From 5129ac8d226f5389bf899c152fc9ceb824aaa475 Mon Sep 17 00:00:00 2001 From: herguejuan Date: Wed, 25 Mar 2020 17:26:09 -0600 Subject: [PATCH 1/4] Modificando archivo About-me a proposito --- module-1/lab-resolving-git-conflicts/your-code/about-me.md | 1 + 1 file changed, 1 insertion(+) diff --git a/module-1/lab-resolving-git-conflicts/your-code/about-me.md b/module-1/lab-resolving-git-conflicts/your-code/about-me.md index 30a999d..584f19f 100755 --- a/module-1/lab-resolving-git-conflicts/your-code/about-me.md +++ b/module-1/lab-resolving-git-conflicts/your-code/about-me.md @@ -5,3 +5,4 @@ Ut porttitor iaculis tellus bibendum euismod. Morbi porta, ante nec tempus porta Suspendisse ut malesuada ex. Nulla ultricies nisl et nisi rhoncus sollicitudin. Vestibulum maximus iaculis ligula, nec commodo nunc ullamcorper nec. Duis quis condimentum sapien. Cras vestibulum interdum felis eu auctor. Quisque semper, magna at dapibus faucibus, felis risus semper ligula, id aliquam lectus ligula vel nisi. In hac habitasse platea dictumst. Donec arcu sapien, suscipit ac dictum et, imperdiet id tortor. Maecenas ornare sodales interdum. Mauris dictum felis eu eros vestibulum cursus. Phasellus accumsan, turpis ut malesuada sollicitudin, augue leo venenatis ante, vel convallis tellus diam sit amet lacus. Aenean eu mauris eros. Praesent ante lacus, gravida sit amet tellus nec, laoreet ultrices lacus. Integer commodo semper vestibulum. Fusce felis massa, consectetur facilisis rutrum nec, pulvinar et nisi. Morbi fermentum ultricies tortor, vehicula ultrices eros elementum a. Duis ornare aliquam facilisis. Proin aliquam tincidunt odio vitae dignissim. Sed malesuada lacinia massa, nec blandit urna auctor elementum. Duis auctor non tortor in consequat. Mauris id vestibulum risus. In eget erat sed lacus efficitur viverra sed eu est. Aliquam interdum consequat molestie. Aliquam metus nisi, blandit non semper ut, blandit vel leo. Cras dictum turpis erat, sed iaculis ligula facilisis dapibus. Aliquam posuere dignissim fermentum. Praesent at neque sit amet lectus ornare iaculis. Curabitur id urna quis lorem varius ultrices eu sit amet sapien. Curabitur maximus volutpat suscipit. Proin imperdiet elementum lacus a eleifend. Sed tempor lacus posuere diam vehicula iaculis. +Agregando cualquier cosa From 484a78b6127d01a89eeedc8029eacf60ecfda819 Mon Sep 17 00:00:00 2001 From: herguejuan Date: Wed, 15 Apr 2020 03:53:20 -0500 Subject: [PATCH 2/4] Descartar cambios --- .../lab-web-scraping/your-code/main.ipynb | 105 +++++++++++++++--- 1 file changed, 87 insertions(+), 18 deletions(-) diff --git a/module-1/lab-web-scraping/your-code/main.ipynb b/module-1/lab-web-scraping/your-code/main.ipynb index d2d7553..0785805 100755 --- a/module-1/lab-web-scraping/your-code/main.ipynb +++ b/module-1/lab-web-scraping/your-code/main.ipynb @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -51,9 +51,9 @@ "# from lxml import html\n", "# from lxml.html import fromstring\n", "# import urllib.request\n", - "# from urllib.request import urlopen\n", + "from urllib.request import urlopen\n", "# import random\n", - "# import re\n", + "import re\n", "# import scrapy" ] }, @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -76,11 +76,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "#your code" + "github_html=requests.get(url).text\n", + "soup = BeautifulSoup(github_html, \"html.parser\")" ] }, { @@ -134,11 +135,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['Product', 'Platform', 'Support', 'Company']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code" + "def elimina(t):\n", + " t = re.sub(' ','',t)\n", + " t = re.sub('\\n\\n',' ',t)\n", + " return t.strip()\n", + "\n", + "dev2 = [d.text for d in soup.find_all('h2')]\n", + "dev2 = list(map(elimina, dev2))\n", + "\n", + "dev2" ] }, { @@ -152,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -162,11 +182,59 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#your code" + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Trending',\n", + " 'zylo117 - Yet-Another-EfficientDet-Pytorch',\n", + " 'shengqiangzhang - examples-of-web-crawlers',\n", + " 'Mascobot - pandemic-ventilator-2.0',\n", + " 'twintproject - twint',\n", + " 'satwikkansal - wtfpython',\n", + " 'tiangolo - fastapi',\n", + " 'diofeher - bbb-v2',\n", + " 'matterport - Mask_RCNN',\n", + " 'alievk - avatarify',\n", + " 'vt-vl-lab - 3d-photo-inpainting',\n", + " 'leisurelicht - wtfpython-cn',\n", + " 'lindawangg - COVID-Net',\n", + " 'grantmcconnaughey - cookiecutter-django-vue-graphql-aws',\n", + " 'awslabs - aws-data-wrangler',\n", + " 'toandaominh1997 - EfficientDet.Pytorch',\n", + " 'DP-3T - reference_implementation',\n", + " 'encode - django-rest-framework',\n", + " 'lazyprogrammer - machine_learning_examples',\n", + " 'allenai - longformer',\n", + " 'eragonruan - text-detection-ctpn',\n", + " 'tensorflow - hub',\n", + " 'darkarp - chromepass',\n", + " 'junyanz - pytorch-CycleGAN-and-pix2pix',\n", + " 'ansible - ansible',\n", + " 'Azure - azure-sdk-for-python']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#your code\n", + "github_html_py = requests.get(url).text\n", + "soup_py = BeautifulSoup(github_html_py, \"html.parser\")\n", + "\n", + "def limpia(t):\n", + " t = re.sub(' ','',t)\n", + " t = re.sub('\\n','',t)\n", + " t = re.sub('/',' - ',t)\n", + " return t\n", + "\n", + "repository = [r.text for r in soup_py.find_all('h1')]\n", + "repository = list(map(limpia, repository))\n", + "repository" ] }, { @@ -178,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -192,7 +260,8 @@ "metadata": {}, "outputs": [], "source": [ - "#your code" + "disney_html = requests.get(url).text\n", + "soup_disney = BeautifulSoup(disney_html, \"html.parser\")" ] }, { @@ -630,7 +699,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.7.6" } }, "nbformat": 4, From 3bbd85e5ad8c769af29c77b52c66f6bc67ff2110 Mon Sep 17 00:00:00 2001 From: herguejuan Date: Fri, 17 Apr 2020 14:18:23 -0500 Subject: [PATCH 3/4] quitando cambios --- .../pymongo-tester-checkpoint.ipynb | 6 + .../starter_code/pymongo-tester.ipynb | 1355 +++++++++++++++++ .../challenge-1-checkpoint.ipynb | 391 +++++ 3 files changed, 1752 insertions(+) create mode 100644 module-2/lab-advance-querying-mongo/starter_code/.ipynb_checkpoints/pymongo-tester-checkpoint.ipynb create mode 100644 module-2/lab-advance-querying-mongo/starter_code/pymongo-tester.ipynb create mode 100755 module-2/lab-matplotlib-seaborn/your-code/.ipynb_checkpoints/challenge-1-checkpoint.ipynb diff --git a/module-2/lab-advance-querying-mongo/starter_code/.ipynb_checkpoints/pymongo-tester-checkpoint.ipynb b/module-2/lab-advance-querying-mongo/starter_code/.ipynb_checkpoints/pymongo-tester-checkpoint.ipynb new file mode 100644 index 0000000..7fec515 --- /dev/null +++ b/module-2/lab-advance-querying-mongo/starter_code/.ipynb_checkpoints/pymongo-tester-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/module-2/lab-advance-querying-mongo/starter_code/pymongo-tester.ipynb b/module-2/lab-advance-querying-mongo/starter_code/pymongo-tester.ipynb new file mode 100644 index 0000000..bce3bb6 --- /dev/null +++ b/module-2/lab-advance-querying-mongo/starter_code/pymongo-tester.ipynb @@ -0,0 +1,1355 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#import pymongo\n", + "#cliente = pymongo.MongoClient()\n", + "#cliente.list_databases.names()\n", + "\n", + "#nueva_base = cliente.nueva_base\n", + "#nueva_base.list_collection_names()\n", + "\n", + "#db.createCollection('test');" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pymongo" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "cliente = pymongo.MongoClient()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Test', 'admin', 'config', 'local', 'nueva_db']" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cliente.list_database_names()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "nueva_base2 = cliente.nueva_base2" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Test', 'admin', 'config', 'local', 'nueva_db']" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cliente.list_database_names()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "coleccion = nueva_base2.coleccion" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nueva_base2.list_collection_names()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dic1 = {'nombre':'Juan', 'edad':27}\n", + "\n", + "coleccion.insert_one(dic1)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'_id': ObjectId('5e98fec6a359803eff02b8d8'), 'nombre': 'Juan', 'edad': 27}]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(coleccion.find())" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Test', 'admin', 'config', 'local', 'nueva_base2', 'nueva_db']" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cliente.list_database_names()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['coleccion']" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nueva_base2.list_collection_names()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'_id': ObjectId('5e98fec6a359803eff02b8d8'), 'nombre': 'Juan', 'edad': 27}]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(coleccion.find())" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dic2 = {'nombre':'anai', 'edad':24}\n", + "coleccion.insert_one(dic2)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'_id': ObjectId('5e98fec6a359803eff02b8d8'), 'nombre': 'Juan', 'edad': 27},\n", + " {'_id': ObjectId('5e99030aa359803eff02b8d9'), 'nombre': 'anai', 'edad': 24}]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(coleccion.find())" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'_id': ObjectId('5e98fec6a359803eff02b8d8'), 'nombre': 'Juan', 'edad': 27},\n", + " {'_id': ObjectId('5e99030aa359803eff02b8d9'), 'nombre': 'anai', 'edad': 24},\n", + " {'_id': ObjectId('5e99034ba359803eff02b8da'), 'nombre': 'Angel', 'edad': 30}]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dic3 = {'nombre':'Angel', 'edad':30}\n", + "coleccion.insert_one(dic3)\n", + "list(coleccion.find())" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'_id': ObjectId('5e98fec6a359803eff02b8d8'), 'nombre': 'Juan', 'edad': 27},\n", + " {'_id': ObjectId('5e99030aa359803eff02b8d9'), 'nombre': 'anai', 'edad': 24},\n", + " {'_id': ObjectId('5e99034ba359803eff02b8da'), 'nombre': 'Angel', 'edad': 30},\n", + " {'_id': ObjectId('5e9903a2a359803eff02b8db'), 'nombre': 'Diana', 'edad': 25}]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dic4 = {'nombre':'Diana', 'edad':25}\n", + "coleccion.insert_one(dic4)\n", + "list(coleccion.find())" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'_id': ObjectId('5e98fec6a359803eff02b8d8'), 'nombre': 'Juan', 'edad': 27},\n", + " {'_id': ObjectId('5e99030aa359803eff02b8d9'), 'nombre': 'anai', 'edad': 24},\n", + " {'_id': ObjectId('5e99034ba359803eff02b8da'), 'nombre': 'Angel', 'edad': 30},\n", + " {'_id': ObjectId('5e9903a2a359803eff02b8db'), 'nombre': 'Diana', 'edad': 25},\n", + " {'_id': ObjectId('5e9903fda359803eff02b8dc'), 'nombre': 'Belen', 'edad': 20}]" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dic5 = {'nombre':'Belen', 'edad':20 }\n", + "coleccion.insert_one(dic5)\n", + "list(coleccion.find())" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['coleccion']" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nueva_base2.list_collection_names()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'_id': ObjectId('5e98fec6a359803eff02b8d8'), 'nombre': 'Juan', 'edad': 27},\n", + " {'_id': ObjectId('5e99030aa359803eff02b8d9'), 'nombre': 'anai', 'edad': 24},\n", + " {'_id': ObjectId('5e99034ba359803eff02b8da'), 'nombre': 'Angel', 'edad': 30},\n", + " {'_id': ObjectId('5e9903a2a359803eff02b8db'), 'nombre': 'Diana', 'edad': 25},\n", + " {'_id': ObjectId('5e9903fda359803eff02b8dc'), 'nombre': 'Belen', 'edad': 20}]" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(coleccion.find())" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'_id': ObjectId('5e98fec6a359803eff02b8d8'), 'nombre': 'Juan', 'edad': 27},\n", + " {'_id': ObjectId('5e99030aa359803eff02b8d9'), 'nombre': 'anai', 'edad': 24},\n", + " {'_id': ObjectId('5e99034ba359803eff02b8da'), 'nombre': 'Angel', 'edad': 30}]" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(coleccion.find().limit(3))" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coleccion.insert_one({'_id':1, 'nombre':'José Juan', 'edad':28})" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'_id': ObjectId('5e98fec6a359803eff02b8d8'), 'nombre': 'Juan', 'edad': 27},\n", + " {'_id': ObjectId('5e99030aa359803eff02b8d9'), 'nombre': 'anai', 'edad': 24},\n", + " {'_id': ObjectId('5e99034ba359803eff02b8da'), 'nombre': 'Angel', 'edad': 30},\n", + " {'_id': ObjectId('5e9903a2a359803eff02b8db'), 'nombre': 'Diana', 'edad': 25},\n", + " {'_id': ObjectId('5e9903fda359803eff02b8dc'), 'nombre': 'Belen', 'edad': 20},\n", + " {'_id': 1, 'nombre': 'José Juan', 'edad': 28}]" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(coleccion.find())" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coleccion.insert_one({'_id':2, 'nombre':'Yona', 'edad':35})" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'_id': ObjectId('5e98fec6a359803eff02b8d8'), 'nombre': 'Juan', 'edad': 27},\n", + " {'_id': ObjectId('5e99030aa359803eff02b8d9'), 'nombre': 'anai', 'edad': 24},\n", + " {'_id': ObjectId('5e99034ba359803eff02b8da'), 'nombre': 'Angel', 'edad': 30},\n", + " {'_id': ObjectId('5e9903a2a359803eff02b8db'), 'nombre': 'Diana', 'edad': 25},\n", + " {'_id': ObjectId('5e9903fda359803eff02b8dc'), 'nombre': 'Belen', 'edad': 20},\n", + " {'_id': 1, 'nombre': 'José Juan', 'edad': 28},\n", + " {'_id': 2, 'nombre': 'Yona', 'edad': 35}]" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(coleccion.find())" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(list(coleccion.find()))" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'_id': ObjectId('5e98fec6a359803eff02b8d8'), 'nombre': 'Juan', 'edad': 27},\n", + " {'_id': ObjectId('5e99030aa359803eff02b8d9'), 'nombre': 'anai', 'edad': 24},\n", + " {'_id': ObjectId('5e99034ba359803eff02b8da'), 'nombre': 'Angel', 'edad': 30},\n", + " {'_id': ObjectId('5e9903a2a359803eff02b8db'), 'nombre': 'Diana', 'edad': 25}]" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(coleccion.find().limit(4))" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(list(coleccion.find()))" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coleccion.insert_many([\n", + " {'_id':3,'nombre':'Esteban', 'edad':45, 'altura':1.7},\n", + " {'_id':4,'nombre':'Alicia', 'edad':34, 'altura':1.5},\n", + " {'_id':5,'nombre':'Luz', 'edad':18, 'altura':1.65}\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'_id': ObjectId('5e98fec6a359803eff02b8d8'), 'nombre': 'Juan', 'edad': 27},\n", + " {'_id': ObjectId('5e99030aa359803eff02b8d9'), 'nombre': 'anai', 'edad': 24},\n", + " {'_id': ObjectId('5e99034ba359803eff02b8da'), 'nombre': 'Angel', 'edad': 30},\n", + " {'_id': ObjectId('5e9903a2a359803eff02b8db'), 'nombre': 'Diana', 'edad': 25},\n", + " {'_id': ObjectId('5e9903fda359803eff02b8dc'), 'nombre': 'Belen', 'edad': 20},\n", + " {'_id': 1, 'nombre': 'José Juan', 'edad': 28},\n", + " {'_id': 2, 'nombre': 'Yona', 'edad': 35},\n", + " {'_id': 3, 'nombre': 'Esteban', 'edad': 45, 'altura': 1.7},\n", + " {'_id': 4, 'nombre': 'Alicia', 'edad': 34, 'altura': 1.5},\n", + " {'_id': 5, 'nombre': 'Luz', 'edad': 18, 'altura': 1.65}]" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(coleccion.find())" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'_id': 1, 'nombre': 'José Juan', 'edad': 28}]" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(coleccion.find({'edad':28}))" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'_id': ObjectId('5e99034ba359803eff02b8da'), 'nombre': 'Angel', 'edad': 30},\n", + " {'_id': 2, 'nombre': 'Yona', 'edad': 35},\n", + " {'_id': 3, 'nombre': 'Esteban', 'edad': 45, 'altura': 1.7},\n", + " {'_id': 4, 'nombre': 'Alicia', 'edad': 34, 'altura': 1.5}]" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(coleccion.find({'edad':{'$gte':30}}))" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'_id': 2, 'nombre': 'Yona', 'edad': 35},\n", + " {'_id': 3, 'nombre': 'Esteban', 'edad': 45, 'altura': 1.7}]" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(coleccion.find({'edad':{'$gte':35}}))" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(coleccion.find({'Edad':{'$gte':35}}))" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'_id': 3, 'nombre': 'Esteban', 'edad': 45, 'altura': 1.7},\n", + " {'_id': 5, 'nombre': 'Luz', 'edad': 18, 'altura': 1.65}]" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(coleccion.find({'altura':{'$gte':1.51}}))" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'_id': 3, 'nombre': 'Esteban', 'edad': 45, 'altura': 1.7},\n", + " {'_id': 2, 'nombre': 'Yona', 'edad': 35},\n", + " {'_id': 4, 'nombre': 'Alicia', 'edad': 34, 'altura': 1.5},\n", + " {'_id': ObjectId('5e99034ba359803eff02b8da'), 'nombre': 'Angel', 'edad': 30},\n", + " {'_id': 1, 'nombre': 'José Juan', 'edad': 28},\n", + " {'_id': ObjectId('5e98fec6a359803eff02b8d8'), 'nombre': 'Juan', 'edad': 27},\n", + " {'_id': ObjectId('5e9903a2a359803eff02b8db'), 'nombre': 'Diana', 'edad': 25},\n", + " {'_id': ObjectId('5e99030aa359803eff02b8d9'), 'nombre': 'anai', 'edad': 24},\n", + " {'_id': ObjectId('5e9903fda359803eff02b8dc'), 'nombre': 'Belen', 'edad': 20},\n", + " {'_id': 5, 'nombre': 'Luz', 'edad': 18, 'altura': 1.65}]" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(coleccion.find().sort('edad',-1))" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'_id': ObjectId('5e99030aa359803eff02b8d9'), 'nombre': 'anai', 'edad': 24},\n", + " {'_id': 2, 'nombre': 'Yona', 'edad': 35},\n", + " {'_id': 5, 'nombre': 'Luz', 'edad': 18, 'altura': 1.65},\n", + " {'_id': ObjectId('5e98fec6a359803eff02b8d8'), 'nombre': 'Juan', 'edad': 27},\n", + " {'_id': 1, 'nombre': 'José Juan', 'edad': 28},\n", + " {'_id': 3, 'nombre': 'Esteban', 'edad': 45, 'altura': 1.7},\n", + " {'_id': ObjectId('5e9903a2a359803eff02b8db'), 'nombre': 'Diana', 'edad': 25},\n", + " {'_id': ObjectId('5e9903fda359803eff02b8dc'), 'nombre': 'Belen', 'edad': 20},\n", + " {'_id': ObjectId('5e99034ba359803eff02b8da'), 'nombre': 'Angel', 'edad': 30},\n", + " {'_id': 4, 'nombre': 'Alicia', 'edad': 34, 'altura': 1.5}]" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(coleccion.find().sort('nombre',-1))" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coleccion.delete_one({'nombre':'anai'})" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'_id': ObjectId('5e98fec6a359803eff02b8d8'), 'nombre': 'Juan', 'edad': 27},\n", + " {'_id': ObjectId('5e99034ba359803eff02b8da'), 'nombre': 'Angel', 'edad': 30},\n", + " {'_id': ObjectId('5e9903a2a359803eff02b8db'), 'nombre': 'Diana', 'edad': 25},\n", + " {'_id': ObjectId('5e9903fda359803eff02b8dc'), 'nombre': 'Belen', 'edad': 20},\n", + " {'_id': 1, 'nombre': 'José Juan', 'edad': 28},\n", + " {'_id': 2, 'nombre': 'Yona', 'edad': 35},\n", + " {'_id': 3, 'nombre': 'Esteban', 'edad': 45, 'altura': 1.7},\n", + " {'_id': 4, 'nombre': 'Alicia', 'edad': 34, 'altura': 1.5},\n", + " {'_id': 5, 'nombre': 'Luz', 'edad': 18, 'altura': 1.65}]" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(coleccion.find())" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coleccion.insert_one({'nomre':'Anai', 'edad':24, 'altura':1.45})" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'_id': ObjectId('5e98fec6a359803eff02b8d8'), 'nombre': 'Juan', 'edad': 27},\n", + " {'_id': ObjectId('5e99034ba359803eff02b8da'), 'nombre': 'Angel', 'edad': 30},\n", + " {'_id': ObjectId('5e9903a2a359803eff02b8db'), 'nombre': 'Diana', 'edad': 25},\n", + " {'_id': ObjectId('5e9903fda359803eff02b8dc'), 'nombre': 'Belen', 'edad': 20},\n", + " {'_id': 1, 'nombre': 'José Juan', 'edad': 28},\n", + " {'_id': 2, 'nombre': 'Yona', 'edad': 35},\n", + " {'_id': 3, 'nombre': 'Esteban', 'edad': 45, 'altura': 1.7},\n", + " {'_id': 4, 'nombre': 'Alicia', 'edad': 34, 'altura': 1.5},\n", + " {'_id': 5, 'nombre': 'Luz', 'edad': 18, 'altura': 1.65},\n", + " {'_id': ObjectId('5e990c68a359803eff02b8dd'),\n", + " 'nomre': 'Anai',\n", + " 'edad': 24,\n", + " 'altura': 1.45}]" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(coleccion.find())" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'_id': 2, 'nombre': 'Yona', 'edad': 35},\n", + " {'_id': 5, 'nombre': 'Luz', 'edad': 18, 'altura': 1.65},\n", + " {'_id': ObjectId('5e98fec6a359803eff02b8d8'), 'nombre': 'Juan', 'edad': 27},\n", + " {'_id': 1, 'nombre': 'José Juan', 'edad': 28},\n", + " {'_id': 3, 'nombre': 'Esteban', 'edad': 45, 'altura': 1.7},\n", + " {'_id': ObjectId('5e9903a2a359803eff02b8db'), 'nombre': 'Diana', 'edad': 25},\n", + " {'_id': ObjectId('5e9903fda359803eff02b8dc'), 'nombre': 'Belen', 'edad': 20},\n", + " {'_id': ObjectId('5e99034ba359803eff02b8da'), 'nombre': 'Angel', 'edad': 30},\n", + " {'_id': 4, 'nombre': 'Alicia', 'edad': 34, 'altura': 1.5},\n", + " {'_id': ObjectId('5e990c68a359803eff02b8dd'),\n", + " 'nomre': 'Anai',\n", + " 'edad': 24,\n", + " 'altura': 1.45}]" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(coleccion.find().sort('nombre',-1))" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'_id': ObjectId('5e990c68a359803eff02b8dd'),\n", + " 'nomre': 'Anai',\n", + " 'edad': 24,\n", + " 'altura': 1.45},\n", + " {'_id': 4, 'nombre': 'Alicia', 'edad': 34, 'altura': 1.5},\n", + " {'_id': ObjectId('5e99034ba359803eff02b8da'), 'nombre': 'Angel', 'edad': 30},\n", + " {'_id': ObjectId('5e9903fda359803eff02b8dc'), 'nombre': 'Belen', 'edad': 20},\n", + " {'_id': ObjectId('5e9903a2a359803eff02b8db'), 'nombre': 'Diana', 'edad': 25},\n", + " {'_id': 3, 'nombre': 'Esteban', 'edad': 45, 'altura': 1.7},\n", + " {'_id': 1, 'nombre': 'José Juan', 'edad': 28},\n", + " {'_id': ObjectId('5e98fec6a359803eff02b8d8'), 'nombre': 'Juan', 'edad': 27},\n", + " {'_id': 5, 'nombre': 'Luz', 'edad': 18, 'altura': 1.65},\n", + " {'_id': 2, 'nombre': 'Yona', 'edad': 35}]" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(coleccion.find().sort('nombre',1))" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'_id': ObjectId('5e990c68a359803eff02b8dd'),\n", + " 'nomre': 'Anai',\n", + " 'edad': 24,\n", + " 'altura': 1.45},\n", + " {'_id': 4, 'nombre': 'Alicia', 'edad': 34, 'altura': 1.5},\n", + " {'_id': ObjectId('5e99034ba359803eff02b8da'), 'nombre': 'Angel', 'edad': 30},\n", + " {'_id': ObjectId('5e9903fda359803eff02b8dc'), 'nombre': 'Belen', 'edad': 20},\n", + " {'_id': ObjectId('5e9903a2a359803eff02b8db'), 'nombre': 'Diana', 'edad': 25},\n", + " {'_id': 3, 'nombre': 'Esteban', 'edad': 45, 'altura': 1.7},\n", + " {'_id': 1, 'nombre': 'José Juan', 'edad': 28},\n", + " {'_id': ObjectId('5e98fec6a359803eff02b8d8'), 'nombre': 'Juan', 'edad': 27},\n", + " {'_id': 5, 'nombre': 'Luz', 'edad': 18, 'altura': 1.65},\n", + " {'_id': 2, 'nombre': 'Yona', 'edad': 35}]" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(coleccion.find().sort('nombre'))" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coleccion.update_one({'_id':'5e990c68a359803eff02b8dd'}, {'$set':{'_id':0}})" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'_id': ObjectId('5e98fec6a359803eff02b8d8'), 'nombre': 'Juan', 'edad': 27},\n", + " {'_id': ObjectId('5e99034ba359803eff02b8da'), 'nombre': 'Angel', 'edad': 30},\n", + " {'_id': ObjectId('5e9903a2a359803eff02b8db'), 'nombre': 'Diana', 'edad': 25},\n", + " {'_id': ObjectId('5e9903fda359803eff02b8dc'), 'nombre': 'Belen', 'edad': 20},\n", + " {'_id': 1, 'nombre': 'José Juan', 'edad': 28},\n", + " {'_id': 2, 'nombre': 'Yona', 'edad': 35},\n", + " {'_id': 3, 'nombre': 'Esteban', 'edad': 45, 'altura': 1.7},\n", + " {'_id': 4, 'nombre': 'Alicia', 'edad': 34, 'altura': 1.5},\n", + " {'_id': 5, 'nombre': 'Luz', 'edad': 18, 'altura': 1.65},\n", + " {'_id': ObjectId('5e990c68a359803eff02b8dd'),\n", + " 'nomre': 'Anai',\n", + " 'edad': 