From 7a6faf55386617293cdf9c71e592506deecc7624 Mon Sep 17 00:00:00 2001 From: Hanne Heggdal Date: Fri, 21 Mar 2025 12:21:55 +0100 Subject: [PATCH 01/18] minor changes --- src/my_package/fetch_data.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/my_package/fetch_data.py b/src/my_package/fetch_data.py index edbc9bc..a3097ef 100644 --- a/src/my_package/fetch_data.py +++ b/src/my_package/fetch_data.py @@ -26,7 +26,7 @@ def fetch_data(start_date, end_date, city_name): # f-string url, to add the "custom" variables to the API-request - url = f"https://history.openweathermap.org/data/2.5/history/city?q={city_name},{"NO"}&units=metric&type=hour&start={start_date}&end={end_date}&appid={API_KEY}" + url = f"https://history.openweathermap.org/data/2.5/history/city?q={city_name},NO&units=metric&type=hour&start={start_date}&end={end_date}&appid={API_KEY}" # Saves the API-request for the url response = requests.get(url) From 7306c5a9c9d6de22e8ffa07d4ba375f88f761f1b Mon Sep 17 00:00:00 2001 From: Hanne Heggdal Date: Wed, 26 Mar 2025 14:47:45 +0100 Subject: [PATCH 02/18] test, one day, test lower/upper case and unix timestamp --- tests/unit/test_letter_one_day.py | 26 +++++++++++++++++++++ tests/unit/test_one_day.py | 39 +++++++++++++++++++++++++++++++ 2 files changed, 65 insertions(+) create mode 100644 tests/unit/test_letter_one_day.py create mode 100644 tests/unit/test_one_day.py diff --git a/tests/unit/test_letter_one_day.py b/tests/unit/test_letter_one_day.py new file mode 100644 index 0000000..53ad395 --- /dev/null +++ b/tests/unit/test_letter_one_day.py @@ -0,0 +1,26 @@ +import unittest +import sys +import os + +# Add the src folder to the Python path +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../../src'))) + +from my_package.fetch_current_data import fetch_current_data + +class TestCityNameCase(unittest.TestCase): + + def test_city_name_case_insensitive(self): + # Test city with big and small letter + city_name_upper = "Oslo" + city_name_lower = "oslo" + + # Test if they return the same, the underscore is for the folder that we dont use here + data_upper, _ = fetch_current_data(city_name_upper) + data_lower, _ = fetch_current_data(city_name_lower) + + # use temperature as an example to see if data is identical + self.assertEqual(data_upper["main"]["temp"], data_lower["main"]["temp"]) + +if __name__ == "__main__": + unittest.main() + diff --git a/tests/unit/test_one_day.py b/tests/unit/test_one_day.py new file mode 100644 index 0000000..ad6e05c --- /dev/null +++ b/tests/unit/test_one_day.py @@ -0,0 +1,39 @@ +import unittest +import sys +import os +from datetime import datetime +from src.my_package.date_to_unix import from_unix_timestamp + +# This will make the absolute path from the root of the project, and will therefor work every time +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../../src"))) + + +class TestGetUnixTimestamp(unittest.TestCase): + + def test_get_unix_timestamp(self): + # Example user input for start and end date + start_date_input = "2000, 03, 05, 11, 00" + end_date_input = "2000, 03, 05, 13, 00" + + # Convert input string to datetime object + start_date = datetime.strptime(start_date_input, "%Y, %m, %d, %H, %M") + end_date = datetime.strptime(end_date_input, "%Y, %m, %d, %H, %M") + + # Get the Unix timestamp by calling .timestamp() + expected_unix_start = int(start_date.timestamp()) + expected_unix_end = int(end_date.timestamp()) + + # Call the function directly with test data + function_unix_start, function_unix_end = from_unix_timestamp(expected_unix_start, expected_unix_end) + + # Assert that the returned timestamps are correct + self.assertEqual(function_unix_start, start_date) + self.assertEqual(function_unix_end, end_date) + + +if __name__ == "__main__": + unittest.main() + + +#this test is to test if the code date matches its timestamp + From 0b6144e83e21353862dee53b0a49593fd1b7c7c0 Mon Sep 17 00:00:00 2001 From: Hanne Heggdal Date: Wed, 26 Mar 2025 14:50:27 +0100 Subject: [PATCH 03/18] notebook add, gets data for a chosen date and place --- notebooks/get_day_data_notebook.ipynb | 845 ++++++++++++++++++++++++++ 1 file changed, 845 insertions(+) create mode 100644 notebooks/get_day_data_notebook.ipynb diff --git a/notebooks/get_day_data_notebook.ipynb b/notebooks/get_day_data_notebook.ipynb new file mode 100644 index 0000000..14af281 --- /dev/null +++ b/notebooks/get_day_data_notebook.ipynb @@ -0,0 +1,845 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Velg hvilken dag du vil sjekke været for\n", + "\n", + "For å kunne hente data og gjøre en analyse trenger programmet å vite hvilken dag du vil hente ut for, også skrives alle timene fra den dagen ut.\n", + "\n", + "Dataen skrives inn slik: (yyyy, mm, dd)\n", + "Her følger et eksempel: \n", + "|Hva|Hvordan|Eksempel|\n", + "|:---|:---:|:---:|\n", + "|år|yyyy|2025|\n", + "|måned|mm|03| \n", + "|dato|dd|01| \n", + "\n", + "Denne dataen skrives da inn på følgende hvis: (2025, 03, 01)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Selected date: 2025-02-21\n", + "Unix Timestamp: 1740092400 -> 2025-02-21 00:00:00\n", + "Unix Timestamp: 1740096000 -> 2025-02-21 01:00:00\n", + "Unix Timestamp: 1740099600 -> 2025-02-21 02:00:00\n", + "Unix Timestamp: 1740103200 -> 2025-02-21 03:00:00\n", + "Unix Timestamp: 1740106800 -> 2025-02-21 04:00:00\n", + "Unix Timestamp: 1740110400 -> 2025-02-21 05:00:00\n", + "Unix Timestamp: 1740114000 -> 2025-02-21 06:00:00\n", + "Unix Timestamp: 1740117600 -> 2025-02-21 07:00:00\n", + "Unix Timestamp: 1740121200 -> 2025-02-21 08:00:00\n", + "Unix Timestamp: 1740124800 -> 2025-02-21 09:00:00\n", + "Unix Timestamp: 1740128400 -> 2025-02-21 10:00:00\n", + "Unix Timestamp: 1740132000 -> 2025-02-21 11:00:00\n", + "Unix Timestamp: 1740135600 -> 2025-02-21 12:00:00\n", + "Unix Timestamp: 1740139200 -> 2025-02-21 13:00:00\n", + "Unix Timestamp: 1740142800 -> 2025-02-21 14:00:00\n", + "Unix Timestamp: 1740146400 -> 2025-02-21 15:00:00\n", + "Unix Timestamp: 1740150000 -> 2025-02-21 16:00:00\n", + "Unix Timestamp: 1740153600 -> 2025-02-21 17:00:00\n", + "Unix Timestamp: 1740157200 -> 2025-02-21 18:00:00\n", + "Unix Timestamp: 1740160800 -> 2025-02-21 19:00:00\n", + "Unix Timestamp: 1740164400 -> 2025-02-21 20:00:00\n", + "Unix Timestamp: 1740168000 -> 2025-02-21 21:00:00\n", + "Unix Timestamp: 1740171600 -> 2025-02-21 22:00:00\n", + "Unix Timestamp: 1740175200 -> 2025-02-21 23:00:00\n" + ] + } + ], + "source": [ + "import datetime\n", + "import time\n", + "\n", + "#makes a function so the star and end date is the same date, with all hours of that date\n", + "def get_unix_timestamps_for_day():\n", + " date_input = input(\"Choose a date (yyyy, mm, dd): \")\n", + " date_components = date_input.split(\",\")\n", + " year = int(date_components[0])\n", + " month = int(date_components[1])\n", + " day = int(date_components[2])\n", + "\n", + "#goes trough all hours of the day, use %Y-%m-%d etc. from pythons strftime to convert datetime into a readable string \n", + " timestamps = []\n", + " for hour in range(24):\n", + " dt = datetime.datetime(year, month, day, hour, 0)\n", + " unix_timestamp = int(time.mktime(dt.timetuple()))\n", + " timestamps.append((unix_timestamp, dt.strftime('%Y-%m-%d %H:%M:%S'))) \n", + "\n", + "#prints the date chosen\n", + " print(f\"\\nSelected date: {year}-{month:02d}-{day:02d}\")\n", + "\n", + "#prints the timestamp and the date an hour of the day after\n", + " for ts, readable in timestamps:\n", + " print(f\"Unix Timestamp: {ts} -> {readable}\")\n", + " \n", + " return [ts[0] for ts in timestamps]\n", + "\n", + "timestamps = get_unix_timestamps_for_day()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Velg en by i Norge og få data\n", + "\n", + "Skriv inn en by du ønsker data fra, foreløpig er det begrenset til Norge\n", + "\n", + "Programmet vil deretter hente data å lagre det i en json fil" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data fetch: ok\n" + ] + } + ], + "source": [ + "import sys\n", + "import os\n", + "\n", + "# Gets the absolute path to the src folder\n", + "sys.path.append(os.path.abspath(\"../src\"))\n", + "\n", + "# Now we can import the fucntion from the module\n", + "from my_package.fetch_data import fetch_data\n", + "\n", + "#user choose a city they want the weather data from\n", + "city_name = input(\"Enter city name: \")\n", + "start_date, end_date = timestamps[0], timestamps[-1]\n", + "weather_data,folder = fetch_data(start_date, end_date, city_name)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Lagre data i en json-fil\n", + "\n", + "Skriv inn navn for til filen du vil lagre med dataen.\n", + "\n", + "Eks. test\n", + "Da vil filen lagres som data_**test**.json, i mappen \"../data/output_stedsnavn/data_{filnavn}.json\"" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has been written to /Users/hanne/Documents/anvendt prosjekt/anvendt_mappe/data/../data/output_stedsnavn/data_test_oslo1.json\n" + ] + } + ], + "source": [ + "# Gets the absolute path to the src folder\n", + "sys.path.append(os.path.abspath(\"../src\"))\n", + "\n", + "from my_package.write_data import write_data\n", + "\n", + "filename = input(\"Write filename: \")\n", + "\n", + "write_data(weather_data, folder, filename)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Lese fra fil\n", + "\n", + "Henter opp data lagret i filen, lagd over, og skriver ut lesbart ved hjelp av pandas" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " message cod city_id calctime cnt \\\n", + "0 Count: 24 200 3143244 0.010569 24 \n", + "1 Count: 24 200 3143244 0.010569 24 \n", + "2 Count: 24 200 3143244 0.010569 24 \n", + "3 Count: 24 200 3143244 0.010569 24 \n", + "4 Count: 24 200 3143244 0.010569 24 \n", + "5 Count: 24 200 3143244 0.010569 24 \n", + "6 Count: 24 200 3143244 0.010569 24 \n", + "7 Count: 24 200 3143244 0.010569 24 \n", + "8 Count: 24 200 3143244 0.010569 24 \n", + "9 Count: 24 200 3143244 0.010569 24 \n", + "10 Count: 24 200 3143244 0.010569 24 \n", + "11 Count: 24 200 3143244 0.010569 24 \n", + "12 Count: 24 200 3143244 0.010569 24 \n", + "13 Count: 24 200 3143244 0.010569 24 \n", + "14 Count: 24 200 3143244 0.010569 24 \n", + "15 Count: 24 200 3143244 0.010569 24 \n", + "16 Count: 24 200 3143244 0.010569 24 \n", + "17 Count: 24 200 3143244 0.010569 24 \n", + "18 Count: 24 200 3143244 0.010569 24 \n", + "19 Count: 24 200 3143244 0.010569 24 \n", + "20 Count: 24 200 3143244 0.010569 24 \n", + "21 Count: 24 200 3143244 0.010569 24 \n", + "22 Count: 24 200 3143244 0.010569 24 \n", + "23 Count: 24 200 3143244 0.010569 24 \n", + "\n", + " list \n", + "0 {'dt': 1740092400, 'main': {'temp': -0.97, 'fe... \n", + "1 {'dt': 1740096000, 'main': {'temp': -0.97, 'fe... \n", + "2 {'dt': 1740099600, 'main': {'temp': -0.97, 'fe... \n", + "3 {'dt': 1740103200, 'main': {'temp': -0.97, 'fe... \n", + "4 {'dt': 1740106800, 'main': {'temp': -0.97, 'fe... \n", + "5 {'dt': 1740110400, 'main': {'temp': -0.42, 'fe... \n", + "6 {'dt': 1740114000, 'main': {'temp': 0.69000000... \n", + "7 {'dt': 1740117600, 'main': {'temp': 1.25, 'fee... \n", + "8 {'dt': 1740121200, 'main': {'temp': 1.81, 'fee... \n", + "9 {'dt': 1740124800, 'main': {'temp': 1.81, 'fee... \n", + "10 {'dt': 1740128400, 'main': {'temp': 3.31, 'fee... \n", + "11 {'dt': 1740132000, 'main': {'temp': 3.31, 'fee... \n", + "12 {'dt': 1740135600, 'main': {'temp': 3.31, 'fee... \n", + "13 {'dt': 1740139200, 'main': {'temp': 3.31, 'fee... \n", + "14 {'dt': 1740142800, 'main': {'temp': 3.87, 'fee... \n", + "15 {'dt': 1740146400, 'main': {'temp': 3.87, 'fee... \n", + "16 {'dt': 1740150000, 'main': {'temp': 3.31, 'fee... \n", + "17 {'dt': 1740153600, 'main': {'temp': 3.31, 'fee... \n", + "18 {'dt': 1740157200, 'main': {'temp': 3.31, 'fee... \n", + "19 {'dt': 1740160800, 'main': {'temp': 3.31, 'fee... \n", + "20 {'dt': 1740164400, 'main': {'temp': 2.92, 'fee... \n", + "21 {'dt': 1740168000, 'main': {'temp': 2.92, 'fee... \n", + "22 {'dt': 1740171600, 'main': {'temp': 2.92, 'fee... \n", + "23 {'dt': 1740175200, 'main': {'temp': 2.92, 'fee... \n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "weather_data = pd.read_json(f'../data/output_stedsnavn/data_{filename}.json')\n", + "\n", + "print(weather_data)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + 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Delete duplicates based on the dt row, all the other values can appear more than once, but the date should only appear once\n", + " df = df.drop_duplicates(subset=['dt'])\n", + "\n", + " # The weather column dosnt have any releated information, therefor we delete it\n", + " df = df.drop(columns=\"weather\")\n", + "\n", + " # Convert 'dt' column from Unix timestamp to datetime and set it as the index\n", + " df['dt'] = pd.to_datetime(df['dt'], unit='s')\n", + " df.set_index('dt', inplace=True)\n", + " \n", + "\n", + " # Ensure the DataFrame is displayed correctly\n", + " display(df)\n", + "\n", + "# Extract main values\n", + " temp = df['main.temp']\n", + " humidity = df['main.humidity']\n", + "\n", + " # Extract wind values\n", + " w_speed = df['wind.speed']\n", + "\n", + " # Extract other variables\n", + " clouds = df['clouds.all']\n", + "\n", + " try:\n", + " rain = df['rain.1h']\n", + " except KeyError:\n", + " print(\"'Rain' is not present in the JSON file.\")\n", + "\n", + " try:\n", + " snow = df['snow.1h']\n", + " except KeyError:\n", + " print(\"'Snow' is not present in the JSON file.\")\n", + "\n", + " # Print the average temperature\n", + " print('Gjennomsnitts temperatur: ', temp.mean().round(2))\n", + "\n", + " # Display the temperature column\n", + " # display(temp)\n", + "\n", + " max_temp = df['main.temp'].max()\n", + " min_temp = df['main.temp'].min()\n", + "\n", + " print(\"max temperature:\", max_temp)\n", + " print(\"min temperature:\", min_temp)\n", + " \n", + "else:\n", + " print(\"The 'list' key is not present in the JSON file.\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "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": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 68063bb6e8a2a407073d2c1d95812b17b83a398c Mon Sep 17 00:00:00 2001 From: Hanne Heggdal Date: Wed, 26 Mar 2025 15:05:08 +0100 Subject: [PATCH 04/18] notebook add, current data for chosen city --- notebooks/get_current_data.ipynb | 240 +++++++++++++++++++++++++++++++ 1 file changed, 240 insertions(+) create mode 100644 notebooks/get_current_data.ipynb diff --git a/notebooks/get_current_data.ipynb b/notebooks/get_current_data.ipynb new file mode 100644 index 0000000..468797b --- /dev/null +++ b/notebooks/get_current_data.ipynb @@ -0,0 +1,240 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data fetch: ok\n" + ] + } + ], + "source": [ + "import sys\n", + "import os\n", + "\n", + "# Gets the absolute path to the src folder\n", + "sys.path.append(os.path.abspath(\"../src\"))\n", + "\n", + "# Now we can import the fucntion from the module\n", + "from my_package.fetch_current_data import fetch_current_data\n", + "\n", + "# User input the city, for the weather\n", + "city_name = input(\"Enter a city in Norway: \")\n", + "\n", + "data, folder = fetch_current_data(city_name)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has been written to /Users/hanne/Documents/anvendt prosjekt/anvendt_mappe/data/../data/output_current_data/data_stavg_current.json\n" + ] + } + ], + "source": [ + "# Gets the absolute path to the src folder\n", + "sys.path.append(os.path.abspath(\"../src\"))\n", + "\n", + "from my_package.write_data import write_data\n", + "\n", + "filename = input(\"Write filename: \")\n", + "\n", + "write_data(data, folder, filename)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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namemain.tempmain.feels_likemain.temp_minmain.temp_maxmain.pressuremain.humiditymain.sea_levelmain.grnd_levelwind.speedwind.gustclouds.allsys.typesys.idsys.countrysys.sunrisesys.sunset
dt
2025-03-26 14:04:13Stavanger8.767.558.379.52101994101910152.243.587522031843NO2025-03-26 05:21:032025-03-26 18:04:12
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" + ], + "text/plain": [ + " name main.temp main.feels_like main.temp_min \\\n", + "dt \n", + "2025-03-26 14:04:13 Stavanger 8.76 7.55 8.37 \n", + "\n", + " main.temp_max main.pressure main.humidity \\\n", + "dt \n", + "2025-03-26 14:04:13 9.52 1019 94 \n", + "\n", + " main.sea_level main.grnd_level wind.speed wind.gust \\\n", + "dt \n", + "2025-03-26 14:04:13 1019 1015 2.24 3.58 \n", + "\n", + " clouds.all sys.type sys.id sys.country \\\n", + "dt \n", + "2025-03-26 14:04:13 75 2 2031843 NO \n", + "\n", + " sys.sunrise sys.sunset \n", + "dt \n", + "2025-03-26 14:04:13 2025-03-26 05:21:03 2025-03-26 18:04:12 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import json\n", + "\n", + "# data = pd.read_json(f'../data/output_current_data/data_{filename}.json')\n", + "\n", + "# Les JSON-filen\n", + "with open(f\"../data/output_current_data/data_{filename}.json\", \"r\", encoding=\"utf-8\") as file:\n", + " data = json.load(file)\n", + "\n", + "# Flate ut JSON-strukturen med json_normalize\n", + "df = pd.json_normalize(data)\n", + "\n", + "# Delete duplicates based on the dt row, all the other values can appear more than once, but the date should only appear once\n", + "df = df.drop_duplicates(subset=['dt'])\n", + "\n", + "# Deleted the columns that was not relevant\n", + "df = df.drop(columns=\"weather\")\n", + "df = df.drop(columns=\"base\")\n", + "df = df.drop(columns=\"visibility\")\n", + "df = df.drop(columns=\"timezone\")\n", + "df = df.drop(columns=\"id\")\n", + "df = df.drop(columns=\"cod\")\n", + "df = df.drop(columns=\"coord.lon\")\n", + "df = df.drop(columns=\"coord.lat\")\n", + "df = df.drop(columns=\"wind.deg\")\n", + "\n", + "#change from unix to datetime for sunrise and sunset\n", + "df['sys.sunrise'] = pd.to_datetime(df['sys.sunrise'], unit='s')\n", + "df['sys.sunset'] = pd.to_datetime(df['sys.sunset'], unit='s')\n", + "\n", + "# Convert 'dt' column from Unix timestamp to datetime and set it as the index\n", + "df['dt'] = pd.to_datetime(df['dt'], unit='s')\n", + "df.set_index('dt', inplace=True)\n", + "\n", + "# Skriv ut DataFrame\n", + "display(df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "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": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From baf7188753f7dfeee711ee694752da149216a844 Mon Sep 17 00:00:00 2001 From: Hanne Heggdal Date: Wed, 26 Mar 2025 15:08:23 +0100 Subject: [PATCH 05/18] function to fetch current data --- src/my_package/fetch_current_data.py | 38 ++++++++++++++++++++++++++++ 1 file changed, 38 insertions(+) create mode 100644 src/my_package/fetch_current_data.py diff --git a/src/my_package/fetch_current_data.py b/src/my_package/fetch_current_data.py new file mode 100644 index 0000000..787f3c3 --- /dev/null +++ b/src/my_package/fetch_current_data.py @@ -0,0 +1,38 @@ +# Import of needed libaries +import requests +import os +from dotenv import load_dotenv + +load_dotenv() + +# Gets the key, from my env file +API_KEY = os.getenv("API_KEY") + +# city_name = "Trondheim" +country_code = "NO" + + +# Gets the data from the API - openweathermap.org +def fetch_current_data(city_name): + + + # f-string url, to add the "custom" variables to the API-request + url = f"https://api.openweathermap.org/data/2.5/weather?q={city_name},NO&units=metric&appid={API_KEY}" + + # Saves the API-request for the url + response = requests.get(url) + + # Checks if the status code is OK + if response.status_code == 200: + + # Converts the data into json + data = response.json() + folder = "../data/output_current_data" + + print("Data fetch: ok") + return data, folder + + + else: + # If html status code != 200, print the status code + print("Failed to fetch data from API. Status code:", response.status_code) \ No newline at end of file From 7d62686e1376ab6823ebc6671572c131986bced3 Mon Sep 17 00:00:00 2001 From: toravest Date: Thu, 27 Mar 2025 16:04:16 +0100 Subject: [PATCH 06/18] rename notebook, add markdown --- notebooks/get_current_data.ipynb | 240 -------------------------- notebooks/notebook_current_data.ipynb | 180 +++++++++++++++++++ 2 files changed, 180 insertions(+), 240 deletions(-) delete mode 100644 notebooks/get_current_data.ipynb create mode 100644 notebooks/notebook_current_data.ipynb diff --git a/notebooks/get_current_data.ipynb b/notebooks/get_current_data.ipynb deleted file mode 100644 index 468797b..0000000 --- a/notebooks/get_current_data.ipynb +++ /dev/null @@ -1,240 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data fetch: ok\n" - ] - } - ], - "source": [ - "import sys\n", - "import os\n", - "\n", - "# Gets the absolute path to the src folder\n", - "sys.path.append(os.path.abspath(\"../src\"))\n", - "\n", - "# Now we can import the fucntion from the module\n", - "from my_package.fetch_current_data import fetch_current_data\n", - "\n", - "# User input the city, for the weather\n", - "city_name = input(\"Enter a city in Norway: \")\n", - "\n", - "data, folder = fetch_current_data(city_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data has been written to /Users/hanne/Documents/anvendt prosjekt/anvendt_mappe/data/../data/output_current_data/data_stavg_current.json\n" - ] - } - ], - "source": [ - "# Gets the absolute path to the src folder\n", - "sys.path.append(os.path.abspath(\"../src\"))\n", - "\n", - "from my_package.write_data import write_data\n", - "\n", - "filename = input(\"Write filename: \")\n", - "\n", - "write_data(data, folder, filename)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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namemain.tempmain.feels_likemain.temp_minmain.temp_maxmain.pressuremain.humiditymain.sea_levelmain.grnd_levelwind.speedwind.gustclouds.allsys.typesys.idsys.countrysys.sunrisesys.sunset
dt
2025-03-26 14:04:13Stavanger8.767.558.379.52101994101910152.243.587522031843NO2025-03-26 05:21:032025-03-26 18:04:12
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" - ], - "text/plain": [ - " name main.temp main.feels_like main.temp_min \\\n", - "dt \n", - "2025-03-26 14:04:13 Stavanger 8.76 7.55 8.37 \n", - "\n", - " main.temp_max main.pressure main.humidity \\\n", - "dt \n", - "2025-03-26 14:04:13 9.52 1019 94 \n", - "\n", - " main.sea_level main.grnd_level wind.speed wind.gust \\\n", - "dt \n", - "2025-03-26 14:04:13 1019 1015 2.24 3.58 \n", - "\n", - " clouds.all sys.type sys.id sys.country \\\n", - "dt \n", - "2025-03-26 14:04:13 75 2 2031843 NO \n", - "\n", - " sys.sunrise sys.sunset \n", - "dt \n", - "2025-03-26 14:04:13 2025-03-26 05:21:03 2025-03-26 18:04:12 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import pandas as pd\n", - "import json\n", - "\n", - "# data = pd.read_json(f'../data/output_current_data/data_{filename}.json')\n", - "\n", - "# Les JSON-filen\n", - "with open(f\"../data/output_current_data/data_{filename}.json\", \"r\", encoding=\"utf-8\") as file:\n", - " data = json.load(file)\n", - "\n", - "# Flate ut JSON-strukturen med json_normalize\n", - "df = pd.json_normalize(data)\n", - "\n", - "# Delete duplicates based on the dt row, all the other values can appear more than once, but the date should only appear once\n", - "df = df.drop_duplicates(subset=['dt'])\n", - "\n", - "# Deleted the columns that was not relevant\n", - "df = df.drop(columns=\"weather\")\n", - "df = df.drop(columns=\"base\")\n", - "df = df.drop(columns=\"visibility\")\n", - "df = df.drop(columns=\"timezone\")\n", - "df = df.drop(columns=\"id\")\n", - "df = df.drop(columns=\"cod\")\n", - "df = df.drop(columns=\"coord.lon\")\n", - "df = df.drop(columns=\"coord.lat\")\n", - "df = df.drop(columns=\"wind.deg\")\n", - "\n", - "#change from unix to datetime for sunrise and sunset\n", - "df['sys.sunrise'] = pd.to_datetime(df['sys.sunrise'], unit='s')\n", - "df['sys.sunset'] = pd.to_datetime(df['sys.sunset'], unit='s')\n", - "\n", - "# Convert 'dt' column from Unix timestamp to datetime and set it as the index\n", - "df['dt'] = pd.to_datetime(df['dt'], unit='s')\n", - "df.set_index('dt', inplace=True)\n", - "\n", - "# Skriv ut DataFrame\n", - "display(df)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "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": "3.9.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/notebook_current_data.ipynb b/notebooks/notebook_current_data.ipynb new file mode 100644 index 0000000..4526089 --- /dev/null +++ b/notebooks/notebook_current_data.ipynb @@ -0,0 +1,180 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Notebook - Current Data\n", + "Denne notebooken er for å hente, skrive og vise nåværende data for ønsket lokasjon." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Velg sted og få nåværende data\n", + "\n", + "Skriv inn et sted du ønsker å få nåværende data fra, foreløpig er det begrenset til Norge" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import os\n", + "\n", + "# Gets the absolute path to the src folder\n", + "sys.path.append(os.path.abspath(\"../src\"))\n", + "\n", + "# Now we can import the fucntion from the module\n", + "from my_package.fetch_current_data import fetch_current_data\n", + "\n", + "# User input the city, for the weather\n", + "city_name = input(\"Enter a city in Norway: \")\n", + "\n", + "# Stores the return of the function\n", + "data, folder = fetch_current_data(city_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Lagre data i en json-fil\n", + "\n", + "Skriv inn navn for til filen du vil lagre med dataen.\n", + "\n", + "Eks. test\n", + "Da vil filen lagres som data_**test**.json, i mappen \"../data/output_stedsnavn/data_{filnavn}.json\"\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Gets the absolute path to the src folder\n", + "sys.path.append(os.path.abspath(\"../src\"))\n", + "\n", + "from my_package.write_data import write_data\n", + "\n", + "# The user choose the filename\n", + "filename = input(\"Write filename: \")\n", + "\n", + "# Writes the data, using user input filename\n", + "write_data(data, folder, filename)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Lese fra fil\n", + "\n", + "Henter opp data lagret i filen, lagd over, og skriver ut lesbart ved hjelp av pandas" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "\n", + "# Read from the json-file\n", + "with open(f\"../data/output_current_data/data_{filename}.json\", \"r\") as file:\n", + " data = json.load(file)\n", + "\n", + "# Display data\n", + "display(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rydde i data\n", + "For å gjøre det enkelre å lese dataen, normaliserer vi json-filen ved hjelp av pandas.\n", + "\n", + "Vi fjerner også irrellevante kolonner som:\n", + "- weather: denne inneholder informasjon om været (beskrivelse, id, icon osv.)\n", + "- coord.lon og coord.lat: vi trengre ikke koordinatene når vi har valgt basert på ønsket sted\n", + "- sys.type, sys.id, base, cod: interne parametre\n", + "- temp_max og temp_min: er ikke store endringer av temperatur innenfor en times tid\n", + "- visibility: sikt avstand i forhold til tåke, vi anser den som urelevant\n", + "\n", + "Deretter konverteres datetime [dt] fra unix_timestamp til vanlig tid, for å brukes som index\n", + "\n", + "Tiden for soloppgang og solnedgang konverteres også fra unix til vanlig tid, for å lettere leses og forstås." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "# Normalize the json-structure, to add better readability\n", + "df = pd.json_normalize(data)\n", + "\n", + "# Delete duplicates based on the dt row, all the other values can appear more than once, but the date should only appear once\n", + "df = df.drop_duplicates(subset=['dt'])\n", + "\n", + "# Delete columns that is not relevant\n", + "df = df.drop(columns=\"weather\")\n", + "df = df.drop(columns=\"base\")\n", + "df = df.drop(columns=\"visibility\")\n", + "df = df.drop(columns=\"timezone\")\n", + "df = df.drop(columns=\"id\")\n", + "df = df.drop(columns=\"cod\")\n", + "df = df.drop(columns=\"coord.lon\")\n", + "df = df.drop(columns=\"coord.lat\")\n", + "df = df.drop(columns=\"wind.deg\")\n", + "df = df.drop(columns=\"main.temp_min\")\n", + "df = df.drop(columns=\"main.temp_max\")\n", + "df = df.drop(columns=\"sys.type\")\n", + "df = df.drop(columns=\"sys.id\")\n", + "\n", + "# Change from unix to datetime for sunrise and sunset\n", + "df['sys.sunrise'] = pd.to_datetime(df['sys.sunrise'], unit='s')\n", + "df['sys.sunset'] = pd.to_datetime(df['sys.sunset'], unit='s')\n", + "\n", + "# Convert 'dt' column from Unix timestamp to datetime and set it as the index\n", + "df['dt'] = pd.to_datetime(df['dt'], unit='s')\n", + "df.set_index('dt', inplace=True)\n", + "\n", + "# Display the df after changes\n", + "display(df)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "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": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 811cc2406c44c00d42575553e96e7fb5b3e765b3 Mon Sep 17 00:00:00 2001 From: toravest Date: Thu, 27 Mar 2025 17:22:30 +0100 Subject: [PATCH 07/18] rename notebook, handel missing data, visulize weather data, add markdown and comments --- notebooks/get_day_data_notebook.ipynb | 845 -------------------------- notebooks/notebook_one_day_data.ipynb | 497 +++++++++++++++ 2 files changed, 497 insertions(+), 845 deletions(-) delete mode 100644 notebooks/get_day_data_notebook.ipynb create mode 100644 notebooks/notebook_one_day_data.ipynb diff --git a/notebooks/get_day_data_notebook.ipynb b/notebooks/get_day_data_notebook.ipynb deleted file mode 100644 index 14af281..0000000 --- a/notebooks/get_day_data_notebook.ipynb +++ /dev/null @@ -1,845 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Velg hvilken dag du vil sjekke været for\n", - "\n", - "For å kunne hente data og gjøre en analyse trenger programmet å vite hvilken dag du vil hente ut for, også skrives alle timene fra den dagen ut.\n", - "\n", - "Dataen skrives inn slik: (yyyy, mm, dd)\n", - "Her følger et eksempel: \n", - "|Hva|Hvordan|Eksempel|\n", - "|:---|:---:|:---:|\n", - "|år|yyyy|2025|\n", - "|måned|mm|03| \n", - "|dato|dd|01| \n", - "\n", - "Denne dataen skrives da inn på følgende hvis: (2025, 03, 01)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Selected date: 2025-02-21\n", - "Unix Timestamp: 1740092400 -> 2025-02-21 00:00:00\n", - "Unix Timestamp: 1740096000 -> 2025-02-21 01:00:00\n", - "Unix Timestamp: 1740099600 -> 2025-02-21 02:00:00\n", - "Unix Timestamp: 1740103200 -> 2025-02-21 03:00:00\n", - "Unix Timestamp: 1740106800 -> 2025-02-21 04:00:00\n", - "Unix Timestamp: 1740110400 -> 2025-02-21 05:00:00\n", - "Unix Timestamp: 1740114000 -> 2025-02-21 06:00:00\n", - "Unix Timestamp: 1740117600 -> 2025-02-21 07:00:00\n", - "Unix Timestamp: 1740121200 -> 2025-02-21 08:00:00\n", - "Unix Timestamp: 1740124800 -> 2025-02-21 09:00:00\n", - "Unix Timestamp: 1740128400 -> 2025-02-21 10:00:00\n", - "Unix Timestamp: 1740132000 -> 2025-02-21 11:00:00\n", - "Unix Timestamp: 1740135600 -> 2025-02-21 12:00:00\n", - "Unix Timestamp: 1740139200 -> 2025-02-21 13:00:00\n", - "Unix Timestamp: 1740142800 -> 2025-02-21 14:00:00\n", - "Unix Timestamp: 1740146400 -> 2025-02-21 15:00:00\n", - "Unix Timestamp: 1740150000 -> 2025-02-21 16:00:00\n", - "Unix Timestamp: 1740153600 -> 2025-02-21 17:00:00\n", - "Unix Timestamp: 1740157200 -> 2025-02-21 18:00:00\n", - "Unix Timestamp: 1740160800 -> 2025-02-21 19:00:00\n", - "Unix Timestamp: 1740164400 -> 2025-02-21 20:00:00\n", - "Unix Timestamp: 1740168000 -> 2025-02-21 21:00:00\n", - "Unix Timestamp: 1740171600 -> 2025-02-21 22:00:00\n", - "Unix Timestamp: 1740175200 -> 2025-02-21 23:00:00\n" - ] - } - ], - "source": [ - "import datetime\n", - "import time\n", - "\n", - "#makes a function so the star and end date is the same date, with all hours of that date\n", - "def get_unix_timestamps_for_day():\n", - " date_input = input(\"Choose a date (yyyy, mm, dd): \")\n", - " date_components = date_input.split(\",\")\n", - " year = int(date_components[0])\n", - " month = int(date_components[1])\n", - " day = int(date_components[2])\n", - "\n", - "#goes trough all hours of the day, use %Y-%m-%d etc. from pythons strftime to convert datetime into a readable string \n", - " timestamps = []\n", - " for hour in range(24):\n", - " dt = datetime.datetime(year, month, day, hour, 0)\n", - " unix_timestamp = int(time.mktime(dt.timetuple()))\n", - " timestamps.append((unix_timestamp, dt.strftime('%Y-%m-%d %H:%M:%S'))) \n", - "\n", - "#prints the date chosen\n", - " print(f\"\\nSelected date: {year}-{month:02d}-{day:02d}\")\n", - "\n", - "#prints the timestamp and the date an hour of the day after\n", - " for ts, readable in timestamps:\n", - " print(f\"Unix Timestamp: {ts} -> {readable}\")\n", - " \n", - " return [ts[0] for ts in timestamps]\n", - "\n", - "timestamps = get_unix_timestamps_for_day()\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Velg en by i Norge og få data\n", - "\n", - "Skriv inn en by du ønsker data fra, foreløpig er det begrenset til Norge\n", - "\n", - "Programmet vil deretter hente data å lagre det i en json fil" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data fetch: ok\n" - ] - } - ], - "source": [ - "import sys\n", - "import os\n", - "\n", - "# Gets the absolute path to the src folder\n", - "sys.path.append(os.path.abspath(\"../src\"))\n", - "\n", - "# Now we can import the fucntion from the module\n", - "from my_package.fetch_data import fetch_data\n", - "\n", - "#user choose a city they want the weather data from\n", - "city_name = input(\"Enter city name: \")\n", - "start_date, end_date = timestamps[0], timestamps[-1]\n", - "weather_data,folder = fetch_data(start_date, end_date, city_name)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Lagre data i en json-fil\n", - "\n", - "Skriv inn navn for til filen du vil lagre med dataen.\n", - "\n", - "Eks. test\n", - "Da vil filen lagres som data_**test**.json, i mappen \"../data/output_stedsnavn/data_{filnavn}.json\"" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data has been written to /Users/hanne/Documents/anvendt prosjekt/anvendt_mappe/data/../data/output_stedsnavn/data_test_oslo1.json\n" - ] - } - ], - "source": [ - "# Gets the absolute path to the src folder\n", - "sys.path.append(os.path.abspath(\"../src\"))\n", - "\n", - "from my_package.write_data import write_data\n", - "\n", - "filename = input(\"Write filename: \")\n", - "\n", - "write_data(weather_data, folder, filename)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Lese fra fil\n", - "\n", - "Henter opp data lagret i filen, lagd over, og skriver ut lesbart ved hjelp av pandas" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " message cod city_id calctime cnt \\\n", - "0 Count: 24 200 3143244 0.010569 24 \n", - "1 Count: 24 200 3143244 0.010569 24 \n", - "2 Count: 24 200 3143244 0.010569 24 \n", - "3 Count: 24 200 3143244 0.010569 24 \n", - "4 Count: 24 200 3143244 0.010569 24 \n", - "5 Count: 24 200 3143244 0.010569 24 \n", - "6 Count: 24 200 3143244 0.010569 24 \n", - "7 Count: 24 200 3143244 0.010569 24 \n", - "8 Count: 24 200 3143244 0.010569 24 \n", - "9 Count: 24 200 3143244 0.010569 24 \n", - "10 Count: 24 200 3143244 0.010569 24 \n", - "11 Count: 24 200 3143244 0.010569 24 \n", - "12 Count: 24 200 3143244 0.010569 24 \n", - "13 Count: 24 200 3143244 0.010569 24 \n", - "14 Count: 24 200 3143244 0.010569 24 \n", - "15 Count: 24 200 3143244 0.010569 24 \n", - "16 Count: 24 200 3143244 0.010569 24 \n", - "17 Count: 24 200 3143244 0.010569 24 \n", - "18 Count: 24 200 3143244 0.010569 24 \n", - "19 Count: 24 200 3143244 0.010569 24 \n", - "20 Count: 24 200 3143244 0.010569 24 \n", - "21 Count: 24 200 3143244 0.010569 24 \n", - "22 Count: 24 200 3143244 0.010569 24 \n", - "23 Count: 24 200 3143244 0.010569 24 \n", - "\n", - " list \n", - "0 {'dt': 1740092400, 'main': {'temp': -0.97, 'fe... \n", - "1 {'dt': 1740096000, 'main': {'temp': -0.97, 'fe... \n", - "2 {'dt': 1740099600, 'main': {'temp': -0.97, 'fe... \n", - "3 {'dt': 1740103200, 'main': {'temp': -0.97, 'fe... \n", - "4 {'dt': 1740106800, 'main': {'temp': -0.97, 'fe... \n", - "5 {'dt': 1740110400, 'main': {'temp': -0.42, 'fe... \n", - "6 {'dt': 1740114000, 'main': {'temp': 0.69000000... \n", - "7 {'dt': 1740117600, 'main': {'temp': 1.25, 'fee... \n", - "8 {'dt': 1740121200, 'main': {'temp': 1.81, 'fee... \n", - "9 {'dt': 1740124800, 'main': {'temp': 1.81, 'fee... \n", - "10 {'dt': 1740128400, 'main': {'temp': 3.31, 'fee... \n", - "11 {'dt': 1740132000, 'main': {'temp': 3.31, 'fee... \n", - "12 {'dt': 1740135600, 'main': {'temp': 3.31, 'fee... \n", - "13 {'dt': 1740139200, 'main': {'temp': 3.31, 'fee... \n", - "14 {'dt': 1740142800, 'main': {'temp': 3.87, 'fee... \n", - "15 {'dt': 1740146400, 'main': {'temp': 3.87, 'fee... \n", - "16 {'dt': 1740150000, 'main': {'temp': 3.31, 'fee... \n", - "17 {'dt': 1740153600, 'main': {'temp': 3.31, 'fee... \n", - "18 {'dt': 1740157200, 'main': {'temp': 3.31, 'fee... \n", - "19 {'dt': 1740160800, 'main': {'temp': 3.31, 'fee... \n", - "20 {'dt': 1740164400, 'main': {'temp': 2.92, 'fee... \n", - "21 {'dt': 1740168000, 'main': {'temp': 2.92, 'fee... \n", - "22 {'dt': 1740171600, 'main': {'temp': 2.92, 'fee... \n", - "23 {'dt': 1740175200, 'main': {'temp': 2.92, 'fee... \n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "weather_data = pd.read_json(f'../data/output_stedsnavn/data_{filename}.json')\n", - "\n", - "print(weather_data)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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main.tempmain.feels_likemain.pressuremain.humiditymain.temp_minmain.temp_maxwind.speedwind.degwind.gustclouds.allsnow.1hrain.1h
dt
2025-02-20 23:00:00-0.97-5.54101691-0.97-0.974.0916212.291001.94NaN
2025-02-21 00:00:00-0.97-5.23101697-0.97-0.973.6715913.071001.54NaN
2025-02-21 01:00:00-0.97-5.37101597-0.97-0.973.8516013.481002.05NaN
2025-02-21 02:00:00-0.97-5.75101597-0.97-0.974.4017612.251000.56NaN
2025-02-21 03:00:00-0.97-5.21101498-0.97-0.973.641729.68100NaNNaN
2025-02-21 04:00:00-0.42-4.16101498-0.42-0.423.181849.60100NaNNaN
2025-02-21 05:00:000.69-2.141014980.690.692.441948.92100NaNNaN
2025-02-21 06:00:001.25-1.091014981.251.252.082009.31100NaNNaN
2025-02-21 07:00:001.811.811015991.811.811.222086.28100NaNNaN
2025-02-21 08:00:001.811.811015991.811.811.231874.22100NaNNaN
2025-02-21 09:00:003.313.311016842.363.311.221804.08100NaNNaN
2025-02-21 10:00:003.313.311016842.923.311.321915.27100NaNNaN
2025-02-21 11:00:003.311.951017842.923.311.551846.56100NaNNaN
2025-02-21 12:00:003.310.991017843.313.472.4017410.09100NaNNaN
2025-02-21 13:00:003.871.741016843.473.872.311679.24100NaNNaN
2025-02-21 14:00:003.871.831016843.473.872.211759.12100NaNNaN
2025-02-21 15:00:003.311.181017882.923.312.2117910.36100NaNNaN
2025-02-21 16:00:003.311.261016882.923.312.1316910.30100NaN0.30
2025-02-21 17:00:003.311.281016882.923.312.1116510.59100NaNNaN
2025-02-21 18:00:003.311.431015882.923.311.9718612.00100NaNNaN
2025-02-21 19:00:002.920.571015992.922.922.3618313.15100NaNNaN
2025-02-21 20:00:002.920.431014992.922.922.5117312.01100NaN0.12
2025-02-21 21:00:002.920.001014992.922.923.0117512.17100NaN0.35
2025-02-21 22:00:002.92-0.241014992.922.923.3317712.50100NaN0.69
\n", - "
" - ], - "text/plain": [ - " main.temp main.feels_like main.pressure main.humidity \\\n", - "dt \n", - "2025-02-20 23:00:00 -0.97 -5.54 1016 91 \n", - "2025-02-21 00:00:00 -0.97 -5.23 1016 97 \n", - "2025-02-21 01:00:00 -0.97 -5.37 1015 97 \n", - "2025-02-21 02:00:00 -0.97 -5.75 1015 97 \n", - "2025-02-21 03:00:00 -0.97 -5.21 1014 98 \n", - "2025-02-21 04:00:00 -0.42 -4.16 1014 98 \n", - "2025-02-21 05:00:00 0.69 -2.14 1014 98 \n", - "2025-02-21 06:00:00 1.25 -1.09 1014 98 \n", - "2025-02-21 07:00:00 1.81 1.81 1015 99 \n", - "2025-02-21 08:00:00 1.81 1.81 1015 99 \n", - "2025-02-21 09:00:00 3.31 3.31 1016 84 \n", - "2025-02-21 10:00:00 3.31 3.31 1016 84 \n", - "2025-02-21 11:00:00 3.31 1.95 1017 84 \n", - "2025-02-21 12:00:00 3.31 0.99 1017 84 \n", - "2025-02-21 13:00:00 3.87 1.74 1016 84 \n", - "2025-02-21 14:00:00 3.87 1.83 1016 84 \n", - "2025-02-21 15:00:00 3.31 1.18 1017 88 \n", - "2025-02-21 16:00:00 3.31 1.26 1016 88 \n", - "2025-02-21 17:00:00 3.31 1.28 1016 88 \n", - "2025-02-21 18:00:00 3.31 1.43 1015 88 \n", - "2025-02-21 19:00:00 2.92 0.57 1015 99 \n", - "2025-02-21 20:00:00 2.92 0.43 1014 99 \n", - "2025-02-21 21:00:00 2.92 0.00 1014 99 \n", - "2025-02-21 22:00:00 2.92 -0.24 1014 99 \n", - "\n", - " main.temp_min main.temp_max wind.speed wind.deg \\\n", - "dt \n", - "2025-02-20 23:00:00 -0.97 -0.97 4.09 162 \n", - "2025-02-21 00:00:00 -0.97 -0.97 3.67 159 \n", - "2025-02-21 01:00:00 -0.97 -0.97 3.85 160 \n", - "2025-02-21 02:00:00 -0.97 -0.97 4.40 176 \n", - "2025-02-21 03:00:00 -0.97 -0.97 3.64 172 \n", - "2025-02-21 04:00:00 -0.42 -0.42 3.18 184 \n", - "2025-02-21 05:00:00 0.69 0.69 2.44 194 \n", - "2025-02-21 06:00:00 1.25 1.25 2.08 200 \n", - "2025-02-21 07:00:00 1.81 1.81 1.22 208 \n", - "2025-02-21 08:00:00 1.81 1.81 1.23 187 \n", - "2025-02-21 09:00:00 2.36 3.31 1.22 180 \n", - "2025-02-21 10:00:00 2.92 3.31 1.32 191 \n", - "2025-02-21 11:00:00 2.92 3.31 1.55 184 \n", - "2025-02-21 12:00:00 3.31 3.47 2.40 174 \n", - "2025-02-21 13:00:00 3.47 3.87 2.31 167 \n", - "2025-02-21 14:00:00 3.47 3.87 2.21 175 \n", - "2025-02-21 15:00:00 2.92 3.31 2.21 179 \n", - "2025-02-21 16:00:00 2.92 3.31 2.13 169 \n", - "2025-02-21 17:00:00 2.92 3.31 2.11 165 \n", - "2025-02-21 18:00:00 2.92 3.31 1.97 186 \n", - "2025-02-21 19:00:00 2.92 2.92 2.36 183 \n", - "2025-02-21 20:00:00 2.92 2.92 2.51 173 \n", - "2025-02-21 21:00:00 2.92 2.92 3.01 175 \n", - "2025-02-21 22:00:00 2.92 2.92 3.33 177 \n", - "\n", - " wind.gust clouds.all snow.1h rain.1h \n", - "dt \n", - "2025-02-20 23:00:00 12.29 100 1.94 NaN \n", - "2025-02-21 00:00:00 13.07 100 1.54 NaN \n", - "2025-02-21 01:00:00 13.48 100 2.05 NaN \n", - "2025-02-21 02:00:00 12.25 100 0.56 NaN \n", - "2025-02-21 03:00:00 9.68 100 NaN NaN \n", - "2025-02-21 04:00:00 9.60 100 NaN NaN \n", - "2025-02-21 05:00:00 8.92 100 NaN NaN \n", - "2025-02-21 06:00:00 9.31 100 NaN NaN \n", - "2025-02-21 07:00:00 6.28 100 NaN NaN \n", - "2025-02-21 08:00:00 4.22 100 NaN NaN \n", - "2025-02-21 09:00:00 4.08 100 NaN NaN \n", - "2025-02-21 10:00:00 5.27 100 NaN NaN \n", - "2025-02-21 11:00:00 6.56 100 NaN NaN \n", - "2025-02-21 12:00:00 10.09 100 NaN NaN \n", - "2025-02-21 13:00:00 9.24 100 NaN NaN \n", - "2025-02-21 14:00:00 9.12 100 NaN NaN \n", - "2025-02-21 15:00:00 10.36 100 NaN NaN \n", - "2025-02-21 16:00:00 10.30 100 NaN 0.30 \n", - "2025-02-21 17:00:00 10.59 100 NaN NaN \n", - "2025-02-21 18:00:00 12.00 100 NaN NaN \n", - "2025-02-21 19:00:00 13.15 100 NaN NaN \n", - "2025-02-21 20:00:00 12.01 100 NaN 0.12 \n", - "2025-02-21 21:00:00 12.17 100 NaN 0.35 \n", - "2025-02-21 22:00:00 12.50 100 NaN 0.69 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Gjennomsnitts temperatur: 1.92\n", - "max temperature: 3.87\n", - "min temperature: -0.97\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "weather_data = pd.read_json(f'../data/output_stedsnavn/data_{filename}.json')\n", - "\n", - "if 'list' in weather_data:\n", - " df = pd.json_normalize(weather_data['list'])\n", - "\n", - " # Delete duplicates based on the dt row, all the other values can appear more than once, but the date should only appear once\n", - " df = df.drop_duplicates(subset=['dt'])\n", - "\n", - " # The weather column dosnt have any releated information, therefor we delete it\n", - " df = df.drop(columns=\"weather\")\n", - "\n", - " # Convert 'dt' column from Unix timestamp to datetime and set it as the index\n", - " df['dt'] = pd.to_datetime(df['dt'], unit='s')\n", - " df.set_index('dt', inplace=True)\n", - " \n", - "\n", - " # Ensure the DataFrame is displayed correctly\n", - " display(df)\n", - "\n", - "# Extract main values\n", - " temp = df['main.temp']\n", - " humidity = df['main.humidity']\n", - "\n", - " # Extract wind values\n", - " w_speed = df['wind.speed']\n", - "\n", - " # Extract other variables\n", - " clouds = df['clouds.all']\n", - "\n", - " try:\n", - " rain = df['rain.1h']\n", - " except KeyError:\n", - " print(\"'Rain' is not present in the JSON file.\")\n", - "\n", - " try:\n", - " snow = df['snow.1h']\n", - " except KeyError:\n", - " print(\"'Snow' is not present in the JSON file.\")\n", - "\n", - " # Print the average temperature\n", - " print('Gjennomsnitts temperatur: ', temp.mean().round(2))\n", - "\n", - " # Display the temperature column\n", - " # display(temp)\n", - "\n", - " max_temp = df['main.temp'].max()\n", - " min_temp = df['main.temp'].min()\n", - "\n", - " print(\"max temperature:\", max_temp)\n", - " print(\"min temperature:\", min_temp)\n", - " \n", - "else:\n", - " print(\"The 'list' key is not present in the JSON file.\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "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": "3.9.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/notebook_one_day_data.ipynb b/notebooks/notebook_one_day_data.ipynb new file mode 100644 index 0000000..d2972f8 --- /dev/null +++ b/notebooks/notebook_one_day_data.ipynb @@ -0,0 +1,497 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Notebook - One day data\n", + "\n", + "Denne notebooken henter data fra ønsket dag og sted, skriver til fil. Visualiserer manglende verdier, retter opp manglende verdier, og visualisere og lagrer data fra plot." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Velg hvilken dag du vil sjekke været for\n", + "\n", + "For å kunne hente data og gjøre en analyse trenger programmet å vite hvilken dag du vil hente ut for, også skrives alle timene fra den dagen ut. Programmet kan ikke hente ut data fra nåværende, eller senere datoer, altså må man velge datoer fra tidligere tidspunkt.\n", + "\n", + "Dataen skrives inn slik: (yyyy, mm, dd)\n", + "Her følger et eksempel: \n", + "|Hva|Hvordan|Eksempel|\n", + "|:---|:---:|:---:|\n", + "|år|yyyy|2025|\n", + "|måned|mm|03| \n", + "|dato|dd|01| \n", + "\n", + "Denne dataen skrives da inn på følgende hvis: (2025, 03, 01)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import datetime\n", + "import time\n", + "\n", + "# Makes a function so the start and end date is the same date, with all hours of that date\n", + "def get_unix_timestamps_for_day():\n", + " date_input = input(\"Choose a date (yyyy, mm, dd): \")\n", + " date_components = date_input.split(\",\")\n", + " year = int(date_components[0])\n", + " month = int(date_components[1])\n", + " day = int(date_components[2])\n", + "\n", + " # Goes through all hours of the day, use %Y-%m-%d etc. from pythons strftime to convert datetime into a readable string \n", + " timestamps = []\n", + " for hour in range(24):\n", + " dt = datetime.datetime(year, month, day, hour, 0)\n", + " unix_timestamp = int(time.mktime(dt.timetuple()))\n", + " timestamps.append((unix_timestamp, dt.strftime('%Y-%m-%d %H:%M:%S'))) \n", + " \n", + " # Prevents from getting data for the current day, or the future\n", + " if dt >= datetime.datetime.now():\n", + " print(\"Failed, cant use future dates\")\n", + " return None\n", + "\n", + " # Prints the date chosen\n", + " print(f\"Selected date: {year}-{month:02d}-{day:02d}\")\n", + "\n", + " # Prints the timestamp and the date an hour of the day after\n", + " for ts, readable in timestamps:\n", + " print(f\"Unix Timestamp: {ts} -> {readable}\")\n", + " \n", + " return date_input, [ts[0] for ts in timestamps]\n", + "\n", + "date, timestamps = get_unix_timestamps_for_day()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Velg en by i Norge og få data\n", + "\n", + "Skriv inn en by du ønsker data fra, foreløpig er det begrenset til Norge\n", + "\n", + "Programmet vil deretter hente data å lagre det i en json fil" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import os\n", + "\n", + "# Gets the absolute path to the src folder\n", + "sys.path.append(os.path.abspath(\"../src\"))\n", + "\n", + "# Now we can import the fucntion from the module\n", + "from my_package.fetch_data import fetch_data\n", + "\n", + "# User choose a city they want the weather data from\n", + "city_name = input(\"Enter city name: \")\n", + "\n", + "# Start_date is the first timestamp, end_date is the last\n", + "start_date, end_date = timestamps[0], timestamps[-1]\n", + "\n", + "# Stores the values in the variables\n", + "weather_data, folder = fetch_data(start_date, end_date, city_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Lagre data i en json-fil\n", + "\n", + "Skriv inn navn for til filen du vil lagre med dataen.\n", + "\n", + "Eks. test\n", + "Da vil filen lagres som data_**test**.json, i mappen \"../data/output_stedsnavn/data_{filnavn}.json\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Gets the absolute path to the src folder\n", + "sys.path.append(os.path.abspath(\"../src\"))\n", + "\n", + "from my_package.write_data import write_data\n", + "\n", + "filename = input(\"Write filename: \")\n", + "\n", + "# Writes the data, with the chosen name\n", + "write_data(weather_data, folder, filename)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Lese fra fil\n", + "\n", + "Henter opp data lagret i filen, lagd over, og skriver ut lesbart ved hjelp av pandas" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "# Reads from file using pandas\n", + "weather_data = pd.read_json(f'../data/output_stedsnavn/data_{filename}.json')\n", + "\n", + "# Checks if 'list' in weather, then proceed because it is the right data\n", + "if 'list' in weather_data:\n", + " # Normalize the json for better readability\n", + " df = pd.json_normalize(weather_data['list'])\n", + "\n", + " # Delete duplicates based on the dt row, all the other values can appear more than once, but the date should only appear once\n", + " df = df.drop_duplicates(subset=['dt'])\n", + "\n", + " # The weather column dosnt have any releated information, therefor we delete it\n", + " df = df.drop(columns=\"weather\")\n", + "\n", + " # Convert 'dt' column from Unix timestamp to datetime and set it as the index\n", + " df['dt'] = pd.to_datetime(df['dt'], unit='s')\n", + " df.set_index('dt', inplace=True)\n", + "\n", + " # Ensure the DataFrame is displayed correctly \n", + " display(df)\n", + " \n", + "else:\n", + " print(\"The 'list' key is not present in the JSON file.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Viser temperaturen\n", + "Regner ut gjennomsnittst-temperatur ved hjelp av innebygde funksjoner. Finner også høyeste og laveste målte temperatur." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Stores the temperature values\n", + "temp = df['main.temp']\n", + "\n", + "temp_mean = temp.mean().round(2)\n", + "\n", + "# Print the average temperature\n", + "print(f'Mean temperatur: {temp_mean}')\n", + "\n", + "# Find the highest and lowest temperatures\n", + "max_temp = df['main.temp'].max().round(2)\n", + "min_temp = df['main.temp'].min().round(2)\n", + "\n", + "print(\"Highest temperature:\", max_temp)\n", + "print(\"Lowest temperature:\", min_temp)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualiserer nedbør\n", + "Ved hjelp av matplotlib visualiserer vi nedbør for ønsket dag." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "import numpy as np\n", + "\n", + "x_axis = df.index\n", + "\n", + "# Checks if the rain is a value, it will not be if it is no rain and then cause a KeyError\n", + "try:\n", + " rain = df['rain.1h']\n", + "\n", + "# If no rain, make the rain column and fill it with NaN\n", + "except KeyError:\n", + " print(\"'Rain' is not present in the JSON file.\")\n", + " df['rain.1h'] = np.nan\n", + "\n", + "# Checks if the snow is a value, it will not be if it is no rain and then cause a KeyError\n", + "try:\n", + " snow = df['snow.1h']\n", + "\n", + "# If no snow, make the snow column and fill it with NaN\n", + "except KeyError:\n", + " print(\"'Snow' is not present in the JSON file.\")\n", + " df['snow.1h'] = np.nan\n", + "\n", + "# Choose the width and height of the plot\n", + "plt.figure(figsize=(15, 6))\n", + "\n", + "# Check with rain, will cause NameError if the try/except over fails\n", + "try:\n", + " plt.bar(x_axis, rain, width=0.02, alpha=0.5, color='tab:blue', label='rain')\n", + "except: NameError\n", + "\n", + "# Check with snow, will cause NameError if the try/except over fails\n", + "try: \n", + " plt.bar(x_axis, snow, width=0.02, alpha=0.5, color='tab:grey', label='snow')\n", + "except: NameError\n", + "\n", + "# Get the current axsis, and store it as ax\n", + "ax = plt.gca()\n", + "\n", + "# Use the current ax, to get a tick-mark on the x_axis for each hour, and print like \"HH:MM\"\n", + "ax.xaxis.set_major_locator(mdates.HourLocator())\n", + "ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))\n", + "\n", + "# Add the label-desciption\n", + "plt.legend()\n", + "\n", + "# Shows the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Vise dataframe, med nye kolonner\n", + "Hvis dataframen ikke inneholdt 'rain.1h' eller 'snow.1h', skal de nå ha blitt lagt til med 'NaN' verdier." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Display df, to see if 'rain.1h' and 'snow.1h' was added with NaN values\n", + "display(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sjekk for manglende verdier\n", + "Missigno sjekker og visualiserer manglende verdier, slik at det blir lettere å se hvilke kolonner feilen ligger i. \n", + "\n", + "Vis the blir \"hull\" i en søyle, tyder the på manglende verdier." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import missingno as msno\n", + "\n", + "# Checks for and display missing values\n", + "msno.matrix(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Endre manglende verdier\n", + "I de fleste tilfeller virker dataene å være tilnærmet \"perfekte\", men de inkluderer bare snø og regn dersom det er snø eller regn. Derfor vil vi fa NaN verdier i de målingene det ikke har regnet/snødd. \n", + "\n", + "Under sjekker vi først om regn eller snø er i målingen, og hvis den er, bytter vi ut NaN med 0." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# If rain is stored, fill the NaN with 0\n", + "try: \n", + " df['rain.1h'] = df['rain.1h'].fillna(0)\n", + "except KeyError:\n", + " print([\"'rain.1h', not in df\"])\n", + "\n", + "# If snow is stored, fill the NaN with 0\n", + "try: \n", + " df['snow.1h'] = df['snow.1h'].fillna(0)\n", + "except KeyError:\n", + " print(\"['snow.1h'], not in df\")\n", + "\n", + "# Display the df, now without NaN (atleast for rain and snow)\n", + "display(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualisere endring av data\n", + "Har lagt inn en ny missigno visualisering, for å se at de manglende dataene \"forsvinner\" når vi kjører cellen over. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import missingno as msno\n", + "\n", + "# Visulaize the same data again, but now it should be no missing values (atleast for rain and snow)\n", + "msno.matrix(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualisere data i en graf\n", + "Ved hjelp av Matplotlib har vi visualiert ønsket data, og ved hjelp av subplot, en modul i matplotlib, kan vi plotte flere verdier i samme graf, og få \"to y-akse\" på samme x-akse. \n", + "\n", + "Temperatur og nedbør får plass i samme graf, hvor man leser temperatur verdiene på venstre side, og nedbørsverdiene på høyre side.\n", + "\n", + "I grafen under, men på samme x-akse, finner vi informasjon om vind, både vindhastighet og vindkast." