From d7482448f71f30222e42e8782130cf7d2c62f3e7 Mon Sep 17 00:00:00 2001 From: EzraMelaku <45677653+ezramlaku@users.noreply.github.com> Date: Tue, 28 Nov 2023 11:20:48 +0200 Subject: [PATCH 1/7] task one --- notebooks/parse_slack_data.ipynb | 21 +++++++++++++++++---- src/loader.py | 4 ++-- 2 files changed, 19 insertions(+), 6 deletions(-) diff --git a/notebooks/parse_slack_data.ipynb b/notebooks/parse_slack_data.ipynb index e3774f8..5ca3c3e 100644 --- a/notebooks/parse_slack_data.ipynb +++ b/notebooks/parse_slack_data.ipynb @@ -12,9 +12,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'wordcloud'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32mc:\\Users\\ezra2\\OneDrive\\Documents\\GitHub\\week0_starter_network_analysis\\notebooks\\parse_slack_data.ipynb Cell 2\u001b[0m line \u001b[0;36m1\n\u001b[0;32m 10\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mseaborn\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39msns\u001b[39;00m\n\u001b[0;32m 12\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mnltk\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mcorpus\u001b[39;00m \u001b[39mimport\u001b[39;00m stopwords\n\u001b[1;32m---> 13\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mwordcloud\u001b[39;00m \u001b[39mimport\u001b[39;00m WordCloud\n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'wordcloud'" + ] + } + ], "source": [ "import os, sys\n", "import re\n", @@ -95,7 +107,8 @@ " \"\"\"\n", "\n", " # specify path to get json files\n", - " combined = []\n", + " combined = []#sa\n", + " \n", " for json_file in glob.glob(f\"{path_channel}*.json\"):\n", " with open(json_file, 'r', encoding=\"utf8\") as slack_data:\n", " combined.append(slack_data)\n", @@ -444,7 +457,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/src/loader.py b/src/loader.py index c75b68d..e63fac1 100644 --- a/src/loader.py +++ b/src/loader.py @@ -5,7 +5,7 @@ import shutil import copy from datetime import datetime -from pick import pick +# from pick import pick from time import sleep @@ -34,7 +34,7 @@ def __init__(self, path): ''' self.path = path self.channels = self.get_channels() - self.users = self.get_ussers() + self.users = self.get_users() def get_users(self): From a9144a8d64add8e155e209d6d3fc2de1313b8fc5 Mon Sep 17 00:00:00 2001 From: EzraMelaku <45677653+ezramlaku@users.noreply.github.com> Date: Tue, 28 Nov 2023 21:55:43 +0300 Subject: [PATCH 2/7] get users and channels in loader.py --- .gitignore | 2 ++ src/loader.py | 18 +++++++----------- 2 files changed, 9 insertions(+), 11 deletions(-) diff --git a/.gitignore b/.gitignore index 8cd10b4..ba8ca27 100644 --- a/.gitignore +++ b/.gitignore @@ -157,3 +157,5 @@ venv.bak/ # mypy .mypy_cache/ + +anonymized/ \ No newline at end of file diff --git a/src/loader.py b/src/loader.py index e63fac1..f94245b 100644 --- a/src/loader.py +++ b/src/loader.py @@ -9,7 +9,6 @@ from time import sleep - # Create wrapper classes for using slack_sdk in place of slacker class SlackDataLoader: ''' @@ -22,12 +21,13 @@ class SlackDataLoader: You'll see reference files for different kinds of conversations: users.json files for all types of users that exist in the slack workspace channels.json files for public channels, - + These files contain metadata about the conversations, including their names and IDs. For secruity reason, we have annonymized names - the names you will see are generated using faker library. - + ''' + def __init__(self, path): ''' path: path to the slack exported data folder @@ -35,7 +35,6 @@ def __init__(self, path): self.path = path self.channels = self.get_channels() self.users = self.get_users() - def get_users(self): ''' @@ -45,7 +44,7 @@ def get_users(self): users = json.load(f) return users - + def get_channels(self): ''' write a function to get all the channels from the json file @@ -58,10 +57,10 @@ def get_channels(self): def get_channel_messages(self, channel_name): ''' write a function to get all the messages from a channel - + ''' - # + # def get_user_map(self): ''' write a function to get a map between user id and user name @@ -71,14 +70,11 @@ def get_user_map(self): for user in self.users: userNamesById[user['id']] = user['name'] userIdsByName[user['name']] = user['id'] - return userNamesById, userIdsByName - - + return userNamesById, userIdsByName if __name__ == "__main__": parser = argparse.ArgumentParser(description='Export Slack history') - parser.add_argument('--zip', help="Name of a zip file to import") args = parser.parse_args() From 01efe7d4d89d32ebff1d959e0bf2c200ae436e91 Mon Sep 17 00:00:00 2001 From: EzraMelaku <45677653+ezramlaku@users.noreply.github.com> Date: Wed, 29 Nov 2023 21:31:45 +0300 Subject: [PATCH 3/7] Update loader.py --- src/loader.py | 22 +++++++++++++--------- 1 file changed, 13 insertions(+), 9 deletions(-) diff --git a/src/loader.py b/src/loader.py index f94245b..c75b68d 100644 --- a/src/loader.py +++ b/src/loader.py @@ -5,10 +5,11 @@ import shutil import copy from datetime import datetime -# from pick import pick +from pick import pick from time import sleep + # Create wrapper classes for using slack_sdk in place of slacker class SlackDataLoader: ''' @@ -21,20 +22,20 @@ class SlackDataLoader: You'll see reference files for different kinds of conversations: users.json files for all types of users that exist in the slack workspace channels.json files for public channels, - + These files contain metadata about the conversations, including their names and IDs. For secruity reason, we have annonymized names - the names you will see are generated using faker library. - + ''' - def __init__(self, path): ''' path: path to the slack exported data folder ''' self.path = path self.channels = self.get_channels() - self.users = self.get_users() + self.users = self.get_ussers() + def get_users(self): ''' @@ -44,7 +45,7 @@ def get_users(self): users = json.load(f) return users - + def get_channels(self): ''' write a function to get all the channels from the json file @@ -57,10 +58,10 @@ def get_channels(self): def get_channel_messages(self, channel_name): ''' write a function to get all the messages from a channel - + ''' - # + # def get_user_map(self): ''' write a function to get a map between user id and user name @@ -70,11 +71,14 @@ def get_user_map(self): for user in self.users: userNamesById[user['id']] = user['name'] userIdsByName[user['name']] = user['id'] - return userNamesById, userIdsByName + return userNamesById, userIdsByName + + if __name__ == "__main__": parser = argparse.ArgumentParser(description='Export Slack history') + parser.add_argument('--zip', help="Name of a zip file to import") args = parser.parse_args() From 7bc13211f882f38bfda9a26e995d26ae14035083 Mon Sep 17 00:00:00 2001 From: EzraMelaku <45677653+ezramlaku@users.noreply.github.com> Date: Thu, 30 Nov 2023 06:38:09 +0300 Subject: [PATCH 4/7] task 1 --- .gitignore | 2 ++ src/__init__.py | 7 +++++++ src/loader.py | 3 ++- 3 files changed, 11 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index ba8ca27..76b0128 100644 --- a/.gitignore +++ b/.gitignore @@ -158,4 +158,6 @@ venv.bak/ # mypy .mypy_cache/ +Data/ +Data/anonymized anonymized/ \ No newline at end of file diff --git a/src/__init__.py b/src/__init__.py index e69de29..fcce97c 100644 --- a/src/__init__.py +++ b/src/__init__.py @@ -0,0 +1,7 @@ +from loader import SlackDataLoader + +data_loader = SlackDataLoader('Data/anonymized') + + +# slack_data = data_loader.get_users() +print(data_loader.get_channels()[0]['name']) \ No newline at end of file diff --git a/src/loader.py b/src/loader.py index c75b68d..99466e8 100644 --- a/src/loader.py +++ b/src/loader.py @@ -13,6 +13,7 @@ # Create wrapper classes for using slack_sdk in place of slacker class SlackDataLoader: ''' + n=SlackDataLoader('Data/anonymized') Slack exported data IO class. When you open slack exported ZIP file, each channel or direct message @@ -34,7 +35,7 @@ def __init__(self, path): ''' self.path = path self.channels = self.get_channels() - self.users = self.get_ussers() + self.users = self.get_users() def get_users(self): From fb23ebb507884f43d56b1da65512aeabce3c8726 Mon Sep 17 00:00:00 2001 From: EzraMelaku <45677653+ezramlaku@users.noreply.github.com> Date: Thu, 30 Nov 2023 10:02:54 +0300 Subject: [PATCH 5/7] starting task 1 --- src/__init__.py | 11 ++++++++--- src/loader.py | 38 +++++++++++++++++++++++++++++++++++--- src/new.py | 39 +++++++++++++++++++++++++++++++++++++++ 3 files changed, 82 insertions(+), 6 deletions(-) create mode 100644 src/new.py diff --git a/src/__init__.py b/src/__init__.py index fcce97c..58d19c3 100644 --- a/src/__init__.py +++ b/src/__init__.py @@ -1,7 +1,12 @@ from loader import SlackDataLoader - +import json data_loader = SlackDataLoader('Data/anonymized') -# slack_data = data_loader.get_users() -print(data_loader.get_channels()[0]['name']) \ No newline at end of file + + + + + + + diff --git a/src/loader.py b/src/loader.py index 99466e8..366ac76 100644 --- a/src/loader.py +++ b/src/loader.py @@ -33,10 +33,21 @@ def __init__(self, path): ''' path: path to the slack exported data folder ''' + self.path = path self.channels = self.get_channels() self.users = self.get_users() - + def get_channel_sub_files(self): + channels_files={} + BS=r"\""[0] + for (current_folder,folders_in_current_folder,files_in_current_folder) in os.walk(self.path): + if(len(folders_in_current_folder) == 0): + current_channel_name=str(current_folder).split(BS)[1] + channels_files[current_channel_name]=files_in_current_folder + return json.dumps(channels_files) + + + def get_users(self): ''' @@ -51,18 +62,39 @@ def get_channels(self): ''' write a function to get all the channels from the json file ''' + + with open(os.path.join(self.path, 'channels.json'), 'r') as f: channels = json.load(f) return channels - + def get_channel_sub_file(self,channel_name): + channel_sub_files=json.loads(self.get_channel_sub_files()) + return channel_sub_files[channel_name] + def get_channel_messages_by_day(self,current_day_path): + with open(current_day_path,'r') as f: + message=json.load(f) + return message def get_channel_messages(self, channel_name): + channel_path=os.path.join(self.path,channel_name) ''' write a function to get all the messages from a channel ''' + message={} + daily_channel_messages_file=self.get_channel_sub_file(channel_name) + for current_day in daily_channel_messages_file: + current_day_path=os.path.join(channel_path,current_day) + message[current_day]=(self.get_channel_messages_by_day(current_day_path)) + #to see if the json object is correct write into file + # with open("sample.json", "w") as outfile: + # outfile.write(json.dumps(message)) + return json.dumps(message) + + + - # + def get_user_map(self): ''' write a function to get a map between user id and user name diff --git a/src/new.py b/src/new.py new file mode 100644 index 0000000..c73be5d --- /dev/null +++ b/src/new.py @@ -0,0 +1,39 @@ +from loader import SlackDataLoader +import json +data_loader = SlackDataLoader('Data/anonymized') + +# print(data_loader.get_channel_sub_files()) +(data_loader.get_channel_messages("week-11-group4")) + + + + + + + +# # slack_data = data_loader.get_users() +# cha=data_loader.get_channels()[0]['name'] +# data_loader.get_channel_messages(cha) +# import os +# import json +# folder_path="Data/anonymized" +# # print(folder_path) + +# channels_files={} +# for (current_folder,folders_in_current_folder,files_in_current_folder) in os.walk(folder_path): +# if(len(folders_in_current_folder) == 0): +# BS=r"\""[0] +# # print("=======================================") +# # print(f'*****current_folder {str(current_folder).split(BS)[1]}') +# # print(f'*****folders_in_current_folder {str(folders_in_current_folder)}') +# # print(f'*****files_in_current_folder {str(files_in_current_folder)}') +# # print("=======================================") +# current_channel=str(current_folder).split(BS)[1] +# # channels_files[current_channel]={} +# channels_files[current_channel]=files_in_current_folder +# # print(current_channel) + + +# # print(json.loads(channels_files)) +# obj = json.dumps(channels_files) +# print(obj) \ No newline at end of file From 0b1883f6425f327316ea4a1905f3dbb2afb9c27b Mon Sep 17 00:00:00 2001 From: EzraMelaku <45677653+ezramlaku@users.noreply.github.com> Date: Thu, 30 Nov 2023 11:46:08 +0300 Subject: [PATCH 6/7] change __init__ dir --- __init__.ipynb | 52 ++++++++++++++++++++++++++++ notebooks/parse_slack_data.ipynb | 55 ++++++++++++++++-------------- src/__init__.py | 12 ------- src/__init__1.ipynb | 58 ++++++++++++++++++++++++++++++++ 4 files changed, 139 insertions(+), 38 deletions(-) create mode 100644 __init__.ipynb delete mode 100644 src/__init__.py create mode 100644 src/__init__1.ipynb diff --git a/__init__.ipynb 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+ "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/parse_slack_data.ipynb b/notebooks/parse_slack_data.ipynb index 5ca3c3e..24869d8 100644 --- a/notebooks/parse_slack_data.ipynb +++ b/notebooks/parse_slack_data.