24,\n", + " 'altura': 1.45}]" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(coleccion.find())" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coleccion.update_one({'nombre':'Anai'}, {'$set':{'nombre':'Argelia Anai'}})" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'_id': ObjectId('5e98fec6a359803eff02b8d8'), 'nombre': 'Juan', 'edad': 27},\n", + " {'_id': ObjectId('5e99034ba359803eff02b8da'), 'nombre': 'Angel', 'edad': 30},\n", + " {'_id': ObjectId('5e9903a2a359803eff02b8db'), 'nombre': 'Diana', 'edad': 25},\n", + " {'_id': ObjectId('5e9903fda359803eff02b8dc'), 'nombre': 'Belen', 'edad': 20},\n", + " {'_id': 1, 'nombre': 'José Juan', 'edad': 28},\n", + " {'_id': 2, 'nombre': 'Yona', 'edad': 35},\n", + " {'_id': 3, 'nombre': 'Esteban', 'edad': 45, 'altura': 1.7},\n", + " {'_id': 4, 'nombre': 'Alicia', 'edad': 34, 'altura': 1.5},\n", + " {'_id': 5, 'nombre': 'Luz', 'edad': 18, 'altura': 1.65},\n", + " {'_id': ObjectId('5e990c68a359803eff02b8dd'),\n", + " 'nomre': 'Anai',\n", + " 'edad': 24,\n", + " 'altura': 1.45}]" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(coleccion.find())" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coleccion.update_one({'nombre':'Juan'}, {'$set':{'nombre':'José Juan'}})" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'_id': ObjectId('5e98fec6a359803eff02b8d8'),\n", + " 'nombre': 'José Juan',\n", + " 'edad': 27},\n", + " {'_id': ObjectId('5e99034ba359803eff02b8da'), 'nombre': 'Angel', 'edad': 30},\n", + " {'_id': ObjectId('5e9903a2a359803eff02b8db'), 'nombre': 'Diana', 'edad': 25},\n", + " {'_id': ObjectId('5e9903fda359803eff02b8dc'), 'nombre': 'Belen', 'edad': 20},\n", + " {'_id': 1, 'nombre': 'José Juan', 'edad': 28},\n", + " {'_id': 2, 'nombre': 'Yona', 'edad': 35},\n", + " {'_id': 3, 'nombre': 'Esteban', 'edad': 45, 'altura': 1.7},\n", + " {'_id': 4, 'nombre': 'Alicia', 'edad': 34, 'altura': 1.5},\n", + " {'_id': 5, 'nombre': 'Luz', 'edad': 18, 'altura': 1.65},\n", + " {'_id': ObjectId('5e990c68a359803eff02b8dd'),\n", + " 'nomre': 'Anai',\n", + " 'edad': 24,\n", + " 'altura': 1.45}]" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(coleccion.find())" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coleccion.update_one({'nombre':'Anai'}, {'$set':{'nombre':'Argelia Anai'}})" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'_id': ObjectId('5e98fec6a359803eff02b8d8'),\n", + " 'nombre': 'José Juan',\n", + " 'edad': 27},\n", + " {'_id': ObjectId('5e99034ba359803eff02b8da'), 'nombre': 'Angel', 'edad': 30},\n", + " {'_id': ObjectId('5e9903a2a359803eff02b8db'), 'nombre': 'Diana', 'edad': 25},\n", + " {'_id': ObjectId('5e9903fda359803eff02b8dc'), 'nombre': 'Belen', 'edad': 20},\n", + " {'_id': 1, 'nombre': 'José Juan', 'edad': 28},\n", + " {'_id': 2, 'nombre': 'Yona', 'edad': 35},\n", + " {'_id': 3, 'nombre': 'Esteban', 'edad': 45, 'altura': 1.7},\n", + " {'_id': 4, 'nombre': 'Alicia', 'edad': 34, 'altura': 1.5},\n", + " {'_id': 5, 'nombre': 'Luz', 'edad': 18, 'altura': 1.65},\n", + " {'_id': ObjectId('5e990c68a359803eff02b8dd'),\n", + " 'nomre': 'Anai',\n", + " 'edad': 24,\n", + " 'altura': 1.45}]" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(coleccion.find())" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coleccion.delete_one({'nombre':'Anai'}) # borrar un elemento" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'_id': ObjectId('5e98fec6a359803eff02b8d8'),\n", + " 'nombre': 'José Juan',\n", + " 'edad': 27},\n", + " {'_id': ObjectId('5e99034ba359803eff02b8da'), 'nombre': 'Angel', 'edad': 30},\n", + " {'_id': ObjectId('5e9903a2a359803eff02b8db'), 'nombre': 'Diana', 'edad': 25},\n", + " {'_id': ObjectId('5e9903fda359803eff02b8dc'), 'nombre': 'Belen', 'edad': 20},\n", + " {'_id': 1, 'nombre': 'José Juan', 'edad': 28},\n", + " {'_id': 2, 'nombre': 'Yona', 'edad': 35},\n", + " {'_id': 3, 'nombre': 'Esteban', 'edad': 45, 'altura': 1.7},\n", + " {'_id': 4, 'nombre': 'Alicia', 'edad': 34, 'altura': 1.5},\n", + " {'_id': 5, 'nombre': 'Luz', 'edad': 18, 'altura': 1.65},\n", + " {'_id': ObjectId('5e990c68a359803eff02b8dd'),\n", + " 'nomre': 'Anai',\n", + " 'edad': 24,\n", + " 'altura': 1.45}]" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(coleccion.find())" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coleccion.delete_one({'nombre':'Anai'}) # borrar un elemento" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coleccion.delete_one({'nombre':'Anai'}) # borrar un elemento" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coleccion.delete_one({'nombre':'Anai'}) # borrar un elemento" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'_id': ObjectId('5e98fec6a359803eff02b8d8'),\n", + " 'nombre': 'José Juan',\n", + " 'edad': 27},\n", + " {'_id': ObjectId('5e99034ba359803eff02b8da'), 'nombre': 'Angel', 'edad': 30},\n", + " {'_id': ObjectId('5e9903a2a359803eff02b8db'), 'nombre': 'Diana', 'edad': 25},\n", + " {'_id': ObjectId('5e9903fda359803eff02b8dc'), 'nombre': 'Belen', 'edad': 20},\n", + " {'_id': 1, 'nombre': 'José Juan', 'edad': 28},\n", + " {'_id': 2, 'nombre': 'Yona', 'edad': 35},\n", + " {'_id': 3, 'nombre': 'Esteban', 'edad': 45, 'altura': 1.7},\n", + " {'_id': 4, 'nombre': 'Alicia', 'edad': 34, 'altura': 1.5},\n", + " {'_id': 5, 'nombre': 'Luz', 'edad': 18, 'altura': 1.65},\n", + " {'_id': ObjectId('5e990c68a359803eff02b8dd'),\n", + " 'nomre': 'Anai',\n", + " 'edad': 24,\n", + " 'altura': 1.45}]" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(coleccion.find())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/module-2/lab-matplotlib-seaborn/your-code/.ipynb_checkpoints/challenge-1-checkpoint.ipynb b/module-2/lab-matplotlib-seaborn/your-code/.ipynb_checkpoints/challenge-1-checkpoint.ipynb new file mode 100755 index 0000000..c522b6f --- /dev/null +++ b/module-2/lab-matplotlib-seaborn/your-code/.ipynb_checkpoints/challenge-1-checkpoint.ipynb @@ -0,0 +1,391 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Before you start :\n", + " - These exercises are related to the Exploratory data analysis using matplotlib and seaborn.\n", + " - Keep in mind that you need to use some of the functions you learned in the previous lessons.\n", + " - The datasets for Challenge 2 and 3 are provided in the `your-code` folder of this lab.\n", + " - Elaborate your codes and outputs as much as you can.\n", + " - Try your best to answer the questions and complete the tasks and most importantly enjoy the process!!!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Import all the libraries that are necessary." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# import libraries here\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Define data." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "x = np.arange(0,100)\n", + "y = x*2\n", + "z = x**2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot (x,y) and (x,z) on the axes.\n", + "\n", + "#### There are 2 ways of doing this. Do in both ways.\n", + "\n", + "*Hint: Check out the `nrows`, `ncols`, and `index` arguments of [subplots](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplot.html)*\n", + "\n", + "#### Also, play around with the linewidth and style. Use the ones you're most happy with." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# your code here-1st way\n", + "plt.subplot(1,2,1)\n", + "plt.plot(x,y,lw=3,color=\"b\", ls='--')\n", + "plt.subplot(1,2,2)\n", + "plt.plot(x,z,lw=3, color=\"r\",ls='-')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here-2st way (call `subplots` only once not using the `index` parameter)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Augmenting your previous code, resize your previous plot.\n", + "\n", + "*Hint: Add the `figsize` argument in `plt.subplots()`*" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "fig,axes=plt.subplots(1,2)\n", + "axes[0].plot(x,y,color=\"b\", lw=3, ls='--')\n", + "axes[1].plot(x,z,color=\"r\", lw=3, ls='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Augmenting your previous code, label your axes.\n", + "\n", + "*Hint: call `set_xlabel` and `set_ylabel`*" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'z')" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "fig, axes = plt.subplots(nrows=1, ncols=2,figsize=(12,2))\n", + "\n", + "axes[0].plot(x,y,color=\"blue\", lw=5)\n", + "axes[0].set_xlabel('x')\n", + "axes[0].set_ylabel('y')\n", + "\n", + "axes[1].plot(x,z,color=\"red\", lw=3, ls='--')\n", + "axes[1].set_xlabel('x')\n", + "axes[1].set_ylabel('z')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot both `y=x^2` and `y=exp(x)` in the same plot using normal and logarithmic scale.\n", + "\n", + "*Hint: Use `set_xscale` and `set_yscale`*" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Logarithmic scale (y)')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "fig, axes = plt.subplots(1, 2, figsize=(10,4))\n", + " \n", + "axes[0].plot(x, x**2,x, np.exp(x))\n", + "axes[0].set_title(\"Normal scale\")\n", + "\n", + "axes[1].plot(x, x**2,x, np.exp(x))\n", + "axes[1].set_yscale(\"log\")\n", + "axes[1].set_title(\"Logarithmic scale (y)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### In the vehicles data set that you have downloaded, use the vehicles.csv file. In this exercise we will conduct some exploratory data analysis using one plot each of scatter plot, box plot, histogram, and bar chart. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Scatter Plot\n", + "\n", + "Please provide a scatter plot between \"Combined MPG\" as X variable and \n", + "\"Highway MPG\" as Y variable" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Box Whisker Plot\n", + "\n", + "Please provide a box plot of the variable \"CO2 Emission Grams/mile\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Histogram\n", + "\n", + "Please provide a histogram of the Fuel Barrels/Year" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Bar Chart\n", + "\n", + "Please provide a bar chart of the Fuel Type on the X axis and \"City MPG\" on the Y axis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From ec1c2ac41db69a77386022e9f67f6ee72442203b Mon Sep 17 00:00:00 2001 From: herguejuan Date: Wed, 13 May 2020 00:12:19 -0500 Subject: [PATCH 4/4] subiendo lab klearn-and-unsupervised-learning --- .../.ipynb_checkpoints/main-checkpoint.ipynb | 2135 +++++++++++++++++ .../your-code/main.ipynb | 1715 ++++++++++++- 2 files changed, 3734 insertions(+), 116 deletions(-) create mode 100755 module-3/lab-sklearn-and-unsupervised-learning/your-code/.ipynb_checkpoints/main-checkpoint.ipynb diff --git a/module-3/lab-sklearn-and-unsupervised-learning/your-code/.ipynb_checkpoints/main-checkpoint.ipynb b/module-3/lab-sklearn-and-unsupervised-learning/your-code/.ipynb_checkpoints/main-checkpoint.ipynb new file mode 100755 index 0000000..1e8526f --- /dev/null +++ b/module-3/lab-sklearn-and-unsupervised-learning/your-code/.ipynb_checkpoints/main-checkpoint.ipynb @@ -0,0 +1,2135 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Before your start:\n", + "- Read the README.md file\n", + "- Comment as much as you can and use the resources in the README.md file\n", + "- Happy learning!\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "#Import your libraries\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge 1 - Explore the Internal Dataset\n", + "\n", + "In this lab, we will start off by working with the wine dataset in scikit-learn. We will select the wine dataset and use a clustering algorithm to learn more about the functionalities of this library. \n", + "\n", + "We start off by loading the dataset using the `load_wine` function ([documentation](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_wine.html)). In the cell below, we will import the function from scikit-learn." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.datasets import load_wine" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the cell below, use the `load_wine` function and assign the wine dataset to a variable called `wine`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "wine = load_wine()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the next step, list the keys of the variable `wine` to examine its contents. Note that the `load_wine` function does not return dataframes. It returns you a Python dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "wine.keys()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, list the feature names. These are the different characteristics of the wine. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['alcohol',\n", + " 'malic_acid',\n", + " 'ash',\n", + " 'alcalinity_of_ash',\n", + " 'magnesium',\n", + " 'total_phenols',\n", + " 'flavanoids',\n", + " 'nonflavanoid_phenols',\n", + " 'proanthocyanins',\n", + " 'color_intensity',\n", + " 'hue',\n", + " 'od280/od315_of_diluted_wines',\n", + " 'proline']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "wine[\"feature_names\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Print the description of the dataset in the cell below using the `DESCR` attribute of the `wine` variable.\n", + "\n", + "*Hint: If your output is ill-formatted by displaying linebreaks as `\\n`, it means you are not using the print function.*" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'.. _wine_dataset:\\n\\nWine recognition dataset\\n------------------------\\n\\n**Data Set Characteristics:**\\n\\n :Number of Instances: 178 (50 in each of three classes)\\n :Number of Attributes: 13 numeric, predictive attributes and the class\\n :Attribute Information:\\n \\t\\t- Alcohol\\n \\t\\t- Malic acid\\n \\t\\t- Ash\\n\\t\\t- Alcalinity of ash \\n \\t\\t- Magnesium\\n\\t\\t- Total phenols\\n \\t\\t- Flavanoids\\n \\t\\t- Nonflavanoid phenols\\n \\t\\t- Proanthocyanins\\n\\t\\t- Color intensity\\n \\t\\t- Hue\\n \\t\\t- OD280/OD315 of diluted wines\\n \\t\\t- Proline\\n\\n - class:\\n - class_0\\n - class_1\\n - class_2\\n\\t\\t\\n :Summary Statistics:\\n \\n ============================= ==== ===== ======= =====\\n Min Max Mean SD\\n ============================= ==== ===== ======= =====\\n Alcohol: 11.0 14.8 13.0 0.8\\n Malic Acid: 0.74 5.80 2.34 1.12\\n Ash: 1.36 3.23 2.36 0.27\\n Alcalinity of Ash: 10.6 30.0 19.5 3.3\\n Magnesium: 70.0 162.0 99.7 14.3\\n Total Phenols: 0.98 3.88 2.29 0.63\\n Flavanoids: 0.34 5.08 2.03 1.00\\n Nonflavanoid Phenols: 0.13 0.66 0.36 0.12\\n Proanthocyanins: 0.41 3.58 1.59 0.57\\n Colour Intensity: 1.3 13.0 5.1 2.3\\n Hue: 0.48 1.71 0.96 0.23\\n OD280/OD315 of diluted wines: 1.27 4.00 2.61 0.71\\n Proline: 278 1680 746 315\\n ============================= ==== ===== ======= =====\\n\\n :Missing Attribute Values: None\\n :Class Distribution: class_0 (59), class_1 (71), class_2 (48)\\n :Creator: R.A. Fisher\\n :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n :Date: July, 1988\\n\\nThis is a copy of UCI ML Wine recognition datasets.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\\n\\nThe data is the results of a chemical analysis of wines grown in the same\\nregion in Italy by three different cultivators. There are thirteen different\\nmeasurements taken for different constituents found in the three types of\\nwine.\\n\\nOriginal Owners: \\n\\nForina, M. et al, PARVUS - \\nAn Extendible Package for Data Exploration, Classification and Correlation. \\nInstitute of Pharmaceutical and Food Analysis and Technologies,\\nVia Brigata Salerno, 16147 Genoa, Italy.\\n\\nCitation:\\n\\nLichman, M. (2013). UCI Machine Learning Repository\\n[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\\nSchool of Information and Computer Science. \\n\\n.. topic:: References\\n\\n (1) S. Aeberhard, D. Coomans and O. de Vel, \\n Comparison of Classifiers in High Dimensional Settings, \\n Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of \\n Mathematics and Statistics, James Cook University of North Queensland. \\n (Also submitted to Technometrics). \\n\\n The data was used with many others for comparing various \\n classifiers. The classes are separable, though only RDA \\n has achieved 100% correct classification. \\n (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) \\n (All results using the leave-one-out technique) \\n\\n (2) S. Aeberhard, D. Coomans and O. de Vel, \\n \"THE CLASSIFICATION PERFORMANCE OF RDA\" \\n Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of \\n Mathematics and Statistics, James Cook University of North Queensland. \\n (Also submitted to Journal of Chemometrics).\\n'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "wine[\"DESCR\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### From the description, we see that all columns are numeric. We also know that there is no missing data \n", + "\n", + "Let's plot the alcohol content histogram. Recall that we are working with a numpy array and will need to use a matplotlib function to produce a histogram. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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alcoholmalic_acidashalcalinity_of_ashmagnesiumtotal_phenolsflavanoidsnonflavanoid_phenolsproanthocyaninscolor_intensityhueod280/od315_of_diluted_winesprolinetarget
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\n", + "
" + ], + "text/plain": [ + " alcohol malic_acid ash alcalinity_of_ash magnesium total_phenols \\\n", + "0 14.23 1.71 2.43 15.6 127.0 2.80 \n", + "1 13.20 1.78 2.14 11.2 100.0 2.65 \n", + "2 13.16 2.36 2.67 18.6 101.0 2.80 \n", + "3 14.37 1.95 2.50 16.8 113.0 3.85 \n", + "4 13.24 2.59 2.87 21.0 118.0 2.80 \n", + "\n", + " flavanoids nonflavanoid_phenols proanthocyanins color_intensity hue \\\n", + "0 3.06 0.28 2.29 5.64 1.04 \n", + "1 2.76 0.26 1.28 4.38 1.05 \n", + "2 3.24 0.30 2.81 5.68 1.03 \n", + "3 3.49 0.24 2.18 7.80 0.86 \n", + "4 2.69 0.39 1.82 4.32 1.04 \n", + "\n", + " od280/od315_of_diluted_wines proline target \n", + "0 3.92 1065.0 0.0 \n", + "1 3.40 1050.0 0.0 \n", + "2 3.17 1185.0 0.0 \n", + "3 3.45 1480.0 0.0 \n", + "4 2.93 735.0 0.0 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "wine_df = pd.DataFrame(data=np.hstack([wine.data, wine.target.reshape(-1,1)]),\n", + " columns=wine.feature_names + ['target'])\n", + "\n", + "wine_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(wine_df[\"alcohol\"]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge 2 - Clustering the Internal Dataset\n", + "\n", + "In this portion of the lab, we will cluster the data to find common traits between the different wines. We will use the k-means clustering algorithm to achieve this goal.\n", + "\n", + "#### We start by importing k-means from scikit-learn and then proceed to create 4 clusters." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "from sklearn.cluster import KMeans" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "kmeans = KMeans(n_clusters=4).fit(wine_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Print the cluster labels." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 2, 0, 0, 3, 0, 0, 0, 2, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 3, 3, 3,\n", + " 2, 2, 3, 3, 0, 0, 2, 2, 0, 0, 2, 0, 2, 2, 2, 2, 2, 3, 3, 2, 2, 3,\n", + " 2, 2, 2, 2, 2, 0, 2, 0, 0, 0, 2, 2, 2, 0, 0, 1, 3, 1, 3, 1, 1, 3,\n", + " 1, 1, 3, 3, 2, 1, 1, 2, 2, 1, 1, 1, 3, 1, 1, 3, 3, 1, 1, 1, 1, 1,\n", + " 3, 3, 1, 1, 1, 1, 1, 2, 3, 1, 3, 1, 3, 1, 1, 1, 3, 1, 1, 1, 1, 3,\n", + " 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 1,\n", + " 1, 3, 3, 3, 3, 1, 3, 3, 3, 3, 1, 1, 3, 3, 1, 3, 3, 1, 1, 1, 1, 3,\n", + " 3, 3, 1, 2, 3, 3, 1, 3, 1, 3, 3, 1, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3,\n", + " 3, 1], dtype=int32)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "kmeans.labels_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Compute the size of each cluster. This can be done by counting the number of occurrences of each unique label in the list above.\n", + "\n", + "Which is the largest cluster of the 4?" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([0, 1, 2, 3], dtype=int32), array([23, 66, 32, 57]))" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "np.unique(kmeans.labels_, return_counts=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Your answer here:\n", + "#El cluster 2 es mayor" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Inspect the shape of `wine['data']`" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(178, 13)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "wine['data'].shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Inspect the first 5 records in `wine['data']`" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.423e+01, 1.710e+00, 2.430e+00, 1.560e+01, 1.270e+02, 2.800e+00,\n", + " 3.060e+00, 2.800e-01, 2.290e+00, 5.640e+00, 1.040e+00, 3.920e+00,\n", + " 1.065e+03],\n", + " [1.320e+01, 1.780e+00, 2.140e+00, 1.120e+01, 1.000e+02, 2.650e+00,\n", + " 2.760e+00, 2.600e-01, 1.280e+00, 4.380e+00, 1.050e+00, 3.400e+00,\n", + " 1.050e+03],\n", + " [1.316e+01, 2.360e+00, 2.670e+00, 1.860e+01, 1.010e+02, 2.800e+00,\n", + " 3.240e+00, 3.000e-01, 2.810e+00, 5.680e+00, 1.030e+00, 3.170e+00,\n", + " 1.185e+03],\n", + " [1.437e+01, 1.950e+00, 2.500e+00, 1.680e+01, 1.130e+02, 3.850e+00,\n", + " 3.490e+00, 2.400e-01, 2.180e+00, 7.800e+00, 8.600e-01, 3.450e+00,\n", + " 1.480e+03],\n", + " [1.324e+01, 2.590e+00, 2.870e+00, 2.100e+01, 1.180e+02, 2.800e+00,\n", + " 2.690e+00, 3.900e-01, 1.820e+00, 4.320e+00, 1.040e+00, 2.930e+00,\n", + " 7.350e+02]])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "wine['data'][:5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You now know the data object is a 2-dimensional array in which there are 178 rows and 13 columns. Each row is a data record and each column is a feature.\n", + "\n", + "#### What is the average ash content for each cluster? \n", + "\n", + "*Hints:* \n", + "\n", + "* *Ash* is the 3rd column.\n", + "\n", + "* The data object is not a Pandas dataframe so you can't apply `pandas.DataFrame.groupby`. Instead, you can use `np.average`." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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alcoholmalic_acidashalcalinity_of_ashmagnesiumtotal_phenolsflavanoidsnonflavanoid_phenolsproanthocyaninscolor_intensityhueod280/od315_of_diluted_winesprolinetargetcluster
014.231.712.4315.6127.02.803.060.282.295.641.043.921065.00.02
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" + ], + "text/plain": [ + " alcohol malic_acid ash alcalinity_of_ash magnesium total_phenols \\\n", + "0 14.23 1.71 2.43 15.6 127.0 2.80 \n", + "1 13.20 1.78 2.14 11.2 100.0 2.65 \n", + "2 13.16 2.36 2.67 18.6 101.0 2.80 \n", + "3 14.37 1.95 2.50 16.8 113.0 3.85 \n", + "4 13.24 2.59 2.87 21.0 118.0 2.80 \n", + "\n", + " flavanoids nonflavanoid_phenols proanthocyanins color_intensity hue \\\n", + "0 3.06 0.28 2.29 5.64 1.04 \n", + "1 2.76 0.26 1.28 4.38 1.05 \n", + "2 3.24 0.30 2.81 5.68 1.03 \n", + "3 3.49 0.24 2.18 7.80 0.86 \n", + "4 2.69 0.39 1.82 4.32 1.04 \n", + "\n", + " od280/od315_of_diluted_wines proline target cluster \n", + "0 3.92 1065.0 0.0 2 \n", + "1 3.40 1050.0 0.0 2 \n", + "2 3.17 1185.0 0.0 0 \n", + "3 3.45 1480.0 0.0 0 \n", + "4 2.93 735.0 0.0 3 " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "wine_df = pd.concat([wine_df, pd.DataFrame(kmeans.labels_)], axis=1)\n", + "wine_df.rename(columns={0: \"cluster\"}, inplace= True)\n", + "wine_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "cluster\n", + "0 2.506957\n", + "1 2.283788\n", + "2 2.370937\n", + "3 2.403158\n", + "Name: ash, dtype: float64" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wine_df.groupby(\"cluster\")[\"ash\"].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge 3 - Load and Explore an External Dataset\n", + "\n", + "We will now load an external dataset using Pandas and use scikit learn to explore the data. In this portion of the lab, we will use a [patient dataset from Kaggle](https://www.kaggle.com/miles99/patient-admission-dataset-for-learning-data-mining). " + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [], + "source": [ + "patients = pd.read_csv('../patient-admission-dataset-for-learning-data-mining.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the next cell, print the first five rows of the data using the `head()` function." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idpatient_namepatient_emaildoctor_phonepatient_genderpatient_dobpatient_diabeticpatient_allergicpatient_weight_kgpatient_height_smpatient_nhs_numberdoctor_nameappointment_datepatient_showis_regular_visitprescribed_medicinesdiagnosis
