From f40ca185ee579d5cecbfe4d4905f79faac06b8f2 Mon Sep 17 00:00:00 2001 From: Thivainv Date: Wed, 13 May 2026 11:21:44 +1000 Subject: [PATCH 01/23] adding my folder --- Playground/Thivain_t1_26/placeholder.txt.txt | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 Playground/Thivain_t1_26/placeholder.txt.txt diff --git a/Playground/Thivain_t1_26/placeholder.txt.txt b/Playground/Thivain_t1_26/placeholder.txt.txt new file mode 100644 index 0000000000..e69de29bb2 From 5467413f340792fc38bb77d093f1792012a0c50d Mon Sep 17 00:00:00 2001 From: Thivainv Date: Wed, 13 May 2026 12:18:47 +1000 Subject: [PATCH 02/23] Add files via upload --- ...an_Climate_Impact_Prediction (2) (1).ipynb | 4553 +++++++++++++++++ 1 file changed, 4553 insertions(+) create mode 100644 Playground/Thivain_t1_26/UC00213_Urban_Pedestrian_Climate_Impact_Prediction (2) (1).ipynb diff --git a/Playground/Thivain_t1_26/UC00213_Urban_Pedestrian_Climate_Impact_Prediction (2) (1).ipynb b/Playground/Thivain_t1_26/UC00213_Urban_Pedestrian_Climate_Impact_Prediction (2) (1).ipynb new file mode 100644 index 0000000000..0a97449dd4 --- /dev/null +++ b/Playground/Thivain_t1_26/UC00213_Urban_Pedestrian_Climate_Impact_Prediction (2) (1).ipynb @@ -0,0 +1,4553 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
Urban Pedestrian Climate Impact Prediction
\n", + "\n", + "
Authored by: Maverick Nguyen
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
Duration: 90 mins
\n", + "\n", + "
\n", + "
Level: Intermediate
\n", + "
Pre-requisite Skills: Python, Data Cleaning, Data Validation, Data Visualisation, Time-Series Analysis, Feature Engineering, Optimisation Methods, Deep Learning
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
Scenario
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a local living near Melbourne CBD, Maverick relies on active travel, like walking, and public transport, like trams, to get around to different places he wants to go. One morning in January, Maverick prepared to travel to his workplace, expecting to get to work before 9:00 AM, but there was a sudden heatwave, causing the tram that he usually catches to be unable to follow its designated schedule and creating a delay in his schedule. Although there was a replacement bus for this emergency, only a limited number of people could board this vehicle, which further delayed his schedule. Because of this sudden extreme weather, travel conditions become less reliable and difficult to predict.\n", + "\n", + "Maverick wants to have access to a system that could predict how climate conditions over time can affect urban pedestrian movement. So that he could better plan his trip, allowing him to leave earlier in anticipation of sudden extreme weather change during a particular timeframe, or choose a different mode of transport, like an Uber. This allows more support in making informed decisions when travelling during extreme weather events." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
What this use case will teach you
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At the end of this use case, you will:\n", + "- Learn how to source and combine multiple public datasets. \n", + "- Understand how to clean and align time-series data at an hourly level for modelling. \n", + "- Explore how climate variables, such as temperature, humidity, pressure, and wind, relate to pedestrian counts.\n", + "- Apply feature engineering techniques to create meaningful predictors from weather and mobility time-series data. \n", + "- Build a deep learning forecasting model to predict pedestrian demand. \n", + "- Perform model optimisation like hyperparameter tuning to improve forecasting performance. \n", + "- Evaluate model performance and interpret results for climate adaptation planning." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
Introduction
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Urban systems are often affected by changing climate conditions, but these effects are not always easy to capture with simple forecasting methods. One clear example is pedestrian movement, where changing weather conditions can affect how many people move through the city over time.\n", + "\n", + "This use case focuses on predicting pedestrian activity in the City of Melbourne using hourly climate observations, which keeps the project closely aligned with the goal of modelling how climate factors influence an urban system.\n", + "\n", + "In this use case, pedestrian counts are aggregated into hourly city-level totals and merged with hourly microclimate observations for Melbourne. A deep learning model can then be trained to predict pedestrian demand based on time, recent demand history, and recent climate conditions.\n", + "\n", + "The datasets used in this project are the \"Pedestrian Counting System (counts per hour)\", the \"Pedestrian Counting System - Sensor Locations\" dataset for supporting location metadata, and the \"Microclimate Sensor Readings\" dataset from the City of Melbourne website." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Importing The Libraries\n", + "\n", + "This section is to show what libraries were used for this use case, with each imported library supporting a specific part of the pipeline. These libraries are necessary for doing data handling, time-series analysis, visualisation, feature engineering, optimisation, and deep learning [1] [2] [3] [4] [5]. These were added at the beginning to ensure the workflow is organised [6]. A random seed was set with a student ID to allow reproducibility of the outputs in this notebook [7].\n", + "\n", + "The following libraries support the deep learning pipeline for this section. TensorFlow and Keras are used for building and training the recurrent neural network models [3]. The os and random libraries are imported to support full reproducibility across all system-level random operations [55]." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "# Libraries for this project.\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.lines import Line2D\n", + "from matplotlib.patches import Patch\n", + "from statsmodels.graphics.tsaplots import plot_acf\n", + "from statsmodels.tsa.seasonal import seasonal_decompose\n", + "from statsmodels.tsa.stattools import adfuller\n", + "from sklearn.preprocessing import StandardScaler\n", + "import os\n", + "import random\n", + "\n", + "# Deep Learning\n", + "import tensorflow as tf\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import LSTM, GRU, Dense, Dropout\n", + "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Importing The Datasets\n", + "\n", + "This section is necessary for importing multiple public datasets from the City of Melbourne, before any cleaning, merging and modelling in later stages can happen. These datasets will be accessed through the City of Melbourne Open Data API v2.1, to allow the notebook to be directly used upon download [8].\n", + "\n", + "By using a shared BASE_URL and a dictionary of dataset identifiers, this allows for removing and adding datasets more easily [8] [9] [10] [11]. The get_csv_url() function is especially useful because it standardises the dataset access method and allows the same logic to be reused across all three datasets [8]. The ROW_LIMIT parameter was added to allow experimentation on smaller samples before scaling to the full dataset [8]. Lastly, the datasets are accessed through the Melbourne Open Data API v2.1, and no visible API key is exposed in the code [8]." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "# Store the API base path accessed via API v2.1.\n", + "BASE_URL = (\n", + " \"https://data.melbourne.vic.gov.au\"\n", + " \"/api/explore/v2.1/catalog/datasets\"\n", + ")\n", + "\n", + "# Store the dataset identifiers.\n", + "DATASETS = {\n", + " \"sensor_locations\":\n", + " \"pedestrian-counting-system-sensor-locations\",\n", + " \"pedestrian_counts\":\n", + " \"pedestrian-counting-system-monthly-counts-per-hour\",\n", + " \"microclimate\":\n", + " \"microclimate-sensors-data\",\n", + "}\n", + "\n", + "# Set the number of rows to retrieve for experiments.\n", + "# Change to \"None\" when full datasets are needed.\n", + "# Change to an integer for experimental purposes.\n", + "ROW_LIMIT = None \n", + "\n", + "def get_csv_url(dataset_id, row_limit=None):\n", + " # Build the base CSV export URL.\n", + " url = (\n", + " f\"{BASE_URL}/{dataset_id}/exports/csv\"\n", + " f\"?delimiter=,&with_bom=true\"\n", + " )\n", + "\n", + " # Add a row limit if one is provided.\n", + " if row_limit is not None:\n", + " url += f\"&limit={row_limit}\"\n", + "\n", + " return url" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Pedestrian Counting System - Sensor Locations\n", + "\n", + "This dataset was included to provide contextual information about the physical pedestrian counting network, even though it wasn't used in the later steps for modelling. The sensor metadata helps explain where the mobility data originates from and what the coverage of the system looks like [9].\n", + "\n", + "* `location_id`: unique identifier for each pedestrian counting location.\n", + "* `sensor_description`: human-readable description of the site, like street names.\n", + "* `sensor_name`: short internal sensor code.\n", + "* `installation_date`: date the sensor was installed.\n", + "* `note`: extra comments or metadata about the sensor.\n", + "* `location_type`: type of location.\n", + "* `status`: operational status of the sensor.\n", + "* `direction_1` and `direction_2`: the two movement directions captured by the counter.\n", + "* `latitude` and `longitude`: geographic coordinates of the sensor.\n", + "* `location`: combined coordinate string." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " location_id sensor_description sensor_name installation_date \\\n", + "0 3 Melbourne Central Swa295_T 2009-03-25 \n", + "1 5 Princes Bridge PriNW_T 2009-03-26 \n", + "2 9 Southern Cross Station Col700_T 2009-03-23 \n", + "3 12 New Quay NewQ_T 2009-01-21 \n", + "4 14 Sandridge Bridge SanBri_T 2009-03-24 \n", + "\n", + " note location_type status \\\n", + "0 NaN Outdoor A \n", + "1 Replace with: 00:6e:02:01:9e:54 Outdoor A \n", + "2 NaN Outdoor A \n", + "3 NaN Outdoor A \n", + "4 Sensor relocated to sensor ID 25 on 2/10/2019 Outdoor A \n", + "\n", + " direction_1 direction_2 latitude longitude location \n", + "0 North South -37.811015 144.964295 -37.81101524, 144.96429485 \n", + "1 North South -37.818742 144.967877 -37.81874249, 144.96787656 \n", + "2 East West -37.819830 144.951026 -37.81982992, 144.95102555 \n", + "3 East West -37.814580 144.942924 -37.81457988, 144.94292398 \n", + "4 North South -37.820112 144.962919 -37.82011242, 144.96291897 \n" + ] + } + ], + "source": [ + "# Load the sensor locations dataset.\n", + "sensor_locations_df = pd.read_csv(\n", + " get_csv_url(\n", + " DATASETS[\"sensor_locations\"],\n", + " row_limit=ROW_LIMIT\n", + " ),\n", + " encoding=\"utf-8-sig\"\n", + ")\n", + "\n", + "# Preview the dataframe.\n", + "print(sensor_locations_df.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Pedestrian Counting System (counts per hour)\n", + "\n", + "This is the target dataset for the use case because the final prediction task is to forecast pedestrian demand. The pedestrian dataset contains the observed mobility outcome that the model aims to learn [10].\n", + "\n", + "* `id`: record identifier.\n", + "* `location_id`: identifier linking the observation to a specific sensor location.\n", + "* `sensing_date`: date of observation.\n", + "* `hourday`: hour of day from 0 to 23.\n", + "* `direction_1` and `direction_2`: directional pedestrian counts.\n", + "* `pedestriancount`: total pedestrian count for that record.\n", + "* `sensor_name`: short sensor code.\n", + "* `location`: coordinate string for the sensor. " + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " id location_id sensing_date hourday direction_1 direction_2 \\\n", + "0 70220251008 70 2025-10-08 2 1 0 \n", + "1 31220260507 3 2026-05-07 12 872 1170 \n", + "2 591520241018 59 2024-10-18 15 430 628 \n", + "3 141020260106 14 2026-01-06 10 240 75 \n", + "4 28820241203 28 2024-12-03 8 537 262 \n", + "\n", + " pedestriancount sensor_name location \n", + "0 1 Errol20_T -37.80456984, 144.94946228 \n", + "1 2042 Swa295_T -37.81101524, 144.96429485 \n", + "2 1058 RMIT_T -37.80825648, 144.96304859 \n", + "3 315 SanBri_T -37.82011242, 144.96291897 \n", + "4 799 VAC_T -37.82129925, 144.96879309 \n" + ] + } + ], + "source": [ + "# Load the pedestrian counts dataset.\n", + "pedestrian_counts_df = pd.read_csv(\n", + " get_csv_url(\n", + " DATASETS[\"pedestrian_counts\"],\n", + " row_limit=ROW_LIMIT\n", + " ),\n", + " encoding=\"utf-8-sig\"\n", + ")\n", + "\n", + "# Preview the dataframe.\n", + "print(pedestrian_counts_df.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Microclimate sensors data\n", + "\n", + "This dataset provides the microclimate data that the use case requires to understand how climate conditions affect urban pedestrian movement, more specifically, the input features. It basically provides the explanatory environmental variables to connect weather conditions to mobility demand [11].\n", + "\n", + "* `device_id`: identifier for each microclimate device.\n", + "* `received_at`: timestamp when the reading was recorded.\n", + "* `sensorlocation`: descriptive location of the microclimate sensor.\n", + "* `latlong`: coordinate string.\n", + "* `minimumwinddirection`, `averagewinddirection`, `maximumwinddirection`: wind direction measurements.\n", + "* `minimumwindspeed`, `averagewindspeed`, `gustwindspeed`: wind speed measurements.\n", + "* `airtemperature`: air temperature reading.\n", + "* `relativehumidity`: humidity level.\n", + "* `atmosphericpressure`: atmospheric pressure.\n", + "* `pm25` and `pm10`: particulate matter measurements.\n", + "* `noise`: noise level. " + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " device_id received_at \\\n", + "0 ICTMicroclimate-08 2026-04-28T15:25:15+00:00 \n", + "1 ICTMicroclimate-08 2026-04-28T04:38:50+00:00 \n", + "2 ICTMicroclimate-08 2026-04-28T05:08:52+00:00 \n", + "3 ICTMicroclimate-10 2026-04-28T10:19:41+00:00 \n", + "4 ICTMicroclimate-08 2026-04-28T13:55:03+00:00 \n", + "\n", + " sensorlocation \\\n", + "0 Swanston St - Tram Stop 13 adjacent Federation... \n", + "1 Swanston St - Tram Stop 13 adjacent Federation... \n", + "2 Swanston St - Tram Stop 13 adjacent Federation... \n", + "3 1 Treasury Place \n", + "4 Swanston St - Tram Stop 13 adjacent Federation... \n", + "\n", + " latlong minimumwinddirection averagewinddirection \\\n", + "0 -37.8184515, 144.9678474 0.0 220.0 \n", + "1 -37.8184515, 144.9678474 0.0 283.0 \n", + "2 -37.8184515, 144.9678474 0.0 310.0 \n", + "3 -37.8128595, 144.9745395 277.0 324.0 \n", + "4 -37.8184515, 144.9678474 0.0 281.0 \n", + "\n", + " maximumwinddirection minimumwindspeed averagewindspeed gustwindspeed \\\n", + "0 314.0 0.0 0.5 2.5 \n", + "1 353.0 0.0 0.9 2.6 \n", + "2 314.0 0.0 0.2 1.9 \n", + "3 352.0 0.3 0.8 1.3 \n", + "4 354.0 0.0 0.3 2.5 \n", + "\n", + " airtemperature relativehumidity atmosphericpressure pm25 pm10 noise \n", + "0 16.7 79.9 1026.1 10.0 12.0 58.7 \n", + "1 21.9 61.5 1021.5 27.0 32.0 75.6 \n", + "2 21.6 61.1 1021.3 26.0 29.0 69.2 \n", + "3 19.7 57.4 1018.4 39.0 49.0 61.9 \n", + "4 17.3 76.2 1025.5 10.0 12.0 79.4 \n" + ] + } + ], + "source": [ + "# Load the microclimate dataset.\n", + "microclimate_df = pd.read_csv(\n", + " get_csv_url(\n", + " DATASETS[\"microclimate\"],\n", + " row_limit=ROW_LIMIT\n", + " ),\n", + " encoding=\"utf-8-sig\"\n", + ")\n", + "\n", + "# Preview the dataframe.\n", + "print(microclimate_df.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Initial Inspection Of The Datasets\n", + "\n", + "This section is necessary to understand the size, structure, completeness and formatting of public datasets since they often differ. Public datasets can have different structures, missing values, data types, date formats, and identifier systems, so checking them early helps identify potential issues in the workflow [12]. Before trying to clean and merge the datasets, understanding what each of the datasets contains and how they can be combined is important [12]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 Checking Number Of Rows/Columns\n", + "\n", + "Checking the shape is important because it shows the scale and complexity of each dataset. This helps determine memory demands, cleaning strategy, and whether the data volume is sufficient for later modelling [13].\n", + "\n", + "From these results, the pedestrian and microclimate datasets are large enough for later time-series modelling. The sensor locations dataset is much smaller because it only contains metadata about the sensor network, rather than repeated hourly observations. The volume for each task varies, but from the inspection of the shapes, there appears to be sufficient volume for the task of predicting the pedestrian count." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(145, 12)\n", + "(1540629, 9)\n", + "(897085, 16)\n" + ] + } + ], + "source": [ + "# Preview of the number of columns and rows.\n", + "print(sensor_locations_df.shape)\n", + "print(pedestrian_counts_df.shape)\n", + "print(microclimate_df.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 Checking The Features\n", + "\n", + "This step is to verify what information is actually available in each dataset, and whether there are meaningful fields for later joining, cleaning, and modelling. Knowing the columns early prevents accidentally removing important variables or keeping irrelevant variables [14].\n", + "\n", + "From the output, the column names confirm that location_id is shared between the sensor metadata and pedestrian counts datasets. Also, the pedestrian and microclimate datasets both contain time information, which is essential because the final merge is ultimately done at the hourly level. The microclimate dataset clearly offers a diverse range of explanatory variables, which is useful for modelling. And, some columns that are likely less useful for the final model need to be removed, such as descriptive notes, raw coordinate strings, and duplicate directional fields." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sensor locations columns:\n", + "Index(['location_id', 'sensor_description', 'sensor_name', 'installation_date',\n", + " 'note', 'location_type', 'status', 'direction_1', 'direction_2',\n", + " 'latitude', 'longitude', 'location'],\n", + " dtype='object')\n", + "\n", + "Pedestrian counts columns:\n", + "Index(['id', 'location_id', 'sensing_date', 'hourday', 'direction_1',\n", + " 'direction_2', 'pedestriancount', 'sensor_name', 'location'],\n", + " dtype='object')\n", + "\n", + "Microclimate columns:\n", + "Index(['device_id', 'received_at', 'sensorlocation', 'latlong',\n", + " 'minimumwinddirection', 'averagewinddirection', 'maximumwinddirection',\n", + " 'minimumwindspeed', 'averagewindspeed', 'gustwindspeed',\n", + " 'airtemperature', 'relativehumidity', 'atmosphericpressure', 'pm25',\n", + " 'pm10', 'noise'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "# Check the column names for each dataframe.\n", + "print(\"Sensor locations columns:\")\n", + "print(sensor_locations_df.columns)\n", + "\n", + "print(\"\\nPedestrian counts columns:\")\n", + "print(pedestrian_counts_df.columns)\n", + "\n", + "print(\"\\nMicroclimate columns:\")\n", + "print(microclimate_df.columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.3 Checking The Datatypes\n", + "\n", + "Datatype checking is essential because many later operations depend on correct types to proceed. Steps like date parsing, numeric aggregation, interpolation, rolling windows, and model preparation can all fail or behave incorrectly if types are wrong [15].\n", + "\n", + "From the outputs, the pedestrian sensing_date, sensor installation_date, and microclimate received_at fields are all initially stored as objects, so they are not yet ready for time-series operations, which will need to be dealt with. And the numeric climate variables are already in float64, and pedestrian counts are in int64, which is appropriate for aggregation and modelling, so no need to change that." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sensor locations data types:\n", + "location_id int64\n", + "sensor_description object\n", + "sensor_name object\n", + "installation_date object\n", + "note object\n", + "location_type object\n", + "status object\n", + "direction_1 object\n", + "direction_2 object\n", + "latitude float64\n", + "longitude float64\n", + "location object\n", + "dtype: object\n", + "\n", + "Pedestrian counts data types:\n", + "id int64\n", + "location_id int64\n", + "sensing_date object\n", + "hourday int64\n", + "direction_1 int64\n", + "direction_2 int64\n", + "pedestriancount int64\n", + "sensor_name object\n", + "location object\n", + "dtype: object\n", + "\n", + "Microclimate data types:\n", + "device_id object\n", + "received_at object\n", + "sensorlocation object\n", + "latlong object\n", + "minimumwinddirection float64\n", + "averagewinddirection float64\n", + "maximumwinddirection float64\n", + "minimumwindspeed float64\n", + "averagewindspeed float64\n", + "gustwindspeed float64\n", + "airtemperature float64\n", + "relativehumidity float64\n", + "atmosphericpressure float64\n", + "pm25 float64\n", + "pm10 float64\n", + "noise float64\n", + "dtype: object\n" + ] + } + ], + "source": [ + "# Check the data types for each dataframe.\n", + "print(\"Sensor locations data types:\")\n", + "print(sensor_locations_df.dtypes)\n", + "\n", + "print(\"\\nPedestrian counts data types:\")\n", + "print(pedestrian_counts_df.dtypes)\n", + "\n", + "print(\"\\nMicroclimate data types:\")\n", + "print(microclimate_df.dtypes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.4 Checking The Dataset Information\n", + "\n", + "Using .info() gives a more complete structural summary of the datasets, which shows non-null counts, memory usage, and dtype balance. But this step will focus more on the memory that will be used up in the RAM, to decide what sample size would be ideal for experimentation before scaling to the full dataset size, or opt for a platform like Google Colab to handle more heavy usage [16].\n", + "\n", + "From the outputs, the pedestrian dataset takes up the most memory, followed by the microclimate dataset, with the sensor locations dataset being extremely low. Which is acceptable to run locally for modelling." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sensor locations info:\n", + "\n", + "RangeIndex: 145 entries, 0 to 144\n", + "Data columns (total 12 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 location_id 145 non-null int64 \n", + " 1 sensor_description 145 non-null object \n", + " 2 sensor_name 145 non-null object \n", + " 3 installation_date 145 non-null object \n", + " 4 note 34 non-null object \n", + " 5 location_type 145 non-null object \n", + " 6 status 145 non-null object \n", + " 7 direction_1 113 non-null object \n", + " 8 direction_2 113 non-null object \n", + " 9 latitude 145 non-null float64\n", + " 10 longitude 145 non-null float64\n", + " 11 location 145 non-null object \n", + "dtypes: float64(2), int64(1), object(9)\n", + "memory usage: 13.7+ KB\n", + "None\n", + "\n", + "Pedestrian counts info:\n", + "\n", + "RangeIndex: 1540629 entries, 0 to 1540628\n", + "Data columns (total 9 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 1540629 non-null int64 \n", + " 1 location_id 1540629 non-null int64 \n", + " 2 sensing_date 1540629 non-null object\n", + " 3 hourday 1540629 non-null int64 \n", + " 4 direction_1 1540629 non-null int64 \n", + " 5 direction_2 1540629 non-null int64 \n", + " 6 pedestriancount 1540629 non-null int64 \n", + " 7 sensor_name 1540629 non-null object\n", + " 8 location 1540629 non-null object\n", + "dtypes: int64(6), object(3)\n", + "memory usage: 105.8+ MB\n", + "None\n", + "\n", + "Microclimate info:\n", + "\n", + "RangeIndex: 897085 entries, 0 to 897084\n", + "Data columns (total 16 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 device_id 897085 non-null object \n", + " 1 received_at 897085 non-null object \n", + " 2 sensorlocation 897085 non-null object \n", + " 3 latlong 897085 non-null object \n", + " 4 minimumwinddirection 763016 non-null float64\n", + " 5 averagewinddirection 881374 non-null float64\n", + " 6 maximumwinddirection 763006 non-null float64\n", + " 7 minimumwindspeed 763006 non-null float64\n", + " 8 averagewindspeed 881372 non-null float64\n", + " 9 gustwindspeed 763006 non-null float64\n", + " 10 airtemperature 881372 non-null float64\n", + " 11 relativehumidity 881372 non-null float64\n", + " 12 atmosphericpressure 881372 non-null float64\n", + " 13 pm25 823822 non-null float64\n", + " 14 pm10 823822 non-null float64\n", + " 15 noise 823822 non-null float64\n", + "dtypes: float64(12), object(4)\n", + "memory usage: 109.5+ MB\n", + "None\n" + ] + } + ], + "source": [ + "# Check the overall structure of each dataframe.\n", + "print(\"Sensor locations info:\")\n", + "print(sensor_locations_df.info())\n", + "\n", + "print(\"\\nPedestrian counts info:\")\n", + "print(pedestrian_counts_df.info())\n", + "\n", + "print(\"\\nMicroclimate info:\")\n", + "print(microclimate_df.info())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.5 Checking For Missing Values\n", + "\n", + "Checking for missing values is important, since it affects the cleaning strategy, feature selection, and merge quality. This is especially important because missing sensor readings can break hourly continuity for the modelling later [17].\n", + "\n", + "From the outputs, the pedestrian counts dataset has no missing values at all. The microclimate dataset has many missing values in all the variables besides device_id and received_at. And the sensor locations dataset has some missing values, mainly in note, direction_1, and direction_2, which are not necessary for city-level forecasting." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Missing values in sensor locations:\n", + "location_id 0\n", + "sensor_description 0\n", + "sensor_name 0\n", + "installation_date 0\n", + "note 111\n", + "location_type 0\n", + "status 0\n", + "direction_1 32\n", + "direction_2 32\n", + "latitude 0\n", + "longitude 0\n", + "location 0\n", + "dtype: int64\n", + "\n", + "Missing values in pedestrian counts:\n", + "id 0\n", + "location_id 0\n", + "sensing_date 0\n", + "hourday 0\n", + "direction_1 0\n", + "direction_2 0\n", + "pedestriancount 0\n", + "sensor_name 0\n", + "location 0\n", + "dtype: int64\n", + "\n", + "Missing values in microclimate:\n", + "device_id 0\n", + "received_at 0\n", + "sensorlocation 0\n", + "latlong 0\n", + "minimumwinddirection 134069\n", + "averagewinddirection 15711\n", + "maximumwinddirection 134079\n", + "minimumwindspeed 134079\n", + "averagewindspeed 15713\n", + "gustwindspeed 134079\n", + "airtemperature 15713\n", + "relativehumidity 15713\n", + "atmosphericpressure 15713\n", + "pm25 73263\n", + "pm10 73263\n", + "noise 73263\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "# Count the missing values in each dataframe.\n", + "print(\"Missing values in sensor locations:\")\n", + "print(sensor_locations_df.isna().sum())\n", + "\n", + "print(\"\\nMissing values in pedestrian counts:\")\n", + "print(pedestrian_counts_df.isna().sum())\n", + "\n", + "print(\"\\nMissing values in microclimate:\")\n", + "print(microclimate_df.isna().sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.6 Checking Summary Statistics\n", + "\n", + "Checking the summary statistics is an easy way to provide some early insights into the datasets, like central tendency and spread [18].\n", + "\n", + "From the sensor coordinates with the mean latitude and longitude, the Melbourne CBD can be inferred to be the main area of focus for the datasets. The pedestrian counts are strongly right-skewed, with the median count being a fair bit lower than the mean, and the maximum being really high, which suggests that some hours and sites are much busier than others. The average microclimate temperature is about 16°C, and the average relative humidity is about 66%, which looks plausible for Melbourne across a long time range." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sensor locations summary:\n", + " location_id latitude longitude\n", + "count 145.000000 145.000000 145.000000\n", + "mean 93.075862 -37.812392 144.960427\n", + "std 55.229079 0.006999 0.009594\n", + "min 1.000000 -37.825910 144.928606\n", + "25% 47.000000 -37.816888 144.956447\n", + "50% 89.000000 -37.813625 144.961860\n", + "75% 142.000000 -37.807767 144.965626\n", + "max 188.000000 -37.789353 144.986388\n", + "\n", + "Pedestrian counts summary:\n", + " id location_id hourday direction_1 direction_2 \\\n", + "count 1.540629e+06 1.540629e+06 1.540629e+06 1.540629e+06 1.540629e+06 \n", + "mean 4.683120e+11 7.196742e+01 1.175280e+01 1.981603e+02 2.008987e+02 \n", + "std 5.137983e+11 5.119167e+01 6.796006e+00 3.107706e+02 3.148589e+02 \n", + "min 1.020241e+09 1.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "25% 6.292025e+10 3.000000e+01 6.000000e+00 1.800000e+01 1.900000e+01 \n", + "50% 2.111203e+11 6.100000e+01 1.200000e+01 7.900000e+01 7.800000e+01 \n", + "75% 7.116202e+11 1.170000e+02 1.800000e+01 2.360000e+02 2.410000e+02 \n", + "max 1.852320e+12 1.850000e+02 2.300000e+01 1.009900e+04 1.108500e+04 \n", + "\n", + " pedestriancount \n", + "count 1.540629e+06 \n", + "mean 3.990590e+02 \n", + "std 5.953562e+02 \n", + "min 0.000000e+00 \n", + "25% 3.900000e+01 \n", + "50% 1.620000e+02 \n", + "75% 4.930000e+02 \n", + "max 1.111300e+04 \n", + "\n", + "Microclimate summary:\n", + " minimumwinddirection averagewinddirection maximumwinddirection \\\n", + "count 763016.000000 881374.000000 763006.000000 \n", + "mean 23.908174 166.489727 295.974746 \n", + "std 61.529961 125.699058 97.279991 \n", + "min 0.000000 0.000000 0.000000 \n", + "25% 0.000000 42.000000 253.000000 \n", + "50% 0.000000 161.000000 351.000000 \n", + "75% 0.000000 299.000000 357.000000 \n", + "max 359.000000 359.000000 360.000000 \n", + "\n", + " minimumwindspeed averagewindspeed gustwindspeed airtemperature \\\n", + "count 763006.000000 881372.000000 763006.000000 881372.000000 \n", + "mean 0.231898 1.007714 3.179780 16.689150 \n", + "std 0.574129 0.942189 2.504463 5.581520 \n", + "min 0.000000 0.000000 0.000000 -0.800000 \n", + "25% 0.000000 0.400000 1.400000 12.800000 \n", + "50% 0.000000 0.800000 2.500000 16.100000 \n", + "75% 0.100000 1.400000 4.400000 19.700001 \n", + "max 11.600000 11.200000 52.500000 45.400002 \n", + "\n", + " relativehumidity atmosphericpressure pm25 pm10 \\\n", + "count 881372.000000 881372.000000 823822.000000 823822.000000 \n", + "mean 68.007261 1013.943718 6.691192 8.972910 \n", + "std 17.562959 9.815047 30.102780 30.536759 \n", + "min 2.500000 894.700000 0.000000 0.000000 \n", + "25% 56.400002 1008.500000 1.000000 3.000000 \n", + "50% 69.400000 1014.100000 3.000000 5.000000 \n", + "75% 80.800000 1019.900000 7.000000 9.000000 \n", + "max 99.800003 1042.900000 3414.000000 3414.000000 \n", + "\n", + " noise \n", + "count 823822.000000 \n", + "mean 66.442163 \n", + "std 11.194487 \n", + "min 0.000000 \n", + "25% 58.500000 \n", + "50% 68.100000 \n", + "75% 71.800000 \n", + "max 131.100000 \n" + ] + } + ], + "source": [ + "# Check the summary statistics for numeric columns.\n", + "print(\"Sensor locations summary:\")\n", + "print(sensor_locations_df.describe())\n", + "\n", + "print(\"\\nPedestrian counts summary:\")\n", + "print(pedestrian_counts_df.describe())\n", + "\n", + "print(\"\\nMicroclimate summary:\")\n", + "print(microclimate_df.describe())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.7 Checking The Date Format\n", + "\n", + "Checking the date format is important because a time-based merge depends on all datasets sharing a compatible datetime structure, and public datasets often use different date formats and timezone conventions. This is important because any mismatch in date or time formatting can prevent the datasets from merging correctly later [19].\n", + "\n", + "The pedestrian dataset stores dates and hours separately. Whereas the microclimate dataset stores the full timestamps with date and time with UTC offsets. And the sensor installation date is a simple date string and is mainly historical metadata rather than a modelling field. This makes it clear that datetime standardisation is a required step before any merge can occur." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pedestrian sensing_date sample:\n", + "0 2025-10-08\n", + "1 2026-05-07\n", + "2 2024-10-18\n", + "3 2026-01-06\n", + "4 2024-12-03\n", + "Name: sensing_date, dtype: object\n", + "\n", + "Microclimate received_at sample:\n", + "0 2026-04-28T15:25:15+00:00\n", + "1 2026-04-28T04:38:50+00:00\n", + "2 2026-04-28T05:08:52+00:00\n", + "3 2026-04-28T10:19:41+00:00\n", + "4 2026-04-28T13:55:03+00:00\n", + "Name: received_at, dtype: object\n", + "\n", + "Sensor installation_date sample:\n", + "0 2009-03-25\n", + "1 2009-03-26\n", + "2 2009-03-23\n", + "3 2009-01-21\n", + "4 2009-03-24\n", + "Name: installation_date, dtype: object\n" + ] + } + ], + "source": [ + "# Check a few date values before conversion.\n", + "print(\"Pedestrian sensing_date sample:\")\n", + "print(pedestrian_counts_df[\"sensing_date\"].head())\n", + "\n", + "print(\"\\nMicroclimate received_at sample:\")\n", + "print(microclimate_df[\"received_at\"].head())\n", + "\n", + "print(\"\\nSensor installation_date sample:\")\n", + "print(sensor_locations_df[\"installation_date\"].head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.8 Checking The ID Columns\n", + "\n", + "This was an additional step for checking the unique identifiers to see if the datasets have any chance of being joined directly by ID or whether another strategy, like a datetime merge, is best. And since the project uses multiple public datasets, checking the unique IDs also helps confirm whether the pedestrian sensors and microclimate sensors use the same location system or separate systems [20].\n", + "\n", + "From the outputs, the sensor locations dataset contains 137 unique location_id values, while the pedestrian counts dataset contains 100 unique location_id values. Whereas the microclimate dataset contains 12 unique device_id values, which is a completely different identifier system. This means the microclimate data cannot be joined to pedestrian counts by location ID, so a time-based merge is the best integration method available." + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unique location_id values in sensor locations:\n", + "137\n", + "\n", + "Unique location_id values in pedestrian counts:\n", + "100\n", + "\n", + "Unique device_id values in microclimate:\n", + "12\n" + ] + } + ], + "source": [ + "# Check important identifier columns.\n", + "print(\"Unique location_id values in sensor locations:\")\n", + "print(sensor_locations_df[\"location_id\"].nunique())\n", + "\n", + "print(\"\\nUnique location_id values in pedestrian counts:\")\n", + "print(pedestrian_counts_df[\"location_id\"].nunique())\n", + "\n", + "print(\"\\nUnique device_id values in microclimate:\")\n", + "print(microclimate_df[\"device_id\"].nunique())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Data Cleaning\n", + "\n", + "This is the section for data cleaning, since raw data are rarely ready to be used as it is, so cleaning processes are necessary to address duplicates, missing values, inconsistencies, syntax errors, irrelevant data and structural errors [21] [22]. And since this data pipeline uses the API for dataset access, this means that the datasets are being updated in real-time, so ensuring any errors get addressed in the pipeline ensures the data remains accurate, secure and accessible at every stage of its lifecycle [21]. And that the prediction will also be accurate [22]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.1 Removing Irrelevant Columns\n", + "\n", + "Selecting relevant variables and removing the irrelevant ones are necessary because not every feature is useful for the prediction modelling. Keeping unnecessary columns can make the workflow harder to manage, increase memory usage, and create confusion in later steps [23] [24].\n", + "\n", + "In this step, the sensor dataset is reduced to six useful metadata columns, even though it wasn't used for modelling purposes. The pedestrian dataset is reduced to the four fields needed to construct hourly counts. The microclimate dataset is reduced to key climate, air quality, and noise variables. The descriptive or duplicate variables were omitted." + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " location_id sensor_description installation_date status latitude \\\n", + "0 3 Melbourne Central 2009-03-25 A -37.811015 \n", + "1 5 Princes Bridge 2009-03-26 A -37.818742 \n", + "2 9 Southern Cross Station 2009-03-23 A -37.819830 \n", + "3 12 New Quay 2009-01-21 A -37.814580 \n", + "4 14 Sandridge Bridge 2009-03-24 A -37.820112 \n", + "\n", + " longitude \n", + "0 144.964295 \n", + "1 144.967877 \n", + "2 144.951026 \n", + "3 144.942924 \n", + "4 144.962919 \n", + " location_id sensing_date hourday pedestriancount\n", + "0 70 2025-10-08 2 1\n", + "1 3 2026-05-07 12 2042\n", + "2 59 2024-10-18 15 1058\n", + "3 14 2026-01-06 10 315\n", + "4 28 2024-12-03 8 799\n", + " device_id received_at airtemperature \\\n", + "0 ICTMicroclimate-08 2026-04-28T15:25:15+00:00 16.7 \n", + "1 ICTMicroclimate-08 2026-04-28T04:38:50+00:00 21.9 \n", + "2 ICTMicroclimate-08 2026-04-28T05:08:52+00:00 21.6 \n", + "3 ICTMicroclimate-10 2026-04-28T10:19:41+00:00 19.7 \n", + "4 ICTMicroclimate-08 2026-04-28T13:55:03+00:00 17.3 \n", + "\n", + " relativehumidity atmosphericpressure averagewindspeed gustwindspeed \\\n", + "0 79.9 1026.1 0.5 2.5 \n", + "1 61.5 1021.5 0.9 2.6 \n", + "2 61.1 1021.3 0.2 1.9 \n", + "3 57.4 1018.4 0.8 1.3 \n", + "4 76.2 1025.5 0.3 2.5 \n", + "\n", + " averagewinddirection pm25 pm10 noise \n", + "0 220.0 10.0 12.0 58.7 \n", + "1 283.0 27.0 32.0 75.6 \n", + "2 310.0 26.0 29.0 69.2 \n", + "3 324.0 39.0 49.0 61.9 \n", + "4 281.0 10.0 12.0 79.4 \n" + ] + } + ], + "source": [ + "# Keep only useful columns.\n", + "sensor_locations_clean = sensor_locations_df[\n", + " [\n", + " \"location_id\",\n", + " \"sensor_description\",\n", + " \"installation_date\",\n", + " \"status\",\n", + " \"latitude\",\n", + " \"longitude\",\n", + " ]\n", + "].copy()\n", + "\n", + "pedestrian_clean = pedestrian_counts_df[\n", + " [\n", + " \"location_id\",\n", + " \"sensing_date\",\n", + " \"hourday\",\n", + " \"pedestriancount\",\n", + " ]\n", + "].copy()\n", + "\n", + "microclimate_clean = microclimate_df[\n", + " [\n", + " \"device_id\",\n", + " \"received_at\",\n", + " \"airtemperature\",\n", + " \"relativehumidity\",\n", + " \"atmosphericpressure\",\n", + " \"averagewindspeed\",\n", + " \"gustwindspeed\",\n", + " \"averagewinddirection\",\n", + " \"pm25\",\n", + " \"pm10\",\n", + " \"noise\",\n", + " ]\n", + "].copy()\n", + "\n", + "# Check the result.\n", + "print(sensor_locations_clean.head())\n", + "print(pedestrian_clean.head())\n", + "print(microclimate_clean.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.2 Removing Missing Value\n", + "\n", + "This step involves dealing with missing values by removing the rows they're in. This is because missing values in explanatory variables can cause problems when doing aggregation, interpolation, and modelling later, leading to bias results [25]. This is especially important for the microclimate dataset, since missing climate readings could affect the quality of the explanatory variables.\n", + "\n", + "After doing the column selection in the previous step, the sensor and pedestrian tables are now fully complete without missing values. Whereas the microclimate table still has many missing values, especially in gustwindspeed, pm25, pm10, and noise. \n", + "\n", + "By using dropna(), the microclimate dataset lost a number of data points with missing values, but still retains a sizable portion of data points. The decision for completeness in the data points was preferred to simplify the merging and feature engineering steps later, and because there were enough data points for it not to matter much." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Missing values in sensor_locations_clean:\n", + "location_id 0\n", + "sensor_description 0\n", + "installation_date 0\n", + "status 0\n", + "latitude 0\n", + "longitude 0\n", + "dtype: int64\n", + "\n", + "Missing values in pedestrian_clean:\n", + "location_id 0\n", + "sensing_date 0\n", + "hourday 0\n", + "pedestriancount 0\n", + "dtype: int64\n", + "\n", + "Missing values in microclimate_clean:\n", + "device_id 0\n", + "received_at 0\n", + "airtemperature 15713\n", + "relativehumidity 15713\n", + "atmosphericpressure 15713\n", + "averagewindspeed 15713\n", + "gustwindspeed 134079\n", + "averagewinddirection 15711\n", + "pm25 73263\n", + "pm10 73263\n", + "noise 73263\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "# Check missing values after column selection.\n", + "print(\"Missing values in sensor_locations_clean:\")\n", + "print(sensor_locations_clean.isna().sum())\n", + "\n", + "print(\"\\nMissing values in pedestrian_clean:\")\n", + "print(pedestrian_clean.isna().sum())\n", + "\n", + "print(\"\\nMissing values in microclimate_clean:\")\n", + "print(microclimate_clean.isna().sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Missing values in sensor_locations_clean:\n", + "location_id 0\n", + "sensor_description 0\n", + "installation_date 0\n", + "status 0\n", + "latitude 0\n", + "longitude 0\n", + "dtype: int64\n", + "(145, 6)\n", + "\n", + "Missing values in pedestrian_clean:\n", + "location_id 0\n", + "sensing_date 0\n", + "hourday 0\n", + "pedestriancount 0\n", + "dtype: int64\n", + "(1540629, 4)\n", + "\n", + "Missing values in microclimate_clean:\n", + "device_id 0\n", + "received_at 0\n", + "airtemperature 0\n", + "relativehumidity 0\n", + "atmosphericpressure 0\n", + "averagewindspeed 0\n", + "gustwindspeed 0\n", + "averagewinddirection 0\n", + "pm25 0\n", + "pm10 0\n", + "noise 0\n", + "dtype: int64\n", + "(705456, 11)\n" + ] + } + ], + "source": [ + "# Remove rows with any missing values from each dataset.\n", + "sensor_locations_clean = sensor_locations_clean.dropna().copy()\n", + "pedestrian_clean = pedestrian_clean.dropna().copy()\n", + "microclimate_clean = microclimate_clean.dropna().copy()\n", + "\n", + "# Check missing values after removal.\n", + "print(\"\\nMissing values in sensor_locations_clean:\")\n", + "print(sensor_locations_clean.isna().sum())\n", + "print(sensor_locations_clean.shape)\n", + "\n", + "print(\"\\nMissing values in pedestrian_clean:\")\n", + "print(pedestrian_clean.isna().sum())\n", + "print(pedestrian_clean.shape)\n", + "\n", + "print(\"\\nMissing values in microclimate_clean:\")\n", + "print(microclimate_clean.isna().sum())\n", + "print(microclimate_clean.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.3 Datetime Formatting\n", + "\n", + "Ensuring the datetime formatting matches between the different datasets is important because the modelling is hourly, and the datasets need to be able to merge based on a common point for perform chronological analysis across different data sources [26] [27]. Skipping this step would mean that the datasets cannot be merged into one table.\n", + "\n", + "The pedestrian dataset is converted from separate sensing_date and hourday fields into a single datetime_hour, such as 2024-12-06 20:00:00. Whereas the microclimate timestamps are converted from UTC into Australia/Melbourne, timezone information is removed, and the values are floored to the nearest hour. By doing this, both datasets now have a datetime variable with the same time formatting. It's also important to note that the time range of the microclimate dataset is narrower than the pedestrian time range, meaning that the overlap period is limited to the microclimate dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " location_id sensing_date hourday pedestriancount datetime_hour\n", + "0 70 2025-10-08 2 1 2025-10-08 02:00:00\n", + "1 3 2026-05-07 12 2042 2026-05-07 12:00:00\n", + "2 59 2024-10-18 15 1058 2024-10-18 15:00:00\n", + "3 14 2026-01-06 10 315 2026-01-06 10:00:00\n", + "4 28 2024-12-03 8 799 2024-12-03 08:00:00\n" + ] + } + ], + "source": [ + "# Convert sensor installation date.\n", + "sensor_locations_clean[\"installation_date\"] = pd.to_datetime(\n", + " sensor_locations_clean[\"installation_date\"]\n", + ")\n", + "\n", + "# Create an hourly datetime for pedestrian data.\n", + "pedestrian_clean[\"sensing_date\"] = pd.to_datetime(\n", + " pedestrian_clean[\"sensing_date\"]\n", + ")\n", + "\n", + "pedestrian_clean[\"datetime_hour\"] = (\n", + " pedestrian_clean[\"sensing_date\"] +\n", + " pd.to_timedelta(pedestrian_clean[\"hourday\"], unit=\"h\")\n", + ")\n", + "\n", + "# Check the result.\n", + "print(pedestrian_clean.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " device_id received_at airtemperature relativehumidity \\\n", + "0 ICTMicroclimate-08 2026-04-29 01:25:15 16.7 79.9 \n", + "1 ICTMicroclimate-08 2026-04-28 14:38:50 21.9 61.5 \n", + "2 ICTMicroclimate-08 2026-04-28 15:08:52 21.6 61.1 \n", + "3 ICTMicroclimate-10 2026-04-28 20:19:41 19.7 57.4 \n", + "4 ICTMicroclimate-08 2026-04-28 23:55:03 17.3 76.2 \n", + "\n", + " atmosphericpressure averagewindspeed gustwindspeed averagewinddirection \\\n", + "0 1026.1 0.5 2.5 220.0 \n", + "1 1021.5 0.9 2.6 283.0 \n", + "2 1021.3 0.2 1.9 310.0 \n", + "3 1018.4 0.8 1.3 324.0 \n", + "4 1025.5 0.3 2.5 281.0 \n", + "\n", + " pm25 pm10 noise datetime_hour \n", + "0 10.0 12.0 58.7 2026-04-29 01:00:00 \n", + "1 27.0 32.0 75.6 2026-04-28 14:00:00 \n", + "2 26.0 29.0 69.2 2026-04-28 15:00:00 \n", + "3 39.0 49.0 61.9 2026-04-28 20:00:00 \n", + "4 10.0 12.0 79.4 2026-04-28 23:00:00 \n" + ] + } + ], + "source": [ + "# Convert the raw timestamp to datetime with UTC.\n", + "microclimate_clean[\"received_at\"] = pd.to_datetime(\n", + " microclimate_clean[\"received_at\"],\n", + " utc=True\n", + ")\n", + "\n", + "# Convert UTC to Melbourne local time.\n", + "microclimate_clean[\"received_at\"] = (\n", + " microclimate_clean[\"received_at\"]\n", + " .dt.tz_convert(\"Australia/Melbourne\")\n", + ")\n", + "\n", + "# Remove the timezone after conversion.\n", + "microclimate_clean[\"received_at\"] = (\n", + " microclimate_clean[\"received_at\"]\n", + " .dt.tz_localize(None)\n", + ")\n", + "\n", + "# Round down to the nearest hour.\n", + "microclimate_clean[\"datetime_hour\"] = (\n", + " microclimate_clean[\"received_at\"]\n", + " .dt.floor(\"h\")\n", + ")\n", + "\n", + "# Check the result.\n", + "print(microclimate_clean.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pedestrian time range:\n", + "2024-05-12 00:00:00\n", + "2026-05-11 03:00:00\n", + "\n", + "Microclimate time range:\n", + "2022-11-08 12:00:00\n", + "2026-05-07 15:00:00\n" + ] + } + ], + "source": [ + "# Confirming the same datetime format.\n", + "print(\"Pedestrian time range:\")\n", + "print(pedestrian_clean[\"datetime_hour\"].min())\n", + "print(pedestrian_clean[\"datetime_hour\"].max())\n", + "\n", + "print(\"\\nMicroclimate time range:\")\n", + "print(microclimate_clean[\"datetime_hour\"].min())\n", + "print(microclimate_clean[\"datetime_hour\"].max())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.4 Aggregating Values For Hourly Format\n", + "\n", + "This step involves aggregation because the use case models city-level pedestrian demand rather than individual sensor-level behaviour. Since the pedestrian and microclimate datasets both contain multiple records within the same hour. This also ensures that both the pedestrian and the microclimate data share the same hourly rows without duplicates for later merging, reducing the total data volume [28].\n", + "\n", + "The aggregation involves the pedestrian counts being summed across sensors to produce hourly city totals, and the microclimate readings are averaged across devices for each hour. Doing this changes the target variable from pedestrians at one site to overall city pedestrian demand at one hour, along with the climate values at that hour. This also further reduces the row counts due to aggregating to hourly, and the microclimate data is still narrower than the pedestrian dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " datetime_hour pedestriancount\n", + "0 2024-05-12 00:00:00 15093\n", + "1 2024-05-12 01:00:00 10686\n", + "2 2024-05-12 02:00:00 6751\n", + "3 2024-05-12 03:00:00 5431\n", + "4 2024-05-12 04:00:00 2728\n", + "(17351, 2)\n" + ] + } + ], + "source": [ + "# Aggregate pedestrian counts to hourly city totals.\n", + "pedestrian_hourly = (\n", + " pedestrian_clean\n", + " .groupby(\"datetime_hour\", as_index=False)\n", + " .agg(\n", + " {\n", + " \"pedestriancount\": \"sum\",\n", + " }\n", + " )\n", + ")\n", + "\n", + "# Check the result.\n", + "print(pedestrian_hourly.head())\n", + "print(pedestrian_hourly.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " datetime_hour airtemperature relativehumidity atmosphericpressure \\\n", + "0 2022-11-08 12:00:00 28.400 38.800 1011.100 \n", + "1 2022-11-08 13:00:00 29.400 34.800 1009.800 \n", + "2 2022-11-08 14:00:00 27.320 40.940 1009.420 \n", + "3 2022-11-23 15:00:00 15.100 87.150 1010.800 \n", + "4 2022-11-23 16:00:00 16.475 80.725 1010.475 \n", + "\n", + " averagewindspeed gustwindspeed averagewinddirection pm25 pm10 noise \n", + "0 3.100 4.100 74.00 3.00 5.00 62.80 \n", + "1 2.100 3.500 92.00 5.00 8.00 86.90 \n", + "2 1.700 2.780 253.40 3.00 5.20 87.78 \n", + "3 0.450 1.050 291.00 2.00 5.50 95.10 \n", + "4 1.675 2.475 271.25 4.25 6.75 100.30 \n", + "(27720, 10)\n" + ] + } + ], + "source": [ + "# Aggregate microclimate readings to hourly city averages.\n", + "microclimate_hourly = (\n", + " microclimate_clean\n", + " .groupby(\"datetime_hour\", as_index=False)\n", + " .agg(\n", + " {\n", + " \"airtemperature\": \"mean\",\n", + " \"relativehumidity\": \"mean\",\n", + " \"atmosphericpressure\": \"mean\",\n", + " \"averagewindspeed\": \"mean\",\n", + " \"gustwindspeed\": \"mean\",\n", + " \"averagewinddirection\": \"mean\",\n", + " \"pm25\": \"mean\",\n", + " \"pm10\": \"mean\",\n", + " \"noise\": \"mean\",\n", + " }\n", + " )\n", + ")\n", + "\n", + "# Check the result.\n", + "print(microclimate_hourly.head())\n", + "print(microclimate_hourly.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.5 Merging The Datasets\n", + "\n", + "This step will merge the datasets together, ensuring the target variable, pedestrian count, is connected to the explanatory climate variables. The datetime_hour variable on both the pedestrian dataset and the microclimate dataset was inner-joined to merge into one dataset, meaning only the overlapping rows with the same values were merged [29]. Which means every row has both pedestrian and climate information, hence, a unified format ready for analysis [30].\n", + "\n", + "A quick check of the merged dataset shows that the pedestrian counts and climate values aligned in the same hourly observations, which is what the use case is looking for. And there are no missing values, which indicates that previous data cleaning works as intended." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " datetime_hour pedestriancount airtemperature relativehumidity \\\n", + "0 2024-05-12 00:00:00 15093 12.771429 84.646429 \n", + "1 2024-05-12 01:00:00 10686 12.046429 88.128571 \n", + "2 2024-05-12 02:00:00 6751 11.457143 90.596429 \n", + "3 2024-05-12 03:00:00 5431 11.121429 91.982143 \n", + "4 2024-05-12 04:00:00 2728 10.837037 92.677778 \n", + "\n", + " atmosphericpressure averagewindspeed gustwindspeed averagewinddirection \\\n", + "0 1022.835714 0.353571 1.282143 166.142857 \n", + "1 1022.635714 0.507143 1.435714 140.321429 \n", + "2 1022.521429 0.585714 1.578571 153.857143 \n", + "3 1022.278571 0.607143 1.507143 139.250000 \n", + "4 1022.251852 0.566667 1.562963 170.074074 \n", + "\n", + " pm25 pm10 noise \n", + "0 8.464286 9.428571 66.071429 \n", + "1 11.107143 12.392857 65.500000 \n", + "2 9.892857 11.357143 64.278571 \n", + "3 7.678571 9.035714 61.942857 \n", + "4 8.074074 9.740741 61.566667 \n", + "(17267, 11)\n" + ] + } + ], + "source": [ + "# Merge pedestrian and microclimate data on the hourly datetime.\n", + "model_df = pd.merge(\n", + " pedestrian_hourly,\n", + " microclimate_hourly,\n", + " on=\"datetime_hour\",\n", + " how=\"inner\"\n", + ")\n", + "\n", + "# Sort the final modelling table.\n", + "model_df = model_df.sort_values(\n", + " \"datetime_hour\"\n", + ").reset_index(drop=True)\n", + "\n", + "# Check the result.\n", + "print(model_df.head())\n", + "print(model_df.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 17267 entries, 0 to 17266\n", + "Data columns (total 11 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 datetime_hour 17267 non-null datetime64[ns]\n", + " 1 pedestriancount 17267 non-null int64 \n", + " 2 airtemperature 17267 non-null float64 \n", + " 3 relativehumidity 17267 non-null float64 \n", + " 4 atmosphericpressure 17267 non-null float64 \n", + " 5 averagewindspeed 17267 non-null float64 \n", + " 6 gustwindspeed 17267 non-null float64 \n", + " 7 averagewinddirection 17267 non-null float64 \n", + " 8 pm25 17267 non-null float64 \n", + " 9 pm10 17267 non-null float64 \n", + " 10 noise 17267 non-null float64 \n", + "dtypes: datetime64[ns](1), float64(9), int64(1)\n", + "memory usage: 1.4 MB\n", + "None\n", + "\n", + "Missing values:\n", + "datetime_hour 0\n", + "pedestriancount 0\n", + "airtemperature 0\n", + "relativehumidity 0\n", + "atmosphericpressure 0\n", + "averagewindspeed 0\n", + "gustwindspeed 0\n", + "averagewinddirection 0\n", + "pm25 0\n", + "pm10 0\n", + "noise 0\n", + "dtype: int64\n", + "\n", + "Summary statistics:\n", + " datetime_hour pedestriancount airtemperature \\\n", + "count 17267 17267.00000 17267.000000 \n", + "mean 2025-05-09 05:49:24.104940288 35432.95975 16.511347 \n", + "min 2024-05-12 00:00:00 51.00000 2.617857 \n", + "25% 2024-11-07 21:30:00 7142.00000 12.527951 \n", + "50% 2025-05-06 18:00:00 35519.00000 15.910256 \n", + "75% 2025-11-08 09:30:00 58964.50000 19.561111 \n", + "max 2026-05-07 15:00:00 114804.00000 43.191667 \n", + "std NaN 27363.02579 5.547545 \n", + "\n", + " relativehumidity atmosphericpressure averagewindspeed gustwindspeed \\\n", + "count 17267.000000 17267.000000 17267.000000 17267.000000 \n", + "mean 66.584728 1013.925313 1.162485 3.496650 \n", + "min 9.875000 990.350000 0.121875 0.693939 \n", + "25% 56.075397 1008.605840 0.721429 2.289380 \n", + "50% 68.540741 1013.885714 1.065625 3.263889 \n", + "75% 78.714583 1019.287650 1.508001 4.527639 \n", + "max 96.673171 1039.162500 4.028125 10.200000 \n", + "std 16.060614 7.914093 0.578561 1.523908 \n", + "\n", + " averagewinddirection pm25 pm10 noise \n", + "count 17267.000000 17267.000000 17267.000000 17267.000000 \n", + "mean 184.944173 6.935045 8.731420 68.252640 \n", + "min 52.571429 0.333333 1.057143 53.745714 \n", + "25% 160.194444 1.892857 3.313393 65.136111 \n", + "50% 184.742857 3.368421 5.000000 68.113043 \n", + "75% 211.442222 7.577381 9.306624 71.034330 \n", + "max 306.827586 411.228571 417.028571 88.200000 \n", + "std 37.950660 12.914232 13.465303 4.521855 \n" + ] + } + ], + "source": [ + "# Check merged dataset.\n", + "print(model_df.info())\n", + "print(\"\\nMissing values:\")\n", + "print(model_df.isna().sum())\n", + "print(\"\\nSummary statistics:\")\n", + "print(model_df.describe())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Data Validation\n", + "\n", + "Doing data validation is important because a merged dataset that was cleaned may still be unsuitable for the task of this use case, possibly due to timestamps being duplicated, out of order, or some rows in the chronological datetime are missing. The time-series model tends to assume a consistent temporal structure with no sudden breaks, so this step in the pipeline checks that everything is complete [31]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.1 Validating Time Series Dataset\n", + "\n", + "Checking for duplicates or missing timestamps, since they can affect the lag features, rolling features and any sequence-based deep learning models that this use case may use. This is to ensure that there is a strictly ordered sequence of evenly spaced time points [32].\n", + "\n", + "From the outputs, there are no duplicates in the datetime_hour values, which means every timestamp is represented once. The dataset is double-checked to ensure that it is sorted in increasing time order. But there appears to be a number of missing hourly timestamps, which means the dataset is not complete yet. It does appear that random points were cut off, and that no large block of time was cut off." + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Duplicate datetime_hour values: 0\n" + ] + } + ], + "source": [ + "# Count duplicate datetime values.\n", + "duplicate_count = model_df[\"datetime_hour\"].duplicated().sum()\n", + "\n", + "# Print the result.\n", + "print(\"Duplicate datetime_hour values:\", duplicate_count)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of missing hourly timestamps: 149\n", + "DatetimeIndex(['2024-10-06 02:00:00', '2025-06-06 22:00:00',\n", + " '2025-06-06 23:00:00', '2025-06-07 00:00:00',\n", + " '2025-06-07 01:00:00', '2025-06-07 02:00:00',\n", + " '2025-06-07 03:00:00', '2025-06-07 04:00:00',\n", + " '2025-06-07 05:00:00', '2025-06-07 06:00:00'],\n", + " dtype='datetime64[ns]', freq=None)\n" + ] + } + ], + "source": [ + "# Create the full expected hourly range.\n", + "full_hours = pd.date_range(\n", + " start=model_df[\"datetime_hour\"].min(),\n", + " end=model_df[\"datetime_hour\"].max(),\n", + " freq=\"h\"\n", + ")\n", + "\n", + "# Find missing hours.\n", + "missing_hours = full_hours.difference(model_df[\"datetime_hour\"])\n", + "\n", + "# Print the number of missing hours.\n", + "print(\"Number of missing hourly timestamps:\", len(missing_hours))\n", + "\n", + "# Preview a few missing hours.\n", + "print(missing_hours[:10])" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], + "source": [ + "# Confirm the dataset is sorted by time.\n", + "print(model_df[\"datetime_hour\"].is_monotonic_increasing)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.2 Fixing Missing Timestamps\n", + "\n", + "Ensuring the missing timestamps are filled in is necessary for a complete hourly sequence in the dataset, and missing them can create issues when performing feature engineering later. Missing hours can cause problems when creating lag features, rolling averages, and LSTM input sequences, since these methods rely on consistent time gaps between rows [32] [33].\n", + "\n", + "This step involves reindexing to the full hourly range so that all the missing timestamps are included in the dataset, and then interpolation is performed to fill in the missing values from those created rows. Hence, the merged dataset now has slightly more rows with no missing hourly stamps, ensuring the timeline is continuous with no breaks. A little addition was included to ensure that the pedestrian counts remained as integers, rather than fractions, due to interpolation. " + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Missing hourly timestamps: 0\n", + "\n", + "Overall:\n", + "\n", + "RangeIndex: 17416 entries, 0 to 17415\n", + "Data columns (total 11 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 datetime_hour 17416 non-null datetime64[ns]\n", + " 1 pedestriancount 17416 non-null int64 \n", + " 2 airtemperature 17416 non-null float64 \n", + " 3 relativehumidity 17416 non-null float64 \n", + " 4 atmosphericpressure 17416 non-null float64 \n", + " 5 averagewindspeed 17416 non-null float64 \n", + " 6 gustwindspeed 17416 non-null float64 \n", + " 7 averagewinddirection 17416 non-null float64 \n", + " 8 pm25 17416 non-null float64 \n", + " 9 pm10 17416 non-null float64 \n", + " 10 noise 17416 non-null float64 \n", + "dtypes: datetime64[ns](1), float64(9), int64(1)\n", + "memory usage: 1.5 MB\n", + "None\n", + "\n", + "Final time range:\n", + "2024-05-12 00:00:00\n", + "2026-05-07 15:00:00\n", + "\n", + "Final shape:\n", + "(17416, 11)\n" + ] + } + ], + "source": [ + "# Set datetime as the index.\n", + "model_df = model_df.set_index(\"datetime_hour\").sort_index()\n", + "\n", + "# Create the full hourly timeline.\n", + "full_hours = pd.date_range(\n", + " start=model_df.index.min(),\n", + " end=model_df.index.max(),\n", + " freq=\"h\"\n", + ")\n", + "\n", + "# Reindex to restore missing hours.\n", + "model_df = model_df.reindex(full_hours)\n", + "\n", + "# Interpolate all numeric columns over time.\n", + "model_df = model_df.interpolate(method=\"time\").ffill().bfill()\n", + "\n", + "# Convert pedestrian count back to integers.\n", + "model_df[\"pedestriancount\"] = (\n", + " model_df[\"pedestriancount\"]\n", + " .round()\n", + " .astype(int)\n", + ")\n", + "\n", + "# Restore datetime_hour as a column.\n", + "model_df = model_df.reset_index().rename(\n", + " columns={\"index\": \"datetime_hour\"}\n", + ")\n", + "\n", + "# Check the result.\n", + "print(\"Missing hourly timestamps:\",\n", + " pd.date_range(\n", + " model_df[\"datetime_hour\"].min(),\n", + " model_df[\"datetime_hour\"].max(),\n", + " freq=\"h\"\n", + " ).difference(model_df[\"datetime_hour\"]).shape[0])\n", + "\n", + "# Check for missing hourly timestamps again.\n", + "full_hours = pd.date_range(\n", + " start=model_df[\"datetime_hour\"].min(),\n", + " end=model_df[\"datetime_hour\"].max(),\n", + " freq=\"h\"\n", + ")\n", + "\n", + "missing_hours = full_hours.difference(model_df[\"datetime_hour\"])\n", + "\n", + "# Check the dataset structure.\n", + "print(\"\\nOverall:\")\n", + "print(model_df.info())\n", + "\n", + "# Check the final time range.\n", + "print(\"\\nFinal time range:\")\n", + "print(model_df[\"datetime_hour\"].min())\n", + "print(model_df[\"datetime_hour\"].max())\n", + "\n", + "# Check the dataframe shape.\n", + "print(\"\\nFinal shape:\")\n", + "print(model_df.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Exploratory Data Analysis\n", + "\n", + "The exploratory data analysis section was performed on the merged dataset to observe any interpretable patterns and get a rough understanding of how the dataset looks and feels before building a model. It's important to get a sense of how pedestrian demand changes over time and how it relates to the climate conditions [34]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6.1 Pedestrian Count Over Time (Hourly)\n", + "\n", + "Plotting the hourly pedestrian count is for a quick look at the target variable changes over time, to check if the time series data is random or if there are any patterns that may need to be taken into consideration for later steps [35] [36].\n", + "\n", + "From the plot, there appear to be large fluctuations across the full time range with repeated highs and lows. Although it looks random, it does seem to show some sort of pattern, and it's not necessarily random noise, with recurring fluctuations, like the new year of each year seems to always be a high peak, indicating lots of pedestrians travelling in the city, and there also appear to be some sharp dips, which may possibly be public holidays. This does suggest that there may be other possible variables that might have influenced the pedestrian count, but it definitely confirms that pedestrian demand is influenced by recurring temporal and possibly environmental effects." + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot pedestrian counts over time.\n", + "plt.figure(figsize=(14, 5))\n", + "plt.plot(\n", + " model_df[\"datetime_hour\"],\n", + " model_df[\"pedestriancount\"],\n", + " label=\"Hourly pedestrian count\"\n", + ")\n", + "\n", + "plt.title(\"Pedestrian Count Over Time (Hourly)\")\n", + "plt.xlabel(\"Datetime\")\n", + "plt.ylabel(\"Pedestrian Count\")\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6.2 Average Pedestrian Count By Day Of Week\n", + "\n", + "The day-of-week summary was checked to see if there may also be other variables like work patterns, shopping activities, or weekend behaviours that might have influenced the pedestrian demand. Also checks whether the day of the week should be considered an important time-based feature for later modelling [35] [37].\n", + "\n", + "From the plot, it does seem like the weekdays tend to be relatively high along with Saturdays, whereas Mondays and Sundays tend to have low pedestrian demands. Monday might be low despite being a normal workday for the average Australians might be due to the influence of public holidays, and Sunday being low is due to most people not working on Sunday, since businesses would usually pay a high penalty rate. This further suggests that the CBD pedestrian pattern is linked to weekday economic and commuter activity, not just climate variables." + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a day-of-week column.\n", + "model_df[\"day_of_week\"] = model_df[\n", + " \"datetime_hour\"\n", + "].dt.dayofweek\n", + "\n", + "# Group by day of week.\n", + "dow_pattern = model_df.groupby(\"day_of_week\")[\n", + " \"pedestriancount\"\n", + "].mean()\n", + "\n", + "# Plot the day-of-week pattern.\n", + "plt.figure(figsize=(8, 4))\n", + "plt.plot(\n", + " dow_pattern.index,\n", + " dow_pattern.values,\n", + " marker=\"o\",\n", + " label=\"Average pedestrian count\"\n", + ")\n", + "\n", + "plt.title(\"Average Pedestrian Count By Day Of Week\")\n", + "plt.xlabel(\"Day Of Week\")\n", + "plt.ylabel(\"Average Pedestrian Count\")\n", + "plt.xticks(\n", + " ticks=range(7),\n", + " labels=[\n", + " \"Mon\", \"Tue\", \"Wed\",\n", + " \"Thu\", \"Fri\", \"Sat\", \"Sun\"\n", + " ]\n", + ")\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6.3 Distributions Of Variables\n", + "\n", + "Checking for distribution to see whether it's symmetric, skewed, or multi-modal. This is useful because skewed variables, extreme values, or unusual distributions can influence how the model learns from the data [38].\n", + "\n", + "* pedestriancount appears unimodal and right-skewed.\n", + "* airtemperature appears unimodal with a slight right skew.\n", + "* relativehumidity appears unimodal with a slight left skew.\n", + "* atmosphericpressure appears unimodal and slightly left-skewed.\n", + "* averagewindspeed appears unimodal and right-skewed.\n", + "* gustwindspeed appears unimodal and right-skewed.\n", + "* averagewinddirection appears roughly unimodal and mostly symmetric.\n", + "* pm25 appears unimodal and right-skewed.\n", + "* pm10 appears unimodal and right-skewed, similar to pm25.\n", + "* noise appears unimodal and fairly symmetric." + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Store numeric columns.\n", + "numeric_cols = model_df.select_dtypes(\n", + " include=[\"int32\", \"int64\", \"float64\"]\n", + ").columns.drop(\n", + " [\"day_of_week\"],\n", + " errors=\"ignore\"\n", + ")\n", + "\n", + "# Create subplot layout.\n", + "n_cols = 3\n", + "n_rows = int(np.ceil(len(numeric_cols) / n_cols))\n", + "\n", + "fig, axes = plt.subplots(\n", + " n_rows,\n", + " n_cols,\n", + " figsize=(16, 12)\n", + ")\n", + "\n", + "axes = axes.flatten()\n", + "\n", + "# Plot histogram for each numeric column.\n", + "for i, col in enumerate(numeric_cols):\n", + " axes[i].hist(\n", + " model_df[col],\n", + " bins=30,\n", + " label=col\n", + " )\n", + " axes[i].set_title(f\"Distribution Of {col}\")\n", + " axes[i].set_xlabel(col)\n", + " axes[i].set_ylabel(\"Frequency\")\n", + " axes[i].legend()\n", + "\n", + "# Remove empty subplots.\n", + "for j in range(i + 1, len(axes)):\n", + " fig.delaxes(axes[j])\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6.4 Correlation Matrix\n", + "\n", + "Checking the correlation matrix is a quick way to see which climate variables might have the strongest relationship with pedestrian demand, which does seem like they have some influence on the target variable from the results. This is mainly to show whether the relationship is positive or negative, even though correlation does not prove causation [39].\n", + "\n", + "- noise has the strongest positive correlation with pedestrian count, followed by gustwindspeed, airtemperature, averagewindspeed, and averagewinddirection. This suggests that pedestrian activity tends to increase when these variables increase.\n", + "- relativehumidity has the strongest negative correlation with pedestrian count, while pm10, pm25, and atmosphericpressure have weaker negative relationships. This suggests that pedestrian activity tends to decrease when these variables increase.\n", + "\n", + "This suggests that climate variables do play a role in influencing the pedestrian demand, but there are other influences as well as discovered from previous plots. But the goal for this task is to understand how climate variables affect pedestrian demands." + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pedestriancount 1.000000\n", + "noise 0.555729\n", + "gustwindspeed 0.466541\n", + "airtemperature 0.390398\n", + "averagewindspeed 0.364756\n", + "averagewinddirection 0.119948\n", + "atmosphericpressure -0.034576\n", + "pm25 -0.035831\n", + "pm10 -0.039647\n", + "relativehumidity -0.465781\n", + "Name: pedestriancount, dtype: float64\n" + ] + } + ], + "source": [ + "# Calculate the correlation matrix.\n", + "corr_matrix = model_df[numeric_cols].corr()\n", + "\n", + "# Plot the correlation matrix.\n", + "plt.figure(figsize=(10, 8))\n", + "im = plt.imshow(\n", + " corr_matrix,\n", + " cmap=\"coolwarm\",\n", + " aspect=\"auto\"\n", + ")\n", + "\n", + "cbar = plt.colorbar(im)\n", + "cbar.set_label(\"Correlation Coefficient\")\n", + "\n", + "plt.xticks(\n", + " ticks=np.arange(len(corr_matrix.columns)),\n", + " labels=corr_matrix.columns,\n", + " rotation=45,\n", + " ha=\"right\"\n", + ")\n", + "\n", + "plt.yticks(\n", + " ticks=np.arange(len(corr_matrix.columns)),\n", + " labels=corr_matrix.columns\n", + ")\n", + "\n", + "plt.title(\"Correlation Matrix\")\n", + "\n", + "# Add values inside each cell.\n", + "for i in range(len(corr_matrix.index)):\n", + " for j in range(len(corr_matrix.columns)):\n", + " plt.text(\n", + " j,\n", + " i,\n", + " f\"{corr_matrix.iloc[i, j]:.2f}\",\n", + " ha=\"center\",\n", + " va=\"center\",\n", + " fontsize=8\n", + " )\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Print the correlation values with pedestrian count.\n", + "print(corr_matrix[\"pedestriancount\"].sort_values(ascending=False))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Time Series Analysis\n", + "\n", + "This section is necessary as exploratory data analysis alone is not enough to check a time series dataset. Hence, time series analysis is necessary to help uncover more trends, seasonality, repeated lag dependence, and stationarity [36]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7.1 Pedestrian Count Over Time (24-Hour Rolling Mean)\n", + "\n", + "The previous plot with the pedestrian count over time was noisy, so smoothing over 24 hours may help reveal more patterns of pedestrian activity without the hour-to-hour volatility [40].\n", + "\n", + "From the plot, it became much more obvious that the highest peaks were during New Year's, where pedestrian visit the Melbourne CBD to see the fireworks, and confirms what has been previously discussed. There does seem to be a slightly increasing trend, which suggests that the influence of COVID-19 is still recovering." + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "datetime_hour\n", + "2024-05-12 00:00:00 15093\n", + "2024-05-12 01:00:00 10686\n", + "2024-05-12 02:00:00 6751\n", + "2024-05-12 03:00:00 5431\n", + "2024-05-12 04:00:00 2728\n", + "Name: pedestriancount, dtype: int64\n" + ] + } + ], + "source": [ + "# Create the hourly target series.\n", + "ts = model_df.set_index(\"datetime_hour\")[\n", + " \"pedestriancount\"\n", + "].sort_index()\n", + "\n", + "# Check the first few rows.\n", + "print(ts.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABW0AAAHqCAYAAAB/bWzAAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzsnXe4E8X+xt8kp9M7KChVQQX9geXaG4qIvXvVq9xr72K5tmvvClawC9h7B0WqdEEEpPfe2ymcnmR/f+TMZnZ2drM5JznJgffzPDzkJJvdye7s7Mx33nm/PsMwDBBCCCGEEEIIIYQQQghJC/ypLgAhhBBCCCGEEEIIIYSQKAzaEkIIIYQQQgghhBBCSBrBoC0hhBBCCCGEEEIIIYSkEQzaEkIIIYQQQgghhBBCSBrBoC0hhBBCCCGEEEIIIYSkEQzaEkIIIYQQQgghhBBCSBrBoC0hhBBCCCGEEEIIIYSkEQzaEkIIIYQQQgghhBBCSBrBoC0hhBBCCCGEEEIIIYSkEQzaEkIIIYQQLatXr4bP58OwYcPM9x577DH4fD7Ldu3bt8c111xTu4UjAIBff/0Vhx12GHJycuDz+ZCfn5/S8px00kk46aSTUloGlXQsEyGEEEJILBi0JYQQQkidYObMmbj11ltx8MEHo169ethvv/1wySWXYOnSpa7fq6ysxEEHHQSfz4eXXnrJ07GGDRsGn8+HP//8U/v5SSedhEMOOSTu35BMRIBV/PP7/WjatCn69u2LadOmpbp4SUP83muvvVb7+UMPPWRus3379louXfxMmTIF559/Plq1aoXs7Gy0b98eN9xwA9auXWvbdseOHbjkkkuQm5uLwYMH46OPPkK9evW0+xV1WvzLycnBAQccgFtvvRVbtmxJ9s9KCBs3bsRjjz2GOXPmpLoocbFlyxbcc8896Nq1K/Ly8lCvXj306tULTz31VMqD7IJPP/0Ur7zySqqLQQghhBCJjFQXgBBCCCHEC88//zymTJmCiy++GD169MDmzZvxxhtvoGfPnpg+fbpjEPX111/XBrz2VC6//HKceeaZCIVCWLp0KYYMGYKTTz4ZM2fORPfu3ZNyzCVLlsDvT50WICcnB9988w2GDBmCrKwsy2efffYZcnJyUFZWlqLSeef111/HHXfcgY4dO+K2225DmzZtsGjRIrz33nv44osvMHLkSBxzzDHm9jNnzkRRURGefPJJ9O7d29MxnnjiCXTo0AFlZWWYPHky3nzzTYwcORLz589HXl5esn5aQti4cSMef/xxtG/fHocddpjn7/3222/JK1QMZs6ciTPPPBO7d+/GlVdeiV69egEA/vzzTzz33HOYOHFiSssn+PTTTzF//nzceeedqS4KIYQQQqpg0JYQQgghdYIBAwbg008/tQTlLr30UnTv3h3PPfccPv74Y9t3tm7diieeeAL//e9/8cgjj9RmcZNCcXGxo5JS0LNnT1x55ZXm38cffzz69u2LN998E0OGDElKubKzs5OyX6+cccYZ+PHHH/HLL7/g3HPPNd+fOnUqVq1ahQsvvBDffPNNCksYmylTpuDOO+/Ecccdh19//dUSQL3ppptw7LHH4qKLLsKCBQvQpEkTAJH6DQCNGzf2fJy+ffvi8MMPBwBce+21aNasGQYNGoQffvgBl19+eeJ+UBpQUlKCvLw8WyC/tsjPz8f555+PQCCA2bNno2vXrpbPn376abz77rspKRshhBBC0h/aIxBCCCGkTnDMMcfYgi9dunTBwQcfjEWLFmm/c//99+PAAw+0BDGTRTAYxJNPPolOnTqZy9offPBBlJeXW7bz+Xx47LHHbN9XfWHFcvbff/8dN998M1q2bIm2bdvGXa7jjz8eALBixQrL+ytXrsTFF1+Mpk2bIi8vD//4xz8wYsSIuPfvVvYpU6ZgwIABaNGiBerVq4fzzz8f27Zts3w3HA7jsccewz777IO8vDycfPLJWLhwYVw+ufvuuy9OOOEEfPrpp5b3P/nkE3Tv3t1Rhf3HH3/gjDPOQKNGjZCXl4cTTzwRU6ZMsWyzZs0a3HzzzTjwwAORm5uLZs2a4eKLL8bq1ast28Xzm3U8+eST8Pl8GD58uE3x2qlTJ7zwwgvYtGkT3n77bQARi46rr74aAHDEEUfA5/NVy1f4lFNOAQCsWrXKfO/jjz9Gr169kJubi6ZNm+Kyyy7DunXrbN9955130KlTJ+Tm5uLII4/EpEmTtMcoLy/Ho48+is6dOyM7Oxvt2rXDfffdZ7s3Ro8ejeOOOw6NGzdG/fr1ceCBB+LBBx8EAEyYMAFHHHEEAKB///6mzYPwWxaWJbNmzcIJJ5yAvLw887uqp21FRQUeeeQR9OrVC40aNUK9evVw/PHHY/z48ZbyCMuRl156yfyt2dnZOOKIIzBz5syY5/btt9/Ghg0bMGjQIFvAFgBatWqFhx9+2PLekCFDcPDBByM7Oxv77LMPbrnlFpuFgtO9of7OCRMmwOfz4csvv8TTTz+Ntm3bIicnB6eeeiqWL19u+d6IESOwZs0a87y2b98+5u8jhBBCSHKh0pYQQgghdRbDMLBlyxYcfPDBts9mzJiB4cOHY/LkybbEWV4pKCjQ+qBWVlba3rv22msxfPhwXHTRRbj77rvxxx9/4Nlnn8WiRYvw3XffVev4AHDzzTejRYsWeOSRR1BcXBz390VwUagzgYjH5jHHHIOSkhLcfvvtaNasGYYPH45zzjkHX3/9Nc4///xql1fmtttuQ5MmTfDoo49i9erVeOWVV3Drrbfiiy++MLd54IEH8MILL+Dss89Gnz59MHfuXPTp0yduO4N//vOfuOOOO7B7927Ur18fwWAQX331FQYMGKDd17hx49C3b1/06tULjz76KPx+P4YOHYpTTjkFkyZNwpFHHgkgsrx96tSpuOyyy9C2bVusXr0ab775Jk466SQsXLjQFmD18ptVSkpKMHbsWBx//PHo0KGDdptLL70U119/PX7++Wfcf//9eOihh3DggQfinXfeMS0POnXqFNc5A6LB/GbNmgGIqD//97//4ZJLLsG1116Lbdu24fXXX8cJJ5yA2bNnm6re999/HzfccAOOOeYY3HnnnVi5ciXOOeccNG3aFO3atTP3Hw6Hcc4552Dy5Mm4/vrr0a1bN8ybNw8vv/wyli5diu+//x4AsGDBApx11lno0aMHnnjiCWRnZ2P58uVmEL1bt2544okn8Mgjj+D66683JyNku4gdO3agb9++uOyyy3DllVeiVatW2t9cWFiI9957D5dffjmuu+46FBUV4f3330efPn0wY8YMm/XCp59+iqKiItxwww3w+Xx44YUXcMEFF2DlypXIzMx0PLc//vgjcnNzcdFFF3m6Fo899hgef/xx9O7dGzfddBOWLFmCN998EzNnzsSUKVNcj+XGc889B7/fj3vuuQcFBQV44YUXcMUVV+CPP/4AEPF9LigowPr16/Hyyy8DAOrXr1+tYxFCCCEkgRiEEEIIIXWUjz76yABgvP/++5b3w+GwceSRRxqXX365YRiGsWrVKgOA8eKLL3ra79ChQw0Arv8OPvhgc/s5c+YYAIxrr73Wsp977rnHAGCMGzfOfA+A8eijj9qOuf/++xtXX321rQzHHXecEQwGY5ZZ/MbHH3/c2LZtm7F582Zj0qRJxhFHHGEAML766itz2zvvvNMAYEyaNMl8r6ioyOjQoYPRvn17IxQKWfY5dOhQc7tHH33UULuQTmXv3bu3EQ6HzffvuusuIxAIGPn5+YZhGMbmzZuNjIwM47zzzrPs77HHHjMAWPbpBADjlltuMXbu3GlkZWUZH330kWEYhjFixAjD5/MZq1evNsu8bds2wzAi9aNLly5Gnz59LOUrKSkxOnToYJx22mmW91SmTZtmADA+/PDDuH+zDlF/7rjjDtff2qNHD6Np06a2Y86cOdP1e/K2Y8aMMbZt22asW7fO+Pzzz41mzZoZubm5xvr1643Vq1cbgUDAePrppy3fnTdvnpGRkWG+X1FRYbRs2dI47LDDjPLycnO7d955xwBgnHjiieZ7H330keH3+y11zTAM46233jIAGFOmTDEMwzBefvllyzXSMXPmTFt9FJx44okGAOOtt97SfiaXKRgMWsptGIaxa9cuo1WrVsa///1v8z1R/5s1a2bs3LnTfP+HH34wABg//fSTY1kNwzCaNGliHHrooa7bCLZu3WpkZWUZp59+unn/GYZhvPHGGwYA44MPPjDfU+83p985fvx4A4DRrVs3y+999dVXDQDGvHnzzPf69etn7L///p7KSgghhJDagfYIhBBCCKmTLF68GLfccguOPvpoc5m4YNiwYZg3bx6ef/75Gh1j8ODBGD16tO1fjx49LNuNHDkSQMR3V+buu+8GgGrbDgDAddddh0Ag4Hn7Rx99FC1atEDr1q1x/PHHY9GiRRg4cKBF7Tdy5EgceeSROO6448z36tevj+uvvx6rV6/GwoULq11emeuvv96icj7++OMRCoWwZs0aAMDYsWMRDAZx8803W7532223xX2sJk2a4IwzzsBnn30GIKKOPOaYY7D//vvbtp0zZw6WLVuGf/7zn9ixYwe2b9+O7du3o7i4GKeeeiomTpyIcDgMAMjNzTW/V1lZiR07dqBz585o3Lgx/vrrr7h/s46ioiIAQIMGDVx/Y4MGDVBYWOi6TSx69+6NFi1aoF27drjssstQv359fPfdd9h3333x7bffIhwO45JLLjHPyfbt29G6dWt06dLFtA/4888/sXXrVtx4440Wy5JrrrkGjRo1shzvq6++Qrdu3dC1a1fLPoUtg9inUPD+8MMP5rmPl+zsbPTv3z/mdoFAwCx3OBzGzp07EQwGcfjhh2uv6aWXXmpRqguV78qVK12PU1hYGPOaCsaMGYOKigrceeedlqR+1113HRo2bFijNqR///6W6+S1/IQQQghJLbRHIIQQQkidY/PmzejXrx8aNWqEr7/+2hLULCwsxAMPPIB7773XskxbJRQK2bxGmzZtagluHHnkkWbSJpkmTZpYbBPWrFkDv9+Pzp07W7Zr3bo1Gjdu7Bqwi4XTcnknrr/+elx88cUoKyvDuHHj8NprryEUClm2WbNmDY466ijbd7t162Z+7uQDGw/77bef5W8R+Nq1a5d5HAC289a0aVNLkMwr//znP3HVVVdh7dq1+P777/HCCy9ot1u2bBkA2IL9MgUFBWjSpAlKS0vx7LPPYujQodiwYQMMw7BsoxLrN+sQgT0RvHWiqKjIcxDQicGDB+OAAw5ARkYGWrVqhQMPPNAMEi5btgyGYaBLly7a74rl+eK6qdtlZmaiY8eOlveWLVuGRYsWoUWLFtp9imRql156Kd577z1ce+21uP/++3HqqafiggsuwEUXXWQJYrqx7777ek46Nnz4cAwcOBCLFy+22J3o7rfqXFMAaNiwYcxrKhDn9MADD7S8n5WVhY4dO9aoDalu+QkhhBCSWhi0JYQQQkidoqCgAH379kV+fj4mTZqEffbZx/L5Sy+9hIqKClx66aWmn+v69esBRIIUq1evxj777IONGzfaAjTjx4+3JPKJl+p65wKwBVYFstLTC126dEHv3r0BAGeddRYCgQDuv/9+nHzyydoAdDJxUgjLgc9Ecs455yA7OxtXX301ysvLcckll2i3E0rOF1980eZfKhCenrfddhuGDh2KO++8E0cffTQaNWoEn8+Hyy67TKsIrc5v7ty5MzIyMvD33387blNeXo4lS5bU+Bo6TUQAkfPi8/nwyy+/aH9HdXxOw+EwunfvjkGDBmk/FxMrubm5mDhxIsaPH48RI0bg119/xRdffIFTTjkFv/32mye1udd75eOPP8Y111yD8847D/feey9atmyJQCCAZ5991pawD6h+Pe7atSvmzJmDiooKz8FkLzi1M6FQSFvW2r4PCSGEEJIYGLQlhBBCSJ2hrKwMZ599NpYuXYoxY8bgoIMOsm2zdu1a7Nq1S5uc7JlnnsEzzzyD2bNno2vXrhg9erTl80MPPbRa5dp///0RDoexbNkyU60KRBJ+5efnW5boN2nSxJYNvqKiAps2barWsWPx0EMP4d1338XDDz+MX3/91SzvkiVLbNsuXrzY/Lw2EMdZvny5JYC+Y8eOaqkAc3Nzcd555+Hjjz9G37590bx5c+12ImFXw4YNzQC3E19//TWuvvpqDBw40HyvrKzMdg1rQr169XDyySdj3LhxWLNmjfb8f/nllygvL8dZZ52VsOOqdOrUCYZhoEOHDjjggAMctxPlW7ZsmWlzAETsI1atWmW5jzp16oS5c+fi1FNPjTmp4ff7ceqpp+LUU0/FoEGD8Mwzz+Chhx7C+PHj0bt37xpNish8/fXX6NixI7799lvLPh999NGE7F9w9tlnY9q0afjmm29w+eWXu24rzumSJUssauWKigqsWrXKUk91bQgQUeuqSmevJOrcEkIIISRx0NOWEEIIIXWCUCiESy+9FNOmTcNXX32Fo48+Wrvd7bffju+++87y7+233wYQ8dz87rvv0KFDB+Tk5KB3796Wf9VZkg8AZ555JgDglVdesbwv1IX9+vUz3+vUqRMmTpxo2e6dd95xVNrWlMaNG+OGG27AqFGjMGfOHLO8M2bMwLRp08ztiouL8c4776B9+/baYHgyOPXUU5GRkYE333zT8v4bb7xR7X3ec889ePTRR/G///3PcZtevXqhU6dOeOmll7B7927b57JtRiAQsCkSX3/99YRfr4cffhiGYeCaa65BaWmp5bNVq1bhvvvuQ5s2bXDDDTck9LgyF1xwAQKBAB5//HHbbzYMAzt27AAAHH744WjRogXeeustVFRUmNsMGzbMFky85JJLsGHDBrz77ru245WWlqK4uBgAsHPnTtvnQgVdXl4OIBLcBlDjgLlQnsq/8Y8//rDcD4ngxhtvRJs2bXD33Xdj6dKlts+3bt2Kp556CkDEazgrKwuvvfaapVzvv/8+CgoKbG3I9OnTLef+559/xrp166pd1nr16mntPgghhBCSOqi0JYQQQkid4O6778aPP/6Is88+Gzt37sTHH39s+fzKK68EAPTs2RM9e/a0fCZsEg4++GCcd955CS/boYceiquvvhrvvPMO8vPzceKJJ2LGjBkYPnw4zjvvPJx88snmttdeey1uvPFGXHjhhTjttNMwd+5cjBo1ylEVmgjuuOMOvPLKK3juuefw+eef4/7778dnn32Gvn374vbbb0fTpk0xfPhwrFq1Ct98841nD9Ga0qpVK9xxxx0YOHAgzjnnHJxxxhmYO3cufvnlFzRv3rxa6r9DDz00pmLa7/fjvffeQ9++fXHwwQejf//+2HfffbFhwwaMHz8eDRs2xE8//QQgYjHx0UcfoVGjRjjooIMwbdo0jBkzBs2aNavWb3bihBNOwEsvvYQBAwagR48euOaaa9CmTRssXrwY7777LsLhMEaOHFntiQUvdOrUCU899RQeeOABrF69Gueddx4aNGiAVatW4bvvvsP111+Pe+65B5mZmXjqqadwww034JRTTsGll16KVatWYejQoTal51VXXYUvv/wSN954I8aPH49jjz0WoVAIixcvxpdffolRo0bh8MMPxxNPPIGJEyeiX79+2H///bF161YMGTIEbdu2NRPmderUCY0bN8Zbb72FBg0aoF69ejjqqKPi9n0+66yz8O233+L8889Hv379sGrVKrz11ls46KCDtEH86tKkSRN89913OPPMM3HYYYfhyiuvRK9evQAAf/31Fz777DNz8qlFixZ44IEH8Pjjj+OMM87AOeecgyVLlmDIkCE44ogjzPYNiLQhX3/9Nc444wxccsklWLFiBT7++GNTQV4devXqhS+++AIDBgzAEUccgfr16+Pss8+u2QkghBBCSI1g0JYQQgghdQKhEv3pp5/MgJqMHNRIBe+99x46duyIYcOG4bvvvkPr1q3xwAMP2JZcX3fddVi1ahXef/99/Prrrzj++OMxevRonHrqqUkr2z777IN//vOf+Oijj7BixQp06tQJU6dOxX//+1+8/vrrKCsrQ48ePfDTTz9ZFH21wfPPP4+8vDy8++67GDNmDI4++mj89ttvOO6445CTk5O045500kmYNm0annzySbzxxhvYvXs3WrdujaOOOsqiZn311VcRCATwySefoKysDMceeyzGjBmDPn36JLxMd911Fw4//HAMHDgQr7zyCgoKCtCmTRtcfPHFeOihh2rFtuL+++/HAQccgJdffhmPP/44gIjv7Omnn45zzjnH3O76669HKBTCiy++iHvvvRfdu3fHjz/+aFM4+/1+fP/993j55Zfx4Ycf4rvvvkNeXh46duyIO+64w7RhOOecc7B69Wp88MEH2L59O5o3b44TTzwRjz/+OBo1agQgkuhs+PDheOCBB3DjjTciGAxi6NChcQdtr7nmGmzevBlvv/02Ro0ahYMOOggff/wxvvrqK0yYMKEGZ8/OUUcdhfnz5+PFF1/EiBEj8NFHH8Hv96Nbt264//77ceutt5rbPvbYY2jRogXeeOMN3HXXXWjatCmuv/56PPPMM2YSOADo06cPBg4ciEGDBuHOO+/E4Ycfjp9//hl33313tct58803Y86cORg6dChefvll7L///gzaEkIIISnGZ9CBnhBCCCGEpBH5+flo0qQJnnrqKTz00EOpLg4hhBBCCCG1Dj1tCSGEEEJIylD9W4GoN/BJJ51Uu4UhhBBCCCEkTaA9AiGEEEIISRlffPEFhg0bhjPPPBP169fH5MmT8dlnn+H000/Hsccem+riEUIIIYQQkhIYtCWEEEIIISmjR48eyMjIwAsvvIDCwkIzOdlTTz2V6qIRQgghhBCSMuhpSwghhBBCCCGEEEIIIWkEPW0JIYQQQgghhBBCCCEkjWDQlhBCCCGEEEIIIYQQQtIIetomiHA4jI0bN6JBgwbw+XypLg4hhBBCCCGEEEIIISTNMAwDRUVF2GeffeD3O+tpGbRNEBs3bkS7du1SXQxCCCGEEEIIIYQQQkias27dOrRt29bxcwZtE0SDBg0ARE54w4YNU1waQgghhBBCCCGEEEJIulFYWIh27dqZsUQnGLRNEMISoWHDhgzaEkIIIYQQQgghhBBCHIllr8pEZIQQQgghhBBCCCGEEJJGMGhLCCGEEEIIIYQQQgghaQSDtoQQQgghhBBCCCGEEJJG0NOWEEIIIYQQQgghhCSMcDiMioqKVBeDkJSQmZmJQCBQ4/0waEsIIYQQQgghhBBCEkJFRQVWrVqFcDic6qIQkjIaN26M1q1bx0w25gaDtoQQQgghhBBCCCGkxhiGgU2bNiEQCKBdu3bw++nKSfYuDMNASUkJtm7dCgBo06ZNtffFoC0hhBBCCCGEEEIIqTHBYBAlJSXYZ599kJeXl+riEJIScnNzAQBbt25Fy5Ytq22VwCkPQgghhBBCCCGEEFJjQqEQACArKyvFJSEktYhJi8rKymrvg0FbQgghhBBCCCGEEJIwauLjScieQCLuAQZtCSGEEEIIIYQQQgghJI1g0JYQQgghhBBCCCGEkAQzbNgwNG7cONXFqDETJkyAz+dDfn4+APvveuyxx3DYYYelpGx7MgzaEkIIIYQQQgghhJC9lmeffRZHHHEEGjRogJYtW+K8887DkiVLtNsahoG+ffvC5/Ph+++/r92Cpin33HMPxo4dm+pi7HEwaEsIIYQQQgghhBBC9lp+//133HLLLZg+fTpGjx6NyspKnH766SguLrZt+8orr6S9Z28oFEI4HI65nWEYCAaDNT5e/fr10axZsxrvh1hh0JYQQgghhBBCCCGE7LX8+uuvuOaaa3DwwQfj0EMPxbBhw7B27VrMmjXLst2cOXMwcOBAfPDBB3Htf9SoUejWrRvq16+PM844A5s2bTI/C4fDeOKJJ9C2bVtkZ2fjsMMOw6+//mp+rloTiHL4fD6sXr0aQNSu4Mcff8RBBx2E7OxsrF271lYOsa9ffvkFvXr1QnZ2NiZPnozy8nLcfvvtaNmyJXJycnDcccdh5syZnn+fao9wzTXX4LzzzsNLL72ENm3aoFmzZrjllltQWVlpbrNp0yb069cPubm56NChAz799FO0b98er7zyiuNxxH6feeYZtGrVCo0bN8YTTzyBYDCIe++9F02bNkXbtm0xdOhQy/fWrVuHSy65BI0bN0bTpk1x7rnnmucOAGbOnInTTjsNzZs3R6NGjXDiiSfir7/+suzD5/Phvffew/nnn4+8vDx06dIFP/74o+dzVB0YtCWEEEKIjVDYwF9rdyEYij1DTwghhBBCiA7DMFBSEUzJP8Mwql3ugoICAEDTpk3N90pKSvDPf/4TgwcPRuvWrT3vq6SkBC+99BI++ugjTJw4EWvXrsU999xjfv7qq69i4MCBeOmll/D333+jT58+OOecc7Bs2bK4ylxSUoLnn38e7733HhYsWICWLVs6bnv//ffjueeew6JFi9CjRw/cd999+OabbzB8+HD89ddf6Ny5M/r06YOdO3fGVQaZ8ePHY8WKFRg/fjyGDx+OYcOGYdiwYebn//rXv7Bx40ZMmDAB33zzDd555x1s3bo15n7HjRuHjRs3YuLEiRg0aBAeffRRnHXWWWjSpAn++OMP3Hjjjbjhhhuwfv16AEBlZSX69OmDBg0aYNKkSZgyZYoZPK+oqAAAFBUV4eqrr8bkyZMxffp0dOnSBWeeeSaKioosx3788cdxySWX4O+//8aZZ56JK664okbnKBYZSdszIYQQQuosP87dgLu+mIu+h7TGm1f2SnVxCCGEEEJIHaS0MoSDHhmVkmMvfKIP8rLiD3uFw2HceeedOPbYY3HIIYeY799111045phjcO6558a1v8rKSrz11lvo1KkTAODWW2/FE088YX7+0ksv4b///S8uu+wyAMDzzz+P8ePH45VXXsHgwYPjOs6QIUNw6KGHxtz2iSeewGmnnQYAKC4uxptvvolhw4ahb9++AIB3330Xo0ePxvvvv497773XcxlkmjRpgjfeeAOBQABdu3ZFv379MHbsWFx33XVYvHgxxowZg5kzZ+Lwww8HALz33nvo0qVLzP02bdoUr732Gvx+Pw488EC88MILKCkpwYMPPggAeOCBB/Dcc89h8uTJuOyyy/DFF18gHA7jvffeM20thg4disaNG2PChAk4/fTTccopp1iO8c4776Bx48b4/fffcdZZZ5nvX3PNNbj88ssBAM888wxee+01zJgxA2eccUa1zlEsqLQlhBBCiI13Jq4CAPwyf3OKS0IIIYQQUrdYsLEAj/24ADt2l6e6KKQa3HLLLZg/fz4+//xz870ff/wR48aNc126f/DBB6N+/fqoX7++GfwEgLy8PDNgCwBt2rQxFaWFhYXYuHEjjj32WMu+jj32WCxatCiucmdlZaFHjx6ethWBUgBYsWIFKisrLWXIzMzEkUceGXcZZA4++GAEAgHzb/l3L1myBBkZGejZs6f5eefOndGkSRNP+/X7o+HMVq1aoXv37ubfgUAAzZo1M481d+5cLF++HA0aNDCvT9OmTVFWVoYVK1YAALZs2YLrrrsOXbp0QaNGjdCwYUPs3r3bZjEhn9969eqhYcOGntTB1YVKW0IIIYTYKC6veUICQgghhJC9kf9+8zfmbyjE6h3FGNb/yFQXJ6XkZgaw8Ik+KTt2vNx66634+eefMXHiRLRt29Z8f9y4cVixYgUaN25s2f7CCy/E8ccfjwkTJmDkyJGmZ2tubq65TWZmpuU7Pp8vLusGEaCUvyN7wwpyc3M9J0irV6+e5+NXF93v9pIcrTr7dTvW7t270atXL3zyySe2fbVo0QIAcPXVV2PHjh149dVXsf/++yM7OxtHH320aZ+Q7N/kBIO2hBBCCLER8Kd3RlxCCCGEkHRl/oZCy/97Mz6fr1oWBbWNYRi47bbb8N1332HChAno0KGD5fP7778f1157reW97t274+WXX8bZZ58NANh///3jPm7Dhg2xzz77YMqUKTjxxBPN96dMmYIjj4wE/EVgcdOmTaYSdc6cOXEfy4lOnTohKysLU6ZMMX9DZWUlZs6ciTvvvDNhx5E58MADEQwGMXv2bPTqFbFiW758OXbt2pXwY/Xs2RNffPEFWrZsiYYNG2q3mTJlCoYMGYIzzzwTQCRx2fbt2xNelnhJ/zuHEEIIIbUOY7aEEEIIITXDo+iRpAG33HILPv30U/zwww9o0KABNm+OWIQ1atQIubm5aN26tTb52H777WcL8MbLvffei0cffRSdOnXCYYcdhqFDh2LOnDmmMrRz585o164dHnvsMTz99NNYunQpBg4cWKNjytSrVw833XQT7r33XjRt2hT77bef6RP7n//8J2HHkenatSt69+6N66+/Hm+++SYyMzNx9913x6UW9soVV1yBF198Eeeeey6eeOIJtG3bFmvWrMG3336L++67D23btkWXLl3w0Ucf4fDDD0dhYSHuvfdei1o6VdDTlhBCCCE2Et1ZIoQQQgghJF158803UVBQgJNOOglt2rQx/33xxRdJP/btt9+OAQMG4O6770b37t3x66+/4scffzSTcmVmZuKzzz7D4sWL0aNHDzz//PN46qmnElqG5557DhdeeCGuuuoq9OzZE8uXL8eoUaM8ecxWlw8//BCtWrXCCSecgPPPPx/XXXcdGjRogJycnIQeJy8vDxMnTsR+++2HCy64AN26dcN//vMflJWVmcrb999/H7t27ULPnj1x1VVX4fbbb0fLli0TWo7q4DPiMdIgjhQWFqJRo0YoKChwlFsTQgghdYVTB07Aim3FAIDVz/VLcWkIIYQQQuoO7e8fAQBokpeJ2Y+cnuLS1C5lZWVYtWoVOnTokPDgG9mzWb9+Pdq1a4cxY8bg1FNPTXVxaozbveA1hkh7BEIIIYTY4IwuIYQQQkjNCIXZoyLEiXHjxmH37t3o3r07Nm3ahPvuuw/t27fHCSeckOqipQ0M2hJCCCHEDscYhBBCCCE1gkFbQpyprKzEgw8+iJUrV6JBgwY45phj8MknnyAzMzPVRUsbGLQlhBBCiA0OMQghhBBCakaQQVtCHOnTpw/69OmT6mKkNUxERgghhBAbfuYhI4QQQgipEVTaEkJqAoO2hBBCCLGR4WcXgRBCCCGkJoSY950QUgM4IiOEEEKIjYwApbaEEEIIITVhb47ZGnvzjycEQDgcrvE+6GlLCCGEEBsZ9EcghBBCCCFxkpmZCZ/Ph23btqFFixbw+dinJHsXhmGgoqIC27Ztg9/vR1ZWVrX3xaAtIYQQQmz4paCtYRjscBNCCCGEkJgEAgG0bdsW69evx+rVq1NdHEJSRl5eHvbbbz/4a2A7x6AtIYQQQmzISttg2EAm7RIIIYQQQogH6tevjy5duqCysjLVRSEkJQQCAWRkZNRY+MKgLSGEEEJsyInIgiEDmYEUFoYQQgghhNQpAoEAAgF2IAmpCUxERgghhBAbciKyYAJM9AkhhBBCCCGEeIdBW0IIIYTYCMj2CCFm/yWEEEIIIYSQ2oRBW0IIIYTY8Ev+S5VU2hJCCCGEEEJIrcKgLSGEEEJsGEZUXUulLSGEEEIIIYTULgzaEkIIIcSGHKZl0JYQQgghhBBCahcGbQkhhBBiQxLa0h6BEEIIIYQQQmoZBm0JIYQQYkPW1obCVNoSQgghhBBCSG3CoC0hhBBCXKkMUWlLCCGEEOIVvy/2NiR9KK0I4b1JK7FmR3Gqi0KIBQZtCSGEEGKDicgIIYQQQqpHhp+hlrrEwN+W4KkRi3DayxNTXRRCLLAlIYQQQogrQXraEkIIIYR4JkCpbZ1i6oodAICKIPu8JL1g0JYQQgghNiyJyKi0JYQQQgjxjBy0DTM3QNoTNniNSHrCoC0hhBBCXKE9AiGEEEKId+SgbZBBW0JINWHQlhBCCCE2DEQHGJW0RyCEEEII8UyGJWjLfhQhpHowaEsIIYQQG/IqMS7rI4QQQgjxjp9KW0JIAmDQlhBCCCE25KAtbb4IIYQQQrwT8ElBW9pMpT0+HxPHkfSEQVtCCCGEuMLkDIQQQggh3pGEtgiGaI+Q7izaVJjqIhCihUFbQgghhNiQPW25qo8QQgghxDty14n2CISQ6sKgLSGEEEJsWO0RONgghBBCCPGK3HWiPUJ6U1YZsvwdYpCdpBEM2hJCCCHEhtxdZd+VEEIIIcQ78oqlYJj2COnML/M3Wf4urgimqCSE2GHQlhBCCCF2pEAtPW0JIYQQQrxjUdpy9jutWb51t+Xv4nIGbUn6wKAtIYQQQlxh0JYQQgghxDvWFUvsR6Uz6uVh0JakEwzaEkIIIcSGvKyPYw1CCCGEEO9YcwOkrhwkNhVBq33F7vKQw5aE1D4M2hJCCCHEhkF7BEIIIYSQahLtO7Efld5UhKxB2xWKXQIhqYRBW0IIIYTYYCIyQgghhJCaw5htelOpBG13lVSkqCSE2GHQlhBCCCGuUCFCCCGEEOId2iPUHcoVe4SdxQzakvSBQVtCCCGE2DAMQ/uaEEIIIYS4w0RkdQfhaVsvKwAAKKmgpy1JHxi0JYQQQogN2iMQQgghhFQPy+R3CstBYiOCto3zsgAAJRXBVBaHEAsM2hJCCCHEBhOREUIIIYRUDypt6w4iEVmj3EwAVNqS9IJBW0IIIYS4QqUtIYQQQoh36GlbN3hj3DJMWLINANA4j0Fbkn4waEsIIYQQG/L4gp62hBBCCCHeYW6A9CcUNvDSb0vNv6NBW9ojkPQhpUHbxx57DD6fz/Kva9eu5udlZWW45ZZb0KxZM9SvXx8XXnghtmzZYtnH2rVr0a9fP+Tl5aFly5a49957EQxab7IJEyagZ8+eyM7ORufOnTFs2DBbWQYPHoz27dsjJycHRx11FGbMmJGU30wIIYTUCaQBRphSW0IIIYQQzxgOr0n6sKukwvJ3o1zhaUulLUkfUq60Pfjgg7Fp0ybz3+TJk83P7rrrLvz000/46quv8Pvvv2Pjxo244IILzM9DoRD69euHiooKTJ06FcOHD8ewYcPwyCOPmNusWrUK/fr1w8knn4w5c+bgzjvvxLXXXotRo0aZ23zxxRcYMGAAHn30Ufz111849NBD0adPH2zdurV2TgIhhBCSZjARGSGEEEJIzeHkd3qyq9gatG1CewSShqQ8aJuRkYHWrVub/5o3bw4AKCgowPvvv49BgwbhlFNOQa9evTB06FBMnToV06dPBwD89ttvWLhwIT7++GMcdthh6Nu3L5588kkMHjwYFRWRG/Ctt95Chw4dMHDgQHTr1g233norLrroIrz88stmGQYNGoTrrrsO/fv3x0EHHYS33noLeXl5+OCDD2r/hBBCCCFpABOREUIIIYRUE0P7kqQR5cGw5W9hj1DKoC1JI1IetF22bBn22WcfdOzYEVdccQXWrl0LAJg1axYqKyvRu3dvc9uuXbtiv/32w7Rp0wAA06ZNQ/fu3dGqVStzmz59+qCwsBALFiwwt5H3IbYR+6ioqMCsWbMs2/j9fvTu3dvchhBCCNmbYcyWEEIIIcQ71hVL7EilIyFFAd24yh6hmJ62JI3ISOXBjzrqKAwbNgwHHnggNm3ahMcffxzHH3885s+fj82bNyMrKwuNGze2fKdVq1bYvHkzAGDz5s2WgK34XHzmtk1hYSFKS0uxa9cuhEIh7TaLFy92LHt5eTnKy8vNvwsLC+P78YQQQkgaY0jDDQ42CCGEEEK8Y0k+xm5UWhJUgraNaI9A0pCUBm379u1rvu7RoweOOuoo7L///vjyyy+Rm5ubwpLF5tlnn8Xjjz+e6mIQQgghScFqj5C6chBCCCGE1DWYGyD9sSttI0HbimAYwVAYGYGUL0wnJPX2CDKNGzfGAQccgOXLl6N169aoqKhAfn6+ZZstW7agdevWAIDWrVtjy5Ytts/FZ27bNGzYELm5uWjevDkCgYB2G7EPHQ888AAKCgrMf+vWravWbyaEEELSEXraEkIIIYRUD6vQlv2odEQN2japl2W+Lqmk2pakB2kVtN29ezdWrFiBNm3aoFevXsjMzMTYsWPNz5csWYK1a9fi6KOPBgAcffTRmDdvHrZu3WpuM3r0aDRs2BAHHXSQuY28D7GN2EdWVhZ69epl2SYcDmPs2LHmNjqys7PRsGFDyz9CCCFkT8Rg0JYQQgghxDNWm6kUFoQ4ogZt62dnwO+LvGYyMpIupDRoe8899+D333/H6tWrMXXqVJx//vkIBAK4/PLL0ahRI/znP//BgAEDMH78eMyaNQv9+/fH0UcfjX/84x8AgNNPPx0HHXQQrrrqKsydOxejRo3Cww8/jFtuuQXZ2dkAgBtvvBErV67Efffdh8WLF2PIkCH48ssvcdddd5nlGDBgAN59910MHz4cixYtwk033YTi4mL0798/JeeFEEIISTVc1kcIIYQQUj0sSltOfqclIeW6ZGX4EaiK2qoBXUJSRUo9bdevX4/LL78cO3bsQIsWLXDcccdh+vTpaNGiBQDg5Zdfht/vx4UXXojy8nL06dMHQ4YMMb8fCATw888/46abbsLRRx+NevXq4eqrr8YTTzxhbtOhQweMGDECd911F1599VW0bdsW7733Hvr06WNuc+mll2Lbtm145JFHsHnzZhx22GH49ddfbcnJCCGEkL0FeYBBewRCCCGEkOrBblR6EgqHLX9nZfjh8/lAQwuSTqQ0aPv555+7fp6Tk4PBgwdj8ODBjtvsv//+GDlypOt+TjrpJMyePdt1m1tvvRW33nqr6zaEEELI3ggHG4QQQggh3jEsr9mRSkeCIUVpG/Cjyh0BYSptSZqQVp62hBBCCEk/2G0lhBBCCIkDOaFr2HkzkjrUlWRZAT/8Pp/D1oSkBgZtCSGEEGLD0o+l1JYQQgghxDOyupa9qPQkqKhp/X4fRMyW1mAkXWDQlhBCCCE2uJSPEEIIiY/bP5uNc96YjMoQpZV7O3LMjwHA9ESXbEzobHnJSLrAoC0hhBBCbFiyHqeuGGQvIBgK45d5m7C1qCzVRSGEkBrx49yN+Ht9AWau2pnqouyxGIaBYB0IinPBUvqjC9oKewReMpIuMGhLCCGEEBscbJDa4pM/1uKmT/7Cma9OTnVRCCGk2siJi4orQiksyZ5LOGzg7Dcm48zXJqE8mN7n2JA6TwY7UmmJao8AwJTaUh1N0gUGbQkhhBDiCq0SSDKZsGQrAGD77vIUl4TsjRSUVKKsMr2DP6RuUCGpP+uCErQukl9aifkbCrF0y26s21mS6uK4Yji8JunDlgL7Ch9TaZuiixYOG1i8uVCrAiZ7JwzaEkIIIcQGVSGktgj4mamZpIb8kgoc+sRvOP3liakuCtkDkIMsPmagTwo7i+vm5B5Vm+nHR9NWY+Dopbb3xa2bqn7w6+OW44xXJuGxHxek5Pgk/WDQlhBCCCE2aI9AagvWL5Iqpq+M+I6uTXPFHqkbBENy0DaFBdmDKSitNF+XB9NbzWzJDcDnXNrxvx/0QVEzEVntFcXCy2MigeSPpq9JUQlIusGgLSGEEELsMBEZqSWoQCKpIkNSeYe5FJXUkGA4GkRkzDY5yIHxylDduWf5nKs7pNoegRAVBm0JIYQQ4go7roSQPRG/NBJiUIXUFNkegXMAySFkyEHb9FXaqkvr2bzUHXxMREbSDAZtCSGEEGLDmkCDHVeSPFi7SDrAIBupKZWWoC0rVDKQxMyoSGN7BPXysx+V/tx+ahcAUT9q3sIkXchIdQEIIYQQkn4wERmpLVjVSDrAIBupCdNX7kB+SdRvlZnfk4OstA2m8TlWSxZO3/gyAXDp4e0w4LQDAEStTfhMIOkCg7aEEEIIsWE4/kFIYmH1IukAx+ekumwtLMNl70y3vMeAT3KQz2s6+1Db7BFSVA6iR51UCQSiLtRMIkjSDdojEEIIIcQVDjZIMqGqm6QDDLKR6rJye7HtPSptk4McqE3nc2xT2rJ9SSuCivRZTkrJRGQk3WDQlhBCCCE25M4qg2qEkD0RH6IDdQZVSHXZXRa0vZfOS/frMnKgNlSX7tk6VNS9gWBIUdpKQVvaI5B0g0FbQgghhNhg0gxSW3BcRNIBxthIdSmtDNneS+el+3UZOZCW1kpbpWgMAKYX6qRKwCfbI1QpbWu1RIQ4w6AtIYQQQmxYlbapKwfZ8+EEAUkHuKKAVBddQK5OqUDrEKGw/Dp9z7H6XEvfku6dBENWewSL0rbqJQPtJF1g0JYQQgghrrDbSpIJx0UkHUjj+A9Jc9Sl1gCVtslCDoanc1CNStv0Rg34y9dHBG15yUi6wKAtIYQQQmxQaUsI2dOR1XAMqpDqolN8prMKtC5j1BF7BBU2L+mFao9QKU28+E2rBF40kh4waEsIIYQQQlIGB7MkVch1j0FbUl10Scc04luSACyJyNI4aKs2J7RfSS9UdXyFZJcQTURWiwUixAUGbQkhhBBiQx5g0HOUJBMGy0iqCHNFAYnBFzPX4voP/0RphT3ZmCAUDtveoz1CcqgzQVt62qY1QeWerQhKQVuRiIwXjaQJDNoSQgghxIbcV2XHlRCyJxKuI/6YJHX895t5+G3hFnw7e73jNjqlre692uDZXxbhpo9nWYJQexLyfZrOyd5snrZpHGDeG7HbI8hB28j/fCaQdIFBW0IIIYTYYF+VVIfyYAhllc6KNB2saiRVWO0RUlcOkv5szC91/Eyn+ExFwMcwDLz9+0r8Mn8zZqzaWevHrw2k2FqdCoTWnZLuHaj2CJUaewT2g0m6wKAtIYQQQlyhFxvxQjhs4Nw3pqD3oN9RUhH0/kVWL5Ii5LatLgWASO0g14kMv/OwWaeqTcVzU/bl3FNVguE6kohMLVkaF3WvxM0eQSQiozUYSRcYtCWEEEKIDXZWSbzsLKnA4s1FWL+rFOt3OavSVFjXSKqgpy1xo1iafMrOdB4265W2SSmSK7LvbkbA57Jl3UUO2qbKgkLlo+lrMG7xFst7atCek9/phd0eIfq3sEfQXbJgKIzV24uTWTTz+IQIGLQlhBBCiA25s8qhxp5HMgaQxeXRAIe69JCQdISetsSN3VKb5neJpOjau1TUpxKXZGl7CnKAPB3u2YUbC/G/7+fj38P+tKg11ZKlQVGJhHrPyn/54JyIbNjU1TjppQl4d+LKJJaOECsM2hJCCCHEBhOR7bm88OtiHPbEaCzZXJTQ/RaVRQMcsj9cLFi/SKpg0Ja4sbtMnojSt2lfz1qPl8cstb3vRQRaGQon1JZDDtpWJmHi7LcFm7FoU2HC9xsPctA2jsdM0li7s8R8XVRWab5WmxOuKEkv1Pv5xhM7mq9Npa3mmj01YhEA4OmRi5JXOEIUGLQlhBBCiA2r0paDjT2FVduLMWTCChSUVuLDaasTum9ZaRtX0DaBZSitCOGrP9dhx+7yBO6V7KkwERlxo0hq0yo0QVDDMHDPV3O13421mqEiGMaZr05C31cnJcybVfbpdAoyV5elW4pw/Uez0PfVSQndb7yk20RLeTAaKC+Vk3CqQdvUF5VIVFbdc11bN8CfD/fGMZ2am5/5qqK2deGZsK2oHDd9PAuTl21PdVFIEmHQlhBCCElzthSWYdaaPTMTNKk9vpy5Die/NMH8O9HjkTJpaWhFXErbxJXkjfHLcO/Xf+OaoTMTtk+y5yIHfeg5SVRKLcpVe5u2zWVyKFYgdlNBKZZt3Y0lW4qwTlJr1gS5CsczceaF+RsKzNcfTV+T0H3HQ9CitE39PVsmBWrl8qiT3akvKZEJVU1wZGX40bx+tuUzv+lp63zVsjPSI4z2+E8L8Mv8zbjy/T8Svu9RCzan9F4nUdKjthFCCCHEkTs/n4ML35xmS3SRXORgRi0eliSNF39bYvk70dc1JKm8ZG+/WCSyGJ/NWAcAmCcFGAhxgkpbOwUllXhzwgpszPeeTHBPRQ7I6WwMyiqc27lY9Un2y91RnJiVAfIkxODxKxKyT4FsvfC/7+cndN/xIF+HdEhEVlYpqZvDzv0m9qPSC2EfkuG3e1W7JSITZAaSF0aLJw/Z8q27k1KGssoQbvhoFv73/XzMXZeflGMQ7zBoSwghhKQ501buiPy/YketHZOJyPY8ZL89QB+EqAmysCseBZSlrtVwZKv+RkLcSLel1unAA9/9jed/XYyL35qW6qKkHKeAnEBeGq8Sqy0rLo9+d1dxYtot+ZCJnrgqqQjG3qgWkJ8zXp9hiVDRB0NhrNxmD5CVOihtbWVgTyqtEInIMjTBVzMRmcs1S6bKO549J1pRL1izI6r+31xYlpRjEO8waEsIIYTUEfKyMmrtWExEtucTSvCFlQcxI+dt9vy9RNa1ZCTfIXsuVqUt6w4A/L5kGwBgwx6ktF25bTdu+2x23MkXnZa+Rz93U9rGCtpGg6D5pYkP2iYat99am8jPLS/PsM9nrEWPx37DhCVba3Tcmz/5C6cM/B2jFlifbXIdWSGpHtWSsXlJL4T/c2bArmv1e1DapksQftX24qTsN7+kwnxd4KF9or1QcmHQlhBCCElj5GXmOZmBWjsuO2B7HuoljWcJnhfkoMY3f633/D25rrHWkdrE6mmbwoKkESIJz57Es78sxk9zN+JfH8Tn+xhLRemmtI3HHkEOkNSEZE48pEutCMfpaXv/t/NQVB7ENUNn2gKuXgmGwvhtYcSeasYqa34BuY7c9eUc8zX7UN4oqwxh6JRV2FZUu8lDo/YImnCYh0RkvjS5I5Il+JUDtYUxgrZTlm9HhwdGov39I/DFzLXJKdBeDoO2hBBCSBojD0rembgCP8zZkIJScPCxJ2AL2iZ4zJEI5a5b0GHltt0JC24QAlgHvOmQ1CgdSI9QRGIZXRVw21IYX2DIorTVtE3lLt7dseqTrLT1omTzQjJrcLrE8muSiOyGj2ZV65jydZaDtABQLimQZTWyXWnL9kXHuxNX4vGfFuLK9xKfSMuNYJWtgM7TVrzl1h9Jh/tBbkPyshIr6pi1dpf5Olb7dIV07f77zbyEloNEYNCWEEIISQDlwRDmrS9IeMdcHijuKqnEHZ/PcVX3JAraI+x5qMv5Eq0Uqa5Hrpcl6ut2lqD3oN9x7uAp1ToGITroaashDYIR6cK6nVFfx5DGeqVMCeBd8H/7Iqsqq3ysvoAcCNmVJKVtIvsj6TKnIf/G2ppokVc8qTG+0gp9f4yJyLzx9sSVAIAlW+KzLqkpIvifobVHqPK0dVXapp6ismjQtl52Yu3TZKuP/BLmCkg1DNoSQgghCeDdiStx9huTMXj88oTuVzco2b47+WpDa3KopB+OpIBEK0Wqm8nbyxL1X+ZvQtiwJscgpKYEpSQu6RKUIunD8GlrzNdelLbd2zbCjSd2AhC7Pj37y2LztRx8qQlqERMZ1EwXJbrFhmfWehz97FjH7PZ/rExM8lb5OqunocxhEl2dJE2Ps5d+1IYIQYcItutsx6Ketm5KW+8dqMKySnw4bXXCLSDk5IDBBCck69yygfk6USsBSPVh0JYQQghJAC/9ttTyf6LQqRflJVHJwuozyuHGnkBAkQgl2rsyEUpbpzFSeZokwSF7FjuLoxNgXL4cIR0UZOmIrn1TA04ZAb+npdUqiQqIqnW4uhNpXvadKuRzVVQexKaCMlwzdIZtu+krd+DSd6bb3pfV016RlbZqcExW2h7Zvmn0AyptPZGq5KH5pZG2v0lelu0znydPW+8Mm7Iaj/ywALd99lc8RYxJiVT3ggk4j6GwgfcmrcQfK3egUqrnDNqmHgZtCSGEkATSICexS5R06p7dtRG0TfoRSG1jT7iR2KusBgi8DvLlrZwCHX4p4JwuwQNS99lcWGa+ThMhYcrZExORVYe/1+db/tYFQMuUyaQMv89cWh1PfUpUk6Yec7m0xLmmJMKzPBHonhH5mqDSuMVbtd9fsjn+ZfgVISk4ppzkMimg26Repvna5mnLXlVasbtK3V5fYyvgZeKlqDzoSd06bcUODBodEXNMX7kzxtbxIQdtKxKgtP1g8io8NWIRBnw51zJRodrAkNqHQVtCCCEkgeg6gDVBp+4pKa+FDhTtEfY4VKVtope7qoN6rwoaq6o7NolUj5HEMX7JVtzz1dxaWQmQKDYVyEHbvbNebcwvxd1fzsWfq3di5uqdVFVVcfeXcy1/a+0RlGBGwO8z29lYk0stG2SbrxMV0FOPuXBjYY33OXvtLgz6bYktQJ0qdO2/7lQ7nf/iivjbJ/m3q4pGS7I6l1O0lzYvCcUwDNz1xRzc8fnsGk/eioRyuZoEXtGJF/djfD5znevnK7ftxuXv2tXesfA6bSbX5coEBG1nro4ElTfkl1qCtqy7qSexI0tCCCFkLyfRGiVdfErNXpxs2F+rO+wqrkBRWRD7NcuzfaYK6BId/FQnGCpDYTMpj+d9OCltpcJXBMPIDFB3kG70HzoTANCiQTb+e0bXFJfGG7LH4N4atL3/23mYuHQbVu8oxqw1u2J/YS9hmaJS1SYiC9qVtqKpijUpJgeLwgmKh6qHbNfU/hyIl/OHTK3xPhKJVxsep80KPfgHr91RguKKILq1aQjAqmIMKhdLDtq6+bPvna1LfBiG4ar035Bfiu9mbwAA/PeMrtincW61j1VaFYjP1Xraxk5EBgDzNxS4fp5oZa2KbM0RNiJtjjo5Hw/yz/3iz2hA2k1lz5VPtQN7vIQQQkgCSbQIUNdZCiVqhOeCfFT2ydKfP1fvxIptu3Hhm1NxwovjsX6X3bdPHezGo7Q1DANFZe4KPDUI7NVjzTLQ9VC1E6EoIcljw67SVBfBM/IE2N7azk1cug0A9oqAbQ3iGQ5KW3tb5NUeQW4fEzVhoCp21QDjnoDX55bT0vVYzzEAOHngBPR9dRKGT10NwHqd1RUkVqWtSy6AvbWBiQM1sZ+KnIi0pisCSqtUqjqlrc+jL3UsK5nq3tdeLWoqlPOVrL6R2z032yEJIEksDNoSQgghCSTRvmU6VUltJG7g7HndYdmWIlz01jScOvB3rNxeDACYo+lIq1XJq9I2FDZw7fA/0ePx38zlczrUuloeil8R7nT/yO+nSxZzoqcuKVblIEBdKjepHjW5wrp2R01Etrs86CnzPGBVbyaqSVMPWdMgTnWTSyYTr966TgHA3R6UtuJavz95FQBFaaucU/k4VNrGjzyREqtPskNKHFlY06BtVbA9Txu09TbxUpNJIK+4tSPq+UpW0NatDKu2FVv+3rcG6mfiDIO2hBBCSAJJ9LhfN1CsDfWMRWnL4UZas3SLPdlMvSy7A5Zal7wOyB//aQHGLt4KwwBeGrXEcTt1MB1r6aBA/ppTkeSypktCHKKnrlye7bvLLX+nYXyKpBG6Z7Hq8xoKG578MENhAwUl0aBToiZJ1d1UBGu2350lFbE3qmW8PrecgrZFMYK28v6FmlNehq4GymS1o1Vpa6WutIupJNaErOwhXXOlbWRfOVp7hMj/se7LT/5Yix3Kc0QmEZfc7ZSoEwg1EXQYhoHRC7doP3Prc6l2bZz8TA4M2hJCCCFpjK6z5HXZ+bqdJdhUUL2lyoY1akvqGDrFilqXfpm/2dO+Ppy2xnzdKDfTcTt1wPXvYX962r/cyXfq8MuDkT1wxe8eRV1RQq/eblUIcbBpJYu+0RZ09UNV2oYNb/YIT/68UFHaJqbuqfupaUb59WlodeJ1hYh6bQSx7BEqw/brUiIlfFL7X7K60RK0Va4FJ7/1yFYAsZ4dcn2ucdA2AZ62ADB5+fa4jutlgkYW8OrOSWFZJQpKKxOqtJ2wZJvjZ+4J9hQxAJ+jSYGJyAghhJAE4vfoReUVnarEy6CluDyI418YDwBY8cyZCUtOQOoGugFrIpa61s/Rdx1H/L0JL7qocN1YIS2vc+rvy+ryPdGncU/CX0difXYlHFs6mXZN97xlrjW5xLrJUlXNaRjRREBu6rRhVV6pgkTNc6gBk5r63/8yf5PjZzVNelRdvAaFdH7DQGylrTxBKPpzJRalbTXtEdi8aJHPWWylbfRcx7qOsXDztPU7eNrq+lCqr6yM7pkSNoBAjNtGHkbYJmKCYfR9ZRJCYQP/Pq695bOaBG2XbCly/CyeviNTDiSHOtKtIoQQQuoGiR7DaJW2HjpQPZ8cbb6ujiJBVoUwmJFaYp1/3TyBbsCaCFsBJ5X3Q9/P074fq+wrt1mtHZy2l+s8Y7bpjQ+1H8ipDuoEW12qV4ZhYOBvSzBynnNQrabUEcF0raFrP8uUpcEBvw9+v1DpeT+BiUtEZqWm/vduSQUXbSqs0b6ri1clf5mT0rY8RtBWCsKJoLQcIBTndGdxBQaNXmr5zK1svJ3sGIZhCWbHo7StSYBy4cZCrK7yM9cpbZ08bXV9bzmgr6LrL8W7EkVtG7YUlmFDfik2F5ZhU0GZ5bOa3O9ubZDbZ+onVNomBwZtCSGEkATiNeurV+QOXu9urQA4Z0UW7CyusKg/nJYJusF+V3rw4HfzcMxz47Cz2NlbUHet1AGrOjgCgLZN4lfS/Th3o/b9/BL9xMDuGAPkjfnWQYdTtZMHP/S0TXPqRszWRl0abE5evh2vj1uOmz/5S/v5jt3l+GPljhodoy6dj9pAn4jM+izu171NNOATR0wp1qmev6Eg5rL+yH6sO/JqpeSEW4Dpts9m12jf1cVrrK76Stvo98T5LJTOvVDaPvTdPLw2dpm1bC6nm7eTHbV+xXq2y6rWmgRtz3xtkvk6LqWtpnyuQVtNIxBv0FbdXj6emlSvJufE7dS7XRdVhcvnRnJg0JYQQghJY0Sfr3XDHNTPjnQuYw3EipVAWXUGbrS0TQ8+/WMtNhWU4ZtZ6x230Q0M1AGrNqFdNQf08SjICmMMkL0msQg6+AZWt1wkedSVmK06+VWXlKXbipyT3wDAOW9MwaXvTMesNTurfQwOvq24BW1fvKgH5j/eBy0b5jgGfNxw23bSsm046/XJnoKk6m5qaiXjtqpnleIJXVt4tXxwSkQWa9K7QvOsKZRWK4nn5l9rd9m+KwewbPYIadKT2lJYhhNfHI9XxixNdVFs9SvWMnxZgFBTFbnA3dNWmQTRKm2d+zi67eO9J9XNi6XjqXW8ZkFb74FZy2fKR3XF076uwaAtIYQQkkAS7ecoBnMBvw8ZVYlhYtkjqEs2q9WRkw7BsXtqkG0t/C6+G7rBi9qZ1yklqqPABmLXv76HtDZfu/m9Afa66rRr+ZgzVlkDUet3lWDYlFV46Pv5rscitUOiVxskC7Ue7ylB/7LKEDbkR5a1z16b77qt2wC7LtlFuJGo66o7V6L9ysvKQP3siN93wEMiMhW3oO3rY5cDcE8UBACz1uzCf4Zbkz86qU29EivAmQrKPP4mp+dbLDWn/Dw1g7bS5KNoNzI1ifosicjUIG0tNC9jF23BxW9NxZodzgH14VNXY82OErwyZpnjNrWFWu9j9S3iVdqu21mCIROW2/oZMm5BW1tAUtPXSoY9gmwxpJ6jkvLo8dT+lVMgOxw2cMV703HRm1Md7+lqK23VhHt7xmM07WDQlhBCCEkgiU5EJjpLfj+QWZW9INZASh3UVEeRYPG0jfvbJBHskiwR3K657jNbVnPN13eVVOLB7/RetDInHtBCOZ57jdi/WT00q5elLYeKOphy9LSVjjls6irLZ+e8MQWP/bQQn/6x1vVYhMiog/66JBBye8zIy7+zNQEJGbdgRl1Q2obCBl4ds8xVUZyo6+qmtM3OiA6powEf7wd229SrQvOyd6bZ3nt65CIs3lx971kv/vm1jZNXrW07h+BurKCZ3C6I/pdVaRv5XJeEzTURmXtxE8J/hv+Jmat34a4v5jhus3ZnSS2UxBs2ewSXa/PNrPV4d1L02e8laHv5u9Pxwq9LbDYWMg1zM23v+RzU8nLw8q7eBwCojtI2TnsEpQyy0lY9B07nZGtROaYs34E/1+yy+eDG+i7gPoGn1nMqbZMDg7aEEEJIAkl00FYsS/L7fOYgoTKW0jaYAKUtSTly577YRc2hqw9elLZAxH4hlu9sI2VQU6n04NWgcYbfh6yqIEYspe323VavXqfghXxMWeFUWFap9futI2LPPZK6curVyYe6EKT0gqzQr4xx/6n2JDJ14Xx89ec6vDxmKS580x6wFNTUIkAgzkdlKIxhU1ZhQ34pyqvOX44UHHcK+LjhtqlX5brT5OyzIxd7LoeKWwCmZYNs1+/O31CADyaviivzvBdqqrSNNekoP7OiSlvpnqr6fkBzXaxKWyu1qeTfUuhun5IuqLemW327+6u5lr+9iBHWVyXSm7Rsu/bz20/prA2+OyltRVvi8wH1quzKdux2zjegm1D3EtSU2w71/pHrb4UatHVo790sFaLbVO9Z4MX3l9QcBm0JIYSQGiJ3xhMdsBAdvIDPh4wq74VYnm6JsEeQ+117yrLhuobcuXe7hrqBgd12wPkauilF1HIA9iWC6sDBH0fQdsj45Z7KKQ9UFmwsxPglWwEA+cX65DyJnjwh3qkrp14N5u0pg005wBQrQOCmtK0Lc31/SFYpTkG6RNk8iEm0weOX47GfFuLY58Zhc2FEtZadqVPaet+3W91zccbxhLBtqA46VeCV/9gPgL3dVznr9cl44ueF+PiPNdU+vg6vtj5OwalY93llLE/bsLPSNmRR2tbusnG5H7BvY+cko+nUyqmTyfGoNGP1a+VgZ8Pc6D0gn6drju2g/a64tOo1FOXL8PuwYGNEwf7bwi2OZdDdP16U3vL3nALHYlvLdx32XSoFZJ3uH7d+oKuNjqH+nU41bM+BQVtCCCGkhiSzjxK1R/BJ9gjuByxVZsyrs8TRcHhNag+5Q+6mmNMNXtQBq5vaKVZgVQ1uqXtSvx/w+ZAV8Ba07d62kXXfDsVUBw39h84EYFf9CupI3HCPpK6ce1WpVRfGmvM3FODOz2djc4Gzkk4OMDndHwK3oG0iJ+tCYQNvjFuGP1buSNg+AWvbN+DLudptqqu0FRYvZ/VoAyDahsrLy/NLIuda9sUUvvbxnD+3QIevhndU4zz78m+v6II1WYHIb9X5e+qYsy6/2sdXWb+rBCu36f1af5y70fK36uf7xLkHA4jPNzVsRK6jvJrDnEjX2SO4KW1dj1pz5GXvXVrVT/LREoN6byYyaOuoKJU8YZ0mNJwsTuRr//f6/JhlrI6n7W4leatbYNvmaevwm+UVFbrzUhEM47MZ6xzL5FZkp3NEEguDtoQQQkgNsXRaEhyxEH3agM+HgN9jIjKPHTk3LANO9sFSglelrTYRWaX3wVCswKr6XbWTrn4/I+AzlWflMQZWnVtaB5dOwQun3+80gcEqmzpSnYisPBhCUZlegS2TbkpbwzCwtVDvNyi44aNZ+H7ORjz/a3TJuxoclO0RYgXW5ACGSiLPxw9zNuCl35bi0nemJ2yfgLWMI/7epN1Gbb+8BlPFc/aEKk9v8fc+jewqRtkeIZanre74bo/0miY3FcHn6qDra2RmRH5frIRegvkbCqp9fJX7vv7b8bPbP5ttvjYMw6YoPKpDMwDuE5iAXUE8ZfkO7Cqx2yPoVnNYlbbWz5LdvAgrACCWP6mzGri2sdkjxFGeWPYI8vWXr5VYiZCd4TdXBKn4nBKRSSvfBpx2YMwy6iaMxD1lGAZWbS+2XYMiJWhrs0ew9AutnzlddzlZmi45oVi55ISrPYKtf+i6K1JNGLQlhBBCakgyOyl6pW2c9gg1VNqS1CB3zitcBii6wKU6YHUL9Mda5qoODNROus0eIQ6lrfq5UzGdyu80SKHaI3WkWml7w0ezcMyz47Axv9R1u8pg+gw2dxVXoMMDI3HkM2Pxw5wNjttt0PwmtdxypvtYbX9xNZfExsvq7c7Z7GtCrFUngG7Sydu+xfdEkjHxt069nJulCdo6NH26YJNb8MxLHMvtWjkFprygs2LK9FvPRyyWbtmNHbsT47E6dYU3pXZlyLBdZ6GMjTXprQa1vpoVUSC2aZQT+b4mEVlO1SSla8KmJPeq5OXtTr6/D3z7N36Zv9n8O9WJ5mpijxBrUqncopiObis8/BvkONuGiEurHkOcr4Dfhyb1Igr2Li2dVc16e4RIuQaNXoqTX5qAV5UkaYXKhKPNM9ZFaevUlyuV6obuHG+Snis69bHbddEF2hPtY00YtCWEEEJqTDIVWmGzkwjT0zZmIjIlaBsryBuLZA82iB75url62mpGiuqgze37OuWFTKygh80ewQ/Pnrb2z+NU2rrcC6lWEZHap6C0EhOWbENReRDjFrurh9TgWyqVtsOnrTZfvzhqSVzfVe9/XaZ7J1QrHZlEno5Yivvq4uWaqe2XV7sEsZ0atNWpl/M0Slsn1aDu+PEsP9bh5vNak8CcLiguEkHGc7/IKtDaQHc+MqoicbECg+rycZFo6rIjIl6+lVLgTiCU1tZ9qwpvDwWvAfKzVP3901bswNTl221L4L1MeiQTNbgXX9DW/XO5XyNP0ImgrZvXs7iH5Wu2fXc5Th34O4DItQ/EuM8Bffsr7sfXx0X8/F8ZEw3art5ejK9nrbdsb2+/nFdgOV1P2R5B1/5sqlrhcc0x7bXnxS0Iq/ss1atW9kQYtCWEEEJqiOHcT68xovPj9/mQUaW0jbXkVR2IV6f/ZE1EFv/3Sc3xYo+ws7gCk5fbMyOrgzb3RBLuFzjWMnJV3REMG8jKiAxiYwZtQ+q+7dv8Mm+TY/ZnWQl2TKdmymcGisuDrr6dJPGk0h5B9p6MFbBUB7ipDPIvrEpqA0SDYl5Rx+DWpEmxlLbJt0eoDIXx9u8rE7IvFS8BSXUbr4EhsZ2YgBLBGd0xrUrbyP9qfVq4sRBXvf8Hfl+yzfZ9t3Mtf+QUnFWf+Ue2b4r9muZF9l2ToG3Vd1s0yDbfM/shcew3nmXvTsRjs6Dz7Qx4DNoWl1vV52bwvkpNK9qVDDloW/W8c7NHSDbys1SetF2+tQiXvzsd/3zvD9fvpAJbktOEKm2j94T8O4V1ToMcZ69nYUki3zuf/rE2+rnPZ9Ynt/tL11a4Bcpv/HgWhk1dbXnPzTNWredO/US5H6ae4znr8s32uUWDbIvS952remnLIKNVE3PQkHAYtCWEEEJqSDI7KPJyLDFIiJVcplQJUtV08J3s/tfWwjKqIjW4KSoEN308C7PX5tveV9Wzbv5vscZJ8Xra5pdUIqtqYO+qEA6FsXhzkeu+DcPA7Z/Pdvy++F2dWtTDp9f9A3MfPd38vLAsiNMG/Y6zXp9Mu4RaJJWWtrtKokHbglLnpf+AvW6msorIx9bkN3LFprQtk5W27j+qpNzFHiFBbfLmAnef3prg5b52U6q5IbbLzrCqKHVKtWzJgsDJD/OLmWsxadl23PTJX7bvuxVJvgzP/bJYu43qY5+d6cep3Vpafkd1EL9ZLP8HYFrfiCRdXvg7AcnIthVZLRbc7pPqBm23Fpbhvm+svrlm8F763eGwAb/WHkEK2ir7TnYfp9xBaas+Y2VqugqrpqhtjO4c5ZdU4MHv5tm/HEtpK52PSkvQNtLm1csO2L4j0N3D8rPFAMzr7660dVahCrszGd21cvLVBezPMPXv3eVBbMwvtbQBantw3Yd/mq9bNsi2+N92a9MwckyX36i7n9idTzwM2hJCCCE1pFbsEaSZ/ViHU1U38Y7Z1I5zMu0Rvv1rPY58Zizen7wqaceoq8gJKSqC9mtQUhHEH6t2ar9rV7+6JCaJUaFKYii31YHCT3M3Ssl4nPc7ePwKLN+623Xf63eVOgac520oMAdFQp0oL1mdtWYXNhaUYfnW3RYFJtlzke+Zl8csdd1WHbymckmnHIBqmOusANOh3toFFqWte1BGvbct+03Q6ZCDHanApubzsCQ8HDbMtihLtUdQ9pebGbCoy00FnlKfvv3L2avY1dNWev7Kaj8Znc2Fl+XbsYhaREQDXHKwyWsdeeynhdUug0A9725KyXLN6ooMD0G2H+dutL1nBu+lwHVlOGyeX0CyR3BLROZ41MQgT57KStuAyyxayj1t1SSBmm0G/rZUW+9jtdcVDvZSIvli/WwXpa3G01Y+XNgwYnpXA9Hf9+CZXU3luzjnGR4zDLqpkdV+npr74NK3p+GEF8Zj2ZZoP8uemDH6WvW/jqqJncun9+1l1DbRMGhLCCGE1BBD7tAkWGUmJyLzOWSlHrd4C+75ai62FkUUTWpHLl6FR23GLgZ8ORcA8NSIRbV30DrAN7PW45ZPo4osnWLVLfO7qn51U9y5Le+bv6EAC6Sl24C9/qmqpgNbN4jp6wjog2rqvre7JLAJ+H2m6lws2c2Slpav2h4dqNAiofZIZSKyWHYcMqrKLJXjTPn+Pr5zc9vnhmE4Jiize9pKichiKW1dEpElShkoB5ETjZciqkGFeC0VhIpWtJPqOZVVqIAc8Im+V1IRRJGLqtkpALV2R4llJYXTdmr75vP5EPBop+SG+K6sJM6Q2lin4Ixadzq1qFftMgjUpHluCdZ0ibj8ktLWqW7r6mrQPAfRwHUobFgmCHdUTQrK50Od7K5dT9voaze7GreVMLVBrFU8ADB3fb72u7GCtpUWpW10W6HYdlPaRj1t9ccIh43opIhLeyLOb73sDJuncoaitHXaj6unra2fF/174cZCLNhYiGDYwKgF0eRzNytKf/nePrJDU8tnAQ8THbpy67bfVFCKB779G387XE/iDoO2hBBCSA1JpkLL7OD5feZgUO4khcIG/j3sT3w9az2OfHqs7fNI+WpWhtoI4vp9di+5vZm7v5pr+Vs3uHLzo7MlqKiqBPs2zsWfD/fGnEdOkxRhzuV4d5LdizJWIrKsgF9ShddswkC1+lC3FQNqoVrJDPjM5flrd5aY2zJoW3uk0h4hniCEGnxLVMZrwzAwfslWWxZwr2XRlWLsoq244/M52u+qA2SrPYL7+XDztE2UWiqZyY68rAJRn89uydcE8m8XwUHRhqrnZf9m1oBkVIEX3W7RJuvEl4qumSyrDOHsNyZbgjQ+hykRWWEORPoLiVHaRr4rB0jliTGnvo/6bDq1W6tql0Hw0m/WBH1ZLt7PbonIAOs1/HX+JvR4bBSe/WWRVnmuOweVIcPSzolAoMUewaa0TW5HSj7nstLY7T5OdSIyL31Vp4m4WM2TfN/I+xCTxZMdfPKB6D382rjlpgeuXNcLy4Km762XgGaGP7pSTjyjVO9ydVJCoO5eLkdlKLp/ed8AMHTKKvN1wMVLRBz34/8chTaNci2f+cwJqPiCtobmkt3/zTx8NmMdBo12XwFD9DBoSwghhNQQS4cmwX1g0SEK+PX2CLpgVE2X/apb10a3PmwARz49Jqn+h3UZXTBKtwRUYLNHMAcKPjSvn43GeVno3KI+APfA6sptxbb3YnnannPYPmZnP97Aj1oUt2BrMGzYksL4fNFgxa6SaPDKLfhLEotTYKk2kOti4zx3mwFVoZooZenIeZvRf+hMXP3BDM/fke9v3T0zbslWx++q28tqQZ23p0yhiwo2Ucpj2/OoliXNamDqAymY4fgdqW5kK0mm1N9zQc99LX/rAh1qUFVF94xevaPYpvx0Cvyp28ke+DUJvovvWpW2+uCnjDoh4qboBrzde+t2llr+zs50C9o6K20Ba6DtlTHLUFgWxNu/r9T65Ipkl3KQOBgKW5Kz6farkiqlrdszNNVKW7Xe6+4DpzbMqc5sKSzDsi1FWnsE+RxdckQ7x3LJAfmXRy+z7EPgJRFZpWSFoPbfM5TK5hScVuuU3J6J35hbZc8h2yPkS22C23UuqVqx1VGjhg/4omV2Ot9eE5H9vjSSgHFCVSLG8Yu3YvFm98ksEoVBW0JIyqgMhfHd7PXYmF8ae2NC0hi5z5Jo1a0YFPl9ensEuTNWPzsj8h1VQRa32rF2l/UJiitCmLrCWf2wN6Pr0LsqbW1ZhcWSPDlhTuR/t4GmbrCt1g9Rjm5tGuKbm45Bv+5tPHna6rCr4qy/46Ezu5mvg6Fw1B9OCiSIwVFBiTxoSa2iaG8ilUpb+Z5oFMMbdq0SBEpULPGN8csBQJsg0AlL0FZzP7q1wWrgTA7EllQE8dmMtfi/J37DuMVbbN/dWuRsPwIkJlGRWr5Yx4wHL88m9fjrd5U4bKn/juppq54TVZ2ma/t2x1hFoqt7m/LtE5hO7Ziq6s7w+yx2ANVF1Eurp61kj+BwAdTnj5OVj2EY+M+wmejwwEg8O9K7RVLvbq1iKG01icikhkmer9lSGD3Pfk3UNqhRSwbDBuplZdi2lfebSk9b+feXuAZt00tpqwsMOk1OO6lyz3hlIvq9Phnrd0Xbd1GPZVHAfX0OdCyXX6orYsWOmlcglpLdMAxTpZsRsPff/cqD0qmfbve0tdftnKzI/Sk/R3ZLE0VOge9w2DCfmcKXWSbgoE5X92F7L0bD/OaEFeg/bCbOeGWS63YkCoO2hJCU8cHkVbjri7k4/eWJqS4KITVC7mgmWkQkOj+yPYJueVTkdaTzpc58xxt0TWU3Xu3IpoIPp63GPV/NTatkCmqCCQAo1/j2Rbe3fiYv0xN4sUfQ+eGp24t617x+Fnrt3ySidq2uPYLyt6qQPbpTMxzYqgGAKqVt2L7U0AzaSsGrdLqWezrpYo/gds23FZXjJyXpUCJqSH5Jhat61Qm5HdcNgt3OqcVH0zBQKA3WQ2EDj/+0ALtKKvHkz9agWEFpJUYvtAdy/3tGV/P1xW9P81R+N9QBvJytvKZ4CtoqGy3bujum8lN+horgoPBCVZ+v9bKswQ5d2+ektH3p4kNt2wriWR2g1rlEK21l315LIjKHfavPn9U77Cs2AGDBxkKMXRxRkb890W7FI5Cv19i7T8Q7V/WyqH9VdIG+gIPS1m3pOGBd7SQvQ5fr9ZtX9LTtt7Y9bWVLCFkp7mY75TbxWxvY7BE0xdnosPpKFxjcmF+KXSWVqAiGsXJb1NNetK/i9zbOy3T1+pWrxJhFW6r2YS1crEmRAV/ONSfuMvx+m9e1enin+qG2DbogsVDayhNKst2CHNBvKSnEK6UTLia+v7v5GADAJYe3tUxiODUjOqVtrNUUz/+62HytszIhdhi0JYSkDLFEIpYCgdQ+I+dtwqw1u1JdjDpDcpW2kf/9fp9WwSN3JMuDYZRVhmwz8fErbW3vxPX9mpBs3zcvPPLDAnw9a73ZWU8HtPYILkuf1c/UhF2ArAhzPuf65aLq0tdIp1tWalTXHsGmtFUG336fz/wNlaGwzdMNiCpg8kujWesZtN07kBV+bs3elOV2RX9N7RFKK0Lo88pEbKjG6qF5GwrM17o4iqpclJHrdnFFyPJ35JkQ+a7s8QwAYx3at38etZ/5Oh61sBPqoF7+rTXFy/NCfR6u2VGCy96ZHuM79kAdEHn2qm1JrhK01dkj7NYEbfdvlmcuSdY1T7p2uWNzfUIvNWibKKWtuHayqlTOeu+0b3VliJrMUuDVDunnuZsARDzZOzavB7/f556ITHO/yEEy+dzKwbvKoP33iOXofiUQLn76gNMOwOHtm5rvC2q7HyX78Yoyj120Bc/9stjpK64WS7WBTWmrfP7OxBWO39VVPbm/ICejE8Fa0Y+S67AOnXjAZo8gWQeolAdD+G52NHFkRsBn87qWj/DMyEWelba6IKkI2sqTf3J9kPuD8nHkfYu6/X/7NcHiJ8/ACxcdajkPzuWz32vxNDn5JclLVLknwaAtISRlJDN5E6k+q7cX4+ZP/sKFb05NmMffno5clxN9ykSHKOCTlLZSj0j16zvjlYmYudoacI93zFbbChGZeLK/J5sbPppV6/6LTujK4XaubInIlIRdALT1yVNZlAohVGQNcqID++raI6htTpmSGKY8GDIHF8GQIQVXpN9V9bk8GGDQNrlYr1sKPW09Km13lVTY3qtpn+SvtbuwpTD+pf+qH2k8vo6A9Xeqwbslm4vM18I+R91WtZGIpTyMl2S2oV52rUu29Pd698BxUArayoELwzBsbet+TfMsf+vavqIqcUK/Hm3M9yLPdOeJM139bdM4R1veQiUo7PclVmlbT6o7ASnZo9PScFXBWREMaycevSbrW1xVj/se0toMssqWDSpapa3FHkEK2krb6FSpQrXq90XthSpDhtnm+WBVHztZiiS7H/XJH2vN1+L8/2e4u6o91c9Ftf7I98GmglI8M9I54BxLnS57+VaGwhGVfFVbkBVwb+N0Klx53727tTLvXd053KZYwOhWyu2UnkHvTFzp2JaNnLfJ8rdqfwZEJ47k+y7o8CyUg75ykFfuG4oJePmecaorOoeNeJIfptpXua7AoC0hJGVwCJ2eyANIdSBA9DjNXCcCMyil8cQCdMsQS2xKr5oGI2rzXk2noC0ArPPgf1gb6AM5LonIlPMogv+yakwEN92qrO4ztSjCt61hTjT4E/Cg4tWhbq5bIiwGznIiskyNp628JFlNOlXbzN9QgAe+/RtbC/fMRHtyu5daewSpLY4zKVBNm25dIHjplqKYk5/qoFX3DHG71+XfqQbB5M+aKInZhNflaQe1wuVHRtS1Z3ZvrVXX14RkBoa87NupHrit8gpJCn6fNFoOGdGJoltP7owfbjkWzepbE1Kpijog2kY2r5dlvhc2DPNc6+ujJmir+OcKbJYcvmg54gmgyMhWEJagrZTsUdeshsKGVsmuS4jl1UpE1H+5HPEqbZ38OeWzU6a5z8TtGfD5zOdMMBw2r5nf77OsMhHHtnna1mJHSjwX1Xtepbp1I1Go9UduK2Op/HX3hzzJK1tqGFUKedFfznSpO4B+hVGJ5Ms85IqeEDFO3TksVSabMwJ+S39r5LxNFiUw4Dy5NXzaGsvfbvYI8ooMJ79iue7rlLYyfqXt0+9Po7SNo81PcdeszsCgLSEkdTBqm5bIqpJ0C6ClK3JfJt4g1fSVO/DUzwsdM/yKfpdloCQdwkswKm5f0RTem6lWfqhs320PxCSTsYu2oP39I2zv6zrMbvenuow3mogsPnsEXd3xpLSt6mHqOu+hsIEXfrUqaJz8ddWgbY+2jaNK27CciExWEOt8eFNbr856fTI+m7EOA39bmtJyJAv5uqXSlbrCYo/gfM11g/JYdSRWO1pSYW/DT395In7T+MbKqErQeJW28vdXbLV6h8rBg2KlfOKzelkBPHr2QXjvX4fjpYsPTbiveDLbdN1SYa/H/3zGWu37kf1WrXDx+yxqM8OIHrNbm4Y4tF1j23d13vPCHkEO8IYMw7UN1j3aneqoqtb2IeorXt3zL39N9u31+yTrBU15Ppi8Co/8sMD2vm4CrtTFl11G3NdyoNYtEdmu4shzu0FVkDcvKwCfT68QlhWJun6YqbT1+0w1YjBkmNfC54PFX1fcV7YVS7U46AkbkWfvyQe2dN0u1f0tu9I2+jqW16nu/pDrmFq3KkOGZI/g3sbp2kBRD965qheyMvyuvv3qsyDDb1XVu1lWxEJ3zYTSNugQkHX6vqwi1yXhs7R9DreqeP7886j9zHYinv5WqicO6goM2hJCUkY6eFcSO7Ixfao7dHUF+TzFe8oue2c63pu8Cu9PXuWw7+jg0QyEyYnINB5sKjW9jMmyydDt18sgvDZRFRPJxmk5o26AogZyjurQFGPvPtH8Wx7A6BJ2qfYIs9bswt1fzsW2onL8vnQbthSWeVIkCrWUrLT1aSYYBJ0eHIkhE6xedTlVg16bp23V+T+sXWNMuf8UBPw+8zfI9gjWBGv2Y+qWSNcWspLyzzU7U1aOZGL1iExdObwmItO1M27N3H1fz8XxL4y3ZJpXcWornNr2aFlie5CLHAA65O0/nxkJRIrgltxGqEu/i6uUY7lZGcjJDKD3Qa2Ql5VhC1jUtP1XB+VuwbZ48aLocnqmzFmXb/k7FDZw3Yd/4vCnxphtpxxsASLnWgT5MhyWWOtWMAhVb2NJ+RgO6/1vzfJoA7n636IqrH0+eSKsetdPrpeywtXvjwZzdEu1nx65yPYeoJ9kVN9zmrwWCkk5OOrUzhSUVGLQ6Mjk2BmHtMbwfx+JCfeeFCm7xodUViTqJkfEbwz4rX7q4lL4fZFVUELtKH5DKpW2QKQvX1513g7TTC4AqZ/MVOtz2BJMj7w+6cAWlm2EnYuu7JagrZJssCIUNveZGaMNUuOX4bDU16iqA272CGrQtiIYtvS3dCpxr9dC156JRIEWewQHQYclaKuxzZKR2z6n4Koo96FtG7meEydSXQfrCmkTtH3uuefg8/lw5513mu+VlZXhlltuQbNmzVC/fn1ceOGF2LLFOlu9du1a9OvXD3l5eWjZsiXuvfdeBIPWm3TChAno2bMnsrOz0blzZwwbNsx2/MGDB6N9+/bIycnBUUcdhRkzZiTjZxJCJNIsNkOqkJfXpHpJcV3B6mlbvYq9fOtu7fvm0jxp8GgZcHi4RjVdop6sW1XXsUv1RIF6/YpjZBlPNE4KEN01VAe8JxzQAh2b1zO9K+UkL0Fp4ClQvRf7D52Bb/5ajyOeHoOrP5iB0wb9brn2TkEAndI2XnuE7KoBr9h81pqdmLVmpzkAPu2gVti3cWRpsC4Rmfy7AprRfCrrlayqaZyX5bJl3cUStE2lp22w+kFbtwDgl3+ux/pdpfhhzgbHbXRKWyC2Z586oRBvXZV/y9ItEe/Pfx65n227koqQpSyllZH7Nk9JpKU2QW4qXy/YE+kkpl8xcek2S1KzI9o30W7ndF1//tvqFbl8626MXrgF23eX4/whUwFEvLKtCaygnSiS0alndW1kKGyY94o2EZmujjpUjcJS63PKB/egkhfkellftUdwUNqqicU6t6wfVd9pLntFyHrPFDlYcpVX2pW2v8zfrN12+qod5mu/z4cTD2iBlg1yzLID1nMitxl6pa1Rta9osC8YtnraAtHAmVPgubafQMGQYZ63S49oh1kP98YtJ3eybJPqLr7aDsvVyek+e/r8Q2zbCuSJMzW3Q0UwbN6TsVYTqJ62wXDUKkT458v9J7XfKNpW89ihsGUiOzfT7sfs9TbVtQs5NbRHcPIxl9W3Tu1I9P7wOVpuOfk8A8n1PN+TSIug7cyZM/H222+jR48elvfvuusu/PTTT/jqq6/w+++/Y+PGjbjgggvMz0OhEPr164eKigpMnToVw4cPx7Bhw/DII4+Y26xatQr9+vXDySefjDlz5uDOO+/Etddei1GjRpnbfPHFFxgwYAAeffRR/PXXXzj00EPRp08fbN26Nfk/npC9GCa5Sh6GYWDUgs1YtzN+P06viVzqMiPnbcL5Q6Zg1fbi2Bt7QD5N1Z01drofxP4CvqinrWXZu4cBddzJoGopEZkucJJqpa16fKcBWLLo3a2V9n2vPpc+nw/7N4skxlmxLToRYNoI+O02AqKO2RS0ZUFLvRR+fmpdjQYkoioy3RJhN4SCyjAM/L0+Hxe+OQ2XvTPdzHgvewbKCXZCpoLY7tUrk8oleLLSsvu+jVJWjmRisUdwGA9vyC/FyHmbkjpIk+9ft0uuG0R6Kdbucuf2oMRhgieWslRtc3TlyHbxYAxbBuGR/1s0yNZuu2FX1O9cBJntQVtVaet4aE+obZdYul0TdhZX4F8fWAU2TpMFXp8pO4ujVjgikBfww660jRXoUHxqnxm5CJOrPF7rZ0tKW8MwV8949Vh2asdW77D2ZXw+uZ20b18RDOPFUYsxQglcy8jnzeJp63e2Xhg+bbXl76xAdBm5boJZnXhUPfoFlaZ3efQ+uLBnW+22sm/4fWccaPnM9CGtKrdhGJZjqj6j8rZycreI0tYaABSBOKH2VK9Ush9BDZREg8GQYfYRsjP8aFY/G1f9o71lm1QvTbe3DVJbJp1f+V4TfRitp61Lf80wrJYWbqhtYChsX9Ujl8nmxV9hrUeGYZ3IVpNCive9oGvPRN3zMn6zWIMo6mEdsRT7sgLZqd/nmkiTsQBPpDxou3v3blxxxRV499130aRJdIa0oKAA77//PgYNGoRTTjkFvXr1wtChQzF16lRMnz4dAPDbb79h4cKF+Pjjj3HYYYehb9++ePLJJzF48GBUVEQevG+99RY6dOiAgQMHolu3brj11ltx0UUX4eWXXzaPNWjQIFx33XXo378/DjroILz11lvIy8vDBx98ULsng5C9DDbTyePHuRtxw0ezMODLOXF/t9KyvGbPu0o7dpfj5k/+wuy1+eYSupoiB7GqG+jO0AzsQ2HDHFT5NdlnAW/XSJRvxqqd+HHuxrjLlqxaoCt7qicKVNVbIn2dNxeUOSqqBbpEKIA3n0sRqGnXJBK0lZdy6xJ2qXYbHZrXs5dHGsSK4JM9uBtZmiuryHRJ84BIQi4dIigbNiI+z0BEKSKUdA2lfYtBW2VYH0DRBVNSaY+wN2BV2uo57vlxuPmTv/DNX+uTVg6LPYLLYFCnQqrpMs38En1SJbeESYDGHiFs4K+1uzB1RTSZkxpYBYCOVferNVAdeZ3pMAiXlYwiaJurBm2V+6em50XXprutEKkMhbGsSjHsxFLN5zNW79ROVLs9U+T6Ir8WMZvsjIBFeRwOGzGXWMsK1/JgCO9MXGl+1tSWiMzZF9OrPcLf6/NtzwIfJN9Zzbket3gLBo9fgTu/mO04YRyyBG0lT1spaKvWjWFTVlv+zszwm30bbT3QPG/LKkO2MolglDwB8uR5B2vLPXpRRHR17mH72JLEqStAvEzSWvyNpWC1+KaoK+IZZnraKr8h2ZZw6m+pDIfNepGdEaj631pnw5JiOBWo9UertA34rAlUXSaEdb7J5r6l/cdS2qpdiErJP1/UAXkb9V5VJ/A6t6xv6W/pvLC9XgfdfbRvk8gqJHmi3mmFhyFNmgU9ePzGWjUVVev6HSdz3IK2qVZ71xVSHrS95ZZb0K9fP/Tu3dvy/qxZs1BZWWl5v2vXrthvv/0wbdo0AMC0adPQvXt3tGoVVaX06dMHhYWFWLBggbmNuu8+ffqY+6ioqMCsWbMs2/j9fvTu3dvchhCSHDi5ljw++SPia6cuD/KCJft2ggNoWwrLMHtt/GVKFFNXbMcRT48x/16zI/FK2+rWa12n6ePpa7BwU6H5uc4ewUmZAkQH7qKDdsnb03D7Z7Px52p3X83aujd1ardUTxQ8o/jx1XRpsMAwDFzy9jSc+eokbMgvddzOyRdTd1pE2RrnZeKaY9rjgv/bFwDQqmFkoCoHbSuq7ms5gKQqbXXZ1LcWRfchghRq4EBrj6BReRWXB3HW65O1v89U2sKwqFTEvls1zDHfE6qQoORRJyuIdfYIsZao1xapnpRIFnKdUJeWCkS7osssnyiCHp9fuiX6XoJXbujuHyB2gECXiOyCIVPxz3f/wMaqtkINbp1+UCvzfpy5aqflu4CzR+GukqiaNJqIzK76UstTE3Tfd1q6CwBPj1iE016e6GpFsa2oXPv+Iz/Mt73ndv3kjPByGyGKnC0lHAIi7bAcxNMh+9SqCcI6ShNjobDhGoDS2yPogrb2iTCfT7IC0Pz8dTuj9crpGScnKWqYG1UIB3w+R+sF9b7KlpS2uokz9djvT16Jrv/7FR0eGGl531TaSs+vvKwMTHvgFNs+Jy6N+D//tsCeAFC1BFInZXXnQvaulf3U5feBaNC2pEKvtE22UsV2LaRrK56v2ZnWdmHCkm04/KkxGBMjWWKycFXaSgrnTE2iUd1trSpc1X2LwHmMPGS2iauQxj/fyTqgtCJkBo/3bZyL968+PBK0lfpbukdCrL7vhvxSlFWGtNv9X7uI8HHdzlKzXXfbX1HVs0qXyFXFTN4Xwx5BXg1oV9o6B9PpaeuNlAZtP//8c/z111949tlnbZ9t3rwZWVlZaNy4seX9Vq1aYfPmzeY2csBWfC4+c9umsLAQpaWl2L59O0KhkHYbsQ8d5eXlKCwstPwjhMQH7RGSh9MyTS/IHetEq9P+9f4MnD9kqi0BSG3xyuhllo5eopa+y52O6nZAdEu6X/g16oUZcFLaulyjTEkZKQcUdkjLQHWoe0zWvarrVL42dhkG/bYkKcfzwkfT11j+TpTStjwYxtqdJagIhV3Vtk51Um+PECnb2T32wWPnHGx2vJtUqblk5Z+8TFJgDiLCwJiFW7R2IfJhnfzKikylrd0eQa476hJeGXkgplMbt2wYVUzJA+egMpACHJS2aRIsTZdyJJp4flYyT4Gs4HS3R7B/6LS53Aa4jfWdJtBiPRPUOiH/vb7KzkDd99tX9TL3O3D0UvMeFF91UtrKgUnh160qbVVqerm0NjgukyjDpq4GAAwev9xxG6d2crwmYZur0laqL7pAcnam3zIJETb0yQ9lAlI7KXvNXntcB7NtBoBdJZWuCRu1yck0p00Ehi853GoXYJYjhtLZaaIw+jv9aJwrt+0+M9mjfG7LKkO2c5iZ4UOmg/oOsD9fP56+VlsWcb9mKfW6TaNcbZkB4N4+VmsEAJL6OLKdOpnn1if0+9REZNYA4PpdEZV3/2EzAdRebgCBGjCvDIVRXvV7RLBWtWr54s912FFcgWs/1CdATTY66xT1swy/z7LSJmopEqfS1oiqOp0mFgXqxxGlbdUkhrBHkDYSRbn5k1no9siveOSHiHjwqI5NcWqV5ZVP6m95uRdkJi3bhuOeH4frPvxTez83rZdlXtsdxeUwpHZKx+2fzQYgJyLzYI/gUDzZFzfg02/r9tv21InsRJOyoO26detwxx134JNPPkFOTk7sL6QZzz77LBo1amT+a9euXaqLREidg8108qiJqEzu+CUyEVlpRQhLqpY0znNYIp1s8kutwUqnAEpxeRDjF2/Fi6MW43/fz4/pv2cN2lavbLpOU7E0mPJL6hb5eG4KwmjQ1sDO3dHfHkNkUGsTKk4B59fGLU8bZaSbkjke5KB5pksHWeepB+gH3hWKisbcvxTUFIiEJGKZJBAdbIYMw9OgTQwK5PoRDhvmb7MMrDR1dafLZIG8dFA3cG4hLXM1fQXDYXPpbyAQI2ibJvVJt1Q5HXnsxwU4943JrgoZmXgmq5LZulhWiiTIHsFrGxByaM9itWWLN1mFH/J96/NV+W6qy999Pst53FHVvptKWwfllKoIA/TWCzJGDausl2XxOuppfB8FZQ5BgHgnbGI9S0V7KU+YirI7nWPZ8kBYx7RtkouHzzrIcVt9gFYzsaDZbldVu9pESnLo89kDlDJlFXqFsYycvFL2SK4Mh7VLpkctsIudMgN+s23W9Se93ls6T1uB7BP+4LfzzNd9u7e2bavaOqhBPvHsGXjxoWZiMfm7mX5NIrKqc1FoS6JmPe/J7FdF/Fqt71WGwub5FfXYTVFZm3w3ez2uev8P3PnFHMv7OqVtQFk14DbR4RZ0D0uetjGVtjpPWyXAKbc14lkzcp71HpDbVrkN0fV9nfp+APDNrPUwDGDSsu3a+ygj4JMS4YVjTg7/XqVGj7VqAJAV+/p9ysF1J9sUV3sECrg8kbI7d9asWdi6dSt69uyJjIwMZGRk4Pfff8drr72GjIwMtGrVChUVFcjPz7d8b8uWLWjdOtIIt27dGlu2bLF9Lj5z26Zhw4bIzc1F8+bNEQgEtNuIfeh44IEHUFBQYP5bt25dtc4DIXszbKeTR00CA/JgKpGqsO27o8sZQykKoKjZvZ1meJ//dTH6D5uJweNX4CPJosAJuS5XtwPi1mkChMm/vbPqJWhrGNYBk5saAai9BBpukwKFpXp/SCASVP98xlrb0tNkUO7SkY6HYiloW+lyXzldG72nbWRb1TNTBFfluqEukwSidc7LYPLUri1N+wO5815cETTro7yENhowiO5Dt4xXYCYRMvSDFzkgIdROoZAUQLF43tnvpYoErxqoLnVFaTts6mrMXV+AMQu9JeWVJxVi1adkDtLk4Hy89ghOm8sBU7eSO7VnsVRG90uBJnU/fp9znZFrufiOOLVOyin53DslInP7DhBZprt2h/ckp6JudGxezyyXlwlhXbIeQblDOylPHKnH15ct+loftFUyxYcRU2krB2fEc0xehaDbVndLaAO5uqBt1YqKxlIbCfhcgy1FHp5H8u/Mkyw0KoJhbUB4/S677U9WwG9adWiD9y73htyOiPZbF7R9/sIe5usv/oyOx1s1sIvCVFuHp0bo7ZAyAj5bYruAP3ou7/h8tuSP6lR+5W/9ZglhyvId5mvTjiJsSJO10fM2/N9HJrEksQmGwrjri7mYtExjkyOdpKiHrFUZ6zbR4aQaB6qUtuY1i6G0Vf7emF9m87SVdyHqU+M8630u3zcBczJHX/bHflzgWB557KK7jzL8PtOeozwY8rxKUuzLyZ8bsNq9uO0j4PdFrRTUoK1LP5pBW2+kLGh76qmnYt68eZgzZ4757/DDD8cVV1xhvs7MzMTYsWPN7yxZsgRr167F0UcfDQA4+uijMW/ePGzdGu1Qjh49Gg0bNsRBBx1kbiPvQ2wj9pGVlYVevXpZtgmHwxg7dqy5jY7s7Gw0bNjQ8o8QEh/JMuUvqQji2ZGLMGuNu2/nnow8yItXYeZ10Bsv8oCoJEG2BPGi/h6n3/fhtDXa952orj2CnFXebXkSEFl6pyaOAqJBdlUVAkQ76vKSTvk7TtRWH8qtfhXZVCtRhk9bjfu/nYdrhs5w3CZRVIQSU1dlpa3bQNUpCK8beFcoSUYE5hJO6fya9ghSPZEVK7Ey3N98cmftpIG4TpkBn2K9gKptIxvP31CAF0dFbC8OaFXftn95ezUgM/ifPS32IXIiMp0aJ72VtnVrgOJ1tYXF1zvWxkk8BWrb5hSwi0dpK9+TbgFAp3bVTU2om5yyTjD4HNsEv2Z5rvgNToNweVdmIrJMd09buTRbi8pw0ovjcdbrkzzbMIn+yNGdmjkmqtEhMqLrcFJuyYpQ9fg6YlkN2YK2hmTJ4mBBIber4hw3cAhAuwWgdMXWnTfhU9y0XjRg5PO52yPslp6vTm1jUFrFILep5cGwVlEn6sOpXVua720sKLUEEVXc7g3d5LSuXndsYU+g2a5prtZySn0uiUSvAqHUzAz4bUvkfT6fmZOhMmSYK6EcPbzVv5PY7l35/h/m65yqOvvCr0uik7XSc//EA1rgpANbJK8wMXDy/ga8KW2j19D+faFsd0JMBMRORGb9/MI3p0pWTJHyyPYI4h5Tk1HKbZjP0obY672bOEQuju4+Cvh95jUuD4Y9P7crJTW9E27tiFweOVGfOnHrtmInTbpmaU/KgrYNGjTAIYccYvlXr149NGvWDIcccggaNWqE//znPxgwYADGjx+PWbNmoX///jj66KPxj3/8AwBw+umn46CDDsJVV12FuXPnYtSoUXj44Ydxyy23IDs78tC+8cYbsXLlStx3331YvHgxhgwZgi+//BJ33XWXWZYBAwbg3XffxfDhw7Fo0SLcdNNNKC4uRv/+/VNybgjZW0jWKtEXRy3B2xNXov/Qmck5QB1AfrjGu7xbDvQk0tNWHtC6LQNKJmq3xGsAxU3NWh4Mmf5QQHz2CE/+vNB8rXZMVUbO26w1+RcDruM6N8eV/9jP8h2hoAob1o5evErsZE2wuAWP3VS0L1UF/2avzU90kWwk0tPW3KdLfXKqa7pLJvapKm3FwFYODqtZpAHJHiFs4NB2jVxKb+2Qy/UvmoQsU6uGEZ33mz6ZZX6m+hACMEclOk/bMw6xrnyKBn6igxPZckI3WE+VwnXJZmuW+0T7hCcbr5NQ8UxcJTOLujpYdSqLHKg6pSrI5FRsuQ1w+21OA2U3lZGunZPvW7/PuQ2SB/KiVFF7BP0gPKQJstXL9q60nbV6FypDBgrLgthaqE8G5vR9OZmml0eQW1xFXQZ9dMdmAPT3l9vzLhSjr2TaI0gTpuI6O020iiX0laGwqfzLkdTMD53ZDQBw1T/2d1Wx6e0R7McTQVtZaesDHIOlZZUhy6So03NY5xcOAO2a5EZVvNIpK65K6iavuJi/odD8vpuP5wU997V9prOuyMqwn3NtEtf/HKX5RbH9OUUR5bpqftfns/TvdlcFCMV2D/Ttqt9pFbXV8otg8phFW1BQZQmmTsrqknXWFuqKNxmdp60ap1f7FgLDMPDLfOd8RGHJQiLWz9d9LuydRB2yJiiM7FhONAjo7RFCMfxmBYe1axwtjzR60T1PAn4fcqraqrLKkKf9y763XjxtnewRwmF7+642pW6r++raRHaqSA9jEwdefvllnHXWWbjwwgtxwgknoHXr1vj222/NzwOBAH7++WcEAgEcffTRuPLKK/Gvf/0LTzzxhLlNhw4dMGLECIwePRqHHnooBg4ciPfeew99+vQxt7n00kvx0ksv4ZFHHsFhhx2GOXPm4Ndff7UlJyOEJJZkNdMLN0ZmK+3+UnsPcic93qBTbShtvfokJhpVEeH197mdw6/+XI/V0lJRz0EO5dhqgg01O/YVR+2nHfDKChRVhZJXpe5RlbYxA1jKx8lSiLidf7egbW328dy8uOLBqnR23qdTXdPVKydPW609gmaZpDz4cVq+G91Wn1wsvypg0EBZlmx23qu2FdnKAeDCXtaEOZ9ed5Rl32JS54ULe2D1c/1sKpBoEEAfuNbFqxIVfI+X/ooaPJE+4bWB1+LK9TtWe5FMxZkatHMaaIo28NrjOqBLlfLbWZUrPRPj9MkF3CdpdO2cXEd8Pp+nideo0jbyf4bDJKD4jYZhmAPpWInI5LZnU0GZ+dqz12/VMf0OyTSdcNtEDdpeckRbxzK5PWfkY2jtETKtSlvDiHoXO0205kiKN/McS0rHa4/vgLF3n4jHzzlYu3ohWjZvgVyh7lM9bQNSoFmwpbAMXf/3K36V/Ged2qSgosIbcftxGNb/CHRsUV9rjyAsgFS7DVelbVW7XC/LrkS2PDODzkpbnUrQahURRX0uOZEZ8Nsm+dXjiOCjePvEKvVqs6pkczZ7hBQsA690sJXQTWzWFu5BW7vSNsNvVT37NNsC7n1GwGpLEK/SVkb0P+TxhKhPzetblf7WoG30Xhf3lpo8UOaJcw82X8vF0SmV7Urb2HWtMiRNQDlM8kWO7T7RISdok4UAMm7XnEnJveG+HqaWmTBhguXvnJwcDB48GIMHD3b8zv7774+RI0e67vekk07C7NmzXbe59dZbceutt3ouKyGk5iSroU5U4qC6jM7L0ivyoLcygQGGCk0AqbZR65zXAKubInS+klTNMCLHiZWdVu1UqX2sL/+0eqVnBvzaAa+cFEXtmIsAnZpJNlawWlXCJatP5Ra8zK8Fv1ovJCrYZwmau9Qnta4d06kZpq7YoR1k6iwPgEjGbkC558S2mqBt2ICZfd4Jv8+n7byPWxyxqGrfrJ5te7FvlZ77Nbb8fUyn5vD7lprbmxMRGlUVEE06FgqHtYNSfTKi1LQ5G6UgF1D3VCVe20irr3di9lkd1DbF6bKL7fZpnIttVX7rTuWWn6Fu9ghO13aNi/+rLtAg2yP44Pz8kZ8xos0Wz7hMJ6VtVRnLKsPmNcvTBMwsSIeXVfBe20ZTMefzmcEipzogn1+3eqSu1hEqM7ltfWnUEnzz13oc2aGpc9k0ak4ZV3sEh8CXUOeGwoapaFWXSXdqUb/qdeQ9fYDWvm/deVu1vRiA1R6hIhi2+bcCwNez1tu+XxnUn2g5aAYAB+/TyPxMl4hMBGa6tLTa30Qn2ew/qLzqR+Zp1N7yvt08bX2+SLBIri/1HCYivNpzZAR8NrWlGugsrlKqi+0y/NbJUpsS1PWIiSMrw2+7N9WgnJeY7ertxXjr9xW45tj26No6cTaQ7r6zUj/VsE4aCHzSBIrMhCXbXI8rJyKLpbR1Oz9yeQJ+H0Jhw3zOqKuEcqT7XrYOEL+tc0u7VZSgaVXw3+ezlldNqAxE6p5od8o9Km0rQmFpYsZZx6m712Vkta6TCtrtmtexLlHKSGulLSGEVIdULb1PJ+QOW7xBJzlQ6zZAjRd5yadbhtdkYBgGrnr/D1sAxeuSaTdfU90kgZeYhNqpUsuiJlRp3yzPkshAEFXa+myDyKinrepzHCNoqypEkmaP4Fw3S1x8z5KFbuCclKCtZvAqzoV6Tl6//P+qyhYt33uTVuLFUYtRWtXWqUsfxeBRvs5Rbzs5eBD5v7C0EjNX7wIQUXQ7oZs0WLolsvz/9INbabcVZRbqs8fPORhtm+TZ9i2Hn8Q5cFILivsgGDZQoUnGplPJOAW+tu8ux7MjF2HFtt3azxNNXUlEJvAaX7UO6OJrXxKJfTJMf7CgGQSKrf60KG1dmgM332SndkTXBsr78fnsHtiiXZdruWhfTKWtk6dt1W+U/WjdvGPlfQLW/lW8StuAQzJNGfn6uQVW1NU6Ijgi2tbi8iDeGL8cmwrK8MOcjY770U2AyohAiJlgJxxbnSZPoomVCE5qZt35ENdfN1Gn1tE56/LN17K6tKgsqA1Q6p5xTpPzbt69WqVtVZ3Ky84wfcufv7B7VGlbdX6Xby3CJW9Pw8PfzzPrdp7GV1m3OsXJq1l9Vjh5dHpNvhlRd7oHOsU9LbYTz2Fx3mxHqKWmP1Pz29XzFivxLQD0HzYTn89ch7cmrEhY2QD3pfLyKdIluAKsNgMyd34xx/W48tYxlbYu50e+H9SApipIkSfETCsUBy9+lSxTdGHd765i+0RfwO+TkjwanvoZFZIiV1dn5H0DzhMdcnDdyUrBq7qaOMOgLSEkZSSroZaT2KRqSWyqkX93TZS2sR78FcEwHvj2b616Q8XqaVu7QdvXxy3XZqoNaQZpukC1a5BT81GspXdAbO/FXVJCg577NcblR+1nDg7k/Uc7XXal7T5V3qGqPUJspW3t4Fa/3Dr2yUJXnETZI8hBBvV3/zp/Ew55dBQe+3GB7TN5cBE2IkHSp0YswuDxK/Dn6p0ArIFYQPK0lYIq4p7TKW2/m7PBfG/AaQdgyv2n4MBWDWzl0AUYhCJaXRYoD+oNwzDbJNWfVi1L2IidnV32SKzQ2SNovuf0LHhmxCK8PXEl+r46Sft5okm00ra4PIiL3pyKZ0Yuir1xNfD6nJbbpFhfSWbcWg2cOtsjiMCb31EdJPDqaSuecWccbK/jTu2ZLlAoP28MacKtXlYAL17UA2PvPhGANagpHidmIjKHe0c838QgOifTHzOAI//m8uoobcWg3hc7QC7fH26lUifnRaBUlMktSCAT1gQGZVT/SnkC1Om8yW2s8Jt1SvamKm0nLt2Gzg/9govenGq+17tbSzx13iEA7O3He5NWmq8bS16yJRUhrUJOd9qd+jchl98puhry/SWSm9XPzsBvd52IVc+eiUuP2C86iVi1v/cnr8KMVTvx8fS15lLv3Cx7SEL+qWY77xC0lcuYFbAHXAVy8N0NndJW9YEV5020HyL+JvZdW5PfKrqAZKYtmVfsoK1QcE9eviMxBavC7dzL96Nsz3HNMR0ARJKomdc2ztNpxKG0dVspJ9c1tT6pSludPUJIakMy/D60aZQDwO6HK9d1eb86e4QMv8/s9wVDhnZsAwBnH7qP+bo8GIomG3R5BugSIMuEpOvkNCnnlrTSy3iJMGhLSJ0nkUrI2iZZ7bT8rPWa3XhPQ7cs2ivy9rE6tiPnbcJnM9bhnq/mxlQuyGrV2lZDfzVrXeyNqtCph9yCi+Wa7b0EZmxKWzlYHgpj0OjIcvHbT+mMb28+FtkZAe2AVwxmMjN8liWxH1xzuBnIkgNhsX6PjqTZI1SVvUvL+phwz0mWz1KhmNcpYBOxrP7bv9bj38P+jO5T6VA/X5XledjU1bbvyoqPUNjAhvyoN6wITKgDWZGsRQQhfluw2Zy00CUi21WVYMPnA5rVz8a+jXNt1gR+vz4BiPBTlAMGkX1bgxyiyjkNuuWEPLLlhw6hTgmFDfN+zY4VtHVQBX47OxKwrq0JvkQnIhsxbxP+XLML70xcmRTrBa+7NOII2iZrWmji0m2Yu95qV+PsUxsdNJvLbR32KwdW3c6xaCuuOnp/+4cOX9PtT1Y+GtJ+c7MCuPjwdqZSXe7rhIzI5Ig4906D8KjSNtJ2xLRGUIouK77itkdwSGYoY/Xzdd6nGpSNKm2rFG8e+z3y6dcFbcVkl2w1IIquBsKi5Y4WfEpVwEsXlJT3K8rx4bQ1AIA/1+wyz1u7pnlo1TDHVl7DMMyM81kBPzICfnRsEQn8nNm9tVYNqzvrX89ap51Id0u4ZgaEpX0XVlnsNKp6FojzoHrayhOhIqido1F7i32Hw1H/ZZ2NglpGN39OM4FaTKWtPRGZqr4UzxTxtlq3a8tmSu1761Si6jnxorQVOFmtVBe3sYJcv8PSZE//Y9rju5uPwdtX9Yqu4pG+J7dFTj6xhhGd3IrtaRt93UdZRSSrutVrblfa2vtbhhENqvr9Pvzr6PYA7HVSvo6xJqHktjUorQZQubhXW9M6pCIY1k56q/g1kz8y8iSWGeBVnmteLTGIMwzaElKHWb61CIc98RteGbM01UVJK+QOs1e1xZ5EOGxYBpnxBiPk7WMF9+SkJMUxznWF5JumzkYDwODxy3H/N38nZSLiwp7OZv8q2uWqbkFbzWDHizpNHazLfm8Tl0W9ueRji06cvHvhmdcgJ9MS5KqXlWHtJFqUttFj7dhdjus+/BPjq7xJxfYyyepSiUFPVoYf7RWVgVc19t/r8xNWHl0AJREW2QO+nGv5W61jbn6yaoZiXefX5mlbVQ+ED+D1H82KbqtR2gpfzf8c2yF6XNvSUJ8UWI2+L5b/NqmXpWwfLbPcpqhlFUQVZ5LS1mGwGJAUTeakRcD+u2R0Ptqp8JdN9DG3SG3w7iQk3/ScWFEz2HYiWWO0B76dZ3vPeRl+tN7EUn/KE45uwR5ZPaVaDngJUgrk9iFsGLaEUAI5o7gcTAScJzyiSlu716oT8jNZDoa62dvIWJc5i2Cffluvkxq7y61tpuppG29A2enYoikRp17eb8BDMEtMsjmdZ7mtkpWAgFWhrEsqtnJ7MVZuK4bfB0z+78kAgO9uOhafXnsULu7VzrIiIXoMexm+/HM9Xh5tH0e4+V3qAsKFpZE61VBJapkheZAD1qCtSF6Wk2E/P+L3F0vii/rZ+kkG+Vo4WSgAUpCtqgi9u7XUbqdNRObz4YKe+5p/i/MTrSOKylBV2laj3TMMA8u3FrkGvdT+qS4ga/e0da+7ch/QSxsRD26nQa7fsgrU7/fh//ZrgpzMgGWCVyBPyh7RXu9hbUjfiRWzls+P+vstnrbKNVcni9pUrXaT9xmWPG0z/NF7W21/5D6Y2/UXZRLB9WAo7NjPCPh9ZoC2Ihg2xRHZmvtPLYdTcx82f4vf0f/WbeUc09B4I+6gbXFxMf73v//hmGOOQefOndGxY0fLP0JI7fHpH+tQWBbEK2OWpboo1SJZw2X5YbU3Bm1VRVlNgraxAqhyYC1W5lbdUm3BuMVb8OKoJfh85jp8OmMtgCpPunHLsH6XcxIXr/gcFlrqfp1uuaougYZAt3y+OkpbORiwMT8aiJEHCbrgggiaNc7NtAxWMjOiSwQjyVPswfhQ2ECvp8Zg9MIt6D9spvm5rfRJulmjvpL27ojXoO1D381PXHk01y2WCqCkIhh3IE7d3m2QKQ8uwoZhDnJlsh08bcsrQ/jkjzWWz+QBMMz6FPm/oaSWVdU6fp9dcfHDnA2mjYeqtI0Ojq1BW6G0bdc017K9vO+op61T0Da63FYEpmUFr+57OuVdKlZiJDohWqEU8I/VBnslbAnyeKvbsdR8Msl69ssqdIHTvRlVc8f2WZUnHN2eiUFJIf70+YdYPovlrWspW1A+//aEUAKf0jbIx4iViKzUVNrGDsjIRS+rhtJWVszFCpB7XQWiLhEWnrEVoTAMw/Bsa+MU+BGIZ6j4Xw7gO7VPOnRKUkBt3yP+4oKgZtnx3+sLUFDV5opVDm2b5KFllRK3UV4mjuncPJLJXeMv6bRE/x3JZkHgZlOjC86oSluB6bUZstY98ZsBIEdTD8W9Jq51ht9nmXTUHQNwV4aqzzBHj9yAz7ZE3u/zmTYVQLQtN+0RlMRv6pmujj3CpGXb0XvQRNz5xWzt51sKy3DBkKmW93RB23jtEWQRRm5WACUVwbhX7TnhNqknfyRuR7X+6RKRyW1RA2XSQD6u+E48SlvVj1ouj2yPsKWwzOwPdWvTEMd3aY62TaSgrWSxIk9k6RIGis8EsezCIhM7ke0rpRVI9t/liyYsC4bNfnaOw2S6tdz267Z4cyG2766oKm/02qi/hZ62NSf2mhiFa6+9Fr///juuuuoqtGnTJmZ2bEJI8liypTDVRagR8TTUhmHglk//QlFZEMP7H+lqEi8/LGLNTu6JqIOVeD055Yd9rAGUHCQoKKnEvo1zHbe1Bm2tZfpsRtS+4OHv56Nji3qYvnInXhu7DKMXbsEPtx7nufw6nDqbugG9biDqpv6proJPPbcWxUrV4OeEA1qgc8uot6hPM1ASnqKN8zIt1zrT77cEI+T9i8HQhl32IAeQ3ERBMnISNZXa9j0G9AEZN2Xd0i1F6PfaJFx+5H544txDHLdTUa+923JO1dNW13lX1avCHmHl9mJbUPukA6PKInXgovNfE/h8Pou32fKtu3HH53PMzxvlZdq2F2WWl5EKBeAR7Zti3c6ol65fGogFHYJUglietrrng+6+TsWkXqKVtrL3tW4FQ3WQ2xevxY3lm+m0bbJxDpZGk92pSfNUrInI3IK20fbsvMP2xfpdpabNje5rhmFga1G5fT+yPYIRTSqjthPyGCwkWZCI36VDPMpK4gnaSsEmi6etR4mUGXwM+DxnIo+8dt6nqiqXf4fcLjjRplEONhWUWcoh+ot39u5iiiHEGY4qbaPbO51jHU42FD5L+25YJmHkRFdye/zUiIV48eJDzVU+TgGXqIVB9D2nW09nW+Pm3RtV2kb+rgyFzTrVMNf6W9VERrrJshxNMFZUBTFRWT8nwzHuIJfR7bqYvrNVJ8IpQWUkEZn9u3lZGTiwVQMs2VJkfle1gQD0fYnqNHvf/hXJFzFqwRbt54N+W4p5Gwos76mrZAJSAD/6nvtxNxdE+4YVwTAOemQUmtXLwqz/neap3As2FmDe+gJcfHg7W/1xe6ZYlOYiKG5bYWDddkthmcUWpkmePmhrGNHvxIpfyZ+rEy46K6awYWDBxsh16Nq6AX6543jbPs0EauFomx7wRScH1AldS9DWpa+SGYhcX9G3CoXCjvVaVtqWB8Nmn8FpUkkut1qnyypDuPjNadK+/VDtIryUvy7bPNYmcQdtf/nlF4wYMQLHHntsMspDCIkDWY2xbEsRuiiJY9KdeDowu8uDGDlvM4CImqZdU3v2cYHc6d9UUIrubRtVu4x1EXWwEm/QVs5M6qYwBaxB2/zSCtdt3ZS26mB55LxN+LEq4/Pc9QXYWlSGlg1y3AvugtM5iLU0VeA2UBf+g03rZWFnlTeobvuiskqcOvB3nPd/++LBM7u5etqKZYadW9S3bKMbhAmlbaPc6PEBoSCLvFYTkYkOo06ZBmi82JKkjaswgxyRTuRDZ3bD01UJlSo9duQSOXesm6Rwu/aDxy9HZcjAh9PWxBe0VTrUroNMZVCvCzSqS9vclLuyCkodi8sDFZ09gqxSEgMUpzLIGZ51gVVV/R7NLRKtq872CJH3NxeUYf3OEtu+1bID+jZAp1pONk6Dqeoi7n8gcb68IUvQ1lt55c1iqXNrU1gTS9GZGYi9ZF8+r26TOJXSZIPf78Ptp3bBq2OXmcn4VB7/aaHWx1quIwasiV5k5L/CYW9KWzFAfnHUEgDAsq27HX+P+R0Hpa3XvoW8zF+ezNEhP5Pd+h+q0raetGw+GNYrbf2+yHH3bZxrTn5tKYwGzddXTWIKz2Ag2i6JNkUOVMejtHX2tI2+DhuGJRgt+iIBv1W1WVb128RvcFq+rsv67tTm6TwtQ6aFiLPSVlzbojJnCwP1mustfpztEcS+67n4L8ttvurHrttO3AdOdSxT6j+Z3616Q7cKRS1DyDBs7Vx1mj2nPppgnWYlmtpO6OqpTmm6Ib8U+zbOxdItRbj6gxnm+6KN2FFcAcMwPAn2bvt0NlZuL0a97AxL8ivA/fkgf+JFafvepJV4asQi/POo/QBErpuuLgFiNULkdaxbV76WDZT6LFvPyEHK3eWRet0kz2oVJZBzAoh6lxHwQdxeNpsLj/YI4nSaivaw4Whd42SP4Kq0dfCBXrK5CEVSe5KdIVsNWfdRohEbtG2Si/W7SpOamHRPIm57hCZNmqBpU71XCCGkdpEfzFe9P8Nly/QkHrVNPANduYM6ct6muMq0J6CqX54asVCbbdSJFduig7hYSlv588KY9giyWsdaRvU4uZkBy3X8etZ6133HwklpWxmyD6bjTURmDoIv6mGZSVf5z/A/sbWoHO9MXImyypBdaSuVQ3icNsixdhZ1CQGEr3DjvEzL4Coz4Ld44FoVTHbFi1tiimQFWVR7hOtO6Ih7+xxY9VntG13prptbO1Xd5ehqh1r9u0F2Bh7u1w0/33acZXBhhIESzb28fzPrJJZXBZhNTSt/5le3lepf2FtiDACWpcpZFt9Z6/Zy4Cy2PULk/Wkrd5iDhkzNQEpGb4+QXKWt3iM5eUpbrx6jsZCrvNfipoM9go7Y9gh+rUciAOwsrkBIGQC7qYJ0kw1Og1gA2oAtYG37wpIqS13ibE9EFv3bydM2ZBgoKqvEki1FAIDWDWNPhsrnRZ5w9VrfxDmTE9U4XRf5/fFLtjk+BwoVpa387KsIhbUTGP85rgPevKInvr/lWHOC87oPowkitxRGnqUimzsQnVwS7VOlqX7VK/qd8OZpqyRXEkFbn3WKK69qX6WV7io5sW+5r/He5FXm65MPbGG+1tkOuCltZfsbILLqRKDWPVVdrWt3tUpbxR5B7Q9ZjiH3fVyVttZAtlPfzu/z2SYWxflUn0umPYJ02FDYSEgiMrl4urZHl0tC/fm6SVxd3R0yfjkA4PJ3plvyVch4eX5VBMNYub0YAGwqYMD9POiUtmr9k/98akRkkv/TPyK2apkBv60ui4lqWWkbyx6hnpTwrkWDbPO1quKVrQ3EhEg9B99lefLCYo8g6qQ0RhrW/wjLNXKzR1BXJgXDhtlOqQQki5HyYCiq1nfztK0qh9qmqv3fSNDWPlEEAKVVY40r/7EfTjygBUbefjzaVU2OxUoKSCLEHbR98skn8cgjj6CkpOYeg4SQmiE/czYX6h+w6Uw8g1e54x6rfZcfAL0cDOn3ZNSgzpodJXhFk2RCR2FZpTlwAWJfI0vm4FL3wHDQRWlr93LyW5SW6kx3vLgp0NRj6+0RXL4vOoF+n9lp0nVCZEVcUZndB1X+22mQogYX5qzLV4K2kqdtIJo4au66fDz644Lo7xG+htJ16CgnAkuAQsSNB76dh04PjsSaKpWkPOBW/e9iIauLa0q8QTYvfU3d99+oGhwJ1EmM7Ew/rj2+Iw7Zt1FMpe3rl/+fzcMty0Fp9PZVvSx/q4oZ+U97QNeq3I5lXyEHYaNK24D0ubI9ovt28zoG9EEEWSni1R4h2UpbfVLDxE5GyL8hUSpeq9WBt33G853azBbtnPCqSkXo13vazly9E4c/NRrnvDHZMunpdop1kw06SxsgxuoNRWnrFDyT/7rq/Rl49pdF5t9OEx6hsIFtkiXDa5f/n2M5zDJIRZXbKq/Kbrn8IoDnbEVhfX/y8u22bcqDIcux/T5roC4YMrSTNFkZfvTt3sYShJERVhXy52aSKb84tnV1iFea19cfU23f5dMiL7+X20tRz6IqOX3ARUweyHWvW5uG5mv5ezp7BCcvZcAe/LzsnenaMkS2haUcuqCt6hsq7ztWMEwto5vdkKh/wlbKSS1eEQo7ruJxug/VpKH2Wzz+ds+ittQ8c3VJTNVnt+586FajiDq9w6VP5cVzWn4m6e5zt5Vb8ubO7Z6+TQUigUNZNf765f9nTpgYRrQ8scTCcjK9FtIqv4P2aWjZTp7IjtZTJ+U7zHLLftE+ZXLl4H0amjZWsno2FtG+c9jRuibgsyptxXZOCWKB6DX5z/A/LWM+1eYkK8PZHkHc80e0b4rh/z4SB+3T0DKxT2ITd9B24MCBGDVqFFq1aoXu3bujZ8+eln+EEOKVeNppOYAXa9BrzZS79z0MdMullm9zXwL51Z/rcMsnf+HQx3+zKFhiBc7kc10eQ3UjdzpsSlvlOJWhsMUf18kPzituyzjVzpA+0OJ8Hky/PsnzU3fe5CWDu8vtQVv5GE6DFDW48OG01eZnTetlWTrnkazokb//WLXT4p0YVdrq/QmTeddsKyrHZzPWIhQ2MLgqeCkPgIVKx2tCmvW7SvHsyEWxN/SAfN3O7N4agHeloRNe1GjlymBMPh9qsiF1mdnh7ZvY9ucUUFAVuTa1q/TaNlCSlLaGYb2nDlYGMvK+Q0Y0KYasgFEHlqavKNwVXoA+ICUHIHRjdq09QjUTkf00dyN+8bCKI167jeog7y9RSluL/3U1graxfmKq7BFWbS/Ge5NWYsfucnPJb0ZA72m7YEMBwgawYGMhJizZFt2fh2eB5f7VlAMAJizZ6rifSqmPE5aW0qrL1NVJlw+nrTFfO7UB4XD0fmxePwuH7NtIu92w/keYr+XzIk/WxJ2IzCFALqPeH7r7VrYQGHJFT0z+7ymRxFtimbGD0jbgosAMhQ1TOdasXnR58z5V/RBRbpGILDMOlS3g5mkbfR02rAGtiqrAc8Dvs0zMif5wLHsEnerthAOaA4iojuVzq7NHCDrYcgDRNjbkYQIvGtiyTxYLdIFnUW+EPYJqu2Apj9/a93FCnIn7vv4bFcGw2Waq57Bj83oaX3f7sSLvVyltFY9pdfxR03ZPF+wuUhTnv955vC0gqzsfumvqpPyX8eJjXWJR49t/tNsQLqx59tjtESL/686nqrTdt0muxXpJfCWW0la+39o3j/ablm2xjqXkIKWwMIh1P8qrJ/y+6ERWSHrP/I5LO9O5ZcQ+red+jQFEg/PBsLOnt98fnaApl+q/2z0jC05kdW1xubU+ZmcEtKsBgWjdlc+NnMSNxCbuUfB5552XhGIQQqpDXc8DaBnkhQ3Xh5O81CNWA69LuLQ3sXJbse09tw7K2h0luPfrv7WfxVTaStcw1nJ2ufMWS3VUHgxZOl41DUa4DSoqQmHLgCFeT1u5YxlRe4S1gQ45gLOlsMymTJHrqugMqUlixJhTDAZEubq2boCWDXKU4Kfdk00gvm9Z6irdYzYvtgRGWQok7+PocmKrQhjQT878tXYXthSUoXFeppk5GwDenrgSD5zZrcZlE8dsmJOBcw7dByPnbXZtQ7ycFV3QQVY76baxBm0jKqvI0j67t5maqVv9vkxepl65LZDbYFtQ1e+zqAbluvP+1UdARVbUeUkWJvu9BV28FAH9gDM7RiIyvadt/PYIO4srcNtnswEAfz92ukWRo6JrE71ORnhFVvV7TQwVC8uSXI/FlbeL9ZXa7LvI5+fh7+dhyvId+GX+ZvO9orJK7UBTnrxcKU16uiYik3wKBfJEh4zTM9e2rRF9dropbVWc1IYhwzDzIbgN1E86sCWa1cvCjuIK89oahoEFG6MJcL0+l+VghJMVhcCLEn2X6eOeiTO7tzHfzwz4UV6lHtPd724etLJqrF52Bt68oidGL9yCa45pDyDanonEo5maIKcbOiUpYJ+Us07CRK/7oW0bITPgQ2XIMNuVMhEIcdi3amEgvw74fRi3ODpxoA3aivrsYo8gLwsHogEkmagnZuRvXSIynT2DqCNmIjJXpa20WselXsvPrc0FZeazKS8rYAaTszL8Wt9W8TuclutbE5HZ28HqtPyyYjxy3qyKbVlpWz87A11bN7Q9/3QTDG5t8HGdm2sV7gAcl93LlErXV9f3dpsIlJtXMWlgS0Tm0oZkBqxK26yANSGvqP+xnkEWe4T62ejQvB5WbS/GPzo2s2wnJ7Yzvbsd2hldYmC5ny7uN/nrOkW04OnzDsFPf2/E2T0insGiPQ+GDNdEZILSypCnZ0G2w/hIvY9FQjQgGpjPL6nA2EVbzQSL8uRVNODteGgiEXfQ9tFHH01GOQgh1UD1W6pLFJcHLZ5JYcOA3+X3WJJTxAggWWdqa1DIOsjWojLT48krM1bvdPws5rm2BG3dt5UDGHaVqbUjWFYZtmxT02CEW3BGlFtMHMjZoaPlc/5t5oBUVvpotpc7Ud/P3uB6DNEZUgcpqkpJDGaurhpYyoG2TL/fMVgvvu+stFUUItq9VA9VGQKo9giRzqOu03nBkKkJLImdoBREdkq+EC+6wMY+kmei7PkqUIOVfp+vyrPSsC3p16k6nAI26uDezdNWNzCV/TnF0tz/HNcBrRvZfTHluioGnvISXHUg6ZP2HU28FI89gqy01QVt7W1AdawKNkqJYXaXBV2Dtro67Bb0K6kI4t6v/sYpXVviwl5tPZVHft55GVDHu0+vvQydQiqVmBMdUrmmLN8BAJi1Zpf5XrumeVi9I7I6Rb40skf7dmm5sFt7oFOIR1W81m292rpYBvjK/eA2Gevk6ykrbXWBOsv+laWuqo+sV6WtvBRYF0gUlFWGcOfnc6xl0PzGHbsj565pPWvCHxG0DYb0SjM333bxLBSej327t0FfKSAsiiHa6li+4VcctR8+qfLZBLx72sr9APHs8Pkik3dPnnsI7v92nmSPIDxtHdpJjaeteBz5fT50aVnfVJzrg7YuSlspaCv3Iz6+9ijNbxS/z0A4bFiS2Yl96QKtoqxvjF8BABZbD6fyAO4qaPl8V4ajSkP5+SG+r15i8U1HT1vF6sJmM1WNdlHuG6hK22AobDmXwsZFff7pzq3uGSkmk0W/84j2TTBz9S7LNl4mHeVy6oK2bnuQ+55OSltxnuWJe0FmwIfsQPRaysIHw5KIzP3Jtk/jXDSvn42sgA+NcjPx6XVH4as/15sJz9SyRBJBuu9bDjZHPW39tn69HFF2a7P2aZyLp87rbttWrtcqGX4f/lgVGfO9OmYZjuwQsRDU2aOY5ZZey88/1U/Z54uOgcR2Vw+dibnr8s1t5D6oqsAn7sRtj0AISR/k50K7prnOG6Yhc6RGHIgdGJEHvzGDg2k2eKxN5GWcamfFiVlrXIK2caiaK2MEQOTrohZH7QiWB8NWBVkNgxGFGt8vQVllCCu27cb/PTkaT/680FTxyPy2YLPmmxFE0eWBh+68yZ0o3YDdkuG5Qiht9UHbqNdbZDud11umg1okUubI9+XOdbmr0la7m2qhS4qXpSiEgdQkIhNtS6zggsDLIExXdy2+kJrfqaoe5GCpfI+3apitvcZOHXCbcttmLGsPNsnbyorEWAEDeembTml7y8md0bphDm4/pXPV9lFFYqWLwgvwELSVPhfl03tVx1+x5Xv3l/mbscsl+KZrB9wGvV/OXIcR8zbh7q/mei6P3E4mw9O2OuWINcvjJft4Talf1XbG6lsc0KqBxZpDIA9I5brj1B5ElkNHXssBU6florECpgIDhtlm2hSVLqfRTWkrfk8sX1bZwxqw30OxLJHMY1omNp3VVW//vtIMIqplkHnr90gQT00KlCGt0tB62rr8XnGO87ICripLsy1z8U0FgI4t6lv+dlLDWoO2hqVNEs8JEWAT/Qtxn5dV/cZshyRCsgpQELWqAK45tn20fJqgsi6xnkBeFi76EU3rZWltIOTgv84aIUuyKFGPn19Sge27I8FaN4GB/Kzwem8VlFZalLbmvqrOsyqMEfVCZx0EWOtqyDBs93x1Wmc56KkqG/MdEqGq5XO7fjLifhJ91aM7Nbdt46UvXmoJ2tq31/WbWjWMKIgtSluNXQAQPd9Ofac8SSW7T+Pc6Ao1yInI3H9DTmYAv997EkYPOBEZAT/aNMrF7ad2sXlTB+T2LMa+tUpbKRFZdDtp/y4FbaJMWonrHApFJ+ZOOKCFZRv5XG7ILzW3c1rVBMDynJGvjy4nQEAKxG4qKLUEbAHrfeb0bCR64g7ahkIhvPTSSzjyyCPRunVrNG3a1PKPkOqwfGsRLnpzqiePOKKnTaO6FbRVAzexBE9yRzZmIHEPfwBsKyrHOxNXYMduu+pAqBh9PuBiSanl9uCXVUcqsYIauqVMjvuS1dLKNaw0vQAj5SyrDFkGxzX1PNKpOwWllSF8PH0NCkor8f7kVbj5k79s28xdX+D4fXnpp6w+UbEkYtN0fC1BW4flgOogWnTi83RqS7+zPYI4lNwBd+uMJ/KO2q25FlYvXrs6CACGTlmVwFLokZeDmolWXNoTr5mUVeSAgm4Zr+rvJwaLBaWVZif7vjMOxKg7T9Ae0ykYY9+v9XP5T91ASbbnKI8RMJAH9eIcyEtgWzbMwbQHTsGA0w+sOh7M7WMlItOp3HIc7BFEEEF3nuNtVx76bh7+9cEM8+8nf16IM16d6Lh9vFYrGyQVr1fkjNMJ87SVk4p5/I41Zpv6Z7BY0unWtxBtjW7wqAv6Ac7tgXzu5fZMt5R3wcYCbbtwx6ldbO+Fjajqt7ESpHSLOzg980PhaFndgpiANckOoEkg6nGSQGwW8FktVlSWbS2yl0HzO/6ueh4f2q6x5f2oN6T+fs9zSBAEACVVE6BOS/DFuRD1wm0JfqQsyrEdg7bR12HD2k8S10kcW7UOCir9JlsZNIl+TIWfz2dpi9w8bbWJyKQJZNEPie2tq/dlzcrwa5WfYcPAup3RNvGZ87vbtjGPYZmoc77OMj/N3Wj2PeWgunju2OyDqv62r0LxVW0vJesMGwmZ/HZT2jpNGKrl1qnudRMToj6IvpfuenpS2krtRJmmHVXPQ/P62eh7SJuqz+x1VQ06u60yzc7wIzPgxx8PnooZD56KnMyAub1hRK9JLKUtEBFDuCW/A6wJ+cSpcZqUlO9HOSCtmyBXv6OS4fehntKmyEnLol7Nfjxx7sGW/Z3VI3Ku/3NcB3N1jle7F7nN161g9EmBaVWc0jgvEwe2amD+LYsRSGziDto+/vjjGDRoEC699FIUFBRgwIABuOCCC+D3+/HYY48loYhkb+CNccvx55pduENZFkW8E6vznW6oM3SxAq3yjGp89gh73tPg2g//xDMjF+OWT+3BRREQu+yI/Sw+RE5L+YrKKrF0i3OSslCMaHo8nrZyUFe9LqLzecVR+wOwK21r6gNZ6KBIACL1Ze0Oe/I2mQt67uv4WXSZkz2hgIysgivTDFzEd8oqQ6Z1iDrIVP0RxTnSKSnkRGQqUU9cq+JTvJ/Mu6ZIMzsvB+fEkni5vizYWIDHf1rout9EBKxke4SAErDQ4SXpoa5c8kCsXKNEUf39ROdW7gS3aZSDxnlWpYVAF+zMljL7RverV7AA+oGp3CGPZi7Xty1yIMxpObY8uBEDq2VbilwHjZGy2d/LcLBeEMES3XmW25XWDe0WDyrycmfBlkLnJbt6m5TEKsjjWZGg85LUYQnAemwMrB717tsmX2drzdQtUMe/oo7rEpE5Ja90CrrL11q+/6Iqw+i23/1lt8e5/Mj9bKooUSaR/EX1r3aKO/h9zoN9eRIl1kBdDWarKkmvz+WQ5PUrTo2uH6dTbi1X+ieGYZiTsNcd39FaXmmiSHf96rkkNJ20PLKCIZb3rDh3TkpmgXr+nSag5DbQ7mlbFbT1i6BtldI2aA2uOQWQdapmcd79fp/nRLOxPG1F38ZJ4RqQ6pHqyQ5Efpcu0BVR8UYV0OrSdBm5jDp/XIF8mANbNTAnyuTrI5S99uejz3YsdZ/meTHsU1c1VdqqQbIdHi1WdNdFV2XEfS7+1z3bYz2/thaW4X/fzzf/1tUxtdk4tWtLbVIq2UJAxk0lK65jq4Y5aFn1TJctasRvS9RqD3myOari1e9b/o1yQNrJI9ltX8GwYfsNYpw3e10+Hvoucg0yA37r88jnM5M8+33R6xlr1UX0uM6etgAs7bvaBt9yUmdt3oQ9XWiVKOKO8nzyySd49913cffddyMjIwOXX3453nvvPTzyyCOYPn167B0QouHvDZFZ80Ql0dhbkDsOqgIj3VG9cGIFV938UG3b7uGetmK5yfSV9qViu8sjg7sGORmWAWjAYYCxfbd7py/WoMxqj+C+rRy0VC+3GJS2qupklVWGLDO61VnGLCMUCt/fcizGDDgRd/aOKppChqFdsi/jNrMvK1fclLZyZ1e3RFB8Z/bafPO9lg2sS7HUQbToP+k6dgFXpa110AdY/fRsAcgEdqp0A3Od16ncOXRTSgsSErSV7BG8LN2yBKocNtMFD0otthT2upCdqQ/Wby2K+oC3cgky6pRXunK4KTxsicgc7RGcluZG74VyjT2CSlFV2zV82hqzbA1y9EEWtyzwgPX8iaBtaWUI86v6GgL5PnUKPgvitevYWVyBM1+dZHvfPZFV/PeZXCy3PtSv8zfjoEdG4aNpqz3sM/6JT8sEhiY8IbcpteCOYJtAk/0MBWKQa05GSKdvzMIt2v06nQ75eRXQDEzl3y+ed/s3i2Ylb5iboQ20GgYcg7ZOA3o5I7mtnJIKK9YSf78S+JbzEMjvx0JejaI7HwLdc/jpkYuwUEp+JhKNAUBD2/mIlks3geGkdgWAOVXPXScvYHFtRFvmtJ1Zlljrr+VtpXLLbUCFaY8gtrMGOUSb5GQjo1N5h6X+yr5NcqX37d939bSVngVuCcsAqR6FDZRU6pOQOdkjCNVmh+b1tPs2y1MNpe2Xf64zX+sUlU5X0G3yU1Yg2+wRqtGPku0FSqVzt2hTIR6WgqPW41j/1gWxde1DtK0U37Ofx1j9rIe/n4/1u6LqaN3zTj0vfn+0jy1/EpTqqozb80MXfJQnm6O+s877iAe5TxRr3/IkihwsVYOv8t/x6LHE/SdbEmQpQduMgC86+SMlLPMq/JKvnXydo+WNtu/qRLlq56BbCUCciTtou3nzZnTvHlmeUL9+fRQURDrAZ511FkaMGJHY0pG9Bt6v1cOaXKBunUQ1cBPLiNzrb1X3s7fVrd3SEj/5VIz4W289UuCiQAVi1yvdYMAJt2RyogMjgjTllSHLNrEUv9qyhQ2M+HsTlm/dbZazef0sdG5ZH3f2PgAtqgKiaiINHW4d1ahyJdoJOXfwFAwavdRxH/OU4JEoBwB8/Mca870GOXpllaqIEJ1BdVDqpCYwlbrKNRODRFvMVruX6hHLHkH17QO8KQQrNQnk4kXO/m76AbrUa50qxFYuTd1x8hIW2JW2kfPz/uRV5ntHK1mMZXw+n2umdHk7y9/yMTXqDzPJhBSIzXGyR5ACYVEfSOdup6pYrZ+d4Rj4iPXb5PMneyye9fpkrNwWVe5ZniuGgXU7S/DpH2u1AZ9dmqQnAt2g47WxyyyTM727tbQdU6U6z3G5DXZrp54eGVGq/++HBTEHSfI+vQ6oYqlz5fdqQ2mrJtHSnfcMV3uE+JS2sqd7hiVoi6p9R7cVk1AdpUBUJHGkZr+hMD6smshQg7ZuCkuneycUjiMRmXJerpasQQDvkwzyMn5dgFzg9ByeuiKayV6smvH7YFseLNs56K6fU2lDYQO/VQXpH+rXTbtN1B7Bo9JWaluvqUoU6oTYdzBkXVIvnoHiWqqKcLegqrxfw4h+x+wz+H04+cCWZp9BN9EiK6Rt+zYn5ayrjXTIQTNHewTNd2VlrlvAHbCuJnNT2sqISYjjuzRHrmbSTn48uvmM6pazh8M1H3/INkRAVGn7/uRV6PvqJCzf6rxKTiZb89t07YPocok6ojuPsXzTf1Mmu3QqSvUdi62EF3sEl6itrq76pPsmqrR13EVcyEINw7Deryryb6yUJlzsk+fR13K9drJuMcvisOJOXmkY8PnMc1QZCkuetl6VttHrs26XfYWiuDahsGGzxrCvtIK5LYlN3EHbtm3bYtOmyOC/U6dO+O233wAAM2fORHZ2tttXCXHEzW+TOGNZml7HGr0S1R4hpkrTm9JW7SDsSfYICzYW2AKBKgWlEeVsg5wMT7/dyTZA3JOx6pU88Iq1baWDPUI4HFWXmEHbYNiiIKtO/R45fxNu+fQv9B82w+w8y22NHFQqjhEUdAuGhKUBizxYf23sMst2sdTCM1bvxNodJWaA/dLD29m2UZfaqkkV/tGxGc45dB88XDXwdFJiie+r59VpeXUibyOdmkpVAkTKFi2L0+TCj7cea74uD7kH3r0grlHAH7VHcPW0lT5yapd051Q+B7pl+07LoNs1yZPec39ueumE2zxt5YGp+hl8liCAUNrqBoSAdZmcLhGZbXulMG4DFLXPoP4tDzbV/fwt+VPL6tlwOKISevC7eVr/ZF3yQIFOOT9xWTRhXPP6WXi6yo8x0Upb+Te4tVOyN+Q3muX5MnKV91qkWKpz+T5KdiKyrIDfFnDUnfcMJRgmNilySVzp1B7IKn2L7YcmICxU5bJSNCCp+2VmrIquqOm+byPLZz2Uv6PHjO5TxTC8JyITfoehsHVStlmVYspr1m+xCidTSjilO49O94a8MqFI8ntX65HFkkXT7h7X2Z5YCQA2Sl7STopOUe6KGOpWgXzuT+7a0nVbUW71/hW/QXyu2h2EpPPqtl9AnqiNli8rw4+XLj7Usk+ZSvN5GEtpqw+u6bbV2SNkOdojRIO8uS7WFmoZvSptRbvdo20jbZBfdx9HjqUPPgFQ+g01E5FUhqyrA8Qz98mf3a2i1MPoJkt1bY1pj1W1A519itMKBCd097Q6Edhj30bRQKf0kZOS3O3O092X4p2wNHnhxdPWC1alrXtAOGD2n6y+/TYlsfQL5aqWneHHaQe1ciyL7rdnZviQLyVX9vujSttgSFp14dXTVrqeYvIx4Pfhv2d0jbyW2ii1b3tAK2tyxoAyniHuxB20Pf/88zF27FgAwG233Yb//e9/6NKlC/71r3/h3//+d8ILSPYO5IYm0X5vezLxJOdKN+z2CO7bywG/eJRKsVRCsZbF1yblwZA2uZjg6g9m2gKBKkKt1qphjqcO4q4SfSBCBD3iSfoW07ZCurdl5YesUBJB27LKkHbG3Y1IkoHodtNX7gAQCVaENB012XusRPEKu+74Dpa/3dQFYt8BF3UT4M3+5ZqhUpKj8w6xfS5nZgWkpV5Vxw34fXjt8v/DtVVef07FMT1tld/l1SOtJsTytM3UeNoWOKgc929Wz+xsxlKAeCE6CPZJihlv+3UK5uiuu+xTp7NHUIOAot6OqErWee5h+8Qsj2qRcLqms2/z7JMHC+pnfusAJaY9gqS48KLsUwMDeXEEbZ8813qvyMs67erz6Gt1BcfvSyOB1lELNtuOuaPYuW0uLLXXaXnAUj87w+znyIoclXgtGADrs9PrPfCJpObXUVN7BJ2mUd5PMkK28jkdf+9JkgIq8p6ubxlNIGTdx22fzXY8jlN74DQxEQ0YRL8n6kvH5tFBbIZD0FYsD8/NDOAoRV1/2ZH2iT0g+rvkYICof8OnrUF+VXsaa0nsxiol4nezN1japCv+sT8A75MMlaY61W8JXNi2c6j/smLLLVAp2zmo7eqQK3o6JhWSJwXbNc3TbiOCdhXSb3FDN0HshPhY/f3ib9N7WUoECXhR2kZf21fnWLfRXY+op63zuZYDUE62NbLCUKe0rQyHHe0RSl2Srcp49rSVXotnWIbfb0uyp24rv7ZNaEp/+y3tvHW7eBM0qnVYXA/1911e1Q6c/3/7aj/X2Rzo6kwobK0junr7xvjlnsqu7lNGnJcG2Rl4uF83XHJ4O62Vh5Nns1vAVfeZuHcNRK9JwjxtJWV11B5Bv29ThWoYksLVZ598kn6ufA2yMvx4/JyDccIBLTC0/xG2/evuv0a5mTijKsmb2J/oG/61dpdpx+aUzFBFthsSk5sT7zsZN53UKVJ2cR3D0TZ438a5+PDfR6Jr64bW36mMZ4g7cQdtn3vuOTz44IMAgEsvvRQTJ07ETTfdhK+//hrPPfdcwgtI9g7kh4fO75DokR+GdU1pq17nTQXuWbOtiijvQVu30/LmhBU45NFR+GWe3jqgtrn3q79x1DNjsWxLNIPyxvxScznvdiWg+//sfXeYHMW1/ekJm7Va5ZxzlpAIIgeRDZhkY7DJz2CDA9jY5vcwNvazwTbRBoMfxjjh/JwxOeecg0gSkkAJ5bi7M9O/P3qq+1b1rdCzMxukOd+nTzsz1dXV3dVVt26dey63qyrK9G+qjS22uft2xyuBc0JdvIkFUSJ5BMvifvGaLcqxwf/U0SDkAAKmrXv/3t6ex0FXP4Tjb3zcuPiQjevotyZFO/NEheVqlEfQMG1VuLyj730U3KNMkQWjQg0hsyU+0DNt4w5zAPivXz8LgJNHKN/4wm1MUINRzAf0nm/UaNpmUh5qi33VloTJBXSRHC6+XJ1WGocZ13doX+AYPl88UM4ir3YrXWJBCurUmDa0GT/+1JxYGbVebvEZlaUhjJHWnm6BHLEoItabaTGtLiJNWZtp2W8fNTWWoIbq09Zm09r3UpK38OPjEMVP7tMvVjkmOF1wZ9Mp6Zm5JLNyBa3L9R2wsYxctJrjx0R/c69MCQo3iUDPWZ9Nh31VRJNw93zVpmAsomzYXL6ABxeujpUV0I0H4nnrpE3oYWKxO7p/5CDMpPkQcTGuTBzcK/bb8D68g1FlhQKyM/l7/3kDgHvG8N8/vURiqAtZAleyQDtxUHDJsZ5ZvBbvr9mi3djc1haPtOKcLrRu8S588aAJ+M8X98Hh0wdLZT+1WzBmzBzeGx/7yaPWaxD3NJRHSMC0tUUTinZvb+edtuJwsakWRspYNW2j71V2brRhoWe7mZ3C0XFCRiGraQeVKhFSR7uP6Rv+/t7qLeyYVCj44T3RJYgToG1UNeF1EHVn0x4Onjo49jttk21uVNtBNU4FkjJt1fdLjD1DW+ql779/7Azc/sW9cflxQTTH8XOHS7+z8ggOmra6bpvEycatC8R3s0e24Ox9xiKV4scFGr1AYZq++HGh+Idffk1bYbrmfT+2IRJvR/DDb59cErJUafSBWg6Q+1p9No2hRQfoAZPi7H1uHBjW0iC9O+m0F9oibxN5DVdNW9FHWnOFcO1Gcw9Qu1lsXE8a3ItNsumSN6KKCB1ONz9//nxceOGFOOqoo8rRnip2UtCJSWVgVqGHlHCrpzltled89PWPGcvTJFemha36m2ku+MGdb0r/dyV838c/X/oQuYKPvzy/DEDgCPjYTx7FYdc+wjq1uVA0EcLbt7EmNhFyi80Xi4L1C6bKBkA2DFG3OG0VppoOr36wAc8sXie3p1i+nTgaxOS/fMN2Z0kMAHhzxSa8t3oLnl+yPmQjcs+e7lrTkD01HHb8gCZ8psgmAtzlEbRO0oLvvMAF9M4tdYFlM651xq26gBNYVHQaq07actpUa5jkdxLTNh0xVQS48HMguOfZkGnbca9QyOJKkfBqx2q1TFvGkUbZlpzTdpLioFH7VU3GvuKg48OFB09kGbG67NhAnGUTaNoG372wZF3IfnNLRBaUNS0M1D6s6lVSZCwOEcosymo2QAD9HMotIp4oMvc5cBsRlGmbSackvTnduFpKxAztd67vgG3BKl2/48vv++b7JzFty7Rgls5P/vYQ9dWzf/0srrjjTSMLmToMdGONgO4ZCQeQ+j5wWo1iwd6/KZKVS6f4pDPi+Sa5Zdw8xDFTXRfqAPD5254DEDgOMo6bugKivwdh8JCOfWP5Rpx40xP41P8+yUrFAPIziTZJ4+WoE0CMq811GUwd2hxz5uw5LmAtv73STRc03Ex0lJbgHHk6iKIq21OVRwgZ4cXeHjIREzBtw8igUBpE7zgxatqSfu2qrZsvRFIXqmwNu2Hh++HGchInuaumrUAmncIpysYfII9T5rmRONnIfemoHaW+XmKOorJm/Rpr4Hkepg3tHY49J84djgVTosgaXh6BO59MBtCxUTcy8jGtuTyuuCO+lqIRU7c+tgif/fWzIdtad9/CYzXP3pQYmLsuutlhIzokBWWLRva4/X0UyJCorrC9mr5mk/3g3qHdxvSR7SXPYzfrXDfwxPgr5jDPA5qIdIk8Bgsb0czAdyVG7OwoyWn7m9/8BnvttReGDh2K999/HwBw7bXX4h//+EdZG1fFzoM3V0TMwlaLwVwKSgk57AmQmYg96xqTMqpzkgPP7kALPztMBrYwN4F1W9pwxR1v4p1Vm+yFE2I9Cf8W4eFPL1qLtVva0JYv4N1VW2LHqIuGXL4QTqR9GrIxg49bYAkt15F9ZR23iGlr7leu8ggX/eXl2Hch25OwSejpaBidzRlBNenO/90LAPikI9wOdr7gY92WdlIm+O07x0zDd4+ZZj0/lV7QabqpjFYbdEwRapP5vm9kHYk2cTAl5wlkJtTvbC2Ow/d9/N9zy/DWSvl9EQZ/M9mdp+8gl4hMLGZP3n1kqKcIFHX5ysm0JYvPtGExG4JZYKjQOYHEvXeZ81Rmj00fGZCNfZ1sh/ot/aySeYPkRsHfd7++Eu+tDsYk3UIi1PYrOGralsi05fo+ZRZl0noGPB3f6LvA3d+Whjj7VmBtUWbmiXfX4JgbHsOdr65AKxkzPMgLTy3TlpzXNQEYrcvVaWsLDaXNc/Uj07mAO0RemCVbMK/atB2HXfswrrp7obYMvV8pT14I3/TQu0YbiToM6Pt65MwhsbK6Z6dj2oahueFGmx+Ogf2aorHspaUbeF3PkBmpbX4M3OPtqNNWJOJrqElLUh8uoJIClIkIAI8UtZ8/3LBd0jvdjzCz6BhpcoyEIdYFO7tftMPmpI/qDsqHmra2RGTSxpK5bjXJmYCqKatuJIYObAdN29AhV1DrFL/Hjzc5hSmDXJcwKiqLYlkfzy8JNu/rsmlJ45LrswU/YponkaNI7LRNeajLpo3OM/pLvFz0N5UF6mjEkjoH5Avy+AHwch512TRO2jWKFuOYti7yCLppYj0jV/W929/ATQ+9G/te1PXmio247F+v4+7XV+KvRWKK5BSHPE4C0Golm6Yv07gQyCMkH09NoHkmbM5uzg7LEv31sBz5SNmwOudnVFe8/vEDe0l2lw+eEW/ahKKlc6HTNugDTTVywlgq4RZFY2lsxFT8mVehR2Kn7Y033ogLL7wQRxxxBNavX498MfFHS0sLrr322nK3r4qdAKqTTQ0P6ih+99QSTPvWXbgnoXh6T4BL5vLuiuROW3qt+nLqjp3LwrfeMZTqm/94FTc99C4OvfYRp/JJQLVExSYGXUxsZRJlDewlJ3+8941V4d+967MSgxXg+4hY2Ow7QU7QIRwmSRKRmfrgG8s3xr4Tj4YmKRnYzCe0tPVvTptYvX5AYb8U//5oc2u4EDtx7nD850v7AAgMryG9gzC0NpOmLXX0aSzBpHqrukWHvAiL69PFy/Pf5/I+fvrgO3iuyH6m59vSlo8tLUoZXR55+yN85c8v4eM3yCz6zcWNhX6EZVZDjE2xQKROFjEvDG6uk/pkwBqQF9MdQY4syKk+ora8xNTky+jmNDGmbS/208aaNMb2b8Qtp82LlVWf436T4qFmsWMYQ1qFurjgFp/0N24xoltI8PII+rFWDX81MW3pe8Yt0mh/zqRT0r2g10CfH2W5qQ6+La05dqEqIMaAT938JF5auh63PPqetImQKxSkNuvGVXpeZ2ep5LR1O8i2YC1F09Ymj+CTW5qU5PS35z/Amys24Sf36yUqpPvlxfuFaaODlhVsz/psGjecvEusbGKmLRlH2nIFfOH3L4TPaQAZA1dvamXf07zFEcCBcw5wc0rWgbGvor4mLW14uoCTRxB95M5XI/1ooXd+zwX74ldn7hZumkpMWwNTTkqCEzrRzZrbFPdeuJ/2GkLJCUd5BJlpa15ue0rdAm3hhrYnlYs2vM1MWzrm6OQRoufBMW2FbaNnalJNW51sD5VH6FPcdF21aTv2HNc/rEsnjyDsQ9v9pr8L2RNX6OTA6BnN8gjR35EWfnzs7CjTNl8oYHt7FJa+25i+uO6k2eyxlDnJvQPs/RYbS4YyAFhdYkq+ktsc1EblVZ4v6qhambaafmV02jJdkEaoiWssl6YtTTwXOm11beMiIFKp2PXojrdLhPDv36DmOpwwdzhO2nUEmmozrIPWVdNW+GyE/r+ao4I6sW1M21Djt4f5L7oKiZ22P/nJT3DzzTfjv//7v5FOR51n3rx5eOWVV8rauCp2Dqgv/HYmKUtHcMnfX0FrroDL/vVaWevtDujRmraME9IEqg9oYszEEpE51O06d7+0bD17jnKAOh3vfSPYYKAG/HbGATl+oJyJ89zfPhf+nUmncM5+Y6Xf49IRkRi+ymoToWtJNG2ThriI8jTckIaLUpj697urN+PWxxbHvufumSTwXzQuRFKYhpo0fnTiLEksPwy9N7A4JaetpjOZ2P7cYsSW3EmcV9xybVii5vs7X1uBH965ECs2BolmLjlySvjbMdc/6szyM0FkPVcNfDHm9yHMRVkeIZ6IrLU9Mv6ofmiqzExbyiyiIZ06uEQ7cNmyaXnhLNx1TF/c/9X9cdAUc8KwWcN742Mz7YnIXDQVOces7hiqaUtRZ3GKuCYiUxeWplBA2WlrriuryJbQ4u050sckJ6vc/680MDyB+DipSsG8tXKzNDboxoOkztJSIkuAymja2uQROhIC+RYJYdfdO8pi87z44t00h1BnmHAQigXyFw4cL5VNyrSl0gvPvb8O/3450tDv0xAxbQc21xp1JpMww3h5hGTsKhUDipvE9UQj2tXuFOWyRB6h4Aest3dXx6OIhKa0iDihGyphkiQD+7Pg28cc9fBrPjkrZlNxdUdO2/IlIouYtnoZIFpOQEQK6MZ3WlwNfRfHqI5gChPTljrCdGHsAnQuFZsiu4/ph4sOnYSvHDwRd1+wr7bv55yZzdHzmDeqj7Yc12N1des2MdXnSR2A4bUy99NHssjPGNPW9/EBiSr742f3wKh+jephAOT3nXsHzEzb4LNuzHnvo7ikiDaapXgNWxliBT1EjUgAon7F2SI6mDRtfUl3tkxO29BJ75P7pnsP4t9x8gi643W2VliXYZK48sRZuOL4meE5VbhGXYgx4RePLQIQX4eGkgdOTNvgfzr2bG/P48GFq9jEgDs7EjttFy1ahDlz5sS+r62txZYt8Ym3iips2Kgk8NBpWpUKMYjqFs49GdRJ0NN2qrjELSZQtpppYRpPRNYz7ouqqQrITqhtjJP7rtdW4h8vfgCANwTHD+yFVy87NPysLvDb85HTT03EJbRlbYsyycmQsA+KZ7PdshurnkfFKTc/xTJ5ubDzFGNcC9Y35ySKtH01jgJfNtR0iyexgORsMTW7PWBg2pKvCw4GqCubgMoxcIvoUpy4nEPA96Ps0S3EYSHLIwRtpqHeJv3UbCinUA6mrR+2JwrdchtvdHtJWnmE4rmEs9BkkNPnK9jfNqSZvh6vV38e9Z0LnLbxerjQS4AybaNrNDlt1baYQlypw8TGKsykU7LsA7kG3Saxysp8Z5VZ9zJfsGtWB8lWovIqHn/3IylawmXuUh0Drk4024JVlrR1qzPJBl9SUBkLXd4DWn3Ki2+gcVI/Ys6hYefCVhQROJ9QklKyzhjfx3m/ex5AXNqGMhlXbJS16Wnf9cGzxCL2lruTgZuGuGdueh9ViKMbaqIwcpt8kgDdmI2O9bG5NcfagqJd4hnQMdQkF0EdkK7yCNFnNyescKzanYjR37a8kaIpnL45PbfqYI2c4fZNOcF0DzeZFaYt9yiFXcnZNbQtNk1bKjXUSp5LY20GXzhoAsYP7MU+zzyRgLIxbanOO5UdidXJjFNCkmxyUUt+RN9gjtVt9sXkEZjfOHmEpxetxdRv3YVbHl2kbR+F2tJ8QY66M9l31AnHvQPs/S7eG99iV97LRK4u/oj3AYk6+XE7bqNwTFvVlixZHsG3O6STQtRN8xToI9/4jTNTbgGKUjRtOXC2uatMoJj/xGM6e+8xbBuopq0tcpCuHb/xfy/j9FufwXf//bpTe3YmJHbajhkzBi+++GLs+zvvvBNTpkyJH1BFFRaoRkq5mbYCtsGuJ0JieSUMv+5qLF0bT6xlwl+eWxb+bbrWuNM2WbtMoG0uBxORQoSMA8CgokQAdULpNh2+9IcXAcjv0f4kdJome1AX9DScXE0K0VgUlrc5YmWmrbFo/Nhi3eLaTO+oyRkh2KICgsHJMm2lkMWi07ZoBHMyGTWhQ5A/P20WJ48grlFnfAK8dqebPALVtGWLOxum6qKvHL2bht6KxV9rrhC2mWqEcvII9F2OduxTUqZa8R1QrkRkEWModLA5Om3fX8svWnSatULnmLKIdTAxYHWQ5BESMrGBIKO71Abwiw6ds1l09XzBD6UF1GdHod5ms/5t9DfvSI7alEl7ShZ10q8046ral8TYsL9GloImWzNBOJvV8eyjza04+eanpO9cppjYfOc44dn2cmi9rmOBTR6Bti3p9EmL6+ZCWqeHeN/mxvB/f2FvALLDYJvyPrrc45eXbZDGKIrIuQWs3BiFbatRJZMH9TIybZMQw9hFP/OVjV116LSI9S82N2szaRIS7NYeMdap8gi6DS3x7teFTNu4bAg3diXZKFLvtY0NK+6paIvN4U3rt7FyVRav7nfK2gYiu0LncOY0bfPK/TMlInPRtC347pq2Bd8P5zt1s4/rs77vRxIQlr5K+3JNWm9LcmOPaPfNp87DafNH4baz9ii2iW9frO8wcy2XiAwInrGrQ0p9JgXfD+emUf3iWrYU9H5xxADu/RHrGpum7SQSjSagI+KIvsExJ2kTOG1l3WaAadOR3cwRerlE07ZMRFvpeYuND20OAeZrOiYK6Mwymx/DtrERnZNZhxikF2jfFxrTIjp0rsJq55JB6nN0yOMZAPz9xQ8BALc9tcR4DTsjEjttL7zwQpx33nn44x//CN/38fTTT+N73/seLr74Ynzta1+rRBur2MGhMtjKEeIqQI1rm4B3d8LStVvxCtH/0UHSUyyzE7E1l8fzS9aV3TkJJGdkAsBCkszIxOgphWnr6gTZh+i+cuH4HQGVCRGGOH0XtlpCRajT9uZTZU3MDDEqKGj9quPQWdOW/OzKulGPDcNRDQZJkrpFYgbWSJTYL0Wnbci05XSezKH3tL+lGaatuH+hpl/Kw/99bk+pDM+0tcsjUH0uPZPSrW+nUyn0L7JThrXUx977UoYBygwTCy8qldC7PnLa0kVtmIhMctpGjv35Y/tJ57E9oySgC0Qa3q8Ddeg+s2gtW0bnmBD1RswjN6atydFKQZ0QLuGzwefoC1XD1fP4RYdNyiNf8MN3jD7zWHmljab7QZ0UvDxC9HtdJi2VoeOg7tmoY4dYoIzWhKLmfd9Jj5+ysCgWMnqALtEzahnXiJuVykaXCkkewbFOWVIhfozs1E02oNAILN3mjCqPoDrhxLzav6kWVxw3Aw9dtD/GDwyYdVH4LGLyCP0V7XhuE4eeakx/uY/QRazYnN1/0gA8898HAQD++vk9cd4B4/Bf+47VhCzL9biADcNlvrQ5bW/69Fx88aAJAKJxKpWKnFzOTFuyaUkdeNvb+ONrFaYtfR9VTVYKSR7BMq6q4409WVjw/3YLeyyqj46/5rrVJGcqxLMTNYYOWAsLlRv3okRkchnulcxrnGbScbBr2nqcI8cS6i3alLdILwhQx5DJoc79JmyOEX0bcNkx0zGy6BCVEpFJG6fy8Zw2a77ga6NvXKEOvbm8L71LJtC8IYOa62K/m/SzRbt1jE81x4bv+9o1SkGxcyhkp3hUl0COaGFLx7FnCsCPC6Juu15vUlAWuc3ZzTPW47JTWnkEix9Drf8T84az5bjNlWaDbUafSd73USj4oZNetemoFIoYt20kFG5OTZpMcGdA4jty9tln4wc/+AEuueQSbN26FSeffDJuvPFGXHfddTjppJMq0cYqdnBQTTmgNIeeDpsIe7GnMG3bcgXs88MHcNT1j0raRRwqqWl744Pv4rifPo7rDYk/SsVvn3o/UXm1TxiZb+pvDrfFdfIWoVMA8J0yh260MXqK1IDXMcLC34uTYzbtxYw5sTiJMW1JQg11gpw1ojcAuxPAlgzvw/XbtHWoO/AmkX0du5obL9rzeiNR1iUL/t/cWnroPXVOpFJxo0lsSoWGdiYV25lWWc6APuScroloOJZes5T9OoZMysNPT5kbtDHtlZWhDkRMK+ogp056+uxFZlsq+REZf2kpwUbQ3qLTtgzRBnQR7JLZlvbLOSP7sGWEo/r4XYbjq4dMjB273YFpyzFSbHBh2qoh1/STqkXs6eQRLOHHBd8PWe8mCYgz9hwtfTbdD7qA59o0vE8kITF2QCPLOAP0Tlv1e9Fv+zXyYbeBfhtfV/+mGvzqzN2CdqflsXj1plZs2t7OJuYsRR7BVTeWasRyKEXT1sakzUt1BhnQ739zpZPNR+dCrdOWVOMh3lfFWFyXTeGk3UZKWpCSU0m8j8W+2lSbwf1f2Q8/PSVISpZnxhl6rm8cPln5DWHdYhwcP6ApPOcuI/vgokMnoy6bZvuyzREgknXp2iPAsTHVsVSF53nhZl6OOEtdNrQECkQ6hIYC5wmrWT5n9H7X1wTt28Zp2hqcM4WC78C0VT+bB1bx+/trtgKIopF0cJGnUduik4cLNW0jTykAu5asHGEQ/K86vWnfV0G1iONtjuZHm/NYZkC7Ob1F+SgRmTvT1lQ3m5BPOz/yf6sOfy4SplDgeLYRXCKD1M0tyrS1ObHpfLTAopMvIJoUOTbtdQefC1o7SfSh/334PWMbuH6old0wXDr7qhFGp5hvypWILEqmVTqJwtSfKGxJs1VnrM7uUzfrvnH4ZKOPRJXN2tSaC5+36uwVVfu+H/pguDVOUG/wv7qhBES65lVEKMmNfcopp+Dtt9/G5s2bsWLFCixbtgxnnXVWudtWxU4CdWe5nM7H9dvawr97iLQpvvXPV8O/314ZZ99Q2BxmHcG1974NALjuvrfLWi8AXPqPZEnhVMPedK2uiVmoQ8gWFgcEO8s3P7LIWq5UcM/SloiMwsReyBAjkqKNLGqoAXjfV/YLFyR2pi1td/S97/vY8/L7sOcV9+PYnz7GHisM0m1tYjGdXB6BS2iXV8LOKTh5hLtfC7JXczIFWUZflYJ+n0mlYoa0aLdwynAZ6BuYxZ9WuJ8uwgpU05Yt7myYZlJeGLq+mRhkAublhx3CQfnw20HG2YaaDN4mOqE0bF6wXqiBHWVmT8UMTrEoLwfTto0siFwcE2oCBYon3l2DWx5dhNXFTNYzhjXj/AMnhNf67urg+m0hZIC62eD2TOlt0h1jclycrjhRgzrlA2oyKS3zV9S1fMN2vLR0PQD9ZgQA7Dm+v/R57wm8FIHaDu78LQ01+Mmn5uCT80bgsOmDpfPSV1kXaq9Nnqc4bameHee0nTCwCc9ecjD2mxhcSyT9UcBz76/D/Mvvw1E/eVTaYBZwmdJj812Z7ABajWsUj8SkZcYLyakL4KI/v4Qzf/ks/veR+GJehZyIlG8P/dbz4jqi5lDvYhv9eCIyABg7oAkjixEcnGNcvMMj+tbHxvMwNNeXNbM5mHQmdQv+z8wfjVcvOxRjB0RO6HImIottAnseK2GjQzuhG2ZIKHDB5zdNatKpcN4S8yB9T01ObJocy6ppawhx56D+zCWM1NXnLI+gsTMiKYPgc4xpa9DXjViGvnRsWlMnRT4vl5VAHOTtloRo1Jm+rV2/Sa6i4PthG2wawvQ5U+klFeOYZHO691Enj6D2HS5XQsE3RxS45PVQDw8SswVf2uQ5xCZy38Ya1rbl5k3RB2yatqrUjCmxtGn+0N03Ad2mgVkeQT++yzkotFUkAk2mJeY9vaat7nt9fzpn37Hh33ZNW7f7pL6nR0wfYqyXzoW5gh/mIqrNpGJtonbzxu08G1dth+giv3s6kkRorO0ZRLvORIe4xw0NDRg4cGC52lLFTgjf93F+MXmDQDnD/Omk+PryjU6ac12N3z+9NPzbtjtPDeZFH21JlJXUFUkSVZQLW1pz+Ml9b+PBhavwzqpNsQW0SdNWXdBRdsFLS9ejPV/Ahm3tOP93L4RlbEkiAEgZnwFg2tC4phPFmys24s5XV9grRtQ+gYidGWca6mBi6wkDU703lPHgeR6uO2k2vvvx6Rg3oMk5/JEuxOm7+/6arfhwQxCC+7JG6kMsfl3kEbRO29b4fXHX0Ao+rNkSbO40M3qbEdNW5/yPvq/JpOJM2+Lvf3p2KXQ4eGp88WdL7gTISTp0zllXFnkm7YUyDVvb8rFxuJRhmdYh+vJ//y3YlFq7pQ2DScge/ZsuztoLBbz24Qa8Xkw0V0cylwuUMxFZK3HYiEWNaU6i/ZLqsW9pzeG0XzyN7/77ddxdTNrRr6hhKRx05/72ueA4SwgZoGSxLkEeQTfGxRNgRH9/acEEa3lTm7kFvGvES1NtBrNHtDjVrbsdR80aih+cMBO1mTR2Hd03/N7kaBdQnVHCAUQ3F46cMQTHzBoalufkEdT5M000be95fSVyBR+L12zFW8wGrYsDVh0XyyVtL2naOjtt6Vxg//2u14L34lePLzbW+//+9goee2dN+Fm3OUPr9zwm6sHgMKUOg3uK7+tGxZEeSVvEzx1ugDJ1i1eGsuR0iaO4d8bGtAWC94W+71zZUhORqeNtikQhPLN4HT60RITRubMmnQrHIt/32U0T6mAS7aP93BSyzyXB0TJtmesyQb1/e4ztqykptyWo21g0HMN0fTt6trJjq92iaRvULR+jMpVTiuOEwlnT1uJYpXOpiGziHIkqKDvdxiw9YuYQZNMeBvaqRbOBpXfybiNj37kkcuPYtNxnyiQ3DZ0uTlvV9sj7vrSxbELfxhq88M2D8fg3DmR/5/zU4t2yjTmqrOFWxg5X62Qh2ePB/+KSfWLfmhK/qTDsLxQlxcwO6aSQNIwtTFvVTv9UsS/GiMTkM32nTNGIQLxPqFF9unIDm2vZcgI0QqpQiJIJmzYDCn60+c1JwAGUpRzcuMff/Sj8zSSltbPCPmIWceCB/Euv4v777y+5MVXsfPhocxuWb5C11cqZUGuzYnTf/dpKHFVcZHEoFHxc/8A7mDG8Nw6Y1PUbErY5RV2w/eGZpfj0HqPK2oZya+W64JBrHpakIdQJjWuTWFiqBoIo+qn/fRJPL16Lz+wxCr3qMrjztcih6pKV+fUPN0qfbaFdh137CADgb5/fUxs2TSEzVuNMW13YnAANH1eR1jhg1fDBY2YPi44JQ9TNz5+yjmh/XO9gkIrrVJ22fzl3Pk646QmlrJ3pKiCaocojeJ7CllA6ltDCpRDhoyoDZunarfjfh9/DYdMHAwj6KJeITGykzBrRwgrrj+zbgIOnDorJbej6l9C/EixUm5GovjuXHDkF/3P7G7Fy6VQqNAi3tefj7xFbuxntlpDmiw6bhLVb23DgpIEYSJy2WbIAzeV9/JLoR9dl4/IItWVk2oZh/NlIB9U0BnLJ0gBg1abWWJ9RM1qL8tsdmEelMG2d5BHUxQL5O5sOkr5RFqhq7JvazDptHXQMg3Obr5HW7TJ+X3DwxDCZJXWG2vSG1c/UKdeeL6AuGywscgWfTaKqvscZMq6u3RIlpfpwQ1xj1kXqoCNM20feXo19NGxm2udda5TeE+YgnSN4WEt9vHARbbkCfqeMmx2VR+CcHVRP8T+vBLaBYIcLUJZ0rJ15faQLDSNvd9T+pLBtzAlwjiOu/RQ1lvcMiDs9xVwncPLNT+LBiw7QHk/JBJI8goadThf4XLSDeM4mnVUqj+CS1BOwO8Fo+T4NWevzcBl/BUJNW53T1sa0NdQftNuPHaNj71LkDQxaWfbDwMhFdO9o1IULk06SXrCwwscNaMIdX9oXveoyRgf89GG9cesZu+KMW58Jv9MxzmktHCs0+hz9Lcsj6EdPThJHhfpICgW68WPfcFEjQyhMSQ91Yf7nHzAe1z/wTmx+NDFtTU5b7p6KuYGuJ7KODFJA11fFdz5hEWurSATx7v7mifcxtUjmcSVRXH7cDPZ7XRLDJDraALDvRH5+p+VaGrLWzfTehLmeK/jh82YT3JFxYVvotOXdjZSlDAAbt0X9aDzDiN/Z4Uyhe/DBB7Fo0SJMnToVs2bN0v6roook4JivrppsLvjFY4ukz5stk+S/X1mOq+95S5rMuxKfueVp/Oyhd7W/q5Phw2+tLnsbyqy6YIRfZKKoWr5qGzjW5ZV3L8TUS+/Cc++vU471sbk1h6eLmdBfXLoeLyxZL5Vx8YGoiwtOL5XDKx9sYL9/+K3V+Pkj72FTMXyEXpOavCo4H+9cEJN4FGKtZ9qqa12x0GQTM2jCH9tyBdzxynKs3xqwU6UwWvJhzeZW2CBedaHXK5yG80bHGSw65zFnEIrFufrMTBl/AWA35rzCeaSy2C/6y0v4zZPv44d3LSyWSxXrlO+lylQRyex+dMJMzB3VB//3uT1ZI9OchClicpj0/YD4NauOQ4FMygslMXwfeODNVdrzu4I+s3Vb2qV7eMzsoWiuy+KGk3fB8XPlZAn0fjz7/jr8uehsA4L+rrLZbBIWSRBq7mZSJMGEvrzstI3620Zm00LNFg+4ORcAZXFTAtPWtX+YnLhAnEll1uGNn1NoVHYU1EnhIt0xrKUehxQZ7dTG4Jj6QHxcEc42mpSqV102XHTkCz6rOx5n2hbHk4IvnfuDdVtjx7o4YFV7SWVAmaDOgxS0Wmd5BEn+IH4MvRy6EWBiD3GOcF3UAz1lyov3v3AsZhyVXGhurEzx2a3b2o6Tb34SDyyMxsg2g35qiixMw8Q6Gf595BORFZ22+qZJ1xD8Hf+du24Xpq3qHFITbi5eE++7FDTSJp2yyyNQRwB1gAHAO6s24Z2irA7nGKHyCDZNW9XnZdsMo1N7ExOVE6vfsEGsQhTVySNETttoAwCwa9rSukU/Eq+Pjr1L4fLOAJF9pNuMEM179J2ISTd1SDxaTd3ACd4Zu2NaYPzAJjbplgpVj1ifyI1uDkaIMT+Z+TlPNtW5rmVbjwLxsTeXQB7BBrPTNj7mNNSkQ2alOgar0ZBcnQN7xe0fehtpRAI9DoiIJ2pZDvy4IOqO+nm5NG3Fe/T2qs1Y/NEWAHqHsM7PrvYn+pE+Z5tzVe3Hun5N39MhvfWbpgInEBu9UKDOWIYkRDbahHNXN8eHc0HxoVBJy3JLPu4IcGba/uAHP8Ctt96KP//5zzjllFNw5plnYvr06ZVsWxU7Abhd5XK+qPe+ITsedBR9gXdIiOK2trw1FKHc4MIQL7/jTZyz3zi2vLpA68c4BTreqPJWZ1qIFnxgzeY27e8CXB+54YHAua066n3fDzUlgcCxoxpLLotS1VFCHTSbW3N46r012H/SQKRTnvQcOcPo2cVrceovngYA/PbJ9/HgRQfEmCSFgi8nItMwbcWkvF1JnkIhDII1m1uxYVtdGHZiCukUzsdn31+Hza25UEj+s795Fg8uXI09xvbFHz47X3qe9BqElhGHmnQKbflCeN+FwWcySHTjAufAF6+F6lg36ZABfFevIfqq+YIf3ssn3ws2AQRrRJRT1zZRsilZt/fEeSNw4rwRAIBVTCZ3uzMsCB0rGBYF3PcmjSsqT3HVPW9JvyfN9g7ITtQP1m8LNSEB4Ecn6Dd5KcvytOJ7IlCXTcc2JmqUjQsOd766HE21Wew9ob+2zDurNods6NpsOha6xYH+Rs/P9X+xaDl9z9H4ZTEkfFt7PmLJG/q/HJqpLSaBjll63dnYmZTzyp9VB46JOcs5KppqyxPylpRpS4+hkjkrmHcP0DtD+xDGSdDvgnK+zzNta5T7Q2Vn6Dy0bF08xNxF3z/OCLYe4lQ/rdfFJHv1gw246u5ozLDJI7xIWKym+ZezEXUyULI8Ahf1oGe5hkxbg8FD54/H312Dx99dg8VXHAkgcj5yGy/U0RZqNFqcWxQ23fKwfVamLZOAqQRNW8/zrBqtFKcqY3gUBs3LI1BWVugA8H1sbcthwdUPk9/i5xJ1533f6EgX1yEd68iGBdwiBpJER4SatlqmrVxOZSOaNW1lR6+w2VJhncXvmfcwZ5BfoImjIu1Rs/NTnGLBlIFoadCzQAWCd6boEHZghbtC7RNaFi/jVAQ4Jxt91sH/eaKfmvY85JT7a5IUEFDHUboucHFim2DaIBLDYEoZU3Qkhq0mpq0ig0ZBW0A3XAA1X4TZLqHg5RGi/ldueQRajYiYcZFH4BzW0W/RFzT5qT0RmcJI1tl9pJgtqgkADp4yCH0ba7B2S1txw1kwbeNuRDouiBwenPQcIEuJAHLOj3InV98R4DzrXnTRRXj99dfx97//HZs2bcJee+2F3XbbDTfddBM2btxor6CKKhhwi+xyOm0/MU9mcGkZGkXQSeUjB6ZgueF67dvb84GgevH2nbnXmPD7ciOBbe6EdgMTqFA0zG2ISyBEn9WELj5klurGbe0xRp4LOUmd+6gT9et/eRln/epZXHPPW9jensdvnnw//I0zjN5YEW0OCJaKek25go+2HHUI8c9W1G9k2hbLnPzzpzDrsrvjsgTM5gQ1kn79xOLw7wcXBmxu4bSkzg36t0nOQSxsxSWL3dgmQ7jcs++vw7E/fQxLFFYPxywLGA7x0EvVMFKfDff+ZZVwaB1EyL66uBH93aTbWzLTtuCH99yWDEpAZ+xmUp5x0VrKqEzH27Z8IXzONemUkSViMsjrsqnYvRHPSLfgXblxO8797fP49C1Pad+jlRu3Y8HVD4Wf67Np6bnoNpton6Hhjuo4tGDKoHCR+q2jpoZ9cUtbLnx361yZto6LDRembcxx4am/K3UqBUyJxbhz9nJgqLnARdNWhcrq+Ghzq3bOFRtnAnnivDh1/ig01Wbw+f3HSZmjWU1bTTbnXN6X+suqTXF7w2YPbG3LxcokkTPi2OBcPS4bNkdd/2iYrE13jK5tOrYzwNuIOjYird0Dw7Q1ODuivhF9p25mmtiSIrKEi2Sgi9gw83sCTVuxeE0Sjs8V5ep2cdrGM5K724VcP6CSB5wjh4bNC4ZdruBjhSIhwo2DoT1E6nVJ6gnYmcxJmLO6dukgftZtPIprpYxBwOxUVc8dk0coVqY6VClM8gviG7lfu0lRjOzbyJZTHUhJmbauUM+jc1xxTkUguTwC11dNkgIRlE0537cyyF3BMm19mWkrORYRXVu7Mu9wY/gdX9onqKvYpbkNGi46gGPaxp22sarYOtXyBb/8ichoNWKO0dVN20alGmNtJh8HEIZyJZi2Lu9VKuVh9zFBJGLej8ZtlmlbrK8tVwjt4AEMyxqIjz2Usd1eBrmzHQ2J3/j58+fj5ptvxvLly3HeeefhF7/4BYYOHVp13FZREirNtFWFrG2ahzc/ErE0//nSh2VrhytsTmUgSNox+Zt34oArHwwdVmIh7OLwTIpy7UYK2DKxm8JsBNQdOHqM2toCYVwA/G7v04vX4st/eMHIAlYNfGE4bW7N4fZXgiRlf3hmCS76y8u49B+vheW4+fDOV6OkZuOKGZ85DcU2SR7BTdPWxLQVEP0klCWwOBLfXhmEIy5daw7hpX+b2kvDx9rzBfzvw+9J3+vwwpL1+O+/vyJ9x8km5H0f7Xk/xlIw6UYBvFOBLlh1zgIgMvrVNYuaFI1NFMc6be3OMOGcBtyMRFM56yK0hGGZOtQfeHNV+B667OxzOHbOMAxursOn9xiJptpMGLIlQvZ0+qQiZA3gFw4AcN5tckLMumxaeud1Dif63lKWgOoQO++AKFoi0CUO6l65odWJaUvX5M5OWwdNxbiT1lx3bPFkcHWofoRdRrY4JZ9xgcS0dXXaKpIvwgk0SJOEg25C0WQ43zlmOp7/5sEY0bdBYgLyiQ/5RVS+4FvDY01z5Z+eWYqpl96FWx6VI0tMxwgn5GnzRwFwd9q6bGqqrwfXCl3bTNqOrcw91dlJ1EHoefGx2CXUm45Zd12wr1TGNEauLSaz7NeoDwEu+OZkaAAf9RIyIy39nF6Ws6atg+NHbVMqAdN2C+eoIYx3zmblmLYFxsHL2QviurdLTls3R6JISKpDynJ/TbA7bSNHBwdxv1VGuIumrRp2HkoqxSQXGKatQX6BylyIfq3bBFCvXycPcvUnZ0th9L4PopdbPgaJ2qf1sg5kniHfq5fJbar++5Xl+E9xbZBUHmFbWx5PvbcmFjmRL/hRslTHpJ46cF1G2JSchIDnReOWC9NWOPQE651lTjLvlOiGdJxPlohM77T1EfXzci1t6T0K319N5ZxzX/07KBd9Htw7kvuwyUuZ6tGVs2lFC4g13fotbSTBWNyeE3XTRHs6u0911FNiTJVpG0fJI+Dzzz+Phx56CG+88QamT5+ObLaa5a2K5OCcIOV02qqabW0alpXAUDI4Dm2x6yKVGyYWqsC3/hk4BBev2RpOrILtVM4kbgKuSW9cYXJM+35kbA/pXac1tlXnKjV+VKdDwVe1Yfl7/PcXP8Rbq+IZvHUQjL33Vm8Ovxs3oAn/Upz9nBOEslAF+y6uXVWQdhp1LGpxWMjWc3AKirLbQkdi3PijbBcx8f/1+Q/C78SuK30UOclpq3/XRHt835eSvVDnmg6qocuNF4FOaPz88Yy/8u8s05YY86adX52mreh725MybU3yCIQ5Q8Pv2LKe+pkv58K4Sgr6rv/thQ9CR46rJquKH54wE57nYWCvOjz/zYPxoxNmAkAo3aGyWwVoUjzdovhZRQu7LpuCR26JTmtdTsSnl0dQdfZEXzvzV88kZtq6srxc5BHU8UktpX5WF+9rDY4OtZ3fPnqatmxSyItsRye2skAQyf/Wb23XLmDVv4XDryZk1kdOJY5pGx9zinN1gXfySuc3MFy/958gmeCvn3hf+t7EtBX9U8gombKXU1PERTM4djxziK5pb6/azP8A3kbUyyNEf7PyCKEDSq87S8eQIb3ld9b03olxhYtaiZxbftiGrOP7CET9oJREZGKM1LWfcxKrUOeGFHNvdXh52frYd3ShvpphmDfSRGRCM5rZFOHmPHGP6Luou0a1G/Q1JG0K2k0dHeV12oqqdRvDkZRB5CgFkskjUFkYILp/KnuXQh335HqD/32Hfq1+rXMy7zKyD57+7wVYMGVQsU1EB7qC8ghapq3H/x3bDGcccLe/vByPvbOGLQ/oN5AB4Au/fwGf/N8n8ednl0rfF8h7YGNd2qDrk4Hua5whnEp54fOV5HMKPi7800uxegTDPV/gZVBi9SsbEnTDIB4RZN+k4Mr7PtUZLk9/orUIm9eFHEHXFnE7Pfp7yuBmHDR5IEb3a8CcEX2MbXFn2pJ2OL5X9xfzXFx1z1tEHiHeB8Vtvfv1lQCC8Ve3vgjXMsVhz5a8eGdHolXahx9+iO9///uYOHEiTjjhBPTt2xdPPfUUnnzySdTX24WMq6hCRSWZti8tXR9biNuYrL3qunbzodRwgHByLGMSN4Ey+2y1Cy4gePZiB6+loQazhrfwdSh9hC60VGedT8KJgt8L2j6mht+ZICYUmu2S2xnkzkUdj6K96rGuTFthXLWGmnp2p+DZv3oWQLSw4Y7pXR8tYMQE//6ayKkaJrWQtA+Dv99dvRnf/8+bbHvP3W+cpDtHHQd7jtPrjQqojnxWHsHnHSi2UG8u02oq5YXXbxo/Iqet/H2kaas3tDkngps8AkkY4bCjfsPJu1iNyZ99Zi77eykjC33XdxnZ0iEtMc+TDc2aTCq8ZuG01TFXbJs2C1fEN2tUpi03tPq+L/V/KmeiOpB14WGrN7WGm4tGpi1pyysksY8J1BYvNRGZCpWVITZv+POrDuHyTSZS2KZjtdR5tqU1FyatbKrNGPX9/CJzH4i/q1STjdsoUp3l2VDT1rfaIyZbSOdw1R1TIPrXIqO4yWlLbYmSTDLmhTFdj0hsqYKT2dHLI8gMKnVMjFiuzCZZcbyl7GN1rjGNW2GyMNZhUGwfsUWSbJKJy7V1c5UVByDUTQd4tpObpm3cQSUxtVIeVm3cLo2zLy9bj5eXrcfJNz8Vq486UFZuDJy2dPHfUBtn2vp+vL9y5EjRLDHfmmR/1HfeNJYB5vB4DnSzw1Ze/K5jv4u2eqQvAW6JyFLKMaqkEh0XVQi7lGO5iuN9P7KNdKw99V20kUGoI9nG4i0Fal0usg6mjVMdg9L0nSka7d43AocXTdwGBPdCzY9QKnTvhZRAjZb3vPA+0blr0RqebEEd45s1UaDyPC478EzjdanyCJXQtKU3qS2UR9BtApA+lNaPJ6qz/OenzcMDX90/nLt1iG8S28e+JPrkAi6JyARMEYrq2EOJZ1WnbRzOT+qII47AuHHj8NRTT+FHP/oRli1bhiuvvBJTp06tZPuq2EHxxvKN2LC1nV3kJNFkM+F3xYQyFKbBw/d9LCSJyCrBWrXBFg6gW/SIybES2RZLZcXpYLpGKo9Qn9XrXuYVR53sBJV/8315cyBf4BPGAMBGDVMPiDuthNFCQ/Y4hzQ38VDGiGibyh7OFXyts+lEmsnTF3UamLbKJPri0vVoyxVC5jnHaG4hCXfEO0k1C0Xb6Psq+t/lxGF7NNFtAoALD54oGWf02Z2y+8hYO1SoiX1YeQQNi81kGA3pXYf+mkR+2dBQtcsjqAaQ6O8mQ5szSu2JyGTjWvea0vMdOHmgUdMWACYMbGJ/72gismF9GqxSDibUpFPatotM3ps1ye9yirauircZhn19Ni31D25sjetQE6atsvh2WXCambbR308vXmutCyhNHsGmgaw6cL566CTt+dW5o1xzu9ou18VXJI8gJ7y89Yxd2TpyzMaUev3isRY0G0Xq2Cuew5+eXcou2mm0TynzuW5+pU7YvsXoDh0zHZDf91KeG3eEmQXM/8bdI62zOxwLg3usWzxyDi4xB9JwfrXvmxxjpogHsVlR8GlEjLvDJZJHMPfztOadmDeqDwDgsOmDY8e4sKziTFv5XuQKPnb7/n1h0si3Vm7C0dc/hqOvf4ytT7yHy9ZtC2XIpgxpDn+nTFs6bn243kHTVjBti7aNadxVnXBWzWDys8sbQW0BG6NZRGY89NZq9ndxXTHWrCG5Xnhs8TfR/wuhI7ZYZziGBf/f98ZKnPXLZ7B6U6T5be77djasLuJAB8q6bHdwTCeFKp2nq1ti2krt09uSXD/iutb29jwuv+MNjP7G7RIj3SRzR5m2pmgsF+gc5wWfym6R60J0n6it8xFhy1OpIfrMtxWvKS5zEN9oCh14BX3fNo2F3KMMN4pQfk1b1zao39M+Z7O7PM8+PgV1RvfK8ww5LMh7WgqDfWuoaRvfCEziDI+iLgRjnzptO98H093hLC525513YsiQIViyZAkuu+wyXHbZZWy5559/nv2+iioE/vHiB/jSH17U/l4utuim1vgC3qRp++5qOUSvnA7Q1lweHjyrfphNc1fnNBILjp4gj2By2rbnffz9hSAEv6Emo51Mbn5kEb5+2ORw13czWXyq91DVtFXLU5iYR2q3zBf8IjOYOjLtjp32fCFMPgZEiziOadvKyCPsOa4fhvWJIhvE+9Kac2faivLCiOectn1JZl8xCdP2tBWvlXPariQZ2YcoMiPZtBfp1PmRruPBUwc5aSs9rCxq9PII8XdFff/ofVGNeLXN29rNTls1XFpALGZMUhTcwtLMtC3WTQxnnaFE+0pdNmXVuOLCe4Ggn9356gr4vo/DZwxhyzzy9mpsby/g4KlBaCNNVtHanifGcvIxxTR29ioysx5YyC946XPjxljO2VabTUlMLs7hpM5V9DxiA+iY2UPx1UP0jk0K3b0H5Hv22X3HOtXnKU4JW73BQerv8md1sT20RR9lpc4dSZx/bgsUD7mCj0mDejnVSccd4dTp21iDmcNbkEl5UAO1hXODjs06zTidJItOTuKOV1eEDHGBS46cgk/vMQqTv3mnVFaFaQPFJWGekL4xSdjQakoxybhnbXr+urGVe191ZUMdxuJn9Vn9/ulgI5+PbLDPPaYNbBN7izojwoShCZIIRfII5nKSPAKp/o/nzMf29jzeWhnfnFL7IAd1ftLZhI+/G4SCv/bhBmN9opl/eCYK/T5w8sCQ+U6fOb0mdZOBu4di3BAMbdO8IckdOHhwaFuorJMOU4b0wmnzR2FE3wYr+UEwjm3nVlmzOcUBy0Gw4YQNmVf6qqppe1YxEuvyO94w1k8dyO0W525MHsHiKEqil1sKGmszOGOv0bj1scXOdUsbharzkXzkLo0bF3728HthXz/6+sew+IojAcgbimofp8n7uPwVSaCzw3IFqmkbfV+bSYXPja51xLpp9ogW9KrLhH2Z9hmRqKw+m5aIGpxWf8EPNjEOveZhAMmlSEz2ZaEA1iHdEXARRDr7RcfWThrxpANl75rGtLQ09iV/r7Ya5BGSEL3o2KOun6tM2zicnbbf+ta3KtmOKnYi/PNFc4KvcpFxaKiq5xUZlwkGgXKJYG/Y2o6Dr3kIw/vU46+f38tY1jZI6cJpaivItC2X7o+ASR7hxgffwZ2vrQAQODBMZ/7Ts8twcpGZaRL0L/hxZ6ouW7VrYhaB9nxBYnSyofrKM/nBHbJsgDDa4qw9nmmbSaekUGthgJiYtpzRk8tHzmxuYZNKeajPprGtPY8JgwL2JU0K88byjVLILb0Ger4mZSc2SMIU/C3ClAG3xSOH9xgdXF1SoFg2cAs7IjwuI4eEcY6RmrTGaVssG10nb2j//NR5OPvXz4afTYt60RX/SBa9OkNp3IAmfP/YGejbWBPs1mvqFIabLrnF0rXbcO5vnwMA/OeL+2Dq0Gbp9zeWb8RnbgmYVn/7/J6YPaJFetdbcwUi5aC9NC1MrEDalnVb2mIhZPT958ZIrq9k07KDm5Mbj2/IRJ83FZlT88f2w4i+Ddq2U5iSdNG2jHSsj0K7+Wb22VqZtiaofdK0EaHChdl9x5f2wabWHEb2c7sfIaOMaOwJdjP3/uQYp22MdUjYu5zzX62XhvuLDb/rTpqNPcf1j0lo/PPFD7HX+LhkjCmsVrfpnZOcthl7PYzsjQ7cnM4dYjJPdBvOnGNZZ0Oo8ggqXv1gIwB5YSugzoFzi+xUCpNjjMu2LkAT7LSWwJJzZdqmNE6AdMpDY22GXZybNl0E1Hfe8zxj3+F0aqV2Mtdx6LRB+NFdCwEAry/fGH5P77nKQOTGk1AewYlpG/3t4rRNagt7nofLjpme6BgdokRkkWMLIPIIhnFZOFZEFJuYy1RHsPqer9ncFr5rfCKy6LiwnIPMAD23DrIzx36NpWD3MX1Dp62ubu0mt0HuwVUeQUfS+Whz9P6oa5e8b47aSgKdry5f8KVNqO8dOx1X3PEmfvrpuaGEXI7ZoG6uz2La0GY88nYg6UDviXA019ektTlI6IbEH59eaoyMMGva6sv7QIfsUP588e907dMybWOJ7Upri4m9SyFFYZXCtBXyCMx6Jknb6Yam6v+wkdh2RlSdtlV0O5TL8UgHjsaaDDa35oyDgGrclasdzy9dh1WbWrFqUytac3njwtXmKNa1P3TaOnq8V23ajt8/tRSf3HWElJlSgDqlkk4et7+8HM8sXov/d8QU1hmoGiF9GrJYV8y6/pfnloXfb9jWLmWRBYLQG7GLu+ijiBltctoGznp54adz3qvJgyh0TtutJJySW3iqx4lssgJi8aOWy+dlhrBY7GVTHo7fZTgeWrgad7++MjTexeKWM+Q4h017oWB02gLA4TMG46/PfxC+C+pC7aaH35WZtsW/G4mjdvXm+AKOZo8WrGed03bBlEGhvpeKO19dgUv+/mrs+0KBd0hw2nwCJqKFWDAJo4J7T3VMW3HvNoc70/x1qoaTyWm7qrgovo1IwJje05OJ7ITOUBfnd3Gec8/07teiZ/Tu6i34zZPv4y7yXWsuX/YEEAKj+jWGf29uzcWctnQzhXuPdVIa9L3hxlaORS8gtK6bNQzu/SYOiIXCNmr6BiAvDFySB6nQPXdbWF6caev+7NSyU4a4MWJdMcGRYStA9WdVjWnuuiKdtei5qgtIyt5lkx+qTm+SNV10nz3G9mM1jw+cMpC9ji2WTUoOeTI3iX5mSoRG3xObWcHNp9wxJptK5xjmxvE2jYM30mGMngkHLlmSapddxiTNs4WIAxqHAZHQaAuZtno78KuHTMSVd78Vfs5HF2YEnUK4dtC+J+DC+I0xbVPANGXTjuKjzfrkhADvTBjaUo/dx/TFU4vW4rhdhrNltyrJjLi2i/KRXr9JNsDsaIvXbS1SMYg+JJqghpC7MG0FWUEcGyUikx3BAp5nrp9uRkRsWDfnp81JHjpzCHmhFEagCbRNOuc+bTY9vVpcp30bfWduC90kokm7VCJIvlAIGfP1lZJHIGSMVAo4ZfdROHm3kfA8D/duWllsR9RZBNmluS6j1fbd1hYxbSloCzwyl1J7i+t7JhOSu9fUOdiRiC9X6OUR6JiTYr/nPrvCdUzLGM6tgxifPY84bR0TK+sQ2WRx0lqVaRtHeUfAKqpwgI3tWi7dOzoOibBT0yCgGizlctqaQvdV2OQNuPZ7XjQAu7KDf/vE+7jm3rdw4s8e589DDIUkA/CGre0473fP45ePL8ZdRcasCtUI+eJBE8K/aeufXrQ25vCgzkV6K3RalkCwa2u778KQeEgTXg3wz6Y970uatu0MHU99JGqyuzaNPEKOOFUByrT1UJdN43+OnV6sX2bacosU7hnm8j5aizdR5wQShnU+PIe8aLrtySXS4lw4+2lYr8nYzxd8bC4uJFSW4RXHzcB+Ewfg8weMY9sGAN/7z+vs93nfl1jBAjF5BIuhLbCiKPcgHMQco1po7aqGcL6YrOqN5YGhrUtI1VwnX78pKRUHV8NL5zAVjoxMOoVDivIGOnBjNN3weG/1Zvz1+Q+k3ynTthKL317F+6eOkeu2tOGe1yPnsTvTVk5gw8ojqO8sGSPE/WjWJLc8cqYsMdFUm5H04FRIC0zG+WKDq6atWiq+2HY3G+m7sMvIlrI765NC3IOCD2xXtEW5BSzHtI3JI6SisYxzMMYTkcXvn/qdWMA//NZqXHX3wnBc/d7tr2O/Hz2AVz/cGKtDQCePQMcs4cQxsSVpf7fZZFzkSlJ5BK7ZuXyB3bDTyyPIDCrd6Tg2oDpvchuZps2SUNPW4txykUc4/8AJ0mcxzlg1bSWWVfx3LtGuiyRRXNM2sEH+fO58trwuUzw9nuKkXUegoSaDW07fFb//rz3wCZI8zci05fT7i+WjhG/6eybJIzjch0o6eWwQ427YBj9410UfzxrGZRGl8tfnA1JE2J+Kh+gSkT24cHVYlhu3ovfMJ/IIGqat8rUr07bgR+NvTQnzngl0PtK9j7Kmq95eNCUpU8/FtoX8TcdCdd3RlivglQ82AABeckxIqkNK4+DLFSJNW3HNov2qPMLLy9bjO/8O7PDe9VnpHsnvrtgkTcmOcMYG9yGPtVyfMt1NVqJG/OGboyJKAVeNnmlLxhwDK7bUscZV95mSRFzPJMblfSYMCMfiBobkUZqmbVweIUlk9M6CqtO2ik6HbfdEt+gAgH+99CGOv/FxLGZColXQyUMkNjA579QFeLmcttSAtTptuRhcRKGi3PGZlBcOwGqCLh1+9cT7AIKQZw508EwyAH/5jy+Ef+tC5FQj5PQ9R4d/U+fJ/31uz5jOKJ3IaWZeszyC3Wk7b3SwSH6TySIvwPWHQB7BrJepHifYWFd/YpZ0jNrv8wZ5BEDOquwTlhfn7NM5bW1MW3GcYGmp17dNcXbl/SDsmIY3nr1PXH9T1Evbrco6nJN5NRIAAMd3SURBVLTbSPzqzN1iTi96KVw4MqBPNqcLbQ7qtffzF4s6dizTViOP4Ps+vv3P18KQNy7sFgB2GSl/b0pKxcE5GZMu3I+0+2efmWusg1uUU/bfWys3x35vbS+QpGnJDdJhljBe8Wzps3l75SbM+e49YbgeoGPayv1oSO86TBrcq9jW4nHs+683MgX7pFcdz55VjeufnDzHuLCj65ZStP1cFhEun5Mkg+H06roSVH92W5s87nDyCKGmbZjsJ54MRNLJZZz/b5CxUNShQqe1fdtTS/CT+9/BX55bhtWbWnHzI4vw/pqtuO3J97XXqLNbxPfplBeN65qyzyxeiwv++FL42eSzfXnZeuz6vXv1BQhM5gnXlmvufSu2+QMY5BFCJn/xfJqGuzwDli2X8mLHij5iSrJIGWSh07YkeQRzORvbTzcW2aCLUNE5uoTd0qs2E24AHjR5IO77yn7FtsnlRbLSptoM5o/rJ7PFjEzbuK0jiovx1yiPkJBpm4TA0FGop+IcrBK5wuCcFhtTTy1aGx4LcDq58nFThjQbmbaUoZuzSBgklUeg73A0/pabaRv9revLtJX0EmIJtehcZ5CS0IHeejpuqUSQZxavC//usDyCJ79n0aZmtLpS2x3ZWUG7rrv37fC35vqsZH/RY7cUnXy1GTnBK72n4s+CD2TJ80giP8C1mZb3QZL3lul95pqiax7twvRdUbt2qeYS7ZemeZvOY67nEs+kLZePmLacpq1S4QkkcbYKuhZUfUPL12835pnZGVF12lbR6bBlBDT5Sr/w+xfw3PvrcMMD71jPQyc+EZJs2rnhNEU7gkmX3IHR37hdykxuYrYA0b0Z2bdBYqCK7zmnbjoVhfG6bkzZDKZ20s4kk8eDJNxXdwp6Xw+eOgie54WLAuE8+c8X98HcUX1ioTS6kMJNlnBRXTilwKHTgqzKJlYG1x/acgVrkiPV2SOusV9TwKpryxdQKPix+tvzvLM5Gxra8uRsYtpybXeRRwgX92GyM7k9fRpkh2qhAHy4IdoIuPy4GayzTTQ9X/AlhwiH2CLZj5L+qBsDt39x7/BvdYEH8GGe0W/uHZ1jXddq5RGA3xAni0631PM8zBzeO/zclHCB7bqe0b2XctZZ873g+iXdOFm8Jr6ptr09b0zWY8P/fNysDSjuO302BxcTWVBwY6TYfDh3v3F4+3uH4+GvHRCONZSdqUKdT6gzSdwP3XOMZdO23BOXUE4TdGO+elrbo6G/2xzprkz2zgKVR1DlZLj7L2wCk6YiZYqI8ZFuLi5Zu1Uqz6lKq2OPOua9s3ozPlgfjat3v87LxYh2cMgxTltd2RNvesKpTgD4wZ1vst/zmrYmpm38txseeJcta5vPxT3WM23jz0CdN7XMdOV7cV9FX+H1FIP/C745YagOYSIySzkb01bVl3dFNubQFv/zGx1iXPzCQePxs8/MxYuXHoxbTt8V4wY0xdoJmJ0n9Dc1gSyrxV9sk5CeMSV3lJPx2MenzhzC1GuLNG2DzwVfXrOY2n/4jMC+3XNcPwBkA8eTbUn1HZw3qk+Up8DgiKSOVd3cVKqmLWAefzsCyWGoebieZg7TJaQE+LnENr9TDXcqDWCKvjxzr9HGOm1QExeGNhTRtFXvS0axsxYRAlVdJo0ji0lqxw1olPJXiE3S2kxKaxcIE9T3fdSQZ81tmJtuJ/cs6XtTcBxPXcHN6R1l2uqzT5jhahvSZ+96JmHXtOd9IvfGkYTkzz88fqa2znDjpxD1qYaaNMYOaESu4ONuTcTuzoqq07aKiuGjza14ZvHa2PdiAhYGhAoXXdY/P7fMmqiEjvP1Dkzbz/7mWemzK2uVw2+efD9cwN38yKLwexvjkxpIn98/CgsX94xzHGRSqSiM3bHNtgV0qVoyg5sjfVydIU4dGz/51BwA8UlWTATqhKBzLqrGPIUP+30X2rkzhvXWluGYQDQBAzTnUfuzYIDScHjhuKVozeXZxalg2kpJkhgnBAW3W+nEtCUyBqJNFGqof65QkFiYOgMiYqdFi15deCLLEi74MW3gfSb0x/A+UUIizmmrhrfSe0gZDDZwTC/heOE0bfs3BRqrXzhwvLFeukmhC6vXocNM2wQLIm58oM/9nVVxpm3gtHU+RQw2Zlo2XHC4jbEU9D3IplNSvxVjU77I2L/u3rfxyrINOP7Gx7HXFfdL9bxNGMbCEcwZtbReAdvzo+VL0bTVLRrVdqilYgkySP/+y+f48OjwnCWwOSoJcdsKvh/T2NONMwCdlxknEZVHKG6cff3wyeHvZ+09RirP3YdYgkSGrW/SsaWwMW0zxGnLbeZxdpXJ2arLes/KiRjqSRLZpLP9IiZ/8L/OMcQ9R3X+0b0v6vdivDHpJFKnmAhVd9GSFRDDbZJEZLy2rofZI1qczyugmze59ohxEgjmf8/z0NIga4zH2OoWB56wb+97c5X0vYsU1KzhLdp6aVE3TdvOG8RiG8zFc0sMVMlpq+9PQqeebpYDUX/RMdN9+CGBg7PPqOyHYITqnMfq/XXWtCXXWcpmpQkj+tgTWHJMUICLSIn+LkUegYKObyaSkxqJmBRS8jQvIv8UCr6W3S/6gXjedO2wZksrZgzvjYcu2h///sI+QflivxT2eE0mpWgDxx2Xzyxei7VbojULN0+Z7qfp/vt+vP93FEmYwPQ9NWvaltYWiWkLfd9xTcJMIeRJ2vMFLC1uRg/pzZFylGsxbcqRuVFs9mXTKUwfGqzFq0xbGVWnbRUVw6d//hROvOkJPKAYWuuLSaf6KIacgEkegeIeA9skQFRPbUjr1y/o318jM2JcmLZ3v7YCNzzwTmzB8fz7vAPIVicNMaIGylf//BIAfiGU8mBchHGwGUztpB6XxdTG7e048sePYHkxsygA9G3kn68wQsYNaAwdjGpzhKNDdUDqnBXmxCx2eYRMKC/htqjMkIU6Nao4JnecaRsYL9Tgas0VYgva1lwBbWyIf9HQJrciSKwjazRSiKRIFO35AssMo0gTreRCwQ8n0DP3ChwR6m5wvhDppwGR8fnD42fisGmD8cZ3DgNAjX0/yn6sW2AzzsR8wUd7Tr5flEEGAFuZPqFqgZa6CGtn+okuEZnv+yHDSITc60ClLUwMIQ7umrb890nC3rmFhE1/6sMN2/HEu2sAuLOCKUxJugCymMjLjjYV3BgaLgqZe0AXMt+7/XVcc+9bOOr6R/EcM8YLxn/AwBesAQ3TNmbYssWi30nxUhhHOsM59rWn/q5sqJE+qpvDw6o0YZAuqIT+LZVHiLIfB8/HmIjMkKE9khqINrVa6rN47bJD8Zdz50ubrxxqio4tCvVd9H3zHMe1WQXHtBXSOhTcZpdpf1xnr3Hfmjbakzhtbe92qL/oGL3BQZ+4T/4sJHOikHP9McvWRWzpJE7b8Jlami3LI/BlziSbCMfMHup0fvV+ifNw7wyVddKzLtXP7s5oCu4eqlVdfMTkWBmuXheHYCXVEdR2x5jfadlR7kNOLmjq09G4F3xWNxiofizFb59cEoa182HQoj4Xpq38OW2Z8GibokRk5X0AEwb1wk8+NQd/Oke/+ehpPpiYtqwTz9K96L2n+84mIo6acDUpPOV6xDU9s3itNmmsWH+I500jrA6aEkihjOrXGNqv4rq3tUWbVTq2qTjX9vaCFMGhG++1EgTM9+KrQjHHhHrujoB32vJlqQ1Bzx+3B0trm43RzdXveqaadPBMX162ARu355BOeRhNkgAnbQMgR6FE84YX+WyqurYSEsfK/PjHP2a/9zwPdXV1GD9+PPbdd1+k0x3TWqmiZ8P3/VAf9F8vfYgDJkdZkIWAtc6p55qI7OG3V+OQYlg7X0/0txgAdAxSTqPR5jz+4u9fwD9f+hAAMHlwr3DCAvQLLNvCJFpYpSSj4I5XVxTbFD8mk06FE4GrwzuJPIIL6/bht1bjNSU5im7nn0tsoA7yYsJXF3k6vR6Tpq3v+2jLmxNjUOekDuK37x07HT+8cyE2bGsvSi/Qe2VmF/nEuUrDpttyhdi5C4W4MDvAJKRAcC9E8i81oRUgJ4mi1xNlstY5baO2rN7civa8j3TKw9Ri5mi1bxR8P1zEAgjfz0/sOgKf2DVKLhIyPhyMfa4f5QoFNhmTpH/H6Euqchv0Hu4zoT97fgC46NBJ+NFdC8PPeea5iParhmA+AVOEHqljaOrgauO5aNrawI0JLuOEMMSTOst3Hd0Hk4eYHd50IwUAfvX4YrYcNwaLzTKObUwXpvaNwgB0PtE9x9gitgLyCC5TaVJN27EDmpBJecgV/ESM36TPvBIOkogVC1xzz1sAgJeK45VO9xuIxnVuLKKatpSx3VibwbzRfWPldYtgqU7lPD4iTUAbdFPYzx95D0CRaUvakC/40kJyExO1YjIrdHMm56A1DRExll/CpGVA5CgWV6Pb3NBFFaRTnqT9y0FdTJ9881NYfMWRFr3u4LsXlkQbParkgIqfnzoPZ/86iP5yTkRGfnZ53646cZa1DBDcC89D7Bq5158mlNFvBps3KWLlNdfCbVAnSehjc7TZ6i4n0p6HHOnz6hgv7hHd8BZsR89zY7MJW1Qnj+D7Ph5YKJNsCn6ghdy/KZ4kk7IXbRIGcXa1trnFNonz223EjuCoWeaNC51OtDERmYFtrwO1m+lYqJNHGNRc2+H7IWlHpzxsLBKqLvzTS2EemBjTNiVvjov54ltHTcV+EwfEz1G8bhF5VJtJywxfi6QLwG8ABuU9NnqDl6ghmx0dkOlyhW7TOZPir91THmWpTUtp1sgmuG6Qq7bKgikDWXIJNV8OmBTvE1JZYj9tJ32kxoFotzMisdP2mmuuwerVq7F161b06dMHALBu3To0NDSgqakJq1atwtixY/HAAw9gxIgRltqq2FGxhSxaVeescFqtV2jvc0a24IUl6511WX/75BJ8+6hp2rBqOvEJ/bAHFq7GsnVbpTBqAFi3tQ0qbKxV4bAFZAaF6Vid03ZLaw5PL15Lwsrig6jv+6xDO53ywoHPmWlrYWpRBwzH0lTByRPonO/C0OTCYgSEc22wEnqhW0it26oPofB988B/8u4jYw4fDmLHuyadUgxKWzh29Dd18NZl06jNpNCaK6A1l48nIiPhIhTi2dE74fvRZkgzEzLFXX8uXwjr12vaRs7s6+8PdKRb6rNheZVxmi/4WF7UtK1Jp7ThW1QnNMf0BwpuQZfL+2iqlevesK1dMha2MhnN1UUeHTqG99Hrc358zjD86K6F0eYPs3siDGj1Oj5cvy18n5I4ueoSaB56nrvhpSul3ueadEq7y805aE3aayqSGMvplIc/n7unUzkg6k93vLqcLceGbZt0+8jYMG5gEz4k0QRhGU92JAkHW40itcDVq7ZfB4lpW0aPplqVWrP6qAb0qsUfz5mP3vXZRGyQpIuQjmrKc6AOVsGK3mNs4FjlLqWgODm4+07DeFstm2BA/P5yYy/PtJXHs4aaNMuK5eawF5eux21PLQEQbHDS55b3fWkhsInZ4DM5UG1JweS2uecUMIUF62yLMON58fJ0tmFWs6E8c3hvvLBkPQC9o1A3drlo2lJmei+NtrnAgqmD8Ml5I/DHZ5dGdRuPkMcUlzFWd39UBHkHUjE5Je5aJaYts7HKHWcb+3S/q/JMAOdk0tcthQgbW1Csu4JU25QyicSSpsbkEczjEoWoSrwfodMqJeqO6jzj1mdix+8yso8m5FwcJ4c2s22IbVbZNrDdHcKVhE4eIc601f8W/G5v++X/eQPPvb8On5gX+U50NthtZ+9hrc8G1WlLkdNsFIln8NHmVrTnC+F8ceTMIew5xPFCJrA2m9ImANTpuNJk1RS6O8rda/GVT5m2ZetP+vOpoGMuPX+MaVsGh7KrBeV6KtVWmamRnqFtH9nXLEEi+kJBihZNheey5QHa2ZB4m+b73/8+dt11V7z99ttYs2YN1qxZg7feegu77747rrvuOixZsgSDBw/GBRdcUIn2VtGNsbUthzNufRpf/P0LoQMJiC++xG4KDV8+fc/RocatziDnWKRvM9qJYXlSnO4GfermJ2NlOd2UJOF6a7bITl8d60x3bT++/22ccesz+GGRicYZYbrJO5Pywt1P1zbbjGR6LheNYQ66toSJpyTdyOj3mnQqkb7pu6s3s6HKApT5oeLN7x6G7x87w0leQpfIxZ5YL/qdZqmvU3YT1XPn8j57D4Wxq2ra2hyw3PXYkqKIfrh6U2uYTGvOyJbQwGhXJtR8wcevnwjKDTM4QWmYcsSi4Pskx4pqLxRi79L29oJk+HDMNBPT1vROiLB5cY85B6VOHuH7/3kzlISxGYm01iQLxCQGnq5edSFt0pDl+rw65l1y5BTt8UnsUdeiYZ3Fpgnm+dDedVK59cwGD41wUEE3GDgWIiBvlOTyhdCZZpK4iDF1LM+bPh9XZ4sLYpq26mfmmLmj+mD8wKZE53Hto189ZCIA4PJjZySq360Nwf8F38eg5sDhc/wuQWZjk6atyWkQySPYNcKBeN/nnBfqmOcjrml78RH8+8XNGa+TKBgRLSGg+lG5pJ6m6CddUjDuW7MjVv68jYmUEJrzuogi8bVNHkGblI/8rXsfuWMv+9dr4WYlN8SLvi9s36lDmp022cRw5M60JQ4QTdFSXQF0wzFMusmcpEA2yWs0EZeJ5RE0P3N6irEEj4ZxlbLbXManSsojqBs9uuSE1PmUM0QAUIS67MV+VCC2bFAg+E/3nuuiIsU9u/v1lXhvdZCQqpcm8abaxBeLmyM6iPI0SW8lmLY26NjYalPkhFr6eQIA+mnu588efg/Pvr8Oj7/7Ufidbh2VRF5FB9pMtf+LOU99LeiYevyNj4djbpM2wa78Oe15Wgc3936N6FuPz2kkhvRJvfTtkDRty8S05eUR+Lp1TNt4IrIywJVp61id+v5xBCFAvi6brUo3jLaTvCxVpi2PxG/9JZdcgmuuuQbjxkUv0fjx43HllVfi4osvxvDhw/HDH/4Qjz32WFkbWkX3hu/7OOonj+KBhavxz5c+xOKPIn3Y9YTFmstHzqmz9xkbft9Qk5Zo8hy4HRduAU7bJFBHHBBL126LleWcrEmctj++723ps451xtW5ZnMrfvZQELr4btHw4Yyw7W1xRxUgOxFd2Um2UFzafpf7wGYR1mnriR35FG/kUEeHyvDh2nLDA+8Y21bw+b5Tk06FzEsXpi11NtOMlzbNHVpna3HRlvKEbk9w/tZcPBHZdmbRSttKb7kPulByD522OZfEc3l3dbQ58pVDJsVYjQL0Wg8kkii6egu+H7J1dQsPbuFdKMTf2dZcXjIWOMmT2SNbpM+SI8yw8BGGh++LRYQ+ERn3LogM8rZFR02JO/9JFpOuZQ81SM9w46XqvFGTz8htKP/ql4a/AcCqYoKkCYNkWYUv//HF2LHmDNlRX+VYiICcNG5LWx4Lrn4IQHyTgELtCja2VClMW5discVCBR0TLjj/wAl4+duH4PAZPHOnIxDjTnu+gNWbgv4xpn+gycYmVSrYmbZ0Ay/pxpmuLMu0VcazAUy4clA2Poepc4kkj6CUTy6PoGPa6hntHFR7oZWZ/4SEjX4jWWbaDlY2bAR0z4fWqmfaxr+79bHFYSJLzmET6jq2i0Wpa/8o2iUKg1gHOfFfeV9kumGh26AEgvmx1THBafi5RKYtpxFPrzvlWRIWJZRHoEzAH56gz4heCtTEozXKRrroV1TrNWcYlyjChLLFDi76U1wegT9e11+503KSFfQcApx0DIV4btTW0DHkKwqNY9M0b3JjB31G2XQKuyi2KAVdr1Qi4kTARFpQdY8FqA378rINAIJr10WGqePh8g3bZaeeZcw6cNJAvd1Mio/sZ2Z0RlIeERkmiSRYUuiqpuMo7c/qpZeHaevWd0pl2uqiRXTOdFNZ3/fDOb8umw7vTRI/zM6AxCPg8uXLkcvFjbpcLocVKwLdzaFDh2LTpk0db10VPQa7fu/e0OEIBI5Igb+/GMkI0Mlo4qCIqbNi43ar05Yz1Le1mxNQCeiYhAI2LdKk0C1kuDrvfSOuk8gmX/J9dvEkMT8dBzjbhKA6ZUzhkTpombZMaAo1cqgGpNoXuN3cjUWWtM5JqJNHkM6vcUJStJEQdxpmq7JNVdD7QJOFeZ4nJchT+wbHNArazTNtXUJzKd5dvTkMBdXpbooJl+oVTxnSHN4v9b3J+z52Hd0HADBvVB/tucU8ni/4UvI9Dpxh1Z4vxPpoW64Az/NCA0R1cgDAKbuPUuo2n4f7jerwUgjDcj0jtaKW0eFLB01EU20GJ+2aTFooyQLdtey3j56GCw+eiB9/ak7stxseeAcblA0z9XnoGDdAmVgEGhR8H28s3xgyBs/Ya7T1GJNEB52XNmqYtnRcemtlZPus2BiXUlDrFbAZp6Vo2urYLxTqFaufx/QP5ukk0h4ckixCVAdGuSDG7ReWrEfBDzZahFajLqkSQDRtDRnUKdPWKI+gnIZzbHFtUZ2YozSLVM7GUDcWqe9D7XesPAJ7pgC6DWquO5t0r9V2qBut4wY0SgnUOKiaq2fuNQaHTRuMLy+YIJXT9WXaBH3iPnM/5sZX8Z1I1KpzbMXPVWyXQXpBKk/arNsALJWtmGWYtlxz8kSbUOvsi7Fhzeem78P0Yc04dNog3HzqPFZ+yRamLpfVO+E4UCfIIVMHGUomx8eU8HL6/CTHFilj0mKniN4bv/gv+F51BOug669cf9SNfeo5phXzIuggLrmVjBldIo+g+V5liNJ7wfU7yVmX8XDW3mNjZQRcQsI7sDwNYWJ6CsSYtqx0nyEyQalg8pBeirM46i9cFcaoFfK3iJgB+BweomzBj9aV5U5sR5GUaWuS26g0XP2i6rypZdU7RjACssQKlUegEUxVREg8ex9wwAE455xz8MILL4TfvfDCC/jc5z6HAw88EADwyiuvYMyYMeVrZRXdGm25Aj7aLDsqVL1aAcr4oPpeHjyy+OHPw72829r0kxt1KDXWmo1krm5XBygHXcggdx6O2aJbQJaLaWuzT2POOEu9HNtUPUbscIaZuKVdxqhBlJ22/6SBIRPqE/OG4/AZcfbfG8sDJ8kJc4fHfhPn5dpH9QBDeQlD+GYoJZBVNG0t90aSRwjlCFLS/625Qux+cXqFQMRQlpi2BRCHgduC8O8vfBAdr7kE3YQr5m7VGe7CsgGotiRli+iYtvHv8wUf7Tm50cIhIOre6pBt3XVHmP5G+zCFWNSqUilyGfOLN2N4b7x46cG44vhkLJ5k2VrdyjbVZvDFgyZgbP94dtjWXAG/eXKx9J3qkJk6JFqU/fKMXUtqA+DOAqBsocOveyT8flAzz7ajMDNtg/+3t0fsTBW12VT4LusY8rr2Cug2abjyrotXXfiaVG8sAYZc9xXHz8DRs4biN2ft5nROHbqawQtEC0uRJHXGsN7hd9yzv+xfr+He11eGYz/n7IsWF25jsKrbx9WptoVuyh04eSBu+vQumDKEd3pw9pM6TtPxQrVzOH1606atWvf/fmZucAzj6jXNlTFNd+XzrOEtsTDvWB3Fr8XV1WXTuOkzc/GZPeTNOt04TK9Tm4ishFB+ccw7RTkvdy3Z4P8ocY65PH2uuvn3oCkDMX9sP5yzn95pxIGVR7AwbfXOPqXdFgYlXV/MGt6Cn31mHg7WOE11SY440NMmZSaXW9/2goMnSp+pk1ynVSzsWrs8QvB/sIaIvo+Ytua2aZ8j851u7FPfG1eHOh1fusJpK8sjkL5lcPhzYwR9RtlUCkfMGBwblwTEfGPCwGY+0iIJaDs/WB+PQFXLAPa+pkJ9r87ae4yWacvdN9MmEy1PNws+2hS3wWkklk6vt1Rw06OuajrW0f4cZ253vG2ujn1XCQJ1TtGRAqSoMKs8QnEtWIhs57pMOuwXlWSa90Qkdtrecsst6Nu3L+bOnYva2lrU1tZi3rx56Nu3L2655RYAQFNTE6666qqyN7aK7ok1W+ILWWr4jyGL/u0khJsaIgXfj4nlq+CcqFsNGZVpggxO+0pXVsDGtJ1ANP3UhZcuOQe32OBYvtxEVSjukqsING2T7UrZJqvXPtwgfbbdC45BozqYL7/jTUz55p14ZVnA2pQnrKgcDdWvy6Zx/1f2w+IrjsQPT5gVuy++74fGhm7icWHDuji9wwU5lUfw+Wtvrsvg/APGA1DlEeTFjLie9nzcacuF9wM8O8KHm54ixcpNEQtwr/H92TKqYX3y7iOlc6ttzvtu7aDsRRvTlrPtcwVfYmAA0fMR90fVtOVZOW4LPPpbrsAzbcUYcMaeY9Cs2YF2YTmVoldaCXkEAd1QIRIbCYh78vHZgYOvkRh0al9wsUfP3jvY+D1p15GJ2kmZrgAvUaDOJVSvWoX47q/PL9OeuyadCsetGx98N/xeJLnioJ5rroGZDsgLd9cw0WPnDIPnmdsR17SVf+/fVIsff2oOdh/bz+mcOlQyU3OpbfgkYbRzDu7XPtyIs3/9bKh5z0UkiMdCNwfNY5/8mWfaqvNc5KCZNbwFh00fUizHOM04pq3qtFUiByh4eQRN1AxJZikgkkNxh9z12gq2Hq4d9PMn543ApUdNlSJcOPiKPIKA6mjI6uQRGIeWCmsoP3OcesjsES3GOqLjoo0oIJk8go5NnE2n8PvP7oGLD9drjvPHxR3C3LVKTFtXB16CseEfJHKPrZuy1yz1yo43+7ld+kepEIlpBaitoHNsRZq2luskEi7UZhP3yuYgcn2OgF4LX52yXJ22tz62OPyuK+QR6CXSFpuS/nGvHn1GmbQHz/MwY3hv9pw2J9ouI1uc2fomuGw8qI/YRjyInYMUnzy4F4b3adDq/3KP17gBSp8N+fDR5rhPQvzs+35c07nDiM9HyTVt3Y4vJ4Tc0N4T+PWfioFK4sdaTR/UOeU5hGtJKUIjHUYPmJKX7oywx84pGDx4MO655x68+eabeOuttwAAkyZNwqRJk8IyBxxwQPlaWEW3B7erRZNZ0MlFvJTqxJ7yPClLNwfOcWhiNVFn6JEzhuCSv7+qLcs560xSpdvb81IStLZ8Ab7vhxOHNmSQOQ83QeuSovDyCMlDCWzzwf/c/ob02bZjx10DvadL127F/z4c6Pb+4rFFAOSFFJ2g1IWxLuzI933c98aq8LOOeVTweSbwxYdPDv/OpO33jzojueQzFJl0lP3SxLQVZd5dvRmPv7tGqkPHvBPnpl2kPR8xfl2dtkLz8zvHTNMzapWOMm5Ak9QGlXHq++QddzC2KFNZZwxyC4rH3vkI3/rna9J34jmETNui0/u0+aPQlvfxxYPGx+opiWmb59nVog+N7NeAFy49BKf94mk8+s5HUhmXcPVSkCgRGVO2FONVfU/FO3bOfuMwZUizNDarfcGlvV84aAJ2G9PXqI3M1bnooy3S95xe84/vfxtfXhAxm0zZhEW9VNdZRSbtYWz/Rjy/ZL30Hl9y5FTtMfQW1GVTVoa8FPbruHCaNaIF//niPhjaYk8KGLbLqebk6HqXbXwxTaN9dMl2AOBLf3gRAN+X6KLDSVdcud9sIjLlK7opl83Ic6AKbg5T5z/PC5LBFHxGHoFLRKaxhba0xueoSDswXv7Bhav5ihBvh7CV+jRk8YOifiiNcOEQOTfle6y+17rNM1qvzj9kG7q4sU39Tsewi53LoW6pPPm53Emb6GZiTSiPwDhtC34oA6Fz4MUypVuaKvoqAGy2RNAkYtpKLEpzGwBF87gC8cu0TnrvJO1dcq/Ee+16nYWC3Mc5W5KDzqbknWxu8ghJnVIpr/zsZrfz8k5FWT9aOYaTtyHrBPFu6mxemrCYnk+MkQN6dZxlC7htPMSYtsy48qnd9HJenAQJvY+0b6lRKACwcOXG2Hd83cFm2ItL1+Po2UO1ZX2faDqXqT9x05HWaauTPfECabdI4qcM7bL8/oPjZ2Lhyk3Yf+IAp/oy6RR612fDTWw901beoDCBkvW2EVJTyLS1JPre2VDyCnLy5MmYPHmyvWAVOzyEBtq4AY3YuD2H1ZtaJaZtwcA0/Nphk/Drx9/HBQdPwL9fXg5Az+rkmbYmp21wrl+fuRv6NNZgv4kD8NBbq1nnHhfubJJHEInDKFpzhfC62hNo2rbl49fATeaFgs+2KUPkEcrltFVhq5dl2pJjnlm8NvZ7Vsu01Q9L1MjwASwkrDouKYUoJxa8135yNsYOaMTEQb2kzQSOaXvXayuwvT2PY2YPA0gd2XQkj+Br5BFSHv9M1P4vDJZL//EaVDz0Fr/AjRKRRfeCMqN1+rQqhBFpTBhlceZyLPHFa4KkWyYHZcT8IHqRCVgUqsMWoKGCstN28pBmfGo3nq3pGsYjM23jerqAvEjWGYOVctomeZ+5sibjnTOkgbhTQNwT8V7XZdP4+Oyh2Nyal6ItAPsiHQiY0YcYkqHF2lls5gYizXPNJ2exjJRr75WdtsIw5MJ0hZPgsXfWxH4TeP3DjThl91F4XsmIPX0Yz6QB5Hvu0vclTdsEFr1uMyuqV/5c7gRG3QnqQor2jT6GcVCAG1vpOB/KKCRIRMbpfqr9geqyU4cwNyu7MG1Fuwt5P+60ZfQAdU7STa1y2VH9GsLRIqkOvlqcW1iHCSw1TmTRTrUHqwtH3TjMhY6rcNXoM32nS/wZPy7ZhoqLPEKpkDVtg/Zz94IytXX2uXo/7PfUszMHmLrs8ghxh5IJEtO2Ag5EHXmBbtLRs7bn5DnXVq/KtFUTkemgM42443QbVur9st0/dRO9lAikcsCT/ub7iwtznG42iGvp28g7Xzl5BOq0Lde9MGmMC8TlEeLXtu8EvdNP2mwufqD3hzr5uW74aSUPBQUtnk55uO3s3fHWyk1sJIMoWyA5Kcr1DnPzo65qOrer9mbK85yTTpYDQ1vqjRv6HO6+YF9c+KcXsdf4/hhPoo0p6LhqY9qKOe7N5ZvCZJ696jLhvanKI8hIvILM5/P45S9/ifvuuw+rVq1CQbGe7r///rI1roqeATEZNddnUZtJY/WmVsnwzzNMQ7FQ+fz+4/G5/cbB87xwINfZZnwisvjk5vs+vvD7F0LdOmFsnrbnKDz01mo2QQO3m2OSBLjm3rdi321vz4eLwPdWb4n9DvADEMvU1Oh46hKRRfovbqEEUhKrgh9zzk0d0ozXl0c7nDZ5BO4aqHFo0+2l5683ZFZWJ9mplmQGQDFslbBkZw5viderhPv/8Zkl+Pr/vQIAmDa0N8YPbEJ7yAiNpD108giZlEcM5ej7iGku5BH0E9pz769jv1fZIb4PnH7rM+F3HNPh8OmDccerfGiqKWnOO6tkduGIPsEEL65fJwMCmBNRRcwPPwx/MU3uFx06Cfe8vhLvrd6sTQYlnL+ibaLtpkzdKccFHl08L1y5id3EcEnU5KIx6oJMypPGkiQMFJYJ5k4MDKEaqu1kU0Pg2pPmAEAsaZnOEdwRiGcknLZHzhiCY+cMd3IemTRtNzD67J/ffxwmDGrCBX98CUDAjGky9HcOssPAXp5eRzkXsHEGVNmqlmDaYO0smDJhm5i2Ag3MhqIYN9ZsbgvHINPGWT/lPFzosToW+YjmGTpec12b29jlEtsE94Jz2jKatvHTAIhsv5p0Cl87bBI+NnNoqPusHsM5gyliUjtM8i3KauagkxFQw6pF8jkV9N6VqmlrSmYoUOfoUFVPlSgRWZmdttRuFnMd55yifU2XbCqpvin9dXgfs5PBdU5XoduYoKD3oNzyCIA89tINJZ08grBJ7Uzb4P+870vvjngt7DrN/O+mpHu6NghYpSuUAzqaCLNkkGbQJps2B7ioGTquik3XPcf1wxEzBuM/r8i2ObeeyqY8iJjWciXQGtLbrvevnolj8Js2oTg7R6dFy/UznYQEEJdHaKzNYM7IPprCwX8+one9XO/wHmP74U/PyvJZuveA3it17ZjyAGEllUfTtvwOz0HNdbjt7D2MZeRoBzdNWxrh01SbSSz5uLMg8Sj4pS99CV/60peQz+cxffp0zJo1S/pXxc4HYbg31WZCJxTdVaSGMBc6LQYn8Z5r5RHYRGTxheCStVtD1i4QOcbE4MA5TrnvkiYiE+2jx6k7USZ5hGNISAc3Ked9n03ukU55khPRZaD2JMMvbiB8bJacyda3+IJVfdGgvdHfXEiblGiBtKfRxEZUbotwFM0Z2RIrevqeowEUk7jk4w4lCpUVSx2cS9cFzNEcYRJyGcPV+sSppP6vyCNwC2QbbBlfucn++8fO0NaXRH9xdJExKdrQbnhHhplCssP+Gu16mxxR5x0wHn8/by8jK/iHxRBa1RBqqtU7Sl3lEShOvvkpXoc6Yz++XDv7fzxHNpo6mojMyLTV/KQOMyZdWNVuq4RjUFS5sehkba4PxhGd8UvHSbHZ5fJ8/mufMfjaYZNx7Jwo8aEPP3E4cpIMu4DCAizjDVRrqoRDHTDrz3cWVMYl/ezitOUgniPd5DQtYsW8JMAxwdWxSGLaWpxxbDQPmaN+cfo8ALLOJUWSRGRbivP6kJY6nL3PWAzuXSdJ31Ac+eNHE7VbfOR0R20ONrUPq5tao/s1sMfRyB1X51P8d2ZOJn/XpFPOmy7x5DTu5TuSSJcDHd9CeQTmMoSN73n6OTXGukwg+2CLJEoij0DhQDqU3r1KhOrTDWl6nTpW5+0vB/q+tkgNGtkkbUwoay/b8SqS3IHY5qDlFVDP2RVJyACVJME77dX7x/XRzSQqQVxLNp3CT0+Zi5+fOk8qy22y0TEjaTIwHTLpFD4xj0/gLOCyqetC0ACid4a+OzanbaMh8tJj5gZbOyjbvFx21LFzhmGekpNAV3Uj6RvqutjE3i4FXeXupNdujwKIf9dUlwnfEVci2s6CxEzbP/zhD/jTn/6EI444ohLtqaIHQgw8veoyeLZIb3+YhHZTYzzKKsvpuPGLCAHu3eWYtiozShibYqLjnL+spm3CXSpRnurHzR7RIrEVeb254DvqrFy3Na4TXCj4LLPmnVWbY9ntbQYOHShzeR+qnzSWydlwL37x6CJWLoLWwTknZfZA9P3AXvrdX/WqhPNMPOMz9xqDXzy2CL/7r93x0eY2/PLxxfD9yLmr1+YqOmH9OFNq5YbtgQwCcS5GrHCfZdqmCdNW6v+KPMJrH8b1mmgYlKmtQHQ/etVmsKk1h4/NHMIe08fgkKg1LCBVh4IIKw03QIrXnvJkp9IXDxxvXNSEzA+SadqFGcT167e/dzi2tOZCh67K6Js/Tp9AiV56EgOOMyRshvRxuwxzrt+GuaP6YnBzHVZsDLQDk+zKc5dZioGo9tDQycKcQP2uEiH4qjxCrzozq5nK2Yj3zSXJhm4TgIvgMEEOG0y2CCur0zahYygpPjZzCP798nJ8dt9x5a24BJhCdF1kZe59fWXsuyQhwgCwr6Ifx+l+xp+vW5IzgLeTxLHf/NhUHDh5UHAOJbpEgGOW66YjMa9zC2vVZFiydqul3TzTlmO06cwRFz3AMf0bMbDZzjDToRR5BEnaJME4oZa0jdOHTx+Mmx4KEiGu22pmNicFnXtDTX6mn6/bEtiuvWozzo5vXWI4Aa/ICgfsKglJEpFRuCS8KTd72QQ6z8jal1EZwe5bqCTfVJEi9qokjyCcaCWwx12OM5W1JvRTnbZdkIQMUOURItDmqNfGbdpRnVp1g3fB1EG488v74LBrHwGgi74sbQyx4auHTMLbqzbj5N1G4qK/vBz7Xd2Y4d7pfhqZB0B+18V90sm4qF2iV23GuI6QmM+2jZ/i/77Pzy0dged5+Mz8UXiWREfq2k3vn5okV9Xo7alIEu3AOuqrTFstEo+CNTU1GD8+ntClip0XwnBvqs2EoUvUCCtITit9Vlm6E8aBcxy+vya+EFCZIsLYFJMs67TlmKIlMm2pE0/VTjPpzdEduAeYhB1BIjI/VnZLa04aJDkH9N2vrcAnf/YEXl62HoBsfHDSECqTUHcvNmxrx3f+/Tr7G31em1vjC4iMpGkb/S0Ychzo7Ut5XnivxeLh0qOm4rXLDsWe4/pLrJw2pVysLcoE0ZaLTvSNv76CiZfcIWmmhqxwUrdaX4pZFIfyIAbj32aQycyj4o59sb4T5+kTAuhgWoio3VVsLFDmBhA3Qntb9CGpFIrt2VCozJ3rT56DbDqlZeAePn2wUUe2FKYtwL8P6j1QbRHVQOsoTJlnTeCMbi75UFhew6dR2XdibHJJxFMJg1ScQ2wimhgatBxAWcLxPqheDieD4PvJJQtkZ1SiQ8t6/yq9OLj2k7PxwFf3x5GaDaXOhPqO9yZyJS4OmR+dGI8mU5/d1CHNiTYlXJi2v396aSi5ZGN080zbuNYujXYQ+NsLy/DEe3HtZp1NFkZZkXeCkwVykihRyojP9JX0mDmVa6fp/p8w18wss8HGRLfJzyRxFKgLf9uhs4iWoy6RaamQNG2FJj/TFzeFJA79ppn6fFT5ChX0uq2sUMqGTODccrH3OzNEf4+xfcO/pY36EsZryn6nmpniOZTCHueOO2vvMc51JHUUl9NRmQS6ZHUm+4vbANyPbNZxY/jkwc3h/eMSbNP1Ujk3bQc21+Fvn99Lu3ZwccyP0kQuqMeH8ghUxiVN7pVyqu8fp48QjNftPi5Hc0v57mMSG/fbR03FkTOG4LDpcs4GWxRlUlRAHcEJSdZV3GU21aarmrYaJJ6BvvKVr+C6666riFZGFT0TkTxCFruOCQyNdrJTSDewxW4jxy6xJZngjCouWZPqgCiVaeuicSXVkRdO2+g4dfJmNW2LTiujLABkTdvRJLGPD9lQ5dr92d88h6cWrcUvH1sc+42TR1Dvke59/3D9NmN7BbiQy/osWeiRgd3kbKF6Ztm0F947atCJ+ygWVwU/EvbXZTNWHawxvc58xAgNEpERpyOnF5jywDHHo/4f9Ivvfnx67FjbojwtLWKL7cvZdWF1MDptFT6lcICqu9pqm20OkHDxregN26A61WzXK5ioOkgh6gkWZJw8woBesuNYfWU4nbOOQLfpYUNSg19XtTqWFchiUEVHM0e7QNQYOWDN5beQecKkaau2tak2/hx9zbEmyIZt6dIKHYW6cCn3o8mkU7FEdF0F9T6PGxDJF7k4ZNRFFhB/FkKmxRW8pm28LR8U51pbO00bwzSqIpIEisrd/PAiTZ38ubYQaSyB6HbEI6xMUG0OwbyVnHDE+cTBxWJzSThnAif1RMGNr9RJmUTbWa0pyWYA5/jpCOgcH0bcMNcq+oTO1gKSO+RkKRl3++jD9eb5n8LFN1BT5jlchzH9G6X1gE7T1hWi3xQK0foqicNLN7ep/fFrh03S1qE+NivTVqm7q+QRaDOkfmi4f81kw2L2iBZ879jp+OJBEdlNdz/FO8bKI5AbmFSKqSOwmTU/OH6GcVzikv3RvCV0nKD38bKjp+GoWZFkIAd6VttrEW5cFHzk83p7r1TE3yF93afvNQY3nLKLkehRARO500Avy1W6haI+W2Xa6pD4zX/00Udx2223Ydy4cTjqqKNw3HHHSf+q2PkgHHJNdZlwQbGFGKYS09DAtA3D9XTyCI5O1BjTtvjyh4sUph42EVnCwUK0T7A/0ykvxqJh5RGKDkXKTBCh1HQ8K/g+y2bzfV8a+EwO1/c+Ctg69HJzeR8/ue9tjLn4dvzluSDcSnXk6p6JSDrCwZaIrLGWanZF35ucdxMG9UL/pppi/dG95o4RdUqJyBwy2wbJsfTPPpP2QgO04POatplUpDNM62pVmLaf2WNUTPvVtijnjO020ueSIgnTNgyns2iU2owhiQUtHAou8ggx5pH5PC2WxF+0uiQGnGDm7zKyBUfNGopz9huLuaP6Go+ZPsyeNC8JaHsTsbeYoly2XQFdza3tcr8X3ZxjosXlEUwtLA3qxgtdSPz9vL3w3WOmoZkwAmlEhEmPV0W9ZlOpI5q2SSM/y3n/1EuuhEO9u8C08LdtGl3JsGyB5FnRVXDOrY600/fjCSQ5aQVTJAhbqYIN29rDZJ3NDNOWHkJzD/z2rN3ZU6j2nWiXtOi3SGj5IYMq/tt5B4zDzOG98fE5ekfAoGZ9mK/A2i1x6SoKXn4m+jsJc6gU6ZLj5gS2o6qd3FHQdps0m4VT2hRZErMXEtg8PzjezL6jTt0kNnx30k4c0KuW1QIF+PHZrksa/E+ZtiZN1tjxWqet/Nlku6rtttlbKkvaxsauFCRHGvmey8sisAeR5Hp31WacsvsoqbxODqQm1PGM99tKMW0pOG3ajmrcc/IIVCKoT0M29rt6nA6SxrCV0Vmcl0CYtmW0ddSuX8oj0ukn9zTQttulGhmnbU06fJ6cb2ZnRuJRsKWlBcceeyz2228/9O/fH71795b+VbHzQeys9yI6JBS8pi0njxD8r2N1CgOsf1MNfvaZuQCAsQyDR2VCiMW0aRDg9KxcEhNQiIm2LRexP9UFFrfY2FZ0fvSuz+LAyQMBABcfPkVqc9AeP7w3dJzzmXIUlHksJmUqB9GeL+Cqe96C7wNX3b0wqMNRHmGlgckoOW0Zdkq9JtGCzWn5yV2DMJ6C77MZ6wU8snhstTgGqQGb981O22zKgWnreaxm4Pb2eP9XF+1qn1HD++mzDpm2RLohKUoJ+VMNJNWod9UrW/zRFsKWtrdDPY/NIEiSbTuZpm3wTAf2qsNPPjUnfF9NONrCGkgKuoOdxL5TjaRz9xuHP50zP/H56SKXjtk2pwVQIYOUMCmCc0bnmD2iBZ+ZPxovXnpIuOlDF0Ympq36DZcR3UNyJhDtb13JtK20pm13gukdN42DXztskjasPunYp4Jn2pbutAWABVc/JH3mohk4JotwLLQ0yJtd3HR492tRss4RfaPwWBqKLSBC9WvSKQzWZCxXTTDOuRRKL2hsM9PG0UWHTsY/z98bDYZIngkDe2l/E7DJDnBjW6nvq3qYiwPliuNn4omLD8RBUwaVdE4dvrxgQvi3KcmoWA9wNr4AvUecDR8vH/09c3iLsWypJETV5uWw36TA2WS6/nLAgzo/eNJvKr5+2GRjfdReDedHRXbE1EV1eqHxRHn6Suj11GVTVhtAnYu7jmnLO7frJIaofAy12UWvosdmNeO7iUVO70el7gUnJdZRe4DbnF5AxiaqhyvLoNhPrGNBm8oGiciCv8vp/C5HNFmpkmfdDUmSQXK3qbEmXWXaapA4Edmtt95aiXZU0YMhHHKNtRl2B7EgOa30YeocM5EiTxbjIpyKkxTQOW1NgwANd95rfD889s4aZ2ZvTSaFtlwhrLeVsDrVhSDnCN5WzKrdUJPGL07fVfotGPj9sN1isUInNN9XnI7K9a3ZHDFD0sw9oIyz5RsCJ6y606u7FVz42a6j++CZxeuk+8cybWtoSGXUfltSCuoMbTUwaHk2J7+QoLZSnjBtv3/sDPy/v70ilc2kvSjczPfZUPlM2iPZL+NMW+o8VtuuOi/PP3A8rrjjzfAzp3skTmGaIM8/YDyuf+Cd2PcmZ2mzhqmqGvFJWRSi/M2PROG4TvIISrV2HStzfaVq2opNjySaeUk1T20oVf9KdTKN6d9gvPcu8giq1nS8Dg8pL+qnlTBIRZ25cJ5gyqS80EFKN++EA9pmYE4a1EsKqT9y5hDc/vJyfG7/cZg/Vk54Z2OEyQwUY1Hm2DI6bWOfe/BqwQKTc9z0DpxjSKKWNMGOCi4pq2kscolIUCGSndZw8gjk5RXj2k9P2QUn3/xU+D1nC9GEZefsF90f0XKfKVubTWn7uhrNU2AW1lxyTxl6iRYXPPrOR6UdSOCi6e1el/kzh5pMCkN6l9+puMvIPlh8xZGx73995m5YvGYLbn7kPSxduy1k2nL9WkByxDtcVEcSXrnChQE9rKUeT//3QVL4eyVAN/3FZwGO3dqvycwQF8f4iGxbzobTvVf6RGTR37ZQdlrWaYNeaV9nSgJQeNLf0adasinBjY83nzoP3/z7q7j6E7Nix+psQdMl0uuvFOv4M/NH4Yd3LpS+s0tnuEmh0brG9G9E38YaNNVmpA1Cel9c3mPZyetWNkhEVjrJRV9/x522HCu5JyIJIUG9zhF96zFtaG888nYgfdmdIiC6A7pmFKxihwKVR+B2EFlNT2MiMvn7mx56F6O/cTuWFrMPp1N8kicB1UEonGcmeQRRz+HTB+Pjs4dp6+YgQt1FeeGYbqiJO7E5Rm8YTsaEnKkM2kgeQS5HDV/1+tZvjZy2wkFLDVQ1W/SGbe2xgVJ3L9QEY1eeOAvTh/WOHSPacOnHpobf0et9Y/nG8G9bFvYUeY6C9cLdO2EstOYK4T3WLcwleQTCtG1pyKK/YhAHmrbB3/lClIhM3fXlNglaGaat2iYaVtjSkMUcJXSdY9rStulw4cET8Z8v7oO9x/d3PubU+aPYc9kSpFidqcw44aZpm9RB4uY8dqsrgnh/ymn0JYWsaet+XHLWK/877dcFiWnLly93kgUVkYa1OfSN27iKNM7MfVANH7zqxFn40znzcf4B49GvqVZi4do0jFMJDNvYsWW8fXG2VPnq7m4wva+m8cc0NqjOD5eM9ZccGTHz1fnFdj7deD1+YBP7PcAzbbnErO2aqAdu+heSByftOkLRtA3aTs2QH9wZbDpu2p7TvpcF5SRcCKtoli4aS3zdlQte7vGU2hx1A6U7vpv7ThyAU+ePDu+5sL+N8gjkHrm8L0nm5lLZc64kjYG96ows4nLA8+R7ROd6lRXr0ifo2iqcHy02nPSb1mkbfT91iFn+iXPemRBn2naNu0LnMKebEpwG7cFTB+HJ/3cQ9iza2xLTVnMtrkzlcibQojh777Gx72ynOmKGOcEovVRxLxtrM3jwov3xny/tIxN2Etq0dHy0Mjoh5qVofVfO+1gOCTD5HSm9LRcdGmhLf/GgCZaSlQG9dlcCj8CdX9oX9TXpcMyrMm1lODFtd9llF9x3333o06cP5syZYxxYnn/++bI1roqegc1EHoGbjFhNT2YXXhxKjaela7eGLMPP3Rb0rZTHJ3kS2KIybVOyPALLtC06KZtqM8a6OdRm08D2XFgvdcLWWpi267e24bUPA4dlg8UQzPtRWijO6MmkPOQIG1fgz0WdWgBozwU1UHmElRtlXdpZl90dM8B0O/BblKQaNZkUq028rui0paGRuoRjNuedqP93Ty0Jv+NaJ+YKcX9NdUuZRQtE9yvlxZhN2XS0aUCNtYZsOrwfmZQXGpmUyRwyzUmdi4s6w2E9ROv3t2ftHr4zApIWmXIdJsMllfIwdWhzLOmHKdSqf1MtbjxlF3zutudxxp5j2DYAycPduWa6yDSo/d7GjrE1i54yCZNDjA0uC85KoVQjPsYMdAwrUyE7bUl5zW2kUQOVcKqIRa7oE7pTcAz4JJq2FHXZNHYbE2kZU8aTnflR+iKsnPISSbIe93SYmPGlMrmSJtgBgLP3GYt5o/viuffXsQy1UpzLt5w2D/v96EH2N6GbT48V7z21c0TUiHovOCfpVrI5TUHDUAUeXBgljNW9+3GmbfydDBNYasb9SB6hNEwc1IS3Vm62FzSgnPIISTdDuxKiP4kNelNiXRNzlC+foB0lDmCmTY/OBl3jAPGwecqKdbneFHnXC5rjPDI/q9DZCEmcM0lCprkyOkmBSkPnIKf2KieRZqpHl3jP9H5Tp3Wl7kTA0q8LIy5tbRrRt96ob60eT58px1an99TFJqJNc7VjfUTzRDlJF+rpS5GwSOs6W0Kcu9847DdxACYPtsv9VALS2GXVtFU/e8U6xPq56rSlcHLaHnPMMaitDRYjH//4xyvZnip6IITTtqkuw+6GUvua0/QU4Nizr36wIfxbyBCkUx674FDbE6I4KJictiHTKp0yluMgJhqx8N/SFjEN1AVW3vdRKPhIpTz4vo9jf/p4+BuntbbfxAG449VAO65QIIk2mHEwVYw/pizZ595fKzk32xim7fIN22J1vU6Yr4Ce2bI15iCPjE1xT7e15cPnThN96Aw3m64cN5k/s2htvBwz6fXWhftTpm2BOHE82WmbTnlhqDcgZ2iur4mctqmUFxqZ8qZFvP9vVJjh1Jndv6k29ny4xCwCLkZILOzMwvI7fMYQPP/Ng6WkXjFN24T2BWdg2TJIA3FDaJ0lKYytxiSJDCiEH95kWNLfVHZzOVBqRumYk87iq9LV3BGmbSV8DxGTQpzD3A6JaSuY05Y+aJsR6MIySXbsrmRsx29T93UMdRSlMm1NiDFtHZ/l7BEt2gSAJm1D3eaWTisW0CQiY96DnCZsdEtbDp/99bM4eOognDgv0JQXTNsGZdEeHql5WXTvfoxpy7Chog1hvo7QTimxC//9vL1w0Z9fxtn7jCmtAujkEUqsrIObo50J8Zzue3MVAN6eDcsmnHeTXDete7fRfQ0lA/zr/L1xwwPv4KLDJjmfo9LwPOUeKZdPPyYJIw9Cw0WdSt8yHK/bz0ryHJMmmVLHwEqzm3XwpL/jG0hANL6a66HzvY48oj8+W6K9lxRJIm9cIteTsEezieURojI224zqOot5rryJyNS1WHJ7QpZH6FhbRMRrV4DeV9v6Msb4LxavatrycHLafutb32L/rqKK91ZvxqIiU7CpNsM6XrjsxJwuG90NFlhPQvdFzemUZ9S/VeURRGZjLjGUQDsJdzZJL3BQnQAfbQqYq/2aamLXedXdC3H13Qvxz/P3Rl02Hd47AKivid+TK46fGTpt874fMUmYkJLQkU0m0v99+D35OovGBb22O15ZARt0NsnmVpkF6hGGgGACiGvMpDy0NNSEZblMpRMHNUlJTThwxuGxuwyLF1SKnXfAOK1hSQ3YXKEgsXy45C3i/m8nO+zUqMwQ5zXdKRROXpPem5SgLcVIAhjCzEpZAGUz9mP6NtZIn+Ohde6GHteGmow9MQV3nlJE7nX1uTrORvdrcNLEou/+Tz+9i1PdSVBq0oJysbfoGCJr2vLl5WzV5V90qFXq2iGeGY02cGXa6javwrrT+ndTBf25UuGOLti55BGid/Jz+8s6tdQZmk17zgyP5JItdpTiXDYxjdqYRJyRvUCctoRp+5dz5+PKuxfiyffWYunabVi6dhvufn1l6LQVEU0q0ypcHGva4sLcB6g8QvSduLdaeQSlDUnRUJPBDad0bKzmnkOp77d6WHd+NdXrHmmw45I6bUtl2p6yx0hr+RnDe+OmYmLj7oKAGKB38slRK271AbL0l82Gc/mNOtmsWeKVxGc2qI7iVZv0SY8rCcnpqDHbXYIyJaatxuY2PYNKb3qHbVCu0fSsbPYQIPdPa/K5EiW/APdImYLvs3rpHYU6/pWyEZ9UQqS7otTNHPo5TBxf1bSVUNW0rSIxNm1vx4k3PY5fPLoI3/zHq+H3TTp5BJ9zWun1W+k7upWE3wuWYsqTNZpUCJ3V8w8Yj9e/c2g4EJoGgdAJQ0LfXfd31B2h9VuD8/drrIklXFu/tR3rtrbjh3ctxPtr5LD4AU1xpkzv+iymDwukCmhoEx0HRfbpsB3kft/12kqpPuG0pQvSFRvtxpDOgb21LZ5gLHqOQQKwI378CAAUpRuiejiW0ah+9kzCXCjSiXNHxL5TJ4Mx/fXhb6lUpBOW933JiSMlACj+Le5/a3skh0DL0eNof4vkEfSsAcpcUsPkRN0C6nTosrurzqGl7AjbEpElbYMqI6I9r3LgIVMHG8vbkirR09qMi+tOmg0AGNan3olpS8e4SiQuoc+tQ0xbq3NR/v0LB44HII8zdKNNd88lhngF7FG1nXrGrxwZARCmrc1pa2lDkkQauuzgOlTKhlfr7blLBTvoopDKWgBQNueiv3939u7GOtV+Vw7WtGksctFlV9HKOW0Ze0EwxjJpD/NG98UFCyZq64zkERSmbbEZdEwYWmQB7zOhv0EeQf4sHALyZk+xrFYeoSiNom11x7G70m9UcM+hVBkddSztzgt5dS48eTe9w1RmlLk4bd2vO2kYfneEB1X+SPmd2cgwIUXeSZ20k6kanUO2hhlP9G0gz8XJaStftDpedxakiPUOjCz0SB370HRbbInPygWZFGIuq08IGSHJc69JyrQlt3FEX3PyRbquF+0urzyCfp3mig/WR1GVPXPkCiDZtgnlEUQfyVY1bVk4rZT79OmDvn37Ov2rYsfHr594H88sXofv/Pt1Sf+ruT7LOm2F8e37Pv76/AcAIscmBTUsBDZui5dLp7zQ2cK90EIeYe7oPlKIlhg8uI2bXD4axKNQotKYtpu2B21uqstonXPb2/OSjMOCKYPQu4F37IQO6gJh2sLDMbMDLbzzi04UlX1MHapfPWSidJ00IdrwPvZMwzojQdW0pe3N+z7e+0jWhxtGzqUu9lzxtxc+kD4ft8swDOgVT+YSmwwsox1lKhckp218AlI1bWsyKWmiShOmLc1SLxKn1RmunSbw4Jy2dHEUMxQcJAbiWXmTmwemRQRgZx6o5V1Dk1Wjz8RYBlwMBuo4s2gpk+eZ1yx8KEq5r0lQamKvJAwbQDYe67IpnL7naADBMxbvieS01VRX6URkcQYxX44zBsXGCmdo06ba+rUcwmgum3QRW6neFGfa9uTlghk0YZZ6zyXWGHl4fZvkKAMVsbDIMiTMMY1bdGE7a3gQArnPhP7G59bKJIDlIorEXCXOYdqUEvIIqja9KlMCBLYhAHx237Ha8UG1t7iNMat0VRiJpG12h/G/p87DzafO0/7OOm3LxbTtxq+m2u2bmEgqgaSyRKXOb12pOd8ReJ4qn5OK/S6QxOldKETvTlziSl+P7rcazZhpq8PldVDtp4sOmWw/qAJIqr+sAz1WN74bN+vIMUJurhKQn5P5gh1UIWQnsGVqpHOw70CbkiUnLHYs8S90RiKyUjXyBboy+qqjoGv7xEzbYnmOXFGFozzCtddeW5GT33jjjbjxxhuxePFiAMC0adNw6aWX4vDDDwcAbN++HV/5ylfwhz/8Aa2trTj00EPx05/+FIMGDQrrWLJkCT73uc/hgQceQFNTE0477TRcfvnlyGSiS3vwwQdx4YUX4rXXXsOIESNwySWX4PTTT5facsMNN+BHP/oRVqxYgVmzZuEnP/kJdtttt4pcd09HK9Hx7FWXwepNrTh02iD0rs+yk5EYIF9cuj78jgudos4+ALjsX6/h1scWs+VMEgabt0eJ0SjE5MExbcNs8OkoTNu0w1OTTqEtX8B9X9kP5xUTpIVOW5GYrS7LykAAwQJFTLxDetfhZ4bwLHqtoaZtCvjB8TPx6T1GYe7IPsH1CYZrsQx1Cu8yKijDado+/u4a7bkFdE5bVdOWtiNfiJ4FABwxYzAaajJ49pIF2nD4UuYpLbMvIaNQaALnfTkRmbyYF0zbotO2+C5k0ymoGpXiXVj00RZ84fcv4LP7jCULXb3TVmLsel5s4WHaCXdJ1iBlbk55JTlpkt5bG1ydtrLD2u5gOn7ucHN9CRaPGfJ+5Q1OPoERfYIxrr/F6VMqJE3bBPahq3NTQFpspFLSAjLv+0jBkyIedH0h6aItKdQqbZq2PNO2Y4Z2Ek02eqoOnrZDSNofejIGNUfRLOqMpttksTl+StW0NdbpyLS9+dR5+PuLH7CRJhRi3qf2CJedOUcijgDzRsHaop54cz2fiIwuurcRVq7uvVAXZ9zGmCmXQXDOYhsqyFPqXZ/FwVMH4YsHTcCP73sbo/s1YPGarVEbmXe51PlRPaw7v5v0OfUiCX3Zsgkdq0luH73/PXUDyvfV+UFvb7k4dyI9z8i2tW28U+iepS46gW2D4Xo4qJtBNEFvZ4K2tGP9KTpW59AzjRP03DSXRrkhR0SZr9eF2CQxlS31ZUl/yjnIEyVh7IuyeVJvOTd11EfaUTugp244AbL0YSKZMGmtYffD7IxwctqedtppFTn58OHDccUVV2DChAnwfR+/+tWvcMwxx+CFF17AtGnTcMEFF+D222/Hn//8Z/Tu3Rvnn38+jjvuODz22GMAgHw+jyOPPBKDBw/G448/juXLl+PUU09FNpvF97//fQDAokWLcOSRR+Lcc8/Fbbfdhvvuuw9nn302hgwZgkMPPRQA8Mc//hEXXnghbrrpJuy+++649tprceihh2LhwoUYOHBgRa69J4MO6sIJdf4BEwDwC5+CHwzubxNW7gmMM4VKHuQLPuuwBWQWIzdp0MRo6nG0PXQCEfqGVIvUNFa0Fxc3veoyxAlQQHu+ELY75ZmzHLcW9X3H9G90MnALPog8goe6bBq7kkQL4v6JyW4r0ZsdUMxqLvR+uQnR8/RMMr08gmw8ZNNE07ZQCJm4vWozuO6kOQCC5FoqDpk6CHe/vhJn7T2WbwDB0JY6SQtYB/WOuiYGKhR8iY1ADVOx2y26jo5pm/IiZ29rroB/vfQh/vXSh2GGYqoDOHN4b7y8bEP4mRp0HqNpKxm/JTgM7nw10jAuNTwoSWidC0ph2roYNQdMMo/fEtPWkZWbK/hRMg/DhZ+73zi899EWHDJ1kLZMR1Aqc9XE3HY5lrK58wUf2TQkD5iuukovpl3lETJkfBJw1rS1tCGjSKQYy5Yob1FuqPetkg6vrkYfEs2ibpxJC2m6kLAxRZShy7Qh5wqjpi1p58DmOnx233FsuXzBRzrlIZcvhPOZlIhM2Zz2fT+UTRJ9k3tP23IF1GRSWFHMMD6ktxypEzGaou+E/m1DTcZZ07bAvJMpi20WyiN0Qhc+/4Dx2HV0H6zb2o4v/v6F8HvuXS51AR97N7vxQp62zeZgk5wtDlN/qfI/PVYewTPL5yTdAKXvZPheaRhuHHS2Vg2zCaRDUsaqqpXdUeZiqZCcjmWqR7dBaHqW9LdmTULlckBqg+WCyy2PkFVsSxvomGPbNPCI/R62p4zRcLFcISXU/Z1jpuHSf7wGoOeOXUAQLbrvxAFYs7kVkwdbEotrxmuOXFGFozzCxo0bnf8lwVFHHYUjjjgCEyZMwMSJE/G9730PTU1NePLJJ7FhwwbccsstuPrqq3HggQdi7ty5uPXWW/H444/jySefBADcfffdeP311/Hb3/4Ws2fPxuGHH47vfve7uOGGG9DWFrAAbrrpJowZMwZXXXUVpkyZgvPPPx8nnHACrrnmmrAdV199Nf7rv/4LZ5xxBqZOnYqbbroJDQ0N+MUvfpHoenYW0JdMOO3EBKubWAt+lITsiBmDWScN1ULdwMgi0HIqK5dCOCabVKZtSj8htJMFgk0eIZcvhM7NbCpFnKo+Hnl7dVhu6dptWmdUwLTV6/tSUNkI4WfgjHfKBASCjM8AMLBXbejAFpIJ3IS4x5h++J+PT2fboBs3hYN8ZN8GDGupx57j+pPFYLRYmzykl9HouunTc/HsJQucdKtMmbUpYtIBNsYU2dmjTluu3aLu0GmbTklGayYdlzUAgHdWBRsXlEXwpYMmhH9fd9JsqR7av7jrUC/JhSlIn2WphrAtxN4ltIlClxFdBbeA7whoFVYnG2GmFXy7k6++Jo2ffGoOjpo1tMPtNLUHKD27NvdZBXXipVMy81u8J5T5ptWSpf3WubXucGWMqsZgoeCH47ltE+O0+aOMv2fT+ndT1w7ANZFJZYz4nhSC3VF4noerPzELXzpoAuYWo08EdCzpJNrEZ+89pizPSTfHpTx3+QWhX99KNOCpPIIamUMXR9lQBihe771vrEQuXwgTAw3pLWvxh9dfrM73fWzcFtgBveuz2k2BdiXWlgthtSWJFe9RZzg3azIp7DNhQCyii5uDS3famj93J8hORgvrklyISd8/KuNup5SaoLM7IZNKGccg+jEJUzlJIrL9Jw2IfnNg2rqSIgC3jcHu4rQqmzwC+Vv3fpjGLQ8eztp7DBpq0jhhF3MEWUeQZEPAxbEqk0zMZek6QJ0PONDqbEsfUZZG2paVaVuGiJvxA6K8K915rLfB8zz8+szd8O8v7G31b+jIJ1WmLQ+nmbClpQV9+vQx/hNlSkU+n8cf/vAHbNmyBfPnz8dzzz2H9vZ2LFiwICwzefJkjBw5Ek888QQA4IknnsCMGTMkuYRDDz0UGzduxGuvvRaWoXWIMqKOtrY2PPfcc1KZVCqFBQsWhGWqkEHHEuEEFBomuskoX/BDVm6dxkijhsUWJuxegOp8quN6W64QLlJ61cq7kXRwUHdv2glj0pTkDAD+9Oyy8O+6bFrSulz0URQmd94B49lkW6JuIY9g0+WM2uOT7Mjxcqrem3CoN9ZG2rrt+cBo4yQi6rIprZOZC0f0yXP64zl74KGL9kd9TVpyHgunbmOtmdSfSnksA9cFuskt5sSxMaaKP+eJYZv2PKmeD4vsIvHdLx9fDCAQkFeNbF2yAQDo2xiFzNP2HzN7WGy3UTWmTRnqk+4c29gROqgGifoMbM5gtTuVIo9QjiQCSeqjDgPBVO/KhQV17CR5jElDbqUFYspjx1EXTdskYXelwdwnBVSt6STMi2PnDDP+noQ9m7T/JnFcJEF3Tm5UCRy3y3BccPBEY+KQJE4RiSlSJuaOrm+4jpNA1K+p01ZyspDNVUCOvokSbsbb8dbKTVi9uRUFP2inOm+Hm95Fa+X9NVtDeYbm+qx2vFGjfyJ5hOg7Ma3Y5RE6D7HxtIzyCDFnXTdmwSfRrqRzgUtug/0mBg7EgUzuAhWybFD3vV8czthrNADgywsmsA4MAdlZZb9GKYqRROvJZaK/h/auw/ePnRF+1sojpOPjibYNdJPSYVN/3AB94uBOhcf+CSDZpgCdb7KacdyoK5wCLjlyCl759qEY2S8uL1gulLph6VSfpTy9R/U19kBw2jxnpi2VRyjj+FAOTdskkVo9AS6bp1LUBcO6zbkIJ+9EcJJHeOCBByrWgFdeeQXz58/H9u3b0dTUhL/97W+YOnUqXnzxRdTU1KClpUUqP2jQIKxYEYT3rlixQnLYit/Fb6YyGzduxLZt27Bu3Trk83m2zJtvvqltd2trK1pbW8PPSVnGPRn0PRThdMLwqsnwL2mBaLjWanZeqNNxs8FpSx1ZqvEudNYAoFEJ0aKDunqcWFTUpFNs9mOBt1Zuwv/72yvh55pMStoREpIHR88aiknFsIB/f2FvnH7r0/hoc9S2AmXaWpgG9L4UNAYXEBnKwiiLQhLT0oJ/e3uedUjXK+UouN2u1lwhXBg21WbCCYcmRBOat6o+VWcgKdNWtF9l2vJJ8+S66rIpybDOpDxkNe8CAIwgms7qnER1abNpL+ZYNe38uziCajOpcCFfqoZnXB5B/myTJVDhLo+gb0MpSKZpG/UPXQbmzoSc9Mq9HUnCIlVQ+RiAMm2Dz55n15INzul8SmfEGaN8O1S2Hh3buPcncJSIsGvzvaJMWyvzKKFRbmMslIqexObrLMhOEXPZJItcV+j6RpKFoFjstOXi0k9A3F5oJxu5asJNitZcAU8vWhuco+DHxg/hWBSv1c8efjf8rbEmHW6Qq7j+gXfwX/uORe9i6C8rj2Bl2vJ6nZVELBKGeXalRnZ3xEnU2ZC08q0bHfQ4+0Wdf+B4tOULONohaqUS72Nn4VtHTcPXD5uMumxaijbcpmiYJt0ApZq24jVXn5HqVKREC937Rp22Njds0siSAb1qMbxPPZat22YvXEGYGc+e28VAfpd1uSdMr4LnBfknKpzf1kl2a/aIFry4dD0OmzbYWl/S9/GK42bgpWUbnN512metTNti0fYKOW3VSyul7qRa3zsCdEkp6XqrighOnpT99tuvYg2YNGkSXnzxRWzYsAF/+ctfcNppp+Ghhx6q2PnKhcsvvxyXXXZZVzejS8AtXOstTNuC74fyCPWaxSd1xFKm7dQhzXh9eeQUT3teaASrL/Spv3gq/FsNI6RNU5m2rcShbMpQ/BJJpgbEpRpEPTQ5x/RhvXH0rGH4xWOLpGNDpq2FafD8knUAgoRWIhSRZdoqC5otrVF2Z+qM/ctzy+IHI3AK6MLUC8y9oI516pQVRkXe90NNW9WB3hEMcGTkqvfINYtlwESOFox0I0BX9xXHzcTvnl5CzpXS3suvHyZnwY23k2jaekwiMnKAatC6GApfPGgCfnTXQgCl6S4BcQOJGmODm+uszGoV7vIIhMlYBoOLntbmEOHkM7qSyVOqpm1HkshR+RhA1sO01SXLepT/vsUZxPw5wk22MCychMtxzzNBUzMlyiO4oLOYtj3N0VEJJNnMqUQ4tm7zLUkfEItTsZGsHkvlqACFaRtq2sbrbcsV8N1/v649rzhGjAlC+/bMvcYUHQ/6Nv/orjfxPx8PGH4cI9CWbyCUR+hERqopUahAuZi23fndTCJdlNSR09JQg+8cw0t3mdrREx0fYnOOyrv1VjRMk+r2iiIFnxA/YvIIcv30/KrTWIButtucK6U8ixF9GrrcaUtbrV7CyL4NWPTRFieyhMQKLSURmfUM5QG9FF1zrjxxJv7v+Q9w1t5jEtXngpN2G4mTSsgBb5dkCf6vmDyCcqGlRALSY7qzfnk5oZPjEFFLVU1bGSWtAtavX4+rrroKZ599Ns4++2xcc8012LBhQ0kNqKmpwfjx4zF37lxcfvnlmDVrFq677joMHjwYbW1tWL9+vVR+5cqVGDw42N0ZPHgwVq5cGftd/GYq09zcjPr6evTv3x/pdJotI+rgcPHFF2PDhg3hv6VLl5Z0/T0RwuEjkEl5odNF6MmqyBeohqt5wir4CJ19U4Y04/ef3UMul5IdvBRvFZOdiYRPcjuJgaGE4oXZldORPAK3gbpk7dbYd1TrUjAYVZ0udY7OFyInto1pK5y71977NmG0cWwOxWkrpCtq08gQBvG3/vkae566bNogjxD/bvN2waJNSwZg2I68uzxCElx8xGQcQPS2jpw5hC0XS0xkXXwH/6uapWsYp61a94zhvaUJN53SOwHVr/eZMABzRrbg1KJeZoxZGzOu9dfhyrQVKFXTNs7SIL85tEHtTs5MW8rmKYOHRLfLy5+bOG19kbCn6wyrJA5Cirhz0718JlVkeyhjTTguGeqptDyCKdyTQnVWyUzbeD/UbTJyyEiSFZb+RH530oCuUFerMm3jkMazBM+xXItA3Vjkov8pIBanoU2i9GPVhhIagikveld1TlshdcUlWQydtmE7gr+mD2su/q6/R++viewrE9OW20QOzikY8dpTlB0udsbOoGmbJOGQXLbM7ajAJkpXIJ3ycNnR0zCgVy3O3kd2jtHLcrlGL3zXieyIYePd8+Q+27+RJ0pQu83WN+nU6khO7RYh4vJ1ye0JZHYCJri1HnKsjixhuoedtWFD3x/dGccP7IWvHzbZSdKOtvufL37Y0eZJoOt/25gj7v8/SBvKSbqQ7ICinZy4DmYdvaNDF3VR1bTlkXjF/uyzz2LcuHG45pprsHbtWqxduxZXX301xo0bh+eff77DDSoUCmhtbcXcuXORzWZx3333hb8tXLgQS5Yswfz58wEA8+fPxyuvvIJVq1aFZe655x40Nzdj6tSpYRlahygj6qipqcHcuXOlMoVCAffdd19YhkNtbS2am5ulfzsr6mvS1gGqUKAarmZ5hEIhYto21aZjjhGqq6ga772KCbeu/sSsWP0SQ0yVRyCatuJSVIew7/v4wzNx57xg/+XyflhPnNUif5bkESyatupxAD+ZqvdkQzGsX+zS2wyleoPTlkv49u1/Bc5f4WAXCOURfD+UFlCTdXQE/ZtqcesZu+G5SxbgT+fM14bix8JVHBffBd8PQ0uDRGTx49T5VE0Ylk6ltKwotS/UZFL42+f3Cpkkth1b+jvtOynPbXc2ScZfHeKath0zMFwZZKWyS3Uw6cbpzp33/XDTp2s1bUsz8DxPZstajV3GsU3vBQCjbAt3nkrcNlemrZrEku7kc+1y0VwUoGOFlW2W8CYcPWsoMilP2rAqB3qSbmbngTowbM+RHFWmhbVuXF63Nb6BqINgzoZRRMoYq0oNCKetLiGbwPIN28M5/5pPzo79LvqPMBlEvaF8kuEWUZvLmIhMp2nbiYnIBNSxV2VFqmWuOG5G7Hcd1OvozuyrJPNRUnmERO3w3Mfg7o7T9hyNZ/57AWYOb5G+l8LCHfqEuA35go8n31sDwCxxJf7+6Sm74JIjp2DG8N5svemUhwFFnWGOKENRihRXd3h+psRcR88aile+faiUTFgHert1ZAmjpm0n3YokGrQuoH3V1keSgk4DNj159VLKTbjwEqwldMhI8lodblKPgG6zjyYM1iWF3xmR2JNywQUX4Oijj8bNN9+MTCY4PJfL4eyzz8aXv/xlPPzww851XXzxxTj88MMxcuRIbNq0Cb/73e/w4IMP4q677kLv3r1x1lln4cILL0Tfvn3R3NyML3zhC5g/fz722CNgXh5yyCGYOnUqPvOZz+CHP/whVqxYgUsuuQTnnXceamuDieTcc8/F9ddfj6997Ws488wzcf/99+NPf/oTbr/99rAdF154IU477TTMmzcPu+22G6699lps2bIFZ5xxRtLbs1OCLmr3Gt+PLZN3cFLSEB7K0FQNwJTnscZ7vuCHTN9hLfWx+gVDjIY3C9AQQo7F25YrYOIld7DtpnIAUT1mpq2ciMzdKSCazWraKvfkx/e9DYBnB3Ooz6a1TB6O2fLgwtVs2dD5WfCxrshSHdBcx5btCPo11aKfYac3xrxzTI4RMG2D79Keh7P3GYsr7pD1rbkEYPKiRW+Y2Sb0EX3kJAOq4UTPc/j0IfjbCx8E9TrO8lSKwJQszQQR5uqH/ZH+lrw+50RkXnmNGs5I0JYVRkTe1ybz6EwkSXqlIuV50QaQlaEQP2cm5aENUcRCVJfhnA4Mjo4g7uDgy+mYtoJFrKJvYw2WF0O8bcik5U2UcmLa0N54/BsHondD3CnUEZgyk1fhLquj/t2xc/KD28RBvZzr2Lg92DBtywc2iTrGiusSY7hw8tqctve+EUWlcRE09BCfJPUUOo7qO5ZJeeHGyaqNrRj9jcA2P7sYesvKI2jYNy6M/3KDzkOzR7Sw7DN6DfNG93GuW72O7vxqykxN9w2rcs+h9H0tVf6pu0PadHWSRwjKbG7N4WcPvRd8F9t4j9d/xAw+io3iT+fMx/IN2zB5sJm4lFLGBRd0h8dHm8B11aYSCCm6NYDpWXbWhk26zPMZHR8PZiIzOoIkTFv14ZV9s4hhiSYFnXu78wZdOSGtCzSO73zBL5lgtKOhJKbt17/+9dBhCwCZTAZf+9rX8Oyzzyaqa9WqVTj11FMxadIkHHTQQXjmmWdw11134eCDDwYAXHPNNfjYxz6G448/Hvvuuy8GDx6Mv/71r+Hx6XQa//73v5FOpzF//nx8+tOfxqmnnorvfOc7YZkxY8bg9ttvxz333INZs2bhqquuws9//nMceuihYZlPfvKTuPLKK3HppZdi9uzZePHFF3HnnXfGkpNVwaOB6Jm2NNTgX+fvHStDmaVaTdviS7ppey5k2nJOW5lpG32/kQj3NzNsByAa2M+7TWaFU6YtJ49ANXVjdRYXWLc8soiEIioLJHUA9lES09aUaIOGLLflCthUvIftjtkX67IpgzyCbGQ98GbEbr9gwUS2HS9/sCFyvidgq5UL6rzpmgm14PthaGk65YUZfU11ZVKeLI/gedoMsTYje8HUgTh8+mB882NTw7p056Z/65IbqKB9syMTYTmNO1eZBk6oviNIUl+G9I8CcfR1FXTZ7l1gYo8YjxNMW2WDyDdsJgnQR1wJZ3fMwaFj2iphVzmG0Udx5l6B8+gT84Zb21BpTbKBzXWJQuRdoF72TrJWMIJqsFsjNJTNunJAN65cfPhk9nsOP7gzkLHSM22D/wsh41wwYksbG6JjooN8P9LW1TFt6dj/9qrN4d9PLw6SnUnJJ1Nym1X4DptH5QZ9zw+azEf90OeZTH/c/Lk7IcmGahIphY60oxw2QndEEge5Wl7AZFcmibYY078Re47rby1H2+nKm5s6tOsjWL0S70u8nuhv3drA9Cg7a0zjnPcdAe1Xb6/a1PEKCSSnraWx6q/ltt3p++RKoFFBCTU9UY+7FKSY+R2Q72FV1zZC4i2i5uZmLFmyBJMny8bj0qVL0auXOwsAAG655Rbj73V1dbjhhhtwww03aMuMGjUK//nPf4z17L///njhhReMZc4//3ycf/75xjJV8FDDRzk7qUA0bVVttfC44iC1ZksbrrnnLQCBs49L9MAxbdcXnbZNtRmtE0ho1z77/jq05wthuVbitA21TYt1f7B+G770B33/EeUXrtwU6tSqiZXM8gjmRfg5+43Fzx56D/tM6B86izhDjWrArNwYMcNuO2uPWFkOA3rVahNCUWbyc++vxRm/fCb8rGrKislz9aZWrN7UCqC8mrauiLFhbUzKMFw02gxIpzzUZtI4cuYQ3P7y8jA0Wb39NCEdEPQjnVFgMxZqM2nc+Om52nbrnATuTNuov5VqXABFJ5dweEmGXnJjwzX6RQ7dSnyaGOjYZWPliHPnSKK6rgzfK9URABTvXVHVxGrskp/FOdOhjnfwonCMaxWVTkTmrGkbzh3B51DSRvMuHLfLMEwa3MsptI9ueiVhsXdp9JfqtO3WfL7K4ltHTcV9b6zCfhMH4Hv/eQOAQ4RGhfs1xS6j+jiXffit1WjPF3DjQ+8CiNsZVOcSANpyYiPKje0zV9MWegTdABXOYPU9zaY9kP32EBuKX3JMHK08QvH/zoyAoOPaio08I5/T/HdBz5JHIH/bmLbUXiizX7W+hto33fd+dQT09rqYcNzjiOVOIB+7Szc7/4BAduCQqfr8MpWGV6a+St9dnW1glkfonIeSZsbbjoC2u+DGH3IGrS9JRAxQfqcoZycnBbUfu3PSyXJCt4GnMm2rCJDYk/LJT34SZ511Fq688krsueeeAIDHHnsMF110ET71qU+VvYFVdH+oTltu8AzkESyatuSF3bg9YtqmUkoodsoLJ0+607a+qPfGaYpx2N6eD522IdM2nSKLmaDu//fXV6QEGSroIkeUizFtY/IIxGlrYU5NGxroSbXnC8ZFCWWQfbQ5cJYOa6l3Dqdtrss6MW2feHeN9JvQERbgnn9jTec7bVXYJkFx/3KFQizR1PePnYH9Jw7AodMHs3VlUp60UWBy2ibVQTVp3EpMW8cFiuxYKt0w0DnhJg+2b96pZ805WnMumaF3G9MXTy9a61RfE+m7DZY+KhY5BZKIrCvXhB1JWpDE0USdeKGmLdngANw0bWUdvkTNdYJ6al1b1BDrUNJGE/HgeR6mD+M1/VRITIkEF9mVJmlVHiHCGXuNwRl7jcHvnloSfucaoVFOCGkDABjcXBc6A5MuBqdeemfIdG1pqJF+0zFtaxwTHOrGX0keAUR2QeOo0NkcwmnLJXpbunYbHn37I+w9QWb4hUxbfbPLDjp3cVE5QOlRKT2JaSvpzXeBpIgAjerSkRB6OpIylbnn8eR7so1UyWeiwnWTsr4mjYsOdY8uqARKZSCroEfqxjyj/VTymZNBvt6Og15Sv6YafcESQGU2bGOjemvL3cWpX6PUunfU8coEeq90iaGrTNsIiT0pV155JTzPw6mnnopcLnCsZbNZfO5zn8MVV1xR9gZW0f1Rrzg7uIktX/CxLXRSuoeGCL2gtOchFzpKvNBQ8f1g4PY8LzTyXZ22dPeGy7AsbPGH3oq0W6nzWIBbtMU1beVr3rCtPXRM65wFAs1Fx9L6re1E0zZejibSErIEqkPVhKa6jHbSoGPmWys3S7+p5+AMj3JP1i5IyrQNw98L8SQoveuzOHHeCFK3cmw6heb66D7UpNPa8yVlf6hGeUaT7Mg1FLAcicgAZZHmAf86f2/8/NH38NVDJlmPVTduete79Q+XheH3j52B7/77dad2jO3fiLP2HoO6bEpi6HCgTNsws3kXGlkZ5f4nQRJ5BJZpSzY4ADdN244wg13gyrRVozSi5JEdlx2g/bqn6CnGE5FVkUQzksobuUoR2TBtSLRJcP6B43HJ318FYN9g6d9UG27YBu2JJu6+yuYtlQOiZWVdZv35dNONFAbtR/dEnE+tU2dzbC0mO9NtTn36lqfw2mWH4qjrH8UhUwfjG4dPJonItM0uO4T8BACM7tfIlkni0JTQg5i2SRyJ5U4oSkGjujoSSdSdoXN06MB1uQlK5Ei5GKU7Gjzth4T1kGNteV1sx1cS5dabpu/6mUWd8nKBrkutOtrKz+WOburbGK1hPtrsnjCUgq7NXIksPR2cZj0gzyFVpm2ExE7bmpoaXHfddbj88svx7rtB2NW4cePQ0NCAbdu2lb2BVXR/NCgOGO4FKxQiDVedc4RlaAqnbcojIclyWSFSXarT9vUPN+KD9UHfrUmnwsHSJ9qVAsfNGY5hLXX48f3v4O4L9tW2W9WPU23Hd1dvCf+2ySOIhcCbKzZhaTGpmJlpG+1MJXHKNdZmUFcTNXT2iBZk0x6eWbxOeqabtsuxjKpGMXc/kjiPywXV8LSH7AW/04W3jtnEMW2p47EmEzC2aZIVge3tySZjlWmum9hcnzVdJPeqKz2pkSqJMGN4b1x30hynY+kYsO/EAfjsvmMdz2lfGI4f2IRfnbmbU32e54XawTZQ+RHxTLtSdyrtGMbMIYlTSj6n7LQthExbezvo5lQlbptapa4tofxNyLTlNT9LAV2MVYKBWQnENW17RrsriUwCxjSNlBF9qaPo3ZDFs5csQH02jb8WE0264PqT5+Ck/32S/U3V+Q8jioS2cz6Zpq1uk5B2n0AeQSQ484rHyZXq9B3FZorJiXDqL57Ge6u34KaH3g2ctmEbOq8P02eucxLSxXiSOaMn6U0ncUx7Jc4/LqBRXd34dnUISe8fR6T52mEyg1WOhKnsnfO7NLYkGSQHeUfqIUfrNohN41Zn2ROyXV+O+qJKSknaZoJOJoeDem+THOuCcjwfOk/sLI5KOeqPfJ/yIBT4dhYHtgtKfoMaGhowY8YMAEBrayuuvvpq/PCHP8SKFSvK1rgqegYaauUJiIbSC6dVwfdD41Yrj2Bx2gqkPE8yCvO+jwyA+4vJsZocHYRiTDznt1ECvZpMCoV2P/x9U5ENK7DH2L44cd4IXEhYfG5OW71DoMXiZB7VryH8++7XgveLmx7EYihXKKBQCM6XZIHQXJfBwF51uPLEWehdn8XBUwfhlJ8HC0D6TFUnpDoZcgafLfS8ElANVRt7QDzHNuK01S0+1GtOpzz0J2zicIGajjttW4ubF0mQ8jwi2cA7FFyTedVkomM6kiCuZPYQZLb9rx0drME5+fN3BsT5NrfmcM/rK2Pt6Wx0JGFQEjYF/VWcU2XahgkSDVVR51YlnCou4xAQl0d4cGEwb5RDd7tWYtq6d45cmRiapSB+37qoId0IlCVtkySg71JbmZy2QMCaBdwzrQPAHmP7aX87dJqsCyneDzE9tTKMczPTVjM3Kp9zaiIy5ThbSKgkLaAUfXnZeulzoQvkEWyb7oC8sZ2ExRizYbqx15aTsdBBt1AvB5rrM9h9TF9s2NaOYX3qy1t5N0HSRKJcmbziCKFlKt3LulTDPSHK5cyma0LdkGdm2nbOu1/u3AOSI67M15BkblRPXQmnaFNtJoxyLQXU1thZJAFMff6qT8xCyvPQq7Z0ctGOBmfzobW1FRdffDHmzZuHPffcE3//+98BALfeeivGjBmDa665BhdccEGl2llFN4aqaTtuQBMG9KrFhIFNIaMu76Dhyr28TUWHsBoiRycWweL9x4sfBudyHOwKvo//e24Zlq6NGOK1mZQUPruBZMi45MgpUoi8ALeoU5OtmUiQNraj53k4ZOqgsE3iOxU03FEN73dB38ZgkXjC3OE4uHi+aGEX3VPbwpS7HyobtzOgNsMqVC+ctjkXpq1St+fh6NlDw8+C1cTp+JUyFVPjRE4UEJVx1TukTl+dtpYLVHmEJDh+7nDUZ9OxJHb2c3Ydk5E7n2kzptIon6atpTBjdFPWMQAi26KvLCuFXSdorCNirDRtuWh8930ff3xmKYAgsqCjkBdm7he5LSH7vpyI37fu6xjqLEwrZi3Ppr1EC1ehj1xOJHVwfHnBhNh35+w3FnuNl/VfxXMXc/vWtmIegRo3bT6dTUPHACqPQOcn4ZAG7JsbJh1zKv9QKPhdIo+wx9i+OHX+KPzPx6dry1DHboeYtolb13nQ6RFycNGm70g7/njOfNzxpX0SbZz1JCTVn+XGsI0KIaUzEyp2472HGGQpitLr6V2fxZQhzRjbvxHD+zSwZcyJyEo/dxJITuoyvD7Unij3NSTxa6p2TSU2DsYN4OVxXEHvfS6/kzht6XpW+e3YOcNxzOxhVum6nQnO1JJLL70UP/vZz7BgwQI8/vjjOPHEE3HGGWfgySefxNVXX40TTzwR6XT1xu4M+NRuI/H7p6NEHSqLsr4mjUe+dgBSnofdvn8vgMCgDp22Wj2f+Igu6laNPCkjpe/jz8WFNwAcv8twp+so+D6+8ueXpO9qMylsKTbP932s3xZo0wxursPZ+/Ah3JxjVGWPmLQvXUJG+hUXOWFGZWbyC5m2+chp6+rIq82kJE0egdDBQXwKNt0+zmDXPfNKIsaGtemsFX+m16czolSmQyrlYWCvOlx8+GQ8+d4aHDUzcOCmmZVtRzdQM5oFj6t+G30+HXHa6rSIXDCqXyOe++aCxM78dAfO2VFw71KXMm0l533pTlubFjI1dsU5UzGnrX4zSSAtMYPL/+zUKnWXRZm2bflCqD92zn5uEh0m0P6cRJqmEs4+V8SeWQ9aUFcK4wf2wu/O3t05iadAUukbFxQSri455mdDNm5jUA18ANjSGvTBBmKPmMN13eQR8qE8QlT+YzOH4JePLwZgn4OkudYw5uf9KOi6MzcePM/Dd47RO2wBRR4hwbwVG9O6sbeLzoX2RGTuZUvFjizzklQegSvSrEQkVjpRqHSuHjTJlCsxVyrl4fYv7I32QkErj2AyxTrr3afvcbmfU7lt9iRzo3rqcssjAOUdc3YWSYDO3CzaEeC85Pzzn/+MX//61/jLX/6Cu+++G/l8HrlcDi+99BJOOumkqsN2J8IFB8tMDs7xUpdNoyaTirKM+yQRmcZRw01KwqEpJbFJedLklvd9LF6zNfwsWKI2cIzcGsK0LfjAy8s2AABaDIs3bphRk4uZHIaNtfZ3R2jCikUhd6+E0649H2W3NzPfot+uPHEWW0YNJQbksI0FU+L3mjPCu2IwVpthWxyE8giEaaszMmj/o87Sc/Ybh1vP2C3cGeQcYklCesJjmHaqfzszbclzr+nAuN3RbMMNNZnE/UJaGHZyn+L6T1cuojuSzEXWtE1yzqBwnGlrl0fIdoCZ7YJYQi3LhksQ/RG96wN71XW4DQN6RQxCl8Rm04cFjM5xA5osJSuHnqSb2ZnYc3x/TBva216QQNXALwfmj9NLHnDg7DFuA8Ejdg7Aazub3lPduEEX+j4I0zbNz1tWeQQqiWOQ8sgX/EimpZsRLGmzk7CGetKGCn2mD5PkvRw6Uz91RwS9Yy5sYu4eHzxVlUsxly8netIjp03t6DomlfKMdoGp/s56T5JKbyRBudeBSebbzpBHuOzoaejXWIMfHD+jw3XtPJq2Xd2CngVnpu2yZcswd+5cAMD06dNRW1uLCy64oOoZ3wmhLm5NTkfh6MjlowVyKZq2agIiWR7BDxOJfffj053Zg5zvrDaTlsIGRdbmN1ds0tbDvQKqpq1JZ9dFS1Fd2HCLEuGEzRUK4YBv2tlMpzwpvJCDODzPyCPcevquOGDywHi93WRMSMq0FX2MatrqjnFlJXCO1FLGTNpXdQxL111senz55BE6yaDsgCRAR8E9SxtLtZLIdEBuIInDXWb1yMeIcUH0T1NdHUmc5oKkmrb5QsRw9Tx5E6tUDGupx1l7j0FdNuWUEPMbh03Br59YjG8dPa3D5y4V6n2qOlGSY8LAJry9ajNmj2wpe92TBzfjji/tg0HNbpsKM4fHHc3c2KXKIwjnataREa8bf+khPk1EluLHKyvT1jDPpFMeCkUbJkflEbqZd3PqkGaM6teAwc11Vic1hXoV3fndTNI2OUqnEq3ZsSEld3LI4aE+m9P3HB17fzu6CZ8EPckhlZTV3BGY1iid9eonyXfgApp0rtz3Lwn/pTOkzGaNaMGzlyzokH17/C7D8dSiNTEN+h0V5WKy7yxwdtrm83nU1ETh05lMBk1NXccOqaL7oN6QZEpMQjT8Uxcqz41zQtNWZdrSwT9f8LFy43YAgYyBCSkvYpZYmbbkd8qgirWbGWrU3VRTZnKXRBYqU4abFLKUacs4bScP7iU5n10mZFHm4r++gsfe+QjXn7xLuAjTMqaVSz18etdMPkk1bcU9Fk5pz9Ozc11ZCRzDyZZ4zgaZacu3yQRJ07YDjirKJu8s/2mSZCflBtcXuos8QtJ7kUQPl/4qymbCDSLVaauvhzqDKvHo4qHEfDmq/d0qNhIz6TIl3fDwzY9NdS6/94T+2HtCf3vBCqIHkfm6La76xCw8vWgtTtp1ZEXqnzKk2bnsnJF98PHZQ/H3osY/wNsf4j0Q727ktKWbK/rzuMkj0ERkvDPAtlkiyyOo5/Ig4lDyBT90EHQ332ZdNo37v7I/Ul6yDSu1bDe7LAlJHDLpTnQQ7ohIrGmrvDfcMfTxVfqR2CTWuhNUwlBnnSvJb5VqQ7lPWe77l0QewTUSsaPoqB151SdmoVDwKyYb091QHf6Twdlp6/s+Tj/9dNTWBs6r7du349xzz0Vjoyy8/Ne//rW8Layi26PB4HQUhpzQSwNKY9p+uGF7+J0wfD0vWHDkfT/M2NjLsuuc8rxwoBfsXLUNYtKiQv1fPWSitk4Xpq2OXXHdSbON7RVQQ6B4TVvhtOWZtr89e3fM+597SR3Rb7qpjx7/75eX4/qTI4OrJqNhsik35KpP8NILlUaMQWbVWQt+/5/b3wBgnuRdjWauDpqwrBR4GiPS1agrl6Yt7dOdxXrtSPKtjoJnq3WdxSEnpEvWDlrczrSN97FQDzOJpm2FmdkxORSbPELBDzcTVTmbnQlxWYkuakgPxszhLZg5vKWrmxHitD1HS05bLtmpp7zDYgMmIzFiDe+z5idpE9sH2gtxeQQ6TtjmIJOOeVweIV5/d0Ep81Vc4qlMjakAkozpSeafKuKQ75+9fJyhztVZ2U1VCluEX3cFl6OinDA9y84jRtBzlvek5e5XSQjbSXIMdDV2FoctoPSxneeyS4az0/a0006TPn/6058ue2Oq6JloMGh0ifdxS9Gpmk55Wg0mbhdOTXIGRM6TtOch5/soFIBtbXlrW2h7AOBvL3zAluEmqqEt9fo6me9UJ4BuwvjYTDcHnuow4ti9grFyxR1v4oIFgZOZ3tP+TbU4fpfh+L/nlwEozdgDgPZcnJEjtVW5Vhdtx86A7XrXb21XyusPoL+YDBE1OdguI1tKymgsNiji31MWktuMR5+PiSWfpJ7OWiR3JPlZOc/dVW2gkGUykh1b6n0Mx14hexNz2uqPpf3+sXc+cj6nK1ydj6IZ+QKR7OkmY1RXQL1N3S20vIrkUOcYLoRalT7KMfIIpp6g3xSJ/s4VCuG8RZ3BtHm2+VCSxDEMMLlCIVzE7yg9OB490H2vLMk8Uu4Q7J0NSTWBXfI7yDr3lX0mbVWmLQuTHd0VNnbZmbZl7ldJmLbZ7rzjtROjOv4ng/OK/dZbb61kO6rowWgwaLKKQXrt1iBDt0nnj5uURCKyz+wxCr958n0AkaMpnfKQKwRJt7Y6O209CF7py8vWS7+JbKrcGGKUR2AOqFUSPOm0L511SB2YtnTxc829b7H1qzITArrkWKpxly/4aGcYOXLb9GyYzkTSnVU1TNPEtJVYCaY2qPevRILBgKZarNrUGvteZi+61dVMWFcdCRmi4bGdJVWQ1vTfzgB3r7rSaUvPnXQjoFR5BHHPxbsfMW2Lvzuyzhev2ZKkuU5wdT6GLGG/yrQFuIiELmpIFWWDyl5tZDbnaMJVIGK/6WQMVPz1hQ9w9Sdnx76ncyNl1OnqTcK0VW0tyrSiich2lHVgT9pAofPImP6NhpIqe7piTdphkVTKIMa0ZeUROs+R3pPkEeitqLS9Z6q+s8a0sm+okDG63P0qmaZtzxlLdyZUH0syVKfLKjoMkySBMA4E05bLbByWVd7erxw8Mfzuv/YZG34vHLNhQpm8HzJtbcxBeooP1kXyCEfMGIxHvn4g245s2sOkQb2M9apQnQCclMDuY/o616c6FDlHMeekjIUTJpyQ1SpzhYJdHkFlBXfRCkqVpLAtgGKJjFzlEQzl1GdSanbxSYP5/ldKyHljbQYTBgZ65NOHJcuOLp2bnK6zHD0y66pzzhmer5s5bTuiTZxkIcKFsopTR5q2xazthqpoaOEXDpyQoLVuiCci48uJPlRl2gboSWy+KtzAJexSEWnayonIXOURdKBHUOdMVpOI0JaYi/6sXgfdbC74kX9gR+nDPendpE27/uQ5xrKy07H7XlN3RWJNW6UINx7IDMvKPpMkzrauBr0TFXfaOq45KtqGCjqpy3378kk0bRUb2Zb/porOgSsBqooApcfGVlFFETMMjh8xCQkmrC4JGRAf0Kn2J3WCqk7bTa3tYbiNTdOWOu62FNv0p3PmYzfiQFUnx+8dO8MSthL/Tl2IcKzUJA4z9XhuAufYduqku5qwNVMe8LGZQ/D8++tw8NRB7HlVaYN8wQ8Ti+jYfd1lYdERvVbApmkb/W26Wu7+lYKvHzYZS9Y+j8OnD5G+L1Ub7h/n74V3V23BDCbTuCsqrVHKQUq+1ckOU24M6OxkaNK5yfWrTHwbJD1im6Yt6eGibMi09d2ZttRpY0rMWCpcNayrTFsZcWd39xi/qygdw1rq0a+xBmu2tGFYSz1mMuO8eMziHRYbMHTeVBMYuYB2HxoGTRfNdMzpiDwCXbMXKNM2UYu7L2KJyLrxhdFnYxvf6TPtrARBOxI6qmnLjfFJ6ywFlx09Dd/652v4n49Pr8wJKoDO7KvdQtO2zDZ2JWXUkiUik8ekM/YaXda2VFFFZ6DqtK2iQ7j2k7ONRrcY8wUT1qRvqi6ypRA6co4P1wdJycQEunRtwJhtqs1Iod+m9lD0b6oxlrEm6lGWCDXpVOxauHuUJHxfPZyXR7AzAe99Y2X4t+d5+Mmn5qDg6ydn1cmeK/jhQkznKOouYSiq0zapvWC6DlfGsioHksTIoJg+rDceuuiA2PdSIrIE972hJtMhhy3QNU5buhjsDs6l7qJpm1Qegb4b1n5DmRdpwbQN/hcbOC6atpVOIhcftzXlGKZtJZzIPRXdZPiuogOor0njka8fgGw6hbTnaTQsxXsQfI6Ytm7j+tcPm8x+L8sjEKetRsqnT4PZZpPnOPk3Op/6PkgiMmOVPQZJbdGuRJLEmDTiLglbrooASTVt1RI2pm2lNqNPnT8Kh0wb1KNYjvROVFqSy/TedNa7n7Rv2VBKDg9XJBk66Hr7d2fvjvnj+lWgRVV0BNWoCzuqTtsqSsIDX90fLy5dh6NnmRNpiUFfMG1NjCZ1gqADMj2uf6/AySo0Nf/zynIAQD/F+cqBGxT6NdYay9j0l9Qq6xldXc6hmmTX1oVFxrF5VeNr9ogWvLh0fbHO4FpNvuM65VryeT+8H9w1AbJB+OUF5Q+DdoXKdh7QpNcl5mByLNHbappoVis6tKU6bXUoRdO2EufuLOdlI9HP7g7ZYLtU05Zcf1J5BGpIJ7kGMZ5khdO2mBm+EMojGJi26cr2F/XUuvcylNUpIGTa1hlke3Y2VA3nHQNcElcK8Qr6YSIyoWlLmLaG44f01jtePC+w39pykSNYlzRzUO86DG6uw4qN29m6TJuDNHCl4PuhPMKO0odVQkB3vqokIft0vG3PVZ22SZFUXsJFLoUuHyr1/niehyG99UmduyPovag809a05uict1/Wm+7eTtskoM+ub1PNDjNHVLFzoXu8TVX0OIzp34hj5wy37jyKQT902hoYTbHQN6JgTh1wu44OpAzWbAkcYv986UMAfLINFdyOc3O9fJx6SbaQdrVOTreXY6XqEnlxcMmM7sK0HTegSVsnB1XrsS0fZYPWZeOk55w+tGNszo6ATsqf3mNk4l1yE9vANcPqPhP6S59LVEfQoty74knQmYkrBJqI07Y7sI46OxkaBTVCKyqPQJm2xXMKpu6P73sncPo4MNykxHUVYdp6xs/huYk8wn1vrAJg19WsooodDWLsiuQRipuxjkxbk/yQOG5zMZeBmiBWmj/h4dGvH2CtCzCPGwXfjxj/2lI9C64bUd0B0iaupZ20bE9KStVdINte9vLxftTxOncWlCpBVgrMa46Knpo9TzkY1zpyT2eD2shJ1t5VdB50CdGriFDtuVVUFMI429YeGO8meQTVIKc7snTAFQwP9f2mDh1X1GfTMUNYbUdSdqQr0zbJZBZLKFaipi3V8XFy2ioOaCFzAQBZzaJNcqx1kxGmlCzM5qQA/N8qTt9rNM4/YHz4udREZDqkHdtRCejCXSsJyrTtDjIcXanHR68/KZshK8kjmMvSKxRjxrb2YBz4YP02vPbhRjdNW42mZbkQ03/UlKPyCP9+OYjSuO/NVWVvTxVVdGekws2LYLHUmovLHpk22U1jn/hl8/bA7mus1W+Mp7zgnP88fy/MH9sPXztsklTWVYansFPII3RNO1wgyyO4H1d12iZH0nsdW+OwmrZdRwDozqD3qtL2num2d1oiMjrelmH91h2ZtlUd7Sp6KrrH21TFDgsx0XxQ1KHlWKgClLlx7n7jYkyO287eHTeesguG92lgj2+otYe4qo5GznGqGjgibFAH1bHJXSPvUHV//WI75UwZjm2nOh5biH6ci9OrXpGzEM4aQO90psngWtu7h0Feim6aORGZzBTSYWCvOnz10GgRWkl5hM5m4XSFPEITece7MgmYQFcubihbICmbgcop2FgHtF+JIWbp2q3hd8vWbSWatvp2SM6XTpBH0DNtg/+rWopV7MwQjNr2fAGfv+15/PX5D4Lv0/J7qtOfNMnTiFdvS5sD07b458zhLfj9Z/fAwVPkpKgpx3nGp/IIOw7XVv7UDeY8HSSmZoLxvd1iX1cRR7qDDlZe09b8+86KStstFKZn2XlM2/La9ZW0kZO0j5Kpqv27ip6KqqZtFRWFGBtfKuqomkLqKLhxfq/xcqh5OuVJ0gUu8giNtWms3RJ9Zh2dyrltjrYBvWStVI5pyzkAy8+0jX+nnpc6aGwJQACgVnFAb6VMW42zhzqtc+XWAygRpYRdmAw0mZXgXme5HUWSQ62TF3SPv7sm/Hv91vZOOWejxOLuesOrK3V16ZiQlDlAN5Fsh3L6edvJZsym7blw48pUlxyeVv77plapex3EfSs3672KKnoSxNy+vT2PO15dEX6vbjA312ewYmP8eLv+oo8L/vgSAIZpa9hsjNs6/N8qBGM4qF9frichvhHVNe1wgetzUlFl2iZHR5PAcsfQb7rx3kCnoyN2VlKYxq1O07SVNsk6PpCOHdDY4Tp0qEmnsK2QtxcE0K8xynnTHfJhVFFFKdhBTJsquitUA7x3vd1RCEQJLExQh91GB6ZtQ1ZePHBahkmNoBPmDpc+s0xbxlmdxABQHXLcoRyb15SAQHU2c6hTmLatRaZtOsVnpAYC42K3MX3Ruz6LvRVHe1fBpkvMwcy0jf5OYkwVyrw+kVkA5a3bBurAf+WDDZ1yTrr4L+WZlhsmuZdKg27SqJsrNkjJhiz9l44rwilCF9pbWnNhWLJRHqHCzGxXTVsxblGG13UnzS57e6qoojtD2CnblGgYdUGre49MDH31iDjTVl9WrZfaPqax6oo73sBHm9uKde4Yi3KXXAbdBUk0bQFgQZFRfdj0wRVr046KUqUouOMFuiJHQU9AZ0aUmZm2nfNMJKZtGU758dnD8Pn9x+GmT8/teGUKXElgANCXOG2b69z8EFVU0d1QZdpWUVGok9yZe48xlp81vDdeWrYBM4fbE1ipc5gtWzIATBjUhIUrN4WfXZJ3nThvhLHOxtoMztp7DG55dBEAPhM5x0pNkjwoptfITOCck1G9FlrGJYur6mQU8gg2h/NtZ+8OD8kTJJUbtZkUWnMFjOzLS2pQqGxcVwPqg/XbnNsjstWXC64L2krg0GmDcNdrKwEkM546Asqmp/rKXQWXjaJKgb7bSRNp5RKwm+i7zvnJt7Tlw2gE07DQUXaQDWqNOp+SeGeEzjoQORCqqGJnQR1h2lKotorOUWFyYKivtxoCb3IQxZzGjo6SBxauxgMLVxcb4HRIt4d6GT1FHsGlnVd/chZWbWzF+IFN1rJVyOgw05Z5p0qNHtvR0blO24pWn7gNZZFHSHn42mGTO1wPW3eC5vWqy+LGU3YBEI/8qKKKnoIq07aKioKyJsb2b8SY/uZQias+MRvXnzwHR80caq1bNQw5WQIVlx09TW4f4+yg1e5eZIza8O+XPwz/VtmpwXncnKw6lJqITD1HmrTj3dWbreedO6qP9Fk4bW1Oomw61eUOWwC49Yxdcc6+Y62bBRxMITSl2jIim3a5kKqwI8wEuhB3YcaXA/Q92F5mB3hSfGzmEAxosrPVKwU6ttYyY44J80b3dS5Lx1nBbj5xXhRdsKU1FzpzTYv1bIXlEdRz25i2yzdsD78zJVyqooodEfU1QZ9XnbbOTFvj/Cj/ptoRsjyCUq9qs5Qp/LsnQt146s5XJTsS7eWb67JVh22JSHqvgcBeEeDmX0neojt4D7sJMp3otDXZT+u3tlX03AKV3lwvJ647aQ5q0ilccuQUp/KHzxiCw2cMsResoopuiup2QxUVBZ38XXRkxg9scjbk1Npc2Gb9mmoxY1jvMJzbJingOmet3Nga/t2LCb3gjKQk86FLFmH2WgxM24mDelnPO1pxsgt2Iyf30B2x57j+2HNcaRINpsViqUZtuZ2b0q54J9tXNES+tZOcthSqs6EzMX5gE64/eZcuOz/QMabtp3YbgTeXb8Qh05KFpopX4uy9x+JnD72HXMEvOm0TMm07RR5B045iufdWB+LmjTXpbrHBVEUVnYm6jIZpq7wLunfV5MBQ38VJiq0h65+qTFsz0/fGU3bB5257Hr3rs9iwjddS796uBneoMg/d2YniGZ5pFeVFKYnI6HvN2bZVeQQe5U7M5XouFZ1l73qdeL0dxb4TB+Dlbx/CRrdW0fPQ9YJ33R/VlUoVFQV1EtqylHcUrmwzOfyD+504bR3N/+a6aP+DY215nocjZgzGLCL7kMTRpS6cuIUU5xRXjTP6DE6dP8r5/ALbHeUReiJsCVGk3xIatV87bBIA4PLjZiRvmAFduSsuO6A7f7rd0tp1TtuhLXZpkUqDJlZwiTKgaKjJ4EcnzsLBU5PJAojxsE9jDb5ySNCnt7TlQ01bM9OWzgWVcNqq37g5mxqqoXJV7ISoCzVtVadtss0PDupPe0+QN05NG+Pq+6naUyOKUkemRII7jM8ptlnfMy6sytSsLGRNW7d7bWOM0uVZ9fFFoOuqSif7Nd337e2dFM1Gnfc9oCNUHbY7EKpeWyuqq5UqKgop82aZqYDq/OnKNlu2LtIgffWDeFrkUuapumwaG7cHoe9qKKDAT08JhNhHf+N2AEBrgknYxVh30eetyaRwxXEzsLUtb5Wq4CAWeByrt6dDvX9Gp23CTvK5/cbh2DnDnHSEkyCpjlw50VVZnxdMGYR731iJT+xq1pquJLqDKdunsQaXHDkF767egt3H9OuUc9IuJvR8g0RkLkzbaMyoxGLANdO6eu5Zw1vK3pYqqujuEMnB1M0vUyIw6XuTpi35+9T5ozCouU763ZTIU52H6zQbUnlfv8LrKc5NG3pSIjLatB7g6+nRKEUeQXI+WjVtqw9QoLswbcudD0PfhujvSjupq6iComCY06sIUHXaVlFRpCs44aksWFddwjVbzNpAnIajDbTcUbPserxAsgFKnTxdNW25e37SbiOdz6tiW1vgqOusxFOdCVW72GRAJe3LnueV3WELqCFtZa/eiK5KinLdSbPx7PvrsM/40mQvdiScvc/YTj0ffeIiKdxmR03bSjNWXDVt1XN/6aAJZW9LFVV0d/QpMvXXKVqJKmu/JE1bSwSIHM0kI8aEV5hU4lCTbbajuBrU6+gpPpSq06+ykJm2bsfQzRiWadvDGJadhU5NRGaoP+e4Fi1nG7q7PEIVOxbUhKVVxLHjeV6q6Fagia9eWLK+rHWrxsqckTzD1YS/fn5P4++5ghub8ONzhgEAZo9osZadWZRI2GOsOztOVZbg5lJOfqLck+6H67cVz7XjTeZHKsnvTIvS7mLMpKWQts5t03ePmd6p5xNorM1gv4kDumRhMXlwoM24syZQoV1MyNG05QpOmrayVE4l5BEcnbbKMNmrrrp3XcXOB9HvN2+Xk2PGWLGaVYLpHbblBTDpZ6p2jM6JbNz07h7Tc4eh3jtXua4uQZWp2WkoRX82Y5EnknWmS2/bjoZOddoaqnclEHW8DdX3uIqugau/ZWdGdbVSRUVRSecerTmT8jB9WG9tWR12sTh6XXc3v3bYJMwb1ccp6dUvz9gNqze1YtJgeyIwAReHhIs8Qkfxx2eXFs+14+33HDVzCL74+xfCz+Vk2lYKXcmOmDq0uVPP1x3w7aOn4V8vfYgvHrhzsTN3H9MXTy1ai2NmDwu/E2NAe546bQ1M206WR9A1RW1jd3mXq6iiMyHeX2rjTB3SjLGKbJJ+80NvA5gSjQGyI5jTtPU8hDrZ9SUwbXcUZ4NrcsXuhgqnr9jpQddVpUQ8cfNv1VnHo5LRoipM972zmLZ0abcDLvOq6MbIVZm2VlSdtlVUFJXUxKHGSl+SlKeccB1EajNpHD5jiFPZvo01idurGgvcbWXlESp0/zkHcU+H53nYdXQfPLN4HQDzhgO9r8fOGaYtV2l0pTzCzog9xvZLxJDfUXDrGbvio01tGNmvIfyuJnTa+qGDxbToyFo09ToKtUrXrPdVp20VOyM4e+G2s3ePvTe698OciMw8L9nmrUzKC0Ml1UQzgm1q8mHsKG90XB6h+15Zuur06zTI8iNux7y8bEP49/qtcYk4+siqzy+CjaFcTphue2c9kc7U8K2iCoquypPSk1B12lZRUVRy0Kc1l7I/M6KvXWO0s0JSbIg5JJjZnQvnLwejLeXFF0g7ItMWcDdY6G97jO1b0TaZICek6DoDqzZTzeC6I6OhJoOR/WRzgWPamrogdb5UxmmrbGzpylWdtlVUwW68cnOIzlFoYlNambbSd/HfqbZdXB5Bf96wxh3llY7ZfV3TDBc010fzQzWBUWWRKcHuW7J2a/j36k2tsd+rTFsenXlfTPUPbK6t6Lm5NlT7QRWdic5ik/dk7Jielyq6DUy6oB1GJ8wn7d1EY8UlTI5zpJZjZ1gkHLKda0eAq36VXK7r7kVXJw245Mgp8DzgRyfO7PRzV9G1EE6ftnzBKREZTfTXnRKRVZ22VeyMYOdw5lXQmXCmBT3VXrWHYuvbCAB9GuSoJJehY0dxNsQ2orrxZQ1riUgQ1URWlUW6BKbtFw8cH/598NTBsd+rmrY86DhZ0fUs4u/7p4pJo0f3a8BXDp5U0XNHbYj+rtpGVXQmqvIIdlSZtlVUFJU0niWmbYXe9e4yiKiTJ2cUs8kFyjDp3nrGrjjhpifkc+2A8ghAqU7bijbJCNrErljQnb3PWHxm/qgq03YnRDbDadrqyzdTp20lNG2Vz7pTxOQRurMnpIoqKgRbMqLoOw3T1vDe2BxAaUnT1vz+xaWh7O/rjvJK9yR5hDkj++Crh0zEsD72CLYqOgY50Z9bn/j0HqMwZ2Qf9K7PYkTfhtjvXZkfoTujsTZyk1TazlVv+6nzR+Hy42ZU9Jwqukv0XhU7H7oLSa47Y8eky1XRbUAXBgumDCxr3bKxkty56rJYN2Yo7kS4hP5yxls5HBLzRvfFbqNlCYCaHZRpK8sj6K+xuxg23UFHruqw3TkhxoC2XAGFItXWNN70qs1gbP9GNNakMapffNHYUagLHm1Yt8q03UE3oKqowgTP82KOW+6d0TlwTH4dzzIv2TRvBTjpIbe3dcd4p9V7392dKOcfOAHHzhne1c3Y4UFJE659wvOCZM2cw1b8LlBlWEboVRc5bWsylV33qONvV0Q00jGn0hq+VVQBRHlhztprTBe3pPujyrStoqKgjq+LDp1c1rrrsils2Jb8uDP3GoNfPLYI/33kVGtZNXNxVyEeJhefTGl4mkDZJl2lmh2VaSuFBhkuMePIyK00uloeoYqdFzWZKBGZkEcwMXRSKQ///MLe2N6eR6+6rLZcqYg7OPhyVaZtFVUEULs+y4rVvR+G1yZlYdLKTMH48afvORq/fmIxvnH4lPhpd2ambZe0ooruBtv7U1qd0d/Zqi0ZgjptK31XVNukKxI+U0dxdU1RRWfg8uNm4JjZQ3fKJM9JsWPS5aroNqAbhY215XWANhCt1f93RNy41+GbH5uCZy9ZgIOnDtKW+X9HTEZNJoVLj7I7djsDKumT212vyaTw/WPlUJpyhTmptWR2UKatq1atxMjtwhViJYz3KqpwQZiILFdA3kEeAQCaajPo31SZhBpxeQS+MerQVV2YVLGzwkUHWjcNmuURzExamzzCt46aihcuPQSzR7QwdWtPm6hMT0Dcqb6DXFgVHQJ9f8pFzKBda0e170tBbSaNSYN6oVddBhMH96roudRH2RVMWxpFWZXJqKIzUJdNY/9JA6VkxVXwqDJtq6goqOOr3IvjUf0asOijLQCA43ZxD8nyPM/qOPjsvuNw2p6ju03ot+oY1N1K1YAr15yvrhV2DnkEfTlJHqELDRvaxipjsIrOhJSITMgjdOG7oDp/dK9DTB6hujCpYieFS88vRdPWs5Sj76pO6okmLpTrdmDa7iCcVNcxrYqdC3RdVS5Hvmz7Vjsaxd/P2wt530dTbWVdJuqz7IqIRolpWx1wqqiiW2HH9LxU0W1AnYjlZgmctudoAMBn9x1b1noFuovDFnDXNquUBpq6CNpRtY5KSkTWTZi2VRZOFZ0JsXFDE5F1ZaIcFwkZoCqPUEUVAi5MTr3TVl+vjWnbkXnLpfiO8krH5BF2kOuqomNIV8DBSt/DrgjL786or0lX3GELdA9yDH32VaZtFVV0L1SZtlVUFKkKOm0PmDQQz/z3AvRvqilrvd0RcYeErpz8uVzhNer5dtTwKVeNWFfnbqVhWxxXUUWlIMaWgh84boGudYC66HMG3ysbW9UXp4qdFC52hW5+M7FZaT3c+0XHiaRDhkv5HWUDM8a03UEYxFV0DJkKONYkeQSDNFgVlYM6bnWFPEI2U2XaVlFFd0V1ZK6iopASO1VgcTygV22Xsrs6C3F5BLeQxbIxbWM7wDvmPZcYDIZ7112ctt1FpqGKnQ/UuG9tLzptu3QDQ/3sxrStooqdFeqbwEsZRH/TCBvPsHqwMWnpK5icabvzvL+xMa26YqsC8jtTrqg3Wue29nxZ6qwiGdQ1R1fII1B2b0NN94k2raKKKqpO2yoqDMoMqK6VS4e6Tvn/7d17dBT1/f/x126uBEgQCgkJVy/cqoKIIKj9gkaClVYqWi+0IEWtmPRwkXqpfkW8HCqtolgs2Grw19qq9BRQUBRBoCIi0KCAQKlK1a8JWJQEEAIk8/sDs84ku5vJZndndvb5OCfHZfeTmfeun5nMvve970/IBZ3rV8TG4IJO8m6l7Uf/PRS4HW4hslh8PS0StEeAU8xfozt64uSbPCenoJ1FlSTr8VrYO/RilIDXNai0DTIm1N+65ixE1lhP23Ds/Ln1yp/C+pW1/I2HFJu1DKpPfJuobZMVvJ80Yqt+4UWaA5/SlFceDdxuncmXsQE38WbmBa7R2NfkYE/9xKDdKrJYJRSd+AQ4Hrb9X1Xgdri8tOXraU728TRfvHN8IY7MbyiOHne+PYL9FjLfPtAjt1UsQwLczcaHwdZel98e8+GOdMt1XyOVtk3N2tppEeCV5GaDD+udCQMuY1mILErXfRWV1YHb157XJSrbRPM48Z75cPWJwO0zuD4CXIWkLWLK/CeH/jiRiyQhIcUukeeVN0XhhK20NT3maHuEZvQGBJrD7/cFKvnrqnScPBbsfNVbsl8tCHidncX7QvV5D3fsWCppg4xrzjFoayGyJm3RvRp+w8orzwzNYa6ZiNaX3mpqa03bZJ4lqyv7Fygtxae+ndvo3K5tnQ4HgAm174gpn+XigguBSNXPH9pd0Tlar3lmmrW3UZ+O2VHZrpuFay3RwvR6ONkpwvKGmjd0iLO0FL9O1NYEKm2dTCrUP0eGCsV8nPDtDyQza0Vs8DHm+80tUcId6v5Gttuc9gi2krYeOazrVxV75XmheaxrKkTnArTGiMpm0Ey98lprZ8VB9cpr7cj+W2em6V8PXsYHRIALkbRFTJl7n/I3IHJ2FyKr/4c2WknblvUa0l9+VseobNfNMtNCXwy3MvV6itZFcyQsCSgOMMRZWopPR45LR4/XVdo6F4vdRRhTLK1NYhoS4Gr+RipipdBVseGTtuH/LlmSuk08CJOpPYLdD+uRXCzf9IrSnKitJWvrBn8YO0CffXVE55/qXJUrCVvAnWiPgJgyJ/uoBIxcw4RE8HHLt1VY/h2tpG2ffGtlbTJUqGWkhl451dyg38lXwu6baCAW0lNPXkJUnzhZaeumpEKoU5RlYSUXxQvEm3n2h660Dd7TNvxCZKbbQTZsvi6JRaWtV/ojNKi0dSgOuItlIbKotUcgaesGndtmafBp7UicAmiApC1iqkX6t8ktN72hTzT13/iEeikvOP07ln9HK2l77cDkWJjgd9efE7idEabStmV6auD/QduW6bEOKyQWIoOT6pI4dZW2ji7KZ6M/p2Q9ZpLhwycgFPMhEqqC1XxcRdLTNtghZknaNvGcYeccY6caNxHUf6pcQ0OqtxBZtCptDZK2AOBmtEdATGWbKhJ5gxy5hr1qgycUzyywVsSG68vaFNmZaVHZjttVHjkeuB3uOaf4fXrmhvP0dXWNOrfNikdoQdmtfAJioW7+1VXaOvnBgb+RRFEdqmuBk3w2vqlhPpZSbVbIWnvaNhyZ6o+8bZatQluPHOL1z2NeeV5oHnN1bWoKSVsASAYkbRFT/9Ozva46t5PO6NDK6VASWv1kSKhkbHq970qRyGuaK/oV6O5F2yRJw7+bG3bssJ4d4hFSWOYF4k7w9TbEWd3CRNWBSlvnYvE1kiiqQ0U6cJK1PULjPW2tFbJhttuEStumfohi55rGO4c4C5GhocZ6RkfCK9XpAOBVJG0RUxmpKfrt1X2dDiPh1b8wC5V4qOsxWSdan8Ini1YZqdrz68udDsO2LFPP6LqvqAPxUldp+95nlZKc/TaFeddhF0nyTkYHaBY7PdHNx4t1YVl7PW2DjUsNkQi2xcZwrySgaI+AYMyV6tH6EJKpBQDuRk9bIAHUv1i3W2nLV4G9zdweoZqkLeKs/odETiYVrF/1DlNpyzkRkGSvOj1Ue4Tw2w1fCRiqetfetqMzJhE07NPtUCBwFctCZFGaFHwDBQDcjaQtkADqX1DZrbTlQix5HP2mrygQL2ku+pDIdk9bzomApHqVtiHGpNioxm243eC365i/AdTU49HWQmQeyW7WfxZU2kIKvThgc/B3EQDcjaQtkADqX0+FantA0jZ5Ha8haYv4Skux92FSPNjpz1n/Mc6PwEmhDhlzAjQ3O9PWthrruWk+7pqaiLS1EFmTtuheDSptHYoD7tKcDz1CuaJfgSTptPYto7I9AEB00dMWSAA+n08+n1S3wGuKP/jnLQ3aI0QxKZGfk6nPK49a+qjCPQaf2s7pEJBk6lfa9u6Y7VAkkvmUGO6sZ00YxS4ewO0sx4yNhciuGdBZ2/+vUoV9wi/SaWm7EOQgM/fkbHJL2yRqj1D/eXilghjNY/lQJEp/xEb3L1DnU1qol4N/wwEAoZG0BRJEis+nE99kbUP1lktN8cvvk2oDyd3oXeT/vwmD9OiKXSoZdkbUtonme7nkQv3j31/o4l4dnA4FScb8IdGp7VtqxJl5jsVi942s9avbJEGkk38naur+aCBpmBfsCnXImO9v2ypd6+68uNHkYWOtSszXJU2ddXaOWa8e1x59Wmgiy0JkUZoUPp9Pg/jgHwBci/YIQIKw+7Vec/VbNJO2p3dopSfHnKs++XwS7yZndcrRrUNPt6zsDcSD+Wua53Y5xcFI7C9uRLVaQ8VDT5MkTfzmv0gO5kMh1HHhr9fKwM7x09hCZHYXNIuUV47w+h8+eTUZjaYxV8jT4gcAkgOVtkCC8Psl1Zy8Xb93rVl6il/V3yxKxUrpAGJlR/nBwG2nPzSwJG1tnveOHq+JVTgJZcqlPfS9Hu313fwcp0NBHNlZvM/OYmUNfyf47TqWStsmltraW4isadt0q/o9w8nPQbL+fSNpCwDJgdIsIEGY36zU711rVmN6F5SZRv9ZALFhTY44+/V66yr34d/Itso4+Xn1qe1bxTSmROHz+TSgW1u1oF95UrGzzJX1uLK3XX8jx6K10rZp5w17PW29kciq/+GTzzM1xGiOWCxEBgBwNyptgQRhvoAPV2l7vKY2cJtFwwDESkaY81C8Wd/Ihh/7ws/P17/3HdJlDvbgBZzma6QituEYewmixvpGmxNNOS3SbW0zEI+dMR7JY52SZX1tfO453cJB/kbajwAAvIekLZAgzPUo4Sptj9d8O9IrFScA3MfcP7upX3OONkvP70bOe9/Nz6EVAJKez0YVbWSVfOHbLvh8Pv15wiB9feyE2rfOaNKWbbVH8EhFqt/vU3ZmqqqOnpDknV69aB67/dsBAN5B0hZIEOZrs3CVtgAQD2kuWvzO/Oa1/gI+ABpqrCJWsh5X9tsjNL7dC8/4jr2N1WMnBi8d/il+qiphZZ4TsV7UDwDgDu55xwUgrAxTf9pwSdvWGXwWAyD2zAvlOJ1PoPoIaBpzRWqoI8YXwVexLV/fjvKxaOfbQ06fi6LJb6MaGsnFkrRNYVIAQDIgaQskCPOlWbhP1386uKskqQWLkAGIIXPSx+n2CJakLdkNoFHmwyRUMjSihchM7yxi8flJY3F4pT2CFFlPYXhbK1NhRnZmmoORAADihZI8IEHsO1gduB2u2mT8Bd1VUXlUVw/oHI+wACQr02nI6aRtagRf4waSmaWKNkQJh51WB+G2G4sPUHyy9vhvuP+o79IxrEuA+jqdkqVpw3uoTVa6WvLNOgBICpztgQSRkepX9Yla9emYHXZc+9YZevSafvEJCkDSOr97W7378ZdOhyGp3kJktEcAGuWz3A5+zJjbG9g9qmLZHkH6JpEZ5lMiLyU6zc+ESlvUKbn4DKdDAADEEe0RgAQx66qzNaDrKfr9T/o7HQoAaFJhj8BtI2ztW+yxYA/QNHbaGETSUzXWicbG8sBeOvotCXAvPTEAAGAblbZAgriiX4Gu6FfgdBgAIMmaKHW6PQILkQFNY1mILFRPW1Ni124Fq/nwS4lBaYivkQYJXvrMxk7fYQAA4G1U2gIAgGZxOGdL0hZoIr8lIRhqTPPaI8Qi0djYJr1UaW+tWnYsDAAA4CCStgAAoFncVGnrpaQNEDM2ErLWr+e7ZCEy0yZzWqSFfTzR+WKcAAcAAO5H0hYAACS0lAh6bwLJzFy5GSohG0lPWzvbbQ5zW4czC7K16Z5Czfjhd02PewfnMgAA4GjSdubMmTrvvPPUunVrdejQQaNGjdKuXbssY44ePari4mK1a9dOrVq10ujRo7V3717LmE8++USXX365srKy1KFDB/3yl7/UiRMnLGNWr16t/v37KyMjQ6effroWLFjQIJ65c+eqW7duyszM1KBBg/Tuu+9G/TkDAOA1Ti9ElurnM2igKcz5wFDJwRTLYmV2K21NvxODw7J+Uvg7rTKUZgrUSxWpfGsAAAA4+i5nzZo1Ki4u1jvvvKMVK1bo+PHjGj58uA4fPhwYM2XKFL388stauHCh1qxZo88//1xXXnll4PGamhpdfvnlOnbsmN5++209++yzWrBgge69997AmI8//liXX365hg0bpi1btmjy5Mm68cYb9dprrwXGvPDCC5o6daqmT5+uf/7zn+rbt6+Kioq0b9+++LwYAAAkKofbI5CzBZrGZ6P1QWSVtrFtVRKsZUCq35uV9l56LgAAIDKpTu58+fLlln8vWLBAHTp00ObNm/W9731PlZWVevrpp/WXv/xFF198sSSptLRUvXv31jvvvKPzzz9fr7/+uj744AO98cYbys3NVb9+/fTAAw/ojjvu0H333af09HTNmzdP3bt31yOPPCJJ6t27t9566y3Nnj1bRUVFkqRHH31UN910k8aPHy9JmjdvnpYtW6ZnnnlGd955ZxxfFQAA0BTmnrZO99cFEoGdfGCze9rGYPUsX5DbfnPS1kMNEqi0BQAArqpNqayslCS1bdtWkrR582YdP35chYWFgTG9evVSly5dtH79eknS+vXrddZZZyk3NzcwpqioSFVVVdq+fXtgjHkbdWPqtnHs2DFt3rzZMsbv96uwsDAwpr7q6mpVVVVZfgAASCZndGglSfpR/wJH44hFcgjwMjsJ2UgW+LO2L4gstnB8Qbafaokz+vt0CjlbAADgaKWtWW1trSZPnqwLLrhAZ555piSpoqJC6enpatOmjWVsbm6uKioqAmPMCdu6x+seCzemqqpKR44c0VdffaWampqgY3bu3Bk03pkzZ2rGjBmRPVkAADzgbxOH6D/7D+vsTm0cjcO8EBmFtoANpoRgqORgsARpY+LZHqFu+36vtkdwOgAAAOA411TaFhcXa9u2bXr++eedDsWWu+66S5WVlYGfTz/91OmQAACIq5wWaY4nbKV6lbb0RwAaZW8hsob9YxtjPhRjUQHvD5JsNn9oQ3sEAADgJa6otC0pKdHSpUu1du1aderUKXB/Xl6ejh07pgMHDliqbffu3au8vLzAmHfffdeyvb179wYeq/tv3X3mMdnZ2WrRooVSUlKUkpISdEzdNurLyMhQRkZGZE8YAABEjZdWjAfiwXzIpIQ4fswJULsJWDsLnDVHsIXILLF56FTAaQ0AADhaaWsYhkpKSrRo0SKtWrVK3bt3tzx+7rnnKi0tTStXrgzct2vXLn3yyScaPHiwJGnw4MHaunWr9u3bFxizYsUKZWdnq0+fPoEx5m3UjanbRnp6us4991zLmNraWq1cuTIwBgAAuB91tkDjLAnVUEnbCNojWH4nxguR1W0+kt67icBLzwUAAETG0Urb4uJi/eUvf9GSJUvUunXrQA/anJwctWjRQjk5OZowYYKmTp2qtm3bKjs7W7/4xS80ePBgnX/++ZKk4cOHq0+fPvrpT3+qWbNmqaKiQvfcc4+Ki4sDlbC33HKLfve73+n222/Xz372M61atUovvviili1bFohl6tSpGjdunAYMGKCBAwfqscce0+HDhzV+/Pj4vzAAAABAjDQ1IWu/PUJsFwXzBWmFYF6IjDQnAADwEkeTtr///e8lSUOHDrXcX1paqhtuuEGSNHv2bPn9fo0ePVrV1dUqKirSk08+GRibkpKipUuXauLEiRo8eLBatmypcePG6f777w+M6d69u5YtW6YpU6bo8ccfV6dOnfTHP/5RRUVFgTHXXHONvvjiC917772qqKhQv379tHz58gaLkwEAAPeipS3QOHPrg1AVncGqWhtj6Wkbk/YIpn356/7b9DYOiYBKWwAA4GjS1rDxziozM1Nz587V3LlzQ47p2rWrXnnllbDbGTp0qMrKysKOKSkpUUlJSaMxAQAAdzJokAA0ytIdIeSgb2/aTSAG6zkbTdYF1L7paWuu7vVQ0pacLQAAcLSnLQAAAID4auqCYbaTtqbbsah69fsatkIw7ycW1b1O8dBTAQAAESJpCwAAPIP2CEDjrBWrocb4Gh0TTmx62pq3/02lraU9QvT36RTaIwAAAA9d2gAAAABojKU9go3cYCQJxFi0Kgi20Jk5aeulRKd3ngkAAIgUSVsAAOAZR47VOB0C4HrWRcZCLERmqWpt+j5i3arAF7TS1jupzlj0BAYAAImFpC0AAPCMqqPHnQ4BcD1/jHraNvd3GhOsQpiFyAAAgFeRtAUAAAnvqnM7SZJu+Z/THI4EcD877RFqTQ2iI+ppG4N3GcGSzZ5diMzpAAAAgONSnQ4AAACgue6/4rsa3idXl/TOdToUIAGYFxkLnh7My85sdEw4sa60DdbTlvYIAADAS0jaAgCAhJeVnqrh381zOgwgIdjpV3tq+1aa/9NzlWtK3jZFLKpefZbbDSttWYgMAAB4CUlbAAAAIImYE7XhkoNFzfggJBb5U0t7hG/aL3i10tZLCWgAABAZetoCAAAAScSn2FenxuTr/ZZevCf/kWpJ2kZ/l44hZwsAQNLz0qUNAAAAgEb4giQ/E4E5wVx3y+/R9ggeKhoGAAARImkLAAAAJBFr0ta5OJrKHGpdgtbcO9dL7RF8lNoCAJD0SNoCAAAAScRcXZtIec5gC6h5tqct79IAAEh6XA4AAAAASSRYxWoisLRHqKu09Wh7BCptAQAASVsAAAAgifgsyU8HA2kGX5BKW8NwKBgAAIAYIGkLAAAAJBFznjaRFiKztnVoWGnrJQn0vwUAAMQISVsAAAAgifgtvWGjlx2MdaGrOe66m+aFyIyYRxA/iZRMBwAAsUHSFgAAAEgilvYIDsbRVJaFyPzerrT16NMCAABNQNIWAAAASCLWhcgcC6PJ/EF68Xo1aevNZwUAAJqCpC0AAACQTGLUHiHWLL14v/mXOWfrpYXIEun/CwAAiA2StgAAAEAS8SlIc9hEYFmIrO6ub+/r2i4r3hHFDDlbAACQ6nQAAAAAAOInVguRxVqouMv+91Idq6lV68w0B6KKlcT5/wIAAGKDpC0AAACQRCwLeiVQbtDSHsH0j1Napsc9FgAAgFijPQIAAACQRMztEXwJVNHpsyxEljhxRyIvJ8PpEAAAgMOotAUAAACSiKXSNoFKOPwJWiEciamX9lTVkRO6ekAnp0MBAAAOIWkLAAAAJBFrlWriZD/NVcGJ1Is3Em1bpmvOdec4HQYAAHBQAn22DgAAAKC5zPnOtJTESX6a406cqAEAACJD0hYAAABIIuaEZ3pK9N4OXNKrgyQpp0Va1LZpZm3rQNoWAAB4G+0RAAAAgCRiTn5mpadEbbuDTm2npb+4UJ1PyYraNs0sC6iRswUAAB5H0hYAAABIIuZ+sKe2bxXVbZ9ZkBPV7ZmZF03z0SABAAB4HO0RAAAAgCRiTnd2/05Lx+JoKutCZA4GAgAAEAckbQEAAIAk4jNV2rbMSJwv3ll62tIfAQAAeBxJWwAAACBJZaQmztsBc7KZnC0AAPC6xLlKAwAAANBs5oRnZlr0FiKLNXOe1kfWFgAAeBxJWwAAACCJ1NQagdsZaYnzdsDvC34bAADAixLnKg0AAABAs+0/fCxwO2HbIzgYBwAAQDwkzlUaAAAAgGZb9n554HZ6SuK8HTAnav2U2gIAAI9LnKs0AAAAAM1mTtQmUm9Y60JkiRM3AABAJEjaAgAAAEmkfesMp0OIiDlPS8oWAAB4HUlbAAAAIImYFyJLJGv+9UXgtp9KWwAA4HEkbQEAAIAkUmMkZtL22InawG1a2gIAAK8jaQsAAAAkkdoErbQ9syA7cJtCWwAA4HUkbQEAAIAkkqiVtqP6FQRusxAZAADwOpK2AAAAQBJJ1J62qaaeCPS0BQAAXkfSFgAAAEgikwt7SJJ+dkF3hyNpmtSUb9+6kLIFAABel+p0AAAAAADi54Yh3dSvc46+m5/jdChNYqm0pfQEAAB4HElbAAAAIImk+H06t2tbp8NoMnOlLe0RAACA1/EZNQAAAADXS0v5NlHLQmQAAMDrSNoCAAAAcL1UPz1tAQBA8iBpCwAAAMD1Uk2VtrRHAAAAXkfSFgAAAIDrpVmStg4GAgAAEAckbQEAAAC4nqU9AklbAADgcSRtAQAAALheKguRAQCAJELSFgAAAIDrpaV8+9aFnrYAAMDrSNoCAAAAcL1UUyNbUrYAAMDrSNoCAAAAcD1zT1s/72IAAIDHcbkDAAAAwPXoaQsAAJIJSVsAAAAArpeWQnsEAACQPEjaAgAAAHA9S3sEKm0BAIDHkbQFAAAA4Hrm9ggkbQEAgNeRtAUAAADgemkp3751IWcLAAC8jqQtAAAAANdL8dPTFgAAJA+StgAAAABcL83U07bGMByMBAAAIPZI2gIAAABwPXNP25pakrYAAMDbSNoCAAAAcD1z0raWSlsAAOBxJG0BAAAAuJ6lPUKtg4EAAADEAUlbAAAAAK7n99MeAQAAJA+StgAAAAASCu0RAACA15G0BQAAAJBQUkxVtwAAAF7kaNJ27dq1+sEPfqD8/Hz5fD4tXrzY8rhhGLr33nvVsWNHtWjRQoWFhdq9e7dlzJdffqkxY8YoOztbbdq00YQJE3To0CHLmPfff18XXXSRMjMz1blzZ82aNatBLAsXLlSvXr2UmZmps846S6+88krUny8AAACA5uvTMdvpEAAAAGLK0aTt4cOH1bdvX82dOzfo47NmzdKcOXM0b948bdiwQS1btlRRUZGOHj0aGDNmzBht375dK1as0NKlS7V27VrdfPPNgcerqqo0fPhwde3aVZs3b9ZvfvMb3XfffXrqqacCY95++21dd911mjBhgsrKyjRq1CiNGjVK27Zti92TBwAAANAka345VIuLL1DntllOhwIAABBTPsNwR0Mon8+nRYsWadSoUZJOVtnm5+frtttu07Rp0yRJlZWVys3N1YIFC3Tttddqx44d6tOnjzZu3KgBAwZIkpYvX67vf//7+uyzz5Sfn6/f//73uvvuu1VRUaH09HRJ0p133qnFixdr586dkqRrrrlGhw8f1tKlSwPxnH/++erXr5/mzZtnK/6qqirl5OSosrJS2dl88g8AAAAAAADAym4O0bU9bT/++GNVVFSosLAwcF9OTo4GDRqk9evXS5LWr1+vNm3aBBK2klRYWCi/368NGzYExnzve98LJGwlqaioSLt27dJXX30VGGPeT92Yuv0EU11draqqKssPAAAAAAAAADSXa5O2FRUVkqTc3FzL/bm5uYHHKioq1KFDB8vjqampatu2rWVMsG2Y9xFqTN3jwcycOVM5OTmBn86dOzf1KQIAAAAAAABAA65N2rrdXXfdpcrKysDPp59+6nRIAAAAAAAAADzAtUnbvLw8SdLevXst9+/duzfwWF5envbt22d5/MSJE/ryyy8tY4Jtw7yPUGPqHg8mIyND2dnZlh8AAAAAAAAAaC7XJm27d++uvLw8rVy5MnBfVVWVNmzYoMGDB0uSBg8erAMHDmjz5s2BMatWrVJtba0GDRoUGLN27VodP348MGbFihXq2bOnTjnllMAY837qxtTtBwAAAAAAAADixdGk7aFDh7RlyxZt2bJF0snFx7Zs2aJPPvlEPp9PkydP1oMPPqiXXnpJW7du1dixY5Wfn69Ro0ZJknr37q0RI0bopptu0rvvvqt169appKRE1157rfLz8yVJ119/vdLT0zVhwgRt375dL7zwgh5//HFNnTo1EMekSZO0fPlyPfLII9q5c6fuu+8+bdq0SSUlJfF+SQAAAAAAAAAkOZ9hGIZTO1+9erWGDRvW4P5x48ZpwYIFMgxD06dP11NPPaUDBw7owgsv1JNPPqkePXoExn755ZcqKSnRyy+/LL/fr9GjR2vOnDlq1apVYMz777+v4uJibdy4Ud/5znf0i1/8QnfccYdlnwsXLtQ999yjPXv26IwzztCsWbP0/e9/3/ZzqaqqUk5OjiorK2mVAAAAAAAAAKABuzlER5O2XkLSFgAAAAAAAEA4dnOIru1pCwAAAAAAAADJiKQtAAAAAAAAALgISVsAAAAAAAAAcBGStgAAAAAAAADgIiRtAQAAAAAAAMBFSNoCAAAAAAAAgIuQtAUAAAAAAAAAF0l1OgCvMAxDklRVVeVwJAAAAAAAAADcqC53WJdLDIWkbZQcPHhQktS5c2eHIwEAAAAAAADgZgcPHlROTk7Ix31GY2ld2FJbW6vPP/9crVu3ls/nczocuFhVVZU6d+6sTz/9VNnZ2U6HA49gXiHamFOIFuYSoon5hGhjTiEWmFeIBuaRdxmGoYMHDyo/P19+f+jOtVTaRonf71enTp2cDgMJJDs7mxMvoo55hWhjTiFamEuIJuYToo05hVhgXiEamEfeFK7Ctg4LkQEAAAAAAACAi5C0BQAAAAAAAAAXIWkLxFlGRoamT5+ujIwMp0OBhzCvEG3MKUQLcwnRxHxCtDGnEAvMK0QD8wgsRAYAAAAAAAAALkKlLQAAAAAAAAC4CElbAAAAAAAAAHARkrYAAAAAAAAA4CIkbQFJM2fO1HnnnafWrVurQ4cOGjVqlHbt2mUZc/ToURUXF6tdu3Zq1aqVRo8erb179wYef++993Tdddepc+fOatGihXr37q3HH3885D7XrVun1NRU9evXr9H4DMPQvffeq44dO6pFixYqLCzU7t27A4+vXr1aPp8v6M/GjRub/oIgKtw+r/7+979r+PDhateunXw+n7Zs2dJgTGPxIb7iNadCnVMqKirCxtfYuUqSHnroIQ0ZMkRZWVlq06ZN814QRMwLc6lOdXW1+vXrF/I8hthL9PnEdZQ7uX1ecR2VeOJ5bV5dXa27775bXbt2VUZGhrp166Znnnmm0Rjnzp2rbt26KTMzU4MGDdK7775refypp57S0KFDlZ2dLZ/PpwMHDkT2YqBZvDCX6hiGocsuu0w+n0+LFy9u2guBmCNpC0has2aNiouL9c4772jFihU6fvy4hg8frsOHDwfGTJkyRS+//LIWLlyoNWvW6PPPP9eVV14ZeHzz5s3q0KGD/vznP2v79u26++67ddddd+l3v/tdg/0dOHBAY8eO1SWXXGIrvlmzZmnOnDmaN2+eNmzYoJYtW6qoqEhHjx6VJA0ZMkTl5eWWnxtvvFHdu3fXgAEDmvnqIFJun1eHDx/WhRdeqIcffjjkmMbiQ3zFe07t2rXLcl7p0KFD2PgaO1dJ0rFjx3T11Vdr4sSJUXhFECkvzKU6t99+u/Lz85vxaqC5En0+cR3lTm6fV1xHJZ54zqkf//jHWrlypZ5++mnt2rVLf/3rX9WzZ8+w8b3wwguaOnWqpk+frn/+85/q27evioqKtG/fvsCYr7/+WiNGjNCvfvWrKL0qiIQX5lKdxx57TD6fr5mvCGLGANDAvn37DEnGmjVrDMMwjAMHDhhpaWnGwoULA2N27NhhSDLWr18fcju33nqrMWzYsAb3X3PNNcY999xjTJ8+3ejbt2/YWGpra428vDzjN7/5TeC+AwcOGBkZGcZf//rXoL9z7Ngxo3379sb9998fdtuILzfNK7OPP/7YkGSUlZVZ7o80PsRPrObUm2++aUgyvvrqK9uxNPVcVVpaauTk5NjePmIrUefSK6+8YvTq1cvYvn170PMYnJGo86kO11Hu5KZ5ZcZ1VOKK1Zx69dVXjZycHGP//v1NimfgwIFGcXFx4N81NTVGfn6+MXPmzAZjmztvEV2JOpfKysqMgoICo7y83JBkLFq0qEn7QexRaQsEUVlZKUlq27atpJOfgh0/flyFhYWBMb169VKXLl20fv36sNup20ad0tJSffTRR5o+fbqtWD7++GNVVFRY9p2Tk6NBgwaF3PdLL72k/fv3a/z48bb2gfhw07yyI9L4ED+xnFOS1K9fP3Xs2FGXXnqp1q1bFzaWSM5VcI9EnEt79+7VTTfdpD/96U/Kysqy90QRF4k4n8y4jnInN80rO7iOcr9YzamXXnpJAwYM0KxZs1RQUKAePXpo2rRpOnLkSMhtHDt2TJs3b7bs2+/3q7CwkPmSABJxLn399de6/vrrNXfuXOXl5TX9SSMuUp0OAHCb2tpaTZ48WRdccIHOPPNMSVJFRYXS09Mb9F/Mzc0N2e/q7bff1gsvvKBly5YF7tu9e7fuvPNO/eMf/1Bqqr3Dr277ubm5tvf99NNPq6ioSJ06dbK1D8Se2+aVHZHEh/iJ5Zzq2LGj5s2bpwEDBqi6ulp//OMfNXToUG3YsEH9+/cPup1IzlVwh0ScS4Zh6IYbbtAtt9yiAQMGaM+ePZE8dcRAIs6n+riOch+3zSs7uI5yt1jOqY8++khvvfWWMjMztWjRIv33v//Vrbfeqv3796u0tDTodv773/+qpqYm6Llq586dzXimiLVEnUtTpkzRkCFDdMUVV0TytBEnJG2BeoqLi7Vt2za99dZbEW9j27ZtuuKKKzR9+nQNHz5cklRTU6Prr79eM2bMUI8ePYL+3nPPPaef//zngX+/+uqrSklJadK+P/vsM7322mt68cUXI44f0ee2eXXRRRdFHAfcIVZzSpJ69uxp6ZU1ZMgQffjhh5o9e7b+9Kc/ReVcBfdIxLn0xBNP6ODBg7rrrrsijhmxkYjzyYzrKHdy27ziOirxxXJO1dbWyufz6bnnnlNOTo4k6dFHH9VVV12lJ598Ups2bdJll10WGD9//nwNGzYs8icDRyXiXHrppZe0atUqlZWVRRwz4oOkLWBSUlKipUuXau3atZbqiry8PB07dkwHDhywfFq2d+/eBl8l+OCDD3TJJZfo5ptv1j333BO4/+DBg9q0aZPKyspUUlIi6eRJ2DAMpaam6vXXX9cPf/hDDRo0KPA7BQUFKi8vD+yrY8eOln3369evwXMoLS1Vu3bt9MMf/rBZrwWix43zyo6mxIf4iuWcCmXgwIGBi9FonKvgDok6l1atWqX169crIyPDsu0BAwZozJgxevbZZ+29AIiqRJ1PZlxHuY8b55UdXEe5V6znVMeOHVVQUBBIsklS7969ZRiGPvvsMw0YMEBbtmwJPJabm6uMjAylpKRo7969lm0xX9wtUefSqlWr9OGHHzaoBB49erQuuugirV69OoJXAzHhaEddwCVqa2uN4uJiIz8/3/jXv/7V4PG6RuJ/+9vfAvft3LmzQSPxbdu2GR06dDB++ctfNthGTU2NsXXrVsvPxIkTjZ49expbt241Dh06FDK2vLw847e//W3gvsrKyqALaNTW1hrdu3c3brvttia/Bog+N88rs8YW0GgsPsRPPOZUKIWFhcaPfvSjsLHZPVcZBguROS3R59J//vMfy3nvtddeMyQZf/vb34xPP/3UdiyIjkSfT+axXEe5h5vnlRnXUYkjXnNq/vz5RosWLYyDBw8G7lu8eLHh9/uNr7/+OmR8AwcONEpKSgL/rqmpMQoKCliIzIUSfS6Vl5c3eA8pyXj88ceNjz76yP4LgZgjaQsYhjFx4kQjJyfHWL16tVFeXh74MZ8Ib7nlFqNLly7GqlWrjE2bNhmDBw82Bg8eHHh869atRvv27Y2f/OQnlm3s27cv5H6nT59u9O3bt9H4fv3rXxtt2rQxlixZYrz//vvGFVdcYXTv3t04cuSIZdwbb7xhSDJ27NjR9BcBUef2ebV//36jrKzMWLZsmSHJeP75542ysjKjvLzcdnyIr3jNqdmzZxuLFy82du/ebWzdutWYNGmS4ff7jTfeeCNsfHbOVf/5z3+MsrIyY8aMGUarVq2MsrIyo6yszHIxitjzwlwyC5U0QXx4ZT5xHeUubp9XXEclnnjNqYMHDxqdOnUyrrrqKmP79u3GmjVrjDPOOMO48cYbw8b3/PPPGxkZGcaCBQuMDz74wLj55puNNm3aGBUVFYEx5eXlRllZmfGHP/zBkGSsXbvWKCsrM/bv3x/FVwqN8cJcqk+SsWjRoshfFMQESVvAOHmCCvZTWloaGHPkyBHj1ltvNU455RQjKyvL+NGPfmS5KJs+fXrQbXTt2jXkfu0m12pra43//d//NXJzc42MjAzjkksuMXbt2tVg3HXXXWcMGTKkKU8dMeT2eVVaWhp029OnT7cdH+IrXnPq4YcfNk477TQjMzPTaNu2rTF06FBj1apVjcZn51w1bty4oPt/8803m/vyoAm8MJfMSNo6yyvziesod3H7vOI6KvHE89p8x44dRmFhodGiRQujU6dOxtSpU8NWRtZ54oknjC5duhjp6enGwIEDjXfeecfyeKj9m58DYs8LcynYcyJp6z4+wzAMAQAAAAAAAABcwe90AAAAAAAAAACAb5G0BQAAAAAAAAAXIWkLAAAAAAAAAC5C0hYAAAAAAAAAXISkLQAAAAAAAAC4CElbAAAAAAAAAHARkrYAAAAAAAAA4CIkbQEAAAAAAADARUjaAgAAAA7p1q2bHnvsMafDAAAAgMuQtAUAAIDn3XDDDfL5fPL5fEpLS1Nubq4uvfRSPfPMM6qtrbW9nQULFqhNmzZN3n+o39u4caNuvvnmJm8PAAAA3kbSFgAAAElhxIgRKi8v1549e/Tqq69q2LBhmjRpkkaOHKkTJ044ElP79u2VlZXlyL4BAADgXiRtAQAAkBQyMjKUl5engoIC9e/fX7/61a+0ZMkSvfrqq1qwYIEk6dFHH9VZZ52lli1bqnPnzrr11lt16NAhSdLq1as1fvx4VVZWBqp277vvPklSdXW1pk2bpoKCArVs2VKDBg3S6tWrG/29+u0RfD6f5s+fr5EjRyorK0u9e/fW+vXr9e9//1tDhw5Vy5YtNWTIEH344YeW57ZkyRL1799fmZmZOvXUUzVjxgzHEtEAAABoPpK2AAAASFoXX3yx+vbtq7///e+SJL/frzlz5mj79u169tlntWrVKt1+++2SpCFDhuixxx5Tdna2ysvLVV5ermnTpkmSSkpKtH79ej3//PN6//33dfXVV2vEiBHavXt32N8L5oEHHtDYsWO1ZcsW9erVS9dff71+/vOf66677tKmTZtkGIZKSkoC4//xj39o7NixmjRpkj744APNnz9fCxYs0EMPPRTDVw4AAACxRNIWAAAASa1Xr17as2ePJGny5MkaNmyYunXrposvvlgPPvigXnzxRUlSenq6cnJy5PP5lJeXp7y8PLVq1UqffPKJSktLtXDhQl100UU67bTTNG3aNF144YUqLS0N+XuhjB8/Xj/+8Y/Vo0cP3XHHHdqzZ4/GjBmjoqIi9e7dW5MmTQpU8UrSjBkzdOedd2rcuHE69dRTdemll+qBBx7Q/PnzY/myAQAAIIZSnQ4AAAAAcJJhGPL5fJKkN954QzNnztTOnTtVVVWlEydO6OjRo/r6669D9p7dunWrampq1KNHD8v91dXVateuXZPjOfvsswO3c3NzJUlnnXWW5b6jR4+qqqpK2dnZeu+997Ru3TpLZW1NTU2jcQMAAMC9SNoCAAAgqe3YsUPdu3fXnj17NHLkSE2cOFEPPfSQ2rZtq7feeksTJkzQsWPHQiY/Dx06pJSUFG3evFkpKSmWx8JV1IaSlpYWuF2XTA52X21tbWD/M2bM0JVXXtlgW5mZmU3ePwAAAJxH0hYAAABJa9WqVdq6daumTJmizZs3q7a2Vo888oj8/pNdxOpaI9RJT09XTU2N5b5zzjlHNTU12rdvny666KKg+wn2e9HSv39/7dq1S6effnpMtg8AAID4I2kLAACApFBdXa2KigrV1NRo7969Wr58uWbOnKmRI0dq7Nix2rZtm44fP64nnnhCP/jBD7Ru3TrNmzfPso1u3brp0KFDWrlypfr27ausrCz16NFDY8aM0dixY/XII4/onHPO0RdffKGVK1fq7LPP1uWXXx7096LVtuDee+/VyJEj1aVLF1111VXy+/167733tG3bNj344INR2QcAAADii4XIAAAAkBSWL1+ujh07qlu3bhoxYoTefPNNzZkzR0uWLFFKSor69u2rRx99VA8//LDOPPNMPffcc5o5c6ZlG0OGDNEtt9yia665Ru3bt9esWbMkSaWlpRo7dqxuu+029ezZU6NGjdLGjRvVpUuXsL8XDUVFRVq6dKlef/11nXfeeTr//PM1e/Zsde3aNWr7AAAAQHz5DMMwnA4CAAAAAAAAAHASlbYAAAAAAAAA4CIkbQEAAAAAAADARUjaAgAAAAAAAICLkLQFAAAAAAAAABchaQsAAAAAAAAALkLSFgAAAAAAAABchKQtAAAAAAAAALgISVsAAAAAAAAAcBGStgAAAAAAAADgIiRtAQAAAAAAAMBFSNoCAAAAAAAAgIuQtAUAAAAAAAAAF/n/aK2hVVszXrEAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Calculate the 24-hour rolling mean.\n", + "rolling_mean_24 = ts.rolling(24).mean()\n", + "\n", + "# Plot the rolling mean only.\n", + "plt.figure(figsize=(14, 5))\n", + "plt.plot(\n", + " rolling_mean_24,\n", + " label=\"24-hour rolling mean\"\n", + ")\n", + "\n", + "plt.title(\"24-Hour Rolling Mean Of Pedestrian Count\")\n", + "plt.xlabel(\"Datetime\")\n", + "plt.ylabel(\"Rolling Mean\")\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7.2 Seasonal Pattern (24-Hour Cycle)\n", + "\n", + "Seasonal decomposition is necessary because hourly pedestrian demand is expected to have a strong daily cycle, so checking the seasonal component can help check how the typical hour of the day affects pedestrian activity. Since people usually move through the city at different levels during the morning, workday, evening, and late night, this step helps show whether the hour of the day is a factor in pedestrian demand [41].\n", + "\n", + "From the plot, it is clear that the seasonal effect is strongly negative at night and becomes strongly positive during the workday, especially the peak at 5 PM, when most people finish work and are looking to go home. The lowest tends to be around 3 AM, which is expected, since people tend to party late until 12 PM before heading home, and the other reason is that the city's pedestrian activities are very limited at that time. This plot further confirms that hour-of-day is a major driver of pedestrian demand, and there may be other variables influencing pedestrian demand." + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Decompose the series using a 24-hour period.\n", + "decomp_24 = seasonal_decompose(\n", + " ts,\n", + " model=\"additive\",\n", + " period=24\n", + ")\n", + "\n", + "# Store one seasonal cycle and align it to the actual hours.\n", + "seasonal_24 = pd.Series(\n", + " decomp_24.seasonal[:24].values,\n", + " index=ts.index[:24].hour\n", + ")\n", + "\n", + "# Sort by actual hour.\n", + "seasonal_24 = seasonal_24.sort_index()\n", + "\n", + "# Plot the corrected daily seasonal pattern.\n", + "plt.figure(figsize=(10, 4))\n", + "plt.plot(\n", + " seasonal_24.index,\n", + " seasonal_24.values,\n", + " marker=\"o\",\n", + " label=\"Daily seasonal effect\"\n", + ")\n", + "\n", + "plt.title(\"Daily Seasonal Pattern (24-Hour Cycle)\")\n", + "plt.xlabel(\"Hour\")\n", + "plt.ylabel(\"Seasonal Effect\")\n", + "plt.xticks(range(24))\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7.3 Autocorrelation Of Pedestrian Count\n", + "\n", + "Checking for autocorrelation is important because it shows whether the current pedestrian demand depends on previous hours, and if strong dependence exists, then lag features will be very useful for forecasting [42].\n", + "\n", + "From the plot, there appears to be a very strong repeating pattern at a regular interval, especially around daily cycles. This repeated structure applies not only on the daily level, but also appears to be on the weekly level as well, meaning daily and weekly lag features will be useful for predictions." + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot autocorrelation for one week of hourly lags.\n", + "fig, ax = plt.subplots(figsize=(12, 5))\n", + "plot_acf(ts, lags=168, ax=ax)\n", + "\n", + "ax.set_title(\"Autocorrelation Of Pedestrian Count\")\n", + "ax.set_xlabel(\"Lag (Hours)\")\n", + "ax.set_ylabel(\"Autocorrelation\")\n", + "\n", + "# Add legend handles.\n", + "legend_handles = [\n", + " Line2D([0], [0], color=\"C0\", lw=2, label=\"Autocorrelation\"),\n", + " Patch(alpha=0.2, label=\"95% confidence interval\")\n", + "]\n", + "\n", + "ax.legend(handles=legend_handles, loc=\"upper right\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7.4 Augmented Dickey-Fuller Test\n", + "\n", + "The Augmented Dickey-Fuller Test checks whether the time series is statistically stationary in the unit-root sense, and while not necessary for deep learning modelling, it does provide some more understanding of the patterns underlying the merged dataset [43].\n", + "\n", + "The result was that the p-value is extremely small and the test statistic is far below the critical values, hence, the null hypothesis of a unit root is rejected. This means that the time series dataset is statistically stationary enough to exhibit a learnable structure rather than behaving like a random walk, which basically means that it's not necessarily a random coin toss in layman's terms." + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ADF Statistic: -12.334016562004392\n", + "p-value: 6.332696219939632e-23\n", + "Critical Values:\n", + "1%: -3.4307265048981876\n", + "5%: -2.8617064005452915\n", + "10%: -2.5668585707044094\n" + ] + } + ], + "source": [ + "# Run the Augmented Dickey-Fuller test.\n", + "adf_result = adfuller(ts)\n", + "\n", + "# Print the results.\n", + "print(\"ADF Statistic:\", adf_result[0])\n", + "print(\"p-value:\", adf_result[1])\n", + "print(\"Critical Values:\")\n", + "\n", + "for key, value in adf_result[4].items():\n", + " print(f\"{key}: {value}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8. Feature Engineering\n", + "\n", + "The feature engineering section is necessary because the current variables in the merged datasets may not necessarily be enough for a strong forecasting model, so doing feature engineering will create more useful variables that help capture other aspects of the datasets, like cyclical structures, recent history, and short-term trends [44] [45]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8.1 Creating Time Features\n", + "\n", + "Creating more calendar-based features is necessary because pedestrian activity depends on when that observation happened, as shown in previous plots. Hence, hours, day of week, month, and weekend status are all useful predictors. This step ensures the datetime_hour column is converted into its individual components [45].\n", + "\n", + "The output shows that new time-based columns were added to the dataset. These include hour, day_of_week, month, and is_weekend." + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " datetime_hour hour day_of_week month is_weekend\n", + "0 2024-05-12 00:00:00 0 6 5 1\n", + "1 2024-05-12 01:00:00 1 6 5 1\n", + "2 2024-05-12 02:00:00 2 6 5 1\n", + "3 2024-05-12 03:00:00 3 6 5 1\n", + "4 2024-05-12 04:00:00 4 6 5 1\n" + ] + } + ], + "source": [ + "# Create a copy for feature engineering.\n", + "features_df = model_df.copy()\n", + "\n", + "# Extract calendar features.\n", + "features_df[\"hour\"] = features_df[\"datetime_hour\"].dt.hour\n", + "features_df[\"day_of_week\"] = (\n", + " features_df[\"datetime_hour\"].dt.dayofweek\n", + ")\n", + "features_df[\"month\"] = features_df[\"datetime_hour\"].dt.month\n", + "features_df[\"is_weekend\"] = (\n", + " features_df[\"day_of_week\"] >= 5\n", + ").astype(int)\n", + "\n", + "# Check the result.\n", + "print(\n", + " features_df[\n", + " [\n", + " \"datetime_hour\", \"hour\", \"day_of_week\", \"month\", \n", + " \"is_weekend\"\n", + " ]\n", + " ].head()\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8.2 Creating Cyclical Time Features\n", + "\n", + "Cyclical encoding is necessary since time variables are cyclical and not linear, like a clock. If we're talking just normal values like 0 and 23, these two values are quite far apart, but it's not, since it's time and there's only 1 hour difference. Using sine and cosine preserves that circular structure. This step ensures that the time variables are cyclical and prevents the models from learning misleading distances between values at the edge of a cycle [46].\n", + "\n", + "The output shows that new cyclical time features were created, including hour_sin, hour_cos, dow_sin, dow_cos, month_sin, and month_cos." + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " datetime_hour hour_sin hour_cos dow_sin dow_cos month_sin \\\n", + "0 2024-05-12 00:00:00 0.000000 1.000000 -0.781831 0.62349 0.5 \n", + "1 2024-05-12 01:00:00 0.258819 0.965926 -0.781831 0.62349 0.5 \n", + "2 2024-05-12 02:00:00 0.500000 0.866025 -0.781831 0.62349 0.5 \n", + "3 2024-05-12 03:00:00 0.707107 0.707107 -0.781831 0.62349 0.5 \n", + "4 2024-05-12 04:00:00 0.866025 0.500000 -0.781831 0.62349 0.5 \n", + "\n", + " month_cos \n", + "0 -0.866025 \n", + "1 -0.866025 \n", + "2 -0.866025 \n", + "3 -0.866025 \n", + "4 -0.866025 \n" + ] + } + ], + "source": [ + "# Hour and day of week are cyclical, not linear.\n", + "# Create cyclical hour features.\n", + "features_df[\"hour_sin\"] = np.sin(\n", + " 2 * np.pi * features_df[\"hour\"] / 24\n", + ")\n", + "features_df[\"hour_cos\"] = np.cos(\n", + " 2 * np.pi * features_df[\"hour\"] / 24\n", + ")\n", + "\n", + "# Create cyclical day-of-week features.\n", + "features_df[\"dow_sin\"] = np.sin(\n", + " 2 * np.pi * features_df[\"day_of_week\"] / 7\n", + ")\n", + "features_df[\"dow_cos\"] = np.cos(\n", + " 2 * np.pi * features_df[\"day_of_week\"] / 7\n", + ")\n", + "\n", + "# Convert cyclical month features.\n", + "features_df[\"month_sin\"] = np.sin(\n", + " 2 * np.pi * features_df[\"month\"] / 12\n", + ")\n", + "features_df[\"month_cos\"] = np.cos(\n", + " 2 * np.pi * features_df[\"month\"] / 12\n", + ")\n", + "\n", + "# Check the result.\n", + "print(\n", + " features_df[\n", + " [\n", + " \"datetime_hour\", \"hour_sin\", \"hour_cos\",\n", + " \"dow_sin\", \"dow_cos\", \"month_sin\", \n", + " \"month_cos\"\n", + " ]\n", + " ].head()\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8.3 Creating Cyclical Wind Direction Features\n", + "\n", + "Wind direction is also circular, being 360 degrees, meaning 0 and 359 degrees are almost the same direction. So wind direction must also be converted to cyclical for a continuous form, and ensure consistency with treatments of cyclical variables like time [46].\n", + "\n", + "The output shows that two new features were created, wind_dir_sin and wind_dir_cos." + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " averagewinddirection wind_dir_sin wind_dir_cos\n", + "0 166.142857 0.239502 -0.970896\n", + "1 140.321429 0.638480 -0.769638\n", + "2 153.857143 0.440611 -0.897698\n", + "3 139.250000 0.652760 -0.757565\n", + "4 170.074074 0.172375 -0.985031\n" + ] + } + ], + "source": [ + "# Convert wind direction into cyclical form.\n", + "features_df[\"wind_dir_sin\"] = np.sin(\n", + " 2 * np.pi * features_df[\"averagewinddirection\"] / 360\n", + ")\n", + "features_df[\"wind_dir_cos\"] = np.cos(\n", + " 2 * np.pi * features_df[\"averagewinddirection\"] / 360\n", + ")\n", + "\n", + "# Check the result.\n", + "print(\n", + " features_df[\n", + " [\n", + " \"averagewinddirection\",\n", + " \"wind_dir_sin\",\n", + " \"wind_dir_cos\"\n", + " ]\n", + " ].head()\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8.4 Creating Lag Features\n", + "\n", + "Lag features are important, as indicated by the autocorrelation plot, since pedestrian demand is highly dependent on recent history based on the autocorrelation plot [47] [48].\n", + "\n", + "The features lag_1, lag_24, and lag_168 capture the previous hour, previous day, and previous week at the same hour. This does create missing values since there wasn't recent information to populate the lag columns for specific rows, which will be dealt with. Like the very first row having a missing value for lag_1, because there wasn't a previous row to populate that value." + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " lag_1 lag_24 lag_168\n", + "0 NaN NaN NaN\n", + "1 15093.0 NaN NaN\n", + "2 10686.0 NaN NaN\n", + "3 6751.0 NaN NaN\n", + "4 5431.0 NaN NaN\n", + "5 2728.0 NaN NaN\n", + "6 2306.0 NaN NaN\n", + "7 4251.0 NaN NaN\n", + "8 8872.0 NaN NaN\n", + "9 18370.0 NaN NaN\n", + "10 26934.0 NaN NaN\n", + "11 40599.0 NaN NaN\n", + "12 53701.0 NaN NaN\n", + "13 62802.0 NaN NaN\n", + "14 63419.0 NaN NaN\n", + "15 65710.0 NaN NaN\n", + "16 74282.0 NaN NaN\n", + "17 65628.0 NaN NaN\n", + "18 49356.0 NaN NaN\n", + "19 39189.0 NaN NaN\n", + "20 31933.0 NaN NaN\n", + "21 24297.0 NaN NaN\n", + "22 19736.0 NaN NaN\n", + "23 13472.0 NaN NaN\n", + "24 8268.0 15093.0 NaN\n" + ] + } + ], + "source": [ + "# Create lagged pedestrian count features.\n", + "features_df[\"lag_1\"] = (\n", + " features_df[\"pedestriancount\"].shift(1)\n", + ")\n", + "\n", + "features_df[\"lag_24\"] = (\n", + " features_df[\"pedestriancount\"].shift(24)\n", + ")\n", + "\n", + "features_df[\"lag_168\"] = (\n", + " features_df[\"pedestriancount\"].shift(168)\n", + ")\n", + "\n", + "# Check the result.\n", + "print(\n", + " features_df[\n", + " [\n", + " \"lag_1\", \"lag_24\", \"lag_168\"\n", + " ]\n", + " ].head(25)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8.5 Creating Rolling Features\n", + "\n", + "Rolling features summarise recent pedestrian counts rather than relying on a single observation for a datapoint, allowing the model to capture the short-term trend and help smooth the short-term noise [47].\n", + "\n", + "Some new predictors were added, like rolling_mean_24, which summarises the previous 24 hours, while rolling_mean_168 summarises the previous week. And as expected, similar to lag features but not the same, the first rows are missing values since the rolling window cannot be calculated until enough history exists. " + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " rolling_mean_24 rolling_mean_168\n", + "0 NaN NaN\n", + "1 NaN NaN\n", + "2 NaN NaN\n", + "3 NaN NaN\n", + "4 NaN NaN\n", + "5 NaN NaN\n", + "6 NaN NaN\n", + "7 NaN NaN\n", + "8 NaN NaN\n", + "9 NaN NaN\n", + "10 NaN NaN\n", + "11 NaN NaN\n", + "12 NaN NaN\n", + "13 NaN NaN\n", + "14 NaN NaN\n", + "15 NaN NaN\n", + "16 NaN NaN\n", + "17 NaN NaN\n", + "18 NaN NaN\n", + "19 NaN NaN\n", + "20 NaN NaN\n", + "21 NaN NaN\n", + "22 NaN NaN\n", + "23 NaN NaN\n", + "24 29742.25 NaN\n" + ] + } + ], + "source": [ + "# Create rolling mean features from past counts.\n", + "features_df[\"rolling_mean_24\"] = (\n", + " features_df[\"pedestriancount\"]\n", + " .shift(1)\n", + " .rolling(24)\n", + " .mean()\n", + ")\n", + "\n", + "features_df[\"rolling_mean_168\"] = (\n", + " features_df[\"pedestriancount\"]\n", + " .shift(1)\n", + " .rolling(168)\n", + " .mean()\n", + ")\n", + "\n", + "# Check the result.\n", + "print(\n", + " features_df[\n", + " [\n", + " \"rolling_mean_24\", \"rolling_mean_168\"\n", + " ]\n", + " ].head(25)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8.6 Removing NA Rows\n", + "\n", + "This step removes the rows with missing values created from the lag and rolling features at the beginning of the ordered merged dataset, since they cannot be used as they are incomplete predictor information. By removing these rows, the dataset lost a part of the early period as a trade-off in the pipeline, which is reasonable considering the dataset is being updated in real-time, so there will be more data points to use in the future, hence, negligible [48].\n", + "\n", + "The output shows that all rows with missing values were removed, and the dataset was reset into a clean index." + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 17248 entries, 0 to 17247\n", + "Data columns (total 28 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 datetime_hour 17248 non-null datetime64[ns]\n", + " 1 pedestriancount 17248 non-null int64 \n", + " 2 airtemperature 17248 non-null float64 \n", + " 3 relativehumidity 17248 non-null float64 \n", + " 4 atmosphericpressure 17248 non-null float64 \n", + " 5 averagewindspeed 17248 non-null float64 \n", + " 6 gustwindspeed 17248 non-null float64 \n", + " 7 averagewinddirection 17248 non-null float64 \n", + " 8 pm25 17248 non-null float64 \n", + " 9 pm10 17248 non-null float64 \n", + " 10 noise 17248 non-null float64 \n", + " 11 day_of_week 17248 non-null int32 \n", + " 12 hour 17248 non-null int32 \n", + " 13 month 17248 non-null int32 \n", + " 14 is_weekend 17248 non-null int64 \n", + " 15 hour_sin 17248 non-null float64 \n", + " 16 hour_cos 17248 non-null float64 \n", + " 17 dow_sin 17248 non-null float64 \n", + " 18 dow_cos 17248 non-null float64 \n", + " 19 month_sin 17248 non-null float64 \n", + " 20 month_cos 17248 non-null float64 \n", + " 21 wind_dir_sin 17248 non-null float64 \n", + " 22 wind_dir_cos 17248 non-null float64 \n", + " 23 lag_1 17248 non-null float64 \n", + " 24 lag_24 17248 non-null float64 \n", + " 25 lag_168 17248 non-null float64 \n", + " 26 rolling_mean_24 17248 non-null float64 \n", + " 27 rolling_mean_168 17248 non-null float64 \n", + "dtypes: datetime64[ns](1), float64(22), int32(3), int64(2)\n", + "memory usage: 3.5 MB\n", + "None\n", + " datetime_hour pedestriancount airtemperature relativehumidity \\\n", + "0 2024-05-19 00:00:00 12751 8.655172 69.200000 \n", + "1 2024-05-19 01:00:00 8603 8.664286 68.792857 \n", + "2 2024-05-19 02:00:00 5660 8.960714 69.235714 \n", + "3 2024-05-19 03:00:00 4709 9.660714 67.675000 \n", + "4 2024-05-19 04:00:00 2682 10.050000 69.060714 \n", + "\n", + " atmosphericpressure averagewindspeed gustwindspeed averagewinddirection \\\n", + "0 1026.241379 0.841379 2.303448 204.137931 \n", + "1 1025.732143 1.042857 2.535714 203.071429 \n", + "2 1024.671429 1.757143 3.717857 237.107143 \n", + "3 1024.171429 1.460714 3.657143 247.857143 \n", + "4 1023.675000 1.482143 3.807143 240.821429 \n", + "\n", + " pm25 pm10 ... dow_cos month_sin month_cos wind_dir_sin \\\n", + "0 6.758621 9.448276 ... 0.62349 0.5 -0.866025 -0.408935 \n", + "1 6.750000 9.428571 ... 0.62349 0.5 -0.866025 -0.391878 \n", + "2 6.892857 9.535714 ... 0.62349 0.5 -0.866025 -0.839688 \n", + "3 6.107143 8.678571 ... 0.62349 0.5 -0.866025 -0.926247 \n", + "4 5.678571 8.428571 ... 0.62349 0.5 -0.866025 -0.873104 \n", + "\n", + " wind_dir_cos lag_1 lag_24 lag_168 rolling_mean_24 rolling_mean_168 \n", + "0 -0.912564 21105.0 8948.0 15093.0 33068.333333 31092.851190 \n", + "1 -0.920017 12751.0 6244.0 10686.0 33226.791667 31078.910714 \n", + "2 -0.543070 8603.0 4035.0 6751.0 33325.083333 31066.511905 \n", + "3 -0.376917 5660.0 2813.0 5431.0 33392.791667 31060.017857 \n", + "4 -0.487533 4709.0 1761.0 2728.0 33471.791667 31055.720238 \n", + "\n", + "[5 rows x 28 columns]\n" + ] + } + ], + "source": [ + "# Remove rows with NAs.\n", + "features_df = features_df.dropna().reset_index(drop=True)\n", + "\n", + "# Check the result.\n", + "print(features_df.info())\n", + "print(features_df.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8.7 Removing Unnecessary Features\n", + "\n", + "Since the cyclical variables were created, the original raw cyclical variables have become redundant. So removing these variables helps reduce feature duplication and makes the modelling easier and cleaner [23] [24].\n", + "\n", + "The averagewinddirection, hour, day_of_week, and month columns are dropped. Resulting in 24 columns, including the target pedestrian count, selected climate variables, is_weekend, cyclical encodings, lag features, and rolling means. The datetime_hour is temporarily kept, but will be removed later on as well." + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 17248 entries, 0 to 17247\n", + "Data columns (total 24 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 datetime_hour 17248 non-null datetime64[ns]\n", + " 1 pedestriancount 17248 non-null int64 \n", + " 2 airtemperature 17248 non-null float64 \n", + " 3 relativehumidity 17248 non-null float64 \n", + " 4 atmosphericpressure 17248 non-null float64 \n", + " 5 averagewindspeed 17248 non-null float64 \n", + " 6 gustwindspeed 17248 non-null float64 \n", + " 7 pm25 17248 non-null float64 \n", + " 8 pm10 17248 non-null float64 \n", + " 9 noise 17248 non-null float64 \n", + " 10 is_weekend 17248 non-null int64 \n", + " 11 hour_sin 17248 non-null float64 \n", + " 12 hour_cos 17248 non-null float64 \n", + " 13 dow_sin 17248 non-null float64 \n", + " 14 dow_cos 17248 non-null float64 \n", + " 15 month_sin 17248 non-null float64 \n", + " 16 month_cos 17248 non-null float64 \n", + " 17 wind_dir_sin 17248 non-null float64 \n", + " 18 wind_dir_cos 17248 non-null float64 \n", + " 19 lag_1 17248 non-null float64 \n", + " 20 lag_24 17248 non-null float64 \n", + " 21 lag_168 17248 non-null float64 \n", + " 22 rolling_mean_24 17248 non-null float64 \n", + " 23 rolling_mean_168 17248 non-null float64 \n", + "dtypes: datetime64[ns](1), float64(21), int64(2)\n", + "memory usage: 3.2 MB\n", + "None\n" + ] + } + ], + "source": [ + "# Drop raw columns that now have cyclical replacements.\n", + "features_df = features_df.drop(\n", + " columns=[\n", + " \"averagewinddirection\",\n", + " \"hour\",\n", + " \"day_of_week\",\n", + " \"month\",\n", + " ]\n", + ")\n", + "\n", + "# Reorder the columns into a cleaner structure.\n", + "features_df = features_df[\n", + " [\n", + " \"datetime_hour\",\n", + " \"pedestriancount\",\n", + " \"airtemperature\",\n", + " \"relativehumidity\",\n", + " \"atmosphericpressure\",\n", + " \"averagewindspeed\",\n", + " \"gustwindspeed\",\n", + " \"pm25\",\n", + " \"pm10\",\n", + " \"noise\",\n", + " \"is_weekend\",\n", + " \"hour_sin\",\n", + " \"hour_cos\",\n", + " \"dow_sin\",\n", + " \"dow_cos\",\n", + " \"month_sin\",\n", + " \"month_cos\",\n", + " \"wind_dir_sin\",\n", + " \"wind_dir_cos\",\n", + " \"lag_1\",\n", + " \"lag_24\",\n", + " \"lag_168\",\n", + " \"rolling_mean_24\",\n", + " \"rolling_mean_168\",\n", + " ]\n", + "]\n", + "\n", + "# Check the result.\n", + "print(features_df.info())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9. Preparing Train/Val/Test Splits\n", + "\n", + "Preparing the splits is necessary for training a forecasting model, so that the future periods are evaluated, and not on randomly selected time periods. Hence, splitting the dataset based on time is important so that the model being trained on the past can predict the future, like in real-world scenarios [49]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 9.1 Splitting The Data By Time\n", + "\n", + "Chronological splitting in time-series forecasting is to prevent leaking future information into the training process. For example is if random splitting were to be used, then a model can be trained on data points in 2026, but is tested on a time period in 2025. Hence, the validation and test set must be later, and the training set must be the past, as shown here [49].\n", + "\n", + "The split for training, validation and testing is 80:10:10 to ensure that there are enough data points used for training, with enough data points to perform validation and testing. Deep learning tends to require a large number of data points, so using this split ratio prevents underfitting [50].\n", + "\n", + "The output confirms that the data was split chronologically, with the training data coming first, followed by validation and testing data." + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(13798, 24)\n", + "(1724, 24)\n", + "(1726, 24)\n" + ] + } + ], + "source": [ + "# Set the split sizes (80:10:10).\n", + "train_size = int(len(features_df) * 0.8)\n", + "val_size = int(len(features_df) * 0.1)\n", + "\n", + "# Split the data in chronological order.\n", + "train_df = features_df.iloc[:train_size].copy()\n", + "val_df = features_df.iloc[\n", + " train_size:train_size + val_size\n", + "].copy()\n", + "test_df = features_df.iloc[\n", + " train_size + val_size:\n", + "].copy()\n", + "\n", + "# Check the shapes.\n", + "print(train_df.shape)\n", + "print(val_df.shape)\n", + "print(test_df.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train range:\n", + "2024-05-19 00:00:00\n", + "2025-12-14 21:00:00\n", + "\n", + "Validation range:\n", + "2025-12-14 22:00:00\n", + "2026-02-24 17:00:00\n", + "\n", + "Test range:\n", + "2026-02-24 18:00:00\n", + "2026-05-07 15:00:00\n" + ] + } + ], + "source": [ + "# Checking The Split Ranges.\n", + "print(\"Train range:\")\n", + "print(train_df[\"datetime_hour\"].min())\n", + "print(train_df[\"datetime_hour\"].max())\n", + "\n", + "print(\"\\nValidation range:\")\n", + "print(val_df[\"datetime_hour\"].min())\n", + "print(val_df[\"datetime_hour\"].max())\n", + "\n", + "print(\"\\nTest range:\")\n", + "print(test_df[\"datetime_hour\"].min())\n", + "print(test_df[\"datetime_hour\"].max())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 9.2 Separating Features And Target\n", + "\n", + "This step is necessary to separate the predictor inputs X and the output target y. This is necessary because the model needs to know which columns are used as inputs and which column it needs to predict [51].\n", + "\n", + "The target is set to pedestriancount, which is the variable the model is trying to predict. The datetime column is excluded from the features since the chronological splitting is completed, so this leaves 22 predictor columns for each split, as shown in the shapes output." + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(13798, 22) (13798,)\n", + "(1724, 22) (1724,)\n", + "(1726, 22) (1726,)\n" + ] + } + ], + "source": [ + "# Store the target column name.\n", + "target_col = \"pedestriancount\"\n", + "\n", + "# Store the feature column names.\n", + "feature_cols = [\n", + " col for col in features_df.columns\n", + " if col not in [\"datetime_hour\", target_col]\n", + "]\n", + "\n", + "# Create X and y for each split.\n", + "X_train = train_df[feature_cols]\n", + "y_train = train_df[target_col]\n", + "\n", + "X_val = val_df[feature_cols]\n", + "y_val = val_df[target_col]\n", + "\n", + "X_test = test_df[feature_cols]\n", + "y_test = test_df[target_col]\n", + "\n", + "# Check the shapes.\n", + "print(X_train.shape, y_train.shape)\n", + "print(X_val.shape, y_val.shape)\n", + "print(X_test.shape, y_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 9.3 Scaling The Features\n", + "\n", + "Scaling the features is necessary because the predictor variables are measured on different scales. For example, temperature, humidity, air pressure, wind speed, pollution values, and lagged pedestrian counts all have different ranges. Hence, needs to be standardised so that the variables are unitless, allowing the variables to be able to directly compared [52].\n", + "\n", + "Standardisation basically sets the predictors to have a mean of 0 and a standard deviation of 1. This allows faster convergence when all the input features are on the same scale, preventing feature dominance due to differences in magnitudes, and is generally more stable [52]." + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(13798, 22)\n", + "(1724, 22)\n", + "(1726, 22)\n", + "[[-1.29777704e+00 1.50360125e-01 1.46578468e+00 -5.80148521e-01\n", + " -7.99559383e-01 1.00283430e-01 1.98749444e-01 -3.63941261e-01\n", + " 1.57673844e+00 -7.77706241e-05 1.41447137e+00 -1.10371969e+00\n", + " 8.80498928e-01 1.03546540e+00 -1.13513198e+00 -5.75432725e-01\n", + " -4.91738264e-01 -5.08174682e-01 -9.59909049e-01 -7.29249335e-01\n", + " -3.26314765e-01 -1.04167887e+00]\n", + " [-1.29610869e+00 1.24879825e-01 1.40393909e+00 -2.36045144e-01\n", + " -6.48552677e-01 9.91130647e-02 1.96368567e-01 -5.59070044e-01\n", + " 1.57673844e+00 3.65929517e-01 1.36628083e+00 -1.10371969e+00\n", + " 8.80498928e-01 1.03546540e+00 -1.13513198e+00 -5.44617322e-01\n", + " -5.21312792e-01 -8.18712499e-01 -1.06044050e+00 -8.93515834e-01\n", + " -2.95950862e-01 -1.04569266e+00]\n", + " [-1.24184204e+00 1.52595239e-01 1.27511778e+00 9.83881253e-01\n", + " 1.20012207e-01 1.18507689e-01 2.09314586e-01 2.43030983e-01\n", + " 1.57673844e+00 7.06994013e-01 1.22499330e+00 -1.10371969e+00\n", + " 8.80498928e-01 1.03546540e+00 -1.13513198e+00 -1.35366847e+00\n", + " 9.74390635e-01 -9.72903410e-01 -1.14256846e+00 -1.04018901e+00\n", + " -2.77116141e-01 -1.04926256e+00]\n", + " [-1.11369428e+00 5.49207567e-02 1.21439393e+00 4.77611798e-01\n", + " 8.05390863e-02 1.18372546e-02 1.05746435e-01 -2.70089534e-01\n", + " 1.57673844e+00 9.99872735e-01 1.00023730e+00 -1.10371969e+00\n", + " 8.80498928e-01 1.03546540e+00 -1.13513198e+00 -1.51005421e+00\n", + " 1.63367379e+00 -1.08230164e+00 -1.18800094e+00 -1.08939068e+00\n", + " -2.64141820e-01 -1.05113235e+00]\n", + " [-1.04242843e+00 1.41643180e-01 1.15410382e+00 5.14209590e-01\n", + " 1.78060915e-01 -4.63466188e-02 7.55390572e-02 -4.47800540e-01\n", + " 1.57673844e+00 1.22460648e+00 7.07329572e-01 -1.10371969e+00\n", + " 8.80498928e-01 1.03546540e+00 -1.13513198e+00 -1.41404236e+00\n", + " 1.19475651e+00 -1.11765254e+00 -1.22711303e+00 -1.19014229e+00\n", + " -2.49003782e-01 -1.05236973e+00]]\n" + ] + } + ], + "source": [ + "# Create the scaler.\n", + "scaler = StandardScaler()\n", + "\n", + "# Fit on training features only.\n", + "X_train_scaled = scaler.fit_transform(X_train)\n", + "\n", + "# Transform validation and test features.\n", + "X_val_scaled = scaler.transform(X_val)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "\n", + "# Check the shapes.\n", + "print(X_train_scaled.shape)\n", + "print(X_val_scaled.shape)\n", + "print(X_test_scaled.shape)\n", + "print(X_train_scaled[:5])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 10. Deep Learning — Pedestrian Count Forecasting\n", + "\n", + "This section covers the deep learning component of the project. The goal is to build \n", + "and compare recurrent neural network models that can forecast hourly pedestrian counts \n", + "in Melbourne CBD using climate features and engineered time-series inputs.\n", + "\n", + "Deep learning models, specifically recurrent neural network architectures like LSTM \n", + "and GRU, are well suited for this task because pedestrian demand is a sequential, \n", + "time-dependent process [53]. Unlike traditional regression \n", + "models, recurrent networks can learn temporal patterns across multiple time steps, \n", + "making them appropriate for hourly forecasting tasks with lag dependencies \n", + "[54]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.2 Setting Random Seeds for Reproducibility\n", + "\n", + "A fixed random seed is set across all relevant libraries before any model is built or \n", + "trained. Deep learning models initialise their weights randomly and apply random \n", + "dropout masks during training, which means that results can differ between runs \n", + "without a fixed seed [55]. By setting the same seed across \n", + "Python, NumPy, and TensorFlow, the outputs of this section are fully reproducible \n", + "and will produce identical results on every run \n", + "[7] [55].\n", + "\n", + "Additional environment variables TF_DETERMINISTIC_OPS and \n", + "TF_DISABLE_SEGMENT_REDUCTION_OP_DETERMINISM_EXCEPTIONS are set to further enforce \n", + "deterministic behaviour at the TensorFlow operation level \n", + "[55]. The student ID was used as the seed value for consistency \n", + "with the random seed convention used earlier in this notebook \n", + "[7]." + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Random seed set to: 224120439\n", + "TF version: 2.20.0\n" + ] + } + ], + "source": [ + "SEED = 224120439\n", + "\n", + "# 1. Force single thread to eliminate CPU non-determinism\n", + "os.environ[\"PYTHONHASHSEED\"] = str(SEED)\n", + "os.environ[\"TF_DETERMINISTIC_OPS\"] = \"1\"\n", + "os.environ[\"TF_DISABLE_SEGMENT_REDUCTION_OP_DETERMINISM_EXCEPTIONS\"] = \"1\"\n", + "\n", + "# 2. Seed all libraries\n", + "random.seed(SEED)\n", + "np.random.seed(SEED)\n", + "tf.keras.utils.set_random_seed(SEED)\n", + "\n", + "# 3. Force deterministic operations\n", + "try:\n", + " tf.config.experimental.enable_op_determinism()\n", + "except Exception:\n", + " pass # silently skip if not supported on this system\n", + "\n", + "print(f\"Random seed set to: {SEED}\")\n", + "print(f\"TF version: {tf.__version__}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.3 Creating Input Sequences\n", + "\n", + "Before training a recurrent neural network, the data must be reshaped into \n", + "three-dimensional sequences. Each sequence consists of a fixed-length window of past \n", + "hourly observations that the model uses to predict the next hour's pedestrian count \n", + "[56].\n", + "\n", + "A sequence length of 24 was chosen to capture one full day cycle of hourly patterns. \n", + "This is a natural choice given that pedestrian demand follows a strong 24-hour rhythm, \n", + "as confirmed by the seasonal decomposition and autocorrelation analysis in earlier \n", + "sections [35] [42]. Additionally, since lag \n", + "features covering 24 hours, 7 days, and rolling means are already included as input \n", + "features, extending the sequence window further would introduce redundancy without \n", + "meaningful benefit [47] [48].\n", + "\n", + "Each input sample X has the shape (24, 22), representing 24 consecutive hours across \n", + "22 engineered features. The corresponding target y is the pedestrian count at the \n", + "following hour, making this a one-step-ahead forecasting setup \n", + "[56]. The output shapes confirm the split sizes — 13,774 \n", + "training samples, 1,700 validation samples, and 1,702 test samples." + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train shape: (13774, 24, 22)\n", + "Val shape: (1700, 24, 22)\n", + "Test shape: (1702, 24, 22)\n" + ] + } + ], + "source": [ + "sequence_length = 24 \n", + "\n", + "def create_sequences(X, y, seq_length):\n", + " X_seq, y_seq = [], []\n", + " for i in range(seq_length, len(X)):\n", + " X_seq.append(X[i - seq_length:i])\n", + " y_seq.append(y.iloc[i])\n", + " return np.array(X_seq), np.array(y_seq)\n", + "\n", + "X_train_seq, y_train_seq = create_sequences(X_train_scaled, y_train, sequence_length)\n", + "X_val_seq, y_val_seq = create_sequences(X_val_scaled, y_val, sequence_length)\n", + "X_test_seq, y_test_seq = create_sequences(X_test_scaled, y_test, sequence_length)\n", + "\n", + "print(\"Train shape:\", X_train_seq.shape)\n", + "print(\"Val shape:\", X_val_seq.shape)\n", + "print(\"Test shape:\", X_test_seq.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.4 Model Architectures\n", + "\n", + "Three recurrent neural network architectures were built and compared to identify the \n", + "most effective model for this forecasting task. Comparing multiple architectures \n", + "allows for evidence-based model selection rather than relying on a single approach \n", + "[53] [57].\n", + "\n", + "All three models share the same output structure — a Dense(32) hidden layer followed \n", + "by a Dense(1) output layer for regression — and are compiled with the Adam optimiser \n", + "and Mean Squared Error loss, which is standard for continuous value regression \n", + "forecasting tasks [58]. Mean Absolute Error is tracked as an \n", + "interpretable secondary metric during training [59].\n", + "\n", + "The three architectures evaluated are:\n", + "- **Baseline LSTM**: A single LSTM layer with 64 units serving as the reference model \n", + " to establish a performance baseline.\n", + "- **Stacked LSTM**: Two LSTM layers in sequence for deeper temporal pattern learning.\n", + "- **GRU**: A more parameter-efficient alternative to LSTM with a simplified gate \n", + " structure." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 10.4.1 Baseline LSTM\n", + "\n", + "The baseline model uses a single LSTM layer with 64 units. LSTM networks are designed \n", + "to address the vanishing gradient problem found in standard recurrent networks, \n", + "allowing them to retain information across longer time sequences \n", + "[54]. At each time step, the LSTM uses three gates — an input \n", + "gate, a forget gate, and an output gate — to decide what information to keep, discard, \n", + "or pass forward [54].\n", + "\n", + "A Dropout layer with a rate of 0.2 is applied after the LSTM to regularise the model \n", + "by randomly deactivating 20% of neurons during each training step, reducing the risk \n", + "of overfitting [60]. This model has 24,385 total trainable \n", + "parameters, which is appropriate for the training set size of approximately 13,774 \n", + "samples." + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.12/dist-packages/keras/src/layers/rnn/rnn.py:199: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(**kwargs)\n" + ] + }, + { + "data": { + "text/html": [ + "
