC71Auh2uzV48OBKl1+McmaHAmj1tX0uZw7S0tLSi/qpyrr8v5ekr7/+Wg888IDi4uIUEhKiZs2a6de//rVycnJqZf2XegGcMWOGAgICKr3vn0t1f8bM7XbX+D2nvLxcL774oqKiovTdd9+prKysRu8d33zzjf773//WeJthHxTAC+B2u3XjjTeqtLRUpaWl8ng8uvnmmxUXF3fW2xw/ftzn9i6XS9HR0fr222/Ny60WwGPHjlV5eXUF0Lttl8Ieic8//1yGYehf//qXednatWvVsmVLOZ1O/fe//zWfP5fLpebNm5vXu9Dn72wD8KWXXjrv5+TMN+Oz/Z/UJn8vgLX5HF/Ia7um21fXe1LqugD27t1b11xzjT788EPt379fmzdv1syZM5WZmVkr67/UC2CbNm2UkpKitm3b1vg23hyWlpbq8ssvV0hIiM9lZWVl5/VL55mz4FzvHRfjvQt1iwJ4Aap6I92wYYMMw9A333xjviCXL1+u5ORkOZ1OLVy4UJK0cOFCRUZGKiAgQCEhIbrhhhvMdfz1r381b9e7d28FBwfL5XIpODhYsbGxatmypcLDwzVgwAB98803io+P11NPPSW3263Q0FBFRkbK6XSqefPmatiwoUJDQzVkyBA9/vjjMgxDGRkZ6tu3r4KCguRyufT4448rISFBDofDPNw5f/58DRkyRKGhoWrTpo3effddn8e5c+dODRw4UOHh4WratKnuvvtuHThwwPz5mW/C8fHxmjFjhkaOHKkGDRooLi5Of/3rX8/5/F522WU+vyVPnjxZDz74oDp27Kh//OMfcrvdGjRokMLCwtShQwdJp9+shgwZIsMwFBYWpoSEBIWHh5vr2L9/v3r06KHAwEAZhqHg4GDdddddys/Pr/RbdUBAgKKionwui4+PlyS98cYbatSokQzDkMPhULNmzZSVlWXez2233SbDMDRu3DjFxsYqPj5eO3fuVMeOHeVwOGQYhlwulwYNGmTe5qqrrlJsbKycTqccDodCQkL00EMP+TwnxcXFuuOOO9SwYUOFhYWpe/fu2rRpk6Sf3sRff/11xcfHKzIyUnfccYfPnti3335bV155pQICAhQcHKx+/frp+++/93mODcNQSEiI2rRpo+7du6tFixYKDQ1V+/btffITGhqq8PBwDR06VBMmTFB0dLSaNm2qv/71r/r+++917733qkGDBkpMTNTatWvN+/AWjczMTHXq1ElOp1M9e/bU9u3bzetUNZCef/558/mXfnr9zZw503yOJSk7O1udO3eW0+lU9+7dzV8IzmdvbHWvbUn68ssvNWzYMEVHRysmJka33nqr9u3bV+32nesQsPc9o+Jz3KlTp1rbO3a2xyZVLoDZ2dnq06ePXC6XWrZsqYceesgnK3PmzFGbNm3kdDrVtGlT3XbbbebPvDlzuVyKiYkxc/bdd9/JMAw99thjSkpKktPpVPv27TVnzhzztlOnTpVhGHI6nXI6nQoMDNQVV1zh8xxs3LhR1113nUJDQxUdHa1f//rX5i/RycnJeuihhzRp0iQ1bNhQzZo1U2pqqs9jTU1NNfdAxsbG+rzOjh07pkmTJumyyy5TWFiYevbsqXXr1pk/95an9957T0lJSeZ7cUlJSbXPfVZWllq0aKHjx4/rsssu0/r16yttV3Wv4fbt2yskJKTS46uYncsvv1wOh0MOh0NNmjTRmDFjdOTIEXP9FQvgnXfeWem9r2vXrjXKbXVz4t1331WbNm3kcrnUt29fvfbaa5V+yaguZxcyO3B+KIAX4Mw30iNHjmjUqFFq06aNTp06Zb4gW7durYyMDBUWFuqrr77SvHnzFBsbq759+6p///7m0E1LS5P0UwFMSkrSSy+9pICAALVs2VIdO3ZU+/bt5XQ69cQTT6hNmzYaPXq0+UYxdOhQNWnSRC+//LJWrlwph8Oh0NBQzZo1Sy+++KIiIyPN9WZmZmrcuHEKDAyUy+VSbm6utm3bZhbAli1baunSpcrPz9f48ePVoEED/ec//5EklZSUqHHjxpoyZYp27dqlTz75RP3799evfvUr87moqgDGxMRozpw5ys/P19NPP62AgADt2rXrrM/v8OHD9etf/9r8d48ePfT2229rzJgxevzxx+V2u3XLLbcoJCREwcHBKi4u1vDhw82iUlBQoGHDhskwDO3Zs0eS1LNnTwUFBempp57SunXr9OSTT6p58+aaNm2aMjIyZBiG2rVrp169emnDhg1as2aNYmNjZRiGFi5caB4GiY2N1fXXX6/Vq1crIyNDLVq0kNPp1A8//CDppwJ4880367PPPtOHH36oqKgoORwO/eUvf1FmZqauueYan70AHTt2VGhoqB566CG988476tKliwzD0HvvvWfmKzExUX369NFHH32k/Px8LV++3ByMqampatCggX77299qx44d2rBhg5o3b67HH3/c/H8LCgpSWlqabrvtNv3qV7/SnDlzdOTIEUnSvHnzFBMTI8Mw9Omnn2revHkKDQ3V9OnTtXfvXqWmppolODMzU7t371bjxo3lcDg0ffp07dmzR0899ZQCAgI0cOBAzZs3T3v27NGYMWPUqFEj8/CRt2h06NBB77//vrZv365BgwapdevW5l7omhbABg0aaMSIEfrss8+0Y8cOHT58WDExMbr77ru1c+dOrV27Vu3atbNcAM98bf/3v/9V27Ztdd9992n79u3Ky8szs+fdY1LV9kk1K4De1+ju3bs1dOhQxcfH1+jUgwt5bF4VC+D27dvVoEEDPf/889qzZ4+ys7PVtWtX3XvvvZKkLVu2KDAwUEuXLtX+/fv1ySef6MUXX5Tkm7N9+/Zp+/btZs5OnDghp9Op8PBwpaenq7CwUBkZGYqJidFrr70mSfrTn/4kwzCUmJioGTNmKCEhQYmJieZz4PF45HQ6NWbMGH366af67LPP9NJLL5m/gCYnJysyMlJPPPGE9uzZo8WLF8vhcOj999+XdLqcRkZGau3atSoqKtLmzZs1b94883kYPny4+fovKCjQs88+K6fTab6HLFq0SMHBwbrhhhu0ZcsWbd26VR06dNDw4cOrfe5HjBihRx99VJL0hz/8Qffcc4/Pz6t7DUsy39/OfHyvv/66mZ3f//73Wrx4sQYOHKhmzZqpXbt2GjNmTJUF8JlnnlFkZKSuuOIKlZaWatiwYTXO7bnmxL59+xQcHKxHH31Un3/+uZYtW6YWLVr4FMDqcua93/OdHTg/FMAL4Ha7FRgYqPDwcPMDArGxsdq6daukn97MX3jhBZ/bxcXFaenSpT5vxHFxcWrSpImknwrgq6++quHDh6t///5atmyZDMPQP//5T02aNEkdO3bU008/rfbt2ys+Pl5Dhgwx1yud/q3uxhtv1FNPPaVf/OIXkqRBgwaZ65VOv9kEBQXJMAyfF5NhGPrjH/9o/vv777+Xw+HQ3/72N0mn36ArFjPp9J6piufhVVUA7777bvPfP/74o5o2baq5c+ee9fmdN2+ewsPDdeLECR0+fFhBQUH6+uuvlZ6erl69esntdqt3797mb6xDhw6Vw+HQggULzEPAixYtUlBQkKZMmSJJCgsLU79+/Xzu54033lBsbKw5AAMDA1VcXGz+3HuOoXdIL1iwQO3bt9ePP/5oXufLL7+UYRjmb6beApibm2s+Z507d1ZkZKT523xVz5n3/Ebp9Dk3hmHovvvuk3Q6FxEREeYb7JlSU1MVFhbms7dg0qRJuuaaayRJW7dulWEY2r9/f6XshoeHy+FwKDg42OcNumJ+vHk+s5RXzM/JkycVHh6uESNGmNcpLS31OZzvfZ7T09PN6/znP/9RaGioli9fbj6WmhTAZs2a+Ryimjt3rho1amQWcUmaP3/+BRXAc722q8rAsWPHFBoaqr///e9n3T6pZgXQ+xqVTu9tP/M1akVV//fh4eFyuVzm//2IESP0wAMP+Nzuo48+UkBAgH744QdlZGT4ZLmiijmrSqNGjcz769Wrl6ZMmaIHH3ywUs68z8Fbb72l6Oho8zm488479ctf/vKsj+/M15F0OqePPfaYJOm5555Tu3btfE7H8SooKJDD4dBXX33lc3m/fv3M95BFixaZv2B6zZkzR82aNTvrNkmn/4xaWFiYPv30U0mSx+NRWFiYz59Vq+41LJ0ugIGBgZUe3+jRo8+aneeff16NGjWqsgA+//zzioqKMl9v55Pbc82Jxx57TFdeeaXPOrx7d73vL9XlzHu/5zs7cH4ogBfA7XbrhhtuUH5+vvLz87V582a53W41bdpU+/fvN9/INm7caN7GO9RDQ0MVFBRkvhF7B+/OnTvNApibm6uuXbvqiSee0IcffmgefnrnnXcUHBysV199VQ0bNlR8fLymTJlirjc8PNw8xOc9PCNJ06ZN8yklqampSkxMlGEYPociDMPQW2+95fNYIyMjtXjxYknSTTfdpODg4EoDxDAM81BfVQXwmWee8Vlnp06dNH36dEnSjTfeaK6nY8eOkmQels3JydGaNWvMy0tLS81Dt0lJSWrVqpXWr1+vgIAA89CRYZz+1G5ISIgMw9CwYcMkyfy39/kJDQ01B9/f/vY3n8O8XjNnzvQZ0mPHjlVgYKCCgoLMw7neZezYsZJ+KoDe0nHTTTf5XD8wMNDcTu9z1rNnT7Vt21YJCQmKiIgwn1Pv6QFjxozRddddd9Y8pqamms+RV1pamhISEiSdLmf9+vVTRESE4uPj1aFDB3388cfKz8/Xpk2bzEPi3sPnYWFhCgwMVGBgoGJiYhQWFibDMHwK9C9/+ctK+WnVqpXP/7V3r7L38JC3ABYVFflsa5cuXfTEE0+Yj6UmBbDiqROSNHHiRJ890ZK0bdu2CyqA53ptezNw5mvA4XDolVdeOev2STUrgN7XqCR9++23lZ5jK858bN5lyZIl5nDu2LGjQkJCfB6b9/8/Ly9Phw8f1lVXXaXGjRvr7rvv1pIlS8w9vBVzNnToUM2bN888POt9//OeuxscHGy+biMiIiRJS5culWEYatKkiRo0aGC+Pr3PQYcOHTRt2rSzPr7k5GTzdeh16623auTIkZKkL774QnFxcWrZsqV+//vfa+XKlebe1bfeest876i4BAUFme8hixYtUlhYmM/6vUdcpNOnClS87ZIlSySd/uXkzEJ05ZVX+hzOrO41LP10CPjMxzd06FAzOx9++KFuuOEGNW/e3DylwzAMTZkypUYFsKa5PdecGDJkiPmce7377rs+BbC6nHnv91yzA9ZRAC9AVYdSvHtApk6dWuVJuf/+979lGIaWLFmi3/zmNz5vxH379tXgwYPNAujxeNSlSxdNnz7d5/DMqlWrFBwcrAULFigqKkrx8fF68sknzfXm5+crKSlJ48ePV35+vgoLCyX9VAC925Oamqorr7xShmH4nONScSB5RUVFadGiRZJOl7Xf/va3lQZIfn6+ee5GVQXwzBPfO3fubJ6b8+WXX5rrqLjnoGXLlpoxY4YeffRRjRkzxry8Xbt26t+/vxo1amQeLujatasMw9DLL78swzCUn5+vWbNmKSIiQqWlpZIkl8ulKVOmaPr06erfv7+CgoL0pz/9Sfn5+frnP/9ZZQF8+umnfZ6T0aNHy+VyqXfv3lq8eLH+9re/mZ8U9r7Zn1kAvc/Zrl279Nprr+n3v/+94uLiFB8fry+//FLS6b2TrVq10gcffKC8vDx99tlnMgzDLDSPPPJItQWwutL0448/auPGjebeyCZNmqiwsNDMpfc80a1bt5rnGKWlpenTTz81M9i3b19zfd4CWDE/Vf1fV3z+zlUAvW/q06dPV6dOnXx+/swzz1R5DmBFEyZM0PXXX+9z2aeffmr5ELDk+9oePXq0evbsWeVroKys7KzrkGpWACtuq/e8uYrPsRU1OQSclJSkhx56qMrH590zdOLECf3jH//QpEmTlJiYqDZt2piD3ZuzadOm6aqrrqqUM+/7lHe5/fbbddlll2n//v3mL0aLFy/W7t27zT363uegW7du1RbAMz8EcubXnZSXl+vdd9/VQw89pObNm+sXv/iFjh8/rvT0dAUGBurzzz+v9Li97yFVfZjuzG8eqHg77968Hj16yOFwmL9UBQYGyuFwqGfPnuZ6avIarqoADh482HzPWbNmjVwulyZOnKj3339fhmFo0qRJZgHs1KlTpUPAZxbA882tV8U5MXjwYPPohdc777zjUwBrkrPqZgesowBegKpeKKdOnVJERIQeeeSRs34qq0WLFnryyScr3X7btm0KCAgwDzl6PB7zEHDFN+dJkybpiiuuMF/E3heId73S6UPAAwcO9LnfW265pVYK4OOPP6727duf85yk8y2AZzNixAj1799fV199tXl4UJLuv/9+dezYUQEBAeZvnN7fLr0nNUuV36x79erl86aUkpKiq666StLpk5G9e+cqHgIaNWqUz3OSlpZm7jH0+uijj3yuc2YBrOo5+/777xUUFKSMjAwdPHhQhmFo6NChldbpLYCvvfaaIiMjz3kIuLrh4eV2u3XrrbeqRYsWeu655ySdzuXIkSPNnA0aNMjnudq7d2+tFsCK/5/ffvutwsLCzMteeeUVNW3a1OcQ6/Dhw6stgHPnzlXjxo19vlbl1VdfrZUCWPG1PW/ePDVs2NDn8F1N1iHZowAOHz68UpE+l4pZPtPJkycr5cz7PuX13HPPqVGjRlqxYoV5Wor3OXjqqad8CuC9995b7SHg6gpgRd5vHNi6dat2794twzC0YcOGs66/ugJYle3bt8vhcGj9+vXasWOHuXz00UdyOBzmeXa1UQCfffZZBQUF6dSpU2Z27rvvPhmGob/85S9q2rSpFi5caD6G4cOHKzo62tw7WVsF8LHHHjPfW73++Mc/+hTAmuSMAvjzowBegDO/KiIvL09jx46Vw+HQunXrzloA58+fr9DQUPXo0UP9+vXT9u3btXDhQj333HMaMWKE+Ruwx+PR1q1bFRAQYA7mV155RaGhoVq0aFGlAuhd7wsvvKDly5fL4XDo9ttvV0pKil566SXzQyBWC+BXX32lJk2aaOjQodq8ebP27t2rv//97xo5cqROnjwpqfYK4MKFC83D5f/+97/Ny5csWWIOii+++MK83PtpZsMwVFhYqGnTpsnlcmnNmjWSTh+WCAwM1Pjx4/XWW2/pqquu0rXXXqupU6ea5/Fddtll6tOnj3JycrRhwwa1bNnS5zk5cuSIAgIC1KxZMy1dulRLliwxT8yeP3++pMoF8KuvvlJkZKQ6d+6sN954Qxs2bNC4cePkcDi0bds2nTp1SkFBQUpKSjL3RnrPr/MWwGPHjqldu3bq06ePNm7cqL1792rFihU+HwI51/DYtGmTZsyYoS1btmjo0KG6+uqrFRISYh6Cnj9/vnmoaMuWLbr77rsVExOjcePGKS8vT3fccUetFsArrrhCH3zwgXbs2KFbb71VrVq1Mn/rz8vLk8Ph0KxZs1RQUKCXX37ZPN3Bq6pBdejQIcXExOiee+5RXl6e+UlNwzDMc69qorrXtvdDIH379tWGDRtUWFiorKwsjR8/3jx/1M4FcNu2bQoNDdXYsWPl8Xi0Z88evfvuuxo3bpwkafXq1XrxxRfl8Xi0f/9+vfLKKwoICNBnn33mk7OioiK99dZbZs4OHjyo9u3bKzg4WJMnT9YHH3ygv/zlL4qMjFTPnj3l8XjMsrd69Wq9/vrr5gcHvM/B7t27FRISojFjxmjbtm3atWuXXnnlFZ8PgZyrAC5atEivvvqqduzYob1792rq1KkKDQ3VwYMHJUl33XWXzwf3cnNzNWvWLPM95EIK4IQJE3zO46uoV69emjhxoqTaKYDp6ekyjNPnnnufz8aNG8swDG3atEkOh0NDhw5VgwYNzNdV48aNFR4eLo/HozvuuEO33HJLpe083wJYWFho/j/v3r1by5cvN99LvXvJq8tZVfcrUQBrGwXwArjP+LLYiIgI9ejRQytWrJB07i8DfvPNN9WwYUM5HA41bNhQ1113nVauXKn9+/ebQ9h7uxUrVig+Pl6GcfpTV88++6wkVSqA3vV26dJFISEhCgsLU0hIiEJCQnTLLbeYh/esFkBJ2rNnj37zm98oOjpaoaGhSkpK0sSJE809NrVVACt+KrIi7wcozjwXJz8/3+crXiIjIxUcHGx+xYj3a1m8JTE4OFjdunUzPwX45JNPmm+WAQEBateuXaU9gJK0fPly8ytiHA6HLrvsMhmGoTfffFNS5QIonT63yfupWcM4fa7iTTfdZD5nnTp1UkxMjJxOpzp16qSsrCyfAiid/hqb2267TZGRkQoLC9PVV1+tzZs3S6p+eOTl5WnAgAFq0qSJAgICFB4erpdeesnn+t6TtIODgxUVFaVGjRrJ5XKpadOmGjduXK0WwNWrV+uKK65QSEiIevToUamgzZ07V3FxcQoPD9c999yjGTNmVFsApdN7cjt16qSQkBB1797dPKfs888/r3Tds6nutS2dPhf1nnvuUePGjeV0OpWYmKj777/f3Cto5wIoSbm5uerfv78aNGig8PBwderUSTNmzJB0eu90cnKy+TVTnTp1MvfeVsyZ0+lUu3btzJwdPXpUKSkpat26tXnuX0BAgFq1aqVly5ZJ+mkvkcvl0oABA8xPt1Z8DrKystSrVy85nU5FR0drwIAB5nZXVwBXrVqla665RpGRkQoPD9e1116rDz74wLzu8ePHNW3aNLVu3VrBwcFq3ry5fvOb35jvIedbAI8dO6ZGjRpVOo/N67nnnlPjxo117NixWimAHo9HaWlpio2NNc+f9J4j/t1332nu3Lnmp/29r6tWrVrptttuMz9s07Vr10rbeb4FUPrpa2CcTqf69u2ruXPnyjAMnw9pnStnVd2vRAGsbRRAAPXSkiVLFBwcrPLy8rreFMCv/fnPf1bLli3rejNwBgoggHph8eLF+uijj1RYWKhVq1apRYsWuuuuu+p6swC/M2fOHOXm5mrv3r16/fXXFRUVpalTp9b1ZuEMFEAA9cLs2bMVHx8vp9Op1q1ba+LEifwNU6AOTJw40fzrRm3bttWTTz5Za19ojtpDAQQAAPAzFEAAAAA/QwEEAADwMxRAAAAAP0MBBAAA8DMUQAB+6WxfjHwhzvXl77Wtqi/IBYDzRQEEUGfO/MsbMTExGjBggLZt23ZR7psCCMBfUQAB1Jkz//aux+PRzTffrLi4uIty3xRAAP6KAgigzlRVwjZs2CDDMPTNN99U+ju1ksw/dL9v3z5JP/2N1vfee09JSUkKDw/XgAEDVFJSYt7m5MmTevjhhxUVFaWYmBhNmjRJ99xzj899//jjj5o9e7YSEhLkcrnUqVMnvf322+bPv/32Ww0fPlyNGzeWy+VSmzZttHDhQkmVC+DJkyd13333qXXr1nK5XGrXrp1eeOGFKh/7s88+q+bNmysmJkZjx47V8ePHzet8/fXXGjRokFwul1q3bq0lS5ZQAAHUCgoggDpzZgE8cuSIRo0apTZt2ujUqVM1LoDBwcG64YYbtGXLFm3dulUdOnTQ8OHDzdvMnj1bUVFRWrFihfLy8vS73/1OERERPvf9+OOPKykpSe+995727t2rRYsWyel0KisrS5L04IMPqkuXLtqyZYv27dunf/zjH/q///s/SZUL4PHjxzVt2jTl5uaqsLBQS5YsUVhYmJYvX+7z2CMjIzV69Gjt2rVLq1evVlhYmObNm2deZ+DAgbryyiuVk5Ojjz/+WL169VJoaCgFEIBlFEAAdcbtdiswMFDh4eEKDw+XYRiKjY3V1q1bJanGBdAwDBUUFJjXmTNnjpo1a2b+OzY2VrNmzTL/feLECbVs2dIsgN9//71cLpdycnJ8tu93v/ud7rzzTknSLbfcopEjR1b5OGpyCHjs2LG67bbbfB57fHy8Tp48aV52++2364477pAk7d69W4ZhaNOmTebPd+3aJcMwKIAALKMAAqgzbrdbN9xwg/Lz85Wfn6/NmzfL7XaradOm2r9/f40LYFhYmM96V65cKYfDIUkqKyuTYRhav369z3WGDBliFsDc3FwZhmEWUe8SHBysnj17SpLWrl2r0NBQde7cWZMmTVJ2dra5rqoK4Ny5c9W9e3c1btzYXFePHj18HvtNN93ks03jx4/Xr371K0nSO++8o6CgIJ+CKEnR0dEUQACWUQAB1JmqzgE8efKkwsPDNXXqVK1fv16GYejbb781f+4ta2eeA1jRqlWrZBin395qUgA3bdokwzCUlZVlllHv8sUXX5i3+eabb7Ro0SLdddddcrlc+sMf/iCpcgFcvny5XC6X5syZo08++UT5+fl64IEH1Llz53M+9gkTJig5Odl8DEFBQTp16pTPdSiAAGoDBn3VCwAAAhxJREFUBRBAnamqBJ06dUoRERF65JFHlJeXJ8MwtHPnTvPn8+bNO68CKJ0+BDx79mzz3ydOnFBcXJx534cPH5bT6dTrr79e423/3//9X0VEREiqXADHjRun66+/3uf6/fr1O68C+Pnnn8swDG3evNn8ufcyCiAAqyiAAOrMmV8Dk5eXp7Fjx8rhcGjdunU6fvy44uLidPvtt2v37t3KzMxU+/btz7sAzpo1Sw0bNtTKlSu1a9cu3X///ZU+BDJ16lQ1atRIr732mgoKCvTJJ5/o5Zdf1muvvSZJ+tOf/qR33nlH+fn5+uyzzzRo0CDz8PCZBfCFF15QZGSk3nvvPe3evVt//OMfFRkZeV4FUJJuvPFGderUSZs2bdLHH3+s3r178yEQALWCAgigzpz5RdARERHq0aOHVqxYYV5n48aNuuqqq+RyudSnTx+9/fbb510AT5w4oQkTJigyMlLR0dF65JFHqvwamBdffFHt27dXcHCwmjRpogEDBpiHjp966il16NBBoaGhiomJ0eDBg1VYWCipcgE8evSo7r33XkVFRSk6OlpjxoxRSkrKeRfA0tJS3XzzzXI6nWrVqpVef/11vgYGQK2gAAIAAPgZCiAAAICfoQACAAD4GQogAACAn6EAAgAA+BkKIAAAgJ+hAAIAAPgZCiAAAICfoQACAAD4GQogAACAn6EAAgAA+BkKIAAAgJ+hAAIAAPiZ/w8fLIalM4ntzQAAAABJRU5ErkJggg==\" 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\" width=\"1000\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
@@ -1705,2069 +1550,39 @@
{
"data": {
"text/plain": [
- "<AxesSubplot:xlabel='Bundesland'>"
+ "Text(58, 1.01, 'Korrelation = -0.823')"
]
},
- "execution_count": 4,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "sterb_21 = df[df.Jahr==2021].groupby(['Bundesland']).mean()\n",
- "uebersterb = sterb_21 / sterb_norm\n",
- "uebersterb = uebersterb.mean(axis=1)\n",
- "uebersterb.name = \"Übersterblichkeit\"\n",
- "uebersterb.sort_values().plot(style='.')\n",
+ "fig, ax = plt.subplots(figsize=(10,7))\n",
+ "plot1 = df_total.plot.scatter(x=\"Impfquote\",y=\"Übersterblichkeit\",title=\"Übersterblichkeit vs Impfquote\", grid=True,\n",
+ " ax=ax, style='o', legend=False, s=150, fontsize=18,\n",
+ " color=range(len(df_total)), colormap='Spectral')\n",
+ "#ax.legend(plot1,[\"1\",\"2\"],fancybox=True)\n",
+ "#df_total.head()\n",
"\n",
- "#uebersterb.mean(axis=1).plot.scatter(x=\"Bundesland\")\n",
- "#df_totalPerWeek = df[df.Jahr<2020].groupby('Bundesland','Jahr').sum()\n",
- "#df_totalPerWeek=df_totalPerWeek.drop('Jahr', axis=1)\n",
- "#df_totalPerWeek.head()"
+ "for k, v in df_total.iterrows():\n",
+ " ax.annotate(laender_short[k], v,\n",
+ " xytext=(9,-3), textcoords='offset points',\n",
+ " family='sans-serif', fontsize=11)\n",
+ "ax.set(title='Übersterblichkeit vs Impfquote',\n",
+ " ylabel='Todesfälle KW1-43 (2021)\\nverglichen mit Durchschnitt 2016-2019',\n",
+ " xlabel=\"Impfquote (2.-Impfungen, Stand 30.11.2021)\\nDaten von DESTATIS, sowie RKI (Lizenz CC-BY)\")\n",
+ "ax.text(58,1.01,\"Korrelation = %0.3f\"%correlation[\"Übersterblichkeit\"][\"Impfquote\"])"
]
},
{
"cell_type": "code",
- "execution_count": 7,
- "id": "c9612c26",
+ "execution_count": null,
+ "id": "277003c9",
"metadata": {},
"outputs": [],
- "source": [
- "#https://github.com/robert-koch-institut/COVID-19-Impfungen_in_Deutschland"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "9d77bb4a",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/javascript": [
- "/* Put everything inside the global mpl namespace */\n",
- "/* global mpl */\n",
- "window.mpl = {};\n",
- "\n",
- "mpl.get_websocket_type = function () {\n",
- " if (typeof WebSocket !== 'undefined') {\n",
- " return WebSocket;\n",
- " } else if (typeof MozWebSocket !== 'undefined') {\n",
- " return MozWebSocket;\n",
- " } else {\n",
- " alert(\n",
- " 'Your browser does not have WebSocket support. ' +\n",
- " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
- " 'Firefox 4 and 5 are also supported but you ' +\n",
- " 'have to enable WebSockets in about:config.'\n",
- " );\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
- " this.id = figure_id;\n",
- "\n",
- " this.ws = websocket;\n",
- "\n",
- " this.supports_binary = this.ws.binaryType !== undefined;\n",
- "\n",
- " if (!this.supports_binary) {\n",
- " var warnings = document.getElementById('mpl-warnings');\n",
- " if (warnings) {\n",
- " warnings.style.display = 'block';\n",
- " warnings.textContent =\n",
- " 'This browser does not support binary websocket messages. ' +\n",
- " 'Performance may be slow.';\n",
- " }\n",
- " }\n",
- "\n",
- " this.imageObj = new Image();\n",
- "\n",
- " this.context = undefined;\n",
- " this.message = undefined;\n",
- " this.canvas = undefined;\n",
- " this.rubberband_canvas = undefined;\n",
- " this.rubberband_context = undefined;\n",
- " this.format_dropdown = undefined;\n",
- "\n",
- " this.image_mode = 'full';\n",
- "\n",
- " this.root = document.createElement('div');\n",
- " this.root.setAttribute('style', 'display: inline-block');\n",
- " this._root_extra_style(this.root);\n",
- "\n",
- " parent_element.appendChild(this.root);\n",
- "\n",
- " this._init_header(this);\n",
- " this._init_canvas(this);\n",
- " this._init_toolbar(this);\n",
- "\n",
- " var fig = this;\n",
- "\n",
- " this.waiting = false;\n",
- "\n",
- " this.ws.onopen = function () {\n",
- " fig.send_message('supports_binary', { value: fig.supports_binary });\n",
- " fig.send_message('send_image_mode', {});\n",
- " if (fig.ratio !== 1) {\n",
- " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
- " }\n",
- " fig.send_message('refresh', {});\n",
- " };\n",
- "\n",
- " this.imageObj.onload = function () {\n",
- " if (fig.image_mode === 'full') {\n",
- " // Full images could contain transparency (where diff images\n",
- " // almost always do), so we need to clear the canvas so that\n",
- " // there is no ghosting.\n",
- " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
- " }\n",
- " fig.context.drawImage(fig.imageObj, 0, 0);\n",
- " };\n",
- "\n",
- " this.imageObj.onunload = function () {\n",
- " fig.ws.close();\n",
- " };\n",
- "\n",
- " this.ws.onmessage = this._make_on_message_function(this);\n",
- "\n",
- " this.ondownload = ondownload;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._init_header = function () {\n",
- " var titlebar = document.createElement('div');\n",
- " titlebar.classList =\n",
- " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
- " var titletext = document.createElement('div');\n",
- " titletext.classList = 'ui-dialog-title';\n",
- " titletext.setAttribute(\n",
- " 'style',\n",
- " 'width: 100%; text-align: center; padding: 3px;'\n",
- " );\n",
- " titlebar.appendChild(titletext);\n",
- " this.root.appendChild(titlebar);\n",
- " this.header = titletext;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
- "\n",
- "mpl.figure.prototype._init_canvas = function () {\n",
- " var fig = this;\n",
- "\n",
- " var canvas_div = (this.canvas_div = document.createElement('div'));\n",
- " canvas_div.setAttribute(\n",
- " 'style',\n",
- " 'border: 1px solid #ddd;' +\n",
- " 'box-sizing: content-box;' +\n",
- " 'clear: both;' +\n",
- " 'min-height: 1px;' +\n",
- " 'min-width: 1px;' +\n",
- " 'outline: 0;' +\n",
- " 'overflow: hidden;' +\n",
- " 'position: relative;' +\n",
- " 'resize: both;'\n",
- " );\n",
- "\n",
- " function on_keyboard_event_closure(name) {\n",
- " return function (event) {\n",
- " return fig.key_event(event, name);\n",
- " };\n",
- " }\n",
- "\n",
- " canvas_div.addEventListener(\n",
- " 'keydown',\n",