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "import os\n", + "\n", + "# Where the figure should be saved when exported\n", + "output_folder = \"../data/output_fig\"\n", + "\n", + "# Creates the folder if it does not exist\n", + "os.makedirs(output_folder, exist_ok=True)\n", + "\n", + "# x_axis set to the index, which mean the datetime\n", + "x_axis = df.index\n", + "\n", + "# Gets the values\n", + "rain = df['rain.1h']\n", + "temp = df['main.temp']\n", + "snow = df['snow.1h']\n", + "wind_gust = df['wind.gust']\n", + "wind_speed = df['wind.speed']\n", + "\n", + "# Two vertically stacked axis, (2 rows, 1 column), width and height of the figure, and the axis share the same x_axis\n", + "fig, (ax1, ax3) = plt.subplots(2, 1,figsize=(15, 8), sharex=True)\n", + "\n", + "\n", + "# Set the title for the diagram, above the first axis, with city_name and input_date\n", + "ax1.set_title(f'Weather data for {city_name} ({date}) ')\n", + "\n", + "# Plot temperature on the primary y-axis\n", + "ax1.plot(x_axis, temp, color='tab:red', label='Temperature (°C)')\n", + "\n", + "# Design the y-axis for temperatur\n", + "ax1.set_ylabel('Temperature (°C)', color='tab:red')\n", + "ax1.tick_params(axis='y', labelcolor='tab:red')\n", + "\n", + "# Plot Precipitation as bars on the secondary y-axis\n", + "ax2 = ax1.twinx()\n", + "\n", + "# Add rain\n", + "ax2.bar(x_axis, rain, color='tab:blue', alpha=0.5, width=0.02, label='Rain (mm)')\n", + "\n", + "# Add snow\n", + "ax2.bar(x_axis, snow, color='tab:grey', alpha=0.5, width=0.02, label='Snow (mm)')\n", + "\n", + "# Design the y-axis for precipiation\n", + "ax2.set_ylabel(\"Precipitation (mm)\", color='tab:blue')\n", + "ax2.tick_params(axis='y', labelcolor='tab:blue')\n", + "\n", + "\n", + "# Format the x-axis to show all hours, in the format \"HH:MM\"\n", + "ax1.xaxis.set_major_locator(mdates.HourLocator()) \n", + "ax1.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))\n", + "\n", + "# Add label-description for both axis\n", + "ax1.legend(loc='upper left')\n", + "ax2.legend(loc='upper right')\n", + "\n", + "# Add grid, but only vertically\n", + "ax1.grid(axis = 'x')\n", + "\n", + "\n", + "# Plot the wind at the second x-axis (the axis below)\n", + "ax3.plot(x_axis, wind_gust, color='tab:purple', label='Wind_gust')\n", + "ax3.plot(x_axis, wind_speed, color='tab:purple', linestyle='dashed', label='Wind_speed')\n", + "ax3.set_ylabel('Wind (m/s)')\n", + "\n", + "# Add x_label visible for both x-axis\n", + "ax3.set_xlabel('Datetime')\n", + "\n", + "# Add label-description\n", + "ax3.legend(loc='upper right')\n", + "\n", + "# Format the x-axis to show all hours, in the format \"HH:MM\"\n", + "ax3.xaxis.set_major_locator(mdates.HourLocator())\n", + "ax3.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))\n", + "\n", + "# Add grid, but only vertically\n", + "ax3.grid(axis = 'x')\n", + "\n", + "# Adjust layout\n", + "plt.tight_layout()\n", + "\n", + "# Save the plot to the data/output_fig folder\n", + "plot_path = os.path.join(output_folder, f\"weather_data_plot{city_name}.png\")\n", + "plt.savefig(plot_path) # Save the plot as a PNG file\n", + "\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "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": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 890950087b27a0f1741c03d979fe39e1802d25ee Mon Sep 17 00:00:00 2001 From: toravest Date: Thu, 27 Mar 2025 17:24:24 +0100 Subject: [PATCH 08/18] add missingno-dependency, minor change (kelvin-number) --- requirements.txt | 7 +++++-- src/my_package/get_record.py | 23 +++++++++++++++++++++++ 2 files changed, 28 insertions(+), 2 deletions(-) create mode 100644 src/my_package/get_record.py diff --git a/requirements.txt b/requirements.txt index 78d1b7b..d671daa 100644 --- a/requirements.txt +++ b/requirements.txt @@ -13,7 +13,7 @@ certifi==2025.1.31 cffi==1.17.1 charset-normalizer==3.4.1 comm==0.2.2 -contourpy==1.3.1 +contourpy==1.2.0 cycler==0.12.1 debugpy==1.8.13 decorator==5.2.1 @@ -27,6 +27,7 @@ httpcore==1.0.7 httpx==0.28.1 idna==3.10 ipykernel==6.29.5 +ipympl==0.9.7 ipython==9.0.1 ipython_pygments_lexers==1.1.1 ipywidgets==8.1.5 @@ -54,6 +55,7 @@ kiwisolver==1.4.8 MarkupSafe==3.0.2 matplotlib==3.10.1 matplotlib-inline==0.1.7 +missingno==0.5.2 mistune==3.1.2 narwhals==1.29.0 nbclient==0.10.2 @@ -62,7 +64,7 @@ nbformat==5.10.4 nest-asyncio==1.6.0 notebook==7.3.2 notebook_shim==0.2.4 -numpy==2.2.3 +numpy==1.26.4 overrides==7.7.0 packaging==24.2 pandas==2.2.3 @@ -92,6 +94,7 @@ requests==2.32.3 rfc3339-validator==0.1.4 rfc3986-validator==0.1.1 rpds-py==0.23.1 +scipy==1.15.2 seaborn==0.13.2 Send2Trash==1.8.3 setuptools==75.8.2 diff --git a/src/my_package/get_record.py b/src/my_package/get_record.py new file mode 100644 index 0000000..7071615 --- /dev/null +++ b/src/my_package/get_record.py @@ -0,0 +1,23 @@ +import pandas as pd + +def get_records(df, city_name): + if df.empty: + print("df is empty") + + else: + max_temp_mean = df['temp.mean_celsius'].max() + min_temp_mean = df['temp.mean_celsius'].min() + + max_temp = df['temp.record_max'].max() - 272.15 + min_temp = df['temp.record_min'].min() - 272.15 + + summary_data = { + "Metric": ["Max Temp mean (°C)", "Min Temp Mean (°C)", "Max Temp (°C)", "Min temp (°C)"], + "Values": [max_temp_mean, min_temp_mean, max_temp, min_temp] + } + + summary_df = pd.DataFrame(summary_data) + folder = "../data/output_record" + filename = f"records_{city_name}" + + return summary_df, filename, folder \ No newline at end of file From 4975f4e15ed799422469949f6431efbc760ba4be Mon Sep 17 00:00:00 2001 From: toravest Date: Thu, 27 Mar 2025 19:39:18 +0100 Subject: [PATCH 09/18] rename notebook, add missigno, plot, comments, markdown to one_week, minor change to one_day --- notebooks/get_data_notebook.ipynb | 548 ------------------------- notebooks/notebook_one_day_data.ipynb | 49 ++- notebooks/notebook_one_week_data.ipynb | 540 ++++++++++++++++++++++++ 3 files changed, 582 insertions(+), 555 deletions(-) delete mode 100644 notebooks/get_data_notebook.ipynb create mode 100644 notebooks/notebook_one_week_data.ipynb diff --git a/notebooks/get_data_notebook.ipynb b/notebooks/get_data_notebook.ipynb deleted file mode 100644 index c3b6ad0..0000000 --- a/notebooks/get_data_notebook.ipynb +++ /dev/null @@ -1,548 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Velg start dato og sluttdato\n", - "\n", - "For å kunne hente data og gjøre en analyse trenger programmet å vite hvilken periode du vil hente ut for.\n", - "\n", - "Dataen skrives inn slik: (yyyy, mm, dd, hh, mm)\n", - "Her følger et eksempel: \n", - "|Hva|Hvordan|Eksempel|\n", - "|:---|:---:|:---:|\n", - "|år|yyyy|2025|\n", - "|måned|mm|03| \n", - "|dato|dd|01| \n", - "|time|hh|12| \n", - "|minutt|mm|00| \n", - "\n", - "Denne dataen skrives da inn på følgende hvis: (2025, 03, 01, 12, 00)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Start date => unix timestamp: 1742202600\n", - "End date => unix timestamp: 1742548200\n", - "Unix timestamp => start date: 2025-03-17 10:10:00\n", - "Unix timestamp => end date: 2025-03-21 10:10:00\n" - ] - } - ], - "source": [ - "import sys\n", - "import os\n", - "\n", - "# Gets the absolute path to the src folder\n", - "sys.path.append(os.path.abspath(\"../src\"))\n", - "\n", - "# Now we can import the fucntion from the module\n", - "from my_package.date_to_unix import get_unix_timestamp\n", - "from my_package.date_to_unix import from_unix_timestamp\n", - "\n", - "# Runs the function and store the data\n", - "unix_start_date, unix_end_date = get_unix_timestamp()\n", - "\n", - "# Prints the unix_timestamp\n", - "print(\"Start date => unix timestamp:\", unix_start_date)\n", - "print(\"End date => unix timestamp:\", unix_end_date)\n", - "\n", - "# Run the function to convert from unix_timestamp to date, and store the variables\n", - "start_date, end_date = from_unix_timestamp(unix_start_date, unix_end_date)\n", - "\n", - "# prints the date\n", - "print(\"Unix timestamp => start date:\", start_date)\n", - "print(\"Unix timestamp => end date:\", end_date)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Velg et sted i Norge og få data\n", - "\n", - "Skriv inn et sted du ønsker data fra, foreløpig er det begrenset til Norge\n", - "\n", - "Programmet vil deretter hente data å lagre det i en json fil" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data fetch: ok\n" - ] - } - ], - "source": [ - "import sys\n", - "import os\n", - "\n", - "# Gets the absolute path to the src folder\n", - "sys.path.append(os.path.abspath(\"../src\"))\n", - "\n", - "# Now we can import the fucntion from the module\n", - "from my_package.fetch_data import fetch_data\n", - "\n", - "# User input the city, for the weather\n", - "city_name = input(\"Enter a city in Norway: \")\n", - "\n", - "data = fetch_data(unix_start_date, unix_end_date, city_name)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Lagre data i en json-fil\n", - "\n", - "Skriv inn navn for til filen du vil lagre med dataen.\n", - "\n", - "Eks. test\n", - "Da vil filen lagres som data_**test**.json, i mappen \"../data/output_stedsnavn/data_{filnavn}.json\"\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data has been written to /Users/toravestlund/Documents/ITBAITBEDR/TDT4114 - Anvendt programmering/anvendt_mappe/data/output_stedsdata/data_test6.json\n" - ] - } - ], - "source": [ - "# Gets the absolute path to the src folder\n", - "sys.path.append(os.path.abspath(\"../src\"))\n", - "\n", - "from my_package.write_data import write_data\n", - "\n", - "filename = input(\"Write filename: \")\n", - "\n", - "write_data(data, filename)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Lese fra fil\n", - "\n", - "Henter opp data lagret i filen, lagd over, og skriver ut lesbart ved hjelp av pandas" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " message cod city_id calctime cnt \\\n", - "0 Count: 96 200 3133880 0.021173 96 \n", - "1 Count: 96 200 3133880 0.021173 96 \n", - "2 Count: 96 200 3133880 0.021173 96 \n", - "3 Count: 96 200 3133880 0.021173 96 \n", - "4 Count: 96 200 3133880 0.021173 96 \n", - ".. ... ... ... ... ... \n", - "91 Count: 96 200 3133880 0.021173 96 \n", - "92 Count: 96 200 3133880 0.021173 96 \n", - "93 Count: 96 200 3133880 0.021173 96 \n", - "94 Count: 96 200 3133880 0.021173 96 \n", - "95 Count: 96 200 3133880 0.021173 96 \n", - "\n", - " list \n", - "0 {'dt': 1742205600, 'main': {'temp': 1.98, 'fee... \n", - "1 {'dt': 1742209200, 'main': {'temp': 3.05, 'fee... \n", - "2 {'dt': 1742212800, 'main': {'temp': 3.6, 'feel... \n", - "3 {'dt': 1742216400, 'main': {'temp': 4.16, 'fee... \n", - "4 {'dt': 1742220000, 'main': {'temp': 4.11, 'fee... \n", - ".. ... \n", - "91 {'dt': 1742533200, 'main': {'temp': -0.24, 'fe... \n", - "92 {'dt': 1742536800, 'main': {'temp': -0.24, 'fe... \n", - "93 {'dt': 1742540400, 'main': {'temp': 0.62, 'fee... \n", - "94 {'dt': 1742544000, 'main': {'temp': 2.18, 'fee... \n", - "95 {'dt': 1742547600, 'main': {'temp': 5.03, 'fee... \n", - "\n", - "[96 rows x 6 columns]\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "data = pd.read_json(f'../data/output_stedsdata/data_{filename}.json')\n", - "\n", - "print(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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main.tempmain.feels_likemain.pressuremain.humiditymain.temp_minmain.temp_maxwind.speedwind.degwind.gustclouds.allrain.1h
dt
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96 rows × 11 columns

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" - ], - "text/plain": [ - " main.temp main.feels_like main.pressure main.humidity \\\n", - "dt \n", - "2025-03-17 10:00:00 1.98 0.11 1021 92 \n", - "2025-03-17 11:00:00 3.05 0.84 1021 93 \n", - "2025-03-17 12:00:00 3.60 1.49 1021 91 \n", - "2025-03-17 13:00:00 4.16 1.75 1021 92 \n", - "2025-03-17 14:00:00 4.11 0.75 1021 89 \n", - "... ... ... ... ... \n", - "2025-03-21 05:00:00 -0.24 -2.29 1024 91 \n", - "2025-03-21 06:00:00 -0.24 -2.14 1024 90 \n", - "2025-03-21 07:00:00 0.62 -0.97 1025 89 \n", - "2025-03-21 08:00:00 2.18 0.78 1025 92 \n", - "2025-03-21 09:00:00 5.03 3.85 1025 78 \n", - "\n", - " main.temp_min main.temp_max wind.speed wind.deg \\\n", - "dt \n", - "2025-03-17 10:00:00 1.07 2.77 1.79 203 \n", - "2025-03-17 11:00:00 2.73 3.33 2.24 225 \n", - "2025-03-17 12:00:00 3.03 3.88 2.24 248 \n", - "2025-03-17 13:00:00 3.84 4.44 2.68 270 \n", - "2025-03-17 14:00:00 3.88 5.03 4.02 293 \n", - "... ... ... ... ... \n", - "2025-03-21 05:00:00 -1.16 0.55 1.67 122 \n", - "2025-03-21 06:00:00 -1.16 0.55 1.57 136 \n", - "2025-03-21 07:00:00 -0.60 2.03 1.45 125 \n", - "2025-03-21 08:00:00 2.18 3.03 1.47 94 \n", - "2025-03-21 09:00:00 5.03 5.03 1.60 85 \n", - "\n", - " wind.gust clouds.all rain.1h \n", - "dt \n", - "2025-03-17 10:00:00 3.58 100 0.36 \n", - "2025-03-17 11:00:00 5.36 100 0.79 \n", - "2025-03-17 12:00:00 4.02 100 1.38 \n", - "2025-03-17 13:00:00 8.05 100 0.16 \n", - "2025-03-17 14:00:00 8.05 100 0.14 \n", - "... ... ... ... \n", - "2025-03-21 05:00:00 1.86 42 NaN \n", - "2025-03-21 06:00:00 1.67 44 NaN \n", - "2025-03-21 07:00:00 1.77 97 NaN \n", - "2025-03-21 08:00:00 1.99 88 NaN \n", - "2025-03-21 09:00:00 2.29 67 NaN \n", - "\n", - "[96 rows x 11 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "data = pd.read_json(f'../data/output_stedsdata/data_{filename}.json')\n", - "\n", - "if 'list' in data:\n", - " df = pd.json_normalize(data['list'])\n", - "\n", - " # Delete duplicates based on the dt row, all the other values can appear more than once, but the date should only appear once\n", - " df = df.drop_duplicates(subset=['dt'])\n", - "\n", - " # The weather column dosnt have any releated information, therefor we delete it\n", - " df = df.drop(columns=\"weather\")\n", - "\n", - " # Convert 'dt' column from Unix timestamp to datetime and set it as the index\n", - " df['dt'] = pd.to_datetime(df['dt'], unit='s')\n", - " df.set_index('dt', inplace=True)\n", - " \n", - "\n", - " \n", - "\n", - " # Ensure the DataFrame is displayed correctly\n", - " display(df)\n", - "\n", - " # # Extract main values\n", - " # temp = df['main.temp']\n", - " # humidity = df['main.humidity']\n", - "\n", - " # # Extract wind values\n", - " # w_speed = df['wind.speed']\n", - "\n", - " # # Extract other variables\n", - " # clouds = df['clouds.all']\n", - "\n", - " # try:\n", - " # rain = df['rain.1h']\n", - " # except KeyError:\n", - " # print(\"'Rain' is not present in the JSON file.\")\n", - "\n", - " # try:\n", - " # snow = df['snow.1h']\n", - " # except KeyError:\n", - " # print(\"'Snow' is not present in the JSON file.\")\n", - "\n", - " # # Print the average temperature\n", - " # print('Gjennomsnitts temperatur: ', temp.mean().round(2))\n", - "\n", - " # Display the temperature column\n", - " # display(temp)\n", - "else:\n", - " print(\"The 'list' key is not present in the JSON file.\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# \"komprimere oversikten over\"\n", - "# Som i å, finne gjennomsnitt av alle aktuelle data, \n", - "# høyeste, laveste (spesielt temp) i gitte periode" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "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": "3.12.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/notebook_one_day_data.ipynb b/notebooks/notebook_one_day_data.ipynb index d2972f8..1083d65 100644 --- a/notebooks/notebook_one_day_data.ipynb +++ b/notebooks/notebook_one_day_data.ipynb @@ -182,7 +182,9 @@ "metadata": {}, "source": [ "### Viser temperaturen\n", - "Regner ut gjennomsnittst-temperatur ved hjelp av innebygde funksjoner. Finner også høyeste og laveste målte temperatur." + "Regner ut gjennomsnittst-temperatur ved hjelp av innebygde funksjoner. Finner også høyeste og laveste målte temperatur.\n", + "\n", + "Plotter temperaturen ved hjelp av matplotlib." ] }, { @@ -191,6 +193,9 @@ "metadata": {}, "outputs": [], "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "\n", "# Stores the temperature values\n", "temp = df['main.temp']\n", "\n", @@ -204,7 +209,38 @@ "min_temp = df['main.temp'].min().round(2)\n", "\n", "print(\"Highest temperature:\", max_temp)\n", - "print(\"Lowest temperature:\", min_temp)" + "print(\"Lowest temperature:\", min_temp)\n", + "\n", + "# Set the x_axis to the index, which means the time\n", + "x_axis = df.index\n", + "\n", + "# Choose the width and height of the plot\n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "# Plotting temperatur\n", + "plt.plot(x_axis, temp, color='tab:red', label='Temperatur')\n", + "\n", + "# Get the current axsis, and store it as ax\n", + "ax = plt.gca()\n", + "\n", + "# Customize the x-axis to show ticks for each hour\n", + "ax.xaxis.set_major_locator(mdates.HourLocator(interval=1)) # Tick marks for every hour\n", + "ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M')) # Format as \"Day Month Hour:Minute\"\n", + "\n", + "# Adjust layout\n", + "plt.tight_layout()\n", + "\n", + "# Add title for the plot, with city_name and start to end date\n", + "plt.title(f'Temperatur {city_name}, ({date})')\n", + "\n", + "# Shows a grid\n", + "plt.grid()\n", + "\n", + "# Show the label-description\n", + "plt.legend(loc = 'upper right')\n", + "\n", + "# Show the plot\n", + "plt.show()" ] }, { @@ -266,7 +302,10 @@ "ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))\n", "\n", "# Add the label-desciption\n", - "plt.legend()\n", + "plt.legend(loc = 'upper right')\n", + "\n", + "# Add title to the plot, with date\n", + "plt.title(f'Precipitation {city_name}, ({date}))')\n", "\n", "# Shows the plot\n", "plt.show()" @@ -405,7 +444,6 @@ "# Two vertically stacked axis, (2 rows, 1 column), width and height of the figure, and the axis share the same x_axis\n", "fig, (ax1, ax3) = plt.subplots(2, 1,figsize=(15, 8), sharex=True)\n", "\n", - "\n", "# Set the title for the diagram, above the first axis, with city_name and input_date\n", "ax1.set_title(f'Weather data for {city_name} ({date}) ')\n", "\n", @@ -429,7 +467,6 @@ "ax2.set_ylabel(\"Precipitation (mm)\", color='tab:blue')\n", "ax2.tick_params(axis='y', labelcolor='tab:blue')\n", "\n", - "\n", "# Format the x-axis to show all hours, in the format \"HH:MM\"\n", "ax1.xaxis.set_major_locator(mdates.HourLocator()) \n", "ax1.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))\n", @@ -441,7 +478,6 @@ "# Add grid, but only vertically\n", "ax1.grid(axis = 'x')\n", "\n", - "\n", "# Plot the wind at the second x-axis (the axis below)\n", "ax3.plot(x_axis, wind_gust, color='tab:purple', label='Wind_gust')\n", "ax3.plot(x_axis, wind_speed, color='tab:purple', linestyle='dashed', label='Wind_speed')\n", @@ -467,7 +503,6 @@ "plot_path = os.path.join(output_folder, f\"weather_data_plot{city_name}.png\")\n", "plt.savefig(plot_path) # Save the plot as a PNG file\n", "\n", - "\n", "# Show the plot\n", "plt.show()" ] diff --git a/notebooks/notebook_one_week_data.ipynb b/notebooks/notebook_one_week_data.ipynb new file mode 100644 index 0000000..5d43ebc --- /dev/null +++ b/notebooks/notebook_one_week_data.ipynb @@ -0,0 +1,540 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Notebook - One week data\n", + "Denne notebooken henter data fra ønsket periode (inntil 7-dager) og sted, skriver til fil. Visualiserer manglende verdier, retter opp manglende verdier, og visualisere og lagrer data fra plot." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Velg start dato og sluttdato\n", + "\n", + "For å kunne hente data og gjøre en analyse trenger programmet å vite hvilken periode du vil hente ut for.\n", + "\n", + "Dataen skrives inn slik: (yyyy, mm, dd, hh, mm)\n", + "Her følger et eksempel: \n", + "|Hva|Hvordan|Eksempel|\n", + "|:---|:---:|:---:|\n", + "|år|yyyy|2025|\n", + "|måned|mm|03| \n", + "|dato|dd|01| \n", + "|time|hh|12| \n", + "|minutt|mm|00| \n", + "\n", + "Denne dataen skrives da inn på følgende hvis: (2025, 03, 01, 12, 00)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import os\n", + "\n", + "# Gets the absolute path to the src folder\n", + "sys.path.append(os.path.abspath(\"../src\"))\n", + "\n", + "# Now we can import the fucntion from the module\n", + "from my_package.date_to_unix import get_unix_timestamp\n", + "from my_package.date_to_unix import from_unix_timestamp\n", + "\n", + "# Runs the function and store the data\n", + "unix_start_date, unix_end_date = get_unix_timestamp()\n", + "\n", + "# Prints the unix_timestamp\n", + "print(\"Start date => unix timestamp:\", unix_start_date)\n", + "print(\"End date => unix timestamp:\", unix_end_date)\n", + "\n", + "# Run the function to convert from unix_timestamp to date, and store the variables\n", + "start_date, end_date = from_unix_timestamp(unix_start_date, unix_end_date)\n", + "\n", + "# Prints the date\n", + "print(\"Unix timestamp => start date:\", start_date)\n", + "print(\"Unix timestamp => end date:\", end_date)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Velg et sted i Norge og få data\n", + "\n", + "Skriv inn et sted du ønsker data fra, foreløpig er det begrenset til Norge\n", + "\n", + "Programmet vil deretter hente data å lagre det i en json fil" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import os\n", + "\n", + "# Gets the absolute path to the src folder\n", + "sys.path.append(os.path.abspath(\"../src\"))\n", + "\n", + "# Now we can import the fucntion from the module\n", + "from my_package.fetch_data import fetch_data\n", + "\n", + "# User input the city, for the weather\n", + "city_name = input(\"Enter a city in Norway: \")\n", + "\n", + "# Stores the values in the variables\n", + "data, folder = fetch_data(unix_start_date, unix_end_date, city_name)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Lagre data i en json-fil\n", + "\n", + "Skriv inn navn for til filen du vil lagre med dataen.\n", + "\n", + "Eks. test\n", + "Da vil filen lagres som data_**test**.json, i mappen \"../data/output_stedsnavn/data_{filnavn}.json\"\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Gets the absolute path to the src folder\n", + "sys.path.append(os.path.abspath(\"../src\"))\n", + "\n", + "from my_package.write_data import write_data\n", + "\n", + "# User chose the name for the file\n", + "filename = input(\"Write filename: \")\n", + "\n", + "# Write the data, with the choosen filename\n", + "write_data(data, folder, filename)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Lese fra fil\n", + "\n", + "Henter opp data lagret i filen, lagd over, og skriver ut lesbart ved hjelp av pandas" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "# Read json-file using pandas\n", + "data = pd.read_json(f'../data/output_stedsnavn/data_{filename}.json')\n", + "\n", + "# Display the data\n", + "display(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rensking av riktig data\n", + "Vi går inn i 'list' for å finne den relevante informasjonen, og ikke bare meta-informasjon.\n", + "\n", + "Sørger for å fjerne duplikater, og andre irelevante kolonner. Samt setter index kolonnen til tid." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Goes into the 'list' to get the needed and relevant information\n", + "if 'list' in data:\n", + " # Normalize the json, for better readability\n", + " df = pd.json_normalize(data['list'])\n", + "\n", + " # Delete duplicates based on the dt row, all the other values can appear more than once, but the date should only appear once\n", + " df = df.drop_duplicates(subset=['dt'])\n", + "\n", + " # The weather column does not have any releated information, therefor we delete it\n", + " df = df.drop(columns=\"weather\")\n", + "\n", + " # Convert 'dt' column from Unix timestamp to datetime and set it as the index\n", + " df['dt'] = pd.to_datetime(df['dt'], unit='s')\n", + " df.set_index('dt', inplace=True)\n", + "\n", + " # Display the datafram, with the changes\n", + " display(df)\n", + "else:\n", + " print(\"The 'list' key is not present in the JSON file.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Viser temperaturen\n", + "Regner ut gjennomsnittst-temperatur ved hjelp av innebygde funksjoner. Finner også høyeste og laveste målte temperatur.\n", + "\n", + "Plotter temperaturen for perioden." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "\n", + "# Extract main values\n", + "temp = df['main.temp']\n", + "temp_mean = temp.mean().round(2)\n", + "temp_max = temp.max().round(2)\n", + "temp_min = temp.min().round(2)\n", + "\n", + "# Print the average temperature\n", + "print(f'Mean temperatur: {temp_mean}')\n", + "print(f'Highest temperatur: {temp_max}')\n", + "print(f'Lowest temperatur: {temp_min}')\n", + "\n", + "# Set the x_axis to the index, which means the time\n", + "x_axis = df.index\n", + "\n", + "# Choose the width and height of the plot\n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "# Plotting temperatur\n", + "plt.plot(x_axis, temp, color='tab:red', label='Temperatur')\n", + "\n", + "# Get the current axsis, and store it as ax\n", + "ax = plt.gca()\n", + "\n", + "# Customize the x-axis to show ticks for each hour\n", + "ax.xaxis.set_major_locator(mdates.HourLocator(interval=12)) # Tick marks for every hour\n", + "ax.xaxis.set_major_formatter(mdates.DateFormatter('%d %b %H')) # Format as \"Day Month Hour:Minute\"\n", + "\n", + "# Adjust layout\n", + "plt.tight_layout()\n", + "\n", + "# Add title for the plot, with city_name and start to end date\n", + "plt.title(f'Temperatur {city_name}, from ({start_date}) to ({end_date})')\n", + "\n", + "# Shows a grid\n", + "plt.grid()\n", + "\n", + "# Show the label-description\n", + "plt.legend(loc = 'upper right')\n", + "\n", + "# Show the plot\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualiserer nedbør\n", + "Ved hjelp av matplotlib visualiserer vi nedbør for ønsket periode." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "import numpy as np\n", + "\n", + "x_axis = df.index\n", + "\n", + "# Checks if the rain is a value, it will not be if it is no rain and then cause a KeyError\n", + "try:\n", + " rain = df['rain.1h']\n", + "\n", + "# If no rain, make the rain column and fill it with NaN\n", + "except KeyError:\n", + " print(\"'Rain' is not present in the JSON file.\")\n", + " df['rain.1h'] = np.nan\n", + "\n", + "# Checks if the snow is a value, it will not be if it is no rain and then cause a KeyError\n", + "try:\n", + " snow = df['snow.1h']\n", + "\n", + "# If no snow, make the snow column and fill it with NaN\n", + "except KeyError:\n", + " print(\"'Snow' is not present in the JSON file.\")\n", + " df['snow.1h'] = np.nan\n", + "\n", + "# Choose the width and height of the plot\n", + "plt.figure(figsize=(15, 6))\n", + "\n", + "# Check with rain, will cause NameError if the try/except over fails\n", + "try:\n", + " plt.bar(x_axis, rain, width=0.02, alpha=0.5, color='tab:blue', label='rain')\n", + "except: NameError\n", + "\n", + "# Check with snow, will cause NameError if the try/except over fails\n", + "try: \n", + " plt.bar(x_axis, snow, width=0.02, alpha=0.5, color='tab:grey', label='snow')\n", + "except: NameError\n", + "\n", + "# Get the current axsis, and store it as ax\n", + "ax = plt.gca()\n", + "\n", + "# Customize the x-axis to show ticks for each hour\n", + "ax.xaxis.set_major_locator(mdates.HourLocator(interval=12)) # Tick marks for every hour\n", + "ax.xaxis.set_major_formatter(mdates.DateFormatter('%d %b %H')) # Format as \"Day Month Hour:Minute\"\n", + "\n", + "# Add the label-desciption\n", + "plt.legend(loc = 'upper right')\n", + "\n", + "# Add title to the plot, with city_name and start to end date\n", + "plt.title(f'Precipitation {city_name}, from ({start_date}) to ({end_date})')\n", + "\n", + "# Shows the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Vise dataframe, med nye kolonner\n", + "Hvis dataframen ikke inneholdt 'rain.1h' eller 'snow.1h', skal de nå ha blitt lagt til med 'NaN' verdier." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Display df, to see if 'rain.1h' and 'snow.1h' was added with NaN values\n", + "display(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sjekk for manglende verdier\n", + "Missigno sjekker og visualiserer manglende verdier, slik at det blir lettere å se hvilke kolonner feilen ligger i. \n", + "\n", + "Vis the blir \"hull\" i en søyle, tyder the på manglende verdier." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import missingno as msno\n", + "\n", + "# Checks for and display missing values\n", + "msno.matrix(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Endre manglende verdier\n", + "I de fleste tilfeller virker dataene å være tilnærmet \"perfekte\", men de inkluderer bare snø og regn dersom det er snø eller regn. Derfor vil vi fa NaN verdier i de målingene det ikke har regnet/snødd. \n", + "\n", + "Under sjekker vi først om regn eller snø er i målingen, og hvis den er, bytter vi ut NaN med 0." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# If rain is stored, fill the NaN with 0\n", + "try: \n", + " df['rain.1h'] = df['rain.1h'].fillna(0)\n", + "except KeyError:\n", + " print([\"'rain.1h', not in df\"])\n", + "\n", + "# If snow is stored, fill the NaN with 0\n", + "try: \n", + " df['snow.1h'] = df['snow.1h'].fillna(0)\n", + "except KeyError:\n", + " print(\"['snow.1h'], not in df\")\n", + "\n", + "# Display the df, now without NaN (atleast for rain and snow)\n", + "display(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualisere endring av data\n", + "Har lagt inn en ny missigno visualisering, for å se at de manglende dataene \"forsvinner\" når vi kjører cellen over. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import missingno as msno\n", + "\n", + "# Visulaize the same data again, but now it should be no missing values (atleast for rain and snow)\n", + "msno.matrix(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualisere data i en graf\n", + "Ved hjelp av Matplotlib har vi visualiert ønsket data, og ved hjelp av subplot, en modul i matplotlib, kan vi plotte flere verdier i samme graf, og få \"to y-akse\" på samme x-akse. \n", + "\n", + "Temperatur og nedbør får plass i samme graf, hvor man leser temperatur verdiene på venstre side, og nedbørsverdiene på høyre side.\n", + "\n", + "I grafen under, men på samme x-akse, finner vi informasjon om vind, både vindhastighet og vindkast." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "import os\n", + "\n", + "# Where the figure should be saved when exported\n", + "output_folder = \"../data/output_fig\"\n", + "\n", + "# Creates the folder if it does not exist\n", + "os.makedirs(output_folder, exist_ok=True)\n", + "\n", + "# x_axis set to the index, which mean the datetime\n", + "x_axis = df.index\n", + "\n", + "# Gets the values\n", + "rain = df['rain.1h']\n", + "temp = df['main.temp']\n", + "snow = df['snow.1h']\n", + "wind_gust = df['wind.gust']\n", + "wind_speed = df['wind.speed']\n", + "\n", + "# Two vertically stacked axis, (2 rows, 1 column), width and height of the figure, and the axis share the same x_axis\n", + "fig, (ax1, ax3) = plt.subplots(2, 1,figsize=(15, 8), sharex=True)\n", + "\n", + "\n", + "# Set the title for the diagram, above the first axis, with city_name and input_date\n", + "ax1.set_title(f'Weather data for {city_name} ({start_date}) to ({end_date}) ')\n", + "\n", + "# Plot temperature on the primary y-axis\n", + "ax1.plot(x_axis, temp, color='tab:red', label='Temperature (°C)')\n", + "\n", + "# Design the y-axis for temperatur\n", + "ax1.set_ylabel('Temperature (°C)', color='tab:red')\n", + "ax1.tick_params(axis='y', labelcolor='tab:red')\n", + "\n", + "# Plot Precipitation as bars on the secondary y-axis\n", + "ax2 = ax1.twinx()\n", + "\n", + "# Add rain\n", + "ax2.bar(x_axis, rain, color='tab:blue', alpha=0.5, width=0.02, label='Rain (mm)')\n", + "\n", + "# Add snow\n", + "ax2.bar(x_axis, snow, color='tab:grey', alpha=0.5, width=0.02, label='Snow (mm)')\n", + "\n", + "# Design the y-axis for precipiation\n", + "ax2.set_ylabel(\"Precipitation (mm)\", color='tab:blue')\n", + "ax2.tick_params(axis='y', labelcolor='tab:blue')\n", + "\n", + "\n", + "# Customize the x-axis to show ticks for each hour\n", + "ax1.xaxis.set_major_locator(mdates.HourLocator(interval=12)) # Tick marks for every hour\n", + "ax1.xaxis.set_major_formatter(mdates.DateFormatter('%d %b %H')) # Format as \"Day Month Hour:Minute\"\n", + "\n", + "# Add label-description for both axis\n", + "ax1.legend(loc='upper left')\n", + "ax2.legend(loc='upper right')\n", + "\n", + "# Add grid, but only vertically\n", + "ax1.grid(axis = 'x')\n", + "\n", + "\n", + "# Plot the wind at the second x-axis (the axis below)\n", + "ax3.plot(x_axis, wind_gust, color='tab:purple', label='Wind_gust')\n", + "ax3.plot(x_axis, wind_speed, color='tab:purple', linestyle='dashed', label='Wind_speed')\n", + "ax3.set_ylabel('Wind (m/s)')\n", + "\n", + "# Add x_label visible for both x-axis\n", + "ax3.set_xlabel('Datetime')\n", + "\n", + "# Add label-description\n", + "ax3.legend(loc='upper right')\n", + "\n", + "# Customize the x-axis to show ticks for each hour\n", + "ax3.xaxis.set_major_locator(mdates.HourLocator(interval=12)) # Tick marks for every hour\n", + "ax3.xaxis.set_major_formatter(mdates.DateFormatter('%d %b %H')) # Format as \"Day Month Hour:Minute\"\n", + "\n", + "# Add grid, but only vertically\n", + "ax3.grid(axis = 'x')\n", + "\n", + "# Adjust layout\n", + "plt.tight_layout()\n", + "\n", + "# Save the plot to the data/output_fig folder\n", + "plot_path = os.path.join(output_folder, f\"weather_data_plot{city_name}.png\")\n", + "plt.savefig(plot_path) # Save the plot as a PNG file\n", + "\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "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": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 704d63ffd053b409b377ebb3dba3176d5141497b Mon Sep 17 00:00:00 2001 From: toravest Date: Sun, 30 Mar 2025 12:29:31 +0200 Subject: [PATCH 10/18] rename notebook, check outliers, missing data (data cleaning) --- notebooks/notebook_statistic_data.ipynb | 581 ++++++++++++++++++++++++ notebooks/statistic_data_notebook.ipynb | 180 -------- 2 files changed, 581 insertions(+), 180 deletions(-) create mode 100644 notebooks/notebook_statistic_data.ipynb delete mode 100644 notebooks/statistic_data_notebook.ipynb diff --git a/notebooks/notebook_statistic_data.ipynb b/notebooks/notebook_statistic_data.ipynb new file mode 100644 index 0000000..836d891 --- /dev/null +++ b/notebooks/notebook_statistic_data.ipynb @@ -0,0 +1,581 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Notebook - Statistic data\n", + "Denne notebooken henter data fra en API som samler alle historiske data for ønsket sted, å regner ut statistiske verdier for alle dagene i året. Vi fjerner uønskede kolonner, utelukker ekstremverdier og visualiserer data gjennom plotter. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Velg et sted i Norge å få statistisk data\n", + "\n", + "Denne API-en henter statistisk historisk data, herunder, statistisk data basert på de historiske dataene, ikke reele statistisk historisk. \n", + "\n", + "Statistikken er basert på de historiske datane total sett, ikke for hvert år." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import os\n", + "\n", + "# Gets the absolute path to the src folder\n", + "sys.path.append(os.path.abspath(\"../src\"))\n", + "\n", + "# Now we can import the fucntion from the module\n", + "from my_package.year_data import fetch_data\n", + "\n", + "# User input the city, for the weather\n", + "city_name = input(\"Enter a city in Norway: \")\n", + "\n", + "for letter in city_name:\n", + " if letter in 'æøå':\n", + " city_name = city_name.replace('æ', 'ae')\n", + " city_name = city_name.replace('ø', 'o')\n", + " city_name = city_name.replace('å', 'aa')\n", + "\n", + "data, folder = fetch_data(city_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Lagre data i json-fil\n", + "\n", + "Skriv inn navn for til filen du vil lagre med dataen.\n", + "\n", + "Eks. test\n", + "Da vil filen lagres som data_**test**.json, i mappen \"../data/output_statistikk/data_{filnavn}.json\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Gets the absolute path to the src folder\n", + "sys.path.append(os.path.abspath(\"../src\"))\n", + "\n", + "from my_package.write_data import write_data\n", + "\n", + "filename = input(\"Write filename: \")\n", + "\n", + "write_data(data, folder, filename)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Lese fra fil\n", + "\n", + "Henter opp data lagret i filen over, og lagrer i en variabel." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "data = pd.read_json(f'../data/output_statistikk/data_{filename}.json')\n", + "\n", + "# Display data\n", + "display(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Lesbar data\n", + "Sørger for at dataen lagret over blir mer lesbar." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "# Checks if the 'result' column is in the data\n", + "if 'result' in data:\n", + " # Normalize the json and store it as a dataframe for better readability\n", + " df = pd.json_normalize(data['result'])\n", + "\n", + " # Display the dataframe\n", + " display(df)\n", + "else:\n", + " print(\"'result' not in data\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rydder i data\n", + "Fjerner alle kolonner vi ikke trenger, som standardavvik for alle kategorier for alle dager, vi kan regne ut en felles ved å bruke statistisc modulen. \n", + "\n", + "Ettersom alle kateogirene har lik data, ogg vi vil fjerne noen av verdiene fra alle kategoriene. Kan vi bruke filter funksjonen til å filtrere ut dataene som inneholder f.eks. '.st_dev'. Dette gjør at alle kategoirene fjernes på likt å vi slipper å skrive alle flere ganger." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Drop all columns that end with '...' using the filter function\n", + "df = df.drop(columns=df.filter(like='.p25').columns)\n", + "df = df.drop(columns=df.filter(like='.p75').columns)\n", + "df = df.drop(columns=df.filter(like='.st_dev').columns)\n", + "df = df.drop(columns=df.filter(like='.num').columns)\n", + "\n", + "display(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotter temperatur\n", + "Denne koden plotter data basert på gjennomsnitts temperatur gjennom året. For å sikre lagring av de ulike kjøringene, vil grafen bli lagret i mappen \"../data/output_fig/mean_temp_plot_{city_name}.json\"\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "import os\n", + "\n", + "output_folder = \"../data/output_fig\"\n", + "os.makedirs(output_folder, exist_ok=True) # Create the folder if it doesn't exist\n", + "\n", + "# Converts to and make a new column with celsius temp, and not kelvin\n", + "df['temp.mean_celsius'] = df['temp.mean'] - 272.15\n", + "temp = df['temp.mean_celsius']\n", + "\n", + "# Convert from day and month, to datetime\n", + "# df['date'] = pd.to_datetime(df[['month', 'day']].assign(year=2024))\n", + "\n", + "# Create a new column that concatenates month and day (e.g., \"03-01\" for March 1)\n", + "df['month_day'] = df[['month', 'day']].apply(lambda x: f\"{x['month']:02d}-{x['day']:02d}\",axis=1)\n", + "\n", + "# Plot the graph of the mean temperature\n", + "plt.figure(figsize=(12, 6))\n", + "plt.plot(df['month_day'], temp)\n", + "\n", + "# Label for easier reading and understanding of the plot\n", + "plt.title(f\"Mean temp - statistic historical {city_name}\")\n", + "plt.xlabel(\"Date\")\n", + "plt.ylabel(\"Temperature (°C)\")\n", + "\n", + "# Customize the x-axis to show ticks and labels only at the start of each month\n", + "plt.gca().xaxis.set_major_locator(mdates.MonthLocator()) \n", + "# Format ticks to show abbreviated month names (e.g., Jan, Feb)\n", + "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b')) \n", + "\n", + "plt.xticks(rotation=45)\n", + "plt.yticks(range(-20, 30, 2))\n", + "plt.tight_layout()\n", + "plt.grid()\n", + "\n", + "# Save the plot to the data/output_fig folder\n", + "plot_path = os.path.join(output_folder, f\"mean_temp_plot_{city_name}.png\")\n", + "plt.savefig(plot_path) # Save the plot as a PNG file\n", + "\n", + "# Show the plot\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotter data\n", + "Her plottes temperatur og regn på samme akse, med vind i en egen graf under, men de deler samme x-akse, som er month_date." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "import os\n", + "\n", + "# Defines the output folder for the figure, and makes it if is does not exsist\n", + "output_folder = \"../data/output_fig\"\n", + "os.makedirs(output_folder, exist_ok=True) \n", + "\n", + "# Converts to and make a new column with celsius temp, and not kelvin\n", + "df['temp.mean_celsius'] = df['temp.mean'] - 272.15\n", + "temp = df['temp.mean_celsius']\n", + "precipitation = df['precipitation.mean']\n", + "wind = df['wind.mean']\n", + "\n", + "# Create a new column that concatenates month and day (e.g., \"03-01\" for March 1)\n", + "df['month_day'] = df[['month', 'day']].apply(lambda x: f\"{x['month']:02d}-{x['day']:02d}\",axis=1)\n", + "\n", + "x_axis = df['month_day']\n", + "\n", + "fig, (ax1, ax3) = plt.subplots(2, 1, figsize = (15, 8), sharex=True)\n", + "\n", + "# Plot temperature on the primary y-axis\n", + "ax1.plot(x_axis, temp, color='tab:red', label='Temperature (°C)')\n", + "# ax1.set_xlabel('Datetime')\n", + "ax1.set_ylabel('Temperature (°C)', color='tab:red')\n", + "ax1.tick_params(axis='y', labelcolor='tab:red')\n", + "\n", + "# Plot precipitation as bars on the secondary y-axis\n", + "ax2 = ax1.twinx()\n", + "ax2.bar(x_axis, precipitation, color='tab:blue', alpha=0.5, width=1, label='Precipitation (mm)')\n", + "ax2.set_ylabel(\"Precipitation (mm)\", color='tab:blue')\n", + "ax2.tick_params(axis='y', labelcolor='tab:blue')\n", + "\n", + "ax1.grid(axis = 'x')\n", + "ax1.legend(loc='upper left')\n", + "ax2.legend(loc='upper right')\n", + "\n", + "ax3.plot(x_axis, wind, color='tab:purple', label='Wind (m/s)')\n", + "# ax3.plot(x_axis, wind_speed, color='tab:purple', linestyle='dashed', label='Wind_speed')\n", + "ax3.set_ylabel('Wind (m/s)')\n", + "ax3.set_xlabel('Datetime')\n", + "ax3.legend(loc='upper right')\n", + "\n", + "ax3.grid(axis = 'x')\n", + "\n", + "\n", + "# Customize the x-axis to show ticks and labels only at the start of each month\n", + "plt.gca().xaxis.set_major_locator(mdates.MonthLocator()) \n", + "# Format ticks to show abbreviated month names (e.g., Jan, Feb)\n", + "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b')) \n", + "\n", + "plt.tight_layout()\n", + "\n", + "# Show the plot\n", + "plt.show()\n", + "\n", + "print(df['precipitation.max'].max())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualiserer målte tempraturer\n", + "\n", + "Ved hjelp av matplotlib visualiserer vi temperaturen målt for alle dagene.\n", + "\n", + "Forklaring til grafen:\n", + "- Grå graf: gjennomsnitt av alle målingene\n", + "- Rød graf: høyeste målte temperatur\n", + "- Blå graf: laveste målte temperatur" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "\n", + "# Converts to and make a new column with celsius temp, and not kelvin\n", + "df['temp.mean_celsius'] = df['temp.mean'] - 272.15\n", + "temp_mean = df['temp.mean_celsius']\n", + "\n", + "df['temp.record_max_celsius'] = df['temp.record_max'] - 272.15\n", + "temp_record_max = df['temp.record_max_celsius']\n", + "\n", + "df['temp.record_min_celsius'] = df['temp.record_min'] - 272.15\n", + "temp_record_min = df['temp.record_min_celsius']\n", + "\n", + "# Create a new column that concatenates month and day (e.g., \"03-01\" for March 1)\n", + "df['month_day'] = df[['month', 'day']].apply(lambda x: f\"{x['month']:02d}-{x['day']:02d}\",axis=1)\n", + "\n", + "# Set the month_date as values for the x_axis\n", + "x_axis = df['month_day']\n", + "\n", + "# Defines the height and width of the figure\n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "# Plots the temperatur\n", + "plt.plot(x_axis, temp_mean, color='tab:gray', label='Mean temperatur')\n", + "plt.plot(x_axis, temp_record_max, color='tab:red', label = 'Max temperatur')\n", + "plt.plot(x_axis, temp_record_min, color='tab:blue', label = 'Min temperatur')\n", + "\n", + "\n", + "# Customize the x-axis to show ticks and labels only at the start of each month\n", + "plt.gca().xaxis.set_major_locator(mdates.MonthLocator()) \n", + "# Format ticks to show abbreviated month names (e.g., Jan, Feb)\n", + "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b')) \n", + "\n", + "plt.tight_layout()\n", + "\n", + "# Plot title with city_name\n", + "plt.title(f'Temperatur {city_name}')\n", + "\n", + "# Add grid\n", + "plt.grid()\n", + "\n", + "# Show the label description\n", + "plt.legend(loc = 'upper right')\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sjekker uteliggere\n", + "Denne koden sjekker om det er noen uteliggere i de ulike temperatur grafene, altså om noen verdier ligger mer enn 3 standardavvik i fra gjennomsnittet." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import statistics\n", + "\n", + "# Ensure 'month_day' is set as the index\n", + "if 'month_day' in df.columns:\n", + " df.set_index('month_day', inplace=True)\n", + "else:\n", + " print('month_day not in')\n", + "\n", + "# Extract temperature columns\n", + "temp_mean = df['temp.mean_celsius']\n", + "temp_record_min = df['temp.record_min_celsius']\n", + "temp_record_max = df['temp.record_max_celsius']\n", + "\n", + "# Calculate means\n", + "temp_mean_mean = temp_mean.mean()\n", + "temp_record_min_mean = temp_record_min.mean()\n", + "temp_record_max_mean = temp_record_max.mean()\n", + "\n", + "# Calculate standard deviations\n", + "temp_mean_stdev = statistics.stdev(temp_mean)\n", + "temp_record_min_stdev = statistics.stdev(temp_record_min)\n", + "temp_record_max_stdev = statistics.stdev(temp_record_max)\n", + "\n", + "# Calculate 3 standard deviation limits\n", + "mean_lower_limit = temp_mean_mean - (temp_mean_stdev * 3)\n", + "mean_upper_limit = temp_mean_mean + (temp_mean_stdev * 3)\n", + "\n", + "min_lower_limit = temp_record_min_mean - (temp_record_min_stdev * 3)\n", + "min_upper_limit = temp_record_min_mean + (temp_record_min_stdev * 3)\n", + "\n", + "max_lower_limit = temp_record_max_mean - (temp_record_max_stdev * 3)\n", + "max_upper_limit = temp_record_max_mean + (temp_record_max_stdev * 3)\n", + "\n", + "# Identify outliers\n", + "mean_outliers = df.loc[(df['temp.mean_celsius'] > mean_upper_limit) | (df['temp.mean_celsius'] < mean_lower_limit), 'temp.mean_celsius']\n", + "min_outliers = df.loc[(df['temp.record_min_celsius'] > min_upper_limit) | (df['temp.record_min_celsius'] < min_lower_limit), 'temp.record_min_celsius']\n", + "max_outliers = df.loc[(df['temp.record_max_celsius'] > max_upper_limit) | (df['temp.record_max_celsius'] < max_lower_limit), 'temp.record_max_celsius']\n", + "\n", + "# Print the outliers\n", + "print(\"Outliers in temp.mean_celsius:\")\n", + "print(mean_outliers)\n", + "\n", + "print(\"Outliers in temp.record_min_celsius:\")\n", + "print(min_outliers)\n", + "\n", + "print(\"Outliers in temp.record_max_celsius:\")\n", + "print(max_outliers)\n", + "\n", + "# Replace outliers with NaN\n", + "df.loc[(df['temp.mean_celsius'] > mean_upper_limit) | (df['temp.mean_celsius'] < mean_lower_limit), 'temp.mean_celsius'] = np.nan\n", + "df.loc[(df['temp.record_min_celsius'] > min_upper_limit) | (df['temp.record_min_celsius'] < min_lower_limit), 'temp.record_min_celsius'] = np.nan\n", + "df.loc[(df['temp.record_max_celsius'] > max_upper_limit) | (df['temp.record_max_celsius'] < max_lower_limit), 'temp.record_max_celsius'] = np.nan\n", + "\n", + "# Interpolate to replace NaN values with linear interpolation\n", + "df['temp.mean_celsius'] = df['temp.mean_celsius'].interpolate(method='linear')\n", + "df['temp.record_min_celsius'] = df['temp.record_min_celsius'].interpolate(method='linear')\n", + "df['temp.record_max_celsius'] = df['temp.record_max_celsius'].interpolate(method='linear')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualiserer temperatur etter endringer\n", + "Hvis det er uteliggere i dataen, som skal ha blitt endret, vil denne plotten vise en mer riktig og \"feilfri\" plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "\n", + "# Ensure 'month_day' is set as the index for proper plotting\n", + "if 'month_day' in df.columns:\n", + " df.set_index('month_day', inplace=True)\n", + "\n", + "# Extract updated temperature columns\n", + "temp_mean = df['temp.mean_celsius']\n", + "temp_record_max = df['temp.record_max_celsius']\n", + "temp_record_min = df['temp.record_min_celsius']\n", + "\n", + "# Plot the updated temperature data\n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "# Plot mean, max, and min temperatures\n", + "plt.plot(temp_mean.index, temp_mean, color='tab:gray', label='Mean Temperature')\n", + "plt.plot(temp_record_max.index, temp_record_max, color='tab:red', label='Max Temperature')\n", + "plt.plot(temp_record_min.index, temp_record_min, color='tab:blue', label='Min Temperature')\n", + "\n", + "# Customize the x-axis to show ticks and labels only at the start of each month\n", + "plt.gca().xaxis.set_major_locator(mdates.MonthLocator()) \n", + "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b')) # Format ticks to show abbreviated month names (e.g., Jan, Feb)\n", + "\n", + "# Add labels, title, and legend\n", + "plt.xlabel('Month-Day')\n", + "plt.ylabel('Temperature (°C)')\n", + "plt.title(f'Temperature Data for {city_name}')\n", + "plt.legend(loc='upper right')\n", + "\n", + "# Add grid for better readability\n", + "plt.grid()\n", + "\n", + "# Adjust layout to prevent overlap\n", + "plt.tight_layout()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rekorder\n", + "\n", + "Denne funksjonen regner ut ulike rekorder for året, for angitt sted." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import os\n", + "\n", + "# Gets the absolute path to the src folder\n", + "sys.path.append(os.path.abspath(\"../src\"))\n", + "\n", + "from my_package.get_record import get_records\n", + "\n", + "summary_df, filename, folder = get_records(df, city_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Skriver dataen til fil\n", + "Lagrer rekord-dataen i en fil, med stedsnavn." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Gets the absolute path to the src folder\n", + "sys.path.append(os.path.abspath(\"../src\"))\n", + "\n", + "from my_package.write_data import write_data\n", + "# makes the data 'json-compatible'\n", + "json_data = summary_df.to_dict(orient=\"records\")\n", + "\n", + "write_data(json_data, folder, filename)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Leser fra fil, og printer data\n", + "Denne funksjonen henter rekordene fra filen den ble skrevet til, og displayer de som en fin lettlest tabell." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import json\n", + "\n", + "# Reads data from file and store it\n", + "with open(f\"../data/output_record/data_{filename}.json\", \"r\", encoding=\"utf-8\") as file:\n", + " data = json.load(file)\n", + "\n", + "# Normalize the data for better readability\n", + "df = pd.json_normalize(data)\n", + "\n", + "\n", + "# Displays the dataframe\n", + "display(df)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "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": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/statistic_data_notebook.ipynb b/notebooks/statistic_data_notebook.ipynb deleted file mode 100644 index e4b10d1..0000000 --- a/notebooks/statistic_data_notebook.ipynb +++ /dev/null @@ -1,180 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Velg et sted i Norge å få statistisk data\n", - "\n", - "Denne API-en henter statistisk historisk data, herunder, statistisk data basert på de historiske dataene, ikke reele statistisk historisk. \n", - "\n", - "Statistikken er basert på de historiske datane total sett, ikke for hvert år." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "import os\n", - "\n", - "# Gets the absolute path to the src folder\n", - "sys.path.append(os.path.abspath(\"../src\"))\n", - "\n", - "# Now we can import the fucntion from the module\n", - "from my_package.year_data import fetch_data\n", - "\n", - "# User input the city, for the weather\n", - "city_name = input(\"Enter a city in Norway: \")\n", - "\n", - "data, folder = fetch_data(city_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Lagre data i json-fil\n", - "\n", - "Skriv inn navn for til filen du vil lagre med dataen.\n", - "\n", - "Eks. test\n", - "Da vil filen lagres som data_**test**.json, i mappen \"../data/output_statistikk/data_{filnavn}.json\"\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Gets the absolute path to the src folder\n", - "sys.path.append(os.path.abspath(\"../src\"))\n", - "\n", - "from my_package.write_data import write_data\n", - "\n", - "filename = input(\"Write filename: \")\n", - "\n", - "write_data(data, folder, filename)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Lese fra fil\n", - "\n", - "Henter opp data lagret i filen, lagd over, og skriver ut lesbart ved hjelp av pandas" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "data = pd.read_json(f'../data/output_statistikk/data_{filename}.json')\n", - "\n", - "display(data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "if 'result' in data:\n", - " df = pd.json_normalize(data['result'])\n", - "\n", - " display(df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plotter data\n", - "Denne koden plotter data basert på gjennomsnitts temperatur gjennom året. For å sikre lagring av de ulike kjøringene, vil grafen bli lagret i mappen \"../data/output_fig/mean_temp_plot_{city_name}.json\"\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import matplotlib.dates as mdates\n", - "import os\n", - "\n", - "output_folder = \"../data/output_fig\"\n", - "os.makedirs(output_folder, exist_ok=True) # Create the folder if it doesn't exist\n", - "\n", - "# Converts to and make a new column with celsius temp, and not kelvin\n", - "df['temp.mean_celsius'] = df['temp.mean'] - 273.15\n", - "temp = df['temp.mean_celsius']\n", - "\n", - "# Convert from day and month, to datetime\n", - "# df['date'] = pd.to_datetime(df[['month', 'day']].assign(year=2024))\n", - "\n", - "# Create a new column that concatenates month and day (e.g., \"03-01\" for March 1)\n", - "df['month_day'] = df[['month', 'day']].apply(lambda x: f\"{x['month']:02d}-{x['day']:02d}\",axis=1)\n", - "\n", - "# Plot the graph of the mean temperature\n", - "plt.figure(figsize=(12, 6))\n", - "plt.plot(df['month_day'], temp)\n", - "\n", - "# Label for easier reading and understanding of the plot\n", - "plt.title(f\"Mean temp - statistic historical {city_name}\")\n", - "plt.xlabel(\"Date\")\n", - "plt.ylabel(\"Temperature (°C)\")\n", - "\n", - "# Customize the x-axis to show ticks and labels only at the start of each month\n", - "plt.gca().xaxis.set_major_locator(mdates.MonthLocator()) \n", - "# Format ticks to show abbreviated month names (e.g., Jan, Feb)\n", - "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b')) \n", - "\n", - "plt.xticks(rotation=45)\n", - "plt.yticks(range(-20, 30, 2))\n", - "plt.tight_layout()\n", - "plt.grid()\n", - "\n", - "# Save the plot to the data/output_fig folder\n", - "plot_path = os.path.join(output_folder, f\"mean_temp_plot_{city_name}.png\")\n", - "plt.savefig(plot_path) # Save the plot as a PNG file\n", - "\n", - "# Show the plot\n", - "plt.show()\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "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": "3.12.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 2d9de07d6552cebe4492549e746260566da04915 Mon Sep 17 00:00:00 2001 From: toravest Date: Sun, 30 Mar 2025 12:30:43 +0200 Subject: [PATCH 11/18] =?UTF-8?q?handling=20nordic=20(=C3=A6=C3=B8=C3=A5),?= =?UTF-8?q?=20data=20cleaning?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- notebooks/notebook_current_data.ipynb | 159 ++++++++++++++++++++++++-- 1 file changed, 152 insertions(+), 7 deletions(-) diff --git a/notebooks/notebook_current_data.ipynb b/notebooks/notebook_current_data.ipynb index 4526089..0287d13 100644 --- a/notebooks/notebook_current_data.ipynb +++ b/notebooks/notebook_current_data.ipynb @@ -19,9 +19,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data fetch: ok\n" + ] + } + ], "source": [ "import sys\n", "import os\n", @@ -35,6 +43,12 @@ "# User input the city, for the weather\n", "city_name = input(\"Enter a city in Norway: \")\n", "\n", + "for letter in city_name:\n", + " if letter in 'æøå':\n", + " city_name = city_name.replace('æ', 'ae')\n", + " city_name = city_name.replace('ø', 'o')\n", + " city_name = city_name.replace('å', 'aa')\n", + "\n", "# Stores the return of the function\n", "data, folder = fetch_current_data(city_name)" ] @@ -54,9 +68,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has been written to /Users/toravestlund/Documents/ITBAITBEDR/TDT4114 - Anvendt programmering/anvendt_mappe/data/../data/output_current_data/data_aaa_maura.json\n" + ] + } + ], "source": [ "# Gets the absolute path to the src folder\n", "sys.path.append(os.path.abspath(\"../src\"))\n", @@ -81,9 +103,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'coord': {'lon': 11.0167, 'lat': 60.25},\n", + " 'weather': [{'id': 500,\n", + " 'main': 'Rain',\n", + " 'description': 'light rain',\n", + " 'icon': '10d'}],\n", + " 'base': 'stations',\n", + " 'main': {'temp': 3.14,\n", + " 'temp_min': 2.52,\n", + " 'temp_max': 3.84,\n", + " 'humidity': 93,\n", + " 'sea_level': 1003,\n", + " 'grnd_level': 965},\n", + " 'visibility': 10000,\n", + " 'wind': {'speed': 1.54, 'deg': 70},\n", + " 'rain': {'1h': 0.11},\n", + " 'clouds': {'all': 75},\n", + " 'dt': 1743329160,\n", + " 'sys': {'type': 1,\n", + " 'id': 1624,\n", + " 'country': 'NO',\n", + " 'sunrise': 1743309996,\n", + " 'sunset': 1743357233},\n", + " 'timezone': 7200,\n", + " 'id': 3146270,\n", + " 'name': 'Maura',\n", + " 'cod': 200}" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import json\n", "\n", @@ -118,7 +175,92 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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namemain.tempmain.humiditymain.sea_levelmain.grnd_levelwind.speedrain.1hclouds.allsys.countrysys.sunrisesys.sunset