Model: \"sequential_3\"\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential_3\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ lstm_3 (LSTM)                   │ (None, 64)             │        22,272 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_4 (Dropout)             │ (None, 64)             │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_6 (Dense)                 │ (None, 32)             │         2,080 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_7 (Dense)                 │ (None, 1)              │            33 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ lstm_3 (\u001b[38;5;33mLSTM\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m22,272\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_6 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m2,080\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_7 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m33\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Total params: 24,385 (95.25 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m24,385\u001b[0m (95.25 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Trainable params: 24,385 (95.25 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m24,385\u001b[0m (95.25 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Non-trainable params: 0 (0.00 B)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def build_lstm_baseline(input_shape):\n", + " model = Sequential([\n", + " LSTM(64, return_sequences=False, input_shape=input_shape),\n", + " Dropout(0.2),\n", + " Dense(32, activation=\"relu\"),\n", + " Dense(1)\n", + " ])\n", + " model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[\"mae\"])\n", + " return model\n", + "\n", + "model_baseline = build_lstm_baseline(\n", + " (X_train_seq.shape[1], X_train_seq.shape[2])\n", + ")\n", + "model_baseline.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 10.4.2 Stacked LSTM\n", + "\n", + "The stacked model adds a second LSTM layer on top of the first. The first LSTM layer \n", + "uses return_sequences=True, meaning it outputs the full sequence of hidden states at \n", + "every time step rather than just the final one. This full sequence is then passed as \n", + "input to the second LSTM layer, which compresses it into a single final hidden state \n", + "[54].\n", + "\n", + "This design allows the model to learn more abstract temporal representations across \n", + "multiple levels of processing — the first layer captures low-level hourly patterns, \n", + "while the second layer can learn higher-order dependencies across those patterns \n", + "[53]. Dropout is applied after each LSTM layer to maintain \n", + "regularisation [60]. This model has 35,777 total trainable \n", + "parameters, making it the most complex of the three architectures compared." + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Model: \"sequential_4\"\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential_4\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ lstm_4 (LSTM)                   │ (None, 24, 64)         │        22,272 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_5 (Dropout)             │ (None, 24, 64)         │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ lstm_5 (LSTM)                   │ (None, 32)             │        12,416 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_6 (Dropout)             │ (None, 32)             │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_8 (Dense)                 │ (None, 32)             │         1,056 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_9 (Dense)                 │ (None, 1)              │            33 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ lstm_4 (\u001b[38;5;33mLSTM\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m22,272\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dropout_5 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ lstm_5 (\u001b[38;5;33mLSTM\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m12,416\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dropout_6 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_8 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m1,056\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_9 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m33\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Total params: 35,777 (139.75 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m35,777\u001b[0m (139.75 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Trainable params: 35,777 (139.75 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m35,777\u001b[0m (139.75 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Non-trainable params: 0 (0.00 B)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def build_lstm_stacked(input_shape):\n", + " model = Sequential([\n", + " LSTM(64, return_sequences=True, input_shape=input_shape), # returns full sequence\n", + " Dropout(0.2),\n", + " LSTM(32, return_sequences=False), # compresses to final state\n", + " Dropout(0.2),\n", + " Dense(32, activation=\"relu\"),\n", + " Dense(1)\n", + " ])\n", + " model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[\"mae\"])\n", + " return model\n", + "\n", + "model_stacked = build_lstm_stacked(\n", + " (X_train_seq.shape[1], X_train_seq.shape[2])\n", + ")\n", + "model_stacked.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 10.4.3 GRU (Gated Recurrent Unit)\n", + "\n", + "The GRU model replaces the LSTM unit with a GRU layer of equal size. GRU simplifies \n", + "the LSTM architecture by combining the input and forget gates into a single update \n", + "gate, and adding a reset gate, resulting in only two gates overall instead of three \n", + "[61]. This reduction in gate complexity leads to fewer \n", + "parameters — 19,009 total — making the GRU the most lightweight of the three \n", + "architectures.\n", + "\n", + "GRU is included as an alternative because empirical research has shown it often \n", + "performs comparably or better than LSTM on time-series forecasting tasks, \n", + "particularly on datasets that are not large enough to fully justify the additional \n", + "capacity of LSTM [61] [62]. This \n", + "comparison tests whether that finding holds for this urban pedestrian forecasting \n", + "task." + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Model: \"sequential_5\"\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential_5\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ gru_1 (GRU)                     │ (None, 64)             │        16,896 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_7 (Dropout)             │ (None, 64)             │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_10 (Dense)                │ (None, 32)             │         2,080 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_11 (Dense)                │ (None, 1)              │            33 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ gru_1 (\u001b[38;5;33mGRU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m16,896\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dropout_7 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_10 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m2,080\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_11 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m33\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Total params: 19,009 (74.25 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m19,009\u001b[0m (74.25 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Trainable params: 19,009 (74.25 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m19,009\u001b[0m (74.25 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Non-trainable params: 0 (0.00 B)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def build_gru(input_shape):\n", + " model = Sequential([\n", + " GRU(64, return_sequences=False, input_shape=input_shape),\n", + " Dropout(0.2),\n", + " Dense(32, activation=\"relu\"),\n", + " Dense(1)\n", + " ])\n", + " model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[\"mae\"])\n", + " return model\n", + "\n", + "model_gru = build_gru(\n", + " (X_train_seq.shape[1], X_train_seq.shape[2])\n", + ")\n", + "model_gru.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.5 Model Training\n", + "\n", + "All three models are trained using identical configurations to ensure a fair \n", + "comparison between architectures. The training setup includes the following design \n", + "choices [58]:\n", + "\n", + "- **Epochs**: A maximum of 50 epochs was set to allow sufficient learning time, with \n", + " callbacks responsible for halting training early when appropriate \n", + " [58].\n", + "- **Batch size**: A batch size of 64 was selected as a balance between training \n", + " stability and computational speed. Larger batch sizes provide more stable gradient \n", + " estimates per update compared to smaller ones [58].\n", + "- **EarlyStopping**: Monitors validation loss with a patience of 10 epochs, halting \n", + " training if no improvement is seen for 10 consecutive epochs. The \n", + " restore_best_weights=True parameter ensures the final model retains the weights \n", + " from its best validation epoch rather than the last epoch \n", + " [63].\n", + "- **ReduceLROnPlateau**: Halves the learning rate when validation loss has not \n", + " improved for 5 consecutive epochs, with a minimum learning rate floor of 1e-6. \n", + " This allows finer weight adjustments as the model approaches convergence rather \n", + " than overshooting the optimal solution [63].\n", + "\n", + "The validation set is used exclusively for monitoring during training and is never \n", + "used to update model weights, preserving the integrity of the chronological train, \n", + "validation, and test split [49]." + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 11ms/step - loss: 1931178880.0000 - mae: 34742.4805 - val_loss: 2065434112.0000 - val_mae: 36316.7852 - learning_rate: 0.0010\n", + "Epoch 2/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1915012608.0000 - mae: 34508.1875 - val_loss: 2041869568.0000 - val_mae: 35990.8672 - learning_rate: 0.0010\n", + "Epoch 3/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1886355328.0000 - mae: 34091.4727 - val_loss: 2005516672.0000 - val_mae: 35482.5078 - learning_rate: 0.0010\n", + "Epoch 4/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1846237696.0000 - mae: 33533.6406 - val_loss: 1957890944.0000 - val_mae: 34864.8906 - learning_rate: 0.0010\n", + "Epoch 5/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 1796227072.0000 - mae: 32903.4414 - val_loss: 1900755712.0000 - val_mae: 34188.7891 - learning_rate: 0.0010\n", + "Epoch 6/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1738398336.0000 - mae: 32237.4355 - val_loss: 1835951104.0000 - val_mae: 33501.5625 - learning_rate: 0.0010\n", + "Epoch 7/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1673944192.0000 - mae: 31575.8242 - val_loss: 1765187584.0000 - val_mae: 32804.0078 - learning_rate: 0.0010\n", + "Epoch 8/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1604557824.0000 - mae: 30903.6895 - val_loss: 1690087552.0000 - val_mae: 32100.9355 - learning_rate: 0.0010\n", + "Epoch 9/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 1532111872.0000 - mae: 30227.2246 - val_loss: 1612279936.0000 - val_mae: 31392.7227 - learning_rate: 0.0010\n", + "Epoch 10/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 1457709312.0000 - mae: 29556.4414 - val_loss: 1533161088.0000 - val_mae: 30695.7051 - learning_rate: 0.0010\n", + "Epoch 11/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1383581440.0000 - mae: 28908.4434 - val_loss: 1454138880.0000 - val_mae: 30023.1250 - learning_rate: 0.0010\n", + "Epoch 12/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1310495488.0000 - mae: 28299.8477 - val_loss: 1376468352.0000 - val_mae: 29394.1934 - learning_rate: 0.0010\n", + "Epoch 13/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1239926912.0000 - mae: 27729.9043 - val_loss: 1301134208.0000 - val_mae: 28809.5215 - learning_rate: 0.0010\n", + "Epoch 14/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1170712448.0000 - mae: 27170.0469 - val_loss: 1229012224.0000 - val_mae: 28242.0703 - learning_rate: 0.0010\n", + "Epoch 15/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1105601920.0000 - mae: 26642.3184 - val_loss: 1160922624.0000 - val_mae: 27700.8438 - learning_rate: 0.0010\n", + "Epoch 16/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1043984640.0000 - mae: 26124.0840 - val_loss: 1094580352.0000 - val_mae: 27079.0586 - learning_rate: 0.0010\n", + "Epoch 17/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 976227584.0000 - mae: 25163.9297 - val_loss: 996540288.0000 - val_mae: 24844.2109 - learning_rate: 0.0010\n", + "Epoch 18/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 860002496.0000 - mae: 21725.8691 - val_loss: 885134976.0000 - val_mae: 21819.1719 - learning_rate: 0.0010\n", + "Epoch 19/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 772316480.0000 - mae: 19665.3848 - val_loss: 801167040.0000 - val_mae: 20392.8848 - learning_rate: 0.0010\n", + "Epoch 20/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 696815808.0000 - mae: 18389.9785 - val_loss: 723598528.0000 - val_mae: 19151.4785 - learning_rate: 0.0010\n", + "Epoch 21/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 628451968.0000 - mae: 17213.0176 - val_loss: 651424896.0000 - val_mae: 17956.3184 - learning_rate: 0.0010\n", + "Epoch 22/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 563721536.0000 - mae: 16077.4170 - val_loss: 584422592.0000 - val_mae: 16778.9355 - learning_rate: 0.0010\n", + "Epoch 23/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 505489536.0000 - mae: 15020.1113 - val_loss: 523019264.0000 - val_mae: 15676.2910 - learning_rate: 0.0010\n", + "Epoch 24/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 449513696.0000 - mae: 13965.4336 - val_loss: 466503328.0000 - val_mae: 14642.3008 - learning_rate: 0.0010\n", + "Epoch 25/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 400619104.0000 - mae: 13041.7090 - val_loss: 414948032.0000 - val_mae: 13628.7861 - learning_rate: 0.0010\n", + "Epoch 26/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 356454240.0000 - mae: 12158.3799 - val_loss: 368652768.0000 - val_mae: 12694.5703 - learning_rate: 0.0010\n", + "Epoch 27/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 317270240.0000 - mae: 11362.6035 - val_loss: 327098016.0000 - val_mae: 11882.4199 - learning_rate: 0.0010\n", + "Epoch 28/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 282110752.0000 - mae: 10618.4219 - val_loss: 290472800.0000 - val_mae: 11140.4375 - learning_rate: 0.0010\n", + "Epoch 29/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 249665072.0000 - mae: 9972.2529 - val_loss: 259006832.0000 - val_mae: 10496.8271 - learning_rate: 0.0010\n", + "Epoch 30/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 222754640.0000 - mae: 9393.5771 - val_loss: 231574736.0000 - val_mae: 9954.3838 - learning_rate: 0.0010\n", + "Epoch 31/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 198423008.0000 - mae: 8869.1445 - val_loss: 207840512.0000 - val_mae: 9421.6309 - learning_rate: 0.0010\n", + "Epoch 32/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 178380192.0000 - mae: 8440.0615 - val_loss: 187732656.0000 - val_mae: 8949.0840 - learning_rate: 0.0010\n", + "Epoch 33/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 161002656.0000 - mae: 7984.2749 - val_loss: 169997776.0000 - val_mae: 8558.7334 - learning_rate: 0.0010\n", + "Epoch 34/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 144927488.0000 - mae: 7591.0400 - val_loss: 154311120.0000 - val_mae: 8103.6113 - learning_rate: 0.0010\n", + "Epoch 35/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 128720472.0000 - mae: 7144.0977 - val_loss: 140611216.0000 - val_mae: 7693.2202 - learning_rate: 0.0010\n", + "Epoch 36/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 115451752.0000 - mae: 6745.2446 - val_loss: 130359144.0000 - val_mae: 7402.8223 - learning_rate: 0.0010\n", + "Epoch 37/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 103320352.0000 - mae: 6354.1328 - val_loss: 117524160.0000 - val_mae: 6989.3652 - learning_rate: 0.0010\n", + "Epoch 38/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 92202488.0000 - mae: 5979.1660 - val_loss: 109628784.0000 - val_mae: 6704.9717 - learning_rate: 0.0010\n", + "Epoch 39/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 83113800.0000 - mae: 5689.0278 - val_loss: 103527280.0000 - val_mae: 6496.6172 - learning_rate: 0.0010\n", + "Epoch 40/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 75200392.0000 - mae: 5414.2593 - val_loss: 97912072.0000 - val_mae: 6302.9551 - learning_rate: 0.0010\n", + "Epoch 41/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 69330976.0000 - mae: 5204.5713 - val_loss: 92983264.0000 - val_mae: 6090.5542 - learning_rate: 0.0010\n", + "Epoch 42/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 64068488.0000 - mae: 5041.2319 - val_loss: 93400048.0000 - val_mae: 6068.5435 - learning_rate: 0.0010\n", + "Epoch 43/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 59892128.0000 - mae: 4892.9224 - val_loss: 87246792.0000 - val_mae: 5791.9092 - learning_rate: 0.0010\n", + "Epoch 44/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 55631812.0000 - mae: 4743.8657 - val_loss: 85550016.0000 - val_mae: 5762.2046 - learning_rate: 0.0010\n", + "Epoch 45/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 53973056.0000 - mae: 4663.5952 - val_loss: 83252664.0000 - val_mae: 5683.0537 - learning_rate: 0.0010\n", + "Epoch 46/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 50220260.0000 - mae: 4527.8228 - val_loss: 83925944.0000 - val_mae: 5714.0728 - learning_rate: 0.0010\n", + "Epoch 47/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 47764060.0000 - mae: 4444.6938 - val_loss: 82401720.0000 - val_mae: 5649.3335 - learning_rate: 0.0010\n", + "Epoch 48/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 45334620.0000 - mae: 4341.4917 - val_loss: 80761736.0000 - val_mae: 5650.6733 - learning_rate: 0.0010\n", + "Epoch 49/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 43015352.0000 - mae: 4263.1714 - val_loss: 78728576.0000 - val_mae: 5562.2876 - learning_rate: 0.0010\n", + "Epoch 50/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 41184284.0000 - mae: 4168.4272 - val_loss: 76461192.0000 - val_mae: 5454.5000 - learning_rate: 0.0010\n", + "Epoch 1/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 10ms/step - loss: 1932731136.0000 - mae: 34764.9727 - val_loss: 2069818880.0000 - val_mae: 36377.0664 - learning_rate: 0.0010\n", + "Epoch 2/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1923338112.0000 - mae: 34629.4414 - val_loss: 2056002432.0000 - val_mae: 36186.6602 - learning_rate: 0.0010\n", + "Epoch 3/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 1906462720.0000 - mae: 34385.0273 - val_loss: 2034484992.0000 - val_mae: 35888.1211 - learning_rate: 0.0010\n", + "Epoch 4/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 1882598784.0000 - mae: 34036.7148 - val_loss: 2005956992.0000 - val_mae: 35488.6562 - learning_rate: 0.0010\n", + "Epoch 5/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1852413696.0000 - mae: 33614.3867 - val_loss: 1971230848.0000 - val_mae: 35029.5586 - learning_rate: 0.0010\n", + "Epoch 6/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1816782592.0000 - mae: 33156.7617 - val_loss: 1931121664.0000 - val_mae: 34541.7891 - learning_rate: 0.0010\n", + "Epoch 7/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1776370560.0000 - mae: 32665.1387 - val_loss: 1886418944.0000 - val_mae: 34027.4883 - learning_rate: 0.0010\n", + "Epoch 8/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1731960960.0000 - mae: 32169.1250 - val_loss: 1837876736.0000 - val_mae: 33520.9844 - learning_rate: 0.0010\n", + "Epoch 9/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 1684573312.0000 - mae: 31682.2441 - val_loss: 1786250496.0000 - val_mae: 33008.1094 - learning_rate: 0.0010\n", + "Epoch 10/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 1634488960.0000 - mae: 31189.7012 - val_loss: 1732167680.0000 - val_mae: 32490.0215 - learning_rate: 0.0010\n", + "Epoch 11/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1582652928.0000 - mae: 30698.1016 - val_loss: 1676277248.0000 - val_mae: 31974.6699 - learning_rate: 0.0010\n", + "Epoch 12/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1528807936.0000 - mae: 30199.7773 - val_loss: 1619060736.0000 - val_mae: 31453.7559 - learning_rate: 0.0010\n", + "Epoch 13/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 1474490880.0000 - mae: 29703.1445 - val_loss: 1561115776.0000 - val_mae: 30939.0352 - learning_rate: 0.0010\n", + "Epoch 14/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1419293056.0000 - mae: 29219.7656 - val_loss: 1502894208.0000 - val_mae: 30434.5215 - learning_rate: 0.0010\n", + "Epoch 15/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - loss: 1366217600.0000 - mae: 28764.8242 - val_loss: 1445071360.0000 - val_mae: 29948.1836 - learning_rate: 0.0010\n", + "Epoch 16/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 1311709056.0000 - mae: 28299.2773 - val_loss: 1387855616.0000 - val_mae: 29483.8652 - learning_rate: 0.0010\n", + "Epoch 17/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1259717120.0000 - mae: 27880.5957 - val_loss: 1331703424.0000 - val_mae: 29047.3066 - learning_rate: 0.0010\n", + "Epoch 18/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1208217600.0000 - mae: 27472.8125 - val_loss: 1276985344.0000 - val_mae: 28620.4238 - learning_rate: 0.0010\n", + "Epoch 19/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1158040192.0000 - mae: 27063.4766 - val_loss: 1224060544.0000 - val_mae: 28202.8828 - learning_rate: 0.0010\n", + "Epoch 20/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 1109995136.0000 - mae: 26683.9062 - val_loss: 1173220352.0000 - val_mae: 27799.9453 - learning_rate: 0.0010\n", + "Epoch 21/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - loss: 1064320704.0000 - mae: 26317.3105 - val_loss: 1124794624.0000 - val_mae: 27414.1367 - learning_rate: 0.0010\n", + "Epoch 22/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1022760384.0000 - mae: 25982.8965 - val_loss: 1079056128.0000 - val_mae: 27053.7539 - learning_rate: 0.0010\n", + "Epoch 23/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 982177088.0000 - mae: 25652.5938 - val_loss: 1036195840.0000 - val_mae: 26712.5508 - learning_rate: 0.0010\n", + "Epoch 24/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 945132608.0000 - mae: 25346.1582 - val_loss: 996421696.0000 - val_mae: 26395.3066 - learning_rate: 0.0010\n", + "Epoch 25/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 911153472.0000 - mae: 25062.0684 - val_loss: 959820032.0000 - val_mae: 26097.6113 - learning_rate: 0.0010\n", + "Epoch 26/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 880028288.0000 - mae: 24827.4922 - val_loss: 926537280.0000 - val_mae: 25821.6191 - learning_rate: 0.0010\n", + "Epoch 27/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - loss: 853682432.0000 - mae: 24604.5742 - val_loss: 896631808.0000 - val_mae: 25565.9453 - learning_rate: 0.0010\n", + "Epoch 28/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 828166080.0000 - mae: 24403.7070 - val_loss: 870131136.0000 - val_mae: 25342.5938 - learning_rate: 0.0010\n", + "Epoch 29/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 810969728.0000 - mae: 24281.2656 - val_loss: 846989760.0000 - val_mae: 25147.4238 - learning_rate: 0.0010\n", + "Epoch 30/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 790364480.0000 - mae: 24090.2852 - val_loss: 827042432.0000 - val_mae: 24978.3867 - learning_rate: 0.0010\n", + "Epoch 31/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 776585792.0000 - mae: 24009.8965 - val_loss: 810190016.0000 - val_mae: 24829.4160 - learning_rate: 0.0010\n", + "Epoch 32/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - loss: 764959104.0000 - mae: 23917.3750 - val_loss: 796246976.0000 - val_mae: 24708.1191 - learning_rate: 0.0010\n", + "Epoch 33/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - loss: 755435648.0000 - mae: 23878.0566 - val_loss: 784937344.0000 - val_mae: 24605.6172 - learning_rate: 0.0010\n", + "Epoch 34/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - loss: 747755712.0000 - mae: 23794.9590 - val_loss: 775911296.0000 - val_mae: 24518.1973 - learning_rate: 0.0010\n", + "Epoch 35/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 743594688.0000 - mae: 23785.2285 - val_loss: 768952064.0000 - val_mae: 24446.5508 - learning_rate: 0.0010\n", + "Epoch 36/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 739301056.0000 - mae: 23742.7988 - val_loss: 763610304.0000 - val_mae: 24390.0273 - learning_rate: 0.0010\n", + "Epoch 37/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 738923584.0000 - mae: 23775.4492 - val_loss: 759721984.0000 - val_mae: 24347.7969 - learning_rate: 0.0010\n", + "Epoch 38/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - loss: 735023744.0000 - mae: 23729.2637 - val_loss: 756937152.0000 - val_mae: 24316.7676 - learning_rate: 0.0010\n", + "Epoch 39/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 734647168.0000 - mae: 23751.9238 - val_loss: 754886144.0000 - val_mae: 24292.0117 - learning_rate: 0.0010\n", + "Epoch 40/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 732123008.0000 - mae: 23701.5078 - val_loss: 753405888.0000 - val_mae: 24274.6113 - learning_rate: 0.0010\n", + "Epoch 41/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 479316192.0000 - mae: 15964.3057 - val_loss: 403580480.0000 - val_mae: 13544.6035 - learning_rate: 0.0010\n", + "Epoch 42/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 343708096.0000 - mae: 12115.6484 - val_loss: 341869088.0000 - val_mae: 12275.1152 - learning_rate: 0.0010\n", + "Epoch 43/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 10ms/step - loss: 294795616.0000 - mae: 11060.8252 - val_loss: 294227808.0000 - val_mae: 11263.7822 - learning_rate: 0.0010\n", + "Epoch 44/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - loss: 255411248.0000 - mae: 10183.4551 - val_loss: 256555760.0000 - val_mae: 10408.2881 - learning_rate: 0.0010\n", + "Epoch 45/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 226594400.0000 - mae: 9585.3291 - val_loss: 226511520.0000 - val_mae: 9840.6309 - learning_rate: 0.0010\n", + "Epoch 46/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 201210784.0000 - mae: 8997.6709 - val_loss: 201730768.0000 - val_mae: 9280.6221 - learning_rate: 0.0010\n", + "Epoch 47/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 180951744.0000 - mae: 8508.0166 - val_loss: 180386640.0000 - val_mae: 8658.5586 - learning_rate: 0.0010\n", + "Epoch 48/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 161437456.0000 - mae: 8037.3340 - val_loss: 162152432.0000 - val_mae: 8236.7969 - learning_rate: 0.0010\n", + "Epoch 49/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 143073152.0000 - mae: 7514.1865 - val_loss: 141376416.0000 - val_mae: 7570.7739 - learning_rate: 0.0010\n", + "Epoch 50/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - loss: 127263440.0000 - mae: 7038.0229 - val_loss: 127391912.0000 - val_mae: 7156.2104 - learning_rate: 0.0010\n", + "Epoch 1/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 1930911488.0000 - mae: 34738.7148 - val_loss: 2063673600.0000 - val_mae: 36292.5195 - learning_rate: 0.0010\n", + "Epoch 2/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1910318336.0000 - mae: 34439.6289 - val_loss: 2033005824.0000 - val_mae: 35867.5156 - learning_rate: 0.0010\n", + "Epoch 3/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1872711552.0000 - mae: 33894.9219 - val_loss: 1984276864.0000 - val_mae: 35195.7305 - learning_rate: 0.0010\n", + "Epoch 4/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1818348032.0000 - mae: 33175.7305 - val_loss: 1919553280.0000 - val_mae: 34405.4180 - learning_rate: 0.0010\n", + "Epoch 5/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1750787072.0000 - mae: 32374.7637 - val_loss: 1842820480.0000 - val_mae: 33571.0039 - learning_rate: 0.0010\n", + "Epoch 6/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 1673256576.0000 - mae: 31570.9395 - val_loss: 1757159040.0000 - val_mae: 32726.9746 - learning_rate: 0.0010\n", + "Epoch 7/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1589680640.0000 - mae: 30760.4844 - val_loss: 1665691136.0000 - val_mae: 31878.0684 - learning_rate: 0.0010\n", + "Epoch 8/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1501466496.0000 - mae: 29948.0781 - val_loss: 1570926464.0000 - val_mae: 31025.0703 - learning_rate: 0.0010\n", + "Epoch 9/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 1412167296.0000 - mae: 29152.6699 - val_loss: 1475466880.0000 - val_mae: 30201.8711 - learning_rate: 0.0010\n", + "Epoch 10/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1322615424.0000 - mae: 28367.2754 - val_loss: 1379597312.0000 - val_mae: 29309.5332 - learning_rate: 0.0010\n", + "Epoch 11/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1220498816.0000 - mae: 26533.4805 - val_loss: 1260929408.0000 - val_mae: 26583.1484 - learning_rate: 0.0010\n", + "Epoch 12/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1112165120.0000 - mae: 24360.7832 - val_loss: 1153268480.0000 - val_mae: 25070.1992 - learning_rate: 0.0010\n", + "Epoch 13/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1014172736.0000 - mae: 22965.8555 - val_loss: 1049243264.0000 - val_mae: 23642.1152 - learning_rate: 0.0010\n", + "Epoch 14/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 918478592.0000 - mae: 21610.4453 - val_loss: 949395648.0000 - val_mae: 22258.7539 - learning_rate: 0.0010\n", + "Epoch 15/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - loss: 828141056.0000 - mae: 20268.5742 - val_loss: 854286336.0000 - val_mae: 20897.9004 - learning_rate: 0.0010\n", + "Epoch 16/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 742179200.0000 - mae: 18959.4883 - val_loss: 764294528.0000 - val_mae: 19546.4355 - learning_rate: 0.0010\n", + "Epoch 17/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 660350528.0000 - mae: 17696.7012 - val_loss: 680099328.0000 - val_mae: 18261.0137 - learning_rate: 0.0010\n", + "Epoch 18/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 587759936.0000 - mae: 16515.5098 - val_loss: 602156032.0000 - val_mae: 16998.6445 - learning_rate: 0.0010\n", + "Epoch 19/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 516721568.0000 - mae: 15305.6777 - val_loss: 530436352.0000 - val_mae: 15776.6455 - learning_rate: 0.0010\n", + "Epoch 20/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 454774592.0000 - mae: 14225.0361 - val_loss: 465422528.0000 - val_mae: 14620.3174 - learning_rate: 0.0010\n", + "Epoch 21/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 400378592.0000 - mae: 13204.1484 - val_loss: 406941792.0000 - val_mae: 13484.5117 - learning_rate: 0.0010\n", + "Epoch 22/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - loss: 351022656.0000 - mae: 12238.7500 - val_loss: 355148032.0000 - val_mae: 12487.7051 - learning_rate: 0.0010\n", + "Epoch 23/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 305887232.0000 - mae: 11356.9395 - val_loss: 309833376.0000 - val_mae: 11593.4004 - learning_rate: 0.0010\n", + "Epoch 24/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 265417856.0000 - mae: 10562.7656 - val_loss: 270137696.0000 - val_mae: 10775.0537 - learning_rate: 0.0010\n", + "Epoch 25/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 234852512.0000 - mae: 9912.8330 - val_loss: 235926240.0000 - val_mae: 10042.0000 - learning_rate: 0.0010\n", + "Epoch 26/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 204994608.0000 - mae: 9289.3477 - val_loss: 208168032.0000 - val_mae: 9438.7832 - learning_rate: 0.0010\n", + "Epoch 27/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 181129088.0000 - mae: 8759.8398 - val_loss: 183661520.0000 - val_mae: 8844.0303 - learning_rate: 0.0010\n", + "Epoch 28/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 159850000.0000 - mae: 8249.1475 - val_loss: 162752464.0000 - val_mae: 8357.9189 - learning_rate: 0.0010\n", + "Epoch 29/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 141057840.0000 - mae: 7771.1348 - val_loss: 144473072.0000 - val_mae: 7830.9922 - learning_rate: 0.0010\n", + "Epoch 30/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 125178776.0000 - mae: 7342.7188 - val_loss: 130381672.0000 - val_mae: 7483.4707 - learning_rate: 0.0010\n", + "Epoch 31/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 113134424.0000 - mae: 6976.5171 - val_loss: 116524848.0000 - val_mae: 7097.4570 - learning_rate: 0.0010\n", + "Epoch 32/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 101647680.0000 - mae: 6615.7490 - val_loss: 107708008.0000 - val_mae: 6762.4595 - learning_rate: 0.0010\n", + "Epoch 33/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 91810328.0000 - mae: 6306.7373 - val_loss: 97756488.0000 - val_mae: 6498.3496 - learning_rate: 0.0010\n", + "Epoch 34/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 82927784.0000 - mae: 6015.1938 - val_loss: 90644216.0000 - val_mae: 6210.5337 - learning_rate: 0.0010\n", + "Epoch 35/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 76303432.0000 - mae: 5791.7852 - val_loss: 84712688.0000 - val_mae: 5904.9824 - learning_rate: 0.0010\n", + "Epoch 36/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 71719856.0000 - mae: 5631.6865 - val_loss: 80555576.0000 - val_mae: 5748.7349 - learning_rate: 0.0010\n", + "Epoch 37/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 67707120.0000 - mae: 5521.0171 - val_loss: 78097168.0000 - val_mae: 5696.1294 - learning_rate: 0.0010\n", + "Epoch 38/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 63857660.0000 - mae: 5421.4185 - val_loss: 73372440.0000 - val_mae: 5529.1011 - learning_rate: 0.0010\n", + "Epoch 39/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 61203508.0000 - mae: 5300.4053 - val_loss: 70816400.0000 - val_mae: 5323.6621 - learning_rate: 0.0010\n", + "Epoch 40/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 59436396.0000 - mae: 5238.1948 - val_loss: 67635208.0000 - val_mae: 5235.2432 - learning_rate: 0.0010\n", + "Epoch 41/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 56774304.0000 - mae: 5124.1748 - val_loss: 66004324.0000 - val_mae: 5169.6328 - learning_rate: 0.0010\n", + "Epoch 42/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 53282044.0000 - mae: 4981.6040 - val_loss: 63594312.0000 - val_mae: 5073.1543 - learning_rate: 0.0010\n", + "Epoch 43/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 51761064.0000 - mae: 4912.4688 - val_loss: 60992804.0000 - val_mae: 4910.9961 - learning_rate: 0.0010\n", + "Epoch 44/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 49697432.0000 - mae: 4781.2227 - val_loss: 59893640.0000 - val_mae: 4884.7842 - learning_rate: 0.0010\n", + "Epoch 45/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 47390824.0000 - mae: 4688.0669 - val_loss: 57193784.0000 - val_mae: 4715.9609 - learning_rate: 0.0010\n", + "Epoch 46/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - loss: 44774588.0000 - mae: 4550.4604 - val_loss: 55175160.0000 - val_mae: 4646.7446 - learning_rate: 0.0010\n", + "Epoch 47/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 45075144.0000 - mae: 4574.6504 - val_loss: 54762232.0000 - val_mae: 4689.1626 - learning_rate: 0.0010\n", + "Epoch 48/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 43724572.0000 - mae: 4516.4062 - val_loss: 53836544.0000 - val_mae: 4628.6943 - learning_rate: 0.0010\n", + "Epoch 49/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 42422604.0000 - mae: 4467.8540 - val_loss: 52762536.0000 - val_mae: 4572.4634 - learning_rate: 0.0010\n", + "Epoch 50/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 42222404.0000 - mae: 4455.9072 - val_loss: 51867440.0000 - val_mae: 4502.6763 - learning_rate: 0.0010\n" + ] + } + ], + "source": [ + "def get_callbacks():\n", + " early_stop = EarlyStopping(\n", + " monitor=\"val_loss\",\n", + " patience=10, # increased from 5 — prevents premature stopping\n", + " restore_best_weights=True\n", + " )\n", + " reduce_lr = ReduceLROnPlateau(\n", + " monitor=\"val_loss\",\n", + " factor=0.5, # halve LR when stuck\n", + " patience=5,\n", + " min_lr=1e-6,\n", + " verbose=1\n", + " )\n", + " return [early_stop, reduce_lr]\n", + "\n", + "# Train all three models\n", + "history_baseline = model_baseline.fit(\n", + " X_train_seq, y_train_seq,\n", + " validation_data=(X_val_seq, y_val_seq),\n", + " epochs=50, batch_size=64,\n", + " callbacks=get_callbacks(), verbose=1\n", + ")\n", + "\n", + "history_stacked = model_stacked.fit(\n", + " X_train_seq, y_train_seq,\n", + " validation_data=(X_val_seq, y_val_seq),\n", + " epochs=50, batch_size=64,\n", + " callbacks=get_callbacks(), verbose=1\n", + ")\n", + "\n", + "history_gru = model_gru.fit(\n", + " X_train_seq, y_train_seq,\n", + " validation_data=(X_val_seq, y_val_seq),\n", + " epochs=50, batch_size=64,\n", + " callbacks=get_callbacks(), verbose=1\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.6 Saving Trained Models\n", + "\n", + "The three trained models are saved to disk immediately after training. This ensures \n", + "that the evaluation results, plots, and metrics produced in subsequent cells are \n", + "fully reproducible on every run without needing to retrain, since loading a saved \n", + "model always produces identical predictions regardless of any residual random seed \n", + "behaviour [58]." + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ model_baseline saved\n", + "✓ model_stacked saved\n", + "✓ model_gru saved\n" + ] + } + ], + "source": [ + "# Save all three trained models to disk\n", + "model_baseline.save(\"model_baseline.keras\")\n", + "model_stacked.save(\"model_stacked.keras\")\n", + "model_gru.save(\"model_gru.keras\")\n", + "\n", + "print(\"✓ model_baseline saved\")\n", + "print(\"✓ model_stacked saved\")\n", + "print(\"✓ model_gru saved\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.7 Loading Saved Models\n", + "\n", + "The saved models are loaded back from disk before evaluation begins. All subsequent \n", + "evaluation, visualisation, and comparison cells use these loaded models, guaranteeing \n", + "that the results in this notebook are consistent and reproducible on every run \n", + "[58]." + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ model_baseline loaded\n", + "✓ model_stacked loaded\n", + "✓ model_gru loaded\n" + ] + } + ], + "source": [ + "# Load all three models from disk\n", + "# This guarantees identical results on every run\n", + "model_baseline = tf.keras.models.load_model(\"model_baseline.keras\")\n", + "model_stacked = tf.keras.models.load_model(\"model_stacked.keras\")\n", + "model_gru = tf.keras.models.load_model(\"model_gru.keras\")\n", + "\n", + "print(\"✓ model_baseline loaded\")\n", + "print(\"✓ model_stacked loaded\")\n", + "print(\"✓ model_gru loaded\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.8 Model Evaluation\n", + "\n", + "Each model is evaluated on the held-out test set using three standard regression \n", + "metrics [59]. The test set represents the final unseen time \n", + "period and was not used at any point during training or validation, ensuring the \n", + "evaluation reflects genuine out-of-sample forecasting performance \n", + "[49].\n", + "\n", + "- **MAE (Mean Absolute Error)**: The average absolute difference between predicted \n", + " and actual pedestrian counts. This is the most directly interpretable metric since \n", + " it is expressed in the same unit as the target variable — pedestrians per hour \n", + " [59].\n", + "- **RMSE (Root Mean Squared Error)**: Similar to MAE but penalises larger errors \n", + " more heavily due to the squaring operation. A substantially higher RMSE relative \n", + " to MAE indicates the presence of occasional large prediction errors \n", + " [59].\n", + "- **R² (Coefficient of Determination)**: Measures the proportion of variance in \n", + " pedestrian counts that the model successfully explains. A value of 1.0 represents \n", + " a perfect fit, while 0.0 means the model performs no better than simply predicting \n", + " the mean value [59]." + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m54/54\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n", + "\n", + "Baseline LSTM\n", + " MAE: 5624.15\n", + " RMSE: 8725.10\n", + " R²: 0.9158\n", + "\u001b[1m54/54\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step\n", + "\n", + "Stacked LSTM\n", + " MAE: 9084.27\n", + " RMSE: 14262.18\n", + " R²: 0.7750\n", + "\u001b[1m54/54\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n", + "\n", + "GRU\n", + " MAE: 4921.98\n", + " RMSE: 7325.04\n", + " R²: 0.9407\n" + ] + } + ], + "source": [ + "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", + "\n", + "def evaluate_model(model, X_test, y_test, name=\"Model\"):\n", + " y_pred = model.predict(X_test).flatten()\n", + " mae = mean_absolute_error(y_test, y_pred)\n", + " rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n", + " r2 = r2_score(y_test, y_pred)\n", + " print(f\"\\n{name}\")\n", + " print(f\" MAE: {mae:.2f}\")\n", + " print(f\" RMSE: {rmse:.2f}\")\n", + " print(f\" R²: {r2:.4f}\")\n", + " return y_pred, {\"MAE\": mae, \"RMSE\": rmse, \"R2\": r2}\n", + "\n", + "pred_baseline, metrics_baseline = evaluate_model(model_baseline, X_test_seq, y_test_seq, \"Baseline LSTM\")\n", + "pred_stacked, metrics_stacked = evaluate_model(model_stacked, X_test_seq, y_test_seq, \"Stacked LSTM\")\n", + "pred_gru, metrics_gru = evaluate_model(model_gru, X_test_seq, y_test_seq, \"GRU\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Evaluation Discussion\n", + "\n", + "The GRU model achieved the best performance across all three metrics, with an MAE of \n", + "4,921.98, RMSE of 7,325.04, and an R² of 0.9407. This means the GRU explains 94.1% \n", + "of the variance in hourly pedestrian demand on completely unseen test data, which \n", + "represents strong predictive performance for a real-world urban climate forecasting \n", + "task [59].\n", + "\n", + "Notably, the Stacked LSTM performed worst of the three models despite having the most \n", + "parameters at 35,777. This outcome highlights an important principle in deep learning \n", + "— increased model complexity does not always lead to better generalisation, \n", + "particularly when the dataset size does not justify the added capacity \n", + "[53] [62]. The Baseline LSTM achieved a \n", + "strong R² of 0.9158, confirming that even a single-layer recurrent model can capture \n", + "the majority of the temporal structure in this dataset \n", + "[54]. The GRU's improvement over the baseline demonstrates that \n", + "architectural efficiency can outperform raw model capacity on medium-sized datasets \n", + "[61] [62]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.9 Training Curves\n", + "\n", + "The training and validation loss curves are plotted for each model to assess how \n", + "learning progressed across epochs and to check for any signs of overfitting \n", + "[53].\n", + "\n", + "A well-behaved training curve shows both the training and validation loss decreasing \n", + "together and converging toward a stable low value. If the validation loss begins to \n", + "rise while training loss continues to fall, this indicates overfitting — meaning the \n", + "model is memorising the training data rather than learning generalisable patterns \n", + "[60]." + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n", + "\n", + "for ax, history, name in zip(\n", + " axes,\n", + " [history_baseline, history_stacked, history_gru],\n", + " [\"Baseline LSTM\", \"Stacked LSTM\", \"GRU\"]\n", + "):\n", + " ax.plot(history.history[\"loss\"], label=\"Train Loss\")\n", + " ax.plot(history.history[\"val_loss\"], label=\"Val Loss\")\n", + " ax.set_title(f\"{name} — Training Curves\")\n", + " ax.set_xlabel(\"Epoch\")\n", + " ax.set_ylabel(\"MSE Loss\")\n", + " ax.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Training Curve Discussion\n", + "\n", + "All three models demonstrate healthy learning behaviour across the 50 training \n", + "epochs. Both the training and validation loss curves decrease consistently and \n", + "converge without significant divergence, indicating that no meaningful overfitting \n", + "occurred in any of the three models [60]. The Dropout \n", + "regularisation layers and the ReduceLROnPlateau callback both contributed to this \n", + "stable training behaviour \n", + "[60] [63].\n", + "\n", + "The Stacked LSTM validation curve is notably observed to plateau earlier and at a \n", + "higher loss value compared to the Baseline LSTM and GRU, which is consistent with \n", + "its weaker test set performance. The GRU and Baseline LSTM both show smooth \n", + "convergence with training and validation loss tracking closely throughout all 50 \n", + "epochs [54] [61]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.10 Actual vs Predicted Visualisation\n", + "\n", + "To assess prediction quality beyond summary metrics, the actual and predicted \n", + "pedestrian counts are plotted across the first 200 hours of the test set. This \n", + "visualisation reveals how well each model captures the shape, magnitude, and timing \n", + "of the daily pedestrian demand cycle, and whether any systematic prediction errors \n", + "are present [3] [59]." + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(3, 1, figsize=(14, 14))\n", + "\n", + "for ax, preds, name in zip(\n", + " axes,\n", + " [pred_baseline, pred_stacked, pred_gru],\n", + " [\"Baseline LSTM\", \"Stacked LSTM\", \"GRU\"]\n", + "):\n", + " ax.plot(y_test_seq[:200], label=\"Actual\", color=\"black\")\n", + " ax.plot(preds[:200], label=\"Predicted\", color=\"steelblue\", alpha=0.8)\n", + " ax.set_title(f\"{name} — Actual vs Predicted (First 200 Hours)\")\n", + " ax.set_xlabel(\"Hour\")\n", + " ax.set_ylabel(\"Pedestrian Count\")\n", + " ax.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Visualisation Discussion\n", + "\n", + "The actual versus predicted plots reveal clear qualitative differences between the \n", + "three models that are consistent with the quantitative evaluation results. The GRU \n", + "most closely follows the actual pedestrian demand curve across the 200-hour window, \n", + "including the sharp daytime peaks during business hours and the near-zero counts \n", + "during the early morning hours \n", + "[61] [62].\n", + "\n", + "The Baseline LSTM captures the general daily rhythm but slightly underestimates the \n", + "magnitude of peak values, producing a smoother curve that misses some of the sharper \n", + "intraday fluctuations [54]. The Stacked LSTM shows the most \n", + "pronounced smoothing effect, with predicted values appearing noticeably flattened at \n", + "daily peaks — capped around 55,000 pedestrians regardless of the actual peak — which \n", + "directly explains its substantially higher RMSE and lower R² relative to the other \n", + "two models [53].\n", + "\n", + "These visual results are fully consistent with the evaluation metrics and further \n", + "support the selection of the GRU as the best performing architecture for this \n", + "pedestrian forecasting task." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.11 Model Comparison Summary\n", + "\n", + "The table below consolidates the test set performance metrics for all three models, \n", + "providing a direct side-by-side comparison to support final model selection \n", + "[59] [57]." + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Model MAE RMSE R²\n", + "Baseline LSTM 5624.145508 8725.096217 0.915807\n", + " Stacked LSTM 9084.274414 14262.183844 0.775039\n", + " GRU 4921.983887 7325.042252 0.940659\n" + ] + } + ], + "source": [ + "comparison = pd.DataFrame({\n", + " \"Model\": [\"Baseline LSTM\", \"Stacked LSTM\", \"GRU\"],\n", + " \"MAE\": [metrics_baseline[\"MAE\"], metrics_stacked[\"MAE\"], metrics_gru[\"MAE\"]],\n", + " \"RMSE\": [metrics_baseline[\"RMSE\"], metrics_stacked[\"RMSE\"], metrics_gru[\"RMSE\"]],\n", + " \"R²\": [metrics_baseline[\"R2\"], metrics_stacked[\"R2\"], metrics_gru[\"R2\"]],\n", + "})\n", + "print(comparison.to_string(index=False))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.12 Conclusion\n", + "\n", + "The deep learning segment compared three recurrent neural network architectures — a \n", + "Baseline LSTM, a Stacked LSTM, and a GRU — for hourly pedestrian count forecasting \n", + "in Melbourne CBD using climate and engineered time-series features.\n", + "\n", + "The GRU model was identified as the best performing architecture, achieving an R² of \n", + "0.9407, MAE of 4,921.98 pedestrians per hour, and RMSE of 7,325.04 across the unseen \n", + "test set [59] [61]. These results \n", + "demonstrate that a well-designed recurrent model, even without excessive architectural \n", + "complexity, can effectively learn the temporal structure of urban pedestrian demand \n", + "from climate and time-series inputs \n", + "[53] [62].\n", + "\n", + "The finding that the Stacked LSTM underperformed despite having the most parameters \n", + "highlights an important consideration in deep learning model selection — that added \n", + "complexity must be justified by the volume and complexity of the data \n", + "[53] [57]. The GRU's parameter efficiency \n", + "and strong generalisation make it the most suitable architecture for this dataset size \n", + "and forecasting task [61].\n", + "\n", + "From an applied perspective, a model explaining 94.1% of the variance in hourly \n", + "pedestrian demand could realistically support urban planning decisions and help \n", + "commuters anticipate pedestrian congestion during varying climate conditions, directly \n", + "addressing the use case scenario outlined at the beginning of this notebook \n", + "[53] [56]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "[1] pandas development team (2026) _pandas - Python Data Analysis Library_, pandas, Online.\n", + "\n", + "[2] pandas development team (2026) _Time series / date functionality_, pandas, Online.\n", + "\n", + "[3] Matplotlib Development Team (Unknown date) _Matplotlib: Visualization with Python_, Matplotlib, Online.\n", + "\n", + "[4] scikit-learn Developers (Unknown date) _Preprocessing data_, scikit-learn, Online.\n", + "\n", + "[5] TensorFlow (2023) _Keras: The high-level API for TensorFlow_, Google, Online.\n", + "\n", + "[6] Guido van Rossum, Barry Warsaw and Alyssa Coghlan (2001) _PEP 8 – Style Guide for Python Code_, Python Enhancement Proposals, Online.\n", + "\n", + "[7] TensorFlow (2024) _tf.keras.utils.set_random_seed_, Google, Online.\n", + "\n", + "[8] Opendatasoft (Unknown date) _Huwise's Explore API Reference Documentation_, Opendatasoft, Online.\n", + "\n", + "[9] City of Melbourne (2023) _Pedestrian Counting System - Sensor Locations_, City of Melbourne Open Data Portal, Online.\n", + "\n", + "[10] City of Melbourne (2023) _Pedestrian Counting System: Counts per Hour_, City of Melbourne Open Data Portal, Online.\n", + "\n", + "[11] City of Melbourne (2023) _Microclimate Sensors Data_, City of Melbourne Open Data Portal, Online.\n", + "\n", + "[12] IBM (Unknown date) _What is data profiling?_, IBM, Online.\n", + "\n", + "[13] pandas development team (2026) _pandas.DataFrame.shape_, pandas, Online.\n", + "\n", + "[14] pandas development team (2026) _pandas.DataFrame.columns_, pandas, Online.\n", + "\n", + "[15] pandas development team (2026) _pandas.DataFrame.dtypes_, pandas, Online.\n", + "\n", + "[16] pandas development team (2026) _pandas.DataFrame.info_, pandas, Online.\n", + "\n", + "[17] pandas development team (2026) _pandas.DataFrame.isna_, pandas, Online.\n", + "\n", + "[18] pandas development team (2026) _pandas.DataFrame.describe_, pandas, Online.\n", + "\n", + "[19] pandas development team (2026) _pandas.to_datetime_, pandas, Online.\n", + "\n", + "[20] pandas development team (2026) _pandas.DataFrame.nunique_, pandas, Online.\n", + "\n", + "[21] IBM (Unknown date) _What is Data Cleaning?_, IBM, Online.\n", + "\n", + "[22] Tableau (Unknown date) _Data Cleaning: Definition, Benefits, and How-To_, Tableau, Online.\n", + "\n", + "[23] Max Kuhn and Kjell Johnson (2026) _Feature Selection_, Applied Machine Learning for Tabular Data, Online.\n", + "\n", + "[24] IBM (Unknown date) _What is Feature Selection?_, IBM, Online.\n", + "\n", + "[25] Youran Zhou, Sunil Aryal and Mohamed Reda Bouadjenek (2024) _A Comprehensive Review of Handling Missing Data: Exploring Special Missing Mechanisms_, arXiv, Online.\n", + "\n", + "[26] Cribl (Unknown date) _What is Data Normalization?_, Cribl, Online.\n", + "\n", + "[27] The Epidemiologist R Handbook Contributors (Unknown date) _Working with dates_, The Epidemiologist R Handbook, Online.\n", + "\n", + "[28] Skforecast Team (Unknown date) _Time series aggregation_, Skforecast, Online.\n", + "\n", + "[29] Microsoft Support (Unknown date) _Join tables and queries_, Microsoft, Online.\n", + "\n", + "[30] IBM (Unknown date) _What is Data Integration?_, IBM, Online.\n", + "\n", + "[31] RudderStack (Unknown date) _What Is Data Validation? Why, When, and How To Use It_, RudderStack, Online.\n", + "\n", + "[32] Google Cloud (Unknown date) _What is Time Series?_, Google, Online.\n", + "\n", + "[33] Ali Suliman AlSalehy and Mike Bailey (2025) _Improving Time Series Data Quality: Identifying Outliers and Handling Missing Values in a Multilocation Gas and Weather Dataset_, MDPI, Basel, Switzerland.\n", + "\n", + "[34] NIST/SEMATECH (Unknown date) _What is EDA?_, NIST/SEMATECH e-Handbook of Statistical Methods, Online.\n", + "\n", + "[35] Rob J. Hyndman and George Athanasopoulos (2018) _Time series patterns_, OTexts, Melbourne, Australia.\n", + "\n", + "[36] NIST/SEMATECH (Unknown date) _Introduction to Time Series Analysis_, NIST/SEMATECH e-Handbook of Statistical Methods, Online.\n", + "\n", + "[37] NIST/SEMATECH (Unknown date) _Seasonality_, NIST/SEMATECH e-Handbook of Statistical Methods, Online.\n", + "\n", + "[38] NIST/SEMATECH (Unknown date) _Histogram_, NIST/SEMATECH e-Handbook of Statistical Methods, Online.\n", + "\n", + "[39] Penn State Eberly College of Science (Unknown date) _3.4.2 - Correlation_, The Pennsylvania State University, Online.\n", + "\n", + "[40] Jim Frost (Unknown date) _Using Moving Averages to Smooth Time Series Data_, Statistics By Jim, Online.\n", + "\n", + "[41] Josef Waples and Laiba Siddiqui (Unknown date) _Time Series Decomposition: Trends, Seasonality, and Noise_, DataCamp, Online.\n", + "\n", + "[42] Penn State Eberly College of Science (Unknown date) _10.2 - Autocorrelation and Time Series Methods_, The Pennsylvania State University, Online.\n", + "\n", + "[43] Minitab (Unknown date) _Interpret all statistics and graphs for Augmented Dickey-Fuller Test_, Minitab Support, Online.\n", + "\n", + "[44] IBM (Unknown date) _What is Feature Engineering?_, IBM, Online.\n", + "\n", + "[45] dotData (Unknown date) _Practical Guide for Feature Engineering of Time Series Data_, dotData, Online.\n", + "\n", + "[46] Skforecast Team (Unknown date) _Cyclical features in time series_, Skforecast, Online.\n", + "\n", + "[47] Feature-engine Developers (Unknown date) _Forecasting Features_, Feature-engine, Online.\n", + "\n", + "[48] Feature-engine Developers (Unknown date) _LagFeatures_, Feature-engine, Online.\n", + "\n", + "[49] ApX Machine Learning (2026) _Train-Test Split for Time Series_, ApX Machine Learning, Online.\n", + "\n", + "[50] Pragati Baheti (2021) _Train Test Validation Split: How To and Best Practices_, V7 Labs, Online.\n", + "\n", + "[51] IBM (Unknown date) _What is Supervised Learning?_, IBM, Online.\n", + "\n", + "[52] ApX Machine Learning (2026) _Feature Scaling: Normalization and Standardization_, ApX Machine Learning, Online.\n", + "\n", + "[53] IBM (Unknown date) _What is Deep Learning?_, IBM, Online.\n", + "\n", + "[54] Christopher Olah (2015) _Understanding LSTM Networks_, Colah's Blog, Online.\n", + "\n", + "[55] TensorFlow (2024) _tf.keras.utils.set_random_seed_, Google, Online.\n", + "\n", + "[56] TensorFlow (2023) _Time series forecasting_, TensorFlow, Online.\n", + "\n", + "[57] ApX Machine Learning (2026) _Model Selection and Evaluation_, ApX Machine Learning, Online.\n", + "\n", + "[58] TensorFlow (2023) _tf.keras.Model.fit_, TensorFlow, Online.\n", + "\n", + "[59] scikit-learn Developers (Unknown date) _Regression metrics_, scikit-learn, Online.\n", + "\n", + "[60] Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever and Ruslan Salakhutdinov (2014) _Dropout: A Simple Way to Prevent Neural Networks from Overfitting_, Journal of Machine Learning Research, Online.\n", + "\n", + "[61] Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk and Yoshua Bengio (2014) _Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation_, arXiv,Online.\n", + "\n", + "[62] Junyoung Chung, Caglar Gulcehre, KyungHyun Cho and Yoshua Bengio (2014) _Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling_, arXiv, Online.\n", + "\n", + "[63] TensorFlow (2023) _tf.keras.callbacks.EarlyStopping_, TensorFlow, Online." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.12.13" + }, + "vscode": { + "interpreter": { + "hash": "369f2c481f4da34e4445cda3fffd2e751bd1c4d706f27375911949ba6bb62e1c" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 8988b254db49bfe028cb10283a2bbec1b67ecf0a Mon Sep 17 00:00:00 2001 From: Thivainv Date: Sun, 17 May 2026 19:49:42 +1000 Subject: [PATCH 03/23] Create test.txt --- .../READY TO PUBLISH/Tourism_and_Hospitality/2026/test.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 datascience/usecases/READY TO PUBLISH/Tourism_and_Hospitality/2026/test.txt diff --git a/datascience/usecases/READY TO PUBLISH/Tourism_and_Hospitality/2026/test.txt b/datascience/usecases/READY TO PUBLISH/Tourism_and_Hospitality/2026/test.txt new file mode 100644 index 0000000000..8b13789179 --- /dev/null +++ b/datascience/usecases/READY TO PUBLISH/Tourism_and_Hospitality/2026/test.txt @@ -0,0 +1 @@ + From 8de1bc80474d05b005bca5ebbeb5db23ce5d628e Mon Sep 17 00:00:00 2001 From: Thivainv Date: Sun, 17 May 2026 19:50:13 +1000 Subject: [PATCH 04/23] Create test.txt --- .../READY TO PUBLISH/Tourism_and_Hospitality/2026/T1/test.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 datascience/usecases/READY TO PUBLISH/Tourism_and_Hospitality/2026/T1/test.txt diff --git a/datascience/usecases/READY TO PUBLISH/Tourism_and_Hospitality/2026/T1/test.txt b/datascience/usecases/READY TO PUBLISH/Tourism_and_Hospitality/2026/T1/test.txt new file mode 100644 index 0000000000..8b13789179 --- /dev/null +++ b/datascience/usecases/READY TO PUBLISH/Tourism_and_Hospitality/2026/T1/test.txt @@ -0,0 +1 @@ + From 26e0070803054c58e13fbb5819f4cf9d57715ca4 Mon Sep 17 00:00:00 2001 From: Thivainv Date: Sun, 17 May 2026 20:06:01 +1000 Subject: [PATCH 05/23] Create test.txt --- .../UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt diff --git a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt new file mode 100644 index 0000000000..8b13789179 --- /dev/null +++ b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt @@ -0,0 +1 @@ + From 62f4b2d101b157588ced772577d686d2ed7920d5 Mon Sep 17 00:00:00 2001 From: Thivainv Date: Sun, 17 May 2026 20:09:08 +1000 Subject: [PATCH 06/23] Delete datascience/usecases/READY TO PUBLISH/Tourism_and_Hospitality/2026 directory --- .../READY TO PUBLISH/Tourism_and_Hospitality/2026/T1/test.txt | 1 - .../READY TO PUBLISH/Tourism_and_Hospitality/2026/test.txt | 1 - 2 files changed, 2 deletions(-) delete mode 100644 datascience/usecases/READY TO PUBLISH/Tourism_and_Hospitality/2026/T1/test.txt delete mode 100644 datascience/usecases/READY TO PUBLISH/Tourism_and_Hospitality/2026/test.txt diff --git a/datascience/usecases/READY TO PUBLISH/Tourism_and_Hospitality/2026/T1/test.txt b/datascience/usecases/READY TO PUBLISH/Tourism_and_Hospitality/2026/T1/test.txt deleted file mode 100644 index 8b13789179..0000000000 --- a/datascience/usecases/READY TO PUBLISH/Tourism_and_Hospitality/2026/T1/test.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/datascience/usecases/READY TO PUBLISH/Tourism_and_Hospitality/2026/test.txt b/datascience/usecases/READY TO PUBLISH/Tourism_and_Hospitality/2026/test.txt deleted file mode 100644 index 8b13789179..0000000000 --- a/datascience/usecases/READY TO PUBLISH/Tourism_and_Hospitality/2026/test.txt +++ /dev/null @@ -1 +0,0 @@ - From 57aee515799ecd7ed6a4cbb47b18bc21377e1d12 Mon Sep 17 00:00:00 2001 From: Thivainv Date: Sun, 17 May 2026 20:09:38 +1000 Subject: [PATCH 07/23] Delete datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction directory --- .../UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt | 1 - 1 file changed, 1 deletion(-) delete mode 100644 datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt diff --git a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt deleted file mode 100644 index 8b13789179..0000000000 --- a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt +++ /dev/null @@ -1 +0,0 @@ - From b3cf82f00ff30626b10aba09600c2bd8b74d580f Mon Sep 17 00:00:00 2001 From: Thivainv Date: Sun, 17 May 2026 20:11:55 +1000 Subject: [PATCH 08/23] Create test.txt --- .../READY TO PUBLISH/Transport_and_Mobility/2026/test.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/test.txt diff --git a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/test.txt b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/test.txt new file mode 100644 index 0000000000..8b13789179 --- /dev/null +++ b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/test.txt @@ -0,0 +1 @@ + From 213e30dc5f194065217bad977d460d1a0e241446 Mon Sep 17 00:00:00 2001 From: Thivainv Date: Sun, 17 May 2026 20:12:27 +1000 Subject: [PATCH 09/23] Create test.txt --- .../READY TO PUBLISH/Transport_and_Mobility/2026/T1/test.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/test.txt diff --git a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/test.txt b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/test.txt new file mode 100644 index 0000000000..8b13789179 --- /dev/null +++ b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/test.txt @@ -0,0 +1 @@ + From 9dbded612283cb285543b9eb6eb524e309b17d27 Mon Sep 17 00:00:00 2001 From: Thivainv Date: Sun, 17 May 2026 20:13:07 +1000 Subject: [PATCH 10/23] Create test.txt --- .../UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt diff --git a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt new file mode 100644 index 0000000000..8b13789179 --- /dev/null +++ b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt @@ -0,0 +1 @@ + From df37f93ce1defefca45fb8b544cfe68b13ddd660 Mon Sep 17 00:00:00 2001 From: Thivainv Date: Sun, 17 May 2026 20:15:58 +1000 Subject: [PATCH 11/23] Add files via upload --- ..._Pedestrian_Climate_Impact_Prediction.html | 12507 ++++++++++++++++ ...Pedestrian_Climate_Impact_Prediction.ipynb | 4553 ++++++ ..._Pedestrian_Climate_Impact_Prediction.json | 66 + 3 files changed, 17126 insertions(+) create mode 100644 datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.html create mode 100644 datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.ipynb create mode 100644 datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.json diff --git a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.html b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.html new file mode 100644 index 0000000000..48dcd0e6f1 --- /dev/null +++ b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.html @@ -0,0 +1,12507 @@ + + + + + +UC00213_Urban_Pedestrian_Climate_Impact_Prediction + + + + + + + + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + diff --git a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.ipynb b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.ipynb new file mode 100644 index 0000000000..0a97449dd4 --- /dev/null +++ b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.ipynb @@ -0,0 +1,4553 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
Urban Pedestrian Climate Impact Prediction
\n", + "\n", + "
Authored by: Maverick Nguyen
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
Duration: 90 mins
\n", + "\n", + "
\n", + "
Level: Intermediate
\n", + "
Pre-requisite Skills: Python, Data Cleaning, Data Validation, Data Visualisation, Time-Series Analysis, Feature Engineering, Optimisation Methods, Deep Learning
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
Scenario
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a local living near Melbourne CBD, Maverick relies on active travel, like walking, and public transport, like trams, to get around to different places he wants to go. One morning in January, Maverick prepared to travel to his workplace, expecting to get to work before 9:00 AM, but there was a sudden heatwave, causing the tram that he usually catches to be unable to follow its designated schedule and creating a delay in his schedule. Although there was a replacement bus for this emergency, only a limited number of people could board this vehicle, which further delayed his schedule. Because of this sudden extreme weather, travel conditions become less reliable and difficult to predict.\n", + "\n", + "Maverick wants to have access to a system that could predict how climate conditions over time can affect urban pedestrian movement. So that he could better plan his trip, allowing him to leave earlier in anticipation of sudden extreme weather change during a particular timeframe, or choose a different mode of transport, like an Uber. This allows more support in making informed decisions when travelling during extreme weather events." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
What this use case will teach you
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At the end of this use case, you will:\n", + "- Learn how to source and combine multiple public datasets. \n", + "- Understand how to clean and align time-series data at an hourly level for modelling. \n", + "- Explore how climate variables, such as temperature, humidity, pressure, and wind, relate to pedestrian counts.\n", + "- Apply feature engineering techniques to create meaningful predictors from weather and mobility time-series data. \n", + "- Build a deep learning forecasting model to predict pedestrian demand. \n", + "- Perform model optimisation like hyperparameter tuning to improve forecasting performance. \n", + "- Evaluate model performance and interpret results for climate adaptation planning." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
Introduction
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Urban systems are often affected by changing climate conditions, but these effects are not always easy to capture with simple forecasting methods. One clear example is pedestrian movement, where changing weather conditions can affect how many people move through the city over time.\n", + "\n", + "This use case focuses on predicting pedestrian activity in the City of Melbourne using hourly climate observations, which keeps the project closely aligned with the goal of modelling how climate factors influence an urban system.\n", + "\n", + "In this use case, pedestrian counts are aggregated into hourly city-level totals and merged with hourly microclimate observations for Melbourne. A deep learning model can then be trained to predict pedestrian demand based on time, recent demand history, and recent climate conditions.\n", + "\n", + "The datasets used in this project are the \"Pedestrian Counting System (counts per hour)\", the \"Pedestrian Counting System - Sensor Locations\" dataset for supporting location metadata, and the \"Microclimate Sensor Readings\" dataset from the City of Melbourne website." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Importing The Libraries\n", + "\n", + "This section is to show what libraries were used for this use case, with each imported library supporting a specific part of the pipeline. These libraries are necessary for doing data handling, time-series analysis, visualisation, feature engineering, optimisation, and deep learning [1] [2] [3] [4] [5]. These were added at the beginning to ensure the workflow is organised [6]. A random seed was set with a student ID to allow reproducibility of the outputs in this notebook [7].\n", + "\n", + "The following libraries support the deep learning pipeline for this section. TensorFlow and Keras are used for building and training the recurrent neural network models [3]. The os and random libraries are imported to support full reproducibility across all system-level random operations [55]." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "# Libraries for this project.\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.lines import Line2D\n", + "from matplotlib.patches import Patch\n", + "from statsmodels.graphics.tsaplots import plot_acf\n", + "from statsmodels.tsa.seasonal import seasonal_decompose\n", + "from statsmodels.tsa.stattools import adfuller\n", + "from sklearn.preprocessing import StandardScaler\n", + "import os\n", + "import random\n", + "\n", + "# Deep Learning\n", + "import tensorflow as tf\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import LSTM, GRU, Dense, Dropout\n", + "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Importing The Datasets\n", + "\n", + "This section is necessary for importing multiple public datasets from the City of Melbourne, before any cleaning, merging and modelling in later stages can happen. These datasets will be accessed through the City of Melbourne Open Data API v2.1, to allow the notebook to be directly used upon download [8].\n", + "\n", + "By using a shared BASE_URL and a dictionary of dataset identifiers, this allows for removing and adding datasets more easily [8] [9] [10] [11]. The get_csv_url() function is especially useful because it standardises the dataset access method and allows the same logic to be reused across all three datasets [8]. The ROW_LIMIT parameter was added to allow experimentation on smaller samples before scaling to the full dataset [8]. Lastly, the datasets are accessed through the Melbourne Open Data API v2.1, and no visible API key is exposed in the code [8]." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "# Store the API base path accessed via API v2.1.\n", + "BASE_URL = (\n", + " \"https://data.melbourne.vic.gov.au\"\n", + " \"/api/explore/v2.1/catalog/datasets\"\n", + ")\n", + "\n", + "# Store the dataset identifiers.\n", + "DATASETS = {\n", + " \"sensor_locations\":\n", + " \"pedestrian-counting-system-sensor-locations\",\n", + " \"pedestrian_counts\":\n", + " \"pedestrian-counting-system-monthly-counts-per-hour\",\n", + " \"microclimate\":\n", + " \"microclimate-sensors-data\",\n", + "}\n", + "\n", + "# Set the number of rows to retrieve for experiments.\n", + "# Change to \"None\" when full datasets are needed.\n", + "# Change to an integer for experimental purposes.\n", + "ROW_LIMIT = None \n", + "\n", + "def get_csv_url(dataset_id, row_limit=None):\n", + " # Build the base CSV export URL.\n", + " url = (\n", + " f\"{BASE_URL}/{dataset_id}/exports/csv\"\n", + " f\"?delimiter=,&with_bom=true\"\n", + " )\n", + "\n", + " # Add a row limit if one is provided.\n", + " if row_limit is not None:\n", + " url += f\"&limit={row_limit}\"\n", + "\n", + " return url" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Pedestrian Counting System - Sensor Locations\n", + "\n", + "This dataset was included to provide contextual information about the physical pedestrian counting network, even though it wasn't used in the later steps for modelling. The sensor metadata helps explain where the mobility data originates from and what the coverage of the system looks like [9].\n", + "\n", + "* `location_id`: unique identifier for each pedestrian counting location.\n", + "* `sensor_description`: human-readable description of the site, like street names.\n", + "* `sensor_name`: short internal sensor code.\n", + "* `installation_date`: date the sensor was installed.\n", + "* `note`: extra comments or metadata about the sensor.\n", + "* `location_type`: type of location.\n", + "* `status`: operational status of the sensor.\n", + "* `direction_1` and `direction_2`: the two movement directions captured by the counter.\n", + "* `latitude` and `longitude`: geographic coordinates of the sensor.\n", + "* `location`: combined coordinate string." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " location_id sensor_description sensor_name installation_date \\\n", + "0 3 Melbourne Central Swa295_T 2009-03-25 \n", + "1 5 Princes Bridge PriNW_T 2009-03-26 \n", + "2 9 Southern Cross Station Col700_T 2009-03-23 \n", + "3 12 New Quay NewQ_T 2009-01-21 \n", + "4 14 Sandridge Bridge SanBri_T 2009-03-24 \n", + "\n", + " note location_type status \\\n", + "0 NaN Outdoor A \n", + "1 Replace with: 00:6e:02:01:9e:54 Outdoor A \n", + "2 NaN Outdoor A \n", + "3 NaN Outdoor A \n", + "4 Sensor relocated to sensor ID 25 on 2/10/2019 Outdoor A \n", + "\n", + " direction_1 direction_2 latitude longitude location \n", + "0 North South -37.811015 144.964295 -37.81101524, 144.96429485 \n", + "1 North South -37.818742 144.967877 -37.81874249, 144.96787656 \n", + "2 East West -37.819830 144.951026 -37.81982992, 144.95102555 \n", + "3 East West -37.814580 144.942924 -37.81457988, 144.94292398 \n", + "4 North South -37.820112 144.962919 -37.82011242, 144.96291897 \n" + ] + } + ], + "source": [ + "# Load the sensor locations dataset.\n", + "sensor_locations_df = pd.read_csv(\n", + " get_csv_url(\n", + " DATASETS[\"sensor_locations\"],\n", + " row_limit=ROW_LIMIT\n", + " ),\n", + " encoding=\"utf-8-sig\"\n", + ")\n", + "\n", + "# Preview the dataframe.\n", + "print(sensor_locations_df.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Pedestrian Counting System (counts per hour)\n", + "\n", + "This is the target dataset for the use case because the final prediction task is to forecast pedestrian demand. The pedestrian dataset contains the observed mobility outcome that the model aims to learn [10].\n", + "\n", + "* `id`: record identifier.\n", + "* `location_id`: identifier linking the observation to a specific sensor location.\n", + "* `sensing_date`: date of observation.\n", + "* `hourday`: hour of day from 0 to 23.\n", + "* `direction_1` and `direction_2`: directional pedestrian counts.\n", + "* `pedestriancount`: total pedestrian count for that record.\n", + "* `sensor_name`: short sensor code.\n", + "* `location`: coordinate string for the sensor. " + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " id location_id sensing_date hourday direction_1 direction_2 \\\n", + "0 70220251008 70 2025-10-08 2 1 0 \n", + "1 31220260507 3 2026-05-07 12 872 1170 \n", + "2 591520241018 59 2024-10-18 15 430 628 \n", + "3 141020260106 14 2026-01-06 10 240 75 \n", + "4 28820241203 28 2024-12-03 8 537 262 \n", + "\n", + " pedestriancount sensor_name location \n", + "0 1 Errol20_T -37.80456984, 144.94946228 \n", + "1 2042 Swa295_T -37.81101524, 144.96429485 \n", + "2 1058 RMIT_T -37.80825648, 144.96304859 \n", + "3 315 SanBri_T -37.82011242, 144.96291897 \n", + "4 799 VAC_T -37.82129925, 144.96879309 \n" + ] + } + ], + "source": [ + "# Load the pedestrian counts dataset.\n", + "pedestrian_counts_df = pd.read_csv(\n", + " get_csv_url(\n", + " DATASETS[\"pedestrian_counts\"],\n", + " row_limit=ROW_LIMIT\n", + " ),\n", + " encoding=\"utf-8-sig\"\n", + ")\n", + "\n", + "# Preview the dataframe.\n", + "print(pedestrian_counts_df.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Microclimate sensors data\n", + "\n", + "This dataset provides the microclimate data that the use case requires to understand how climate conditions affect urban pedestrian movement, more specifically, the input features. It basically provides the explanatory environmental variables to connect weather conditions to mobility demand [11].\n", + "\n", + "* `device_id`: identifier for each microclimate device.\n", + "* `received_at`: timestamp when the reading was recorded.\n", + "* `sensorlocation`: descriptive location of the microclimate sensor.\n", + "* `latlong`: coordinate string.\n", + "* `minimumwinddirection`, `averagewinddirection`, `maximumwinddirection`: wind direction measurements.\n", + "* `minimumwindspeed`, `averagewindspeed`, `gustwindspeed`: wind speed measurements.\n", + "* `airtemperature`: air temperature reading.\n", + "* `relativehumidity`: humidity level.\n", + "* `atmosphericpressure`: atmospheric pressure.\n", + "* `pm25` and `pm10`: particulate matter measurements.\n", + "* `noise`: noise level. " + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " device_id received_at \\\n", + "0 ICTMicroclimate-08 2026-04-28T15:25:15+00:00 \n", + "1 ICTMicroclimate-08 2026-04-28T04:38:50+00:00 \n", + "2 ICTMicroclimate-08 2026-04-28T05:08:52+00:00 \n", + "3 ICTMicroclimate-10 2026-04-28T10:19:41+00:00 \n", + "4 ICTMicroclimate-08 2026-04-28T13:55:03+00:00 \n", + "\n", + " sensorlocation \\\n", + "0 Swanston St - Tram Stop 13 adjacent Federation... \n", + "1 Swanston St - Tram Stop 13 adjacent Federation... \n", + "2 Swanston St - Tram Stop 13 adjacent Federation... \n", + "3 1 Treasury Place \n", + "4 Swanston St - Tram Stop 13 adjacent Federation... \n", + "\n", + " latlong minimumwinddirection averagewinddirection \\\n", + "0 -37.8184515, 144.9678474 0.0 220.0 \n", + "1 -37.8184515, 144.9678474 0.0 283.0 \n", + "2 -37.8184515, 144.9678474 0.0 310.0 \n", + "3 -37.8128595, 144.9745395 277.0 324.0 \n", + "4 -37.8184515, 144.9678474 0.0 281.0 \n", + "\n", + " maximumwinddirection minimumwindspeed averagewindspeed gustwindspeed \\\n", + "0 314.0 0.0 0.5 2.5 \n", + "1 353.0 0.0 0.9 2.6 \n", + "2 314.0 0.0 0.2 1.9 \n", + "3 352.0 0.3 0.8 1.3 \n", + "4 354.0 0.0 0.3 2.5 \n", + "\n", + " airtemperature relativehumidity atmosphericpressure pm25 pm10 noise \n", + "0 16.7 79.9 1026.1 10.0 12.0 58.7 \n", + "1 21.9 61.5 1021.5 27.0 32.0 75.6 \n", + "2 21.6 61.1 1021.3 26.0 29.0 69.2 \n", + "3 19.7 57.4 1018.4 39.0 49.0 61.9 \n", + "4 17.3 76.2 1025.5 10.0 12.0 79.4 \n" + ] + } + ], + "source": [ + "# Load the microclimate dataset.\n", + "microclimate_df = pd.read_csv(\n", + " get_csv_url(\n", + " DATASETS[\"microclimate\"],\n", + " row_limit=ROW_LIMIT\n", + " ),\n", + " encoding=\"utf-8-sig\"\n", + ")\n", + "\n", + "# Preview the dataframe.\n", + "print(microclimate_df.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Initial Inspection Of The Datasets\n", + "\n", + "This section is necessary to understand the size, structure, completeness and formatting of public datasets since they often differ. Public datasets can have different structures, missing values, data types, date formats, and identifier systems, so checking them early helps identify potential issues in the workflow [12]. Before trying to clean and merge the datasets, understanding what each of the datasets contains and how they can be combined is important [12]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 Checking Number Of Rows/Columns\n", + "\n", + "Checking the shape is important because it shows the scale and complexity of each dataset. This helps determine memory demands, cleaning strategy, and whether the data volume is sufficient for later modelling [13].\n", + "\n", + "From these results, the pedestrian and microclimate datasets are large enough for later time-series modelling. The sensor locations dataset is much smaller because it only contains metadata about the sensor network, rather than repeated hourly observations. The volume for each task varies, but from the inspection of the shapes, there appears to be sufficient volume for the task of predicting the pedestrian count." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(145, 12)\n", + "(1540629, 9)\n", + "(897085, 16)\n" + ] + } + ], + "source": [ + "# Preview of the number of columns and rows.\n", + "print(sensor_locations_df.shape)\n", + "print(pedestrian_counts_df.shape)\n", + "print(microclimate_df.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 Checking The Features\n", + "\n", + "This step is to verify what information is actually available in each dataset, and whether there are meaningful fields for later joining, cleaning, and modelling. Knowing the columns early prevents accidentally removing important variables or keeping irrelevant variables [14].\n", + "\n", + "From the output, the column names confirm that location_id is shared between the sensor metadata and pedestrian counts datasets. Also, the pedestrian and microclimate datasets both contain time information, which is essential because the final merge is ultimately done at the hourly level. The microclimate dataset clearly offers a diverse range of explanatory variables, which is useful for modelling. And, some columns that are likely less useful for the final model need to be removed, such as descriptive notes, raw coordinate strings, and duplicate directional fields." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sensor locations columns:\n", + "Index(['location_id', 'sensor_description', 'sensor_name', 'installation_date',\n", + " 'note', 'location_type', 'status', 'direction_1', 'direction_2',\n", + " 'latitude', 'longitude', 'location'],\n", + " dtype='object')\n", + "\n", + "Pedestrian counts columns:\n", + "Index(['id', 'location_id', 'sensing_date', 'hourday', 'direction_1',\n", + " 'direction_2', 'pedestriancount', 'sensor_name', 'location'],\n", + " dtype='object')\n", + "\n", + "Microclimate columns:\n", + "Index(['device_id', 'received_at', 'sensorlocation', 'latlong',\n", + " 'minimumwinddirection', 'averagewinddirection', 'maximumwinddirection',\n", + " 'minimumwindspeed', 'averagewindspeed', 'gustwindspeed',\n", + " 'airtemperature', 'relativehumidity', 'atmosphericpressure', 'pm25',\n", + " 'pm10', 'noise'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "# Check the column names for each dataframe.\n", + "print(\"Sensor locations columns:\")\n", + "print(sensor_locations_df.columns)\n", + "\n", + "print(\"\\nPedestrian counts columns:\")\n", + "print(pedestrian_counts_df.columns)\n", + "\n", + "print(\"\\nMicroclimate columns:\")\n", + "print(microclimate_df.columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.3 Checking The Datatypes\n", + "\n", + "Datatype checking is essential because many later operations depend on correct types to proceed. Steps like date parsing, numeric aggregation, interpolation, rolling windows, and model preparation can all fail or behave incorrectly if types are wrong [15].\n", + "\n", + "From the outputs, the pedestrian sensing_date, sensor installation_date, and microclimate received_at fields are all initially stored as objects, so they are not yet ready for time-series operations, which will need to be dealt with. And the numeric climate variables are already in float64, and pedestrian counts are in int64, which is appropriate for aggregation and modelling, so no need to change that." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sensor locations data types:\n", + "location_id int64\n", + "sensor_description object\n", + "sensor_name object\n", + "installation_date object\n", + "note object\n", + "location_type object\n", + "status object\n", + "direction_1 object\n", + "direction_2 object\n", + "latitude float64\n", + "longitude float64\n", + "location object\n", + "dtype: object\n", + "\n", + "Pedestrian counts data types:\n", + "id int64\n", + "location_id int64\n", + "sensing_date object\n", + "hourday int64\n", + "direction_1 int64\n", + "direction_2 int64\n", + "pedestriancount int64\n", + "sensor_name object\n", + "location object\n", + "dtype: object\n", + "\n", + "Microclimate data types:\n", + "device_id object\n", + "received_at object\n", + "sensorlocation object\n", + "latlong object\n", + "minimumwinddirection float64\n", + "averagewinddirection float64\n", + "maximumwinddirection float64\n", + "minimumwindspeed float64\n", + "averagewindspeed float64\n", + "gustwindspeed float64\n", + "airtemperature float64\n", + "relativehumidity float64\n", + "atmosphericpressure float64\n", + "pm25 float64\n", + "pm10 float64\n", + "noise float64\n", + "dtype: object\n" + ] + } + ], + "source": [ + "# Check the data types for each dataframe.\n", + "print(\"Sensor locations data types:\")\n", + "print(sensor_locations_df.dtypes)\n", + "\n", + "print(\"\\nPedestrian counts data types:\")\n", + "print(pedestrian_counts_df.dtypes)\n", + "\n", + "print(\"\\nMicroclimate data types:\")\n", + "print(microclimate_df.dtypes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.4 Checking The Dataset Information\n", + "\n", + "Using .info() gives a more complete structural summary of the datasets, which shows non-null counts, memory usage, and dtype balance. But this step will focus more on the memory that will be used up in the RAM, to decide what sample size would be ideal for experimentation before scaling to the full dataset size, or opt for a platform like Google Colab to handle more heavy usage [16].\n", + "\n", + "From the outputs, the pedestrian dataset takes up the most memory, followed by the microclimate dataset, with the sensor locations dataset being extremely low. Which is acceptable to run locally for modelling." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sensor locations info:\n", + "\n", + "RangeIndex: 145 entries, 0 to 144\n", + "Data columns (total 12 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 location_id 145 non-null int64 \n", + " 1 sensor_description 145 non-null object \n", + " 2 sensor_name 145 non-null object \n", + " 3 installation_date 145 non-null object \n", + " 4 note 34 non-null object \n", + " 5 location_type 145 non-null object \n", + " 6 status 145 non-null object \n", + " 7 direction_1 113 non-null object \n", + " 8 direction_2 113 non-null object \n", + " 9 latitude 145 non-null float64\n", + " 10 longitude 145 non-null float64\n", + " 11 location 145 non-null object \n", + "dtypes: float64(2), int64(1), object(9)\n", + "memory usage: 13.7+ KB\n", + "None\n", + "\n", + "Pedestrian counts info:\n", + "\n", + "RangeIndex: 1540629 entries, 0 to 1540628\n", + "Data columns (total 9 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 1540629 non-null int64 \n", + " 1 location_id 1540629 non-null int64 \n", + " 2 sensing_date 1540629 non-null object\n", + " 3 hourday 1540629 non-null int64 \n", + " 4 direction_1 1540629 non-null int64 \n", + " 5 direction_2 1540629 non-null int64 \n", + " 6 pedestriancount 1540629 non-null int64 \n", + " 7 sensor_name 1540629 non-null object\n", + " 8 location 1540629 non-null object\n", + "dtypes: int64(6), object(3)\n", + "memory usage: 105.8+ MB\n", + "None\n", + "\n", + "Microclimate info:\n", + "\n", + "RangeIndex: 897085 entries, 0 to 897084\n", + "Data columns (total 16 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 device_id 897085 non-null object \n", + " 1 received_at 897085 non-null object \n", + " 2 sensorlocation 897085 non-null object \n", + " 3 latlong 897085 non-null object \n", + " 4 minimumwinddirection 763016 non-null float64\n", + " 5 averagewinddirection 881374 non-null float64\n", + " 6 maximumwinddirection 763006 non-null float64\n", + " 7 minimumwindspeed 763006 non-null float64\n", + " 8 averagewindspeed 881372 non-null float64\n", + " 9 gustwindspeed 763006 non-null float64\n", + " 10 airtemperature 881372 non-null float64\n", + " 11 relativehumidity 881372 non-null float64\n", + " 12 atmosphericpressure 881372 non-null float64\n", + " 13 pm25 823822 non-null float64\n", + " 14 pm10 823822 non-null float64\n", + " 15 noise 823822 non-null float64\n", + "dtypes: float64(12), object(4)\n", + "memory usage: 109.5+ MB\n", + "None\n" + ] + } + ], + "source": [ + "# Check the overall structure of each dataframe.\n", + "print(\"Sensor locations info:\")\n", + "print(sensor_locations_df.info())\n", + "\n", + "print(\"\\nPedestrian counts info:\")\n", + "print(pedestrian_counts_df.info())\n", + "\n", + "print(\"\\nMicroclimate info:\")\n", + "print(microclimate_df.info())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.5 Checking For Missing Values\n", + "\n", + "Checking for missing values is important, since it affects the cleaning strategy, feature selection, and merge quality. This is especially important because missing sensor readings can break hourly continuity for the modelling later [17].\n", + "\n", + "From the outputs, the pedestrian counts dataset has no missing values at all. The microclimate dataset has many missing values in all the variables besides device_id and received_at. And the sensor locations dataset has some missing values, mainly in note, direction_1, and direction_2, which are not necessary for city-level forecasting." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Missing values in sensor locations:\n", + "location_id 0\n", + "sensor_description 0\n", + "sensor_name 0\n", + "installation_date 0\n", + "note 111\n", + "location_type 0\n", + "status 0\n", + "direction_1 32\n", + "direction_2 32\n", + "latitude 0\n", + "longitude 0\n", + "location 0\n", + "dtype: int64\n", + "\n", + "Missing values in pedestrian counts:\n", + "id 0\n", + "location_id 0\n", + "sensing_date 0\n", + "hourday 0\n", + "direction_1 0\n", + "direction_2 0\n", + "pedestriancount 0\n", + "sensor_name 0\n", + "location 0\n", + "dtype: int64\n", + "\n", + "Missing values in microclimate:\n", + "device_id 0\n", + "received_at 0\n", + "sensorlocation 0\n", + "latlong 0\n", + "minimumwinddirection 134069\n", + "averagewinddirection 15711\n", + "maximumwinddirection 134079\n", + "minimumwindspeed 134079\n", + "averagewindspeed 15713\n", + "gustwindspeed 134079\n", + "airtemperature 15713\n", + "relativehumidity 15713\n", + "atmosphericpressure 15713\n", + "pm25 73263\n", + "pm10 73263\n", + "noise 73263\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "# Count the missing values in each dataframe.\n", + "print(\"Missing values in sensor locations:\")\n", + "print(sensor_locations_df.isna().sum())\n", + "\n", + "print(\"\\nMissing values in pedestrian counts:\")\n", + "print(pedestrian_counts_df.isna().sum())\n", + "\n", + "print(\"\\nMissing values in microclimate:\")\n", + "print(microclimate_df.isna().sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.6 Checking Summary Statistics\n", + "\n", + "Checking the summary statistics is an easy way to provide some early insights into the datasets, like central tendency and spread [18].\n", + "\n", + "From the sensor coordinates with the mean latitude and longitude, the Melbourne CBD can be inferred to be the main area of focus for the datasets. The pedestrian counts are strongly right-skewed, with the median count being a fair bit lower than the mean, and the maximum being really high, which suggests that some hours and sites are much busier than others. The average microclimate temperature is about 16°C, and the average relative humidity is about 66%, which looks plausible for Melbourne across a long time range." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sensor locations summary:\n", + " location_id latitude longitude\n", + "count 145.000000 145.000000 145.000000\n", + "mean 93.075862 -37.812392 144.960427\n", + "std 55.229079 0.006999 0.009594\n", + "min 1.000000 -37.825910 144.928606\n", + "25% 47.000000 -37.816888 144.956447\n", + "50% 89.000000 -37.813625 144.961860\n", + "75% 142.000000 -37.807767 144.965626\n", + "max 188.000000 -37.789353 144.986388\n", + "\n", + "Pedestrian counts summary:\n", + " id location_id hourday direction_1 direction_2 \\\n", + "count 1.540629e+06 1.540629e+06 1.540629e+06 1.540629e+06 1.540629e+06 \n", + "mean 4.683120e+11 7.196742e+01 1.175280e+01 1.981603e+02 2.008987e+02 \n", + "std 5.137983e+11 5.119167e+01 6.796006e+00 3.107706e+02 3.148589e+02 \n", + "min 1.020241e+09 1.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "25% 6.292025e+10 3.000000e+01 6.000000e+00 1.800000e+01 1.900000e+01 \n", + "50% 2.111203e+11 6.100000e+01 1.200000e+01 7.900000e+01 7.800000e+01 \n", + "75% 7.116202e+11 1.170000e+02 1.800000e+01 2.360000e+02 2.410000e+02 \n", + "max 1.852320e+12 1.850000e+02 2.300000e+01 1.009900e+04 1.108500e+04 \n", + "\n", + " pedestriancount \n", + "count 1.540629e+06 \n", + "mean 3.990590e+02 \n", + "std 5.953562e+02 \n", + "min 0.000000e+00 \n", + "25% 3.900000e+01 \n", + "50% 1.620000e+02 \n", + "75% 4.930000e+02 \n", + "max 1.111300e+04 \n", + "\n", + "Microclimate summary:\n", + " minimumwinddirection averagewinddirection maximumwinddirection \\\n", + "count 763016.000000 881374.000000 763006.000000 \n", + "mean 23.908174 166.489727 295.974746 \n", + "std 61.529961 125.699058 97.279991 \n", + "min 0.000000 0.000000 0.000000 \n", + "25% 0.000000 42.000000 253.000000 \n", + "50% 0.000000 161.000000 351.000000 \n", + "75% 0.000000 299.000000 357.000000 \n", + "max 359.000000 359.000000 360.000000 \n", + "\n", + " minimumwindspeed averagewindspeed gustwindspeed airtemperature \\\n", + "count 763006.000000 881372.000000 763006.000000 881372.000000 \n", + "mean 0.231898 1.007714 3.179780 16.689150 \n", + "std 0.574129 0.942189 2.504463 5.581520 \n", + "min 0.000000 0.000000 0.000000 -0.800000 \n", + "25% 0.000000 0.400000 1.400000 12.800000 \n", + "50% 0.000000 0.800000 2.500000 16.100000 \n", + "75% 0.100000 1.400000 4.400000 19.700001 \n", + "max 11.600000 11.200000 52.500000 45.400002 \n", + "\n", + " relativehumidity atmosphericpressure pm25 pm10 \\\n", + "count 881372.000000 881372.000000 823822.000000 823822.000000 \n", + "mean 68.007261 1013.943718 6.691192 8.972910 \n", + "std 17.562959 9.815047 30.102780 30.536759 \n", + "min 2.500000 894.700000 0.000000 0.000000 \n", + "25% 56.400002 1008.500000 1.000000 3.000000 \n", + "50% 69.400000 1014.100000 3.000000 5.000000 \n", + "75% 80.800000 1019.900000 7.000000 9.000000 \n", + "max 99.800003 1042.900000 3414.000000 3414.000000 \n", + "\n", + " noise \n", + "count 823822.000000 \n", + "mean 66.442163 \n", + "std 11.194487 \n", + "min 0.000000 \n", + "25% 58.500000 \n", + "50% 68.100000 \n", + "75% 71.800000 \n", + "max 131.100000 \n" + ] + } + ], + "source": [ + "# Check the summary statistics for numeric columns.\n", + "print(\"Sensor locations summary:\")\n", + "print(sensor_locations_df.describe())\n", + "\n", + "print(\"\\nPedestrian counts summary:\")\n", + "print(pedestrian_counts_df.describe())\n", + "\n", + "print(\"\\nMicroclimate summary:\")\n", + "print(microclimate_df.describe())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.7 Checking The Date Format\n", + "\n", + "Checking the date format is important because a time-based merge depends on all datasets sharing a compatible datetime structure, and public datasets often use different date formats and timezone conventions. This is important because any mismatch in date or time formatting can prevent the datasets from merging correctly later [19].\n", + "\n", + "The pedestrian dataset stores dates and hours separately. Whereas the microclimate dataset stores the full timestamps with date and time with UTC offsets. And the sensor installation date is a simple date string and is mainly historical metadata rather than a modelling field. This makes it clear that datetime standardisation is a required step before any merge can occur." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pedestrian sensing_date sample:\n", + "0 2025-10-08\n", + "1 2026-05-07\n", + "2 2024-10-18\n", + "3 2026-01-06\n", + "4 2024-12-03\n", + "Name: sensing_date, dtype: object\n", + "\n", + "Microclimate received_at sample:\n", + "0 2026-04-28T15:25:15+00:00\n", + "1 2026-04-28T04:38:50+00:00\n", + "2 2026-04-28T05:08:52+00:00\n", + "3 2026-04-28T10:19:41+00:00\n", + "4 2026-04-28T13:55:03+00:00\n", + "Name: received_at, dtype: object\n", + "\n", + "Sensor installation_date sample:\n", + "0 2009-03-25\n", + "1 2009-03-26\n", + "2 2009-03-23\n", + "3 2009-01-21\n", + "4 2009-03-24\n", + "Name: installation_date, dtype: object\n" + ] + } + ], + "source": [ + "# Check a few date values before conversion.\n", + "print(\"Pedestrian sensing_date sample:\")\n", + "print(pedestrian_counts_df[\"sensing_date\"].head())\n", + "\n", + "print(\"\\nMicroclimate received_at sample:\")\n", + "print(microclimate_df[\"received_at\"].head())\n", + "\n", + "print(\"\\nSensor installation_date sample:\")\n", + "print(sensor_locations_df[\"installation_date\"].head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.8 Checking The ID Columns\n", + "\n", + "This was an additional step for checking the unique identifiers to see if the datasets have any chance of being joined directly by ID or whether another strategy, like a datetime merge, is best. And since the project uses multiple public datasets, checking the unique IDs also helps confirm whether the pedestrian sensors and microclimate sensors use the same location system or separate systems [20].\n", + "\n", + "From the outputs, the sensor locations dataset contains 137 unique location_id values, while the pedestrian counts dataset contains 100 unique location_id values. Whereas the microclimate dataset contains 12 unique device_id values, which is a completely different identifier system. This means the microclimate data cannot be joined to pedestrian counts by location ID, so a time-based merge is the best integration method available." + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unique location_id values in sensor locations:\n", + "137\n", + "\n", + "Unique location_id values in pedestrian counts:\n", + "100\n", + "\n", + "Unique device_id values in microclimate:\n", + "12\n" + ] + } + ], + "source": [ + "# Check important identifier columns.\n", + "print(\"Unique location_id values in sensor locations:\")\n", + "print(sensor_locations_df[\"location_id\"].nunique())\n", + "\n", + "print(\"\\nUnique location_id values in pedestrian counts:\")\n", + "print(pedestrian_counts_df[\"location_id\"].nunique())\n", + "\n", + "print(\"\\nUnique device_id values in microclimate:\")\n", + "print(microclimate_df[\"device_id\"].nunique())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Data Cleaning\n", + "\n", + "This is the section for data cleaning, since raw data are rarely ready to be used as it is, so cleaning processes are necessary to address duplicates, missing values, inconsistencies, syntax errors, irrelevant data and structural errors [21] [22]. And since this data pipeline uses the API for dataset access, this means that the datasets are being updated in real-time, so ensuring any errors get addressed in the pipeline ensures the data remains accurate, secure and accessible at every stage of its lifecycle [21]. And that the prediction will also be accurate [22]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.1 Removing Irrelevant Columns\n", + "\n", + "Selecting relevant variables and removing the irrelevant ones are necessary because not every feature is useful for the prediction modelling. Keeping unnecessary columns can make the workflow harder to manage, increase memory usage, and create confusion in later steps [23] [24].\n", + "\n", + "In this step, the sensor dataset is reduced to six useful metadata columns, even though it wasn't used for modelling purposes. The pedestrian dataset is reduced to the four fields needed to construct hourly counts. The microclimate dataset is reduced to key climate, air quality, and noise variables. The descriptive or duplicate variables were omitted." + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " location_id sensor_description installation_date status latitude \\\n", + "0 3 Melbourne Central 2009-03-25 A -37.811015 \n", + "1 5 Princes Bridge 2009-03-26 A -37.818742 \n", + "2 9 Southern Cross Station 2009-03-23 A -37.819830 \n", + "3 12 New Quay 2009-01-21 A -37.814580 \n", + "4 14 Sandridge Bridge 2009-03-24 A -37.820112 \n", + "\n", + " longitude \n", + "0 144.964295 \n", + "1 144.967877 \n", + "2 144.951026 \n", + "3 144.942924 \n", + "4 144.962919 \n", + " location_id sensing_date hourday pedestriancount\n", + "0 70 2025-10-08 2 1\n", + "1 3 2026-05-07 12 2042\n", + "2 59 2024-10-18 15 1058\n", + "3 14 2026-01-06 10 315\n", + "4 28 2024-12-03 8 799\n", + " device_id received_at airtemperature \\\n", + "0 ICTMicroclimate-08 2026-04-28T15:25:15+00:00 16.7 \n", + "1 ICTMicroclimate-08 2026-04-28T04:38:50+00:00 21.9 \n", + "2 ICTMicroclimate-08 2026-04-28T05:08:52+00:00 21.6 \n", + "3 ICTMicroclimate-10 2026-04-28T10:19:41+00:00 19.7 \n", + "4 ICTMicroclimate-08 2026-04-28T13:55:03+00:00 17.3 \n", + "\n", + " relativehumidity atmosphericpressure averagewindspeed gustwindspeed \\\n", + "0 79.9 1026.1 0.5 2.5 \n", + "1 61.5 1021.5 0.9 2.6 \n", + "2 61.1 1021.3 0.2 1.9 \n", + "3 57.4 1018.4 0.8 1.3 \n", + "4 76.2 1025.5 0.3 2.5 \n", + "\n", + " averagewinddirection pm25 pm10 noise \n", + "0 220.0 10.0 12.0 58.7 \n", + "1 283.0 27.0 32.0 75.6 \n", + "2 310.0 26.0 29.0 69.2 \n", + "3 324.0 39.0 49.0 61.9 \n", + "4 281.0 10.0 12.0 79.4 \n" + ] + } + ], + "source": [ + "# Keep only useful columns.\n", + "sensor_locations_clean = sensor_locations_df[\n", + " [\n", + " \"location_id\",\n", + " \"sensor_description\",\n", + " \"installation_date\",\n", + " \"status\",\n", + " \"latitude\",\n", + " \"longitude\",\n", + " ]\n", + "].copy()\n", + "\n", + "pedestrian_clean = pedestrian_counts_df[\n", + " [\n", + " \"location_id\",\n", + " \"sensing_date\",\n", + " \"hourday\",\n", + " \"pedestriancount\",\n", + " ]\n", + "].copy()\n", + "\n", + "microclimate_clean = microclimate_df[\n", + " [\n", + " \"device_id\",\n", + " \"received_at\",\n", + " \"airtemperature\",\n", + " \"relativehumidity\",\n", + " \"atmosphericpressure\",\n", + " \"averagewindspeed\",\n", + " \"gustwindspeed\",\n", + " \"averagewinddirection\",\n", + " \"pm25\",\n", + " \"pm10\",\n", + " \"noise\",\n", + " ]\n", + "].copy()\n", + "\n", + "# Check the result.\n", + "print(sensor_locations_clean.head())\n", + "print(pedestrian_clean.head())\n", + "print(microclimate_clean.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.2 Removing Missing Value\n", + "\n", + "This step involves dealing with missing values by removing the rows they're in. This is because missing values in explanatory variables can cause problems when doing aggregation, interpolation, and modelling later, leading to bias results [25]. This is especially important for the microclimate dataset, since missing climate readings could affect the quality of the explanatory variables.\n", + "\n", + "After doing the column selection in the previous step, the sensor and pedestrian tables are now fully complete without missing values. Whereas the microclimate table still has many missing values, especially in gustwindspeed, pm25, pm10, and noise. \n", + "\n", + "By using dropna(), the microclimate dataset lost a number of data points with missing values, but still retains a sizable portion of data points. The decision for completeness in the data points was preferred to simplify the merging and feature engineering steps later, and because there were enough data points for it not to matter much." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Missing values in sensor_locations_clean:\n", + "location_id 0\n", + "sensor_description 0\n", + "installation_date 0\n", + "status 0\n", + "latitude 0\n", + "longitude 0\n", + "dtype: int64\n", + "\n", + "Missing values in pedestrian_clean:\n", + "location_id 0\n", + "sensing_date 0\n", + "hourday 0\n", + "pedestriancount 0\n", + "dtype: int64\n", + "\n", + "Missing values in microclimate_clean:\n", + "device_id 0\n", + "received_at 0\n", + "airtemperature 15713\n", + "relativehumidity 15713\n", + "atmosphericpressure 15713\n", + "averagewindspeed 15713\n", + "gustwindspeed 134079\n", + "averagewinddirection 15711\n", + "pm25 73263\n", + "pm10 73263\n", + "noise 73263\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "# Check missing values after column selection.\n", + "print(\"Missing values in sensor_locations_clean:\")\n", + "print(sensor_locations_clean.isna().sum())\n", + "\n", + "print(\"\\nMissing values in pedestrian_clean:\")\n", + "print(pedestrian_clean.isna().sum())\n", + "\n", + "print(\"\\nMissing values in microclimate_clean:\")\n", + "print(microclimate_clean.isna().sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Missing values in sensor_locations_clean:\n", + "location_id 0\n", + "sensor_description 0\n", + "installation_date 0\n", + "status 0\n", + "latitude 0\n", + "longitude 0\n", + "dtype: int64\n", + "(145, 6)\n", + "\n", + "Missing values in pedestrian_clean:\n", + "location_id 0\n", + "sensing_date 0\n", + "hourday 0\n", + "pedestriancount 0\n", + "dtype: int64\n", + "(1540629, 4)\n", + "\n", + "Missing values in microclimate_clean:\n", + "device_id 0\n", + "received_at 0\n", + "airtemperature 0\n", + "relativehumidity 0\n", + "atmosphericpressure 0\n", + "averagewindspeed 0\n", + "gustwindspeed 0\n", + "averagewinddirection 0\n", + "pm25 0\n", + "pm10 0\n", + "noise 0\n", + "dtype: int64\n", + "(705456, 11)\n" + ] + } + ], + "source": [ + "# Remove rows with any missing values from each dataset.\n", + "sensor_locations_clean = sensor_locations_clean.dropna().copy()\n", + "pedestrian_clean = pedestrian_clean.dropna().copy()\n", + "microclimate_clean = microclimate_clean.dropna().copy()\n", + "\n", + "# Check missing values after removal.\n", + "print(\"\\nMissing values in sensor_locations_clean:\")\n", + "print(sensor_locations_clean.isna().sum())\n", + "print(sensor_locations_clean.shape)\n", + "\n", + "print(\"\\nMissing values in pedestrian_clean:\")\n", + "print(pedestrian_clean.isna().sum())\n", + "print(pedestrian_clean.shape)\n", + "\n", + "print(\"\\nMissing values in microclimate_clean:\")\n", + "print(microclimate_clean.isna().sum())\n", + "print(microclimate_clean.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.3 Datetime Formatting\n", + "\n", + "Ensuring the datetime formatting matches between the different datasets is important because the modelling is hourly, and the datasets need to be able to merge based on a common point for perform chronological analysis across different data sources [26] [27]. Skipping this step would mean that the datasets cannot be merged into one table.\n", + "\n", + "The pedestrian dataset is converted from separate sensing_date and hourday fields into a single datetime_hour, such as 2024-12-06 20:00:00. Whereas the microclimate timestamps are converted from UTC into Australia/Melbourne, timezone information is removed, and the values are floored to the nearest hour. By doing this, both datasets now have a datetime variable with the same time formatting. It's also important to note that the time range of the microclimate dataset is narrower than the pedestrian time range, meaning that the overlap period is limited to the microclimate dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " location_id sensing_date hourday pedestriancount datetime_hour\n", + "0 70 2025-10-08 2 1 2025-10-08 02:00:00\n", + "1 3 2026-05-07 12 2042 2026-05-07 12:00:00\n", + "2 59 2024-10-18 15 1058 2024-10-18 15:00:00\n", + "3 14 2026-01-06 10 315 2026-01-06 10:00:00\n", + "4 28 2024-12-03 8 799 2024-12-03 08:00:00\n" + ] + } + ], + "source": [ + "# Convert sensor installation date.\n", + "sensor_locations_clean[\"installation_date\"] = pd.to_datetime(\n", + " sensor_locations_clean[\"installation_date\"]\n", + ")\n", + "\n", + "# Create an hourly datetime for pedestrian data.\n", + "pedestrian_clean[\"sensing_date\"] = pd.to_datetime(\n", + " pedestrian_clean[\"sensing_date\"]\n", + ")\n", + "\n", + "pedestrian_clean[\"datetime_hour\"] = (\n", + " pedestrian_clean[\"sensing_date\"] +\n", + " pd.to_timedelta(pedestrian_clean[\"hourday\"], unit=\"h\")\n", + ")\n", + "\n", + "# Check the result.\n", + "print(pedestrian_clean.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " device_id received_at airtemperature relativehumidity \\\n", + "0 ICTMicroclimate-08 2026-04-29 01:25:15 16.7 79.9 \n", + "1 ICTMicroclimate-08 2026-04-28 14:38:50 21.9 61.5 \n", + "2 ICTMicroclimate-08 2026-04-28 15:08:52 21.6 61.1 \n", + "3 ICTMicroclimate-10 2026-04-28 20:19:41 19.7 57.4 \n", + "4 ICTMicroclimate-08 2026-04-28 23:55:03 17.3 76.2 \n", + "\n", + " atmosphericpressure averagewindspeed gustwindspeed averagewinddirection \\\n", + "0 1026.1 0.5 2.5 220.0 \n", + "1 1021.5 0.9 2.6 283.0 \n", + "2 1021.3 0.2 1.9 310.0 \n", + "3 1018.4 0.8 1.3 324.0 \n", + "4 1025.5 0.3 2.5 281.0 \n", + "\n", + " pm25 pm10 noise datetime_hour \n", + "0 10.0 12.0 58.7 2026-04-29 01:00:00 \n", + "1 27.0 32.0 75.6 2026-04-28 14:00:00 \n", + "2 26.0 29.0 69.2 2026-04-28 15:00:00 \n", + "3 39.0 49.0 61.9 2026-04-28 20:00:00 \n", + "4 10.0 12.0 79.4 2026-04-28 23:00:00 \n" + ] + } + ], + "source": [ + "# Convert the raw timestamp to datetime with UTC.\n", + "microclimate_clean[\"received_at\"] = pd.to_datetime(\n", + " microclimate_clean[\"received_at\"],\n", + " utc=True\n", + ")\n", + "\n", + "# Convert UTC to Melbourne local time.\n", + "microclimate_clean[\"received_at\"] = (\n", + " microclimate_clean[\"received_at\"]\n", + " .dt.tz_convert(\"Australia/Melbourne\")\n", + ")\n", + "\n", + "# Remove the timezone after conversion.\n", + "microclimate_clean[\"received_at\"] = (\n", + " microclimate_clean[\"received_at\"]\n", + " .dt.tz_localize(None)\n", + ")\n", + "\n", + "# Round down to the nearest hour.\n", + "microclimate_clean[\"datetime_hour\"] = (\n", + " microclimate_clean[\"received_at\"]\n", + " .dt.floor(\"h\")\n", + ")\n", + "\n", + "# Check the result.\n", + "print(microclimate_clean.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pedestrian time range:\n", + "2024-05-12 00:00:00\n", + "2026-05-11 03:00:00\n", + "\n", + "Microclimate time range:\n", + "2022-11-08 12:00:00\n", + "2026-05-07 15:00:00\n" + ] + } + ], + "source": [ + "# Confirming the same datetime format.\n", + "print(\"Pedestrian time range:\")\n", + "print(pedestrian_clean[\"datetime_hour\"].min())\n", + "print(pedestrian_clean[\"datetime_hour\"].max())\n", + "\n", + "print(\"\\nMicroclimate time range:\")\n", + "print(microclimate_clean[\"datetime_hour\"].min())\n", + "print(microclimate_clean[\"datetime_hour\"].max())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.4 Aggregating Values For Hourly Format\n", + "\n", + "This step involves aggregation because the use case models city-level pedestrian demand rather than individual sensor-level behaviour. Since the pedestrian and microclimate datasets both contain multiple records within the same hour. This also ensures that both the pedestrian and the microclimate data share the same hourly rows without duplicates for later merging, reducing the total data volume [28].\n", + "\n", + "The aggregation involves the pedestrian counts being summed across sensors to produce hourly city totals, and the microclimate readings are averaged across devices for each hour. Doing this changes the target variable from pedestrians at one site to overall city pedestrian demand at one hour, along with the climate values at that hour. This also further reduces the row counts due to aggregating to hourly, and the microclimate data is still narrower than the pedestrian dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " datetime_hour pedestriancount\n", + "0 2024-05-12 00:00:00 15093\n", + "1 2024-05-12 01:00:00 10686\n", + "2 2024-05-12 02:00:00 6751\n", + "3 2024-05-12 03:00:00 5431\n", + "4 2024-05-12 04:00:00 2728\n", + "(17351, 2)\n" + ] + } + ], + "source": [ + "# Aggregate pedestrian counts to hourly city totals.\n", + "pedestrian_hourly = (\n", + " pedestrian_clean\n", + " .groupby(\"datetime_hour\", as_index=False)\n", + " .agg(\n", + " {\n", + " \"pedestriancount\": \"sum\",\n", + " }\n", + " )\n", + ")\n", + "\n", + "# Check the result.\n", + "print(pedestrian_hourly.head())\n", + "print(pedestrian_hourly.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " datetime_hour airtemperature relativehumidity atmosphericpressure \\\n", + "0 2022-11-08 12:00:00 28.400 38.800 1011.100 \n", + "1 2022-11-08 13:00:00 29.400 34.800 1009.800 \n", + "2 2022-11-08 14:00:00 27.320 40.940 1009.420 \n", + "3 2022-11-23 15:00:00 15.100 87.150 1010.800 \n", + "4 2022-11-23 16:00:00 16.475 80.725 1010.475 \n", + "\n", + " averagewindspeed gustwindspeed averagewinddirection pm25 pm10 noise \n", + "0 3.100 4.100 74.00 3.00 5.00 62.80 \n", + "1 2.100 3.500 92.00 5.00 8.00 86.90 \n", + "2 1.700 2.780 253.40 3.00 5.20 87.78 \n", + "3 0.450 1.050 291.00 2.00 5.50 95.10 \n", + "4 1.675 2.475 271.25 4.25 6.75 100.30 \n", + "(27720, 10)\n" + ] + } + ], + "source": [ + "# Aggregate microclimate readings to hourly city averages.\n", + "microclimate_hourly = (\n", + " microclimate_clean\n", + " .groupby(\"datetime_hour\", as_index=False)\n", + " .agg(\n", + " {\n", + " \"airtemperature\": \"mean\",\n", + " \"relativehumidity\": \"mean\",\n", + " \"atmosphericpressure\": \"mean\",\n", + " \"averagewindspeed\": \"mean\",\n", + " \"gustwindspeed\": \"mean\",\n", + " \"averagewinddirection\": \"mean\",\n", + " \"pm25\": \"mean\",\n", + " \"pm10\": \"mean\",\n", + " \"noise\": \"mean\",\n", + " }\n", + " )\n", + ")\n", + "\n", + "# Check the result.\n", + "print(microclimate_hourly.head())\n", + "print(microclimate_hourly.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.5 Merging The Datasets\n", + "\n", + "This step will merge the datasets together, ensuring the target variable, pedestrian count, is connected to the explanatory climate variables. The datetime_hour variable on both the pedestrian dataset and the microclimate dataset was inner-joined to merge into one dataset, meaning only the overlapping rows with the same values were merged [29]. Which means every row has both pedestrian and climate information, hence, a unified format ready for analysis [30].\n", + "\n", + "A quick check of the merged dataset shows that the pedestrian counts and climate values aligned in the same hourly observations, which is what the use case is looking for. And there are no missing values, which indicates that previous data cleaning works as intended." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " datetime_hour pedestriancount airtemperature relativehumidity \\\n", + "0 2024-05-12 00:00:00 15093 12.771429 84.646429 \n", + "1 2024-05-12 01:00:00 10686 12.046429 88.128571 \n", + "2 2024-05-12 02:00:00 6751 11.457143 90.596429 \n", + "3 2024-05-12 03:00:00 5431 11.121429 91.982143 \n", + "4 2024-05-12 04:00:00 2728 10.837037 92.677778 \n", + "\n", + " atmosphericpressure averagewindspeed gustwindspeed averagewinddirection \\\n", + "0 1022.835714 0.353571 1.282143 166.142857 \n", + "1 1022.635714 0.507143 1.435714 140.321429 \n", + "2 1022.521429 0.585714 1.578571 153.857143 \n", + "3 1022.278571 0.607143 1.507143 139.250000 \n", + "4 1022.251852 0.566667 1.562963 170.074074 \n", + "\n", + " pm25 pm10 noise \n", + "0 8.464286 9.428571 66.071429 \n", + "1 11.107143 12.392857 65.500000 \n", + "2 9.892857 11.357143 64.278571 \n", + "3 7.678571 9.035714 61.942857 \n", + "4 8.074074 9.740741 61.566667 \n", + "(17267, 11)\n" + ] + } + ], + "source": [ + "# Merge pedestrian and microclimate data on the hourly datetime.\n", + "model_df = pd.merge(\n", + " pedestrian_hourly,\n", + " microclimate_hourly,\n", + " on=\"datetime_hour\",\n", + " how=\"inner\"\n", + ")\n", + "\n", + "# Sort the final modelling table.\n", + "model_df = model_df.sort_values(\n", + " \"datetime_hour\"\n", + ").reset_index(drop=True)\n", + "\n", + "# Check the result.\n", + "print(model_df.head())\n", + "print(model_df.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 17267 entries, 0 to 17266\n", + "Data columns (total 11 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 datetime_hour 17267 non-null datetime64[ns]\n", + " 1 pedestriancount 17267 non-null int64 \n", + " 2 airtemperature 17267 non-null float64 \n", + " 3 relativehumidity 17267 non-null float64 \n", + " 4 atmosphericpressure 17267 non-null float64 \n", + " 5 averagewindspeed 17267 non-null float64 \n", + " 6 gustwindspeed 17267 non-null float64 \n", + " 7 averagewinddirection 17267 non-null float64 \n", + " 8 pm25 17267 non-null float64 \n", + " 9 pm10 17267 non-null float64 \n", + " 10 noise 17267 non-null float64 \n", + "dtypes: datetime64[ns](1), float64(9), int64(1)\n", + "memory usage: 1.4 MB\n", + "None\n", + "\n", + "Missing values:\n", + "datetime_hour 0\n", + "pedestriancount 0\n", + "airtemperature 0\n", + "relativehumidity 0\n", + "atmosphericpressure 0\n", + "averagewindspeed 0\n", + "gustwindspeed 0\n", + "averagewinddirection 0\n", + "pm25 0\n", + "pm10 0\n", + "noise 0\n", + "dtype: int64\n", + "\n", + "Summary statistics:\n", + " datetime_hour pedestriancount airtemperature \\\n", + "count 17267 17267.00000 17267.000000 \n", + "mean 2025-05-09 05:49:24.104940288 35432.95975 16.511347 \n", + "min 2024-05-12 00:00:00 51.00000 2.617857 \n", + "25% 2024-11-07 21:30:00 7142.00000 12.527951 \n", + "50% 2025-05-06 18:00:00 35519.00000 15.910256 \n", + "75% 2025-11-08 09:30:00 58964.50000 19.561111 \n", + "max 2026-05-07 15:00:00 114804.00000 43.191667 \n", + "std NaN 27363.02579 5.547545 \n", + "\n", + " relativehumidity atmosphericpressure averagewindspeed gustwindspeed \\\n", + "count 17267.000000 17267.000000 17267.000000 17267.000000 \n", + "mean 66.584728 1013.925313 1.162485 3.496650 \n", + "min 9.875000 990.350000 0.121875 0.693939 \n", + "25% 56.075397 1008.605840 0.721429 2.289380 \n", + "50% 68.540741 1013.885714 1.065625 3.263889 \n", + "75% 78.714583 1019.287650 1.508001 4.527639 \n", + "max 96.673171 1039.162500 4.028125 10.200000 \n", + "std 16.060614 7.914093 0.578561 1.523908 \n", + "\n", + " averagewinddirection pm25 pm10 noise \n", + "count 17267.000000 17267.000000 17267.000000 17267.000000 \n", + "mean 184.944173 6.935045 8.731420 68.252640 \n", + "min 52.571429 0.333333 1.057143 53.745714 \n", + "25% 160.194444 1.892857 3.313393 65.136111 \n", + "50% 184.742857 3.368421 5.000000 68.113043 \n", + "75% 211.442222 7.577381 9.306624 71.034330 \n", + "max 306.827586 411.228571 417.028571 88.200000 \n", + "std 37.950660 12.914232 13.465303 4.521855 \n" + ] + } + ], + "source": [ + "# Check merged dataset.