- " on_keyboard_event_closure('key_press')\n",
- " );\n",
- " canvas_div.addEventListener(\n",
- " 'keyup',\n",
- " on_keyboard_event_closure('key_release')\n",
- " );\n",
- "\n",
- " this._canvas_extra_style(canvas_div);\n",
- " this.root.appendChild(canvas_div);\n",
- "\n",
- " var canvas = (this.canvas = document.createElement('canvas'));\n",
- " canvas.classList.add('mpl-canvas');\n",
- " canvas.setAttribute('style', 'box-sizing: content-box;');\n",
- "\n",
- " this.context = canvas.getContext('2d');\n",
- "\n",
- " var backingStore =\n",
- " this.context.backingStorePixelRatio ||\n",
- " this.context.webkitBackingStorePixelRatio ||\n",
- " this.context.mozBackingStorePixelRatio ||\n",
- " this.context.msBackingStorePixelRatio ||\n",
- " this.context.oBackingStorePixelRatio ||\n",
- " this.context.backingStorePixelRatio ||\n",
- " 1;\n",
- "\n",
- " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
- "\n",
- " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
- " 'canvas'\n",
- " ));\n",
- " rubberband_canvas.setAttribute(\n",
- " 'style',\n",
- " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
- " );\n",
- "\n",
- " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
- " if (this.ResizeObserver === undefined) {\n",
- " if (window.ResizeObserver !== undefined) {\n",
- " this.ResizeObserver = window.ResizeObserver;\n",
- " } else {\n",
- " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
- " this.ResizeObserver = obs.ResizeObserver;\n",
- " }\n",
- " }\n",
- "\n",
- " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
- " var nentries = entries.length;\n",
- " for (var i = 0; i < nentries; i++) {\n",
- " var entry = entries[i];\n",
- " var width, height;\n",
- " if (entry.contentBoxSize) {\n",
- " if (entry.contentBoxSize instanceof Array) {\n",
- " // Chrome 84 implements new version of spec.\n",
- " width = entry.contentBoxSize[0].inlineSize;\n",
- " height = entry.contentBoxSize[0].blockSize;\n",
- " } else {\n",
- " // Firefox implements old version of spec.\n",
- " width = entry.contentBoxSize.inlineSize;\n",
- " height = entry.contentBoxSize.blockSize;\n",
- " }\n",
- " } else {\n",
- " // Chrome <84 implements even older version of spec.\n",
- " width = entry.contentRect.width;\n",
- " height = entry.contentRect.height;\n",
- " }\n",
- "\n",
- " // Keep the size of the canvas and rubber band canvas in sync with\n",
- " // the canvas container.\n",
- " if (entry.devicePixelContentBoxSize) {\n",
- " // Chrome 84 implements new version of spec.\n",
- " canvas.setAttribute(\n",
- " 'width',\n",
- " entry.devicePixelContentBoxSize[0].inlineSize\n",
- " );\n",
- " canvas.setAttribute(\n",
- " 'height',\n",
- " entry.devicePixelContentBoxSize[0].blockSize\n",
- " );\n",
- " } else {\n",
- " canvas.setAttribute('width', width * fig.ratio);\n",
- " canvas.setAttribute('height', height * fig.ratio);\n",
- " }\n",
- " canvas.setAttribute(\n",
- " 'style',\n",
- " 'width: ' + width + 'px; height: ' + height + 'px;'\n",
- " );\n",
- "\n",
- " rubberband_canvas.setAttribute('width', width);\n",
- " rubberband_canvas.setAttribute('height', height);\n",
- "\n",
- " // And update the size in Python. We ignore the initial 0/0 size\n",
- " // that occurs as the element is placed into the DOM, which should\n",
- " // otherwise not happen due to the minimum size styling.\n",
- " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
- " fig.request_resize(width, height);\n",
- " }\n",
- " }\n",
- " });\n",
- " this.resizeObserverInstance.observe(canvas_div);\n",
- "\n",
- " function on_mouse_event_closure(name) {\n",
- " return function (event) {\n",
- " return fig.mouse_event(event, name);\n",
- " };\n",
- " }\n",
- "\n",
- " rubberband_canvas.addEventListener(\n",
- " 'mousedown',\n",
- " on_mouse_event_closure('button_press')\n",
- " );\n",
- " rubberband_canvas.addEventListener(\n",
- " 'mouseup',\n",
- " on_mouse_event_closure('button_release')\n",
- " );\n",
- " // Throttle sequential mouse events to 1 every 20ms.\n",
- " rubberband_canvas.addEventListener(\n",
- " 'mousemove',\n",
- " on_mouse_event_closure('motion_notify')\n",
- " );\n",
- "\n",
- " rubberband_canvas.addEventListener(\n",
- " 'mouseenter',\n",
- " on_mouse_event_closure('figure_enter')\n",
- " );\n",
- " rubberband_canvas.addEventListener(\n",
- " 'mouseleave',\n",
- " on_mouse_event_closure('figure_leave')\n",
- " );\n",
- "\n",
- " canvas_div.addEventListener('wheel', function (event) {\n",
- " if (event.deltaY < 0) {\n",
- " event.step = 1;\n",
- " } else {\n",
- " event.step = -1;\n",
- " }\n",
- " on_mouse_event_closure('scroll')(event);\n",
- " });\n",
- "\n",
- " canvas_div.appendChild(canvas);\n",
- " canvas_div.appendChild(rubberband_canvas);\n",
- "\n",
- " this.rubberband_context = rubberband_canvas.getContext('2d');\n",
- " this.rubberband_context.strokeStyle = '#000000';\n",
- "\n",
- " this._resize_canvas = function (width, height, forward) {\n",
- " if (forward) {\n",
- " canvas_div.style.width = width + 'px';\n",
- " canvas_div.style.height = height + 'px';\n",
- " }\n",
- " };\n",
- "\n",
- " // Disable right mouse context menu.\n",
- " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
- " event.preventDefault();\n",
- " return false;\n",
- " });\n",
- "\n",
- " function set_focus() {\n",
- " canvas.focus();\n",
- " canvas_div.focus();\n",
- " }\n",
- "\n",
- " window.setTimeout(set_focus, 100);\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function () {\n",
- " var fig = this;\n",
- "\n",
- " var toolbar = document.createElement('div');\n",
- " toolbar.classList = 'mpl-toolbar';\n",
- " this.root.appendChild(toolbar);\n",
- "\n",
- " function on_click_closure(name) {\n",
- " return function (_event) {\n",
- " return fig.toolbar_button_onclick(name);\n",
- " };\n",
- " }\n",
- "\n",
- " function on_mouseover_closure(tooltip) {\n",
- " return function (event) {\n",
- " if (!event.currentTarget.disabled) {\n",
- " return fig.toolbar_button_onmouseover(tooltip);\n",
- " }\n",
- " };\n",
- " }\n",
- "\n",
- " fig.buttons = {};\n",
- " var buttonGroup = document.createElement('div');\n",
- " buttonGroup.classList = 'mpl-button-group';\n",
- " for (var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " /* Instead of a spacer, we start a new button group. */\n",
- " if (buttonGroup.hasChildNodes()) {\n",
- " toolbar.appendChild(buttonGroup);\n",
- " }\n",
- " buttonGroup = document.createElement('div');\n",
- " buttonGroup.classList = 'mpl-button-group';\n",
- " continue;\n",
- " }\n",
- "\n",
- " var button = (fig.buttons[name] = document.createElement('button'));\n",
- " button.classList = 'mpl-widget';\n",
- " button.setAttribute('role', 'button');\n",
- " button.setAttribute('aria-disabled', 'false');\n",
- " button.addEventListener('click', on_click_closure(method_name));\n",
- " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
- "\n",
- " var icon_img = document.createElement('img');\n",
- " icon_img.src = '_images/' + image + '.png';\n",
- " icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
- " icon_img.alt = tooltip;\n",
- " button.appendChild(icon_img);\n",
- "\n",
- " buttonGroup.appendChild(button);\n",
- " }\n",
- "\n",
- " if (buttonGroup.hasChildNodes()) {\n",
- " toolbar.appendChild(buttonGroup);\n",
- " }\n",
- "\n",
- " var fmt_picker = document.createElement('select');\n",
- " fmt_picker.classList = 'mpl-widget';\n",
- " toolbar.appendChild(fmt_picker);\n",
- " this.format_dropdown = fmt_picker;\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = document.createElement('option');\n",
- " option.selected = fmt === mpl.default_extension;\n",
- " option.innerHTML = fmt;\n",
- " fmt_picker.appendChild(option);\n",
- " }\n",
- "\n",
- " var status_bar = document.createElement('span');\n",
- " status_bar.classList = 'mpl-message';\n",
- " toolbar.appendChild(status_bar);\n",
- " this.message = status_bar;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.send_message = function (type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function () {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1], msg['forward']);\n",
- " fig.send_message('refresh', {});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
- " var x0 = msg['x0'] / fig.ratio;\n",
- " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
- " var x1 = msg['x1'] / fig.ratio;\n",
- " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0,\n",
- " 0,\n",
- " fig.canvas.width / fig.ratio,\n",
- " fig.canvas.height / fig.ratio\n",
- " );\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch (cursor) {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
- " for (var key in msg) {\n",
- " if (!(key in fig.buttons)) {\n",
- " continue;\n",
- " }\n",
- " fig.buttons[key].disabled = !msg[key];\n",
- " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
- " if (msg['mode'] === 'PAN') {\n",
- " fig.buttons['Pan'].classList.add('active');\n",
- " fig.buttons['Zoom'].classList.remove('active');\n",
- " } else if (msg['mode'] === 'ZOOM') {\n",
- " fig.buttons['Pan'].classList.remove('active');\n",
- " fig.buttons['Zoom'].classList.add('active');\n",
- " } else {\n",
- " fig.buttons['Pan'].classList.remove('active');\n",
- " fig.buttons['Zoom'].classList.remove('active');\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function () {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message('ack', {});\n",
- "};\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = 'image/png';\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src\n",
- " );\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data\n",
- " );\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " } else if (\n",
- " typeof evt.data === 'string' &&\n",
- " evt.data.slice(0, 21) === 'data:image/png;base64'\n",
- " ) {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig['handle_' + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\n",
- " \"No handler for the '\" + msg_type + \"' message type: \",\n",
- " msg\n",
- " );\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\n",
- " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
- " e,\n",
- " e.stack,\n",
- " msg\n",
- " );\n",
- " }\n",
- " }\n",
- " };\n",
- "};\n",
- "\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function (e) {\n",
- " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
- " var targ;\n",
- " if (!e) {\n",
- " e = window.event;\n",
- " }\n",
- " if (e.target) {\n",
- " targ = e.target;\n",
- " } else if (e.srcElement) {\n",
- " targ = e.srcElement;\n",
- " }\n",
- " if (targ.nodeType === 3) {\n",
- " // defeat Safari bug\n",
- " targ = targ.parentNode;\n",
- " }\n",
- "\n",
- " // pageX,Y are the mouse positions relative to the document\n",
- " var boundingRect = targ.getBoundingClientRect();\n",
- " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
- " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
- "\n",
- " return { x: x, y: y };\n",
- "};\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys(original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object') {\n",
- " obj[key] = original[key];\n",
- " }\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function (event, name) {\n",
- " var canvas_pos = mpl.findpos(event);\n",
- "\n",
- " if (name === 'button_press') {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " var x = canvas_pos.x * this.ratio;\n",
- " var y = canvas_pos.y * this.ratio;\n",
- "\n",
- " this.send_message(name, {\n",
- " x: x,\n",
- " y: y,\n",
- " button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event),\n",
- " });\n",
- "\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We want\n",
- " * to control all of the cursor setting manually through the\n",
- " * 'cursor' event from matplotlib */\n",
- " event.preventDefault();\n",
- " return false;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.key_event = function (event, name) {\n",
- " // Prevent repeat events\n",
- " if (name === 'key_press') {\n",
- " if (event.which === this._key) {\n",
- " return;\n",
- " } else {\n",
- " this._key = event.which;\n",
- " }\n",
- " }\n",
- " if (name === 'key_release') {\n",
- " this._key = null;\n",
- " }\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.which !== 17) {\n",
- " value += 'ctrl+';\n",
- " }\n",
- " if (event.altKey && event.which !== 18) {\n",
- " value += 'alt+';\n",
- " }\n",
- " if (event.shiftKey && event.which !== 16) {\n",
- " value += 'shift+';\n",
- " }\n",
- "\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
- " return false;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
- " if (name === 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message('toolbar_button', { name: name });\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "\n",
- "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
- "// prettier-ignore\n",
- "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
- "\n",
- "mpl.default_extension = \"png\";/* global mpl */\n",
- "\n",
- "var comm_websocket_adapter = function (comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.close = function () {\n",
- " comm.close();\n",
- " };\n",
- " ws.send = function (m) {\n",
- " //console.log('sending', m);\n",
- " comm.send(m);\n",
- " };\n",
- " // Register the callback with on_msg.\n",
- " comm.on_msg(function (msg) {\n",
- " //console.log('receiving', msg['content']['data'], msg);\n",
- " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data']);\n",
- " });\n",
- " return ws;\n",
- "};\n",
- "\n",
- "mpl.mpl_figure_comm = function (comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = document.getElementById(id);\n",
- " var ws_proxy = comm_websocket_adapter(comm);\n",
- "\n",
- " function ondownload(figure, _format) {\n",
- " window.open(figure.canvas.toDataURL());\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element;\n",
- " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
- " if (!fig.cell_info) {\n",
- " console.error('Failed to find cell for figure', id, fig);\n",
- " return;\n",
- " }\n",
- " fig.cell_info[0].output_area.element.on(\n",
- " 'cleared',\n",
- " { fig: fig },\n",
- " fig._remove_fig_handler\n",
- " );\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
- " var width = fig.canvas.width / fig.ratio;\n",
- " fig.cell_info[0].output_area.element.off(\n",
- " 'cleared',\n",
- " fig._remove_fig_handler\n",
- " );\n",
- " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable();\n",
- " fig.parent_element.innerHTML =\n",
- " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
- " fig.close_ws(fig, msg);\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var width = this.canvas.width / this.ratio;\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] =\n",
- " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function () {\n",
- " // Tell IPython that the notebook contents must change.\n",
- " IPython.notebook.set_dirty(true);\n",
- " this.send_message('ack', {});\n",
- " var fig = this;\n",
- " // Wait a second, then push the new image to the DOM so\n",
- " // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () {\n",
- " fig.push_to_output();\n",
- " }, 1000);\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function () {\n",
- " var fig = this;\n",
- "\n",
- " var toolbar = document.createElement('div');\n",
- " toolbar.classList = 'btn-toolbar';\n",
- " this.root.appendChild(toolbar);\n",
- "\n",
- " function on_click_closure(name) {\n",
- " return function (_event) {\n",
- " return fig.toolbar_button_onclick(name);\n",
- " };\n",
- " }\n",
- "\n",
- " function on_mouseover_closure(tooltip) {\n",
- " return function (event) {\n",
- " if (!event.currentTarget.disabled) {\n",
- " return fig.toolbar_button_onmouseover(tooltip);\n",
- " }\n",
- " };\n",
- " }\n",
- "\n",
- " fig.buttons = {};\n",
- " var buttonGroup = document.createElement('div');\n",
- " buttonGroup.classList = 'btn-group';\n",
- " var button;\n",
- " for (var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " /* Instead of a spacer, we start a new button group. */\n",
- " if (buttonGroup.hasChildNodes()) {\n",
- " toolbar.appendChild(buttonGroup);\n",
- " }\n",
- " buttonGroup = document.createElement('div');\n",
- " buttonGroup.classList = 'btn-group';\n",
- " continue;\n",
- " }\n",
- "\n",
- " button = fig.buttons[name] = document.createElement('button');\n",
- " button.classList = 'btn btn-default';\n",
- " button.href = '#';\n",
- " button.title = name;\n",
- " button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
- " button.addEventListener('click', on_click_closure(method_name));\n",
- " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
- " buttonGroup.appendChild(button);\n",
- " }\n",
- "\n",
- " if (buttonGroup.hasChildNodes()) {\n",
- " toolbar.appendChild(buttonGroup);\n",
- " }\n",
- "\n",
- " // Add the status bar.\n",
- " var status_bar = document.createElement('span');\n",
- " status_bar.classList = 'mpl-message pull-right';\n",
- " toolbar.appendChild(status_bar);\n",
- " this.message = status_bar;\n",
- "\n",
- " // Add the close button to the window.\n",
- " var buttongrp = document.createElement('div');\n",
- " buttongrp.classList = 'btn-group inline pull-right';\n",
- " button = document.createElement('button');\n",
- " button.classList = 'btn btn-mini btn-primary';\n",
- " button.href = '#';\n",
- " button.title = 'Stop Interaction';\n",