dt
2025-03-30 10:06:00Maura3.149310039651.540.1175NO2025-03-30 04:46:362025-03-30 17:53:53
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" + ], + "text/plain": [ + " name main.temp main.humidity main.sea_level \\\n", + "dt \n", + "2025-03-30 10:06:00 Maura 3.14 93 1003 \n", + "\n", + " main.grnd_level wind.speed rain.1h clouds.all \\\n", + "dt \n", + "2025-03-30 10:06:00 965 1.54 0.11 75 \n", + "\n", + " sys.country sys.sunrise sys.sunset \n", + "dt \n", + "2025-03-30 10:06:00 NO 2025-03-30 04:46:36 2025-03-30 17:53:53 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import pandas as pd\n", "\n", @@ -151,6 +293,9 @@ "df['dt'] = pd.to_datetime(df['dt'], unit='s')\n", "df.set_index('dt', inplace=True)\n", "\n", + "# Drops the whole column, if all values is 'NaN' value.\n", + "df = df.dropna(axis='columns', how='all')\n", + "\n", "# Display the df after changes\n", "display(df)" ] From 0fbde5cef78e84b4e5ee0ca38b152865aef4b8e3 Mon Sep 17 00:00:00 2001 From: toravest Date: Sun, 30 Mar 2025 12:31:54 +0200 Subject: [PATCH 12/18] =?UTF-8?q?handling=20nordic=20(=C3=A6=C3=B8=C3=A5),?= =?UTF-8?q?=20handlel=20missing=20values,=20data=20cleaning?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- notebooks/notebook_one_week_data.ipynb | 1212 +++++++++++++++++++++--- 1 file changed, 1063 insertions(+), 149 deletions(-) diff --git a/notebooks/notebook_one_week_data.ipynb b/notebooks/notebook_one_week_data.ipynb index 5d43ebc..9f5def1 100644 --- a/notebooks/notebook_one_week_data.ipynb +++ b/notebooks/notebook_one_week_data.ipynb @@ -31,9 +31,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Start date => unix timestamp: 1742202600\n", + "End date => unix timestamp: 1742721000\n", + "Unix timestamp => start date: 2025-03-17 10:10:00\n", + "Unix timestamp => end date: 2025-03-23 10:10:00\n" + ] + } + ], "source": [ "import sys\n", "import os\n", @@ -73,9 +84,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data fetch: ok\n" + ] + } + ], "source": [ "import sys\n", "import os\n", @@ -89,6 +108,12 @@ "# User input the city, for the weather\n", "city_name = input(\"Enter a city in Norway: \")\n", "\n", + "for letter in city_name:\n", + " if letter in 'æøå':\n", + " city_name = city_name.replace('æ', 'ae')\n", + " city_name = city_name.replace('ø', 'o')\n", + " city_name = city_name.replace('å', 'aa')\n", + "\n", "# Stores the values in the variables\n", "data, folder = fetch_data(unix_start_date, unix_end_date, city_name)\n" ] @@ -108,9 +133,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has been written to /Users/toravestlund/Documents/ITBAITBEDR/TDT4114 - Anvendt programmering/anvendt_mappe/data/../data/output_stedsnavn/data_test.json\n" + ] + } + ], "source": [ "# Gets the absolute path to the src folder\n", "sys.path.append(os.path.abspath(\"../src\"))\n", @@ -135,9 +168,177 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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messagecodcity_idcalctimecntlist
0Count: 14420031338800.024779144{'dt': 1742205600, 'main': {'temp': 1.98, 'fee...
1Count: 14420031338800.024779144{'dt': 1742209200, 'main': {'temp': 3.05, 'fee...
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4Count: 14420031338800.024779144{'dt': 1742220000, 'main': {'temp': 4.11, 'fee...
.....................
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144 rows × 12 columns

\n", + "
" + ], + "text/plain": [ + " main.temp main.feels_like main.pressure main.humidity \\\n", + "dt \n", + "2025-03-17 10:00:00 1.98 0.11 1021 92 \n", + "2025-03-17 11:00:00 3.05 0.84 1021 93 \n", + "2025-03-17 12:00:00 3.60 1.49 1021 91 \n", + "2025-03-17 13:00:00 4.16 1.75 1021 92 \n", + "2025-03-17 14:00:00 4.11 0.75 1021 89 \n", + "... ... ... ... ... \n", + "2025-03-23 05:00:00 6.03 4.19 1020 75 \n", + "2025-03-23 06:00:00 6.03 4.15 1020 67 \n", + "2025-03-23 07:00:00 6.03 4.40 1020 64 \n", + "2025-03-23 08:00:00 7.03 7.03 1020 61 \n", + "2025-03-23 09:00:00 8.03 8.03 1020 61 \n", + "\n", + " main.temp_min main.temp_max wind.speed wind.deg \\\n", + "dt \n", + "2025-03-17 10:00:00 1.07 2.77 1.79 203 \n", + "2025-03-17 11:00:00 2.73 3.33 2.24 225 \n", + "2025-03-17 12:00:00 3.03 3.88 2.24 248 \n", + "2025-03-17 13:00:00 3.84 4.44 2.68 270 \n", + "2025-03-17 14:00:00 3.88 5.03 4.02 293 \n", + "... ... ... ... ... \n", + "2025-03-23 05:00:00 6.03 6.03 2.42 100 \n", + "2025-03-23 06:00:00 6.03 6.03 2.46 87 \n", + "2025-03-23 07:00:00 6.03 6.03 2.18 89 \n", + "2025-03-23 08:00:00 7.03 7.03 1.19 82 \n", + "2025-03-23 09:00:00 8.03 8.03 1.05 4 \n", + "\n", + " wind.gust clouds.all rain.1h snow.1h \n", + "dt \n", + "2025-03-17 10:00:00 3.58 100 0.36 NaN \n", + "2025-03-17 11:00:00 5.36 100 0.79 NaN \n", + "2025-03-17 12:00:00 4.02 100 1.38 NaN \n", + "2025-03-17 13:00:00 8.05 100 0.16 NaN \n", + "2025-03-17 14:00:00 8.05 100 0.14 NaN \n", + "... ... ... ... ... \n", + "2025-03-23 05:00:00 2.72 84 NaN NaN \n", + "2025-03-23 06:00:00 3.32 74 NaN NaN \n", + "2025-03-23 07:00:00 3.20 8 NaN NaN \n", + "2025-03-23 08:00:00 2.34 5 NaN NaN \n", + "2025-03-23 09:00:00 2.23 3 NaN NaN \n", + "\n", + "[144 rows x 12 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ + "import numpy as np\n", + "\n", "# Goes into the 'list' to get the needed and relevant information\n", "if 'list' in data:\n", " # Normalize the json, for better readability\n", @@ -179,6 +660,24 @@ " df['dt'] = pd.to_datetime(df['dt'], unit='s')\n", " df.set_index('dt', inplace=True)\n", "\n", + " # Checks if the rain is a value, it will not be if it is no rain and then cause a KeyError\n", + " try:\n", + " rain = df['rain.1h']\n", + "\n", + " # If no rain, make the rain column and fill it with NaN\n", + " except KeyError:\n", + " print(\"'Rain' is not present in the JSON file.\")\n", + " df['rain.1h'] = np.nan\n", + "\n", + " # Checks if the snow is a value, it will not be if it is no rain and then cause a KeyError\n", + " try:\n", + " snow = df['snow.1h']\n", + "\n", + " # If no snow, make the snow column and fill it with NaN\n", + " except KeyError:\n", + " print(\"'Snow' is not present in the JSON file.\")\n", + " df['snow.1h'] = np.nan\n", + "\n", " # Display the datafram, with the changes\n", " display(df)\n", "else:\n", @@ -190,20 +689,25 @@ "metadata": {}, "source": [ "### Viser temperaturen\n", - "Regner ut gjennomsnittst-temperatur ved hjelp av innebygde funksjoner. Finner også høyeste og laveste målte temperatur.\n", - "\n", - "Plotter temperaturen for perioden." + "Regner ut gjennomsnittst-temperatur ved hjelp av innebygde funksjoner. Finner også høyeste og laveste målte temperatur." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean temperatur: 5.33\n", + "Highest temperatur: 13.03\n", + "Lowest temperatur: -0.24\n" + ] + } + ], "source": [ - "import matplotlib.pyplot as plt\n", - "import matplotlib.dates as mdates\n", - "\n", "# Extract main values\n", "temp = df['main.temp']\n", "temp_mean = temp.mean().round(2)\n", @@ -213,124 +717,7 @@ "# Print the average temperature\n", "print(f'Mean temperatur: {temp_mean}')\n", "print(f'Highest temperatur: {temp_max}')\n", - "print(f'Lowest temperatur: {temp_min}')\n", - "\n", - "# Set the x_axis to the index, which means the time\n", - "x_axis = df.index\n", - "\n", - "# Choose the width and height of the plot\n", - "plt.figure(figsize=(12, 6))\n", - "\n", - "# Plotting temperatur\n", - "plt.plot(x_axis, temp, color='tab:red', label='Temperatur')\n", - "\n", - "# Get the current axsis, and store it as ax\n", - "ax = plt.gca()\n", - "\n", - "# Customize the x-axis to show ticks for each hour\n", - "ax.xaxis.set_major_locator(mdates.HourLocator(interval=12)) # Tick marks for every hour\n", - "ax.xaxis.set_major_formatter(mdates.DateFormatter('%d %b %H')) # Format as \"Day Month Hour:Minute\"\n", - "\n", - "# Adjust layout\n", - "plt.tight_layout()\n", - "\n", - "# Add title for the plot, with city_name and start to end date\n", - "plt.title(f'Temperatur {city_name}, from ({start_date}) to ({end_date})')\n", - "\n", - "# Shows a grid\n", - "plt.grid()\n", - "\n", - "# Show the label-description\n", - "plt.legend(loc = 'upper right')\n", - "\n", - "# Show the plot\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Visualiserer nedbør\n", - "Ved hjelp av matplotlib visualiserer vi nedbør for ønsket periode." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import matplotlib.dates as mdates\n", - "import numpy as np\n", - "\n", - "x_axis = df.index\n", - "\n", - "# Checks if the rain is a value, it will not be if it is no rain and then cause a KeyError\n", - "try:\n", - " rain = df['rain.1h']\n", - "\n", - "# If no rain, make the rain column and fill it with NaN\n", - "except KeyError:\n", - " print(\"'Rain' is not present in the JSON file.\")\n", - " df['rain.1h'] = np.nan\n", - "\n", - "# Checks if the snow is a value, it will not be if it is no rain and then cause a KeyError\n", - "try:\n", - " snow = df['snow.1h']\n", - "\n", - "# If no snow, make the snow column and fill it with NaN\n", - "except KeyError:\n", - " print(\"'Snow' is not present in the JSON file.\")\n", - " df['snow.1h'] = np.nan\n", - "\n", - "# Choose the width and height of the plot\n", - "plt.figure(figsize=(15, 6))\n", - "\n", - "# Check with rain, will cause NameError if the try/except over fails\n", - "try:\n", - " plt.bar(x_axis, rain, width=0.02, alpha=0.5, color='tab:blue', label='rain')\n", - "except: NameError\n", - "\n", - "# Check with snow, will cause NameError if the try/except over fails\n", - "try: \n", - " plt.bar(x_axis, snow, width=0.02, alpha=0.5, color='tab:grey', label='snow')\n", - "except: NameError\n", - "\n", - "# Get the current axsis, and store it as ax\n", - "ax = plt.gca()\n", - "\n", - "# Customize the x-axis to show ticks for each hour\n", - "ax.xaxis.set_major_locator(mdates.HourLocator(interval=12)) # Tick marks for every hour\n", - "ax.xaxis.set_major_formatter(mdates.DateFormatter('%d %b %H')) # Format as \"Day Month Hour:Minute\"\n", - "\n", - "# Add the label-desciption\n", - "plt.legend(loc = 'upper right')\n", - "\n", - "# Add title to the plot, with city_name and start to end date\n", - "plt.title(f'Precipitation {city_name}, from ({start_date}) to ({end_date})')\n", - "\n", - "# Shows the plot\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Vise dataframe, med nye kolonner\n", - "Hvis dataframen ikke inneholdt 'rain.1h' eller 'snow.1h', skal de nå ha blitt lagt til med 'NaN' verdier." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Display df, to see if 'rain.1h' and 'snow.1h' was added with NaN values\n", - "display(df)" + "print(f'Lowest temperatur: {temp_min}')" ] }, { @@ -345,9 +732,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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1bPB1IDOS/q1btzZPPPGEadSokR3QRPgEVyyq6sPFF19sbrzxRlu61k0GiEz69+jRw0yYMMFs2LDBnqNztSe7SrerKkCxYsXSlPtHcvnzzz9tm6HJQtoiJMj9m1epUsV+/e6772zcRFJsKWnSvHlzW22kXLlytoLEV199lUmfAlmFWzUbbC/c90db3SHa79HHSm7EEWLBxcxrr71m+8itWrWy5fsHDx5sHnroIbtd0euvv25OOOEEM378eJL+AAAAAIBMRU1mIIuWidSg0tatW03RokXZLx1xSfpfcMEFpm3btiZv3rzEYMgokeHaogceeMAObB9//PHm3nvvtSuvRZMB1Ga5pL9+fvPNN+0qN+1pXLNmTTNp0iRbul0VI04++WT/75PsT14FChQw99xzj/n5559N586d7Wv//POPKVSokL/HdbVq1Uz9+vXtOZMnTza33HKLHxPB+6AmjpQvX97GV0pKim2TEB7uvqM2ZOzYsWbhwoX2NZVQv/rqq03FihWP+G8G91kfMWKEnYikOKVNSl7EEWJp9+7d5rPPPrP3KfWJtL2Mi4eSJUva+1T+/PltbCnpr3ua4k79agAAAAAAjqVsHksRgCwhmOTQ4KH2LZ45c6ZNnJUuXdom3ZQwU9IEiCZy1XRwQBo4Gg8++KBduXb++eebQYMGmVNPPTXdc5XUVWJWJfznzZtn8uXLZ9urgQMHmptuusmew8r+cNCKRv07K8nWr18/s2bNGvPkk0/aBJvz1FNP2WSJztM9r0uXLnaygPP999/bhMlZZ51lhg8f7r9OuxauPtHmzZtNu3btbJsSpKoPd999t+nYsaOtAnE4grEzatQo07NnT7tNhKpSHHfccbRNSYg4Qqz99ddfpmHDhnbSiCaPFCxYMM19SVsbPf300+bhhx82e/fuNZdccold/U/SHwAAAABwLJHwB7KAYBJMCRAlQkSDSHpdiTStGtEqyCuvvNKceOKJcb5iZNVBba080gBknTp1TO7cuTP0N0nOhttHH31kk7CNGze25dm1ii1o48aNdsWkVms7Gtj+448/bOl1JXeV/HCTBEjUhs+CBQvMOeecY6vV6P6lPY+DSf9evXqZ559/3rYzer9p06Z2L2RNdlOyZO7cuXZlpKsUgHBR+6KS2bqnKWHWqVMns2nTJrtqVls8qLrI9ddfb6uKHGqVdrD9eeutt2wsqr3S36lXr14mfSLEA3GEWNqyZYvd7mr16tV2pX+bNm2inqctjbQtjSY/7tq1y1bM0oRI+tUAAAAAgGOFkXcgi+0JqRWyGhRSwmPJkiXm22+/Nd26dbNJ/yFDhtjE2++//x7vS0YWolVGSvZr1dFtt91mV7EpkXa0+9KKftfFpRK7CB+1QVqp7UrWOl9//bVdEVm9enWb4NAK7F9++cW+p9VrWjHZvXt3m+h1yX5NHiHZHz4nnXSSefnll02NGjXMq6++ah555BFbpt957rnnzH333WcnJ2nbB93rdK6Sclrhr8lvJPvDude6aLuHRYsW2b2xVQVCpbJvuOEGm2RT9RHFjao/vP/++/aeld49L1qSdseOHebLL78kSZukiCMcK5qArec09ZFnzJhhV/MHufg5/fTTzWmnnWb7UNryQZMnSfYDAAAAAI4lVvgDWaSMv1xxxRV2RaNWHWmFdvC8xx9/3E4G0P/LDh061Fx++eWsmIUfQ1pxpFVGSrxqRZHiRTGUJ0+eI/6bwbjSnuyKNx1aeYvwxJVWQSohopWQRYsWta8/88wzNra0ir9q1apm586ddgKSBrKV9DiaeENychVCVHXk008/tYm15cuX20SbyvwHV/p/+OGH9t6nrWzUnqnt0sQlldkW7nXh4P6dNXnt119/NVOmTLGxoZXZ7n3FlWJEk5G03YhKZiuWNDkyWvWjaElatVtK1AX7WUgexBEyKr0KV67PrRi45ppr7Or9kSNH2klqkb+rmNM9TJPXVAlJFY8O9rcBAAAAAMgoEv5AFqDSxfnz57erHTWApBWPbkDIDS5pUFJJEq3yV6Ltu+++M8WLF4/3pSOOXIxo31iVwl63bp258847zQMPPBB1n9DDGWSMHNTu27ev2bZtm03GaeUtkk9kXLjVaaoS8eKLL9qV+prsocSGyhZr1faAAQPMRRddZBMqem/VqlVm+vTpdrIJwidaQj4YV5FJf5XPVjsVTPqL9sFW26UjZ86c6f5tJK9///3XropV5ZoSJUrYrY3U7qgP5O5rLiZ0Tvv27c0XX3xhV9E+9thjqeIuvRXZSuqSpE1uxBFiMRlbkzo06VExo60fdDg9evSwlWuU9H/hhRdMixYtTKVKlex7s2bNsv1nVbTRxMnKlSvb17mfATjahSEAAADA4eCJE4izSZMmmXvuuce88cYbdp9jDUYGy5HqQU8DRBpsGjhwoKlbt65ZtmyZmT17dpyvHJktcn6WWz2rMv4pKSmmT58+UZP9ek9bQhyqxH96g9panUSyPzkFt25Yu3at/aoY0KH2pkGDBnblvhL88+fPt22VKpBcdtllJm/evKZ8+fI24aEB7xNOOCHOnwbxoHuVazcWL15sV9FqO5rgJBLFilbsDxo0yE4YUVntYHl/t+JWyRTFUnCAk+RIuGgCm0phq3LInDlzbL/I9YHcPUwxobjTpBBVOwpuPePiLriNyNtvv02SNmSII2Q0wabtaFS6X/csJexr1qxpqxypaoR7/8orrzS7du0yt956q13lr0m3d911l92KRtsf9ezZ00/2C/czANFEPqPv3bvXb4tGjx6dZgwAiIY4AQAAwlMnEGcq83jzzTfbASSVyVZSTTQAGRyU1EQAJULq169vX1u9enVcrxuZSw/+ruJDkGJGK6+bNGliV8+6ZL/OHzNmjOnatatp1KiROfPMM+0KpOD+2YeT7KdcbTgStfo3Vwl/rXJ0AwYq4z916lTzzjvvmGHDhtmJH5oE4NogDURpxaQGtc866yy7ihLhonbDDUhqVWzr1q1tW6OVtUp8KDYOlfRXgi0yCUK54/CqWLGi3Wv9lltusXHx888/2ySb6GfXL3IxUqpUqah/x72vtk2xqMlxJGnDgzjCkXLbPIgqPSiJv2DBAluWX6v39+zZYyuw6VDfR1TOv3///qZ69epm3rx5tlKbtl9T1Qj1mxR/7m8DQHpcP1jP32p3cufObX/WfUcTi1RxDTgUN44IAADCjYQ/kAUS/r1797artPWw9/7775tXXnkl1eojDRS5RK6S/tonW2X9EQ7dunUzLVu2tAl4DUYGk/6q9rB+/Xp/Bb5iRSvbunfvbq666ipbRlvbRWjVrfap1Wptd55DudpwJ2qVFNGkoxUrVthE7ZYtW/zJJVpx3aVLF/u+StW6MuuuZK0SthrYvvHGG02RIkXi+IkQD67dUMJDW86ojPYpp5xi25ehQ4fapMnEiRPTJP21X7aS/i5ZolW4JPnDKXgvcve2KlWq2DbnjjvusDGmWBo1alSqCZD6qt/VPtmi6keRf0+luL/55ht7/ldffcX9LIkRR8godw9SnDz11FP2XqWtivRcNnnyZFuav1y5cnZLNfWPVTVCdA9TtTa9r+e3jz/+2E4IUOxFVlICgPSoOsgTTzxhJxWpfVG/WpOIVD3k3HPPjfflIYvTs7iewd599914XwoAAIg3D0Cm+O+//1J9lX379vnfp6SkeL169fJy5MjhnXTSSd5bb72V5m98++23XvHixb2yZct6P//8cyZdOeJp3bp1XrFixbxs2bJ5F110kffvv/+mip0VK1Z4J554oleuXDlv6NCh3n333Wd/1vmtWrXyFi9e7K1evdq7++677Wvt27dPFYcHDhzw/1sjR460sXXcccd5v/76a1w+L469YBt077332rg477zzvO+++y7q+cEYcSZMmOCddtpp9nefe+65qH8b4TBnzhyvYsWK3sUXX+z9+OOP9rVp06Z5V1xxhZc9e3avdu3a3nvvvZfqd3bt2mVjSPc6xdD06dPjdPWIl/379x/ynKVLl3o9e/a0cVS+fHlvyJAhqd5/9tlnvcKFC3u1atXyNm/eHPVvqN+keyCSE3GEWFKsKA6OP/54b+7cuan6Nf3797f3q3bt2nmzZ89Ot38URJ8IwOGaOXOm16JFC9vOVKpUyX69/PLLvYULF8b70pAA+vXrZ2NGx7vvvhvvywEAAHFEwh/I5AHJv/76yyZxow0ELVu2zLv99tvtoKQS+w888IC3atUqb+fOnd7HH3/stWzZ0nbiX3nllUz/DIifX375xatZs6b9t7/gggv8pL/iR4fiREl695B3xhlneC+99JJ/nixatMi+d+ONN0b9b7z55pt2gFOD3iT7w+GNN97wk/0LFixI8/6OHTvSDGgrkatJI/q9UqVKpWqLDjXwjeT0/vvve4UKFfJ++OGHVK9rspG7n9WpUydN0l/3tTFjxnhvv/12Jl8xskqfSP2he+65x+vWrZvXtWtX78MPP/Q2bdqUpl+kZK0mQ6rdadSokde2bVvv1FNP9fLnz2+Tc+onRbZBJNqSH3GEWJsyZYqXM2dOb+DAgaleHzBggJ/s/+mnn/zXN2zYkOo84gVARqxcudI+j+teVaZMGW/y5Mn+fYn2BYcaZ3z88cdJ+gMAABL+wLEWHDjUqiKtii1SpIjXtGlT75lnnvHWrl2bZlDyjjvu8AcllVTTqut8+fLZVZRK5Do8+IUr6V+9evU0SX8XY5MmTfKef/557+WXX/b+/PPPVHGn73v06GF/d/To0WliR79bunRpr2DBgiT7Q0IxoYoRBQoUsCu0g+bNm2dX7derV89r1qyZN3jwYH9lo75qxXbHjh29qVOnpvp7SH7R/p1ff/11r0GDBlHP0f3stttuS3elf3CQihgKB/fvrJXUigndl1x/R9VstJpt+fLlaVbd3nnnnf55p5xyik3eaqLa+vXrD3ulN5IHcYRjQX0fxYb6046S/y7ZH6yupn64Ktv873//i9PVIt5oLxBrjzzyiG1vSpQo4bc7uncJ4z6I5GJi9+7d/muPPvooSX8AAELu/2/GC+CY7nH8wAMPmEcffdTkzp3bHHfccWbmzJlm9uzZdh/IJ5980u5n7PYcvfXWW+3+2tpHUnuRtm7d2vTu3dvukV25cuU0+64j+WlfWe3Jdumll9r9Qbt3727Gjh1rChQoYOOgQ4cOqc53e9hqYtdLL71kxo8fb5o1a2bOP/98+3pwP9Fdu3aZqlWrmmHDhrE3bUj88ccfZurUqaZixYqmXr16/uvDhw+3e9f+9ttv/mvz5s2ze7M/9NBDpkKFCrbt2rNnjylZsqQfY7RFyU9tiu5LMmHCBLNs2TKzevVqkz9/fntfc3teB9sW3c+0f7aofRk8eLC9d3Xp0sW+5v6eEEPhoH9n3XOuuuoqs3btWnPLLbeYrl27mi+++MLe28aMGWPbpxdeeMHGj+j+dNNNN9kYU78oT548plWrVv59T3ur58qVK86fDJmJOMKxUKxYMfs1JSXFfn3kkUfMgAEDTNu2be39K9hfeu+998yHH35o2rVrF7frRXy5Pswrr7xiKlWqZNq0aRPvS0KC0f0o2G8+4YQTzG233WaaN29un98/++wzs3//fvPiiy/ae5k7P/L3ED733nuv2b59u40T9Wf0bK6v999/v32/X79+tl8k7rkLAACERLxnHABhoJWwJUuW9C688EK7J6RWYKuMsUqKupLaWgkZlJKS4u85qpW2KunvuP3bET7prfTfu3dv1JUnffr0savdKlSo4FeTiLaSduvWrZlw9chKWrVq5RUtWtQbMWKEbY+uvvpqG1d67cUXX7T7sT/55JNe7ty5bUn2PXv2xPuSESfBVUXBPSKDx0cffRT1/GDlGp2nPbQj73cIlyVLlthStX379vXbFW3x8M0333hnnXVWuv0irXK79dZb/YoRwZVLVIgIH+IIGRV5r1JMaYsa7Z/dpUsXG0MdOnSwlY+CvvrqK++kk06y1W30vIbwxs24ceNsnDRp0sRuCQEcruD9ZsuWLf7327dvt1/V7px99tlp7mXByhLaAgDho2oz7vnr3nvv9WOJlf4AAEBI+AOZ4Omnn7al2ZTsd5Sg1Z7HKpntHuRcyTZHD3ZuD2QNSo4fP95/j7Ju4eP+zVV2v0aNGv5A5D///JNmP1slcLV9hM4544wzvDVr1qQ6x2FwO3wUA4qlF154wStXrpw/GKAtHa699lpv1qxZqc7VOSeccIKNK4SbYkaxoiSHymBr3+xzzz3Xvqbkx4wZMw6a9Fd8DR06NA5XjniKvM988skndquiXbt2pZrEqJjR/tiuX9S6des0/aLffvvN7xfVrVs3Vb8IyY04QkYdqs+rPo+SJ3ny5LGx07hxYxtLwd/99ttvvRYtWtgtkcaOHZsp142sI/I5Su2QJvQrZjSR9osvvojbtSEx40jJWG2V9uCDD6Y6R22OJl8H72XBCUYaV1IfXL+LcFG/R5ON1AdSbNx9991+TAWT/o899pj/nP/OO+/E8YoBAEBmIuEPZMJg0s033+xdeeWV/kCkO0ffRz7IHWxQUvuNjho1KpM+CeLlcJLw6SX9FVM6brrpJu/kk0+2K9+0x62w12S44yhyhb4GBD744AO7YluJWE1Aiqz08Nlnn9kYu+WWW6L+TSS3yDajZcuWdrWR2h/3vu5hXbt2tXFSv379gyb9t23blu57SO4Y0srrDRs22MHqadOm2aoz+jmyTYnsF0Vboa1+kSpG5MyZ0ytVqpRtu5DciCPE8n6m2FCSbfDgwTZpoupZjhL62jdbcaHJIHru0mSS33//3Rs5cqTtWyumgpPXuJ+FL4b076/nr8KFC3uVK1e2MZErVy7b5qiyH3A4cTRgwAAvf/78Xt68eb2HH37YVoE82L1Mk41WrVplJ2dr3EivKamL8NFzvJL46r+Q9AcAAEEk/IFj9ACnh/3Ro0d77733nnf99dd7559/vl3VH1mOP/JBrm3btmnKQ2pQslevXv5qbVfqDckbQ0rgazD78ccftxUiNDAZTJYdLOkvGvzW5BH3wEeiNrxt0YcffmhXrGlg6KKLLrKrtN2KtchB6mCcfP/997Ys8nHHHed9/vnnmXj1yGpeffVVmxBRQl/JkcgYW7FihdetWzfbFmli2sGS/um9huTjYkSlapVAq1ixol8NQquSVD5bDtUv0vZHirHIfpEmUl5xxRWZ+IkQD8QRMirYt3nkkUfsNmvBLWkUR5og66j/3alTJ5v01/uqclSkSBH7fenSpb1XXnkl6t9GOKhPrYn4p59+uvfyyy/bVf7aJsRtuda0aVOS/ogq2P/Vtnsuia+taNI7z93LdJ7O1wQBHfp+yJAhUX8H4UDSHwAAREPCH4iR4ENW//79/XKQ7qhVq5afsI1cNRn5IKekvs4NDiJpEoAeDBcuXJiJnwqZycWFVuSrLKRm+wdjSAlbrTQKxsXBkv4OAwDhEowPrd7PkSOHjQ+Vn3WxpH3UtXrfiWyTlODXgKXOfemllzL1+pG16N9fcaCVjtqaJr1ytcuXL0+V9NcqSeCPP/6wWxK5GKpatapXrFgxf5DbtT3R+kWamKTfKVq0qF+pJmjTpk3+9yTdkhtxhKMV7APff//9Nmbq1KnjvfHGG95bb71lV/m7/tHVV1/tn6tVtNrq4ZxzzrH7syu5q0m43333nX8O8RI+Y8aMsbGi57T58+enem/KlCm2vL/e14RZ/QwcrG/dvn37NHGU3gS29evX2zZKk92aN29uF5Y4tEXhdTRJf7YxAgAguZHwB2LMdaZV8lErADp37uzvr6WErFb5pzcoOW/ePLuC8qmnnor6t93vIvm4B3UNamuFiFYVKXk2adIk77XXXvMuu+wyOwGgSpUq3vDhw1P9bjDprwGmHTt2xOlTICsZOHCgjQkNCmnQUQNFSnz07t3bvq5JSXPmzEn1Oz///LOdWJQ7d267mu3FF1/032MwKZxUBvu0006zMaN2yQ0wRg5GRq701yS3L7/8Mg5XjHgLbl2kBJsGIbWqVlQWW1uJqBS74qR79+5+LEXrFy1YsMBPyKbXBjGpLTkRR4gl3bs0AVKTZ4Ml/OWqq66yK2a1ZU3k1g9uO6RoCTiEh/v3vuGGG2yb87///c9/LxgbM2fOtPuqqwKAJiMFzwNE26dpcYcm0eq5K0gVazSpRBNHbrzxRjsBO7Kyo8r+//333/7PPJ/haJL+EydOjOMVAwCAY4mEP5BB7iFfD1t//fWXd+qpp9qSo26PY63U14pINyipZMjBBiWDq48YTAqXXbt2eZdeeqmNk0GDBqV6T4PVxYsX98qVK+c9//zzaQYetTpAJUo1mBltBRvCReVota+oVqRFrhxRwkQJfe1pHNyzWO2RBgI0QUmDlR999JH/HoNJ4RB5T3LtzMqVK+3gpNom3cs0eSTa+S7pf8kll9hzNWiJcHExo/uZqC1p06ZNmgmL2n/2cPpFDm1QuBBHiDVNCtHE2eCWM64qm+LnggsuSDMJUlxM6ZmM57LwUtuh9kgVjBQvrm/tYiIYG5qYrXPU11bS/6uvvorbdSPrUVLfLQQJevvtt+04UrC6nxaQuNXYbvJREG1SuAX7NIeb9H/wwQdTtWEAACD5kPAHYkQDSJptrf2utSo7kvbDPpJBSR7gwkez/PWQpoHtYEL/33//teVHtfro4Ycf9lfwR8bO4sWL/UQcg9rhpiohamuCSfvgwLYmJWkbEcdtN6LBTJViX7Nmjf8esRQ+WqEWLenvtnnQ14Ml/bVCUquSEE5aSZ0rVy7v+uuvt0k0t31IZLI2vX5RtOoRCB/iCLGyYcMG279WFaxolZDUJwqutNWkbfZgRzQXX3xxqgmNwXYm+OyuRK2r8KfV2tqaD+ETjAn3PKX2SPcsPduruqPamltvvdWvvqaJ2Vp97SpmqVokws09aymeNPFDMbRz58405x0s6R+cMKKKkgBwpMhRAIkjuwGQYXfeeac5++yzzZVXXmkqVKhgTj/9dPv6f//955+j18aPH2/Kly9v3nnnHXvu/v37TY4cOezXSNmyZcvUz4D4mzVrltmyZYu56KKLTM6cOe1rO3bsMI0bNzYpKSmmX79+5p577jH58+e3r3/77bf2q1O9enVTtmxZc+DAAZM9O817WATbGdmzZ4+ZMWOGjYFatWr5rz/88MP2aNu2rRk8eLCpX7++fX316tXmtddeM6tWrTJ58+Y1TZo0se2UaGIgsRQud999t2natKl5/vnn7c9qi3SPqlSpkhk1apS9l82cOdN06dLFbNiwwd7D1OYEValSxbRp0yZqfCL5/frrrzZmXn/9dfPxxx+bH374wb6eK1euVOdF9ouuueYa+3vu/odwI44QK7t27TK7d+82O3futH0kUX9owIABfp+oXr16/vkvvviiee6558zWrVvjeNXIStSX0VGtWjX78xtvvGG/qp0J9oFcn0ftlJ7nzj//fPP555+bb775xu9XIxwUF8HxHD1P6d5UuHBh06pVK7NgwQJz1llnmXPPPdfe5zp37my++OIL+7x/8cUX23ZJv6/ns7179xI7IeXGC3UfGzhwoH2+Ur9HsfP222+blStX+ufmyZPHtjt6hitZsqR5+umnzX333WdjMXfu3DaOpFixYnH8RAAStS3SPUntifrT69evN/v27fPf5x4FZC2M4gMx4BL8kyZNsp3uX375Jd3zgoOS3bp1Y1ASPhcHbnBg+/bt5owzzjBLly41/fv3txNLlJCVf//91w5Sjh49Os3f0UMhwiE4ueO3336zX/VArxjQoKMb2Fb8pDewrWT/kCFDzD///JPm7zPxKHxKly5tv/bq1cu88MILaZL+48aNs5OQXNJ/48aNUZP+DhNGwkcD2RMnTvR/1j3MDQhExonrF5144olmzJgxNu4AIY4QK0p8VK1a1d6v1DdSnye9PtGHH35ohg8fbieuFSxYMK7XjcznBqwjB67Vl9Fx8803mzJlypjp06ebq666yr6nPpDOV59Z53z55Zd2Mq2S/ddee61ttx5//HH77Ea/Ojzc8/jVV19tOnTo4PenNXFf8aBns/POO8/e6z744AObpNUCEmfevHk2ri688EL7bIfwUV9HMaO2Q3GiiWqaKJIvXz67GOSWW26xcaRYSS/pr6+33367n/QX2iEAh8P1hVxbpET/ddddZxcInXTSSXai2ptvvum3KyT9gSwk3iUGgGSh0mtuv7WbbrrJfz1auWPtm12mTBl7LmWP4YwaNcrfR3T58uVe3bp17f6PgwcP9vexdaWULr/8clv2b8qUKXG9ZmSNkloq2afYcSVoBwwYYH9+/PHHvSFDhtjv27Ztm6pkrUyePNluQ6KYo7wfnGHDhvn3s+effz5qef8zzjjDvt+8eXO/vD8Q3AJkwoQJfhw98MADB+0XaVukRo0aeStWrMi0a0XWRRwhVuWP3ddrr73WxlD58uXt14suusibPXt2qt/77rvvbPyo3PY333wTl2tH/ATbFPVzFB/qV6vsepC27itWrJiNo44dO9o+kLb1E8VNq1atvNKlS3uLFi2yr6u8v0psb968OdM/E+JH7Y5Kr7v715VXXhn1PhetRLK2qjn99NO9EiVKeF999VWmXTOyDhcfKt2v7Why5szp3XLLLXa7Ixk+fLiNjwIFCthtQ4Jb9bny/uPHj/dy5MjhFS1a1P89ADiUV155xfvpp59S9Y20zWyDBg3s/axcuXJ2DNHd3wYNGuT/LmX/gayBhD8Qw8GBYNJfe2VHO8fRPtmvv/56pl0nsh7XGXIPdP/884932mmn2Qc3DRQp2f/EE094O3bsSPU72p9dHSzt7affQbi99NJLNlbOOeccO5nIDTi6tsjtH6o9aSMHtps1a+aVLFnS+/jjj+N09ciqXnzxxUMm/Zs2bWrf1+SkYDuF8CVl03v9gw8+OOx+kdubnb3Xw4U4QqxjKFpMKdlRs2ZNG0PaW13PYUFK7CqpovdHjBhxzK8ZWUuwLXnmmWfs/uq5cuWy8aA+9sUXX2wnzSqJpmexMWPGeMWLF7fvK6Gv2NFzWaFChexrzz77rP1bmkyrgfFTTjnF/i7C95yvuFHCVXFx2WWX+e+7eIi8j2kyv57PdP6rr76ayVeNrET9GCX58+fPbyf3K+Emq1atsos/FCNuEZEm78+dOzfV72vBiMYnU1JS4vQJACQa9YHVpqhfs2DBAv/Z6qqrrvIKFy7s3Xnnnd7WrVu9+fPn23HIaM9nJP2B+CPhDxymyMGjPXv2+IOKQR9++OFhD0qm97cRjhVHf/75Z5qYeuGFF+zAkOLn/PPP91avXp3qnEcffdQOGtSuXdtfVUuHKlwi25KWLVvaFWmuQ+7i4bnnnvPborvuuivVe5988omdXKL31FF3iKVwxlB6/+6HSvprFa0SKMFZ3QiH4Gz/r7/+2t67NBlNK9Ei71tH0y9COBBHyKjgv7+qQdx3333eueee6/Xq1cv2b4IVsqZNm2aTs4qhhg0bem+99Zb35ptvev369fNXbLtErdAnCofgv/O9997rr15Tou3222/3qlevbl/Ts9dHH33k94GUXGvcuLGdQKL3CxYsaONLK+OcPn362PfuuOMO2qoQcv/mSvorURKZ9A/GxNKlS73HHnvMK1KkiJ38H+x30xaFk6qMqDpImzZt/GT/smXLvCuuuMLG0o033mjP0TO9Kj+qcs2cOXPifdkAEphW9rdu3dq2MZp8tnDhQvt65cqVvauvvtpfcObuX+kteuS+BcQXCX/gKFbx6+FdSbbzzjvPlj52q2odBiURyQ0OaTakSvR36NDBrh7RbOynn37aT96vW7fODlKqRJsS+yoV+fbbb9vEm0pEKqbU2dLMbiGmwksDiirJX7VqVW/06NF+x9pNIFKsDRw40G+LFGsaZFLsabWSjqFDh/p/j4lH4aNSjxpgPJhg0l+TSCLbtO3bt/uv8WAXDu6+o/LE6gupzGiwosiZZ57pvffee4fVL6LdCS/iCBkVvOfcf//9qeLHHZoQGSzJrrLHStJGnnfqqaf6fSkhpsLZr462BZYm+OuZzSXu1b92sbdt2zZbuv/dd9+1iTc3MC6acKIV/3puU1UkJK/I9iLYNh0q6a/+tJK51113nX1PW2YF7320ReH1zjvv2PbDrdD//fff7SQkxcn111+fZms/TRTR2FHk2CQAHAktJFKuQ+3K2Wef7Y0cOdI76aST/MnYkWPQ6T2fMTYExA8Jf+AQgg9Zffv2tftg6Uam2dfupqaEm1aJpHfTU9IN4eU6RConqpKOigmVgVQJtnz58tmf69Wr5yfelPzXCjeVyQ4ORqrMv8pF6mEv+HcRPtoORDGhQWsNJr7//vvplrVV2VGtOHKxphVIl1xyiV2lFDwP4aKVQ4oHrRI52H7X2juyZ8+e9lytHgkm/YMPcTzQhYNrK1SmuFq1ajZJ27VrV++LL76wMdWjRw8bKyo/Om7cuHT7RRqcRHgRR4gl9Zld+VFNzP7ss8/sa1WqVLGv16hRw5s+fXqqilqqdKSErCbdzpgxw1uzZo3/Pn2icFH/ZcuWLXaSkSo9RJbFfvzxx/3tsWbNmpXmd6NRZS1tmaXVua4CF5JTMAa0+vpwk/7du3dP9Xf0fK+JI8GJuLRFUGUaPYuJnveV1NdzfJC27dN2j25ikvpTbCECICPUd1F1ETcpVovRfvnll1Rj0MH7XPD57OGHH47TVQNwSPgDh8mtlG3RooUdkNSKpJkzZ3oPPvigfV0P9NpzLSh401NVAIRDtMEfrQapX7++TbpqgForQtauXWtX9Lt9+lSOTYPfbra/vtfefRqQ1AC4Ol2unBvJ/nDTgJHaIte+qAzywQaGNm7caFcHfPfdd3ZQW4PdDoNJ4aN/cyVF1Cap0oP2ZFu+fHm656tMsuLMrcB98sknM/V6kbWoRHbnzp297Nmz24o1wTZEA9aqUFOxYkX7nu5Vwfc10ci1W3///XecPgGyAuIIRyvYB1YcNWnSJNVeo462ztLrihMlQlw1rYNh8lrycv+20Z6hlDBTnNxwww2pXh8wYIB9vV27dqlW/Qe/D8aMVr9pSza3bYRW/yMcevfubceDNE50sKT/N998Y5O2ipFLL7003b9HWxRu0Sbxa1KS+kbuXuee57WXtiZIalK2VvgHK40AwNFSW6Ntstwz18cff3zQRR/B/IcmSwKIHxL+wGH48ssv7WxslYVUhzpI+z66Wf9uRUDwpuf2tGGVfzhoIkhkDOgBzZUa1d6irhS2e69WrVpe+fLlvXvuucefwX0wDADAdcBVnt9VHFGZ2sNddU0MQSVqtQpSE42UcIuW9HeDk6oAoMkBKmWreNOKSISX+kFqczTrP3g/04Q07XGsShCDBg3yduzY4cdakLYicdvS0BaFF3GEjNI9TEeuXLm8sWPHpooFF1NK+rvqWlr1eLDS20heai+0v3V6E6e//fZbGyOqaHSoZL9of1ttwRbNiBEj7HZIriIbkp8m8nfq1MmfXBRcBBI5JqCfFWeuamTkam0gGvV3VCHyxBNPTDN57c477/SOP/54u1gkOKkf4RFtAQf9G8TqeU1bg+p+ValSJbvK/2BjjqpUo2e4yH4TgMxFwh84DEOGDLE3uGAJ7OCq//bt26faH1KruYMOtnISyUOlsVU21CX9HQ06aj8+lRZ1A03BQW2tmtUKNpfs16CB+57V13BcLLgOtb6qA+6S/pr17yYk8YAHJ9rDmIslJdA+/fTTqEn/YAJu2LBhNqGiiiSsVoPbUuTtt99OdT/TILcqRuh+plW3oq+aNPnbb7+l+TvBGEP4EEeIxbOZ+t4agFTVtch4cEldTchWokQTbFXxCOGidkVbhyhelJR1k4iCSX+X8G/evHmqZ/xoyf7Ro0dHrXYUfGajEltyi/Z8ruoO2lddsVGzZs2oSX/3VXscK3HrKmdpQgpwMP/8848dN9JWfuPHj/cT+6+88op3wgkn2LbKtW0IF9fvUUz88MMP9tleY0KRE2WBIN2HPv/880Oe58Yc1ca4flK0Mcfg92qvAMRXdgMgXf/995/Zt2+f+eqrr+zP1atX9997+OGHzYABA0zbtm3NI488Yk499VT7+sqVK83o0aPN2rVr/XNPPPFE/+8hOfXo0cP+uysO9u7dm+o9xcSvv/5qKlasaHLnzm1f27FjhznjjDPM0qVLzcCBA82dd95p8uXLZ9+bOXOmeemll8zu3btN9uw002F14MCBVD+7uMqWLZv/tWbNmubRRx815513npk1a5a57bbbzIIFC+x7mtSHcFMMuXhx7Y64diVXrlzm3HPPtfez+vXrm1GjRpkHH3zQtks5c+a053z33Xdm+PDhpmnTpqZIkSKmRo0a9nXuZ+Frh9z3O3futF937dplv27bts3ez1JSUkz//v3t/Sxv3rz2vS1btpgbb7zRfPPNN2n+tosxJD/iCLFWtGhRc/LJJ5t33nnHrF692syYMSNNPOTIkcN+LVOmjClevLhZtGiRWb58edyuGfFRoEABM3bsWNtnnjhxounevbttfxQf+/fvt+c0aNDANG/e3Pz000/mwgsvtM/47dq1M4MHDzb16tXz/9a3335rHnvsMTsm0Lp161T/neAzm4s9JB/1f92/tcaJRM9cFSpUMA899JC55pprzOLFi03Pnj3N1KlT7fvqiyvWXJ/8zz//NM2aNbPPbnrm13MccDD58+c3Xbt2tTHXp08f07FjRzsOqTEovfb000/bc5D89uzZ43+vdkX9Hj3jd+nSxbYl7du3N40bNzZ33323vWcBkXSv0vjPpZde6uc60qP7Vu3atc0TTzxh+z1ff/111DHH4PfqdwGIszhPOACylMjZ+G729mWXXWZXP7rSoQ899FC6s/5Vuv24445Ls48kkpdmVruSfEuXLvVXo+3evdt+v3btWq9o0aJ2L0fZvn27naEduYLNxdzJJ59sV20fTnl/JH9bpNWPN954o12dpJVJd999t7dhwwZ/Zr9iRrNszzvvPBuHzZo1Y6V/yOnfPRhDr776qtelSxevQoUKXo8ePbxRo0alOt+t9FcbpRhSpRKVo3300Uf9VXFvvPFGHD4JMlNke+HaGJXE1so154MPPrAxce2119pz6tatG/V+Jt27d7fvTZ8+PZM+BeKNOEKspdeX0Uprd99q3Lhxqmprkb/XqlUru/ex66cjfH766Sf7jOW24nOrYbU6UrGiFftuxbUqH2nVf9BXX33ltWjRwsubN2+qyiQIp5tuusk+c7k4cu3NmjVr7H3NrfSPXEH59ddfe+XKlUuzJQRVIXAo6kPddtttXsWKFW18FStWzDv77LO9JUuWxPvSkEkeeeQR7/nnn7cVQYNVbFxfSFsYaassxYa2DTnrrLPs1keAo/6LYkX5DX1VBSxVUTscynEw5ggkBhL+CK3IUmzBn1UmctmyZf7rffr0sTc1fdVApL5v27ZtmmS/HuiU2FWydsuWLZn0SRBv1113nY0JNxCtB31t8zBp0iQ7iKQEvzrbOkeDk6eeeqoduNbetMFBbXWUtAebOucvvPAC5fxDKthhvvfee23cuMPt96gYGj58uE2gREv6a0Dy119/jeOnQGbT/WjWrFlpXncx5GJHD3f58+e3r0cm/fWw58q1BWNOAwsOD3TJ7fvvv081OK19QrUvqLYM+fvvv/3XSpUqZeOjTJkyNkGiRInudcE4eeqpp+we7Zo0SWm/cCGOECvBvrD6PJGTQTSBTZNFdK/S5MiFCxemuV+pf65taZTE1SRchDeGUlJS/ImMelYLbrWmZza1M24AXG3PjBkzvB9//NE+l6kN03vPPPOM/zv0icIZR9q+UTHiJo+4OIqW9Nd5b775pp1spO0hNTlJk0Y+/vjjuH0WxI+b2KFYOZKS6y62/vjjD7u92ogRI7w5c+ak2UoSyet///ufbVPKli1rt8Vy28j269fPLjjTojTXFmlLES1G0vmaDKCJ/YDGi5o0aeJvqaYFIRlJ+rds2dJOpgSQ9ZDwR+hdffXV3vvvv+//3Lt3bz+57zrhGjwqXLiwnwDRAMHixYtT/Z3vvvvOzrAtWbKk98knn2T650D8VtEqhoL70GovUf08ZswY/9xnn33Wj588efLYBFpwnzX9raFDh9r40Sokl8hFeClGlJxVR3rq1Kn2oV6DQ6effrqNo/Lly3vPPfecnzxRDKkD7hK29evXt4MCSH6a3KF/cyXTlGhzXn75ZZtE0wQQJTx0PP7443b/R53fs2fPNH9LbZp+77777vOeeOIJb9q0af57TEJKbkqEuOSr9oBUlRrtMat40URIxYYbcNT9ThNHdL4e+iNXpmlypCZAas9sJXaFxEg4EEeIlWA8jB071uvWrZs3YMCANBM/1N9WZRol/VUNQhNug6tp1a9WjI0cOTJTrx9ZK4bUV9Je6X379rUTQBQTmqQffB5T0v+WW27xk7mKKZ2r/rie75RkcegThTOONC6kZNqDDz5oE/eRceTuUZpcpNXY7vk/OJak5zeEd691TVxTFbXrr7/eW7ly5UF/J7LPQx8ovDTmo+S++sWagKZJH+pjayGaVvW76qKOFoNcc801JP1hKbeh+5biQePXzkUXXXRUSX835qjciKvmBiDrIOGPUHNJWJVb08pIleN3pdldQt89zCv55gYlVcItSEk4rRrRe8OGDfNfp0MeDhMnTvQf4FUuVA/0epCP7HS7SgBK+L/33nv2NZ2j0v0q06740qC4W33EQFK4RCY6NEDdoEEDv1SW8/vvv3s33HCDrRKh0qSRHXMNaCrxq5WSCAcl1ZTU18B069atvZkzZ/oPcJr48csvv6SKM60wcgOPwaT/wVaa0B4lv3Xr1tlkmeLipJNOsglbJWnVVwpuISKbNm3yBg4c6PeLtLL23XfftcmQ888/375WuXJlv4Q7pWrDgzhCLAT/rRUj+fLls/GgldZuMmPwvqQJAZoY4vrjZ5xxhn02071OFSI0qdbh+SwcgvHRv39/u62RYkOr21y1o8jy/i72tJJSA+N674ILLrArtINbRtAnCm8cuYn9KmesSbUFChRIE0eujdHXl156yWvevLlXtWpV7+KLL061IIA4Cl+yXyuwlaBVG6R7k6qIpBcHLo5+++0375tvvknzOsLDxYiqYD3wwAO2X62kv8ao1ba47frcFjXBCUok/eHo+Ur3MPVxgnGie9ORJv1VLUDbjUaOVQLIGkj4I9SWL1/u3Xrrrf4eWPqqUn7R9sHSAObDDz/sP9QpuaJzNZtSD3tKvgUHk3iACwf376zkqhtk7Ny5s/9+cLbjqlWr/A63K4Gk5Jw66fpZJUkZ1IZK9avqyAknnODvm64OuQ4Xb1rpqDhzs2ojBStEMCgQDrNnz7YrjBQTmiyi/fo0IcStaIy8J+n9I036I7npvqN71uWXX+6vbFQCNnKw0tmwYYP36quv2pUmwW0g9LMmTmpykvu7CA/iCBkV7Lfcc889Nh7OOeecNHuqR97btG2WJkrqfCV39ZymKhJuElzk+QgHTRhRTChxr+SazJ07107O1kTraEn/g6FfHe44Uqy4ONJ2D0q4RYuj4L1OVUm0OleT/B3aovBw/9ZK9muPdY0banW/q9J3MNoKQmNEmqikRSYIr8ikf8GCBW11UFWh0Wr/9AST/loU8sEHH2TiVSOrCVYqDo5VHyzpn94zGCv7gayLhD9CTyW1ateubQckNVPytddeS/fG9tdff9mOtpIoriSyBiS7du1qV0w6PMCFi1YaVaxY0V99pCNY8jEYR3r4V0ntevXq2U66SgFqLz+tItFKt8jzES5Kzip+zj33XDvr3yVrgzHhBho1AKD92lQxQuX+o2FQMvkF/4210t8l/TUZrXTp0v69KVq7Ekz69+rVK1OvG1mT+jkauFafSHGh/o62gjhYm6KJalpZon2NtR2E9hZ1e0hyPwsn4gixoKppbmKjqhdFUp86cpKaEvxKqGgytqpnuQkjQhyFj/rH6itrcrVK0AbbH8WOVs2qEombEOCStUx+RJD61xr70b0sMo7UDqUXR+klQ3g+Cx/Fgkpgq1+krWlc/yY97n6lFdm6p7mtjw71ewhP0l/l/bXFo2JDsRXs70RL+muSiVt0RByFT+R9x8VScHJatKR/8H1tkwUgMZDwR+hpVrZuam5mtvaAVBLkYA9kWj2rKgBaabJmzZpUpdtJ9odzEKBatWp2sogbnNShAev0Bhk1SUAr/jXDUu+5uCF+wk1tiitjrEOzt6O1Ra7jrQd/nac9SRFekUl/t6eajkOVMdb9zlW40bYjCDcNZGvCkVayuYEhDXB//vnnaeJI96uDDVozoB1exBFiMWmkadOm9v6kCjaR8aXEvia4aXDyq6++SpWgVcns6tWr26R/7969vRUrVsThEyAr0BZqan8eeugh+7OeuSLblE8++cROkHSTS1yylgkicLTVjOJDidr04kiJWW1hQxyFW2QVIxcnqt6n2NA9KzLZqolJjz76qK1KM2jQIH97R0cLSXQ/1ERIhMO2bdvSHRt0r6lCRN++fW27o76SxiEPVjVCW/ypuq2S/0BQekn/qVOn+q+rH6UFSWwbCiQGEv4IPXWwdfPSYNFtt92W7qAkg5E4GD2Yuc53ekl/vU9CH4fy/fffe126dLHxo9XXX3zxRbptkTrk2oNUJSURbsG4mDVrlh9Dmvmv+1u084KD3Tr3sccey7TrRda1ZcsWe6/S5MYbbrghar8oODCgVSbCoDaCiCNkhPYsVgWjiy66yP7s+s9vvfWWv4Lf9bU1AKn9SIPGjh3rJ/21LcCyZcvi8jkQH66v8+KLL9oY0cSPg7VVWvHo4kkTb1n9iGAcPfvsszY27rrrroPGkapruTjSljSuhD9jReEwZMgQO+FMFUQjKZEfOUlf/Z0+ffrYPbWDWxo1b97cbncUdDjl/5EctHJf2xi5rT4PlvR35f01ZnT88cfbrSEPFiuUYMeRJP1LlChhV/W79kvVjVNSUuJ6nQAODwl/hFqwpJ9L2mqfUTcoGW2lv1Zkuw44D2+I5GJCq/3dQ5smADgk/HG4CdvOnTv7e61F7qMlKh+pbSSqVKnCQDbSxJAmjnTq1MnfIuK7776Lep6jiiMHex/hozhYt26dd9NNN0XtF+l+pkltGpRauXJlXK8VWRdxhKOhbYu0N62S+TNnzvSmTZvm3XzzzTaGNNHx4YcfthMir7rqKvva2WefbVfUBvvZ48aN8+rUqWPf1yo4yrSHj5Jr+vdv2LBh1EoPLl40mK3t2cqWLWvPnzRpUhyuFlmVVu8rLk4//fSo9ykXR9q2T1UjK1SoYM+///77mcQWEtoXXf/mmmg2YcIEP+nvnqm0faPev/322+1EfU3GbtSokX1NW4688sortipb/fr1vdy5c/v9JMaOwkWVsRQT2vZTY0GqJnuopP8///xj40tJ/3Llytmkv6sQABxt0l+T1hSL2hbJrfinOgSQOEj4AxHJjeCgpDrfesBz5s6d67Vq1cp25NWJIimCSMHOOEl/ZLQ0+4UXXugPHmhvY/fwppVsrVu3tu+NGDEijleMrB5D2kv0cJP+QvuESMF+kbavcSu0tSWSyiCrjOTB9o0EhDjCkerZs6eNF63019cCBQp4Xbt2TbWHqFa3FSxY0Cb83RZrwfvbm2++aZMqDFKGk1ZIuooQwaprEkzEqsxxs2bN7GRJ9beBICX569WrZ+NIidn04khtltoijR8p8Va3bl1v/fr1cbhiZDYtHNKkNE3Gr1mzpi3hH1zpr0VDp512mr2XFSpUyH7VBKM777zT27x5s3/eHXfckaZKJMIzacT1d1zVh44dOx510p+qEDgawXbLTZrVMxr9aCCxkPAHDjEoqVnamuWvFbZafUTZYxxt0j9ygAA4nIStK6nlVkYq+a9Z39qvTYmSaL+HcMto0h+I1i9yq2t1NGjQwN8ywq12YxUbDoU4wpH2o7WSX1vU6P6lyY6RZY61il8xpIHu4H0t+DcY9A437Y2tGMmVK5f39ttv+3urB6tqqX+trUeCmACJI4kj9a81mU2TR5R8U6JO52u1N5Kbu+9ockePHj1sjLikv9vWQaXUFSOXXnqpTfyrvdEEo8g40jY2esZfsGBBXD4L4kPVGrWdjNqMJ554wm63pxg62qS/Kta88MIL9H9CKrhS/0gEx4bcPU/J/kWLFsXw6gBkBhL+wEEGJW+77TZ/UFIdd319+umn/XNIluBIk/7aexQ40tLsGhxQ/Jxwwgl24FsTkObNm+efw6AkDjfpr8EElUcGjtTWrVtt2WOtcKtcubJ33nnn2RVNQpIWh4s4Ci+X2Dic56fIWIj2O0rUavW+KkRMnz49zfs8p4U7hoJ941tuucX2gdTuaAXte++9523atMkmY7V1lp7zlZxDOMQijnr16mVjZsuWLd7EiRNtHKkU+zvvvGPP1d7sOnf06NHH8JMgqyXYNm7caCd9qPKMJumrrfn3339TnfvXX39F/RsvvfSSjSE9s6mvhPD4+OOPbXvRrl07748//rBtlNqSo0n69+/f3/5OjRo1iKMQcvc1tTtPPvmk7escKU22pYw/kNhI+AMHoZm42ktLg0lKuL377rv+eyTYwiM4GHAkg4fBGHnuuedsia5o+0cC0QRjTYPamvGfPXt2mxz59ddfMzyDF+GtFtG4ceM0qyQRDkd7PwvSg78StCqlLSRpw4c4wpGaMmWKrZTm+i+HEzfBcyKfu7TaXyXYqaAVHkcTQ8F25Z577vHLJbtVa+57PachHDIaR3fffbdNyrrYUULEfR9cGKKt11S6Xfu1I7m5Z3GVwtbz1Ysvvui1bNnSxkTDhg1t0t+t9E+P2iCV+K9QoYJd7Y1wWbJkib1HjR8/3n9NSX+NPx9p0l+r+gcPHmz/JsJJ96xOnTrZuJk9e7b/2uHQs5nGG6k0AiS2bPo/BkhCCu1s2bLZr6Lv//vvP5M9e/Yj/lu7du0yuXPnNjly5LA/H+3fQWI5cOCA/2+eXnwdyd/YsWOHKVCggNm/f7/JmTNnzK8XydcWBeNs9uzZ5pFHHjGffPKJadWqlRk4cKA544wz0pyH5BOrGPrhhx/M/fffby644ALTq1evY37dSK77WbSYo+0JF+IIR2Pnzp3m4osvNlOmTLH9l+eee87UrFnzqP7d58yZY//OM888Y3bv3m0ee+wxc/vtt9v3iKPklZEYCrZbEyZMMHPnzjWff/65fRarU6eO6dChg+nYsaN9n2f85BarOHrvvfdsWzR58mQ/jtq2bWsuvfRS+/6TTz5p7rvvPtO6dWvzzjvvmCJFimTK50Pmc3GhcZ5u3bqZmTNn2tcVF1u2bLHf16tXzzzwwAOmXbt2Jm/evKnGGJcuXWoGDx5sn+/Lli1rPv74YxuTCJ+tW7eaokWLproXqc1SbGjcZ/Hixbb9UrtVvnx5e47armj9cvpD4bZv3z4zaNAgO3bYrFkzM3XqVBsnhxsXGndUe6Q4A5CYSPgjKQUfyHSz+/fff/3OU3oP89GSsAxKhpeLh23btpkRI0aY1atX20kfGhQ67bTTTKFChdLEh4uN3377zeTKlctUrFgxVTwSO+ETq7Yo2PnWoIAGA9q0aWOTt2edddYx/hRIphjatGmTKV26tP2eNikcYnk/Q3gRR8iIX375xfTt29cmWps3b25efPHFI07669x+/fqZxx9/3DRp0sT07t2bRG2IZCSGIicr/f3337ZNUhvm+kvEUDjEMo50P1QcKYby5MljXxs6dKh9VlMszZgxw1StWvWYfybElxL3iqVFixaZa6+91jz00ENm+/btZs2aNTbWZs2aZWNMSVtNDMmXL59tbzQR+8EHHzTTp083nTt3tve2ypUrx/vjIM4i26KDJf0dTWZTH7tBgwZxumpkFa4vo36Okv2//vqrGTJkiO0zB+PKnafxJd3Hgq8BSALxLjEAxFqwVM3LL7/stW3b1itfvrwtgfTGG2/4+xhFK4Okkm7ab50yWuHmYmjz5s3eqaee6pfpc3uo33bbbXa/vmhx9O2333qFCxf2rrzySm/x4sVxuX4kd1sULM2ur4cqEYjEdSzvZ+xtHA7czxALxBEySvccldA+99xzbdyopLYrFZre/Si9e9tHH33kLV269KDnIfnEIoYiz6MvFD7HIo50j9S+2+qfq4y/yrJTCjn5KQ509OvXz8bSrbfeavdQD5b5X716te3/6P169ep577//vv/sri2NPvnkE3v89ddfcf0syNoiy/trq0fX7+7bt6997YYbbvB2794d70tFJnNtTfA+5V6bNm2aV6RIEa9Jkya2LYo8b+7cud79999P/gNIQiT8kbTuvfdefzAyR44c9qsewK677jo7YBnt4a179+72vK5du7LnUcht27bN7rmWK1cu7/LLL/feeecd78Ybb/ROPPFEGyPdunWLGkfaU9TFXc+ePdmTFjFri4KDSzNnzrQxyGBSOHA/Q0ZwP0MsEEc4WsF/85SUFK9NmzZ+oi29fbQjByTTm9xIwjYcjmUMITyOVRzpNe3BXq5cOa99+/apJiQh+V144YVegQIF/ISaizMXO3pdMaZYUz/qvffeswnc4DnA4Sb9a9SoYWNJe7T36tXLfl+wYEHGhULItR///vuvjYeRI0emen/9+vXeZZddZmNk0KBB/utqo/7++2+vSpUq9j21YcuXL8/06wdw7JDwR1IaMWKElzt3bq9169beN9984y1atMh79tlnvZNPPtlPgEQblJw4caI/KKkBSoRLZCwcd9xx3uDBg729e/emmoVdu3btNHEUHEAYM2aMV6xYMW/FihVx+BQIS1u0a9euTPsciB/uZzga3M8QC8QRMioYBz/99JONhbvvvtvLmzevlz9//kOuru3cubNNkGiQ2yVIEC7EEBIhjjZt2uT9+OOPfvUtJD/FiSZ7nHTSSfZZbd68ebbfFIwf9/3333/vP5fVrVvXxh/P8jhSStJ++OGHXq1atbycOXPaeFL/euHChfG+NMTJnj17vNNOO81vX6699lr7bOZ88cUX/nv6PmjChAn+e7///nscrh7AsULCH0khcrWQbnIafPzll1/819QZnzJlilenTp00g5Ku5I2MGzfOGzJkSCZePbLSoLYe0ufMmePdddddNlYcFyOKta+//jpqHLkBcHGz/4OxheRHW4SMIoaQUdzPEAvEEWI5YeShhx7ySpcubStENGrUyCtatKhfsSa9RJtWJqkEss5RQoWSo+FDDCEWiCMcCy65ry3XFBvDhw+Pep76PSq/rthRaW2de/rpp9tJk8Dhcv1nJXgvueQSkv3wn8MaN25s40FV1zSBTRUfNIbktgrRYhG9f/XVV9vXgvfESZMmUR0CSEIk/JFUtMexZstWqlTJ3tSCe2uJbmyHMyjpUGIreUXb30oPXVo1q4f/li1b2g6RRM6+Vqfqq6++OmiyjRKj4UZbhIwihnC4uJ8hFogjHCsqI+pKhqoktij58cILL3hnnnlm1ESbu2fpteOPP957/PHH4/oZEF/EEGKBOEIs9smOnJyt+HHJtm+//dZ/PRg/UqFCBa9Pnz62soQqtgFH45FHHvGT/cRRuLl2af78+d4JJ5zgNWvWzE7SPuOMM2yMqGS/2iet7Nd9T5MBvvzyy3THiwAkDxL+SDgbN260pYwijR071t7UWrRoYRMkWtkYbSVR5Eok7WmjEmzCYGQ4PPDAA94zzzyTJo40O1Z7+am0n2Lj3HPP9d+LjI3IODr//PO9P/74I9M+A+KPtggZRQwho7ifIRaIIxwrKp1dokQJey9ze2S7e5kmmcycOdM7++yz/USbBi0jkypuhZJwbwsfYgixQBwho/755x+7ml9lsIPPZIoLJdNcP0mxJMFk/1NPPWX7UsGKbcCRuvfee22clSpVipX9IRStSpraGU3Q7tWrl40NjSMpmf/iiy96p5xyin1NXzVx220p4saLACQvEv5IKJoxW758ebva0ZUGdX744QevS5cuXr58+eyN7Pbbbz/koGT9+vXtue3bt0/z95CcPv74Y/tvrhn6S5YsSfO+9l678sorvTx58ti92EaNGnXQONKe2uXKlfOKFy/OwHaI0BYho4ghZBT3M8QCcYRj6bPPPrPxdc8999ifI1dG6ufJkyd7lStXtiW2IxNtwco2JNjCiRhCLBBHiNXKaq2a1R7ZboWs4kF9KVdWu3r16t7o0aO9VatW2YpITzzxhH3m07OaJnsDR+uVV16xW5C4tgnh8++//9otG7/77rs0z2uq+lCtWjVv6dKl/rZsGkfSBJHs2bPb9knHk08+yX0MSHIk/JEw9KDVr18/e4O68cYbo5agUWm2q666yg5KqlP9wQcfHHRQUuVsVFqLPY7DQ52evn37em+88YY/o1+ztYNUBumKK67wcubM6TVt2tSWQDpYHM2aNcvu7SeUzU5+tEXIKGIIscD9DLFAHOFYcHGh7WncNg/pxYJiTpUkdJ5WQGqPY1ZBghhCLBBHiBX1jW655RYbH6oUoaS/2w5JMfXRRx957dq185NqJUuW9MqUKWO/11dWZIdTeu3N4faPI89Tvx3hpGesjh072jZF40NDhw61Cz1cjGhVv94bPHhwqt/ThKRbb73Vb5uWL18ep08AILOQ8EdCUedGJWpcJ0cDkNu2bUuTJFFZY818bN68ufe///3voIOS69atS/d9JBfXEXLJNe3Zp4Fr7WsUWcZWMyS7detmZ0IeKo6cyJUCSF60RcgoYggZwf0MsUAc4VhTxRrtGdqgQQNvzZo1aQavXexp1ZpWRZ5++ul2MPL999+P2zUjayGGEAvEETLC9We0uvamm26KmvRXX0iTHbV6tlGjRl7p0qW9hg0b2gncJNjCXYJ9z549tiKfJoV8/vnnB/0d1y6pTx6MPyqMwFWrcVs76OjQoYM3cuRI2w4pzrQlpBaMaBubIN3jpk6d6i1atChu1w4g85DwR8KInNn44IMP2tKiI0aMiDooqdnbRzIoSccpfJ5++mn/Ye3111+PSRwh+dEWIaOIIcQa9zPEAnGEjIqMAyXW6tWrZ+NK97rgecEJIXfeead30kkneTNmzPA+/PDDTL1mZC3EEGKBOEIsYif4zOa+P1jS39mxY4f3119/2e+VhEO4J4mcd9559lnfJWkvvPBCWw0rWpU/+fnnn71atWrZrf+AaJOo1e5o6xltAaltaC666CJ7n9OkNcVap06dUk0a4TkNCBcS/khI6lD37NnTztKuWLEig5I4KlpNqwf+QoUKeSeccMJhxdGUKVPidr3IemiLkFHEEGKB+xligTjCkTqckrTjx4/3B7m1B3Ik7UOqFbXXXHNNuokWJC9iCLFAHCGWK7KDidhgoi2Y9L/55pttLGmCyKeffpoqsR+MGZ7VwksTPxo3bmzjRF+vu+4627/Wz6o2MmnSpDSTRfTzgAED/LbqpZdeitv1I34O576zYsUK7+233/ZOPvlkGytVq1b1HnvsMa9KlSq25L8mBQgV14DwIeGPhKUByCMZlGzZsuUhyychfB0oxYz20j5UHKmMrUojaWWAynEBDm0RMooYQkZwP0MsEEc4UsEBRJW91p6h3bt3tyVrf/vtt1TnapsIN3itZNobb7zhrVy50nv33XftIHjOnDm9cePGxeFTIJ6IIcQCcYRY+ueff7xixYrZhP6hkv4XXHCBjSUl3JT0T2/FNsIjOMHjqaee8ooWLeo99NBDfmJfbdKll15qt+yrW7eubacik/7z58+3/ezChQt7CxcuzPTPgKy1FcTzzz9vx4o++OAD75dffklzvraIvOWWW+xzm1b3a9W/2iVt0wYgnEj4I+EHJfv27XvIQUntgawb3qmnnuqtXr06TleNrNLxdt+7jtThxNGPP/7onX/++Xam5KZNmzL56pFV0RYho4ghHCnuZ4gF4gixih9NEHEJNB0FChTw2rRp402fPj3V72jbGk1ac+cFy9tStjZ8iCHEAnGEWFOlBxcPffr0iZr0d99v2LDBK126tL/Sf+LEiX5fCuHj/u3d17Zt23pNmjTxdu3aZX92VSBWrVrl3XDDDTbpr4mz0ZL+2mtd8YXwbgWhrR/y5s2b6r5WsmRJ77XXXvPPd5OMFD/ffPONd+2116Y6f/369XH7LADih4Q/srSDlb8KDkoeaiXS3Llzvfbt23tDhw495teMrNlhcrGkGdvBcmvu/cOJI820/eOPP+z3lPYLF9oiZBQxhIzifoZYII4QS48//rgdUNSA9fDhw7377rvPbvWg17T/7OTJk1Odr8HIhx9+2FaqadasmdejRw9b0tYhjsKHGEIsEEeIpS+++OKQSX8l2rZv3+7VqFHDTsR28adEHcIjsqqD/v2rVatm+9AdOnTwRo8eneo81//WxP3rr78+VdI/2B9H+Lj7jraCqF+/vo0NjfuMHDnSu/POO+0ENtcu6Z7nRE4yevXVV+1kgcWLF2f6ZwCQNZDwR5YV7Exv3LjR+/nnn70FCxbYfWqOZmXk5s2b/e/ZRyscXMdHJY4UH+ecc46Nj1atWtmyfZEOJ46EAYBwoS1CRhFDyCjuZ4gF4ggZFfy3VizUqVPHa926tffrr7+mmgiiUtkakNR+2JGJNvd3FI/skx0+xBBigTjCsaA4cLGgeImW9Fd8BJ/tNKHkmWee8e655x4bcwgHTSxyCfpg+zFhwoRUK6zvvffeNL8bLel/2mmnee+99x5J/5DT/ejKK6+0sdO/f/9UlR90r3vyySf92Hr55ZdT/W7w3qVJAwDCi4Q/sqRgB/rFF1/0Z8xqT7VSpUrZEjaRMykjByXffPPNqIOSJEfCFUNKjLn4KVOmjJ1t6zpI2ksrsgxtMI5OPPFEG39a/YZwoi1CRhFDyCjuZ4gF4ggZFbznqOSxVsgqLqZNm5ZmoHHt2rW2XK1LtP3vf/+LuhKJ+1i4EEOIBeIIsZDexI7gs5vixfWRlNCPNHDgQC9PnjyspA0ZTfBQTDRu3Dhq2/HKK694+fPnt+dccsklUbe+Cib9b7rpJn/PdfrY4abtHo4//njv9NNP95P9kWNFQ4YMsfFSsGBB74cffkj1HvcyAELCH1m6463ZkLqRFSlSxOvatat39dVX+x1ulWnTSskgV360aNGiNpEybNiwNDdHhCeG/vzzT69u3bpevnz5vNtuu80vO/vUU095uXLlsnF01113RR3cfvDBB+37DRo0oCxbSNEWIaOIIWQU9zPEAnGEo+H2nI2kMthKcKgEtvYs1iQSJUgiBxmVaNPKNZdoU4lkhAsxhFggjhBrbrKHnq20V/q4ceO8sWPHevPmzfO2bNmS6tzgSn8lZmfNmmX7U1ppW7p0aa9Ro0apqq8h+a1bt86rUKGCfYZPr63Ss7tW7itunnjiiah/x7VVK1eu9Hr27GljEeH2+eef25jp1auX/TlY8cHFiyYCdO7c2Z6nhSEAEImEP7L8Pmxt27b1Zs+e7b8eTJJoUDIySaJ9tFxiZcSIEXG4cmQFO3fu9K644gqvQIECdjDAzY5cuHChd+mll9r4cLNuVaJtw4YNqX5/27ZttmO+Zs2aOH0CZBW0RcgoYggZwf0MsUAc4UhoxawmhSg+IuPogQcesJPRNElEx5IlS9JdVRRMtFWuXNn7+OOPM+0zIL6IIcQCcYRjlezX5EVNwla1I/c8pjhq166dLa0epEkiWk2rc1RNwv2OEv4kacMZP65kuibFXnfddX5iNliCXSv9XWw9/fTTUf+ea68i92FHOE2aNMnGy1lnnXXQCdZ6ltN5t9xyS6ZeH4DEQMIfWdLUqVNtGZszzzzT++mnn/wOlTpJefPmte+pc+32RIo2KKnybgj3zEgNaushznW6tWd2p06dbNxo5ewnn3ySam+tyDiKVtYN4UJbhIwihpBR3M8QC8QRDpdWN6oShJtsFpk8U2UITf7Qilqdo4FuVy0ivUSbm+D21ltvZdrnQPwQQ4gF4gjHquKREmn169f3E2sq19+lSxevbNmy9rXChQvbPdqDtLJfEyc1YaRmzZp2he2yZcvi9EkQT8H25cILL7Qx06JFC78aXzDp/+qrrx4y6Q84mlhdvnx5e2jCWyQXY1OmTPGf1wAgEgl/ZDnqHLl91r788kv/pvbss8/aWbVVqlSxP0+cODHVflqRKyMPtTcXktuoUaPsSjWV23Jlsq655hobL3feead/nls961bYrl+/Po5XjayEtggZRQwhFrifIRaIIxyJkSNH2rjYunWr/dkl0YKJOFWv0aS1EiVK2P1sNUEtvUSb9qjVBDiEBzGEWCCOEGt69lJyP3v27N7AgQNTPV/NnDnTrphVH0iTJFXqP/J3NVlAFSaCSV2El/rJmgDiJo9EW+n/2muv+X1r7b+OcAvemyK/V8WIiy++2K8OqYlqTrC8/6233mrPmTBhQpq/AwAk/JElDR482JZcE3XA33nnHbuH8Yknnphq4LF79+5+x0k3vPRWIiGc5s6d65fa0t5GKtHm4sp1ilSuLWfOnDbxpjjSQx+dJTi0RcgoYgixwP0MsUAc4VCCiQ9XXlZ71F5++eXe0qVLU52rxJsSbSVLlrSrIg+VaIv230DyIYYQC8QRjlVi7fvvv/eOO+44uyLb9YmCe69rUoi2kVAf6JxzzrErbl2s0B9CkGubNm3a5FWrVu2wk/6DBg2K2zUjvlyVNH3VodiJpC1sXAVIbTGiNkuTjIKxVKRIEe+UU05hzAhAVCT8kWVt3rzZftWN7bzzzrM3vF9++SVVh7xHjx52JrcblIxW8gbhEu0hTA9y9erVswMAboak63zPmTPHq1Chgt0DsEGDBt6qVasy/ZqRtdEWIaOIIRwN7meIBeIIRyq4ZcP8+fP9Ese33357mkTbn3/+eVSJNiQ3YgixQBzhaKVXLU20PYTiSDGS3jZFmiB58skn24mRPJPBcW2JJoC4748k6e9iL3fu3LbNQri4WNFzmKpAnnHGGV6hQoW8a6+91i4KCZo3b54dG1K8aNuaNm3aeI8++qjdii1Pnjz2vUWLFsXpkwDI6kj4I0vNto02y1o3Pt3kVHY0skOumbea8TZ69Og0e2wh+R1sD9lgfP3666+2JNtpp51mk2uuo6V40yoBJdiC+yG59xEOtEXIKGIIGcX9DLFAHCGjgvcu9++ve5f2Olb5Y1WgOVSi7bnnnvMTbQgfYgixQBzhaGl7tAsuuMD2daJRXOh57Oqrr7b9m/QqPVx33XXsu45D9q/FJfcPJ+n/1ltv2RXcCBfXzmhLEE2oVozoXqX7mQ49lz3xxBOpfkcT3ZTod5PddGh7NlUeWbJkSZw+CYBEkNMAcXDgwAGTI0cO+/3evXvNjh07TNGiRU327Nnta//995///Z49e+zXMmXK2K/u9W+++caMHz/eXHnlleayyy7z/3bwd5H8MbRt2zbz8ssvm3Xr1tkYatu2rTnttNNMnjx5/FgoUKCAKVasmFm1apVZv369qVy5sv0bzz//vJkyZYpp3bq1jcNcuXLZ13PmpGkMC9oiZBQxhIzifoZYII4QC+6ec+utt5rt27ebUaNGmUsvvdS+/sgjj9jYkjvuuMNUrVrVfq9YuuGGG+z3Q4cONYMGDTI7d+40d911l8mdO3ccPw3igRhCLBBHOBrLli0zH330kf2qPtDdd99tateunaqfVK1aNduvmT9/vu3r5M+fP9XznJ7X1GeqU6eO/Vl9J4TX/v37bbwoLiZMmGDjZvXq1X7/ukaNGrZ9USyVKlXKPtefffbZ5ttvvzWtWrUyU6dOtfG0e/dukzdvXvu8j/DRvUuxdOONN5qUlBTTs2dP88ADD9jvv/76a/v9fffdZ/bt22f69etnf0dt15gxY8zmzZvNDz/8YF+rV6+eqVixom3fACBd8Z5xgPAJzqAdOnSod+6553rHH3+8XVE0ZswYf28at5pIe4lqJlv58uXtLF29P23aNK9Zs2a2/M0nn3wSt8+C+MaQymSrJK2b7aijcuXK3r333uv9888/qX7nyiuvtO8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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import missingno as msno\n", "\n", @@ -367,9 +775,280 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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main.tempmain.feels_likemain.pressuremain.humiditymain.temp_minmain.temp_maxwind.speedwind.degwind.gustclouds.allrain.1hsnow.1h
dt
2025-03-17 10:00:001.980.111021921.072.771.792033.581000.360.0
2025-03-17 11:00:003.050.841021932.733.332.242255.361000.790.0
2025-03-17 12:00:003.601.491021913.033.882.242484.021001.380.0
2025-03-17 13:00:004.161.751021923.844.442.682708.051000.160.0
2025-03-17 14:00:004.110.751021893.885.034.022938.051000.140.0
.......................................
2025-03-23 05:00:006.034.191020756.036.032.421002.72840.000.0
2025-03-23 06:00:006.034.151020676.036.032.46873.32740.000.0
2025-03-23 07:00:006.034.401020646.036.032.18893.2080.000.0
2025-03-23 08:00:007.037.031020617.037.031.19822.3450.000.0
2025-03-23 09:00:008.038.031020618.038.031.0542.2330.000.0
\n", + "

144 rows × 12 columns

\n", + "
" + ], + "text/plain": [ + " main.temp main.feels_like main.pressure main.humidity \\\n", + "dt \n", + "2025-03-17 10:00:00 1.98 0.11 1021 92 \n", + "2025-03-17 11:00:00 3.05 0.84 1021 93 \n", + "2025-03-17 12:00:00 3.60 1.49 1021 91 \n", + "2025-03-17 13:00:00 4.16 1.75 1021 92 \n", + "2025-03-17 14:00:00 4.11 0.75 1021 89 \n", + "... ... ... ... ... \n", + "2025-03-23 05:00:00 6.03 4.19 1020 75 \n", + "2025-03-23 06:00:00 6.03 4.15 1020 67 \n", + "2025-03-23 07:00:00 6.03 4.40 1020 64 \n", + "2025-03-23 08:00:00 7.03 7.03 1020 61 \n", + "2025-03-23 09:00:00 8.03 8.03 1020 61 \n", + "\n", + " main.temp_min main.temp_max wind.speed wind.deg \\\n", + "dt \n", + "2025-03-17 10:00:00 1.07 2.77 1.79 203 \n", + "2025-03-17 11:00:00 2.73 3.33 2.24 225 \n", + "2025-03-17 12:00:00 3.03 3.88 2.24 248 \n", + "2025-03-17 13:00:00 3.84 4.44 2.68 270 \n", + "2025-03-17 14:00:00 3.88 5.03 4.02 293 \n", + "... ... ... ... ... \n", + "2025-03-23 05:00:00 6.03 6.03 2.42 100 \n", + "2025-03-23 06:00:00 6.03 6.03 2.46 87 \n", + "2025-03-23 07:00:00 6.03 6.03 2.18 89 \n", + "2025-03-23 08:00:00 7.03 7.03 1.19 82 \n", + "2025-03-23 09:00:00 8.03 8.03 1.05 4 \n", + "\n", + " wind.gust clouds.all rain.1h snow.1h \n", + "dt \n", + "2025-03-17 10:00:00 3.58 100 0.36 0.0 \n", + "2025-03-17 11:00:00 5.36 100 0.79 0.0 \n", + "2025-03-17 12:00:00 4.02 100 1.38 0.0 \n", + "2025-03-17 13:00:00 8.05 100 0.16 0.0 \n", + "2025-03-17 14:00:00 8.05 100 0.14 0.0 \n", + "... ... ... ... ... \n", + "2025-03-23 05:00:00 2.72 84 0.00 0.0 \n", + "2025-03-23 06:00:00 3.32 74 0.00 0.0 \n", + "2025-03-23 07:00:00 3.20 8 0.00 0.0 \n", + "2025-03-23 08:00:00 2.34 5 0.00 0.0 \n", + "2025-03-23 09:00:00 2.23 3 0.00 0.0 \n", + "\n", + "[144 rows x 12 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# If rain is stored, fill the NaN with 0\n", "try: \n", @@ -383,7 +1062,46 @@ "except KeyError:\n", " print(\"['snow.1h'], not in df\")\n", "\n", - "# Display the df, now without NaN (atleast for rain and snow)\n", + "# If wind_gust is stored, fill the NaN with 0\n", + "try: \n", + " df['wind.gust'] = df['wind.gust'].fillna(0)\n", + "except KeyError:\n", + " print(\"['wind.gust'], not in df\")\n", + "\n", + "# If wind_deg is stored, fill the NaN with 0\n", + "try: \n", + " df['wind.deg'] = df['wind.deg'].fillna(0)\n", + "except KeyError:\n", + " print(\"['wind.deg'], not in df\")\n", + "\n", + "# If wind_speed is stored, fill the NaN with 0\n", + "try: \n", + " df['wind.speed'] = df['wind.speed'].fillna(0)\n", + "except KeyError:\n", + " print(\"['wind.speed'], not in df\")\n", + "\n", + "# If temperature is missing, take the same as the one before\n", + "df['main.temp'] = df['main.temp'].fillna('obj.ffill()')\n", + "\n", + "# Forward fill missing values in what the temperature feels like\n", + "df['main.feels_like'] = df['main.feels_like'].fillna('obj.ffill()')\n", + "\n", + "# Forward fill missing values in the pressure\n", + "df['main.pressure'] = df['main.pressure'].fillna('obj.ffill()')\n", + "\n", + "# Forward fill missing values in the humidity\n", + "df['main.humidity'] = df['main.humidity'].fillna('obj.ffill()')\n", + "\n", + "# Forward fill missing values in the lowest temperature \n", + "df['main.temp_min'] = df['main.temp_min'].fillna('obj.ffill()')\n", + "\n", + "# Forward fill missing values in the highest temperature \n", + "df['main.temp_max'] = df['main.temp_max'].fillna('obj.ffill()')\n", + "\n", + "# Forward fill missing values of clouds\n", + "df['clouds.all'] = df['clouds.all'].fillna('obj.ffill()')\n", + "\n", + "# Display the df, now without NaN\n", "display(df)" ] }, @@ -397,9 +1115,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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733777akyO0RVY4gqxxCJ/sMOOywlQKJFdmw6Kdx0002pE0DEE82h2BC00UYbpX//wu67757tsMMOacNIbDiKVtgx7jz99NOpkn/llVdO41ckZWeeeea0iSQqZ2P8KSffaF7Rkj82D0WyNjaLxGa03XbbLX0d48/dd9+dHXfcca0m/eNn4qz1lVZaSXK2yQ1qHD3xxBPp9TEORWeIl19+OXvooYeyxx9/PLv++usrmyDNiwAAAIAhpaU/ABWx4BxJ+mhHG21n77rrrtQiOyoZoy12iARHtNAuqrKLpFzRhnaCCSbIll122WyxxRZLrfzjbNto/188X07+0hyKGIkE7KmnnpoSIieccELa/BGi+8Pzzz+fXXrppSlJcuSRR7ZI9kclZLRCXmihhVo8Tud3+umnZ9dcc032zDPPZJNNNlmq4I+NH2uttVbakFQc7xDjS7du3dJHxNBzzz2XKv/PP//81Do7jhYJ8buWWGKJ9HM6jDSP6uNj4jiR2BQS3R8iFuJaFV0gll566dShJq5VkawN++23X7boootWkv4xfsUGk2jRLknbXNoTRyHmRcVmt2LDW/l4AAAAAIAhJeEPQEUsOEeFfyQyrr766pRQu+eee1LSP6rQoqrx66+/zn799ddKwr9YAK+udOzatWv6iNa1ZZJszSniJ5KvkYyNiu2IgxVXXDF9fdVVV6X27JEMOe2009IREIWo4o4NALFxJKomy0kSOp/yxqCIkTgm5P3330/nW0dFdXQXWWONNVIHiOmnn77yc8X4U/xcdBWJjzXXXDN1LIkq2zgrOzYBxO+IjUk0hyIpH8c5fPLJJ2ksiaRsXM9iI0gkZuP7IvYWXnjhlOSPr2PTW9h///2zRRZZJH091lhjpc+StM2lFnEUY1MRR2U6RQAAAADtJeEPQEqqRQX/Pvvskyr8Y2E7kv7XXnttJem/7rrrpkr/IsERSY9I/kdL7Wj1H1X/sVkgFrv//fff7N13303tbcudAWge1ZWvcTREbBIpqveLxGy0N47kSbQ2jvOQv/vuu5Twj00l0Z49ErtvvfVWqvRee+210+8qEip0PjGmTDzxxCk2olp27LHHThX9UaUfif9RRx01W2aZZVok+8uqO47EMSPxseCCC6Z27HHcSGw8iYpccdQ841BUYW+88cbZK6+8kmIsrl9xlEhsAll++eXTayMWipiIpOy+++6bHo+ON9G6PbqTzDfffJXfLXaax9CMIwAAAIBaGC6PFQkAmlbv3r0rbWYjKRtnXoeiNe0vv/xSSfpHJdtee+2VbbHFFmljQCT2I+EfSbZI4JaNN954KVGrkra5HXPMMSlOYrNInLV+zjnnpMeLhEh8vv3221MFd5xpHGKjSCRGvvjii5TwPeqoo9KZ7a21VKbzePDBB7OePXumavzYfFS47777UmeIKaaYIvv000/TESFxDES8NsaogSli5umnn06bBVZeeeWU+Kc5RCV2jD1xPZpnnnnSNSuStfERXSAuuOCCbI455qi8vrwRJDqMRJX2Rx99lJK6rmfNSxwBAAAAHZmEPwDZeeedl6qr42zi+Byt+ENU7Ediv5z0j7a10aZ/rrnmSoncqLiNs2sj8TbaaKOlxFp8vfvuu6f266pom9cjjzySzjKefPLJUwxEDEUyN+ImukKUk/6RCIkk7E033ZRiMGIoErORoI0z14Nkf+cWlffbb799ttRSS6WxJsaViI9I8j/55JPZDDPMkF1xxRWp4n/KKafMzjrrrGy55ZZLrwutjTXxWHxE3ESHgEjURcV//L4Yu+icymPFYYcdlp177rnp+hZJ12IDUlz3YsNbHPtw6KGHpg1thXIsvfDCC1m3bt1SktYY1FzEEQAAANAoJPwBmliR0C989dVXqcL/kEMOSQn76qT/Ouusk/3vf/9LybY77rijRTVba4rELs3pm2++SVWPF110UWrbH7EVZ6hHwqOc7C8naaOyv0j0lhOyEiSdVzkGou31vPPOm45/ePbZZ7MFFlggPV6ckR1HPUSXkYir1pL+4b333kuvm3322VuMP5Gs23HHHdM4duWVV7b4GTpf+/WIgdiEFseGfPnll2kTSREP8ZrHHnssO+KII9JxImussUZK6LaVrA3GoOYijgAAAIBGYrUBoAlde+212W+//VZpy1+uyI7zaPfcc8+UUAvxmkj6jzHGGNn111+fzqmN5O2GG26Yvfrqq/1V0pZJ9jePSHxUm3DCCbNtt90223rrrVNl4zvvvJMStD///HN/yf4idqJ7RCRXItlfPiZCgqRzin/jiIEYY0K06Y9kfyTmF1pooZSkD5Hsj2NGRh999OyUU05JcRXj0E477ZQ2IcVz4cUXX0zxFkdAfPbZZ5X/Tjwex0tEXEVyTrK/84okbbRfn2mmmbIVV1wxVVavtdZa6XoU8RZjTbymR48eKTkbbdpvueWW9PUbb7xR+T3V3SKMQc1FHAEAAACNxIoDQJM58MADU7I+qmR///33tHhdJGuj5Xq01Q7bbbddalVbTvpHMjaS/rH4/eabb2YbbLBB+lwsamvd37wi8RGicvqJJ56oPB7V/Ntss01Kwo4//vhpI8mll16aOkYUSf9QxE45GSIx0rlFon6VVVZJG0BijClvGpluuunS50j8F+NQHPMQif1I2p988slpjIqkfyT3I+7iY5999kmVtiuttFI21VRTVX7f2GOPncarp59+Oh0NQOfWp0+f1BUiNrF98MEH2XfffZceL28yivElkrSRoI1jQyJZe+SRR2Yvv/xynd89HYU4AgAAABqFlv4ATSSq0m699dZUwR+JsqiSjcRZJNAikRYJtfJZ2uGcc85JibXq9v6RPLv77rtTQjcSbDPOOGNd/zbq75JLLsm22mqrbOWVV84OOOCASjv28O2336Zkf1Rnd+3aNSVmN99889Q5orWz1+ncYrPR/PPPn7311lsp6R/J+oiFf/75p1J9f+GFF6YxqnocKsaq+B37779/2kASbbcLEWNxznZ1++zyGEfnFp1rYuPRLrvskjalRZV2HCcSXUeqW6rH93ENO/roo7OHHnoojWERbzrUII4AAACARiHhD9BkIqEW52RHK+y+ffsOcdI/EmwrrLBCqpj99NNPs0kmmaSufxf198ADD2QnnXRSqoaMLhD77rtvtuCCC1ael/Sn7LXXXss22WST9LlXr17pqJGIhWKMGdA4VIxVf/75Z3bzzTenJFwk3+L3rLrqquk1zspubpGsjQTsHnvskWJsnXXWSTEUR0a0lqyNJG2MT3E9nHLKKev63uk4xBEAAADQCCT8AZo06R/ttCPpPySV/sVrfvvtt1RlG1X+0Y67aOtO84rEyLHHHps2lUTl9oCS/nEe+6677pptueWW6bgImk+cdb3uuutmb7/9dkrWX3PNNSkWBifpXyh/L9lPOVkb17p33nkn22ijjbLTTjutzWRtXBuL4yVczyiII1oTHWqmn376SlcaAAAAqCcJf4AmT/rvvPPOQ1TpL7lGWblC/7HHHsuOO+64ASb9L7744tT6OBIivXv3zuacc846vnuGtfKY8eGHH6aq/GiZ3bNnz+yGG24Y5KS/hBoDEzESY9KgJGuhLeKIsmeeeSZbeOGFUzejW265RdIfAACAurMyAdCkYnFyueWWy84888ysW7duKaG25557por9SORHQj/ERoBzzz03fb3DDjtkp556avq6XFlrobu5kh6F8p7BSPYX3y+xxBLZfvvtl5K3d9xxR3bCCSekox8K448/fqrqj+r+qPSX7G++GCrGjGiRHcmzSPjHWdexSSQq/n/55ZeU7I+kf2vjUHSJCJL9DEzESIxJZ511VjqD/aqrrsp222237IcffkhxGMlaGBhxRHU8jD322Nndd9+dbbzxxmkTLQAAANSTCn+AJjeo7f0vvPDC9FxsFPj+++/T885cb14nnnhiSnqssMIKKVHbVqX/QQcdlD311FPZ2muvnbpJLLLIIpXXxvnrXbt2TV+rkGwO5X/nQw89NLv00kuzzz77LMVFVExGkj82HUVcXXfdddkYY4zRZqV/bD6KTSMwJBXa0X3ksssuS0k7GFTiiMILL7yQrbXWWmnuvM4662RXXnmlSn8AAADqxso6QJMrKv2jam3KKadss9J/6623zq644oq0wD3aaKPV+21TRzfddFNq0x/VjQ8//HA637itSv8tttgifR8/E90kIvlfKJL9QbK/ORT/zkcccUR25JFHZnPPPXf2xBNPpI9InsRRENNOO2127733Zuuvv36rlf5nn312+vq3336r699CY1ZoR/xEl5E4SqTcsQQGhTii2Lw277zzZtdff3021VRTpaNoNtxwQ5X+AAAA1I0KfwAGWulfrrANkeAtV3XTXH788cds9913zy6//PKUnI3NIksvvXSblf6zzDJL9vPPP2dffPFFttRSS6VWyBNPPHEd/wLq6aWXXkrxEsmy2267LZt11lkr8RJjy3PPPZc2irz77rup0v/aa6/NxhxzzBYdR954441sttlmq/efQgOKGHv22WezaaaZJptkkkl0F2GIiKPmFRs8ysfJPP7446nC/5tvvsnWWGONdM1S6Q8AAMCwZlUCgDYr/ffZZ59URVtO9gfJ/ubR2r7AaF18+umnZ5tvvnn2wQcfpE0iDz30UItK//i54mcjEbLiiitmiy66aLbyyitL9jeBosqxtfjp27dv9tNPP2XrrrtuSvaXq2NjbFl44YVTfEUSLSr9I5ESG0Yi2V9U+sfPBedmM7gixuIIiYiviD1JWoaEOGpO5WT/JZdckm288cbZAQccULmO3XLLLdlGG22k0h8AAIBhzsoEAP0l/aNVbVRun3POOdlRRx1V77dFnUQytajSf/vtt1PL9UJUXMcZ6uWkf7T3LxKykfyIn73zzjvTGe1bbrllquYuzlzXYKjzivb80QHi008/rcRP+d/866+/Tp8jiR8ieVJ+XYhWyUUF//3335+tueaa6fXF5qPi9ZJszaE8XtRy7ChX6dL5iSPaI2Km+Lfef//9s+222y579dVX0xEPO++8c7bssstm4403XnbjjTemjQCS/gAAAAxLVkmhgykWIMvVseXHYVgk/Xv27Jkdf/zx2fzzz58WNGk+5YrF6Pqw+uqrZ9tss01qXVtsBqhO+m+//fbZzTffnH355ZfpNfHaOJM9WrdHV4Bxxx23v3b/dC7fffddGjNis1AcEVJW/JtPN9106fNTTz2V4qZaxFYkTXr06JG6jUw66aSpg8Sjjz46jP4KOoqiarY8XhRfD2l3h9Z+zhyrcxNH1EIRMxdccEGaIy+zzDKpff/RRx+dHXLIIem4oosvvjibfPLJsxtuuEHSHwAAgGFKT2booG0iY1Hphx9+yMYZZxznpVOXpP8qq6yS9erVK+vatasYbDKRyCjGooMOOigtbE822WTZvvvumyqvQ2wGiDGrSPrH95deemmqcoszjWeZZZbsjjvuSK3bo2PEDDPMUPn9kv2d12ijjZbtvffe2SuvvJKttdZa6bFffvklG2OMMSpnXM8444zZXHPNlV5z3333ZTvssEMlJsrXwdg4MsUUU6T46tOnTxqTaB7FdSfGkGuuuSZ7880302PRQn2zzTbLunXrNti/s3zO+kUXXZQ2IkWcGpM6L3FELf3555/ZPffck65TMSeK42WKeJhgggnSdWrUUUdNsRVJ/7imRdzFvBoAAACGpuFypQjQIZSTHLF4GOcW9+7dOyXOJppoopR0i4RZJE2gNdVV0+UFaRgSBx98cKpcW2GFFbIjjzwym3vuudt8bSR1IzEbLfxffPHFbJRRRknj1eGHH55tu+226TUq+5tDVDTGv3Mk2Q488MDsk08+yU444YSUYCuceOKJKVkSr4tr3jrrrJM2CxSeeeaZlDBZbLHFsgsvvLDyuHGtueZE33zzTbbiiiumMaUsuj7stdde2RprrJG6QAyKcuxceeWV2S677JKOiYiuFGONNZaxqRMSR9Ta999/n80333xp00hsHhl99NH7uy7F0UYnn3xydsQRR2R///13tvbaa6fqf0l/AAAAhiYJf+gAykmwSIBEIiTEIlI8Hom0qBqJKshNNtkkm3rqqev8jumoi9pReRQLkLPPPns20kgjtet3Ss42t9tvvz0lYRdccMHUnj2q2Mq++uqrVDEZ1dqFWNj+9ttvU+v1SO5G8qPYJCBR23zeeOONbMkll0zdauL6FWcel5P+u+22W3bGGWekcSaeX3TRRdNZyLHZLZIlL7zwQqqMLDoF0FxifImW2XFNi4TZmmuumX399depajaOeIjuIltttVXqKjKwKu3y+HP55ZenWIzxKn5P9+7dh9FfRD2II2qpX79+6birvn37pkr/5ZdfvtXXxZFGcSxNbH78448/Uses2BBpXg0AAMDQYuUdOtiZkFEhG4tCkfB45513sieffDJbf/31U9L/pJNOSom3zz//vN5vmQ4kqowi2R9VRzvttFOqYotE2pCeSxviZ4u4jMQuzSfGoKjULlrWFh577LFUETnTTDOlBEdUYL/66qvpuahei4rJDTbYICV6i2R/bB6R7G8+0047bXbuuedmM888c3b++ednRx11VGrTXzjttNOy/fbbL21OimMf4loXr42kXFT4x+Y3yf7mPGs9xHEPb731VjobO7pARKvsrbfeOiXZovtIxE10f7jpppvSNauta15rSdrffvste+SRRyRpOylxxNASG7DjPi3myE888USq5i8r4meBBRbI5plnnjSHiiMfYvOkZD8AAABDkwp/6CBt/MPGG2+cKhqj6igqtMuvO+6449JmgPi/7Omnn55ttNFGKmapxFBUHEWVUSReo6Io4iViaOSRRx7s31mOqziTPeItPqLyluaJq6iCjIRIVEKOM8446fFTTjklxVZU8U8//fTZ77//njYgxUJ2JD2GJN7onIoOIdF15O67706JtQ8++CAl2qLNf7nS/7bbbkvXvjjKJsazGLti41K02Q6udc2h+HeOzWuvvfZa9sADD6TYiMrs4vmIq4iR2IwUx41Ey+yIpdgc2Vr3o9aStDFuRaKuPM+i8xBHtFdbHa6KOXfEwOabb56q9y+77LK0Sa36ZyPm4hoWm9eiE1J0PBrQ7wYAAID2kvCHDiBaF4866qip2jEWkKLisVgQKhaXYlEykiRR5R+Jtqeeeiobb7zx6v3WqaMiRuLc2GiF/dlnn2V77LFHdtBBB7V6TuigLDJWL2ofcMAB2Y8//piScVF5S+dTHRdFdVp0iTjrrLNSpX5s9ojERrQtjqrtww47LFtttdVSQiWe+/jjj7OHH344bTah+bSWkC/HVXXSP9pnxzhVTvqHOAc7xq746NKlS5u/m87r119/TVWx0blm/PHHT0cbxbgTc6DiulbERLxmpZVWyu6///5URXvssce2iLu2KrIjqStJ27mJI2qxGTs2dcSmx4iZOPohPgrbb7996lwTSf8zzzwzW2qppbKpppoqPff000+n+XN0tImNk9NMM0163PUMGNLCEAAAGBTuOKHO7rjjjmzvvffOLrnkknTOcSxGltuRxo1eLBDFYtPhhx+ezTHHHNl7772XPffcc3V+5wxr1fuziurZaOPfp0+fbJ999mk12R/PxZEQA2vx39aidlQnSfZ3TuWjGz799NP0OWIgPmK8mXfeeVPlfiT4X3/99TRWRQeSDTfcMOvatWs2xRRTpIRHLHhPPvnkdf5rqIe4VhXjxttvv52qaOM4mvImkoiVqNg/8sgj04aRaKtdbu9fVNxGMiViqbzAKTnSXGIDW7TCjs4hzz//fJoXFXOg4hoWMRFxF5tCottR+eiZIu7Kx4hcccUVkrRNRhzR3gRbHEcTrfvjmhUJ+1lmmSV1OYquEcXzm2yySfbHH39kO+64Y6ryj023e+65ZzqKJo4/2mWXXSrJ/uB6BrSm+h7977//roxFV111VX9rANAacQIABHedUGfR5nG77bZLC0jRJjuSaiEWIMuLkrERIBIhc801V3qsb9++dX3fDFtx4190fCiLmInK60UWWSRVzxbJ/nj91Vdfna233nrZ/PPPny288MKpAql8fvagJPu1q22ORG38m0cL/6hyLBYMoo3/gw8+mF133XXZOeeckzZ+xCaAYgyKhaiomIxF7cUWWyxVUdJcYtwoFiSjKrZnz55prInK2kh8RGwMLOkfCbbqJIh2x82rW7du6az1HXbYIcXFK6+8kpJsIb4v5kVFjEw44YSt/p7i+RjbIhZjc5wkbfMQRwyu4piHEJ0eIon/xhtvpLb8Ub3/119/pQ5s8RFznxDt/A899NBspplmyl588cXUqS2OX4uuETFvivgrfjdAW4p5cNx/x7gz0kgjpe/juhMbi6LjGgxMsY4IADQ3CX/oAAn/3XffPVVpx83eTTfdlJ133nktqo9ioahI5EbSP87Jjrb+NIf1118/W3rppVMCPhYjy0n/6PbwxRdfVCrwI1aism2DDTbINt1009RGO46LiKrbOKc2qrWL1xW0q23uRG0kRWLT0YcffpgStf369atsLomK63XWWSc9H61qizbrRcvaSNjGwvY222yTjT322HX8i6iHYtyIhEccORNttOecc840vpx++ukpaXLLLbf0l/SP87Ij6V8kS6IKV5K/OZWvRcW1bbrppktjzq677ppiLGLpyiuvbLEBMj7Hz8Y52SG6H1X/vmjF/fjjj6fXP/roo65nnZg4or2Ka1DEyYknnpiuVXFUUdyX3Xfffak1/6STTpqOVIv5cXSNCHENi25t8Xzcv915551pQ0DEXnUnJYC2RHeQ448/Pm0qivEl5tWxiSi6hyy77LL1fnt0cHEvHvdg119/fb3fCgBQbzkwTPz3338tPod//vmn8nWfPn3y3XbbLR9hhBHyaaedNr/88sv7+x1PPvlkPt544+WTTDJJ/sorrwyjd049ffbZZ/m4446bDzfccPlqq62W//rrry1i58MPP8ynnnrqfNJJJ81PP/30fL/99kvfx+uXWWaZ/O2338779u2b77XXXumxlVZaqUUc/t///V/lv3XZZZel2BprrLHy1157rS5/L0NfeQzad999U1wst9xy+VNPPdXq68sxUrj55pvzeeaZJ/3saaed1urvpjk8//zzebdu3fLVV189f+mll9JjDz30UL7xxhvnww8/fD7bbLPlN954Y4uf+eOPP1IMxbUuYujhhx+u07unXv7999+Bvubdd9/Nd9lllxRHU0wxRX7SSSe1eP7UU0/NxxxzzHzWWWfNv/nmm1Z/R8yb4hpI5ySOqKWIlYiDySabLH/hhRdazGsOPfTQdL1accUV8+eee67N+VGZOREwqHr37p0vtdRSaZyZaqqp0ueNNtoof/PNN+v91mgABx54YIqZ+Lj++uvr/XYAgDqS8IdhvCD5/fffpyRuawtB7733Xr7zzjunRclI7B900EH5xx9/nP/+++/5nXfemS+99NJpEn/eeecN87+B+nn11VfzWWaZJf3br7LKKpWkf8RPfEScRJK+uMlbaKGF8rPPPrvyuvDWW2+l57bZZptW/xuXXnppWuCMRW/J/uZwySWXVJL9b7zxRn/P//bbb/0taEciNzaNxM9NOOGELcaigS180znddNNN+RhjjJE/++yzLR6PzUbF9Wz22WfvL+kf17Wrr746v+KKK4bxO6ajzIliPrT33nvn66+/fr7eeuvlt912W/7111/3Ny+KZG1shoxxZ/7558979eqVzz333Pmoo46aknMxT6oegyTaOj9xRK098MADeZcuXfLDDz+8xeOHHXZYJdn/8ssvVx7/8ssvW7xOvADt8dFHH6X78bhWTTzxxPl9991XuS4ZXxjYOuNxxx0n6Q8ASPjD0FZeOIyqoqiKHXvssfNFF100P+WUU/JPP/20v0XJXXfdtbIoGUm1qLoeZZRRUhVlJHILbvyaK+k/00wz9Zf0L2LsjjvuyM8444z83HPPzb/77rsWcRdfb7/99ulnr7rqqv5iJ352ookmykcffXTJ/iYRMREdI0YbbbRUoV324osvpqr97t2750sssUR+9NFHVyob43NUbK+xxhr5gw8+2OL30fm19u988cUX5/POO2+rr4nr2U477dRmpX95kUoMNYfi3zkqqSMm4rpUzHeim01Us33wwQf9Vd3uscceldfNOeecKXkbG9W++OKLQa70pvMQRwwNMfeJ2Ij5dCGS/0Wyv9xdLebh0dnmf//7X53eLfVmvKDWjjrqqDTejD/++JVxJ65dwboP1YqY+PPPPyuPHXPMMZL+ANDk/v9hvMBQPeP4oIMOyo455phspJFGysYaa6ysd+/e2XPPPZfOgTzhhBPSecbFmaM77rhjOl87zpGMs0h79uyZ7b777umM7Gmmmaa/c9fp/OJc2TiTbd11103ng26wwQbZNddck4022mgpDlZeeeUWry/OsI2NXWeffXZ2ww03ZEsssUS2wgorpMfL54n+8ccf2fTTT5+dc845zqZtEt9++2324IMPZt26dcu6d+9eefzCCy9MZ9e+//77lcdefPHFdDb7IYcckk055ZRp7Prrr7+yCSaYoBJjxqLOL8aUuC6Fm2++OXvvvfeyvn37ZqOOOmq6rhVnXpfHlriexfnZIcaXo48+Ol271llnnfRY8fuCGGoO8e8c15xNN900+/TTT7MddtghW2+99bL7778/XduuvvrqND6deeaZKX5CXJ+23XbbFGMxLxp55JGzZZZZpnLdi7PVRxxxxDr/ZQxL4oihYdxxx02f+/Tpkz4fddRR2WGHHZb16tUrXb/K86Ubb7wxu+2227IVV1yxbu+X+irmMOedd1421VRTZcsvv3y93xINJq5H5Xnz5JNPnu20005Zjx490v37Pffck/3777/ZWWedla5lxeurf47ms++++2Y///xzipOYz8S9eXzef//90/MHHnhgmheF4r4LAGgS9d5xAM0gKmEnmGCCfNVVV01nQkYFdrQxjpaiRUvtqIQs69OnT+XM0ai0jZb+heL8dppPW5X+f//9d6uVJ/vss0+qdptyyikr3SRaq6T94YcfhsG7pyNZZpll8nHGGSe/6KKL0ni02WabpbiKx84666x0HvsJJ5yQjzTSSKkl+19//VXvt0ydlKuKymdElj9uv/32Vl9f7lwTr4sztKuvdzSXd955J7WqPeCAAyrjShzx8Pjjj+eLLbZYm/OiqHLbcccdKx0jypVLOkQ0H3FEe1VfqyKm4oiaOD97nXXWSTG08sorp85HZY8++mg+7bTTpu42cb9G88bNtddem+JkkUUWSUdCwKAqX2/69etX+frnn39On2PcWXzxxfu7lpU7S8QRADSf6DZT3H/tu+++lVhS6Q8ABAl/GAZOPvnk1Jotkv2FSNDGmcfRMru4kStathXixq44AzkWJW+44YbKc9q6NZ/i3zza7s8888yVhchffvmlv/NsI4Ebx0fEaxZaaKH8k08+afGagsXt5hMxELF05pln5pNOOmllMSCOdNhiiy3yp59+usVr4zWTTz55iiuaW8RMxEokOaINdpybveyyy6bHIvnxxBNPDDDpH/F1+umn1+GdU0/V15m77rorHVX0xx9/tNjEGDET52MX86KePXv2Ny96//33K/OiOeaYo8W8iM5NHNFeA5vzxpwnkicjjzxyip0FF1wwxVL5Z5988sl8qaWWSkciXXPNNcPkfdNxVN9HxTgUG/ojZmIj7f3331+390ZjxlEkY+OotIMPPrjFa2LMic3X5WtZeYNRrCvFHDx+luYS857YbBRzoIiNvfbaqxJT5aT/scceW7nPv+666+r4jgGAYUnCH4bBYtJ2222Xb7LJJpWFyOI18XX1jdyAFiXjvNErr7xyGP0l1MugJOHbSvpHTMXHtttum88wwwyp8i3OuA3OmmzuOKqu0I8FgVtvvTVVbEciNjYgVXd6uOeee1KM7bDDDq3+Tjq36jFj6aWXTtVGMf4Uz8c1bL311ktxMtdccw0w6f/jjz+2+RydO4ai8vrLL79Mi9UPPfRQ6joT31ePKdXzotYqtGNeFB0junTpkk844YRp7KJzE0fU8noWsRFJtqOPPjolTaJ7ViES+nFudsRFbAaJ+67YTPL555/nl112WZpbR0yVN6+5njVfDMW/f9x/jTnmmPk000yTYmLEEUdMY0509oNBiaPDDjssH3XUUfOuXbvmRxxxROoCOaBrWWw2+vjjj9Pm7Fg3isciqUvzifv4SOLH/EXSHwAok/CHoXQDFzf7V111VX7jjTfmW221Vb7CCiukqv7qdvzVN3K9evXqrz1kLErutttulWrtotUbnTeGIoEfi9nHHXdc6hARC5PlZNmAkv4hFr9j80hxwydR27xj0W233ZYq1mJhaLXVVktV2kXFWvUidTlOnnnmmdQWeayxxsrvvffeYfju6WjOP//8lBCJhH4kR6pj7MMPP8zXX3/9NBbFxrQBJf3beozOp4iRaFUbCbRu3bpVukFEVVK0zw4DmxfF8UcRY9XzothIufHGGw/Dv4h6EEe0V3luc9RRR6Vj1spH0kQcxQbZQsy/11xzzZT0j+ejy9HYY4+dvp5ooony8847r9XfTXOIOXVsxF9ggQXyc889N1X5xzEhxZFriy66qKQ/rSrPf+PYvSKJH0fRtPW64loWr4vXxwaB+IivTzrppFZ/huYg6Q8AtEbCH2qkfJN16KGHVtpBFh+zzjprJWFbXTVZfSMXSf14bXkRKTYBxI3hm2++OQz/KoalIi6iIj/aQsZu/3IMRcI2Ko3KcTGgpH/BAkBzKcdHVO+PMMIIKT6i/WwRS3GOelTvF6rHpEjwx4JlvPbss88epu+fjiX+/SMOotIxjqZpq13tBx980CLpH1WS8O2336YjiYoYmn766fNxxx23sshdjD2tzYtiY1L8zDjjjFPpVFP29ddfV76WdOvcxBFDqjwH3n///VPMzD777Pkll1ySX3755anKv5gfbbbZZpXXRhVtHPWw5JJLpvPZI7kbm3CfeuqpymvES/O5+uqrU6zEfdrrr7/e4rkHHnggtfeP52PDbHwPA5pbr7TSSv3FUVsb2L744os0RsVmtx49eqTCkoKxqHkNSdLfMUYA0LlJ+EONFZPpaPkYFQBrrbVW5XytSMhGlX9bi5IvvvhiqqA88cQTW/3dxc/S+RQ36rGoHRUiUVUUybM77rgjv+CCC/INN9wwbQCYbrrp8gsvvLDFz5aT/rHA9Ntvv9Xpr6AjOfzww1NMxKJQLDrGQlEkPnbffff0eGxKev7551v8zCuvvJI2Fo000kipmu2ss86qPGcxqTlFG+x55pknxUyMS8UCY/ViZHWlf2xye+SRR+rwjqm38tFFkWCLRcioqg3RFjuOEolW7BEnG2ywQSWWWpsXvfHGG5WEbFtjkE1tnZM4opbi2hUbIGPzbLmFf9h0001TxWwcWVN99ENxHFJrCTiaR/HvvfXWW6cx53//+1/luXJs9O7dO52rHh0AYjNS+XUQ4vi0KO6ITbRx31UWHWtiU0lsHNlmm23SBuzqzo7R9v+nn36qfO/+jCFJ+t9yyy11fMcAwNAk4Q/tVNzkx83W999/n88999yp5WhxxnFU6kdFZLEoGcmQAS1KlquPLCY1lz/++CNfd911U5wceeSRLZ6Lxerxxhsvn3TSSfMzzjijv4XHqA6IFqWxmNlaBRvNJdrRxrmiUZFWXTkSCZNI6MeZxuUzi2M8ioWA2KAUi5W333575TmLSc2h+ppUjDMfffRRWpyMsSmuZbF5pLXXF0n/tddeO702Fi1pLkXMxPUsxFiy/PLL97dhMc6fHZR5UcEY1FzEEbUWm0Ji42z5yJmiK1vEzyqrrNLfJshQxFTck7kva14xdsR4FB2MIl6KuXURE+XYiI3Z8ZqYa0fS/9FHH63b+6bjiaR+UQhSdsUVV6R1pHJ3vyggKaqxi81HZcak5lae0wxq0v/ggw9uMYYBAJ2PhD/USCwgxW7rOO86qrKrxXnYg7Mo6Qau+cQu/7hJi4XtckL/119/Te1Ho/roiCOOqFTwV8fO22+/XUnEWdRubtElJMaactK+vLAdm5LiGJFCcdxILGZGK/ZPPvmk8pxYaj5RodZa0r845iE+DyjpHxWSUZVEc4pK6hFHHDHfaqutUhKtOD6kOlnb1ryote4RNB9xRK18+eWXaX4dXbBa64QUc6JypW1s2nYGO61ZffXVW2xoLI8z5Xv3SNQWHf6iWjuO5qP5lGOiuJ+K8SiuWXFvH90dY6zZcccdK93XYmN2VF8XHbOiWyTNrbjXiniKjR8RQ7///nt/rxtQ0r+8YSQ6SgIMLjkKaBzDZ0C77bHHHtniiy+ebbLJJtmUU06ZLbDAAunx//77r/KaeOyGG27Ipphiiuy6665Lr/3333+zEUYYIX2uNtxwww3Tv4H6e/rpp7N+/fplq622WtalS5f02G+//ZYtuOCCWZ8+fbIDDzww23vvvbNRRx01Pf7kk0+mz4WZZpopm2SSSbL/+7//y4Yf3vDeLMrjTPjrr7+yJ554IsXArLPOWnn8iCOOSB+9evXKjj766GyuueZKj/ft2ze74IILso8//jjr2rVrtsgii6RxKsTGQLHUXPbaa69s0UUXzc4444z0fYxFcY2aaqqpsiuvvDJdy3r37p2ts8462ZdffpmuYTHmlE033XTZ8ssv32p80vm99tprKWYuvvji7M4778yeffbZ9PiII47Y4nXV86LNN988/Vxx/aO5iSNq5Y8//sj+/PPP7Pfff09zpBDzocMOO6wyJ+revXvl9WeddVZ22mmnZT/88EMd3zUdScxl4mPGGWdM319yySXpc4wz5TlQMeeJcSru51ZYYYXs3nvvzR5//PHKvJrmEHFRXs+J+6m4No055pjZMsssk73xxhvZYostli277LLpOrfWWmtl999/f7rfX3311dO4FD8f92d///232GlSxXphXMcOP/zwdH8V856InSuuuCL76KOPKq8deeSR07gT93ATTDBBdvLJJ2f77bdfisWRRhopxVEYd9xx6/gXAY06FsU1KcaTmE9/8cUX2T///FN53jUKOhar+FADRYL/jjvuSJPuV199tc3XlRcl119/fYuSVBRxUCwO/Pzzz9lCCy2Uvfvuu9mhhx6aNpZEQjb8+uuvaZHyqquu6u/3xE0hzaG8ueP9999Pn+OGPmIgFh2Lhe2In7YWtiPZf9JJJ2W//PJLf7/fxqPmM9FEE6XPu+22W3bmmWf2l/S/9tpr0yakIun/1VdftZr0L9gw0nxiIfuWW26pfB/XsGJBoDpOinnR1FNPnV199dUp7iCII2olEh/TTz99ul7F3CjmPG3NiW677bbswgsvTBvXRh999Lq+b4a9YsG6euE65jLxsd1222UTTzxx9vDDD2ebbrppei7mQPH6mDPHax555JG0mTaS/VtssUUat4477rh072Ze3TyK+/HNNtssW3nllSvz6di4H/EQ92bLLbdcutbdeuutKUkbBSSFF198McXVqquumu7taD4x14mYibEj4iQ2qsVGkVFGGSUVg+ywww4pjiJW2kr6x+edd965kvQPxiFgUBRzoWIsikT/lltumQqEpp122rRR7dJLL62MK5L+0IHUu8UAdBbReq04b23bbbetPN5au+M4N3viiSdOr9X2mMKVV15ZOUf0gw8+yOeYY450/uPRRx9dOce2aKW00UYbpbZ/DzzwQF3fMx2jpVa07IvYKVrQHnbYYen74447Lj/ppJPS17169WrRsjbcd9996RiSiDnt/Sicc845levZGWec0Wp7/4UWWig936NHj0p7fygfAXLzzTdX4uiggw4a4LwojkWaf/758w8//HCYvVc6LnFErdofF5+32GKLFENTTDFF+rzaaqvlzz33XIufe+qpp1L8RLvtxx9/vC7vnfopjykxz4n4iHl1tF0vi6P7xh133BRHa6yxRpoDxbF+IeJmmWWWySeaaKL8rbfeSo9He/9osf3NN98M87+J+olxJ1qvF9evTTbZpNXrXGstkuOomgUWWCAff/zx80cffXSYvWc6jiI+onV/HEfTpUuXfIcddkjHHYULL7wwxcdoo42Wjg0pH9VXtPe/4YYb8hFGGCEfZ5xxKj8HMDDnnXde/vLLL7eYG8Uxs/POO2+6nk066aRpDbG4vh155JGVn9X2HzoGCX+o4eJAOekfZ2W39ppCnJN98cUXD7P3ScdTTIaKG7pffvkln2eeedKNWywURbL/+OOPz3/77bcWPxPns8cEK872i5+huZ199tkpVpZccsm0mahYcCzGouL80DiTtnphe4kllsgnmGCC/M4776zTu6ejOuusswaa9F900UXT87E5qTxO0XxJ2bYev/XWWwd5XlScze7s9eYijqh1DLUWU5HsmGWWWVIMxdnqcR9WFondSKrE8xdddNFQf890LOWx5JRTTknnq4844ogpHmKOvfrqq6dNs5FEi3uxq6++Oh9vvPHS85HQj9iJ+7IxxhgjPXbqqaem3xWbaWNhfM4550w/S/Pd50fcRMI14mLDDTesPF/EQ/V1LDbzx/1ZvP78888fxu+ajiTmMZHkH3XUUdPm/ki4hY8//jgVf0SMFEVEsXn/hRdeaPHzUTAS65N9+vSp018ANJqYA8eYEvOaN954o3Jvtemmm+Zjjjlmvscee+Q//PBD/vrrr6d1yNbuzyT9of4k/GEQVS8e/fXXX5VFxbLbbrttkBcl2/rdNEfF0XfffddfTJ155plpYSjiZ4UVVsj79u3b4jXHHHNMWjSYbbbZKlW1JlTNpXosWXrppVNFWjEhL+LhtNNOq4xFe+65Z4vn7rrrrrS5JJ6LiXpBLDVnDLX17z6wpH9U0UYCpbyrm+ZQ3u3/2GOPpWtXbEaLSrTq69aQzItoDuKI9ir/+0c3iP322y9fdtll89122y3Nb8odsh566KGUnI0Ymm+++fLLL788v/TSS/MDDzywUrFdJGqDOVFzKP8777vvvpXqtUi07bzzzvlMM82UHot7r9tvv70yB4rk2oILLpg2kMTzo48+eoqvqIwr7LPPPum5XXfd1VjVhIp/80j6R6KkOulfjol33303P/bYY/Oxxx47bf4vz7uNRc0puoxEd5Dll1++kux/77338o033jjF0jbbbJNeE/f00fkxOtc8//zz9X7bQAOLyv6ePXumMSY2n7355pvp8WmmmSbfbLPNKgVnxfWrraJH1y2oLwl/GIIq/rh5jyTbcsstl1ofF1W1BYuSVCsWh2I3ZLToX3nllVP1SOzGPvnkkyvJ+88++ywtUkaLtkjsR6vIK664IiXeokVkxFRMtmJndxBTzSsWFKMl//TTT59fddVVlYl1sYEoYu3www+vjEURa7HIFLEX1Urxcfrpp1d+n41HzSdaPcYC44CUk/6xiaR6TPv5558rj7mxaw7FdSfaE8dcKNqMljuKLLzwwvmNN944SPMi407zEke0V/mas//++7eIn+IjNkSWW7JH2+NI0la/bu65567MpYKYas55dWtHYMUG/7hnKxL3Mb8uYu/HH39Mrfuvv/76lHgrFsZDbDiJiv+4b4uuSHRe1eNFeWwaWNI/5tORzN1yyy3Tc3FkVvnaZyxqXtddd10aP4oK/c8//zxtQoo42Wqrrfo72i82isTaUfXaJMDgiEKiyHXEuLL44ovnl112WT7ttNNWNmNXr0G3dX9mbQjqR8IfBqJ8k3XAAQekc7DiQha7r4uLWiTcokqkrYteJN1oXsWEKNqJRkvHiIloAxkt2EYZZZT0fffu3SuJt0j+R4VbtMkuL0ZGm/9oFxk3e+XfS/OJ40AiJmLROhYTb7rppjbb2kbb0ag4KmItKpDWXnvtVKVUfh3NJSqHIh6iSmRA513H2ZG77LJLem1Uj5ST/uWbODd0zaEYK6JN8YwzzpiStOutt15+//33p5jafvvtU6xE+9Frr722zXlRLE7SvMQRtRRz5qL9aGzMvueee9Jj0003XXp85plnzh9++OEWHbWi01EkZGPT7RNPPJF/8sknlefNiZpLzF/69euXNhlFp4fqttjHHXdc5Xisp59+ur+fbU101oojs6I6t+jARedUjoGovh7UpP8GG2zQ4vfE/X1sHClvxDUWEZ1p4l4sxP1+JPXjPr4sju2L4x6LjUkxn3KECNAeMXeJ7iLFptgoRnv11VdbrEGXr3Pl+7MjjjiiTu8aKEj4wyAqKmWXWmqptCAZFUm9e/fODz744PR43NDHmWtl5YtedAWgObS2+BPVIHPNNVdKusYCdVSEfPrpp6mivzinL9qxxeJ3sds/vo6z+2JBMhbAY9JVtHOT7G9usWAUY1ExvkQb5AEtDH311VepOuCpp55Ki9qx2F2wmNR84t88kiIxJkWnhziT7YMPPmjz9dEmOeKsqMA94YQThun7pWOJFtlrrbVWPvzww6eONeUxJBaso0NNt27d0nNxrSo/HxuNinHrp59+qtNfQEcgjhhS5TlwxNEiiyzS4qzRQhydFY9HnEQipOimNSA2r3Vexb9ta/dQkTCLONl6661bPH7YYYelx1dcccUWVf/lr8sxE9VvcSRbcWxEVP/THHbfffe0HhTrRANK+j/++OMpaRsxsu6667b5+4xFza21TfyxKSnmRsW1rrifj7O0Y4NkbMqOCv9ypxGAIRVjTRyTVdxz3XnnnQMs+ijnP2KzJFA/Ev4wCB555JG0GzvaQsaEuizOfSx2/RcVAeWLXnGmjSr/5hAbQapjIG7QilajcbZo0Qq7eG7WWWfNp5hiinzvvfeu7OAeEAsAFBPwaM9fdByJNrWDWnUthogWtVEFGRuNIuHWWtK/WJyMDgCxOSBa2Ua8RUUkzSvmQTHmxK7/8vUsNqTFGcfRCeLII4/Mf/vtt0qslcVRJMWxNMai5iWOaK+4hsXHiCOOmF9zzTUtYqGIqUj6F921oupxQK236bxivIjzrdvaOP3kk0+mGImORgNL9oc43zaOYGvNRRddlI5DKjqy0fnFRv4111yzsrmoXARSvSYQ30ecFV0jq6u1oTUx34kOkVNPPXV/m9f22GOPfLLJJkvFIuVN/TSP1go4zG+o1f1aHA0a16upppoqVfkPaM0xOtXEPVz1vAkYtiT8YRCcdNJJ6QJXboFdrvpfaaWVWpwPGdXcZQOqnKTziNbY0Ta0SPoXYtExzuOL1qLFQlN5UTuqZqOCrUj2x6JB8bXqawpFLBQT6vgcE/Ai6R+7/osNSW7wKLR2M1bEUiTQ7r777laT/uUE3DnnnJMSKtGRRLUaxZEiV1xxRYvrWSxyR8eIuJ5F1W2Iz7Fp8v333+/v95RjjOYjjqjFvVnMvWMBMrquVcdDkdSNDdmRKIkNttHxiOYS40ocHRLxEknZYhNROelfJPx79OjR4h6/tWT/VVdd1Wq3o/I9m05snVtr9+fR3SHOVY/YmGWWWVpN+hef44zjSNwWnbNiQwoMyC+//JLWjeIovxtuuKGS2D/vvPPyySefPI1VxdhGcynmPRETzz77bLq3jzWh6o2yUBbXoXvvvXegryvWHGOMKeZJra05lr+O8Qqor+EzoE3//fdf9s8//2SPPvpo+n6mmWaqPHfEEUdkhx12WNarV6/sqKOOyuaee+70+EcffZRdddVV2aefflp57dRTT135fXRO22+/ffp3jzj4+++/WzwXMfHaa69l3bp1y0YaaaT02G+//ZYttNBC2bvvvpsdfvjh2R577JGNMsoo6bnevXtnZ599dvbnn39mww9vmG5W//d//9fi+yKuhhtuuMrnWWaZJTvmmGOy5ZZbLnv66aeznXbaKXvjjTfSc7Gpj+YWMVTESzHuhGJcGXHEEbNll102Xc/mmmuu7Morr8wOPvjgNC516dIlveapp57KLrzwwmzRRRfNxh577GzmmWdOj7ueNd84VHz9+++/p89//PFH+vzjjz+m61mfPn2yQw89NF3Punbtmp7r169fts0222SPP/54f7+7iDE6P3FErY0zzjjZDDPMkF133XVZ3759syeeeKK/eBhhhBHS54knnjgbb7zxsrfeeiv74IMP6vaeqY/RRhstu+aaa9Kc+ZZbbsk22GCDNP5EfPz777/pNfPOO2/Wo0eP7OWXX85WXXXVdI+/4oorZkcffXTWvXv3yu968skns2OPPTatCfTs2bPFf6d8z1bEHp1PzH+Lf+tYJwpxzzXllFNmhxxySLb55ptnb7/9drbLLrtkDz74YHo+5uIRa8Wc/LvvvsuWWGKJdO8W9/xxHwcDMuqoo2brrbdeirl99tknW2ONNdI6ZKxBxWMnn3xyeg2d319//VX5OsaVmPfEPf4666yTxpKVVlopW3DBBbO99torXbOgWlyrYv1n3XXXreQ62hLXrdlmmy07/vjj07znsccea3XNsfx1zLuAOqvzhgPoUKp34xe7tzfccMNU/Vi0Dj3kkEPa3PUfrdvHGmus/s6RpPOKndVFS7533323Uo32559/pq8//fTTfJxxxklnOYaff/457dCurmArYm6GGWZIVduD0t6fzj8WRfXjNttsk6qTojJpr732yr/88svKzv6Imdhlu9xyy6U4XGKJJVT6N7n4dy/H0Pnnn5+vs846+ZRTTplvv/32+ZVXXtni9UWlf4xREUPRqSTa0R5zzDGVqrhLLrmkDn8Jw1L1eFGMMdESOyrXCrfeemuKiS222CK9Zo455mj1ehY22GCD9NzDDz88jP4K6k0cUWttzWWi0rq4bi244IItuq1V/9wyyyyTzj4u5uk0n5dffjndYxVH8RXVsFEdGbESFftFxXV0Poqq/7JHH300X2qppfKuXbu26ExCc9p2223TPVcRR8V488knn6TrWlHpX11B+dhjj+WTTjppf0dC6ArBwMQcaqeddsq7deuW4mvcccfNF1988fydd96p91tjGDnqqKPyM844I3UELXexKeZCcYRRHJUVsRHHhiy22GLp6CMoxPwlYiXyG/E5OmBFF7VBETkOa47QGCT8aVrVrdjK30ebyPfee6/y+D777JMuavE5FiLj6169evWX7I8bukjsRrK2X79+w+gvod623HLLFBPFQnTc6McxD3fccUdaRIoEf0y24zWxODn33HOnhes4m7a8qB0TpTiDLSbnZ555pnb+Tao8Yd53331T3BQfxXmPEUMXXnhhSqC0lvSPBcnXXnutjn8Fw1pcj55++un+Hi9iqIiduLkbddRR0+PVSf+42SvatZVjLhYWCm7oOrdnnnmmxeJ0nBMa54LGkSE//fRT5bEJJ5wwxcfEE0+cEiSRKIlrXTlOTjzxxHRGe2ya1NqvuYgjaqU8F445T/VmkNjAFptF4loVmyPffPPN/q5XMT+PY2kiiRubcGneGOrTp09lI2Pcq5WPWot7thhnigXwGHueeOKJ/KWXXkr3ZTGGxXOnnHJK5WfMiZozjuL4xoiRYvNIEUetJf3jdZdeemnabBTHQ8bmpNg0cuedd9btb6F+io0dESuD03K9iK1vv/02Ha920UUX5c8//3x/R0nSef3vf/9LY8okk0ySjsUqjpE98MADU8FZFKUVY1EcKRLFSPH62AwQG/sh1osWWWSRypFqURDSnqT/0ksvnTZTAh2PhD9Nb7PNNstvuummyve77757JblfTMJj8WjMMcesJEBigeDtt99u8XueeuqptMN2ggkmyO+6665h/ndQvyraiKHyObRxlmh8f/XVV1dee+qpp1biZ+SRR04JtPI5a/G7Tj/99BQ/UYVUJHJpXhEjkZyNifSDDz6YbupjcWiBBRZIcTTFFFPkp512WiV5EjEUE/AiYTvXXHOlRQE6v9jcEf/mkUyLRFvh3HPPTUm02AASCY/4OO6449L5j/H6XXbZpb/fFWNa/Nx+++2XH3/88flDDz1Uec4mpM4tEiFF8jXOgIwuNXHGbMRLbISM2CgWHON6FxtH4vVx019dmRabI2MDZJyZHYndIDHSHMQRtVKOh2uuuSZff/3188MOO6y/jR8x347ONJH0j24QseG2XE0b8+qIscsuu2yYvn86VgzFXCnOSj/ggAPSBpCIidikX74fi6T/DjvsUEnmRkzFa2M+Hvd3kWQpmBM1ZxzFulAk0w4++OCUuK+Oo+IaFZuLohq7uP8vryXF/RvNe9Z6bFyLLmpbbbVV/tFHHw3wZ6rnPOZAzSvWfCK5H/Pi2IAWmz5ijh2FaFHVX3QXLUQxyOabby7pTxK5jbhuRTzE+nVhtdVWG6Kkf7HmGLmRopsb0HFI+NPUiiRstFuLyshox1+0Zi8S+sXNfCTfikXJaOFWFkm4qBqJ584555zK4ybkzeGWW26p3MBHu9C4oY8b+epJd9EJIBL+N954Y3osXhOt+6NNe8RXLIoX1UcWkppLdaIjFqjnnXfeSquswueff55vvfXWqUtEtCatnpjHgmYkfqNSkuYQSbVI6sfCdM+ePfPevXtXbuBi48err77aIs6iwqhYeCwn/QdUaWI86vw+++yzlCyLuJh22mlTwjaStDFXKh8hEr7++uv88MMPr8yLorL2+uuvT8mQFVZYIT02zTTTVFq4a1XbPMQRtVD+t44YGWWUUVI8RKV1sZmxfF2KDQGxMaSYjy+00ELp3iyuddEhIjbVFtyfNYdyfBx66KHpWKOIjahuK7odVbf3L2IvKiljYTyeW2WVVVKFdvnICHOi5o2jYmN/tDOOTbWjjTZaf3FUjDHx+eyzz8579OiRTz/99Pnqq6/eoiBAHDVfsj8qsCNBG2NQXJuii0hbcVDE0fvvv58//vjj/T1O8yhiJLpgHXTQQWleHUn/WKOOsaU4rq84oqa8QUnSn0LcX8U1LOY45TiJa9PgJv2jW0AcN1q9Vgl0DBL+NLUPPvgg33HHHStnYMXnaOXX2jlYsYB5xBFHVG7qIrkSr43dlHGzF8m38mKSG7jmUPw7R3K1WGRca621Ks+Xdzt+/PHHlQl30QIpknMxSY/voyWpRW2iVX90HZl88skr56bHhDw+iniLSseIs2JXbbVyhwiLAs3hueeeSxVGEROxWSTO64sNIUVFY/U1KZ4f3KQ/nVtcd+KatdFGG1UqGyMBW71YWfjyyy/z888/P1WalI+BiO9j42RsTip+L81DHNFe5XnL3nvvneJhySWX7O9M9eprWxybFRsl4/WR3I37tOgiUWyCq349zSE2jERMROI+kmvhhRdeSJuzY6N1a0n/ATGvbu44ilgp4iiOe4iEW2txVL7WRVeSqM6NTf4FY1HzKP6tI9kfZ6zHumFU9xdd+gYkjoKINaLYqBRFJjSv6qT/6KOPnrqDRheaqPZvSznpH0Uht9566zB813Q05U7F5bXqASX927oHU9kPHZeEP00vWmrNNttsaUEydkpecMEFbV7Yvv/++zTRjiRK0RI5FiTXW2+9VDFZcAPXXKLSqFu3bpXqo/got3wsx1Hc/EdL7e7du6dJerQCjLP8oookKt2qX09zieRsxM+yyy6bdv0XydpyTBQLjbEAEOe1RceIaPffGouSnV/53zgq/Yukf2xGm2iiiSrXptbGlXLSf7fddhum75uOKeY5sXAdc6KIi5jvxFEQAxpTYqNaVJbEucZxHEScLVqcIel61pzEEbUQXdOKjY3RvahazKmrN6lFgj8SKrEZO7pnFRtGgjhqPjE/jrlybK6OFrTl8SdiJ6pmoxNJsSGgSNba/EhZzK9j7SeuZdVxFONQW3HUVjLE/VnziViIFtgxL4qjaYr5TVuK61VUZMc1rTj6aGA/R/Mk/aO9fxzxGLERsVWe77SW9I9NJkXRkThqPtXXnSKWypvTWkv6l5+PY7KAxiDhT9OLXdlxUSt2ZscZkJEEGdANWVTPRheAqDT55JNPWrRul+xvzkWAGWecMW0WKRYn4yMWrNtaZIxNAlHxHzss47kibsRPc4sxpWhjHB+xe7u1saiYeMeNf7wuziSleVUn/Ysz1eJjYG2M43pXdLiJY0dobrGQHRuOopKtWBiKBe577723vziK69WAFq0taDcvcUQtNo0suuii6foUHWyq4ysS+7HBLRYnH3300RYJ2miZPdNMM6Wk/+67755/+OGHdfgL6AjiCLUYfw455JD0fdxzVY8pd911V9ogWWwuKZK1NohQiKNmIj4iUdtWHEViNo6wEUfNrbqLUREn0b0vYiOuWdXJ1tiYdMwxx6SuNEceeWTleMdCFJLE9TA2QtIcfvzxxzbXBovHokPEAQcckMadmCvFOuSAukbEEX/R3TaS/1DWVtL/wQcfrDwe86goSHJsKDQGCX+aXkyw4+IVi0U77bRTm4uSFiMZkLgxKybfbSX943kJfQbmmWeeyddZZ50UP1F9ff/997c5FsWEPM4gjZaSNLdyXDz99NOVGIqd/3F9a+115cXueO2xxx47zN4vHVe/fv3StSo2N2699datzovKCwNRZRIsalMmjmiPOLM4Ohitttpq6fti/nz55ZdXKviLuXYsQMZ5pGXXXHNNJekfxwK89957dfk7qI9irnPWWWelGImNHwMaq6LisYin2Hir+pFyHJ166qkpNvbcc88BxlF01yriKI6kKVr4WytqDieddFLacBYdRKtFIr96k37Md/bZZ590pnb5SKMePXqk447KBqX9P51DVO7HMUbFUZ8DSvoX7f1jzWiyySZLR0MOKFa0YGdwkv7jjz9+quovxq/obtynT5+6vk9g0Ej409TKLf2KpG2cM1osSrZW6R8V2cUE3M0b1YqYiGr/4qYtNgAUJPwZ1ITtWmutVTlrrfocrRDtI+MYiemmm85CNv3FUGwcWXPNNStHRDz11FOtvq4QHUcG9DzNJ+Lgs88+y7fddttW50VxPYtNbbEo9dFHH9X1vdJxiSOGRBxbFGfTRjK/d+/e+UMPPZRvt912KYZio+MRRxyRNkRuuumm6bHFF188VdSW59nXXnttPvvss6fnowpOm/bmE8m1+Pefb775Wu30UMRLLGbH8WyTTDJJev0dd9xRh3dLRxXV+xEXCyywQKvXqSKO4ti+6Bo55ZRTptfvv//+NrE1iTgXPf7NY6PZzTffXEn6F/dUcXxjPL/zzjunjfqxGXv++edPj8WRI+edd17qyjbXXHPlI400UmWeZO2ouURnrIiJOPYz1oKim+zAkv6//PJLiq9I+k866aQp6V90CIAhTfrHprWIxTgWqaj41x0CGoeEP1QlN8qLkjH5jhu8wgsvvJAvs8wyaSIfkyhJEaqVJ+OS/rS3Nfuqq65aWTyIs42Lm7eoZOvZs2d67qKLLqrjO6ajx1CcJTqoSf9gfKJaeV4Ux9cUFdpxJFK0QY42kgM6NxKCOGJw7bLLLileotI/Po822mj5euut1+IM0ahuG3300VPCvzhirXx9u/TSS1NSxSJlc4oKyaIjRLnrWignYqPN8RJLLJE2S8Z8G8oiyd+9e/cUR5GYbSuOYsyKsSjWjyLxNsccc+RffPFFHd4xw1oUDsWmtNiMP8sss6QW/uVK/ygammeeedK1bIwxxkifY4PRHnvskX/zzTeV1+266679dYmkeTaNFPOdouvDGmusMcRJf10hGBLlcavYNBv3aObR0Fgk/GEgi5KxSzt2+UeFbVQfaXvMkCb9qxcIYFAStkVLraIyMpL/ses7zmuLRElrP0dza2/SH1qbFxXVtfEx77zzVo6MKKrdVLExMOKIwZ1HRyV/HFET16/Y7Fjd5jiq+COGYqG7fF0r/w6L3s0tzsaOGBlxxBHzK664onK2ermrVsyv4+iRMhsgGZw4ivl1bGaLzSORfItEXbw+qr3p3IrrTmzu2H777VOMFEn/4liHaKUeMbLuuuumxH+MN7HBqDqO4hibuMd/44036vK3UB/RrTGOk4kx4/jjj0/H7UUMDWnSPzrWnHnmmeY/TapcqT84ymtDxTUvkv1vvfVWDd8dMCxI+MMAFiV32mmnyqJkTNzj88knn1x5jWQJg5v0j7NHYXBbs8fiQMTP5JNPnha+YwPSiy++WHmNRUkGNekfiwnRHhkG1w8//JDaHkeF2zTTTJMvt9xyqaIpSNIyqMRR8yoSG4Ny/1QdC639TCRqo3o/OkQ8/PDD/T3vPq25Y6g8N95hhx3SHCjGnaigvfHGG/Ovv/46JWPj6Ky4z4/kHM2hFnG02267pZjp169ffsstt6Q4ilbs1113XXptnM0er73qqquG4l9CR0uwffXVV2nTR3SeiU36Mdb8+uuvLV77/ffft/o7zj777BRDcc8WcyWax5133pnGixVXXDH/9ttv0xgVY8mQJP0PPfTQ9DMzzzyzOGpCxXUtxp0TTjghzXUGV2y21cYfGpuEPwxA7MSNs7RiMSkSbtdff33lOQm25lFeDBicxcNyjJx22mmpRVdr50dCa8qxFovaseN/+OGHT8mR1157rd07eGnebhELLrhgf1WSNIchvZ6VxY1/JGijlXaQpG0+4ojB9cADD6ROacX8ZVDipvya6vuuqPaPFuw6aDWPIYmh8riy9957V9olF1Vrxddxn0ZzaG8c7bXXXikpW8ROJESKr8uFIXH0WrRuj/Pa6dyKe/FohR33V2eddVa+9NJLp5iYb775UtK/qPRvS4xB0eJ/yimnTNXeNJd33nknXaNuuOGGymOR9I/158FN+kdV/9FHH51+J80prllrrrlmipvnnnuu8tigiHuzWG/UaQQa23DxPxl0QhHaww03XPoc4uv//vsvG3744Qf7d/3xxx/ZSCONlI0wwgjp+yH9PTSW//u//6v8m7cVX4PzO3777bdstNFGy/7999+sS5cuNX+/dL6xqBxnzz33XHbUUUdld911V7bMMstkhx9+eLbQQgv19zo6n1rF0LPPPpvtv//+2SqrrJLttttuQ/1907muZ63FnLGnuYgjhsTvv/+erb766tkDDzyQ5i+nnXZaNsssswzRv/vzzz+ffs8pp5yS/fnnn9mxxx6b7bzzzuk5cdR5tSeGyuPWzTffnL3wwgvZvffem+7FZp999mzllVfO1lhjjfS8e/zOrVZxdOONN6ax6L777qvEUa9evbJ11103PX/CCSdk++23X9azZ8/suuuuy8Yee+xh8vcx7BVxEes866+/fta7d+/0eMRFv3790tfdu3fPDjrooGzFFVfMunbt2mKN8d13382OPvrodH8/ySSTZHfeeWeKSZrPDz/8kI0zzjgtrkUxZkVsxLrP22+/ncavGLemmGKK9JoYu1qbl5sPNbd//vknO/LII9Pa4RJLLJE9+OCDKU4GNS5i3THGo4gzoDFJ+NMplW/I4mL366+/ViZPbd3Mt5aEtSjZvIp4+PHHH7OLLroo69u3b9r0EYtC88wzTzbGGGP0Fx9FbLz//vvZiCOOmHXr1q1FPIqd5lOrsag8+Y5FgVgMWH755VPydrHFFhvKfwWdKYa+/vrrbKKJJkpfG5OaQy2vZzQvcUR7vPrqq9kBBxyQEq09evTIzjrrrMFO+sdrDzzwwOy4447LFllkkWz33XeXqG0i7Ymh6s1KP/30UxqTYgwr5ktiqDnUMo7iehhxFDE08sgjp8dOP/30dK8WsfTEE09k008//VD/m6ivSNxHLL311lvZFltskR1yyCHZzz//nH3yyScp1p5++ukUY5G0jY0ho4wyShpvYiP2wQcfnD388MPZWmutla5t00wzTb3/HOqseiwaUNK/EJvZYo4977zz1uld01EUc5mY50Sy/7XXXstOOumkNGcux1XxulhfiutY+TGgE6h3iwGotXKrmnPPPTfv1atXPsUUU6QWSJdccknlHKPW2iBFS7c4b10breZWxNA333yTzz333JU2fcUZ6jvttFM6r6+1OHryySfzMcccM99kk03yt99+uy7vn849FpVbs8fngbUIpHENzeuZs42bg+sZtSCOaK+45kQL7WWXXTbFTbTULlqFtnU9auvadvvtt+fvvvvuAF9H51OLGKp+nblQ8xkacRTXyDh3O+bn0cY/2rJrhdz5RRzEx4EHHphiaccdd0xnqJfb/Pft2zfNf+L57t275zfddFPl3j2ONLrrrrvSx/fff1/Xv4WOrbq9fxz1WMy7DzjggPTY1ltvnf/555/1fqsMY8VYU75OFY899NBD+dhjj50vssgiaSyqft0LL7yQ77///vIf0AlJ+NNp7bvvvpXFyBFGGCF9jhuwLbfcMi1YtnbztsEGG6TXrbfees48anI//vhjOnNtxBFHzDfaaKP8uuuuy7fZZpt86qmnTjGy/vrrtxpHcaZoEXe77LKLM2mp2VhUXlzq3bt3ikGLSc3B9Yz2cD2jFsQRQ6r8b96nT598+eWXryTa2jpHu3pBsq3NjRK2zWFoxhDNY2jFUTwWZ7BPOumk+UorrdRiQxKd36qrrpqPNtpolYRaEWdF7MTjEWMRazGPuvHGG1MCt/waGNSk/8wzz5xiKc5o32233dLXo48+unWhJlSMH7/++muKh8suu6zF81988UW+4YYbphg58sgjK4/HGPXTTz/l0003XXouxrAPPvhgmL9/YOiR8KdTuuiii/KRRhop79mzZ/7444/nb731Vn7qqafmM8wwQyUB0tqi5C233FJZlIwFSppLdSyMNdZY+dFHH53//fffLXZhzzbbbP3FUXkB4eqrr87HHXfc/MMPP6zDX0GzjEV//PHHMPs7qB/XM4aE6xm1II5or3IcvPzyyykW9tprr7xr1675qKOOOtDq2rXWWislSGKRu0iQ0FzEEI0QR19//XX+0ksvVbpv0flFnMRmj2mnnTbdq7344otp3lSOn+LrZ555pnJfNsccc6T4cy/P4Iok7W233ZbPOuuseZcuXVI8xfz6zTffrPdbo07++uuvfJ555qmML1tssUW6Nyvcf//9lefi67Kbb7658tznn39eh3cPDC0S/nQK1dVCcZGLxcdXX3218lhMxh944IF89tln729Rsmh5E6699tr8pJNOGobvno60qB036c8//3y+5557plgpFDESsfbYY4+1GkfFAngodv+XY4vOz1hEe4kh2sv1jFoQR9Ryw8ghhxySTzTRRKlDxPzzz5+PM844lY41bSXaojIpWiDHayKhouVo8xFD1II4Ymgokvtx5FrExoUXXtjq62LeE+3XI3aitXa8doEFFkibJmFQFfPnSPCuvfbakv1U7sMWXHDBFA/RdS02sEXHh1hDKo4KiWKReH6zzTZLj5WviXfccYfuENAJSfjTqcQZx7FbdqqppkoXtfLZWiEubIOyKFnQYqvzau18q7jpiqrZuPlfeuml04QoVO++jknVo48+OsBkmxajzc1YRHuJIQaV6xm1II4YWqKNaNEyNFpih0h+nHnmmfnCCy/caqKtuGbFY5NNNll+3HHH1fVvoL7EELUgjqjFOdnVm7Mjfopk25NPPll5vBw/Ycopp8z32Wef1FkiOrbBkDjqqKMqyX5x1NyKcen111/PJ5988nyJJZZIm7QXWmihFCPRsj/Gp6jsj+tebAZ45JFH2lwvAjoPCX8azldffZVaGVW75ppr0kVtqaWWSgmSqGxsrZKouhIpzrSJFmzBYmRzOOigg/JTTjmlvziK3bFxll+09ovYWHbZZSvPVcdGdRytsMIK+bfffjvM/gbqz1hEe4kh2sv1jFoQRwwt0Tp7/PHHT9ey4ozs4loWm0x69+6dL7744pVEWyxaVidVigql4NrWfMQQtSCOaK9ffvklVfNHG+zyPVnERSTTinlSxFIoJ/tPPPHENJcqd2yDwbXvvvumOJtwwglV9jeh1rqkxTgTG7R32223FBuxjhTJ/LPOOiufc84502PxOTZuF0eKFOtFQOcl4U9DiR2zU0wxRap2LFqDFp599tl8nXXWyUcZZZR0Idt5550Huig511xzpdeutNJK/f0+Oqc777wz/ZvHDv133nmnv+fj7LVNNtkkH3nkkdNZbFdeeeUA4yjO1J500knz8cYbz8J2EzEW0V5iiPZyPaMWxBFD0z333JPia++9907fV1dGxvf33XdfPs0006QW29WJtnJnGwm25iSGqAVxRK0qq6NqNs7ILipkIx5iLlW01Z5pppnyq666Kv/4449TR6Tjjz8+3fPFvVps9oYhdd5556UjSIqxiebz66+/piMbn3rqqf7u16Lrw4wzzpi/++67lWPZYh0pNogMP/zwaXyKjxNOOMF1DDo5CX8aRtxoHXjggekCtc0227TagiZas2266aZpUTIm1bfeeusAFyWjnU201nLGcfOISc8BBxyQX3LJJZUd/bFbuyzaIG288cZ5ly5d8kUXXTS1QBpQHD399NPpbL+gbXbnZyyivcQQteB6Ri2II4aGIi7ieJrimIe2YiFiLjpJxOuiAjLOOFYFiRiiFsQRtRJzox122CHFR3SKiKR/cRxSxNTtt9+er7jiipWk2gQTTJBPPPHE6ev4rCK7ObU13gzq/Lj6dTFvpznFPdYaa6yRxpRYHzr99NNToUcRI1HVH88dffTRLX4uNiTtuOOOlbHpgw8+qNNfAAwrEv40lJjcRIuaYpITC5A//vhjf0mSaGscOx979OiR/+9//xvgouRnn33W5vN0LsVEqEiuxZl9sXAd5xpVt7GNHZLrr79+2gk5sDgqVFcK0HkZi2gvMUR7uJ5RC+KIoS061sSZofPOO2/+ySef9Ld4XcReVK1FVeQCCyyQFiNvuummur1nOhYxRC2II9qjmM9Ede22227batI/5kKx2TGqZ+eff/58ookmyuebb760gVuCrblbsP/111+pI19sCrn33nsH+DPFuBRz8nL86TBC0a2mONohPlZeeeX8sssuS+NQxFkcCRkFI3GMTVlc4x588MH8rbfeqtt7B4YdCX8aRvXOxoMPPji1Fr3oootaXZSM3duDsyhp4tR8Tj755MrN2sUXX1yTOKLzMxbRXmKIWnM9oxbEEe1VHQeRWOvevXuKq7jWlV9X3hCyxx575NNOO23+xBNP5Lfddtswfc90LGKIWhBH1CJ2yvdsxdcDSvoXfvvtt/z7779PX0cSjubeJLLccsule/0iSbvqqqumblitdfkLr7zySj7rrLOmo/+gtU3UMe7E0TNxBGQcQ7Paaqul61xsWotYW3PNNVtsGnGfBs1Fwp+GFBPqXXbZJe3S7tatm0VJhkhU08YN/xhjjJFPPvnkgxRHDzzwQN3eLx2PsYj2EkPUgusZtSCOGFyD0pL2hhtuqCxyxxnI1eIc0qio3XzzzdtMtNB5iSFqQRxRy4rsciK2nGgrJ/232267FEuxQeTuu+9ukdgvx4x7teYVGz8WXHDBFCfxecstt0zz6/g+uo3ccccd/W0Wie8PO+ywylh19tln1+39Uz+Dct358MMP8yuuuCKfYYYZUqxMP/30+bHHHptPN910qeV/bAoIOq5B85Hwp2HFAuTgLEouvfTSA22fRPNNoCJm4iztgcVRtLGN1khRGRDtuKBgLKK9xBDt4XpGLYgjBld5ATHaXseZoRtssEFqWfv++++3eG0cE1EsXkcy7ZJLLsk/+uij/Prrr0+L4F26dMmvvfbaOvwV1JMYohbEEbX0yy+/5OOOO25K6A8s6b/KKqukWIqEWyT926rYpnmUN3iceOKJ+TjjjJMfcsghlcR+jEnrrrtuOrJvjjnmSONUddL/9ddfT/PsMcccM3/zzTeH+d9AxzoK4owzzkhrRbfeemv+6quv9vf6OCJyhx12SPdtUd0fVf8xLsUxbUBzkvCn4RclDzjggIEuSsYZyHHBm3vuufO+ffvW6V3TUSbexdfFRGpQ4uill17KV1hhhbRT8uuvvx7G756OylhEe4khBpfrGbUgjqhV/MQGkSKBFh+jjTZavvzyy+cPP/xwi5+JY2ti01rxunJ7W21rm48YohbEEbUWnR6KeNhnn31aTfoXX3/55Zf5RBNNVKn0v+WWWypzKZpP8W9ffO7Vq1e+yCKL5H/88Uf6vugC8fHHH+dbb711SvrHxtnWkv5x1nrEF817FEQc/dC1a9cW17UJJpggv+CCCyqvLzYZRfw8/vjj+RZbbNHi9V988UXd/hagfiT86dAG1P6qvCg5sEqkF154IV9ppZXy008/fai/ZzrmhKmIpdixXW63Vjw/KHEUO22//fbb9LXWfs3FWER7iSHay/WMWhBH1NJxxx2XFhRjwfrCCy/M99tvv3TUQzwW58/ed999LV4fi5FHHHFE6lSzxBJL5Ntvv31qaVsQR81HDFEL4ohauv/++wea9I9E288//5zPPPPMaSN2EX+RqKN5VHd1iH//GWecMc2hV1555fyqq65q8bpi/h0b97faaqsWSf/yfJzmU1x34iiIueaaK8VGrPtcdtll+R577JE2sBXjUlzzCtWbjM4///y0WeDtt98e5n8D0DFI+NNhlSfTX331Vf7KK6/kb7zxRjqnZkgqI7/55pvK187Rag7FxCdaHEV8LLnkkik+lllmmdS2r9qgxFGwANBcjEW0lxiivVzPqAVxRHuV/60jFmafffa8Z8+e+WuvvdZiI0i0yo4FyTgPuzrRVvyeiEfnZDcfMUQtiCOGhoiDIhYiXlpL+kd8lO/tYkPJKaecku+9994p5mgOsbGoSNCXx4+bb765RYX1vvvu29/Ptpb0n2eeefIbb7xR0r/JxfVok002SbFz6KGHtuj8ENe6E044oRJb5557boufLV+7YtMA0Lwk/OmQyhPos846q7JjNs5Um3DCCVMLm+qdlNWLkpdeemmri5KSI80VQ5EYK+Jn4oknTrttiwlSnKVV3Ya2HEdTTz11ir+ofqM5GYtoLzFEe7meUQviiPYqX3Oi5XFUyEZcPPTQQ/0tNH766aepXW2RaPvf//7XaiWS61hzEUPUgjiiFtra2FG+d4t4KeZIkdCvdvjhh+cjjzyyStomExs8IiYWXHDBVseO8847Lx911FHTa9Zee+1Wj74qJ/233Xbbypnr5tjNLY57mGyyyfIFFligkuyvXis66aSTUryMPvro+bPPPtviOdcyIEj406En3rEbMi5kY489dr7eeuvlm222WWXCHW3aolKyrGg/Os4446REyjnnnNPfxZHmiaHvvvsun2OOOfJRRhkl32mnnSptZ0888cR8xBFHTHG05557trq4ffDBB6fn5513Xm3ZmpSxiPYSQ7SX6xm1II4YEsWZs9WiDXYkOKIFdpxZHJtIIkFSvcgYibaoXCsSbdEimeYihqgFcUStFZs94t4qzkq/9tpr82uuuSZ/8cUX8379+rV4bbnSPxKzTz/9dJpPRaXtRBNNlM8///wtuq/R+X322Wf5lFNOme7h2xqr4t49Kvcjbo4//vhWf08xVn300Uf5LrvskmKR5nbvvfemmNltt93S9+WOD0W8xEaAtdZaK70uCkMAqkn40+HPYevVq1f+3HPPVR4vJ0liUbI6SRLnaBWJlYsuuqgO75yO4Pfff8833njjfLTRRkuLAcXuyDfffDNfd911U3wUu26jRduXX37Z4ud//PHHNDH/5JNP6vQX0FEYi2gvMUR7uJ5RC+KIwREVs7EpJOKjOo4OOuigtBktNonExzvvvNNmVVE50TbNNNPkd9555zD7G6gvMUQtiCOGVrI/Ni/GJuzodlTcj0Ucrbjiiqm1ellsEolq2nhNdJMofiYS/pK0zRk/Rcv02BS75ZZbVhKz5RbsUelfxNbJJ5/c6u8rxqvqc9hpTnfccUeKl8UWW2yAG6zjXi5et8MOOwzT9wc0Bgl/OqQHH3wwtbFZeOGF85dffrkyoYpJUteuXdNzMbkuzkRqbVEy2rvR3DsjY1E7buKKSXecmb3mmmumuInK2bvuuqvF2VrVcdRaWzeai7GI9hJDtJfrGbUgjhhUUd0YnSCKzWbVybPoDBGbP6KiNl4TC91Ft4i2Em3FBrfLL798mP0d1I8YohbEEUOr41Ek0uaaa65KYi3a9a+zzjr5JJNMkh4bc8wx0xntZVHZHxsnY8PILLPMkips33vvvTr9JdRTeXxZddVVU8wstdRSlW585aT/+eefP9CkPxRiY/UUU0yRPmLDW7Uixh544IHK/RpANQl/OpyYHBXnrD3yyCOVi9qpp56adtVON9106ftbbrmlxXla1ZWRAzubi87tyiuvTJVq0W6raJO1+eabp3jZY489Kq8rqmeLCtsvvviiju+ajsRYRHuJIWrB9YxaEEcMjssuuyzFxQ8//JC+L5Jo5URcdK+JTWvjjz9+Os82Nqi1lWiLM2pjAxzNQwxRC+KIWot7r0juDz/88Pnhhx/e4v6qd+/eqWI25kCxSTJa/Vf/bGwWiA4T5aQuzSvmybEBpNg80lql/wUXXFCZW8f56zS38rWp+uvoGLH66qtXukPGRrVCub3/jjvumF5z88039/d7ACT86ZCOPvro1HItxAT8uuuuS2cYTz311C0WHjfYYIPKxCkueG1VItGcXnjhhUqrrTjbKFq0FXFVTIqiXVuXLl1S4i3iKG76TJYoGItoLzFELbieUQviiIEpJz6K9rJxRu1GG22Uv/vuuy1eG4m3SLRNMMEEqSpyYIm21v4bdD5iiFoQRwytxNozzzyTjzXWWKkiu5gTlc9ej00hcYxEzIGWXHLJVHFbxIr5EGXF2PT111/nM8444yAn/Y888si6vWfqq+iSFp/jI2KnWhxhU3SAjCNGYsyKTUblWBp77LHzOeec05oR0CoJfzqsb775Jn2OC9tyyy2XLnivvvpqiwn59ttvn3ZyF4uSrbW8obm0dhMWN3Ldu3dPCwDFDsli8v3888/nU045ZToDcN55580//vjjYf6e6diMRbSXGGJIuJ5RC+KIwVU+suH111+vtDjeeeed+0u0fffdd0OUaKNzE0PUgjhiSLXVLS3E8RARRxEjbR1TFBskZ5hhhrQx0j0ZhWIsiQ0gxdeDk/QvYm+kkUZKYxbNpYiVuA+LLpALLbRQPsYYY+RbbLFFKgope/HFF9PaUMRLHFuz/PLL58ccc0w6im3kkUdOz7311lt1+kuAjk7Cnw6127a1XdZx4YuLXLQdrZ6Qx87b2PF21VVX9XfGFp3fgM6QLcfXa6+9llqyzTPPPCm5Vky0It6iSiASbOXzkIrnaQ7GItpLDNFermfUgjiivcrXruLfP65dcdZxtD+ODjQDS7SddtpplUQbzUcMUQviiCEVx6Otssoqaa7TmoiLuB/bbLPN0vymrU4PW265pXPXGej8OhTJ/UFJ+l9++eWpgpvmUowzcSRIbKiOGIlrVVzP4iPuy44//vgWPxMb3SLRX2x2i484ni06j7zzzjt1+kuARtAlgzr4v//7v2yEEUZIX//999/Zb7/9lo0zzjjZ8MMPnx7777//Kl//9ddf6fPEE0+cPhePP/7449kNN9yQbbLJJtmGG25Y+d3ln6Xzx9CPP/6YnXvuudlnn32WYqhXr17ZPPPMk4088siVWBhttNGycccdN/v444+zL774IptmmmnS7zjjjDOyBx54IOvZs2eKwxFHHDE93qWLobFZGItoLzFEe7meUQviiFoorjk77rhj9vPPP2dXXnlltu6666bHjzrqqBRbYdddd82mn3769HXE0tZbb52+Pv3007Mjjzwy+/3337M999wzG2mkker411APYohaEEcMiffeey+7/fbb0+eYA+21117ZbLPN1mKeNOOMM6Z5zeuvv57mOqOOOmqL+7m4X4s50+yzz56+j7kTzevff/9N8RJxcfPNN6e46du3b2V+PfPMM6fxJWJpwgknTPf1iy++ePbkk09myyyzTPbggw+mePrzzz+zrl27pvt9mk9cuyKWttlmm6xPnz7ZLrvskh100EHp68ceeyx9vd9++2X//PNPduCBB6afibHr6quvzr755pvs2WefTY91794969atWxrfANpU7x0HNJ/yDtrTTz89X3bZZfPJJpssVRRdffXVlbNpimqiOEs0drJNMcUUaZduPP/QQw/lSyyxRGp/c9ddd9Xtb6G+MRRtsqMlbbHbMT6mmWaafN99981/+eWXFj+zySabpOcj1rbbbrvUVrt4/WeffZZeo91fczEW0V5iiPZyPaMWxBG1Ev/mcYxDET8333xz5bmbbropn2OOOQZYXXv00UfnXbp00ammiYkhakEcMaTuu+++fJFFFklxs/HGG6cq2bJvv/22UoW97rrrVh4vztQubLjhhqnq9oknnhim75+Oo4iHqMqOSuvy/Do+ouNI+f69tUr/qMYuHqf5lLukRbeaqaaaKt9qq61a3JdFnF122WWVuDrqqKMqz7XVgQRgQCT8qZsDDjggXcziRiw+4utxxx0333XXXftblFxttdUqLW9mmWWWvGvXrun7SLDQnGLS3aNHj3QTFmceXXvttant2qSTTppiI85E+vnnn1v8TLR2KyZREUOLLrpo5ezagbXpovMyFtFeYoj2cD2jFsQRtXTOOeekuFh77bXzL774YpATbf369cuff/75OrxjOhoxRC2IIwZVeZPi//73v3zBBRdsM+n/+OOP5+ONN156fv31168cG1GIjSIxL1p44YXTZkqaT5FojfPW4wisGGvWWGONNK48+uij+QYbbJDiJ85SL29IKif9Z5111vSaXr161e3voP5iPWivvfbKzz777HziiSfOP//881bvta644opWk/6OVgMGl4Q/dXH77bfnY489dpr4PP3006nSMc6riarHuLjFjrfqRcmYUHXr1q2yIHnllVdWnrPrrflE3Ew00UT54YcfXrlB+/7771MFbVSntbW4HTu+o/L22WefzX/44Yf0mEXt5mUsor3EEO3lekYtiCOGRPU1p/i3/+STT/JlllkmH2mkkfI77rijxWuqE23vvffeIP1uOicxRC2II2qh/G8d85uFFlqo1aR/JNBi7lMk/RdYYIF89913T49tvvnm+VhjjZUSuW+//Xad/hI6gkjeRxeIuGc/+OCDKxv5Y6yJDhBFcnb00UfPb7311hY/F7788st8vvnmy1988cW6/Q3Uf0yKjdgRJ0svvXQ++eST53379m3zXquc9D/mmGOG+fsFOgcJf+pWDTnllFPmL7/8covKpEicDGhRMtq6xU3fTz/9VHnMDVxzqG4re9FFF6UYKnY7FhOmWOS+9957K3HUWrKtTPw0N2MR7SWGGFyuZ9SCOKK9youNb731VmUhu4itc889N8VMtB/96KOPWk20RRJu2223zd95551h/O7pCMQQtSCOqIVi/lKex9x9990tKv1jY3Yh5kcPP/xwZX5U/ogNAJL9XHfddalzViT3i7lznz59UiwVc+r9998/fR2bRMqV/n/++Wf6bF7d3PdocX2LcSiOcCzGl9iMNCCR9I9rWrz2pJNOGgbvGOhsJPwZ6lqb4Ky88sr5PvvsU3m+uCDGIuU999zT6qJka+eIOlu0uRYBYtIcLbWiAi0m37PPPnt6rDrGquMokm3FwoGqteZlLKK9xBDt5XpGLYgjammHHXZIXWm23377tBGtLNrXRszE5raItfK16pZbbslnmmmm9PwjjzxSh3dORyGGqAVxxJAq5jJRWR3xcP7557fZ3r+c9C9ar19zzTX5YYcdlj4iOaeNf3MqjyuxIWSPPfbIJ5xwwvyrr75Kj0Vldsyhi7l0iKNGomNfcaRfudK/+nfSPGNR3IsVhR0RSw888EC+3HLLpTiZf/75+xuHql1wwQX5OOOMk7/xxhvD5H0DnYuEP0NVeRHxueeeSzvZop3RmmuumVplxeSneqGxelEydmoPKElC51bEx7fffpuvtdZa6czrEUccMS1qx9ffffddi9e1FUfRiiuqbmlOxiLaSwzRXq5n1II4opbiLNqi4iiq02Jh+5JLLqmch/3SSy+lBNyMM85YeaxoVRsiSXLVVVfV7f1Tf2KIWhBHDKmis1HMadZee+3Ufn2CCSZosfljYEl/KOKoOBYr3HXXXWnuHGJD7amnnpqOENloo41a/GzRsr34iE0jNG8M/fHHH+mIx9ioFutGxX3Zgw8+mC+55JIpRtZZZ538zTffHODvK3eCBBgcEv4MNeXqoiOOOCKdcVyeBK2wwgqV59talJxhhhnSa9dff/0WEy+aSyxqzzrrrCkWYqE6zlMbYYQR0vdxXt+A4iiScmOOOWZaNCgWwWkuxiLaSwxRK65n1II4olYiBiL5EZtG4pzsWIiMxewePXrkN954Y3rNLrvskmJrvfXW629Rs0zb2uYkhqgFccSQKP6tI9k/11xz5SOPPHLa0Bhnp1ffb1Un/V9//fVW50s2ZTdvHEX3q+mnnz7ffffdK8/9/vvvleRr3M/HBtvvv/8+PRbdRsI555yTzmffbLPNUnw5DqK5Nx4VlfzTTjttfscdd1TGohhnHnrooXyxxRZLz8cGpYEl/QGGhIQ/Q10kR+JiFjuyY2IdZ9cUi5JFG+S2FiVjR2W0RTr55JPr8M7pCBPviItYvI5d2tFiLb5/9dVX8zPOOKMSR4ceemibcVSczfb5559XfifNyVhEe4khhpTrGbUgjmiP8r91OaERx0GMPvroKckWLUePOuqoyoa2PffcM7/yyivT9SsSKRdffHGd3j0dgRiiFsQRtRSdHlZfffU0B4r5T9G9qNxau62kv3bZlONoxRVXTLERFfxR0V922223tWjlX35+2WWXzRdeeOE0x+7Xr98wf+90nI1Hc889d7pGRZy0VqFfVPoXx0BI+gNDg4Q/NVeeWH/88cf5zDPPnK+66qqVHbRxxtFFF100yIuS8TsKdts21+7I4vMcc8yRdmoXOyMjDuIjWvcNShwN7HE6J2MR7SWGaC/XM2pBHNFeA9vYEe1oRxpppPzqq69O30cniFiEjMeiarJbt255ly5d0jESFrObkxiiFsQRtVLcS0UiNuJjpZVWqiT7q+OstaR/xNFqq62Wv/XWW8P4ndNRlDuExGbY8cYbLz/ooIMqlfvl+/Wbb745za9jw39R9R/OO++89HMHH3zwMH73dCSxAWTllVeu3IOVY6RQHEMT49Gjjz6aL7LIIpUOkI4ZAWpJwp+aKk+I3n///dRKK1qxxQ7tatdee+1gLUqqQGouETsTTzxxfuKJJ+Y9e/bMX3jhhfR4dWu2wV3cpjkYi2gvMUStuJ5RC+KIWohk2nzzzZfOly0nyz788MPUqjaOiigWJGNTW7wuNruVj7GJs7RpXmKIWhBH1Ep0WYt4uPfeewf5PiteO9NMM6Vj2oqORzTnvX608X/mmWdSN77onlUcd1UdRz///HM+55xzpvWADTfcMFVpxzEj0XVkqqmmSmMXzSuuUVHZH91Gqu/NXn755XT/FhvVTj/99Py9995Ljz/yyCPp+JoYv+I4iOKaB9BeEv4MFVtuuWW6aG2zzTZp11qhetJkUZK2PPfccy1u6C+88MI2X1tOth1++OHD9H3SsRmLaC8xRHu5nlEL4oj2+uabb/LJJpssxcWEE06Yr7nmmpWKoqhyK9pn77bbbi1+LhbDDzzwwFRde9xxx9Xp3dMRiCFqQRxRSxE/MeeJitm2uqjF/Vg8/uKLL1Yei4TtRx99NEzfKx1LxEUk+WO8icTr4osv3urr4r4/PuLYkdiQVJ6Px/daslMc/Xj99ddXHouY2XfffVNnmnLMrLfeemmjUcRfbD7q1auXGAJqSsKfoSImz8XFLHY8FruvW5t8lxcl4wYOCo899lgljvbbb7/K423FUdeuXdNrnZFNwVhEe4khasH1jFoQR9SiU8TZZ5+dzz///Ck24rzsqDqKTjbRwjaOi4hOEk8++WR6fVGlFJ8//fTTyu/RqaZ5iSFqQRzRXsXcJxL+1Ruuy4oYiddH54iTTjppmL5POrbo3lfMraeZZppK4rW1ufUff/yRv/LKK6kQYNttt81POeWUFuMRzSuOdogYiqMd3nnnnfz++++vXN+mm266/PLLL0/XuIixeOzWW2+tjE/FERIAtSLhz1ATO2yLiVNc2AqtTZxip2Tx2minBK3FUVTQDiiOLr3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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import missingno as msno\n", "\n", @@ -421,9 +1160,179 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "import os\n", + "\n", + "# Where the figure should be saved when exported\n", + "output_folder = \"../data/output_fig\"\n", + "\n", + "# Creates the folder if it does not exist\n", + "os.makedirs(output_folder, exist_ok=True)\n", + "\n", + "# x_axis set to the index, which mean the datetime\n", + "x_axis = df.index\n", + "\n", + "# Gets the values\n", + "rain = df['rain.1h']\n", + "temp = df['main.temp']\n", + "snow = df['snow.1h']\n", + "wind_gust = df['wind.gust']\n", + "wind_speed = df['wind.speed']\n", + "temp_mean = temp.mean().round(2)\n", + "\n", + "# Two vertically stacked axis, (2 rows, 1 column), width and height of the figure, and the axis share the same x_axis\n", + "fig, (ax1, ax3) = plt.subplots(2, 1,figsize=(15, 8), sharex=True)\n", + "\n", + "\n", + "# Set the title for the diagram, above the first axis, with city_name and input_date\n", + "ax1.set_title(f'Weather data for {city_name} ({start_date}) to ({end_date}) ')\n", + "\n", + "# Plot temperature on the primary y-axis\n", + "ax1.plot(x_axis, temp, color='tab:red', label='Temperature (°C)')\n", + "ax1.axhline(y=temp_mean, color='tab:red', linestyle='dashed', label='Mean temperature (°C)')\n", + "ax1.axhline(y=0, color='black', linewidth=1.5)\n", + "\n", + "# Design the y-axis for temperatur\n", + "ax1.set_ylabel('Temperature (°C)', color='tab:red')\n", + "ax1.tick_params(axis='y', labelcolor='tab:red')\n", + "\n", + "# Plot Precipitation as bars on the secondary y-axis\n", + "ax2 = ax1.twinx()\n", + "\n", + "# Add rain\n", + "# ax2.bar(x_axis, rain, color='tab:blue', alpha=0.5, width=0.02, label='Rain (mm)')\n", + "ax2.hist(x_axis, bins=len(x_axis), weights=rain, color='tab:blue', alpha=0.5, label= 'Rain (mm)', bottom=snow)\n", + "\n", + "# Add snow\n", + "# ax2.bar(x_axis, snow, color='tab:grey', alpha=0.5, width=0.02, label='Snow (mm)')\n", + "ax2.hist(x_axis, bins=len(x_axis), weights=snow, color='tab:gray', alpha=0.5, label= 'Snow (mm)')\n", + "\n", + "# Design the y-axis for precipiation\n", + "ax2.set_ylabel(\"Precipitation (mm)\", color='tab:blue')\n", + "ax2.tick_params(axis='y', labelcolor='tab:blue')\n", + "\n", + "\n", + "# Customize the x-axis to show ticks for each hour\n", + "ax1.xaxis.set_major_locator(mdates.HourLocator(interval=12)) # Tick marks for every hour\n", + "ax1.xaxis.set_major_formatter(mdates.DateFormatter('%d %b %H')) # Format as \"Day Month Hour:Minute\"\n", + "\n", + "# Add label-description for both axis\n", + "ax1.legend(loc='upper left')\n", + "ax2.legend(loc='upper right')\n", + "\n", + "# Add grid, but only vertically\n", + "ax1.grid(axis = 'x')\n", + "\n", + "\n", + "# Plot the wind at the second x-axis (the axis below)\n", + "ax3.plot(x_axis, wind_gust, color='tab:purple', linestyle='dashed', label='Wind_gust')\n", + "ax3.plot(x_axis, wind_speed, color='tab:purple', label='Wind_speed')\n", + "ax3.set_ylabel('Wind (m/s)')\n", + "\n", + "# Add x_label visible for both x-axis\n", + "ax3.set_xlabel('Datetime')\n", + "\n", + "# Add label-description\n", + "ax3.legend(loc='upper right')\n", + "\n", + "# Customize the x-axis to show ticks for each hour\n", + "ax3.xaxis.set_major_locator(mdates.HourLocator(interval=12)) # Tick marks for every hour\n", + "ax3.xaxis.set_major_formatter(mdates.DateFormatter('%d %b %H')) # Format as \"Day Month Hour:Minute\"\n", + "\n", + "# Add grid, but only vertically\n", + "ax3.grid(axis = 'x')\n", + "\n", + "# Adjust layout\n", + "plt.tight_layout()\n", + "\n", + "# Save the plot to the data/output_fig folder\n", + "plot_path = os.path.join(output_folder, f\"weather_data_plot{city_name}.png\")\n", + "plt.savefig(plot_path) # Save the plot as a PNG file\n", + "\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Outliers in main.temp:\n", + "Series([], Name: main.temp, dtype: float64)\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import statistics\n", + "\n", + "# Extract temperature columns\n", + "temp_mean = df['main.temp']\n", + "\n", + "# Calculate means\n", + "temp_mean_mean = temp_mean.mean()\n", + "\n", + "\n", + "# Calculate standard deviations\n", + "temp_mean_stdev = statistics.stdev(temp_mean)\n", + "\n", + "\n", + "# Calculate 3 standard deviation limits\n", + "mean_lower_limit = temp_mean_mean - (temp_mean_stdev * 3)\n", + "mean_upper_limit = temp_mean_mean + (temp_mean_stdev * 3)\n", + "\n", + "# Identify outliers\n", + "mean_outliers = df.loc[(df['main.temp'] > mean_upper_limit) | (df['main.temp'] < mean_lower_limit), 'main.temp']\n", + "\n", + "# Print the outliers\n", + "print(\"Outliers in main.temp:\")\n", + "print(mean_outliers)\n", + "\n", + "# Replace outliers with NaN\n", + "df.loc[(df['main.temp'] > mean_upper_limit) | (df['main.temp'] < mean_lower_limit), 'main.temp'] = np.nan\n", + "\n", + "# Interpolate to replace NaN values with linear interpolation\n", + "df['main.temp'] = df['main.temp'].interpolate(method='linear')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.dates as mdates\n", @@ -444,6 +1353,7 @@ "snow = df['snow.1h']\n", "wind_gust = df['wind.gust']\n", "wind_speed = df['wind.speed']\n", + "temp_mean = temp.mean().round(2)\n", "\n", "# Two vertically stacked axis, (2 rows, 1 column), width and height of the figure, and the axis share the same x_axis\n", "fig, (ax1, ax3) = plt.subplots(2, 1,figsize=(15, 8), sharex=True)\n", @@ -454,6 +1364,8 @@ "\n", "# Plot temperature on the primary y-axis\n", "ax1.plot(x_axis, temp, color='tab:red', label='Temperature (°C)')\n", + "ax1.axhline(y=temp_mean, color='tab:red', linestyle='dashed', label='Mean temperature (°C)')\n", + "ax1.axhline(y=0, color='black', linewidth=1.5)\n", "\n", "# Design the y-axis for temperatur\n", "ax1.set_ylabel('Temperature (°C)', color='tab:red')\n", @@ -463,10 +1375,12 @@ "ax2 = ax1.twinx()\n", "\n", "# Add rain\n", - "ax2.bar(x_axis, rain, color='tab:blue', alpha=0.5, width=0.02, label='Rain (mm)')\n", + "# ax2.bar(x_axis, rain, color='tab:blue', alpha=0.5, width=0.02, label='Rain (mm)')\n", + "ax2.hist(x_axis, bins=len(x_axis), weights=rain, color='tab:blue', alpha=0.5, label= 'Rain (mm)', bottom=snow)\n", "\n", "# Add snow\n", - "ax2.bar(x_axis, snow, color='tab:grey', alpha=0.5, width=0.02, label='Snow (mm)')\n", + "# ax2.bar(x_axis, snow, color='tab:grey', alpha=0.5, width=0.02, label='Snow (mm)')\n", + "ax2.hist(x_axis, bins=len(x_axis), weights=snow, color='tab:gray', alpha=0.5, label= 'Snow (mm)')\n", "\n", "# Design the y-axis for precipiation\n", "ax2.set_ylabel(\"Precipitation (mm)\", color='tab:blue')\n", @@ -486,8 +1400,8 @@ "\n", "\n", "# Plot the wind at the second x-axis (the axis below)\n", - "ax3.plot(x_axis, wind_gust, color='tab:purple', label='Wind_gust')\n", - "ax3.plot(x_axis, wind_speed, color='tab:purple', linestyle='dashed', label='Wind_speed')\n", + "ax3.plot(x_axis, wind_gust, color='tab:purple', linestyle='dashed', label='Wind_gust')\n", + "ax3.plot(x_axis, wind_speed, color='tab:purple', label='Wind_speed')\n", "ax3.set_ylabel('Wind (m/s)')\n", "\n", "# Add x_label visible for both x-axis\n", From 40d09e63bde346d2790d386e13649181fd258586 Mon Sep 17 00:00:00 2001 From: toravest Date: Sun, 30 Mar 2025 12:32:46 +0200 Subject: [PATCH 13/18] =?UTF-8?q?handel=20nordic(=C3=A6=C3=B8=C3=A5),=20ha?= =?UTF-8?q?ndel=20missing=20values,=20data=20cleaning?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- notebooks/notebook_one_day_data.ipynb | 62 +++++++++++++++++++++++++-- 1 file changed, 58 insertions(+), 4 deletions(-) diff --git a/notebooks/notebook_one_day_data.ipynb b/notebooks/notebook_one_day_data.ipynb index 1083d65..5cbc500 100644 --- a/notebooks/notebook_one_day_data.ipynb +++ b/notebooks/notebook_one_day_data.ipynb @@ -74,9 +74,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Velg en by i Norge og få data\n", + "### Velg et sted i Norge og få data\n", "\n", - "Skriv inn en by du ønsker data fra, foreløpig er det begrenset til Norge\n", + "Skriv inn et sted du ønsker data fra, foreløpig er det begrenset til Norge\n", "\n", "Programmet vil deretter hente data å lagre det i en json fil" ] @@ -99,6 +99,12 @@ "# User choose a city they want the weather data from\n", "city_name = input(\"Enter city name: \")\n", "\n", + "for letter in city_name:\n", + " if letter in 'æøå':\n", + " city_name = city_name.replace('æ', 'ae')\n", + " city_name = city_name.replace('ø', 'o')\n", + " city_name = city_name.replace('å', 'aa')\n", + "\n", "# Start_date is the first timestamp, end_date is the last\n", "start_date, end_date = timestamps[0], timestamps[-1]\n", "\n", @@ -358,7 +364,11 @@ "### Endre manglende verdier\n", "I de fleste tilfeller virker dataene å være tilnærmet \"perfekte\", men de inkluderer bare snø og regn dersom det er snø eller regn. Derfor vil vi fa NaN verdier i de målingene det ikke har regnet/snødd. \n", "\n", - "Under sjekker vi først om regn eller snø er i målingen, og hvis den er, bytter vi ut NaN med 0." + "Under sjekker vi først om regn eller snø er i målingen, og hvis de er, bytter vi ut NaN med 0. \n", + "\n", + "Så sjekker vi om alle verdiene i en kolonne er 'NaN', isåfall så fjerner vi hele kolonnen. Grunne til at dette ikke inkluderer snø og regn, er fordi vi senere plotter disse verdiene, og da får vi ikke feil om verdien er 0, men vil få om hele kolonnen mangler.\n", + "\n", + "Deretter sjekker vi andre verdier, og bytter enten 'NaN' med 0, eller med verdien før. Verdiene vi setter til 0 gjelder da snø, regn og vind, resten blir satt til verdien før." ] }, { @@ -379,7 +389,49 @@ "except KeyError:\n", " print(\"['snow.1h'], not in df\")\n", "\n", - "# Display the df, now without NaN (atleast for rain and snow)\n", + "# Drops all the columns, if it has 'NaN' value.\n", + "df = df.dropna(axis='columns', how='all')\n", + "\n", + "# If wind_gust is stored, fill the NaN with 0\n", + "try: \n", + " df['wind.gust'] = df['wind.gust'].fillna(0)\n", + "except KeyError:\n", + " print(\"['wind.gust'], not in df\")\n", + "\n", + "# If wind_deg is stored, fill the NaN with 0\n", + "try: \n", + " df['wind.deg'] = df['wind.deg'].fillna(0)\n", + "except KeyError:\n", + " print(\"['wind.deg'], not in df\")\n", + "\n", + "# If wind_speed is stored, fill the NaN with 0\n", + "try: \n", + " df['wind.speed'] = df['wind.speed'].fillna(0)\n", + "except KeyError:\n", + " print(\"['wind.speed'], not in df\")\n", + "\n", + "# If temperature is missing, take the same as the one before\n", + "df['main.temp'] = df['main.temp'].fillna('obj.ffill()')\n", + "\n", + "# Forward fill missing values in what the temperature feels like\n", + "df['main.feels_like'] = df['main.feels_like'].fillna('obj.ffill()')\n", + "\n", + "# Forward fill missing values in the pressure\n", + "df['main.pressure'] = df['main.pressure'].fillna('obj.ffill()')\n", + "\n", + "# Forward fill missing values in the humidity\n", + "df['main.humidity'] = df['main.humidity'].fillna('obj.ffill()')\n", + "\n", + "# Forward fill missing values in the lowest temperature \n", + "df['main.temp_min'] = df['main.temp_min'].fillna('obj.ffill()')\n", + "\n", + "# Forward fill missing values in the highest temperature \n", + "df['main.temp_max'] = df['main.temp_max'].fillna('obj.ffill()')\n", + "\n", + "# Forward fill missing values of clouds\n", + "df['clouds.all'] = df['clouds.all'].fillna('obj.ffill()')\n", + "\n", + "# Display the df, now without NaN\n", "display(df)" ] }, @@ -440,6 +492,7 @@ "snow = df['snow.1h']\n", "wind_gust = df['wind.gust']\n", "wind_speed = df['wind.speed']\n", + "temp_mean = temp.mean().round(2)\n", "\n", "# Two vertically stacked axis, (2 rows, 1 column), width and height of the figure, and the axis share the same x_axis\n", "fig, (ax1, ax3) = plt.subplots(2, 1,figsize=(15, 8), sharex=True)\n", @@ -452,6 +505,7 @@ "\n", "# Design the y-axis for temperatur\n", "ax1.set_ylabel('Temperature (°C)', color='tab:red')\n", + "ax1.axhline(y=temp_mean, color='tab:red', linestyle='dashed', label='Mean temperature (°C)')\n", "ax1.tick_params(axis='y', labelcolor='tab:red')\n", "\n", "# Plot Precipitation as bars on the secondary y-axis\n", From 6b94d688af08d82738e2e9b928193e0d04a69442 Mon Sep 17 00:00:00 2001 From: toravest Date: Sun, 30 Mar 2025 12:33:07 +0200 Subject: [PATCH 14/18] minor change, kelvin to celsius --- src/my_package/get_record.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/my_package/get_record.py b/src/my_package/get_record.py index 7071615..7454681 100644 --- a/src/my_package/get_record.py +++ b/src/my_package/get_record.py @@ -8,8 +8,8 @@ def get_records(df, city_name): max_temp_mean = df['temp.mean_celsius'].max() min_temp_mean = df['temp.mean_celsius'].min() - max_temp = df['temp.record_max'].max() - 272.15 - min_temp = df['temp.record_min'].min() - 272.15 + max_temp = df['temp.record_max_celsius'].max() + min_temp = df['temp.record_min_celsius'].min() summary_data = { "Metric": ["Max Temp mean (°C)", "Min Temp Mean (°C)", "Max Temp (°C)", "Min temp (°C)"], From e276f2ec2a0b3a21939557974ff22b688432b462 Mon Sep 17 00:00:00 2001 From: toravest Date: Sun, 30 Mar 2025 19:23:54 +0200 Subject: [PATCH 15/18] add util.py, with nordic replace and kelvin to celsius function --- notebooks/notebook_current_data.ipynb | 159 +---- notebooks/notebook_one_day_data.ipynb | 19 +- notebooks/notebook_one_week_data.ipynb | 884 +----------------------- notebooks/notebook_statistic_data.ipynb | 41 +- 4 files changed, 81 insertions(+), 1022 deletions(-) diff --git a/notebooks/notebook_current_data.ipynb b/notebooks/notebook_current_data.ipynb index 0287d13..c99efdd 100644 --- a/notebooks/notebook_current_data.ipynb +++ b/notebooks/notebook_current_data.ipynb @@ -19,17 +19,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data fetch: ok\n" - ] - } - ], + "outputs": [], "source": [ "import sys\n", "import os\n", @@ -40,14 +32,13 @@ "# Now we can import the fucntion from the module\n", "from my_package.fetch_current_data import fetch_current_data\n", "\n", + "# Import function to replace nordic (æøå)\n", + "from my_package.util import replace_nordic\n", + "\n", "# User input the city, for the weather\n", "city_name = input(\"Enter a city in Norway: \")\n", "\n", - "for letter in city_name:\n", - " if letter in 'æøå':\n", - " city_name = city_name.replace('æ', 'ae')\n", - " city_name = city_name.replace('ø', 'o')\n", - " city_name = city_name.replace('å', 'aa')\n", + "city_name = replace_nordic(city_name)\n", "\n", "# Stores the return of the function\n", "data, folder = fetch_current_data(city_name)" @@ -68,17 +59,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data has been written to /Users/toravestlund/Documents/ITBAITBEDR/TDT4114 - Anvendt programmering/anvendt_mappe/data/../data/output_current_data/data_aaa_maura.json\n" - ] - } - ], + "outputs": [], "source": [ "# Gets the absolute path to the src folder\n", "sys.path.append(os.path.abspath(\"../src\"))\n", @@ -103,44 +86,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'coord': {'lon': 11.0167, 'lat': 60.25},\n", - " 'weather': [{'id': 500,\n", - " 'main': 'Rain',\n", - " 'description': 'light rain',\n", - " 'icon': '10d'}],\n", - " 'base': 'stations',\n", - " 'main': {'temp': 3.14,\n", - " 'temp_min': 2.52,\n", - " 'temp_max': 3.84,\n", - " 'humidity': 93,\n", - " 'sea_level': 1003,\n", - " 'grnd_level': 965},\n", - " 'visibility': 10000,\n", - " 'wind': {'speed': 1.54, 'deg': 70},\n", - " 'rain': {'1h': 0.11},\n", - " 'clouds': {'all': 75},\n", - " 'dt': 1743329160,\n", - " 'sys': {'type': 1,\n", - " 'id': 1624,\n", - " 'country': 'NO',\n", - " 'sunrise': 1743309996,\n", - " 'sunset': 1743357233},\n", - " 'timezone': 7200,\n", - " 'id': 3146270,\n", - " 'name': 'Maura',\n", - " 'cod': 200}" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import json\n", "\n", @@ -175,92 +123,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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namemain.tempmain.humiditymain.sea_levelmain.grnd_levelwind.speedrain.1hclouds.allsys.countrysys.sunrisesys.sunset
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2025-03-30 10:06:00Maura3.149310039651.540.1175NO2025-03-30 04:46:362025-03-30 17:53:53
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" - ], - "text/plain": [ - " name main.temp main.humidity main.sea_level \\\n", - "dt \n", - "2025-03-30 10:06:00 Maura 3.14 93 1003 \n", - "\n", - " main.grnd_level wind.speed rain.1h clouds.all \\\n", - "dt \n", - "2025-03-30 10:06:00 965 1.54 0.11 75 \n", - "\n", - " sys.country sys.sunrise sys.sunset \n", - "dt \n", - "2025-03-30 10:06:00 NO 2025-03-30 04:46:36 2025-03-30 17:53:53 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "\n", diff --git a/notebooks/notebook_one_day_data.ipynb b/notebooks/notebook_one_day_data.ipynb index 5cbc500..1eb90fb 100644 --- a/notebooks/notebook_one_day_data.ipynb +++ b/notebooks/notebook_one_day_data.ipynb @@ -55,7 +55,9 @@ " # Prevents from getting data for the current day, or the future\n", " if dt >= datetime.datetime.now():\n", " print(\"Failed, cant use future dates\")\n", - " return None\n", + "\n", + " # If \n", + " raise ValueError\n", "\n", " # Prints the date chosen\n", " print(f\"Selected date: {year}-{month:02d}-{day:02d}\")\n", @@ -96,18 +98,19 @@ "# Now we can import the fucntion from the module\n", "from my_package.fetch_data import fetch_data\n", "\n", + "# Import function to replace nordic (æøå)\n", + "from my_package.util import replace_nordic\n", + "\n", "# User choose a city they want the weather data from\n", "city_name = input(\"Enter city name: \")\n", "\n", - "for letter in city_name:\n", - " if letter in 'æøå':\n", - " city_name = city_name.replace('æ', 'ae')\n", - " city_name = city_name.replace('ø', 'o')\n", - " city_name = city_name.replace('å', 'aa')\n", + "city_name = replace_nordic(city_name)\n", "\n", "# Start_date is the first timestamp, end_date is the last\n", "start_date, end_date = timestamps[0], timestamps[-1]\n", "\n", + "city_name = replace_nordic(city_name)\n", + "\n", "# Stores the values in the variables\n", "weather_data, folder = fetch_data(start_date, end_date, city_name)" ] @@ -533,8 +536,8 @@ "ax1.grid(axis = 'x')\n", "\n", "# Plot the wind at the second x-axis (the axis below)\n", - "ax3.plot(x_axis, wind_gust, color='tab:purple', label='Wind_gust')\n", - "ax3.plot(x_axis, wind_speed, color='tab:purple', linestyle='dashed', label='Wind_speed')\n", + "ax3.plot(x_axis, wind_gust, color='tab:purple', linestyle='dashed', label='Wind_gust')\n", + "ax3.plot(x_axis, wind_speed, color='tab:purple', label='Wind_speed')\n", "ax3.set_ylabel('Wind (m/s)')\n", "\n", "# Add x_label visible for both x-axis\n", diff --git a/notebooks/notebook_one_week_data.ipynb b/notebooks/notebook_one_week_data.ipynb index 9f5def1..dcaef29 100644 --- a/notebooks/notebook_one_week_data.ipynb +++ b/notebooks/notebook_one_week_data.ipynb @@ -31,20 +31,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Start date => unix timestamp: 1742202600\n", - "End date => unix timestamp: 1742721000\n", - "Unix timestamp => start date: 2025-03-17 10:10:00\n", - "Unix timestamp => end date: 2025-03-23 10:10:00\n" - ] - } - ], + "outputs": [], "source": [ "import sys\n", "import os\n", @@ -84,17 +73,9 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data fetch: ok\n" - ] - } - ], + "outputs": [], "source": [ "import sys\n", "import os\n", @@ -105,14 +86,13 @@ "# Now we can import the fucntion from the module\n", "from my_package.fetch_data import fetch_data\n", "\n", + "# Import function to replace nordic (æøå)\n", + "from my_package.util import replace_nordic\n", + "\n", "# User input the city, for the weather\n", "city_name = input(\"Enter a city in Norway: \")\n", "\n", - "for letter in city_name:\n", - " if letter in 'æøå':\n", - " city_name = city_name.replace('æ', 'ae')\n", - " city_name = city_name.replace('ø', 'o')\n", - " city_name = city_name.replace('å', 'aa')\n", + "city_name = replace_nordic(city_name)\n", "\n", "# Stores the values in the variables\n", "data, folder = fetch_data(unix_start_date, unix_end_date, city_name)\n" @@ -133,17 +113,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data has been written to /Users/toravestlund/Documents/ITBAITBEDR/TDT4114 - Anvendt programmering/anvendt_mappe/data/../data/output_stedsnavn/data_test.json\n" - ] - } - ], + "outputs": [], "source": [ "# Gets the absolute path to the src folder\n", "sys.path.append(os.path.abspath(\"../src\"))\n", @@ -168,177 +140,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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4Count: 14420031338800.024779144{'dt': 1742220000, 'main': {'temp': 4.11, 'fee...
.....................
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144 rows × 12 columns

\n", - "
" - ], - "text/plain": [ - " main.temp main.feels_like main.pressure main.humidity \\\n", - "dt \n", - "2025-03-17 10:00:00 1.98 0.11 1021 92 \n", - "2025-03-17 11:00:00 3.05 0.84 1021 93 \n", - "2025-03-17 12:00:00 3.60 1.49 1021 91 \n", - "2025-03-17 13:00:00 4.16 1.75 1021 92 \n", - "2025-03-17 14:00:00 4.11 0.75 1021 89 \n", - "... ... ... ... ... \n", - "2025-03-23 05:00:00 6.03 4.19 1020 75 \n", - "2025-03-23 06:00:00 6.03 4.15 1020 67 \n", - "2025-03-23 07:00:00 6.03 4.40 1020 64 \n", - "2025-03-23 08:00:00 7.03 7.03 1020 61 \n", - "2025-03-23 09:00:00 8.03 8.03 1020 61 \n", - "\n", - " main.temp_min main.temp_max wind.speed wind.deg \\\n", - "dt \n", - "2025-03-17 10:00:00 1.07 2.77 1.79 203 \n", - "2025-03-17 11:00:00 2.73 3.33 2.24 225 \n", - "2025-03-17 12:00:00 3.03 3.88 2.24 248 \n", - "2025-03-17 13:00:00 3.84 4.44 2.68 270 \n", - "2025-03-17 14:00:00 3.88 5.03 4.02 293 \n", - "... ... ... ... ... \n", - "2025-03-23 05:00:00 6.03 6.03 2.42 100 \n", - "2025-03-23 06:00:00 6.03 6.03 2.46 87 \n", - "2025-03-23 07:00:00 6.03 6.03 2.18 89 \n", - "2025-03-23 08:00:00 7.03 7.03 1.19 82 \n", - "2025-03-23 09:00:00 8.03 8.03 1.05 4 \n", - "\n", - " wind.gust clouds.all rain.1h snow.1h \n", - "dt \n", - "2025-03-17 10:00:00 3.58 100 0.36 NaN \n", - "2025-03-17 11:00:00 5.36 100 0.79 NaN \n", - "2025-03-17 12:00:00 4.02 100 1.38 NaN \n", - "2025-03-17 13:00:00 8.05 100 0.16 NaN \n", - "2025-03-17 14:00:00 8.05 100 0.14 NaN \n", - "... ... ... ... ... \n", - "2025-03-23 05:00:00 2.72 84 NaN NaN \n", - "2025-03-23 06:00:00 3.32 74 NaN NaN \n", - "2025-03-23 07:00:00 3.20 8 NaN NaN \n", - "2025-03-23 08:00:00 2.34 5 NaN NaN \n", - "2025-03-23 09:00:00 2.23 3 NaN NaN \n", - "\n", - "[144 rows x 12 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import numpy as np\n", "\n", @@ -694,19 +220,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean temperatur: 5.33\n", - "Highest temperatur: 13.03\n", - "Lowest temperatur: -0.24\n" - ] - } - ], + "outputs": [], "source": [ "# Extract main values\n", "temp = df['main.temp']\n", @@ -732,30 +248,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import missingno as msno\n", "\n", @@ -775,280 +270,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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main.tempmain.feels_likemain.pressuremain.humiditymain.temp_minmain.temp_maxwind.speedwind.degwind.gustclouds.allrain.1hsnow.1h
dt
2025-03-17 10:00:001.980.111021921.072.771.792033.581000.360.0
2025-03-17 11:00:003.050.841021932.733.332.242255.361000.790.0
2025-03-17 12:00:003.601.491021913.033.882.242484.021001.380.0
2025-03-17 13:00:004.161.751021923.844.442.682708.051000.160.0
2025-03-17 14:00:004.110.751021893.885.034.022938.051000.140.0
.......................................