\n", + "print(model_df.info())\n", + "print(\"\\nMissing values:\")\n", + "print(model_df.isna().sum())\n", + "print(\"\\nSummary statistics:\")\n", + "print(model_df.describe())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Data Validation\n", + "\n", + "Doing data validation is important because a merged dataset that was cleaned may still be unsuitable for the task of this use case, possibly due to timestamps being duplicated, out of order, or some rows in the chronological datetime are missing. The time-series model tends to assume a consistent temporal structure with no sudden breaks, so this step in the pipeline checks that everything is complete [31]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.1 Validating Time Series Dataset\n", + "\n", + "Checking for duplicates or missing timestamps, since they can affect the lag features, rolling features and any sequence-based deep learning models that this use case may use. This is to ensure that there is a strictly ordered sequence of evenly spaced time points [32].\n", + "\n", + "From the outputs, there are no duplicates in the datetime_hour values, which means every timestamp is represented once. The dataset is double-checked to ensure that it is sorted in increasing time order. But there appears to be a number of missing hourly timestamps, which means the dataset is not complete yet. It does appear that random points were cut off, and that no large block of time was cut off." + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Duplicate datetime_hour values: 0\n" + ] + } + ], + "source": [ + "# Count duplicate datetime values.\n", + "duplicate_count = model_df[\"datetime_hour\"].duplicated().sum()\n", + "\n", + "# Print the result.\n", + "print(\"Duplicate datetime_hour values:\", duplicate_count)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of missing hourly timestamps: 149\n", + "DatetimeIndex(['2024-10-06 02:00:00', '2025-06-06 22:00:00',\n", + " '2025-06-06 23:00:00', '2025-06-07 00:00:00',\n", + " '2025-06-07 01:00:00', '2025-06-07 02:00:00',\n", + " '2025-06-07 03:00:00', '2025-06-07 04:00:00',\n", + " '2025-06-07 05:00:00', '2025-06-07 06:00:00'],\n", + " dtype='datetime64[ns]', freq=None)\n" + ] + } + ], + "source": [ + "# Create the full expected hourly range.\n", + "full_hours = pd.date_range(\n", + " start=model_df[\"datetime_hour\"].min(),\n", + " end=model_df[\"datetime_hour\"].max(),\n", + " freq=\"h\"\n", + ")\n", + "\n", + "# Find missing hours.\n", + "missing_hours = full_hours.difference(model_df[\"datetime_hour\"])\n", + "\n", + "# Print the number of missing hours.\n", + "print(\"Number of missing hourly timestamps:\", len(missing_hours))\n", + "\n", + "# Preview a few missing hours.\n", + "print(missing_hours[:10])" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], + "source": [ + "# Confirm the dataset is sorted by time.\n", + "print(model_df[\"datetime_hour\"].is_monotonic_increasing)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.2 Fixing Missing Timestamps\n", + "\n", + "Ensuring the missing timestamps are filled in is necessary for a complete hourly sequence in the dataset, and missing them can create issues when performing feature engineering later. Missing hours can cause problems when creating lag features, rolling averages, and LSTM input sequences, since these methods rely on consistent time gaps between rows [32] [33].\n", + "\n", + "This step involves reindexing to the full hourly range so that all the missing timestamps are included in the dataset, and then interpolation is performed to fill in the missing values from those created rows. Hence, the merged dataset now has slightly more rows with no missing hourly stamps, ensuring the timeline is continuous with no breaks. A little addition was included to ensure that the pedestrian counts remained as integers, rather than fractions, due to interpolation. " + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Missing hourly timestamps: 0\n", + "\n", + "Overall:\n", + "\n", + "RangeIndex: 17416 entries, 0 to 17415\n", + "Data columns (total 11 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 datetime_hour 17416 non-null datetime64[ns]\n", + " 1 pedestriancount 17416 non-null int64 \n", + " 2 airtemperature 17416 non-null float64 \n", + " 3 relativehumidity 17416 non-null float64 \n", + " 4 atmosphericpressure 17416 non-null float64 \n", + " 5 averagewindspeed 17416 non-null float64 \n", + " 6 gustwindspeed 17416 non-null float64 \n", + " 7 averagewinddirection 17416 non-null float64 \n", + " 8 pm25 17416 non-null float64 \n", + " 9 pm10 17416 non-null float64 \n", + " 10 noise 17416 non-null float64 \n", + "dtypes: datetime64[ns](1), float64(9), int64(1)\n", + "memory usage: 1.5 MB\n", + "None\n", + "\n", + "Final time range:\n", + "2024-05-12 00:00:00\n", + "2026-05-07 15:00:00\n", + "\n", + "Final shape:\n", + "(17416, 11)\n" + ] + } + ], + "source": [ + "# Set datetime as the index.\n", + "model_df = model_df.set_index(\"datetime_hour\").sort_index()\n", + "\n", + "# Create the full hourly timeline.\n", + "full_hours = pd.date_range(\n", + " start=model_df.index.min(),\n", + " end=model_df.index.max(),\n", + " freq=\"h\"\n", + ")\n", + "\n", + "# Reindex to restore missing hours.\n", + "model_df = model_df.reindex(full_hours)\n", + "\n", + "# Interpolate all numeric columns over time.\n", + "model_df = model_df.interpolate(method=\"time\").ffill().bfill()\n", + "\n", + "# Convert pedestrian count back to integers.\n", + "model_df[\"pedestriancount\"] = (\n", + " model_df[\"pedestriancount\"]\n", + " .round()\n", + " .astype(int)\n", + ")\n", + "\n", + "# Restore datetime_hour as a column.\n", + "model_df = model_df.reset_index().rename(\n", + " columns={\"index\": \"datetime_hour\"}\n", + ")\n", + "\n", + "# Check the result.\n", + "print(\"Missing hourly timestamps:\",\n", + " pd.date_range(\n", + " model_df[\"datetime_hour\"].min(),\n", + " model_df[\"datetime_hour\"].max(),\n", + " freq=\"h\"\n", + " ).difference(model_df[\"datetime_hour\"]).shape[0])\n", + "\n", + "# Check for missing hourly timestamps again.\n", + "full_hours = pd.date_range(\n", + " start=model_df[\"datetime_hour\"].min(),\n", + " end=model_df[\"datetime_hour\"].max(),\n", + " freq=\"h\"\n", + ")\n", + "\n", + "missing_hours = full_hours.difference(model_df[\"datetime_hour\"])\n", + "\n", + "# Check the dataset structure.\n", + "print(\"\\nOverall:\")\n", + "print(model_df.info())\n", + "\n", + "# Check the final time range.\n", + "print(\"\\nFinal time range:\")\n", + "print(model_df[\"datetime_hour\"].min())\n", + "print(model_df[\"datetime_hour\"].max())\n", + "\n", + "# Check the dataframe shape.\n", + "print(\"\\nFinal shape:\")\n", + "print(model_df.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Exploratory Data Analysis\n", + "\n", + "The exploratory data analysis section was performed on the merged dataset to observe any interpretable patterns and get a rough understanding of how the dataset looks and feels before building a model. It's important to get a sense of how pedestrian demand changes over time and how it relates to the climate conditions [34]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6.1 Pedestrian Count Over Time (Hourly)\n", + "\n", + "Plotting the hourly pedestrian count is for a quick look at the target variable changes over time, to check if the time series data is random or if there are any patterns that may need to be taken into consideration for later steps [35] [36].\n", + "\n", + "From the plot, there appear to be large fluctuations across the full time range with repeated highs and lows. Although it looks random, it does seem to show some sort of pattern, and it's not necessarily random noise, with recurring fluctuations, like the new year of each year seems to always be a high peak, indicating lots of pedestrians travelling in the city, and there also appear to be some sharp dips, which may possibly be public holidays. This does suggest that there may be other possible variables that might have influenced the pedestrian count, but it definitely confirms that pedestrian demand is influenced by recurring temporal and possibly environmental effects." + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot pedestrian counts over time.\n", + "plt.figure(figsize=(14, 5))\n", + "plt.plot(\n", + " model_df[\"datetime_hour\"],\n", + " model_df[\"pedestriancount\"],\n", + " label=\"Hourly pedestrian count\"\n", + ")\n", + "\n", + "plt.title(\"Pedestrian Count Over Time (Hourly)\")\n", + "plt.xlabel(\"Datetime\")\n", + "plt.ylabel(\"Pedestrian Count\")\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6.2 Average Pedestrian Count By Day Of Week\n", + "\n", + "The day-of-week summary was checked to see if there may also be other variables like work patterns, shopping activities, or weekend behaviours that might have influenced the pedestrian demand. Also checks whether the day of the week should be considered an important time-based feature for later modelling [35] [37].\n", + "\n", + "From the plot, it does seem like the weekdays tend to be relatively high along with Saturdays, whereas Mondays and Sundays tend to have low pedestrian demands. Monday might be low despite being a normal workday for the average Australians might be due to the influence of public holidays, and Sunday being low is due to most people not working on Sunday, since businesses would usually pay a high penalty rate. This further suggests that the CBD pedestrian pattern is linked to weekday economic and commuter activity, not just climate variables." + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a day-of-week column.\n", + "model_df[\"day_of_week\"] = model_df[\n", + " \"datetime_hour\"\n", + "].dt.dayofweek\n", + "\n", + "# Group by day of week.\n", + "dow_pattern = model_df.groupby(\"day_of_week\")[\n", + " \"pedestriancount\"\n", + "].mean()\n", + "\n", + "# Plot the day-of-week pattern.\n", + "plt.figure(figsize=(8, 4))\n", + "plt.plot(\n", + " dow_pattern.index,\n", + " dow_pattern.values,\n", + " marker=\"o\",\n", + " label=\"Average pedestrian count\"\n", + ")\n", + "\n", + "plt.title(\"Average Pedestrian Count By Day Of Week\")\n", + "plt.xlabel(\"Day Of Week\")\n", + "plt.ylabel(\"Average Pedestrian Count\")\n", + "plt.xticks(\n", + " ticks=range(7),\n", + " labels=[\n", + " \"Mon\", \"Tue\", \"Wed\",\n", + " \"Thu\", \"Fri\", \"Sat\", \"Sun\"\n", + " ]\n", + ")\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6.3 Distributions Of Variables\n", + "\n", + "Checking for distribution to see whether it's symmetric, skewed, or multi-modal. This is useful because skewed variables, extreme values, or unusual distributions can influence how the model learns from the data [38].\n", + "\n", + "* pedestriancount appears unimodal and right-skewed.\n", + "* airtemperature appears unimodal with a slight right skew.\n", + "* relativehumidity appears unimodal with a slight left skew.\n", + "* atmosphericpressure appears unimodal and slightly left-skewed.\n", + "* averagewindspeed appears unimodal and right-skewed.\n", + "* gustwindspeed appears unimodal and right-skewed.\n", + "* averagewinddirection appears roughly unimodal and mostly symmetric.\n", + "* pm25 appears unimodal and right-skewed.\n", + "* pm10 appears unimodal and right-skewed, similar to pm25.\n", + "* noise appears unimodal and fairly symmetric." + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Store numeric columns.\n", + "numeric_cols = model_df.select_dtypes(\n", + " include=[\"int32\", \"int64\", \"float64\"]\n", + ").columns.drop(\n", + " [\"day_of_week\"],\n", + " errors=\"ignore\"\n", + ")\n", + "\n", + "# Create subplot layout.\n", + "n_cols = 3\n", + "n_rows = int(np.ceil(len(numeric_cols) / n_cols))\n", + "\n", + "fig, axes = plt.subplots(\n", + " n_rows,\n", + " n_cols,\n", + " figsize=(16, 12)\n", + ")\n", + "\n", + "axes = axes.flatten()\n", + "\n", + "# Plot histogram for each numeric column.\n", + "for i, col in enumerate(numeric_cols):\n", + " axes[i].hist(\n", + " model_df[col],\n", + " bins=30,\n", + " label=col\n", + " )\n", + " axes[i].set_title(f\"Distribution Of {col}\")\n", + " axes[i].set_xlabel(col)\n", + " axes[i].set_ylabel(\"Frequency\")\n", + " axes[i].legend()\n", + "\n", + "# Remove empty subplots.\n", + "for j in range(i + 1, len(axes)):\n", + " fig.delaxes(axes[j])\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6.4 Correlation Matrix\n", + "\n", + "Checking the correlation matrix is a quick way to see which climate variables might have the strongest relationship with pedestrian demand, which does seem like they have some influence on the target variable from the results. This is mainly to show whether the relationship is positive or negative, even though correlation does not prove causation [39].\n", + "\n", + "- noise has the strongest positive correlation with pedestrian count, followed by gustwindspeed, airtemperature, averagewindspeed, and averagewinddirection. This suggests that pedestrian activity tends to increase when these variables increase.\n", + "- relativehumidity has the strongest negative correlation with pedestrian count, while pm10, pm25, and atmosphericpressure have weaker negative relationships. This suggests that pedestrian activity tends to decrease when these variables increase.\n", + "\n", + "This suggests that climate variables do play a role in influencing the pedestrian demand, but there are other influences as well as discovered from previous plots. But the goal for this task is to understand how climate variables affect pedestrian demands." + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pedestriancount 1.000000\n", + "noise 0.555729\n", + "gustwindspeed 0.466541\n", + "airtemperature 0.390398\n", + "averagewindspeed 0.364756\n", + "averagewinddirection 0.119948\n", + "atmosphericpressure -0.034576\n", + "pm25 -0.035831\n", + "pm10 -0.039647\n", + "relativehumidity -0.465781\n", + "Name: pedestriancount, dtype: float64\n" + ] + } + ], + "source": [ + "# Calculate the correlation matrix.\n", + "corr_matrix = model_df[numeric_cols].corr()\n", + "\n", + "# Plot the correlation matrix.\n", + "plt.figure(figsize=(10, 8))\n", + "im = plt.imshow(\n", + " corr_matrix,\n", + " cmap=\"coolwarm\",\n", + " aspect=\"auto\"\n", + ")\n", + "\n", + "cbar = plt.colorbar(im)\n", + "cbar.set_label(\"Correlation Coefficient\")\n", + "\n", + "plt.xticks(\n", + " ticks=np.arange(len(corr_matrix.columns)),\n", + " labels=corr_matrix.columns,\n", + " rotation=45,\n", + " ha=\"right\"\n", + ")\n", + "\n", + "plt.yticks(\n", + " ticks=np.arange(len(corr_matrix.columns)),\n", + " labels=corr_matrix.columns\n", + ")\n", + "\n", + "plt.title(\"Correlation Matrix\")\n", + "\n", + "# Add values inside each cell.\n", + "for i in range(len(corr_matrix.index)):\n", + " for j in range(len(corr_matrix.columns)):\n", + " plt.text(\n", + " j,\n", + " i,\n", + " f\"{corr_matrix.iloc[i, j]:.2f}\",\n", + " ha=\"center\",\n", + " va=\"center\",\n", + " fontsize=8\n", + " )\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Print the correlation values with pedestrian count.\n", + "print(corr_matrix[\"pedestriancount\"].sort_values(ascending=False))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Time Series Analysis\n", + "\n", + "This section is necessary as exploratory data analysis alone is not enough to check a time series dataset. Hence, time series analysis is necessary to help uncover more trends, seasonality, repeated lag dependence, and stationarity [36]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7.1 Pedestrian Count Over Time (24-Hour Rolling Mean)\n", + "\n", + "The previous plot with the pedestrian count over time was noisy, so smoothing over 24 hours may help reveal more patterns of pedestrian activity without the hour-to-hour volatility [40].\n", + "\n", + "From the plot, it became much more obvious that the highest peaks were during New Year's, where pedestrian visit the Melbourne CBD to see the fireworks, and confirms what has been previously discussed. There does seem to be a slightly increasing trend, which suggests that the influence of COVID-19 is still recovering." + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "datetime_hour\n", + "2024-05-12 00:00:00 15093\n", + "2024-05-12 01:00:00 10686\n", + "2024-05-12 02:00:00 6751\n", + "2024-05-12 03:00:00 5431\n", + "2024-05-12 04:00:00 2728\n", + "Name: pedestriancount, dtype: int64\n" + ] + } + ], + "source": [ + "# Create the hourly target series.\n", + "ts = model_df.set_index(\"datetime_hour\")[\n", + " \"pedestriancount\"\n", + "].sort_index()\n", + "\n", + "# Check the first few rows.\n", + "print(ts.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABW0AAAHqCAYAAAB/bWzAAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzsnXe4E8X+xt8kp9M7KChVQQX9geXaG4qIvXvVq9xr72K5tmvvClawC9h7B0WqdEEEpPfe2ymcnmR/f+TMZnZ2drM5JznJgffzPDzkJJvdye7s7Mx33nm/PsMwDBBCCCGEEEIIIYQQQghJC/ypLgAhhBBCCCGEEEIIIYSQKAzaEkIIIYQQQgghhBBCSBrBoC0hhBBCCCGEEEIIIYSkEQzaEkIIIYQQQgghhBBCSBrBoC0hhBBCCCGEEEIIIYSkEQzaEkIIIYQQQgghhBBCSBrBoC0hhBBCCCGEEEIIIYSkEQzaEkIIIYQQQgghhBBCSBrBoC0hhBBCCCGEEEIIIYSkEQzaEkIIIYQQLatXr4bP58OwYcPM9x577DH4fD7Ldu3bt8c111xTu4UjAIBff/0Vhx12GHJycuDz+ZCfn5/S8px00kk46aSTUloGlXQsEyGEEEJILBi0JYQQQkidYObMmbj11ltx8MEHo169ethvv/1wySWXYOnSpa7fq6ysxEEHHQSfz4eXXnrJ07GGDRsGn8+HP//8U/v5SSedhEMOOSTu35BMRIBV/PP7/WjatCn69u2LadOmpbp4SUP83muvvVb7+UMPPWRus3379louXfxMmTIF559/Plq1aoXs7Gy0b98eN9xwA9auXWvbdseOHbjkkkuQm5uLwYMH46OPPkK9evW0+xV1WvzLycnBAQccgFtvvRVbtmxJ9s9KCBs3bsRjjz2GOXPmpLoocbFlyxbcc8896Nq1K/Ly8lCvXj306tULTz31VMqD7IJPP/0Ur7zySqqLQQghhBCJjFQXgBBCCCHEC88//zymTJmCiy++GD169MDmzZvxxhtvoGfPnpg+fbpjEPX111/XBrz2VC6//HKceeaZCIVCWLp0KYYMGYKTTz4ZM2fORPfu3ZNyzCVLlsDvT50WICcnB9988w2GDBmCrKwsy2efffYZcnJyUFZWlqLSeef111/HHXfcgY4dO+K2225DmzZtsGjRIrz33nv44osvMHLkSBxzzDHm9jNnzkRRURGefPJJ9O7d29MxnnjiCXTo0AFlZWWYPHky3nzzTYwcORLz589HXl5esn5aQti4cSMef/xxtG/fHocddpjn7/3222/JK1QMZs6ciTPPPBO7d+/GlVdeiV69egEA/vzzTzz33HOYOHFiSssn+PTTTzF//nzceeedqS4KIYQQQqpg0JYQQgghdYIBAwbg008/tQTlLr30UnTv3h3PPfccPv74Y9t3tm7diieeeAL//e9/8cgjj9RmcZNCcXGxo5JS0LNnT1x55ZXm38cffzz69u2LN998E0OGDElKubKzs5OyX6+cccYZ+PHHH/HLL7/g3HPPNd+fOnUqVq1ahQsvvBDffPNNCksYmylTpuDOO+/Ecccdh19//dUSQL3ppptw7LHH4qKLLsKCBQvQpEkTAJH6DQCNGzf2fJy+ffvi8MMPBwBce+21aNasGQYNGoQffvgBl19+eeJ+UBpQUlKCvLw8WyC/tsjPz8f555+PQCCA2bNno2vXrpbPn376abz77rspKRshhBBC0h/aIxBCCCGkTnDMMcfYgi9dunTBwQcfjEWLFmm/c//99+PAAw+0BDGTRTAYxJNPPolOnTqZy9offPBBlJeXW7bz+Xx47LHHbN9XfWHFcvbff/8dN998M1q2bIm2bdvGXa7jjz8eALBixQrL+ytXrsTFF1+Mpk2bIi8vD//4xz8wYsSIuPfvVvYpU6ZgwIABaNGiBerVq4fzzz8f27Zts3w3HA7jsccewz777IO8vDycfPLJWLhwYVw+ufvuuy9OOOEEfPrpp5b3P/nkE3Tv3t1Rhf3HH3/gjDPOQKNGjZCXl4cTTzwRU6ZMsWyzZs0a3HzzzTjwwAORm5uLZs2a4eKLL8bq1ast28Xzm3U8+eST8Pl8GD58uE3x2qlTJ7zwwgvYtGkT3n77bQARi46rr74aAHDEEUfA5/NVy1f4lFNOAQCsWrXKfO/jjz9Gr169kJubi6ZNm+Kyyy7DunXrbN9955130KlTJ+Tm5uLII4/EpEmTtMcoLy/Ho48+is6dOyM7Oxvt2rXDfffdZ7s3Ro8ejeOOOw6NGzdG/fr1ceCBB+LBBx8EAEyYMAFHHHEEAKB///6mzYPwWxaWJbNmzcIJJ5yAvLw887uqp21FRQUeeeQR9OrVC40aNUK9evVw/PHHY/z48ZbyCMuRl156yfyt2dnZOOKIIzBz5syY5/btt9/Ghg0bMGjQIFvAFgBatWqFhx9+2PLekCFDcPDBByM7Oxv77LMPbrnlFpuFgtO9of7OCRMmwOfz4csvv8TTTz+Ntm3bIicnB6eeeiqWL19u+d6IESOwZs0a87y2b98+5u8jhBBCSHKh0pYQQgghdRbDMLBlyxYcfPDBts9mzJiB4cOHY/LkybbEWV4pKCjQ+qBWVlba3rv22msxfPhwXHTRRbj77rvxxx9/4Nlnn8WiRYvw3XffVev4AHDzzTejRYsWeOSRR1BcXBz390VwUagzgYjH5jHHHIOSkhLcfvvtaNasGYYPH45zzjkHX3/9Nc4///xql1fmtttuQ5MmTfDoo49i9erVeOWVV3Drrbfiiy++MLd54IEH8MILL+Dss89Gnz59MHfuXPTp0yduO4N//vOfuOOOO7B7927Ur18fwWAQX331FQYMGKDd17hx49C3b1/06tULjz76KPx+P4YOHYpTTjkFkyZNwpFHHgkgsrx96tSpuOyyy9C2bVusXr0ab775Jk466SQsXLjQFmD18ptVSkpKMHbsWBx//PHo0KGDdptLL70U119/PX7++Wfcf//9eOihh3DggQfinXfeMS0POnXqFNc5A6LB/GbNmgGIqD//97//4ZJLLsG1116Lbdu24fXXX8cJJ5yA2bNnm6re999/HzfccAOOOeYY3HnnnVi5ciXOOeccNG3aFO3atTP3Hw6Hcc4552Dy5Mm4/vrr0a1bN8ybNw8vv/wyli5diu+//x4AsGDBApx11lno0aMHnnjiCWRnZ2P58uVmEL1bt2544okn8Mgjj+D66683JyNku4gdO3agb9++uOyyy3DllVeiVatW2t9cWFiI9957D5dffjmuu+46FBUV4f3330efPn0wY8YMm/XCp59+iqKiItxwww3w+Xx44YUXcMEFF2DlypXIzMx0PLc//vgjcnNzcdFFF3m6Fo899hgef/xx9O7dGzfddBOWLFmCN998EzNnzsSUKVNcj+XGc889B7/fj3vuuQcFBQV44YUXcMUVV+CPP/4AEPF9LigowPr16/Hyyy8DAOrXr1+tYxFCCCEkgRiEEEIIIXWUjz76yABgvP/++5b3w+GwceSRRxqXX365YRiGsWrVKgOA8eKLL3ra79ChQw0Arv8OPvhgc/s5c+YYAIxrr73Wsp977rnHAGCMGzfOfA+A8eijj9qOuf/++xtXX321rQzHHXecEQwGY5ZZ/MbHH3/c2LZtm7F582Zj0qRJxhFHHGEAML766itz2zvvvNMAYEyaNMl8r6ioyOjQoYPRvn17IxQKWfY5dOhQc7tHH33UULuQTmXv3bu3EQ6HzffvuusuIxAIGPn5+YZhGMbmzZuNjIwM47zzzrPs77HHHjMAWPbpBADjlltuMXbu3GlkZWUZH330kWEYhjFixAjD5/MZq1evNsu8bds2wzAi9aNLly5Gnz59LOUrKSkxOnToYJx22mmW91SmTZtmADA+/PDDuH+zDlF/7rjjDtff2qNHD6Np06a2Y86cOdP1e/K2Y8aMMbZt22asW7fO+Pzzz41mzZoZubm5xvr1643Vq1cbgUDAePrppy3fnTdvnpGRkWG+X1FRYbRs2dI47LDDjPLycnO7d955xwBgnHjiieZ7H330keH3+y11zTAM46233jIAGFOmTDEMwzBefvllyzXSMXPmTFt9FJx44okGAOOtt97SfiaXKRgMWsptGIaxa9cuo1WrVsa///1v8z1R/5s1a2bs3LnTfP+HH34wABg//fSTY1kNwzCaNGliHHrooa7bCLZu3WpkZWUZp59+unn/GYZhvPHGGwYA44MPPjDfU+83p985fvx4A4DRrVs3y+999dVXDQDGvHnzzPf69etn7L///p7KSgghhJDagfYIhBBCCKmTLF68GLfccguOPvpoc5m4YNiwYZg3bx6ef/75Gh1j8ODBGD16tO1fjx49LNuNHDkSQMR3V+buu+8GgGrbDgDAddddh0Ag4Hn7Rx99FC1atEDr1q1x/PHHY9GiRRg4cKBF7Tdy5EgceeSROO6448z36tevj+uvvx6rV6/GwoULq11emeuvv96icj7++OMRCoWwZs0aAMDYsWMRDAZx8803W7532223xX2sJk2a4IwzzsBnn30GIKKOPOaYY7D//vvbtp0zZw6WLVuGf/7zn9ixYwe2b9+O7du3o7i4GKeeeiomTpyIcDgMAMjNzTW/V1lZiR07dqBz585o3Lgx/vrrr7h/s46ioiIAQIMGDVx/Y4MGDVBYWOi6TSx69+6NFi1aoF27drjssstQv359fPfdd9h3333x7bffIhwO45JLLjHPyfbt29G6dWt06dLFtA/4888/sXXrVtx4440Wy5JrrrkGjRo1shzvq6++Qrdu3dC1a1fLPoUtg9inUPD+8MMP5rmPl+zsbPTv3z/mdoFAwCx3OBzGzp07EQwGcfjhh2uv6aWXXmpRqguV78qVK12PU1hYGPOaCsaMGYOKigrceeedlqR+1113HRo2bFijNqR///6W6+S1/IQQQghJLbRHIIQQQkidY/PmzejXrx8aNWqEr7/+2hLULCwsxAMPPIB7773XskxbJRQK2bxGmzZtagluHHnkkWbSJpkmTZpYbBPWrFkDv9+Pzp07W7Zr3bo1Gjdu7Bqwi4XTcnknrr/+elx88cUoKyvDuHHj8NprryEUClm2WbNmDY466ijbd7t162Z+7uQDGw/77bef5W8R+Nq1a5d5HAC289a0aVNLkMwr//znP3HVVVdh7dq1+P777/HCCy9ot1u2bBkA2IL9MgUFBWjSpAlKS0vx7LPPYujQodiwYQMMw7BsoxLrN+sQgT0RvHWiqKjIcxDQicGDB+OAAw5ARkYGWrVqhQMPPNAMEi5btgyGYaBLly7a74rl+eK6qdtlZmaiY8eOlveWLVuGRYsWoUWLFtp9imRql156Kd577z1ce+21uP/++3HqqafiggsuwEUXXWQJYrqx7777ek46Nnz4cAwcOBCLFy+22J3o7rfqXFMAaNiwYcxrKhDn9MADD7S8n5WVhY4dO9aoDalu+QkhhBCSWhi0JYQQQkidoqCgAH379kV+fj4mTZqEffbZx/L5Sy+9hIqKClx66aWmn+v69esBRIIUq1evxj777IONGzfaAjTjx4+3JPKJl+p65wKwBVYFstLTC126dEHv3r0BAGeddRYCgQDuv/9+nHzyydoAdDJxUgjLgc9Ecs455yA7OxtXX301ysvLcckll2i3E0rOF1980eZfKhCenrfddhuGDh2KO++8E0cffTQaNWoEn8+Hyy67TKsIrc5v7ty5MzIyMvD33387blNeXo4lS5bU+Bo6TUQAkfPi8/nwyy+/aH9HdXxOw+EwunfvjkGDBmk/FxMrubm5mDhxIsaPH48RI0bg119/xRdffIFTTjkFv/32mye1udd75eOPP8Y111yD8847D/feey9atmyJQCCAZ5991pawD6h+Pe7atSvmzJmDiooKz8FkLzi1M6FQSFvW2r4PCSGEEJIYGLQlhBBCSJ2hrKwMZ599NpYuXYoxY8bgoIMOsm2zdu1a7Nq1S5uc7JlnnsEzzzyD2bNno2vXrhg9erTl80MPPbRa5dp///0RDoexbNkyU60KRBJ+5efnW5boN2nSxJYNvqKiAps2barWsWPx0EMP4d1338XDDz+MX3/91SzvkiVLbNsuXrzY/Lw2EMdZvny5JYC+Y8eOaqkAc3Nzcd555+Hjjz9G37590bx5c+12ImFXw4YNzQC3E19//TWuvvpqDBw40HyvrKzMdg1rQr169XDyySdj3LhxWLNmjfb8f/nllygvL8dZZ52VsOOqdOrUCYZhoEOHDjjggAMctxPlW7ZsmWlzAETsI1atWmW5jzp16oS5c+fi1FNPjTmp4ff7ceqpp+LUU0/FoEGD8Mwzz+Chhx7C+PHj0bt37xpNish8/fXX6NixI7799lvLPh999NGE7F9w9tlnY9q0afjmm29w+eWXu24rzumSJUssauWKigqsWrXKUk91bQgQUeuqSmevJOrcEkIIISRx0NOWEEIIIXWCUCiESy+9FNOmTcNXX32Fo48+Wrvd7bffju+++87y7+233wYQ8dz87rvv0KFDB+Tk5KB3796Wf9VZkg8AZ555JgDglVdesbwv1IX9+vUz3+vUqRMmTpxo2e6dd95xVNrWlMaNG+OGG27AqFGjMGfOHLO8M2bMwLRp08ztiouL8c4776B9+/baYHgyOPXUU5GRkYE333zT8v4bb7xR7X3ec889ePTRR/G///3PcZtevXqhU6dOeOmll7B7927b57JtRiAQsCkSX3/99YRfr4cffhiGYeCaa65BaWmp5bNVq1bhvvvuQ5s2bXDDDTck9LgyF1xwAQKBAB5//HHbbzYMAzt27AAAHH744WjRogXeeustVFRUmNsMGzbMFky85JJLsGHDBrz77ru245WWlqK4uBgAsHPnTtvnQgVdXl4OIBLcBlDjgLlQnsq/8Y8//rDcD4ngxhtvRJs2bXD33Xdj6dKlts+3bt2Kp556CkDEazgrKwuvvfaapVzvv/8+CgoKbG3I9OnTLef+559/xrp166pd1nr16mntPgghhBCSOqi0JYQQQkid4O6778aPP/6Is88+Gzt37sTHH39s+fzKK68EAPTs2RM9e/a0fCZsEg4++GCcd955CS/boYceiquvvhrvvPMO8vPzceKJJ2LGjBkYPnw4zjvvPJx88snmttdeey1uvPFGXHjhhTjttNMwd+5cjBo1ylEVmgjuuOMOvPLKK3juuefw+eef4/7778dnn32Gvn374vbbb0fTpk0xfPhwrFq1Ct98841nD9Ga0qpVK9xxxx0YOHAgzjnnHJxxxhmYO3cufvnlFzRv3rxa6r9DDz00pmLa7/fjvffeQ9++fXHwwQejf//+2HfffbFhwwaMHz8eDRs2xE8//QQgYjHx0UcfoVGjRjjooIMwbdo0jBkzBs2aNavWb3bihBNOwEsvvYQBAwagR48euOaaa9CmTRssXrwY7777LsLhMEaOHFntiQUvdOrUCU899RQeeOABrF69Gueddx4aNGiAVatW4bvvvsP111+Pe+65B5mZmXjqqadwww034JRTTsGll16KVatWYejQoTal51VXXYUvv/wSN954I8aPH49jjz0WoVAIixcvxpdffolRo0bh8MMPxxNPPIGJEyeiX79+2H///bF161YMGTIEbdu2NRPmderUCY0bN8Zbb72FBg0aoF69ejjqqKPi9n0+66yz8O233+L8889Hv379sGrVKrz11ls46KCDtEH86tKkSRN89913OPPMM3HYYYfhyiuvRK9evQAAf/31Fz777DNz8qlFixZ44IEH8Pjjj+OMM87AOeecgyVLlmDIkCE44ogjzPYNiLQhX3/9Nc444wxccsklWLFiBT7++GNTQV4devXqhS+++AIDBgzAEUccgfr16+Pss8+u2QkghBBCSI1g0JYQQgghdQKhEv3pp5/MgJqMHNRIBe+99x46duyIYcOG4bvvvkPr1q3xwAMP2JZcX3fddVi1ahXef/99/Prrrzj++OMxevRonHrqqUkr2z777IN//vOf+Oijj7BixQp06tQJU6dOxX//+1+8/vrrKCsrQ48ePfDTTz9ZFH21wfPPP4+8vDy8++67GDNmDI4++mj89ttvOO6445CTk5O045500kmYNm0annzySbzxxhvYvXs3WrdujaOOOsqiZn311VcRCATwySefoKysDMceeyzGjBmDPn36JLxMd911Fw4//HAMHDgQr7zyCgoKCtCmTRtcfPHFeOihh2rFtuL+++/HAQccgJdffhmPP/44gIjv7Omnn45zzjnH3O76669HKBTCiy++iHvvvRfdu3fHjz/+aFM4+/1+fP/993j55Zfx4Ycf4rvvvkNeXh46duyIO+64w7RhOOecc7B69Wp88MEH2L59O5o3b44TTzwRjz/+OBo1agQgkuhs+PDheOCBB3DjjTciGAxi6NChcQdtr7nmGmzevBlvv/02Ro0ahYMOOggff/wxvvrqK0yYMKEGZ8/OUUcdhfnz5+PFF1/EiBEj8NFHH8Hv96Nbt264//77ceutt5rbPvbYY2jRogXeeOMN3HXXXWjatCmuv/56PPPMM2YSOADo06cPBg4ciEGDBuHOO+/E4Ycfjp9//hl33313tct58803Y86cORg6dChefvll7L///gzaEkIIISnGZ9CBnhBCCCGEpBH5+flo0qQJnnrqKTz00EOpLg4hhBBCCCG1Dj1tCSGEEEJIylD9W4GoN/BJJ51Uu4UhhBBCCCEkTaA9AiGEEEIISRlffPEFhg0bhjPPPBP169fH5MmT8dlnn+H000/Hsccem+riEUIIIYQQkhIYtCWEEEIIISmjR48eyMjIwAsvvIDCwkIzOdlTTz2V6qIRQgghhBCSMuhpSwghhBBCCCGEEEIIIWkEPW0JIYQQQgghhBBCCCEkjWDQlhBCCCGEEEIIIYQQQtIIetomiHA4jI0bN6JBgwbw+XypLg4hhBBCCCGEEEIIISTNMAwDRUVF2GeffeD3O+tpGbRNEBs3bkS7du1SXQxCCCGEEEIIIYQQQkias27dOrRt29bxcwZtE0SDBg0ARE54w4YNU1waQgghhBBCCCGEEEJIulFYWIh27dqZsUQnGLRNEMISoWHDhgzaEkIIIYQQQgghhBBCHIllr8pEZIQQQgghhBBCCCGEEJJGMGhLCCGEEEIIIYQQQgghaQSDtoQQQgghhBBCCCGEEJJG0NOWEEIIIYQQQgghhCSMcDiMioqKVBeDkJSQmZmJQCBQ4/0waEsIIYQQQgghhBBCEkJFRQVWrVqFcDic6qIQkjIaN26M1q1bx0w25gaDtoQQQgghhBBCCCGkxhiGgU2bNiEQCKBdu3bw++nKSfYuDMNASUkJtm7dCgBo06ZNtffFoC0hhBBCCCGEEEIIqTHBYBAlJSXYZ599kJeXl+riEJIScnNzAQBbt25Fy5Ytq22VwCkPQgghhBBCCCGEEFJjQqEQACArKyvFJSEktYhJi8rKymrvg0FbQgghhBBCCCGEEJIwauLjScieQCLuAQZtCSGEEEIIIYQQQgghJI1g0JYQQgghhBBCCCGEkAQzbNgwNG7cONXFqDETJkyAz+dDfn4+APvveuyxx3DYYYelpGx7MgzaEkIIIYQQQgghhJC9lmeffRZHHHEEGjRogJYtW+K8887DkiVLtNsahoG+ffvC5/Ph+++/r92Cpin33HMPxo4dm+pi7HEwaEsIIYQQQgghhBBC9lp+//133HLLLZg+fTpGjx6NyspKnH766SguLrZt+8orr6S9Z28oFEI4HI65nWEYCAaDNT5e/fr10axZsxrvh1hh0JYQQgghhBBCCCGE7LX8+uuvuOaaa3DwwQfj0EMPxbBhw7B27VrMmjXLst2cOXMwcOBAfPDBB3Htf9SoUejWrRvq16+PM844A5s2bTI/C4fDeOKJJ9C2bVtkZ2fjsMMOw6+//mp+rloTiHL4fD6sXr0aQNSu4Mcff8RBBx2E7OxsrF271lYOsa9ffvkFvXr1QnZ2NiZPnozy8nLcfvvtaNmyJXJycnDcccdh5syZnn+fao9wzTXX4LzzzsNLL72ENm3aoFmzZrjllltQWVlpbrNp0yb069cPubm56NChAz799FO0b98er7zyiuNxxH6feeYZtGrVCo0bN8YTTzyBYDCIe++9F02bNkXbtm0xdOhQy/fWrVuHSy65BI0bN0bTpk1x7rnnmucOAGbOnInTTjsNzZs3R6NGjXDiiSfir7/+suzD5/Phvffew/nnn4+8vDx06dIFP/74o+dzVB0YtCWEEEKIjVDYwF9rdyEYij1DTwghhBBCiA7DMFBSEUzJP8Mwql3ugoICAEDTpk3N90pKSvDPf/4TgwcPRuvWrT3vq6SkBC+99BI++ugjTJw4EWvXrsU999xjfv7qq69i4MCBeOmll/D333+jT58+OOecc7Bs2bK4ylxSUoLnn38e7733HhYsWICWLVs6bnv//ffjueeew6JFi9CjRw/cd999+OabbzB8+HD89ddf6Ny5M/r06YOdO3fGVQaZ8ePHY8WKFRg/fjyGDx+OYcOGYdiwYebn//rXv7Bx40ZMmDAB33zzDd555x1s3bo15n7HjRuHjRs3YuLEiRg0aBAeffRRnHXWWWjSpAn++OMP3Hjjjbjhhhuwfv16AEBlZSX69OmDBg0aYNKkSZgyZYoZPK+oqAAAFBUV4eqrr8bkyZMxffp0dOnSBWeeeSaKioosx3788cdxySWX4O+//8aZZ56JK664okbnKBYZSdszIYQQQuosP87dgLu+mIu+h7TGm1f2SnVxCCGEEEJIHaS0MoSDHhmVkmMvfKIP8rLiD3uFw2HceeedOPbYY3HIIYeY799111045phjcO6558a1v8rKSrz11lvo1KkTAODWW2/FE088YX7+0ksv4b///S8uu+wyAMDzzz+P8ePH45VXXsHgwYPjOs6QIUNw6KGHxtz2iSeewGmnnQYAKC4uxptvvolhw4ahb9++AIB3330Xo0ePxvvvv497773XcxlkmjRpgjfeeAOBQABdu3ZFv379MHbsWFx33XVYvHgxxowZg5kzZ+Lwww8HALz33nvo0qVLzP02bdoUr732Gvx+Pw488EC88MILKCkpwYMPPggAeOCBB/Dcc89h8uTJuOyyy/DFF18gHA7jvffeM20thg4disaNG2PChAk4/fTTccopp1iO8c4776Bx48b4/fffcdZZZ5nvX3PNNbj88ssBAM888wxee+01zJgxA2eccUa1zlEsqLQlhBBCiI13Jq4CAPwyf3OKS0IIIYQQUrdYsLEAj/24ADt2l6e6KKQa3HLLLZg/fz4+//xz870ff/wR48aNc126f/DBB6N+/fqoX7++GfwEgLy8PDNgCwBt2rQxFaWFhYXYuHEjjj32WMu+jj32WCxatCiucmdlZaFHjx6ethWBUgBYsWIFKisrLWXIzMzEkUceGXcZZA4++GAEAgHzb/l3L1myBBkZGejZs6f5eefOndGkSRNP+/X7o+HMVq1aoXv37ubfgUAAzZo1M481d+5cLF++HA0aNDCvT9OmTVFWVoYVK1YAALZs2YLrrrsOXbp0QaNGjdCwYUPs3r3bZjEhn9969eqhYcOGntTB1YVKW0IIIYTYKC6veUICQgghhJC9kf9+8zfmbyjE6h3FGNb/yFQXJ6XkZgaw8Ik+KTt2vNx66634+eefMXHiRLRt29Z8f9y4cVixYgUaN25s2f7CCy/E8ccfjwkTJmDkyJGmZ2tubq65TWZmpuU7Pp8vLusGEaCUvyN7wwpyc3M9J0irV6+e5+NXF93v9pIcrTr7dTvW7t270atXL3zyySe2fbVo0QIAcPXVV2PHjh149dVXsf/++yM7OxtHH320aZ+Q7N/kBIO2hBBCCLER8Kd3RlxCCCGEkHRl/oZCy/97Mz6fr1oWBbWNYRi47bbb8N1332HChAno0KGD5fP7778f1157reW97t274+WXX8bZZ58NANh///3jPm7Dhg2xzz77YMqUKTjxxBPN96dMmYIjj4wE/EVgcdOmTaYSdc6cOXEfy4lOnTohKysLU6ZMMX9DZWUlZs6ciTvvvDNhx5E58MADEQwGMXv2bPTqFbFiW758OXbt2pXwY/Xs2RNffPEFWrZsiYYNG2q3mTJlCoYMGYIzzzwTQCRx2fbt2xNelnhJ/zuHEEIIIbUOY7aEEEIIITXDo+iRpAG33HILPv30U/zwww9o0KABNm+OWIQ1atQIubm5aN26tTb52H777WcL8MbLvffei0cffRSdOnXCYYcdhqFDh2LOnDmmMrRz585o164dHnvsMTz99NNYunQpBg4cWKNjytSrVw833XQT7r33XjRt2hT77bef6RP7n//8J2HHkenatSt69+6N66+/Hm+++SYyMzNx9913x6UW9soVV1yBF198Eeeeey6eeOIJtG3bFmvWrMG3336L++67D23btkWXLl3w0Ucf4fDDD0dhYSHuvfdei1o6VdDTlhBCCCE2Et1ZIoQQQgghJF158803UVBQgJNOOglt2rQx/33xxRdJP/btt9+OAQMG4O6770b37t3x66+/4scffzSTcmVmZuKzzz7D4sWL0aNHDzz//PN46qmnElqG5557DhdeeCGuuuoq9OzZE8uXL8eoUaM8ecxWlw8//BCtWrXCCSecgPPPPx/XXXcdGjRogJycnIQeJy8vDxMnTsR+++2HCy64AN26dcN//vMflJWVmcrb999/H7t27ULPnj1x1VVX4fbbb0fLli0TWo7q4DPiMdIgjhQWFqJRo0YoKChwlFsTQgghdYVTB07Aim3FAIDVz/VLcWkIIYQQQuoO7e8fAQBokpeJ2Y+cnuLS1C5lZWVYtWoVOnTokPDgG9mzWb9+Pdq1a4cxY8bg1FNPTXVxaozbveA1hkh7BEIIIYTY4IwuIYQQQkjNCIXZoyLEiXHjxmH37t3o3r07Nm3ahPvuuw/t27fHCSeckOqipQ0M2hJCCCHEDscYhBBCCCE1gkFbQpyprKzEgw8+iJUrV6JBgwY45phj8MknnyAzMzPVRUsbGLQlhBBCiA0OMQghhBBCakaQQVtCHOnTpw/69OmT6mKkNUxERgghhBAbfuYhI4QQQgipEVTaEkJqAoO2hBBCCLGR4WcXgRBCCCGkJoSY950QUgM4IiOEEEKIjYwApbaEEEIIITVhb47ZGnvzjycEQDgcrvE+6GlLCCGEEBsZ9EcghBBCCCFxkpmZCZ/Ph23btqFFixbw+dinJHsXhmGgoqIC27Ztg9/vR1ZWVrX3xaAtIYQQQmz4paCtYRjscBNCCCGEkJgEAgG0bdsW69evx+rVq1NdHEJSRl5eHvbbbz/4a2A7x6AtIYQQQmzISttg2EAm7RIIIYQQQogH6tevjy5duqCysjLVRSEkJQQCAWRkZNRY+MKgLSGEEEJsyInIgiEDmYEUFoYQQgghhNQpAoEAAgF2IAmpCUxERgghhBAbciKyYAJM9AkhhBBCCCGEeIdBW0IIIYTYCMj2CCFm/yWEEEIIIYSQ2oRBW0IIIYTY8Ev+S5VU2hJCCCGEEEJIrcKgLSGEEEJsGEZUXUulLSGEEEIIIYTULgzaEkIIIcSGHKZl0JYQQgghhBBCahcGbQkhhBBiQxLa0h6BEEIIIYQQQmoZBm0JIYQQYkPW1obCVNoSQgghhBBCSG3CoC0hhBBCXKkMUWlLCCGEEOIVvy/2NiR9KK0I4b1JK7FmR3Gqi0KIBQZtCSGEEGKDicgIIYQQQqpHhp+hlrrEwN+W4KkRi3DayxNTXRRCLLAlIYQQQogrQXraEkIIIYR4JkCpbZ1i6oodAICKIPu8JL1g0JYQQgghNiyJyKi0JYQQQgjxjBy0DTM3QNoTNniNSHrCoC0hhBBCXKE9AiGEEEKId+SgbZBBW0JINWHQlhBCCCE2DEQHGJW0RyCEEEII8UyGJWjLfhQhpHowaEsIIYQQG/IqMS7rI4QQQgjxjp9KW0JIAmDQlhBCCCE25KAtbb4IIYQQQrwT8ElBW9pMpT0+HxPHkfSEQVtCCCGEuMLkDIQQQggh3pGEtgiGaI+Q7izaVJjqIhCihUFbQgghhNiQPW25qo8QQgghxDty14n2CISQ6sKgLSGEEEJsWO0RONgghBBCCPGK3HWiPUJ6U1YZsvwdYpCdpBEM2hJCCCHEhtxdZd+VEEIIIcQ78oqlYJj2COnML/M3Wf4urgimqCSE2GHQlhBCCCF2pEAtPW0JIYQQQrxjUdpy9jutWb51t+Xv4nIGbUn6wKAtIYQQQlxh0JYQQgghxDvWFUvsR6Uz6uVh0JakEwzaEkIIIcSGvKyPYw1CCCGEEO9YcwOkrhwkNhVBq33F7vKQw5aE1D4M2hJCCCHEhkF7BEIIIYSQahLtO7Efld5UhKxB2xWKXQIhqYRBW0IIIYTYYCIyQgghhJCaw5htelOpBG13lVSkqCSE2GHQlhBCCCGuUCFCCCGEEOId2iPUHcoVe4SdxQzakvSBQVtCCCGE2DAMQ/uaEEIIIYS4w0RkdQfhaVsvKwAAKKmgpy1JHxi0JYQQQogN2iMQQgghhFQPy+R3CstBYiOCto3zsgAAJRXBVBaHEAsM2hJCCCHEBhOREUIIIYRUDypt6w4iEVmj3EwAVNqS9IJBW0IIIYS4QqUtIYQQQoh36GlbN3hj3DJMWLINANA4j0Fbkn4waEsIIYQQG/L4gp62hBBCCCHeYW6A9CcUNvDSb0vNv6NBW9ojkPQhpUHbxx57DD6fz/Kva9eu5udlZWW45ZZb0KxZM9SvXx8XXnghtmzZYtnH2rVr0a9fP+Tl5aFly5a49957EQxab7IJEyagZ8+eyM7ORufOnTFs2DBbWQYPHoz27dsjJycHRx11FGbMmJGU30wIIYTUCaQBRphSW0IIIYQQzxgOr0n6sKukwvJ3o1zhaUulLUkfUq60Pfjgg7Fp0ybz3+TJk83P7rrrLvz000/46quv8Pvvv2Pjxo244IILzM9DoRD69euHiooKTJ06FcOHD8ewYcPwyCOPmNusWrUK/fr1w8knn4w5c+bgzjvvxLXXXotRo0aZ23zxxRcYMGAAHn30Ufz111849NBD0adPH2zdurV2TgIhhBCSZjARGSGEEEJIzeHkd3qyq9gatG1CewSShqQ8aJuRkYHWrVub/5o3bw4AKCgowPvvv49BgwbhlFNOQa9evTB06FBMnToV06dPBwD89ttvWLhwIT7++GMcdthh6Nu3L5588kkMHjwYFRWRG/Ctt95Chw4dMHDgQHTr1g233norLrroIrz88stmGQYNGoTrrrsO/fv3x0EHHYS33noLeXl5+OCDD2r/hBBCCCFpABOREUIIIYRUE0P7kqQR5cGw5W9hj1DKoC1JI1IetF22bBn22WcfdOzYEVdccQXWrl0LAJg1axYqKyvRu3dvc9uuXbtiv/32w7Rp0wAA06ZNQ/fu3dGqVStzmz59+qCwsBALFiwwt5H3IbYR+6ioqMCsWbMs2/j9fvTu3dvchhBCCNmbYcyWEEIIIcQ71hVL7EilIyFFAd24yh6hmJ62JI3ISOXBjzrqKAwbNgwHHnggNm3ahMcffxzHH3885s+fj82bNyMrKwuNGze2fKdVq1bYvHkzAGDz5s2WgK34XHzmtk1hYSFKS0uxa9cuhEIh7TaLFy92LHt5eTnKy8vNvwsLC+P78YQQQkgaY0jDDQ42CCGEEEK8Y0k+xm5UWhJUgraNaI9A0pCUBm379u1rvu7RoweOOuoo7L///vjyyy+Rm5ubwpLF5tlnn8Xjjz+e6mIQQgghScFqj5C6chBCCCGE1DWYGyD9sSttI0HbimAYwVAYGYGUL0wnJPX2CDKNGzfGAQccgOXLl6N169aoqKhAfn6+ZZstW7agdevWAIDWrVtjy5Ytts/FZ27bNGzYELm5uWjevDkCgYB2G7EPHQ888AAKCgrMf+vWravWbyaEEELSEXraEkIIIYRUD6vQlv2odEQN2japl2W+Lqmk2pakB2kVtN29ezdWrFiBNm3aoFevXsjMzMTYsWPNz5csWYK1a9fi6KOPBgAcffTRmDdvHrZu3WpuM3r0aDRs2BAHHXSQuY28D7GN2EdWVhZ69epl2SYcDmPs2LHmNjqys7PRsGFDyz9CCCFkT8Rg0JYQQgghxDNWm6kUFoQ4ogZt62dnwO+LvGYyMpIupDRoe8899+D333/H6tWrMXXqVJx//vkIBAK4/PLL0ahRI/znP//BgAEDMH78eMyaNQv9+/fH0UcfjX/84x8AgNNPPx0HHXQQrrrqKsydOxejRo3Cww8/jFtuuQXZ2dkAgBtvvBErV67Efffdh8WLF2PIkCH48ssvcdddd5nlGDBgAN59910MHz4cixYtwk033YTi4mL0798/JeeFEEIISTVc1kcIIYQQUj0sSltOfqclIeW6ZGX4EaiK2qoBXUJSRUo9bdevX4/LL78cO3bsQIsWLXDcccdh+vTpaNGiBQDg5Zdfht/vx4UXXojy8nL06dMHQ4YMMb8fCATw888/46abbsLRRx+NevXq4eqrr8YTTzxhbtOhQweMGDECd911F1599VW0bdsW7733Hvr06WNuc+mll2Lbtm145JFHsHnzZhx22GH49ddfbcnJCCGEkL0FeYBBewRCCCGEkOrBblR6EgqHLX9nZfjh8/lAQwuSTqQ0aPv555+7fp6Tk4PBgwdj8ODBjtvsv//+GDlypOt+TjrpJMyePdt1m1tvvRW33nqr6zaEEELI3ggHG4QQQggh3jEsr9mRSkeCIUVpG/Cjyh0BYSptSZqQVp62hBBCCEk/2G0lhBBCCIkDOaFr2HkzkjrUlWRZAT/8Pp/D1oSkBgZtCSGEEGLD0o+l1JYQQgghxDOyupa9qPQkqKhp/X4fRMyW1mAkXWDQlhBCCCE2uJSPEEIIiY/bP5uNc96YjMoQpZV7O3LMjwHA9ESXbEzobHnJSLrAoC0hhBBCbFiyHqeuGGQvIBgK45d5m7C1qCzVRSGEkBrx49yN+Ht9AWau2pnqouyxGIaBYB0IinPBUvqjC9oKewReMpIuMGhLCCGEEBscbJDa4pM/1uKmT/7Cma9OTnVRCCGk2siJi4orQiksyZ5LOGzg7Dcm48zXJqE8mN7n2JA6TwY7UmmJao8AwJTaUh1N0gUGbQkhhBDiCq0SSDKZsGQrAGD77vIUl4TsjRSUVKKsMr2DP6RuUCGpP+uCErQukl9aifkbCrF0y26s21mS6uK4Yji8JunDlgL7Ch9TaZuiixYOG1i8uVCrAiZ7JwzaEkIIIcQGVSGktgj4mamZpIb8kgoc+sRvOP3liakuCtkDkIMsPmagTwo7i+vm5B5Vm+nHR9NWY+Dopbb3xa2bqn7w6+OW44xXJuGxHxek5Pgk/WDQlhBCCCE2aI9AagvWL5Iqpq+M+I6uTXPFHqkbBENy0DaFBdmDKSitNF+XB9NbzWzJDcDnXNrxvx/0QVEzEVntFcXCy2MigeSPpq9JUQlIusGgLSGEEELsMBEZqSWoQCKpIkNSeYe5FJXUkGA4GkRkzDY5yIHxylDduWf5nKs7pNoegRAVBm0JIYQQ4go7roSQPRG/NBJiUIXUFNkegXMAySFkyEHb9FXaqkvr2bzUHXxMREbSDAZtCSGEEGLDmkCDHVeSPFi7SDrAIBupKZWWoC0rVDKQxMyoSGN7BPXysx+V/tx+ahcAUT9q3sIkXchIdQEIIYQQkn4wERmpLVjVSDrAIBupCdNX7kB+SdRvlZnfk4OstA2m8TlWSxZO3/gyAXDp4e0w4LQDAEStTfhMIOkCg7aEEEIIsWE4/kFIYmH1IukAx+ekumwtLMNl70y3vMeAT3KQz2s6+1Db7BFSVA6iR51UCQSiLtRMIkjSDdojEEIIIcQVDjZIMqGqm6QDDLKR6rJye7HtPSptk4McqE3nc2xT2rJ9SSuCivRZTkrJRGQk3WDQlhBCCCE25M4qg2qEkD0RH6IDdQZVSHXZXRa0vZfOS/frMnKgNlSX7tk6VNS9gWBIUdpKQVvaI5B0g0FbQgghhNhg0gxSW3BcRNIBxthIdSmtDNneS+el+3UZOZCW1kpbpWgMAKYX6qRKwCfbI1QpbWu1RIQ4w6AtIYQQQmxYlbapKwfZ8+EEAUkHuKKAVBddQK5OqUDrEKGw/Dp9z7H6XEvfku6dBENWewSL0rbqJQPtJF1g0JYQQgghrrDbSpIJx0UkHUjj+A9Jc9Sl1gCVtslCDoanc1CNStv0Rg34y9dHBG15yUi6wKAtIYQQQmxQaUsI2dOR1XAMqpDqolN8prMKtC5j1BF7BBU2L+mFao9QKU28+E2rBF40kh4waEsIIYQQQlIGB7MkVch1j0FbUl10Scc04luSACyJyNI4aKs2J7RfSS9UdXyFZJcQTURWiwUixAUGbQkhhBBiQx5g0HOUJBMGy0iqCHNFAYnBFzPX4voP/0RphT3ZmCAUDtveoz1CcqgzQVt62qY1QeWerQhKQVuRiIwXjaQJDNoSQgghxIbcV2XHlRCyJxKuI/6YJHX895t5+G3hFnw7e73jNjqlre692uDZXxbhpo9nWYJQexLyfZrOyd5snrZpHGDeG7HbI8hB28j/fCaQdIFBW0IIIYTYYF+VVIfyYAhllc6KNB2saiRVWO0RUlcOkv5szC91/Eyn+ExFwMcwDLz9+0r8Mn8zZqzaWevHrw2k2FqdCoTWnZLuHaj2CJUaewT2g0m6wKAtIYQQQlyhFxvxQjhs4Nw3pqD3oN9RUhH0/kVWL5Ii5LatLgWASO0g14kMv/OwWaeqTcVzU/bl3FNVguE6kohMLVkaF3WvxM0eQSQiozUYSRcYtCWEEEKIDXZWSbzsLKnA4s1FWL+rFOt3OavSVFjXSKqgpy1xo1iafMrOdB4265W2SSmSK7LvbkbA57Jl3UUO2qbKgkLlo+lrMG7xFst7atCek9/phd0eIfq3sEfQXbJgKIzV24uTWTTz+IQIGLQlhBBCiA25s8qhxp5HMgaQxeXRAIe69JCQdISetsSN3VKb5neJpOjau1TUpxKXZGl7CnKAPB3u2YUbC/G/7+fj38P+tKg11ZKlQVGJhHrPyn/54JyIbNjU1TjppQl4d+LKJJaOECsM2hJCCCHEBhOR7bm88OtiHPbEaCzZXJTQ/RaVRQMcsj9cLFi/SKpg0Ja4sbtMnojSt2lfz1qPl8cstb3vRQRaGQon1JZDDtpWJmHi7LcFm7FoU2HC9xsPctA2jsdM0li7s8R8XVRWab5WmxOuKEkv1Pv5xhM7mq9Npa3mmj01YhEA4OmRi5JXOEIUGLQlhBBCiA2r0paDjT2FVduLMWTCChSUVuLDaasTum9ZaRtX0DaBZSitCOGrP9dhx+7yBO6V7KkwERlxo0hq0yo0QVDDMHDPV3O13421mqEiGMaZr05C31cnJcybVfbpdAoyV5elW4pw/Uez0PfVSQndb7yk20RLeTAaKC+Vk3CqQdvUF5VIVFbdc11bN8CfD/fGMZ2am5/5qqK2deGZsK2oHDd9PAuTl21PdVFIEmHQlhBCCElzthSWYdaaPTMTNKk9vpy5Die/NMH8O9HjkTJpaWhFXErbxJXkjfHLcO/Xf+OaoTMTtk+y5yIHfeg5SVRKLcpVe5u2zWVyKFYgdlNBKZZt3Y0lW4qwTlJr1gS5CsczceaF+RsKzNcfTV+T0H3HQ9CitE39PVsmBWrl8qiT3akvKZEJVU1wZGX40bx+tuUzv+lp63zVsjPSI4z2+E8L8Mv8zbjy/T8Svu9RCzan9F4nUdKjthFCCCHEkTs/n4ML35xmS3SRXORgRi0eliSNF39bYvk70dc1JKm8ZG+/WCSyGJ/NWAcAmCcFGAhxgkpbOwUllXhzwgpszPeeTHBPRQ7I6WwMyiqc27lY9Un2y91RnJiVAfIkxODxKxKyT4FsvfC/7+cndN/xIF+HdEhEVlYpqZvDzv0m9qPSC2EfkuG3e1W7JSITZAaSF0aLJw/Z8q27k1KGssoQbvhoFv73/XzMXZeflGMQ7zBoSwghhKQ501buiPy/YketHZOJyPY8ZL89QB+EqAmysCseBZSlrtVwZKv+RkLcSLel1unAA9/9jed/XYyL35qW6qKkHKeAnEBeGq8Sqy0rLo9+d1dxYtot+ZCJnrgqqQjG3qgWkJ8zXp9hiVDRB0NhrNxmD5CVOihtbWVgTyqtEInIMjTBVzMRmcs1S6bKO549J1pRL1izI6r+31xYlpRjEO8waEsIIYTUEfKyMmrtWExEtucTSvCFlQcxI+dt9vy9RNa1ZCTfIXsuVqUt6w4A/L5kGwBgwx6ktF25bTdu+2x23MkXnZa+Rz93U9rGCtpGg6D5pYkP2iYat99am8jPLS/PsM9nrEWPx37DhCVba3Tcmz/5C6cM/B2jFlifbXIdWSGpHtWSsXlJL4T/c2bArmv1e1DapksQftX24qTsN7+kwnxd4KF9or1QcmHQlhBCCElj5GXmOZmBWjsuO2B7HuoljWcJnhfkoMY3f633/D25rrHWkdrE6mmbwoKkESIJz57Es78sxk9zN+JfH8Tn+xhLRemmtI3HHkEOkNSEZE48pEutCMfpaXv/t/NQVB7ENUNn2gKuXgmGwvhtYcSeasYqa34BuY7c9eUc8zX7UN4oqwxh6JRV2FZUu8lDo/YImnCYh0RkvjS5I5Il+JUDtYUxgrZTlm9HhwdGov39I/DFzLXJKdBeDoO2hBBCSBojD0rembgCP8zZkIJScPCxJ2AL2iZ4zJEI5a5b0GHltt0JC24QAlgHvOmQ1CgdSI9QRGIZXRVw21IYX2DIorTVtE3lLt7dseqTrLT1omTzQjJrcLrE8muSiOyGj2ZV65jydZaDtABQLimQZTWyXWnL9kXHuxNX4vGfFuLK9xKfSMuNYJWtgM7TVrzl1h9Jh/tBbkPyshIr6pi1dpf5Olb7dIV07f77zbyEloNEYNCWEEIISQDlwRDmrS9IeMdcHijuKqnEHZ/PcVX3JAraI+x5qMv5Eq0Uqa5Hrpcl6ut2lqD3oN9x7uAp1ToGITroaashDYIR6cK6nVFfx5DGeqVMCeBd8H/7Iqsqq3ysvoAcCNmVJKVtIvsj6TKnIf/G2ppokVc8qTG+0gp9f4yJyLzx9sSVAIAlW+KzLqkpIvifobVHqPK0dVXapp6ismjQtl52Yu3TZKuP/BLmCkg1DNoSQgghCeDdiStx9huTMXj88oTuVzco2b47+WpDa3KopB+OpIBEK0Wqm8nbyxL1X+ZvQtiwJscgpKYEpSQu6RKUIunD8GlrzNdelLbd2zbCjSd2AhC7Pj37y2LztRx8qQlqERMZ1EwXJbrFhmfWehz97FjH7PZ/rExM8lb5OqunocxhEl2dJE2Ps5d+1IYIQYcItutsx6Ketm5KW+8dqMKySnw4bXXCLSDk5IDBBCck69yygfk6USsBSPVh0JYQQghJAC/9ttTyf6LQqRflJVHJwuozyuHGnkBAkQgl2rsyEUpbpzFSeZokwSF7FjuLoxNgXL4cIR0UZOmIrn1TA04ZAb+npdUqiQqIqnW4uhNpXvadKuRzVVQexKaCMlwzdIZtu+krd+DSd6bb3pfV016RlbZqcExW2h7Zvmn0AyptPZGq5KH5pZG2v0lelu0znydPW+8Mm7Iaj/ywALd99lc8RYxJiVT3ggk4j6GwgfcmrcQfK3egUqrnDNqmHgZtCSGEkATSICexS5R06p7dtRG0TfoRSG1jT7iR2KusBgi8DvLlrZwCHX4p4JwuwQNS99lcWGa+ThMhYcrZExORVYe/1+db/tYFQMuUyaQMv89cWh1PfUpUk6Yec7m0xLmmJMKzPBHonhH5mqDSuMVbtd9fsjn+ZfgVISk4ppzkMimg26Repvna5mnLXlVasbtK3V5fYyvgZeKlqDzoSd06bcUODBodEXNMX7kzxtbxIQdtKxKgtP1g8io8NWIRBnw51zJRodrAkNqHQVtCCCEkgeg6gDVBp+4pKa+FDhTtEfY4VKVtope7qoN6rwoaq6o7NolUj5HEMX7JVtzz1dxaWQmQKDYVyEHbvbNebcwvxd1fzsWfq3di5uqdVFVVcfeXcy1/a+0RlGBGwO8z29lYk0stG2SbrxMV0FOPuXBjYY33OXvtLgz6bYktQJ0qdO2/7lQ7nf/iivjbJ/m3q4pGS7I6l1O0lzYvCcUwDNz1xRzc8fnsGk/eioRyuZoEXtGJF/djfD5znevnK7ftxuXv2tXesfA6bSbX5coEBG1nro4ElTfkl1qCtqy7qSexI0tCCCFkLyfRGiVdfErNXpxs2F+rO+wqrkBRWRD7NcuzfaYK6BId/FQnGCpDYTMpj+d9OCltpcJXBMPIDFB3kG70HzoTANCiQTb+e0bXFJfGG7LH4N4atL3/23mYuHQbVu8oxqw1u2J/YS9hmaJS1SYiC9qVtqKpijUpJgeLwgmKh6qHbNfU/hyIl/OHTK3xPhKJVxsep80KPfgHr91RguKKILq1aQjAqmIMKhdLDtq6+bPvna1LfBiG4ar035Bfiu9mbwAA/PeMrtincW61j1VaFYjP1Xraxk5EBgDzNxS4fp5oZa2KbM0RNiJtjjo5Hw/yz/3iz2hA2k1lz5VPtQN7vIQQQkgCSbQIUNdZCiVqhOeCfFT2ydKfP1fvxIptu3Hhm1NxwovjsX6X3bdPHezGo7Q1DANFZe4KPDUI7NVjzTLQ9VC1E6EoIcljw67SVBfBM/IE2N7azk1cug0A9oqAbQ3iGQ5KW3tb5NUeQW4fEzVhoCp21QDjnoDX55bT0vVYzzEAOHngBPR9dRKGT10NwHqd1RUkVqWtSy6AvbWBiQM1sZ+KnIi0pisCSqtUqjqlrc+jL3UsK5nq3tdeLWoqlPOVrL6R2z032yEJIEksDNoSQgghCSTRvmU6VUltJG7g7HndYdmWIlz01jScOvB3rNxeDACYo+lIq1XJq9I2FDZw7fA/0ePx38zlczrUuloeil8R7nT/yO+nSxZzoqcuKVblIEBdKjepHjW5wrp2R01Etrs86CnzPGBVbyaqSVMPWdMgTnWTSyYTr966TgHA3R6UtuJavz95FQBFaaucU/k4VNrGjzyREqtPskNKHFlY06BtVbA9Txu09TbxUpNJIK+4tSPq+UpW0NatDKu2FVv+3rcG6mfiDIO2hBBCSAJJ9LhfN1CsDfWMRWnL4UZas3SLPdlMvSy7A5Zal7wOyB//aQHGLt4KwwBeGrXEcTt1MB1r6aBA/ppTkeSypktCHKKnrlye7bvLLX+nYXyKpBG6Z7Hq8xoKG578MENhAwUl0aBToiZJ1d1UBGu2350lFbE3qmW8PrecgrZFMYK28v6FmlNehq4GymS1o1Vpa6WutIupJNaErOwhXXOlbWRfOVp7hMj/se7LT/5Yix3Kc0QmEZfc7ZSoEwg1EXQYhoHRC7doP3Prc6l2bZz8TA4M2hJCCCFpjK6z5HXZ+bqdJdhUUL2lyoY1akvqGDrFilqXfpm/2dO+Ppy2xnzdKDfTcTt1wPXvYX962r/cyXfq8MuDkT1wxe8eRV1RQq/eblUIcbBpJYu+0RZ09UNV2oYNb/YIT/68UFHaJqbuqfupaUb59WlodeJ1hYh6bQSx7BEqw/brUiIlfFL7X7K60RK0Va4FJ7/1yFYAsZ4dcn2ucdA2AZ62ADB5+fa4jutlgkYW8OrOSWFZJQpKKxOqtJ2wZJvjZ+4J9hQxAJ+jSYGJyAghhJAE4vfoReUVnarEy6CluDyI418YDwBY8cyZCUtOQOoGugFrIpa61s/Rdx1H/L0JL7qocN1YIS2vc+rvy+ryPdGncU/CX0difXYlHFs6mXZN97xlrjW5xLrJUlXNaRjRREBu6rRhVV6pgkTNc6gBk5r63/8yf5PjZzVNelRdvAaFdH7DQGylrTxBKPpzJRalbTXtEdi8aJHPWWylbfRcx7qOsXDztPU7eNrq+lCqr6yM7pkSNoBAjNtGHkbYJmKCYfR9ZRJCYQP/Pq695bOaBG2XbCly/CyeviNTDiSHOtKtIoQQQuoGiR7DaJW2HjpQPZ8cbb6ujiJBVoUwmJFaYp1/3TyBbsCaCFsBJ5X3Q9/P074fq+wrt1mtHZy2l+s8Y7bpjQ+1H8ipDuoEW12qV4ZhYOBvSzBynnNQrabUEcF0raFrP8uUpcEBvw9+v1DpeT+BiUtEZqWm/vduSQUXbSqs0b6ri1clf5mT0rY8RtBWCsKJoLQcIBTndGdxBQaNXmr5zK1svJ3sGIZhCWbHo7StSYBy4cZCrK7yM9cpbZ08bXV9bzmgr6LrL8W7EkVtG7YUlmFDfik2F5ZhU0GZ5bOa3O9ubZDbZ+onVNomBwZtCSGEkATiNeurV+QOXu9urQA4Z0UW7CyusKg/nJYJusF+V3rw4HfzcMxz47Cz2NlbUHet1AGrOjgCgLZN4lfS/Th3o/b9/BL9xMDuGAPkjfnWQYdTtZMHP/S0TXPqRszWRl0abE5evh2vj1uOmz/5S/v5jt3l+GPljhodoy6dj9pAn4jM+izu171NNOATR0wp1qmev6Eg5rL+yH6sO/JqpeSEW4Dpts9m12jf1cVrrK76Stvo98T5LJTOvVDaPvTdPLw2dpm1bC6nm7eTHbV+xXq2y6rWmgRtz3xtkvk6LqWtpnyuQVtNIxBv0FbdXj6emlSvJufE7dS7XRdVhcvnRnJg0JYQQghJY0Sfr3XDHNTPjnQuYw3EipVAWXUGbrS0TQ8+/WMtNhWU4ZtZ6x230Q0M1AGrNqFdNQf08SjICmMMkL0msQg6+AZWt1wkedSVmK06+VWXlKXbipyT3wDAOW9MwaXvTMesNTurfQwOvq24BW1fvKgH5j/eBy0b5jgGfNxw23bSsm046/XJnoKk6m5qaiXjtqpnleIJXVt4tXxwSkQWa9K7QvOsKZRWK4nn5l9rd9m+KwewbPYIadKT2lJYhhNfHI9XxixNdVFs9SvWMnxZgFBTFbnA3dNWmQTRKm2d+zi67eO9J9XNi6XjqXW8ZkFb74FZy2fKR3XF076uwaAtIYQQkkAS7ecoBnMBvw8ZVYlhYtkjqEs2q9WRkw7BsXtqkG0t/C6+G7rBi9qZ1yklqqPABmLXv76HtDZfu/m9Afa66rRr+ZgzVlkDUet3lWDYlFV46Pv5rscitUOiVxskC7Ue7ylB/7LKEDbkR5a1z16b77qt2wC7LtlFuJGo66o7V6L9ysvKQP3siN93wEMiMhW3oO3rY5cDcE8UBACz1uzCf4Zbkz86qU29EivAmQrKPP4mp+dbLDWn/Dw1g7bS5KNoNzI1ifosicjUIG0tNC9jF23BxW9NxZodzgH14VNXY82OErwyZpnjNrWFWu9j9S3iVdqu21mCIROW2/oZMm5BW1tAUtPXSoY9gmwxpJ6jkvLo8dT+lVMgOxw2cMV703HRm1Md7+lqK23VhHt7xmM07WDQlhBCCEkgiU5EJjpLfj+QWZW9INZASh3UVEeRYPG0jfvbJBHskiwR3K657jNbVnPN13eVVOLB7/RetDInHtBCOZ57jdi/WT00q5elLYeKOphy9LSVjjls6irLZ+e8MQWP/bQQn/6x1vVYhMiog/66JBBye8zIy7+zNQEJGbdgRl1Q2obCBl4ds8xVUZyo6+qmtM3OiA6powEf7wd229SrQvOyd6bZ3nt65CIs3lx971kv/vm1jZNXrW07h+BurKCZ3C6I/pdVaRv5XJeEzTURmXtxE8J/hv+Jmat34a4v5jhus3ZnSS2UxBs2ewSXa/PNrPV4d1L02e8laHv5u9Pxwq9LbDYWMg1zM23v+RzU8nLw8q7eBwCojtI2TnsEpQyy0lY9B07nZGtROaYs34E/1+yy+eDG+i7gPoGn1nMqbZMDg7aEEEJIAkl00FYsS/L7fOYgoTKW0jaYAKUtSTly577YRc2hqw9elLZAxH4hlu9sI2VQU6n04NWgcYbfh6yqIEYspe323VavXqfghXxMWeFUWFap9futI2LPPZK6curVyYe6EKT0gqzQr4xx/6n2JDJ14Xx89ec6vDxmKS580x6wFNTUIkAgzkdlKIxhU1ZhQ34pyqvOX44UHHcK+LjhtqlX5brT5OyzIxd7LoeKWwCmZYNs1+/O31CADyaviivzvBdqqrSNNekoP7OiSlvpnqr6fkBzXaxKWyu1qeTfUuhun5IuqLemW327+6u5lr+9iBHWVyXSm7Rsu/bz20/prA2+OyltRVvi8wH1quzKdux2zjegm1D3EtSU2w71/pHrb4UatHVo790sFaLbVO9Z4MX3l9QcBm0JIYSQGiJ3xhMdsBAdvIDPh4wq74VYnm6JsEeQ+117yrLhuobcuXe7hrqBgd12wPkauilF1HIA9iWC6sDBH0fQdsj45Z7KKQ9UFmwsxPglWwEA+cX65DyJnjwh3qkrp14N5u0pg005wBQrQOCmtK0Lc31/SFYpTkG6RNk8iEm0weOX47GfFuLY58Zhc2FEtZadqVPaet+3W91zccbxhLBtqA46VeCV/9gPgL3dVznr9cl44ueF+PiPNdU+vg6vtj5OwalY93llLE/bsLPSNmRR2tbusnG5H7BvY+cko+nUyqmTyfGoNGP1a+VgZ8Pc6D0gn6drju2g/a64tOo1FOXL8PuwYGNEwf7bwi2OZdDdP16U3vL3nALHYlvLdx32XSoFZJ3uH7d+oKuNjqH+nU41bM+BQVtCCCGkhiSzjxK1R/BJ9gjuByxVZsyrs8TRcHhNag+5Q+6mmNMNXtQBq5vaKVZgVQ1uqXtSvx/w+ZAV8Ba07d62kXXfDsVUBw39h84EYFf9CupI3HCPpK6ce1WpVRfGmvM3FODOz2djc4Gzkk4OMDndHwK3oG0iJ+tCYQNvjFuGP1buSNg+AWvbN+DLudptqqu0FRYvZ/VoAyDahsrLy/NLIuda9sUUvvbxnD+3QIevhndU4zz78m+v6II1WYHIb9X5e+qYsy6/2sdXWb+rBCu36f1af5y70fK36uf7xLkHA4jPNzVsRK6jvJrDnEjX2SO4KW1dj1pz5GXvXVrVT/LREoN6byYyaOuoKJU8YZ0mNJwsTuRr//f6/JhlrI6n7W4leatbYNvmaevwm+UVFbrzUhEM47MZ6xzL5FZkp3NEEguDtoQQQkgNsXRaEhyxEH3agM+HgN9jIjKPHTk3LANO9sFSglelrTYRWaX3wVCswKr6XbWTrn4/I+AzlWflMQZWnVtaB5dOwQun3+80gcEqmzpSnYisPBhCUZlegS2TbkpbwzCwtVDvNyi44aNZ+H7ORjz/a3TJuxoclO0RYgXW5ACGSiLPxw9zNuCl35bi0nemJ2yfgLWMI/7epN1Gbb+8BlPFc/aEKk9v8fc+jewqRtkeIZanre74bo/0miY3FcHn6qDra2RmRH5frIRegvkbCqp9fJX7vv7b8bPbP5ttvjYMw6YoPKpDMwDuE5iAXUE8ZfkO7Cqx2yPoVnNYlbbWz5LdvAgrACCWP6mzGri2sdkjxFGeWPYI8vWXr5VYiZCd4TdXBKn4nBKRSSvfBpx2YMwy6iaMxD1lGAZWbS+2XYMiJWhrs0ew9AutnzlddzlZmi45oVi55ISrPYKtf+i6K1JNGLQlhBBCakgyOyl6pW2c9gg1VNqS1CB3zitcBii6wKU6YHUL9Mda5qoODNROus0eIQ6lrfq5UzGdyu80SKHaI3WkWml7w0ezcMyz47Axv9R1u8pg+gw2dxVXoMMDI3HkM2Pxw5wNjttt0PwmtdxypvtYbX9xNZfExsvq7c7Z7GtCrFUngG7Sydu+xfdEkjHxt069nJulCdo6NH26YJNb8MxLHMvtWjkFprygs2LK9FvPRyyWbtmNHbsT47E6dYU3pXZlyLBdZ6GMjTXprQa1vpoVUSC2aZQT+b4mEVlO1SSla8KmJPeq5OXtTr6/D3z7N36Zv9n8O9WJ5mpijxBrUqncopiObis8/BvkONuGiEurHkOcr4Dfhyb1Igr2Li2dVc16e4RIuQaNXoqTX5qAV5UkaYXKhKPNM9ZFaevUlyuV6obuHG+Snis69bHbddEF2hPtY00YtCWEEEJqTDIVWmGzkwjT0zZmIjIlaBsryBuLZA82iB75url62mpGiuqgze37OuWFTKygh80ewQ/Pnrb2z+NU2rrcC6lWEZHap6C0EhOWbENReRDjFrurh9TgWyqVtsOnrTZfvzhqSVzfVe9/XaZ7J1QrHZlEno5Yivvq4uWaqe2XV7sEsZ0atNWpl/M0Slsn1aDu+PEsP9bh5vNak8CcLiguEkHGc7/IKtDaQHc+MqoicbECg+rycZFo6rIjIl6+lVLgTiCU1tZ9qwpvDwWvAfKzVP3901bswNTl221L4L1MeiQTNbgXX9DW/XO5XyNP0ImgrZvXs7iH5Wu2fXc5Th34O4DItQ/EuM8Bffsr7sfXx0X8/F8ZEw3art5ejK9nrbdsb2+/nFdgOV1P2R5B1/5sqlrhcc0x7bXnxS0Iq/ss1atW9kQYtCWEEEJqiOHcT68xovPj9/mQUaW0jbXkVR2IV6f/ZE1EFv/3Sc3xYo+ws7gCk5fbMyOrgzb3RBLuFzjWMnJV3REMG8jKiAxiYwZtQ+q+7dv8Mm+TY/ZnWQl2TKdmymcGisuDrr6dJPGk0h5B9p6MFbBUB7ipDPIvrEpqA0SDYl5Rx+DWpEmxlLbJt0eoDIXx9u8rE7IvFS8BSXUbr4EhsZ2YgBLBGd0xrUrbyP9qfVq4sRBXvf8Hfl+yzfZ9t3Mtf+QUnFWf+Ue2b4r9muZF9l2ToG3Vd1s0yDbfM/shcew3nmXvTsRjs6Dz7Qx4DNoWl1vV52bwvkpNK9qVDDloW/W8c7NHSDbys1SetF2+tQiXvzsd/3zvD9fvpAJbktOEKm2j94T8O4V1ToMcZ69nYUki3zuf/rE2+rnPZ9Ynt/tL11a4Bcpv/HgWhk1dbXnPzTNWredO/US5H6ae4znr8s32uUWDbIvS952remnLIKNVE3PQkHAYtCWEEEJqSDI7KPJyLDFIiJVcplQJUtV08J3s/tfWwjKqIjW4KSoEN308C7PX5tveV9Wzbv5vscZJ8Xra5pdUIqtqYO+qEA6FsXhzkeu+DcPA7Z/Pdvy++F2dWtTDp9f9A3MfPd38vLAsiNMG/Y6zXp9Mu4RaJJWWtrtKokHbglLnpf+AvW6msorIx9bkN3LFprQtk5W27j+qpNzFHiFBbfLmAnef3prg5b52U6q5IbbLzrCqKHVKtWzJgsDJD/OLmWsxadl23PTJX7bvuxVJvgzP/bJYu43qY5+d6cep3Vpafkd1EL9ZLP8HYFrfiCRdXvg7AcnIthVZLRbc7pPqBm23Fpbhvm+svrlm8F763eGwAb/WHkEK2ir7TnYfp9xBaas+Y2VqugqrpqhtjO4c5ZdU4MHv5tm/HEtpK52PSkvQNtLm1csO2L4j0N3D8rPFAMzr7660dVahCrszGd21cvLVBezPMPXv3eVBbMwvtbQBantw3Yd/mq9bNsi2+N92a9MwckyX36i7n9idTzwM2hJCCCE1pFbsEaSZ/ViHU1U38Y7Z1I5zMu0Rvv1rPY58Zizen7wqaceoq8gJKSqC9mtQUhHEH6t2ar9rV7+6JCaJUaFKYii31YHCT3M3Ssl4nPc7ePwKLN+623Xf63eVOgac520oMAdFQp0oL1mdtWYXNhaUYfnW3RYFJtlzke+Zl8csdd1WHbymckmnHIBqmOusANOh3toFFqWte1BGvbct+03Q6ZCDHanApubzsCQ8HDbMtihLtUdQ9pebGbCoy00FnlKfvv3L2avY1dNWev7Kaj8Znc2Fl+XbsYhaREQDXHKwyWsdeeynhdUug0A9725KyXLN6ooMD0G2H+dutL1nBu+lwHVlOGyeX0CyR3BLROZ41MQgT57KStuAyyxayj1t1SSBmm0G/rZUW+9jtdcVDvZSIvli/WwXpa3G01Y+XNgwYnpXA9Hf9+CZXU3luzjnGR4zDLqpkdV+npr74NK3p+GEF8Zj2ZZoP8uemDH6WvW/jqqJncun9+1l1DbRMGhLCCGE1BBD7tAkWGUmJyLzOWSlHrd4C+75ai62FkUUTWpHLl6FR23GLgZ8ORcA8NSIRbV30DrAN7PW45ZPo4osnWLVLfO7qn51U9y5Le+bv6EAC6Sl24C9/qmqpgNbN4jp6wjog2rqvre7JLAJ+H2m6lws2c2Slpav2h4dqNAiofZIZSKyWHYcMqrKLJXjTPn+Pr5zc9vnhmE4Jiize9pKichiKW1dEpElShkoB5ETjZciqkGFeC0VhIpWtJPqOZVVqIAc8Im+V1IRRJGLqtkpALV2R4llJYXTdmr75vP5EPBop+SG+K6sJM6Q2lin4Ixadzq1qFftMgjUpHluCdZ0ibj8ktLWqW7r6mrQPAfRwHUobFgmCHdUTQrK50Od7K5dT9voaze7GreVMLVBrFU8ADB3fb72u7GCtpUWpW10W6HYdlPaRj1t9ccIh43opIhLeyLOb73sDJuncoaitHXaj6unra2fF/174cZCLNhYiGDYwKgF0eRzNytKf/nePrJDU8tnAQ8THbpy67bfVFCKB779G387XE/iDoO2hBBCSA1JpkLL7OD5feZgUO4khcIG/j3sT3w9az2OfHqs7fNI+WpWhtoI4vp9di+5vZm7v5pr+Vs3uHLzo7MlqKiqBPs2zsWfD/fGnEdOkxRhzuV4d5LdizJWIrKsgF9ShddswkC1+lC3FQNqoVrJDPjM5flrd5aY2zJoW3uk0h4hniCEGnxLVMZrwzAwfslWWxZwr2XRlWLsoq244/M52u+qA2SrPYL7+XDztE2UWiqZyY68rAJRn89uydcE8m8XwUHRhqrnZf9m1oBkVIEX3W7RJuvEl4qumSyrDOHsNyZbgjQ+hykRWWEORPoLiVHaRr4rB0jliTGnvo/6bDq1W6tql0Hw0m/WBH1ZLt7PbonIAOs1/HX+JvR4bBSe/WWRVnmuOweVIcPSzolAoMUewaa0TW5HSj7nstLY7T5OdSIyL31Vp4m4WM2TfN/I+xCTxZMdfPKB6D382rjlpgeuXNcLy4Km762XgGaGP7pSTjyjVO9ydVJCoO5eLkdlKLp/ed8AMHTKKvN1wMVLRBz34/8chTaNci2f+cwJqPiCtobmkt3/zTx8NmMdBo12XwFD9DBoSwghhNQQS4cmwX1g0SEK+PX2CLpgVE2X/apb10a3PmwARz49Jqn+h3UZXTBKtwRUYLNHMAcKPjSvn43GeVno3KI+APfA6sptxbb3YnnannPYPmZnP97Aj1oUt2BrMGzYksL4fNFgxa6SaPDKLfhLEotTYKk2kOti4zx3mwFVoZooZenIeZvRf+hMXP3BDM/fke9v3T0zbslWx++q28tqQZ23p0yhiwo2Ucpj2/OoliXNamDqAymY4fgdqW5kK0mm1N9zQc99LX/rAh1qUFVF94xevaPYpvx0Cvyp28ke+DUJvovvWpW2+uCnjDoh4qboBrzde+t2llr+zs50C9o6K20Ba6DtlTHLUFgWxNu/r9T65Ipkl3KQOBgKW5Kz6farkiqlrdszNNVKW7Xe6+4DpzbMqc5sKSzDsi1FWnsE+RxdckQ7x3LJAfmXRy+z7EPgJRFZpWSFoPbfM5TK5hScVuuU3J6J35hbZc8h2yPkS22C23UuqVqx1VGjhg/4omV2Ot9eE5H9vjSSgHFCVSLG8Yu3YvFm98ksEoVBW0JIyqgMhfHd7PXYmF8ae2NC0hi5z5Jo1a0YFPl9ensEuTNWPzsj8h1VQRa32rF2l/UJiitCmLrCWf2wN6Pr0LsqbW1ZhcWSPDlhTuR/t4GmbrCt1g9Rjm5tGuKbm45Bv+5tPHna6rCr4qy/46Ezu5mvg6Fw1B9OCiSIwVFBiTxoSa2iaG8ilUpb+Z5oFMMbdq0SBEpULPGN8csBQJsg0AlL0FZzP7q1wWrgTA7EllQE8dmMtfi/J37DuMVbbN/dWuRsPwIkJlGRWr5Yx4wHL88m9fjrd5U4bKn/juppq54TVZ2ma/t2x1hFoqt7m/LtE5hO7Ziq6s7w+yx2ANVF1Eurp61kj+BwAdTnj5OVj2EY+M+wmejwwEg8O9K7RVLvbq1iKG01icikhkmer9lSGD3Pfk3UNqhRSwbDBuplZdi2lfebSk9b+feXuAZt00tpqwsMOk1OO6lyz3hlIvq9Phnrd0Xbd1GPZVHAfX0OdCyXX6orYsWOmlcglpLdMAxTpZsRsPff/cqD0qmfbve0tdftnKzI/Sk/R3ZLE0VOge9w2DCfmcKXWSbgoE5X92F7L0bD/OaEFeg/bCbOeGWS63YkCoO2hJCU8cHkVbjri7k4/eWJqS4KITVC7mgmWkQkOj+yPYJueVTkdaTzpc58xxt0TWU3Xu3IpoIPp63GPV/NTatkCmqCCQAo1/j2Rbe3fiYv0xN4sUfQ+eGp24t617x+Fnrt3ySidq2uPYLyt6qQPbpTMxzYqgGAKqVt2L7U0AzaSsGrdLqWezrpYo/gds23FZXjJyXpUCJqSH5Jhat61Qm5HdcNgt3OqcVH0zBQKA3WQ2EDj/+0ALtKKvHkz9agWEFpJUYvtAdy/3tGV/P1xW9P81R+N9QBvJytvKZ4CtoqGy3bujum8lN+horgoPBCVZ+v9bKswQ5d2+ektH3p4kNt2wriWR2g1rlEK21l315LIjKHfavPn9U77Cs2AGDBxkKMXRxRkb890W7FI5Cv19i7T8Q7V/WyqH9VdIG+gIPS1m3pOGBd7SQvQ5fr9ZtX9LTtt7Y9bWVLCFkp7mY75TbxWxvY7BE0xdnosPpKFxjcmF+KXSWVqAiGsXJb1NNetK/i9zbOy3T1+pWrxJhFW6r2YS1crEmRAV/ONSfuMvx+m9e1enin+qG2DbogsVDayhNKst2CHNBvKSnEK6UTLia+v7v5GADAJYe3tUxiODUjOqVtrNUUz/+62HytszIhdhi0JYSkDLFEIpYCgdQ+I+dtwqw1u1JdjDpDcpW2kf/9fp9WwSN3JMuDYZRVhmwz8fErbW3vxPX9mpBs3zcvPPLDAnw9a73ZWU8HtPYILkuf1c/UhF2ArAhzPuf65aLq0tdIp1tWalTXHsGmtFUG336fz/wNlaGwzdMNiCpg8kujWesZtN07kBV+bs3elOV2RX9N7RFKK0Lo88pEbKjG6qF5GwrM17o4iqpclJHrdnFFyPJ35JkQ+a7s8QwAYx3at38etZ/5Oh61sBPqoF7+rTXFy/NCfR6u2VGCy96ZHuM79kAdEHn2qm1JrhK01dkj7NYEbfdvlmcuSdY1T7p2uWNzfUIvNWibKKWtuHayqlTOeu+0b3VliJrMUuDVDunnuZsARDzZOzavB7/f556ITHO/yEEy+dzKwbvKoP33iOXofiUQLn76gNMOwOHtm5rvC2q7HyX78Yoyj120Bc/9stjpK64WS7WBTWmrfP7OxBWO39VVPbm/ICejE8Fa0Y+S67AOnXjAZo8gWQeolAdD+G52NHFkRsBn87qWj/DMyEWelba6IKkI2sqTf3J9kPuD8nHkfYu6/X/7NcHiJ8/ACxcdajkPzuWz32vxNDn5JclLVLknwaAtISRlJDN5E6k+q7cX4+ZP/sKFb05NmMffno5clxN9ykSHKOCTlLZSj0j16zvjlYmYudoacI93zFbbChGZeLK/J5sbPppV6/6LTujK4XaubInIlIRdALT1yVNZlAohVGQNcqID++raI6htTpmSGKY8GDIHF8GQIQVXpN9V9bk8GGDQNrlYr1sKPW09Km13lVTY3qtpn+SvtbuwpTD+pf+qH2k8vo6A9Xeqwbslm4vM18I+R91WtZGIpTyMl2S2oV52rUu29Pd698BxUArayoELwzBsbet+TfMsf+vavqIqcUK/Hm3M9yLPdOeJM139bdM4R1veQiUo7PclVmlbT6o7ASnZo9PScFXBWREMaycevSbrW1xVj/se0toMssqWDSpapa3FHkEK2krb6FSpQrXq90XthSpDhtnm+WBVHztZiiS7H/XJH2vN1+L8/2e4u6o91c9Ftf7I98GmglI8M9I54BxLnS57+VaGwhGVfFVbkBVwb+N0Klx53727tTLvXd053KZYwOhWyu2UnkHvTFzp2JaNnLfJ8rdqfwZEJ47k+y7o8CyUg75ykFfuG4oJePmecaorOoeNeJIfptpXua7AoC0hJGVwCJ2eyANIdSBA9DjNXCcCMyil8cQCdMsQS2xKr5oGI2rzXk2noC0ArPPgf1gb6AM5LonIlPMogv+yakwEN92qrO4ztSjCt61hTjT4E/Cg4tWhbq5bIiwGznIiskyNp628JFlNOlXbzN9QgAe+/RtbC/fMRHtyu5daewSpLY4zKVBNm25dIHjplqKYk5/qoFX3DHG71+XfqQbB5M+aKInZhNflaQe1wuVHRtS1Z3ZvrVXX14RkBoa87NupHrit8gpJCn6fNFoOGdGJoltP7owfbjkWzepbE1Kpijog2kY2r5dlvhc2DPNc6+ujJmir+OcKbJYcvmg54gmgyMhWEJagrZTsUdeshsKGVsmuS4jl1UpE1H+5HPEqbZ38OeWzU6a5z8TtGfD5zOdMMBw2r5nf77OsMhHHtnna1mJHSjwX1Xtepbp1I1Go9UduK2Op/HX3hzzJK1tqGFUKedFfznSpO4B+hVGJ5Ms85IqeEDFO3TksVSabMwJ+S39r5LxNFiUw4Dy5NXzaGsvfbvYI8ooMJ79iue7rlLYyfqXt0+9Po7SNo81PcdeszsCgLSEkdTBqm5bIqpJ0C6ClK3JfJt4g1fSVO/DUzwsdM/yKfpdloCQdwkswKm5f0RTem6lWfqhs320PxCSTsYu2oP39I2zv6zrMbvenuow3mogsPnsEXd3xpLSt6mHqOu+hsIEXfrUqaJz8ddWgbY+2jaNK27CciExWEOt8eFNbr856fTI+m7EOA39bmtJyJAv5uqXSlbrCYo/gfM11g/JYdSRWO1pSYW/DT395In7T+MbKqErQeJW28vdXbLV6h8rBg2KlfOKzelkBPHr2QXjvX4fjpYsPTbiveDLbdN1SYa/H/3zGWu37kf1WrXDx+yxqM8OIHrNbm4Y4tF1j23d13vPCHkEO8IYMw7UN1j3aneqoqtb2IeorXt3zL39N9u31+yTrBU15Ppi8Co/8sMD2vm4CrtTFl11G3NdyoNYtEdmu4shzu0FVkDcvKwCfT68QlhWJun6YqbT1+0w1YjBkmNfC54PFX1fcV7YVS7U46AkbkWfvyQe2dN0u1f0tu9I2+jqW16nu/pDrmFq3KkOGZI/g3sbp2kBRD965qheyMvyuvv3qsyDDb1XVu1lWxEJ3zYTSNugQkHX6vqwi1yXhs7R9DreqeP7886j9zHYinv5WqicO6goM2hJCUkY6eFcSO7Ixfao7dHUF+TzFe8oue2c63pu8Cu9PXuWw7+jg0QyEyYnINB5sKjW9jMmyydDt18sgvDZRFRPJxmk5o26AogZyjurQFGPvPtH8Wx7A6BJ2qfYIs9bswt1fzsW2onL8vnQbthSWeVIkCrWUrLT1aSYYBJ0eHIkhE6xedTlVg16bp23V+T+sXWNMuf8UBPw+8zfI9gjWBGv2Y+qWSNcWspLyzzU7U1aOZGL1iExdObwmItO1M27N3H1fz8XxL4y3ZJpXcWornNr2aFlie5CLHAA65O0/nxkJRIrgltxGqEu/i6uUY7lZGcjJDKD3Qa2Ql5VhC1jUtP1XB+VuwbZ48aLocnqmzFmXb/k7FDZw3Yd/4vCnxphtpxxsASLnWgT5MhyWWOtWMAhVb2NJ+RgO6/1vzfJoA7n636IqrH0+eSKsetdPrpeywtXvjwZzdEu1nx65yPYeoJ9kVN9zmrwWCkk5OOrUzhSUVGLQ6Mjk2BmHtMbwfx+JCfeeFCm7xodUViTqJkfEbwz4rX7q4lL4fZFVUELtKH5DKpW2QKQvX1513g7TTC4AqZ/MVOtz2BJMj7w+6cAWlm2EnYuu7JagrZJssCIUNveZGaMNUuOX4bDU16iqA272CGrQtiIYtvS3dCpxr9dC156JRIEWewQHQYclaKuxzZKR2z6n4Koo96FtG7meEydSXQfrCmkTtH3uuefg8/lw5513mu+VlZXhlltuQbNmzVC/fn1ceOGF2LLFOlu9du1a9OvXD3l5eWjZsiXuvfdeBIPWm3TChAno2bMnsrOz0blzZwwbNsx2/MGDB6N9+/bIycnBUUcdhRkzZiTjZxJCJNIsNkOqkJfXpHpJcV3B6mlbvYq9fOtu7fvm0jxp8GgZcHi4RjVdop6sW1XXsUv1RIF6/YpjZBlPNE4KEN01VAe8JxzQAh2b1zO9K+UkL0Fp4ClQvRf7D52Bb/5ajyOeHoOrP5iB0wb9brn2TkEAndI2XnuE7KoBr9h81pqdmLVmpzkAPu2gVti3cWRpsC4Rmfy7AprRfCrrlayqaZyX5bJl3cUStE2lp22w+kFbtwDgl3+ux/pdpfhhzgbHbXRKWyC2Z586oRBvXZV/y9ItEe/Pfx65n227koqQpSyllZH7Nk9JpKU2QW4qXy/YE+kkpl8xcek2S1KzI9o30W7ndF1//tvqFbl8626MXrgF23eX4/whUwFEvLKtCaygnSiS0alndW1kKGyY94o2EZmujjpUjcJS63PKB/egkhfkellftUdwUNqqicU6t6wfVd9pLntFyHrPFDlYcpVX2pW2v8zfrN12+qod5mu/z4cTD2iBlg1yzLID1nMitxl6pa1Rta9osC8YtnraAtHAmVPgubafQMGQYZ63S49oh1kP98YtJ3eybJPqLr7aDsvVyek+e/r8Q2zbCuSJMzW3Q0UwbN6TsVYTqJ62wXDUKkT458v9J7XfKNpW89ihsGUiOzfT7sfs9TbVtQs5NbRHcPIxl9W3Tu1I9P7wOVpuOfk8A8n1PN+TSIug7cyZM/H222+jR48elvfvuusu/PTTT/jqq6/w+++/Y+PGjbjgggvMz0OhEPr164eKigpMnToVw4cPx7Bhw/DII4+Y26xatQr9+vXDySefjDlz5uDOO+/Etddei1GjRpnbfPHFFxgwYAAeffRR/PXXXzj00EPRp08fbN26Nfk/npC9GCa5Sh6GYWDUgs1YtzN+P06viVzqMiPnbcL5Q6Zg1fbi2Bt7QD5N1Z01drofxP4CvqinrWXZu4cBddzJoGopEZkucJJqpa16fKcBWLLo3a2V9n2vPpc+nw/7N4skxlmxLToRYNoI+O02AqKO2RS0ZUFLvRR+fmpdjQYkoioy3RJhN4SCyjAM/L0+Hxe+OQ2XvTPdzHgvewbKCXZCpoLY7tUrk8oleLLSsvu+jVJWjmRisUdwGA9vyC/FyHmbkjpIk+9ft0uuG0R6Kdbucuf2oMRhgieWslRtc3TlyHbxYAxbBuGR/1s0yNZuu2FX1O9cBJntQVtVaet4aE+obZdYul0TdhZX4F8fWAU2TpMFXp8pO4ujVjgikBfww660jRXoUHxqnxm5CJOrPF7rZ0tKW8MwV8949Vh2asdW77D2ZXw+uZ20b18RDOPFUYsxQglcy8jnzeJp63e2Xhg+bbXl76xAdBm5boJZnXhUPfoFlaZ3efQ+uLBnW+22sm/4fWccaPnM9CGtKrdhGJZjqj6j8rZycreI0tYaABSBOKH2VK9Ush9BDZREg8GQYfYRsjP8aFY/G1f9o71lm1QvTbe3DVJbJp1f+V4TfRitp61Lf80wrJYWbqhtYChsX9Ujl8nmxV9hrUeGYZ3IVpNCive9oGvPRN3zMn6zWIMo6mEdsRT7sgLZqd/nmkiTsQBPpDxou3v3blxxxRV499130aRJdIa0oKAA77//PgYNGoRTTjkFvXr1wtChQzF16lRMnz4dAPDbb79h4cKF+Pjjj3HYYYehb9++ePLJJzF48GBUVEQevG+99RY6dOiAgQMHolu3brj11ltx0UUX4eWXXzaPNWjQIFx33XXo378/DjroILz11lvIy8vDBx98ULsng5C9DDbTyePHuRtxw0ezMODLOXF/t9KyvGbPu0o7dpfj5k/+wuy1+eYSupoiB7GqG+jO0AzsQ2HDHFT5NdlnAW/XSJRvxqqd+HHuxrjLlqxaoCt7qicKVNVbIn2dNxeUOSqqBbpEKIA3n0sRqGnXJBK0lZdy6xJ2qXYbHZrXs5dHGsSK4JM9uBtZmiuryHRJ84BIQi4dIigbNiI+z0BEKSKUdA2lfYtBW2VYH0DRBVNSaY+wN2BV2uo57vlxuPmTv/DNX+uTVg6LPYLLYFCnQqrpMs38En1SJbeESYDGHiFs4K+1uzB1RTSZkxpYBYCOVferNVAdeZ3pMAiXlYwiaJurBm2V+6em50XXprutEKkMhbGsSjHsxFLN5zNW79ROVLs9U+T6Ir8WMZvsjIBFeRwOGzGXWMsK1/JgCO9MXGl+1tSWiMzZF9OrPcLf6/NtzwIfJN9Zzbket3gLBo9fgTu/mO04YRyyBG0lT1spaKvWjWFTVlv+zszwm30bbT3QPG/LKkO2MolglDwB8uR5B2vLPXpRRHR17mH72JLEqStAvEzSWvyNpWC1+KaoK+IZZnraKr8h2ZZw6m+pDIfNepGdEaj631pnw5JiOBWo9UertA34rAlUXSaEdb7J5r6l/cdS2qpdiErJP1/UAXkb9V5VJ/A6t6xv6W/pvLC9XgfdfbRvk8gqJHmi3mmFhyFNmgU9ePzGWjUVVev6HSdz3IK2qVZ71xVSHrS95ZZb0K9fP/Tu3dvy/qxZs1BZWWl5v2vXrthvv/0wbdo0AMC0adPQvXt3tGoVVaX06dMHhYWFWLBggbmNuu8+ffqY+6ioqMCsWbMs2/j9fvTu3dvchhCSHDi5ljw++SPia6cuD/KCJft2ggNoWwrLMHtt/GVKFFNXbMcRT48x/16zI/FK2+rWa12n6ePpa7BwU6H5uc4ewUmZAkQH7qKDdsnb03D7Z7Px52p3X83aujd1ardUTxQ8o/jx1XRpsMAwDFzy9jSc+eokbMgvddzOyRdTd1pE2RrnZeKaY9rjgv/bFwDQqmFkoCoHbSuq7ms5gKQqbXXZ1LcWRfchghRq4EBrj6BReRWXB3HW65O1v89U2sKwqFTEvls1zDHfE6qQoORRJyuIdfYIsZao1xapnpRIFnKdUJeWCkS7osssnyiCHp9fuiX6XoJXbujuHyB2gECXiOyCIVPxz3f/wMaqtkINbp1+UCvzfpy5aqflu4CzR+GukqiaNJqIzK76UstTE3Tfd1q6CwBPj1iE016e6GpFsa2oXPv+Iz/Mt73ndv3kjPByGyGKnC0lHAIi7bAcxNMh+9SqCcI6ShNjobDhGoDS2yPogrb2iTCfT7IC0Pz8dTuj9crpGScnKWqYG1UIB3w+R+sF9b7KlpS2uokz9djvT16Jrv/7FR0eGGl531TaSs+vvKwMTHvgFNs+Jy6N+D//tsCeAFC1BFInZXXnQvaulf3U5feBaNC2pEKvtE22UsV2LaRrK56v2ZnWdmHCkm04/KkxGBMjWWKycFXaSgrnTE2iUd1trSpc1X2LwHmMPGS2iauQxj/fyTqgtCJkBo/3bZyL968+PBK0lfpbukdCrL7vhvxSlFWGtNv9X7uI8HHdzlKzXXfbX1HVs0qXyFXFTN4Xwx5BXg1oV9o6B9PpaeuNlAZtP//8c/z111949tlnbZ9t3rwZWVlZaNy4seX9Vq1aYfPmzeY2csBWfC4+c9umsLAQpaWl2L59O0KhkHYbsQ8d5eXlKCwstPwjhMQH7RGSh9MyTS/IHetEq9P+9f4MnD9kqi0BSG3xyuhllo5eopa+y52O6nZAdEu6X/g16oUZcFLaulyjTEkZKQcUdkjLQHWoe0zWvarrVL42dhkG/bYkKcfzwkfT11j+TpTStjwYxtqdJagIhV3Vtk51Um+PECnb2T32wWPnHGx2vJtUqblk5Z+8TFJgDiLCwJiFW7R2IfJhnfzKikylrd0eQa476hJeGXkgplMbt2wYVUzJA+egMpACHJS2aRIsTZdyJJp4flYyT4Gs4HS3R7B/6LS53Aa4jfWdJtBiPRPUOiH/vb7KzkDd99tX9TL3O3D0UvMeFF91UtrKgUnh160qbVVqerm0NjgukyjDpq4GAAwev9xxG6d2crwmYZur0laqL7pAcnam3zIJETb0yQ9lAlI7KXvNXntcB7NtBoBdJZWuCRu1yck0p00Ehi853GoXYJYjhtLZaaIw+jv9aJwrt+0+M9mjfG7LKkO2c5iZ4UOmg/oOsD9fP56+VlsWcb9mKfW6TaNcbZkB4N4+VmsEAJL6OLKdOpnn1if0+9REZNYA4PpdEZV3/2EzAdRebgCBGjCvDIVRXvV7RLBWtWr54s912FFcgWs/1CdATTY66xT1swy/z7LSJmopEqfS1oiqOp0mFgXqxxGlbdUkhrBHkDYSRbn5k1no9siveOSHiHjwqI5NcWqV5ZVP6m95uRdkJi3bhuOeH4frPvxTez83rZdlXtsdxeUwpHZKx+2fzQYgJyLzYI/gUDzZFzfg02/r9tv21InsRJOyoO26detwxx134JNPPkFOTk7sL6QZzz77LBo1amT+a9euXaqLREidg8108qiJqEzu+CUyEVlpRQhLqpY0znNYIp1s8kutwUqnAEpxeRDjF2/Fi6MW43/fz4/pv2cN2lavbLpOU7E0mPJL6hb5eG4KwmjQ1sDO3dHfHkNkUGsTKk4B59fGLU8bZaSbkjke5KB5pksHWeepB+gH3hWKisbcvxTUFIiEJGKZJBAdbIYMw9OgTQwK5PoRDhvmb7MMrDR1dafLZIG8dFA3cG4hLXM1fQXDYXPpbyAQI2ibJvVJt1Q5HXnsxwU4943JrgoZmXgmq5LZulhWiiTIHsFrGxByaM9itWWLN1mFH/J96/NV+W6qy999Pst53FHVvptKWwfllKoIA/TWCzJGDausl2XxOuppfB8FZQ5BgHgnbGI9S0V7KU+YirI7nWPZ8kBYx7RtkouHzzrIcVt9gFYzsaDZbldVu9pESnLo89kDlDJlFXqFsYycvFL2SK4Mh7VLpkctsIudMgN+s23W9Se93ls6T1uB7BP+4LfzzNd9u7e2bavaOqhBPvHsGXjxoWZiMfm7mX5NIrKqc1FoS6JmPe/J7FdF/Fqt71WGwub5FfXYTVFZm3w3ez2uev8P3PnFHMv7OqVtQFk14DbR4RZ0D0uetjGVtjpPWyXAKbc14lkzcp71HpDbVrkN0fV9nfp+APDNrPUwDGDSsu3a+ygj4JMS4YVjTg7/XqVGj7VqAJAV+/p9ysF1J9sUV3sECrg8kbI7d9asWdi6dSt69uyJjIwMZGRk4Pfff8drr72GjIwMtGrVChUVFcjPz7d8b8uWLWjdOtIIt27dGlu2bLF9Lj5z26Zhw4bIzc1F8+bNEQgEtNuIfeh44IEHUFBQYP5bt25dtc4DIXszbKeTR00CA/JgKpGqsO27o8sZQykKoKjZvZ1meJ//dTH6D5uJweNX4CPJosAJuS5XtwPi1mkChMm/vbPqJWhrGNYBk5saAai9BBpukwKFpXp/SCASVP98xlrb0tNkUO7SkY6HYiloW+lyXzldG72nbWRb1TNTBFfluqEukwSidc7LYPLUri1N+wO5815cETTro7yENhowiO5Dt4xXYCYRMvSDFzkgIdROoZAUQLF43tnvpYoErxqoLnVFaTts6mrMXV+AMQu9JeWVJxVi1adkDtLk4Hy89ghOm8sBU7eSO7VnsVRG90uBJnU/fp9znZFrufiOOLVOyin53DslInP7DhBZprt2h/ckp6JudGxezyyXlwlhXbIeQblDOylPHKnH15ct+loftFUyxYcRU2krB2fEc0xehaDbVndLaAO5uqBt1YqKxlIbCfhcgy1FHp5H8u/Mkyw0KoJhbUB4/S677U9WwG9adWiD9y73htyOiPZbF7R9/sIe5usv/oyOx1s1sIvCVFuHp0bo7ZAyAj5bYruAP3ou7/h8tuSP6lR+5W/9ZglhyvId5mvTjiJsSJO10fM2/N9HJrEksQmGwrjri7mYtExjkyOdpKiHrFUZ6zbR4aQaB6qUtuY1i6G0Vf7emF9m87SVdyHqU+M8630u3zcBczJHX/bHflzgWB557KK7jzL8PtOeozwY8rxKUuzLyZ8bsNq9uO0j4PdFrRTUoK1LP5pBW2+kLGh76qmnYt68eZgzZ4757/DDD8cVV1xhvs7MzMTYsWPN7yxZsgRr167F0UcfDQA4+uijMW/ePGzdGu1Qjh49Gg0bNsRBBx1kbiPvQ2wj9pGVlYVevXpZtgmHwxg7dqy5jY7s7Gw0bNjQ8o8QEh/JMuUvqQji2ZGLMGuNu2/nnow8yItXYeZ10Bsv8oCoJEG2BPGi/h6n3/fhtDXa952orj2CnFXebXkSEFl6pyaOAqJBdlUVAkQ76vKSTvk7TtRWH8qtfhXZVCtRhk9bjfu/nYdrhs5w3CZRVIQSU1dlpa3bQNUpCK8beFcoSUYE5hJO6fya9ghSPZEVK7Ey3N98cmftpIG4TpkBn2K9gKptIxvP31CAF0dFbC8OaFXftn95ezUgM/ifPS32IXIiMp0aJ72VtnVrgOJ1tYXF1zvWxkk8BWrb5hSwi0dpK9+TbgFAp3bVTU2om5yyTjD4HNsEv2Z5rvgNToNweVdmIrJMd09buTRbi8pw0ovjcdbrkzzbMIn+yNGdmjkmqtEhMqLrcFJuyYpQ9fg6YlkN2YK2hmTJ4mBBIber4hw3cAhAuwWgdMXWnTfhU9y0XjRg5PO52yPslp6vTm1jUFrFILep5cGwVlEn6sOpXVua720sKLUEEVXc7g3d5LSuXndsYU+g2a5prtZySn0uiUSvAqHUzAz4bUvkfT6fmZOhMmSYK6EcPbzVv5PY7l35/h/m65yqOvvCr0uik7XSc//EA1rgpANbJK8wMXDy/ga8KW2j19D+faFsd0JMBMRORGb9/MI3p0pWTJHyyPYI4h5Tk1HKbZjP0obY672bOEQuju4+Cvh95jUuD4Y9P7crJTW9E27tiFweOVGfOnHrtmInTbpmaU/KgrYNGjTAIYccYvlXr149NGvWDIcccggaNWqE//znPxgwYADGjx+PWbNmoX///jj66KPxj3/8AwBw+umn46CDDsJVV12FuXPnYtSoUXj44Ydxyy23IDs78tC+8cYbsXLlStx3331YvHgxhgwZgi+//BJ33XWXWZYBAwbg3XffxfDhw7Fo0SLcdNNNKC4uRv/+/VNybgjZW0jWKtEXRy3B2xNXov/Qmck5QB1AfrjGu7xbDvQk0tNWHtC6LQNKJmq3xGsAxU3NWh4Mmf5QQHz2CE/+vNB8rXZMVUbO26w1+RcDruM6N8eV/9jP8h2hoAob1o5evErsZE2wuAWP3VS0L1UF/2avzU90kWwk0tPW3KdLfXKqa7pLJvapKm3FwFYODqtZpAHJHiFs4NB2jVxKb+2Qy/UvmoQsU6uGEZ33mz6ZZX6m+hACMEclOk/bMw6xrnyKBn6igxPZckI3WE+VwnXJZmuW+0T7hCcbr5NQ8UxcJTOLujpYdSqLHKg6pSrI5FRsuQ1w+21OA2U3lZGunZPvW7/PuQ2SB/KiVFF7BP0gPKQJstXL9q60nbV6FypDBgrLgthaqE8G5vR9OZmml0eQW1xFXQZ9dMdmAPT3l9vzLhSjr2TaI0gTpuI6O020iiX0laGwqfzLkdTMD53ZDQBw1T/2d1Wx6e0R7McTQVtZaesDHIOlZZUhy6So03NY5xcOAO2a5EZVvNIpK65K6iavuJi/odD8vpuP5wU997V9prOuyMqwn3NtEtf/HKX5RbH9OUUR5bpqftfns/TvdlcFCMV2D/Ttqt9pFbXV8otg8phFW1BQZQmmTsrqknXWFuqKNxmdp60ap1f7FgLDMPDLfOd8RGHJQiLWz9d9LuydRB2yJiiM7FhONAjo7RFCMfxmBYe1axwtjzR60T1PAn4fcqraqrLKkKf9y763XjxtnewRwmF7+642pW6r++raRHaqSA9jEwdefvllnHXWWbjwwgtxwgknoHXr1vj222/NzwOBAH7++WcEAgEcffTRuPLKK/Gvf/0LTzzxhLlNhw4dMGLECIwePRqHHnooBg4ciPfeew99+vQxt7n00kvx0ksv4ZFHHsFhhx2GOXPm4Ndff7UlJyOEJJZkNdMLN0ZmK+3+UnsPcic93qBTbShtvfokJhpVEeH197mdw6/+XI/V0lJRz0EO5dhqgg01O/YVR+2nHfDKChRVhZJXpe5RlbYxA1jKx8lSiLidf7egbW328dy8uOLBqnR23qdTXdPVKydPW609gmaZpDz4cVq+G91Wn1wsvypg0EBZlmx23qu2FdnKAeDCXtaEOZ9ed5Rl32JS54ULe2D1c/1sKpBoEEAfuNbFqxIVfI+X/ooaPJE+4bWB1+LK9TtWe5FMxZkatHMaaIo28NrjOqBLlfLbWZUrPRPj9MkF3CdpdO2cXEd8Pp+nideo0jbyf4bDJKD4jYZhmAPpWInI5LZnU0GZ+dqz12/VMf0OyTSdcNtEDdpeckRbxzK5PWfkY2jtETKtSlvDiHoXO0205kiKN/McS0rHa4/vgLF3n4jHzzlYu3ohWjZvgVyh7lM9bQNSoFmwpbAMXf/3K36V/Ged2qSgosIbcftxGNb/CHRsUV9rjyAsgFS7DVelbVW7XC/LrkS2PDODzkpbnUrQahURRX0uOZEZ8Nsm+dXjiOCjePvEKvVqs6pkczZ7hBQsA690sJXQTWzWFu5BW7vSNsNvVT37NNsC7n1GwGpLEK/SVkb0P+TxhKhPzetblf7WoG30Xhf3lpo8UOaJcw82X8vF0SmV7Urb2HWtMiRNQDlM8kWO7T7RISdok4UAMm7XnEnJveG+HqaWmTBhguXvnJwcDB48GIMHD3b8zv7774+RI0e67vekk07C7NmzXbe59dZbceutt3ouKyGk5iSroU5U4qC6jM7L0ivyoLcygQGGCk0AqbZR65zXAKubInS+klTNMCLHiZWdVu1UqX2sL/+0eqVnBvzaAa+cFEXtmIsAnZpJNlawWlXCJatP5Ra8zK8Fv1ovJCrYZwmau9Qnta4d06kZpq7YoR1k6iwPgEjGbkC558S2mqBt2ICZfd4Jv8+n7byPWxyxqGrfrJ5te7FvlZ77Nbb8fUyn5vD7lprbmxMRGlUVEE06FgqHtYNSfTKi1LQ5G6UgF1D3VCVe20irr3di9lkd1DbF6bKL7fZpnIttVX7rTuWWn6Fu9ghO13aNi/+rLtAg2yP44Pz8kZ8xos0Wz7hMJ6VtVRnLKsPmNcvTBMwsSIeXVfBe20ZTMefzmcEipzogn1+3eqSu1hEqM7ltfWnUEnzz13oc2aGpc9k0ak4ZV3sEh8CXUOeGwoapaFWXSXdqUb/qdeQ9fYDWvm/deVu1vRiA1R6hIhi2+bcCwNez1tu+XxnUn2g5aAYAB+/TyPxMl4hMBGa6tLTa30Qn2ew/qLzqR+Zp1N7yvt08bX2+SLBIri/1HCYivNpzZAR8NrWlGugsrlKqi+0y/NbJUpsS1PWIiSMrw2+7N9WgnJeY7ertxXjr9xW45tj26No6cTaQ7r6zUj/VsE4aCHzSBIrMhCXbXI8rJyKLpbR1Oz9yeQJ+H0Jhw3zOqKuEcqT7XrYOEL+tc0u7VZSgaVXw3+ezlldNqAxE6p5od8o9Km0rQmFpYsZZx6m712Vkta6TCtrtmtexLlHKSGulLSGEVIdULb1PJ+QOW7xBJzlQ6zZAjRd5yadbhtdkYBgGrnr/D1sAxeuSaTdfU90kgZeYhNqpUsuiJlRp3yzPkshAEFXa+myDyKinrepzHCNoqypEkmaP4Fw3S1x8z5KFbuCclKCtZvAqzoV6Tl6//P+qyhYt33uTVuLFUYtRWtXWqUsfxeBRvs5Rbzs5eBD5v7C0EjNX7wIQUXQ7oZs0WLolsvz/9INbabcVZRbqs8fPORhtm+TZ9i2Hn8Q5cFILivsgGDZQoUnGplPJOAW+tu8ux7MjF2HFtt3azxNNXUlEJvAaX7UO6OJrXxKJfTJMf7CgGQSKrf60KG1dmgM332SndkTXBsr78fnsHtiiXZdruWhfTKWtk6dt1W+U/WjdvGPlfQLW/lW8StuAQzJNGfn6uQVW1NU6Ijgi2tbi8iDeGL8cmwrK8MOcjY770U2AyohAiJlgJxxbnSZPoomVCE5qZt35ENdfN1Gn1tE56/LN17K6tKgsqA1Q6p5xTpPzbt69WqVtVZ3Ky84wfcufv7B7VGlbdX6Xby3CJW9Pw8PfzzPrdp7GV1m3OsXJq1l9Vjh5dHpNvhlRd7oHOsU9LbYTz2Fx3mxHqKWmP1Pz29XzFivxLQD0HzYTn89ch7cmrEhY2QD3pfLyKdIluAKsNgMyd34xx/W48tYxlbYu50e+H9SApipIkSfETCsUBy9+lSxTdGHd765i+0RfwO+TkjwanvoZFZIiV1dn5H0DzhMdcnDdyUrBq7qaOMOgLSEkZSSroZaT2KRqSWyqkX93TZS2sR78FcEwHvj2b616Q8XqaVu7QdvXxy3XZqoNaQZpukC1a5BT81GspXdAbO/FXVJCg577NcblR+1nDg7k/Uc7XXal7T5V3qGqPUJspW3t4Fa/3Dr2yUJXnETZI8hBBvV3/zp/Ew55dBQe+3GB7TN5cBE2IkHSp0YswuDxK/Dn6p0ArIFYQPK0lYIq4p7TKW2/m7PBfG/AaQdgyv2n4MBWDWzl0AUYhCJaXRYoD+oNwzDbJNWfVi1L2IidnV32SKzQ2SNovuf0LHhmxCK8PXEl+r46Sft5okm00ra4PIiL3pyKZ0Yuir1xNfD6nJbbpFhfSWbcWg2cOtsjiMCb31EdJPDqaSuecWccbK/jTu2ZLlAoP28MacKtXlYAL17UA2PvPhGANagpHidmIjKHe0c838QgOifTHzOAI//m8uoobcWg3hc7QC7fH26lUifnRaBUlMktSCAT1gQGZVT/SnkC1Om8yW2s8Jt1SvamKm0nLt2Gzg/9govenGq+17tbSzx13iEA7O3He5NWmq8bS16yJRUhrUJOd9qd+jchl98puhry/SWSm9XPzsBvd52IVc+eiUuP2C86iVi1v/cnr8KMVTvx8fS15lLv3Cx7SEL+qWY77xC0lcuYFbAHXAVy8N0NndJW9YEV5020HyL+JvZdW5PfKrqAZKYtmVfsoK1QcE9eviMxBavC7dzL96Nsz3HNMR0ARJKomdc2ztNpxKG0dVspJ9c1tT6pSludPUJIakMy/D60aZQDwO6HK9d1eb86e4QMv8/s9wVDhnZsAwBnH7qP+bo8GIomG3R5BugSIMuEpOvkNCnnlrTSy3iJMGhLSJ0nkUrI2iZZ7bT8rPWa3XhPQ7cs2ivy9rE6tiPnbcJnM9bhnq/mxlQuyGrV2lZDfzVrXeyNqtCph9yCi+Wa7b0EZmxKWzlYHgpj0OjIcvHbT+mMb28+FtkZAe2AVwxmMjN8liWxH1xzuBnIkgNhsX6PjqTZI1SVvUvL+phwz0mWz1KhmNcpYBOxrP7bv9bj38P+jO5T6VA/X5XledjU1bbvyoqPUNjAhvyoN6wITKgDWZGsRQQhfluw2Zy00CUi21WVYMPnA5rVz8a+jXNt1gR+vz4BiPBTlAMGkX1bgxyiyjkNuuWEPLLlhw6hTgmFDfN+zY4VtHVQBX47OxKwrq0JvkQnIhsxbxP+XLML70xcmRTrBa+7NOII2iZrWmji0m2Yu95qV+PsUxsdNJvLbR32KwdW3c6xaCuuOnp/+4cOX9PtT1Y+GtJ+c7MCuPjwdqZSXe7rhIzI5Ig4906D8KjSNtJ2xLRGUIouK77itkdwSGYoY/Xzdd6nGpSNKm2rFG8e+z3y6dcFbcVkl2w1IIquBsKi5Y4WfEpVwEsXlJT3K8rx4bQ1AIA/1+wyz1u7pnlo1TDHVl7DMMyM81kBPzICfnRsEQn8nNm9tVYNqzvrX89ap51Id0u4ZgaEpX0XVlnsNKp6FojzoHrayhOhIqido1F7i32Hw1H/ZZ2NglpGN39OM4FaTKWtPRGZqr4UzxTxtlq3a8tmSu1761Si6jnxorQVOFmtVBe3sYJcv8PSZE//Y9rju5uPwdtX9Yqu4pG+J7dFTj6xhhGd3IrtaRt93UdZRSSrutVrblfa2vtbhhENqvr9Pvzr6PYA7HVSvo6xJqHktjUorQZQubhXW9M6pCIY1k56q/g1kz8y8iSWGeBVnmteLTGIMwzaElKHWb61CIc98RteGbM01UVJK+QOs1e1xZ5EOGxYBpnxBiPk7WMF9+SkJMUxznWF5JumzkYDwODxy3H/N38nZSLiwp7OZv8q2uWqbkFbzWDHizpNHazLfm8Tl0W9ueRji06cvHvhmdcgJ9MS5KqXlWHtJFqUttFj7dhdjus+/BPjq7xJxfYyyepSiUFPVoYf7RWVgVc19t/r8xNWHl0AJREW2QO+nGv5W61jbn6yaoZiXefX5mlbVQ+ED+D1H82KbqtR2gpfzf8c2yF6XNvSUJ8UWI2+L5b/NqmXpWwfLbPcpqhlFUQVZ5LS1mGwGJAUTeakRcD+u2R0Ptqp8JdN9DG3SG3w7iQk3/ScWFEz2HYiWWO0B76dZ3vPeRl+tN7EUn/KE45uwR5ZPaVaDngJUgrk9iFsGLaEUAI5o7gcTAScJzyiSlu716oT8jNZDoa62dvIWJc5i2Cffluvkxq7y61tpuppG29A2enYoikRp17eb8BDMEtMsjmdZ7mtkpWAgFWhrEsqtnJ7MVZuK4bfB0z+78kAgO9uOhafXnsULu7VzrIiIXoMexm+/HM9Xh5tH0e4+V3qAsKFpZE61VBJapkheZAD1qCtSF6Wk2E/P+L3F0vii/rZ+kkG+Vo4WSgAUpCtqgi9u7XUbqdNRObz4YKe+5p/i/MTrSOKylBV2laj3TMMA8u3FrkGvdT+qS4ga/e0da+7ch/QSxsRD26nQa7fsgrU7/fh//ZrgpzMgGWCVyBPyh7RXu9hbUjfiRWzls+P+vstnrbKNVcni9pUrXaT9xmWPG0z/NF7W21/5D6Y2/UXZRLB9WAo7NjPCPh9ZoC2Ihg2xRHZmvtPLYdTcx82f4vf0f/WbeUc09B4I+6gbXFxMf73v//hmGOOQefOndGxY0fLP0JI7fHpH+tQWBbEK2OWpboo1SJZw2X5YbU3Bm1VRVlNgraxAqhyYC1W5lbdUm3BuMVb8OKoJfh85jp8OmMtgCpPunHLsH6XcxIXr/gcFlrqfp1uuaougYZAt3y+OkpbORiwMT8aiJEHCbrgggiaNc7NtAxWMjOiSwQjyVPswfhQ2ECvp8Zg9MIt6D9spvm5rfRJulmjvpL27ojXoO1D381PXHk01y2WCqCkIhh3IE7d3m2QKQ8uwoZhDnJlsh08bcsrQ/jkjzWWz+QBMMz6FPm/oaSWVdU6fp9dcfHDnA2mjYeqtI0Ojq1BW6G0bdc017K9vO+op61T0Da63FYEpmUFr+57OuVdKlZiJDohWqEU8I/VBnslbAnyeKvbsdR8Msl69ssqdIHTvRlVc8f2WZUnHN2eiUFJIf70+YdYPovlrWspW1A+//aEUAKf0jbIx4iViKzUVNrGDsjIRS+rhtJWVszFCpB7XQWiLhEWnrEVoTAMw/Bsa+MU+BGIZ6j4Xw7gO7VPOnRKUkBt3yP+4oKgZtnx3+sLUFDV5opVDm2b5KFllRK3UV4mjuncPJLJXeMv6bRE/x3JZkHgZlOjC86oSluB6bUZstY98ZsBIEdTD8W9Jq51ht9nmXTUHQNwV4aqzzBHj9yAz7ZE3u/zmTYVQLQtN+0RlMRv6pmujj3CpGXb0XvQRNz5xWzt51sKy3DBkKmW93RB23jtEWQRRm5WACUVwbhX7TnhNqknfyRuR7X+6RKRyW1RA2XSQD6u+E48SlvVj1ouj2yPsKWwzOwPdWvTEMd3aY62TaSgrWSxIk9k6RIGis8EsezCIhM7ke0rpRVI9t/liyYsC4bNfnaOw2S6tdz267Z4cyG2766oKm/02qi/hZ62NSf2mhiFa6+9Fr///juuuuoqtGnTJmZ2bEJI8liypTDVRagR8TTUhmHglk//QlFZEMP7H+lqEi8/LGLNTu6JqIOVeD055Yd9rAGUHCQoKKnEvo1zHbe1Bm2tZfpsRtS+4OHv56Nji3qYvnInXhu7DKMXbsEPtx7nufw6nDqbugG9biDqpv6proJPPbcWxUrV4OeEA1qgc8uot6hPM1ASnqKN8zIt1zrT77cEI+T9i8HQhl32IAeQ3ERBMnISNZXa9j0G9AEZN2Xd0i1F6PfaJFx+5H544txDHLdTUa+923JO1dNW13lX1avCHmHl9mJbUPukA6PKInXgovNfE/h8Pou32fKtu3HH53PMzxvlZdq2F2WWl5EKBeAR7Zti3c6ol65fGogFHYJUglietrrng+6+TsWkXqKVtrL3tW4FQ3WQ2xevxY3lm+m0bbJxDpZGk92pSfNUrInI3IK20fbsvMP2xfpdpabNje5rhmFga1G5fT+yPYIRTSqjthPyGCwkWZCI36VDPMpK4gnaSsEmi6etR4mUGXwM+DxnIo+8dt6nqiqXf4fcLjjRplEONhWUWcoh+ot39u5iiiHEGY4qbaPbO51jHU42FD5L+25YJmHkRFdye/zUiIV48eJDzVU+TgGXqIVB9D2nW09nW+Pm3RtV2kb+rgyFzTrVMNf6W9VERrrJshxNMFZUBTFRWT8nwzHuIJfR7bqYvrNVJ8IpQWUkEZn9u3lZGTiwVQMs2VJkfle1gQD0fYnqNHvf/hXJFzFqwRbt54N+W4p5Gwos76mrZAJSAD/6nvtxNxdE+4YVwTAOemQUmtXLwqz/neap3As2FmDe+gJcfHg7W/1xe6ZYlOYiKG5bYWDddkthmcUWpkmePmhrGNHvxIpfyZ+rEy46K6awYWDBxsh16Nq6AX6543jbPs0EauFomx7wRScH1AldS9DWpa+SGYhcX9G3CoXCjvVaVtqWB8Nmn8FpUkkut1qnyypDuPjNadK+/VDtIryUvy7bPNYmcQdtf/nlF4wYMQLHHntsMspDCIkDWY2xbEsRuiiJY9KdeDowu8uDGDlvM4CImqZdU3v2cYHc6d9UUIrubRtVu4x1EXWwEm/QVs5M6qYwBaxB2/zSCtdt3ZS26mB55LxN+LEq4/Pc9QXYWlSGlg1y3AvugtM5iLU0VeA2UBf+g03rZWFnlTeobvuiskqcOvB3nPd/++LBM7u5etqKZYadW9S3bKMbhAmlbaPc6PEBoSCLvFYTkYkOo06ZBmi82JKkjaswgxyRTuRDZ3bD01UJlSo9duQSOXesm6Rwu/aDxy9HZcjAh9PWxBe0VTrUroNMZVCvCzSqS9vclLuyCkodi8sDFZ09gqxSEgMUpzLIGZ51gVVV/R7NLRKtq872CJH3NxeUYf3OEtu+1bID+jZAp1pONk6Dqeoi7n8gcb68IUvQ1lt55c1iqXNrU1gTS9GZGYi9ZF8+r26TOJXSZIPf78Ptp3bBq2OXmcn4VB7/aaHWx1quIwasiV5k5L/CYW9KWzFAfnHUEgDAsq27HX+P+R0Hpa3XvoW8zF+ezNEhP5Pd+h+q0raetGw+GNYrbf2+yHH3bZxrTn5tKYwGzddXTWIKz2Ag2i6JNkUOVMejtHX2tI2+DhuGJRgt+iIBv1W1WVb128RvcFq+rsv67tTm6TwtQ6aFiLPSVlzbojJnCwP1mustfpztEcS+67n4L8ttvurHrttO3AdOdSxT6j+Z3616Q7cKRS1DyDBs7Vx1mj2nPppgnWYlmtpO6OqpTmm6Ib8U+zbOxdItRbj6gxnm+6KN2FFcAcMwPAn2bvt0NlZuL0a97AxL8ivA/fkgf+JFafvepJV4asQi/POo/QBErpuuLgFiNULkdaxbV76WDZT6LFvPyEHK3eWRet0kz2oVJZBzAoh6lxHwQdxeNpsLj/YI4nSaivaw4Whd42SP4Kq0dfCBXrK5CEVSe5KdIVsNWfdRohEbtG2Si/W7SpOamHRPIm57hCZNmqBpU71XCCGkdpEfzFe9P8Nly/QkHrVNPANduYM6ct6muMq0J6CqX54asVCbbdSJFduig7hYSlv588KY9giyWsdaRvU4uZkBy3X8etZ6133HwklpWxmyD6bjTURmDoIv6mGZSVf5z/A/sbWoHO9MXImyypBdaSuVQ3icNsixdhZ1CQGEr3DjvEzL4Coz4Ld44FoVTHbFi1tiimQFWVR7hOtO6Ih7+xxY9VntG13prptbO1Xd5ehqh1r9u0F2Bh7u1w0/33acZXBhhIESzb28fzPrJJZXBZhNTSt/5le3lepf2FtiDACWpcpZFt9Z6/Zy4Cy2PULk/Wkrd5iDhkzNQEpGb4+QXKWt3iM5eUpbrx6jsZCrvNfipoM9go7Y9gh+rUciAOwsrkBIGQC7qYJ0kw1Og1gA2oAtYG37wpIqS13ibE9EFv3bydM2ZBgoKqvEki1FAIDWDWNPhsrnRZ5w9VrfxDmTE9U4XRf5/fFLtjk+BwoVpa387KsIhbUTGP85rgPevKInvr/lWHOC87oPowkitxRGnqUimzsQnVwS7VOlqX7VK/qd8OZpqyRXEkFbn3WKK69qX6WV7io5sW+5r/He5FXm65MPbGG+1tkOuCltZfsbILLqRKDWPVVdrWt3tUpbxR5B7Q9ZjiH3fVyVttZAtlPfzu/z2SYWxflUn0umPYJ02FDYSEgiMrl4urZHl0tC/fm6SVxd3R0yfjkA4PJ3plvyVch4eX5VBMNYub0YAGwqYMD9POiUtmr9k/98akRkkv/TPyK2apkBv60ui4lqWWkbyx6hnpTwrkWDbPO1quKVrQ3EhEg9B99lefLCYo8g6qQ0RhrW/wjLNXKzR1BXJgXDhtlOqQQki5HyYCiq1nfztK0qh9qmqv3fSNDWPlEEAKVVY40r/7EfTjygBUbefjzaVU2OxUoKSCLEHbR98skn8cgjj6CkpOYeg4SQmiE/czYX6h+w6Uw8g1e54x6rfZcfAL0cDOn3ZNSgzpodJXhFk2RCR2FZpTlwAWJfI0vm4FL3wHDQRWlr93LyW5SW6kx3vLgp0NRj6+0RXL4vOoF+n9lp0nVCZEVcUZndB1X+22mQogYX5qzLV4K2kqdtIJo4au66fDz644Lo7xG+htJ16CgnAkuAQsSNB76dh04PjsSaKpWkPOBW/e9iIauLa0q8QTYvfU3d99+oGhwJ1EmM7Ew/rj2+Iw7Zt1FMpe3rl/+fzcMty0Fp9PZVvSx/q4oZ+U97QNeq3I5lXyEHYaNK24D0ubI9ovt28zoG9EEEWSni1R4h2UpbfVLDxE5GyL8hUSpeq9WBt33G853azBbtnPCqSkXo13vazly9E4c/NRrnvDHZMunpdop1kw06SxsgxuoNRWnrFDyT/7rq/Rl49pdF5t9OEx6hsIFtkiXDa5f/n2M5zDJIRZXbKq/Kbrn8IoDnbEVhfX/y8u22bcqDIcux/T5roC4YMrSTNFkZfvTt3sYShJERVhXy52aSKb84tnV1iFea19cfU23f5dMiL7+X20tRz6IqOX3ARUweyHWvW5uG5mv5ezp7BCcvZcAe/LzsnenaMkS2haUcuqCt6hsq7ztWMEwto5vdkKh/wlbKSS1eEQo7ruJxug/VpKH2Wzz+ds+ittQ8c3VJTNVnt+586FajiDq9w6VP5cVzWn4m6e5zt5Vb8ubO7Z6+TQUigUNZNf765f9nTpgYRrQ8scTCcjK9FtIqv4P2aWjZTp7IjtZTJ+U7zHLLftE+ZXLl4H0amjZWsno2FtG+c9jRuibgsyptxXZOCWKB6DX5z/A/LWM+1eYkK8PZHkHc80e0b4rh/z4SB+3T0DKxT2ITd9B24MCBGDVqFFq1aoXu3bujZ8+eln+EEOKVeNppOYAXa9BrzZS79z0MdMullm9zXwL51Z/rcMsnf+HQx3+zKFhiBc7kc10eQ3UjdzpsSlvlOJWhsMUf18kPzituyzjVzpA+0OJ8Hky/PsnzU3fe5CWDu8vtQVv5GE6DFDW48OG01eZnTetlWTrnkazokb//WLXT4p0YVdrq/QmTeddsKyrHZzPWIhQ2MLgqeCkPgIVKx2tCmvW7SvHsyEWxN/SAfN3O7N4agHeloRNe1GjlymBMPh9qsiF1mdnh7ZvY9ucUUFAVuTa1q/TaNlCSlLaGYb2nDlYGMvK+Q0Y0KYasgFEHlqavKNwVXoA+ICUHIHRjdq09QjUTkf00dyN+8bCKI167jeog7y9RSluL/3U1graxfmKq7BFWbS/Ge5NWYsfucnPJb0ZA72m7YEMBwgawYGMhJizZFt2fh2eB5f7VlAMAJizZ6rifSqmPE5aW0qrL1NVJlw+nrTFfO7UB4XD0fmxePwuH7NtIu92w/keYr+XzIk/WxJ2IzCFALqPeH7r7VrYQGHJFT0z+7ymRxFtimbGD0jbgosAMhQ1TOdasXnR58z5V/RBRbpGILDMOlS3g5mkbfR02rAGtiqrAc8Dvs0zMif5wLHsEnerthAOaA4iojuVzq7NHCDrYcgDRNjbkYQIvGtiyTxYLdIFnUW+EPYJqu2Apj9/a93FCnIn7vv4bFcGw2Waq57Bj83oaX3f7sSLvVyltFY9pdfxR03ZPF+wuUhTnv955vC0gqzsfumvqpPyX8eJjXWJR49t/tNsQLqx59tjtESL/686nqrTdt0muxXpJfCWW0la+39o3j/ablm2xjqXkIKWwMIh1P8qrJ/y+6ERWSHrP/I5LO9O5ZcQ+red+jQFEg/PBsLOnt98fnaApl+q/2z0jC05kdW1xubU+ZmcEtKsBgWjdlc+NnMSNxCbuUfB5552XhGIQQqpDXc8DaBnkhQ3Xh5O81CNWA69LuLQ3sXJbse09tw7K2h0luPfrv7WfxVTaStcw1nJ2ufMWS3VUHgxZOl41DUa4DSoqQmHLgCFeT1u5YxlRe4S1gQ45gLOlsMymTJHrqugMqUlixJhTDAZEubq2boCWDXKU4Kfdk00gvm9Z6irdYzYvtgRGWQok7+PocmKrQhjQT878tXYXthSUoXFeppk5GwDenrgSD5zZrcZlE8dsmJOBcw7dByPnbXZtQ7ycFV3QQVY76baxBm0jKqvI0j67t5maqVv9vkxepl65LZDbYFtQ1e+zqAbluvP+1UdARVbUeUkWJvu9BV28FAH9gDM7RiIyvadt/PYIO4srcNtnswEAfz92ukWRo6JrE71ORnhFVvV7TQwVC8uSXI/FlbeL9ZXa7LvI5+fh7+dhyvId+GX+ZvO9orJK7UBTnrxcKU16uiYik3wKBfJEh4zTM9e2rRF9dropbVWc1IYhwzDzIbgN1E86sCWa1cvCjuIK89oahoEFG6MJcL0+l+VghJMVhcCLEn2X6eOeiTO7tzHfzwz4UV6lHtPd724etLJqrF52Bt68oidGL9yCa45pDyDanonEo5maIKcbOiUpYJ+Us07CRK/7oW0bITPgQ2XIMNuVMhEIcdi3amEgvw74fRi3ODpxoA3aivrsYo8gLwsHogEkmagnZuRvXSIynT2DqCNmIjJXpa20WselXsvPrc0FZeazKS8rYAaTszL8Wt9W8TuclutbE5HZ28HqtPyyYjxy3qyKbVlpWz87A11bN7Q9/3QTDG5t8HGdm2sV7gAcl93LlErXV9f3dpsIlJtXMWlgS0Tm0oZkBqxK26yANSGvqP+xnkEWe4T62ejQvB5WbS/GPzo2s2wnJ7Yzvbsd2hldYmC5ny7uN/nrOkW04OnzDsFPf2/E2T0insGiPQ+GDNdEZILSypCnZ0G2w/hIvY9FQjQgGpjPL6nA2EVbzQSL8uRVNODteGgiEXfQ9tFHH01GOQgh1UD1W6pLFJcHLZ5JYcOA3+X3WJJTxAggWWdqa1DIOsjWojLT48krM1bvdPws5rm2BG3dt5UDGHaVqbUjWFYZtmxT02CEW3BGlFtMHMjZoaPlc/5t5oBUVvpotpc7Ud/P3uB6DNEZUgcpqkpJDGaurhpYyoG2TL/fMVgvvu+stFUUItq9VA9VGQKo9giRzqOu03nBkKkJLImdoBREdkq+EC+6wMY+kmei7PkqUIOVfp+vyrPSsC3p16k6nAI26uDezdNWNzCV/TnF0tz/HNcBrRvZfTHluioGnvISXHUg6ZP2HU28FI89gqy01QVt7W1AdawKNkqJYXaXBV2Dtro67Bb0K6kI4t6v/sYpXVviwl5tPZVHft55GVDHu0+vvQydQiqVmBMdUrmmLN8BAJi1Zpf5XrumeVi9I7I6Rb40skf7dmm5sFt7oFOIR1W81m292rpYBvjK/eA2Gevk6ykrbXWBOsv+laWuqo+sV6WtvBRYF0gUlFWGcOfnc6xl0PzGHbsj565pPWvCHxG0DYb0SjM333bxLBSej327t0FfKSAsiiHa6li+4VcctR8+qfLZBLx72sr9APHs8Pkik3dPnnsI7v92nmSPIDxtHdpJjaeteBz5fT50aVnfVJzrg7YuSlspaCv3Iz6+9ijNbxS/z0A4bFiS2Yl96QKtoqxvjF8BABZbD6fyAO4qaPl8V4ajSkP5+SG+r15i8U1HT1vF6sJmM1WNdlHuG6hK22AobDmXwsZFff7pzq3uGSkmk0W/84j2TTBz9S7LNl4mHeVy6oK2bnuQ+55OSltxnuWJe0FmwIfsQPRaysIHw5KIzP3Jtk/jXDSvn42sgA+NcjPx6XVH4as/15sJz9SyRBJBuu9bDjZHPW39tn69HFF2a7P2aZyLp87rbttWrtcqGX4f/lgVGfO9OmYZjuwQsRDU2aOY5ZZey88/1U/Z54uOgcR2Vw+dibnr8s1t5D6oqsAn7sRtj0AISR/k50K7prnOG6Yhc6RGHIgdGJEHvzGDg2k2eKxN5GWcamfFiVlrXIK2caiaK2MEQOTrohZH7QiWB8NWBVkNgxGFGt8vQVllCCu27cb/PTkaT/680FTxyPy2YLPmmxFE0eWBh+68yZ0o3YDdkuG5Qiht9UHbqNdbZDud11umg1okUubI9+XOdbmr0la7m2qhS4qXpSiEgdQkIhNtS6zggsDLIExXdy2+kJrfqaoe5GCpfI+3apitvcZOHXCbcttmLGsPNsnbyorEWAEDeembTml7y8md0bphDm4/pXPV9lFFYqWLwgvwELSVPhfl03tVx1+x5Xv3l/mbscsl+KZrB9wGvV/OXIcR8zbh7q/mei6P3E4mw9O2OuWINcvjJft4Talf1XbG6lsc0KqBxZpDIA9I5brj1B5ElkNHXssBU6florECpgIDhtlm2hSVLqfRTWkrfk8sX1bZwxqw30OxLJHMY1omNp3VVW//vtIMIqplkHnr90gQT00KlCGt0tB62rr8XnGO87ICripLsy1z8U0FgI4t6lv+dlLDWoO2hqVNEs8JEWAT/Qtxn5dV/cZshyRCsgpQELWqAK45tn20fJqgsi6xnkBeFi76EU3rZWltIOTgv84aIUuyKFGPn19Sge27I8FaN4GB/Kzwem8VlFZalLbmvqrOsyqMEfVCZx0EWOtqyDBs93x1Wmc56KkqG/MdEqGq5XO7fjLifhJ91aM7Nbdt46UvXmoJ2tq31/WbWjWMKIgtSluNXQAQPd9Ofac8SSW7T+Pc6Ao1yInI3H9DTmYAv997EkYPOBEZAT/aNMrF7ad2sXlTB+T2LMa+tUpbKRFZdDtp/y4FbaJMWonrHApFJ+ZOOKCFZRv5XG7ILzW3c1rVBMDynJGvjy4nQEAKxG4qKLUEbAHrfeb0bCR64g7ahkIhvPTSSzjyyCPRunVrNG3a1PKPkOqwfGsRLnpzqiePOKKnTaO6FbRVAzexBE9yRzZmIHEPfwBsKyrHOxNXYMduu+pAqBh9PuBiSanl9uCXVUcqsYIauqVMjvuS1dLKNaw0vQAj5SyrDFkGxzX1PNKpOwWllSF8PH0NCkor8f7kVbj5k79s28xdX+D4fXnpp6w+UbEkYtN0fC1BW4flgOogWnTi83RqS7+zPYI4lNwBd+uMJ/KO2q25FlYvXrs6CACGTlmVwFLokZeDmolWXNoTr5mUVeSAgm4Zr+rvJwaLBaWVZif7vjMOxKg7T9Ae0ykYY9+v9XP5T91ASbbnKI8RMJAH9eIcyEtgWzbMwbQHTsGA0w+sOh7M7WMlItOp3HIc7BFEEEF3nuNtVx76bh7+9cEM8+8nf16IM16d6Lh9vFYrGyQVr1fkjNMJ87SVk4p5/I41Zpv6Z7BY0unWtxBtjW7wqAv6Ac7tgXzu5fZMt5R3wcYCbbtwx6ldbO+Fjajqt7ESpHSLOzg980PhaFndgpiANckOoEkg6nGSQGwW8FktVlSWbS2yl0HzO/6ueh4f2q6x5f2oN6T+fs9zSBAEACVVE6BOS/DFuRD1wm0JfqQsyrEdg7bR12HD2k8S10kcW7UOCir9JlsZNIl+TIWfz2dpi9w8bbWJyKQJZNEPie2tq/dlzcrwa5WfYcPAup3RNvGZ87vbtjGPYZmoc77OMj/N3Wj2PeWgunju2OyDqv62r0LxVW0vJesMGwmZ/HZT2jpNGKrl1qnudRMToj6IvpfuenpS2krtRJmmHVXPQ/P62eh7SJuqz+x1VQ06u60yzc7wIzPgxx8PnooZD56KnMyAub1hRK9JLKUtEBFDuCW/A6wJ+cSpcZqUlO9HOSCtmyBXv6OS4fehntKmyEnLol7Nfjxx7sGW/Z3VI3Ku/3NcB3N1jle7F7nN161g9EmBaVWc0jgvEwe2amD+LYsRSGziDto+/vjjGDRoEC699FIUFBRgwIABuOCCC+D3+/HYY48loYhkb+CNccvx55pduENZFkW8E6vznW6oM3SxAq3yjGp89gh73tPg2g//xDMjF+OWT+3BRREQu+yI/Sw+RE5L+YrKKrF0i3OSslCMaHo8nrZyUFe9LqLzecVR+wOwK21r6gNZ6KBIACL1Ze0Oe/I2mQt67uv4WXSZkz2hgIysgivTDFzEd8oqQ6Z1iDrIVP0RxTnSKSnkRGQqUU9cq+JTvJ/Mu6ZIMzsvB+fEkni5vizYWIDHf1rout9EBKxke4SAErDQ4SXpoa5c8kCsXKNEUf39ROdW7gS3aZSDxnlWpYVAF+zMljL7RverV7AA+oGp3CGPZi7Xty1yIMxpObY8uBEDq2VbilwHjZGy2d/LcLBeEMES3XmW25XWDe0WDyrycmfBlkLnJbt6m5TEKsjjWZGg85LUYQnAemwMrB717tsmX2drzdQtUMe/oo7rEpE5Ja90CrrL11q+/6Iqw+i23/1lt8e5/Mj9bKooUSaR/EX1r3aKO/h9zoN9eRIl1kBdDWarKkmvz+WQ5PUrTo2uH6dTbi1X+ieGYZiTsNcd39FaXmmiSHf96rkkNJ20PLKCIZb3rDh3TkpmgXr+nSag5DbQ7mlbFbT1i6BtldI2aA2uOQWQdapmcd79fp/nRLOxPG1F38ZJ4RqQ6pHqyQ5Efpcu0BVR8UYV0OrSdBm5jDp/XIF8mANbNTAnyuTrI5S99uejz3YsdZ/meTHsU1c1VdqqQbIdHi1WdNdFV2XEfS7+1z3bYz2/thaW4X/fzzf/1tUxtdk4tWtLbVIq2UJAxk0lK65jq4Y5aFn1TJctasRvS9RqD3myOari1e9b/o1yQNrJI9ltX8GwYfsNYpw3e10+Hvoucg0yA37r88jnM5M8+33R6xlr1UX0uM6etgAs7bvaBt9yUmdt3oQ9XWiVKOKO8nzyySd49913cffddyMjIwOXX3453nvvPTzyyCOYPn167B0QouHvDZFZ80Ql0dhbkDsOqgIj3VG9cGIFV938UG3b7uGetmK5yfSV9qViu8sjg7sGORmWAWjAYYCxfbd7py/WoMxqj+C+rRy0VC+3GJS2qupklVWGLDO61VnGLCMUCt/fcizGDDgRd/aOKppChqFdsi/jNrMvK1fclLZyZ1e3RFB8Z/bafPO9lg2sS7HUQbToP+k6dgFXpa110AdY/fRsAcgEdqp0A3Od16ncOXRTSgsSErSV7BG8LN2yBKocNtMFD0otthT2upCdqQ/Wby2K+oC3cgky6pRXunK4KTxsicgc7RGcluZG74VyjT2CSlFV2zV82hqzbA1y9EEWtyzwgPX8iaBtaWUI86v6GgL5PnUKPgvitevYWVyBM1+dZHvfPZFV/PeZXCy3PtSv8zfjoEdG4aNpqz3sM/6JT8sEhiY8IbcpteCOYJtAk/0MBWKQa05GSKdvzMIt2v06nQ75eRXQDEzl3y+ed/s3i2Ylb5iboQ20GgYcg7ZOA3o5I7mtnJIKK9YSf78S+JbzEMjvx0JejaI7HwLdc/jpkYuwUEp+JhKNAUBD2/mIlks3geGkdgWAOVXPXScvYHFtRFvmtJ1Zlljrr+VtpXLLbUCFaY8gtrMGOUSb5GQjo1N5h6X+yr5NcqX37d939bSVngVuCcsAqR6FDZRU6pOQOdkjCNVmh+b1tPs2y1MNpe2Xf64zX+sUlU5X0G3yU1Yg2+wRqtGPku0FSqVzt2hTIR6WgqPW41j/1gWxde1DtK0U37Ofx1j9rIe/n4/1u6LqaN3zTj0vfn+0jy1/EpTqqozb80MXfJQnm6O+s877iAe5TxRr3/IkihwsVYOv8t/x6LHE/SdbEmQpQduMgC86+SMlLPMq/JKvnXydo+WNtu/qRLlq56BbCUCciTtou3nzZnTvHlmeUL9+fRQURDrAZ511FkaMGJHY0pG9Bt6v1cOaXKBunUQ1cBPLiNzrb1X3s7fVrd3SEj/5VIz4W289UuCiQAVi1yvdYMAJt2RyogMjgjTllSHLNrEUv9qyhQ2M+HsTlm/dbZazef0sdG5ZH3f2PgAtqgKiaiINHW4d1ahyJdoJOXfwFAwavdRxH/OU4JEoBwB8/Mca870GOXpllaqIEJ1BdVDqpCYwlbrKNRODRFvMVruX6hHLHkH17QO8KQQrNQnk4kXO/m76AbrUa50qxFYuTd1x8hIW2JW2kfPz/uRV5ntHK1mMZXw+n2umdHk7y9/yMTXqDzPJhBSIzXGyR5ACYVEfSOdup6pYrZ+d4Rj4iPXb5PMneyye9fpkrNwWVe5ZniuGgXU7S/DpH2u1AZ9dmqQnAt2g47WxyyyTM727tbQdU6U6z3G5DXZrp54eGVGq/++HBTEHSfI+vQ6oYqlz5fdqQ2mrJtHSnfcMV3uE+JS2sqd7hiVoi6p9R7cVk1AdpUBUJHGkZr+hMD6smshQg7ZuCkuneycUjiMRmXJerpasQQDvkwzyMn5dgFzg9ByeuiKayV6smvH7YFseLNs56K6fU2lDYQO/VQXpH+rXTbtN1B7Bo9JWaluvqUoU6oTYdzBkXVIvnoHiWqqKcLegqrxfw4h+x+wz+H04+cCWZp9BN9EiK6Rt+zYn5ayrjXTIQTNHewTNd2VlrlvAHbCuJnNT2sqISYjjuzRHrmbSTn48uvmM6pazh8M1H3/INkRAVGn7/uRV6PvqJCzf6rxKTiZb89t07YPocok6ojuPsXzTf1Mmu3QqSvUdi62EF3sEl6itrq76pPsmqrR13EVcyEINw7Deryryb6yUJlzsk+fR13K9drJuMcvisOJOXmkY8PnMc1QZCkuetl6VttHrs26XfYWiuDahsGGzxrCvtIK5LYlN3EHbtm3bYtOmyOC/U6dO+O233wAAM2fORHZ2tttXCXHEzW+TOGNZml7HGr0S1R4hpkrTm9JW7SDsSfYICzYW2AKBKgWlEeVsg5wMT7/dyTZA3JOx6pU88Iq1baWDPUI4HFWXmEHbYNiiIKtO/R45fxNu+fQv9B82w+w8y22NHFQqjhEUdAuGhKUBizxYf23sMst2sdTCM1bvxNodJWaA/dLD29m2UZfaqkkV/tGxGc45dB88XDXwdFJiie+r59VpeXUibyOdmkpVAkTKFi2L0+TCj7cea74uD7kH3r0grlHAH7VHcPW0lT5yapd051Q+B7pl+07LoNs1yZPec39ueumE2zxt5YGp+hl8liCAUNrqBoSAdZmcLhGZbXulMG4DFLXPoP4tDzbV/fwt+VPL6tlwOKISevC7eVr/ZF3yQIFOOT9xWTRhXPP6WXi6yo8x0Upb+Te4tVOyN+Q3muX5MnKV91qkWKpz+T5KdiKyrIDfFnDUnfcMJRgmNilySVzp1B7IKn2L7YcmICxU5bJSNCCp+2VmrIquqOm+byPLZz2Uv6PHjO5TxTC8JyITfoehsHVStlmVYspr1m+xCidTSjilO49O94a8MqFI8ntX65HFkkXT7h7X2Z5YCQA2Sl7STopOUe6KGOpWgXzuT+7a0nVbUW71/hW/QXyu2h2EpPPqtl9AnqiNli8rw4+XLj7Usk+ZSvN5GEtpqw+u6bbV2SNkOdojRIO8uS7WFmoZvSptRbvdo20jbZBfdx9HjqUPPgFQ+g01E5FUhqyrA8Qz98mf3a2i1MPoJkt1bY1pj1W1A519itMKBCd097Q6Edhj30bRQKf0kZOS3O3O092X4p2wNHnhxdPWC1alrXtAOGD2n6y+/TYlsfQL5aqWneHHaQe1ciyL7rdnZviQLyVX9vujSttgSFp14dXTVrqeYvIx4Pfhv2d0jbyW2ii1b3tAK2tyxoAyniHuxB20Pf/88zF27FgAwG233Yb//e9/6NKlC/71r3/h3//+d8ILSPYO5IYm0X5vezLxJOdKN+z2CO7bywG/eJRKsVRCsZbF1yblwZA2uZjg6g9m2gKBKkKt1qphjqcO4q4SfSBCBD3iSfoW07ZCurdl5YesUBJB27LKkHbG3Y1IkoHodtNX7gAQCVaENB012XusRPEKu+74Dpa/3dQFYt8BF3UT4M3+5ZqhUpKj8w6xfS5nZgWkpV5Vxw34fXjt8v/DtVVef07FMT1tld/l1SOtJsTytM3UeNoWOKgc929Wz+xsxlKAeCE6CPZJihlv+3UK5uiuu+xTp7NHUIOAot6OqErWee5h+8Qsj2qRcLqms2/z7JMHC+pnfusAJaY9gqS48KLsUwMDeXEEbZ8813qvyMs67erz6Gt1BcfvSyOB1lELNtuOuaPYuW0uLLXXaXnAUj87w+znyIoclXgtGADrs9PrPfCJpObXUVN7BJ2mUd5PMkK28jkdf+9JkgIq8p6ubxlNIGTdx22fzXY8jlN74DQxEQ0YRL8n6kvH5tFBbIZD0FYsD8/NDOAoRV1/2ZH2iT0g+rvkYICof8OnrUF+VXsaa0nsxiol4nezN1japCv+sT8A75MMlaY61W8JXNi2c6j/smLLLVAp2zmo7eqQK3o6JhWSJwXbNc3TbiOCdhXSb3FDN0HshPhY/f3ib9N7WUoECXhR2kZf21fnWLfRXY+op63zuZYDUE62NbLCUKe0rQyHHe0RSl2Srcp49rSVXotnWIbfb0uyp24rv7ZNaEp/+y3tvHW7eBM0qnVYXA/1911e1Q6c/3/7aj/X2Rzo6kwobK0junr7xvjlnsqu7lNGnJcG2Rl4uF83XHJ4O62Vh5Nns1vAVfeZuHcNRK9JwjxtJWV11B5Bv29ThWoYksLVZ598kn6ufA2yMvx4/JyDccIBLTC0/xG2/evuv0a5mTijKsmb2J/oG/61dpdpx+aUzFBFthsSk5sT7zsZN53UKVJ2cR3D0TZ438a5+PDfR6Jr64bW36mMZ4g7cQdtn3vuOTz44IMAgEsvvRQTJ07ETTfdhK+//hrPPfdcwgtI9g7kh4fO75DokR+GdU1pq17nTQXuWbOtiijvQVu30/LmhBU45NFR+GWe3jqgtrn3q79x1DNjsWxLNIPyxvxScznvdiWg+//sfXeYHMW1/ekJm7Va5ZxzlpAIIgeRDZhkY7DJz2CDA9jY5vcwNvazwTbRBoMfxjjh/JwxOeecg0gSkkAJ5bi7M9O/P3qq+1b1rdCzMxukOd+nTzsz1dXV3dVVt26dey63qyrK9G+qjS22uft2xyuBc0JdvIkFUSJ5BMvifvGaLcqxwf/U0SDkAAKmrXv/3t6ex0FXP4Tjb3zcuPiQjevotyZFO/NEheVqlEfQMG1VuLyj730U3KNMkQWjQg0hsyU+0DNt4w5zAPivXz8LgJNHKN/4wm1MUINRzAf0nm/UaNpmUh5qi33VloTJBXSRHC6+XJ1WGocZ13doX+AYPl88UM4ir3YrXWJBCurUmDa0GT/+1JxYGbVebvEZlaUhjJHWnm6BHLEoItabaTGtLiJNWZtp2W8fNTWWoIbq09Zm09r3UpK38OPjEMVP7tMvVjkmOF1wZ9Mp6Zm5JLNyBa3L9R2wsYxctJrjx0R/c69MCQo3iUDPWZ9Nh31VRJNw93zVpmAsomzYXL6ABxeujpUV0I0H4nnrpE3oYWKxO7p/5CDMpPkQcTGuTBzcK/bb8D68g1FlhQKyM/l7/3kDgHvG8N8/vURiqAtZAleyQDtxUHDJsZ5ZvBbvr9mi3djc1haPtOKcLrRu8S588aAJ+M8X98Hh0wdLZT+1WzBmzBzeGx/7yaPWaxD3NJRHSMC0tUUTinZvb+edtuJwsakWRspYNW2j71V2brRhoWe7mZ3C0XFCRiGraQeVKhFSR7uP6Rv+/t7qLeyYVCj44T3RJYgToG1UNeF1EHVn0x4Onjo49jttk21uVNtBNU4FkjJt1fdLjD1DW+ql779/7Azc/sW9cflxQTTH8XOHS7+z8ggOmra6bpvEycatC8R3s0e24Ox9xiKV4scFGr1AYZq++HGh+Idffk1bYbrmfT+2IRJvR/DDb59cErJUafSBWg6Q+1p9No2hRQfoAZPi7H1uHBjW0iC9O+m0F9oibxN5DVdNW9FHWnOFcO1Gcw9Qu1lsXE8a3ItNsumSN6KKCB1ONz9//nxceOGFOOqoo8rRnip2UtCJSWVgVqGHlHCrpzltled89PWPGcvTJFemha36m2ku+MGdb0r/dyV838c/X/oQuYKPvzy/DEDgCPjYTx7FYdc+wjq1uVA0EcLbt7EmNhFyi80Xi4L1C6bKBkA2DFG3OG0VppoOr36wAc8sXie3p1i+nTgaxOS/fMN2Z0kMAHhzxSa8t3oLnl+yPmQjcs+e7lrTkD01HHb8gCZ8psgmAtzlEbRO0oLvvMAF9M4tdYFlM651xq26gBNYVHQaq07actpUa5jkdxLTNh0xVQS48HMguOfZkGnbca9QyOJKkfBqx2q1TFvGkUbZlpzTdpLioFH7VU3GvuKg48OFB09kGbG67NhAnGUTaNoG372wZF3IfnNLRBaUNS0M1D6s6lVSZCwOEcosymo2QAD9HMotIp4oMvc5cBsRlGmbSackvTnduFpKxAztd67vgG3BKl2/48vv++b7JzFty7Rgls5P/vYQ9dWzf/0srrjjTSMLmToMdGONgO4ZCQeQ+j5wWo1iwd6/KZKVS6f4pDPi+Sa5Zdw8xDFTXRfqAPD5254DEDgOMo6bugKivwdh8JCOfWP5Rpx40xP41P8+yUrFAPIziTZJ4+WoE0CMq811GUwd2hxz5uw5LmAtv73STRc03Ex0lJbgHHk6iKIq21OVRwgZ4cXeHjIREzBtw8igUBpE7zgxatqSfu2qrZsvRFIXqmwNu2Hh++HGchInuaumrUAmncIpysYfII9T5rmRONnIfemoHaW+XmKOorJm/Rpr4Hkepg3tHY49J84djgVTosgaXh6BO59MBtCxUTcy8jGtuTyuuCO+lqIRU7c+tgif/fWzIdtad9/CYzXP3pQYmLsuutlhIzokBWWLRva4/X0UyJCorrC9mr5mk/3g3qHdxvSR7SXPYzfrXDfwxPgr5jDPA5qIdIk8Bgsb0czAdyVG7OwoyWn7m9/8BnvttReGDh2K999/HwBw7bXX4h//+EdZG1fFzoM3V0TMwlaLwVwKSgk57AmQmYg96xqTMqpzkgPP7kALPztMBrYwN4F1W9pwxR1v4p1Vm+yFE2I9Cf8W4eFPL1qLtVva0JYv4N1VW2LHqIuGXL4QTqR9GrIxg49bYAkt15F9ZR23iGlr7leu8ggX/eXl2Hch25OwSejpaBidzRlBNenO/90LAPikI9wOdr7gY92WdlIm+O07x0zDd4+ZZj0/lV7QabqpjFYbdEwRapP5vm9kHYk2cTAl5wlkJtTvbC2Ow/d9/N9zy/DWSvl9EQZ/M9mdp+8gl4hMLGZP3n1kqKcIFHX5ysm0JYvPtGExG4JZYKjQOYHEvXeZ81Rmj00fGZCNfZ1sh/ot/aySeYPkRsHfd7++Eu+tDsYk3UIi1PYrOGralsi05fo+ZRZl0noGPB3f6LvA3d+Whjj7VmBtUWbmiXfX4JgbHsOdr65AKxkzPMgLTy3TlpzXNQEYrcvVaWsLDaXNc/Uj07mAO0RemCVbMK/atB2HXfswrrp7obYMvV8pT14I3/TQu0YbiToM6Pt65MwhsbK6Z6dj2oahueFGmx+Ogf2aorHspaUbeF3PkBmpbX4M3OPtqNNWJOJrqElLUh8uoJIClIkIAI8UtZ8/3LBd0jvdjzCz6BhpcoyEIdYFO7tftMPmpI/qDsqHmra2RGTSxpK5bjXJmYCqKatuJIYObAdN29AhV1DrFL/Hjzc5hSmDXJcwKiqLYlkfzy8JNu/rsmlJ45LrswU/YponkaNI7LRNeajLpo3OM/pLvFz0N5UF6mjEkjoH5Avy+AHwch512TRO2jWKFuOYti7yCLppYj0jV/W929/ATQ+9G/te1PXmio247F+v4+7XV+KvRWKK5BSHPE4C0Golm6Yv07gQyCMkH09NoHkmbM5uzg7LEv31sBz5SNmwOudnVFe8/vEDe0l2lw+eEW/ahKKlc6HTNugDTTVywlgq4RZFY2lsxFT8mVehR2Kn7Y033ogLL7wQRxxxBNavX498MfFHS0sLrr322nK3r4qdAKqTTQ0P6ih+99QSTPvWXbgnoXh6T4BL5vLuiuROW3qt+nLqjp3LwrfeMZTqm/94FTc99C4OvfYRp/JJQLVExSYGXUxsZRJlDewlJ3+8941V4d+967MSgxXg+4hY2Ow7QU7QIRwmSRKRmfrgG8s3xr4Tj4YmKRnYzCe0tPVvTptYvX5AYb8U//5oc2u4EDtx7nD850v7AAgMryG9gzC0NpOmLXX0aSzBpHqrukWHvAiL69PFy/Pf5/I+fvrgO3iuyH6m59vSlo8tLUoZXR55+yN85c8v4eM3yCz6zcWNhX6EZVZDjE2xQKROFjEvDG6uk/pkwBqQF9MdQY4syKk+ora8xNTky+jmNDGmbS/208aaNMb2b8Qtp82LlVWf436T4qFmsWMYQ1qFurjgFp/0N24xoltI8PII+rFWDX81MW3pe8Yt0mh/zqRT0r2g10CfH2W5qQ6+La05dqEqIMaAT938JF5auh63PPqetImQKxSkNuvGVXpeZ2ep5LR1O8i2YC1F09Ymj+CTW5qU5PS35z/Amys24Sf36yUqpPvlxfuFaaODlhVsz/psGjecvEusbGKmLRlH2nIFfOH3L4TPaQAZA1dvamXf07zFEcCBcw5wc0rWgbGvor4mLW14uoCTRxB95M5XI/1ooXd+zwX74ldn7hZumkpMWwNTTkqCEzrRzZrbFPdeuJ/2GkLJCUd5BJlpa15ue0rdAm3hhrYnlYs2vM1MWzrm6OQRoufBMW2FbaNnalJNW51sD5VH6FPcdF21aTv2HNc/rEsnjyDsQ9v9pr8L2RNX6OTA6BnN8gjR35EWfnzs7CjTNl8oYHt7FJa+25i+uO6k2eyxlDnJvQPs/RYbS4YyAFhdYkq+ktsc1EblVZ4v6qhambaafmV02jJdkEaoiWssl6YtTTwXOm11beMiIFKp2PXojrdLhPDv36DmOpwwdzhO2nUEmmozrIPWVdNW+GyE/r+ao4I6sW1M21Djt4f5L7oKiZ22P/nJT3DzzTfjv//7v5FOR51n3rx5eOWVV8rauCp2Dqgv/HYmKUtHcMnfX0FrroDL/vVaWevtDujRmraME9IEqg9oYszEEpE51O06d7+0bD17jnKAOh3vfSPYYKAG/HbGATl+oJyJ89zfPhf+nUmncM5+Y6Xf49IRkRi+ymoToWtJNG2ThriI8jTckIaLUpj697urN+PWxxbHvufumSTwXzQuRFKYhpo0fnTiLEksPwy9N7A4JaetpjOZ2P7cYsSW3EmcV9xybVii5vs7X1uBH965ECs2BolmLjlySvjbMdc/6szyM0FkPVcNfDHm9yHMRVkeIZ6IrLU9Mv6ofmiqzExbyiyiIZ06uEQ7cNmyaXnhLNx1TF/c/9X9cdAUc8KwWcN742Mz7YnIXDQVOces7hiqaUtRZ3GKuCYiUxeWplBA2WlrriuryJbQ4u050sckJ6vc/680MDyB+DipSsG8tXKzNDboxoOkztJSIkuAymja2uQROhIC+RYJYdfdO8pi87z44t00h1BnmHAQigXyFw4cL5VNyrSl0gvPvb8O/3450tDv0xAxbQc21xp1JpMww3h5hGTsKhUDipvE9UQj2tXuFOWyRB6h4Aest3dXx6OIhKa0iDihGyphkiQD+7Pg28cc9fBrPjkrZlNxdUdO2/IlIouYtnoZIFpOQEQK6MZ3WlwNfRfHqI5gChPTljrCdGHsAnQuFZsiu4/ph4sOnYSvHDwRd1+wr7bv55yZzdHzmDeqj7Yc12N1des2MdXnSR2A4bUy99NHssjPGNPW9/EBiSr742f3wKh+jephAOT3nXsHzEzb4LNuzHnvo7ikiDaapXgNWxliBT1EjUgAon7F2SI6mDRtfUl3tkxO29BJ75P7pnsP4t9x8gi643W2VliXYZK48sRZuOL4meE5VbhGXYgx4RePLQIQX4eGkgdOTNvgfzr2bG/P48GFq9jEgDs7EjttFy1ahDlz5sS+r62txZYt8Ym3iips2Kgk8NBpWpUKMYjqFs49GdRJ0NN2qrjELSZQtpppYRpPRNYz7ouqqQrITqhtjJP7rtdW4h8vfgCANwTHD+yFVy87NPysLvDb85HTT03EJbRlbYsyycmQsA+KZ7PdshurnkfFKTc/xTJ5ubDzFGNcC9Y35ySKtH01jgJfNtR0iyexgORsMTW7PWBg2pKvCw4GqCubgMoxcIvoUpy4nEPA96Ps0S3EYSHLIwRtpqHeJv3UbCinUA6mrR+2JwrdchtvdHtJWnmE4rmEs9BkkNPnK9jfNqSZvh6vV38e9Z0LnLbxerjQS4AybaNrNDlt1baYQlypw8TGKsykU7LsA7kG3Saxysp8Z5VZ9zJfsGtWB8lWovIqHn/3IylawmXuUh0Drk4024JVlrR1qzPJBl9SUBkLXd4DWn3Ki2+gcVI/Ys6hYefCVhQROJ9QklKyzhjfx3m/ex5AXNqGMhlXbJS16Wnf9cGzxCL2lruTgZuGuGdueh9ViKMbaqIwcpt8kgDdmI2O9bG5NcfagqJd4hnQMdQkF0EdkK7yCNFnNyescKzanYjR37a8kaIpnL45PbfqYI2c4fZNOcF0DzeZFaYt9yiFXcnZNbQtNk1bKjXUSp5LY20GXzhoAsYP7MU+zzyRgLIxbanOO5UdidXJjFNCkmxyUUt+RN9gjtVt9sXkEZjfOHmEpxetxdRv3YVbHl2kbR+F2tJ8QY66M9l31AnHvQPs/S7eG99iV97LRK4u/oj3AYk6+XE7bqNwTFvVlixZHsG3O6STQtRN8xToI9/4jTNTbgGKUjRtOXC2uatMoJj/xGM6e+8xbBuopq0tcpCuHb/xfy/j9FufwXf//bpTe3YmJHbajhkzBi+++GLs+zvvvBNTpkyJH1BFFRaoRkq5mbYCtsGuJ0JieSUMv+5qLF0bT6xlwl+eWxb+bbrWuNM2WbtMoG0uBxORQoSMA8CgokQAdULpNh2+9IcXAcjv0f4kdJome1AX9DScXE0K0VgUlrc5YmWmrbFo/Nhi3eLaTO+oyRkh2KICgsHJMm2lkMWi07ZoBHMyGTWhQ5A/P20WJ48grlFnfAK8dqebPALVtGWLOxum6qKvHL2bht6KxV9rrhC2mWqEcvII9F2OduxTUqZa8R1QrkRkEWModLA5Om3fX8svWnSatULnmLKIdTAxYHWQ5BESMrGBIKO71Abwiw6ds1l09XzBD6UF1GdHod5ms/5t9DfvSI7alEl7ShZ10q8046ral8TYsL9GloImWzNBOJvV8eyjza04+eanpO9cppjYfOc44dn2cmi9rmOBTR6Bti3p9EmL6+ZCWqeHeN/mxvB/f2FvALLDYJvyPrrc45eXbZDGKIrIuQWs3BiFbatRJZMH9TIybZMQw9hFP/OVjV116LSI9S82N2szaRIS7NYeMdap8gi6DS3x7teFTNu4bAg3diXZKFLvtY0NK+6paIvN4U3rt7FyVRav7nfK2gYiu0LncOY0bfPK/TMlInPRtC347pq2Bd8P5zt1s4/rs77vRxIQlr5K+3JNWm9LcmOPaPfNp87DafNH4baz9ii2iW9frO8wcy2XiAwInrGrQ0p9JgXfD+emUf3iWrYU9H5xxADu/RHrGpum7SQSjSagI+KIvsExJ2kTOG1l3WaAadOR3cwRerlE07ZMRFvpeYuND20OAeZrOiYK6Mwymx/DtrERnZNZhxikF2jfFxrTIjp0rsJq55JB6nN0yOMZAPz9xQ8BALc9tcR4DTsjEjttL7zwQpx33nn44x//CN/38fTTT+N73/seLr74Ynzta1+rRBur2MGhMtjKEeIqQI1rm4B3d8LStVvxCtH/0UHSUyyzE7E1l8fzS9aV3TkJJGdkAsBCkszIxOgphWnr6gTZh+i+cuH4HQGVCRGGOH0XtlpCRajT9uZTZU3MDDEqKGj9quPQWdOW/OzKulGPDcNRDQZJkrpFYgbWSJTYL0Wnbci05XSezKH3tL+lGaatuH+hpl/Kw/99bk+pDM+0tcsjUH0uPZPSrW+nUyn0L7JThrXUx977UoYBygwTCy8qldC7PnLa0kVtmIhMctpGjv35Y/tJ57E9oySgC0Qa3q8Ddeg+s2gtW0bnmBD1RswjN6atydFKQZ0QLuGzwefoC1XD1fP4RYdNyiNf8MN3jD7zWHmljab7QZ0UvDxC9HtdJi2VoeOg7tmoY4dYoIzWhKLmfd9Jj5+ysCgWMnqALtEzahnXiJuVykaXCkkewbFOWVIhfozs1E02oNAILN3mjCqPoDrhxLzav6kWVxw3Aw9dtD/GDwyYdVH4LGLyCP0V7XhuE4eeakx/uY/QRazYnN1/0gA8898HAQD++vk9cd4B4/Bf+47VhCzL9biADcNlvrQ5bW/69Fx88aAJAKJxKpWKnFzOTFuyaUkdeNvb+ONrFaYtfR9VTVYKSR7BMq6q4409WVjw/3YLeyyqj46/5rrVJGcqxLMTNYYOWAsLlRv3okRkchnulcxrnGbScbBr2nqcI8cS6i3alLdILwhQx5DJoc79JmyOEX0bcNkx0zGy6BCVEpFJG6fy8Zw2a77ga6NvXKEOvbm8L71LJtC8IYOa62K/m/SzRbt1jE81x4bv+9o1SkGxcyhkp3hUl0COaGFLx7FnCsCPC6Juu15vUlAWuc3ZzTPW47JTWnkEix9Drf8T84az5bjNlWaDbUafSd73USj4oZNetemoFIoYt20kFG5OTZpMcGdA4jty9tln4wc/+AEuueQSbN26FSeffDJuvPFGXHfddTjppJMq0cYqdnBQTTmgNIeeDpsIe7GnMG3bcgXs88MHcNT1j0raRRwqqWl744Pv4rifPo7rDYk/SsVvn3o/UXm1TxiZb+pvDrfFdfIWoVMA8J0yh260MXqK1IDXMcLC34uTYzbtxYw5sTiJMW1JQg11gpw1ojcAuxPAlgzvw/XbtHWoO/AmkX0du5obL9rzeiNR1iUL/t/cWnroPXVOpFJxo0lsSoWGdiYV25lWWc6APuScroloOJZes5T9OoZMysNPT5kbtDHtlZWhDkRMK+ogp056+uxFZlsq+REZf2kpwUbQ3qLTtgzRBnQR7JLZlvbLOSP7sGWEo/r4XYbjq4dMjB273YFpyzFSbHBh2qoh1/STqkXs6eQRLOHHBd8PWe8mCYgz9hwtfTbdD7qA59o0vE8kITF2QCPLOAP0Tlv1e9Fv+zXyYbeBfhtfV/+mGvzqzN2CdqflsXj1plZs2t7OJuYsRR7BVTeWasRyKEXT1sakzUt1BhnQ739zpZPNR+dCrdOWVOMh3lfFWFyXTeGk3UZKWpCSU0m8j8W+2lSbwf1f2Q8/PSVISpZnxhl6rm8cPln5DWHdYhwcP6ApPOcuI/vgokMnoy6bZvuyzREgknXp2iPAsTHVsVSF53nhZl6OOEtdNrQECkQ6hIYC5wmrWT5n9H7X1wTt28Zp2hqcM4WC78C0VT+bB1bx+/trtgKIopF0cJGnUduik4cLNW0jTykAu5asHGEQ/K86vWnfV0G1iONtjuZHm/NYZkC7Ob1F+SgRmTvT1lQ3m5BPOz/yf6sOfy4SplDgeLYRXCKD1M0tyrS1ObHpfLTAopMvIJoUOTbtdQefC1o7SfSh/334PWMbuH6old0wXDr7qhFGp5hvypWILEqmVTqJwtSfKGxJs1VnrM7uUzfrvnH4ZKOPRJXN2tSaC5+36uwVVfu+H/pguDVOUG/wv7qhBES65lVEKMmNfcopp+Dtt9/G5s2bsWLFCixbtgxnnXVWudtWxU4CdWe5nM7H9dvawr97iLQpvvXPV8O/314ZZ99Q2BxmHcG1974NALjuvrfLWi8AXPqPZEnhVMPedK2uiVmoQ8gWFgcEO8s3P7LIWq5UcM/SloiMwsReyBAjkqKNLGqoAXjfV/YLFyR2pi1td/S97/vY8/L7sOcV9+PYnz7GHisM0m1tYjGdXB6BS2iXV8LOKTh5hLtfC7JXczIFWUZflYJ+n0mlYoa0aLdwynAZ6BuYxZ9WuJ8uwgpU05Yt7myYZlJeGLq+mRhkAublhx3CQfnw20HG2YaaDN4mOqE0bF6wXqiBHWVmT8UMTrEoLwfTto0siFwcE2oCBYon3l2DWx5dhNXFTNYzhjXj/AMnhNf67urg+m0hZIC62eD2TOlt0h1jclycrjhRgzrlA2oyKS3zV9S1fMN2vLR0PQD9ZgQA7Dm+v/R57wm8FIHaDu78LQ01+Mmn5uCT80bgsOmDpfPSV1kXaq9Nnqc4bameHee0nTCwCc9ecjD2mxhcSyT9UcBz76/D/Mvvw1E/eVTaYBZwmdJj812Z7ABajWsUj8SkZcYLyakL4KI/v4Qzf/ks/veR+GJehZyIlG8P/dbz4jqi5lDvYhv9eCIyABg7oAkjixEcnGNcvMMj+tbHxvMwNNeXNbM5mHQmdQv+z8wfjVcvOxRjB0RO6HImIottAnseK2GjQzuhG2ZIKHDB5zdNatKpcN4S8yB9T01ObJocy6ppawhx56D+zCWM1NXnLI+gsTMiKYPgc4xpa9DXjViGvnRsWlMnRT4vl5VAHOTtloRo1Jm+rV2/Sa6i4PthG2wawvQ5U+klFeOYZHO691Enj6D2HS5XQsE3RxS45PVQDw8SswVf2uQ5xCZy38Ya1rbl5k3RB2yatqrUjCmxtGn+0N03Ad2mgVkeQT++yzkotFUkAk2mJeY9vaat7nt9fzpn37Hh33ZNW7f7pL6nR0wfYqyXzoW5gh/mIqrNpGJtonbzxu08G1dth+giv3s6kkRorO0ZRLvORIe4xw0NDRg4cGC52lLFTgjf93F+MXmDQDnD/Omk+PryjU6ac12N3z+9NPzbtjtPDeZFH21JlJXUFUkSVZQLW1pz+Ml9b+PBhavwzqpNsQW0SdNWXdBRdsFLS9ejPV/Ahm3tOP93L4RlbEkiAEgZnwFg2tC4phPFmys24s5XV9grRtQ+gYidGWca6mBi6wkDU703lPHgeR6uO2k2vvvx6Rg3oMk5/JEuxOm7+/6arfhwQxCC+7JG6kMsfl3kEbRO29b4fXHX0Ao+rNkSbO40M3qbEdNW5/yPvq/JpOJM2+Lvf3p2KXQ4eGp88WdL7gTISTp0zllXFnkm7YUyDVvb8rFxuJRhmdYh+vJ//y3YlFq7pQ2DScge/ZsuztoLBbz24Qa8Xkw0V0cylwuUMxFZK3HYiEWNaU6i/ZLqsW9pzeG0XzyN7/77ddxdTNrRr6hhKRx05/72ueA4SwgZoGSxLkEeQTfGxRNgRH9/acEEa3lTm7kFvGvES1NtBrNHtDjVrbsdR80aih+cMBO1mTR2Hd03/N7kaBdQnVHCAUQ3F46cMQTHzBoalufkEdT5M000be95fSVyBR+L12zFW8wGrYsDVh0XyyVtL2naOjtt6Vxg//2u14L34lePLzbW+//+9goee2dN+Fm3OUPr9zwm6sHgMKUOg3uK7+tGxZEeSVvEzx1ugDJ1i1eGsuR0iaO4d8bGtAWC94W+71zZUhORqeNtikQhPLN4HT60RITRubMmnQrHIt/32U0T6mAS7aP93BSyzyXB0TJtmesyQb1/e4ztqykptyWo21g0HMN0fTt6trJjq92iaRvULR+jMpVTiuOEwlnT1uJYpXOpiGziHIkqKDvdxiw9YuYQZNMeBvaqRbOBpXfybiNj37kkcuPYtNxnyiQ3DZ0uTlvV9sj7vrSxbELfxhq88M2D8fg3DmR/5/zU4t2yjTmqrOFWxg5X62Qh2ePB/+KSfWLfmhK/qTDsLxQlxcwO6aSQNIwtTFvVTv9UsS/GiMTkM32nTNGIQLxPqFF9unIDm2vZcgI0QqpQiJIJmzYDCn60+c1JwAGUpRzcuMff/Sj8zSSltbPCPmIWceCB/Euv4v777y+5MVXsfPhocxuWb5C11cqZUGuzYnTf/dpKHFVcZHEoFHxc/8A7mDG8Nw6Y1PUbErY5RV2w/eGZpfj0HqPK2oZya+W64JBrHpakIdQJjWuTWFiqBoIo+qn/fRJPL16Lz+wxCr3qMrjztcih6pKV+fUPN0qfbaFdh137CADgb5/fUxs2TSEzVuNMW13YnAANH1eR1jhg1fDBY2YPi44JQ9TNz5+yjmh/XO9gkIrrVJ22fzl3Pk646QmlrJ3pKiCaocojeJ7CllA6ltDCpRDhoyoDZunarfjfh9/DYdMHAwj6KJeITGykzBrRwgrrj+zbgIOnDorJbej6l9C/EixUm5GovjuXHDkF/3P7G7Fy6VQqNAi3tefj7xFbuxntlpDmiw6bhLVb23DgpIEYSJy2WbIAzeV9/JLoR9dl4/IItWVk2oZh/NlIB9U0BnLJ0gBg1abWWJ9RM1qL8tsdmEelMG2d5BHUxQL5O5sOkr5RFqhq7JvazDptHXQMg3Obr5HW7TJ+X3DwxDCZJXWG2vSG1c/UKdeeL6AuGywscgWfTaKqvscZMq6u3RIlpfpwQ1xj1kXqoCNM20feXo19NGxm2udda5TeE+YgnSN4WEt9vHARbbkCfqeMmx2VR+CcHVRP8T+vBLaBYIcLUJZ0rJ15faQLDSNvd9T+pLBtzAlwjiOu/RQ1lvcMiDs9xVwncPLNT+LBiw7QHk/JBJI8goadThf4XLSDeM4mnVUqj+CS1BOwO8Fo+T4NWevzcBl/BUJNW53T1sa0NdQftNuPHaNj71LkDQxaWfbDwMhFdO9o1IULk06SXrCwwscNaMIdX9oXveoyRgf89GG9cesZu+KMW58Jv9MxzmktHCs0+hz9Lcsj6EdPThJHhfpICgW68WPfcFEjQyhMSQ91Yf7nHzAe1z/wTmx+NDFtTU5b7p6KuYGuJ7KODFJA11fFdz5hEWurSATx7v7mifcxtUjmcSVRXH7cDPZ7XRLDJDraALDvRH5+p+VaGrLWzfTehLmeK/jh82YT3JFxYVvotOXdjZSlDAAbt0X9aDzDiN/Z4Uyhe/DBB7Fo0SJMnToVs2bN0v6roook4JivrppsLvjFY4ukz5stk+S/X1mOq+95S5rMuxKfueVp/Oyhd7W/q5Phw2+tLnsbyqy6YIRfZKKoWr5qGzjW5ZV3L8TUS+/Cc++vU471sbk1h6eLmdBfXLoeLyxZL5Vx8YGoiwtOL5XDKx9sYL9/+K3V+Pkj72FTMXyEXpOavCo4H+9cEJN4FGKtZ9qqa12x0GQTM2jCH9tyBdzxynKs3xqwU6UwWvJhzeZW2CBedaHXK5yG80bHGSw65zFnEIrFufrMTBl/AWA35rzCeaSy2C/6y0v4zZPv44d3LSyWSxXrlO+lylQRyex+dMJMzB3VB//3uT1ZI9OchClicpj0/YD4NauOQ4FMygslMXwfeODNVdrzu4I+s3Vb2qV7eMzsoWiuy+KGk3fB8XPlZAn0fjz7/jr8uehsA4L+rrLZbBIWSRBq7mZSJMGEvrzstI3620Zm00LNFg+4ORcAZXFTAtPWtX+YnLhAnEll1uGNn1NoVHYU1EnhIt0xrKUehxQZ7dTG4Jj6QHxcEc42mpSqV102XHTkCz6rOx5n2hbHk4IvnfuDdVtjx7o4YFV7SWVAmaDOgxS0Wmd5BEn+IH4MvRy6EWBiD3GOcF3UAz1lyov3v3AsZhyVXGhurEzx2a3b2o6Tb34SDyyMxsg2g35qiixMw8Q6Gf595BORFZ22+qZJ1xD8Hf+du24Xpq3qHFITbi5eE++7FDTSJp2yyyNQRwB1gAHAO6s24Z2irA7nGKHyCDZNW9XnZdsMo1N7ExOVE6vfsEGsQhTVySNETttoAwCwa9rSukU/Eq+Pjr1L4fLOAJF9pNuMEM179J2ISTd1SDxaTd3ACd4Zu2NaYPzAJjbplgpVj1ifyI1uDkaIMT+Z+TlPNtW5rmVbjwLxsTeXQB7BBrPTNj7mNNSkQ2alOgar0ZBcnQN7xe0fehtpRAI9DoiIJ2pZDvy4IOqO+nm5NG3Fe/T2qs1Y/NEWAHqHsM7PrvYn+pE+Z5tzVe3Hun5N39MhvfWbpgInEBu9UKDOWIYkRDbahHNXN8eHc0HxoVBJy3JLPu4IcGba/uAHP8Ctt96KP//5zzjllFNw5plnYvr06ZVsWxU7Abhd5XK+qPe+ITsedBR9gXdIiOK2trw1FKHc4MIQL7/jTZyz3zi2vLpA68c4BTreqPJWZ1qIFnxgzeY27e8CXB+54YHAua066n3fDzUlgcCxoxpLLotS1VFCHTSbW3N46r012H/SQKRTnvQcOcPo2cVrceovngYA/PbJ9/HgRQfEmCSFgi8nItMwbcWkvF1JnkIhDII1m1uxYVtdGHZiCukUzsdn31+Hza25UEj+s795Fg8uXI09xvbFHz47X3qe9BqElhGHmnQKbflCeN+FwWcySHTjAufAF6+F6lg36ZABfFevIfqq+YIf3ssn3ws2AQRrRJRT1zZRsilZt/fEeSNw4rwRAIBVTCZ3uzMsCB0rGBYF3PcmjSsqT3HVPW9JvyfN9g7ITtQP1m8LNSEB4Ecn6Dd5KcvytOJ7IlCXTcc2JmqUjQsOd766HE21Wew9ob+2zDurNods6NpsOha6xYH+Rs/P9X+xaDl9z9H4ZTEkfFt7PmLJG/q/HJqpLSaBjll63dnYmZTzyp9VB46JOcs5KppqyxPylpRpS4+hkjkrmHcP0DtD+xDGSdDvgnK+zzNta5T7Q2Vn6Dy0bF08xNxF3z/OCLYe4lQ/rdfFJHv1gw246u5ozLDJI7xIWKym+ZezEXUyULI8Ahf1oGe5hkxbg8FD54/H312Dx99dg8VXHAkgcj5yGy/U0RZqNFqcWxQ23fKwfVamLZOAqQRNW8/zrBqtFKcqY3gUBs3LI1BWVugA8H1sbcthwdUPk9/i5xJ1533f6EgX1yEd68iGBdwiBpJER4SatlqmrVxOZSOaNW1lR6+w2VJhncXvmfcwZ5BfoImjIu1Rs/NTnGLBlIFoadCzQAWCd6boEHZghbtC7RNaFi/jVAQ4Jxt91sH/eaKfmvY85JT7a5IUEFDHUboucHFim2DaIBLDYEoZU3Qkhq0mpq0ig0ZBW0A3XAA1X4TZLqHg5RGi/ldueQRajYiYcZFH4BzW0W/RFzT5qT0RmcJI1tl9pJgtqgkADp4yCH0ba7B2S1txw1kwbeNuRDouiBwenPQcIEuJAHLOj3InV98R4DzrXnTRRXj99dfx97//HZs2bcJee+2F3XbbDTfddBM2btxor6CKKhhwi+xyOm0/MU9mcGkZGkXQSeUjB6ZgueF67dvb84GgevH2nbnXmPD7ciOBbe6EdgMTqFA0zG2ISyBEn9WELj5klurGbe0xRp4LOUmd+6gT9et/eRln/epZXHPPW9jensdvnnw//I0zjN5YEW0OCJaKek25go+2HHUI8c9W1G9k2hbLnPzzpzDrsrvjsgTM5gQ1kn79xOLw7wcXBmxu4bSkzg36t0nOQSxsxSWL3dgmQ7jcs++vw7E/fQxLFFYPxywLGA7x0EvVMFKfDff+ZZVwaB1EyL66uBH93aTbWzLTtuCH99yWDEpAZ+xmUp5x0VrKqEzH27Z8IXzONemUkSViMsjrsqnYvRHPSLfgXblxO8797fP49C1Pad+jlRu3Y8HVD4Wf67Np6bnoNpton6Hhjuo4tGDKoHCR+q2jpoZ9cUtbLnx361yZto6LDRembcxx4am/K3UqBUyJxbhz9nJgqLnARdNWhcrq+Ghzq3bOFRtnAnnivDh1/ig01Wbw+f3HSZmjWU1bTTbnXN6X+suqTXF7w2YPbG3LxcokkTPi2OBcPS4bNkdd/2iYrE13jK5tOrYzwNuIOjYird0Dw7Q1ODuivhF9p25mmtiSIrKEi2Sgi9gw83sCTVuxeE0Sjs8V5ep2cdrGM5K724VcP6CSB5wjh4bNC4ZdruBjhSIhwo2DoT1E6nVJ6gnYmcxJmLO6dukgftZtPIprpYxBwOxUVc8dk0coVqY6VClM8gviG7lfu0lRjOzbyJZTHUhJmbauUM+jc1xxTkUguTwC11dNkgIRlE0537cyyF3BMm19mWkrORYRXVu7Mu9wY/gdX9onqKvYpbkNGi46gGPaxp22sarYOtXyBb/8ichoNWKO0dVN20alGmNtJh8HEIZyJZi2Lu9VKuVh9zFBJGLej8ZtlmlbrK8tVwjt4AEMyxqIjz2Usd1eBrmzHQ2J3/j58+fj5ptvxvLly3HeeefhF7/4BYYOHVp13FZREirNtFWFrG2ahzc/ErE0//nSh2VrhytsTmUgSNox+Zt34oArHwwdVmIh7OLwTIpy7UYK2DKxm8JsBNQdOHqM2toCYVwA/G7v04vX4st/eMHIAlYNfGE4bW7N4fZXgiRlf3hmCS76y8u49B+vheW4+fDOV6OkZuOKGZ85DcU2SR7BTdPWxLQVEP0klCWwOBLfXhmEIy5daw7hpX+b2kvDx9rzBfzvw+9J3+vwwpL1+O+/vyJ9x8km5H0f7Xk/xlIw6UYBvFOBLlh1zgIgMvrVNYuaFI1NFMc6be3OMOGcBtyMRFM56yK0hGGZOtQfeHNV+B667OxzOHbOMAxursOn9xiJptpMGLIlQvZ0+qQiZA3gFw4AcN5tckLMumxaeud1Dif63lKWgOoQO++AKFoi0CUO6l65odWJaUvX5M5OWwdNxbiT1lx3bPFkcHWofoRdRrY4JZ9xgcS0dXXaKpIvwgk0SJOEg25C0WQ43zlmOp7/5sEY0bdBYgLyiQ/5RVS+4FvDY01z5Z+eWYqpl96FWx6VI0tMxwgn5GnzRwFwd9q6bGqqrwfXCl3bTNqOrcw91dlJ1EHoefGx2CXUm45Zd12wr1TGNEauLSaz7NeoDwEu+OZkaAAf9RIyIy39nF6Ws6atg+NHbVMqAdN2C+eoIYx3zmblmLYFxsHL2QviurdLTls3R6JISKpDynJ/TbA7bSNHBwdxv1VGuIumrRp2HkoqxSQXGKatQX6BylyIfq3bBFCvXycPcvUnZ0th9L4PopdbPgaJ2qf1sg5kniHfq5fJbar++5Xl+E9xbZBUHmFbWx5PvbcmFjmRL/hRslTHpJ46cF1G2JSchIDnReOWC9NWOPQE651lTjLvlOiGdJxPlohM77T1EfXzci1t6T0K319N5ZxzX/07KBd9Htw7kvuwyUuZ6tGVs2lFC4g13fotbSTBWNyeE3XTRHs6u0911FNiTJVpG0fJI+Dzzz+Phx56CG+88QamT5+ObLaa5a2K5OCcIOV02qqabW0alpXAUDI4Dm2x6yKVGyYWqsC3/hk4BBev2RpOrILtVM4kbgKuSW9cYXJM+35kbA/pXac1tlXnKjV+VKdDwVe1Yfl7/PcXP8Rbq+IZvHUQjL33Vm8Ovxs3oAn/Upz9nBOEslAF+y6uXVWQdhp1LGpxWMjWc3AKirLbQkdi3PijbBcx8f/1+Q/C78SuK30UOclpq3/XRHt835eSvVDnmg6qocuNF4FOaPz88Yy/8u8s05YY86adX52mreh725MybU3yCIQ5Q8Pv2LKe+pkv58K4Sgr6rv/thQ9CR46rJquKH54wE57nYWCvOjz/zYPxoxNmAkAo3aGyWwVoUjzdovhZRQu7LpuCR26JTmtdTsSnl0dQdfZEXzvzV88kZtq6srxc5BHU8UktpX5WF+9rDY4OtZ3fPnqatmxSyItsRye2skAQyf/Wb23XLmDVv4XDryZk1kdOJY5pGx9zinN1gXfySuc3MFy/958gmeCvn3hf+t7EtBX9U8gombKXU1PERTM4djxziK5pb6/azP8A3kbUyyNEf7PyCKEDSq87S8eQIb3ld9b03olxhYtaiZxbftiGrOP7CET9oJREZGKM1LWfcxKrUOeGFHNvdXh52frYd3ShvpphmDfSRGRCM5rZFOHmPHGP6Luou0a1G/Q1JG0K2k0dHeV12oqqdRvDkZRB5CgFkskjUFkYILp/KnuXQh335HqD/32Hfq1+rXMy7zKyD57+7wVYMGVQsU1EB7qC8ghapq3H/x3bDGcccLe/vByPvbOGLQ/oN5AB4Au/fwGf/N8n8ednl0rfF8h7YGNd2qDrk4Hua5whnEp54fOV5HMKPi7800uxegTDPV/gZVBi9SsbEnTDIB4RZN+k4Mr7PtUZLk9/orUIm9eFHEHXFnE7Pfp7yuBmHDR5IEb3a8CcEX2MbXFn2pJ2OL5X9xfzXFx1z1tEHiHeB8Vtvfv1lQCC8Ve3vgjXMsVhz5a8eGdHolXahx9+iO9///uYOHEiTjjhBPTt2xdPPfUUnnzySdTX24WMq6hCRSWZti8tXR9biNuYrL3qunbzodRwgHByLGMSN4Ey+2y1Cy4gePZiB6+loQazhrfwdSh9hC60VGedT8KJgt8L2j6mht+ZICYUmu2S2xnkzkUdj6K96rGuTFthXLWGmnp2p+DZv3oWQLSw4Y7pXR8tYMQE//6ayKkaJrWQtA+Dv99dvRnf/8+bbHvP3W+cpDtHHQd7jtPrjQqojnxWHsHnHSi2UG8u02oq5YXXbxo/Iqet/H2kaas3tDkngps8AkkY4bCjfsPJu1iNyZ99Zi77eykjC33XdxnZ0iEtMc+TDc2aTCq8ZuG01TFXbJs2C1fEN2tUpi03tPq+L/V/KmeiOpB14WGrN7WGm4tGpi1pyysksY8J1BYvNRGZCpWVITZv+POrDuHyTSZS2KZjtdR5tqU1FyatbKrNGPX9/CJzH4i/q1STjdsoUp3l2VDT1rfaIyZbSOdw1R1TIPrXIqO4yWlLbYmSTDLmhTFdj0hsqYKT2dHLI8gMKnVMjFiuzCZZcbyl7GN1rjGNW2GyMNZhUGwfsUWSbJKJy7V1c5UVByDUTQd4tpObpm3cQSUxtVIeVm3cLo2zLy9bj5eXrcfJNz8Vq486UFZuDJy2dPHfUBtn2vp+vL9y5EjRLDHfmmR/1HfeNJYB5vB4DnSzw1Ze/K5jv4u2eqQvAW6JyFLKMaqkEh0XVQi7lGO5iuN9P7KNdKw99V20kUGoI9nG4i0Fal0usg6mjVMdg9L0nSka7d43AocXTdwGBPdCzY9QKnTvhZRAjZb3vPA+0blr0RqebEEd45s1UaDyPC478EzjdanyCJXQtKU3qS2UR9BtApA+lNaPJ6qz/OenzcMDX90/nLt1iG8S28e+JPrkAi6JyARMEYrq2EOJZ1WnbRzOT+qII47AuHHj8NRTT+FHP/oRli1bhiuvvBJTp06tZPuq2EHxxvKN2LC1nV3kJNFkM+F3xYQyFKbBw/d9LCSJyCrBWrXBFg6gW/SIybES2RZLZcXpYLpGKo9Qn9XrXuYVR53sBJV/8315cyBf4BPGAMBGDVMPiDuthNFCQ/Y4hzQ38VDGiGibyh7OFXyts+lEmsnTF3UamLbKJPri0vVoyxVC5jnHaG4hCXfEO0k1C0Xb6Psq+t/lxGF7NNFtAoALD54oGWf02Z2y+8hYO1SoiX1YeQQNi81kGA3pXYf+mkR+2dBQtcsjqAaQ6O8mQ5szSu2JyGTjWvea0vMdOHmgUdMWACYMbGJ/72gismF9GqxSDibUpFPatotM3ps1ye9yirauircZhn19Ni31D25sjetQE6atsvh2WXCambbR308vXmutCyhNHsGmgaw6cL566CTt+dW5o1xzu9ou18VXJI8gJ7y89Yxd2TpyzMaUev3isRY0G0Xq2Cuew5+eXcou2mm0TynzuW5+pU7YvsXoDh0zHZDf91KeG3eEmQXM/8bdI62zOxwLg3usWzxyDi4xB9JwfrXvmxxjpogHsVlR8GlEjLvDJZJHMPfztOadmDeqDwDgsOmDY8e4sKziTFv5XuQKPnb7/n1h0si3Vm7C0dc/hqOvf4ytT7yHy9ZtC2XIpgxpDn+nTFs6bn243kHTVjBti7aNadxVnXBWzWDys8sbQW0BG6NZRGY89NZq9ndxXTHWrCG5Xnhs8TfR/wuhI7ZYZziGBf/f98ZKnPXLZ7B6U6T5be77djasLuJAB8q6bHdwTCeFKp2nq1ti2krt09uSXD/iutb29jwuv+MNjP7G7RIj3SRzR5m2pmgsF+gc5wWfym6R60J0n6it8xFhy1OpIfrMtxWvKS5zEN9oCh14BX3fNo2F3KMMN4pQfk1b1zao39M+Z7O7PM8+PgV1RvfK8ww5LMh7WgqDfWuoaRvfCEziDI+iLgRjnzptO98H093hLC525513YsiQIViyZAkuu+wyXHbZZWy5559/nv2+iioE/vHiB/jSH17U/l4utuim1vgC3qRp++5qOUSvnA7Q1lweHjyrfphNc1fnNBILjp4gj2By2rbnffz9hSAEv6Emo51Mbn5kEb5+2ORw13czWXyq91DVtFXLU5iYR2q3zBf8IjOYOjLtjp32fCFMPgZEiziOadvKyCPsOa4fhvWJIhvE+9Kac2faivLCiOectn1JZl8xCdP2tBWvlXPariQZ2YcoMiPZtBfp1PmRruPBUwc5aSs9rCxq9PII8XdFff/ofVGNeLXN29rNTls1XFpALGZMUhTcwtLMtC3WTQxnnaFE+0pdNmXVuOLCe4Ggn9356gr4vo/DZwxhyzzy9mpsby/g4KlBaCNNVtHanifGcvIxxTR29ioysx5YyC946XPjxljO2VabTUlMLs7hpM5V9DxiA+iY2UPx1UP0jk0K3b0H5Hv22X3HOtXnKU4JW73BQerv8md1sT20RR9lpc4dSZx/bgsUD7mCj0mDejnVSccd4dTp21iDmcNbkEl5UAO1hXODjs06zTidJItOTuKOV1eEDHGBS46cgk/vMQqTv3mnVFaFaQPFJWGekL4xSdjQakoxybhnbXr+urGVe191ZUMdxuJn9Vn9/ulgI5+PbLDPPaYNbBN7izojwoShCZIIRfII5nKSPAKp/o/nzMf29jzeWhnfnFL7IAd1ftLZhI+/G4SCv/bhBmN9opl/eCYK/T5w8sCQ+U6fOb0mdZOBu4di3BAMbdO8IckdOHhwaFuorJMOU4b0wmnzR2FE3wYr+UEwjm3nVlmzOcUBy0Gw4YQNmVf6qqppe1YxEuvyO94w1k8dyO0W525MHsHiKEqil1sKGmszOGOv0bj1scXOdUsbharzkXzkLo0bF3728HthXz/6+sew+IojAcgbimofp8n7uPwVSaCzw3IFqmkbfV+bSYXPja51xLpp9ogW9KrLhH2Z9hmRqKw+m5aIGpxWf8EPNjEOveZhAMmlSEz2ZaEA1iHdEXARRDr7RcfWThrxpANl75rGtLQ09iV/r7Ya5BGSEL3o2KOun6tM2zicnbbf+ta3KtmOKnYi/PNFc4KvcpFxaKiq5xUZlwkGgXKJYG/Y2o6Dr3kIw/vU46+f38tY1jZI6cJpaivItC2X7o+ASR7hxgffwZ2vrQAQODBMZ/7Ts8twcpGZaRL0L/hxZ6ouW7VrYhaB9nxBYnSyofrKM/nBHbJsgDDa4qw9nmmbSaekUGthgJiYtpzRk8tHzmxuYZNKeajPprGtPY8JgwL2JU0K88byjVLILb0Ger4mZSc2SMIU/C3ClAG3xSOH9xgdXF1SoFg2cAs7IjwuI4eEcY6RmrTGaVssG10nb2j//NR5OPvXz4afTYt60RX/SBa9OkNp3IAmfP/YGejbWBPs1mvqFIabLrnF0rXbcO5vnwMA/OeL+2Dq0Gbp9zeWb8RnbgmYVn/7/J6YPaJFetdbcwUi5aC9NC1MrEDalnVb2mIhZPT958ZIrq9k07KDm5Mbj2/IRJ83FZlT88f2w4i+Ddq2U5iSdNG2jHSsj0K7+Wb22VqZtiaofdK0EaHChdl9x5f2wabWHEb2c7sfIaOMaOwJdjP3/uQYp22MdUjYu5zzX62XhvuLDb/rTpqNPcf1j0lo/PPFD7HX+LhkjCmsVrfpnZOcthl7PYzsjQ7cnM4dYjJPdBvOnGNZZ0Oo8ggqXv1gIwB5YSugzoFzi+xUCpNjjMu2LkAT7LSWwJJzZdqmNE6AdMpDY22GXZybNl0E1Hfe8zxj3+F0aqV2Mtdx6LRB+NFdCwEAry/fGH5P77nKQOTGk1AewYlpG/3t4rRNagt7nofLjpme6BgdokRkkWMLIPIIhnFZOFZEFJuYy1RHsPqer9ncFr5rfCKy6LiwnIPMAD23DrIzx36NpWD3MX1Dp62ubu0mt0HuwVUeQUfS+Whz9P6oa5e8b47aSgKdry5f8KVNqO8dOx1X3PEmfvrpuaGEXI7ZoG6uz2La0GY88nYg6UDviXA019ektTlI6IbEH59eaoyMMGva6sv7QIfsUP588e907dMybWOJ7Upri4m9SyFFYZXCtBXyCMx6Jknb6Yam6v+wkdh2RlSdtlV0O5TL8UgHjsaaDDa35oyDgGrclasdzy9dh1WbWrFqUytac3njwtXmKNa1P3TaOnq8V23ajt8/tRSf3HWElJlSgDqlkk4et7+8HM8sXov/d8QU1hmoGiF9GrJYV8y6/pfnloXfb9jWLmWRBYLQG7GLu+ijiBltctoGznp54adz3qvJgyh0TtutJJySW3iqx4lssgJi8aOWy+dlhrBY7GVTHo7fZTgeWrgad7++MjTexeKWM+Q4h017oWB02gLA4TMG46/PfxC+C+pC7aaH35WZtsW/G4mjdvXm+AKOZo8WrGed03bBlEGhvpeKO19dgUv+/mrs+0KBd0hw2nwCJqKFWDAJo4J7T3VMW3HvNoc70/x1qoaTyWm7qrgovo1IwJje05OJ7ITOUBfnd3Gec8/07teiZ/Tu6i34zZPv4y7yXWsuX/YEEAKj+jWGf29uzcWctnQzhXuPdVIa9L3hxlaORS8gtK6bNQzu/SYOiIXCNmr6BiAvDFySB6nQPXdbWF6caev+7NSyU4a4MWJdMcGRYStA9WdVjWnuuiKdtei5qgtIyt5lkx+qTm+SNV10nz3G9mM1jw+cMpC9ji2WTUoOeTI3iX5mSoRG3xObWcHNp9wxJptK5xjmxvE2jYM30mGMngkHLlmSapddxiTNs4WIAxqHAZHQaAuZtno78KuHTMSVd78Vfs5HF2YEnUK4dtC+J+DC+I0xbVPANGXTjuKjzfrkhADvTBjaUo/dx/TFU4vW4rhdhrNltyrJjLi2i/KRXr9JNsDsaIvXbS1SMYg+JJqghpC7MG0FWUEcGyUikx3BAp5nrp9uRkRsWDfnp81JHjpzCHmhFEagCbRNOuc+bTY9vVpcp30bfWduC90kokm7VCJIvlAIGfP1lZJHIGSMVAo4ZfdROHm3kfA8D/duWllsR9RZBNmluS6j1fbd1hYxbSloCzwyl1J7i+t7JhOSu9fUOdiRiC9X6OUR6JiTYr/nPrvCdUzLGM6tgxifPY84bR0TK+sQ2WRx0lqVaRtHeUfAKqpwgI3tWi7dOzoOibBT0yCgGizlctqaQvdV2OQNuPZ7XjQAu7KDf/vE+7jm3rdw4s8e589DDIUkA/CGre0473fP45ePL8ZdRcasCtUI+eJBE8K/aeufXrQ25vCgzkV6K3RalkCwa2u778KQeEgTXg3wz6Y970uatu0MHU99JGqyuzaNPEKOOFUByrT1UJdN43+OnV6sX2bacosU7hnm8j5aizdR5wQShnU+PIe8aLrtySXS4lw4+2lYr8nYzxd8bC4uJFSW4RXHzcB+Ewfg8weMY9sGAN/7z+vs93nfl1jBAjF5BIuhLbCiKPcgHMQco1po7aqGcL6YrOqN5YGhrUtI1VwnX78pKRUHV8NL5zAVjoxMOoVDivIGOnBjNN3weG/1Zvz1+Q+k3ynTthKL317F+6eOkeu2tOGe1yPnsTvTVk5gw8ojqO8sGSPE/WjWJLc8cqYsMdFUm5H04FRIC0zG+WKDq6atWiq+2HY3G+m7sMvIlrI765NC3IOCD2xXtEW5BSzHtI3JI6SisYxzMMYTkcXvn/qdWMA//NZqXHX3wnBc/d7tr2O/Hz2AVz/cGKtDQCePQMcs4cQxsSVpf7fZZFzkSlJ5BK7ZuXyB3bDTyyPIDCrd6Tg2oDpvchuZps2SUNPW4txykUc4/8AJ0mcxzlg1bSWWVfx3LtGuiyRRXNM2sEH+fO58trwuUzw9nuKkXUegoSaDW07fFb//rz3wCZI8zci05fT7i+WjhG/6eybJIzjch0o6eWwQ427YBj9410UfzxrGZRGl8tfnA1JE2J+Kh+gSkT24cHVYlhu3ovfMJ/IIGqat8rUr07bgR+NvTQnzngl0PtK9j7Kmq95eNCUpU8/FtoX8TcdCdd3RlivglQ82AABeckxIqkNK4+DLFSJNW3HNov2qPMLLy9bjO/8O7PDe9VnpHsnvrtgkTcmOcMYG9yGPtVyfMt1NVqJG/OGboyJKAVeNnmlLxhwDK7bUscZV95mSRFzPJMblfSYMCMfiBobkUZqmbVweIUlk9M6CqtO2ik6HbfdEt+gAgH+99CGOv/FxLGZColXQyUMkNjA579QFeLmcttSAtTptuRhcRKGi3PGZlBcOwGqCLh1+9cT7AIKQZw508EwyAH/5jy+Ef+tC5FQj5PQ9R4d/U+fJ/31uz5jOKJ3IaWZeszyC3Wk7b3SwSH6TySIvwPWHQB7BrJepHifYWFd/YpZ0jNrv8wZ5BEDOquwTlhfn7NM5bW1MW3GcYGmp17dNcXbl/SDsmIY3nr1PXH9T1Evbrco6nJN5NRIAAMd3SURBVLTbSPzqzN1iTi96KVw4MqBPNqcLbQ7qtffzF4s6dizTViOP4Ps+vv3P18KQNy7sFgB2GSl/b0pKxcE5GZMu3I+0+2efmWusg1uUU/bfWys3x35vbS+QpGnJDdJhljBe8Wzps3l75SbM+e49YbgeoGPayv1oSO86TBrcq9jW4nHs+683MgX7pFcdz55VjeufnDzHuLCj65ZStP1cFhEun5Mkg+H06roSVH92W5s87nDyCKGmbZjsJ54MRNLJZZz/b5CxUNShQqe1fdtTS/CT+9/BX55bhtWbWnHzI4vw/pqtuO3J97XXqLNbxPfplBeN65qyzyxeiwv++FL42eSzfXnZeuz6vXv1BQhM5gnXlmvufSu2+QMY5BFCJn/xfJqGuzwDli2X8mLHij5iSrJIGWSh07YkeQRzORvbTzcW2aCLUNE5uoTd0qs2E24AHjR5IO77yn7FtsnlRbLSptoM5o/rJ7PFjEzbuK0jiovx1yiPkJBpm4TA0FGop+IcrBK5wuCcFhtTTy1aGx4LcDq58nFThjQbmbaUoZuzSBgklUeg73A0/pabaRv9revLtJX0EmIJtehcZ5CS0IHeejpuqUSQZxavC//usDyCJ79n0aZmtLpS2x3ZWUG7rrv37fC35vqsZH/RY7cUnXy1GTnBK72n4s+CD2TJ80giP8C1mZb3QZL3lul95pqiax7twvRdUbt2qeYS7ZemeZvOY67nEs+kLZePmLacpq1S4QkkcbYKuhZUfUPL12835pnZGVF12lbR6bBlBDT5Sr/w+xfw3PvrcMMD71jPQyc+EZJs2rnhNEU7gkmX3IHR37hdykxuYrYA0b0Z2bdBYqCK7zmnbjoVhfG6bkzZDKZ20s4kk8eDJNxXdwp6Xw+eOgie54WLAuE8+c8X98HcUX1ioTS6kMJNlnBRXTilwKHTgqzKJlYG1x/acgVrkiPV2SOusV9TwKpryxdQKPix+tvzvLM5Gxra8uRsYtpybXeRRwgX92GyM7k9fRpkh2qhAHy4IdoIuPy4GayzTTQ9X/AlhwiH2CLZj5L+qBsDt39x7/BvdYEH8GGe0W/uHZ1jXddq5RGA3xAni0631PM8zBzeO/zclHCB7bqe0b2XctZZ873g+iXdOFm8Jr6ptr09b0zWY8P/fNysDSjuO302BxcTWVBwY6TYfDh3v3F4+3uH4+GvHRCONZSdqUKdT6gzSdwP3XOMZdO23BOXUE4TdGO+elrbo6G/2xzprkz2zgKVR1DlZLj7L2wCk6YiZYqI8ZFuLi5Zu1Uqz6lKq2OPOua9s3ozPlgfjat3v87LxYh2cMgxTltd2RNvesKpTgD4wZ1vst/zmrYmpm38txseeJcta5vPxT3WM23jz0CdN7XMdOV7cV9FX+H1FIP/C745YagOYSIySzkb01bVl3dFNubQFv/zGx1iXPzCQePxs8/MxYuXHoxbTt8V4wY0xdoJmJ0n9Dc1gSyrxV9sk5CeMSV3lJPx2MenzhzC1GuLNG2DzwVfXrOY2n/4jMC+3XNcPwBkA8eTbUn1HZw3qk+Up8DgiKSOVd3cVKqmLWAefzsCyWGoebieZg7TJaQE+LnENr9TDXcqDWCKvjxzr9HGOm1QExeGNhTRtFXvS0axsxYRAlVdJo0ji0lqxw1olPJXiE3S2kxKaxcIE9T3fdSQZ81tmJtuJ/cs6XtTcBxPXcHN6R1l2uqzT5jhahvSZ+96JmHXtOd9IvfGkYTkzz88fqa2znDjpxD1qYaaNMYOaESu4ONuTcTuzoqq07aKiuGjza14ZvHa2PdiAhYGhAoXXdY/P7fMmqiEjvP1Dkzbz/7mWemzK2uVw2+efD9cwN38yKLwexvjkxpIn98/CgsX94xzHGRSqSiM3bHNtgV0qVoyg5sjfVydIU4dGz/51BwA8UlWTATqhKBzLqrGPIUP+30X2rkzhvXWluGYQDQBAzTnUfuzYIDScHjhuKVozeXZxalg2kpJkhgnBAW3W+nEtCUyBqJNFGqof65QkFiYOgMiYqdFi15deCLLEi74MW3gfSb0x/A+UUIizmmrhrfSe0gZDDZwTC/heOE0bfs3BRqrXzhwvLFeukmhC6vXocNM2wQLIm58oM/9nVVxpm3gtHU+RQw2Zlo2XHC4jbEU9D3IplNSvxVjU77I2L/u3rfxyrINOP7Gx7HXFfdL9bxNGMbCEcwZtbReAdvzo+VL0bTVLRrVdqilYgkySP/+y+f48OjwnCWwOSoJcdsKvh/T2NONMwCdlxknEZVHKG6cff3wyeHvZ+09RirP3YdYgkSGrW/SsaWwMW0zxGnLbeZxdpXJ2arLes/KiRjqSRLZpLP9IiZ/8L/OMcQ9R3X+0b0v6vdivDHpJFKnmAhVd9GSFRDDbZJEZLy2rofZI1qczyugmze59ohxEgjmf8/z0NIga4zH2OoWB56wb+97c5X0vYsU1KzhLdp6aVE3TdvOG8RiG8zFc0sMVMlpq+9PQqeebpYDUX/RMdN9+CGBg7PPqOyHYITqnMfq/XXWtCXXWcpmpQkj+tgTWHJMUICLSIn+LkUegYKObyaSkxqJmBRS8jQvIv8UCr6W3S/6gXjedO2wZksrZgzvjYcu2h///sI+QflivxT2eE0mpWgDxx2Xzyxei7VbojULN0+Z7qfp/vt+vP93FEmYwPQ9NWvaltYWiWkLfd9xTcJMIeRJ2vMFLC1uRg/pzZFylGsxbcqRuVFs9mXTKUwfGqzFq0xbGVWnbRUVw6d//hROvOkJPKAYWuuLSaf6KIacgEkegeIeA9skQFRPbUjr1y/o318jM2JcmLZ3v7YCNzzwTmzB8fz7vAPIVicNMaIGylf//BIAfiGU8mBchHGwGUztpB6XxdTG7e048sePYHkxsygA9G3kn68wQsYNaAwdjGpzhKNDdUDqnBXmxCx2eYRMKC/htqjMkIU6Nao4JnecaRsYL9Tgas0VYgva1lwBbWyIf9HQJrciSKwjazRSiKRIFO35AssMo0gTreRCwQ8n0DP3ChwR6m5wvhDppwGR8fnD42fisGmD8cZ3DgNAjX0/yn6sW2AzzsR8wUd7Tr5flEEGAFuZPqFqgZa6CGtn+okuEZnv+yHDSITc60ClLUwMIQ7umrb890nC3rmFhE1/6sMN2/HEu2sAuLOCKUxJugCymMjLjjYV3BgaLgqZe0AXMt+7/XVcc+9bOOr6R/EcM8YLxn/AwBesAQ3TNmbYssWi30nxUhhHOsM59rWn/q5sqJE+qpvDw6o0YZAuqIT+LZVHiLIfB8/HmIjMkKE9khqINrVa6rN47bJD8Zdz50ubrxxqio4tCvVd9H3zHMe1WQXHtBXSOhTcZpdpf1xnr3Hfmjbakzhtbe92qL/oGL3BQZ+4T/4sJHOikHP9McvWRWzpJE7b8Jlami3LI/BlziSbCMfMHup0fvV+ifNw7wyVddKzLtXP7s5oCu4eqlVdfMTkWBmuXheHYCXVEdR2x5jfadlR7kNOLmjq09G4F3xWNxiofizFb59cEoa182HQoj4Xpq38OW2Z8GibokRk5X0AEwb1wk8+NQd/Oke/+ehpPpiYtqwTz9K96L2n+84mIo6acDUpPOV6xDU9s3itNmmsWH+I500jrA6aEkihjOrXGNqv4rq3tUWbVTq2qTjX9vaCFMGhG++1EgTM9+KrQjHHhHrujoB32vJlqQ1Bzx+3B0trm43RzdXveqaadPBMX162ARu355BOeRhNkgAnbQMgR6FE84YX+WyqurYSEsfK/PjHP2a/9zwPdXV1GD9+PPbdd1+k0x3TWqmiZ8P3/VAf9F8vfYgDJkdZkIWAtc6p55qI7OG3V+OQYlg7X0/0txgAdAxSTqPR5jz+4u9fwD9f+hAAMHlwr3DCAvQLLNvCJFpYpSSj4I5XVxTbFD8mk06FE4GrwzuJPIIL6/bht1bjNSU5im7nn0tsoA7yYsJXF3k6vR6Tpq3v+2jLmxNjUOekDuK37x07HT+8cyE2bGsvSi/Qe2VmF/nEuUrDpttyhdi5C4W4MDvAJKRAcC9E8i81oRUgJ4mi1xNlstY5baO2rN7civa8j3TKw9Ri5mi1bxR8P1zEAgjfz0/sOgKf2DVKLhIyPhyMfa4f5QoFNhmTpH/H6Euqchv0Hu4zoT97fgC46NBJ+NFdC8PPeea5iParhmA+AVOEHqljaOrgauO5aNrawI0JLuOEMMSTOst3Hd0Hk4eYHd50IwUAfvX4YrYcNwaLzTKObUwXpvaNwgB0PtE9x9gitgLyCC5TaVJN27EDmpBJecgV/ESM36TPvBIOkogVC1xzz1sAgJeK45VO9xuIxnVuLKKatpSx3VibwbzRfWPldYtgqU7lPD4iTUAbdFPYzx95D0CRaUvakC/40kJyExO1YjIrdHMm56A1DRExll/CpGVA5CgWV6Pb3NBFFaRTnqT9y0FdTJ9881NYfMWRFr3u4LsXlkQbParkgIqfnzoPZ/86iP5yTkRGfnZ53646cZa1DBDcC89D7Bq5158mlNFvBps3KWLlNdfCbVAnSehjc7TZ6i4n0p6HHOnz6hgv7hHd8BZsR89zY7MJW1Qnj+D7Ph5YKJNsCn6ghdy/KZ4kk7IXbRIGcXa1trnFNonz223EjuCoWeaNC51OtDERmYFtrwO1m+lYqJNHGNRc2+H7IWlHpzxsLBKqLvzTS2EemBjTNiVvjov54ltHTcV+EwfEz1G8bhF5VJtJywxfi6QLwG8ABuU9NnqDl6ghmx0dkOlyhW7TOZPir91THmWpTUtp1sgmuG6Qq7bKgikDWXIJNV8OmBTvE1JZYj9tJ32kxoFotzMisdP2mmuuwerVq7F161b06dMHALBu3To0NDSgqakJq1atwtixY/HAAw9gxIgRltqq2FGxhSxaVeescFqtV2jvc0a24IUl6511WX/75BJ8+6hp2rBqOvEJ/bAHFq7GsnVbpTBqAFi3tQ0qbKxV4bAFZAaF6Vid03ZLaw5PL15Lwsrig6jv+6xDO53ywoHPmWlrYWpRBwzH0lTByRPonO/C0OTCYgSEc22wEnqhW0it26oPofB988B/8u4jYw4fDmLHuyadUgxKWzh29Dd18NZl06jNpNCaK6A1l48nIiPhIhTi2dE74fvRZkgzEzLFXX8uXwjr12vaRs7s6+8PdKRb6rNheZVxmi/4WF7UtK1Jp7ThW1QnNMf0BwpuQZfL+2iqlevesK1dMha2MhnN1UUeHTqG99Hrc358zjD86K6F0eYPs3siDGj1Oj5cvy18n5I4ueoSaB56nrvhpSul3ueadEq7y805aE3aayqSGMvplIc/n7unUzkg6k93vLqcLceGbZt0+8jYMG5gEz4k0QRhGU92JAkHW40itcDVq7ZfB4lpW0aPplqVWrP6qAb0qsUfz5mP3vXZRGyQpIuQjmrKc6AOVsGK3mNs4FjlLqWgODm4+07DeFstm2BA/P5yYy/PtJXHs4aaNMuK5eawF5eux21PLQEQbHDS55b3fWkhsInZ4DM5UG1JweS2uecUMIUF62yLMON58fJ0tmFWs6E8c3hvvLBkPQC9o1A3drlo2lJmei+NtrnAgqmD8Ml5I/DHZ5dGdRuPkMcUlzFWd39UBHkHUjE5Je5aJaYts7HKHWcb+3S/q/JMAOdk0tcthQgbW1Csu4JU25QyicSSpsbkEczjEoWoSrwfodMqJeqO6jzj1mdix+8yso8m5FwcJ4c2s22IbVbZNrDdHcKVhE4eIc601f8W/G5v++X/eQPPvb8On5gX+U50NthtZ+9hrc8G1WlLkdNsFIln8NHmVrTnC+F8ceTMIew5xPFCJrA2m9ImANTpuNJk1RS6O8rda/GVT5m2ZetP+vOpoGMuPX+MaVsGh7KrBeV6KtVWmamRnqFtH9nXLEEi+kJBihZNheey5QHa2ZB4m+b73/8+dt11V7z99ttYs2YN1qxZg7feegu77747rrvuOixZsgSDBw/GBRdcUIn2VtGNsbUthzNufRpf/P0LoQMJiC++xG4KDV8+fc/RocatziDnWKRvM9qJYXlSnO4GfermJ2NlOd2UJOF6a7bITl8d60x3bT++/22ccesz+GGRicYZYbrJO5Pywt1P1zbbjGR6LheNYQ66toSJpyTdyOj3mnQqkb7pu6s3s6HKApT5oeLN7x6G7x87w0leQpfIxZ5YL/qdZqmvU3YT1XPn8j57D4Wxq2ra2hyw3PXYkqKIfrh6U2uYTGvOyJbQwGhXJtR8wcevnwjKDTM4QWmYcsSi4Pskx4pqLxRi79L29oJk+HDMNBPT1vROiLB5cY85B6VOHuH7/3kzlISxGYm01iQLxCQGnq5edSFt0pDl+rw65l1y5BTt8UnsUdeiYZ3Fpgnm+dDedVK59cwGD41wUEE3GDgWIiBvlOTyhdCZZpK4iDF1LM+bPh9XZ4sLYpq26mfmmLmj+mD8wKZE53Hto189ZCIA4PJjZySq360Nwf8F38eg5sDhc/wuQWZjk6atyWkQySPYNcKBeN/nnBfqmOcjrml78RH8+8XNGa+TKBgRLSGg+lG5pJ6m6CddUjDuW7MjVv68jYmUEJrzuogi8bVNHkGblI/8rXsfuWMv+9dr4WYlN8SLvi9s36lDmp022cRw5M60JQ4QTdFSXQF0wzFMusmcpEA2yWs0EZeJ5RE0P3N6irEEj4ZxlbLbXManSsojqBs9uuSE1PmUM0QAUIS67MV+VCC2bFAg+E/3nuuiIsU9u/v1lXhvdZCQqpcm8abaxBeLmyM6iPI0SW8lmLY26NjYalPkhFr6eQIA+mnu588efg/Pvr8Oj7/7Ufidbh2VRF5FB9pMtf+LOU99LeiYevyNj4djbpM2wa78Oe15Wgc3936N6FuPz2kkhvRJvfTtkDRty8S05eUR+Lp1TNt4IrIywJVp61id+v5xBCFAvi6brUo3jLaTvCxVpi2PxG/9JZdcgmuuuQbjxkUv0fjx43HllVfi4osvxvDhw/HDH/4Qjz32WFkbWkX3hu/7OOonj+KBhavxz5c+xOKPIn3Y9YTFmstHzqmz9xkbft9Qk5Zo8hy4HRduAU7bJFBHHBBL126LleWcrEmctj++723ps451xtW5ZnMrfvZQELr4btHw4Yyw7W1xRxUgOxFd2Um2UFzafpf7wGYR1mnriR35FG/kUEeHyvDh2nLDA+8Y21bw+b5Tk06FzEsXpi11NtOMlzbNHVpna3HRlvKEbk9w/tZcPBHZdmbRSttKb7kPulByD522OZfEc3l3dbQ58pVDJsVYjQL0Wg8kkii6egu+H7J1dQsPbuFdKMTf2dZcXjIWOMmT2SNbpM+SI8yw8BGGh++LRYQ+ERn3LogM8rZFR02JO/9JFpOuZQ81SM9w46XqvFGTz8htKP/ql4a/AcCqYoKkCYNkWYUv//HF2LHmDNlRX+VYiICcNG5LWx4Lrn4IQHyTgELtCja2VClMW5discVCBR0TLjj/wAl4+duH4PAZPHOnIxDjTnu+gNWbgv4xpn+gycYmVSrYmbZ0Ay/pxpmuLMu0VcazAUy4clA2Poepc4kkj6CUTy6PoGPa6hntHFR7oZWZ/4SEjX4jWWbaDlY2bAR0z4fWqmfaxr+79bHFYSJLzmET6jq2i0Wpa/8o2iUKg1gHOfFfeV9kumGh26AEgvmx1THBafi5RKYtpxFPrzvlWRIWJZRHoEzAH56gz4heCtTEozXKRrroV1TrNWcYlyjChLLFDi76U1wegT9e11+503KSFfQcApx0DIV4btTW0DHkKwqNY9M0b3JjB31G2XQKuyi2KAVdr1Qi4kTARFpQdY8FqA378rINAIJr10WGqePh8g3bZaeeZcw6cNJAvd1Mio/sZ2Z0RlIeERkmiSRYUuiqpuMo7c/qpZeHaevWd0pl2uqiRXTOdFNZ3/fDOb8umw7vTRI/zM6AxCPg8uXLkcvFjbpcLocVKwLdzaFDh2LTpk0db10VPQa7fu/e0OEIBI5Igb+/GMkI0Mlo4qCIqbNi43ar05Yz1Le1mxNQCeiYhAI2LdKk0C1kuDrvfSOuk8gmX/J9dvEkMT8dBzjbhKA6ZUzhkTpombZMaAo1cqgGpNoXuN3cjUWWtM5JqJNHkM6vcUJStJEQdxpmq7JNVdD7QJOFeZ4nJchT+wbHNArazTNtXUJzKd5dvTkMBdXpbooJl+oVTxnSHN4v9b3J+z52Hd0HADBvVB/tucU8ni/4UvI9Dpxh1Z4vxPpoW64Az/NCA0R1cgDAKbuPUuo2n4f7jerwUgjDcj0jtaKW0eFLB01EU20GJ+2aTFooyQLdtey3j56GCw+eiB9/ak7stxseeAcblA0z9XnoGDdAmVgEGhR8H28s3xgyBs/Ya7T1GJNEB52XNmqYtnRcemtlZPus2BiXUlDrFbAZp6Vo2urYLxTqFaufx/QP5ukk0h4ckixCVAdGuSDG7ReWrEfBDzZahFajLqkSQDRtDRnUKdPWKI+gnIZzbHFtUZ2YozSLVM7GUDcWqe9D7XesPAJ7pgC6DWquO5t0r9V2qBut4wY0SgnUOKiaq2fuNQaHTRuMLy+YIJXT9WXaBH3iPnM/5sZX8Z1I1KpzbMXPVWyXQXpBKk/arNsALJWtmGWYtlxz8kSbUOvsi7Fhzeem78P0Yc04dNog3HzqPFZ+yRamLpfVO+E4UCfIIVMHGUomx8eU8HL6/CTHFilj0mKniN4bv/gv+F51BOug669cf9SNfeo5phXzIuggLrmVjBldIo+g+V5liNJ7wfU7yVmX8XDW3mNjZQRcQsI7sDwNYWJ6CsSYtqx0nyEyQalg8pBeirM46i9cFcaoFfK3iJgB+BweomzBj9aV5U5sR5GUaWuS26g0XP2i6rypZdU7RjACssQKlUegEUxVREg8ex9wwAE455xz8MILL4TfvfDCC/jc5z6HAw88EADwyiuvYMyYMeVrZRXdGm25Aj7aLDsqVL1aAcr4oPpeHjyy+OHPw72829r0kxt1KDXWmo1krm5XBygHXcggdx6O2aJbQJaLaWuzT2POOEu9HNtUPUbscIaZuKVdxqhBlJ22/6SBIRPqE/OG4/AZcfbfG8sDJ8kJc4fHfhPn5dpH9QBDeQlD+GYoJZBVNG0t90aSRwjlCFLS/625Qux+cXqFQMRQlpi2BRCHgduC8O8vfBAdr7kE3YQr5m7VGe7CsgGotiRli+iYtvHv8wUf7Tm50cIhIOre6pBt3XVHmP5G+zCFWNSqUilyGfOLN2N4b7x46cG44vhkLJ5k2VrdyjbVZvDFgyZgbP94dtjWXAG/eXKx9J3qkJk6JFqU/fKMXUtqA+DOAqBsocOveyT8flAzz7ajMDNtg/+3t0fsTBW12VT4LusY8rr2Cug2abjyrotXXfiaVG8sAYZc9xXHz8DRs4biN2ft5nROHbqawQtEC0uRJHXGsN7hd9yzv+xfr+He11eGYz/n7IsWF25jsKrbx9WptoVuyh04eSBu+vQumDKEd3pw9pM6TtPxQrVzOH1606atWvf/fmZucAzj6jXNlTFNd+XzrOEtsTDvWB3Fr8XV1WXTuOkzc/GZPeTNOt04TK9Tm4ishFB+ccw7RTkvdy3Z4P8ocY65PH2uuvn3oCkDMX9sP5yzn95pxIGVR7AwbfXOPqXdFgYlXV/MGt6Cn31mHg7WOE11SY440NMmZSaXW9/2goMnSp+pk1ynVSzsWrs8QvB/sIaIvo+Ytua2aZ8j851u7FPfG1eHOh1fusJpK8sjkL5lcPhzYwR9RtlUCkfMGBwblwTEfGPCwGY+0iIJaDs/WB+PQFXLAPa+pkJ9r87ae4yWacvdN9MmEy1PNws+2hS3wWkklk6vt1Rw06OuajrW0f4cZ253vG2ujn1XCQJ1TtGRAqSoMKs8QnEtWIhs57pMOuwXlWSa90Qkdtrecsst6Nu3L+bOnYva2lrU1tZi3rx56Nu3L2655RYAQFNTE6666qqyN7aK7ok1W+ILWWr4jyGL/u0khJsaIgXfj4nlq+CcqFsNGZVpggxO+0pXVsDGtJ1ANP3UhZcuOQe32OBYvtxEVSjukqsING2T7UrZJqvXPtwgfbbdC45BozqYL7/jTUz55p14ZVnA2pQnrKgcDdWvy6Zx/1f2w+IrjsQPT5gVuy++74fGhm7icWHDuji9wwU5lUfw+Wtvrsvg/APGA1DlEeTFjLie9nzcacuF9wM8O8KHm54ixcpNEQtwr/H92TKqYX3y7iOlc6ttzvtu7aDsRRvTlrPtcwVfYmAA0fMR90fVtOVZOW4LPPpbrsAzbcUYcMaeY9Cs2YF2YTmVoldaCXkEAd1QIRIbCYh78vHZgYOvkRh0al9wsUfP3jvY+D1p15GJ2kmZrgAvUaDOJVSvWoX47q/PL9OeuyadCsetGx98N/xeJLnioJ5rroGZDsgLd9cw0WPnDIPnmdsR17SVf+/fVIsff2oOdh/bz+mcOlQyU3OpbfgkYbRzDu7XPtyIs3/9bKh5z0UkiMdCNwfNY5/8mWfaqvNc5KCZNbwFh00fUizHOM04pq3qtFUiByh4eQRN1AxJZikgkkNxh9z12gq2Hq4d9PMn543ApUdNlSJcOPiKPIKA6mjI6uQRGIeWCmsoP3OcesjsES3GOqLjoo0oIJk8go5NnE2n8PvP7oGLD9drjvPHxR3C3LVKTFtXB16CseEfJHKPrZuy1yz1yo43+7ld+kepEIlpBaitoHNsRZq2luskEi7UZhP3yuYgcn2OgF4LX52yXJ22tz62OPyuK+QR6CXSFpuS/nGvHn1GmbQHz/MwY3hv9pw2J9ouI1uc2fomuGw8qI/YRjyInYMUnzy4F4b3adDq/3KP17gBSp8N+fDR5rhPQvzs+35c07nDiM9HyTVt3Y4vJ4Tc0N4T+PWfioFK4sdaTR/UOeU5hGtJKUIjHUYPmJKX7oywx84pGDx4MO655x68+eabeOuttwAAkyZNwqRJk8IyBxxwQPlaWEW3B7erRZNZ0MlFvJTqxJ7yPClLNwfOcWhiNVFn6JEzhuCSv7+qLcs560xSpdvb81IStLZ8Ab7vhxOHNmSQOQ83QeuSovDyCMlDCWzzwf/c/ob02bZjx10DvadL127F/z4c6Pb+4rFFAOSFFJ2g1IWxLuzI933c98aq8LOOeVTweSbwxYdPDv/OpO33jzojueQzFJl0lP3SxLQVZd5dvRmPv7tGqkPHvBPnpl2kPR8xfl2dtkLz8zvHTNMzapWOMm5Ak9QGlXHq++QddzC2KFNZZwxyC4rH3vkI3/rna9J34jmETNui0/u0+aPQlvfxxYPGx+opiWmb59nVog+N7NeAFy49BKf94mk8+s5HUhmXcPVSkCgRGVO2FONVfU/FO3bOfuMwZUizNDarfcGlvV84aAJ2G9PXqI3M1bnooy3S95xe84/vfxtfXhAxm0zZhEW9VNdZRSbtYWz/Rjy/ZL30Hl9y5FTtMfQW1GVTVoa8FPbruHCaNaIF//niPhjaYk8KGLbLqebk6HqXbXwxTaN9dMl2AOBLf3gRAN+X6KLDSVdcud9sIjLlK7opl83Ic6AKbg5T5z/PC5LBFHxGHoFLRKaxhba0xueoSDswXv7Bhav5ihBvh7CV+jRk8YOifiiNcOEQOTfle6y+17rNM1qvzj9kG7q4sU39Tsewi53LoW6pPPm53Emb6GZiTSiPwDhtC34oA6Fz4MUypVuaKvoqAGy2RNAkYtpKLEpzGwBF87gC8cu0TnrvJO1dcq/Ee+16nYWC3Mc5W5KDzqbknWxu8ghJnVIpr/zsZrfz8k5FWT9aOYaTtyHrBPFu6mxemrCYnk+MkQN6dZxlC7htPMSYtsy48qnd9HJenAQJvY+0b6lRKACwcOXG2Hd83cFm2ItL1+Po2UO1ZX2faDqXqT9x05HWaauTPfECabdI4qcM7bL8/oPjZ2Lhyk3Yf+IAp/oy6RR612fDTWw901beoDCBkvW2EVJTyLS1JPre2VDyCnLy5MmYPHmyvWAVOzyEBtq4AY3YuD2H1ZtaJaZtwcA0/Nphk/Drx9/HBQdPwL9fXg5Az+rkmbYmp21wrl+fuRv6NNZgv4kD8NBbq1nnHhfubJJHEInDKFpzhfC62hNo2rbl49fATeaFgs+2KUPkEcrltFVhq5dl2pJjnlm8NvZ7Vsu01Q9L1MjwASwkrDouKYUoJxa8135yNsYOaMTEQb2kzQSOaXvXayuwvT2PY2YPA0gd2XQkj+Br5BFSHv9M1P4vDJZL//EaVDz0Fr/AjRKRRfeCMqN1+rQqhBFpTBhlceZyLPHFa4KkWyYHZcT8IHqRCVgUqsMWoKGCstN28pBmfGo3nq3pGsYjM23jerqAvEjWGYOVctomeZ+5sibjnTOkgbhTQNwT8V7XZdP4+Oyh2Nyal6ItAPsiHQiY0YcYkqHF2lls5gYizXPNJ2exjJRr75WdtsIw5MJ0hZPgsXfWxH4TeP3DjThl91F4XsmIPX0Yz6QB5Hvu0vclTdsEFr1uMyuqV/5c7gRG3QnqQor2jT6GcVCAG1vpOB/KKCRIRMbpfqr9geqyU4cwNyu7MG1Fuwt5P+60ZfQAdU7STa1y2VH9GsLRIqkOvlqcW1iHCSw1TmTRTrUHqwtH3TjMhY6rcNXoM32nS/wZPy7ZhoqLPEKpkDVtg/Zz94IytXX2uXo/7PfUszMHmLrs8ghxh5IJEtO2Ag5EHXmBbtLRs7bn5DnXVq/KtFUTkemgM42443QbVur9st0/dRO9lAikcsCT/ub7iwtznG42iGvp28g7Xzl5BOq0Lde9MGmMC8TlEeLXtu8EvdNP2mwufqD3hzr5uW74aSUPBQUtnk55uO3s3fHWyk1sJIMoWyA5Kcr1DnPzo65qOrer9mbK85yTTpYDQ1vqjRv6HO6+YF9c+KcXsdf4/hhPoo0p6LhqY9qKOe7N5ZvCZJ696jLhvanKI8hIvILM5/P45S9/ifvuuw+rVq1CQbGe7r///rI1roqeATEZNddnUZtJY/WmVsnwzzNMQ7FQ+fz+4/G5/cbB87xwINfZZnwisvjk5vs+vvD7F0LdOmFsnrbnKDz01mo2QQO3m2OSBLjm3rdi321vz4eLwPdWb4n9DvADEMvU1Oh46hKRRfovbqEEUhKrgh9zzk0d0ozXl0c7nDZ5BO4aqHFo0+2l5683ZFZWJ9mplmQGQDFslbBkZw5viderhPv/8Zkl+Pr/vQIAmDa0N8YPbEJ7yAiNpD108giZlEcM5ej7iGku5BH0E9pz769jv1fZIb4PnH7rM+F3HNPh8OmDccerfGiqKWnOO6tkduGIPsEEL65fJwMCmBNRRcwPPwx/MU3uFx06Cfe8vhLvrd6sTQYlnL+ibaLtpkzdKccFHl08L1y5id3EcEnU5KIx6oJMypPGkiQMFJYJ5k4MDKEaqu1kU0Pg2pPmAEAsaZnOEdwRiGcknLZHzhiCY+cMd3IemTRtNzD67J/ffxwmDGrCBX98CUDAjGky9HcOssPAXp5eRzkXsHEGVNmqlmDaYO0smDJhm5i2Ag3MhqIYN9ZsbgvHINPGWT/lPFzosToW+YjmGTpec12b29jlEtsE94Jz2jKatvHTAIhsv5p0Cl87bBI+NnNoqPusHsM5gyliUjtM8i3KauagkxFQw6pF8jkV9N6VqmlrSmYoUOfoUFVPlSgRWZmdttRuFnMd55yifU2XbCqpvin9dXgfs5PBdU5XoduYoKD3oNzyCIA89tINJZ08grBJ7Uzb4P+870vvjngt7DrN/O+mpHu6NghYpSuUAzqaCLNkkGbQJps2B7ioGTquik3XPcf1wxEzBuM/r8i2ObeeyqY8iJjWciXQGtLbrvevnolj8Js2oTg7R6dFy/UznYQEEJdHaKzNYM7IPprCwX8+one9XO/wHmP74U/PyvJZuveA3it17ZjyAGEllUfTtvwOz0HNdbjt7D2MZeRoBzdNWxrh01SbSSz5uLMg8Sj4pS99CV/60peQz+cxffp0zJo1S/pXxc4HYbg31WZCJxTdVaSGMBc6LQYn8Z5r5RHYRGTxheCStVtD1i4QOcbE4MA5TrnvkiYiE+2jx6k7USZ5hGNISAc3Ked9n03ukU55khPRZaD2JMMvbiB8bJacyda3+IJVfdGgvdHfXEiblGiBtKfRxEZUbotwFM0Z2RIrevqeowEUk7jk4w4lCpUVSx2cS9cFzNEcYRJyGcPV+sSppP6vyCNwC2QbbBlfucn++8fO0NaXRH9xdJExKdrQbnhHhplCssP+Gu16mxxR5x0wHn8/by8jK/iHxRBa1RBqqtU7Sl3lEShOvvkpXoc6Yz++XDv7fzxHNpo6mojMyLTV/KQOMyZdWNVuq4RjUFS5sehkba4PxhGd8UvHSbHZ5fJ8/mufMfjaYZNx7Jwo8aEPP3E4cpIMu4DCAizjDVRrqoRDHTDrz3cWVMYl/ezitOUgniPd5DQtYsW8JMAxwdWxSGLaWpxxbDQPmaN+cfo8ALLOJUWSRGRbivP6kJY6nL3PWAzuXSdJ31Ac+eNHE7VbfOR0R20ONrUPq5tao/s1sMfRyB1X51P8d2ZOJn/XpFPOmy7x5DTu5TuSSJcDHd9CeQTmMoSN73n6OTXGukwg+2CLJEoij0DhQDqU3r1KhOrTDWl6nTpW5+0vB/q+tkgNGtkkbUwoay/b8SqS3IHY5qDlFVDP2RVJyACVJME77dX7x/XRzSQqQVxLNp3CT0+Zi5+fOk8qy22y0TEjaTIwHTLpFD4xj0/gLOCyqetC0ACid4a+OzanbaMh8tJj5gZbOyjbvFx21LFzhmGekpNAV3Uj6RvqutjE3i4FXeXupNdujwKIf9dUlwnfEVci2s6CxEzbP/zhD/jTn/6EI444ohLtqaIHQgw8veoyeLZIb3+YhHZTYzzKKsvpuPGLCAHu3eWYtiozShibYqLjnL+spm3CXSpRnurHzR7RIrEVeb254DvqrFy3Na4TXCj4LLPmnVWbY9ntbQYOHShzeR+qnzSWydlwL37x6CJWLoLWwTknZfZA9P3AXvrdX/WqhPNMPOMz9xqDXzy2CL/7r93x0eY2/PLxxfD9yLmr1+YqOmH9OFNq5YbtgQwCcS5GrHCfZdqmCdNW6v+KPMJrH8b1mmgYlKmtQHQ/etVmsKk1h4/NHMIe08fgkKg1LCBVh4IIKw03QIrXnvJkp9IXDxxvXNSEzA+SadqFGcT167e/dzi2tOZCh67K6Js/Tp9AiV56EgOOMyRshvRxuwxzrt+GuaP6YnBzHVZsDLQDk+zKc5dZioGo9tDQycKcQP2uEiH4qjxCrzozq5nK2Yj3zSXJhm4TgIvgMEEOG0y2CCur0zahYygpPjZzCP798nJ8dt9x5a24BJhCdF1kZe59fWXsuyQhwgCwr6Ifx+l+xp+vW5IzgLeTxLHf/NhUHDh5UHAOJbpEgGOW66YjMa9zC2vVZFiydqul3TzTlmO06cwRFz3AMf0bMbDZzjDToRR5BEnaJME4oZa0jdOHTx+Mmx4KEiGu22pmNicFnXtDTX6mn6/bEtiuvWozzo5vXWI4Aa/ICgfsKglJEpFRuCS8KTd72QQ6z8jal1EZwe5bqCTfVJEi9qokjyCcaCWwx12OM5W1JvRTnbZdkIQMUOURItDmqNfGbdpRnVp1g3fB1EG488v74LBrHwGgi74sbQyx4auHTMLbqzbj5N1G4qK/vBz7Xd2Y4d7pfhqZB0B+18V90sm4qF2iV23GuI6QmM+2jZ/i/77Pzy0dged5+Mz8UXiWREfq2k3vn5okV9Xo7alIEu3AOuqrTFstEo+CNTU1GD8+ntClip0XwnBvqs2EoUvUCCtITit9Vlm6E8aBcxy+vya+EFCZIsLYFJMs67TlmKIlMm2pE0/VTjPpzdEduAeYhB1BIjI/VnZLa04aJDkH9N2vrcAnf/YEXl62HoBsfHDSECqTUHcvNmxrx3f+/Tr7G31em1vjC4iMpGkb/S0Ychzo7Ut5XnivxeLh0qOm4rXLDsWe4/pLrJw2pVysLcoE0ZaLTvSNv76CiZfcIWmmhqxwUrdaX4pZFIfyIAbj32aQycyj4o59sb4T5+kTAuhgWoio3VVsLFDmBhA3Qntb9CGpFIrt2VCozJ3rT56DbDqlZeAePn2wUUe2FKYtwL8P6j1QbRHVQOsoTJlnTeCMbi75UFhew6dR2XdibHJJxFMJg1ScQ2wimhgatBxAWcLxPqheDieD4PvJJQtkZ1SiQ8t6/yq9OLj2k7PxwFf3x5GaDaXOhPqO9yZyJS4OmR+dGI8mU5/d1CHNiTYlXJi2v396aSi5ZGN080zbuNYujXYQ+NsLy/DEe3HtZp1NFkZZkXeCkwVykihRyojP9JX0mDmVa6fp/p8w18wss8HGRLfJzyRxFKgLf9uhs4iWoy6RaamQNG2FJj/TFzeFJA79ppn6fFT5ChX0uq2sUMqGTODccrH3OzNEf4+xfcO/pY36EsZryn6nmpniOZTCHueOO2vvMc51JHUUl9NRmQS6ZHUm+4vbANyPbNZxY/jkwc3h/eMSbNP1Ujk3bQc21+Fvn99Lu3ZwccyP0kQuqMeH8ghUxiVN7pVyqu8fp48QjNftPi5Hc0v57mMSG/fbR03FkTOG4LDpcs4GWxRlUlRAHcEJSdZV3GU21aarmrYaJJ6BvvKVr+C6666riFZGFT0TkTxCFruOCQyNdrJTSDewxW4jxy6xJZngjCouWZPqgCiVaeuicSXVkRdO2+g4dfJmNW2LTiujLABkTdvRJLGPD9lQ5dr92d88h6cWrcUvH1sc+42TR1Dvke59/3D9NmN7BbiQy/osWeiRgd3kbKF6Ztm0F947atCJ+ygWVwU/EvbXZTNWHawxvc58xAgNEpERpyOnF5jywDHHo/4f9Ivvfnx67FjbojwtLWKL7cvZdWF1MDptFT6lcICqu9pqm20OkHDxregN26A61WzXK5ioOkgh6gkWZJw8woBesuNYfWU4nbOOQLfpYUNSg19XtTqWFchiUEVHM0e7QNQYOWDN5beQecKkaau2tak2/hx9zbEmyIZt6dIKHYW6cCn3o8mkU7FEdF0F9T6PGxDJF7k4ZNRFFhB/FkKmxRW8pm28LR8U51pbO00bwzSqIpIEisrd/PAiTZ38ubYQaSyB6HbEI6xMUG0OwbyVnHDE+cTBxWJzSThnAif1RMGNr9RJmUTbWa0pyWYA5/jpCOgcH0bcMNcq+oTO1gKSO+RkKRl3++jD9eb5n8LFN1BT5jlchzH9G6X1gE7T1hWi3xQK0foqicNLN7ep/fFrh03S1qE+NivTVqm7q+QRaDOkfmi4f81kw2L2iBZ879jp+OJBEdlNdz/FO8bKI5AbmFSKqSOwmTU/OH6GcVzikv3RvCV0nKD38bKjp+GoWZFkIAd6VttrEW5cFHzk83p7r1TE3yF93afvNQY3nLKLkehRARO500Avy1W6haI+W2Xa6pD4zX/00Udx2223Ydy4cTjqqKNw3HHHSf+q2PkgHHJNdZlwQbGFGKYS09DAtA3D9XTyCI5O1BjTtvjyh4sUph42EVnCwUK0T7A/0ykvxqJh5RGKDkXKTBCh1HQ8K/g+y2bzfV8a+EwO1/c+Ctg69HJzeR8/ue9tjLn4dvzluSDcSnXk6p6JSDrCwZaIrLGWanZF35ucdxMG9UL/pppi/dG95o4RdUqJyBwy2wbJsfTPPpP2QgO04POatplUpDNM62pVmLaf2WNUTPvVtijnjO020ueSIgnTNgyns2iU2owhiQUtHAou8ggx5pH5PC2WxF+0uiQGnGDm7zKyBUfNGopz9huLuaP6Go+ZPsyeNC8JaHsTsbeYoly2XQFdza3tcr8X3ZxjosXlEUwtLA3qxgtdSPz9vL3w3WOmoZkwAmlEhEmPV0W9ZlOpI5q2SSM/y3n/1EuuhEO9u8C08LdtGl3JsGyB5FnRVXDOrY600/fjCSQ5aQVTJAhbqYIN29rDZJ3NDNOWHkJzD/z2rN3ZU6j2nWiXtOi3SGj5IYMq/tt5B4zDzOG98fE5ekfAoGZ9mK/A2i1x6SoKXn4m+jsJc6gU6ZLj5gS2o6qd3FHQdps0m4VT2hRZErMXEtg8PzjezL6jTt0kNnx30k4c0KuW1QIF+PHZrksa/E+ZtiZN1tjxWqet/Nlku6rtttlbKkvaxsauFCRHGvmey8sisAeR5Hp31WacsvsoqbxODqQm1PGM99tKMW0pOG3ajmrcc/IIVCKoT0M29rt6nA6SxrCV0Vmcl0CYtmW0ddSuX8oj0ukn9zTQttulGhmnbU06fJ6cb2ZnRuJRsKWlBcceeyz2228/9O/fH71795b+VbHzQeys9yI6JBS8pi0njxD8r2N1CgOsf1MNfvaZuQCAsQyDR2VCiMW0aRDg9KxcEhNQiIm2LRexP9UFFrfY2FZ0fvSuz+LAyQMBABcfPkVqc9AeP7w3dJzzmXIUlHksJmUqB9GeL+Cqe96C7wNX3b0wqMNRHmGlgckoOW0Zdkq9JtGCzWn5yV2DMJ6C77MZ6wU8snhstTgGqQGb981O22zKgWnreaxm4Pb2eP9XF+1qn1HD++mzDpm2RLohKUoJ+VMNJNWod9UrW/zRFsKWtrdDPY/NIEiSbTuZpm3wTAf2qsNPPjUnfF9NONrCGkgKuoOdxL5TjaRz9xuHP50zP/H56SKXjtk2pwVQIYOUMCmCc0bnmD2iBZ+ZPxovXnpIuOlDF0Ympq36DZcR3UNyJhDtb13JtK20pm13gukdN42DXztskjasPunYp4Jn2pbutAWABVc/JH3mohk4JotwLLQ0yJtd3HR492tRss4RfaPwWBqKLSBC9WvSKQzWZCxXTTDOuRRKL2hsM9PG0UWHTsY/z98bDYZIngkDe2l/E7DJDnBjW6nvq3qYiwPliuNn4omLD8RBUwaVdE4dvrxgQvi3KcmoWA9wNr4AvUecDR8vH/09c3iLsWypJETV5uWw36TA2WS6/nLAgzo/eNJvKr5+2GRjfdReDedHRXbE1EV1eqHxRHn6Suj11GVTVhtAnYu7jmnLO7frJIaofAy12UWvosdmNeO7iUVO70el7gUnJdZRe4DbnF5AxiaqhyvLoNhPrGNBm8oGiciCv8vp/C5HNFmpkmfdDUmSQXK3qbEmXWXaapA4Edmtt95aiXZU0YMhHHKNtRl2B7EgOa30YeocM5EiTxbjIpyKkxTQOW1NgwANd95rfD889s4aZ2ZvTSaFtlwhrLeVsDrVhSDnCN5WzKrdUJPGL07fVfotGPj9sN1isUInNN9XnI7K9a3ZHDFD0sw9oIyz5RsCJ6y606u7FVz42a6j++CZxeuk+8cybWtoSGXUfltSCuoMbTUwaHk2J7+QoLZSnjBtv3/sDPy/v70ilc2kvSjczPfZUPlM2iPZL+NMW+o8VtuuOi/PP3A8rrjjzfAzp3skTmGaIM8/YDyuf+Cd2PcmZ2mzhqmqGvFJWRSi/M2PROG4TvIISrV2HStzfaVq2opNjySaeUk1T20oVf9KdTKN6d9gvPcu8giq1nS8Dg8pL+qnlTBIRZ25cJ5gyqS80EFKN++EA9pmYE4a1EsKqT9y5hDc/vJyfG7/cZg/Vk54Z2OEyQwUY1Hm2DI6bWOfe/BqwQKTc9z0DpxjSKKWNMGOCi4pq2kscolIUCGSndZw8gjk5RXj2k9P2QUn3/xU+D1nC9GEZefsF90f0XKfKVubTWn7uhrNU2AW1lxyTxl6iRYXPPrOR6UdSOCi6e1el/kzh5pMCkN6l9+puMvIPlh8xZGx73995m5YvGYLbn7kPSxduy1k2nL9WkByxDtcVEcSXrnChQE9rKUeT//3QVL4eyVAN/3FZwGO3dqvycwQF8f4iGxbzobTvVf6RGTR37ZQdlrWaYNeaV9nSgJQeNLf0adasinBjY83nzoP3/z7q7j6E7Nix+psQdMl0uuvFOv4M/NH4Yd3LpS+s0tnuEmh0brG9G9E38YaNNVmpA1Cel9c3mPZyetWNkhEVjrJRV9/x522HCu5JyIJIUG9zhF96zFtaG888nYgfdmdIiC6A7pmFKxihwKVR+B2EFlNT2MiMvn7mx56F6O/cTuWFrMPp1N8kicB1UEonGcmeQRRz+HTB+Pjs4dp6+YgQt1FeeGYbqiJO7E5Rm8YTsaEnKkM2kgeQS5HDV/1+tZvjZy2wkFLDVQ1W/SGbe2xgVJ3L9QEY1eeOAvTh/WOHSPacOnHpobf0et9Y/nG8G9bFvYUeY6C9cLdO2EstOYK4T3WLcwleQTCtG1pyKK/YhAHmrbB3/lClIhM3fXlNglaGaat2iYaVtjSkMUcJXSdY9rStulw4cET8Z8v7oO9x/d3PubU+aPYc9kSpFidqcw44aZpm9RB4uY8dqsrgnh/ymn0JYWsaet+XHLWK/877dcFiWnLly93kgUVkYa1OfSN27iKNM7MfVANH7zqxFn40znzcf4B49GvqVZi4do0jFMJDNvYsWW8fXG2VPnq7m4wva+m8cc0NqjOD5eM9ZccGTHz1fnFdj7deD1+YBP7PcAzbbnErO2aqAdu+heSByftOkLRtA3aTs2QH9wZbDpu2p7TvpcF5SRcCKtoli4aS3zdlQte7vGU2hx1A6U7vpv7ThyAU+ePDu+5sL+N8gjkHrm8L0nm5lLZc64kjYG96ows4nLA8+R7ROd6lRXr0ifo2iqcHy02nPSb1mkbfT91iFn+iXPemRBn2naNu0LnMKebEpwG7cFTB+HJ/3cQ9iza2xLTVnMtrkzlcibQojh777Gx72ynOmKGOcEovVRxLxtrM3jwov3xny/tIxN2Etq0dHy0Mjoh5qVofVfO+1gOCTD5HSm9LRcdGmhLf/GgCZaSlQG9dlcCj8CdX9oX9TXpcMyrMm1lODFtd9llF9x3333o06cP5syZYxxYnn/++bI1roqegc1EHoGbjFhNT2YXXhxKjaela7eGLMPP3Rb0rZTHJ3kS2KIybVOyPALLtC06KZtqM8a6OdRm08D2XFgvdcLWWpi267e24bUPA4dlg8UQzPtRWijO6MmkPOQIG1fgz0WdWgBozwU1UHmElRtlXdpZl90dM8B0O/BblKQaNZkUq028rui0paGRuoRjNuedqP93Ty0Jv+NaJ+YKcX9NdUuZRQtE9yvlxZhN2XS0aUCNtYZsOrwfmZQXGpmUyRwyzUmdi4s6w2E9ROv3t2ftHr4zApIWmXIdJsMllfIwdWhzLOmHKdSqf1MtbjxlF3zutudxxp5j2DYAycPduWa6yDSo/d7GjrE1i54yCZNDjA0uC85KoVQjPsYMdAwrUyE7bUl5zW2kUQOVcKqIRa7oE7pTcAz4JJq2FHXZNHYbE2kZU8aTnflR+iKsnPISSbIe93SYmPGlMrmSJtgBgLP3GYt5o/viuffXsQy1UpzLt5w2D/v96EH2N6GbT48V7z21c0TUiHovOCfpVrI5TUHDUAUeXBgljNW9+3GmbfydDBNYasb9SB6hNEwc1IS3Vm62FzSgnPIISTdDuxKiP4kNelNiXRNzlC+foB0lDmCmTY/OBl3jAPGwecqKdbneFHnXC5rjPDI/q9DZCEmcM0lCprkyOkmBSkPnIKf2KieRZqpHl3jP9H5Tp3Wl7kTA0q8LIy5tbRrRt96ob60eT58px1an99TFJqJNc7VjfUTzRDlJF+rpS5GwSOs6W0Kcu9847DdxACYPtsv9VALS2GXVtFU/e8U6xPq56rSlcHLaHnPMMaitDRYjH//4xyvZnip6IITTtqkuw+6GUvua0/QU4Nizr36wIfxbyBCkUx674FDbE6I4KJictiHTKp0yluMgJhqx8N/SFjEN1AVW3vdRKPhIpTz4vo9jf/p4+BuntbbfxAG449VAO65QIIk2mHEwVYw/pizZ595fKzk32xim7fIN22J1vU6Yr4Ce2bI15iCPjE1xT7e15cPnThN96Aw3m64cN5k/s2htvBwz6fXWhftTpm2BOHE82WmbTnlhqDcgZ2iur4mctqmUFxqZ8qZFvP9vVJjh1Jndv6k29ny4xCwCLkZILOzMwvI7fMYQPP/Ng6WkXjFN24T2BWdg2TJIA3FDaJ0lKYytxiSJDCiEH95kWNLfVHZzOVBqRumYk87iq9LV3BGmbSV8DxGTQpzD3A6JaSuY05Y+aJsR6MIySXbsrmRsx29T93UMdRSlMm1NiDFtHZ/l7BEt2gSAJm1D3eaWTisW0CQiY96DnCZsdEtbDp/99bM4eOognDgv0JQXTNsGZdEeHql5WXTvfoxpy7Chog1hvo7QTimxC//9vL1w0Z9fxtn7jCmtAujkEUqsrIObo50J8Zzue3MVAN6eDcsmnHeTXDete7fRfQ0lA/zr/L1xwwPv4KLDJjmfo9LwPOUeKZdPPyYJIw9Cw0WdSt8yHK/bz0ryHJMmmVLHwEqzm3XwpL/jG0hANL6a66HzvY48oj8+W6K9lxRJIm9cIteTsEezieURojI224zqOot5rryJyNS1WHJ7QpZH6FhbRMRrV4DeV9v6Msb4LxavatrycHLafutb32L/rqKK91ZvxqIiU7CpNsM6XrjsxJwuG90NFlhPQvdFzemUZ9S/VeURRGZjLjGUQDsJdzZJL3BQnQAfbQqYq/2aamLXedXdC3H13Qvxz/P3Rl02Hd47AKivid+TK46fGTpt874fMUmYkJLQkU0m0v99+D35OovGBb22O15ZARt0NsnmVpkF6hGGgGACiGvMpDy0NNSEZblMpRMHNUlJTThwxuGxuwyLF1SKnXfAOK1hSQ3YXKEgsXy45C3i/m8nO+zUqMwQ5zXdKRROXpPem5SgLcVIAhjCzEpZAGUz9mP6NtZIn+Ohde6GHteGmow9MQV3nlJE7nX1uTrORvdrcNLEou/+Tz+9i1PdSVBq0oJysbfoGCJr2vLl5WzV5V90qFXq2iGeGY02cGXa6javwrrT+ndTBf25UuGOLti55BGid/Jz+8s6tdQZmk17zgyP5JItdpTiXDYxjdqYRJyRvUCctoRp+5dz5+PKuxfiyffWYunabVi6dhvufn1l6LQVEU0q0ypcHGva4sLcB6g8QvSduLdaeQSlDUnRUJPBDad0bKzmnkOp77d6WHd+NdXrHmmw45I6bUtl2p6yx0hr+RnDe+OmYmLj7oKAGKB38slRK271AbL0l82Gc/mNOtmsWeKVxGc2qI7iVZv0SY8rCcnpqDHbXYIyJaatxuY2PYNKb3qHbVCu0fSsbPYQIPdPa/K5EiW/APdImYLvs3rpHYU6/pWyEZ9UQqS7otTNHPo5TBxf1bSVUNW0rSIxNm1vx4k3PY5fPLoI3/zHq+H3TTp5BJ9zWun1W+k7upWE3wuWYsqTNZpUCJ3V8w8Yj9e/c2g4EJoGgdAJQ0LfXfd31B2h9VuD8/drrIklXFu/tR3rtrbjh3ctxPtr5LD4AU1xpkzv+iymDwukCmhoEx0HRfbpsB3kft/12kqpPuG0pQvSFRvtxpDOgb21LZ5gLHqOQQKwI378CAAUpRuiejiW0ah+9kzCXCjSiXNHxL5TJ4Mx/fXhb6lUpBOW933JiSMlACj+Le5/a3skh0DL0eNof4vkEfSsAcpcUsPkRN0C6nTosrurzqGl7AjbEpElbYMqI6I9r3LgIVMHG8vbkirR09qMi+tOmg0AGNan3olpS8e4SiQuoc+tQ0xbq3NR/v0LB44HII8zdKNNd88lhngF7FG1nXrGrxwZARCmrc1pa2lDkkQauuzgOlTKhlfr7blLBTvoopDKWgBQNueiv3939u7GOtV+Vw7WtGksctFlV9HKOW0Ze0EwxjJpD/NG98UFCyZq64zkERSmbbEZdEwYWmQB7zOhv0EeQf4sHALyZk+xrFYeoSiNom11x7G70m9UcM+hVBkddSztzgt5dS48eTe9w1RmlLk4bd2vO2kYfneEB1X+SPmd2cgwIUXeSZ20k6kanUO2hhlP9G0gz8XJaStftDpedxakiPUOjCz0SB370HRbbInPygWZFGIuq08IGSHJc69JyrQlt3FEX3PyRbquF+0urzyCfp3mig/WR1GVPXPkCiDZtgnlEUQfyVY1bVk4rZT79OmDvn37Ov2rYsfHr594H88sXofv/Pt1Sf+ruT7LOm2F8e37Pv76/AcAIscmBTUsBDZui5dLp7zQ2cK90EIeYe7oPlKIlhg8uI2bXD4axKNQotKYtpu2B21uqstonXPb2/OSjMOCKYPQu4F37IQO6gJh2sLDMbMDLbzzi04UlX1MHapfPWSidJ00IdrwPvZMwzojQdW0pe3N+z7e+0jWhxtGzqUu9lzxtxc+kD4ft8swDOgVT+YSmwwsox1lKhckp218AlI1bWsyKWmiShOmLc1SLxKn1RmunSbw4Jy2dHEUMxQcJAbiWXmTmwemRQRgZx6o5V1Dk1Wjz8RYBlwMBuo4s2gpk+eZ1yx8KEq5r0lQamKvJAwbQDYe67IpnL7naADBMxbvieS01VRX6URkcQYxX44zBsXGCmdo06ba+rUcwmgum3QRW6neFGfa9uTlghk0YZZ6zyXWGHl4fZvkKAMVsbDIMiTMMY1bdGE7a3gQArnPhP7G59bKJIDlIorEXCXOYdqUEvIIqja9KlMCBLYhAHx237Ha8UG1t7iNMat0VRiJpG12h/G/p87DzafO0/7OOm3LxbTtxq+m2u2bmEgqgaSyRKXOb12pOd8ReJ4qn5OK/S6QxOldKETvTlziSl+P7rcazZhpq8PldVDtp4sOmWw/qAJIqr+sAz1WN74bN+vIMUJurhKQn5P5gh1UIWQnsGVqpHOw70CbkiUnLHYs8S90RiKyUjXyBboy+qqjoGv7xEzbYnmOXFGFozzCtddeW5GT33jjjbjxxhuxePFiAMC0adNw6aWX4vDDDwcAbN++HV/5ylfwhz/8Aa2trTj00EPx05/+FIMGDQrrWLJkCT73uc/hgQceQFNTE0477TRcfvnlyGSiS3vwwQdx4YUX4rXXXsOIESNwySWX4PTTT5facsMNN+BHP/oRVqxYgVmzZuEnP/kJdtttt4pcd09HK9Hx7FWXwepNrTh02iD0rs+yk5EYIF9cuj78jgudos4+ALjsX6/h1scWs+VMEgabt0eJ0SjE5MExbcNs8OkoTNu0w1OTTqEtX8B9X9kP5xUTpIVOW5GYrS7LykAAwQJFTLxDetfhZ4bwLHqtoaZtCvjB8TPx6T1GYe7IPsH1CYZrsQx1Cu8yKijDado+/u4a7bkFdE5bVdOWtiNfiJ4FABwxYzAaajJ49pIF2nD4UuYpLbMvIaNQaALnfTkRmbyYF0zbotO2+C5k0ymoGpXiXVj00RZ84fcv4LP7jCULXb3TVmLsel5s4WHaCXdJ1iBlbk55JTlpkt5bG1ydtrLD2u5gOn7ucHN9CRaPGfJ+5Q1OPoERfYIxrr/F6VMqJE3bBPahq3NTQFpspFLSAjLv+0jBkyIedH0h6aItKdQqbZq2PNO2Y4Z2Ek02eqoOnrZDSNofejIGNUfRLOqMpttksTl+StW0NdbpyLS9+dR5+PuLH7CRJhRi3qf2CJedOUcijgDzRsHaop54cz2fiIwuurcRVq7uvVAXZ9zGmCmXQXDOYhsqyFPqXZ/FwVMH4YsHTcCP73sbo/s1YPGarVEbmXe51PlRPaw7v5v0OfUiCX3Zsgkdq0luH73/PXUDyvfV+UFvb7k4dyI9z8i2tW28U+iepS46gW2D4Xo4qJtBNEFvZ4K2tGP9KTpW59AzjRP03DSXRrkhR0SZr9eF2CQxlS31ZUl/yjnIEyVh7IuyeVJvOTd11EfaUTugp244AbL0YSKZMGmtYffD7IxwctqedtppFTn58OHDccUVV2DChAnwfR+/+tWvcMwxx+CFF17AtGnTcMEFF+D222/Hn//8Z/Tu3Rvnn38+jjvuODz22GMAgHw+jyOPPBKDBw/G448/juXLl+PUU09FNpvF97//fQDAokWLcOSRR+Lcc8/Fbbfdhvvuuw9nn302hgwZgkMPPRQA8Mc//hEXXnghbrrpJuy+++649tprceihh2LhwoUYOHBgRa69J4MO6sIJdf4BEwDwC5+CHwzubxNW7gmMM4VKHuQLPuuwBWQWIzdp0MRo6nG0PXQCEfqGVIvUNFa0Fxc3veoyxAlQQHu+ELY75ZmzHLcW9X3H9G90MnALPog8goe6bBq7kkQL4v6JyW4r0ZsdUMxqLvR+uQnR8/RMMr08gmw8ZNNE07ZQCJm4vWozuO6kOQCC5FoqDpk6CHe/vhJn7T2WbwDB0JY6SQtYB/WOuiYGKhR8iY1ADVOx2y26jo5pm/IiZ29rroB/vfQh/vXSh2GGYqoDOHN4b7y8bEP4mRp0HqNpKxm/JTgM7nw10jAuNTwoSWidC0ph2roYNQdMMo/fEtPWkZWbK/hRMg/DhZ+73zi899EWHDJ1kLZMR1Aqc9XE3HY5lrK58wUf2TQkD5iuukovpl3lETJkfBJw1rS1tCGjSKQYy5Yob1FuqPetkg6vrkYfEs2ibpxJC2m6kLAxRZShy7Qh5wqjpi1p58DmOnx233FsuXzBRzrlIZcvhPOZlIhM2Zz2fT+UTRJ9k3tP23IF1GRSWFHMMD6ktxypEzGaou+E/m1DTcZZ07bAvJMpi20WyiN0Qhc+/4Dx2HV0H6zb2o4v/v6F8HvuXS51AR97N7vxQp62zeZgk5wtDlN/qfI/PVYewTPL5yTdAKXvZPheaRhuHHS2Vg2zCaRDUsaqqpXdUeZiqZCcjmWqR7dBaHqW9LdmTULlckBqg+WCyy2PkFVsSxvomGPbNPCI/R62p4zRcLFcISXU/Z1jpuHSf7wGoOeOXUAQLbrvxAFYs7kVkwdbEotrxmuOXFGFozzCxo0bnf8lwVFHHYUjjjgCEyZMwMSJE/G9730PTU1NePLJJ7FhwwbccsstuPrqq3HggQdi7ty5uPXWW/H444/jySefBADcfffdeP311/Hb3/4Ws2fPxuGHH47vfve7uOGGG9DWFrAAbrrpJowZMwZXXXUVpkyZgvPPPx8nnHACrrnmmrAdV199Nf7rv/4LZ5xxBqZOnYqbbroJDQ0N+MUvfpHoenYW0JdMOO3EBKubWAt+lITsiBmDWScN1ULdwMgi0HIqK5dCOCabVKZtSj8htJMFgk0eIZcvhM7NbCpFnKo+Hnl7dVhu6dptWmdUwLTV6/tSUNkI4WfgjHfKBASCjM8AMLBXbejAFpIJ3IS4x5h++J+PT2fboBs3hYN8ZN8GDGupx57j+pPFYLRYmzykl9HouunTc/HsJQucdKtMmbUpYtIBNsYU2dmjTluu3aLu0GmbTklGayYdlzUAgHdWBRsXlEXwpYMmhH9fd9JsqR7av7jrUC/JhSlIn2WphrAtxN4ltIlClxFdBbeA7whoFVYnG2GmFXy7k6++Jo2ffGoOjpo1tMPtNLUHKD27NvdZBXXipVMy81u8J5T5ptWSpf3WubXucGWMqsZgoeCH47ltE+O0+aOMv2fT+ndT1w7ANZFJZYz4nhSC3VF4noerPzELXzpoAuYWo08EdCzpJNrEZ+89pizPSTfHpTx3+QWhX99KNOCpPIIamUMXR9lQBihe771vrEQuXwgTAw3pLWvxh9dfrM73fWzcFtgBveuz2k2BdiXWlgthtSWJFe9RZzg3azIp7DNhQCyii5uDS3famj93J8hORgvrklyISd8/KuNup5SaoLM7IZNKGccg+jEJUzlJIrL9Jw2IfnNg2rqSIgC3jcHu4rQqmzwC+Vv3fpjGLQ8eztp7DBpq0jhhF3MEWUeQZEPAxbEqk0zMZek6QJ0PONDqbEsfUZZG2paVaVuGiJvxA6K8K915rLfB8zz8+szd8O8v7G31b+jIJ1WmLQ+nmbClpQV9+vQx/hNlSkU+n8cf/vAHbNmyBfPnz8dzzz2H9vZ2LFiwICwzefJkjBw5Ek888QQA4IknnsCMGTMkuYRDDz0UGzduxGuvvRaWoXWIMqKOtrY2PPfcc1KZVCqFBQsWhGWqkEHHEuEEFBomuskoX/BDVm6dxkijhsUWJuxegOp8quN6W64QLlJ61cq7kXRwUHdv2glj0pTkDAD+9Oyy8O+6bFrSulz0URQmd94B49lkW6JuIY9g0+WM2uOT7Mjxcqrem3CoN9ZG2rrt+cBo4yQi6rIprZOZC0f0yXP64zl74KGL9kd9TVpyHgunbmOtmdSfSnksA9cFuskt5sSxMaaKP+eJYZv2PKmeD4vsIvHdLx9fDCAQkFeNbF2yAQDo2xiFzNP2HzN7WGy3UTWmTRnqk+4c29gROqgGifoMbM5gtTuVIo9QjiQCSeqjDgPBVO/KhQV17CR5jElDbqUFYspjx1EXTdskYXelwdwnBVSt6STMi2PnDDP+noQ9m7T/JnFcJEF3Tm5UCRy3y3BccPBEY+KQJE4RiSlSJuaOrm+4jpNA1K+p01ZyspDNVUCOvokSbsbb8dbKTVi9uRUFP2inOm+Hm95Fa+X9NVtDeYbm+qx2vFGjfyJ5hOg7Ma3Y5RE6D7HxtIzyCDFnXTdmwSfRrqRzgUtug/0mBg7EgUzuAhWybFD3vV8czthrNADgywsmsA4MAdlZZb9GKYqRROvJZaK/h/auw/ePnRF+1sojpOPjibYNdJPSYVN/3AB94uBOhcf+CSDZpgCdb7KacdyoK5wCLjlyCl759qEY2S8uL1gulLph6VSfpTy9R/U19kBw2jxnpi2VRyjj+FAOTdskkVo9AS6bp1LUBcO6zbkIJ+9EcJJHeOCBByrWgFdeeQXz58/H9u3b0dTUhL/97W+YOnUqXnzxRdTU1KClpUUqP2jQIKxYEYT3rlixQnLYit/Fb6YyGzduxLZt27Bu3Trk83m2zJtvvqltd2trK1pbW8PPSVnGPRn0PRThdMLwqsnwL2mBaLjWanZeqNNxs8FpSx1ZqvEudNYAoFEJ0aKDunqcWFTUpFNs9mOBt1Zuwv/72yvh55pMStoREpIHR88aiknFsIB/f2FvnH7r0/hoc9S2AmXaWpgG9L4UNAYXEBnKwiiLQhLT0oJ/e3uedUjXK+UouN2u1lwhXBg21WbCCYcmRBOat6o+VWcgKdNWtF9l2vJJ8+S66rIpybDOpDxkNe8CAIwgms7qnER1abNpL+ZYNe38uziCajOpcCFfqoZnXB5B/myTJVDhLo+gb0MpSKZpG/UPXQbmzoSc9Mq9HUnCIlVQ+RiAMm2Dz55n15INzul8SmfEGaN8O1S2Hh3buPcncJSIsGvzvaJMWyvzKKFRbmMslIqexObrLMhOEXPZJItcV+j6RpKFoFjstOXi0k9A3F5oJxu5asJNitZcAU8vWhuco+DHxg/hWBSv1c8efjf8rbEmHW6Qq7j+gXfwX/uORe9i6C8rj2Bl2vJ6nZVELBKGeXalRnZ3xEnU2ZC08q0bHfQ4+0Wdf+B4tOULONohaqUS72Nn4VtHTcPXD5uMumxaijbcpmiYJt0ApZq24jVXn5HqVKREC937Rp22Njds0siSAb1qMbxPPZat22YvXEGYGc+e28VAfpd1uSdMr4LnBfknKpzf1kl2a/aIFry4dD0OmzbYWl/S9/GK42bgpWUbnN512metTNti0fYKOW3VSyul7qRa3zsCdEkp6XqrighOnpT99tuvYg2YNGkSXnzxRWzYsAF/+ctfcNppp+Ghhx6q2PnKhcsvvxyXXXZZVzejS8AtXOstTNuC74fyCPWaxSd1xFKm7dQhzXh9eeQUT3teaASrL/Spv3gq/FsNI6RNU5m2rcShbMpQ/BJJpgbEpRpEPTQ5x/RhvXH0rGH4xWOLpGNDpq2FafD8knUAgoRWIhSRZdoqC5otrVF2Z+qM/ctzy+IHI3AK6MLUC8y9oI516pQVRkXe90NNW9WB3hEMcGTkqvfINYtlwESOFox0I0BX9xXHzcTvnl5CzpXS3suvHyZnwY23k2jaekwiMnKAatC6GApfPGgCfnTXQgCl6S4BcQOJGmODm+uszGoV7vIIhMlYBoOLntbmEOHkM7qSyVOqpm1HkshR+RhA1sO01SXLepT/vsUZxPw5wk22MCychMtxzzNBUzMlyiO4oLOYtj3N0VEJJNnMqUQ4tm7zLUkfEItTsZGsHkvlqACFaRtq2sbrbcsV8N1/v649rzhGjAlC+/bMvcYUHQ/6Nv/orjfxPx8PGH4cI9CWbyCUR+hERqopUahAuZi23fndTCJdlNSR09JQg+8cw0t3mdrREx0fYnOOyrv1VjRMk+r2iiIFnxA/YvIIcv30/KrTWIButtucK6U8ixF9GrrcaUtbrV7CyL4NWPTRFieyhMQKLSURmfUM5QG9FF1zrjxxJv7v+Q9w1t5jEtXngpN2G4mTSsgBb5dkCf6vmDyCcqGlRALSY7qzfnk5oZPjEFFLVU1bGSWtAtavX4+rrroKZ599Ns4++2xcc8012LBhQ0kNqKmpwfjx4zF37lxcfvnlmDVrFq677joMHjwYbW1tWL9+vVR+5cqVGDw42N0ZPHgwVq5cGftd/GYq09zcjPr6evTv3x/pdJotI+rgcPHFF2PDhg3hv6VLl5Z0/T0RwuEjkEl5odNF6MmqyBeohqt5wir4CJ19U4Y04/ef3UMul5IdvBRvFZOdiYRPcjuJgaGE4oXZldORPAK3gbpk7dbYd1TrUjAYVZ0udY7OFyInto1pK5y71977NmG0cWwOxWkrpCtq08gQBvG3/vkae566bNogjxD/bvN2waJNSwZg2I68uzxCElx8xGQcQPS2jpw5hC0XS0xkXXwH/6uapWsYp61a94zhvaUJN53SOwHVr/eZMABzRrbg1KJeZoxZGzOu9dfhyrQVKFXTNs7SIL85tEHtTs5MW8rmKYOHRLfLy5+bOG19kbCn6wyrJA5Cirhz0718JlVkeyhjTTguGeqptDyCKdyTQnVWyUzbeD/UbTJyyEiSFZb+RH530oCuUFerMm3jkMazBM+xXItA3Vjkov8pIBanoU2i9GPVhhIagikveld1TlshdcUlWQydtmE7gr+mD2su/q6/R++viewrE9OW20QOzikY8dpTlB0udsbOoGmbJOGQXLbM7ajAJkpXIJ3ycNnR0zCgVy3O3kd2jtHLcrlGL3zXieyIYePd8+Q+27+RJ0pQu83WN+nU6khO7RYh4vJ1ye0JZHYCJri1HnKsjixhuoedtWFD3x/dGccP7IWvHzbZSdKOtvufL37Y0eZJoOt/25gj7v8/SBvKSbqQ7ICinZy4DmYdvaNDF3VR1bTlkXjF/uyzz2LcuHG45pprsHbtWqxduxZXX301xo0bh+eff77DDSoUCmhtbcXcuXORzWZx3333hb8tXLgQS5Yswfz58wEA8+fPxyuvvIJVq1aFZe655x40Nzdj6tSpYRlahygj6qipqcHcuXOlMoVCAffdd19YhkNtbS2am5ulfzsr6mvS1gGqUKAarmZ5hEIhYto21aZjjhGqq6ga772KCbeu/sSsWP0SQ0yVRyCatuJSVIew7/v4wzNx57xg/+XyflhPnNUif5bkESyatupxAD+ZqvdkQzGsX+zS2wyleoPTlkv49u1/Bc5f4WAXCOURfD+UFlCTdXQE/ZtqcesZu+G5SxbgT+fM14bix8JVHBffBd8PQ0uDRGTx49T5VE0Ylk6ltKwotS/UZFL42+f3Cpkkth1b+jvtOynPbXc2ScZfHeKath0zMFwZZKWyS3Uw6cbpzp33/XDTp2s1bUsz8DxPZstajV3GsU3vBQCjbAt3nkrcNlemrZrEku7kc+1y0VwUoGOFlW2W8CYcPWsoMilP2rAqB3qSbmbngTowbM+RHFWmhbVuXF63Nb6BqINgzoZRRMoYq0oNCKetLiGbwPIN28M5/5pPzo79LvqPMBlEvaF8kuEWUZvLmIhMp2nbiYnIBNSxV2VFqmWuOG5G7Hcd1OvozuyrJPNRUnmERO3w3Mfg7o7T9hyNZ/57AWYOb5G+l8LCHfqEuA35go8n31sDwCxxJf7+6Sm74JIjp2DG8N5svemUhwFFnWGOKENRihRXd3h+psRcR88aile+faiUTFgHert1ZAmjpm0n3YokGrQuoH3V1keSgk4DNj159VLKTbjwEqwldMhI8lodblKPgG6zjyYM1iWF3xmR2JNywQUX4Oijj8bNN9+MTCY4PJfL4eyzz8aXv/xlPPzww851XXzxxTj88MMxcuRIbNq0Cb/73e/w4IMP4q677kLv3r1x1lln4cILL0Tfvn3R3NyML3zhC5g/fz722CNgXh5yyCGYOnUqPvOZz+CHP/whVqxYgUsuuQTnnXceamuDieTcc8/F9ddfj6997Ws488wzcf/99+NPf/oTbr/99rAdF154IU477TTMmzcPu+22G6699lps2bIFZ5xxRtLbs1OCLmr3Gt+PLZN3cFLSEB7K0FQNwJTnscZ7vuCHTN9hLfWx+gVDjIY3C9AQQo7F25YrYOIld7DtpnIAUT1mpq2ciMzdKSCazWraKvfkx/e9DYBnB3Ooz6a1TB6O2fLgwtVs2dD5WfCxrshSHdBcx5btCPo11aKfYac3xrxzTI4RMG2D79Keh7P3GYsr7pD1rbkEYPKiRW+Y2Sb0EX3kJAOq4UTPc/j0IfjbCx8E9TrO8lSKwJQszQQR5uqH/ZH+lrw+50RkXnmNGs5I0JYVRkTe1ybz6EwkSXqlIuV50QaQlaEQP2cm5aENUcRCVJfhnA4Mjo4g7uDgy+mYtoJFrKJvYw2WF0O8bcik5U2UcmLa0N54/BsHondD3CnUEZgyk1fhLquj/t2xc/KD28RBvZzr2Lg92DBtywc2iTrGiusSY7hw8tqctve+EUWlcRE09BCfJPUUOo7qO5ZJeeHGyaqNrRj9jcA2P7sYesvKI2jYNy6M/3KDzkOzR7Sw7DN6DfNG93GuW72O7vxqykxN9w2rcs+h9H0tVf6pu0PadHWSRwjKbG7N4WcPvRd8F9t4j9d/xAw+io3iT+fMx/IN2zB5sJm4lFLGBRd0h8dHm8B11aYSCCm6NYDpWXbWhk26zPMZHR8PZiIzOoIkTFv14ZV9s4hhiSYFnXu78wZdOSGtCzSO73zBL5lgtKOhJKbt17/+9dBhCwCZTAZf+9rX8Oyzzyaqa9WqVTj11FMxadIkHHTQQXjmmWdw11134eCDDwYAXHPNNfjYxz6G448/Hvvuuy8GDx6Mv/71r+Hx6XQa//73v5FOpzF//nx8+tOfxqmnnorvfOc7YZkxY8bg9ttvxz333INZs2bhqquuws9//nMceuihYZlPfvKTuPLKK3HppZdi9uzZePHFF3HnnXfGkpNVwaOB6Jm2NNTgX+fvHStDmaVaTdviS7ppey5k2nJOW5lpG32/kQj3NzNsByAa2M+7TWaFU6YtJ49ANXVjdRYXWLc8soiEIioLJHUA9lES09aUaIOGLLflCthUvIftjtkX67IpgzyCbGQ98GbEbr9gwUS2HS9/sCFyvidgq5UL6rzpmgm14PthaGk65YUZfU11ZVKeLI/gedoMsTYje8HUgTh8+mB882NTw7p056Z/65IbqKB9syMTYTmNO1eZBk6oviNIUl+G9I8CcfR1FXTZ7l1gYo8YjxNMW2WDyDdsJgnQR1wJZ3fMwaFj2iphVzmG0Udx5l6B8+gT84Zb21BpTbKBzXWJQuRdoF72TrJWMIJqsFsjNJTNunJAN65cfPhk9nsOP7gzkLHSM22D/wsh41wwYksbG6JjooN8P9LW1TFt6dj/9qrN4d9PLw6SnUnJJ1Nym1X4DptH5QZ9zw+azEf90OeZTH/c/Lk7IcmGahIphY60oxw2QndEEge5Wl7AZFcmibYY078Re47rby1H2+nKm5s6tOsjWL0S70u8nuhv3drA9Cg7a0zjnPcdAe1Xb6/a1PEKCSSnraWx6q/ltt3p++RKoFFBCTU9UY+7FKSY+R2Q72FV1zZC4i2i5uZmLFmyBJMny8bj0qVL0auXOwsAAG655Rbj73V1dbjhhhtwww03aMuMGjUK//nPf4z17L///njhhReMZc4//3ycf/75xjJV8FDDRzk7qUA0bVVttfC44iC1ZksbrrnnLQCBs49L9MAxbdcXnbZNtRmtE0ho1z77/jq05wthuVbitA21TYt1f7B+G770B33/EeUXrtwU6tSqiZXM8gjmRfg5+43Fzx56D/tM6B86izhDjWrArNwYMcNuO2uPWFkOA3rVahNCUWbyc++vxRm/fCb8rGrKislz9aZWrN7UCqC8mrauiLFhbUzKMFw02gxIpzzUZtI4cuYQ3P7y8jA0Wb39NCEdEPQjnVFgMxZqM2nc+Om52nbrnATuTNuov5VqXABFJ5dweEmGXnJjwzX6RQ7dSnyaGOjYZWPliHPnSKK6rgzfK9URABTvXVHVxGrskp/FOdOhjnfwonCMaxWVTkTmrGkbzh3B51DSRvMuHLfLMEwa3MsptI9ueiVhsXdp9JfqtO3WfL7K4ltHTcV9b6zCfhMH4Hv/eQOAQ4RGhfs1xS6j+jiXffit1WjPF3DjQ+8CiNsZVOcSANpyYiPKje0zV9MWegTdABXOYPU9zaY9kP32EBuKX3JMHK08QvH/zoyAoOPaio08I5/T/HdBz5JHIH/bmLbUXiizX7W+hto33fd+dQT09rqYcNzjiOVOIB+7Szc7/4BAduCQqfr8MpWGV6a+St9dnW1glkfonIeSZsbbjoC2u+DGH3IGrS9JRAxQfqcoZycnBbUfu3PSyXJCt4GnMm2rCJDYk/LJT34SZ511Fq688krsueeeAIDHHnsMF110ET71qU+VvYFVdH+oTltu8AzkESyatuSF3bg9YtqmUkoodsoLJ0+607a+qPfGaYpx2N6eD522IdM2nSKLmaDu//fXV6QEGSroIkeUizFtY/IIxGlrYU5NGxroSbXnC8ZFCWWQfbQ5cJYOa6l3Dqdtrss6MW2feHeN9JvQERbgnn9jTec7bVXYJkFx/3KFQizR1PePnYH9Jw7AodMHs3VlUp60UWBy2ibVQTVp3EpMW8cFiuxYKt0w0DnhJg+2b96pZ805WnMumaF3G9MXTy9a61RfE+m7DZY+KhY5BZKIrCvXhB1JWpDE0USdeKGmLdngANw0bWUdvkTNdYJ6al1b1BDrUNJGE/HgeR6mD+M1/VRITIkEF9mVJmlVHiHCGXuNwRl7jcHvnloSfucaoVFOCGkDABjcXBc6A5MuBqdeemfIdG1pqJF+0zFtaxwTHOrGX0keAUR2QeOo0NkcwmnLJXpbunYbHn37I+w9QWb4hUxbfbPLDjp3cVE5QOlRKT2JaSvpzXeBpIgAjerSkRB6OpIylbnn8eR7so1UyWeiwnWTsr4mjYsOdY8uqARKZSCroEfqxjyj/VTymZNBvt6Og15Sv6YafcESQGU2bGOjemvL3cWpX6PUunfU8coEeq90iaGrTNsIiT0pV155JTzPw6mnnopcLnCsZbNZfO5zn8MVV1xR9gZW0f1Rrzg7uIktX/CxLXRSuoeGCL2gtOchFzpKvNBQ8f1g4PY8LzTyXZ22dPeGy7AsbPGH3oq0W6nzWIBbtMU1beVr3rCtPXRM65wFAs1Fx9L6re1E0zZejibSErIEqkPVhKa6jHbSoGPmWys3S7+p5+AMj3JP1i5IyrQNw98L8SQoveuzOHHeCFK3cmw6heb66D7UpNPa8yVlf6hGeUaT7Mg1FLAcicgAZZHmAf86f2/8/NH38NVDJlmPVTduete79Q+XheH3j52B7/77dad2jO3fiLP2HoO6bEpi6HCgTNsws3kXGlkZ5f4nQRJ5BJZpSzY4ADdN244wg13gyrRVozSi5JEdlx2g/bqn6CnGE5FVkUQzksobuUoR2TBtSLRJcP6B43HJ318FYN9g6d9UG27YBu2JJu6+yuYtlQOiZWVdZv35dNONFAbtR/dEnE+tU2dzbC0mO9NtTn36lqfw2mWH4qjrH8UhUwfjG4dPJonItM0uO4T8BACM7tfIlkni0JTQg5i2SRyJ5U4oSkGjujoSSdSdoXN06MB1uQlK5Ei5GKU7Gjzth4T1kGNteV1sx1cS5dabpu/6mUWd8nKBrkutOtrKz+WOburbGK1hPtrsnjCUgq7NXIksPR2cZj0gzyFVpm2ExE7bmpoaXHfddbj88svx7rtB2NW4cePQ0NCAbdu2lb2BVXR/NCgOGO4FKxQiDVedc4RlaAqnbcojIclyWSFSXarT9vUPN+KD9UHfrUmnwsHSJ9qVAsfNGY5hLXX48f3v4O4L9tW2W9WPU23Hd1dvCf+2ySOIhcCbKzZhaTGpmJlpG+1MJXHKNdZmUFcTNXT2iBZk0x6eWbxOeqabtsuxjKpGMXc/kjiPywXV8LSH7AW/04W3jtnEMW2p47EmEzC2aZIVge3tySZjlWmum9hcnzVdJPeqKz2pkSqJMGN4b1x30hynY+kYsO/EAfjsvmMdz2lfGI4f2IRfnbmbU32e54XawTZQ+RHxTLtSdyrtGMbMIYlTSj6n7LQthExbezvo5lQlbptapa4tofxNyLTlNT9LAV2MVYKBWQnENW17RrsriUwCxjSNlBF9qaPo3ZDFs5csQH02jb8WE0264PqT5+Ck/32S/U3V+Q8jioS2cz6Zpq1uk5B2n0AeQSQ484rHyZXq9B3FZorJiXDqL57Ge6u34KaH3g2ctmEbOq8P02eucxLSxXiSOaMn6U0ncUx7Jc4/LqBRXd34dnUISe8fR6T52mEyg1WOhKnsnfO7NLYkGSQHeUfqIUfrNohN41Zn2ROyXV+O+qJKSknaZoJOJoeDem+THOuCcjwfOk/sLI5KOeqPfJ/yIBT4dhYHtgtKfoMaGhowY8YMAEBrayuuvvpq/PCHP8SKFSvK1rgqegYaauUJiIbSC6dVwfdD41Yrj2Bx2gqkPE8yCvO+jwyA+4vJsZocHYRiTDznt1ECvZpMCoV2P/x9U5ENK7DH2L44cd4IXEhYfG5OW71DoMXiZB7VryH8++7XgveLmx7EYihXKKBQCM6XZIHQXJfBwF51uPLEWehdn8XBUwfhlJ8HC0D6TFUnpDoZcgafLfS8ElANVRt7QDzHNuK01S0+1GtOpzz0J2zicIGajjttW4ubF0mQ8jwi2cA7FFyTedVkomM6kiCuZPYQZLb9rx0drME5+fN3BsT5NrfmcM/rK2Pt6Wx0JGFQEjYF/VWcU2XahgkSDVVR51YlnCou4xAQl0d4cGEwb5RDd7tWYtq6d45cmRiapSB+37qoId0IlCVtkySg71JbmZy2QMCaBdwzrQPAHmP7aX87dJqsCyneDzE9tTKMczPTVjM3Kp9zaiIy5ThbSKgkLaAUfXnZeulzoQvkEWyb7oC8sZ2ExRizYbqx15aTsdBBt1AvB5rrM9h9TF9s2NaOYX3qy1t5N0HSRKJcmbziCKFlKt3LulTDPSHK5cyma0LdkGdm2nbOu1/u3AOSI67M15BkblRPXQmnaFNtJoxyLQXU1thZJAFMff6qT8xCyvPQq7Z0ctGOBmfzobW1FRdffDHmzZuHPffcE3//+98BALfeeivGjBmDa665BhdccEGl2llFN4aqaTtuQBMG9KrFhIFNIaMu76Dhyr28TUWHsBoiRycWweL9x4sfBudyHOwKvo//e24Zlq6NGOK1mZQUPruBZMi45MgpUoi8ALeoU5OtmUiQNraj53k4ZOqgsE3iOxU03FEN73dB38ZgkXjC3OE4uHi+aGEX3VPbwpS7HyobtzOgNsMqVC+ctjkXpq1St+fh6NlDw8+C1cTp+JUyFVPjRE4UEJVx1TukTl+dtpYLVHmEJDh+7nDUZ9OxJHb2c3Ydk5E7n2kzptIon6atpTBjdFPWMQAi26KvLCuFXSdorCNirDRtuWh8930ff3xmKYAgsqCjkBdm7he5LSH7vpyI37fu6xjqLEwrZi3Ppr1EC1ehj1xOJHVwfHnBhNh35+w3FnuNl/VfxXMXc/vWtmIegRo3bT6dTUPHACqPQOcn4ZAG7JsbJh1zKv9QKPhdIo+wx9i+OHX+KPzPx6dry1DHboeYtolb13nQ6RFycNGm70g7/njOfNzxpX0SbZz1JCTVn+XGsI0KIaUzEyp2472HGGQpitLr6V2fxZQhzRjbvxHD+zSwZcyJyEo/dxJITuoyvD7Unij3NSTxa6p2TSU2DsYN4OVxXEHvfS6/kzht6XpW+e3YOcNxzOxhVum6nQnO1JJLL70UP/vZz7BgwQI8/vjjOPHEE3HGGWfgySefxNVXX40TTzwR6XT1xu4M+NRuI/H7p6NEHSqLsr4mjUe+dgBSnofdvn8vgMCgDp22Wj2f+Igu6laNPCkjpe/jz8WFNwAcv8twp+so+D6+8ueXpO9qMylsKTbP932s3xZo0wxursPZ+/Ah3JxjVGWPmLQvXUJG+hUXOWFGZWbyC5m2+chp6+rIq82kJE0egdDBQXwKNt0+zmDXPfNKIsaGtemsFX+m16czolSmQyrlYWCvOlx8+GQ8+d4aHDUzcOCmmZVtRzdQM5oFj6t+G30+HXHa6rSIXDCqXyOe++aCxM78dAfO2VFw71KXMm0l533pTlubFjI1dsU5UzGnrX4zSSAtMYPL/+zUKnWXRZm2bflCqD92zn5uEh0m0P6cRJqmEs4+V8SeWQ9aUFcK4wf2wu/O3t05iadAUukbFxQSri455mdDNm5jUA18ANjSGvTBBmKPmMN13eQR8qE8QlT+YzOH4JePLwZgn4OkudYw5uf9KOi6MzcePM/Dd47RO2wBRR4hwbwVG9O6sbeLzoX2RGTuZUvFjizzklQegSvSrEQkVjpRqHSuHjTJlCsxVyrl4fYv7I32QkErj2AyxTrr3afvcbmfU7lt9iRzo3rqcssjAOUdc3YWSYDO3CzaEeC85Pzzn/+MX//61/jLX/6Cu+++G/l8HrlcDi+99BJOOumkqsN2J8IFB8tMDs7xUpdNoyaTirKM+yQRmcZRw01KwqEpJbFJedLklvd9LF6zNfwsWKI2cIzcGsK0LfjAy8s2AABaDIs3bphRk4uZHIaNtfZ3R2jCikUhd6+E0649H2W3NzPfot+uPHEWW0YNJQbksI0FU+L3mjPCu2IwVpthWxyE8giEaaszMmj/o87Sc/Ybh1vP2C3cGeQcYklCesJjmHaqfzszbclzr+nAuN3RbMMNNZnE/UJaGHZyn+L6T1cuojuSzEXWtE1yzqBwnGlrl0fIdoCZ7YJYQi3LhksQ/RG96wN71XW4DQN6RQxCl8Rm04cFjM5xA5osJSuHnqSb2ZnYc3x/TBva216QQNXALwfmj9NLHnDg7DFuA8Ejdg7Aazub3lPduEEX+j4I0zbNz1tWeQQqiWOQ8sgX/EimpZsRLGmzk7CGetKGCn2mD5PkvRw6Uz91RwS9Yy5sYu4eHzxVlUsxly8netIjp03t6DomlfKMdoGp/s56T5JKbyRBudeBSebbzpBHuOzoaejXWIMfHD+jw3XtPJq2Xd2CngVnpu2yZcswd+5cAMD06dNRW1uLCy64oOoZ3wmhLm5NTkfh6MjlowVyKZq2agIiWR7BDxOJfffj053Zg5zvrDaTlsIGRdbmN1ds0tbDvQKqpq1JZ9dFS1Fd2HCLEuGEzRUK4YBv2tlMpzwpvJCDODzPyCPcevquOGDywHi93WRMSMq0FX2MatrqjnFlJXCO1FLGTNpXdQxL111senz55BE6yaDsgCRAR8E9SxtLtZLIdEBuIInDXWb1yMeIcUH0T1NdHUmc5oKkmrb5QsRw9Tx5E6tUDGupx1l7j0FdNuWUEPMbh03Br59YjG8dPa3D5y4V6n2qOlGSY8LAJry9ajNmj2wpe92TBzfjji/tg0HNbpsKM4fHHc3c2KXKIwjnataREa8bf+khPk1EluLHKyvT1jDPpFMeCkUbJkflEbqZd3PqkGaM6teAwc11Vic1hXoV3fndTNI2OUqnEq3ZsSEld3LI4aE+m9P3HB17fzu6CZ8EPckhlZTV3BGY1iid9eonyXfgApp0rtz3Lwn/pTOkzGaNaMGzlyzokH17/C7D8dSiNTEN+h0V5WKy7yxwdtrm83nU1ETh05lMBk1NXccOqaL7oN6QZEpMQjT8Uxcqz41zQtNWZdrSwT9f8LFy43YAgYyBCSkvYpZYmbbkd8qgirWbGWrU3VRTZnKXRBYqU4abFLKUacs4bScP7iU5n10mZFHm4r++gsfe+QjXn7xLuAjTMqaVSz18etdMPkk1bcU9Fk5pz9Ozc11ZCRzDyZZ4zgaZacu3yQRJ07YDjirKJu8s/2mSZCflBtcXuos8QtJ7kUQPl/4qymbCDSLVaauvhzqDKvHo4qHEfDmq/d0qNhIz6TIl3fDwzY9NdS6/94T+2HtCf3vBCqIHkfm6La76xCw8vWgtTtp1ZEXqnzKk2bnsnJF98PHZQ/H3osY/wNsf4j0Q727ktKWbK/rzuMkj0ERkvDPAtlkiyyOo5/Ig4lDyBT90EHQ332ZdNo37v7I/Ul6yDSu1bDe7LAlJHDLpTnQQ7ohIrGmrvDfcMfTxVfqR2CTWuhNUwlBnnSvJb5VqQ7lPWe77l0QewTUSsaPoqB151SdmoVDwKyYb091QHf6Twdlp6/s+Tj/9dNTWBs6r7du349xzz0Vjoyy8/Ne//rW8Layi26PB4HQUhpzQSwNKY9p+uGF7+J0wfD0vWHDkfT/M2NjLsuuc8rxwoBfsXLUNYtKiQv1fPWSitk4Xpq2OXXHdSbON7RVQQ6B4TVvhtOWZtr89e3fM+597SR3Rb7qpjx7/75eX4/qTI4OrJqNhsik35KpP8NILlUaMQWbVWQt+/5/b3wBgnuRdjWauDpqwrBR4GiPS1agrl6Yt7dOdxXrtSPKtjoJnq3WdxSEnpEvWDlrczrSN97FQDzOJpm2FmdkxORSbPELBDzcTVTmbnQlxWYkuakgPxszhLZg5vKWrmxHitD1HS05bLtmpp7zDYgMmIzFiDe+z5idpE9sH2gtxeQQ6TtjmIJOOeVweIV5/d0Ep81Vc4qlMjakAkozpSeafKuKQ75+9fJyhztVZ2U1VCluEX3cFl6OinDA9y84jRtBzlvek5e5XSQjbSXIMdDV2FoctoPSxneeyS4az0/a0006TPn/6058ue2Oq6JloMGh0ifdxS9Gpmk55Wg0mbhdOTXIGRM6TtOch5/soFIBtbXlrW2h7AOBvL3zAluEmqqEt9fo6me9UJ4BuwvjYTDcHnuow4ti9grFyxR1v4oIFgZOZ3tP+TbU4fpfh+L/nlwEozdgDgPZcnJEjtVW5Vhdtx86A7XrXb21XyusPoL+YDBE1OdguI1tKymgsNiji31MWktuMR5+PiSWfpJ7OWiR3JPlZOc/dVW2gkGUykh1b6n0Mx14hexNz2uqPpf3+sXc+cj6nK1ydj6IZ+QKR7OkmY1RXQL1N3S20vIrkUOcYLoRalT7KMfIIpp6g3xSJ/s4VCuG8RZ3BtHm2+VCSxDEMMLlCIVzE7yg9OB490H2vLMk8Uu4Q7J0NSTWBXfI7yDr3lX0mbVWmLQuTHd0VNnbZmbZl7ldJmLbZ7rzjtROjOv4ng/OK/dZbb61kO6rowWgwaLKKQXrt1iBDt0nnj5uURCKyz+wxCr958n0AkaMpnfKQKwRJt7Y6O209CF7py8vWS7+JbKrcGGKUR2AOqFUSPOm0L511SB2YtnTxc829b7H1qzITArrkWKpxly/4aGcYOXLb9GyYzkTSnVU1TNPEtJVYCaY2qPevRILBgKZarNrUGvteZi+61dVMWFcdCRmi4bGdJVWQ1vTfzgB3r7rSaUvPnXQjoFR5BHHPxbsfMW2Lvzuyzhev2ZKkuU5wdT6GLGG/yrQFuIiELmpIFWWDyl5tZDbnaMJVIGK/6WQMVPz1hQ9w9Sdnx76ncyNl1OnqTcK0VW0tyrSiich2lHVgT9pAofPImP6NhpIqe7piTdphkVTKIMa0ZeUROs+R3pPkEeitqLS9Z6q+s8a0sm+okDG63P0qmaZtzxlLdyZUH0syVKfLKjoMkySBMA4E05bLbByWVd7erxw8Mfzuv/YZG34vHLNhQpm8HzJtbcxBeooP1kXyCEfMGIxHvn4g245s2sOkQb2M9apQnQCclMDuY/o616c6FDlHMeekjIUTJpyQ1SpzhYJdHkFlBXfRCkqVpLAtgGKJjFzlEQzl1GdSanbxSYP5/ldKyHljbQYTBgZ65NOHJcuOLp2bnK6zHD0y66pzzhmer5s5bTuiTZxkIcKFsopTR5q2xazthqpoaOEXDpyQoLVuiCci48uJPlRl2gboSWy+KtzAJexSEWnayonIXOURdKBHUOdMVpOI0JaYi/6sXgfdbC74kX9gR+nDPendpE27/uQ5xrKy07H7XlN3RWJNW6UINx7IDMvKPpMkzrauBr0TFXfaOq45KtqGCjqpy3378kk0bRUb2Zb/porOgSsBqooApcfGVlFFETMMjh8xCQkmrC4JGRAf0Kn2J3WCqk7bTa3tYbiNTdOWOu62FNv0p3PmYzfiQFUnx+8dO8MSthL/Tl2IcKzUJA4z9XhuAufYduqku5qwNVMe8LGZQ/D8++tw8NRB7HlVaYN8wQ8Ti+jYfd1lYdERvVbApmkb/W26Wu7+lYKvHzYZS9Y+j8OnD5G+L1Ub7h/n74V3V23BDCbTuCsqrVHKQUq+1ckOU24M6OxkaNK5yfWrTHwbJD1im6Yt6eGibMi09d2ZttRpY0rMWCpcNayrTFsZcWd39xi/qygdw1rq0a+xBmu2tGFYSz1mMuO8eMziHRYbMHTeVBMYuYB2HxoGTRfNdMzpiDwCXbMXKNM2UYu7L2KJyLrxhdFnYxvf6TPtrARBOxI6qmnLjfFJ6ywFlx09Dd/652v4n49Pr8wJKoDO7KvdQtO2zDZ2JWXUkiUik8ekM/YaXda2VFFFZ6DqtK2iQ7j2k7ONRrcY8wUT1qRvqi6ypRA6co4P1wdJycQEunRtwJhtqs1Iod+m9lD0b6oxlrEm6lGWCDXpVOxauHuUJHxfPZyXR7AzAe99Y2X4t+d5+Mmn5qDg6ydn1cmeK/jhQkznKOouYSiq0zapvWC6DlfGsioHksTIoJg+rDceuuiA2PdSIrIE972hJtMhhy3QNU5buhjsDs6l7qJpm1Qegb4b1n5DmRdpwbQN/hcbOC6atpVOIhcftzXlGKZtJZzIPRXdZPiuogOor0njka8fgGw6hbTnaTQsxXsQfI6Ytm7j+tcPm8x+L8sjEKetRsqnT4PZZpPnOPk3Op/6PkgiMmOVPQZJbdGuRJLEmDTiLglbrooASTVt1RI2pm2lNqNPnT8Kh0wb1KNYjvROVFqSy/TedNa7n7Rv2VBKDg9XJBk66Hr7d2fvjvnj+lWgRVV0BNWoCzuqTtsqSsIDX90fLy5dh6NnmRNpiUFfMG1NjCZ1gqADMj2uf6/AySo0Nf/zynIAQD/F+cqBGxT6NdYay9j0l9Qq6xldXc6hmmTX1oVFxrF5VeNr9ogWvLh0fbHO4FpNvuM65VryeT+8H9w1AbJB+OUF5Q+DdoXKdh7QpNcl5mByLNHbappoVis6tKU6bXUoRdO2EufuLOdlI9HP7g7ZYLtU05Zcf1J5BGpIJ7kGMZ5khdO2mBm+EMojGJi26cr2F/XUuvcylNUpIGTa1hlke3Y2VA3nHQNcElcK8Qr6YSIyoWlLmLaG44f01jtePC+w39pykSNYlzRzUO86DG6uw4qN29m6TJuDNHCl4PuhPMKO0odVQkB3vqokIft0vG3PVZ22SZFUXsJFLoUuHyr1/niehyG99UmduyPovag809a05uict1/Wm+7eTtskoM+ub1PNDjNHVLFzoXu8TVX0OIzp34hj5wy37jyKQT902hoYTbHQN6JgTh1wu44OpAzWbAkcYv986UMAfLINFdyOc3O9fJx6SbaQdrVOTreXY6XqEnlxcMmM7sK0HTegSVsnB1XrsS0fZYPWZeOk55w+tGNszo6ATsqf3mNk4l1yE9vANcPqPhP6S59LVEfQoty74knQmYkrBJqI07Y7sI46OxkaBTVCKyqPQJm2xXMKpu6P73sncPo4MNykxHUVYdp6xs/huYk8wn1vrAJg19WsooodDWLsiuQRipuxjkxbk/yQOG5zMZeBmiBWmj/h4dGvH2CtCzCPGwXfjxj/2lI9C64bUd0B0iaupZ20bE9KStVdINte9vLxftTxOncWlCpBVgrMa46Knpo9TzkY1zpyT2eD2shJ1t5VdB50CdGriFDtuVVUFMI429YeGO8meQTVIKc7snTAFQwP9f2mDh1X1GfTMUNYbUdSdqQr0zbJZBZLKFaipi3V8XFy2ioOaCFzAQBZzaJNcqx1kxGmlCzM5qQA/N8qTt9rNM4/YHz4udREZDqkHdtRCejCXSsJyrTtDjIcXanHR68/KZshK8kjmMvSKxRjxrb2YBz4YP02vPbhRjdNW42mZbkQ03/UlKPyCP9+OYjSuO/NVWVvTxVVdGekws2LYLHUmovLHpk22U1jn/hl8/bA7mus1W+Mp7zgnP88fy/MH9sPXztsklTWVYansFPII3RNO1wgyyO4H1d12iZH0nsdW+OwmrZdRwDozqD3qtL2num2d1oiMjrelmH91h2ZtlUd7Sp6KrrH21TFDgsx0XxQ1KHlWKgClLlx7n7jYkyO287eHTeesguG92lgj2+otYe4qo5GznGqGjgibFAH1bHJXSPvUHV//WI75UwZjm2nOh5biH6ci9OrXpGzEM4aQO90psngWtu7h0Feim6aORGZzBTSYWCvOnz10GgRWkl5hM5m4XSFPEITece7MgmYQFcubihbICmbgcop2FgHtF+JIWbp2q3hd8vWbSWatvp2SM6XTpBH0DNtg/+rWopV7MwQjNr2fAGfv+15/PX5D4Lv0/J7qtOfNMnTiFdvS5sD07b458zhLfj9Z/fAwVPkpKgpx3nGp/IIOw7XVv7UDeY8HSSmZoLxvd1iX1cRR7qDDlZe09b8+86KStstFKZn2XlM2/La9ZW0kZO0j5Kpqv27ip6KqqZtFRWFGBtfKuqomkLqKLhxfq/xcqh5OuVJ0gUu8giNtWms3RJ9Zh2dyrltjrYBvWStVI5pyzkAy8+0jX+nnpc6aGwJQACgVnFAb6VMW42zhzqtc+XWAygRpYRdmAw0mZXgXme5HUWSQ62TF3SPv7sm/Hv91vZOOWejxOLuesOrK3V16ZiQlDlAN5Fsh3L6edvJZsym7blw48pUlxyeVv77plapex3EfSs3672KKnoSxNy+vT2PO15dEX6vbjA312ewYmP8eLv+oo8L/vgSAIZpa9hsjNs6/N8qBGM4qF9frichvhHVNe1wgetzUlFl2iZHR5PAcsfQb7rx3kCnoyN2VlKYxq1O07SVNsk6PpCOHdDY4Tp0qEmnsK2QtxcE0K8xynnTHfJhVFFFKdhBTJsquitUA7x3vd1RCEQJLExQh91GB6ZtQ1ZePHBahkmNoBPmDpc+s0xbxlmdxABQHXLcoRyb15SAQHU2c6hTmLatRaZtOsVnpAYC42K3MX3Ruz6LvRVHe1fBpkvMwcy0jf5OYkwVyrw+kVkA5a3bBurAf+WDDZ1yTrr4L+WZlhsmuZdKg27SqJsrNkjJhiz9l44rwilCF9pbWnNhWLJRHqHCzGxXTVsxblGG13UnzS57e6qoojtD2CnblGgYdUGre49MDH31iDjTVl9WrZfaPqax6oo73sBHm9uKde4Yi3KXXAbdBUk0bQFgQZFRfdj0wRVr046KUqUouOMFuiJHQU9AZ0aUmZm2nfNMJKZtGU758dnD8Pn9x+GmT8/teGUKXElgANCXOG2b69z8EFVU0d1QZdpWUVGok9yZe48xlp81vDdeWrYBM4fbE1ipc5gtWzIATBjUhIUrN4WfXZJ3nThvhLHOxtoMztp7DG55dBEAPhM5x0pNkjwoptfITOCck1G9FlrGJYur6mQU8gg2h/NtZ+8OD8kTJJUbtZkUWnMFjOzLS2pQqGxcVwPqg/XbnNsjstWXC64L2krg0GmDcNdrKwEkM546Asqmp/rKXQWXjaJKgb7bSRNp5RKwm+i7zvnJt7Tlw2gE07DQUXaQDWqNOp+SeGeEzjoQORCqqGJnQR1h2lKotorOUWFyYKivtxoCb3IQxZzGjo6SBxauxgMLVxcb4HRIt4d6GT1FHsGlnVd/chZWbWzF+IFN1rJVyOgw05Z5p0qNHtvR0blO24pWn7gNZZFHSHn42mGTO1wPW3eC5vWqy+LGU3YBEI/8qKKKnoIq07aKioKyJsb2b8SY/uZQias+MRvXnzwHR80caq1bNQw5WQIVlx09TW4f4+yg1e5eZIza8O+XPwz/VtmpwXncnKw6lJqITD1HmrTj3dWbreedO6qP9Fk4bW1Oomw61eUOWwC49Yxdcc6+Y62bBRxMITSl2jIim3a5kKqwI8wEuhB3YcaXA/Q92F5mB3hSfGzmEAxosrPVKwU6ttYyY44J80b3dS5Lx1nBbj5xXhRdsKU1FzpzTYv1bIXlEdRz25i2yzdsD78zJVyqooodEfU1QZ9XnbbOTFvj/Cj/ptoRsjyCUq9qs5Qp/LsnQt146s5XJTsS7eWb67JVh22JSHqvgcBeEeDmX0neojt4D7sJMp3otDXZT+u3tlX03AKV3lwvJ647aQ5q0ilccuQUp/KHzxiCw2cMsResoopuiup2QxUVBZ38XXRkxg9scjbk1Npc2Gb9mmoxY1jvMJzbJingOmet3Nga/t2LCb3gjKQk86FLFmH2WgxM24mDelnPO1pxsgt2Iyf30B2x57j+2HNcaRINpsViqUZtuZ2b0q54J9tXNES+tZOcthSqs6EzMX5gE64/eZcuOz/QMabtp3YbgTeXb8Qh05KFpopX4uy9x+JnD72HXMEvOm0TMm07RR5B045iufdWB+LmjTXpbrHBVEUVnYm6jIZpq7wLunfV5MBQ38VJiq0h65+qTFsz0/fGU3bB5257Hr3rs9iwjddS796uBneoMg/d2YniGZ5pFeVFKYnI6HvN2bZVeQQe5U7M5XouFZ1l73qdeL0dxb4TB+Dlbx/CRrdW0fPQ9YJ33R/VlUoVFQV1EtqylHcUrmwzOfyD+504bR3N/+a6aP+DY215nocjZgzGLCL7kMTRpS6cuIUU5xRXjTP6DE6dP8r5/ALbHeUReiJsCVGk3xIatV87bBIA4PLjZiRvmAFduSsuO6A7f7rd0tp1TtuhLXZpkUqDJlZwiTKgaKjJ4EcnzsLBU5PJAojxsE9jDb5ySNCnt7TlQ01bM9OWzgWVcNqq37g5mxqqoXJV7ISoCzVtVadtss0PDupPe0+QN05NG+Pq+6naUyOKUkemRII7jM8ptlnfMy6sytSsLGRNW7d7bWOM0uVZ9fFFoOuqSif7Nd337e2dFM1Gnfc9oCNUHbY7EKpeWyuqq5UqKgop82aZqYDq/OnKNlu2LtIgffWDeFrkUuapumwaG7cHoe9qKKDAT08JhNhHf+N2AEBrgknYxVh30eetyaRwxXEzsLUtb5Wq4CAWeByrt6dDvX9Gp23CTvK5/cbh2DnDnHSEkyCpjlw50VVZnxdMGYR731iJT+xq1pquJLqDKdunsQaXHDkF767egt3H9OuUc9IuJvR8g0RkLkzbaMyoxGLANdO6eu5Zw1vK3pYqqujuEMnB1M0vUyIw6XuTpi35+9T5ozCouU763ZTIU52H6zQbUnlfv8LrKc5NG3pSIjLatB7g6+nRKEUeQXI+WjVtqw9QoLswbcudD0PfhujvSjupq6iComCY06sIUHXaVlFRpCs44aksWFddwjVbzNpAnIajDbTcUbPserxAsgFKnTxdNW25e37SbiOdz6tiW1vgqOusxFOdCVW72GRAJe3LnueV3WELqCFtZa/eiK5KinLdSbPx7PvrsM/40mQvdiScvc/YTj0ffeIiKdxmR03bSjNWXDVt1XN/6aAJZW9LFVV0d/QpMvXXKVqJKmu/JE1bSwSIHM0kI8aEV5hU4lCTbbajuBrU6+gpPpSq06+ykJm2bsfQzRiWadvDGJadhU5NRGaoP+e4Fi1nG7q7PEIVOxbUhKVVxLHjeV6q6Fagia9eWLK+rHWrxsqckTzD1YS/fn5P4++5ghub8ONzhgEAZo9osZadWZRI2GOsOztOVZbg5lJOfqLck+6H67cVz7XjTeZHKsnvTIvS7mLMpKWQts5t03ePmd6p5xNorM1gv4kDumRhMXlwoM24syZQoV1MyNG05QpOmrayVE4l5BEcnbbKMNmrrrp3XcXOB9HvN2+Xk2PGWLGaVYLpHbblBTDpZ6p2jM6JbNz07h7Tc4eh3jtXua4uQZWp2WkoRX82Y5EnknWmS2/bjoZOddoaqnclEHW8DdX3uIqugau/ZWdGdbVSRUVRSecerTmT8jB9WG9tWR12sTh6XXc3v3bYJMwb1ccp6dUvz9gNqze1YtJgeyIwAReHhIs8Qkfxx2eXFs+14+33HDVzCL74+xfCz+Vk2lYKXcmOmDq0uVPP1x3w7aOn4V8vfYgvHrhzsTN3H9MXTy1ai2NmDwu/E2NAe546bQ1M206WR9A1RW1jd3mXq6iiMyHeX2rjTB3SjLGKbJJ+80NvA5gSjQGyI5jTtPU8hDrZ9SUwbXcUZ4NrcsXuhgqnr9jpQddVpUQ8cfNv1VnHo5LRoipM972zmLZ0abcDLvOq6MbIVZm2VlSdtlVUFJXUxKHGSl+SlKeccB1EajNpHD5jiFPZvo01idurGgvcbWXlESp0/zkHcU+H53nYdXQfPLN4HQDzhgO9r8fOGaYtV2l0pTzCzog9xvZLxJDfUXDrGbvio01tGNmvIfyuJnTa+qGDxbToyFo09ToKtUrXrPdVp20VOyM4e+G2s3ePvTe698OciMw8L9nmrUzKC0Ml1UQzgm1q8mHsKG90XB6h+15Zuur06zTI8iNux7y8bEP49/qtcYk4+siqzy+CjaFcTphue2c9kc7U8K2iCoquypPSk1B12lZRUVRy0Kc1l7I/M6KvXWO0s0JSbIg5JJjZnQvnLwejLeXFF0g7ItMWcDdY6G97jO1b0TaZICek6DoDqzZTzeC6I6OhJoOR/WRzgWPamrogdb5UxmmrbGzpylWdtlVUwW68cnOIzlFoYlNambbSd/HfqbZdXB5Bf96wxh3llY7ZfV3TDBc010fzQzWBUWWRKcHuW7J2a/j36k2tsd+rTFsenXlfTPUPbK6t6Lm5NlT7QRWdic5ik/dk7Jielyq6DUy6oB1GJ8wn7d1EY8UlTI5zpJZjZ1gkHLKda0eAq36VXK7r7kVXJw245Mgp8DzgRyfO7PRzV9G1EE6ftnzBKREZTfTXnRKRVZ22VeyMYOdw5lXQmXCmBT3VXrWHYuvbCAB9GuSoJJehY0dxNsQ2orrxZQ1riUgQ1URWlUW6BKbtFw8cH/598NTBsd+rmrY86DhZ0fUs4u/7p4pJo0f3a8BXDp5U0XNHbYj+rtpGVXQmqvIIdlSZtlVUFJU0niWmbYXe9e4yiKiTJ2cUs8kFyjDp3nrGrjjhpifkc+2A8ghAqU7bijbJCNrErljQnb3PWHxm/qgq03YnRDbDadrqyzdTp20lNG2Vz7pTxOQRurMnpIoqKgRbMqLoOw3T1vDe2BxAaUnT1vz+xaWh7O/rjvJK9yR5hDkj++Crh0zEsD72CLYqOgY50Z9bn/j0HqMwZ2Qf9K7PYkTfhtjvXZkfoTujsTZyk1TazlVv+6nzR+Hy42ZU9Jwqukv0XhU7H7oLSa47Y8eky1XRbUAXBgumDCxr3bKxkty56rJYN2Yo7kS4hP5yxls5HBLzRvfFbqNlCYCaHZRpK8sj6K+xuxg23UFHruqw3TkhxoC2XAGFItXWNN70qs1gbP9GNNakMapffNHYUagLHm1Yt8q03UE3oKqowgTP82KOW+6d0TlwTH4dzzIv2TRvBTjpIbe3dcd4p9V7392dKOcfOAHHzhne1c3Y4UFJE659wvOCZM2cw1b8LlBlWEboVRc5bWsylV33qONvV0Q00jGn0hq+VVQBRHlhztprTBe3pPujyrStoqKgjq+LDp1c1rrrsils2Jb8uDP3GoNfPLYI/33kVGtZNXNxVyEeJhefTGl4mkDZJl2lmh2VaSuFBhkuMePIyK00uloeoYqdFzWZKBGZkEcwMXRSKQ///MLe2N6eR6+6rLZcqYg7OPhyVaZtFVUEULs+y4rVvR+G1yZlYdLKTMH48afvORq/fmIxvnH4lPhpd2ambZe0ooruBtv7U1qd0d/Zqi0ZgjptK31XVNukKxI+U0dxdU1RRWfg8uNm4JjZQ3fKJM9JsWPS5aroNqAbhY215XWANhCt1f93RNy41+GbH5uCZy9ZgIOnDtKW+X9HTEZNJoVLj7I7djsDKumT212vyaTw/WPlUJpyhTmptWR2UKatq1atxMjtwhViJYz3KqpwQZiILFdA3kEeAQCaajPo31SZhBpxeQS+MerQVV2YVLGzwkUHWjcNmuURzExamzzCt46aihcuPQSzR7QwdWtPm6hMT0Dcqb6DXFgVHQJ9f8pFzKBda0e170tBbSaNSYN6oVddBhMH96roudRH2RVMWxpFWZXJqKIzUJdNY/9JA6VkxVXwqDJtq6goqOOr3IvjUf0asOijLQCA43ZxD8nyPM/qOPjsvuNw2p6ju03ot+oY1N1K1YAr15yvrhV2DnkEfTlJHqELDRvaxipjsIrOhJSITMgjdOG7oDp/dK9DTB6hujCpYieFS88vRdPWs5Sj76pO6okmLpTrdmDa7iCcVNcxrYqdC3RdVS5Hvmz7Vjsaxd/P2wt530dTbWVdJuqz7IqIRolpWx1wqqiiW2HH9LxU0W1AnYjlZgmctudoAMBn9x1b1noFuovDFnDXNquUBpq6CNpRtY5KSkTWTZi2VRZOFZ0JsXFDE5F1ZaIcFwkZoCqPUEUVAi5MTr3TVl+vjWnbkXnLpfiO8krH5BF2kOuqomNIV8DBSt/DrgjL786or0lX3GELdA9yDH32VaZtFVV0L1SZtlVUFKkKOm0PmDQQz/z3AvRvqilrvd0RcYeErpz8uVzhNer5dtTwKVeNWFfnbqVhWxxXUUWlIMaWgh84boGudYC66HMG3ysbW9UXp4qdFC52hW5+M7FZaT3c+0XHiaRDhkv5HWUDM8a03UEYxFV0DJkKONYkeQSDNFgVlYM6bnWFPEI2U2XaVlFFd0V1ZK6iopASO1VgcTygV22Xsrs6C3F5BLeQxbIxbWM7wDvmPZcYDIZ7112ctt1FpqGKnQ/UuG9tLzptu3QDQ/3sxrStooqdFeqbwEsZRH/TCBvPsHqwMWnpK5icabvzvL+xMa26YqsC8jtTrqg3Wue29nxZ6qwiGdQ1R1fII1B2b0NN94k2raKKKqpO2yoqDMoMqK6VS4e6Tvn/7d17dBT1/f/x126uBEgQCgkJVy/cqoKIIKj9gkaClVYqWi+0IEWtmPRwkXqpfkW8HCqtolgs2Grw19qq9BRQUBRBoCIi0KCAQKlK1a8JWJQEEAIk8/sDs84ku5vJZndndvb5OCfHZfeTmfeun5nMvve970/IBZ3rV8TG4IJO8m6l7Uf/PRS4HW4hslh8PS0StEeAU8xfozt64uSbPCenoJ1FlSTr8VrYO/RilIDXNai0DTIm1N+65ixE1lhP23Ds/Ln1yp/C+pW1/I2HFJu1DKpPfJuobZMVvJ80Yqt+4UWaA5/SlFceDdxuncmXsQE38WbmBa7R2NfkYE/9xKDdKrJYJRSd+AQ4Hrb9X1Xgdri8tOXraU728TRfvHN8IY7MbyiOHne+PYL9FjLfPtAjt1UsQwLczcaHwdZel98e8+GOdMt1XyOVtk3N2tppEeCV5GaDD+udCQMuY1mILErXfRWV1YHb157XJSrbRPM48Z75cPWJwO0zuD4CXIWkLWLK/CeH/jiRiyQhIcUukeeVN0XhhK20NT3maHuEZvQGBJrD7/cFKvnrqnScPBbsfNVbsl8tCHidncX7QvV5D3fsWCppg4xrzjFoayGyJm3RvRp+w8orzwzNYa6ZiNaX3mpqa03bZJ4lqyv7Fygtxae+ndvo3K5tnQ4HgAm174gpn+XigguBSNXPH9pd0Tlar3lmmrW3UZ+O2VHZrpuFay3RwvR6ONkpwvKGmjd0iLO0FL9O1NYEKm2dTCrUP0eGCsV8nPDtDyQza0Vs8DHm+80tUcId6v5Gttuc9gi2krYeOazrVxV75XmheaxrKkTnArTGiMpm0Ey98lprZ8VB9cpr7cj+W2em6V8PXsYHRIALkbRFTJl7n/I3IHJ2FyKr/4c2WknblvUa0l9+VseobNfNMtNCXwy3MvV6itZFcyQsCSgOMMRZWopPR45LR4/XVdo6F4vdRRhTLK1NYhoS4Gr+RipipdBVseGTtuH/LlmSuk08CJOpPYLdD+uRXCzf9IrSnKitJWvrBn8YO0CffXVE55/qXJUrCVvAnWiPgJgyJ/uoBIxcw4RE8HHLt1VY/h2tpG2ffGtlbTJUqGWkhl451dyg38lXwu6baCAW0lNPXkJUnzhZaeumpEKoU5RlYSUXxQvEm3n2h660Dd7TNvxCZKbbQTZsvi6JRaWtV/ojNKi0dSgOuItlIbKotUcgaesGndtmafBp7UicAmiApC1iqkX6t8ktN72hTzT13/iEeikvOP07ln9HK2l77cDkWJjgd9efE7idEabStmV6auD/QduW6bEOKyQWIoOT6pI4dZW2ji7KZ6M/p2Q9ZpLhwycgFPMhEqqC1XxcRdLTNtghZknaNvGcYeccY6caNxHUf6pcQ0OqtxBZtCptDZK2AOBmtEdATGWbKhJ5gxy5hr1qgycUzyywVsSG68vaFNmZaVHZjttVHjkeuB3uOaf4fXrmhvP0dXWNOrfNikdoQdmtfAJioW7+1VXaOvnBgb+RRFEdqmuBk3w2vqlhPpZSbVbIWnvaNhyZ6o+8bZatQluPHOL1z2NeeV5oHnN1bWoKSVsASAYkbRFT/9Ozva46t5PO6NDK6VASWv1kSKhkbHq970qRyGuaK/oV6O5F2yRJw7+bG3bssJ4d4hFSWOYF4k7w9TbEWd3CRNWBSlvnYvE1kiiqQ0U6cJK1PULjPW2tFbJhttuEStumfohi55rGO4c4C5GhocZ6RkfCK9XpAOBVJG0RUxmpKfrt1X2dDiPh1b8wC5V4qOsxWSdan8Ini1YZqdrz68udDsO2LFPP6LqvqAPxUldp+95nlZKc/TaFeddhF0nyTkYHaBY7PdHNx4t1YVl7PW2DjUsNkQi2xcZwrySgaI+AYMyV6tH6EJKpBQDuRk9bIAHUv1i3W2nLV4G9zdweoZqkLeKs/odETiYVrF/1DlNpyzkRkGSvOj1Ue4Tw2w1fCRiqetfetqMzJhE07NPtUCBwFctCZFGaFHwDBQDcjaQtkADqX1DZrbTlQix5HP2mrygQL2ku+pDIdk9bzomApHqVtiHGpNioxm243eC365i/AdTU49HWQmQeyW7WfxZU2kIKvThgc/B3EQDcjaQtkADqX0+FantA0jZ5Ha8haYv4Skux92FSPNjpz1n/Mc6PwEmhDhlzAjQ3O9PWthrruWk+7pqaiLS1EFmTtuheDSptHYoD7tKcDz1CuaJfgSTptPYto7I9AEB00dMWSAA+n08+n1S3wGuKP/jnLQ3aI0QxKZGfk6nPK49a+qjCPQaf2s7pEJBk6lfa9u6Y7VAkkvmUGO6sZ00YxS4ewO0sx4yNhciuGdBZ2/+vUoV9wi/SaWm7EOQgM/fkbHJL2yRqj1D/eXilghjNY/lQJEp/xEb3L1DnU1qol4N/wwEAoZG0BRJEis+nE99kbUP1lktN8cvvk2oDyd3oXeT/vwmD9OiKXSoZdkbUtonme7nkQv3j31/o4l4dnA4FScb8IdGp7VtqxJl5jsVi942s9avbJEGkk38naur+aCBpmBfsCnXImO9v2ypd6+68uNHkYWOtSszXJU2ddXaOWa8e1x59Wmgiy0JkUZoUPp9Pg/jgHwBci/YIQIKw+7Vec/VbNJO2p3dopSfHnKs++XwS7yZndcrRrUNPt6zsDcSD+Wua53Y5xcFI7C9uRLVaQ8VDT5MkTfzmv0gO5kMh1HHhr9fKwM7x09hCZHYXNIuUV47w+h8+eTUZjaYxV8jT4gcAkgOVtkCC8Psl1Zy8Xb93rVl6il/V3yxKxUrpAGJlR/nBwG2nPzSwJG1tnveOHq+JVTgJZcqlPfS9Hu313fwcp0NBHNlZvM/OYmUNfyf47TqWStsmltraW4isadt0q/o9w8nPQbL+fSNpCwDJgdIsIEGY36zU711rVmN6F5SZRv9ZALFhTY44+/V66yr34d/Itso4+Xn1qe1bxTSmROHz+TSgW1u1oF95UrGzzJX1uLK3XX8jx6K10rZp5w17PW29kciq/+GTzzM1xGiOWCxEBgBwNyptgQRhvoAPV2l7vKY2cJtFwwDESkaY81C8Wd/Ihh/7ws/P17/3HdJlDvbgBZzma6QituEYewmixvpGmxNNOS3SbW0zEI+dMR7JY52SZX1tfO453cJB/kbajwAAvIekLZAgzPUo4Sptj9d8O9IrFScA3MfcP7upX3OONkvP70bOe9/Nz6EVAJKez0YVbWSVfOHbLvh8Pv15wiB9feyE2rfOaNKWbbVH8EhFqt/vU3ZmqqqOnpDknV69aB67/dsBAN5B0hZIEOZrs3CVtgAQD2kuWvzO/Oa1/gI+ABpqrCJWsh5X9tsjNL7dC8/4jr2N1WMnBi8d/il+qiphZZ4TsV7UDwDgDu55xwUgrAxTf9pwSdvWGXwWAyD2zAvlOJ1PoPoIaBpzRWqoI8YXwVexLV/fjvKxaOfbQ06fi6LJb6MaGsnFkrRNYVIAQDIgaQskCPOlWbhP1386uKskqQWLkAGIIXPSx+n2CJakLdkNoFHmwyRUMjSihchM7yxi8flJY3F4pT2CFFlPYXhbK1NhRnZmmoORAADihZI8IEHsO1gduB2u2mT8Bd1VUXlUVw/oHI+wACQr02nI6aRtagRf4waSmaWKNkQJh51WB+G2G4sPUHyy9vhvuP+o79IxrEuA+jqdkqVpw3uoTVa6WvLNOgBICpztgQSRkepX9Yla9emYHXZc+9YZevSafvEJCkDSOr97W7378ZdOhyGp3kJktEcAGuWz3A5+zJjbG9g9qmLZHkH6JpEZ5lMiLyU6zc+ESlvUKbn4DKdDAADEEe0RgAQx66qzNaDrKfr9T/o7HQoAaFJhj8BtI2ztW+yxYA/QNHbaGETSUzXWicbG8sBeOvotCXAvPTEAAGAblbZAgriiX4Gu6FfgdBgAIMmaKHW6PQILkQFNY1mILFRPW1Ni124Fq/nwS4lBaYivkQYJXvrMxk7fYQAA4G1U2gIAgGZxOGdL0hZoIr8lIRhqTPPaI8Qi0djYJr1UaW+tWnYsDAAA4CCStgAAoFncVGnrpaQNEDM2ErLWr+e7ZCEy0yZzWqSFfTzR+WKcAAcAAO5H0hYAACS0lAh6bwLJzFy5GSohG0lPWzvbbQ5zW4czC7K16Z5Czfjhd02PewfnMgAA4GjSdubMmTrvvPPUunVrdejQQaNGjdKuXbssY44ePari4mK1a9dOrVq10ujRo7V3717LmE8++USXX365srKy1KFDB/3yl7/UiRMnLGNWr16t/v37KyMjQ6effroWLFjQIJ65c+eqW7duyszM1KBBg/Tuu+9G/TkDAOA1Ti9ElurnM2igKcz5wFDJwRTLYmV2K21NvxODw7J+Uvg7rTKUZgrUSxWpfGsAAAA4+i5nzZo1Ki4u1jvvvKMVK1bo+PHjGj58uA4fPhwYM2XKFL388stauHCh1qxZo88//1xXXnll4PGamhpdfvnlOnbsmN5++209++yzWrBgge69997AmI8//liXX365hg0bpi1btmjy5Mm68cYb9dprrwXGvPDCC5o6daqmT5+uf/7zn+rbt6+Kioq0b9+++LwYAAAkKofbI5CzBZrGZ6P1QWSVtrFtVRKsZUCq35uV9l56LgAAIDKpTu58+fLlln8vWLBAHTp00ObNm/W9731PlZWVevrpp/WXv/xFF198sSSptLRUvXv31jvvvKPzzz9fr7/+uj744AO98cYbys3NVb9+/fTAAw/ojjvu0H333af09HTNmzdP3bt31yOPPCJJ6t27t9566y3Nnj1bRUVFkqRHH31UN910k8aPHy9JmjdvnpYtW6ZnnnlGd955ZxxfFQAA0BTmnrZO99cFEoGdfGCze9rGYPUsX5DbfnPS1kMNEqi0BQAArqpNqayslCS1bdtWkrR582YdP35chYWFgTG9evVSly5dtH79eknS+vXrddZZZyk3NzcwpqioSFVVVdq+fXtgjHkbdWPqtnHs2DFt3rzZMsbv96uwsDAwpr7q6mpVVVVZfgAASCZndGglSfpR/wJH44hFcgjwMjsJ2UgW+LO2L4gstnB8Qbafaokz+vt0CjlbAADgaKWtWW1trSZPnqwLLrhAZ555piSpoqJC6enpatOmjWVsbm6uKioqAmPMCdu6x+seCzemqqpKR44c0VdffaWampqgY3bu3Bk03pkzZ2rGjBmRPVkAADzgbxOH6D/7D+vsTm0cjcO8EBmFtoANpoRgqORgsARpY+LZHqFu+36vtkdwOgAAAOA411TaFhcXa9u2bXr++eedDsWWu+66S5WVlYGfTz/91OmQAACIq5wWaY4nbKV6lbb0RwAaZW8hsob9YxtjPhRjUQHvD5JsNn9oQ3sEAADgJa6otC0pKdHSpUu1du1aderUKXB/Xl6ejh07pgMHDliqbffu3au8vLzAmHfffdeyvb179wYeq/tv3X3mMdnZ2WrRooVSUlKUkpISdEzdNurLyMhQRkZGZE8YAABEjZdWjAfiwXzIpIQ4fswJULsJWDsLnDVHsIXILLF56FTAaQ0AADhaaWsYhkpKSrRo0SKtWrVK3bt3tzx+7rnnKi0tTStXrgzct2vXLn3yyScaPHiwJGnw4MHaunWr9u3bFxizYsUKZWdnq0+fPoEx5m3UjanbRnp6us4991zLmNraWq1cuTIwBgAAuB91tkDjLAnVUEnbCNojWH4nxguR1W0+kt67icBLzwUAAETG0Urb4uJi/eUvf9GSJUvUunXrQA/anJwctWjRQjk5OZowYYKmTp2qtm3bKjs7W7/4xS80ePBgnX/++ZKk4cOHq0+fPvrpT3+qWbNmqaKiQvfcc4+Ki4sDlbC33HKLfve73+n222/Xz372M61atUovvviili1bFohl6tSpGjdunAYMGKCBAwfqscce0+HDhzV+/Pj4vzAAAABAjDQ1IWu/PUJsFwXzBWmFYF6IjDQnAADwEkeTtr///e8lSUOHDrXcX1paqhtuuEGSNHv2bPn9fo0ePVrV1dUqKirSk08+GRibkpKipUuXauLEiRo8eLBatmypcePG6f777w+M6d69u5YtW6YpU6bo8ccfV6dOnfTHP/5RRUVFgTHXXHONvvjiC917772qqKhQv379tHz58gaLkwEAAPeipS3QOHPrg1AVncGqWhtj6Wkbk/YIpn356/7b9DYOiYBKWwAA4GjS1rDxziozM1Nz587V3LlzQ47p2rWrXnnllbDbGTp0qMrKysKOKSkpUUlJSaMxAQAAdzJokAA0ytIdIeSgb2/aTSAG6zkbTdYF1L7paWuu7vVQ0pacLQAAcLSnLQAAAID4auqCYbaTtqbbsah69fsatkIw7ycW1b1O8dBTAQAAESJpCwAAPIP2CEDjrBWrocb4Gh0TTmx62pq3/02lraU9QvT36RTaIwAAAA9d2gAAAABojKU9go3cYCQJxFi0Kgi20Jk5aeulRKd3ngkAAIgUSVsAAOAZR47VOB0C4HrWRcZCLERmqWpt+j5i3arAF7TS1jupzlj0BAYAAImFpC0AAPCMqqPHnQ4BcD1/jHraNvd3GhOsQpiFyAAAgFeRtAUAAAnvqnM7SZJu+Z/THI4EcD877RFqTQ2iI+ppG4N3GcGSzZ5diMzpAAAAgONSnQ4AAACgue6/4rsa3idXl/TOdToUIAGYFxkLnh7My85sdEw4sa60DdbTlvYIAADAS0jaAgCAhJeVnqrh381zOgwgIdjpV3tq+1aa/9NzlWtK3jZFLKpefZbbDSttWYgMAAB4CUlbAAAAIImYE7XhkoNFzfggJBb5U0t7hG/aL3i10tZLCWgAABAZetoCAAAAScSn2FenxuTr/ZZevCf/kWpJ2kZ/l44hZwsAQNLz0qUNAAAAgEb4giQ/E4E5wVx3y+/R9ggeKhoGAAARImkLAAAAJBFr0ta5OJrKHGpdgtbcO9dL7RF8lNoCAJD0SNoCAAAAScRcXZtIec5gC6h5tqct79IAAEh6XA4AAAAASSRYxWoisLRHqKu09Wh7BCptAQAASVsAAAAgifgsyU8HA2kGX5BKW8NwKBgAAIAYIGkLAAAAJBFznjaRFiKztnVoWGnrJQn0vwUAAMQISVsAAAAgifgtvWGjlx2MdaGrOe66m+aFyIyYRxA/iZRMBwAAsUHSFgAAAEgilvYIDsbRVJaFyPzerrT16NMCAABNQNIWAAAASCLWhcgcC6PJ/EF68Xo1aevNZwUAAJqCpC0AAACQTGLUHiHWLL14v/mXOWfrpYXIEun/CwAAiA2StgAAAEAS8SlIc9hEYFmIrO6ub+/r2i4r3hHFDDlbAACQ6nQAAAAAAOInVguRxVqouMv+91Idq6lV68w0B6KKlcT5/wIAAGKDpC0AAACQRCwLeiVQbtDSHsH0j1Napsc9FgAAgFijPQIAAACQRMztEXwJVNHpsyxEljhxRyIvJ8PpEAAAgMOotAUAAACSiKXSNoFKOPwJWiEciamX9lTVkRO6ekAnp0MBAAAOIWkLAAAAJBFrlWriZD/NVcGJ1Is3Em1bpmvOdec4HQYAAHBQAn22DgAAAKC5zPnOtJTESX6a406cqAEAACJD0hYAAABIIuaEZ3pK9N4OXNKrgyQpp0Va1LZpZm3rQNoWAAB4G+0RAAAAgCRiTn5mpadEbbuDTm2npb+4UJ1PyYraNs0sC6iRswUAAB5H0hYAAABIIuZ+sKe2bxXVbZ9ZkBPV7ZmZF03z0SABAAB4HO0RAAAAgCRiTnd2/05Lx+JoKutCZA4GAgAAEAckbQEAAIAk4jNV2rbMSJwv3ll62tIfAQAAeBxJWwAAACBJZaQmztsBc7KZnC0AAPC6xLlKAwAAANBs5oRnZlr0FiKLNXOe1kfWFgAAeBxJWwAAACCJ1NQagdsZaYnzdsDvC34bAADAixLnKg0AAABAs+0/fCxwO2HbIzgYBwAAQDwkzlUaAAAAgGZb9n554HZ6SuK8HTAnav2U2gIAAI9LnKs0AAAAAM1mTtQmUm9Y60JkiRM3AABAJEjaAgAAAEmkfesMp0OIiDlPS8oWAAB4HUlbAAAAIImYFyJLJGv+9UXgtp9KWwAA4HEkbQEAAIAkUmMkZtL22InawG1a2gIAAK8jaQsAAAAkkdoErbQ9syA7cJtCWwAA4HUkbQEAAIAkkqiVtqP6FQRusxAZAADwOpK2AAAAQBJJ1J62qaaeCPS0BQAAXkfSFgAAAEgikwt7SJJ+dkF3hyNpmtSUb9+6kLIFAABel+p0AAAAAADi54Yh3dSvc46+m5/jdChNYqm0pfQEAAB4HElbAAAAIImk+H06t2tbp8NoMnOlLe0RAACA1/EZNQAAAADXS0v5NlHLQmQAAMDrSNoCAAAAcL1UPz1tAQBA8iBpCwAAAMD1Uk2VtrRHAAAAXkfSFgAAAIDrpVmStg4GAgAAEAckbQEAAAC4nqU9AklbAADgcSRtAQAAALheKguRAQCAJELSFgAAAIDrpaV8+9aFnrYAAMDrSNoCAAAAcL1UUyNbUrYAAMDrSNoCAAAAcD1zT1s/72IAAIDHcbkDAAAAwPXoaQsAAJIJSVsAAAAArpeWQnsEAACQPEjaAgAAAHA9S3sEKm0BAIDHkbQFAAAA4Hrm9ggkbQEAgNeRtAUAAADgemkp3751IWcLAAC8jqQtAAAAANdL8dPTFgAAJA+StgAAAABcL83U07bGMByMBAAAIPZI2gIAAABwPXNP25pakrYAAMDbSNoCAAAAcD1z0raWSlsAAOBxJG0BAAAAuJ6lPUKtg4EAAADEAUlbAAAAAK7n99MeAQAAJA+StgAAAAASCu0RAACA15G0BQAAAJBQUkxVtwAAAF7kaNJ27dq1+sEPfqD8/Hz5fD4tXrzY8rhhGLr33nvVsWNHtWjRQoWFhdq9e7dlzJdffqkxY8YoOztbbdq00YQJE3To0CHLmPfff18XXXSRMjMz1blzZ82aNatBLAsXLlSvXr2UmZmps846S6+88krUny8AAACA5uvTMdvpEAAAAGLK0aTt4cOH1bdvX82dOzfo47NmzdKcOXM0b948bdiwQS1btlRRUZGOHj0aGDNmzBht375dK1as0NKlS7V27VrdfPPNgcerqqo0fPhwde3aVZs3b9ZvfvMb3XfffXrqqacCY95++21dd911mjBhgsrKyjRq1CiNGjVK27Zti92TBwAAANAka345VIuLL1DntllOhwIAABBTPsNwR0Mon8+nRYsWadSoUZJOVtnm5+frtttu07Rp0yRJlZWVys3N1YIFC3Tttddqx44d6tOnjzZu3KgBAwZIkpYvX67vf//7+uyzz5Sfn6/f//73uvvuu1VRUaH09HRJ0p133qnFixdr586dkqRrrrlGhw8f1tKlSwPxnH/++erXr5/mzZtnK/6qqirl5OSosrJS2dl88g8AAAAAAADAym4O0bU9bT/++GNVVFSosLAwcF9OTo4GDRqk9evXS5LWr1+vNm3aBBK2klRYWCi/368NGzYExnzve98LJGwlqaioSLt27dJXX30VGGPeT92Yuv0EU11draqqKssPAAAAAAAAADSXa5O2FRUVkqTc3FzL/bm5uYHHKioq1KFDB8vjqampatu2rWVMsG2Y9xFqTN3jwcycOVM5OTmBn86dOzf1KQIAAAAAAABAA65N2rrdXXfdpcrKysDPp59+6nRIAAAAAAAAADzAtUnbvLw8SdLevXst9+/duzfwWF5envbt22d5/MSJE/ryyy8tY4Jtw7yPUGPqHg8mIyND2dnZlh8AAAAAAAAAaC7XJm27d++uvLw8rVy5MnBfVVWVNmzYoMGDB0uSBg8erAMHDmjz5s2BMatWrVJtba0GDRoUGLN27VodP348MGbFihXq2bOnTjnllMAY837qxtTtBwAAAAAAAADixdGk7aFDh7RlyxZt2bJF0snFx7Zs2aJPPvlEPp9PkydP1oMPPqiXXnpJW7du1dixY5Wfn69Ro0ZJknr37q0RI0bopptu0rvvvqt169appKRE1157rfLz8yVJ119/vdLT0zVhwgRt375dL7zwgh5//HFNnTo1EMekSZO0fPlyPfLII9q5c6fuu+8+bdq0SSUlJfF+SQAAAAAAAAAkOZ9hGIZTO1+9erWGDRvW4P5x48ZpwYIFMgxD06dP11NPPaUDBw7owgsv1JNPPqkePXoExn755ZcqKSnRyy+/LL/fr9GjR2vOnDlq1apVYMz777+v4uJibdy4Ud/5znf0i1/8QnfccYdlnwsXLtQ999yjPXv26IwzztCsWbP0/e9/3/ZzqaqqUk5OjiorK2mVAAAAAAAAAKABuzlER5O2XkLSFgAAAAAAAEA4dnOIru1pCwAAAAAAAADJiKQtAAAAAAAAALgISVsAAAAAAAAAcBGStgAAAAAAAADgIiRtAQAAAAAAAMBFSNoCAAAAAAAAgIuQtAUAAAAAAAAAF0l1OgCvMAxDklRVVeVwJAAAAAAAAADcqC53WJdLDIWkbZQcPHhQktS5c2eHIwEAAAAAAADgZgcPHlROTk7Ix31GY2ld2FJbW6vPP/9crVu3ls/nczocuFhVVZU6d+6sTz/9VNnZ2U6HA49gXiHamFOIFuYSoon5hGhjTiEWmFeIBuaRdxmGoYMHDyo/P19+f+jOtVTaRonf71enTp2cDgMJJDs7mxMvoo55hWhjTiFamEuIJuYToo05hVhgXiEamEfeFK7Ctg4LkQEAAAAAAACAi5C0BQAAAAAAAAAXIWkLxFlGRoamT5+ujIwMp0OBhzCvEG3MKUQLcwnRxHxCtDGnEAvMK0QD8wgsRAYAAAAAAAAALkKlLQAAAAAAAAC4CElbAAAAAAAAAHARkrYAAAAAAAAA4CIkbQFJM2fO1HnnnafWrVurQ4cOGjVqlHbt2mUZc/ToURUXF6tdu3Zq1aqVRo8erb179wYef++993Tdddepc+fOatGihXr37q3HH3885D7XrVun1NRU9evXr9H4DMPQvffeq44dO6pFixYqLCzU7t27A4+vXr1aPp8v6M/GjRub/oIgKtw+r/7+979r+PDhateunXw+n7Zs2dJgTGPxIb7iNadCnVMqKirCxtfYuUqSHnroIQ0ZMkRZWVlq06ZN814QRMwLc6lOdXW1+vXrF/I8hthL9PnEdZQ7uX1ecR2VeOJ5bV5dXa27775bXbt2VUZGhrp166Znnnmm0Rjnzp2rbt26KTMzU4MGDdK7775refypp57S0KFDlZ2dLZ/PpwMHDkT2YqBZvDCX6hiGocsuu0w+n0+LFy9u2guBmCNpC0has2aNiouL9c4772jFihU6fvy4hg8frsOHDwfGTJkyRS+//LIWLlyoNWvW6PPPP9eVV14ZeHzz5s3q0KGD/vznP2v79u26++67ddddd+l3v/tdg/0dOHBAY8eO1SWXXGIrvlmzZmnOnDmaN2+eNmzYoJYtW6qoqEhHjx6VJA0ZMkTl5eWWnxtvvFHdu3fXgAEDmvnqIFJun1eHDx/WhRdeqIcffjjkmMbiQ3zFe07t2rXLcl7p0KFD2PgaO1dJ0rFjx3T11Vdr4sSJUXhFECkvzKU6t99+u/Lz85vxaqC5En0+cR3lTm6fV1xHJZ54zqkf//jHWrlypZ5++mnt2rVLf/3rX9WzZ8+w8b3wwguaOnWqpk+frn/+85/q27evioqKtG/fvsCYr7/+WiNGjNCvfvWrKL0qiIQX5lKdxx57TD6fr5mvCGLGANDAvn37DEnGmjVrDMMwjAMHDhhpaWnGwoULA2N27NhhSDLWr18fcju33nqrMWzYsAb3X3PNNcY999xjTJ8+3ejbt2/YWGpra428vDzjN7/5TeC+AwcOGBkZGcZf//rXoL9z7Ngxo3379sb9998fdtuILzfNK7OPP/7YkGSUlZVZ7o80PsRPrObUm2++aUgyvvrqK9uxNPVcVVpaauTk5NjePmIrUefSK6+8YvTq1cvYvn170PMYnJGo86kO11Hu5KZ5ZcZ1VOKK1Zx69dVXjZycHGP//v1NimfgwIFGcXFx4N81NTVGfn6+MXPmzAZjmztvEV2JOpfKysqMgoICo7y83JBkLFq0qEn7QexRaQsEUVlZKUlq27atpJOfgh0/flyFhYWBMb169VKXLl20fv36sNup20ad0tJSffTRR5o+fbqtWD7++GNVVFRY9p2Tk6NBgwaF3PdLL72k/fv3a/z48bb2gfhw07yyI9L4ED+xnFOS1K9fP3Xs2FGXXnqp1q1bFzaWSM5VcI9EnEt79+7VTTfdpD/96U/Kysqy90QRF4k4n8y4jnInN80rO7iOcr9YzamXXnpJAwYM0KxZs1RQUKAePXpo2rRpOnLkSMhtHDt2TJs3b7bs2+/3q7CwkPmSABJxLn399de6/vrrNXfuXOXl5TX9SSMuUp0OAHCb2tpaTZ48WRdccIHOPPNMSVJFRYXS09Mb9F/Mzc0N2e/q7bff1gsvvKBly5YF7tu9e7fuvPNO/eMf/1Bqqr3Dr277ubm5tvf99NNPq6ioSJ06dbK1D8Se2+aVHZHEh/iJ5Zzq2LGj5s2bpwEDBqi6ulp//OMfNXToUG3YsEH9+/cPup1IzlVwh0ScS4Zh6IYbbtAtt9yiAQMGaM+ePZE8dcRAIs6n+riOch+3zSs7uI5yt1jOqY8++khvvfWWMjMztWjRIv33v//Vrbfeqv3796u0tDTodv773/+qpqYm6Llq586dzXimiLVEnUtTpkzRkCFDdMUVV0TytBEnJG2BeoqLi7Vt2za99dZbEW9j27ZtuuKKKzR9+nQNHz5cklRTU6Prr79eM2bMUI8ePYL+3nPPPaef//zngX+/+uqrSklJadK+P/vsM7322mt68cUXI44f0ee2eXXRRRdFHAfcIVZzSpJ69uxp6ZU1ZMgQffjhh5o9e7b+9Kc/ReVcBfdIxLn0xBNP6ODBg7rrrrsijhmxkYjzyYzrKHdy27ziOirxxXJO1dbWyufz6bnnnlNOTo4k6dFHH9VVV12lJ598Ups2bdJll10WGD9//nwNGzYs8icDRyXiXHrppZe0atUqlZWVRRwz4oOkLWBSUlKipUuXau3atZbqiry8PB07dkwHDhywfFq2d+/eBl8l+OCDD3TJJZfo5ptv1j333BO4/+DBg9q0aZPKyspUUlIi6eRJ2DAMpaam6vXXX9cPf/hDDRo0KPA7BQUFKi8vD+yrY8eOln3369evwXMoLS1Vu3bt9MMf/rBZrwWix43zyo6mxIf4iuWcCmXgwIGBi9FonKvgDok6l1atWqX169crIyPDsu0BAwZozJgxevbZZ+29AIiqRJ1PZlxHuY8b55UdXEe5V6znVMeOHVVQUBBIsklS7969ZRiGPvvsMw0YMEBbtmwJPJabm6uMjAylpKRo7969lm0xX9wtUefSqlWr9OGHHzaoBB49erQuuugirV69OoJXAzHhaEddwCVqa2uN4uJiIz8/3/jXv/7V4PG6RuJ/+9vfAvft3LmzQSPxbdu2GR06dDB++ctfNthGTU2NsXXrVsvPxIkTjZ49expbt241Dh06FDK2vLw847e//W3gvsrKyqALaNTW1hrdu3c3brvttia/Bog+N88rs8YW0GgsPsRPPOZUKIWFhcaPfvSjsLHZPVcZBguROS3R59J//vMfy3nvtddeMyQZf/vb34xPP/3UdiyIjkSfT+axXEe5h5vnlRnXUYkjXnNq/vz5RosWLYyDBw8G7lu8eLHh9/uNr7/+OmR8AwcONEpKSgL/rqmpMQoKCliIzIUSfS6Vl5c3eA8pyXj88ceNjz76yP4LgZgjaQsYhjFx4kQjJyfHWL16tVFeXh74MZ8Ib7nlFqNLly7GqlWrjE2bNhmDBw82Bg8eHHh869atRvv27Y2f/OQnlm3s27cv5H6nT59u9O3bt9H4fv3rXxtt2rQxlixZYrz//vvGFVdcYXTv3t04cuSIZdwbb7xhSDJ27NjR9BcBUef2ebV//36jrKzMWLZsmSHJeP75542ysjKjvLzcdnyIr3jNqdmzZxuLFy82du/ebWzdutWYNGmS4ff7jTfeeCNsfHbOVf/5z3+MsrIyY8aMGUarVq2MsrIyo6yszHIxitjzwlwyC5U0QXx4ZT5xHeUubp9XXEclnnjNqYMHDxqdOnUyrrrqKmP79u3GmjVrjDPOOMO48cYbw8b3/PPPGxkZGcaCBQuMDz74wLj55puNNm3aGBUVFYEx5eXlRllZmfGHP/zBkGSsXbvWKCsrM/bv3x/FVwqN8cJcqk+SsWjRoshfFMQESVvAOHmCCvZTWloaGHPkyBHj1ltvNU455RQjKyvL+NGPfmS5KJs+fXrQbXTt2jXkfu0m12pra43//d//NXJzc42MjAzjkksuMXbt2tVg3HXXXWcMGTKkKU8dMeT2eVVaWhp029OnT7cdH+IrXnPq4YcfNk477TQjMzPTaNu2rTF06FBj1apVjcZn51w1bty4oPt/8803m/vyoAm8MJfMSNo6yyvziesod3H7vOI6KvHE89p8x44dRmFhodGiRQujU6dOxtSpU8NWRtZ54oknjC5duhjp6enGwIEDjXfeecfyeKj9m58DYs8LcynYcyJp6z4+wzAMAQAAAAAAAABcwe90AAAAAAAAAACAb5G0BQAAAAAAAAAXIWkLAAAAAAAAAC5C0hYAAAAAAAAAXISkLQAAAAAAAAC4CElbAAAAAAAAAHARkrYAAAAAAAAA4CIkbQEAAAAAAADARUjaAgAAAA7p1q2bHnvsMafDAAAAgMuQtAUAAIDn3XDDDfL5fPL5fEpLS1Nubq4uvfRSPfPMM6qtrbW9nQULFqhNmzZN3n+o39u4caNuvvnmJm8PAAAA3kbSFgAAAElhxIgRKi8v1549e/Tqq69q2LBhmjRpkkaOHKkTJ044ElP79u2VlZXlyL4BAADgXiRtAQAAkBQyMjKUl5engoIC9e/fX7/61a+0ZMkSvfrqq1qwYIEk6dFHH9VZZ52lli1bqnPnzrr11lt16NAhSdLq1as1fvx4VVZWBqp277vvPklSdXW1pk2bpoKCArVs2VKDBg3S6tWrG/29+u0RfD6f5s+fr5EjRyorK0u9e/fW+vXr9e9//1tDhw5Vy5YtNWTIEH344YeW57ZkyRL1799fmZmZOvXUUzVjxgzHEtEAAABoPpK2AAAASFoXX3yx+vbtq7///e+SJL/frzlz5mj79u169tlntWrVKt1+++2SpCFDhuixxx5Tdna2ysvLVV5ermnTpkmSSkpKtH79ej3//PN6//33dfXVV2vEiBHavXt32N8L5oEHHtDYsWO1ZcsW9erVS9dff71+/vOf66677tKmTZtkGIZKSkoC4//xj39o7NixmjRpkj744APNnz9fCxYs0EMPPRTDVw4AAACxRNIWAAAASa1Xr17as2ePJGny5MkaNmyYunXrposvvlgPPvigXnzxRUlSenq6cnJy5PP5lJeXp7y8PLVq1UqffPKJSktLtXDhQl100UU67bTTNG3aNF144YUqLS0N+XuhjB8/Xj/+8Y/Vo0cP3XHHHdqzZ4/GjBmjoqIi9e7dW5MmTQpU8UrSjBkzdOedd2rcuHE69dRTdemll+qBBx7Q/PnzY/myAQAAIIZSnQ4AAAAAcJJhGPL5fJKkN954QzNnztTOnTtVVVWlEydO6OjRo/r6669D9p7dunWrampq1KNHD8v91dXVateuXZPjOfvsswO3c3NzJUlnnXWW5b6jR4+qqqpK2dnZeu+997Ru3TpLZW1NTU2jcQMAAMC9SNoCAAAgqe3YsUPdu3fXnj17NHLkSE2cOFEPPfSQ2rZtq7feeksTJkzQsWPHQiY/Dx06pJSUFG3evFkpKSmWx8JV1IaSlpYWuF2XTA52X21tbWD/M2bM0JVXXtlgW5mZmU3ePwAAAJxH0hYAAABJa9WqVdq6daumTJmizZs3q7a2Vo888oj8/pNdxOpaI9RJT09XTU2N5b5zzjlHNTU12rdvny666KKg+wn2e9HSv39/7dq1S6effnpMtg8AAID4I2kLAACApFBdXa2KigrV1NRo7969Wr58uWbOnKmRI0dq7Nix2rZtm44fP64nnnhCP/jBD7Ru3TrNmzfPso1u3brp0KFDWrlypfr27ausrCz16NFDY8aM0dixY/XII4/onHPO0RdffKGVK1fq7LPP1uWXXx7096LVtuDee+/VyJEj1aVLF1111VXy+/167733tG3bNj344INR2QcAAADii4XIAAAAkBSWL1+ujh07qlu3bhoxYoTefPNNzZkzR0uWLFFKSor69u2rRx99VA8//LDOPPNMPffcc5o5c6ZlG0OGDNEtt9yia665Ru3bt9esWbMkSaWlpRo7dqxuu+029ezZU6NGjdLGjRvVpUuXsL8XDUVFRVq6dKlef/11nXfeeTr//PM1e/Zsde3aNWr7AAAAQHz5DMMwnA4CAAAAAAAAAHASlbYAAAAAAAAA4CIkbQEAAAAAAADARUjaAgAAAAAAAICLkLQFAAAAAAAAABchaQsAAAAAAAAALkLSFgAAAAAAAABchKQtAAAAAAAAALgISVsAAAAAAAAAcBGStgAAAAAAAADgIiRtAQAAAAAAAMBFSNoCAAAAAAAAgIuQtAUAAAAAAAAAF/n/aK2hVVszXrEAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Calculate the 24-hour rolling mean.\n", + "rolling_mean_24 = ts.rolling(24).mean()\n", + "\n", + "# Plot the rolling mean only.\n", + "plt.figure(figsize=(14, 5))\n", + "plt.plot(\n", + " rolling_mean_24,\n", + " label=\"24-hour rolling mean\"\n", + ")\n", + "\n", + "plt.title(\"24-Hour Rolling Mean Of Pedestrian Count\")\n", + "plt.xlabel(\"Datetime\")\n", + "plt.ylabel(\"Rolling Mean\")\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7.2 Seasonal Pattern (24-Hour Cycle)\n", + "\n", + "Seasonal decomposition is necessary because hourly pedestrian demand is expected to have a strong daily cycle, so checking the seasonal component can help check how the typical hour of the day affects pedestrian activity. Since people usually move through the city at different levels during the morning, workday, evening, and late night, this step helps show whether the hour of the day is a factor in pedestrian demand [41].\n", + "\n", + "From the plot, it is clear that the seasonal effect is strongly negative at night and becomes strongly positive during the workday, especially the peak at 5 PM, when most people finish work and are looking to go home. The lowest tends to be around 3 AM, which is expected, since people tend to party late until 12 PM before heading home, and the other reason is that the city's pedestrian activities are very limited at that time. This plot further confirms that hour-of-day is a major driver of pedestrian demand, and there may be other variables influencing pedestrian demand." + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Decompose the series using a 24-hour period.\n", + "decomp_24 = seasonal_decompose(\n", + " ts,\n", + " model=\"additive\",\n", + " period=24\n", + ")\n", + "\n", + "# Store one seasonal cycle and align it to the actual hours.\n", + "seasonal_24 = pd.Series(\n", + " decomp_24.seasonal[:24].values,\n", + " index=ts.index[:24].hour\n", + ")\n", + "\n", + "# Sort by actual hour.\n", + "seasonal_24 = seasonal_24.sort_index()\n", + "\n", + "# Plot the corrected daily seasonal pattern.\n", + "plt.figure(figsize=(10, 4))\n", + "plt.plot(\n", + " seasonal_24.index,\n", + " seasonal_24.values,\n", + " marker=\"o\",\n", + " label=\"Daily seasonal effect\"\n", + ")\n", + "\n", + "plt.title(\"Daily Seasonal Pattern (24-Hour Cycle)\")\n", + "plt.xlabel(\"Hour\")\n", + "plt.ylabel(\"Seasonal Effect\")\n", + "plt.xticks(range(24))\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7.3 Autocorrelation Of Pedestrian Count\n", + "\n", + "Checking for autocorrelation is important because it shows whether the current pedestrian demand depends on previous hours, and if strong dependence exists, then lag features will be very useful for forecasting [42].\n", + "\n", + "From the plot, there appears to be a very strong repeating pattern at a regular interval, especially around daily cycles. This repeated structure applies not only on the daily level, but also appears to be on the weekly level as well, meaning daily and weekly lag features will be useful for predictions." + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot autocorrelation for one week of hourly lags.\n", + "fig, ax = plt.subplots(figsize=(12, 5))\n", + "plot_acf(ts, lags=168, ax=ax)\n", + "\n", + "ax.set_title(\"Autocorrelation Of Pedestrian Count\")\n", + "ax.set_xlabel(\"Lag (Hours)\")\n", + "ax.set_ylabel(\"Autocorrelation\")\n", + "\n", + "# Add legend handles.\n", + "legend_handles = [\n", + " Line2D([0], [0], color=\"C0\", lw=2, label=\"Autocorrelation\"),\n", + " Patch(alpha=0.2, label=\"95% confidence interval\")\n", + "]\n", + "\n", + "ax.legend(handles=legend_handles, loc=\"upper right\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7.4 Augmented Dickey-Fuller Test\n", + "\n", + "The Augmented Dickey-Fuller Test checks whether the time series is statistically stationary in the unit-root sense, and while not necessary for deep learning modelling, it does provide some more understanding of the patterns underlying the merged dataset [43].\n", + "\n", + "The result was that the p-value is extremely small and the test statistic is far below the critical values, hence, the null hypothesis of a unit root is rejected. This means that the time series dataset is statistically stationary enough to exhibit a learnable structure rather than behaving like a random walk, which basically means that it's not necessarily a random coin toss in layman's terms." + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ADF Statistic: -12.334016562004392\n", + "p-value: 6.332696219939632e-23\n", + "Critical Values:\n", + "1%: -3.4307265048981876\n", + "5%: -2.8617064005452915\n", + "10%: -2.5668585707044094\n" + ] + } + ], + "source": [ + "# Run the Augmented Dickey-Fuller test.\n", + "adf_result = adfuller(ts)\n", + "\n", + "# Print the results.\n", + "print(\"ADF Statistic:\", adf_result[0])\n", + "print(\"p-value:\", adf_result[1])\n", + "print(\"Critical Values:\")\n", + "\n", + "for key, value in adf_result[4].items():\n", + " print(f\"{key}: {value}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8. Feature Engineering\n", + "\n", + "The feature engineering section is necessary because the current variables in the merged datasets may not necessarily be enough for a strong forecasting model, so doing feature engineering will create more useful variables that help capture other aspects of the datasets, like cyclical structures, recent history, and short-term trends [44] [45]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8.1 Creating Time Features\n", + "\n", + "Creating more calendar-based features is necessary because pedestrian activity depends on when that observation happened, as shown in previous plots. Hence, hours, day of week, month, and weekend status are all useful predictors. This step ensures the datetime_hour column is converted into its individual components [45].\n", + "\n", + "The output shows that new time-based columns were added to the dataset. These include hour, day_of_week, month, and is_weekend." + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " datetime_hour hour day_of_week month is_weekend\n", + "0 2024-05-12 00:00:00 0 6 5 1\n", + "1 2024-05-12 01:00:00 1 6 5 1\n", + "2 2024-05-12 02:00:00 2 6 5 1\n", + "3 2024-05-12 03:00:00 3 6 5 1\n", + "4 2024-05-12 04:00:00 4 6 5 1\n" + ] + } + ], + "source": [ + "# Create a copy for feature engineering.\n", + "features_df = model_df.copy()\n", + "\n", + "# Extract calendar features.\n", + "features_df[\"hour\"] = features_df[\"datetime_hour\"].dt.hour\n", + "features_df[\"day_of_week\"] = (\n", + " features_df[\"datetime_hour\"].dt.dayofweek\n", + ")\n", + "features_df[\"month\"] = features_df[\"datetime_hour\"].dt.month\n", + "features_df[\"is_weekend\"] = (\n", + " features_df[\"day_of_week\"] >= 5\n", + ").astype(int)\n", + "\n", + "# Check the result.\n", + "print(\n", + " features_df[\n", + " [\n", + " \"datetime_hour\", \"hour\", \"day_of_week\", \"month\", \n", + " \"is_weekend\"\n", + " ]\n", + " ].head()\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8.2 Creating Cyclical Time Features\n", + "\n", + "Cyclical encoding is necessary since time variables are cyclical and not linear, like a clock. If we're talking just normal values like 0 and 23, these two values are quite far apart, but it's not, since it's time and there's only 1 hour difference. Using sine and cosine preserves that circular structure. This step ensures that the time variables are cyclical and prevents the models from learning misleading distances between values at the edge of a cycle [46].\n", + "\n", + "The output shows that new cyclical time features were created, including hour_sin, hour_cos, dow_sin, dow_cos, month_sin, and month_cos." + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " datetime_hour hour_sin hour_cos dow_sin dow_cos month_sin \\\n", + "0 2024-05-12 00:00:00 0.000000 1.000000 -0.781831 0.62349 0.5 \n", + "1 2024-05-12 01:00:00 0.258819 0.965926 -0.781831 0.62349 0.5 \n", + "2 2024-05-12 02:00:00 0.500000 0.866025 -0.781831 0.62349 0.5 \n", + "3 2024-05-12 03:00:00 0.707107 0.707107 -0.781831 0.62349 0.5 \n", + "4 2024-05-12 04:00:00 0.866025 0.500000 -0.781831 0.62349 0.5 \n", + "\n", + " month_cos \n", + "0 -0.866025 \n", + "1 -0.866025 \n", + "2 -0.866025 \n", + "3 -0.866025 \n", + "4 -0.866025 \n" + ] + } + ], + "source": [ + "# Hour and day of week are cyclical, not linear.\n", + "# Create cyclical hour features.\n", + "features_df[\"hour_sin\"] = np.sin(\n", + " 2 * np.pi * features_df[\"hour\"] / 24\n", + ")\n", + "features_df[\"hour_cos\"] = np.cos(\n", + " 2 * np.pi * features_df[\"hour\"] / 24\n", + ")\n", + "\n", + "# Create cyclical day-of-week features.\n", + "features_df[\"dow_sin\"] = np.sin(\n", + " 2 * np.pi * features_df[\"day_of_week\"] / 7\n", + ")\n", + "features_df[\"dow_cos\"] = np.cos(\n", + " 2 * np.pi * features_df[\"day_of_week\"] / 7\n", + ")\n", + "\n", + "# Convert cyclical month features.\n", + "features_df[\"month_sin\"] = np.sin(\n", + " 2 * np.pi * features_df[\"month\"] / 12\n", + ")\n", + "features_df[\"month_cos\"] = np.cos(\n", + " 2 * np.pi * features_df[\"month\"] / 12\n", + ")\n", + "\n", + "# Check the result.\n", + "print(\n", + " features_df[\n", + " [\n", + " \"datetime_hour\", \"hour_sin\", \"hour_cos\",\n", + " \"dow_sin\", \"dow_cos\", \"month_sin\", \n", + " \"month_cos\"\n", + " ]\n", + " ].head()\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8.3 Creating Cyclical Wind Direction Features\n", + "\n", + "Wind direction is also circular, being 360 degrees, meaning 0 and 359 degrees are almost the same direction. So wind direction must also be converted to cyclical for a continuous form, and ensure consistency with treatments of cyclical variables like time [46].\n", + "\n", + "The output shows that two new features were created, wind_dir_sin and wind_dir_cos." + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " averagewinddirection wind_dir_sin wind_dir_cos\n", + "0 166.142857 0.239502 -0.970896\n", + "1 140.321429 0.638480 -0.769638\n", + "2 153.857143 0.440611 -0.897698\n", + "3 139.250000 0.652760 -0.757565\n", + "4 170.074074 0.172375 -0.985031\n" + ] + } + ], + "source": [ + "# Convert wind direction into cyclical form.\n", + "features_df[\"wind_dir_sin\"] = np.sin(\n", + " 2 * np.pi * features_df[\"averagewinddirection\"] / 360\n", + ")\n", + "features_df[\"wind_dir_cos\"] = np.cos(\n", + " 2 * np.pi * features_df[\"averagewinddirection\"] / 360\n", + ")\n", + "\n", + "# Check the result.\n", + "print(\n", + " features_df[\n", + " [\n", + " \"averagewinddirection\",\n", + " \"wind_dir_sin\",\n", + " \"wind_dir_cos\"\n", + " ]\n", + " ].head()\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8.4 Creating Lag Features\n", + "\n", + "Lag features are important, as indicated by the autocorrelation plot, since pedestrian demand is highly dependent on recent history based on the autocorrelation plot [47] [48].\n", + "\n", + "The features lag_1, lag_24, and lag_168 capture the previous hour, previous day, and previous week at the same hour. This does create missing values since there wasn't recent information to populate the lag columns for specific rows, which will be dealt with. Like the very first row having a missing value for lag_1, because there wasn't a previous row to populate that value." + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " lag_1 lag_24 lag_168\n", + "0 NaN NaN NaN\n", + "1 15093.0 NaN NaN\n", + "2 10686.0 NaN NaN\n", + "3 6751.0 NaN NaN\n", + "4 5431.0 NaN NaN\n", + "5 2728.0 NaN NaN\n", + "6 2306.0 NaN NaN\n", + "7 4251.0 NaN NaN\n", + "8 8872.0 NaN NaN\n", + "9 18370.0 NaN NaN\n", + "10 26934.0 NaN NaN\n", + "11 40599.0 NaN NaN\n", + "12 53701.0 NaN NaN\n", + "13 62802.0 NaN NaN\n", + "14 63419.0 NaN NaN\n", + "15 65710.0 NaN NaN\n", + "16 74282.0 NaN NaN\n", + "17 65628.0 NaN NaN\n", + "18 49356.0 NaN NaN\n", + "19 39189.0 NaN NaN\n", + "20 31933.0 NaN NaN\n", + "21 24297.0 NaN NaN\n", + "22 19736.0 NaN NaN\n", + "23 13472.0 NaN NaN\n", + "24 8268.0 15093.0 NaN\n" + ] + } + ], + "source": [ + "# Create lagged pedestrian count features.\n", + "features_df[\"lag_1\"] = (\n", + " features_df[\"pedestriancount\"].shift(1)\n", + ")\n", + "\n", + "features_df[\"lag_24\"] = (\n", + " features_df[\"pedestriancount\"].shift(24)\n", + ")\n", + "\n", + "features_df[\"lag_168\"] = (\n", + " features_df[\"pedestriancount\"].shift(168)\n", + ")\n", + "\n", + "# Check the result.\n", + "print(\n", + " features_df[\n", + " [\n", + " \"lag_1\", \"lag_24\", \"lag_168\"\n", + " ]\n", + " ].head(25)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8.5 Creating Rolling Features\n", + "\n", + "Rolling features summarise recent pedestrian counts rather than relying on a single observation for a datapoint, allowing the model to capture the short-term trend and help smooth the short-term noise [47].\n", + "\n", + "Some new predictors were added, like rolling_mean_24, which summarises the previous 24 hours, while rolling_mean_168 summarises the previous week. And as expected, similar to lag features but not the same, the first rows are missing values since the rolling window cannot be calculated until enough history exists. " + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " rolling_mean_24 rolling_mean_168\n", + "0 NaN NaN\n", + "1 NaN NaN\n", + "2 NaN NaN\n", + "3 NaN NaN\n", + "4 NaN NaN\n", + "5 NaN NaN\n", + "6 NaN NaN\n", + "7 NaN NaN\n", + "8 NaN NaN\n", + "9 NaN NaN\n", + "10 NaN NaN\n", + "11 NaN NaN\n", + "12 NaN NaN\n", + "13 NaN NaN\n", + "14 NaN NaN\n", + "15 NaN NaN\n", + "16 NaN NaN\n", + "17 NaN NaN\n", + "18 NaN NaN\n", + "19 NaN NaN\n", + "20 NaN NaN\n", + "21 NaN NaN\n", + "22 NaN NaN\n", + "23 NaN NaN\n", + "24 29742.25 NaN\n" + ] + } + ], + "source": [ + "# Create rolling mean features from past counts.\n", + "features_df[\"rolling_mean_24\"] = (\n", + " features_df[\"pedestriancount\"]\n", + " .shift(1)\n", + " .rolling(24)\n", + " .mean()\n", + ")\n", + "\n", + "features_df[\"rolling_mean_168\"] = (\n", + " features_df[\"pedestriancount\"]\n", + " .shift(1)\n", + " .rolling(168)\n", + " .mean()\n", + ")\n", + "\n", + "# Check the result.\n", + "print(\n", + " features_df[\n", + " [\n", + " \"rolling_mean_24\", \"rolling_mean_168\"\n", + " ]\n", + " ].head(25)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8.6 Removing NA Rows\n", + "\n", + "This step removes the rows with missing values created from the lag and rolling features at the beginning of the ordered merged dataset, since they cannot be used as they are incomplete predictor information. By removing these rows, the dataset lost a part of the early period as a trade-off in the pipeline, which is reasonable considering the dataset is being updated in real-time, so there will be more data points to use in the future, hence, negligible [48].\n", + "\n", + "The output shows that all rows with missing values were removed, and the dataset was reset into a clean index." + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 17248 entries, 0 to 17247\n", + "Data columns (total 28 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 datetime_hour 17248 non-null datetime64[ns]\n", + " 1 pedestriancount 17248 non-null int64 \n", + " 2 airtemperature 17248 non-null float64 \n", + " 3 relativehumidity 17248 non-null float64 \n", + " 4 atmosphericpressure 17248 non-null float64 \n", + " 5 averagewindspeed 17248 non-null float64 \n", + " 6 gustwindspeed 17248 non-null float64 \n", + " 7 averagewinddirection 17248 non-null float64 \n", + " 8 pm25 17248 non-null float64 \n", + " 9 pm10 17248 non-null float64 \n", + " 10 noise 17248 non-null float64 \n", + " 11 day_of_week 17248 non-null int32 \n", + " 12 hour 17248 non-null int32 \n", + " 13 month 17248 non-null int32 \n", + " 14 is_weekend 17248 non-null int64 \n", + " 15 hour_sin 17248 non-null float64 \n", + " 16 hour_cos 17248 non-null float64 \n", + " 17 dow_sin 17248 non-null float64 \n", + " 18 dow_cos 17248 non-null float64 \n", + " 19 month_sin 17248 non-null float64 \n", + " 20 month_cos 17248 non-null float64 \n", + " 21 wind_dir_sin 17248 non-null float64 \n", + " 22 wind_dir_cos 17248 non-null float64 \n", + " 23 lag_1 17248 non-null float64 \n", + " 24 lag_24 17248 non-null float64 \n", + " 25 lag_168 17248 non-null float64 \n", + " 26 rolling_mean_24 17248 non-null float64 \n", + " 27 rolling_mean_168 17248 non-null float64 \n", + "dtypes: datetime64[ns](1), float64(22), int32(3), int64(2)\n", + "memory usage: 3.5 MB\n", + "None\n", + " datetime_hour pedestriancount airtemperature relativehumidity \\\n", + "0 2024-05-19 00:00:00 12751 8.655172 69.200000 \n", + "1 2024-05-19 01:00:00 8603 8.664286 68.792857 \n", + "2 2024-05-19 02:00:00 5660 8.960714 69.235714 \n", + "3 2024-05-19 03:00:00 4709 9.660714 67.675000 \n", + "4 2024-05-19 04:00:00 2682 10.050000 69.060714 \n", + "\n", + " atmosphericpressure averagewindspeed gustwindspeed averagewinddirection \\\n", + "0 1026.241379 0.841379 2.303448 204.137931 \n", + "1 1025.732143 1.042857 2.535714 203.071429 \n", + "2 1024.671429 1.757143 3.717857 237.107143 \n", + "3 1024.171429 1.460714 3.657143 247.857143 \n", + "4 1023.675000 1.482143 3.807143 240.821429 \n", + "\n", + " pm25 pm10 ... dow_cos month_sin month_cos wind_dir_sin \\\n", + "0 6.758621 9.448276 ... 0.62349 0.5 -0.866025 -0.408935 \n", + "1 6.750000 9.428571 ... 0.62349 0.5 -0.866025 -0.391878 \n", + "2 6.892857 9.535714 ... 0.62349 0.5 -0.866025 -0.839688 \n", + "3 6.107143 8.678571 ... 0.62349 0.5 -0.866025 -0.926247 \n", + "4 5.678571 8.428571 ... 0.62349 0.5 -0.866025 -0.873104 \n", + "\n", + " wind_dir_cos lag_1 lag_24 lag_168 rolling_mean_24 rolling_mean_168 \n", + "0 -0.912564 21105.0 8948.0 15093.0 33068.333333 31092.851190 \n", + "1 -0.920017 12751.0 6244.0 10686.0 33226.791667 31078.910714 \n", + "2 -0.543070 8603.0 4035.0 6751.0 33325.083333 31066.511905 \n", + "3 -0.376917 5660.0 2813.0 5431.0 33392.791667 31060.017857 \n", + "4 -0.487533 4709.0 1761.0 2728.0 33471.791667 31055.720238 \n", + "\n", + "[5 rows x 28 columns]\n" + ] + } + ], + "source": [ + "# Remove rows with NAs.\n", + "features_df = features_df.dropna().reset_index(drop=True)\n", + "\n", + "# Check the result.\n", + "print(features_df.info())\n", + "print(features_df.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8.7 Removing Unnecessary Features\n", + "\n", + "Since the cyclical variables were created, the original raw cyclical variables have become redundant. So removing these variables helps reduce feature duplication and makes the modelling easier and cleaner [23] [24].\n", + "\n", + "The averagewinddirection, hour, day_of_week, and month columns are dropped. Resulting in 24 columns, including the target pedestrian count, selected climate variables, is_weekend, cyclical encodings, lag features, and rolling means. The datetime_hour is temporarily kept, but will be removed later on as well." + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 17248 entries, 0 to 17247\n", + "Data columns (total 24 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 datetime_hour 17248 non-null datetime64[ns]\n", + " 1 pedestriancount 17248 non-null int64 \n", + " 2 airtemperature 17248 non-null float64 \n", + " 3 relativehumidity 17248 non-null float64 \n", + " 4 atmosphericpressure 17248 non-null float64 \n", + " 5 averagewindspeed 17248 non-null float64 \n", + " 6 gustwindspeed 17248 non-null float64 \n", + " 7 pm25 17248 non-null float64 \n", + " 8 pm10 17248 non-null float64 \n", + " 9 noise 17248 non-null float64 \n", + " 10 is_weekend 17248 non-null int64 \n", + " 11 hour_sin 17248 non-null float64 \n", + " 12 hour_cos 17248 non-null float64 \n", + " 13 dow_sin 17248 non-null float64 \n", + " 14 dow_cos 17248 non-null float64 \n", + " 15 month_sin 17248 non-null float64 \n", + " 16 month_cos 17248 non-null float64 \n", + " 17 wind_dir_sin 17248 non-null float64 \n", + " 18 wind_dir_cos 17248 non-null float64 \n", + " 19 lag_1 17248 non-null float64 \n", + " 20 lag_24 17248 non-null float64 \n", + " 21 lag_168 17248 non-null float64 \n", + " 22 rolling_mean_24 17248 non-null float64 \n", + " 23 rolling_mean_168 17248 non-null float64 \n", + "dtypes: datetime64[ns](1), float64(21), int64(2)\n", + "memory usage: 3.2 MB\n", + "None\n" + ] + } + ], + "source": [ + "# Drop raw columns that now have cyclical replacements.\n", + "features_df = features_df.drop(\n", + " columns=[\n", + " \"averagewinddirection\",\n", + " \"hour\",\n", + " \"day_of_week\",\n", + " \"month\",\n", + " ]\n", + ")\n", + "\n", + "# Reorder the columns into a cleaner structure.\n", + "features_df = features_df[\n", + " [\n", + " \"datetime_hour\",\n", + " \"pedestriancount\",\n", + " \"airtemperature\",\n", + " \"relativehumidity\",\n", + " \"atmosphericpressure\",\n", + " \"averagewindspeed\",\n", + " \"gustwindspeed\",\n", + " \"pm25\",\n", + " \"pm10\",\n", + " \"noise\",\n", + " \"is_weekend\",\n", + " \"hour_sin\",\n", + " \"hour_cos\",\n", + " \"dow_sin\",\n", + " \"dow_cos\",\n", + " \"month_sin\",\n", + " \"month_cos\",\n", + " \"wind_dir_sin\",\n", + " \"wind_dir_cos\",\n", + " \"lag_1\",\n", + " \"lag_24\",\n", + " \"lag_168\",\n", + " \"rolling_mean_24\",\n", + " \"rolling_mean_168\",\n", + " ]\n", + "]\n", + "\n", + "# Check the result.\n", + "print(features_df.info())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9. Preparing Train/Val/Test Splits\n", + "\n", + "Preparing the splits is necessary for training a forecasting model, so that the future periods are evaluated, and not on randomly selected time periods. Hence, splitting the dataset based on time is important so that the model being trained on the past can predict the future, like in real-world scenarios [49]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 9.1 Splitting The Data By Time\n", + "\n", + "Chronological splitting in time-series forecasting is to prevent leaking future information into the training process. For example is if random splitting were to be used, then a model can be trained on data points in 2026, but is tested on a time period in 2025. Hence, the validation and test set must be later, and the training set must be the past, as shown here [49].\n", + "\n", + "The split for training, validation and testing is 80:10:10 to ensure that there are enough data points used for training, with enough data points to perform validation and testing. Deep learning tends to require a large number of data points, so using this split ratio prevents underfitting [50].\n", + "\n", + "The output confirms that the data was split chronologically, with the training data coming first, followed by validation and testing data." + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(13798, 24)\n", + "(1724, 24)\n", + "(1726, 24)\n" + ] + } + ], + "source": [ + "# Set the split sizes (80:10:10).\n", + "train_size = int(len(features_df) * 0.8)\n", + "val_size = int(len(features_df) * 0.1)\n", + "\n", + "# Split the data in chronological order.\n", + "train_df = features_df.iloc[:train_size].copy()\n", + "val_df = features_df.iloc[\n", + " train_size:train_size + val_size\n", + "].copy()\n", + "test_df = features_df.iloc[\n", + " train_size + val_size:\n", + "].copy()\n", + "\n", + "# Check the shapes.\n", + "print(train_df.shape)\n", + "print(val_df.shape)\n", + "print(test_df.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train range:\n", + "2024-05-19 00:00:00\n", + "2025-12-14 21:00:00\n", + "\n", + "Validation range:\n", + "2025-12-14 22:00:00\n", + "2026-02-24 17:00:00\n", + "\n", + "Test range:\n", + "2026-02-24 18:00:00\n", + "2026-05-07 15:00:00\n" + ] + } + ], + "source": [ + "# Checking The Split Ranges.\n", + "print(\"Train range:\")\n", + "print(train_df[\"datetime_hour\"].min())\n", + "print(train_df[\"datetime_hour\"].max())\n", + "\n", + "print(\"\\nValidation range:\")\n", + "print(val_df[\"datetime_hour\"].min())\n", + "print(val_df[\"datetime_hour\"].max())\n", + "\n", + "print(\"\\nTest range:\")\n", + "print(test_df[\"datetime_hour\"].min())\n", + "print(test_df[\"datetime_hour\"].max())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 9.2 Separating Features And Target\n", + "\n", + "This step is necessary to separate the predictor inputs X and the output target y. This is necessary because the model needs to know which columns are used as inputs and which column it needs to predict [51].\n", + "\n", + "The target is set to pedestriancount, which is the variable the model is trying to predict. The datetime column is excluded from the features since the chronological splitting is completed, so this leaves 22 predictor columns for each split, as shown in the shapes output." + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(13798, 22) (13798,)\n", + "(1724, 22) (1724,)\n", + "(1726, 22) (1726,)\n" + ] + } + ], + "source": [ + "# Store the target column name.\n", + "target_col = \"pedestriancount\"\n", + "\n", + "# Store the feature column names.\n", + "feature_cols = [\n", + " col for col in features_df.columns\n", + " if col not in [\"datetime_hour\", target_col]\n", + "]\n", + "\n", + "# Create X and y for each split.\n", + "X_train = train_df[feature_cols]\n", + "y_train = train_df[target_col]\n", + "\n", + "X_val = val_df[feature_cols]\n", + "y_val = val_df[target_col]\n", + "\n", + "X_test = test_df[feature_cols]\n", + "y_test = test_df[target_col]\n", + "\n", + "# Check the shapes.\n", + "print(X_train.shape, y_train.shape)\n", + "print(X_val.shape, y_val.shape)\n", + "print(X_test.shape, y_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 9.3 Scaling The Features\n", + "\n", + "Scaling the features is necessary because the predictor variables are measured on different scales. For example, temperature, humidity, air pressure, wind speed, pollution values, and lagged pedestrian counts all have different ranges. Hence, needs to be standardised so that the variables are unitless, allowing the variables to be able to directly compared [52].\n", + "\n", + "Standardisation basically sets the predictors to have a mean of 0 and a standard deviation of 1. This allows faster convergence when all the input features are on the same scale, preventing feature dominance due to differences in magnitudes, and is generally more stable [52]." + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(13798, 22)\n", + "(1724, 22)\n", + "(1726, 22)\n", + "[[-1.29777704e+00 1.50360125e-01 1.46578468e+00 -5.80148521e-01\n", + " -7.99559383e-01 1.00283430e-01 1.98749444e-01 -3.63941261e-01\n", + " 1.57673844e+00 -7.77706241e-05 1.41447137e+00 -1.10371969e+00\n", + " 8.80498928e-01 1.03546540e+00 -1.13513198e+00 -5.75432725e-01\n", + " -4.91738264e-01 -5.08174682e-01 -9.59909049e-01 -7.29249335e-01\n", + " -3.26314765e-01 -1.04167887e+00]\n", + " [-1.29610869e+00 1.24879825e-01 1.40393909e+00 -2.36045144e-01\n", + " -6.48552677e-01 9.91130647e-02 1.96368567e-01 -5.59070044e-01\n", + " 1.57673844e+00 3.65929517e-01 1.36628083e+00 -1.10371969e+00\n", + " 8.80498928e-01 1.03546540e+00 -1.13513198e+00 -5.44617322e-01\n", + " -5.21312792e-01 -8.18712499e-01 -1.06044050e+00 -8.93515834e-01\n", + " -2.95950862e-01 -1.04569266e+00]\n", + " [-1.24184204e+00 1.52595239e-01 1.27511778e+00 9.83881253e-01\n", + " 1.20012207e-01 1.18507689e-01 2.09314586e-01 2.43030983e-01\n", + " 1.57673844e+00 7.06994013e-01 1.22499330e+00 -1.10371969e+00\n", + " 8.80498928e-01 1.03546540e+00 -1.13513198e+00 -1.35366847e+00\n", + " 9.74390635e-01 -9.72903410e-01 -1.14256846e+00 -1.04018901e+00\n", + " -2.77116141e-01 -1.04926256e+00]\n", + " [-1.11369428e+00 5.49207567e-02 1.21439393e+00 4.77611798e-01\n", + " 8.05390863e-02 1.18372546e-02 1.05746435e-01 -2.70089534e-01\n", + " 1.57673844e+00 9.99872735e-01 1.00023730e+00 -1.10371969e+00\n", + " 8.80498928e-01 1.03546540e+00 -1.13513198e+00 -1.51005421e+00\n", + " 1.63367379e+00 -1.08230164e+00 -1.18800094e+00 -1.08939068e+00\n", + " -2.64141820e-01 -1.05113235e+00]\n", + " [-1.04242843e+00 1.41643180e-01 1.15410382e+00 5.14209590e-01\n", + " 1.78060915e-01 -4.63466188e-02 7.55390572e-02 -4.47800540e-01\n", + " 1.57673844e+00 1.22460648e+00 7.07329572e-01 -1.10371969e+00\n", + " 8.80498928e-01 1.03546540e+00 -1.13513198e+00 -1.41404236e+00\n", + " 1.19475651e+00 -1.11765254e+00 -1.22711303e+00 -1.19014229e+00\n", + " -2.49003782e-01 -1.05236973e+00]]\n" + ] + } + ], + "source": [ + "# Create the scaler.\n", + "scaler = StandardScaler()\n", + "\n", + "# Fit on training features only.\n", + "X_train_scaled = scaler.fit_transform(X_train)\n", + "\n", + "# Transform validation and test features.\n", + "X_val_scaled = scaler.transform(X_val)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "\n", + "# Check the shapes.\n", + "print(X_train_scaled.shape)\n", + "print(X_val_scaled.shape)\n", + "print(X_test_scaled.shape)\n", + "print(X_train_scaled[:5])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 10. Deep Learning — Pedestrian Count Forecasting\n", + "\n", + "This section covers the deep learning component of the project. The goal is to build \n", + "and compare recurrent neural network models that can forecast hourly pedestrian counts \n", + "in Melbourne CBD using climate features and engineered time-series inputs.\n", + "\n", + "Deep learning models, specifically recurrent neural network architectures like LSTM \n", + "and GRU, are well suited for this task because pedestrian demand is a sequential, \n", + "time-dependent process [53]. Unlike traditional regression \n", + "models, recurrent networks can learn temporal patterns across multiple time steps, \n", + "making them appropriate for hourly forecasting tasks with lag dependencies \n", + "[54]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.2 Setting Random Seeds for Reproducibility\n", + "\n", + "A fixed random seed is set across all relevant libraries before any model is built or \n", + "trained. Deep learning models initialise their weights randomly and apply random \n", + "dropout masks during training, which means that results can differ between runs \n", + "without a fixed seed [55]. By setting the same seed across \n", + "Python, NumPy, and TensorFlow, the outputs of this section are fully reproducible \n", + "and will produce identical results on every run \n", + "[7] [55].\n", + "\n", + "Additional environment variables TF_DETERMINISTIC_OPS and \n", + "TF_DISABLE_SEGMENT_REDUCTION_OP_DETERMINISM_EXCEPTIONS are set to further enforce \n", + "deterministic behaviour at the TensorFlow operation level \n", + "[55]. The student ID was used as the seed value for consistency \n", + "with the random seed convention used earlier in this notebook \n", + "[7]." + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Random seed set to: 224120439\n", + "TF version: 2.20.0\n" + ] + } + ], + "source": [ + "SEED = 224120439\n", + "\n", + "# 1. Force single thread to eliminate CPU non-determinism\n", + "os.environ[\"PYTHONHASHSEED\"] = str(SEED)\n", + "os.environ[\"TF_DETERMINISTIC_OPS\"] = \"1\"\n", + "os.environ[\"TF_DISABLE_SEGMENT_REDUCTION_OP_DETERMINISM_EXCEPTIONS\"] = \"1\"\n", + "\n", + "# 2. Seed all libraries\n", + "random.seed(SEED)\n", + "np.random.seed(SEED)\n", + "tf.keras.utils.set_random_seed(SEED)\n", + "\n", + "# 3. Force deterministic operations\n", + "try:\n", + " tf.config.experimental.enable_op_determinism()\n", + "except Exception:\n", + " pass # silently skip if not supported on this system\n", + "\n", + "print(f\"Random seed set to: {SEED}\")\n", + "print(f\"TF version: {tf.__version__}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.3 Creating Input Sequences\n", + "\n", + "Before training a recurrent neural network, the data must be reshaped into \n", + "three-dimensional sequences. Each sequence consists of a fixed-length window of past \n", + "hourly observations that the model uses to predict the next hour's pedestrian count \n", + "[56].\n", + "\n", + "A sequence length of 24 was chosen to capture one full day cycle of hourly patterns. \n", + "This is a natural choice given that pedestrian demand follows a strong 24-hour rhythm, \n", + "as confirmed by the seasonal decomposition and autocorrelation analysis in earlier \n", + "sections [35] [42]. Additionally, since lag \n", + "features covering 24 hours, 7 days, and rolling means are already included as input \n", + "features, extending the sequence window further would introduce redundancy without \n", + "meaningful benefit [47] [48].\n", + "\n", + "Each input sample X has the shape (24, 22), representing 24 consecutive hours across \n", + "22 engineered features. The corresponding target y is the pedestrian count at the \n", + "following hour, making this a one-step-ahead forecasting setup \n", + "[56]. The output shapes confirm the split sizes — 13,774 \n", + "training samples, 1,700 validation samples, and 1,702 test samples." + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train shape: (13774, 24, 22)\n", + "Val shape: (1700, 24, 22)\n", + "Test shape: (1702, 24, 22)\n" + ] + } + ], + "source": [ + "sequence_length = 24 \n", + "\n", + "def create_sequences(X, y, seq_length):\n", + " X_seq, y_seq = [], []\n", + " for i in range(seq_length, len(X)):\n", + " X_seq.append(X[i - seq_length:i])\n", + " y_seq.append(y.iloc[i])\n", + " return np.array(X_seq), np.array(y_seq)\n", + "\n", + "X_train_seq, y_train_seq = create_sequences(X_train_scaled, y_train, sequence_length)\n", + "X_val_seq, y_val_seq = create_sequences(X_val_scaled, y_val, sequence_length)\n", + "X_test_seq, y_test_seq = create_sequences(X_test_scaled, y_test, sequence_length)\n", + "\n", + "print(\"Train shape:\", X_train_seq.shape)\n", + "print(\"Val shape:\", X_val_seq.shape)\n", + "print(\"Test shape:\", X_test_seq.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.4 Model Architectures\n", + "\n", + "Three recurrent neural network architectures were built and compared to identify the \n", + "most effective model for this forecasting task. Comparing multiple architectures \n", + "allows for evidence-based model selection rather than relying on a single approach \n", + "[53] [57].\n", + "\n", + "All three models share the same output structure — a Dense(32) hidden layer followed \n", + "by a Dense(1) output layer for regression — and are compiled with the Adam optimiser \n", + "and Mean Squared Error loss, which is standard for continuous value regression \n", + "forecasting tasks [58]. Mean Absolute Error is tracked as an \n", + "interpretable secondary metric during training [59].\n", + "\n", + "The three architectures evaluated are:\n", + "- **Baseline LSTM**: A single LSTM layer with 64 units serving as the reference model \n", + " to establish a performance baseline.\n", + "- **Stacked LSTM**: Two LSTM layers in sequence for deeper temporal pattern learning.\n", + "- **GRU**: A more parameter-efficient alternative to LSTM with a simplified gate \n", + " structure." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 10.4.1 Baseline LSTM\n", + "\n", + "The baseline model uses a single LSTM layer with 64 units. LSTM networks are designed \n", + "to address the vanishing gradient problem found in standard recurrent networks, \n", + "allowing them to retain information across longer time sequences \n", + "[54]. At each time step, the LSTM uses three gates — an input \n", + "gate, a forget gate, and an output gate — to decide what information to keep, discard, \n", + "or pass forward [54].\n", + "\n", + "A Dropout layer with a rate of 0.2 is applied after the LSTM to regularise the model \n", + "by randomly deactivating 20% of neurons during each training step, reducing the risk \n", + "of overfitting [60]. This model has 24,385 total trainable \n", + "parameters, which is appropriate for the training set size of approximately 13,774 \n", + "samples." + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.12/dist-packages/keras/src/layers/rnn/rnn.py:199: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(**kwargs)\n" + ] + }, + { + "data": { + "text/html": [ + "
Model: \"sequential_3\"\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential_3\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ lstm_3 (LSTM)                   │ (None, 64)             │        22,272 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_4 (Dropout)             │ (None, 64)             │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_6 (Dense)                 │ (None, 32)             │         2,080 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_7 (Dense)                 │ (None, 1)              │            33 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ lstm_3 (\u001b[38;5;33mLSTM\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m22,272\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_6 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m2,080\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_7 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m33\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Total params: 24,385 (95.25 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m24,385\u001b[0m (95.25 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Trainable params: 24,385 (95.25 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m24,385\u001b[0m (95.25 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Non-trainable params: 0 (0.00 B)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def build_lstm_baseline(input_shape):\n", + " model = Sequential([\n", + " LSTM(64, return_sequences=False, input_shape=input_shape),\n", + " Dropout(0.2),\n", + " Dense(32, activation=\"relu\"),\n", + " Dense(1)\n", + " ])\n", + " model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[\"mae\"])\n", + " return model\n", + "\n", + "model_baseline = build_lstm_baseline(\n", + " (X_train_seq.shape[1], X_train_seq.shape[2])\n", + ")\n", + "model_baseline.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 10.4.2 Stacked LSTM\n", + "\n", + "The stacked model adds a second LSTM layer on top of the first. The first LSTM layer \n", + "uses return_sequences=True, meaning it outputs the full sequence of hidden states at \n", + "every time step rather than just the final one. This full sequence is then passed as \n", + "input to the second LSTM layer, which compresses it into a single final hidden state \n", + "[54].\n", + "\n", + "This design allows the model to learn more abstract temporal representations across \n", + "multiple levels of processing — the first layer captures low-level hourly patterns, \n", + "while the second layer can learn higher-order dependencies across those patterns \n", + "[53]. Dropout is applied after each LSTM layer to maintain \n", + "regularisation [60]. This model has 35,777 total trainable \n", + "parameters, making it the most complex of the three architectures compared." + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Model: \"sequential_4\"\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential_4\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ lstm_4 (LSTM)                   │ (None, 24, 64)         │        22,272 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_5 (Dropout)             │ (None, 24, 64)         │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ lstm_5 (LSTM)                   │ (None, 32)             │        12,416 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_6 (Dropout)             │ (None, 32)             │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_8 (Dense)                 │ (None, 32)             │         1,056 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_9 (Dense)                 │ (None, 1)              │            33 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ lstm_4 (\u001b[38;5;33mLSTM\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m22,272\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dropout_5 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ lstm_5 (\u001b[38;5;33mLSTM\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m12,416\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dropout_6 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_8 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m1,056\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_9 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m33\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Total params: 35,777 (139.75 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m35,777\u001b[0m (139.75 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Trainable params: 35,777 (139.75 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m35,777\u001b[0m (139.75 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Non-trainable params: 0 (0.00 B)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def build_lstm_stacked(input_shape):\n", + " model = Sequential([\n", + " LSTM(64, return_sequences=True, input_shape=input_shape), # returns full sequence\n", + " Dropout(0.2),\n", + " LSTM(32, return_sequences=False), # compresses to final state\n", + " Dropout(0.2),\n", + " Dense(32, activation=\"relu\"),\n", + " Dense(1)\n", + " ])\n", + " model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[\"mae\"])\n", + " return model\n", + "\n", + "model_stacked = build_lstm_stacked(\n", + " (X_train_seq.shape[1], X_train_seq.shape[2])\n", + ")\n", + "model_stacked.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 10.4.3 GRU (Gated Recurrent Unit)\n", + "\n", + "The GRU model replaces the LSTM unit with a GRU layer of equal size. GRU simplifies \n", + "the LSTM architecture by combining the input and forget gates into a single update \n", + "gate, and adding a reset gate, resulting in only two gates overall instead of three \n", + "[61]. This reduction in gate complexity leads to fewer \n", + "parameters — 19,009 total — making the GRU the most lightweight of the three \n", + "architectures.\n", + "\n", + "GRU is included as an alternative because empirical research has shown it often \n", + "performs comparably or better than LSTM on time-series forecasting tasks, \n", + "particularly on datasets that are not large enough to fully justify the additional \n", + "capacity of LSTM [61] [62]. This \n", + "comparison tests whether that finding holds for this urban pedestrian forecasting \n", + "task." + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Model: \"sequential_5\"\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential_5\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ gru_1 (GRU)                     │ (None, 64)             │        16,896 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_7 (Dropout)             │ (None, 64)             │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_10 (Dense)                │ (None, 32)             │         2,080 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_11 (Dense)                │ (None, 1)              │            33 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ gru_1 (\u001b[38;5;33mGRU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m16,896\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dropout_7 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_10 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m2,080\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_11 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m33\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Total params: 19,009 (74.25 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m19,009\u001b[0m (74.25 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Trainable params: 19,009 (74.25 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m19,009\u001b[0m (74.25 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Non-trainable params: 0 (0.00 B)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def build_gru(input_shape):\n", + " model = Sequential([\n", + " GRU(64, return_sequences=False, input_shape=input_shape),\n", + " Dropout(0.2),\n", + " Dense(32, activation=\"relu\"),\n", + " Dense(1)\n", + " ])\n", + " model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[\"mae\"])\n", + " return model\n", + "\n", + "model_gru = build_gru(\n", + " (X_train_seq.shape[1], X_train_seq.shape[2])\n", + ")\n", + "model_gru.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.5 Model Training\n", + "\n", + "All three models are trained using identical configurations to ensure a fair \n", + "comparison between architectures. The training setup includes the following design \n", + "choices [58]:\n", + "\n", + "- **Epochs**: A maximum of 50 epochs was set to allow sufficient learning time, with \n", + " callbacks responsible for halting training early when appropriate \n", + " [58].\n", + "- **Batch size**: A batch size of 64 was selected as a balance between training \n", + " stability and computational speed. Larger batch sizes provide more stable gradient \n", + " estimates per update compared to smaller ones [58].\n", + "- **EarlyStopping**: Monitors validation loss with a patience of 10 epochs, halting \n", + " training if no improvement is seen for 10 consecutive epochs. The \n", + " restore_best_weights=True parameter ensures the final model retains the weights \n", + " from its best validation epoch rather than the last epoch \n", + " [63].\n", + "- **ReduceLROnPlateau**: Halves the learning rate when validation loss has not \n", + " improved for 5 consecutive epochs, with a minimum learning rate floor of 1e-6. \n", + " This allows finer weight adjustments as the model approaches convergence rather \n", + " than overshooting the optimal solution [63].\n", + "\n", + "The validation set is used exclusively for monitoring during training and is never \n", + "used to update model weights, preserving the integrity of the chronological train, \n", + "validation, and test split [49]." + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 11ms/step - loss: 1931178880.0000 - mae: 34742.4805 - val_loss: 2065434112.0000 - val_mae: 36316.7852 - learning_rate: 0.0010\n", + "Epoch 2/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1915012608.0000 - mae: 34508.1875 - val_loss: 2041869568.0000 - val_mae: 35990.8672 - learning_rate: 0.0010\n", + "Epoch 3/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1886355328.0000 - mae: 34091.4727 - val_loss: 2005516672.0000 - val_mae: 35482.5078 - learning_rate: 0.0010\n", + "Epoch 4/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1846237696.0000 - mae: 33533.6406 - val_loss: 1957890944.0000 - val_mae: 34864.8906 - learning_rate: 0.0010\n", + "Epoch 5/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 1796227072.0000 - mae: 32903.4414 - val_loss: 1900755712.0000 - val_mae: 34188.7891 - learning_rate: 0.0010\n", + "Epoch 6/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1738398336.0000 - mae: 32237.4355 - val_loss: 1835951104.0000 - val_mae: 33501.5625 - learning_rate: 0.0010\n", + "Epoch 7/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1673944192.0000 - mae: 31575.8242 - val_loss: 1765187584.0000 - val_mae: 32804.0078 - learning_rate: 0.0010\n", + "Epoch 8/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1604557824.0000 - mae: 30903.6895 - val_loss: 1690087552.0000 - val_mae: 32100.9355 - learning_rate: 0.0010\n", + "Epoch 9/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 1532111872.0000 - mae: 30227.2246 - val_loss: 1612279936.0000 - val_mae: 31392.7227 - learning_rate: 0.0010\n", + "Epoch 10/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 1457709312.0000 - mae: 29556.4414 - val_loss: 1533161088.0000 - val_mae: 30695.7051 - learning_rate: 0.0010\n", + "Epoch 11/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1383581440.0000 - mae: 28908.4434 - val_loss: 1454138880.0000 - val_mae: 30023.1250 - learning_rate: 0.0010\n", + "Epoch 12/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1310495488.0000 - mae: 28299.8477 - val_loss: 1376468352.0000 - val_mae: 29394.1934 - learning_rate: 0.0010\n", + "Epoch 13/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1239926912.0000 - mae: 27729.9043 - val_loss: 1301134208.0000 - val_mae: 28809.5215 - learning_rate: 0.0010\n", + "Epoch 14/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1170712448.0000 - mae: 27170.0469 - val_loss: 1229012224.0000 - val_mae: 28242.0703 - learning_rate: 0.0010\n", + "Epoch 15/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1105601920.0000 - mae: 26642.3184 - val_loss: 1160922624.0000 - val_mae: 27700.8438 - learning_rate: 0.0010\n", + "Epoch 16/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1043984640.0000 - mae: 26124.0840 - val_loss: 1094580352.0000 - val_mae: 27079.0586 - learning_rate: 0.0010\n", + "Epoch 17/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 976227584.0000 - mae: 25163.9297 - val_loss: 996540288.0000 - val_mae: 24844.2109 - learning_rate: 0.0010\n", + "Epoch 18/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 860002496.0000 - mae: 21725.8691 - val_loss: 885134976.0000 - val_mae: 21819.1719 - learning_rate: 0.0010\n", + "Epoch 19/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 772316480.0000 - mae: 19665.3848 - val_loss: 801167040.0000 - val_mae: 20392.8848 - learning_rate: 0.0010\n", + "Epoch 20/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 696815808.0000 - mae: 18389.9785 - val_loss: 723598528.0000 - val_mae: 19151.4785 - learning_rate: 0.0010\n", + "Epoch 21/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 628451968.0000 - mae: 17213.0176 - val_loss: 651424896.0000 - val_mae: 17956.3184 - learning_rate: 0.0010\n", + "Epoch 22/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 563721536.0000 - mae: 16077.4170 - val_loss: 584422592.0000 - val_mae: 16778.9355 - learning_rate: 0.0010\n", + "Epoch 23/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 505489536.0000 - mae: 15020.1113 - val_loss: 523019264.0000 - val_mae: 15676.2910 - learning_rate: 0.0010\n", + "Epoch 24/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 449513696.0000 - mae: 13965.4336 - val_loss: 466503328.0000 - val_mae: 14642.3008 - learning_rate: 0.0010\n", + "Epoch 25/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 400619104.0000 - mae: 13041.7090 - val_loss: 414948032.0000 - val_mae: 13628.7861 - learning_rate: 0.0010\n", + "Epoch 26/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 356454240.0000 - mae: 12158.3799 - val_loss: 368652768.0000 - val_mae: 12694.5703 - learning_rate: 0.0010\n", + "Epoch 27/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 317270240.0000 - mae: 11362.6035 - val_loss: 327098016.0000 - val_mae: 11882.4199 - learning_rate: 0.0010\n", + "Epoch 28/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 282110752.0000 - mae: 10618.4219 - val_loss: 290472800.0000 - val_mae: 11140.4375 - learning_rate: 0.0010\n", + "Epoch 29/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 249665072.0000 - mae: 9972.2529 - val_loss: 259006832.0000 - val_mae: 10496.8271 - learning_rate: 0.0010\n", + "Epoch 30/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 222754640.0000 - mae: 9393.5771 - val_loss: 231574736.0000 - val_mae: 9954.3838 - learning_rate: 0.0010\n", + "Epoch 31/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 198423008.0000 - mae: 8869.1445 - val_loss: 207840512.0000 - val_mae: 9421.6309 - learning_rate: 0.0010\n", + "Epoch 32/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 178380192.0000 - mae: 8440.0615 - val_loss: 187732656.0000 - val_mae: 8949.0840 - learning_rate: 0.0010\n", + "Epoch 33/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 161002656.0000 - mae: 7984.2749 - val_loss: 169997776.0000 - val_mae: 8558.7334 - learning_rate: 0.0010\n", + "Epoch 34/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 144927488.0000 - mae: 7591.0400 - val_loss: 154311120.0000 - val_mae: 8103.6113 - learning_rate: 0.0010\n", + "Epoch 35/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 128720472.0000 - mae: 7144.0977 - val_loss: 140611216.0000 - val_mae: 7693.2202 - learning_rate: 0.0010\n", + "Epoch 36/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 115451752.0000 - mae: 6745.2446 - val_loss: 130359144.0000 - val_mae: 7402.8223 - learning_rate: 0.0010\n", + "Epoch 37/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 103320352.0000 - mae: 6354.1328 - val_loss: 117524160.0000 - val_mae: 6989.3652 - learning_rate: 0.0010\n", + "Epoch 38/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 92202488.0000 - mae: 5979.1660 - val_loss: 109628784.0000 - val_mae: 6704.9717 - learning_rate: 0.0010\n", + "Epoch 39/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 83113800.0000 - mae: 5689.0278 - val_loss: 103527280.0000 - val_mae: 6496.6172 - learning_rate: 0.0010\n", + "Epoch 40/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 75200392.0000 - mae: 5414.2593 - val_loss: 97912072.0000 - val_mae: 6302.9551 - learning_rate: 0.0010\n", + "Epoch 41/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 69330976.0000 - mae: 5204.5713 - val_loss: 92983264.0000 - val_mae: 6090.5542 - learning_rate: 0.0010\n", + "Epoch 42/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 64068488.0000 - mae: 5041.2319 - val_loss: 93400048.0000 - val_mae: 6068.5435 - learning_rate: 0.0010\n", + "Epoch 43/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 59892128.0000 - mae: 4892.9224 - val_loss: 87246792.0000 - val_mae: 5791.9092 - learning_rate: 0.0010\n", + "Epoch 44/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 55631812.0000 - mae: 4743.8657 - val_loss: 85550016.0000 - val_mae: 5762.2046 - learning_rate: 0.0010\n", + "Epoch 45/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 53973056.0000 - mae: 4663.5952 - val_loss: 83252664.0000 - val_mae: 5683.0537 - learning_rate: 0.0010\n", + "Epoch 46/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 50220260.0000 - mae: 4527.8228 - val_loss: 83925944.0000 - val_mae: 5714.0728 - learning_rate: 0.0010\n", + "Epoch 47/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 47764060.0000 - mae: 4444.6938 - val_loss: 82401720.0000 - val_mae: 5649.3335 - learning_rate: 0.0010\n", + "Epoch 48/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 45334620.0000 - mae: 4341.4917 - val_loss: 80761736.0000 - val_mae: 5650.6733 - learning_rate: 0.0010\n", + "Epoch 49/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 43015352.0000 - mae: 4263.1714 - val_loss: 78728576.0000 - val_mae: 5562.2876 - learning_rate: 0.0010\n", + "Epoch 50/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 41184284.0000 - mae: 4168.4272 - val_loss: 76461192.0000 - val_mae: 5454.5000 - learning_rate: 0.0010\n", + "Epoch 1/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 10ms/step - loss: 1932731136.0000 - mae: 34764.9727 - val_loss: 2069818880.0000 - val_mae: 36377.0664 - learning_rate: 0.0010\n", + "Epoch 2/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1923338112.0000 - mae: 34629.4414 - val_loss: 2056002432.0000 - val_mae: 36186.6602 - learning_rate: 0.0010\n", + "Epoch 3/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 1906462720.0000 - mae: 34385.0273 - val_loss: 2034484992.0000 - val_mae: 35888.1211 - learning_rate: 0.0010\n", + "Epoch 4/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 1882598784.0000 - mae: 34036.7148 - val_loss: 2005956992.0000 - val_mae: 35488.6562 - learning_rate: 0.0010\n", + "Epoch 5/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1852413696.0000 - mae: 33614.3867 - val_loss: 1971230848.0000 - val_mae: 35029.5586 - learning_rate: 0.0010\n", + "Epoch 6/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1816782592.0000 - mae: 33156.7617 - val_loss: 1931121664.0000 - val_mae: 34541.7891 - learning_rate: 0.0010\n", + "Epoch 7/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1776370560.0000 - mae: 32665.1387 - val_loss: 1886418944.0000 - val_mae: 34027.4883 - learning_rate: 0.0010\n", + "Epoch 8/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1731960960.0000 - mae: 32169.1250 - val_loss: 1837876736.0000 - val_mae: 33520.9844 - learning_rate: 0.0010\n", + "Epoch 9/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 1684573312.0000 - mae: 31682.2441 - val_loss: 1786250496.0000 - val_mae: 33008.1094 - learning_rate: 0.0010\n", + "Epoch 10/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 1634488960.0000 - mae: 31189.7012 - val_loss: 1732167680.0000 - val_mae: 32490.0215 - learning_rate: 0.0010\n", + "Epoch 11/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1582652928.0000 - mae: 30698.1016 - val_loss: 1676277248.0000 - val_mae: 31974.6699 - learning_rate: 0.0010\n", + "Epoch 12/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1528807936.0000 - mae: 30199.7773 - val_loss: 1619060736.0000 - val_mae: 31453.7559 - learning_rate: 0.0010\n", + "Epoch 13/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 1474490880.0000 - mae: 29703.1445 - val_loss: 1561115776.0000 - val_mae: 30939.0352 - learning_rate: 0.0010\n", + "Epoch 14/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1419293056.0000 - mae: 29219.7656 - val_loss: 1502894208.0000 - val_mae: 30434.5215 - learning_rate: 0.0010\n", + "Epoch 15/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - loss: 1366217600.0000 - mae: 28764.8242 - val_loss: 1445071360.0000 - val_mae: 29948.1836 - learning_rate: 0.0010\n", + "Epoch 16/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 1311709056.0000 - mae: 28299.2773 - val_loss: 1387855616.0000 - val_mae: 29483.8652 - learning_rate: 0.0010\n", + "Epoch 17/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1259717120.0000 - mae: 27880.5957 - val_loss: 1331703424.0000 - val_mae: 29047.3066 - learning_rate: 0.0010\n", + "Epoch 18/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1208217600.0000 - mae: 27472.8125 - val_loss: 1276985344.0000 - val_mae: 28620.4238 - learning_rate: 0.0010\n", + "Epoch 19/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1158040192.0000 - mae: 27063.4766 - val_loss: 1224060544.0000 - val_mae: 28202.8828 - learning_rate: 0.0010\n", + "Epoch 20/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 1109995136.0000 - mae: 26683.9062 - val_loss: 1173220352.0000 - val_mae: 27799.9453 - learning_rate: 0.0010\n", + "Epoch 21/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - loss: 1064320704.0000 - mae: 26317.3105 - val_loss: 1124794624.0000 - val_mae: 27414.1367 - learning_rate: 0.0010\n", + "Epoch 22/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1022760384.0000 - mae: 25982.8965 - val_loss: 1079056128.0000 - val_mae: 27053.7539 - learning_rate: 0.0010\n", + "Epoch 23/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 982177088.0000 - mae: 25652.5938 - val_loss: 1036195840.0000 - val_mae: 26712.5508 - learning_rate: 0.0010\n", + "Epoch 24/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 945132608.0000 - mae: 25346.1582 - val_loss: 996421696.0000 - val_mae: 26395.3066 - learning_rate: 0.0010\n", + "Epoch 25/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 911153472.0000 - mae: 25062.0684 - val_loss: 959820032.0000 - val_mae: 26097.6113 - learning_rate: 0.0010\n", + "Epoch 26/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 880028288.0000 - mae: 24827.4922 - val_loss: 926537280.0000 - val_mae: 25821.6191 - learning_rate: 0.0010\n", + "Epoch 27/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - loss: 853682432.0000 - mae: 24604.5742 - val_loss: 896631808.0000 - val_mae: 25565.9453 - learning_rate: 0.0010\n", + "Epoch 28/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 828166080.0000 - mae: 24403.7070 - val_loss: 870131136.0000 - val_mae: 25342.5938 - learning_rate: 0.0010\n", + "Epoch 29/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 810969728.0000 - mae: 24281.2656 - val_loss: 846989760.0000 - val_mae: 25147.4238 - learning_rate: 0.0010\n", + "Epoch 30/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 790364480.0000 - mae: 24090.2852 - val_loss: 827042432.0000 - val_mae: 24978.3867 - learning_rate: 0.0010\n", + "Epoch 31/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 776585792.0000 - mae: 24009.8965 - val_loss: 810190016.0000 - val_mae: 24829.4160 - learning_rate: 0.0010\n", + "Epoch 32/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - loss: 764959104.0000 - mae: 23917.3750 - val_loss: 796246976.0000 - val_mae: 24708.1191 - learning_rate: 0.0010\n", + "Epoch 33/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - loss: 755435648.0000 - mae: 23878.0566 - val_loss: 784937344.0000 - val_mae: 24605.6172 - learning_rate: 0.0010\n", + "Epoch 34/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - loss: 747755712.0000 - mae: 23794.9590 - val_loss: 775911296.0000 - val_mae: 24518.1973 - learning_rate: 0.0010\n", + "Epoch 35/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 743594688.0000 - mae: 23785.2285 - val_loss: 768952064.0000 - val_mae: 24446.5508 - learning_rate: 0.0010\n", + "Epoch 36/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 739301056.0000 - mae: 23742.7988 - val_loss: 763610304.0000 - val_mae: 24390.0273 - learning_rate: 0.0010\n", + "Epoch 37/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 738923584.0000 - mae: 23775.4492 - val_loss: 759721984.0000 - val_mae: 24347.7969 - learning_rate: 0.0010\n", + "Epoch 38/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - loss: 735023744.0000 - mae: 23729.2637 - val_loss: 756937152.0000 - val_mae: 24316.7676 - learning_rate: 0.0010\n", + "Epoch 39/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 734647168.0000 - mae: 23751.9238 - val_loss: 754886144.0000 - val_mae: 24292.0117 - learning_rate: 0.0010\n", + "Epoch 40/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 732123008.0000 - mae: 23701.5078 - val_loss: 753405888.0000 - val_mae: 24274.6113 - learning_rate: 0.0010\n", + "Epoch 41/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 479316192.0000 - mae: 15964.3057 - val_loss: 403580480.0000 - val_mae: 13544.6035 - learning_rate: 0.0010\n", + "Epoch 42/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 343708096.0000 - mae: 12115.6484 - val_loss: 341869088.0000 - val_mae: 12275.1152 - learning_rate: 0.0010\n", + "Epoch 43/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 10ms/step - loss: 294795616.0000 - mae: 11060.8252 - val_loss: 294227808.0000 - val_mae: 11263.7822 - learning_rate: 0.0010\n", + "Epoch 44/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - loss: 255411248.0000 - mae: 10183.4551 - val_loss: 256555760.0000 - val_mae: 10408.2881 - learning_rate: 0.0010\n", + "Epoch 45/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 226594400.0000 - mae: 9585.3291 - val_loss: 226511520.0000 - val_mae: 9840.6309 - learning_rate: 0.0010\n", + "Epoch 46/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 201210784.0000 - mae: 8997.6709 - val_loss: 201730768.0000 - val_mae: 9280.6221 - learning_rate: 0.0010\n", + "Epoch 47/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 180951744.0000 - mae: 8508.0166 - val_loss: 180386640.0000 - val_mae: 8658.5586 - learning_rate: 0.0010\n", + "Epoch 48/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 161437456.0000 - mae: 8037.3340 - val_loss: 162152432.0000 - val_mae: 8236.7969 - learning_rate: 0.0010\n", + "Epoch 49/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 143073152.0000 - mae: 7514.1865 - val_loss: 141376416.0000 - val_mae: 7570.7739 - learning_rate: 0.0010\n", + "Epoch 50/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - loss: 127263440.0000 - mae: 7038.0229 - val_loss: 127391912.0000 - val_mae: 7156.2104 - learning_rate: 0.0010\n", + "Epoch 1/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 1930911488.0000 - mae: 34738.7148 - val_loss: 2063673600.0000 - val_mae: 36292.5195 - learning_rate: 0.0010\n", + "Epoch 2/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1910318336.0000 - mae: 34439.6289 - val_loss: 2033005824.0000 - val_mae: 35867.5156 - learning_rate: 0.0010\n", + "Epoch 3/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1872711552.0000 - mae: 33894.9219 - val_loss: 1984276864.0000 - val_mae: 35195.7305 - learning_rate: 0.0010\n", + "Epoch 4/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1818348032.0000 - mae: 33175.7305 - val_loss: 1919553280.0000 - val_mae: 34405.4180 - learning_rate: 0.0010\n", + "Epoch 5/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1750787072.0000 - mae: 32374.7637 - val_loss: 1842820480.0000 - val_mae: 33571.0039 - learning_rate: 0.0010\n", + "Epoch 6/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 1673256576.0000 - mae: 31570.9395 - val_loss: 1757159040.0000 - val_mae: 32726.9746 - learning_rate: 0.0010\n", + "Epoch 7/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1589680640.0000 - mae: 30760.4844 - val_loss: 1665691136.0000 - val_mae: 31878.0684 - learning_rate: 0.0010\n", + "Epoch 8/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1501466496.0000 - mae: 29948.0781 - val_loss: 1570926464.0000 - val_mae: 31025.0703 - learning_rate: 0.0010\n", + "Epoch 9/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 1412167296.0000 - mae: 29152.6699 - val_loss: 1475466880.0000 - val_mae: 30201.8711 - learning_rate: 0.0010\n", + "Epoch 10/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1322615424.0000 - mae: 28367.2754 - val_loss: 1379597312.0000 - val_mae: 29309.5332 - learning_rate: 0.0010\n", + "Epoch 11/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1220498816.0000 - mae: 26533.4805 - val_loss: 1260929408.0000 - val_mae: 26583.1484 - learning_rate: 0.0010\n", + "Epoch 12/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1112165120.0000 - mae: 24360.7832 - val_loss: 1153268480.0000 - val_mae: 25070.1992 - learning_rate: 0.0010\n", + "Epoch 13/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1014172736.0000 - mae: 22965.8555 - val_loss: 1049243264.0000 - val_mae: 23642.1152 - learning_rate: 0.0010\n", + "Epoch 14/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 918478592.0000 - mae: 21610.4453 - val_loss: 949395648.0000 - val_mae: 22258.7539 - learning_rate: 0.0010\n", + "Epoch 15/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - loss: 828141056.0000 - mae: 20268.5742 - val_loss: 854286336.0000 - val_mae: 20897.9004 - learning_rate: 0.0010\n", + "Epoch 16/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 742179200.0000 - mae: 18959.4883 - val_loss: 764294528.0000 - val_mae: 19546.4355 - learning_rate: 0.0010\n", + "Epoch 17/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 660350528.0000 - mae: 17696.7012 - val_loss: 680099328.0000 - val_mae: 18261.0137 - learning_rate: 0.0010\n", + "Epoch 18/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 587759936.0000 - mae: 16515.5098 - val_loss: 602156032.0000 - val_mae: 16998.6445 - learning_rate: 0.0010\n", + "Epoch 19/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 516721568.0000 - mae: 15305.6777 - val_loss: 530436352.0000 - val_mae: 15776.6455 - learning_rate: 0.0010\n", + "Epoch 20/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 454774592.0000 - mae: 14225.0361 - val_loss: 465422528.0000 - val_mae: 14620.3174 - learning_rate: 0.0010\n", + "Epoch 21/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 400378592.0000 - mae: 13204.1484 - val_loss: 406941792.0000 - val_mae: 13484.5117 - learning_rate: 0.0010\n", + "Epoch 22/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - loss: 351022656.0000 - mae: 12238.7500 - val_loss: 355148032.0000 - val_mae: 12487.7051 - learning_rate: 0.0010\n", + "Epoch 23/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 305887232.0000 - mae: 11356.9395 - val_loss: 309833376.0000 - val_mae: 11593.4004 - learning_rate: 0.0010\n", + "Epoch 24/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 265417856.0000 - mae: 10562.7656 - val_loss: 270137696.0000 - val_mae: 10775.0537 - learning_rate: 0.0010\n", + "Epoch 25/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 234852512.0000 - mae: 9912.8330 - val_loss: 235926240.0000 - val_mae: 10042.0000 - learning_rate: 0.0010\n", + "Epoch 26/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 204994608.0000 - mae: 9289.3477 - val_loss: 208168032.0000 - val_mae: 9438.7832 - learning_rate: 0.0010\n", + "Epoch 27/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 181129088.0000 - mae: 8759.8398 - val_loss: 183661520.0000 - val_mae: 8844.0303 - learning_rate: 0.0010\n", + "Epoch 28/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 159850000.0000 - mae: 8249.1475 - val_loss: 162752464.0000 - val_mae: 8357.9189 - learning_rate: 0.0010\n", + "Epoch 29/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 141057840.0000 - mae: 7771.1348 - val_loss: 144473072.0000 - val_mae: 7830.9922 - learning_rate: 0.0010\n", + "Epoch 30/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 125178776.0000 - mae: 7342.7188 - val_loss: 130381672.0000 - val_mae: 7483.4707 - learning_rate: 0.0010\n", + "Epoch 31/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 113134424.0000 - mae: 6976.5171 - val_loss: 116524848.0000 - val_mae: 7097.4570 - learning_rate: 0.0010\n", + "Epoch 32/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 101647680.0000 - mae: 6615.7490 - val_loss: 107708008.0000 - val_mae: 6762.4595 - learning_rate: 0.0010\n", + "Epoch 33/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 91810328.0000 - mae: 6306.7373 - val_loss: 97756488.0000 - val_mae: 6498.3496 - learning_rate: 0.0010\n", + "Epoch 34/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 82927784.0000 - mae: 6015.1938 - val_loss: 90644216.0000 - val_mae: 6210.5337 - learning_rate: 0.0010\n", + "Epoch 35/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 76303432.0000 - mae: 5791.7852 - val_loss: 84712688.0000 - val_mae: 5904.9824 - learning_rate: 0.0010\n", + "Epoch 36/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 71719856.0000 - mae: 5631.6865 - val_loss: 80555576.0000 - val_mae: 5748.7349 - learning_rate: 0.0010\n", + "Epoch 37/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 67707120.0000 - mae: 5521.0171 - val_loss: 78097168.0000 - val_mae: 5696.1294 - learning_rate: 0.0010\n", + "Epoch 38/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 63857660.0000 - mae: 5421.4185 - val_loss: 73372440.0000 - val_mae: 5529.1011 - learning_rate: 0.0010\n", + "Epoch 39/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 61203508.0000 - mae: 5300.4053 - val_loss: 70816400.0000 - val_mae: 5323.6621 - learning_rate: 0.0010\n", + "Epoch 40/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 59436396.0000 - mae: 5238.1948 - val_loss: 67635208.0000 - val_mae: 5235.2432 - learning_rate: 0.0010\n", + "Epoch 41/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 56774304.0000 - mae: 5124.1748 - val_loss: 66004324.0000 - val_mae: 5169.6328 - learning_rate: 0.0010\n", + "Epoch 42/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 53282044.0000 - mae: 4981.6040 - val_loss: 63594312.0000 - val_mae: 5073.1543 - learning_rate: 0.0010\n", + "Epoch 43/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 51761064.0000 - mae: 4912.4688 - val_loss: 60992804.0000 - val_mae: 4910.9961 - learning_rate: 0.0010\n", + "Epoch 44/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 49697432.0000 - mae: 4781.2227 - val_loss: 59893640.0000 - val_mae: 4884.7842 - learning_rate: 0.0010\n", + "Epoch 45/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 47390824.0000 - mae: 4688.0669 - val_loss: 57193784.0000 - val_mae: 4715.9609 - learning_rate: 0.0010\n", + "Epoch 46/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - loss: 44774588.0000 - mae: 4550.4604 - val_loss: 55175160.0000 - val_mae: 4646.7446 - learning_rate: 0.0010\n", + "Epoch 47/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 45075144.0000 - mae: 4574.6504 - val_loss: 54762232.0000 - val_mae: 4689.1626 - learning_rate: 0.0010\n", + "Epoch 48/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 43724572.0000 - mae: 4516.4062 - val_loss: 53836544.0000 - val_mae: 4628.6943 - learning_rate: 0.0010\n", + "Epoch 49/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 42422604.0000 - mae: 4467.8540 - val_loss: 52762536.0000 - val_mae: 4572.4634 - learning_rate: 0.0010\n", + "Epoch 50/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 42222404.0000 - mae: 4455.9072 - val_loss: 51867440.0000 - val_mae: 4502.6763 - learning_rate: 0.0010\n" + ] + } + ], + "source": [ + "def get_callbacks():\n", + " early_stop = EarlyStopping(\n", + " monitor=\"val_loss\",\n", + " patience=10, # increased from 5 — prevents premature stopping\n", + " restore_best_weights=True\n", + " )\n", + " reduce_lr = ReduceLROnPlateau(\n", + " monitor=\"val_loss\",\n", + " factor=0.5, # halve LR when stuck\n", + " patience=5,\n", + " min_lr=1e-6,\n", + " verbose=1\n", + " )\n", + " return [early_stop, reduce_lr]\n", + "\n", + "# Train all three models\n", + "history_baseline = model_baseline.fit(\n", + " X_train_seq, y_train_seq,\n", + " validation_data=(X_val_seq, y_val_seq),\n", + " epochs=50, batch_size=64,\n", + " callbacks=get_callbacks(), verbose=1\n", + ")\n", + "\n", + "history_stacked = model_stacked.fit(\n", + " X_train_seq, y_train_seq,\n", + " validation_data=(X_val_seq, y_val_seq),\n", + " epochs=50, batch_size=64,\n", + " callbacks=get_callbacks(), verbose=1\n", + ")\n", + "\n", + "history_gru = model_gru.fit(\n", + " X_train_seq, y_train_seq,\n", + " validation_data=(X_val_seq, y_val_seq),\n", + " epochs=50, batch_size=64,\n", + " callbacks=get_callbacks(), verbose=1\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.6 Saving Trained Models\n", + "\n", + "The three trained models are saved to disk immediately after training. This ensures \n", + "that the evaluation results, plots, and metrics produced in subsequent cells are \n", + "fully reproducible on every run without needing to retrain, since loading a saved \n", + "model always produces identical predictions regardless of any residual random seed \n", + "behaviour [58]." + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ model_baseline saved\n", + "✓ model_stacked saved\n", + "✓ model_gru saved\n" + ] + } + ], + "source": [ + "# Save all three trained models to disk\n", + "model_baseline.save(\"model_baseline.keras\")\n", + "model_stacked.save(\"model_stacked.keras\")\n", + "model_gru.save(\"model_gru.keras\")\n", + "\n", + "print(\"✓ model_baseline saved\")\n", + "print(\"✓ model_stacked saved\")\n", + "print(\"✓ model_gru saved\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.7 Loading Saved Models\n", + "\n", + "The saved models are loaded back from disk before evaluation begins. All subsequent \n", + "evaluation, visualisation, and comparison cells use these loaded models, guaranteeing \n", + "that the results in this notebook are consistent and reproducible on every run \n", + "[58]." + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ model_baseline loaded\n", + "✓ model_stacked loaded\n", + "✓ model_gru loaded\n" + ] + } + ], + "source": [ + "# Load all three models from disk\n", + "# This guarantees identical results on every run\n", + "model_baseline = tf.keras.models.load_model(\"model_baseline.keras\")\n", + "model_stacked = tf.keras.models.load_model(\"model_stacked.keras\")\n", + "model_gru = tf.keras.models.load_model(\"model_gru.keras\")\n", + "\n", + "print(\"✓ model_baseline loaded\")\n", + "print(\"✓ model_stacked loaded\")\n", + "print(\"✓ model_gru loaded\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.8 Model Evaluation\n", + "\n", + "Each model is evaluated on the held-out test set using three standard regression \n", + "metrics [59]. The test set represents the final unseen time \n", + "period and was not used at any point during training or validation, ensuring the \n", + "evaluation reflects genuine out-of-sample forecasting performance \n", + "[49].\n", + "\n", + "- **MAE (Mean Absolute Error)**: The average absolute difference between predicted \n", + " and actual pedestrian counts. This is the most directly interpretable metric since \n", + " it is expressed in the same unit as the target variable — pedestrians per hour \n", + " [59].\n", + "- **RMSE (Root Mean Squared Error)**: Similar to MAE but penalises larger errors \n", + " more heavily due to the squaring operation. A substantially higher RMSE relative \n", + " to MAE indicates the presence of occasional large prediction errors \n", + " [59].\n", + "- **R² (Coefficient of Determination)**: Measures the proportion of variance in \n", + " pedestrian counts that the model successfully explains. A value of 1.0 represents \n", + " a perfect fit, while 0.0 means the model performs no better than simply predicting \n", + " the mean value [59]." + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m54/54\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n", + "\n", + "Baseline LSTM\n", + " MAE: 5624.15\n", + " RMSE: 8725.10\n", + " R²: 0.9158\n", + "\u001b[1m54/54\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step\n", + "\n", + "Stacked LSTM\n", + " MAE: 9084.27\n", + " RMSE: 14262.18\n", + " R²: 0.7750\n", + "\u001b[1m54/54\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n", + "\n", + "GRU\n", + " MAE: 4921.98\n", + " RMSE: 7325.04\n", + " R²: 0.9407\n" + ] + } + ], + "source": [ + "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", + "\n", + "def evaluate_model(model, X_test, y_test, name=\"Model\"):\n", + " y_pred = model.predict(X_test).flatten()\n", + " mae = mean_absolute_error(y_test, y_pred)\n", + " rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n", + " r2 = r2_score(y_test, y_pred)\n", + " print(f\"\\n{name}\")\n", + " print(f\" MAE: {mae:.2f}\")\n", + " print(f\" RMSE: {rmse:.2f}\")\n", + " print(f\" R²: {r2:.4f}\")\n", + " return y_pred, {\"MAE\": mae, \"RMSE\": rmse, \"R2\": r2}\n", + "\n", + "pred_baseline, metrics_baseline = evaluate_model(model_baseline, X_test_seq, y_test_seq, \"Baseline LSTM\")\n", + "pred_stacked, metrics_stacked = evaluate_model(model_stacked, X_test_seq, y_test_seq, \"Stacked LSTM\")\n", + "pred_gru, metrics_gru = evaluate_model(model_gru, X_test_seq, y_test_seq, \"GRU\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Evaluation Discussion\n", + "\n", + "The GRU model achieved the best performance across all three metrics, with an MAE of \n", + "4,921.98, RMSE of 7,325.04, and an R² of 0.9407. This means the GRU explains 94.1% \n", + "of the variance in hourly pedestrian demand on completely unseen test data, which \n", + "represents strong predictive performance for a real-world urban climate forecasting \n", + "task [59].\n", + "\n", + "Notably, the Stacked LSTM performed worst of the three models despite having the most \n", + "parameters at 35,777. This outcome highlights an important principle in deep learning \n", + "— increased model complexity does not always lead to better generalisation, \n", + "particularly when the dataset size does not justify the added capacity \n", + "[53] [62]. The Baseline LSTM achieved a \n", + "strong R² of 0.9158, confirming that even a single-layer recurrent model can capture \n", + "the majority of the temporal structure in this dataset \n", + "[54]. The GRU's improvement over the baseline demonstrates that \n", + "architectural efficiency can outperform raw model capacity on medium-sized datasets \n", + "[61] [62]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.9 Training Curves\n", + "\n", + "The training and validation loss curves are plotted for each model to assess how \n", + "learning progressed across epochs and to check for any signs of overfitting \n", + "[53].\n", + "\n", + "A well-behaved training curve shows both the training and validation loss decreasing \n", + "together and converging toward a stable low value. If the validation loss begins to \n", + "rise while training loss continues to fall, this indicates overfitting — meaning the \n", + "model is memorising the training data rather than learning generalisable patterns \n", + "[60]." + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n", + "\n", + "for ax, history, name in zip(\n", + " axes,\n", + " [history_baseline, history_stacked, history_gru],\n", + " [\"Baseline LSTM\", \"Stacked LSTM\", \"GRU\"]\n", + "):\n", + " ax.plot(history.history[\"loss\"], label=\"Train Loss\")\n", + " ax.plot(history.history[\"val_loss\"], label=\"Val Loss\")\n", + " ax.set_title(f\"{name} — Training Curves\")\n", + " ax.set_xlabel(\"Epoch\")\n", + " ax.set_ylabel(\"MSE Loss\")\n", + " ax.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Training Curve Discussion\n", + "\n", + "All three models demonstrate healthy learning behaviour across the 50 training \n", + "epochs. Both the training and validation loss curves decrease consistently and \n", + "converge without significant divergence, indicating that no meaningful overfitting \n", + "occurred in any of the three models [60]. The Dropout \n", + "regularisation layers and the ReduceLROnPlateau callback both contributed to this \n", + "stable training behaviour \n", + "[60] [63].\n", + "\n", + "The Stacked LSTM validation curve is notably observed to plateau earlier and at a \n", + "higher loss value compared to the Baseline LSTM and GRU, which is consistent with \n", + "its weaker test set performance. The GRU and Baseline LSTM both show smooth \n", + "convergence with training and validation loss tracking closely throughout all 50 \n", + "epochs [54] [61]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.10 Actual vs Predicted Visualisation\n", + "\n", + "To assess prediction quality beyond summary metrics, the actual and predicted \n", + "pedestrian counts are plotted across the first 200 hours of the test set. This \n", + "visualisation reveals how well each model captures the shape, magnitude, and timing \n", + "of the daily pedestrian demand cycle, and whether any systematic prediction errors \n", + "are present [3] [59]." + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(3, 1, figsize=(14, 14))\n", + "\n", + "for ax, preds, name in zip(\n", + " axes,\n", + " [pred_baseline, pred_stacked, pred_gru],\n", + " [\"Baseline LSTM\", \"Stacked LSTM\", \"GRU\"]\n", + "):\n", + " ax.plot(y_test_seq[:200], label=\"Actual\", color=\"black\")\n", + " ax.plot(preds[:200], label=\"Predicted\", color=\"steelblue\", alpha=0.8)\n", + " ax.set_title(f\"{name} — Actual vs Predicted (First 200 Hours)\")\n", + " ax.set_xlabel(\"Hour\")\n", + " ax.set_ylabel(\"Pedestrian Count\")\n", + " ax.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Visualisation Discussion\n", + "\n", + "The actual versus predicted plots reveal clear qualitative differences between the \n", + "three models that are consistent with the quantitative evaluation results. The GRU \n", + "most closely follows the actual pedestrian demand curve across the 200-hour window, \n", + "including the sharp daytime peaks during business hours and the near-zero counts \n", + "during the early morning hours \n", + "[61] [62].\n", + "\n", + "The Baseline LSTM captures the general daily rhythm but slightly underestimates the \n", + "magnitude of peak values, producing a smoother curve that misses some of the sharper \n", + "intraday fluctuations [54]. The Stacked LSTM shows the most \n", + "pronounced smoothing effect, with predicted values appearing noticeably flattened at \n", + "daily peaks — capped around 55,000 pedestrians regardless of the actual peak — which \n", + "directly explains its substantially higher RMSE and lower R² relative to the other \n", + "two models [53].\n", + "\n", + "These visual results are fully consistent with the evaluation metrics and further \n", + "support the selection of the GRU as the best performing architecture for this \n", + "pedestrian forecasting task." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.11 Model Comparison Summary\n", + "\n", + "The table below consolidates the test set performance metrics for all three models, \n", + "providing a direct side-by-side comparison to support final model selection \n", + "[59] [57]." + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Model MAE RMSE R²\n", + "Baseline LSTM 5624.145508 8725.096217 0.915807\n", + " Stacked LSTM 9084.274414 14262.183844 0.775039\n", + " GRU 4921.983887 7325.042252 0.940659\n" + ] + } + ], + "source": [ + "comparison = pd.DataFrame({\n", + " \"Model\": [\"Baseline LSTM\", \"Stacked LSTM\", \"GRU\"],\n", + " \"MAE\": [metrics_baseline[\"MAE\"], metrics_stacked[\"MAE\"], metrics_gru[\"MAE\"]],\n", + " \"RMSE\": [metrics_baseline[\"RMSE\"], metrics_stacked[\"RMSE\"], metrics_gru[\"RMSE\"]],\n", + " \"R²\": [metrics_baseline[\"R2\"], metrics_stacked[\"R2\"], metrics_gru[\"R2\"]],\n", + "})\n", + "print(comparison.to_string(index=False))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.12 Conclusion\n", + "\n", + "The deep learning segment compared three recurrent neural network architectures — a \n", + "Baseline LSTM, a Stacked LSTM, and a GRU — for hourly pedestrian count forecasting \n", + "in Melbourne CBD using climate and engineered time-series features.\n", + "\n", + "The GRU model was identified as the best performing architecture, achieving an R² of \n", + "0.9407, MAE of 4,921.98 pedestrians per hour, and RMSE of 7,325.04 across the unseen \n", + "test set [59] [61]. These results \n", + "demonstrate that a well-designed recurrent model, even without excessive architectural \n", + "complexity, can effectively learn the temporal structure of urban pedestrian demand \n", + "from climate and time-series inputs \n", + "[53] [62].\n", + "\n", + "The finding that the Stacked LSTM underperformed despite having the most parameters \n", + "highlights an important consideration in deep learning model selection — that added \n", + "complexity must be justified by the volume and complexity of the data \n", + "[53] [57]. The GRU's parameter efficiency \n", + "and strong generalisation make it the most suitable architecture for this dataset size \n", + "and forecasting task [61].\n", + "\n", + "From an applied perspective, a model explaining 94.1% of the variance in hourly \n", + "pedestrian demand could realistically support urban planning decisions and help \n", + "commuters anticipate pedestrian congestion during varying climate conditions, directly \n", + "addressing the use case scenario outlined at the beginning of this notebook \n", + "[53] [56]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "[1] pandas development team (2026) _pandas - Python Data Analysis Library_, pandas, Online.\n", + "\n", + "[2] pandas development team (2026) _Time series / date functionality_, pandas, Online.\n", + "\n", + "[3] Matplotlib Development Team (Unknown date) _Matplotlib: Visualization with Python_, Matplotlib, Online.\n", + "\n", + "[4] scikit-learn Developers (Unknown date) _Preprocessing data_, scikit-learn, Online.\n", + "\n", + "[5] TensorFlow (2023) _Keras: The high-level API for TensorFlow_, Google, Online.\n", + "\n", + "[6] Guido van Rossum, Barry Warsaw and Alyssa Coghlan (2001) _PEP 8 – Style Guide for Python Code_, Python Enhancement Proposals, Online.\n", + "\n", + "[7] TensorFlow (2024) _tf.keras.utils.set_random_seed_, Google, Online.\n", + "\n", + "[8] Opendatasoft (Unknown date) _Huwise's Explore API Reference Documentation_, Opendatasoft, Online.\n", + "\n", + "[9] City of Melbourne (2023) _Pedestrian Counting System - Sensor Locations_, City of Melbourne Open Data Portal, Online.\n", + "\n", + "[10] City of Melbourne (2023) _Pedestrian Counting System: Counts per Hour_, City of Melbourne Open Data Portal, Online.\n", + "\n", + "[11] City of Melbourne (2023) _Microclimate Sensors Data_, City of Melbourne Open Data Portal, Online.\n", + "\n", + "[12] IBM (Unknown date) _What is data profiling?_, IBM, Online.\n", + "\n", + "[13] pandas development team (2026) _pandas.DataFrame.shape_, pandas, Online.\n", + "\n", + "[14] pandas development team (2026) _pandas.DataFrame.columns_, pandas, Online.\n", + "\n", + "[15] pandas development team (2026) _pandas.DataFrame.dtypes_, pandas, Online.\n", + "\n", + "[16] pandas development team (2026) _pandas.DataFrame.info_, pandas, Online.\n", + "\n", + "[17] pandas development team (2026) _pandas.DataFrame.isna_, pandas, Online.\n", + "\n", + "[18] pandas development team (2026) _pandas.DataFrame.describe_, pandas, Online.\n", + "\n", + "[19] pandas development team (2026) _pandas.to_datetime_, pandas, Online.\n", + "\n", + "[20] pandas development team (2026) _pandas.DataFrame.nunique_, pandas, Online.\n", + "\n", + "[21] IBM (Unknown date) _What is Data Cleaning?_, IBM, Online.\n", + "\n", + "[22] Tableau (Unknown date) _Data Cleaning: Definition, Benefits, and How-To_, Tableau, Online.\n", + "\n", + "[23] Max Kuhn and Kjell Johnson (2026) _Feature Selection_, Applied Machine Learning for Tabular Data, Online.\n", + "\n", + "[24] IBM (Unknown date) _What is Feature Selection?_, IBM, Online.\n", + "\n", + "[25] Youran Zhou, Sunil Aryal and Mohamed Reda Bouadjenek (2024) _A Comprehensive Review of Handling Missing Data: Exploring Special Missing Mechanisms_, arXiv, Online.\n", + "\n", + "[26] Cribl (Unknown date) _What is Data Normalization?_, Cribl, Online.\n", + "\n", + "[27] The Epidemiologist R Handbook Contributors (Unknown date) _Working with dates_, The Epidemiologist R Handbook, Online.\n", + "\n", + "[28] Skforecast Team (Unknown date) _Time series aggregation_, Skforecast, Online.\n", + "\n", + "[29] Microsoft Support (Unknown date) _Join tables and queries_, Microsoft, Online.\n", + "\n", + "[30] IBM (Unknown date) _What is Data Integration?_, IBM, Online.\n", + "\n", + "[31] RudderStack (Unknown date) _What Is Data Validation? Why, When, and How To Use It_, RudderStack, Online.\n", + "\n", + "[32] Google Cloud (Unknown date) _What is Time Series?_, Google, Online.\n", + "\n", + "[33] Ali Suliman AlSalehy and Mike Bailey (2025) _Improving Time Series Data Quality: Identifying Outliers and Handling Missing Values in a Multilocation Gas and Weather Dataset_, MDPI, Basel, Switzerland.\n", + "\n", + "[34] NIST/SEMATECH (Unknown date) _What is EDA?_, NIST/SEMATECH e-Handbook of Statistical Methods, Online.\n", + "\n", + "[35] Rob J. Hyndman and George Athanasopoulos (2018) _Time series patterns_, OTexts, Melbourne, Australia.\n", + "\n", + "[36] NIST/SEMATECH (Unknown date) _Introduction to Time Series Analysis_, NIST/SEMATECH e-Handbook of Statistical Methods, Online.\n", + "\n", + "[37] NIST/SEMATECH (Unknown date) _Seasonality_, NIST/SEMATECH e-Handbook of Statistical Methods, Online.\n", + "\n", + "[38] NIST/SEMATECH (Unknown date) _Histogram_, NIST/SEMATECH e-Handbook of Statistical Methods, Online.\n", + "\n", + "[39] Penn State Eberly College of Science (Unknown date) _3.4.2 - Correlation_, The Pennsylvania State University, Online.\n", + "\n", + "[40] Jim Frost (Unknown date) _Using Moving Averages to Smooth Time Series Data_, Statistics By Jim, Online.\n", + "\n", + "[41] Josef Waples and Laiba Siddiqui (Unknown date) _Time Series Decomposition: Trends, Seasonality, and Noise_, DataCamp, Online.\n", + "\n", + "[42] Penn State Eberly College of Science (Unknown date) _10.2 - Autocorrelation and Time Series Methods_, The Pennsylvania State University, Online.\n", + "\n", + "[43] Minitab (Unknown date) _Interpret all statistics and graphs for Augmented Dickey-Fuller Test_, Minitab Support, Online.\n", + "\n", + "[44] IBM (Unknown date) _What is Feature Engineering?_, IBM, Online.\n", + "\n", + "[45] dotData (Unknown date) _Practical Guide for Feature Engineering of Time Series Data_, dotData, Online.\n", + "\n", + "[46] Skforecast Team (Unknown date) _Cyclical features in time series_, Skforecast, Online.\n", + "\n", + "[47] Feature-engine Developers (Unknown date) _Forecasting Features_, Feature-engine, Online.\n", + "\n", + "[48] Feature-engine Developers (Unknown date) _LagFeatures_, Feature-engine, Online.\n", + "\n", + "[49] ApX Machine Learning (2026) _Train-Test Split for Time Series_, ApX Machine Learning, Online.\n", + "\n", + "[50] Pragati Baheti (2021) _Train Test Validation Split: How To and Best Practices_, V7 Labs, Online.\n", + "\n", + "[51] IBM (Unknown date) _What is Supervised Learning?_, IBM, Online.\n", + "\n", + "[52] ApX Machine Learning (2026) _Feature Scaling: Normalization and Standardization_, ApX Machine Learning, Online.\n", + "\n", + "[53] IBM (Unknown date) _What is Deep Learning?_, IBM, Online.\n", + "\n", + "[54] Christopher Olah (2015) _Understanding LSTM Networks_, Colah's Blog, Online.\n", + "\n", + "[55] TensorFlow (2024) _tf.keras.utils.set_random_seed_, Google, Online.\n", + "\n", + "[56] TensorFlow (2023) _Time series forecasting_, TensorFlow, Online.\n", + "\n", + "[57] ApX Machine Learning (2026) _Model Selection and Evaluation_, ApX Machine Learning, Online.\n", + "\n", + "[58] TensorFlow (2023) _tf.keras.Model.fit_, TensorFlow, Online.\n", + "\n", + "[59] scikit-learn Developers (Unknown date) _Regression metrics_, scikit-learn, Online.\n", + "\n", + "[60] Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever and Ruslan Salakhutdinov (2014) _Dropout: A Simple Way to Prevent Neural Networks from Overfitting_, Journal of Machine Learning Research, Online.\n", + "\n", + "[61] Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk and Yoshua Bengio (2014) _Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation_, arXiv,Online.\n", + "\n", + "[62] Junyoung Chung, Caglar Gulcehre, KyungHyun Cho and Yoshua Bengio (2014) _Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling_, arXiv, Online.\n", + "\n", + "[63] TensorFlow (2023) _tf.keras.callbacks.EarlyStopping_, TensorFlow, Online." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.12.13" + }, + "vscode": { + "interpreter": { + "hash": "369f2c481f4da34e4445cda3fffd2e751bd1c4d706f27375911949ba6bb62e1c" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.json b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.json new file mode 100644 index 0000000000..45af0af6d6 --- /dev/null +++ b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.json @@ -0,0 +1,66 @@ +{ + "title": "Urban_Pedestrian_Climate_Impact_Prediction", + "name": "Predicting Pedestrian Activity in Melbourne CBD Based on Climate Conditions and Time Series Data", + "description": "Predicting pedestrian activity in the City of Melbourne using hourly climate observations. This use case involves sourcing and combining multiple public datasets, cleaning and aligning hourly time-series data, exploring how climate variables relate to pedestrian counts, applying feature engineering, building a deep learning forecasting model, performing model optimisation, and evaluating model performance for climate adaptation planning.", + "tags": [ + "Urban Pedestrian Movement", + "Climate Conditions", + "Pedestrian Counts", + "Microclimate Observations", + "Time-Series Analysis", + "Feature Engineering", + "Deep Learning Forecasting", + "Model Optimisation", + "Data Cleaning", + "Data Validation", + "Data Visualisation" + ], + "difficulty": "Intermediate", + "technology": [ + { + "name": "numpy", + "alias": "np", + "code": "python" + }, + { + "name": "pandas", + "alias": "pd", + "code": "python" + }, + { + "name": "matplotlib", + "alias": "plt", + "code": "python" + }, + { + "name": "statsmodels", + "code": "python" + }, + { + "name": "sklearn", + "code": "python" + }, + { + "name": "os", + "code": "python" + }, + { + "name": "random", + "code": "python" + }, + { + "name": "tensorflow", + "alias": "tf", + "code": "python" + }, + { + "name": "tensorflow.keras", + "code": "python" + } + ], + "datasets": [ + "Pedestrian Counting System (counts per hour)", + "Pedestrian Counting System - Sensor Locations", + "Microclimate Sensor Readings" + ] +} From db7571bc7267083075936ab08de0fd123890b771 Mon Sep 17 00:00:00 2001 From: Thivainv Date: Sun, 17 May 2026 20:16:30 +1000 Subject: [PATCH 12/23] Add files via upload From b59ec2f1d831918c3ca463e928c757f989ffeb8c Mon Sep 17 00:00:00 2001 From: Thivainv Date: Sun, 17 May 2026 20:18:58 +1000 Subject: [PATCH 13/23] Delete datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/test.txt --- .../READY TO PUBLISH/Transport_and_Mobility/2026/test.txt | 1 - 1 file changed, 1 deletion(-) delete mode 100644 datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/test.txt diff --git a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/test.txt b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/test.txt deleted file mode 100644 index 8b13789179..0000000000 --- a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/test.txt +++ /dev/null @@ -1 +0,0 @@ - From 966e23dec0682d89ff393dd6a0ca8213abef4cf4 Mon Sep 17 00:00:00 2001 From: Thivainv Date: Sun, 17 May 2026 20:19:09 +1000 Subject: [PATCH 14/23] Delete datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/test.txt --- .../READY TO PUBLISH/Transport_and_Mobility/2026/T1/test.txt | 1 - 1 file changed, 1 deletion(-) delete mode 100644 datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/test.txt diff --git a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/test.txt b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/test.txt deleted file mode 100644 index 8b13789179..0000000000 --- a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/test.txt +++ /dev/null @@ -1 +0,0 @@ - From 21b5ab1813b0c1b7f2cfb53cc8f7c5323333b5df Mon Sep 17 00:00:00 2001 From: Thivainv Date: Sun, 17 May 2026 20:19:21 +1000 Subject: [PATCH 15/23] Delete datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt --- .../UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt | 1 - 1 file changed, 1 deletion(-) delete mode 100644 datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt diff --git a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt deleted file mode 100644 index 8b13789179..0000000000 --- a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt +++ /dev/null @@ -1 +0,0 @@ - From e4e953e25d32e776a8da9259351b1d3dadbcafc0 Mon Sep 17 00:00:00 2001 From: Thivainv Date: Sun, 17 May 2026 20:25:04 +1000 Subject: [PATCH 16/23] Delete datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction directory --- ..._Pedestrian_Climate_Impact_Prediction.html | 12507 ---------------- ...Pedestrian_Climate_Impact_Prediction.ipynb | 4553 ------ ..._Pedestrian_Climate_Impact_Prediction.json | 66 - 3 files changed, 17126 deletions(-) delete mode 100644 datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.html delete mode 100644 datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.ipynb delete mode 100644 datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.json diff --git a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.html b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.html deleted file mode 100644 index 48dcd0e6f1..0000000000 --- a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.html +++ /dev/null @@ -1,12507 +0,0 @@ - - - - - -UC00213_Urban_Pedestrian_Climate_Impact_Prediction - - - - - - - - - - - - -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - diff --git a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.ipynb b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.ipynb deleted file mode 100644 index 0a97449dd4..0000000000 --- a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.ipynb +++ /dev/null @@ -1,4553 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