- " button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
- " button.addEventListener('click', function (_evt) {\n",
- " fig.handle_close(fig, {});\n",
- " });\n",
- " button.addEventListener(\n",
- " 'mouseover',\n",
- " on_mouseover_closure('Stop Interaction')\n",
- " );\n",
- " buttongrp.appendChild(button);\n",
- " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
- " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
- " var fig = event.data.fig;\n",
- " if (event.target !== this) {\n",
- " // Ignore bubbled events from children.\n",
- " return;\n",
- " }\n",
- " fig.close_ws(fig, {});\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function (el) {\n",
- " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
- " // this is important to make the div 'focusable\n",
- " el.setAttribute('tabindex', 0);\n",
- " // reach out to IPython and tell the keyboard manager to turn it's self\n",
- " // off when our div gets focus\n",
- "\n",
- " // location in version 3\n",
- " if (IPython.notebook.keyboard_manager) {\n",
- " IPython.notebook.keyboard_manager.register_events(el);\n",
- " } else {\n",
- " // location in version 2\n",
- " IPython.keyboard_manager.register_events(el);\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager) {\n",
- " manager = IPython.keyboard_manager;\n",
- " }\n",
- "\n",
- " // Check for shift+enter\n",
- " if (event.shiftKey && event.which === 13) {\n",
- " this.canvas_div.blur();\n",
- " // select the cell after this one\n",
- " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
- " IPython.notebook.select(index + 1);\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
- " fig.ondownload(fig, null);\n",
- "};\n",
- "\n",
- "mpl.find_output_cell = function (html_output) {\n",
- " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
- " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
- " // IPython event is triggered only after the cells have been serialised, which for\n",
- " // our purposes (turning an active figure into a static one), is too late.\n",
- " var cells = IPython.notebook.get_cells();\n",
- " var ncells = cells.length;\n",
- " for (var i = 0; i < ncells; i++) {\n",
- " var cell = cells[i];\n",
- " if (cell.cell_type === 'code') {\n",
- " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
- " var data = cell.output_area.outputs[j];\n",
- " if (data.data) {\n",
- " // IPython >= 3 moved mimebundle to data attribute of output\n",
- " data = data.data;\n",
- " }\n",
- " if (data['text/html'] === html_output) {\n",
- " return [cell, data, j];\n",
- " }\n",
- " }\n",
- " }\n",
- " }\n",
- "};\n",
- "\n",
- "// Register the function which deals with the matplotlib target/channel.\n",
- "// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel !== null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target(\n",
- " 'matplotlib',\n",
- " mpl.mpl_figure_comm\n",
- " );\n",
- "}\n"
- ],
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\" width=\"640\">"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "df_impf = pd.read_csv('Impfquoten.csv')\n",
- "df_impf = df_impf[df_impf.Bundesland!=\"Deutschland\"].drop('Datum', axis=1) # Drop column 'Datum', row \"Deutschland\"\n",
- "df_impf = df_impf.set_index('Bundesland')\n",
- "#df_impf.columns\n",
- "df_impf.sort_values(by='Impfquote').plot(style='.')\n",
- "#Merge both dataset into a combined one\n",
- "df_total = pd.merge(df_impf,uebersterb, left_index=True, right_index=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "8e99105a",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/javascript": [
- "/* Put everything inside the global mpl namespace */\n",
- "/* global mpl */\n",
- "window.mpl = {};\n",
- "\n",
- "mpl.get_websocket_type = function () {\n",
- " if (typeof WebSocket !== 'undefined') {\n",
- " return WebSocket;\n",
- " } else if (typeof MozWebSocket !== 'undefined') {\n",
- " return MozWebSocket;\n",
- " } else {\n",
- " alert(\n",
- " 'Your browser does not have WebSocket support. ' +\n",
- " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
- " 'Firefox 4 and 5 are also supported but you ' +\n",
- " 'have to enable WebSockets in about:config.'\n",
- " );\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
- " this.id = figure_id;\n",
- "\n",
- " this.ws = websocket;\n",
- "\n",
- " this.supports_binary = this.ws.binaryType !== undefined;\n",
- "\n",
- " if (!this.supports_binary) {\n",
- " var warnings = document.getElementById('mpl-warnings');\n",
- " if (warnings) {\n",
- " warnings.style.display = 'block';\n",
- " warnings.textContent =\n",
- " 'This browser does not support binary websocket messages. ' +\n",
- " 'Performance may be slow.';\n",
- " }\n",
- " }\n",
- "\n",
- " this.imageObj = new Image();\n",
- "\n",
- " this.context = undefined;\n",
- " this.message = undefined;\n",
- " this.canvas = undefined;\n",
- " this.rubberband_canvas = undefined;\n",
- " this.rubberband_context = undefined;\n",
- " this.format_dropdown = undefined;\n",
- "\n",
- " this.image_mode = 'full';\n",
- "\n",
- " this.root = document.createElement('div');\n",
- " this.root.setAttribute('style', 'display: inline-block');\n",
- " this._root_extra_style(this.root);\n",
- "\n",
- " parent_element.appendChild(this.root);\n",
- "\n",
- " this._init_header(this);\n",
- " this._init_canvas(this);\n",
- " this._init_toolbar(this);\n",
- "\n",
- " var fig = this;\n",
- "\n",
- " this.waiting = false;\n",
- "\n",
- " this.ws.onopen = function () {\n",
- " fig.send_message('supports_binary', { value: fig.supports_binary });\n",
- " fig.send_message('send_image_mode', {});\n",
- " if (fig.ratio !== 1) {\n",
- " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
- " }\n",
- " fig.send_message('refresh', {});\n",
- " };\n",
- "\n",
- " this.imageObj.onload = function () {\n",
- " if (fig.image_mode === 'full') {\n",
- " // Full images could contain transparency (where diff images\n",
- " // almost always do), so we need to clear the canvas so that\n",
- " // there is no ghosting.\n",
- " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
- " }\n",
- " fig.context.drawImage(fig.imageObj, 0, 0);\n",
- " };\n",
- "\n",
- " this.imageObj.onunload = function () {\n",
- " fig.ws.close();\n",
- " };\n",
- "\n",
- " this.ws.onmessage = this._make_on_message_function(this);\n",
- "\n",
- " this.ondownload = ondownload;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._init_header = function () {\n",
- " var titlebar = document.createElement('div');\n",
- " titlebar.classList =\n",
- " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
- " var titletext = document.createElement('div');\n",
- " titletext.classList = 'ui-dialog-title';\n",
- " titletext.setAttribute(\n",
- " 'style',\n",
- " 'width: 100%; text-align: center; padding: 3px;'\n",
- " );\n",
- " titlebar.appendChild(titletext);\n",
- " this.root.appendChild(titlebar);\n",
- " this.header = titletext;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
- "\n",
- "mpl.figure.prototype._init_canvas = function () {\n",
- " var fig = this;\n",
- "\n",
- " var canvas_div = (this.canvas_div = document.createElement('div'));\n",
- " canvas_div.setAttribute(\n",
- " 'style',\n",
- " 'border: 1px solid #ddd;' +\n",
- " 'box-sizing: content-box;' +\n",
- " 'clear: both;' +\n",
- " 'min-height: 1px;' +\n",
- " 'min-width: 1px;' +\n",
- " 'outline: 0;' +\n",
- " 'overflow: hidden;' +\n",
- " 'position: relative;' +\n",
- " 'resize: both;'\n",
- " );\n",
- "\n",
- " function on_keyboard_event_closure(name) {\n",
- " return function (event) {\n",
- " return fig.key_event(event, name);\n",
- " };\n",
- " }\n",
- "\n",
- " canvas_div.addEventListener(\n",
- " 'keydown',\n",
- " on_keyboard_event_closure('key_press')\n",
- " );\n",
- " canvas_div.addEventListener(\n",
- " 'keyup',\n",
- " on_keyboard_event_closure('key_release')\n",
- " );\n",
- "\n",
- " this._canvas_extra_style(canvas_div);\n",
- " this.root.appendChild(canvas_div);\n",
- "\n",
- " var canvas = (this.canvas = document.createElement('canvas'));\n",
- " canvas.classList.add('mpl-canvas');\n",
- " canvas.setAttribute('style', 'box-sizing: content-box;');\n",
- "\n",
- " this.context = canvas.getContext('2d');\n",
- "\n",
- " var backingStore =\n",
- " this.context.backingStorePixelRatio ||\n",
- " this.context.webkitBackingStorePixelRatio ||\n",
- " this.context.mozBackingStorePixelRatio ||\n",
- " this.context.msBackingStorePixelRatio ||\n",
- " this.context.oBackingStorePixelRatio ||\n",
- " this.context.backingStorePixelRatio ||\n",
- " 1;\n",
- "\n",
- " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
- "\n",
- " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
- " 'canvas'\n",
- " ));\n",
- " rubberband_canvas.setAttribute(\n",
- " 'style',\n",
- " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
- " );\n",
- "\n",
- " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
- " if (this.ResizeObserver === undefined) {\n",
- " if (window.ResizeObserver !== undefined) {\n",
- " this.ResizeObserver = window.ResizeObserver;\n",
- " } else {\n",
- " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
- " this.ResizeObserver = obs.ResizeObserver;\n",
- " }\n",
- " }\n",
- "\n",
- " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
- " var nentries = entries.length;\n",
- " for (var i = 0; i < nentries; i++) {\n",
- " var entry = entries[i];\n",
- " var width, height;\n",
- " if (entry.contentBoxSize) {\n",
- " if (entry.contentBoxSize instanceof Array) {\n",
- " // Chrome 84 implements new version of spec.\n",
- " width = entry.contentBoxSize[0].inlineSize;\n",
- " height = entry.contentBoxSize[0].blockSize;\n",
- " } else {\n",
- " // Firefox implements old version of spec.\n",
- " width = entry.contentBoxSize.inlineSize;\n",
- " height = entry.contentBoxSize.blockSize;\n",
- " }\n",
- " } else {\n",
- " // Chrome <84 implements even older version of spec.\n",
- " width = entry.contentRect.width;\n",
- " height = entry.contentRect.height;\n",
- " }\n",
- "\n",
- " // Keep the size of the canvas and rubber band canvas in sync with\n",
- " // the canvas container.\n",
- " if (entry.devicePixelContentBoxSize) {\n",
- " // Chrome 84 implements new version of spec.\n",
- " canvas.setAttribute(\n",
- " 'width',\n",
- " entry.devicePixelContentBoxSize[0].inlineSize\n",
- " );\n",
- " canvas.setAttribute(\n",
- " 'height',\n",
- " entry.devicePixelContentBoxSize[0].blockSize\n",
- " );\n",
- " } else {\n",
- " canvas.setAttribute('width', width * fig.ratio);\n",
- " canvas.setAttribute('height', height * fig.ratio);\n",
- " }\n",
- " canvas.setAttribute(\n",
- " 'style',\n",
- " 'width: ' + width + 'px; height: ' + height + 'px;'\n",
- " );\n",
- "\n",
- " rubberband_canvas.setAttribute('width', width);\n",
- " rubberband_canvas.setAttribute('height', height);\n",
- "\n",
- " // And update the size in Python. We ignore the initial 0/0 size\n",
- " // that occurs as the element is placed into the DOM, which should\n",
- " // otherwise not happen due to the minimum size styling.\n",
- " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
- " fig.request_resize(width, height);\n",
- " }\n",
- " }\n",
- " });\n",
- " this.resizeObserverInstance.observe(canvas_div);\n",
- "\n",
- " function on_mouse_event_closure(name) {\n",
- " return function (event) {\n",
- " return fig.mouse_event(event, name);\n",
- " };\n",
- " }\n",
- "\n",
- " rubberband_canvas.addEventListener(\n",
- " 'mousedown',\n",
- " on_mouse_event_closure('button_press')\n",
- " );\n",
- " rubberband_canvas.addEventListener(\n",
- " 'mouseup',\n",
- " on_mouse_event_closure('button_release')\n",
- " );\n",
- " // Throttle sequential mouse events to 1 every 20ms.\n",
- " rubberband_canvas.addEventListener(\n",
- " 'mousemove',\n",
- " on_mouse_event_closure('motion_notify')\n",
- " );\n",
- "\n",
- " rubberband_canvas.addEventListener(\n",
- " 'mouseenter',\n",
- " on_mouse_event_closure('figure_enter')\n",
- " );\n",
- " rubberband_canvas.addEventListener(\n",
- " 'mouseleave',\n",
- " on_mouse_event_closure('figure_leave')\n",
- " );\n",
- "\n",
- " canvas_div.addEventListener('wheel', function (event) {\n",
- " if (event.deltaY < 0) {\n",
- " event.step = 1;\n",
- " } else {\n",
- " event.step = -1;\n",
- " }\n",
- " on_mouse_event_closure('scroll')(event);\n",
- " });\n",
- "\n",
- " canvas_div.appendChild(canvas);\n",
- " canvas_div.appendChild(rubberband_canvas);\n",
- "\n",
- " this.rubberband_context = rubberband_canvas.getContext('2d');\n",
- " this.rubberband_context.strokeStyle = '#000000';\n",
- "\n",
- " this._resize_canvas = function (width, height, forward) {\n",
- " if (forward) {\n",
- " canvas_div.style.width = width + 'px';\n",
- " canvas_div.style.height = height + 'px';\n",
- " }\n",
- " };\n",
- "\n",
- " // Disable right mouse context menu.\n",
- " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
- " event.preventDefault();\n",
- " return false;\n",
- " });\n",
- "\n",
- " function set_focus() {\n",
- " canvas.focus();\n",
- " canvas_div.focus();\n",
- " }\n",
- "\n",
- " window.setTimeout(set_focus, 100);\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function () {\n",
- " var fig = this;\n",
- "\n",
- " var toolbar = document.createElement('div');\n",
- " toolbar.classList = 'mpl-toolbar';\n",
- " this.root.appendChild(toolbar);\n",
- "\n",
- " function on_click_closure(name) {\n",
- " return function (_event) {\n",
- " return fig.toolbar_button_onclick(name);\n",
- " };\n",
- " }\n",
- "\n",
- " function on_mouseover_closure(tooltip) {\n",
- " return function (event) {\n",
- " if (!event.currentTarget.disabled) {\n",
- " return fig.toolbar_button_onmouseover(tooltip);\n",
- " }\n",
- " };\n",
- " }\n",
- "\n",
- " fig.buttons = {};\n",
- " var buttonGroup = document.createElement('div');\n",
- " buttonGroup.classList = 'mpl-button-group';\n",
- " for (var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " /* Instead of a spacer, we start a new button group. */\n",
- " if (buttonGroup.hasChildNodes()) {\n",
- " toolbar.appendChild(buttonGroup);\n",
- " }\n",
- " buttonGroup = document.createElement('div');\n",
- " buttonGroup.classList = 'mpl-button-group';\n",
- " continue;\n",
- " }\n",
- "\n",
- " var button = (fig.buttons[name] = document.createElement('button'));\n",
- " button.classList = 'mpl-widget';\n",
- " button.setAttribute('role', 'button');\n",
- " button.setAttribute('aria-disabled', 'false');\n",
- " button.addEventListener('click', on_click_closure(method_name));\n",
- " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
- "\n",
- " var icon_img = document.createElement('img');\n",
- " icon_img.src = '_images/' + image + '.png';\n",
- " icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
- " icon_img.alt = tooltip;\n",
- " button.appendChild(icon_img);\n",
- "\n",
- " buttonGroup.appendChild(button);\n",
- " }\n",
- "\n",
- " if (buttonGroup.hasChildNodes()) {\n",
- " toolbar.appendChild(buttonGroup);\n",
- " }\n",
- "\n",
- " var fmt_picker = document.createElement('select');\n",
- " fmt_picker.classList = 'mpl-widget';\n",
- " toolbar.appendChild(fmt_picker);\n",
- " this.format_dropdown = fmt_picker;\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = document.createElement('option');\n",
- " option.selected = fmt === mpl.default_extension;\n",
- " option.innerHTML = fmt;\n",
- " fmt_picker.appendChild(option);\n",
- " }\n",
- "\n",
- " var status_bar = document.createElement('span');\n",
- " status_bar.classList = 'mpl-message';\n",
- " toolbar.appendChild(status_bar);\n",
- " this.message = status_bar;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.send_message = function (type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function () {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1], msg['forward']);\n",
- " fig.send_message('refresh', {});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
- " var x0 = msg['x0'] / fig.ratio;\n",
- " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
- " var x1 = msg['x1'] / fig.ratio;\n",
- " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0,\n",
- " 0,\n",
- " fig.canvas.width / fig.ratio,\n",
- " fig.canvas.height / fig.ratio\n",
- " );\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch (cursor) {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
- " for (var key in msg) {\n",
- " if (!(key in fig.buttons)) {\n",
- " continue;\n",
- " }\n",
- " fig.buttons[key].disabled = !msg[key];\n",
- " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
- " if (msg['mode'] === 'PAN') {\n",
- " fig.buttons['Pan'].classList.add('active');\n",
- " fig.buttons['Zoom'].classList.remove('active');\n",
- " } else if (msg['mode'] === 'ZOOM') {\n",
- " fig.buttons['Pan'].classList.remove('active');\n",
- " fig.buttons['Zoom'].classList.add('active');\n",
- " } else {\n",
- " fig.buttons['Pan'].classList.remove('active');\n",
- " fig.buttons['Zoom'].classList.remove('active');\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function () {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message('ack', {});\n",
- "};\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = 'image/png';\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src\n",
- " );\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data\n",
- " );\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " } else if (\n",