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -12,21 +12,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 20, "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'wordcloud'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32mc:\\Users\\ezra2\\OneDrive\\Documents\\GitHub\\week0_starter_network_analysis\\notebooks\\parse_slack_data.ipynb Cell 2\u001b[0m line \u001b[0;36m1\n\u001b[0;32m 10\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mseaborn\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39msns\u001b[39;00m\n\u001b[0;32m 12\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mnltk\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mcorpus\u001b[39;00m \u001b[39mimport\u001b[39;00m stopwords\n\u001b[1;32m---> 13\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mwordcloud\u001b[39;00m \u001b[39mimport\u001b[39;00m WordCloud\n", - "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'wordcloud'" - ] - } - ], + "outputs": [], "source": [ "import os, sys\n", "import re\n", @@ -45,17 +33,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'Data/anonymized\\\\channels.json'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32mc:\\Users\\ezra2\\OneDrive\\Documents\\10Acadmy\\week0_starter_network_analysis\\notebooks\\parse_slack_data.ipynb Cell 3\u001b[0m line \u001b[0;36m6\n\u001b[0;32m 3\u001b[0m \u001b[39m# if rpath not in sys.path:\u001b[39;00m\n\u001b[0;32m 4\u001b[0m sys\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39minsert(\u001b[39m0\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mC:\u001b[39m\u001b[39m\\\\\u001b[39;00m\u001b[39mUsers\u001b[39m\u001b[39m\\\\\u001b[39;00m\u001b[39mezra2\u001b[39m\u001b[39m\\\\\u001b[39;00m\u001b[39mOneDrive\u001b[39m\u001b[39m\\\\\u001b[39;00m\u001b[39mDocuments\u001b[39m\u001b[39m\\\\\u001b[39;00m\u001b[39m10Acadmy\u001b[39m\u001b[39m\\\\\u001b[39;00m\u001b[39mweek0_starter_network_analysis\u001b[39m\u001b[39m\\\\\u001b[39;00m\u001b[39msrc\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m----> 6\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39msrc\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mloader\u001b[39;00m \u001b[39mimport\u001b[39;00m SlackDataLoader\n\u001b[0;32m 7\u001b[0m \u001b[39m# import src.utils as utils\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\ezra2\\OneDrive\\Documents\\10Acadmy\\week0_starter_network_analysis\\src\\__init__.py:3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mloader\u001b[39;00m \u001b[39mimport\u001b[39;00m SlackDataLoader \n\u001b[0;32m 2\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mjson\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m data_loader \u001b[39m=\u001b[39m SlackDataLoader(\u001b[39m'\u001b[39;49m\u001b[39mData/anonymized\u001b[39;49m\u001b[39m'\u001b[39;49m)\n", + "File \u001b[1;32m~\\OneDrive\\Documents\\10Acadmy\\week0_starter_network_analysis\\src\\loader.py:38\u001b[0m, in \u001b[0;36mSlackDataLoader.__init__\u001b[1;34m(self, path)\u001b[0m\n\u001b[0;32m 33\u001b[0m \u001b[39m\u001b[39m\u001b[39m'''\u001b[39;00m\n\u001b[0;32m 34\u001b[0m \u001b[39mpath: path to the slack exported data folder\u001b[39;00m\n\u001b[0;32m 35\u001b[0m \u001b[39m'''\u001b[39;00m\n\u001b[0;32m 37\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpath \u001b[39m=\u001b[39m path\n\u001b[1;32m---> 38\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mchannels \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mget_channels()\n\u001b[0;32m 39\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39musers \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_users()\n", + "File \u001b[1;32m~\\OneDrive\\Documents\\10Acadmy\\week0_starter_network_analysis\\src\\loader.py:67\u001b[0m, in \u001b[0;36mSlackDataLoader.get_channels\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 61\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mget_channels\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[0;32m 62\u001b[0m \u001b[39m \u001b[39m\u001b[39m'''\u001b[39;00m\n\u001b[0;32m 63\u001b[0m \u001b[39m write a function to get all the channels from the json file\u001b[39;00m\n\u001b[0;32m 64\u001b[0m \u001b[39m '''\u001b[39;00m\n\u001b[1;32m---> 67\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39;49m(os\u001b[39m.\u001b[39;49mpath\u001b[39m.\u001b[39;49mjoin(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpath, \u001b[39m'\u001b[39;49m\u001b[39mchannels.json\u001b[39;49m\u001b[39m'\u001b[39;49m), \u001b[39m'\u001b[39;49m\u001b[39mr\u001b[39;49m\u001b[39m'\u001b[39;49m) \u001b[39mas\u001b[39;00m f:\n\u001b[0;32m 68\u001b[0m channels \u001b[39m=\u001b[39m json\u001b[39m.\u001b[39mload(f)\n\u001b[0;32m 70\u001b[0m \u001b[39mreturn\u001b[39;00m channels\n", + "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Data/anonymized\\\\channels.json'" + ] + } + ], "source": [ "# Add parent directory to path to import modules from src\n", "rpath = os.path.abspath('..')\n", - "if rpath not in sys.path:\n", - " sys.path.insert(0, rpath)\n", + "# if rpath not in sys.path:\n", + "sys.path.insert(0, 'C:\\\\Users\\\\ezra2\\\\OneDrive\\\\Documents\\\\10Acadmy\\\\week0_starter_network_analysis\\\\src')\n", "\n", "from src.loader import SlackDataLoader\n", - "import src.utils as utils" + "# import src.utils as utils" ] }, { @@ -372,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -381,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -390,7 +393,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -399,7 +402,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -408,7 +411,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -457,7 +460,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.4" } }, "nbformat": 4, diff --git a/src/__init__.py b/src/__init__.py deleted file mode 100644 index 58d19c3..0000000 --- a/src/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -from loader import SlackDataLoader -import json -data_loader = SlackDataLoader('Data/anonymized') - - - - - - - - - diff --git a/src/__init__1.ipynb b/src/__init__1.ipynb new file mode 100644 index 0000000..6a4a918 --- /dev/null +++ b/src/__init__1.ipynb @@ -0,0 +1,58 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'Data/anonymized\\\\channels.json'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32mc:\\Users\\ezra2\\OneDrive\\Documents\\10Acadmy\\week0_starter_network_analysis\\src\\__init__.ipynb Cell 1\u001b[0m line \u001b[0;36m9\n\u001b[0;32m 3\u001b[0m sys\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39minsert(\u001b[39m1\u001b[39m,\u001b[39m\"\u001b[39m\u001b[39mC:/Users/ezra2/OneDrive/Documents/10Acadmy/week0_starter_network_analysis/src\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m 7\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39msrc\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mloader\u001b[39;00m \u001b[39mimport\u001b[39;00m SlackDataLoader\n\u001b[1;32m----> 9\u001b[0m n\u001b[39m=\u001b[39mSlackDataLoader(\u001b[39m\"\u001b[39;49m\u001b[39mData/anonymized\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n", + "File \u001b[1;32mC:\\Users/ezra2/OneDrive/Documents/10Acadmy/week0_starter_network_analysis\\src\\loader.py:38\u001b[0m, in \u001b[0;36mSlackDataLoader.__init__\u001b[1;34m(self, path)\u001b[0m\n\u001b[0;32m 33\u001b[0m \u001b[39m\u001b[39m\u001b[39m'''\u001b[39;00m\n\u001b[0;32m 34\u001b[0m \u001b[39mpath: path to the slack exported data folder\u001b[39;00m\n\u001b[0;32m 35\u001b[0m \u001b[39m'''\u001b[39;00m\n\u001b[0;32m 37\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpath \u001b[39m=\u001b[39m path\n\u001b[1;32m---> 38\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mchannels \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mget_channels()\n\u001b[0;32m 39\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39musers \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_users()\n", + "File \u001b[1;32mC:\\Users/ezra2/OneDrive/Documents/10Acadmy/week0_starter_network_analysis\\src\\loader.py:67\u001b[0m, in \u001b[0;36mSlackDataLoader.get_channels\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 61\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mget_channels\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[0;32m 62\u001b[0m \u001b[39m \u001b[39m\u001b[39m'''\u001b[39;00m\n\u001b[0;32m 63\u001b[0m \u001b[39m write a function to get all the channels from the json file\u001b[39;00m\n\u001b[0;32m 64\u001b[0m \u001b[39m '''\u001b[39;00m\n\u001b[1;32m---> 67\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39;49m(os\u001b[39m.\u001b[39;49mpath\u001b[39m.\u001b[39;49mjoin(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpath, \u001b[39m'\u001b[39;49m\u001b[39mchannels.json\u001b[39;49m\u001b[39m'\u001b[39;49m), \u001b[39m'\u001b[39;49m\u001b[39mr\u001b[39;49m\u001b[39m'\u001b[39;49m) \u001b[39mas\u001b[39;00m f:\n\u001b[0;32m 68\u001b[0m channels \u001b[39m=\u001b[39m json\u001b[39m.\u001b[39mload(f)\n\u001b[0;32m 70\u001b[0m \u001b[39mreturn\u001b[39;00m channels\n", + "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Data/anonymized\\\\channels.json'" + ] + } + ], + "source": [ + "import sys\n", + "sys.path.insert(1,\"C:/Users/ezra2/OneDrive/Documents/10Acadmy/week0_starter_network_analysis\")\n", + "\n", + "\n", + "\n", + "from src.loader import SlackDataLoader\n", + "\n", + "n=SlackDataLoader(\"Data/anonymized\")\n", + "\n", + " \n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "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": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From a22b964bfe086e3a40629558629e4bb20803b176 Mon Sep 17 00:00:00 2001 From: EzraMelaku <45677653+ezramlaku@users.noreply.github.com> Date: Fri, 1 Dec 2023 03:18:07 +0300 Subject: [PATCH 7/7] Update __init__.ipynb and Complete Task One In this commit, I have significantly updated the '__init__.ipynb' file, addressing various aspects to enhance its functionality and readability. Additionally, I am pleased to share that Task One has been completed. This comprehensive update includes improvements, bug fixes, and optimizations, ensuring a more robust and efficient implementation. Please review the changes for a detailed overview of the enhancements made. --- __init__.ipynb | 349 +++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 336 insertions(+), 13 deletions(-) diff --git a/__init__.ipynb b/__init__.ipynb index df76ca0..7aa74b9 100644 --- a/__init__.ipynb +++ b/__init__.ipynb @@ -2,29 +2,352 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 475, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import json\n", + "from src.loader import SlackDataLoader\n", + "import random\n", + "import pandas as pd\n", + "\n", + "from matplotlib import pyplot as plt \n", + "import numpy as np\n", + "from datetime import datetime\n", + "\n", + "\n", + "\n", + "SDL=SlackDataLoader(\"Data/anonymized\")" + ] + }, + { + "cell_type": "code", + "execution_count": 274, + "metadata": {}, + "outputs": [], + "source": [ + "#who are the top and bottom 10 users\n", + "users=SDL.get_users()\n", + "channels=SDL.get_channels()\n", + "\n", + "users_by_replay={}\n", + "users_by_mention={}\n", + "users_by_message={}\n", + "users_by_reaction={}\n", + "\n", + "message_by_replay={}\n", + "message_by_reaction={}\n", + "\n", + "\n", + "for user in users:\n", + " users_by_mention[user['id']],users_by_replay[user['id']],users_by_message[user['id']],users_by_reaction[user['id']]=0,0,0,0" + ] + }, + { + "cell_type": "code", + "execution_count": 275, + "metadata": {}, + "outputs": [], + "source": [ + "# by reply count\n", + "for channel in channels:\n", + " current_channel_messages=json.loads(SDL.get_channel_messages(channel['name']))\n", + " for current_message in current_channel_messages:\n", + " if('reply_count' in current_channel_messages[current_message][0]):\n", + " users_by_replay[current_channel_messages[current_message][0]['user']]+=1\n", + "\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 276, + "metadata": {}, + "outputs": [], + "source": [ + "def top_and_bottom(by):\n", + " by=({k: v for k, v in sorted(by.items(), key=lambda item: item[1],reverse=True)})\n", + " top_10_users_id=list(by)[0:10]\n", + " bottom_10_users_id=list(by)[-10:]\n", + " return [top_10_users_id,bottom_10_users_id]\n", + "# def top_and_bottom_message(by):\n", + "# by=({k: v for k, v in sorted(by.items(), key=lambda item: item[1],reverse=True)})\n", + "# top_10_bottom_10_message_id=list(by)[0:10]\n", + "# bottom_10_bottom_10_message_id=list(by)[-10:]\n", + "# return [top_10_bottom_10_message_id,bottom_10_bottom_10_message_id]\n", + " \n", + "def display_top_and_bottom_users(top,bottom,by,by_txt):\n", + " print(f\"top 10 users by {by_txt} \\n \")\n", + " i=1\n", + " for user_id in top:\n", + " print(f\"{i},\",list(filter(lambda x:x[\"id\"]==user_id,users))[0]['real_name'],f'with {by[user_id]} {by_txt}')\n", + " i+=1\n", + " print(f\"Bottom 10 users by {by_txt}\")\n", + " i=len(users)\n", + " for user_id in bottom:\n", + " print(f\"{i},\",list(filter(lambda x:x[\"id\"]==user_id,users))[0]['real_name'],f'with {by[user_id]} {by_txt}')\n", + " i-=1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# to display who are the top and bottom 10 users by reply count\n", + "top_10_users_id,bottom_10_users_id=top_and_bottom(users_by_replay)\n", + "display_top_and_bottom_users(top_10_users_id,bottom_10_users_id,users_by_replay,\"replay Count\")" + ] + }, + { + "cell_type": "code", + "execution_count": 278, + "metadata": {}, + "outputs": [], + "source": [ + "# by mention\n", + "for channel in channels:\n", + " current_channel_message=json.loads(SDL.get_channel_messages(channel['name']))\n", + " for current_message in current_channel_message:\n", + " users_by_mention[current_channel_message[current_message][0]['user']]+=1\n", + " if('replies' in current_channel_message[current_message][0]):\n", + " for u in current_channel_message[current_message][0]['replies']:\n", + " users_by_mention[u['user']]+=1\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# to display who are the top and bottom 10 users by mention\n", + "top_10_users_id,bottom_10_users_id=top_and_bottom(users_by_mention)\n", + "display_top_and_bottom_users(top_10_users_id,bottom_10_users_id,users_by_mention,\"mention\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 280, + "metadata": {}, + "outputs": [], + "source": [ + "# by Message\n", + "for channel in channels:\n", + " current_channel_message=json.loads(SDL.get_channel_messages(channel['name']))\n", + " for current_message in current_channel_message:\n", + " users_by_message[current_channel_message[current_message][0]['user']]+=1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# to display who are the top and bottom 10 users by message\n", + "top_10_users_id,bottom_10_users_id=top_and_bottom(users_by_message)\n", + "display_top_and_bottom_users(top_10_users_id,bottom_10_users_id,users_by_message,\"message\")" + ] + }, + { + "cell_type": "code", + "execution_count": 282, + "metadata": {}, + "outputs": [], + "source": [ + "# by Reaction\n", + "for channel in channels:\n", + " current_channel_message=json.loads(SDL.get_channel_messages(channel['name']))\n", + " for current_message in current_channel_message:\n", + " if('replies' in current_channel_message[current_message][0]):\n", + " for u in current_channel_message[current_message][0]['replies']:\n", + " users_by_reaction[u['user']]+=1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# to display who are the top and bottom 10 users by Reaction\n", + "top_10_users_id,bottom_10_users_id=top_and_bottom(users_by_reaction)\n", + "display_top_and_bottom_users(top_10_users_id,bottom_10_users_id,users_by_reaction,\"Reaction\")" + ] + }, + { + "cell_type": "code", + "execution_count": 284, + "metadata": {}, + "outputs": [], + "source": [ + "# which message are top and bottom 10 message by replies\n", + "for channel in channels:\n", + " current_channel_messages=json.loads(SDL.get_channel_messages(channel['name']))\n", + " for current_message in current_channel_messages:\n", + " if('client_msg_id' in current_channel_messages[current_message][0]):\n", + " if(\"reply_count\" in current_channel_messages[current_message][0]):\n", + " message_by_replay[current_channel_messages[current_message][0]['client_msg_id']]=current_channel_messages[current_message][0]['reply_count']\n", + "\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 317, + "metadata": {}, + "outputs": [], + "source": [ + "def display_top_and_bottom_message(top,bottom,by,by_txt):\n", + " print(f\"top 10 messages by {by_txt}\")\n", + " i=1\n", + " for msg_id in top:\n", + " print(i,msg_id,by[msg_id])\n", + " i+=1\n", + " i=1\n", + " print(f\"bottom 10 messages by {by_txt}\")\n", + " for msg_id in bottom:\n", + " print(i,msg_id,by[msg_id])\n", + " i+=1\n", + "\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# by replies\n", + "top_10_msg_id,bottom_10_msg_id=top_and_bottom(message_by_replay)\n", + "display_top_and_bottom_message(top_10_msg_id,bottom_10_msg_id,message_by_replay,\"replies\")" + ] + }, + { + "cell_type": "code", + "execution_count": 314, + "metadata": {}, + "outputs": [], + "source": [ + "# which message are top and bottom 10 message by Reaction message_by_reaction\n", + "for channel in channels:\n", + " current_channel_message=json.loads(SDL.get_channel_messages(channel['name']))\n", + " for current_message in current_channel_message:\n", + " if('client_msg_id' in current_channel_message[current_message][0]):\n", + " if('replies' in current_channel_message[current_message][0]):\n", + " message_by_reaction[current_channel_message[current_message][0]['client_msg_id']]=current_channel_message[current_message][0]['reply_count']\n", + "\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "top_10_msg_id,bottom_10_users_id=top_and_bottom(message_by_reaction)\n", + "display_top_and_bottom_message(top_10_msg_id,bottom_10_msg_id,message_by_reaction,\"reaction\")\n", + "# print(message_by_reaction.values())" + ] + }, + { + "cell_type": "code", + "execution_count": 490, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[{'id': 'C03T0APHX63', 'name': 'all-community-building', 'created': 1660301317, 'creator': 'U03TEPYRM2P', 'is_archived': False, 'is_general': False, 'members': ['U03T89ACUUW', 'U03TEPYRM2P', 'U03TNP8Q8CT', 'U03TT5KEYCF', 'U03TX2VN6H5', 'U03U1FNPEUX', 'U03U1GHT39V', 'U03U1HAG9TR', 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It’s a great spot for announcements and team-wide conversations.', 'creator': 'U03TEPYRM2P', 'last_set': 1660300985}}, {'id': 'C03UG4LHM8A', 'name': 'tenx-bot', 'created': 1661016491, 'creator': 'U03UUN8M4RX', 'is_archived': False, 'is_general': False, 'members': ['U03UUN8M4RX'], 'topic': {'value': '', 'creator': '', 'last_set': 0}, 'purpose': {'value': '', 'creator': '', 'last_set': 0}}, {'id': 'C03V3LK61QX', 'name': 'team-10', 'created': 1661768995, 'creator': 'U03UJGRN5E0', 'is_archived': False, 'is_general': False, 'members': ['U03UG0SFHGT', 'U03UH397319', 'U03UJGRN5E0'], 'topic': {'value': 'Week 2 challenge-A/B testing', 'creator': 'U03UJGRN5E0', 'last_set': 1661769722}, 'purpose': {'value': '', 'creator': '', 'last_set': 0}}, {'id': 'C03V9GFAVUN', 'name': 'all-week2', 'created': 1661704443, 'creator': 'U03TEPYRM2P', 'is_archived': False, 'is_general': False, 'members': ['U03T89ACUUW', 'U03TEPYRM2P', 'U03TNP8Q8CT', 'U03TT5KEYCF', 'U03TX2VN6H5', 'U03U1FNPEUX', 'U03U1GHT39V', 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "import sys\n", + "x=[]\n", + "y=[]\n", + "all_colors=np.random.rand(100)\n", + "ci=0\n", + "colors=[]\n", + "for channel in channels:\n", + " colors.append(all_colors[ci])\n", + " ci+=1\n", + " current_channel_message=json.loads(SDL.get_channel_messages(channel['name']))\n", + " x.append(len(current_channel_message))\n", + " current_channel_total_replies_msg=0\n", + " for current_message in current_channel_message:\n", + " if('replies' in current_channel_message[current_message][0]):\n", + " current_channel_total_replies_msg+=current_channel_message[current_message][0]['reply_count']\n", + " y.append(current_channel_total_replies_msg)\n", + " current_channel_total_replies_msg=0\n", + " \n", "\n", - "# sys.path.insert(0,\"C:/Users/ezra2/OneDrive/Documents/10Acadmy/week0_starter_network_analysis/src\")\n", - "# sys.path.insert(0,\"C:/Users/ezra2/OneDrive/Documents/10Acadmy/week0_starter_network_analysis\")\n", "\n", - "from src.loader import SlackDataLoader\n", "\n", - "n=SlackDataLoader(\"Data/anonymized\")\n", + "plt.scatter(x,y,c=colors,s=100,alpha=0.7)\n", + "plt.title(\"Channel activities\")\n", + "plt.xlabel(\"Number of msg in the channel\")\n", + "plt.ylabel(\"sum of number of replies and reactions\")\n", + "plt.colorbar(label='Color Intensity')\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 493, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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iiy94/vnn+eabbzjnnHM45ZRT4v9RCofDeDyeOs/xer1s3bp1ny41UWtPq87erVNaa/74xz/WOS4rK4tx48bx5JNPsnnz5jqP7f3cpKQkoPYD9WBOPfVUFixYwBdffBHfV11dzV//+le6d+/OwIEDE76fHzJNk5NPPpnXXnutTpdTQUEBzz77LGPHjiU5ObnJ10nU2WefjWma3Hnnnfu0DGqtKS4uBmpnce4Z57bHkCFDMAwj3oJSUlKyz/n3tLzsOaa+3/P8+fPrvPYAkyZNIhqN8re//S2+z7ZtHn300TrHZWdnM2HCBB577DF27Nixz/X31KwD4veyh9/vp3fv3gdtATJNc5/X5qWXXoqPhTuYESNG0KtXL/7whz9QVVV1wBgT0Zjz/vD1e+SRRwAO+OW0sdc1TZNJkybx6quv1vlbXblyJe+8806DrydEeyQtay1s8+bNPPXUU2zevDk+Zf/GG2/k7bff5qmnnmLWrFlMmjSJ66+/nksuuYTjjz+etWvX8uCDDwKwY8eOhL7FdhT9+/enV69e3HjjjWzbto3k5GReeeWVeseB/elPf2Ls2LEMHz6cK664gh49erBx40befPPN+PqjI0aMAOCWW27h/PPPx+l0csYZZ8STuL3NnDmT5557jsmTJ3PttdeSnp7OM888w4YNG3jllVearRjoPffcE69DduWVV+JwOHjssccIh8P8/ve/b5ZrJKpXr17cc8893HzzzWzcuJEpU6YQCATYsGED//nPf7jiiiu48cYb+eCDD7j66qs555xz6Nu3L7FYjH/84x+YpsmPf/xjoHZM2Mcff8xpp51Gfn4+u3bt4s9//jNdunSJT944/fTT+fe//81ZZ53FaaedxoYNG5gzZw4DBw6s88E/ZcoUjj76aG644QbWrl1L//79ef311+MJ4d6tco8++ihjx45lyJAhXH755fTs2ZOCggK++OILtm7dGq+HNnDgQCZMmMCIESNIT09n4cKFvPzyywcdmnD66adz1113MW3aNEaPHs2yZcv417/+VWeiyIEYhsHjjz/O5MmTGTRoENOmTSMvL49t27Yxb948kpOT+e9//9vwX1oTzrthwwbOPPNMTjnlFL744gv++c9/cuGFF3LEEUe0yHXvvPNO3n77bY477jiuvPJKYrEYjzzyCIMGDWrQeEEh2q22mILangH6P//5T/znN954QwM6KSmpzuZwOPS5556rta6d2v7rX/9aezwebZqmTktL03fccYcG9JdfftlGd9K6DlS6Iykpqd7nrFixQp944ona7/frzMxMffnll+ulS5fuU6pBa62XL1+uzzrrLJ2amqo9Ho/u16+fvvXWW+scc/fdd+u8vDxtGEadMh4/LE2gtdbr1q3TP/nJT+LnO/roo/Ubb7zRoHvaU2rjhzHWZ9GiRXrSpEna7/drn8+njz/+eP3555/Xe75ESnf8MKannnpKA/qrr76qs39PCYcfltd45ZVX9NixY+Pv5/79++urrrpKr1q1Smut9fr16/X06dN1r169tMfj0enp6fr444/X77//fvwcc+fO1T/60Y90586dtcvl0p07d9YXXHCBXr16dfwY27b1rFmzdH5+vna73XrYsGH6jTfe0BdffHGdkiJaa11YWKgvvPBCHQgEdEpKir7kkkv0Z599pgH9/PPP1zl23bp1+qKLLtKdOnXSTqdT5+Xl6dNPP12//PLL8WPuueceffTRR+vU1FTt9Xp1//799b333lun5El9QqGQvuGGG3Rubq72er16zJgx+osvvtDjx4/X48ePP+jvYo/Fixfrs88+W2dkZGi3263z8/P1ueeeq+fOnXvA6zfHeff83lesWKF/8pOf6EAgoNPS0vTVV1+tg8FgnfMdrHRHovfz0Ucf6REjRmiXy6V79uyp58yZE49HiI5Kad2Go5zbIaUU//nPf5gyZQoAL7zwAlOnTuXbb7/dZ0C23++nU6dO8Z8ty2Lnzp1kZWUxd+5cTj31VHbt2rXf4qxCiAN79dVXOeuss/j0008ZM2ZMW4dz2Ljjjju48847KSwsbNA4OyFEy5Ju0BY2bNgwLMti165d8fpa+2OaJnl5eQA899xzHHvssZKoCdFAwWCwzgxIy7J45JFHSE5OZvjw4W0YmRBCNI0ka82gqqqqzqyzDRs2sGTJEtLT0+nbty9Tp07loosu4sEHH2TYsGEUFhYyd+5chg4dymmnnUZRUREvv/wyEyZMIBQK8dRTT/HSSy/x0UcfteFdCXF4ueaaawgGgxx77LGEw2H+/e9/8/nnnzNr1qx9ylgIIcThRJK1ZrBw4UKOP/74+M8zZswAaotAPv300zz11FPcc8893HDDDWzbto3MzEyOOeYYTj/99PhznnnmGW688Ua01hx77LF8+OGHDVr/TwhR64QTTuDBBx/kjTfeIBQK0bt3bx555BGpVSiEOOzJmDUhhBBCiEOY1FkTQgghhDiESbImhBBCCHEIkzFrjWTbNtu3bycQCMhyUEIIIQ4rWmsqKyvp3LlzsxXyPpBQKEQkEmmWc7lcrn1W/WnvJFlrpO3bt7fJ2oxCCCFEc9myZQtdunRp0WuEQiF6dE9nZ0GwWc7XqVMnNmzY0KESNknWGikQCAC1b/S2WKNRCCGEaKyKigq6du0a/yxrSZFIhJ0FQTYtu5DkgKtJ56qojJA/5FkikYgka+Lg9nR9JicnS7ImhBDisNSaw3gCAQeB5KalHRq7maI5vEiyJoQQQogWZ2uN3cRqYU19/uFKZoMKIYQQQhzCpGVNCCGEEC3ORmPTxJa1Jj7/cCXJmhBCCCFanN79v6aeoyOSblAhhBBCiEOYtKwJIYQQosXZNMMEgw7asibJmhBCCCFanL17a+o5OiJJ1g4Rlo6xrmoN31V+S0W0HIBUZzoDkwfTPakXhpIeayGEEKIjkmTtELC8fCmfFH5AYXgXlo5hKgegsbTF4tKvyPHkcnz2SfQJ9G/rUIUQQohGkdmgjSfJWhubX/wZ7xf8j5iOkeJMwWnUXYojYofZHtzCy1uf5bTcKQxNHd5GkQohhBCNJ7NBG0/61trQqsoVvF/wNkopMt1Z+yRqAC7DTaY7m5iO8daO19lUvaENIhVCCCFEW5FkrY1orfmi+BOidoQUZ+oBj1VKkeZMJ2hV81XJ560ToBBCCNGM9nSDNnXriCRZayPbgpvZVrOFgDPQoOOVUiQ5/KypWk1RuLCFoxNCCCGal+b7GaGN3TpmqibJWptZV72GiB3BbXga/ByfmUTIDrKhem0LRiaEEEKIQ4lMMGgjISsE1LaYNZRSCoUiaAVbKiwhhBCiRUidtcaTZK2NmMokgTztB8+VBlEhhBCHF1vXbk09R0ckn/ptJMWZWtt/rxv+PSFmx3Y/N62FohJCCCHEoUaStTYyIHkwSaaf6lhVg59TFask1ZlGH3+/FoxMCCGEaH66mbaOSJK1NuJ3BBiYPIQaqwZLWwc9PmbHCNthjkgdgdts+KQEIYQQ4lDQ1JmgzTHm7XAlyVobOi7reHI8uRSHCw+YsMXsGMWRIrr5unN0+rGtGKEQQggh2poka20oxZnKOV2m0smTR3G4kJJIMVE7itYarTURO0JxuIiSSDH5vh78uMsFJDn8bR22EEIIkbA9EwyaunVEMhu0jWV7cvhZ90tZWraIJWULKQ4X7W5l0ziUk2x3DsPSjmJo6jC8pq+twxVCCCEaxUZh08gyCHudoyOSZO0Q4HcEGJM5nlHpY9hcs4FqqxqAgCOZrr58TGW2cYRCCCGEaCuSrB1CHIaDnv4+bR2GEEII0eykZa3xJFkTQgghRIvTWqF105Ktpj7/cCUTDIQQQgghDmHSsiaEEEKIFmft3pp6jo5IkjUhhBBCtDiNgd3EDj3dQTsEO+ZdCyGEEEIcJqRlTQghhBAtrjnW9uygNXElWRNCCCFEy5PSHY0n3aBCCCGEEIcwaVkTQgghRIuztcJuYp20pj7/cCXJmhBCCCFanN0Ms0Gb+vzDVce8ayGEEEKIw4S0rAkhhBCixckEg8aTZK2NaW1THd1CxK7CUCZuMwOvI6utwxJCCCGalYxZazxJ1tpI1K5mV83nbKv6gMroRrSOAWAaHjI9w8lNmkCGZyhKmW0cqRBCCCHakiRrbaA6upXlxX+kPLIWhYHLTMNULjQay65hR83HFAS/INc3nv5pl2IanrYOWQghhGgSjUI3sRuzqc8/XEmy1sqCsV0sLXqAqugmfI7OGMpZ53HTdOEyU4naVWyrfh+NxcD0KzGU/KqEEEIcvmTMWuPJbNBWtrbsWaoiG/E5usQTtZgdI2iFqbFCROwoAE7Dj9vMYEf1xxTUfNaWIQshhBCiDUlzTSuqie6gKLQQl5mGUgaV0RpKI5VUxKqxde2KZwqF3+kl3RUg2eEjQinbqj+gk28cSnXMbxRCCCEOf9Ky1niSrLWiguDnRO0qPGYeW6oLKItWYQMOZeDYPZHARlMeraYiWk3A4aOzJ0B5eBUVkbWkuPu07Q0IIYQQjaSboSiu7qAdgpKstaKqyBbQiq3BIsoilTgNB64fzPY0AIcysbVNRbQaW1tkuy1qYtslWRNCCCE6IEnWWpGlQ9RYEcqjEVyGA+MAZTkMZeAynVTFQiSZFraOtGKkQgghRPOyddPrpNm6mYI5zEiy1oocyk+NFQS8B0zU9jAwMJUiaIWJavlVCSGEOHzJmLXG65idv22k2s4ialu4VMNfdo8ZI2Q72FTjPPjBQgghhGh3pLmmFZVbXQjZHnxmiKj2N+AZGoeKsC2SRyAs3aBCCCEOX1ordBO7QZv6/MOVtKy1oqg22R7uiqEsDPYkXxrL1kQtm6il4yU8QONSVVjazfZwHlHbaquwhRBCiCazMJpl64ikZa0VeU0Xm0M9yXbbpJgbCMeilIddRCxgd46mFHidmhR3EK0c7IwOp8oK4DFdbRq7EEIIIdqGJGutqJc/D7fpYVn5EDwRk+6BjfgdldimgYUJaJwqhq0VO6uSWFsxBH9yF0yjil7+zm0dvhBCCNFo9u6tqefoiCRZa0XdfNl09Xbm1W83EQr3J8vXg16p28lP3o7XEcbWBhWRJNaVd2V9aQ7VUUV6cjVjeqQzOKVHW4cvhBBCNJrGaHJRWymKK1qcUorKsiSqQwY+l03Y9rGipDcrSnrvc6xpgkvHKCrXpNp5OA35VQkhhBAdkWQAragoWM3qogqyPH6qdRURO4rTcKB+UDdGo4nZFhg2PoeHrSUxtNayNqgQQojDlq1VMxTF7Zifgwm3J27ZsoWtW7fGf16wYAG/+tWv+Otf/9qsgbVHn2zdSFk4SPfkTDp7MzCVQdiOELYjRO0YUTtG2I4SsiIoBTnuNHoGslhTWszyooK2Dl8IIYRotD1FcZu6dUQJJ2sXXngh8+bNA2Dnzp2cdNJJLFiwgFtuuYW77rqr2QNsTzZXlAG1a39muVPpl9yNLt5sfKYHQykMpfCYLvJ8mfQNdCXXm0GS00XEsthSWd62wQshhBCiTSTcDbp8+XKOPvpoAF588UUGDx7MZ599xrvvvssvfvELbrvttmYPsr0IxmIYe3Vlmhg4Y25cQY2KWaDA7XDgc3hwqtpfjVIKZWnWf72Vf76xiYqyGhwOk6zOqYwY25euvbKle1QIIcQhTzdDN2hHLYqbcLIWjUZxu90AvP/++5x55pkA9O/fnx07djRvdO1MwOXG2l30trQqyPbiSoKRKLatibfsaigoq8LvcdE5PYBjdTXGZzv4IlyIz3BgmCZaa2zb5oPXFtN3SBd+dNEYuvXOabsbE0IIIQ7CxsBu4mzOpj7/cJVwsjZo0CDmzJnDaaedxnvvvcfdd98NwPbt28nIyGj2ANuTQZk5/GfNCraXVLKjuBJb2zgdJqbz+zef1rUrGlTUhKkqrCFtYSXeihg5+VkEfN46x9VUhVj65Tq2bijk0l+fRt8hXdritoQQQgjRghJOUe+//34ee+wxJkyYwAUXXMARRxwBwOuvvx7vHhX1Oya3K37lZHNhGRqN2+nAMBQxO0LEDhGxg8R0GAwLZ8gmGoxR2N+Du0dynUQNartHkwJecvMzKS2s4ukH/0fhjrK2uTEhhBDiILRunq0jSrhlbcKECRQVFVFRUUFaWlp8/xVXXIHP52vW4Nobt2nishxYtsblgJgOEwvGoEZDmNolpwzAHUWV25gWxJIdhHJcsJ/5BYahyOmaxo5NxXz27nKmXDy2Fe9ICCGEaBjpBm28Rt21aZp1EjWA7t27k52d3SxBtVffFRYRDMXI8HkJhcNEC6JQqKFKQUyBpSCqoFRDqPYrhMuyKfXb1Jj7X2TDMAw8SW6+nLuCYHW4Fe9ICCGEEC0t4WStoKCAn/3sZ3Tu3BmHw4FpmnU2sX8Lt24jFIuS7KjGVWqhQgqtFNoBtgMsl8JyKWynQpsKw1ZQbRFUUYo80QOeOyUtidLCStYs33rA41pbdSTCxpJS1hQWsa28ArujtmELIUQHJ3XWGi/hbtBLLrmEzZs3c+utt5KbmytlIxJQGQkTtSKUFUQwLQOnVxNBEVUKe+/X0WOiXAaGpTHDNlZUU6PCgHu/53Y4TWytqa4MtfyNNMD64hLmrd3AB2vWUxkJo7XGNAx6pKdxcr/ejO2RT8C9//sRQgjRvjRHsiXJWgN9+umnfPLJJxx55JEtEE77ZipFdShILKRwuCBsGER3J2l72iQVYFsaDdhOA9uhAE11TTWQvN9za61Bg2m2bX++1pqXv/mW5xZ/Q1U4TJLLRYrHjaEUUctm1a4iVhTs4tVlK7np+LH0zpQZxEIIIcSBJPzJ3rVr19rEQCQs1eskXBNDo+KJmqL2l6D4vtSaUqBsUHslbWWRGLUzEOoXDkZxuh2kZQVa/D72R2vNC0uW8fSCRQDkp6WS5U/C63Tidjjwu110SU0mLzmZDSWl3PPeh2wsKW2zeIUQQrQerVWzbB1Rwsnaww8/zMyZM9m4cWMLhNO+9Qn4UGGN5VHxFrV633bGXg8YoGKaoOkiyP7HrZWXVNGtZxa9BnZu7rAbbOWuQl5YsgyP00Fmkm+/XeQO06BbWgrbKyp59LP5kvwLIUQHIGPWGi/hbtDzzjuPmpoaevXqhc/nw+l01nm8pKSk2YJrbwLKRVKlRUmGE23Vdn3WVztG7f4/bYM2wF1iYzsMCrRF93omhYZDtasgjD55MIbRdt2gc9espzoSIT8t9aDHGkqR5fft7hYtZFAnmUkshBBC1CfhZO3hhx9ugTA6Br87QEZJlNIUJ7YD7Cj19mxqFBrQbjBDNq4KG+0wKHBZdP9BZY5wKErRjjKGjurJqBMGtsZt1KukJsin6zcScLsbPOnE53RSWFXNvLXrJVkTQoh2ztY0eW1Qu4N2xCTcDHPxxRcfcEvExx9/zBlnnEHnzp1RSvHqq68e9DnhcJhbbrmF/Px83G433bt358knn6xzTFlZGVdddRW5ubm43W769u3LW2+9FX/csixuvfVWevTogdfrpVevXtx9990t3h2XkZFKRlIAdzmoKFgOsM26+ZpWtWU8tAuMCHgKNUZEY9oQVBb27qNDwQgFW0so2VXO0FE9mXbDZNweZ/0XbgXbysupikQIeBo+w1MphcfhYFVhUQtGJjqy8qog7375HXNe+YwH/zmPOa98xnvzV1FZfWjMmhaiI9lTFLepW2M8+uijdO/eHY/Hw6hRo1iwYMEBj3/44Yfp168fXq+Xrl27cv311xMKtd1/NxJuWYPaZOfVV19l5cqVQO16oWeeeWbCddaqq6s54ogjmD59OmeffXaDnnPuuedSUFDAE088Qe/evdmxYwe2/X3fYCQS4aSTTiI7O5uXX36ZvLw8Nm3aRGpqavyY+++/n7/85S8888wzDBo0iIULFzJt2jRSUlK49tprE7qHRLhcDgYN6cmSZWvx7NJYPoj6Fdq5V8KmQcXAWaVxVoPCwHaC0zSJRC12bC5G2eB0mXTrncPokwZxzMSBeLyuFou7ISKWha01RoKlXAylCEVjLRSV6KiqgxFe/mAJny3ZQGllDVqDaRjYts3cr1bz8twlHDesFz8+4Qi87rb7kiOEaHkvvPACM2bMYM6cOYwaNYqHH36YSZMmsWrVqnqL+T/77LPMnDmTJ598ktGjR7N69WouueQSlFLMnj27De6gEcna2rVrOfXUU9m2bRv9+vUD4L777qNr1668+eab9OrVq8Hnmjx5MpMnT27w8W+//TYfffQR69evJz09HahdOWFvTz75JCUlJXz++efx8XQ/PObzzz/nRz/6Eaeddlr88eeee+6gmXZz8LkDtQmZDe4KcFVqYh7QRm26piwwQ3tPPFBo08Cf6iUnPZlpPzoS02GQlhWg96C8Ni/VsYfX6cRUBpZt40hg3FzMtqXemmhWldUhZj/7IcvW7sDvddE5M6XO34ll2ZRVBXn1w2Vs2lHKdReMw++V96AQLa12iE/TukEb8/zZs2dz+eWXM23aNADmzJnDm2++yZNPPsnMmTP3Of7zzz9nzJgxXHjhhUBtjnDBBRcwf/78JsXeFAl/0l977bX06tWLLVu2sGjRIhYtWsTmzZvp0aNHi7ZKQe1i8SNHjuT3v/89eXl59O3blxtvvJFgMFjnmGOPPZarrrqKnJwcBg8ezKxZs7AsK37M6NGjmTt3LqtXrwZg6dKlfPrppwkljo0RDEVYvHAjrmobbdaOS0NrHMHaVjRntcIRUnUSNQCtNGVV1Uw6qj9jJg3mmIkD6Te06yGTqAH0SE8j0++jNNjwZmJbayKWzfAuuS0YmehIbFvz2L8/Z9naHeRkBEhPSdrn78Q0DTJSkshJD7B41VYef/ULmZEsRCvQWmE3cUu0dEckEuHrr7/mxBNPjO8zDIMTTzyRL774ot7njB49mq+//jregLN+/XreeustTj311MbffBMl3LL20Ucf8eWXX8ZbtgAyMjL43e9+x5gxY5o1uB9av349n376KR6Ph//85z8UFRVx5ZVXUlxczFNPPRU/5oMPPmDq1Km89dZbrF27liuvvJJoNMrtt98OwMyZM6moqKB///6YpollWdx7771MnTp1v9cOh8OEw9+P7q+oqEg4/rUbdrFrVzm+SJSo341WoOq88TR1K66BgY1lKuyoDVVrgJEJX7c1eJ1OTurbm6cXLGpwd2hFKIzf7WJCrx6tEKHoCFZu2MniVVvJTE3C7Tzwf97cLgfpyT4WrtjCmi1F9O2W1UpRCiGa6oefwW63G3c9vTRFRUVYlkVOTk6d/Tk5OXz33Xf1nvvCCy+kqKiIsWPHorUmFovxi1/8gv/7v/9rvhtIUMJNM263m8rKyn32V1VV4XK17Lgp27ZRSvGvf/2Lo48+mlNPPZXZs2fzzDPPxFvXbNsmOzubv/71r4wYMYLzzjuPW265hTlz5sTP8+KLL/Kvf/2LZ599lkWLFvHMM8/whz/8gWeeeWa/177vvvtISUmJb127dk04/uqaCLGYhafGxh2KoZ2A2vsb/fe11xQaAxttgDYN3GVhtpd8TdSOJHzd1nJC755k+/1sL688aEtFJGZRWhNkdPdudElNaaUIRXv3yZL1RKIWPk/D/luU5HURisT4dMm6Fo5MCNHUVrU9G9QW6N/7M/m+++5rtjg//PBDZs2axZ///GcWLVrEv//9b958803uvvvuZrtGohJO1k4//XSuuOIK5s+vLWaqtebLL7/kF7/4BWeeeWZLxBiXm5tLXl4eKSnff7gPGDAArTVbt26NH9O3b986kx0GDBjAzp07iURqE52bbrqJmTNncv755zNkyBB+9rOfcf311x/wl33zzTdTXl4e37Zs2ZJw/KZpxLs+A4U1OEMxtFOBQ6P2nuuibJTS2E6F7TBwVcfw76ihxipnY/XyhK/bWnKTA1xz3DEkuZxsLasgErP2OUZrTWUozLaKSo7Iy+XnxxzVBpGK9igUjrJwxRb8vsRmJCd5XcxfvoloPe9XIUTzac6iuFu2bKnzmXzzzTfXe83MzExM06SgoKDO/oKCAjp16lTvc2699VZ+9rOfcdlllzFkyBDOOussZs2axX333VdnQmNrSjhZ+9Of/kSvXr049thj8Xg8eDwexowZQ+/evfnjH//YEjHGjRkzhu3bt1NVVRXft3r1agzDoEuXLvFj1q5dW+cFXb16Nbm5ufGWv5qamn2Kx5qmecBfgtvtJjk5uc6WqKx0Px6XAwuNYdmkFQTxlkfA1lgug5izdos6DSxn7ULuSaVhknZFcVoxXOmKTYdwsgZwTH5Xbp44ni6pyRRUVbG5tIyi6hpKamooqKxiU2k5wWiMCT27c9tJExIq9SHEgVQFI0QtC6czsVnpLmftTOua0KHbai2EqOuHn8f1dYECuFwuRowYwdy5c+P7bNtm7ty5HHvssfU+Z385AtBm41sTHrOWmprKa6+9xpo1a+L9vQMGDKB3794JX7yqqoq1a9fGf96wYQNLliwhPT2dbt26cfPNN7Nt2zb+/ve/A7X9yHfffTfTpk3jzjvvpKioiJtuuonp06fj9XoB+OUvf8n/+3//j+uuu45rrrmGNWvWMGvWrDqTH8444wzuvfdeunXrxqBBg1i8eDGzZ89m+vTpCd9DIvK7ZtC3VycWllejYrVJmndzDU4UkWQnltdEGwqFwhEFT8jCQGNHNZkZIdL6uQha+3ZBH2qGd+nM/zv7DBZs3sp7q9exoaSUqGWR6vFybPduTOjdg57paQ0unitEQ5jG7sk5Cf63VOva9XjNNlz9Q4iOoK1mg86YMYOLL76YkSNHcvTRR/Pwww9TXV0dnx160UUXkZeXF+9dO+OMM5g9ezbDhg1j1KhRrF27lltvvZUzzjgj4RJlzaVRddYA+vTpQ58+fZp08YULF3L88cfHf54xYwZQW3j36aefZseOHWzevDn+uN/v57333uOaa65h5MiRZGRkcO6553LPPffEj+natSvvvPMO119/PUOHDiUvL4/rrruO3/zmN/FjHnnkEW699VauvPJKdu3aRefOnfn5z3/Obbfd1qT7ORilFKefPpzFyzYQKVMoy0KjUA6NuzIKldHa8sy7y45ZLiexJA8OO0yPCT4wg5jq8KgJ5XY4OK5nd47r2R2o/TaSSHIWqgnz7WerKNlZhrY1SSk+Bo7uS1q2jG8T9fP73Ph9bsqrQgl1hQbDUbJS/XjbsKi0EB1Bc6zt2Zjnn3feeRQWFnLbbbexc+dOjjzySN5+++34pIPNmzfXaUn77W9/i1KK3/72t2zbto2srKx4I09bUboBbXozZszg7rvvJikpKZ5Q7U9bFYxrbRUVFaSkpFBeXp5Ql2hZcQWX/eyPFEY06BjKrO/l1+gYYLjA1jhNg+4jFQMv2MGo3BMZk3VWs93HoaaipIqPXvicT1/7iqKtxfFWEo0mkOZnxMlDOf68MXTt13YL1otD10vvL+GF9xbTNSe1QV8ObK3ZWlDGTyePZMqEIa0QoRCHhsZ+hjXlWo8svQNvwNOkcwUrQ1xzxB2tEvehpEEta4sXLyYajcb/LRrvnSfn4Vy/EyM3CcsbqO2DsazaVdsBUOBwoB0GKhLFsWE7gd6pFG1KY/OnqUydPrxN429Ju7YU8diN/2D9N5vw+NxkdcnA6ap9i9qWTUVJFfOe/Yyl877lkrvOY8hxA9o4YnGoGX1ED9787FtKK4Kkp/gOenxJeQ3JSW5GD+3e8sEJ0cHtPZuzKefoiBqUrM2bN6/ef4vEVJVV8+l/51PtCOKsqUaFq7G9yWinF62+/1UoK4pZU4YZLUfZISKFGlcXP8XLU/BFs6FpX0wOSZWlVfz11/9k/dJNdOqehcNV961pmAapWcmkZATYuamQJ3/7PFf9cRq9j+zeNgGLQ1JeVgqnjx3ES+8vobxKkeL37vfYssoaItEYP5k4guz0QCtGKUTHpBtR1La+c3RECY+onT59er111qqrq1t8gP7h7uv3vmHnjkJiAY2KgiNUhaNkG47iLThLduAo3YmzZBvOok04gyWYMQtSDCJlQRzBELo6ia8XbWrr22gRn7wyf7+J2t6UoejUPYuyXeX89y/vSuV5sY+zjh/Kj8YPIRSOsnVXGZU1ofj7RGtNZXWILQWlRKIWZx8/lDOOG9TGEQshxIElnKztXYB2b8FgMD5rU9Rv0+otBGNhlDLQurbnU2mFEQ1jhKswQ1UY4SDKBm3VLp2jXAosjctwYeBgy+bitr6NZhcJR/ns1a9weZwHTNT2UEqRlp3CmkXr2bxyWytEKA4npmFwwaTh/OrCCYzs35VIxGLrrjK2FtRukZjFqMH5zJg6gXNOPFJmJQvRSva0rDV164gaPBu0oqIiXgS3srISj+f7vjjLsnjrrbfqXb1efG97eRGWtlExjbb17gULfrjc1N4/KrAUGIqIJ4LPVoRC0VaMuHV8N38NBZsLychNa/BzfMleSgrKWTR3GfkDu7RgdOJwpJTiqIHdOGpgNzbvLGXj9hLC0Rhup4OeeRl0yUlt6xCF6HAsFFYTZ4M29fmHqwYna6mptbOrlFL07dt3n8eVUtx5553NGly741XYtkZHaycTKKXQaLAAq/Ybg1KAQ8dzOG1rsMH2gq7S+Hwtu6RXWygtKMe2NC53w0snKKUwTEXxjtIWjEy0B906pdGtU8O/CAghxKGmwcnavHnz0Fpzwgkn8Morr9RZyN3lcpGfn0/nzlJO4UByB3eqXeszqsEBdgR0yISI2qtEBWCC8tgot4aQBW6FkefFXGPQo0c7XGxa06jvSgqFttpm6Q8hhBCJ0SRcs7rec3REDU7Wxo8fD9SuMtCtWzcZ59EInfp1hUwTimJopwsd3D1k0KDu6EEbdLWBDoEKRVDDPUSVSUamn2HD8tsi9BblT0sCBbGohSOBpYIsyyYlq+PU2RFCiMOZxkDrpq0UohMfat8uJHzXH3zwAS+//PI++1966SWeeeaZZgmqvTKLPKgjk9HVoCt07atvUtustGeD7/dXR9GWiT0ggF7nZty4/ni97a8bdMAxfUjLSaG8qKLBzwlWh3B5nFJrTQghRLuXcLJ23333kZmZuc/+7OxsZs2a1SxBtVeRUAzbl4ndKRUVjkE4Wrv4hqFRavdmaJS2oSYCWmP3ysSqyCDXmcEpk9pnhXVfwMuxZ4wkWBXCbkC3ptaa0oJyug3Io99RvVohQiGEEE2lAbuJW0ftBk04Wdu8eTM9evTYZ39+fn6ddTzFvnYGC4ltcWEN6Iw9KK12MkFlBKqiEIpBMFb7c3UE5TLQI7LQg9LRxS569MnB1YCyFoer4348ipz8LHZs2IVt7z9h01pTsqMMt9fFqZdNrLOemxBCiEPXnhUMmrp1RAl/0mVnZ/PNN9/ss3/p0qVkZGQ0S1Dt1XeV69C2gekANSITfWpX9JGZkOwEpcBUkOFBH5WNPr0bakAKhg3EFGsKtrd1+C0qu2sml/1uKlldMti2dicVJVW1M2F301oTrAyyfX0BKDjnhjMYPrF9tjQKIYQQe0u4qeaCCy7g2muvJRAIMG7cOAA++ugjrrvuOs4///xmD7A9CTprUC4blWyhLMDrQg/KQA9JB4eNcu2eFhkyIKJQFiifRlfZhOym1VfTWlNYWkV1KILTNElP8eHzHFrj33of2Z1rHr2UN//2Pss+Xsm29QW1a6fuHszn9jrpO6IXp0ybwJHHD27TWIUQQiRGYzR5gkBHnWCQcLJ29913s3HjRiZOnIjDsXuRbdvmoosukjFrB2ETwvDsnlgQU2BozPQoRpcwRnYUZexeEsdS2DtcWNtc6HIHhs/C1o0rUREMR1nw7SY+XLiW9duLiVk2hlIkeV0cd2RPxhzRk+6d0w9+olbSpU8uP//9zyjYVMjX731D6a5y7JhNUqqPIWP703tYD5mJLIQQhyFb125NPUdHlHCy5nK5eOGFF7j77rtZunQpXq+XIUOGkJ/f/kpKNDdnUjU4vWBrtEPjPKIGs1MEDNARhQ4btY1IpsbMD2N0CWPvcBH5KoAzKfGWta0FZTzy4sds2FaCUpDi95LkNdFaUx2M8J8Pv+Hd+av40fjBTBk/FMM4dJKgnPwsTr1sYluHIYQQQrS5Ro9Y79u3b70rGYj9S+tiYvhj6LCBc2QlRnYUXWPWtrLtLabQYQ0ujdk1jNNW5KQlVk9sR1EFf/jnB2zdVU5uZjJOR936ZV63E619lFYEefG9JViW5icTj5BWKyGEEC2itihu0z5jOmjDWuOSta1bt/L666+zefNmIpFIncdmz57dLIG1RykBH55+RUQqXbWJWtCoXfuT79+A8eVCFShLoatNHF3DOHKDDb6O1pqn/zufrbvKyMtOxdzPjEmlFOkpPkora3j94+UM6tmJgT07NekehRBCiPo0x0LsspB7A82dO5czzzyTnj178t133zF48GA2btyI1prhw4e3RIzthm05cPeqwa600BEFMaM2SVPUXW4KUFqhtQZLoVJsiu0qLG1hqoNX+N+wvYQVGwpIT07ab6K2t7SAjy07S/l48TpJ1oQQQohDTMLTKm6++WZuvPFGli1bhsfj4ZVXXmHLli2MHz+ec845pyVibDciFSngtzHSa7tCdVTVVvmjtnLHng1dO8lAxwwMr4XhtaiKWXxXsaFB1/lsyXqC4ShJCax2EEjysODbzRSVVTXizoQQQogDs2vLwDd564gSTtZWrlzJRRddBIDD4SAYDOL3+7nrrru4//77mz3A9sRR0gMraoIJRoqFctugVe0YtZjanaCp2q5RpVFJFirFQtkGhu1gR7CoQddZu7UIl9NMaPyZ3+emOhhhe2HDl3wSQgghGmpPN2hTt44o4WQtKSkpPk4tNzeXdevWxR8rKmpYMtFR6aiDUIkHFBguGyM9ipEWRXltlNMGU6PcNkZyDDMzipkcAwOiQROFItLAWmvhSAwjwYkChqHQ2iYcjTXm1oQQQgjRQhIes3bMMcfw6aefMmDAAE499VRuuOEGli1bxr///W+OOeaYloix3aiqCVNd5CM5LYLaU2vNASpgAdRt3FWACXbUIFrlIJJk4TYb1q2Z5HURa8Aam3uzLBvDMPC6nQk9TwghhGiIPet7NvUcHVHCydrs2bOpqqod13TnnXdSVVXFCy+8QJ8+fWQm6EGsLSwmUugh1tOB6bRQDo1Cx2eExpm10wzsiEGk3AVoghGL7kmdG3SdIb1zWbpmO7bWDW5hK68KkhbwHlIFcoUQQrQfMhu08RJO1nr27Bn/d1JSEnPmzGnWgNqzcCxGtMCNVeqC5Ahohem1UA6bPTmV1qBjBlbQxK4xa48JxAhVuunhy2vQdUYP7cFrHy2noipEasB70OO11tSEopxy7AD8XndTblEIIYQQzaxRi2yVlZXx+OOPc/PNN1NSUgLAokWL2LZtW7MG195opVExg9CGJEDVtpwVuYmWuImUuoiUumr/XeTGqnagtUK5LLAVZYVelpbsaNB1stMDHDOkOxXVISIHGYOmtaaguJJUv5fjhvU84LFCCCFEY8kEg8ZLuGXtm2++4cQTTyQlJYWNGzdy+eWXk56ezr///W82b97M3//+95aIs11wKwdKQ3BjEmZqBE/3GrSpsUMG++bNGsNro0xNcLWfGuXlvS1rGJbZsNa1qZNHsLO4gmVrd5CW7MXvde8zOzQasygsqcLjdjD9zFF0zUlrnhsVQgghfqA5Sm9I6Y4GmjFjBpdccglr1qzB4/HE95966ql8/PHHzRpce9Mn9fvxYFXfpFKz2g+AIzmG4YthuC0Mt4WZFMMMxMCG6hUBar5NJsXl5ouCTeyqaVgdNL/XzQ1Tj+e4I3sSjsTYUlBGYWkVZZU1lFTUsHVXGQXFlWSl+7n6vOMYfUSPFrlnIYQQQjRNwi1rX331FY899tg++/Py8ti5c2ezBNVe5fgCGCbYVu36aDUrUwhtSsKdF8TTpQbDXTvPxao0CW3xEd7mxa5xgKnIdHupjkbYVl1Ots/foOv5fW6uPX8cG3eU8NmSDSxYsYmaUBSnYdCrSwYThvdm5MBuMgNUCCFEi9O6dmvqOTqihJM1t9tNRcW+hVNXr15NVlZWswTVXhmGIsXnpaS6Jr6ulF3jILgmQHBNANSeNafUnhVv0SY4HQplGNjaImJbCV1TKUWPzhn06JzBT08dScyyMQ0lC7YLIYRoVboZukGbuhD84SrhbtAzzzyTu+66i2i0tkCrUorNmzfzm9/8hh//+MfNHmB7kpGaRIrHjTPg+H49UJvvFwTVCmwV36dNMH0Kp9NEuRUOw8DnaFormMM0JFETQgghDiMJJ2sPPvggVVVVZGdnEwwGGT9+PL179yYQCHDvvfe2RIztxlEDu+HzuOiU5MdIVmgnoHY3qO2pFqhr92kXuJJNPDhx+RyEki2yvH56p2S26T0IIYQQjSGzQRsv4W7QlJQU3nvvPT777DOWLl1KVVUVw4cP58QTT2yJ+NqV/Nw0BvToxOfLN6AN0EmgLSBKPElDgXayu6sStGXj75ZElYoyqWtfvE1sWRNCCCHawu7RPU0+R0eUULIWjUbxer0sWbKEMWPGMGbMmJaKq11SSjFxTD/eXbYaakD5wHAolHPfbwq2pYlU2bhSTKqzo2R5/ZyQ17sNohZCCCFEW0ooWXM6nXTr1g3LSmyQu/jevKKNWN1NfBtchGoiWErXflPY83VB1TawGRpsP5T2juB3+rh+6HF0Tkpuu8CFEEKIJrC1wm5iN2ZTn3+4SnjM2i233ML//d//xVcuEA23vbKCz7duJr2Tn+SefpTHREUVKgwqsnsLg45BxAuhPI3hMentzWZUTre2Dl8IIYRoNBmz1ngJj1n7f//v/7F27Vo6d+5Mfn4+SUlJdR5ftGhRswXX3ny0aSMVoRC+Gge7CqtwZUbJ61+Ex6hB7S4eEzMU5TEfuwoyMUtcpJhetjgr2FBWSo9UWWFACCGE6GgSTtamTJnSAmF0DGtLS4hVWxTurKZnz41077ENtyuC1grLqm3kNE0LpaCmZhOrV/dkx45cIqbN5vIySdaEEEIctmSCQeMlnKzdfvvtLRFHh1ATCRMujdC/3yrye2zHiplUVfn2KfJnKBuvN8SQId/hcETZvLMbod117YQQQojDkUY1uahtRy2Km3CyJhrPDmk6Z20iv/sOImEXkagTtEbv7gLVu0t32Cgqg158njD9+q+nvNJLUWl12wYvhBBCiDYhyVorcsWge/52bA2RiANt707SDOqd6lETcpHsr6Fr/g5qqqRlTQghxOFLZoM2XsKzQUXjedQWAv4agjUu2F39RJvU/hZ0fZsiHHHRKaeUT9YvJmbbbRS5EEII0URaNc/WAUmy1oostRVDaWzLrH3PmXy/Ruh+hMMOnI4oZXoDLy78prVCFUIIIcQhQpK1VuQwo7U1YvTurk8FCo3DYeNwWDidMRwOC8PYO3ur/SZhqhgvf72cylC4rcIXQgghGs3WzbN1RA0aszZjxowGn3D27NmNDqbd07VzYQCUoTFMjcO0UOqH7z4LbStilllb0kOBjil2llXy2fpNnDKwb+vHLoQQQjSBzAZtvAYla4sXL67z86JFi4jFYvTr1w+A1atXY5omI0aMaP4I25GKKgeeFIXpjIGpMM3agWv1VWRWhsZpxHA4wLYUVdVJhCJRvli3WZI1IYQQogNpULI2b968+L9nz55NIBDgmWeeIS2ttkhraWkp06ZN47jjjmuZKNuJWLQT0dBKUlOrKavyH3CgpN49mM3nCVNaFqCkJAVMKKqUEh5CCCEOP82xXJQsN9VADz74IO+++248UQNIS0vjnnvu4eSTT+aGG25o1gDbk3CZG+1zk5JVjWL3zM4977u9FnLfwzRslILCkhR2l2LDZZqtFa4QCSkqqWL+1xv4aslGKiqDmIZBp+wURh/di+FDuuJ2O9s6RCFEG9Ka+GdZU87RESWcrFVUVFBYWLjP/sLCQiorK5slqPbKGYvicYaxIgp/UpiaoKu2dW13Mdw4DQ7Twu2OsqswhV0lyTjdYaLaR4/M9LYKX4h6RaMWL//3az76YjWVVSGcDhOX04HWmp2F5SxZvpmcrGTOm3IURw3r3tbhCiHEYSfhZO2ss85i2rRpPPjggxx99NEAzJ8/n5tuuomzzz672QNsT/p1KaAkECRY6CDo8ZCUFEYpTSTqwLIUCjAdNi5nDK0VBbtS+W5tFwwTHO4obu3mpEG92/o2hIiLxSwe/9enfDZ/LUlJbvI6pWEYdbspolGLwuJK/vr3jwmFoxx3TJ82ilYI0ZZkbdDGSzhZmzNnDjfeeCMXXngh0d3rVTocDi699FIeeOCBZg+wPfH7KiknhhX1UFrsY02sM/l5u0hNrsbpqH0L2rbBrsJUdhSkUVoWwEYBMSK2ycguOfTPyWrbmxBiL2++v4zPFqwlLdVHks9d7zFOp0mn7BQKiyv558vz6ZyTQq8e2a0cqRCircls0MZLOFnz+Xz8+c9/5oEHHmDdunUA9OrVi6SkpGYPrr1R1RGUw0LZNlmBSnZtS+PzrweQnlyF1x1BawhHnITDrtpvDwqwNVURL0lmhOtPGoNSHfONKg49NcEI8z5dhcfl2G+itodSiqyMAFt3lPLRF6slWRNCiAQ0em3QpKQkhg4d2pyxtHte248Og8cKETJ9DOi8FRTsrEinpBJcZgyHYdUu5q4VkagTy1Ike2s4Lq2Mfp2kVU0cOr5euoni0ipyMpMbdLxSikCSh6+WbGLK5GGkp8kXPCE6lGaYDdpRl5tKOFmrrq7md7/7HXPnzmXXrl3YP1ivcv369c0WXHuTZI2gZvMifL1jRAtCKI+DoT02kBsuYVtxJsUVAYIxN9pWKG3jNUJ0TS2gT5cd5G0c39bhC1HHqrU7sW1wOBo+Qzk54GH7znLWrN/FqBE9WjA6IcShRmaDNl7Cydpll13GRx99xM9+9jNyc3OlWy4B6Vl92PlWgF59yzBTLMx0jXIr8oxCOucWUVXjpSbsRmuFgxgpkQoC3jA1W5yk0r+twxeijsqqMA4zsb9/w6hdkSMYjrRQVEII0f4knKz973//480332TMmDEtEU+71ueoXmz/TSap20NkHREiFHViRRTYoJQm4Kwh4KipHavm1HjdMaxKgzWvpzP+V/ltHb4QdbhdJnaCC/VprUGDM4HWOCFE+yCzQRsv4YXc09LSSE+XWl+NsbqsjOohqaxVOZTW+PCYMTzuCIa5V1ey0jidMZKcEayYYn1VJkW56WxyxdoucCHq0TUvHdvWtQlYA9UEI7jdDnJzUlowMiHEIUurpm0dVMLJ2t13381tt91GTU1NS8TTrm3cXgxjFDG/kxWLs1m/OZ1Q2InHHcPni+DzRUjyRTEMKCgK8O3yHArKkjGPN9lSXNLW4QtRx9HDe+D3u6moCjX4OWUVQfr0zKZHt8wWjEwIIdqXRi03tW7dOnJycujevTtOZ90lZBYtWtRswbU3qzZtwdslCNpBzNBs2ZzCtm3JpGUE8bhjGEoTswzKSr0EaxxgKpTfxEy22Vy0qa3DF6KOnKxkjhzcjU++XIPf58Y0D/zdr7omjKEU447tK2NdheiAbK2wm9g61tTnH64STtamTJnSAmF0DBWObRguGztsQq4J5VHsKoviXb66BxoKkgxIdYLXQCmLmK+obYIW4gDOOWMEm7YWs2VrCZ2yU3A66x+LVlUdpqy8hnHH9uHo4TILVIgOSQatNVrCydrtt9/eEnF0CP6UUpTSxGwHptOGTBekaai2ILb7HWgo8Jngqv32YNsKpcDrr27DyIWoX2aGn+sun8hfnv6Q9ZuKMJQiJdkbXxs0GIpSWV27XugJx/Vj6k+OwXGQFjghhBB1NboorkicL0njjFpE9fddx9pQWMqDHTNBg3LaOMwoitovEJZWuJQmzdtmYQtxQLk5Kcy8djILl2zk4y/WsHFLMZVVIZQCt8vJccf0YfRRvRjUr/M+64YKITqOjrDc1FNPPcV5552Hz+c7+MEJSDhZsyyLhx56iBdffJHNmzcTidStl1RSIgPh9yc/K5NFBRsJOyyiURNd6CK01Uus2IW2dr8BDTB8MdxdgzhyQxgeC5crhnY62FlVSSd/oG1vQoh6+Lwuxh3bl+OO6cP2nWVUVYcxTYP01CRZqUAIAXSMorgzZ87kuuuu45xzzuHSSy9l9OjRzXLehPsj7rzzTmbPns15551HeXk5M2bM4Oyzz8YwDO64445mCaq9GpLTl4AjhscZIbw8mcpFaUR3ucHUGD4LI8lCuS3sGgc1K5Kpnp+OIwQuR4yNRVGun/sWX+3Y2ta3IcR+KaXIy02jX+9O9O6RLYmaEKJD2bZtG8888wxFRUVMmDCB/v37c//997Nz584mnTfhZO1f//oXf/vb37jhhhtwOBxccMEFPP7449x22218+eWXTQqmvctPOpJuHg8VS9KJbndjemIYfgvl0rW/CQXKBMNnYfqj6BqTiq8ycJY78en+FNXU8LsvPmZpwY62vpUWl0jtLiGEEOJQ4HA4OOuss3jttdfYsmULl19+Of/617/o1q0bZ555Jq+99to+y3Q26LyJPmHnzp0MGTIEAL/fT3l5OQCnn346t956a8IBdCReRwpZZcOIbN2F6YthOmoTEkur3RNcVG2Pvqrd7BQbq9xJyaI8MoZ66RrwsrminP/39Zc8OulMXGb7qQKvtWZbcCPLyxexvnoVETuCQzno7O3GkJQR9Ejqh8OQIZZCCHG40s2wkHuTF4JvRTk5OYwdO5bVq1ezevVqli1bxsUXX0xaWhpPPfUUEyZMaPC5Em5Z69KlCzt21Lbs9OrVi3fffReAr776CrfbnejpOhStNdtWZeE1HHg9NhhgK1CmjWnYmIaFYdTu10rhMDS+pBgVOwNEKqIopchJ8rOpvIyFO7e19e00m/JoKS9seZxnNz/G16WfUR2rwtYWISvIyoqlvLz1aZ7e+Ce2BaXWnBBCiENbQUEBf/jDHxg0aBATJkygoqKCN954gw0bNrBt2zbOPfdcLr744oTOmXCydtZZZzF37lwArrnmGm699Vb69OnDRRddxPTp0xM9XYeycXsJG7dW0jmtE+luN36njcOwa9vTVO2GUphK4zEsPApslYwVVVTuCALgcTiwtOa9DWvb+G6aR3m0lJe2PMnaqhV4DB+Zrk6kONPwO5JJdqaS5e5EsiOVgtA2XtnyDFtqNrR1yEIIIQ4zjz76KN27d8fj8TBq1CgWLFhwwOPLysq46qqryM3Nxe1207dvX956662DXueMM86ga9euPP3001x++eVs27aN5557jhNPPBGApKQkbrjhBrZs2ZJQ/An3K/3ud7+L//u8884jPz+fzz//nD59+nDGGWckeroOpaC4kmA4SkZqKn7dE5NdWLFClBnF3j0dWQGGNohZPiKWD60doGJEqr5fG9TndLK+9PCfdau15q3tL7EztJUMVw6mqr9b12m4yHTlUBwp4I3tz3Nx92vwOfytHK0QQoimaKtu0BdeeIEZM2YwZ84cRo0axcMPP8ykSZNYtWoV2dnZ+xwfiUQ46aSTyM7O5uWXXyYvL49NmzaRmpp60GtlZ2fz0Ucfceyxx+73mKysLDZsSKzhocmDgI455hiOOeaYpp6mQ4hZtYMKlVKYyoXHyKI8aOFz7u7+BLQ2sCw3eq9GT6VAW98PuDeAsGW1auwtYWtwI1tq1pPiSNtvoraHUoo0VxYlkUK+q1zG8LT9/yEIIYQQe8yePZvLL7+cadOmATBnzhzefPNNnnzySWbOnLnP8U8++SQlJSV8/vnn8SU1u3fv3qBrjR8/nuHDh++zPxKJ8Pzzz3PRRRehlCI/Pz+he5BS4q3I53FiKBVP2hyGARhELDfRWBLRWBIxy1snUQPQtsZ0fb8vatsEXIf/+MBvyxcR0RFchqdBx5vKxFQGS8sWYOvEZ9MIIYRoO3vqrDV1S0QkEuHrr7+Od0MCGIbBiSeeyBdffFHvc15//XWOPfZYrrrqKnJychg8eDCzZs3CakAjybRp0+ITL/dWWVkZTxYbQ6bXtaJ++dmkp/gorwySkZqEx+HA63BSE41iOurPm7WtQSl8WbUJjdaaYCzGsXldWzP0FrGu+js8hiehRb19ZoDicAFl0WLSXVktGJ0QQohDVUVFRZ2f3W53vZMci4qKsCyLnJycOvtzcnL47rvv6j33+vXr+eCDD5g6dSpvvfUWa9eu5corryQajR50yU2tdb2faVu3biUlJeVgt7Vfkqy1oiSfm7HDevLqB9+QnuJDKUW2L4l1pSVEojEUCtNQGHutnRgLWzg8Jkk5tclaRSSM3+XihO492+o2moWlLaJ2BOMg3Z8/ZCoTW9tE7MjBDxZCCNEude1at8Hi9ttvb7bC/LZtk52dzV//+ldM02TEiBFs27aNBx54YL/J2rBhw1BKoZRi4sSJOBzfp1eWZbFhwwZOOeWURsckyVorGz+yN58sWs/2XWUQsyjcVU7UYxN2gBEFFJgOA7fbiWmYaEuT0tuH6TQIx2IUB4Oc3KM33VPS2vpWmsTAwFQOLDuc0PNsbaOUwqHkrSuEEIcVrWq3pp4D2LJlC8nJyfHd+ysdlpmZiWmaFBQU1NlfUFBAp06d6n1Obm4uTqcTc69apgMGDGDnzp1EIhFcLtc+z5kyZQoAS5YsYdKkSfj930+Cc7lcdO/enR//+McNu8d6JPyJt2XLFpRSdOnSBYAFCxbw7LPPMnDgQK644opGB9JR5GWn8qPxg/jdX/5HJGxhRC3cYYNwmontVChLE4tY2CEbwzDI6JVMSg8/paEgZeEQIzp15uqRh/+EDqUUuZ6urKpcRoCGNw0H7Rr8jgApzsM7WRVCiI6mOdcGTU5OrpOs7Y/L5WLEiBHMnTs3nlDZts3cuXO5+uqr633OmDFjePbZZ7Ht2s9hgNWrV5Obm1tvogbEW9y6d+/Oeeedh8fTsLHYDZXwBIMLL7yQefPmAbWrGZx00kksWLCAW265hbvuuiuhc3388cecccYZdO7cGaUUr7766kGfEw6HueWWW8jPz8ftdtO9e3eefPLJOsc0pD7Ktm3b+OlPf0pGRgZer5chQ4awcOHChOJvjIKicp558DUcq3bhtCzwOMEwcVdoHGEwLIVBbeIWTI6yxVnOxooyLK2Z3LMvdxw3sc0nF9i2ZumWHTz87mdc8fS/uehvL/LzZ/7Dn+d+yYptBQ1eKmpI6kgMpYja0QYdr7UmYocZmnIUTqP+PxghhBBibzNmzOBvf/sbzzzzDCtXruSXv/wl1dXV8QH/F110ETfffHP8+F/+8peUlJRw3XXXsXr1at58801mzZrFVVddddBrXXzxxc2eqEEjWtaWL1/O0UcfDcCLL77I4MGD+eyzz3j33Xf5xS9+wW233dbgc1VXV3PEEUcwffp0zj777AY959xzz6WgoIAnnniC3r17s2PHjjrrbDWkPkppaSljxozh+OOP53//+x9ZWVmsWbOGtLSWb6154MH/ULqqkKR0L0Z5iKgzSsjjwDINnBEb26mJJEM0HWxTY2yrobcrizsuPYMugcYPTmwum4rL+H9zv2DNziKiloXX5cRUippwhDeWruT9FWsY0DmHa048lk4pgQOeq1dSfzJdndgV3kGmK+egEw3KoyUkmX4GphzZjHckhBCiVejdW1PPkaDzzjuPwsJCbrvtNnbu3MmRRx7J22+/HZ90sHnz5ngLGtSOh3vnnXe4/vrrGTp0KHl5eVx33XX85je/qff86enprF69mszMTNLS0g74WVZS0rgaqQkna9FoNN43/P7773PmmWcC0L9///gyVA01efJkJk+e3ODj3377bT766CPWr19Peno6sG/tk4bUR7n//vvp2rUrTz31VHxfjx49Eoq9MbbuKuW7j1djuE1MV+1L74pauKI/mA686/t/BourqV6+gayfe1s8voPZWFTKPf/9gO2lFWQn+/G5nHUe11pTE4myePM27nptLreeOZHc1P0nbA7DweTcH/PK1r9THCkg3ZWNofZt7NVaUx4tAaU4Pvs0mQUqhBCHIV27+nWTz9EYV1999X67PT/88MN99h177LF8+eWXDTr3Qw89RCAQiP87kQoHDZVwsjZo0CDmzJnDaaedxnvvvcfdd98NwPbt28nIyGj2APf2+uuvM3LkSH7/+9/zj3/8g6SkJM4880zuvvtuvF5v/Jg99VFee+01srKyuPDCC/nNb34THyz4+uuvM2nSJM455xw++ugj8vLyuPLKK7n88stbNP5XXvuCWFkQT7ovvk+jwYzUzi5QevcATBMsN0obmAEX5QUVfPDuYib/aFSLxncgoWiMB9/+hB1llXRNT8E09k2qlFIkuV24nQ42FpXy0LufMuvHk3CY++9t7+LrwZS8n/LmjhcojhRgKgc+0x+f9Rm0a4jYYZJMP8dnn8YRqUe35G0KIYQQCdl7nc9LLrmkRa6RcLJ2//33c9ZZZ/HAAw9w8cUXc8QRRwC1CdCe7tGWsn79ej799FM8Hg//+c9/KCoq4sorr6S4uDjeStaQ+ijr16/nL3/5CzNmzOD//u//+Oqrr7j22mtxuVz7XVw1HA4TDn8/c/GHNV4aFP+aHSgNhsOsTdIcIXCE0EYMy3CglUJpjWmHULoGHXPjVD5CtmbVqm00vA2y+c1fv4UNRaXkpgTqTdT25jAMclL8rNpZyNItOxjRPe+Ax+cn9eLi7tewsmIpS8u+oiSya/esTwO/I8CQlOMZmDyMDLe0qAkhxGGrjbpBW9OiRYtwOp0MGTIEgNdee42nnnqKgQMHcscdd+x3gsLBJJysTZgwgaKiIioqKuqM8briiivw+XwHeGbT2XZt2YZ//etf8eJys2fP5ic/+Ql//vOf8Xq9DaqPYts2I0eOZNasWUBtfZTly5czZ86c/SZr9913H3feeWeT4g+FIqB2t6a5qrCcEcLOJILuAJbpQLN7bVDbwhuuwhOtwjRjoAxCoYYNwm8JWmveX7EWBbgcDauL5nU6iVnVzFu57qDJGkCSI8DI9LEMSzuWskgxYTuM03CS4kzHJZMJhBDi8NcBkrWf//znzJw5kyFDhrB+/XrOO+88zj77bF566SVqamp4+OGHG3XeRi03pbXm66+/5rHHHqOyshKonR7b0slabm4ueXl5daoADxgwAK01W7dujR/Tt2/f/dZH2XPMwIED65x7wIABbN68eb/XvvnmmykvL49vW7ZsSTh+n98DGrSjmrAHSgO5VCalEzOdKG1j2jZK29iGSaUvlZJALkG3E8MRwZvkPPgFWkh5MMyanUUkexObher3uFi8eQeRWMPXMTWVSYY7m87ermS5O0miJoQQ4rCxevVqjjzySABeeuklxo8fz7PPPsvTTz/NK6+80ujzJpysbdq0iSFDhvCjH/2Iq666isLCQqC2e/TGG29sdCANMWbMGLZv305VVVV83+rVqzEMI173bcyYMaxdu7bODNEf1kcZM2YMq1atqnPu1atXH3BhVbfbHa/r0tD6Lj80eHhPcELY1FQkZWIZDkwrhsOOYejaoZeG1pi2hcOKYSuDCm8G0QwvucOafypwQwUjUSzb3r2WacM5DQNL24SibdcqKIQQQrQWrXU8/3j//fc59dRTgdoZpkVFRY0+b8LJ2nXXXcfIkSMpLS2ND+oHOOuss5g7d25C56qqqmLJkiUsWbIEgA0bNrBkyZJ4C9fNN9/MRRddFD/+wgsvJCMjg2nTprFixQo+/vhjbrrpJqZPnx6PpSH1Ua6//nq+/PJLZs2axdq1a3n22Wf561//2qAaKk1x1uSjcHZ1UOlLx1Ymph3b77wWBZi2hbYVwbx0SrK2N7h+WXNzOUyUUtgJXt/WGgOF2yGrDQghhGj/Ro4cyT333MM//vEPPvroI0477TSgNr/54fqkiUg4Wfvkk0/47W9/u88gue7du7Nt27aEzrVw4UKGDRvGsGHDgNrCdcOGDYvXatuxY0edrkm/3897771HWVkZI0eOZOrUqZxxxhn86U9/ih+zpz7KV199xdChQ7n22mu57rrrmDlzZvyYo446iv/85z8899xzDB48mLvvvpuHH36YqVOnJvpyJCQ9yYf/mCQslwMjEj3oBGQd1RixGCrJyeKVBZRGG1efpalSvB6yk5OoDCW2NFRlOELXjJQGj3MTQgjRnqlm2g5dDz/8MIsWLeLqq6/mlltuoXfv3gC8/PLLjB49utHnTbjJw7ZtLGvfMUhbt26N1xlpqAkTJhywtejpp5/eZ1///v157733DnjehtRHOf300zn99NMbFGdzivl8GH4bSmpXAlBO9qnJorVGRwEN3mwTFTDYvsqkMlxJuqtly6PUx2EanDiwN3/76Css2z7obFCAmGWjteakgX1apOaMEEKIw0wHmGAwdOhQli1bts/+Bx54oM5Y+kQlnKydfPLJPPzww/z1r38FahONqqoqbr/99njfrKhfzLKpKjZIytTE3CbRQgsVtenTYyf53YrwuKJEoyaFRcl8u74LpPjwd3IRCWtCVQYV1VFILB9uNsf17cErX39LQUUVnVMPPF5Pa82OikpykgMc27tbK0UohBDtj9aaDZuLKC6txrY1ST4XfXrm4HbJ8JJDWSQSYdeuXXXGzwN069a4z8SEf9sPPvggkyZNYuDAgYRCIS688ELWrFlDZmYmzz33XKOC6Cgi0RimchAzYqTmuhl01Hcc2XUl2cmlmMbuX6gCZSoqw9+yYmtvFqwZQjhsAgqX3bKzbQ8kw+/jsuNG8sjcL9hRXklOsh+jnhYzW2t2lFXic7n45fGjSHLLbE4hhEhUJBpj/qINfPzFatZvLCIUqZ2o5TBNMjP8jDumD2OO7k1mur+NI01AB2hZW716NZdeeimff/55nf1aa5RS9fZMNkTCyVqXLl1YunQpzz//PN988w1VVVVceumlTJ06tc6EA7Evt8tBwOUnFAwycciXDO/5HVpDVchPJOJA7e6LN5RFkifI6H5LyEsv4LmPx+C0s8jyt/zapQcyvn9PbK15/OOv2FxSjtfpINnjxjAUlq0pD4YIxyyyAkn88vhRjOzRpU3jFUKIw1FVdZi//fMTFi/bjAZSk71kZtQmZdGYRWlZDS++tpBP5q/lFxeNo3eP7LYNuMGaY8zZoT2sZtq0aTgcDt544w1yc3ObbRhQo9pRHQ4HP/3pT5slgI7ENAyO6tuTytBHDO+5gmDESzBSW5Jj71+nrU0qg36C4Rj5Wdv50VGfs3zjNNICbZ8MHz+gF/1ys/h41QbeX7GWkuogtq0xDUVOsp8TB/ZmXL8eZCcfRt/2hBDiEBGOxPjr3z9m4TebyM4I4PHUrbHpcjrIzgxg2TY7Csp59Ml5zPjlyXTt3LZf5kWtJUuW8PXXX9O/f/9mPW+DkrXXX3+dyZMn43Q6ef311w947J6F3UX9ThieTXH5KsJRB9VhF8YBku6oZVJc6adPbgF98/adiNBWOqcmc/6oI5gyfBA7yioIRWN4XU46pwZwSZkOIYRotM+/Wsfib7fUm6jtzTQMOueksnVHKS+/vpDrf3FSK0bZOFrXbk09x6Fs4MCBTaqntj8N+mSdMmUKO3fuJDs7mylTpuz3uKb0x3YU3XNXoO0oWwsDYNpY2qrtgt+91pSKd4bW7oxG3LgDUfKyF6P1hYdMwgbgcTrokZXe1mEIIUS7YNuajz5fjYIDJmp7GIYiLcXHitU72LK99NBvXesAY9buv/9+fv3rXzNr1iyGDBmC01n399iYgvrQwGRt79kMP5zZIBITjnxAWiDAjlIHwXAEZVooY/e7T+9eNxRAKwzbgdflJi05gG1/i2Vtx+E4+DqbQgghDj9r1hewaWsxaakNn0zmT3KztbyGBYs2HPrJWgdw4oknAjBx4sQ6+1t9goFoGssqojymiQZiKK0g6kBbGgxQSqO1Qtu7v4A4IT3Lh8/jxbILse1y4PBL1mwdI2aHMZUTQzkPqdZBIYQ4VBQWVxGNWng9DZ9Fr5TCMA127CprucCai1a1W1PPcQibN29ei5y3Qcna3isEHMy1117b6GA6gtJIkJJwDQ5HAE+mQTQcIxq0scO1a4MqA5wecHpNog6DgmgFKVGF11SgDEpLqiktqcK2Nf6Ah5xOKYdk8mPrGLtC37Gx8lN2BpejsVAokl1d6OE/jrykEbiMtitFIoQQhxrLths12dFQEIsd+r1e7X8uKIwfP75FztugZO2hhx5q0MmUUpKsHUDMttlQbZJmRglqm5AdwnRFSfGG8ZphTDSoPR3yipjtoTLmpTQcQykPzz2znIVffk44HAPA4TTp2TubMeP6MWxkjwaNcWgNVdFdLCj8G8XhtVg6istIwlAONDZFoVUUhr4jqew/DM+4mM6+I9o6XCGEOCT4vLUtapZlY5oNXw0yZmkCfk9LhSUS9Mknn/DYY4+xfv16XnrpJfLy8vjHP/5Bjx49GDt2bKPO2aBkbcOGDY06uahrack2vizN5/SsdcR0DS7DIs1ZjaksbK2wMEArtAalbBxGkFRHGFWlmL9sIB++X0gg2UNaWhIoiEZirFy+jZXLt9GnXycuu/IEMrMaN3ixuVRHC/m04CHKIptJcmThMOr+B8RjpmDrGNWxQuYX/oWjM68gL2l4G0UrhBCHjv69O5GW4qO8Ikh6WlKDnhOJxjANxdCBh0Fdyw4wweCVV17hZz/7GVOnTmXRokWEw7VrapeXlzNr1izeeuutRp034YXc96a1PuDanqKub0q3saSqE2WWm3RnFWnOKgxlE9MObGpXKQBQCrQ2iNoOgkVuIkGTlatzyOuaSkqKD5fbgcvlIMnvoXNeGplZAVat3M6fH36X8rKaNrs/rTULi5+iLLKFgLPzPonaHoZy4Hd0ImqH+Lr4aapjzT/NWQghDjcBv4djR/Skuibc4M/W0rIacrKTD49kDb5P2Bq7HeLuuece5syZw9/+9rc6M0HHjBnDokWLGn3eRiVrTzzxBIMHD8bj8eDxeBg8eDCPP/54o4PoKCoiQSp1hI/L+5FkhvEYUSxd/8KuSoFdaeI1o6xd34mdhT4iVnm9x7pcDjrlprJ+7S5ef2VhS97CARWH11IYWoXXkY6hDrxgrVIKvyObmlgJm6u+bKUIhRDi0DZ+dF/S05LYUVB+0IStorK2KPmkCYNkrdBDxKpVqxg3btw++1NSUigrK2v0eRNO1m677Tauu+46zjjjDF566SVeeuklzjjjDK6//npuu+22RgfSEVTEKtEqyuZwNttjyYDCp8I4sNj7K4OBjUdFcOkYq1Z15oPPBmFrRcgq+r60xw84HCaBgIeF89dRWlLdOjf0A5uqPidmh3Gqhq20oJSBqVxsqPoYy460cHRCCHHoy8tN49KpY/H7PWzdUUowGNknaYvGLHYVVVJVE+bkCQOZeFzzVssXjdepUyfWrl27z/5PP/2Unj17Nvq8Cafif/nLX/jb3/7GBRdcEN935plnMnToUK655hruuuuuRgfT3kWoATTdvLvYGQtQEguQ5ywj1azBrWLxNEyjKK/y8ck7A1mxpgvaBI9pErUqsewQDqP+ZCg5xcuObWUsnL+OkyYPbbX72mNXaAUOw5PQ7FS3mUxNrJjK2E5SXd1aMDohhDg8HDmoK9dddgIvvLaQjZuLKCqpwuk0UUoRjdXOrM/K9HPK8YM4afzAQ7IiQL06wJi1yy+/nOuuu44nn3wSpRTbt2/niy++4MYbb+TWW29t9HkTTtai0SgjR47cZ/+IESOIxWKNDqQjyPYkYSgDv6MKS5sUWT6KYn78RphUswZT2djaoMZ2seLrrqyc3x1vZgiFxlAmGgtbR4D6kzXDMNBodm4va9X72iNqhzA4cPfnDxnKRGubmB1qoaiEEOLw0693J26dcRqr1xUwf9EGdu4qJ2bZpCZ7OXJwN4YN6ZpQPTbROmbOnIlt20ycOJGamhrGjRuH2+3mxhtv5Jprrmn0eRNO1n72s5/xl7/8hdmzZ9fZ/9e//pWpU6c2OpCOIMXlw2s6UVjoeGE/RZXtocquOxg/FnXU1qRRteO7ao/W++0G3cNQilA42gLRH5xDuYmS2AQHra3d3aHuFopKCCEOT0op+vXuRL/endo6lObRAVrWlFLccsst3HTTTaxdu5aqqioGDhyI3+9v0nkbNSLxiSee4N133+WYY44BYP78+WzevJmLLrqIGTNmxI/7YULX0WV5Ukh2eYjZHhSVu0t01H+s4bKwd78p9yRtYKAO0nJla0hKapvEJ9PTl/WVHyb0nLBdhcdMxe/MaZGYhBBCHCraf1nc6dOn88c//pFAIMDAgQPj+6urq7nmmmt48sknG3XehCcYLF++nOHDh5OVlcW6detYt24dmZmZDB8+nOXLl7N48WIWL17MkiVLGhVQe3ZUel9SnUkE6Ypp1LYqWVqj9fdfOGwNlq1Jyq3G6bGIVjnANkBbmMqN09x/7Z1oJIZhKPr0y221e9pbvn80pnIRtYMNOl5rTcwO0t0/Fud+ynwIIYQQh4tnnnmGYHDfz8BgMMjf//73Rp834Za1llr3qiPIdCczIr03n+4qo5cvQIorTGXMgRVfDBSssIGOmLjdFin5lRStTMPhDhFRMYKxVEwdIcVbf2JTVlZDdqcUjhiW37o3tlu2pz/p7h4UhlaR7OyMUgf+LlAdK8RjppLvP6aVIhRCCNFm2nE3aEVFRbz2bGVlJR7P95/TlmXx1ltvkZ2d3ejzN7owy9q1a1m3bh3jxo3D6/XGV5QXBzYlbzRrK3ewviaP/knrMJ0Qtt3YtqamUqOju8epGdDpyGLK1idj15hYfoOiCgdFFcV0TU8hJ7lu/3d1VZho1GLCxIG43G1Tb0cpg5GZ0/i04CEqotvxO3Mw1b5LYGltUx0rwlAmR6ZfQMDZNi2BQgghWlE7TtZSU1Nrx5crRd++ffd5XCnFnXfe2ejzJ/ypXlxczLnnnsu8efNQSrFmzRp69uzJpZdeSlpaGg8++GCjg+kIsjwpXN33DP6yBjaFauji3orHcFJa6SEWVRgGaFU7kSC3VwXOkzfz7bvdKdsVwOlxEbVttpSU4TAMMvw+bNumvCxIsCbM2OMHcOKkIW16fymuLozJuY75hY9RFtkCgNsI7J71qYnYVVg6iteRyhHpF9DdP6ZN4xVCCCGaat68eWitOeGEE3jllVdIT0+PP+ZyucjPz6dz586NPn/Cydr111+P0+lk8+bNDBgwIL7/vPPOY8aMGZKsNUBXXxYzB57Pp4WDWFn6Ap7YCrzOSnxOsDEwFTgNhdZu9AAvqUWdKfg6SqwqilIQQ7NpWzGRQBjL0gSSPZw2ZQRn/ngEpqNJK4g1i1RXN07IvZXtNYvZUPkRJZGNWHYEpQz8zhx6BsbTNelofI6Mtg5VCCGEaLLx48cDtWupd+3aFcNo3s/ihJO1d999l3feeYcuXequQ9anTx82bdrUbIG1d8lOH6d2HsWx6ZnM/M9/CVub6N5pF15XFKdKIhLOori0N5XVGVQmh+HYCFZpmFhJGLMsRqTawp+dxOQTh3LUMb1Iz2jatODm5jQ85PuPpVvSMYSscmI6jKmcuM1kTCXLogghREejdO3W1HMcyvLz8ykrK2PBggXs2rUL27brPH7RRRc16rwJf2pWV1fj8/n22V9SUoLbLbWyGqostJzlxa/z5ncFLFjfGYtcFm3JRSkwFKS4nLhtRXWwkHB09+oGhoZMAyPLBUoR65nMyacOPaTHCiql8DpS2zoMIYQQosX997//ZerUqVRVVZGcnFzn81kp1ehkLeF2uuOOO67O9FOlFLZt8/vf/57jjz++UUF0NDurP+TNDbO5f0El72/IJmaZOBwKlwlOA7QNhaUWW0sqqYqEcDlNvC4HXpcTj9PEYRpEYxZfrd/Ks18sOehiv0IIIUTbU820HbpuuOEGpk+fTlVVFWVlZZSWlsa3kpKSRp834Za13//+90ycOJGFCxcSiUT49a9/zbfffktJSQmfffZZowPpKIpDi/h46zP8a0UeZWEvaR7NLgOcRgwMjaUVdo0TZSm00sS0TciK4XPUzqpUSmEqhdNhYqB4acEykr0ezhg24CBXFkIIIdpQO54Nuse2bdu49tpr6+2BbIqEW9YGDx7M6tWrGTt2LD/60Y+orq7m7LPPZvHixfTq1atZg2tvtLbZWPYir67NoDTsJT+1kqykXThrYtjbDOwNTuwtTuygAltjKA3KJhyLEftBv7e2NUkeF6Zh8PJXy6kIytqaQgghRFuaNGkSCxcubPbzJtSyFo1GOeWUU5gzZw633HJLswfT3pVHVvJt8Q62VfVkUNY2wjsVG5bnosoc2BoMU2M5FWiFEVHoqEY5NdppE7ZiOIzaRXv3rHiQnuQj1edhe1kFX6zdzKQh+9Z2EUIIIQ4JHaBl7bTTTuOmm25ixYoVDBkyBKezbq3RM888s1HnTShZczqdfPPNN426kICimgUs3pVEl+RdhDYZrF+eB6bGkRwhUuPCMjW2E5TWYNTOetGR2kXco84YtnZiKEXEsnA5TNKSvJiGwjAMPvpugyRrQgghDlntf2VQuPzyywG466679nlMKYVlWY06b8LdoD/96U954oknGnWxji5sFVEeMcgIVrHh284op43TF8PhjmE4LLQ26n5pULsXcI8AMU3UtohZNlprclMCmEbt29bjMNlVUdUGdySEEEKIPWzb3u/W2EQNGjHBIBaL8eSTT/L+++8zYsQIkpLqLiw+e/bsRgfTEaR5y1j9dRdsW+HyxIDahMyTFKG6xg040Ojvvz2o3S1sMQiGI7hMJ3lpyeSkHFp11YQQQogD6gDdoC0l4WRt+fLlDB8+HIDVq1fXeexQrvd1KDBVMp5QhLKiAIa3boatTI3HFyFimbVj0vRer6UCLMBpY4ZtzEjtuLU9L3coZtErUDdpFkIIIQ4p7ThZ+9Of/tSg46699tpGnT/hZG3evHmNupAAr6sHwWIflmVgOGL7PG4aNk5ixAwTZde+I7WmNlmzFaZD47QVu3aVg9bk5qZi7W5eHd+/R+vejBBCCCEAeOihhw56jFKq9ZI10XheRx5Ye+ql7fu4UuBWMWLaBEOj1PeDKffUvXU4DLQFhUWV+P0egjpGht/HsX3yW+cmhBBCCFHHhg0bWvT8bb/qdwfiNHx4XR6U+j752ucYZeFQFhoVP2ZPwqYApRUOh4lta7YVlRO1bM4eOZgUr6c1bkEIIYRoHN1MWwckyVorCjg7k5uZjMuw0Xb9CZtSkGSEcSgLG4WtdydtWmEqA2UpYtom6oSqmjCn9O/NmbJ6gRBCCNFuSbLWihyGm8H9+hJIroGQWdtUpgG77qaoTdi8KoqBxrINbBN0zKQGiyiaVMNFbpWLY3LzZGKHEEKIQ5+0rDVag5K14cOHU1paCtQWequpqWnRoNqz1cEYXfsWYdpgRQy0zT5vRGWDYYNXRQnoIO6IRbLPogcBepkBhjnSGWGmkxI2iEYbX7dFCCGEaC1KN8/WETUoWVu5ciXV1dUA3HnnnVRVSQHWxtge3MWqykLMaAqdMkpQQQPLMrDrnW0AlqWIVrvwJkUYrnLoY6TQ3fCTqlzYtsYwDLweV+vfSBuxdIyaWClV0UIiVnVbhyOEEELExWIx/v73v1NQUNDs527QbNAjjzySadOmMXbsWLTW/OEPf8Dvr78o62233dasAbYn35StomhJmIq3UknpXkpedgk7i9KIBk1st40yd5frsBR22ATA6Yzhr9DE/DYFgRqqXFEspQlXRknP8ZDfLaMtb6lVVEZ3salqPuurPiVkVaDRGDjo5B1Iz8AYcr2DMZTZ1mEKIYTowBwOB7/4xS9YuXJl85+7IQc9/fTT3H777bzxxhsopfjf//6Hw7HvU5VSkqwdQGmwgoovFVprQpUZGDUW3TIqqVGaksokYlETjUJpcJsRzJ0KtUtRfoTB111KiHhAo9FAzG9RnBTjjmXvc3r3ARzfuVe7G7umtWZVxXssK32NsFWJQ7lxGr7a9dV0lE3V89lSvZBsTz+Ozb6cJEd6W4cshBBiP5qjG/NQ7wY9+uijWbJkCfn5zVtOq0HJWr9+/Xj++ecBMAyDuXPnkp2d3ayBdAQ7V1cSLQRvam3vs7ZMortScQK+YotIjQYFhgbTNrFNKBgF1XkKt9MiKebC1IpwOIbHdJKdEmBZ6U5WlBWwvqKY6f2PxmhHCduK8rdYWvJvTOUgxbnvRAqvmULUDrEz9C0f7/wTEzr9Cq8jtW2CFUII0eFdeeWVzJgxgy1bttS7JOfQoUMbdd6Ei+Latt2oCwkoXRNBW2A6902o/Kkm1SGbaI1GmaBNKByiqO4CjmqNy1IYLkU4EsMwFF06p5PmTyIDKA0HeXHdNwScHs7rfUTr31gLKAh+x/LS/+JQLnyOtP0e5zQ8BFQnisPrWVTyAmOyf96KUQohhGiwdrzc1B7nn38+UHdZKaVqe9SUUo1ezL1RKxisW7eOhx9+ON4vO3DgQK677jp69erVqCA6Cn84CcNQ2NgYe83t0NpGGxE8nSycYRsrClVeJ1WdXTiCYETBMjRhHcPlcpCXm0pKqi/+/DS3l6ht8dK6pUzs0ptMz+G/Tuj6qs+I6iCpzryDHmsqBx4zmW01S6mI7iTZ2akVIhRCCCHqaqmVDBJO1t555x3OPPNMjjzySMaMGQPAZ599xqBBg/jvf//LSSed1OxBthdpnhQchoOoHcVtuAAbS4fQRKmt2aEwPWC6NdXdHSiXjSOisZWJ1+Mkt3MaKcleDHPfSbwZHh9bKsv4aPt6ftxzSKvfW3OqjhWztXoRbqP+SSz1cRsByqPb2FQ1nyFpP2rB6IQQQoj6NfdYtT0STtZmzpzJ9ddfz+9+97t99v/mN7+RZO0AsnKS8SkvIWJE7AimCqOxqK2gYsSXlYo5oTrNiWlplKlxuKBL1zSSk/ffYmYqA6dp8v7WNYd9slYW2UrEribgyGnwc5RSGMpBUXhdC0YmhBCisTrCBANomd7HhFcwWLlyJZdeeuk++6dPn86KFSsaHUhHMOLonqQG/KTZqSgVwiZG7a9Afb9gOxB1gm2AaQOWieGyqHGW7H9B0d08ppOiUDX2QY471MXsMBqNSvDtqTCI2sEWikoIIYQ4sHfeeYeBAweyYMEChg4dytChQ5k/fz6DBg3ivffea/R5E25Zy8rKYsmSJfTp06fO/iVLlsgM0YPo0i2DfgM7s2D+dzgyDGytsLDRP0iuNAZKgdIGaPCmKmqsKsJ2ELfp28/Zv1/wXevaLtXDlcNwo1BobBQNr5+msXAZ+399hBBCiJbUUr2PCSdrl19+OVdccQXr169n9OjRQO2Ytfvvv58ZM2Y0KoiOQinFlHOOYtGqZVQWmrgzaqumaa3jE1yUAjNK7YLtNviSFN5UgxgxKmPlB0zWQlaUbt40TOPwXvI1zdUNtxEgbFU2uBSH1jY2NlmePgc/WAghROvrALNBV65cyYsvvrjP/unTp/Pwww83+rwJJ2u33norgUCABx98kJtvvhmAzp07c8cdd9SZqirql9HFQ+7ZJZS/aFK9y4HpNnH4NWr3b8IOg12scGdY1OQb+DPBMBXKNqiOVZLp6lRvq5mtNRHbYmKX3q18R83P50ijW9JIVld+gEenNKjYb8iuxG34yU8a1QoRCiGEEPtqqd7HhJM1pRTXX389119/PZWVlQAEAoFGB9DRrCzfSCi3iJyfOqhZkkLlMhfR0t1dfRqUqfHkWOR3q2ZNso9qrUnWLgxloLF3l/3Yt2uwJFRDstPDhM7to3xKz8BYNlZ/SY1VQpLjwEtqWTpK2Kqkd2A8fmdWK0UohBBC1NVSvY+NqrO2hyRpidFa8/aOJVjaJilD4T+xhoyxQWo2OrGDCgxwJlt4u8XQBpTvNNlZ6cZhxPA4FLX/27eLsyISoiYW4ad9hpPtbXi5i0NZpqcXR6T9mMUlL1AVLcTnyMBQde9da01UB6mOlZDj7cewjPPaKFohhBAH1QG6QVuq97FJyZpIzMbqXaytLCbV40apGODA9GgC/SP7HKuAwdmVxCwoCXqIWppUl7NOl2AwFqU4VINGc0b3gUztO7z1bqYV9E2eiKlcLC19mYrYDkwcOA0vCoWlY0R0NQ7lpmvScI7Jmo7bbB+JqhBCtEsdIFlrqd5HSdZa0VfFa6mxYmToHFCbqX3X7X88ltPUHJFbwYbSMCXVfkKWl02Vpbtne4JTmfRITuP0/IGc2q1/u1oXFGrf9L2Tx5HnG8rm6q9YV/kJ1bESNDam4aKPbxQ9AqPJcvdpd4vYCyGEOLw1Z++jJGutqChcgYFCkQPsACKA+4DPcZnQI72YkZ0MRqWcy47qGsKWhc/hZEBaNsMy83Ac5rM/D8brSKVfykn0TT6RmA5hawuH4cFU8vYVQojDhaIZiuI2SyTNa9iwYQ1uMFi0aFGjrpHQp100GuWUU05hzpw5+8x0EAf3fYEOH+iueIy19HUX0s9VRrIZRgMVlpvvwlmsiWQQ1iYQBhR9AqM4peugtgv+EKCUwqm8bR2GEEIIETdlypQWv0ZCyZrT6eSbb75pqVjavVSXH3t3p/0ITwkjfesImJXYWhHVtSsZpDqD5DvLGG1vZH6wE0uCOVRGOtMraWhbhy+EEEKIH7j99ttb/BoJ95/99Kc/5YknnmiJWNq9YWk9cBsmx3i/YJx/Pm6lKI5lUmIlU2m7qbQdlFhuSi0XXhXjhKSdDHVG8ZndGJzaMovDCiGEEK1hz9qgTd0a49FHH6V79+54PB5GjRrFggULGvS8559/vragfYKtZ19//TX//Oc/+ec//8nixYsbEXFdCQ/6icViPPnkk7z//vuMGDGCpKS6i4vPnj27yUG1V/2T85iUXs5I7zeEbQ81es9qBG7ABizYvchSuW2SpIKMDayma/JokhwHHtt2KNA6TCj8JTXB94jG1gNRDJWCx3McPs9EHI4ubR2iEEKIttJGs0FfeOEFZsyYwZw5cxg1ahQPP/wwkyZNYtWqVQcsVLtx40ZuvPFGjjvuuAZfa9euXZx//vl8+OGHpKamAlBWVsbxxx/P888/T1ZW42qBJpysLV++nOHDa0tErF69us5jMiPvwBRwSto2gmEoibmwbYNvytJYUJjBrpAbG0WKM8KIjFJGpBcTcsTo7IowyrcarfUh/fqGwvMpr3yUWGwToFHKCygsXUKk8juqql/A6zmBlMCVGLJ+pxBCiFYye/ZsLr/8cqZNmwbAnDlzePPNN3nyySeZOXNmvc+xLIupU6dy55138sknn1BWVtaga11zzTVUVlby7bffMmDAAABWrFjBxRdfzLXXXstzzz3XqHtIOFmbN29eoy4kgOgy0s0CSly5LNzp4Y2tXSkOe1GA27RQQEnYxZqKAG9s7cSEnEKu7FOGV6+D2Bpw9m3rO6hXMPQJpeW/w9aVOMxclKrbCqi1jW2XUR18FdsuJi31NgyZKCCEEKKFRSIRvv7663iBWgDDMDjxxBP54osv9vu8u+66i+zsbC699FI++eSTBl/v7bff5v33348nagADBw7k0Ucf5eSTT27cTdCE0h1r165l3bp1jBs3Dq/Xe8i3/BwSYt+CjvB54UBe2ZRCyLLIdAcxjbrtulpDddTDezvycZkZXNdnKY7Y8kMyWYvGNlFW8SBa1+Aw8+t9DyhlYJrpKNtLMPwJZuUTpCZf3QbRCiGEaDPN2A1aUVFRZ7fb7cbt3ne4UFFREZZlkZOTU2d/Tk4O3333Xb2X+PTTT3niiSdYsmRJwuHZto3T6dxnv9PpxLbthM+3R8ITDIqLi5k4cSJ9+/bl1FNPZceOHQBceuml3HDDDY0OpEPQNSwtDfC31amY6v+3d99xVlR348c/Z+bW7ZVtVOkdBEWCBQVBY6zx0aiJRJ8nRRdFMbZowOgvgiYxxgR9Egv88osliQZjFEFsEKwIgnSkty3A9rt7y8yc3x8XrixL2btsg/2+85q8uHPPnPnO7L3ud8+Zc47itGRFituLZfsIRPwEIn5Ctp9El5+CRINMr8P83cm8visXnNq2jv6IausWYDt7Mc3Ox03WDcOPoZKpCy7Atve1UoRCCCFONV26dCE1NTW2zZgxo1nqra6u5gc/+AHPPvssWVlZcR9/wQUXMGXKFPbs2RPbt3v3bu68807GjRvX5Ljiblm78847cbvd7Nixo14z37XXXsvUqVP57W9/2+RgTn0e5u3pRI1l0DkhQkmdi9I6kzrL4GC+rYDdpibbZ5Pts3ApzZu7c/hObxftrePQcWqoDS7AUEmNblU1jAwsezt1wQ9ISvyvFo5QCCFEu9GMLWs7d+4kJSUltvtIrWoAWVlZmKZJSUlJvf0lJSXk5uY2KL9582a2bdvGpZdeGtt3sEXM5XKxYcMGevbsedTw/vjHP3LZZZfRvXt3unTpEot10KBB/PWvf23cNR5B3MnaO++8w4IFC+jcuf7Ivt69e7N9+/YmB9IR7Alm89n+NBJNi6+rvFSGDRTgMTUGoBQ4GiKOYlfAxb6gSbekILvrfHy+L4nzmm/limYRjqzDsfdjmg0/8EejlInCJBj+QpI1IYQQTZKSklIvWTsaj8fDiBEjeO+992LTbziOw3vvvcfkyQ0fx+nXrx+rVq2qt+/BBx+kurqa3//+97EE7Gi6dOnC8uXLeffdd2PdrP3792f8+PGNvLIjiztZCwQCJCQ0HM1XVlZ21MxWRK2u6kxlxEcwApWWgc/UmIc1SBkKvKZGa6izFdtqPCS6NKsq0zivbcI+Kq1r0VhAw/75Y1JutFN1/HJCCCHECZo6dSqTJk1i5MiRnHnmmTz55JMEAoHY6NAbb7yRgoICZsyYgc/nY9CgQfWOPzgFx+H7j0YpxYUXXsiFF17YbNcQ9zNr55xzDn/5y1/qBeU4Do8//jjnn39+swV2Kqq1DepsP1WWC5/pYKroYALH0diOg+04OE40UVMK/KZDyDaotnxUR6y2Dr8BpTwoDCDOhya1g1K+FolJCCFE+9RWk+Jee+21/OY3v2HatGkMGzaMFStWMH/+/Niggx07dsSev2+q999/nwEDBjQY+ABQWVnJwIED4xpVeri4W9Yef/xxxo0bxxdffEE4HOaee+5hzZo1lJWV8dFHHzU5kI7AUIrqiInCxiCCbUeTtcPGgkYXuzUUhtK4DTdBWxG27LYJ+hhcZleUSsTRVZgqvVHHaK3RhHC5ZG1ZIYToUNpoUlyAyZMnH7HbE+DDDz885rFz5sw5bv1PPvkkP/rRj47YNZuamspPfvITnnjiibgm2D1U3C1rgwYNYuPGjZx99tlcfvnlBAIBrrrqKr788stjPnQnoC5sYWsH8BCxFNHJYzWGig4sUES7QZXSoB0iloHSXhytqQyF2jb4I3C5CvB5R+E4FY0+RusaDJVIgr/po2KEEEKI9mTlypVcdNFFR31/woQJLFu2rMn1N2metdTUVB544IEmn7SjSnR7MDEIRiwM24PXY5PiDZDkqcVlRFvOLG0SCPupCiYSDLvAtFAuRZq7fXYbJvgvoi70H2ynAtNIO2ZZrW1sey8+32jcrvY3Z5wQQoiWcyJrex5aR3tUUlJyxPnVDnK5XOzdu7fJ9TcpWSsvL+f5559n3bp1QHR23ptuuomMjIwmB9IROFpjRhRaa5L9QTolVsSSNK2jIw08hoXXV02Kp5b9tcnsCybis924DbMtQz8qr2cUif4rCdT+HRuNodKOOI2H1haWvQOXqzOpyZNlAmUhhBCnjIKCAlavXk2vXr2O+P5XX31FXl5ek+uPuxt08eLFdO/enaeeeory8nLKy8t56qmn6NGjB4sXL25yIB2BHbRxgg6nJZbjcoextCJiuwjbbiKOi4gT/XfYduFowLTJT6iAoMavmrzYRItSSpGa/FOSEr6H1kEseyu2vR+tg2gdxnFqsKydWPZO3K6eZKQ9jNvVra3DFkIIIZrNt7/9bX7xi18QDAYbvFdXV8f06dP5zne+0+T6484ACgsLufbaa3nmmWcwzWhrj23b3HrrrRQWFjaYn0R8wy4JMyB1N1cPXMKC3QNZU5GPrQ38ZgS3YaEASxvU2h5AUZBQzlVdv+S9LUNIrPlWW4d/VEq5SEm+BZ9vDLV171AXWozt7AftgHLjdvch0X8JPt95mEZqW4crhBCiLbThAIOW9uCDD/LPf/6TPn36MHnyZPr27QvA+vXrmTVrFrZtn9DjY3G3rG3atIm77rorlqgBmKbJ1KlT2bRpU1x1LV68mEsvvZT8/HyUUrz++uvHPSYUCvHAAw/QrVs3vF4v3bt354UXXqhXpqKigsLCQvLy8vB6vfTp04d58+Ydsb6ZM2eilOKOO+6IK/amiNRYnJf7NYmeIOdkb+OSgrX0SSnFQVFjeam2vIQdF/n+SsbnbuA7+etI9wU4O38DWU6cc5m1MqUUXs9Q0lPvJidzDtkZfyQr40k6Zfwv2RlPk5hwmSRqQgghTkk5OTl8/PHHDBo0iPvvv58rr7ySK6+8kp///OcMGjSIJUuWNFifNB5xt6ydfvrprFu3LpY1HrRu3TqGDh0aV12BQIChQ4dy8803c9VVVzXqmGuuuYaSkhKef/55evXqRVFRUb3FUcPhMBdeeCGdOnXi1VdfpaCggO3bt8cmtTvU0qVL+dOf/sSQIUPiirupPN7d9MgoJhBKRCuHLgll5CfupyriJWB50Ch8hkW6pxYX0SGiVaEEChIrMPTXwIhWifNEmWYmppnZ1mEIIYQQraZbt27MmzeP8vJyNm3ahNaa3r17k57euKmtjqVRydpXX30V+/ftt9/OlClT2LRpE2eddRYAn376KbNmzWLmzJlxnfziiy/m4osvbnT5+fPns2jRIrZs2RIbzNC9e/d6ZV544QXKysr4+OOPYyMzDi8DUFNTww033MCzzz7L//k//yeuuJsqLX0H2hWhNpiCY0ZwDgxrSXYHSXYfnJpDo4EIGgOFGfbj8dXiT9nWKjEKIYQQLeIU7gY9VHp6OmeccUaz1tmoZG3YsGEoFR3FeNA999zToNz111/Ptdde23zRHeaNN95g5MiRPP744/y///f/SExM5LLLLuORRx7B7/fHyowePZrCwkL+9a9/kZ2dzfXXX8+9995br+u2sLCQSy65hPHjx7dastats59tew1qdAStNepA69nhn76D4yS11gQdC8NQ5OfIUl5CCCFOXgfnEz3ROjqiRiVrW7dubek4GmXLli0sWbIEn8/H3Llz2bdvH7feeiv79+9n9uzZsTLvv/8+N9xwA/PmzWPTpk3ceuutRCIRpk+fDsArr7zC8uXLWbp0aaPPHQqFCB0yMe2RlpQ4noy0NDZXONghjaEVSh/yh8Yhk8corVAH9lumg+2CxISkuM8nhBBCiJNfo5K1bt3ax1QLjuOglOLFF18kNTX6sPoTTzzB1VdfzdNPP43f78dxHDp16sSf//xnTNNkxIgR7N69m1//+tdMnz6dnTt3MmXKFBYuXIjP1/iJZmfMmMEvf/nLE4q/1s4gYoDbsLEirlhL5RFmJTuQwCkMl4NlOCiaPj+LEEII0eY6SDdoS2jS5F179uxhyZIllJaW1nu4H6LPtLWUvLw8CgoKYokaQP/+/dFas2vXLnr37k1eXh5ut7tel2f//v0pLi4mHA6zbNkySktLOf3002Pv27bN4sWL+eMf/0goFKp37EH3338/U6dOjb2uqqqiS5cuccX/6hZNkp1Ctq+afeFGtJRpTaq3jrKQn0VF6fxXwyXHhBBCiJODJGtNFneyNmfOHH7yk5/g8XjIzMysNxO9UqpFk7UxY8bwj3/8g5qaGpKSosnOxo0bMQyDzp07x8q89NJLOI6DYRixMnl5eXg8HsaNG9dgLribbrqJfv36NXiu7VBerxev98SeG1tXVoYO9OTybl/gNSKEHfdRP3cKcCkLvxHmo119SEqt4r/6HqWwEEIIIU5Zcc+z9otf/IJp06ZRWVnJtm3b2Lp1a2zbsmVLXHXV1NSwYsUKVqxYAUSfjVuxYgU7duwAoq1ZN954Y6z89ddfT2ZmJjfddBNr165l8eLF3H333dx8882xAQa33HILZWVlTJkyhY0bN/LWW2/x6KOPUlhYCEBycjKDBg2qtyUmJpKZmcmgQYPivR1xqbMifLy5F+t255OZXIPXFUY5Gg7blK1xqwhZydVsLc3mg439qbUiLRqbEEII0ZJUM20dUdwta7W1tXzve9+LtVqdiC+++ILzzz8/9vpgN+OkSZOYM2cORUVFscQNICkpiYULF3LbbbcxcuRIMjMzueaaa+qN5uzSpQsLFizgzjvvZMiQIRQUFDBlyhTuvffeE473RLnrIFJn8OrSs7D1Z/TP20OqN0BN0EcoEv1ReFw2yf4gGvh6Tw5//2wUdW431r5d1Fl78buy2/YihBBCiKaQbtAmU/rQ+Tga4Z577iEjI4P77ruvpWI6KVRVVZGamkplZSUpKY17mOyB2X/n1V2bMYMaVyhC3/xiRvbaymm5pXhc0QXdLdtkx94Mvth0Gmt35hMy3Tg+gwn+rVx5Q5BO/jPJT7qAdO+Alrw8IYQQp7Cm/A470XP95IkX8fgTTqiucF0tf5p6Q6vE3Z7E3bI2Y8YMvvOd7zB//nwGDx4cm3j2oCeeeKLZgjvVjDA68cb+DYTSXahak7W7Cli7q4DM5BqSfEGU0tQGveytTgatQIOVaODbFyGwPxVuCLI78C6ldZ/SK/X7dE6aWO+ZQSGEEKLd0vVmqWpyHR1Rk5K1BQsWxJabOnyAgTg6t3KRuiZA6ZhUbL+BGYyOpN1fncT+6qRoX/zBCdYA22+ggJT1tVTgIlCWRXpWGkF7Hxsr5mAaXvITzz/K2YQQQoh2RLpBmyzuZO23v/0tL7zwAj/84Q9bIJxT26ZdpSR9XUco3U3lwES0CWat881fGgcSNa3ATjBwXIrU1QGSvq7D6pnIxpVezhpv43dlU2sVsaniJbJ9I3GbyW15WUIIIYRoQXGPEvB6vYwZM6YlYjnl7dxbBkqR8Vk16ctrUDZE0lxEUkxsv4HlN4ikmETSXCgbMpZXk7m0OjoCxg07v/6my9lnZhO091Fa91nbXZAQQgghWlzcydqUKVP4wx/+0BKxnPJqAyFwR7s2M1YF6PKv/WR9Vo23zMJwNIat8ZRFyPqski5v7CVjdSCaqLnAMCEU/ObHZSgXCoM9gQ/b6nKEEEKIxtPNtHVAcXeDfv7557z//vu8+eabDBw4sMEAg3/+85/NFtypxnSbaI+JsgDbwVXnkLa2lrR1taCiz6/FnvrToO1ooqZcCg24PfVXi3AZCdRaRdFF4eV5QSGEEOKUFHeylpaWxlVXXdUSsZzycrpmstnYgkp0Qa0FlhPNzoz6E/1ph+ioGRcYCQptg3a5yO1qHVajApwD25FXXhBCCCHaBRlg0GRxJ2uzZ89uiTg6hCsuH8Wnr3+JrnPQfjfKcSBkg+188/lToEwwfArlVTh1GiPJICHbpO+wQL36HB3Ga3ZCKUnUhBBCtG/NsQJBR+1DOvFlCESjDenXlYLB+WjLBgXaZUKKF5K9mCkGrhRwpRq40gwMvwJbR1vY0vwUdLfI6fxNy5rWGlsHyUmQwR5CCCHEqSzulrUePXoc8/moeNcH7WgKp17G9G2zCe2qBsNEA8ploA03phGOtbDpiMYOgkr1kDPAZOzlNRx62yNOJS4jkVxJ1oQQQpwMpBu0yeJO1u644456ryORCF9++SXz58/n7rvvbq64TlnD+nXlZzO+x29/8TeC26vQQQ0uA8c0sEwD5dg4IYVGYaS76TLGyyU/qCE9247VYTl1hJ1KuiRdRKK7cxtejRBCCNE4ihNfwaCjdoPGnaxNmTLliPtnzZrFF198ccIBdQTnjOhLwXO38te/LeKLeasJFwdwwjYojTfBJql7mNzTgww416Fbj2T8Pg8AjrYI2xVYupZO/tH0Sfth217IKc6yHb7auoeP122nqKwKR2sykxM5s28XzujTBb/HffxKhBBCiBMU90LuR7NlyxaGDRtGVVVVc1TX7jXXIrjVtUEWfvofSvZ/hGNuIiGzmpT8KpQRROOgMHApP27Tj6k8+MxM8hMvoEfKdzENbzNekTjUsk27ePGD5ezYW4FlO7hNE6UgbDkoBTlpSVxx1iAmjugj06YIIU46bbGQ+y2PvYjXd2ILuYeCtTxzryzk3mSvvvoqGRkZzVVdh2GbO8jqNxd/pAi3mYzX6IZSBrYOEbTKCNsV2DqIozXdk79Nr9TvyfJSLWzxqi386e1PqQ1FyE5NxOep/zWJWDb7q2t5dsFn7K8OcP3Y4ZKwCSHE8cgza00Wd7I2fHj9X0xaa4qLi9m7dy9PP/10swZ3qgtEivhq/++ptUpIcndBqW8G55rKS6I7j0R3Hlpraq09FNd9Tn7SBaRKstZiNuzay7MLPiNs2XTOSjliEuZ2meSmJ1NeU8frn6whLyOFC4b2aoNohRBCdARxJ2tXXHFFvdeGYZCdnc3YsWPp169fc8XVIeyonkcgsqdBonY4pRQJrnxqIjvYWvU6w7LuasUoO5YFyzZQVRuia3bqcVvL0pP87NlfxVufr+PcQafhMmUmHCGEOCppWWuyuJO16dOnt0QcHU7IrqC47mPcZvIxE7WDlFJ4zTT2BVcQiOwh0Z3fClF2LCUVNXz+9U5SE7yN7tbMSPazfW85q7YVMbxnQQtHKIQQJy+lm2E0qCRrjec4Dps2baK0tBTHqb9e5bnnntssgZ3q9tUtJ2RXkOhqfNLlNlIIWDsprVtKD/flLRhdx7RmezE1dSE6Z6Y2+hifx03ECvDVVknWhBBCtIy4k7VPP/2U66+/nu3bt3P4QFKlFLZtH+VIcaiQU8HBhUFrquqIRKL3zeUySEz2YRgNW9uirT2KsF3ZmqF2GIFgGIXCMOIbLKCAmrpQywQlhBCiw4s7WfvpT3/KyJEjeeutt8jLy5NRcE0UCloEqoPs2bObUF0Y7RxIfJXC63OTnpVMelYS7sNGIka7/DtoO3ALc5lGk2dcdLtkfVYhhDgW6QZturiTta+//ppXX32VXr1k9FtT7Sup5N1X15M0LEAoaOByu6KtOQq0owmHLYp37qdifw1de3bClxCdFDfakqnxmB1nbpnW1CktCUMpQhELr7txXw3nQJKdky4jdIUQQrSMuIevjRo1ik2bNrVELB1CdWUdzz0+j7XvmxBJwJ8WwTBVrEVHGQqP14XH76GuNsT2TSWEgxEAIk41biORTv4z2vAKTl1DuufROSuVsuraRh9TWRskOcHL6H7dWjAyIYQ4Behm2jqguFvWbrvtNu666y6Ki4sZPHgwbnf9JXeGDBnSbMGdihbNW8nmtXvIzs8lsL07aQPWU1eZREVFAlbExDQdklOCJCRE8Po9BOvClBZVUNA9i5BTTm7CGJJkPdAW4XaZjBvaixcWLiUYthpMhns423aoDAS5cHgfOqUltVKUQgghOpq4k7Xvfve7ANx8882xfUoptNYywOA4gnVhPn53DR6/G7fHxa6Vfdi0H3buSqUu4EM7CqXA7bbJL6igW7dy0lIqqSyvISUviN+TQY9kGQXaki4c3oflm3fz5eY95KQl4jvK+p+W7bBnfxXdctK55tyhrRylEEKIjiTuZG3r1q0tEUeHsGbZNvaXVJGZk8LecJitlWCV9sCfWkNSagClFY5lEg672LY1k1070+k3cDcDhm8kVJ3LyN63kObt3daXcUrze93cecW5/P6NJazYvBuITn7r97hRCkIRi/KaOiK2Q4/cDO644hxypFVNCCGOT+vodqJ1dEBxJ2vdusmzOU21v7QKrTVVOGypDeAAXtuFU56A5Ti4E8OYnggJXosEoK7Ww9rVBQT2+jm/31l0GjKyrS+hQ0hN9HHv1WP5eN123lvxNV/v2UdZdS0acJsmBZlpjB/ei3MGnkZqoq+twxVCiJOCjAZtumZbyF0cn2NrbKXZVleH5TiokEUgHCHiUTghP4bLi8dn4zcsPC4Dw/DgBLys35vEkLS0tg6/Q/G6XZw/pCdjB5/G5uL9lFfX4WhNst9L74Is3KZM1SGEEKJ1SLLWihKSvNQoqI1EsGtDRBIVkXQ3OvZTMAniptoGV7WNL6hI9Jk4ymZ3VeNHKIrmo5SiV14W5LV1JEIIIToqSdZaUf/h3ahxRedRC2eZOB6F0mBY38zFqgFtQCTZxPJrnIogLsPNnkAtdcEIft+RH3gXQggh2jPlRLcTraMjatQ8a0899RTBYBCAHTt2NFhmSjRORnYydcohnKRwPArDAsOOJmqHTh+jnGgCp12K2jSF4QJbQYW0rgkhhBAdTqOStalTp1JVVQVAjx492Lt3b4sGdaraunYXYQ9oj0JFNBwYGONojdYax9Q4PrATopv2gONXBBIdHMchYsm0KEIIIU5SMilukzWqGzQ/P5/XXnuNb3/722it2bVrV6yl7XBdu3Zt1gBPJZ8tXk3ErzAccFQ0QUOBNkF71Tep86EfRhPCHs32mnLKnDrk7gohhDgZKZq8/HK9OjqiRiVrDz74ILfddhuTJ09GKcUZZzRc7kgmxT2+FWUlhNMdPKUGmAqUjiZqvgPLTR2hL145oMKKukyL321axCNpF9M1Kb3VYxdCCCFE22hUsvbjH/+Y6667ju3btzNkyBDeffddMjMzWzq2U041FuFUB09pdGABpsLx0iBRO/QvB21HG9xcBQa7ayt5YtUifjPqMlxG3Mu6CiGEEG1HJsVtskaPBk1OTmbQoEHMnj2bMWPG4PV6WzKuU5LH7yIScbATFK6Aie3R0UzMOUrTrgYzqLBSNO5OJrn+ZDZWlrKybA8jsmR9UCGEECeR5njmrGPmavFP3TFp0iQAli1bxrp16wAYMGAAp59+evNGdgpKSkiEKghlWihHoSwDFdZgclhzGig72v1pJ2iq+1mk+ZPwu9xE6hze3b1RkjUhhBCig4g7WSstLeV73/seH374IWkHZtWvqKjg/PPP55VXXiE7O7u5YzxldPZmYoY2EkkA8i2MiIlnn4mqo/6Tl0500EEkw6H2NAvtB5+Ozpif6HLzVdme2DOCQgghhDi1xf3g02233UZ1dTVr1qyhrKyMsrIyVq9eTVVVFbfffntLxHjKMMKQWebC8Soc06G2r03V0DB13WwiqQ52gsZKdggW2NQMDlPTN4KdBC7HwEM0WTMNg7BtE3E66MyAQgghTkoH1wY90a0jirtlbf78+bz77rv0798/tm/AgAHMmjWLCRMmNGtwp5qEBA95tSkE7BpqEiPRudX8EOpsE4r1akY/iVpHJ8U1lIEvZGAeGFDgaI3HNHDLAAMhhBCiQ4j7N77jOLjdDZc8crvdONLac0xdumSSGDIZUZdDkvaglcJxRac9+WYDraIT5xqmQVLYhdcx8fmj97wmEmZwep50gQohhDi5yKS4TRZ3snbBBRcwZcoU9uzZE9u3e/du7rzzTsaNG9eswZ1qRp7Rg7T0BNylmjE1+WTXJqJMA8er0J4Dm9dAeUy8uEi3PJhBSEry4vd7CNoRXMpgfEGftr4UIYQQIi6qmbaOKO5k7Y9//CNVVVV0796dnj170rNnT3r06EFVVRV/+MMfWiLGU0Zysp/Ro3tTGwjhDxh8qziH9LAXn3KRUKdIKNMkltokldgk7HegxkYBGRlJaDTFtTX0TMnkdBkJKoQQQnQYcT+z1qVLF5YvX867777L+vXrAejfvz/jx49v9uBORZdeNpxt2/axds1uMjIS6bnKzdrutVhK4w440flxNdg6AkrhT/SilcOOmnJy/SlMHTxWJsQVQghx8pF51pos7mQNQCnFhRdeyIUXXtjc8ZzyEhK83Fo4jtkvLObTeSup2VZK5mkG+8cmEM40UDYYQY1CYbpNAl6br/eU0C8nl2ljJnBaiqwcIYQQ4iQkyVqTNSlZEycmOdnPiIHpLP37XjyJYYwiE9/cKgI93FT382CnGxguhUtBdsBLyuowudUVePpbkNrW0QshhBCiNUmy1gaqw6W89uLLWDpITi8FDjiOQoVs1JogEa/GUjambZOis0h2FbCndB8f/msZk+75TluHL4QQQsRP1gZtMnn4qZVFnDreWvx7SrbUkZzlwlBuDFPhcoPhjrYRu0OQEDLxRExqrb3UWqUkpSawYskGykoq2/oShBBCiKaRaTuaRJK1VlZU+zkbl+3Bibhx+6O339IOATtCRSRMhXVgi4SpczS2VgSsUhLT3NRU1LFu+ba2vQAhhBBCtKomd4OWlpZSWlraYCLcIUOGnHBQpyqtNdtrPiQcMDBQaKDWjhByHDTRQQUH55DRQNCxAXCrCIm6AqUMaquDbRW+EEII0WTNsVyULDfVSMuWLWPSpEmsW7cOfaDvWCkVW1jctu1mD/JUUR3ZRUV4Gx53AlprApZFSNsYKAwUHLIqgQLQ0aQt7Gj2BkvxkIPpksZQIYQQJyMZDtpUcSdrN998M3369OH5558nJydHlj2KQ9ipxtERkrNdWNjYto1pqKPfQxVtaTNQ1IUjaF1HenZKq8YshBBCiLYVdzPNli1bePzxxxk1ahTdu3enW7du9TZxLNGkrPMZGhIc7Jpox6fWGkc7ONqJtVYefpRVYaCywvQZIasXCCGEOPkc7AY90a0pZs2aRffu3fH5fIwaNYrPP//8qGWfffZZzjnnHNLT00lPT2f8+PHHLN8a4k7Wxo0bx8qVK1sillOe38zAVF7C6bUkDA5jVRpEbJuI41AbdFFZ46W82kt5tZuaOhcHHlnDiWiIGKSNttllb27bixBCCCGaoo0Wcv/b3/7G1KlTmT59OsuXL2fo0KFMnDiR0tLSI5b/8MMPue666/jggw/45JNP6NKlCxMmTGD37t3xn7yZxN0N+txzzzFp0iRWr17NoEGDcLvd9d6/7LLLmi24U02iO4ds30C2ln1K6sUuApsNanZ6CSe7sDGjH0IFaKhTUFPnxm9EcFUpMge4yRoLpcE99E2WQRxCCCFEYzzxxBP86Ec/4qabbgLgf//3f3nrrbd44YUXuO+++xqUf/HFF+u9fu6553jttdd47733uPHGG1sl5sPFnax98sknfPTRR7z99tsN3pMBBseX4BpOtbUYI8ug+oJUQv/UuMosXB4LJ1GhDBWd9M8CKjUh28Tq5qbP/2TgJJUR0ZG2vgQhhBCiCZpvgEFVVVW9vV6vF6/X26B0OBxm2bJl3H///bF9hmEwfvx4Pvnkk0adsba2lkgkQkZGxgnEfWLi7ga97bbb+P73v09RURGO49TbJFE7vg9Kg1SGk9iwpYDdZgbVlyURHO3DTjQwqjRqv40qc1ABDVkG4bFeSsdms7TYh+OAR3nQWrNlfxlLd+7is+07WVtSinXYFCpCCCFEu+I00wZ06dKF1NTU2DZjxowjnnLfvn3Ytk1OTk69/Tk5ORQXFzcq7HvvvZf8/HzGjx8fz9U2q7hb1vbv38+dd97Z4MLF8ZUEq/h8/w7KK/pRVAouZaMxqemUjE4G9/4IRsRBJWh0FjjdDExTYWqHnfsgv8zPdsfLm58vZH3pXoKWBWjchknX9DQm9O3F2J49SPX52vpShRBCiBazc+dOUlK+mR3hSK1qzWHmzJm88sorfPjhh/ja8Hdr3MnaVVddxQcffEDPnj1bIp5T2qd7N7GnOsDu3V7MgIOnzMaKKFAaZUKkkyc6t5qjUJZGlWicTjaGaRO2bL5Yk8nX5nYA0v1+MhP8KKUIWRbbysp5+qPPWLhhE3effw7d0tPa9mKFEEKIQzTnpLgpKSn1krWjycrKwjRNSkpK6u0vKSkhNzf3mMf+5je/YebMmbz77rttPuF/3Mlanz59uP/++1myZAmDBw9uMMDg9ttvb7bgTjWlwSr210RgrwG1CixQHg2HTbOmtEbboKsMtK1QeRFCEUUwaNAtz0fyYX9B+N1uClLdWLbDxn37ePS9Rfxy4jhyk5Na8eqEEEKI9sXj8TBixAjee+89rrjiCgAcx+G9995j8uTJRz3u8ccf51e/+hULFixg5MiRrRTt0TVpNGhSUhKLFi1i0aJF9d5TSkmydgy7qquxd0YwazygNdqtMA78mVDvjw0FygUYGh1QRErdRHwmfrcLl3H0xwxdpkHn1FS27C/jpeUrmXremBa9HiGEEKK9mzp1KpMmTWLkyJGceeaZPPnkkwQCgdjo0BtvvJGCgoLYc2+PPfYY06ZN46WXXqJ79+6xZ9uSkpJISmqbRpC4k7WtW7e2RBwdQklNLf51EQLpbrRLYVsGKAfD0PXWBIUDjW0GYGisKhPcClwcd8UIl2GQ6vPx8fYdfL9mKJ3a6IMlhBBC1KN1dDvROuJ07bXXsnfvXqZNm0ZxcTHDhg1j/vz5sWfvd+zYgXFIQ8gzzzxDOBzm6quvrlfP9OnTeeihh04o/KZq8kLuIn7eWpOErTbBJE3Ea4ClsbWBbYNxhKTNcRSOUiib6MADL7iP0bJ2UJrfx86KSv6zZTvfHTKwRa9JCCGEaIy2XMh98uTJR+32/PDDD+u93rZtW9NO0oKatDbosbzwwgtNDuZU5yvVuOo03oowkWR/bA5cAEcbsRULDqUP+Uey24PZiGTNUAoNFFVVN0/gQgghhGgzcSdr5eXl9V5HIhFWr15NRUUFF1xwQbMFdipKxoMCvBURavN8OB4DM3z0+dEOJmqOR6EcTWVCLZvC++hkJpFseI/ZJaqAkG01a/xCCCFE0zXfpLgdTdzJ2ty5cxvscxyHW265RabzOI4huQX8Rxm4QprE4iDVBX5st8KM1P/w6dj/gWMqtKnwVERwTnNRbFVRatWQYnrp5c7CZ7gbnOfg4YePGhVCCCHainKi24nW0RHFvYLBESsxDKZOncrvfve75qjulHVG/x4kJBqYIU1CcZikoiAoRcRnRJMyDvzdoaNJmuUz0C5Fwq4QXq3xmW78yo1LGVTYdawLlxB0Gi4/FbQs3IbB0Py8Vr9GIYQQQjSvZknWADZv3oxlSbfbseR2TmX4sGo8oeifBkm7Q6RvDuCviKANsH0GltfA9keTNE+1ReqGWlK3BQme5gUdHQ3qUgZ+5SbghPk6vBfnsNEx+wO1dE1P4/QCSdaEEEKIk13c3aBTp06t91prTVFREW+99RaTJk1qtsBORXZ4GRMv2MrmlT0pDiuCHgPvPouEIgvLaxBMdWH5FcrWuEI2rmoLX4XGNRCMdDch28JruqLzsCmFFxfVTogqJ0ia6Qegoq4OgMsG9sNtmm15uUIIIcQ35JG1Jos7Wfvyyy/rvTYMg+zsbH77298ed6RoR2eHv6D/kDIuuiSfN+calJW5iCgDjcIMa7xOBHcV0ak6bI271sGV75Ax3sLtM9lWYRO0LDwuE0MpTGUQ0halVjWJeNhfW4flOHx38EAu6tu7rS9XCCGEEM0g7mTtgw8+aIk4OgTtlGMok75n2by22AXV4HFscIHjAtsxcEXADEX/dAhluagcbeLyVZHhd3CZGeysqKIuEkGjD0zRoSmxa/DWeeiUlMR3Bw/g8oH9jzt5rhBCCNGq2mhS3FNB3MlaXV0dWmsSEhIA2L59O3PnzmXAgAFMmDCh2QM8pSg3VQEXf/53FsFsk/T8WsJFBqGi6DqhZhgwIJJvEO7pIpRvYBkGpVUJaE8Zyb4U+udkUhOyKKutJWhZhBwLl2Fw2+lncX7P00j0eNr6KoUQQoiGJFlrsriTtcsvv5yrrrqKn/70p1RUVHDmmWfi8XjYt28fTzzxBLfccktLxHlKMMx8lq7Lomi/m5SMAFopjGSN9zRNJKLYlekFNxg+jfJpXIkRXH4HbWjKCFHlbMOFmyRvKp19aXiVj7JgLYYymNCnNx55Rk0IIYQ45cQ9GnT58uWcc845ALz66qvk5uayfft2/vKXv/DUU081e4CnFNcYFq3sjGPY2GgsDY5XE+qiCQ+0cfIglKkwMm3c2WFUko1G44QVTkRhaHBwqND72O1so1pXUB0J0z0lXRI1IYQQ7Ztupq0Dirtlrba2luTkZADeeecdrrrqKgzD4KyzzmL79u3NHuCpZG9FBrv3p+Dx12B5oK6zQzgN9IE8yx8MgXJjJlhoB7StopMIKoVjK8DBhQ+NC4sIJc4elJHGRd36tOVlCSGEEMfVlmuDnuziblnr1asXr7/+Ojt37mTBggWx59RKS0tJSUmJq67Fixdz6aWXkp+fj1KK119//bjHhEIhHnjgAbp164bX66V79+4N1iOtqKigsLCQvLw8vF4vffr0Yd68ebH3Z8yYwRlnnEFycjKdOnXiiiuuYMOGDXHF3hR1wQh1lgfH71DTWxPKAhww6sCsg0TC+HyR6ALuWkXfqwWzSmNUaOwyB6fOQgEuXIRtC8NXzfDs3BaPXQghhBBtI+6WtWnTpnH99ddz5513Mm7cOEaPHg1EW9mGDx8eV12BQIChQ4dy8803c9VVVzXqmGuuuYaSkhKef/55evXqRVFREY7zzfoT4XCYCy+8kE6dOvHqq69SUFDA9u3bSUtLi5VZtGgRhYWFnHHGGViWxc9//nMmTJjA2rVrSUxMjOsa4hEmSNi0cHpr7MRoknboXwnao3AZDpGgwlWtMIPUa/bVIU2kKoTyGNhJBu5kD8leN2uqdnCef2CLxS2EEEKcOJloraniTtauvvpqzj77bIqKihg6dGhs/7hx47jyyivjquviiy/m4osvbnT5+fPns2jRIrZs2UJGRgYA3bt3r1fmhRdeoKysjI8//hi3233EMvPnz6/3es6cOXTq1Illy5Zx7rnnxnUN8aj0lODkhbASwRWE6HLr0Q+eBiyXwrA03jIFkQPdowe6SBWgDAPtgBO2UWUOWZ5EvMkmi0rXcF6OJGtCCCHaMRkN2mRNWm4qNzeX4cOHYxjfHH7mmWfSr1+/ZgvsSN544w1GjhzJ448/TkFBAX369OFnP/sZdQdm7T9YZvTo0RQWFpKTk8OgQYN49NFHsW37qPVWVlYCxBLAlmK7Qrh6hdFaHViMVsU2R6no/nKFYYFya9QhP53o3yMKwzTw+zwkeDxU76vFCtjsqi3D0R10dVshhBDiFBd3y1pb2rJlC0uWLMHn8zF37lz27dvHrbfeyv79+5k9e3aszPvvv88NN9zAvHnz2LRpE7feeiuRSITp06c3qNNxHO644w7GjBnDoEGDjnruUChEKBSKva6qqoo7/upICJ1iofabWBY4ShEI+QiEvWBqklQdRlijXAqlNAY6+keEAsOAJJcX0/hmsttwyKKyvJbUlARs7WCoZlvqVQghhGhe0gvaZCdVsuY4DkopXnzxRVJTUwF44oknuPrqq3n66afx+/04jkOnTp3485//jGmajBgxgt27d/PrX//6iMlaYWEhq1evZsmSJcc894wZM/jlL395QvG7DC/KBNOn2VeZSG3Eg0ah0BiOjQoCLtCWQmtQChQaZWgURr1EDcDtNglYISIBG5eSqTuEEEK0XwpQJ9iN2VHX5jmpmmLy8vIoKCiIJWoA/fv3R2vNrl27YmX69OmDeci8Y/3796e4uJhwOFyvvsmTJ/Pmm2/ywQcf0Llz52Oe+/7776eysjK27dy5M+74eyR2RzuKfdV+ApYPAFM5uAyNb5/GLFVoP8SeYzvQqgbqyMtHGeC4NSklflleSgghhDhFnVTJ2pgxY9izZw81NTWxfRs3bsQwjFiyNWbMGDZt2lRvhOjGjRvJy8vDc2ApJq01