2025-03-23 05:00:006.034.191020756.036.032.421002.72840.000.0
2025-03-23 06:00:006.034.151020676.036.032.46873.32740.000.0
2025-03-23 07:00:006.034.401020646.036.032.18893.2080.000.0
2025-03-23 08:00:007.037.031020617.037.031.19822.3450.000.0
2025-03-23 09:00:008.038.031020618.038.031.0542.2330.000.0
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144 rows × 12 columns

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" - ], - "text/plain": [ - " main.temp main.feels_like main.pressure main.humidity \\\n", - "dt \n", - "2025-03-17 10:00:00 1.98 0.11 1021 92 \n", - "2025-03-17 11:00:00 3.05 0.84 1021 93 \n", - "2025-03-17 12:00:00 3.60 1.49 1021 91 \n", - "2025-03-17 13:00:00 4.16 1.75 1021 92 \n", - "2025-03-17 14:00:00 4.11 0.75 1021 89 \n", - "... ... ... ... ... \n", - "2025-03-23 05:00:00 6.03 4.19 1020 75 \n", - "2025-03-23 06:00:00 6.03 4.15 1020 67 \n", - "2025-03-23 07:00:00 6.03 4.40 1020 64 \n", - "2025-03-23 08:00:00 7.03 7.03 1020 61 \n", - "2025-03-23 09:00:00 8.03 8.03 1020 61 \n", - "\n", - " main.temp_min main.temp_max wind.speed wind.deg \\\n", - "dt \n", - "2025-03-17 10:00:00 1.07 2.77 1.79 203 \n", - "2025-03-17 11:00:00 2.73 3.33 2.24 225 \n", - "2025-03-17 12:00:00 3.03 3.88 2.24 248 \n", - "2025-03-17 13:00:00 3.84 4.44 2.68 270 \n", - "2025-03-17 14:00:00 3.88 5.03 4.02 293 \n", - "... ... ... ... ... \n", - "2025-03-23 05:00:00 6.03 6.03 2.42 100 \n", - "2025-03-23 06:00:00 6.03 6.03 2.46 87 \n", - "2025-03-23 07:00:00 6.03 6.03 2.18 89 \n", - "2025-03-23 08:00:00 7.03 7.03 1.19 82 \n", - "2025-03-23 09:00:00 8.03 8.03 1.05 4 \n", - "\n", - " wind.gust clouds.all rain.1h snow.1h \n", - "dt \n", - "2025-03-17 10:00:00 3.58 100 0.36 0.0 \n", - "2025-03-17 11:00:00 5.36 100 0.79 0.0 \n", - "2025-03-17 12:00:00 4.02 100 1.38 0.0 \n", - "2025-03-17 13:00:00 8.05 100 0.16 0.0 \n", - "2025-03-17 14:00:00 8.05 100 0.14 0.0 \n", - "... ... ... ... ... \n", - "2025-03-23 05:00:00 2.72 84 0.00 0.0 \n", - "2025-03-23 06:00:00 3.32 74 0.00 0.0 \n", - "2025-03-23 07:00:00 3.20 8 0.00 0.0 \n", - "2025-03-23 08:00:00 2.34 5 0.00 0.0 \n", - "2025-03-23 09:00:00 2.23 3 0.00 0.0 \n", - "\n", - "[144 rows x 12 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# If rain is stored, fill the NaN with 0\n", "try: \n", @@ -1115,30 +339,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import missingno as msno\n", "\n", @@ -1160,20 +363,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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v3y5ms1mSk5NlzJgxEhbmP4UjdMwR5RIe5fpL6ODThsth54wSYzAHQAQyFVnhj3EuaudAaW6NnHjVXn5VRO8tjiNTqrpxZIqaq6C0/D4XbKuUjx75TWKSI3y6iJ6Y0TwkNb9GrFabGI28XwIAAAAA4FNF9G3btsm///1vefPNN2Xnzp06F9shNDRUDjroILn44ovlpJNOEoOBf/wDoogeHeLyfcIiXb8t/EtJbrUuBqqjEcKb41xUnIc/qaux/0yERfnf61zldatit+r2Tupn6tZj/LU4TxrqrDJ0SopERIeKqXm4cHVZXZfX4hzAGbcrziUxw57dXllUq9fpa0NrVc776m+yZfBeSRISbtTfq/ICs/PrAgAAAAAAnuNypfuKK66QCRMmyNatW+Xuu++WtWvXSkVFhdTX10t+fr58+umncuCBB8ptt90m48ePl2XLlvXuyuEVcS7hflgwhPt98cyf8trtiyV/S4UMm5oqex83yDmw0m8G7db478/E5lVF8soti+T719Z3+/uz4rPt8uObG2T7nyX6uuiE5iJ6qaVLWevqvUffPkgkImZXET0yJtT5cWlejfia8kKzFO2okpryemc3ujuGuQIAAAAAgJ5zuVUvKipKtmzZIomJiXt8LiUlRQ477DB9uv322+Xzzz+X7Oxs2Xvvvd2wRHgjNSDy5BumSJNYZcnvO1y6T21VvfzyziapMzfIcZdO6PU1wns4C8zRIZI5Il78TWO9TWyN9kKwY3CqP0nMbC7q5tboTnA1BLMrVHFYdVUHhxhk8F7J+rqouDD9OLbmzvLoeNcicGqao1wiTKF7RJ2o4vPOynopzamRtEGx4kvMzcNSI2NDJaEpWvK3VEpJTo0Iv0YBAAAAAPA4l6s9CxYscPlBZ8yY0d31wEeobtvwQSHS0NAg8rtr91FZ6CqyQPHFuAV0jyq6dif+pz3qNbTo/c0y4+9jvaZQ6thJYDAG6WG7/iY2OUL//DZarFJZUiuxyZFduv+GpQX6fOCEJOfPvcEQJNFxYVJTaRFzpcXlInpMUoQcd9kEaWyw7vE5dXTDzr/KutTBXbyzWj55YrVMOmqAjJvWTzzFMSxV7VxwRF+V0okOAAAAAIBX6FZweW1trR4o6qAGjD7yyCPyxRdfuHNt8MOBko6hkirCAYFBZZ87xieoInp9baPOSFedyd3x9QtrpabcIu/et0K8hcW8Kw89KKhrXdq+wGA0SIIjYmRn16JSVKf5xuX2IvrwqWmtPjfn5r3l/x6dJikDYlx+vLCIYBkwNlGGTEzpoGPe9eLzl/9ZI9VlFh0140k1LYalJvWLlrjUSGfkDQAAAAAA8MEi+oknnigvv/yyvlxeXi777LOPPPjggzJz5kw9eBT+b/3iPFn11Q6d49sVpgR7t2lVF4cJwnepGB9F7UBR8RuqoPrm/KXyy7ubuvV4R140xnm5u8NJv3v1L/nprQ2ycVmBLur3VF2N/TEcQ1P9kTOnuwsFaiVnfZmOawmLCpb+oxP2OKKlq9EwHa6xOWdfxaC0HHzdkSMv3PV66ko2e+91oodKxrA4OfPOfeWQ00d4bD0AAAAAAKCHRfSVK1fKQQcdpC+/8847kpqaqrvRVWH90Ucf7c5Dwses+SlXfn13k5Tmdq2IvmuYIEX0QLF7lIsjqqK7xeuhk1MkPt1e0N2yqrDL91cd8Gt/zpXfv9upu5CLd1aJOwrMR//fONl/9lDxV4n9mgvUO7tWRN+wzN6FPnRSio6E6akda0r0TryKoj3fexLSo2TamSPk2EvGu/x4qsNeZbUrFV3cKeguVqvNubNJdaIDAAAAAADv0q2KhopyMZlM+vKXX34ps2fPFoPBIPvuu68upsP/1ToKo1Fd67w1xduL6FUU0QPutRLhKKI3R/o4IlC6SsWlDJ+a2ipruztFXYfywlrpKTXkUg3MHDg+SfxVYkZzET23a3Eu1gabzj/fPcpFyd9SIZ8/80eXjkr444cc+frFdZK9rmyPzwWHGmXMQZmSNjjW5VgdtTZnVI0a5OkBaoeSyp1XQ2lVd76D6qZvqN8z+x0AAAAAAPStbmUPDB06VD744AOZNWuWzkG/+uqr9fWFhYUSE+N6ti18V3cHRUY3x7mQiR44YlMiZMoxAyUyJlR/HNoceWIxd70TXcVt/PbVDl2YVXauL9P56GoYoytUUXLDUvtw29Bwo9TXWaXCDUX0QJDUP1omzRig87q7QsWl1J46TMKbj0DYfSDr5pVFzi53V5gd2eEubvP2qOf+4OFVMmRissSnRUnh9iodVaOOdOhrEdGhctb8/Vpd98f3O2XxB5tl+D5pxLoAAAAAAOCLnei33XabXHvttTJw4ECdh77ffvs5u9InTpzo7jXCy6hCZl1zF3HLrklXmFScS5DQXRlAVAfzPicMlnHT+umPVbdtd/PM682Nsuj9zbL8022SMsAk0iSSva7U5furQqkqmqv4jr2O6K+vaysWpKtUR/WGZflSlu+ZTua+KvTuN3OIDJuS2q37tpV9bkrseryTMzs81r5TZnfqKJc1P+XI+iX2nSXt2bKqSEfTbFpRKElZ9iJ+qYc60dvi2MlTktO1+BwAAAAAAOAlnegnn3yyHHjggZKXlycTJkxwXn/44Yfr7nT4Nx3D0Tx/r6txLkMmpsgQN2Ujwzc5iugNdVaxWW1iMLr+Wqit3jWk9ODTRkhohFF3EbtqY3P8y6AJSZKcZY+kqijqeSf6ul9yZe0vebLPCYNkyjGDevx4/qC+rlEfsRKTFNFpvJM6KkHdPjQ8uNMdeObKjrPDC7dXyvevrZfk/iYZsc+eETIOjiMSVDRQ/zGJOgpG75jxEgnNQ1JLc+1DUl2NpwEAAAAAAO7XpQpo//795YQTTtCnww47TNLSWhcopk6d6u71wYujXFQxtCsFUMXYPMAPgaNcDWtsEomKD5OQUKMugDvU11olPNrQrXz11EFdi45SBdiNy+1F9GFT03TMjKI603tapKxrjqZxDE31VyoCpWh7lRiMQZI5Ir7D225aXijfvfqXjNo/XQ47Z1Sbt1GvBfU+ooroKuIpIaPjX0lq+GaTTW0rlUMf0mF2e2lejd7mKvN8d9VldZKzsVxfHrZ3qsQkRuihpJ6y8svt+vs1+sAMGXtwpr4uIS1Kd++r742KLIpu3uEAAAAAAAD6Xpcqmq+88oqEhYXJpZdeKklJSXLqqafKa6+9JuXl9mIEAkNtVfeiXBCYfnl7o7x2+2JnF7jRaJCJR/SXvY8bJEHGILdk8atiaWcsNQ2SMjBGZ7P3H52gC6eqGNtgsTpf092lHjsQfia2/FYkHz36myz/bFunt3V0eselRnZ4u+guDBs2N0e5qEGu7e3Ai0mO0HE9Kje/Qu3AacPGZYV6x0760Fj9OvC0snyzFO2ocr6+HTscHd87Tw08BQAAAAAA3ehEP+SQQ/TpwQcflDVr1shHH30kjz32mFx44YWy//77O7vUBw8e3JWHhY9R+cEn3zhFbNbOC5dt+eWdjTqb+sA5w5yRGvBftW0Uvvc/aWi3HquuRSe6o6P457c36iLkabdO7bCbXBVej71kvC6uOuKE1DpUUT04tGdHSNTVNHeidzHeyNckNkeMdJbT3bLTe+iUjgd1mhLC9OO5UkRXHdmdDRVVnecJGc2DQnNq2oz7Ufn1yvCpu46mUp3r+Zsr9H3TBsdKX9o1LLV1zntiZpSU5dXo78+AsYl9uiYAAAAAALBLtytHY8aMkXnz5snixYtl69atcvrpp8s333wjY8eO1adPPvmkuw8NL6dyi1MHxkj6kO4Vmgq2VUruxnIdowH/1173eHc4MtEdj6XiU7avKdW50ep11dVIob2m99eF1M6yuF2aExAAneiqwKwGA6vOfUc2eVs2LCtwudM7OiFcDMFBOhO9M6mDY+S4yybIvjMHu5QnXpK7Z7FfvVaKs6t1sX3opF0F/rU/5er4GUfkT1+qKW87590RTdPW1wEAAAAAAPqOW9om09PT5W9/+5s+mc1m+eKLL3TsC9B+fEOFS52n8I8c7Zbd445sa3NVve4Cj4gO7UZB3n6fkDCjDJ6QJBuWFuhT2qC2d+yowqnqNu9oyKU7vkZ/z0RXmfaxyRF6B9jbC5dJcIhRxk3LlPGHZjnz7z954ndnx3jLTu/2HHDSUDn41OE6/7sz6rXiSkd2oir2q+3eRgxKkEFkxL5p+kialjt2EjKjPBadUtPciR65WxFd7TRQX2/qQNd3WKrM+J/e2iDBYUbZf3b3jvhQdv5VKss+2SaHnDHCo3nxAAAAAAD4fBG9sLBQn2w2W6vrZ82a1dN1wYtt+6NYyvLMkjkiTuIzul6UNCW4noEM32a12vRgRKVlwfLbV/6Sbb8Xy7QzR8iYg+yDFF0xblo/yRqdINFxu4YsqsGQqoC+aXmBHHjy0Dazshd/uFm2ri6Wg08brh+jZVG+YLu9g33AmO7FZTQ2WKWx3v4eGO7ncS6KGiiqiuhqEKjSMsfb1tgk5QVm59DQlp3e7QkONfZp7IyKd5l+3uh271Pax13fKl7I8T3cPc4la2SCPnWFOsrnjx9y9OXJRw+UsBaDfLviw0d+0+dfPb9GTr2ZoeEAAAAAgMDWrf+uV6xYIeeee66sW7dOmppa52KrTGKr1equ9cELbV5RKH8tzpf9Zg3pZhHd3m1ZTRHd71mas8JVBEjLLm1HYc9RYHeV6iTfvZtcFdVVgV5FjOz8q0z671YMV13i2/8s0ZczhsW1+lzupnL57Kk/JLm/qdtFdNVBffT/jdPP09NYGF+gdkSM2i/dORMhuvnnWTElhsusaybpy6pj3R0RPi1tWlGod1r0GxHvHEjaltRBMXLi1RN1pnh3o2rUURJ9oabSvjPCYAxySxzQppWFzsu1lfXdLqI7VBYRuwUAAAAAQLf+u77gggtk+PDh8p///EdSU1M7HOYH/1PbHF3R3QKZykBW6ET3f84M88gQnUHtEBbZXESv7VoRvS1Go0GGTU7R3beqI333IvrmlYW64KsKqo5uY4fYFHtBvqKoVu8Q7M57mXr+wXslS6BQX297gzdVvM7uOyo6o7LQv315nY6AmXXt5Favk92t/GK7FO2o0gNiOyqiq50ZqtC+uy2rinShXw1H3n1b66iapAj9WlAd7JExXesA765Gi02/DtWw2/Zef478+c4K++rID/V6d96vql7iUiPdvGIAAAAAAAJPt4roW7ZskXfffVeGDu1+3ip8lyN6oGXGdVc4il+OOAj4r7CIEJlyzEDZvTYYGtm9TvQ/f8zRHbuqaN2ya3fY1DRdRN/yW5E01Ft1QdRBFdbby+dW3dKq+7i+tlG/riNMfdN9jNZxLlt+K9ZZ3uaKeomOb3+ehiNrPSqu6zM3VIH5u9f+0ttZdam3VWRP7BftLKJnjeqbIrrqgD9r/n7tfv7n/26U1d9my+SjB8i+Jw7p8LHUkRiqk145Zd4UiXdDlvn0C8b0+DHgn9SOx1Vf7tDvx2rnVm11g4zcr+eDmgEAAADAG3XrP53DDz9cVq9eTRE9QKl/lFsOd+xWnEuQiDE4SOcBG0P2zLCGf1AF0X1OGLzH9aozXak378rTdsWi9zfrgnf6kNhWRfS0wTEycHyS7oJWxVgHdbSDyoh2ZKfvTg3GjI4Lk+oyiy6edqeIru5XsLVCYpIj2h1sivapznO1DdS2qi6ra7eIbrPadGe1Ehnb+XYq3F4pm5YX6i5vlbufvba0eUdJiGQMjW13IKnqVi/J7fvhou2JSQ53eeCp6lQfsU+azlZPGRDTo+JoygCT7oBPHdj9x4F/W/HZdlny0Rb57ZtsHR2kZI2Kl9A0iugAAAAA/E+3/tN57rnndCb6n3/+KWPHjpWQkNYdySeccIK71gc/7ERXAwf/77FpOr4AgUm9Broa56I6iVUBXYnYbQeOisFQER+727jc3oWuiuuOgba7U0VWXUQvNLcbU9KRnA1l8t0rf8mAsYly3GUTunx/2HPVVRFdndrbBrrDusmeQe/Kzo7indWy6qsdehCqKqI7jkgYOiW1zeGzjqMV0ofGSVK/1rE/ntTRkNTdJWeZZPr5ew5N7Sr183TKvL17/DjwX6u/ydYFdGXSkf31kUAqP7/loGEAAAAAkEAvoi9atEh++eUX+eyzz/b4HINF/VvLQmZ3M9HVa0R1ocP/qc7ixnqb7hxueYh/WDfiXBzFGRUN47h/Z7b+VtxuF7pDbHKk5Kwvl/JuDlB0DE8Ni6L7srvUDo48qehwTkJNhcXZbd1RbvruxefS3GppsFhl6+oi/fHwqe2/FlR+eF9niP/8zkbJ3VAuE4/sL8OmpLb7dVSV1On8eFeiMlS0xo61JRKXEilDJqV0a12q83/NT7m6+3/KjIEcMQSnNT/lyM9vb9SXpx4/SPaa3l82LiuQSvU+3cWILgAAAADwFd36r/jyyy+Xs846S/Ly8sRms7U6UUD3b60Kmc3dxEB7fvsqW167fbE+7L+l+LRIGX9YPx090dXXntp5o7qR26J28Py1KE+2/WEvnp9w5V5y5IVjZOjk9guJzuGihd0rotc1R9I4ImrQdY6jBKpL6jrPQ3chysWRNa5io1QH+9qfc/XOHBW5423xJKW5NbroraKt2qJiixxfs7pte/5anCdF2VU6iiVvc4Us/mCLLmx2l/oZU4XS5Z9sc8boAOuX5Mv3r6/Xl9WOHzXzQnHEa1maB48DAAAAgL/pVhW0pKRErr76aklNbb+jD/5J/aOsBtapDmJdyOzmPpN1v+bpYqcaSDbh8Cx3LxNeoramvs2jFuLTouSgOcO79FjOInqLLPTdrfk5V359d5OkD42VgeOSJCTM2GEXujJwbJJExYRKYj+TdIejaBTWwbrQsejmInpVWfvDhmsqHHnorg0VVcNlY5MidGa9ytJXhu+dqo+E6ciONSWSs6FcZ+yr7P3etmvnQPtfl+pGr6ko1ZEubcXdWMwN8t2rf4mtsUlOv30f3a2vdLf4vXllofz6/maxWe3zBWqr6tuNQ0LgUD8b37y0TscqjTskU/abNcT58+R4/6ujiA4AAADAT3WriD579mz57rvvZMiQIe5fEbyayjHvycA6B3OlRQ98jEmkMOPPepqf3/ZA2/YfS8Vh/PreJsnbVCGVJbUSk2jvMu+sY1l3LXdTXXOcSzhxLt2mCrSG4CDdRd2eQROSxJQY7lKcScvisyqiWxttnUa5tMzR/2tRvo6c6pMiuiOmpoMO+4TMaNmxVhXR2+5E37yqSBfQ9Ws5PUoXvZ058t2gYnVUvrWDGjAKpAyM0QNn1ZFEB506vNUOKcfOTYroAAAAAPxVt6o+w4cPl3nz5snPP/8s48aN22Ow6BVXXOGu9cFPRcc7Ok/bj2+A72sZwdKSKpaaK+p1FEp8amS7gx5bP1Z9m0NFW4qOD5PM4XE64/yVmxfJgacM6/UjHVQXsBJGnEu3ZY1OkP97dFq7MT2OTu2OurXbkpgZJVt+K9KxQaMPzNBHQLg8yLOD6BR3UREujkz9qLj2v7b+oxLE1mjT36e2OGJbHDsJnJ3o3Sx+O4rw7X2MwKQK5SoiKzjUuMfPqmMmhOP1DAAAAAD+pltF9Oeee06io6Plhx9+0KeWVGcSRXT/lb+lQnf5JvePln4j2y7ouMIRDVBV2n58A3xfR93jL837RVTj8XkLD+iwgOgwaK9kPQQ0tJMs/uF7p+kiuqIyol2hjopQURkDxiZKTFLn3estOYajdhQzg465Mii0O5xDOUvrJGNYnGv3yWguoudUd+m5ygvMYqlt7FLmuqMLXR3h09GwXFU8b6+Arh4jZ32ZvuwYTBphCnXOCGhssEpwiLFLX4t5tw727na0+6PK4lo9P6G97eHv2jsSZNjkVEnOMvX5YF4AAAAA8Ooi+tatW92/EviE7HWlsvR/W3VXZ0+K6NEJ9qJpdVmdNNmaOuxAhf/FuaidbaGRwbprUXWju1JEd7UTecikZJ0PrXQ0ULSlJR9t0YX0Iy4Y3eUi+n4zh0h1eZ0k9bMXX9E71v6Sq4vtA8Yldng0Qkuq0HnOvfvrIxRclZBp71ZXMTAN9Vadrd4Zm9WmB+gq5y5Qz+daTJUj5z0qLrTTrPb2bFpeqHdGpQ2Ocb52VUFefa9stiZdADcldK2IXtvcwa66i9XPKHEuu7x66yL9/T7phsmSNqj34368wet3LNYxLcdfvpck9zf1SiwWAAAAAHi7zjMUABfiObpKFU1VzUjl+HZ3+B28m+qAbbDYJ8+Gt1H0dMSf1Dd3cruLetwjLxoj+5wwWHeWuyI2xV58LC/clQPtKlWoHbV/hks7AtC+X97dJG8vXC47m7uqd7f4wy16qGF1B8NH2+qaVUe9dKVAraJQ9Ptbk0hZnmuRLiqOyJFpXrjNtaMfHMV31bkbm9J5967qKs/bXKG76lva0BzlMmzvNOd16uuNMIV0O4rFUTR3dOVTRLdrrLfqAnqgxZao7a92xhhD+JMRAAAAQOByuRN94cKFcuWVV0pEROddmkuWLJHi4mI59thje7o+eGk8R08HRRqNBl10VAWx6lJLl7OO4f2abCJTjhmoOxhDw/fshA1rjmVRERiuUAMfG+qsumjtiANqjyPWwlWxyfb3tYoic5fuB/cpz6+Rwm2VOhal34j4Vp+zWm3OYnBvv1eoArTKUleRQCrSxdVBylkjE2T9knwpzauWwROTXbpP5vB4OfPOfV26rTq6YtOKQtlv9hCZdOQAfZ362aoqqdVH8ux+1MWMv4+T4FCDxLlQoN+d43s9blo/vTNKDXSFSFm+2Rnd1H9MYMS5qCPF1A4cpaPIIfU+vv2PYrE2Nsmo/dP7cIUAAAAA0Ddcbitau3at9O/fXy655BL57LPPpKioyPm5xsZG+f333+XJJ5+U/fffX0499VQxmdo+5LercnJy5KyzzpLExERdwFeDTJcvX+6Wx0bXqaKNOzrRFVWYUYX0ekvgdPQFkpAwoy7AHXL6iDY7gR0FGUemeGdWfblDFxK7mlXtCpW1rqis465QnfYblubLjrUlbl9ToHHsGKnerdPaGS/SZM9O7+kOPFc4OrBL88xdjoEpyemdgaSqsK/X1OLxVTH33IUHyEnXTXYOE3VIGxwrSf1Meghkd7aFisBJGWjSWfKd7bQKFI73HrUtuhu/42vUe5yj+76jIrr6Gf3q+bXy03839N3iAAAAAMAbO9FffvllWb16tTz++ONyxhlnSGVlpRiNRgkLCxOz2V5omDhxolx00UVy3nnnSXh4z//pLisrkwMOOEAOPfRQXbhPTk6WjRs3Snx86y5FeCDOxQ1DFGfNnUQWegDrahG9ttreHetqHnZXxKVGdKuIrqI1VOFIfS0XPXSw29cVSKIdw4bL9iyi15Tbt72KTOmL94y9jugvEw7PcqkDW+1EWftzrjOSpTd28igJjoGnudV7HNWTOsj1YaaumH3dZLc+nr9wbFu1LVTnteNoGn+mZlY4ht92NKDW8TeBOlrI2mjTtwcAAAAAf9Kl/wAnTJggzz77rDz99NO683z79u1SW1srSUlJstdee+lzd7rvvvskKytLXnjhBed1gwYNcutzoGvcWcikgO7/rxW100V1yDryz9vMRK+1F2n6Ko+/LY6BjOpIC3VydSeRYwdAmBt2KgW6XZ3oe2ae11TYr4vso9inrnRer19SIDkbyp1FdJWrb22wuZQf/cVzf+r4mgNOGtrpoObETHsRvSzPrLPUVWxGcIih3ffRgm2VsvOvUolPi5LBe7kWL7M79XWoga5qbsXexw7SRwIEspJc+1EAf3y/U3I3lctpt0wVf+eIclGDoDuiP69eHk3298Xdj4wAAAAAAF/XrVYhg8Ggi+YnnniinHbaaTJ9+nS3F9CVjz76SKZMmSKnnHKKpKSk6E53VcSH5/RmIRP+ZeOyQnn9jiU6gqUt/UbGy4TDsiRlYOddtA31Vmmst+nLvRHnoQZQOoo+lcWud6NbHPFGnRSY0IVO9DbiXGrK7UX0qObhnd5CZYdnryvVlyce0V8fkaAypEvzXYt0UdEvxdnVqu7YqZjEcAkOM+ouX1WoX/bxVnn55l91DntbcjeUy+IPtugc9e4KMoj8+OYGWf7JNudrPZC1PMqgrdghf+TYUdjZe5zaweLozHf8nQAAAAAA/sSrKz9btmyRf//73zJ37ly56aabZNmyZXLFFVdIaGionHvuuW3ex2Kx6JODip2BezQ1Ncmsaybpf5Cj43reEVq0o0p+fnujLjwd84/xblkjvEdd81EL4e0ctaCGf7o6ANRRlDEEB0lIG0NK3eHQs0dKaESw7tztatSBO+KNAp0pwf6eUlNmEZutqVXXs1lloqsiuhved1yluo1z1pfpaJfErLaHc25eWaiL5sn9TRKXGinDp6bpLnHVIe4Kc3OHvSvDUlXHeWJGlBRsrdTFXDVoVw1mVsND2xIZE9JqSKirtv5eLD//d4P0G5Ugh545Ur+21dEZahtEmLxrJ0ZfO/XmqZK/pUI+e+oPXVyur2vUO+D8mTqiwtVcfPVaUd8Xx/siAAAAAPgTr/7vz2az6U70e++9V3+sOtH//PNPeeqpp9otoi9YsEDuvPPOPl5pYFCD1FIGuC97V3U55m4slwgTBUh/5Ch8u6Nz3PlYUSG9NtBv4LiuH01jqSHOxV1UVIvqtFZHBFjMDa0io0YfmCHpQ2J1JnpfUR3mW1cXS/rQuHaL6BuWFejz4VPtO4MOPm24y4+vjq5wdPm62mHvKKL/+UOOLqCHhhtlwNjENm/rKHh3tYheU1YnlcV19mGu6nFiQnURvauP44/Ua1NF46gdv2rbqaMmHENo/VXaoFi989wV+n2wqJZOdAAAAAB+yasnP6Wnp8vo0aNbXTdq1CjZsWNHu/eZN2+eVFRUOE/Z2dl9sFJ0R3S8vbOttqpBGuutnl4O3KzWEXXSToHZarXpmI7KktouxAh5VyesKi4qxLn0nOo8v/iRg+Xsu/bbY+aC6oLtPyZRkvqZ+mw9jgzy9gaFqgJq3qYKnQM9dLJrR1S01YWuutbVERCuGLZ3qhx06jDd/a4Mnpjc7rBHVfzWz1PVtYKmo+vfEW8U2byTU+Wio/Xvrrby+wOZ471e7QQDAAAAAH/j1ZWfAw44QNavX9/qug0bNsiAAQPavU9YWJg+wf3UADzVmRmXGiGDJnRvUF1LqpsvJMwoDRar7qpUcQgInPz8nevK5OPHV0tSVrSOSehI8gCTnHDlXtJLTejO4uGW34rEZm2S8Yf2c+k+jpxoOtHdo7eOMuiNIvrG5i70zGFxEh2/63eO2iFYVmCW5KyOC/415c3F6rgwl79uNXw0fVicLPt4m/54+N5p7d42srkTva6qfo94nI44iu6OTnZHMb62MrALo79/t1Nqyuv0jgxTYrh+XbSV3x/IJh3VX8YclCEpA/puZxcAAAAA9BWvLqJfffXVsv/+++s4lzlz5sjSpUvlmWee0Sf0vcLtlfLre5skc0S8W4roqnCkhgmW5dVIVUkdRXQ/U9tJnIvaiaLU19ojLTrrcMwalSC9SXXF//D6eh0v5GoRfeT+6brA7yi4onf89vUOCYsMkSGTkvssgzox056NX5pX4+z8bikhPUqyRsXL0Ba5/mqH4LNX/6hvf+GDB3WYlV/jzEPv2tEV2WtL9REQqridOSKu3duFN3eQNzXZd/a4mmfuiG1xdKI77hfonegblubrKJ2kLJOYmneaBMJw0V/f3SR/Lc6TiUcO0MNzO5I5PL7P1gUAAAAAfc3lasTs2bNdftD33ntP3GHvvfeW999/X0e0zJ8/XwYNGiSPPPKInHnmmW55fLi3KNrdYYK6iF7m/8WIQNNZJ7qjiO7Ihfa0mOQIZ7yQKuy7ErGhZgS4c05AoFOFytXf7tTF6X1PHKKvszba5Jd3NunLA8e3nf/dG2JTIvVQxcZ6m1SW7Pn+NHB8kj61pI6sUV3paqeg6lTuqKiodiKqHYdxKV3bebh5RaGzA95gbD+RzWg0SFhUsM7t78pQUGcWevPtHR3tjusDkdopUpJboy+rHWbqaBX1PU3IcH0Isa9SO0/Ue2JbO5IAAAAAIJC4XESPjY11Xm5qatLFbXWdGvyprFixQsrLy7tUbHfFcccdp0/w/qJod6hOdIXD4v3P2IMzdbetIz94d44itaW2URdogjqIm9ixpkRH/qQNidUdwL0hLCJYd6GrglFFUa0k9yeSoK/V11mlcFulswu6ZUa3wRjUYWe3u6n4E/VaK9pRJaXNBVRXqCKrvYhe02ERfejkFH3qKlWoV6cpxw7s9LbH/mO8hIQHS2yKfQdR1zLRQ5xDUzOHx+kIk0CldqI0WqxiDDZIXEqEfl2M2Kf9KB1/4tjJ6djp2RE13yJ/c4U+aqS9gbcAAAAA4PdF9BdeeMF5+YYbbtDxKk899ZQYjfahZlarVS655BKJiaEr01/1RhE9JjFcxxm4mtcL3zHlmI6LfM6iTJMqnjbqwkt71v6SJ5tXFspBpw7vtSK6EpscKbVVFVJeaO60iJ69rlTHO6giY0yS60VKtE8NEN19p9qu2BPXs8PdJTEjShfQ1Y6VljuR1/yUKwPHJbXKQm95n22/F0tJbttZ6j110GnD5YBThumCbmfSh7Yf99LRjk019Dcyxv61qdd2oL++Hbn48emRHXb/+yPHkNCO3p8dVAH9q+fX6sg3iugAAAAA/E23wmWff/55+fnnn50FdEVdnjt3rs4wv//++925RvhxnMukowbI5Bmdd1TC/wSHGHVchrXBprsdOyrS1NXUu/211xbVsZu/pUJ3onfmzx9y9CDSBkuj7DdraK+uK1BEJ+yZNW1uHsAZFde17HB3OHDOMDn07JFitVll26er9HUF2yp1dv6v4ZvkwvsP0q/hNgeS7uydIrrakWAM7r2dCTOvnthrj+3rRfTEjF2zD9TRMyrqRMXddHQUja9zzKxQR+p0xjFg2VF4BwAAAAB/0q2WqsbGRvnrr7/2uF5dZ7PZ3LEueCFHIdOdneh93VmKvqE6y8vya/QAxI64movu6AR252uvLbHNueidFdHV17Xtz2J9efjUwIh16Aum5ugf9XpQr6GWneiRsXt2ffc2tWNn987jjUsL9Pmg8Ul7FNBbFtFVB3tHOdLvPbBC3rpnqRTvrJLeonYILf9sm+6M766Geqv8/t1OWfq/LboLPxCpaB4loXnYrNquaoDsizf84nx9+ivHe3OoC3Eu4c07Qjt73wcAAACAgOlEP//88+XCCy+UzZs3y9SpU/V1S5YskYULF+rPwb/jXCKi+r4jFL4lb1OFfPz4aknKipZTb7a/R7Rl5H7pYq23dZq32xtRQm1xDHmsKDR3eLstq4rE1tgkiZlRzqIpek7l5KvXgircqUgX1flbU74rzsXT1EDJjc2DPYftndrmbWJTI8QQHCQNFqv+GtqLQlGd6ioD3pVYlu7a+VepLPloq4zaP32PIaguaxL56a0N+uJeR/SX0PBu/dng06pK7DvVHD/rqvNcvRfZt3H7cx/8KhPdhU708Gj7bepqvGNYNAAAAAC4U7f+G37ggQckLS1NHnzwQcnLy9PXpaeny3XXXSfXXHONWxcI7zH9/DFirrC4deCi6uj78F+r9CC+k66f0mqgIHxXXXXzUQudDILcb+aQTh9Ldb86d+D0chFdZfnOumaixDYX09uzYVm+PqcL3f1UQdJirpbqUou9iO7IRPdAnIuiCsg5G8vEOMAguRvLpbayXr+us0YntHl7o9EgE4/oL2ERIRIcuivyrCVVfFUF9N7eORBhsn/PaqvsP4+d2bG2REfVZAyLk8PPHa2vU0NMg8OMerCmGjoaiEX0k2+coneIOL6fjvx+9XtLRw8N2TV43Z+o38/q970qpLsy1NcRyaVeKyqmq60jNQAAgO/9PVCwvVLikiN7vaEJALxdt/4bNhgMcv311+tTZWWlvo6Bov4vqV+0iDq5keroK883S01FvVSX1VFE97f8/BZFp+5SxUZbcyyGK4WcnlCvv85eg9VlFsnZUK4vD52S0qvrCURxKRE6QsRmtUeD7X3sIN317anhlmpAaMnOGomPN8imFUX6uiGTU3SxvD37ntjxziHHjgFVnA4Jb7vQ7g6Onz9V/HaF6vqvLK6TuNTWt480hUilxapjleIC8CWvYsdiEiPazO9vOQTX36jfz7OumeTy7VW3ukpoU6k/deYGrzh6BAAA9IyKafvwoVW6yWjOzXuLwY9nwQBAZ3rcUkbxHD0VnRCui+iqGJEygNeTP3A1fqWxwSqWmkbdsdhegdzR1a4Kju119valjcsKdMRF+tDYPQpr6LkZfx/X6mNVPPdUAV1R3fA568ulvsIoWzc25+Dv3bNKsjqiR4mKDe3VuRCOHUKOmQKdrqu52K6GZe5ejFfFddWFj9b5/S2H4AY6VXRX3ejqn211oogOAIDvUf+f7VhTKoP3SnbORWlssOlB65tWFMjwvTkSF0Dg6taxtgUFBXL22WdLRkaGBAcHi9FobHWC/6mtrpeVX26X9UvsMRbupA6LV1R8A/xDbY1r8Su/vrNJXrzxF1n9TXa7t4mMCZMTrtxLjjjfHi/R27auLpJf39ukhzK2RXVJq4I+US6BwZGDbc4JEVujTaLjwyR9SFyH91FHTqjBotv+aHugZ025vRjd20VGRxHdXFXv0lDQ2sq2jyBp+TiBZtVXO+Tzp/+Q7X+W7LHzV6kq4/dWS9POHCHH/GOcX+fEAwDgj+rrGmXVlzvklZsXyWdP/eH8X0gdfbnPCYP15WUfb3MeLQoAgahbnejnnXee7NixQ2699Vadhd6bnXTwDpVFdbLovc26gDRiH/cWD53FCDr6/K8TvZP4FdXZ0HJ4XVtUJnPWqLbzp3vDphWFsmFpgV572uA9s46nHjdIZ14Lb3u9TuUqr/p6h+7YVu87hg4iVHpLQmaUPg8yNsnZd+8rNWUNuuO2s07zN+Yv0bf7+78O2SMb2pnzHtu78VWOYrj6Pqoc9s7yzB1F8ojdIo26mq3uT3auK5Uda0ul327vQaYA+L21c32ZfPncn5I6KFaOvWS8S/cZMikA834AAPDx/9tWf5ctf3y30/k/mfqfXx1V5jD+sH666am8wCwblhXIyH3TPbhiAPCxIvrPP/8sP/30k+y1117uXxG8thNd6Y1hIqYAyJYNNK7GuTgG0VnMrsVN9AXHUNGKQnOHhX30DvXH+VcvrNWXj7pojCz5cIsYgw0ycj/P/LGekG4votvqDWJttLkUORUVFyZhkcH6H5HS/BpJzmo9jFnFEsWnRXY6wLanujoU1FEkVxnobXaiB2CcS3FO9a6ZIC3EpUbIkEnJEp9mf3346/u4igLypvdnAADgPjnry+SL5/50Rv/FpUbKpKP66yNu1d/fDupvyIlH9pdF72+WZZ9s0/OKOpoPBAD+qltF9KysLJcODYd3y15bKgZjkGSOiO/0tnUuxnN0h+Ow78riWud1ORvK9PpaUt2co/ZP79Fh4tvXlEjeRvtQSEeRd6/p/Z0fr/42+//ZOw+wuM4r/R9JgADRmwDRQQiBei9WL+6923GNN8k/Pdlk0za7m7KbuhunbdYptmPHvcpVtiRbvXeJLorooorepf/znjvfMAxT7p250+D75eERkQUMM3e++33vec97CMIBBkYiC1niGFmL8fxNMwqQ1oDQCPp7rTvRL1V0UHNNJ8WmhLolMz88Vsnfbm8auR4B1jxEdEQlTpPdNy7EL2AyNVZ28NAiDHEFwS7ODrcFDg0QkSEgt9b1UFiUfdEUjxXXSf2Fdmqt7Rojos9ZO4M/3MHNX5nPv4PI8LaFEMnNneg5K+MpJTfKo9n0ngD3gp525TnB62lKeGwwXfe50fn9440Bw7osip1qwFrdUtvNaz/WbIlEIpFIJN7J0MAwffy3fBbQYe5YdnMGZSyMtTo4dO76JDq9s4o6mnqp+HAD5a5OdPtjlkgkEp8U0Z988kn67ne/S0899RSlpaXp/6gkbhEH3vndaf78id+spalBfiqdxfrHD4THKcLMytszjX+HDLYT2y+O+bftjb202cFsbAgCH/zpLF0ZGikAodpuKqIX7K9joRRV+Tu+vdihnyNRNllqENcdhotao/x0E5386CLN35jsHhE9zrKI3lDeQW/+6gRNTw+jO/9lsRTSXURw+FSOQUGueFNVJ/+dpwcUhkRNZYHZtK1VTZY6RHQIip4kMct2frspaN0d7Bse83xDMMbHRAMDtEBodKBdF/94pM/gQLe3PzCl8GA9nf2khhZdmypFdIlEIpFIvBh0Rm55PJcF8bUPzCL/gCl2Oxxxfz/w+gWqv3BZiugSiWRC4tCp8N5776Wenh7KzMyk4OBg8vcf7VJqbR3tIJZ4b4s6uFTeTil50Tb/fW+X65zocHyve2DWKNdyXEoYZ68Zf37nIJUeu8QOdUeBMA8BPSjUn1vQLA3QS58XwyJ6S103O4+lUOpa1DjR+1wYJWSJCINYCBc0hoiKDWXJUWWobkRcsLwuXAjcLyERUzne6VKFMtBoWoRrs8PtsenRHPrg5X2UNtf2OmlpIGlL3cha6+3c/BUZ0WaKeO3Ea2nO1StXOUceh1AtQrOvMGDIRRXrtBrEHAwhwEskEolEIvEeYBLqaOml5Bxl1ktSThR/qAWdlCiSz8i238kukUgk4xGHnegS36alZkTYUePuVZtx7Sjm0QbJuVH8IcBQPAx87G4f4KF8jjhT68sUQQ4FgzX3ZFv8N0tuSGMHPFzrcJ0GucB5P94ZHr7CbX64Vuw9f2oy0UUBx10i+tRpfsY8a/weENDwO+H6A9nLlAKMxHXA+QsRvaGiwyuc6Hg8oemDYwaE2gJxRsCSE/35fz1I/oF+dPOX53N+uiupu3CZ6kov84En1U6x1BoDfUNUdKie3xNLb0yniYJ47cRrac6HT52jijPNXAR2VzyPOxHFTTEAWg3GNd2wbkskEolEIvEOEGu642/5bAK467tLHJrrAuOAFNAlEslExiER/ZFHHtH/kUg84rCDaKxGnOxzoRNdDWgfu+Pbi9it7mhbPdrOQEJmuM2NASIN4EJGdIwU0bXT2dxHL/7HEX7NPvfbdTb/LbKuc1bEj8lg9uS1B5c5ctEbL3byNQARHfn8eBzoYkjKkRtHd8SngM4WZdiwq4VmVxBlcC93X+7ngpxw6EKQ7mhWfi//QNcPqK3Kb6ETH16kOetmOCyiXxm+SvteKeXP0cZrOmhqPHNl6Ar/rtFmQ0UF6JgYz0OxUTQBWlz2gSHKv5VOdIlEIpFIvAOI5jCJHXm3nOgqcTQlzmnOgoH0ly/1UIKG6ECJRCLxdVSfjDo6OigsLMz4uS3Ev5N4vxPdWpu6OStuy6C8tYmcIe4p4tOti99qgFsQjkx70TXIxGYRvamX4jOc+5kTEZEbraY4A4fxpkdtZ9y724kONj2SSwFBU4wO6JKjl/jPrCXTabKcRO9yzIdgotjia0B4XH1XFg/jNHWwQ1QXAro7crYxFFUcdGyBqKxPniuk6enhtPWzeWN+l8mGnHp8H2eGO/sSWJs2PJRD1uaoh0Qpz0PXOBXRUVDGHkHL6x1ocKL3dVmP6JJI3DnXABFsWrqIJBKJZDyB7uqdzxZw5xzIvSaR1t6b7fS6eKmig95+8hQFBE6hh36yko1oEolEMhFQfYKPjIyk+vp6iouLo4iICIuZwCJDenh4WO/HKdG5Gt1ar7SpDw9doRPbKyl9XixFWWlZB2j3cqTly5tQ+ztggF5t8WVqb+xxy+Mab+idn+/qKCFLmL4X4ByuONPEn8soF/cQFhvEH3EpoTy0yNba5M2YDi0W9LQPuDWiRsx9wGBUW0Dch0M+1CAMm4JBr+jCQJwW5lNMFBEd2CqaiedqvDrRV92RRavu0PY1Yp22FdElkbgDRLB99JfzLBht+EyOpx+ORCKReIRPni9iAX2y3yQWz/PW6BM/F5McQoHT/KirtZ/y99XR/E3JunxfiUQiGTci+ieffEJRUVHGz+VgPd/m7u8uZYdOybFLVHm2mUUSbxeq4II89OYFqrvQTjd/Zb4xHkFvkmZFckEIGcIS7WgdBDo0MMyxAXg9zV0ReM1FLICnonUqzzXT0MAVFnWnp8kuG3cA4Rwf4xHMdHDnsNRgg4gO8dsW4r9bi1bC30NEtyfGTySQlQ9wgJSMzkQXxU+JxFOc213Df4oB1RKJRDLRQNRK2SllptOtX1tIiTP1i11B3N3SG9Lp038UsSEPBUtbETEdzb10dncNrbw9k6bIrl6JRDIRRPR169ZRRUUFpaen0/r16137qCQuRQjm+Oi63M8iev2FdqKt1p3rp3ZUsSg6a1m8x9piESdQea6FNwQNZe2UNi9G9dfi8fv5T6aMhbF2HaAzl07nD4ljaI1fefFHRzj7+s7vLLYY2XPL1xawIAO3g7tAZMXpXdXU3z1Ia+7L5tiN4cErsngo0dxCi5gURBzNXqUUBbovu9mJrjLORYjjwrluTYyfKCL6qY+rqPhoAw8MtTY0VDjycR9FwQ/3qInOtPAA2vjwbF6vRXeiROLJaLllN2V4+qFIJBKJRwgI8qOFW1K421BPAV0wa2U8C+joZDy3p4YWbU0d82+w/zz+YSWd31PLM3YQsTUeh7FLJJKJgyZVKjMzk1JTU2nDhg20ceNGFtOTkpJc9+gkLichSxEtIUpDLIfAbk5/7xAdequMP5+1PJ48/XghoteXXVYtouP3Orn9Ih+o4C53l3g1UTEOAp2mzmk7NdiPOltGhtiZAlEqOUfpgHE3uGZoEtE198yktLnqCzYSfYBYW7C/jt2+nl53HAUxHx/86Rznn+esTGBBsbvD4ER30zokxG+8vxDfZW0oaI9BZBf/3hwhrtsT48cLjVUdPDsEhRBrIKtfZMX3tPePu5ib575/kIvmt31zoerrFZmos1cluPyxSST29iGtdd2j9rkSiUQy0cBcHESzuQo4ypfckM4zdU59VMXiuJj3M9g/TGd2VdHJj6tosE+J+k2eHSm7eiUSycQS0RHjsnv3bv546aWXaGBggDIyMlhQh7COj+nTpYPX2zn7aTUND16lzEWxFJscyg5tCMxtl3ooKmFspIsQTTA4xJoA4y4SMiOo8EC94pxXCX4v/H74PdVGtAwNDlNHUx+FxQaSn78clOLKDHMMLQQDFkR0T4HHjut9oE+5Drw96mg88vy/HuQYHeCrInpEfDBNnjKJDw/otsCQUQxejIwP5nggd4AilZqhoGKdR/a5LUe7ENvHOy213XaHb+N5nX1NIvkFTLZYgPZlcA8UWe9yWJjE16i7cNn4eT06F+dGy6HgEolE4gJmLZ9OJz6spPamXo7Rghs9f38dHXuvwti9iPz0VbdnUXKuZ4xREolE4jERHc5zEeXS19dHBw8eNIrqf//732lwcJBycnIoPz9f1wcp0Zezn9ZQe2MvxSSFsKgzPSOMB2nWX7hsUUT3xGBHawhH0aWLHXzIVyNw4/cC09PDVBcB/vHDw9z6dtd3lvDXSdSTPDuKCxZqnzeRoYuOB3PQdYA4DLT+zZgVSe4CjuHwuGBqquqk7X8+R7d+Q70TU6IPQkD3ZeDQgWAOQbalrpvX2yU3pPGHu4C4i+sXYrq1vHPQazjowLVkibxrEiljQSyFxYwvt7UlEN2EtQdEz7BdQFv/wCwajxg7gyYRBdjIOLVEXellFuBnZEeMO3e+xDcQ+z7w4f+do0d/sVrewyUSyYQBLvBdfy+kOWsT+fzkymg1FCiX3qS40dlxPkkZ7AwBHXvGFbdmUtbiOKPZ4Myuaqo420zX3J1FMUly/phEIvE9HA4ZDgwMZAf6Nddcww70Dz/8kJ566ikqKirS9xFKdGVwYJgrxSA6KcTo7lZE9HaLE7tHMq49M9jRlPDYIBZ5cGNurOxUle8mXOsJWRGafg5E9PamHodEdDy+no7+Cbk50JopHxCsLEP9hgGi5mLM7heK2UXmThEdiP1mW0MP0VW3/mjJOAJOZhbRa7soXcMcBz1Rs05Oi5hKoV2D/KclIqYHU4QLGs2Qm433eVxaGPm72PEMRyqKGvaGUrc2dHMMGAoP1p6P8Y6IsUGnkFaX/aG3LlBDeQdd//m5UkSXeASsKeaDk6WILpFIJgqFB+uo7GQjNVV30oM/WmE807gKnPuw1wyNUu75q+7IpIZyRVcwN7BVF7VSbXEbVRe0TchzskQi8X009zYiwmXv3r30ox/9iMXziIgI+sIXvkBtbW30hz/8gYePSrwXzoi8qrTsC8ehcHc3VnXaHM4U5AVOdFTSxeNFLroaxL9LyAzXJKIDUXDQyus/P06v/PQYb14ktoFQBSxlovd2DXisCwKioWCiCmmeRDieIXr6MiIOpLW2i7yZG/7fPHr4P1dRXKp7O2/gSHr7f07R4beVuRuu4lJFB7393yc5Dswe4rVCjJM99xbE9u72fupocexe4a2I9RhDybQiihRi7yCRuBMU5lLyonnPh1g2072ERCKRjHeuDF+h0zur+fOFm5PdMvQcP0MI6AB7yXkbki12gCcZTFE1Ra0uf1wSiUTiCjSdjuA8P3LkCKWnp9O6devo85//PL344ouUkCCHSPkKcEOCqMSRnFc40e/8zmKreeHeFOciHi8chWroauvnieHQQeIzNIjocQYRvbHXqSzZyrPNnDs/kWhr6GYRBR9qHIwiE91SnItxSKkHuiBW3ZnFm8IFW1Lc/rMlRDd9eT4d/7CSllzvvugTVyDy9BHnAnfvP/7tEAWHT6W7v7vEbTMm4PjBmonoLkcH9aJTpOhwAw0NDNPi6/R7TQ68foH/PPtJDa25J5tcxaXKds6FR1F1IaU4nYcuKDhQp3TLzIuhG784j8abiC6KnFqYKkV0iQdB4Wv5LRn8+Vv/fZJd6X2d8lqUSLyhwOXKWBGJQtmpJp7Dg3M7htp7Y+ynmF2B+DwMMJdIJBJfQtPpaN++fSyYQ0xHNjqE9OjoaNc9OonutBrEgRgTccB/6hSKT7cuMPd6mYg+Z/0MmrcxSdVGrLWuiwV0RNdocdSFxyruV8S5aKWtvmeMCDyR3A8v/ugIdzs89strrOYrm4LiTc6KeEqwUOTwZAEHrd+bHs11+8+VKETGT6Mtj+WRryOE2MsNPexWRqzA8NBVtw5prjzXzK6k+ZuTHRbRkVG//9VSLoxhaJQegzRxoF57XzbtfbnEGIOlZs1wBBRTTbuMbDF1mh93QKgpgIYYnFc4sI4n+nsHHRbRMTzXWkSXROJOxKBk6USXSMaSv6+Wyk810cZHZrss7qjwYD3f6y+Vt1NtyWW674fL5LBqF4Ln+tTHVfz53PVJXvlcw1yCtRn74YaKdpqR7d64TolEInEWTaejy5cvs5COQaK/+MUv6P7776fs7GwW04WoHhsb6/SDkriOZuFEtzMszZS562ZQ8uxIr4m0wLA+taCl94nfrKWedm0HKKMT3YE4l5a6LoufTwTYvWjIDw+cpm55SZsbwx++UMCRSLQSEjmVtn42jw8NEInBtHD3dlaIgaK9nZbXQRxiPv5rPhe0kGNtiUCDGIX4kr6eQV26Q1AIxSEPB3m4vxvK2iljoWv2EGItx8Dg5pouOvB6KW15PM+iaA+nvVq3fagh87urbXyJ6BjajQIQni+tBIb4TcgissQ7QA5vZMI07nIT6xTEGolEMhp0UYGmqk6aNneqS4ZbHnzjwqiupOqiNo/Nh5kIoFCB19PPfzLNXT92zpk3gL1fUk4UlR67RDVFbVJEl0gkPocmK9y0adPouuuuo5///Occ69Lc3Ey//OUvKTg4mP9MSkqiOXPmuO7RSpymrd7gRDcMFRXAIfnpC0W0/c/nxnxNWEwQpeRGU7RJBIy3VNsH+sZGgJgTEOg3Kt9aDcKtiIOXpZgRW6TmRdPyW5VW4om2MRCiN9yLmNau1/fzhjx+icTRwwIGLkGQFCI64lzcSXCoQUgy/HxzMEQZTmr8aat4CYe28n0GdY/oEq29rqK9UekQCo8Jok+fL+SD26f/KOL7iDOERE01FhDFMM7xQMaCWHYMbngwR/PXTjU40fsszLmQSFwJogEwY+Gv39xLHc29xuKf2EtIJBKFwYFh4+fT01wzC6Vgfx0L6JhxM2edIuiWn24iV4LYtomMcKHnrErwSBSmWmDOA9WFMhddIpH4Htr7dM1E9aioKP6IjIwkPz8/Kiws1O/RSXTnoZ+upNb67jGCOASSgn11HH0C0VjkVHsrpccv0b5XSriSDZen3kB4h0OSXYoaRZag0ADOcfb1LGdH6HNgEChELBx8B/qGx7hCR76f924EJRK1CJF6WoRnnOg9VtyYQly3F6UCMb6/e4h6OgcoitR3M1miuaaTLp5voZlLpvOw6PN7a6neRSI6DtXtzcKJHkQbHppNr/38GM+swCE/b82IW2t46ArPQlAbV4N7BYqGENE72/ooOsi7is2eQAwW7ZeZ6BI3c+liB7+HseaFRgdS6pxoFpJik+X7UiIxBRFzYr+Oc4veYF9/6uOL/Pmia1O5q+n8nlqqPNPM0Y96GG1MgVgP1ztmt6y9dybNWZdEEw2cpzIXxfJcrgWbk8mbwfndb+oUvvbQ4ahHRKBEIpG4C01K6ZUrV+j48eMc5/Lpp5/SgQMHqLu7m2bMmEEbNmygP/7xj/ynxHtBNhomZpuDqBY4BZAbi1ZYuKkFEDcmT5lEGfNjvSZWAzdduMQhulgbVIPq9pF3yilzYRwt3Kp9OCSyeiWud44jQ/6lHx9h4eWz/71m1H/b9Mhs6m4f4HxiicRXwYGm7GQjHXqrjP+/q7JHHXWiC3FdiO221t22hh6r30drTiqGiSLGZfVdWbTugVksprsCRK1cGbrK9zFkmEMkX3FrJh+4979Wyh1Dolspf18dHX67jPLWzqDVd2ap+v74nv09XdTV2u91HVueYHp6GG18eDbvKSQSd4IhoiAxK5z3hZj3Y2vmj0QyUYGhCkQlTGPz1IkPKykhM5zS5+sTqVZ0uJ7374ivy1mRQJMmK/NGIHZj0LnenbooymNfAU5+XMX38Ik2xBS/b+7qRJq9KsHrf/fQqEB64n/WaIpolUgkEp8U0SMiIlg0j4+PZ7H8N7/5DWehZ2Zmuu4RStxGQlYEdTQ3sDBtKqJDUIDLDpsrbxHR0XoIIaSrrZ8FqrDoIIuHqUsVHZqjXJwBm8Nzu2s4ugHt8MiEHRq8wrnIE4GRQaDqXS1icB028eYFkcSZEysORzI+6WrtowOvXzD+f3eL6MJlhiKXJceP0Ylux40mvg+c6M4AF1rp8Ub+PHvZdH4+5qyd4dLnX0ST4b4BFmxKpovnm6m2+DLteKaA7vj2Ij7MYY4FclwhuGs5DLbUdPG9aLyAeLe6ksu07OZ07hbQAp5nfEgk7kZ0syTOVCKiJBKJ7XhPzA84+0k1x4CUxQbxLClnB5/jHg8hGyzYkkJT/JXvlz43hp3iiHTRQ0Q3dbRjLwFxvuRIA8fTYcYKzrUTEW8X0AVSQJdIJL6KptXrV7/6Fce11NbW0j/+8Q/67Gc/KwV0H+LE9kra/WIxXarssPjfEw2bjfoL7aM2KDws0suGO/pPnUIxKaFjHq+lwxTEf0dAS3BbQzc1VXeq/hoIKUffrWB348mPL9LfvrWPjmxT3KcTAUcGgQYYRHSIexCvJJLxRtSMEXdycHiA2x26QWGjh4KaIwaO2mvpFnEvzjrRIVzje6D7JDk3ilwNinGf/906uuVrC4x/h0LCpkdyKSDIjxorO+jEB5X8962G4dvRGoZvp8+Pofmbkik60bmIG2+io6mXLl/qoStDVzz9UCQSVWC/ChENCPEMkRKYtYDoJolEYsmJHsz3L3SiYd2HEchZLpxo5O+Fe7xpXFr6AsXlDhHdmXkkeK+f2lFFL/7HEePcKgjH6x+YZRxOXnLsEk0kzuyq5s7xIZOse1+hu936PB6JRCLxeRH985//PGVny4gLX6XsZBPl762l7jbLNyvRSg+RHQcP0NdtGAw2aWRYmLcgHq84NJkL4A0VHU45krDJwwZt38slqr8GLkYAJ7oYTtpSp2xUJwJxKaGcJZ+co95hggnywvUpCjYAQxjz99VS5Tl5+JX4NpgxIQZQXvvEHN3apbW4fW775kK679+WWZx3IZzl9jLR566fQXf+y2Kat9G5rM2SYw38Z9biOKMTqbdrgA+AKPa6KsoMjnFT8P/XPZBtvI/gYC7Way2xLGifvubumePK9SaGpDpy30exBq31xUca+F4skbiD5pouGuwb5sIY9mAARcO3fn2S3v/T2Qk/cFAiMQXRbMKJjtkeK27J4P9//INKY1epo6C7DLFe8zclselJgKI5CtRZi6cbz5laaarqpNd/cYLj2NqbenmuiSlwpAshf3h4Ytx/cL8++m457XmxmGqK28hXwP7gxR8doWe/c4A7yyUSicRX8O7pkRLdwOHB6Dqw4rBD7AlcA4gkgfs6PiN8JJ4j2N/YBu8twDl/Zme1xWF02GRhg4bfx9E4l4g45esuNykD6dSAfF8AR6IQYfC84/n3tufPFaANFB9agHsEkS7IuBfCDWit66LdLxRzHnra3BgXPFqJxH1A1EFmdkttl0eiBmy1TouZGMgutUVkvPNOa7ikyk418eczDYdd0NM+wAdADJpauCVF96Fj1sheGk/+AVModW4Mx75AhENRL2KCz2EQBU3RKaSJSUQf/K8iWuK6myhxZhLPIroSYbAQ+y1jV9xVJW7OXqFQIpko3P3dJdTa0M2Z6CBnVQKd/RRzSrro2PsVtOZex01zM2ZFcsEdBVVTcK+974fLHfqecK4f3lbOsTP4vjg3rLoji/O/TUmaFcmuenS7VRe0TojzA2a5DPQN83nJNI7V20FskH+AsterKW7l7HyJRCLxBaSIPkFob+xhUdkvYDKFW8kqhZiJwwdEX3GA7use8LooF4GIaWmt62bhH4K5pcOUo9lwYQYnOTZiA31D7NSwBzafQjDD18NljUx0PP96CFDjFbgdIaL3m0RNOBINI5F4KyiqXTzXYlwjvAm4491F5bkWFqrhzE/IGInawkEeh2Lce+AotTQA21F2PVdI0NQWX59mMatbdAYIFzrWai1ZnTjcY/1CS3JsshIz5uuIFnlLnQuqCqPTlMIo7s1SRJe4A8QqIXs5JGLkesP7WKwr6HaRIrpEooCODdOhuyg8Ycj3O789Tef31NKcdTOcOrfgPjBJw2wRe8BZfnL7Rf48a0kcd39Zmi+DAvyym9Jpit+kcdUdZsvNfeaTamP+vPnMG28naXYUNV7spJrCNimiSyQSn0FOdJggCId0VGKIzRvstf80hz7z45WUOkepZOMQDIK8UMhEfu/MJXHsWrwyPNrtUF8m8tAd30BBPAgKVX7v9kb7bnQ4IyDoCxEdG9IoQ0aueP59lYv5LfTifxymCju5oh3NvRzDorVt2jhc1CTORXRBBGkYUiqReCsisgiOIU9QXdTK7rJaJ1p98Z5E7ubJj5SDrCN0X+7nYm720umj7kX4PN5QGLU258IRsC5fOHaJCg7U212XkAEOxLqvlu7LA/TMv+yn1352fFxERqAoINZiR2PcRFG7v9u5WACJRC0okGFAcdq80c5TMeuhz7CflUgklkmeHUVpc6P5PnbknQrNX19X2kbHP6jg4qk94bcqv0V1hAcez7H3lMez+LpULvzbGtCOdWD2qkSHisC+ROPFDnr392d4X4V5O7OWxZOvIeI/sUd1JidfIpFI3IkU0ScIIw5p264C84ns3u4G3vrEHFp1Z9YYdxFa+fCRMNOxoaICkWuO3D17dCIKoH+YJvtNovDpyteJXE6Rle6LwF258+kCzk/c+UwB/57W2PbkKRaTMKhPC4iAyVkRT8FhI5tisQn31mtPItFC7jWJ7JIUBUp3U3G6mYceVxe2OuVOxtBkHGYdPexggNnjv1pDC7emWu0ushTR5Sjd7QPcDQSRPjQ6UFWhI2elNjcUDq8omkKw7xkHA7JwHxNt+KLAqRUhvtsTUyQSVyOKYmI/K5FMdIoO1dPel0ssFtVxpkK0C5zeWjn6XiWL78fftz3bZPufz7P4K+ajqCng4yyA+9HCa8fuHSYipccvceEeryH2LqvvzOI9pq8B8wQeNyL92uoVI4NEIpF4O+O7RCsZK6KrHJaGAWuo/KM9FvEv/kEjg2F8gQ0P5tD6B5yvaIfHBlNDeQe1N/Wofo5NowCMInqN74roIC4tlKryWzmzfNffC+jWry202NFgzNA3idZRA1ovzfH2Ao5EogUMsXz056spINAza2lwmEFIMgwRNZ0f8cH/neW16qYvzVclRkGUhtCqJuLKEhg0ZjpszHTOBai7cJlFekejuEwRazcEdHsRLfM3JrOTS+uaAwF9WuRU6mzpo87WfgqJtC3WezuIfsP1MNg/xF0DjiCeQymiS9wBioPoJMHgQjHPRiC62czXPolkolJ5tplnk2AWCvLLTcEZZtPDszV/z4bydkXQnTyJ5m+2PXw8JTeKH0PF6SZaZKGgbg6MUrf/8yI28ah1l+Peg2IB3O6OFAS8EdN9EbLPsSfDmrf85gyLUXW+gJ//FDZQ1BS1sRtddHBLJBKJN+N7JUuJQ4iDbHSSfRH94JsX6K//vI+KDzdwuxxu0Ka5ed4GhF20BQ4NDo/N43NShAmPC1Id55IyJ5ru/7fltP6BWca/QyY7cgVnLh0ZoOdr4Bq46cvzeUgQBJXa4st0epeSv2fenonBNnoJ3yNxLlJEl4wPUFxy18BMc0SkQY9ZpAE6TTDwFO3A9oBoLkRVRwQpxD3ZAjno6IZCjJiaNVcNoosowtBVZA9H1y4USQCGk/o6uFbu++Eyeuinqxy+hwYaHOxSRJe4g8KDirO2+MhYZ2ugdKJLJKPA7CsghoraArME1HDCkFeevSLeeD+0hphD0lDRwXsQNeBeFBYdpOkMceD1CzwsFTGTaoGBbNezBbTj6XyviWfDXK6j71VwXr3oAkSm/YM/WkFbHsvzWQHdNEYIQEiXSCQSX0A60ScId3xrMR9mMRndHhAxMPgNubR5a2aQt/Pij46wAASXQuLMCBZ3IITo4WIUN3aR1WsLuBzNK+gQhfQckOdO4DQVblE8l/EZ4ezm2P1CMR3eVsbPTYxJUUaIJXCoa80hxKYQ7tarw1d5Y8jfz7Bxl050iUQ/Ed1c/Bb/P9jw39V8HziuezoGKVw5B6sCa/TzPzzE3VB3fmexxXsRWnrR9VJf1s4RWBHTRztKHUGI8SKay1UI0cBW3NVEYqph3e7vHplzIZG4AuwfRAQU9oDmzFwcx+tOfIZv7sUkEj2B4UXcFyNtiOgQz3f/o5gd5g/+eIXNzjMMA4eznDDAW0XcCoZNx6WFcfRjxZlmzjC3+FiHr9CZndUch6e1wxX7h7jUUB5aWXaykeauT1J99sEAdJxpsJ54+hxccKCODr9dZpxRVlXQyi50Z2aWeBuIOYThwVNxhxKJRKIV6USfQGADoiYvDe5pMZwTmWsF++uoo0UfV6ArEAcjMUz09V8cp2e+c4Caazp1+N7htPTGdErOUcT0ibTJfuu/T9Ku5wrZASHARlYM7RLxNWOjXPw0T4c/vaOa/vzVPbT3lRLj3y2/NZO2PpFn8VAskUi0IeZGmDuyjMOjzeZK2Ps+Wp3ouJfQVbjZp9gs5m58aDY98T9rKXNhHOmBiHMJN4t40JuQqKnjxomuB1mL42jTI7Mpe7nvdmFJfAMU9RDZgBgJS12TSTlRNG9Dks8aGiQSPYGADoc1TDIQs60RMNWPmmu7eM+w/9VSqjzXzAXu1rruMe5xMWw8a1Gc6uJ3xgLlLIFIF2ugI/rQW2V8rhNzOrSQbRi0WXJUXfY6gAloyQ1p/PmRd8pHnYHcTXVBK336fBHv02AEuPaf5nAUzngD8XEbPpNDGQs0ODMkEonEg0gnusSicAwTd0dzHw+Rw7CPG784T1MbnTtJyIygspNNVFfaTrOW9/PjxuN35+NFhuynLxSx22nepqRR2bvYgKF1EhszZA36AmgbRFYyiicrbskwOlDgSMdGB5tqUxf66AxzdWKcKQGGzP3+npHN6vS0MP6QSCSuc6ILUV38d7XfR0t7NCg5eon/tBdtpYf73BSxpkgnunrg2ju8rZwP62vuzXboe0DM9OYYOMn4ATMUQGxqqMVZCxKJZGyUC1zotjp2YbpadUcmbX/qPMcl4UOAjtF/+s1a4xnn4vkW/nzRdeqHfkIwPfx2OdUUt/HQcvMOVph5xIBSONW1mnNA1pI4OvB6qWG2Va/qfQBiOM/tqeGCw8ntF2nFbZnkblDo2P96KX8+e1UCrXtwlt25LhKJRCJxD3I1ngAcebectj15iirOWK/2mwLBNCY5lD+HgO7tkRrCqYyWw7oLSp4aHr+IBXEWbLywQbQlGrU2dLNj4sT2SnZDmXL0nQp64xcn6PzeWvIF6kovG10lGNA6LWLqGCequYDubIa5aElEvr1EItEf41DQAcwuGHI4ziVY5AtrcKK3NXRzUQ6HYDiU3cmtX19In/vtOmM0l6uAy3X+pmSfnn8hgMsQQxq1FkokEk9QX3p51GBiS/EM2Ndg+KhEMtEZyUMPViV0w5WdlBNJsSmhPIh06jQ/7jg1PTOi+I1/G2s4O6oBpqLI+GC6MnyVaorGvjch2qMojTOHtbgXNTOdxODU0mO23eiIhPr4b/l8/0Os6ao7svjvT++s9kg3duGBOnb9Tw32o1V3Zo17Af3K8BU+x2MYrEQikXg70ok+AagrucwHiFnLlbY2NWBSNkQPgTeL6NEzprH7CAJs/t464+PXCwyXuVTRQdd9bg5lLrIsALUaok2QiW7u7IiaobjPW2qVjas3g+dw5zMFHLuQszLe6u8ruFTZwVl9aDHE5nruhiQKi7Y9UMgSwoHS3zNodPYXHqpnQR4bc0ccKBKJhEYddG/7xkJ2kvuZxKkIoTQ4TN0av2BLCs1enahpkFXpMcWFDiFbjeMdg8AuHL9EC7emGAeQOYM73KkQGPAxHhDu/QDDcFCHvkfvEIsSmIGWbogAk0hcQd2Fdv4zwUr02+XGHo6nQyHx8V+tcfOjk0i8CxE5ZisPXYDzzPJbMsb8vRhuKdjwmVkU4UBk2tr7snlPYD5PamhwmE58qLjQF1+fOmrP4kikCwZWohtu8fVKTIslAXfPS8V8ToNove7+WZQ+P4ZmZEdQbclldsxv/WweuQtE10C8B4gU1ZoH74ugYPLGL0/Q5CmTKGXuSk8/HIlEIrHJ+C5rSnijgwFtInNMLQlmjh5H3MXuYvKUycZcdBQLLD1+ZxDtf3CkW0MI5JaeY+HabjW8Dt7MvldKeCMTGh1Ia+7JtrvJ2/X3Qt6cYvMJAWntvdm0YHOK5p8LZ4upeNPTOUB7Xiymj5/O50FFEonEeeDIwmHVtFsGTi0UwMw7Tmy5xxD5JbLR1dyDSgwievYydS7ttvpuzl6tLVbWc4l7Eeuw1gHRpnQ09dL7fzxLu18o0vGRSSSjwfA/3ptNsm6eCDJEzPV1DzmUqyyRjCc2PjybHv/1NZS7OtHh72FuFopJCnVI6Ma8ApybzL9fwf56nnOAzHbMYnKGzIWx3JmMogG6UqwV7llAn+ZHy25O57/DY1p910xeW2AEQEedu4Bx6I5vL6IlN6ZxtMxEAMYMnD3RmdBQphRGJRKJxFvxKRH95z//Od/Uvv71r3v6ofgMiGPp7x7iG3KkitY90+GiwqWAr9UrGsVVmIvmejrRjSJ6ozKgzhK2ChX8PE5SBvh5c3s8cnCLDjdwnvzmx3Ltvua4LjY9PJv/vHC80Zh57AjiZ8G9OCoaZpq/zcxGiUTiHHivP/TTVXyYdQXoaEKuqJ//ZHZ2qcF0uLUzlJ1qpLd/c4rO7FIcXa4G6zu6lkRHja8i1mE48hzFWBiFcGnmWpRI9AIOzSf+ew3d/s1FVt2awgQCAd107opEMlFBYclb3c1DA8McjQngHPfzd66TDOeLx365mq7//FzjfCdTui/38xwosPK2TGPRDcActPzmDLr16wvcPtMKjwM/G9EyEwGc9ZJzlOgdaaCQSCTejs+szMeOHaOnnnqK5s2b5+mH4lNgsjqIiAvStBGBO3HrZ3ONUS7eLmQi8kMINFpclWoIN7QoqnKim7UkAv+AKUYhvsXwengjcJGg3XnRtalWs0XNmZ4eRktvVNojEQODeBe0RWpFiDWDfcP89b1dAw4PKZVIJJapym+hY+9XGAfxOSoUQ5Q+vbNK1b9HYfHGL82jlXdkWTzA2iqKNlV3jcpv10pzdRfVFrdR2yXrBVA9eed3p+n1XxznIWa+zIBwohtmVTiCEGgwHA45/BKJq4BIJmbjWBuQKAr1Ym8hkUjcB+4DyLu2VFBFoR1Z5PtfU4ZoDg1eobS5MXxuwkBNPbB1/sXgUZw9cJ6x5M5XMuFdO1PFlKbqzglbeBbPsxTRJRKJt+Pd9mIDXV1d9OCDD9Jf/vIX+ulPf+rph+NTCNFWS5SLoNeJQZGjHkNdF1f6U3KjyVXg91txayYL3s60oDsS54J2Yvx+IMrK84zHBzcmXg9XD7hzlNQ50XTfD5drdh8uvi6VB6/Cgfn6z4/TpkdmU85KbRtfvGZp82IoMNiPhoevsmvf27P41QIRr6HCdmvilatXaLDLuUJVc00XdTT3ckHJUVDAuHCiUXV2tcS3QLdJwYF6WmZjCJ890CWCwy7WCTXRTXBR4UCshdCoQAqJmkpdrf28rji6ZiIL2XQNdzV43C01XRyJ5csIJ70z91Lk0CPbFK3ZuEe6I5feF8Fzg/snBu5OFMehJ8A+FjNfsLeIVD8eSCIZV1QXtNLpXdWUkhdF8zcmu+VnQhDe/tQ5qjzXQitvz2Sjjil9PYMclwITDwZoogC74TM5PBtJ7zURA7OHhkcK89VFrVR6vJE7cJGDbm/+EoaOYu/jrDveGig0IBccr8+NX5zHcaUTCTEEFsa0hFnebd6TSCQTG58Q0b/0pS/RjTfeSJs3b7Yrovf39/OHoKPDtx1hztJqzOrW3oaGKeu3fG2BU5nU2Dy994cznG334H+s4AnurgJZv6vvVKap64kYloPfAW2G5rl/EGqwAQuJDLQqOsChXn6qySud6BjgIzaEanOOTcEmb8vjufSPHx7m/z8tcqpD3wMbRoExzsXHRXSIUe/+/gy7cOwxZWow9d04SP4R2n9niOdv/foEDfQN0+3/vMimK88WOFwderOMclYlcFSPZHwRZHh/iyJVa303vfu70xQZH0y3fG2hyu+hXJ+IRcB17SrhLyEzgkpbL/FwSkdFdBQu3SqiG9Y+XxfRUUBDESMw1PH1F91rU6f5U2/HAK+DKDBIxnLo7TIq2FfHQro7B9eNBxA79N7vT/NaseL2zFGzHsyBQAcjhHSiSyYy6BZFR5o799boXIOADo5/UEmzVsRzt7MA+1UI09iXNJRdpsSZkcYOEj05/kEFHXmngmatnE5k2CKL4aVz1ifZHQx+bncNHXyrjJbekDamEKAHOC/DFQ+Cw6dOOAFdnEGjk0LYjNDfIgvvEonEe/H6Ffrll1+mkydP0s9+9jNV/x7/Ljw83PiRnOyeSru3MiVgMlf1oxK1O9HxdRAvkp1oY4OIATchXfXuKBN72a7Cnd3ePNaNHp8eTp/77Tq69RsLrH4PuKyvuXsmzVmXRN4E2gaf+8Ehdh87Q3hsMN3xrUW0/NYMSspWNsDOuvPGgxMdsQ4QGvF7oC3V2gcEq+H+ybT3pVLNbZxwj+94uoAFdFB8pMGhx4qfW3xY+Vq4fyXjD9FdIGYz9LT3c3EQH2oJDPY3urWEGG+NI++Us0iIIo9WRCEIA0YdvZ5F91B4nHtE9BCDUNzl4yL6tf80hx75r9VO3fsBOotMi6KSsUBAB3BiyqGX2kCBDffY8jNNNgV002g4e2uWRDKeQeEcaJmR5QyNFzvo0FtlxjMlBnse3lY+6t9MmTLSrfbWf59y2VkRxTZQcbqZrhrmi97whXm0+PpUWn5Lht2v9w+cQkP9w3T8w0qXzLfCOQzrmd/UKbRCxePRCzj+4YBHseP83lru2PEkIhe9v1WK6BKJxHvxaid6dXU1fe1rX6MdO3ZQYKA6F9P3vvc9+uY3vznKiT6RhfQND+bQ+gdmsYjtCUyzd21linszcNStuC2T29FN3ROmwJ0OIdkacalh/OFNDA4M046/5bNTEMJr5qJYp7LvkWNsPuBVq+iFLEQchkWUkK+L6Djkg7S50bTRhrO7rqyV3vr1Kao800KFB+op95qxuYzWgLMHG2A8b1euXOXIjrX3Zmt28aB9srWu29jyyq20OjuBJJ5FdJr0dhpEdMOfWjpQIKDDxYZDJL5PiJXOE3S4nP20hg9kKblRFBajTcjGcGi8/x3pjhGFOHEYDNf4sx0lNDpwXDjR9UKs333dcpijNR7+r1X03PcPGgtGjnYRTUTE/VVNNFXu6gRehxyNsZJIxgNtDcoezxFjlVZw//3or/kc6ZW5MJYWbEnhqJKiQ/U0d92MUWcixBAKA8j2P5+nB/59ud1oFa1gbcV+BaaBviZF/sCsBESBqmHWsng6+0kNZ7hjECmfrXUC+yVRbFi0NUXXuV7mdLX1sViPmMlL5e3UWNVJV4ZGRIKDb17gM8i89Uma9216kLdmBqXMjaITBfvd/rMlEolkXIjoJ06coMbGRlq0aJHx74aHh2nv3r30hz/8gWNbpkwZXamcOnUqf0hGYGF0kmcPOaDdkE/ri8xZO4PGGwdev0BtDT0UHB5AGx/O8fjw2G1PnuJhMnBBYoOdNCuSYyZ8GeGitVdcQBtpeHY/tRcH0r5XSyghK5wi46epen9BRAcbH5lNh968QN3tA3Qxv0VzNnrJ0REHOxyRrQ3dHOkkGX9OdCGi93YYYpM05t8jFgYiuhDhLVF1vpUP0Ti0OiJcIZ7r8V9d4/C6JKJc8PPNI7hchYgs4e4rCWfm564eorg0uY7YumZyVsZT0aEGXoOliK6eulKDiK7iOUuf7/isEIlkPACTBfb8IMrFTnSYYna/WEwdTb3cabn+MznsRM9eNp1Kjl6i/a+W0u3fWmS8vyfnjXQ9Lbo2RXcBHeB7zlwynU7tqKLWc4Gauz7x9dfcncVu+YJ9tTR3/QyK1qkYAXG+s6WPxXMUG/QGBhkMlYfhxlLnIQre8elhbHbDNXJmZzWd3VVNGQvjaMHmZIrPCCd3gdjXaVH+NKnIbT9SIpFIxpeIvmnTJjp37tyov3vssccoJyeHvvOd74wR0CWjwQbB08Jo/YV2n3ei23uO3/ntaQqLDaJVt2fS1GDrzmm4e5FHGJMU4tCgVz0pP91E+Xtr+fPNj+ZSkKHV2ZP4T1WWI+Tn4vnx9HPkLHByi1gUuGrtEZI+SCFX46m25DJ9/Ld8uutflth0guN5QowLzgGzlsfzB+J5sPnFIUWLiA7RHHECABnXiKBpre2SIvo4I1jEuZg50UVWuvrv409IOEUXizVKjilFGRxaHTkQO3vvQl4ycpDdlYcOMBcDdF3uZ8HCXsSEN4LnDQOiEWGGiC5nclmdGXI8kchepojoF0420hp0EckBo3bBfJrGi512i9QQjGAU8MX3okSiJ50tvcYOw9Bo194XsZ5hT4l7/9bPzmEBHWCwKM4fMJggvgT7A+AfMIWu//xcarvUTbNWJLjscc1cpojoV4cm0Vu/Ok13f2+pprUBee0ZC2N5xtXBNy7QzV+xHuOpFhgSEBEDVt6Wwc+F3l3HmE0mOuSwtULuOKJI4zPCaHpGOO+TsOfCWaCqoJXO7Kqi6sI27mzFx/T0MC6KZyyIcWtWO84i/r7dkCyRSMYpXi2ih4aG0pw5c0b93bRp0yg6OnrM30ss59GWHLlEC7em0Nz17s/ixhRzU+Hcl0V0CAuXKtppaODKKGEAzoGaojaaXHqZ1t6XbfN7nPjoIhUdrKelN6Z5VCDGofKT5wv5czgeHB3apzcidx4DC8cDaJHEBhBCnpqButjYrnsom974+Slqru6iw++UWx2Ui+LNnheLeVMcFhNovPbQbgoRvfJcM7uA0aqqNnYJ1wX+PWJ9ECnTXNtN+jWrSrwB41DQ7iEaHr5idKQLcV399xmdrW5pvaw822I8tDoDrnVEs2gt9KXmRdPjv1qjaqivXkCsm785mUIjA3lWweTJvlfo7+8e5IKvn/9kVYdl/J6lxxvp7CfVLMxc9zm5N1MLZgWc3V1DkdODObYI7ycIGOnzlHxgiXXglkRMBMQ53AOtDQLc+3IJzduYRMtuSjdkLU+Sbn/JhKS1XnGho8PTlUUlRMbsfbmYP19+S/ooEwkKzRjKefTdCo4NwVonOsUgThO5tvAKExMEY5xH0SHlyPOAQkDl2Waqym/lgdCpc6Kdekzdl/t5DxYQ58cFVb05+dFFPiugK2/To7kUlxpKAYGWzwYoeuD3wQfWy9O7YMppYEPQR385z51T19wz0+UFcoj5lwun0j/2HKF7vr/UI7Ey3gaek5qSNjY3iaKURCLxHF4tokucAxnHuHFq7FjTjQZDlIXIoMMHKuJ6V9ndAVy57/7uDLclmm4eWgwZ0ojewHAcW0QnThv1NZ66Ce/6ewGLaIgQWXGr+4bX2GOqQfCFAIfMRGysU/KirG72vB0RZYRhRmpdtWjl3PBQDn34f+fo9I4qznC1VORAdiSEK2x4t3w2zyiWxySH8AEJAkPZqSYeWqqGEoMLHbmV09PCWETHNS8ZX2Ao6G3fWMjxLZMnTTI6yVHo0cLia9O4MGvN5Q2XFsRrXIs4tDoKxNy3f3OKhdrHfulYtIs7Xb04kF9z10zyZUQRM8BQ1LSV4Yqi8MmPq7iYDOAMNi3e4Z7fXNPJBz53toP7CmixR9ET9+JVd2bxPTA51zuK2t4O1gaANcbSulBV0EL7XinhzxsrO6mhooPe+/0ZNjDc98Nlbn+8EomnQfci1mY1UYGOgvvCR3/JZ8NRUk4kLdqaOubfwLxTsL+OY89O76yiJTekq/reuJ/gPY1zLUw3WC/xZ0Cwv/FzfKAgmZgdaVEgx1qx6bEc2vn2YVpxm7qfa05EXDDN25DEESxaI2EsgfX//n9fzkVUvWNsUCw49VEVf776rpkck6kWrJWbHp7N50QMHD2/p5af+x1P59ODP1ph7LxzBXgeBjsn837k3J5aq4aiicJA3xB3HqN4k7d2hq55/BKJxDF8Tp3avXu3px+Cz9BSo4hgMUmu2zCpGSqaNi+G2/pwM0Q+ni/GdITHBRs3cdgk+vkrhQAxRT56hv3nWPzerpo8rwZEDEQmTOMD5ZbHc72qbdzoRO8epE+fL+LH+sjPVvmuiG7MQ9cmHqFIg00S4nZ2PlvAB35TF257Uw/tfUkRB+CuQ0um6QFh5tLp7PIpPdagSkSH2Fl2opE/R16lcAV58jqVuO5gMsPkEIXDJqKotB6GkFduC1x7AK4qZ2JZ4Hrq6xrkaxQZ52o6OiTOgSKmaVHT0mEOB2qIvz0mRZiB3mF+nRBlE2X42ovnm2n3C8W8B7jxi/Pc+Fv4BqK9HkYDxHFJ1IMh5Fi/LK0JcMJCyBP6Vk9HPw9DBr1d1iOoJJLxTM6KBF5n8N5xFQffKOO9I+4Jmx/LtSgKw0i18o5M2vG3Ajqx/SLlrEy0OqBc0FLXxUUwS3nelsA954YvzLX482E2CcsccGpWytIb0ykpJ8opFzr2NihWQ+zHWUzMVNGT/a+V8n0ZBQ10mTrCtPCptPzmDFp8bSpte/I056offLOMtn42j1xJSNoA9bf4UeGBOj7r+E/1PQOeXh1r7//vWS66Y77dilu8x/wmkUxkfFOdktgFbjBxQHPHFHZbeegYKtdY2cEutXYfFdGxIfQPnEKDfcPU0dxHUQmKiCTcump+J/Fv8Bx4ypGPjdrae7N5M+TK6e+OIPLkO1uVPGHgqy1rcPwbnegODFVcfVcW1ZW0saP8k+eK6Ib/N5fFSERwwI0w2D/MLemLrhvr8oEQDhEdMUOIVMIG2BZoR0WBC1EUcO8gaxZgQCk2+Rg4JBmfbHhotu7fE84sFB2bqrq4oOMMyG5FyzXuJSjKahHRX/2vYxQ4zY82Ppxr94CuJ4iegeAfEDTFpY4/VzFgcKKLoqYA4iOcd4jIEG51PK+Ii5u9OpFz1HHI627rN94fxZoOB6RkLF1tyh7NFeLJeAcFYnyIvYLp+w+CA/bAWC/gWEexR9zHcE/zhnlBEoknwHXvqrMHss5xfwCIDbG190QW+rlPa1mQPfx2GQvu1qgtaePuTNx30Hmy4tZMNjPh//NH7xAN9Azyn/j/2C/AsXv0vQpa7iLBEY5+UwEd5pbuywOqo6IwH2v7U+fYaIB4GFeAWEc8DxDpMWvD2TUPRQdER776s2NsjJuzboZDQ+PVEhg7TKExgdTZ3MeRMnlrZtBEA9f+9qfO830NZ7QbvjBPnskkEi/Be2yoEl0RkSEQSj0hRMKt1lwthj6FG53cEBd8EWw+RHSBaba7eJ7tOTMBXFMcm3BVaeN2J1eHFRe6wNsEdFPRpr1RadP2mzrFKaeIJ2lt6ObNvF/AZHa9aAWHHMS0TPabxJtgMQT22HsVnE2I5wqHDkvtquGxwTwECC68C8cVh7ktxEBRCJ74fnD+i4xZ6UYff+Bgdez9CmqoGBn67EiGJ9qwz+yqtrhWrrt/Fj32y9W6DPUURSjR2aEGHDiaqjp5MBbEbHeC5+X1Xxync58qYoKv0WcQvE2HZKP1/rnvH6TjH1TyugZxcuPDs+kzP1lJ8zYk83oVYrinwIkuQBGDv2f3+JhzoTcoGIMQg4iO99Wht8uMM0sk9jG9B6LIjNxe7DMRvXfTl+fz3yNaAhn/ADnqENglEol+wLT1yXPKurVwSwrPJLEF9gnI1hbxhNb2I6XHL9E7vzvN9x1kq9/x7cWcnQ7xGZFyS25I46gPGAKu+9xcuvXrC2nDZ5SoC9yvMBTT1WDd3vab0/w4L+Yrs2BsUXCgjt769Ul21aPwAFOM3qDIsO/VUv583qZkY2Fbj+iZ3NWJ/DmidcyLmHoCzT9vjdJNe/ZTfaJzPAHObIfeKhv1XKGYaw90/L3z5GnezyLH/u7vLuWznTAm1BS1uvRxSyQS20gRfZwyEjMS4rGbBu53cFghKmBEgFYEUl8E4qSpyIsp95cbejQ9z6IrwN3i5OWCqfT+788auxO8EcRKoAUzIl55noN81IVu2oUxPT3cbla+NTA8ZuVtikNl/+sXeEOF1lew/sEcm+5FMZwI7g1bQEyoONtsHEo65jqtkyL6eAOHUnQq1JUqnRKOAGfngdcv0MmPlevREmqGUqpBDCUTnR1q219F4dLdcVDifenNa60thMBoOpQYw8UgRKIgeO0/zeH8VriATePARGG2+/LI7210/3ZLJ7olugzXiLhm0KF2crsygNza0F6JdQ68doE7sFCAR3wQ9p0iAgBxQ/h70Nspr0fJxAL3o3/82yHa/ufzuouRmFmy42/5LHTHpYXRcpWzljB/J2eFsu/c/2rpmMeFIv3Hf8unK0NXWTi/5WsLVJnCZq1IoPmbkvnznX8vdPl5C6aWqBnT+Ez4wf+epbJTloV7/PfdLxRxXCUiVnDeueu7S1wSU3J6RzXHp8K9vPTGNF2/NzLSsT9oru7iqBVXMmtFPJuRYDyrK3F8z+rJ9937fzrLw11PGfbL6I569rsH6NPnC417VUsdwnteLGbhHQan2/95kbGjEl///A8P0Qd/Oif3CRKJB5Ei+jhFS1a3K/PQE2YqAkh4nCKiX/ZRJzqIMPwOwk2PqAxsULCBUhsXIF6P1lr3OdEx5K+7JoDqyzpGuei9DbQF4uA7Z63SsufLLWv1ZSLKxblhevM3JvNwUWy+saFCFwPEq6zFcTa/Dv8dWZCIUBID2CwBFwy+N5ylpo55cZ2KuQreCsQ55N+OF7BhhiPLle6e4NAAY6Hz7987QG/990nN30MMIu3rHOToIgGcVRC79Tyks4g+SVl3seaq4bKh0CnuO+5EuIpb67up4kwTfzRe7DD+dzw3+DsckiCaehsofsDFOy18ZA5Dj+F5R/cL1hZLHTDTDPdA08xaY5xLtxKhIRlNp5mIjoF1EKHwVKHY5W7giBzo8P5jAa4xrF3v/v608bpCkVlESWx5LJdikkKNhTTQ0zlAwYZ1q1eFC1AiGU9AhMQ9FPslPaOM8P7b83IJd4oFBE7hnGwts5ZW3JbJxS3sR0qOKmse9hT7Xy/lPG/seeE4R/FWS2fqqjsyOQd8qH+YPvjTWVXOX/O1WW0hHI/r+s/PpcxFcdzpgnkMcNebu9Xf+p+TlL+vjvczy29J58x2a7NHnAGP+8SHlfw5XPp6GwkwmB4Z5eDwtnKXFslxvhbzQs4a1ndfMiS8/8cz1NsxwEY7XMfGc9fQFSo4UE8v/Pth2v1i8ZhrDec+7LVW3JbB88tMr30Uh8Njgvh+je5kiUTiGbx/tyxxiLCYIG77iUtVWn885cRNyIwY7eL2ZSe6ENENvwOe40d+tpo/1G5KIYDe8MV5nCPrDnCj3v9qGX++YHOypsnsnkJsdsUgMF+kvnRkHoAzQAjf+Mhso2iJa1C0wNoCwkHy7Ei7bvQSQ5QLctRNr2HjEFw3xw5pBa37L/34KDV7udivFsT2vPGLE7Tr2QKX/QwcgACeM4hRIpfZke8BsV/kY4PCg3X05q9P0q5n9YujgBAbbeiMUOtEEoVOESPmToQgitkZcArh49SOqpF/cJX47977wxlFJPAy5m1Iokf+azWtvktZZzAjQbzGtjJuRZwLxAKBcA1CWHBFy7ovg+gR8VyhaCHAWmwas+VOMBQQXWtqh/d5irZL3fwYYQrAfaumuI32vawM24YLFsO5BTA6gJ525KIrn/d2SveeZGKBoi7QK9ZDABGvYF8dR28gB11rhBs6mDCjCSDyAoLsx0/n8+BqgLzwNffOtFi4tVcMvvaJORxNiHvxR389z455NWemI++U0/P/eohe/slR1YV7FA62fjaXnfUoAux8toALe/w9h6/Qm78+YYxihFloyQ3pFoee6sGB10p5eCzy2Z2dS2ONOetnUGTCND6vuVrInbshieZvTqZVd2SRr4BrDddcS203n8du/NI8YzFj0bWpHEuEIg/2Rtj3o0vk3d+dNt57cW1sfSKPFl+XNkZfwH9bdafyXOTvrxtXRiKJxJeQIvo4BZl0d31niV3HqivAhuGSId9OOHGFixs3CGS1+SIzsiNp0yOz2TlhipZWPLij0ufFuC2TvKqglTc5k6deoSU3uke4d9bVIkQ9X3WiCwcLNjoiv84ZIFwh6zF1bjS7XdS6SkYiXS5ZdIHicFBT2DpKuLEkops6jb0JzF2oLW7jx1elIofSF6g812J8zUqO2Y7icRThzESrr6kgrgUcGMUMA9FOimtMOMmSDAUcvchePp3mbUyi6CR1sVmi40aPTHatYPAZBm7hvS8+4DC29P6qOu/91614fU1fc0tMs5CJjjZs4UqUkS6jgSj0yM9X8z5NdIeIgXu4d0BwER0V7gDv34vnWmng8hSvF9FFjF4kBoc29vCAPtH2vths2LbRid4xQPM3JvFgPF8cbi+ROEObQUSH8KkXEImPva84ntfeP2tU8UoLMPig+Iyi4ov/cZhn+UyeMokduBAcHXXO4wxxw/+bx053xDwdfFMxFFkDc1Re+9kxzlLHvhJOYsRraRHuMStk7roZXCxH92jx4XqOdMSAU8zOQnxL2twYchXVBa1UdqqJ7yF6DBO1Bn6nNXcrhfZze2pdGv0IE8U1d8106X4O5wnEB+FPPe6lyKOvym/lWRwQ0M3jN9Fhifz+2/95IRc7EFmE8/qB10dijWy9dtAjEAeE69TedS2RSFyDe8NCJROC5qouzk/lnLj4acbNDFr9BvqG2RWgtxvCHcB5jg9fotTgQg5OGNIto9hVYPPyt2/t483E5kdn+9xzLWgwDECMSQrRrY0Smyx8aCF9fgxv4CAoNlZ2jhH0cVDBXg1/LzpFBCh6QfxCK2xHS++Y/+4NXCpX5i4Y87INbiafjnIpHxmutefFEu7ksZV97wjmorkjIrr4OjiUFVfnNM7HRHTQFP/JlDHfscO0NRZt1fbaGp3oHhDRxWBVq/998iS6818W01+/sZfFShTc9H6N9aS7fcAoRto61IlIM1MnuhgeBzFdiXbxzoKcJ8BzgwKpubufu4hyIvlADTf60huVtnlXg/cud4FNxjwO7xaZRURZaHQgZxCLLOaND+WMuUaDw5Tnt6ejn7ssJJKJiN5OdMRE7n2pmD9fcmOaMYbRERBVAWctOgsxrwBnxeu+MJeSc6KcfpwomG1+ZDZnwUMkRWxh5uKYMe5zCOeYOQRREudVDM9EjvX5fbXcOYzZXmpg8fq+bPIPnMKmiNQ5MUZTS+biOM0zkvB4Cg/Vc1EBcTEY4m0N/B77XlU6ciDk4wziSpJzo/icUXGmmTPtkVnvKtFeb2AiOvHhRVp5RyY/pxiejjk/x96v4PsEBqY7auTCENTze2o5tmfL43k2EwESZ0bS7f8cyd1UJ7dX8muIrj01Z0dEFiEWsPJsM9WWtLGwLpFI3Id3q2oSH8+DjjC2q+HGKlrrxWBOX+fV/zpGb//mlNXBILacAkffq6Dmmk5ytSgthkYGJ3i/CxCO/qvDisiSlBPF148vIgYgOpuH7izYhGGDCyy5mkXMi7kLHaDgEpmgvF/RjuiN1BnWGYA8Tm91zGvJLIXzCe8DFDbw+a6/F+j+ewlnprX/rxYRMYSsYdPrCZ02pkMpPUHgND8+AJk7wL0FvMYxKUpmszMDXl3Bjqfz6fVfHDfONRF56CIWwxohEYrIABEEcxYEEFdyViS4JPt1vCLWZGtdRK4AaygICB/mQ7w3D5UWIjreO20NPdwFccP/m2sxMzk4zLBOyQFskgkK1pARJ7rz90S87zDwE0tT7jWJxnxsZ8hcFMvZ1+g2u/1bi3QR0Ee+dxwtuUEZrrn7H8U8K0iAeSWm7nN0bz/w78s5i1o4hCG2agHn3ZW3Z3Gx3FSI1Sqg4z6645kCHkSKiLy/f/cAR8BZm3N05pNqXg+xN1t2s3uKr4h9g+EGTv+K08p505X3KOTbFx2ud+r74HlF9xJmaOC5BSiSYDYUCrLorvj7Dw6yK9zUFKAWFMZhJkEUEQbiqgFRq7d8bSF3Tqg1X0XGT6O8NYn8+cE3Lvj8GUgi8TWkiC5xYR76aBFRuAK9ebilPTD0Dy2MuJmj9Q9xElNVTIsf0wL5XgVvOlwJqtPoCAiLDST/cPtZgJ4GG88AQ1yAac6yr1F3QZ88dD0QkS6lxxtH5UFiE46DBIpcWYstZyYaI10MQ4q9tVghrhfhtPJVxO8TnxFGmx/N5Rbk2uLLdHqXkg2qF0L8dlZEN41JgIteZDi7KoMTP6OmqHV0vrgVbvzSfPrsr9eMGpbrbYgimxAvvQVk5SNKBI4oUye6vQiyqdP8+OCofI13x4F4A3ByYngeiurmpC+IZWf/jOwIzrZ1B/WGYs6VgUn0/A+O0J4XFJepN9JmEJFQ4MU9DAK6tbz+ESf6APV2DbBjD9e3RDJR6L48wF3AeK84W1jGfhBCJu4PiJNYd78+kSH4Hhhcfd+/LjMOBdYTCP14vHjcO/5aQEO9k+jYe5X0+i9O8DqCfRGGl+IDXXZ4PEKILjhQxx2ZWnGmE7W/d4je/cNp3lch+gtzM7DPhZsewyi3PXmKyk6N7OvR1XbcEK0DAV8M9XY1ONcv2JLMn+N+hhkqrtwjw/WO58DR4jK+bu/LxdRQruTTi9cYxZP7/305v/7YN6IL9/TOanruXw/S7heKNAnU/L3+bRnH6roadKrh98B5zV17BYlEoiBFdImu4AZl6kS3KKIbWu19kdMfV3HO3ZldipCDjY1Wh13UjGluGdqICvh1n59DS2/CYBLyCcTGD611vugc6+8ZNDr44s2KSJ4gOS+Kh/thOjzaBc0HimL4qDURVQxz9EYRneculHeMGsrnbWKk4x0MEeyIueYuZXDQ4W1lug5Oxet92zcXcj6nJVFdy+Ydec5wj8GZBrEVm/nUvGhyBZ0tfbTtydN06M0LqkVab24tTjQM3TYtBnkDooAZaFiLhRN9mp1iC8eTiFx0k0xtDL2qPNds1T03UakqaOHhecLxby6+PPyfq2j9gzk22/f1pM6wfoamDxr2ce2au+zcAUQaRCAJQqOm2myXNxb72geo8mwLvf0/p+jIu+VueawSibfsS2NTQjneQ8yocAS87979/Rm+R8RnhNO1T+R5fUykAAWELY/l8swSFBUa9kyjUx9VG93n9//b8jEzxBCPIYY/anWjOwPmQr316xNsokDX2o1fnkcP/XQVZ2unzY3mmBCYsLY/dZ6e+8Eh7mze90oJdxChixHDTd0Jhl/i3o892umd9k0OjoKIHRTqER0oYjO1gpiVggP1fCbe+tm8UUUlFCtwDdzz/aV005fns9EBnQiY5yK66mHmwABcFDBM96FwrIt5XgARmO7Yf+L+9pmfrOQsfi3z2SQSifPI/lqJruCgjHZu3OjiDO3qAmOcS5PvHqbDDQNS4SIDjgyoEuJkq4vFST//KZS5MI4GBwep+APyCURBAm2J6QtiHHbJegq4GxD7i4KRNWecO0H7KDaF6H5ANEBKbrRhAKSIcrG+2Y5Omua1cS48d2HwChcIclYmsAMHYqQzuZyeBK+J6GAQxUe0SSNTEx0liNi4+3tL+D3tLDj04nCIVmW0tarN+jTHdO2DSAoyF8Ya3ch6g/cUDohwkSLPf/4mxf3kq+CAhm6thJkR/Pp7i+APBxwQkTzdhmJmsIr1LCRiKg+s7bo8cpg89XEVFR6s58Fq87c4//5kB9pVRRTxZTpblQO4tTx8d/5+EAN40PAkoqD4QQrqn051pe1UevwSCyTeBAS8hIxwHiiKvaa9DgkRQ4SivCgYKnMcJJKJAe7VEAYtuXfRCXPhZCP/G3TBIU7FUuwIhMR3f3eaxUIMJ4Wgayk+yZvBPQ1xGa/9/BgN9A5z1Arml5iL56YsuzmDaopOUNHBeh5y6uo5KzDhvPf7M1yIxvkHYi4KIAADSfGB4mb+/joqPFDHrwc6m5lJxIOT3X1vhHi76s5M2vG3Ai42zFqR4JI5L3i9EHVWeKCezu6u0Rz5WVfaxtntYMXtmZRixfCBvVjqnGj+gEHENJIHsUjIyhfg95yeEcZ/j5kiN31lvks6KWyBc5DENih+oGtDjzOURCKQIrpEV4QbdHpa2BgxRQjQvhznIgYsir2oEMS1EG1woiMDGQs7qt8SBThZBY4OdfEKN7HGIaCuZOay6Syio/Az9MAwx56gGwRDR0Vmui2RFDMM4P7zpgOTcG/C7S9ic0SMlC8ChxcOQ1gLsHaKjfyGz+TQyz85wmvF4bfL6Zq7Z+r2M20Nv9TK6juyKGN+DAVOc23RCwcoiOjopLAmop/ZVUNFBxood02i5oGk7gQt43d8ezF5W4cH2phN12K1mehACJrdbQNjDnh9PfrM5cAgyabqLrrvh8s8fnjc+3IJH8zWPTBLcxFEuNZEJ40lIHpx23mQn7FzxBWIrp7oxGk02b+TMpbGKSL6Me8T0XGN4X0DxyMGwdkV0Q2FeHRjGa9FDFCVSCYY5msUzh+YuyIiuwD2hRjSC0F9eno4O84xJPP9P57hvG3ETN38lfkeX3sdBV1+N31lHu186zDd9vgKCo20HW+DQndKXhRV5bfS8Q8qaNMjuS57bIia+uBP53geDh4nnuewmLGiPf5u5W2ZtOzGdCo73cjuaux/F2xKttmV40pmLplufBzoFtz6xBzjf4PbHy55FOjRFYFCKLqOHRl8iqGfENHLTjZxoUEMNLcH4ngwXBbXPCIH1UatwGxiCjo5sLfEPbO1rov37qIzCkVa/6mek9XQ9Yc89zX3zqSgEN8yoLkS7KM+/L9zPIQVhZHZKxModW60U105EgmQIrrENSKihSgLUcFHyxdy6XxxAROFAHO3rrbvEcwFBjhp4f7CZklvsDFGhXzOuiTyD/Idkd40Q9AXNwF1Nq5/TwHXHq4FbPQwaFYIJhDQbWU2QnxAIQOCA4R3T23O7Q1vhTsZzhuxmXWFA8bViAIAhk2atmTiNUCb5vt/PMs5kNj46TFwq+JMEwuRaA129HXFc112spGd7TjYJM6MJFeD/H44iRorO7ijSRQ1Tbnc0MOFWtPhlhJ1DJjMojA60UUmuhonuuFAa+pER1Y66O92XrhEMQ/dGaD8dBO3d3sKuLcxmAwsvj5N07qDQx32QcDW16FwBtdbzqoE2vTwbHIV6Pr6zE9WUGdbL50obKD0+dG0/9UL3IWEOC9HOu5cjRj4Zk9ER7EKQDyZ4qfsheBg96buD4nElUDEtOROhmiL9T0gcAqL5Q0VHSzgwn1rOvAaBVVF+PRjp60v7rFMQeZ1WNaAaqPOspsyWEQvPtzARUVXnNnQ9bPz2QKOD8FrceMX59l9fDhHZi+N5w+I054c6I61dM292fTafx3jGUxtl47ytYTrBn+OaYKYRBwHKEwjaoHLG8I2rs/8fbXc4aYGEQ+K137DQzkOr/147Tc8mMOfD/QN0aXKDrpU3s4FJhg7XN2pYA3cz3Y+U8CzroJC/Pm1kIx026CjF+BPfOC9hVhLR4yQEonA91RMiW8MFbXQZgVBCAIRbqbemLWpBnPRxpEFGG7TqASRi65/pAvEraLDDVyRFsPhfLFIIcQXXwGiXWNlp9cMFRXg8AQ3Oig+0sCbdXtRLvx1kyYZuya8KdJF5PWK5xmFAOFoEfMYfA1RFEg0DJs0Be27eYaYml3PFnJbtbPgOkALcEO54+59uIDgBHVlBqalewgySoEYZGqOuLd46jCjFRx+vSXPX+ShQ1QRHVIOOdENAicwun+7nR8WjQgPgRChPQXat42fN2hbHyEqwJkHQmwIUnBNgfKTjTQ06LqBbVjrsbdBQRLAJShmGyAGzJsQA97ENYYIIVvArCGuweFh5WuxLxLPv0QynsF+6dnvHqCXfnxk1CwB0/d21tLpdPNXF9AT/72GhytCZMxdnaB0v0xS7gsQbG/44rwJKTphXYThAGfXYx8YolN0BHuoj/+azwJ6xoJYuvXrCzR34mLN9nRRMDY5lPLWKHtV5JZ3NPfxtSME9Ml+kygoLEDpcrtqfQ9nj7nrk/hPiOhqzRLx6eF09/eWcpyPXnNGcPaAqWXJDem05fE8jxqN8NqvuC2TP0dHgJxBM7JfOLxNmYGCWQGLrk3hcwTeKqbFMHS46nG2kkwsfEulkng1cGZxVMsky0MV+aAWF8Q3V/y7yHjXtSe7imlmQkJEvGOOBIiTTVWdHNOQuZB0BXnB2KCgWg/HCDLRfYXZqxI4QxebLEu5jN5MY1UnH87R0mfeseBpspdOp5PbL9JFg4sTBYrkXPuOZhyYMNzIm4aLYnMIdzwOdSIrEmI63k8o4sGV42sIEdVaxuPqO7OopqiVY3j2vFTMA5GcOTBdNZw79r1SygcSR75XcJi/Uczc+1Ixrboryy15gzOXxlN1YRsLAHAAmz/29iZFKLDkUvc2IGo894ODLFj/02/WejwyCesX3OT+hg4VxLvAtavZiW4yWFQImHo40dvqRw6G6EbwJHCeGT+v7+F5E1rz0CGU2DrQo6MJcS9drf08FNNWdq/eoMhacaaZ85JX3JbhcYFG8OavT1JPR7+xbd6eE10UgHBAHuxTxEAIL7iubXViSSTjAVzncOFiDgAcqgIU5VCcE/tDYbiAwQcfossHERyNFzt4/Rfmn4kIstHRBcX7juvSdHsuMDheDC3FXuyae2b6dMQnIgdxtpg8ZRLf++GOx3kOkWRYe3EfQbTkh0+d4z9X35Wl+d6SsSCGZsyK5D+v4n9Xr1JtcRsFh03ltR4/T3xPmBRQYAC+3kFhj+TZUcboIWTGr5VudO4UwNlQye3P4s40dC9AgxIaA4T27X85R70dg7T4+lRaemO6px+2xEeQO0iJ7i50tP6KAZHmwB3IInqjbzrReZOZOI3Fb7Q1Oir0YhPmqiE1JceUoZHIffM1eg1Zpb6YtzgSMRLhNYKDAO9JfAgxHLEYauKUMGQKeJOILtYZnrtg+B0Q64JhtL6Yiw5xB+uJrRggbADhdHnjlye4SAZ3+qzljhcLTAeMOXqtipgEgJigNfe5Z8OesTCW9rxYzK5SFFNMH8eVITinlbZdbytkWROdg0MDWORAG655/qa7wRrxyM9WG/8/cqTFfc9UgHHMie68iI6s3tmrEzgTtcvkZ3jaiS4c3GoRjlB7h3o871wA/aiKh0G7QkRHW/zZT6opbX4MZS4emZEB5yWG1OFnesv9DOsWXP9wN04zFGxUiehhAbzG9nRgvfDnokRv14DPdKtIJI6C3GaRo21apIWhYqBvmO9BtjoncZbTI0LO14FhAy5xxIgde7+CrjXJ/HYUfC8hoK+8PZMWbk3xmrXWUSCU43myRXJeFGfv4z4IPUCYYdSCCMHbvjHiPsPeYtuTp0ceg99kXvPheofpZtMjs+0+pvECCjEQ0S8cv0TX3JXFz9VEBtFI9/7rUjY6iLMCnhNTEyciraYGKfuCo+9WsIFA7g0kapAiukQ3RJRCoo08aOEO9OXhonAKQDx3ZCiKwBWZegAHTGxK4GTIWuQ+15pe9Blcj57M9nPFPABvAEMZD73VZfxcDaJ1t8Ug8uoFNr07ni7guALkaTuahy4QnS+IRzJ1nviSCx1rgqkgbA6KBktvTONN3qfPF9HRd5UWRWsk50bT+gf0Gx5qjml2e9LsKLcd/nCov+/flvEm1/xnDvdONnZa+EIhDo8f1zGGZGGegqdFdHNEHnpwqL/FTF1zQiICjV8nhmZP1VFEx2uOIZ7LbkpXJZ66kvT5sezYT5odyQc1LaTNiaZHf7GaBvvsR4rgQAcR/WJ+i+ImtbFGOEJ1USuVnWqiKQGTR4noENxE27w3uWpF5FBf54CqOBcgnjMUq5Zcn0ZXhq+Oe1eiRAJaDd07kWbOaRHlArONmrVdQrT0pnQWvi+caKQl1zs3KwIC8ifPFfLnCzYns6lqooDuq5Q50exEx/OpVUQ3B9FcuL4RPYf7AzrqTAd+YqDkeBDRkcGev7eOnf7W9Af8N3S44V5ZU9RGKYZYtokMcvTxYQ0UEiG0v/Pb0/ycwbAg3egSNfieUiXxyTx0gXAHtptkm/oaeroy4KxCpVyvaJsSQ8YcKv1aM/W8AfGYLU2k92bQDmYvksPTQIw5sf0ihUYH8rBRNYhMTDhSIUDA3aEHRYfqqSq/hYVDRPiYCrL2qLPwPKPVGAIbinN4HeDUHg956OYsvi6Vqgtbea1F3qQt8vfW0vKb0y2Kbgs2p3BUgzMHCwjAaKm9VNHOj8udRMRZLkIOdU/2mSgXQUJmBIvoruiigPA6PHCFi1VwiGllJA9dnWCNVmoIMlgPIfjifRkaNZVbtvUSf1HADon0vACKw6qaSCxL4DnieBwVyzCEGogMaElGB8a1n5uja8HKuG/L9M77limXL3UbD70iMkhNVr/4N7iHLdyS5eJHKZF4D6JjxjR+BEaDyvPNqmbjSEaAcJm5KI4HqmOmzHWfn+vQ97kyfIV2PJ3Pgm9caqgxy3oiwa5+g4iudjioNVAQfeDfl/PniOrq7ujntR5diSiYIvbF10Gh4P0/nuXOscCP/NlIYiliD/sjdI8hFx2FsokqouM6wIB7tWcB7KlyViawiI6ZcktuGBsVKZGYI0V0iW4V0ubqzjEOUXNEi8xlH3ai63lTxEYKk6Pv+cFSp4V0CPKlBneJyDj0NeDEfPi/VnncZaiVVkOLud/UKRSb7J2DlyA8fObHK5RcQpXOI4jb4TGKOA2Xd3CYPgUk4YIa6h/mSelqo4cwd6GjqZeHwpgXAhJmRvioiK6++II2xFu+uoCaEa8zksgyBuRN4gCB58OSeGl8n6kQoGxx05fm8TqmtztWy0YZP99YMJx0lWJTFNHRVxDucwx5Fe5tvcBBHwOT0D01f2Oy3X9fcKCOCvbXUebCOG4tF050tdcJHjsKbYhzgciJQx66QlC0Ac7M58BzU3y4nu+TiHXxltxY/K6t9d0c6eKqjO31D86iN35xguPOcL3r9XOQeY8imK19G+Lh4H6DU1IMOvV0Dr0Q0dFtomYOgyj+IktdIplIYG0CUQkjYhI6TzDEEiYJMTxeog50QZWdauTnsKm6k4dpauXYB5W87/MPnEJbn8hTFa043kBcGO7hiNmCkUyv7mycb8Kig/hjvDA4MEzv/+8ZFtBFVx+6GG768nyLQi8KY1UFrYoJaoJyfm8tHXzjAmecY56B2sIOzvA4YzaUd3htV7nEe5h4K7fEJTRWdvIEbrhcbbnEhIMQw+hwgJvIIBMOB+KhwSu085kCp5+PoYErLCTCdZU2z3dEREuuAm8RSLQKofHpYV6dQQexU6sAI1pWW2u7dYscgqtSgNY5zXMXkkLGRP6IDY9wdvsCQwPDPLTLXvHRFMQsxKeHc4SEtY/IePuxWfw+c/JaxWPxlIBeeLCOnvnOfjr8zkisTdD0Ybr92wtdGmOjNxAxUKwa6B0yZuPrQXtTDwvowkEHZ5b9r+nlr4GDxxEnuulw0W6T4aJ60NHcS588V0Rv/+YUtdR00ft/PEPb/3yePAGKeYhBwZ+v//I4twE316ifG3HknXI68HopXVbZkReXGka3f2sR3fqNhboK9c1VXbxvQPxRlJUi/qXyDj68Fx9Rv067CogtQBTN1BbbpwkRvX2AX7PakjZNr5dE4qtgv2Ue5yL2XIj1k25LbUCYnLlEMX0gWk8rNcVtdPyDSmNx1Je65vQExfUZOZH8OdzoEutnhA/+9yzVFl/mosvGh2dzoQC553CbWyI+I4wNUxMpIsjc1HlieyUbL7QY8rAPz1qodOfCsCGR2MN71R6JT9FQpk4IgsAL8Rjt3hDSJzJwA2PgCSaJY6jc8feVjZWj4Aaw6eHZPBjOVY44iW/moTtDlMGpxO5nHV3owiWBzSCGvGl7nse6tsVwLExjHxq0nzXsDUBAR7spnJJ6RhgZZ0/4cGyWPbA5RvdH2YlGny7IopCBQ4/eBaBSQ7QXePt/TqkqVom8aTEYvNsgvKuJzBCIQ4vp4E8UzSrPNfMgWEe5bHAhoxA/ecokqjzXwpFQOCi5G/zcd548zXMdhPhsOmhUTZzV6Z3V1N+tPN9qQHHMtLhsOhzYURCnJdZTa91JMw3zMyrONHHR3xtEdP+pyvWp9oAcHKb8OxSS8Nzj/XB6Z5ULH6lE4nnQqYXINRRqRacrOjhqS5T3vdoOQMloMJsGtQd0UQoThBoQcbbz6XzuIkSMYfbSiR2lI+IE4eqXjAXnmA/+7xxHjOB8f/NXFvB1gyG04MAbF4ydJqagMDaRi2MYlI5MeCQf5KxK0PS1s1bEc8QSuh0lEntIEV2iCw2GnGJbU94BFvaRXHQZ6QLXPgalgRMfVhpztZ3B11zc4wEtkRy+RozRie68iA7hR4h5aLOLSQ5hEQxCqBpGcufHFiuwrgSF+nObctPFEae7N1Nnct3ouek1xmaN4zU2aVYkv95obUUkFtBBV/QIc9YlsSstda4+cRnK+0wR0cUAxRYV79+BHkXkFoN54dwFlrI3rSEGPXYb3Oxg198LOM/TGfcvIrNAZEIwd1r4BSidXEJcdydthmF9UfF4LAYRXeXjQMEHETAgJGqqQ63de18uoZMfXSR3FH8x0DgsNogd6xVnPSt2RCWGcGwOjBggJEJdcUcUgUwHs+KQLZGMZ+BYvfaJOXTfD5cb586UHr/EIi72UOMp8sKdYM3PXq4I4B/95TwXVdXck3c9V8gRabh/rbk3myY66fNjeOZSY2UHdbVNbFOdpQLYh/93nve2iBhBdIu4T89bn8TzWIZFF/uQZRMJ/jty5/UY6u4r4Hc99bFSIEfWPjLitYCi493fW0q5qxNd9Agl4wkpokuc5uoVuD87VQ+nMrokm8avS1ILaA3MXj6dBaCdz+RzK5IjLZtwROjhTpNoQ0yBh5MPB/zxhjHOpa7badcnoiIwEBObwvR5scahVmIgri0Qd2Gcu2BhnYEILYoYehSj3Ft80beDwVioHMezJ+DgzjK0VUMYQCxW7cch9MpPjvO14muOrLw1M3QTNZqru1jUhYiy9KY0/rsWFXFM/YbnDd1RpnEu05x0ogtRXovz2hwhUkPAwGsvcu8bq9Q7AV0RkQBR3/Tv7AEBHbfpyX6TKNiBKKSL51ro3O4aOvpOhSYXpCXQEYiiu63iL9ZVMWNFzFzxFHDg3fWdJTRpyiSNTnSDcN41aOyy6FPZ/SSRjCeMUS7She4Uy25O5/UH+9l3f3+Gtj91js8B1jj7SQ2v3cg/3/rEHGNRYyKD4ryYbVR+Whl0KyEWxbf/+RwXZ1AwxuwhMTvH2MX+8GyOYUOXH2bfWOKd353m+UgXVJqUxgMwFwz0DXPkJwasamUiO/gl2pEiusRpBjsmc8UTC7rI4lXjkpRO9BHW3jeLXWnYkO1/rVTz15/aUUWv/ew4Hd42kg8scW8XBgaKjscYHbgQsZGDSImBK84g3LEZC2L4EMHZkpMUMbmjxfb3bqhoZ/EpLAZzFyyLJ8KpIWIKvBkUJBrKLqvq4NHKRClUCiEABzCO07gyiSMbkB05kRFCCQbsioIT2n7tFcFEnIuYNyAGizqbiS4yrJ1xRIm4FLHHQE64mMfiblqNgr6JE93gTrdHV6vBhR4ZqHrAsymZi2Ipc2Esv5ZwocGZ7ig3fmk+PfHkWpqeZns4noh9QPSWM5E8eiGc/GpFdAwg5ecaxQuDAC+d6JLxDAqiyAU2jTrDPQAFVhTOMh0QmCQjoOD9wL8vp/mbknltQSTJi/9xWHnOzZzBKHYefPMCf776riyeUSJRSDdEupSfnjhCry1w7aC7AXF1MEHc+KV5NCNbyY43Bfe+9Q/kGIVjS2ce7P+0zp3yZbAvOPdpDX++4tYMh/ZXputnwf467l6TSKwhRXSJ0/S3TbGbqznRXJJagTtqy2O5LBDmrNCW4SVatkDK7CgXPUKJmlzZ8QgOXCK/XE0khDVwmLtwQhHRRRYkBLcZ2RFjMpxturZtPM/CUYnCBlyW3kxrXRc7JlBMQGapnohCJZy/47mVE50fWDOH+ofp3Ke1xt/dF90kyHuGw1iPApCI8cHgOFEEG1ZRBDNmogf78fun15CJ7qwTPdDgbBffXyvosBJ52FGGAXlxBuHXWTe2Izmlnc29Y5zocCGqyQwXbsVQB6JcAK7t9Q/mcEQJ3PmH3iwjZ/APmGJ3wDCecxG9deGkZ8QOFAuEIKhVRMe+NDhUKeSIZj21czgkEk+ANc/RzlJ83Z4Xi+nw2+W08+kC498LMS0lL4qCQjwzEHw8gWLzNXfPpHu+v5S7CRF5hef85Z8cpepCJWIOXXEf/TWfZ9+g42zOuhmefthemYteV9o+4ddk3N8+/ls+VZxpVgT0L86jpBzrZ3o4rXNWxBu62AvGdGBy8VulSWk80FTdyV1qmB+TOse5aMT3/3CGPv1HkRJ/JZFYQYroEv1EdJWRBOFxwiU5/hd1LSTOjKQHfrRiVNuWGi7mt7A4gQNlgsavlTiPUdydOf6GigqiDJEuzojoGI4D9x9yrJNnjzgrjJEuRy/ZPDTWG1zbttYZCD3ISsb7wdLAHW+8buIzw+2KWFqBMC9ygMfzOgtBkbsZiOjCcaWQCFHdFzm/r5azrkW3hjPg8HX/vy+n1LxoLoJB7FXz/sV1Axc/RHREX7BzfRJRkCEOQ6sTXbyfp07zd0pER3cBvha1EQwWBXEpihMdzkprmaCu4PKlXj60QkBBTAjEKOG0F0K/LUT2a2ik49cpfh6GkgMUXrAH0IrWYbwo7kN889T7C7/nU1/ZQ/tfLR3JlLfSkWQJ0U0BMQtA8HLGxS+RuAqsZ+//71l65T+PUYehYKeFkiMNbEpA8QhOaYC1WBgVxJ5Log9wlt/+z4to06OzeX+L+8A7vz3NjuJPnivk4jU6jTc8lOOTBX5XAtMD9u0o2mNQ60QF7/Pt/3eODXGIervhC3MpWYUpDtn6odGB1NnSR/teKRn137SYlHwB7FlsOcPhvH/opyt5b+Ts+yxridKpU3x4Yrj4JY4hRXSJU2BjNtA6RVMkgXBJYmNxReNBbrxjOgQDjjU1ThRxc8SiL4eKupf+nkFqqesa10500+GianKVrSFcUFmLp48SjRFNgE0jMtetfX8cKi+VK45TW/m9eP/AheALuehqhvo5gzE2a5xHusxaEU9LbkijREMRC85rX0TcP8V14SxwD8PNBESngz0RHY66zz25jmKSQqmnQxEqg0L8NQ1nEkNIEf8kRHPEaQBHuyLQqXXLVxewCNHTOcDuc3S0oXAMR7o7I0aMeejxwSyWYN1aemM6PzY1zmhjnIth4KujpORG07wNSfz5J38v1Ozie/nHR+nV/zqmOssdYtzNX1nAP9cT4LmG0IIiqYhiUetEB2KgKGbOjES6TGzno8Q7ObKtnPOzW2q6WIxFEVEtuN/veUkR05bdlGbcD5nOo0mbp8Q8SPQDoh0KjQ/+aAXN3ZDEBV9kUSPmBcWMrY/nGe+DEstu9ImYiw6HOBzPL/zbYY5wwVno+s/PpZQ8dfdZFPM3P5bL11vR4YYx+eei61cPc4a7wZ7G1JiAM+DT395PL/34CO19pcTi0FSYGiKm248VtgfMOdBTGi928h5PIrGEFNElToFc8yuDk/mwHptsO1dTEBIxlYerwOXWaThQSkZTcKCO8/XO71EiCqyBA6Go3stBQe6nAcLuVSWiSAwvG49EqRThrIGYA7FBRsSEKRg6aC+7D216EOVwCLE3d8GYi17qvbnoKI7VGYeKuqb4Yuz4GeezJ5BJvfyWDKMwFh7rm050cd1iw+6o2Ixik6WhqhkL42jJjWmqD2aj8tDDtMWO+AVMMYoFwjEc6KQTHd8zOTeKZq9KZFHp9V+c4O/9yM9W0R3fWqxJTNUjQmjDZ3Jo7voZ9MYvT9DrvzxOs1cnUO7qRFX3gLX3ZdOjv1htFMCdHbSJLgO87moz2UF3ez8fTrGuasm79yTiMB0UplxLEBu0iFKiMweCJJ63dfdnj8sZJhLfBkWtUzur+HO4mtFJhgGBMGyocWrueLqA91vo2Ft0nTJUWrjTTefRSFwD9rNr782mu7+/lOIzlG6pFbdluGyfN55E9OqCVj7TTgTQkYbIJYjnyN6GHoI9zp3fXmw8D2kxYCy8NpU/3/1ikXHfJWaoCJNSc43jncTuBsaIbU+epjf/+6RRxBbnT/x/ZJ9jaOrfvrWP/vrNvZwL72j8lbWie4ohEqbYsHZKJObIHaTEeRGRh3yFGF1v9kBVHm5BDAqDa0I4JiUjYBOMduODb1zgAzKKDgI4S2JTlIJF0aF6FhdReRV/J1FHdVErt9Q7U7U25qGP8w1ydKLiRG9v7uVrU+shrOJsE+dWIwoAIpQ5KADBVYCuipW3ZY6ZrWAafWKvTU+8FiL+xR7YvOJnI0bJmUIINnjY+M2YNXYIkDlovcRGF04HS8+Hvk708S2iC9qblZiMsJggl7fddrT0UZKK11nrph1rEcRCZPo74hasPNfMIkremkRac0+28e/T58XwhxaMudMa8tAF0yKnciGgq62fomeE8D0L2bGhMQF0rvIiOQoO2EJMxYEQwzk9MVAu95ogjosSRQF01eH3VAPWNuHWdxYUF677pzkUEDRF03Mh1lM8Zrj8tYAOOYgdudckkjsRr3vAVOXx4jnU0rIt1naI6BC5JBJvLQojygFnKxTn3vz1SXakv//Hs3TL1xbwe94ax96rYMc53KlbHs8zdqbyPBrDLAMZ5eIeYCq749uLuWtmPBts9AAzl7BfxV4Vw6uR9T1ewb7qxEcXKX9fLV0ZUkTfpJxIWnZTulPnSHw97stNVZ206+8F3DWGvQablObEUPnpJqopavWJobbYO2777Sle9xAlOMkgf8xdn8TXRm3JZaotaaPa4jaeC4N92KG3ylgLwfOgF8ibh0kR5q7lt2bITn/JGKSILnEYRLEUH2owiltaiIgziOhwSea66AH6MPPWJ9HFc81UXdhGB15XproLVt6RaRTMhbg5c0mczNrTACrLGMSCnMKHfrrK4Zsjcr5dGcnhLXD2b1gADxqEC0Cr8FtqaCXEAc7SdZo6N5oPfhDdUJgwn0ZvjD5RMXcBjw2bR8QmQPAJtRObcPitMjq1o4o38nd/bwn5+U9xSOB5/ZcnuFCAPGp7AqiImolNDeXBfi4V0ce5E10QERtEnS29xsHVrgBOl3d/f4Zfb1wrcan6FkBwfeN7owDkiIiOll0MEDUtuqrlcmOPsiZGBtJ1n5tDPcKJ7oCIjm4zHICEEI/3Fj4GBwfpXKXmb0eFB+s4AgrfV2CaFYxsa1e9j6xhOqRVEdQHqfvygDLMy42Ioc9aEOup2gg+0yIGnHMo7E/PCDPuP9xxqBYRLmS4V5teC5pEdMN1LZF4K+nzY/kD3PLV+fTWf5/iuTSTbazrKODDnQnWPzhr1L6nptBkHk2OvsVfiXWw15UCurrnKWNhLJ36uIrKTzWOSxEd97DjH1TS+b21vEcDMO4suzl9zHnHEbDnQ6wLItqgGxQerDcWuvEz0A2BAp23g30Uug0x6wbr1W1fXzjqccNsgutDXCPoqqsrucz7V72H9qIjAPOBcC6FaJ9sY8irZGIi41wkDnNi+0W6VIFpyFcpZ6U2d8NEc0lqBSLg5sfy2ImSuShu1EekiXMajr/cNYk0b6MyPEhiH1xze14q5s8htNaVtDn4fXqosbKDs+icnQTuC0QbxBqRAa8l1w7uEmBNYIJwjWx0UGI2AAfCpRCd1Yg+aNGPTQ5R5UavLmxlAR2gOHDozTLSCgSlHU/ns4AOdj1XyBs7T+ahAzGAcbxnogsyF8dSRG6/Sw+tcAcKRyyuHb0RcxXqStsdOnzAiW4pMgngIID/btrqawpEFrgYm6qU7rIewzXsSNwH7kvKz1S6A5zlyDsVLPA3VLSPWsf7e4c49uyv39jrliGR6FqBgwxRUTi0mXahQOja+WyBzTkveLzb/3yeDrxeyvneegPX1Ht/PGN3aKhYT9UOgzddW9Fy7u5BZeI9h9iefkPUkdYIH7EuIAcdxVW42NQMgpVI3EHF2WaL6yXmU9z7g6UcP2TL7IFhw3d/bymLZWLYtr15NBKJt0W6VJ5vMYrM44mP/5ZPZ3ZV8++Gff+tX19At31zoS4CuukcnBW3ZvDnR98tpyHDnggdZz4hoPcO0Tu/O8Nueqxnt359oV2TADrScK7EXBrkoesJ0hWylkznMz4ek0RijrybShwChzC0DoKIvD6eDu1YXq88xNg69G18aDa7Ak0/hENFCGUbHsyRA2tUAoFj5zP5NNg3zDdGZwauiK9Lmh2lW3u+NyPiCrTmopedaGTxKSY5hDd51phpEP7w7yFMCyB0wGXFcxdURhYJMbLehhgJcR+il6mYdPbTGqMQqRZsVjF8Bo4FbPjwWHf9vdCmSObqPHTTAZsQRy3lZI83spdPp5AU1w6YNM3sd8XgWjEctbm6c9R7QA0YYIb2YFyDlqJFcK0jEqCqoMXi14vMXbT/gm7DMDtH4lyES1gI9lh3G8rbqep8K2mNrcS1O/J9ro5yggcETqG+niH+ezjfXQ3c77tfKGanlKkTHi4zDOzD47BlDECMU9nJRh4AZh5ZpccBFK8xBhIee1fZm1lzk+P6crSIJwo0ENH1zCC1hRC7EXdkjBnSKKKL6xhxLqd3VtHbvznFHQ4SiadBAf+jv5ynl39y1GJhBxFlooMPBbKiw/UW33t4fyw2yUE3zqM5Y724KpF4A9PTwniNxtkMUZvjCRTcEbWCt/BNX55Pt39rESXlRLmke3zuuiTusMZMm3O7x85Us1dg9xTY5737u9NsTIOeAQFdbUSeK1l8XSo98rPVtGirkjkvkZgiRXSJQ4c1OC+xh8taEkvTZmgXaKQTXeIJjn1QyW5SiC8bH5nNfwdRY2hQm4sRBxghok+Ug4mjIvrI82S7WwWODERHIN/u4vmWMfm92GSrjakQorg1Jzpev0+fL+LWfgwqvfmrC4yD/j55rpCFFjUgY/Dkx4qTfcNDOXTtP80hP//JvGE+80m1xa+ByI4oK1c70ZF1jHZIINdZ5+Fc2RNKrixAbrnebmKIJbd8fQE99qtrNEeymK5Hlg5n0cbhwMq1Z47I90YxaJQTXeNgUVOBs8tE/MYQzu1P5dNVjdsFZF4qjyPAOOxUXNP4PaenKoW1S5WKg96ViMcCsarDkMEP8LnoEBP/xhJdrcrX2IuYcvT9vv7BHP4cmatwWlviUnkH791gfHAkUx4xQygY4HdG54I7CIlU3GYpuVEOi+jiOsZ1LRxrPSIiRiLxENh7fvx0PjtUscexNSMK+5btT52nXc8W0lFDoaz0+CWqsfJeHzWPJjbIZfNXJBJnQVE53eBGR373eKL4sGK+SJ4dxV3LroxehdlI5IKf+KjSaKDBuWP7n8/R898/qNmg4Q6wtmFfgv0n9sDekt2OvZo7B9dLfAspoks0g4nScFThEHbNPVkOfQ+RW4tBhXCpSiSuBi34Jz5QAnkhNsxaFs+H84G+4VGirRqQ1wbHEDYsGSadAeMZowhX063agQi3Jjt2JyG333axAa3KIu7FtDtADG9FfqBahMO7pa6bXaLm5O+ro4ozzTy1fstn8zhPGbMG8DvCuY3BPPYEUmxKETFBV4mzBzMXxrHTfvXdM/m/Y9CNpRbA+nKlKADxHvl+rkQWK/UDhRG85ihMQESE6IwsbD3B4Qq5i4jN0ILIbATW3mf2imDisCUGTQrBWg8nOobhobgErgxqO0C2NSjPcWRCMHWYRBNBxMV7NC5NEYYaL7pBRK8feSym7yl8jr8zfbyWQIwIwH3HFSAnFBFwWJN2PFNgce3D7AmssyI+SytYKzMWxDjVxaUVuPa2fjaPFl2barwuHc1Ex/0eg1hBX6fMR5doAzFeeq41h98qVwbohfqzscNWhwruD6l5SpwS8pUPvHGBPnm+iLY9ecq4/lstri61XFyVSLwF5KID7M3Hiy6APQo6R0DOygS3/MxZy+P5fNHfPUSndiomn4BgPz6L4f5Zla/tvOsO0AGJwcm3//MiHsrrjWD/L5GYIkV0ieaBjGjjxUYPhxocyBwBDigIWGg/1ys3VSKxBqIKdjyjdE/MWhHPIgKuYSE4aRUDig2xDunzYhx+D/gaEIhxBoMwo9apDZcUmJEdoUo4wkEPIFJFiHqO5IdDMOFC3VXkWLePaZ3e/1opf77ytkzjhg257BDUURhBhjuiXazBTvZ/FPGGFK7UawzCOchbk0jp82PYfYuOHfOsZnfkoQvCYydWLrorEWsEMhLjDY4+8Vp6Gn6fXVWuKbjZLSGGQFoT0YUTHYctXN9iAKMjLpyRTPSR/HVkXDomoivXbuT0aaOEa7ipMHtADHdtuuj6zEohkLMTvWXksXS19BlnELTV23CiG/Y6WuPvtIC1SMSeYI0yL3jCjYq92+q7RtYsrYiuogsnLtnMgHcFortB63XpHzhSyJk8WfmzVx6KJSbgPV1woI6OvFPO9+43f3WCnv3Ofo5+M5078NrPj7OI7Wwn0sX8FmPH2saHZ6uKBZyzLomW36LkHp/eUcUuc+yvLM2LweOuNsyjmSgdkxLfBUYZOJEhVqLTbzyA4hZmb+GciHOBO8Dcg2U3K2vEmZ3VPAdkshPnXVeBvUnFmZGuAxg4TCNcMH/r8LYyjrlCJyNmmRXsr+MiptrcfHSQYt+IrmFHc82x13znt6fomX/ZP2pPK5FMDPVHovtAxqU3plF8RjgNDjp2CMGCHh4TxAfk9sZeCou23sIokTh7o979YjFvZMJiAmntfdmjcpQxWBKiLYR2kQdsCzgkxFC1iXQwgZsUswzgwIcQZ+/ANzryRt3gYWSeQwDCz0BLJ9of4TiFeI/1Rgtwo2NtQRwMpqybt05jQN58s4G8EBpX35lFe18uoYNvXaAZsyJ4sJc52Mjh8U2eohQT/acqzkYAtxeiXS5VHuX17cBrpcaYhVEiugvz0Md0/DRKJ7ozIEdabPbxnkeRpaaojbPtIWro/bOOvV/JudWIGbI1TE7Ah6OriIOxLs7ygKZJSkY+imDmA1gR0wawBkJQFy2/iFjSinAJ4/ugiAT3Mr4vDiBXBhxzoodPD6LO/f1GQRTZqdiTxBniXNou9XDhzZVFTSHowzUKAwBeGxRj8VyJiBCbTvQW18W5CNDFsOXxXD50lp9qosID9dwpoydJOZH8HOA1QLePK51juN92tvRSaDRyoUe6G7SK6FiXg8IC+DW4OkkRPyEuSCS4DiCKn99bO2ruggDXjHh/cwHsKoYdYxZKB21+NNehNQdrMGangLnrk4x7FDUsvj6VzQwYVAjREY/B3MGOdffA6xf4/YN9lS8MFpRMbKZMmczGJMwMqTzbQjQOZIGiQ4rhCsYtnKHcReaiWH7fQzg+sf0iF9exd8WagSHG2Gdq7Xh0RXf4B386x13AInMc3cswhZQeaxxj+DA1RGHvhe6/mORQ3n/g3Ih1vKOljzqbe/lPFEW72/pHzeFZuCWFVt6eqWkmDaIVhwav8FqKuUjoiJNIgBTRJaqreeyq7BvmvOHF148eXuNo1ACL6E29lKzEU0skLsmju3C8kW+acBqbbhxQ9Ya4BHcyBvPlrk5U5SyASxOHl5S8aJpIIO5EEdG7KSXX9u+ODRCeV3ScqI0OgNCBjR7yPrFZEZvO6KQQzQdVuHKLDtaPykU3bZ3eZKV1es66GVRV0MqOs4//VkB3f28Ji48CiGT7X1Wc7CtuzbQ47BQH7s2P5fIAQkTH4LlCq+rQwDAPITXNbXclMs5FH3AtDA1cUXJl08LY/ecqJzrcsvn7avlei/ePmmxIdHks3Jpi89+g0AOXOgZywuETHKbEAggmGcRpuIGECx1rHDo0tIL3KiJv8DzhEIMDTmCIn1NO9MBgf3Z+olMEnQDVhW18XWOWAkRpRKU0VnVS0qxIcgUoCoo4F/GcQFBDIQ2PEeuceLz4t5aiEzpbFQHYkSxyLcCdv/zWDDr0Zhk1mxxE4dxHYQOt3s5EO0DsuPGL8/kQ6+qDOAT0f/zwMF9Tn/nxCqMDbVqE9uJOsBDRDUKpdKJLQOGhemPnWXxGGEUnhVJYdCC/v7Fmmg5EX7A5ha9FGIoQO/H6L47T9V+Yq1mkhmjf2zHA+89Vd2Rq+lq8d1Hsh3MXX2++nkDc3/F0gXFI6YLNo80CEom3gn0yi+hnWih8Ofk0KOpj5hbIWanOSKQXWCNW3JZB7/7uDJ3fU0vzNyWPMSnlrHBPvIw1zn6irLmttV28/sKcNkoonzKJz9gzl8Tx/2+q7mJzSVN1J0fV4ByKD5E5b0sExx4Z+0WY5tDNtunh2byXVAueKxiycG1iry2jsSRAiugSVRx/v5KHSGHzCHFIjTtOXdRAC7U3yqgBieumou95uYQ/X3ZzOsWnh1sUbQ+/Xc6uaTUieqnBXZ25OE7z8D9fB0WHspNNqoaLChc6HFZqHP4CIaLD6RsQ5O+wa1u0N2PgIBzotSWXR1qnH7LeOo1rYuNDOdxCCNHs4OsXaNXdSmskBJyP/5bPrgS4MW0dTpFtvXBzCm/aPvlHIWc3dzT3sNMN7l5rsRsuiXORa6xTmOfKTk8P5wIMnNUQb/V0FqMVF10XyGCHSK/ngKXoxGksorfWdvP1acqqO7P4AwKwGFRn7lZXC54juNFxWMOBhUV0wxqgVUS/6cvzuXAlxFO8b9ARI0R0sRb3dQ0Y89xdAQoLyNPG2Uk8FnR6YC8E4RxrzLr7sxUxDRqthV+zp6Pf5U50AdaeuJRQzhM3vY4PvnGBXXHooHEGdw0pFEWU0KipxhixwGn+DhV3xPUsuixQqMJrqeUwLRkfpiB0NIgO2Hnrk1ggwXBxdL/ZA/tEdK19+NQ5vj4R7wI3eIZhMKI1TItrcF9ijci7JtEhhyruP+Y/D9FKJz+6SMfeq2TXJOZZIGfdnuFBIvEW8P5DAR57q+AO316XL5xs5LMCitYwX3jiuUShDY7v4+9X0IaHZpuYlC55VESH41wMkMVjKT5iiJiZhPjPSN5ro6CCe70ge9nIOorrQxHUFWEde0Hc37E/5OKnoQCKz4NDA3i9RDb9p88VsViPvQSKn2r3jNhjokMZZ0LMRLNknpJMPKSILrFLXWkbHf9QDGScpVv0ijFqQLokJa7qnoDg2T/MGwlrLViIQoCIztl1bf02s7shlAhnwawJFOWidjih4Kpp5I0h51yL8AuBBkU78Vw7kh+O9QWOc8RXVJ1vpd2GKCpunZ5nu3UaAz9xKH7nd6e5xTsxR/n5OJxiA4WNnaUWanPgBoUgiZbKnc8WUNIsRdhPyIxwi5NBrLHIbh/sHx4VOyNRB1pE0ZlgGt+E5zE2OYS7CiB0h6qMK1ILrnchouN6tQYOE5/8vZBm5ETyUEl7wiK6LNAVYqsohetSONGDVWT0WmOaQUQX8RtTRSa6xjgXdFPg49zumlH/H6AgAODKdDVTp/nRbd9YyLnmYqAsIulQ9IAZoLOl3+7j+MyPV1JP54CxoOBKsDaZCui4VkTnREyyfoUZgA4bV7WqCzctMucdzUMXiOt5oHeYi+pY569yxUMyEcB7ACYA5OzCAHHvvy7jIhiu3Ru/OE/T98Ie5Z7vL6WP/nKeRapPnivkbHJLhgF0gGCeFKIdrv/8HC60IeJq7b0j0YLOgrknGHTeUK4MPc1cFMfnNVMRSiLxdvBexPBcvE97L/m2RFV0aGSgqCecy4obPZNnOxQegoM6lQvobFIqbOW9jKu74qxxbk+t8XPErcBkhLMi9rH27u/4vWBEwEf6fPUD0lE0gNC+/anzVFvcRm/9+iSbNNTM64LYnr4ghrvaMbQdxU/MV5Pr68TGt1coictB7h7aAnHOQDuSGEyhB1JEl7gSbBQgcnFmpI3uCVSrIVphcjkGpaFd1xoXz7WwGxE3XQihEw3EuYjheXA9KQLSWOouXOaCBDpXUudqd0FBrISILnDkucZGC2IhcoF3PFvAxRQtrdPITIfT/PTOatrzQgkFZ/hRzTll47fx4RxVQg4O6sgnfvW/jvGmDa54d0W5AGzwcP0jwgHOD9OhPRJ1XDjRyEUh81xZXFuKiN6uOvNfLULkRua6tWgQgPcI2ksvnGqizIVKy6st1DoSIfoAOBkdRRxMxDDNrEVxFB4XSGV15xz6fu3Nyj4BArro4nDn3gEFihmGqJjtf1Z+B8T7iDVQTbcHhG01wwP1Bq/nJ88VUV2J0mFgaQihI0A83PdqCR/EtYqQmkV0w7BUp0R0gxMdhTHTORUS349NOPHRRV5rsH8TGf0wAOXvrzP+O+xbxHA5FNjxnnUmKxzX0y1fX8CxSXB9mgroV68QVZ5pppKjTXTxfItxCCmEdGTy6gXuD5jRsv/1C7zHCQicQmvvn8V7KBk5IPFF0GHBInqDEuHmq13Q2BviLaj3/lALONumzY2mynMtdOTdcrr2iTlsIkrJjfLIXgTA0IPIQsG6B2bRnLUz3PKzsQe+/Z8X0Xt/OMNmsDd+eZxu+sp87iyyx4JNKbymw42+/7VSPtsKEd3WPl0yfpEi+gQGbej2DqHnPq1hMQwH1zU6uiZGRQ009fKNUsugB8nEBa3YjZUdNGxhAJQAuadoawU4LNtrn8eBAyI62spsiegi1gHV/Il4vaILRWQdlx5vtCpmnN+juEbhenWk7T5r8XTa/9oFXhcwLFGNU8DaBhIiOh4vBG1EGGhxTCLzHE5yuM/7zwUZ3bxa3A84pGPt/PT5ImOWtl4ilhqwdkPsxabeG0V0bD4hlCGiQ4+YML0ZGY47uoCM1xBDmlAw0hu4HPFcQDSEUIn2VoBhUCJTH0A8ARkLYpzuMkA0Aa5PXKt6OdFB92Xle0Fkis8KpeoPzqj+Hmj3ba7potS8aKPrfJQT3SCsAxT14BCHwO7qnG6xbwo3EdHxWDDICjFUKFypKWq4i9M7qqgqv4U/R3SJXq3IcOdjbUR2PwwXrnBlGUX0+GDjYFZH8tBNRXQRC6MW/G5w23vKtSex/dq8+7vTxnURRh8horc39VGJiAkwgHVywZYULpDrsU5gPgCG9pmCYb51u0KodkgZHCrW9NmrEihLRyMSruNP/1HEMzsAnPCbHs11S1yUROIqUucq+5nBbmIxffZK9wiseiJyumHGcfT8ohfLb82kyvMt7KJetLWTbvh/cz0q+BYerOMoNQBBP2+NvoPP7YH9z53/spje/f0Z3l/AkY7nJHGm7Xk6WMMf+s9VbKyBAI/uOMHHf1ViPuGmx+8ku34nBlJEn6Bg4Xj1Z8eMC5ktJlsYyKgHyLjE90YmJdp05cZPogZkuoohUPaYvTqB28PU5J3te6WUXUrI37XkTurvGaTK88phxZPOAk+CwgFyleGAReuwPWY6GHkDsSM5J5JjNBzJQxcgxkew6s5MzSIyBCcI76/85zFep5Bt6Eh0BA7PVedbeHgthjcKR787gDgNgaG90Ts7frAhxQZ00bUptPJ218dyaAHCKA86mqSIM6bEGyKGXCEgotU/NjWU32clxy4ZRXQ8nm2/OTXm32cvVb8eQZTHYCZumzURyRFnhWFNyOzv0cOJHjHaie7otYFYqCl+k4zCNdzf+ADosBDP/as/O85Dg9GemzpH/wxgOKcgmKfkRRkFfTwOiGigvbmPn1sUy7DumIvoELELDtRxxIq7XFeCZbdksBsW+c3IZtVrlgfcWxj6jOcd0Vt5a/T/vdpMnOgodOvhRIf4qBQ9+rhIayuiENnSb/zyBLvXP/OTlbJ924vA6/jOb0/xcDm8Lhj4Zrp3i0sLpdV3jdxTcN0j5sTRWQ9qwDp19J1Kujo0iYLC/ClneQLlrEoYNZzU0XhCOOnFYD0Ur7BfhasTQ41X3JLJhYGJaO6QjC8QnTF/SxIdf+8iHXmnkmYujndZXJgrgPnHNMrF02CuDvav2Esdeaec90gCGDOwhrjTlX5iu2Jwg9C84TM5HhH0Yba489uL6f3/Pct7/G2/PU1bHsuzqxfg3oHZGeadUOVnmujK0FUuaGKIq3lhVTI+kSL6BASOLeTzQkBHSyNyIa2BtW3ehmSXDMXAgTQ0JpDFHWw8pYgusQeETGRLikM1pndbAy5BtTeyoJAASs6L4rgWOE+X36IMkjQFAihukogE0XPYn6+Bg+rxDypZbLMFhtoJ8c8RkOUHAcNWZ4A94EjLXZ1A/oF+NrOlbYFD+YaHZ9G+t87R5sdzHBsCNmkSrf9MDqfvQmCzFoPjCrw9NqvijFKYKthfz2KfECW9AQjYIGlW5BjhDptprEEoSGMTjgG6eoIZDsferzC6KoUIhPXHFBSGkmerf5+hDRXiC9xRQuhFN8BAz5DRXYwMfYABuM470RVBHsBJ3FEWwAVJ/3D7YiQKmgDPs3CdI4ccRQY8NjjmcV1DQENxD2Ju48UOl4joiAeDaIfWX0R6iccC0Qr7JLj4g8OmjnrcpjRWdbKrDo/d3SI6fuZ1n5tL+18v5QOensB5daimi++beovoOJyKrohIkzgXUaDRirie8T0PvV3GzjzsEWw9JyiM4D2O6xnxZBLv6aTd9uQpPj/gdb31awvHro0o8qho09dbAJy3cQaV1xXS7Q9vpamBjq2h6GqpPNtCzTWKYN5S18X7T3Ow/iOucCLvSSXjj3kbZtCpnRXU3dZPp3dV05Lr08hXqDHM18L9In2+vvtCR8EMkLITjVxMR/ckOikRL/nxX89TaHQQ3fq1BW4pwFUXtRrv6RsfynFpQdMegSH+dOvXF3BkMboeP/rreerryqY567SdFfE6Yz4GihT4yFriPV2IEtcid4QTkOMfXmSHG974d39vqUfFa7TDsIje2MNChURii6qCFnYfwiF5/78v1zX+AXENiojewBsO8+o4/l78u4kMhDd3RBWg5e7Wry906ntgU4iJ9M6CuIyiul6KdMJNBqHv+s/PJXcTYXDtYvCYNyKGHcJRjGGaeovRjgJh2TS+yRLItofAhuxLvR83ckHxYV7Quf/fljv1fSE2Q0SHezPT8PaCEwnDnYQIZMxEN4jCTmWim4joH/25gDrqplLFmRaau3akFdaam0tEeQSHBtDQwBUWq0OjA41FUhzG4ApHkT8uNYxfL0R96Q1EfxEBIgq3uAeJghpiPiDqiVsGhhn3dQ3yIU3Q1ao48kM8tN+CwHjLVxfo/n3x3jj0VhkfzPEc6LmfRBF1yQ1pLJ4jb1qvTHS8lsJRDoe5LcSAbAy788a4qYkICmpv/88p4/V26zcWGOMhPQ3e8wuvTaH6D87bNHnYAkVZDDU37xRG3nlMcigPBkZxFZ/jfS2vS8l4A/fW8Fn91HomiE5uv8jdnJ7K8NZK0cF6473RkThLV2kt6MzO31dHh98u41xwxM7xHqq5j87vrXXYaKQW7PF3PatEXOWtnaFrtJUz19m1n5tD+18p4WGn+14t5dfN0oBoW3DB9tYQiwY8yfjFeyxfFvjZz35GS5cupdDQUIqLi6PbbruNiouLPf2wfJqGinZ2kYJ1D2R73P0tsk29NWpA4l0IZyhuvnofHNLnxXLeNzYUpkMtAVwFtSWXbQpqEok3gjgXb11jIYLgvWUuWHkDyOLGACERAWAJMfBWFAJ8gShDpFFrbZfx71CYBIgFQIRRj45OdAiWiCIAmYuVokDZ8Sa7X9/Z1sfCOYQodM8JAVpEkcAFbtphEWfolrt0sZMLIHqCGBT+ncIDqNcgpotIGdPPUXwQe6pWMzd6Z6tynXt6z6U3+H14UPJVYme3nkDoxqF048NKIVQvER1zVQKCFHGjt2vQZucbYmrATEOEG/bQmJMh8RwwAKFjBh0qt39rkdcI6HqAvee7BgEdUUmLr0+l6z43h6OEnvjNWha/1tyTzTERcJ9LAV0yXglKGOJYOxT50QnmC/Qj2uOUsr+Z7QVRLqYsuSGd93cwfVTlt7KwvvIOZcjxwTcvuLRbFXuy3S8U8T0c3bGOxGK6Cqyha+7L5rhOdFhjCKujwHwnB4xOHLxaRN+zZw996UtfosOHD9OOHTtocHCQtm7dSt3dY1tlJfZB9tXOpwvY4QUhUEuO6kSNGpB41/WLydiucoMjny3D0HonHKiC0uOXWCSAWGArP1Ui8TZEoRJiNYbjeRNCfIYjBpSfaeYDkzcg1oC0udHszrYEi4cs3HbQ0KB3PG57iDz+lrruMSI6fk88/+I1cMb5Bfc4CzxXlfgMkLlIEdFrSy8b3e72hGsUgYQALa5lU+G63RDzAncmOk8gcptGyOgBBpYCdKKYDhUdYwZoQreKIuihAGOKyIYfbyK66YyQkmNKt5YrgKANh78zcS5wBYpIFhEbZcuJLjrfUExCDNfF/BZ681cnacff8qm3S9twUom+xRV0VUBQHk/vJ0RRwYGOuChcb8jsxXBzFHGxxkhxRjKRwOW+8o50/rzwQB0bG7wdRKZgwCQEWcxk8CbQHSjc5oe3lbEWNHddEq81MCx88lwh/52r9tOIswP4md42eBNra8ZCZX9acdq+yUMi8XoRffv27fToo49SXl4ezZ8/n5599lmqqqqiEydOePqh+SQH3lAqjVhI192fTd6AcJB4a9SAxLuyk7E5QeEFUR+uFAMunLhkdD+OinKRLnSJj4H2crSBA3RZeBNwxICcFQk84A+50hjM42lwkBCueFtDhCFsBIUFcFYthrf6AmK4LiLURFFloFcRJ9HCKgRvv4DJPATXUSBom+ei4zUOiBhW5VoWInRUfPCooaLmBXgx5BO53yITubFS39dCCPqI0xE/z9T9KkR0/Dcx2FB8jXBhIQ8ehET5Rku6FrIWxXEO6NIb03XtAoBogmxovB+7O/qN3RKmMTlaMc9gFcK8JcQagKFsKAjNmBnB7md0V+x+oVj3jgeJdTBU9tzukYHymOXkyTxdvUHE1ju/Pc1zABIyw+nGL83zOqFJInE38RnhXETCUnvg9VKvX3NNB4p6Y9Fr8bWpfB7AnAUI6dinodMLXdiY/3Fuz8gaqxe4h+95aSRFwtkhy65CxCeiWO5thiOJd+LVIro57e3KgTsqKsrqv+nv76eOjo5RHxIIkE1UsK+OaBLRpkdzNec9uSPOxdtvjhLPYhS1lk532eYEA/owbBcH6+qiNqMLERsOHKIz7Uzulki8DbxXjJEuXlashDACEmaGG2OSRGSTJ8FhAsIvXKspc6JsPreJBje6r0S6QHiCCInbrXBYozgJwZwzMg1iJVzozq6zQkQ3jewJThwcVZi0RpshD32U+9sQ4aJ8LmKKRq7p6amhxs4APRGDQuEus+REDzOJlsG/Mf0aADezcPd7KhPdleB6uvaJOXwI1fPevPPZAnr+B4eo8nwLdV8e0OW6FMKr2G5ac5Sj863CrPMN+albHsvliCG07AvBROJaqgtb6Z3fnqK9L5fwALjxBoaHbvvtKV4n4jPCeHhxQKAcWSaRgJW3Z3LxtKaojQdjeis8H6esnR30s5Z7vtPf2r163QOz+POTH1VRwYE63susNsS6HHqzjC6b7Kn0mGuCHHQx3yFiehAlzfbO+Xcw58HkAFd+VUGrpx+OxAfwGRH9ypUr9PWvf51Wr15Nc+bMsZmjHh4ebvxITk6miQ5cM5/+o4g/X7A5xasGeIbGBHIlFId44YCTSMxBy7W4qbkyk3zylMmUtXj6KJFH/JmSF0VBIePH+SSZOJjGTXgLGNbYUtdlzBYX8WJV+S08gMiTiPc84kfsDYYayUVXCgLeDgRIY6RLrSL0puRG0+eeXMfxAd065KGbDxc1jVcJih+iSZMRXdBpHBxqibX3ZdMD/7Gccq9JHBGuDe5z/tyYQz5gdA3Bsbb8lvQxA1mdRbjiTQV9i6745l5KnRPDURMbTQYaYw8mHNRwzEvsA/d5uyikTA82XkOORrkIxHWN7FOAAbDWfv7CrSmUkhc9qvMNn4vhYfteKfWqNXW8gVkKR94tp3d/f4aFjZTcKErOtV7U9EVaarto229OU3/3EE1PD6Obv7JACugSiQm418/foGg5B9+4YJyx4m0UHVaKqsm50Q7P7XAH6K5cemMaf77nhWKqLmqlvDUzKCknktdXPTtgTu+sYlOKYP6mFK906BsjXQx7x/FYrJVMYBEd2ejnz5+nl19+2ea/+973vseOdfFRXV1NExm4uz95vpCdtWjjXuFlk4ORSxkarTiz9Kx+SsYXGO6FQy0OsKJd3lUI11n5aSWfWU2sg0TizXjjAOeG8g6O9cBjgzsUURwYpAZxSwzz8wQ8TNAwGErNe17kosOB5Ko8Sb1Zfksm3fkvi40Z5QIUtEUx25k89DFOdBMRfcrUqzRjViTHxUBAsrU3wFqPzOORCJUR4RqDBUW+tchFh+CJ4VnTDUNG9eK2by6im786n8VcCOLmj0V8jn0WWqVxEDU9RKN9+Qu/W08P/scKGs/AfX/knXKOpnAWXDMwV6ADDGYLZ4eKWhouioILXI6WuiDRrbns5gy6+Svzxxz6F2xJ4fc99gc7nykYFf0m0e9aevOXJ+j4+5W8rsLZecP/mzeuilAoIm978hQXjeNSQ/laE2uaRCIZYfENaVyERkwad9W7Ebw/cb+w57guPqyYL3JWev9ZcelN6WxIw+Pe/tR57vy7/gtz+UOPvR9oqu6kI9vKjf8fnY7e6tAXZBpy0REr6a3FGon34BN36y9/+cv03nvv0d69eykpSRmKYI2pU6fyh0Qhf18dXTzXQlP8JtOWx3N5MrO3EREbxIdkOHpmZHuPS17iPYiIB1e60AVwAyG7F/nRh98u4z+RF5c2Txk6KpH4GiMDnL2nUCniT4QILaKaDtV0ceEKzhhPgDxEtNVPMwwTtEdMUgg7d5Bli3gUkTnuzSBz1xpi4KeuTnTDUE3BNfdkUWhkkCrHZX/vkLEzQcSmGGOKYoNYsMX+ITrRdc87hHx8CNEfh0EMNxTg9xAxYNjHWJrZgQKFM1nevsCx9yv5vYv3grNzSy4bMuXh+EdBpUtnER2dMNfcPdOh7wFhf/OjufTyT49SQ3k7lR5v9HpxwJdATM6eF4u5iIL32rr7Z7ll7+dOcK/Y9ptTvGbgvXLzVxd4TcymROJtYOj58pvTac9LJXT03Qo2O+n9fuHup+Zeju9sru6kJsOfKJxjHcLgSaxD0Cl4aLoJtUVtHFuHf5c+3/vPitg/bXw4h7pa+9gA8v4fz9Bd31kyak+GIrGjrnSI0IhjgykmKMSfersGafbqRK+f8xCfGWHcy9WVXKbk2eOr80kygUR0uEO+8pWv0FtvvUW7d++m9HRlSrNEHWiVxiAOsOK2DK893HujS1LiPXTiJo+ohEkY8BXnls0FHKjHP6iks58qQ1YyFsR4/c1fIrE/wLnX+/LQs0aEagwnPPRWGdWWXuYDiRBhPRHlgsOS+UHJWgQUcmyrC9u4MOCt91lbnNlVzTE6s1bGu9yJDlCk9Pf3s+lgOrOzmuIzw42uchxszEV3iOoQ0U2vaziWGyra+b/FJus7gNoY5WIi5ps+FiGiQ/SvLWnj+InEmRPHGABhAyJ66YlGWn1XFr83HEVk4mOQJzA60Z28LoPDlK8XHQWWqC5o5Uz01LnRVuOc8Hqvf3AWx3CI7rWJBByMatZHR0D2PAR0xAtsemQ2hUQGjjuXvRDQY5JD6JavLRhVlJNIJGNBtBvOZHCj43y2+i7tRdDBgWHe4/S09/M9AB/QSrDnwCBrkd1tDowVhQfq+QOF2KzFcbxHhOkKZ8ZCw3wMDKG2FwHoLeBxwnn++i+Os1nsw/87R7d8fQFHZ+17pYRnUczbkESzViSwiUAN6Mo6vK2cTn1cxf8f0YGIDUQz19x1njHGaAH3tPR5MVRwoJ4jXaSILvFZER0RLi+++CJt27aNQkNDqaFBOdwi6zwoaOwhRjIC2lB2PJ3PiyE2ovM3em82vLcOvZN4ByJOJTErwm2HKWyOsEkTyCgXyXhwone29HFbKjqTPAkew6XKjjGu6LDoIHamo2h24cQlnuHhTuCgrTzbovk9j0IARPS6C+00Z53tbjlvAQOlcGhcdmM6u60wcwIxK7o60SPGZqKbg8GO5rMmGis7qPhIA8/CEOKSaXyKQPydiHsBJz6spHN7amn+pmRdRPQLJxrZgZ46J9riUFHTx3KpooNz0eFmEy3nENERcYKD/9z1M8Z1tx1ibPB69XYMUE0xigjRDn8vkZePCJ1RInqkc9eluK6FeIICD+J2hEMdHP+wknNcV92ZRQu3WF+DxByHiQZc1NufOscD6sT1jKIDjAaO5t3i9RCvAQQq/8D5lDI7ijs4xhNwaO54poDvNRCYpIAukagDRVkI5+/94QyL6XPWzTAaRMwLfNjT1BZfpsaqDkU07xjgvY01kVyAvTGiBWOTQygmOZQ/ohOncbG+5PglKjvRyN8LPx8fMARgjpbI0M5ZmUC+RFBoAN305fn0+i9OsLHlk+eK2HRZW9zGRb4j71TQkXcrWEyevTKB0hfEWC0S4B790V/PG+cDQYBHXBrupY1VnRbNB95IxsI4o4i+9t7scXcPkkwQEf1Pf/oT/7l+/fpRf//MM8/Qo48+6qFH5RtAAMTgLrQWwcnhzYuAEHhsDRnTq1Vr78slVF9uewAcV0zXJ1Hu6kSHf9a53TVUW3KZnUrevkGGa+7T54s4ugDCgzO/M4SYDZ/JGXUgdZbS4yKT3H1uLxyq4RBCWx8ckMk541f4kIx/8H5EBjWKqhDShbvTFthEn/zoIgtJerurcSBB9jjiLcwfCyJdsAkvOaqviI51DoVlMTjTEkP9wyzwR8YH8/tfLaIQICJqfAGIzXAfZcyP4cgUgDxe4dCdZnDs6uVEN8+dxv1++5/PscPr4f9cNWqPAsEZIBNdFNdNB3mO6WIzZKKDODjX99RS4YE6FnJN7+v3/mCZ8f+j4yExO4JS82wLvcjnh5AOFzwEcmuPRfwdhHbcP0x/j5qiNo79gDg4nkHsCn7H83traeezhYpjb1Es59SLCJW3/ueU1a9PmxtNK27N5M9F51dEfPCobganB4sa9iYQVj79RxEf8Ld+Ns8YF4LONx6EprHzDSJyxZnmcR/rUnmumT7+Wz6LURjyd9d3l/Dff/zXfF47MRBYy9waFNHwfS6eb6H7frjc+PrYe1+6AhHJhfsSHoeeAz5x/jj2QSUde6+C/398Rjjd8P/mymH1EokGUvKU4cLoFjr0Zhld9/m5/N7CfAGI5rjnYx+GfYU1/PwnczEVXUn4Ey5r7PdQdMf9Bvcxc2AwwAdEVfxsRIxWnG3mPRT2yWLweFyavt1v7gDr9fWfn0Pv/u4Mm9Yi4oLowR+v5L1P4cF6vh/id8YHNKVlN6fTPMOgVwGGk+74Wz4L7/6BU3iwutjvYFYNPnyFpFmR/DtgjwCzD9ZqicTnRHRLw34k9kGOVdFBpbUIThFvb4VEpiwOuGj5waKducg1B01UjXG4U8PBNy/wYcgRxyYOUwcwQRxDsaZM4gOaN7/Hdv+jiCuu+IiaMY2Sc7S3L+Emu/eVEh4UiIMpDgd6TOBuretmIRvPY+ZC9woQc9bOoN0vFHOOmzNt6RKJp1Hyo4PZUYsBzmpE9ENvlynO2pbzdM/3l+oaZyScKhCfzdcJrP97XylloR1Cq5rHqgYcBqryW1X9W7zntaxf09PDuQ0UETQQ4dS2vnoSFEZwAMR9Vxw4cUDS04kuRPQrQ1epr2uQ/AJHntOQqKlc0BnoG6b6ssujYk8QdwAiE4LZlQ7CY2wL1wIUg/Fa4Pu21JgMLp00ulMP11b+/lq671+X24wNMn0sOCjyY7El6Df1GodTtdUrX9tlyIT3hevCWWavTqDz+2rZjY6P+PSRIa9Xr9Do18SMGJNi3dIb0+jkxxcpPj2c9yl6DxZFl0N8ZphRyDUv2mvpfMOe75WfHuX3E95DaXO9PxNXK3gNTu+opoNvXeB9Ht5n131uDq+TuKdAvMKe9+WfHKUFm5Np8fVpVkVovB8wuB17TuwdeSDzJOL4AE8VISrONPHeFSKQALNwpoUFGAS3EdENEQ4QWtTeI1CkxABauNABHLTI4/d0R5hE4mvgPbf6zix6pfAoD4B/9/dn6FJlO8dqmYIB31ij0CUYGh3I719EgeFPCKSOnk/xnsV8LHxAa8F7GoI6iuS4Z+lx7vUESTlRtO7BWWyow2wTJATAVY8PGBmKDjXwrArscf2njqzrPfV+9PKPj1FHk7LHwXOLYgT+LT6W35qhe6yeq8HsQNzDUVAoP9UkRXSJb4roEseA2AHHFQ4DyOfydnBQWXRtKp3YfpE+faGIFyxnD0rmQDyC8wzgRpeQaX1gHIZhwI2HDb0jhyG4kXCYAFiE4a7y1jgQTBPHRkSw69lCuu+HyzS558UBAQcrgE1Fwf46XQYDigMtqtjuHsqG/D1ci3AXSCS+Djp+sA6qyUXHv4GADiA2wimIgqxeQDQFltZhtJeidRQZ3TicLLspXddYqIVbU2wWCiGcQCTRes+NSQllwReikC+4USGi414FB5cQ0dGtIA6jemSi48AZFBbAYioOXxEJI6IkWoIzFsVxwR9dB6NF9BEnunjdbAnXEOORxYliJ1qG7//35fx3VrmqCHn4XT95roBu/soCi916aAu/fKnXxBVvI84lZiRaRtwzMOcFGaxCAEbhYLwTlxrGe4iey4aOBpMCBQ7Yt3x1gdWvNS3cTPabTPf/23J29aOLROypnL0uMeQMgi08OgEGMcBUOBXXm5ZBlhCL0+fF0plPqumT54vo/h8u43VsvDA0OMyGAuwXQe6aRHZkChE4Ii6YHvj35bTv1VLe/538qIrf04heyFwUaxSWcC9BlIkojAngAl1zbzYXLtwNCiD7XyvlrGOAve/Q0BXuSsIH3vOW7pmIfEDnJjokbWUgwwjywf+d5bUAzxfuo7NX+Vbkg0TibXuX2dckcmQa9oli35aYFc7xUnCMI47F1eYn7PtwnxgvQ4/Rfd/e2MPr9yfPF3LhFGs7OhRR+MPeuaGsnWfViEJ0R2kADXWP7LXQocSdXAbmb/beKGFbZCyI5b1A2ekmWnlHps8WRySuRYro4xQIjogk8RWW3pTOUSBwH+56rpBu/vJ83SJocPja8XQBt5piUBR+lq0FEcPtzn5Sw4cAR0R0fB1AVhqcSZgmztVwL3OhoTWd3eNEtOSGNG5ZxyFnz4vFtPWJPNU3DQwggfsSvy8GkKBdFYcSbGaccZHiBi6G/CHiwd3g9/fFIYESiSVMnbJqi1dw8ECMRAcP1k49HJZ4Xxud6FmWHR4QJnA4wiZWD3cP3MS4t8ChjIOAK1ro8btAHEKupC+I6BCBADvReweNTmEw2W8STZ2mz/YQ8RsQ0SEkm4roYl2HiH7hZCOLaBCZIDrjfiLc38ZhnhayT/G98TW4t0OkF5mbuO/YuvfgazY/lkuv/ucxzrI/u7vG4twY3CN5hgDavyMCjMK8JRFduOIhziMCDId6iHDstL2qPKfB40hYtUV0YghFJ1p+3tGKrwa8V0UsjihC4JrE0ElngLACIR3C+WR/RWTp7VKuf0R5cOfb5EmUpbEjcsXtGdypANEUjmYMbBsPB2+IzO/89jQXVbEnh4Ma2f4oRGFuAZ6rlDnR/N678YvzOOJg/6slvPf96C/nWXwRubIwx6CQiyIGOgwgVGQsjLGYa+wOsFbvfCafHyseE/Lvl9+cwe93/N48eNCQp9zT0c9RYF2tfVx85Nf5+SI6/HYZz8FA56J5jCFcjLtfKOV1AN0uuCZQZJJIJM6x6vZMJZYlLIDPmrGpoRZjWCTaQJza5cZeXrtgqjOH94bB/jQ1yI/6ugdoqHsKd4rPWhE/ar6RIEpDtJe3xQZhvwJTBNZ6qQVILCFFdIlXgMVqy+OGQ21BK53bUzMmc8tRDr9Tzht3FBaQ123vYIOqMkR0tHeiXUtLjAEqs3Cwgxu/OJ+ruTh87Hq2gG79+kKvyaaHww7ucVSNIf6gsID2tDd+eYLFdHyuRgjCv4U7CU/p5kdz2bkN0QB5ynAc3fHtRQ5vbBorO/lwA3ckHo9EItFBRG9UIaIb3JgormEDeWaXfg5LFOrgLIVQEZtiuc0zfX4M/3f8W4jfzgoP4vdJmh3lsgzaxMwIOrOz2mdy0cWhAMKh4MqQoqLjYKqXAAjhDK+hyLQ2BY4x4VQXnV+X4UK/qhgB4BQWX2dJuMb9FMVbONchtmsZXAVnOfL+MScFXWroThCFBXNHPAT5HuS6X7nKexVLbmieO2AQzrta+3kgJn7vakOEEDruvOX+72volYcuQCwHi+iG1wP7NtN1AgdorZ1vcCNjD/vaz4+zyIr4KGfm6ngL2P/ifYW1+NrPzeH3iXk8CUSUpJxIFsXT58dyB8GJjy7SqY+quGAqrnt8LwjJiHDUo9PFUVAYg9kDWcYocKFDBPtX06G/6C7AB5yY5iDbv2B/PZ39tJqLd/y9tl+k7OXT2Z0eEh1A7cUBtPPDIuM6d+0TeeOqO0Ei8SQQctfck+3phzHuwFoNg8GBkFKeq4e1Duv9QM8Qr5WI5hNRbcBv2hW67atLaHqq+7uIXAnWfhT8cY9Dt74U0SWWkCK6xGswPdQefLOMkmaNPdRqBTmNp3dW8ecQ0NVs3KenhRld5BVnmyh7qXpXIURlHLTjUkP5seNm9Mp/HuMho6d3VrML0hs49fFFduGgvRqHBxwm8XvD9Xn03Qra+1Ixi+th0dZFCRwedr+gHBIWXZfKbnuw+dHZnIsJV+aJDypp2c0ZDj3GkmOKCx2HMj3zmCWSiQgyDoEY1GgNFBwhnMNxgmxnuMIhcOrlsMS6A7DeWMuEVeIRYng9hbDljIjOHS3HXD+cWLjq8TyhSODtA6UxPArPP0RfwYDhcz0FLiF8CjexKbjvzFwcx/NKROcXXOi4vDDctaOllwV1rP9wd1sCDnAhoifP1vbY4JKtPNfCXQ87nsmnu76zZNQ1KTLNo+JNHPExlsVwnjsQMxKZBBc9RPQqQ1E9dAJEubgKvfLQTWNjWkzG4wgRXeTfz3RwnYhJCqXlt2TwwDtEm8zIjvCYy1ovcF1vfCiHXdgoZGF9+/Cpcyyq472CbiV8jnkT+Nj9YjElZIRTxsJYuukr81hERzcoiqKuGhiKe1rpsUa+Z+EcgbUDwr8okpiCoiEKAHhvAjgo0QUDZ6UWAQ97+Xmbktixib099ruIhMEH3uudrcq1umBLCq28LUPO1ZFIJD6Bf8AUWv9gzqi/g64BUyEEdRbWe4ZooH+QTpceclqn8VZQFIaIjtkdesVKSsYXUkSXeBXKobaZN+OWDrVawEIPBzgO4bmrE3hBVHtoQIb58Q8qeWOuRUQXMQgiIw0uljV3z2Tx6fC2MkrOjeSDlidpvNhBR9+p4M/RZmvq3lt8XSoLCg3lcM8X0q3fWGjxIIIb6q6/F/CNFAUDONkFcNytu38Wffy3fDr+4UXOM9c6mANO+QvHG10ufEkkEwVjfnTzSH60rTiqtDkxLBaAUQ7LA/U8L8BRhFPbUuunKVhDhYi+8o4si+uQGiCWwH2P1l843F0FXIZwLENQQm6kt3fP4PWH0Iv4ihu+OI9S86KMg7fNYwmcQWRii+Ga5kCwhIguOr9wn/7c79ZxXnlTdadRKLdWuDHNIndIIHw4h4u+eB7QcYH5LAIMTATIOBff35Ij3vhYYoWI3sOHrmU3ZdDF88104PULFOrlA94nlIhuuL6xDgIMvQXXfW4uC+khTkTvLdicQlXnW9g4sf+1Cxxx4msM9A7xXhbdiIjPwQeu7bKTjbTr74X8PjWNJ8FzxsPpTzWxexGFUlEsjU4K4T08xGo9i3PYI14810zn99RyFKQ5ENSx/xaiOtY6RLMc3lbOoj6igdY/kENZix0fWI8uS8ydwvfAnvnMzip+Hjpb+2nSlKu04aEcmr3C+dlAEolE4klgHEA2Oj5ENO3g4CCdLadxC4w8+L0xDB3GCFt7P8nERIroEq9COdTOppd/rBxqj75bTitvz3LoeyGLnHNSY4No9d0zNX0tBByI6DgM4YClprUXrjnO+p1ElLV4RPidvTqBcyJR0UQ2+93fW2JzEJErQd4sXDg4gGDgEw425sIKu+d/eoxjWU7vqBolKgggetQUtbEwhX9vHtmC5w/FEAhyiHW59wdL2V2qltqSNs6hhJtTbYaqRCKxjml+NNy+lhyScG1bGqyHwt+KWzLp4JsXaN9rpZSYHWGxzV0NI3notts/4VicGuzHDsj60svcEu8IwoWeNj9G0xrkqBsdIjoGp3q7iA62PJ7H+dAiZgBrLtBzsLctJ7roSJi7IYmHyUL4Arg/+kVM4ax0YOvwIrLI25u1i+gAwh661CDEmc+RgRsLhWWIiLgXmv48m4+FD1zK+wOxdHPXJdGQYTCmxHtEdGzW4ByHm1oA0dUZUOzb9GguRwQhP9wXKThQx4UfZJ7f8a3FvF88sq2c40/AjFkRdO0Tc4zrBp6zxdfhI43vLSiIQUyuK21nAeJQTReL1yjUzV6VyPM1HDXHYI3C4Pr8/bUcm8RMIkqZHUUBwX7clYI1GEI5XPP4MCclN4rPGXpdTzi3oCickDmX5yigi7Ky5bxTAr1EIpFIPAd0n8SZERxPi/sZZmZIJKZIEV3idYhDLVpGT35cRalzoilxpjYBBZtYiEGoIm55LFezeIKBVnDQ4ABQdqqR8tbYd5MI5zRaeOHSMW+HfeknR3lDf/itcrrmHs8crg69cYEPGWhnhgvHkrsPh388PgxNOvJOOYsbptnFcNrhgAhW35Vl9dC59r5sFuLh4DvwWilteEh9r32pwQ0LoV8Oi5FInIfzoxF9Ud/NzmxLIjrcdBBBEJ+RNnd02/2Czcl0Mb+ZaosvcyHujm8t0tyi3t3er8RiYLBchu2IFrT/IxIAzncI4Y6I6NzRIooCS1zf0ZKQGcGPVxQKvB0xuFEApyaYFu4KJ7plER33IHREWcLo/raRda5lYK41eMChhU41CKLifdKu0olu+rhN33syksx5EV2/THTl+oajGnMfsE6gTV1LpIct4NTb+tk88kXgzsdMIAAnOkwkHz+dz7OKxH1g5e2ZVtd+/O4oHOEDX4v9c9Gher63IDoJHxAn8L1nr0pQlTWrDKO+TOf21LLb/crwVf57mCzwPfLWJo66n+H1xBBgOORZVDf82dczxANAMRjVVUNf0dk5f3MS1X5w1iXfXyKRSCTuAftCFtFPSRFdMhYpoku8Eogn2BxjONPOZwrp3h8uU33AgQi058US/nzx9amao0QE2Uuns4MGYrwaEd2Yu2sh/gWOHQjp7//xLJ35pJqdOBCn3QkicnAIAZsemW3TXY/nHs55xDfASX4P3PMBU9jdg/8PNyuKG3lrrT8viIJA3vrbT56iggP1lDo3RlWkztDgMA/yADLKRSLRj3AholsRHEuPNhjXX7zfzYXATY/kcvQFhiWf2H6Rlt6oLScQMScgOjHEGBVjbw2GKI0YARTltLoXUcSDkx2Odldk8ZqTOFO511y62MHrmKc6jrTGe8FliqIxnisQ7KZMdEsC3rYnT1N4XBC7eIW7PEylcA2xzVlxDFFlF/NbOJ/dlA7xWGwJ+ob/Jt5fJ7ZXUnNNF11z10xd3f0TdbConpnooKej39j59v4fztKs5dM1FfvVggM4Oup8oZBSfrqZ99DYH0LgfvVnx1iQRtch3NumHUr2wPfA3hkfELSxn8cgerjJEZ2EDxg0cL/B+07J21WG2PX3Krm7/NE9SAN9I7MbUICFGJ65OM7iGqsUv4L4I22ubk+NRCKRSCYQGQtiaN8rJdRQ0c4mIE8OxJZ4H9LiKfFa4IbGEC9s6Pe9rIji9hBZ3ch0jEsLY5eRo4jDQm3pZat5roKWui52rU+eMokPBJbAoRwbf4CsdgyfcxfDA5OMhYV5G5IoJde2oAQhAnmOcGxBdBPOczjT8XvicIT/bk+wgHt04WaleotceNyE7FF1vpVfP7j54eyUSCT6AHESwIluScAU8RkQr625DNfdr7iGj71fyWK6FkROrr08dEFidiQLXhBSLOXe2kNE02BAqhhs50ogsGLNvDJ0lRorlTxvbwbF0I/+ms/PLeK5hKioaya6QfiECIZ13RoQ2T7+az4XPvC6YbiVmhxyHn49SXEV93Y6d0+Fg/Xd35/mYveB10vp47+ep/N7alicb29W9gC2YoyE2I+h5NiLQDBEh9qL/3GYeruUAoXEiTgXnQ6wwSK+qH2AB02i6w7vBXJw7oItDrxxgbsqcT35Aqd3Vhk7Krf95hQL6NiH3/mdJZoEdHPQsbjqjix65GerOCce+2TslzGzAkU8DLQ/s7Oaig7Wc+s8Op4Q6Yifj7XDL2Ay5a5JpHt+sJTu/JclNGtFgk8UKSUSiUTim2DG2/T0MJ6tB1OhRGKKFNElXgsiWDY/lkfQaZHNKIZ22uL0rmrefGPDjRgXZ6JAIBgh4xaLZ6khqsWeWIMhmmgxtcaqu7J4+Bwcf7tfKObDuavBz2g7P5V6OwZ5QBpacdUQFBLAziORgX7k3XI6ZThgIW5H7YEWmaNwOaK1F4dVe79ziUn8AtyvEolEH4QAiMGH5kBEhQgZFOpPSTnWo1MwdBliCkRCDH+GeKl5qCjWVRXAUShiWIRLXi3omoGDHTgj/mjOxjX8bshF93YgYgmhuqe93+hE19Ntg/s4hlEB8f0tgQOK6EDCPRJ3CeFEtyWiozgi4tOEW9xRcL1NT1dev9M7q/m+fzG/lZ2zQ/3DvBcxzc82JzRqKn8PCLIoGAtHvyICSsHPEfBciuKIfpnoyvfB6/reH86wUGureOgMyAEH+fvqeDaON9NQ3s6F0UlTiKoLWznHPyUviu7+3lKKSbIfu6IGxMBgXsT1n59Lj/58NXecYH2GQL5wawqtuC2DC7WIw7npK/Ppzn9ZTA/8x3J6/FdraMODORSbPBItKJFIJBKJKxEd9CjuSiSmyDgXiVcDx+Li69N4yOeeF4tZsMEh1RL4b4e3KY5pbMxxEHcWHKqQbwuR3FoelukwPnvxI3DXbXk8l974xQkWeI5sC9LlcdqiubaT+i75s2CCwoKWwzwiW+aum8ExMMffr+S/y12doCqWxVTkwM997WfH6eL5Fjr4xgWrOZh4LjGQ1J3Cl0QyUbCVHy2KV1mL4uxmnSNaBYI4HO1wWGIAoz2wPjdVd6kaKmoK1gG0/UNkHegbUj3foqqghR3scLLD0e4u0D1TdrLJa3LR0YYaGhloUYA07SRigXvS6LgLvcDPhgvdVqQLXmfR8YTXGP8Wjn7c701njFi7rjFkENe1o/FtgiU3plFVfgs1XlQ6CaISgo2FBriSbEUK4X0TEh3I/x6PxT9g8qh7v7X3Be55KPo4CxxTzgzG7O8ZpL6mKW4p7qtFdK9h/4IhuHogrm8RFyJI1LAuqSUpJ4pzxFGU+fT5Qrpy/yw2WiDiLyrRuSGm1vPD29m4oDXjHY8RzMiOpJrCNu5IufFL863uuZ0Fz8H8Tcku+d4SiUQikTgL9A7sTWuL2jhBwJZRUjKxkCK6xOsxPdTCyWwPDMTLvSZRl5+NzMV9r5RyyynazS0dUC9VdnD7th+G8c0bnaNqibjUMFp6UxodeaeCc4XdxZIbU0cNCFXLyjuzqKa4jQczoV199d3ah6JCNIcDfv9rpcaDmi0i44MpJlkf55NEIjET0Zt7ObpCiCNDA8Oc2wtmLhs708EcbCI3PZpL2548xQ5LzDtIt7P2XapoZ/c6BFF0+aglLjWUHzdEyXO7a2jxdeoiukRhE052V4lAlkicGWHMWYYz2laGtqtBfM6bvzrBgtq9P1hqMX4LazpEX6y5WOPxTyBu6Qlec8SCdV+27kQ3vSbwWEShB85ve0UdZJGjA629cWyHhVbQvbbl8Tx65T+P0tDAFe7eEo/FVja78bEYnk98TWxqGA9StAUOZ7iu9QDZ//f+6zJN7y9Tx/f7fzhPzdXBdGp6NS2/WV3HmqsR1wy6I/TqTMPzBFEeAypxbWLoLYovrup8W3FrJju7W2q7afufz/Pf4T15378uM/6b1352jHq7BikiLojW3j/LZmyQLQF970sldH5vLQv0d39XmWWjBtwPONJmEhmz2yEeuHPtlEgkEonEm4DREffT1rpuuniumaPEJBIgRXSJ14ND7dYn5tChNy/Q4IDt6AAMq1tzz0ynh4uZRppgIBQc1BBllt2cMebflB5VxJqM+TFW3WbmLLo2lfq6h1iYdzUQrtr6LtG8TUkOfT1+p+s+P5dOfFjJj1utE9QcZLGjfbq5xnZWMA5t8zYl6/YaSiQShZAoCJKT2OGLOQ+cJ03EQh8csRDfMLRNDUmzImnBphGH5fQfLreZpW3MQ9fo9sQ6gG6kT54r5NxczHOwVwyEY13kF7p7ODGKf+igwu+789kCuu2bizwmRBUdrOM/MccChWAUcM255avzudMLMzsg8AWFBej+eIUL3t5MDAjA5z6tpuW3ZlDF6Wa7US4CIW6L+Bc9Dk2456F9N3NRHJ00FLvVPBYI+igTQ0hffEMaO+qRyW8JiJYlhpgiFF8QQ+cobfU9PD8GM2Fu/dpCzYLwsfcqOIManPjwIqXNjaXpaerWArfkoes4mBVrCtYqiOcrbs2g+vIOWnHL2L2dXqAb7/ovzKXD28qpq7WP3e/m1xJeO8TWIFrmrV+fpFu+tsBqx541AX3fq6UsoAMc+A+9XUZr7lFmWNgD73lklaP48/rPj/PfZSxS33HLSEPVAAAxOElEQVQokUgkEsl4BAVl3FMxeFuK6BKBFNElPgEOHDjUegK0mUNER9zB0pvSR4m7GMZXekJ77i6cdYiccQeDg4P0wQdVTgkjUQnT2J3nDDjUq81jl0gk+oM1AM7oy5d6WCwRIrrRtb10uqbilanDEkL6DV+cZ/XrjXnoKoeKmpKzMp4qzjSxML7jmQK653u2HZb4d8jzxX3Dke4bZ8Dvv/mxXHr5J0c5VuHUxxdVu+f1hDPhDd0FAPcvSyJ6eGwwbXoklyoNec165qELQoSIDkHUxsuB3OUNDylzOIT7W5VwHau4dkXsih6k5kXzh+bHIob3NiPOZQrPD7EGOuxE5NCt31jo1D0a72m45+HIP/NJNS0wDPRWQ92Fy3TyI6VQ4B86TIOdU2gn3mffX2p0JY8nER0IET0g2J/WPzCLXA2u0WufmGP1v9/+z4tYRN/7cjGvp2/9z0m65asLLL5nLQnoB167QOc+rWEned6aGZS/t5bOflLDn2P/ppaOll5jy/oMQ1eNRCKRSCQTWUSH2QR7Npg51RomJeMbOVhUIrFD+vwY8vOfzPm/cPOZggNrb8cAHzjgWJdIJBJvxijyNfYac5Arzzvm2uZ5B4/n0WS/SexmL9ivOJ/NQbGxobxD01BRc2F6w0M5LHwhFgQOS1sYiwLLtBUF9AKFijX3Kg5QuOfN7xvu4KJBoBWOZDwniGywhnCJ652HPsqJbiPOxRytESqmX6M3YmCpmmge8W/UCPolR/WLHIJ7XhTm8f5oqVVc5fZAVj0Ec8SgZy+Lo9hlPTQtIoBFecwv8TRdBhFdFGL0Qgx9xUBdbwBRgehGQOdKXFoY9XcP0bbfnOIChz3QRVRb2safo2iDosCSG9K4qKlGQK8rvcxOeFB+Uim8pS+IsRujJJFIJBLJeAcdpogWhDmnOr/V0w9H4iXIHZJEYgfEl6TNjxk1fE9QcvySMTsdsTMSiUTizZgLjnArI94FmX9a4gNGzTu4TekwwcwDiG/mwFkJoScgcApFJYY4HK218WHFpQyHZXWB5Y1sX9eg8b9hMLSngHs+Y2Es5y7veDqfc+fdiYgJmbNuBmdA97QPcE67NRC1BabZiORxFDEY1NZgUWvCtZY4Fzh5EeWjN9pc8eoEfYjXFWf1jRzCLBjMhMH7ma+5QfvX3L5XSzhCBAfEVXdl0uQAovWfUQpAiAYRg77HoxPd9Lr3FmDIuPXrC1hQH+gbpuPvV9gd9Io96q1fX0jX/tMcyl2tzANafkuG3TkVosCJ2Knn//UQu+wQYQQyF8bp9BtJJBKJROK7wIwDNzrY/WIRHX233Ov2DhL3I1U/iUQFcIqBCyZuPhxQy082elyskUgkErWI6AsxhNE0ysVR5m9MphmzInkQI+JWhoevjPrv9WWKmzI+M9wpx23qnGiau24Gf47sZ8QOmIPMQqzRcI5YGgTtzk33+gdnsbMbAzsPvmXbPa8nEGgrzyoDLWevTOAir6nz2RLd7QOjHLp6IgRQCPlqgGioxYk+NciPxUdT8V3P5xKFGa2CProALF2fgvIzTRy5Awe5XpFDSsfGbAoK9efC1ZFt5Tb/fdnJRio61MDDZDc/mksBQUrCI97L8zcl8+eYReDJw6JRRI8MmBAiuhDFb/rKfF5XIYxb6qbBe+RShdLdA3D9Zxne5+Zg/kVVgeUBt1gvUURBoW3SZOX5wHWQlBOp428kkUgkEonvMm9jEoXFBLJZ49j7lfTc9w/SJ88Xqu76k4w/pIgukagA2ag4ZEBoQOsrQE46nEJw2TmS8yuRSCQei3Np6uUIj9riNqcLgYgM2fzobF4jGys76MQHlaP+O7LBQUKm8xm7K+/Mosj4YF6Ld79QNMaleeG4KGzGk6eBe36TwT2PvGI4Pd0BugswtBLPE4oJswxOZxR9rbmTRayF3o5fUyc6Dh9XVRjyEWUB8VoM6tR6XeuJ+H4QpoXIbAtkZYpIHFuPRRQ0snWOHII4vNGQK4+hvzVFrVbF6U9fKOLPF16bys5nU1bclsHdKXjNPv3H2PeZu0V03eNcvFhEF9fRNffMpKnBSnEItNSNHNZPfFhFr//iOJ1FDroN8DWYz4ChwZYKTGd2VfGfc9bO4EguAAf7FD95PJRIJBKJBGCG1AM/WkFbn8jjyDXssQsP1PP99Z3fneYIRU/tkySeQe6SJBKV2b+ZC2NHOTeNDs4l0425sxKJROLNCDctMpshOGPPF58Rpirv2RYhkYG0zjCg7/iHF6mhXBHOsal0ZqioJXEJgzvhaC872UTFR5TYEjDUO4kayjp4uN7Mpd4RR5CSF01z1yfx57ueKzS6mt0R5SIEWhQvIGSj6HvRIJRZEyuFuKgncMkKUW643/698nKT0iUxLTzA5gBZU8T16yoRXcv7w/Q9ZgkUr2oKW53uALFG2rwYylujxHrs+nvhGEf81StX+VpEsQIu+GU3pY/5Hn7+U2jL47nKvIOzzXxYdDdYO1we56KyO8Jdvy9eG0uc3lnFh/Wigw3UcSGATn5YZYxjsUXk9GDuyBnsG+bYFtO5CFijMasCr3He2kQqNwwiRgyVRCKRSCSSERDbC83nru8spju+vZh1IXggECH53u/P0Es/Pkr5+2rdHt8o8QxSRJdIVDJzWbyxBbq3c8DYLo/hdRKJROILIPsYRT8MyDnzSTX/3UydXNvYXEK4hRCEWBfkUyMqAK5xiN5x6WG6/Jy41DBaerMi/O19ucTosOytV5zCiVkQjQPJW1h5Rya7wiHYwf3rSreKaXeBeF3xegux1lqkizET3QVxLhDyMawSDPfZ33Z2aIhyUStcO4qWbHbjY7Ej6Ivi1fT0MIqIU+KV9Gb1XTPZnd/V1s/vEVPO7VFmCsAcgIKUNddxTFIorbhFmXewD/MODBFQ7gKROFinXHFdjjjRvWOwKNj7Ugn9+et76Mg75TRocgjneCMMgr6Kf1NKHaXKc7HqjixasDnF5vfEcFC8xv5Tp3BH0KmPLxr/25ldyvqfvSyeutsG+FrxmzqFUnKjXPY7SiQSiUTiyyjmlHC67vNz6TM/WcnRa7jHttV30+4Xiunw27aj9CTjAymiSyQqQbsz2rRxsNv9YvFIu3ySY4PyJBKJxBNOCgjpAAI3XBTWsnQdYe192RQSNZXFzAOvlVJ9meJIj00NZRe5Xiy6NpU3saYOy546f5e5e50Bv/eWx/O4kAC3J3KoXYWpQGsq/EIoA5Xnmy06k4UjV0SR6I0oagz3TdJ1kKfWgZ5aEbMDtAj64t+2W8lnFwPK9Rooagkc6LY8lscFFHTNie6E1rpuOvimks+/+s4sikqwPTdgwWbMO4igof5h2vlMgV3ns54IFzpiotR2JKhFXOc9na7vDFHDpcoOHuSKuRLHP6ikl350hCrONBkP7Gvvz+bXQrDs5jRauNW2gG763lhz70z+/Oi7FdRU1cnFIRhCwIJNyVR2Svkcg2n1fq4lEolEIhmPoEsR0WuP/Hw1rbozi89X6OySjH+kiC6RqAQCiBgwKtpeIdbomWcqkUgkribCRBBMmh2la4QHMnwxpBCRKgUH6unkR4rzUe+5EViP2WEZqDgs4dAc7JzComHWIu+IcjGFYzNuUdzz+14p0V3sHRvlMrq7AMVeZFxfGbpK5aeV+5cAorqIeXBFnItpHIeaOJcOB0T0MFeJ6I440W244i9f6uG5AXydLnZtsQeFlKU3pvHne14qYSf5jmfyeaBpSl4UzTEM6bUFHuemR5ShoxhkeWL7iJPZ1bgqygUEhSrXOYoD6JjxJHCaHzIMHoZZA9FLKHB+8Kdz9P4fz/A1jX0mDugbH51FUQt6acHWEUFdDTkrEzim5crwVdrxdD6/lhDLMUAU64IQ1DMXet/aKZFIJBKJN4MB9wu3pNBDP1nJEWqS8Y8U0SUSDZg7x7zN8SiRSCT2GOVQdsEaNiM7khYaYgbgfAUJWc4PFbXkAFlzTzZ/XnJYcfcm50ZSYMjIMD5vYuHWVErICqfB/mHaZZZPrAcs0F7sNAi0cVbvX0JoFyBuB+B5c9VAQTEYUk2cixCuHYlz6Wrt4y4xvTC64mP0EfSFCz05J9JlBQtTFl+XymI6BrW+9rPj1FzdxRn1Gx+erdoAEBoVSOvuV95nx96vpIYKpbvE1XS5aKgoCAj0Y7e+NwwXrS5s5QgmZJNvenQ2PfAfK7jTZvKUSTzsE670o+9VcPED7+vgBO2iP17r9Q/O4muuraGHCyqP/GwV/11zTRd1NPdxvA+KKxKJRCKRSLQjZ+RNHJQAUYlEotpNiMM6DseYzuyqPFOJRCJxFeGGdQuCacYC1wyRW35LBlUVtlJLTRf///gMfZ3ogpyV8XTxXDOVGbqDshZ771A8ds8/mksv//Qox9z87Z/30aTJtoW+a/9pDk1PU5clX2wQx5OtdBeg6IusxtqSy5x/DMcr6Gk3OH5dFOXC39sghA71aIlzUX9/xe/rFzCZ4zDg4o2Y7vy9GaIlnietgn6E4XHDSY0BUyIeA47jUjdEuVjKxH7lP4+xkA7Wf2aW5oxxdDZA0MXjf/u/T5HfVOsX7iSaRDmrEjguxlud6OKawbUGEd1TezlEKQkX+ty1SRQWrVxnK2/P5LUNefY1RW107L0KKj5cT6vuVDLqHSEoJIA2PTKbB8Uu2JLCMVPoHEIGO0AWOtYciUQikUgkEol1pBNdItHo5pm/SWmjnbchydMPRyKRSDSDFn4IurmrEzimwRXA1bjl8VzOM+Z5Ei5y3SoOyxzOIZwSeIVS50aTNwP3/PoHZvHnEDX7u61/QAz++K/njeKnLVigPWpboIVAByc8BhQKMdfUiR7sgqGigrjUUP6zr9GP6kouW/13GKgo8tm1RKjgOsBzayuLXCsdLcowRwxb1HL9Tp3mZ3xfweErQBY1ugX8/CdTuouKV5aAQLzuvmyOWMpbO8PhyA7MOwiLCWSnv63rFvFAp3dUUelxy0NsvUZEF7nohuvNE1w42cjdAYilWnxD6qj/hpbwW762gLY+kccFLlxL25/Kp5b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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.dates as mdates\n", @@ -1272,18 +464,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Outliers in main.temp:\n", - "Series([], Name: main.temp, dtype: float64)\n" - ] - } - ], + "outputs": [], "source": [ "import numpy as np\n", "import statistics\n", @@ -1319,20 +502,9 