01Celestyna Dillimorecdillimore0@dion.ne.jp674-914-1212Female10/18/2018FalseTrue591768.200152e+09Sarena Waliszek5/1/2018TrueTruetriamcinolone acetonideI669
12Meta Michielimmichieli1@loc.gov172-580-3586Female2/8/2018FalseTrue77186NaNFarris Robinet12/7/2017TrueTrueNaNNaN
23Cordie Sanctocsancto2@cafepress.com794-222-5085Female10/9/2018TrueTrue901776.145594e+09Kaspar Spitaro10/5/2018FalseFalseNaNNaN
34Josh De Ambrosisjde3@amazon.co.jp856-540-5195Male9/10/2018TrueTrue70150NaNRafferty Fowls10/21/2018FalseTrueNaNNaN
45Delinda Alfonsinidalfonsini4@opensource.org938-978-1131Female2/26/2018FalseTrue821404.804758e+08Glenna MacNeachtain11/15/2018FalseFalseNaNNaN
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" + ], + "text/plain": [ + " id patient_name patient_email doctor_phone \\\n", + "0 1 Celestyna Dillimore cdillimore0@dion.ne.jp 674-914-1212 \n", + "1 2 Meta Michieli mmichieli1@loc.gov 172-580-3586 \n", + "2 3 Cordie Sancto csancto2@cafepress.com 794-222-5085 \n", + "3 4 Josh De Ambrosis jde3@amazon.co.jp 856-540-5195 \n", + "4 5 Delinda Alfonsini dalfonsini4@opensource.org 938-978-1131 \n", + "\n", + " patient_gender patient_dob patient_diabetic patient_allergic \\\n", + "0 Female 10/18/2018 False True \n", + "1 Female 2/8/2018 False True \n", + "2 Female 10/9/2018 True True \n", + "3 Male 9/10/2018 True True \n", + "4 Female 2/26/2018 False True \n", + "\n", + " patient_weight_kg patient_height_sm patient_nhs_number \\\n", + "0 59 176 8.200152e+09 \n", + "1 77 186 NaN \n", + "2 90 177 6.145594e+09 \n", + "3 70 150 NaN \n", + "4 82 140 4.804758e+08 \n", + "\n", + " doctor_name appointment_date patient_show is_regular_visit \\\n", + "0 Sarena Waliszek 5/1/2018 True True \n", + "1 Farris Robinet 12/7/2017 True True \n", + "2 Kaspar Spitaro 10/5/2018 False False \n", + "3 Rafferty Fowls 10/21/2018 False True \n", + "4 Glenna MacNeachtain 11/15/2018 False False \n", + "\n", + " prescribed_medicines diagnosis \n", + "0 triamcinolone acetonide I669 \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN " + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "patients.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, print the column types and check which columns have been misclassified by pandas." + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 1000 entries, 0 to 999\n", + "Data columns (total 17 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 1000 non-null int64 \n", + " 1 patient_name 1000 non-null object \n", + " 2 patient_email 1000 non-null object \n", + " 3 doctor_phone 1000 non-null object \n", + " 4 patient_gender 1000 non-null object \n", + " 5 patient_dob 1000 non-null object \n", + " 6 patient_diabetic 1000 non-null bool \n", + " 7 patient_allergic 1000 non-null bool \n", + " 8 patient_weight_kg 1000 non-null int64 \n", + " 9 patient_height_sm 1000 non-null int64 \n", + " 10 patient_nhs_number 796 non-null float64\n", + " 11 doctor_name 942 non-null object \n", + " 12 appointment_date 1000 non-null object \n", + " 13 patient_show 1000 non-null bool \n", + " 14 is_regular_visit 1000 non-null bool \n", + " 15 prescribed_medicines 512 non-null object \n", + " 16 diagnosis 512 non-null object \n", + "dtypes: bool(4), float64(1), int64(3), object(9)\n", + "memory usage: 105.6+ KB\n" + ] + } + ], + "source": [ + "# Your code here:\n", + "patients.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### We can see that none of the date columns have been correctly classified. Also, some columns contain qualitative data that can be dropped.\n", + "\n", + "First, transform the `patient_dob` and `appointment_date` columns to datetime using the `pd.to_datetime` function." + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "patients['patient_dob'] = pd.to_datetime(patients['patient_dob'])\n", + "patients['appointment_date'] = pd.to_datetime(patients['appointment_date'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, drop the `id`, `patient_name`, `patient_email`, `patient_nhs_number`, and `doctor_phone` columns. These are not quantitative columns and will not contribute to our analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "patients.drop(columns=['id', 'patient_name', 'patient_email', 'patient_nhs_number', 'doctor_phone'], inplace=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0Female2018-10-18FalseTrue59176Sarena Waliszek2018-05-01TrueTruetriamcinolone acetonideI669
1Female2018-02-08FalseTrue77186Farris Robinet2017-12-07TrueTrueNaNNaN
2Female2018-10-09TrueTrue90177Kaspar Spitaro2018-10-05FalseFalseNaNNaN
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4Female2018-02-26FalseTrue82140Glenna MacNeachtain2018-11-15FalseFalseNaNNaN
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" + ], + "text/plain": [ + " patient_gender patient_dob patient_diabetic patient_allergic \\\n", + "0 Female 2018-10-18 False True \n", + "1 Female 2018-02-08 False True \n", + "2 Female 2018-10-09 True True \n", + "3 Male 2018-09-10 True True \n", + "4 Female 2018-02-26 False True \n", + "\n", + " patient_weight_kg patient_height_sm doctor_name appointment_date \\\n", + "0 59 176 Sarena Waliszek 2018-05-01 \n", + "1 77 186 Farris Robinet 2017-12-07 \n", + "2 90 177 Kaspar Spitaro 2018-10-05 \n", + "3 70 150 Rafferty Fowls 2018-10-21 \n", + "4 82 140 Glenna MacNeachtain 2018-11-15 \n", + "\n", + " patient_show is_regular_visit prescribed_medicines diagnosis \n", + "0 True True triamcinolone acetonide I669 \n", + "1 True True NaN NaN \n", + "2 False False NaN NaN \n", + "3 False True NaN NaN \n", + "4 False False NaN NaN " + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "patients.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we work on the missing data. Most ML algorithms will not perform as intended if there are missing data.\n", + "\n", + "In the cell below, count how many rows contain missing data in each column. You should see three columns contain missing data:\n", + "\n", + "* `doctor_name`: 58 missing data\n", + "* `prescribed_medicines`: 488 missing data\n", + "* `diagnosis`: 488 missing data" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "patient_gender 0\n", + "patient_dob 0\n", + "patient_diabetic 0\n", + "patient_allergic 0\n", + "patient_weight_kg 0\n", + "patient_height_sm 0\n", + "doctor_name 58\n", + "appointment_date 0\n", + "patient_show 0\n", + "is_regular_visit 0\n", + "prescribed_medicines 488\n", + "diagnosis 488\n", + "dtype: int64" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "patients.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The main issues are found in the `prescribed_medicines` and `diagnosis` columns. Can we simply drop these rows?\n", + "\n", + "The answer is not yet. Because when there are missing data in these columns, it doesn't mean the data records are broken. Instead, it means no medication was prescribed and no diagnosis was recorded. Therefore, once we fill in the missing data these columns will be fine. But we'll revisit these columns and decide whether we will eventually drop them when we look at how many unique values are there in these categorical columns. \n", + "\n", + "For the `prescribed_medicines` column, fill the missing values with the value `no prescription`. For the `diagnosis` column, fill the missing values with `no diagnosis`.\n", + "\n", + "*Hint: Use [`pandas.DataFrame.fillna`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.fillna.html).*" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "patients.fillna({'prescribed_medicines': 'no prescription', 'diagnosis': 'no diagnosis'}, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How about `doctor_name`? Since a doctor visit without a doctor name might not be meaningful, we will drop these rows." + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "patients.dropna(inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Another step in preprocessing that can be performed by scikit-learn is label encoding. \n", + "\n", + "We have 4 columns that are of `bool` type. We would like to convert them to an integer column containing either zero or one. We can do this using [scikit-learn's label encoder](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html).\n", + "\n", + "In the cell below, import the label encoder and encode the 4 boolean columns (*patient_diabetic*, *patient_allergic*, *patient_show*, *is_regular_visit*) with `0` and `1`. " + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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patient_genderpatient_dobpatient_diabeticpatient_allergicpatient_weight_kgpatient_height_smdoctor_nameappointment_datepatient_showis_regular_visitprescribed_medicinesdiagnosis
0Female2018-10-180159176Sarena Waliszek2018-05-0111triamcinolone acetonideI669
1Female2018-02-080177186Farris Robinet2017-12-0711no prescriptionno diagnosis
2Female2018-10-091190177Kaspar Spitaro2018-10-0500no prescriptionno diagnosis
3Male2018-09-101170150Rafferty Fowls2018-10-2101no prescriptionno diagnosis
4Female2018-02-260182140Glenna MacNeachtain2018-11-1500no prescriptionno diagnosis
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" + ], + "text/plain": [ + " patient_gender patient_dob patient_diabetic patient_allergic \\\n", + "0 Female 2018-10-18 0 1 \n", + "1 Female 2018-02-08 0 1 \n", + "2 Female 2018-10-09 1 1 \n", + "3 Male 2018-09-10 1 1 \n", + "4 Female 2018-02-26 0 1 \n", + "\n", + " patient_weight_kg patient_height_sm doctor_name appointment_date \\\n", + "0 59 176 Sarena Waliszek 2018-05-01 \n", + "1 77 186 Farris Robinet 2017-12-07 \n", + "2 90 177 Kaspar Spitaro 2018-10-05 \n", + "3 70 150 Rafferty Fowls 2018-10-21 \n", + "4 82 140 Glenna MacNeachtain 2018-11-15 \n", + "\n", + " patient_show is_regular_visit prescribed_medicines diagnosis \n", + "0 1 1 triamcinolone acetonide I669 \n", + "1 1 1 no prescription no diagnosis \n", + "2 0 0 no prescription no diagnosis \n", + "3 0 1 no prescription no diagnosis \n", + "4 0 0 no prescription no diagnosis " + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "from sklearn.preprocessing import LabelEncoder\n", + "\n", + "label = LabelEncoder()\n", + "\n", + "label.fit([False, True])\n", + "\n", + "patients[['patient_diabetic', 'patient_allergic', 'patient_show', 'is_regular_visit']] = patients[['patient_diabetic', 'patient_allergic', 'patient_show', 'is_regular_visit']].apply(label.fit_transform)\n", + "\n", + "patients.head()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Print the data dtypes to confirm those four `bool` columns are converted to `int64`." + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "patient_gender object\n", + "patient_dob datetime64[ns]\n", + "patient_diabetic int64\n", + "patient_allergic int64\n", + "patient_weight_kg int64\n", + "patient_height_sm int64\n", + "doctor_name object\n", + "appointment_date datetime64[ns]\n", + "patient_show int64\n", + "is_regular_visit int64\n", + "prescribed_medicines object\n", + "diagnosis object\n", + "dtype: object" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "patients.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### The last step is to handle the `object` data.\n", + "\n", + "There are 4 `object` columns now: `patient_gender`, `doctor_name`, `prescribed_medicines`, and `diagnosis`. The gender columns\n", + "\n", + "In the next cell, check the unique values of each of the `object` columns using `value_counts()`." + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Female 476\n", + "Male 466\n", + "Name: patient_gender, dtype: int64\n", + "-----\n", + "Sarena Waliszek 74\n", + "Cissy Markey 57\n", + "Flossy Canlin 55\n", + "Farris Robinet 55\n", + "Glenna MacNeachtain 53\n", + "Jilly McGrill 53\n", + "Gabie Brafield 53\n", + "Nerte Elmer 51\n", + "Oralie Swaffer 49\n", + "Kaspar Spitaro 48\n", + "Wyn Grassett 47\n", + "Humfried Cartmel 47\n", + "Carly SperaJillie Katt 47\n", + "Madelina Scurrell 46\n", + "Rafferty Fowls 45\n", + "Dulce McKerley 42\n", + "Rouvin McKinstry 42\n", + "Ryley Swallow 40\n", + "Hermina Domeny 38\n", + "Name: doctor_name, dtype: int64\n", + "-----\n", + "0 triamcinolone acetonide\n", + "1 no prescription\n", + "2 no prescription\n", + "3 no prescription\n", + "4 no prescription\n", + " ... \n", + "994 no prescription\n", + "996 no prescription\n", + "997 no prescription\n", + "998 IRON SUPPLEMENT\n", + "999 aluminum hydroxide, magnesium carbonate, sodiu...\n", + "Name: prescribed_medicines, Length: 942, dtype: object\n", + "-----\n", + "no diagnosis 457\n", + "T2169 2\n", + "B3323 1\n", + "S12101 1\n", + "S0280XD 1\n", + " ... \n", + "T8511 1\n", + "S21049S 1\n", + "S72452A 1\n", + "W621XXS 1\n", + "S46899 1\n", + "Name: diagnosis, Length: 485, dtype: int64\n" + ] + } + ], + "source": [ + "# Your code here:\n", + "\n", + "print(patients.patient_gender.value_counts())\n", + "\n", + "print('-----')\n", + "\n", + "print(patients.doctor_name.value_counts())\n", + "\n", + "print('-----')\n", + "\n", + "print(patients.prescribed_medicines)\n", + "\n", + "print('-----')\n", + "\n", + "print(patients.diagnosis.value_counts())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### The number of unique values is large for all three columns except `patient_gender`. We will handle these columns differently.\n", + "\n", + "For `diagnosis`, there are too many unique values which will make ML difficult. However, we can re-encode the values to either with or without diagnosis. Remember at an earlier step we filled in the missing values of this column with *no diagnosis*? We can re-encode *no diagnosis* to `0` and all other values to `1`. In this way we can tremendously simply this column.\n", + "\n", + "For `prescribed_medicines`, we can drop this column because it is perfectly correlated with `diagnosis`. Whenever there is no diagnosis, there is no prescribed medicine. So we don't need to keep this duplicated data.\n", + "\n", + "How about `doctor_name`? There are not excessive unique values but still quite many (19). We may either drop or keep it but keeping it will make the analysis more complicated. So due to the length of this lab let's drop it.\n", + "\n", + "How about `gender`? This one is easy. Just like re-encoding the boolean values, we can re-encode gender to `0` and `1` because there are only 2 unique values.\n", + "\n", + "In the next cells, do the following:\n", + "\n", + "1. Create a new column called `diagnosis_int` that has `0` and `1` based on the values in `diagnosis`.\n", + "\n", + "1. Create a new column called `patient_gender_int` that has `0` and `1` based on the values in `patient_gender`.\n", + "\n", + "1. Drop the following columns: `doctor_name`, `diagnosis`, `prescribed_medicines`, and `patient_gender`." + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "patients['diagnosis_int'] = np.where(patients.diagnosis == \"no diagnosis\", 0, 1)\n", + "\n", + "patients['patient_gender_int'] = np.where(patients.patient_gender == \"Male\", 0, 1)\n", + "\n", + "patients.drop(columns=['doctor_name', 'diagnosis', 'prescribed_medicines', 'patient_gender'], inplace=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's look at the head again to ensure the re-encoding and dropping are successful:" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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patient_dobpatient_diabeticpatient_allergicpatient_weight_kgpatient_height_smappointment_datepatient_showis_regular_visitdiagnosis_intpatient_gender_int
02018-10-1801591762018-05-011111
12018-02-0801771862017-12-071101
22018-10-0911901772018-10-050001
32018-09-1011701502018-10-210100
42018-02-2601821402018-11-150001
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" + ], + "text/plain": [ + " patient_dob patient_diabetic patient_allergic patient_weight_kg \\\n", + "0 2018-10-18 0 1 59 \n", + "1 2018-02-08 0 1 77 \n", + "2 2018-10-09 1 1 90 \n", + "3 2018-09-10 1 1 70 \n", + "4 2018-02-26 0 1 82 \n", + "\n", + " patient_height_sm appointment_date patient_show is_regular_visit \\\n", + "0 176 2018-05-01 1 1 \n", + "1 186 2017-12-07 1 1 \n", + "2 177 2018-10-05 0 0 \n", + "3 150 2018-10-21 0 1 \n", + "4 140 2018-11-15 0 0 \n", + "\n", + " diagnosis_int patient_gender_int \n", + "0 1 1 \n", + "1 0 1 \n", + "2 0 1 \n", + "3 0 0 \n", + "4 0 1 " + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "patients.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An interesting observation is that all patients are no older than 2 years. However, their weights and heights indicate that they are adults. This cannot be true. Therefore, we can either trust the weight and height columns or the DOB column. Since there are other columns that indicate that these are adults (they have emails, some have diabetes) we will drop the `patient_dob` column. We will also drop the `appointment_date` column since it has too many unique values to transform to a dummy variable. Drop the two columns in the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "patients.drop(columns=['patient_dob', 'appointment_date'], inplace=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Our data is now ready for clustering. Let's use k-means again.\n", + "\n", + "We start by initializing and fitting a model in the cell below. Call this model patients_cluster." + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n", + " n_clusters=4, n_init=10, n_jobs=None, precompute_distances='auto',\n", + " random_state=None, tol=0.0001, verbose=0)" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "patients_cluster = KMeans(n_clusters=4)\n", + "\n", + "patients_cluster.fit(patients)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Attach the labels to the dataframe. Do this by accessing the `labels_` in the `patients_cluster` model and assign them to a new column in `patients` that you will call `labels`." + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "patients['labels'] = patients_cluster.labels_\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now using a `groupby`, find the mean of every variable in `patients` and group by the `labels` column. This summary will allow us to see how the patients differ between the clusters. Your output should look similar to the image below.\n", + "\n", + "![groupby mean](../groupby-mean.png)\n", + "\n", + "Additionally, add a comment to describe which columns have the largest difference between clusters." + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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00.5043480.49130465.365217152.3043480.5086960.5565220.5043480.447826
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" + ], + "text/plain": [ + " patient_diabetic patient_allergic patient_weight_kg \\\n", + "labels \n", + "0 0.504348 0.491304 65.365217 \n", + "1 0.485944 0.485944 94.112450 \n", + "2 0.508333 0.533333 62.900000 \n", + "3 0.560538 0.529148 97.381166 \n", + "\n", + " patient_height_sm patient_show is_regular_visit diagnosis_int \\\n", + "labels \n", + "0 152.304348 0.508696 0.556522 0.504348 \n", + "1 178.847390 0.506024 0.530120 0.510040 \n", + "2 176.954167 0.475000 0.495833 0.454167 \n", + "3 153.529148 0.488789 0.506726 0.596413 \n", + "\n", + " patient_gender_int \n", + "labels \n", + "0 0.447826 \n", + "1 0.542169 \n", + "2 0.545833 \n", + "3 0.479821 " + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "patients.groupby('labels').mean()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your comment here:\n", + "# Las diferencias mayores son entre los clusters 0 y 3 y tambien entre los clusters 1 y 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bonus Challenge: Visualize K-Means Clusters\n", + "\n", + "How did k-means cluster the data? You can obtain an intuitive view with a scatter plot. Generate a 2-d cluster plot below using `matplotlib`. You need to choose 2 of the features from your cleaned and transformed dataset, and use color to represent the cluster label generated from k-means.\n", + "\n", + "If the scatter plot does not make any sense to you, it means the features you chose to visualize are not the right ones. You should be able to see 4 clear clusters with different colors in your visualization that suggests how k-means had clustered your data.\n", + "\n", + "![Cluster Visualization](../clusters.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Your code here:\n", + "fig = plt.figure()\n", + "\n", + "ax = fig.add_subplot(111)\n", + "\n", + "scatter = ax.scatter(patients['patient_weight_kg'], patients['patient_height_sm'],\n", + " c=patients['labels'])\n", + "\n", + "plt.colorbar(scatter)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Additionally, you can visualize the clusters in 3-D scatter plot. Give it a try below." + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "from mpl_toolkits.mplot3d import Axes3D" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(12,6))\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "ax.scatter(patients[\"patient_height_sm\"],patients[\"patient_weight_kg\"],\n", + " patients[\"patient_diabetic\"],c=patients.labels);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/module-3/lab-sklearn-and-unsupervised-learning/your-code/main.ipynb b/module-3/lab-sklearn-and-unsupervised-learning/your-code/main.ipynb index ba2d1b9..1e8526f 100755 --- a/module-3/lab-sklearn-and-unsupervised-learning/your-code/main.ipynb +++ b/module-3/lab-sklearn-and-unsupervised-learning/your-code/main.ipynb @@ -12,11 +12,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "#Import your libraries\n" + "#Import your libraries\n", + "import pandas as pd" ] }, { @@ -32,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -48,11 +49,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "# Your code here:\n" + "# Your code here:\n", + "wine = load_wine()" ] }, { @@ -64,11 +66,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here:\n" + "# Your code here:\n", + "wine.keys()" ] }, { @@ -80,11 +94,35 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n" + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['alcohol',\n", + " 'malic_acid',\n", + " 'ash',\n", + " 'alcalinity_of_ash',\n", + " 'magnesium',\n", + " 'total_phenols',\n", + " 'flavanoids',\n", + " 'nonflavanoid_phenols',\n", + " 'proanthocyanins',\n", + " 'color_intensity',\n", + " 'hue',\n", + " 'od280/od315_of_diluted_wines',\n", + " 'proline']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "wine[\"feature_names\"]" ] }, { @@ -98,11 +136,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'.. _wine_dataset:\\n\\nWine recognition dataset\\n------------------------\\n\\n**Data Set Characteristics:**\\n\\n :Number of Instances: 178 (50 in each of three classes)\\n :Number of Attributes: 13 numeric, predictive attributes and the class\\n :Attribute Information:\\n \\t\\t- Alcohol\\n \\t\\t- Malic acid\\n \\t\\t- Ash\\n\\t\\t- Alcalinity of ash \\n \\t\\t- Magnesium\\n\\t\\t- Total phenols\\n \\t\\t- Flavanoids\\n \\t\\t- Nonflavanoid phenols\\n \\t\\t- Proanthocyanins\\n\\t\\t- Color intensity\\n \\t\\t- Hue\\n \\t\\t- OD280/OD315 of diluted wines\\n \\t\\t- Proline\\n\\n - class:\\n - class_0\\n - class_1\\n - class_2\\n\\t\\t\\n :Summary Statistics:\\n \\n ============================= ==== ===== ======= =====\\n Min Max Mean SD\\n ============================= ==== ===== ======= =====\\n Alcohol: 11.0 14.8 13.0 0.8\\n Malic Acid: 0.74 5.80 2.34 1.12\\n Ash: 1.36 3.23 2.36 0.27\\n Alcalinity of Ash: 10.6 30.0 19.5 3.3\\n Magnesium: 70.0 162.0 99.7 14.3\\n Total Phenols: 0.98 3.88 2.29 0.63\\n Flavanoids: 0.34 5.08 2.03 1.00\\n Nonflavanoid Phenols: 0.13 0.66 0.36 0.12\\n Proanthocyanins: 0.41 3.58 1.59 0.57\\n Colour Intensity: 1.3 13.0 5.1 2.3\\n Hue: 0.48 1.71 0.96 0.23\\n OD280/OD315 of diluted wines: 1.27 4.00 2.61 0.71\\n Proline: 278 1680 746 315\\n ============================= ==== ===== ======= =====\\n\\n :Missing Attribute Values: None\\n :Class Distribution: class_0 (59), class_1 (71), class_2 (48)\\n :Creator: R.A. Fisher\\n :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n :Date: July, 1988\\n\\nThis is a copy of UCI ML Wine recognition datasets.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\\n\\nThe data is the results of a chemical analysis of wines grown in the same\\nregion in Italy by three different cultivators. There are thirteen different\\nmeasurements taken for different constituents found in the three types of\\nwine.\\n\\nOriginal Owners: \\n\\nForina, M. et al, PARVUS - \\nAn Extendible Package for Data Exploration, Classification and Correlation. \\nInstitute of Pharmaceutical and Food Analysis and Technologies,\\nVia Brigata Salerno, 16147 Genoa, Italy.\\n\\nCitation:\\n\\nLichman, M. (2013). UCI Machine Learning Repository\\n[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\\nSchool of Information and Computer Science. \\n\\n.. topic:: References\\n\\n (1) S. Aeberhard, D. Coomans and O. de Vel, \\n Comparison of Classifiers in High Dimensional Settings, \\n Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of \\n Mathematics and Statistics, James Cook University of North Queensland. \\n (Also submitted to Technometrics). \\n\\n The data was used with many others for comparing various \\n classifiers. The classes are separable, though only RDA \\n has achieved 100% correct classification. \\n (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) \\n (All results using the leave-one-out technique) \\n\\n (2) S. Aeberhard, D. Coomans and O. de Vel, \\n \"THE CLASSIFICATION PERFORMANCE OF RDA\" \\n Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of \\n Mathematics and Statistics, James Cook University of North Queensland. \\n (Also submitted to Journal of Chemometrics).\\n'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here:\n" + "# Your code here:\n", + "wine[\"DESCR\"]" ] }, { @@ -116,20 +166,203 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "# imports\n" + "# imports\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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alcoholmalic_acidashalcalinity_of_ashmagnesiumtotal_phenolsflavanoidsnonflavanoid_phenolsproanthocyaninscolor_intensityhueod280/od315_of_diluted_winesprolinetarget