- " typeof evt.data === 'string' &&\n",
- " evt.data.slice(0, 21) === 'data:image/png;base64'\n",
- " ) {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig['handle_' + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\n",
- " \"No handler for the '\" + msg_type + \"' message type: \",\n",
- " msg\n",
- " );\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\n",
- " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
- " e,\n",
- " e.stack,\n",
- " msg\n",
- " );\n",
- " }\n",
- " }\n",
- " };\n",
- "};\n",
- "\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function (e) {\n",
- " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
- " var targ;\n",
- " if (!e) {\n",
- " e = window.event;\n",
- " }\n",
- " if (e.target) {\n",
- " targ = e.target;\n",
- " } else if (e.srcElement) {\n",
- " targ = e.srcElement;\n",
- " }\n",
- " if (targ.nodeType === 3) {\n",
- " // defeat Safari bug\n",
- " targ = targ.parentNode;\n",
- " }\n",
- "\n",
- " // pageX,Y are the mouse positions relative to the document\n",
- " var boundingRect = targ.getBoundingClientRect();\n",
- " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
- " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
- "\n",
- " return { x: x, y: y };\n",
- "};\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys(original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object') {\n",
- " obj[key] = original[key];\n",
- " }\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function (event, name) {\n",
- " var canvas_pos = mpl.findpos(event);\n",
- "\n",
- " if (name === 'button_press') {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " var x = canvas_pos.x * this.ratio;\n",
- " var y = canvas_pos.y * this.ratio;\n",
- "\n",
- " this.send_message(name, {\n",
- " x: x,\n",
- " y: y,\n",
- " button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event),\n",
- " });\n",
- "\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We want\n",
- " * to control all of the cursor setting manually through the\n",
- " * 'cursor' event from matplotlib */\n",
- " event.preventDefault();\n",
- " return false;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.key_event = function (event, name) {\n",
- " // Prevent repeat events\n",
- " if (name === 'key_press') {\n",
- " if (event.which === this._key) {\n",
- " return;\n",
- " } else {\n",
- " this._key = event.which;\n",
- " }\n",
- " }\n",
- " if (name === 'key_release') {\n",
- " this._key = null;\n",
- " }\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.which !== 17) {\n",
- " value += 'ctrl+';\n",
- " }\n",
- " if (event.altKey && event.which !== 18) {\n",
- " value += 'alt+';\n",
- " }\n",
- " if (event.shiftKey && event.which !== 16) {\n",
- " value += 'shift+';\n",
- " }\n",
- "\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
- " return false;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
- " if (name === 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message('toolbar_button', { name: name });\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "\n",
- "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
- "// prettier-ignore\n",
- "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
- "\n",
- "mpl.default_extension = \"png\";/* global mpl */\n",
- "\n",
- "var comm_websocket_adapter = function (comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.close = function () {\n",
- " comm.close();\n",
- " };\n",
- " ws.send = function (m) {\n",
- " //console.log('sending', m);\n",
- " comm.send(m);\n",
- " };\n",
- " // Register the callback with on_msg.\n",
- " comm.on_msg(function (msg) {\n",
- " //console.log('receiving', msg['content']['data'], msg);\n",
- " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data']);\n",
- " });\n",
- " return ws;\n",
- "};\n",
- "\n",
- "mpl.mpl_figure_comm = function (comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = document.getElementById(id);\n",
- " var ws_proxy = comm_websocket_adapter(comm);\n",
- "\n",
- " function ondownload(figure, _format) {\n",
- " window.open(figure.canvas.toDataURL());\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element;\n",
- " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
- " if (!fig.cell_info) {\n",
- " console.error('Failed to find cell for figure', id, fig);\n",
- " return;\n",
- " }\n",
- " fig.cell_info[0].output_area.element.on(\n",
- " 'cleared',\n",
- " { fig: fig },\n",
- " fig._remove_fig_handler\n",
- " );\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
- " var width = fig.canvas.width / fig.ratio;\n",
- " fig.cell_info[0].output_area.element.off(\n",
- " 'cleared',\n",
- " fig._remove_fig_handler\n",
- " );\n",
- " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable();\n",
- " fig.parent_element.innerHTML =\n",
- " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
- " fig.close_ws(fig, msg);\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var width = this.canvas.width / this.ratio;\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] =\n",
- " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function () {\n",
- " // Tell IPython that the notebook contents must change.\n",
- " IPython.notebook.set_dirty(true);\n",
- " this.send_message('ack', {});\n",
- " var fig = this;\n",
- " // Wait a second, then push the new image to the DOM so\n",
- " // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () {\n",
- " fig.push_to_output();\n",
- " }, 1000);\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function () {\n",
- " var fig = this;\n",
- "\n",
- " var toolbar = document.createElement('div');\n",
- " toolbar.classList = 'btn-toolbar';\n",
- " this.root.appendChild(toolbar);\n",
- "\n",
- " function on_click_closure(name) {\n",
- " return function (_event) {\n",
- " return fig.toolbar_button_onclick(name);\n",
- " };\n",
- " }\n",
- "\n",
- " function on_mouseover_closure(tooltip) {\n",
- " return function (event) {\n",
- " if (!event.currentTarget.disabled) {\n",
- " return fig.toolbar_button_onmouseover(tooltip);\n",
- " }\n",
- " };\n",
- " }\n",
- "\n",
- " fig.buttons = {};\n",
- " var buttonGroup = document.createElement('div');\n",
- " buttonGroup.classList = 'btn-group';\n",
- " var button;\n",
- " for (var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " /* Instead of a spacer, we start a new button group. */\n",
- " if (buttonGroup.hasChildNodes()) {\n",
- " toolbar.appendChild(buttonGroup);\n",
- " }\n",
- " buttonGroup = document.createElement('div');\n",
- " buttonGroup.classList = 'btn-group';\n",
- " continue;\n",
- " }\n",
- "\n",
- " button = fig.buttons[name] = document.createElement('button');\n",
- " button.classList = 'btn btn-default';\n",
- " button.href = '#';\n",
- " button.title = name;\n",
- " button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
- " button.addEventListener('click', on_click_closure(method_name));\n",
- " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
- " buttonGroup.appendChild(button);\n",
- " }\n",
- "\n",
- " if (buttonGroup.hasChildNodes()) {\n",
- " toolbar.appendChild(buttonGroup);\n",
- " }\n",
- "\n",
- " // Add the status bar.\n",
- " var status_bar = document.createElement('span');\n",
- " status_bar.classList = 'mpl-message pull-right';\n",
- " toolbar.appendChild(status_bar);\n",
- " this.message = status_bar;\n",
- "\n",
- " // Add the close button to the window.\n",
- " var buttongrp = document.createElement('div');\n",
- " buttongrp.classList = 'btn-group inline pull-right';\n",
- " button = document.createElement('button');\n",
- " button.classList = 'btn btn-mini btn-primary';\n",
- " button.href = '#';\n",
- " button.title = 'Stop Interaction';\n",
- " button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
- " button.addEventListener('click', function (_evt) {\n",
- " fig.handle_close(fig, {});\n",
- " });\n",
- " button.addEventListener(\n",
- " 'mouseover',\n",
- " on_mouseover_closure('Stop Interaction')\n",
- " );\n",
- " buttongrp.appendChild(button);\n",
- " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
- " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
- " var fig = event.data.fig;\n",
- " if (event.target !== this) {\n",
- " // Ignore bubbled events from children.\n",
- " return;\n",
- " }\n",
- " fig.close_ws(fig, {});\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function (el) {\n",
- " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
- " // this is important to make the div 'focusable\n",
- " el.setAttribute('tabindex', 0);\n",
- " // reach out to IPython and tell the keyboard manager to turn it's self\n",
- " // off when our div gets focus\n",
- "\n",
- " // location in version 3\n",
- " if (IPython.notebook.keyboard_manager) {\n",
- " IPython.notebook.keyboard_manager.register_events(el);\n",
- " } else {\n",
- " // location in version 2\n",
- " IPython.keyboard_manager.register_events(el);\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager) {\n",
- " manager = IPython.keyboard_manager;\n",
- " }\n",
- "\n",
- " // Check for shift+enter\n",
- " if (event.shiftKey && event.which === 13) {\n",
- " this.canvas_div.blur();\n",
- " // select the cell after this one\n",
- " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
- " IPython.notebook.select(index + 1);\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
- " fig.ondownload(fig, null);\n",
- "};\n",
- "\n",
- "mpl.find_output_cell = function (html_output) {\n",
- " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
- " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
- " // IPython event is triggered only after the cells have been serialised, which for\n",
- " // our purposes (turning an active figure into a static one), is too late.\n",
- " var cells = IPython.notebook.get_cells();\n",
- " var ncells = cells.length;\n",
- " for (var i = 0; i < ncells; i++) {\n",
- " var cell = cells[i];\n",
- " if (cell.cell_type === 'code') {\n",
- " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
- " var data = cell.output_area.outputs[j];\n",
- " if (data.data) {\n",
- " // IPython >= 3 moved mimebundle to data attribute of output\n",
- " data = data.data;\n",
- " }\n",
- " if (data['text/html'] === html_output) {\n",
- " return [cell, data, j];\n",
- " }\n",
- " }\n",
- " }\n",
- " }\n",
- "};\n",
- "\n",
- "// Register the function which deals with the matplotlib target/channel.\n",
- "// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel !== null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target(\n",
- " 'matplotlib',\n",
- " mpl.mpl_figure_comm\n",
- " );\n",
- "}\n"
- ],
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HFjQi16bNm3aaM6cOcrKyir18exoij4pcNRRR+nMM88sdf1pp52mv/71r0pPT9eCBQv03HPP6cgjj9QZZ5xR7nGrs6BXJNNlZbXo7O4DBgzQ7NmzNXbs2Khncc/Pz1ebNm3UokULpaWladasWRo4cKCOOeaYUtkpOot7nz59NHPmTE2YMEHHHXdcibO4F59TamqqPvjgA2VlZUXW7MCBA1W7dm0NGTJEb775pubNm6dJkybplltuKXFyQACAPSjoABByhYWFevbZZ3XKKaeodu3aiouL0/HHH6977rlHu3btKnHb4h+pHjRokBo1aqT4+Hidc845+vjjj0sd+9NPP9WVV16pxo0bq1atWkpJSdH555+v5557LnKbskrPvn37dPPNN+ukk05S3bp1lZiYqDZt2ig1NbXEu6zZ2dnq3bu36tevr5iYmBKFOC8vT48//njkd6CTk5PVtm1b/elPf9Lq1asjtztYQb/tttv0zDPPqHXr1qpVq5batm2rSZMmRX1tDlbQJenll1/WqaeeGplTx44dS3xS4VALuvTzf89BgwYpNjZWY8eOrdTrEK2gz507Vx07dlR8fLw8r+zfQS9u586dSkxMlOd5kTkUd++99+qUU05RgwYNFB8fr2OPPVZ333135GfYylKdBb0imS4rq4WFhRo5cqSOPvpoxcXF6aSTTtKbb74Z9fVctWqVLrroItWtW1eNGjXSHXfcETmRXvHsSNK4ceN00kknKS4uTvXq1VOPHj20fPnyErfJzc3VjTfeqEaNGkXyX/wfUV588UWdfvrpSkpKUmJiolq3bq3rr78+6noFAJhHQQcAAKEVrfRXlWgFHQCA8lDQAQBAaFHQAQA2oaADAIDQoqADAGxCQQcAAAAAwAIUdAAAAAAALEBBBwAAAADAAhR0AAAAAAAsQEEPmYKCAm3cuFE7duzQzp07GQwGg8FgMBgMRkjHjh07tHHjRhUUFJiuKfgFBT1kNm7cKM/zGAwGg8FgMBgMBkOe52njxo2mawp+QUEPmR07dkQWoel/sQvL2LZtm9LS0rRt2zbjc2GEb5A/hslB/hgmB/ljmByu5K/ozbsdO3aYrin4BQU9ZHbu3CnP87Rz507TUwmN/fv3Kz09Xfv37zc9FYQQ+YNJ5A8mkT+Y5Er+6Ab2oaCHDIvQf65s0Agm8geTyB9MIn8wyZX80Q3sQ0EPGRah/1zZoBFM5A8mkT+YRP5gkiv5oxvYh4IeMixC/7myQSOYyB9MIn8wifzBJFfyRzewDwU9ZFiE/nNlg0YwkT+YRP5gEvmDSa7kj25gHwp6yLAI/efKBo1gIn8wifzBJPIHk1zJH93APhT0kGER+s+VDRrBRP5gEvmDSeQPJrmSP7qBfSjoIcMi9J8rGzSCifzBJPIHk8gfTHIlf3QD+1DQQ4ZF6D9XNmgEE/mDSeQPJpE/mORK/ugG9qGghwyL0H+ubNAIJvIHk8gfTCJ/MMmV/NEN7ENBDxkWof9c2aARTOQPJpE/mET+YJIr+aMb2IeCHjIsQv+5skEjmMgfTCJ/MIn8wSRX8kc3sA8FPWRYhP5zZYNGMJE/mET+YBL5g0mu5I9uYB8KesiwCP3nygbtl217s7Xoh/9qzsb3lPH1fM39drGWbVuhvfn7TE8tkMgfTCJ/MIn8wSRX8kc3sA8FPWRYhP5zZYOuToWFhVq9Y4Omr52tp7+YqDFfTNKYLyZF/v+nv5ioZ5anae63i7Vt73bT0w0U8geTyB9MIn8wyZX80Q3sQ0EPGRah/1zZoKtLQWGB3v12SYkyXtYY88UkPbs8TWt3flPuMVNTU+V5XmQkJCSoXbt2euKJJ1RYWBi5XfHbeJ6nJk2aqFevXlqxYkV1P21rhD1/MIv8wSTyB5NcyR/dwD4U9JBhEfrPlQ26OhQWFuqdjYvKLeVljfU/fVvmcVNTU5WYmKglS5ZoyZIlevfdd3XPPffI8zw99dRTkdt5nqc77rhDS5Ys0eLFizVlyhSdcMIJatGihXbv3u3HS2BcmPMH88gfTCJ/MMmV/NEN7ENBDxkWof9c2aCrw+c/rjykcv70FxP17PLJ2rU/J+pxU1NTlZSUVOryzp07q3PnzpG/Pc/TY489VuI2CxYskOd5WrBgQdU+WUuFOX8wj/zBJPIHk1zJH93APhT0kGER+s+VDbqqFRYW6qWVMw65oI/5YpI+2Lws6rHLKuh/+MMfdPbZZ0f+jlbQP/nkE3mep7lz51btE7ZUWPMHO5A/mET+YJIr+aMb2IeCHjIsQv+5skFXta93fX/I5bxojFsxTfkF+aWOXVTQ8/LylJeXp507d2ratGmKi4vTv/71r8jtPM/TqFGjlJeXp/3792vNmjXq1q2bmjVrpj179vj5chgT1vzBDuQPJpE/mORK/ugG9qGghwyL0H+ubNBVbdY3Cw56UriKjGgnjDvwJHFFo1+/fuWeJM7zPDVu3FgffPCBny+FUWHNH+xA/mAS+YNJruSPbmAfCnrIsAj958oGXdUmrX7zsMv5mC8m6b9bvyh17KKTxGVlZSkrK0vvv/++nnzySdWrV08DBw6M3M7zPN15552R22VkZOiSSy7REUccoc8//9zPl8OYsOYPdiB/MIn8wSRX8kc3sA8FPWRYhP5zZYOuav/56rXDLujPfDFJH2wq/T30sr6D/vjjj8vzPH3xxc+lPtp30Pft26eGDRuqd+/e1fPELRPW/MEO5A8mkT+Y5Er+6Ab2oaCHDIvQf65s0FWtut9Bj1bQZ86cKc/zNGXKFEnRC7oknXbaaWrbtm3VP2kLhTV/sAP5g0nkDya5kj+6gX0o6CHDIvSfKxt0Vavu76BHK+iPPvpoiTO0Ryvoe/fu1ZFHHlnibO9BFtb8wQ7kDyaRP5jkSv7oBvahoIcMi9B/rmzQVa26z+KemJioJUuWaMmSJVqwYIH++c9/qm7dumrXrl3ktfY8T3fccUfkdm+88Ya6du0qz/M0ceJEv18SI8KaP9iB/MEk8geTXMkf3cA+FPSQYRH6z5UNuqpV9++gFz8ze82aNXXMMcfo1ltv1ebNmyO3O/AM7vXr19cZZ5yhGTNm+PUyGBfW/MEO5A8mkT+Y5Er+6Ab2oaCHDIvQf65s0NXh8x9XHnJBf3b5ZO3an2P6KTgvzPmDeeQPJpE/mORK/ugG9qGghwyL0H+ubNDVobCwUO9sXHRIBX39T9+ann4ghDl/MI/8wSTyB5NcyR/dwD4U9JBhEfrPlQ26uhQUFmjet0t++dj6wT/W/uzytKgnhsOhCXv+YBb5g0nkDya5kj+6gX0o6CHDIvSfKxt0dSosLNTqHRs0fe2cSBEfU6yUP/3FRD2zPE3vfrtE2/ZuNz3dQCF/MIn8wSTyB5NcyR/dwD4U9JBhEfrPlQ3aL9v2bteiH/6rORvfV8bX8zX328Vatm2F9ubvMz21QCJ/MIn8wSTyB5NcyR/dwD4U9JBhEfrPlQ0awUT+YBL5g0nkDya5kj+6gX0o6CHDIvSfKxs0gon8wSTyB5PIH0xyJX90A/tQ0EOGReg/VzZoBBP5g0nkDyaRP5jkSv7oBvahoIcMi9B/rmzQCCbyB5PIH0wifzDJlfzRDexDQQ8ZFqH/XNmgEUzkDyaRP5hE/mCSK/mjG9iHgh4yLEL/ubJBI5jIH0wifzCJ/MEkV/JHN7APBT1kWIT+c2WDRjCRP5hE/mAS+YNJruSPbmAfCnrIsAj958oGjWAifzCJ/MEk8geTXMkf3cA+FPSQYRH6z5UNGsFE/mAS+YNJ5A8muZI/uoF9KOghwyL0nysbNIKJ/MEk8geTyB9MciV/dAP7UNBDhkXoP1c2aAQT+YNJ5A8mkT+Y5Er+6Ab2oaCHDIvQf65s0Agm8geTyB9MIn8wyZX80Q3sQ0EPGRah/1zZoBFM5A8mkT+YRP5gkiv5oxvYh4IeMixC/7myQSOYyB9MIn8wifzBJFfyRzewDwU9ZFiE/nNlg0YwkT+YRP5gEvmDSa7kj25gHwp6yLAI/efKBo1gIn8wifzBJPIHk1zJH93APhT0kGER+s+VDRrBRP5gEvmDSeQPJrmSP7qBfSjoIcMi9J8rGzSCifzBJPIHk8gfTHIlf3QD+1DQQ4ZF6D9XNmgEE/mDSeQPJpE/mORK/ugG9qGghwyL0H+ubNAIJvIHk8gfTCJ/MMmV/NEN7ENBDxkWof9c2aARTOQPJpE/mET+YJIr+aMb2IeCHjIsQv+5skEjmMgfTCJ/MIn8wSRX8kc3sA8FPWRYhP5zZYNGMJE/mET+YBL5g0mu5I9uYB8KesiwCP3nygaNYCJ/MIn8wSTyB5NcyR/dwD4U9JBhEfrPlQ0awUT+YBL5g0nkDya5kj+6gX0o6CHDIvSfKxs0gon8wSTyB5PIH0xyJX90A/tQ0EOGReg/VzZoBBP5g0nkDyaRP5jkSv7oBvahoIcMi9B/rmzQCCbyB5PIH0wifzDJlfzRDexDQQ8ZFqH/XNmgEUzkDyaRP5hE/mCSK/mjG9iHgh4yLEL/ubJBI5jIH0wifzCJ/MEkV/JHN7APBT1kWIT+c2WDRjCRP5hE/mAS+YNJruSPbmAfCnrIsAj958oGjWAifzCJ/MEk8geTXMkf3cA+FPSQYRH6z5UNGsFE/mAS+YNJ5A8muZI/uoF9KOghwyL0nysbNIKJ/MEk8geTyB9MciV/dAP7UNBDhkXoP1c2aAQT+YNJ5A8mkT+Y5Er+6Ab2CW1Bz8nJ0cyZMzV8+HD16tVLLVq0kOd58jxPqamph3Xs7du3Kz09XcOGDVP37t2VkpISOfb48eMPev/Vq1frH//4hy699FK1aNFCcXFxql27to4//njdcMMN+vjjjw95bixC/7myQSOYyB9MIn8wifzBJFfyRzewT2gLemZmZqQ0HzgOt6CPHz++zGMfrKC///77pe5Tp04dxcXFRf6OjY3VsGHDDmluLEL/ubJBI5jIH0wifzCJ/MEkV/JHN7BPqAt6gwYN1KVLFw0ZMkSTJ0+OvNNdFQU9JSVF3bp10/33369XX321wgU9MzNTNWrUUM+ePTVt2jRt27ZNkpSfn6+PPvpIZ599duRY48aNq/TcWIT+c2WDRjCRP5hE/mAS+YNJruSPbmCf0Bb0/Pz8Upe1bNmySgp6Xl5eqcsqWtA3btyoVatWlXl9bm6uTjrpJHmep9atW1d6bixC/7myQSOYyB9MIn8wifzBJFfyRzewT2gLejRVVdCjqcx30A/m0UcfjRwvOzu7UvdlEfrPlQ0awUT+YBL5g0nkDya5kj+6gX0o6MW4UtBHjx4dOd7WrVsrdV8Wof9c2aARTOQPJpE/mET+YJIr+aMb2IeCXowrBf2yyy6T53lq2rSpCgsLK3VfFqH/XNmgEUzkDyaRP5hE/mCSK/mjG9iHgl6MCwV98eLFio2Nled5h3Qmdxah/1zZoBFM5A8mkT+YRP5gkiv5oxvYh4JejO0FfcuWLZE5Hn/88dq1a9dB77Nv3z7t3LkzMjZu3CjP87Rt2zbt37+f4cPIyclRenq6cnJyjM+FEb5B/hgmB/ljmBzkj2FyuJK/bdu2UdAtQ0EvxuaCvmvXLv3ud7+L/C76smXLKnS/1NTUqL/HnpaWpvT0dAaDwWAwGAwGgxHSkZaWRkG3DAW9GFsL+u7du3XuuefK8zwlJyfrvffeq/B9eQfd/HDlX1AZwRzkj2FykD+GyUH+GCaHK/njHXT7UNCLsbGg7969W506dZLneUpKStKCBQsOax58z8R/+/e78R0kBBP5g0nkDyaRP5jkSv7oBvahoBdjW0EvXs5r166t+fPnH/Y8WIT+c2WDRjCRP5hE/mAS+YNJruSPbmAfCnoxNhX04h9rT0pKqpJyLrEITXBlg0YwkT+YRP5gEvmDSa7kj25gHwp6MbYU9APL+eF+rL04FqH/XNmgEUzkDyaRP5hE/mCSK/mjG9gn1AU9OztbW7dujYyjjz5anudpyJAhJS4/8OfMip8Zff369VGPXfz+W36XbTcAACAASURBVLdujdz+qaeeKnF5Tk5Oifvl5OTovPPOi5wQbuHChVX6nFmE/nNlg0YwkT+YRP5gEvmDSa7kj25gn1AX9KJ3zA82+vbtW+J+FSnoFTlutHfrX3rppch1CQkJatKkSblj0aJFlXrOLEL/ubJBI5jIH0wifzCJ/MEkV/JHN7APBd2ygj5+/PgK39fzPGVmZlbqObMI/efKBu2S7B9ztGblVn352Q/asPZH7dnDa1sW8geTyB9MIn8wyZX80Q3sE+qCHkYsQv+5skHbLj+/QFlLvtbIB+bo+h4vlxgDrpikF8cs0dfrfjQ9TeuQP5hE/mAS+YNJruSPbmAfCnrIsAj958oGbbN1q7fpzhum6/oeL6tfrwmlCnrxyx9/eK725OSWeayKfDJl/PjxWr9+vTzP07Rp00odo7zrbEP+YBL5g0nkDya5kj+6gX0o6CHDIvSfKxu0rb787AcN6D2xzGIeraj/5Y7XteunfVGPt2TJkhLD8zzdcccdJS7bsmULBR2oAuQPJpE/mORK/ugG9qGghwyL0H+ubNA2+u6bHbrpqjT17XnwYn5gSR9+zyzl5RUc9DE8z9Njjz1W6nIKOnD4yB9MIn8wyZX80Q3sQ0EPGRah/1zZoG00+pH5FX7nPNr44L31B30MCjpQfcgfTCJ/MMmV/NEN7ENBDxkWof9c2aBtk/1jzmGV876/vIt+MAcr6FOnTlVeXl6JsWbNGgo6UAHkDyaRP5jkSv7oBvahoIcMi9B/rmzQtpkx5dNKf7Q92vhmfXa5j3Owgl7eoKAD5SN/MIn8wSRX8kc3sA8FPWRYhP5zZYO2zcNDZh52Oe/b82XNen15uY9zsII+atQoZWVllRhvvPEGBR2oAPIHk8gfTHIlf3QD+1DQQ4ZF6D9XNmjb/PlPMw67oPe/bIKmT1xa7uPwHXSg+pA/mET+YJIr+aMb2IeCHjIsQv+5skHbZsgtVVPQX0tbVu7jUNCB6kP+YBL5g0mu5I9uYB8KesiwCP3nygZtm7/dO6tKPuL+9psryn0cCjpQfcgfTCJ/MMmV/NEN7ENBDxkWof9c2aBtM+v15VVykrgtm34q93Eo6ED1IX8wifzBJFfyRzewDwU9ZFiE/nNlg7bN7l37dMPlEw+5mPfrNUGPPzz3oI9DQQeqD/mDSeQPJrmSP7qBfSjoIcMi9J8rG7SNxo5edFi/hb4s61vTT8E48geTyB9MIn8wyZX80Q3sQ0EPGRah/1zZoG3047Yc3d73lUqX9L49X9boR+aroKDQ9FMwjvzBJPIHk8gfTHIlf3QD+1DQQ4ZF6D9XNmhbfbM+W7deN7XCJb1vz5f1yLC3lZubb3rqViB/MIn8wSTyB5NcyR/dwD4U9JBhEfrPlQ3aZls27dLDQ2dGvlteVjHvf9kEvfzvD5WXV2B6ytYgfzCJ/MEk8geTXMkf3cA+FPSQYRH6z5UN2gUb1v6oF55erAG9S5487s7+0/Tm9M+1c/se01O0DvmDSeQPJpE/mORK/ugG9qGghwyL0H+ubNAuyc8v0M4de7V18y7t3pWrwkK+a14W8geTyB9MIn8wyZX80Q3sQ0EPGRah/1zZoBFMYc3fxt3Zyvj6C01d+7GmrPlYb339mdbt3Mo/5vgsrPmDHcgfTHIlf3QD+1DQQ4ZF6D9XNmgEU5jyl19YoA83r9cjS+do4MJJunlhmm55L023vDdZNy9M08CFk/TX/2bovR/WaH8BJxH0Q5jyB/uQP5jkSv7oBvahoIcMi9B/rmzQCKaw5G9P3n7989O5Grhwkv70SxmPNv70y//9+yez9FPu3nKPmZqaKs/z1KxZMxUUlD7xYLdu3eR5nrp3767HH39cMTEx+uabb6Iea8WKFfI8T//+97+r5Pm6Iiz5g53IH0xyJX90A/tQ0EOGReg/VzZoBFMY8pebn6cRn8wqt5gfOG5emKZhWW9o9/7cMo+bmpqqWrVqKS4uTu+++26J67Zu3aqaNWsqOTlZ3bt313fffafY2Fg9+uijUY/1wAMPKC4uTj/++GOVPnfbhSF/sBf5g0mu5I9uYB8KesiwCP3nygaNYApD/l78alHknfHKjJsXpulfn71b5nFTU1OVlJSkHj16aMCAASWuGzNmjFq0aKFzzz1X3bt3lyR17txZHTp0iHqs1q1bq0ePHlX3pB0RhvzBXuQPJrmSP7qBfSjoIcMi9J8rGzSCKej5y96Xc0jlvPj4Zld21GMXFfSpU6eqfv36ys3937vtZ511loYOHapOnTpFCvq4cePkeZ5WrFhR4jgffPCBPM/T1KlTq++FsFTQ8we7kT+Y5Er+6Ab2oaCHDIvQf65s0AimoOfv9fWfVuqj7dHeRZ+w6sOoxy4q6Hv27FGdOnWUnp4uSdqwYYNiYmK0bNmyEgV9x44dio+P17Bhw0ocZ9CgQapTp4727NlTvS+GhYKeP9iN/MEkV/JHN7APBT1kWIT+c2WDRjAFOX8FhQUavHj6Yb17PnDhJN363mTtzSv9+hQVdEnq06ePrrzySknSiBEj1L59e0kqUdAlqVevXjruuOP+N8eCAqWkpKhv377V+ErYK8j5g/3IH0xyJX90A/tQ0EOGReg/VzZoBFOQ8/dT7t7DLudF49vd20sdv3hBnzVrlhITE7Vr1y6deOKJ+vvf/y6pdEGfPn26PM9TVlaWJOmdd96R53maM2eOD6+IfYKcP9iP/MEkV/JHN7APBT1kWIT+c2WDRjAFOX+b9/xUZQV99Y4tpY5fvKDn5+ercePGGjp0qDzP07p16ySVLuj79u1TvXr1dPfdd0uS+vfvryZNmig/P5y/ux7k/MF+5A8muZI/uoF9KOghwyL0nysbNIIpyPnbkbunygp6tBPFFS/oknT77bcrNjZWZ5xxRuSyAwu69HMpb9asmfbs2aN69erpzjvvrL4XwXJBzh/sR/5gkiv5oxvYh4IeMixC/7myQSOYgpy/vIJ83fre5MMu539aOEk/5e4tdfwDC/rSpUvVo0cPTZs2LXJZtII+d+5ceZ6n22+/XZ7n6cMPo5+ELgyCnD/Yj/zBJFfyRzewDwU9ZFiE/nNlg0YwBT1/L6/8QDcf5lncn/oiM+qxDyzo0UQr6AUFBWrWrJliYmJKnDAujIKeP9iN/MEkV/JHN7APBT1kWIT+c2WDRjAFPX/f7Mo+7HfQl2d/H/XYh1rQJWnw4MHyPE8PPvhglTxPVwU9f7Ab+YNJruSPbmAfCnrIsAj958oGjWAKQ/5GLZ1zSO+i37wwTfd/9LoKCgtNP4XACkP+YC/yB5NcyR/dwD4U9JBhEfrPlQ0awRSG/P24b7f+b8n0SpX0Py1M06BFU/V9zg7T0w+0MOQP9iJ/MMmV/NEN7ENBDxkWof9c2aARTGHJ3w85O3TPBzMqVNJvXpimuxdP0/qftpmeduCFJX+wE/mDSa7kj25gHwp6yLAI/efKBo1gClP+dubu0eQ1Wbr9/amRs7MfWM5vfW+yXlr5gbbt3W16uqEQpvzBPuQPJrmSP7qBfSjoIcMi9J8rGzSCKYz525u/Xwu/X63Rn2fq75/M0t/+O1NPfj5Pc79doZy8XNPTC5Uw5g/2IH8wyZX80Q3sQ0EPGRah/1zZoBFM5A8mkT+YRP5gkiv5oxvYh4IeMixC/7myQSOYyB9MIn8wifzBJFfyRzewDwU9ZFiE/nNlg0YwFeXvoxU/6O3/btTMj77Rws9/0JYde01PDSHA/geTyB9MciV/dAP7UNBDhkXoP1c2aATP5u179OKs5UpPT9fF976pzkPeiozzh76lYf/J0n9XbVUhvwOOasL+B5PIH0xyJX90A/tQ0EOGReg/VzZoBEv6ovXqMvQtdf3Lm1ELeuchb+mCezLUechbuvu5Jdq15+D5nDhxok499VTVrVtXderUUdu2bTVgwABt3ry5QnPKz89X48aN5Xme1q1bV+r69evXy/M81ahRQytXrox63bRp0yr2AsAK7H8wifzBJFfyRzewDwU9ZFiE/nNlg0ZwTJi7KlLCL7637IJeNLoMfUv9Hp9fbkkfOXKkYmJiNHjwYM2aNUszZ87Uo48+qt/85jdaunRpheY1a9YseZ4nz/P0t7/9rdT1RSXc8zz16dMn6nUUdLew/8Ek8geTXMkf3cA+FPSQYRH6z5UNGsHw7tLvSpTvihT0opJ+93NLyvy4e7NmzdS/f/+o1xUUFFRobtddd50aNGigU089VSeccEKp64tK+Pnnn68aNWpo1apVpa6joLuF/Q8mkT+Y5Er+6Ab2oaCHDIvQf65s0HBfQUGhrv77u4dU0IvGJ6u3Rj127dq1dd999x3y3HJycpScnKybbrpJY8aMked5pd55LyrhkyZN0lFHHaW+ffuWuo6C7hb2P5hE/mCSK/mjG9iHgh4yLEL/ubJBw30ffbWlVOGuTEG/4J4MPfhSVtRjn3POOTriiCM0duxY/fDDD5We2+TJk+V5nubNm6etW7eqZs2a+vOf/1ziNsVL+NNPP60aNWpo9erVpa6DO9j/YBL5g0mu5I9uYB8KesiwCP3nygYN9/3lxY/U5ZcTvx3qO+jnD30r6k+wff755zruuOMi3xE/5phjNGjQIK1fv75Cc7v00kvVvHnzyMfhL7nkkhJ/SyVL+L59+9S8eXP169ev1HVwB/sfTCJ/MMmV/NEN7ONEQf/+++81bNgwde7cWW3btlX79u116aWXaty4ccrPzzc9PaewCP3nygYN9/1+2OzDege9aLz3efR3yHNzc5WRkaE777xTHTp0kOd5qlOnTuSj6gUFBcrLy4uMovK9bds21apVS4MHD44ca9KkSfI8T5mZmZHLDizhTz75pGrWrKm1a9dS0B3F/geTyB9MciV/dAP7WF/Qs7KyVK9ePXXo0EFnnHGGYmNj1adPH1111VWqX7++zjjjDP3000+mp+kMFqH/XNmg4b4uQzOqpKDPyvqmQo83e/Zs1ahRQ7169ZIkpaamRt5h9zxPqampkqRnn31Wnufp3Xff1fbt27V9+3Z9++23SkhI0I033hg53oElfO/evWratKluuOEGCrqj2P9gEvmDSa7kj25gH+sL+llnnaWHHnoo8veECRN0+umnS5Kys7PVoUMHDRo0yNT0nMMi9J8rGzTcd9G9M6ukoM/95NsKP+Zvf/tbtW3bVpL03XffKSsrKzK+++47SdLZZ59dorgXH/Xr11dubq6k6B9jf+KJJ1SzZk3NmzePgu4g9j+YRP5gkiv5oxvYx/qCnpiYqLVr10b+LigoUK1atbRp0yZJ0ttvv61mzZqZmp5zWIT+c2WDhvuu/NvcKino/11V+kzuRXtucXv27FGjRo103nnnlTmnr7/+WjExMbr55puVmZlZYowePVqe52nGjBmSohf0PXv2KCUlRV26dKGgO4j9DyaRP5jkSv7oBvaxvqC3bNlS77//fuTv77//XjExMdqzZ4+kn/8HXUJCgqnpOYdF6D9XNmi47/mZK9Rl6OEV9J4Pva39eaV/17xRo0a64YYbNHXqVC1cuFBTpkzRWWedpZiYmEjBjmbkyJHyPK/EP7QWyc/PV9OmTdW7d29JZZ8I7h//+EfkHXcKulvY/2AS+YNJruSPbmAf6wv6nXfeqRNPPFGzZs3SvHnz1Llz5xLv1syePVutW7c2OEO3sAj958oGDff98GPOYb2D3mXoWxo/Z2XUY48ZM0Zdu3ZV8+bNFRcXp2bNmqlr166aN29euXM68cQTdfbZZ5d5/Z///GclJCRo586dZRb0nJwcNW7cmILuIPY/mET+YJIr+aMb2Mf6gr5r1y5deeWVqlmzpmJiYnTmmWdq3bp1kevnzJmjV155xeAM3cIi9J8rGzSC4d4XPtQFxX5qrXIFPUNbtu8x/RQQIOx/MIn8wSRX8kc3sI/1Bb3I3r17tWvXLtPTcB6L0H+ubNAIhk3Ze9TrobcjH3WvTEFPX7zB9PQRMOx/MIn8wSRX8kc3sI8zBR1Vg0XoP1c2aATHmu93qtdDb+uCezIqXNAnvrva9LQRQOx/MIn8wSRX8kc3sI/zBX3NmjXq3Lmz6Wk4g0XoP1c2aATLpuw9uveFD8ss6EUfg796xLuat/Q709NFQLH/wSTyB5NcyR/dwD7OF/Rly5YpNjbW9DScwSL0nysbNIJp4+adSk9P17Uj3tFFf5mpLkMz9PsHZ+u+Fz/SRyu3qKCg0PQUEWDsfzCJ/MEkV/JHN7CP9QX9ySefLHcMHTqUgl4JLEL/ubJBI5jIH0wifzCJ/MEkV/JHN7CP9QU9JiZGzZo1U6tWraKOZs2aUdArgUXoP1c2aAQT+YNJ5A8mkT+Y5Er+6Ab2sb6gt2rVSlOnTi3z+qVLl1LQK4FF6D9XNmgEE/mDSeQPJpE/mORK/ugG9rG+oF9++eUaOnRomdcvW7ZMMTExPs7IbSxC/7myQSOYyB9MIn8wifzBJFfyRzewj/UFffny5crKyirz+v3792vDBn67t6JYhP5zZYNGMJE/mET+YBL5g0mu5I9uYB/rCzqqFovQf65s0Agm8geTyB9MIn8wyZX80Q3s41RBz8/P16ZNm7R582bl5+ebno6TWIT+c2WDRjCRP5hE/mAS+YNJruSPbmAfJwr6a6+9pjPPPFNxcXGKjY1VbGys4uLidOaZZ2rGjBmHdMycnBzNnDlTw4cPV69evdSiRQt5nifP85SamnpY892+fbvS09M1bNgwde/eXSkpKZFjjx8/vsLHWbNmjQYOHKhWrVopPj5ejRo10kUXXaTp06cf8txYhP5zZYNGMJE/mET+YBL5g0mu5I9uYB/rC/pzzz2nuLg43XzzzZoxY4YWL16sRYsWacaMGbr55psVHx+v559/vtLHzczMjJTmA8fhFvTx48eXeeyKFvSMjAzVrl07cr+6desqNjY28nf//v1VWFhY6bmxCP3nygaNYCJ/MIn8wSTyB5NcyR/dwD7WF/TWrVtr3LhxZV7/wgsv6Nhjj630cTMzM9WgQQN16dJFQ4YM0eTJkyPvdFdFQU9JSVG3bt10//3369VXX61UQV+3bp2SkpLkeZ7OOussrVy5UpK0a9cuPfjgg5FjjRo1qtJzYxH6z5UNGsFE/mAS+YNJ5A8muZI/uoF9rC/oCQkJ+uqrr8q8fsWKFUpISKj0caN9h71ly5ZVUtDz8vJKXVaZgn7dddfJ8zylpKRo+/btpa4fOHBg5F317OzsSs2NReg/VzZoBBP5g0nkDyaRP5jkSv7oBvaxvqCffPLJGjx4cJnXDx48WCeffHKVPFZVFfRoKlrQd+/ercTERHmep4cffjjqbdavXx853osvvlipebAI/efKBo1gIn8wifzBJPIHk1zJH93APtYX9Pnz5yspKUnt2rXTXXfdpZEjR+qRRx7RXXfdpfbt2ys5OVkLFy6skseyoaDPnj07ctuPPvqozNudcMIJ8jxPV199daXmwSL0nysbNIKJ/MEk8geTyB9MciV/dAP7WF/QpZ/fMR46dKjOPfdc/epXv9KvfvUrnXvuubrnnnu0fv36KnscGwr6448/HrltTk5Ombe74oor5Hmefv3rX1dqHixC/7myQSOYyB9MIn8wifzBJFfyRzewjxMF3S82FPTBgwfL8zw1aNCg3Nvddddd8jxPRx55ZLm327dvn3bu3BkZGzdulOd52rZtm/bv38/wYeTk5Cg9PV05OTnG58II3yB/DJOD/DFMDvLHMDlcyd+2bdso6JahoBdjQ0G/6aab5HmemjdvXu7t7rvvPnmep7i4uHJvl5qaGvXn3tLS0pSens5gMBgMBoPBYDBCOtLS0ijolnGioL/11lsaMGCAhgwZoi+//LLEddnZ2ercuXOVPE4QCzrvoJsfrvwLKiOYg/wxTA7yxzA5yB/D5HAlf7yDbh/rC/qkSZNUo0YNde/eXWeffbYSEhI0ceLEyPWbNm1SbGxslTyWDQW9qj/ifiC+Z+K//fvd+A4Sgon8wSTyB5PIH0xyJX90A/tYX9A7duyo0aNHR/6eNm2akpOTNW7cOEnBK+icJC54XNmgEUzkDyaRP5hE/mCSK/mjG9jH+oKelJSkdevWlbgsMzNTderU0bPPPhu4gs7PrAWPKxs0gon8wSTyB5PIH0xyJX90A/tYX9CbNm2qJUuWlLp8/vz5Sk5O1v333x+ogr57924lJibK8zz99a9/jXqbDRs2RI734osvVmoeLEL/ubJBI5jIH0wifzCJ/MEkV/JHN7CP9QW9R48eevDBB6Nel5mZqaSkpEAVdEm67rrr5HmemjZtqh07dpS6/pZbbpHneapTp46ys7MrNQ8Wof9c2aARTOQPJpE/mET+YJIr+aMb2Mf6gj5//nyNGDGizOszMzPVr1+/Qzp2dna2tm7dGhlHH320PM/TkCFDSly+a9euEvcr/tNl69evj3rs4vffunVr5PZPPfVUicujfc983bp1SkpKkud5Ouecc7Rq1SpJP7+7/vDDDysmJkae52nUqFGVfs4sQv+5skEjmMgfTCJ/MIn8wSRX8kc3sI/1Bb06Fb1jfrDRt2/fEverSEGvyHHLe7c+IyNDtWvXjtyuXr16qlGjRuTvfv36qbCwsNLPmUXoP1c2aAQT+YNJ5A8mkT+Y5Er+6Ab2cbKgX3LJJfr+++8P+zg2F3RJWrNmjW666Sa1atVKcXFxOvLII3XhhRdq+vTph/ycWYT+c2WDRjCRP5hE/mAS+YNJruSPbmAfJwt6cnKy1q5da3oaTmIR+s+VDRrBRP5gEvmDSeQPJrmSP7qBfSjoIcMi9J8rGzSCifzBJPIHk8gfTHIlf3QD+zhZ0Nu3b69vvvnG9DScxCL0nysbNIKJ/MEk8geTyB9MciV/dAP7OFnQcehYhP5zZYNGMJE/mET+YBL5g0mu5I9uYB/nCvr27dv1/PPP64EHHtDYsWOj/k44ysYi9J8rGzSCifzBJPIHk8gfTHIlf3QD+1hf0C+//HK9+uqrkqTly5erYcOGatSokU4//XQ1adJEKSkp+vLLLw3P0h0sQv+5skEjmMgfTCJ/MIn8wSRX8kc3sI/1Bb1hw4ZatWqVJKlbt2669tprlZubK+nn4A8YMEAXXXSRySk6hUXoP1c2aAQT+YNJ5A8mkT+Y5Er+6Ab2sb6gJyYmas2aNZKkpk2b6pNPPilx/cqVK1WvXj0TU3MSi9B/rmzQCCbyB5PIH0wifzDJlfzRDexjfUE//fTT9fzzz0uSOnbsqBkzZpS4/u2331ZKSoqJqTmJReg/VzZoBBP5g0nkDyaRP5jkSv7oBvaxvqC/9dZbOuKIIzR+/HiNHz9erVq10rhx47Ro0SK9+OKLOvroozVkyBDT03QGi9B/rmzQCCbyB5PIH0wifzDJlfzRDexjfUGXpOnTp+uoo45SbGysYmJiIiMhIUF33XWX8vPzTU/RGSxC/7myQSOYyB9MIn8wifzBJFfyRzewjxMFXZLy8/P10UcfacqUKUpLS1NmZqZ++ukn09NyDovQf65s0Agm8geTyB9MIn8wyZX80Q3s40tB/+qrr5Samqrzzz9fxx57rFJSUvTrX/9a119/vSZNmqR9+/b5MQ2IRWiCKxs0gon8wSTyB5PIH0xyJX90A/tUa0H/5JNPdMEFFyg+Pl6dO3fWPffco9GjR2vs2LEaNWqU+vTpo1atWumII47QI488ckhFPTs7Wy+99FI1zD6YWIT+c2WDRjCRP5hE/mAS+YNJruSPbmCfai3oLVq00FNPPaUff/yx3NstXrxYV1xxhf7+979X+jGWLVum2NjYQ51i6LAI/efKBo1gIn8wifzBJPIHk1zJH93APtVa0HNzcw/79jt37ix3vPfeexT0SmAR+s+VDRrBRP5gEvmDSeQPJrmSP7qBfaw/SVxMTIxiY2PLHEXXo2JYhP5zZYNGMJE/mET+YBL5g0mu5I9uYB/jBX3Tpk16+OGHy7y+bt26GjVqlObPnx91jB07loJeCSxC/7myQSOYyB9MIn8wifzBJFfyRzewj/GCfrDvkJ933nkaNWpUufePiYmpjqkFEovQf65s0Agm8geTyB9MIn8wyZX80Q3sU+0F/dNPPy13TJ06tdyC/vzzz+vJJ58s8/pNmzbpoYceqo6pBxKL0H+ubNAIJvIHk8gfTCJ/MMmV/NEN7FPtBb3oO+IxMTGlBt8h9x+L0H+ubNAIJvIHk8gfTCJ/MMmV/NEN7FPtBb1hw4Z64YUXtGHDhqgjIyODgu4jFqH/XNmgEUzkDyaRP5hE/mCSK/mjG9in2gv6xRdfrOHDh5d5fUW+Q7579249//zz6tevn7p27apu3bqpX79+Gjt2rHbv3l3VUw40FqH/XNmgEUzkDyaRP5hE/mCSK/mjG9in2gv6a6+9pgkTJpR5fXZ2tv7zn/+Uef3y5cvVrFkz1a9fXz169NDAgQN10003qUePHqpfv76aN2+u5cuXV8fUA4lF6D9XNmgEE/mDSeQPJpE/mORK/ugG9jF+FveDOe+883T11VcrNze31HW5ubm65pprdN555xmYmZtYhP5zZYNGMJE/mET+YBL5g0mu5I9uYB/rC3piYmK575B//vnnSkxM9HFGbrNhEe7Ny9fCdT8offkGTftsnWat/EYbsncZm091c2WDRjCRP5hE/mAS+YNJruTPhm6Aknwp6Hv27NF7770XtWjv3btXL730Upn3bdasmdLT08u8fsaMGWrWrFmVzDMMTC7CjTt2acyS5br0P7PVZWyGLhiboQvHZajL2J/HXW8u1vy13yuvoMD3uVUnVzZoBBP5g0nkDyaRP5jkSv4o6Pap9oK+cuVKtWzZMvJzap06ddL3338fuX7Tpk3lnsU9NTVV9erV02OPPaZly5bphx9+0KZNm7Rs2TI99thjatCggR5++OHqfhqBYWIRFhYWatpna9VlbIYuHDczUsgPHEVlfeCrr6tdegAAIABJREFUC/Vjzr6DHnfixIk69dRTVbduXdWpU0dt27bVgAEDtHnz5qi379ixozzPU2ZmZhU/w/K5skEjmMgfTCJ/MIn8wSRX8kdBt0+1F/SePXvq0ksv1datW7V69Wr9/ve/1zHHHKOvv/5a0sELuiQ98sgjatq0aaTkF/1+etOmTTVq1KjqfgqBYmIR/ufjlWWW8mjjonEzdXXau+WW9JEjRyomJkaDBw/WrFmzNHPmTD366KP6zW9+o6VLl5a6/YoVK+R5njzP04033lidT7cUVzZoBBP5g0nkDyaRP5jkSv4o6Pap9oLeuHFjffbZZyUuu/XWW9WiRQutXbu2QgW9yLp167R48WItXrxY69atq47pBp7fi3Du6m8rVc6Ll/SBr76n/DI+7t6sWTP1798/6nUFUe7zwAMPqEaNGurSpYvq168f9aSD1cWVDRrBRP5gEvmDSeQPJrmSPwq6faq9oNepU0dffvllqctvv/12HXXUUVq4cGGFCzoOn5+LsKCwUNdOnndIBb1ovL9hU9Rj165dW/fdd1+F53LsscfqwgsvVEZGhjzP04wZM6rqaR6UKxs0gon8wSTyB5PIH0xyJX8UdPtUe0E/9dRT9fLLL0e97rbbblP9+vUPWtAP5yRzKMnPRZi1ccthlfMLx2XozxkfRD32OeecoyOOOEJjx47VDz/8UO48lixZIs/z9OKLLyovL08NGzZU7969q+MpR+XKBo1gIn8wifzBJPIHk1zJHwXdPtVe0EeMGKFu3bqVef0tt9yimJiYMq8/3JPMoSQ/F+EDc7J0UTknhavo+HbH7lLH/vzzz3XcccdFvld+zDHHaNCgQVq/fn2p295+++2Kj4/Xjh07JP38FYuEhATfNiJXNmgEE/mDSeQPJpE/mORK/ijo9rH+d9Cr4iRz+B8/F+FlE9457HLeZWyG5q35Lurxc3NzlZGRoTvvvFMdOnSQ53mqU6dOiZPE5efnq3Hjxrrssssily1atEie52n8+PHV/RJIcmeDRjCRP5hE/mAS+YNJruSPgm4f6wt6VZ5kDv4uwq4vzqqSgv768g0VerzZs2erRo0a6tWrV+SyWbNmRcr49u3bI+Ooo47SBRdcUF1PvQRXNmgEE/mDSeQPJpE/mORK/ijo9vGloH/00Ue69tpr1apVKyUkJCgxMVGtWrXStddeq6ysrHLvy0nmqpafi/D3/5ldJQV91spvKvyYv/3tb9W2bdvI39ddd13kY/AHjtjY2IN+f70quLJBI5jIH0wifzCJ/MEkV/JHQbdPtRf0GTNmqFatWurataueeOIJpaWladKkSXriiSfUrVs3xcXFKT09vcz7V8VJ5vA/fi7Cvq/Mr5KC/uE3W0ode9Om0md337Nnjxo1aqTzzjtPkpSTk6Pk5GT17NlTmZmZJcYrr7wiz/P0xBNPVPvr4MoGjWAifzCJ/MEk8geTXMkfBd0+1V7Q27dvr5EjR5Z5/SOPPKJ27dqVef3hnmQOJfm5CCctXa0LDrOcX/by29qfX/p3zRs1aqQbbrhBU6dO1cKFCzVlyhSdddZZiomJifyE2uTJk+V5nubNmxd1fqeeeqpOOeWUan0NJHc2aAQT+YNJ5A8mkT+Y5Er+KOj2qfaCHh8fr5UrV5Z5/VdffaX4+PjqngZ+4ecizN6z77DO4n7huAyN/zh6dsaMGaOuXbuqefPmiouLU7NmzdS1a9cSZfzSSy9VixYtVFhYGPUYTz/9tDzPKzefVcGVDRrBRP5gEvmDSeQPJrmSPwq6faq9oLdr106jRo0q8/pRo0bphBNOqO5p4Bd+L8K/z1uqCw+xpF84bqY279rjyzyrkysbNIKJ/MEk8geTyB9MciV/FHT7VHtBnz59umrWrKlLLrlE//rXvzR58mRNmTJF//rXv9S9e3fVqlVLr776anVPA7/wexFu37NP106ed0jvpM/8quInh7OZKxs0gon8wSTyB5PIH0xyJX8UdPv4chb3xYsX66qrrlKLFi0UFxenuLg4tWjRQldddZUWL17sxxTwCxOL8LudOfrjlHkVeie96Dvr0z5b69v8qpsrGzSCifzBJPIHk8gfTHIlfxR0+1j/O+ioWqYW4Y69uRo1f5kufmFm1BPHFb3DPmD6Ai3aUPoM7S5zZYNGMJE/mET+YBL5g0mu5I+Cbh9nCvqCBQuUl5dX6vK8vDwtWLDAwIzcZHoR7tibq6mfrtWN0xfqsgnv6A//maNr0t7V3+ct1Rebsss8oZvLXNmgEUzkDyaRP5hE/mCSK/kz3Q1Qmi8FfdmyZRo+fLjGjBmjrVu3lrhu586d6t+//0GPERsbq82bN5e6fNu2bfwOeiWwCP3nygaNYCJ/MIn8wSTyB5NcyR/dwD7VXtDnzJmjuLg4tW/fXi1atFDDhg1L/BTWpk2bKlSwY2JitGXLllKXr1y5UnXq1KnSOQcZi9B/rmzQCCbyB5PIH0wifzDJlfzRDexT7QX9jDPO0H333SdJKiws1KOPPqrk5GTNmjVL0sELeq9evdSrVy/Fxsbqkksuifzdq1cv/eEPf1CrVq108cUXV/fTCAwWof9c2aARTOQPJpE/mET+YJIr+aMb2KfaC3rdunW1Zs2aEpelpaUpKSlJb7zxxkELer9+/dSvXz/FxMToqquuivzdr18/DRw4UCNGjCj1sXmUjUXoP1c2aAQT+YNJ5A8mkT+Y5Er+6Ab2qfaC3qhRI3388celLp8yZYpq166tZ599tkIfcX/ooYeUk5NTHVMMFRah/1zZoBFM5A8mkT+YRP5gkiv5oxvYp9oL+oUXXqjHHnss6nVpaWmqVatWhQp6586dtX379lKX79y5U507dz7seYYFi9B/rmzQCCbyB5PIH0wifzDJlfzRDexT7QX9tdde01133VXm9WlpaTrvvPMOepyyzuK+efNm1axZ87DmGCYsQv+5skEjmMgfTCJ/MIn8wSRX8kc3sI/1v4P+6aef6tNPP1VMTIwyMzMjf3/66af65JNPNGLECLVs2dL0NJ3BIvSfKxs0gon8wSTyB5PIH0xyJX90A/tYVdALCwtLXRYTE6PY2FjFxsYqJiam1Khdu7ZeeOEFA7N1E4vQf65s0Agm8geTyB9MIn8wyZX80Q3sU60FvW3btpo0aZJyc3PLvd2qVat08803a+TIkaWu27Bhg9avX6+YmBhlZWVpw4YNkfH9998rPz+/uqYfSCxC/7myQSOYyB9MIn8wifzBJFfyRzewT7UW9HfffVennHKK6tevryuvvFKPPvqoJk6cqOnTp2vs2LG6++67deqppyopKUn33HMPwfABi9B/rmzQCCbyB5PIH0wifzDJlfzRDezjy0fcFy1apEGDBqlDhw6qX7++4uPj1bx5c1166aV66qmnop6dXZJef/31SKhff/31cgcqhkXoP1c2aAQT+YNJ5A8mkT+Y5Er+6Ab2seo76AeKiYmJnLk92vfPi0ZFfqYNP2MR+s+VDRrBRP5gEvmDSeQPJrmSP7qBfawu6Kh6LEL/ubJBI5jIH0wifzCJ/MEkV/JHN7APBT1kWIT+c2WDRjCRP5hE/mAS+YNJruSPbmAfpwr63Llz9Ze//EUDBgxQ//79SwxUDIvQf65s0Agm8geTyB9MIn8wyZX80Q3s40xBf+ihhxQbG6vTTjtNPXr0UM+ePUsMVAyL0H+ubNAIJvIHk8gfTCJ/MMmV/NEN7ONMQU9JSdHLL79sehrOYxH6z5UNGsFE/mAS+YNJ5A8muZI/uoF9nCnoRxxxhNasWWN6Gs5jEfrPlQ0awUT+YBL5g0nkDya5kj+6gX18KehjxoxRly5ddMUVV+jdd98tcd3WrVt1zDHHHPQYQ4cO1V//+tfqmmJosAj958oGjWAifzCJ/MEk8geTXMkf3cA+1V7Qn3zySdWuXVu33XabrrvuOsXHx2vEiBGR6zdt2lSh3zEfNGiQ6tevr3PPPVe333677r777hIDFcMi9J8rGzSCifzBJPIHk8gfTHIlf3QD+1R7QW/Xrp0mTZoU+Xvx4sVq3Lixhg0bJqniBf28884rc3Tu3Lna5h80LEL/ubJBI5jIH0wifzCJ/MEkV/JHN7BPtRf0xMRErV+/vsRlX3zxhZo0aaJ77723wgW9quXk5GjmzJkaPny4evXqpRYtWsjzPHmep9TU1Cp5jE2bNmnw4MH61a9+pYSEBDVo0EBnn322xo4dq8LCwnLvm5eXp3HjxumCCy5Qo0aNVLNmTSUnJ+vEE0/UoEGDDvn7+CxC/7myQSOYyB9MIn8wifzBJFfyRzewT7UX9KOPPloLFy4sdfny5cvVpEkT9enTx0hBz8zMjBTyA0dVFPSPP/5YRx55ZOSYycnJqlmzZuTviy66SPv27Yt63+zsbJ1++ukl5lSnTp0S94+Pj9crr7xS6XmxCP3nygaNYCJ/MIn8wSTyB5NcyR/dwD7VXtCvueYa3XnnnVGv++KLL9SoUaMKFfTdu3frgQce0BlnnKHWrVvrmGOOKTEqKzMzUw0aNFCXLl00ZMgQTZ48WSkpKVVS0Hfs2BE5Vtu2bZWVlSVJys3N1dNPP61atWrJ8zzdcsstUe9//fXXR4r4Qw89pG3btkmS8vPzNX/+fLVv316e5ynx/9m78/CoqsN94Gcm+54IKgUFQUEWqYDFUoms6k+LgLRWLbUKilW0SsFS2m+RoKiVRTYVQRSJIAqiBlRkTwQkaqKC7LIkbAn7lj0zc9/fHyHXDDOTdeaee+68n+c5j80sN2fS95zwZmbuREXhyJEjdZobF6HxVNmgyZqYP5KJ+SOZmD+SSZX8sRuYT8AL+tatWzFv3jyf12/fvh3jx4+v8TgPPPAAfvWrX+Ff//oXpk2bhunTp7uNunI6nR6XtWjRwi8FfezYsXqBPnDggMf1L7/8MoQQCAkJwZ49e9yuKy0tRUREBIQQePjhh70ef9++fXqBnz17dp3mxkVoPFU2aLIm5o9kYv5IJuaPZFIlf+wG5qPM56AnJCRg06ZNAf0e/irole9nHzp0qNfrCwoKEBsbCyEExo0b53Zdfn6+Xr5fe+01n9/jsssugxACU6ZMqdPcuAiNp8oGTdbE/JFMzB/JxPyRTKrkj93AfKQV9CFDhuDo0aO1vv0111yDnTt3BnBG/inou3fv1gt2de8Rv+uuuyCEQLdu3dwu1zQNMTExtX4GPSMjo07z4yI0niobNFkT80cyMX8kE/NHMqmSP3YD8zHkJe7eRlhYGD799FP965osWLAA9957L4qKigI2V38U9KVLl+rlubo/KIwePRpCCMTHx3tcN2rUqFq9B/3ee++t8/y4CI2nygZN1sT8kUzMH8nE/JFMquSP3cB8Al7QbTYb7HY7bDabx6i83NdJ4jp16oTOnTvrIy4uTv+osaqXd+7c2S9z9UdBnzlzpl6uqwv69OnT9dsVFBS4XVdSUuJ2orjKIl95FvdWrVph4sSJXt9Hf6nS0lKcP39eH4cPH4YQAqdOnUJ5eTmHAaOoqAhpaWkoKiqSPheO4BvMH4fMwfxxyBzMH4fMoUr+Tp06xYJuMgEv6DfeeCP69euHXbt2ITc3F7m5ucjJyUFoaCjWrFmjX+bN+PHjaz38wR8F/aWXXtJLtcPh8Hm7t956S79dXl6ex/Xl5eWYPHmyfsb3qqNJkyYYM2ZMrV5NkJKS4vWj5BYtWoS0tDQODg4ODg4ODg4OjiAdixYtYkE3mYAX9LKyMowYMQLt27fHDz/8oF8eGhqKHTt2BPrb14lZCvqBAwfQsWNHCCHwwAMPIDs7GwUFBTh06BDmz5+PX/3qVxBC4KabbvJ49v1SfAZd/lDlL6gc1hzMH4fMwfxxyBzMH4fMoUr++Ay6+Rh2krgVK1bgqquuwssvvwyXy1Xngv7dd9/hm2++8bj8m2++0T9nvKHM8BJ3p9Opl/OHHnrI63137typfxTb2LFj6zQ/vs/EeOXlarwHiayJ+SOZmD+SifkjmVTJH7uB+Rh6Fvdjx47hrrvuQnJycp0LeteuXfHRRx95XP7xxx/j5ptv9sv8zHCSuBUrVuj3/+mnn3ze/w9/+AOEEGjXrl2d5sdFaDxVNmiyJuaPZGL+SCbmj2RSJX/sBuYj5WPWZsyYgXvuuQeHDx+u9X1iYmKwf/9+j8sPHDiA2NhYv8zLDB+zNmXKFP3+1b3HfMyYMRBCIDIysk7z4yI0niobNFkT80cyMX8kE/NHMqmSP3YD85H2Oeh1ddlll2Hz5s0el3/99ddITEz0y/fwR0EHgObNm0MIgUceecTr9YWFhYiNjYUQAuPGjXO7burUqbV6Bn7IkCEQQuDyyy+v09y4CI2nygZN1sT8kUzMH8nE/JFMquSP3cB8pBT0uLg4r8+GV+f+++9Hz549ce7cOf2ys2fPomfPnvjTn/7kl3n5q6CPHTsWQghER0cjJyfH4/qJEydCCIGQkBDs2bPH7bqMjAy9oD/99NNej5+fn4+EhAQIITBgwIA6zY2L0HiqbNBkTcwfycT8kUzMH8mkSv7YDcxHSkGPjY2tc0E/cuQIWrVqhYSEBPTq1Qu9evVCYmIirr/+ehw6dKhe8zhz5gxOnjypj6uvvhpCCIwePdrt8kvPlF71o8u8FfBz586hSZMmEEKgffv2yM7OBlBxRvtZs2YhPDwcQggMHz7c474ulws33ngjhBCw2WwYOXIkjh49CqDi89G//PJLtG7dWr8+IyOjTo+Zi9B4qmzQZE3MH8nE/JFMzB/JpEr+2A3MR5mCDlS8NHzOnDl48skn8eyzzyI1NbVBoa98xrym8fDDD7vdr6aCDgDZ2dlo1KiRfru4uDi3zzS/4447UFpa6vW++/btQ6tWrdzmEBsbC7vdrn8dEhKCGTNm1PkxcxEaT5UNmqyJ+SOZmD+SifkjmVTJH7uB+Ugp6E888QROnjwp41u7CWRBByrOWj9y5Ei0bt0akZGRSExMRHJyMubOnQuXy1Xt3AoLCzFt2jT06tULjRs3RmhoKKKjo9G2bVs8/vjj2Lp1a70eMxeh8VTZoMmamD+SifkjmZg/kkmV/LEbmI8yJ4mbP38+Pv/8c/3r0aNHIyEhAb/73e+Qm5srcWZq4SI0niobNFkT80cyMX8kE/NHMqmSP3YD85Fe0I8dO4bnn3++xtu1adMG69atAwBs3rwZUVFRmDNnDvr3749BgwYFepqWwUVoPFU2aLIm5o9kYv5IJuaPZFIlf+wG5iO9oG/ZsgV2u73G20VFReHgwYMAgH/961/461//CgDYvn07GjduHNA5WgkXofFU2aDJmpg/kon5I5mYP5JJlfyxG5hPwAv61q1bqx2LFy+uVUG//PLL8cMPPwAAOnXqhNTUVAAVJ1SLiYkJ6GOwEi5C46myQZM1MX8kE/NHMjF/JJMq+WM3MJ+AF3SbzQa73Q6bzeYxKi+vTUEfPHgwunTpgkcffRTR0dE4deoUAGDZsmXo0KFDoB+GZXARGk+VDZqsifkjmZg/kon5I5lUyR+7gfkEvKA3btwY77zzDnJzc72OL774olYF/ezZs3jqqacwYMAAfPnll/rl48aNw4svvhjIh2ApXITGU2WDJmti/kgm5o9kYv5IJlXyx25gPgEv6P/v//0/TJgwwef1W7Zsgc1mC/Q06CIuQuOpskGTNTF/JBPzRzIxfySTKvljNzCfgBf0Tz75BAsWLPB5/ZkzZzB//vxaHevs2bNYtWoVFixYgNTUVH289957/pqu5XERGk+VDZqsifkjmZg/kon5I5lUyR+7gflIP4t7bS1fvhxxcXGw2+1ISEhAYmKiPpKSkmRPTxlchMZTZYMma2L+SCbmj2Ri/kgmVfLHbmA+AS/oa9asQXFxcYOP07p1a4wYMQJFRUV+mFXw4iI0niobNFkT80cyMX8kE/NHMqmSP3YD8zHkLO4RERG49dZbMW7cOKSnp6OsrKzOx4mOjsb+/fsDMMPgwkVoPFU2aLIm5o9kYv5IJuaPZFIlf+wG5hPwgn7kyBG89957ePTRR9GqVSvYbDZERUWhT58+mDBhAjZt2gSHw1HjcQYNGoTFixcHerqWx0VoPFU2aLIm5o9kYv5IJuaPZFIlf+wG5mP4e9APHTqE1NRUDB06FC1btoTdbkdsbKzX2y5btkwfb7/9Npo3b46UlBQsXbrU7bply5YZ/CjUxUVoPFU2aLIm5o9kYv5IJuaPZFIlf+wG5iPlJHH79u3DW2+9hcGDByM+Ph4xMTFeb2ez2Wo1avM56lSBi9B4qmzQZE3MH8nE/JFMzB/JpEr+2A3Mx5CCvn//frzzzjt48MEH0axZM8THx+POO+/Eyy+/jK+//tr0wbUSLkLjqbJBkzUxfyQT80cyMX8kkyr5Yzcwn4AX9ObNmyMxMRF33303Jk2ahG+//RZOpzPQ35Z84CI0niobNFkT80cyMX8kE/NHMqmSP3YD8wl4Qb/yyiuRmJiI/v3749VXX0V2djY0TavzcZ5++mnMmDHD4/LXXnsNI0aM8MdUgwIXofFU2aDJmpg/kon5I5mYP5JJlfyxG5iPIS9x37VrF958803cd999uPLKK5GQkIB+/fph8uTJ+O677+ByuWo8RtOmTZGdne1x+ffff49mzZoFYtqWxEVoPFU2aLIm5o9kYv5IJuaPZFIlf+wG5iPlJHE7d+7EG2+8gT/96U+Ij49HQkJCjfeJiIjA3r17PS7fu3cvIiIiAjFNS+IiNJ4qGzRZE/NHMjF/JBPzRzKpkj92A/MxvKAfO3YMH374IR5//HG0adMGNpsNkZGRNd6vQ4cOeO211zwunzlzJtq1axeIqVoSF6HxVNmgyZqYP5KJ+SOZmD+SSZX8sRuYT8AL+vHjx7F48WIMHz4cbdu2hd1uR0REBG699VaMGzcO6enpKC0trfE477zzDqKiojBu3DhkZGQgIyMDzz33HKKjo/HWW28F+mFYBheh8VTZoMmamD+SifkjmZg/kkmV/LEbmE/AC7rNZkN4eDhuueUW/Pe//8WaNWtQXFxcr2PNmjULzZo10z//vGXLlkhNTfXzjK2Ni9B4qmzQZE3MH8nE/JFMzB/JpEr+2A3MJ+AFfcGCBSgsLKz2NgsXLqz2eofDgfnz5yM/Px8AcOLECRQUFPhtjsGEi9B4qmzQZE3MH8nE/JFMzB/JpEr+2A3MJ+AFvV27djhz5ozP699//32EhYXVeJyoqCjk5ub6c2pBiYvQeKps0GRNzB/JxPyRTMwfyaRK/tgNzCfgBb1nz564+eabvT6L/sEHHyAsLAxTp06t8Ti9evXCp59+GogpBhUuQuOpskGTNTF/JBPzRzIxfySTKvljNzCfgBf0goIC3HTTTejbt69bQBcvXoywsDBMnjy5VsdZsmQJWrVqhddeew2bN2/G1q1b3QbVDheh8VTZoMmamD+SifkjmZg/kkmV/LEbmI8hH7N24sQJtG3bFn/4wx+gaRqWLFmCsLAwvPLKK7U+RuWJ4aoOu92u/5dqh4vQeKps0GRNzB/JxPyRTMwfyaRK/tgNzMewz0E/dOgQmjdvjj59+iA8PBwvvvhine6fm5tb7aDa4SI0niobNFkT80cyMX8kE/NHMqmSP3YD8wl4Qa/6MvTFixcjIiIC999/P1+iLgkXofFU2aDJmpg/kon5I5mYP5JJlfyxG5iPIZ+DXvWl6Je+PL22L1FPTU2tdlDtcBEaT5UNmqyJ+SOZmD+SifkjmVTJH7uB+QS8oNf00vTavkQ9MTHRbcTExMBmsyEiIgJJSUmBfhiWwUVoPFU2aLIm5o9kYv5IJuaPZFIlf+wG5mPYe9AD4eeff0bfvn2xcuVK2VNRBheh8VTZoMmamD+SifkjmZg/kkmV/LEbmI/SBR0AsrKycP3118uehjK4CI2nygZN1sT8kUzMH8nE/JFMquSP3cB8lC/oP/zwA+Li4mRPQxlchMZTZYMma2L+SCbmj2Ri/kgmVfLHbmA+yhT0ZcuWuY20tDS8+eab6NChA+68807Z01MGF6HxVNmgyZqYP5KJ+SOZmD+SSZX8sRuYjzIFvfKM71XP/H7llVfiz3/+M/Ly8mRPTxlchMZTZYMma2L+SCbmj2Ri/kgmVfLHbmA+hhZ0h8OBNWvWYPbs2bhw4QIA4OjRoygoKDByGkGNi9B4qmzQZE3MH8nE/JFMzB/JpEr+2A3Mx7CCnpubi7Zt2yI6OhohISHYv38/AGDEiBF4/PHHjZpG0OMiNJ4qGzRZE/NHMjF/JBPzRzKpkj92A/MxrKAPHDgQDz74IMrKyhAbG6sX9IyMDFx33XXV3rewsBDPPfccOnTogJiYGMTGxqJjx454/vnnUVRUZMT0LYOL0HiqbNBkTcwfycT8kUzMH8mkSv7YDczHsILeqFEj7N69GwDcCnpOTg6ioqJ83q+srAw33XQTIiIicM899+Df//43xowZgwEDBiA8PBzdunUzffDNhIvQeKps0GRNzJ//aJoG7ew+uHYuhOv7qXB9+z+4vp8G1+4PoZ0/KHt6psT8kUzMH8mkSv7YDczHsIKelJSEHTt2AHAv6Bs3bsQVV1zh837Tp0/HlVdeqZf7qnbt2oUrr7wSM2fODMykLYiL0HiqbNBkTcxfw2maBi3vG7i+fQmu9BFwZYys+G/lqPw6axK04z9InavZMH8kE/NHMqmSP3YD8zGsoN9333147LHHAFQU9AMHDqCgoAB9+vTBkCFDfN6vR48eeP31131eP3PmTPTo0cPv87UqLkLjqbJBkzUxfw2juZxw7foAboXc5/hHxX/3LYOmaT6PmZKSgpiYmFpdJ4TwOkJCQvzzAAOM+SOZmD+SSZX8sRuYj2EF/ejRo2jTpg3atWuH0NBQdOvWDY0aNcL111+P48eP+7xf48aNsX37dp/Xb9u2DY0bNw7ElC2Ji9B4qmzQZE3MX/1pmgbX7g9Ru3J+ydj/uc94CD7BAAAgAElEQVTj1rWgP/3008jMzHQb33zzjT8eYsAxfyQT80cyqZI/dgPzMfRj1oqLi/HOO+/gqaeewvDhwzF37lwUFxdXe5/Q0FDk5+f7vD4vLw9hYWH+nqplcREaT5UNmqyJ+as/7fj3qFc5vzi0055vzQLqXtAnT57snwckAfNHMjF/JJMq+WM3MB9DC3p92O12nDhxwuf1x44dg91uN3BGauMiNJ4qGzRZE/NXf67sV6G/bL3OYyRcW+d4PS4LOpExmD+SSZX8sRuYT0AL+rJly2o9fLHZbOjYsSM6d+7sdXTs2JEFvQ64CI2nygZN1sT81Y924TAa8uy5/ix68SmPY1eWcIfD4TGee+45j4I+ceJEj9s5nc4APnr/Yf5IJuaPZFIlf+wG5hPQgm6z2Wo1qivY48ePr9Wg2uEiNJ4qGzRZE/NXP66fP/Y8W3s9nkXXclZ6HDslJcXnyd+EELU6SVzPnj0D9+D9iPkjmZg/kkmV/LEbmI/pX+JO/sVFaDxVNmiyJuavflzb3kaDn0HPGAnX7g88jp2SkoKoqChkZWV5jMcee8yjoI8YMcLjdt4+etSMmD+SifkjmVTJH7uB+bCgBxkuQuOpskGTNTF/9ePa8gYa/hL3f8C1I9Xj2HwPOpExmD+SSZX8sRuYT0AL+owZM2o9yBhchMZTZYMma2L+6se1fR7qf4K4Ks+g71nscWwWdCJjMH8kkyr5Yzcwn4AW9GuuuaZWo2XLloGcBlXBRWg8VTZosibmr360nC/R4IKePgLakY0ex2ZBJzIG80cyqZI/dgPz4UvcgwwXofFU2aDJmpi/+tFKz6LBBf2rZ6E5ij2OXdeC/vTTTyMzM9NjlJaW+u3xBgrzRzIxfySTKvljNzAfZQp6amqq13+MlJWVITXV8z1+5B0XofFU2aDJmpi/+nNteweu9HqeyT1jJFy7PV/eDtS9oPsaOTk5/nmgAcT8kUzMH8mkSv7YDczH0IJ++PBhvPHGGxgzZgxGjhzpNmpit9tx/Phxj8tPnTrFz0GvAy5C46myQZM1MX/1p53LQb2fRc8YBa0wX8a0TYX5I5mYP5JJlfyxG5hPQAv6smXL9FCuXbsW0dHR6NChA0JDQ9GpUyckJiYiISEBvXv3rvFYNpsNJ06c8Lh8y5YtSEpK8vvcrYqL0HiqbNBkTcxfw2h5majXe89PbJUyX7Nh/kgm5o9kUiV/7AbmE9CCbrPZ9Ge9b775Zjz33HMAgNjYWOzfvx8FBQUYMGAAZs2a5fMYnTp1QufOnWG329GxY0d07txZH7/+9a8RFxeHP/3pT4F8GJbCRWg8VTZosibmr+G0/G/hSv9HxcvWqy3mI+HKeBbayW3S5mo2zB/JxPyRTKrkj93AfAx7iXtsbCz27t0LAEhMTMT27dsBVDwD3qJFC5/3Gz9+PMaPHw+bzYZ//vOf+tfjx4/Hyy+/jEWLFqGsrMyIh2AJXITGU2WDJmti/vxDK8yHa89HcH31T1S+hL1iXCztG/4F1940aMUnZU/VVJg/kon5I5lUyR+7gfkEtKAPHToUFy5cAABceeWV2LlzJwCgffv2WLZsGYCKgu7rZDlVzZ8/HyUlJYGbbJDgIjSeKhs0WRPz51+ao6TiZe/7P4fr54/hOvAFtPwsaE7+odgb5o9kYv5IJlXyx25gPgEt6Pfcc4/+f3b//v3x9ttvAwBGjx6N6667Di+++CK6dOmCvn37BnIaVAUXofFU2aDJmpg/kon5I5mYP5JJlfyxG5iPYS9x3717N7KzswEARUVFGD58ODp27IhBgwYhNzfX632SkpJw8mTFywUTExORlJTkc1DtcBEaT5UNmqwpmPKnaeVwaifh1PLg1PLh0s5A0zTZ0wpqwZQ/Mh/mj2RSJX/sBuYT0II+Y8YM/WXpBw8erPM/lObPn69/9vn8+fOrHVQ7XITGU2WDJmsKhvy5tHMod21BifNzlDiXXzJWw+H6GZpWKnuaQSkY8kfmxfyRTKrkj93AfAJa0ENCQvSzuPv6HHMyFheh8VTZoMmarJw/TXOizJl9sYh/5qWcVx2fweHy/mqtqlJSUiCE0EdkZCTat2+PadOmYffu3YiIiMDzzz/vcb+8vDzEx8fj73//eyAeqrKsnD8yP+aPZFIlf+wG5hPQgn711Vdj1qxZyM3Nhc1mw/fff4+DBw96HbV1/PhxbNu2DVu3bnUbVDtchMZTZYMma7Jq/jTNiVLnphpKuedwuPZWe9yUlBRERUUhMzMTmZmZWLduHcaMGQMhBF577TWkpKQgMjJS/1SSSvfffz+aNm3KvfUSVs0fqYH5I5lUyR+7gfkEtKDPmTMH4eHhsNvtPofNZoPdbq/xWNnZ2ejQoYN+n6qjNvenClyExlNlgyZrsmr+fnnmvO7D6Tri87gpKSleP1mkd+/e6N27N0pLS3H99dfj9ttv169bvXo1hBD4+OOPA/JYVWbV/JEamD+SSZX8sRuYT8BPEnfhwgVs27YNNpsN69atw5YtW7yOmlSeUO6bb75BTk4OcnNz3QbVDheh8VTZoMmarJg/l3a23uW88n3pvs6J4qugDxgwAMnJyQCAjIwMCCHwwQcfoLS0FK1bt0b//v0D+phVZcX8kTqYP5JJlfyxG5iPYWdxr3rCt/qIjY31eElhQxQVFWHFihWYMGECBg0ahObNm+vvOUxJSfHL9zh27BhGjRqFNm3aIDIyEklJSUhOTsbcuXNrfcK8vXv3YtSoUejQoQPi4+MRHR2Nli1bYuDAgXjjjTfqPCcuQuOpskGTNVkxf2XOH2vxnvMankXXjnk9dmVBdzgccDgcOH/+PD766COEh4dj+vTp+u2GDh2KJk2aYOTIkYiNja3TW7WCiRXzR+pg/kgmVfLHbmA+hhX0Q4cO4fDhw/rX3377LUaMGIE5c+bU6v4DBw7E0qVL/Taf9PR0txMBVR3+KOjZ2dlo1KiRfszY2FiEhobqX99xxx01/sFi2rRpiIiI0O8TFRWFuLg4/euEhIQ6z4uL0HiqbNBkTVbLn6aV+Thbe13GZyhzZno9/qUniascQ4YMcfvD6unTp3H55ZdDCIGpU6ca9fCVY7X8kVqYP5JJlfyxG5iPYQU9OTkZ7733HgAgPz8fcXFx+N3vfodGjRp5PSPupU6ePInf//73GD9+PJYuXYply5a5jbpKT09HUlIS+vbti9GjR+ODDz5AkyZN/FLQz507px+rbdu2yMrKAgCUlZXh9ddfR1hYGIQQGD58uM9jvPrqqxBCwGaz4amnnsLu3bv1686ePYvVq1fj2WefrfPcuAiNp8oGTdZktfw5tRMNLOeV43Ovx688SVxWVhaysrKwadMmzJgxAwkJCfjb3/7mdtv//Oc/iIqKgtPpNOKhK8lq+SO1MH8kkyr5YzcwH8MKemJiol4yZ8yYgVtuuQUAsGrVKrRs2bLG+y9btgzx8fEeJ4ir70nivP2DqkWLFn4p6GPHjtWf8T5w4IDH9S+//DKEEAgJCcGePXs8rv/pp5/0Ej9jxowGzeVSXITGU2WDJmuyWv6crqN+KujLoWkuj+P7eg/6lClTIITA9u3ba7wt/cJq+SO1MH8kkyr5YzcwH8MKekxMDHJycgAA/fv3xyuvvAIAOHjwICIjI2u8f4sWLfDUU0/h2DHv7xv0B38V9Mr3sw8dOtTr9QUFBYiNjYUQAuPGjfO4/oEHHoAQAjfffHOD5uENF6HxVNmgyZqslj+nlufHgu55LhBfpXvFihUQQuDDDz+s8bb0C6vlj9TC/JFMquSP3cB8DCvoN998M8aMGYMNGzYgMjJSP3N7ZmYmmjVrVuP9Y2NjsW/fvoDO0R8Ffffu3fp7FpcsWeLzdnfddReEEOjWrZvb5YWFhfqz57Nnz673PHzhIjSeKhs0WZPV8ufSTvmpoK/wenxfpXvSpEkQQmDt2rU13pZ+YbX8kVqYP5JJlfyxG5iPYQU9PT0diYmJsNvtbs8s/+c//8GgQYNqvP9DDz2EuXPnBnKKfinoS5cu1Qv6zp07fd5u9OjREEIgPj7e7fL169fr99+1axfWr1+Pfv36oVGjRoiIiEDLli3xyCOPuL3Msi64CI2nygZN1mS1/GmaCyXOlQ0+SVy5a6vX41e+Bz0zMxOZmZn46quvMHXqVMTHx6N9+/ZuP0cW9JpZLX+kFuaPZFIlf+wG5mNYQQcq3vd95swZt8tycnJw/PjxGu/74osvonHjxnj44YcxZcoUzJgxw234gz8K+syZM/WCXV3Qp0+frt+uoKBAv3z27Nn6yeEmTJgAm80GIQRiYmL0l8ULIRAeHo7U1NQ6z4+L0HiqbNBkTVbMn8O1p8HPoLs073vgpWdxDw0NRcuWLfHkk096/K5iQa+ZFfNH6mD+SCZV8sduYD6GFnSHw4E1a9Zg9uzZuHDhAgDg6NGjbgXVl2uuucbnqM1J5mrDHwX9pZde0v9h53A4fN7urbfe0m+Xl5enX/6///1PL+hCCNxwww3YvHmzfv13332HTp066f9wrDxDvC+lpaU4f/68Pg4fPgwhBE6dOoXy8nIOA0ZRURHS0tJQVFQkfS4cwTesmL+ysgIUlCxHQcmyeozlKCz5SvpjCJZhxfxxqDOYPw6ZQ5X8nTp1igXdZAwr6Lm5uWjbti2io6MREhKC/fv3AwBGjBiBxx9/3KhpVMsMBb3q/SMiIpCbm+tx3yNHjiA6OhpCCAwcOLDa+fj6TN9FixYhLS2Ng4ODg4ODg4ODgyNIx6JFi1jQTcawgj5w4EA8+OCDKCsrQ2xsrF7QMzIycN111xk1jWqZ4SXuVe//4IMP+rz/sGHD9Je+V/cZvHwGXf5Q5S+oHNYcVs5fcenOOj1zXlDyOUrLjkmfdzANK+ePw/yD+eOQOVTJH59BNx/DCnqjRo30z0GvWtBzcnIQFRVl1DSqZYaTxH388cf6/V966SWf93/11Vf129XmPfyV+D4T45WXq/EeJLImq+fP6TqEEucq/eRv3k4IV+JcjlLnBp/vO6fAsXr+yNyYP5JJlfyxG5iPYQU9KSkJO3bsAOBe0Ddu3IgrrrjCqGlUywwfs7Z37179/i+//LLP+0+ZMkW/3YkTJ2o9Py5C46myQZM1BUP+NM0Fp5aPUufXl5T0FSh3bYVLOyd7ikErGPJH5sX8kUyq5I/dwHwMK+j33XcfHnvsMQAVBf3AgQMoKChAnz59MGTIEKOmUS1/FHQAaN68OYQQeOSRR7xeX1hYqJ+Rfdy4cR7Xt2rVqsaXuD/66KP6M/Aul6vWc+MiNJ4qGzRZUzDmT9Oc0LTa74sUOMGYPzIP5o9kUiV/7AbmY1hBP3r0KNq0aYN27dohNDQU3bp1Q6NGjXD99dfX6SXageSvgj527FgIIRAdHY2cnByP6ydOnAghBEJCQrBnzx6P61944QX9JHHe7l/1JHEPPPBAnebGRWg8VTZosibmj2Ri/kgm5o9kUiV/7AbmY+jHrBUXF+Odd97BU089heHDh2Pu3LkoLi6u9f3Pnj2LVatWYcGCBUhNTXUb9XHmzBmcPHlSH1dffTWEEBg9erTb5Zd+DFzVM6N7K9Dnzp1DkyZNIIRA+/btkZ2dDQAoKyvDrFmzEB4eDiEEhg8f7nVehYWF+h8LOnbsiMzMTP26rKws/WPWoqKiqn2fuzdchMZTZYMma2L+SCbmj2Ri/kgmVfLHbmA+hhb0hli+fDni4uJgt9uRkJCAxMREfSQlJdXrmJUluKbx8MMPu92vpoIOANnZ2WjUqJF+u7i4OISFhelf33HHHSgtLfU5t127dqFZs2b67WNjY/WXxVd+/dlnn9X5MXMRGk+VDZqsifkjmZg/kon5I5lUyR+7gfkEtKAvW7as1qMmrVu3xogRI1BUVOS3+QWyoAPAsWPHMHLkSLRu3RqRkZFITExEcnIy5s6dW6v3jZ87dw7jxo3Dr3/9a8TGxiIqKgrXX389nnnmGa+fj14bXITGU2WDJmti/kgm5o9kYv5IJlXyx25gPgEt6DabzW3Y7Xavl9nt9hqPFR0drZ/5neqPi9B4qmzQZE3MH/nidDmQV/Qz9pzNxM4zG7D77Nc4cP5HFDn89/uB+SOZmD+SSZX8sRuYj2EvcV+zZg26dOmClStX4vz587hw4QJWrlyJ3/zmN1i9enWN9x80aBAWL15swEytjYvQeKps0GRNzB9dqthRgL3nvsOGvIVIPzofGUdTkX40Vf9v+tH5+PHkKpwsOQRN0xr0vZg/kon5I5lUyR+7gfkYVtA7dOiAjRs3ely+YcMGtG3btsb7v/3222jevDlSUlKwdOnSOr9EnipwERpPlQ2arIn5o6rOlOZdLOYVRdz3qLh+z9lMuGr4yLqqb/sSQiAyMhLt27fHtGnTUFZWpufP19vIQkJCDHr0FGy4/5FMquSP3cB8DCvokZGR+Omnnzwu37p1KyIjI2u8/6Uvjb/0ZfJUO1yExlNlgyZrYv6o0tnSYxefJa+umHuOXWc2VftMekpKCqKiopCZmYnMzEysW7cOY8aMgRAC06dPdyvoTz/9tH67yvHNN98Y+FOgYML9j2RSJX/sBuZjWEG/9dZb0adPH+Tl5emX5efn47bbbkOPHj2MmkbQ4yI0niobNFkT80cAUOYswYa892vxzLn3caRwl89jp6SkICYmxuPy3r17o1evXm4FffLkyYF8mERuuP+RTKrkj93AfAwr6Hv37sUNN9yAsLAwXHvttbj22msRFhaGDh06YO/evXU6VklJSYBmaX1chMZTZYMma2L+CAAOXvipXsW8cmzO/8jns+i+CvqAAQPQvXt3FnSShvsfyaRK/tgNzMfQz0HXNA2rVq3CjBkzMH36dKxevbrWJ6BxOp144YUX0LRpU4SEhOhndB87dizefvvtQE7bUrgIjafKBk3WxPyRprmwOX9Jgwp6+tH5OFVyxOvxKwu6w+GAw+HA+fPn8dFHHyE8PByvvvqqW0GfOHGifrvK4XQ6Df6JULDg/kcyqZI/dgPzMbSgN8Tzzz+PVq1aYeHChYiKitIL+uLFi9GtWzfJs1MHF6HxVNmgyZqYPzpdcqTB5Tz9aCp+OrXO6/EvPUlc5RgyZEitThLXs2dPY38gFDS4/5FMquSP3cB8DC3oGRkZuPvuu3HttdfiuuuuQ//+/bFhw4Za3ffaa6/F2rVrAQCxsbF6Qd+1axcSExMDNmer4SI0niobNFkT80eHC3b4oaDPR+axj70ev/IkcVlZWcjKysKmTZswY8YMJCQkYNiwYW4FfcSIEfrtKsfu3bsN/olQsOD+RzKpkj92A/MxrKAvWLAAoaGhuO+++/SXuN93330ICwvD+++/X+P9IyMjkZubC8C9oO/YscPre9/IOy5C46myQZM1MX+Uc2FLvc7efunYlP+h1+P7eg/6lClTIITAzJkz+R50koL7H8mkSv7YDczHsILetm1bTJ061ePyV199tVafg37TTTdhwYIFANwL+vjx45GcnOzfyVoYF6HxVNmgyZqYPzpUsKPeZ293fwZ9qdfj+yroK1asgBAC//znP1nQSQrufySTKvljNzAfwwp6eHi417O17927FxERETXef/ny5UhISMArr7yC6OhoTJ48GcOGDUN4eDhWr14diClbEheh8VTZoMmamD86WXLQL+9B33LK++9aXwV90qRJEELg+eefZ0EnKbj/kUyq5I/dwHwMK+jXXnstZs+e7XH57Nmzcd1119XqGCtXrkSPHj0QExODqKgodO/eHatWrfL3VC2Ni9B4qmzQZE3MH7k0Fzblf9Dgkn6iONfr8Svfg56ZmYnMzEx89dVXmDp1KuLj49GuXTssXbpUL+hPP/20fruqo7S01OCfCgUD7n8kkyr5Yzcwn4AX9KFDh+LChQuYNWsWwsPD8cQTT+C9997DggUL8PjjjyMiIsJrcafA4CI0niobNFkT80cAcOD8jw16mfum/A/h0lxej33pWdxDQ0PRsmVLPPnkkzhy5EiNZ3EXQiAnJ8fYHwgFBe5/JJMq+WM3MJ+AF3S73Y7jx48DAD755BN0794dl112GS677DJ0794daWlpdTpeWVkZDh8+jIMHD7oNqh0uQuOpskGTNTF/BAClzkJ8dXRBvQv6wQs/1ev7Mn8kE/NHMqmSP3YD8wl4QbfZbHpBb4iff/4ZycnJsNvtbsNms8Fut/thpsGBi9B4qmzQZE3MH1Wq73vRfzq1DpqPZ89rwvyRTMwfyaRK/tgNzMeQgn7ixIkGH+eWW25Bjx49sGLFCvz444/YsmWL26Da4SI0niobNFkT80dVHS/OQcbR92r9cvdtp9fDqTnr/f2YP5KJ+SOZVMkfu4H5GFLQExMTkZSUVO2oSXR0NHbt2hXo6VoeF6HxVNmgyZqYP7rUhfLT2HE6w+tno1de9u3xNBwt3ANN0xr0vZg/kon5I5lUyR+7gfkYUtBnzJiB+fPnVztq8pvf/AYbN24M9HQtj4vQeKps0GRNzB/5UuYsRu6Fn7D9dDp+PLkKP51aiz1nN+Nc2fEGF/NKzB/JxPyRTKrkj93AfEz9HvTz58/rY926dfjd736H9PR0nDp1yu06Bqr2uAiNp8oGTdbE/JFMzB/JxPyRTKrkj93AfAw9i3tdVZ4A7tITwvEkcfXHRWg8VTZosibmj2Ri/kgm5o9kUiV/7AbmY+pn0DMyMmo9qHa4CI2nygZN1sT8kUzMH8nE/JFMquSP3cB8Al7QyVy4CI2nygZN1sT8kUzMH8nE/JFMquSP3cB8lCno8+bNw5IlSzwuX7JkSa1OMkcVuAiNp8oGTdbE/JFMzB/JxPyRTKrkj93AfJQp6G3atMH69es9Ls/IyECbNm0kzEhNXITGU2WDJmti/kgm5o9kYv5IJlXyx25gPsoU9IiICOTk5HhcnpOTg8jISOMnpCguQuOpskGTNTF/JBPzRzIxfySTKvljNzAfZQr61VdfjWXLlnlcnpaWhmbNmkmYkZq4CI2nygZN1sT8kUzMH8nE/JFMquSP3cB8lCnoo0ePRosWLbB+/Xo4nU44nU6sW7cOLVq0wLPPPit7esrgIjSeKhs0WRPzRzIxfyQT80cyqZI/dgPzUaagl5WV4b777oPNZkNYWBjCwsIQEhKCoUOHoqysTPb0lMFFaDxVNmiyJuaPZGL+SCbmj2RSJX/sBuajTEGvtGfPHixZsgSfffYZcnNzZU9HOVyExlNlgyZrYv5IJuaPZGL+SCZV8sduYD7KFXRqGC5C46myQZM1MX8kE/NHMjF/JJMq+WM3MB9TF/SRI0eisLBQ/9/VDaodLkLjqbJBkzUxfyQT80cyMX8kkyr5YzcwH1MX9F69euHs2bP6//Y1evfuLXmm6uAiNJ4qGzRZE/NHMjF/JBPzRzKpkj92A/MxdUEn/+MiNJ4qGzRZE/NHMjF/JBPzRzKpkj92A/NhQQ8yXITGU2WDJmti/kgm5o9kYv5IJlXyx25gPqYu6IMGDar1oNrhIjSeKhs0WRPzRzIxfyQT80cyqZI/dgPzMXVBHzJkSK0H1Q4XofFU2aDJmpg/kon5I5mYP5JJlfyxG5iPqQs6+R8XofFU2aDJmpg/kon5I5mYP5JJlfyxG5gPC3qQ4SI0niobNFkT80cyMX8kE/NHMqmSP3YD81GmoHfq1AmdO3f2GF26dMEtt9yChx56COvXr5c9TdPjIjSeKhs0WVNQ5E/TAM1R8V8ylaDIH5kW80cyqZI/dgPzUaag//vf/0ZCQgKSk5MxatQojBw5ErfeeisSEhIwYsQI3H777bDb7UhLS5M9VVPjIjSeKhs0WZNl86c5AMcBoHgtUPQpUPTJxfEFULYDcBXJniHBwvkjJTB/JJMq+WM3MB9lCvqwYcPwwgsveFw+YcIEDBs2DAAwbtw43HTTTUZPTSlchMZTZYMma7Jc/jQNKNsJFC2rUsovHRcLe8k3gFZa4yFTUlIghNBHZGQk2rdvj2nTpkHTNMyfPx9CCPz8889u93vjjTcghMD//d//uV1eUFCAkJAQr7+zgo3l8keG0FwuXNi6G6fWbsaJFV/hzMZslJ8+V+fjMH8kkyr5YzcwH2UKenx8PPbu3etx+d69exEfHw8A2LVrF2JjY42emlK4CI2nygZN1mSp/GkuoCSzmmLupagXr6zx2fSUlBRERUUhMzMTmZmZWLduHcaMGQMhBF577TXs27cPQgi8++67bvf7y1/+gujoaPTs2dPt8jVr1kAIgXXr1vn5B6AeS+WPAq787Hkcfmcpvus7BBvb93MfHftj1z8n4fz3O6DV8u0szB/JpEr+2A3MR5mCfsUVVyA1NdXj8tTUVFxxxRUAgB07dqBRo0ZGT00pXITGU2WDJmuyTP40DSjNrkM5r1rSVwFamc9Dp6SkICYmxuPy3r17o3fv3gCAJk2a6K/WqnTNNdfgySefRFRUlNvPd/z48QgNDUVhYaGfHry6LJM/CrgTn2dgU6d7sLHD3Z7l/OLY1HEANrbvh23DxsJRUPMf3mJiYrzm79I1L4TA5MmTvR6nuuuIqqPK/sduYD7KFPQJEyYgKioKzzzzDBYsWICFCxfimWeeQXR0NF588UUAwNSpU3HbbbdJnqm5cREaT5UNmqzJMvlzHqtHOa9S0su2+jy0r4I+YMAAJCcnAwD+8Ic/oF27dvp1R48ehRACu3fvRnh4OL799lv9uttvvx1du3b144NXl2XyRwGV98EXFSW8g/di7jFu6I/vBz4JxwXffwRjQSfZVNn/2A3MR5mCDgALFy5Et27dkJSUhKSkJHTr1g3vv/++fn1xcTFKSkokztD8uAiNp8oGTdZkmfyVbL7kZHB1HcsrTiznReU/1h0OBxwOB86fP4+PPvoI4eHhmD59OoCKPwDbbDacPn0aALBkyRI0bdoUANCtWzdMnToVAOByuRAXF4d//OMftXpYmuMctKK90AjMF40AACAASURBVAq2QyvcDa30aK1fvqsCy+SPAub0hqxqnzWvrqT/NOQ/0Fwur8dlQSfZVNn/2A3MR4mC7nA4MH78eBw6dEj2VJTHRWg8VTZosiZL5M9V1IBiXmU4crwe/tKTxFWOIUOG6GX522+/hRACn332GQDgH//4B+69914AwKhRo/DHP/4RALBlyxYIIbB06VKfD0fTXNCK9kE79hG03Mme4/Bb0M5nQXOq/wdnS+SPAkbTNHw/YHj9CvrFcebrH7wemwWdZFNl/2M3MB8lCjoAxMTEICcnR/Y0lMdFaDxVNmiyJkvkr3yvfwp6ySavh688SVxWVhaysrKwadMmzJgxAwkJCfjb3/5WMYXyckRHR2PMmDEAgK5du2LatGkAgKVLl6JJkyYAfjmze35+vtfvpTkKoB2df7GMT/Fe0CvHwWnQivbV+OO59A8MjRo1Qvfu3fHFF1+43a5Fixb6bUJCQtCqVSsMHz4cJ0+erN3/D/VgifxRwJz/YWe9i3nlieN2POX90xIqS/jHH3+M4uJi/RUyDocDzz33nEdBnzhxotttKgcLOtWXKvsfu4H5KFPQBw4c6HEGXao7LkLjqbJBkzVZIn9l2xv48vaLo3it18P7eg/6lClTIITA9u3bAQC9evVCcnIyioqKEBYWpr/vPC8vD0II7Nu3D4MHD8a1117r9ftojgvQDr9ZczG/dBTurPbHc+lZ6D/++GN07doVdrsdX3/9tX67Fi1a4N5770VmZiY2bNiASZMmITo6GrfccgtcPl4m3FCWyB8FzO7Rk7Dx4onf6j063I3SvBMex/b1ypjKcWlBr26woFN9qLL/sRuYjzIFffbs2WjSpAmeffZZLFq0CMuWLXMbVDtchMZTZYMma7JE/sq2+amgr/Z6eF8FfcWKFRBC4MMPPwQA/Pe//0VkZCRWrVrlceb2a665BqmpqWjRogUefvhhj2NpmgPa0Xl1L+eVz7SXHPH54/E2/7y8PNhsNv0VAEBFQX/qqafcbvfCCy9ACIGsrCyfx28IS+SPAuabHg82rJxfHCe+yPA4duUfrqZMmYLMzEz9FTJZWVl47LHHPAr6iBEj3G5TOVjQqb5U2f/YDcxHmYJus9l8DrvdLnt6yuAiNJ4qGzRZkyXyV77bPwW9xPMf8YDvgj5p0iQIIbB2bcUz75WFvV+/fujRo4fbbQcPHoy7774bQgjMnTvX41hawbZ6FPMqBT1/sc8fj6/5X3HFFbjzzjv1r70V9C+++AJCCCxZssTn8RuiLvnTnE6U5eagZPs2lOzcgfLDhy11wjzy9HWXP/iloOd98IXHsfkedJJNld+/7Abmo0xBJ//gIjSeKhs0WZMl8uc85Z/3oJft8Hr4S18i/tVXX2Hq1KmIj49H+/bt9Z/duXPnYLfbYbPZ8O9//9vtGK+//jpsNhuEENi50/Ml6drR1Ho+e15llJ/2Of9LC3pBQQFCQ0PdCrm3gv76669DCIFNm7y/P7+hapM/x5kzOLv4Axx66EHkDOjnNg4/PgznP1sOFz9T3pI2d73XLwU9f+kqj2OzoJNsqvz+ZTcwHyULOj9Krf64CI2nygZN1mSJ/GkaULym4QXdVez18Je+VzU0NBQtW7bEk08+iePHj7vdtmPHjhBCYPny5W6X//DDD/oJ2i591lcrzW9YMa98Fv30ep/zr/oxcYcOHcLgwYNx2WWX4eeff9Zv16JFCzz55JNwOBwoLi5Geno6mjZtilatWgXs92p1+dM0DWcWLkDOPf2RM7C/RznPGdAPOQPvRs7Au5F77yBcWLWyxu9X+f/lrbfe6vW62pYyMkbWHY/6paCfWv+Nx7FZ0Ek2VX7/shuYjzIF3el04oUXXkDTpk0REhKC/fv3AwDGjh2Lt99+W/Ls1MFFaDxVNmiyJsvkz3GgAeX8U6AkU9rUtfNZDX/2PHcytKPzvR7f28mwQkJCqj2Le+Xo2rUrfvzxx4A9dl/50zQNJ2ZM817Kqxlnl/h+qT/g/rNYt26dx3Us6OaSO+M9bLyhf4PK+ebf3AtnsecfmFjQSTZVfv+yG5iPMgX9+eefR6tWrbBw4UJERUXpBX3x4sXo1q2b5Nmpg4vQeKps0GRNlsmf5rj4LHp93oueBrjOyZv62U3Qcl9teEE/PNvr8at+TNy3336LhQsX4qqrrsJll12GvLw8/XYtWrTAfffdh6ysLGzZsgWnT3t/ybw/+crfmYUL6lzOK0fBOu9n4wd+KV6//e1vPc4TwIJuPqV5Jxr0GegbO/bH/lfe8npsFnSSTZXfv+wG5qNMQb/22mv1E/XExsbqBX3Xrl1ITEyUOTWlcBEaT5UNmqzJUvlzFQNFK+pY0j8FnN4/k9wo2rlM/xT0I54nnwO8vwc9OzsbdrsdTzzxhH6Zt/egB5q3/DlOnap4WXs9C/rBwQ9A85Hnyp9F5cnv0tPTPa6rxOJlDjv+PgEbO9b/WfTiHN+fcGCp/Y+Uo0r+2