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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58+ap21IeQeqUCilJIMOepcarkDIPcrtiSQOpHSv1bi3v10oMWJo9e7YaVmw53Lm6ci5SgsCy1ICUlZH7pXazZuTIkaYzzzyzXH1qGUov9UWlHqpGhmHLc2XYfnXD04XUhJblv/nmm3IlJNq1a1epnEttX6+1dUgZD6mrLKU0qmOtXIkMBZd6w2PGjDGXsRBSa7Vi3WwpbSH3ST1ce1T3uYWGhqrtW5L60FKbVspdWNbilVquljWGtdcyY8aMcs+XesSyP2i2bt2qlrvpppvKLXfJJZfYLOdS3Tod2cevvPJKU8uWLSu99piYGFNGRob5fikbIPdXHH4vQ/Tl86murvpll11Wro1VkRITUrdaagxbkmO84msXS5cuVW2Tkk62yOcln1vFkhtSvkHqa0s9azkuZX+Xdsr2//7772rbqpU6kTIrFfXt21fVV65Iq4EsF3nvrr/+elUqxFGOlHORc41s76233nJ4O1WVc3Hk9Wv7luxztlR1bpHHZL+2RvZXqWFesUyP5fEjJSbkvnXr1lV6/vnnn6/qU1u+t9bOWdp3T8VzzIkTJ9T9UnqjKrbKach5UvZPOVda7gtLliyxWsu/IqmxL8vJMV4dKVsipTSkLJrlsSxlctq0aVNpefm+kHVLvfSqSN11OWdKjXhL8h0m30NyfEntcHmN9u6L2vsl9fwrknrk8ljFc48cw9rxJRf5brW2f9rL1usSUiveclt9+vQx/fPPPw6tv6pyLtpjTzzxRKXH5G8ceWz37t2VvgNtqaqci6PvtXxvWH53OHKOrsl5U/4G6Ny5s83XRkRE5G6YiU5ERG5BMq0kq1yGz4tt27ap4fzaUGz5X8tYkiHoxcXFKptPSGafZHdJxp9kkGkXGe4sWbtSMkBjmbkm65flZN2ScVhdVp6lCy+8EBEREebbUgpEaGUlpASAlAjRMoy1NklWtWQ979u3r9KQfslSlIzF6ixbtgwJCQmqTIxGsnstyxQ48/VariM9PV2NCpDX+/fff6MmJPNbsv0kk9oyu01ev2SJVsxwl6HrklXsDJLxLkPRNVKGQjJQpQSKZKVrunXrprLa5b2u6IYbbih3W94L+VwlG1loz5k1a1a55eT12lLdOh3Zx22R0QOS/ac566yz1P9SXsJy+L3cL59PdSUnpH2Wx4Atkq0o25b9SLLyK2aYyudbkWSPa49bI+/FpZdeqrJ5ZWSBJdm/pXTAjBkzVBkdGdUh5aAka96ejGltm7baZa1N8rpWrVqFDz74QI0+kffP0TIyjpLMXxnhIPuEMzny+iUDXs4lluVLHN2WZI1bU3FbUtpJtmVZEsqRtjq6r2n7tmX5GEfIyAfJApYRRto2hIxIkVFU1Y3kkePLsh1VkRFJMopJsvQtj+WaHl9CRh3JuVr2bSkzZUm+i2+99VZ1fMm5S7LmpeyWjMKoap2W23SkXRdffLEq2yP7vFa6qLrt1OR1CRm1ItuSUVzy2uQYk+9OZ3H09UvJoNIy9HW/Lcn0rzj6yt59qCbnTdm3a3p8ERERucJ/f2URERG5kAS4JPi1bt06FSCTH+lS61sbQi2PacP4tWC6FkSXgLT8yJRgojXyI9iyvqiUS5G65BIQtiTBYXtZ1he1DHRo65S62dKmhx9+WF2skQBL06ZNzbel7rM9jhw5ot6XinXaO3bsWGlZZ7zeJUuWqJI1Emy2rLturU68ve231l4Jpkm9bu1xjbxHtgJtjqr4Httqi9axIyVzJIAiZQvs+eylE0DWKZ0DMlTdkrVt2LtOR/Zxe7ehBdSlJre1+yvuL9ZUF9yRzi6p2y4BvuXLl6uh/ZYksG6tlr9WZ9lWuQYpSSMlaD755BN079692nbK8SK13qUzQtoknVXS0WVZU1m2Ja9d26atdllrk5QO0UinhJQ/kI4ZrQ6wrW3VVE5OjqolLB1yWkkrZ6nJ66/NtmzVtbZnW4601dF9Tdu3nX2eExJE1zqMa3uMSVmx9957T81rIPMOOOP4+vrrr1WZJ6kXL6XAqiPnZwnkawF1+W6WfVQuGjnmpAOzus/MWruknIo2J4IE1KXDWGpq79mzRy1ra1s1eV1S/1suQjqqJeguHapyDra3DnlVavL6Xbkte/ehmpw3ZN+u6fFFRETkCgyiExGR25Af3lKPWercavXQNXL97rvvVtmxEnyQYJwEXIUE3eWHmATprGVyywRzQoJn8mNYAlpS/1kCGRIclXVKwMty4tLq2MoY1wIe2rqktrQEuqypWGPVmcEpZ71eqbUu2YZS41fqaUsGvARspSatvTVwa8vZQbvaqu6zr4t12ruP12QbNX09ErytLtAuowukE0bqsY8YMaLS47I/VZyUUmj3VQy6C6kVL/uiZJJefvnlsJd0FkjAVjpFpGNC6vNLXXjNlVdeqTKqpU1aGyp2MMh9Wj3pqgKKcsxI+yT7UvY5W9uqqQULFqgMf8nGd7bavn5HtyXnKelQlE5TjXxOkolt7fO31daK5D7L58uy1kZX2NrXtH3bWt3s+qB1jkg7bE00LfuQnNsleC3B4YrkNcsolYrByqqOL8nCljrckjH/zjvv2N1ebV/RJmKVWuKW8zpIEFyynKv7zGREkLVsZksS3JaOA+l0l+9XW9tyxuuSbcm8I9JpJfNu1Jb2+hw979WEM95re8/RNTlvyL7tquOLiIioJhhEJyIit6FllkuQXILoluUvZJI3+bEnQ5tl8lHLjDvJ+JUggWQZy8RxtkhwXibBkonJLCfWlB/XFdU2O0oL8EvAWTLmnEkCBDJxYcXAiGTlOfv1fv/992ootmRkW/7YliB6bdqvtVd7n7TA2aFDh2r1fjn6uVm2pSKZJFJ+4Ftmodu7Tgl6ywRxllmo1rZhL3v38foknTISHJcRDdayqqXTS/YTmWBVsketkQxu6aiR98uytI8c41KiqOJrffPNN1U5Dzk3SPDQEVJqSfZlrcNBJpa17ATQgkFaVrmU47AM/Jw4cQKJiYlWyyZVJMFz+byklJME0W1tq6bkfZfXIcF6Z3PG66/JtizP6XJb9gnLDH9rpISIlC+R5S3L2si5REbOWN4n65KAspRIspxcVJvMuuK25FwkqpvU0dY5x/LcUrEDSe7THq/q+NLaceaZZ1Z6XIK6MoGxdNDIcWGNvKb3339fTW4tk39W95rlfpkMWiaHlElrLUvDVEcrZaZlgMt3jvadbtmBKSOLZBlrE/3K5LHVfeZCKw2ijaaytS1nvK6K26otOc/J52nt9Us75TtRr9c7ZVvOeK/tPUfX5Lwh+7Y9I4mIiIjcBWuiExGR25AfuBLkkgCRZAxaZqJLAFdKJEiwQDJJLX8wSxBBsmklE61i9qzc1mrLahm3lsvI9Tlz5lRqixY4lZrLNSFZlVLDd+7cuVazuFJSUlBTEmySH6ZaqQghWanvvvtuueWc8XplHRIkkmxRjWT4SSZsTUmQXLJ1X3vttXJtk3rSEqiQTMGacvRzk+w5+fEvHQ2Wz5FOCqlxXbE8gj3Gjx+v/pfXZ0mCyTVl7z5enwYMGKC2LeUbrJWYkOxQqZEstZOryvJMSkpSZVY0UiNX6hFPnjy5XMeNlGKQOvOSff3yyy/bXKe1Y0vmWJCSRmPGjDEHgqRjTvZF7aIFGbt27aoCmHI8We73b7/9tjoWLOcikAzqimQ/ks4nycbUsqttbasm5PVJTWcJCkoQy9kcef0Gg0F1Nlk7x9lDgsuSDSvrtiS35bVZngvkHCfbsqyhLJ038n5+9tlnqsNC8+mnn6ryHlKLXyPtltdjeZ6U0hPS0SPzAFTMnpX9Wl6v7Oc1OefI95l8/pL1bFniQkaTSFC7uvOc7DNynrQWAJUMbCmTJCOE5PvSMrhpSUoYSUeujNzQyDErbZIAq+V3rNYmqXMvo0dsjdqxdnzJey/nN+l0lHYLCQZb7vODBg0qNzeFbOPYsWPm+3788UfV6Wv5mdn6npTvCvls5G+C6rZl7+uS/cra6BvphNA+T2eRfXHTpk3lPlvpWJF5VCxfv1aSTfb7mrL3vRbS8SuXmpyjHTlvCPmul21Z7oNERETujpnoRETkNiRg0LdvX5X1JD/MtB/jGvmxJRmdwjKILlm6UrNbJg2UAO+0adNUJpdkOc2fP19lQElZFfmBJ8vKdQnSSzaiBLuslaTQti1BOxkuLgFMCVo4QgL+0k7JOpOyFvJDX36MysSokpklgb2akHVJfXjJvpNAjwSCJWhUMaDmjNcrwQcJWI4bN05N6CZBQ3ldUormn3/+qVH7JTNOPisJCMt6JZtWAggS6JHPX2pK11RNPjcJ+ErgW4JlUitXMg9lskoJ0FlOYmgvCcpL5rW8HgkUyH4rQQupk19T9u7j9Un2bSk5IQFdy0xbac8999yj6rdLFq8EOC1JiSHLmsMyEadMHCt10yUIJ++bBGEsyzNI1qTs77K9kSNHqsChJXmPtVENMumvBMrkPgliynolsCPHR8WJTavaJ2S/lKC77D/SqSLHnGT+WmYmy34jpTYkCCvbkoCXBGWlk0uC/vaQgKhctKChdBLKZy0kSCoXS7JembTU0VIusi9qk7Bq80rIa5LJFeUiNa0dff1yXpHbFcvTSD1wOScJLVCovSbJwNbK8MjnJLW8b775ZhXQk2NWzv+yzzz99NPlJvuVfUAmfXz00UfLHZeynHzWw4YNU8eBnFvle0LaLucXjXxGsg05huQ8Jucw6TyT40mCshXJiB0JxlZXc16OTXn/JDAtx6QE1WVbMmrkueeeU/u2tE3OCXL+l05MCejefvvtVa5XOpTlNcjx9cQTT5R7b+Wz0QKTEsy0JJMiy0XIvimjNuTzlA4POb9KB6i8x3IMaR2tEgSX916+G2QEScVJT+U1ap0Jcv6XdUgAVeZZkA6UDz/8UO378pnbM3+FdK5Ju+XzlE426fCQNsp3peUk0vLZyr4qn6NsS0rFyHeYBKBlXoSKJdEqcuR1yT4nn6GcW+VcIs+VEViyH8hrtVaOqiJ5/fL5SIePkONa2+9ln9dGH8hks1KORr5f5bwtHR3yPSvnxTvvvLPcOuW8J6WgKgb45XiUjhs51wgphSf7vpD3RhsdZO97LeTcKixL4dh7jnbkvCFkv5bXJB09REREDYaJiIjIjdx///3yS9E0cODASo/98MMP6jG9Xm8yGo2VHv/+++9NgwcPNgUHB6tLp06dTDfffLNpz5495mV27txpGjVqlCkkJMQUHR1tuvbaa03btm1T6/3oo4/My8n6Z86caYqJiTF5eXmpx8WhQ4fU9RdeeKHS9uX+Rx99tNx9Bw4cMF1xxRWm+Ph4k06nMzVt2tQ0adIk03fffWdeRrYrz920aZPd79ORI0dMU6ZMMQUFBanXceutt5pWrFih1rN27VqnvV7xwQcfmNq3b2/y9/dX76k8T16nPX9GaMulpKRUeuyNN95Q65P3JS4uznTjjTea0tPTyy0zbNgwU9euXe1+X2ryuYk1a9aYBg0aZAoMDDSFhoaaJk+erN47e16L9vnJNjT5+fmmWbNmmaKiotS+KOs7duxYpX3EkXXau49feeWVppYtW5pv23rtsp/I/d9++63VbduzP8prbNeundX3ydbFcv8UaWlppquvvlq9V7I/y2decdtam2xdLPflOXPmmPr162eKjIw0+fr6mhISEkyXXXaZad++fSZHzJ8/39SjRw+13zdr1sz00EMPmYqKiirtw/J5yLEl25L9Tj7rdevW2b2dqt6viucT0b9/f1NsbKzVc2BVtP3A2sVyf3Hk9WvrlH3O2r5l7SKfb0XvvvuuqWPHjiY/Pz9T27ZtTa+88oqppKTE6jqtvSe//vqr+s4ICAhQn4EcE1lZWZWWk+PyrrvuUudjeV19+/ZV582KMjIyVFvef/99kz0WLlxo6tKli9oHKu6PX3/9talnz55qe7JPXnrppabExES71ivfeXIeO3r0aKX3wd59pri42PTMM8+oz1hek5xPP/vsM7v3jYqf76pVq0yjR482f6eFh4ebxowZY/rxxx9Njti+fbt6nhzzsg55X06dOlVuGdmWfF82adJEbUu+++U8Le9vxf3DGkdel5xzzj//fFOLFi3UZyXn1169eplefvllk8FgsOs1yb5t73lPvg/OO+889X0j38/yOq2do7R1ViSfp61tVfzesOe91tZp7VxgzznakfOGuPDCC9W5k4iIqCHxkn9cHcgnIiIiooZH6iDLiAcpUaFlMRI1dFKa5Pnnn1flJpw94bMjJNtXSv9IbXfJ2CfyBKdOnVIjNb766itmohMRUYPCIDoRERER1diNN96oStVYm7CWqKGRsidS5uO+++5TZTdcTcr3yDEm5VK0SXGJGjI5tqT+u5RoIiIiakgYRCciIiIiIiIiIiIissH6VO5ERERERERERERERMQgOhERERERERERERGRLQyiExERERERERERERHZwCA6EREREREREREREZENvvBwRqMRW7ZsQVxcHLy92WdAREREREREREREVFMlJSVISkpCz5494evr8eFlxeNfpQTQ+/Xr5+pmEBEREREREREREXmMP//8E3379kVj4PFBdMlA1z7UhIQEVzfHIzP9f/zxR4wcObLR9Dw1NPyM3B8/I/fGz8f98TNyf/yM3Bs/H/fHz8j98TNyb/x83B8/I/fHz8i91ffnc/LkSZW0rMVdGwOP3+u1Ei4SQG/WrJmrm+NxDAYDoqOj0bRpU+h0Olc3h6zgZ+T++Bm5N34+7o+fkfvjZ+Te+Pm4P35G7o+fkXvj5+P++Bm5P35G7s1Vn493Iyqd3XheKRERERERERERERGRgxhEJyIiIiIiIiIiIiKygUF0IiIiIiIiIiIiIqLGWhPdXsXFxap+EDlG3jOZsKCgoEC9h9Q4PyOpt+Xj41Mn6yYiIiIiIiIi18bLGP9xb87+fBjnqazRB9FNJhNOnTqFjIwMVzelwb5/8fHxOHbsGLy8vFzdHHLhZxQeHq62w/2AiIiIiIiIyLPiZYz/uLe6+HwY5ymv0QfRtRNCbGwsgoKCuGM4qKSkBDk5OQgJCWlUM/I2JHX9GcmJOi8vD8nJyep2QkKC07dBRERERERERK6Ll8lvf8Z/Gkfsh3Ee6xp1EF2GN2gnhKioKFc3p8EepEVFRQgICOBJtBF/RoGBgep/OcHK8cQhP0RERERERESeEy9j/Me9OfvzYZynska912s10KVHjYhqRzuOOLcAERERERERUcPFeBkJxnnKa9RBdA1LuBDVHo8jIiIiIiIiIs/B3/mNGz//8hhEJyIiIiIiIiIiIiKygUF0Ijs9/PDDuO6665y2PqlV1apVK2zevNlp6yQiIiIiIiIioto5++yzcdttt9V6PadPn1Y1xQ8fPoz6tnPnTjRr1gy5ubn1vm1P1KgnFvXEoRSPPvooHnvsMXgSCTTLicsZJ6/azEo9Z84c/Pvvv+b75CR09dVX45dfflEn1w8++KBcvTB5ztNPP42lS5fi+PHj6qTZo0cP9TpGjhwJPz8/3HXXXbj33nvx448/uuiVEREREREREbmWMTkZPjk5rm4G2eHVNfvqrczH7aM7OPyc//3vf5g3b5667uvrq4LI559/Pp544gk16aa9fvjhB+h0OtSWxIWmTp2qYlv1rUuXLujfvz9efvlllRhKtcNM9Abm5MmT5surr76K0NDQcvdJULYhMJlMMBqN9bpNyfyuqffffx8DBw5Ey5YtzffJ+x8SEoJVq1apWYvltkZ6GHv37o2ffvoJL7zwggq+r1ixAsOHD8fNN99sXu7SSy/Fb7/9hh07dtTilRERERERERE1TMbTp3F02nS0eO11mGrxu51IM27cOBUjO3jwIF555RXMnTtXJZ06IjIyEnq9vlbtyMvLUwmXkoDpKldddRXefvvteo/BeSIG0RuY+Ph48yUsLEz1/lne99VXX6Fz586qd61Tp0546623ygV2ZflvvvkGQ4YMUYHfvn37Yu/evdi0aRP69OmjgsLjx49HSkpKuV68adOm4fHHH0dMTIwK3N9www3lgtIlJSWYPXs2WrdurdbbvXt3fPfdd+bHf/75Z7Xt5cuXq+Cyv7+/Ch4fOHBA9cjFxcWpbUt71qxZY36eZHgfOXIEt99+u3q+1tsp2faS1W1JgtiWPXtau6XXr0mTJujYsaO6/9ixY7jgggsQHh6uToqy/eqG1cj7Onny5HL3paeno0OHDjjzzDPVe52RkWF+7KabblJt/fPPP3Huueeq5bp27Yo77rgDGzduNC8XERGBQYMGqfUTERERERERNTaZixejJDsbusxM5P78s6ubQx5AYk4SI2vevLmKC40aNQqrV68uV2Ll4osvRtOmTVVFAYnrfPnll1WWc5F40zPPPIMZM2ao4HqLFi3w7rvvVtmOZcuWqbZINnjF+NjKlSvRs2dPFUMbMWIEkpOTVcxMYnoSd7vkkktUEN6yPTNnzlRtkliSxNHee+89VSVBAuUSI+zVq5dah6XRo0cjLS1NVVGg2mE5lwrZ0ab8fJds2yswsNbDYT7//HM88sgjeOONN9SBuGXLFlx77bUIDg7GlVdeaV5Oet8k4CwHvBz8cmDKCUDKlcjJQwLMsh7pqdJIuREJzMvBLgFnOUCjoqLw5JNPqsefffZZtf133nkH7du3x7p163DZZZepoPuwYcPM67nvvvvw4osvok2bNuqgl4D2hAkTVKBbTiyffPKJClbv2bNHtU+Gz0hAXmqRy2txlLRbTj7aydJgMGDs2LEYMGAAfv31VzW056mnnlK9lP/8848qsVKRnGykjpR0Mli65ZZbVFmWBx98EO3atTMH/2V5yTqX1yTvfUUSvLfUr18/1RYiInfwyuq9Th9SSURERERkKw6T+cN88+2shYsQMXGiS9tEnmX79u3YsGFDucoCBQUFKsFTyutKzEjK8F5++eVo27atitHY8tJLL6k42AMPPKASR2+88UYV89KSNiuSWI9sxxpJDpX4nRaHk4vExb744gvk5ORg+vTpeP3111UbNVKm5p577lEJm19//bXa/vz589WyEm97/vnnVfzv6NGj5nLDEueSJFRpi8SwqOYYRLcgAfQ9vazv3HWt499/wcuinnZNSHBcDuhzzjlH3ZascAn+yrAVyyC6lHyRQLK49dZbVe+bBJslI1rIMJOPP/643LrloPvwww/VQSgZ1VJL6u6771bZ6YWFhSoLXYLIEpwWEiSXTHPZtmUQXZ4nvWAayQSXILlGTkZyAli0aJEKUsvjPj4+KsgvvYiOkiC2lGLRguOfffaZypqX+7ROi48++kgFtqWDYMyYMZXWIScf+WKXbHZL0gu5b98+1VsoPYDa+vbv36+Wl+x0e8h6JdueiIiIiIiIqDEp3LULhXv3SvFqwGhE3vr1MCQnQxcb6+qmUQO2ZMkSVe1ASphIzMrb21sFrDWSgW5ZDlkyvCUzXCo3VBVElyRQqTwgJLgtpWLWrl1rM4gusZ6KsSSNJHRaxuHuv/9+Va1B4mnivPPOU+u2DKJL/Oyhhx5S12V5SWiNjo5WSacS65IAu8TuJEnUMvudcSfnYBDdQ8jwDTnY5MCzzNiWE4YM6bDUrVs383UJ/goZumJ5nwSGLcmBajlppgTLpWdMMsllAk0ZYmIZHBdS7kUy4i1VzOaWdUjvm/T6Sb0qaW9+fr4KXDuDvC7L7PJt27apIHfFulbSCynvnzXSHmFtAgo5EVcM7ksA3REydMdyiA4RERERERFRY5Axf4H6P2TkSKTs2oXAo0eRtXgJoq6e4eqmUQMm89FJdQWJlUmgW6oQSKldTXFxsSrNIkHz48ePq/iVBNst417WWMbTtPLKFeNnFeNJtiYzrRibk21rAXTtPsk4t/UcSTiVChGW8bzYss6nim1i3Mk5GESvUFJFMsJdte3akGC0kHpIZ511VrnH5MCyZDm7sJY9XfE+6cGyl5yUhATCpTfPkgxFsVSxvIn0/EmpFSnxIiVR5MCW3rbqJgGV4HXFYLWUaqmo4vbkfZKhNFJ6piIpPWON9OppNdBtLWNJytnIe7h7927YQ8q/2LNeIiIiIiIiIk8hk4hmLV6sruunTsGB4CAVRM9cMB+RM66qdclbarwkFiQxJiGZ2ZIYajnB5wsvvKBKGkupYwlCy/JSa7y6WJRl7Mye+JnEkySWVN26ZD32rNvaMtZifBWfJ3EnKVVDtcMgugU1cWUtS6q4ivRQyfAMmXn40ksvdfr6JYNbetAkyC1kckwZGiOTNEiPngTLJXvcsnSLPdavX68mAJX6TVqQu+Ikn5JJLr2EliToLBnwEkjXThJbt26tdnsyyYLUjZLeOal7ZQ850ciyUhpHJgitjpSgkXI5b775JmbNmlUpkC8TkFrWRZf6XBUz9omIiIiIiIg8WfYvv6A4IwO+MTEIGjAA2cnJiF+6DIX79qNg+w4EnnmGq5tIHkCSMKWG+R133KHmBJS4lsSipk6dquby04LOe/fuRZcuXZy6bYn1SFlhV5O4kySsUu141/L55EakPrnUJn/ttdfUwf/vv/+qet8vv/xyrdctvXHSYyeBZJldWOqvS81yORlJaZQ777wTt99+u5rkQMqi/P3332oCBLldXda2TB4qAXAJ1MsJrWKPmdQel4lKZYhNamqqeVbilJQUNWmCbE8C1hVnILZGOhikJ1BOljKpwqFDh1QtdAl2JyYmWn2OvEaZyVlqvNtL2iOBf6ml9f3336va6bt27VKfjVY3XiPtsFaLnYiIiIiIiMhTZZaVcgmbOgVevr4oCQxE8IgRZY/9N9koUW2df/75qkqDxGq0WJRURZAJRyVWc/311yMpKcnp25UEyx07dtjMRq8Pkqgq8TSJa1HtMIjuQa655ho1YaYEzmU4imSFywShMsFobckMvnKSGTp0KC688EJMmTJF1TK3nDD04YcfVkH8zp07Y9y4caq8S3XblgB/REQEBg4ciMmTJ6sTjGSLW5J1y0EvGeFa2RPZxltvvaVOgDIsR+pEWU4KYYvUmJKAfIsWLdQErLIe6RyQmuhVZabLe/vVV1/ZXeZG6lhJR4LU4ZIOhjPOOEPVjJcJXKUul+b3339HZmYmewSJiIiIiIio0TCePo2cdevU9bBp08z3h06bqv7PXLoUJdWU1iCyl1RQkERQScSUksQyOafEniQGJUmaUtt8msV+6CwSm5PtSO11V/nyyy9V4mbLli1d1gZP4WVydBbEBkayi6XkiEyA2axZs3KPSeBUMpEl0Gur0D9BlVuREiQLFpT2EluSoHJWVpYKQEvGtqeSw0RqzUu2/cUXX+y09UqHhHQCyNCiulJfnxGPp5qTev4ywkNm+q5Y44xcr7F9Pq+s3lvl47ePrr6sVX1rbJ9RQ8TPyL3x83F//IzcHz8j98bPx/2kzZuHpNnPIuDMM9H622/Mn9H4sWNxZOw4GJOS0PTVVxE6bqyrm9ooWft931jiP84mCaZ33323KqlSl++btc9HqkpIQuwXX3yBQYMGOTXOk1hFvNVTca8nsoPUXX/33XdhNBqdtk45mUmvpATmiYiIiIiIiBqLDK2Uy/Ty2b9ePj4ImzJFXWdJF/IEEydOxHXXXadKqtQ3mbtQkjZrEkCnyjixKJGdevTooS7OIhOmyhAiIiIiIiIiosaiYNcuFO7eDS+dDmETJlR6XALrp997Dzm//QZjSoqaeJSoIbvttttcst127dqpCzkHM9GpWlJX3VopFyIiIiIiIiIiR2SUZZiHjBwJn/DwSo/7t2mDwO7dgeJiZC5e4oIWEhFVxiA6ERERERERERHVOVNREbLKAuPhFUq5WAqbPt1c0sXDp/IjanTe+nk/Wt23FI8v3lHlckv/OYkRL/2MDg8tx9hX1mHt7mS4EoPoRERERERERERU53LWrUNxejp8YqIRXEWd5tAJ4+Hl54fCfftQsGNnvbaRiOrOtmMZ+OKPo+gUr69yub+OpGHWV1twYZ/mWDZrMMZ0jcN1n27GnlPZcBUG0YmIiIiIiIiIqP4mFJ0yBV6+tqfp8wkNhX7UKHWdE4wSeYbcQiNu+3ornj2nG8ICdVUu++H6wxjWIQbXD2uLdrF63DmmI7o2CcO83w/DVRhEJyIiIiIiIiKiOmU8fRo5v/yirodPs13KpWJJl6wlS1BSVFTn7SOiuvXwwu0Y3jEWg9tHV7vsliPpGNSu/HJDO8Tg7yPpcBUG0YmIiIiIiIiIqE5JMBxGIwLOPBP+7dtXu3zwwAHwjY1FcWYmctb+XC9tJCLHZGdnIysry3wpLCy0utyibSew43gW7hnX0a71puQUIjrEr9x9MSF+SM2xvv76wCA6ERERERERERHVTymXKiYUteTl44OwqVPVdZZ0IXJPXbp0QVhYmPkye/bsSsucyMjHE4t34NWLeiBA54OGikF0Ig9SVFSEdu3aYcOGDU5b54oVK9CjRw+UlJQ4bZ1ERERERETUeBTs2oXC3bvhpdMhbMIEu5+nBdxzfv0VxpSUOmwhUf378ccf0blzZxQXF9f7tt955x1Mnjy51uvZuXMnMjMzzZf777+/0jL/Hs9Eak4RJr3+G9o+sExd/jiUho83HFbXi0tMlZ4TE+KvnmMpJacI0SH+cBXbszjUg7xNm3D6gw9RsGOHOhk2e+N188QRJoMBKXPmIOeXdShKTIRPSIgayhNzx53QxcWiMfvf//6HefPm4frrr1c7vaWbb74Zb731Fq688kp8/PHHcKXHHnsMCxYswNatW+Hpfv75ZwwfPhzp6ekIDw93WTtkf2jdujUGDhxovu/333/HDTfcoIbYPPzww7j66qvLPWft2rV44YUX8McffyA/Px+tWrXC+PHjcccdd6Bp06YYN26cet7nn3+Oyy+/3AWvioiIiIiIiBqyzAWlWeghI0fCx4HfzP5t2iCwe3fkb9uGzMVLEDXjqjpsJdkb//Dy8qqXbUmcxVEpKSl45JFHsHTpUiQlJSEiIgLdu3dX9w0aNAju5J577sFDDz0EH5/6z86eMWMGnnzySfz6668YMmRIjdej1+sRGhpa5TJS23zlbUPL3Xf3d9vQNiYENwxrCx/vyvtTz5YR2LA/FVcPbm2+77d9KejVMgKNMhO9JD8f/p06Iu6Rhys/VlCAgp07EX3TjWj9/fdo9vprKDx0GIk33eSStrqb5s2b46uvvlJBT01BQQG++OILtGjRwqVt87TM7vpmMBhq9DyTyYQ33nijUpD82muvxd13343PPvtMDas5duyY+bG5c+di1KhRiI+Px/fff696ECUQL72HL730UrmOm9dee60Wr4qIiIiIiIgaI1NRETIXLVbXw6aVlmdxhDbBqJR0kd+9RFU599xzsWXLFpV8unfvXixatAhnn302Tp8+DXfy22+/4cCBA6q9ruDn54dLLrmkXmI9If6+6BivL3cJ1PkgPEinros7vt6K51bsNj9nxqBW+GVvCt5bdxD7k3Pwyuq9KqP9ygGt0CiD6CFDhyL2ttsQOnp0pcd89Hq0+PBDhI4fD/82rRHYowfiH35IZa0bTpxAY9erVy8VSP/hhx/M98l1CaD37Nmz3LJShkOCp5KhHBgYqHrgvvvuO/PjMmxEAq/a4x07dsScOXPKrUOCqNOmTcOLL76IhIQEREVFqax3WwFfyYJ//PHHsW3bNtVDKRctMz4jIwPXXHMNYmJiVG/ViBEj1HKWGexSPuTDDz9UryckJAQ33XSTaufzzz+vAr6xsbF4+umny21TtvH222+rLGp5HW3atCn3OoUEkC+44AKVLR4ZGYmpU6fi8OHDlV6nrLtJkybqvRCffvop+vTpo3rYZPtyoklOTlaPyfO13lHp4ZR2yHqEZHW/+uqr5dogr01eY8V2T5kyBcHBwebXtXDhQvU5BwQEqNci76fRaIQtf/31lzoBT5w4sdz9ubm56jOXi7RPMtJFYmIiZs2apS7yXsuXirR36NCheP/991UvrUaG+GzevFmtn4iIiIiIiMheUoqlOD0dPtHRCBk82OHnh04YDy8/PxTu24eCHTvrpI3kGSTeJJnVzz33nIrTtGzZEv369VMlRiTmYhmHkbjH9OnTERQUhPbt26tgu6VffvlFPdff31/Fwe677z5zTGbJkiUqrqSVYZEKDLJOWUYjca/LLrvMZlslMXb06NEq5uOMeJgkSV544YXqOVIiRqoS7N+/X8V6JNYkFQsqxnQk1iOv2zJB11WOZ+QjOeu/SUN7t4zEnIt64ss/j2LCnF+xfPtJvHt5H3PQvdGVc3FUiQT/vLzgXc0wgVpvJy/P9oM+PvD297dvWW9veFscDLaW9Q4KqvHQi48++giXXnqpui0H2VVXXaWG1liSALpkIUuGsZwY1q1bpw5kCWIPGzZMBdmbNWuGb7/9VgXHpZ72ddddp04SEnC2LPsh98n/ciDKwdmtWzf1f0Vy3/bt21U97TVr1qj7ZIIBcf7556sg9/Lly9V9cqCPHDlS9RBKYFvIgS2Py/Pl+nnnnYeDBw+iQ4cO6kQmbZTXL1nUZ511lnm7Unbk2WefVZ0AEvi+6KKL8O+//6oTiAT8x44diwEDBqiTqq+vL5566ilVruSff/5RvXBaTSoJ7q9evdq8XnmuDHORoLoEz6XUiQTKly1bpjozJItbeg/37NmjniuvzxFyopR2S8Bd2iXtu+KKK1SPoAyrkfdAPhPx6KOPWl2HPEfeHwn0W5KhQfIeycn+xhtvVJM+CPm8JdNehg9ZY1mWRk7ecXFxahtt27Z16LURERERERFRw2NMT8eRyy+H/uyzEXvXXTVeT0bZpKBhU6bAy9fxMJRPaKgq/Zu1bBkOn3eeigvVKS8vhJ97DhKefLJut0NOJwFkuUhp4f79+6sAuC2SqCiBaSlv+/rrr6vY2pEjR1Rc6vjx45gwYYKK+3zyySfYvXu3GuUvAW+J30icRhIUJeNdEi4lThUdHV0uHif33XvvvTa3L/EVSdCsqKbxMAmsS9xK4mHSaSDrloRMuS4xHXnOLbfcotatkbZLrEjK+0qwvT59ff2AKm+Lid0S1MVdNJggeklhIZJffAmhEyeq+ui2FBYWqotGy7p1xJ5evW0+FjxsKFrMnWu+vXfQYJhs9NgE9e2Llp9+Yr69f+Qo1ftaUefdu1ATEgiXg0EOcrF+/XrVk2V50Mp78cwzz6hAtgSPhRxEMmxEgtcSRNfpdOrkoZGMdOmx+uabb8oF0SWLWcqFSK2mTp06qYznn376yWoQXYLIcuKSgLD0lGlku3/++acKRGsnM8lulxOcZI1rgWIJ7EungASEJegrPYgSoJagtbe3twpmS8+iBPQtTxoSoJfePiEnDwmEy8lQ6sR//fXXar3S26jV75JOCAkWy3s2ZswYdZ/00MkyWlBdyMlGI++fBLf79u2LnJwc9Tq14L/0CNakJrqc3KQDxHJ70oMpte21bcrrkYC3rSC67AeSPV+RjDKQk7+c7KWTRLNv3z4V8JeOEXvIurV9jYiIiIiIiDxb7rp1KNp/AKcPH0HkVVfB1+L3pL2MaWnI+fmXGpdy0URcfhmyJNFNRsPXdUkXkwkZ332P6BtugK5p07rdFjmVxKCkCoIEvCWRVEb3S9xLEiwlCdSSBMgvvvhidV3iZhLnkXiVJFpKDEkSJiUGJvEjiYGdOHFCBcVl1L4khErGuMSSJBAt/99+++0qtiZxIimRK8mnsm1bbMVwahoPk9cjmfUS55F2SgxQEk0lmVTceuut5eJOQrLw5bUw1uNBQXSZZPT4bbfDBBPiH7MeQLTMurYMCHsyySSXQLacIKQumFyXni9LctDm5eWpISKWJAPZsuzLm2++qQ7So0ePqmEc8ricECx17dq13GQHEnyVLG9HSNkWOaFYBnOFbNNyWImUFbHMqJYsaNm2nDAs79NKqmi0jgLL29rEprJteT8qZmpLLXnLbZ955pnlAuhaqRTpbZR1yOShclIT8n5pmd21ISddS7Id6RSxHKIjw3ekrfJ5yomuInkPLYcBWZKOgYoTPcg+48hkINIxItsmIiIiIiIiz1ewq6w+sdGIrCVLEFmW5OUIeZ48P+CMMxDQoUON2xLUsyc6bvxdza1X147fcSfy/vwTmYsWIfrGG+t8e+RcUiVA4mOS6b1x40aVeS0Z55IsqZXeFZZBdS1mosWYdu3apeJJljETmZRU4llSGlcyuyVALsHzO++8U21L4pGSjCrJo2lpaSpALtUgbLEVw6lpPExiWZaPW7tPYkpZWVnl4kOM9XhQEF0C6Im3367qoLf4+KMqs9CFZGZLqQ2NDMFwNMjZ8e+/bD9YYcbcDut/s72sxQ4u2v1YWtbEmbThGFogvCI5wIXMSty0Qg+qlgku2et33XWXmkhSThJysMpwFhnOYUky1i3JyUQLJttL2iPB94olZ4RlBre1bdV2+7Lt3r174/PPP7faIWF58qxYU1x67uQiz5VlJXgut6ubeFROchUnPrFWR77iNqWt0hl0zjnnVFrWVqBcOlAc6dSQoUDSO3ry5Em7stHlS8DyfSIiIiIiIiLPVbD7v0n+MuYvqFEQXZ4nwqZPq3V7vIOD1aWuSSkXCaJL26NuuMGh5DNyDxI3kWRSuUg2tlQskFH9lkH02saYpPyJJKNKEqSsS7LV5T6Jd0nyZVVZ6FoMR5arqKbxMMtltH3W2n0Vn8dYj4cE0c0B9CNH0GLePPhGRFT7HAkMW9Y8kh4WRzlSo7yulrWXDDORQK4cDNoQDUvSgSDvhwR9bR3AkvEsEwzIZAUaZ0wgKdnc2iQLGhlKc+rUKTXERnrXnE16GaWWuOVtLeNeti0lXaTkSsWs7KpI7SuZxVlqlstwHiGTbFrSMtcrvl45EUmQ2nJ/PHToULXblLbKcJ127drZ3U55nTJBqb0Z5lJXS0rGSI/sK6+8YnVCDq1jQ8vWrzhpLREREREREXke+V1ZuOu/0rOFu3ejYNcuBHTubPc6ZHlZh5dOh9AJE9BQ6EePhvfjT8AgI/X//htBvW2X/KWGQWJjUkbYXjKvnsx9ZxlfkdiZJJ3KnIJCq4su8RQt3iZBdIkdSXBcMtSrIvGVnTtdO1GuxHkk3sNYj33Kp0rXs5LcXHVSlYsoSkxU1yXrXAXQb70NBdt3oMkLL0h0EsaUFHUxVZP925jIkA4ZZiIHnmWpFY0c4JJlLrWZ5s2bpw6Qv//+W9UJl9tChpdIUHjlypVqck/ppdu0aVOt2yZBcgkYSzmV1NRUVZ9dJj6QbPdp06Zh1apVOHz4sJoU4cEHH6wUmK4JmSxTegLldUgvo9Sz0jL1ZZII6embOnWqGmojbZMewlmzZqnhOLbIMB0Jkst7JpM5yMzFUp/cksz4LCdWmaE5JSXFPAJgxIgRaoJT2Z5kiUuNc2ufU0VSY0smr5Bs9B07dqjPWEYMyCShtkidLNmuLG8P6RCQk71MOiF102WCCqmDJV8M119/fbnXKJ0R0hlTsVwOEREREREReR7jqVMozsyUItMIGTGi3ASh9sosC1rK8+1JinQXkgCpHzeuRq+ZXEsSICUO89lnn+Gff/5RcR+JE0nyoMSC7CVJpseOHcPMmTNVYuXChQtVjEkqX2hlVWTeQCkJIxULtEk5hw4dqmJuEpOqLhNdEmGl9IsrSaxK5uBr27atS9vRULg0iJ6/fQcOTT9HXUTys8+p6ymvvQ5DUjJyfvpJnbgPTZuOfUOGmi95W0prXFMpyaquKrNagqESGJf6TNKbJtnrUt5FJhAVEjCVsiEyQahMSiAnHcus9NrUoZJtSXBXMrK//PJLFWiWyRDkxCITGkhJEZngQYK3Ws2m2pCgswSb5UQmQWjZplbOR+qIr1u3TgXF5fXKeyHBY+l1q+r9k7ZL3Xk58cq6pFdRJkO1JKVyZNuS2S2vQwvcS3khOXFOmjRJ1eSSzgN7Tk5yMpWAvHQ0yASmMqu0BLwlWG+L1JmXSSSslauxRT5n2YaUPZLnyvAjGeYk74d0vmjkfZROCGu12ImIiIiIiMgz66H7t22LiAsvUNezFi+xO6lRlstctNhppVzqW3hZm7OXr0AJ60U3GCEhISquJfETiTudccYZKh4mE43KJKH2khiPxK4kMbN79+644YYbVPyoYmKjxHukIoEWRI+MjFRxo/j4eDUBaFUkxiJJkFKFwFUk1iPvDdnHy1SxYLOHkQxjybiVHiRtyIVGgqfSKyXBZFt1pqlqUktJm5TAcpIDV5AA/fz581WgurGSnlap+SUjDuTLwxmfkYwikJO/jBTQOl6s4fFUc1InX76gJ0yYUKnWGbleY/t8Xlm9t8rHbx9d8wmh6kpj+4waIn5G7o2fj/vjZ+T++Bm5N34+jkt56y2kvvY6wqZOQcLTT2Pf8OEoTklFszffgH7kyGqfn/3jj0i8+Rb4REej/c9r4eXr26A+I1NJCQ6MHQfDsWNo8vxzCJsyBY2Jtd/37hT/8RR33323ek/nzp1b63U5+vlIAF+y9iVrPiwszOE4T2IV8VZPxb2eyINIBv5zzz1nV911e0nJnbfeeqvKADoRERERERF5jkItE71TZxUAD5s8xaHyJtpyEnyuLoDujry8vRE2bWq5sjREzialjaXigCMTmjqLzN8nFRxsBdCpMgbRiTyMzDZ95plnOm19ffr0UaV+iIiIiIiIqHEo2F0aRA/o3En9rwWUc37+Bca0tCqfK4/LcpbPa4jCppaOcs/9faOau4/I2cLDw/HAAw+4JLNf5iyUUsJkPwbRyWNIZaLGXMqFiIiIiIiIqLaKs7NVGRPhX1bXOaBDBwSccQZgNCJryZIqn5+1ZKlaTpaX5zVUfs2aIuissyTYgMxFi1zdHCJyMQbRiYiIiIiIiIhIKSyb6NA3IQG+ERHm+7UJQjPmV13eJGNBWSkXD0hy++81z1eJe0TUeDGITkRERERERERESkFZPfSATqWlXDShEybAS6dD4a5dKNi1y/pzd+9G4c5dgE6H0IkT0NCFjhkD76AgGI4cRf6WLa5uDhG5EIPoZTPYElHt8DgiIiIiIiJq+Ap27ypXD10jWekhI0ZUOdlmZlmWun748HJZ7A2VBND148ap65l2TqrqSfg7v3Hj519ew5si2Yn8/PxU8f4TJ04gJiZG3fby8nJ1sxrcAVVUVISCggKXTIRArv+MZEibrD8lJUWtX44jIiIiIiIiapgKyzLR/StkomvlTbJXrkTmosWIvfNOeFn8/jMZDMhcvNi8nKeQyVEzf/gBWcuWI04mgQwMRGOMl2m//Rn/8fzYD+M81jXqILrsCK1bt8bJkyfViYFqdmDl5+cjMDCQHRCN/DMKCgpCixYt+GVKRERERETUQEkgvHDfPnU9oHPnSo+HDB4Mn+hoFKemIufXX6EfOdL8mNwuTktTj8tyniKoTx/omjWDITER2WvWIGzyZDTGeBnjP+6tLj4fxnnKa9RBdCG9KbJDGI1GFBcXu7o5DY7BYMC6deswdOhQ6HQ6VzeHXPQZ+fj4wNfXl1+kREREREREDVjhwUMqkO4dEgJd06aVHvfy9UXYlClI+/BDNdmmZRBdK3ciQWapne4pvLy91SSpqW+8oV5jYwiiW4uXMf7j3pz9+TDOU1mjD6IL2SFkB+NJoGYHlZxQAwIC+P65KX5GREREREREZI/Csnro/p06quCxrfImEkTP+fkXGNPS4BsZqf7PXvtz2eOeU8rF8jVLED33940wnDgBXZMmaGzxMsYW3Bs/n7rHfHwiIiIiIiIiIkJBWT30gE6VS7loAjp0QMAZZwBGI7KWLFH3ZS1Zqm4HdO2KgI4d4Gn8mjVDUL9+UjMDmYsWubo5ROQCDKITEREREREREREK9pQF0TtXnlTUkjZxaMb8BaX/Lygr5TJ9OjyV9tqkjI3UnyaixoVBdCIiIiIiIiKiRk4Cw4Vlmej+naoOoodOmKDqnhfu2qUyswt37gJ0OoROnABPFTpmNLyCgmA4chT5W7a4ujlEVM8YRCciIiIiIiIiauSMSUkozsgAfH3h365dlcv6RkQgZMQIdf3kY4+r//XDh6v7PZV3cDBCx44tN4kqETUeDKITERERERERETVyBbvKJhVt0wbe/v7VLq+VdDHl5ZW77cm015i1bDlK8vNd3RwiqkcMohMRERERERERNXKFu+2rh64JGTwYPtHR6rr8L7c9XVCfPtA1bYqS3Fxkr17t6uYQUT1iEJ2IiIiIiIiIqJErMNdD72zX8l6+vgg/71x1Pfycc1SNdE/n5e2N0MmT1PWcdb+6ujlEVI9863NjRERERERERETkfgoczEQXMTffjKA+fRF8Vj80FoE9eqj/C/eUvl9E1DgwE52IiIiIiIiIqBErzsmB4ehRdd2/Y0e7nyfZ5yGDBzWKLHRNQOfSTP3Cg4dQUlDg6uYQUT1hEJ2IiIiIiIiIqBEr3LNH/e+bkADfiAhXN8et+cbGwkfeo+JiFO7b7+rmEFE9YRCdiIiIiIiIiKgR0+qhB3Syv5RLY+Xl5WUueVOwe5erm0NE9YQ10YmIiIiIiIiIGjEtGOzfyf5SLo2ZTL6au+F3FJZ1PhBR1T7deASfbzyCxPR8dbt9XAhmjWyP4R1jrS7/7eZjuPu7f8rd5+frjb1PjYerMIhORERERERERNSIacHggE6l9b6pav9lojOITmSPhNAA3DuuE1pFB8NkMuH7vxNx3SebsXTWEHSI01t9jt7fFz/eNcx82wtecCUG0YmIiIiIiIiIGimTwYDCffvKBYepalrZm8Ldu2EqKYGXN6slE1VlVJe4crfvHtsJn208ii1H020G0SVmHqsPgLtgEJ2IiIiIiIiIqJEqPHQIpqIieAcHQ9esmaub0yD4tW4NLz8/lOTlwXDsGPxatnR1k4hcIjs7G1lZWebb/v7+6lKV4hITlv57EvlFxejVwvZExnlFxRj07E8oMZnQtUkY7hnX0XbAvR6wq4yIiIiIiIiIqJGSbGrh36kTM6rt5OXrC/8OHcpNykrUGHXp0gVhYWHmy+zZs20uu/tUFro8sgIdHlqOB+f/i7mX90Z7G0HxNjEheP7cbnj3it545cIeqgTMuW9twMnM0prqrsBMdCIiIiIiIiKiRkoLAmslSsg+UvqmYPt2NSlr6Lixrm4OkUvs3LkTTZs2Nd+uKgu9TXQIls0aguwCI5ZtP4k7v92Gr6/rbzWQ3rtlhLpY3h718i/44o+juHOMayZAZhCdiIiIiIiIiKiRkiCwYD10x0jmvuWkrESNkV6vR2hoqF3L+vl6q4lFxZnNwvBPYgY+XH8Ys885s9rn6ny80bVJKA6fzoOrcJwOEREREREREVEjJCUStCCwf6fOrm5OgxLQufT9Kigrh0NEjikpAYqMJXYtK3XUd5/KRqy+6nrrdYmZ6EREREREREREjZAxKQnFGRmAjw/827dzdXMaFP8OHc3voTEtDb6Rka5uEpHbem7FbpzdIQZNwgORW2TEwq0nsPHQaXwyo596/I6vtyIuLAD3jisd4TFnzT70bBGOVlHByCowYO66gzieno+L+jZ32WtgEJ2IiIiIiIiIqBEq2FVaysW/TRt4V1HLmCrzCQmGrmULGI4cVZOz+g4c6OomEbmt0zmFuOObbUjJLoQ+wBedEvQqgD6kfYx6/HhGPry8vMzLZ+YbcP8P/6rlQwN1OLNpKL6/caDNiUjrA4PoRERERERERESNkAR/hT/roddIQKfOKoguk7MGM4hOZNPz53W3/SCAr68fUO72I5O7qIs7YU10IiIiIiIiIqJGSIK/WjCYHKdNxsq66ESej0F0IiIiIiIiIqJGSAv+asFgcox/p9L3rXB3aVkcIvJcDKITERERERERETUyxTk5MBw9Wi4YTI4J6FyawV948BBKCgpc3RwiqkMMohMRERERERERNTKFe/eq/33j4+EbEeHq5jRIvrGx8JH3rrgYhfv2u7o5ROSpQfS8TZtw7IYbsW/IUOzq1BnZa9aUe9xkMiHltdewd8gQ7O7eA0euugpFhw+7rL1ERERERERERJ6gYFdpCZIAZqHXmJeXl0VddJZ0IfJkLg2il+Tnw79TR8Q98rDVx0+//z7SPv0MCY89hlbffA3vwCAcveZalBQW1ntbiYiIiIiIiIg8RWFZPXR/1kOvFf+ySVkLd+9xdVOIyFOD6CFDhyL2ttsQOnp0pcckCz3tk08QfcMN0I8ciYCOHdHkuWdhTE6ulLFORERERERE5OlyfluPomPHXN0M8hAFu8omFS0LAlPN/JeJXvp+EpFnctua6IbERBSnpCJ44ADzfT56PQK7dUP+1m02n1dYWIisrCzzJTs7u55aTERERERERFQ3CvbswbFrrsHRq6+BqaTE1c2hBq44OxuFe0ozpwM6dXR1cxo0rRyOZPbz2CTyXG4bRDempKr/faKiyt3vEx0NY2qKzefNnj0bYWFh5kuXLl3qvK1EREREREREdUmbtNBw9Cjy/tzk6uZQA5e1YgVMBgP82raFrkULVzenQfNr3Rpefn4oyc1VCaFE5JncNoheU/fffz8yMzPNl507d7q6SURERERERES1Ykw6Zb6eOX++S9tCDV/m/AXq//Dp09TkmFRzXr6+8O/QoVyJHCLyPG4bRPeNiVb/F58+Xe7+4tRU+EbH2Hyev78/QkNDzRe9Xl/nbSUiIiIiIiKqS4aT/wXRs1atQnFOrkvbQw1X0eHDyP/7b8DbG6GTp7i6OR5WF32Xq5tCRI0tiK5r1gw+MdHI/X2j+b7inBzk//MPAnt0d2nbiIiIiIiIiFyViW7Kz0f2ypUubQ81XBkLSrPQgwcPgi4u1tXN8Qj+Wl10ZqITeSyXBtGlXlTBrl3qIooSE9V1w4kTajhR5BVXIPWdd5D9008o2LMXJ+69D76xsdCPGuXKZhMRERERERG5JBM9oHs39T9LulBNmIqLkblgoboePn26q5vjMQI6d1b/F+xmEJ3IU/m6cuP523fg6JVXmm8nP/uc+j9s2jQ0eXY2oq65RvWwn3zkUZRkZSGwdy80f+9dePv7u7DVRERERERERPXLUJaJHn399Ui8+Rbkbd6MoqNH4cdJIckBeX/8AeOpU/AODUXIiBGubo7H8O/QUf0v760xPR2+ERGubhIReVIQPfisfuhcRb0oyUaPmTVLXYiIiIiIiIgaI1NREYpTS+cLC+zeHcEDByJ3/XqVURwza6arm0cNSEbZhKKhEycwQdGJfEKCoWvZAoYjR1G4ezd8BwxwdZOIqLHURCciIiIiIiIiwJCcAphM8NLp4BMZibCyMhyZCxbAVFLi6uZRA1GcnY3s1avVdZZycb6ATmUlXVgXncgjMYhORERERERE1AAmFfWNj1cjtvWjRsI7JETNJ5b35yZXN48aiKwVK2AqKIBf27YIOPNMVzfH4wR0Lp1ctKCKigtE1HAxiE5ERERERETUACYV1cXHq/+9AwIQOmGCus4JRsle/00oOk11xpBz+XcqDaIXMhOdyCMxiE5ERERERETUQDLRNWHTp6n/s1atQnFOrsvaRg1D0ZEjyP/rL8DbG6GTp7i6OR4poHNpOZfCgwdRUljo6uYQkZMxiE5ERERERETUgDLRRWCPHvBr1Qqm/Hxkr1rlwtZRQ5CxoHRC0eBBg6CLi3V1czySb2wsfCIigOJiFO7b7+rmEJGTMYhORERERERE1CAy0ePM90k5DvMEoyzpQlWQyWctS7lQ3ZBjUquLXsi66EQeh0F0IiIiIiIiooaQiZ6QUO7+sKlTJHKHvE2bUHTsmItaR+4u748/YDx5Et56PUJGjnR1czyaf6fSki4FrItO5HEYRCciIiIiIiJyYwYtEz3uv0x0rbxL8IAB6rqWaUxUUUbZSIXQiRPg7e/v6uZ4NC0TvWA3g+hEnoZBdCIiIiIiIiI3ZSoqQnHq6Uo10TXmki4LFqiyHUSWinNykL1qtboeXravUN0J6KSVc9nN45HIwzCITkREREREROSmDMkpgMkEL50OPpGRlR7XjxoJ75AQGI4fR96mzS5pI7mv7BUrYCoogF+bNgjo1s3VzfF4fq1bw8vPDyW5uTAkJrq6OUTkRAyiExEREREREbkp46mT6n/f+Hg1cWFF3oGBCB0/Xl3nBKNUUcb8Ber/sOnTrO4/5Fxevr7w79BBXWdddCLPwiA6ERERERERkZsynEqyWcqlYkmXrFWrVAYskSg6cgT5f/0FeHsjbMoUVzenEdZF3+XqphCRE/k6c2VEREREREREVDeZ6LYE9uwBv5YtVdA0a+UqhJ/D2tcEZCwozUIPHjQIugqT0lLd8dfqojMTncjs041H8PnGI0hMz1e328eFYNbI9hjeMRa2LP3nJF5avUc9p3VUMO4b3wnDO9levq4xE52IiIiIiIioAWeiS5kO8wSjLOlCMiFtSQkyFyxU18OnT3N1cxqVgM6d1f8FuxlEJ9IkhAbg3nGdsHjmYCy6ZRAGto3CdZ9sxt6kbFjz15E0zPpqCy7s0xzLZg3GmK5xuO7Tzdhzyvry9YGZ6ERERERERNQomYqKYDIa4R0UBHdlTDql/veNrzqTOGzqFKTMmYO8TZtQdOwY/Jo3r6cWkjvK++MPGE+ehLdej5CRI13dnEbFv0NH9b/x1CnkbdkC7+DgOt2ed1Aw/Jo1rdNtENXWqC7lv8PuHtsJn208ii1H09EhTl9p+Q/XH8awDjG4flhbdfvOMR3x675UzPv9MJ6ZfqZd2zQUlyAluxD5hmJEBfshPMivVq+BQXQiIiIiIiJqlBJvvQ25Gzei7YrlblvuwnCyNIiuS0iocjl5PHjAAORu2KAykGNm3lJPLSR3lFE2IiF04gR4+/u7ujmNik9IMHQtW8Bw5CiOXHxJvWyzyfPPse49NRjFJSYs/fck8ouK0atFhNVlthxJx9VD2pS7b2iHGKzaUfqdaEtOoRHztxzH4m0nsO1Yhgqkm2TElmTDhwViSPtoXNyvBbo3D3e43QyiExERERERUaNTePAgctauVdcL/v3XfYPoWia6He0Lmz6tNIi+cCGib74JXt6s4NoYFefkIHvVanU9fBpLubhC5GWXI/XduRItrNPtmAwGlGRnI+3zzxlEJ5fIzs5GVlaW+ba/v7+6WLP7VBbOeWsDCo0lCPLzwdzLe6O9lSx0kZJTiOiQ8pnjMSF+SM0ptNmW9389iDfW7kfLyCCM7ByHm4e3Q1yoPwJ8fZCRb8DeU9n483AaLv/gD/RoEYHHp3RF62j7R4owiE5ERERERESNTub80kkXLeuOu2O5meLU03Zlogv9qFGqdIQhMRF5mzcjuF+/emgluZvslSthKiiAX+vWCOje3dXNaZQiL79MXeqa8fRp7Bt2Ngq2/YPCAwfg37a09AVRfenSpUu5248++igee+wxq8u2iQ7BsllDkF1gxLLtJ3Hnt9vw9XX9bQbSHbUtMRPfXD/AankY0aN5OC7o2xyF08/At5sTselQmkNBdHZLExERERERUaNiKi5W2doaY5J7BtENySmAyQQvPz/4RFgf8m7JOzAQoRPGV+okoMZZykUmm5VJZ8lz+UZFIWToUHU9cwGPeap/O3fuRGZmpvly//3321zWz9cbraKDcWazMDXJaOcEvap9bk1MiD9Sc4rK3ZeSU4ToENvlqV6/uKfNALolf18fXNa/pQqoO8KhILr0aqW89jqOXPk/7B89BnuHDMHBKVNx4t77kLl4CUqKyr84IiIiIiIiIneTu+F3GJOTK5VMcTfGUyfV/77x8XYHQyVwKrJWrkRJbm6dto/cT9GRI8jf/Bfg7a0mmyXPJ2WcRObCRaqDkKg+6fV6hIaGmi+2SrlYU1ICFBmtlzzq2TICG/anlrvvt30p6NWy+g7lumJXOZf8HTuQ/OKLyP/rbwT26oXAbt3UMDGvAH+UZGaicN8+pLz6KpKeegqR11yNyCuvhLdf7WY8JSIiIiIiIqoLmWWZur4xMTCmpMCY9F9A3Z1oZWYcqdce2LOneVLDrFWrEV4WYKPGQRthETxwoNvW+Sfn0g8bBp/wcNUxKHMihAwZ4uomEVXy3IrdOLtDDJqEByK3yIiFW09g46HT+GRGadmxO77eiriwAJWhLmYMaoUL527Ee+sOYninWDVR6L/HMzH7nG6wR4GhGPM2HMbvB0/jdE4RSkwyveh/ls4aUjdB9OOzbkXk1TPQbM4c+ISG2lwub8sWpH/6KdI+/AjRN1zvcGOIiIiIiIiI6lJxVhay16xR1yNnzEDyc8/BeMrNM9ET4u1+jmSsh0+fjpRX56jOAgbRGw9TSQkyykp6aNnJ5Pmk3FPopElI/+wzdcwziE7u6HROIe74ZhtSsguhD/BFpwS9CqAPaR+jHj+ekV9uxFXvlpGYc1FPvLRqD15YuQetooPw7uV90DHevvrp937/D37dl4rxZ8Sje7NwOKOylV1B9LYrlsNLp6t2uaCePdVFZgcmIiIiIiIicjdZy1eoCTv927eHfsRwFUQ3JCXBJLXH3ax+9H+Z6PYH0UXYlClImfMa8v78E0WJifBr1qyOWkjuRD5v44mT8NbroR850tXNoXoknSYSRM9e8yOKMzPhExbm6iYRlfP8eVVPcvz19QMq3TexW4K61MRPu5Lx0VV90adVJJzFrpro9gTQa7M8ERERERERUX2WcpHa4b5l5S5MBQUoycqCuzHUIBNd6Jo0QfCA/up65oL/JlClxrFvh06YAO+AAFc3h+pRQJcu8O/QQXUQZi1f7urmELmclIYJ9rcrd9z5E4vmbtyIAxMnoTgnp9JjxdnZODBpEvI2b3Zq44iIiIiIiIicpfDgIeRv3Qr4+CBs8iQVaJRawpZZ3+7EqGWixzsWRLecYDRzwQJV5oM8m8RqslauUtdZwqfxkVE05mN+fmlJH6LG7MGJnfHs8t1ITM9z2jrtDsmnzfsE4eefB5+QkEqP+ej1iLjgQpz++GME9enjtMYREREREREROYsElIXUDJZJRYVkoxdnZMCYnAR07AB3Yiir1V6TILp+1Ch4BwfDkJioEt6C+5VO3kaeKXvlSjWiwq91awR0r7psAnkm6RhMfvFF5G/bhsKDB+Hfpo2rm0TkMt2ahqHQWIyhz69FoM4Hvj7l88i3PTqm7oLoBXt2I/auO20+Hjx4EE5/9JHDDSAiIiIiIiKqa6biYmQuLC1tomVsCt/4OBTu2WMOWLsLKctQnJqqrvvWIIjuHRiI0AnjkfHtdyozlUF0z5ZhUabI3Wr7U/3wjY5GyNChyFm7Vh3zsXfe4eomEbnMrK+2ICmrEHeP7YToED+nnBftDqIXp56Gl6/txb18fFCcllbrBhERERERERE5W+7vG2FMSlIT7oUMP9t8vy62tC66MSkZ7sSQnKL+9/Lzg09ERI3WETZtmgqiZ61cifiHHlSZ6eR5io4cQf7mvwBvb4RNneLq5pCLJxhVQfSFCxFz260qVkfUGP11JB0/3DgIXZqE1n9NdBniVrhvn83HC/bsMQ+HIyIiIiIiInLLSRcnToS3n1+5THRhTHKvTHSjNqlofHyNM+gCe/WCrkULmPLykLVqtZNbSO5CG2ERPHAgdGWT5VLjpD/7bDXPgzE5Gbkbfnd1c4hcpm1MCAqMxU5dp91BdBkSkjLnNZQUFlZ6rKSgAKmvv4GQs//rzSciIiIiIiJyB8VZWches6ZSKRfLeuPuNrGo1p7aBEUl+K5NMqnVgyfPIpPGZpR9tpKFTI2bjFwJnTSpXMchUWN077hOeHrpLvx+4DTSc4uQXWAod6nTci7RN96AQ6tX48C48Yi89BI1WYWQyQrSv/gSKC5G9A3X16gRRERERERERHUla/kKmAoL4d++HQLO6FruMV9zOZck98xET3C8HrqlsKlTkfLa68j74w8UJR6HX7OmTmohuYO8P/+E8cRJeOv10I8c6ermkBuQzpT0zz5THYfSgegT6rxyFkQNxZUf/an+v/T9jeXuN0lnE4CDsyfWXRBdJiho9eUXOPn440h++RXAZNK6ttWkovGPPKKWISIiIiIiInInWkZm2LTKky7qysq5GJLcNRO9dkF0XZMmCOp/FvJ+34jMhQsQc/PNTmohuVWZovHj4R0Q4OrmkBsI6NIF/h06oHDvXmQtW46Iiy50dZOI6t2X1/Z3+jrtDqILXdOmaPHuuyjOzETR0aMqkO7XsqWamIWIiIiIiIjI3RQePIT8rVsBHx+ETi4tc1Bx/i9RkpmJkvx8eAcGwh0YnJSJLsKnTy8Nos9fgOgbb4SXt92VXcmNFefkmmvds5QLaaSjUCYVTn7+edXJwiA6NUb920S5NoiukaB54JlnOr0xRERERERERHUx6WLI4MHQxcZWelzKYHgFBanJN6Wki1+rVnAHRi0Tvaxme23oR42Cd3AwDImJyP/rLwT17euEFpKrZa9cCVN+vtpnA3v0cHVzyI2ETZ6E5JdeQv62baoMs3+bNq5uElG9KzAUY/epbJzOKURJWUEVzegucXUXRJcJRdPmfYKS7CxEXH651T8+iIiIiIiIiNyFqbjYHESvOKGoZdam/L4tOnwYhqRktwmiG06dcloQ3TsoCPrx45D53ffImL+AQXRPK1M0vXKZImrcfGNiEDJkCHJ+/lmNQIm98w5XN4moXv28Jxl3frMNaXlFlR6r85roJx98CF7+fqr36uiMGWi7ZAnq4w+elDfeQNaixTCmpsI3NlYNUVLDz/gFQURERERERFXI3bgRxlOn4B0WhpARw20u5xsfr4LoxqTSwLWrmYqKUJyaam6bM0hJFwmiZ61YgfgHH1CZ6dRwSYndvM2b1Tx1YVOnuLo55Iakc0UF0RcuRMxtt8LLx8fVTSKqN48t2oEJZyZg1sj2iNH7O2WddhdCk5m8o/73P0RdfTWKjhyF8fRp1LXT772PjC+/QtzDD6HN0qWIvfNOpL3/AdI//azOt01EREREREQNm2RgirCJE+Ht52dzOV1crFtNLmpITlb/e/n5wSciwinrDOzVC7oWLVTZmqzVpXW0qeHKXFA6wiJ44ECnjFYgzxMy/GxVjtmYnIzcDb+7ujlE9So1pwjXDGnttAC6Q5noMtwr7ZNP1dA2XUICfKOcX6C9ovwtWxAycgT0Z5+tbvs1a4qspUuR/++/db5tIiKiV1bvRUlJMfYd88b+n/bD29vx7I3bR3eok7YRERFR1Yqzs5FdFiy2VcpF4xsXX64OuatJ9ryWhe6sUdiynvDp05Ay5zXVuRA+jRNRNlSmkhJkLlhg175NjZd0HIZOmoT0zz9X+0vIkMGubpLHKcnNhZe/P7x8azTlJNWh8WfEY+PB02gZ5bxRV3Z/yglPP6VqoksGesuPPkR9COzZExnffIPCQ4fg37o1CnbvRt7ffyPuvnvrZftERERERETUMEnZElNhIfzbt0PAGV2rXNY3vnSCMYOblHMxnHRePXRLYVOmqCC6jDTfN2RolcsGDeiPJs89x1Kqbijvz00wnDgB75AQ6EeNdHVzyI1JJ4sE0bPXrEFxVhZ8QkNd3SSPcvqDD5H+zTeIvf02hJ97rqubQxaemHoGbvr8L/x5KB2d4vXw9Sn/XXbVoNaosyC6d2Agom+4HvUp6rprUZKbg4MTJgJSu6m4GDG33YawyZNtPqewsFBdNNnZ2fXUWiIiIiIiInIX+Vu2qv/1o8dUGwjWxZUG0Y1JpWVUXE2rza4F951F17QpQkaORM6PP8KYklLlsjI3WeRllyGwWzentoGcN6Fo6IQJ8A4IcHVzyI0FdO0C//btUbhvH7KWr0DEhRe4ukkeNyJE5q/w4nHodhZtO45f96XC39dbZaRb/hkg1+s0iO4KWcuXI3PxEjR58QX4t2uPwt27kPTMbDXBqAxDs2b27Nl4/PHH672tRERERERE5D4Mx4+r//1at6p22f/KubhJJnpZWRldfILT193s1VdQePAgUFJic5nUt95C9uo1yJg/n0F0N1Ock4usVavU9TAbcREijXQgSjZ68vPPq84XBtHrYESIXg/9SI4IcTcvrNyrSqveOKwtvL296m9i0ZOPPgaDnX9MZC1bhszFi+EMyS+8iKhrr1GTwAR07ICwqVMR+b8rcfrdd20+5/7770dmZqb5snPnTqe0hYiIiIiIiBpeEF2yr6ujTSxqTE2FyWCAqxlOnayTTHThpdMhoGNHBHTubPMScfHFatmspctQYjHSm1wve+VKmPLz1Xx1gT16uLo51ACETZ6kqjvkb92KwoOHXN0cj8ERIe7NUFyCSd0SnBZAtzuI7hMZgYOTJuPoddch/csv1cSeMmu5MT0dRUeOIPunn5D0wgvYN3wETs+bB/8OzplETb4YvLwrNFEmdauix9zf3x+hoaHmi16vd0pbiIiIiIiIqGEwGY3mRDB7gug+UVGATAxnMqlAuqsZ6zAT3R5BZ50F34QElGRlIeenn1zSBqo6cCfZxaxXT/bwjYlByJAh6ro2IS05b0SIrUoZ5Frn9mqGJf+Udkg7i13lXGJvvRWRl16KjO++Q/oXX6LwwIFyj3sHByN4wAAkPPG4+cB0hpDhw5H6zlz15S3lXAp27UTaxx8j/NxznLYNIiIiIiIi8izG5GTAaAR0OhVAqo4kb/nGxsB44iSMSUnQJbgmeK0xdwDUQSa6Pbx8fBA2dQpOvzNXlXQJHT/eJe2g8oqOHkXe5s2qoK98PkT2kk6XnJ9/RubChYi5dZY6xskJI0Jat0ZA9+6ubg5ZUWIy4Z1fDuCXvSnorCYWLZ+k/fCkLqizmui+0dGIvuEGdSnOzITh5EmYCgrgExEBXYsWddIDGvfQQ0h5bQ5OPfEEik+nldZCv/ACxNx0k9O3RURERERERB5WyqVJQuXRzTbo4uJVEF3qkQfCdUxFRWqiOiEJZa4SPm2aCqLn/rYehqRkc8kbcp3MBQvV/5LEqIsvreNPZI+Q4WfDJyxMdRLm/r4RIYMHubpJDRpHhLi/3aey0LVJqLq+Jym73GNeqNlnVqOJReXAk0td8wkJRvwDD6gLERERERERkT2KtElF7SjlotHqjxuTXDu5qEGy6OVHvr8/fMLDXdYOVXO7Vy/k//03spYsRtTVV7usLQSYSkrMpTgkcEfkCG8/P4ROnIj0L75QAWAG0Z0wIsTbmyNC3NhX1w1w+jrt65InIiIiIiIi8sBJRTW62NIgumRdu5KxrJSLBPVdneEYVlbrV0q6mEwml7alscv7cxMMJ07AOyQE+lEjXd0caoC0zpfsNWtQnJXl6uY0/BEhAwdCF+eaklvkGgyiExERERERkUcxHD/hcBDdt6w8hhbEdhXDybJ66HGuL9cROm4cvAICULT/AAq2b3d1cxo1LQtd6tN7B7qy4BA1VAFndIV/+3YwFRYia/kKVzen4Y8ImcYJRd3NA/P/xcnMfLuWXbztBBZsKe1wtxeD6ERERERERORRapSJXlbz25CcBFfSyslo5WVcyUevh3706HI1gKn+leTmImvVKnWdpVyopmRkS9i00v2Hx3PNcESIe4sK9sOYl9fhfx/9iU83HsG2Yxk4lVmA9NwiHE7NxeqdSZi9bBcGzv4RH/x2CB3j9XVfE52IiIiIiIjIk4Lo/2WiJ7lHJnq86yYVtRQ+fRqyFi9G5tJliL33Xnj7+7u6SY1O1spVMOXlwa9lSwT27OHq5lADFjp5EpJffhn5W7ei8NAh+Ldu7eomNcwRIRMmwDsgwNXNoQruHNMRVwxoha83HcVnvx/BvuTyE4oG+/ticLtoPHPOmTi7o+OTZdcoiG4yGpH3558oOnoMoZMmqQlApW6c/O8dHFyTVRIRERERERHVmvxeNZSVZHEoiF5WE92YlKTqf7uqHrnBjTLRRdBZZ8E3IQHGkyeRs3atKvFC9UvLGpYsdFfXyaeGTRcbi5DBg5Hzyy+qtnfs7be5ukkNdEQIS7k44s21+7FyxykcSM5BgM4HvVpG4L7xndA2JsTmc77dfAx3f/dPufv8fL2x96nxVW4rRu+PW0a0V5fMPAOOZ+SjwFiMyCA/tIwKqtU51LcmPfpHr70OhpMnYSoqQvCggSp4fvr999XthMcfq3FjiIiIiIiIiGrDmJwMGI2ATgffmBi7n6eLLV3WZDCgOD0dvpGRcAWjm2Wie/n4IGzqFJx+Z66aYJRB9PpVdOwY8jZtkloc6nMgqi3pjFFB9IULETNrpjrGyYERIa1aIbAHR4Q44o9Dabi8f0t0bx4OY7EJL6zcjSs++BOr7xiKID/boWm9vy9+vGuY+bYXHAuAhwXp1MVZHK6JfuqZ2Woygo5/bCw3jEs/ahRyN/7utIYRERERERER1biUS5MEeHnb/5PXy88PPlFR5mx0VzGUbVvnJpnoIrxsAr3cX3+DQTopqN5ItrAIHjAAugT36Fihhi1kxHB4h4WpSZRzN250dXMaDI4IqblPZvTD+X2ao0OcHl2ahOLF87urDPF/EzOrfqIXEKsPMF8ky9yVHA6i52/ejOgbblR/YFiSYXLGJH6ZEhERERERkesUlQXR/Rwo5aLRxZUGrrVyMPVNRncXp6aq61JCxV2ozMtevYCSElUfneqHqaTEXIOZE4qSs3j7+SFs4kR1PXN+6f5FVeOIEOfKLjCq/8ODyseWK8orKsagZ3/CgNk/4pp5m7E3qXyNc7cPokttOJQUW51BnPXQiYiIiIiIqKFNKlppclEXZaJrWd5e/v7wCQ+HO9FqAEtJFxUXoDqXt2mz2p+9Q0KgHzXS1c0hD6J1ymSvXo3ibNcGJhvUiJCBA6Er+56gUtnZ2cjKyjJfCgsLUZWSEhOeWLITfVpGoGO83uZybWJC8Py53fDuFb3xyoU91PfOuW9twMnMfDSYILrUQE+b98l/d3h5qeL6Ka+/gZChQ53cPCIiIiIiIiL7GY6fqHkQPS62dB0uCqJLeQXVjvg4tysXILXQvQICULT/AAq2b3d1cxpV+YjQ8ePhHRjo6uaQB5Eyzf7t28FUWIis5ctd3Ry3xhEhVevSpQvCwsLMl9mzZ1e5/MMLt2PPqWy8fknPKpfr3TIC5/Zuhq5NwtC/TRTeubw3IkP88MUfR9Fgguhx99yDvC1/48DESSgpKsKJO+/C/pGjVE997F131k0riYiIiIiIiByqid7E4efq4soy0U+5KBNdm1S0rB3uxEevV3OhWQZ3qe5IsmLWqlXqOgN35GzSSRc2rXS/YkmXqnFESNV27tyJzMxM8+X++++3uewjC7fjp93J+Oq6/kgIc6xjUOfjja5NQnH4dB5cxfYUqDbIRBZtFixQPVUFu3ejJC8PYeedi7DJk+EdEFA3rSQiIiIiIiKq63IuZTXRXVXORcqkCl2C+wXRtZIuWUuWIHPpMsTeey+8/V07yZsny1q5Cqa8PPi1bInAnj1c3RzyQKGTJyH55ZeRv2ULCg8dgn/r1q5uknuPCJkwgXFPK/R6PUJDQ1EVKcXy6KIdWLnjFL66bgCaRwbBUcUlJuw+lY3hHUtHjFUnJbsQzyzbhfX7U3E6t6hSGbKDs0vnBaizILrJYMCBCRPR/J23VdBcLkRERA3RK6v3uroJRERE5GQmo9E8KWhNgui6+LKJRZNcm4nu64aZ6CK4f39VN17KzuSsXatKvFDdBu6k48LdSvuQZ9DFxiJ48CDk/rJO1fyOvf02VzfJzUeElM4LQY6TEi4Lt57Ae1f0QbC/D5KzC9T9oQE6BOh81PU7vt6KuLAA3Duuk7o9Z80+9GwRjlZRwcgqMGDuuoM4np6Pi/o2t2ubd327DScy8jFzZHvE6v3hjLOoQ0F0L51O1UsiIiIiIiIicjdGmZjTaAR0OvjGxDS4THSDm2eie/n4IGzqVJyeO1dNMMoget0oOnYMeZs2qTnowqZMcXVzyIOFT59eGkRfuBAxs2aqY5ysjAhp1QqBPTgipKY+21hax/yidzeWu/+F87rh/D6lQfHjGfnlOgwz8w24/4d/VUZ5aKAOZzYNxfc3DkT7ONuTkVrafDgN39wwQNVUd1k5l4hLLsHp995HwlNPwsvX4acTERERERER1W0pl4SEGgWDfGNLg+glOTkozsmBT0gI6pPRzTPRRdi00iB67q+/wZCcrLJZybkkK1gED+hfo9r+RPYKGT4c3mFhanRJ7saNCBk0yNVNctMRIdM5IqQWDj9bfemUr68fUO72I5O7qEtNJYQHokIFl1pzOAqev/1f5P2+Ebnr18O/Qwd4B5UvBN/s9ded2T4iIiIiIiIiuxSZ66HXLPDoExKsJo+TILpko9d3EF0rI+OumehC6iYH9uyp6iifuONO6Jo1s7msd3Awom+5Gb4RETXalqmkBKfffRdFh4/UosVASUkJojMyUDJihBqlUKN1FBUh9fXXYUxJRV3L+fVX9T8nFKW6JvMahE2cgPQvvkTys88hq2vXKo+juOOJSFq/Ad7e3laX8QkPVxnt3kGO17x26xEhUzkipKF5ZFIXPLdiN56ZfmaNarA7JYjuow+FfswYp2yciIiIiIiIyB0mFdX4xsehaH9pEN2/bVvUFwnSFqeWBmil7rg7Cz/3HBVEz9u8GZBLFWQEe9z999VoOznr1iHl1TlwhkgpzfDtt4iZMaNGz8/84Qc1Kr++eOv10I8aVW/bo8Yr7JxzVRC9cN8+dalyWQDZf/1d5TI+UZGIvvZaeM6IkAHQufk5mSq75Yu/UWAowbAX1iJQ5wNfn/IdP9seHVP3QfQms59xeCNEREREREREdc1w/IT6368WQXRdXDyK9h+A4VRS/ddzl6Czv7/K5nRnKkPaywvF6ek2lzGcOIn0L75A5pIliL3rTjXHmqMy5y9Q/wcPHozg/mfVuL15u3YjZ+lSZC1aXOMgutSAF6ETxiOgS81LDNgr6Kz+8A4sP/KfqC4EntEVzd56E0UHD1a5XHFxMXbv3oNOnTrCx0q5rML9B5C5YIEKPkddc02DLn8io2CkTrzgiJCG6ZHJtkdV1BSLmhMREREREZFHcEomuja5aHI9B9FPnizdfnyc2wefpN58+LnnVrmMyWBA1sqVKD59Gjm//gb9iOEObcOYno6cn35S1yUIH9CpU43bG5ySgqwVK1C0ezcKdu1CQOfODj2/8MABFGz7B5Cs+gcfhG9UVI3bQuSO9FLqSC5VMBgMSF+2DBETJkBnpVOsODu79DiT4+XffxHYrRsaKhllY0hMVOW99KNGuro5VAPn9bZdaqzeguj7R45SPc62tFuzurZtIiIiIiIiInJJEF0XXxpEN5wqneSzvmiZ77r4BHgCyTwPmzwZaR9/rCbnczSInrVsmQrE+3fuXKsAupDM/twuXaD/91+VKetoEF2eI0KGDmUAncgGHylBNHo0shYvViM3GnIQXRsFEzp+PEeENGDFJSas2nEK+5Nz1O32cXqM7hIHH2+v+gmiR155RbnbJoNR9eTm/vorIq++ukaNICIiIiIiIqoNk9FoDnzXKhM9tiwTPam0vEp9MSadKhfE9wRh06epIHr2zz+rzHJHJhjVgljh06c5pS1ZvXuXBtEXLUbsnXfCy8/PrueZiouRuXCR+fUQkW1yvEoQPWvpMsTdd5+auLShKcnNVaNoBEu5NFyHU3Nx1cebcCqzAG1igtV9b/18AAnhAfjof33RMqr0vroNol9RPoiuSfv8cxRs3+FwA4iIiIiIiIicUlPcaAR0OvjGxNR4PVJORRjKgtr1xXCydHu+cZ4zgV1Ax46qfnjBzp3IWrIUkZdfZtfzCvbuRcH27ap8SuikSU5pS26H9vCJiiorL/Mr9CPtK9GQu2GD2rckm10/bJhT2kLkqYLOOgu+CQmqPJWUY5JM7oYma9VqmPLy4NeyJQJ79nB1c6iGHlu8Ay0igzD/poEIDyrtNE3PLcJtX2/FY4t24KOr+jm8zvJTk9aCDGvKXrXKWasjIiIiIiIicryUS0KCqtldUzqtJno9TyyqBe11CZ4TRLfM5JSSLvaSiQlFyNnD4BsZ6ZyG+PhAP3lSuUlC7WqLNqHo5Ml2Z68TNVZy7g2bOsXh48ydaMe8nLvcfX4Ksu2Pg2m4f0IncwBdRAT74d5xnfDHoTTUhNOC6NkrV8InLMxZqyMiIiIiIiKyW5G5HnqTWq3HN740iF2cloaSoiI4i6mkRJUGsXUxemAmugidNFGNDpBs9II9e+0qy5O5qLR8SriTSynop5QG93J+/gXGtOqDKMWZmche82NZW1jKhcge4dNKj5Xc39bDUM9lsWqrKDEReX/+qeaC1DoDqGHy8/VGbqGx0v15RUbofGoWDne4nMvB6ecAlh0xJsCYmoLitHTEP/JIjRpBRERERERE5OpJRYWU7ZCMY1NRkSrj4desWa3blvLmm0h98y2gpKTaZT0tE13qoOvPPhvZq1eXTup57z1VLp/z228oTk2FT2SkGvHuTP7t2yPgjDNUqZisJUtslqvVZC1frvYD/44d1QSnRFQ9v1atENirF/L//htZSxYjqgHNn6iNggkeMECNaqKGa2SnWNz/w7947txu6NE8XN235VgGHpy/HaM6x9VPEF0/YoTqkTHz9lLDq4L69YN/mzY1agQRERERERFRbRhOnFD/+9UyiC7D933j4mA4dgzGpKRaB9Hz/voLqW+8CZhM1S6ra9ECfq1bw9NIWQQVRF+8GLF33A4vna7aCUXDJk+qcrmat2WaCqJnzF9QbRBdK0chz2FZByL7yTEjQXQ5hiJnzGgQx4+MFpKOPsEJRRu+R6d0xZ3fbMM5b2+Azrs089xYUqIC6I9O6VI/QfSYmbfUaENEREREREREdcVw/IRTMtHVOsqC6IZTtZtctCQ3Fyfuu18F0MOmTUPcffdWubx3SAi8fB3+me72QoYMLp3UMzVVZZrrhw+3upwxPV1NRliXQazQCROQ/OxzKNy1CwW7diHARoZ54YEDKNj2j5rcNGzy5DppC5GnCh03DklPP4Oi/QdUp1XgmWfC3eVt3gxDYqI6D+tH2TfxMLmvsEAd3r+yDw6l5uJAco66r11sCFpFB9d4nQ4XgdnVpSuMp09b/bKTx4iIiIiIiIgaajkXIZnowljLer5JL76ogvG+CQmIe/ABVSqmqosnBtCFZJRrgWgt09yarGXLYDIYVOmUgE6d6qy8TIiMsFelG2y3RXtMSsr4RkXVSVuIPJWPXg/96NEOTyrsStq5KXT8eHgHBrq6OeQkraODMapLnLrUJoAuHP+GtjEEzVRkqJOhVkRERERERERVkYk5DSdPOi+IHq8F0WueiZ7z23pkfPmVut7k6adUUKmxl3dI+/hjZK9dq5LwJJhtK4hV15N4SluyV65E5qLFiL3zTlUDv+L+lLlwkXlZInKcHMdZixcjc+kyxN57L7z9/eGuZNRQ1sqV6jpLuTRcTy7ZiTvHdECQn6+6XpWHJ3WpuyB62iefll7x8kLGt9/BOyjI/JippFgNe/BjTXQiIiIiIiKqZzIBKIxGQKeDb0yMU8q5CEMNM9GLs7Jw8qGH1PWISy5B8MCBaOwCZHLOLp1RuHMXspYuQ+Rll5Z7vHDfPlX2QcqnhE6aVKdtCRk8GD7R0aXlZX79FfqR5Us35G7YoPYpGR2gHzasTttC5KmCzjoLvvHxMJ46hZy1a1WJF3eVtWo1THl58GvZEoE9e7i6OVRDO05kwlBsMl93NvuD6PPmlV4xmZD+9dfwKivKLiQDXXr7Ex571OkNJCJqyCQDyVRUWDopMxERERHVbSmXhAR4+fjUen2+cfHqfwn+1ITUApbn6lq2QOxdd9a6PZ4ifNp0JO3cpco7VAyiZ2jlU4YNg29kZJ22Q8rmSHmZtI8+UmVbKgbRzWUdJk2qlKVORPaRc3HY1Kk4PXeummDUnYPo/00oykmEG7Kvrhtg9bqz2F0Tvd2Pa9QlqG9ftFkw33xbLm1XLEeLD95HYPfuTm8gEVFDZUxJwbEbbkDiTTfjxAMPoqSgoM62lfPLL8iYvwAmGyW3iIiIiBpHPfQmTlmfLi62dL1JSQ4/N3vNGmQuXAh4e6PJ7GfLjeJu7EInT1KjBQp27EDBnr3m+01GIzIXLaqXUi4amehVZK/9Gca0tHKjCOQzVMuwlAtRrYRNm6r+z/31NxhkxJAbKko8jrw//lCVN8KmTHF1c8hJ7v52G3IKjZXuzysyqsfqZWLRlp/Mg09YWI02RkTUmGStWlU6rFh6tn/4AYcvvgRFR486dRsy0XPirbfh2PU34OT99yPv99+dun4iIiKihqDIiZOKCilBoCVFSH1sexWnpeHko4+p61FXz0BQr55OaY+nkDro+rOHVZrUM3f9ehSnpMJHJv0cOrRe2hLQsQMCunZVf69nLVlqvj9r2XKYiorg36EDAro4XjOXiP7j37o1Anv2BEpKVH10d5S5sPRcFDygP3RNnNMRS673/d+JKDBU/v4uMJTghy2lfzPUeRBdGE6dQtoXXyD5pZeQNPvZchciIiqVvXyF+j904kT4REaicNcuHDr3PGT/tNYp689asQIHJ01WkyKZ71u+3CnrJiIiImqImeh+zgqiR0erTHIJsErSgl1MJiQ/+SSKT5+Gf/v2iJ450ylt8TTapH2ZixerDHQhIyq1TPX6LJ+itSVjwXzzfVJqRnuMZR2Iak8b0SEdZ+42ctpUUmIu38QJRT1DdoEBWQUGyJ6WW2hUt7VLZp4Ba3cnIyrYr25romtyf/8dx266GX7NmqHw0CH1x4H6g8VkYi8tEVEZmYQq76+/1HVVB9PLC8dvvQ3527Yh8aabEHX99YiZNbNGNTtluOmpJ55E9orSIL1kyYSdMx3Jzz6H7FWrEf/II2quCiIiIqLGwnD8hFMz0aVmtgTSZXJJY1IydLGl5V2qot+6FblrflQTYzZ57ll4s5a2VSFDhqgEEzWp52+/IahHD+T8+KN6LLyeg1ihEycg6bnn1GSnBbt3qwC+/L0OqeUspWeIqNZCx49X80QU7tuPgu07EHjmGXAX+X/9BUNiIryDg6EfNcrVzSEn6Pb4Kkj3p1yGv/hzpcelc/T2Ue3rJ4ie/PIriLrqKhX82dOrN5q9NkdN+nH87nsQMmRwjRpBRORpVHa4yaSGrskEV6Llp58g6fkXkP7ZZ2pylfx/tqHpiy8CoaEOZZ+fevwJFKenqz/uo667FjE33qgypU6/977KfMrd+AfPx0RERNRIa6I7J4iulXQpDaKfAqoJ+hiTkhArddABRN90IxPMqiDJHmpSz3nzVAao4cQJmAwG+HfqhIDOneu/vMzw4chetUq1RcuCl5IyajQCEdWaj14P/ejRyFqyRI30cKcgunkUzITx8A4MdHVzyAm+vLa/hGJwyfsb8falvREe9F+Coc7HG80iAhEXGlA/5VyKDhwwTwwgPeymggLVYyNBdQngOJtM5CIB+r1n9cfu7j1wcPIU5P+73enbISJyJq2sivS6a+SP8viHHkSTF1+EV2Ag8n7fiEPnnIv8rdvsyj5PvO12HL/tdhVAl1FArb7+GrG33qrWK9lS+jGjS7e9sjRDnYiIiKgxkJrlhpMnnR5Et3dyUSlPkPzoY/DJL4B/166IvvZap7XBU8koSpHz009I/+KLep1Q1GapicWLSyeE5YSiRHV3nC1dipKiIriDktxclaQmWMrFc/RvE4UBbaPw6z3DMaZLnLqtXXq3jKhxAL1GmeheQUGql1g9OSYGRceOqWCOMGZkwJmKMzNx5OJLEHTWWWj+3rtqyFfR4SPwCbM/a5OIqL7Jj7j8LVtUCRf92LGVHg+bNFFNZJQ461YUHTqE41ddhWYtmuP4d9/brLtYuHcviuUcW5Z9Hn3jjZWGCIeOHYeML79C9uo1MD36KEu6EBGRU5Tk5SH17beRv+2fqhf09kb4+echbOLE+moakSLZ4moyd51O/UZ1Ft+4sslFT1UdRM/49lvkrV+PEl9fxD3zNP8Gs0NAx47w79JZlVEp2n9AJeiFTnJN+ZSQwYPhEx2tyssIn/Bw6M8+2yVtIfJUwf37l47uOXUKOT+tRei4yr+T61vW6tUw5eVB17JF6eSn5FGaRQSp//OLinE8Ix+G4pJyj3dOCK37IHpg9+6qzq9/27ZqiJOqH7Z3r6rDG9i9G5zp9PvvwzchAU1mP2O+T2qxExG5s6yyiT6Devc2ZzBVpDLJv/0WJx98UJV+CTp4CPkHD1W5XnlOwuzZCDyjq9XHg/r2gU9UVFlJl42q3iQREVFtSG3gE/fci6IjR+xavnDfPjUKy0smZCSq71IuCQk1mm/GFl9zJvqpKrct89KI02PHoEObNk7bvqcLnzYdSTt3qeshw4bBNyrKteVlPvpI3ZZgfn1ObkrUGMi5OWzqVFXWNO2TT8yTCrtS+ufaKBhOIuyJTucU4u7v/sHPe5KtPn5w9sS6D6LH3XevykYRMTNvUdezli2HX8uW6jFnyv5pLUIGD0Lirbchb9Mm+MbFIeLiixBxwQU2n1NYWKgu5nVkZzu1TURE9pZy0Y8fV+VyPiHBaPrqK8j+fSM2rVmDnj17wtfX+g8/Kf8SPHBglRNUyR8moWPHIP2LL9WwNAbRiYiopmTkqWSfp859FyguVtlj0TffBJ+QEBtPMOHEQw+rjlxJsAno1Km+m0yN2H/10Js4db26+LJM9KRkm2VcTj78sPpNHNCzJ9IHc04aR4ROnoSkF14ADAaXlXLRhE2bZg6is5QLUd2Q0tBqbrC//1YXt+DlhbApU1zdCqoDTyzZiax8AxbcPAgXvbsRcy/vjdScQrz+0348NLFm82/4OlprToZe+HfsqG57BwUh4fHHUFcMx44h/cuvEPm//yH6+utULXSZ0ddL52fzS3b27Nl4/PHH66xNRERVKUo8jgIZ7u7tjdAxY6pdXnq8A/v2QU5KMvTjxkJXy+G/+rHjVBA9e82PpSVdmEVDREQOKjx4ECfuvgcFO3ao26GTJyP+4YfgU81E2BkLFyL3l3XIXb+eQXSqV0V1MKmokCQuIb+Brcn4+hvkbvgdXgEBiH3icWDnTqdu39PJpJ4Jjz2qzjkhLi6fIqUWY+++CyaDkZPCEtUR/9atEXPHHcj9fQPcRcjQYdA1cW4HLLmHDQdO470r+qBbs3B4e3mhaXgghrSPQYi/Dm+tPYARnUq/4+ssiC5ZjkevvgZtli2t9o9oZ5Ce/cCuXRF7x+3qtnyZyRDRjK++shlEv//++3HHHXeYbx8/fhxd+CVIRPUku2xSz6C+fZ1ak9NeQX16m2s6qpIuQ4fWexuIiKhhMpWUIO3Tz5D84oswFRbCOyxMBbgsJ8muSsigQWVB9A2IuvrqOm8vUcVMdD8nB9F1ZUF0Q3Ky+m1qOdxfEieSn39eXY+9/Tb4tWrFIHoNhJ97LtwFz1tEdS9a5ve6jpMvU92TWuhRwaVJhWGBOqTlFqFNDNApXo/tJzJrtE6HixVKTV7JEK8PvjHR8GvXtvz227Yxz7xujb+/P0JDQ80XvV5fDy0lIiqVtbw0iB5aTSmXuqJKupRlwGetKK3NTkREVB3fzEycuOFGJD39tAqgBw8ahDaLFtodQBdSdkzkbd6MkoKCOmwtUXmG4yfqNBPdlJ+Pkqysch1OJx96SJVxCezdGxGXX+7U7RIREVHttIkJxsHUXHW9c4IeX/xxFKcyC/DZH0cQqw+o0Todrokec9utSHr+BcTMmomArl1VSRdLNusk1kBQz14oOnS43H1Fhw9zqAURuaWio0dRsH27KuWit6OUS12RsjDpX3yB7DVrYHqs8ZZ0KcnPx+kPP1T1TN0py4mIyN3krFmDlq+8gvz8gtKyFHffhYhLLnF4ki2/tm1V0NGYlIS8v/5SmelE9VsT3blBdO+AAPiEhaE4MxOGpCR1XWR8/TXyNm5Ux0uTZ54unUi3uNip2yYiIvIUb67dj5U7TuFAcg4CdD7o1TIC943vhLYxVceQl/5zEi+t3oPE9Hy0jgpWzxneqXTS7+pcNagVkrNKkzpuHdkBV370JxZsPQ6djzdePL97/QTRj113vfo/8aabVQF+M5NJ3e68s7R2ojNE/u9KHL74EqS+M1dldeb/8y/Sv/kWCVJvjojIzWiZ38H9z4JvZKTL2hHUuzd8YqJRnJKK3N9/R8iwYXY975XVe6t8/PbRHWq9DnvX44wf08dmzkThzl3m90QNs27k7Pl8iKjxkPmOUl57XU3yJdNa+59xBpo+/zz827Su0fok6C7Z6Jnz56uSLgyiU33tx9pIZWcH0YVMqitBdOkcQocOKEpMRNILL6rHYu+4A34tWzp9m0RERJ7kj0NpuLx/S3RvHg5jsQkvrNyNKz74E6vvGIogP+uh6b+OpGHWV1twz9iOGNk5Fgu3nsB1n27GkplD0DG++qoj03s2M18/s1kY1t87AgdSctAkPBCRZWVe6jyI3mLex6gvgWeeiWavv4aUl19B6ltvQdesGeLuvw9hkyfXWxuIiOyVtXy5+l/vwND3uivpMhbpn3+uAvv2BtE9hdSCP37b7SjOyDDfJzV+ZVI8IiIqVZyVheN33YXcdb+q2+mDB6PPa3PgV2GUqaOkDIwKom9wn0nDyLMZk5MBoxHQ6epkPhrfuFgU7tmjguiqjMuDD8GUl4egPn0QcdmlTt8eERGRp/lkRr9ytyUTvPdTa/BvYibOahNl9Tkfrj+MYR1icP2w0jLfd47piF/3pWLe74fxzPQzq93mnDX7cN3QNgj0k1QRqP/PaBqGAkOxeuzWUe3rPoge3K/8C69r+uHD1YWIyJ0VHjqEwl27AB8f6EeNcnVzEColXT7/vLSky+OPNYqSLjLhV9q8eUiW7LDiYjUZdcSll6gfuxnz5yPm1ln1Mik2EZG7K9y3D8duuQWGI0fh5e+P2Mcfw14vL3jpdLVed/CA/qXb2L0bxtRU+EZHO6HFRHaUcklIUIkEzqaLiy/dzqkkpH/5JfL++ANegYFI0Mq4EBERNWLZ2dnIspg3ROaqlEuVzykwqv/Dg2zHKbYcScfVQ9qUu29ohxis2nHKrnbN+XEvLu3fwhxEt5xwVB6rSRC9Rt/6MlnQ8bvvweGLLla14UTmwoWq9iERUWOUvbKslMuAAfCNiHB1cxDYq5fKxirJzkZOI8gGlAnsTtxzL5KffU4F0MOmTkHLLz5H2DnnwL99O5UxlvH9D65uJhGRy2WtXIVDF16kAugyz1CrL7+AfuJEp63fNyoK/l06q+tSUoyo/uqh1828Wdrkonl/bUbySy+r67F33gm/Fi3qZHtEREQNSZcuXRAWFma+zJ49u8rlS0pMeGLJTvRpGVFlWZaUnEJEh5QPsseE+CE1p9CudplklL6V+3edzKoyeO/UTHT5w/vEvfcibPIkFOzcCVNRkbq/ODsHmXPnosW779aoIUREDVnWstJSLqEuLuWikUws/dixSP/sM2QvXwH92WfDU5Wrf+7jg7h770XE5ZeZJ8SLuPxynHrkUfVeRF5xeZ1kqRERNaT65yKof380feVl1fFrMBicuq2QgQPVOTn3t/Usw0h1rqiOJhXV6OLLgui/b1T/B/Xrh4hLLq6TbRERETU0O3fuRFOL7+DqstAfXrgde05l47sbB9RJe7o9tlLFAiQaMPzFn81xAS2An1tkxKVntayfIHrqO+8g/rFHET5tGrKWLjPfH9Srp3qMiKixKTxwAIV796panPpRI+EuVEkXCaL/9BNKiorg7YElXXI3/oHjt9+O4vR0+EREoOmrryL4rPJlx8KmTFFza0iwXd6L0NGjXdZeIiJ3qH8eeeWViL37Lnj5OvxTwO666Kff/0DVRZdSW5Y/XojqKhPdr46C6L5l5VyEV1AQEp5+imVciIiIyuj1eoTaWTb1kYXb8dPuZHxz/QAkhAVWuWxMiD9Sc0oTtzUpOUWIDqk6SP/I5K7q7897vv8Ht4/uAH3Af+UKdT5eaBYRhN4ta1Y9wOG/nIsOHUJQn76V7vfW61FiUQOHiKixyFq+wpx55xMWBnehlXQxpqQgd/16j5pfQib2SvvoYyS//LK5/nmzN15XpQkq8g4IQPgFF+D0u+8ifd4nDKITUaMLMB6ZMcNc/zzhqSfrPDtcvn9kW/L9I/XXAzp0qNPtUeNmOH6iTjPRZWJRTexdd8KvefM62Q4REZGnMplMeHTRDqzccQpfXTcAzSOrn8i+Z8sIbNifiqsHtzbf99u+FPSqJgB+Xu9m6n/ZhgTLdT7O6/h2OIgukwMZjh6BX7Pyf6RIPXQd/6CgBuCV1XurfFx6qogckbWitJSLfvw4uBPJktKPG4f0Tz9F9ooVHhNEl4DQifsfQN6ff6rboVMmI+GJJ1Sw3BYZdn36ww/VnB5SikyC7o3t3EZEjY90OJ64735z/XPpbKyP85+3vz+C+vZF7m+/qWx0BtGpfmqi100Q3b9tW4SMGqlKH0VcdFGdbIOIiMiTPbxwOxZuPYH3ruiDYH8fJGcXqPtDA3QI0JWWW73j662ICwvAveM6qdszBrXChXM34r11BzG8UywWbzuBf49nYvY53WxuJ7vAYM4879okFAWGYnWxxjJDvc6C6OHnn49TzzyDJk8/DXh5wZicjPytW5H8/AuIvvFGhxtARNSQFezdi6L9B+AlpVxGuk8pl3IlXSSI/mPDL+kivddZixbh1JNPoSQnRw2plvrn4RecX22pAF18PELHjEHWsmVI++RTNHm26slOiIg8QfrnXyBv0yZ1vmwx7+N6zaANHjiwNIi+fgOi/ve/etsuNb5a/4aTJ+s0iC5zqTR/4406WTcREVFj8NnGo+r/i94tnV9E88J53XB+n9K/T49n5Jf7Xd+7ZSTmXNQTL63agxdW7kGr6CC8e3mfKicj7f74Kvz54ChV8qXb46usTiyqTTh6cPbEug+iR113raS14MhVM2DKz8eRyy6Hl58fImdchcjLL3O4AUREDZlkeIvgIUPgo7d9MneVwJ494Rsbqzo8ZYI3/YiGmY1uTE/HqcceR/bKlep2YI8eaPLcs/Braf+EIJFXXqGC6FlLl6rh2EREnqzo6NHSklcuKkEhddGFBPEbeicuuS/5+wZGo5qXRkrYERERkfs5/Gz1Aeuvr6880ejEbgnqYq8vru2P8MDSDPMvr+0PZ3M4iC69AtE33ICoGTPUH+cleXlqiJt3cDDcWUl+vmprJT4+asipeTlry2i8vcuVC3Bo2fx8SaO0vqyXF7wDA2u2bEGBTC9ruxlBQTVbtrBQ1Rm2Z1kvg0G9FyU660MhvAIDzb1J8iNK/aFrg0PLBgSYJ/UxFRXBZOey8rpkeHNVGS2ScWLXev39/1vWYFAXm8v6+Zkn8HJoWaNRtcPmsjqduthatsRggFdRkfqMTEFB/y0r74N8zrbW6+ur2uHwsiUlMMm+5oRl4etr/tEtWcjSceeUZR057qtYVraTuWSpuh4ycoQ6xmp6jtA+I6vHUS3OEbI/SNsyvvwKmUsWI7j/WTaPZbWv21pvBbbOEdp+re1n6r5iI1Bisvm+VHfc56xfj5OPP46S1NPq84iZORNR11yt9p+q3uOK5wj/9u3hf8YZKNy+HWmffgZTx/9qo6vjTev1rnCOMJUUl94nx613Cbx8faRWjtVlK7VBlrV4b+ryHGFreS+pAedd1g455qo4t5db1lQCk7Hystp77m7nCNl3q/oucsU5opJG/HeE5XeRfEaO/M3hbn9HOPS3gYv+jpD9/cQDD6j9PLBfPzXBsq39rdz52mhUn5Vdf3NUc9z7tWoJn5hoFKekqlJaQT162F5vI/07wpFzRMVjyNPOETVdViZ3F7qEBHUsufIcoZFjs8rjyA3OEc78rWFzWTf7rVHpGPKwc0S1yzaAc4R0hjn7HKGazL8jnHaOsHYceco5wqoGdo4oqXAc1PU5oqSq1+gi/dtEWb3usiC6RnY6CZxrF3d3YNw45OoqZ8AEDxuKFnPnmm/vHTTY5s4utR1bfvqJ+fb+kaNQnJ5uddmAM85A6+++Nd8+OHESDCdKJ72pyK9dW7RdssR8+9D556vyENZIPct2P/1ovi0jAQq2b7e6rE9EBDr8vsF8+9i116lsIFtfFp22/G2+nThrFnJ/WQdbOu/eZb4e//U3OPjQwzaX7fj3X2oYsTj1yKPIXLDA5rLtN6yHb2Skup787LNI/+JLm8u2XbPGXJs/+dU5SPvwQ5vLtlm8SAXQtPr9tt4HUdD8GgSeeaa6nvbpp0h+4UWby7aYNw/BZ/VT19O/+QZJTz5lc9lm77wN/dlnq+uZi5fg5AMP2Fy26auvIHRcaX3t7DVrcPy2220um/DMMwg/Z7q6nvPbb0i8oXJZJXnlBx9+BHEPP4TISy9V9+Vt/gtHr7zS5npj774LUVdfra5LDenD519gc9nom29GzMxb1PWiAwdwcPIUm8tGzpiBuHvuVtcNJ07iwKhRVdaxjn/kEXVdjrV9A0uz2qwJmzbNXKJDjuE9vXrbXFY/diyazXnVfLuqZe09R5x68CFkLVhY43NEq5dfVp9RXZ4jspctx55lpfXbrZ0jshYvtnmekj8KMOGlas8RpzuNMe8TmuzVa9R+odlzx6oanyOav/eumsBVJD3xRI3PEafnzsXpTofMtyMuvgg+kVE2zxHySPratep6+PnnwTc2Tl3P/+cfVe+3qv0S6FQv5wjtva8oZMRIBHQubYN0fksmvi3BQ4eaz39yfFr7LLTPz93OET65uTh4Vn+3PUeIxv53hPZdVPHviBP33GseZdIQ/o5InfsuUt980+ayrb79xuV/R4RNn4b8zaXvW+jYMdjbp2+Vf0cET56krudt2ICTN5ceq9Y4+neEnK8zFy5C1qLFODaj9HxhDf+OsO8cYXkMeeI5wpIj5wjV7qZNXH6O8G7VSl1Pe+89pL/9jlufI5z9W0Pj7r81LI8hTzxHWGqI54i2f/5RJ+cI/h3h3HNExePIk84RFTW0c0RAnz7A+efV2znilMF2h4m7yMwz4OvNR7E/OUfdbh+rx/l9miE8qGYjJB2eolR6lpLnzMGePn3VByIXuZ786qtVZ8QQEVGjkff331VmPNdUQKfSYDAREdmmjZKSH4m+Uc7PwnGkLrrI37bNZW2gxqGu6qETERFRw/THwdMY/NxP+Hj9YWTmG9Tl4w2HMeS5teqxmvAyyTgDB5x87DGVVSjD6QN7lg7LzN+yFSlvvqEm1Ut47DG4k8TERDRv3hxH9u5FM2t/XHH4lPVl7Rw+ZTAYsHzhQowdPRq6BlLO5ZUVu6osv3D72E5uO3zK6rLVDJ+Sz2jlqlUYO2YM/FjOxSnDp+T9kOyBtI/nqfsSnnsOoaNH1fgcoY6jBQvUZ2T1OHLCOSL5xRdVJoV+zJhyk2paHvcvL99ZZTmXOyZ0rfYc8fwnv5jrxPvKZJ7jxsErwL9cOZfbzm5d5XGfKZOHPvEkUFQEn9hYJDzxOIL79XPaOeL0hx8h9Y038OXgixF+zjlqWpGqyrmUlBRj/4GDaNe2Dby9fRwu53L7mE71co549edD9VLORfv83OkcIcfQsqVLMW74cJvfRe4wxLIx/x1h+V0knxGHYVtZ1gnnCDlmjl5zLQq2bkXQgP5oIVlxcnxW83eEtHDZsmUYP2YMfKv4HnB0GLYxIwP7hw5Tt9v+uAa+ERE2l21Mf0fU5BxR8RjytHNETZeVcm9ZCxchetZMxNx0k0vPEcbi4tLjaPToKod7u3OpBqvLekiphkrHkIedI6pdtgGcI4p1OnUMTZgwAT6yHMu5uN05wtpx5CnnCKsa2DnCUFyMFT/9pI4h+Xzq+hyRePw4WnbogGPHjqFZs2ZwN2NfWYdeLcPx1LQz4eNdelwXl5jw0ILt+PtIOlbePrTuy7lkLVmKpi+/hJCh/20soGNH6BLicfzOu9wuiK6RD9zyRGtzOTuWqdGyFjucU5e12OmduqzFQVodU9mPYW9bgQvL9cpJxc6JpRxZVp3k7Z2wysfH/EVjdV0WjzmyXssvBKcuKyf5si+wmizrLV+QUn7JIoCulpX3wc592KFlvb3rZlkvrzpZ1tFjWf4oOH7X3cj97Td1O/LqGQibMrncLNI1Wm/ZZ2TXcVSDc0To5MkqiJ69ahVOBvgj/oEH4BMeXm5Ze/czW+eI4qws5P76q/m28dQpZHzzDfTjx0EXn1Dt+yJ/dKW89BLS5pUOUw0ZNgxNnn8OPmFhTj1HRFx6CU6/9x6KU0/DmJKqhqVWdY7wKvEuvU+OWy3AbGNZV54j7FpejjltfohqV+wNL13lZa19fu5wjlB/3Nl5DNXlOYJ/R1g/R1h+F1X8jBz5m8Md/o6os2WdcI5ImzdPBdDlfW7y1FOl3032/B2hzWchPwDtbYMdx70uNhb+HTqgcO9e5G/dirCJ1U8q5el/R9R02aqOIU84R9R0WWNSsvrfryxZyqXniLIgnhybdh9HHvBbw+aybvZbo7pjqKGfI6pdtgGcI4otgrrOOkdUWpZ/Rzi+rMVxX+1x1IDPEdUu2wDOEfL51Oc5wtuB57jC4dO5eOuyXuYAupDr1wxpjQl/J9ZPORc5gK0Nl9M1a2b3QUhE1BAV7NmLQ+dfoALokk3Q5KUXEXf33VYD6O4msEcPRN90kwqiSm3aA5MnI/un0hrfzpI0+1mU5OaqoHf4RRfCJzJS9X5nzl+Agh07qnyuMS0NR2dcbQ6gR990I5q9/ZbNAHptSCakTLInWGKAiDxF4aFDSH75FXU99p573Ka8RfCg0vqhVc0fQVRThuPH1f/usr8TERGRezijaZi5Frolua9zQmj9BNElgy/1rbdLh7aUkeup78xFRNkEAUREniZrxQocvvhiGI4dUz/UWn31pV0Zde5CAv0xs2ai1Refw69NGxSnpCLxpptw/J57UJyRUev1Z//8MzLnz1fXQ0aOhG9UNMLPPRd+bduqYZs5P/+sLtaGWubv2IFD552HvD//VL3lTV9/DTGzZtmfMV0DkVdcrv4vOnRIZdATETVkMsz55AMPqqHOwQMHIPxC25Nvuaoueu76DWp4M5Ez93vDyZPqOoPoREREZOl/A1vhicU78e66A9h0OE1d5PqTS3bi6sGtsetklvlSZ+VcCnbtQt7vG7F/2Nnw79RR3Ve4e4+qpxQ8oD8SZ840L9vs9dcdXT0Rkdv9QEt5dY4q/yGkxmzTl1+2Wde1IWSkt/7he6S8/jrSPvpYZaXLOT3+8cdlHvYarbM4MxOnymZoD+zeA7qEBPPIpdBxY5G3+W/k/bFRZaMbT5+GoVeUGuIvpP75yYcfUYEfv5Yt0ezNN+Dfrh3qmn/79tA1b646RQr+/decKUlE1BClffIp8rdsgXdwMBK0Mi5uIqhPb/V9ICW+pOPSv00bVzeJPIQxObm0XrGvL3zL/q4gIiIiErO+2qL+n718N6w9Jn8tS3qH/H9w9sS6CaL76EPVxHSWLGvdEhF5CgkOq/rnZXW+I2fMQOwdtztUO9wdST1CKUMTOno0Ttz/gApqSFZ69tSZCBkyGF7+9tc2FEnPzIYxJQV+rVohqP9ZFR71UgEU35hoVY9dgiiHzz0PTV95GdmrV5vLtwQPG4qmL7wAn9CaDauqicBu3UqD6Dt3IqhfX3jp7JxXgYjIjRQePISUV19V12PvvafyPA8uJvUyA3v3Uh22ub+tZxCdnF/KJSHB7vlJiIiIqHH49Z7hTl+nw5GgJrOfcXojiIicRSanTP/iC+Rt2lTrdRXs2AnDiROq/rlk9oVNajjlW+zOSp//gzkrvXDPHhVUDhk+XAXE7SF11TMXLlS11hNmPwOvVOtfK5JlHn7++chavgzG3Sk4cllpORURdeMNiJk5s07Lt9hqk9Rcl86SzEWLbU94ajIhJCcH2QcO1Ci7M3GRc0dl+cbEIPrGG9X/rpS1bBkKdu9B1HXXwickxKVtIWqsTCUlOPnAA6VlXAYNUudZdxQyaFBpEH39enM5LaLaKkosnRSMpVyIiIioomYR9k+saq+GnU5JRGSh6OhRnLjnXuRv3eq0dcoPs2ZvvI6Azp3hibSsdP2oUfCZs0DVR89auhQBXboiePDAKrOzZdmTj5aWcYm86n8I6tkTWL3X5vI+4eEIP/c86IPTVBa6BK0Tnp2N0Aqjm+qNl5fqSMj55ReVIV8Vf8l4S0mp0Wayd6+pYQOrWOePP6HZ66+pbHpXyPt7ixqlIfXus3/8UR0j/q1bu6QtRI1Z9po16juvtIzLk25VxqVyXfSXkLtpE0xFRaq8C1FtGY6VBtH9mjd3dVOIiIjIDazemYSzO8ZA5+OtrldldJe4ug+iG9PTkfr668j9408Unz5daYKgjn9sdLgRRES1IeehjG+/RdKzz8GUlwfvkBBEXXMNfMLDarVeLz9/6EeOUNnKnk4C4OEX+iNv40bkb9uGgp07UHQ8EfpRI22W7Dr1zDNqglKZqFQmArWHBE6aznlVlciRSUf9mjWDKwV07Qovf3+VxWlLicmE5ORkxMbGwrsGAar4iwbAmVmn6Z9/gaIDB3Dk0ssQ/9ijagLX+lSSl4cT99+nAugyAkHacvj8C9DkheehH+78IXNEZPu77/T7H6jrEZdfZp6Pwh35d+oEn8hIFKelIW/rVgT36+fqJpEHMGiZ6C7+W4KIiIjcw3WfbsamB0chOsRfXbfFkTrotQqin7j3XhiOHEXYeefCNypaZfIREbmK1OKWiSlzfv5Z3Q7q10+VneLQXsdJrffgwYNVKRfJLi6RMic/zEdgr14I7tu33LLyuExKKkFUeb+9/f3t3463N0KGDYNb8PJSk4xWpaSkGIV+fgho3w7e3o7XXI0Y3QHOFDZlCk7cex9yfvwRJx98SE3YGnfffagvyS+9rP4O8I2LQ4uPPsLJhx9G/l9/IfGmmxE98xZE33BDvZfmIWqMpGxZwT//qM7JyMvdu0SKnBOCBwxQI51yN2xgEJ2cWs7FrzmD6ERERAQcsgiMW153Fod/5eZv/ktlEUZfey3Cz5mO8OnTyl2IiOpL1urVODhlqgqge+l0iL33XrT4+CMG0GtJMroiLroI/h07SKqjCpBmfP89CvfvN49IOvnoY+p61NUzENi9u4tb3LhI/XEp5SIBa5H+xZc4ctUMlSFe1wyJx5D++efqesLTT8O/TWu0/OhDRFxysdpXUl97HYmzZqE4J7fO20LU2J3+oDQLPeyc6fCNioK7k5rtInf9Blc3hTyEzOMimIlORERE9cHhILoM2zcVFNRNa4iI7FCck4MT9z+A4zNnoTg9XQ0Tb/X9d4i66n/MgHUSKXGiHzUa+rFj1XXJ+D90zrlI++QTJD31NIpTU+HXri2ibykN5FL9kv085uab0eytt1T5ItXR8c03MCZVXfetNkxFhcj+6Sd1PfziixAyuDQgJlmw8Y88goSnn1KdWTlrfsThCy9E4aFDddYWosauYM9e5P6yTo0GirrqKjQEwYOkLjpQsH27mlODqDZKCgthTE5W13WsiU5EREQVPLZoBz5aX/k36bwNh/H44h2oCYfLucQ/+ogayh19041qCLz8YK6YIUdEVFcKdu5E4i0zYThxQpXiiLrmakTPnAlvTlJWJ/zbtVN1diV4atpdhKRnZpc+IGVcnnGsjAs5n37EcLT65hsk3nILSnJzkTF/viqVUxcT4eb8uh4l2TkqWBF3112VHpfa7PJ3QeLMWVbrpEs9d5lLxXAqCcbkJBiSkmBMSoYxNQUwFteqbSWmEoSbTDCNH1+r9RA1FGkflmah68eMgV/LlmgIdHFxqvO1aP8B5G7ciNBx41zdJGrADMePq/9lUl2ZuJyIiIjI0vLtJ/H+FeXL0oreLSPw9s8H8OjkrqjzILqPXo+SnBwc/V+FrBeZYNTLC5131iyaT0RUHSkRIQE6CaBLyZYmzz2LoD59XN0sjyc/UMMmT0L8gCZIeu55NRop6uqrEditm6ubRtLR0aY1Wn3zNfwefh9Fhw4h56efVEa6lE6o2NFdU7Lewt271HVVAz842Opysk+0/u5bJN52u7lOesAZZ8CYmqpGM8BoRF2JlUD/4MGInDq1zrZB5A7kOzBz6TJ1Xc7FDUnI4CFI238AaR99rDoAOHqMnFHKxYtzdBEREVEF6XkG6AMqh71D/H2RlleEmnA4iH787nvU5HNNX3wBPmpi0Rptl4jIYcnPPacyjySA3nr+D/AJDXV1kxoRL0RcfLGaeFQmstSPHu3qBlGFUWCh48cjb/Nm5P35p/qMZMI1/YgR0DVpUqt1lxTkI7ts4t7AHj2r7bjyjYlRddKTnn1W1Wsv+Pff/x708oJvdDR84+PhGxcLXWwcfGNjah3sz9u5CzlLlyLl6Weg7z8AujgJqRN5prR581SHVFD//gg88ww0JJFX/Q8Z336L/G3bVAkqmX+DqDaTiuo4qSgRERFZ0SoqCL/sTUGr6PIJYD/vSUaLyCDUSxC9cN8+tP7hB5X5RkRUX3J++UX98BYJs59hAN1F/Jo3VxdyQ15eCOrbF75xcchZuxYlmZnInD8fAd26IXhAf3j51ixQnfPLOpjy8uATGYGg/v3sa0pZnfTQSZNVBrouPk61SwLo0hHvbPq8PKRu3YqA48dx8uGH0HzuXGYmkkeSWuLp337XILPQtZIuMbfdhqSnn1blIfUjR6qONyJHGY6VBtH9mvFvEiIiIqrsmsFt8Mii7TidW4SBbaPUfRv2p+K9Xw/hkcldUBMOj6EMOKMrjKdO1mhjREQ1DRqcfOhhdT3yyisQ3M++QB5RY+TXogUiLr4I/mV10Qv++QfpX31dOo9ADTrOi/bvVwF6/chR8PJxLAAe1KsnQseOQWD37tDFx9dJAF1IJvupCy9Qwfvcdb8i47vSICORp0n/8kvVqSUTageXTe7b0ERccrEq81SSnY2k2c+6ujnUQBUl/lfOhYiIiKiiC/o2x4MTu+CbTcdw8Xsb1WX+1uN4atoZuLhfC9SEw79mIy+7DKeeeQZRM66Gf4cO8NKVX0VAx441aggRkS2nnnpaZbP6tW6NmNtvd3VziNyel5+/KuXi364tctb+XKOsdJmoVEaACCnh4hvr3iVSiuLiEDlrJk6/+BKSZz+L4AED4MfgCnmQkoICpH36mTkLvaGOtvDy8UH844+pyYezli1D2PRpCBkyxNXNogbGkFg6sej/2bsL8DbL9Y/jd5N6m7p3nbsyYTgMGDB8GzDcOZz/wRk6nCEbBz3YwQ6uB+fg7vONAduYS1d3S5u2Sf/X86TJ2q2Stmlj38915UqaRp72TdP2fu/3d4cS5wIAANpx9r4D9Kmk2iLhIUaJCutZU1eXO9Fzrp4r9Zu3SN7NN8u2U06RrTNnydZZs53nAOBOlZ9/IZUffyxiNOpBoobwcE8vCfAZof0HtN2VntdZV3qTjoRpslh01ELklMniC+LOOksipkwWm9ksefNukiabzdNLAtxG7QizlpbqOQcxR88QXxYxZowknH22vpx/53yx1dZ6eknwIU1NTbsGixIxBwAA2tFotcnPG4vl8zX50tR8XUFlndRYGqU7ulyCH/r1V916IgD+S3WsFj78iL3o1tRx0Spi7DhJvfkmnYvamcbiYsm/4w59OfHiv0nE+PFuWzMQcF3pQ4bsykp/730xREe3Pxzc1qR/rtXOK9P0w0UMRvGVDteMe++VLTNniXnZMil75RVJOPdctzy2tapKCh96SBp2ZEvG/f+U4IQEtzwu4Iomq1VKnn9BX044//xei0bqS8lXXC6VX34pDTt3SvGT/5aUa+Z6eknwoZg//TtKFdEzMz29HAAA4IV2lpnl3OeXSm55ndRbbXLQ0GSJDguWf3+/WX9876xxvd+Jrv5Q6egEILCYV62SLbNmS9mrr0pDTo405uZ1eKr68kvZcsKJ+hDuzrqM8m67Xf+jpLpok//xjz77mgB/FDpAdaWf7uxKt1VXi62qnVNzcSJq333FmGAfwuJLmfCp11+nLxc+9LBYtmzp8WPWLF0qW048UcrfeFNqfvlF8u+4U79HAX1F/e5UnbfGuDiJO8k/jvw0REVJ2q236MslL7wgdes3eHpJ8BFqx4uiYsYMYWGeXg4AAPBCd/5vrYzvFyerbz9SwoN3lb+PGpOmB4x2R7faWCo+/NB+OPjOnTLwzTd08bz0pZf0YBfT4Yd3ayEAfEtTfb0UPfmklDzzrIjNJsHp6ZJ2800SnJrW7n1UxELhAw9I3R9/SM7ca6Tqm28l7bZbxRgbu8dtKz74UKq//VYkJEQyFi7UAwMB9ExQmL0rXWWcN9XVdXzb4GAxJsSLL4o79VSp+uprXfDOveFGGfjG693q3LVZLFL0r0el9IUX1J49HaPRUFioC5pVn30mMccc0yvrB1pSO2xKnvuPvhx/5pliiIwUf6Hej0xHTNc/r/m33y4DXn9Nggxd7vFBgCHKBQAAdGbZtlJ59x/7S2iLArrSLz5C8is7/l+4PV3+K7XsjTekYOF9En3IwfrQZkfeqMEUI6UvvdytRQDwLZZNm2TbaadLyVNP6wJ67IknyOAPPxDT9OkSMW5su6eofabKwNdfk6RLL9UxEZWffKK70mt+/bXV4zfk5UnBPffoy8mXXy7hI4Z76CsF/JMxJkZ38HV0Muq4Eh8dXBgUJOn33C0Gk0nvtCt59tkuP0bdX3/JtpNPkdLnn9cF9LhTTpZBH30kSX//u/58/vy7dOQU0NvMixdL3Zo1EhQeLvFnnSn+JvXmm/WOgdrffpPyt9/x9HLgA+qz7Z3oof04ChoAALTNpiJKbXsePawK6CrWpU+K6KWvvibpd82XpP/7v1adIuFjx4hlA4dhAv5M7TQrffll2Tr7JKlbu1Z3kGc+8ohk3HefLsq5IigkRJIvv0x3hqp4icaCAtlxwYWSf++9Yqur08+hBherqImICRMk8cILev3rAuB/QtLSnFERRU88KXXr1rmcPV387LOy9ZQ5Ytm4UYyJidLvySck/a67xBgdJUl/v1jCRo7UUVP5dxLrgt7n6EKPO+kkCY73zaNDOvtZTb7qSn258MEHpbGoyNNLgo/EuYT0oxMdAAC07aDhyfL8L1udHwcFiR4o+vBXG2TaiBTpkyK6+qMlvDlPtdUDhYaKrba2W4sA4P1Ud/iOCy+UgnsX6CiXqIMOkkH/+0hiZhzVrcdTQ0IHvf+exJ9xuv647OVXZOtJJ0vhP++Xml8X6Y679IUL9KBAAOiOmOOP11ER0tgoudffILb6+g5vX79zp2w/51wpevAhkYYGiT78cBn80Yc6csJBRUtlLFwgEhysIygqP+l4vgPQE2rnj4olUkdvJZx/nvgrFVMTPmaM2Cor9RGvQEfqdzriXPp5eikAAMBFS7aUyIUvLpOp93wtA2/8RL5Yk9/h7RdtLtG32/1UWOVaFMvNx4yS5dvKZPpDP4il0SZXvLlKDrzvW92JfuPRI6U7uty/rnLP1SHOuw8Rrf7pZwkdMlh6U/Ezz0rRQw9J/DlnS9pNN/Xqc8E1NT/+KFmPPyF5n30uYQP6644Q9QdtaFaWfo0YwsM9vUSfZV6+XIqeeEKCDEYJ6Z8loS2/t1lZYoyObnV71Q1pLS7Wh7g27MyW+uxsadiRLZbsHZJms0nT9Ok6X7xba1mxQrL/cYn+5zYoIkJSb7heZw6ryISeUIdvp912m0Qfeqjk3XSz1G/eLKWbN+vPpVxzjYQNGtSjxwcQ2NR7VNodd4h5xUrdVb7l6GPEEBnR7u3rd+ZIU22tfm9KvfkmiZ09u833ufCRIyXpH/8nxY89LgV33aWjqoKTk3v5q0Egd6HHzJghof38t2Codpin3XmnbJszR0e9xc6cKdEHHejpZcFLNezM0ef+/DMBAIC/MTdYZVR6jJwyJUv+79UVLt/v22sOkejwXeXrpCjXhopnxEXIZ1ceJB//nifr8iqlpr5RTp2SJTMnZkp4iLF3i+iqmJd4wQWScN65Oge0yWIRdQBz7e+/6z92VYFbxbz0lto//pDyt96SsBEjeu050DVquFrBvJskorJSarKzpaaN26hcXVXwjRg7VhccjHFxHlipb1HF8LLXX5eCBQt196T2y563U99LXUyPj5PGvDxdPG9vUKAKWil96ilJu+aaLq/HWl0tOddepwvo4ePHS8Z9C91e3I5WXe0ffSj5d86Xqs8/l6gDDpD4M88QT1KH+HTm6iPIage8XXBioqTPv1N2Xna5NOTYCy8diZgyWQ8z7qw4k3TxxVL1zTdiWbtO8u64U/o9/liPdywCLTXk5krl55/ry4EQbRYxdowknH2WnrGUP3++nqHCzinsrqmxUf9sKAwWBQDAdxw6IkWfuioxOkxiI7rWENpgtcnhD/4gz583RRfN1ckdXC6iFz/xpMSfdprEn3KK7i4u/Ne/dLdW7rXX6UJp2k3zJPbYY6U32Gpq9POoIn3xv5/qledA1wu9+bffoQurdZmZknXB+WLNzdWdz+pw+IYdO/R2ayws1KfaFSuk4tNPJP3O+WI67FBPL99r2SwWXUiueO89/XHMMcfogrI6bLUhe6fUZ+/Q59bSUp3Hq06tGAw6WzSkf38JzeqnjwywWixS+uSTUvb8CxJ75FF6wGdXFN73T12kV0ehDHjheTFERUlvUDmvmQ8/JPVXXKHX3nLmAgD0hBp6rHbUqffOjhgiIiR87FiXYqTUfIeMBQtk68mnSPU330jlxx9L7PHHu3HVCHRqh7pYrRK5zz4SPnq0BIKky6+Qyi++lIbsbNl83PGSdsvNEnPcceygglNDfr7+uVDRWuxkAQDA/x3zr5+k3mqTEakmuWr6MJkyMKHT+4QYDWJptLp9La7HubQYnKX+SVQnlYFuM5t1l1dvUp3v0dMOkaj996eI7iUqP/pIqr/7TseD5M85RcbOmSMhLaJCdLRIebnO0K/fulWKn3pa6rdskZ2XXCKxJ54gqTfdpIdSovU/BTuvuFLqfv9dF8NTrr1W55+29Y+jtbpGGnJ26n8yG8vKJCQ9w140T0/X/1S0etyGBtn2yy8Ss3q15N00Twa++66eYeCK6p9/kfK339aX0++5p9cK6A7qaw0bTIQLAPcLH+7+I0fCR4yQ5Ev+IUX/elTy775HFztDUro3pAZoSTUilP3X/vs34dxzJFCo4b39n3tWcq6/Xh/lkXvd9VL5+ReSfsftFEzReqhoZiYNFwAAeIGqqiqprKx0fhwWFqZPPZUSEyb3zBor4zPjpN5qlTeXZstpzyyWDy49QMZmdl5PPGe/gfLv77fIfSeNk2Cje/5m6Fom+m7FPNWxpU69qeKTT6Ru7VoZ+I79H4nOWCwWfWq5MeFeDQWFkn/Pvfpywj/+TzakpbVZDFWdxeoUMW6cmI46Soofe0xKnn9BKj78SA+OVNmXdKXvyj/feeVVYi0p0TsXMh56UKIPOKDDfzKNI0boAo4rCk88QeKzs8WycZMUP/6EpMy9utP7WKuqJO+WW/Tl+LPO0pm/AIDWEi+6SA8YVX+rqCO0+j35BF2z6LHyDz7QR/uFDOgv0dOmSSAJGzpUBr31lhQ/+6xunlFHemxevpyudGhq5o/CUFEAALzD6N2OmLz99tvljjvu6PHjDkmO1ieHyQMSZHupWf7z81Z5+NS9Or3/6uxy+XVzify0sUhGpJkkMrT10cZPnz2ly2vqUil+84yjZf0++3Z4cqeGvDwpuHeBZDxwvxhc3IuxYMECiY2NdZ5235hwQ4zLbbfZ87HHjpX488936X5q+6nOapVvGTp4sDQWFemu9NwbbhBrRYUE8vez9PXXZft55+sCusr8H/juOx0W0LvDFhUlybfdqi+XPPecnmXQmYL77pPG/HwdDeNK0R0AApGKdUlfuEAfmaWO0FJHagE90WSzSdnLr+jLCWefE5DdturnKvmSS2TQO29L2OhRYquo0F3pOy+/XP8NicClYg0VhooCAOAd1q5dKxUVFc7TvHnzeu259sqKk20lbU1k3FNMRIjMGJsmBw9PltSYcDGFh7Q69XonevJll4nBZJK+UrdmjS4sbp190q4rrVbdtVv22usy8vfVe+SWqo01d+5c58c5OTkU0t2o4oMPpfqHH+xZsAsXSFBw1w5miNhrLxn0/ntS9OijUvrCi7u60uffKaZDDw3w/POjJf3uu8UQGdkrzxd9+OG6g0vl9ubOu0kGvfduuzunqn/6SSreeVdfzrin99YEAP4SFZN86aVS9Mgj+kityH33k5BUYl3QPdXf/yD127eLISZG4mbNlECmjrhr1ZX+9TeyZdlySb3lFok57li60gM5zqUfQ0UBAPAGJpNJYmJi+uS51uZWSorJtSbrB06Z4Pbn71IFNObYY3o9/7wl9U+oGgTWUt5NN0vo4EH68Om2Bn/tnr3TMpcHPdNQUCAF99pjXJIuv1wfbqvytrtKFW5Tr7tOYo44QhdzVWb6zn9cIvHnnC1pN90kgUD9c5xz7XVS98cf9vzza+ZKwgUX9Po/g6k33yQ1ixdL/ebNUvz445JyzTV73MZaWSl5t9i71tU2idx7715dEwD4g8SLLpSqr7+Wuj//lK2zZ4uxgz8k1Q7ouFNOlvizz6YIiD2UvvyyPlevkd6eReJLXemmww+X3HnzmrPSr5Oqr76S9HvvEWP0rsN84f/qHUV04lwAAPApNZbGVl3k2aVmWZNbIXGRoZIZFyH3ff6XFFTUyUPNUS0qtiUrPkKGp5rE0miTN5ftkF83F8srF+7T4fPYbE3y9I9b5Ot1BdJgtcn+Q5L0QNLwkD1ryL1XRPfAP3k693m3QWAqg90YF9crA8LQcexI3q23iq2qSsLHj5fEC1yLcXGpK/2xx6T0+Rf0ocuRkyZJzIwZ4s/fx/K3/qujUppqa8UQGyuZneSfu5PKqE+/8w7ZeellUvKf58U0fbpETGi9d65g4X3SWFCgc1hTribGBQBcoQrjGQvula2nzNFH0alTR1RcXe3q3yX97rt6fb4MfEfdX3+JefFiEaNREs4809PL8equ9KovvxTLpk3S7/HHGUoeQBqaM9GJcwEAwLf8vrNCTn92sfPjuz9Zp89PmtRPHpwzQQorLZJTXuv8vCqA3/PpOsmvqJOIUKOMTDPJqxfto4viHXn8u03yyNcb5IChSRIeEiov/LJVSqotcr8bOtNdL6I3NfX4yeC7Kt57X2p+/EmCQkN1kaCrMS7tMYSH66509bgl/35K8u+4UyInT5bg5GTxNw2FhXpHRM0PP+qPI/fZR38vQzIy+nQdqpMr5oTjpfKj/9ljXd5/zxnroqJ6dLxMUJBk3HsvhR0A6IKwYcNkyKefSENuboe3U8XzwocekspPPhHLli3S77HHJLRfZp+tE96r9CV7F7rpyCP6/O8DX+pKjz7wQNl5+RVSv2WLbJszRzLu/2fAxQIGImt1jVjLyvTlEIroAAD4lP2GJMq2hce2+3lVSG/p/w4Zok9d9d7KnXLXzLFy5j4D9Mc/byyWC15cJvedNF4Mhp41iLs8qWjUurV9GuXSngGvvBwwkR/eQg94XbBAX06+4nIJG9L1F3Fnkv/xDwkbNUqs5eWSd/sdumPbn1R++aVsPeFEXUBXOwxSbrxB+r/wvMf+QVY/Q8bkJP3Pp8qnV9SA17xbb9OXE845R+/MAAB0jXpfj5wypcNT4oUX6N8BxoQEsaxbJ9tOPllqFi3y9NLhYWpgpppboiSee66nl+PVIsaP10NHIyZPFlt1tY4FLHryST2UFf6rIcce5aKOSjb24ZwuAADgO3LL6+TQEbvmUx04LEkkSKSgqq7Hj+2edmL4eYzLbfoflPAJ4yXh/J7HuLRFd7gvXKgLCdXffqsHmPrDMC1rVZUU3HOvVHzwgf5Y7SjI/Od9ulvRk9Q/H+l3zpedl1yiB7yqfPqyN9+SxsJCCR0wQJKvutKj64P7PfzVBk8vAUALUVOnyqB339HdtCpHfceFF0nKdddJwnnnkpMeoMreeFOaGhp0zJqKvEPH1FGLA154XgoWLpSy19+Q4kcfk7q1ayVj4X06EhL+G+VCFzoAAGhPo80mYcGte8ZDDEHSaO15sy5FdHSo4t13pebnn5tjXBa0OczVXcJHDJekKy6XogcfkoJ77pGofab69KHMNUuXSu6NN0pjbp4eHqqG4SZfdqn+XnoD02GHSuyJJ0jFhx9J9qWX2fN7g4IkfQExLgDQF0LS02XAq6/oKDO1s7Xwvvukbs0aSb9rPu/DAcZmsUjZm2/qy2pHClyj/qZKu+02CR8zRv8cVX/9jWw79VTp9/hjEjaInHR/w1BRAADQGVUqv/bt1RLaopCuBpPe9P4fEhm6q6b59NlTpKsooqNdKtO1YMFCeXXkkRK1//4SsblRZHPrblabzSpD3ficiRdcoP8Bql29WvJuuUWynntOggwupw71CfOyZVL40MO6W6xdNpvUrVunZwmEZGVJxn0L9dBUb5N6001S8+sifQi5knDeeV65TgDwV2o2iNp5qYqAqqNWxXlYNm/ucU56fXa2FN7/gISNHCFJF1/stlkm6B1qu1tLSyU4PV1MRxzh6eX4nLiTTpKwoUPtOembN8u2U+ZIxgP3S/Qhh4itokIaCgqlsbBAGvLzpVFdLiiQhoJ8kcZGXYQPHTjQ018CXNCQbS+ih/bL8vRSAACAl1KDSnc3c6J75k/xHxXapDIlc2++WWw1NRKcliYRe/V8iq0rVKe7KjhvmTlLF3fL3nhDEs48U7yFymzPmXuNs+jcmbhTTpaUG2702sOKjbGxknbXfJ0lGjp4sCRfeYUEAqJN4G2vt6uPGN4na4F3UvEtCWefJWEjhkvOVVc7c9LV+7OK2+qqqm+/00dC2SorperLL8W8dJlkPviAV8y2QdvReY6BoglnnckOj25SMTg6IunKq6R25Ur9t01QWJg01XWcf5l78y165pK3NW1gT/U7iXMBAAAde+CU3qtf8lc62qSK1+ZFiyUoPFxMhx8mEtR3/1iobqCUa6+VgrvvlsIHHpToAw7wmg6h/Lvu1gX00EGDJOWG6zvMrQ1OTZXwkSPF25mmTZPB//tIZ4uqjkgAgAdz0t95256TvmaN5Fx+hVQdd5yk3nyTBMfHd3r/JqtVih59TEqefto5h6N++3YxL14sW2fNlsxHHpHISRP74CtBV5gXLRLLhg0SFBkpcaec4unl+H5O+osvOHPSHQV0NQtG/V0WnJYqISmp9suJCVJw/wNSu2KFlL/7rsTzvfd6DTtz9HkocS4AAMADKKJjD/XbtunitZJyzTVijOv8H3d3iz/jdKn6+mv9j3/uvJt0Zmxv5rG7ovLzz6Xyk09EmrvlI8aPF3+hDoEGAHiemgUy4PXXpPiJJ6Xkued0zEfN4sWSfsftYpo+vd37NZaUSM611+od4Er8WWdJ6vXX6ViXnVdcqSMutp9zjqRef73En30Ww0u9SMlLL+nzuFmzxBgT4+nl+E1OesIFF4rYrBKcktJuk4DKoi9ceJ+OPjIdeqgEJyX1+Xrh+hEbDc5MdOJcAABA36OIjj262FTRuqm2ViL33VfizzxD5JtNfb4OdUhtxj13y5YTTpTaVauk9IUX9GBOT1Hd52pglZJ48d/8qoAOAN4YYRPIMTiGsDBJmXu1mKYfrn8nqwL4zssul5h2utJrf/tNR1ionOegiAhJv+suiT3uWP25sCFDZNB/35K8W2+Vyk8/k4J775Xa31bp2xiivDNqLJBYtmyRmh9+1IO9E84529PL8SuuzBRIOOssqfjoI7GsXScF9/1TMu//Z5+sDd37W7zJYhExGCQkLc3TywEAAAGI8D+0oorVqmit/rFWRWxP5kOGZGbqwZdK0b8elbr1GzzW+ZJ3+x06D10dGp/8j394ZB0AgMCidtgOeu9dSfzb33ThSHWlbzn+BKn65ptdWdqvvibbzj5HF9BV1JgqmDsK6A76d/qDD0rqzTeLBAfrYvrWOafqAabwrNJXXtHn0YceKqEDBnh6OQFH5c+n3znf/vP1v/9J9S+/eHpJaIezCz09XYJCQjy9HAAAEIDoRIeTKlKrYrWSetM8XcTuS212HcaMkcoZF+uImQtvvFEGvPxSz2Ndmpp0x5erKt7/QKq//VYkJEQyFi7UhwkDANBnXenXzBXTEdN3daVfepnuSle/z3TMmJpvMWOGpN99d7uDrB3DS8PHjJGcq67Sj7P1lDmScfddEnPMMX38VUFRO+crPvhQX04491xPLydgRYwbK/Fnnillr7wi+XfOl8EffejyjBh1BGflp59K2LBhPjEHx5c1ZDNUFAAAeBZFdGhN9fWSO+9GaWpokOhp0yR29mzxDkESfeg0PRzKsnqdbNh7ao8fsXTqyTr30pU/whtyc/Wh70ry5ZdL+Aj/jA4AAPhGV3rx449LyX+e113pWnCwpF53rcSfc45LOedqsOig99+TnLnXiHnJEn1uq62TuJO85fd+4Ch/5x0dnxc2cqRETt3b08sJaMlXXiFVX34pDTt2SPFTT0nKVVd1eh9bba3kXHedVH/9jRiio2XwJx9LSGpqn6w3ENU789ApogMAAM+giA6t+KmndR6kMTZW0ubf6VUDxwyRUWI67DCRtZ+LNDT0+PFslZVS8eGHEjFhgkTut68EGdv+MWiy2ST35pvFVl2tb5t4wfk9fm50LJAzkAHAta70a8R0xBGSd+ttYquqkoz7/ymRkyd36XGCExOl/3+ek4KF90nZq69K8b//LbEzT/T4AO9AU/HR//S5mj/jTX93BSJjdLSk3nKz5Fx+hd5JFXvccR0OXW8sLZWd/7hEalev1h+rvxVVF3u/Jx5nW/aShmx7ET20H0NFAQCAZ1BEh9T+8YcUP/20vpx2+20SkpIi3iZ08GAZuXKFNDU29uhx1ECi8Pv/K3Vr1uh/fOqzd4hp+hESnJy8x23L3nhDzIsWS1B4uKQvXKBzMwEA8Iqu9A/e13Eu3Z1don6nqZgYlQOtsoarv/tOTNOnu32taJvKo7ds2KCj4mKOPNLTy4GKRJo+XaIPO0xH+KlZOANeebnNn6/67dtlx8UXS8P2HWKIjZWUuXMl/+679f2qPv9cYo4+2iPr93f1O4lzAQAAnsVg0QBnq6uT3BvniVitEnPM0V6di6qGCBkiInp0MsbF6biamGOPlaDISLGWlunDqc0rVqjWc+dzqQz2wgce1JdV11/YoEEe/MoBAGhNdbv2dPi3+r0YN2eOvlz6sn3AJfpG5Wef6/Oo/ffTRwHCO36m0m65Wf99WLtihVS8994et1ENGNtOO10X0EMyMmTg669J/KlzJOnii/Xn8++6WxrLyjywev/XsDNHn4cS5wIAADyEInqAK3rkX3q4mDEpSVJvvVUCRejAgRJ/2mm6w11sNjEvXizl770v1ooKPSRKDW9TOaWR++yjD7MGAMAfxZ9xuojRKOalS6Vu3TpPLydgVH7+mT6PmUHXsjdRhXE1A0cpuP8BaSwpcX6u6ttvZfu554m1rEzCR4+WgW+9KWFDhujPJf79YgkdOkSspaVSuPA+j63fX9ksFmksKNCXQ7KIcwEAAJ5BET2AmZctk9KXXtKX0++aL8Hx8RJIVAdezNEzJPrww3WXe2N+vpS99ZbkXHWV1K5aJYaoKMm4954ed/oBAOCtQtLTJeYoe5xI6Suveno5AaFuwwap37RZ/+1hOvwwTy8Hu0k4+ywJGzVKbBUVUnCfvSBe+vrrsvOyy6Wprk6iDj5IR720jAI0hIZK+l13qXZ2PXen+qefPfgV+J+GnFx7fFVkpBgD7P8VAADgPagOBqimhgbJvelm/Qdp7EmzxXTooRKYgiR85EiJO+003X2kBpdWffW1/kzqTfMkJDPT0wsEAKBXxZ99tj5X+egtO2/RO1RuthJ14IFijInx9HLQxryA9Pl36oJ45Uf/k51XXCkF8+/SRy7GnXKyZD35pG602F3kxIkSf/ZZ+nLe7beJrabGA6v3Tw05jqGi/RjcCgAAPIZJiQGqZtEiacjO1t0cqTfeKP7i4a82dOt+6p/Y2JknSu1vqyVo+09iOvxwiZ092+3rAwDA20TstZeEjx8vdb//ro/ISr7kEk8vyW81NTU589DVLBp4p4hx4yT+zDOl7NVXperLL/V1SVdcLkn/+EeHRdyUK6+U6q+/kYbcXCl85F+SdvNNfbhq/1Wf3TxUlCgXAADgQXSiB6iKjz/W52qQqNFk8vRyvEOQQSImTpQRS5dIxv3/pNMFABAQ1O+7hOZu9LI33pCm+npPL8lvWdavl/qtWyUoNFSiA/YoQN+QfNWVEtKvn4jqTL/3Xr1zqbO/DVWHetr8+fqyKsCbV63qo9X6t4ZsRyc6R4gCAADPoYgegGy1tbpLRok57lhPL8frqIxSCugAgECictGDU1LEWlQslc1xI3A/Rxd69CEHizE62tPLQQfU9hn0/nsy7PvvJG72LJfvF33gARI7c6aOTMy75VaxsVOqxxp22ovoIf3oRAcAAJ5DET0AVX//vdjMZp33rQ7hBgAAgU11Rsefcbq+XPrSyzp2BL0R5fKZvmyaMcPTy4EL1NGawUlJXb5f6o03iDExUeo3b5aSp57ulbUFknpHET2rn6eXAgAAAhhF9ABU8fEn+jzm2GPpuAYAAFrcnDkSFBYmdWvWSC0xFG5Xt3atNOzYIUHh4WKaNs3Ty0EvMsbFSdqtt+jLxc88I3XruzezB/adT2qOk2OwKAAAgKdQRA8w1ooKqfnxR32ZKBcAAOAQnJAgMccf5+xG70qRq8lq7fQU6KqaY3KiDzlEZ2fDv5mOOkqiDz9cpLFR8m65hZ+BbrJVVIitulpfVkfRAgAAeEqwx54ZHlH11VfS1NAgYcOGSfjw4Z5eDgAA8CIJZ58jFe+8q/9eaMjJ6bRoVbd+veRee61YNm7q+IGDgiTpkINFjjlGAjbK5VN7lEvM0US5BAJ1tGfabbfJlqVLpe6PP6Tsrbck4YwzPL0sn1PfPFQ0ODlZDBERnl4OAAAIYHSiB2qUy3H2TjMAAACH8BHDJXK/fUVsNil9/fUOb6vyvbeddnrnBXSlqUkSvv9BzIsWSyCq+/NPvVMiKCJCd6IjMISkpkjy5Zfpy+XvvOPp5fikhp32KJcQolwAAICHUUQPIA2FhWJeskRfjjk2MDvBAABA593oSvnb7+hB5LtTsRSFDzwgOVfPlabaWonaf38Z+u03MmzRr+2eYk6do+9beOutYq2qkkDj6EI3HTqNbtoAE3P88SJGo1jWrpP6bds8vRyfw1BRAADgLSiiB5Cqzz7TnWARe+3FYB4AANCm6GmHSEj//mKrrJSKDz9s9TlreblkX/x3KXnuP/rjxIsulKxnn5GQjAwJjo9v95Q0d67UJyZKY0GBFNxzrwRclEtzHrrp6KM9vRz0MfX6j9p3X33Z8TqA6xqa41z43wUAAHgaRfQAQpQLAADoTJDBIAlnnaUvl778ijTZbM78862nzJGaX37RsSSZDz0oKddeK0FGY6ePaYiMlPw5p+hs9IoPPpCqb76RQFG3erU05uXp70H0QQd5ejnwgJhj7DtPKj+jiN5VDY5O9H5Znl4KAAAIcBTRA0T99u16qJEYDBIz4yhPLwcAAHix2NmzxRAdLfVbt+qiuSP/vCE7W2cTD3zjdYnp4pDQuoEDJe688/TlvNtul8bSUgkE6nunRB92mBjCwz29HHiA6fDDRYKDxbJ+vVi2bPH0cnwyziWUOBcAAHzaki0lcuGLy2TqPV/LwBs/kS/W5Hd6n0WbS+TYR3+S4Td/Jofc/528vdw+K8VTKKIHiIpP7F3oUfvtJ8FJSZ5eDgAA8GLG6CiJO2m2vpx7002t8s8Hvv1fCR85sluPm3DpJRI2bKhYS0ok/447ddSJP1Nd/JWff9GqGxmBxxgXJ1H779dqpwo6p+YvNOTm6ssMFgUAwLeZG6wyKj1G5p841qXbZ5ea5YIXl8l+gxPl0ysPlAsOGCQ3vveH/LChSDyFInqgZHES5QIAALogXkW6BAWJtahYf5xw4QWS9czTOuO5uwxhYZK+cKHuyq368kvn3yf+qnbVKp0Dr7r6ow480NPLgQfFHG0/cqOKXHSXNebnizQ2SlBIiASnpHh6OQAAoAcOHZEi1x41QmaMTXPp9q8u2S5ZCRFyy3GjZWiKSc7df6AcPTZN/vPzVvEUiugBwPLXX1K/ZYsEhYaK6Yjpnl4OAADwAaFZWRJ/9lliTEyUjAcfkNTrrpOg4OAeP27EmDGS9I//05fz77pLGgoKxV85MrBVnIchNNTTy4EHmQ4/TBeDLRs3iWXjRk8vxyfUNw8VDcnMdGn2AgAA6HtVVVVSWVnpPFksFrc87qrt5XLA0NZJGgcPT5ZV28vEUyiiB4CKjz/W59HTpokxOtrTywEAAD4idd48GfbzTxJ77LFufdykiy+W8DFjxFZZKXm33OKXsS4qiqLqC3uUi+noGZ5eDjzMGBPjPBqBAaOuadhpzz0lygUAAO81evRoiY2NdZ4WLFjglsctqrZIUnRYq+uSo8OkytIodQ1W8QSK6H5OZ3F+8qm+HHOce/8BBgAA/i0oKEif3P64ISGScd9CfZRczU8/Sfnbb4u/Ma9YIY1FRWKIiZHo/ff39HLgBWKad6aoXHR/3HHUW0NFQxgqCgCA11q7dq1UVFQ4T/PmzRN/RRHdz9WuXKnzBFUWZ/Qhh3h6OQAAAFrY0KGSfNVV+nLhwvucBTN/4ci+Nk2frncWANGHHaZfC/Vbt4plwwZPL8frNTTHuYT2y/L0UgAAQDtMJpPExMQ4T2FhrbvHu0t1nRdXW/boTjeFBUt4iGdi3noebAmfiHIxHXGEHuYFAADgLRLOPUeqvv1GapevkLwb50nK9deJNzAmJEpov8xu37+psVEqv/iyVfcxoGIVow4+SKq//kZ3o4ePGOHpJXm1euJcAAAIWBMHxMn3fxW1uu7njcUycUC8x9ZEEd2PNTU0SNXn9ixOolzQkYe/6rgb6uojhvfZWgAAgUMNC8xYsEC2nDhTzMuXy7Y5p4q3SLz4Ykm+6koJMhi6HKVX9NjjYi0pEWNsrETtu2+vrRG+J2bG0c4ievKVV/ZKXJK/aNiZo89DiXMBAMDn1VgaZVtJjfPj7FKzrMmtkLjIUMmMi5D7Pv9LCirq5KFT99KfP2ufAfLyr9tlwafr5JQpWbJoc7F88keePH/e3h77Gry6iF789DNS9dVXUr9liwSFh0vExImScs01EjZ4kKeX5hNqfv1VrOXlYkxMlKh99vH0cgAAAPYQmpUl6XfNl+JHH9Md3B7X1CQNublS8swzYtm8WTLuu0+M0VEu3dVmNkvujfOk6kt7F3ri3/+u898BB9Oh0yQoLEwatu8Qy7p1Ej56tKeX5JUai4v1jigJCpKQ/v09vRwAANBDv++skNOfXez8+O5P1unzkyb1kwfnTJDCSovklNc6P5+VEKkL5nd9vFZe+GWbpMWGy8LZ4+SQ4cniKV5dRDcvWybxZ5whEePGSpPVKoUPPyw7LrpQhnz8sRgiIz29PK9X8fEn+jzm6KMlKNirNzUAAAhgscceq0/eouKjjyTvllul+ptvZPsZZ0i/J5/sNN5FFd6zL71MF0ZV4TztzjslbvasPlszfIMhKkrPKVI7WnSkC0X0NpmXr9DnYcOH6xgcAADg2/YbkijbFrb/974qpLd1n0+vPEi8hVcPFu3/3LP6n4+wYcMkfORIfbhvY26e1K1Z4+mleT1bba1UffONvhxLlAsAAIDLYk84QQa8/JIYk5L0AMhtp5yi42baY165SraeMkcX0NURgP1feokCOtoVc8zR+rzys8+lqanJ08vxSuYV9iJ65JQpnl4KAACA5lPtybaqKn1uiI1t9zYWi0WfHKqa7xNoqr/7TprMZj2IJ3zCnntzAMBXM/oVcvoB9LaIvfaSQW//V3ZeepnUrV0r28+/QNJvv03iTj651e3K3/9A8m+7Tc+iCRs5UrKefEJCMjI8tm54v+iDD5agiAhp2LlT6v5co4+6RWvmFfadVpFTJnt6KQAAAN7fib77kKaCexdIxKRJEj68/eLJggULJDY21nkaHaCHSDqjXI49loFFAAAA3RCSni4DXntVTDNmiDQ06IiXggULdXa7ihosuP9+yZs3TxfQTUccIQNfe5UCOjqlYimjpx2iL6tIF7RmraoSy7q/9OWIyRTRAQCAd/CZTvT8+fPFsnGjDHj9tQ5vN2/ePJk7d67z45ycnIArpKt/6qwVFfoyUS5dRxcsAABwMERESObDD8m9KVPEvHSpyJI8CZn7L92kUL99p8jII3XkxE03nSlBBp/pT4GHqZlFVZ99LpWffyYp111L00sLtatW6QG/IQP6S0hKiqeXAwAA4DtF9Pz5d0n19z/IgFdfkZC0tA5vGxYWpk8OlZWVEmiCjEbdCVW/M6fTIVgAEMg7xADAFarAGbn33mKMj9czZxp27LB/wmgU0+GH6/k9FNDR1UgX1ZGu5z2tXq3jg2BnXuaIciEPHQAAeA+v/mtfDdpRBfSqr7+WAS++IKH9+nl6ST6FAjoAAID7hA0dKnGzZ4shJkYMJpO+rAroQFcZwsMl+rDDnANG0cZQ0ckU0QEAgPcweHuES8X//icZD9wvhqgoaSwq0idbXZ2nlwYAAIAAFJycLAlnnSkJZ58lwURNoAdijp6hzys//1zPf4Lo//Nq//hDX47cmyI6AADwHl4d51L+xpv6fMc557a6Pv3eeyVu9iwPrQoAAHhTZI/NZpWN2QbZ9O0mMRiMrW7DDAv0iiCv7kOBj4g68EAxREdLY0GB1P72m0ROmiSBrvb33/UQX7WDKoSjkAEAgBfx6iL6qL/WeXoJAAAAAOB2hrAwMR1+mFR8+JGOdKGILmJevisPnWGrAADAm3h1ER29j8F7AAAAgGeYZszQRfSqzz+X1BtvkCBj66NpAk3tcnseesSUyZ5eCgAAQCsciwoAAAAAHhB9wAF6SK2a+1T1zTcSyJoaG8X822/OTnQAAABvQhEdAAAAADwgKDRU4k87VV/Ov+12aSgokEBVt26dNJnNYoiNlbChQz29HAAAgFYoogMAAACAhyRdfrmEjx4t1vJyyb32OmmyWiUQmZc156FPmiRBBv5NBQAA3oW/TgAAAADAQwyhoZL50INiiIwU87JlUvzvpyQQmVfY89CJcgEAAN6IIjoAAAAAeFDowIGSducd+nLxk09KzdKlEkiabDapXd7cic5QUQAA4IUoogMAAACAh8Uef7zEzpolYrNJ7nXXS2NZmQSK+s2bxVpRIUERETraBgAAwNtQRAcAAAAAL5B2y80SOmiQNBYUSN68m6SpqUkCgbm5Cz1irwkSFBLi6eUAAADsgSI6AAAAAHgBQ1SUzkcPCg2V6u+/l7JXXpFAYF5OHjoAAPBuFNEBAAAAwEuEjxolKTdcry8X3P+A1P65RvyZ6rZ3dKJHTqaIDgAAvBNFdAAAAADwIvFnnCHR0w8XaWiQnGvmiq2mRvxVQ06Ojq+RkBCJmDDe08sBAABoE0V0AAAAAPAiQUFBknH33RKcni4N23dI4fy7VMu2+CPzsuY89DFjxBAR4enlAAAAtIkiOgAAAAB4GWNcnGQ++ICI0SjVn34qMc254f7GvKI5ymVvolwAAID3oogOAAAAAF4octIkSb78Mn059b33pPSpp6WpsVH8Sa2jE33yZE8vBQAAoF3B7X8KAADAtz381YYOP3/1EcP7bC0A0B2Jf/ub1G7cJNWffCKlTzwh5p9/ksz77pPQgQPF1zUWFUn99u0qv0bvMAAAAPBWdKIDAAAAgJcKMholdcG9knfaqWIwmaRu9e+yZdZsKXvzTWny8Zx08wp7RE3YiBFijInx9HIAAADaRREdAAAAALx80GjVxImS9d67ErnvvtJUWyv5d9wp2X//uzQUFoqvMjfnvEdOIQ8dAAB4N4roAAAAAOADQtLSpP/z/5HUeTdKUGio1Pz4k2w94USp/OJL8UXm5c1DRaeQhw4AALwbmegAAAAA4COCDAZJOPdcidp/f8m54QaxrF0nOVdeKdUnniCpt9wiRpNJfIG1slIs69fry5EMFQUAwO+9vGibPP3DFimqtsio9Bi584QxsldWXJu3fXt5tlz3zu+trgsNNsiGu48WT6GIDgAAAAA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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.dates as mdates\n", diff --git a/notebooks/notebook_statistic_data.ipynb b/notebooks/notebook_statistic_data.ipynb index 836d891..996d9cc 100644 --- a/notebooks/notebook_statistic_data.ipynb +++ b/notebooks/notebook_statistic_data.ipynb @@ -34,14 +34,13 @@ "# Now we can import the fucntion from the module\n", "from my_package.year_data import fetch_data\n", "\n", + "# Import function to replace nordic (æøå)\n", + "from my_package.util import replace_nordic\n", + "\n", "# User input the city, for the weather\n", "city_name = input(\"Enter a city in Norway: \")\n", "\n", - "for letter in city_name:\n", - " if letter in 'æøå':\n", - " city_name = city_name.replace('æ', 'ae')\n", - " city_name = city_name.replace('ø', 'o')\n", - " city_name = city_name.replace('å', 'aa')\n", + "city_name = replace_nordic(city_name)\n", "\n", "data, folder = fetch_data(city_name)" ] @@ -167,12 +166,19 @@ "import matplotlib.pyplot as plt\n", "import matplotlib.dates as mdates\n", "import os\n", + "import sys\n", + "\n", + "# Gets the absolute path to the src folder\n", + "sys.path.append(os.path.abspath(\"../src\"))\n", + "\n", + "# Import the kelvin to celsius function\n", + "from my_package.util import kelvin_to_celsius\n", "\n", "output_folder = \"../data/output_fig\"\n", "os.makedirs(output_folder, exist_ok=True) # Create the folder if it doesn't exist\n", "\n", "# Converts to and make a new column with celsius temp, and not kelvin\n", - "df['temp.mean_celsius'] = df['temp.mean'] - 272.15\n", + "df['temp.mean_celsius'] = kelvin_to_celsius(df['temp.mean'])\n", "temp = df['temp.mean_celsius']\n", "\n", "# Convert from day and month, to datetime\n", @@ -225,13 +231,20 @@ "import matplotlib.pyplot as plt\n", "import matplotlib.dates as mdates\n", "import os\n", + "import sys\n", + "\n", + "# Gets the absolute path to the src folder\n", + "sys.path.append(os.path.abspath(\"../src\"))\n", + "\n", + "# Import the kelvin to celsius function\n", + "from my_package.util import kelvin_to_celsius\n", "\n", "# Defines the output folder for the figure, and makes it if is does not exsist\n", "output_folder = \"../data/output_fig\"\n", "os.makedirs(output_folder, exist_ok=True) \n", "\n", "# Converts to and make a new column with celsius temp, and not kelvin\n", - "df['temp.mean_celsius'] = df['temp.mean'] - 272.15\n", + "df['temp.mean_celsius'] = kelvin_to_celsius(df['temp.mean'])\n", "temp = df['temp.mean_celsius']\n", "precipitation = df['precipitation.mean']\n", "wind = df['wind.mean']\n", @@ -303,15 +316,23 @@ "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.dates as mdates\n", + "import os\n", + "import sys\n", + "\n", + "# Gets the absolute path to the src folder\n", + "sys.path.append(os.path.abspath(\"../src\"))\n", + "\n", + "# Import the kelvin to celsius function\n", + "from my_package.util import kelvin_to_celsius\n", "\n", "# Converts to and make a new column with celsius temp, and not kelvin\n", - "df['temp.mean_celsius'] = df['temp.mean'] - 272.15\n", + "df['temp.mean_celsius'] = kelvin_to_celsius(df['temp.mean'])\n", "temp_mean = df['temp.mean_celsius']\n", "\n", - "df['temp.record_max_celsius'] = df['temp.record_max'] - 272.15\n", + "df['temp.record_max_celsius'] = kelvin_to_celsius(df['temp.record_max'])\n", "temp_record_max = df['temp.record_max_celsius']\n", "\n", - "df['temp.record_min_celsius'] = df['temp.record_min'] - 272.15\n", + "df['temp.record_min_celsius'] = kelvin_to_celsius(df['temp.record_min'])\n", "temp_record_min = df['temp.record_min_celsius']\n", "\n", "# Create a new column that concatenates month and day (e.g., \"03-01\" for March 1)\n", From ba86807b48a261f53bd108439492473ad74dcd14 Mon Sep 17 00:00:00 2001 From: toravest Date: Sun, 30 Mar 2025 21:45:34 +0200 Subject: [PATCH 16/18] add easy setup for API-key, replace nordic and kelvin to celsius --- src/my_package/setup.py | 25 +++++++++++++++++++++++++ src/my_package/util.py | 12 ++++++++++++ 2 files changed, 37 insertions(+) create mode 100644 src/my_package/setup.py create mode 100644 src/my_package/util.py diff --git a/src/my_package/setup.py b/src/my_package/setup.py new file mode 100644 index 0000000..0f18581 --- /dev/null +++ b/src/my_package/setup.py @@ -0,0 +1,25 @@ +import os + +def set_up_API(): + # Define the path to the .env file at the root of the project + env_filepath = os.path.join(os.path.dirname(__file__), "../../.env") + + # Stores the API_EMAIL and API_KEY + API_EMAIL = input("Write your API - email: ") + API_KEY = input("Write your API - key: ") + + # Prints the file path + print(f".env file created at: {env_filepath}") + + # Writes the API_EMAIL and API_KEY + with open (env_filepath, "w") as env_file: + env_file.write(f'API_EMAIL = "{API_EMAIL}"') + env_file.write("\n") + env_file.write(f'API_KEY = "{API_KEY}"') + + # Confirmation messages + print("Values are stored!") + print("You can now run the notebooks, and get data!") + +print("Add your info to OpenWeatherMap.com, and the function will create and add the info to env.") +set_up_API() \ No newline at end of file diff --git a/src/my_package/util.py b/src/my_package/util.py new file mode 100644 index 0000000..2eb13c3 --- /dev/null +++ b/src/my_package/util.py @@ -0,0 +1,12 @@ +def replace_nordic(city_name): + for letter in city_name: + if letter in 'æøå': + city_name = city_name.replace('æ', 'ae') + city_name = city_name.replace('ø', 'o') + city_name = city_name.replace('å', 'aa') + return city_name + + +def kelvin_to_celsius(temp_in_kelvin): + temp_in_celsius = temp_in_kelvin - 273.15 + return temp_in_celsius \ No newline at end of file From ed6bd34c7874d594d4184f247c5acf9443df51ac Mon Sep 17 00:00:00 2001 From: toravest Date: Sun, 30 Mar 2025 21:45:53 +0200 Subject: [PATCH 17/18] rename notebook --- ...est_notebook.ipynb => notebook_test.ipynb} | 23 ++++++++----------- 1 file changed, 10 insertions(+), 13 deletions(-) rename notebooks/{test_notebook.ipynb => notebook_test.ipynb} (75%) diff --git a/notebooks/test_notebook.ipynb b/notebooks/notebook_test.ipynb similarity index 75% rename from notebooks/test_notebook.ipynb rename to notebooks/notebook_test.ipynb index c887b20..d7ff57d 100644 --- a/notebooks/test_notebook.ipynb +++ b/notebooks/notebook_test.ipynb @@ -1,21 +1,18 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Notebook - Test\n", + "Dette er bare en test notebook, for å se om venv funker og det å importere funksjoner fra packager." + ] + }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Hello World!'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import sys\n", "import os\n", From bdae9fac5e04c47df26bb683c6c6d3301ee2f893 Mon Sep 17 00:00:00 2001 From: toravest Date: Sun, 30 Mar 2025 21:46:47 +0200 Subject: [PATCH 18/18] add documentation, markdown, and files to .gitignore --- .gitignore | 3 ++- README.md | 47 ++++++++++++++++++++++++++++++++++++++++++++- data/README.md | 16 +++++++-------- notebooks/README.md | 15 ++++++++++++++- resources/README.md | 29 +++++++++++++++++++++++++++- src/README.md | 17 +++++++++++++++- tests/README.md | 9 ++++++++- 7 files changed, 121 insertions(+), 15 deletions(-) diff --git a/.gitignore b/.gitignore index 701031c..a733132 100644 --- a/.gitignore +++ b/.gitignore @@ -4,4 +4,5 @@ /venv/ .env /data/output*/ -old_* \ No newline at end of file +old_* +.DS_Store \ No newline at end of file diff --git a/README.md b/README.md index d5c10d8..827bc63 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,28 @@ # Anvendt mappe +## Installering +- For å starte må man aktivere venv-en. Dette gjøres på følgende måte: + 1. Skriv en av disse i terminalen, dette kan variere fra pc og operativsystem: + - `python3 -m venv venv` + - `python -m venv venv` + 2. Aktiver vevn, det gjøres på en av følgende måter: + **Mac/linux**: `source venv/bin/activate` + **Windows**: `./venv/Scripts/activate` + 3. Installere nødvendige biblioteker, med en av disse: + - `pip3 install -r requirements.txt` + - `pip install -r requirements.txt` + +## Oversikt +Her kommer oversikt over strukturen i prosjektet: +- `data` denne mappen inneholder output data +- `docs` denne mappen inneholder dokumentasjon +- `notebooks` denne mappen inneholder notebookene med all funksjonalitet +- `resources` denne mappen inneholder våre kilder +- `src` denne mappen inneholder python filene +- `tests` denne mappen inneholder våre unittester + +Det kan leses mer om disse i deres tilhørende `README.md` filer. + ### Mappe del 1 #### Vår visjon av oppgaven @@ -32,6 +55,10 @@ Ettersom ingen av de fra MET funket etter vårt ønske, søkte vi videre på net - [OpenWeatherMap API](https://openweathermap.org/) - Denne inneholder forecast data, men det er også mulig å hente historiske data. - Med en student profil, får vi gratis tilgang på masse data. Dermed vil vi kunne requeste historiske data fra API-en. + - Det finnes også flere ulike API-er, som vil hjelpe oss å oppnå vår visjon. Blant annet: + - [Current Data](https://openweathermap.org/current): for å hente ut data fra ønsket sted på nåværende tidspunkt. + - [History API](https://openweathermap.org/history): for å hente data fra ønsket sted og tidsperiode (inntil 7 dager). + - [Statistic Historical Data](https://openweathermap.org/api/statistics-api): for å hente statistisk historisk data som kan brukes til regresjon. Den tar utganspunkt i all historisk data og oppsummerer det for hver dag i løpet av et år. ##### Henting av data For å hente data fra OpenWeatherMap API-en har vi skrevet en funskjon som tar inn stedsnavn, startdato og sluttdato, den legger da ønskede verdier inn i url-en og requester for ønsket sted og tidsperiode, sammen med API-key som er lagret i en env-fil og importert. @@ -47,7 +74,25 @@ Funksjonen returnerer en print setning når dataen er skrevet, og legger ved fil ##### Hente data fra fil +For å hente data fra json-fil, bruker vi pandas sin innebygde funksjon _read_json_, for deretter å lagra dataene i en pandas dataframe. + +#### Oppgave 3 - Databehandling +Vi har hele tiden fokusert på å forstå dataen vi har, derfor har vi lagret den i en json fil for å lettere kunne lese ut hvilke verdier vi har, og hvilke vi kanskje ikke trenger. De kolonnene vi mener vi ikke trenger har vi da fjernet. Så har vi sjekket etter feil og mangler i dataen, både med 'NaN' verdier, manglende kolonner eller ekstremverdier. +##### Metoder for å identifisere og håndtere manglende data +Metoder vi har brukt  er for eksempel pd.json_normalize, df.drop_duplicates og df.drop(columns = «name»). Ved json.normalize har vi fått konvertert dataene våre til en tabell, DataFrame, fordi det er lettere å manipulere. Df.drop_duplicates bruker vi for å enkelt håndtere duplikatene i datasettet. Vi har også kolonner som inneholder informasjon som ikke er relevant til det vi ønsker å finne og da bruker vi df.drop(column= «name») og setter inn kolonnenavnet i parentes bak, eksempel: df = df.drop(columns = «base») eller df = df.drop(columns = «visability»). Denne metoden er nyttig for å rydde opp i datasettet og håndtere fjerning av kolonner som ikke er relevant, og dermed blir det mer oversiktlig og ryddig å jobbe med. +Vi har også brukt missingno.matrix for å visualisere hvilke kolonner som mangler data, før vi har brukt enten fillna(0) for å endre 'NaN' verider til 0, eller fillna('obj.ffill()) for å bruke forrige lagret data. -#### Oppgave 3 - Databehandling +##### List comprehensions +I den ene koden til statistic_data_notebook er et eksempel på hvor vi har brukt list comprehension for å manipulere data. Vi bruker den til å manipulere temperaturene til celsius og lagre det resultatet i en ny kolonne, temp.mean_celsius. Vi har gjort dette fordi den metoden er mer effektiv å bruke enn for eksempel en direkte for-løkke.  + +Dette er også brukt i statistic_data_notebook for å lage en kolonne bestående av måned og dag. + +##### Pandas SQL vs tradisjonell Pandas +Pandas-syntaks kan være noe kompleks og da kan man for eksempel med sqldf, bruke SQL-spørringer på Pandas DataFrames. Dette kan gi en enkel måte å filtrere, transformere og gruppere data på, på en mindre kompleks måte. SQL-spørringer kan også være enklere å lese og vedlikeholde enn Pandas-operasjoner, når man jobber med komplekse datasett. Man kan også bruke effektive og enklere SQL-kommandoer som for eksempel JOIN og GROUP BY.  + +##### Uregelmessigheter i dataene +Uregelmessigheter vi kan forvente å møte på er blant annet manglende verdier. For å håndtere disse kan vi bruke metoder som for eksempel fillna(), som fyller manglende verdier med en standardverdi. Eller så kan vi bruke dropna(), som fjerner radene med manglende verdi. Vi kan også møte på ufullstendige datoer eller datoer i ukjent format. Da kan vi bruke pd.to_datetime() for å sikre at datoene blir riktig konvertert til datetime format.  + +Vi kan også møte ekstremverdier, som vi kan fjerne ved å sjekke om de er "uteliggere" ved å ligge mer enn tre standardavvik i fra gjennomsnittet. Da kan vi bruke verdien før med fillna('obj.ffill()') eller bruke interpolate linear metoden for å få den mest "smoothe" overgangen mellom manglende verdier. Da den "gjetter" seg frem til manglende verdier. \ No newline at end of file diff --git a/data/README.md b/data/README.md index 401b18a..2721585 100644 --- a/data/README.md +++ b/data/README.md @@ -1,13 +1,11 @@ # Data-description -### Possible API -- **API from openweathermap** -[API_OPEN_WEATHER_MAP](https://openweathermap.org/) +Her vil det opprettes ulike mapper som et resultat av dataene som lagres gjennom kjøringen av de ulike notebookene. -- **API from meterologisk institutt** -[API_FROST](https://frost.met.no/index.html) - -### Possible dataset -- **Natural Disasters:** -[DATASET_1](https://www.kaggle.com/datasets/brsdincer/all-natural-disasters-19002021-eosdis) +Funksjonen er bygd slik at den først sjekker om det eksisterer en mappe, før den eventuelt lager. Alle mapper som starter med output (altså output data) er lagt til i `.gitignore`. Dette for å ikke laste opp masse unødvendig til github, men også for at brukere ikke 'deler' data. Mine kjøringer vil være mine, og dine vil kun vises hos deg. +Dette er eksempel på noen av mappene: +- `output_current_data` lagrer dataen for ønsket sted, kjørt fra `notebook_current_data.ipynb` +- `output_fig` lagrer grafer, kjørt fra `notebook_statistic_data.ipynb` +- `output_record` lagrer rekord data fra ønsket sted, kjørt fra `notebook_statistic_data.ipynb` +- `output_statistikk` lagrer dataen for ønsket sted, kjørt fra`notebook_statistic_data.ipynb` \ No newline at end of file diff --git a/notebooks/README.md b/notebooks/README.md index 8c1d051..23f68fb 100644 --- a/notebooks/README.md +++ b/notebooks/README.md @@ -1 +1,14 @@ -# Notebook - description \ No newline at end of file +# Notebook - description + +Her finnes informasjon om de ulike notebookene og deres innhold. + +- [Current data](notebook_current_data.ipynb) + Denne notebooken er for å hente, skrive og vise nåværende data for ønsket lokasjon. +- [One day data](notebook_one_day_data.ipynb) + Denne notebooken henter data fra ønsket dag og sted, skriver til fil. Visualiserer manglende verdier, retter opp manglende verdier, og visualisere og lagrer data fra plot. +- [One week data](notebook_one_week_data.ipynb) + Denne notebooken henter data fra ønsket periode (inntil 7-dager) og sted, skriver til fil. Visualiserer manglende verdier, retter opp manglende verdier, og visualisere og lagrer data fra plot. +- [Statistic year data](notebook_statistic_data.ipynb) + Denne notebooken henter data fra en API som samler alle historiske data for ønsket sted, å regner ut statistiske verdier for alle dagene i året. Vi fjerner uønskede kolonner, utelukker ekstremverdier og visualiserer data gjennom plotter. +- [Test notebook](test_notebook.ipynb) + Dette er bare en test notebook, for å se om venv funker og det å importere funksjoner fra packager. \ No newline at end of file diff --git a/resources/README.md b/resources/README.md index 142fb2d..ec71c31 100644 --- a/resources/README.md +++ b/resources/README.md @@ -1 +1,28 @@ -# Resources - description \ No newline at end of file +# Resources - description + +Kilden til våre API-er er: [Open Weather](https://openweathermap.org/) + +Her finner vi API-er for: +- Current Data (Now) +- Historical Data (7 days) +- Statistic Historical Data (A year) + +For å benytte denne API-en må man lage en bruker, og som student for man tilgang på en del "ekstra" ressurser gratis. Her kommer en oversikt over hvordan lage bruker: +1. Du kan registrere bruker [HER](https://home.openweathermap.org/users/sign_up?student=true) +2. Når du logger inn trykker du til din profil å finner fanen 'API keys' +3. Kopier koden +4. Gå inn i `src/my_package/setup.py` kjør funksjonen, og du kan lime inn mail og API-key i terminalen +5. Finn en notebook, og kjør kode! +6. Du skal nå få data fra API-en + +### Possible API +- **API from openweathermap** +[API_OPEN_WEATHER_MAP](https://openweathermap.org/) + +- **API from meterologisk institutt** +[API_FROST](https://frost.met.no/index.html) + +### Possible dataset +- **Natural Disasters:** +[DATASET_1](https://www.kaggle.com/datasets/brsdincer/all-natural-disasters-19002021-eosdis) + diff --git a/src/README.md b/src/README.md index 42a797b..fca3fd8 100644 --- a/src/README.md +++ b/src/README.md @@ -1 +1,16 @@ -# Src - description \ No newline at end of file +# Src - description + +Mye av funksjonaliteten og funksjonener er skrevet i en vanlig `.py` fil, før de er importert til notebooken og kjøres der. + +`my_package` med en `__init__.py` gjør at funksjonene funker som 'moduler' og blir mulig å importere til videre bruk. + +Her kommer en kjapp forklaring av de ulike filene og deres funksjoner: +- `date_to_unix.py` bruker innebygde moduler som datetime og time, for å gjøre om datoer og tider til unix timestamp, sekunder fra 1. januar 1970. +- `fetch_current_data.py` funksjon for å hente nåværende data for ønsket sted fra API-en. Sender feilkode dersom statusen ikke har 200, altså ok. +- `fetch_data.py` henter data for ønsket sted, fra ønsket starttid til sluttid. Sender feilkode dersom statusen ikke har 200, altså ok. +- `get_record.py` brukt i `notebook_statistic_data.ipynb` for å finne rekord-målinger som høyeste og laveste målte temperatur. +- `setup.py` funskjon for å hjelpe brukeren å lage en .env fil for å lagre API-key og email. +- `test_module.py` en test funksjon for å sjekke at venv og implementering til notebook funker som det skal. +- `util.py` inneholder funksjoner for å erstatte nordiske (æøå) og å omgjøre temperaturer fra kelvin til celsius. Altså funksjoner som bare er en enkel del av noe større. +- `write_data.py` lagrer data i json-format, med ønsket filnavn til en 'passende' mappe basert på hvor funksjonen brukes. +- `year_data.py` henter statistisk værdata basert på historikk for ønsket sted. Sender feilkode dersom statusen ikke har 200, altså ok. \ No newline at end of file diff --git a/tests/README.md b/tests/README.md index aa1174a..69f95b0 100644 --- a/tests/README.md +++ b/tests/README.md @@ -1 +1,8 @@ -# Test - description \ No newline at end of file +# Test - description + +Her har vi lagd noen enkle tester for å sjekke deler av funksjonaliteten. Det skal legges til at det gjøres flere 'tester' av koden inne i koden, som `try and except`, `if-else` og `raise Error`. Dette sørger for å raskere oppfatte feil når man kjører koden. + +Her er litt info om testene: +- `test_letter_one_day.py` Tester at man får samme data av et sted med stor og liten bokstav. +- `test_one_day.py` Tester at funksjonaliteten for å gjøre om fra unix-timestamp blir den samme som input date. +- `test_test.py` Bare en første test for å sjekke at unittest funker. \ No newline at end of file