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\n", + "
" + ], + "text/plain": [ + " alcohol malic_acid ash alcalinity_of_ash magnesium total_phenols \\\n", + "0 14.23 1.71 2.43 15.6 127.0 2.80 \n", + "1 13.20 1.78 2.14 11.2 100.0 2.65 \n", + "2 13.16 2.36 2.67 18.6 101.0 2.80 \n", + "3 14.37 1.95 2.50 16.8 113.0 3.85 \n", + "4 13.24 2.59 2.87 21.0 118.0 2.80 \n", + "\n", + " flavanoids nonflavanoid_phenols proanthocyanins color_intensity hue \\\n", + "0 3.06 0.28 2.29 5.64 1.04 \n", + "1 2.76 0.26 1.28 4.38 1.05 \n", + "2 3.24 0.30 2.81 5.68 1.03 \n", + "3 3.49 0.24 2.18 7.80 0.86 \n", + "4 2.69 0.39 1.82 4.32 1.04 \n", + "\n", + " od280/od315_of_diluted_wines proline target \n", + "0 3.92 1065.0 0.0 \n", + "1 3.40 1050.0 0.0 \n", + "2 3.17 1185.0 0.0 \n", + "3 3.45 1480.0 0.0 \n", + "4 2.93 735.0 0.0 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "wine_df = pd.DataFrame(data=np.hstack([wine.data, wine.target.reshape(-1,1)]),\n", + " columns=wine.feature_names + ['target'])\n", + "\n", + "wine_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "# Your code here:\n" + "plt.hist(wine_df[\"alcohol\"]);" ] }, { @@ -145,20 +378,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ - "# imports\n" + "# imports\n", + "from sklearn.cluster import KMeans" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "# Your code here:\n" + "# Your code here:\n", + "kmeans = KMeans(n_clusters=4).fit(wine_df)" ] }, { @@ -170,11 +405,31 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n" + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 2, 0, 0, 3, 0, 0, 0, 2, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 3, 3, 3,\n", + " 2, 2, 3, 3, 0, 0, 2, 2, 0, 0, 2, 0, 2, 2, 2, 2, 2, 3, 3, 2, 2, 3,\n", + " 2, 2, 2, 2, 2, 0, 2, 0, 0, 0, 2, 2, 2, 0, 0, 1, 3, 1, 3, 1, 1, 3,\n", + " 1, 1, 3, 3, 2, 1, 1, 2, 2, 1, 1, 1, 3, 1, 1, 3, 3, 1, 1, 1, 1, 1,\n", + " 3, 3, 1, 1, 1, 1, 1, 2, 3, 1, 3, 1, 3, 1, 1, 1, 3, 1, 1, 1, 1, 3,\n", + " 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 1,\n", + " 1, 3, 3, 3, 3, 1, 3, 3, 3, 3, 1, 1, 3, 3, 1, 3, 3, 1, 1, 1, 1, 3,\n", + " 3, 3, 1, 2, 3, 3, 1, 3, 1, 3, 3, 1, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3,\n", + " 3, 1], dtype=int32)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "kmeans.labels_" ] }, { @@ -188,20 +443,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(array([0, 1, 2, 3], dtype=int32), array([23, 66, 32, 57]))" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here:\n" + "# Your code here:\n", + "np.unique(kmeans.labels_, return_counts=True)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "# Your answer here:\n" + "# Your answer here:\n", + "#El cluster 2 es mayor" ] }, { @@ -213,11 +481,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(178, 13)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here:\n" + "# Your code here:\n", + "wine['data'].shape" ] }, { @@ -229,11 +509,37 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n" + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.423e+01, 1.710e+00, 2.430e+00, 1.560e+01, 1.270e+02, 2.800e+00,\n", + " 3.060e+00, 2.800e-01, 2.290e+00, 5.640e+00, 1.040e+00, 3.920e+00,\n", + " 1.065e+03],\n", + " [1.320e+01, 1.780e+00, 2.140e+00, 1.120e+01, 1.000e+02, 2.650e+00,\n", + " 2.760e+00, 2.600e-01, 1.280e+00, 4.380e+00, 1.050e+00, 3.400e+00,\n", + " 1.050e+03],\n", + " [1.316e+01, 2.360e+00, 2.670e+00, 1.860e+01, 1.010e+02, 2.800e+00,\n", + " 3.240e+00, 3.000e-01, 2.810e+00, 5.680e+00, 1.030e+00, 3.170e+00,\n", + " 1.185e+03],\n", + " [1.437e+01, 1.950e+00, 2.500e+00, 1.680e+01, 1.130e+02, 3.850e+00,\n", + " 3.490e+00, 2.400e-01, 2.180e+00, 7.800e+00, 8.600e-01, 3.450e+00,\n", + " 1.480e+03],\n", + " [1.324e+01, 2.590e+00, 2.870e+00, 2.100e+01, 1.180e+02, 2.800e+00,\n", + " 2.690e+00, 3.900e-01, 1.820e+00, 4.320e+00, 1.040e+00, 2.930e+00,\n", + " 7.350e+02]])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "wine['data'][:5]" ] }, { @@ -253,11 +559,200 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n" + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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alcoholmalic_acidashalcalinity_of_ashmagnesiumtotal_phenolsflavanoidsnonflavanoid_phenolsproanthocyaninscolor_intensityhueod280/od315_of_diluted_winesprolinetargetcluster
014.231.712.4315.6127.02.803.060.282.295.641.043.921065.00.02
113.201.782.1411.2100.02.652.760.261.284.381.053.401050.00.02
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413.242.592.8721.0118.02.802.690.391.824.321.042.93735.00.03
\n", + "
" + ], + "text/plain": [ + " alcohol malic_acid ash alcalinity_of_ash magnesium total_phenols \\\n", + "0 14.23 1.71 2.43 15.6 127.0 2.80 \n", + "1 13.20 1.78 2.14 11.2 100.0 2.65 \n", + "2 13.16 2.36 2.67 18.6 101.0 2.80 \n", + "3 14.37 1.95 2.50 16.8 113.0 3.85 \n", + "4 13.24 2.59 2.87 21.0 118.0 2.80 \n", + "\n", + " flavanoids nonflavanoid_phenols proanthocyanins color_intensity hue \\\n", + "0 3.06 0.28 2.29 5.64 1.04 \n", + "1 2.76 0.26 1.28 4.38 1.05 \n", + "2 3.24 0.30 2.81 5.68 1.03 \n", + "3 3.49 0.24 2.18 7.80 0.86 \n", + "4 2.69 0.39 1.82 4.32 1.04 \n", + "\n", + " od280/od315_of_diluted_wines proline target cluster \n", + "0 3.92 1065.0 0.0 2 \n", + "1 3.40 1050.0 0.0 2 \n", + "2 3.17 1185.0 0.0 0 \n", + "3 3.45 1480.0 0.0 0 \n", + "4 2.93 735.0 0.0 3 " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "wine_df = pd.concat([wine_df, pd.DataFrame(kmeans.labels_)], axis=1)\n", + "wine_df.rename(columns={0: \"cluster\"}, inplace= True)\n", + "wine_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "cluster\n", + "0 2.506957\n", + "1 2.283788\n", + "2 2.370937\n", + "3 2.403158\n", + "Name: ash, dtype: float64" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wine_df.groupby(\"cluster\")[\"ash\"].mean()" ] }, { @@ -271,7 +766,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 80, "metadata": {}, "outputs": [], "source": [ @@ -287,11 +782,199 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n" + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idpatient_namepatient_emaildoctor_phonepatient_genderpatient_dobpatient_diabeticpatient_allergicpatient_weight_kgpatient_height_smpatient_nhs_numberdoctor_nameappointment_datepatient_showis_regular_visitprescribed_medicinesdiagnosis
01Celestyna Dillimorecdillimore0@dion.ne.jp674-914-1212Female10/18/2018FalseTrue591768.200152e+09Sarena Waliszek5/1/2018TrueTruetriamcinolone acetonideI669
12Meta Michielimmichieli1@loc.gov172-580-3586Female2/8/2018FalseTrue77186NaNFarris Robinet12/7/2017TrueTrueNaNNaN
23Cordie Sanctocsancto2@cafepress.com794-222-5085Female10/9/2018TrueTrue901776.145594e+09Kaspar Spitaro10/5/2018FalseFalseNaNNaN
34Josh De Ambrosisjde3@amazon.co.jp856-540-5195Male9/10/2018TrueTrue70150NaNRafferty Fowls10/21/2018FalseTrueNaNNaN
45Delinda Alfonsinidalfonsini4@opensource.org938-978-1131Female2/26/2018FalseTrue821404.804758e+08Glenna MacNeachtain11/15/2018FalseFalseNaNNaN
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" + ], + "text/plain": [ + " id patient_name patient_email doctor_phone \\\n", + "0 1 Celestyna Dillimore cdillimore0@dion.ne.jp 674-914-1212 \n", + "1 2 Meta Michieli mmichieli1@loc.gov 172-580-3586 \n", + "2 3 Cordie Sancto csancto2@cafepress.com 794-222-5085 \n", + "3 4 Josh De Ambrosis jde3@amazon.co.jp 856-540-5195 \n", + "4 5 Delinda Alfonsini dalfonsini4@opensource.org 938-978-1131 \n", + "\n", + " patient_gender patient_dob patient_diabetic patient_allergic \\\n", + "0 Female 10/18/2018 False True \n", + "1 Female 2/8/2018 False True \n", + "2 Female 10/9/2018 True True \n", + "3 Male 9/10/2018 True True \n", + "4 Female 2/26/2018 False True \n", + "\n", + " patient_weight_kg patient_height_sm patient_nhs_number \\\n", + "0 59 176 8.200152e+09 \n", + "1 77 186 NaN \n", + "2 90 177 6.145594e+09 \n", + "3 70 150 NaN \n", + "4 82 140 4.804758e+08 \n", + "\n", + " doctor_name appointment_date patient_show is_regular_visit \\\n", + "0 Sarena Waliszek 5/1/2018 True True \n", + "1 Farris Robinet 12/7/2017 True True \n", + "2 Kaspar Spitaro 10/5/2018 False False \n", + "3 Rafferty Fowls 10/21/2018 False True \n", + "4 Glenna MacNeachtain 11/15/2018 False False \n", + "\n", + " prescribed_medicines diagnosis \n", + "0 triamcinolone acetonide I669 \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN " + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "patients.head()" ] }, { @@ -303,11 +986,43 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n" + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 1000 entries, 0 to 999\n", + "Data columns (total 17 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 1000 non-null int64 \n", + " 1 patient_name 1000 non-null object \n", + " 2 patient_email 1000 non-null object \n", + " 3 doctor_phone 1000 non-null object \n", + " 4 patient_gender 1000 non-null object \n", + " 5 patient_dob 1000 non-null object \n", + " 6 patient_diabetic 1000 non-null bool \n", + " 7 patient_allergic 1000 non-null bool \n", + " 8 patient_weight_kg 1000 non-null int64 \n", + " 9 patient_height_sm 1000 non-null int64 \n", + " 10 patient_nhs_number 796 non-null float64\n", + " 11 doctor_name 942 non-null object \n", + " 12 appointment_date 1000 non-null object \n", + " 13 patient_show 1000 non-null bool \n", + " 14 is_regular_visit 1000 non-null bool \n", + " 15 prescribed_medicines 512 non-null object \n", + " 16 diagnosis 512 non-null object \n", + "dtypes: bool(4), float64(1), int64(3), object(9)\n", + "memory usage: 105.6+ KB\n" + ] + } + ], + "source": [ + "# Your code here:\n", + "patients.info()" ] }, { @@ -321,11 +1036,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 83, "metadata": {}, "outputs": [], "source": [ - "# Your code here:\n" + "# Your code here:\n", + "patients['patient_dob'] = pd.to_datetime(patients['patient_dob'])\n", + "patients['appointment_date'] = pd.to_datetime(patients['appointment_date'])" ] }, { @@ -337,11 +1054,164 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 84, "metadata": {}, "outputs": [], "source": [ - "# Your code here:\n" + "# Your code here:\n", + "patients.drop(columns=['id', 'patient_name', 'patient_email', 'patient_nhs_number', 'doctor_phone'], inplace=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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patient_genderpatient_dobpatient_diabeticpatient_allergicpatient_weight_kgpatient_height_smdoctor_nameappointment_datepatient_showis_regular_visitprescribed_medicinesdiagnosis
0Female2018-10-18FalseTrue59176Sarena Waliszek2018-05-01TrueTruetriamcinolone acetonideI669
1Female2018-02-08FalseTrue77186Farris Robinet2017-12-07TrueTrueNaNNaN
2Female2018-10-09TrueTrue90177Kaspar Spitaro2018-10-05FalseFalseNaNNaN
3Male2018-09-10TrueTrue70150Rafferty Fowls2018-10-21FalseTrueNaNNaN
4Female2018-02-26FalseTrue82140Glenna MacNeachtain2018-11-15FalseFalseNaNNaN
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" + ], + "text/plain": [ + " patient_gender patient_dob patient_diabetic patient_allergic \\\n", + "0 Female 2018-10-18 False True \n", + "1 Female 2018-02-08 False True \n", + "2 Female 2018-10-09 True True \n", + "3 Male 2018-09-10 True True \n", + "4 Female 2018-02-26 False True \n", + "\n", + " patient_weight_kg patient_height_sm doctor_name appointment_date \\\n", + "0 59 176 Sarena Waliszek 2018-05-01 \n", + "1 77 186 Farris Robinet 2017-12-07 \n", + "2 90 177 Kaspar Spitaro 2018-10-05 \n", + "3 70 150 Rafferty Fowls 2018-10-21 \n", + "4 82 140 Glenna MacNeachtain 2018-11-15 \n", + "\n", + " patient_show is_regular_visit prescribed_medicines diagnosis \n", + "0 True True triamcinolone acetonide I669 \n", + "1 True True NaN NaN \n", + "2 False False NaN NaN \n", + "3 False True NaN NaN \n", + "4 False False NaN NaN " + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "patients.head()" ] }, { @@ -359,11 +1229,35 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n" + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "patient_gender 0\n", + "patient_dob 0\n", + "patient_diabetic 0\n", + "patient_allergic 0\n", + "patient_weight_kg 0\n", + "patient_height_sm 0\n", + "doctor_name 58\n", + "appointment_date 0\n", + "patient_show 0\n", + "is_regular_visit 0\n", + "prescribed_medicines 488\n", + "diagnosis 488\n", + "dtype: int64" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "patients.isnull().sum()" ] }, { @@ -381,11 +1275,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 87, "metadata": {}, "outputs": [], "source": [ - "# Your code here:\n" + "# Your code here:\n", + "patients.fillna({'prescribed_medicines': 'no prescription', 'diagnosis': 'no diagnosis'}, inplace=True)" ] }, { @@ -397,11 +1292,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 88, "metadata": {}, "outputs": [], "source": [ - "# Your code here:\n" + "# Your code here:\n", + "patients.dropna(inplace=True)" ] }, { @@ -417,11 +1313,163 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n" + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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patient_genderpatient_dobpatient_diabeticpatient_allergicpatient_weight_kgpatient_height_smdoctor_nameappointment_datepatient_showis_regular_visitprescribed_medicinesdiagnosis
0Female2018-10-180159176Sarena Waliszek2018-05-0111triamcinolone acetonideI669
1Female2018-02-080177186Farris Robinet2017-12-0711no prescriptionno diagnosis
2Female2018-10-091190177Kaspar Spitaro2018-10-0500no prescriptionno diagnosis
3Male2018-09-101170150Rafferty Fowls2018-10-2101no prescriptionno diagnosis
4Female2018-02-260182140Glenna MacNeachtain2018-11-1500no prescriptionno diagnosis
\n", + "
" + ], + "text/plain": [ + " patient_gender patient_dob patient_diabetic patient_allergic \\\n", + "0 Female 2018-10-18 0 1 \n", + "1 Female 2018-02-08 0 1 \n", + "2 Female 2018-10-09 1 1 \n", + "3 Male 2018-09-10 1 1 \n", + "4 Female 2018-02-26 0 1 \n", + "\n", + " patient_weight_kg patient_height_sm doctor_name appointment_date \\\n", + "0 59 176 Sarena Waliszek 2018-05-01 \n", + "1 77 186 Farris Robinet 2017-12-07 \n", + "2 90 177 Kaspar Spitaro 2018-10-05 \n", + "3 70 150 Rafferty Fowls 2018-10-21 \n", + "4 82 140 Glenna MacNeachtain 2018-11-15 \n", + "\n", + " patient_show is_regular_visit prescribed_medicines diagnosis \n", + "0 1 1 triamcinolone acetonide I669 \n", + "1 1 1 no prescription no diagnosis \n", + "2 0 0 no prescription no diagnosis \n", + "3 0 1 no prescription no diagnosis \n", + "4 0 0 no prescription no diagnosis " + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "from sklearn.preprocessing import LabelEncoder\n", + "\n", + "label = LabelEncoder()\n", + "\n", + "label.fit([False, True])\n", + "\n", + "patients[['patient_diabetic', 'patient_allergic', 'patient_show', 'is_regular_visit']] = patients[['patient_diabetic', 'patient_allergic', 'patient_show', 'is_regular_visit']].apply(label.fit_transform)\n", + "\n", + "patients.head()\n" ] }, { @@ -433,11 +1481,35 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n" + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "patient_gender object\n", + "patient_dob datetime64[ns]\n", + "patient_diabetic int64\n", + "patient_allergic int64\n", + "patient_weight_kg int64\n", + "patient_height_sm int64\n", + "doctor_name object\n", + "appointment_date datetime64[ns]\n", + "patient_show int64\n", + "is_regular_visit int64\n", + "prescribed_medicines object\n", + "diagnosis object\n", + "dtype: object" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "patients.dtypes" ] }, { @@ -453,13 +1525,84 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 91, "metadata": { "scrolled": false }, - "outputs": [], - "source": [ - "# Your code here:\n" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Female 476\n", + "Male 466\n", + "Name: patient_gender, dtype: int64\n", + "-----\n", + "Sarena Waliszek 74\n", + "Cissy Markey 57\n", + "Flossy Canlin 55\n", + "Farris Robinet 55\n", + "Glenna MacNeachtain 53\n", + "Jilly McGrill 53\n", + "Gabie Brafield 53\n", + "Nerte Elmer 51\n", + "Oralie Swaffer 49\n", + "Kaspar Spitaro 48\n", + "Wyn Grassett 47\n", + "Humfried Cartmel 47\n", + "Carly SperaJillie Katt 47\n", + "Madelina Scurrell 46\n", + "Rafferty Fowls 45\n", + "Dulce McKerley 42\n", + "Rouvin McKinstry 42\n", + "Ryley Swallow 40\n", + "Hermina Domeny 38\n", + "Name: doctor_name, dtype: int64\n", + "-----\n", + "0 triamcinolone acetonide\n", + "1 no prescription\n", + "2 no prescription\n", + "3 no prescription\n", + "4 no prescription\n", + " ... \n", + "994 no prescription\n", + "996 no prescription\n", + "997 no prescription\n", + "998 IRON SUPPLEMENT\n", + "999 aluminum hydroxide, magnesium carbonate, sodiu...\n", + "Name: prescribed_medicines, Length: 942, dtype: object\n", + "-----\n", + "no diagnosis 457\n", + "T2169 2\n", + "B3323 1\n", + "S12101 1\n", + "S0280XD 1\n", + " ... \n", + "T8511 1\n", + "S21049S 1\n", + "S72452A 1\n", + "W621XXS 1\n", + "S46899 1\n", + "Name: diagnosis, Length: 485, dtype: int64\n" + ] + } + ], + "source": [ + "# Your code here:\n", + "\n", + "print(patients.patient_gender.value_counts())\n", + "\n", + "print('-----')\n", + "\n", + "print(patients.doctor_name.value_counts())\n", + "\n", + "print('-----')\n", + "\n", + "print(patients.prescribed_medicines)\n", + "\n", + "print('-----')\n", + "\n", + "print(patients.diagnosis.value_counts())\n" ] }, { @@ -487,11 +1630,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 92, "metadata": {}, "outputs": [], "source": [ - "# Your code here:\n" + "# Your code here:\n", + "patients['diagnosis_int'] = np.where(patients.diagnosis == \"no diagnosis\", 0, 1)\n", + "\n", + "patients['patient_gender_int'] = np.where(patients.patient_gender == \"Male\", 0, 1)\n", + "\n", + "patients.drop(columns=['doctor_name', 'diagnosis', 'prescribed_medicines', 'patient_gender'], inplace=True)\n" ] }, { @@ -503,11 +1651,143 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n" + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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patient_dobpatient_diabeticpatient_allergicpatient_weight_kgpatient_height_smappointment_datepatient_showis_regular_visitdiagnosis_intpatient_gender_int
02018-10-1801591762018-05-011111
12018-02-0801771862017-12-071101
22018-10-0911901772018-10-050001
32018-09-1011701502018-10-210100
42018-02-2601821402018-11-150001
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" + ], + "text/plain": [ + " patient_dob patient_diabetic patient_allergic patient_weight_kg \\\n", + "0 2018-10-18 0 1 59 \n", + "1 2018-02-08 0 1 77 \n", + "2 2018-10-09 1 1 90 \n", + "3 2018-09-10 1 1 70 \n", + "4 2018-02-26 0 1 82 \n", + "\n", + " patient_height_sm appointment_date patient_show is_regular_visit \\\n", + "0 176 2018-05-01 1 1 \n", + "1 186 2017-12-07 1 1 \n", + "2 177 2018-10-05 0 0 \n", + "3 150 2018-10-21 0 1 \n", + "4 140 2018-11-15 0 0 \n", + "\n", + " diagnosis_int patient_gender_int \n", + "0 1 1 \n", + "1 0 1 \n", + "2 0 1 \n", + "3 0 0 \n", + "4 0 1 " + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "patients.head()" ] }, { @@ -519,11 +1799,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 94, "metadata": {}, "outputs": [], "source": [ - "# Your code here:\n" + "# Your code here:\n", + "patients.drop(columns=['patient_dob', 'appointment_date'], inplace=True)\n" ] }, { @@ -537,11 +1818,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n" + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n", + " n_clusters=4, n_init=10, n_jobs=None, precompute_distances='auto',\n", + " random_state=None, tol=0.0001, verbose=0)" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "patients_cluster = KMeans(n_clusters=4)\n", + "\n", + "patients_cluster.fit(patients)\n" ] }, { @@ -553,11 +1850,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 96, "metadata": {}, "outputs": [], "source": [ - "# Your code here:\n" + "# Your code here:\n", + "patients['labels'] = patients_cluster.labels_\n" ] }, { @@ -573,11 +1871,131 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n" + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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patient_diabeticpatient_allergicpatient_weight_kgpatient_height_smpatient_showis_regular_visitdiagnosis_intpatient_gender_int
labels
00.5043480.49130465.365217152.3043480.5086960.5565220.5043480.447826
10.4859440.48594494.112450178.8473900.5060240.5301200.5100400.542169
20.5083330.53333362.900000176.9541670.4750000.4958330.4541670.545833
30.5605380.52914897.381166153.5291480.4887890.5067260.5964130.479821
\n", + "