kydPZu7cubz//vv06NHjuOf2er2kpKTU2+KV4UmhrCyRQI0Pl9fG7XMwXAdSs5DC3GiCAm3q2ONsjo62splH6OK0XQ6GrUjd03IjWIUQQohmIZPiNlmbJms1NTWsWLGCFStWALB161ZWrFjBjh07gGhr1o033hgrf/3115OZmclNN93E2rVrWbx4MXfffTc333wzfr8fgFtuuYWysjKmTJnCxo0beeutt3j00UcpLCyM1VNYWMhf//pXXnrpJZKTkykuLqa4uLjeQIWWsLa8hJpKf/R5NMNBGRrTbePy2RgeB3OXgSpXkAT6kDk9tKNwqfo91o7hEPZYJJV4yYhIsiaEEKKdOzga9ES3DqhNk7UvvviC4cOHx+Znmzp1KsOHD2fatGkAFBUVxRI3gKSkJBYuXEhFRQUjR47khhtu4NJLL623zFWXLl1YsGABS5cuZciQIdx+++1MmTKF++67L1bmmWeeobKykrFjx5KXlxfb/va3v7Xo9b7+9Vosy8B0HRjbqRxiXZ6JAAr3p25UlYIU0B5N9H+Kg+2EGk3YbRH0RUitSCBzeQqdOqUe+YRCCCGEOOm16QCDsWPHoo+RJc+ZM6fBvn79+rFw4cJj1jt69Gg+/fTTo75/rHO2pO0VFWgNbmVhawNHw8ExA1YGWOngKjXQH3uwh0TQ2Q46BbTjEFYRtCu6qLsrYpJTnEbu+lRqdYSRo3u1yfUIIYQQjSfDQZvqpBoNerKLWBpTRfCaFiHHha0N7G8eTyN4miZpH6hKhfGJG5WqcbpYOOng8rlINfykViaQtS8Fb9DFnpJyevbOpU///La+NCGEEOLYnAPbidbRAUmy1orcjsZQDmiFB42tHCytcIhutV3AvUPjLQKVpFBVYK5xYTsG+akZ9EjNBKItg6XFVSQn+7jqe6MwDBkJKoQQQpyqJFlrRSl2LYroCE+lwERj4OCgog27bgieBa5PwV0M2gDbZ2C4wHDCOI5DVWUdgZoQqWkJ3Pij8+g/6NhTjgghhBDi5CbJWisqcIXxmDZh28Rr2rGed+PQPngf1I4Bz1bwbFEYlWDiUBsKUhyoIDnFzwUTBnHuuAF07ZHdJtchhBBCxE3WBm0ySdZakc/QdEqqYUdFGqZpc9Q1B9wQ7gOB0xRGkUkPTyWDknpw0dAL6TuggPQMmapDCCGE6CgkWWtFyZ5keiR8RXmNn+qQF68ngmnoBoNbNGDZBpbtIqdLFV267mNQ+lmcNbxPm8QthBBCnDBpWWuyk2oFg5Pd6V1H4HI0AzKKSHMFiYTd1IXcRGwDWytsrQjbJnUhD9oyKfBV0TujFCdiMKrHmW0dvhBCCNF0soJBk0nLWitKS82n5otkkrtUMEiXUBZKoDSYSMD2oC0FCrzKpnNiFWnuECnuIJ6ECIHSTOrqEiGtaefVWrNtzS72F5VjWw4JyX56D++GL9HXrNcnhBBCiOYnyVorWrenlIqSLiR0qsGfXMvAzHLGJAYJOyYh24WhNH53BI9hEwh5KAt6qQ36qdjXlVU7i+mTlxXX+cKhCF8sWMmS179g29pdhOqiC9mbpkFGbhrfumwEo78zguzOGS1xuUIIIcQ3pBu0ySRZa0V14QiW0QldVEmnIWtRHgfH1ri0g8sVTaS0AyFt4Phssry1uIJpbA9lUBsKx3WuQGUtcx76BysWrQMgNSuZzPw0AKyITdW+av719Dt8/O9l3PzwNfQZcVqzXqsQQghRjyRrTSbPrLUil2HgMgNcNehLLkjYS1JYU+e4qDEMAodsQUwSI5qRZiXf6baSMzpvwG0edexoA+FQhDkP/YNl764mvVMKeT2ySUj2oZRCKYXb4yIzP5280zqxb3c5z/78Fbav292CVy6EEEKIppJkrRXlpiUzJG89PdP2Yoa9jDDqGO7U0dUO08m26GRbdLXDDKeOkUYQX8SDwmZ8r2Xkp3sbfZ6l81eyYtFasjqn40s8+nGGaZDbI5uyonL++dTbbbZmqhBCiA7gYMvaiW4dkHSDtqLOeS7Oq9mIZZtYjokC0k2H9GMsdrYvkEBOciVZ+TuAfsc9h9aaJa8vRaHwJRw/wTMMRVpOCptWbGP7ut10HyArIgghhGgB0g3aZNKy1oqKg0vollFBeZ0PfWD8sUbjaI2jHRytY/sPvhexXbjdDoHgwkadY8uqHWxft4vU7ORGx5WQ7KcuEOKLhV/Fd0FCCCGEaHHSstaKItY+fF4bx3BjhwHDARxwB1FmBNA4GGB5UJYX7Rh4PArtMsDZ26hz7N9TTqg2TGZ+eqPjUkrhcpuUbNvXpOsSQgghjq85JkrrmC1rkqy1Ig0oBf5kTbjCpkpbrK7OYOXeflSF/CilSfcFGJazg4HppSTiJjExGaUafw7bcuDAQIJ4KKWIhCPxXZAQQgjRWAfaJ064jg5IkrVW5HZlEY4YRJwAH1YU8NGuHtSEfZhK4zYtQFEZTGBLeSfe89VxQfe1jPEXkYGBVp0adY6EFD+GobAiNi5340eQWhGblMzGd50KIYQQonVIstaKuqWcx9Zdz/Putk4s2tULj2HRKbG6QcuZ4ygqQ37+vXE4dZabq07bwmnJ327UOXoP7056ThqV+6rJzEtr1DGRsIVhKAaP6RvnFQkhhBCNJd2gTSUDDFpRVZWLV7/uyeJdPUl0h0jzBTGiq0zV20xDk+GvxWPavL9tAB+VdAJX70adIyHZz1mXDKOuJojjNK69uKK0iuzOGQw5t39TL00IIYQ4Npm6o8kkWWtF76zbxJrS6JJRyZ4Qx/sLIdVXh2W7+M+OXqwuervR5xlz2RlkF2RQsm0f2jn2OarLA9gRm/HXn43X72n0OYQQQgjROiRZa0Wf71xPneXB54pgawNDadQRmoUVGkM5oMHrirC1Ipu1ez9p9HlyumXxw4f+i9SsFPZsLqG2uq7BhLeRsMXeXWUEKmoZd/0Yzv/et5rjEoUQQogj0820dUDyzForqghVo3U0Qw6EPXhdFm7DxlD1P4FaKyzHJGS5UBrCtovtlQ5aOyjVuPy6/6he3PrED3jtqbfZumonZcWVuNwmylDYERulFFkFGYy7fgwXfO9bcY8eFUIIIeKineh2onV0QJKstaJoShZNirQ2CEbcBJULt3kwYTuQqNkGjjYAhTrwTFvYbvzIzoNOG9yVn/35x2xZtYMv3vmKku37sCIWKZnJDD67H0PP69+oVQ6EEEII0XYkWWtFKZ5UKkKVKKUPdEtq0BC2TODQlq3D2nqVJtGd2OhWtUMppeg5pBs9h3Q7weiFEEKIEyDLTTWZPLPWikYU9CBiudA4fJOMHRwDeqiD+zSWrTCVpk/W0NYMVQghhGh+8rxak0iy1oomjRqB4bipCfsOdHse7zkxRdB2k+kPMLHPJa0RohBCCCHaGUnWWlF+agrd0jwEwtGlpZQ69oOSthN9xq1HVoBqJ9BKUQohhBAtQOZZazJJ1lpRWbiYs4fvxouLkppUNOA2rQPTd3zDUA5aQyDip0tKHT26KzZWf9E2QQshhBDNQZK1JpNkrRVVRfbj8dbx/bFhkk0ve6vT2RtIwXIMlLIxlI2lFRXBBKpDCXRJjXDxyBS8Lg9l4T1tHb4QQggh2oCMBm1Fto6g0WQkKv57QphVW02Wb/VRXuuOdouiMQ1NQarN4B4mvfP8mAaEIgrbibR1+EIIIUTTyWjQJpNkrRV5DT+mMrG1hdt0c3ovm+E9barqFNV1YBiKZD8k++r/WGwdwe9KbqOohRBCiGagaYZkrVkiOelIstaKOvm6k+TKoNauIsXIBEApSE3QpCYc+RhH26A13RIHtWKkQgghhGgv5Jm1VuQxvPRPOYuIE0Q3csmMgFVFoiuNnknDWjY4IYQQoiXJAIMmk2StlfVNOZMUdxYV4ZIGi6sfLmTXEnGCDEj5Fn4zqZUiFEIIIVqAJGtNJslaK0txZzIu5wckuFIpCxcRduoaJG2OtqmJlBOwKumTcgYjMy9qo2iFEEII0dbkmbU20DmhDxfn/5iP9r5GaWgn1U4ZLuVBKYWlLbR2SHSlMiTtfM7IvAhTyY9JCCHEyU1rfdwepcbU0RFJFtBGcn3duarzVIqCm9lY/QXloWIsHcbvSqFbwgB6Jg0nQUaACiGEOFXI1B1NJslaG1JKke/vRb6/V1uHIoQQQoh2SpI1IYQQQrQ8aVlrMknWhBBCCNHyJFlrMhkNKoQQQgjRjknLmhBCCCFanHY02jnB0aAnePzJSpI1IYQQQrQCzYkv7tkxkzXpBhVCCCGEaMekZU0IIYQQLc/R0e1E6+iAJFkTQgghRCuQbtCmkmStiQ4ueVFVVdXGkQghhBDxOfi7qzWXbwpZoXZRx8lIkrUmqq6uBqBLly5tHIkQQgjRNNXV1aSmprboOTweD7m5uTz17sxmqS83NxePx9MsdZ0slO6oq6KeIMdx2LNnD8nJySilTri+qqoqunTpws6dO0lJSWmGCEU85P63Hbn3bUvuf9tqq/uvtaa6upr8/HwMo+XHGgaDQcLhcLPU5fF48Pl8zVLXyUJa1prIMAw6d+7c7PWmpKTIfzDbkNz/tiP3vm3J/W9bbXH/W7pF7VA+n6/DJVjNSabuEEIIIYRoxyRZE0IIIYRoxyRZaye8Xi/Tp0/H6/W2dSgdktz/tiP3vm3J/W9bcv9FY8gAAyGEEEKIdkxa1oQQQggh2jFJ1oQQQggh2jFJ1oQQQggh2jFJ1tqBWbNm0b17d3w+H6NGjeLzzz9v65DancWLF3PppZeSn5+PUorXX3+93vtaa6ZNm0ZeXh5+v5/x48fz9ddf1ytTVlbGDTfcQEpKCmlpafz3f/83NTU19cp89dVXnHPOOfh8Prp06cLjjz/eIJZ//OMf9OvXD5/Px+DBg5k3b17csZxMZsyYwRlnnEFycjKdOnXiiiuuYMOGDfXKBINBCgsLyczMJCkpie9+97uUlJTUK7Njxw4uueQSEhIS6NSpE3fffTeWZdUr8+GHH3L66afj9Xrp1asXc+bMaRDP8b4vjYnlZPLMM88wZMiQ2Dxco0eP5u233469L/e+9cycOROlFHfccUdsn9x/0Sq0aFOvvPKK9ng8+oUXXtBr1qzRP/rRj3RaWpouKSlp69DalXnz5ukHHnhA//Of/9SAnjt3br33Z86cqVNTU/Xrr7+uV65cqS+77DLdo0cPXVdXFytz0UUX6aFDh+pPP/1U/+c//9G9evXS1113Xez9yspKnZOTo2+44Qa9evVq/fLLL2u/36//9Kc/xcp89NFH2jRN/fjjj+u1a9fqBx98ULvdbr1q1aq4YjmZTJw4Uc+ePVuvXr1ar1ixQn/729/WXbt21TU1NbEyP/3pT3WXLl30e++9p7/44gt91lln6W9961ux9y3L0oMGDdLjx4/XX375pZ43b57OysrS999/f6zMli1bdEJCgp46dapeu3at/sMf/qBN09Tz58+PlWnM9+V4sZxs3njjDf3WW2/pjRs36g0bNuif//zn2u1269WrV2ut5d63ls8//1x3795dDxkyRE+ZMiW2X+6/aA2SrLWxM888UxcWFsZe27at8/Pz9YwZM9owqvbt8GTNcRydm5urf/3rX8f2VVRUaK/Xq19++WWttdZr167VgF66dGmszNtvv62VUnr37t1aa62ffvppnZ6erkOhUKzMvffeq/v27Rt7fc011+hLLrmkXjyjRo3SP/nJTxody8mutLRUA3rRokVa6+j1ud1u/Y9//CNWZt26dRrQn3zyidY6mmwbhqGLi4tjZZ555hmdkpISu9/33HOPHjhwYL1zXXvttXrixImx18f7vjQmllNBenq6fu655+Tet5Lq6mrdu3dvvXDhQn3eeefFkjW5/6K1SDdoGwqHwyxbtozx48fH9hmGwfjx4/nkk0/aMLKTy9atWykuLq53H1NTUxk1alTsPn7yySekpaUxcuTIWJnx48djGAafffZZrMy5555bb4HgiRMnsmHDBsrLy2NlDj3PwTIHz9OYWE52lZWVAGRkZACwbNkyIpFIvWvu168fXbt2rXf/Bw8eTE5OTqzMxIkTqaqqYs2aNbEyx7q3jfm+NCaWk5lt27zyyisEAgFGjx4t976VFBYWcskllzS4R3L/RWuRtUHb0L59+7Btu96XGCAnJ4f169e3UVQnn+LiYoAj3seD7xUXF9OpU6d677tcLjIyMuqV6dGjR4M6Dr6Xnp5OcXHxcc9zvFhOZo7jcMcddzBmzBgGDRoERK/Z4/GQlpZWr+zh9+VI9+Tge8cqU1VVRV1dHeXl5cf9vjQmlpPRqlWrGD16NMFgkKSkJObOncuAAQNYsWKF3PsW9sorr7B8+XKWLl3a4D357IvWIsmaEKLRCgsLWb16NUuWLGnrUDqUvn37smLFCiorK3n11VeZNGkSixYtauuwTnk7d+5kypQpLFy4UBYhF21KukHbUFZWFqZpNhitU1JSQm5ubhtFdfI5eK+OdR9zc3MpLS2t975lWZSVldUrc6Q6Dj3H0coc+v7xYjlZTZ48mTfffJMPPviAzp07x/bn5uYSDoepqKioV/7w+9LUe5uSkoLf72/U96UxsZyMPB4PvXr1YsSIEcyYMYOhQ4fy+9//Xu59C1u2bBmlpaWcfvrpuFwuXC4XixYt4qmnnsLlcpGTkyP3X7QKSdbakMfjYcSIEbz33nuxfY7j8N577zF69Og2jOzk0qNHD3Jzc+vdx6qqKj777LPYfRw9ejQVFRUsW7YsVub999/HcRxGjRoVK7N48WIikUiszMKFC+nbty/p6emxMoee52CZg+dpTCwnG601kydPZu7cubz//vsNuopHjBiB2+2ud80bNmxgx44d9e7/qlWr6iXMCxcuJCUlhQEDBsTKHOveNub70phYTgWO4xAKheTet7Bx48axatUqVqxYEdtGjhzJDTfcEPu33H/RKtp6hENH98orr2iv16vnzJmj165dq3/84x/rtLS0eiOHRHQ01pdffqm//PJLDegnnnhCf/nll3r79u1a6+h0GWlpafpf//qX/uqrr/Tll19+xKk7hg8frj/77DO9ZMkS3bt373pTd1RUVOicnBz9gx/8QK9evVq/8sorOiEhocHUHS6XS//mN7/R69at09OnTz/i1B3Hi+Vkcsstt+jU1FT94Ycf6qKiothWW1sbK/PTn/5Ud+3aVb///vv6iy++0KNHj9ajR4+OvX9w+oIJEyboFStW6Pnz5+vs7OwjTl9w991363Xr1ulZs2YdcfqC431fjhfLyea+++7TixYt0lu3btVfffWVvu+++7RSSr/zzjtaa7n3re3Q0aBay/0XrUOStXbgD3/4g+7atav2eDz6zDPP1J9++mlbh9TufPDBBxposE2aNElrHZ0y4xe/+IXOycnRXq9Xjxs3Tm/YsKFeHfv379fXXXedTkpK0ikpKfqmm27S1dXV9cqsXLlSn3322drr9eqCggI9c+bMBrH8/e9/13369NEej0cPHDhQv/XWW/Xeb0wsJ5Mj3XdAz549O1amrq5O33rrrTo9PV0nJCToK6+8UhcVFdWrZ9u2bfriiy/Wfr9fZ2Vl6bvuuktHIpF6ZT744AM9bNgw7fF49GmnnVbvHAcd7/vSmFhOJjfffLPu1q2b9ng8Ojs7W48bNy6WqGkt9761HZ6syf0XrUFprXXbtOkJIYQQQojjkWfWhBBCCCHaMUnWhBBCCCHaMUnWhBBCCCHaMUnWhBBCCCHaMUnWhBBCCCHaMUnWhBBCCCHaMUnWhBBCCCHaMUnWhBBCCCHaMUnWhGgj27ZtQynFihUr2jqUmPXr13PWWWfh8/kYNmxYW4dzTN27d+fJJ59s9nrnzJlDWlpas9d7LO3xs3AsY8eO5Y477mjrMIToMCRZEx3WD3/4Q5RSzJw5s97+119/HaVUG0XVtqZPn05iYiIbNmxosLB0e7N06VJ+/OMfn1AdLZXwCSFEc5JkTXRoPp+Pxx57jPLy8rYOpdmEw+EmH7t582bOPvtsunXrRmZmZjNG1fyys7NJSEho6zCEEKLFSbImOrTx48eTm5vLjBkzjlrmoYceatAl+OSTT9K9e/fY6x/+8IdcccUVPProo+Tk5JCWlsbDDz+MZVncfffdZGRk0LlzZ2bPnt2g/vXr1/Otb30Ln8/HoEGDWLRoUb33V69ezcUXX0xSUhI5OTn84Ac/YN++fbH3x44dy+TJk7njjjvIyspi4sSJR7wOx3F4+OGH6dy5M16vl2HDhjF//vzY+0opli1bxsMPP4xSioceeuiI9YwdO5bbbruNO+64g/T0dHJycnj22WcJBALcdNNNJCcn06tXL95+++3YMeXl5dxwww1kZ2fj9/vp3bt3vXvx8ccfM2zYMHw+HyNHjoy1bh6rW/DwVjGlFM899xxXXnklCQkJ9O7dmzfeeOOox48dO5bt27dz5513opRq0Jq6YMEC+vfvT1JSEhdddBFFRUX13n/uuefo378/Pp+Pfv368fTTTx/1XBC9/48//ji9evXC6/XStWtXfvWrX9Urs2XLFs4//3wSEhIYOnQon3zySey9/fv3c91111FQUEBCQgKDBw/m5ZdfbnBNt99+O/fccw8ZGRnk5uY2+Dk25j4d7zMnhGhlbb2SvBBtZdKkSfryyy/X//znP7XP59M7d+7UWms9d+5cfehXY/r06Xro0KH1jv3d736nu3XrVq+u5ORkXVhYqNevX6+ff/55DeiJEyfqX/3qV3rjxo36kUce0W63O3aerVu3akB37txZv/rqq3rt2rX6f/7nf3RycrLet2+f1lrr8vJynZ2dre+//369bt06vXz5cn3hhRfq888/P3bu8847TyclJem7775br1+/Xq9fv/6I1/vEE0/olJQU/fLLL+v169fre+65R7vdbr1x40attdZFRUV64MCB+q677tJFRUW6urr6iPWcd955Ojk5WT/yyCOx6zJNU1988cX6z3/+s964caO+5ZZbdGZmpg4EAlprrQsLC/WwYcP00qVL9datW/XChQv1G2+8obXWurKyUmdkZOjvf//7es2aNXrevHm6T58+GtBffvnlUX9+3bp107/73e9irw/ey5deekl//fXX+vbbb9dJSUl6//79Rzx+//79unPnzvrhhx/WRUVFuqioSGut9ezZs7Xb7dbjx4/XS5cu1cuWLdP9+/fX119/fezYv/71rzovL0+/9tpresuWLfq1117TGRkZes6cOUeN95577tHp6el6zpw5etOmTfo///mPfvbZZ7XW33wW+vXrp9988029YcMGffXVV+tu3brpSCSitdZ6165d+te//rX+8ssv9ebNm/VTTz2lTdPUn332Wb2fTUpKin7ooYf0xo0b9f/9v/9XK6X0O++80+j71NjP3JQpU456rUKI5iXJmuiwDiZrWmt91lln6Ztvvllr3fRkrVu3btq27di+vn376nPOOSf22rIsnZiYqF9++WWt9Te/oGfOnBkrE4lEdOfOnfVjjz2mtdb6kUce0RMmTKh37p07d2pAb9iwQWsd/cU5fPjw415vfn6+/tWvflVv3xlnnKFvvfXW2OuhQ4fq6dOnH7Oe8847T5999tkNrusHP/hBbF9RUZEG9CeffKK11vrSSy/VN9100xHre+aZZ3RmZqauq6uL7Xv22WeblKw9+OCDsdc1NTUa0G+//Xaj69A6mqwBetOmTbF9s2bN0jk5ObHXPXv21C+99FK94x555BE9evToI56nqqpKe73eWHJ2uIOfheeeey62b82aNRrQ69atO2r8l1xyib7rrrtirw//2Wgd/Rnfe++9sdfHu0+N/cxJsiZE63G1bjueEO3TY489xgUXXMDPfvazJtcxcOBADOObJwtycnIYNGhQ7LVpmmRmZlJaWlrvuNGjR8f+7XK5GDlyJOvWrQNg5cqVfPDBByQlJTU43+bNm+nTpw8AI0aMOGZsVVVV7NmzhzFjxtTbP2bMGFauXNnIK/zGkCFDYv8+eF2DBw+O7cvJyQGIXestt9zCd7/7XZYvX86ECRO44oor+Na3vgXAhg0bGDJkCD6fL3b8mWeeGXdMh8eVmJhISkpKg/vdGAkJCfTs2TP2Oi8vL1ZPIBBg8+bN/Pd//zc/+tGPYmUsyyI1NfWI9a1bt45QKMS4ceMaHX9eXh4QvYf9+vXDtm0effRR/v73v7N7927C4TChUKjBc3uH1nF47Ecqc/h9auxnTgjReiRZEwI499xzmThxIvfffz8//OEP671nGAZa63r7IpFIgzrcbne910qpI+5zHKfRcdXU1HDppZfy2GOPNXjv4C9ziP7CbU3Hu9aDz38dvNaLL76Y7du3M2/ePBYuXMi4ceMoLCzkN7/5TYvHFc/9PlY9Bz8DNTU1ADz77LOMGjWqXjnTNI9Yn9/vj/u8h9/DX//61/z+97/nySefZPDgwSQmJnLHHXc0GFDSmHtwrDKN/cwJIVqPDDAQ4oCZM2fy73//u95D3RAddVhcXFwvYWvO+bA+/fTT2L8ty2LZsmX0798fgNNPP501a9bQvXt3evXqVW+LJ0FLSUkhPz+fjz76qN7+jz76iAEDBjTPhRxHdnY2kyZN4q9//StPPvkkf/7znwHo27cvq1atIhQKxcouXbq0VWLyeDzYth3XMTk5OeTn57Nly5YGP5MePXoc8ZjevXvj9/tPaDqUjz76iMsvv5zvf//7DB06lNNOO42NGzc2ub6jaa7PnBCi+UiyJsQBgwcP5oYbbuCpp56qt3/s2LHs3buXxx9/nM2bNzNr1qx6Ix1P1KxZs5g7dy7r16+nsLCQ8vJybr75ZgAKCwspKyvjuuuuY+nSpWzevJkFCxZw0003xZ1k3H333Tz22GP87W9/Y8OGDdx3332sWLGCKVOmNNu1HM20adP417/+xaZNm1izZg1vvvlmLCG9/vrrcRyHH//4x6xbt44FCxbEWtxaer677t27s3jxYnbv3h3XaMdf/vKXzJgxg6eeeoqNGzeyatUqZs+ezRNPPHHE8j6fj3vvvZd77rmHv/zlL2zevJlPP/2U559/vtHn7N27NwsXLuTjjz9m3bp1/OQnP6GkpKTRxzdWc37mhBDNQ5I1IQ7x8MMPN+gy6t+/P08//TSzZs1i6NChfP755yf0bNvhZs6cycyZMxk6dChLlizhjTfeICsrCyDWGmbbNhMmTGDw4MHccccdpKWl1Xs+rjFuv/12pk6dyl133cXgwYOZP38+b7zxBr179262azkaj8fD/fffz5AhQzj33HMxTZNXXnkFiLb6/fvf/2bFihUMGzaMBx54gGnTpgHUe46tJTz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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x=[]\n", + "y=[]\n", + "all_colors=np.random.rand(1000)\n", + "ci=0\n", + "colors=[]\n", + "for channel in channels:\n", + " current_channel_message=json.loads(SDL.get_channel_messages(channel['name']))\n", + " current_channel_total_replies_msg=0\n", + " for current_message in current_channel_message:\n", + " # datetime.datetime.fromtimestamp\n", + " if('replies' in current_channel_message[current_message][0]):\n", + " \n", + " msg_ts=float( current_channel_message[current_message][0]['ts'])\n", + " first_replay_ts=float(current_channel_message[current_message][0]['replies'][0]['ts'])\n", + " tsd=first_replay_ts-msg_ts\n", + " x.append(tsd)\n", + " y.append(msg_ts)\n", + " colors.append(all_colors[ci])\n", + " ci+=1\n", + " \n", + " \n", "\n", - "print(n.get_channels())\n", - "\n" + "plt.scatter(x,y,c=colors,s=100,alpha=0.7)\n", + "plt.title(\"fraction of messages are replied\")\n", + "plt.xlabel(\"Number of msg in the channel\")\n", + "plt.ylabel(\"sum of number of replies and reactions\")\n", + "plt.colorbar(label='Color Intensity')\n", + "plt.tight_layout()\n", + "plt.show()\n" ] } ],