A3MR5mCHhkZidzcXADuBX3Hjh1ez15L3nERGk+VDZqsyXL500qA4nW/lO9qy/lngPOk7BlDu7DVD+9Bnwwtf5HX4/s6i/tf/vIXREREID+/4g8UZinoZz9YVPHe8noW9JwB/VDwVc1n5O/atSt69uzp9TqABd0sSo4eR2b3P9frpe4H3/yg2mNbbv8jpaiSP3YD81GmoN90001YsGABAPeCPn78eCQnJ8ucmlK4CI2nygZN1mTJ/Gka4DwGlGz28XnnawFHTsXL4k1Acxb65xn0C97fK+6roO/evRt2ux1jxowBYI6CrjmdOPjQXxpUznPu6Y+80aO8fr+qP4vPPvsMQghkZGR4XAewoJtJwa79yLzlgTo9k75/4twaP4bPkvsfKUOV/LEbmI8yBX358uVISEjAK6+8gujoaEyePBnDhg1DeHg4Vq9eLXt6yuAiNJ4qGzRZk+Xz5yquKOuOIxUvZXedqyjwJqOd+LxhJ4o7OA2aq8zrsX0VdKDiWfT4+HicO3fOFAW9LCenYeW8ynCVlnp8v0t/Fl26dEHv3r29XseCbi4lR47hp0f+T39vuUcpv/gM+zfdByNv8YpaHdPy+x+Zmir5YzcwH2UKOgCsXLkSPXr0QExMDKKiotC9e3esWuX52ZfkGxeh8VTZoMmamD9z0EqPNvAj1tbIfgj1cmn+Srb95LeC7jh1yuP7XVrC09LSIITAhg0bLFXQNZcLR1ZlYc2A/2LhZQPxbvjtSI2+C0uu+TO+HzsPBYeO13wQkyrOPYL9E+ci85Y/Y2OHinK+qdM92PrXf+Hkyo1wldf+lTHc/0gmVfLHbmA+ShV0ajguQuOpskGTNTF/5qGdXl+/cn5kLjSn989wNzuPgr59m/8K+pkzHt/P26sJOnXqhL59+1qmoO//cD2WtBqMebY+eDfsdsyz9XEb74behnn2vlg76DkUHj5R4/EWLlyIrl27Ij4+HnFxcWjbti0effRRHD/+S8nv2bMn+vXr5/X+1V3XUJrLBVdZ/fcu7n8kkyr5YzcwH2UK+pAhQ7B27doa329E1eMiNJ4qGzRZE/NnHpqmQTv5Zd3K+eG3oDnkfURcQ3m8xP3QIb8VdG9ncvdW0D/55BMIIdC3b1+/FnSt+BycP6bBsXIKHGkpcHz+IhzrZ8F1aAs0zVXv41Zny4sLPQq5r/Fu2O1Y1OSPOLM9x+fx/ve//8Fms2HUqFH48ssvsWLFCkyaNAk33ngjfvzxl3MeyCroDcX9j2RSJX/sBuajTEHv378/IiIi0LRpU4waNQo//PCD7CkpiYvQeKps0GRNzJ+5aJoG7XwWtEOv/VLCvRXz3CnQTixX9pnzSh4niXO5cPixRxt8krj8lOe8fj9vBV3TNPz617+GEMIvBV07fQiOtTNRPmcwymc/gPLZ9/8y5vy54r/v/x3OrZ9Dc/rvRIU7X/+01uW86rPpH/zqXp/PpDdt2hRDhw71ep3L9csfGVjQiepOlfyxG5iPMgUdAM6ePYs5c+agZ8+esNvtaNeuHV566SXk5OTInpoyuAiNp8oGTdbE/JmTpjmhFe6Clr8I2sHpF4v5q9AOz4F2djM0R4HsKfqFt/ydW5bW4I9ZK8r6zuv383XCvKVLl/qloLtys1E+98Ffini14wE4lk+AVlZU7TFTUlIghMCtt97q8/EUHDyGd0NugxBCHyHChitEFG4TV2GmuFUv5Y1FJLqJK92eSV97z3O44oorEBYWhqKiX+YTHR2Nbt26ITIyEmVl3k9CCLCgE9WHKvljNzAfpQp6VYcPH8akSZPQtm1bhISEyJ6OMrgIjafKBk3WxPypwapv3/KWP2dBAXL+eE/9yvnAu3Fo6MPQnE7DH4vr8E8Xi/kDtSjnvzyj7khLgeb0vf4qC7oQAuvWrfO4LiYmBtn/9zbeDa0o6H3FVfivuAljRGfcI1oiRNhwg7hML+S/E1eikYh0eyb9f7bfQQiBqKgopKen68e/9dZbERoaitatWyM/P9/nHHv27Inf//73cDgcHqNHjx4s6EReqJI/dgPzUbKgl5eX49NPP8Uf//hHREZGomnTprKnpAwuQuOpskGTNTF/JJOv/F34ckW9C3rx99mGPw6tpADlbz9Ut3Je5Zl0Z+ZCn8euLOG//e1v0aNHD6/Xvd9oIObZ+kAIgfvEdW7lu5doCiEEporumGfrg7+K6yGEwKsXv55n64NH7e3R/LIrcdttt+HFF1/Uj//DDz/AZrPpfyBo2bIlnnnmGY9XJvbs2dPt2ftLBws6kSdV8sduYD5KFfT169dj2LBhSEpKQnx8PIYMGYI1a9a4vU+qtoqKirBixQpMmDABgwYNQvPmzfVfNCkpKX6Z77FjxzBq1Ci0adMGkZGRSEpKQnJyMubOnVuvZ0vuvPNOfY49e/as15y4CI2nygZN1sT8kUzV5e/sh4vqVMxzBt6NgvXrvHyXwHNu/bye5fziePthaOUlXo9dWcK/+OILCCHcnuFOSUlBdGSUXrS9FfS/ijYQQuC/4ibMs/XBC+JmCCHwhOig36aHaIpesa0wbtw43HXXXfrxv/76awghMH78eIwYMQKdOnWCEAJxcXEeJ4lLTk5GVlaWx+jSpQsLOpEXquSP3cB8lCnozZo1Q2RkJAYOHIglS5agpMT7L7raSk9P9/mXYH8U9OzsbDRq1Eg/ZmxsLEJDQ/Wv77jjDpSWltb6eO+++67bHFnQ1aHKBk3WxPyRTDXl78Ka1Tg4+H795G/eTgiXM6AfDg19SMoz5wCgaS6UL/x7/cv5xeHa5f2PC1XfN9+1a1e33+8pKSmIjoistqD/XrSATQhME8mYZ+uDd0RvRItQ3Cau0m/TVMRgaMQNWLlyJRITE/UnCSZNmgSbzYYzVT6ybuXKlQgJCcGgQYP0y/gedKK6UyV/7Abmo0xBnzNnjtsvkIZKT09HUlIS+vbti9GjR+ODDz5AkyZN/FLQz507px+rbdu2yMrKAgCUlZXh9ddfR1hYGIQQGD58eK2Ol5+fj6SkJCQmJqJdu3Ys6IpRZYMma2L+SKba5E8rL0dB+noc/edIj4Ken/IcirK+k/Ke80quozsaXM7LZz+A8o//z+vxqxb0zz77DEIIZGRk6NddWtD/JK7FXNELb4qe+If4NaJFKPpWKePzbH3QUTRCCxGHebY+eE3cCpsQeCn0Fpw7dw52ux3btm0DANxzzz244YYbPObUpUsXtG3bVv+aBZ2o7lTJH7uB+ShT0P3N6eWXfYsWLfxS0MeOHaufjOXAgQMe17/88ssVZ2ANCcGePXtqPN6gQYMghMDcuXP194GxoKtDlQ2arIn5I5nqmj9XaQkcp07Ccfo0XNWcVdxIrt3pfijo96P83Ue9Hv/SM8936dIFvXv31q+7tKBfOlqLBMwVvdwK+iDRCnZhwyzRA/8Qv0aMCMXCywYCAG644Qa8+eabAIDGjRvjiSeecJtPcXExLr/8cvTq1Uu/jAWdqO5UyR+7gfkEbUH3xl8FvfL97L4+W7SgoACxsbEQQmDcuHHVHmvx4sV6Idc0jQVdQaps0GRNzB/JZIX8Obd92bD3n1eOuX/1evxLC3paWhqEENiwYUPFddHRmGfvqxf028RVeE78BmNEZ/QQv4IQAr8XLdwK+hjRGUII/FN0Qj/RAh1tjbD2norPjv/b3/6GBx98ED///DOEEOjRowcWL16MDRs24MMPP0T37t1hs9nw6aef6nNiQSeqO1Xyx25gPizoVfijoO/evVv/q/aSJUt83u6uu+6CEALdunXzeZtTp07hiiuuQEREhP5MOwu6elTZoMmamD+SyQr5c+35yj/PoM9/zOvxvX12e6dOndC3b1/9utX9/w/vht3m9T3onUVjhAobpohb9Mtmi54IETbcI1riepGIQaIljq6peA///Pnz0bJlS/3cNj169ECzZs0QHh6Opk2b4s4778T69evd5sOCTlR3quSP3cB8WNCr8EdBX7p0qV7Qd+7c6fN2o0ePhhAC8fHxPm8zePBgCCHcPhKFBV09qmzQZE3MH8lkhfy5ju9reDmf82c4lo33enxvBf2TTz6p+Mzzvn0RExODIyu/83mSuJfEb2G7+Mx61ctbini0FYkIF3aMa9oX2sVPvKl85rxfv35o1qxZwH9+Mlkhf6QuVfLHbmA+LOhV+KOgz5w5Uy/o1QV9+vTp+u0KCgo8rl++fDmEELjhhhvcFnZdC3ppaSnOnz+vj8OHD0MIgVOnTqG8vJzDgFFUVIS0tDQUFRVJnwtH8A3mj0PmsEL+ysrKULR4DIrmPIiiOX+p9yjds8nr8ceOHYuYmBiP79mxY0cIIRATE4Oy0lJ81ndkRUEPux7zou90G91CfoVwEYKZUX30y+4IvQY2IRAibNia+oXb8S+//HLYbDbce++90n++zB+HVYcq+Tt16hQLusmwoFfhj4L+0ksv6cXb4XD4vN1bb72l3y4vL8/tunPnzqFZs2aw2+3IzMx0u66uBT0lJcXrSWUWLVqEtLQ0Dg4ODg4ODonj/vvvR2RkpMfl//rXvyCEcLtOCIEhQ4Z43PaNN96A3W7H/fffr182ZswYCCHQqlUrj9vffHPFZ6UPGzZM+uPn4OCQOxYtWsSCbjIs6FWYpaA/+uijEELg73//u8f9+Ay6ekOVv6ByWHMwfxwyh1XyV1ZUgKLUJ1H01l/r9ex5yfdpfplH4fHT+KzvSMyLvhPvxt3l8Ux65Xg37i68G3sXts3yz/dVdVglfxxqDlXyx2fQzYcFvQozvMR9zZo1EELgqquuwoULFzzux/egq6e8XI33IJE1MX8kk5Xyp53MQfncv6J8zp/r9PnnjjXToWku/83D5cKR1VlYM3Csfnb3quP9xvfg++fmoeDQcb99T1VZKX+kHlXyx25gPizoVZjhJHHXXHON/hL0goICj5GcnAwhBJKTk/XLvH2muy9chMZTZYMma2L+SCar5c91Yh/K33205pJ+8XrH+lnQnL5fTddQBYeOI2fpV9jz9hfYt2A1jqzOgrPMGj9rf7Ba/kgtquSP3cB8WNCrMMPHrHl7v3hNo+pnldaEi9B4qmzQZE3MH8lkxfxphWfg/O7DiqI++36UvzW4opBX/nf2/XAsfwGuA99B0zTZ0w1qVswfqUOV/LEbmA8LehX+KOgA0Lx5cwgh8Mgjj3i9vrCwELGxsRBCYNy4cW7XsaBbjyobNFkT80cyWTl/mtMB196v4fw6FY70N+HcMBfOrI+gnT0qe2p0kZXzR+anSv7YDcyHBb0KfxX0sWPHQgiB6Oho5OTkeFw/ceJECCEQEhKCPXv21OnYfA+6elTZoMmamD+SifkjmZg/kkmV/LEbmE9QF/QzZ87g5MmT+rj66qshhMDo0aPdLr/0c8qrfnSZtwJ+7tw5NGnSBEIItG/fHtnZ2QCAsrIyzJo1C+Hh4RBCYPjw4XWeMwu6elTZoMmamD+SifkjmZg/kkmV/LEbmE9QF/TKZ8xrGg8//LDb/Woq6ACQnZ2NRo0a6beLi4tDWFiY/vUdd9yB0tLSOs+ZBV09qmzQZE3MH8nE/JFMzB/JpEr+2A3MhwU9QAUdAI4dO4aRI0eidevWiIyMRGJiIpKTkzF37ly4XPX7yBUWdPWoskGTNTF/JBPzRzIxfySTKvljNzCfoC7owYiL0HiqbNBkTcwfycT8kUzMH8mkSv7YDcyHBT3IcBEaT5UNmqyJ+SOZmD+SifkjmVTJH7uB+bCgBxkuQuOpskGTNTF/JBPzRzIxfySTKvljNzAfFvQgw0VoPFU2aLIm5o9kYv5IJuaPZFIlf+wG5sOCHmS4CI2nygZN1sT8kUzMH8nE/JFMquSP3cB8WNCDDBeh8VTZoMmamD+SifkjmZg/kkmV/LEbmA8LepDhIjSeKhs0WRPzRzIxfyQT80cyqZI/dgPzYUEPMlyExlNlgyZrYv5IJuaPZGL+SCZV8sduYD4s6EGGi9B4qmzQZE3MH8nE/JFMzB/JpEr+2A3MhwU9yHARGk+VDZqsifkjmZg/kon5I5lUyR+7gfmwoAcZLkLjqbJBkzUxfyQT80cyMX8kkyr5YzcwHxb0IMNFaDxVNmiyJuaPZGL+SCbmj2RSJX/sBubDgh5kuAiNp8oGTdbE/JFMzB/JxPyRTKrkj93AfFjQgwwXofFU2aDJmpg/kon5I5mYP5JJlfyxG5gPC3qQ4SI0niobNFkT80cyMX8kE/NHMqmSP3YD82FBDzJchMZTZYMma2L+SCbmj2Ri/kgmVfLHbmA+LOhBhovQeKps0GRNzB/JxPyRTMwfyaRK/tgNzIcFPchwERpPlQ2arIn5I5mYP5KJ+SOZVMkfu4H5sKAHGS5C46myQZM1MX8kE/NHMjF/JJMq+WM3MB8W9CDDRWg8VTZosibmj2Ri/kgm5o9kUiV/7Abmw4IeZLgIjafKBk3WxPyRTMwfycT8kUyq5I/dwHxY0IMMF6HxVNmgyZqYP5KJ+SOZmD+SSZX8sRuYDwt6kOEiNJ4qGzRZE/NHMjF/JBPzRzKpkj92A/NhQQ8yXITGU2WDJmti/kgm5o9kYv5IJlXyx25gPizoQYaL0HiqbNBkTcwfycT8kUzMH8mkSv7YDcyHBT3IcBEaT5UNmqyJ+SOZmD+SifkjmVTJH7uB+bCgBxkuQuOpskGTNTF/JBPzRzIxfySTKvljNzAfFvQgw0VoPFU2aLIm5o9kYv5IJuaPZFIlf+wG5sOCHmS4CI2nygZN1sT8kUzMH8nE/JFMquSP3cB8WNCDDBeh8VTZoMmamD+SifkjmZg/kkmV/LEbmA8LepDhIjSeKhs0WRPzRzIxfyQT80cyqZI/dgPzYUEPMlyExlNlgyZrYv5IJuaPZGL+SCZV8sduYD4s6EGGi9B4qmzQZE3MH8nE/JFMzB/JpEr+2A3MhwU9yHARGk+VDZqsifkjmZg/kon5I5lUyR+7gfmwoAcZLkLjqbJBkzUxfyQT80cyMX8kkyr5YzcwHxb0IMNFaDxVNmiyJuaPZGL+SCbmj2RSJX/sBubDgh5kuAiNp8oGTdbE/JFMzB/JxPyRTKrkj93AfFjQgwwXofFU2aDJmpg/kon5I5mYP5JJlfyxG5gPC3qQ4SI0niobNFkT80cyMX8kE/NHMqmSP3YD82FBDzJchMZTZYMma2L+SCbmj2Ri/kgmVfLHbmA+LOhBhovQeKps0GRNzB/JxPyRTMwfyaRK/tgNzIcFPchwERpPlQ2arIn5I5mYP5KJ+SOZVMkfu4H5sKAHGS5C46myQZM1MX8kE/NHMjF/JJMq+WM3MB8W9CDDRWg8VTZosibmj2Ri/kgm5o9kUiV/7Abmw4IeZLgIjafKBk3WxPyRTMwfycT8kUyq5I/dwHxY0IMMF6HxVNmgyZqYP5KJ+SOZmD+SSZX8sRuYDwt6kOEiNJ4qGzRZE/NHMjF/JIPTVYjC8p9wunAz0tLScKboW5Q4D0LTNNlToyCiyv7HbmA+LOhBhovQeKps0GRNzB/JxPyRkUqdR3G6ZBXyiuYgr2g2Dp+fi7S0NBw+/xbyimbjWPFCFJT/CJdWKnuqFARU2f/YDcyHBT3IcBEaT5UNmqyJ+SOZmD8ygqZpOF+2GXlFs/VyXlHQ51ws6L9cllc0G8eK3kO560yNx124cCG6du2K+Ph4xMXFoW3btnj00Udx/Phxr7fv3LkzhBBIT0/38yMkFamy/7EbmA8LepDhIjSeKhs0WRPzRzIxfxRomqbhbOlXbgW8poKeVzQH+UXz4KimpP/vf/+DzWbDqFGj8OWXX2LFihWYNGkSbrzxRvz4448et9+1axeEEBBCYNiwYYF8yKQIVfY/dgPzYUEPMlyExlNlgyZrYv5IJuaPAq2wfLvXcl59Qa8o6ceKF0DTHF6P27RpUwwdOtTrdS6Xy+Oy/9/encdFUf9/AP9w7LLAcogiqCieeFTemSmF5u23r2d5VV++2qlfUTuwMr9imLd5opWUmBWaV2pe+c00kihFNETlULEw8RYTkPv1+4PfTgy7C8s5u+zr+XjMo3bmM7Ozw3s+zmtndmb27Nmws7NDv3794O7ujtzc3Gr9nGR5LKX/YzYwPwzoVoY7Ye2zlA6a6ibWHymJ9Uc1qaioENeyNlUyoBcPWfmJBpft5OSEWbNmmbwuLVu2xIABA7Bv3z4IIfDNN99U18ckC2Up/R+zgflhQLcy3Alrn6V00FQ3sf5ISaw/qkkP8lONBm/TAvonuJG93eCyn3jiCXh4eCA8PBzp6ellrkdMTAyEENiwYQPy8/PRoEEDPPPMMzXxkcmCWEr/x2xgfhjQrQx3wtpnKR001U2sP1IS649q0u0He2U3havMGfSrWR8jr/CG3rLPnDmD1q1bS78rb9GiBaZNm4bU1FS9tlOnToWDgwMyMjIAAFOmTIFGo+GxlpWzlP6P2cD8MKBbGe6Etc9SOmiqm1h/T9riLAAAIABJREFUpCTWH9Wka1mfV/EMuu4y9ySDy8/NzcW+ffswffp0dO7cGUIIuLi4yG4SV1BQgIYNG2LUqFHSuOjoaAghEBERUdObgMyYpfR/zAbmhwHdynAnrH2W0kFT3cT6IyWx/qgmpWd9Wi0BPTMv3qT3O3jwIOzs7DBy5Ehp3IEDB6QwfvfuXWnw8fFB//79a+qjkwWwlP6P2cD8MKBbGe6Etc9SOmiqm1h/pCTWH9Wk9KyIajqDft7k9+zatSvatWsnvX7++eely+BLD7a2tuX+fp3qLkvp/5gNzA8DupXhTlj7LKWDprqJ9UdKYv1RTbqZvb1aAnpOQZresq9du6Y3Ljs7G56enujTpw8AICsrC1qtFiNGjMCRI0dkw9atWyGEwIoVK2p8O5B5spT+j9nA/DCgWxnuhLXPUjpoqptYf6Qk1h/VpMy8s1UO6NeyNqGoSP+55p6enpg0aRK+/vprREVFYcuWLejduzdsbGykR6ht3rwZQgj88MMPBtfv0UcfRffu3Wt0G5D5spT+j9nA/DCgWxnuhLXPUjpoqptYf6Qk1h/VpMKiXFwt43fopjxm7X7eSYPLXrt2LQYPHowmTZpArVajcePGGDx4sCyMP/3002jWrBmKiooMLiMsLAxCCCQlGb4JHdVtltL/MRuYHwZ0K8OdsPZZSgdNdRPrj5TE+qOalpF7zOij1soP6OEoKMpS+iNQHWUp/R+zgflhQLcy3Alrn6V00FQ3sf5ISaw/qmlFRXn//1t0/RBeXkB/kH9J6dWnOsxS+j9mA/PDgG5luBPWPkvpoKluYv2Rklh/VBsKix7g5oOdeiHdcED/BFezPkF2frLSq011nKX0f8wG5ocB3cpwJ6x9ltJBU93E+iMlsf6othQV5eOvvBO4lrVRCuLygF4c0m8/2IfcAv07tBNVN0vp/5gNzA8DupXhTlj7LKWDprqJ9UdKYv1RbSsqKkB2/kXcfnAQ6X9tx65du5B+fxfu5f6C/EIe+1DtsZT+j9nA/DCgWxnuhLXPUjpoqptYf6Qk1h8pifVHSrKU+mM2MD9WG9CzsrKwf/9+zJs3DyNHjkSzZs0ghIAQAiEhIdXyHteuXcMbb7wBPz8/aDQa1KtXD/7+/ggPDzf6SA4ASElJwYcffig9vkOtVsPJyQlt2rTBpEmTEBsbW+l14k5Y+yylg6a6ifVHSmL9kZJYf6QkS6k/ZgPzY7UB/ciRI1IgLz1UR0CPjY1F/fr1pWVqtVrY29tLrwcOHIicnBy9+Y4dO6a3Pi4uLlCr1dJrW1tb/Pe//63UenEnrH2W0kFT3cT6IyWx/khJrD9SkqXUH7OB+bHqgF6vXj3069cPwcHB2Lx5M7y9vasloGdkZEjLateuHU6cOAEAyM3NRVhYGFQqFYQQmDx5ssH1srOzw4gRI7Bt2zbcunULAFBQUIDjx4/D399fCuqffvpphdeNO2Hts5QOmuom1h8pifVHSmL9kZIspf6YDcyP1Qb0goICvXG+vr7VEtBnz54NIQQcHR1x6ZL+MzYXLFgAIQTs7OyQlJQkm5aWlobkZOOP/sjNzUXHjh0hhECrVq0qvG7cCWufpXTQVDex/khJrD9SEuuPlGQp9cdsYH6sNqAbUl0BXfd79okTJxqcfv/+fWi1WgghMGfOnAovf8mSJdJZ9Dt37lRoXu6Etc9SOmiqm1h/pCTWHymJ9UdKspT6YzYwPwzoJVRHQE9MTJTC89atW422GzJkCIQQ6NmzZ4XfY/Xq1dJ73Lx5s0LzciesfZbSQVPdxPojJbH+SEmsP1KSpdQfs4H5YUAvoToC+vbt26XwfO7cOaPtgoODIYSAq6trhd9j1KhREEKgUaNGZd4N3hDuhLXPUjpoqptYf6Qk1h8pifVHSrKU+mM2MD8M6CVUR0AveXa7rEJfuXKl1O7+/fsmL//nn3+Gra0thBAm3ck9JycH9+7dk4a0tDQIIXDr1i3k5eVxqIUhKysLu3btQlZWluLrwsH6BtYfByUH1h8HJQfWHwclB0upv1u3bjGgmxkG9BKqI6DPnz9fCt75+flG261fv15qd/XqVZOWfePGDWkd27RpY1KwDwkJMfgoucjISOzatYsDBw4cOHDgwIEDBw5WOkRGRjKgmxkG9BLMOaDfv38fPXv2lJ6Lfvr0aZPWh2fQlR8s5RtUDnVzYP1xUHJg/XFQcmD9cVBysJT64xl088OAXoK5XuKemZmJJ598EkIIaLVa/PTTT5VeP/7OpPbl5VnGb5CobmL9kZJYf6Qk1h8pyVLqj9nA/DCgl2CON4nLzMxEQEAAhBBwdnbGjz/+WOl1A7gTKsFSOmiqm1h/pCTWHymJ9UdKspT6YzYwPwzoJZjbY9ZKhnMnJyccPXq00uulw52w9llKB011E+uPlMT6IyWx/khJllJ/zAbmhwG9hOoI6ADQrFkzCCEwadIkg9MzMzOh1WohhMCcOXOMttFd1u7s7Fwt4RzgTqgES+mgqW5i/ZGSWH+kJNYfKclS6o/ZwPwwoJdQXQF99uzZ0lnv1NRUvemLFy+GEAJ2dnZISkrSm146nFf1svaSuBPWPkvpoKluYv2Rklh/pCTWHynJUuqP2cD8WHVAv3PnDm7evCkNTZs2hRACwcHBsvGlb+JW8tFlhgJ4RkYGvL29IYRAhw4dEBsbCwDIzc3FunXroFarIYTA5MmT9ebNyspCnz59pBvCRUVFVetn5k5Y+yylg6a6ifVHSmL9kZJYf6QkS6k/ZgPzY9UBXXfGvLwhMDBQNl95AR0AYmNjUb9+famdi4sLVCqV9HrgwIHIycnRm+/zzz+X2mg0Gnh5eZU5REdHV+gzcyesfZbSQVPdxPojJbH+SEmsP1KSpdQfs4H5YUCvoYAOANeuXcPrr7+ONm3aQKPRwN3dHf7+/ggPD0dhYaHBeSIiIkxaJ91w5MiRCn1m7oS1z1I6aKqbWH+kJNYfKYn1R0oqXX9FOdnIO/Ytcr5cggfhc/AgYh5ytq9FQeo5FBUVKbaezAbmx6oDujXiTlj7eIBASmL9kZJYf6Qk1h8pSVd/OdfSkLNtNTLfHIrMoH7InD6w+L/TBkj/n7XwZeT9vB9FhQW1vp7MBuaHAd3KcCesfTxAICWx/khJrD9SEuuPlKSrv4x3RiFz+oDiUG5smNYfmUH98GD9f1GUq/8T2JJKXsmr+0lshw4dsGLFCtmZ+JJtVCoVWrVqhWnTpuH27duy5TEbmB8GdCvDnbD28QCBlMT6IyWx/khJrD9S0oOU+OKA/vrQssO5LKgPQPa6d1BUYPxMekhICBwdHRETE4OYmBgcPnwYb7/9NoQQWLNmjdROCIGgoCDExMTg6NGjeP/996FSqTB48GDZ8pgNzA8DupXhTlj7eIBASmL9kZJYf6Qk1h8ppSg7Exmzni0O6DMGmx7Qg/ohM6g/cvdGGF12SEgInJ2d9cb37dsXffv2lV4LIbB06VJZm1dffRVCCFy9elUax2xgfhjQrQx3wtrHAwRSEuuPlMT6IyWx/kgpeVHFwbxyAb0fMoOHGb3U3VhAHzZsGPz9/aXXhgL6unXrIITAL7/8Io1jNjA/DOhWhjth7eMBAimJ9UdKYv2Rklh/pISioiJkzQtExowhlQ/oQf2Q98t3BpevC+j5+fnIz8/HvXv3sG3bNqjVaqxcuVJqZyigv/POO7C1tcW1a9ekccwG5ocB3cpwJ6x9PEAgJbH+SEmsP1IS64+UUHDpLDKD+lXtDPq0/sheNtXg8kvfJE43/Pvf/9a7SdzixYuRn5+PzMxM7Nu3D+7u7ggKCpItj9nA/DCgWxnuhLWPBwikJNYfKYn1R0pi/ZES8n49VPWAHtQPmW+PMLh83U3iTpw4gRMnTuDYsWNYtWoV3Nzc8Morr0jtDIV4f39/5Ofny5bHbGB+GNCtDHfC2scDBFIS64+UxPojJbH+SAl5UbuQGdS/6gH99cEGl2/sN+jLli2DEAIJCQkAigP69OnTceLECRw9ehQvvfQShBB45513ZPMxG5gfBnQrw52w9vEAgZTE+iMlsf5ISaw/UkJtnEE3FND3798PIQS2bNkCwPBv0IcPHw61Wo0//vhDGsdsYH4Y0K0Md8LaxwMEUhLrj5TE+iMlsf5ICbXxG3RDAX3JkiUQQuD7778HYDigJyYmwtbWFtOmTZPGMRuYHwZ0K8OdsPbxAIGUxPojJbH+SEmsP1JCbdzF3dHRETExMYiJicGPP/6I5cuXw9XVFR06dJDq3VBAB4AJEybAyckJt27dAsBsYI4Y0K0Md8LaxwMEUhLrj5TE+iMlsf5IKTX9HPSSN36zt7dHixYtMGXKFFy/fl1qZyygJyYmws7ODiEhIQCYDcwRA7qV4U5Y+3iAQEpi/ZGSWH+kJNYfKaUoOxMZs56tZEDvj9y9EbW2rswG5ocB3cpwJ6x9PEAgJbH+SEmsP1IS64+U9CAlvjigvz60Ar89H4Dsde+gqKCg1taT2cD8MKBbGe6EtY8HCKQk1h8pifVHSmL9kZJ09ZfxzihkTh9Q7k3hMoP64cH6/xq9tL2mMBuYHwZ0K8OdsPbxAIGUxPojJbH+SEmsP1KSrv5yrqUhZ9saZL75j+IwPn2gdLZc9//Zi15BXswBFBXW3plzHWYD88OAbmW4E9Y+HiCQklh/pCTWHymJ9UdKKl1/RTnZyDv2LXK+XIIH4SF4EPEBcnasRcHl84quJ7OB+WFAtzLcCWsfDxBISaw/UhLrj5TE+iMlWUr9MRuYHwZ0K8OdsPZZSgdNdRPrj5TE+iMlsf5ISZZSf8wG5ocB3cpwJ6x9ltJBU93E+iMlsf5ISaw/UpKl1B+zgflhQLcy3An/FhERATc3txpfjqV00FQ3sf5ISaw/UhLrj5RkKfXHbGB+GNCtjJI7YWBgIIYPHy4bt23bNjg4OGDx4sW1vj6VCei+vr5YsWKFbFx2djauX79udB5z7qCLiooQEhKCRo0aQaPRICAgAAkJCeXOt2LFCvj5+UGj0cDHxwczZszAgwcPZG3Wrl2L5s2bw8HBAV27dkVUVJRs+rVr1xAYGIhGjRrB0dERgwYNQnJycrV+PjLv+qO6j/VHSmL9kZIspf4Y0M0PA7qVMaeAHh4eDrVajfDw8EovMzc3V29cQUEBCgsLy523ugJ6ecy5g160aBFcXFywY8cOnDlzBmPHjkWjRo3w119/GZ3nyy+/hIODA7766iukpqbiu+++Q6NGjTBjxgypzZYtW6BSqRAeHo5z585h+vTpcHZ2xu+//w6g+IuBnj174oknnsDx48eRmJiIV155Bc2aNUNmZmaNf25rYs71R3Uf64+UxPojJVlK/TGgmx8GdCtjLgF98eLFcHBwwPbt22Vttm/fjg4dOkCtVsPX1xfLli2TTff19cW8efMQGBgIV1dX/Otf/5KC9rfffov27dvDzs4Oly5dQm5uLoKDg9G4cWM4OTmhR48eOHLkiLSs0gH9woULGDZsGBo2bAhnZ2d0794d//vf/6TpAQEBEELIBkPLAYB169ahZcuWUKlUaNOmDaZPny7roIUQCA8Px4gRI+Do6IjWrVtj9+7dVdvAFVRUVARvb28sWrRIGpeTkwM3Nzd8/PHHRuf7z3/+g6eeeko27o033oC/v7/0ukePHnjttddkbdq1a4d33nkHAJCUlAQhhOxsfUFBATw8PKr0hQ3ps5QDBKqbWH+kJNYfKclS6o8B3fwwoFsZcwjob7/9NrRarSz8AkBsbCxsbW0RGhqKpKQkREREwNHREREREVIbX19fuLq6YunSpUhJSUFKSgoiIiKgUqnQq1cvREdHIzExEZmZmZgwYQJ69eqFqKgoXLhwAUuXLoWDg4N0GXXpYH369Gl8/PHHiI+PR3JyMt577z1oNBrprO/t27fh4+OD0NBQpKenIz093eBydu7cCZVKhbVr1yIpKQlLliyBra0tDh06JLURQsDHxweRkZFISUnBtGnToNVqcfv2baPb79VXX4Wzs3OZg25dTXHx4kUIIRAXFycbP2zYMPzrX/8yOt/mzZvh5uaGX3/9VVpOu3btsHDhQgDFVzXY2dlh586dsvmmTZuGJ598EgAQHx8PIQQuXLgga+Pt7Y3AwECTPwOVz1IOEKhuYv2Rklh/pCRLqT8GdPPDgG5llA7oarUaQggcPnxYb/qECRMwYMAA2bjg4GB06NBBeu3r64sRI0bI2kREREAIgdOnT0vjLly4ABsbG/z555+ytv369cO7774rzVfeJe4dOnTAmjVrZO9f+hL30svp1asXXn75Zel1Xl4eevXqhSFDhkjjhBCYPXu29DozMxM2NjY4cOCA0XW5fv269KWEsSE/P7/Mz1NSdHQ0hBB62+jll1/GwIEDy5x39erVUKlUsLe3hxACkydPlqb9+eefEEIgOjpaNs/8+fPh5+cHoHib+Pr64tlnn8WdO3eQm5uLhQsXQghR7ntTxVjKAQLVTaw/UhLrj5RkKfXHgG5+GNCtjNIBvXv37mjevDl69+6t9zvnLl26YO7cubJxu3btgkqlQkFBAYDigPzBBx/I2kRERECtVqOoqEgat3XrVggh9M4w29vbY8yYMdJ8JYN1ZmYmgoOD0b59e7i5ucHZ2Rm2trYIDg6W2pgS0OvVq4eNGzdKr/Py8vDiiy+iRYsW0jghBLZu3SpbjqurKz7//PMytmDlffnll7LtEBUVJQX0q1evytq+9NJLGDRokNFlHTlyBF5eXggPD0d8fDx27tyJpk2bIjQ0FMDfAf3nn3+WzffBBx+gbdu20uvY2Fh06tQJQgjY2dlh0KBBGDJkiOyLDKo6SzlAoLqJ9UdKYv2Rkiyl/hjQzQ8DupVROqAPHz4cv//+O1q2bIlevXrJQnrnzp3x/vvvy+b55ptv9AJ6eQEZKL5JmZ2dHRITE/XOMhu7NH3y5Mlo2bIldu7cifj4eKSkpKBTp06YPn261MbUgF4yaOfl5WHSpElo2bKlNE4IgW+++Ua2HDc3N9nl/KVV5RL3v/76S7YNsrOzK32Ju7+/P9566y3ZuC+++AKOjo4oLCw06RL3kjIyMnDjxg0Axb9dnzJlitH3poqzlAMEqptYf6Qk1h8pyVLqjwHd/DCgWxlzCOgAkJaWhtatW6Nnz57Suhi7xP2hhx6SXpsa0HU3ISv9aK+y5nv44Yels8AAcP/+fbi5uckCeps2bfRuXGfqJe5Dhw6VxlUmoFf3Je66m8SVfMRdbm5uuTeJ69q1K2bOnCkbFxkZCY1GI32R0qNHD9ll7wDQvn176SZxhiQnJ8PW1hbfffedyZ+BymcpBwhUN7H+SEmsP1KSpdQfA7r5YUC3MuYS0AHgypUraNOmDR577DFkZGTg5MmTspvEbdy40eBN4kwJ6ADw3HPPoXnz5tixYwcuXbqE48ePY9GiRdi3b5/B+UaMGIHOnTvj1KlTOH36NP75z3/CxcVFFtAHDBiAYcOG4cqVK7h586bB5ejO+n/00UdITk6WbhJX8qZ4lQnoNWHRokVwc3PDzp07cebMGYwfP17vMWsvvPCCLFiHhITAxcUFmzdvxqVLl3Do0CG0atVK+ukA8Pdj1j777DOcO3cOM2bMgLOzMy5fviy12bp1K44cOYKLFy9i165d8PX1xahRo2rng1sRSzlAoLqJ9UdKYv2Rkiyl/hjQzQ8DupUxp4AOAFevXkXbtm3x6KOP4u7du9Jj1lQqFZo1a4alS5fK2lckoOfl5WHOnDlo3rw5VCoVvL29MXLkSMTHxxucLzU1FX379oWjoyOaNm2KsLAwBAQEyAJ6TEwMOnbsCAcHhyo/Zs0cAnpRURFCQkLg7e0NBwcHPPnkkzhz5oysTUBAgOzO6vn5+Zg7dy5atWoFjUaDpk2bYsqUKbh7965svrVr18LX1xdqtRpdu3bFjz/+KJu+atUq+Pj4SH/r2bNnG3yuPVWNpRwgUN3E+iMlsf5ISZZSfwzo5ocB3cpkZGRACIG0tDTcu3ePQy0Mt27dQmRkJG7duqX4unCwvoH1x0HJgfXHQcmB9cdBycFS6i8tLQ1CCGRkZCgdU+j/MaBbGd1OyIEDBw4cOHDgwIEDBw5CFJ+8I/PAgG5lCgsLkZaWhoyMDMW/sbOWQfelCK9a4KDEwPrjoOTA+uOg5MD646DkYCn1l5GRgbS0NBQWFiodU+j/MaAT1bB79/jbHlIO64+UxPojJbH+SEmsP6osBnSiGsYOmpTE+iMlsf5ISaw/UhLrjyqLAZ2ohrGDJiWx/khJrD9SEuuPlMT6o8piQCeqYTk5OQgJCUFOTo7Sq0JWiPVHSmL9kZJYf6Qk1h9VFgM6ERERERERkRlgQCciIiIiIiIyAwzoRERERERERGaAAZ2IiIiIiIjIDDCgExEREREREZkBBnSiKrh37x4WLVqExx9/HA0aNIBarUaTJk3Qp08fhISE4O7duwbnu3btGt544w34+flBo9GgXr168Pf3R3h4OIqKimr5U5Clqmj9hYSEQAhR7pCSkqLQJyJzZkrt6IY+ffoYXQ77P6qMqtYf+z+qLocOHcKzzz6LZs2awcHBARqNBi1atMCECRNw9OjRMudl/0emYEAnqqQffvgBXl5e0j/q9vb2cHd3l/1Df+rUKb35YmNjUb9+famNVquFvb299HrgwIF8JAeVqzL1pztAValU8PLyMjqkpqYq86HIrJVVM15eXvDw8JBqLzg42OAy2P9RZVW1/tj/UVUVFRXh1Vdflf07q9Fo4OjoKBv3+uuvG5yf/R+ZigGdqBKOHTsmdcj9+/fHsWPHUFhYCADIzs5GbGws3nvvPVy6dEk2X0ZGBry9vSGEQLt27XDixAkAQG5uLsLCwqBSqSCEwOTJk2v