" + ], + "text/plain": [ + " patient_diabetic patient_allergic patient_weight_kg \\\n", + "labels \n", + "0 0.504348 0.491304 65.365217 \n", + "1 0.485944 0.485944 94.112450 \n", + "2 0.508333 0.533333 62.900000 \n", + "3 0.560538 0.529148 97.381166 \n", + "\n", + " patient_height_sm patient_show is_regular_visit diagnosis_int \\\n", + "labels \n", + "0 152.304348 0.508696 0.556522 0.504348 \n", + "1 178.847390 0.506024 0.530120 0.510040 \n", + "2 176.954167 0.475000 0.495833 0.454167 \n", + "3 153.529148 0.488789 0.506726 0.596413 \n", + "\n", + " patient_gender_int \n", + "labels \n", + "0 0.447826 \n", + "1 0.542169 \n", + "2 0.545833 \n", + "3 0.479821 " + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "patients.groupby('labels').mean()\n" ] }, { @@ -586,7 +2004,8 @@ "metadata": {}, "outputs": [], "source": [ - "# Your comment here:\n" + "# Your comment here:\n", + "# Las diferencias mayores son entre los clusters 0 y 3 y tambien entre los clusters 1 y 2" ] }, { @@ -604,11 +2023,42 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n" + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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mmvnbrBtIj/T+AbW77RxsKfePUVcFwwZXOx6DpweJpMVto6XDY6ifOt0eWjq8OqaiCFbMm8iKeRPZtL+IX724Fo/BxOS8CencuXIht/71LcPJrqYQ9N5QSR0ay6+v73njPH/muD7Pabc7OXCsmujwMCaPTMLudJN7rJpwi5kpo5K5adkcbloWOC/e3GFHM/SYdfJK69hxuJRpmaksyx5PakIsNqebaaOTsVpMrPrqIA1tnVw8dwK3rZjP4qmZ5JXW8th7m7E7Bz7x3W4/ri2PTk7goZsu6rF/8sgkCivqSU6IYXRygn97Y1to8z8mRaGl8+T9ngaCd/LvDDbMUsrvBdn1eJDjHwIe+jqd6k2MOZxh1hiq7D0nNgSCWfFer8ite/j1/rfYUn+kT3000TqEKJPVcN+LRV/wbNEGzIrq1RSlxK73/YW2ay7eLN3GW2XbsSgqf511A5OHjAg4bkbcSKTB3SVctTB/6Ng+r9FFo7Odu3NeorSj3qtf6xrfTZ/HT7Mu8hvHgaBLnT8cWs0nVXuxKCbsQXR4i2IiJTwO8HrVTxWux+Qbo4SwKP6RfROpEfEsGJrFhppD2HsZUk3qzIgbScM4BxaTCbur55hazWZmjg2cI546Ohm3gSG3WkwsmjKa0Unx6AY3bEXAgomhebDfBK9v3Mvf3/8Sk6oipcRiUrG53Jh97yOtFp74yRWMNfAUs8eNQPXpsr3ZsK+QTblFaJqO2aygaRIhBC6PhsdzfJrqXx9s9cpBQqDACRllgBmZhvP26Lrk9699xpodh7GYVNyazsSMRP5250qiw8M4a/JISmoaDX833XFrOuNHGM9VfNN445hPX8N8RmT+CSG4f9JlWBWzXxM2CYVIUxh3ZXlDrJ85+jlf1R/x66NdmnLX0CsIrIqZ+yddZmjEttUX8HzxRv/5ds3Vr1Huwq5743xb3DZ+uvt5HAYeXnrkUC5OnYlVPa45WxUzY6IS/ZOL/fE/+17naHuNNxbb440rfrd8B+uq94d0fm/eKt3Ouqp9fh1e6zVmwtfH+yZeiklR2d1YxNOFn+HsNkaVtmZ+tvtFpJQsSZzEqMjhPXT1cNXCyrRsUiPimTo6mbnj03toyOEWE9lZacwYE2gE4qIjuPHC2T2ODzObSI6PYcX8iVjMJuKjAhNddAmZKQkB278N9hVV8o9VW/wxv11xxq5u7+tbO7n98XcMJYaJGYksnjI6QGcHjmu0bg/tNm9bnQ4Xbk9g7IDd5Y0D7zQwyl16eX/38oykOMPtr2/cy9pdR7yatE9DPnismt+9vB6AzOSEfo2y1WLirksXEBViUs43gS5FSK9TwZmRpgPMHTqW5+bfzsvFmyntbGBqXDrXj1xEYrh3Jvzdsp049UBdSyDIiklhZOQwvj96EWOjkw3bf7Nsa8hxzsnWOBQB1fbWgEIouoQv645wfvLUgPPum7iS2QmZvFe2E7vm4qKU6awcMbuH/BGMBkcbh1orAiYaHZqbN0q3sixlekh9786bpVsD4pS7yIpOJjUigetHLfI/AbxVui3geImk3tlGQXs1WTEpPDX3VlaV72Jd9X7CVQvfSZ/LkkSvfiqE4M+3rWDNjsOs2noIkKxcMJnlcyYE9fhvXzGfySOTeGPjXlptDs6bMY7vLp5KuMVMVWMrDUEemz/clsfKBZMN932TvPXFfpz96KsALrfGrvxyf0Zjdx6+aTmf7DrC+1sO0NRuo6qxzVACGijx0eEkxcUwd0I6c7JGsGrrQUprmymoaDCMOlmfU8C9V54TsP0Nn3bdHbemsym3CLvTzepthwyvryoKo5LiSU6I5tqlM5k7/tRkwMLp7zGfMYYZYGx0Mg9Ou7rHtoK2Kko7G7Brxo/hEsl1IxcyInIoY6KSWFu5j7XV+xgbncztY84jr62Sz2oPUNxunHxhxIq0mbS4Onm7LDCpQZMae5qOYVXNzE7I7BG9IITg3KQpnJs0JeRrddHhcaAahAUCtAXR0/uj02Mcn2oSKo9n/zBAv27pQ7fv0vrDVDPLU2cwPDwWq2ImO2F0D6OrKgqXzJ/EJb7JLvBqzNvySvBoOrOzRgR4UWdNHsVZkwMjDdptzoBoCX9fBxB7a0RJTRMFlfWkDR3ChPThVDa0kldWS1JcDFNGJQXcSOpbOthXVEVpbXNIka9S19mWV4Km68zJGtEjokNRBBfPncDFcyfw/pYD/PntL77WZ+kiMS6aV+6/1v9+7oQMbA4XZ9/zL8M5nNYOB395+wtmjkll6YzjcltHH7HiDpfbX0ujN1aLygM3XMCE9MQT/xAnCYlAO40FgzPKMHen0+Pk7t0vkt9ehYrSZ6WoPx5ajUtz4+kWNLe1oYCXjm06oWuvSJlJcWcdH1XuCdBTnbqHNVV7WVu1Dx3JH6Z/L2gUw0AYETmUMMUUcD2TUFk8LDQppDfzho7j0+r9AaGEw6wxxFkiA45fkjiRw62VOHt5zZquMyk2DfBGrzx6+CP/U4CC4LFZNzA1zljz3ZVfzs+f/MD/3qPp/Pr681g+p//P5J1wCjTMihCcM+3EIjLcmsZ9z3zMtrxSTKqCpumEWUzYnW7MJq+unpIQw1M/u5L4mAiklPxj1RZe27AXs0k11MSNsLk8vLflAO9/dRABPHbnSmaNTQs4bnbWCMPJwIFiMassmT4mYHuE1UJGYhzF1YEhmy6Pxmsb9vLahr3ERISx+oGbiI2ysmDiSNbtzg/wshPjoxkSFc6SaZmU17UEePlCCMaknIoIDGNOlUwRCqfvLaMfHj38IXmtFTg0b+2Lrom1Lg26638JdGpO3Ccp633x8AkkR8Qxf+hYpg5JJ1w1Bxxj11y+WGcXv9j72kmpjaEKhV9OvqKHzh6mmIizRHJD5tkn1Oad4y4g2hyOxVfOREXBqpr51eQrDKWFy9LmkBI+xK8hC59u/9PxFxFhCqOovYZHD3/s16A7Pd5Y7btzXjSUiTrsTu7+92o6HS7/y+n28LtXPqO8vv+Ub7NJJSYiUKPUpSQ1IXagwwHAC2t3si2v1F8HwuH20NrpwOXxasR2p5uSmiZ++cInAGzOLeaNL/b593cZo1D+5Ls04g6Hi5/9c5XhJF1CTOTXrkoYZjaROCSKq88xlrt+fd35WC2moE8fAG02J7c9/jYAd61cSHREGBaT73ujCKwWE7+5/nyEEFx/7iwSYiL9sdqK8Baj+uX3zsVsOi1K5yARuKQa0utUcEZ6zLrUWVe933B1AVUozIhLJ6+lErt+Yum546NTGBU1nOWpM/igYjc7Go4SbrLw/VGLuCpjAQCKUHhs1g2sr8nlk6p91DnaKO2oNwyp+7zmAN9Jn3tCfenO2YkTeW7+7bxRspVqezNzEsZwRfrcE670lhQ+hLcW3c3bpdvZ11xCeuRQvpexMGg1vXCThRfn38UHFbvZXHeYuLAorkqf76+W92FFDm4DnV+XsK2hwK81d7Ep1zgJRNN11uw4zG0rAuPTu1Ne3xKQPtzFB9sOcflZA5eM3v3yQL/1HjRdsudoJa2dDt7YtM8wZtekKmSNGEZLh4PKxlZCsa2bc4u5cHbPp6svDxZjNql4QtCtwy1mzCaFeRPSWblgMmt2HKGutYNFk0dx+VlTiLQGJgUBTMtM4Y1ffp/XNuyhuKqB3YXGWX4FFd7SBCkJMbz7mxt484t97CuqYmRSHN9bMsNfHS8m0sqbv7qed788wNa8EhKHRPG9pTNOCwmjC2+Cyenrl56hhlkapu6C93Hp33Nu5dzPHjzhtJ2bMpdwTpLXiMztI5TNpKhclDKDi1Jm8EzhZzzbEVhdyyN1bEH0b/BO3m2tz8emuZiTkMlwa9+e3tjoZH495Tv+97rU2dl4lBp7CxNiU4NObgYjzhLFj8b2XTKzrLOB/c2lxIdFMTdhDFePXMDVIxcEHNceJMNSlzpvlW4jp7GY749eTKLvM9ocLnSD36Om6X3qmF3Yne6gXl7383VdklNYQWVjKxNGDGdc2jCvHlzXzJiUoUxMH87BklqKaxpDui54Q/IcLnfQWscWs4l7vnsOr2/cS4WvMl5faLpk+5FSJJKFk0YSHeEN6bQ53MgQJ6nOmjKKR2652P9+/sSRIZ0HkDY0hvThcdidLghimLsTHxPBHZf2/A4UVTVwsKSW4UOimDN+BDdckM0NFwRdw/SUMzj5d5IxKSrjY1I43Bb4BeqKa14wLIv11bkDFjAEsGD4wDXhBcOyeNmgGpsqFBYMNU5G2N9cyt05LyJl14oKOj8cfQ43j1ka0jXrHW3ctvNpmpyd6NL7SeckZPLIjOtCivToDyklDx18n3XV+1CEgkAQrpp5cs6thl71OYkT+azmQIAO7tDd5DQVk9NUzFtl27hp9DncPu4C5k3MgHcDr2u1mFkcQtbe6OQETGqg12Mxqf4EkIbWTm597G3qWzrQpURKiaooSN/nA++jtq5LEISsEQ+JDGf4kCjOmzmWw2V1AXqrrutMGDGcc6eP5cvcYwGx271xuj2s253P+pwCNF3y+xuXce7MscydkG548+pNuMXMuTMCNeRQOFbdyDUPvWIYO90dI9kIvPMC//PcGr48eAxFCITw1s145udXkZIQc0J9+qaRUqDJ09djPn171g9Gk1Pdt/84axmxlgjCFJ/OFWK7N2cuxaIM/H41MTaN85KnEN4tCsOqmrk0bRaZ0UkBx7t0Dz/PeYlOjxObT4926R5eOraJ3ObQVmf/Te5bVNuasWlOHLobp+5mV2MRrx7bMuD+G7Guej+f1uzHqXv8daubXB3cu/dlQ91z4bAsZsSN8o9BMH/k+eIvKO9sZMSwIVy7ZAZWi8l/bLjFzMLJI8keFzgR1huTqvDADRf20EfDLd4452uXelf7+d//rKOivgWb043D5cHp1rA53dh97x0ub01jh9v7s6YfN9Z9fYaYSCtCCIbFRhkmDlktZswm74TblFFJ/nRp4Wvb7LuhdG+/qy9Ot4dfvbiWpjYbyfEx3Hjh7B5jZFYVrwHsNmaTRyaxdHpoiUq9+dHf3unXKANMHWX8NPbO5ly2HDrmraHt8tZAqW3u4BfPfnxC/fm20BEhvU4FZ6TH7H18LzLct7nuMACJ1ljeXvRzVpXv4kBLGaOihjEnLpO/5n9MaWcD0WYrt2WeT05zMTlNx0gIi+KuccuYP+zEvtxCCH49+TuclzSFNVX7UICLU2cyJ8HYi9ndWGTozTs1D6sqdgWNYuiizW0nt7k0oCyhQ3fzfsXOE54Q7M47ZdsDJu0kUOtopayzIcBrVoTCo7O+z6baPNbX5LKvqSRoedAXijfymylX8pPLF7Fg0ihWbz2Iy6Nx0ezxLJoyOuRMxrOnZvLa/dfx1ub91DS1s3DSSJbPnUC4xYzN4WJXfrnf2A6Uc6ZlklNYQbuBXFFS20xLh53VWw8a6scOl4fCygbGpQ3jiZ9cwcZ9R1mfU0BUeBgrF06iqc3Gut35lNe3UFBRH9BHIbyZflcunsbtK+Yzd3w6q746iMPtYVl2FgmxkazacpAOu5PzZo1l6fSxhk8P/dFhc4acQh2swtzbm/cH6Oy6lBRU1NPQ2snQWGMn6lTinfw7fc3f6dszAzy6xraGAso7Gw2L0IO34NEbJV+RETmM2QmZTB4yAouikhoRz/SEUbx21s96HH95Rs96BUfba8hpKmaIOZKzEycEVFHb3VjEu2U7iDSFcXPmUpIjjmdHCSFYMCwrpPA4o2QY8Eoa3TMHHZqbzXV5NDs7mRE/inExXq/FrXu87paBUXAFaXugBEu4URABIXNdqEJhadJkliZN5ofb/hXUMNs9xz/jrHFpzArBQw7GyKR4/vuqJf73bo/G53sLKattPuGIBl16lV09iFEXvusEq3inKMI/iWhSFc6fNY7zZx2XtKoa26hv7cTp9pBfHhhDr+sSh/v4d9zhcrO3qBK3WyN7bCpLpo8J6sGCt/bx5v3FNLXbmDUujXFpxhO6A0lc6S7zSCnZV1TF4bLaoPWaFSFweYJ/F0trm9l2uJQoq4VzpmV+q1mAg5N/J4laRyu3bn+KNrcdt/QgEIaPkJrU+UfBWkwoaEgUIdCkjlmYiDWH88y82wwn2HSp8+CBd/i85hASiUmoPJK3mn/OvmuUvIQAACAASURBVJkJsd504Zu3P8mBljL/OR9U5nDH2Av4YeY5A/482fGjDQvlh6sWzk/yZg0WtFVxx87n0KSGR+ooCBYnTuDBqVeREBbN8LBYKu2B8aehpnj3x4Up0ygtrA+4iVgUk6E805uVabM51GpcQ7kruuVkU1bXws2PvonD6cat6UENayhs3G/8VAaQFB/N0NhIlmVnkVdaG6AxSykZn25cB+KFdTt56qPt3qcCiWH6shCwyJdU84M/vsbB0lr/vj+8sZF/frCVLx6907D9I+V13PbYO2i6jkfTURTBOdMy+f2NF6H0miyNj4kgzKzidPdtoBUhvHMCeJ8G7vz7u+RX1KNpelANPD4mguT4QI1ZSsmj72zi3S8PAF7N/49vbOBvd64ke1xgnZlvCm0wjvnr80DuO9Q5WrFpTty65pcBurLhVJ8WpCNx6xp23Y1L9+DQ3Lh1DZvmpM7RyoMHDGabgE+rc9lYm4fTd55Nc9LhcfBfe172RRVs7WGUu/h34ae09LGaRzCizeHcM+ESwhSzv+/hqoWZ8aNYnDgRKSX37nmFdo8dm09/duhuNtcd5pOqff7jjYgMUqRpoHw3fR4jo4b5r2MWKlbFzANTrwqahdidS1NnkREZ6KnNih/FjPiRJ6WPvfnFsx/T3G6n0+nGZVBDoosuTTrMrCKE9/9QsJhUIsLM/O7GZQghiAoPM7yGxaR6S5P2Ir+8jmc+3oHLo+F0e3B28yi79GerxcR1S2eSkRjH3qMVPYxyF202J4+8EVgQX0rJ//v3B7Tbndh8Y+Bwedi0v5g1uw73ORbBsFpMxEZaue9q71PJs2u2c7isFruvfU8vXd5iUgm3mPn9D5cZSlLbD5fx/hZvWGKX5m9zuvn5kx+GPPn6denK/AvldSo4Izxmm8fJvuZjhppsmDCxJGUS66r201+wqIZkT1MxDs0VIFGsrtgVEE0A3hCwgrZq3ioNvqbcq8e+4q6sga9AfNmI2UwZks5HlTl0eByckziJ+UPHogiFwvZqw9KlDs3N++W7OGvYeEo6jZfkWl+dy0+yeq+fO3CsqoXn593BhppD7GgsJNEayyWp2aREGBe36Y2iKLy58Ge8WbqVVRW7UYXCtaMWsiJ11tfumxH1rR0UVzf2u9oIeJMuzp0xlgnpiZwzPZONe49ypLyOD7fnBT1nWXYWo5LjuWzhZIbFetPVP9h2yFAucXo08ivqA6qnrdl5xFA+CDOrTB2dwuikeC6aM8G/Inaw1UgAVm09xH3XnNtjW2FlA222QGnB7nLz/paDrJg7scf2NpsDmzO43HDJvIlkjRjOJfMn+lcB/3B7XlAP+6LZ48lIjOOyhZODrk7ywbaDhhKQlJLdBRWG9UO+CfTTOCrjjDDMuqFo4cWimvjfKd/ls+oDhrWAjTBK3zZanw+82W0eqQcUgO9xrjz+JSvvbGRTXR4CwZLESf0asczoRH42fnnAdo8efD64wdnGq8e2BB0VoxVVjrersbWhgKPtNYyISODsxIl9RqGYFRMXpkzjwpRpfX6OYCiKwsioRFIi4lCFQkbEMCptTXxRmwdIzkmcRGpE/IDabOt08OmeAto6Hcwal8bUUckIIfBoer8V07oIM5t44AbvzdStaSTGR9Pp7DuGeX9xNQ1tnayYd1wqChZPr/Qq4l9QUc/WQyXkldYY3jgUobB8zgRWLuiZhNNXkXuPR+eFdTtZOGmUX0P2aMGjcz1a4He498Kwvekao1DOURTBfVcvISay7yc2l8HSWH318ZvAW8Ro0DB/LaJMVrJiUjjcWtHDFJmEylJfQaCzho3ni9pD/S6eOCJiKJGmwEmG5SkzKGivDpjwMisK42NSWJE6k6ePGnsv12QsBODl4s08ffQz7x+e8Mocd2ctDyhMHwrjYpKxKGZsBl58naOV/xzbZGiYzULl/CBFktrcdm7Z/iR1jlbsmptw1czfjqzh+fl3+JM+TjZ37HyGnKZj/veb6w6jIFB9j/n/LlzPneMu4NqRZ4XU3p7CCn7yz1VIKXF7NCwmlQUTR/LHWy8mKS6axLhoyur6T+c+Z1omAM3tNm7885s0tnVid7mDzacCUN3URnVTGxf/8nl+de25XLFoKivmTqCgoj4gKsGkqowfMRwpJY+8sZHV2w7h0XWCqQaarnPW5JEB23+6ciH3PmMcdiaR/OuDrTzz8Q5WzJvI/d9bStaI4VjMpoA1Ba0WExfPDZx7iIuKICLMHHA8EHQNw3OmjeG9LQcCtmcMj+vXKAMsnzOe7XmlAbHdmq5/axqzROA+RenWoXD63jJ68b9TriTaHO6vZxyhWkgOH8IdviWZ7pmwgoSw6KC6axe1DuNVhC9Jm8Xk2BH+8y1CxaqaeWja9zApKjeNXkKKNdD7vTxtNknhQyjtbODpo95axW6p4dY1XLqHv+Wvocbev6HojSoUHp5+DVbV7Pdou/6mPVLv8RRh8um94aqFtIh4bhlzbmCDwD/y11Jha8KmuZBIbJqLRmc7Dx98b8D9C4UNNQd7GOUuuuYBusbo3wWfUhFkRe7uaLrOvU9/5I9B1nSJ3eVha14J63bnI4Tg4ZuWE2m1+Os0BPOgTar3j/Ivb2+iuqkNm9PtS/QJjS6JYeWCyUwemUR4mPd7aTGpWC0m/njLxZhUhZ355b5Hfw+apuPu5S2aVIUws4mfX3k2CTGBYWVLZ44jKc5YEtClN2PQ4fbw8Y7D7DhS5p1Iu3k5VovJX8siPMzMxIxELl9ofMP+060rArxsVRH87c6VhscHC8szqaE9riyZNoYFEzMIDzMj8MZlh5lNPPCDC/3j+E0jJWhSCel1KjgjPGaAUVHDWbX4v1hbtY9yWyMTY9NYkjTJb7SGWmN4Z/E9fFZzgIK2Kt4o3WrYjk1z0+BoZ6g1usd2s2Liidk3sb2hkJ2NR4m3RLE8ZQZDrd5ZZUVReG/xPbxbsZOPKnOIUC3cknkusxK8GWobaw4GlVK+qD3ENSMXDvgzZydk8t7ie/mkai8lHfV8UrXX8BoxpgguSJnKlCHpnJM4EXMQaeLzmtwASUZHsrOxCI+unZRswe68XhJaoosuJRtrD/H9UYv7PO5QSa3h5JDd5WH1VwdZPmcCEzMS+fB3N7Fm52HKapt5Z8sBQw14w75C7v/eUjbsO2ooFwgB41KGkV9prONLYHd+GdlZ6Tz5syvZllfCjiNlJMREsnzueL8GvWbHYcOsP6tvVeuxqcNYlp3lrzNhxJqHb+XZNdt5aX0OLre3RqLWa4lnu8vNxzsOM29CBnPGp/PBgzfx8Y7DNLZ1Mmd8OgsmjgyIyOhiwaSRrHn4Fh5//0tKqpuZPDqJn648i6ggmX4b9h413F5U3US7zeFPJw+Gogj+9KMV5BRW8OWBYqIjrCyfM+FbzhI8OckjQogRwH+AJLxFIJ6WUj7e65hzgNVAl5fynpTywb7aPWMMM3jjhFXFm/XUPfOpC5fm5mBLGYdb+8/1N0IRSp9xyEIIsqKTqYsfQ4TJQnJ4HB9X7uH98p00ONqNFxrVdbbWF9DitrE0cbI/DjnkPiFQUVB9KdHG/fJ6zd4xCf5l+7ZWaz/YUs7mujxq7P3XiADvI3ko4cbBZxp6fjbF9z1R+0i4cLg8/O3dzcH1UiF47ZfXMevOv/XbL49HZ11OPtvySomJCGNi+jBKa5v56lAJhZUNhueoisLFcyey1KAUpxG3LJ/HLcvn8cX+In794lo6DSSuoqoGHn//SxZM9GZO9lWnot3mYO2ufCobWpkyKpnF00bz8E2Bcx1G9PV7CBUhBNnjRnyr4XHdkXCyvGEPcI+Uco8QIhrIEUKsl1L2nkX+Ukq5ItRGzxjDXNpRz807nsSlecPGwlULTxau5/l5dxBriaCgrZofbH2i39oYkaawAG85FKSUPHDgHTbUHsSpuVGFwpOF6/v9irrxFhna2XiUV49t4ZqMBSFHcOxpKubunJfQpewzaaTVbeOVki1EqBaeCf+cZ+feTpQ50Gs5a+h41tUELkPVexHZE0VKyR8OrWJt1T6cuttfnrQ/VKEEVJ4zYlJGkj+VuTvhFrN/tZIj5XXc+te30XS9z5WaHU43//ksJ6gXOTtrBEIIEodEUdsSGA4pgOysdNrtdpbe+5Q/c6+xzcZtj7+Hqgg0XRqGzIFXlpl3Ait4zMkaEfRmcrSykSPl9by1aT9zskbwl9su8Wv53SmsbOCWR9/CrXlD6SLCzCQnxPDCvVeHlORx0ezxvLVpf4/oEkUIJqQn9ustn06cjMk/KWU1UO37uV0IcRhIxbsg9QlzxmjMvz3wNu1uh79IkF1zUWNv4amj3nXG7sn5T79GWSD4y4zvn9D1tzcUsrH2EA7NjcSr8wa7WpeX24WOREfi1L3LQBW0Vfd7PU3q3L/vdRyau99Mvi55w6a5KO9s5PmiDcZtBim311cUx0DIaSpmXfV+HLrbN+ttPEIKArNQMQmFMMXEbWPPY0Rk/2v0mVSFP//oEsItZqxmE0J4a2PMm5DOstlZSCm5/7k13jrK/ZTJ7IrMCZaA0mVon73nKsPby33XeGN673z8PcOU765t3aM2FEUQZlYJM5v43Y3LiAhShrMvIqwWHvrhRYSZTYSZ1R4xyF3Xsjvd7DxSzvqcAsM2fvn8GtrtTv8Y2ZxuyutaePaTHSH14bYV8xmZFEeETyMODzMTG2nldzd+/RDNbwtJaOv9DaSYvhBiJDADMBrI+UKI/UKIT4QQ/XohZ4TH3OFxcKStKuARyi01Pqs+yH9PXEmtM/hj86y4UWREDeOOsRcQa4k4oT6sq95vGOdsxLiYFIZbY9heX4Crl6br1j18UXuoX0mjsK0aZ4hrEPZoX2qsq87lpwYheFvrjf9QD7WU49Y9QbXpUFlfHVhZDrwF/UdEJJAcHsf1oxYzzBrNxtpDSAlLEieFZJS7mDUujY8fupn1OQW0djrIzkpj2ugUhBDUNLVT09T2tT5DFzmFFei6JHVoLNse/ykPvvop+4uqGB4XxYM3LCN1qDeK5XCZsQbdG6vFxAWzshiXNpTzZ43za9AnwpLpY/jwdzexPqeAgop61uXkB9yI7C43H20/zLLZ43tsb2zrpNQgasXl0Vi3O5+7r+hb5weItFp49f7r+OpQCfnldSQnxHDujLH+Qk1nAhJwh14rY6gQYne3909LKZ/ufoAQIgpvrcS7pZS9v4R7gAwpZYcQYjmwCuizKM8ZYZiDq6vQ6bZz5ea/9nl+SngcaREJgYZd97Ch5hC7m4pJtMZwSVp20LAxr8Ybmk47PiaFcTHJ7Gw4CkGK+feHIpQTVvLsHhe/P/Aek2LTOGv4eDbUHKSwvSZofRHRjzbdF7rU2dZQyJe1h8lrLTdMlVeFyg8zl/RYoLa/ib7ulNQ08eH2PNpsDhZPGc3CSaP47tmBcdWqIkLSqkOhq3wlgMWi8vsfXuTf5/ZorN11hF355SHX4lAVhUvmTWTWuDR0XeeVz3JYvfUQFpPKDy6YxYXZ4/tvpBttNgd1rR20dNiDfuaK+hZ+98p65k/M4JxpYzCpCoqiBE3E6i57tNscfLj9MIWV3iSZeRMyePLDrRwpr2NUUgL3XHk2i6eMDqk8qxG6Lvnq0DE2HygmJsLKJfMm9jkBevIRA6nH3CClDCrYCyHMeI3yq1LKgBCn7oZaSrlGCPEvIcRQKaXxBARniGGOMIUxLW4k+5pLAh673eiU2YJ+PgA+rNpDmGLi+aKNPD33R4yJTsLucXHzjieptDVh11yYFZX/HNvMYzNv8EdadOfi1Jmsr84Nuqp0d74/ajHhqpm/5H1ouP+8EBZjHRudRIw5PGQvvTt23ckHlbtZV72fP+atxiJUnNLjT/3ujorC/KHjTkhj1qTOPTkvs7f5GHbNhRKkfolL9zB/mHFN6v5Ys+Mwv3v1M3/dhzU7jzAjM5XH71oZoJ8OGxLFqKR4Cirrv7aBnjt+hGE6sc3h4sa/vEllQ6vhUlDB0DSdqZnJ6LrOZf/7Yo/i+fc/9wlrdhzh8bsuC6mtVV8d5JE3N+LRtD4r51U2tlL2VQtrd+eTmZzDMz//LnFR4aQPj6OoOjA88QJfkaXy+hZueOR1fynUT0yHeySFlNa18EVuEf+46zIWGiyS2x+arnP3v1azp7ASu8u72MFrG/byq2vP4+J5J6fOS39ITk7mn/B+SZ4DDkspDT1EIUQSUCullEKIOXgl5D7jQ88cjXnKlQwNiyZCDcMsBm5EnLqHDo+D3/lqZbxe+hXlnQ1+w+fWNRyam1/nvoluoLnOjB/FdzPmE6aYsCgmwlWL4eTWtRkLGRGZgEN3+2sHdEcRCp19rGjShRCCP8+4nmiTlQjVgllRsSgmzEIlXDH3OQZd+qlTdyOROH2ZiV2ab5fGG6GGkRgey/9Mvrzf/hixsfaQ3ygDfWj8Epc28Ip3NoeL37/6GU63xx/SZne62Xu0ks/2FBqe88itF/uTJsyqSpjBGnMCrzZtVtWgk3/2IGnKr36+h7La5gEZZfBGMng0PeiKJl8ePMaRssCaGL1ptzl45M0N3rjoXkbZYlJ7TI527bc73RRWNviKBnkr1fVGAHaHd/sfXv+cNttxDTpYpt4vnlvTb3+N+Hxvod8od/XT6fbw0Guf0RniCjInA83nNff36oeFwPeBpUKIfb7XciHE7UKI233HXAkcFELsB/4OXCP7edQ6IzxmgMTwIby/+F621OdT1tnAEwVrgx67aNh4djYeNSytWdBeTYfHwafV+w33d3qclHTWMzoqcH2yn2QtY2VaNtvqC7CaLCxJnERRey1vlm7Fopj4YebZjPKd91VdPopQoJeR16TO5rrDTIztv8zl+NhUPl7yC76ozaPJ1cHMuFGMihrO5rrDHG6t4JUQ44SNuHPchaRHDmX+0LEnHJGxvjo3JI/eopjY0VjIRSkzBtT+nqOV3pC3XnbE7nKzdtcRLswODGtMHx7HmoduZlNuMbUtHUwZmcTYtASeXbOTYzXNzM4awXcXT2H74TJKa5t5/P0tGAlU+4qq0HUZYLjX7s4fUKnMLkyqyv6iKj7YFnyy/vWN+wxToLuzM78ck2pcDW5c2jCmjU7mvS0HAmpRON0e1u46wgWzxlHf2hlwrgQ25hbx39csYeeR8pDqjXQ6XNhdLsItA5vEXLcr3zC2W1UVcgorTlgeGQhSipPiMUspt9DP2rtSyieAJwbS7hljmMG7pNQ5iRPx6J4+DfOjs37ARRsexmlQ9U3gjfkNVh9Cl5Iw3yrQHl3j85qDbKrLI9YcweUj5jAuJpn0yONLsM+IH2lYKc2sqIYeteqLROjicGslq8p30eGxszRpMmcPn9jDUFpVC8tSeq5ufH7yVCbGpp2wYbYoJq4bFVoKtFNzs656P1vrC0i0xnDFiLn+AvlhijlE3V30WY9jT2EFH2w7hNujs2x2FmdNHoUQos8VlY9VN/Hfz3zE4imjuTA7q8exFrOpR+1jgJ9evqjH+0VTRrNoCjz50TZszkCPUFWEYdag5QRXefZoOi+u22loFLsIs3jHqKuYz0fb89B0nYvmTKC6oZVXN+yhzebEZbBYrBCQmZLAJfMn8d6Wg8btm02YTWpQXbxrDFVVQQ/x5mMUjtcfXVmZhvu+pVW0vZN/p29K9hllmLswKSYSrbHUOgIfCTN9Husladm8VrKlR6iZKhTmJIzFqlr4zoi5PHrkox61MQSCtIh4UiPicese7tj5LIXtNX799KPKPdw7cQUr02b328clSZN47EhgjQNVCP8k2JslW3miYB1u3ZvNtaU+n8mxO3k8+8Z+vdjUiHiiTGF0ePqXRbpjFmrIBYk6PU5u2vZvahzN2H2x2++X7+L3069h8fAJrEidyafV+/tNOJBIFgw1Ttr55+qveHXDHpwuDxL4Yn8Ri6eO4uGbljNzbGrQ9N+KhhbK6lv46mAJr23Yy/P3Xo3VMvCv80VzxvPBtrweGYVmX2F7I435ysVTefSdTf2G4/XG6fawM9+4NnUXNy/zLtrw2LubeffLXL/X+8muI/1q5mFmEysXTGJs6lDiosOxN/b0SMMtJq5cNJXYSCuTRyWTW1zVQwoJM5u44qwpCCGYPzGDzUFWMO/O8CFRWEwDH/PLz5rCF7lFAWOoKgozv8aCCQNjcM2/b4R/zb4lwAsLVy38I/smAG7OXMKUIelYVTNWxUyEaiE1PJ5fT7kC8NbGOHv4RMKUrv1hJIRF8ciM6wBYW7WfgvbqHvqpU3fzl7yP6AzBGMZZonhg6lWE+a4drlqwKCbumXAJaREJtLhs/KNgLU7d7ddm7ZqLg63lbKw9FNIYPDH75oAIDwVBuGrxf+5w1YJVOf7zmOgkw2p2RrxZupVKexN2381LkzoO3c2DB97Bo2s0uToMdXTwrncYoYYRrlr484zrCTcFPu5W1Lfw8mc5OHxGGXwrb+QeY09hJWZV5fE7LyPKaiEizIy1WzhWl02xu9yU1Dax6itjL7E/7r5iMeNShxIeZsZqMRERZmZ0coK/9nBvLls4mbOnZhJmNmG1mE5oOScjbl8xn8S4aIqrG3l7c24PKSKYUTabVMJ9NTFuuCCb6ZmpCCF4/M6VxEWFE2m1YLWYCDObWDZ7PBdke58iHr7pIhLjookI8+63WkzMzhrB9efNBAi6Iknvaz9195Un9FlnZ43guqUz/XWbI60WoqwWHr9rJWb12/OYT3Yc88nkjPSYAUZEJrD5vN/yVtk2DrdWMj1uJCvTsr3hQECYaubfc24hr7WCgrZqUiLiyI4f7dV98U7C/W7a1RR31JLbXMbQsGjmddNbP6s5YLi0kklRyG0uDSnKYGnSZGYnZPJVfT6a1FkwbBxxFm/86t6mYsxCxUWv+FPNxec1B3uElgVjYmwam87/La8e20JJRx0LhmVxfvIU9jQdo8LWxNjoJCbEpLK/pZTSzgZGRQ1n6pD0kNfT21Bz0DC5xaNrFHXU8nmQ+iDhioVLUmcxLS6DhcOyiDCo5gewLa/UsC8Ol5tNuUXekp6jk/n0kdvYcrCYvNJa3ty0P2DizeHy8GlOPtcsmR7QVn9EWi38577vsb+4iuLqJjIS45g5JjXoGKmKwh9uXk5RVQO5xdW8tH53SNXsemO1mJgyMonRyQnceOFsEuO82ahb80oNJ5+NiI8K5/ZLFjBvQrr/fIDMlKGs/cOtbD1UQlO7jZlj08hIPF6AKzEumtUP/pCdR8qpbmpjUkYiWb660VJKcouNE6AURbBs9ngmjBjG95bM8P+tnQh3rVzI5WdNYWd+GVFWC2dNHn1CTzxfh8Gyn98Qtc5Wah2tNLraqbI3U9Bezec1BzncVsHY6GSuzljAxNg0/0Rbq8vGu+U72NNUTFpEAtdkLGB0VKJ/os/ucfFu2Q621B+hrNM4BE9KDL0/wKd9r2Nt1T4EgotTZqIIrwSio1PQXs1Pxl2ISTEFFOrvQiAoaKvip7te4OzECVycOstfUc8I76TjOT22ZSdkkp2Q6X8/I34UM+JDC2tqd9t5r2wnu5qKaHC2Gx6jSYlVtRBpCjOMWxbCuwRWsIVouwi3mg2jIoQi2JpXwrEnmrgwO4sLZ2dx3sxxpA6N5a1NgSnlAJHhx8ezuLrRG/1Q38rsrBHMm5DBh9sPUVLTzPTMFCZkJPLUR9uobGhlZFI8P7p4Prvzy8krqyUrbRgpCTGGSyJ1JzNlKJkpQ/k0p+CEDLOqKPzg/OyAcLNwiwlVUXAHydLszrAhUQH1m7swm1TOnnb8O1BY2cAbG/dS3dTG3PEZXLFoStCC9GaTYji5aDWb+L0vu6+53cbbm3PZe7SSjMQ4rl06g/Thx41/p8PFe1sOsC2vhMQh0VyzZLrf+HeRkhDDZb5U+m+brsy/0xXRX4C8EOJ5YAVQJ6Wc7Ns2HXgSsOIt4nGnlHKnL6bvcWA5YANulFLu6a8T2dnZcvfu3f0d1oMjrZXctvMZ3LqGR2qYhIJH6t3+V7EoKk/N/RFZMSk0ONr4/tYnaPc4cOnemF6zYuJPM69n3tCxdHqc3Ljtn9TYW3HqwevyDg2L5sNz7guQEHRdZ+WmP/eZgQgwLCyaD8++Dx3Jso0P0+a2Bz3WqphJi4jn+fl3BDXkJ5MmZwff3/oEbW4bTt14XUWBYFTUMN44625ym0v58e7nA54shpgjWbPkF/3q5O12J0vvfbLPYu3hFm/Jyn//7DuoiuCSXz9PdWNbj16FW8z88ZblLJoymq8OlfBfT3uXKNJ0iVlVcGveOsi6BJMi/EshdUcVoEmvvmw2qTx3z1UBhsSIT3Ye4VcvfDLgZKCYiDDWP3JbwARnS4ed8+97KqSVvf92x6UsnprZ73Ff7C/i/ufW4NY0dF0SZjYxJMrKa/9zPXFR4QHH/+6V9Xy843CP6BOLSeXysyZz39VLqW5q47o/vIbN4cLl0VAV70TtP358ObPGptFuc3Dtw6/S0GbD6fag+CZyH7jhAi6Y1f9Cxf0hhMjpK+EjFJImxcvrXzs/pGMfnf7W177eQAnFl38R6J0E/yfgASnldOA3vvcAF+FNNRwL/Aj498npZiB/zFuNXXP5y1h2PVIf/1/Dprl45NBqAJ45+jktbpv/0VxD4tDd/P7Ae0gpebdsOzX2Fv/qz93/LCIUC5FqGEMskfxt1o2GmXurKnb1a5QB6p3tvF22HZOi8vfsHxJjDidSDSPCwPA6dDcVtiY+rMgJfWC+Bi8Ub6TZ1eEPI+xulCNUizfu2RrLozN/AMDUuAxuHXMuFsVEhBpGpBpGrDmCv4cweQnQ1GYLWji+C7vLTV5ZLZv2FyGE4B8/vpyE2EgirWYiwsxYTCrXLJnOoimj0XXJA//51F+rGcDti3/usnNGRhm8RrnreJvTzR/eMK430pvqpragsdARFhORPn3cZFKIsFqItFqIiQjjHz++3DDqpL61M6hu350VpfN1yAAAIABJREFUcyeEZJQ1XefBlz/F6fb464I43R6a2my89Okuw3PuufJsJo1M8mruPp166ugUf2TLE6u+oq3T4Tfcmi5xuDw8+PJ6pJS88tke/wrg4I10cro9PPTq57i/pRVK+kNKcOtKSK9TQb9ShpRys684R4/NQNezXixQ5ft5JfAfX/D0diHEECFEsq8C00nDo2shl/Y81FqBJnW2+HTe3rS6O6lztPoWYg3UUyNUC9ePWszkISPIjh8d1OB8Wm38iG14bE0uV4/0yiyfLLmfXY1F5DaX8kbp1oAVSxy6m421h/huxvyQ2z9Rvqw7YqgZhykmbhtzPuNikpkRP9Kv04M3y3FF6kx2NxYTZbIyOyEz5LjoHUfKvEatn79Vu9OrOZ87cyyjkuL55OFb2J1fTkung5ljUhnmW1uuuqmNDsfAolSCceBYtWEcc2827Dtq6N1GhJn5wfnZTBmVzOysEbTbnezOLycizMyc8elBQwF3HC4Neq3pmclMHpnMNUumk5IQ2oozpbXNOA1C39yazhf7iwxrY0RYLTx3z1UcKa+jpKaJ0ckJ/qWrALbllRjGOdc0t9HS6WDj/qOGsd66lBRVNQasg3gq8EoZ///TmO8G1gkh/oLX6+5aiz4VKO92XIVvW4BhFkL8CK9XTXr6wMofqkLBrKj9Vl0Db9zyLdufDJoI4dY1/mvvq9QbhN6BNxpjSeIkMqMDE066E20OvThSdLdVrF8s3sTbpdtwaG6/t96b2AG0PVDqHW28VrKFvc0ltLuNZ+MlcGHKNOLDjAvvxFmigk5WenTt/2PvrKOcuPf3/xqJ7WY3WXcBdnH34rRcqFCgLdSo263brdz2urS3duvut05bSpVSWlpocbfFWdbdNTKZ+f2R3XQ3mWwCpcLvfJ9z9hwyGcuQvOczz+d5Pw9flG1jaelWZEFiTvooFE3l89It1JS50YTQj+yiKGCL/PGRWxJFxg34kR/dkV/GW99spbSm8ZiaP/RgkuWg6Sf1Le289+021u0tpLJen4dXNY0ZI3PpnRKHw6WwbOM+lm/ZT6TFhEfTmNyh1faH1WJC7qBfukIWReqb28krrGBtXgFzJwzuVtw37C3k3e+20dDiYPrwHOZPGepVO1hMeILkBkZ1sfjML6/lja83c7islqG9Uzl5eA5fbtrH/uIqBmYlMWt0X5ZvOUheQUXwa6x5eejoINafikfFegyOej8XjsIr4xfHsRbm64DbNE1bLAjCuXh7xWeg3wGj+8vrcGd6Ebwc89EcXBAETk8dwdKybSGLs4pGXmNJUJMeDdjXpD/6FhFIMcfQ2xr6Dv/73BmsqgrPgvWajuinq9Y/z86Goh7XNYsGFhxDZmA4KG2r45K1z3SjhPSQZokNWpR7gqqp3LLldXbVF/k8RrbU5SMIAh5NRYsCjxpPiMYpNE1j9kn6HgqfrcvjP+9969NBH4+fmtEgMWfCIN3CWdvUyvn3vUVzmzNogRIFgbQ4G71T4nC5Fa54ZBEFlXU+3e62gyWcP204N/k1vQCcMiKHhz9YGbBcUVUKqxoorGpgb1EVSzfu46XbFyCJIq9/tYkXl6737X9/SRUfr9nN2/dcSKLdysCsJHYdKe82sjcbZRae4pXHbT5QzM3PfIxL8XLQ+4qrePe7bYiCgKp5X3/w/U6fx7TeNTZIIlOG9sZiMnDhKSPZW1TZTfIniQK9U2JJT9DPEfyl0SmX+63iWMfylwKdLkofAGM7/l0CdI0kSOdHmuO44vYBZzAiphcm0dChDugZ/nxpp6GPXnNEpOzV36ZGxPLYqEvDkpflRCX7Cm5PkAWRFo+TA41lQYuyhECkZMIoylyTO4ORsT9Pi+ozB76iVXH0WJQBal3Nx+TZvKHmELsbirsZP6lovn0JMkjjG8CgYjbJQfPeZFGkoDwwq9HlVnjo/ZXddNC+HERJxGo2YpAl4qIjMBkkIkOM1qwdWYGjczOC2l++tmxTN361KyLNRiwmA+kJNp8h0fItByisrO/WTNHuUnj7221U6RjwR0WYeeL6uURZTN796VhpOlwK+0uq+X5nPk2tDl74Yl23/TvdHirrm/l4rVcP/9DVs+mdEofFaMBqNmKUJRZMGeprab/v7RU4XD9y0J0FvJOu8Pgt77zGQsdnNhtlBmYl8deLvJNp04f14YLpIzDKEtaOa5KZGMOj187Rvaa/DrxURjh/vwaOdcRcBkwFVgInA52OMp8CNwqC8B4wDmg83vxyJ8ySkafGXE5hSzVFbbXcve2toJl7XSEJIn8adDZPH1hGuSNQ5mQRDVyfO4uBtjQG2tLD1vwCXJVzCudmnsTHJZvYXl/AuuoDAWbxiqayqeYwbi34SN9qMPP3oecyxJ5JtCFw1vx4YXPt4ZDhAuC1Ry1tq+vWih4ONtYeCumlIcQpyKfWMcsyDnt9DO9/H6hTdntUftidz6wx3Wf0j1TUBd1vUkwUd507jf4ZiSTYrRwuq6GouoE/PK/v+GeQJf59xWlkJcZ00/z6Y3VeQQDNAF6Z283zJjEgK4lBWUm+780Pu47o+kLIksi2Q6W6fh+j+2bwzUO/Z9uhUn7Ylc/i1bsC/ZadbtbuKcDUYcbkL29zuBVW7TzMhSePIN4WyXt/uoh9xVXUNLYyIDOJeJs3+LXV4aKk5ujlfp3456UzyUiIISftx++GIAjcOG8SF548krzCCuKiIxiQmXRUv6VfAscj8+/nQsjCLAjCu8A0vGbRJcDfgKuBJwRBkAEHHVwxsBSvVO4QXrnc5cfzZPMaink9fyVFrTUM7ggeXVq6jfyWqrD9hCVBZGryQN4vWqdbmD1oTE0aQKKOL3Or4mRRwRq+qdxNhGRkfuZ4ZqUM833h3KrC0rJtfF2+iyalzbvcb5LEKMrEmCJ79PF1eBSe3r+MyYn9WZg9uZu5/9tHfuD1/FW0KU6yIhP485CzezREOtRcwWuHv+NQcwW50SnMSh7Gispd7G0s053s1IPTo/CHrW+QZLazMHtS2BaeMcZIjIIUEBbgD7MsM6hPIilNSXz4w86A9yVR0E2Qjo40B53lT46JYnIXM5w+qfH0TonDZJB9aoGuiLKYwjLPibGaKaoKXO5RNaYN6+ObiOxEnC3CJ9XrDg27jlStEwZZYmz/TFocLt2uRoMkEhsVgT3S7HMT7ApBgPgu10zoiH7yh9EgIYkiHvXouXmjQWbq0JygE6Sx0RHd/g9+S/CqMk5grwxN0y4I8tYonXU14IafelJ6WF21j3u3v9thZQmFrdV8VrolbPN68BbFmSlDO0x8JrN/Z1k3Da4siAy2ZegWZafHzRXrnqOsvc5X0A40l7OzoYi7Bs7x8qmbXw94dPeHKAjMShlGlMHCE/u/1F3Hqbo53FJJUWsNy8p28M7Em7EazPxz14d8XvqjLPxQSwWXrXuWV8ddy+CYwAnUHfWF3LTpVZyqgoZGQWs1y8t3IiKENVLuhIpGYWsNha01Xu1yv1M5NwyVyOmpI3j50Le6YQFdIQgCv0seSkSaCUWn0Kqqxpk6Pr0psdEk2qy6NppzdBovBEHgpAFZrNx5OOC9k4eHlp4BTB3Wmx06nXFRZmNAUQY4c/xAFn23PWC5qsHoMHwhJg3KRpYl8HuKEEWRuRMGkRIbTYItkpLqxm5KCZNBDqsT0iBJnDa2P19u2odLp6mkJ4zvnxlStfJbxW+9weS3qxfpAk3TeHDPJ74sOcDXF+VfXgTAKpsxijJpllgMguh7PTq2D3cN9PJc05IGcmmvqZhEmUjZhEk00D86jQdGXKh7Dl+V76DC0dBtlOnwuPm0ZDNlbfVsrs0nr7FEtyibRJlIyUS0wcIjIy4mzhRFvasVOcTld2se6l0tLC7aQJvi6FaUu+KfuxfrLn9kz6cd16w7NxhOUY6UTb7Ulq5wqG6eObAMRxh2n/HmaB4aeRFRsoVI2avVjjZYOjymTURIJuyGCB4bdSk2YwQVdc26oz9ZFtlXHBjh5HAp1DQFurWJgsB+vWEtUFCpT38cLuvRt9yHL9bv1V1e29JOS1ugVG9/cbW3sPrBo2q6HLM/jAaZF26dT5LdSoSpw1fCYuSBq04nNc6GIAg8e/PZZCXF+HwnLEYDdy6YxpBe4SWy33XedMb1z8RkkLBaTBgkkbR4m5cjtgSfvymoDOT9TySoCGH9/Ro4IVqyG93t1DlDf4nBm8jx8IiLSI+MI8lso87ZQkFrFSmWGFIs3bnDK3NO5rysCRxoLiPOFE1WDxzq+pqDunypLIjsaiiioLVK931JEJmTPppZKcMZaEvzaXx3NRRhkgwoIUzznarCupoD9O5BrlfcFlhUNE3jQPOx0fsGQeKvg+fzzIFlFOnsW0Qkv6UqLE/p8fG5fHXyvexpLEUWRfpHp6LhtTvV0BhoS/c17OzIL8MoSSh+xuxuRWXd3kJmjx/YbXlBZZ2u7aSqaazX0QO7PR4Kq/SLSV5haJN6QDcvrxMb9xdx8ojuUW5r9xR0c67rhEES2Xm4PGTrN3h9lr+47yr2FVfhUhRvWniXYp8aZ+PDv17CobJamtocDMxKOqr8PYvRwBPXz6Ostony2iZ6JccSGx1BVUMLRVX13PjUEt3JzuLqBlxuBWMPNp6/VfzWVRknxBWNkI0dfG3odaONEb5oqMPNFbx4aAV7G0tJj4jlzLRRrKnZz876IpItdq7oM53x8blhqR6SzTYkhIDJPE2DWJOVZqUdk2gI0CJrmsZHRRtYUryRYTFZ/Gf4BdiNVuKM1pB2meBtgU4y20iPCD4hZdLxOhYEgUjZTIsS2ilMb9uTEvqypHijbmF2ax5ijeHL52RRYmgH1eL0uHmvYC2flXq7GU9LGY5UaOWzNXtpaXfoFjFZEkmyRwUsj7FadNcHiLcHctKyKGI2yAEm8kA37e3B0hpe/GIdeworSU+wM2FQNotWbqe6oaXHVunMxEApWJI9yicz80ecLXx9uigKDMzy3pxVVeOj1btYtHI7bU43Jw/P4YpTx5KbFnxgsWrnYV77ahPVDS2M6pvB788Y7wuU7URqXDSpcd4bxa4j5bzwxXrye3iSMBkk5DDd4KobW3h56QZW5xVgjzRz9qQhHCmvY+XOw1gtJi6YPpw5J+lLFH8u/P/YYPKLwijKnJY6gmVl23qcsDJLBhZmTwRgf1MZV294AafHy69WOBrYXJfv83+ocDRw19a3uGPgmcxJD90Gf2rqCN7WNabXGBXbi9yoZN2MPx9toGlsqTvCnJUP882MvzAsRt9Axh8mUea87An0siYRY4yk3hX46H5e1gSdLeG8rJN4+8jqsHIKux7vdylDMUsGLuo1me31Bd22lwWJIbYMki1Hr0fVNI0bN7/KvsYy3w3spYMrUBtllAobwVTIsiRy9qRAs5ukmCiG90ll26HSbkoJs1Hm0t8FemYLgsD8KcN4f9WObhOAZqPss7zcW1TJlf99H6dbQdOgvK6ZTfuLA/blD4tRJictIWD57PEDeOfbQApK02BkzrF5D//r7a/5avOPydjvfbeNFdsO8v6fLyZCRxL43nfbePLj1b71l27Yy8odh3jvTxfpdhBu2FfEbc990qPntNkgc87koWFxzPXNbVxw39s0tjnweFTKa5u4750VPp00wEOLvmNPYSX3XBBacno8oGkCym+4MP92z8wPdwyYzZTEARg7OGGjIJFjTfrxtSgzJ200C3t5RftP7f8Sh8cdMCrt+tqhunly/5coYcxI72oowqDTauxBo7Stnj2NJbrv+8Ohunnl0LdUtDfiDnJcCYEIyYRVNvOnwWf5KIP/nXQDdr8uwEkJ/bmu70zd/VzZ52ROTR3uu0Y9/YQ6ddMTE/pz18C5AIyNz+Hm/qdhkYy+94fFZPFAh2f10WJT3WEONJV3e6pQRQ3N6kZI6BJYIHgf9SNMBuxWMw9fc2bQxoSHrp7NiJw0jLLk0xHfctbkoM5pN86byMxRfX3rGw0SZ00czEWneOeyH//oB68u+ihdiTr9IPyxM78cWQ78mSmqh7La0N4q/iitaeTLjfu6FU23R6WuuY3PNwQ2ODndCk9/sqbb+qqm0e5089LSDbrHePj973SLcqdu2ShLzBjVl5vOCi8F593vttHS7gzoQOw6WdnuUvhkbV7QTsqfA//nx3wcYJIM3Df8AuqcLVQ5GkmPjMMqm2lwtVLe3kBaRGw3zW9eY89pEZ1weRSqnU0B/LM/ttTl49IppLIgktdYTGFrddjys021h8iNTsEgyrpyr9Fxfbix36n0tiZi6EJTJFvsLD/lz+xpLKGopYbxCTnYe6AUZFHi3sFncUPfWZS113PNhhd0z1FE4IlRl5EWGUucqTtlMD9zPLPTRlHQUkWMMZKkYxgpdyKvoQSnjsc1MhDjhmrvaE/TYNboflw0YxR9UuN6jC+KjjTz/K3zqaxvpq65jV7JcT36+hokiX9cOotbz5lCeW0TGQk2orrQGHvC5Jr9IYkixVUN3fS8AJv2F+PWCTM1SBJ5BZVH3QmXV1iBLIkBnK/DpbBxXzHnTu2uxCip1ufEParGloOBvxFN08gv158gFUWBF26dT0pctK4rXTBs3F8cVqu8QZbYU1jZzVv658L/cczHGbEma7f2YLsxErsxkE+MNVrDShpxqgqXrn2GWFMUl/We2k2X3BWplhhkQQroknOpCv/d+xmahs9yNBQONVfywO5PcOpNFiKQE5VMv+jUoNt39ZgOBzZjBDZjBDFGKxU62m2TZGBwTEY3c6KuMEsG+tvSwj5eMCSYojCKciC14gEcPx7bZJDJTU+gb3oCHlXlnW+3smjlDlodLiYNzub6ORNJ9JOmJcVEBf1Bf78rnxc+X0d5XTP9MxLJTrLz6bo9tDvdWC1Gbpo3mflTvF4fsVGWY0pqdise4qIDOePU+GhkSfSlfHdFgg4PHgoJNv0bsdyhpPBHbHSk7rHBq/X2hyAIREeYaNJRmNgiLT6ee09hJc98uoa9RVWkxEZxzRnjmRrE7S4tzsbuIxUhA149HjXg//XnxG+5MJ8wVMbR4vLe0zCLoWemNTQa3G3kt1Ryf94SXstfqbveWRljkXVGboqm0uhup0lpD6sog5fOaFLaAiYSwUuN+IevHi9MT9I3VR8XlxO0KB9PTEsaFOhtogEiaGU/cqOSKDB7nFe3/K+3vubpT9ZQXN1AXXMbX2zYy4X3v01jGPFH4PXS+OPLX7C3qIqGlnbW7y3kvZU7aHN6pZfN7S7uf3cFb6/wTkbOHBVe84w/0hPsxEQFFub5k4cGxE9JokC8LZIROUd/sxveJ5V4WySSH7crSyILpgQaScVYLUwa3CsgRNZslLl81tiA9QEuOmVUwFOH2Shzye+8dM+ewgquevR91u0ppKGlnb1FVdzzylI+XacfibbwlJEYDaFpPlkWfYX/50anjvm3SmX8f1uYz0gbyeV9pmGRDL68vSG2DF8OnV6CtcPj5vXDK2lXAkdMaRGx/HfkJSSaojGLhg6N7/H/TzOIElvqQgdhHgu21xfoLt/TWNJjJ+Lxwq6GosC0bAFETcCQ6DVwT4u38dwt5xATFUFZbRPLNu3vxnd6VI1Wh4vFOh2C/lBVjcc6OONQeOaTtQBsORienaw/ymubdFuv0xPsPHbdXBLskZg78vkGZyfzwq3zj0mBIAgCL962gMHZyRhlCbNRJsEWyWPXzglKi/zrslOZMqS3N2PPZMBqMXLngmlBefgrTh3L/MlDMRm8GYgmg8z504b7ePinPl4TcE0dLoXHP/pBN/RgYFYS/7r0VOxWCxajAVHQTyB3uT2U1zUd5RU5dvyfjvlXgCAIXN5nOhdkT6LS0UicyYpVNuNSFcrbG7h182uUtgdqWiVBpLitlr7RgeL8MXF9+Gza3ZS21/HSoW/5smxbyPOQEJmXMRqLZOL9wrUh25PdqodtdQVcmB3exMrRIL9Fnz+tcTbjVJUeI6yOB/Y2leq7AUoaF8wbzIKUCaTGRfsK1oGSagyyFMBPOt0KWw6WcMWp+iO+TjS2ttMWJi3h6Ji4O1AS2MgCIArw4NVn8OgH31OuM0ElSSIl1Y26krVx/TNZdv/VlNQ0dhTSn/a4nmi38tqd51PT2Eq7y016vK3HIm8xGXjomtk0tjpoaGknNT66x9BTURS4ff5Urp19ElUNLSTFRHUzmNpbpP89anO4aGhp122fP2VkLtOG96G0ppH731nBRh2li0GW2FdcHbbX9E+BpoHyK5ngh4MTqjD/d8/nLC5ej6KpWCQjs1KGsauhiLL2ejIj4zklaTArKnZT1FZDqiWG6/vOZHLigG6NI0ZRJisynszIBN3C7NY8JJqDi/4FQSA9Io6+Ucl8q6Nb9odJkhkR2wubIYKPijfgCpHgIAsSmZFxgNc2c1HhOt4pWE2zu52hMVnc0u/0kN7QwRBvitL9zACzvr2PBFM0V+WcfFyplAZXK0/tX8bKyjwUTe0mkeqESTSQE5sUwJGmxEbpjsBkSSQ7KTbksa0WU9gtw53rJdqtuh1tJoOBqUNzWPzDbt3C3OZwcdnD7xEdYWbhySO48OSR3Y4tCAIZx8ny0q14ePnLDXzUYW40YVA2t509heRYL2esqhrvfreNt1ZspbnNwfCcNG4/Zwq9U+KwRXonOj9fv4eXlm6gpqmVfukJ3Hr2FIb2/nEw8v2ufJ7+ZA2lNY1kJtjpn5HAV5sP+G5gehAFoZvHsz8kUSQz0Wt4tPVQaQD3raqaT0f9S+D/OObjgL9sf49FRWt9PG67x8XHJZs43FJJu8fF/qYynj24nP3NZbR7XBxuqeRP29/jm3L9R97Lek8N4KBNoszUxAG6k4n+OCNtpC7n3BUCAmbJyNTEgYyO60OM0eqzGw0GgygxP9Prv/zYvi947sByKh2NtHlcbKg5yJXrn6O0LbirWk+YGoRjVtFo97goaqvh/rwlvF+47pj27w+XqnDZumf5smwbzYqjw/c5sNC6VIVpiYFeGP0yEumdEofBj6OVJZHzpoXhA9FhbxlO+nKnb8XVp4/T5VfPnz4cWRK58tSxuvvT8Dq+VdY38+xna3no/e9CHvNYceeLn/HG11uobWqj1eFixdaDLPzP2zR18O4Pv/8dz3y6hsr6ZtqcbtblFXDJg+9S2uEp8uY3W7j/3RUUVzfQ7nSz/XAZ1z7+IXkFFQCs2HqQP778BYdKa2h3utlfUs0n6/b0WJTNRpn5U4aG1QV43rThAby7LIlkJ8f8Yukm/8cxHwcoqsLyitCcoj+8OuVluu+NiO3FX4fMJ9ZoxSjKGDsaK/4yZH5Y+7YbI3lh7NX0sSZhECRkQWJgdFr317Y0Xh7/e0ySl5N+cdw1DI/JRhYkDIJEVkQ8Q+2Z3teiREZEHE+OvpwUSwyNrjaWFG/qpmDQ8Dq9vZH//VFfC/DafIaCw+PmhYNfh6XtDoVvK3ZT72oNOSlqFGW21B3Rfe+Zm85m/MAsX0hqWryNp244S7fLTg83zZvMOZOHYjbImHooGrWN3sad08YO4OZ5k7BaTJgMMmajzHlTh3P9HG8Tz6i+6fz9kpnERnk9nkUhsC3G4VL4eM1u6luCB+0eK/LLa9mwr7ibZlrVNNqcbpas2U1DSztL1uzuxgFreOmf/y3fhNvj4cUv1gdyxG6FZz718uyPh8nLg7f7z2SQOWfSEF8mYChkJNh56sazSI+3YZAlDJLISQOzeOams8Pa/nhB04Sw/n4NnBBURmlb/VGnEHeiwtGAR1N1A1RnpAzh5ORB1DlbsBrMR51E3Tc6lXcn3UK9qwVZkIjq0FE3uNoQoJtdJ0CCOZrnxl1Ni9uBW1OI6dAgtygOnB43sUarjyssbK3BKMoBnKwHld2NPaeeBENhqz5/6g+XqtDobgvQNB8t9jaWhvRjBq+b3qHmCqYmDQx4zxZp5onr59HqcOFwuYmNijiqSTNZEvnD/KncOHcidU1tnPHnV3TXK67+sdnj/OkjmD9lGPUtbdgizAGjwJmj+jFjRF9qm1u56emPdXlpo0GisLKOGOtPlxl2xcHSmgBFBngL764j5Qzvk4pRh5f3qBq7CyqobWwLmkp+sKQaTdMoPYrGl3fvvYiUuOgeb3p6GJWbzif/vJy65jbMHeZLvzROaD/m3wKSdGw4w4XdEKFblDshCiLxHZzyV2U7eGDPx7QqTgRguD2bJ0ZfhlnW/9JUtjfw372fs7bmAJIgMiVxIC7Vzdrq/YDAycmDuK3/bOx+Bdpq8PJ8Do+bZw58xeelW3B6FEbF9iLVEsM3Fbto87hQdUaaAgK9Ir2Pex6Ph0vXPcuBlh/Niualj+HewWfpnm+i2aZreBR4TYSwDfoLW6p5ZO9nbKk7glkyMNSeycbawyFTUTqhuQWEvGheXLqLl7TdnDQwi6gIM99uO4hb8TB+QBZ3njuN9AT7Mf14NU1j8epdvLx0A7VNrbocNxCgn5Ul0TdJt3FfEY9+uIrD5bXEREVw+cwxnD99OAk2KzmpcRwqrQnYp8vtCTqJVVHXzEPvf8eavAJkSeS0Mf259ezJWDv42cZWB498sJIV2w6iahpTh/bhzgXTiLdFkpFg083wk0WBPilxpMZF6zZzCEB+eR2z//xKUD1xWoJ3EjE2KoK65jbddfz3mZloRwxB6YF3UvWh979jZ345ESYjC6YM5ZrZ43UnCn8JaNrx4ZgFQcgA3gCS8Zpevqhp2hN+6wjAE3i96tuAyzRN07eK7MAJQWWYZSNpITrzgmF8fG7olYCNNYf4y85FvqYUDdjWUMCFa57UXb9NcXLZumf5vmovLlWh3ePiq/LtfFeZh1NVcKpuvi7fyVXrnw8ay3Tn1jdZUryRVsWJonnYUHuIJSWbaFYc3kw8nW1MoswlvacCsGD1Y92KMsDHJZt4bN8Xuse7JmdGWNruAdHp3ToOg6HW2cwV65/zFeIWxcHamgPhF2UNPKvtuIvtJK7KAAAgAElEQVQMuN0qLsXDqp35fL5+D21ON26PypoOfrQpTN2yP95asZVHP1xFVYcBkV5RMhtlfj9b3196R34Ztz77CQdKa/CoGjWNrTz1yWpe/tLbznzpzDEBGl2TQWLi4GzdZolWh4uLH3yH73fm41Y8tDvdfLY+j2sfX4ymaXhUlSseWeTzwnC5PXy77SCXPPguLrfCgMwk3QQVRdUYOyCTBLuVyUN6YfI7Jw28mX49yCJnjPBquPV4dj3E2yLDKsqlNY1c8cgith70Tvg1tTl4a8VW/v6/5SG3/fkg4FHFsP5CQAH+oGnaAGA8cIMgCP6PfqcBuR1/1wDPhdrpCVGYPR1NHHoQEZAFUVeXDLC5NjxN8CN79SOHStrrONAUaJ+5rGw7bR5nj97GiqZS42xibfWBgPcON1ewvb4wZJisgICIgCSIZETE8cjIi+kbnUKjq5WSdv1JwEUFa3WXz0odxu0DziDGGNnjNdvXVBoWx7y4eIPPhD8cSILIyJheDLVnIQkiYq0RsU2GHkYuqqbhcCt8EqR5oScoHpWXlm7Q5UtFQUCWROxWC7fPn8oZ4/TDXp/7dG3ApJfDpfD68s243Aq5afE8ft1cMhPtSKKAUZY4Y9xA7rv8dN39fblxL60OV7cC6VZUjlTWsf1wGev2eJO3uyoWPKpGU5uDFdsO8e32Q0GL68tfrgfg35efxuzxAzEZJF3aIxh+2O39rZw7dRg3zJlIdIQpYJKuK6obW1GD0CJd8daKLbqSxxXbDoblSf1z4XhwzJqmlXeOfjVNawb2Av781VzgDc2L9YBdEIQezbJPCCqj2d0eVJYWIZv4dNpdzFzxb90vbE1HkGhPdIaieihvC276vb3+SICu+UBzBe16vg9+cHoU8lsqmZzYv9vy/JaqHs+pExoaw2KyeHjExd046531wXnmnm4W8zLGMjd9DK0eJ/NWPkyTEnjD82gqtc4WEs3RQflcj6ayp6Ek5I2lE0PsmTw39ipfg4nT4+b9lTt4hrW46Pkm4HApAdpZ7+hS67FwNLc5cAVREkSajXxx35VEmIw9SuqCGehrmkZlQwvp8TbG9s/k4394+VKr2YDREPypZF9xte6NQlU1DpfV0Opw61IRbU43h8pqcOg0sfifq8kg86cLZ3DXedPZV1jF9U99FFabeef2giCw8JSRXDB9BE1tDk6+8/mg29Q2tREXHeEbOSseFUkUun1v9hZW6baFGw0S+WW1JNgif/E8wKP0yogXBGFzl9cvapr2ov9KgiBkAyMAf3eoNKCrcLukY1lQw/QTojB3JpDoubFlRHjNjJLMdkp1RpCxhsigBXBT7WEe3vNpx6RY8P+kofbADqkcaxJm0RDSUtMkGciKDLSDzLYm6HLI/hAQ2FVfxMxv/01vaxJ3D5rL8JhsBsVkBN0m2EjYt09BwCqbyYyMZ3djoNDfpSrMWfUgEbKJ87MmcFXOKb5rmNdQzAN7PuFAUzniUfyYhtuzu3X9mSQDuakJuoY8/jAZZPqle3l1l9vbYfbxmt043Qp90xO454JTumlwOxEVYdZtUAEvN2rtQXPbiezkWGp1+FanW2HuX18jwmRgeJ9UNh8o8R2nX0YCr9x+rq4FZ25aPGajHFCcJVEgOzmWVodL11vDZJDplRKLQRJ5VyeqCqCXn7bbIEn0SonF5Q7P9jU7+Ue6cPEPO3nus3UhueZZ97wEwMDMRJranZRWNxJhNnLRKSO56vRxSKJIv4wE8gorAjypWx0ubnj6IwySxOnjBnDngmlBk9KPO7SAOM6eUKNpWo/ewIIgWIHFwK2apvm3L+r9UHo8+glBZciixGCbfiGa0qF/TbfoNxwEc0Pb21jKH7a8QUFrNRoEfRxPNtt0DXxOTRuOOUhrdyckQcRujGBSQmAScm5UCgNsaRhDWIVqaHg6zu5wSyU3b3qNw82VxBqtREr6hWWCzvH0cG3u7zDpcM5ax1+r4uTtI6t5Yt9SAIpba7l+0yvsbyrznleY3iAA1+XMCFg2tl8mybFRPY56BcHL2c7tyPC799UvvXIwt4IG7C+p5tonPqRQpylElkQumzkmUJdskLl+zsSwzvvaM0/SVRx0/qjbnG7W7insVvz3F1cz/5//093f6eMGYDbK3W5qnQZEo3LTGZWboTuidroVxvbNZNbo/kEnQe88d1rAMqvFhMUUetLUbJC5brZXErhk9S7+++GqsCYAO7GnqIqS6kbv98bh4n9fb+bxxV5Z58JTRmGU9a+hpnm576Ub9nLLs5+EfbzjgePVki0IggFvUX5b07SPdFYpAboWsHSgrKd9nhCFWVE97GrQf3RfVeX1oN1cr88l72vS//yvHv4uZNfegOhU3pt0i+57VtnMayddx9j4nA6eW2Ja4kDGx+UiCSKSIDI5cQCvjL/WFyflj8dGXcrpqSMxijICAsPsWZyaPAyzaEBA/zbrUhXeyF+FS1XwoF8Ym9zh/aDGxudw//ALSI/wdhrqPVk4VDdLijfRpjh5p2A1Lk941EVXGJHY0xzoQSGKAi/ffi6njMhFlkREQWBs/wxmjMjBIEuIgsCo3HRev/N87FYLFXXNrM47EuB77FY8vPXNFt1jX3HqGG6YM9GXSJ0eb+P+K08L6hPhj1G56TxyzWwyE72jyZ5uIl1RUd/CvuLA3MEoi4k37rqAsf0zEAUBgyTyu5F9eem2BQiCwP+Wbwq6zxeXeht/lvz9UrK66LitZiOPXTdH16i/vK5J1ykOfvx+ZSbG8NA1sxnV0WTz/OfrwtYxB4PDpfDhD7tod7rJTLTz4m3zGZSVhID36cD/aculeNhdUE5+eXjZiz8V2nGa/OtQXLwC7NU07dEgq30KXCJ4MR5o1DStx9y3E4LKaFEcuIPM9Jd0dMEFG71paNQ4m7EbIpBFCU3TaFYcHG6p1B0jR8omnh1zJQPCsNVMi4jlydGX+wyAOnmyNrcTQQCL7B3RNrhaUFSNeHN3XXCEbOLewWdxz6B5aGg+hzdN09hRX8RtW18PsC5V0TjUUkG9qzXoo1hRaw1N7nassimka9zkxP5MTuyPqqnMW/UwFY5ADaskCFQ7mjjYXB70ZtATJEmisLWGoTqpLXarhf9ceXrANfR/Dd6MOaMsBaQ5e1SNA6XdtcRujweH043VYmLhKSNZeMpIVFU7plTniYN7MWFQNo0tDi556B1KasIz2tl5uKxbJ1tLuxODLJGeYOfZm8/R/Yz7dUJnu77X6nARb7Oy5B+Xo6oqqoquEX8n9vbgLy2KAhueuqXbNdE0jerGwJQc73nC1w/8nhl3vxB0n10hiQLVjS1kJsYwKDuZN/94IaqqccuzH7MmryBgfVkUKaisp3dKXFj7/6k4Tr5dE4GLgV2CIHRyTPcCmd5jaM8DS/FK5Q7hlctdHmqnJ0RhjjJYMIsGXY45u4O/7ckLee7KhzCIEmPjc9hVX0Sjuz0ov6uoHjJ6CGXVQ+cPq6Stln/s+pDdDV7eNseaRElbHa0dgasRkpH/DF/ISQm5Adt3daoTBIFsa7zu55UQGBCdTqwxOHfe5G7ntG/vJ1I2cX3fmczL6NnsB7x67tyoFCodjQE3LFXTSLLYGGBLZ09jSdj2pp3Q0EL6e/hP/uhNBmUnxQQUZfCOYjvtIhWPypNLfuDDH3aieFRirBbuPHcaM0b2PaaiDLBs0z4e/fB7GlrbQ3oKd8Wovt6n1z2FlfzjzeUcKa8DASYP6cVfL5rp863oisG9klmdp98Fuaeokml/eI5+GQn8/eKZ5KTFE0qt1lNSdmxURMA1EQSBlNgoyusC/UAEBE6796WeD9gFqqaR4CcZFEWBQVlJbNIxz3d7VPr8QkUZOC5dfZqmraanCSrvOhpww9Hs94SgMiRB9GpwJX9vCwPXd8QqjY3LCbq9W/PQ5nGxsnIPta4WFM2jq1wwiwbOyRyHVQ78wYSCw+PiyvXPs7O+CI+m4tFU9jeX+4oyQJvHxa1bXqPGETo+x26M5IzUkQG6Y6Nk4JLeUzCIMn2j9H90KhpuzUODu41H937B10H8Qvxxdc4pAZyzWTJwYfZEzJKRhdmTMIahg+52vqJMv6hUBkQfB6N9u5WZo/pi9uN8jbLExTO8lpQPv7+SD7/ficOloHhUqhtb+ev/vmLjvmPrlly3p5B/vvU1NU2tKB4VtYcw1q6wGGX6pMZR1dDCNY99wMHSGhRVRfGorN51hOue+FDXanXhKSOC7lPTwKOq7C2s5Ir/LqIhjJbvBLsVu84NALxe0Xq4ce6kgGsM3kKrp6HWg9koc/GMUbpp3QumDsNkkLtZf5oMEuP6Z5CVdGz9CkcLL7/9223JPiEKM8B52RO4Y8CZpJjtGATJG3468iJGx3lTE/JbAvm8cCDgdXSLM1q5JncGN/c77Zj2803Fbt2MQX9owNMH9P07/HHXoDlc0WcaMcZIDILEiJhsXhx3DZmR8Tg9bvaEEZ/lUN28eOibsI7X35bGU2OuYJAtHYMgEW+K4vrcmfw+93eAN9rqlfG/Z0xcHwyihN0QwYKM8ZwU3xejKBMlWzgrYwxTEvpjEmWsspl5GWN4cvTlx00O9deLZ3LpzNHYrRYMssTovum8duf5pMbZaHO4+HTdbl3dcbB8u1B44Ytj41s9qkZlfTOLv98ZoLBwe1QKKxvI06EZ1u8twhKiuUPDq33+bH1obXdTq4M2p/5cyo4j+jTnaWP788/LZpGZaEeWxJC8uigKTBiQzcCsRAySSII9kpvmTuLaIE07cdGRvHH3BUwYmI1RloiOMHP+9BE8dPXskJ/neOK3bGJ0QlAZnZiTPjpoonWN89gMtk2SgUWTbg2Z+RcKJa21YflCAOQ3V1LjbCa+w4uiyd2O0+Mm3hTVrYBJgshlfaZzWZ/p3bZvU5wUtFZ71w1jAFfpaGR3fRHZUYm6TwOK6qHW2YzNGMGwmCxeHn8tNc5momULFr929D5RyTwz5soej6eqKoeaK7DIJjIij++jqSyJ/H72SSw8ZSRtTjfx0ZGIokCbw8X+kqoOPW0g3VFc3UBFXTNxtogevYj90enIdrQwGiTK65rIr6jTb5EWBUqrGxmcndxteUl1Y1ijUqdb4WBJDdUNLcT3oAOuamgJKhksqgqu3Z8xsi8zRno7AcffpN/9KgoCa5+4MSxHOX9kJcXw5A3zqGlsxWyUu+Uu/lL4BbIhjhknVGHuCRkR8RxpPfpRs4joK5A/Bf1sqURIRtrCKM77m8uYt+phEs3R2AwRHGgqRxAEEkxR/HXIfEbE9tLdzulx82DeJ16nPQ1cWngjOYfHzRUbvE0Co2J68cyYK30NAR8WrefZA8tRVK8ob6g9i/1NZb6OvlkpQ7l70LzA5JEg+KR4Mw/u+djHQ1tlE0+PufKoMgp7QnO7k7//7ytW5x3x+v9GmMlKjGFXx+gvmCa6prGVs//xOrIkcsOciWHZhgL0z0hkbV7BUZtoudwespNiGdo7hR925Qecl+LxkJseOJfRPyMBoywFzenrhAB8uXEfy7ccIMFu5R+XzGRkbuA1Tou3oQTxAB+Ulay73B+9kmPZr2PUFG+LxCCHf5Prik37i/n7m8upbWxF0zTG9MvgX5efdlQhrz8FGgLqb9go/7d7ZkeJUUGKWU8wSwauzjklLF+IUJic0J9Esw2DEPqLquGVvZW01ZHXWIJb8+BSFUrb67lly+uUBelC/NeuxXxdsROXqoRdlP2xpf4If9z+DuC15Xxy35e0KA4cqhunqrCp7jBNirfT0qUqLC/fyf27l4S17wONZdyX91G3ycEWxcmV659HCbNDMBTueOEzVucV4FZUnG4PNY2tbDnobe7oqVFF1TQcLoWWdhdPLPmB5Vv2h3W86+dMwBSGb0RXmI0yZ08agt1qYfyATN3zkkWRXsmB2vsx/TLJSooJyOjzhwZ4NA2X4qG0ppGbnl5CWW3gU6PFZCA9Xl/Lf9KA8CSDt54zJYBzNhtkbj178jFRVEVVDdzy7MeU1zbhUjy4PSob9xdzw5Mf/SIRZ53Qwvz7NfD/TWFeVbVXd7kkCERKJnpbE7mh7yxGxfYmUjKRFRnPPQPnsbDX8YlwkkWJV8Zfy9z00dgMEdgNkcxNG02uNfmozAUVVWVx8fqA5Y2uNlZW7cEZRoEziXLQ5hP48Vq9cvjbkJ2LTlXhm4pdNAfxKumKx/cv1V3u0VTeOw7m+8XVDezML8cdolMQvMUx0mzUzZY7Gs55QGYSL922gFG56USajUHNfQS8bd4psdHcNG8SdyyYBsCyTft1AxU0TWNnfiDHK4oCL922gAVThhFjtRBpNobld6F4ND78PnCSt6GlneLqwGR0gKWb9H8z/hjXP5MnbpjH4OxkIs1GctPi+c9Vp3PqmP6hN9bBopXbA54IFI9KYWW9rvb7Z8FvfPLvhKMymt3tVDubSLXEYpYMtCgOqhyN1Lv0zVA8msY3M/7ik5adnzWB0vY64oxRAX7JenCpCptrDhNviqKvLRVVUyluq8MsGQLsSKMMFu4aNJe7Bs0N2M/HxZt4dN/nOEL4ayiah6LWQJF9rasZWRAJh8WOkE18Oe0exi//s+77Ghr5LZVUtofHn0qCSJ2zxec3HQzlQWKrAI4c4+RsJ6obWth+qAxZEggyl9UNKbHRvHz7Ak6792XdEevRmOcMyk7m6ZvOorSmkZuf+Vh3ZBphNvLqHecFZP4VVTWg6Bj9CKJIeV0Tw/qk6u7rDwum8ocFU1n8w07+++EqPCEmIN0eDwdKqzhSUddhqOT9vtc0tmII0vZeUt1IfnktafE23e7GVoeLyvpmkmOiGNMvgzfuvqDb+w6XQlltI/HRkUQHUX50heJRKa5u4FBZjS5VI4oC5XXNDMj8ZZKyf7XhcBgIWZgFQXgVmA1UaZo2uGPZIqCz79cONGiaNrzjvXuAK/HOwNysadpXx+NE3arCf/I+Znn5TmRBQtVUcqKS2d9UhkGUgzaYpEfE+YryOwWreeHgNwh4nd+mJQ7kz0POCRpC+tyB5byev9L3/2cWDZglGYeqoGoaOVHJPDD8QpKDtH13xUBbWlhfBLNoYKQOLZNmiQ37ezTQlo4oet3jghkaXbHu+bAnKwUEUiJCT44OtWcFzRScGGabuD+qG1u4+6Uv2FNYiSgKYSkkJFFgRE4atkjviNOlIysbeBQ//je/2cLzn69DgKDHV1WV9IRA/+WROWmsyTsSsJ3Ho/q01z1hUHZ4PLAgwIa9RVz0wDuYZIm/XPQ7pg/PIT3BTjCFX0VdE5c8+C4acO0Z47n4d96JdY+q8uiHq/ho9S4kUcSjapw7dRi3nDUZURTQNI3XvtrEK19uQBAEFI/KzFF9+fPCGUEnAr/ecoD731mBy+PB5VYQhMDJN7fi+cWipeD46Jh/LoRDZbwOnNp1gaZp52maNryjGC8GPgLo8CE9HxjUsc2zghAG6RoGHt+3lK/Ld+FSFdo8Thyqm92NxR0aZWcQ72IDt/c/A4DvKnbz/MGvafe4aPO4cKkKK6v28J88ff50ZUUer3UpyuCVnjW423F4vPzrvsZSrtv4clhmRH2jUxkd17ubTtj/ayELIlEGC2emjwr8LB18eDhJ1iNisgFI6eGG4b1moUu9WTJwbe6MsCb/bhtwum7TS5zRysnJg0Nu7w9N07j+iY/YdaQcl+LRLYr+11AUBCwmA1ecOgZRFLjtnCnd6AcBL81x07zwKKxvth7guc/W0u500+Z0B/Vzvvr08bqa3TkTBmGLNHeTnJmNMtOG9va1efeE/hmJjMpN7zaiFXTirLwaZ412p5uGVgd/eu1LDpRUYzbKXHPGeF0KRtW8Xh/tTjfPfb6Or7d47Wlf+XIjS9bsxun20OZ043QrfPD9Dt78xmuwtnTDXl7+cgPtLoU2p9cN7+utB3nkg1W6n2FPYQV/+99XNLY5aHe68ahaQFE2G2VOHdPvFwtj1QBVFcL6+zUQsjBrmvY9oGv829Enfi7wbseiucB7mqY5NU07grcFMXTbWQi4VYVPSzaH9LYAryY51mhleEw2j4+6lEkddpuv5a8MoBFcqsKKil0Bbc8AzxwMbeKtolHvamF7fUFYn+OhERdxdc7JpFliSTBFsyDzJK7L/R0ZEXHEm6KYmz6GNybcELTB5aJek/nrkPn0jUohxhAZ1EBpWfkOgKCjV3+ICMQYIhlkS+fugXOZljSIWKOVflEp/H3IAs7PDs/wx260smjSreRGJfs8pCfG92XJlDvC2t4fe4sqKattCnAlEwSIjjARFx3B6eMGcMeCaeSmxRMXHcHvRuby1h8X+tJDZo8fyMNXz2ZwdjJxURFMGJTNq3ecF9ZoFeCVZRuD+jnHRlnol57A3y6eyWWzxuhuH2k28vYfF3LWpCEk2CLJTLRzw5yJ/PuK8PXyj147h6vPGEd6vI1Eu5Xzp3kzCDMT7cRYLboctEvx8M632wC4dOZo/n7JTPqlJxAbZQnKu7+6bCMAb3+7NTAT0KXwRocfyatfbQp43+lW+HR9XoCPCcBb32zFqQQul0QBu9VCVlIMt5w1mb9eNDO8C3I8oOH1AQ/n71fAT+WYJwOVmqYd7HidBnSduer0HQ2AIAjX4HXzJzMzs8eDtCqusFthNU3lhr4zGWzPoJf1xx9fjVO/205EpMndRqTcfbKs3hk+B1nlCE9DLYsSF/eawuTEAbhUhT7WJGRR4nI/nXJJWy1N7nZyopIDRqozkocwxJbBweYK/rjtbVw6HiK1zuawDMy74pNpd/lG4+dkjuv2nsPjIr+lihhjZEi9d2ZkPG9PvLnHdVRNJb+lytuqbU0K8PPQNI2Cynq2HirVbaPWNBjcK4Wnb/wxQuvCk4N3zE0c3IuJg7vTQ3VNbZTVNZGRYNdtje5ETRDfCKNB4s0/XkhKrHeE53ApHC6rwW61kBZvw+VWOFRWg9ViJjPRzq1nT2JY7xRsEeaAcwkFgyxx+cwxTBvaB5fiITctHkkUufK0cazJK+Cel7+gxc9vWVU1jlTUkldQQZ/UeGaO6sfMUf2orG9m3t9e1y2gNR3SteYgpkedKTK1TUFMsjRobXcF8NVldU26mmGL0cCDV53BmH7BLWx/Tvz/rGO+gB9Hy3AUvqMdRtMvAowePbrHS2QzWLAbI6gOUly7woPGv3Z7nffSLLG8O/FmzLKR4fYsVlTmBTy+GyWZBFPg49Mgezrrag4GLPeHW/Mw2B7eF+tISxV3bH2TamcTIgJGUeafw87zxV9VORq5Y+ubHGmpQhYkNOCugWdyetpIwDvxec/2d9leX4BBlHSLMsBQWyaiKGIUJVxhJJGkWGKCUiQfFK7jqQPLkAQRRfUw0JbOgyMuCsgxDBd5DcXcve1tmhXvj9wqm3lgxIUMsXtvzoWV9dz23CdU1Hv/r/VGq2ajzLj+Pd/Mg8GtePjnm8v5eutBX2jpvImDuevc6bo3gRF90nRTQ0wG2RcdtfiHnTy6+HvEDr41KcZKTWOrj3+1WozdiplBFnnqxrMY2y+8z5BfXsttz31KdWMLoiBgNMjcf8VpjB+QxYCMRN0iC7CnsIprn1iMqmncdvYU5k8ZSrwtEotRDthGFASG9UlFEAT6pMbpBgTkdrjXDe2dzOrdBQHvR0WYfC5+XTG+fyb7iqoCJiBdHg990wMd8X4x/IYL8zHL5QRBkIGzgUVdFh+172iYx+KOAWd2840QwhChlbbXce2mlwH4fd+ZWCRjNw7ULBq4rf8Zurac9ww6K6ThPICEGFbXoKJ6uG7jy5S01eLwuGnzuGhwt3HX1reoaG9A0zRu3vw6B5srcKoKrR4nbR4nD+R94mu9/vOORWyrO4JLVXTpl04M6rhR3JA7K+R5mUUDdw48U/e9DTUHeWr/MhweN62KE6eqsKuhmLu3vRVyv3pocTu4cdOrVDmbaPe4aPe4qHY2cdOm12h2t6N4VK557AMKq+pxuBTdomyQROyRFs6aePScNcCTS37gm22HcCkeWhwuXIqHT9fl8cY3m3XXv37OBK9/cpeibTbK3DF/GpIosuVACf/9cBXtTjetDhdOt0JRVQNtXV77jzDdisr1T3yEooR+qnErHq5+9ANKqhtwdHC6DS3t3P78p1TUNWO3mnVVH+CdxGt1uGh3unl08So2HyhGEkXuWDCtG+csigJmo8wNc72U1V3nTsdskH3ffkHwfuZOv+dgjSnZSTG6N7fzpo8gyi+mymKUuWTG6B6fVn5ehCeVOxG9MmYA+zRN62rY8ClwviAIJkEQeuENH9z4U06wE9OTB/PE6MsYG5dDqiWGGcmD+few85mY0I/UHgrjnsYSVFUlKzKeNyfcyKyUoaRaYhgV25tHRl7MGR2jUX8kW+wsmnQrA6JTff7KehAQ2Fqn7wbWFetqDnR4aXSHR1P5tGQzB5vLKW+vC1CXOFWF9wrWUudsYUtdflD7065YVu51H7yg1yT+NmS+z4ku1mjlhtyZTE8aRKolhgnxfXl6zJVBjfXfOvJDgM5Z0TzsaSyhol1fG9sTvqnYhapjG6qi8k3FLjbtL6bN6Qp4xBQFgbioCNITbFxw8gjeuXdhWOkj/tA0jY9W7woYLTpcCu+s0A8tzk6O5Z17F3Lq6H6kxUUzum86j103lzPGewMa3lqx5Zi8NFRN472V20KutzavAJfiCfzeeFQ+XZfH8i0Hwnokd7gUn2f16eMG8Ph1cxnTL4O0uGhOHd2Pd+5d6Gt4GdMvg5f/sIDJQ3qRFhfN1KF9ePWO8xiR42Ull23ap3uMXUcqdK9FjNXCu/dexPwpQ0mPtzE4O5m/XTKT687U99L4xfAb7jAJRy73LjANb+5VCfA3TdNewau+6EpjoGlaniAI7wN78KbH3qBpYUYmh4ERsb34ozmasvZ6+liTiDNFMTPF65A1dtm9QbdbX3uQrMgEMiLj+PvQc3s8RoviYF9jKXZjJLZHpQUAACAASURBVH2sSTw/9hr2NJbwWv5KNtYeClhfEKAuDD66ztWqq95wax6qHE3UuVp1vZM1NKocjTS628LWMde7fuRFz0gbyejY3hS21ZAVmUCS2ca+xlI21R1msC2ToTHBH6eD8fKyIFHnaglLJgjeydu8xhL2NZXi1DHad3rc1DlbMDRbdYuMqmn0y0zkspmjGZSdrKt+CAeKqgYYHHWisc3Jhn1FZCbafbxxJzITY/j35fqTdT8lTFRPD+2P2uY2PDojYrdHpaqh5ag674qrG9m4r4h+GYmM7Z+JqmnsL65mypBeAQqRgVnJPH79vG7LqhtayK+oo7ZJv9lIA9ocLl0FSLwtkhvmTGT6sBxskWZy0+J/8Zy/btBA+5UUF+EgZGHWNO2CIMsvC7L8PuC+n3ZagWhTnNy97R221x/BIMq4VIXZaSO5a+AcREHs0afizzsW4VIVRsb24sHhCwOMeTrx9pHVPH9wOQZRQtFUrJKZZqUdWZRweRQEAm+giqYyTMcA3h/D7Vm68jSLZGRcfA4DotN0/ZdNoszExH5kRMSFNL0Hr8JiVGxvwFsQ/7rjfX6o3oex45pJgthNvxxjtPL+pFt1m21Ois/lcEugA5qieuhtDU/VsK76AH/a8R4aGoqq6tutSkZGxGaTZI/TbTwQgM37i9lxuAyPqnLXudOZdwxUhkGSyE6K5UhFoMhIUTzc+eJnuNweJg3uxf1XnBaWOc/EQdkcLqsNmVuoh9PC6JwbkZOmP3FmMjB+QCa5aQk895l+KnpXCEBRZT13vPgZTpcCguDroHxiyQ/0y0jg7T9e6PNQ6QrFo/LPt75m+eb9GA1SULe62CgLMVH6TUjvfreNJ5esxiB5ddHJsVE8fdNZATfBXxa/3cJ8wrRkP5D3Mdvqj+BUFVoUBy5VYWnpNt7vaPW9a2Bgt10nOtffWpfPg3v0c8U21hzihUNfd+zficPjpsbV7OV7FSduLfBx0iwZmJc+JqyRY5Y1gZkpw7pNsplEA1mR8UxPGoTNGMFlvad1e98oysSarJyVMRZZlLh9wBndeHbJ779PFkQiZBPX5XplR88cWM7q6n24ulwz/6aSelcLV2/QT6QIpoRR0cLSU1c5Grl729u0KI4OjjrwB22WDAyzZzEiphdp8TbmnDQoYMSl4ZV/tTpcOFwKDy36jj09JHP0hK6RTP7HaGn3cs5r8gp48uPVYe3vwpNHYos0dzPzESBkUG1uejxDdAJk/dErOZaZo/p2swI1GWR6JccydVgfspJiGJXbs9d154DCo2m0tLtwe9SAtvb9xdX87Q19iehryzbyzdYDXl6+PVAh1akNv+eCU3RHwVsOlPDUx6txuhVaHC7aXW4Kq+q56eklv6g3RgBOZCrjtwCnx82Kit0B/KpDdfNe4VrOz57I6WkjsBksPLL3M6odzbpm+C7Vw9cVO7l38FkBMrT3CteEbJcGMAgSyRY78aYo5meOZ0bykLA/x58Gn8WYuD4sLtqAU3UzM2UY8zPH+yYfr8o5mX7RKbxbsIZGdxtTEwdyfvZEn655dtooUi2xvHXkeyraGxkT15sxcTl8UrKZkrZahsdkc0nvKb7JyI+LN4blrVHQWo1LVQKuyWel+jl6bs3D1tp8Rsb17nG/X5Zt1x0hGwSJJLONaGMEZ6aNYm76aN8P+p4LTmZUbjrvr9pObXMb5XVNuP0myVyKhw9W7eBvlxyd7lVVNdbtDW2Y73QrLFm9iz/MnxrycdtutfDeny/mrW+2sGb3EWKjIzln0mAOl9fx7baD2KwWZo8bwLLN+9l6sBSDJDJnwiBuP2dK2Of9t4tnMrZ/Jot/8AYAnDa2PwumDPPZl750+7k888ka3l+1A6dbYUivZOZOGMyXm/ZRUddMYWVdWDz08s37+ddlpwYsX7Rqh76WWxTolRxLVmIMl88aE7RL8Z3vtgVsr6oa5bXNHCyt+fWUGb9hVcYJUZj1Js060ex2+P49MbE/GZHxHGmp4m8739elNjTNW+j9i1CdS1+v6g+DKDErdTijYnoxLCaLWmcLS0o2YpYMLMgYj9mPJnGpCtvqjqBqGiNiszk1dTinpga3nJycOIDJHcnf3vPV2FlfRIO7lcG2DEbG9gpo2e5sounE4eaK/8feeUdJUW3f/3Orc09mZmCGnKPkHAQEQVAUUBGzTzGAOYdnesozPvNTH6IoBkRFUYwgknPOUXJmYPJ07q77++P2NDPT1TNNMPBb373WLOiudKu6+9SpfffZh/0n4Q8N4A36sVbIVI344FKU7Q34e9FhJu6aR6rVyZ3NBqFLyZq83WwtPIjf4MYghGBkvR6MrK+6Mgf0IMuP7yAoQ7RPa0D/Dk2oluxkxfb9fD5rdVRg1qVk95E85q3fSZuGNaOsIqWUbNmXw7GCEprXrU6NtCS2HzjGvmP5MaVlUdfDH0RKDIsxKiIt0cFdw3qVqybsD9x6UbfI64u7t4rruEbQNMFFXVtwUdcWMde5Y2jPiKqiFEO6tWTX4Vyuf2lyTPqhLGKpO1zeGN8jCZMevTom5SNDxyCwnrxC4z6GyQ4f+Ocg/Q3A0oEzVCQcH0oLTP6mOCsCc7LFQZY9hQOe8tygQNA5nLX59SCPrfmc5bk7MGummNlvtiPVsLKud/UW7AxL1SqDO+Tn890Lmbx7IQE9WE5L/N9t03mk5dBIgcaK3J08smZS5HFNR/JMmxH0rRHfj/SwJ587VnxInq8YIQQBPcQ19XsxpqlxplgS9HL/qo/ZWngoYmITD6yaiWQDjrlZck02FBpnmL1rqCBxzcL/8nvJCZe0KfuWYkLDbrLE7A0oEHQKf25r8nbz4OpP1eOxAH+eQFtWDXTlyRAroGzZd5QnPppOIBhi1OCu3HKhuuZ5RW7GvPUNB44VommKR010WHF5/ZhNJsN5AiO0rFfjlHsE/p1Qt3pa3J29a6VHe30AtG9ck2Vb9kVdt0Y10w2DspQSWfwKuD8GYaV3oxZs3d8eX/BE4L2i83ruGbAUq8WBzJcgnJD2EcLSNO5zO138nQtMzgqOWQjBY+cMx65ZIrI1izCRaLZxZzP16PXe9pksz90R4YRLH6FLf1omoWHXLPyz1XDDx9Mr6nYn3ZaErUwmXdp2qiLcIR+ukM+wwOOlzdPI85VQHPDw4OpPFb8aXt8T8vPUuq84atCJ2ggPrPqEQ+483CE/rqAPvx5k8t5FzI9hcfrypmlsKjiAV1e649LvXenZxtJl39/cuKXP020uN9zmklqdSDTbGbf913JBuRQhdFwhY07ZYbIwMLsNjZKycAV93LfqE4rD16jE58OzMAGXW2mAK8vyAiGl0fUHQ0ycsZwlm/cC8NiHP7P7SB4efyCyPK/Ygy+gOOpYv8XI90RTXhuPXtkv5rHPJphNGv+8uj9264kee0Y3HAGMvTGaxgB4cERfnHZrJMCbwrrnf17d3/igvl/B8xngB1nC5Z3WUj25BJtZ/V5a1TrKXecvw2YJISgB6QL9GDL/Rs6giKtq6CK+v78AZ0XGDNA5vRETe9zOpN0L2es6Rtu0elxZryeZdjWr+92BFYbZriYELZNr0yCxBtc06EWDRGP3qkSLnc963sU3e5ex8NhW0m1JDK/TmV0lOcw5upEcTxFHvAUx3drK4sOdc2iabDyxE0JnxqF1XN+wco5xT0kOB9x5UcfzhgJ8uWcxvauXf6wN6iFDHh7UJGLT5Jo0T65Jp7SGTNg1h/3uXKrbU7i/+ZCort2lqJuQwbe9H+Tfm6ayqWA/SRYHoxr1Y1gd5QsRr8eyVZipk6D8QIbV6Uy/GkpRsSBnC2XzV3nMekq8n8cf5Mu5a2heJ5N1Ow9V2f3DCBazieZ1qtOsTibXnt+ROpnxSQHPBgzs2IzaGSl89ttqDucV0a1FPbLSkvhg+jLyiz00rpnBE9f0p3EtY663YXY6Xz15HZNmrWbj7iM0qZXBted3jNk4Vbo+BnlCUpdgC/DZrVOYuqoNc3cOZkyfFdgtBgFYuiGwCqynba8TF8TfOGM+awIzQMPEGjzZ+jLDZbEM33UJ47vdFrNApCwSzXYG1WxLDUcKqdYEOlZrSNeMJlxVvydPrP2CQ0fiMwUqCnhwBX0EDYJkQA9REqzadN4V9MUcc1GZ7XVd5+v9S9lefNjweAAmoUz8S1E7MZ0dxUeo48ygVUptdpfksLXoIDUd1WiTWrfcE0W2My1mj79AnF1JTJrGtQ3OjSrmKQl6CZV9ngzE18PQCIXhpqOnSj9IVPfmBllp1M5IYcfB42w7cIzamSm0aZB90prbrfuO8t2iTVRLdnLdgA44rMYSzcjx9WLwLwIEWHshtIRTOo9YaFkvi+dHXXjieFJySWcPhA6BpTXCXD4oSykhuBGCu8DchOxqLXlgWA0IFoIpEyyV3LhktD47wRbguh6buW5gb/AFIGD0QQvQq7ZdOCP4K9uTxIGzKjBXhg5pDVme+3vUtT4ntU5cQVlKyZvbfubrfcswCw0QOM1W3u08ivqJ1elToyWzj26MyZuWxdDanUi1Gru/OUzWmJV2ZdEkOdtQZmnTzPQPZ5w7io9ww+J3qqwGLOXhfaEAD67+lHX5e9GEQCIxCaXRLu2ykeVI5d3ON1PNlljlGFun1mV1ftVVjyGpR7qZl0WX9MaU/XWIjMApTcjYLWYGdGhKdrVkkhy2U+tqHdJ5YfIsdF1GmpeaNIEE6mSmMu6eywx9IIxw82tfsfr3g5HX7/24hBdvvogBHY35U93zExQ+BsKE+tBDkPIGwn6e4fqnCxk6hsy7DvQj6ngyhLT1RaS+hhBmpF6CzB8Fwa3h5TpSWEH6w2OUYGoE1T5CaAY6ZNsFENwDUeVQHih5D6NmuWpgPrAaN1s+8/jrnOPiwVnBMceDB1sMIcFsj6gtLMKE02TjkUr0zWUxL2cL3+5fEfZ79uMO+cj1FXPfqk+QUpJlTy2f3cVAy+RadExvSKOkGgyp1RGH6USmpIJyU9qmVl2QYtXMPNZyGDbNEgnwds1CDXsqI+qpUtbRy96Pq0Q7I2zSNP7331iTvwevrrw6PKGA0jfL0nP2s891nGc2TKlynwDXNTi3ynXsJgvXNTg3qtsLKKrk0rpdI5po4dCxNvOhmU/ckywmrZwywmwSCHFCJ2y3mqmVkcLwXq3RNMG/rr8Au9UcscIs1ReXvo6V+OrhiUZvIEixx4cvEIx4Fe8+nMvYz2bGc0n4+NeV5YIyqFvPoxN+wm9gfSlDh6DwUcCruFZZAtKDLLgHqRu67Z42ZOEDENqrqAPpUsf2zUW6PlHLi5+HwCZFR0h3eGxFZcbohuBWZNG/DPcvEv4BpiygdJK97EV3A7F8XswgzuyTQqX4Px3zH496iZl8de59fL13CZuLDtI0KZsR9bobBgQjfLNvaZS8TAJ5/hJ2lBzhp0PGvgZmYcJhsuA02xhRtxvXN+wTWfZwy0voVb05Px5YRUjqDK7Zjj41Wsb9WDywZlvqJ1Znyr4l5HiL6JnZjItrdcRhtnLYnV+O0qgMs49u5JFWQ/n+4CpD+VpZBKXOitxduIK+KCvUiph5ZIOhysGmmWmaVJNsZxrDancyzJZLcW+zC+me0ZTvD6wkoAe5oF07kgtT+G7hRty+AAM7NSWrWhLfLtxIkctL/w5NqF89jW8XbSS3yEXvNg0Z0u1EUUr3lvWY9Ng1fDl3LQeOF9K5aR26Nq/Dj8u2sOdIHku27I3nkpVDIKSzYONufIGgYQumspg8x9hzQ0qYtngTI3q3Lb/A+zPGv34B3l/BeeVJj7cySL0Q/KuIzlq94JmMTLgRPD8Qne1WRAC8M5AyFCVzE1oSpE9Der4G3wII/g569CRxFIQG/hVg+5M8NE5+KuJPw1kdmHWpsypvN/tcx2mUVIM2KXXpmtGETHsKdRMyqG5g5xkLsdzaNASeYABX0GtYUm3RTJyf3YZ2afXolVleTyyEoGdms1NuqwRQPzGTHpnNyPeV0L5ag0g5ealtZjwoDcZVBeUIpOSnj36jTlYNOg9qhzlGMCpVfogjAcxr3eDQCHRNwJxg4vqGvelTo6Xhdvu3HWTKKz8gpeTyBy6mW4smEetTALKIssTs0Lh2uddtDHrllaJBVrUoVUXzujUIhnS63vUWVadBknNqHaVZVi4H8pNZvqu26hAS0qGKgkdfIPYTjJHPsdTdKFuZigiFs9UzDOkj5oNyZMIujqaKAASR7s/BXA+sPcsFaKElILVaoKUQf9opyk0a/qH4Px3zH4MCv5vRy8dzxFNASMoyFoUCXUpMQpDtSGNcl1viaro6ILtN2HKzwpdSQIuUmvTLOoeZh9dHqSQ8IT8/H1zDjENreUF+x3PtruLc6qfWPbgidhYfYfTyDwjoIXSpI5Gcn92aJ8+5jMaJNTALrUrOW0PQI0PdGLplNGHe0c3lz0ESxWXL/V4+ufdzTGYTjiQ7r88fS3bDaG+M87Nas+o/C9G+zQvXIYMD8I+tS8e+xlWBr49+j5/H/xZ5Pf3D2Qwa1Y8H3h8TzyU5LZhNGm0aZLN+16HIFTCJELoUyHCwspmD/PfaH2mefQwNSUhq5JY4eWH6KJz2yifwAPq0acT3SzYZLhvWI9rfQ9j7Il0fABVvtAJs8VcHxg0tE7QM0A9WWCDA1g8hBNLaGfzLiSugFv8HKUygpUG1yQhTDXQ9CMcHGByjCsjAn6bIgDOnyjDqi1pheV9gGlA6ITNVSvlsZfs8aznmlzdPY5/rOO6QH58ewBv+84Rfu0N+9rqO8Z8t38e1v+F1utAgMTPCCZfqnp885zIsmpk8b0lMqVzp8bx6gH+unUxx4PTv+lJKHlj9KYUBd6THoU8PMuvIRn49vB5N03ioxSWG21rCk512zUKqNYG7mytntPuaX0SK1Rnx2zDp4Y6Y3nBw9+vg1rG/egSfx4+72EPekQLGjnzN8DgZWwTmaQUIv0T4JMKj/pKePYJNRuu/t67YUS4ol2L6hNlsWbb9pK/RqeCJa/qT4LBis6jxje63iiSHH5tZ3ZBH911Gq5o5OK1B7NYQCbYA2alF/Oeqqo2CAB6+oi8JBgF8eM9zqJYcnSAISxtwXAKidGJRAA5wXoMwNz6lc6wMQogYPK4ETSktRPIzIJKAUiorVpiQRHjn0GFk4cPq7aKnqgjKpvA+Sx8/NMAOyU8gtKonnc8YzhzHPJEKfVENsKC0T2pVQRnO0oxZlzpzj26qMlsMSp3ZRzYi28gqeV27ycKEbqP57cgGFuVsI8OWxPA6XaiXqGREX+xbFNfYTEKwIGcrF9aK3eooHuwsOVrOvrMU3lCAqfuXM6hmO4bX7ULT5Gze3PYzhzwFtEmpy42N+jIvZzO7SnJonVqXi2t1JNGiJmGyHKl8fe79fH9gFZsK97N63DL83x8l2CmBUCsH2gE/1umFaLknHselLtmzaT/HD+aSUSu93Fh+/Wgu0hv96G5GY93czXQaWJ5P/eIF48a3AJOf/5Znpz1yUtfoVNCoZgbf/etGvl28ge0HjnNt9w+5tONGpq1pzpZD1bm882ZsFTS2FpPEwkqk9CJE5cbuTruVmS/dxjvTFjFrze8kO22MHtKdvu1iB1mRPBbsFyK9PwAawjEMYTXuIXi6kHo+hGIoabw/QtL9CHNDyPwV6Z4CwS3gnUXsCbtShMC/AqmXhHnzGLB0AUtLcFyGCO1Ben8FkYpwjkBYzsyT5p8NKeV8IUT9M7nPszIwS2I7n1WELiUSGVfHE4tmZnDN9gyuGR1UjSw5DY9H5Vxufk4hC6cuI+AN0HVIB2o1Ni5ECeihmGMuu/8mjiyu2duao3uP0axzY2qbUpEfHEZs3IfsacP+UPmP2CEsMPkoYvFuzHOPEXIHMf1YCD/GrkbUhCBgIEELxPBQ0EM686Ys5sjuHHpd2oXUTDUB66+kkq+yZVIvVBNhslhpfCuU7UrdDb6ZoB8HSyewtKn0RpyWZOWmvsUqyysOkeIIcn2PdTHXP3GgUFxOkXarmfuH1eD+C3eoTNheuUWqEAJsPRC2HlXvPNbQ9GLwzgBZqPjeWEFOBol5EvLEZyC0aojE2wDQj7Y9CXVCkJhyOADnNWiOsLe1pRnCXnWXnT8KJ0FlZAghyra4GR9ujXcy6C6EWIfq6PSglNKY7wrjrAzMJqHRLLkmW4oq57AEgo7pDePyMa4Kg2u2Z8LO2VWup0s95mTfgm+W8uL1/0UI0EOSCY9/zogHL+Yfz0TPvDdJysKimaK+43bNwqBslYke3nWUe3o+gdftxe8NYDKZ8JcJlst/XsOkf3/DxG1vkVk7nZx9x7ix+T34vfFO7iikZaWSVT+6YrLvlT1ZMWMtXlf5bMrr8jHr0/nMmbyQ/90/kcc+u5tew7ty8ZgLWPGLsbrl4jHGP1DpW4wsCPPPMgi8gXQMRyT