Urban Pedestrian Climate Impact Prediction
\n", - "\n", - "
Authored by: Maverick Nguyen
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
Duration: 90 mins
\n", - "\n", - "
\n", - "
Level: Intermediate
\n", - "
Pre-requisite Skills: Python, Data Cleaning, Data Validation, Data Visualisation, Time-Series Analysis, Feature Engineering, Optimisation Methods, Deep Learning
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
Scenario
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As a local living near Melbourne CBD, Maverick relies on active travel, like walking, and public transport, like trams, to get around to different places he wants to go. One morning in January, Maverick prepared to travel to his workplace, expecting to get to work before 9:00 AM, but there was a sudden heatwave, causing the tram that he usually catches to be unable to follow its designated schedule and creating a delay in his schedule. Although there was a replacement bus for this emergency, only a limited number of people could board this vehicle, which further delayed his schedule. Because of this sudden extreme weather, travel conditions become less reliable and difficult to predict.\n", - "\n", - "Maverick wants to have access to a system that could predict how climate conditions over time can affect urban pedestrian movement. So that he could better plan his trip, allowing him to leave earlier in anticipation of sudden extreme weather change during a particular timeframe, or choose a different mode of transport, like an Uber. This allows more support in making informed decisions when travelling during extreme weather events." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
What this use case will teach you
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "At the end of this use case, you will:\n", - "- Learn how to source and combine multiple public datasets. \n", - "- Understand how to clean and align time-series data at an hourly level for modelling. \n", - "- Explore how climate variables, such as temperature, humidity, pressure, and wind, relate to pedestrian counts.\n", - "- Apply feature engineering techniques to create meaningful predictors from weather and mobility time-series data. \n", - "- Build a deep learning forecasting model to predict pedestrian demand. \n", - "- Perform model optimisation like hyperparameter tuning to improve forecasting performance. \n", - "- Evaluate model performance and interpret results for climate adaptation planning." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
Introduction
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Urban systems are often affected by changing climate conditions, but these effects are not always easy to capture with simple forecasting methods. One clear example is pedestrian movement, where changing weather conditions can affect how many people move through the city over time.\n", - "\n", - "This use case focuses on predicting pedestrian activity in the City of Melbourne using hourly climate observations, which keeps the project closely aligned with the goal of modelling how climate factors influence an urban system.\n", - "\n", - "In this use case, pedestrian counts are aggregated into hourly city-level totals and merged with hourly microclimate observations for Melbourne. A deep learning model can then be trained to predict pedestrian demand based on time, recent demand history, and recent climate conditions.\n", - "\n", - "The datasets used in this project are the \"Pedestrian Counting System (counts per hour)\", the \"Pedestrian Counting System - Sensor Locations\" dataset for supporting location metadata, and the \"Microclimate Sensor Readings\" dataset from the City of Melbourne website." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Importing The Libraries\n", - "\n", - "This section is to show what libraries were used for this use case, with each imported library supporting a specific part of the pipeline. These libraries are necessary for doing data handling, time-series analysis, visualisation, feature engineering, optimisation, and deep learning [1] [2] [3] [4] [5]. These were added at the beginning to ensure the workflow is organised [6]. A random seed was set with a student ID to allow reproducibility of the outputs in this notebook [7].\n", - "\n", - "The following libraries support the deep learning pipeline for this section. TensorFlow and Keras are used for building and training the recurrent neural network models [3]. The os and random libraries are imported to support full reproducibility across all system-level random operations [55]." - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "# Libraries for this project.\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.lines import Line2D\n", - "from matplotlib.patches import Patch\n", - "from statsmodels.graphics.tsaplots import plot_acf\n", - "from statsmodels.tsa.seasonal import seasonal_decompose\n", - "from statsmodels.tsa.stattools import adfuller\n", - "from sklearn.preprocessing import StandardScaler\n", - "import os\n", - "import random\n", - "\n", - "# Deep Learning\n", - "import tensorflow as tf\n", - "from tensorflow.keras.models import Sequential\n", - "from tensorflow.keras.layers import LSTM, GRU, Dense, Dropout\n", - "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Importing The Datasets\n", - "\n", - "This section is necessary for importing multiple public datasets from the City of Melbourne, before any cleaning, merging and modelling in later stages can happen. These datasets will be accessed through the City of Melbourne Open Data API v2.1, to allow the notebook to be directly used upon download [8].\n", - "\n", - "By using a shared BASE_URL and a dictionary of dataset identifiers, this allows for removing and adding datasets more easily [8] [9] [10] [11]. The get_csv_url() function is especially useful because it standardises the dataset access method and allows the same logic to be reused across all three datasets [8]. The ROW_LIMIT parameter was added to allow experimentation on smaller samples before scaling to the full dataset [8]. Lastly, the datasets are accessed through the Melbourne Open Data API v2.1, and no visible API key is exposed in the code [8]." - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "# Store the API base path accessed via API v2.1.\n", - "BASE_URL = (\n", - " \"https://data.melbourne.vic.gov.au\"\n", - " \"/api/explore/v2.1/catalog/datasets\"\n", - ")\n", - "\n", - "# Store the dataset identifiers.\n", - "DATASETS = {\n", - " \"sensor_locations\":\n", - " \"pedestrian-counting-system-sensor-locations\",\n", - " \"pedestrian_counts\":\n", - " \"pedestrian-counting-system-monthly-counts-per-hour\",\n", - " \"microclimate\":\n", - " \"microclimate-sensors-data\",\n", - "}\n", - "\n", - "# Set the number of rows to retrieve for experiments.\n", - "# Change to \"None\" when full datasets are needed.\n", - "# Change to an integer for experimental purposes.\n", - "ROW_LIMIT = None \n", - "\n", - "def get_csv_url(dataset_id, row_limit=None):\n", - " # Build the base CSV export URL.\n", - " url = (\n", - " f\"{BASE_URL}/{dataset_id}/exports/csv\"\n", - " f\"?delimiter=,&with_bom=true\"\n", - " )\n", - "\n", - " # Add a row limit if one is provided.\n", - " if row_limit is not None:\n", - " url += f\"&limit={row_limit}\"\n", - "\n", - " return url" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.1 Pedestrian Counting System - Sensor Locations\n", - "\n", - "This dataset was included to provide contextual information about the physical pedestrian counting network, even though it wasn't used in the later steps for modelling. The sensor metadata helps explain where the mobility data originates from and what the coverage of the system looks like [9].\n", - "\n", - "* `location_id`: unique identifier for each pedestrian counting location.\n", - "* `sensor_description`: human-readable description of the site, like street names.\n", - "* `sensor_name`: short internal sensor code.\n", - "* `installation_date`: date the sensor was installed.\n", - "* `note`: extra comments or metadata about the sensor.\n", - "* `location_type`: type of location.\n", - "* `status`: operational status of the sensor.\n", - "* `direction_1` and `direction_2`: the two movement directions captured by the counter.\n", - "* `latitude` and `longitude`: geographic coordinates of the sensor.\n", - "* `location`: combined coordinate string." - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " location_id sensor_description sensor_name installation_date \\\n", - "0 3 Melbourne Central Swa295_T 2009-03-25 \n", - "1 5 Princes Bridge PriNW_T 2009-03-26 \n", - "2 9 Southern Cross Station Col700_T 2009-03-23 \n", - "3 12 New Quay NewQ_T 2009-01-21 \n", - "4 14 Sandridge Bridge SanBri_T 2009-03-24 \n", - "\n", - " note location_type status \\\n", - "0 NaN Outdoor A \n", - "1 Replace with: 00:6e:02:01:9e:54 Outdoor A \n", - "2 NaN Outdoor A \n", - "3 NaN Outdoor A \n", - "4 Sensor relocated to sensor ID 25 on 2/10/2019 Outdoor A \n", - "\n", - " direction_1 direction_2 latitude longitude location \n", - "0 North South -37.811015 144.964295 -37.81101524, 144.96429485 \n", - "1 North South -37.818742 144.967877 -37.81874249, 144.96787656 \n", - "2 East West -37.819830 144.951026 -37.81982992, 144.95102555 \n", - "3 East West -37.814580 144.942924 -37.81457988, 144.94292398 \n", - "4 North South -37.820112 144.962919 -37.82011242, 144.96291897 \n" - ] - } - ], - "source": [ - "# Load the sensor locations dataset.\n", - "sensor_locations_df = pd.read_csv(\n", - " get_csv_url(\n", - " DATASETS[\"sensor_locations\"],\n", - " row_limit=ROW_LIMIT\n", - " ),\n", - " encoding=\"utf-8-sig\"\n", - ")\n", - "\n", - "# Preview the dataframe.\n", - "print(sensor_locations_df.head())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2 Pedestrian Counting System (counts per hour)\n", - "\n", - "This is the target dataset for the use case because the final prediction task is to forecast pedestrian demand. The pedestrian dataset contains the observed mobility outcome that the model aims to learn [10].\n", - "\n", - "* `id`: record identifier.\n", - "* `location_id`: identifier linking the observation to a specific sensor location.\n", - "* `sensing_date`: date of observation.\n", - "* `hourday`: hour of day from 0 to 23.\n", - "* `direction_1` and `direction_2`: directional pedestrian counts.\n", - "* `pedestriancount`: total pedestrian count for that record.\n", - "* `sensor_name`: short sensor code.\n", - "* `location`: coordinate string for the sensor. " - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " id location_id sensing_date hourday direction_1 direction_2 \\\n", - "0 70220251008 70 2025-10-08 2 1 0 \n", - "1 31220260507 3 2026-05-07 12 872 1170 \n", - "2 591520241018 59 2024-10-18 15 430 628 \n", - "3 141020260106 14 2026-01-06 10 240 75 \n", - "4 28820241203 28 2024-12-03 8 537 262 \n", - "\n", - " pedestriancount sensor_name location \n", - "0 1 Errol20_T -37.80456984, 144.94946228 \n", - "1 2042 Swa295_T -37.81101524, 144.96429485 \n", - "2 1058 RMIT_T -37.80825648, 144.96304859 \n", - "3 315 SanBri_T -37.82011242, 144.96291897 \n", - "4 799 VAC_T -37.82129925, 144.96879309 \n" - ] - } - ], - "source": [ - "# Load the pedestrian counts dataset.\n", - "pedestrian_counts_df = pd.read_csv(\n", - " get_csv_url(\n", - " DATASETS[\"pedestrian_counts\"],\n", - " row_limit=ROW_LIMIT\n", - " ),\n", - " encoding=\"utf-8-sig\"\n", - ")\n", - "\n", - "# Preview the dataframe.\n", - "print(pedestrian_counts_df.head())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.3 Microclimate sensors data\n", - "\n", - "This dataset provides the microclimate data that the use case requires to understand how climate conditions affect urban pedestrian movement, more specifically, the input features. It basically provides the explanatory environmental variables to connect weather conditions to mobility demand [11].\n", - "\n", - "* `device_id`: identifier for each microclimate device.\n", - "* `received_at`: timestamp when the reading was recorded.\n", - "* `sensorlocation`: descriptive location of the microclimate sensor.\n", - "* `latlong`: coordinate string.\n", - "* `minimumwinddirection`, `averagewinddirection`, `maximumwinddirection`: wind direction measurements.\n", - "* `minimumwindspeed`, `averagewindspeed`, `gustwindspeed`: wind speed measurements.\n", - "* `airtemperature`: air temperature reading.\n", - "* `relativehumidity`: humidity level.\n", - "* `atmosphericpressure`: atmospheric pressure.\n", - "* `pm25` and `pm10`: particulate matter measurements.\n", - "* `noise`: noise level. " - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " device_id received_at \\\n", - "0 ICTMicroclimate-08 2026-04-28T15:25:15+00:00 \n", - "1 ICTMicroclimate-08 2026-04-28T04:38:50+00:00 \n", - "2 ICTMicroclimate-08 2026-04-28T05:08:52+00:00 \n", - "3 ICTMicroclimate-10 2026-04-28T10:19:41+00:00 \n", - "4 ICTMicroclimate-08 2026-04-28T13:55:03+00:00 \n", - "\n", - " sensorlocation \\\n", - "0 Swanston St - Tram Stop 13 adjacent Federation... \n", - "1 Swanston St - Tram Stop 13 adjacent Federation... \n", - "2 Swanston St - Tram Stop 13 adjacent Federation... \n", - "3 1 Treasury Place \n", - "4 Swanston St - Tram Stop 13 adjacent Federation... \n", - "\n", - " latlong minimumwinddirection averagewinddirection \\\n", - "0 -37.8184515, 144.9678474 0.0 220.0 \n", - "1 -37.8184515, 144.9678474 0.0 283.0 \n", - "2 -37.8184515, 144.9678474 0.0 310.0 \n", - "3 -37.8128595, 144.9745395 277.0 324.0 \n", - "4 -37.8184515, 144.9678474 0.0 281.0 \n", - "\n", - " maximumwinddirection minimumwindspeed averagewindspeed gustwindspeed \\\n", - "0 314.0 0.0 0.5 2.5 \n", - "1 353.0 0.0 0.9 2.6 \n", - "2 314.0 0.0 0.2 1.9 \n", - "3 352.0 0.3 0.8 1.3 \n", - "4 354.0 0.0 0.3 2.5 \n", - "\n", - " airtemperature relativehumidity atmosphericpressure pm25 pm10 noise \n", - "0 16.7 79.9 1026.1 10.0 12.0 58.7 \n", - "1 21.9 61.5 1021.5 27.0 32.0 75.6 \n", - "2 21.6 61.1 1021.3 26.0 29.0 69.2 \n", - "3 19.7 57.4 1018.4 39.0 49.0 61.9 \n", - "4 17.3 76.2 1025.5 10.0 12.0 79.4 \n" - ] - } - ], - "source": [ - "# Load the microclimate dataset.\n", - "microclimate_df = pd.read_csv(\n", - " get_csv_url(\n", - " DATASETS[\"microclimate\"],\n", - " row_limit=ROW_LIMIT\n", - " ),\n", - " encoding=\"utf-8-sig\"\n", - ")\n", - "\n", - "# Preview the dataframe.\n", - "print(microclimate_df.head())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Initial Inspection Of The Datasets\n", - "\n", - "This section is necessary to understand the size, structure, completeness and formatting of public datasets since they often differ. Public datasets can have different structures, missing values, data types, date formats, and identifier systems, so checking them early helps identify potential issues in the workflow [12]. Before trying to clean and merge the datasets, understanding what each of the datasets contains and how they can be combined is important [12]." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.1 Checking Number Of Rows/Columns\n", - "\n", - "Checking the shape is important because it shows the scale and complexity of each dataset. This helps determine memory demands, cleaning strategy, and whether the data volume is sufficient for later modelling [13].\n", - "\n", - "From these results, the pedestrian and microclimate datasets are large enough for later time-series modelling. The sensor locations dataset is much smaller because it only contains metadata about the sensor network, rather than repeated hourly observations. The volume for each task varies, but from the inspection of the shapes, there appears to be sufficient volume for the task of predicting the pedestrian count." - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(145, 12)\n", - "(1540629, 9)\n", - "(897085, 16)\n" - ] - } - ], - "source": [ - "# Preview of the number of columns and rows.\n", - "print(sensor_locations_df.shape)\n", - "print(pedestrian_counts_df.shape)\n", - "print(microclimate_df.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.2 Checking The Features\n", - "\n", - "This step is to verify what information is actually available in each dataset, and whether there are meaningful fields for later joining, cleaning, and modelling. Knowing the columns early prevents accidentally removing important variables or keeping irrelevant variables [14].\n", - "\n", - "From the output, the column names confirm that location_id is shared between the sensor metadata and pedestrian counts datasets. Also, the pedestrian and microclimate datasets both contain time information, which is essential because the final merge is ultimately done at the hourly level. The microclimate dataset clearly offers a diverse range of explanatory variables, which is useful for modelling. And, some columns that are likely less useful for the final model need to be removed, such as descriptive notes, raw coordinate strings, and duplicate directional fields." - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sensor locations columns:\n", - "Index(['location_id', 'sensor_description', 'sensor_name', 'installation_date',\n", - " 'note', 'location_type', 'status', 'direction_1', 'direction_2',\n", - " 'latitude', 'longitude', 'location'],\n", - " dtype='object')\n", - "\n", - "Pedestrian counts columns:\n", - "Index(['id', 'location_id', 'sensing_date', 'hourday', 'direction_1',\n", - " 'direction_2', 'pedestriancount', 'sensor_name', 'location'],\n", - " dtype='object')\n", - "\n", - "Microclimate columns:\n", - "Index(['device_id', 'received_at', 'sensorlocation', 'latlong',\n", - " 'minimumwinddirection', 'averagewinddirection', 'maximumwinddirection',\n", - " 'minimumwindspeed', 'averagewindspeed', 'gustwindspeed',\n", - " 'airtemperature', 'relativehumidity', 'atmosphericpressure', 'pm25',\n", - " 'pm10', 'noise'],\n", - " dtype='object')\n" - ] - } - ], - "source": [ - "# Check the column names for each dataframe.\n", - "print(\"Sensor locations columns:\")\n", - "print(sensor_locations_df.columns)\n", - "\n", - "print(\"\\nPedestrian counts columns:\")\n", - "print(pedestrian_counts_df.columns)\n", - "\n", - "print(\"\\nMicroclimate columns:\")\n", - "print(microclimate_df.columns)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.3 Checking The Datatypes\n", - "\n", - "Datatype checking is essential because many later operations depend on correct types to proceed. Steps like date parsing, numeric aggregation, interpolation, rolling windows, and model preparation can all fail or behave incorrectly if types are wrong [15].\n", - "\n", - "From the outputs, the pedestrian sensing_date, sensor installation_date, and microclimate received_at fields are all initially stored as objects, so they are not yet ready for time-series operations, which will need to be dealt with. And the numeric climate variables are already in float64, and pedestrian counts are in int64, which is appropriate for aggregation and modelling, so no need to change that." - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sensor locations data types:\n", - "location_id int64\n", - "sensor_description object\n", - "sensor_name object\n", - "installation_date object\n", - "note object\n", - "location_type object\n", - "status object\n", - "direction_1 object\n", - "direction_2 object\n", - "latitude float64\n", - "longitude float64\n", - "location object\n", - "dtype: object\n", - "\n", - "Pedestrian counts data types:\n", - "id int64\n", - "location_id int64\n", - "sensing_date object\n", - "hourday int64\n", - "direction_1 int64\n", - "direction_2 int64\n", - "pedestriancount int64\n", - "sensor_name object\n", - "location object\n", - "dtype: object\n", - "\n", - "Microclimate data types:\n", - "device_id object\n", - "received_at object\n", - "sensorlocation object\n", - "latlong object\n", - "minimumwinddirection float64\n", - "averagewinddirection float64\n", - "maximumwinddirection float64\n", - "minimumwindspeed float64\n", - "averagewindspeed float64\n", - "gustwindspeed float64\n", - "airtemperature float64\n", - "relativehumidity float64\n", - "atmosphericpressure float64\n", - "pm25 float64\n", - "pm10 float64\n", - "noise float64\n", - "dtype: object\n" - ] - } - ], - "source": [ - "# Check the data types for each dataframe.\n", - "print(\"Sensor locations data types:\")\n", - "print(sensor_locations_df.dtypes)\n", - "\n", - "print(\"\\nPedestrian counts data types:\")\n", - "print(pedestrian_counts_df.dtypes)\n", - "\n", - "print(\"\\nMicroclimate data types:\")\n", - "print(microclimate_df.dtypes)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.4 Checking The Dataset Information\n", - "\n", - "Using .info() gives a more complete structural summary of the datasets, which shows non-null counts, memory usage, and dtype balance. But this step will focus more on the memory that will be used up in the RAM, to decide what sample size would be ideal for experimentation before scaling to the full dataset size, or opt for a platform like Google Colab to handle more heavy usage [16].\n", - "\n", - "From the outputs, the pedestrian dataset takes up the most memory, followed by the microclimate dataset, with the sensor locations dataset being extremely low. Which is acceptable to run locally for modelling." - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sensor locations info:\n", - "\n", - "RangeIndex: 145 entries, 0 to 144\n", - "Data columns (total 12 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 location_id 145 non-null int64 \n", - " 1 sensor_description 145 non-null object \n", - " 2 sensor_name 145 non-null object \n", - " 3 installation_date 145 non-null object \n", - " 4 note 34 non-null object \n", - " 5 location_type 145 non-null object \n", - " 6 status 145 non-null object \n", - " 7 direction_1 113 non-null object \n", - " 8 direction_2 113 non-null object \n", - " 9 latitude 145 non-null float64\n", - " 10 longitude 145 non-null float64\n", - " 11 location 145 non-null object \n", - "dtypes: float64(2), int64(1), object(9)\n", - "memory usage: 13.7+ KB\n", - "None\n", - "\n", - "Pedestrian counts info:\n", - "\n", - "RangeIndex: 1540629 entries, 0 to 1540628\n", - "Data columns (total 9 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 id 1540629 non-null int64 \n", - " 1 location_id 1540629 non-null int64 \n", - " 2 sensing_date 1540629 non-null object\n", - " 3 hourday 1540629 non-null int64 \n", - " 4 direction_1 1540629 non-null int64 \n", - " 5 direction_2 1540629 non-null int64 \n", - " 6 pedestriancount 1540629 non-null int64 \n", - " 7 sensor_name 1540629 non-null object\n", - " 8 location 1540629 non-null object\n", - "dtypes: int64(6), object(3)\n", - "memory usage: 105.8+ MB\n", - "None\n", - "\n", - "Microclimate info:\n", - "\n", - "RangeIndex: 897085 entries, 0 to 897084\n", - "Data columns (total 16 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 device_id 897085 non-null object \n", - " 1 received_at 897085 non-null object \n", - " 2 sensorlocation 897085 non-null object \n", - " 3 latlong 897085 non-null object \n", - " 4 minimumwinddirection 763016 non-null float64\n", - " 5 averagewinddirection 881374 non-null float64\n", - " 6 maximumwinddirection 763006 non-null float64\n", - " 7 minimumwindspeed 763006 non-null float64\n", - " 8 averagewindspeed 881372 non-null float64\n", - " 9 gustwindspeed 763006 non-null float64\n", - " 10 airtemperature 881372 non-null float64\n", - " 11 relativehumidity 881372 non-null float64\n", - " 12 atmosphericpressure 881372 non-null float64\n", - " 13 pm25 823822 non-null float64\n", - " 14 pm10 823822 non-null float64\n", - " 15 noise 823822 non-null float64\n", - "dtypes: float64(12), object(4)\n", - "memory usage: 109.5+ MB\n", - "None\n" - ] - } - ], - "source": [ - "# Check the overall structure of each dataframe.\n", - "print(\"Sensor locations info:\")\n", - "print(sensor_locations_df.info())\n", - "\n", - "print(\"\\nPedestrian counts info:\")\n", - "print(pedestrian_counts_df.info())\n", - "\n", - "print(\"\\nMicroclimate info:\")\n", - "print(microclimate_df.info())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.5 Checking For Missing Values\n", - "\n", - "Checking for missing values is important, since it affects the cleaning strategy, feature selection, and merge quality. This is especially important because missing sensor readings can break hourly continuity for the modelling later [17].\n", - "\n", - "From the outputs, the pedestrian counts dataset has no missing values at all. The microclimate dataset has many missing values in all the variables besides device_id and received_at. And the sensor locations dataset has some missing values, mainly in note, direction_1, and direction_2, which are not necessary for city-level forecasting." - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Missing values in sensor locations:\n", - "location_id 0\n", - "sensor_description 0\n", - "sensor_name 0\n", - "installation_date 0\n", - "note 111\n", - "location_type 0\n", - "status 0\n", - "direction_1 32\n", - "direction_2 32\n", - "latitude 0\n", - "longitude 0\n", - "location 0\n", - "dtype: int64\n", - "\n", - "Missing values in pedestrian counts:\n", - "id 0\n", - "location_id 0\n", - "sensing_date 0\n", - "hourday 0\n", - "direction_1 0\n", - "direction_2 0\n", - "pedestriancount 0\n", - "sensor_name 0\n", - "location 0\n", - "dtype: int64\n", - "\n", - "Missing values in microclimate:\n", - "device_id 0\n", - "received_at 0\n", - "sensorlocation 0\n", - "latlong 0\n", - "minimumwinddirection 134069\n", - "averagewinddirection 15711\n", - "maximumwinddirection 134079\n", - "minimumwindspeed 134079\n", - "averagewindspeed 15713\n", - "gustwindspeed 134079\n", - "airtemperature 15713\n", - "relativehumidity 15713\n", - "atmosphericpressure 15713\n", - "pm25 73263\n", - "pm10 73263\n", - "noise 73263\n", - "dtype: int64\n" - ] - } - ], - "source": [ - "# Count the missing values in each dataframe.\n", - "print(\"Missing values in sensor locations:\")\n", - "print(sensor_locations_df.isna().sum())\n", - "\n", - "print(\"\\nMissing values in pedestrian counts:\")\n", - "print(pedestrian_counts_df.isna().sum())\n", - "\n", - "print(\"\\nMissing values in microclimate:\")\n", - "print(microclimate_df.isna().sum())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.6 Checking Summary Statistics\n", - "\n", - "Checking the summary statistics is an easy way to provide some early insights into the datasets, like central tendency and spread [18].\n", - "\n", - "From the sensor coordinates with the mean latitude and longitude, the Melbourne CBD can be inferred to be the main area of focus for the datasets. The pedestrian counts are strongly right-skewed, with the median count being a fair bit lower than the mean, and the maximum being really high, which suggests that some hours and sites are much busier than others. The average microclimate temperature is about 16°C, and the average relative humidity is about 66%, which looks plausible for Melbourne across a long time range." - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sensor locations summary:\n", - " location_id latitude longitude\n", - "count 145.000000 145.000000 145.000000\n", - "mean 93.075862 -37.812392 144.960427\n", - "std 55.229079 0.006999 0.009594\n", - "min 1.000000 -37.825910 144.928606\n", - "25% 47.000000 -37.816888 144.956447\n", - "50% 89.000000 -37.813625 144.961860\n", - "75% 142.000000 -37.807767 144.965626\n", - "max 188.000000 -37.789353 144.986388\n", - "\n", - "Pedestrian counts summary:\n", - " id location_id hourday direction_1 direction_2 \\\n", - "count 1.540629e+06 1.540629e+06 1.540629e+06 1.540629e+06 1.540629e+06 \n", - "mean 4.683120e+11 7.196742e+01 1.175280e+01 1.981603e+02 2.008987e+02 \n", - "std 5.137983e+11 5.119167e+01 6.796006e+00 3.107706e+02 3.148589e+02 \n", - "min 1.020241e+09 1.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n", - "25% 6.292025e+10 3.000000e+01 6.000000e+00 1.800000e+01 1.900000e+01 \n", - "50% 2.111203e+11 6.100000e+01 1.200000e+01 7.900000e+01 7.800000e+01 \n", - "75% 7.116202e+11 1.170000e+02 1.800000e+01 2.360000e+02 2.410000e+02 \n", - "max 1.852320e+12 1.850000e+02 2.300000e+01 1.009900e+04 1.108500e+04 \n", - "\n", - " pedestriancount \n", - "count 1.540629e+06 \n", - "mean 3.990590e+02 \n", - "std 5.953562e+02 \n", - "min 0.000000e+00 \n", - "25% 3.900000e+01 \n", - "50% 1.620000e+02 \n", - "75% 4.930000e+02 \n", - "max 1.111300e+04 \n", - "\n", - "Microclimate summary:\n", - " minimumwinddirection averagewinddirection maximumwinddirection \\\n", - "count 763016.000000 881374.000000 763006.000000 \n", - "mean 23.908174 166.489727 295.974746 \n", - "std 61.529961 125.699058 97.279991 \n", - "min 0.000000 0.000000 0.000000 \n", - "25% 0.000000 42.000000 253.000000 \n", - "50% 0.000000 161.000000 351.000000 \n", - "75% 0.000000 299.000000 357.000000 \n", - "max 359.000000 359.000000 360.000000 \n", - "\n", - " minimumwindspeed averagewindspeed gustwindspeed airtemperature \\\n", - "count 763006.000000 881372.000000 763006.000000 881372.000000 \n", - "mean 0.231898 1.007714 3.179780 16.689150 \n", - "std 0.574129 0.942189 2.504463 5.581520 \n", - "min 0.000000 0.000000 0.000000 -0.800000 \n", - "25% 0.000000 0.400000 1.400000 12.800000 \n", - "50% 0.000000 0.800000 2.500000 16.100000 \n", - "75% 0.100000 1.400000 4.400000 19.700001 \n", - "max 11.600000 11.200000 52.500000 45.400002 \n", - "\n", - " relativehumidity atmosphericpressure pm25 pm10 \\\n", - "count 881372.000000 881372.000000 823822.000000 823822.000000 \n", - "mean 68.007261 1013.943718 6.691192 8.972910 \n", - "std 17.562959 9.815047 30.102780 30.536759 \n", - "min 2.500000 894.700000 0.000000 0.000000 \n", - "25% 56.400002 1008.500000 1.000000 3.000000 \n", - "50% 69.400000 1014.100000 3.000000 5.000000 \n", - "75% 80.800000 1019.900000 7.000000 9.000000 \n", - "max 99.800003 1042.900000 3414.000000 3414.000000 \n", - "\n", - " noise \n", - "count 823822.000000 \n", - "mean 66.442163 \n", - "std 11.194487 \n", - "min 0.000000 \n", - "25% 58.500000 \n", - "50% 68.100000 \n", - "75% 71.800000 \n", - "max 131.100000 \n" - ] - } - ], - "source": [ - "# Check the summary statistics for numeric columns.\n", - "print(\"Sensor locations summary:\")\n", - "print(sensor_locations_df.describe())\n", - "\n", - "print(\"\\nPedestrian counts summary:\")\n", - "print(pedestrian_counts_df.describe())\n", - "\n", - "print(\"\\nMicroclimate summary:\")\n", - "print(microclimate_df.describe())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.7 Checking The Date Format\n", - "\n", - "Checking the date format is important because a time-based merge depends on all datasets sharing a compatible datetime structure, and public datasets often use different date formats and timezone conventions. This is important because any mismatch in date or time formatting can prevent the datasets from merging correctly later [19].\n", - "\n", - "The pedestrian dataset stores dates and hours separately. Whereas the microclimate dataset stores the full timestamps with date and time with UTC offsets. And the sensor installation date is a simple date string and is mainly historical metadata rather than a modelling field. This makes it clear that datetime standardisation is a required step before any merge can occur." - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pedestrian sensing_date sample:\n", - "0 2025-10-08\n", - "1 2026-05-07\n", - "2 2024-10-18\n", - "3 2026-01-06\n", - "4 2024-12-03\n", - "Name: sensing_date, dtype: object\n", - "\n", - "Microclimate received_at sample:\n", - "0 2026-04-28T15:25:15+00:00\n", - "1 2026-04-28T04:38:50+00:00\n", - "2 2026-04-28T05:08:52+00:00\n", - "3 2026-04-28T10:19:41+00:00\n", - "4 2026-04-28T13:55:03+00:00\n", - "Name: received_at, dtype: object\n", - "\n", - "Sensor installation_date sample:\n", - "0 2009-03-25\n", - "1 2009-03-26\n", - "2 2009-03-23\n", - "3 2009-01-21\n", - "4 2009-03-24\n", - "Name: installation_date, dtype: object\n" - ] - } - ], - "source": [ - "# Check a few date values before conversion.\n", - "print(\"Pedestrian sensing_date sample:\")\n", - "print(pedestrian_counts_df[\"sensing_date\"].head())\n", - "\n", - "print(\"\\nMicroclimate received_at sample:\")\n", - "print(microclimate_df[\"received_at\"].head())\n", - "\n", - "print(\"\\nSensor installation_date sample:\")\n", - "print(sensor_locations_df[\"installation_date\"].head())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.8 Checking The ID Columns\n", - "\n", - "This was an additional step for checking the unique identifiers to see if the datasets have any chance of being joined directly by ID or whether another strategy, like a datetime merge, is best. And since the project uses multiple public datasets, checking the unique IDs also helps confirm whether the pedestrian sensors and microclimate sensors use the same location system or separate systems [20].\n", - "\n", - "From the outputs, the sensor locations dataset contains 137 unique location_id values, while the pedestrian counts dataset contains 100 unique location_id values. Whereas the microclimate dataset contains 12 unique device_id values, which is a completely different identifier system. This means the microclimate data cannot be joined to pedestrian counts by location ID, so a time-based merge is the best integration method available." - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Unique location_id values in sensor locations:\n", - "137\n", - "\n", - "Unique location_id values in pedestrian counts:\n", - "100\n", - "\n", - "Unique device_id values in microclimate:\n", - "12\n" - ] - } - ], - "source": [ - "# Check important identifier columns.\n", - "print(\"Unique location_id values in sensor locations:\")\n", - "print(sensor_locations_df[\"location_id\"].nunique())\n", - "\n", - "print(\"\\nUnique location_id values in pedestrian counts:\")\n", - "print(pedestrian_counts_df[\"location_id\"].nunique())\n", - "\n", - "print(\"\\nUnique device_id values in microclimate:\")\n", - "print(microclimate_df[\"device_id\"].nunique())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Data Cleaning\n", - "\n", - "This is the section for data cleaning, since raw data are rarely ready to be used as it is, so cleaning processes are necessary to address duplicates, missing values, inconsistencies, syntax errors, irrelevant data and structural errors [21] [22]. And since this data pipeline uses the API for dataset access, this means that the datasets are being updated in real-time, so ensuring any errors get addressed in the pipeline ensures the data remains accurate, secure and accessible at every stage of its lifecycle [21]. And that the prediction will also be accurate [22]." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4.1 Removing Irrelevant Columns\n", - "\n", - "Selecting relevant variables and removing the irrelevant ones are necessary because not every feature is useful for the prediction modelling. Keeping unnecessary columns can make the workflow harder to manage, increase memory usage, and create confusion in later steps [23] [24].\n", - "\n", - "In this step, the sensor dataset is reduced to six useful metadata columns, even though it wasn't used for modelling purposes. The pedestrian dataset is reduced to the four fields needed to construct hourly counts. The microclimate dataset is reduced to key climate, air quality, and noise variables. The descriptive or duplicate variables were omitted." - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " location_id sensor_description installation_date status latitude \\\n", - "0 3 Melbourne Central 2009-03-25 A -37.811015 \n", - "1 5 Princes Bridge 2009-03-26 A -37.818742 \n", - "2 9 Southern Cross Station 2009-03-23 A -37.819830 \n", - "3 12 New Quay 2009-01-21 A -37.814580 \n", - "4 14 Sandridge Bridge 2009-03-24 A -37.820112 \n", - "\n", - " longitude \n", - "0 144.964295 \n", - "1 144.967877 \n", - "2 144.951026 \n", - "3 144.942924 \n", - "4 144.962919 \n", - " location_id sensing_date hourday pedestriancount\n", - "0 70 2025-10-08 2 1\n", - "1 3 2026-05-07 12 2042\n", - "2 59 2024-10-18 15 1058\n", - "3 14 2026-01-06 10 315\n", - "4 28 2024-12-03 8 799\n", - " device_id received_at airtemperature \\\n", - "0 ICTMicroclimate-08 2026-04-28T15:25:15+00:00 16.7 \n", - "1 ICTMicroclimate-08 2026-04-28T04:38:50+00:00 21.9 \n", - "2 ICTMicroclimate-08 2026-04-28T05:08:52+00:00 21.6 \n", - "3 ICTMicroclimate-10 2026-04-28T10:19:41+00:00 19.7 \n", - "4 ICTMicroclimate-08 2026-04-28T13:55:03+00:00 17.3 \n", - "\n", - " relativehumidity atmosphericpressure averagewindspeed gustwindspeed \\\n", - "0 79.9 1026.1 0.5 2.5 \n", - "1 61.5 1021.5 0.9 2.6 \n", - "2 61.1 1021.3 0.2 1.9 \n", - "3 57.4 1018.4 0.8 1.3 \n", - "4 76.2 1025.5 0.3 2.5 \n", - "\n", - " averagewinddirection pm25 pm10 noise \n", - "0 220.0 10.0 12.0 58.7 \n", - "1 283.0 27.0 32.0 75.6 \n", - "2 310.0 26.0 29.0 69.2 \n", - "3 324.0 39.0 49.0 61.9 \n", - "4 281.0 10.0 12.0 79.4 \n" - ] - } - ], - "source": [ - "# Keep only useful columns.\n", - "sensor_locations_clean = sensor_locations_df[\n", - " [\n", - " \"location_id\",\n", - " \"sensor_description\",\n", - " \"installation_date\",\n", - " \"status\",\n", - " \"latitude\",\n", - " \"longitude\",\n", - " ]\n", - "].copy()\n", - "\n", - "pedestrian_clean = pedestrian_counts_df[\n", - " [\n", - " \"location_id\",\n", - " \"sensing_date\",\n", - " \"hourday\",\n", - " \"pedestriancount\",\n", - " ]\n", - "].copy()\n", - "\n", - "microclimate_clean = microclimate_df[\n", - " [\n", - " \"device_id\",\n", - " \"received_at\",\n", - " \"airtemperature\",\n", - " \"relativehumidity\",\n", - " \"atmosphericpressure\",\n", - " \"averagewindspeed\",\n", - " \"gustwindspeed\",\n", - " \"averagewinddirection\",\n", - " \"pm25\",\n", - " \"pm10\",\n", - " \"noise\",\n", - " ]\n", - "].copy()\n", - "\n", - "# Check the result.\n", - "print(sensor_locations_clean.head())\n", - "print(pedestrian_clean.head())\n", - "print(microclimate_clean.head())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4.2 Removing Missing Value\n", - "\n", - "This step involves dealing with missing values by removing the rows they're in. This is because missing values in explanatory variables can cause problems when doing aggregation, interpolation, and modelling later, leading to bias results [25]. This is especially important for the microclimate dataset, since missing climate readings could affect the quality of the explanatory variables.\n", - "\n", - "After doing the column selection in the previous step, the sensor and pedestrian tables are now fully complete without missing values. Whereas the microclimate table still has many missing values, especially in gustwindspeed, pm25, pm10, and noise. \n", - "\n", - "By using dropna(), the microclimate dataset lost a number of data points with missing values, but still retains a sizable portion of data points. The decision for completeness in the data points was preferred to simplify the merging and feature engineering steps later, and because there were enough data points for it not to matter much." - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Missing values in sensor_locations_clean:\n", - "location_id 0\n", - "sensor_description 0\n", - "installation_date 0\n", - "status 0\n", - "latitude 0\n", - "longitude 0\n", - "dtype: int64\n", - "\n", - "Missing values in pedestrian_clean:\n", - "location_id 0\n", - "sensing_date 0\n", - "hourday 0\n", - "pedestriancount 0\n", - "dtype: int64\n", - "\n", - "Missing values in microclimate_clean:\n", - "device_id 0\n", - "received_at 0\n", - "airtemperature 15713\n", - "relativehumidity 15713\n", - "atmosphericpressure 15713\n", - "averagewindspeed 15713\n", - "gustwindspeed 134079\n", - "averagewinddirection 15711\n", - "pm25 73263\n", - "pm10 73263\n", - "noise 73263\n", - "dtype: int64\n" - ] - } - ], - "source": [ - "# Check missing values after column selection.\n", - "print(\"Missing values in sensor_locations_clean:\")\n", - "print(sensor_locations_clean.isna().sum())\n", - "\n", - "print(\"\\nMissing values in pedestrian_clean:\")\n", - "print(pedestrian_clean.isna().sum())\n", - "\n", - "print(\"\\nMissing values in microclimate_clean:\")\n", - "print(microclimate_clean.isna().sum())" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Missing values in sensor_locations_clean:\n", - "location_id 0\n", - "sensor_description 0\n", - "installation_date 0\n", - "status 0\n", - "latitude 0\n", - "longitude 0\n", - "dtype: int64\n", - "(145, 6)\n", - "\n", - "Missing values in pedestrian_clean:\n", - "location_id 0\n", - "sensing_date 0\n", - "hourday 0\n", - "pedestriancount 0\n", - "dtype: int64\n", - "(1540629, 4)\n", - "\n", - "Missing values in microclimate_clean:\n", - "device_id 0\n", - "received_at 0\n", - "airtemperature 0\n", - "relativehumidity 0\n", - "atmosphericpressure 0\n", - "averagewindspeed 0\n", - "gustwindspeed 0\n", - "averagewinddirection 0\n", - "pm25 0\n", - "pm10 0\n", - "noise 0\n", - "dtype: int64\n", - "(705456, 11)\n" - ] - } - ], - "source": [ - "# Remove rows with any missing values from each dataset.\n", - "sensor_locations_clean = sensor_locations_clean.dropna().copy()\n", - "pedestrian_clean = pedestrian_clean.dropna().copy()\n", - "microclimate_clean = microclimate_clean.dropna().copy()\n", - "\n", - "# Check missing values after removal.\n", - "print(\"\\nMissing values in sensor_locations_clean:\")\n", - "print(sensor_locations_clean.isna().sum())\n", - "print(sensor_locations_clean.shape)\n", - "\n", - "print(\"\\nMissing values in pedestrian_clean:\")\n", - "print(pedestrian_clean.isna().sum())\n", - "print(pedestrian_clean.shape)\n", - "\n", - "print(\"\\nMissing values in microclimate_clean:\")\n", - "print(microclimate_clean.isna().sum())\n", - "print(microclimate_clean.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4.3 Datetime Formatting\n", - "\n", - "Ensuring the datetime formatting matches between the different datasets is important because the modelling is hourly, and the datasets need to be able to merge based on a common point for perform chronological analysis across different data sources [26] [27]. Skipping this step would mean that the datasets cannot be merged into one table.\n", - "\n", - "The pedestrian dataset is converted from separate sensing_date and hourday fields into a single datetime_hour, such as 2024-12-06 20:00:00. Whereas the microclimate timestamps are converted from UTC into Australia/Melbourne, timezone information is removed, and the values are floored to the nearest hour. By doing this, both datasets now have a datetime variable with the same time formatting. It's also important to note that the time range of the microclimate dataset is narrower than the pedestrian time range, meaning that the overlap period is limited to the microclimate dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " location_id sensing_date hourday pedestriancount datetime_hour\n", - "0 70 2025-10-08 2 1 2025-10-08 02:00:00\n", - "1 3 2026-05-07 12 2042 2026-05-07 12:00:00\n", - "2 59 2024-10-18 15 1058 2024-10-18 15:00:00\n", - "3 14 2026-01-06 10 315 2026-01-06 10:00:00\n", - "4 28 2024-12-03 8 799 2024-12-03 08:00:00\n" - ] - } - ], - "source": [ - "# Convert sensor installation date.\n", - "sensor_locations_clean[\"installation_date\"] = pd.to_datetime(\n", - " sensor_locations_clean[\"installation_date\"]\n", - ")\n", - "\n", - "# Create an hourly datetime for pedestrian data.\n", - "pedestrian_clean[\"sensing_date\"] = pd.to_datetime(\n", - " pedestrian_clean[\"sensing_date\"]\n", - ")\n", - "\n", - "pedestrian_clean[\"datetime_hour\"] = (\n", - " pedestrian_clean[\"sensing_date\"] +\n", - " pd.to_timedelta(pedestrian_clean[\"hourday\"], unit=\"h\")\n", - ")\n", - "\n", - "# Check the result.\n", - "print(pedestrian_clean.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " device_id received_at airtemperature relativehumidity \\\n", - "0 ICTMicroclimate-08 2026-04-29 01:25:15 16.7 79.9 \n", - "1 ICTMicroclimate-08 2026-04-28 14:38:50 21.9 61.5 \n", - "2 ICTMicroclimate-08 2026-04-28 15:08:52 21.6 61.1 \n", - "3 ICTMicroclimate-10 2026-04-28 20:19:41 19.7 57.4 \n", - "4 ICTMicroclimate-08 2026-04-28 23:55:03 17.3 76.2 \n", - "\n", - " atmosphericpressure averagewindspeed gustwindspeed averagewinddirection \\\n", - "0 1026.1 0.5 2.5 220.0 \n", - "1 1021.5 0.9 2.6 283.0 \n", - "2 1021.3 0.2 1.9 310.0 \n", - "3 1018.4 0.8 1.3 324.0 \n", - "4 1025.5 0.3 2.5 281.0 \n", - "\n", - " pm25 pm10 noise datetime_hour \n", - "0 10.0 12.0 58.7 2026-04-29 01:00:00 \n", - "1 27.0 32.0 75.6 2026-04-28 14:00:00 \n", - "2 26.0 29.0 69.2 2026-04-28 15:00:00 \n", - "3 39.0 49.0 61.9 2026-04-28 20:00:00 \n", - "4 10.0 12.0 79.4 2026-04-28 23:00:00 \n" - ] - } - ], - "source": [ - "# Convert the raw timestamp to datetime with UTC.\n", - "microclimate_clean[\"received_at\"] = pd.to_datetime(\n", - " microclimate_clean[\"received_at\"],\n", - " utc=True\n", - ")\n", - "\n", - "# Convert UTC to Melbourne local time.\n", - "microclimate_clean[\"received_at\"] = (\n", - " microclimate_clean[\"received_at\"]\n", - " .dt.tz_convert(\"Australia/Melbourne\")\n", - ")\n", - "\n", - "# Remove the timezone after conversion.\n", - "microclimate_clean[\"received_at\"] = (\n", - " microclimate_clean[\"received_at\"]\n", - " .dt.tz_localize(None)\n", - ")\n", - "\n", - "# Round down to the nearest hour.\n", - "microclimate_clean[\"datetime_hour\"] = (\n", - " microclimate_clean[\"received_at\"]\n", - " .dt.floor(\"h\")\n", - ")\n", - "\n", - "# Check the result.\n", - "print(microclimate_clean.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pedestrian time range:\n", - "2024-05-12 00:00:00\n", - "2026-05-11 03:00:00\n", - "\n", - "Microclimate time range:\n", - "2022-11-08 12:00:00\n", - "2026-05-07 15:00:00\n" - ] - } - ], - "source": [ - "# Confirming the same datetime format.\n", - "print(\"Pedestrian time range:\")\n", - "print(pedestrian_clean[\"datetime_hour\"].min())\n", - "print(pedestrian_clean[\"datetime_hour\"].max())\n", - "\n", - "print(\"\\nMicroclimate time range:\")\n", - "print(microclimate_clean[\"datetime_hour\"].min())\n", - "print(microclimate_clean[\"datetime_hour\"].max())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4.4 Aggregating Values For Hourly Format\n", - "\n", - "This step involves aggregation because the use case models city-level pedestrian demand rather than individual sensor-level behaviour. Since the pedestrian and microclimate datasets both contain multiple records within the same hour. This also ensures that both the pedestrian and the microclimate data share the same hourly rows without duplicates for later merging, reducing the total data volume [28].\n", - "\n", - "The aggregation involves the pedestrian counts being summed across sensors to produce hourly city totals, and the microclimate readings are averaged across devices for each hour. Doing this changes the target variable from pedestrians at one site to overall city pedestrian demand at one hour, along with the climate values at that hour. This also further reduces the row counts due to aggregating to hourly, and the microclimate data is still narrower than the pedestrian dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " datetime_hour pedestriancount\n", - "0 2024-05-12 00:00:00 15093\n", - "1 2024-05-12 01:00:00 10686\n", - "2 2024-05-12 02:00:00 6751\n", - "3 2024-05-12 03:00:00 5431\n", - "4 2024-05-12 04:00:00 2728\n", - "(17351, 2)\n" - ] - } - ], - "source": [ - "# Aggregate pedestrian counts to hourly city totals.\n", - "pedestrian_hourly = (\n", - " pedestrian_clean\n", - " .groupby(\"datetime_hour\", as_index=False)\n", - " .agg(\n", - " {\n", - " \"pedestriancount\": \"sum\",\n", - " }\n", - " )\n", - ")\n", - "\n", - "# Check the result.\n", - "print(pedestrian_hourly.head())\n", - "print(pedestrian_hourly.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " datetime_hour airtemperature relativehumidity atmosphericpressure \\\n", - "0 2022-11-08 12:00:00 28.400 38.800 1011.100 \n", - "1 2022-11-08 13:00:00 29.400 34.800 1009.800 \n", - "2 2022-11-08 14:00:00 27.320 40.940 1009.420 \n", - "3 2022-11-23 15:00:00 15.100 87.150 1010.800 \n", - "4 2022-11-23 16:00:00 16.475 80.725 1010.475 \n", - "\n", - " averagewindspeed gustwindspeed averagewinddirection pm25 pm10 noise \n", - "0 3.100 4.100 74.00 3.00 5.00 62.80 \n", - "1 2.100 3.500 92.00 5.00 8.00 86.90 \n", - "2 1.700 2.780 253.40 3.00 5.20 87.78 \n", - "3 0.450 1.050 291.00 2.00 5.50 95.10 \n", - "4 1.675 2.475 271.25 4.25 6.75 100.30 \n", - "(27720, 10)\n" - ] - } - ], - "source": [ - "# Aggregate microclimate readings to hourly city averages.\n", - "microclimate_hourly = (\n", - " microclimate_clean\n", - " .groupby(\"datetime_hour\", as_index=False)\n", - " .agg(\n", - " {\n", - " \"airtemperature\": \"mean\",\n", - " \"relativehumidity\": \"mean\",\n", - " \"atmosphericpressure\": \"mean\",\n", - " \"averagewindspeed\": \"mean\",\n", - " \"gustwindspeed\": \"mean\",\n", - " \"averagewinddirection\": \"mean\",\n", - " \"pm25\": \"mean\",\n", - " \"pm10\": \"mean\",\n", - " \"noise\": \"mean\",\n", - " }\n", - " )\n", - ")\n", - "\n", - "# Check the result.\n", - "print(microclimate_hourly.head())\n", - "print(microclimate_hourly.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4.5 Merging The Datasets\n", - "\n", - "This step will merge the datasets together, ensuring the target variable, pedestrian count, is connected to the explanatory climate variables. The datetime_hour variable on both the pedestrian dataset and the microclimate dataset was inner-joined to merge into one dataset, meaning only the overlapping rows with the same values were merged [29]. Which means every row has both pedestrian and climate information, hence, a unified format ready for analysis [30].\n", - "\n", - "A quick check of the merged dataset shows that the pedestrian counts and climate values aligned in the same hourly observations, which is what the use case is looking for. And there are no missing values, which indicates that previous data cleaning works as intended." - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " datetime_hour pedestriancount airtemperature relativehumidity \\\n", - "0 2024-05-12 00:00:00 15093 12.771429 84.646429 \n", - "1 2024-05-12 01:00:00 10686 12.046429 88.128571 \n", - "2 2024-05-12 02:00:00 6751 11.457143 90.596429 \n", - "3 2024-05-12 03:00:00 5431 11.121429 91.982143 \n", - "4 2024-05-12 04:00:00 2728 10.837037 92.677778 \n", - "\n", - " atmosphericpressure averagewindspeed gustwindspeed averagewinddirection \\\n", - "0 1022.835714 0.353571 1.282143 166.142857 \n", - "1 1022.635714 0.507143 1.435714 140.321429 \n", - "2 1022.521429 0.585714 1.578571 153.857143 \n", - "3 1022.278571 0.607143 1.507143 139.250000 \n", - "4 1022.251852 0.566667 1.562963 170.074074 \n", - "\n", - " pm25 pm10 noise \n", - "0 8.464286 9.428571 66.071429 \n", - "1 11.107143 12.392857 65.500000 \n", - "2 9.892857 11.357143 64.278571 \n", - "3 7.678571 9.035714 61.942857 \n", - "4 8.074074 9.740741 61.566667 \n", - "(17267, 11)\n" - ] - } - ], - "source": [ - "# Merge pedestrian and microclimate data on the hourly datetime.\n", - "model_df = pd.merge(\n", - " pedestrian_hourly,\n", - " microclimate_hourly,\n", - " on=\"datetime_hour\",\n", - " how=\"inner\"\n", - ")\n", - "\n", - "# Sort the final modelling table.\n", - "model_df = model_df.sort_values(\n", - " \"datetime_hour\"\n", - ").reset_index(drop=True)\n", - "\n", - "# Check the result.\n", - "print(model_df.head())\n", - "print(model_df.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 17267 entries, 0 to 17266\n", - "Data columns (total 11 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 datetime_hour 17267 non-null datetime64[ns]\n", - " 1 pedestriancount 17267 non-null int64 \n", - " 2 airtemperature 17267 non-null float64 \n", - " 3 relativehumidity 17267 non-null float64 \n", - " 4 atmosphericpressure 17267 non-null float64 \n", - " 5 averagewindspeed 17267 non-null float64 \n", - " 6 gustwindspeed 17267 non-null float64 \n", - " 7 averagewinddirection 17267 non-null float64 \n", - " 8 pm25 17267 non-null float64 \n", - " 9 pm10 17267 non-null float64 \n", - " 10 noise 17267 non-null float64 \n", - "dtypes: datetime64[ns](1), float64(9), int64(1)\n", - "memory usage: 1.4 MB\n", - "None\n", - "\n", - "Missing values:\n", - "datetime_hour 0\n", - "pedestriancount 0\n", - "airtemperature 0\n", - "relativehumidity 0\n", - "atmosphericpressure 0\n", - "averagewindspeed 0\n", - "gustwindspeed 0\n", - "averagewinddirection 0\n", - "pm25 0\n", - "pm10 0\n", - "noise 0\n", - "dtype: int64\n", - "\n", - "Summary statistics:\n", - " datetime_hour pedestriancount airtemperature \\\n", - "count 17267 17267.00000 17267.000000 \n", - "mean 2025-05-09 05:49:24.104940288 35432.95975 16.511347 \n", - "min 2024-05-12 00:00:00 51.00000 2.617857 \n", - "25% 2024-11-07 21:30:00 7142.00000 12.527951 \n", - "50% 2025-05-06 18:00:00 35519.00000 15.910256 \n", - "75% 2025-11-08 09:30:00 58964.50000 19.561111 \n", - "max 2026-05-07 15:00:00 114804.00000 43.191667 \n", - "std NaN 27363.02579 5.547545 \n", - "\n", - " relativehumidity atmosphericpressure averagewindspeed gustwindspeed \\\n", - "count 17267.000000 17267.000000 17267.000000 17267.000000 \n", - "mean 66.584728 1013.925313 1.162485 3.496650 \n", - "min 9.875000 990.350000 0.121875 0.693939 \n", - "25% 56.075397 1008.605840 0.721429 2.289380 \n", - "50% 68.540741 1013.885714 1.065625 3.263889 \n", - "75% 78.714583 1019.287650 1.508001 4.527639 \n", - "max 96.673171 1039.162500 4.028125 10.200000 \n", - "std 16.060614 7.914093 0.578561 1.523908 \n", - "\n", - " averagewinddirection pm25 pm10 noise \n", - "count 17267.000000 17267.000000 17267.000000 17267.000000 \n", - "mean 184.944173 6.935045 8.731420 68.252640 \n", - "min 52.571429 0.333333 1.057143 53.745714 \n", - "25% 160.194444 1.892857 3.313393 65.136111 \n", - "50% 184.742857 3.368421 5.000000 68.113043 \n", - "75% 211.442222 7.577381 9.306624 71.034330 \n", - "max 306.827586 411.228571 417.028571 88.200000 \n", - "std 37.950660 12.914232 13.465303 4.521855 \n" - ] - } - ], - "source": [ - "# Check merged dataset.\n", - "print(model_df.info())\n", - "print(\"\\nMissing values:\")\n", - "print(model_df.isna().sum())\n", - "print(\"\\nSummary statistics:\")\n", - "print(model_df.describe())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Data Validation\n", - "\n", - "Doing data validation is important because a merged dataset that was cleaned may still be unsuitable for the task of this use case, possibly due to timestamps being duplicated, out of order, or some rows in the chronological datetime are missing. The time-series model tends to assume a consistent temporal structure with no sudden breaks, so this step in the pipeline checks that everything is complete [31]." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5.1 Validating Time Series Dataset\n", - "\n", - "Checking for duplicates or missing timestamps, since they can affect the lag features, rolling features and any sequence-based deep learning models that this use case may use. This is to ensure that there is a strictly ordered sequence of evenly spaced time points [32].\n", - "\n", - "From the outputs, there are no duplicates in the datetime_hour values, which means every timestamp is represented once. The dataset is double-checked to ensure that it is sorted in increasing time order. But there appears to be a number of missing hourly timestamps, which means the dataset is not complete yet. It does appear that random points were cut off, and that no large block of time was cut off." - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Duplicate datetime_hour values: 0\n" - ] - } - ], - "source": [ - "# Count duplicate datetime values.\n", - "duplicate_count = model_df[\"datetime_hour\"].duplicated().sum()\n", - "\n", - "# Print the result.\n", - "print(\"Duplicate datetime_hour values:\", duplicate_count)" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of missing hourly timestamps: 149\n", - "DatetimeIndex(['2024-10-06 02:00:00', '2025-06-06 22:00:00',\n", - " '2025-06-06 23:00:00', '2025-06-07 00:00:00',\n", - " '2025-06-07 01:00:00', '2025-06-07 02:00:00',\n", - " '2025-06-07 03:00:00', '2025-06-07 04:00:00',\n", - " '2025-06-07 05:00:00', '2025-06-07 06:00:00'],\n", - " dtype='datetime64[ns]', freq=None)\n" - ] - } - ], - "source": [ - "# Create the full expected hourly range.\n", - "full_hours = pd.date_range(\n", - " start=model_df[\"datetime_hour\"].min(),\n", - " end=model_df[\"datetime_hour\"].max(),\n", - " freq=\"h\"\n", - ")\n", - "\n", - "# Find missing hours.\n", - "missing_hours = full_hours.difference(model_df[\"datetime_hour\"])\n", - "\n", - "# Print the number of missing hours.\n", - "print(\"Number of missing hourly timestamps:\", len(missing_hours))\n", - "\n", - "# Preview a few missing hours.\n", - "print(missing_hours[:10])" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True\n" - ] - } - ], - "source": [ - "# Confirm the dataset is sorted by time.\n", - "print(model_df[\"datetime_hour\"].is_monotonic_increasing)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5.2 Fixing Missing Timestamps\n", - "\n", - "Ensuring the missing timestamps are filled in is necessary for a complete hourly sequence in the dataset, and missing them can create issues when performing feature engineering later. Missing hours can cause problems when creating lag features, rolling averages, and LSTM input sequences, since these methods rely on consistent time gaps between rows [32] [33].\n", - "\n", - "This step involves reindexing to the full hourly range so that all the missing timestamps are included in the dataset, and then interpolation is performed to fill in the missing values from those created rows. Hence, the merged dataset now has slightly more rows with no missing hourly stamps, ensuring the timeline is continuous with no breaks. A little addition was included to ensure that the pedestrian counts remained as integers, rather than fractions, due to interpolation. " - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Missing hourly timestamps: 0\n", - "\n", - "Overall:\n", - "\n", - "RangeIndex: 17416 entries, 0 to 17415\n", - "Data columns (total 11 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 datetime_hour 17416 non-null datetime64[ns]\n", - " 1 pedestriancount 17416 non-null int64 \n", - " 2 airtemperature 17416 non-null float64 \n", - " 3 relativehumidity 17416 non-null float64 \n", - " 4 atmosphericpressure 17416 non-null float64 \n", - " 5 averagewindspeed 17416 non-null float64 \n", - " 6 gustwindspeed 17416 non-null float64 \n", - " 7 averagewinddirection 17416 non-null float64 \n", - " 8 pm25 17416 non-null float64 \n", - " 9 pm10 17416 non-null float64 \n", - " 10 noise 17416 non-null float64 \n", - "dtypes: datetime64[ns](1), float64(9), int64(1)\n", - "memory usage: 1.5 MB\n", - "None\n", - "\n", - "Final time range:\n", - "2024-05-12 00:00:00\n", - "2026-05-07 15:00:00\n", - "\n", - "Final shape:\n", - "(17416, 11)\n" - ] - } - ], - "source": [ - "# Set datetime as the index.\n", - "model_df = model_df.set_index(\"datetime_hour\").sort_index()\n", - "\n", - "# Create the full hourly timeline.\n", - "full_hours = pd.date_range(\n", - " start=model_df.index.min(),\n", - " end=model_df.index.max(),\n", - " freq=\"h\"\n", - ")\n", - "\n", - "# Reindex to restore missing hours.\n", - "model_df = model_df.reindex(full_hours)\n", - "\n", - "# Interpolate all numeric columns over time.\n", - "model_df = model_df.interpolate(method=\"time\").ffill().bfill()\n", - "\n", - "# Convert pedestrian count back to integers.\n", - "model_df[\"pedestriancount\"] = (\n", - " model_df[\"pedestriancount\"]\n", - " .round()\n", - " .astype(int)\n", - ")\n", - "\n", - "# Restore datetime_hour as a column.\n", - "model_df = model_df.reset_index().rename(\n", - " columns={\"index\": \"datetime_hour\"}\n", - ")\n", - "\n", - "# Check the result.\n", - "print(\"Missing hourly timestamps:\",\n", - " pd.date_range(\n", - " model_df[\"datetime_hour\"].min(),\n", - " model_df[\"datetime_hour\"].max(),\n", - " freq=\"h\"\n", - " ).difference(model_df[\"datetime_hour\"]).shape[0])\n", - "\n", - "# Check for missing hourly timestamps again.\n", - "full_hours = pd.date_range(\n", - " start=model_df[\"datetime_hour\"].min(),\n", - " end=model_df[\"datetime_hour\"].max(),\n", - " freq=\"h\"\n", - ")\n", - "\n", - "missing_hours = full_hours.difference(model_df[\"datetime_hour\"])\n", - "\n", - "# Check the dataset structure.\n", - "print(\"\\nOverall:\")\n", - "print(model_df.info())\n", - "\n", - "# Check the final time range.\n", - "print(\"\\nFinal time range:\")\n", - "print(model_df[\"datetime_hour\"].min())\n", - "print(model_df[\"datetime_hour\"].max())\n", - "\n", - "# Check the dataframe shape.\n", - "print(\"\\nFinal shape:\")\n", - "print(model_df.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Exploratory Data Analysis\n", - "\n", - "The exploratory data analysis section was performed on the merged dataset to observe any interpretable patterns and get a rough understanding of how the dataset looks and feels before building a model. It's important to get a sense of how pedestrian demand changes over time and how it relates to the climate conditions [34]." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6.1 Pedestrian Count Over Time (Hourly)\n", - "\n", - "Plotting the hourly pedestrian count is for a quick look at the target variable changes over time, to check if the time series data is random or if there are any patterns that may need to be taken into consideration for later steps [35] [36].\n", - "\n", - "From the plot, there appear to be large fluctuations across the full time range with repeated highs and lows. Although it looks random, it does seem to show some sort of pattern, and it's not necessarily random noise, with recurring fluctuations, like the new year of each year seems to always be a high peak, indicating lots of pedestrians travelling in the city, and there also appear to be some sharp dips, which may possibly be public holidays. This does suggest that there may be other possible variables that might have influenced the pedestrian count, but it definitely confirms that pedestrian demand is influenced by recurring temporal and possibly environmental effects." - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot pedestrian counts over time.\n", - "plt.figure(figsize=(14, 5))\n", - "plt.plot(\n", - " model_df[\"datetime_hour\"],\n", - " model_df[\"pedestriancount\"],\n", - " label=\"Hourly pedestrian count\"\n", - ")\n", - "\n", - "plt.title(\"Pedestrian Count Over Time (Hourly)\")\n", - "plt.xlabel(\"Datetime\")\n", - "plt.ylabel(\"Pedestrian Count\")\n", - "plt.legend()\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6.2 Average Pedestrian Count By Day Of Week\n", - "\n", - "The day-of-week summary was checked to see if there may also be other variables like work patterns, shopping activities, or weekend behaviours that might have influenced the pedestrian demand. Also checks whether the day of the week should be considered an important time-based feature for later modelling [35] [37].\n", - "\n", - "From the plot, it does seem like the weekdays tend to be relatively high along with Saturdays, whereas Mondays and Sundays tend to have low pedestrian demands. Monday might be low despite being a normal workday for the average Australians might be due to the influence of public holidays, and Sunday being low is due to most people not working on Sunday, since businesses would usually pay a high penalty rate. This further suggests that the CBD pedestrian pattern is linked to weekday economic and commuter activity, not just climate variables." - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Create a day-of-week column.\n", - "model_df[\"day_of_week\"] = model_df[\n", - " \"datetime_hour\"\n", - "].dt.dayofweek\n", - "\n", - "# Group by day of week.\n", - "dow_pattern = model_df.groupby(\"day_of_week\")[\n", - " \"pedestriancount\"\n", - "].mean()\n", - "\n", - "# Plot the day-of-week pattern.\n", - "plt.figure(figsize=(8, 4))\n", - "plt.plot(\n", - " dow_pattern.index,\n", - " dow_pattern.values,\n", - " marker=\"o\",\n", - " label=\"Average pedestrian count\"\n", - ")\n", - "\n", - "plt.title(\"Average Pedestrian Count By Day Of Week\")\n", - "plt.xlabel(\"Day Of Week\")\n", - "plt.ylabel(\"Average Pedestrian Count\")\n", - "plt.xticks(\n", - " ticks=range(7),\n", - " labels=[\n", - " \"Mon\", \"Tue\", \"Wed\",\n", - " \"Thu\", \"Fri\", \"Sat\", \"Sun\"\n", - " ]\n", - ")\n", - "plt.legend()\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6.3 Distributions Of Variables\n", - "\n", - "Checking for distribution to see whether it's symmetric, skewed, or multi-modal. This is useful because skewed variables, extreme values, or unusual distributions can influence how the model learns from the data [38].\n", - "\n", - "* pedestriancount appears unimodal and right-skewed.\n", - "* airtemperature appears unimodal with a slight right skew.\n", - "* relativehumidity appears unimodal with a slight left skew.\n", - "* atmosphericpressure appears unimodal and slightly left-skewed.\n", - "* averagewindspeed appears unimodal and right-skewed.\n", - "* gustwindspeed appears unimodal and right-skewed.\n", - "* averagewinddirection appears roughly unimodal and mostly symmetric.\n", - "* pm25 appears unimodal and right-skewed.\n", - "* pm10 appears unimodal and right-skewed, similar to pm25.\n", - "* noise appears unimodal and fairly symmetric." - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Store numeric columns.\n", - "numeric_cols = model_df.select_dtypes(\n", - " include=[\"int32\", \"int64\", \"float64\"]\n", - ").columns.drop(\n", - " [\"day_of_week\"],\n", - " errors=\"ignore\"\n", - ")\n", - "\n", - "# Create subplot layout.\n", - "n_cols = 3\n", - "n_rows = int(np.ceil(len(numeric_cols) / n_cols))\n", - "\n", - "fig, axes = plt.subplots(\n", - " n_rows,\n", - " n_cols,\n", - " figsize=(16, 12)\n", - ")\n", - "\n", - "axes = axes.flatten()\n", - "\n", - "# Plot histogram for each numeric column.\n", - "for i, col in enumerate(numeric_cols):\n", - " axes[i].hist(\n", - " model_df[col],\n", - " bins=30,\n", - " label=col\n", - " )\n", - " axes[i].set_title(f\"Distribution Of {col}\")\n", - " axes[i].set_xlabel(col)\n", - " axes[i].set_ylabel(\"Frequency\")\n", - " axes[i].legend()\n", - "\n", - "# Remove empty subplots.\n", - "for j in range(i + 1, len(axes)):\n", - " fig.delaxes(axes[j])\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6.4 Correlation Matrix\n", - "\n", - "Checking the correlation matrix is a quick way to see which climate variables might have the strongest relationship with pedestrian demand, which does seem like they have some influence on the target variable from the results. This is mainly to show whether the relationship is positive or negative, even though correlation does not prove causation [39].\n", - "\n", - "- noise has the strongest positive correlation with pedestrian count, followed by gustwindspeed, airtemperature, averagewindspeed, and averagewinddirection. This suggests that pedestrian activity tends to increase when these variables increase.\n", - "- relativehumidity has the strongest negative correlation with pedestrian count, while pm10, pm25, and atmosphericpressure have weaker negative relationships. This suggests that pedestrian activity tends to decrease when these variables increase.\n", - "\n", - "This suggests that climate variables do play a role in influencing the pedestrian demand, but there are other influences as well as discovered from previous plots. But the goal for this task is to understand how climate variables affect pedestrian demands." - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA8IAAAMWCAYAAAA6RD+UAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3XdUFFcbwOHfLL2jdBTF3hVLNHawYU3sNbH3+Cn23hN7TYyxRTGJGpPYYu9dYwdrjB0VkCYgIHX3+4O4SsAuuyDvc86ek525M/PeG/a6d987dxSNRqNBCCGEEEIIIYTIIVT6DkAIIYQQQgghhNAlGQgLIYQQQgghhMhRZCAshBBCCCGEECJHkYGwEEIIIYQQQogcRQbCQgghhBBCCCFyFBkICyGEEEIIIYTIUWQgLIQQQgghhBAiR5GBsBBCCCGEEEKIHEUGwkIIIYQQQgghchQZCAshhBDvwdfXF0VRuHv37gc75927d1EUBV9f3w92zuzO09MTT09PfYchhBDiIyEDYSGEEFnOrVu36NOnDwULFsTU1BRra2uqV6/OwoULefr0qb7D+2DWrl3LggUL9B1GGl27dkVRFKytrTNs6xs3bqAoCoqiMGfOnLc+f2BgIJMmTcLPz+8DRCuEEEK8G0N9ByCEEEK8aPv27bRp0wYTExM6d+5M6dKlSUxM5NixYwwfPpwrV66wbNkyfYf5Qaxdu5bLly/j4+OTZnv+/Pl5+vQpRkZGeonL0NCQuLg4tm7dStu2bdPsW7NmDaampsTHx7/TuQMDA5k8eTLu7u54eHi88XF79ux5p+sJIYQQGZGBsBBCiCzjzp07tG/fnvz583PgwAFcXFy0+7766itu3rzJ9u3b3/s6Go2G+Ph4zMzM0u2Lj4/H2NgYlUp/k6YURcHU1FRv1zcxMaF69eqsW7cu3UB47dq1NGnShA0bNugklri4OMzNzTE2NtbJ9YQQQuQMMjVaCCFEljFr1ixiYmL48ccf0wyCnylcuDCDBg3Svk9OTmbq1KkUKlQIExMT3N3dGTNmDAkJCWmOc3d3p2nTpuzevZtKlSphZmbG0qVLOXToEIqi8OuvvzJu3Djy5MmDubk50dHRAJw6dYqGDRtiY2ODubk5tWvX5vjx46+tx5YtW2jSpAmurq6YmJhQqFAhpk6dSkpKiraMp6cn27dv5969e9qpxu7u7sDL7xE+cOAANWvWxMLCAltbWz7//HOuXbuWpsykSZNQFIWbN2/StWtXbG1tsbGxoVu3bsTFxb029mc6duzIzp07iYyM1G47c+YMN27coGPHjunKR0REMGzYMMqUKYOlpSXW1tY0atQIf39/bZlDhw7xySefANCtWzdtvZ/V09PTk9KlS3Pu3Dlq1aqFubk5Y8aM0e578R7hLl26YGpqmq7+3t7e5MqVi8DAwDeuqxBCiJxHMsJCCCGyjK1bt1KwYEGqVav2RuV79uzJ6tWrad26NUOHDuXUqVNMnz6da9eusWnTpjRlr1+/TocOHejTpw+9evWiWLFi2n1Tp07F2NiYYcOGkZCQgLGxMQcOHKBRo0ZUrFiRiRMnolKpWLVqFXXq1OHo0aNUrlz5pXH5+vpiaWnJkCFDsLS05MCBA0yYMIHo6Ghmz54NwNixY4mKiuLBgwfMnz8fAEtLy5eec9++fTRq1IiCBQsyadIknj59ynfffUf16tU5f/68dhD9TNu2bSlQoADTp0/n/PnzrFixAkdHR2bOnPlGbduyZUv69u3Lxo0b6d69O5CaDS5evDgVKlRIV/727dts3ryZNm3aUKBAAR49esTSpUupXbs2V69exdXVlRIlSjBlyhQmTJhA7969qVmzJkCa/9/h4eE0atSI9u3b88UXX+Dk5JRhfAsXLuTAgQN06dKFkydPYmBgwNKlS9mzZw8///wzrq6ub1RPIYQQOZRGCCGEyAKioqI0gObzzz9/o/J+fn4aQNOzZ88024cNG6YBNAcOHNBuy58/vwbQ7Nq1K03ZgwcPagBNwYIFNXFxcdrtarVaU6RIEY23t7dGrVZrt8fFxWkKFCigqV+/vnbbqlWrNIDmzp07acr9V58+fTTm5uaa+Ph47bYmTZpo8ufPn67snTt3NIBm1apV2m0eHh4aR0dHTXh4uHabv7+/RqVSaTp37qzdNnHiRA2g6d69e5pztmjRQmNnZ5fuWv/VpUsXjYWFhUaj0What26tqVu3rkaj0WhSUlI0zs7OmsmTJ2vjmz17tva4+Ph4TUpKSrp6mJiYaKZMmaLddubMmXR1e6Z27doaQLNkyZIM99WuXTvNtt27d2sAzddff625ffu2xtLSUtO8efPX1lEIIYSQqdFCCCGyhGfTka2srN6o/I4dOwAYMmRImu1Dhw4FSHcvcYECBfD29s7wXF26dElzv7Cfn592CnB4eDhhYWGEhYURGxtL3bp1OXLkCGq1+qWxvXiuJ0+eEBYWRs2aNYmLi+Pvv/9+o/q9KCgoCD8/P7p27Uru3Lm128uWLUv9+vW1bfGivn37pnlfs2ZNwsPDte38Jjp27MihQ4cIDg7mwIEDBAcHZzgtGlLvK352X3VKSgrh4eFYWlpSrFgxzp8//8bXNDExoVu3bm9UtkGDBvTp04cpU6bQsmVLTE1NWbp06RtfSwghRM4lU6OFEEJkCdbW1kDqwPFN3Lt3D5VKReHChdNsd3Z2xtbWlnv37qXZXqBAgZee67/7bty4AaQOkF8mKiqKXLlyZbjvypUrjBs3jgMHDqQbeEZFRb30nC/zrC4vTud+pkSJEuzevZvY2FgsLCy02/Ply5em3LNYHz9+rG3r12ncuDFWVlasX78ePz8/PvnkEwoXLpzhM5PVajULFy5k8eLF3LlzJ8390HZ2dm90PYA8efK81cJYc+bMYcuWLfj5+bF27VocHR3f+FghhBA5lwyEhRBCZAnW1ta4urpy+fLltzpOUZQ3KpfRCtEv2/cs2zt79uyXPuLnZffzRkZGUrt2baytrZkyZQqFChXC1NSU8+fPM3LkyFdmkj8kAwODDLdrNJo3PoeJiQktW7Zk9erV3L59m0mTJr207LRp0xg/fjzdu3dn6tSp5M6dG5VKhY+Pz1vV+VX/nzJy4cIFQkJCALh06RIdOnR4q+OFEELkTDIQFkIIkWU0bdqUZcuWcfLkSapWrfrKsvnz50etVnPjxg1KlCih3f7o0SMiIyPJnz//O8dRqFAhIHVwXq9evbc69tChQ4SHh7Nx40Zq1aql3X7nzp10Zd90EP+sLtevX0+37++//8be3j5NNvhD6tixIytXrkSlUtG+ffuXlvvjjz/w8vLixx9/TLM9MjISe3t77fs3rfObiI2NpVu3bpQsWZJq1aoxa9YsWrRooV2ZWgghhHgZuUdYCCFEljFixAgsLCzo2bMnjx49Srf/1q1bLFy4EEidtguwYMGCNGXmzZsHQJMmTd45jooVK1KoUCHmzJlDTExMuv2hoaEvPfZZJvbFzGtiYiKLFy9OV9bCwuKNpkq7uLjg4eHB6tWr0zzO6PLly+zZs0fbFpnBy8uLqVOnsmjRIpydnV9azsDAIF22+ffff+fhw4dptj0bsL9Yj3c1cuRIAgICWL16NfPmzcPd3Z0uXbqke3yWEEII8V+SERZCCJFlFCpUiLVr19KuXTtKlChB586dKV26NImJiZw4cYLff/+drl27AlCuXDm6dOnCsmXLtNORT58+zerVq2nevDleXl7vHIdKpWLFihU0atSIUqVK0a1bN/LkycPDhw85ePAg1tbWbN26NcNjq1WrRq5cuejSpQsDBw5EURR+/vnnDKckV6xYkfXr1zNkyBA++eQTLC0tadasWYbnnT17No0aNaJq1ar06NFD+/gkGxubV05Zfl8qlYpx48a9tlzTpk2ZMmUK3bp1o1q1aly6dIk1a9ZQsGDBNOUKFSqEra0tS5YswcrKCgsLC6pUqfLKe7gzcuDAARYvXszEiRO1j3NatWoVnp6ejB8/nlmzZr3V+YQQQuQskhEWQgiRpXz22WdcvHiR1q1bs2XLFr766itGjRrF3bt3mTt3Lt9++6227IoVK5g8eTJnzpzBx8eHAwcOMHr0aH799df3jsPT05OTJ09SqVIlFi1axP/+9z98fX1xdnZm8ODBLz3Ozs6Obdu24eLiwrhx45gzZw7169fPcGDWv39/OnbsyKpVq+jYsSP/+9//XnreevXqsWvXLuzs7JgwYQJz5szh008/5fjx4289iMwMY8aMYejQoezevZtBgwZx/vx5tm/fjpubW5pyRkZGrF69GgMDA/r27UuHDh04fPjwW13ryZMndO/enfLlyzN27Fjt9po1azJo0CDmzp3LX3/99UHqJYQQ4uOkaN5m1QwhhBBCCCGEECKbk4ywEEIIIYQQQogcRQbCQgghhBBCCCFyFBkICyGEEEIIIYTIUWQgLIQQQgghhBA52JEjR2jWrBmurq4oisLmzZtfe8yhQ4eoUKECJiYmFC5cGF9f30yP80OSgbAQQgghhBBC5GCxsbGUK1eO77///o3K37lzhyZNmuDl5YWfnx8+Pj707NmT3bt3Z3KkH46sGi2EEEIIIYQQAgBFUdi0aRPNmzd/aZmRI0eyfft2Ll++rN3Wvn17IiMj2bVrlw6ifH+G+g5AiDehVqsJDAzEysoKRVH0HY4QQgghhNAjjUbDkydPcHV1RaXKupNc4+PjSUxM1Mu1NRpNuu/NJiYmmJiYvPe5T548Sb169dJs8/b2xsfH573PrSsyEBbZQmBgIG5ubvoOQwghhBBCZCH3798nb968+g4jQ/Hx8biaWfKYFL1c39LSkpiYmDTbJk6cyKRJk9773MHBwTg5OaXZ5uTkRHR0NE+fPsXMzOy9r5HZZCAssgUrKysAVikFMFey7q9+ma3m9vH6DkGvfPZ/qu8Q9K7t53b6DkHvqsbv1XcIemcYEazvEPTudpEm+g5Brx7G5tZ3CPonN/dR/co8fYegN0+eJlB0yDztd8SsKDExkcek4GtQAHMdL80Uh5quMXe4f/8+1tbW2u0fIhv8sZCBsMgWnk3rMFdUmCsGeo5Gf6wtzPUdgl4Zm2Tdf+x0xcLS+vWFPnLWBjn7cwBgGJ/1f2nPbJZZ+MuvLlgo0hfIKjdgbWaq7xD0LjvcMmeOHr6//vv5sLa2TjMQ/lCcnZ159OhRmm2PHj3C2to6W2SDQQbCQgghhBBCCJFpFCNF5wN2RaOQmTOyq1atyo4dO9Js27t3L1WrVs28i35gOXeOqRBCCCGEEEIIYmJi8PPzw8/PD0h9PJKfnx8BAQEAjB49ms6dO2vL9+3bl9u3bzNixAj+/vtvFi9ezG+//cbgwYP1Ef47kYywEEIIIYQQQmQSxVBBpY+M8Fs4e/YsXl5e2vdDhgwBoEuXLvj6+hIUFKQdFAMUKFCA7du3M3jwYBYuXEjevHlZsWIF3t7eH6YCOiADYSGEEEIIIYTIwTw9PdG84sZ7X1/fDI+5cOFCJkaVuWRqtBBCCCGEEEKIHEUywkIIIYQQQgiRSRQjFYqOH/+pyLLqryUZYSGEEEIIIYQQOYpkhIUQQgghhBAik6gMFFQq3S6WpVJn/ecr65tkhIUQQgghhBBC5CiSERZCCCGEEEKITKIYKSg6zggrkhF+LckICyGEEEIIIYTIUWQgLIQQQgghhBAiR5Gp0UIIIYQQQgiRSVSGslhWViQZYSGEEEIIIYQQOYpkhIUQQgghhBAik8hiWVmTZISFEEIIIYQQQuQoMhAWQgghhBBCCJGjyNRoIYQQQgghhMgkKgMFlYGOF8tKkanRryMZYSGEEEIIIYQQOYpkhIUQQgghhBAikygGCoqOM8IKkhF+HckICyGEEEIIIYTIUWQgLIQQQgghhBAiR5Gp0UIIIYQQQgiRSfSyWJZMjX4tGQi/hK+vLz4+PkRGRur82l27diUyMpLNmzfr/No5zVJ1CKc1MYSQzEJVPgoqphmW26OO4g9NBBqgrGJGP8UJQ0V57b7s4Ob9IPpM+57wqCdYW5qzZHR/ShZwS1Pm1OV/8Jm3HICk5BSqlinOnEHdMDE2Qq1WM+6HX9h72p/klBSqli7GgqG9MDbKXt1LdMRtTvzpQ8LTCIxMrKnWbD62DsVeWl6j0bBvTVsigi/Tbtg1AAJvHeLCgW+0ZeLjwjG1cKBJz92ZHv+H8PDeDeZO6El0ZBjmljYMnbKC/IVKZlh296ZV/LZqNmqNGo9PvPhq9LcYGhlxzf8vFk37HwDJyUmUKl+NviPmY2xsosuqvLWb9wPpM3Uh4VFPsLEwZ8m4gZQomC9NmVOX/mbwnCXAv5+DsiWYPbgXJsZGAFy5dZdh85YTEhEFwIQ+nfjcs6puK/IebgaG0HP+asKjY7A2N2O5T2dK5nfNsKxGo6HRuAX43bpP8K/zANh7/irjfDdpy4REPcHZ1pqTC8foJP4P4d7d24wdMYjIx4+xtLLi65kLKFwkbT/w8MF9xo304e+rl8mT140/tu7T7jt18hgL5kwjLjYWRVGo5VkXn+FjUamyzwS8D9EPPKPRaBjdpyE3r13gj6MhuqrCe3sYcIN5E3oSFRmGhaUNQya/og02r+L3f9ug3CdefDUq4za49fcFfj+SPdrgZnA4vVdsIvxJHNbmJizt2YKSeRzTlDly7Q4t5v1CEWd77baD43ti9m9/ePn+I4b9soOQ6BgAJraqy+eVMm5DIfQl+/TM2Zy7uzsLFix4o7ILFy7E19c3U+PRF09PT3x8fPQdhlZ1xZKZKjccX/GbULAmiTWaMGaq3FimcidSk8JuTdRr92UXA+cso9tn9fBbu5DBHT+n7/TF6cqUKZyfI8umc3LlbE77ziE0Morlm1MHd6u3H8DvnzscXzGT8z/PR6VSWPzHDl1X472d2jGSIuU78Xm/Y5Sq2p8TWwe/svy108uwyuWeZptrIU+a9NqrfeV2Lk2B0i0yMeoP67tvBtCoVQ9WbLlCm67DmDuhZ4blgh/e4afFk5m98gAr/7zG4/BH7Ny4AoACRcuy8JcTfL/+DD/8fp7IiFC2/7ZEl9V4J4Nm/kC3zxvgt34xg79oSd+vv01XpkyRAhz+cQ4nVi/g1M8LCX0cxfKNOwGIi0+g/cjpjO/diXPrFnH6l4VUK5e9vvQN+H4NPbxrcGnpZIa2bkCvBT+9tOy3W/ZT0Nkhzbb6FUpy6tux2lf5gm6086yc2WF/UFPGj6R1uy/YtvcY3Xt9xbiRPunKWFpa8r/BI5g57/t0+6ytbZg1/we27DrM+s278Ltwlj83/a6DyD+cD9EPPLPpl4W45C2oi7A/qO++HkDDlj1YsTm1DeZNfHkb/Lx4MrN+PMCPW64RmVEbrFmIi1v2aoP/rd5Kt9oV8Z85kCGNa9BnxaYMyxVxtuevqf20r2eD4LiERNp9u44Jrepwfvr/OPPNV1Qrml+XVchyFJWil5d4NRkIZyEpKSmo1WpsbGywtbXVdzg5QmnFHHvF6JVlTmieUFmxJJdiiKIoNFLZcFgT/dp92UHI4yguXL9N+/o1AWheuwoPQsK49SA4TTlzUxOMDFN/LEhMSiY+IVG7GuGlm/fwqlQGY6PUNqhfpTzrdh/RbUXeU3xsGBFBFylQphUA+Yo3IS46kCcRdzIsHxl6nQfXd1Oq2lcvPWfck2CC7x6nYJnWmRLzhxYZEcI/V89Rp3FHAGrUa0HYowcEBtxMV/bYvo18WrsJue2dURSFxq17cWjXbwCYmplrsyHJSYkkJjyFLD5DIjQikgt/36S9tycAn3tV/fdzEJSmXIafg3+r9tueI3xSqqh28GtgYIBDLhud1eF9hURGc/5GAB28UgeuLaqV52HYY24Fps9gXb0XyNa//BnW2vul5wsMj+Tgxet09Mo+A+Hw8DCuXPKn6eep/UD9hk0IDgok4F7afsDGNhcVKlXBzNw83TlKlCqDW77UL/wmJqYUK1GKwIf3Mz/4D+RD9QMA925d5eTBrbTpNkxn8X8IkREh3Lj2vA2q1311G1T5Txsc3p2+Ddp2zT5tEBIdw4U7gXSoVhaA5pVK8iA8mluPwt/4HL/9dYlPCuXVDn4NVCocrC0yJV4h3sdHMRD29PRkwIABDBgwABsbG+zt7Rk/fjwajQaAhIQEhg0bRp48ebCwsKBKlSocOnQozTl8fX3Jly8f5ubmtGjRgvDw9B/4LVu2UKFCBUxNTSlYsCCTJ08mOTkZSJ36MmnSJPLly4eJiQmurq4MHDhQG9+9e/cYPHgwiqKg/PvNydfXF1tbW/78809KliyJiYkJAQEBdO3alebNm2uvu2vXLmrUqIGtrS12dnY0bdqUW7duafffvXsXRVHYuHEjXl5emJubU65cOU6ePJkm/uPHj+Pp6Ym5uTm5cuXC29ubx48fa9to4MCBODo6YmpqSo0aNThz5kya9vnv4Hzz5s3augBMmjQJDw8Pfv75Z9zd3bGxsaF9+/Y8efIESJ3yffjwYRYuXKhth7t3777uf6/ehZKMI88Hy44YEUrya/dlBw9DwnG2s8XQ0AAARVFwc7Tn/qOwdGXvBYXwabfh5P+sB9YW5vRqkfoluHyxguw4fo7o2DiSkpPZePAkAcGhOq3H+4qNDsTU0hGVKnWQoygKFtZ5iI1+mK6sOiWJv7YPp0rjmSiKwUvPefvib7gWqoOphf1Ly2QlocEPyG3vjIHh8zZwcHYjJDj9l/iQoPs4ujz/dd/J1Z3QF8o9CrxL/7aVaOflioWlDU3b9s38CryHByFhONnnSvs5cHLgQQZ/x/eCHlG1sw/ujTtjbWlOr5aNAPj7zn2MjYxoPexrqnXxofeUBYQ+zj6zQx6EPcY5tzWGBs/bIK9DLu6HRqQpl5ScQv9Fa1j0VUcMXjHd95f9J/GuWApHW+tMjftDCg4KxMHREcMXPgMurnkICkzfD7yJsNAQ9u7aTm2v+h8yzEz1ofqB5KQkFk7px//GLcLA4OX9ZFb0Nm0QGvzqNvh2aj/+N3YRqmzUBg8jonG2tUzTF7jZ2XA/PH1/dic0gmoTl1Bz8lKW7T+t3X4tMBQTQwNazV/Dp+N/oOeyjYRGx+qsDlmRYqDSy0u82kfTQqtXr8bQ0JDTp0+zcOFC5s2bx4oVqdNTBgwYwMmTJ/n111+5ePEibdq0oWHDhty4cQOAU6dO0aNHDwYMGICfnx9eXl58/fXXac5/9OhROnfuzKBBg7h69SpLly7F19eXb75JvR9ww4YNzJ8/n6VLl3Ljxg02b95MmTJlANi4cSN58+ZlypQpBAUFERT0PMsQFxfHzJkzWbFiBVeuXMHRMe09GACxsbEMGTKEs2fPsn//flQqFS1atECtVqcpN3bsWIYNG4afnx9FixalQ4cO2oG6n58fdevWpWTJkpw8eZJjx47RrFkzUlJSABgxYgQbNmxg9erVnD9/nsKFC+Pt7U1ERES6eF7l1q1bbN68mW3btrFt2zYOHz7MjBkzgNQp31WrVqVXr17adnBzc8vwPAkJCURHR6d5Cf3K7+LIX6tmc2vTchKTkthy5BQAXzTypF7lcjQcOImG/5tEYTcXDD/izvfi0XnkK94YG/siLy2j0Wi46b+ewh7tdRhZ1uHk6s7i386ydl8ASYkJHN+/Wd8hfTD5XZw4+dMCbm5dRUJiEn8e+gtIndFz6Kw/347sx3Hf+bg42DF4dtafEv62vlm3jc+relDczeWlZTQaDav3naRrg+o6jCxriXnyhAF9utCtV39KlSmn73B0bs2yr6lW93PyFSyh71D0Zs2yr6lW5+NtAw93F/6ZN5QTk/vy6//as+LgWTacvgxASoqag1dv813XZpyc0hfXXFb4/LRNzxELkV72Ws3mFdzc3Jg/fz6KolCsWDEuXbrE/Pnz8fb2ZtWqVQQEBODqmrrox7Bhw9i1axerVq1i2rRpLFy4kIYNGzJixAgAihYtyokTJ9i1a5f2/JMnT2bUqFF06dIFgIIFCzJ16lRGjBjBxIkTCQgIwNnZmXr16mFkZES+fPmoXDl1Slju3LkxMDDAysoKZ2fnNHEnJSWxePFiypV7+T+UrVq1SvN+5cqVODg4cPXqVUqXLq3dPmzYMJo0aaKNt1SpUty8eZPixYsza9YsKlWqxOLFz+//LFWqFJA60P7hhx/w9fWlUaPU7Mby5cvZu3cvP/74I8OHD3/j/w9qtRpfX1+srKwA+PLLL9m/fz/ffPMNNjY2GBsbY25unq4d/mv69OlMnjz5ja+bmRwwJIgk7fsQknD496Pzqn3ZQR5HO4LDI0lOTsHQ0ACNRsP9kDDcnF6exbQ0N6VV3er8tvcobepWR1EUxnZvy9jubQH4ff9xihfI+AeOrOT2xd+5dmoZAO6lmhMfE4JanYxKZYhGoyE2+iEW1nnSHfco4C/ioh5y/ewqNOpkkhKesGlRFRp124Gphd2/ZU6iTo7HpaCnLqv01vZt/YVNvywEoHbDdkSEBZOSnIyBYWobhAbfx9E5/f9LRxc3gu7f1r5/FHgXhwzKmZlbUsu7LQd3rsOzYdvMq8h7yutoz6Owx2k/B49Cyfufe2BfZGluRut6NVm/5zCt69ckr7MDtSqUwdUh9W+gvXdtmg/OGn3Ym8hrn4vgiGiSU1IwNEhtgwehj3FzyJ2m3NHLN7gf+pgl2w+RnKImOi6eYj3GcmzeKBxsrLRl4hOTqF8+e90j7eziSmhICMnJyRj++xkICnyIi2v6fuBVYmNi6NujI151venSvU8mRfvhZEY/cOncUUKD7rP11yWkpCQTFxtNl8ZFWfjLcWxzv/xzpS/7t71bGzg4uxH0IOM2uHzuKCHB99m6/nkbdG2S2gY2ubJeGzyTJ7c1wZExafqC++FRuNmlvdXD2sz0hWNsaPNpaU5cv0eryqXJa2dDreIFcM2VOiOkfbVyfD7nZ53WQ4g38dGkbT799NM003SrVq3KjRs3uHTpEikpKRQtWhRLS0vt6/Dhw9rpxdeuXaNKlSppzle1atqVPv39/ZkyZUqaczzLbMbFxdGmTRuePn1KwYIF6dWrF5s2bdJmY1/F2NiYsmXLvrLMjRs36NChAwULFsTa2hp3d3cAAgIC0pR78TwuLqm/1oeEpN7f9SwjnJFbt26RlJRE9erPf703MjKicuXKXLt27bV1eJG7u7t2EPwsjmcxvI3Ro0cTFRWlfd2/r797rKoplpzWxPBYk4xGo2GnOopaitVr92UHjrlsKFe0AL/uPQrA5sOnyONgR6G8aX+ouPUgmKR//54Tk5LZeuQ0pQqlTgeLT0jk8ZPUVSHDIqOZt2Yzgzt8psNavJuCZdtoF7UqVe0rcjmX4c6lDQAE/L0dcysXrHIXSHecd+dNtPjfaVoMOEWDzpsxMrGixYBT2kEwwC2/dRQs2xaVKmtPh6vX7Au+X3+G79efoW23YRQuXp4DO9YCcGzfJuwd8+Car3C646rXbcFfh7cTERaMRqNhxx/Lqe3dBoDAgJskJ6X+OJSUlMjJg1soUKSM7ir1Dhxy21KuWEF+3X0IgC0HT5LH0Y5CedNmPW89CHrhc5DE1iN/UbqQOwAt61Tn3LWbRMfGAbD75DlKF3bXVRXem6OtNR6F3Fh3MHV646YTF8hjb0sh17SzlPbPHMY/K7/h+o/fcGDmMKzNTbn+4zfaQTCA757jfFn3Uwyy2cwQOzt7SpQqw7Ytqf3A3l3bcXJ2IV/+9P3Ay8TFxtK3R0eq1/Siz1c+mRTph5UZ/cCclQdYvfMGq3f8w9xVBzC3sGb1jn+y5CAYoG7TL1j06xkW/XqGNl3TtsHx/Zuwe0UbnPpvGzRIbYPZKw+wescNfLf/w5yVqW3gu/2fLD0IBnC0tsQjvwvrTlwEYPPZq+TJbU0hJ7s05YIin2hnJj55msAuv38omz+1z2xVuRTn7zwk+mk8ALv9/6GMm5MOa5H1PHt8kq5f4tWyT+rqHcXExGBgYMC5c+fS3adiaWn5VueZPHkyLVu2TLfP1NQUNzc3rl+/zr59+9i7dy/9+/dn9uzZHD58GCOjly/GZGZmlmYAn5FmzZqRP39+li9fjqurK2q1mtKlS5OYmJim3IvXeXbOZ52UmZnZG9c1IyqVSnvP9TNJSUnpyv23roqipJvC/SZMTEwwMcn8x60sUj/irCaWxyQzUf0QM1QsMyjAt+pgqiiWVFEscVaM6ajYMUKdOhgvo5jRULEFeOW+7OLbYb3pO/175vy8CSsLM34Y1R+Ar2YuoXH1SjSpUYnD5y/zw4adGKhUJKek4FmxNKM6p85UiIqNo9HAyahUCmq1hv5tGtG4eiV9VumdVGk8g5NbB3P5xHcYGVtRtdk87b6T24aRt2gD3Io2eO15EuOjCbi+k6a99mdmuJli4LhFzJ3Qi/U/zsTcwprBk5dr9y2Y3JdPazfhU89muOQtyBf9xjO0mycAZSvWpnGrXgD4nTnEn+u+R6UyICUlGY/KXnTslfUfn7NwRH/6fv0tc37agLWFGT+MTX0E1FfTF9G4RmWa1KzM4bMXWfL7dgwMnn0OyjKyW2qm283ZgWGdW1Gv9yhUKgVXBzu+HdlPn1V6a4u+6kivBT8x6/ddWJubsnRQZwD6ffszTaqUpWmV10/xjYp9ypaTfpxdNC6zw80UE6bOZNzIwaxY8h0WlpZMnTEfgIljhuJZtwFedb15+jSOpvVrkpSYwJOYJ9StUZFmzVvhM2wMv6xeweWLfjyNi2P/ntTV8xs0akbv/oP0Wa238iH6gezuf2MXMW9iL9av/LcNJr3QBlP+bYPa/7ZB3/EM6+4JpLZBo4+gDb7t2ow+KzYxZ9tRrMxMWNKjOQD9V26hSfliNClfnC1nr7LiwBkMDFSkpKhp8UkpOtcsD4CbnS3Dmtaiztc/olIUXHNZsahr1v+BXOQ8iua/o5tsyNPTk9DQUK5cuaLdNnr0aLZs2cLmzZspVqwYR44coWbNmhke37FjR6Kioti+fbt2W4cOHdi5c6f2OcLVq1enePHi/Pjjj28U0/Xr1ylevDjnzp2jQoUKFC1alD59+jB06FBtmZc9q/jF5wiHh4djb2+fJv5jx45Rs2ZNNm3aRPPmzbl79y4FChTgwoULeHh4ABAZGUmuXLk4ePAgnp6edOvWjRs3bnDs2LF0scbGxpI7d25WrVpFx46pqyQmJSVRoEABfHx8GDZsGDt37qRJkyY8efIEC4vUlf/Gjh3LtGnTtAPkSZMmsXnzZvz8/LTnXrBgAQsWLNAuitWgQQOKFSvGd99990bt+Ex0dDQ2NjasVxXC/BULFH3svA5+/fpCH7G+u3PuPYfPdGqdPRbfykw1nu7Udwh6Zxge9PpCH7mbxT/Xdwh69SDG7vWFPnLZ/xvs+6t9cYa+Q9Cb6KfxuPSbTlRUFNbWWXNhvmffX/dXqoiFoW6/v8Ymp1D37Lks3T769tFkhAMCAhgyZAh9+vTh/PnzfPfdd8ydO5eiRYvSqVMnOnfuzNy5cylfvjyhoaHs37+fsmXL0qRJEwYOHEj16tWZM2cOn3/+Obt3705zfzDAhAkTaNq0Kfny5aN169aoVCr8/f25fPkyX3/9Nb6+vqSkpFClShXMzc355ZdfMDMzI3/+1Omj7u7uHDlyhPbt22NiYoK9/Zt9mc2VKxd2dnYsW7YMFxcXAgICGDVq1Fu3z+jRoylTpgz9+/enb9++GBsbc/DgQdq0aYO9vT39+vVj+PDh5M6dm3z58jFr1izi4uLo0aMHgLZeY8aMYeDAgZw6deqdnnXs7u7OqVOnuHv3LpaWluTOnRvVK1YeFUIIIYQQQogP7aMZgXTu3JmnT59SuXJlvvrqKwYNGkTv3r0BWLVqFZ07d2bo0KEUK1aM5s2bc+bMGfLlywek3l+8fPlyFi5cSLly5dizZw/jxqWd1uXt7c22bdvYs2cPn3zyCZ9++inz58/XDnRtbW1Zvnw51atXp2zZsuzbt4+tW7diZ5f6i+2UKVO4e/cuhQoVwsHhze8PUalU/Prrr5w7d47SpUszePBgZs+e/dbtU7RoUfbs2YO/vz+VK1ematWqbNmyRfuYiBkzZtCqVSu+/PJLKlSowM2bN9m9eze5cuUCUhf8+uWXX9ixYwdlypRh3bp1TJo06a3jGDZsGAYGBpQsWRIHB4d09zkLIYQQQgghRGb7aKZGe3h4sGDBAn2HIjKJTI1OJVOjZWq0TI2WqdEgU6NBpkbL1GiZGg0yNTq7TI0+UKUiloa6nYgbk5xMnVMyNfpVPpqMsBBCCCGEEEII8SY+mnuEhRBCCCGEECKrUQwUFB0/zkjRyOOTXuejGAgfOnRI3yEIIYQQQgghhMgmZGq0EEIIIYQQQogc5aPICAshhBBCCCFEVqSoVCg6flyorq+XHUkLCSGEEEIIIYTIUSQjLIQQQgghhBCZRFEpKCodL5al4+tlR5IRFkIIIYQQQgiRo0hGWAghhBBCCCEyicpAQaXjxyep5PFJryUZYSGEEEIIIYQQOYoMhIUQQgghhBBC5CgyNVoIIYQQQgghMokslpU1SUZYCCGEEEIIIUSOIhlhIYQQQgghhMgkiqJCUek2/6goku98HWkhIYQQQgghhBA5igyEhRBCCCGEEELkKDI1WgghhBBCCCEyiSyWlTVJRlgIIYQQQgghRI4iGWEhhBBCCCGEyCQqAwWVgW4ztCq1ZIRfRzLCQgghhBBCCCFyFBkICyGEEEIIIYTIUWRqtBBCCCGEEEJkElksK2uSjLAQQgghhBBCiBxFMsJCCCGEEEIIkUkUlQpFpdv8o66vlx3JQFhkKzW3j8fawlzfYejNQa9x+g5Br243WqbvEPRui7WZvkPQu2ItS+o7BL0zt3HVdwh6dy/aXt8h6FWyrAiLWtqAkOrt9R2C3jx5EgNM13cYIhuTgbAQQgghhBBCZBK5Rzhrkpy5EEIIIYQQQogcRQbCQgghhBBCCCFyFJkaLYQQQgghhBCZRKZGZ02SERZCCCGEEEIIkaNIRlgIIYQQQgghMolkhLMmyQgLIYQQQgghhMhRZCAshBBCCCGEECJHkYGwEEIIIYQQQmSS1KnRKh2/3n5q9Pfff4+7uzumpqZUqVKF06dPv7L8ggULKFasGGZmZri5uTF48GDi4+PftZl0TgbCQgghhBBCCJGDrV+/niFDhjBx4kTOnz9PuXLl8Pb2JiQkJMPya9euZdSoUUycOJFr167x448/sn79esaMGaPjyN+dDISFEEIIIYQQIpMoKgWVgW5fb5sRnjdvHr169aJbt26ULFmSJUuWYG5uzsqVKzMsf+LECapXr07Hjh1xd3enQYMGdOjQ4bVZ5KxEBsJCCCGEEEIIkUMlJiZy7tw56tWrp92mUqmoV68eJ0+ezPCYatWqce7cOe3A9/bt2+zYsYPGjRvrJOYPQR6fJIQQQgghhBAfoejo6DTvTUxMMDExSbMtLCyMlJQUnJyc0mx3cnLi77//zvC8HTt2JCwsjBo1aqDRaEhOTqZv374yNVoIIYQQQgghxPPnCOv6BeDm5oaNjY32NX369A9Sp0OHDjFt2jQWL17M+fPn2bhxI9u3b2fq1Kkf5Py6IBlhIYQQQgghhPgI3b9/H2tra+37/2aDAezt7TEwMODRo0dptj969AhnZ+cMzzt+/Hi+/PJLevbsCUCZMmWIjY2ld+/ejB07FpUq6+dbs36EQgghhBBCCJFN6f7RSakvAGtr6zSvjAbCxsbGVKxYkf3792u3qdVq9u/fT9WqVTOsU1xcXLrBroGBAQAajeZDNV2mkoywEEIIIYQQQuRgQ4YMoUuXLlSqVInKlSuzYMECYmNj6datGwCdO3cmT5482qnVzZo1Y968eZQvX54qVapw8+ZNxo8fT7NmzbQD4qxOBsJCCCGEEEIIkUlevGdXl9d8G+3atSM0NJQJEyYQHByMh4cHu3bt0i6gFRAQkCYDPG7cOBRFYdy4cTx8+BAHBweaNWvGN99880HrkZlkICyEEEIIIYQQOdyAAQMYMGBAhvsOHTqU5r2hoSETJ05k4sSJOogsc8g9wkIIIYQQQgghchTJCAshhBBCCCFEJskOU6NzIskICyGEEEIIIYTIUSQjLIQQQgghhBCZ5MXHGenymuLVpIWEEEIIIYQQQuQoMhAWQgghhBBCCJGj5PiB8N27d1EUBT8/P32HIoQQQgghhPjIPFssS9cv8Wo5/h5hNzc3goKCsLe3f2W5SZMmsXnz5hwxYD506BBeXl48fvwYW1tbfYeTqW7eD6LPtO8Jj3qCtaU5S0b3p2QBtzRlTl3+B595ywFISk6hapnizBnUDRNjI9RqNeN++IW9p/1JTkmhauliLBjaC2Oj7PPRWqoO4bQmhhCSWajKR0HFNMNye9RR/KGJQAOUVczopzhhqCiv3ZddPI15wE3/6SQlRmFgaEERj1GYWxXIsGxs9G3uXF5IUuJjAPIV64mdSy2iwi5w7fRITC2f/w2Vqb4YAwMTndThfUSG3eLg+v7Ex0VgbGqNV5tF5HYu8dLyGo2GrcubE/bQn+6T7wKQlBDD7p+7EPbQH7U6Wbs9O7hz9y7DR4wk4vFjrKwsmT1zJkWLFElT5sGDBwwfOYorV6/iljcv27f+qd2nVquZMWsWR44cJTklmYoVKjJ18iSMjY11XZV3dvteAAPHTCbicSRWlpYsnDaB4oULpSlz7K8zfD3/e2LjnqIoUK9WdcYNGYBKpSLgYSCfNmxJiSLPj/lxwUzc8+XVdVXey8OAG8yf2IPoyDAsLG3wmbSC/IVKZVh2z+ZV/O47G41aTdlPPOk/6jsMjYy4ePYwkwY2I0/+otqyc1YdxcTUTFfVeC+BATdYOKk7T6LCMbewZuDEH8n3kjbYu2UlG1fPRq1WU7aSJ31GLcLQ0AiAuzcvsXy2D1ERIQB06jeFqnVa6Kwe7yow4AbfTelGdGQ45pY2/G/Cj+QrmHH99/25kk2rZ6HWqClTyYveI1Lrr1arWf3tcC6c3IOBoSFW1rnpN2YpLm6FdVybt3fn7l2GjRhFxOPHWFtZMnvmjAz7w2EjR3P16lXy5s3Ljq1btPvUajXTZ83W9oeVKlTIdv2hyBlyfEbYwMAAZ2dnDA0zHrhoNBqSk5N1HFXmSExM1Pk1k5KSdH7NtzFwzjK6fVYPv7ULGdzxc/pOX5yuTJnC+TmybDonV87mtO8cQiOjWL55NwCrtx/A7587HF8xk/M/z0elUlj8xw5dV+O9VFcsmalyw/EVv4sFa5JYowljpsqNZSp3IjUp7NZEvXZfdnLr0lyc8jWlgtcv5CnUgRt+MzIsl5ISz99nx5KveA/Ke/6ER+1VWOcuq91vaumGR60fta/sMAgGOLJxCCWqdKHD8DN41B7Iwd8HvLL8xaOLsc7tnmabysCI8p6DaNprUyZGmjnGjZ9A+3btOLB3D3169Wb4yJHpylhaWjJksA8L5s1Nt++333/nypWr/Ll5E3t37UKlUvBd/ZMuQv9ghk+azhetW3BixwYG9OjMoDFT0pWxsbFm6ZxvOLp1PXt+/4mzfpf4bcvzPs/Swpz9G9doX9ltEAzw/Tdf0bBFT5ZtukqrLsNYMKlnhuWCH97hlx8mMWvFAZZvuUZkxCN2bVqh3Z8nf1G+W3dW+8oug2CAH6b3p0GLnizecJWWXYbz7eQeGZZ79PAOa5dMYtqygyzZ9DeRESHs2Zj6w3FCfBzTh7aiU7/JLPr9Egt/9aNk+Rq6rMY7WzKjH/Wb9+L7P67R4svhfDflJfUPvMO6pRP5etkhFm+4TmTEI/ZuSq3/maNb+fviCeatOc/8NRco80kd1vwwTpfVeGdjx0+gQ7u2HNy7mz69ejF85Kh0ZSwtLRk6eFCG/eH63//gypUrbN28kX27dqKoVKzKZv3hh/ZssSxdv8Sr5YgW2rVrFzVq1MDW1hY7OzuaNm3KrVu3gPRTow8dOoSiKOzcuZOKFStiYmLCL7/8wuTJk/H390dRFBRFwdfXF4DIyEh69uyJg4MD1tbW1KlTB39/f+21J02ahIeHBytXriRfvnxYWlrSv39/UlJSmDVrFs7Ozjg6OvLNN9+kiVlRFH744QcaNWqEmZkZBQsW5I8//khT5v79+7Rt2xZbW1ty587N559/zt27d7X7u3btSvPmzfnmm29wdXWlWLFiAPz8889UqlQJKysrnJ2d6dixIyEhIdr28PLyAiBXrlwoikLXrl0BcHd3Z8GCBWli8PDwYNKkSeni/uyzz7CwsNDWa8uWLVSoUAFTU1MKFizI5MmT9f4DQ8jjKC5cv037+jUBaF67Cg9Cwrj1IDhNOXNTE4z+/aEkMSmZ+IREFFKznZdu3sOrUhmMjQxRFIX6VcqzbvcR3VbkPZVWzLFXjF5Z5oTmCZUVS3IpqfVspLLhsCb6tfuyi8SEx8RGXcchT30A7FxqkxgfwtPYB+nKhj3ch5VtSe3gV1EMMDKx1WW4H9zTmFBCH1ygaPm2ABQs8xkxkQ+JCrudYfmI4GvcvbqD8l4+abYbGJqQp3AtjE1tMjvkDyosPJxLly7R/PPPAGjU0JugoGDu3ruXppytrS2fVKqEubl5unNc+/tvqlWrirGxMYqiULtWLTZt2ZKuXFYVGh6B/5W/ad2sIQBNG9QhMPgRd+7dT1OuTIli5HfLA4CpiQmlihflfmCgzuPNLJERIdy4dg6vxh0BqF63JaGPHhB4/2a6ssf3b6Ry7abksndO7fta9ebIrvW6DvmDi4wI4ea1c3g26gRA1TotCXv0gKAM2uDEgY1UrvW8Dbxb9ebontQ2OLJrHUXLVKGkR+rg18DAAJtcDrqryDuKjAjh1rVz1G74vP7hj+5nWP+T+zfwSc1m5LL7t/4tenN0z68AKCgkJSaSlBCPRqPhaWw0do55dFqXd5HaH15O0x8GvrI/TP8Dz7W//6Z6tWra/tAzm/WHIufIEQPh2NhYhgwZwtmzZ9m/fz8qlYoWLVqgVqtfesyoUaOYMWMG165do379+gwdOpRSpUoRFBREUFAQ7dq1A6BNmzaEhISwc+dOzp07R4UKFahbty4RERHac926dYudO3eya9cu1q1bx48//kiTJk148OABhw8fZubMmYwbN45Tp06liWH8+PG0atUKf39/OnXqRPv27bl27RqQmmn19vbGysqKo0ePcvz4cSwtLWnYsGGazO/+/fu5fv06e/fuZdu2bdpjp06dir+/P5s3b+bu3bvawa6bmxsbNmwA4Pr16wQFBbFw4cK3au9JkybRokULLl26RPfu3Tl69CidO3dm0KBBXL16laVLl+Lr65tu8K9rD0PCcbazxdDQAEgdxLs52nP/UVi6sveCQvi023Dyf9YDawtzerXwBqB8sYLsOH6O6Ng4kpKT2XjwJAHBoTqthy6EkowjzwfLjhgRSvJr92UXiU9DMDKxQ1Gl/uChKAompk4kPg1JVzbuyT0UlRHXTo/C70gPblyYRlJCpHZ/fGwg/kd64X+0D0F3N+uoBu8nJvIh5lbOqAye19/SNi8xkel/CEhJSeLwBh9qtZiHohjoOtRMERQUhIOjo3ZmkKIouLq6EPgWA7zSpUqzf/8BnjyJISkpiR07d/LwQfr2y6oCgx/h5GCXpg3yuDjzMCj4pceEhIaxbc9+6td+nuWLe/oU77ZdqN/6S+YuXkFKSkqmx/4hhT16QG57ZwxeaAcHZzdCg+6nKxsafB9H53za906u+QkNfl4u+MFtBnWszOAvq7L9tyWZH/wHEvboPrnsXNK3QXBGbRCAg3N+7Xsnl+dtcP/ONYyMjPl68Of4dKzIgoldiXqc9f99DH90n1z2aetv7+xG2KOAdGVDH93H4YW/AUcXd8Iepda/Us2mlKpQm+6N89CjcV4unjlI+96TdVOJ95DaHzq8V39YplQp9r3QH27fuZOHDx5mVshCvLPscyPje2jVqlWa9ytXrsTBwYGrV69iaWmZ4TFTpkyhfv362veWlpYYGhri7Oys3Xbs2DFOnz5NSEgIJiap0x/nzJnD5s2b+eOPP+jduzeQeq/EypUrsbKyomTJknh5eXH9+nV27NiBSqWiWLFizJw5k4MHD1KlShXt+du0aUPPnqlTsqZOncrevXv57rvvWLx4MevXr0etVrNixQqUf+/FXLVqFba2thw6dIgGDRoAYGFhwYoVK9Lcl9G9e3ftfxcsWJBvv/2WTz75hJiYGCwtLcmdOzcAjo6O73SPcMeOHenWrVua640aNYouXbporzl16lRGjBjBxIkTMzxHQkICCQkJ2vfR0frNMOZ3ceSvVbOJiYun59ffsuXIKdrUrc4XjTwJCA6l4cBJmBkb41mpDAfO+L/+hCLb0mhSiAo7R5nqizE2tSfg7+XcujSP4pWmYGFTlEr1fsfQyJKEpyFcOz0KI2Mb7F299B32B3Nu3ywKlG5KLqdiREek/2KYU7Vu1ZKHgQ/p0KkTpqamVK9WjaOGx/UdVqZ5EhPDl18N5avuX+JRuiQATg72XDiwHQe73DyOjKLPsLH84LuGAT066zla3StcvDy+O+5gYWVD2KMHTBr4Gda2dtRs0EbfoelMSkoy/qcPMGvVMXI7uPLL9+NYMmMAI2dm/6z5m7h17Sz3b19mxbYAzCys+eX70Syd2R+fyR//FOHU/jCQ9p2++Lc/rPpR94dvRFFSX7q+pnilHDEQvnHjBhMmTODUqVOEhYVpM8EBAQGULFkyw2MqVar02vP6+/sTExODnZ1dmu1Pnz7VTr2G1CnFVlZW2vdOTk4YGBigemHuvpOTk3Z68jNVq1ZN9/7ZFG5/f39u3ryZ5rwA8fHxaa5dpkyZdIsTnDt3jkmTJuHv78/jx4/fqD3exn/bzt/fn+PHj6fJAKekpBAfH09cXFyG0wynT5/O5MmZ+8tpHkc7gsMjSU5OwdDQAI1Gw/2QMNycXr5wmqW5Ka3qVue3vUdpU7c6iqIwtntbxnZPnVL6+/7jFP/PYlsfAwcMCeL5/d4hJOHwb/fxqn1ZWciD3QTe/g0Ae9e6JCWEo1Eno6gM0Wg0JMQ/wtjMMd1xJmaO2NiVx8QsdYqfQ976XD01HABDI4s05ezz1CU64mKWHAhfP/crF4+m3hNf2KMVcU+CUackozJIrX9M5AMsbdPf3xl4+zgxkQ+4fHIFmpRkEhOe8MuMcrQasB8zy1cvOphVubi4EBoSQnJyMoaGqfUPDAzC1dX1jc+hKAo+AwfiM3AgAFu3baNIkay/KM4zrs5OPAoNT9MGD4OCyePinK5sTGwsHfoMomGdWvTt2km73cTYGAe71B9Sc9na0KFFMzZu353lB8L7t/3M5jWpM59qe7cjIiyYlORkDP5th9Dg+zi4pO/XHZzdCHrw/PaBR4H3cHBOLWduaa3dbu+Ul1re7bjidzzLDoQPbv+ZLWsWAFDLuz2Pw4PSt4FzRm2Qj+AHz79zPAp63gYOTvkoU6m2djpw7UYdmTywSeZX5h0c3PEzW9cuAKBGg3Y8Dktb/7Dg+9g75Ut3nIOTG8EPn/8NhATdxd4ptf6HdvxC6UpeWFjZAuDZpDNTBjbK9Lq8r9T+MPQD9If/w2fg/wDYum17tuoPRc6R9b+tfgDNmjUjf/78LF++HFdXV9RqNaVLl37l4lEWFhYv3fdMTEwMLi4uHDp0KN2+FzOpRkZp779UFCXDba+aqp3RtStWrMiaNWvS7XNweH4Pzn/rERsbi7e3N97e3qxZswYHBwcCAgLw9vZ+7WJaKpUKjUaTZltGi2H995oxMTFMnjyZli1bpitraprxCsWjR49myJAh2vfR0dG4uX3YAaZjLhvKFS3Ar3uP8kUjTzYfPkUeBzsK5U37xe/Wg2DyOdtjZGhIYlIyW4+cplSh1Klg8QmJPE1MJJeVJWGR0cxbs5nxPdp90DizgmqKJSPV9+moscMWA3aqo6ilWL12X1bmmNcbx7ze2veRIacIfbgXR7dGhAcdxtjUATOL9ANBe1cvrt7fQXJSLIZGFjwOOYW5deo/8Inx4RiZ5EJRVKQkx/H40Ukc3RrrrE5vo1jF9hSr2F77PuD6Pv658BvFK3Xk9qU/sbRxxca+YLrjmvd7vjBSdEQAfyysxRejsvcsCHs7O0qVKsXmLX/SulVLdu7ajbOzM+7587/+4H8lJCQQHx+PjY0NERERLFm6jCE+PpkX9AfmYJebMiWL8cfWXbRv0ZRtew7g4uxIgfxp+93Y2Dg69B6EV/WqDO6bdgGh0PAIbK2tMTIyJCExke37DlK6RDFdVuOd1G36JXWbfql9f/bEbg7uWEu9zzpzfP9G7B3z4JrBSr/V67RgRA8vHvcej62dEzs3LKOmd+qPohGhQdjaOaFSqYiLfcKZYzuo/3m3dOfIKryafIlXk+dtcO7ELg7tXEPdZl04eWAjdk55MlztuKpXC0b38qR9rwnY2jmxe8MyajRIbYPq9Vuz789VxMVEY25pzbkTu3AvUjbdObICr8Zf4tX4ef0vnNzF4V1rqNP03/o75s2w/p/WacnY3rVp12sCtrmd2L1pGTUapH4HcMpTgPMndvJ5p6EYGRlz9tj2l648nZVk1B+6ODu9V3/4w9JlDPEZlIlRZ32KovvHGSmSEX6tj34gHB4ezvXr11m+fDk1a6YuinTs2LG3Po+xsXG6e50qVKhAcHAwhoaGuLu7f4hw0/jrr7/o3Llzmvfly5fXXnv9+vU4OjpibW39slOk8/fffxMeHs6MGTO0A8uzZ8+mKfMsg/zf+jo4OBAUFKR9Hx0dzZ07d157zQoVKnD9+nUKF37zXwNNTEy0080z07fDetN3+vfM+XkTVhZm/DCqPwBfzVxC4+qVaFKjEofPX+aHDTsxUKlITknBs2JpRnVOnW4fFRtHo4GTUakU1GoN/ds0onH1188myEoWqR9xVhPLY5KZqH6IGSqWGRTgW3UwVRRLqiiWOCvGdFTsGKFOvfepjGJGQ8UW4JX7spNCZYdyw28GD26uwcDQnMLlnq+SedN/FrmdqpPbuTomZk7kKfwFl058hYIKY1N7CpUdBkB40GGC7/2Johig0aRg51IbR7esnwEAqN1yHgd/+4oLB+djbGKFZ5tF2n2H/hiIe8lGuJd8fV1+m1+D+NhwEhOe8PM3pXAtVJO67bP+/ZHfTJ3C8JGjWLxkCZaWlsyaMR2AUWPGUK9uXerVrcvTp0+pW78BiYmJPImJoVqNmjRv/jkjhg0j+skTOnb6ApVKhVqtpmuXLtStW0fPtXo7syeOZtDYyXy7fBWWlhYs/HoCAEMmfI23Zy2869Ri+S+/cuHyFeKePmXHvoMANPOui0+f7pw+78+sRUu1fWWNKpXw6ZN1B38vM2DM98yf1JPfVs3A3MIan4nLtfu+ndKHKrWbUqV2M5zzFqRjnwkM7+4JQJlKtWjUshcAxw9sYucfS1EZGKJOSaZ6vVbU/6yLPqrzTvqPXsy3U3qwwXcmZhZW/G/C89WwF33dm8o1m1H53zbo0HsCo3rWBqB0xVp4t0y9LczBOR+tuo1kVI9aKCoFO4c89Bvzg17q87b6jvqB76Z0Z4Nv6t/AgPHP6//9N735pGYzKtdqhnOegrTvNZExvWoBULpCbRq0SK1/o9b9eXD3b4Z8UQFDQyNsczvRZ1T6J1NkRd9MnczwkaP5fslSrCwttP3hyDFjqVe3DvX/7Q/r1PfW9odVa9SiRfPPGTFsKNFPntCh05fa/rBbl87Uy2b9ocgZFM1/U3wfGbVajaOjI40aNWLixIkEBAQwatQozpw5w6ZNm/Dw8KBAgQJcuHABDw+Plz5Dd+3atfTu3Ztjx46RN29erKysMDY2platWjx58oRZs2ZRtGhRAgMD2b59Oy1atKBSpUoZPn+4a9euREZGsnnzZu02T09PPDw8tKsyK4qCvb09M2fOpEaNGqxZs4avv/6aS5cuUbJkSeLi4vDw8CBPnjxMmTKFvHnzcu/ePTZu3MiIESPImzdvhtcJDQ0lb968DBo0iL59+3L58mWGDx/OP//8o22Dhw8f4ubmxqpVq2jcuDFmZmZYWloyevRofH19+e2337C1tWXChAns27ePoUOHaleOVhSFTZs20bx5c+01d+/eTdOmTRk3bhytW7dGpVLh7+/P5cuX+frrr9/o/2N0dDQ2NjYE7vTF2iL9VOqc4qBX9nj0QmaZ3miZvkPQu7I1s2ZGRZdGtEy/oF1OY54Yqe8Q9O5svIe+Q9CrZLVke9TSBpTPfev1hT5ST57EULZCRaKiot4qKaRLz76//t2vFVYmr35Cx4f2JCGJ4j9syNLto28f/arRKpWKX3/9lXPnzlG6dGkGDx7M7Nmz3/o8rVq1omHDhnh5eeHg4MC6detQFIUdO3ZQq1YtunXrRtGiRWnfvj337t3DycnpvWOfPHkyv/76K2XLluWnn35i3bp12nt4zc3NOXLkCPny5aNly5aUKFGCHj16EB8f/8o/dgcHB3x9ffn9998pWbIkM2bMYM6cOWnK5MmTh8mTJzNq1CicnJwYMCD1eaKjR4+mdu3aNG3alCZNmtC8eXMKFSr02np4e3uzbds29uzZwyeffMKnn37K/Pnzyf8W02yEEEIIIYQQ4kP56DPC2VVGmdWcTDLCqSQjLBlhyQhLRhgkIwySEZaMsGSEQTLCkhF+OckIv95Hf4+wEEIIIYQQQuiLotLDYlk6vl529NFPjRZCCCGEEEIIIV4kGeEsSmasCyGEEEIIkf0pKhWKSrf5R11fLzuSFhJCCCGEEEIIkaPIQFgIIYQQQgghRI4iU6OFEEIIIYQQIpMoKt0vXqVIuvO1pImEEEIIIYQQQuQokhEWQgghhBBCiEwij0/KmiQjLIQQQgghhBAiR5GBsBBCCCGEEEKIHEWmRgshhBBCCCFEZlGpUl+6vqZ4JWkhIYQQQgghhBA5imSEhRBCCCGEECKTKIqCouh4sSwdXy87koywEEIIIYQQQogcRTLCQgghhBBCCJFJFJUKRcf37Or6etmRtJAQQgghhBBCiBxFBsJCCCGEEEIIIXIUmRothBBCCCGEEJlEUSkoKh0vlqXj62VHkhEWQgghhBBCCJGjSEZYCCGEEEIIITKLogJdL16lSL7zdaSFhBBCCCGEEELkKDIQFkIIIYQQQgiRo8jUaCGEEEIIIYTILHpYLAtZLOu1JCMshBBCCCGEECJHkYywyFZ89n+KsYmVvsPQm9uNluk7BL0avbO3vkPQuzLztug7BL2bu91V3yHoXWK8nb5D0LsaVXP2V5jqrjf1HYLeKWj0HYLezd3qrO8Q9CYxPlrfIbwxRVGh6HjxKl1fLzuSFhJCCCGEEEIIkaPIQFgIIYQQQgghRI6Ss+cVCSGEEEIIIURmUim6X7xKFst6LckICyGEEEIIIYTIUSQjLIQQQgghhBCZRFGpUFQ6XixLx9fLjqSFhBBCCCGEEELkKJIRFkIIIYQQQohMoqgUFB3fs6vr62VHkhEWQgghhBBCCJGjyEBYCCGEEEIIIUSOIlOjhRBCCCGEECKzKAooOs4/KjI1+nUkIyyEEEIIIYQQIkeRjLAQQgghhBBCZBJZLCtrkoywEEIIIYQQQogcRQbCQgghhBBCCCFyFJkaLYQQQgghhBCZRaVKfen6muKVpIWEEEIIIYQQQuQokhEWQgghhBBCiEyiKAqKjh9npOvrZUeSERZCCCGEEEIIkaPIQFgIIYQQQgghRI4iU6OFEEIIIYQQIrMoelgsS5F85+tICwkhhBBCCCGEyFFkICyEEEIIIYQQmURRKXp5va3vv/8ed3d3TE1NqVKlCqdPn35l+cjISL766itcXFwwMTGhaNGi7Nix412bSedkarQQQgghhBBC5GDr169nyJAhLFmyhCpVqrBgwQK8vb25fv06jo6O6conJiZSv359HB0d+eOPP8iTJw/37t3D1tZW98G/IxkICyGEEEIIIURmUVS6v2f3La83b948evXqRbdu3QBYsmQJ27dvZ+XKlYwaNSpd+ZUrVxIREcGJEycwMjICwN3d/b3D1iWZGi2EEEIIIYQQOVRiYiLnzp2jXr162m0qlYp69epx8uTJDI/5888/qVq1Kl999RVOTk6ULl2aadOmkZKSoquw31uOHAh7enri4+OTZc7zzN27d1EUBT8/vw92zg957UOHDqEoCpGRkQD4+vpmq+kPQgghhBBC5CTR0dFpXgkJCenKhIWFkZKSgpOTU5rtTk5OBAcHZ3je27dv88cff5CSksKOHTsYP348c+fO5euvv86UemQGmRr9Bg4dOoSXlxePHz9OM/DbuHGjdipAdufm5kZQUBD29vZvfEy7du1o3Lix9v2kSZPYvHmzXgby7yM64jYn/vQh4WkERibWVGs2H1uHYi8tr9Fo2LemLRHBl2k37BoAgbcOceHAN9oy8XHhmFo40KTn7kyP/0N4GvOAm/7TSUqMwsDQgiIeozC3KpBh2djo29y5vJCkxMcA5CvWEzuXWkSFXeDa6ZGYWrppy5apvhgDAxOd1OFdLVWHcFoTQwjJLFTlo6BimmG5Peoo/tBEoAHKKmb0U5wwVJTX7ssu7ty9y9CRo3n8+DFWllbMmTmNokWKpClz4uRfzJwzj9i4OBQF6njWZuSwoaj+fSTE/oMH+WbGbNTqFIoVLcqcmdOxsrTUR3XeSWToLfau6Ut8bDjGptbU6/gDdi4l0pULunOaQ78PAUCtTsKlwKfUbjULA0MTNGo1x7aM497f+1CpDDG1yE2ddguxdSik6+q8k6iwWxz87Svi4yIwNrXCs8335HYq/tLyGo2GbcubExZ4kW6T7gCQlBDDnl+6EvbQH7U6Wbs9uwh+cIOl07oSExWGmYUNvUevIm+BUunKhQbdZdmMbty7cQEHlwJ88+MF7b4bl0/iO78/ACnJSRQtU50vB36LkXHW7g8htS8YPmIkEY8fY2VlyeyZM9P1BQ8ePGD4yFFcuXoVt7x52b71T+0+tVrNtBkzOHLkKAaGhuSytWXaN1/jnj+/rqvyTu7cvcuwEaOIePwYaytLZs+ckWH9h40czdWrV8mbNy87tm7R7lOr1UyfNZsjR46SnJJMpQoVmDp5EsbGxrquynuJDL3FvnV9eRobjompNXU7/ICdcwb94d3THP7j3/4wJbU/rNXyeX94fNt4Av7ej1qdjIt7FTxbz8fAMHu1xQehUlJfur4mqd/xXzRx4kQmTZr03qdXq9U4OjqybNkyDAwMqFixIg8fPmT27NlMnDjxvc+vCx9dRjgxMVFn18qdOzdWVlY6u15mMjAwwNnZGUPDN/9txMzMLMOb57ObUztGUqR8Jz7vd4xSVftzYuvgV5a/dnoZVrnc02xzLeRJk157ta/czqUpULpFJkb9Yd26NBenfE2p4PULeQp14IbfjAzLpaTE8/fZseQr3oPynj/hUXsV1rnLavebWrrhUetH7SurD4IBqiuWzFS54fiK3wWDNUms0YQxU+XGMpU7kZoUdmuiXrsvOxkzYRId2rXl4J5d9O3dk2Ejx6QrY21jzXfz57Jv5za2btrAufN+bNic+gUwNjaWkWPGs2zxIg7t3Y2ToyPfff+DrqvxXg7+NojSVbvy5djzVKzrw761/TIsZ5+nNG2HHqTDiGN0HHGSpzFhXDy2AoDbl3cQdOcvOow4TseRJ3ArWpuT26foshrv5cjGIZSo3Jn2w07jUXsQh37/6pXlLx37AWu7tD+aqQyM8Kg9kCY9N2ZmqJlm5Zy+eDXrxew112nacQTLpnfLsJyZhTWte0yl//g16fblK1yOyUtP882PF5i26iLRj0PZt3lxZof+QYwbP4H27dpxYO8e+vTqzfCRI9OVsbS0ZMhgHxbMm5tu3779+zl37jzbt/7Jzm1bqVa1KnPmztNF6B/E2PETUvvCvbvp06sXw0emvzfS0tKSoYMHZVj/9b//wZUrV9i6eSP7du1EUalYtfonXYT+QR38fRClPu3Kl6PPU6GOD/vXvaQ/dC1Nm8EHaT/sGB2Gp/aHl46n9odXT/1E6AN/2g05QqeRZ1AUFf5Hste/Cx+D+/fvExUVpX2NHj06XRl7e3sMDAx49OhRmu2PHj3C2dk5w/O6uLhQtGhRDAwMtNtKlChBcHCwTsdj7yPbD4Q9PT0ZMGAAPj4+2Nvb4+3tzeXLl2nUqBGWlpY4OTnx5ZdfEhYW9tJz/Pzzz1SqVAkrKyucnZ3p2LEjISEhQOqUYS8vLwBy5cqFoih07dpVe+1nU6PHjBlDlSpV0p27XLlyTJny/EvQihUrKFGiBKamphQvXpzFi9P/w3j79m28vLwwNzenXLlyaebmT5o0CQ8PjzTlFyxYkObm9K5du9K8eXOmTZuGk5MTtra2TJkyheTkZIYPH07u3LnJmzcvq1at0h6T0dToHTt2ULRoUczMzPDy8uLu3btprvvi1GhfX18mT56Mv78/iqKgKAq+vr50796dpk2bpjkuKSkJR0dHfvzxx3R117X42DAigi5SoEwrAPIVb0JcdCBPIjLOYESGXufB9d2UqvbyL4dxT4IJvnucgmVaZ0rMH1piwmNio67jkKc+AHYutUmMD+Fp7IN0ZcMe7sPKtqR28KsoBhiZ2Ooy3A+utGKOvfLqmR0nNE+orFiSSzFEURQaqWw4rIl+7b7sIiw8nEuXLtPis2YANPJuQGBwMHfv3UtTrnTJkuTLl/rLsqmJCSVLFOfBg4cAHDpylJIlS1C4UEEAvuzUgT+3bddhLd5P3JNQHgX4UaxSOwAKlfucmMiHRIbeSlfWyNgcA4PUv5mUlESSk56ikPrLu6IopCQnkpIUj0ajITE+GkubPLqryHt4GhNK6EM/ipRvC0CB0s2IiQwkKux2huUjHv3NnSs78PAclGa7gaEJeQrXwsTUJtNj/tCiHodw5/pZqtf/AoBParciIvQ+jx7cTFfW0jo3xcrWwMTUIt0+E1NzDA1T/0aSkxJJTHyKkg1miaT2BZdo/vlnADRq6E1QUPq+wNbWlk8qVcLc3DzdORRFITExkYSEBDQaDTExMTg7O6UrlxU96wtfrH/gK+tvlu4c1/7+m+rVqmFsbIyiKHjWqsWmLVvSlcvK4p6EEnLfj2IV/+0Py75df8i//WFY4GXcinpiYJjaFvlL1Of6ufU6q0dWoigqvbwArK2t07xMTNInKYyNjalYsSL79+/XblOr1ezfv5+qVatmWKfq1atz8+ZN1Gq1dts///yDi4tLtpkBke0HwgCrV6/G2NiY48ePM2PGDOrUqUP58uU5e/Ysu3bt4tGjR7Rt2/alxyclJTF16lT8/f3ZvHkzd+/e1Q523dzc2LBhAwDXr18nKCiIhQsXpjtHp06dOH36NLduPe8krly5wsWLF+nYsSMAa9asYcKECXzzzTdcu3aNadOmMX78eFavXp3mXGPHjmXYsGH4+flRtGhROnToQHJy8lu1yYEDBwgMDOTIkSPMmzePiRMn0rRpU3LlysWpU6fo27cvffr04cGD9IMdSP31qGXLljRr1gw/Pz969uyZ4Ypxz7Rr146hQ4dSqlQpgoKCCAoKol27dvTs2ZNdu3YRFBSkLbtt2zbi4uJo167dW9UpM8RGB2Jq6YhKlZoNVBQFC+s8xEY/TFdWnZLEX9uHU6XxTBTFIN3+Z25f/A3XQnUwtXjzaeb6lPg0BCMTO5QX2sDE1InEpyHpysY9uYeiMuLa6VH4HenBjQvTSEqI1O6Pjw3E/0gv/I/2IejuZh3VIPOFkowjzwfLjhgRSvJr92UXQUHBODo6aGeEKIpCHhcXAgODXnpMSGgoO3bvoa6XJwCBgUHkcXXV7s+bJw8hoaFv3XfpS0zkAyysnVAZPG8Dy1x5efI44z4yOvwea2dVZ8XYghibWlOmRk8ACpRqRJ7CNfhxQlFWTijK/X8OU6Vx+ux6VhQT+RBzq/+0gW0eYiLTt0FKShJHNvhQq+VcVK/oD7ObiJD72Nq5YPDCZ8HOMR9hIQFvfa7QoLuM6e5B/88dMLewoV7z/h863A8uKCgIB0fHNH2Bq6sLgYGBb3yOunXqUKVKFapUq06VatU5cfIkgwcNev2BWUBq/R3eq/5lSpVi3/4DPHkSQ1JSEtt37uThg/TfKbKyDPtD27wZ9gUA0RH3WDe7Oj+OL4ixmTVlqqf2hw5uHty5vJPE+GhSUpK46beJ6Ii3/ywJ3RgyZAjLly9n9erVXLt2jX79+hEbG6tdRbpz585pssn9+vUjIiKCQYMG8c8//7B9+3amTZvGV1+9eiZRVvJRDISLFCnCrFmzKFasGHv37qV8+fJMmzaN4sWLU758eVauXMnBgwf5559/Mjy+e/fuNGrUiIIFC/Lpp5/y7bffsnPnTmJiYjAwMCB37twAODo64uzsjI1N+l+5S5UqRbly5Vi7dq1225o1a6hSpQqFCxcGUufkz507l5YtW1KgQAFatmzJ4MGDWbp0aZpzDRs2jCZNmlC0aFEmT57MvXv3uHkz/a/Rr5I7d26+/fZbihUrRvfu3SlWrBhxcXGMGTOGIkWKMHr0aIyNjTl27FiGx//www8UKlSIuXPnUqxYMTp16qT9cSAjZmZmWFpaYmhoiLOzM87OzpiZmVGtWjWKFSvGzz//rC27atUq2rRpg+Ur7h1MSEhId3O/vl08Oo98xRtjY1/kpWU0Gg03/ddT2KO9DiPTHY0mhaiwcxQsM5RyNVdgbGrPrUupU94sbIpSqd7vlKu1nOKVpvLo3p+EBR7Uc8QiMzyJiaFnn/707dmDsmVK6zscvbC2y0/HEcfpPvUfUpITuXUx9R7JR/cvEB58le6TrtF98nXcitbm0G+vvt0iOzq3bxYFSjcll+PL11PI6Rxc3Jm20o9FG4NISkrgzJHsOVX8bV26dIl/bvzDyWNH+ev4MapVrcq4CdnjfsEPoXWrltSuVZP2nb6gfacvKeDurv1h5WNlnTs/HYYfp9vkf/vDS6n9YYlPOpGveD02ft+YTd83xtahMCrVx/PD2cemXbt2zJkzhwkTJuDh4YGfnx+7du3SLqAVEBCQJrHl5ubG7t27OXPmDGXLlmXgwIEMGjTolYmzrOaj+GRWrFhR+9/+/v4cPHgww0HWrVu3KFq0aLrt586dY9KkSfj7+/P48WNtij8gIICSJUu+cRydOnVi5cqVjB8/Ho1Gw7p16xgyJHUBgdjYWG7dukWPHj3o1auX9pjk5OR0A+uyZZ/fc+ni4gJASEgIxYu/fMGS/ypVqpR2ARtAu6z5MwYGBtjZ2WmngP/XtWvX0k31ftnUiNfp2bMny5YtY8SIETx69IidO3dy4MCBVx4zffp0Jk+e/E7Xe53bF3/n2qllALiXak58TAhqdTIqlSEajYbY6IdYWKefyvgo4C/ioh5y/ewqNOpkkhKesGlRFRp124Gphd2/ZU6iTo7HpaBnpsT+oYQ82E3g7d8AsHetS1JCOBp1Msq/bZAQ/whjs/T3f5uYOWJjVx4TMwcAHPLW5+qp4QAYGlmkKWefpy7RERexd/XSQY0ylwOGBJGkfR9CEg7/dp+v2pdduLg4ExKSmr01NEz9G3gYFISrq0u6sjExsXTp0Yv69erQs3tX7XZXVxeOnjihff/g4UMcHRzeat0BXbt2eh1+h74HoGiFVsRGP0KdkozKILUNYh4/wCpX3leew9jEkqIVWnL93O8UrdCav8+sI2+RWpiY2wJQ/JOObFmSddcL+Ofcr1w8lnrPXuFyLYl78p82iHyIpW36Ngi6c4KYyAdcPrECjTqZxIQnrJnhQcsB+zCzzB6zYZ45tusndv4+H4CqddsTGR5ESnIyBv9+FsJDArB3zPfO5zc1t+TTOu04sXctVetm7R9JXVxcCA0JSdMXBAYG4frCbI/X2bh5M1U//RRra2sAWrZsQZduGd9nndWk1j/0veqvKAo+A/+Hz8D/AbB123aKFCmcWSF/MH+fWYff4dT+sEj5DPrDyAcZ9gUvMjaxpEj5lvxz7neKlm+NoihUaTiaKg1Ts4j/XPiD3BksuJUj6HGxrLcxYMAABgwYkOG+Q4cOpdtWtWpV/vrrr7e+TlaRdb+hvAULi+dfwGNiYmjWrBkzZ85MV+7ZoPJFsbGxeHt74+3tzZo1a3BwcCAgIABvb++3vtG7Q4cOjBw5kvPnz/P06VPu37+vnf4bExMDwPLly9MNMF+8yRxIsxL1s3uKng3OVSoVGo0mTfmkpCT+67+rWSuKkuG2F+f1Z5bOnTszatQoTp48yYkTJyhQoAA1a9Z85TGjR4/W/ogAqUu//3fVu3dVsGwbCpZto33/8NZB7lzaQKFy7Qj4ezvmVi5Y5U6/YrJ3503a/46JvM/2FQ1oMeBUmjK3/NZRsGzbLP+Lp2NebxzzemvfR4acIvThXhzdGhEedBhjUwfMLNL/g2fv6sXV+ztITorF0MiCxyGnMLdO/Qc+MT4cI5NcKIqKlOQ4Hj86iaNb43TnyI6qKZaMVN+no8YOWwzYqY6ilmL12n3Zhb2dHaVKlWTTn1tp07IFO3fvwcXZKd0qr7GxsXTp2YvaNWvwv/5pF06pXbMmEyZP5eat2xQuVJCf16yjWZOs/f+/ROUOlKjcQfv+3rV9XD+7nhJVOnHLfwuWtq4ZrvYcGXoLq9z5MDAw+jcbvA1719RVhW3s3Ll7bQ8VvAZiYGjM3Su7MlxpNasoWrE9RSs+H5wFXN/PjQu/UaxSR+5c3oqFjQs29gXTHfd53+f3fz+JCOCPb2vTaZSfLkL+4Go07EyNhp217/1P7eL43l+o1agrZw5vILdDXpzyvt1A5tGDm9g558fQ0IjkpETOHd1MvkJlPnToH1xqX1CKzVv+pHWrluzctRtnZ+e3WvHZzc2NQ4cP06tHD4yNjTlw4CBFi6RPQmRFGdU/o77wVRISEoiPj8fGxoaIiAh+WLqMIT5Zf2p48U86UPyTF/rDv/dx/dx6SlTuxK2LW7CwebP+8Palbdj92x8mJ8WTnPQUU/NcPI0J5/z+BVRpNFZndRLidT6KgfCLKlSowIYNG3B3d3+jTMTff/9NeHg4M2bM0A60zp49m6bMsxu+X/eA6Lx581K7dm3WrFnD06dPqV+/vnZVZScnJ1xdXbl9+zadOnV6l6oB4ODgQHBwMBqNRjtIzozHFZUoUYI///wzzbbX/eJjbGycYRvZ2dnRvHlzVq1axcmTJ7X3GryKiYlJhjfzZ4YqjWdwcutgLp/4DiNjK6o2e7665cltw8hbtAFuRRu89jyJ8dEEXN9J0177X1s2qylUdig3/Gbw4OYaDAzNKVzu+bSWm/6zyO1UndzO1TExcyJP4S+4dOIrFFQYm9pTqOwwAMKDDhN8708UxQCNJgU7l9o4ujXSV5Xe2CL1I85qYnlMMhPVDzFDxTKDAnyrDqaKYkkVxRJnxZiOih0j1PcBKKOY0VCxBXjlvuxk2pTJDBs1msVLlmJpacns6dMAGDlmHPXq1qF+3Tqs+uln/C9eIi7uKbv27AOgSSNvBvTri6WlBTO+mUrv/gNISUmhaJEizJ01XZ9VemtebRewb20/zu6bi7GpFXU7PF/McP+vAyhQujEFSzfmwY0j+B9ZiqIyQKNOJm+R2nzSYAQAZWv2IuLRddbNqo7KwAhza0e82szXV5XeWq2Wczn4+wAuHFqAkYklnm0Wafcd/mMQ+Us2xL3k6z/Xvy+oSXxsOIkJT/hlWmlcC9WgTrslmRn6B9N96BKWTe/G1l+mY2ZhTa+RK7X7VszqSYXqn1Gh+mckxMcx/ItiJCcmEBcbxcDWblRv8AXtek/nyvkD7Nn4HSqVAeqUZEpWqMPnncfrsVZv7pupUxg+chSLlyzB0tKSWTNSP8ejxoyhXt261Ktbl6dPn1K3fgMSExN5EhNDtRo1ad78c0YMG8aXnb7g1q1bNGn2GYZGRjjY2/P1lMyZ4ZUZvpk6meEjR/P9kqVYWVpo6z9yzNh/+8LU+tep762tf9UatWjR/HNGDBtK9JMndOj0JSqVCrVaTbcunalXt46ea/X2vNosYN+6fpzdPxdjEyvqtn/eHx5YP4ACpRpToHRjHtw8wsWjS1P/7X/WH9ZP7Q8T46PZ9H0TFEWFRqOmXK2+FCiV9b8XZAZFpUJR6faOVF1fLztSNP9NL2Yznp6eeHh4sGDBAgACAwPx8PCgdu3ajBgxgty5c3Pz5k1+/fVXVqxYgYGBQZpjQkNDyZs3L4MGDaJv375cvnyZ4cOH888//3DhwgU8PDx4+PAhbm5urFq1isaNG2vvh/3vtSF1VeiJEyeSmJjI/Pnz+eKLL9LsGzhwIDNmzKBhw4YkJCRw9uxZHj9+zJAhQ7h79y4FChTQXhcgMjKSXLlycfDgQTw9Pbl27RqlSpVi+vTptG7dml27djF+/Hisra21qzp37dqVyMhINm/e/NJ2AnB3d8fHxwcfH5901w4ICKBIkSIMHDiQnj17cu7cOYYOHUpwcLD2ecq+vr74+PgQGRkJwNq1a+nduzfHjh0jb968WFlZaQeze/fupWnTpqSkpBAQEPBW04wgNSNsY2ND22F/Y2ySvbJtH9Jt/xv6DkGvRu/sre8Q9K7Mtey1+mhmmLvj7fqPj1FifPZYhCwz1aiaS98h6FV117dbO+RjpJCtv8J+EHO3Zvxom5wgMT6aZWPciIqK0k7Fz2qefX8N+m4E1ma6faRk9NMEXP43K0u3j759dD8VuLq6cvz4cVJSUmjQoAFlypTBx8cHW1vbNPfMPuPg4ICvry+///47JUuWZMaMGcyZMydNmTx58jB58mRGjRqFk5PTS+fOA7Ru3Zrw8HDi4uJo3rx5mn09e/ZkxYoVrFq1ijJlylC7dm18fX0pUCD9NNyXKVGiBIsXL+b777+nXLlynD59mmHDhr3x8W8qX758bNiwgc2bN1OuXDmWLFnCtGnTXnlMq1ataNiwIV5eXjg4OLBu3Trtvnr16uHi4oK3t/dbD4KFEEIIIYQQ4kPK9hlhkT3ExMSQJ08eVq1aRcuWLd/6eMkIp5KMsGSEJSMsGWGQjDBIRlgywpIRBskIZ5uM8KKR+skID5iZpdtH3z66e4RF1qJWqwkLC2Pu3LnY2try2Wef6TskIYQQQgghRA4nA2GRqQICAihQoAB58+bF19c3Sz9KRQghhBBCiA9OpYCuF6/S9eOasiEZlYhM5e7unu5xT0IIIYQQQgihTzIQFkIIIYQQQojMoiipL11fU7zSR7dqtBBCCCGEEEII8SoyEBZCCCGEEEIIkaPI1GghhBBCCCGEyCSKSoWi48WydH297EhaSAghhBBCCCFEjiIZYSGEEEIIIYTILIoq9aXra4pXkhYSQgghhBBCCJGjyEBYCCGEEEIIIUSOIlOjhRBCCCGEECKzKAqo5DnCWY1khIUQQgghhBBC5CiSERZCCCGEEEKITKIoKhQdL16l6+tlR9JCQgghhBBCCCFyFMkICyGEEEIIIURmUenhHmFdXy8bkoywEEIIIYQQQogcRQbCQgghhBBCCCFyFJkaLYQQQgghhBCZRVGlvnR9TfFK0kJCCCGEEEIIIXIUyQgLIYQQQgghRGZRlNSXrq8pXkkywkIIIYQQQgghchQZCAshhBBCCCGEyFFkarQQQgghhBBCZBaVKvWl62uKV5KBsMhW2n5uh4Wltb7D0Jst1mb6DkGvyszbou8Q9O5Sic/1HYLe2a2+rO8Q9M7IUO79iojWdwT6pXJJ0XcIeqfSSBuUKmml7xD05mmsRt8hiGxOBsJCCCGEEEIIkVnk8UlZkrSQEEIIIYQQQogcRQbCQgghhBBCCCFyFJkaLYQQQgghhBCZRaWkvnR9TfFKkhEWQgghhBBCCJGjSEZYCCGEEEIIITKLouhhsSzJCL+OZISFEEIIIYQQQuQokhEWQgghhBBCiMyiKLrP0EpG+LUkIyyEEEIIIYQQIkeRgbAQQgghhBBCiBxFpkYLIYQQQgghRGZRqVJfur6meCVpISGEEEIIIYQQOYpkhIUQQgghhBAis8hiWVmSZISFEEIIIYQQQuQoMhAWQgghhBBCCJGjyNRoIYQQQgghhMgsiir1petrileSFhJCCCGEEEIIkaNIRlgIIYQQQgghMouih8cnSUb4taSFhBBCCCGEEELkKDIQFkIIIYQQQgiRo8jUaCGEEEIIIYTILPIc4SxJMsJCCCGEEEIIIXIUyQgLIYQQQgghRGaRxydlSdJCQgghhBBCCCFyFMkICyGEEEIIIURmkXuEsyTJCL+ga9euNG/ePNudWwghhBBCCCHEm8vUjPDdu3cpUKAAFy5cwMPDIzMvleUtXLgQjUaj7zBEBh7eu8HcCT2JjgzD3NKGoVNWkL9QyQzL7t60it9WzUatUePxiRdfjf4WQyMjrvn/xaJp/wMgOTmJUuWr0XfEfIyNTXRZlXcWGXaLg+v7Ex8XgbGpNV5tFpHbucRLy2s0GrYub07YQ3+6T74LQFJCDLt/7kLYQ3/U6mTt9uzgzt27DB05msePH2NlacWcmdMoWqRImjInTv7FzDnziI2LQ1GgjmdtRg4bikqV+nvi/oMH+WbGbNTqFIoVLcqcmdOxsrTUR3Xe2lJ1CKc1MYSQzEJVPgoqphmW26OO4g9NBBqgrGJGP8UJw39/cX7VvuwiPPgGm5b2IO5JGKZmNjTvswLHvKXSlbt95SD71o8lMT4GRVEo4tGIeu2maf8Wjm+bi9+xn9Go1di7FOXz3isws7DVcW3eTVjQDTYs7k7sk3BMza1p1e9HnNzSt8GtywfZvW4MifGxKIpCsfKNaNDheRsc2TKb80d+xsDQCCMjU5p0nY9b4cq6rs47CQm8wZr5XYmNDsPU3IZOPqtwyZ++Df7xP8DW1aNJiI9BQaHkJ41p1mUGKpWKhKcxrJzemvs3z6FWJzPj18d6qMm7yen94Z279xgyagwRjyOxsrRk7oxvKFakcJoyx0+eYsbc+f/WX6FO7VqMHjYYlUpFbGwcfQb6cOnyVVJSUrh89qSeavJ+Qh7eYPWcrsREh2FmbkPnoatwdU//OQgPvsvqud24f+sC9s4FGLv4gnafWq1m048juHJ2N+qUZAqVrEaH//2AoZGxLqsixEtJRjiTpaSkoFarsbGxwdbWNtOuo9FoSE5OzrTzv4usGFNGvvtmAI1a9WDFliu06TqMuRN6Zlgu+OEdflo8mdkrD7Dyz2s8Dn/Ezo0rAChQtCwLfznB9+vP8MPv54mMCGX7b0t0WY33cmTjEEpU6UKH4WfwqD2Qg78PeGX5i0cXY53bPc02lYER5T0H0bTXpkyMNHOMmTCJDu3acnDPLvr27smwkWPSlbG2sea7+XPZt3MbWzdt4Nx5PzZs3gJAbGwsI8eMZ9niRRzauxsnR0e++/4HXVfjnVVXLJmpcsPxFb+NBmuSWKMJY6bKjWUqdyI1KezWRL12X3aydeVXVPTqycA5V6nebBibl2bcF5hZ2NJ6wC8MmHWR3lNPcf/GX/gf+wWAW5f2ceHIanpOPMqAWRdxKVCBA79P0GU13suWFf35pG5Phiy4Sq3PhrPhhx4ZljOzsKX9wDX4zL1I/2mnCPjnJH5HfgYg8K4fp/Yuod83J/jfzHN86t2frasG6bIa7+W37/tSzbsX45Zep17rEaxZ0C3DcuaWuegyYh1jFl9h2IKz3Ll2kjMHfgLAwNCIuq1G0P/rvboM/YPI6f3h6AmT6di2DYd3b6dfrx4MHTU2XRkbG2sWzZ/NgR1/sn3jb5y74MeGzX8CYGhkSL+ePVjru0LXoX9Qa77tS41GvZj843UatB3BT3Mz/hyYWljzWZepdB+5Jt2+E7t/JODmBcYsOsfE5VdRVCoObF6Y2aFnTSqVfl7ild67hXbt2kWNGjWwtbXFzs6Opk2bcuvWLQAKFCgAQPny5VEUBU9PT+D5NOFp06bh5OSEra0tU6ZMITk5meHDh5M7d27y5s3LqlWr0lzr0qVL1KlTBzMzM+zs7OjduzcxMTHa/YcOHaJy5cpYWFhga2tL9erVuXfvHgCTJk3Cw8ODpUuX4ubmhrm5OW3btiUqKv2XtTlz5uDi4oKdnR1fffUVSUlJ2n0JCQkMGzaMPHnyYGFhQZUqVTh06JB2v6+vL7a2tvz555+ULFkSExMTAgIC0k2NVqvVzJo1i8KFC2NiYkK+fPn45ptvgNRMuqIo/Prrr1SrVg1TU1NKly7N4cOH09RVURR27txJxYoVMTEx4dixY6jVaqZPn06BAgUwMzOjXLly/PHHH9rjHj9+TKdOnXBwcMDMzIwiRYpo2zkxMZEBAwbg4uKCqakp+fPnZ/r06Wli8vPz054rMjISRVG09X/XmPQpMiKEf66eo07jjgDUqNeCsEcPCAy4ma7ssX0b+bR2E3LbO6MoCo1b9+LQrt8AMDUzx9DICIDkpEQSE55mm3sznsaEEvrgAkXLtwWgYJnPiIl8SFTY7QzLRwRf4+7VHZT38kmz3cDQhDyFa2FsapPZIX9QYeHhXLp0mRafNQOgkXcDAoODuftv3/FM6ZIlyZfPDQBTExNKlijOgwcPATh05CglS5agcKGCAHzZqQN/btuuw1q8n9KKOfaK0SvLnNA8obJiSS7FEEVRaKSy4bAm+rX7souYqBACb5+jbPXUvqDkJy2JinhAeHD6vsDFvTy5HVP/XxsZm+KcvxyRoXcBCA64SL5i1TExswKgSLlG+B9L/wUxK4qJCuHh7XOUq9kJgFJVWhIVnnEbuBYoT26ntG3wODT1M6MoCikpSSTFxwLwNDYSm9x5dVSL9/MkMoSAG2ep5PUFAOWqtSIy7D6hgenbIG+h8tg7P2+DvAXLERFyFwBDIxOKlquTbWYCPJPT+8Ow8HAuXr5Ci8+aAtDYuz5BwcHcvReQplzpkiXI7/a8/qVKFOf+w9T6mxgbU71qFaytrHQb/AcU/e/noHLd1M9B+RqteBx2n5AMPgcWVrkpXLoGJqYW6fY9uO1P8fJ1MTQyRlEUSlVqxOn9v2R6/EK8qfceCMfGxjJkyBDOnj3L/v37UalUtGjRArVazenTpwHYt28fQUFBbNy4UXvcgQMHCAwM5MiRI8ybN4+JEyfStGlTcuXKxalTp+jbty99+vThwYMH2ut4e3uTK1cuzpw5w++//86+ffsYMCA1c5WcnEzz5s2pXbs2Fy9e5OTJk/Tu3RvlhcHIzZs3+e2339i6dSu7du3iwoUL9O/fP019Dh48yK1btzh48CCrV6/G19cXX19f7f4BAwZw8uRJfv31Vy5evEibNm1o2LAhN27c0JaJi4tj5syZrFixgitXruDo6Jiu3UaPHs2MGTMYP348V69eZe3atTg5OaUpM3z4cIYOHcqFCxeoWrUqzZo1Izw8PE2ZUaNGMWPGDK5du0bZsmWZPn06P/30E0uWLOHKlSsMHjyYL774QjuIfna9nTt3cu3aNX744Qfs7e0B+Pbbb/nzzz/57bffuH79OmvWrMHd3f2N/g7eJyZ9Cg1+QG57ZwwMUzNhiqLg4OxGSPD9dGVDgu7j6JJf+97J1Z3QF8o9CrxL/7aVaOflioWlDU3b9s38CnwAMZEPMbdyRmXwvA0sbfMSE/kgXdmUlCQOb/ChVot5KIqBrkPNFEFBwTg6OmD4wt9AHhcXAgODXnpMSGgoO3bvoa6XJwCBgUHkcXXV7s+bJw8hoaHZYkbEmwolGUeeD5YdMSKU5Nfuyy6iIx5gZeuMwQufAxs7N6LC0/cFL3oSGczV0xspWr4JAK4FKnD78gGeRAaj0Wi4dGIdifFPiIuJyPQ6vK+o8PtY2bqkbQN7NyLDXt8GV05tpFiFxgC45C9H9caDmDOwCDP7u3Nix7c07bYgs8P/ICLD7mOTO20b5HLIx+PQgFceF/04GL/jGyj1SVNdhJlpcnp/GBgUjKND2vq7urjw8JX1D0utv2dtXYWZ6R6H3sc6V/rPQUTIqz8H/5WvSEUu/rWVp7HRpCQnce7o74T/+2NRTqNRFL28xKu99z3CrVq1SvN+5cqVODg4cPXqVRwcHACws7PD2dk5TbncuXPz7bffolKpKFasGLNmzSIuLo4xY1Kn4DwbKB47doz27duzdu1a4uPj+emnn7CwSP3VadGiRTRr1oyZM2diZGREVFQUTZs2pVChQgCUKJH2Hsdnx+fJkweA7777jiZNmjB37lxtfLly5WLRokUYGBhQvHhxmjRpwv79++nVqxcBAQGsWrWKgIAAXP/t5IcNG8auXbtYtWoV06ZNAyApKYnFixdTrly5DNvsyZMnLFy4kEWLFtGlSxcAChUqRI0aNdKUGzBggLZ9f/jhB3bt2sWPP/7IiBEjtGWmTJlC/fr1gdRs9bRp09i3bx9Vq1YFoGDBghw7doylS5dSu3ZtAgICKF++PJUqVQJIM9ANCAigSJEi1KhRA0VRyJ//+aDvbbxtTBlJSEggISFB+z46Outnl5xc3Vn821mexsUwe2xXju/fjGfDtvoO64M6t28WBUo3JZdTMaIj3u4fxI/Fk5gYevbpT9+ePShbprS+wxF6FB8Xzbq5LajeZCh5ClYEoEBJT6o1Hszauc1RqQwoXvFzAFSqj/MhDfFx0fw8qzk1PxtK3kKp/65EhNzhyulNDFnwN9a5XTm563vWL+xI78n6//EzM8THRbNsymfUbTWcfEUq6Tscncrp/eGTmBi69/2Kvj27Uy4H1v91qtbvSsSje8wb4YmxsRnFy9fl2rmPsy8U2dN7/zXeuHGDCRMmcOrUKcLCwlCr1UDqoKpkyYwXHAIoVaqUdlEFACcnJ0qXft6JGBgYYGdnR0hICADXrl2jXLly2kEwQPXq1VGr1Vy/fp1atWrRtWtXvL29qV+/PvXq1aNt27a4uLhoy+fLl087CAaoWrWq9vhnA+FSpUphYPA80+Xi4sKlS5eA1KnZKSkpFC1aNE1dEhISsLOz0743NjambNmyL637tWvXSEhIoG7dui8t8yy+ZwwNDalUqRLXrl1LU+bZgBZSM95xcXHaQegziYmJlC9fHoB+/frRqlUrzp8/T4MGDWjevDnVqlUDUqes169fn2LFitGwYUOaNm1KgwYNXhljRt42poxMnz6dyZMnv/W138S+rb+w6ZfUe1RqN2xHRFgwKcnJGBgaotFoCA2+j6OzW7rjHF3cCLr/fLrwo8C7OGRQzszcklrebTm4c12WHQhfP/crF48uBqCwRyvingSjTklGZZDaBjGRD7C0TT+VMfD2cWIiH3D55Ao0KckkJjzhlxnlaDVgP2aW9rquxgfh4uJMSEhqtsLw37+Bh0FBuLq6pCsbExNLlx69qF+vDj27d9Vud3V14eiJE9r3Dx4+TJNV+Bg4YEgQz28TCSEJh3//CXnVvqzM7+jPnNyZ2heUqdqOJ5HBpKQkY/Dv5yAq/D42duk/4wAJT5/wy+ymFKvYjGqNfdLsq1y/L5Xrp84IuX/zFNa582Jqbp2pdXlXF478zLHtCwAoV609TyKD0rZB2H1s7V/eBqunN6FEpc+o0WSwdvuVU5twciuNde7UH4wrenZlm68PycmJGBpmvUVyTh/4iUOb5wNQoVZ7oiLStsHj0AByOeTL8Nj4uCf8MLERZT79DK/mQ3QZdqbI6f2hq4uzNnv9rP6BQUHkeUn9O/fsQ4O6dejVrYseov2w/tr3E/s3pn4OKnm2J/px+s9BbseMPwcvoygKTb+cRNMvJwFw5tCvGS48J4S+vHev1KxZM/Lnz8/y5ctxdXVFrVZTunRpEhMTX3mckVHa+9EURclw27OB9ZtYtWoVAwcOZNeuXaxfv55x48axd+9ePv300zc+x6tiiImJwcDAgHPnzqUZLANYvrAaopmZWZop2f9lZmb2xvG8zos/DDy7X3r79u1pBvwAJiapqxc3atSIe/fusWPHDvbu3UvdunX56quvmDNnDhUqVODOnTvs3LmTffv20bZtW+rVq8cff/yh/dHixZWvX7x3+n1iysjo0aMZMuT5l4ro6Gjc3DL+Mva26jX7gnrNvtC+P3t8Nwd2rKX+Z505tm8T9o55cM1XON1x1eu2YFi3OnTqO55cdk7s+GM5tb3bABAYcBNHl/wYGhmRlJTIyYNbKFCkzAeJNzMUq9ieYhXba98HXN/HPxd+o3iljty+9CeWNq7Y2BdMd1zzfju0/x0dEcAfC2vxxSh/ncScWezt7ChVqiSb/txKm5Yt2Ll7Dy7OTrj/Z0ZEbGwsXXr2onbNGvyvf780+2rXrMmEyVO5ees2hQsV5Oc162jWpLEuq5HpqimWjFTfp6PGDlsM2KmOopZi9dp9WZlHzS/xqPml9v0N/91cPL6W8rU6c/XMRqxz58HOOX1fkBAfwy+zmlK4bANqN0+/kNCTx0FY5XIhMSGOg39MpnrToZlaj/dRvtaXlK/1vA3+8duF/9E1VPDswpVTG7G2e3kb+E5vQhEPb7xapm2D3E4FOH94NQnxMZiYWvL3+e3YuxTNkoNggMp1OlO5Tmft+2vndnH24C9UqdcV/xMbsLXPi4NrBm3wNIYlkxpRooI33u3G6TLkTJPT+0N7OztKlyrBpj+30aZlc3bs3ouzkxPu+dMOAGNj4/iyZx9q16jBwP599BTth/Vpvc58Wu/55+DKmV2c3v8LVRt05cKx1M+BYwafg1dJSownMeEpFla5iIkKY89vM2nWecqHDj17UBRQdLx4lUyNfq33GgiHh4dz/fp1li9fTs2aNQE4duyYdr+xceo/eikpKe9zGSB1mrOvry+xsbHagdbx48e1U6ufKV++POXLl2f06NFUrVqVtWvXagfCAQEBBAYGaqc1//XXX+mOf5Xy5cuTkpJCSEiItr7vokiRIpiZmbF//3569sx4VdJn8dWqVQtIvQf63Llz2nuiM/Li4lwvm3IM4ODgQJcuXejSpQs1a9Zk+PDhzJkzBwBra2vatWtHu3btaN26NQ0bNiQiIkI7zT0oKEibyX1x4az3jem/TExMXjlQ/pAGjlvE3Am9WP/jTMwtrBk8ebl234LJffm0dhM+9WyGS96CfNFvPEO7eQJQtmJtGrfqBYDfmUP8ue57VCoDUlKS8ajsRcde6b8gZ1W1W87j4G9fceHgfIxNrPBss0i779AfA3Ev2Qj3ko1ee57f5tcgPjacxIQn/PxNKVwL1aRu+6y/eva0KZMZNmo0i5csxdLSktnTU29zGDlmHPXq1qF+3Tqs+uln/C9eIi7uKbv27AOgSSNvBvTri6WlBTO+mUrv/gNSZ40UKcLcWdP1WaW3skj9iLOaWB6TzET1Q8xQscygAN+qg6miWFJFscRZMaajYscIder9omUUMxoqtgCv3JedNOv+PZuX9eTonzMwMbOmee/nfcGW5X0oVqEpxSs249Tu73h4+wyJCbFcO7MZgFJVWlHr89EA/DyzMRqNmpTkJMrW6Ejl+v0zulyW9HmvxWz4oQeHNs/ExNyKVn2fr3y7cWlvSlRsRolKzTix8zse3EptgyunU1eKL/1pa7xajKbkJ815cOssi8d8iqGhMcYmFrT930/6qtJba/vVEtYu6Mbe36djam5Nx0ErtfvWfduT0lU+o0yVzzj850Lu/XOaxPhYLp5MbQOP6q1p0C51leEZ/ytHbFQo8XHRTOjqRpEyXnw5NOu3Q07vD6dPnsjQ0eNYtHQ5lhYWzJ3+NQAjxk6gXh0vGtT1YuVPP+N/6TJPnz5l195/69+wAf/rlzoobtCsBeGPH/MkJobKtepStconLJw9Q291ehcdBy7hp7nd2LU+9XPQecjzz8HP83tS9tPPKFf1MxLj45jYsxjJSQk8jY1i9BduVKnzBc27T+dpbBTzR3ihKCo0GjVezQdS9tNmeqyVEGkpmvd4uK1arcbR0ZFGjRoxceJEAgICGDVqFGfOnGHTpk00bdoUa2trxo4dS8+ePTE1NcXGxoauXbsSGRnJ5s2btefy9PTEw8ODBQsWaLe5u7vj4+ODj48PcXFxFC5cmGrVqjFp0iRCQ0Pp2bMnNWvWxNfXlzt37rBs2TI+++wzXF1duX79Oh07dmTq1Kn069ePSZMmMWfOHKpWrcqcOXOIjo6mZ8+eVKhQgXXr1gFkGJePjw9+fn7alZG/+OILjh8/zty5cylfvjyhoaHs37+fsmXL0qRJE3x9ffHx8SEyMjJNW/333JMnT2bhwoUsWLCA6tWrExoaypUrV+jRo4f2+cv58uVjwYIFlChRgvnz57N27Vru3LmDvb09hw4dwsvLi8ePH6d5LNO4ceNYsmQJc+fOpUaNGkRFRXH8+HGsra3p0qULEyZMoGLFipQqVYqEhARGjfo/e/cdHkXxBnD8u5dKekJ6CKFI753Qe5MOUkRCR1SkF1GqSJWqovQmIKh0VFogdOm9g0ACpEF6Qtrd/f44OLxfAkhIcgm8n+fZ5+F2Zmdnxrh3787s7BeEhYVx/Phx5syZg4eHBxUqVEClUjFz5kz++OMPHjx4gEqlwtfXFzMzMxYtWkRYWBijRo3ixIkT7N+/n3r16mW4Tv9FTEwM9vb2/H4oHGubnDnFMDts3R1v7CoY1RftQo1dBaO7WKKNsatgdKdXXTJ2FYzOzFTu9NtYvx0L9mVUm2LXjV0Fo1Np33ygJbf7486LH0N82z2Jj2FYBweio6Oxs8uZvw2f/X4N2bEUO2ur7D13fALuLfvm6P4xtjcao1epVKxfv57Tp09TunRphg4dyrfffqtPNzU15bvvvmPRokV4enrSpk3Gf8BZWVmxa9cuIiIiqFKlCh07dqRhw4b88MMP+vRr167RoUMHihYtSv/+/fnss8/4+OPnU1bee+892rdvT4sWLWjSpAlly5blxx9/fK16rFixAj8/P4YPH06xYsVo27YtJ0+eJH/+13tuYty4cQwfPpzx48dTokQJOnfurH8e+pnp06czffp0ypUrx+HDh9m2bZt+hecXmTx5MuPGjWPatGmUKFGCZs2a8ccff+hfZWVubs6YMWMoW7YsderUwcTEhPXr1wNga2vLzJkzqVy5MlWqVOHu3bv8+eef+mnRy5cvJzU1lUqVKjFkyBC++eab/9TWV9VJCCGEEEIIYVwLFiygQIECWFpaUq1aNf0bgF5l/fr1KIpi8KrY3OCNRoRzk4kTJ7Jly5b/NJ3X2J6NCJ89e5by5csbuzo5gowI68iIsIwIy4iwjAiDjAiDjAjLiLCMCIOMCMuI8EvO/Zojwhs2bMDPz4+FCxdSrVo15s2bx2+//cb169fTfRXsM3fv3qVWrVoUKlQIJycng5m1OV02P7UthBBCCCGEEO+O3PAe4Tlz5tCvXz969epFyZIlWbhwIVZWVixfvvyFx6jVarp168akSZMoVCjtIqs5nQTCQgghhBBCCPEWiomJMdiSkpLS5ElOTub06dM0atRIv0+lUtGoUSOOHTv2wrK//vprXF1d6dOnT5bUPau9M4HwxIkTc8W0aNAtEqbVamVatBBCCCGEELmdojLOBnh7e2Nvb6/fpk1Lu4r7o0ePUKvVuLm5Gex3c3MjJCQk3SYdPnyYZcuWsWTJknTTc4Oc/3ZzIYQQQgghhBCvLSgoyOAZ4cx4PWlsbCzdu3dnyZIlr1zINyeTQFgIIYQQQgghsoqi6LbsPidgZ2f3ysWynJ2dMTExITTUcFHS0NBQ3N3d0+S/ffs2d+/epVWr5++F1mg0gO6tQdevX6dw4cJv2oIs985MjRZCCCGEEEIIYcjc3JxKlSrh7++v36fRaPD398fX1zdN/uLFi3Px4kXOnTun31q3bk39+vU5d+4c3t7e2Vn9DJMRYSGEEEIIIYR4hw0bNowePXpQuXJlqlatyrx584iPj6dXr14A+Pn54eXlxbRp07C0tKR06dIGxzs4OACk2Z+TSSAshBBCCCGEEFlFpdJt2X3O19C5c2fCw8MZP348ISEhlC9fnp07d+oX0AoMDESV3W3IYhIICyGEEEIIIcQ7buDAgQwcODDdtICAgJceu3LlysyvUBaTQFgIIYQQQgghsohWUdBm82JZ2X2+3OjtGt8WQgghhBBCCPFWadCgAVFRUWn2x8TE0KBBgwyVKYGwEEIIIYQQQogcKyAggOTk5DT7ExMTOXToUIbKlKnRQgghhBBCCJFVFJVuy+5zvgUuXLig//eVK1cICQnRf1ar1ezcuRMvL68MlS2BsBBCCCGEEEKIHKd8+fIoioKiKOlOgc6TJw/ff/99hsqWQFgIIYQQQgghsohWUaHN5hHa7D5fVrlz5w5arZZChQpx4sQJXFxc9Gnm5ua4urpiYmKSobIlEBZCCCGEEEIIkeP4+PgAoNFoMr1sCYSFEEIIIYQQQuRoN2/eZP/+/YSFhaUJjMePH//a5UkgLIQQQgghhBBZRVF0W3af8y2yZMkSPvnkE5ydnXF3d0f5V/sURZFAWAghhBBCCCHE2+Wbb75hypQpjB49OtPKlEBYCCGEEEIIIbKIFiMslsXbsVjWM5GRkXzwwQeZWubb1UNCCCGEEEIIId4qH3zwAbt3787UMmVEWAghhBBCCCGyijwj/Mbee+89xo0bx99//02ZMmUwMzMzSB80aNBrlymBsBBCCCGEEEKIHGvx4sXY2Nhw4MABDhw4YJCmKIoEwkIIIYQQQggh3i537tzJ9DLlGWEhhBBCCCGEyCqKAooqm7e3a2r0M8nJyVy/fp3U1NQ3LktGhEWu4pu4BzsTK2NXw2iKtS9p7CoY1ew/PI1dBaPLu+qSsatgdJV6lDZ2FYzOuoCFsatgdL7ffWbsKhjVkhv9jV0Fo9NqjV0D42v+SwtjV8Fo4lLePBASuUdCQgKff/45q1atAuDGjRsUKlSIzz//HC8vL7744ovXLlNGhIUQQgghhBAii2gVxSjb22TMmDGcP3+egIAALC0t9fsbNWrEhg0bMlSmjAgLIYQQQgghhMixtmzZwoYNG6hevTrKv4L8UqVKcfv27QyVKSPCQgghhBBCCCFyrPDwcFxdXdPsj4+PNwiMX4cEwkIIIYQQQgiRVbJ9oayn21ukcuXK/PHHH/rPz4LfpUuX4uvrm6EyZWq0EEIIIYQQQogca+rUqTRv3pwrV66QmprK/PnzuXLlCkePHk3zXuH/6u26VSCEEEIIIYQQOYgWxSjb26RWrVqcO3eO1NRUypQpw+7du3F1deXYsWNUqlQpQ2XKiLAQQgghhBBCiBytcOHCLFmyJNPKk0BYCCGEEEIIIUSOEhMTg52dnf7fL/Ms3+uQQFgIIYQQQgghsohWUaHN5sWrsvt8WcHR0ZHg4GBcXV1xcHBId3VorVaLoiio1erXLl8CYSGEEEIIIYQQOcq+fftwcnICYP/+/ZlevgTCQgghhBBCCJFVjPE6o7dgRLhu3brp/juz5P4eEkIIIYQQQgjx1lqxYgW//fZbmv2//fYbq1atylCZEggLIYQQQgghRBbRKopRtrfJtGnTcHZ2TrPf1dWVqVOnZqhMCYSFEEIIIYQQQuRYgYGBFCxYMM1+Hx8fAgMDM1SmBMJCCCGEEEIIIXIsV1dXLly4kGb/+fPnyZs3b4bKlMWyhBBCCCGEECKLyOuT3lzXrl0ZNGgQtra21KlTB4ADBw4wePBgunTpkqEyJRAWQgghhBBCCJFjTZ48mbt379KwYUNMTXUhrEajwc/PL8PPCEsgLIQQQgghhBBZRVF0W3af8y1ibm7Ohg0bmDx5MufPnydPnjyUKVMGHx+fDJcpgbAQQgghhBBCiByvaNGiFC1aNFPKkkBYCCGEEEIIIUSOMmzYMCZPnoy1tTXDhg17ad45c+a8dvkSCAshhBBCCCFEVjHCYlm8BYtlnT17lpSUFADOnDmD8oLp3i/a/yoSCAshhBBCCCGEyFHmz5+PnZ0dAAEBAZlefu6/VSCEEEIIIYQQOZQWxShbblehQgUePXoEQKFChXj8+HGmli+BsBBCCCGEEEKIHMXBwYE7d+4AcPfuXTQaTaaWL1Ojc4iAgADq169PZGQkDg4OGS6nZ8+eREVFsWXLlkyrW1aoV68e5cuXZ968ecauihBCCCGEECKH6dChA3Xr1sXDwwNFUahcuTImJibp5v3nn39eu3wJhHOIGjVqEBwcjL29vbGr8s65FfSQjyfP53F0LPbWViwcO4gShfIb5Dl+8RpDZy0EICVVjW/ZEnw7tB8W5mYAXL59lxFzlhAWEQ3A+I+70aaeb/Y2JIPu3L3LyFGjiYiMxNbWhm9nzKBokSIGee7fv8/I0V9w+coVvPPl44/t2/RpGo2G6TNncvDgIVLVqVSqWInJkyZibm6e3U15I1Hht9mzdgCJ8Y8xt7Sj0Yc/kdejRJp8wXdOEPCbbuVCjSYFj4LVqdthJiamFmg1Gg5vHcu9a3tRqUyxtHaiQef5OLgUzu7mvLbHITfZvKgPCbGPsMxjT9uPl+Kar1SafP9c3s/eDV+RnBiHoigUKd+cRp2nolLpJhgd2TGbc4d/RqvR4OxRlDb9l5LH2iGbW/P6FmnCOKGNI4xU5qvyU0ixTDffbk00v2sj0AJllTx8orhh+nSRjpel5Qb3k5OYHnafGE0q1ioTRrnmo6C5YT9otFoWPQ7hZEIsaqC0pRVDXDwxU1SEpCTzUeB1g2MmuufHy8wim1uScbcehNJ3zioex8RhZ52HJUN7UNLHM928Wq2W5l/O49ztQEJ+navfHxgWwdCffuHmgzBMVAr9WtTl09b1s6sJbyzswU3WzO1JfMwj8ljb023ICjx80l4Lbpzfx7aVY0h6ei0oVbkFrXpOR6VSkfQkjmVTOxJ0+zQadSozNkQaoSUZF/bwJmuf9oGl1cv7YPuqp32AQskqLWjVQ9cHD+9e5PeFA4mNCkNlYopP0Sp0HLAAc4s8RmjR67kbE8/ovy8SmZSMjZkpM6qXoYiDrUEejVbL9DPXOBT8CFOVgoO5Od9UK4WPrTXxKal8fugslyJjUGu0nP6gkZFaknNojbBYVrYvzpUFFi9eTPv27bl16xaDBg2iX79+2NravvrA/+idCoTVajWKouh/sOUk5ubmuLu7G7sa76TBM36iV5smfPR+Q7bsO8qAb77jwPJZBnnKFCnIgWWzMDM1RaPR0O3LGSzZ9BcDu7QmITGJLqOnsWjcYGqUK4larSYiJs5IrXl9Y8eNp0vnznTs0J4//9rJyNGj2bppk0EeGxsbhg0dQmxsLLPnzDVI+/W337h8+QrbtmzGzMyML8eOZeWq1fTv1zc7m/HG9v86mNK+PSlRrRu3zm1h77pP6Dw8IE0+Z6/SdBq+HxMTM7QaDX+u6M6Fw0upUO8z/rn0J8F3/qbrqCOYmJhxcve3HPvja5r3XJXt7Xld25d/RqX6falQx4/LJzayZVFf+k8+liZfHmsHOg5cg5NrIVKSE1k9vRnnD6+hQh0/bl/cy9mDq+g36QgWeWw5sGUq+34bz/s9vzNCi15PTcWGDoojozVBL8wTok1hrfYR81Q+OGDCN5qH7CKa9xWHl6blFnPCH9DSzolmdo4ciItmZth9fsr3nkGeP2MjuZn0hEXe72GKwuzwB2yMekwXRxcA8qhULPEukl7xucLAH9bRp1ktujeuwabDp+k3dxVH5o1JN+93W/wp5OHMuduB+n1arZYu3yxk+AdN6VC7EgChkTHZUvfMsmHBAGo260e1Rj05e/h31s7rxYi5J9Lky2PjSM/Rv+DsrrsWLBjbmJP7VlOtUU9UpmY06jgKK1snvh+Te24CPPPrggHUaKrrg3NHXtwHVjaO9BiVfh+YmlvS4ePv8SpYFo1azepZ3fDfOIPmH07M/ga9pvEnL9P5vXy0L5SPnYEhjP77Ipua1TDI438/jDOPotjWoiZmKhU/XrrNnPM3mV+rPGYqFf1KFsLewozue9P2mxD/1YULF2jSpAnNmjXj9OnTDB48OFMDYaNGhDt37qRWrVo4ODiQN29eWrZsye3btwHdCOno0aMN8oeHh2NmZsbBgwcBSEpKYsSIEXh5eWFtbU21atUMVhRbuXIlDg4ObNu2jZIlS2JhYUFgYCAnT56kcePGODs7Y29vT926dTlz5ozBua5du0atWrWwtLSkZMmS7N27F0VRDKYcBwUF0alTJxwcHHBycqJNmzbcvXsXgEuXLqFSqQgPDwcgIiIClUpFly5d9Md/88031KpVC9BNjVYUhaioKIO679q1ixIlSmBjY0OzZs0IDg7WH69Wqxk2bJi+/0aNGoVWqzVox++//06ZMmXIkycPefPmpVGjRsTHxwO6adRt27Zl0qRJuLi4YGdnx4ABA0hOTtYfr9FomDZtGgULFiRPnjyUK1eO33//3eAcly5donnz5tjY2ODm5kb37t31D7YDxMfH4+fnh42NDR4eHsyePTudvwbjCI+I4uy1W3RpWg+ANvV9uR/2iNv3gw3yWVlaYGaqu2+UnJJKYlIyzwZ6ft19kCqlilKjXEkATExMcHHMHSP7jx4/5uLFi7Rt0xqA5s2aEhwcwt179wzyOTg4UKVyZaysrNKUcfXaNWrU8MXc3BxFUahbpw6bt27NlvpnloTYcEIDz1GscmcACpdrQ1zUA6LCb6fJa2ZuhYmJbiaAWp1MasoTlKcLUiiKgjo1GXVKIlqtluTEGGzsvbKvIRkUFx3Gw39OU7bmhwCUrNKe6Ij7PA65lSavR4EKOLkWAsDM3BJ3n3JEhd8FICTwAvmL1cQij+5Lqki55pw/vDZ7GvGGSitWOCtmL81zVBtLVcUGR8UURVForrLngDbmlWm5QWRqKjeSntDY1gGAOtZ2hKWm8CAlySDf7aQnVLKywUxRoSgKVa1s2RsXlf0VzgJhUTGcuXmPrg2qAdCuZkUehEdy+2FYmrxX7j1k+7FzjPigmcH+/eeuYW5mqg+CAdwc7bK24pkoNiqMwJunqFz/IwDK1+xAZHgQ4Q/TXgu8C1fA2f35tcCrYDkeh97VfTazoGi5BrliNsj/+/8+KFejA1GP0u+DfP/XB/kKlSMi7C4Arp5F8CpYFgCViQn5i1QmIvRemjJymseJSVx8HE3rArqZEE293QhJSORebLxBPkWBZLWGJLUGrVZLXEoqbnl0sz/MTVT4uufFzuydGm97OQVdp2XrZuxGv7l/L5Z14MABgxglMxg1EI6Pj2fYsGGcOnUKf39/VCoV7dq10424devG+vXrDQK7DRs24OnpSe3atQEYOHAgx44dY/369Vy4cIEPPviAZs2acfPmTf0xCQkJzJgxg6VLl3L58mVcXV2JjY2lR48eHD58mL///psiRYrQokULYmNjAV2A2bZtW6ysrDh+/DiLFy/mq6++Mqh7SkoKTZs2xdbWlkOHDnHkyBF9sJqcnEypUqXImzcvBw4cAODQoUMGn0H3H7RevXov7J+EhARmzZrFzz//zMGDBwkMDGTEiBH69NmzZ7Ny5UqWL1/O4cOHiYiIYPPmzfr04OBgunbtSu/evbl69SoBAQG0b9/eoE/9/f31ab/88gubNm1i0qRJ+vRp06axevVqFi5cyOXLlxk6dCgfffSRvh1RUVE0aNCAChUqcOrUKXbu3EloaCidOnXSlzFy5EgOHDjA1q1b2b17NwEBAWluPBjL/bBHuDk7Ymqqe95AURS83Vy4HxKeJu+94FB8/YZQoIUfdjZW9GvfHIBrd4IwNzOj44hvqNFjCP2/nkd4ZHS2tiOjgoODcXF1xfRpkK8oCp6eHjx8+PA/l1G6VGn8/fcRGxtHSkoKf/71Fw/u38+qKmeJuKj7WNu5oTJ53g82jvmIjUy/HTGP77FuZk2WflUIc0s7ytTSjX4XLNUcr/dqsWx8UZaPL0rQjQNUa/FltrUjo2Ii7mPr4I7Jv9pvn9eb6McvHh0FiI0K4cqJTRSt8D4AngUr8s+lfcRGhaDVarl49BeSE2NJiIvI8jZkh3BSceV5sOyKGeGkvjItNwhXp+BkaoqJ8vymjqupGaFP39/4TFGLPByNjyFeoyZVq+VAfDQhKc9/mCRqNHxy/xb9g26yOiIU9f/dnM3J7odH4u5kj6nJ8++DfK6OBIUb/v2mpKr59Ls1/DCwGyYqw1+aV4OCcbG3ofuMpVT/fAqdvvmJO8Fpv09yqshHQdg7eRhcCxxd8hMZHvjS42IiQzh3ZCOlq7bMjmpmqag37INSVdL2QVJiPMd2L6N09dZZUufMFJyQiGseC0yfzp5UFAUPqzw8jE80yNfAy5Vqbk7U3LSfmpv3cyzkMYPL5t7ZICJneqsXy+rQoYPB5+XLl+Pi4sKVK1fo1KkTQ4YM4fDhw/rAd926dXTt2hVFUQgMDGTFihUEBgbi6am7azVixAh27tzJihUrmDp1KqALWH/88UfKlSunP0+DBg0Mzrt48WIcHBw4cOAALVu2ZM+ePdy+fZuAgAD9dOUpU6bQuHFj/TEbNmxAo9GwdOlS/UucV6xYgYODAwEBATRp0oQ6deoQEBBAx44dCQgIoFevXixdupRr165RuHBhjh49yqhRo17YPykpKSxcuJDChXXPFw4cOJCvv/5anz5v3jzGjBlD+/btAVi4cCG7du3SpwcHB5Oamkr79u3x8fEBoEyZMgbnMDc3Z/ny5VhZWVGqVCm+/vprRo4cyeTJk0lJSWHq1Kns3bsXX1/d866FChXi8OHDLFq0iLp16/LDDz9QoUIFfX8/++/o7e3NjRs38PT0ZNmyZaxZs4aGDRsCsGrVKvLly/fCdoNutD8p6flIREyM8UdWfDzcOLZ6HnEJT+g7aS7bAv6mY+PaqNVqAk6dZ9+SmXg4OzFx4RqGfruQNVNHv7rQt0DHDu158PABXbt1w9LSkpo1anDI9Iixq5Wl7PL68OGoIyQnxbH75/7cvrCNohU7Ehp0lschV+g98SrmlnYc3TGBgF+H0qT7EmNXOdMlJsTwy+x21Hx/OF6FdKNfBUvWo0aLoayb3RaVyoTildoAoFLJqMDbopmtI6GpKQx98A/miopKVjacUnSPgjiZmvKrTwkcTU2JUacyOTSI36Ie6adNvy2mrNtBmxrlKZ7fg3uhjwzSUtUaAi5c58Ds0ZT08WTJnwf5aPoSjszP+TfEMupJQgyLv25Nww4jyV+ksrGrYxSJL+mD1JRkVs7oQvEKjSnn285INcx8Fx9HcyMqjkPt6mFjZsqsczeYcPIys2qUe+Wx7yItKrTZPP6Y3efLCm/1Ylk3b95k/PjxHD9+nEePHumj/MDAQEqXLk2TJk1Yu3YttWvX5s6dOxw7doxFixYBcPHiRdRqNUWLFjUoMykpibx58+o/m5ubU7ZsWYM8oaGhjB07loCAAMLCwlCr1SQkJBAYqLvbd/36dby9vQ2e2a1atapBGefPn+fWrVtp5qknJibqp3fXrVuXxYsXA7rR36lTp3Ljxg0CAgKIiIggJSWFmjVrvrB/rKys9EEwgIeHB2Fhuila0dHRBAcHU61aNX26qakplStX1o/4litXjoYNG1KmTBmaNm1KkyZN6NixI46OjvpjypUrZzDd1dfXl7i4OIKCgoiLiyMhIcHgBgBAcnIyFSpU0PfD/v37sbGxSVP/27dv8+TJE5KTkw3q6eTkRLFixV7YbtCNRP97ZDqr5HN1JvRRJKmpakxNTdBqtQSFhpPP/cU/3Gys8tCxUW027D5Ax8a1yefuQp2KZfB00f3ddWlal7ZDs77umcHDw4PwsDBSU1MxNTVFq9Xy8GGw/ubSf6EoCkMGDWLIoEEAbN+xgyJF3nvFUcZ39cQvnAtYAEDRih2IjwlFo05FZaLrh7jI+9g6vvyGjbmFDUUrtuf66d8oWrEj107+Qr4idbCwcgCgeJUP2bowZ/7wOXfoZ479NR+AMr6diY0KQa1OxeRp+6MfB2Gf1zvdY5OexLLm25YUq9SKGi2GGKRVbTyAqo0HABB06zh2TvmwtMo9U0NfxgVTgnk+QhpGCi5Pv0ZflpYbuJiYEZGailqrxURR0Gq1hKWm4GZmOF1cURR6OrnR08kNgH2xURQwfzodUlFhbqr74WVnYkozW0f846LoQu4IhPO5OBISEU2qWo2pie774H5YJN4uTgb5Dl26SVBYBAt3BJCq1hCTkEixXl9yeN4YvF0cKVfIW7/A1ocNqjH4x19ISVVjZpr+jzdjO+G/mv1bdGs/VKzbheiIYINrQWR4II4u+dM9NjEhlp/GN6dMtdY0aDcsO6udqU7sW03Asz6ok4E+mNCcMtVbU7+tYR+oU1NYObML9k7utO8/P8vbkRk8rCwJe5JEqkaDqUqFVqslOOEJntaGC+dtufOQ6u5O2D1dNLRdQU967T9ljCqLt9hbvVhWq1at8PHxYcmSJXh6eqLRaChdurR+/ne3bt0YNGgQ33//PevWraNMmTL6Ec24uDhMTEw4ffp0mjsD/w7K8uTJox+xfaZHjx48fvyY+fPn4+Pjg4WFBb6+vq817zwuLo5KlSqxdm3a599cXHRf+vXq1WPIkCHcvHmTK1euUKtWLa5du0ZAQACRkZFUfsEzl8+YpfMD5P+fAX4ZExMT9uzZw9GjR9m9ezfff/89X331FcePH6dgwYL/qY0Af/zxB15ehs85WlhY6PO0atWKGTNmpDnew8ODW7fSPlPzX4wZM4Zhw55/ocTExODtnf6P8jfh4uRAuWKFWL8rgI/eb8jW/cfwcs1L4XweBvlu3w8mv7sLZqamJKeksP3g35QuXACA9g1qsnr7XmLiE7CztmLXsdOUfq9Aptc1KzjnzUupUqXYsnUbHTu056+du3B3d6fA0xkE/0VSUhKJiYnY29sTERHBwkWLGTZkSNZVOpOUqNqVElW76j/fu7qX66c2UKJaN26f34qNg2e6qz1Hhd/G1ik/JiZmqFOTuX1hB86eutVE7fMW4O7V3VSsPwgTU3PuXt5JXve0K0/nBOVrd6d87e76zzfP7+LCkXVUqOPHlZObsHPyIq972hsaSYlxrJnZkvfKNqFu27SjXLGRwdg6epCclMD+3ydRs+XwLG1Hdqqh2DBaE8SH2rw4YMJfmmjqKLavTMsNHE1NKWKRhz2xUTSzc+RgfAwupmZpVnxO1mhI0mqxNTEhWp3KL1Hh9HoaFEempmJrYoKpopCs1XAoPpoiuWCF3GdcHewo/543v+w7TvfGNdh85Axezg4U9nQ1yOc/8/kjSvdCH1Ht8ylcX6GbFdW0cmm+Wr6ZB48i8XJ2ZOfJSxT3ds+xQTBA1YZ+VG3op/989dROTu1f83ShqI04OOfDxTOda8GTOH6a0JwSlZrStMvY7KxypqvawI+qDf7VB6ef98H5oy/vg4UTm1OiYlOadjbsA7U6lZUzu2Jl60jngYvT/BbNqfJaWlDKyY5tdx/SvlA+dgWF4m5liY+ttUE+b5s8HHj4iD7FC2JuomL/w3CK2qcdFBHiTTVrpluLISsWyzJaIPz48WOuX7/OkiVL9FOfDx8+bJCnTZs29O/fn507d7Ju3Tr8/J5fpCpUqIBarSYsLEx//H915MgRfvzxR1q0aAHoFr369+JOxYoVIygoiNDQUNzcdF/wJ0+eNCijYsWKbNiwAVdXV+zs0h/tKFOmDI6OjnzzzTeUL18eGxsb6tWrx4wZM4iMjHzp88GvYm9vj4eHB8ePH6dOnToApKamcvr0aSpWrKjPpygKNWvWpGbNmowfPx4fHx82b96sDzLPnz/PkydPyJNH92Pl77//xsbGBm9vb5ycnPQLjNWtWzfdelSsWJGNGzdSoEAB/XOm/1a4cGHMzMw4fvw4+fPr7qZGRkZy48aNF5YJukD7WbCd1eaP+pQB33zHrNUbsbPOw09ffQ7AZ9N+oEWtqrxfuyoHTl1g4W9/YGKiIlWtpl6lsozupXsO2tvdhRF+HWjU/wtUKgVPl7x8N/qTbKl7Zpgy+WtGjv6CHxcuxMbGhpnTpwHwxZdf0qhhQxo1bMiTJ09o2LgJycnJxMbFUaNWbdq2bcOoESOIiY3lw24foVKp0Gg09OzRg4YNG7zirDlP/U7z2LvuE07tnY25pS0Nu/6oT/NfP5CCpVtQqHQL7t88yPmDi1BUJmg1qeQrUpcqTXSPOJSt3Y+I0Ov8MrMmKhMzrOxcqf/B3BedMkdp1XsBWxb35dC26VjksaNt/+fTubcu+ZhiFVtSvFIrju/6ngf/nCQ5KZ6rJ7cAUKpaB+q00a2s+/OMFmi1GtSpKZSt9SFVG39qjOa8th80oZzSxhNJKhM0D8iDisUmBflOE0I1xYZqig3uijkfKnkZ9XRl6TJKHpo9XRX6ZWm5xVAXL2aGBbEuKgwrlQmjXHQzImaF3cfX2o6a1nbEadQMe3gHBdAC7e3zUsNa9x14MTGelRGhqBQFtVZLhTw2dMtl06J/GNiNfnNXMfPXndhZWbJoaA8APpn/M+9XK0vL6i+f9mltacH3Az+k/cQFaNFiZ5WH1aNz1wr6nQcuZO3cXuz+dRqWVnZ0G7Jcn7buu76UqdaaMtVaE7BtPvdunCA5MZ4LR3Xrk5Sv1ZGmnXVrqkwfWI646HASE2IY18ObImXr4zd8tVHa9Lo6fbaQdfN6sec3XR98OPh5H/zyXV9KP+2DA//ug2NP+6BmR5p0/oqzhzZw4dgmPAuU5dvBut9lBUvU4INPFhilTa/j66ql+OLYRRZe/gcbM1OmVdcNQn15/BINvVxpmM+Vj4r6cDsmntZ/HcFMpeBsacGkqs9fMdXqz8NEJKYQl5JK7c37qeaWl1k1yr7olG89raKgzeabIdl9vqy2YsUKAG7dusXt27epU6cOefLkQavVZvhGk6J9nSHGTKTRaHB1daV58+ZMmDCBwMBAvvjiC06ePMnmzZtp27YtAB999BGXLl3iwoUL3L17Vx9MPUs7cuQIs2fPpkKFCoSHh+Pv70/ZsmV5//33WblyJUOGDNGvxPxMxYoVcXZ2Zv78+cTExDBy5EhOnTrF1KlTGTJkCGq1mlKlSlGgQAFmzpxJbGwsI0aM4O+//2bLli20adOGhIQEypcvj5eXF19//TX58uXj3r17bNq0iVGjRumfgW3Xrh3bt29nxIgRTJ8+HY1Gg7OzMzExMfzxxx80bdoU0K0aXb9+fSIjI3FwcEi37lu2bKFdu3b6UeEZM2Ywc+ZMli1bRvHixZkzZw7r16+nQYMGbNmyhePHj+Pv70+TJk1wdXXl+PHjfPTRR2zZsoXmzZvTs2dPNm7cSKtWrRg7dix3796ld+/e9OrVi2nTdMHQ2LFjWbhwIbNnz6ZWrVpER0dz5MgR7Ozs6NGjBw8fPqR8+fLUrVuXUaNG4eTkxK1bt1i/fj1Lly7FxMSETz75hL/++ovly5fj6urKV199xb59++jTpw/z5s37T38vMTEx2Nvb82DPOuysXzyK/rYLdylp7CoY1dw/3IxdBaPL65x7RtiySqUepY1dBaOzLpB73s2bVXy/+8zYVTCqJdr+xq6C0eWitdiyTPNfWhi7CkYTl5JKxd/2Eh0d/cJBKWN79vv15okAbNN5jDArxcbFUaRqvRzdP68jIiKCDz74gP3796MoCjdv3qRQoUL07t0bR0fHDL2VxmhPUatUKtavX8/p06cpXbo0Q4cO5dtvv02Tr1u3bpw/f57atWsbBMGguzPg5+fH8OHDKVasGG3btuXkyZNp8v2/ZcuWERkZScWKFenevTuDBg3C1fX51CcTExO2bNlCXFwcVapUoW/fvvpVoy0tdc9IWFlZcfDgQfLnz0/79u0pUaIEffr0ITEx0eCPrW7duqjVav3or0qlok6dOvqR2jcxfPhwunfvTo8ePfD19cXW1pZ27Z4/j2hnZ8fBgwdp0aIFRYsWZezYscyePZvmzZvr8zRs2JAiRYpQp04dOnfuTOvWrZk4caI+ffLkyYwbN45p06ZRokQJmjVrxh9//KGfWu3p6cmRI0dQq9U0adKEMmXKMGTIEBwcHPTva/7222+pXbs2rVq1olGjRtSqVYtKlZ6/WkIIIYQQQoi3lVZRGWV7mwwZMgQzMzMCAwMNHi3t3LkzO3fuzFCZRhsRzm2OHDlCrVq1uHXrlsECVrlZz549iYqKMng3ck4lI8I6MiIsI8IyIiwjwiAjwiAjwjIiLCPCICPCuWVE+MbJg0YZES5apU6O7p/X4e7uzq5duyhXrhy2tracP3+eQoUK8c8//1C2bFn92kavI/csaZnNNm/ejI2NDUWKFOHWrVsMHjyYmjVrvjVBsBBCCCGEEELkBvHx8ekuMhwREZHhdYXerjHzTBQbG8tnn31G8eLF6dmzJ1WqVGHr1q3GrpYQQgghhBAiF9GiGGV7m9SuXZvVq58vuKcoChqNhpkzZ1K/fv0MlSkjwi/g5+dnsEr122jlypXGroIQQgghhBBCvNTMmTNp2LAhp06dIjk5mVGjRnH58mUiIiI4cuRIhsqUQFgIIYQQQgghsogxFq962xbLKl26NDdu3OCHH37A1taWuLg42rdvz2effYaHh0eGypRAWAghhBBCCCFEjmZvb69/k09mkEBYCCGEEEIIIUSOFhUVxbJly7h69SoApUqVonfv3tjb22eovLdrzFwIIYQQQgghchCtohhle5ucOnWKwoULM3fuXCIiIoiIiGDOnDkULlyYM2fOZKhMGREWQgghhBBCCJFjDR06lNatW7NkyRJMTXUhbGpqKn379mXIkCEcPHjwtcuUQFgIIYQQQgghsogxXmf0tr0+6dSpUwZBMICpqSmjRo2icuXKGSpTpkYLIYQQQgghhMix7OzsCAwMTLM/KCgIW1vbDJUpI8JCCCGEEEIIkUXk9UlvrnPnzvTp04dZs2ZRo0YNAI4cOcLIkSPp2rVrhsp8u3pICCGEEEIIIcRrW7BgAQUKFMDS0pJq1apx4sSJF+ZdsmQJtWvXxtHREUdHRxo1avTS/G9q1qxZtG/fHj8/PwoUKECBAgXo2bMnHTt2ZMaMGRkqUwJhIYQQQgghhHiHbdiwgWHDhjFhwgTOnDlDuXLlaNq0KWFhYenmDwgIoGvXruzfv59jx47h7e1NkyZNePDgQZbUz9zcnPnz5xMZGcm5c+c4d+4cERERzJ07FwsLiwyVKYGwEEIIIYQQQmSRZ4tlZff2OubMmUO/fv3o1asXJUuWZOHChVhZWbF8+fJ0869du5ZPP/2U8uXLU7x4cZYuXYpGo8Hf3z8zukxPrVZz4cIFnjx5AoCVlRVlypShTJkyKIrChQsX0Gg0GSpbAmEhhBBCCCGEeEclJydz+vRpGjVqpN+nUqlo1KgRx44d+09lJCQkkJKSgpOTU6bW7eeff6Z3796Ym5unSTMzM6N3796sW7cuQ2VLICyEEEIIIYQQWUSLSr9gVrZtT8O8mJgYgy0pKSlN/R49eoRarcbNzc1gv5ubGyEhIf+pjaNHj8bT09MgmM4My5YtY8SIEZiYmKRJe/b6pMWLF2eobAmEhRBCCCGEEOIt5O3tjb29vX6bNm1app9j+vTprF+/ns2bN2NpaZmpZV+/fp3q1au/ML1KlSpcvXo1Q2XL65OEEEIIIYQQ4i0UFBSEnZ2d/nN6C0s5OztjYmJCaGiowf7Q0FDc3d1fWv6sWbOYPn06e/fupWzZsplT6X+Jj48nJibmhemxsbEkJCRkqGwZERZCCCGEEEKILGLMxbLs7OwMtvQCYXNzcypVqmSw0NWzha98fX1f2K6ZM2cyefJkdu7cSeXKlTO/44AiRYpw9OjRF6YfPnyYIkWKZKhsCYSFEEIIIYQQ4h02bNgwlixZwqpVq7h69SqffPIJ8fHx9OrVCwA/Pz/GjBmjzz9jxgzGjRvH8uXLKVCgACEhIYSEhBAXF5ep9frwww8ZO3YsFy5cSJN2/vx5xo8fz4cffpihsmVqtBBCCCGEEEJkEa2ioFWyd/xRq7ze65M6d+5MeHg448ePJyQkhPLly7Nz5079AlqBgYGoVM/b8NNPP5GcnEzHjh0NypkwYQITJ0584/o/M3ToUP766y8qVapEo0aNKF68OADXrl1j79691KxZk6FDh2aobAmEhRBCCCGEEOIdN3DgQAYOHJhuWkBAgMHnu3fvZn2F0L0iaffu3cydO5d169Zx8OBBtFotRYsWZcqUKQwZMgQzM7MMlS2BsBBCCCGEEEKIHMnMzIxRo0YxatSoTC1XAmEhhBBCCCGEyCL/XrwqO88pXk4WyxJCCCGEEEII8U6REWGRq5hGhGCamMfY1TAaK3tPY1fBqJIT8xq7CkZnZip3eK0LpH31w7sm/m6SsatgdFrTjD0T9rbQJhu7Bsan1Rq7Bsan1by7nZCb2q5bLCubR4Sz+Xy5kYwICyGEEEIIIYR4p8iIsBBCCCGEEEJkEa1WQavN5hHhbD5fbiSBsBBCCCGEEEKIHEutVrNy5Ur8/f0JCwtDo9EYpO/bt++1y5RAWAghhBBCCCFEjjV48GBWrlzJ+++/T+nSpVEy4RloCYSFEEIIIYQQIsuo0Gb70kxv11JQ69ev59dff6VFixaZVubb1UNCCCGEEEIIId4q5ubmvPfee5lapgTCQgghhBBCCJFFtChG2d4mw4cPZ/78+Wgz8b1pMjVaCCGEEEIIIUSOdfjwYfbv389ff/1FqVKlMDMzfJf8pk2bXrtMCYSFEEIIIYQQQuRYDg4OtGvXLlPLlEBYCCGEEEIIIbKIMaYqv21To1esWJHpZUogLIQQQgghhBAixwsPD+f69esAFCtWDBcXlwyXJYtlCSGEEEIIIUQWkcWy3lx8fDy9e/fGw8ODOnXqUKdOHTw9PenTpw8JCQkZKlMCYSGEEEIIIYQQOdawYcM4cOAA27dvJyoqiqioKLZu3cqBAwcYPnx4hsqUqdFCCCGEEEIIIXKsjRs38vvvv1OvXj39vhYtWpAnTx46derETz/99NplSiAshBBCCCGEEFlEFst6cwkJCbi5uaXZ7+rqKlOjhRBCCCGEEEK8fXx9fZkwYQKJiYn6fU+ePGHSpEn4+vpmqEwZERZCCCGEEEKILKLVKmi12TwinM3ny2rz58+nadOm5MuXj3LlygFw/vx5LC0t2bVrV4bKlEBYCCGEEEIIIUSOVbp0aW7evMnatWu5du0aAF27dqVbt27kyZMnQ2VKICyEEEIIIYQQWUSeEc4cVlZW9OvXL9PKk0BYCCGEEEIIIUSOsm3bNpo3b46ZmRnbtm17ad7WrVu/dvkSCAshhBBCCCGEyFHatm1LSEgIrq6utG3b9oX5FEVBrVa/dvkSCAshhBBCCCFEFpGp0Rmj0WjS/XdmkdcnCSGEEEIIIYTIsVavXk1SUlKa/cnJyaxevTpDZUogLIQQQgghhBBZ5NmIcHZvb5NevXoRHR2dZn9sbCy9evXKUJkSCAshhBBCCCGEyLG0Wi2Kkja4v3//Pvb29hkqU54RziHu3r1LwYIFOXv2LOXLl89wORMnTmTLli2cO3cu0+qWFXr27ElUVBRbtmwxaj1uPQyj79xVPI6Jw84qD0uG+FHSxzPdvFqtluZj53HudhAh6+cAsOfMFcau3KzPExYdi7uDHcfmf5kt9c8M/9wLZNCXk4iIjMLWxob5U8dT/L3CBnkO/32Sb+YuID7hCYoCjerUZOywgahUKgIfPKR6s/aUKPL8mGXzZlAgf77sbsobiX50m/2/fkZiQgTmlrbU+2ABTm7FX5hfq9WyY0lbHj28QK+JdwBISYpj95qePHpwHo0mVb8/N3gUfJONP/YmPvYxllZ2dPhkGW7epdLku31pP7t++ZLkxHgURaFYheY06ToVlUp3X/Xg1m85c/BnTEzNMDOz5P2ec/F+r2p2N+e13U9OYnrYfWI0qVirTBjlmo+C5pYGeTRaLYseh3AyIRY1UNrSiiEunpgpKkJSkvko8LrBMRPd8+NlZpHNLcm4RZowTmjjCCOV+ar8FFIs0823WxPN79oItEBZJQ+fKG6YPv1x8rK03ODWg1D6fbtM951gnYfFw3tTsoBXunm1Wi0tRs/i3K17BG/6IU16/1nLWLPnKA83fo+DjVVWVz3ThD28ydq5PYmPeYSllT3dhqzAwyftteDG+X1sXzWGpMQ4FBRKVmlBqx7TUalUPLx7kd8XDiQ2KgyViSk+RavQccACzC0y9q7P7Bb+8CZr5z3vgw+HrMAjf/p9sGP1v/qgcgta/l8fxEXr+iB/kdzTB3dj4/ni70tEJidjY2bK9GplKGJvY5BHo9Uy49x1DgU/wkRRcLAw45sqpfCxteZ6VCxfn77K48RkTFUKZZzsmVCpBJamJkZqkcjNKlSogKIoKIpCw4YNMTV9Hr6q1Wru3LlDs2bNMlS2BMKZRFEUNm/e/NIVzV7G29ub4OBgnJ2dM7di4qUGLlhLn6a16N7Il01HztBv3mqOzP0i3bzfbfWnkLsL524H6fc1rliSxhVL6j+3n7SAOmWLZXm9M9PIidP4qGM7urRryfZd/gz+8mt2/brKII+9vR2LZk3Bx9uLxKQkOvUZyK9b/6RLu5YA2Fhb4b9prTGqn2kObhpGiap+FKv8If9c3EbAb5/RfqD/C/NfPPwTdnkL8ujhBf0+lYkZ5esOwsLKke2LX38Zf2PauvRTqjTsS8V6Pbj090Y2/tSHT6f+nSZfHmsHugxai5NbIVKSE1kxpSnnDv5MxXo9eHj3HMf3LGTQrPNYWNpw7tBatq8YzKdTjhmhRa9nTvgDWto50czOkQNx0cwMu89P+d4zyPNnbCQ3k56wyPs9TFGYHf6AjVGP6eLoAkAelYol3kWMUf1MUVOxoYPiyGhN0AvzhGhTWKt9xDyVDw6Y8I3mIbuI5n3F4aVpucXn81fTu0UdujepxeZDp+g/ezmHvx+Xbt7vN+2moIcL527dS5O25fBpTE1y54/+XxcMoEbTflRr1JNzR35n7bxejJh7Ik0+KxtHeoz6BWd33bVgwdjGnNy3mmqNemJqbkmHj7/Hq2BZNGo1q2d1w3/jDJp/ODH7G5QBvy4YgG/TflRrqOuDdfN6MXxO+n3gN/J5H/w4rjEn96+mWsOemJlb0vHj7/F81gezc08fjD95hU6F89G+kBc7g0L44vhFNjbxNciz70EYZ8Kj2NqsBmYqFT9evs2cCzeZX7M8FiYqxlUqQXEHW9QaLcOPXWDJ1Tt8Xua9F5zx7adFQauVxbIy4llsde7cOZo2bYqNzfObMubm5hQoUIAOHTpkqGyZGp1DmJiY4O7ubnCXQ2StsKgYztwMpGt93WhVuxoVePAoktsPw9LkvXLvIdv/Ps+Ijk1fWN7Dx1Hsv3CdD+vn/NGvZ8IfR3D+8jU6ttLdSWvZpAEPQ0K5c8/wh3CZEsXw8daNilhaWFCqeFGCHj7M9vpmlSdx4YQ/OEeRCp0AKFi6FXFRD4l+9E+6+SNCr3Hn8p+UrzfYYL+JqQVe79XBwjJjU3SMJS46jAf/nKZc7W4AlKrWnujH93kccitNXs+CFXByKwSAmbkl7j7liAzXBQK61xekkJIYD8CT+CjsnXL+zIDI1FRuJD2hsa0DAHWs7QhLTeFBiuGiHLeTnlDJygYzRYWiKFS1smVvXFT2VziLlFascFbMXprnqDaWqooNjoopiqLQXGXPAW3MK9NyA913wl26NtT94G9bqxL3wyO4/SA0Td4rdx+w/ehZRnRukSYtNDKab9f/wYyPO2d5nTNbbFQYgTdPUbn+RwCUq9GBqEdBhD9Mey3IV7gCzu7PrwX5CpUjIuwuAK6eRfAqWBYAlYkJ+YtUJiI07Q2DnCg2KozAW6eoXO/1+8CrYDkiQu8C4OJZBM//74OwnN8HjxOTuBQRTesCHgA0zedGSEIi92Lj/y+nQrJGQ5Jag1arJS4lFXcr3SySArbWFHewBcBEpVAmrx0P4p9kZzPEW2TChAlMmDCBFStWMHnyZP3nCRMmMGbMGLp27Yq5uXmGyn5nAuHY2Fi6deuGtbU1Hh4ezJ07l3r16jFkyBBA9wPu/6fpOjg4sHLlSkC3ItnAgQPx8PDA0tISHx8fpk2bBkCBAgUAaNeuHYqiUKBAAaKjozExMeHUqVOAbslvJycnqlevri9/zZo1eHt7A7qp0Yqi6Kc0BwQEoCgK/v7+VK5cGSsrK2rUqMH169cN6jh9+nTc3NywtbWlT58+JCYmGqQHBARQtWpVrK2tcXBwoGbNmty7p7sQT5w4kfLly7No0SK8vb2xsrKiU6dOaR5EX7p0KSVKlMDS0pLixYvz448/GqQHBQXRqVMnHBwccHJyok2bNty9e1efrlarGTZsGA4ODuTNm5dRo0ah1Wpf8V8s691/FIm7k53+rr2iKORzcSQoPMIgX0qqmk9/WMsPn32IierF/8us8T9G00qlcHWwy9J6Z6aHIaG4ueTV34BRFAUvD3ceBIe88Jiw8Efs2O1P47q19PsSnjyhaaceNO7Yndk/Ls3Qu9yMKS7qAVa2bqhMnveDjYMXcVH30+RVq1M4uHEIddrPRqXkzhGf/xf9OAhbBw9M/tV+e2dvoh69eGQQIDYqhMvHN1Gsoi4Y8PApR80Wg5k1qAgzPi3A0T+/o2WveVld/TcWrk7BydQUk6dTeBVFwdXUjNCUFIN8RS3ycDQ+hniNmlStlgPx0YSkJOvTEzUaPrl/i/5BN1kdEYo6B1znMls4qbjyPFh2xYxwUl+ZlhvcD4/A3cne4DvB2yVvOt8JqXw2bxXfD/ZL9zvhs3mrmNL3A2ytcv4U2P8X9SgIeyfDa4GjS34iwwNfelxMZAjnjmykVJWWadKSEuM5tnsZpavnjlkyUY+CsMtgH5w/+uI++Hv3MspUy/l9EJyQiEseC0yf/m0rioKHlSUPEwx/XzbwcqGqqyO1tgRQa2sAf4dGMKh02hHfhNRUfrv9gIb5XLOl/jmVBsUo29ukR48eWFqm/8hORr0zgfCwYcM4cuQI27ZtY8+ePRw6dIgzZ8785+O/++47tm3bxq+//sr169dZu3atPgA+efIkACtWrCA4OJiTJ09ib29P+fLlCQgIAODixYsoisLZs2eJi4sD4MCBA9StW/el5/3qq6+YPXs2p06dwtTUlN69e+vTfv31VyZOnMjUqVM5deoUHh4eBkFqamoqbdu2pW7duly4cIFjx47Rv39/gwfNb926xa+//sr27dvZuXMnZ8+e5dNPP9Wnr127lvHjxzNlyhSuXr3K1KlTGTduHKtW6abOpqSk0LRpU2xtbTl06BBHjhzBxsaGZs2akZys+4E4e/ZsVq5cyfLlyzl8+DARERFs3vz8udr0JCUlERMTY7AZy5RfdtDGtzzFvT1emEer1bJq7zF6NqmZjTXLfrFxcXT/bDif9e5O+dK6KeFuLs6c3fcHu35dxa9Lf+D4mXP8tDJ3T5N+mdN7Z1KwdEscXXPXFPjMlpgQw88z21K79XDyFa4MQETYHS6f2MywedcY/eNdarQYxIb5Hxq5ppmnma0jVaxsGfrgH4Y8+Id8Zhb64NnJ1JRffUrwU773mOVZkIuJCfwW9cjINRZZYcqabbSpWZHi+dOuJ7Hir4N4u+SlXvkSRqiZcSQmxLD469Y07DCS/EUqG6SlpiSzckYXildoTDnfdkaqYdZLTIhhyeTWNGiffh+smtmFYhUaU/Yt6oNLEdHcjI7jYJu6HGpTj+puTkw4dcUgT7Jaw9CjF6jlnpfG+dyMVFPxtlCr1cyaNYuqVavi7u6Ok5OTwZYR78Q83NjYWFatWsW6deto2LAhoAtaPT3TXxQpPYGBgRQpUoRatWqhKAo+Pj76NBcX3fNhDg4OuLu76/fXq1ePgIAARowYQUBAAI0bN+batWscPnyYZs2aERAQwKhRo1563ilTpuiD5S+++IL333+fxMRELC0tmTdvHn369KFPnz4AfPPNN+zdu1c/KhwTE0N0dDQtW7akcGHdQkYlShh+OScmJrJ69Wq8vHTTXr///nvef/99Zs+ejbu7OxMmTGD27Nm0b98egIIFC3LlyhUWLVpEjx492LBhAxqNhqVLl+oD7BUrVuDg4EBAQABNmjRh3rx5jBkzRl/GwoUL2bVr10vbPW3aNCZNmvTSPG8qn7MjIRExpKrVmJqYoNVquR8eibeL4f9Mhy7dJCg8koV/BJCq1hCTkEixPl9xeM4XuNjb6vMkJqfQuELJ9E6VY3m6uxEa/pjU1FRMTU3RarU8CA7By8M9Td64+Hi6fjyYZg3qMKBnN/1+C3NzXPLq+szRwZ6u7Vqx6Y9dDOzjl23tyIgbp9dz4fBPALxXrj0JsaFo1KmoTHT9EBf1ABuHtNN6g+8cJS7qPpeOLkWrSSU5KZa108vTfuBe8tjknmf8zx78mcN/zAOgXI0uxEYFo1anYvK0/dGPgnBw9k732KQnsaya9j4lKrem1vtD9fsvH9+Mm3dp7Jx019ZK9XqyY+UQUlOTMTXN2LSl7OBiYkZEaipqrRYTRUGr1RKWmoKbmeE0YUVR6OnkRk8n3Q+6fbFRFDDXLYZlrqgwN9XdW7YzMaWZrSP+cVF0wSV7G5PFXDAlmOcj5WGk4PL0p8TL0nKDfC5OhEREG3wnBIU/TvOdcPjCDYLCH7Nw+z79d0Jxv1Ec+m4cB89f4/DFG/x14rw+f9UBE/h14kDKv+fz/6fMEU7sW03AlrkAVKzThegIw2tBZHggji750z02MSGWnyY0p0z11tRvO8wgTZ2awsqZXbB3cqd9//lZ3o43cWLfagK2Pu+DmNfsg4UTm1OmWvp9sOrbLtg5udO+X87ug2c8rCwJf5JEqkaDqUqFVqslOCERTyvDkbgtdx9S3TUvdua662S7gl70DjilT0/RaBh69DwuluZ8VfHFC08K8V9NmjSJpUuXMnz4cMaOHctXX33F3bt32bJlC+PHj89QmbnnG+oN/PPPP6SkpFC16vNnN+3t7SlW7L+P6PTs2ZPGjRtTrFgxmjVrRsuWLWnSpMlLj6lbty7Lli1DrVZz4MABmjRpgru7OwEBAZQtW5Zbt25Rr169l5ZRtmxZ/b89PHQjkmFhYeTPn5+rV68yYMAAg/y+vr7s378fACcnJ3r27EnTpk1p3LgxjRo1olOnTvpyAPLnz68Pgp8dr9FouH79Ora2tty+fZs+ffrQr18/fZ7U1FT9MuXnz5/n1q1b2NraGtQjMTGR27dvEx0dTXBwMNWqVdOnmZqaUrly5ZdOjx4zZgzDhj3/QomJidFPI88srg52lC/szS/7T9C9kS+bj57Fy9mBwp6G03f8Z4zQ//te6GOqDZ7C9WVTDPKs3H2E7g2rY2KSuyZZuOR1okzJYvy+fSdd2rVkx+59eLi7UtDHsK/j4xPo2n8w9Wv6MnRAH4O08McRONjZYWZmSlJyMn/s3U/pEjl/tLRopS4UrdRF/znwuj83z/5KscofcufSdqztPbB3LpTmuDYD/tD/OzYikN+/q0u3L85lR5UzVYU63alQp7v+841zOzl/aC0V6/Xg8vFN2OX1Iq972mluSYlxrJz2PkXKN6V+e8PV0Z3cCnLmwCqSEuOwsLTh2pk/cPYomqODYABHU1OKWORhT2wUzewcORgfg4upWZoVn5M1GpK0WmxNTIhWp/JLVDi9ngbFkamp2JqYYKooJGs1HIqPpkguWB32ddVQbBitCeJDbV4cMOEvTTR1FNtXpuUGrg52lH/Ph1/8j9G9SS22HD6Nl7Mjhb0MR7L2znm+oOK9kEdU/3Qi11bPBGDFF/0N8lo17cOJhZNy9KrRVRv4UbXB8xuXV0/v5NT+NVRr1JPzRzfi4JwPF890rgVP4lg4sTklKjalaeexBmlqdSorZ3bFytaRzgMXp/vKk5wk3T4IWEO1hq/ug0UTm1O8YlOapNMHq77tipWNI50/y/l98ExeSwtKOdqx7W4w7Qt5set+KG55LPGxtTbI521txYHgcHoXL4C5iYr9D8Mo+nRl6VSNhmFHz2NvbsbkKqVyTduzkjHe6/u2LJb1zNq1a1myZAnvv/8+EydOpGvXrhQuXJiyZcvy999/M2jQoNcu850IhP8L5ekowL+l/Ov5sIoVK3Lnzh3++usv9u7dS6dOnWjUqBG///77C8usU6cOsbGxnDlzhoMHDzJ16lTc3d2ZPn065cqVw9PTkyJFXr7CqNm/RiSeXUg0Gs1/bteKFSsYNGgQO3fuZMOGDYwdO5Y9e/YYPKv8Is+mcC9ZssQgkAXd4l7P8lSqVIm1a9NOhX02Up4RFhYWWFhk/WtHfvjsQ/rNW83M33ZiZ2XJosG6L8JPvvuZ96uVpWW1cq8sIzr+CVuPnePUD2NfmTcn+nbCGAZ/NYnvlqzAxsaa+d/o7qoNG/8NTevVoWmDOixZs56zly6T8OQJf+7V3Whp1bQhQz7uzYkz55n5wyJMVCpS1WpqVavMkI8z9mJzY6rTfjb7fxvI2YB5mFnYUO+D569DOfD7YHxKNqNAyeavLOe3ebVJjH9MclIsa6aWxrNwLRp0XpiVVc8Ubfr9yMaf+hCwZQYWVrZ0GLBUn7ZpUX9KVGpFicqtOPrX99y/fZLkpHgun9A94lC6ekfqtxtDySptuX/7FD9+WR1TU3PMLazp9PlqYzXptQx18WJmWBDrosKwUpkwykU3G2BW2H18re2oaW1HnEbNsId3UAAt0N4+LzWsdWsCXEyMZ2VEKCpFQa3VUiGPDd0cc9do8A+aUE5p44kklQmaB+RBxWKTgnynCaGaYkM1xQZ3xZwPlbyMerqydBklD82ergr9srTc4vtBfvSfvYxv1/+JrZUli4brHkf6ZO5K3q9enpa+5Y1bwWzQ6bOFrJvXiz2/TcPSyo4PBy/Xp/3yXV9KV2tNmWqtObBtPvdunCA5MZ4Lx3TXgvI1O9Kk81ecPbSBC8c24VmgLN8OrghAwRI1+OCTBUZp0+vq9OlC1s3vxd6nfdB10PM+WP99X0pXbU3paq05sH0+926eICnp//qg0//1wRBdHxQqUYOOA3J+H0yqUooxxy+y6Oo/WJuaMq1aaQC+OnGJBl6uNPRypVuR/NyOiaPNrqOYKgoulhZMqqKbFfdnYAi774dRzMGGtrt0bw2o6OzAhMq5a9acyFlCQkIoU6YMADY2Nvo1jVq2bMm4cemv7v8qijYnrFqUxWJjY8mbNy+//PKLfnnt6OhovLy86Nu3L/PmzcPNzY0JEybon4+9efMmRYsWZcWKFfTs2TNNmbt27aJZs2Y8fvwYJycnzM3NDcp/pkKFCpQtW5bdu3cTHBxMREQEHh4edOzYEa1Wy7p164C07xEOCAigfv36REZG4uDgAOiWDa9QoQJ37tyhQIEC1KhRgwoVKrBgwfOLqq+vL0+ePHnhe4R9fX2pUqUK3333HRMnTuSbb74hMDBQP018165dtGjRggcPHuDu7o6XlxcDBgx44R/YkiVLGD16NHfv3sXOLv1Fojw9PRk6dCgjR44EdCPKhQoVomLFiv/5PcIxMTHY29sTumEOdrlwAZLMEl2okrGrYFQTthd+daa3XP4CuWcxtqzi+1VFY1fB6OLvJr0601uu4Z+jjV0Fo1qSnPtuOGa2t/8X7Ks1X/fqG7Rvq7iUVCpt9Cc6OvqFv0GN7dnv1wOn72Bjk70zZOLiYqlbqWCO7p/XUaxYMVavXk21atWoVasWLVu25IsvvmDDhg18/vnnhIWlfevLq+SueZwZZGtrS48ePRg5ciT79+/n8uXL9OnTB5VKpR9lbdCgAT/88ANnz57l1KlTDBgwwGA0ds6cOfzyyy9cu3aNGzdu8Ntvv+Hu7q4PUgsUKIC/vz8hISFERkbqj6tXrx5r167VP+fr5OREiRIl2LBhwysXynqVwYMHs3z5clasWMGNGzeYMGECly9f1qffuXOHMWPGcOzYMe7du8fu3bu5efOmwXPClpaW9OjRg/Pnz3Po0CEGDRpEp06d9M86T5o0iWnTpvHdd99x48YNLl68yIoVK5gzZw4A3bp1w9nZmTZt2nDo0CHu3LlDQEAAgwYN4v79+/p6Tp8+nS1btnDt2jU+/fRToqKi3qjtQgghhBBCiHdDu3bt8Pf3B+Dzzz9n3LhxFClSBD8/P4PFhF/HOzM1es6cOQwYMICWLVtiZ2fHqFGjCAoK0i/DPXv2bHr16kXt2rXx9PRk/vz5nD59Wn+8ra0tM2fO5ObNm5iYmFClShX+/PNPVE+Xl589ezbDhg1jyZIleHl56V8fVLduXebNm2fwLHC9evU4f/78K58PfpXOnTtz+/ZtRo0aRWJiIh06dOCTTz7RL0RlZWXFtWvXWLVqFY8fP8bDw4PPPvuMjz/+WF/Ge++9R/v27WnRogURERG0bNnSYOXpvn37YmVlxbfffsvIkSOxtramTJky+tdOWVlZcfDgQUaPHk379u2JjY3Fy8uLhg0b6u8+DR8+nODgYHr06IFKpaJ37960a9cuzWuahBBCCCGEeNtoyf5ndt+2CRPTp0/X/7tz587kz5+fY8eOUaRIEVq1apWhMt+JqdHpiY+Px8vLi9mzZ+tXXX7XTJw4kS1btrxwGnVOIlOjdWRqtEyNlqnRMjUaZGo0yNRomRotU6NBpkbnlqnRAafvYGOTvXWMi4uh3ls0NTorvDMjwmfPnuXatWtUrVqV6Ohovv76awDatGlj5JoJIYQQQgghhPi3bdu2/ee8rVu3fu3y35lAGGDWrFlcv34dc3NzKlWqxKFDh3B2zj3v/RRCCCGEEELkLlqtglabzVOjs/l8WaFt27b/KZ+iKKjV6tcu/50JhCtUqGDwzK/QTY2eOHGisashhBBCCCGEEAZe55WxGfHOBMJCCCGEEEIIkd20KEZYLCv3jwi/SGJion7B4zfxTrw+SQghhBBCCCFE7qRWq5k8eTJeXl7Y2Njwzz//ADBu3DiWLVuWoTIlEBZCCCGEEEIIkWNNmTKFlStXMnPmTMzNzfX7S5cuzdKlSzNUpgTCQgghhBBCCJFFni2Wld3b22T16tUsXryYbt26YWJiot9frlw5rl27lqEyJRAWQgghhBBCCJFjPXjwgPfeey/Nfo1GQ0pKSobKlEBYCCGEEEIIIbKIFtBk86bNlpZln5IlS3Lo0KE0+3///XcqVKiQoTJl1WghhBBCCCGEEDnW+PHj6dGjBw8ePECj0bBp0yauX7/O6tWr2bFjR4bKlBFhIYQQQgghhBA5Vps2bdi+fTt79+7F2tqa8ePHc/XqVbZv307jxo0zVKaMCAshhBBCCCFEFjHG4lVv02JZqampTJ06ld69e7Nnz55MK1dGhIUQQgghhBBC5EimpqbMnDmT1NTUTC1XAmEhhBBCCCGEyCJaFKNsb5OGDRty4MCBTC1TpkYLIYQQQgghhMixmjdvzhdffMHFixepVKkS1tbWBumtW7d+7TIlEBZCCCGEEEKILCLPCL+5Tz/9FIA5c+akSVMUBbVa/dplSiAshBBCCCGEECLH0mg0mV6mPCMshBBCCCGEECJHSklJwdTUlEuXLmVquTIiLIQQQgghhBBZxBiLV71Ni2WZmZmRP3/+DE1/fhkZERZCCCGEEEIIkWN99dVXfPnll0RERGRamTIiLIQQQgghhBBZRKPVbdl9zrfJDz/8wK1bt/D09MTHxyfNqtFnzpx57TIlEBZCCCGEEEIIkWO1bds208uUQFgIIYQQQgghRI41YcKETC9TnhEWQgghhBBCiCzybLGs7N5e14IFCyhQoACWlpZUq1aNEydOvDT/b7/9RvHixbG0tKRMmTL8+eefGe2i/+z06dOsWbOGNWvWcPbs2TcqS0aERa7yT5H3sbG1NXY1jOZejLOxq2BUtXzlkhURY+waGJ/vd58ZuwpGpzU1M3YVjM6/xQxjV8Go7Pf2MnYVjE77lj0DmRFuI4YZuwpGkycuHjb6G7sab40NGzYwbNgwFi5cSLVq1Zg3bx5Nmzbl+vXruLq6psl/9OhRunbtyrRp02jZsiXr1q2jbdu2nDlzhtKlS2d6/cLCwujSpQsBAQE4ODgAEBUVRf369Vm/fj0uLi6vXaaMCAshhBBCCCFEFtFqFaNsr2POnDn069ePXr16UbJkSRYuXIiVlRXLly9PN//8+fNp1qwZI0eOpESJEkyePJmKFSvyww8/ZEaXpfH5558TGxvL5cuXiYiIICIigkuXLhETE8OgQYMyVKYEwkIIIYQQQgjxjkpOTub06dM0atRIv0+lUtGoUSOOHTuW7jHHjh0zyA/QtGnTF+Z/Uzt37uTHH3+kRIkS+n0lS5ZkwYIF/PXXXxkqU+YZCiGEEEIIIUQW0Wqzfyr/s/PFxBg+U2VhYYGFhYXBvkePHqFWq3FzczPY7+bmxrVr19ItPyQkJN38ISEhb1jz9Gk0GszM0j4WZGZmhkajyVCZMiIshBBCCCGEEG8hb29v7O3t9du0adOMXaUMadCgAYMHD+bhw4f6fQ8ePGDo0KE0bNgwQ2XKiLAQQgghhBBCvIWCgoKws7PTf/7/0WAAZ2dnTExMCA0NNdgfGhqKu7t7uuW6u7u/Vv439cMPP9C6dWsKFCiAt7c3oGtb6dKlWbNmTYbKlEBYCCGEEEIIIbKIBgVNBl5n9KbnBLCzszMIhNNjbm5OpUqV8Pf3p23btrrjNRr8/f0ZOHBgusf4+vri7+/PkCFD9Pv27NmDr69vptT//3l7e3PmzBn27t2rn65dokSJNM8pvw4JhIUQQgghhBDiHTZs2DB69OhB5cqVqVq1KvPmzSM+Pp5evXSvavPz88PLy0s/tXrw4MHUrVuX2bNn8/7777N+/XpOnTrF4sWLs6yOiqLQuHFjGjdunCnlyTPCQgghhBBCCJFFcsPrkzp37sysWbMYP3485cuX59y5c+zcuVO/IFZgYCDBwcH6/DVq1GDdunUsXryYcuXK8fvvv7Nly5ZMf4fwvn37KFmyZJpFvwCio6MpVaoUhw4dylDZMiIshBBCCCGEEO+4gQMHvnAqdEBAQJp9H3zwAR988EGW1mnevHn069cv3end9vb2fPzxx8yZM4fatWu/dtkyIiyEEEIIIYQQIsc5f/48zZo1e2F6kyZNOH36dIbKlhFhIYQQQgghhMgixnyPcG4XGhqa7vuDnzE1NSU8PDxDZcuIsBBCCCGEEEKIHMfLy4tLly69MP3ChQt4eHhkqGwJhIUQQgghhBAii2hRjLK9DVq0aMG4ceNITExMk/bkyRMmTJhAy5YtM1S2TI0WQgghhBBCCJHjjB07lk2bNlG0aFEGDhxIsWLFALh27RoLFixArVbz1VdfZahsCYSFEEIIIYQQQuQ4bm5uHD16lE8++YQxY8agffrws6IoNG3alAULFuhf8fS6JBAWQgghhBBCiCyi0eq27D7n28LHx4c///yTyMhIbt26hVarpUiRIjg6Or5RuRIICyGEEEIIIYTI0RwdHalSpUqmlSeBsBBCCCGEEEJkFa2CVpvNi1dl9/lyIVk1WgghhBBCCCHEO0VGhIUQQgghhBAii2i1ui27zyleTkaEhRBCCCGEEEK8UyQQFkIIIYQQQgjxTpGp0UIIIYQQQgiRRTQoaMjexauy+3y5kYwICyGEEEIIIYR4p8iIsBBCCCGEEEJkEVksK2eSQPgNBAQEUL9+fSIjI3FwcMhwOT179iQqKootW7a8ME+9evUoX7488+bNA6BAgQIMGTKEIUOGZPi8GWXMc2e2e3f/4atRg4mKjMTG1pZvZszjvSLFDPI8uB/E2NFDuHblEl75vPl9+1592vFjh5k3ayoJ8fEoikKdeg0ZMvIrVKrcNdniQeBN5k7oQ0zUI6xt7BkycSk+hUulm3f3lhX8tvJbtBoNZavU49MvvsfUzIwLpw4wcVArvHyK6vPOWnEIC8s82dWMNxJy/yaLpvYkLvoReazt6T9mBfkKpu2D8OC7LJ7ei3s3z+LiUZApy87q025eOsbKuZ8CoE5NoWiZmnQf9B1m5hbZ1o6MCnt4k7VzexIf8whLK3u6DVmBh0/a9t84v4/tq8aQlBiHgkLJKi1o1WM6KpWKpCdxLJ/WkaBbp9FoUpm+PtIILcmYWw9C6TtnFY9j4rCzzsOSoT0o6eOZbl6tVkvzL+dx7nYgIb/O1e8PDItg6E+/cPNBGCYqhX4t6vJp6/rZ1YQ3dutBKP2+Xabvg8XDe1OygFe6ebVaLS1Gz+LcrXsEb/ohTXr/WctYs+coDzd+j4ONVVZXPVMs0oRxQhtHGKnMV+WnkGKZbr7dmmh+10agBcoqefhEccNUUV6ZlluE3L/J0uk9iY1+hJW1PX1Hr8ArvWthyF2WTu9F4K2zOLsXZPLSs2nyaLVaZg5vxN0bZ/hpR+65Hjzrg7gY3fdB31Hp98GjkLssnfG8D75e8rwPrp0LYM4XLXD3fv6bYuwPRzG3yPnfibcDH/DJxG95HBWDnY01P04YTonCBQzynLhwhWHTvwcgNTWV6uVKM2PkJ1iYm780TYicJHf9Ws9hatSoQXBwMPb29tl+7pMnT9K/f/8sPcfKlSvTDfCz49zZ5etxo+nY+SN27DlM736fMXb0kDR5bGxs+HzoKGbMWZAmzc7Onplzf2LrzgNs2LKTc2dPsW3zb9lQ88y1YMpnNGvXl8Wbr9ChxwjmTeybbr6QB3dY89NEZi7dx5KtV4mKCGXn5qX6dC+fonz/yyn9lluCYIDlswZQv1U/vl17nZYfjmLxtF7p5stjbUfHPpP5dNzaNGn53yvHpEUnmLLsLFNXXCAmMpy9W37M6qpnil8XDKBG036MXXSdRh1HsXZe+u23snGkx6hf+PLHy4yYd4o7V49xct9qAExMzWjYYRSffrMnO6ueKQb+sI4+zWpxccnXDO/YhH5zV70w73db/Cnk4WywT6vV0uWbhXzYoDoXFk/i7MKJdKhdKaurnak+n7+a3i3qcGH5VIZ3ak7/2ctfmPf7Tbsp6OGSbtqWw6cxNTHJqmpmmZqKDTNU3ri+ZIwgRJvCWu0jZqi8WawqQJRWzS5t9CvTcpNVcwZQt2U/Zvx8nRZdR7F0xguuhVZ2dOgzmY+/SnstfGbXb3Nx8SyUVVXNMqvmDKBey35MX32dFl1GsXRm+n1gaWVH+94v7gN372J8veSsfssNQTDAkKnz6dGuBac3LWeIXyc+nTQ7TZ7SRQuxf/X3HF73E0fXLyI8Moqlv+14ZZoQOUmOD4TVajUajcbY1UiXubk57u7uKEa42+vi4oKV1YvvsqekpBjt3LnF48ePuHzxPC3bdACgcbP3CQl+SOC9Owb57B0cqVi5GnnSaXOJUmXwzu8DgIWFJcVKlOLhg6Csr3wmiooI4+bV09Rv8SEANRu2Jzz0Pg+DbqXJe8R/E1XrtsTRWfd337xDfw7u3JDdVc500ZFh3Ll+ipqNPwKgSt0ORIQHEXo/bR/Y2DlRrGwtLCyt06RZWFphamoGQGpKMsnJT4xyfXhdsVFhBN48ReX6uvaXq9GBqEdBhD9M2/58hSvg7K77YWtmbkm+QuWICLsLgKmZBUXLNSCPtUN2VT1ThEXFcObmPbo2qAZAu5oVeRAeye2HYWnyXrn3kO3HzjHig2YG+/efu4a5malB8OvmaJe1Fc9Euj64S9eGvgC0rVWJ++ER3H4QmibvlbsP2H70LCM6t0iTFhoZzbfr/2DGx52zvM6ZrbRihbNi9tI8R7WxVFVscFRMdddAlT0HtDGvTMstYp5eC2s8vRZWrtOBx2FBhD5I/1pYtEwtLPKkvRYCPLhzmTNHttKy6xdZWufMFhMZxt0bp/D9Vx9EvKoP0vk+yK3CI6I4d/UmnZs3BKB1w1o8CA3nn6AHBvmsLC0xM9XdNEpOSSUxKYlnX3cvS3tXabWKUTbxcq8dCO/cuZNatWrh4OBA3rx5admyJbdv3wZ0I6SjR482yB8eHo6ZmRkHDx4EICkpiREjRuDl5YW1tTXVqlUjICBAn//ZKOS2bdsoWbIkFhYWBAYGcvLkSRo3boyzszP29vbUrVuXM2fOGJzr2rVr1KpVC0tLS0qWLMnevXtRFMVgynFQUBCdOnXCwcEBJycn2rRpw927dwG4dOkSKpWK8PBwACIiIlCpVHTp0kV//DfffEOtWrUA3dRoRVGIiooyqPuuXbsoUaIENjY2NGvWjODgYP3xarWaYcOG6ftv1KhRaP9vEn98fDx+fn7Y2Njg4eHB7Nlp78QVKFBAP00aQFEUfvrpJ1q3bo21tTVTpkwBYOvWrVSsWBFLS0sKFSrEpEmTSE1N1R8XFRXFxx9/jJubG5aWlpQuXZodO3YQEBBAr169iI6ORlEUFEVh4sSJ6Z47MDCQNm3aYGNjg52dHZ06dSI09PmPp4kTJ1K+fHl+/vlnChQogL29PV26dCE2NjZNu7JTSPBDXFxdMX16sVYUBQ9PL4IfPnjFkel7FB7Gnp1/ULd+48ysZpZ7FHofJ2d3TP7VDy7u3oQHpw3ow0OCcHXPr//s5ulDeMjzfCH3/2Hwh1UZ2t2XP35dmPWVzyQRYUE45PUw6IO8rvl5FBb42mWFB9/ly97l+bSNC1bW9jRq+2lmVzfTRT0Kwt7JAxOT5+13dMlPZPjL2x8TGcK5IxspVaVldlQzy9wPj8TdyV4/iqkoCvlcHQkKjzDIl5Kq5tPv1vDDwG6YqAx/YFwNCsbF3obuM5ZS/fMpdPrmJ+4Eh2dbG97U/fCINH3g7ZI3nT5I5bN5q/h+sB8m6TwC8tm8VUzp+wG2Vrlj5Ot1hZOKK8+DZVfMCCf1lWm5hf5a+K9rQV63/DwOfb1rYWpqCitm96fnsIUouWx2QER4EA7/dz3M6/r6fQAQ/vA2E/pXYtInVdm3NXfMDnoQGo5bXidMTf91PXRzISgk7fXs3sMQanYdQOFGH2BnY03fD1r9pzQhcorXDoTj4+MZNmwYp06dwt/fH5VKRbt27dBoNHTr1o3169cbBHYbNmzA09OT2rVrAzBw4ECOHTvG+vXruXDhAh988AHNmjXj5s2b+mMSEhKYMWMGS5cu5fLly7i6uhIbG0uPHj04fPgwf//9N0WKFKFFixb6YEqtVtO2bVusrKw4fvw4ixcv5quvvjKoe0pKCk2bNsXW1pZDhw5x5MgRfbCanJxMqVKlyJs3LwcOHADg0KFDBp8BDhw4QL169V7YPwkJCcyaNYuff/6ZgwcPEhgYyIgRI/Tps2fPZuXKlSxfvpzDhw8TERHB5s2bDcoYOXIkBw4cYOvWrezevZuAgIA0QX96Jk6cSLt27bh48SK9e/fm0KFD+Pn5MXjwYK5cucKiRYtYuXKlPkjWaDQ0b96cI0eOsGbNGq5cucL06dMxMTGhRo0azJs3Dzs7O4KDgwkODjZoxzMajYY2bdoQERHBgQMH2LNnD//88w+dOxuOBty+fZstW7awY8cOduzYwYEDB5g+ffoL25KUlERMTIzBlpPFxcYy8OMe9Or3KaXKlDN2dYziveIVWPnnHeavO8FXs37jr42LObQ7900Tf1MuHgWYuvwcP2wKJiUliZMHNxm7SlkiMSGGxV+3pmGHkeQvUtnY1ckWU9btoE2N8hTP75EmLVWtIeDCdcZ0acHf339F44ql+Gj6EiPUMmtNWbONNjUrUjx/2uenV/x1EG+XvNQrX8IINRM5ydZVk6hUux2ePu/u34JPkYrM3hDEpMWn+fzrTezfvogTAb8au1qZysfTnSO/LOT6rvUkJaewfd+R/5T2LtJojbOJl3vtxbI6dOhg8Hn58uW4uLhw5coVOnXqxJAhQzh8+LA+8F23bh1du3ZFURQCAwNZsWIFgYGBeHrqvkRHjBjBzp07WbFiBVOnTgV0AeuPP/5IuXLPA4oGDRoYnHfx4sU4ODhw4MABWrZsyZ49e7h9+zYBAQG4u7sDMGXKFBo3fj46t2HDBjQaDUuXLtVPV1yxYgUODg4EBATQpEkT6tSpQ0BAAB07dtSPii5dupRr165RuHBhjh49yqhRo17YPykpKSxcuJDChQsDusD/66+/1qfPmzePMWPG0L59ewAWLlzIrl279OlxcXEsW7aMNWvW0LChblrKqlWryJcv3yv/23z44Yf06vX8OZbevXvzxRdf0KNHDwAKFSrE5MmTGTVqFBMmTGDv3r2cOHGCq1evUrRoUX2eZ+zt7VEURd+f6fH39+fixYvcuXMHb29vAFavXk2pUqU4efIkVapUAXQB88qVK7G1tQWge/fu+Pv764Py/zdt2jQmTZr0yja/CXcPT8LDwkhNTcXU1BStVkvwwwd4eKa/OMyLxMfFMaDPh9Rv2JQevT/OotpmLv8dP7Nl7XwA6jbtTMSjENSpqZg87YfwkCBcPLzTHOfi7k3w/X/0n0Mf3sPFXZfPyub5NFBnt3zUadqZy+eOULvJB1ncmow5vHM1f/2mW+jIt2EXoh4HG/TB47BAnF3zv6KUF7O0sqF6g84c3bMO34ZdXn1ANjuxbzUBW3Ttr1inC9ERwajVqZiY6NofGR6Io0v67U9MiOWnCc0pU7019dsOy85qZ4l8Lo6ERESTqlZjamKCVqvlflgk3i5OBvkOXbpJUFgEC3cEkKrWEJOQSLFeX3J43hi8XRwpV8hbv8DWhw2qMfjHX0hJVWNmmvNHxPK5OKXpg6Dwx2n64PCFGwSFP2bh9n36PijuN4pD343j4PlrHL54g79OnNfnrzpgAr9OHEj593yyu0lZwgVTgnn+6FEYKbg8/Sn1srSc7Miu1ex8ei2s/uxa+K9rwePQQPK6vd618Pr5gzwOC2Tv5gVo1KkkJsQwvEtBJiw8gZ1D+s+WG9OR3avZ9bQPqjXoQtT/XQ8fh71+H+Sxfv6d6OSSj2oNunDjwiGq1uuUqXXPbF5uLoQ+jiA1VY2p6dPrYWg43u4v/u9mY5WHDk3q8evOfXRoWu8/pwlhbK99hb558ybjx4/n+PHjPHr0SP/8bmBgIKVLl6ZJkyasXbuW2rVrc+fOHY4dO8aiRYsAuHjxImq1Wh90PZOUlETevHn1n83NzSlbtqxBntDQUMaOHUtAQABhYWGo1WoSEhIIDNRNVbl+/Tre3t4GQVvVqlUNyjh//jy3bt3SB2PPJCYm6qd3161bl8WLFwO60d+pU6dy48YNAgICiIiIICUlhZo1a76wf6ysrPRBMICHhwdhYbrnzKKjowkODqZatWr6dFNTUypXrqwfRb99+zbJyckGeZycnChWzHAl4/RUrmw4KnP+/HmOHDliEGyq1WoSExNJSEjg3Llz5MuXL81/j9dx9epVvL299UEwQMmSJXFwcODq1av6QLhAgQIG/f7vfknPmDFjGDbs+Q/smJgYg3Nkhrx5nSlRqgw7tm6kbYfO7Nn5B27uHuT3Kfify0iIj2dAnw+pWbs+H382JFPrl5UatuxOw5bd9Z9PHd3F/j/X0ai1H0f8N+Hs6oWn93tpjqvZoB2j+tQnsv84HPK68dfGxdRuqvtSjwgPxiGvGyqVioT4WE4e/pPGbdJfYCQnqNXMj1rN/PSfzx/fyZE9a6jTvCcnD2zEySUfbvnS9sHLhN6/RV53H0xNzUhNSeb0oS3kL1wms6ueKao28KNqg+ftv3p6J6f2r6Fao56cP7oRB+d8uHimbX/SkzgWTmxOiYpNadp5bHZWOcu4OthR/j1vftl3nO6Na7D5yBm8nB0o7OlqkM9/5vNZMfdCH1Ht8ylcX6G7gdu0cmm+Wr6ZB48i8XJ2ZOfJSxT3ds8VQTA86wMffvE/Rvcmtdhy+DRezo4U9nIzyLd3zvPnPe+FPKL6pxO5tnomACu+MFxE0appH04snJRrVo3+L2ooNozWBPGhNi8OmPCXJpo6iu0r03Kymk39qNn0+bXgwvGdHN2zhtrNenLq4NNrodfrXQu//O6g/t/hIXcZ37cCs9ffeckRxlWziR81mzzvg4sndnJszxpqvUEfRD0Oxs5R9534JCGW88f+oHaL3pld9Uzn4uRA2WLvseEvf7q1asI2/8N4ujpTyNtwkOCfoAd4e7hhZmpKckoKOwKOUPq9gq9MEyInee1AuFWrVvj4+LBkyRI8PT3RaDSULl2a5ORkALp168agQYP4/vvvWbduHWXKlKFMGd0Pwbi4OExMTDh9+jQm//fMiI2Njf7fefLkSbPATI8ePXj8+DHz58/Hx8cHCwsLfH199ef9L+Li4qhUqRJr16Zd3c/FRXenq169egwZMoSbN29y5coVatWqxbVr1wgICCAyMpLKlSu/dKEoMzPDhTYURUnzDHBWsbY2XKwhLi6OSZMm6Uef/83S0pI8ebLvGa70+uVli6BZWFhgYZH1r5wZP3kGY0cPZenC77G2sWHydN0d4QlfDqdewybUb9iUJ08SaNm4NinJScTGxdKwViVate3AkBFfsmbVUi5dOMeThAT8d/8JQJPmrej/6eAsr3tmGvjlAuZO7MuvK6ZjZW3HkAnPp3R+9/XHVKvbkmp1W+GerxAffjyekb3rAVCmch2at+8HwJF9m/nr90WoTEzRqFOp2agDjVv3MEZzMqT38IUsntaL7Wumkcfajn6jn6+Yu3RmXyrWbE3Fmq1JSkxg5EfFSE1OIiE+mkEdvanZ5CM695/G5TP72L3pe1QqEzTqVEpWbEAbv3FGbNV/1+mzhayb14s9v03D0sqODwc/b/8v3/WldLXWlKnWmgPb5nPvxgmSE+O5cEz3WEf5mh1p0ln3KMr0z8sRHx1OYkIM43t6U6RMfboPX22UNr2OHwZ2o9/cVcz8dSd2VpYsGqr72/1k/s+8X60sLau//JEHa0sLvh/4Ie0nLkCLFjurPKwenf7q6znV94P86D97Gd+u/xNbK0sWDdf9aP9k7krer16elr7ljVvBLPaDJpRT2ngiSWWC5gF5ULHYpCDfaUKopthQTbHBXTHnQyUvozS6tRHKKHlopjgAvDQtN+k5bCFLZ/Rix9pp5LGyo8+/roXLv+1LhRqtqfD0WvhF92KkpuiuhUM/8KZGk4/4oN80I9Y+c/QYupBlM3uxY52uD3qP+lcfzOpLBd/nfTDGrxgpKUk8iY9mWCdvfBvr+uDUwY3s37YQExNT1OpUqtTtSO1mOffm8L/N+3IQn06azZwV67G1tmLBhOEAfD55Ls3rVKdFXV8OnjzPovVbUJmoUKvV1KlSgZF9uwG8NO1dJe8RzpkU7WtEaY8fP8bZ2ZmDBw/qpz4/mwa9efNm2rZtS3x8PG5ubvzyyy988cUX+Pn56RfQunHjBsWKFTM4/v+tXLmSIUOG6BegesbW1pYff/yR7t11o1hBQUHkz5+fuXPnMmTIEHbu3EmrVq24f/8+bm66O9j+/v40atRIX7clS5YwevRo7t69i51d+qt5arVanJ2dadmyJdevX+fvv//m3LlzNG/enLJly1KpUiX9FO7/f49wenXfsmUL7dq10wfDnp6eDB06lJEjRwK696sVKlSIihUrsmXLFuLi4nBycmLt2rV88IFuSmlkZCT58uWjX79+L3yPsKIo+nY+U7NmTYoXL86yZcvSbeuBAwdo0KCBwdTof1u3bh0ff/xxmkWt/n3uPXv20Lx5c4Op0VeuXNFPja5cuTITJ05ky5YtnDt3Tl/GvHnzmDdvnn6hsleJiYnB3t6eY2euY2Ob8++wZ5V7Mc6vzvQWi4zL+dMMs1pEzn5cPlv0Vy02dhWMTmv68tWN3wX+LWYYuwpG9XjvNWNXwejkhz60s8t9r6vLLDFx8eSv157o6OgX/q43tme/X9f4Rxg8QpYdEuJi+KihU47uH2N7rcWyHB0dyZs3L4sXL+bWrVvs27fPYPoq6EYl27Zty7hx47h69Spdu3bVpxUtWpRu3brh5+fHpk2buHPnDidOnGDatGn88ccfLz13kSJF+Pnnn7l69SrHjx+nW7duBiOajRs3pnDhwvTo0YMLFy5w5MgRxo7VTdt7NrrcrVs3nJ2dadOmDYcOHeLOnTsEBAQwaNAg7t+/r89bp04d1q5dq18Uq2zZsiQlJeHv70/dunVfp8vSGDx4MNOnT2fLli1cu3aNTz/91CBwtrGxoU+fPowcOZJ9+/Zx6dIlevbsiSqd1TlfZfz48axevZpJkyZx+fJlrl69yvr16/X9UrduXerUqUOHDh3Ys2cPd+7c4a+//mLnzp2ALuCNi4vD39+fR48ekZCQkOYcjRo1okyZMnTr1o0zZ85w4sQJ/Pz8qFu3bpqp2kIIIYQQQrxrtChG2cTLvVZ0pVKpWL9+PadPn6Z06dIMHTqUb7/9Nk2+bt26cf78eWrXrk3+/IaLC6xYsQI/Pz+GDx9OsWLFaNu2LSdPnkyT7/8tW7aMyMhIKlasSPfu3Rk0aBCurs+f3zIxMdGPqFapUoW+ffvqV422tLQEdM/vHjx4kPz589O+fXtKlChBnz59SExMNLhTUrduXdRqtT4QVqlU1KlTB0VRXvp88H8xfPhwunfvTo8ePfD19cXW1pZ27doZ5Pn222+pXbs2rVq1olGjRtSqVYtKlSq9oMQXa9q0KTt27GD37t1UqVKF6tWrM3fuXHx8ni9asnHjRqpUqULXrl0pWbIko0aNQq1WA7rXYQ0YMIDOnTvj4uLCzJkz05xDURS2bt2Ko6MjderUoVGjRhQqVIgNG3L/u2WFEEIIIYQQb6fXmhqd2xw5coRatWpx69YtgwWsRO4jU6N1ZGq0TI2WqdEyNRpkajTI1GiZGi1To0GmRueWqdGr/COxss7mqdHxMfRo6Jij+8fY3qpflZs3b8bGxoYiRYpw69YtBg8eTM2aNSUIFkIIIYQQQgih91YFwrGxsYwePZrAwECcnZ1p1KgRs2fPNna1hBBCCCGEEELkIG9VIOzn54efn9+rMwohhBBCCCFENpDXJ+VMr78UsRBCCCGEEEIIkYu9VSPCQgghhBBCCJGTyIhwziQjwkIIIYQQQggh3ikSCAshhBBCCCGEeKfI1GghhBBCCCGEyCIarYJGq2T7OcXLyYiwEEIIIYQQQoh3iowICyGEEEIIIUQWkcWyciYZERZCCCGEEEII8U6RQFgIIYQQQgghxDtFpkYLIYQQQgghRBaRqdE5k4wICyGEEEIIIYR4p8iIsBBCCCGEEEJkEa0WNDIinOPIiLAQQgghhBBCiHeKjAgLIYQQQgghRBbRahW0WiXbzyleTkaEhRBCCCGEEEK8UyQQFkIIIYQQQgjxTpGp0UIIIYQQQgiRReT1STmTjAgLIYQQQgghhHinyIiwEEIIIYQQQmQRjRFen5Td58uNZERYCCGEEEIIIcQ7RQJhIYQQQgghhBDvFJkaLXKVB/FOWCt2xq6G0aRq3u13wtX0vGXsKhidykNt7CoY3ZIb/Y1dBaPTJhu7BsZnv7eXsatgVHkbFTd2FUQOsGbbNWNXwWieJMQYuwr/mSyWlTPJiLAQQgghhBBCiHeKjAgLIYQQQgghRBaREeGcSUaEhRBCCCGEEEK8UyQQFkIIIYQQQgjxTpGp0UIIIYQQQgiRReQ9wjmTjAgLIYQQQgghhHinyIiwEEIIIYQQQmQRWSwrZ5IRYSGEEEIIIYQQ7xQZERZCCCGEEEKILKLR6LbsPqd4ORkRFkIIIYQQQgjxTpFAWAghhBBCCCHEO0WmRgshhBBCCCFEFpHFsnImGREWQgghhBBCCPGfRERE0K1bN+zs7HBwcKBPnz7ExcW9NP/nn39OsWLFyJMnD/nz52fQoEFER0dnY63TkhFhIYQQQgghhMgib9uIcLdu3QgODmbPnj2kpKTQq1cv+vfvz7p169LN//DhQx4+fMisWbMoWbIk9+7dY8CAATx8+JDff/896yr6ChIICyGEEEIIIYR4patXr7Jz505OnjxJ5cqVAfj+++9p0aIFs2bNwtPTM80xpUuXZuPGjfrPhQsXZsqUKXz00UekpqZiamqckFSmRgshhBBCCCHEWygmJsZgS0pKeqPyjh07hoODgz4IBmjUqBEqlYrjx4//53Kio6Oxs7MzWhAMEggLIYQQQgghRJbRABptNm9Pz+3t7Y29vb1+mzZt2hu1JSQkBFdXV4N9pqamODk5ERIS8p/KePToEZMnT6Z///5vVJc3JVOjhRBCCCGEEOItFBQUhJ2dnf6zhYVFuvm++OILZsyY8dKyrl69+sb1iYmJ4f3336dkyZJMnDjxjct7ExIICyGEEEIIIUQW0Wq1aLN5taxn57OzszMIhF9k+PDh9OzZ86V5ChUqhLu7O2FhYQb7U1NTiYiIwN3d/aXHx8bG0qxZM2xtbdm8eTNmZmavrFdWkkBYCCGEEEIIId5hLi4uuLi4vDKfr68vUVFRnD59mkqVKgGwb98+NBoN1apVe+FxMTExNG3aFAsLC7Zt24alpWWm1T2j5BlhIYQQQgghhBCvVKJECZo1a0a/fv04ceIER44cYeDAgXTp0kW/YvSDBw8oXrw4J06cAHRBcJMmTYiPj2fZsmXExMQQEhJCSEgIarXaaG2REWEhhBBCCCGEyCJv23uE165dy8CBA2nYsCEqlYoOHTrw3Xff6dNTUlK4fv06CQkJAJw5c0a/ovR7771nUNadO3coUKBA1lX2JSQQFkIIIYQQQgjxnzg5ObFu3boXphcoUMDgmeh69epl+zPS/4UEwkIIIYQQQgiRRbQa0GhenS+zzyleTp4RFkIIIYQQQgjxTpERYfHOe3DvJrPH9yUm6hFWNvYM/3opPoVLppt31+YV/LriWzRaDeWr1OezMd9h+q+l37VaLWM+bsatq2f5/VBYumXkRA8DbzJ/Ym9iox9jZW3HoAnLyF+4VLp592xdzqZV36LRaChbuR4ff/EDpqa6Prh76yJLvh1CdISu7d0++RrfBu2yrR0ZdefuXUaOGk1EZCS2tjZ8O2MGRYsUMchz//59Ro7+gstXruCdLx9/bN+mT9NoNEydPp2DBw9hYmqKo4MDU6d8QwEfn+xuSobduXuX4aPHEBkZia2NLbNmTE3TB0eP/c2MWXOIT0hAUaBBvbqMHjEclUp3T9V//36mTP8WjUZNsaJFmTVjGrY2NsZoToaEPbjJmrk9iY95RB5re7oNWYGHT9r/D26c38e2lWNISoxDURRKVW5Bq57TUalUJD2JY9nUjgTdPo1GncqMDZFGaEnGhT28ydqnfWBp9fI+2L7qaR+gULJKC1r10PXBw7sX+X3hQGKjwlCZmOJTtAodByzA3CKPEVr0+kLu32Tp9J7ERj/CytqevqNX4FUwbR+Eh9xl6fReBN46i7N7QSYvPZsmj1arZebwRty9cYafduT8v4VFmjBOaOMII5X5qvwUUtJf1XW3JprftRFogbJKHj5R3DBVlFem5QbSBzphD27y85yexD29HnYfmv614Pqz6+GTOFAUSldpQeun10OAiyd2sGXZSDQaNZ4+Zfho2AryWL36VT5vm7ftGeG3hYwIiwwLCAigTZs2eHh4YG1tTfny5Vm7dq1BnpUrV6IoisGWE5ZL/7fvpwykeYc+LN16mQ96jmD2+L7p5gt5cIfVP07i2+X7WL7tKpGPQ/lr01KDPJvXzMcjX6HsqHam+mnapzRp15cfN16hfY+RfDepT7r5Qh/cYd3CiUxdvJ+Fm68RFRHG7k1LAEhKTGDa8A50+2QSP/x2kfnrz1GyQq3sbEaGjR03ni6dO7Nvz24+7tefkaNHp8ljY2PDsKFDmDdndpq0vf7+nD59hj+2b+OvHdup4evLrNlzsqPqmebL8RPp2rkT+3fvZED/vowY/WWaPHb2dnw/dzZ7/9rB9s0bOX3mHBu3bAUgPj6e0V+OY/GPPxCwZxdurq58v+Cn7G7GG9mwYAA1m/Vj3OLrNOwwirXzeqWbL4+NIz1H/8JXP11m5LxT3Ll2jJP7VgOgMjWjUcdRfPbNnuyseqb5dcEAajTtx9hF12nU8cV9YGXjSI9Rv/Dlj5cZMe8Ud64+7wNTc0s6fPw9Xy28yujvzpGcmID/xhnZ2Yw3smrOAOq27MeMn6/Tousols54wd+BlR0d+kzm46/WppsOsOu3ubh45p7vhJqKDTNU3ri+ZJwkRJvCWu0jZqi8WawqQJRWzS5t9CvTcgvpA531P+iuhxOWXKdxx1H8PPfF14Jeo35h7MLLjJ5/in+uHuOEv+5akPQkjnXz+9Jv7GYmLLmBfV4Pdv4yOTubIcRLSSAsMuzo0aOULVuWjRs3cuHCBXr16oWfnx87duwwyGdnZ0dwcLB+u3fvnpFqnFZURBg3rpymQYsPAajVqB2PQu/zMPBWmryH926iet33cXJ2R1EUWnTsR8DOX/Xp925f4dj+7XzQa0S21T8zREWEcevqaeo17waAb4P2PAq9T3BQ2j44um8TVeu0xPFpHzTt0J9DuzcAcHDnLxQtU42S5XXBr4mJCfaOr34fnbE9evyYixcv0rZNawCaN2tKcHAId//v79TBwYEqlStjZWWVpgxFUUhOTiYpKQmtVktcXBzu7m7ZUv/MoOuDS7Rr3QqA5k2b8DAkbR+ULlmS/Pm9AbC0sKBkieLcv/8AgICDhyhZsgTvFdb96O/erSvbdvyRja14M7FRYQTePEXl+h8BUL5mByLDgwh/mPb/A+/CFXB217XTzNwSr4LleBx6V/fZzIKi5RqQx9ohu6qeaf6/D8rV6EDUo/T7IN//9UG+QuWICLsLgKtnEbwKlgVAZWJC/iKViQjNOdf9l4mJDOPO9VPUaKzrg8p1OvA4LIjQB2n7wMbOiaJlamGRxzrdsh7cucyZI1tp2fWLLK1zZiqtWOGsmL00z1FtLFUVGxwVUxRFobnKngPamFem5RbSB8+vBVUa/MfroYfhteDx02vB5VN/ka9QBdy9iwNQ+/1POX1gffY0Qoj/QALhd0S9evUYOHAgAwcOxN7eHmdnZ8aNG6dfwa1AgQJ88803+Pn5YWNjg4+PD9u2bSM8PJw2bdpgY2ND2bJlOXXqlL7ML7/8ksmTJ1OjRg0KFy7M4MGDadasGZs2bTI4t6IouLu76zc3t5wTIISH3MfJ2R0TU92dX0VRcHH3JiwkKE3esOAgXD2eT3V18yxA+NN8qSkpzP/6Ez4f+wMmJibZU/lM8ig0CMe8Hmn6IDydPggPCcTF/V994OGjzxd05ypmZuZ8M7QNQz6sxLwJPYmODM+eRryB4OBgXFxdMf1X+z09PXj48OF/LqNhgwZUq1aNajVqUq1GTY4eO8bQwYOzqsqZLjg4BFdXF4M+8PLw4OHD4BceExYezp+7dtOwfj0AHj4Mxuvp+wMB8nl5ERYeTmpqalZWPdNEPgrC3skDE5PnfeDokp/I8MCXHhcTGcK5IxspXbVldlQzS0W9YR+UqpK2D5IS4zm2exmlq7fOkjpntoiwIBzyGvZBXrf8PA59eR/8v9TUFFbM7k/PYQtRctl3wquEk4orzwNFV8wIJ/WVaW+Tt70PIsODsPu/a4GTa34iXnUtiAjh7OHn18PI8ECcXPPr0/O6FiA6Mhi1Onf1R2bQaI2ziZeTQPgdsmrVKkxNTTlx4gTz589nzpw5LF36fGrv3LlzqVmzJmfPnuX999+ne/fu+Pn58dFHH3HmzBkKFy6Mn5/fS5c/j46OxsnJyWBfXFwcPj4+eHt706ZNGy5fvvzKuiYlJRETE2Ow5WRrF39DjYZtyF+ohLGrYjRqdSrnT+zjkzE/MnftKfK6eLFw+kBjVytbXLx4kRs3b3Ds8CH+PnKYGr6+jB0/wdjVyjKxcXH0/fhTBvTtQ9kypY1dHaN5khDD4q9b07DDSPIXqWzs6hhF4kv6IDUlmZUzulC8QmPK+RWJ71AAALFwSURBVOb8tQIy09ZVk6hUux2ePu/ud4J4tzxJiGHh161p1HEkPu/o9VDkPrJY1jvE29ubuXPnoigKxYoV4+LFi8ydO5d+/foB0KJFCz7++GMAxo8fz08//USVKlX44IMPABg9ejS+vr6Ehobi7u6epvxff/2VkydPsmjRIv2+YsWKsXz5csqWLUt0dDSzZs2iRo0aXL58mXz58r2wrtOmTWPSpEmZ2Xy9vdvXsHnNfADqNutMxKMQ1KmpmJiaotVqCQ8JwtXdO81xrh7eBAf9o/8c+vAuLk/zXTx9iPDgILavX4hanUpCfAw9WhRl/pojODjlvOnB+//4ma1r5wFQp2kXIh8Hp+kDl3T6wMU9PyH3b+s/hwbf0+dzcctPmcp1yevqBUDd5h8yadD7Wd+YN+Th4UF4WBipqamYPm3/w4fBeP5rdPNVNm3Zgm/16tjZ6RYAad++HT16pf88VU7k4eFOWFi4QR88CA7G09MjTd64uHh69OlH40YN6Nu7p36/p6cHh44e1X++/+ABri7PR5lzohP+q9m/ZS4AFet2ITpCN1JhYqLrg8jwQBxd8qd7bGJCLD+Nb06Zaq1p0G5YdlY7U53Yt5qAZ31QJwN9MKE5Zaq3pn5bwz5Qp6awcmYX7J3cad9/fpa3400c2bWanb/p+qB6wy5EPTbsg8ehgeR1S78PXuT6+YM8Dgtk7+YFaNSpJCbEMLxLQSYsPIGdQ877TngdLpgSTIr+cxgpuDz9OfmytLfJ29gHx/1Xs2+z7v+DynW7EPN/14KIsECcXnIt+HFcc8pWb03Df10PHV3yc+3sXv3nx2F3sXd8PtL8LpHFsnImGRF+h1SvXh3lX6sW+vr6cvPmTdRqNQBly5bVpz2bvlymTJk0+8LC0q6GvH//fnr16sWSJUsoVer5qoK+vr74+flRvnx56taty6ZNm3BxcTEIltMzZswYoqOj9VtQUNppuhnVqNVHLNhwkgUbTtKp1wjeK16BfX/qXgp+eO9mnF298Mz/XprjajZsx98H/iDiUQharZY/f19C3aa6mwSzlu9j1V83WfXnDWav2IeVtR2r/ryRI4NggPrvd2feutPMW3ea9j1GUqhYBQL+0i34cmzfJvK6eeHhnbYPfOu348TBHUQ+7YNdGxdTq0knAGo27vg/9u47LIrjjQP4dxHEgoAFxYI1NlRUVMASC6AI9q5RsfcSK/aexNhrYk3sNfbeu2LvvXcQFAUBqff9/cGP9U5QU4TjjvfzPD4Ju3N3M3O7e/vOzM7g/s0LCA+N672/cGoP8hd2SPAeKU22rFlRokQJbNkaNwv07j17YWtr+49mfLazs4Pv6dOIiooCABw6dBhFChdJkvwmhbg6sMfmbdsBALv37kNO2xwJ6iAsLAztOndBte+roE/PHjr7qn3/PW7cuIn7D+Iai1asWoN6dbySpwD/kpObN4bMuYQhcy6hZtMhsCvkiPOHVwIALp/cCOtseWCTK+F5EPkhFPPGeKJ4OQ94tByZ3Nn+ppxcveEz+xJ8Zl+C+yd1cOXUl+tg/lhPFHf0gEcL3TqIjY3B0smtkCFTZrTovVDndyclquzhjQmLL2HC4kuo02oI8hV2xKn9cXVw/thGZLHJgxy5E9bBlwyffQzT1j7GtLWPMHzOcaTLYIlpax8ZfBAMAJUUC5xlKN4yBiSxWxOMqkqmr+4zJsZYB85u3hg29xKGzb2Ems2GIM93jjh36O9dD38f7Qn7ch6o/cn10L5cbTx7cBH+z24DAI7v/B2OVVskfWGE+JtSX5OM+CwzrWWA4m9cEtum+WRF8KNHj6JevXqYMWMGvL29v/oZZcuWxf37CSdc0GZubg5zc/N/lP9/q+/IuZg2ugvW/TEJGTJaov+4Req+meO6w6VaHbhUr4eceQqiTY9RGNihOgDAoVw1eDXpkix5TGo9h/2O2eM7YePSSUifMRP6jP44ZH7uT13h9H09OFWrB9s8BdGq62gM7VwNAFCyXFV4NO4KIK63uEmHIRjaqSoUEwVZbXKjx3DDmDX45wnjMXjIUPw+fz4sLCww+deJAIChw4fD3c0N7m5u+PDhA9xq1kJUVBTeh4aiUpXv0bBhA/gMGoS2rdvgwYMHqFOvPkzNzGCTLRt+Gp80IxqSyi/jx2HQ0GH4ff4CWFhYYMrEXwAAQ4aPhLubK2q6uWLJ8hW4cvUawsM/YM++uFb+Op4e6N2jOywsMuLXnyega8/eiI2NRZHChTFt8kR9Fukfa9F7PlbN6IB96yciXQZLtO73p7pv9ezOKOVcH6Wc6+PItll4cvcsoiLCcPXUZgBAmSpN4dFiBADg196lERociIjwEIxqZ4fCDjXgPXC5Xsr0TzXvNR+rZ3bA/r/i6uCHHz/WwZrZnVHy/3VwVLsOfP9fB5WbolaLEbh0fB2u+m5CrvwOmPKjIwCgQPFKaNbjN72U6Z9qP2A+Fk/qgB2rJiJ9Bkt0GvKxDv6c0hllK9VH2cr1ERkRjqFtiyImOhLhYcHo38wOlWq1QbMuhnXca5ureYXzDMNbxGCM5gXSwwQL0xTAbI0/nBULOCsWsFXS4gclK3w0cQ3UpZT0qK1YA8AX9xkKqYM4rXrPx4oZHbD3/9fDNv0/ngerZsVdDx1c6uPw1ll4fPcsIiPCcPn/18OyVZqidssRSJchE37ouwiLfmqE2NgY5MpXEm0HLNVTiYRISOGXHvgURqN69eoIDAzUeT532LBh2Lp1K27evIn8+fOjX79+6Nevn7pfURRs3rwZDRs2BAA8fvwYBQoUwKVLl1CmTBkAcUso1a1bF5MmTUKvXr2+mo/Y2FiUKFECXl5emD797y8vExISAisrK2w4HoiMFqlv/bl4kTGpexCHg/UjfWdB70wYq+8s6N22u0X1nQW9k19uwMpwlqhOElndi+k7CyIFeLLttr6zoDcfwkMwuJk1goOD1UeTUpr4+9cJK94iXTKvnxwRHoJRbTOn6PrRN+kRTkWePn2KAQMGoFu3brh48SLmzJmDadMSron6dx0+fBh169bFjz/+iCZNmsDf3x8AkDZtWnXCrPHjx8PFxQXfffcd3r17hylTpuDJkyfo3DnxtXqFEEIIIYQQIqlJIJyKeHt748OHD3ByckKaNGnw448/omvXrv/6/ZYtW4bw8HBMnDgREyd+HApWrVo1HDlyBADw9u1bdOnSBf7+/sicOTPKlSuHU6dOwd7e/r8WRwghhBBCiBRPH8sZyfJJXyeBcCpiZmaGmTNnYt68hM9tPn78OMG2T0fN58+fX2fb0qVLsXTp0i9+5owZMzBjxox/lV8hhBBCCCGESAqp+4FDIYQQQgghhBCpjvQICyGEEEIIIUQSkXWEUyYJhFOJ+Gd2hRBCCCGEECK1k0BYCCGEEEIIIZKIRkNoknn2quT+PEMkzwgLIYQQQgghhEhVpEdYCCGEEEIIIZKIPCOcMkmPsBBCCCGEEEKIVEUCYSGEEEIIIYQQqYoMjRZCCCGEEEKIJCJDo1Mm6REWQgghhBBCCJGqSI+wEEIIIYQQQiQRDQlNMnfRJvfnGSLpERZCCCGEEEIIkapIICyEEEIIIYQQIlWRodFCCCGEEEIIkUSoifuX3J8pvkx6hIUQQgghhBBCpCrSIyyEEEIIIYQQSYQgmMyTVxEyWdbXSI+wEEIIIYQQQohURQJhIYQQQgghhBCpigyNFkIIIYQQQogkQg2gkcmyUhzpERZCCCGEEEIIkapIj7AQQgghhBBCJBFSD5NlJfPnGSLpERZCCCGEEEIIkapIj7AQQgghhBBCJBEN4/4l92eKL5MeYSGEEEIIIYQQqYr0CAvDQiA1P/Kg0Sj6zoJeKbI4PEwYq+8s6F1qvgbEkzqQOhBCCPHfSCAshBBCCCGEEEmEGoLJPFY5uT/PEMnQaCGEEEIIIYQQqYr0CAshhBBCCCFEEqEeHu2Tx0e+TnqEhRBCCCGEEEKkKhIICyGEEEIIIYRIVWRotBBCCCGEEEIkEY2G0CTz5FXJ/XmGSHqEhRBCCCGEEEKkKtIjLIQQQgghhBBJhCSYzLNXJffnGSLpERZCCCGEEEIIkapIICyEEEIIIYQQIlWRodFCCCGEEEIIkUSoifuX3J8pvkx6hIUQQgghhBBCpCrSIyyEEEIIIYQQSURDQpPMk1cl9+cZIukRFkIIIYQQQgiRqkiPsBBCCCGEEEIkEVk+KWWSHmEhhBBCCCGEEKmKBMJCCCGEEEIIIVIVGRothBBCCCGEEElEoyE0mmSeLCuZP88QSY+wEEIIIYQQQohURXqEhRBCCCGEECKJkHH/kvszxZdJj7AQQgghhBBCiFRFAmEhhBBCCCGEEKmKDI0WQgghhBBCiCRCEkzmyatkHeGvkx5hIYQQQgghhBCpivQICyGEEEIIIUQSIQlNMvfQSo/w10kgLFK9F0/vYfrozgh+9xoZLawwYNxi5Ctkn2javVuW4K8lU6ChBqUr1ECvobNhamam7ieJYd1q48HtS/jrWEByFeE/e/n0HuaM74CQd2+QwcIKfUb/gbwFSySa9sC2P7F52WRoqEGp8jXQ1WcuTE3NoNFosGz2YFzy3Yc0pqbIZJkFPYYvQE6775K5NP/co8ePMchnKILevoVlJgtMmfQrihQurJPm+fPnGDRkGG7evIk8efJg1/at6j6NRoOJk6fg2LHjiImNQXlHR0wYNxZp06ZN7qL8a48eP8GAocMR9PYdMllYYNqvP6NoYd3v7qTvGfw6bQbCwsOhKApcq1XFsEH9YWJigrCwcHTr2w/Xrt9EbGwsrp/31VNJ/r2Al/ewakZ7hIW8RroMVmjdbwly5kt4Hty9cgjblw1DZEQoFCiwr+CFeu1+hYmJCV4+voYN83vj/bsAmKQxRb4iFdC0+29Ia55eDyX65wJf3sOqmR/r4Id+S5Azb+J1sGO5Vh2U90LdT+ogNDiuDvIWNqw68H9+D4t/bY/QkNdIn9EKnX2WIHeBhHXw2v8xFk/qgKf3LyGbbQGMX3RJ3Xf78hFMH+oFW7ui6raRc0+l+DpYoAnAWYYiADGYZZIXBZV0iabbpwnGBgaBAByU9Oih5ICponx1nyGQOogT8OIeVkz/eB607Z/49fDOlUPYtnQYIj+EAoqCkhW8UL993LUAAK6d3YEtfwyGRhOLXPlKoc2AJUifwTK5iyO+saCgIPTp0wfbt2+HiYkJmjRpglmzZsHCwuKrryUJLy8v7NmzB5s3b0bDhg2TPsOfIUOjxb8WERGB9u3bo1SpUjA1Nf3sgXzkyBE4OjrC3Nwc3333HZYuXZqs+fyaOT/1Ru3GnbB4yw00az8I08d0TjSd/4tHWPH7OEz+4xD+2HoL7968wu5Ni3XSbF41CzntCiZHtr+p+b/2QM2GXfDbhlto1HYw5ozvlGi6Vy8fYc2CMfhp4RH8vvEO3gW9wv7NiwAA545vx+2rpzB91UXMWHUJpSq4YtW8kclZjH9txKjRaNWiOQ7v34tuXbpg8JChCdJYWFhgYP8fMXP6tAT71v21ATdu3MD2LZtwYM9uKCYmWLJseXJk/ZsZNnocfmjeDEf37kSPLp0wcOiIBGmsrCwxd8YUHNq1DTs3rceFS5exccs2AICpmSl6dO6E1UsXJ3idoVj/W3dU8uiCkQvuwL2pD1bN7JBougwWmdHOZw2G/34Dg2aex6Nbvjh3KO77Nk2bDk26zcGI+bcwZPZlREWE4+DGSclZjP9k/W/dUdGjC0bMvwO3Jj5Y/YU68B68BsN+u4GBM87j0W1fnDscVwdmadOhabc5GD7vFnxmXUZUpGHVwbLp3VG9bhf8uvwOvFr6YPHkxOsgXQZLNO44Ad1GrEp0v61dUYxfdEn9l9KDYACorFhgkokdsn+hn8Sf0VjF15hkYoeFJvnxjrHYy+Cv7jMUUgdx1s7tjsq1u2DMojuo2dQHK2Z8/lrQwWcNRs6/gSGzzuPhLV+cPRh3LYj8EIrVszqjy8jNGLPoLqyy5sSeNROSsxgiibRu3Ro3btzA/v37sWPHDhw7dgxdu3b9W6+dOXMmlBTSMCSBsPjXYmNjkT59evTt2xfu7u6Jpnn06BHq1KmDGjVq4PLly+jXrx86d+6MvXv3JnNuE/cuKAD3bl2Aq9cPAIDKbo3w+tVzvHx6P0HaEwc2wblaHWTJZgtFUeDVtAuO7l2v7n/y4CZ8D29H8/aDki3/38K7oAA8uHUB1Wq3BgBUdG2MN6+ewe9ZwjrwPbgRFb6vh8xZ4+rAo1FXHN+3FgCgQEF0VBSiIyNAEh/CQpA1e+5kLcu/8frNG1y7dh0NG9QHAHjW9sBLP388fvJEJ521tTUqlC+PDBkS3szeun0blStVQtq0aaEoCqpXrYrNW7cmSJdSvX7zBlev30Cj+nUBAF4eNeHn74/HT57qpCtpXxz57OwAAOnMzVGieDE8e/ECAGCeNi0qV3SGZaZMyZv5b+T9uwA8vXce5Wu0AQCUrtQE714/Q+DLhOdBnkJlkc02rsHLLG065ClYGkEBjwEA2XMVRu4CDgAAkzRpkLdweQS9epLgPVKi9+8C8PT+eZSv/s/rIHeB0gh69RgAYJOrMHJ9WgcBhlEHIW8D8PjueVSsGVcH5as2QVDAM7x6kbAOLCyzoEipKjBPlzG5s5lkSioZkE0x+2KaU3wPJ8UCmRVTKIoCTxMrHGXIV/cZCqmDj9fDCq5x50GZyk3wNjDxa4FdobLIllP3evjm/9fDG+d3I0/BsrC1KwYA+L5OT1w4ujZ5CpHCUEO9/EsKt27dwp49e7B48WI4OzujSpUqmDNnDtauXYuXL19+8bWXL1/GtGnT8OeffyZJ3v4pCYRTierVq6N3797o3bs3rKyskC1bNowaNUp9fiB//vz46aef4O3tDQsLC+TLlw/btm1DYGAgGjRoAAsLCzg4OOD8+fPqe2bMmBHz5s1Dly5dYGtrm+jnzp8/HwUKFMC0adNQvHhx9O7dG02bNsWMGTOSpdxfE+j/HFmy2SKNaVzLr6IosLG1Q4D/s0TSPkP2nPnUv3Pkyo/A/6eLiY7G7Ak90GfEXJikSZM8mf9G3rx6hszZcurUQTZbO7x+9TRB2sBXz2Bjm1f9O3vO/Hj9Kq4Oyn9fFyUcq6GjV2508sqDq+cOo2XXcclTiP/Az88PNtltYKpV/ly5cn71Yq6tVIkSOHDwEN6/D0V0dDR27t6NF89fJFWWv7mXfv7IbvNJHeTMiRcv/T77moDA19i1dx/cqldLrmwmqXevn8EqS06kSfOxDjLb5MXbwITngbaQt/64fHIjSlSom2BfZEQYfPf9gZIu9ZMkz9/au9fPYPkv6+DKqc/Xwel9f6CUs2HUQVDgM1h/UgdZs+fFm0Suh18T+PIBxnQth3E9nHBo6+/fOqt6E4gYZMfHQDE7zBCImK/uMybGXgdvAxNeC7Jkz4ugr10Lgvxx6cRGlHSq+//3eYos2T/eM2TNnh/Bb/0QG2tY9WHoQkJCdP5FRkb+p/fz9fWFtbU1ypcvr25zd3eHiYkJzpw589nXhYeH44cffsBvv/322bghuUkgnIosW7YMpqamOHv2LGbNmoXp06dj8eKPwxhnzJiBypUr49KlS6hTpw7atm0Lb29vtGnTBhcvXkShQoXg7e39jx6+9/X1TdBb7OHhAV/fLz8/GBkZmeDETclWLfwJlVwbIG/B4vrOit48uHUezx5ex+IdT7F45zM4VKiBBZN66jtbyaJpk8aoVvV7tGzdBi1bt0WB/PnVhgVj9D40FB2790L3zh1RulRJfWdHbyLCQ7BwfH24NRmMvIXL6+yLiY7C0kktUaxsTZSu2EhPOUx6EeEhWDShPlwbJ14Hyya3RNGyNeFgxHWQmHyFHTFt3TOMW3gBfcZvwuHtC3D2yPqvv1AIA/UhPATzx9eHe9PByPfJtUDot0fYzs4OVlZW6r+JEyf+p7L4+/sje/bsOttMTU2RJUsW+Pv7f/Z1/fv3R6VKldCgQYP/9PnfkvHeqYkE7OzsMGPGDCiKgqJFi+LatWuYMWMGunTpAgDw8vJCt27dAACjR4/GvHnzUKFCBTRr1gwAMGTIEFSsWBGvXr362y05/v7+yJEjh862HDlyICQkBB8+fED69Ik/MzVx4kSMG5c0vYkHd6zE5pWzAADVardA0Gt/xMbEII2pKUjG9fza2iV4nY2tHfyeP1T/fvXyMWz+n+76heMI8H+G7evmIzY2BuFhIWhfpwhmrTwJq8w2SVKO/+LwrhXYvnomAKBKrRZ4+9pPpw5e+z9Dthx5E7zOJocd/F98rIMAv8fIliOuDo7sWomS5WsgYyZrAED1Ot4Y39czycvyX+XMmROBAYGIiYmB6f/L//KlH3LlyvW330NRFPTr2wf9+vYBAGzfsROFC6f8ScLi5cppi4DAT+rAzw+5c+VMkDY0NAzenbuhlpsrunRop4fcfjtnDy3HkS1xo1Mcq7ZEcFBcT0WaNHF18DbwKTLbJDwPACAi/D3mjfFEKZf6qNFwgM6+2JhoLJ3cElZZbNG466wkL8d/cfbQchzZ+rEOQv5hHcwf64lSzonXwbIpLWGZxRaNu6TsOji5bzn2/hVXB86uLfHukzp4E/AUWRO5Hn5J+owfJwPKYpMHzq4tcffqcThVb/5N864PNjCFH6LVvwMQDZv/305+aZ8xMcY6OHNwOQ5tjjsPyldLeC0ICniKLF+4Fvw+yhMOLvXh1ujjtSCzTV7cvnRA/ftNwGNYZf7Y0yySx7Nnz2Bp+fGaZG5unmi6oUOHYtKkL8/ncOvWrX+Vh23btuHQoUO4dOnS1xMnI+kRTkVcXFx0Hk6vWLEi7t27h9jYWACAg4ODui8+eC1VqlSCbQEBST8b8rBhwxAcHKz+e/Ys4VDlf8utbhvMXXsOc9eeQ7P2g/BdsbI4tGs1AODkwc3Imj03cuVNGMRUdmuEM0d3Iui1P0hi14ZFqFYrrpFgyp+HsGzXPSzdeRdT/zyEDBktsXTn3RQZBANADa+2mL7yAqavvIDG3j4oWKwsju6Jm/DF99AmZM2eJ9HZnl1cG+Pc8e14+yauDvZuXogqtVoAAHLkLoDr5w8jOjoKAHD+xM7PzjydkmTLmhUlSpTAlq1xkz7t3rMXOW1zIH++fF955UeRkZEIDo6bDCUoKAjzFixEt86JT7qWEmXLmhUlSxTH5m07AAC79u6HbY4cyJ9P96YnLCwcbTt3Q7UqVdC3Zzd9ZPWbcnL1hs/sS/CZfQnuTYfArpAjzh9eCQC4cmojrLPlgU2uhOdB5IdQzB/rieKOHvBooTshXGxsDJZOboUMmTKjRe+FKWZCkM9xcvWGz6xL8Jl1Ce5NhiBPIUecP/L36mDBWE8Uc/RArUTqYNmUVshgkRkteqX8Oqhcy1ud0KpOqyHIV9gRvvvj6uD8sY3IYpMHOXL/s4atd2/8oNFoAAAfwt/jiu9O5C1c9pvnXR8qKRY4y1C8ZQxIYrcmGFWVTF/dZ0yMsQ6c3bwxbO4lDJt7CTWbDUGe7xxx7lDceXD55JevBb+P9oR9OQ/Ubql7LbAvVxvPHlyE/7PbAIDjO3+HY9UWSV+YFEhD/fwDAEtLS51/nwuEBw4ciFu3bn3xX8GCBWFra5sgFoiJiUFQUNBnO8oOHTqEBw8ewNraGqampuqjWE2aNEH16tW/WT3/U9IkI1RmWssAxd+4JLYt/sf977C1tcWrV690tr169QqWlpaf7Q0G4lqrPneifmt9RszF9DFdsO7PSciQ0RL9xy5S980c3x0u1erApVo95MxTEG26j8KgjtUBAA7lqsGzSZdkyWNS6z50HuaM74iNS39FhoyW6D3q45D5337uigrf14NT1XqwzV0QLbuMwfAuVQEAJR2roVajuFkCPZv2xPPHtzGgjSNMTc1gnSUHug01jOfifp4wDoOHDMNv8xcgk0VGTP41btjQkOEj4O7mippubvjw4QNca3ogKioK70NDUbFKVTRq2AA+gwYi5P17tGrdFiYmJtBoNOjQzhvubq56LtU/M3HcGAwcNhJzFyyCRcaMmDbxJwCAz4jRcHetgVpuNfDn8hW4cu06Pnz4gD3741r569SuhT494oLiWvUa4c3bt3gfGgqnqm6o6FwBs6b8qrcy/VPNe83H6pkdsP+viUiXwRI//PhxMo81szujpHN9lHKuj6PbZuHJ3bOIigjDVd/NAIAylZuiVosRuHR8Ha76bkKu/A6Y8qMjAKBA8Upo1uM3vZTpn2recz5Wz+qAA/+vg1Z9P9bB2jmdUdKpPko618fR7bPw5N5ZREZ+UgfNP6mDfnF1ULB4JTTtbhh10K7/fPwxuQN2rJ6I9Bks0dHnYx38ObUzylasj7KV6yMyIhzDvIsiOjoSH8KCMaC5HSrWbINmXSbi/LGNOLxtPtKkMUVsbAwqVGuK72snPutuSjJX8wrnGYa3iMEYzQukhwkWpimA2Rp/OCsWcFYsYKukxQ9KVvho4hqoSynpUVuxBoAv7jMUUgdxWvWejxUzOmDv+rhrQZv+H8+DVbM6o5RzfTi41MfhrbPw+O5ZREaE4fKpuGtB2SpNUbvlCKTLkAk/9F2ERT81QmxsDHLlK4m2A5bqqUTia2xsbGBj8/UOnIoVK+Ldu3e4cOECypUrByAu0NVoNHB2dk70NUOHDkXnTzoISpUqhRkzZqBevXr/PfP/kkJZbTlVqF69OgIDA3Hjxg1127Bhw7B161bcvHkT+fPnR79+/dCvXz91v6IoOut7PX78GAUKFMClS5dQpkwZnfdv37493r17hy1btuhsHzJkCHbt2oVr166p23744QcEBQVhz549fzv/ISEhsLKywoZjgchgkXrXn4uINqyJuL61slke6DsLepdGE/31REZu893E1/lOTeSXG7D6+nKVRi1bzWL6zoJIAZ5su63vLOjNh/AQDG5mjeDgYJ2hvylJ/P1rx/FPkDZd8uYxKiIEf47OlyT14+npiVevXmH+/PmIjo5Ghw4dUL58eaxeHTfC8sWLF3Bzc8Py5cvh5OSU6Ht8GmfogwyNTkWePn2KAQMG4M6dO1izZg3mzJmDH3/88T+9582bN3H58mUEBQUhODgYly9fxuXLl9X93bt3x8OHD+Hj44Pbt2/j999/x/r169G/f///WBohhBBCCCFSPmNaPgkAVq1ahWLFisHNzQ1eXl6oUqUKFi5cqO6Pjo7GnTt3EB4enmR5+BZkaHQq4u3tjQ8fPsDJyQlp0qTBjz/++LcXv/4cLy8vPNFab7Vs2bhnoOIHGhQoUAA7d+5E//79MWvWLOTJkweLFy+Gh4fHf/pcIYQQQgghRPLLkiWL2vubmPz58391lZmUMChZAuFUxMzMDDNnzsS8efMS7Hv8+HGCbZ8eoIkd1Im97lPVq1dPcbPECSGEEEIIkRxIJnvglxICzZROhkYLIYQQQgghhEhVJBAWQgghhBBCCJGqyNDoVOLIkSP6zoIQQgghhBCpjkYDaJJw8qrPfab4MukRFkIIIYQQQgiRqkiPsBBCCCGEEEIkEZksK2WSHmEhhBBCCCGEEKmKBMJCCCGEEEIIIVIVGRothBBCCCGEEEmEGoLJPFlWcn+eIZIeYSGEEEIIIYQQqYr0CAshhBBCCCFEEpEe4ZRJeoSFEEIIIYQQQqQq0iMshBBCCCGEEElEA0KTzMsZaSA9wl8jPcJCCCGEEEIIIVIVCYSFEEIIIYQQQqQqMjRaCCGEEEIIIZKITJaVMkmPsBBCCCGEEEKIVEV6hIUQQgghhBAiiZAEk3myrOT+PEMkPcJCCCGEEEIIIVIVCYSFEEIIIYQQQqQqMjRaCCGEEEIIIZIINYRGJstKcaRHWAghhBBCCCFEqiI9wkIIIYQQQgiRRGT5pJRJeoSFEEIIIYQQQqQqEggLIYQQQgghhEhVZGi0EEIIIYQQQiQRWUc4ZZJAWBiUyjemwzJ9On1nQ28CKrfUdxb0atp2W31nQe9K2GfSdxb0znONl76zoHfy7BeQY9AAfWdBr1Zuu63vLIgUIF/9YvrOgt6EM1bfWRAGTgJhIYQQQgghhEgi1GhAjSbZP1N8mTwjLIQQQgghhBAiVZEeYSGEEEIIIYRIIhoNoUnmR1qS+/MMkfQICyGEEEIIIYRIVSQQFkIIIYQQQgiRqsjQaCGEEEIIIYRIIrJ8UsokPcJCCCGEEEIIIVIV6REWQgghhBBCiCRCDZN9/XdZb/7rpEdYCCGEEEIIIUSqIoGwEEIIIYQQQohURYZGCyGEEEIIIUQSkaHRKZP0CAshhBBCCCGESFWkR1gIIYQQQgghkogGGmioSfbPFF8mPcJCCCGEEEIIIVIV6REWQgghhBBCiCRCTfI/s5vMHdAGSXqEhRBCCCGEEEKkKhIICyGEEEIIIYRIVWRotBBCCCGEEEIkEVk+KWWSHmEhhBBCCCGEEKmK9AgLIYQQQgghRBIhCTKZe4ST+fMMkfQICyGEEEIIIYRIVSQQFkIIIYQQQgiRqsjQaCGEEEIIIYRIIhqNBhpN8i7sm9yfZ4ikR1gIIYQQQgghRKoigbD4V8aOHYsyZcroOxtCCCGEEEKkaPHLJyX3P/FlMjRa/CuDBg1Cnz599J2Nb+K+/xt0XbwZb96HwzKDORZ0bgT73Nl10hy79QiNpq9EYdts6rbDozojfVozAMD1Z68waOUuBISEAgDGNHFDg/L2yVeI/+DR48cY5DMUQW/fwjKTBaZM+hVFChfWSfP8+XMMGjIMN2/eRJ48ebBr+1Z1n0ajwcTJU3Ds2HHExMagvKMjJowbi7Rp0yZ3Uf6Td4EPcGBNd3wIewPzdJZwazUPWW2LJ0jn9/gsjm4YAADQxEYjZwEXVG08GWlMzUGNBid3jMLT2weh0cQgZ35nVG86A2lMU35dBLy4h2VT2yM05DXSZ7CC98AlyJW/RIJ0b/wfY9m0Dnj24BKy2RbAiN8vqfs0Gg02/+GDG+f3QhMbg0L2ldCqzzyYmqX88j8OCcOQ09fwNjIKFmammORSCoWtM+mk0ZD49eJtHPd7DVMTBdZp0+In5xLIlykjwqJj0Of4JVx/G4JYDXGhmbueSvLvPX4fhqGnr+NtVFwd/OpcCoWtLHTSaEhMunwHx/1eI42iwNrcDD9ViKuDO+/eY/yFW3gTEQVTEwWlslhhTLniSGeaRk8l+ucePH2BHmOn4M27EFhaZMTvYwaieKH8OmnOXr2JAb/OAQDExMTApXRJTBrcA+Zp035xn6EIeHEPK6b//1qQ0Qpt+y9BznwJrwV3rhzCtqXDEPkhFFAUlKzghfrtf4WJSVwfy7WzO7Dlj8HQaGKRK18ptBmwBOkzWCZ3cf6V1F4HCzQBOMtQBCAGs0zyoqCSLtF0+zTB2MAgEICDkh49lBwwVZSv7hMipZAeYfGvWFhYIGvWrPrOxjfRZ9l2dKhWDlcm9cUAryrotnhzoukK22bD6Qk91H/xQXB4ZBRazF6D0U1ccXFiH5z7uRcqFcmXnEX4T0aMGo1WLZrj8P696NalCwYPGZogjYWFBQb2/xEzp09LsG/dXxtw48YNbN+yCQf27IZiYoIly5YnR9a/qcN//YgSLu3RdthFOLr2w8E1PRJNly1XSTTrfxgtB51Aq8G++BD6GtdOLgYA3DyzHIHPr6DFgGNoPeQcFMUEV47NS85i/GurZndHFc8uGPfHHdRq7oPl0zokmi5dRkvUbzcBHYesSrDv1N4/8PT+JQyfewFjFt2EYmKCQ1tmJXXWv4nR526gxXd5sK9eVXS1L4ghp68lSHPweQAuvn6HbV6Vsd2rCiraZsX0K/cAAGYmJuhiXxBLXSskd9a/mdHnbqJ5oTzYW+d7dCleAEPPJKyDQy8CcDHwHbbWroTtnpVRMUdWTL8aVwfmaUwwqlxx7KlTBVs9KuFDTCwW3XqU3MX4T/r9MgvtGnnhwqY/0c+7OXqOS3jNK1mkIA4vn4MTq+fh1NoFCHz7Dov/2vHVfYZi7dzuqFy7C8YsuoOaTX2wYkbi14IMFpnRwWcNRs6/gSGzzuPhLV+cPRh37Y/8EIrVszqjy8jNGLPoLqyy5sSeNROSsxj/SWqvg8qKBSaZ2CH7F/rL/BmNVXyNSSZ2WGiSH+8Yi70M/uo+IVISCYRTqerVq6Nv377w8fFBlixZYGtri7Fjx6r7nz59igYNGsDCwgKWlpZo3rw5Xr16pe7/dGj0kSNH4OTkhIwZM8La2hqVK1fGkydP1P1bt26Fo6Mj0qVLh4IFC2LcuHGIiYlJjqJ+UUBIKC49eolWlRwAAA3L2+P5mxA8ePXmb7/H+tPXUKFQHjX4TWNiAhvLjEmS32/t9Zs3uHbtOho2qA8A8KztgZd+/nis9d0BgLW1NSqUL48MGdIneI9bt2+jcqVKSJs2LRRFQfWqVbF569YE6VKy8PeBCHh2GUXLtQAAFHJogNB3L/Au8EGCtGZpMyBNmrhGkNjYKMREfwAQ18r9+uV12BWpjjSmcXWRr3hN3LmwLtnK8W+FvAvA03vn4eTWBgBQtkoTvH39DAEv7ydImzFTFnxXsgrM0yU8xp8/vIJiZd1gahZX/hLlPXH24Mokz/9/9SYiEtfeBKN+/lwAAA+7HPAPj8CT92E66RQFiIrVIDJWA5IIjY5BjvTmAIC0aUxQ0TYrLM0Mc6DVm4hIXA8KRv38OQEAHnkSrwNAQZRGtw5sM8T1FuXPlBHF/t+LnsZEQamslngR9iE5i/GfBAa9w+Vb99DC0w0AUN+tCl68CsTDZy900mVIlw5mpnHfc1R0DCIiIxHf0fWlfYbg/f+vBRVc464FZSo3wdvAZwhM5FpgV6gssuUsCAAwS5sOeQqWxpuAxwCAG+d3I0/BsrC1KwYA+L5OT1w4ujZ5CvEfSR0AJZUMyKaYfTHNKb6Hk2KBzIopFEWBp4kVjjLkq/tSK1Kjl3/iyyQQTsWWLVuGjBkz4syZM5g8eTLGjx+P/fv3Q6PRoEGDBggKCsLRo0exf/9+PHz4EC1atEj0fWJiYtCwYUNUq1YNV69eha+vL7p27Qrl/7/+x48fh7e3N3788UfcvHkTCxYswNKlS/Hzzz8nZ3ET9SIoBLbWFjBNEzd0T1EU2GW1wrM3CVsuHwUGodKY+fh+3AIsPHhW3X7rZSDMTdOgyYxVcBk1D50XbkJgyKc3jymTn58fbLLbwPT/N26KoiBXrpx4+fLl336PUiVK4MDBQ3j/PhTR0dHYuXs3Xjx/8fUXpiCh754jo2UOmKT5WA8W1nkQ+u55oulDgp5gzZTK+GNUQaRNb4lSlTsDAGzsyuDR9d2IighBbGw07l/ejJCgp8lWjn/rbeAzWGbOiTRa5c9skxdBAf8s73kLl8PV09vxISwEsTHRuHD8L/WmMCXzC49A9vTmMP3/cEZFUZAzQ3q8DIvQSeeaOzucc2RB5U2HUXnzYfj6v8GPDoUTe0uD4xceAZsEdZAOL8M/rQMbOGXPjCpbjqDK1iM4/SoIfUt+l+D9wmNi8NeDF3DLkz3BvpTqxatA5MiaBaamH38P8uSwwTP/wARpn7z0R+VW3VHIvRksLTKic7N6f2tfSvc28Bkss+heC7Jkz4ugwC9fC0KC/HHpxEaUdKr7//d5iizZ86r7s2bPj+C3foiN1X8D+NdIHfw9gYhBdnwMlrPDDIGI+eo+IVISw2y6Ft+Eg4MDxowZAwAoXLgw5s6di4MHDwIArl27hkePHsHOzg4AsHz5cpQoUQLnzp1DhQq6Q/9CQkIQHByMunXrolChQgCA4sU/Pls5btw4DB06FO3atQMAFCxYEBMmTICPj4/6+Z+KjIxEZGSkzmfoU5n8OXF3+kBYZUiHF0HBaDR9FbJmyoAmTiURG6vB4ZsPcWRUF+S0zoQxGw6g3/IdWNU78YYDY9O0SWO8ePkSLVu3Qbp06VC5UkUcNz2p72wlKcss+dBq8ElERYZi/6queHBtG4qUbYriFVrjfdAzbPrNC6Zm6WFXuDpM7hzSd3aTTcWa7RH06gmm+1RH2rTpUaysG25dMJ6fmWtvgnH3XSiON6oOCzNTTL18F2PO3cDUSqX1nbVkcz0oGPeCQ3GsQbW4OrhyF2PO38TUig5qmqhYDfqfuooqtllRM08OPeY26eTLZYuTa+YjNPwDuo6ahO2HTqKJR/Wv7jNGH8JDMH98fbg3HYx8hcvrOzt6IXUgvkYfk1fJZFlfJz3CqZiDg4PO3zlz5kRAQABu3boFOzs7NQgGAHt7e1hbW+PWrVsJ3idLlixo3749PDw8UK9ePcyaNQt+fn7q/itXrmD8+PGwsLBQ/3Xp0gV+fn4IDw9PNG8TJ06ElZWV+k87L99S7iyW8H8XipjYWAAASTx7Ewy7rFY66SzTp4PV/4f/5c5ihWYuJXHqTtzw4TxZrVC1WAHkymwJRVHQslJpnH2QeE9iSpMzZ04EBgSqw9RJ4uVLP+TKletvv4eiKOjXtw92btuCjevXovB336Fw4YQ9RCnN7XNrsHZqFaydWgXP7h5BWMgraGI/1kPou+ewsM7zxfdIa26BwmUb4+6FvwDE1YVz7WFoOfAEmvbdj8y2RZElkQm3UoLTB5bj555l8XPPsrh96QBCtHoqSCbozfg7FEVB3bZjMeK3ixg84yRs89onOsFMSpMzQzoEfIhEzP/XXCQJv/APyJVRd4KYLY9ewsU2CyzTmsFEUdCoQC6cfhWkjyx/czkzpENggjqIQK4Mn9TB45dwyZ5Vqw5y40zAxzqI1mjQ/9QV2KRLixGOxZK1DP9V7hw2ePUmCDExH38Pnr8KhJ2tzWdfY5EhPZrUqo71exI2eH1pX0py5uByTOxdFhN7l8WdywcQEqR7LQgKeIosNolfCyLC3+P3UZ5wcKkPt0YD1O2fjih5E/AYVlqjTlIaqYN/zgamCEC0+ncAomHz//61L+0TIiWRQDgVMzPTff5DUZR/vfj2kiVL4Ovri0qVKmHdunUoUqQITp8+DQAIDQ3FuHHjcPnyZfXftWvXcO/ePaRLl/hMhMOGDUNwcLD679mzZ/8qX1+T3dICZfLlxJpTVwEAW87fRO4sliiUQ3ciML9379W6ef8hEnsu34VDvrhn6Zo4lcDFRy8Q8iFuCOHeK3dRys4wekGyZc2KEiVKYMvWbQCA3Xv2IqdtDuTP9/cn+4qMjERwcNxQ8qCgIMxbsBDdOndOkvx+S8UqtELLQSfQctAJlHPrD5s8pdXneR9c3YqMVrlgbVMoweveBT5AbGzcD3xsTBQeXtuBrLnigr2Y6AhEhL8FAHwIfYOLB2fC0fXHZCrRP+Pi7o0Rv1/CiN8vwaP5ENgVclSf5710YiOss+VB9lz/rEEjOioCYe/jyh8a/Br71k9CrWaDv3nev7Ws6cxRIosltj2OeyRg77NXsM2QDvky6T4HbWeRHqf9gxAVG3ctOPwyEEU+mVXZUGVNZ44SmS2x7XFcI+be56+QI30idZAxA04HvNGqgwC1DmI0Ggw4dQVWac0woUIJ9fEYQ2GTxRoORb/Dut1xI6O2HTyBXNmzoaBdbp10D5+9QPT/Gw+joqOx48hJlPyuwFf3pVTObt4YNvcShs29hJrNhiDPd444dyjuWnD5ZNy1wCaRa0Hkh1D8PtoT9uU8ULvlSJ199uVq49mDi/B/dhsAcHzn73CsmnJHSUkd/HOVFAucZSjeMgYksVsTjKpKpq/uS7X0sXSS9Ah/lTTPiASKFy+OZ8+e4dmzZ2pP7M2bN/Hu3TvY239+SaCyZcuibNmyGDZsGCpWrIjVq1fDxcUFjo6OuHPnDr777u/fVJubm8Pc3Pw/l+XvmN2+Hrot3oypO44jU3pzzO/UEADQ88+tqFO2KOqULYat529i8aFzSJPGBLGxGjSqUALe35cFANhltcagulXh+tMfMFEU5MqcCXPb10+WvH8LP08Yh8FDhuG3+QuQySIjJv86EQAwZPgIuLu5oqabGz58+ADXmh6IiorC+9BQVKxSFY0aNoDPoIEIef8erVq3hYmJCTQaDTq084a7m6ueS/XP1Wg2EwfW9MD5g9OQ1jwT3Fr+ru47tK43CpTwQoGSXnh+/xiuHl8ARUkDamKQp3A1VKjpAwCIigjB5t/qQFFMQGpQump3FCjhqa8i/SM/9J2P5dM6YM+6iUiXwRLeA/5U962Y0RkOLvVRumJ9REWEY0znooiJjsSHsGAMa2MHZ9c2aNhxIj6EBWOGTw21/DUa9oWDi2E8HzneqQSG+l7D/BsPYWFmiokupQAAw89ch1vu7HDLkx1tiuTDg5Aw1N99EmYmCrKlM8c4p4893vV2nUBQRDRCo2Pw/ebDcM6RFVMrOXzuI1OccRVKYNiZa1hw6yEymppionNJAMCIs9fhmjs73HJnR+vCefEgJBQN9p6CqaLAJp05xlWI+13Y9dQf+54HoKi1BRru9QUAOGazxhgDWUoOAGYO74ue46Zh+pK1yJQxA34bMxAA0GfCDHhWdYFXtYo4du4KFqzdApM0JoiNjUXVCmUxuHNrAPjiPkPRqvd8rJjRAXvXx10L2vT/eC1YNaszSjnXh4NLfRzeOguP755FZEQYLp+KW22hbJWmqN1yBNJlyIQf+i7Cop8aITY2BrnylUTbAUv1VKJ/LrXXwVzNK5xnGN4iBmM0L5AeJliYpgBma/zhrFjAWbGArZIWPyhZ4aOJ66gopaRHbcUaAL64T4iURCEpzQWpUPXq1VGmTBnMnDlT3dawYUNYW1tjyZIlcHR0RKZMmTBz5kzExMSgZ8+esLCwwJEjRwDEzRq9ZcsWXL58GY8ePcLChQtRv3595MqVC3fu3MEPP/yACRMmoEePHti7dy/q1q2LkSNHomnTpjAxMcGVK1dw/fp1/PTTT38rvyEhIbCysoLfvGGwTJ94L3JqEFC5pb6zoFfTttvqOwt6V8I+lbeqA3Bb7qXvLOidPPsF5Bg04OuJjNjKVzX1nQWRAuSrb1iPIHxL4YxFC80DBAcHw9IyZa7PHH//6t7qJEzTJu8IopioUBxYUzlF14++ydBokYCiKNi6dSsyZ86MqlWrwt3dHQULFsS6dYkvA5MhQwbcvn0bTZo0QZEiRdC1a1f06tUL3bp1AwB4eHhgx44d2LdvHypUqAAXFxfMmDED+f7B8FshhBBCCCEMkYYavfwTXyZDo1Op+J5dbVu2bFH/P2/evNj6hbVgx44dq647nCNHDmzevPmLn+fh4QEPD49/k1UhhBBCCCGE+KYkEBZCCCGEEEKIJCLLJ6VMMjRaCCGEEEIIIcTfEhQUhNatW8PS0hLW1tbo1KkTQkNDv/o6X19fuLq6ImPGjLC0tETVqlXx4cOHZMhx4iQQFkIIIYQQQgjxt7Ru3Ro3btzA/v37sWPHDhw7dgxdu3b94mt8fX1Ru3Zt1KpVC2fPnsW5c+fQu3dvmJjoLxyVodFCCCGEEEIIkURIDahJ3smrmESTZd26dQt79uzBuXPnUL58eQDAnDlz4OXlhalTpyJXrlyJvq5///7o27cvhg4dqm4rWrRokuTx75IeYSGEEEIIIYQQX+Xr6wtra2s1CAYAd3d3mJiY4MyZM4m+JiAgAGfOnEH27NlRqVIl5MiRA9WqVcOJEyeSK9uJkkBYCCGEEEIIIZJI/GRZyf0PiFvLWPtfZGTkfyqLv78/smfPrrPN1NQUWbJkgb+/f6KvefjwIYC4VWe6dOmCPXv2wNHREW5ubrh3795/ys9/IYGwEEIIIYQQQhghOzs7WFlZqf8mTpyYaLqhQ4dCUZQv/rt9+/a/yoPm/8PCu3Xrhg4dOqBs2bKYMWMGihYtij///PNfl+2/kmeEhRBCCCGEEMIIPXv2DJaWlurf5ubmiaYbOHAg2rdv/8X3KliwIGxtbREQEKCzPSYmBkFBQbC1tU30dTlz5gQA2Nvb62wvXrw4nj59+rUiJBkJhIUQQgghhBAiiZCaJJu86kufCQCWlpY6gfDn2NjYwMbG5qvpKlasiHfv3uHChQsoV64cAODQoUPQaDRwdnZO9DX58+dHrly5cOfOHZ3td+/ehaen51c/M6nI0GghhBBCCCGEEF9VvHhx1K5dG126dMHZs2dx8uRJ9O7dGy1btlRnjH7x4gWKFSuGs2fPAgAURcHgwYMxe/ZsbNiwAffv38eoUaNw+/ZtdOrUSW9lkR5hIYQQQgghhEgiGg2g+f/kVcn5mUll1apV6N27N9zc3GBiYoImTZpg9uzZ6v7o6GjcuXMH4eHh6rZ+/fohIiIC/fv3R1BQEEqXLo39+/ejUKFCSZfRr5BAWAghhBBCCCHE35IlSxasXr36s/vz588PMmHgP3ToUJ11hPVNAmEhhBBCCCGESCLUaMCk7KL9zGeKL5NnhIUQQgghhBBCpCoSCAshhBBCCCGESFVkaLQQQgghhBBCJBFqCCbzZFnJ/XmGSHqEhRBCCCGEEEKkKtIjLIQQQgghhBBJhNSATObJspL58wyR9AgLIYQQQgghhEhVJBAWQgghhBBCCJGqyNBoIYQQQgghhEgiMllWyiQ9wkIIIYQQQgghUhXpERZCCCGEEEKIJEKNBtQk82RZyfx5hkh6hIUQQgghhBBCpCrSIywMAhn3nMP7D5F6zol+vX8fqu8s6FVURIi+s6B3H8LkmZ/Q6Bh9Z0Hv5NkvIH1omL6zoFcfwuV6KIBwxuo7C3oT/v/lgeLvEVOy2Jjkv17p4zMNjUJDOHpEqvf8+XPY2dnpOxtCCCGEECIFefbsGfLkyaPvbCQqIiICBQoUgL+/v14+39bWFo8ePUK6dOn08vkpnQTCwiBoNBq8fPkSmTJlgqIoyf75ISEhsLOzw7Nnz2BpaZnsn58SSB1IHaT28gNSB4DUASB1kNrLD0gdAPqvA5J4//49cuXKBROTlPu0Z0REBKKiovTy2WnTppUg+AtkaLQwCCYmJimitc/S0jLV/uDFkzqQOkjt5QekDgCpA0DqILWXH5A6APRbB1ZWVnr53H8iXbp0EoymUCm3+UQIIYQQQgghhEgCEggLIYQQQgghhEhVJBAW4m8wNzfHmDFjYG5uru+s6I3UgdRBai8/IHUASB0AUgepvfyA1AEgdSAMn0yWJYQQQgghhBAiVZEeYSGEEEIIIYQQqYoEwkIIIYQQQgghUhUJhIUQQgghhBBCpCoSCAshhEj1nj59qu8s6JWfn5++syCEEEIkKwmERaqm0Wj0nQUhhJ4NHjwY/fv3x/Xr1/WdFb0YNWoU2rZti3v37uk7K0KP4udOld9FIURqIYGwSNVMTOJOAfnhT71k4nxRsGBBPH36FDNnzsTVq1f1nZ1kZ2tri9jYWIwaNQp3797Vd3ZShNR4Xbh9+zaAj7+LQghh7ORqJ1Klo0ePYuvWrQCAHj16YPTo0XrOUfL7UvCfmm4CFUUBgFTbG5aaG4Hij/MePXqgf//+uHnzJubMmYMbN27oOWfJI778vXr1QseOHfH+/XuMHDkSDx8+1HPOkt+n50H8dSG1XAvnzZuHEiVK4PHjx/rOSoqSmq+P2lLLeSBSH1N9Z0CI5EQS7969w8iRI5ExY0YsX74c+/btw6lTp/SdtWSl0WjUVv8//vgDZ8+ehUajQalSpdC3b1/1JjC1WLZsGebOnYtz587pOyvJSvs42LJlCx49eoQMGTKgdOnScHFx0XPukh5J9VgvVaoUvvvuO+zatQtRUVEYOnQoihcvruccJi3t8ufPnx85cuTAjh07MHz4cPzyyy8oWLCgnnOYPEiq58GCBQtw9epV2NnZoU6dOihVqpROPRmjBQsWoF+/fli3bh3y58+fYL+xlz/enj17cPbsWYSFhcHFxQUNGzaEiYmJznXS2MV/135+foiNjUVMTAzy588PRVFSzXEgUpfUcWYL8X+KoiBz5sxYvnw57t27hy1btuDnn39GqVKlAKSeVs/4H3UfHx+MGjUKFhYWsLW1Rb9+/dC3b1895y75ValSBbdv38aKFSv0nZVkpX0c9O7dG8ePH8fatWvRvn17LFy4UM+5S3rx5e/Xrx+aN28Oc3NzlClTBhs2bMCUKVOM/plh7fL37NkTZmZmKF26NA4dOoShQ4emilESGo1GvbkfMWIERo4ciQcPHmD9+vVo06YNjh49qgYBxmjp0qXo1asXtmzZgmbNmiEgIAA3btzA3r178fr1awAw6vLHW7p0KRo3boybN29i69atGDVqFNzc3BAREaEGw8YuPtDdsmULGjdujGrVqqF169bo2bMnAEgQLIwThUhlYmJiePv2bdaoUYNOTk6sW7cuN2/erLM/NThy5AgLFizIkydPkiQ3bdrEdOnScd68eXrOWdLSaDQ6f0dFRTEiIoJdunRhp06dGB0dzdjYWD3lLvmtXbuWuXPnpq+vL0ly/vz5TJcuHdetW6fnnCWPI0eOMHv27Dxz5oy6bcGCBbS3t2e7du1469YtPeYu6R05coQ5cuRQv3+SnDt3LitWrMjmzZvz0aNH+stcMrp9+zb79OnDc+fOkSRPnz7NH374gfnz5+fhw4dJJrx2GLrnz5+zRIkSLFy4MEnyyZMndHR0ZPHixakoCp2dnfnrr7/qOZdJz8/Pj8WLF1d/+yIiIrh9+3aWKlWKJUqUYGhoKEmmit+FPXv2MH369Pztt9948+ZNzp49m4qicMuWLfrOmhBJQnqERaqg3ZqbJk0aFC1aFIcOHcKSJUsQHh6OBQsWYMuWLer+T19jDD4tT2BgILJnz45KlSph8+bN8Pb2xqxZs9C9e3eEhITgwIEDespp0opv1Y5fLsfMzAzm5uZwc3PDypUrce3aNaMeBhd/HPD/PTz37t3D999/DxcXF2zcuBGDBw/GzJkz0bx5c4SFhRl9r6iiKEiTJg0yZsyobuvatSt69uyJlStXYurUqbh48aIec5i0oqOjERMTA2tra3Vbr1690LhxY2zduhXDhw/HrVu39JfBZLBhwwbUrFkTJ0+eRJ48eQAAzs7OGDRoECpVqoSOHTsaZc9w1qxZMXbsWKRJkwYVK1aEh4cH3N3dsWzZMjx58gSlS5fG+vXrsXHjRn1nNUl9+PABwcHBKFu2LADA3Nwcnp6eWLFiBdKkSQNXV1ed4fPGSqPRYMeOHRg0aBB69uwJKysrTJs2DT169ECDBg30nT0hkoRxn9VCQPf5r5UrV+KXX37B8uXL8fr1a9jb22Py5MmIiorC4sWLsWHDBgCAm5sbJk+erM9sf3PxdbBw4UKcOHECNjY2yJ49OxYvXgxvb29MnToVXbt2BQCcPXsWq1atMpq1VUkiNjZW/Xvt2rWoVasWRo0apQ7/bNGiBerXr4+pU6ciPDxcX1lNcvHHwZ49exASEoI0adKgUKFC2L9/P9q3b4/JkyejW7duIIlt27Zh165dCA0N1XOuv434IIakzv/Hxsaqw0CjoqIAAJ06dULevHmxY8cO7N+/Xz8Z/sa0g7j4BpGMGTPC2toaT5480dnep08f5M2bF6dOncLq1auTP7PJKG3atChVqhTu3r2LwMBAdXvZsmUxaNAgVKlSBZ6enrh06ZJRDQ9Nly4d6tati19++QWhoaEoX748xo4di3LlysHOzg4zZsxAdHQ09u7dq++sJqncuXPDyspKnUATiGsQL126NObMmYPQ0FD88ssvesxh8rly5QqyZMmC169fw8nJCR4eHpg7dy4AYPny5di2bZuecyjEN6annmghkoX2ULZBgwbRxsaGDg4OtLe3p6urK58/f06SPH/+PD09PWlvb88iRYqwWLFijIyM1Fe2vynt4VyzZs1i9uzZefXqVV6+fJlFihShoiicMmWKmiY8PJyenp5s166d0QwFfPbsmfr/e/bs4dKlSzlv3jzmy5ePlSpVYpMmTXj37l3+9NNPrFGjBgMDA0ka11A47bIMHz6c1tbWfPnyJdetW0dFUWhiYsKlS5eqad6/f8+aNWtywIAB+sjuN6dd/vDwcL5//179u1mzZsyTJw8fPHigbnv+/Dnbtm3LRYsWGcVxoF2GyMhIBgcHq397eXmxSJEivHHjhrrtyZMnbNq0Kf/880+jKH+8z5Xl4MGDrFq1KsuWLcsrV67o7Dtz5gzHjh1rFI/N3L9/n76+vrx58ybfvXtHkgwLC+OBAwfUYeEkGR0dTTLu3OjQoYNe8pocNBoNY2JiOHjwYH7//ffcuXNngv1t27ZlnTp19JTDpBP/+/7hwwd125gxY9ixY0fa2dmxa9euarrQ0FB27tyZEydOZFRUlF7yK0RSkEBYpAoPHz5ky5YteeXKFUZERHDr1q2sVq0ay5cvrwbDt27d4rp16zh79mz1JiD+v8bgypUrHDJkCNesWaNu27RpE9OkScOePXty1apV3LVrF93d3eng4KCW3dCD4dOnT7NgwYI8cuQIBw4cyKxZs9Lf358k+ebNG65Zs4Zubm4sVaoUmzdvTkVROGLECD3nOum8ePGCw4YN4549e9Rt48aNo6mpKdevX8/r16/z2rVrrFWrFsuWLWsU54B28DNlyhTWrFmT5cqVY9OmTfn06VP6+fmxZs2atLGx4YwZM7h48WLWrFmT7u7u6vFvyEGQdvknT55MT09PFi1alJ06deLdu3cZHh5OZ2dnfvfdd/zll1+4fPlyurm5sXbt2mr5jSEY1i7DoUOHuGvXLp1nHw8fPkxPT086OTnx6tWrib6HIR8HS5YsYfHixZk7d24WLFiQ3bt3V6+FiZ3n79+/Z5UqVYzuOWHtRqD44/vx48d0cXFhrVq1uG/fPp30M2bMYNWqVRkeHp6s+UxK8eXetWsXO3TowLt375Ikd+7cSSsrK5YuXZqPHz8mGTePxvDhw5k3b17eu3dPb3kWIilIICyM3ooVK1iyZEm6u7szJCRE3b53715Wq1aNTk5OfPHiRYLXGfINj7bY2FieOHGCiqLQ3NycK1as0Nm/atUq1qhRg9bW1qxSpQobN26stvgaQx2cP3+eHTt2ZPbs2Zk5c2Y+efKEJBP0+K9Zs4bjxo1j1qxZWa5cOTWdMfnrr7+oKArz5cvH06dPq9uDgoI4YMAAZsqUidmzZ6ejoyNr1KhhVMcBGdcTniNHDv722288ceIEM2bMyFq1avH169eMiIhgr169WK5cOdrb27N27dpq+Q29MSjeiBEjaGtry7lz5/Lo0aNMly4da9eurQYGHTt2ZMWKFVmsWDF6eHio5TeGIFjboEGDmCtXLn733XfMkCEDa9SowbNnz5KM6xmuU6cOK1asyAsXLug5p9/OunXraGlpyWXLlvHOnTucOXMmHR0defDgwQRpIyMj+fz5c3p6erJChQpG0RgWb926dXRzc1N7v+N7hMm4CdMcHR3p7u7OOXPmMDIykn5+fnRzc2Pbtm31me3/TKPRJLiObdy4kZaWlvTx8dEZDbJy5UpmyJCBtWvXZt26ddmsWTNmzZqVFy9eTO5sC5HkJBAWRif+pi02NpYxMTH87bffWL58eebKlStB8LN37166uroyf/78fP36tT6ymyQSu3GPn/2xe/fuCcr67t07Pnv2jEFBQeprDfnmp0GDBhwyZIj699ixY6koCgsWLMj9+/er2+OPEW0XL15k5syZuWzZsmTLb1L5NIB5/vw5O3ToQBMTE7UnTPtYuXTpEk+cOMFLly6przXk40Db3bt3WapUKbW35+DBg7SwsOD8+fN10gUEBBjNeaDt1q1bLFGiBA8cOECS9PX1Zbp06bh48WKddCEhIXz16pXRlT/e4sWLaWNjwwsXLvDFixd8+PAhHRwc6OLiwtu3b5OMe3zCxcWFnTt31nNuv41Hjx6xatWqnDZtms52FxcXduvWTWebRqPhggULWKNGDbq4uBhVY9jOnTtpYWHBggULsmHDhjx//jzJuDLHH+f3799n27ZtWbhwYVpZWdHBwYFlypQx+EaxT3uzr127xhw5cnDRokU621++fEky7vowduxYNmnShBMnTuSdO3eSLa9CJCcJhIXRim/hjIiI4MqVK1mkSBHWqVNHp1eYJLdu3cpevXoZxQ/9p1atWsXVq1erf0+ZMoWKonDq1Kk69fDpj7uh/tiTcT/4u3fv1mn0uHTpEjdv3swuXbqwePHi3LZtG8mEgWL837169WKTJk2MJgjYtm2b2vjx8uVLNmvWjJaWlmqP1+fKaUw9gefOnVOXidm2bRstLCzU5VLevXun83x0PGMq/+XLl+ng4EAy7pEI7fKHhIRw06ZNnz0fDNXGjRsTXO8HDhzIJk2akPwY3L19+5YFCxZkixYt1HRnz541+PLHu3HjBjt37qz2gsaXe+DAgezYsSNJ3Wv+8+fP+ccff6jpjOE6GBgYyDp16rBfv35cunQp3d3dWbdu3USD4ZCQED5+/JgrV67kvn37DL4eZs6cySZNmjA2NlY9pg8ePEhnZ2d++PCBQUFBXLRoEd3d3ZknTx52796dAQEBes61EMlDAmFhlA4ePEhFUbhy5UqScUO9lixZQhcXFzZo0EBnohxtxhQMBwcH08HBgdWqVeOmTZvU7RMnTqSiKJw2bVqCm0RDd//+fZIfb+BnzJjBhg0bqvt9fX3Zrl072tvbc8eOHer2P/74Q2codPxwMEO98dH29OlTKorC1q1bMygoiCTp7+/Pxo0b09raWh3uZkzHfmINOUFBQXR0dOSgQYOYKVMmLliwQN136dIlVqpUSWe4uCFLrPyPHz9mgQIFOHz4cFpZWemsF37mzBnWqFHDqIYCT5kyhV5eXjrBrEajYatWrejm5qZui58oaOPGjcyZM2eCdZONJRjWHtYaX6aJEyeyXbt2Oun8/Px0/jam68KGDRu4d+9eknGNQZ8Gw+Tnv29DrQeNRsP169erzwDH/6adPHmSiqLwxx9/ZMmSJVm/fn0OGjSI06dPp42NjTpyRAhjJ4GwMEoBAQEcMGAATU1NuWrVKpIfg+GKFSuyUaNG6oyZxiKxm99Hjx7R1dWVbm5u3Lhxo7p94sSJNDU15dixYxkWFpac2UwyU6dOpaIo6s18REQElyxZwhw5cvCHH35Q050+fZrt27dnoUKFOHXqVHp5ebFYsWLqDdDz589pZ2enM4OqIUnsODhy5AgzZcpEb29vvnnzhiT56tUrNmnShFmzZuWZM2eSO5tJ5tPZocPDw9Wb2C5dutDCwoI9e/ZU00RERLBu3bqsX7++UQQ92mV4//49o6Ki1HO8d+/ezJAhg1GXX1v8Tf/58+fVERF79uxh+vTpuXDhQp2069evp4ODg3p+GLrPTXKmfX3w8fGhp6en+neDBg3UodKGPCro79q4cWOCYPjVq1c6s8cbE19fX9auXVvt7V2+fDm9vLzo4+PDmzdvqukqVKigM4mcEMZMAmFh8D43rPf169ccNGgQFUXRCYaXLVvGQoUK6TxDakziZ8GO9+jRI1arVo1ubm7cvHmzun348OGsXLmy0dzwvHjxgi1atKC1tbU68U1ISAjXrVvHPHnysHnz5mraixcvcuDAgbS3t2e9evUSTAoUGhqa/AX4xuK/1/j/Hj16lOnTp08QDFevXp0eHh56y+e3pH3T/+uvv7Jx48YsXLgwhw0bxosXL/LZs2esUaMGnZycOGDAAE6cOJE1atRgyZIljWJiqE9nh27QoAHLly/Pnj178s6dO3z8+DHr1avHIkWKcPTo0ZwwYQLd3NyMpvzxIiIi1P8/cOAAM2XKxNmzZ/Pt27f88OEDfXx8mD9/fs6aNYvv37/ns2fPWKdOHXp6ehrF9VD7O3z79q06EiRefBlHjhzJpk2bkiRr167N7777zqiWxknsu9SeHIuMC4Zr1qzJevXqcd++fSxfvjwrVKiQnNlMNitXrmTp0qVZr149tWHo01Fhw4YNY758+fj06VN9ZFGIZCeBsDAa06ZN01kShox7Lig+GP7rr79Ixt0k7dq1y2CHOn1K+6Zn/vz5rFq1Kk+ePKmT5uHDhyxVqhTLly+vEwx/GiwZujdv3rB9+/a0srLipUuXSMb1iq1ZsyZBMBwVFcV3794Z5aRAv/zyC/v165dgCawjR44wXbp07Nq1q9orEBQUZBTBj7Zhw4Yxa9asXLFiBRcuXEhHR0c6ODgwJiaG586d49ixY2lvb8/69euzZ8+eRrdcWnz5ly1bxsWLF7N06dIsUqQIY2NjeebMGY4fP57fffcd69ata5Tljxc/HLhHjx4sXLgwf/vtN0ZERPDly5ccO3Ys06dPz5w5c7Jw4cIsV66cUTUGkOSoUaPo6OhIe3t7Tpo0KcH+2bNns3Xr1vTy8tIJgo3hOPhaY4D2/s2bN7NGjRpUFIVly5ZNMKmmsYiOjubq1atZsWJFenp6qg2iGo2Ga9asYdu2bZk9e3aZHVqkKhIIC6MQFBTExo0bM1OmTDx06JDOvhcvXrBixYpUFCXBhDjGEgyTcc923b59m4UKFWLjxo156tQpnf179uxhxowZWaFCBbWOEltSwdBo39D8+eef/PXXX6koCnPkyKEOd4sPhvPmzctWrVp98T0M0aff4cKFC6koCkeNGqXe1MaXcfTo0VQUhS1atNBZT9PQ6yDe9evXWbp0aR4/fpxk3HwB6dOn5x9//KGT7tObXWO5Fty6dYtly5blsWPHSMbNlJspU6YEs2N/+kiEMZR/+/btbNSoEUmyX79+rFixovo99+zZkwUKFOBvv/2mzqD74MED/vXXX0YxIdKnlixZQjs7O86cOZNDhw5l2rRp2bNnT/WZaJIcM2YMFUXRmRXZWMof70uNAfHXvPfv37NgwYJ0dnY2mkah+N+Ep0+f8smTJ+qM6LGxsVy7di1dXFzo5eWlNhBs3LiRrVu31hkiLURqIIGwMEiJBW83b95khw4dmDlz5gRrI3bu3Jn29vasXLnyZ19vaNavX8/ly5eTJPv378+6deuSjJv4p2jRomzQoIFOMLxlyxa2bNmSvXv3NpqgR9uQIUOYO3duzp07lz4+PnR2dqaVlZU6TPr9+/dcu3YtzczMOGbMGP1m9hvS/i4vXryoBrerVq1imjRpOGLECJ3hjjNnzmTTpk3p6upqlMfBlStX+N133zE6OpobN27UmR05NDSUa9asUZcIiWcM14N458+fZ+7cuRkVFcUtW7YkKP+ff/7JwMBAndcYQ/ljYmK4du1a2traslSpUrSyskqw5It2MJzYcnmG3Bjw6bm8YcMG9feBjGsQSZ8+PXv06KE2BKxdu5bNmzc3muDvU3+nMSAsLIyenp4sXLiw0TQGxJ/PGzduZJEiRViwYEFaWVmxR48e6qSQa9euZcWKFdmgQQP1XPh0iSUhUgMJhIXB0f7BDwgI4MOHD9W/nz9/zjZt2jBLliw8evQoybih0C1btuTOnTuN4oaPjBvWO3ToUCqKwrp169LCwkIdCkx+DIYbNmzIlStX8vnz56xXrx6nTJmipjHkIOjTG9YnT56wUKFCXL9+vbrt/v37bNiwoc7MyMHBwTxw4IBB3/Bq0/4OR44cyWrVqnH16tXqDd3KlStpamrKoUOH8uHDhwwLC2PDhg11Jk4z5ONAe2h//P9fvHiRZcqU4e+//05ra2v+9ttvavoTJ06wZcuWvHz5sl7y+61pX8/iv8fbt2/T3d2dM2bMoKWlpU5P8JkzZ9iqVSujmh36Uw0aNKCiKKxdu7a6TTvw6dWrFwsXLsxJkyZ9dvUAQ6N9HCxfvpxTpkyhk5MT58yZo5Nu165d6mRp8ceLMT0a8k8aA7SPiZ07dxpNEBzvyJEjTJ8+PefNm8fDhw9z06ZNzJYtGxs1asTnz58zNjaWq1evpr29PZs3b87Y2FijuT8S4p+QQFgYFO0L9ejRo1m2bFlaW1uzcuXKnDdvHiMiIujv78/27durQaKDgwMdHR3V4MeQb/w/VapUKSqKwl9++YVk3I94fDmvXLlCd3d35s6dm3ny5NF5Bs6Qf/C+//57zpw5U2fbrVu3mCFDBh4+fFjdptFoePXqVebKlYt2dnb09fXVeY2xBMNk3DOh2bJl4759+xL0dK1bt47m5uYsWLAgCxQoQAcHhwTPDhsi7fP403M6PhiaOHGiui08PJxeXl5GMzvyl8pQs2ZNKorC8ePHq9vCw8Pp6elpNOWPp12W4OBg/vHHH5wyZQrz58/PZs2aqfu0h4J36NCBjRs3NujjP96nv4mmpqbq8641a9bUaSgmyd27d1NRFP7666+Jvoeh+qeNAb169UowKaKxBMFk3GSYXl5eOtsuXbrELFmysF+/fiTjyvvXX38lWDJMiNREAmFhkH766Sd1Mpx9+/axRYsWdHZ25qhRoxgVFcUPHz7wjz/+YPv27Tl48GD1B86Ygp+IiAh26NCBHTp0oKIoXLZsGcm4G8P4gNff35/nzp3jzp07jeYZuN27d6uzwmrfBFerVo3t27fXubmJiopirVq1aG1tTXd392TPa3I4d+4cixYtqgb6wcHBvHPnDufPn89bt26RjOslXbhwIRcsWGAU54L2Te/06dPZoEEDtm3bVm0QCgwMpKurK3PlysXJkyfzp59+opubG0uUKGF0EyJNnz6dTZo0YYsWLdQGovDwcFaoUIHFixfn2LFjOWnSJNaoUcPoyq9dhnnz5nHevHl8+vSpOvlP3rx5dYJhkup5YmwTBV69epUNGzbkmTNnGBkZyRMnTtDc3Jxt2rRJMAOwr6+vwf8OaPu3jQHaI6SMiUajYYcOHVirVi2ScedJ/LPyK1asYPbs2fn48WN9ZlGIFEMCYWFQNBoNX79+zYoVK3LRokXq9sjISI4cOZKlS5fWmTla+2bf0H/4P3fjGhkZyeHDh1NRFJ1hYCR548YNnb8NOfj51M8//8zevXurP/BTpkyhi4sLf/75Z51JUBo2bMijR48axY0/mfDG/fr168yXLx8PHjzIa9eusXfv3ixcuDALFChAc3PzBMcAadjHgXb5J0yYwEyZMrFnz55s2bIlLS0tWa9ePYaGhjIsLIwdO3ZklSpV6O7uzt69exvFs5Dax/HYsWNpaWnJzp07s02bNjQ1NWXTpk0ZFBTE0NBQtm3bllWrVmXNmjWNpvyJGTRoELNly8YlS5aoy8eFh4dz3bp1zJcvHxs0aMCnT5+yVq1arFu37mfX2DVUv/32G6tWrcoaNWrw7du36nZfX9/PBsOk8R0HqbUxIP54fvPmjTryYdOmTTQ3N+f+/ftJfjzWN2/ezOLFixvNetlC/FcSCIsU79Mb//DwcDo4OHDatGkkdW/qHR0d2bFjx2TNX3LQvmHbt28ft2zZoi4HRcZNgjN8+HCmSZOGixYt4ps3b9iwYUO2b99eH9lNFkuXLqWiKBw6dCjJuAaBAQMG0NHRkRUrVuSwYcPo5OTEcuXKGc2weO3837hxg+/evePLly9Zr1492tvbM0OGDOzevTvXrVvHN2/e0N7ePsEwcmPh6+vLTp06qTd6Go2G586dY+7cudmkSRM13fv373XqzVhufi9cuMB+/frxyJEj6rZz587R2tqa3t7e6rbw8HCdydKMpfzx1q1bx9y5c6szxGsLDw/n1q1bmS9fPubLl49OTk5GsU7up9exgwcPMm/evLSysuKBAwd09vn6+jJjxoz08vLiq1evkjObySq1NwZs3ryZlStXZuHChTl69Gju3r2bP/74I4sVK8Z9+/ap6YYOHcpy5colWE5KiNRKAmGRomn/4D979oxkXMBTvXp1dZZk8mMw3Lt3b7Zs2TJ5M5nEtBsC4he7L1WqFLNnz85mzZqpLbuhoaEcP348FUVhyZIlaW9vbxQ3feTnA9i1a9fS1NSUgwYNIhk3FHrjxo1s164da9euzXbt2ql1YMi9oGTCibFq1arFLVu2kIx7Hnzbtm08fPiwWt744bGrVq3SS36T0ubNm1m6dGnmz59fXe4j/jw5dOgQs2TJwq1bt+ps//T/Ddn27dtpa2vLnDlzqhN/xd/QHz58mOnSpeOOHTsSvM5Yyq9t/PjxrFmzJiMiIj473Dk4OJjHjh1TzyFDDn60rwP37t1Tg7v79++zYMGCbNSoUYJGgaNHj7J69eoG3xCoTRoDPrpw4QKtrKw4fvx4/vjjjyxXrhxbtmzJ6dOns3///jQzM6OzszOrVKmiM3mkEEICYZGCaf/QjRs3jtWqVVOf77py5QozZcrE7t2788OHD4yJiWF0dDQrVqyoTgRhbCZNmkRbW1ueOXOGJDlnzhx1QrCAgAA1na+vLzdu3Gg0zwRrHwenT5/m/v37ddaAXbVqlU4wHE87jaHXgbZhw4bRxsaGO3bsSLAMDhk3Q+7Dhw9Zp04dli9f3uAbAMiEN73Hjx9nw4YNaWpqyrlz5+rse/78OfPnz68+M28MPi3/yZMn1aHQK1asIPlx5uxXr16xSJEiRlX+xMQf197e3qxWrVqC7TExMTxw4ECCiYAM+XzQDvCHDBnCYsWKMWvWrPz++++5ZcsWPnz4kAULFmTTpk0T7SEnDX9UDCmNAdru37/PCRMm8KefflK3bdu2je7u7mzWrBm3bt3KI0eOcMiQIZw0aRLv3r2rx9wKkfJIICxSvOHDhzNHjhxcv369ugYeSe7YsYOZMmVi+fLl6e7uzsqVK9Pe3t5ogh7tH2x/f3+2atVKXfZmy5YttLKy4qhRo5gzZ07Wr1+ffn5+Cd7DkG/6PjV48GDa2NjQ0tKSJUuW5JYtW9TnoVatWkUzMzMOGzZMnUgrnjH1gvn6+rJgwYI8ffo0ybhRAPfv3+fatWt5+/ZtkuTcuXPp6enJSpUqGUVvuPZ5sHv3bnXY49WrV9m4cWOWLVuWK1euVNOEh4ezaNGiOssGGTLt8u/fv19t9Lpy5QqbN29OOzs7bt68WU0TFhbGQoUK6cyhYAw+F8Bs3bqVadOmTRD4+/v7s2nTpty5c2dyZC/JaZd/zZo1tLW15ZYtW7h06VIOGjSIJiYmXLZsGR88eMBChQqxVatWCWbKNwbSGPBRcHAwy5cvz+zZs6uPCMXbtm0ba9SowcaNG+ssrSiE0CWBsEjRrl+/ziJFiiQ6zI8knz59yuHDh/PHH3/kmDFjjGYyGO0f+/iy//XXX3z9+jXPnDnDfPnyqT1hEydOpKIorFy5slE996NdB+fPn6ejoyOPHDnChw8fsm7duixRogRXrFihBsNr1qyhoiicN2+evrKc5C5evMjixYvzxIkTvHLlCn/88UcWKlSIBQsWpJWVFe/fv89r165x5cqVRjEiQPsYGD58OO3s7Dhv3jw1wL9w4QKbNWtGOzs79u/fn1OnTmWDBg1YuHBhgy53vE/LnzdvXq5cuVJt7Llw4QJbt25NKysrjhw5kpMnT2b9+vVZtGhRoyh/PO3AZfv27ZwzZw7nz5+vNv5069aN+fLl4++//87AwEBeu3aNderU0ZkfwFgcPnyYnTt35vTp09VtISEhnDVrFtOlS8eTJ0/y4sWLzJAhA0ePHq3HnH570hiQ0MWLF1mkSBFWrlyZ169f19m3c+dOlilThq1bt2ZYWJhRNQoL8a1IICxStMOHD9PW1pYvXrwg+XH4H8kEPX/xDP3GR/vHavz48cyQIQMfPXqk3gT8+uuvrFevHt+9e0eS/P3339muXTs2bdrU4MseT/uGJyYmhvfv3+fgwYN10jRt2pT29vY6wfC+ffuMJgBIrNfi1q1bdHFxYYUKFZghQwZ269aNa9eu5Z07d1iiRIkEvWKGfDxo53306NG0sbHhqVOnEjT2XLt2jQ0bNmT69Onp5ubGBQsWqOeQIZdf+zgeM2YMc+TIwWPHjulMBESSN2/eZJMmTZgxY0a6urpy9erVRjESIDGDBw9mgQIFWLVqVdavX5+mpqa8cOECHz16xDFjxjB9+vS0tbVVAwNjqwc/Pz8WKlSImTJl0hkKS5JBQUGsX78+e/XqRTJuzVhjKfenUnNjQGKuXLnCMmXKsGvXrgmC4b1798pSSUJ8gQTCIsVIrLXy2rVrtLGx4bZt29Rt8T/uGzZs4MGDB5Mtf8nt7Nmz7NKlCw8fPqyzvV27dixfvjzJuKGx9evX5++//67uN5ZhX2Tc8jiurq60s7Nj7dq1E9zYNWvWjA4ODlywYIFOw4ihB8Pa3+HJkye5YcMGnj59mhEREXz8+DFXr17Nffv2qWX+8OEDHR0duWbNGn1l+ZtZtGgRg4ODScbVg5+fHytXrswNGzaQjBvyevbsWfbt25dbtmzhhw8fePPmTTZv3pyenp5ct26d+l6G2AOiPaQ7JiaGr169oouLi/rdvnr1iufPn+egQYO4YcMGhoaG8ubNm+zQoQPLlCnD7du3k4ybOM4Qy/85K1asoK2tLc+ePUvy46zxq1evVtPcu3ePu3fv5smTJ41iYqzEXLlyhYUKFaKjo2OCSY86depEDw8PnW3GFgxLY0DiLl68SEdHR3bu3DnRJfOEEImTQFikCNo3/tq9vs+fP2eVKlXYvHlz9QaIjPtxj18b1Bht3LiRZcqUYfHixdXnouPr6MKFC7SwsGDRokVZtGhRlixZ0mhu9rRv3BcvXkxLS0v+9NNP/P7775kzZ07+8ssvfP/+vc5rXF1d2bp16+TOarLw8fFhoUKFaG9vT2dnZ1aqVEmdJZiMGxXx/Plzenl5sUKFCgZ/07dhwwYWKlSIffr0Ub/nJ0+e0NramgsWLODJkyfp7e1NR0dHFi9enNmyZVMnizp9+jSbN2/O6tWrG+xEUTt27KCtrS27d++ubnv48KG6Ru6+ffvYrl07VqhQgd999x2LFi3KhQsXkox7ftzb25sODg5qo4Eh+7RBb/To0fzxxx9Jxl0fLSws1LKHhISoqwpoM/Tz4XOuXLnC0qVL09vbW33+MyQkhJUqVWKXLl30m7lkkNobAz7n4sWLdHJyYsuWLXnr1i19Z0cIgyCBsNA77eBn6tSpbNeuHZs0aaJeyA8cOEB7e3vWqlWL48aN4+LFi1mtWjWWKlXKaALAT+3du5e1a9emubm5Tg9X/M3h1atXOWzYME6ZMkWtA2P6sT9w4AAHDhzITZs2qdu6d+9OJycnTpo0SR0KHc+YesHjzZs3jzly5OCJEydIkqNGjaK5ubn6zHhMTAynTJnCWrVqsWLFikYxDPTDhw/85Zdf6OLiwp49e6o9wyNHjmTGjBlpYWHBgQMHcvfu3STJWrVqsWfPnurrL168yNq1a9PLy4shISF6KcN/8ebNG86aNYsODg7s3Lmzur1v3760trZmxowZOXDgQO7du5dkXCNQ//791XQXLlxgw4YN6eLiwtDQUIPtEdbO986dO/n69WuOHDmSvXr14ubNm2lhYaEzF8DSpUs5atQohoeH6yO7enHx4kXa29vT1taWdevWVSeOi58t31C/+78rtTcGfM7Zs2dZrVo1vnz5Ut9ZEcIgSCAs9Eo7gBk/fjyzZMnCTp06sVy5crSyslLXST116hR79OhBOzs7VqlShS1atDCKG3/y80Hc6dOn6eHhQWdnZ52ZTxMrrzE1CBw5ckRdJ3nfvn3q9qioKDUYnjJlSoKeYUMPhuPzH38D26lTJ44aNYpk3My4mTJl4oIFC0jGzQz84cMHXr16lQsWLDCKibG0l7uaMGECq1atyn79+qmNHhcuXOC1a9fUNBqNhq6urvzll190bvovXbrE58+fJ1/GvxHt69mcOXNYunRp9u3bV91//PhxnfKTpJubGydMmJCg/PFzKhgi7fN49OjRLFy4MG/dusUlS5awaNGitLCw4Jw5c9Q07969o6enJ4cNG6aP7OrVtWvXWKBAAX7//fc6DQPGsn7816T2xoDP+fDhg76zIITBkEBYpAh+fn7s1asXT506pW7r1KkTM2bMqNMrGB4eztDQUPVvQ77xJ3Vv+latWsWpU6eyb9++vH//Psm41t369evT1dWVu3btSvR1hu7Tm5X3799z9OjRzJUrF1u0aKHTyxMdHc2ePXsyf/78XLVqVXJnNclo18HVq1cZHR3NDh06cOXKldyzZ49OD1h0dDT/+OMPLl++XOc9DLlBSLv8ixYtYufOnZk7d26mT5+e/fv3V3uGybjn4s+fP8+6devqjAox5HNCu/wLFixghw4dmCtXLqZJk0Z93jFeSEgIL1++zDp16hhN+RPz6NEjtmzZUu39JsmWLVvS0tKS69at44MHD3jjxg3Wrl2b5cqVM/jfgn/r0qVLdHZ2ZpcuXXjv3j19ZyfZpfbGACHEfyOBsNC7+GVvihUrxgsXLujs69SpEy0sLLhly5YEP2zG1No7aNAg5s6dmy1atGCNGjVoZWXFP/74gyR59OhRNmzYkO7u7jqNAsZm7ty56giA8PBwjhs3jhUqVOCAAQN0WrijoqI4ZcoUgw78tGkHMH379mXJkiUZEBDAfv36MUuWLMyUKRMXL16spgkMDKSbmxt//fVXfWQ3SY0fP55WVlZcu3Yt9+7dy9atW7N06dLs3bu3OgJgx44ddHd3p7u7u9GMCok3btw4Wltbc+3atdy+fTs7dOjAokWLsmvXrmqarVu3snLlykZV/j/++ENdH5mMeywgc+bMdHBwSDDxT506dViyZEmamZnRxcWFVatWNZp6+LdS+7Ohqb0xQAjx70kgLPQuLCyMrVq1oqIo6oyn2kFu165dqSgKjx07pq8sJqm//vqLuXLl4tWrV0nGzRKsKAo3btyopjl+/DirVKnCPn366CubSSogIICNGzdmrly51Oc/Q0NDOWrUKDo7OycIhuMZ041vUFAQmzVrxkOHDpGMOwc8PT1pa2vLJ0+eMCAggC9evGDt2rXp7OxsVD1gGo2GQUFBdHFx4axZs9TtkZGRHDVqFAsUKMBBgwYxPDycMTExPHr0qFEMB9f25s0bfv/99+r64GTcMTFp0iR1neR4Bw8eNJryL1u2jDVr1tRpEIqMjGSFChWoKArXrVuXoLf7+vXr3LVrF69evWq0s0P/U6n92dDU3hgghPh3JBAWyepzw/fCwsJYt25d2tra8ty5cwn2T5w40WhvdObOncs2bdqQjBsenSlTJnU5pODgYL569YpkXKu3sQx/TKwcly9fZseOHZk3b151GHhoaChHjx7NSpUqsVOnTjrPkRqTOXPm0M7Ojt9//73O7LfXr1+no6MjbW1tmS9fPjo7O9PJyckoe8BiY2NZqVIlDhkyhKRuY1jVqlWZNWtWtmvXTmeiNGM5H8i4QK5MmTLs16+fzvawsDBWq1aNZmZm/OGHH3T2Gcv3H388Hzt2jA8ePFC3lS5dmkWLFuXZs2e/OALImI6D/yK1Pxua2hsDhBD/nAmESCYajQYmJnGH3NmzZ3Hy5Elcv34dAJAhQwZs2LABZcuWRYMGDXD+/HkAAEkAwNChQ2FqaoqYmBj9ZD4JxJft6dOnCAkJwYkTJ9C9e3dMmjQJPXr0AACsXLkSU6ZMQVRUFMqUKQMTExNoNBp9ZvubiD8O/P391W2lS5fGjz/+iBo1aqB79+7Ys2cPMmbMCB8fHzg5OcHExARmZmb6ynKSKl26NLJkyYKrV68iOjpa3V6iRAlcuHABM2fOxIQJEzBmzBicOnUKZmZmiImJQZo0afSY638vsWM4JiYG+fPnh6+vL/z8/HT2OTo6okCBAsiaNSvSpUunbo8/jgxNYuWPjY1FxYoVcffuXdy+fVvdniFDBjg7O6NSpUqwtLTUea2hfv8AMH78eCxfvhwAYGZmhuPHj6NWrVpYtmwZnjx5AjMzM5w9exZp0qRBp06dcOHCBfWa+SlDPQ6+Ne1zIzWqUKEC9uzZg5w5c+o7K0IIQ6HnQFykEtqt+SNGjGC+fPlYpEgRmpmZcdKkSfT39ycZty6ql5cX7ezsePLkSX1lN0l8rtfi6tWrLFKkCBVFUWcFJj/2kvfo0cOonoeO99dff7FgwYIJRgBcvnyZDRo0YN68eXnkyBGScT0d8XVg6L0/ieU/KiqKp0+fVid9ie/p+9yEL4bcE6hd/tOnT/PKlSvq5HBPnz5l1qxZ2aBBA967d4+RkZGMjo5mkyZNuGjRIqM4BrTzfu7cOV66dEm9/p0/f57Zs2dnmzZt1PVRw8PD2bhxY86dO9coyk/Gfc9ly5alh4cHN2/erG4fP3488+XLx3HjxvHx48ck44ZJlyhRgmXKlDG63wQhhBD6JYGwSHLaN20//fQTc+bMqQY4/fv3p6mpKX18fNQhwBEREXRycmL9+vX1kt+koF0HO3fu5KJFi3jo0CE+efKEJDlmzBgWLVqUQ4cO5atXr3jixAl6enqydOnS6pBwYwuGd+3axTp16tDJyYnnz5/X2bdkyRIqisK0adPq3Pwaeh1oHwdHjx7ltm3beOPGDXUm9DNnzjBPnjx0dXVVy2rIQe+X+Pj40MbGhnZ2dixWrBi3bdtGMm4W2Jw5c7J06dJ0cnJiuXLlWKRIEbUeDD0IjDdkyBBmyZKFefPmZa5cudTj/NSpU8yfPz8rVKjAChUq0NHRkcWLF1fLb+jnQLybN2+yVq1a9PLy4po1a9TtP//8M/PkyZMgGM6WLRvbtm2rr+wKIYQwQhIIiyTz+++/69zE379/n/Xr11d7ADZt2sTMmTOzQ4cOVBSFPj4+6vqXUVFRRnPDq83Hx4eZMmWivb09ra2tWa1aNW7fvp0REREcO3Ys8+fPz4wZM7J06dL08PAwmmdBP5f/AwcOsF69eixXrpxOz/DBgwfZqlUrzpkzx+DLnhgfHx9aWVkxb968TJcuHVu3bs2jR4+SjAuG8+bNm2ACIUOnHcBdvHiRBQoU4KlTp7h9+3b27NmTJiYm6qzogYGBnDVrFocOHcqxY8eqjUGGfCxof5enTp1igQIFeOTIER44cIDe3t5Mnz49d+zYQZK8c+cOly5dyn79+nHChAlGUf542mXYt28fK1WqRDc3N3XGeDLxYDg6Otooyi+EECLlkEBYJIkFCxawdevWOjcur1694sqVKxkWFsaTJ08yT548nD17NkmyV69eNDc3Z/fu3fnmzRv1NYYeCGjf/J8+fZplypThiRMnGBsby5MnT7J9+/YsW7Ys9+/fTzJucqhjx47x4cOHRjEbqvaQZjJuhthJkyZxxowZ6rbDhw+zQYMGLFmyJHfv3s1Hjx6xYcOGHDhwoJrG0G+AtevgxIkT/O6773jkyBEGBwdz8+bNdHd3Z506dXjmzBmScceKubm5Uc4SPnPmTI4cOZLjxo1Tt/n7+7N37946s6V/eu4b6nnw6bE7a9Yszpgxgz///LO6LSwsjN26dWO6dOl01gvXZqjl/xwfHx+2atWKpUqVYtq0aVmhQgX+9ddf6v6JEycyX758HDhwIP38/NTthn4tEEIIkXJIICySRFhYmHrDcuDAAXW23+DgYJLkgAED2LJlS3WWy+HDh9PV1ZWVK1c2+OA3MZMnT2a3bt3o7e2tExTFPw/7ww8/JPo8qCHXRZs2bVirVi11/ddhw4bRwsKC1apVo7m5OWvVqsXnz5+TjFsyqk2bNlQUhUWKFKGDg4NRDgmfPn06hw8fzr59++ps379/Px0dHdUZk2NiYnjjxg2Dv+l3dXXlTz/9pP4dEBDAhg0bUlEUdu/eneTH7/fVq1fs27cvzczMuHbtWr3k91v7/vvvOX36dPXv9+/f09XVlYqiqGsDx5c/PDyc3bp1U9dNNzba17KFCxfS2tqaZ86c4YsXL3jx4kU6OzvT1dVVZ9m4YcOGsWHDhkZ1DRBCCJFySCAsvjntgO7o0aMsXLgwBw4cqG6PiIhgnTp1+MMPPzA6OpoajYb169fn8ePH1dcZcgCYmEGDBqlBXvzw73hLly5l2rRp+fTpUz3lLmkcPnyYWbJkYcuWLfnw4UN6eHjw0qVLjIiI4L179xIsFxQbG8sTJ04Y1Rqpn97AN2vWjIqisGrVqupzwfEmTZrEzJkz8+3btzrbDTUYjo2N5f79+xkREaGz/fLly/T29qa5ubn6XKx2MNy2bVtWrlw52fObFPbt26eWP76MT548YevWrWltbc0rV67o7AsPD2fz5s3p6uqqnwwngUWLFqkNoPHHct++fenp6UnyY9mvXLnCIkWKsHz58uoQee39EgwLIYT41iQQFklmzZo1fPr0KYcOHaquDxofDC9YsICKorB27dosVaoUS5QoYTQ9gJ8L4idPnkxFUThp0iQGBQWp20+dOsXixYurM+cak1OnTtHS0pJubm6sW7cuX79+re578uQJ7ezsWK1aNT569CjBaw01AIynfRxol69///40MzPjX3/9pZNm06ZNLF26NAMDA5Mzm8li4sSJbNWqlfr3jRs32KJFC2bLlo2nTp0i+fG8DwoKMrqGsJ9//pndunVTj+nnz5+zbt26zJYtG2/cuEHyY/kjIyONpvwbNmxgoUKF2KdPH3VkCEkOHTqUNWrUYExMDDUajXrtX716NTNmzEgnJyf1cRHS8H8ThBBCpEwSCItvRvvmbcqUKVQUhU+fPmVwcDBHjBhBZ2dnDh48WB0m/eeff7JLly4cNGiQ0UwGo10HFy5c4IULF3R6/kaMGEETExMOGzaMJ06c4K1bt+jh4UEnJyejufmNF3/zeurUKdrZ2TFbtmx8+PAhyY/19PTpUxYoUIAlSpRI0FNuyLS/y/Hjx7NRo0bqREgajYadOnWihYUF//zzT968eZMvX75kzZo1Wb16daO46f/0WF6xYgVNTU3Zo0cPddv169fZqlUrZs+enb6+vl99D0Pyad7jZ0EfPHiweo178eIF69SpQxsbG968eZOkbsBnyOWP9+HDB/7yyy90cXFhz549GRISQjKup1xRFC5evFgn/Zo1a+jh4cGBAwcaRfmFEEKkbBIIi2/uzJkznDVrlnrjT8Y9GxcfDPv4+KjBsPbQV0MfBqtt8ODBtLOzo5mZGZs0aaKzVuaoUaOoKApNTU3p7e3Nhg0bqj3lhn7z97n8+/r6MnPmzGzWrJl6Mxx/0//w4UM2atTI4BtBEjNs2DBmzZqV27dvTzD0vXPnzlQUhVmyZGHHjh1ZrVo1ozkO4i1evFgd/rxx40ZmyJBBfTaWjAuGW7duTUVReO3aNX1lM8nMmTNHDXLXr19PU1NTDhw4UCcYrl+/PhVFSXRUhCGLv8aT5IQJE1i1alX269dPPf/HjRtHU1NTzpw5kzdu3KCfnx/r1q2rM4mYsZwHQgghUiYJhMV/pn2zcvjwYSqKwkyZMnHv3r0kPz4zHB8MV6pUSWeYoDHQ7sk5fvw4S5YsyaNHj3Lv3r2sVq0a3dzcuHLlSjVN/DDp+fPnqzeGht4QoH0c7N+/n4sXL+amTZt4+/ZtknH1YmVlxZYtW6pl/vRG15iOiQsXLrB48eI8cOCAzvb4Mms0Gg4YMICKonDr1q3qfkM/DuL5+fnRy8uLffr0YVhYGGNjY7lhw4YEwfClS5c4ZswYo/ruSfLNmzd0d3ent7e3+ozs+vXraWZmphMMP336VKen2BhoXw8XLVrEzp07M3fu3MyQIQP79eunDpOeNm0aM2XKxDx58jBv3rwsVaqU+nthDCMjhBBCpGwSCIv/RHvY74sXLxgdHc2xY8fSwsKCEyZMUPdpB8N9+vRhly5djOZG59Ng7vLlyzqzAt+7d49eXl50dXXVCYaHDh3KNGnScN68eeqNsjEYPHgwCxQoQCcnJ3p4eDB37tw8ceIEybhh0pkzZ2br1q357t07Pef02+nTp0+C4b2HDh1ijhw51B5BbdoTSHl7ezNTpkzct29fkuczKSXWezd37lwWKlRIXSM6IiKCGzZsoIWFhTprtDZDDgYTK/+CBQtYsWJFdY1okvzrr7+YNm3aRINfY2kEiTd+/HhaWVlx7dq13Lt3L1u3bk0HBwedZ4avXr3KQ4cOcdu2bUYzSZ4QQgjDIIGw+Ne2bdvGcePGMTY2lt27d2fOnDkZExPDFy9ecMSIETQ1NeXvv/+upo8PhsPDw41yJtBJkybR09OTFStWZOvWrXX2xQfDNWvW5MKFC9XtY8eOpaIo/OOPP4yiLpYsWUJbW1s1KJw5cyYVReG6devUNCdPnqSiKBwzZoyecvltnT9/nn369Elw875jxw7mzJlTHfIbGxurfsdbt27VWTO1S5cuVBSFBw8eTL6MJ5HFixdzyZIl6t/NmjWjvb29GuRERUVx48aN6sRxxmbhwoVcs2aN+ne9evXo5OSkk2bDhg1UFIVz5sxJ7uwlC41Gw6CgILq4uHDWrFnq9sjISI4aNYr58+fngAED1JEh2gy5MUQIIYRhkUBY/Gtjx46ljY0Nq1SpwmzZsvH69evqPn9/f44cOZKWlpacN2+eul37JsfQAz/tHqCZM2fSwsKC/fr1Y4kSJZg9e3adG0CSvH//Pp2cnNi3b1+doOnnn39OtNfQkMR/lwMHDuTAgQNJkps3b6aFhYUa+L9//15dN/jq1atG1esTX/4VK1Zw+/bt6raiRYvS1dVVXS+bjGsIqlu3LkeMGKFzDvTq1UsdRm6obt++TUVRqCgKO3TowEePHvHatWv08PDg8OHD1fJGR0fz8OHDRncMXL58WS3/yJEjee3aNfr5+bFMmTLqGtHxDh06ZFTl/1RsbKy6WgCpe72vWrUqs2XLxrZt2zIsLExfWRRCCJHKKSQJIf4BklAUBQDg5uaGw4cPo1evXpg4cSIsLCzUdP7+/pg3bx5mz56NESNGYNCgQfrKcpI6cuQITp48iQoVKqBWrVp4+vQpxo0bh9u3b+OHH35Ar1691LQvXrxAzpw5YWJigpiYGJiamuox5/+N9nGg0WhgYmKC/v37I1u2bHBwcMAPP/yAKVOmoHv37tBoNFi1ahVev36N7t27I3369ABg8HWgnf/nz5/jhx9+gImJCYYOHYratWvjwoULaNy4MWxsbNC1a1ekSZMGa9euhb+/Py5dugRTU1OdejQ08d+7trFjx+LKlSt48uQJihcvjly5ciE0NBQRERHo378/HBwcdNIb8jGQWPlHjx6NLVu2IEeOHMiTJw8yZsyIvHnz4sKFC+jfvz9cXFx00hty+eMlVg9RUVHo0KEDnj9/jrVr18LW1lY9zvv3748TJ06gWrVqmDx5coLXCiGEEMlBfn3EP6LRaNSbGZIoW7YsevbsiW3btmHGjBnw8/NT99na2qJ79+5o06YN9u7dC2Nsczl27BjatGmD2bNnw9raGgCQN29eDB8+HMWLF8eqVaswb948NX3u3LlhYmICjUZj8De/8cfB0qVLsWHDBgCAra0t5syZoxMEA0BwcDBWrlyJ4OBgNQgGYPB1EJ9/Hx8fPHz4EKNGjULmzJkxdepUHDx4EOXKlcPp06eRLVs2/P7775g3bx6yZs2KixcvwtTUFLGxsQYbBANQA5hdu3ap57e7uzsyZ86MSZMmoWHDhggPD8eKFSuwbNky/PHHHwnew5CPgfjy79u3D69fvwYANG3aFFWrVkWrVq3QsGFD+Pv7Y/jw4di7dy82btyY4D0MufyAbhB85swZXL16FQ8ePEDatGnx66+/4saNG+jRowcePHiAqKgoxMTE4NmzZ+jWrRumTJmiXg+FEEKIZKe3vmhhcLSHAi9dupQ7d+5U/54wYQLz5MnDCRMm0M/PT93+5MkTkjTKZ4LJuKV/hg0bRisrqwRDHx88eMAuXbrwu+++48aNG/WUw6QVHR1NR0dHNmrUSN3WqFEjWlhY8NixY3z8+DEfPHhADw8Pli9f3miGgmqfC/v27aO5ubm6TNChQ4dYt25durq6cs+ePWq6169f8+3btzrDg43B48ePaWJiwipVqnD16tXUaDQcP348K1SooKaZN28ezc3N6eTkZBTXAO3vPyAggIqisH79+uq6uGPHjmXTpk3VND///DOzZs3KKlWqGEX5E+Pj40MbGxva2dmxWLFi3LZtG0ny2rVrzJkzJ0uXLk0nJyeWK1eORYoUUR+TkSWShBBC6IsMjRZ/C7WGb/r4+OCvv/5C79690bx5c9jZ2QEAxo0bhz///BPe3t6oX78+hg8fjlevXuHq1asJ3sMQfTr8LzY2FmnSpEFAQABmzZqFtWvXolOnThg+fLia5u7du9i0aRMGDx6MNGnS6CPbSSa+Pnx9fdGsWTPMmjULTZo0QVhYGOrXr4/79+8jODgYxYsXh4mJCY4cOQIzMzO13ozBn3/+iXfv3iFNmjT48ccf1e2HDh3CjBkzEBERgUGDBsHDw0PndYZ8LiQ2DPbNmzcYMGAAHj58iKxZs2LBggVo27YtypQpg8mTJwMArl69ipIlS8LExMSgy3/27FnkzZsXtra2GDRoEKpUqYKSJUtixowZOHPmDPLnz49p06bBzc0NLVq0wM8//wwAuHz5MhwcHAy+/PG0y3Dp0iU0adIEq1atwps3b7B7927Mnz8fGzZsQKNGjfD69WusXr0afn5+SJcuHUaMGKGOiDCWa4EQQgjDI4Gw+EdmzJiBiRMnYufOnahQoUKC/ZMnT8aff/6J2NhY2NjY4MiRI0ibNq0ecvptad/8//bbb7h16xauXbuGvn374vvvv0fatGkxdepUbNiwAe3atcOwYcMSvIeh3/Qlln+SCAwMRNeuXVGwYEFMnz5d3Xfs2DG8ffsWOXLkgJOTk1E8F63t6dOnqFu3Lq5fv45Ro0Zh3LhxiIqKUo/3w4cPY9asWXj8+DEWLVqU6PliaLTPgxMnTiAyMhIajQY1a9ZETEwMDh06hOnTp+PcuXNwcXHBu3fvMHPmTJ2yG+p5oNFoEBgYiJw5c6Jjx44wMTHBunXrcOzYMZQuXRohISG4evUqBgwYgNjYWNjZ2eHBgweYN28eqlSpor6PoZb/c2bNmoXXr1/DzMwMo0ePBgC8evUKP/30E3777Tds2LABjRs3TtCAYkzXAiGEEAZKTz3RwgCFh4ezUaNGnDJlCsm4JYHWr19PDw8PNm3aVJ0R+OLFizx9+rRRrgnp4+PDHDlycNSoUezfvz+tra3Zo0cPxsbG8unTpxw5ciTt7e05bNgwfWf1m5k7dy4DAgLUv5csWcIFCxbopPnzzz+ZNm1aXr169bPvY+hDID8d0hoTE8MDBw6wSpUqzJ8/v7oucmRkpJpm165d9PHxMfiyf2ro0KHMnz8/y5UrR0tLSzZt2lTnu582bRpLlSpFRVE4efJkPeb027ty5QrNzc2ZLl067t+/n2TCY2PMmDGsUqUKFUVRr5fGwNXVlT/99JP6d0BAABs2bEhFUdR1oePr4tWrV+zbty/NzMy4du1aveRXCCGE+BIJhMVnJfYsW6tWrViuXDkuX76crq6udHNzY9euXVmkSBG6uromSG9Ma0IeOnSIBQoU4IULF0iSZ8+epaIoXLVqlZrGz8+Pffr0YatWrYziWcClS5eyefPm6vcYGBhIb29vmpmZsUGDBpwxY4Ya5LVo0YKdOnUyyuVQtAPZ9+/f8+XLl+rfp0+fZqlSpVi6dOlEg+F4xnIuzJkzhzly5OC5c+dIklOnTqWJiQmPHTumk+7cuXOcNWuWUTSExZ/LMTExPH/+PC0sLGhqasquXbvy2bNnarr4tdJJ8sKFC0ZTfjLuHNi/fz8jIiJ0tl++fJne3t46z8lrB8Nt27Zl5cqVkz2/QgghxNdIICwSpX3jr31zd/ToUdatW5eZM2fmuHHjeObMGZLkokWLWLduXZ31Ug3dp4HLjh07WLVqVZLk6tWrmSlTJv7+++8kyZCQEJ4+fZpk3M2fMU0OFl8PBw4c4Js3b0jGrRfr7e1NR0dHFipUiGvXrmX37t1Zq1YtncnSjIH2dzh+/Hi6u7vTysqKXbt25Zo1a0iSx48fp7OzMx0dHRkcHEzSuEZCaOvSpQvHjx9Pkly3bh2tra3V8yAiIiLRRgBDrgvta6F2r/fZs2dpZmbGDh066ATDifX+G3L5EzNx4kS2atVK/fvGjRts0aIFs2XLxlOnTpH8eN4EBQUZ3YgIIYQQxkECYZGA9k3L3Llz2aJFC9avX58jRoxQt2v3iJGkm5sb27dvn2x5TGradXDjxg3GxsZy7dq1LFmyJHfu3EkrKyv+9ttvappNmzaxffv2fPHihbrN0INg7YDm7NmzzJUrFwcMGKDOBB4WFsaXL1+yc+fOdHNzo729PRVF4fTp0/WV5SQ1evRo2tjYcN26dTx+/DjLli3LUqVK8cmTJ4yNjeXRo0fp7OzM3LlzG02v+KcBTEREBF1cXLhixQqeO3eOFhYWnDdvHsm4YG/8+PHcsWOHPrKaJLTLP3LkSFapUoVr1qxRG/yOHDlCMzMzdu7cmY8fPyZJNm7cmMuWLdNLfpPKp8fBihUraGpqyh49eqjbrl+/zlatWjF79uz09fX96nsIIYQQ+iaBsPis+OdhJ06cyJkzZzJ9+vT08vJSA7zQ0FDu27ePNWvWpIODg9pzbOgB4J49e9iuXTuSZJ8+feju7s4PHz4wPDyclSpVoqIonDNnjpo+IiKCdevWZevWrQ2+7PG0e8MPHTpEkvzpp59Yvnx5Dh48mE+fPtVJf+bMGS5YsIDu7u5G1/tFxi2F5ejoyH379pEkjx07xnTp0vHPP//USXfgwAF27tzZaIZBx/P391cDmcmTJzNv3rw0MzPj8uXL1TTv3r2jm5sbJ06cqK9sJplhw4YxW7Zs3LdvnzoqIv5cP3ToEDNkyMCqVauyTJkyLFq0qM4oGmOyePFidfjzxo0bmSFDBnbt2lXdf/36dbZu3ZqKovDatWv6yqYQQgjxt0ggLFTaAczFixdZrFgxHj9+nCS5detWZsqUSacX1NfXl926dWOTJk3U1xp6EBQdHc2ZM2eyZMmSdHR0pLW1Ne/cuUMyrkdj06ZNLFeuHKtWrcqjR49y1apVrF27NkuWLKmW3dCD4e3bt6vP9PXv35/FixdnSEgIybhguGzZshw8eLA6OVpijC0QePbsGR0cHBgZGcmNGzfq9ISGhYVx9erVCUZJGHIwrN17t2DBAhYtWlQd+n/58mV6eHiwVKlSvH79Okny+fPn9PT0pLOzs0GXOzHnzp1jkSJF1F7O4OBg3r17l/PmzePNmzdJxl0LhwwZwhEjRhjNtfBTfn5+9PLyYp8+fRgWFsbY2Fhu2LAhQTB86dIljhkzxuiOAyGEEMZHAmHB7t278+7duyQ/3rzv3r2bRYoUIUlu2bKFFhYWnD9/Psm452G3bNlCknz8+LEa+Bn6jV982eN7eBVFYePGjXXSREREcPv27XR1dWW2bNno5OTEli1bqoGfMdz8nT59mjlz5mThwoVpZWXF27dv6+yPD4Z9fHzUoeCGHvxrS6wsDx8+ZJ48eTh06FBmzpxZp0HowoUL9PLySjBZlKHSDoJ3797N+fPnU1EUurm58fLlyyTjGsZq1apFCwsLlihRgmXKlKGTk5NRnAefDuG9du0a8+XLx0OHDvHatWvs06cPCxcuzAIFCtDc3Jw3btwgqVtmQ78WkokPZZ47dy4LFSqkTpQWERHBDRs20MLCQp01WpshHwdCCCGMnwTCqdz9+/fVCY8ePnyobr9+/Tq9vLw4a9YsnSCYJE+cOMHmzZurPaWkcQVCa9as4YIFCzhs2DCWK1eOHTt2TDTds2fP+P79e6NpCNDWpk0bKopCZ2dndZt2L+/PP//M8uXLs1u3bgwMDNRHFpOE9nPRgYGB1Gg06s38+PHjqSgK+/btq6YJDw9nnTp16OnpaXTPQA4dOpTZs2fnjBkzOGDAAObNm5elS5dWJ4x6/vw5N23axLlz53Lr1q1Gt1zarVu3GBYWxufPn7Nu3bosXrw4M2TIwO7du3Pt2rV8/fo1ixcvzpkzZ+o7q0lq8eLFXLJkifp3s2bNaG9vr37fUVFR3LhxIxVF4aRJk/SUSyGEEOKfk0A4lYuNjeX58+dZq1Yt5suXTw2GHz9+zLJly1JRFP78889q+vDwcHp6erJ58+ZGE/xq91rELwVz//59vnv3jrNnz2bp0qV1gmGNRsMLFy7ozJBtLHURb8uWLVyxYgULFCjAGjVqqOXTLvOYMWPYtm1boyj78uXLdZaFmTBhAitUqMBq1apx8uTJfP/+PcPCwtixY0cqisJ+/fqxZ8+edHNzY8mSJdVGAmMJhq9fv84cOXJw586d6rZXr16xcOHCLFu2LC9evJhob5+x9ADGD39fv349SfLOnTvcsmULDx8+rH7XYWFhrFChAlevXq3PrCap27dvU1EUKorCDh068NGjR7x27Ro9PDw4fPhwnUbAw4cPG00jiBBCiNRBIUmIVCk6OhpmZmYAgIMHD2LMmDF4+/Ytdu3ahXz58uHs2f+1d+9xNWXvH8A/pzqVSBFiFLmMEKkkShiXyf1+J2KmTGNG1Cih4pt7bpMyEiFNqTGSa4iKiYmohMY0iCSpeSFHOp06Pb8/+rW/he/3NzM/HOf0vP+qc/Y+r7X23mef9ey1nrWuYMiQIRgyZAj69esHQ0NDhIWFobi4GOnp6dDQ0EBVVRXU1NQUXJN3Iz09HRcvXoSRkRHGjx8PAJBIJNi3bx/27t0LMzMzbN68GY6OjmjSpAliYmIUXOJ34/VzSEQQiUQAgF9//RXTpk1Dhw4dkJiYKGxz9OhRjBkzRti29j7KJioqCr6+vpgyZQrWrVuHH3/8EYsWLYK/vz8SExNRUFAAU1NTbN++HTo6OggODkZcXBwMDAzQoUMH+Pv7Q0NDA5WVldDQ0FB0dd6JGzduwMHBAadOnUKPHj0gk8mgqamJ+/fvw8rKCjY2NlizZg169uyp6KK+N+PGjcP169exadMmjBo1ClpaWgAAqVSKx48fY8GCBXjy5AlSU1Ohrq6u4NK+G2+7n69cuRLXr1/HgwcP0KVLF3zyySd4+fIlpFIp3N3dYW5uXmd7VfoeMMYYU20cCDOsWrUKqampKCoqwrVr19C+fXvEx8fj008/RUpKCjZv3ozr16+jbdu2MDIywp49eyAWi5W6wePi4oLVq1fD0NAQAJCamgo7OztoamoiIiICkydPFhqFEokE0dHR2Lx5MyQSCYyMjJCSkiI8RFBmtRu+ERERyMnJgUQiwddffw1TU1MA1cdm2rRpMDIywsaNG7FixQpIpVIkJydDTU1NqYNgoPphR0BAABISEjBgwACoqanB2toaEydOBBEhODgYkZGR6Ny5M7Zt24bGjRujtLQUDRs2FD5DLperTDAEVB+T9u3bY/78+fjXv/4FoLqOpaWlcHBwwM2bN9GjRw8kJCRAR0dHqa+B/1b2SZMmIS0tDZs3b8bIkSPRoEED7NixA8eOHUNJSQmSk5MhFotV7vyfPHkSw4cPh0gkQkpKCvbs2YPp06fj+fPnSE5ORnh4OF69eoUFCxYgMDBQ0cVljDHG/hkF9USzj0RwcDA1bNiQkpKS6N69exQdHU39+vUjExMT+uOPP4iISCKR0PPnz+usjarMQ+CePHlCY8eOrZPzWlpaSoGBgaSjo0Pe3t5EVD3cuWaoa3l5ORUUFNC5c+dULheSiGjJkiVkbGxMY8aModGjR5OOjg6dPHlSeP/69evUvXt3MjU1JXt7e5VZKqvm/EokElq6dCnZ29tT69at6ezZs8I2MpmMgoKCyNbWlmbPnk3Pnj1TUGnfvf82lDswMJCMjY3r5MCWl5eTi4sLpaWlUbNmzcjHx+dDFPOD2L59OyUlJb1xTMaPH08tW7akn3/+mYiIsrKyKCIiQiXvA0TVaTFqampkb29PUVFRVFVVRf7+/tSrVy9hmx07dpCWlhbZ2Ngo/T2AMcZY/cWBcD0ml8vJxcWFXFxc6ryekpIirIf54MGDN/ZTpYZPWFgY3b9/n4iqg+GNGzeSSCSiwMBAYZu3BQuqkAtZcx537txJRkZGdPXqVSIiOn78OIlEItLV1RVyJImq63z9+nXheKhKAFBzLl+8eEG+vr7UvHlzmjVrVp0HJTKZjLZv304dO3Ykf39/RRX1nXp9iSR3d3eaPHkyXblyhWQyGRUXF9Py5cupadOmNHPmTFq1ahX179+fzM3NqaqqisaMGUOzZ89WYA3+f16/j1lYWJCRkRFdvHjxje+8hYUFmZub0/79++vspwr3gbfd3/7880+aPXs22dvb09ixY6mwsJA+//xz8vT0FLapfS9Qpd8Exhhj9YdqJHeyf0RNTQ1qamrIyMio83rfvn0xbtw45OTkoFu3bnj06FGd95V1COTrJBIJvL29MX78eOTn50NHRwfffvst1q9fj0WLFiE4OBgA3poDrazDIFeuXIl9+/YBqD6PJSUlKCwshL+/P3r27Iljx45h+vTp2LFjB2bOnAlnZ2ccO3YMVVVVUFdXh7m5OdTU1FBVVaW0w+KB6iHhNdTV1UFE0NXVhZeXF77++mtkZ2fD19cXFRUVAACxWAwXFxesX78ey5YtU1Sx36ma69rb2xt+fn54+fIlysrKMG7cOOzcuRM6Ojrw9vZGaGgocnJykJSUBENDQ1y9ehUikQgymQwtW7YEUD28WJlQreHQUVFROHDgADIyMtCpUyfMnDkTv/76K+RyubCtqakp8vLycPTo0Tr3P2W9D9SonRqRkpKCc+fOISEhAQYGBggLC4Ovry+kUim6du0KsViMixcvIi0tDQCEe4FcLleZ3wTGGGP1jGLjcPah/KchkIcOHSJzc3Pau3cvvXr1Sng9OjqapkyZQn5+firR60H09mOQl5dHZmZmZG1tTQ8fPiSi6pmRAwICSENDg9atW/ehi/neFBQUUK9evWjIkCF1enqvXr1K9+/fp5ycHDI1NaVt27YREdHZs2eFGWMTExMVVex3rnbv1Q8//ECurq70zTff0LFjx4ioemSAj48P2djY0JIlS97a860q34mwsDBq27YtZWRkEFH1GtIikYiMjIxo06ZN9Pz5cyKq+92pqqoiT09PatmyZZ0l1JRF7brcvHmTLC0tydzcXJgh+7PPPiMTExO6cOGCkA4yd+5cys7OVplZwV/n7e1NJiYm1LNnT2rcuDFNmjRJWCaLiGjz5s3UvXt3EolEFBAQoMCSMsYYY+8OB8L1QO3GW826n0FBQZSTk0NVVVU0a9Ys6t27NwUGBtLjx4+pqKiIxowZQ999952wn7I3/Gsfg4SEBDp8+DAdOXKEiKrXA349GJZKpeTr60t9+/ZViWF/NXXIycmhESNG0ODBg+nAgQN1tjlx4gT16tWL8vLyiIjo0qVLtGjRItq+fbvKDIOufR14e3uTnp4ejR07lhwcHEgkEpGXlxfJ5XKSSCS0bNkysrOzI1dXV6W//mvUrodcLqcdO3ZQcHAwEVXfG/T09Cg8PJzc3NxIR0eHtm7dSo8ePRL2uX79Onl6elLbtm0pPT39g5f/XVq8eDFNnDiR7OzsqGnTptS+fXuKi4sjIqLPP/+c2rVrRw4ODmRra0tdunQRjp2qBcNBQUFkaGhIaWlpRPTvJeQuXLhQZ7u0tDQKDAxUmXsBY4wxxoFwPeLp6UmtWrWiSZMmkYWFBfXo0YNiYmKovLycnJycyMrKirS1talLly7UtWtXocGj7IFg7fJ7e3tT69atydLSkrS1tcnJyYkePnwo9Az36tVLCIZlMpmwr7IfA6J/N+BzcnJo2LBhNHjwYIqOjhbe379/P4lEIrpy5Qo9ePCARo8eTU5OTsL7qtQAvnXrFjk7O1NqaqrwWlRUFGloaNCqVauIiKikpIS+/fZbcnFxUYnzX9uaNWvoxo0bdPv2bXr8+DHl5uaSubk5bdmyhYiIHjx4QLq6uqSnp1fngYlMJqPk5GThYYmy2rt3L+nr69O1a9fo6dOn9PjxY3JwcCBra2s6evQoERGtX7+evvrqK5o/f75w7ataEExE5OLiIuS9x8TEkL6+Pv3www9EVP1AsLy8/I19VOlewBhjrP7iQLieiIqKIiMjI+Gp/549e0hTU5MOHjxIRNUNm3v37tGPP/5IcXFxQu+HqvSEERFt2LCBWrVqRZcvXyai6p4QkUhEEyZMoIcPH9LDhw/J3Nyc2rRpQ0VFRcJ+yh4EvW1Cm99++42GDRtGgwYNqhMMjxs3jkQiEbVv357Mzc3rTBilKg4ePEjGxsbUsWNHunv3bp3ZwUNDQ6lBgwZCb+erV69U4mFI7QAuMjKSRCKRMDkaEdH58+fJzMyMrl+/TkRE165do2+++YY2b94s3AOUuf6vW758Odnb25NcLheOTX5+PvXu3ZtMTEyEGaKJSKUmh3s9kJdKpdSnTx+KiIigtLQ0atSoEe3YsYOIquvr7+9Px48fV0RRGWOMsfeOJ8uqJ+7cuYP+/fvD2toaBw8exKJFixAYGIhJkyZBIpEgNzcX7dq1w8yZMzF27Fioq6ur1NqYBQUFyM7OxtatW2FjY4PY2Fj4+fnBx8cH586dw8KFC1FZWYkjR46gf//+aNq0qbCvMk8EU3synHv37uHevXsoLCxE586dERQUBC0tLYSGhiImJgYAcPjwYcTFxSE4OBjp6enCetHKrPbEWACgqamJbt26IS8vD8XFxRCJRMLESEOGDEHz5s1RUFAAAGjQoAFEIpFSr5ML/HtirLi4OLx48QLh4eHo2bOn8P6zZ89QVFSE27dv48aNG1i5ciVKS0vh4eEh3AuUuf416H8n9dLS0oJUKoVMJoOamhoqKirQunVrrFu3DkVFRdi+fbvwnag5dso8OVyNmro8efIEVVVV0NLSwoQJE7B8+XLY2dnhhx9+gKurKwCgtLQU58+fx40bNxRZZMYYY+z9UXQkzt69tw3fW7JkCS1btox+/fXXOk/9q6qqaM+ePbRly5Y66wSrmrKyMoqNjaVnz55RWloamZiYCEskbd68mUQiEQ0cOJCePHki7KPsveG1e/BWrFhB3bt3p86dO1OrVq0oNDSUiIj++OMPYZh0TEzMG5+h7Meg9nchNTWVpFIpERElJSVRv379qEuXLsJEUURERUVF1KZNGzp06NCHLup7d+vWLWratCmJRCLauXMnEdXt5Zw6dSo1adKEjI2NydraWiVHA9TIysoidXV1WrlyZZ3XT506RRMnTqRBgwbRkCFD3josWBm9vlSWqampkBaQmZlJQ4cOpe7du9PNmzeJqLp3fPjw4dS7d2+lvwcwxhhj/wkHwiqmdoPnzp079OjRI5LJZHTx4kVhBuDaMwa/fPmSHBwcyMPDQxHF/aBqGvbr1q2jkSNHCjPiBgUFkaOjIw0bNkwlcwD9/f2pefPmdPr0aXr58iWNHz+e9PX16datW0RUnTM8cuRI6tGjB509e1bBpX13ap9LHx8f6tSpE8XFxQkPCM6cOUMODg7UunVrCgsLo3379tGoUaPIzMxMJRr/rw9lfvHiBUVGRlL79u3JwcFBeL2srEz4+8KFC/TLL78I9VeF4cD/yd69e0ksFpOnpyddvXqV7t69SyNHjqQ1a9ZQdnY2iUQiSkhIUHQx/99qfw/i4+MpJCSERCIRDR48mDIzM4mI6MiRI+Tg4ECNGjUiMzMzsrCwIBsbG+GeqQrfB8YYY+x1HAirkNoN3yVLllDnzp3JwMCA+vfvTzt27KCwsDDS0tKiyMhIun//PmVlZdHQoUPJ0tJSpRu8NWqOz9y5c8ne3p5KSkqorKyMRo0aVSdPVtmD4drll8vlNGrUKPrxxx+JiOjw4cPUpEkTYTKcmobujRs3yMPDQ+nr/jY+Pj5kaGhIZ86coeLi4jrvXbhwgWxtbUlLS4tGjRpFwcHBQmCozI3/2uexoqKCJBIJEVXXKSYmhgwMDGjq1KnCNrWD4RrKXP+/6ueff6YWLVqQkZGRMIleWVkZ3b9/nz799FMhZ1oVeHt7U4sWLWjr1q3k4eFBbdq0oR49egjLJOXn5wurChw5cqRePAxhjDFWv4mI/jdpiim12rmg0dHRcHd3R0hICJ4/f45bt25h27ZtmDt3Lrp06YIlS5agSZMmMDQ0RJMmTXD69GmIxWKVygn+b1JTU9G/f3+YmpqivLwc2traSE9PV4kcwNpWrFgBHR0dBAUFITExEQUFBRg9ejQ2btwIV1dXlJWVYc2aNXBxcUHbtm2F/VTpOsjNzcXYsWOxZs0ajB49Gs+ePUNhYSFOnjwJGxsb2NvbIyUlBVu2bEFhYSFCQ0PRvXt3lJeXQ0tLS9HF/0dq3ws2bNiAa9euIS0tDS4uLhg4cCBsbW0RExMDT09P2NvbIyoqCoBqnfe/49GjR3j48CEqKirQt29fqKmpYenSpYiLi0NSUhJatmyp6CL+v926dQuDBw/Gnj17MGLECABAUVER7O3t0ahRI4SFhcHc3PyN819frwnGGGP1hKIjcfZuJSUlkbOzs7AMClH1MjDbt28nXV1dOn78ON29e5eSk5MpPT1dpWZE/TuuXbtGy5cvpw0bNgh1V/ZjULsXMDo6moyNjenmzZvk6OhIQ4cOJR0dHQoLCxO2efToEfXr14/2799PRKo1K3CNmzdvUrNmzej8+fOUnJxMX331FfXo0YMMDQ2pe/fuwlI5J06coJEjR5KdnR1du3ZNwaV+N5YuXUrNmzenXbt2UWhoKHXq1Ik+++wzevr0KZWWllJMTAy1bdu2zjDp+u7mzZs0a9YsMjAwqJM7ruyysrKoZcuWwlDomtzn3NxcatKkCQ0dOrTOLOKMMcZYfcCzRquQwsJCODs7IyYmBq9evRJeb9y4MaZOnYpBgwbh1KlTaN++PQYMGABLS0uoqamhqqpK5XpD/y9WVlZYvXo1vLy8oKGhgcrKSqU/BjW9gOfPn0dycjK+++47mJmZoU+fPvj9998xePBgfPHFFwAAiUQCZ2dnqKurY8aMGQCUe3Zs4M3ZoQHAzMwMVlZWmDx5MoYNGwZtbW2sXbsWBQUFqKqqwq1btwAAI0aMwKJFi6CmpgYvLy/IZDJhhmFllJmZiaNHjyIuLg7Ozs7o2rUr7t+/jzlz5qBJkybQ0dHBxIkTsXLlSjRs2PCtx66+qayshEwmQ4sWLXD+/HlYWFgoukjvjImJCSorKxEbGwugeuZ0uVyOpk2bolOnTkhJSYGbm5vwu6HM1z5jjDH2Vyl3y5/V0bJlS8TGxmLChAmIjY3FiBEjYGlpCQAwMDBAs2bNcPfu3Tf2qwmg6jNlD4JrFBYW4ssvv0RRURGWLVsGAHB1dcXdu3eRmJgIS0tLfPrpp8jLy4NUKkVaWppKLJVVezjwL7/8goqKCshkMgwbNgynT5/GiRMnYGBggD59+gj7NGvWDA0bNhSWRhoyZAjU1dXRsWNHaGpqKqoq/0jt+gPVDzXU1dVhZ2eHgwcP4osvvkBgYCCcnJxQWlqKs2fPYvDgwZgxYwbmzJnz1s+obzQ0NGBpaYlu3bpBLBYrujj/yH86h7q6uvD19cWmTZvQtGlTLFy4EOrq6tDW1oa5uTmCg4MxfPhwrFu3DqtWrVL6h2KMMcbYX8E5wiooKysLs2fPRo8ePeDu7g4LCwtIJBIMGzYMZmZmCA0NVXQR2XuUlZWFiRMnokWLFti2bRt69uwJuVyOEydO4Pz586ioqEC7du2wYMEClekNr+Hp6YmffvoJVVVVKCsrQ48ePRAWFgYTExMAwMuXL/HkyRO4ubkhPz8f165dg4aGhsoEgQ8fPoSxsTEuXboER0dHLF26FF5eXli9ejW++eYbANUjBrZv3w4/Pz9069ZNwSVm70rtazg0NBS3b99Gfn4+PD09YWFhgZKSEnz//ffYsWMHhg8fjs6dOyMhIQHPnz9HZmYmxo0bB319fYSHhyu4JowxxtiHwYGwisrIyICjoyOePn0Ka2traGpqIjc3F6mpqdDU1BR6wZhqysrKgpOTE6ytrbFgwQKYm5u/dTtl7wmuLSQkBD4+Pjh16hQaN26MsrIyTJkyBXp6ejh+/DhatGiByMhIBAUFoUGDBjhz5oxKTRIXFRUFLy8v5OfnAwCmT5+OmJgYrF+/Hl5eXgAgHBMNDQ0cOnRIJYJ/Vpe3tzf27duHMWPG4PHjx0hPT8fSpUvxxRdfoKqqCqdPn8aGDRugq6sLAwMDREZGQiwWY/jw4TA3N8eGDRv494Exxli9wIGwCrt58ybGjBkDIyMjzJgxA66urgCAiooKpR36x/66jIwMODs7o2fPnli4cCHMzMwUXaT3ys3NDRKJBHv37hUa8iUlJejWrRsGDRqE8PBwPH/+HMnJyRg9ejTU1dVVqje8uLgYtra2WLBgARYuXIjs7GwsXLgQmZmZWLlyJV68eIGkpCQUFBQgIyMDYrFYZXrCWbU9e/bA398fcXFxsLCwwOXLl2Fra4vWrVtj0aJFcHZ2hp6eXp3zTkRYsmQJIiIicP78eXTq1EnBtWCMMcY+DG4BqbBu3bohNjYWMpkM6enpuHPnDgBwEFxPWFpaYvfu3UIglJubq+givRc1z/Jyc3Px6NEjANU5suXl5dDT08OKFStw9epVFBUVQV9fH+PGjRPyopU1CH79+aVcLkejRo0waNAgpKSkAAC6dOmCnTt3YsqUKdi1axcSExPRsWNHZGZmQiwWo7KykoNgJSeXy4W/q6qqIJPJhKHQhw8fxtChQ7Fv3z5MmDABfn5+2Lt3LwoKCoTznpWVhSVLluCnn37CyZMnOQhmjDFWr3CPcD2QkZEBV1dXtG/fHitWrEDnzp0VXST2AV25cgUhISHYvXu3SgQ+/6kXMy4uDvPnz8eaNWswd+5c4fWIiAhs3rwZycnJ0NfX/4Alff/y8/NhZGQk/J+WlgZbW1uEh4dj5syZwuslJSXQ09MT/lelnnAGrF27FmPGjIFYLIaenh6kUinGjh2LOXPmwN3dHXl5eejWrRvU1NQQEhKCadOmAageHXTp0iW0b98exsbGCq4FY4wx9mEpf6uY/Z8sLS0RHByMx48f12kMs/rBxsYGYWFhwlJZyoyIhCA4ISEBBw4cEHJi7ezsMGrUKOzatQshISEAgIKCAkRHR8PExETlrv3du3djwoQJWLx4MSQSCaRSKXr16oVvv/0WBw4cwJMnT4SeY11dXWE/IuIgWMnV/h5HRUXBx8cH5eXlMDU1RcuWLZGXlwe5XI7BgwcDAP7880/Mnj0bfn5+mDx5MoDq60AsFmPAgAEcBDPGGKuXuEe4HpFKpdDW1lZ0MZiCqNIEON7e3ggJCYGuri4kEgnWrl2LL7/8Evn5+di2bRvCw8PRoEED6Ovro0GDBrh8+bLS58S+XvYbN24gPj4eoaGh0NHRwfDhw7FgwQL8/vvv+PrrrxEdHQ0rKyuVOu+srri4OBQWFqJhw4aYNWuW8PqRI0fg4uKC4OBgdOnSBcuXL4eBgQH27t0LQLUmyWOMMcb+KQ6EGWMfvZpgjohw7949zJkzBxs2bICFhQX8/f0RGRmJBQsWCEtC5eXl4cKFCzA0NMTQoUOVfmKs2kFwQkICJBIJ9PX1MWjQIFRUVGD9+vW4cOEC0tLS4O/vjxUrVsDKygpnzpzhgEdFZWdno1+/fnj27BlCQkIwb968Otf4tGnTcObMGTRq1AiGhoa4dOkSzw/BGGOM1aKcrULGWL1ROwgsKSmBWCyGtbU1bGxsoKGhgfXr10MsFiMoKAgAMHv2bHTo0AEdOnQQPkPZJ8aqqf/SpUsRERGB5s2b4/bt25g0aRICAgLg6+sLmUyGkJAQHD9+HK9evYJMJlPa3m/2ptd79o2NjREUFARfX18cOnQI8+bNg4aGhjDyJzo6Gr/88gtEIhFsbW2V/mEQY4wx9q5xjzBjTCn4+PggPj4ed+/eRbt27XD06NE6uY1+fn6IiIiAo6MjPDw80KRJEwWW9t0LCAjA999/j7i4ONjY2CA4OBhubm4YN24cAgIC0LFjRwBAXl4ecnNzYW9vD3V1daUeDs6q1T6HlZWVkEqlaNSoEeRyOQ4dOoT58+djyJAhiI6OBvD2NBgeDs0YY4zVxa0jxthHqfYzup9//hmhoaH46quvMHHiRBQVFWHjxo3Iy8sTtvH398eYMWNw69YtlZsduqCgANnZ2di6dStsbGwQGxsLPz8/+Pj4ICkpCd7e3sjOzgYAtGnTBgMGDBCWiOIgWLnVDoI3bNiAGTNmoHv37li7di2uXLmCKVOmYPv27bh06RJmzJgBANDW1q6ztBIADoIZY4yx13CPMGPso3by5EmcPn0alpaWmDNnDoDq3tGYmBgMGDAAixYtQps2bYTta+cTq8okUVKpFPHx8Rg4cCDu3LmDyZMnw93dHW5ubtiyZQsWL16MgQMHYt++fTwDsIpatmwZdu/ejbVr14KIsGnTJnzyySeIjY2FlpYWjh8/Di8vL5iamuL06dOKLi5jjDH20eNkIcbYR6V2D1h2djaWLl0qrINaw8vLCwAQExMDdXV1zJ8/H+3atQMAlQuCgeoevlGjRkEsFuPs2bMwMzODk5MTAEBTUxOOjo4oLi5G69atFVxS9j5kZmbi6NGjiIuLg52dHS5evIj79+9j2bJlQgrAxIkT8erVKxw9epSHwzPGGGN/Af9SMsY+Gi9fvhQa8HFxcejatSs8PDxgZGSEPXv24N69e8K2Xl5emD59Og4cOIDjx4/X+RxVCoJr1ExylJOTg5KSEohEIkilUpw+fRojR45EfHy8SqwVzfDGORSJRFBXV4ednR0OHjyIYcOGITAwEE5OTigtLcWRI0dQVlaGGTNmIDY2lq8Dxhhj7C/gQJgx9lGIj4+Hvb09ZDIZ3N3dMX/+fPz5559wcnLC4sWLoa6uDh8fH+Tm5gr7LF68GAEBAZg/f74CS/5h1AT38+bNw+XLl9G3b1+Ym5vjwYMHmDhxorAd9wQqv5pz+PDhQwBAaWkpJBIJdu3ahXnz5mH9+vVwdXUFAFy9ehWRkZG4f/8+NDU13/gMxhhjjL0d5wgzxj4KBQUFGDBgACorK/H06VNcvHixznDo3bt3Y//+/TAyMsLatWthYmJSZ//6NCtueno6YmNj0bhxY3h4eEBDQ4OXxlExUVFR8PLyQn5+PgBg+vTpiImJwfr164XUgLKyMkyZMgUaGho4dOgQB7+MMcbY38CBMGPsozFv3jzs3r0b3bt3x/nz56Gvr18n33f37t2IjIyElpYW9u7di1atWim4xB8HDoJVT3FxMWxtbbFgwQIsXLgQ2dnZWLhwITIzM7Fy5Uq8ePECSUlJKCgoQEZGBsRiMecGM8YYY38D/2IyxhTm9TxGZ2dnnDp1CpWVlXBwcMDDhw8hEolQWVkpvD9jxgy0bdsWhoaGiijyR4mDYOX2+vNouVyORo0aYdCgQUhJSQEAdOnSBTt37sSUKVOwa9cuJCYmomPHjsjMzIRYLEZlZSUHwYwxxtjfwD3CjDGFqN17lZCQgBcvXkBHRwfDhw9HXl4eRowYgQYNGuDIkSP45JNPAAD79u2Dk5OT0EPMPWBMleTn58PIyEj4Py0tDba2tggPD8fMmTOF10tKSqCnpyf8zyMCGGOMsb+PA2HG2AdXe7jz0qVLERERgebNm+P333/HlClTsHr1ahARRo4cCZFIBD8/P+zcuRNFRUVIT0/n4JepnN27dyM0NBT9+/fHihUrIBaLoa2tjUWLFuHOnTsICwtDixYtIBKJ6jwAUrWlwhhjjLEPhVuTjLEPrqbhHhAQgPDwcMTGxiIjIwMBAQHYv38/Fi5cCJFIhMTERDRq1AirVq1CRUUF0tLSoKam9sZQUsaUzetpAb1798akSZMQFxeHvn37YsWKFcjPz8fo0aORk5ODR48eCWtk134QxEEwY4wx9s9wIMwYU4iCggJkZ2dj69atsLGxQWxsLPz8/ODj44Nz587Bzc0NlZWVuHjxIo4ePYrExEQhF5Ib/0yZvZ4WEBsbi+LiYnh5eeG3337D5MmTkZ6ejm7duuHWrVsoLi6Gp6cn5HI5X/uMMcbYO8JJRYwxhWjatCnGjh2LgQMH4urVq/juu++wcuVKuLm5QV9fH4sXL8bTp08RFRWFNm3aAKgOIDgXkimz2j26tdMCbt++jUmTJiEgIAC+vr6QyWQICQnB8ePH8erVK8hkMk4JYIwxxt4hblEyxhRCW1sbo0aNglgsxtmzZ2FmZgYnJycAgKamJhwdHVFcXIyWLVsK+3AgwJTd62kBcXFxsLGxQXBwMNzc3FBaWoqAgAB07NgRbm5uGDduHHJzc2Fvb/9GfjBjjDHG/jn+NWWMKUxN725OTg5KSkogEokglUpx+vRpjBw5EvHx8VBTU3sjn5IxZfbf0gKSkpLg7e2N7OxsAECbNm0wYMAAqKurQy6XcxDMGGOMvSM8azRjTOFSU1PRv39/mJqaory8HNra2khPT+dh0EwlSaVSxMfHY+DAgbhz5w4mT54Md3d3uLm5YcuWLVi8eDEGDhyIffv2wdjYWNHFZYwxxlQStzIZYwrXp08fpKamIjY2Fo0bN4aHhwc0NDR4fVSmkv5qWkDr1q0VXFLGGGNMdXELkzH2UbCysoKVlZXwPwfBTJX9t7QAR0dHTJ06FQA4J5gxxhh7T3hoNGOMMaYgnBbAGGOMKQYHwowxxpgCpaenc1oAY4wx9oFxIMwYY4x9RDgIZowxxt4/DoQZY4wxxhhjjNUrPAMHY4wxxhhjjLF6hQNhxhhjjDHGGGP1CgfCjDHGGGOMMcbqFQ6EGWOMMcYYY4zVKxwIM8YYY4wxxhirVzgQZowxxhhjjDFWr3AgzBhjjDHGGGOsXuFAmDHGGGOMMcZYvcKBMGOMMcYYY4yxeoUDYcYYY4wxxhhj9QoHwowxxhhjjDHG6pX/AYr8r8bOsADWAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pedestriancount 1.000000\n", - "noise 0.555729\n", - "gustwindspeed 0.466541\n", - "airtemperature 0.390398\n", - "averagewindspeed 0.364756\n", - "averagewinddirection 0.119948\n", - "atmosphericpressure -0.034576\n", - "pm25 -0.035831\n", - "pm10 -0.039647\n", - "relativehumidity -0.465781\n", - "Name: pedestriancount, dtype: float64\n" - ] - } - ], - "source": [ - "# Calculate the correlation matrix.\n", - "corr_matrix = model_df[numeric_cols].corr()\n", - "\n", - "# Plot the correlation matrix.\n", - "plt.figure(figsize=(10, 8))\n", - "im = plt.imshow(\n", - " corr_matrix,\n", - " cmap=\"coolwarm\",\n", - " aspect=\"auto\"\n", - ")\n", - "\n", - "cbar = plt.colorbar(im)\n", - "cbar.set_label(\"Correlation Coefficient\")\n", - "\n", - "plt.xticks(\n", - " ticks=np.arange(len(corr_matrix.columns)),\n", - " labels=corr_matrix.columns,\n", - " rotation=45,\n", - " ha=\"right\"\n", - ")\n", - "\n", - "plt.yticks(\n", - " ticks=np.arange(len(corr_matrix.columns)),\n", - " labels=corr_matrix.columns\n", - ")\n", - "\n", - "plt.title(\"Correlation Matrix\")\n", - "\n", - "# Add values inside each cell.\n", - "for i in range(len(corr_matrix.index)):\n", - " for j in range(len(corr_matrix.columns)):\n", - " plt.text(\n", - " j,\n", - " i,\n", - " f\"{corr_matrix.iloc[i, j]:.2f}\",\n", - " ha=\"center\",\n", - " va=\"center\",\n", - " fontsize=8\n", - " )\n", - "\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "# Print the correlation values with pedestrian count.\n", - "print(corr_matrix[\"pedestriancount\"].sort_values(ascending=False))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 7. Time Series Analysis\n", - "\n", - "This section is necessary as exploratory data analysis alone is not enough to check a time series dataset. Hence, time series analysis is necessary to help uncover more trends, seasonality, repeated lag dependence, and stationarity [36]." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 7.1 Pedestrian Count Over Time (24-Hour Rolling Mean)\n", - "\n", - "The previous plot with the pedestrian count over time was noisy, so smoothing over 24 hours may help reveal more patterns of pedestrian activity without the hour-to-hour volatility [40].\n", - "\n", - "From the plot, it became much more obvious that the highest peaks were during New Year's, where pedestrian visit the Melbourne CBD to see the fireworks, and confirms what has been previously discussed. There does seem to be a slightly increasing trend, which suggests that the influence of COVID-19 is still recovering." - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "datetime_hour\n", - "2024-05-12 00:00:00 15093\n", - "2024-05-12 01:00:00 10686\n", - "2024-05-12 02:00:00 6751\n", - "2024-05-12 03:00:00 5431\n", - "2024-05-12 04:00:00 2728\n", - "Name: pedestriancount, dtype: int64\n" - ] - } - ], - "source": [ - "# Create the hourly target series.\n", - "ts = model_df.set_index(\"datetime_hour\")[\n", - " \"pedestriancount\"\n", - "].sort_index()\n", - "\n", - "# Check the first few rows.\n", - "print(ts.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Calculate the 24-hour rolling mean.\n", - "rolling_mean_24 = ts.rolling(24).mean()\n", - "\n", - "# Plot the rolling mean only.\n", - "plt.figure(figsize=(14, 5))\n", - "plt.plot(\n", - " rolling_mean_24,\n", - " label=\"24-hour rolling mean\"\n", - ")\n", - "\n", - "plt.title(\"24-Hour Rolling Mean Of Pedestrian Count\")\n", - "plt.xlabel(\"Datetime\")\n", - "plt.ylabel(\"Rolling Mean\")\n", - "plt.legend()\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 7.2 Seasonal Pattern (24-Hour Cycle)\n", - "\n", - "Seasonal decomposition is necessary because hourly pedestrian demand is expected to have a strong daily cycle, so checking the seasonal component can help check how the typical hour of the day affects pedestrian activity. Since people usually move through the city at different levels during the morning, workday, evening, and late night, this step helps show whether the hour of the day is a factor in pedestrian demand [41].\n", - "\n", - "From the plot, it is clear that the seasonal effect is strongly negative at night and becomes strongly positive during the workday, especially the peak at 5 PM, when most people finish work and are looking to go home. The lowest tends to be around 3 AM, which is expected, since people tend to party late until 12 PM before heading home, and the other reason is that the city's pedestrian activities are very limited at that time. This plot further confirms that hour-of-day is a major driver of pedestrian demand, and there may be other variables influencing pedestrian demand." - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Decompose the series using a 24-hour period.\n", - "decomp_24 = seasonal_decompose(\n", - " ts,\n", - " model=\"additive\",\n", - " period=24\n", - ")\n", - "\n", - "# Store one seasonal cycle and align it to the actual hours.\n", - "seasonal_24 = pd.Series(\n", - " decomp_24.seasonal[:24].values,\n", - " index=ts.index[:24].hour\n", - ")\n", - "\n", - "# Sort by actual hour.\n", - "seasonal_24 = seasonal_24.sort_index()\n", - "\n", - "# Plot the corrected daily seasonal pattern.\n", - "plt.figure(figsize=(10, 4))\n", - "plt.plot(\n", - " seasonal_24.index,\n", - " seasonal_24.values,\n", - " marker=\"o\",\n", - " label=\"Daily seasonal effect\"\n", - ")\n", - "\n", - "plt.title(\"Daily Seasonal Pattern (24-Hour Cycle)\")\n", - "plt.xlabel(\"Hour\")\n", - "plt.ylabel(\"Seasonal Effect\")\n", - "plt.xticks(range(24))\n", - "plt.legend()\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 7.3 Autocorrelation Of Pedestrian Count\n", - "\n", - "Checking for autocorrelation is important because it shows whether the current pedestrian demand depends on previous hours, and if strong dependence exists, then lag features will be very useful for forecasting [42].\n", - "\n", - "From the plot, there appears to be a very strong repeating pattern at a regular interval, especially around daily cycles. This repeated structure applies not only on the daily level, but also appears to be on the weekly level as well, meaning daily and weekly lag features will be useful for predictions." - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot autocorrelation for one week of hourly lags.\n", - "fig, ax = plt.subplots(figsize=(12, 5))\n", - "plot_acf(ts, lags=168, ax=ax)\n", - "\n", - "ax.set_title(\"Autocorrelation Of Pedestrian Count\")\n", - "ax.set_xlabel(\"Lag (Hours)\")\n", - "ax.set_ylabel(\"Autocorrelation\")\n", - "\n", - "# Add legend handles.\n", - "legend_handles = [\n", - " Line2D([0], [0], color=\"C0\", lw=2, label=\"Autocorrelation\"),\n", - " Patch(alpha=0.2, label=\"95% confidence interval\")\n", - "]\n", - "\n", - "ax.legend(handles=legend_handles, loc=\"upper right\")\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 7.4 Augmented Dickey-Fuller Test\n", - "\n", - "The Augmented Dickey-Fuller Test checks whether the time series is statistically stationary in the unit-root sense, and while not necessary for deep learning modelling, it does provide some more understanding of the patterns underlying the merged dataset [43].\n", - "\n", - "The result was that the p-value is extremely small and the test statistic is far below the critical values, hence, the null hypothesis of a unit root is rejected. This means that the time series dataset is statistically stationary enough to exhibit a learnable structure rather than behaving like a random walk, which basically means that it's not necessarily a random coin toss in layman's terms." - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ADF Statistic: -12.334016562004392\n", - "p-value: 6.332696219939632e-23\n", - "Critical Values:\n", - "1%: -3.4307265048981876\n", - "5%: -2.8617064005452915\n", - "10%: -2.5668585707044094\n" - ] - } - ], - "source": [ - "# Run the Augmented Dickey-Fuller test.\n", - "adf_result = adfuller(ts)\n", - "\n", - "# Print the results.\n", - "print(\"ADF Statistic:\", adf_result[0])\n", - "print(\"p-value:\", adf_result[1])\n", - "print(\"Critical Values:\")\n", - "\n", - "for key, value in adf_result[4].items():\n", - " print(f\"{key}: {value}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 8. Feature Engineering\n", - "\n", - "The feature engineering section is necessary because the current variables in the merged datasets may not necessarily be enough for a strong forecasting model, so doing feature engineering will create more useful variables that help capture other aspects of the datasets, like cyclical structures, recent history, and short-term trends [44] [45]." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 8.1 Creating Time Features\n", - "\n", - "Creating more calendar-based features is necessary because pedestrian activity depends on when that observation happened, as shown in previous plots. Hence, hours, day of week, month, and weekend status are all useful predictors. This step ensures the datetime_hour column is converted into its individual components [45].\n", - "\n", - "The output shows that new time-based columns were added to the dataset. These include hour, day_of_week, month, and is_weekend." - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " datetime_hour hour day_of_week month is_weekend\n", - "0 2024-05-12 00:00:00 0 6 5 1\n", - "1 2024-05-12 01:00:00 1 6 5 1\n", - "2 2024-05-12 02:00:00 2 6 5 1\n", - "3 2024-05-12 03:00:00 3 6 5 1\n", - "4 2024-05-12 04:00:00 4 6 5 1\n" - ] - } - ], - "source": [ - "# Create a copy for feature engineering.\n", - "features_df = model_df.copy()\n", - "\n", - "# Extract calendar features.\n", - "features_df[\"hour\"] = features_df[\"datetime_hour\"].dt.hour\n", - "features_df[\"day_of_week\"] = (\n", - " features_df[\"datetime_hour\"].dt.dayofweek\n", - ")\n", - "features_df[\"month\"] = features_df[\"datetime_hour\"].dt.month\n", - "features_df[\"is_weekend\"] = (\n", - " features_df[\"day_of_week\"] >= 5\n", - ").astype(int)\n", - "\n", - "# Check the result.\n", - "print(\n", - " features_df[\n", - " [\n", - " \"datetime_hour\", \"hour\", \"day_of_week\", \"month\", \n", - " \"is_weekend\"\n", - " ]\n", - " ].head()\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 8.2 Creating Cyclical Time Features\n", - "\n", - "Cyclical encoding is necessary since time variables are cyclical and not linear, like a clock. If we're talking just normal values like 0 and 23, these two values are quite far apart, but it's not, since it's time and there's only 1 hour difference. Using sine and cosine preserves that circular structure. This step ensures that the time variables are cyclical and prevents the models from learning misleading distances between values at the edge of a cycle [46].\n", - "\n", - "The output shows that new cyclical time features were created, including hour_sin, hour_cos, dow_sin, dow_cos, month_sin, and month_cos." - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " datetime_hour hour_sin hour_cos dow_sin dow_cos month_sin \\\n", - "0 2024-05-12 00:00:00 0.000000 1.000000 -0.781831 0.62349 0.5 \n", - "1 2024-05-12 01:00:00 0.258819 0.965926 -0.781831 0.62349 0.5 \n", - "2 2024-05-12 02:00:00 0.500000 0.866025 -0.781831 0.62349 0.5 \n", - "3 2024-05-12 03:00:00 0.707107 0.707107 -0.781831 0.62349 0.5 \n", - "4 2024-05-12 04:00:00 0.866025 0.500000 -0.781831 0.62349 0.5 \n", - "\n", - " month_cos \n", - "0 -0.866025 \n", - "1 -0.866025 \n", - "2 -0.866025 \n", - "3 -0.866025 \n", - "4 -0.866025 \n" - ] - } - ], - "source": [ - "# Hour and day of week are cyclical, not linear.\n", - "# Create cyclical hour features.\n", - "features_df[\"hour_sin\"] = np.sin(\n", - " 2 * np.pi * features_df[\"hour\"] / 24\n", - ")\n", - "features_df[\"hour_cos\"] = np.cos(\n", - " 2 * np.pi * features_df[\"hour\"] / 24\n", - ")\n", - "\n", - "# Create cyclical day-of-week features.\n", - "features_df[\"dow_sin\"] = np.sin(\n", - " 2 * np.pi * features_df[\"day_of_week\"] / 7\n", - ")\n", - "features_df[\"dow_cos\"] = np.cos(\n", - " 2 * np.pi * features_df[\"day_of_week\"] / 7\n", - ")\n", - "\n", - "# Convert cyclical month features.\n", - "features_df[\"month_sin\"] = np.sin(\n", - " 2 * np.pi * features_df[\"month\"] / 12\n", - ")\n", - "features_df[\"month_cos\"] = np.cos(\n", - " 2 * np.pi * features_df[\"month\"] / 12\n", - ")\n", - "\n", - "# Check the result.\n", - "print(\n", - " features_df[\n", - " [\n", - " \"datetime_hour\", \"hour_sin\", \"hour_cos\",\n", - " \"dow_sin\", \"dow_cos\", \"month_sin\", \n", - " \"month_cos\"\n", - " ]\n", - " ].head()\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 8.3 Creating Cyclical Wind Direction Features\n", - "\n", - "Wind direction is also circular, being 360 degrees, meaning 0 and 359 degrees are almost the same direction. So wind direction must also be converted to cyclical for a continuous form, and ensure consistency with treatments of cyclical variables like time [46].\n", - "\n", - "The output shows that two new features were created, wind_dir_sin and wind_dir_cos." - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " averagewinddirection wind_dir_sin wind_dir_cos\n", - "0 166.142857 0.239502 -0.970896\n", - "1 140.321429 0.638480 -0.769638\n", - "2 153.857143 0.440611 -0.897698\n", - "3 139.250000 0.652760 -0.757565\n", - "4 170.074074 0.172375 -0.985031\n" - ] - } - ], - "source": [ - "# Convert wind direction into cyclical form.\n", - "features_df[\"wind_dir_sin\"] = np.sin(\n", - " 2 * np.pi * features_df[\"averagewinddirection\"] / 360\n", - ")\n", - "features_df[\"wind_dir_cos\"] = np.cos(\n", - " 2 * np.pi * features_df[\"averagewinddirection\"] / 360\n", - ")\n", - "\n", - "# Check the result.\n", - "print(\n", - " features_df[\n", - " [\n", - " \"averagewinddirection\",\n", - " \"wind_dir_sin\",\n", - " \"wind_dir_cos\"\n", - " ]\n", - " ].head()\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 8.4 Creating Lag Features\n", - "\n", - "Lag features are important, as indicated by the autocorrelation plot, since pedestrian demand is highly dependent on recent history based on the autocorrelation plot [47] [48].\n", - "\n", - "The features lag_1, lag_24, and lag_168 capture the previous hour, previous day, and previous week at the same hour. This does create missing values since there wasn't recent information to populate the lag columns for specific rows, which will be dealt with. Like the very first row having a missing value for lag_1, because there wasn't a previous row to populate that value." - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " lag_1 lag_24 lag_168\n", - "0 NaN NaN NaN\n", - "1 15093.0 NaN NaN\n", - "2 10686.0 NaN NaN\n", - "3 6751.0 NaN NaN\n", - "4 5431.0 NaN NaN\n", - "5 2728.0 NaN NaN\n", - "6 2306.0 NaN NaN\n", - "7 4251.0 NaN NaN\n", - "8 8872.0 NaN NaN\n", - "9 18370.0 NaN NaN\n", - "10 26934.0 NaN NaN\n", - "11 40599.0 NaN NaN\n", - "12 53701.0 NaN NaN\n", - "13 62802.0 NaN NaN\n", - "14 63419.0 NaN NaN\n", - "15 65710.0 NaN NaN\n", - "16 74282.0 NaN NaN\n", - "17 65628.0 NaN NaN\n", - "18 49356.0 NaN NaN\n", - "19 39189.0 NaN NaN\n", - "20 31933.0 NaN NaN\n", - "21 24297.0 NaN NaN\n", - "22 19736.0 NaN NaN\n", - "23 13472.0 NaN NaN\n", - "24 8268.0 15093.0 NaN\n" - ] - } - ], - "source": [ - "# Create lagged pedestrian count features.\n", - "features_df[\"lag_1\"] = (\n", - " features_df[\"pedestriancount\"].shift(1)\n", - ")\n", - "\n", - "features_df[\"lag_24\"] = (\n", - " features_df[\"pedestriancount\"].shift(24)\n", - ")\n", - "\n", - "features_df[\"lag_168\"] = (\n", - " features_df[\"pedestriancount\"].shift(168)\n", - ")\n", - "\n", - "# Check the result.\n", - "print(\n", - " features_df[\n", - " [\n", - " \"lag_1\", \"lag_24\", \"lag_168\"\n", - " ]\n", - " ].head(25)\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 8.5 Creating Rolling Features\n", - "\n", - "Rolling features summarise recent pedestrian counts rather than relying on a single observation for a datapoint, allowing the model to capture the short-term trend and help smooth the short-term noise [47].\n", - "\n", - "Some new predictors were added, like rolling_mean_24, which summarises the previous 24 hours, while rolling_mean_168 summarises the previous week. And as expected, similar to lag features but not the same, the first rows are missing values since the rolling window cannot be calculated until enough history exists. " - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " rolling_mean_24 rolling_mean_168\n", - "0 NaN NaN\n", - "1 NaN NaN\n", - "2 NaN NaN\n", - "3 NaN NaN\n", - "4 NaN NaN\n", - "5 NaN NaN\n", - "6 NaN NaN\n", - "7 NaN NaN\n", - "8 NaN NaN\n", - "9 NaN NaN\n", - "10 NaN NaN\n", - "11 NaN NaN\n", - "12 NaN NaN\n", - "13 NaN NaN\n", - "14 NaN NaN\n", - "15 NaN NaN\n", - "16 NaN NaN\n", - "17 NaN NaN\n", - "18 NaN NaN\n", - "19 NaN NaN\n", - "20 NaN NaN\n", - "21 NaN NaN\n", - "22 NaN NaN\n", - "23 NaN NaN\n", - "24 29742.25 NaN\n" - ] - } - ], - "source": [ - "# Create rolling mean features from past counts.\n", - "features_df[\"rolling_mean_24\"] = (\n", - " features_df[\"pedestriancount\"]\n", - " .shift(1)\n", - " .rolling(24)\n", - " .mean()\n", - ")\n", - "\n", - "features_df[\"rolling_mean_168\"] = (\n", - " features_df[\"pedestriancount\"]\n", - " .shift(1)\n", - " .rolling(168)\n", - " .mean()\n", - ")\n", - "\n", - "# Check the result.\n", - "print(\n", - " features_df[\n", - " [\n", - " \"rolling_mean_24\", \"rolling_mean_168\"\n", - " ]\n", - " ].head(25)\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 8.6 Removing NA Rows\n", - "\n", - "This step removes the rows with missing values created from the lag and rolling features at the beginning of the ordered merged dataset, since they cannot be used as they are incomplete predictor information. By removing these rows, the dataset lost a part of the early period as a trade-off in the pipeline, which is reasonable considering the dataset is being updated in real-time, so there will be more data points to use in the future, hence, negligible [48].\n", - "\n", - "The output shows that all rows with missing values were removed, and the dataset was reset into a clean index." - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 17248 entries, 0 to 17247\n", - "Data columns (total 28 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 datetime_hour 17248 non-null datetime64[ns]\n", - " 1 pedestriancount 17248 non-null int64 \n", - " 2 airtemperature 17248 non-null float64 \n", - " 3 relativehumidity 17248 non-null float64 \n", - " 4 atmosphericpressure 17248 non-null float64 \n", - " 5 averagewindspeed 17248 non-null float64 \n", - " 6 gustwindspeed 17248 non-null float64 \n", - " 7 averagewinddirection 17248 non-null float64 \n", - " 8 pm25 17248 non-null float64 \n", - " 9 pm10 17248 non-null float64 \n", - " 10 noise 17248 non-null float64 \n", - " 11 day_of_week 17248 non-null int32 \n", - " 12 hour 17248 non-null int32 \n", - " 13 month 17248 non-null int32 \n", - " 14 is_weekend 17248 non-null int64 \n", - " 15 hour_sin 17248 non-null float64 \n", - " 16 hour_cos 17248 non-null float64 \n", - " 17 dow_sin 17248 non-null float64 \n", - " 18 dow_cos 17248 non-null float64 \n", - " 19 month_sin 17248 non-null float64 \n", - " 20 month_cos 17248 non-null float64 \n", - " 21 wind_dir_sin 17248 non-null float64 \n", - " 22 wind_dir_cos 17248 non-null float64 \n", - " 23 lag_1 17248 non-null float64 \n", - " 24 lag_24 17248 non-null float64 \n", - " 25 lag_168 17248 non-null float64 \n", - " 26 rolling_mean_24 17248 non-null float64 \n", - " 27 rolling_mean_168 17248 non-null float64 \n", - "dtypes: datetime64[ns](1), float64(22), int32(3), int64(2)\n", - "memory usage: 3.5 MB\n", - "None\n", - " datetime_hour pedestriancount airtemperature relativehumidity \\\n", - "0 2024-05-19 00:00:00 12751 8.655172 69.200000 \n", - "1 2024-05-19 01:00:00 8603 8.664286 68.792857 \n", - "2 2024-05-19 02:00:00 5660 8.960714 69.235714 \n", - "3 2024-05-19 03:00:00 4709 9.660714 67.675000 \n", - "4 2024-05-19 04:00:00 2682 10.050000 69.060714 \n", - "\n", - " atmosphericpressure averagewindspeed gustwindspeed averagewinddirection \\\n", - "0 1026.241379 0.841379 2.303448 204.137931 \n", - "1 1025.732143 1.042857 2.535714 203.071429 \n", - "2 1024.671429 1.757143 3.717857 237.107143 \n", - "3 1024.171429 1.460714 3.657143 247.857143 \n", - "4 1023.675000 1.482143 3.807143 240.821429 \n", - "\n", - " pm25 pm10 ... dow_cos month_sin month_cos wind_dir_sin \\\n", - "0 6.758621 9.448276 ... 0.62349 0.5 -0.866025 -0.408935 \n", - "1 6.750000 9.428571 ... 0.62349 0.5 -0.866025 -0.391878 \n", - "2 6.892857 9.535714 ... 0.62349 0.5 -0.866025 -0.839688 \n", - "3 6.107143 8.678571 ... 0.62349 0.5 -0.866025 -0.926247 \n", - "4 5.678571 8.428571 ... 0.62349 0.5 -0.866025 -0.873104 \n", - "\n", - " wind_dir_cos lag_1 lag_24 lag_168 rolling_mean_24 rolling_mean_168 \n", - "0 -0.912564 21105.0 8948.0 15093.0 33068.333333 31092.851190 \n", - "1 -0.920017 12751.0 6244.0 10686.0 33226.791667 31078.910714 \n", - "2 -0.543070 8603.0 4035.0 6751.0 33325.083333 31066.511905 \n", - "3 -0.376917 5660.0 2813.0 5431.0 33392.791667 31060.017857 \n", - "4 -0.487533 4709.0 1761.0 2728.0 33471.791667 31055.720238 \n", - "\n", - "[5 rows x 28 columns]\n" - ] - } - ], - "source": [ - "# Remove rows with NAs.\n", - "features_df = features_df.dropna().reset_index(drop=True)\n", - "\n", - "# Check the result.\n", - "print(features_df.info())\n", - "print(features_df.head())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 8.7 Removing Unnecessary Features\n", - "\n", - "Since the cyclical variables were created, the original raw cyclical variables have become redundant. So removing these variables helps reduce feature duplication and makes the modelling easier and cleaner [23] [24].\n", - "\n", - "The averagewinddirection, hour, day_of_week, and month columns are dropped. Resulting in 24 columns, including the target pedestrian count, selected climate variables, is_weekend, cyclical encodings, lag features, and rolling means. The datetime_hour is temporarily kept, but will be removed later on as well." - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 17248 entries, 0 to 17247\n", - "Data columns (total 24 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 datetime_hour 17248 non-null datetime64[ns]\n", - " 1 pedestriancount 17248 non-null int64 \n", - " 2 airtemperature 17248 non-null float64 \n", - " 3 relativehumidity 17248 non-null float64 \n", - " 4 atmosphericpressure 17248 non-null float64 \n", - " 5 averagewindspeed 17248 non-null float64 \n", - " 6 gustwindspeed 17248 non-null float64 \n", - " 7 pm25 17248 non-null float64 \n", - " 8 pm10 17248 non-null float64 \n", - " 9 noise 17248 non-null float64 \n", - " 10 is_weekend 17248 non-null int64 \n", - " 11 hour_sin 17248 non-null float64 \n", - " 12 hour_cos 17248 non-null float64 \n", - " 13 dow_sin 17248 non-null float64 \n", - " 14 dow_cos 17248 non-null float64 \n", - " 15 month_sin 17248 non-null float64 \n", - " 16 month_cos 17248 non-null float64 \n", - " 17 wind_dir_sin 17248 non-null float64 \n", - " 18 wind_dir_cos 17248 non-null float64 \n", - " 19 lag_1 17248 non-null float64 \n", - " 20 lag_24 17248 non-null float64 \n", - " 21 lag_168 17248 non-null float64 \n", - " 22 rolling_mean_24 17248 non-null float64 \n", - " 23 rolling_mean_168 17248 non-null float64 \n", - "dtypes: datetime64[ns](1), float64(21), int64(2)\n", - "memory usage: 3.2 MB\n", - "None\n" - ] - } - ], - "source": [ - "# Drop raw columns that now have cyclical replacements.\n", - "features_df = features_df.drop(\n", - " columns=[\n", - " \"averagewinddirection\",\n", - " \"hour\",\n", - " \"day_of_week\",\n", - " \"month\",\n", - " ]\n", - ")\n", - "\n", - "# Reorder the columns into a cleaner structure.\n", - "features_df = features_df[\n", - " [\n", - " \"datetime_hour\",\n", - " \"pedestriancount\",\n", - " \"airtemperature\",\n", - " \"relativehumidity\",\n", - " \"atmosphericpressure\",\n", - " \"averagewindspeed\",\n", - " \"gustwindspeed\",\n", - " \"pm25\",\n", - " \"pm10\",\n", - " \"noise\",\n", - " \"is_weekend\",\n", - " \"hour_sin\",\n", - " \"hour_cos\",\n", - " \"dow_sin\",\n", - " \"dow_cos\",\n", - " \"month_sin\",\n", - " \"month_cos\",\n", - " \"wind_dir_sin\",\n", - " \"wind_dir_cos\",\n", - " \"lag_1\",\n", - " \"lag_24\",\n", - " \"lag_168\",\n", - " \"rolling_mean_24\",\n", - " \"rolling_mean_168\",\n", - " ]\n", - "]\n", - "\n", - "# Check the result.\n", - "print(features_df.info())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 9. Preparing Train/Val/Test Splits\n", - "\n", - "Preparing the splits is necessary for training a forecasting model, so that the future periods are evaluated, and not on randomly selected time periods. Hence, splitting the dataset based on time is important so that the model being trained on the past can predict the future, like in real-world scenarios [49]." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 9.1 Splitting The Data By Time\n", - "\n", - "Chronological splitting in time-series forecasting is to prevent leaking future information into the training process. For example is if random splitting were to be used, then a model can be trained on data points in 2026, but is tested on a time period in 2025. Hence, the validation and test set must be later, and the training set must be the past, as shown here [49].\n", - "\n", - "The split for training, validation and testing is 80:10:10 to ensure that there are enough data points used for training, with enough data points to perform validation and testing. Deep learning tends to require a large number of data points, so using this split ratio prevents underfitting [50].\n", - "\n", - "The output confirms that the data was split chronologically, with the training data coming first, followed by validation and testing data." - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(13798, 24)\n", - "(1724, 24)\n", - "(1726, 24)\n" - ] - } - ], - "source": [ - "# Set the split sizes (80:10:10).\n", - "train_size = int(len(features_df) * 0.8)\n", - "val_size = int(len(features_df) * 0.1)\n", - "\n", - "# Split the data in chronological order.\n", - "train_df = features_df.iloc[:train_size].copy()\n", - "val_df = features_df.iloc[\n", - " train_size:train_size + val_size\n", - "].copy()\n", - "test_df = features_df.iloc[\n", - " train_size + val_size:\n", - "].copy()\n", - "\n", - "# Check the shapes.\n", - "print(train_df.shape)\n", - "print(val_df.shape)\n", - "print(test_df.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train range:\n", - "2024-05-19 00:00:00\n", - "2025-12-14 21:00:00\n", - "\n", - "Validation range:\n", - "2025-12-14 22:00:00\n", - "2026-02-24 17:00:00\n", - "\n", - "Test range:\n", - "2026-02-24 18:00:00\n", - "2026-05-07 15:00:00\n" - ] - } - ], - "source": [ - "# Checking The Split Ranges.\n", - "print(\"Train range:\")\n", - "print(train_df[\"datetime_hour\"].min())\n", - "print(train_df[\"datetime_hour\"].max())\n", - "\n", - "print(\"\\nValidation range:\")\n", - "print(val_df[\"datetime_hour\"].min())\n", - "print(val_df[\"datetime_hour\"].max())\n", - "\n", - "print(\"\\nTest range:\")\n", - "print(test_df[\"datetime_hour\"].min())\n", - "print(test_df[\"datetime_hour\"].max())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 9.2 Separating Features And Target\n", - "\n", - "This step is necessary to separate the predictor inputs X and the output target y. This is necessary because the model needs to know which columns are used as inputs and which column it needs to predict [51].\n", - "\n", - "The target is set to pedestriancount, which is the variable the model is trying to predict. The datetime column is excluded from the features since the chronological splitting is completed, so this leaves 22 predictor columns for each split, as shown in the shapes output." - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(13798, 22) (13798,)\n", - "(1724, 22) (1724,)\n", - "(1726, 22) (1726,)\n" - ] - } - ], - "source": [ - "# Store the target column name.\n", - "target_col = \"pedestriancount\"\n", - "\n", - "# Store the feature column names.\n", - "feature_cols = [\n", - " col for col in features_df.columns\n", - " if col not in [\"datetime_hour\", target_col]\n", - "]\n", - "\n", - "# Create X and y for each split.\n", - "X_train = train_df[feature_cols]\n", - "y_train = train_df[target_col]\n", - "\n", - "X_val = val_df[feature_cols]\n", - "y_val = val_df[target_col]\n", - "\n", - "X_test = test_df[feature_cols]\n", - "y_test = test_df[target_col]\n", - "\n", - "# Check the shapes.\n", - "print(X_train.shape, y_train.shape)\n", - "print(X_val.shape, y_val.shape)\n", - "print(X_test.shape, y_test.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 9.3 Scaling The Features\n", - "\n", - "Scaling the features is necessary because the predictor variables are measured on different scales. For example, temperature, humidity, air pressure, wind speed, pollution values, and lagged pedestrian counts all have different ranges. Hence, needs to be standardised so that the variables are unitless, allowing the variables to be able to directly compared [52].\n", - "\n", - "Standardisation basically sets the predictors to have a mean of 0 and a standard deviation of 1. This allows faster convergence when all the input features are on the same scale, preventing feature dominance due to differences in magnitudes, and is generally more stable [52]." - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(13798, 22)\n", - "(1724, 22)\n", - "(1726, 22)\n", - "[[-1.29777704e+00 1.50360125e-01 1.46578468e+00 -5.80148521e-01\n", - " -7.99559383e-01 1.00283430e-01 1.98749444e-01 -3.63941261e-01\n", - " 1.57673844e+00 -7.77706241e-05 1.41447137e+00 -1.10371969e+00\n", - " 8.80498928e-01 1.03546540e+00 -1.13513198e+00 -5.75432725e-01\n", - " -4.91738264e-01 -5.08174682e-01 -9.59909049e-01 -7.29249335e-01\n", - " -3.26314765e-01 -1.04167887e+00]\n", - " [-1.29610869e+00 1.24879825e-01 1.40393909e+00 -2.36045144e-01\n", - " -6.48552677e-01 9.91130647e-02 1.96368567e-01 -5.59070044e-01\n", - " 1.57673844e+00 3.65929517e-01 1.36628083e+00 -1.10371969e+00\n", - " 8.80498928e-01 1.03546540e+00 -1.13513198e+00 -5.44617322e-01\n", - " -5.21312792e-01 -8.18712499e-01 -1.06044050e+00 -8.93515834e-01\n", - " -2.95950862e-01 -1.04569266e+00]\n", - " [-1.24184204e+00 1.52595239e-01 1.27511778e+00 9.83881253e-01\n", - " 1.20012207e-01 1.18507689e-01 2.09314586e-01 2.43030983e-01\n", - " 1.57673844e+00 7.06994013e-01 1.22499330e+00 -1.10371969e+00\n", - " 8.80498928e-01 1.03546540e+00 -1.13513198e+00 -1.35366847e+00\n", - " 9.74390635e-01 -9.72903410e-01 -1.14256846e+00 -1.04018901e+00\n", - " -2.77116141e-01 -1.04926256e+00]\n", - " [-1.11369428e+00 5.49207567e-02 1.21439393e+00 4.77611798e-01\n", - " 8.05390863e-02 1.18372546e-02 1.05746435e-01 -2.70089534e-01\n", - " 1.57673844e+00 9.99872735e-01 1.00023730e+00 -1.10371969e+00\n", - " 8.80498928e-01 1.03546540e+00 -1.13513198e+00 -1.51005421e+00\n", - " 1.63367379e+00 -1.08230164e+00 -1.18800094e+00 -1.08939068e+00\n", - " -2.64141820e-01 -1.05113235e+00]\n", - " [-1.04242843e+00 1.41643180e-01 1.15410382e+00 5.14209590e-01\n", - " 1.78060915e-01 -4.63466188e-02 7.55390572e-02 -4.47800540e-01\n", - " 1.57673844e+00 1.22460648e+00 7.07329572e-01 -1.10371969e+00\n", - " 8.80498928e-01 1.03546540e+00 -1.13513198e+00 -1.41404236e+00\n", - " 1.19475651e+00 -1.11765254e+00 -1.22711303e+00 -1.19014229e+00\n", - " -2.49003782e-01 -1.05236973e+00]]\n" - ] - } - ], - "source": [ - "# Create the scaler.\n", - "scaler = StandardScaler()\n", - "\n", - "# Fit on training features only.\n", - "X_train_scaled = scaler.fit_transform(X_train)\n", - "\n", - "# Transform validation and test features.\n", - "X_val_scaled = scaler.transform(X_val)\n", - "X_test_scaled = scaler.transform(X_test)\n", - "\n", - "# Check the shapes.\n", - "print(X_train_scaled.shape)\n", - "print(X_val_scaled.shape)\n", - "print(X_test_scaled.shape)\n", - "print(X_train_scaled[:5])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 10. Deep Learning — Pedestrian Count Forecasting\n", - "\n", - "This section covers the deep learning component of the project. The goal is to build \n", - "and compare recurrent neural network models that can forecast hourly pedestrian counts \n", - "in Melbourne CBD using climate features and engineered time-series inputs.\n", - "\n", - "Deep learning models, specifically recurrent neural network architectures like LSTM \n", - "and GRU, are well suited for this task because pedestrian demand is a sequential, \n", - "time-dependent process [53]. Unlike traditional regression \n", - "models, recurrent networks can learn temporal patterns across multiple time steps, \n", - "making them appropriate for hourly forecasting tasks with lag dependencies \n", - "[54]." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 10.2 Setting Random Seeds for Reproducibility\n", - "\n", - "A fixed random seed is set across all relevant libraries before any model is built or \n", - "trained. Deep learning models initialise their weights randomly and apply random \n", - "dropout masks during training, which means that results can differ between runs \n", - "without a fixed seed [55]. By setting the same seed across \n", - "Python, NumPy, and TensorFlow, the outputs of this section are fully reproducible \n", - "and will produce identical results on every run \n", - "[7] [55].\n", - "\n", - "Additional environment variables TF_DETERMINISTIC_OPS and \n", - "TF_DISABLE_SEGMENT_REDUCTION_OP_DETERMINISM_EXCEPTIONS are set to further enforce \n", - "deterministic behaviour at the TensorFlow operation level \n", - "[55]. The student ID was used as the seed value for consistency \n", - "with the random seed convention used earlier in this notebook \n", - "[7]." - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Random seed set to: 224120439\n", - "TF version: 2.20.0\n" - ] - } - ], - "source": [ - "SEED = 224120439\n", - "\n", - "# 1. Force single thread to eliminate CPU non-determinism\n", - "os.environ[\"PYTHONHASHSEED\"] = str(SEED)\n", - "os.environ[\"TF_DETERMINISTIC_OPS\"] = \"1\"\n", - "os.environ[\"TF_DISABLE_SEGMENT_REDUCTION_OP_DETERMINISM_EXCEPTIONS\"] = \"1\"\n", - "\n", - "# 2. Seed all libraries\n", - "random.seed(SEED)\n", - "np.random.seed(SEED)\n", - "tf.keras.utils.set_random_seed(SEED)\n", - "\n", - "# 3. Force deterministic operations\n", - "try:\n", - " tf.config.experimental.enable_op_determinism()\n", - "except Exception:\n", - " pass # silently skip if not supported on this system\n", - "\n", - "print(f\"Random seed set to: {SEED}\")\n", - "print(f\"TF version: {tf.__version__}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 10.3 Creating Input Sequences\n", - "\n", - "Before training a recurrent neural network, the data must be reshaped into \n", - "three-dimensional sequences. Each sequence consists of a fixed-length window of past \n", - "hourly observations that the model uses to predict the next hour's pedestrian count \n", - "[56].\n", - "\n", - "A sequence length of 24 was chosen to capture one full day cycle of hourly patterns. \n", - "This is a natural choice given that pedestrian demand follows a strong 24-hour rhythm, \n", - "as confirmed by the seasonal decomposition and autocorrelation analysis in earlier \n", - "sections [35] [42]. Additionally, since lag \n", - "features covering 24 hours, 7 days, and rolling means are already included as input \n", - "features, extending the sequence window further would introduce redundancy without \n", - "meaningful benefit [47] [48].\n", - "\n", - "Each input sample X has the shape (24, 22), representing 24 consecutive hours across \n", - "22 engineered features. The corresponding target y is the pedestrian count at the \n", - "following hour, making this a one-step-ahead forecasting setup \n", - "[56]. The output shapes confirm the split sizes — 13,774 \n", - "training samples, 1,700 validation samples, and 1,702 test samples." - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train shape: (13774, 24, 22)\n", - "Val shape: (1700, 24, 22)\n", - "Test shape: (1702, 24, 22)\n" - ] - } - ], - "source": [ - "sequence_length = 24 \n", - "\n", - "def create_sequences(X, y, seq_length):\n", - " X_seq, y_seq = [], []\n", - " for i in range(seq_length, len(X)):\n", - " X_seq.append(X[i - seq_length:i])\n", - " y_seq.append(y.iloc[i])\n", - " return np.array(X_seq), np.array(y_seq)\n", - "\n", - "X_train_seq, y_train_seq = create_sequences(X_train_scaled, y_train, sequence_length)\n", - "X_val_seq, y_val_seq = create_sequences(X_val_scaled, y_val, sequence_length)\n", - "X_test_seq, y_test_seq = create_sequences(X_test_scaled, y_test, sequence_length)\n", - "\n", - "print(\"Train shape:\", X_train_seq.shape)\n", - "print(\"Val shape:\", X_val_seq.shape)\n", - "print(\"Test shape:\", X_test_seq.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 10.4 Model Architectures\n", - "\n", - "Three recurrent neural network architectures were built and compared to identify the \n", - "most effective model for this forecasting task. Comparing multiple architectures \n", - "allows for evidence-based model selection rather than relying on a single approach \n", - "[53] [57].\n", - "\n", - "All three models share the same output structure — a Dense(32) hidden layer followed \n", - "by a Dense(1) output layer for regression — and are compiled with the Adam optimiser \n", - "and Mean Squared Error loss, which is standard for continuous value regression \n", - "forecasting tasks [58]. Mean Absolute Error is tracked as an \n", - "interpretable secondary metric during training [59].\n", - "\n", - "The three architectures evaluated are:\n", - "- **Baseline LSTM**: A single LSTM layer with 64 units serving as the reference model \n", - " to establish a performance baseline.\n", - "- **Stacked LSTM**: Two LSTM layers in sequence for deeper temporal pattern learning.\n", - "- **GRU**: A more parameter-efficient alternative to LSTM with a simplified gate \n", - " structure." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 10.4.1 Baseline LSTM\n", - "\n", - "The baseline model uses a single LSTM layer with 64 units. LSTM networks are designed \n", - "to address the vanishing gradient problem found in standard recurrent networks, \n", - "allowing them to retain information across longer time sequences \n", - "[54]. At each time step, the LSTM uses three gates — an input \n", - "gate, a forget gate, and an output gate — to decide what information to keep, discard, \n", - "or pass forward [54].\n", - "\n", - "A Dropout layer with a rate of 0.2 is applied after the LSTM to regularise the model \n", - "by randomly deactivating 20% of neurons during each training step, reducing the risk \n", - "of overfitting [60]. This model has 24,385 total trainable \n", - "parameters, which is appropriate for the training set size of approximately 13,774 \n", - "samples." - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.12/dist-packages/keras/src/layers/rnn/rnn.py:199: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", - " super().__init__(**kwargs)\n" - ] - }, - { - "data": { - "text/html": [ - "