9M5HlqGz96Q5QAwICFFhrquuWLVsmHWgmJibqTWf/RzWpvPpj/0dVtWHDBqnGnnnmGSQnJ0vTEhMTMXz4cGn6zp07ZfOy/6OKYEAnqqCsrCy0bNkSQgiMHj1aCkammD17NoQQcHR01AtPALBgwQIIIWBnZ4ekpKTqXG2qI6pSfzxApZrUvn17CCHg7+9vcDr7P6pJ5dUf+z+qqj59+kAIgdatWyM/P19vel5envTv87hx42TT2P9RRTCgE1XQxx9/LHWyN2/erNC8zZo1gxACEydONDj9/v370Gq1EEJgzpw51bG6VMdUpf54gEo1JTo6WjpztHHjRoNt2P9RTTGl/tj/UVW1bdtW+nLcmFGjRkEIgaefflo2nv0fVQQDOlEF9erVy+C3o+VJTEyUDiC2bt1qtN2QIUMghEDPnj2ruqpUB1W2/gAeoFLNmTRpEoQQcHV1RVZWlt509n9Uk8qrP4D9H1Wdrn8y5Qz63LlzpfHs/6iiGNCJKiAnJwdqtRpCCHz00Ue4ePEiJk2ahCZNmkCtVsPLywvDhg3D/v379ebdvn271EGfO3fO6HsEBwdLBxpEJVWl/oC/D1AbNGiAhx56CBqNBs7OzvDz88NLL72EuLi4Wv5EVBeUPPPz2muvGWzD/o9qiin1B7D/o6rbs2eP7DfoJe/4n5iYiBEjRkAIgVatWuHevXvSNPZ/VFEM6EQVUPJb0Pfeew/Ozs4QQkCtVuvdQbv0gcLq1aulaSU77tJWrlwptbt//35NfySyIFWpP0D+mCFbW1t4eHjI7iBrY2OD9957T4FPRpYsPDxcqqHY2FiDbdj/UU0xpf4A9n9UPVasWCF9Ua77uZnupq3u7u6YPHkybt++LZuH/R9VFAM6UQXExMTI/oF3d3fH5s2bkZeXBwD4448/MG7cOKnNypUrpXnnz58vjTd0aZTO+vXrpXZXr16t8c9ElqMq9QcAX375JZYsWYKkpCRpntzcXHz33Xfo1q2bNN+yZctq/bOR5XrssccghECnTp2MtmH/RzXFlPoD2P9R9dm7dy8aNmwo+1JcF9aff/55vZvAsf+jimJAJ6qAkjeiEUJgy5Ytem0KCwvRpUsX6VI6XWfMDpqqqir1V54HDx7g0UcfhRDFz2bNyMio7tWnOighIUGqxzVr1hhtx/6PaoKp9Vce9n9kiqysLIwZMwZCCHTv3h2HDh3CrVu3cPPmTRw6dAjdu3eX/u397bffpPnY/1FFMaATVUB8fLzUeTZt2tRouy+//FJq98svvwDgJU5UdVWpP1P873//k+bbsWNHdawy1XEzZsyAEAIajQZ37twx2o79H9UEU+vPFOz/qDxTpkyBEAJ+fn7Izs7Wm56dnQ0/Pz8IIX/cH/s/qigGdKIKuH37ttR5DhgwwGi7kydPSu2+/vprALxJCFVdVerPFJmZmdJ8S5curY5VpjosNzcXDRo0gBACzz33XJlt2f9RdatI/ZmC/R+V5a+//pLuWbB69Wqj7UqG8evXrwNg/0cVx4BOVEFNmjSBEAIDBw402iY2NlbqjHWP1OBjNqg6VLb+TMEDVKqIrVu3SvVy5MiRMtuy/6PqVpH6MwX7PypLyX9X9+3bZ7TdgQMH9K5gY/9HFcWATlRBEydOlC4xLioqMtjmiy++kDrjEydOSOObNWsGIQQmTZpkcL7MzEzpcTFz5sypkfUny1aV+ivP999/L823ffv26lplqqMGDhwIIYqfCWysFkti/0fVqaL1Vx72f1SWuLg4qT7WrVtntN3GjRuldmfPnpXGs/+jimBAJ6qgqKgoqfPdvHmz3vSSN+lq0qQJCgsLpWmzZ8+GEAJOTk5ITU3Vm3fx4sUQQsDOzg5JSUk1+THIQlW2/so7gM3JyZHuhuzs7Iy7d+/WyPpT3fD777/D1tYWQggsWLDApHnY/1F1qWj9sf+jqsrOzpYep9a1a1eDN3srKChAr169IIRAvXr1UFBQIE1j/0cVwYBOVAnPPPMMhCh+5uWWLVuMPuZq48aNsvkyMjLg7e0NIQQ6dOggPbM1NzcX69atk56tOXny5Fr/TGQ5KlN/R49QUKh8AAAgAElEQVQeRb9+/fDFF18gLS1NGp+Xl4fvv/9euoOxEAKLFy+u9c9ElkX3TGl7e3uT7zbM/o+qS0Xrj/0fVYegoCCpTgYPHoz4+HgUFhaisLAQv/32m3RVhxAC77//vmxe9n9UEQzoRJWQmZmJJ598UuqIHRwcUK9ePel1WZcoxcbGon79+lI7FxcXqFQq6fXAgQORk5NTy5+ILEll6u/IkSOy6Y6OjmjQoIGs9mxtbTFr1iyFPhVZisLCQvj6+kIIgWHDhlVoXvZ/VFWVqT/2f1QdsrOzMXjwYFktOTg4wMHBQTZu/PjxsrPnOuz/yFQM6ESVVFhYiPDwcDz55JPw8PCASqVCkyZNMG7cOERHR5c577Vr1/D666+jTZs20Gg0cHd3h7+/P8LDw2WXxBMZU9H6u3XrFpYtW4bRo0fDz88PHh4esLe3h6urKzp16oSpU6ciPj5egU9Clua7776TDij37NlT4fnZ/1FVVKb+2P9RdSkqKsK2bdswfPhw+Pj4QK1Ww8HBAU2bNsXo0aOxd+/eMudn/0emYEAnIiIiIiIiMgMM6ERERERERERmgAGdiIiIiIiIyAwwoBMRERERERGZAQZ0IiIiIiIiIjPAgE5ERERERERkBhjQiYiIiIiIiMwAAzoRERERERGRGWBAJyIiIiIiIjIDDOhEREREREREZoABnYjIyn3zzTdo1aoVbG1tMX36dKVXp9ISExPh5eWFv/76S+lVMUlRURFefvll1KtXD0IInDp1SulVsgoBAQEWXefmbvTo0fjwww+VXg0iIovFgE5EZIYCAwMxfPjwWnmvhg0b4u2338aff/5Zq+H2yJEjEELg7t271bK80aNHIzQ0VLb8YcOGwdvbG05OTujUqRO+/PLLcpcjhMA333xTLetUlv3790OlUiE6Ohrp6enIz8+v8fdUwscff4yOHTvCyckJbm5u6Ny5MxYtWiRNr81aB8oP6D/99BN69eoFDw8PaDQatG3bFsuXL9drt337drRv3x5qtRrt27fHzp07y3zfBw8eIDAwEA8//DDs7OwMfuarV69i/Pjx8PPzg42NjUlfJJw+fRrjxo2Dj48PNBoN2rVrh5UrV+q1i4+Px5NPPgmNRoPGjRvj/fffR1FRkTR9x44d6N+/Pxo0aAAXFxf07NkTBw8elC0jISEBo0aNgq+vL4QQWLFihd77/Pbbb/Dw8MC9e/fKXXciItLHgE5EZIZqK7Tcv38fQgj88MMPNf5epVVnQE9LS4NKpUJaWpo0bv78+Zg9ezaio6Nx4cIFrFq1Cra2ttizZ0+Zy6qtgL5mzRo0a9asxt9HSZ9++imcnJzw6aefIiUlBQkJCYiMjMTs2bOlNuYW0OPi4hAZGYmEhASkpqbiiy++gJOTEz755BOpzc8//ww7OzssWLAA58+fx4IFC2Bvb49ffvnF6HIzMzPx2muvYf369Rg0aJDBz5yamopp06bh888/R+fOnU0K6J999hmCgoJw9OhRXLx4EV988QUcHR2xZs0aqc29e/fg5eWFcePG4cyZM9ixYwdcXFywbNkyqc306dOxePFiHD9+HMnJyXj33XehUqkQFxcntTl+/DjeeustbN68Gd7e3gYDOgB07doV69atK3fdiYhIHwM6EZEZKh1aAgICMHXqVEyfPh3u7u5o2LAhPvnkE2RmZuLf//43tFotWrZsif3790vz6ALw3r170bFjRzg4OKBHjx6Ij4+XTS85HDlyBAAQERGBpk2bwtHRESNGjMCyZcvg5uZmdP2A4gP8gIAA6XVOTg6CgoLg6ekJBwcH9O7dG8ePHwdQHERKv3dgYCCA4ku/Fy9ejBYtWkCj0aBjx47Ytm1bmdvrww8/RPfu3cvdrkOHDsXEiRPLbFMyoOvW8+uvv4a/vz80Gg26d++OpKQkHD9+HN26dYOzszMGDRqEGzdu6G2fuXPnwtPTEy4uLnjllVeQm5srTS/52X19fQEAvr6+eqGnU6dOCAkJka1feHg4RowYAUdHR7Ru3Rq7d++WzbN79260bt0aGo0Gffr0wcaNG/W+DImOjsYTTzwBjUYDHx8fBAUFITMzU5ru6+uL+fPnY+LEidBqtWjatKkspJpi+PDh+Pe//210ekhIiNEanDlzJtq0aQNHR0e0aNECs2fPRl5enmzeTp06YdOmTfD19YWrqyvGjh0ruwokMzMTL7zwApydneHt7Y1ly5ZV6hL3kSNH4vnnn5dejxkzBoMHD5a1GTRoEMaNG2fS8kz5UqIql+JPmTIFffv2lV6vW7cObm5uyMnJkcYtXLgQjRs3lp1FL61Dhw54//33DU4zVKs6c+fOxRNPPFGpdScisnYM6EREZshQQHdxccG8efOQnJyMefPmwdbWFkOGDMH69euRnJyMyZMno379+sjKygLwdwBv3749Dh06hPj4eDz99NNo3rw58vLykJubi6SkJAghsGPHDqSnpyM3Nxe//PILbGxssHDhQiQlJWHVqlVwd3evcECfNm0aGjdujP379+Ps2bMIDAxEvXr1cPv2bRQUFGDHjh0QQiApKQnp6enIyMgAAMyaNQvt2rXDwYMHcfHiRURERMDBwQFHjx41ur2GDx+O1157rdzt2rt3b7z55ptltjEU0HXrc+7cOfTs2RNdu3ZFnz59cOzYMcTFxaF169ay9w8MDIRWq8XYsWORkJCAvXv3wtPTE7NmzQIAZGRkIDQ0FD4+PkhPT5fCvakB3cfHB5GRkUhJScG0adOg1Wpx+/ZtaZ1VKhXeeustJCYmYvPmzWjSpIksoMfHx0Or1WLFihVITk5GdHQ0unTpIgvTvr6+8PDwwNq1a5GSkoKFCxfC1tYW58+fL3c767z66qto164dLl++bHD6/fv3pbCbnp4u1SAAzJs3D9HR0UhNTcWePXvg5eWFxYsXS/OGhIRAq9Vi1KhROHPmDKKiouDt7S1tYwCYPHkyfHx8ZPWv1WorFHzj4uLg5eWF8PBwaVzTpk31Lntfvny5yVdE1HRAf+655zB69Gjp9QsvvIBhw4bJ2sTFxUEIgUuXLhlcRmFhIZo2bSo7E19SWQF9//79cHBwkH0hQEREpmFAJyIyQ4YCur+/v/S6oKAAzs7OeOGFF6Rx6enpEEIgJiYGwN8BfcuWLVKb27dvw9HREV9//TUA4O7du7KzlgAwfvx4vbODY8eOrVBAz8zMhEqlwldffSVNz8vLQ+PGjbFkyRLZ+pU8q5uZmQmNRoOff/5ZtuwXX3wR48ePN7q9OnXqJPv9uSHbtm2DWq1GQkJCme0MBfRPP/1Umr5582YIIXD48GFp3MKFC9G2bVvpdWBgIDw8PKQvSwDgo48+glarRWFhIQBgxYoV0plzHVMDeslLxDMzM2FjY4MDBw4AAN5++208/PDDsmW89957sm39wgsv4JVXXpG1+emnn2Bra4sHDx5I61LyrHFRUREaNmyIjz76yNBmM+jq1avo2bMnhBDw8/NDYGAgvv76a2kbAKZf4r5kyRJ069ZNeh0SEgInJyfZGfPg4GA89thjAIrDv1qtNlj/pgTfJk2aQK1Ww9bWVq+2Stc2AHz11VdQq9XlLheo2YD+888/Q6VS4dChQ9K4AQMG4OWXX5a1+/PPPyGE0NvXdJYsWQIPDw9cv37d4PSyAvpvv/0GIYTRL2aIiMg4BnQiIjNkKKBPmTJF1qZZs2ZS2AWKA5QQQrrcWReAf//9d9l8nTt3xty5cwEYDuidO3fWu6x15cqVFQroxg7QR4wYIV1ibiigHz9+HEIIODs7ywaVSoUePXoY3V5+fn6ybVHakSNH4OzsjM8//9xoGx1DAV13aT4A/PDDDxBCyC5p37BhA+rVqye9DgwMlF1iDBTfzKvkNqlKQN+6dausjaurq/TZSm5jnd27d8u2dYcOHaBWq2Xb2MnJCUIInDt3TlqX0tu0Y8eORi95LsuZM2cQFhaGCRMmQKPRYMCAAVJINxZWt23bht69e8PLywvOzs5wcHCAp6enND0kJAQdOnSQzbN8+XK0aNECwN/b21D9mxJ8L126hPj4eKxfvx4eHh6IjIyUpqlUKtlrAPjyyy/h4OBQ7nKBmgvoCQkJ8PT0xLx582TjBwwYoPeFzJUrV2Rf6JUUGRkJJycn/O9//zP6XmUF9OTkZFktERGR6RjQiYjMkKGAXvpg3dABcslwWVZA14UsQwG9U6dO5Qb0iRMn6l0yO2XKFCmgGwtHw4cPx6RJk2TrVzKg//LLLxBC4OjRo0hJSZENf/zxh+GNBaBXr1549913DU47evQotFqtyb+fNhTQSz4CzdB6R0RE6H2BYSyg67aJoYDeokULvUunO3TooBfQS9/Ezs3NDREREQDk21hn165dsnVu164dgoKC9LZxSkqKdIm5KV8WVMZPP/0kuzGhobAaExMDOzs7fPDBBzhx4gSSk5MRGhoq28a636CXVHKbnjp1qkoBvaR58+bBz89Pem2Ol7ifPXsWDRs2lF3ir1ORS9y3bNkCR0dH7N27t8z3Kyug6/bjmzdvmrz+RERUjAGdiMgMVWdA113ODgB37tyBk5NTuZe4DxkyRLbccePGycLRzJkz8eijj8ra9OrVS3aJu1qt1rvEvUmTJli6dCmA4puUCSFw69Ytqc1ff/0FBwcHbNq0qewNVMp//vMfg4FHd+Y8LCzM5GVVV0D38PBAdna2NO7jjz8u9xL3Hj16IDg4WHp97949ODo6Viigv/3223jkkUdk02fPni1b5wkTJuCpp54qczvUVEC/ffs2hBD49ttvAQAvv/wynn76aVmbZcuWoWXLlrJxL774YoUC+v3796FSqQzWf0UDemhoqOxvNWbMGL19ZPDgwYrdJC4hIQENGzaU1U5J69atg7u7u/TlCwAsWrRI7yZxkZGR0Gg0Jj3FoKyA/umnn8LHx8ekdSciIjkGdCIiM1SdAf2hhx7C999/jzNnzmDYsGFo1qyZdKBuKKDHxMTAxsYGixcvRlJSEtasWaN3k7iDBw/CxsYGn3/+OZKTkzFnzhy4urrKbhI3ffp0NG7cGAcOHJDdJO7OnTsAii+xtbGxwcaNG3Hjxg3cv38fQPHvpevXr4+NGzfiwoULiIuLQ1hYGDZu3Gh0e+3ZswcNGzZEQUGBNO7IkSNwcnLCu+++K92ALD09XbqZmjHVFdC1Wi3Gjx+Ps2fPYv/+/fDy8sI777wjtTEU0N955x14e3sjKioKZ86cwYgRI6DVaisU0C9dugSVSoWZM2ciKSkJX3/9NXx8fCCEkG7E99tvv8HR0RFTpkzBqVOnkJycjN27d2Pq1KnSMqsjoL/22msIDQ3FsWPHcPnyZcTExOAf//gHPD09pS9m5s+fj2bNmiExMRE3b95EXl4edu3aBXt7e2zevFl6RJ6Hh0eFArru/Zs1ayar//JuEhcWFoY9e/YgOTkZycnJ2LBhA1xdXfHee+9JbaKjo2FnZ4dFixbh/PnzWLRokd5j1tasWaP3JcjZs2dx6tQp/POf/0SfPn1w6tQpWW0BkMZ169YNEyZMwKlTp3D27Flp+s6dO2X3O9Bd1v7cc8/J6rzkTzAyMjLg5eWF8ePH48yZM9i5cydcXV1lj1mLjIyEvb091q5dK1uOrmYAIDc3V1q/Ro0a4a233sKpU6eQkpIi+wyBgYF6V3EQEZFpGNCJiMxQdQb0b7/9Fg899BDUajUeffRRnD59WmpvKKADxc9W9vHxgaOjI/75z3/qPWYNAObMmQMvLy+4ubnh9ddfx9SpU2UB/cGDBwgKCkKDBg30HrOmExoaCm9vb9jY2Mges7Zq1Sq0bdsWKpUKnp6eGDRoEH788Uej26ugoABNmjTBwYMHZduw9CO8hBCydTSkugL68OHDMWfOHNSvXx9arRYvvfSS7K7WhgL6vXv3MGbMGLi6uqJp06bYuHGjwd+glxXQgb8fs+bg4IA+ffrgo48+ghBCugEcUPx7/wEDBkCr1cLZ2RkdO3bE/PnzpemmBPSAgADp72bI9u3bMXToUDRq1AhqtRqNGzfG6NGjpUf9AcCNGzek9ShZi8HBwdK2Gzt2LFasWFHhgH7//n08//zzcHJygpeXF5YsWVLumenVq1fjoYcegpOTE1xdXdGlSxesW7dOdmM7oPg38roabdeuHXbs2CGbHhISYvAeA4ZqsiRD00suJyIiQjaPoUfVlZ4HKL5z/xNPPAEHBwd4e3tj7ty5srPnAQEBBpdT8u9r6PGIpfepBw8ewNXV1eBv24mIqHwM6EREdZShIFlZpQOoOVq7di0GDhyo9GoAMP3O5LXlgw8+qJFLjn19fWVfDBCFhYVhwIABSq8GEZHFYkAnIqqjrC2g5+fn44MPPpA9dkspSgf0tWvX4vjx47h48SI2bdoENzc32SXa1eH8+fN4+OGH9c4sk3X75JNPkJiYqPRqEBFZLAZ0IqI6ytoCujlROqDPmDEDjRo1goODA9q0aYPQ0FDk5+crtj5ERERkGgZ0IiIiIiIiIjPAgE5ERERERERkBhjQiYiIiP6foTv3ExER1RYGdCIiqjYlH21mb2+Phg0bon///vjss88qfDMxa/jdu+4+AUII2NjYwNXVFZ07d0ZwcDCuXr0qa2vscVoln4l98eJFjBs3Tvr9eZMmTTBs2DAkJSVJj+cqa9A94iwtLQ0qlUq2bGPvX3JITU3Ve/xZZmYmZs6ciRYtWsDBwQENGjRAQEAAvv3225rduJVUUFCA9PT0Kv1mv+TfVQgBDw8P9O3bF8eOHZO1M/SouKioKLi5uWHq1KkoKiqyiv2AiIj+xoBORETVJjAwEIMHD0Z6ejquXLmCkydPYv78+dBqtRgyZEiFQo81BBNdkEtKSkJ6ejqSkpKwefNmdOnSBR4eHrLnhYeEhOChhx5Cenq6bLh58yYAIDc3Fy1btsTQoUMRExODy5cv49ixY5g1axaOHz+O7Oxs2XyPP/44Xn75Zdm43NxcAMC8efPw3HPPoWnTplKovH//vqytj48PQkNDZeMKCgr0Qufzzz8PPz8/7Nu3D6mpqYiNjcXq1auxcePGWtzStav03zU+Ph5jxoyBm5sbrl+/LrUrva327t0LR0dHzJkzRxpnDfsBERH9jQGdiIiqjbG7lx8+fBhCCISHh0vjPvzwQzz88MNwcnKCj48PJk+ejPv37wPQPwMphEBISAiA4iAaHByMxo0bw8nJCT169JDO/AJ/B5qDBw+iXbt2cHZ2xqBBg/TOSOsUFhaiSZMm+Oijj2TjT548CSEELl68CAD4/fffMWzYMDg7O8PFxQXPPvssrl27JrXXha1NmzbB19cXrq6uGDt2bJmPfTN2p/3s7Gy0bdsWvXv31lu+MadOnYIQApcvXzbapqSAgABMnz5db3xRURFatmyJgwcP4u2338bEiRMNzu/r64sVK1bojS+9nm5ublUO47m5ufjPf/4Db29vODg4wNfXFwsWLJCml/W3ycjIgK2tLWJjY6XPV69ePXTv3l2aPzIyEt7e3gAMX+J+9uxZDBkyBM7OzmjYsCGef/556YsRQwz9XePj4yGEwJ49e6RxJbfVV199BbVajVWrVsmWxYBORGRdGNCJiKjalPV4sU6dOmHIkCHS6xUrVuCHH37ApUuXcPjwYbRt2xaTJ08GUBzIVq5cCVdXV+nsrC68T5gwAb169UJUVBQuXLiApUuXwsHBAcnJyQCKA41KpUL//v1x4sQJnDx5Eu3bt8eECROMrvebb74Jf39/vXGPP/44gOJQ16VLF/j7+yM2Nha//PILunbtioCAAKl9SEgItFotRo0ahTNnziAqKgre3t6YNWuW0fct61F4K1asgBBCOuNaXkC/cuUKbG1tsWzZMhQUFBhtp2MsoB8+fBje3t4oKChAQkICnJ2dDX7JYGpAb9u2LcaMGVOl59MvXboUTZs2RVRUFC5fvoyffvoJkZGRAEz723Tt2hXLli0DAJw+fRr16tWDWq3GvXv3AACvvPIKxo4dC0A/oF+9ehUNGjTAu+++i/PnzyMuLg4DBgxA3759ja5v6b9rVlYWXn/9dQghcODAAb1tFRYWBrVajS+++EJvWQzoRETWhQGdiIiqTVkBfezYsWjfvr3Rebdu3Yr69etLrw0FkwsXLsDGxgZ//vmnbHy/fv3w7rvvSvMJIXDhwgVp+tq1a+Hl5WX0vePi4mBjYyOdfdadVV+7di0A4NChQ7Czs8Mff/whzXP27FkIIXD8+HEAxWHLyclJFkSDg4Px2GOPGX3fsgL6gQMHIITAr7/+Ki3f1tYWzs7OsuHFF1+U5gkLC4OTkxNcXFzQt29fhIaGSlcAlGYsoE+YMAEzZsyQXnfq1El25YOOqQH9xx9/hI+PD1QqFbp3744ZM2bo/Ra7PEFBQXjqqadQVFSkN82Uv80bb7yBp59+GgCwcuVKPPPMM+jatSv27dsHAPDz85OuoCgd0P/73/9i4MCBsvdMS0uTLmE3RPd31f2NbGxsIIRAt27dkJeXJ9tWarUaQgh89tlnBpfFgE5EZF0Y0ImIqNqUFdDHjBmDDh06SK9/+OEH9O/fH40bN4ZWq4VGo4EQApmZmQAMB5OtW7fKgo9usLe3x5gxY6T5nJycZPPt3LkTNjY2Za57+/btsXDhQmndVCqVdBnzqlWr0Lx5c7153N3d8fnnnwMoDlslPx8ALF++HC1atDD6nmUF9P379+t9AdC+fXukpKTIhpKX2QPAX3/9hd27d2PWrFl45JFHoNFocOjQIb3lGwrod+/ehUajkS4HB4rPXuuuJCjJ1IAOAHl5eYiKisLChQsxYMAA2NjYIDQ01Oh2Ke3kyZPw8PBAmzZtEBQUhO+++06aZsrfZs+ePXBzc0NhYSFGjhyJsLAwvPHGG5g5cybS09MhhMC5c+cA6Af0oUOHQqVS6dWcEAL79+83uL66v2tcXBySkpKwZcsW+Pr64syZM3rbqmXLlujatSvatm1r8GcYDOhERNaFAZ2IiKpNWQH9kUcewT/+8Q8AwOXLl6HRaDBjxgzExMQgKSkJn332mSysGgomW7ZsgZ2dHRITE/WCanp6utH5vvnmGwhR9j95H3zwAR555BEAwEsvvSSdcQWKz7oaCtpubm7YtGkTAMPBdMWKFfD19TX6nmUF9A8//BBCCNy4ccPo8stTVFSEAQMG4Mknn9SbZiigr127FkII2NnZSYOtrS2EEDh79qysbUUCemnz5s2DSqWSbkpninv37mHLli146aWX4ObmhtGjRwMw7W+j+x36iRMn0KBBAyQkJGD37t147LHHEBkZiYYNG0rz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width=\"1000\">"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": [
- "Text(58, 1.01, 'Korrelation = -0.809')"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "fig, ax = plt.subplots(figsize=(10,7))\n",
- "plot1 = df_total.plot.scatter(x=\"Impfquote\",y=\"Übersterblichkeit\",title=\"Übersterblichkeit vs Impfquote\", grid=True,\n",
- " ax=ax, style='o', legend=False, s=150, fontsize=18,\n",
- " color=range(len(df_total)), colormap='Spectral')\n",
- "#ax.legend(plot1,[\"1\",\"2\"],fancybox=True)\n",
- "#df_total.head()\n",
- "laender_short={\"Schleswig-Holstein\":\"SH\",\n",
- "\"Hamburg\":\"HH\",\n",
- "\"Niedersachsen\":\"NI\",\n",
- "\"Bremen\":\"HB\",\n",
- "\"Nordrhein-Westfalen\":\"NRW\",\n",
- "\"Hessen\":\"HE\",\n",
- "\"Rheinland-Pfalz\":\"RP\",\n",
- "\"Baden-Württemberg\":\"BW\",\n",
- "\"Bayern\":\"BY\",\n",
- "\"Saarland\":\"SA\",\n",
- "\"Berlin\":\"BE\",\n",
- "\"Brandenburg\":\"BB\",\n",
- "\"Mecklenburg-Vorpommern\":\"MV\",\n",
- "\"Sachsen\":\"SA\",\n",
- "\"Sachsen-Anhalt\":\"S-AN\",\n",
- "\"Thüringen\":\"TH\",\n",
- "}\n",
- "c=0\n",
- "correlation = df_total.corr(method=\"pearson\")\n",
- "\n",
- "for k, v in df_total.iterrows():\n",
- " c += 1\n",
- " ax.annotate(laender_short[k], v,\n",
- " xytext=(9,-3), textcoords='offset points',\n",
- " family='sans-serif', fontsize=11)\n",
- "ax.set(title='Übersterblichkeit vs Impfquote',\n",
- " ylabel='Todesfälle KW1-42 (2021)\\nverglichen mit Durchschnitt 2016-2019',\n",
- " xlabel=\"Impfquote (2 Impfungen, Stand 30.11.2021)\\nDaten von DESTATIS, sowie RKI\\n(c)CC-BY\")\n",
- "ax.text(58,1.01,\"Korrelation = %0.3f\"%correlation[\"Übersterblichkeit\"][\"Impfquote\"])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 171,
- "id": "426b3884",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "-0.808719600395588"
- ]
- },
- "execution_count": 171,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "\n"
- ]
+ "source": []
}
],
"metadata": {
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diff --git a/wcorr/__init__.py b/wcorr/__init__.py
new file mode 100644
index 0000000..f1ff733
--- /dev/null
+++ b/wcorr/__init__.py
@@ -0,0 +1 @@
+from .wcorr import WeightedCorr
\ No newline at end of file
diff --git a/wcorr/wcorr.py b/wcorr/wcorr.py
new file mode 100644
index 0000000..3e6cb6f
--- /dev/null
+++ b/wcorr/wcorr.py
@@ -0,0 +1,69 @@
+import numpy as np
+import pandas as pd
+from scipy.stats import rankdata
+
+class WeightedCorr:
+ def __init__(self, xyw=None, x=None, y=None, w=None, df=None, wcol=None):
+ ''' Weighted Correlation class. Either supply xyw, (x, y, w), or (df, wcol). Call the class to get the result, i.e.:
+ WeightedCorr(xyw=mydata[[x, y, w]])(method='pearson')
+ :param xyw: pd.DataFrame with shape(n, 3) containing x, y, and w columns (column names irrelevant)
+ :param x: pd.Series (n, ) containing values for x
+ :param y: pd.Series (n, ) containing values for y
+ :param w: pd.Series (n, ) containing weights
+ :param df: pd.Dataframe (n, m+1) containing m phenotypes and a weight column
+ :param wcol: str column of the weight column in the dataframe passed to the df argument.
+ '''
+ if (df is None) and (wcol is None):
+ if np.all([i is None for i in [xyw, x, y, w]]):
+ raise ValueError('No data supplied')
+ if not ((isinstance(xyw, pd.DataFrame)) != (np.all([isinstance(i, pd.Series) for i in [x, y, w]]))):
+ raise TypeError('xyw should be a pd.DataFrame, or x, y, w should be pd.Series')
+ xyw = pd.concat([x, y, w], axis=1).dropna() if xyw is None else xyw.dropna()
+ self.x, self.y, self.w = (pd.to_numeric(xyw[i], errors='coerce').values for i in xyw.columns)
+ self.df = None
+ elif (wcol is not None) and (df is not None):
+ if (not isinstance(df, pd.DataFrame)) or (not isinstance(wcol, str)):
+ raise ValueError('df should be a pd.DataFrame and wcol should be a string')
+ if wcol not in df.columns:
+ raise KeyError('wcol not found in column names of df')
+ self.df = df.loc[:, [x for x in df.columns if x != wcol]]
+ self.w = pd.to_numeric(df.loc[:, wcol], errors='coerce')
+ else:
+ raise ValueError('Incorrect arguments specified, please specify xyw, or (x, y, w) or (df, wcol)')
+
+ def _wcov(self, x, y, ms):
+ return np.sum(self.w * (x - ms[0]) * (y - ms[1]))
+
+ def _pearson(self, x=None, y=None):
+ x, y = (self.x, self.y) if ((x is None) and (y is None)) else (x, y)
+ mx, my = (np.sum(i * self.w) / np.sum(self.w) for i in [x, y])
+ return self._wcov(x, y, [mx, my]) / np.sqrt(self._wcov(x, x, [mx, mx]) * self._wcov(y, y, [my, my]))
+
+ def _wrank(self, x):
+ (unique, arr_inv, counts) = np.unique(rankdata(x), return_counts=True, return_inverse=True)
+ a = np.bincount(arr_inv, self.w)
+ return (np.cumsum(a) - a)[arr_inv]+((counts + 1)/2 * (a/counts))[arr_inv]
+
+ def _spearman(self, x=None, y=None):
+ x, y = (self.x, self.y) if ((x is None) and (y is None)) else (x, y)
+ return self._pearson(self._wrank(x), self._wrank(y))
+
+ def __call__(self, method='pearson'):
+ '''
+ :param method: Correlation method to be used: 'pearson' for pearson r, 'spearman' for spearman rank-order correlation.
+ :return: if xyw, or (x, y, w) were passed to __init__ returns the correlation value (float).
+ if (df, wcol) were passed to __init__ returns a pd.DataFrame (m, m), the correlation matrix.
+ '''
+ if method not in ['pearson', 'spearman']:
+ raise ValueError('method should be one of [\'pearson\', \'spearman\']')
+ cor = {'pearson': self._pearson, 'spearman': self._spearman}[method]
+ if self.df is None:
+ return cor()
+ else:
+ out = pd.DataFrame(np.nan, index=self.df.columns, columns=self.df.columns)
+ for i, x in enumerate(self.df.columns):
+ for j, y in enumerate(self.df.columns):
+ if i >= j:
+ out.loc[x, y] = cor(x=pd.to_numeric(self.df[x], errors='coerce'), y=pd.to_numeric(self.df[y], errors='coerce'))
+ out.loc[y, x] = out.loc[x, y]
+ return out
--
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