/S/GhgS1Kk0tQ6WyxqFn91LdR3c8q7C90DJk3EvT88ERYPL9OAeYWcRV9SCmRRY+D50fUh2eG4n9D2njEH8SfSv8KZP4t4VMJAG8iHRchkp+PukEJUybSVBtCuyrsxQL2GJ27Ld3AP6fqgZgbI7RUpLU7+OcarCDANqDq/fwZkJxMufVxKeXpCKxXA/WklCVCiAuB71DdnWLirOWYD3sqr8KzmywkWxw81mpYpevFi1GN+hqqPET4z4TAplm4venASJl4WZQUuHjp+v/i9/jxuf0EfAH8Hj9fv/oD21ZEd0YxaybGth2JXbOoAI3SQTdOymJ4HfUDf/6aNyk4Voin2EsoECoXlEvh9/h5fMjzADw2+LlKg7LVYUVoAptDcaRWuwVHop3HPrvbMAPtObwL7fq1xp6guEhTGbOcgD+Iz+3H7/Hz4nVv4Spy07B1XcP9CCFo2Ca6k4qUfmTBnWE9rQcVdLzg+Q7881QQLLhDaWxlqbrBA74l6jHcALLoSQgdDut3y2Z3pZy4nbDD8InXIgmR+lLM61YOvt/A+5MaJwE1HulG5t+OlCd3Q4wHUgaQ+beHz99N5Bp5f1FjMYBIfSXMM4c5ZOEEUy1E4h3GBwntrGIU4WuU8rJ6mfoyJ65fGSQ+jKb9jXLBP0nHLKUsklKWhP//M2ARQmRUts3f6CrFj6AepCAQW0p0ae0uNEqqweCa7SP86unCrJn5vs/DTNq7kF8OrsVhtjCq4Xk4zDZmH92IVbMwuGY7GicZe9Iu+2k1moHLl98bYNakBTTrHM1BdstowpTe9/PjgVUc8xXTNb0R51ZvgVkzUZRbzI41u5F61d+c3RuUQ9y+LbGfMAbf3I+6zWvT98oerJ+3hQ0LNlOjfnUuuKEvaTWMy29NJhPPfPsQq3/bwNIfVrB56e/sWL07yvxcM2ms+GUNuYfyMVvNBCrQFiaLiYXfLGPEgxUmM/0rYozWg3RPRZjqQCi6mzN4wDMFEso335EyBL55GD9qm8ExAsxNwX4BwjcfGVgP5gYIx3CEFp8eXnqmxpB8hcC/Gmxd49pP3AiswfB8pBvp/hphj85QheUcyJyFdH8Lob0Ia0ewD0aIGKqTUCUe1vYRKlN2DkNoyjtD01LRqy+HkteVjlnLhKQH0azxe5f/GfizvDKEEFnAUSmlFEJ0QSXERl/cCM7KwByZ8vPrWBaVoB0MoNe1EuieCBbBo+cYZ8mhUIgVv6xl24odZNbJoPeIbmxdtoPNi7eRlpXKeVf2JDE19uOqpml0ya2FmJODPcFGk9qZFB8pIet7PxabTuIIDZJijDmG1y0S9FBsTq6GPYVRjaOdznRdj78xThxfwNGv/QNnuOS431W96HdVryq2UNA0jU4D29JpYFveGD2eHasrPiKHxxvS0UM6hl6LUiqNcBQq4/VDIHUMK1wA9OPoxW8hrG2Q5o4I30xkcG+MlQFhQkspM1nuvAyBsS9LpYhZiRkutT7TqLTy88Q11YMHoeQ1CB0BWy9w3oKWaOyBAqDrRVD8OgS3VX785MfRNAO1ibCCrTdSJCBMGUhTHUW5+Jaqm5xjCEKrVtXZ/bE4c3K5qL6ohCUnUspxwOXAGCFE+JGOK2UVrVvOysBs1cyk5JvR796OcOvgkeAQ2CYeJ/Fd415wHpeXB/o+zYFth/CUeLEn2Hjr9vGYLGb8Hj82p40PHvmMl397yjB7lVLyxpjxzPpsPgFvAJPFzPuPfIZm0tBDOppJ4+Onv+TOt0cx+KZoO8Qug9sTMugLZ3Na6TuyZ9T7VSE1M4WsBtXZv+1QlevWbqaKMbIaVOfI7pyo5QkpzkhQPh2cd2VPZk2aH8U5hwIhOg1qR1FuCROf+iJqO81koucwAxWCtTPG5VlOhGMomBuBSDbOUPVccL2NdDkAPxIr6jcR43ZmPUOaYcdQ8C8gKgjL4B/jA2HtaPy+cCAcKkHRPdOg8KETywIrwDUePXMemoHXhe7fBHmXUWVpnEgxDMpS+pF5N0NwvcrcsQNjkZhRn4UNSl6F1HGIP6vKzwhnKDDH6otaZvnbwNsns8+zlmNu/pFE5IeUdhYQHol2LEjbr4wfx7588Vv2btqPp0QJ+b0uH6Ggjt+jeFmf24e72MPYK14z7EO2+rf1zJ60AJ/bj65LAr4Aekgn6A9G/vV7A7x95wTyc6IVDikZydz1zs1YHVbMFhOaJrA5rVxw43m06nlqMiERh5OayWLi398/CkCj9vUN16nVxJh+OVm06dOS86/rg82puGqz1YzVYeWecbeSXC2J2k2yue7pEVgdVkxmEyazhs1h5erHL6VOs+i+dUI4ECmvovhKKxGNr60v2M5HCA2R+laYL63oy1BKl3hQQbI0eMf6NZ7Jn8KfZ7AghBWR+jrq/Es5YwdYe4F9kHpSK3zUYIguKHjQeKcFt1B1vbKA1P8ZLpHuLyGwtkzlohf1GZROtnpRfiB3I+XJG06dCQgZ/99fgbMyYw6FQmyfsx1R4bsjQrD2B2PZ02+TFsSlRig8XsSBmwv3eQAAIABJREFU7YeiAsXszxdGZYJG0Mway35azaAbo53BBt3Yj3bnncPcLxfhc/vpMbQzTTpU3jevsnEe3mnckNTmtNKwTT1adGvGjf8eid2pgtaa3zYYrr9j7R4C/gAWa9UNViuDEIJ73r2FQTf1Y+kPKyNPA2UVHVc+MpweQ7sw/+slSCnpfXl36rWoHXuf9v6QORO8PyL1IoStD1jaRyYRhbU9ZM4Gz4/I4C7wfEX8JcVlYKgiOAV4vscwqAmz8qj4AzJEYesDmbPA+wNSL0DYzgWL6qMo/YuJSaH4o7X5uq4r2WEsmNuBpQkkPYAWi4rwTCW6ktEIQQisB2uHqlf9I/AXmeDHg7MqMB/dm8N7D37Cvq0HY056hYI6/73rA+q3qkv/a87FGW6nHq9xkOrzZqAc0JSrWZXyaVl5n7is+tW58pHhcY0F4PihPGZ+MpfcQwV0OL81zTo3Ztak+RzYdjgmb52cnsRbi5USw1Pi4afxM9m1YS/BSnwcTtZvOBaklPg9fjwlXqSUURN9AHWb1+LaJy6Pe5/CVAMSRkVyYRk6jO75FvQ8hLUn2PogEq6D0BFlnHNKAw+iFz4D5mYIx8WVSuOkDIJvFtK3HExZanLQFJ5kr/Q6ivD2EgJrkN4ZIGwIxyWnXeUnTJmQcFOZa3QE3fMd+I1vxgo6euEz4cm/gbEn/8rC0grMTRCUX1fX3YqT9i9RHs/xj/wk1j2z+D+j/DOAJT+s4KlhL1f5lBgKhfj+nRnYE2x88vSXvLX0ebIb1GDgDX354sVvq8ya07NTqdUkuujj/Gt7M++rxVVmzXpIp9uQGLzfSWL1rA08NfQl9JBOwBdg+oRZ+H0BzBYTAV/QkMqw2i0MvKEvAEf3HuPOro/hdXnxunyYzNFl0ppJo+OAtjGNik4GUkr+c9M7LPh6KT63D81k4utXf2DMG//golvOjH5V+uYh8+8iXMqjArG5DVSbgDBlIc31Ibidk6cTQuCZBDiQJW9A+tcIc3QmL6UHmXu1qp6TbsCGdL0DaRMQ1o4Ix6VI/1IDAyINrB3COuenwpm1F9CQronIpIfQEq476ethBOlbpCR0hKjcJU4HzySk91soeRvSv0LTktG1GqAbP43hmQTCgSx5E9K/QpjroQePwvH+VRzLCAIsbU5ymzOIv3FgPms45ueuesPwQgoh0Ewamlmdih7uqOx1+SjKLebN0apA54qHLqFxh4Y4Eu2YzBqORDtmiwmrw4LJrGFPtJOYmsBTXz9omD227duKC2/ujy3MEducVkxmDbPVHOFSrXYrD0wYQ0pG/K52sRAKhXj+qtfxuX2RrNPn8SN1ScCneLnSpwaz1Rw5p4Zt63Ployojf+uO9ynKLY7cTEonHzWTptZPspNeM437x9922uMFWDNrAwu+XorX5VNubMEQPo+fd+75iMLj0V0tThZK13w/KqCFg4B0Q2Cd0jaD4ltFaph3NqFslcyAM/w6VoZW+jThAVmALHraeAyujyC4o0zg9akJroL7VCZsGwD2C1Ccr1nxvcKBSH1bZaSBleGg7EF9oUPqfIpfVl2lTxNSBpEF94X3X1WgDP+gpBtC+5El76rXaRM4oes22swDshBZ+Lh6XXBPJccyATEycRniD1GqxIP/45hPH8cO5OJzG3/wUkpuGnsVH//rK/QK3J6uS9bM3oiu69gcNt5YMJa1czaybcVOqtdJp8ewLvy+ahebFm8jPTuNXpd1xZFgrHsWQjDm9RsZfPP5rPhlDfYEG+de3o2SfBdLfliFxWbm3Mu6kZ5t3AftZLFz7Z5Ky5TLIjk9keF3XUjTTo1o37+14halZNWv65RELepc4B/PXkmtJtl0v6TTGcmWAeZNWWL4RGE2m1g5Yx39r6naWL9SBDZgnOZ4kJ7vlN+CuTFUnwvemUoaZmmDtLRH+H5DBveD6/U4DqSDfxFS6oiKVaOeHzH0jdALIbQbYW6ISHkJ6bwBfItASwb7oIgOWnpmYMi/Ck1prJ3xUzyGCG4hJscuqimddmAF0QExoDwukh9FszRFr74KSt6F4GbwLyXamlSHwEpVNBOspJzdcQX4lxlUGqK6oQTW/4ldSyrgb5wxnxWB2WSOndgLTXDlo8P5/PmpBA38HKSUvHTD29RrUYvBo/rTvl9r2vc7IXRvfW4LWp/bImo7UBnf4mkrWPLDSpIzkhh8Uz/qt6pD/VZ1IuukZqYw4oGakWOtnbOROV8sRNM0+l9zLuf0Kr/v7at28uvHc/G5fZx7WXc6D2pXLkPft/Ugv0yYxYHthwj648smCo8V89lz31C/ZW0eb3Qf2Q2UN4Nm0ggFowOz2WKOZNVnEhabGaGJaP5fgDns5ialZOPCrcz+fAFSSs67qhdtesfbPMBMbB1ymaxML0AGd0PoAEJLQ1jbIhwXgQwhXW9h7H9cETGya4Myb4UgsvgVpLkBwnEFwtJSmfUAUupI72ykbyYEjDucIwWI05t8VajkGpkbItLeROb0ImaRTRia5oRkpdrQj3ZUPiVRUC3Y1L8xvquOKyC03zgwI6nS4PoPREXxwN8JZ0VgrpaVRmJaAiX50W5rzbuoSZO+V/Zk5ifzooKzEILZkxZgtVv48qVpvDrvGRq3a1DlMYOBII8MHMv2lTvD/KzGj//7lXvG3cqA6/oYbvP2XRMiQRcEMz+dz9A7LuCWlxR3+OV/pvHpM18R8AbQdcncLxfT9aIOPD75PjXOyQt47eZxBANBFVDjnBcJBUOEgiG2rdjJ9Y3u5M3F/6Zlt2ace3l35k9ZUu6amK1m+lxx6mY5lWHAdX2Y/uHsqKcbGZJ0HqyMod578GN+eu83fB6Vdc6atIBBN/XjjjdvqvoAltaKopAVvwcOhHOkOpZ/pepXJxW/Kr2/gus9SP8GoaUgbf3AN5vKg7NFVf8Z3SwcI6H4ZU7I70oRVCXQPgvS9Smkvo6w90fKkPL68C1HlUzHrIgB2xno8WduDjJGImO/BKGlIS3nKPqn3BOmPXa2br9EVVKWoyvMYDtP9Qi09orhpWFHs7ZCt10E/oXRi6VPfab/hyicNRzzSzOfisqcnckO/v3jYwDc9sr11G9VB0eiHavdElm39FHe7w3gLvbw6ihj7WVFzJm8KBKUQak9fB4/b44Zj8cV/Sj6++pdzJg4J8KvSinxuX1Me3s6e7cc4PihPD55+suIDhoUD77spzWs/m09XreP1299D5/HfyLLDf9+LXYLVocFq92Cxab8K0yW2BzgE0NeAODOt26idtNsHEn2iO9F3ea1GPP6P+K6BieLZp0bc9Vjl2K1W7A5rTgS7dicNp6c8gDOJAe7N+7jh3Ez8bpLr5G6Br98MIud6/ZUuX8hNETaOOUVHPF6sINjCNguCHtnPBQuOCkNIh6l1igZp/aR8iyY6pTZ3qH2IZzqtUgAUz1E8lPGY3COVJVzEd1wacZXGmyVV4UsfFg95vtmlQnKZdcjvL1T7Sv1dePGpicJKT1AifFC3zx1DqmvgVa9/DWwdkAk3Gy4mUh6QFEg5a5RHUTKWLVC6usQJZ3TIC1swOaaFGO0QaReaWXyH4s/ySvjVHBWZMwATTs05PuSz/jihW/Zu/kA7fu15sJb+qOFuzsnJDt5d+VLrJ+3md0b9/HhPz+PFJOUxe4N+3AVuUkwMC0vizlfLjLkS01mExsXbKHzoPLWoEt/XEXAQPERCuks/2k1yRlJhl4ZXpeXhVOXITTNcDlArcbZDLltAG36tKR202yW/bSadfM28d1bvxiuX5ynMsqktETeW/sKa+dsYt+WA9RrWZt2551zxqRxRrjm8csYcF1vlv+yFpvDSvdLOkXK3Jf9tNqw+jHoD7Lsp9U0alu/yv0LyzlQfZHyCJYFYO0akZrJ4AFV8RcFP3inQ/Ijqgw44xfwL4bgbrA0Q5o7IQLLVW86c0Owdo/mlkuPL8yItHeQgc3gX410fwKhPQZr6hDYhPRO50RQLgunKku2dgBb/7i9OKpEZV7IfmX2L0y1lO7ZtwBCB5UyohKrVKElQvo3ipsObANzA7D2iFwjTXNC9aVKwuidC+a6kDDmRFWgvjn2mErGQcqTp3Kmp4e/cGIvHpw1gRkg/3ABXpcPn9tHSYGLfZsPMPuLhexat5dmnRszZPRA2vZtRdu+rZj8wlTDwCyEiPCdlcGZGMP8SILNGd2k1O60qfLsCr7NmiawJ9iwhZ3bKkIzKUWI3Wk1rDgEqNW4BkPvONEgodfwrtRsXCNmYAYYkTWKhm3rc+dbN9Ghf2s69I/vkdFV5GbGR3NYPWsD2Q2qM+imfmxevI3l09eQWTudS24fVI5jN0L1upkMuS1aHmd32jCZTYQq6Kk1iwm7wTWNBSHs4LjIYIGdmBVregl63q1g7YhwjkTYeoUzXyB0HOlfoSYX9VzQqiN9s5RBkLkxWM8H1zg1UWWqAYn3Kt7UvzCGYREgA8jiV0CPobQQGsLWH2GPj77QSyaC+2MgAPaLQNQF70fq+LaBkPSQCoSikqy7DD8uhAXs0R4sMSHzkf7l4F8Peo66GZrKV4xqjuHgMJq7MBHzc/F8g+6fDwm3oZ3uxOfJ4m8cmEUVXhox+1kJIe4C7kSRdT9JKR8Ov/8YMAo1G3C3lHJGVYPo1KmTXLlyZaXrbF66nUcGjiXoDxL0B7HYLAR8AcxWE0F/CKvdgtVh5e1lL1CrcTYfP/0lX73yfaTkGlR5cudB7RkbR6eM5dPX8MSQF6ImshxJdr7Nm4jJVD64H913jOsb3RmlgtBMGl8cHI/daWVkzVujbhY2h5X/Ln2eeq3qcHXd0eQeKm9nak+w8dSUB6IydIBB1pGGk3tlIQS8Ou9ZWvcynuAsi6LcYsZ0fJjC40X43H5MZuUDYraqa62ZNCw2M498fBfnXtatyv1VRO7hfK6uO9rwGk3a+z8yap6+qY2eO1IF0KjJKA0VHOygJSDSv1W65+AuZO4VIEsleBYUHVH6r8lgX6BymmCZ/VZELC65FFZEjdVxFXXouVdBYFXlK4kkyFwEWCGnpfGYnNeixaBoKoMM7kPmXha+Rj51DGFBVJukJjmrGn/+veCrJJMvhW0gWlrVlhJCiFWn6Y+MvWYdWf+W++Nad9uz95/28U4W8XDME6nQz0oIcR4wFGgjpWwFvBJ+vyVwJdAqvM27omJv81PEq6PexVvijUxklWp7S5ULfm8AV4Gbd++dCMBV/7yUNr1bYHNasSfYcCTaqd20Jg9OiK/pZ+7BvAhNUhZBf9CQ4sg7XGCoHtFMGgVHC3AkOnjmu4dxJNpxJjlwJNqx2Czc8vK1NGhdD03T+PePj5GcnoQz2YE9vHzYnYMNgzLAcz/9s8rzkDKsAY8Dnz//DXlHCiKTd6GgjpQnrrUe0vG5/bx2i5qgPFnkHylAMxk/NeQdrtxfO16I1DfBlK14UOHkxAxqaaDyKtVGsepjKIv+HVYclN7AAxX+jaWMKT3/MgGwlINVe65ipBJkDC64DHT/qqqDMqhzKH4VZWAWI9iLyjupxN718+FrVPq994N0qUKZeJD8Koj0qtfz/apc8P4ECJQqI56/vwJVUhkx+lmNAV6UUvrC65Ralg0Fvgi/v1sIsQPoAiw5nUEW55dwaMeRKteTUrJ2tipBtdosvPDLE+xct4dd6/aS1aA65/RqHje/Ov/rpYZ8qMVmYdOibXQZXD5Yrpi+xljeJiUrpq+lQet6tO/Xmq+OfMCK6WsJeP10GNAm0nYJoHG7Bnxx8D1W/bqe4vwS2vZtRfU6sf20Ow5oy0/eSYy7/xN2rtnN5iXGDU1zD+bFdc6LvlthKDmsiFBIZ/eGfSft87FyxlqM2jTqIZ0V09fStGOjk9qfEYQpCzJ+A/8y5Z1R/BzRCowQ+Oaq//qXcWaeae2QMEYZ1Acr4VQjA7WCbyk4Lqx8PfdX8Q/BNwMCQxRlYXRKvhmQdArFRL7FGGbggY1I6a8y69dMJqixBN0zHdyTwh7SMQpSPF9CUnyZ7Gnh/1OOuSlwrhDiOZRa/kEp5QqgFrC0zHoHwu9FQQhxK3ArQN260d0rysJqt1ThQXACFfnfRm3rRyaVCo4V8t3b01k/dxM1G2cx6KbzWD9vMyt/XUf1uplces+FkeCQmGbslSClxJkcbZGZkOyMYQJvxllmotHutHHupbHN0i1WS7mS7i3Lfufbt37m+MFcul7YgeT0ZCa/MJXC3GKadWrIPeNu5e63b8br9nJxonFJb2UudPlHC/j2rZ/ZuHArroJoOaIR9GDI8BpUBUeSA7PFFHXDM1tMVU7GloX0r0K6PlF8sO08hPPKct4WQmjKLMjaTmV7RhDh4wk7nJHOIhLhuAQZ3BJfYJZBpOs9pGcawnkF2PpFkgYpJfhmIN3fqAnJeKEXI4ueDVMOBggdRc+9Bmw9Ec6rEZpxA4QoCHuMfZqJVSEo9SKkezL45is/kYQb0ByDwDEIPac/6PtjHOtP9Gj+/zAwm4E0oBvQGfhKCNEQY+Wt4emHmxmOB8UxV3Ywm8NGt4s6svSnVZVmdFa7hcGjjCc0jh/MZUyHh3EVeQj4AmxctJUZE+dgtiiOWmhbWfjNUh766A76XNGDQTeex7wvF0dNyAkhaNm9adT++17Zk4+emGx47N6XnzwfCzBj4hz+e+cH+D2BSGFGWc57zayN3NjsHsave5X6repQvW4GOfuincFiFdAc3XuMMZ0exlvijem9URGaJqjVJDtmE9nK0OeK7rz/yKdR7wsh6HNFfK5rumsyFL9AxEIysB7p+RLSpyr1QLn9OpC2PmGZWNngawfn1eq/jsvAPZmqu0BXBpMyPzJlg/NqpHc2Vbur+cJVeluUt4ZzBCL5CQBk0aPgmU60VroquCC4gZgCeHkcAsfUNXN/DunTEKY4KAbnSHB9TPlzsoL9IoyYSqkXII8PBT0P8EFAQ3pnIpPHojmHQsKtUGykxBAnPpc/A3/jwHyqOuYDwFSpsBz1nJMRfr/slH1tVFfY08b9H4ymUdv62BNsOJMdWOwWUjKTsDosOJMd2BxW2vdvzQ3PRjc2Bfj4X19RnF8SyWilLkGe4KilLvF5/LwxejzBQJBDO44YcsYBXwBXYbT8KT07jX9OvjcyPmeyA0eSnae/eZDk9BhtTSqB3+vnnbs/xOf2R24ORo56Upe8dP1bALw+/1kcSeXVJNWy03jup8cMj/Hh45/jyndFeW+AmuS0J9hJTk/EbDVHzierQXWejWPy1AipmSk8+dUDimcPXyN7gp0nvrw/Zvuq8ufqguIXUQGidKxe1e3aHW3ADyBSnldFF8IBIhGwgb0/IkEVtIikB8DaFaVlTgz/Ww01wRV+HcXZaoBFLRdOMNVFhCethLULJN55YnuRACRW2F9FeMD9pZpkC2wGzy+cfFAui9JrYwkfv+L7PtDzka7349qbSLwLbD1QGuZEwKHkdcnGMjfp+igsWyy92ekoP5BnkNKPljASrBWbSQhIeQNNi8Ph7gzh/0evjO+AfsBcIURT1Df3OPA98LkQ4jWgJqoT7PIzMdCktETeXvYCO9bs5vDuHBq1rUfNRlns3rCXA9sPU/+cOoZm66VYMX1tlQoGUOZB+7ceZNG0FYY2mVa7hc1LttP1wmgP2R6XdGbK0Qmsm7MRhKDdea2wOeKXgZXFznV748pgAXZvUI+F1etm8n3hp8z7ajHbVuygw8C2dBrQNuZ2q35dFyl2KQuz1cztb9xIvZZ1aN6lMbmH89my9HeqZaXSsnvT09JBd72wA1OOfsDaOZtAStr1Oyf+axTcpPwVoobsA99MSIwukBBaKiLjGxXwQvtVt2vzCepMCBui2gfI4A4I7gRTQ4SliWpDFdyqCiksLdF9K1WxiKUp2IYiZI6qntNqgKVtuWuiJd6KdF4O/pUgUpQXhHSBf7lqEmtYJSeUzli6iNvYR6Srm4p/nkE1JKooxDEcil8lOtCHi18wMNGveBhhRaSNU5x98Hcw1UdYjDvBA+D9DWMOWartLa3Qqv0PPbgX3FNUcYrz2j81KJcO5++KKgNzjH5WHwIfCiE2oj6BG8I9rDYJIb4CNqNmXO6QstKmZCeNxu0b0Lj9iZLqBq3r0aB1vSq3S0x1xjUJ5vf4ef7qNynOLzH0X9ZDkqRq6pHZ7wvw0/iZzJ60AIvNwpDbBtD3yp50vSg+208pJYu+W87370zHVeSmz4geXDxmII5EB0nVEg0nH2PtZ3T7h2jduwVXPDSUPlf0iKvsOiHFSeHxaA8EIaDPFT0ihk4ZNatVyoufLGwOm+GNLRY+evILfvjfDPweH+d0qcEDr3tJz6pAaWmxH8llYDPSNT5cUNIBEm9WRRZlIMyNlWY58roemE98rzRbJ7CVVUxlRel4I8fTS1QG750JWiqCGxC2vmA/HxnYFC5PrsBrCxNoqaBbOCHVqwLm+ojEm5B5cw0WCtVI1n4Bsvhl4+3j5ZhL92huqApwDCBDOUjXBGV4FMtoXwbVjar08OZ6ET+OPx3yLPfKqKSf1bUx1n8OeO50BvVH4LJ7h/DuPR/hdVftp7xnk/HEhBCC5IwkWnRtQigY4qF+/2Lnur1hbwxVlr1y5joe/ujOuMb0/iOf8cP/ZkTkd3s2HeDXT+bx7ooXqd0kmzrNarFr/V5Dh7iKY965bg97N+9n5qfzeHfFS9RsVHW7qEvvvYj3H5kUGT8oI6JuF3eK6bL3Z+Pe3k+yaeHWyOtV8xK4vmsLJq3aTGpG6Y3LgXAaT3ye8G8Oc9LB35He78N+y1V7ppwspF6CzB0GoaOUPsrLwGpkwq1oiXcov2bXBKIDr0l5ZUg/FP87vmwu8R4wtwYtA0IHqOh9IZzXIkw1kFot0HdHb2+/9JTOsSJk6LDilKWL2DeUMA9v4HH9l+FvnDGfNV4Zp4tBN/Vj8M39sdgtOFOcWO0WMmpXw2JTHLXJYoIYHUpKdcVZDarz4ownEUKw9MdV7N6wr1xQ87p8zP9qCXs3x5hxLoPjB3OZ9vYv5TTRfo+fo3tymDVpAQBjv3+Eus1rRXhrk9VUadViMBDCU+w1bHhqhIvHXMCA63qra5DixOaw0rJbUx54f3Rc2//R2LNpf7mgrCAIBgTvPlHvBGecdI9hU08pJbLwKcpz0kGQJaoq7w+A9HwNoRzKTSZKD5SMQ+r5CHMdSHk1rLMOc9BaBqLaR4pW0ZIQae8T8ZSO8NIV6CPHKDRbN4QQiLQPwVRb8d2l6yc9hLB2UH4d0qj6UAs3FDgD51zydljnbBSUEwAHmBsj0t49I8c7U/j/kWM+6yCE4PY3buSqx4aza/1eMmqnU69FbYpyi/l99S7+d/9E9m46ELWdI8nODc9cQaueLWjasWGES1z123rDkm8ErJ+/hXotKy9b3rR4O2arOaqjitflY9nPqxk8qj8ZtdIZv/5Vdq3fS0FOIU07NSIpLZFF01awdfl2vn71hyjttB7SWTNrY1zXRNM07vnfrVz71Aj2bNxHjXqZ1G5aM65t/wzMnrwgxhLB2sU1EKlPqkkoLcbkqsyP8VgtwX9Gpj6i4ZuDsd+yRVUk2vqgOQYg7UvBv0bpmS1ty6kbhLUTVF+sGpqig6WdShg836qs1Hl5ue7WwlwXMmYqDl4vVPsrVagE92BcmagbO76d0jkvxJgXt0PykwhLS4Tl1BoO/6H4G2fMZ1Vg3rhoK58/P5WDvx+medcmXPvEZZVO+C37eTX/veMDjh/KIzE1gX88eyVDbhtAxwGKW8vZf5zJz09lzeyNFOcZV2HpQZ3Og9pHHSc9KxWTRSMUqNgRVpBaXfFowUCQH8b9yvQJswmFdAZc15thdw3G5rCRWj3ZsGef0AQZtaqV2Z2IMvfpObQzHfqfwzev/mg45qLcYgbZriQ9O407/3sT3S/uHPMagVKUlBr8H9mTw6TnvmHD/M1k1sngqkeH0+H8v6b9T416mTGXJaWnImw9DZdJ32Kk6z0IHiTmRJr0ox8bAOaWiMQ7EJZoCeQpQcvCuExbL8eDC2EDW2wZpRDmcgbyQgAJIytZX4DlnOgFWqridg3HGodULgZkcLfqeBJYG8OrGcAPrneQWhYk3By3L8ifgr/QOS4enDVUxqLvlvPoBWNZ8csaDu04wtwvFnFH50fZvWGv4frzv17CE0Ne4OjeY4QCIQqPFfHmmPF89KR6zD92IJfR7R/ilwmzOfj7YYpyjb9cidUSDIP/uSN6RAdlwO/20fUi1dvt6eEvM+GxSexav5e9m/bz6TNTeKj/M4RCoZjaYiklA6839nsuC0eig57Du2CxRRuN6yGdUCBEzr7jPDX0ZeZMji8zOrz7KKM7PMTMj+dy8PcjrJ29kaeGvcwvH86Ka/szjcGj+hv2KQS4/l9XGL6vu6ci80erpqD6PmKa5+CB0F5VyJE7AumvpAvHSUAkXIuhvE7LAnOrM3KMkxqPKROsXYgypBcORMKoU9qnDPyOzB0O3h/UNYxZWi6VEiawAllwL7pr4ikd74+A4O9NZZwVgVlKydt3TShnwK6HdLwuLx88Zuz1+uYYY43mFy9+i67rTH5hKu5iT5XKh6LcElxF0brln96bGWOssPSHlWxZ9jvr520uN2afx8+ejftZOX0txw/kRrmsAVisZrat2FnpmEpx//ujad//HKx2S7nqwop4644P4trfZ89+jafYW05W6HP7eO+BT07JG+N0oWka/5n9NBZb+Qe7S+4YxHkjo7NlKYPh4hOj4g4NRBLRxRc64EEWv3hGxiwsrSH52fL8sLkJotqHf6jdaqVjSn0dLO05oUO2Q8IdCPupNciVJa+EXfUMbnoikROdTcpGNQ+UvB72i/574O8cmM8KKqM4r4SCY9HNPKWETYu3GW4TKwPWQzpH9x5jzawNhoGxIqw2M7vX741qEbVqZuwMa+HUZTTt1MhQB+0p8bJh4Va8bn/YIa98wAv4gqycuY6Lx1zdXn4DAAAgAElEQVRQ5dgciQ6e+/Gf5Ow/zs61e3hq6EuG65XEWWq9du5GQwWIHtI5sjvnL+GfW/dqwc+eySz7eTV5RwrofXm32OXboUNK1WAEkQ6pb0C+oZgo3E/wzEBzDkM6Bqs2Ulqykpn9hRBaCiL9s7Bf9TF1o6hQJXlS8K/GmAewQMprUPx8DI9qk9KKG1EufwX+xlTGWRGYHUl2tBjFFgFfkJG1bqVB67r849mRNO/SBFCG9rGy4ZTMZDJqp3Ng++Eqj+0p8fHsiFdJTk9i8Kj+HNmTw6Jvl1MUg5MGWPbLatbMNv6hmywmpn84mxkfzSlnSRpZbjaRVb96zH0v/HYZk1+YSu6hfFqf24Ibnr2S2k2ySc2M7cMby4C/ItKzq5GzN3qyzOv2cX/fp6mWlcrIh4fRd2SPPyz783v9PHfV6yz/eQ26LmnYpi5PfHV/pbpn6flZ6ZQrqiHKwpyNsHZCCgcnOlyXh57TE8yNEIl3qwm40v0HdyCL31B8qikbkXB7pXypDOUopYJvDogkZMI/EI4RMa+ZHsqFgjvDk32ApaPSW/t+AqmD4xJEwq3l/EAqg5QSvD8gXR+Anq9M7ZPuDkvVlFxN+uYiS96B0GE1WZh4L8LSJK79o6VDqNBoAcLWHemubRyYpRuZdyvSVE1VXtqH/2VPEWo8f92hq8JZQWVYrBZa9TKe1fV7/OQdzmfVr+t4sN+/2LBANbvsd3Uvw/UbtK6LM9HByIeHGRreV4Qe0sk/WsjezQcY9+DHTHtnerhrd2w9tLvQQ0FOkaGvRynfXZBTaJhRm61mLh490HC/37zxIy9e91+2r9xF7qF85k9Zwh2dH+HgjsNY7VZq1DeeLGvXL74MJRa3LXVJ/pECdq7dw2u3/I9Jz30T1/5OBTc0vZvF01YSDITQQzo71uxhVIt7KTge/cQEoJeMQxY+poyD5HGMH68dKrAJAY5rMS6L9qts0r8UmXcT0qcUIYpPvVxVFuqq2k8W3Isew/VN6vlKx+z5GvSjENoBxc8pcyGj8et+OHZe2NozpP4Cy8E9TvGz+kFwfYjMu1pRNXFAlryFLHxSVS7qR8E7DXl8GDKkHBp19xRk/j2qclHPAd9vyLwRyECc8rmE21AtucrCBo6hSvKXcIvBcgBdfUbB7cjCZ2IXvvwZiJPGiIfKEEJ8KITICRfcGS0XQoi3hBA7hBDrhRBVVledFYFZ13V2r99X5Xo+t59xD0wE4MEPb6d17/L0Q3bDGryx6N8AdBrYltteuU55Iycp7+NG7eorT4hEOyazFn03l8Z+FZVBCIHFZg475Bks1wQmiwl7gp3UzGSemvKAIWXg9/qZ+NSX5W4Iui7xunx8NvZrpJQxqRm/O8bjfQVsWBijg3MZeF0+vnjhW9zFZ54rXPbzao4fiG4NFQrqjLtvYtT7UndDybtElxuHOzeXejIn3ouwq5udSLoXHEOJ9K4zNPzxKp9mQJa8EeZTK/Clxf8xDJTSPRn0YspZjUoPeKYgQzlR6+N6l7gMj0J7Iz37KoPUi8H1AeWvia6yVdeHYR6+YjNZCdKDLHm9yv0DCMcwSLwVcISvoVU1rw2b8Atbd0h+/IRO27DjuAfcnyH1+Cxp/xDIOP+qxkQqeNZXwGCUPUUTlKNmlY1HzwoqoyTfFTdPunuDCuCapvHa3GfJzylg06JtNGhTl1qNyjuiXTz6Ai64sR8Hfz9MamYyaTVSCQaCHNh+mDfHjGdjVHFDNByJdoaMHkj+0QJ++3R+1HKhwbC7LiQhxcnnz0+Noi+kLul1eVeue+pyajerGdUZpRSHdx01dD7VQzqbFm3DU+Il/2iB4bY74mh0CsR1vqDomP3bDtGs0+n7J5fF4mkrYi4zHFtoN8bewxK0espYyFxXtaIKQwgzImUsMukhZX6UOwxDSV1oN1LqYe9gg1+n9Kts01ThJupbiiGdIqzKTc5U3WD9OCDdyMBahL2i+U8FBH9XmmlZcQwBpd3WjxksA+XUF58yRQiBSLwD6bxRVRyaMhFaWrl1NOcVSMcwCO5BFj6iNNZRO7JCYHulssE/EmeqJDuGZ31ZDAU+CdtWLBVCpAohsqWUMbnUsyIwO5JUBhuMI/GTEoZVu4FajbL4x9gr6TyoPb2GG/s87N64jwmPTWLT4m2kZiYz8uFhXHDjedRvVYf6reqwafG2uDLkHpd0Yt/WQyz4Zmk5FQaorPbrV79XBQIGgVUzCVZMV52yuw7uQHaj6vz6yTzchW46DGjLqBeuJrtBDdKyUo2N+FF6X5vTitVuNSx68ZZ4GaCNQGiCwaP6cd97xpV91esZ24ZWRNAfJL2m+iHm7D/OhH9+zopf1uBItHPxmAsY8cDFEZlbUV4xHz/1JfOnLEEza/Qd2YNQUGf+lCVIqSin6/91BQnJTuq2iK1Jr15PNQyQUke6PwP3RFVMYWTeA2CuV6k2WWjJoCUjtWrGfflECkJoSoNrWKTiQx6/GKllQMKtCMel6gnLXE9RERUpFelG5t+P1BLAcYnifr2/xeS7o8fjiPL3iOxahlRTWNcnIIti7zO0B3lsCDFN6rUTHU6kbyGy+HXFFZsbIhLvQ9hO+K9I/zqlzghsVh1jEu9C2E9MWOve2VD0eNhlLpYNaSCm38ifgZNQXGQIIcr2vhsfti2OF7WAsuXApT71MQPzWUFlWKwWhtw6AJuzavepoD+Iq8DN9lW7eObyV1gwdZnhege2H+KeHo+z/OfVlOS7OLD9MO/c/SGfPjsFgP7X9o4rKEspadWzOede1jWqik+tUKbM22B3ekjiLvJQnFfCb5/P59Nnv+bonmMU57tY8M1S7uj8KHlH8kmulkSPYZ0VJVIGNqeNqx4bjslkYugdgyrlzaUu+fn9Wbxw7ZuGy6/+52VVXmOLzUL789uQUbMaRXnF3NHpEeZ+sYjivBJy9h3ns7FTePE6ZUMa8Ae4u/vj/PzBLAqOFZF3uICpb/7M9+/MIP9oIQU5hfzwvxncd+6ThEIhht45SJXGG+DW/1yvzqForHJLCx0IFzYYfUZ2ROKtlZ5HBAnhR/JycEBC2KnOcXGMDXV1/NBuKHoW6XonvP5QYlbaUQz6EXC9rzhomU/8PtBm1YTVALLoCSh+Q/HRsjjG8QkH7FjLHYjE29Vq3lnI/NuVt7MsVrx6/mikTz0RSv86ZN51qvuLLFacccHD6O4v1Zn6lkLB6DIdy2MoOCytEeb6cZ7/GUa8NIYa+nEpZacyfycTlMH4zlRpcDkrAjPALS9fxwU3nofVfqKHX9u+rbA51Gsji0yf28/4Bz8x3N+k577B5/GX88bwun3/j73zjpKiaLv4r7onb2ZZguQcBCQnQVAEBASMiAkFFANijhgRxYSiiFkxBwyIARAFJYgSxQACApLjApsnT9f3R83G6dmZXVaR77z3nDkw09Xd1bUzT1ffus99+PjJL/Dke/hl4e9xVdMOhQx2bdzD70v+xOY4RtvCCCc7pdWe87yqhn3HzOvpfX53rHYrDpedpGqJTHhhbFFm3pWPjGTI1f2KxiQavv/APOGky8D2jJ8+hsS0BOwuO1aHlba9W5GcnqTe2y30GNaZez+4CYB5ry3Cne8tJbHzuf389MVq9m07wI+zV3F0f1bpRVBJqeIDAV+QA9sPsWreOiwWC9OXP1KqOopu0ZjwwlW07NIUGToCnk+IyinjBJEGKY8ibOVnOxbt6Rql7EKFU71wguvy8AIW4IunKpoH8l9VnLfvJ2I/iMYiL22g1Vf/Yge9qSp8apJ6LkMHwPMVkWOihV/lLXBr4etOgqQ7i3TN0lQL7kXmPqa25z9tst0DeU8jZQhyHyznnHZ1XfbeiLSYVOs/i6rjmGOhwj71JwSVAUpGNuH5q7jq8cvIycwl/aQ0rDYrPo+PoweyubLFjaYz3AM7DhEKhSK4240rtphqdnWrzv6/D7H+x02mqomysFh1tv26g12b9par1KgsAr5gkVbb7rRz97s3MuGFq8g7mk9G3fRSmXG6rnPdtNGMfvQSjuw7ypXNb6zw+QaN6ceAUX3J3HNEFYZNchIKhcjcfYSktAQSUoolW+t/3Ggq+bPYLGz7bSebV28x9xMpA0+Bl22/7qDH0M4079yUL7LfYffmveRnF9CiS9PiorjBLSDsJlplCZaTEanTlZytAvV/FV86AZkwTsnt9IxSnDTB+HxHELqaxfvXEFljMB44wHUlOAejhX0lZOgwEELo5RRRDW6OMiaGkt0ljofsCeaUj3YSotrbqvSTUE9iUkoIRVloL5TABaIsEksPGNlhp7soSLwV4ToPoaVEb/MvoDDz71/Cl8ANQoiPgG5ATnn8MpxAgbkQzgRHKUtKu9NO7UY1Sa+dRubuyBX95PQk0wW1Os1qs3dL5NgE/EGq16lG/VZ1+H3Jn7E9kSXUalSDgD+II9GBN45AVBEITbBp1VaGuC6hZbdm6Fad337YgBEycCY5uf7ZKzlrdHE5re8/WMbbD31sqm6IF2W11Lpurq2u17IOa7/9PSIr0AgZ1G5Ug+xDOThc9phWq7qu89ETnzPrqS/oNrgjiWkuln6ygoA3QKeB7aheJ53FHy3H6/bSvmcG10zyUadRyUCkgbVZVEtJwz0bCl5QgdfSBJF0N6LMgpMQdrCYGE/pdaP7C5eEDKqFPUsTCFQiOAsdYe+KsLZU9fLyngLvXEAi7f0RyXcjNOWhIqUPmTddPT1ID+aubjpYmiOsbaPL7CyNlNtdyW4IgRTVQJqrJYwDbYme5u5HZvahXKP//MeQ+Y8jrd0h7RU07fjZy4oKKqyiHsfcs94KIKV8GZgHDAa2Am5gdKxjnjBURiz0GNbZ9PPOA80reFwy8bwIPtXmtNH7vG4kpydx7o2DsdrKv2/pVp1ajWvQqntzTruwB3aHrcoF89KQ+D1+/N4Avy/5k3UL/yia6XvyPDw99iWWfqoet+e9vpBnxr3Cvq0HzPnuMGo3jp7AUhEMH38WFlvpm57FZqFB67o07dCIMy4+FYvdErOObigYwuf24833suTjn5j7ykLyjubjdftY/vlqvpjxDTmH8/C5A6xalMSNg5qTlVnyb2NHuMx9H4yCdyB3ktIE44Pgn8iscUh/dAVIKThiVLFWjcBxlqqWkjCKCF+KmNBV0oatp1rcPHoJeGaDzFczXe/XyCMXIMOzYpl1PbjfAZmtrsk0UNoQCVeqmaljMJHabQci8Trz7iRei7kOORQ+X7TvlkQtLMaSO0gI/AyHz47R7h9ExTjm8g8l5cVSytpSSquUsq6U8g0p5cvhoEy4BN94KWUTKWVbKeWaWMf8fxOY1y00z7QrTDgpi5N7tuCe926iep1qWGxKZ9x/1Gnc9oZaAKnTtDZT5t9L3ea1sdgsWGwWOvRrS/1WdbBYdSxWC13O6sBTCx9ECIEzwcGzyx8pmtVqumbKewtNoOkaulWnUZv6nNL3ZCxWCxarTr2WdejYv506Xxz8diFevOVNpJTMvPfDuOiUqrp51GpYgye+vb/UmHQb0pHHvlFFRRNSEnj2x0do3rkpFquObtVp1b0ZLbo0KRqjaBmd0SAl+H06X79dA7Cq0k9pL5kqMKQMQf50IvlXLzLv6fhOGFU7LFAPnHZwnoNIUbUhhKURotrroDdU/cMKtp6gNw63t4K1K1hahN9bwNYdUe0DVd3b/2OYCigZ/IJhFcd3yMAW8K8mctGwkFO2gt4AUe21okIAImWyKjGFXW3XakDK1FLZjaWuzHWFokBEuFZh1DAhYmyPAWMXht9ERvcv4X9eGVUMKWVEcNn390HTtpm7j5hyzACnntOVnsO7kHc0H0eiA1sZp7a2vVsxc+Nz5GcXYHNYi2rT5WXlFy3AlUTdZrWZ/tOjFOS6+WLGN7z78McREjdpSM67eTCXPXAhiamKr/W6fQR8AZLSlH+Bz+Pj96V/8shF03Dnxk7kyD6YgyffS35WfFrv/dsPYRhGMXdrArMxNkPrHi14Y8OzEWNSuH+DVnWZsfIxCnIK0HQNZ6KaiRXkuvn6lW9558GPy53dm8Hvg00b+iFqTAjL2qLJsXJARqGWglsjm5tdc9DciwUckP4hwtIUIWyl9he2LlB9gTq/cBRx1kYoB6HZEEKNgTTyAB2hlfD+CP5lwhcDsgAZ2BiW+JjdtA2w94PkKWrmHr4O9Xe2IVImIZPvVTNwkVru31bx7uOQCWNA5iIP9SGqeqT6QjgyAuQBk41OSH0BsscTtbisfwXY/n3XPaCqFvb+EZwwM2YpJV+8MJ8Rta9mgGUEV7a4kZ+/Kn4iyKhr7i2bWiMlatIGqC9hcnpSRFAuuT0pLRG7087uzXu5s//DXFBjLBdkjGHaNa/gyY/8wiUku2jYpp6pJadu0Zgz4xvOzxjDxEGPsv/vg0phkVZsKmN32mlySkPTlG4zJKYl4kiw40iMr6ipNCQDLRcxwDKCx0dNL+ULve77P7i63a0MtFzE+Rmj+fCx2aa+0WWRlJaIw2Vn79b93D1wMmfZRjIk4VKeHvsiBbluElIScCY6yTqYzcMjnuaCGmOZOfFDgnHWNSwJi81C43aNSgUgU4gklWxhBr2YWzU8X2McOh15sAXGodMUJ13UrmGUg0uEpTFC2JD+3zAOn4882BLjYEeM3KlAMNw/BzL4N8bRUZDZDXmwG0bO/UijQFUr0coYMukNibQNBRDgfhVybwXMbsCamt1ndkcevggjcyjGgeZwqCXGgXYY7k9VUVUtLe4nJiEsitfWa0drAYdPAxltPcNA2DqUsz9gax9XX/4J/JdnzCdMYP546pe8ftf7KrtNwt4t+3l05DTWfKuyla54+KIIztjhsnP5gxdWyfmzM3O4sce9/Pq9cmDzefx8984SJg6eYtq+2+COpFRPjvATDgUNQkHlA7H2u9+4ofs9FORE/tCq1Urj1HO7YXPGluCNefRiNE3j4nvOK1cmVxbSkCx6bxlTLlW65k2rtnD/0MfZsX43Ukpyj+Tz/qOzef3u9+I6Xl5WPjf2mMgvixQP7vf4WfTBMu7qPxkpJcFAkBt73MtPc1YT9AcxQgZGHJXLy8JqszB8fGz3PSGsYIlSJTycom145kLORKUBBqUzzn0Iwx32A7GY+2arWacKujJrlNL8IhUv7H4HmTMRAGkcRR4ZoTS/GIAXPJ8js8x11tLWm6gVpsuFgVpwlBD6FUIlZ/peyJ2I4V0c4xhRkHgT5v4ihX87syceB7guUsZLyfeaH1erjmaLr3DxP4J/Ty5XYZwQgTkUCvHhlNkRq/s+j5837/sQgDMvPY3x08eQVisVTROkVE9i7OOXRjUEqijmv74Iv9dfRoMbYMsv29nyy98R7XWLznPLH6HroA7oFh2hCYReeqZiGBKf28+375jzmLfPvJ4h487E7lI67XotT6JB62LlgW7VufLhixh81ZkAjLh9GKMmjSCpWiJauBLKgCv6kpKRXC6Xu/TjnzAMg3cnfYKvjPzN5/bx5QsL8BTEVpssePMHfG5/KdliwBdk55+72bhyCz9/uYacI7kRShfdoqFpGpqu0aZXS7oN6YjFakHTBC27NaPnOV2w2q1omqB55yZM/eEhatSPXt2kEFIGo6cZ+75X/+ZPw0yzS/6z4f/OiXLwAxhGLrLgNSJTnL3gnY8MZapq2TJcCLYIfgisRwb+jDis8P9E/IuHhfrtOHn6vEfiPG5paM4hkPyQ4qbRMKdSCvsjlD9GwmhE0j1qf3tvSJpEKU213hSqz6tUf6oEUqVkx/M6HjghOOb8rIKoPGRJydugMf0YeOXp5GcXkJiaUC6HWlFsXbfdtA+aLti9aS/NOhZ77gb8AYQQVKuVxsNf3EUoGGLe6wt55fZ3IlK2fW4ff/+6w/ScNruV66eN5uonL8NX4CMxVdEdwWAQn9sf4UsshODCW4dxwS1DCfgCWO1WJX+SkoAvwBDXpabnkVJ5NkerDq7pGvu27qdhm/rl0kLbftsREdgLsWvjXo7sO1qq+GwhQiGDy++/kEvuPQ+L1RL+TD1VWG1W0/dxQeYSVUEQ3K5M20N7zbcbBzGMAqKXTQICG8D/J+aOdnYVeP1/YO6doUHwb7C2Lv15aFv0PpeF1gCS74KcO4heRaTksQ8ipbe0TjtOaK7zMBznALlw6Ez1bwRsUP1L0Oqjaep7IqUXsKIlXAwJF2MY+YADTTu+oedf1jFXGCfEjDkxLSEiFbkQdVsUm8jMe30hI2pfzYU1r+KCGmOZPX1uqRnusaBZpyamtIIRktRvpWaxB3Yc4o4zJ3F2wmUMSbiUiYMe5fDeI+gWnUZtGyBMbhQOl50mHRqZntPvCzBjwhucm3ol52eMZXSrm/j1h/VYLJboZvGoAG0rId0rfF+2EkjJ9ompCTRsU9908uUp8HJdp7sYnjyKF26aScBvHjiatm8YNaW7fqs61G9V1/TvaHfaaHByvaKgDErbXDIIl30fF0SySr4wgyxAHuxI9GdVDQ6ZqxaKkDUaQlGsMmUeZF8N/ihluWRIaZ7LQm8Svc9lYW2FsDQ3Xyw0hQ95sD1GZr+i9Op4Ybg/g8O94FAPzDluAD8cHgSZnTCybsHI7I882B55sANG7qNI6UfTEo97UC6ClPG9jgNOiMCs6zqX3nd+hA+E3WVjzCMXA/Ddu0t48ea3yMnMxQgZ5B3N5817P+TLF7+pkj4MGnsGNoe11MKJ1W6leefGNO3QCJ/Hx4097+X3xRuKuNO1C3/nxp73EgwEOblnC+q3rIOlhDZa0wT2BDv9Lz/N9JxPXjmD+TO/x+fxY4QM9mzex31DH+fv383rHMbChbcPN/2878ieaJrGqcO7mMcpqfhon8fP/NcX8fRY81TagaPPwO4snR5vtVto2KY+rbo1o12fVhFPDKDSuNv1icLlHgOEsEDCdUTX5IaIrrk1KDdRIu42ZghXxraaXLP9tDBlEEfwSrhBJYiIilQjMSC0G5l1A9L/a3x7eOYqLbihMhGjX7Ok0GIU31xlVVrIq7tnIXOicM3HCf9b/KsCXHDrUK6ZejnVaqcpvrXFSdz/8W1FPhFvPzgrQsPrLfDx7sOfVsn5U6on8/yKx+jUvx26RcORYOesMWfw6Fy1yLP00xV4870YJfhVI2SQn13Az1+uQQjBkwsfoP+oPthddnSLTpdBHZix8rFSac6FOLI/i5++WB2R8hzw+vnoiSi8ZwyMnjySEXcOKzIK0nSN/lf2YeL7NwOw5NPYvhA+j5+ln64g61BkBYvE1ARmrHyMzgPbo1t07C47/Uf14Ylv70cIwfuTo/8t3quiv1NZiISxkHQnaBkUa33NoIe3VXY2F+ZXy/1JaSBc4LwAkfaK+VGEjkj/CByDUOoMS5Rj2iCwHBncYZ5uHRNeVcEkHuQ/S2zP6Njnwzv/+Povl0QVJpj8E/iPPFPEhhCCodcOZOi1pVfjg4EgeUfzyYySgpyTmRtVx2wGKSU5h3NxJDhMdcqFyRMlkXs0jx3rd5n6Qvg9/iIePCHZxa2vXsutr5rbbpbEwR2HsNmtBMrw2oYh2f7HLrIO5ZBSPcmUR3fnezi4I5M6zWtjK/P4f/Xjl3P145ebnnPP5nJ9VYpgtVs4tDOTtBqRfge1G9dkytyJ5B7Nw2K14Eoqnq3u2BDdQ2Hnn+X4KxwDhBCIhEsh4VKk9CMPto3SUkertR7jYAeVXh0BK6LGz8gj54dngmXhgrSXIet6lIObWZMxiMTxRSWipJENWIrq70kjFxBK0pb6NPA00v0RMncKkYHRr7xDLI2Ur7Gpx3IMhCIXrc3bmWmUKwFhU1psW8ciLffxxPFa2IsHJ0xgLgspJe9N/pRPpn5ZJD8zQ0a99LiD8upv1vHsta8WGc73Pr87N788rigpoiz+/n0nT14xg50b9yghvy4wQqVvsTanjUbtGlTgyhTqNj+JgM+EyxWwa+MeLm1wHYlpCYx/bgx9LuwBqEXB2/o+yJ8//VXU9rQLunP/rNviOmfTDo3i9mM+qam5j+5fa7fx1JUvsCd8MzqlT2vufPsGqtVKo2W3pqxbZJ6h2bJbnPXmjglWNXM2TCqJFNpPWppA4PfI7SJRvSwnh9O7y37fCiBrLFG9jgHcbyLdbyMt7VTySTjAS2trFViD28Lv2yJSnlI0haUlCGEyc3OCpR1YmlUuKKOpa4kHloblJNpUADIfssYhEUjHmYjkR46tKOwx4r8cmE8YKqMsZj35Rdii06vUElEeOdr0jo+73PrrdiZdMJVDuw4T8AUJ+IIs+2wlj4w0L7eTeySPW/s8wLbfdihNbtCICMpWm4WaDTKi+nWUh+T0JM4ae0akv7JUFEnAFyDrQDZPjZ7Bb0tUWuudZ04uDsrhtks/WcH061+L65zxeGjYXXbOvqZ/qYSYQmQdzOaOMyaxY8Nugv4gQX+QX3/YwG2nP4RhGFx427Coxx1xR/RtVQUhBCTegalvRNKdqk3i7ZHbhROSbkUIDZE4nuhWmrEW4UJAAIJrVS1AAuoV+E3V5yt6/yvy6EjljWE9JaylLnlOHbQkhHMoQj+pghxzIeyIxBviaqnGpqrMhgKAH7wLkdkTquiYlYDkf4t/VQ0pJbOenBPTtQxg7YL4Fjg+mfplhBwu4Avw6/frObQrssLFt+8sJmCSmadbFbeakOJi4OjTmbZ0ctwz9rIY/9wYrpg0gvQ61bA5rVE9pz+YMhu/P8AfSyN1sQDz34iiDCiDb978Ieo2q8NCRt10Rj8ykmueviLqeco6zYWCIY7sPcofSzeyat66CHoIVLBfOfeXuPp4rNBcwxGpT4W9KxxgaYVIewFhVwuwwt5d8b+WNmq73hCSH0VzXaS2W5sh0t9Xfhc4wq9YOuKKepMULqAtUlRMtTfBdTGIVKURdgxGpH+G0FzI4NYKcMzh/lq7INLfK7fCS6ne23sj0l4I3yAcmGcmQpH/s14HXCSF9VUAACAASURBVNeBtXu4vdkTpx/8a5DB2LU8/yn8lxf/TkgqIxQMURBnDcDcI/mlOOaAP0Dm7iOkZCSTkOwiFAxxaNdhdv65x9TP2Wq3cmjX4YiEhr1b9pt7EVt0rpk6qkKJLdmZOXgLfNRskIEQgtwjeRTkuqnZIIMLbxvGhbcNY+efu5nQ4148JkVQ9287QO7h6Hrbsr7S+dn5bPllOw3b1COtRmrRmHjyoi/w3PTSOHoO62I6Uy7E7s37TLXeUkoO7DjE4b1H8XtNVBkeHwd3mJR3KoGcw7l48r3UqF/9mPXpwjGwVBmkiO32Hgj77OjbrW0Q6Sob0jh8jqrQHdEoCZH2KrLgLfAtqHgnpa9IYy2EE5E8EZInRrYL7Y2fY7Y0RqteuYVjYe+NsPcGwDjYJYpEz4rIWIzQS9sjGEdGQMBsgmRF+n8DLbHI0vRfxX9YxxwzMAshZgJnA4eklG3Cnz0EXA0U/pomSinnhbfdA4xFPbfdKKWsxLcyRqetFmo0yIj5YwbllVwYlL98aQFv3PM+RsggFDRo0bkJO/5Uj90+j78oGaMkAr5AkU65JFp3b8HCd5dGJEwITdA0ii65LI7sz+LRi59l04q/0HSNxLQE0munsX39LjRdw5Xs4rbXrqXbkE7UalQDaeJZUZgdV61WKkITpjcXR2LYRMcwuGvAZH79vtj8Pf2kNApy3UUUSTRMv/51nrv2VboN7sQdb40vtahXiJN7tuTH2SsjxkRKSdMOjUirmYrdZY9YJHUmOKKOWdahHKZc8iwblm9C09QY3T5zPJ0HVJwe+kdg66SMh8r6L0u/4qttncIOdRVUNQhb9HTwkrC0iJNjtoC1Y8X6EA3W1uA3UfAIF2ipkZ/buqhknIjEmXzInYjMlUhbR0TKMwi9etX0MQb+PySYvIV5ae5pUsr24VdhUG4NjARODu/zoqhIOYkKoGXXpjHb2F02rg0/dv/0xWpeveNd3LkevAXKzW398k3kZxXgLfAhDRkRlO0uO0OvHUByemRJnz4jepBWM7WULtnmsNKia9O4+ial5M4zJ/HnT5sIhG8MR/Zl8dfavwn4VGZf1oFsJl80je1/7MTutHPJxPNKUQFCgM1l5/IHLkTTtKiz9KsfVxl/T4yaUSooAxzZl4U332eakVcSfo+fgC/IynlrefRic96932W9VWGCEpalNqeNU/qcTJNTGtJ54CnUaVa7lLmT1W6ldpOadBkUaWYjpeSegY/wx7KNakzCY/TQeU+ye3OUjL1/GSIhXJaq5E9JOMF1iTIxcl4AWjIVezi1q0QTW8+YLYVeK1xnsDyVgyohJRKvqkAfyjln0m1Ecs5OSLrdtHqMcI2KHKMi+CiiNbJGV1lCWExIiTDiex0PxAzMUsqlQLziw+HAR1JKn5RyO8qxv+sx9C9an1j9jTl3bLHpJFVLpGXXpkz6/E5OPUed/oMps+PzKtYEiWkJ1GlWm2ufHhWVT7U5bMxY+RiDr+pHao0UqtetxkV3ncOUeffG5d61ccVfypI0holPwBdg9rNzARh597nc/Mo1NGhdl6RqiXQb0onnf55CvRaqevKEGVdx+YMXquw7AQkpLm5+ZRzDrlf31cWzlsfsV/FAYMppB3xB1n2/nsN7I+WJzgQHM1Y9zlmjTyclI5mMeulcfM+5PPT5HQBomsYzSyZxzoRBVKudSrVaqQy/4ayoPPzWddvZu3U/oTJUTNAfZM6MqkkcOlYIvTYifTbYBygOWG8Iifcgku5W27VEtd15Log0IAlzrwmLylTUakDCFYhq78bvApc8GZJuU455Ig3sQyHxVtUXkQr2/oqT1k+Keay4zmdtp6gcWw8QKYqnT52K5jI3DBN6TUT650qbLVJRY1D22oJK7RJvKa+qwH9YxyziuUMJIRoCX5ehMq5EJcyvAW6TUmYJIWYAK6SU74XbvQHMl1JGZA8IIcYB4wDq16/faefO+LPZgoEggx2XmN5drXYL8zwfRnw+su44juzLinlsV7KTaUsn07gSEreKYMknP/PMVS/hNuGMy6Jdn9Y8/cOkSp8rP7uAAzsOcV3HO+Nqb7VbGD99DB8/9SX7tkZqWJ1JDm5++Rra9GpJjXrVCQVD7Nq4B1eyi5oNYpsLxYP9fx9kxdy1vHX/R6ae1F3Oas+UeVWXSSaNPBUY9Dpx1aOT0qe8LrR0hB5/RRgj5wHwfGSyxYFInohwjaxAr/9dSCNf1QPUT0KYUBZSeiG4HbQMhF5dqUqC20CrhtBrImUAgtuULjuwIvIEIhGR8kRRUdhoEEKslVLGyJcvH0mpdWXH3jfF1Xbp13ce8/kqisou/r0ETEbdTyYDTwNjMF9+No384RLgrwJ07ty5Qvcli9VC7SY1TYNG43YNTfdp3aM5P36+ypSDLYlQMESdZuYa3apE886No3pOlITNYaX96W0qdY5QKMSMCTP59q0fSlEusRDwBXn2mugV2j15Xp695hVCwRC1GtXgyP4sjKBBKGTQ8OR6PPjZ7dSoVzmucP/fB3novKfYu2U/QghT5Y3daaP9GdESRSoGKQ1k3hRwzwJhBRlAOs9FJD+gUrpNYBS8B/lTAaHa27ojUp+NS5MrbJ2Rns+I5FsNsLY75uv5J6DG6Clwv1dijIYikicVFXE1CmZC/nOADtKP1BupG50QIINIvT4Y+1H5/V5UqCjzW5QBsFbuu17xiwKOE00RDyq1vC2lPCilDEkpDeA1iumKCpfprizGPzcGewlTISEKOeVRpu2veHgkDpfd9PG8JGrUr15UqeSfRM0GGaVMe8ygW3QSUlwMH29G8cfG+498xnfvLMbvDcRVCaUiKNSP79q4l4Jst3rv8bN13Xbu6v9wpbjCUCjEbac/yPb1u/B5/KZB2WLVSUxLYPBV/ariMpAFr4L7E8AXdmjzgWcOMv958/a+JZD3pJKzyQIUP7oCmX1LfOfT6mLuHudHRjXlP76Q7rfB/QGlx+hrZN4zarv3WxWUpSe83R/2gy4cIx+EtlBUw5AQkfM1pyrRVZ6pfpVfWJyv44BKBWYhRMnROxcoJIa+BEYKIexCiEZAM2DVsXXRHF0HdeDxBffRoV8bqtepRpezOvD04odp08t8JbtBq7rMWPU4vc/vTvW66biSzRdLDmzP5OiB2JTHsWLD8s2mwUsIlVySUS+dQVedwUu/PGW6+BgPPn9unqlpUFloukb1utVi3rTigREyOLIvi40roriulYNfv19PfrY74qlGaIKU6snUqF+ds68ZwEu/PFVUluuYUfAWZjUBcb9r2lzmv4ZperT/Z2Qojmra+c+V05eXY+9/PFAwE9Mx8nyoZtMFr6qgXGHoKhPT0gyS7kUkV56uqwxOaB1zlNLcfYUQ7VH3kx3ANQBSyg1CiI+BP1H6ofFSysrYb8WFNr1a8eR3D5b6LOtgNnu37Kd2k1qk104j53AuuzftpUaDDOq3rMPNL49j54bdPHHFDNNZpMWmk30ol2q10kzPGfArc3xngp2GberHXKAxDINtv+7ACBk07dioaJHr6IFsND3yvigltOndkkmz4+ODzZC55wgHth8iP06tN8Bbm6czLHkUsgqmCEITHNmfXfQ+FAyx5Ze/0S06Tdo3jNAhB4NBln2ygnXfr8cIRX5dpCHpNPAU7nn3xrj7IIO7Veq1pRlCSy6noZmvMCDzMXyrEJb6CL0W0shR3hTR/JuFFYyjEEvuZZYOXogSx5bSgOBGIASWk03VDhFdDm4FIxesrSvluRwVRrb559KD9K+EYCW9NIQTkfI0wt698n07BhwvxUU8iBmYpZQXm3z8RjntHwUePZZOVQahYIhp177C9+//iM1hxe8NUKN+dQ7uzMTutBHwBciom86h3YexOaxKS2tCcyGhbnPzx6lln61g6tgXATUzTK+dxuSv7i5SRZTF5tVbefDcJ9UNQCjj+/tm3Ur709vQqnsz05p+dpedzgMqVwfN5/Hx2KXTWfXNOmx2q6L34vju1W5cE7vTTq1GNUx5+4oi4A/SqpuSDP6y6A8eHTlN8elSKUUmzbmT5p2UF/EPH/7I46OeL1dDbbVb6HRmfPyrNPKQ2ePBvy6ceOFHJlyFSLzR/CZqbW3ujYGA7OuU8ZFWS/GjwqYojGgo9NsoD7Ze4Nlmvs0+SF2D/zd1DTJf9QMbpE5H2LuZ7iZD+1SpquBuEDpgIJPuR3OdH7s/8cDaFgJrTDZIyL6hApmHZXf3g5n16b+B40hTxIMTMiXbDB9Mmc3ij5YT8AUoyHET8AXYu2U/QX+Qghw3fm+AvVsPEPAFKcjxKF8LSanlSrvLzrinLsfmiEw53bVpL09c8TzuXE+RFnrftgPc0W8SIZNZniffw10DJnNkXxaefC+ePC85h/O4f9jjZB3KoUa96gwa269UjT6r3Ur1OtXoP6pPpcbgxVveYvU36wh41RjECsqFvPyEGWMBVcrKdPlWlP6/Zin+2ghBqZm/I8HO0Gv6U71OOkf2Z/HA8CfIPZKHJ8+LJ9/L4b1Huav/ZLxuH7lH85hy2XPlBmWAoD9Es06Ny21TCJlzB/h/QfGheepf90zwzjUfg6T7UBrgsj8FWby/sRPwhwNlYV9LDooTEu8qqpZdLpJuwdR3QjsJzXkm0shHZo1WM+tCHltmIbPHmVpmSimRR8eEq36HOV7phtxJKquuCiCSJ2I+RoTHyGxMzN6XgZYclwLmn4BKMJFxvY4HTsiUbDN8MWN+XHxqWQghOKlJTWo0yGDEHcOjZpTNffW7iBmulODO8/LbDxuKfKEL8ePnq0wDjhEy+OGDHznv5iGMnz6G1j2aM2fGfApyPfS5sAfn3zzE1E8iFkLBEAvfWRK1BFdJWGwWajWsQYPWdbh44vm06Kxmr217teK5Hx/h6ateYt/WAziTnAwbP5CCbDdrv/2N9JPSGHx1f7b88jcr564lOT2JIeP6s+evfSybvZLEFBfnTBhM34tUYsTC95aaZiuGQiF++mI1m1dtiWvWolk0vntnCeOeNLcrLYQ0ssH3IxFmQtKDLHgD4Tw7Yh9haw/pnyALXoTARgjtJ74sPQF6fSUdSxiHsJ8axz6gaS6MGosh63YIrAI0pe9NDj9k+r4FaXKjkgZ4voaEMovbwQ2qgGyE250P6X4XYTv2DElhbQPVP0Pmv6gy+EIHiOScQd2164OlLjhHgH8N+JcrKkSapEIYBcjAX3F7dlQ5/sPucv9vAnNBTuVUBza7lScXPhC1uGdBrpuNK7aw/Y9dUZJBJNmZkTxlTmauKVXh9waKTOaFEPQdeSq1GtXAnefl5J7NIyxGpZRsXbed7EM5tOjalORq5guBfq8/oshpNBghgzc3mS9Cte7Rgjc2PFvu/qePPLVUkHTneWjTqxWuZCctuzYtogyyDuaY3iiC/hA5mbkcjkNXDhAKhOJbkDVyiVootByDdmFtjkhV12wc7BjnI64NUe2duFUE0nBDYJ3iVa3todqL4F+r6BFrx2IO2cjCXLXhQwb+AN9ysHUq5pCNo5hfs1R1DX1LwdoOoaUiA5vUTNzaxtSbQga3K52ypXnEdQlLU0i6HYJbkNm3RhkjC6L6rOJjOxU1YxwZCQGT8Rd6+HqPD47XbDge/L8JzC27NWX9j5sqvJ8j0U71uumm2754YT6v3fkeulUn4AuactJBf4g2vVpG7HtK35PRLDqUCc6ORAftT1c+uDs27GbioEfJzylAExrBYIjx00czaIySgmXuOcLdAx/h0K5MdItOwBfgkonncel9F0Scz5nopHbjmuz5a3/EtrKIJ2U8Xsx97TteuvktdKuONCSJaYk89s29NGhVl05ntmXuq9/hLeONoemCU/qeTEa9dJZ+ErtqiiPBTtezOsTujF4H5XxmcpO2xTejxdYFfIuJGZ21JNBqxnVIw/0Z5D4c5n8lYFH+FsISfm+Haq8irO3A1k1tNwvO3gVI3yLAQCY/huYcpGxBo3llBDcpGZ/0IbUUMPLVOaUf6RqFSLpd+cMYBcjs68O8vFVtd5yFSHkMISxIGUDm3AnehWGePQqnrFUPZzeWgf30sFdGmX7KIFjj9ISuavyPY/53UKdZ5fSPGfXM3cr+/Hkzr931Hj6PH3euR5nWl+GkHQl2ho0faJpM0axjY7qf3akUh2x32WnVvRkd+rUlFApxZ/+HydyjXN0Kct343D5euHEmW3/dDsD9wx5nz1/78Bb4injyjx6fw8q5a02v5aaXxmF32dHCsrdCLrhQBqfpqiTW9c+NqdRYlcXmNdt46Za3isbIk+/l8J7D3DVgMqFQiE4DTqFF5yalPKUdCXZOPbcbjds1oNe53ajduPzgZnfaqNeyDr0viL1yL4QOepTkIGt8NyORdCeIBIrnLIXfjcJZqUBl6T2MELF/PjKwSdXLK+J/C0DmAN4S748ij45WFaytbcDeF3Pvi8J93JBzl7LMFMkQVYERCHPAfjAyw30o5N3fK+LdZe5DavaOt3i7dwGyQK3xy/yXwLuIYt6+LKesqTFJedh0gVW4LgE9g9Ke0k5Iuvk4GuVXnVeGEOIsIcRmIcRWIcTdJtuvFEJkCiF+Db9impb8v5kxL59TObn0zg27ycvKj7Cz/OKFBaa2nlabhZOa1CL9pGoMu34gPYd3iXrsiR/cxKL3lzH/jUUYIYMBo/oy4Mq+aJrGL4v+wGdiHBTwBZn7ynecf8vZ7PlrXwRP7XX7mP3cPLoN6RSxb/vT2/D8iil8/NQX7Nywh1bdm9HrvK58//6PbPttJ006NOSiO8+hbiVvYmXx9SvfRlAVUoI71836HzdxSp+Teeybe1nw5mK+e3cxFquFwVedyekXF89eZ256lpdueZtF7y3FCBl0G9KR7md3ZsFbP+DJ93L6yFMZMu7MuCpkS+MohLabb/R8Dgmxb0jC0gSqf4XMf0NZVVoaK58L31Lwrwa9HiLxKoQ1vsxD6Z5FbAN9AAN8P4BjECL1GfB+hXR/ogqghvaaHCOI9HyGsJ9BhLNdXPAg3W+BYwB45xE5Q/eq4J14TTi5xIx3F2BpDZZGiISrENbWpmcSWhKkz0G631MBXktHJFwRNy//j6EKqIywSdsLQH9Ugt1qIcSXUsqyXrCzpJTxVSbg/0FgXvLxT/y5YktcnhNmEJqGJ88TEZhzDuea/t2sditXP3k53QbHtlDUNI3+l/eh/+WRKov8rHzTfYyQQXZmLnlZBegWc7704M5Mln76My26NKVG/epsXr2VgzsP06xjIxq1qc9db5euDJGf7caVkkDb3i1Ng/LerfvZum4HtRpm0Lxzk7jNc3Iyc01T3IUQ5Gepx12rzcrZ1/Tn7GvM/Q8sFgsTnh/LhOfHlvq836W94+pDKRj5RH0INKLolcOQgS2qqojeCGFtiUh5oHSDygYRmUVcq0wyBEbh2oOusuCc5yDdHyNzzdSnQcXPylwq/eBr5Kg06Gj9k+HvaDkSQZE+Ky41itCSEYnXQ+L1lejoPwBJVZWW6gpslVL+DSCE+Ahl5mZetSJOnLCB+fC+o4xtffMxpxonV0skw4SKOPWcrqz/cVOEI13QH+Tkni2O6ZwAbU9rbVoBxZFg59RzutLklAbmac1CFWp9euxLBPxBHAl2/F4/uq4TDATpdV537nx7PLquk3Uom9Etb6IgW/2wZj/7NYlpCby5eTqp1ZMJBUM8fvl0fvpiNRarBcMwqNOsNk98ez8p1ctJygij5/CurFv0R4RlaNAfNOXd/3HodUFLBKPs7M6ieE4TSOlFZl0XXojTQRqq5l7aK0VFU48Fwt4P6f0BcxVDqZ6Y23zaumM+I7Yi7KeDtT3mpvWxYAV7P4SWgNQbmBdmtYXpI1tX8C8jgpS1tIhPIvhfRdUs/tUBdpd4vwcwE5yfL4Q4DfgLuEVKudukTRFOWI75tr4PlBuUCz2BLeF/NV3NAgvNfDRdw+6ycetr15rOEAdc0Yc6zWoV8aNK82tnzJRLqiQdOK1GCpfee14EB93w5Hr0GdEDm8PGhBlXYXfZijhiES7KGQyEcOcp3jvvaD4+tx93nge/N8DyOSuZ8/x8AG4/Y1JRUC5EflYBd535MACfPTuXn79ao7w08pQ2e8eG3Tw1Or6y9mdc0ot6LeuU4pDtLjuXP3hhXIG9qiGEhkiZgtIJF3617aClqtmaCWTe00rWhTe8qOVRNffyHquaTjnOAmuLsB9xITRKz4mc4BqJsNSP3F+LlkkYQur1wnx4vPLKwu+5XdEJiePC54jijqcpm1CRPBFVV7AwCFtAuBDJD8d53v8o4vfKqC6EWFPiNa7EUeIxbvsKaCilbAcsBN6O1bW4bD//aXTu3FmuWWOWWWSOYDDIIJtZQqJCuz6tadq+EWdc2ouf5qxmw0+badC6Lv1H9WXNgnX8+sMGTmpai/NvHkKD1vWiHsfn8fHtW4tZNnslydWTGH79WbSNs7hrvPj1h/V8+eICCnIK6HvRqfS77DRsJYzkN6/Zxpzn57Fv6wE2rdoaMxkD4KSmtXj7r+fpr5n74wJ8Z3zC5Y2v54BJFRiLzcLswzOjVgcvCb/Xz3fvLGHJJz+TlJbAsOvP4pS+x2mlPQwZ+Evxp8HdYO+BCJvWm0HJ48xoJTui5u9x0zrl9kf6wfMl0vs1iCRwjkDII0jPlyAcCNcIsPUxPZf0zEPm3muihLBAwhg1I88aE1/2nZahah3aeyFcFyO0ZJXZeLADpioQLQOthvLwlqGDSPc74P8NrC0QrivMbyT/AqrC9jM5sY7s3uaauNp+t/LBqOcTQvQAHpJSDgy/vwdASml6Zw9z0kellOVm1pyQVIYRw1y+pHexruvUblyT2o1r0qJLE1p2bcpl90cPWCVhd9oZet1Ahl4XvT5cWeQezWPNN7+CEHQd1CHm7Lr96W3KtfVs0bkJd709gd2b93J957tiVhoB5fxmmCR2lMQ3M78v10vD5/HHFZhtDhtdB3fEarfiSnbSqnuzmPvEgtftY9W8X/C5/XQa0C6qb0khpAyqRIbQYbB1BEsThGOISoQIa3ij7xxtPP2UlOHIwGYI/AF6bbD1iEuRUQghbOC6AOEqLXMUznOKu2FkI73L1PnsfdSCGYRVG2Z/yyAYeeGAHOfNQ6uFll7GnKlcjrnEE6lWQ9VJ1Bup1HM9+oTmhICkqhJMVgPNwqZte1EVnC4p2UAIUVtKWahjHQZsjHXQEzIw2xw2HImOCH0sQGoNdSPy+wI8MOxx1i/fjBCKBsiol87TiyeRmvHPpIEufH8p065+Bd2igVA3kLvemUDv84/dpKVOs9o4EhxxBeaug9qjaRquZGdUuueFm2YqCaAJajXMiJuKeGfSx8x6Yg66RVNUgiaYMv9eWnevXDbXb4s3cP/wxxGo+ovBYIjRk0dy4W3DTNvL4A7k0cvCAUoqbSw6xaJzibT3RaQ+Y+6vbOumgnqpp08B1k4IoSkNb/ZN4YxCAUIDrRpUe1+VdaoCGO45kHs/iiIAZAiZMhXNOSC86GiSOCRcCMeZYO0QvuZYsKsMw7KH0RKQlmYQLJsDoIFNVQ6X0oM8epWqLiKF6qNeH6q9U/5N7z8MQdWkW0spg0KIG4AFqC/ezLCZ28PAGinll8CNQohhqMWCo6giI+XihOWY75hpwhkKJVED+OCRT/lj2UZ8blXPzpPvZe+WA0wd+9I/0p9DuzKZNu4V/F5/kTeGz+Pn8VHPk3UwijtXBaBpGne+fQN2ly2qWqMQhfRESf66LLwFvqJMxkKvC4tNx5Hg4PaZ4+N6hP9tyQY+mfolfm8AT74Pd56Hghw39539GMFAxSVcXreP+4c/gSfPiztP6aID3gBvPziLv9aaG//IrOuVRlcWhNUDftRCmzv8rxd8i5Hu9033F8n3K3qhiKe1gUgosqCUBW+Hg7JXHU8WQGgvMvu2Cl+faf9De8NB2QcUhG8wXsi5DRk6ospBJVyN0jUX/k2cqqyT7VS1QJl8P6V5dWu4rV7c3lIf4TKn/0TKFBAuijlkB4gURLJyOJR505TRkwyPq3SrSiQ5D5oe74SBlPG9Yh5GzpNSNpdSNgmbuCGlfCAclJFS3iOlPFlKeYqU8nQpZcxMuBNyxgxw2gU9eOmXJ3nhpjfZu2U/jdrU4/rnxtAgXNF6/szvIzS2oWCItQt+xefxVbkZ/pKPf0aa8L8CWPbZSoZdHz8dEg1dBrbn5XVT+eqlBezYsJtfvjNzRYP1y9ST0tH98d0QpJT0GNaZei3qMOz6gXGXh5r/xvemdRRDwRC/Ld5Ap/4V82lYs+BX04fygDfAgrcWFznSFfU7uANCe4idwuUF90eQcEXEFmFpBNUXID0fQWC9ql/nuhihh8fA8xGRGl5DLRAaWQitfJolJjzzMH+mFso3w3UxWtJNSHtPpOdTMLwI5xCwn1lEp2iuC5HWk5Huj8A4gnD0R1qag/tTMA4g7H3BOSyqFajywvgG6f5AmSFZT0G4LiqeDXtmE5G1RwB8C5EyFJcl6X8S/4H1tWg4YQMzQNP2jZi2xHxlOBDFzCcUMnhk5DTqt6rLpfedjysOHrU8/PnzZj6fPo/dm/cRNPGqKAxSmq7R/eyOVK9jnv4dL2rUr06bU1tic1ijBmbDkDE55pIQQnDfrFux2a1IKdm0agubVm4l/aQ0ug/tFDW5w1fgjfrd9pkk5xTi6IEsVny1Fikl3Yd2Jr22Cm5+b8DUD9owJL4CkwQH6VXUQjy/LxndmEjo6YjE8VH2i3YdUgUySxOwn1Fp2ZiUXkypCoxSfZZaRphG0JBaOlpZjltvgLB1Cvsxt0GzNIWyWuxS5w2o1PPQflXOydoBLenWKK2jGWOFwi/zwCwDfyrFi1YdHP0QovRkSAa3K+8PLVHdaP7NLMCq45j/EZzQgbk89BjWhUXvL4sw9pGGZMVXa1nx1Vo+mfolTy18sNIqgolDprB6/rpy2wQDIVbMj2apLgAAIABJREFUXcPKeWt56ZY3ufrJyzjnhsGVOt/uzXu59bQH8HkDhMqhChqcXA9N00iqlkjeUfNElkIIobwzbHYrwUCQB4Y/yR/L/iQUNLDadGxOO9OWTTZNTOkzoidrvv0tUsccCEUd029mfs/zN7yOCKfBv3jzm4yfPobBV51JxzPbEvRHBilHosOcp7c0Qz3Cl+ORDIC1yFCnwrCfCZ73iYz+Ich/GSksgA3S31dGPxWEcJyOLDCriqIWAQGMnKngKVGD0TsXw3YqWrU3AZD+X5BZ4QQdGYQ8kM7zEckPmis9gnuQR0eG6Z+A0m9bToFqr5vfYGx91Oy9VCQTYO1g2l7KkPLo8C1W+wgr5D4E1d5DWJsrq9K8x8BdWDRZB/EQpL2KsHWNON4/BVGBycu/jROWY46FsY9dQlqtlCKe1fQLakjuG1o5verST3+OGZQL4fcE8Ln9+L0BXrvrffZsiW00ZIaHL3ianMO5eMKaZTPoFo2HPr0dgPs/vhVTb/iwHM/utJGQksCtr10LKOvU35duwFvgI+AL4M7zkpOZyyMjnjE9V+8LutO2dysciY6ic9ucyt85IdkV0f7Q7sM8f8Pr+L0BfG4fPrcPvzfACzfO5ODOTFIzUhj31OXYnbYi3tuR6KDzgFPoMijSxEgIHZH6NIp/LZzV21EEUvi9cKnK1wnXml5DTGjRynpJlHdEAchsZNaEKO3Kh7C2AecFFHPImvp/wmiEpTFGcG/poFwI/3IMz9dIGVQJMrKQn/apl3eOSvE263nOrSrVu7BmoQxrtwteM+9j8kS14Fnk3+EAkYRImWx+UZ7PwbcEdbPxF49R9vUqacr/E3hmFfc1zFvLLFWY4N9BnPzy//yYqxbVaqUx889nWfjeMjau+IuF7y01feQtTKpoeHLF5D8fT/2yUv0KBUIsnrWcy0wc4srDwZ2Z7Nt2wPR74kiwk1YzlTa9WjJu6ihSw4qKDme05e2tM3jplrfYsX43jds14MqHL+K3JRvYtHobjdrU46zRZxTVFJz72qIIT2spJbs37yVzzxEyyrjw6brOI1/fw6p561g+ZyWJaYmcNfr0qNrwHz9badp/aUiWfrqCC28byjk3DKLdaa2VV0aeh97nd6fzwPZRFyOF/VTImBf2ldiPsPVCWtuDZw6E9iBs3cA5JOIxOm54viI2VyLVgmBwV6W0vSL5fnAOQnq+BjSEc7jyiQaIEizVtjdAr4kp1SDdSM8nCMcZpT82ssJOb2Vni15wfwYmlI7Qa0H1b5GeOWoR0NIC4TovKr8u3R9imukYyoTQNsWVm9YIlOBfBfZepsetUkj+xzH/Uwj4A/z85Rp2b95Hozb16TakYynFgjPRydBrBzD02gEsen9Z1Hp2/iiyMYC/1m7jl+9+JyHFRZ8RPYuCWDBQuVKG0jAqpVgIBoJRi6Vm1E1n5kZzf+XajWry8Jy7it6HQiH2bz9EfrabmvUzcCYVLwiFolyTNCSzn5tL/ZZ16HV+N5Z/vorFs34ipXoSV04eiadAVSbx5PswnaKXuAYz43xDylLUTON2DbjumSujHkcaOeCdrwzYbd2RehOEXheEHfRaCL0uIqlyM1iTXsfZTpRqKwPrlZpDS1HZf6GDyghJOJVRUYnagEIIsHVB2EwMscqbQcpAWIccbbvJvjJEVN2zcRAj80ywtIPkR9H04vUXoSUiEi6Lfq5SiDJmQiiqJdY1/Vv47zIZJ25gPrzvKDf1vJe8rHx8BT7sCXbSa6fx3PJHTatKN+/chE0rt0R8brFZaN4xsmyRlJKnRr/A0k9XEPQHsNgsvHrnu0z6/E46ntmOs8f157nrTB4xY8DqsNLrXPPabeXhpCa1SM1I4eDO0pl6NoeVfpfFZ/iTl5XPzb3uI3PPkaIxe/n2t5n+0xQy6qbTd2RPPpzyeYRHRyAQZPazc7E5rEwb90qp7d9/8GOptks+/olRky7i8vsjnwh6Du/C2w/OgjI3AItFp0c5Ln0lIf1rkFlXoR5F/ShjrxASK+BTFpjWjpD2CkLEdqSLCedQKHibmA5xWirojVTV6Jy7wPtteB8r5E6mWFdtgbynIHUawtEv9vkTrgTvp+bbXBeBrROmM3rhLJXAUvSxXj3sjRH5W4CAMsoP7YLMrzGqfY1mq4Qe3XkO5G0ngjcXCcqE3zkM6fuRiFm1DIX9qP8d/JeN8k9Yjvm5a19Vs7Q8L4Yh8eR5ObD9EK/c/o5p+wc+uQ2rPfI+dOeb5qvxP32xmmWfrcDnVnpfn9uPt8DHwxc+TcAfwOYo/56m6Rq6RUO36Gi6hqYJ7C4bw8efRdP2jSp8vUIIJn54M85EBzanWnBxJjqo37ou598yNK5jvHbXe+zbdrDUmB3dn820q18Gin1EIiCV6523wGdurFQG7zw4i9yjeRGf121+EiPvPreIQy70K7ng9qFFMsfyIGUImTVe6WilB6UI8KFmaB6UksEN/jVhu81jh0i4TiVTUMiZ2yn0Hy56L1yI1Glq5uv7DrzfhfsTQgWnIIpuCIbfe5E5tyKN2GnUmrU52E0Wi/Um4LwUIeyIlKnh/oQX4oRLBTiH+SKzSJ0a1m7HqKR99NKY/TM9vusSVWRVlBgz4UKkPqskfvb+iq4QLorXAxyQ8jhCi1yb+MfwP465ahEKhVj9zboI34hgIMSyz1Zwh0mwzaibzuwjb/LmfbP4fckGajWqwVVPXEqdJubexN++vdg0y05KyR/LNvHFC99E7V+jdvXpfnZn+o7oidAEi2ctJxQ0OO2C7hFa3IqgdffmvLNtBoveX8ahXYdp27sVPYZ2jplwUoiln/wcUe7KCBmsXfQ7wUCQ79//Ma7AGw/mPD+fUQ+OiPj88gcupOfwLiz55GeQktMu7BH/jSrwB/F5G3vB8xnE/egdHUJLhOpfgPc7ZOBX5WDnGIwIrEP6V4NWG+EaXlROSXo+J7ZKBEBTi2AOczvUUi3TnsXwngsFr4L0gWsEmqt4bIWjH2QsQHq+ACMLYT8NbD2j8/LWVpDxvfLqCGyMPiMnJ47rMDm+sEO1D1Rij3+lSud2nlNE3wihQeoM8K9E+paAloRwDENYYt+cqwxSQhy+M8cLJ2Rghug3svICi8Pl4LpnIpMMKnocpDS3LwijeacmjHmkOMuqUZviBaF92w7ww0fL8Xv99BjambSaqfzw0Y8U5HjoNqQjJ/dsUW7WnZqJ61htFhWQK+CxU941SSmrLCjHQpNTGtLklIZF7/f8tY/Fs5YTDIQ49ZyuNDOhlsK9rMBZqu5ahLCCczDCWWIGqvdHhIOqlAbStwzpWwHBXXF2z0B6vkQGtiCcgxGWhuU21xx9wBHp613UR702IrFYeSJDh5Her5Chwwh7d6SlNcI7Fxk6iLB1Qlo7IISOrCTdo3TQC5H+PxCW+kjbmeB+C/wrQT8Jkm5Hc/Qrn64ROqq8lkX9/9/Gf5jKOCEDs67rdB54CmsW/FZq1myx6lXiSwHQf1RfU69hgDa9W3HOhLN4avSLpvteeLu5r8P8NxYxY8IbhEIGRtDgk6e+JBQyEJogFAwx5/l59BzehbvfvdE0OG9evZU7z3yYUDAUNhlaQP1WdZj6w6S4KmufdkF3Fr67tNTCpaZrdDijLVablQFX9uX9Rz4zrdxSUZwzIT7d8BcvzOfVO94jFAwhDYNPn/mKs68dwLVTTW6g1raox/VYFIAARyS/+k9AyoDivAO/KRol7p+UR2mDfYuQBS8hk+9Dc11UNX3yrURmj0PNHnzKFY4AEhvgRbrfBfxF76PD3C9FGrnIIyPC1bLdSOxAiWSW4G/gm4+RMhXNGflbkNJQOmf/4jAlZUPmzyj2B/m38B8OzCcsx3zzy9dQrVYqzrCG1pnkoEb9DK6ZOirGnvHh1HO60HN4l6IaejanDbvLVpQhN+CK02nVLdJJ7awxp5vypdmZOcyY8AZ+b4BQIISUkoA/iBEylBpCKuneT1+sZtW8XyL2l1LyyEXTcOd5irLqPPletq/fxezn5sZ1TVc/eTm1GtUoHrNEB6k1Urj1VWV/eMEtZ9O0QyO1XRR7WlcUl91/QdRq3iVxeN9RXr3j3aIK34Yh8bn9fP3yd2xevTWivRAWRNrzYW6ypDeEGSqnmqkopPszVYKqqMpHSapIo1hfbbp3uL0Pch9Bho4ce39kSJkuSQ/FadQ+lAShMAh7y7yPgmrm/iIy/7lwKnzhNUcx1sq52zwD1bdQ6ZyLJHN+1ZfcO5GmMrp/ABIwZHyv44ATcsYMijN+e+sMln++ij2b99GwbX16DO2ExVo1l6RpGve8dxObVm1h7be/k5Dqou9FPUs5003/eQrLv1jNVy8twGqzcMm959Gqm/kq9ur5v6qq2VHTWxW8BT4Wvr8soqbf/r8PknUo0vvC7wmw8N2lXHLPeTGvKblaEq+vn8bPX61h+++7qNOsFr3O64bNoRaNbA4b05Y+zC8L/2Djir/45s3vObTzcMRxLDYLTdo3pFbDGoyePJINP//FwneWkJDiYtSkEaWom/Kw8uu1phJAv9dfVDqrLIStK2T8AN55yMA28HxCZGCQ4P0SEsdG7F/l8H4eRZPrAOdwCPwJwT9iH0foKlPOdf6x9Se4kaiBMiZsqmiqpQ0kP4amR0mR9s4jPq4/qGbPttLJQdLzFeY8vKaoEHvfinW7UpCUy0ceZ5ywgRnA7/GTfSibQ3sOk1ojGb83UGWBGZQh/8q5v7D0059JTEukfqs6dOzXrlSbU4d34dQ4pF66RYubDraYzFQ1XYv65JWflc/TV79E216t6HhmWxbP+omdG/fQqmszTr+kF86E4tV33aLT69xuUSV7wUCInMxcMvcciWrKb7Hq3P7G9UVJOXWa1WbAqOj8Z1lsXLmFHz78kZ0bdpvWDAT4Y9lGpo9/jV7ndqNDv7alqB2hpYHrUuVw5vksylmKx1AaR5HuzyH0N8LaIZxwcmweKcWI9n2TSmcdbyabNJCer5HBzQjn2Qhru9j7mEKj0vy6cKryVpZTYvhNV+RJymR8yuWT/6WQJPlPL/6dkBVMAPZs2c+NPf+vvTMPk6I+8/jn7fuY4RY1yKFyyREOAUGMKBAQEIluVNAYdDUajw0x+qhILo1Zk6z7KKxEJWKIxiVeRIioeKEghFu5FBQXRGAFVGBgZnp6pvvdP341Mz3T1dM9h9PFWp/nqae7q6q7vlU989av39973E28NE5ZSZxQNEi4MMzsNb9Ly1BrCPFYnMkdb+DolzVrTUy6ZRy3zMrebbk2xw4Xc+mJ12ZNTAlFg9y78E4GjEzvwnxdn1vZ/eGeNAPt8XpIJpIEwgHKy8rxB/3ES801KWgVZfba32UtNg9QXFTCtLNnsH/3QWLHyvD6vGm1RgBO6tKeJz95uEHdPeZOf5q//9crxGPGYGUyzB6vkEwooWiIsyYMYMb8W9OOp6roF6MhUbt9WhgKp+OJTkbLP0S/utIkNhADIuBpjbR7oSqSojFo6UK06JcZRs1gjFh93CoeIADRa/EUTqu/Hk2iB8+D5Of1fm+11gh4WiBtX6iuspdC8uh/QvE8so/Mg3hOSv+1kIy9A4dvID3DI4icuD5rQaim6GDSMnCinn3i5Jz2fXXPrEYfr74ctz7mh254jGNfFVelEMeKyzhysIhHbv1zk3z+oz/7S5pRBlNP4vAXdXdctiMei5PMcBP0h/z4g34C4QATrh+dsaPJL567jcK2hYQLQ/gCvipDVTmyjZfG0aRWTd7Fiss4tP8Ic+74a04an/3DQvZ9sp/YMfMPV2WUxYySw4UhClpF+dWC2xtklD/94DMWzHqZspIyNKkZjbI5J7XOIcbqxRtY++r7afuICNJqNkhLq/ed34z6gsOruoXokTsxraMq/aklkNxvagw3BaGJEByJ8Xn7qa5pXEmqUfZb/nGftW+Q9H59lu+3+HFT1rSeiHiQ1n80ccpVx8qE31oqzUCl1hJIHkSPPmB/jIKbUuKU/dSsFV21F7S2nxw334ed6dHcf2E0BW4cc9OSSCTYvPzDtPCuZCLJmpdzKyyUjWUvrMq4bfGc17ny7vr5Atct2Ugg5K8yeqn0GtqdsycNZvAF/enYo0PGz+h8xinM3/0oKxeu5bOP9vH0fS9kTKOuJFGR4J+Lcvs1svSZFbZdTfxBP5f+bCKde53C2d8bklMEiB2rXtpA0mYEno1YcRnLnlvJELtCRv6e0H4ZxN40BfMDZ1a5ATRZZOoLp1FhMvMyFeGpByIepNWDaPlWiK81rpWK7TZ7RiD8PcTfG0JjTHnOsqVo2QoTnZDmflBThMh3Tf01+fvACctNOFvFR1bmos3o1tMaIlfBMbubVMJM0tl9voShzTOmrkXFB+DtgPpHQGwBxJeB5xQonIYnUxnP2GJs07YlAPG1ELLvaN605M/o5sJxaZhFBI/HY+sDzZi9Vk98gcx+sHdfWM3B3V8y9przOHygiLefWUkg7Gfs1efTZ3hP2/f4g37bUabH66HbwFO5ZNoEAHZu/pTFf3qDwweKGDZxEKf27cSSeUv5Yu9XDL5gACOnDOe8y4dz9NAx/vu3C3I6l9SMx6KvjvLq3LfYtmYHXfp0ZOQV57Dm5ffY8u42So7YJ0YIMPHGMY2uJe0P+kzVuHrWGRGP1OjEreUfm6Lw+iUSHAGhCUj4Qpt31uHLbGD95Iwf5+8N/t5ofJ29YRZBwhOQwGBUk1CxHi3faKq84SHd3SGm9keOaPk2k+2oh5DgKAhdgIQvgoo9pguLHZ42SPQaE2VhWzgic0SJiEDwLLNgjZejU8wCqMbQkufQsuXgPRmJTDZNCcCkzWf84Kb9XjKigIPLfma1YiLyBHAhcEBV+9TadjvwH8AJqvqFGMszExiPmXa9WlXTY78aicfj4ZxLhvDugtU1fLb+YO51I7Ix8caxzPv532y37XhvJ59s3MUrc9/E4xEqyhOICEvnr+DS2y9i6q/TM97OmjAwY/H+kVcaza8/9Q4zfzynKoxu5cK1VMQrEI/5ab/m5Q0seOglZq78LYWtCzhjaHe2rthWZ+dsf8jPmKnnAaZC3c2D76S0uIx4aZxVL63jr/c+hy/opzxWjscmQsLjEbr07thoowwmjnrudPsQrLoIpJxDsnQxHJmOiW5JoLGlUPwktJ2f1qFDPFE0cBbEV1HT8AUhnFtD3voikcvQ+HIbn3MQ/AONX/zwNIgvt0LsKmto1KYcgrnF9CZLXoCiezCREkk09jaUPGXqH/tOMUX2k3tqK4XQxYgETDW+cptfVaGGdd3R5DH0y0shsQ+Tmu4zFedazURC50P4Yiuyo9bfrSagGesxO3nEnIuPeR5wQe2VItIR+C6Qmuo0DuhmLdcDX0+DPeDfZl9Hx54dCBeECEaChKJBug7ownX3Nyy/vzZT7rqYHkMyFz7XpJJMJKtuDKpKWUkZz/7hxbRCQwDFmUajXuHwgSJKi2PMvPFPlJXGqwxteVk5qpriby1j747P+ccjrwFw99M/oX3HdoQLQwQjAQLhgJkETbkmPQd3Zeo95kbx6G1/4ehXx6p80OVlFahWd3tJWj5fEQhGAoQLw7T5Vmt+/mymzhb1o12Httw290YCoQDhghDhAuMr9/q9hAtChKJBfH6T1RguDBOKBgmE/PzgF9+nx+CuqJZB0QyMv7jS0Jaa6IwS+7Riafl7k0YtUUwd4TAEBtbIkmtSAt+B8BSM7zhsjiuFSJs5pgVTfGWKUYbMERRe0OytwTRZDEX3Uh2bDFBiRu2li8zoXO0ScgT0kHmazDBnkmxYr0otedKKc668OZmJVz1yJ6oVSPIQ9qangro6zTQtVkp2LkseyDpiVtVlItLFZtODwB3AwpR1k4An1Th/V4lIq1qtu5uMFm0Keez9B9i07AP2bN/HqX07ccbQ7g2alLLD4/Hw8Kr72bRsK68+sZTta3awe9verO8Tj7D+tY2M/9HoGuvXv74JX8CXFpWRiCdY+eIavD6v6a6dhXhpnLefWcFlt19Euw5tmffxLDa8sZkDnx6kx+CunNavMxvf3sq+HZ9zWr8u9BzSteqarFvyfpXxrfPcvV6u/s1kOvbowKAx/XKuxZELI6d8h0Fj+7N68QZQrFKtHlYv3kB5vIIh4wYQigZZvXgDseIyBo3tVz1aL9+E/T90zPgtbWpjiLc9tFsC8X8aY+HvhfjTI16aChFBWtyFRq40x/S0hOCIqtG8xt5IMcpZKFsG2bqilL9vws/SXNSlaOwlJDAA++iJJMSWoNEfQ8K+0S3x5bnprE3slQzHLIeKj9DYK9j7mIOWj3lk+ramRk0Ei1NpkEPWasW9V1U31jKEHYDU2KU91ro0wywi12NG1XTqVP/i4tZn0G9Eb/qNaFhrqFz49rm9+fa5vZl50xz2fLQvq2HzeD223anDBaGqdkqpeH0eIi0ihKLBnGtVRAqrY3C9Xi+Dx/avsX3AyL624XaBUMA2xbw24hHGXze6xnGakhZtCvnuVTXjnkdeUdMFdd7lw22ERchYRFcy94szFc1sPu9rRHwdwWfTMMBTgPm3y1bn2ZdSna2uA4XJOOqujMzQDD59iVi1KjIMZuryBdepKWq/XhPWL4gC7F04mts5NxV5yurLhXqHy4lIBJhBjeT46s0262zPXlXnqOogVR10wgm5dWXOJ+OuHVXVkqkuNGkajNZmyPgB2F0Kr9/HmKkj6Dmkq207ptqEokEm3ZzmWcqJcdeOJBCu+xy8Pi8DRvX92oxyo/D1AmlD2p+ZhE2pyeMAUyM5l/GQmuiNbPj7Z7gpha1u3ydbvRFr/atLGIlcZWKGg6NIn+gLQTh9riQXJPIDqttQVeIBXyfE1xmJXI59ydEgBJoxXNjB4XINiWM+HTgV2Cgiu4BTgA0ichJmhJw6TDgF2NdYkU6g+5mn86/3X4E/6CdSGCbSIkwwEsBn+UMjLcxy78I7bY1aOBrivn9MJ9oyYt5fGCYQ8nPTQ1fTuZdpnvrvr8ygVfuWRArDhAtD+IO+qufmtZ/x143inEsaVkz8h7++jH4jehMMBwgXGp9uQeso/pDfHKMgRKczOnDHvAwdo/OMiCBt5piuyxIFopiJvKuaKY238YjvdGjxS0yN4qh1HkFMjLP1WiJIq5k5JcCYuOXHwdM25fMCUHAdEjzb7NP6YfB2qHnNQhNNQXtAWt4Hvu7WCNryxQeHmnjlhhCaAOF/sc7L0uQ5CWll4polMMhqYZVyDaQ10mYuIs2V+acmKiOXJQ/klPln+Zhfqh2VYW3bBQyyojImALdgojLOAmapatZp1oZk/uWLI18U8f7SrYSiQQaO7ktZSZwNb2zCH/QzcHRfguG6Q5zisTgb3thMPBZnwKi+FLauOdpJVCR4760tHDt0jL7n9qJV+xZseucDDh8oovfwHrTv2C7DJ+fOzi272bl5Nx26nUz3M09jz0f72PHeLk7s3K5J/fRfF6oJE2mRPGzilr0n5VtSvdHkEeODJmDcLFoO8RWABwLD610wXrXCuiZHIDAkLWNPNWkiLxIHwN/PuFpqbFfjw098Bv6eDer4naYpsRfi75kbaWBIWpq3Jr40tTE8BRAYlnPHmSbJ/PO202HR3BpMLDk6r9kz/7IaZhGZD5wHtAP2A79S1bkp23dRbZgFeBgTxVECXKOqWS3u8WSYXVxc8kvTGOa2OjQ0Iad9Xyt5qtkNcy5RGVOybO+S8lwBZ/4OdnFxcamksuynQzkuM/9cXFxcGo2Dw+WO2yJGLi4uLg1FoaqQVrYlGyJygYhsF5EdInKXzfagiDxjbV+dIS+kBq5hdnFx+eahVqH8XJY6EBEvMBuT9dwLmCIivWrtdi1wSFW7YhLzfp9NnmuYXVxcvpFoIpHTkoUhwA5V/R9VjQN/w2RApzIJqKwk9TwwSrKEPjnCx7x+/fovROTTerylHZDe88hZuBobj9P1gfM1Ol0f1F9j58Ye8CiHlryhz+caexoSkdSwsTmqOsd6bpftXDvRoGofVa0QkSNAW+o4Z0cYZlWtV+qfiKxr7vCV+uJqbDxO1wfO1+h0fZAfjarasPTZdHLJds45I7oS15Xh4uLi0nByyXau2kdMamNL4Ku6PtQ1zC4uLi4NZy3QTUROFdOscDKwqNY+i4Cp1vPvA29plsw+R7gyGsCc7LvkHVdj43G6PnC+Rqfrg+NDoy2Wz/gWYAmmZc4TqrpVRO4F1qnqImAu8JSI7MCMlLN2gXVEl2wXFxcXl2pcV4aLi4uLw3ANs4uLi4vDOC4Ms4jsEpHNIvJ+ZTyhiLQRkddF5GPrsXUe9bUSkedFZJuIfCgiwxymr4d17SqXIhH5qcM03ioiW0Vki4jMF5GQNaGy2tL3jDW5kjdEZJqlb6uI/NRal9drKCJPiMgBEdmSss5WkxhmWanBm0RkYJ70XWpdw6SIDKq1/3RL33YRaVg32P8HHBeG2eJ8Ve2fEu94F/CmqnYD3rRe54uZwKuq2hPoB3zoJH2qut26dv2BMzElWf/uFI0i0gH4CaZ8bB/MJMpkTOrqg5a+Q5jU1rwgIn2AH2EyvfoBF4pIN/J/DeeR3iw5k6Zma5acRd8W4BJgWepKK5V5MtDbes8frZTnbx6q6vgF2AW0q7VuO3Cy9fxkYHuetLUAdmJNpDpNn43eMcAKJ2mkOjOqDSZS6CVgLCYzymftMwxYksfrdinweMrrX2CaEef9GgJdgC0pr201AY8BU+z2a059KevfxtyMK19PB6anvF4CDMvXd57P5XgZMSvwmoisF9PEFeBEtbpvW4/t86TtNOAg8GcReU9EHheRqIP01WYyMN967giNqroXeADYjWncewRYDxxW1cqupZWNffPFFuBcEWkrpu/leEzSgCOuYS0yacrULNkpOF1fs3G8GObhqjoQ81PsZhE5N9+CUvABA4FHVHUAUEx+3SoZsXy0FwHP5VtLKpYFk8BLAAABwElEQVQPdBKml+S3MI3pxtnsmrfYTlX9EONaeR14FdhI9lbXTqPeqcHNjNP1NRvHhWFW1X3W4wGMb3QIsF9ETgawHg/kSd4eYI+qrrZeP48x1E7Rl8o4YIOq7rdeO0XjaGCnqh5U1XJgAXA20Eqqu3PmvbGvqs5V1YGqei4mUeBjnHMNU8mkyenNkp2ur9lwvGEWkaiIFFY+x/hIt1AzzXEqsDAf+lT1c+AzEelhrRoFfIBD9NViCtVuDHCOxt3AUBGJWOUQK6/hUkwKa771ASAi7a3HTpjJq/k45xqmkknTIuCHVnTGUOBIpcvDISwCJospLH8qZpJyTZ415Yd8O7mzLRgf7kZr2QrMsNa3xcw4f2w9tsmjxv7AOmAT8CLQ2kn6LI0R4EugZco6x2gE7gG2YW66TwFB67tfA+zAuF+Ceb6GyzE3jI3AKCdcQ8zN4X+BcsyI89pMmjCugtnAJ8BmUibemlnfxdbzMkyD5yUp+8+w9G0HxuXz+87n4qZku7i4uDgMx7syXFxcXL5puIbZxcXFxWG4htnFxcXFYbiG2cXFxcVhuIbZxcXFxWG4htnFxcXFYbiG2cXFxcVh/B/YIiXWI7ZJpgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Your code here:\n", + "fig = plt.figure()\n", + "\n", + "ax = fig.add_subplot(111)\n", + "\n", + "scatter = ax.scatter(patients['patient_weight_kg'], patients['patient_height_sm'],\n", + " c=patients['labels'])\n", + "\n", + "plt.colorbar(scatter)\n" ] }, { @@ -620,12 +2070,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 99, "metadata": {}, "outputs": [], "source": [ - "# Your code here:\n" + "# Your code here:\n", + "from mpl_toolkits.mplot3d import Axes3D" ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(12,6))\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "ax.scatter(patients[\"patient_height_sm\"],patients[\"patient_weight_kg\"],\n", + " patients[\"patient_diabetic\"],c=patients.labels);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -644,7 +2127,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.7.6" } }, "nbformat": 4,