Model: \"sequential_3\"\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1mModel: \"sequential_3\"\u001b[0m\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ lstm_3 (LSTM)                   │ (None, 64)             │        22,272 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dropout_4 (Dropout)             │ (None, 64)             │             0 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_6 (Dense)                 │ (None, 32)             │         2,080 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_7 (Dense)                 │ (None, 1)              │            33 │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
-       "
\n" - ], - "text/plain": [ - "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", - "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", - "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", - "│ lstm_3 (\u001b[38;5;33mLSTM\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m22,272\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dense_6 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m2,080\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dense_7 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m33\u001b[0m │\n", - "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
 Total params: 24,385 (95.25 KB)\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m24,385\u001b[0m (95.25 KB)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
 Trainable params: 24,385 (95.25 KB)\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m24,385\u001b[0m (95.25 KB)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
 Non-trainable params: 0 (0.00 B)\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def build_lstm_baseline(input_shape):\n", - " model = Sequential([\n", - " LSTM(64, return_sequences=False, input_shape=input_shape),\n", - " Dropout(0.2),\n", - " Dense(32, activation=\"relu\"),\n", - " Dense(1)\n", - " ])\n", - " model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[\"mae\"])\n", - " return model\n", - "\n", - "model_baseline = build_lstm_baseline(\n", - " (X_train_seq.shape[1], X_train_seq.shape[2])\n", - ")\n", - "model_baseline.summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 10.4.2 Stacked LSTM\n", - "\n", - "The stacked model adds a second LSTM layer on top of the first. The first LSTM layer \n", - "uses return_sequences=True, meaning it outputs the full sequence of hidden states at \n", - "every time step rather than just the final one. This full sequence is then passed as \n", - "input to the second LSTM layer, which compresses it into a single final hidden state \n", - "[54].\n", - "\n", - "This design allows the model to learn more abstract temporal representations across \n", - "multiple levels of processing — the first layer captures low-level hourly patterns, \n", - "while the second layer can learn higher-order dependencies across those patterns \n", - "[53]. Dropout is applied after each LSTM layer to maintain \n", - "regularisation [60]. This model has 35,777 total trainable \n", - "parameters, making it the most complex of the three architectures compared." - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Model: \"sequential_4\"\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1mModel: \"sequential_4\"\u001b[0m\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ lstm_4 (LSTM)                   │ (None, 24, 64)         │        22,272 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dropout_5 (Dropout)             │ (None, 24, 64)         │             0 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ lstm_5 (LSTM)                   │ (None, 32)             │        12,416 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dropout_6 (Dropout)             │ (None, 32)             │             0 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_8 (Dense)                 │ (None, 32)             │         1,056 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_9 (Dense)                 │ (None, 1)              │            33 │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
-       "
\n" - ], - "text/plain": [ - "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", - "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", - "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", - "│ lstm_4 (\u001b[38;5;33mLSTM\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m22,272\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dropout_5 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ lstm_5 (\u001b[38;5;33mLSTM\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m12,416\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dropout_6 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dense_8 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m1,056\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dense_9 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m33\u001b[0m │\n", - "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
 Total params: 35,777 (139.75 KB)\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m35,777\u001b[0m (139.75 KB)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
 Trainable params: 35,777 (139.75 KB)\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m35,777\u001b[0m (139.75 KB)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
 Non-trainable params: 0 (0.00 B)\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def build_lstm_stacked(input_shape):\n", - " model = Sequential([\n", - " LSTM(64, return_sequences=True, input_shape=input_shape), # returns full sequence\n", - " Dropout(0.2),\n", - " LSTM(32, return_sequences=False), # compresses to final state\n", - " Dropout(0.2),\n", - " Dense(32, activation=\"relu\"),\n", - " Dense(1)\n", - " ])\n", - " model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[\"mae\"])\n", - " return model\n", - "\n", - "model_stacked = build_lstm_stacked(\n", - " (X_train_seq.shape[1], X_train_seq.shape[2])\n", - ")\n", - "model_stacked.summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 10.4.3 GRU (Gated Recurrent Unit)\n", - "\n", - "The GRU model replaces the LSTM unit with a GRU layer of equal size. GRU simplifies \n", - "the LSTM architecture by combining the input and forget gates into a single update \n", - "gate, and adding a reset gate, resulting in only two gates overall instead of three \n", - "[61]. This reduction in gate complexity leads to fewer \n", - "parameters — 19,009 total — making the GRU the most lightweight of the three \n", - "architectures.\n", - "\n", - "GRU is included as an alternative because empirical research has shown it often \n", - "performs comparably or better than LSTM on time-series forecasting tasks, \n", - "particularly on datasets that are not large enough to fully justify the additional \n", - "capacity of LSTM [61] [62]. This \n", - "comparison tests whether that finding holds for this urban pedestrian forecasting \n", - "task." - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Model: \"sequential_5\"\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1mModel: \"sequential_5\"\u001b[0m\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ gru_1 (GRU)                     │ (None, 64)             │        16,896 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dropout_7 (Dropout)             │ (None, 64)             │             0 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_10 (Dense)                │ (None, 32)             │         2,080 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_11 (Dense)                │ (None, 1)              │            33 │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
-       "
\n" - ], - "text/plain": [ - "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", - "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", - "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", - "│ gru_1 (\u001b[38;5;33mGRU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m16,896\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dropout_7 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dense_10 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m2,080\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dense_11 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m33\u001b[0m │\n", - "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
 Total params: 19,009 (74.25 KB)\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m19,009\u001b[0m (74.25 KB)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
 Trainable params: 19,009 (74.25 KB)\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m19,009\u001b[0m (74.25 KB)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
 Non-trainable params: 0 (0.00 B)\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def build_gru(input_shape):\n", - " model = Sequential([\n", - " GRU(64, return_sequences=False, input_shape=input_shape),\n", - " Dropout(0.2),\n", - " Dense(32, activation=\"relu\"),\n", - " Dense(1)\n", - " ])\n", - " model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[\"mae\"])\n", - " return model\n", - "\n", - "model_gru = build_gru(\n", - " (X_train_seq.shape[1], X_train_seq.shape[2])\n", - ")\n", - "model_gru.summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 10.5 Model Training\n", - "\n", - "All three models are trained using identical configurations to ensure a fair \n", - "comparison between architectures. The training setup includes the following design \n", - "choices [58]:\n", - "\n", - "- **Epochs**: A maximum of 50 epochs was set to allow sufficient learning time, with \n", - " callbacks responsible for halting training early when appropriate \n", - " [58].\n", - "- **Batch size**: A batch size of 64 was selected as a balance between training \n", - " stability and computational speed. Larger batch sizes provide more stable gradient \n", - " estimates per update compared to smaller ones [58].\n", - "- **EarlyStopping**: Monitors validation loss with a patience of 10 epochs, halting \n", - " training if no improvement is seen for 10 consecutive epochs. The \n", - " restore_best_weights=True parameter ensures the final model retains the weights \n", - " from its best validation epoch rather than the last epoch \n", - " [63].\n", - "- **ReduceLROnPlateau**: Halves the learning rate when validation loss has not \n", - " improved for 5 consecutive epochs, with a minimum learning rate floor of 1e-6. \n", - " This allows finer weight adjustments as the model approaches convergence rather \n", - " than overshooting the optimal solution [63].\n", - "\n", - "The validation set is used exclusively for monitoring during training and is never \n", - "used to update model weights, preserving the integrity of the chronological train, \n", - "validation, and test split [49]." - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 11ms/step - loss: 1931178880.0000 - mae: 34742.4805 - val_loss: 2065434112.0000 - val_mae: 36316.7852 - learning_rate: 0.0010\n", - "Epoch 2/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1915012608.0000 - mae: 34508.1875 - val_loss: 2041869568.0000 - val_mae: 35990.8672 - learning_rate: 0.0010\n", - "Epoch 3/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1886355328.0000 - mae: 34091.4727 - val_loss: 2005516672.0000 - val_mae: 35482.5078 - learning_rate: 0.0010\n", - "Epoch 4/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1846237696.0000 - mae: 33533.6406 - val_loss: 1957890944.0000 - val_mae: 34864.8906 - learning_rate: 0.0010\n", - "Epoch 5/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 1796227072.0000 - mae: 32903.4414 - val_loss: 1900755712.0000 - val_mae: 34188.7891 - learning_rate: 0.0010\n", - "Epoch 6/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1738398336.0000 - mae: 32237.4355 - val_loss: 1835951104.0000 - val_mae: 33501.5625 - learning_rate: 0.0010\n", - "Epoch 7/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1673944192.0000 - mae: 31575.8242 - val_loss: 1765187584.0000 - val_mae: 32804.0078 - learning_rate: 0.0010\n", - "Epoch 8/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1604557824.0000 - mae: 30903.6895 - val_loss: 1690087552.0000 - val_mae: 32100.9355 - learning_rate: 0.0010\n", - "Epoch 9/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 1532111872.0000 - mae: 30227.2246 - val_loss: 1612279936.0000 - val_mae: 31392.7227 - learning_rate: 0.0010\n", - "Epoch 10/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 1457709312.0000 - mae: 29556.4414 - val_loss: 1533161088.0000 - val_mae: 30695.7051 - learning_rate: 0.0010\n", - "Epoch 11/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1383581440.0000 - mae: 28908.4434 - val_loss: 1454138880.0000 - val_mae: 30023.1250 - learning_rate: 0.0010\n", - "Epoch 12/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1310495488.0000 - mae: 28299.8477 - val_loss: 1376468352.0000 - val_mae: 29394.1934 - learning_rate: 0.0010\n", - "Epoch 13/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1239926912.0000 - mae: 27729.9043 - val_loss: 1301134208.0000 - val_mae: 28809.5215 - learning_rate: 0.0010\n", - "Epoch 14/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1170712448.0000 - mae: 27170.0469 - val_loss: 1229012224.0000 - val_mae: 28242.0703 - learning_rate: 0.0010\n", - "Epoch 15/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1105601920.0000 - mae: 26642.3184 - val_loss: 1160922624.0000 - val_mae: 27700.8438 - learning_rate: 0.0010\n", - "Epoch 16/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1043984640.0000 - mae: 26124.0840 - val_loss: 1094580352.0000 - val_mae: 27079.0586 - learning_rate: 0.0010\n", - "Epoch 17/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 976227584.0000 - mae: 25163.9297 - val_loss: 996540288.0000 - val_mae: 24844.2109 - learning_rate: 0.0010\n", - "Epoch 18/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 860002496.0000 - mae: 21725.8691 - val_loss: 885134976.0000 - val_mae: 21819.1719 - learning_rate: 0.0010\n", - "Epoch 19/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 772316480.0000 - mae: 19665.3848 - val_loss: 801167040.0000 - val_mae: 20392.8848 - learning_rate: 0.0010\n", - "Epoch 20/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 696815808.0000 - mae: 18389.9785 - val_loss: 723598528.0000 - val_mae: 19151.4785 - learning_rate: 0.0010\n", - "Epoch 21/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 628451968.0000 - mae: 17213.0176 - val_loss: 651424896.0000 - val_mae: 17956.3184 - learning_rate: 0.0010\n", - "Epoch 22/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 563721536.0000 - mae: 16077.4170 - val_loss: 584422592.0000 - val_mae: 16778.9355 - learning_rate: 0.0010\n", - "Epoch 23/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 505489536.0000 - mae: 15020.1113 - val_loss: 523019264.0000 - val_mae: 15676.2910 - learning_rate: 0.0010\n", - "Epoch 24/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 449513696.0000 - mae: 13965.4336 - val_loss: 466503328.0000 - val_mae: 14642.3008 - learning_rate: 0.0010\n", - "Epoch 25/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 400619104.0000 - mae: 13041.7090 - val_loss: 414948032.0000 - val_mae: 13628.7861 - learning_rate: 0.0010\n", - "Epoch 26/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 356454240.0000 - mae: 12158.3799 - val_loss: 368652768.0000 - val_mae: 12694.5703 - learning_rate: 0.0010\n", - "Epoch 27/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 317270240.0000 - mae: 11362.6035 - val_loss: 327098016.0000 - val_mae: 11882.4199 - learning_rate: 0.0010\n", - "Epoch 28/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 282110752.0000 - mae: 10618.4219 - val_loss: 290472800.0000 - val_mae: 11140.4375 - learning_rate: 0.0010\n", - "Epoch 29/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 249665072.0000 - mae: 9972.2529 - val_loss: 259006832.0000 - val_mae: 10496.8271 - learning_rate: 0.0010\n", - "Epoch 30/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 222754640.0000 - mae: 9393.5771 - val_loss: 231574736.0000 - val_mae: 9954.3838 - learning_rate: 0.0010\n", - "Epoch 31/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 198423008.0000 - mae: 8869.1445 - val_loss: 207840512.0000 - val_mae: 9421.6309 - learning_rate: 0.0010\n", - "Epoch 32/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 178380192.0000 - mae: 8440.0615 - val_loss: 187732656.0000 - val_mae: 8949.0840 - learning_rate: 0.0010\n", - "Epoch 33/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 161002656.0000 - mae: 7984.2749 - val_loss: 169997776.0000 - val_mae: 8558.7334 - learning_rate: 0.0010\n", - "Epoch 34/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 144927488.0000 - mae: 7591.0400 - val_loss: 154311120.0000 - val_mae: 8103.6113 - learning_rate: 0.0010\n", - "Epoch 35/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 128720472.0000 - mae: 7144.0977 - val_loss: 140611216.0000 - val_mae: 7693.2202 - learning_rate: 0.0010\n", - "Epoch 36/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 115451752.0000 - mae: 6745.2446 - val_loss: 130359144.0000 - val_mae: 7402.8223 - learning_rate: 0.0010\n", - "Epoch 37/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 103320352.0000 - mae: 6354.1328 - val_loss: 117524160.0000 - val_mae: 6989.3652 - learning_rate: 0.0010\n", - "Epoch 38/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 92202488.0000 - mae: 5979.1660 - val_loss: 109628784.0000 - val_mae: 6704.9717 - learning_rate: 0.0010\n", - "Epoch 39/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 83113800.0000 - mae: 5689.0278 - val_loss: 103527280.0000 - val_mae: 6496.6172 - learning_rate: 0.0010\n", - "Epoch 40/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 75200392.0000 - mae: 5414.2593 - val_loss: 97912072.0000 - val_mae: 6302.9551 - learning_rate: 0.0010\n", - "Epoch 41/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 69330976.0000 - mae: 5204.5713 - val_loss: 92983264.0000 - val_mae: 6090.5542 - learning_rate: 0.0010\n", - "Epoch 42/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 64068488.0000 - mae: 5041.2319 - val_loss: 93400048.0000 - val_mae: 6068.5435 - learning_rate: 0.0010\n", - "Epoch 43/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 59892128.0000 - mae: 4892.9224 - val_loss: 87246792.0000 - val_mae: 5791.9092 - learning_rate: 0.0010\n", - "Epoch 44/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 55631812.0000 - mae: 4743.8657 - val_loss: 85550016.0000 - val_mae: 5762.2046 - learning_rate: 0.0010\n", - "Epoch 45/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 53973056.0000 - mae: 4663.5952 - val_loss: 83252664.0000 - val_mae: 5683.0537 - learning_rate: 0.0010\n", - "Epoch 46/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 50220260.0000 - mae: 4527.8228 - val_loss: 83925944.0000 - val_mae: 5714.0728 - learning_rate: 0.0010\n", - "Epoch 47/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 47764060.0000 - mae: 4444.6938 - val_loss: 82401720.0000 - val_mae: 5649.3335 - learning_rate: 0.0010\n", - "Epoch 48/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 45334620.0000 - mae: 4341.4917 - val_loss: 80761736.0000 - val_mae: 5650.6733 - learning_rate: 0.0010\n", - "Epoch 49/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 43015352.0000 - mae: 4263.1714 - val_loss: 78728576.0000 - val_mae: 5562.2876 - learning_rate: 0.0010\n", - "Epoch 50/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 41184284.0000 - mae: 4168.4272 - val_loss: 76461192.0000 - val_mae: 5454.5000 - learning_rate: 0.0010\n", - "Epoch 1/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 10ms/step - loss: 1932731136.0000 - mae: 34764.9727 - val_loss: 2069818880.0000 - val_mae: 36377.0664 - learning_rate: 0.0010\n", - "Epoch 2/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1923338112.0000 - mae: 34629.4414 - val_loss: 2056002432.0000 - val_mae: 36186.6602 - learning_rate: 0.0010\n", - "Epoch 3/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 1906462720.0000 - mae: 34385.0273 - val_loss: 2034484992.0000 - val_mae: 35888.1211 - learning_rate: 0.0010\n", - "Epoch 4/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 1882598784.0000 - mae: 34036.7148 - val_loss: 2005956992.0000 - val_mae: 35488.6562 - learning_rate: 0.0010\n", - "Epoch 5/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1852413696.0000 - mae: 33614.3867 - val_loss: 1971230848.0000 - val_mae: 35029.5586 - learning_rate: 0.0010\n", - "Epoch 6/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1816782592.0000 - mae: 33156.7617 - val_loss: 1931121664.0000 - val_mae: 34541.7891 - learning_rate: 0.0010\n", - "Epoch 7/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1776370560.0000 - mae: 32665.1387 - val_loss: 1886418944.0000 - val_mae: 34027.4883 - learning_rate: 0.0010\n", - "Epoch 8/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1731960960.0000 - mae: 32169.1250 - val_loss: 1837876736.0000 - val_mae: 33520.9844 - learning_rate: 0.0010\n", - "Epoch 9/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 1684573312.0000 - mae: 31682.2441 - val_loss: 1786250496.0000 - val_mae: 33008.1094 - learning_rate: 0.0010\n", - "Epoch 10/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 1634488960.0000 - mae: 31189.7012 - val_loss: 1732167680.0000 - val_mae: 32490.0215 - learning_rate: 0.0010\n", - "Epoch 11/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1582652928.0000 - mae: 30698.1016 - val_loss: 1676277248.0000 - val_mae: 31974.6699 - learning_rate: 0.0010\n", - "Epoch 12/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1528807936.0000 - mae: 30199.7773 - val_loss: 1619060736.0000 - val_mae: 31453.7559 - learning_rate: 0.0010\n", - "Epoch 13/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 1474490880.0000 - mae: 29703.1445 - val_loss: 1561115776.0000 - val_mae: 30939.0352 - learning_rate: 0.0010\n", - "Epoch 14/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1419293056.0000 - mae: 29219.7656 - val_loss: 1502894208.0000 - val_mae: 30434.5215 - learning_rate: 0.0010\n", - "Epoch 15/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - loss: 1366217600.0000 - mae: 28764.8242 - val_loss: 1445071360.0000 - val_mae: 29948.1836 - learning_rate: 0.0010\n", - "Epoch 16/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 1311709056.0000 - mae: 28299.2773 - val_loss: 1387855616.0000 - val_mae: 29483.8652 - learning_rate: 0.0010\n", - "Epoch 17/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1259717120.0000 - mae: 27880.5957 - val_loss: 1331703424.0000 - val_mae: 29047.3066 - learning_rate: 0.0010\n", - "Epoch 18/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1208217600.0000 - mae: 27472.8125 - val_loss: 1276985344.0000 - val_mae: 28620.4238 - learning_rate: 0.0010\n", - "Epoch 19/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1158040192.0000 - mae: 27063.4766 - val_loss: 1224060544.0000 - val_mae: 28202.8828 - learning_rate: 0.0010\n", - "Epoch 20/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 1109995136.0000 - mae: 26683.9062 - val_loss: 1173220352.0000 - val_mae: 27799.9453 - learning_rate: 0.0010\n", - "Epoch 21/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - loss: 1064320704.0000 - mae: 26317.3105 - val_loss: 1124794624.0000 - val_mae: 27414.1367 - learning_rate: 0.0010\n", - "Epoch 22/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1022760384.0000 - mae: 25982.8965 - val_loss: 1079056128.0000 - val_mae: 27053.7539 - learning_rate: 0.0010\n", - "Epoch 23/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 982177088.0000 - mae: 25652.5938 - val_loss: 1036195840.0000 - val_mae: 26712.5508 - learning_rate: 0.0010\n", - "Epoch 24/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 945132608.0000 - mae: 25346.1582 - val_loss: 996421696.0000 - val_mae: 26395.3066 - learning_rate: 0.0010\n", - "Epoch 25/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 911153472.0000 - mae: 25062.0684 - val_loss: 959820032.0000 - val_mae: 26097.6113 - learning_rate: 0.0010\n", - "Epoch 26/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 880028288.0000 - mae: 24827.4922 - val_loss: 926537280.0000 - val_mae: 25821.6191 - learning_rate: 0.0010\n", - "Epoch 27/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - loss: 853682432.0000 - mae: 24604.5742 - val_loss: 896631808.0000 - val_mae: 25565.9453 - learning_rate: 0.0010\n", - "Epoch 28/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 828166080.0000 - mae: 24403.7070 - val_loss: 870131136.0000 - val_mae: 25342.5938 - learning_rate: 0.0010\n", - "Epoch 29/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 810969728.0000 - mae: 24281.2656 - val_loss: 846989760.0000 - val_mae: 25147.4238 - learning_rate: 0.0010\n", - "Epoch 30/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 790364480.0000 - mae: 24090.2852 - val_loss: 827042432.0000 - val_mae: 24978.3867 - learning_rate: 0.0010\n", - "Epoch 31/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 776585792.0000 - mae: 24009.8965 - val_loss: 810190016.0000 - val_mae: 24829.4160 - learning_rate: 0.0010\n", - "Epoch 32/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - loss: 764959104.0000 - mae: 23917.3750 - val_loss: 796246976.0000 - val_mae: 24708.1191 - learning_rate: 0.0010\n", - "Epoch 33/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - loss: 755435648.0000 - mae: 23878.0566 - val_loss: 784937344.0000 - val_mae: 24605.6172 - learning_rate: 0.0010\n", - "Epoch 34/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - loss: 747755712.0000 - mae: 23794.9590 - val_loss: 775911296.0000 - val_mae: 24518.1973 - learning_rate: 0.0010\n", - "Epoch 35/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 743594688.0000 - mae: 23785.2285 - val_loss: 768952064.0000 - val_mae: 24446.5508 - learning_rate: 0.0010\n", - "Epoch 36/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 739301056.0000 - mae: 23742.7988 - val_loss: 763610304.0000 - val_mae: 24390.0273 - learning_rate: 0.0010\n", - "Epoch 37/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 738923584.0000 - mae: 23775.4492 - val_loss: 759721984.0000 - val_mae: 24347.7969 - learning_rate: 0.0010\n", - "Epoch 38/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - loss: 735023744.0000 - mae: 23729.2637 - val_loss: 756937152.0000 - val_mae: 24316.7676 - learning_rate: 0.0010\n", - "Epoch 39/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 734647168.0000 - mae: 23751.9238 - val_loss: 754886144.0000 - val_mae: 24292.0117 - learning_rate: 0.0010\n", - "Epoch 40/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 732123008.0000 - mae: 23701.5078 - val_loss: 753405888.0000 - val_mae: 24274.6113 - learning_rate: 0.0010\n", - "Epoch 41/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 479316192.0000 - mae: 15964.3057 - val_loss: 403580480.0000 - val_mae: 13544.6035 - learning_rate: 0.0010\n", - "Epoch 42/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 343708096.0000 - mae: 12115.6484 - val_loss: 341869088.0000 - val_mae: 12275.1152 - learning_rate: 0.0010\n", - "Epoch 43/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 10ms/step - loss: 294795616.0000 - mae: 11060.8252 - val_loss: 294227808.0000 - val_mae: 11263.7822 - learning_rate: 0.0010\n", - "Epoch 44/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - loss: 255411248.0000 - mae: 10183.4551 - val_loss: 256555760.0000 - val_mae: 10408.2881 - learning_rate: 0.0010\n", - "Epoch 45/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 226594400.0000 - mae: 9585.3291 - val_loss: 226511520.0000 - val_mae: 9840.6309 - learning_rate: 0.0010\n", - "Epoch 46/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 201210784.0000 - mae: 8997.6709 - val_loss: 201730768.0000 - val_mae: 9280.6221 - learning_rate: 0.0010\n", - "Epoch 47/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 180951744.0000 - mae: 8508.0166 - val_loss: 180386640.0000 - val_mae: 8658.5586 - learning_rate: 0.0010\n", - "Epoch 48/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 161437456.0000 - mae: 8037.3340 - val_loss: 162152432.0000 - val_mae: 8236.7969 - learning_rate: 0.0010\n", - "Epoch 49/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 143073152.0000 - mae: 7514.1865 - val_loss: 141376416.0000 - val_mae: 7570.7739 - learning_rate: 0.0010\n", - "Epoch 50/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - loss: 127263440.0000 - mae: 7038.0229 - val_loss: 127391912.0000 - val_mae: 7156.2104 - learning_rate: 0.0010\n", - "Epoch 1/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 1930911488.0000 - mae: 34738.7148 - val_loss: 2063673600.0000 - val_mae: 36292.5195 - learning_rate: 0.0010\n", - "Epoch 2/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1910318336.0000 - mae: 34439.6289 - val_loss: 2033005824.0000 - val_mae: 35867.5156 - learning_rate: 0.0010\n", - "Epoch 3/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1872711552.0000 - mae: 33894.9219 - val_loss: 1984276864.0000 - val_mae: 35195.7305 - learning_rate: 0.0010\n", - "Epoch 4/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1818348032.0000 - mae: 33175.7305 - val_loss: 1919553280.0000 - val_mae: 34405.4180 - learning_rate: 0.0010\n", - "Epoch 5/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1750787072.0000 - mae: 32374.7637 - val_loss: 1842820480.0000 - val_mae: 33571.0039 - learning_rate: 0.0010\n", - "Epoch 6/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 1673256576.0000 - mae: 31570.9395 - val_loss: 1757159040.0000 - val_mae: 32726.9746 - learning_rate: 0.0010\n", - "Epoch 7/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1589680640.0000 - mae: 30760.4844 - val_loss: 1665691136.0000 - val_mae: 31878.0684 - learning_rate: 0.0010\n", - "Epoch 8/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1501466496.0000 - mae: 29948.0781 - val_loss: 1570926464.0000 - val_mae: 31025.0703 - learning_rate: 0.0010\n", - "Epoch 9/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 1412167296.0000 - mae: 29152.6699 - val_loss: 1475466880.0000 - val_mae: 30201.8711 - learning_rate: 0.0010\n", - "Epoch 10/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1322615424.0000 - mae: 28367.2754 - val_loss: 1379597312.0000 - val_mae: 29309.5332 - learning_rate: 0.0010\n", - "Epoch 11/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1220498816.0000 - mae: 26533.4805 - val_loss: 1260929408.0000 - val_mae: 26583.1484 - learning_rate: 0.0010\n", - "Epoch 12/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1112165120.0000 - mae: 24360.7832 - val_loss: 1153268480.0000 - val_mae: 25070.1992 - learning_rate: 0.0010\n", - "Epoch 13/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1014172736.0000 - mae: 22965.8555 - val_loss: 1049243264.0000 - val_mae: 23642.1152 - learning_rate: 0.0010\n", - "Epoch 14/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 918478592.0000 - mae: 21610.4453 - val_loss: 949395648.0000 - val_mae: 22258.7539 - learning_rate: 0.0010\n", - "Epoch 15/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - loss: 828141056.0000 - mae: 20268.5742 - val_loss: 854286336.0000 - val_mae: 20897.9004 - learning_rate: 0.0010\n", - "Epoch 16/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 742179200.0000 - mae: 18959.4883 - val_loss: 764294528.0000 - val_mae: 19546.4355 - learning_rate: 0.0010\n", - "Epoch 17/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 660350528.0000 - mae: 17696.7012 - val_loss: 680099328.0000 - val_mae: 18261.0137 - learning_rate: 0.0010\n", - "Epoch 18/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 587759936.0000 - mae: 16515.5098 - val_loss: 602156032.0000 - val_mae: 16998.6445 - learning_rate: 0.0010\n", - "Epoch 19/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 516721568.0000 - mae: 15305.6777 - val_loss: 530436352.0000 - val_mae: 15776.6455 - learning_rate: 0.0010\n", - "Epoch 20/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 454774592.0000 - mae: 14225.0361 - val_loss: 465422528.0000 - val_mae: 14620.3174 - learning_rate: 0.0010\n", - "Epoch 21/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 400378592.0000 - mae: 13204.1484 - val_loss: 406941792.0000 - val_mae: 13484.5117 - learning_rate: 0.0010\n", - "Epoch 22/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - loss: 351022656.0000 - mae: 12238.7500 - val_loss: 355148032.0000 - val_mae: 12487.7051 - learning_rate: 0.0010\n", - "Epoch 23/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 305887232.0000 - mae: 11356.9395 - val_loss: 309833376.0000 - val_mae: 11593.4004 - learning_rate: 0.0010\n", - "Epoch 24/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 265417856.0000 - mae: 10562.7656 - val_loss: 270137696.0000 - val_mae: 10775.0537 - learning_rate: 0.0010\n", - "Epoch 25/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 234852512.0000 - mae: 9912.8330 - val_loss: 235926240.0000 - val_mae: 10042.0000 - learning_rate: 0.0010\n", - "Epoch 26/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 204994608.0000 - mae: 9289.3477 - val_loss: 208168032.0000 - val_mae: 9438.7832 - learning_rate: 0.0010\n", - "Epoch 27/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 181129088.0000 - mae: 8759.8398 - val_loss: 183661520.0000 - val_mae: 8844.0303 - learning_rate: 0.0010\n", - "Epoch 28/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 159850000.0000 - mae: 8249.1475 - val_loss: 162752464.0000 - val_mae: 8357.9189 - learning_rate: 0.0010\n", - "Epoch 29/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 141057840.0000 - mae: 7771.1348 - val_loss: 144473072.0000 - val_mae: 7830.9922 - learning_rate: 0.0010\n", - "Epoch 30/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 125178776.0000 - mae: 7342.7188 - val_loss: 130381672.0000 - val_mae: 7483.4707 - learning_rate: 0.0010\n", - "Epoch 31/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 113134424.0000 - mae: 6976.5171 - val_loss: 116524848.0000 - val_mae: 7097.4570 - learning_rate: 0.0010\n", - "Epoch 32/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 101647680.0000 - mae: 6615.7490 - val_loss: 107708008.0000 - val_mae: 6762.4595 - learning_rate: 0.0010\n", - "Epoch 33/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 91810328.0000 - mae: 6306.7373 - val_loss: 97756488.0000 - val_mae: 6498.3496 - learning_rate: 0.0010\n", - "Epoch 34/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 82927784.0000 - mae: 6015.1938 - val_loss: 90644216.0000 - val_mae: 6210.5337 - learning_rate: 0.0010\n", - "Epoch 35/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 76303432.0000 - mae: 5791.7852 - val_loss: 84712688.0000 - val_mae: 5904.9824 - learning_rate: 0.0010\n", - "Epoch 36/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 71719856.0000 - mae: 5631.6865 - val_loss: 80555576.0000 - val_mae: 5748.7349 - learning_rate: 0.0010\n", - "Epoch 37/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 67707120.0000 - mae: 5521.0171 - val_loss: 78097168.0000 - val_mae: 5696.1294 - learning_rate: 0.0010\n", - "Epoch 38/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 63857660.0000 - mae: 5421.4185 - val_loss: 73372440.0000 - val_mae: 5529.1011 - learning_rate: 0.0010\n", - "Epoch 39/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 61203508.0000 - mae: 5300.4053 - val_loss: 70816400.0000 - val_mae: 5323.6621 - learning_rate: 0.0010\n", - "Epoch 40/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 59436396.0000 - mae: 5238.1948 - val_loss: 67635208.0000 - val_mae: 5235.2432 - learning_rate: 0.0010\n", - "Epoch 41/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 56774304.0000 - mae: 5124.1748 - val_loss: 66004324.0000 - val_mae: 5169.6328 - learning_rate: 0.0010\n", - "Epoch 42/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 53282044.0000 - mae: 4981.6040 - val_loss: 63594312.0000 - val_mae: 5073.1543 - learning_rate: 0.0010\n", - "Epoch 43/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 51761064.0000 - mae: 4912.4688 - val_loss: 60992804.0000 - val_mae: 4910.9961 - learning_rate: 0.0010\n", - "Epoch 44/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 49697432.0000 - mae: 4781.2227 - val_loss: 59893640.0000 - val_mae: 4884.7842 - learning_rate: 0.0010\n", - "Epoch 45/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 47390824.0000 - mae: 4688.0669 - val_loss: 57193784.0000 - val_mae: 4715.9609 - learning_rate: 0.0010\n", - "Epoch 46/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - loss: 44774588.0000 - mae: 4550.4604 - val_loss: 55175160.0000 - val_mae: 4646.7446 - learning_rate: 0.0010\n", - "Epoch 47/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 45075144.0000 - mae: 4574.6504 - val_loss: 54762232.0000 - val_mae: 4689.1626 - learning_rate: 0.0010\n", - "Epoch 48/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 43724572.0000 - mae: 4516.4062 - val_loss: 53836544.0000 - val_mae: 4628.6943 - learning_rate: 0.0010\n", - "Epoch 49/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 42422604.0000 - mae: 4467.8540 - val_loss: 52762536.0000 - val_mae: 4572.4634 - learning_rate: 0.0010\n", - "Epoch 50/50\n", - "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 42222404.0000 - mae: 4455.9072 - val_loss: 51867440.0000 - val_mae: 4502.6763 - learning_rate: 0.0010\n" - ] - } - ], - "source": [ - "def get_callbacks():\n", - " early_stop = EarlyStopping(\n", - " monitor=\"val_loss\",\n", - " patience=10, # increased from 5 — prevents premature stopping\n", - " restore_best_weights=True\n", - " )\n", - " reduce_lr = ReduceLROnPlateau(\n", - " monitor=\"val_loss\",\n", - " factor=0.5, # halve LR when stuck\n", - " patience=5,\n", - " min_lr=1e-6,\n", - " verbose=1\n", - " )\n", - " return [early_stop, reduce_lr]\n", - "\n", - "# Train all three models\n", - "history_baseline = model_baseline.fit(\n", - " X_train_seq, y_train_seq,\n", - " validation_data=(X_val_seq, y_val_seq),\n", - " epochs=50, batch_size=64,\n", - " callbacks=get_callbacks(), verbose=1\n", - ")\n", - "\n", - "history_stacked = model_stacked.fit(\n", - " X_train_seq, y_train_seq,\n", - " validation_data=(X_val_seq, y_val_seq),\n", - " epochs=50, batch_size=64,\n", - " callbacks=get_callbacks(), verbose=1\n", - ")\n", - "\n", - "history_gru = model_gru.fit(\n", - " X_train_seq, y_train_seq,\n", - " validation_data=(X_val_seq, y_val_seq),\n", - " epochs=50, batch_size=64,\n", - " callbacks=get_callbacks(), verbose=1\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 10.6 Saving Trained Models\n", - "\n", - "The three trained models are saved to disk immediately after training. This ensures \n", - "that the evaluation results, plots, and metrics produced in subsequent cells are \n", - "fully reproducible on every run without needing to retrain, since loading a saved \n", - "model always produces identical predictions regardless of any residual random seed \n", - "behaviour [58]." - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✓ model_baseline saved\n", - "✓ model_stacked saved\n", - "✓ model_gru saved\n" - ] - } - ], - "source": [ - "# Save all three trained models to disk\n", - "model_baseline.save(\"model_baseline.keras\")\n", - "model_stacked.save(\"model_stacked.keras\")\n", - "model_gru.save(\"model_gru.keras\")\n", - "\n", - "print(\"✓ model_baseline saved\")\n", - "print(\"✓ model_stacked saved\")\n", - "print(\"✓ model_gru saved\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 10.7 Loading Saved Models\n", - "\n", - "The saved models are loaded back from disk before evaluation begins. All subsequent \n", - "evaluation, visualisation, and comparison cells use these loaded models, guaranteeing \n", - "that the results in this notebook are consistent and reproducible on every run \n", - "[58]." - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✓ model_baseline loaded\n", - "✓ model_stacked loaded\n", - "✓ model_gru loaded\n" - ] - } - ], - "source": [ - "# Load all three models from disk\n", - "# This guarantees identical results on every run\n", - "model_baseline = tf.keras.models.load_model(\"model_baseline.keras\")\n", - "model_stacked = tf.keras.models.load_model(\"model_stacked.keras\")\n", - "model_gru = tf.keras.models.load_model(\"model_gru.keras\")\n", - "\n", - "print(\"✓ model_baseline loaded\")\n", - "print(\"✓ model_stacked loaded\")\n", - "print(\"✓ model_gru loaded\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 10.8 Model Evaluation\n", - "\n", - "Each model is evaluated on the held-out test set using three standard regression \n", - "metrics [59]. The test set represents the final unseen time \n", - "period and was not used at any point during training or validation, ensuring the \n", - "evaluation reflects genuine out-of-sample forecasting performance \n", - "[49].\n", - "\n", - "- **MAE (Mean Absolute Error)**: The average absolute difference between predicted \n", - " and actual pedestrian counts. This is the most directly interpretable metric since \n", - " it is expressed in the same unit as the target variable — pedestrians per hour \n", - " [59].\n", - "- **RMSE (Root Mean Squared Error)**: Similar to MAE but penalises larger errors \n", - " more heavily due to the squaring operation. A substantially higher RMSE relative \n", - " to MAE indicates the presence of occasional large prediction errors \n", - " [59].\n", - "- **R² (Coefficient of Determination)**: Measures the proportion of variance in \n", - " pedestrian counts that the model successfully explains. A value of 1.0 represents \n", - " a perfect fit, while 0.0 means the model performs no better than simply predicting \n", - " the mean value [59]." - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m54/54\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n", - "\n", - "Baseline LSTM\n", - " MAE: 5624.15\n", - " RMSE: 8725.10\n", - " R²: 0.9158\n", - "\u001b[1m54/54\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step\n", - "\n", - "Stacked LSTM\n", - " MAE: 9084.27\n", - " RMSE: 14262.18\n", - " R²: 0.7750\n", - "\u001b[1m54/54\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n", - "\n", - "GRU\n", - " MAE: 4921.98\n", - " RMSE: 7325.04\n", - " R²: 0.9407\n" - ] - } - ], - "source": [ - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "\n", - "def evaluate_model(model, X_test, y_test, name=\"Model\"):\n", - " y_pred = model.predict(X_test).flatten()\n", - " mae = mean_absolute_error(y_test, y_pred)\n", - " rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n", - " r2 = r2_score(y_test, y_pred)\n", - " print(f\"\\n{name}\")\n", - " print(f\" MAE: {mae:.2f}\")\n", - " print(f\" RMSE: {rmse:.2f}\")\n", - " print(f\" R²: {r2:.4f}\")\n", - " return y_pred, {\"MAE\": mae, \"RMSE\": rmse, \"R2\": r2}\n", - "\n", - "pred_baseline, metrics_baseline = evaluate_model(model_baseline, X_test_seq, y_test_seq, \"Baseline LSTM\")\n", - "pred_stacked, metrics_stacked = evaluate_model(model_stacked, X_test_seq, y_test_seq, \"Stacked LSTM\")\n", - "pred_gru, metrics_gru = evaluate_model(model_gru, X_test_seq, y_test_seq, \"GRU\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Evaluation Discussion\n", - "\n", - "The GRU model achieved the best performance across all three metrics, with an MAE of \n", - "4,921.98, RMSE of 7,325.04, and an R² of 0.9407. This means the GRU explains 94.1% \n", - "of the variance in hourly pedestrian demand on completely unseen test data, which \n", - "represents strong predictive performance for a real-world urban climate forecasting \n", - "task [59].\n", - "\n", - "Notably, the Stacked LSTM performed worst of the three models despite having the most \n", - "parameters at 35,777. This outcome highlights an important principle in deep learning \n", - "— increased model complexity does not always lead to better generalisation, \n", - "particularly when the dataset size does not justify the added capacity \n", - "[53] [62]. The Baseline LSTM achieved a \n", - "strong R² of 0.9158, confirming that even a single-layer recurrent model can capture \n", - "the majority of the temporal structure in this dataset \n", - "[54]. The GRU's improvement over the baseline demonstrates that \n", - "architectural efficiency can outperform raw model capacity on medium-sized datasets \n", - "[61] [62]." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 10.9 Training Curves\n", - "\n", - "The training and validation loss curves are plotted for each model to assess how \n", - "learning progressed across epochs and to check for any signs of overfitting \n", - "[53].\n", - "\n", - "A well-behaved training curve shows both the training and validation loss decreasing \n", - "together and converging toward a stable low value. If the validation loss begins to \n", - "rise while training loss continues to fall, this indicates overfitting — meaning the \n", - "model is memorising the training data rather than learning generalisable patterns \n", - "[60]." - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n", - "\n", - "for ax, history, name in zip(\n", - " axes,\n", - " [history_baseline, history_stacked, history_gru],\n", - " [\"Baseline LSTM\", \"Stacked LSTM\", \"GRU\"]\n", - "):\n", - " ax.plot(history.history[\"loss\"], label=\"Train Loss\")\n", - " ax.plot(history.history[\"val_loss\"], label=\"Val Loss\")\n", - " ax.set_title(f\"{name} — Training Curves\")\n", - " ax.set_xlabel(\"Epoch\")\n", - " ax.set_ylabel(\"MSE Loss\")\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Training Curve Discussion\n", - "\n", - "All three models demonstrate healthy learning behaviour across the 50 training \n", - "epochs. Both the training and validation loss curves decrease consistently and \n", - "converge without significant divergence, indicating that no meaningful overfitting \n", - "occurred in any of the three models [60]. The Dropout \n", - "regularisation layers and the ReduceLROnPlateau callback both contributed to this \n", - "stable training behaviour \n", - "[60] [63].\n", - "\n", - "The Stacked LSTM validation curve is notably observed to plateau earlier and at a \n", - "higher loss value compared to the Baseline LSTM and GRU, which is consistent with \n", - "its weaker test set performance. The GRU and Baseline LSTM both show smooth \n", - "convergence with training and validation loss tracking closely throughout all 50 \n", - "epochs [54] [61]." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 10.10 Actual vs Predicted Visualisation\n", - "\n", - "To assess prediction quality beyond summary metrics, the actual and predicted \n", - "pedestrian counts are plotted across the first 200 hours of the test set. This \n", - "visualisation reveals how well each model captures the shape, magnitude, and timing \n", - "of the daily pedestrian demand cycle, and whether any systematic prediction errors \n", - "are present [3] [59]." - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(3, 1, figsize=(14, 14))\n", - "\n", - "for ax, preds, name in zip(\n", - " axes,\n", - " [pred_baseline, pred_stacked, pred_gru],\n", - " [\"Baseline LSTM\", \"Stacked LSTM\", \"GRU\"]\n", - "):\n", - " ax.plot(y_test_seq[:200], label=\"Actual\", color=\"black\")\n", - " ax.plot(preds[:200], label=\"Predicted\", color=\"steelblue\", alpha=0.8)\n", - " ax.set_title(f\"{name} — Actual vs Predicted (First 200 Hours)\")\n", - " ax.set_xlabel(\"Hour\")\n", - " ax.set_ylabel(\"Pedestrian Count\")\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Visualisation Discussion\n", - "\n", - "The actual versus predicted plots reveal clear qualitative differences between the \n", - "three models that are consistent with the quantitative evaluation results. The GRU \n", - "most closely follows the actual pedestrian demand curve across the 200-hour window, \n", - "including the sharp daytime peaks during business hours and the near-zero counts \n", - "during the early morning hours \n", - "[61] [62].\n", - "\n", - "The Baseline LSTM captures the general daily rhythm but slightly underestimates the \n", - "magnitude of peak values, producing a smoother curve that misses some of the sharper \n", - "intraday fluctuations [54]. The Stacked LSTM shows the most \n", - "pronounced smoothing effect, with predicted values appearing noticeably flattened at \n", - "daily peaks — capped around 55,000 pedestrians regardless of the actual peak — which \n", - "directly explains its substantially higher RMSE and lower R² relative to the other \n", - "two models [53].\n", - "\n", - "These visual results are fully consistent with the evaluation metrics and further \n", - "support the selection of the GRU as the best performing architecture for this \n", - "pedestrian forecasting task." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 10.11 Model Comparison Summary\n", - "\n", - "The table below consolidates the test set performance metrics for all three models, \n", - "providing a direct side-by-side comparison to support final model selection \n", - "[59] [57]." - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Model MAE RMSE R²\n", - "Baseline LSTM 5624.145508 8725.096217 0.915807\n", - " Stacked LSTM 9084.274414 14262.183844 0.775039\n", - " GRU 4921.983887 7325.042252 0.940659\n" - ] - } - ], - "source": [ - "comparison = pd.DataFrame({\n", - " \"Model\": [\"Baseline LSTM\", \"Stacked LSTM\", \"GRU\"],\n", - " \"MAE\": [metrics_baseline[\"MAE\"], metrics_stacked[\"MAE\"], metrics_gru[\"MAE\"]],\n", - " \"RMSE\": [metrics_baseline[\"RMSE\"], metrics_stacked[\"RMSE\"], metrics_gru[\"RMSE\"]],\n", - " \"R²\": [metrics_baseline[\"R2\"], metrics_stacked[\"R2\"], metrics_gru[\"R2\"]],\n", - "})\n", - "print(comparison.to_string(index=False))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 10.12 Conclusion\n", - "\n", - "The deep learning segment compared three recurrent neural network architectures — a \n", - "Baseline LSTM, a Stacked LSTM, and a GRU — for hourly pedestrian count forecasting \n", - "in Melbourne CBD using climate and engineered time-series features.\n", - "\n", - "The GRU model was identified as the best performing architecture, achieving an R² of \n", - "0.9407, MAE of 4,921.98 pedestrians per hour, and RMSE of 7,325.04 across the unseen \n", - "test set [59] [61]. These results \n", - "demonstrate that a well-designed recurrent model, even without excessive architectural \n", - "complexity, can effectively learn the temporal structure of urban pedestrian demand \n", - "from climate and time-series inputs \n", - "[53] [62].\n", - "\n", - "The finding that the Stacked LSTM underperformed despite having the most parameters \n", - "highlights an important consideration in deep learning model selection — that added \n", - "complexity must be justified by the volume and complexity of the data \n", - "[53] [57]. The GRU's parameter efficiency \n", - "and strong generalisation make it the most suitable architecture for this dataset size \n", - "and forecasting task [61].\n", - "\n", - "From an applied perspective, a model explaining 94.1% of the variance in hourly \n", - "pedestrian demand could realistically support urban planning decisions and help \n", - "commuters anticipate pedestrian congestion during varying climate conditions, directly \n", - "addressing the use case scenario outlined at the beginning of this notebook \n", - "[53] [56]." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "[1] pandas development team (2026) _pandas - Python Data Analysis Library_, pandas, Online.\n", - "\n", - "[2] pandas development team (2026) _Time series / date functionality_, pandas, Online.\n", - "\n", - "[3] Matplotlib Development Team (Unknown date) _Matplotlib: Visualization with Python_, Matplotlib, Online.\n", - "\n", - "[4] scikit-learn Developers (Unknown date) _Preprocessing data_, scikit-learn, Online.\n", - "\n", - "[5] TensorFlow (2023) _Keras: The high-level API for TensorFlow_, Google, Online.\n", - "\n", - "[6] Guido van Rossum, Barry Warsaw and Alyssa Coghlan (2001) _PEP 8 – Style Guide for Python Code_, Python Enhancement Proposals, Online.\n", - "\n", - "[7] TensorFlow (2024) _tf.keras.utils.set_random_seed_, Google, Online.\n", - "\n", - "[8] Opendatasoft (Unknown date) _Huwise's Explore API Reference Documentation_, Opendatasoft, Online.\n", - "\n", - "[9] City of Melbourne (2023) _Pedestrian Counting System - Sensor Locations_, City of Melbourne Open Data Portal, Online.\n", - "\n", - "[10] City of Melbourne (2023) _Pedestrian Counting System: Counts per Hour_, City of Melbourne Open Data Portal, Online.\n", - "\n", - "[11] City of Melbourne (2023) _Microclimate Sensors Data_, City of Melbourne Open Data Portal, Online.\n", - "\n", - "[12] IBM (Unknown date) _What is data profiling?_, IBM, Online.\n", - "\n", - "[13] pandas development team (2026) _pandas.DataFrame.shape_, pandas, Online.\n", - "\n", - "[14] pandas development team (2026) _pandas.DataFrame.columns_, pandas, Online.\n", - "\n", - "[15] pandas development team (2026) _pandas.DataFrame.dtypes_, pandas, Online.\n", - "\n", - "[16] pandas development team (2026) _pandas.DataFrame.info_, pandas, Online.\n", - "\n", - "[17] pandas development team (2026) _pandas.DataFrame.isna_, pandas, Online.\n", - "\n", - "[18] pandas development team (2026) _pandas.DataFrame.describe_, pandas, Online.\n", - "\n", - "[19] pandas development team (2026) _pandas.to_datetime_, pandas, Online.\n", - "\n", - "[20] pandas development team (2026) _pandas.DataFrame.nunique_, pandas, Online.\n", - "\n", - "[21] IBM (Unknown date) _What is Data Cleaning?_, IBM, Online.\n", - "\n", - "[22] Tableau (Unknown date) _Data Cleaning: Definition, Benefits, and How-To_, Tableau, Online.\n", - "\n", - "[23] Max Kuhn and Kjell Johnson (2026) _Feature Selection_, Applied Machine Learning for Tabular Data, Online.\n", - "\n", - "[24] IBM (Unknown date) _What is Feature Selection?_, IBM, Online.\n", - "\n", - "[25] Youran Zhou, Sunil Aryal and Mohamed Reda Bouadjenek (2024) _A Comprehensive Review of Handling Missing Data: Exploring Special Missing Mechanisms_, arXiv, Online.\n", - "\n", - "[26] Cribl (Unknown date) _What is Data Normalization?_, Cribl, Online.\n", - "\n", - "[27] The Epidemiologist R Handbook Contributors (Unknown date) _Working with dates_, The Epidemiologist R Handbook, Online.\n", - "\n", - "[28] Skforecast Team (Unknown date) _Time series aggregation_, Skforecast, Online.\n", - "\n", - "[29] Microsoft Support (Unknown date) _Join tables and queries_, Microsoft, Online.\n", - "\n", - "[30] IBM (Unknown date) _What is Data Integration?_, IBM, Online.\n", - "\n", - "[31] RudderStack (Unknown date) _What Is Data Validation? Why, When, and How To Use It_, RudderStack, Online.\n", - "\n", - "[32] Google Cloud (Unknown date) _What is Time Series?_, Google, Online.\n", - "\n", - "[33] Ali Suliman AlSalehy and Mike Bailey (2025) _Improving Time Series Data Quality: Identifying Outliers and Handling Missing Values in a Multilocation Gas and Weather Dataset_, MDPI, Basel, Switzerland.\n", - "\n", - "[34] NIST/SEMATECH (Unknown date) _What is EDA?_, NIST/SEMATECH e-Handbook of Statistical Methods, Online.\n", - "\n", - "[35] Rob J. Hyndman and George Athanasopoulos (2018) _Time series patterns_, OTexts, Melbourne, Australia.\n", - "\n", - "[36] NIST/SEMATECH (Unknown date) _Introduction to Time Series Analysis_, NIST/SEMATECH e-Handbook of Statistical Methods, Online.\n", - "\n", - "[37] NIST/SEMATECH (Unknown date) _Seasonality_, NIST/SEMATECH e-Handbook of Statistical Methods, Online.\n", - "\n", - "[38] NIST/SEMATECH (Unknown date) _Histogram_, NIST/SEMATECH e-Handbook of Statistical Methods, Online.\n", - "\n", - "[39] Penn State Eberly College of Science (Unknown date) _3.4.2 - Correlation_, The Pennsylvania State University, Online.\n", - "\n", - "[40] Jim Frost (Unknown date) _Using Moving Averages to Smooth Time Series Data_, Statistics By Jim, Online.\n", - "\n", - "[41] Josef Waples and Laiba Siddiqui (Unknown date) _Time Series Decomposition: Trends, Seasonality, and Noise_, DataCamp, Online.\n", - "\n", - "[42] Penn State Eberly College of Science (Unknown date) _10.2 - Autocorrelation and Time Series Methods_, The Pennsylvania State University, Online.\n", - "\n", - "[43] Minitab (Unknown date) _Interpret all statistics and graphs for Augmented Dickey-Fuller Test_, Minitab Support, Online.\n", - "\n", - "[44] IBM (Unknown date) _What is Feature Engineering?_, IBM, Online.\n", - "\n", - "[45] dotData (Unknown date) _Practical Guide for Feature Engineering of Time Series Data_, dotData, Online.\n", - "\n", - "[46] Skforecast Team (Unknown date) _Cyclical features in time series_, Skforecast, Online.\n", - "\n", - "[47] Feature-engine Developers (Unknown date) _Forecasting Features_, Feature-engine, Online.\n", - "\n", - "[48] Feature-engine Developers (Unknown date) _LagFeatures_, Feature-engine, Online.\n", - "\n", - "[49] ApX Machine Learning (2026) _Train-Test Split for Time Series_, ApX Machine Learning, Online.\n", - "\n", - "[50] Pragati Baheti (2021) _Train Test Validation Split: How To and Best Practices_, V7 Labs, Online.\n", - "\n", - "[51] IBM (Unknown date) _What is Supervised Learning?_, IBM, Online.\n", - "\n", - "[52] ApX Machine Learning (2026) _Feature Scaling: Normalization and Standardization_, ApX Machine Learning, Online.\n", - "\n", - "[53] IBM (Unknown date) _What is Deep Learning?_, IBM, Online.\n", - "\n", - "[54] Christopher Olah (2015) _Understanding LSTM Networks_, Colah's Blog, Online.\n", - "\n", - "[55] TensorFlow (2024) _tf.keras.utils.set_random_seed_, Google, Online.\n", - "\n", - "[56] TensorFlow (2023) _Time series forecasting_, TensorFlow, Online.\n", - "\n", - "[57] ApX Machine Learning (2026) _Model Selection and Evaluation_, ApX Machine Learning, Online.\n", - "\n", - "[58] TensorFlow (2023) _tf.keras.Model.fit_, TensorFlow, Online.\n", - "\n", - "[59] scikit-learn Developers (Unknown date) _Regression metrics_, scikit-learn, Online.\n", - "\n", - "[60] Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever and Ruslan Salakhutdinov (2014) _Dropout: A Simple Way to Prevent Neural Networks from Overfitting_, Journal of Machine Learning Research, Online.\n", - "\n", - "[61] Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk and Yoshua Bengio (2014) _Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation_, arXiv,Online.\n", - "\n", - "[62] Junyoung Chung, Caglar Gulcehre, KyungHyun Cho and Yoshua Bengio (2014) _Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling_, arXiv, Online.\n", - "\n", - "[63] TensorFlow (2023) _tf.keras.callbacks.EarlyStopping_, TensorFlow, Online." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.12.13" - }, - "vscode": { - "interpreter": { - "hash": "369f2c481f4da34e4445cda3fffd2e751bd1c4d706f27375911949ba6bb62e1c" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.json b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.json deleted file mode 100644 index 45af0af6d6..0000000000 --- a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.json +++ /dev/null @@ -1,66 +0,0 @@ -{ - "title": "Urban_Pedestrian_Climate_Impact_Prediction", - "name": "Predicting Pedestrian Activity in Melbourne CBD Based on Climate Conditions and Time Series Data", - "description": "Predicting pedestrian activity in the City of Melbourne using hourly climate observations. This use case involves sourcing and combining multiple public datasets, cleaning and aligning hourly time-series data, exploring how climate variables relate to pedestrian counts, applying feature engineering, building a deep learning forecasting model, performing model optimisation, and evaluating model performance for climate adaptation planning.", - "tags": [ - "Urban Pedestrian Movement", - "Climate Conditions", - "Pedestrian Counts", - "Microclimate Observations", - "Time-Series Analysis", - "Feature Engineering", - "Deep Learning Forecasting", - "Model Optimisation", - "Data Cleaning", - "Data Validation", - "Data Visualisation" - ], - "difficulty": "Intermediate", - "technology": [ - { - "name": "numpy", - "alias": "np", - "code": "python" - }, - { - "name": "pandas", - "alias": "pd", - "code": "python" - }, - { - "name": "matplotlib", - "alias": "plt", - "code": "python" - }, - { - "name": "statsmodels", - "code": "python" - }, - { - "name": "sklearn", - "code": "python" - }, - { - "name": "os", - "code": "python" - }, - { - "name": "random", - "code": "python" - }, - { - "name": "tensorflow", - "alias": "tf", - "code": "python" - }, - { - "name": "tensorflow.keras", - "code": "python" - } - ], - "datasets": [ - "Pedestrian Counting System (counts per hour)", - "Pedestrian Counting System - Sensor Locations", - "Microclimate Sensor Readings" - ] -} From 3f79677d8cfd6caedd582f093afa96ab5633656c Mon Sep 17 00:00:00 2001 From: Thivainv Date: Sun, 17 May 2026 20:26:42 +1000 Subject: [PATCH 17/23] Create test.txt --- .../READY TO PUBLISH/Transport_and_Mobility/2026/test.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/test.txt diff --git a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/test.txt b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/test.txt new file mode 100644 index 0000000000..8b13789179 --- /dev/null +++ b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/test.txt @@ -0,0 +1 @@ + From 38ed5d37e85974cdd9354cad9e3ba7c6b6fb063e Mon Sep 17 00:00:00 2001 From: Thivainv Date: Sun, 17 May 2026 20:27:15 +1000 Subject: [PATCH 18/23] Create test.txt --- .../READY TO PUBLISH/Transport_and_Mobility/2026/T1/test.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/test.txt diff --git a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/test.txt b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/test.txt new file mode 100644 index 0000000000..8b13789179 --- /dev/null +++ b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/test.txt @@ -0,0 +1 @@ + From 6f8e64d871e5c221fa1ae70fb5edf87288ee5fb9 Mon Sep 17 00:00:00 2001 From: Thivainv Date: Sun, 17 May 2026 20:28:37 +1000 Subject: [PATCH 19/23] Create test.txt --- .../UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt diff --git a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt new file mode 100644 index 0000000000..8b13789179 --- /dev/null +++ b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt @@ -0,0 +1 @@ + From 20c7b80f330100e8174fe1fee845f2106be1e4ed Mon Sep 17 00:00:00 2001 From: Thivainv Date: Sun, 17 May 2026 20:32:22 +1000 Subject: [PATCH 20/23] added final UC00213 urban pedestrian climate impact prediction files Commit directly to the Thivain_t1_26 branch --- ..._Pedestrian_Climate_Impact_Prediction.html | 12507 ++++++++++++++++ ...Pedestrian_Climate_Impact_Prediction.ipynb | 4553 ++++++ ..._Pedestrian_Climate_Impact_Prediction.json | 66 + 3 files changed, 17126 insertions(+) create mode 100644 datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.html create mode 100644 datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.ipynb create mode 100644 datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.json diff --git a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.html b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.html new file mode 100644 index 0000000000..48dcd0e6f1 --- /dev/null +++ b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.html @@ -0,0 +1,12507 @@ + + + + + +UC00213_Urban_Pedestrian_Climate_Impact_Prediction + + + + + + + + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + diff --git a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.ipynb b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.ipynb new file mode 100644 index 0000000000..0a97449dd4 --- /dev/null +++ b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.ipynb @@ -0,0 +1,4553 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
Urban Pedestrian Climate Impact Prediction
\n", + "\n", + "
Authored by: Maverick Nguyen
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
Duration: 90 mins
\n", + "\n", + "
\n", + "
Level: Intermediate
\n", + "
Pre-requisite Skills: Python, Data Cleaning, Data Validation, Data Visualisation, Time-Series Analysis, Feature Engineering, Optimisation Methods, Deep Learning
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
Scenario
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a local living near Melbourne CBD, Maverick relies on active travel, like walking, and public transport, like trams, to get around to different places he wants to go. One morning in January, Maverick prepared to travel to his workplace, expecting to get to work before 9:00 AM, but there was a sudden heatwave, causing the tram that he usually catches to be unable to follow its designated schedule and creating a delay in his schedule. Although there was a replacement bus for this emergency, only a limited number of people could board this vehicle, which further delayed his schedule. Because of this sudden extreme weather, travel conditions become less reliable and difficult to predict.\n", + "\n", + "Maverick wants to have access to a system that could predict how climate conditions over time can affect urban pedestrian movement. So that he could better plan his trip, allowing him to leave earlier in anticipation of sudden extreme weather change during a particular timeframe, or choose a different mode of transport, like an Uber. This allows more support in making informed decisions when travelling during extreme weather events." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
What this use case will teach you
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At the end of this use case, you will:\n", + "- Learn how to source and combine multiple public datasets. \n", + "- Understand how to clean and align time-series data at an hourly level for modelling. \n", + "- Explore how climate variables, such as temperature, humidity, pressure, and wind, relate to pedestrian counts.\n", + "- Apply feature engineering techniques to create meaningful predictors from weather and mobility time-series data. \n", + "- Build a deep learning forecasting model to predict pedestrian demand. \n", + "- Perform model optimisation like hyperparameter tuning to improve forecasting performance. \n", + "- Evaluate model performance and interpret results for climate adaptation planning." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
Introduction
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Urban systems are often affected by changing climate conditions, but these effects are not always easy to capture with simple forecasting methods. One clear example is pedestrian movement, where changing weather conditions can affect how many people move through the city over time.\n", + "\n", + "This use case focuses on predicting pedestrian activity in the City of Melbourne using hourly climate observations, which keeps the project closely aligned with the goal of modelling how climate factors influence an urban system.\n", + "\n", + "In this use case, pedestrian counts are aggregated into hourly city-level totals and merged with hourly microclimate observations for Melbourne. A deep learning model can then be trained to predict pedestrian demand based on time, recent demand history, and recent climate conditions.\n", + "\n", + "The datasets used in this project are the \"Pedestrian Counting System (counts per hour)\", the \"Pedestrian Counting System - Sensor Locations\" dataset for supporting location metadata, and the \"Microclimate Sensor Readings\" dataset from the City of Melbourne website." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Importing The Libraries\n", + "\n", + "This section is to show what libraries were used for this use case, with each imported library supporting a specific part of the pipeline. These libraries are necessary for doing data handling, time-series analysis, visualisation, feature engineering, optimisation, and deep learning [1] [2] [3] [4] [5]. These were added at the beginning to ensure the workflow is organised [6]. A random seed was set with a student ID to allow reproducibility of the outputs in this notebook [7].\n", + "\n", + "The following libraries support the deep learning pipeline for this section. TensorFlow and Keras are used for building and training the recurrent neural network models [3]. The os and random libraries are imported to support full reproducibility across all system-level random operations [55]." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "# Libraries for this project.\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.lines import Line2D\n", + "from matplotlib.patches import Patch\n", + "from statsmodels.graphics.tsaplots import plot_acf\n", + "from statsmodels.tsa.seasonal import seasonal_decompose\n", + "from statsmodels.tsa.stattools import adfuller\n", + "from sklearn.preprocessing import StandardScaler\n", + "import os\n", + "import random\n", + "\n", + "# Deep Learning\n", + "import tensorflow as tf\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import LSTM, GRU, Dense, Dropout\n", + "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Importing The Datasets\n", + "\n", + "This section is necessary for importing multiple public datasets from the City of Melbourne, before any cleaning, merging and modelling in later stages can happen. These datasets will be accessed through the City of Melbourne Open Data API v2.1, to allow the notebook to be directly used upon download [8].\n", + "\n", + "By using a shared BASE_URL and a dictionary of dataset identifiers, this allows for removing and adding datasets more easily [8] [9] [10] [11]. The get_csv_url() function is especially useful because it standardises the dataset access method and allows the same logic to be reused across all three datasets [8]. The ROW_LIMIT parameter was added to allow experimentation on smaller samples before scaling to the full dataset [8]. Lastly, the datasets are accessed through the Melbourne Open Data API v2.1, and no visible API key is exposed in the code [8]." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "# Store the API base path accessed via API v2.1.\n", + "BASE_URL = (\n", + " \"https://data.melbourne.vic.gov.au\"\n", + " \"/api/explore/v2.1/catalog/datasets\"\n", + ")\n", + "\n", + "# Store the dataset identifiers.\n", + "DATASETS = {\n", + " \"sensor_locations\":\n", + " \"pedestrian-counting-system-sensor-locations\",\n", + " \"pedestrian_counts\":\n", + " \"pedestrian-counting-system-monthly-counts-per-hour\",\n", + " \"microclimate\":\n", + " \"microclimate-sensors-data\",\n", + "}\n", + "\n", + "# Set the number of rows to retrieve for experiments.\n", + "# Change to \"None\" when full datasets are needed.\n", + "# Change to an integer for experimental purposes.\n", + "ROW_LIMIT = None \n", + "\n", + "def get_csv_url(dataset_id, row_limit=None):\n", + " # Build the base CSV export URL.\n", + " url = (\n", + " f\"{BASE_URL}/{dataset_id}/exports/csv\"\n", + " f\"?delimiter=,&with_bom=true\"\n", + " )\n", + "\n", + " # Add a row limit if one is provided.\n", + " if row_limit is not None:\n", + " url += f\"&limit={row_limit}\"\n", + "\n", + " return url" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Pedestrian Counting System - Sensor Locations\n", + "\n", + "This dataset was included to provide contextual information about the physical pedestrian counting network, even though it wasn't used in the later steps for modelling. The sensor metadata helps explain where the mobility data originates from and what the coverage of the system looks like [9].\n", + "\n", + "* `location_id`: unique identifier for each pedestrian counting location.\n", + "* `sensor_description`: human-readable description of the site, like street names.\n", + "* `sensor_name`: short internal sensor code.\n", + "* `installation_date`: date the sensor was installed.\n", + "* `note`: extra comments or metadata about the sensor.\n", + "* `location_type`: type of location.\n", + "* `status`: operational status of the sensor.\n", + "* `direction_1` and `direction_2`: the two movement directions captured by the counter.\n", + "* `latitude` and `longitude`: geographic coordinates of the sensor.\n", + "* `location`: combined coordinate string." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " location_id sensor_description sensor_name installation_date \\\n", + "0 3 Melbourne Central Swa295_T 2009-03-25 \n", + "1 5 Princes Bridge PriNW_T 2009-03-26 \n", + "2 9 Southern Cross Station Col700_T 2009-03-23 \n", + "3 12 New Quay NewQ_T 2009-01-21 \n", + "4 14 Sandridge Bridge SanBri_T 2009-03-24 \n", + "\n", + " note location_type status \\\n", + "0 NaN Outdoor A \n", + "1 Replace with: 00:6e:02:01:9e:54 Outdoor A \n", + "2 NaN Outdoor A \n", + "3 NaN Outdoor A \n", + "4 Sensor relocated to sensor ID 25 on 2/10/2019 Outdoor A \n", + "\n", + " direction_1 direction_2 latitude longitude location \n", + "0 North South -37.811015 144.964295 -37.81101524, 144.96429485 \n", + "1 North South -37.818742 144.967877 -37.81874249, 144.96787656 \n", + "2 East West -37.819830 144.951026 -37.81982992, 144.95102555 \n", + "3 East West -37.814580 144.942924 -37.81457988, 144.94292398 \n", + "4 North South -37.820112 144.962919 -37.82011242, 144.96291897 \n" + ] + } + ], + "source": [ + "# Load the sensor locations dataset.\n", + "sensor_locations_df = pd.read_csv(\n", + " get_csv_url(\n", + " DATASETS[\"sensor_locations\"],\n", + " row_limit=ROW_LIMIT\n", + " ),\n", + " encoding=\"utf-8-sig\"\n", + ")\n", + "\n", + "# Preview the dataframe.\n", + "print(sensor_locations_df.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Pedestrian Counting System (counts per hour)\n", + "\n", + "This is the target dataset for the use case because the final prediction task is to forecast pedestrian demand. The pedestrian dataset contains the observed mobility outcome that the model aims to learn [10].\n", + "\n", + "* `id`: record identifier.\n", + "* `location_id`: identifier linking the observation to a specific sensor location.\n", + "* `sensing_date`: date of observation.\n", + "* `hourday`: hour of day from 0 to 23.\n", + "* `direction_1` and `direction_2`: directional pedestrian counts.\n", + "* `pedestriancount`: total pedestrian count for that record.\n", + "* `sensor_name`: short sensor code.\n", + "* `location`: coordinate string for the sensor. " + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " id location_id sensing_date hourday direction_1 direction_2 \\\n", + "0 70220251008 70 2025-10-08 2 1 0 \n", + "1 31220260507 3 2026-05-07 12 872 1170 \n", + "2 591520241018 59 2024-10-18 15 430 628 \n", + "3 141020260106 14 2026-01-06 10 240 75 \n", + "4 28820241203 28 2024-12-03 8 537 262 \n", + "\n", + " pedestriancount sensor_name location \n", + "0 1 Errol20_T -37.80456984, 144.94946228 \n", + "1 2042 Swa295_T -37.81101524, 144.96429485 \n", + "2 1058 RMIT_T -37.80825648, 144.96304859 \n", + "3 315 SanBri_T -37.82011242, 144.96291897 \n", + "4 799 VAC_T -37.82129925, 144.96879309 \n" + ] + } + ], + "source": [ + "# Load the pedestrian counts dataset.\n", + "pedestrian_counts_df = pd.read_csv(\n", + " get_csv_url(\n", + " DATASETS[\"pedestrian_counts\"],\n", + " row_limit=ROW_LIMIT\n", + " ),\n", + " encoding=\"utf-8-sig\"\n", + ")\n", + "\n", + "# Preview the dataframe.\n", + "print(pedestrian_counts_df.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Microclimate sensors data\n", + "\n", + "This dataset provides the microclimate data that the use case requires to understand how climate conditions affect urban pedestrian movement, more specifically, the input features. It basically provides the explanatory environmental variables to connect weather conditions to mobility demand [11].\n", + "\n", + "* `device_id`: identifier for each microclimate device.\n", + "* `received_at`: timestamp when the reading was recorded.\n", + "* `sensorlocation`: descriptive location of the microclimate sensor.\n", + "* `latlong`: coordinate string.\n", + "* `minimumwinddirection`, `averagewinddirection`, `maximumwinddirection`: wind direction measurements.\n", + "* `minimumwindspeed`, `averagewindspeed`, `gustwindspeed`: wind speed measurements.\n", + "* `airtemperature`: air temperature reading.\n", + "* `relativehumidity`: humidity level.\n", + "* `atmosphericpressure`: atmospheric pressure.\n", + "* `pm25` and `pm10`: particulate matter measurements.\n", + "* `noise`: noise level. " + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " device_id received_at \\\n", + "0 ICTMicroclimate-08 2026-04-28T15:25:15+00:00 \n", + "1 ICTMicroclimate-08 2026-04-28T04:38:50+00:00 \n", + "2 ICTMicroclimate-08 2026-04-28T05:08:52+00:00 \n", + "3 ICTMicroclimate-10 2026-04-28T10:19:41+00:00 \n", + "4 ICTMicroclimate-08 2026-04-28T13:55:03+00:00 \n", + "\n", + " sensorlocation \\\n", + "0 Swanston St - Tram Stop 13 adjacent Federation... \n", + "1 Swanston St - Tram Stop 13 adjacent Federation... \n", + "2 Swanston St - Tram Stop 13 adjacent Federation... \n", + "3 1 Treasury Place \n", + "4 Swanston St - Tram Stop 13 adjacent Federation... \n", + "\n", + " latlong minimumwinddirection averagewinddirection \\\n", + "0 -37.8184515, 144.9678474 0.0 220.0 \n", + "1 -37.8184515, 144.9678474 0.0 283.0 \n", + "2 -37.8184515, 144.9678474 0.0 310.0 \n", + "3 -37.8128595, 144.9745395 277.0 324.0 \n", + "4 -37.8184515, 144.9678474 0.0 281.0 \n", + "\n", + " maximumwinddirection minimumwindspeed averagewindspeed gustwindspeed \\\n", + "0 314.0 0.0 0.5 2.5 \n", + "1 353.0 0.0 0.9 2.6 \n", + "2 314.0 0.0 0.2 1.9 \n", + "3 352.0 0.3 0.8 1.3 \n", + "4 354.0 0.0 0.3 2.5 \n", + "\n", + " airtemperature relativehumidity atmosphericpressure pm25 pm10 noise \n", + "0 16.7 79.9 1026.1 10.0 12.0 58.7 \n", + "1 21.9 61.5 1021.5 27.0 32.0 75.6 \n", + "2 21.6 61.1 1021.3 26.0 29.0 69.2 \n", + "3 19.7 57.4 1018.4 39.0 49.0 61.9 \n", + "4 17.3 76.2 1025.5 10.0 12.0 79.4 \n" + ] + } + ], + "source": [ + "# Load the microclimate dataset.\n", + "microclimate_df = pd.read_csv(\n", + " get_csv_url(\n", + " DATASETS[\"microclimate\"],\n", + " row_limit=ROW_LIMIT\n", + " ),\n", + " encoding=\"utf-8-sig\"\n", + ")\n", + "\n", + "# Preview the dataframe.\n", + "print(microclimate_df.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Initial Inspection Of The Datasets\n", + "\n", + "This section is necessary to understand the size, structure, completeness and formatting of public datasets since they often differ. Public datasets can have different structures, missing values, data types, date formats, and identifier systems, so checking them early helps identify potential issues in the workflow [12]. Before trying to clean and merge the datasets, understanding what each of the datasets contains and how they can be combined is important [12]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 Checking Number Of Rows/Columns\n", + "\n", + "Checking the shape is important because it shows the scale and complexity of each dataset. This helps determine memory demands, cleaning strategy, and whether the data volume is sufficient for later modelling [13].\n", + "\n", + "From these results, the pedestrian and microclimate datasets are large enough for later time-series modelling. The sensor locations dataset is much smaller because it only contains metadata about the sensor network, rather than repeated hourly observations. The volume for each task varies, but from the inspection of the shapes, there appears to be sufficient volume for the task of predicting the pedestrian count." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(145, 12)\n", + "(1540629, 9)\n", + "(897085, 16)\n" + ] + } + ], + "source": [ + "# Preview of the number of columns and rows.\n", + "print(sensor_locations_df.shape)\n", + "print(pedestrian_counts_df.shape)\n", + "print(microclimate_df.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 Checking The Features\n", + "\n", + "This step is to verify what information is actually available in each dataset, and whether there are meaningful fields for later joining, cleaning, and modelling. Knowing the columns early prevents accidentally removing important variables or keeping irrelevant variables [14].\n", + "\n", + "From the output, the column names confirm that location_id is shared between the sensor metadata and pedestrian counts datasets. Also, the pedestrian and microclimate datasets both contain time information, which is essential because the final merge is ultimately done at the hourly level. The microclimate dataset clearly offers a diverse range of explanatory variables, which is useful for modelling. And, some columns that are likely less useful for the final model need to be removed, such as descriptive notes, raw coordinate strings, and duplicate directional fields." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sensor locations columns:\n", + "Index(['location_id', 'sensor_description', 'sensor_name', 'installation_date',\n", + " 'note', 'location_type', 'status', 'direction_1', 'direction_2',\n", + " 'latitude', 'longitude', 'location'],\n", + " dtype='object')\n", + "\n", + "Pedestrian counts columns:\n", + "Index(['id', 'location_id', 'sensing_date', 'hourday', 'direction_1',\n", + " 'direction_2', 'pedestriancount', 'sensor_name', 'location'],\n", + " dtype='object')\n", + "\n", + "Microclimate columns:\n", + "Index(['device_id', 'received_at', 'sensorlocation', 'latlong',\n", + " 'minimumwinddirection', 'averagewinddirection', 'maximumwinddirection',\n", + " 'minimumwindspeed', 'averagewindspeed', 'gustwindspeed',\n", + " 'airtemperature', 'relativehumidity', 'atmosphericpressure', 'pm25',\n", + " 'pm10', 'noise'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "# Check the column names for each dataframe.\n", + "print(\"Sensor locations columns:\")\n", + "print(sensor_locations_df.columns)\n", + "\n", + "print(\"\\nPedestrian counts columns:\")\n", + "print(pedestrian_counts_df.columns)\n", + "\n", + "print(\"\\nMicroclimate columns:\")\n", + "print(microclimate_df.columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.3 Checking The Datatypes\n", + "\n", + "Datatype checking is essential because many later operations depend on correct types to proceed. Steps like date parsing, numeric aggregation, interpolation, rolling windows, and model preparation can all fail or behave incorrectly if types are wrong [15].\n", + "\n", + "From the outputs, the pedestrian sensing_date, sensor installation_date, and microclimate received_at fields are all initially stored as objects, so they are not yet ready for time-series operations, which will need to be dealt with. And the numeric climate variables are already in float64, and pedestrian counts are in int64, which is appropriate for aggregation and modelling, so no need to change that." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sensor locations data types:\n", + "location_id int64\n", + "sensor_description object\n", + "sensor_name object\n", + "installation_date object\n", + "note object\n", + "location_type object\n", + "status object\n", + "direction_1 object\n", + "direction_2 object\n", + "latitude float64\n", + "longitude float64\n", + "location object\n", + "dtype: object\n", + "\n", + "Pedestrian counts data types:\n", + "id int64\n", + "location_id int64\n", + "sensing_date object\n", + "hourday int64\n", + "direction_1 int64\n", + "direction_2 int64\n", + "pedestriancount int64\n", + "sensor_name object\n", + "location object\n", + "dtype: object\n", + "\n", + "Microclimate data types:\n", + "device_id object\n", + "received_at object\n", + "sensorlocation object\n", + "latlong object\n", + "minimumwinddirection float64\n", + "averagewinddirection float64\n", + "maximumwinddirection float64\n", + "minimumwindspeed float64\n", + "averagewindspeed float64\n", + "gustwindspeed float64\n", + "airtemperature float64\n", + "relativehumidity float64\n", + "atmosphericpressure float64\n", + "pm25 float64\n", + "pm10 float64\n", + "noise float64\n", + "dtype: object\n" + ] + } + ], + "source": [ + "# Check the data types for each dataframe.\n", + "print(\"Sensor locations data types:\")\n", + "print(sensor_locations_df.dtypes)\n", + "\n", + "print(\"\\nPedestrian counts data types:\")\n", + "print(pedestrian_counts_df.dtypes)\n", + "\n", + "print(\"\\nMicroclimate data types:\")\n", + "print(microclimate_df.dtypes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.4 Checking The Dataset Information\n", + "\n", + "Using .info() gives a more complete structural summary of the datasets, which shows non-null counts, memory usage, and dtype balance. But this step will focus more on the memory that will be used up in the RAM, to decide what sample size would be ideal for experimentation before scaling to the full dataset size, or opt for a platform like Google Colab to handle more heavy usage [16].\n", + "\n", + "From the outputs, the pedestrian dataset takes up the most memory, followed by the microclimate dataset, with the sensor locations dataset being extremely low. Which is acceptable to run locally for modelling." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sensor locations info:\n", + "\n", + "RangeIndex: 145 entries, 0 to 144\n", + "Data columns (total 12 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 location_id 145 non-null int64 \n", + " 1 sensor_description 145 non-null object \n", + " 2 sensor_name 145 non-null object \n", + " 3 installation_date 145 non-null object \n", + " 4 note 34 non-null object \n", + " 5 location_type 145 non-null object \n", + " 6 status 145 non-null object \n", + " 7 direction_1 113 non-null object \n", + " 8 direction_2 113 non-null object \n", + " 9 latitude 145 non-null float64\n", + " 10 longitude 145 non-null float64\n", + " 11 location 145 non-null object \n", + "dtypes: float64(2), int64(1), object(9)\n", + "memory usage: 13.7+ KB\n", + "None\n", + "\n", + "Pedestrian counts info:\n", + "\n", + "RangeIndex: 1540629 entries, 0 to 1540628\n", + "Data columns (total 9 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 1540629 non-null int64 \n", + " 1 location_id 1540629 non-null int64 \n", + " 2 sensing_date 1540629 non-null object\n", + " 3 hourday 1540629 non-null int64 \n", + " 4 direction_1 1540629 non-null int64 \n", + " 5 direction_2 1540629 non-null int64 \n", + " 6 pedestriancount 1540629 non-null int64 \n", + " 7 sensor_name 1540629 non-null object\n", + " 8 location 1540629 non-null object\n", + "dtypes: int64(6), object(3)\n", + "memory usage: 105.8+ MB\n", + "None\n", + "\n", + "Microclimate info:\n", + "\n", + "RangeIndex: 897085 entries, 0 to 897084\n", + "Data columns (total 16 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 device_id 897085 non-null object \n", + " 1 received_at 897085 non-null object \n", + " 2 sensorlocation 897085 non-null object \n", + " 3 latlong 897085 non-null object \n", + " 4 minimumwinddirection 763016 non-null float64\n", + " 5 averagewinddirection 881374 non-null float64\n", + " 6 maximumwinddirection 763006 non-null float64\n", + " 7 minimumwindspeed 763006 non-null float64\n", + " 8 averagewindspeed 881372 non-null float64\n", + " 9 gustwindspeed 763006 non-null float64\n", + " 10 airtemperature 881372 non-null float64\n", + " 11 relativehumidity 881372 non-null float64\n", + " 12 atmosphericpressure 881372 non-null float64\n", + " 13 pm25 823822 non-null float64\n", + " 14 pm10 823822 non-null float64\n", + " 15 noise 823822 non-null float64\n", + "dtypes: float64(12), object(4)\n", + "memory usage: 109.5+ MB\n", + "None\n" + ] + } + ], + "source": [ + "# Check the overall structure of each dataframe.\n", + "print(\"Sensor locations info:\")\n", + "print(sensor_locations_df.info())\n", + "\n", + "print(\"\\nPedestrian counts info:\")\n", + "print(pedestrian_counts_df.info())\n", + "\n", + "print(\"\\nMicroclimate info:\")\n", + "print(microclimate_df.info())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.5 Checking For Missing Values\n", + "\n", + "Checking for missing values is important, since it affects the cleaning strategy, feature selection, and merge quality. This is especially important because missing sensor readings can break hourly continuity for the modelling later [17].\n", + "\n", + "From the outputs, the pedestrian counts dataset has no missing values at all. The microclimate dataset has many missing values in all the variables besides device_id and received_at. And the sensor locations dataset has some missing values, mainly in note, direction_1, and direction_2, which are not necessary for city-level forecasting." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Missing values in sensor locations:\n", + "location_id 0\n", + "sensor_description 0\n", + "sensor_name 0\n", + "installation_date 0\n", + "note 111\n", + "location_type 0\n", + "status 0\n", + "direction_1 32\n", + "direction_2 32\n", + "latitude 0\n", + "longitude 0\n", + "location 0\n", + "dtype: int64\n", + "\n", + "Missing values in pedestrian counts:\n", + "id 0\n", + "location_id 0\n", + "sensing_date 0\n", + "hourday 0\n", + "direction_1 0\n", + "direction_2 0\n", + "pedestriancount 0\n", + "sensor_name 0\n", + "location 0\n", + "dtype: int64\n", + "\n", + "Missing values in microclimate:\n", + "device_id 0\n", + "received_at 0\n", + "sensorlocation 0\n", + "latlong 0\n", + "minimumwinddirection 134069\n", + "averagewinddirection 15711\n", + "maximumwinddirection 134079\n", + "minimumwindspeed 134079\n", + "averagewindspeed 15713\n", + "gustwindspeed 134079\n", + "airtemperature 15713\n", + "relativehumidity 15713\n", + "atmosphericpressure 15713\n", + "pm25 73263\n", + "pm10 73263\n", + "noise 73263\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "# Count the missing values in each dataframe.\n", + "print(\"Missing values in sensor locations:\")\n", + "print(sensor_locations_df.isna().sum())\n", + "\n", + "print(\"\\nMissing values in pedestrian counts:\")\n", + "print(pedestrian_counts_df.isna().sum())\n", + "\n", + "print(\"\\nMissing values in microclimate:\")\n", + "print(microclimate_df.isna().sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.6 Checking Summary Statistics\n", + "\n", + "Checking the summary statistics is an easy way to provide some early insights into the datasets, like central tendency and spread [18].\n", + "\n", + "From the sensor coordinates with the mean latitude and longitude, the Melbourne CBD can be inferred to be the main area of focus for the datasets. The pedestrian counts are strongly right-skewed, with the median count being a fair bit lower than the mean, and the maximum being really high, which suggests that some hours and sites are much busier than others. The average microclimate temperature is about 16°C, and the average relative humidity is about 66%, which looks plausible for Melbourne across a long time range." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sensor locations summary:\n", + " location_id latitude longitude\n", + "count 145.000000 145.000000 145.000000\n", + "mean 93.075862 -37.812392 144.960427\n", + "std 55.229079 0.006999 0.009594\n", + "min 1.000000 -37.825910 144.928606\n", + "25% 47.000000 -37.816888 144.956447\n", + "50% 89.000000 -37.813625 144.961860\n", + "75% 142.000000 -37.807767 144.965626\n", + "max 188.000000 -37.789353 144.986388\n", + "\n", + "Pedestrian counts summary:\n", + " id location_id hourday direction_1 direction_2 \\\n", + "count 1.540629e+06 1.540629e+06 1.540629e+06 1.540629e+06 1.540629e+06 \n", + "mean 4.683120e+11 7.196742e+01 1.175280e+01 1.981603e+02 2.008987e+02 \n", + "std 5.137983e+11 5.119167e+01 6.796006e+00 3.107706e+02 3.148589e+02 \n", + "min 1.020241e+09 1.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "25% 6.292025e+10 3.000000e+01 6.000000e+00 1.800000e+01 1.900000e+01 \n", + "50% 2.111203e+11 6.100000e+01 1.200000e+01 7.900000e+01 7.800000e+01 \n", + "75% 7.116202e+11 1.170000e+02 1.800000e+01 2.360000e+02 2.410000e+02 \n", + "max 1.852320e+12 1.850000e+02 2.300000e+01 1.009900e+04 1.108500e+04 \n", + "\n", + " pedestriancount \n", + "count 1.540629e+06 \n", + "mean 3.990590e+02 \n", + "std 5.953562e+02 \n", + "min 0.000000e+00 \n", + "25% 3.900000e+01 \n", + "50% 1.620000e+02 \n", + "75% 4.930000e+02 \n", + "max 1.111300e+04 \n", + "\n", + "Microclimate summary:\n", + " minimumwinddirection averagewinddirection maximumwinddirection \\\n", + "count 763016.000000 881374.000000 763006.000000 \n", + "mean 23.908174 166.489727 295.974746 \n", + "std 61.529961 125.699058 97.279991 \n", + "min 0.000000 0.000000 0.000000 \n", + "25% 0.000000 42.000000 253.000000 \n", + "50% 0.000000 161.000000 351.000000 \n", + "75% 0.000000 299.000000 357.000000 \n", + "max 359.000000 359.000000 360.000000 \n", + "\n", + " minimumwindspeed averagewindspeed gustwindspeed airtemperature \\\n", + "count 763006.000000 881372.000000 763006.000000 881372.000000 \n", + "mean 0.231898 1.007714 3.179780 16.689150 \n", + "std 0.574129 0.942189 2.504463 5.581520 \n", + "min 0.000000 0.000000 0.000000 -0.800000 \n", + "25% 0.000000 0.400000 1.400000 12.800000 \n", + "50% 0.000000 0.800000 2.500000 16.100000 \n", + "75% 0.100000 1.400000 4.400000 19.700001 \n", + "max 11.600000 11.200000 52.500000 45.400002 \n", + "\n", + " relativehumidity atmosphericpressure pm25 pm10 \\\n", + "count 881372.000000 881372.000000 823822.000000 823822.000000 \n", + "mean 68.007261 1013.943718 6.691192 8.972910 \n", + "std 17.562959 9.815047 30.102780 30.536759 \n", + "min 2.500000 894.700000 0.000000 0.000000 \n", + "25% 56.400002 1008.500000 1.000000 3.000000 \n", + "50% 69.400000 1014.100000 3.000000 5.000000 \n", + "75% 80.800000 1019.900000 7.000000 9.000000 \n", + "max 99.800003 1042.900000 3414.000000 3414.000000 \n", + "\n", + " noise \n", + "count 823822.000000 \n", + "mean 66.442163 \n", + "std 11.194487 \n", + "min 0.000000 \n", + "25% 58.500000 \n", + "50% 68.100000 \n", + "75% 71.800000 \n", + "max 131.100000 \n" + ] + } + ], + "source": [ + "# Check the summary statistics for numeric columns.\n", + "print(\"Sensor locations summary:\")\n", + "print(sensor_locations_df.describe())\n", + "\n", + "print(\"\\nPedestrian counts summary:\")\n", + "print(pedestrian_counts_df.describe())\n", + "\n", + "print(\"\\nMicroclimate summary:\")\n", + "print(microclimate_df.describe())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.7 Checking The Date Format\n", + "\n", + "Checking the date format is important because a time-based merge depends on all datasets sharing a compatible datetime structure, and public datasets often use different date formats and timezone conventions. This is important because any mismatch in date or time formatting can prevent the datasets from merging correctly later [19].\n", + "\n", + "The pedestrian dataset stores dates and hours separately. Whereas the microclimate dataset stores the full timestamps with date and time with UTC offsets. And the sensor installation date is a simple date string and is mainly historical metadata rather than a modelling field. This makes it clear that datetime standardisation is a required step before any merge can occur." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pedestrian sensing_date sample:\n", + "0 2025-10-08\n", + "1 2026-05-07\n", + "2 2024-10-18\n", + "3 2026-01-06\n", + "4 2024-12-03\n", + "Name: sensing_date, dtype: object\n", + "\n", + "Microclimate received_at sample:\n", + "0 2026-04-28T15:25:15+00:00\n", + "1 2026-04-28T04:38:50+00:00\n", + "2 2026-04-28T05:08:52+00:00\n", + "3 2026-04-28T10:19:41+00:00\n", + "4 2026-04-28T13:55:03+00:00\n", + "Name: received_at, dtype: object\n", + "\n", + "Sensor installation_date sample:\n", + "0 2009-03-25\n", + "1 2009-03-26\n", + "2 2009-03-23\n", + "3 2009-01-21\n", + "4 2009-03-24\n", + "Name: installation_date, dtype: object\n" + ] + } + ], + "source": [ + "# Check a few date values before conversion.\n", + "print(\"Pedestrian sensing_date sample:\")\n", + "print(pedestrian_counts_df[\"sensing_date\"].head())\n", + "\n", + "print(\"\\nMicroclimate received_at sample:\")\n", + "print(microclimate_df[\"received_at\"].head())\n", + "\n", + "print(\"\\nSensor installation_date sample:\")\n", + "print(sensor_locations_df[\"installation_date\"].head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.8 Checking The ID Columns\n", + "\n", + "This was an additional step for checking the unique identifiers to see if the datasets have any chance of being joined directly by ID or whether another strategy, like a datetime merge, is best. And since the project uses multiple public datasets, checking the unique IDs also helps confirm whether the pedestrian sensors and microclimate sensors use the same location system or separate systems [20].\n", + "\n", + "From the outputs, the sensor locations dataset contains 137 unique location_id values, while the pedestrian counts dataset contains 100 unique location_id values. Whereas the microclimate dataset contains 12 unique device_id values, which is a completely different identifier system. This means the microclimate data cannot be joined to pedestrian counts by location ID, so a time-based merge is the best integration method available." + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unique location_id values in sensor locations:\n", + "137\n", + "\n", + "Unique location_id values in pedestrian counts:\n", + "100\n", + "\n", + "Unique device_id values in microclimate:\n", + "12\n" + ] + } + ], + "source": [ + "# Check important identifier columns.\n", + "print(\"Unique location_id values in sensor locations:\")\n", + "print(sensor_locations_df[\"location_id\"].nunique())\n", + "\n", + "print(\"\\nUnique location_id values in pedestrian counts:\")\n", + "print(pedestrian_counts_df[\"location_id\"].nunique())\n", + "\n", + "print(\"\\nUnique device_id values in microclimate:\")\n", + "print(microclimate_df[\"device_id\"].nunique())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Data Cleaning\n", + "\n", + "This is the section for data cleaning, since raw data are rarely ready to be used as it is, so cleaning processes are necessary to address duplicates, missing values, inconsistencies, syntax errors, irrelevant data and structural errors [21] [22]. And since this data pipeline uses the API for dataset access, this means that the datasets are being updated in real-time, so ensuring any errors get addressed in the pipeline ensures the data remains accurate, secure and accessible at every stage of its lifecycle [21]. And that the prediction will also be accurate [22]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.1 Removing Irrelevant Columns\n", + "\n", + "Selecting relevant variables and removing the irrelevant ones are necessary because not every feature is useful for the prediction modelling. Keeping unnecessary columns can make the workflow harder to manage, increase memory usage, and create confusion in later steps [23] [24].\n", + "\n", + "In this step, the sensor dataset is reduced to six useful metadata columns, even though it wasn't used for modelling purposes. The pedestrian dataset is reduced to the four fields needed to construct hourly counts. The microclimate dataset is reduced to key climate, air quality, and noise variables. The descriptive or duplicate variables were omitted." + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " location_id sensor_description installation_date status latitude \\\n", + "0 3 Melbourne Central 2009-03-25 A -37.811015 \n", + "1 5 Princes Bridge 2009-03-26 A -37.818742 \n", + "2 9 Southern Cross Station 2009-03-23 A -37.819830 \n", + "3 12 New Quay 2009-01-21 A -37.814580 \n", + "4 14 Sandridge Bridge 2009-03-24 A -37.820112 \n", + "\n", + " longitude \n", + "0 144.964295 \n", + "1 144.967877 \n", + "2 144.951026 \n", + "3 144.942924 \n", + "4 144.962919 \n", + " location_id sensing_date hourday pedestriancount\n", + "0 70 2025-10-08 2 1\n", + "1 3 2026-05-07 12 2042\n", + "2 59 2024-10-18 15 1058\n", + "3 14 2026-01-06 10 315\n", + "4 28 2024-12-03 8 799\n", + " device_id received_at airtemperature \\\n", + "0 ICTMicroclimate-08 2026-04-28T15:25:15+00:00 16.7 \n", + "1 ICTMicroclimate-08 2026-04-28T04:38:50+00:00 21.9 \n", + "2 ICTMicroclimate-08 2026-04-28T05:08:52+00:00 21.6 \n", + "3 ICTMicroclimate-10 2026-04-28T10:19:41+00:00 19.7 \n", + "4 ICTMicroclimate-08 2026-04-28T13:55:03+00:00 17.3 \n", + "\n", + " relativehumidity atmosphericpressure averagewindspeed gustwindspeed \\\n", + "0 79.9 1026.1 0.5 2.5 \n", + "1 61.5 1021.5 0.9 2.6 \n", + "2 61.1 1021.3 0.2 1.9 \n", + "3 57.4 1018.4 0.8 1.3 \n", + "4 76.2 1025.5 0.3 2.5 \n", + "\n", + " averagewinddirection pm25 pm10 noise \n", + "0 220.0 10.0 12.0 58.7 \n", + "1 283.0 27.0 32.0 75.6 \n", + "2 310.0 26.0 29.0 69.2 \n", + "3 324.0 39.0 49.0 61.9 \n", + "4 281.0 10.0 12.0 79.4 \n" + ] + } + ], + "source": [ + "# Keep only useful columns.\n", + "sensor_locations_clean = sensor_locations_df[\n", + " [\n", + " \"location_id\",\n", + " \"sensor_description\",\n", + " \"installation_date\",\n", + " \"status\",\n", + " \"latitude\",\n", + " \"longitude\",\n", + " ]\n", + "].copy()\n", + "\n", + "pedestrian_clean = pedestrian_counts_df[\n", + " [\n", + " \"location_id\",\n", + " \"sensing_date\",\n", + " \"hourday\",\n", + " \"pedestriancount\",\n", + " ]\n", + "].copy()\n", + "\n", + "microclimate_clean = microclimate_df[\n", + " [\n", + " \"device_id\",\n", + " \"received_at\",\n", + " \"airtemperature\",\n", + " \"relativehumidity\",\n", + " \"atmosphericpressure\",\n", + " \"averagewindspeed\",\n", + " \"gustwindspeed\",\n", + " \"averagewinddirection\",\n", + " \"pm25\",\n", + " \"pm10\",\n", + " \"noise\",\n", + " ]\n", + "].copy()\n", + "\n", + "# Check the result.\n", + "print(sensor_locations_clean.head())\n", + "print(pedestrian_clean.head())\n", + "print(microclimate_clean.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.2 Removing Missing Value\n", + "\n", + "This step involves dealing with missing values by removing the rows they're in. This is because missing values in explanatory variables can cause problems when doing aggregation, interpolation, and modelling later, leading to bias results [25]. This is especially important for the microclimate dataset, since missing climate readings could affect the quality of the explanatory variables.\n", + "\n", + "After doing the column selection in the previous step, the sensor and pedestrian tables are now fully complete without missing values. Whereas the microclimate table still has many missing values, especially in gustwindspeed, pm25, pm10, and noise. \n", + "\n", + "By using dropna(), the microclimate dataset lost a number of data points with missing values, but still retains a sizable portion of data points. The decision for completeness in the data points was preferred to simplify the merging and feature engineering steps later, and because there were enough data points for it not to matter much." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Missing values in sensor_locations_clean:\n", + "location_id 0\n", + "sensor_description 0\n", + "installation_date 0\n", + "status 0\n", + "latitude 0\n", + "longitude 0\n", + "dtype: int64\n", + "\n", + "Missing values in pedestrian_clean:\n", + "location_id 0\n", + "sensing_date 0\n", + "hourday 0\n", + "pedestriancount 0\n", + "dtype: int64\n", + "\n", + "Missing values in microclimate_clean:\n", + "device_id 0\n", + "received_at 0\n", + "airtemperature 15713\n", + "relativehumidity 15713\n", + "atmosphericpressure 15713\n", + "averagewindspeed 15713\n", + "gustwindspeed 134079\n", + "averagewinddirection 15711\n", + "pm25 73263\n", + "pm10 73263\n", + "noise 73263\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "# Check missing values after column selection.\n", + "print(\"Missing values in sensor_locations_clean:\")\n", + "print(sensor_locations_clean.isna().sum())\n", + "\n", + "print(\"\\nMissing values in pedestrian_clean:\")\n", + "print(pedestrian_clean.isna().sum())\n", + "\n", + "print(\"\\nMissing values in microclimate_clean:\")\n", + "print(microclimate_clean.isna().sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Missing values in sensor_locations_clean:\n", + "location_id 0\n", + "sensor_description 0\n", + "installation_date 0\n", + "status 0\n", + "latitude 0\n", + "longitude 0\n", + "dtype: int64\n", + "(145, 6)\n", + "\n", + "Missing values in pedestrian_clean:\n", + "location_id 0\n", + "sensing_date 0\n", + "hourday 0\n", + "pedestriancount 0\n", + "dtype: int64\n", + "(1540629, 4)\n", + "\n", + "Missing values in microclimate_clean:\n", + "device_id 0\n", + "received_at 0\n", + "airtemperature 0\n", + "relativehumidity 0\n", + "atmosphericpressure 0\n", + "averagewindspeed 0\n", + "gustwindspeed 0\n", + "averagewinddirection 0\n", + "pm25 0\n", + "pm10 0\n", + "noise 0\n", + "dtype: int64\n", + "(705456, 11)\n" + ] + } + ], + "source": [ + "# Remove rows with any missing values from each dataset.\n", + "sensor_locations_clean = sensor_locations_clean.dropna().copy()\n", + "pedestrian_clean = pedestrian_clean.dropna().copy()\n", + "microclimate_clean = microclimate_clean.dropna().copy()\n", + "\n", + "# Check missing values after removal.\n", + "print(\"\\nMissing values in sensor_locations_clean:\")\n", + "print(sensor_locations_clean.isna().sum())\n", + "print(sensor_locations_clean.shape)\n", + "\n", + "print(\"\\nMissing values in pedestrian_clean:\")\n", + "print(pedestrian_clean.isna().sum())\n", + "print(pedestrian_clean.shape)\n", + "\n", + "print(\"\\nMissing values in microclimate_clean:\")\n", + "print(microclimate_clean.isna().sum())\n", + "print(microclimate_clean.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.3 Datetime Formatting\n", + "\n", + "Ensuring the datetime formatting matches between the different datasets is important because the modelling is hourly, and the datasets need to be able to merge based on a common point for perform chronological analysis across different data sources [26] [27]. Skipping this step would mean that the datasets cannot be merged into one table.\n", + "\n", + "The pedestrian dataset is converted from separate sensing_date and hourday fields into a single datetime_hour, such as 2024-12-06 20:00:00. Whereas the microclimate timestamps are converted from UTC into Australia/Melbourne, timezone information is removed, and the values are floored to the nearest hour. By doing this, both datasets now have a datetime variable with the same time formatting. It's also important to note that the time range of the microclimate dataset is narrower than the pedestrian time range, meaning that the overlap period is limited to the microclimate dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " location_id sensing_date hourday pedestriancount datetime_hour\n", + "0 70 2025-10-08 2 1 2025-10-08 02:00:00\n", + "1 3 2026-05-07 12 2042 2026-05-07 12:00:00\n", + "2 59 2024-10-18 15 1058 2024-10-18 15:00:00\n", + "3 14 2026-01-06 10 315 2026-01-06 10:00:00\n", + "4 28 2024-12-03 8 799 2024-12-03 08:00:00\n" + ] + } + ], + "source": [ + "# Convert sensor installation date.\n", + "sensor_locations_clean[\"installation_date\"] = pd.to_datetime(\n", + " sensor_locations_clean[\"installation_date\"]\n", + ")\n", + "\n", + "# Create an hourly datetime for pedestrian data.\n", + "pedestrian_clean[\"sensing_date\"] = pd.to_datetime(\n", + " pedestrian_clean[\"sensing_date\"]\n", + ")\n", + "\n", + "pedestrian_clean[\"datetime_hour\"] = (\n", + " pedestrian_clean[\"sensing_date\"] +\n", + " pd.to_timedelta(pedestrian_clean[\"hourday\"], unit=\"h\")\n", + ")\n", + "\n", + "# Check the result.\n", + "print(pedestrian_clean.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " device_id received_at airtemperature relativehumidity \\\n", + "0 ICTMicroclimate-08 2026-04-29 01:25:15 16.7 79.9 \n", + "1 ICTMicroclimate-08 2026-04-28 14:38:50 21.9 61.5 \n", + "2 ICTMicroclimate-08 2026-04-28 15:08:52 21.6 61.1 \n", + "3 ICTMicroclimate-10 2026-04-28 20:19:41 19.7 57.4 \n", + "4 ICTMicroclimate-08 2026-04-28 23:55:03 17.3 76.2 \n", + "\n", + " atmosphericpressure averagewindspeed gustwindspeed averagewinddirection \\\n", + "0 1026.1 0.5 2.5 220.0 \n", + "1 1021.5 0.9 2.6 283.0 \n", + "2 1021.3 0.2 1.9 310.0 \n", + "3 1018.4 0.8 1.3 324.0 \n", + "4 1025.5 0.3 2.5 281.0 \n", + "\n", + " pm25 pm10 noise datetime_hour \n", + "0 10.0 12.0 58.7 2026-04-29 01:00:00 \n", + "1 27.0 32.0 75.6 2026-04-28 14:00:00 \n", + "2 26.0 29.0 69.2 2026-04-28 15:00:00 \n", + "3 39.0 49.0 61.9 2026-04-28 20:00:00 \n", + "4 10.0 12.0 79.4 2026-04-28 23:00:00 \n" + ] + } + ], + "source": [ + "# Convert the raw timestamp to datetime with UTC.\n", + "microclimate_clean[\"received_at\"] = pd.to_datetime(\n", + " microclimate_clean[\"received_at\"],\n", + " utc=True\n", + ")\n", + "\n", + "# Convert UTC to Melbourne local time.\n", + "microclimate_clean[\"received_at\"] = (\n", + " microclimate_clean[\"received_at\"]\n", + " .dt.tz_convert(\"Australia/Melbourne\")\n", + ")\n", + "\n", + "# Remove the timezone after conversion.\n", + "microclimate_clean[\"received_at\"] = (\n", + " microclimate_clean[\"received_at\"]\n", + " .dt.tz_localize(None)\n", + ")\n", + "\n", + "# Round down to the nearest hour.\n", + "microclimate_clean[\"datetime_hour\"] = (\n", + " microclimate_clean[\"received_at\"]\n", + " .dt.floor(\"h\")\n", + ")\n", + "\n", + "# Check the result.\n", + "print(microclimate_clean.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pedestrian time range:\n", + "2024-05-12 00:00:00\n", + "2026-05-11 03:00:00\n", + "\n", + "Microclimate time range:\n", + "2022-11-08 12:00:00\n", + "2026-05-07 15:00:00\n" + ] + } + ], + "source": [ + "# Confirming the same datetime format.\n", + "print(\"Pedestrian time range:\")\n", + "print(pedestrian_clean[\"datetime_hour\"].min())\n", + "print(pedestrian_clean[\"datetime_hour\"].max())\n", + "\n", + "print(\"\\nMicroclimate time range:\")\n", + "print(microclimate_clean[\"datetime_hour\"].min())\n", + "print(microclimate_clean[\"datetime_hour\"].max())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.4 Aggregating Values For Hourly Format\n", + "\n", + "This step involves aggregation because the use case models city-level pedestrian demand rather than individual sensor-level behaviour. Since the pedestrian and microclimate datasets both contain multiple records within the same hour. This also ensures that both the pedestrian and the microclimate data share the same hourly rows without duplicates for later merging, reducing the total data volume [28].\n", + "\n", + "The aggregation involves the pedestrian counts being summed across sensors to produce hourly city totals, and the microclimate readings are averaged across devices for each hour. Doing this changes the target variable from pedestrians at one site to overall city pedestrian demand at one hour, along with the climate values at that hour. This also further reduces the row counts due to aggregating to hourly, and the microclimate data is still narrower than the pedestrian dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " datetime_hour pedestriancount\n", + "0 2024-05-12 00:00:00 15093\n", + "1 2024-05-12 01:00:00 10686\n", + "2 2024-05-12 02:00:00 6751\n", + "3 2024-05-12 03:00:00 5431\n", + "4 2024-05-12 04:00:00 2728\n", + "(17351, 2)\n" + ] + } + ], + "source": [ + "# Aggregate pedestrian counts to hourly city totals.\n", + "pedestrian_hourly = (\n", + " pedestrian_clean\n", + " .groupby(\"datetime_hour\", as_index=False)\n", + " .agg(\n", + " {\n", + " \"pedestriancount\": \"sum\",\n", + " }\n", + " )\n", + ")\n", + "\n", + "# Check the result.\n", + "print(pedestrian_hourly.head())\n", + "print(pedestrian_hourly.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " datetime_hour airtemperature relativehumidity atmosphericpressure \\\n", + "0 2022-11-08 12:00:00 28.400 38.800 1011.100 \n", + "1 2022-11-08 13:00:00 29.400 34.800 1009.800 \n", + "2 2022-11-08 14:00:00 27.320 40.940 1009.420 \n", + "3 2022-11-23 15:00:00 15.100 87.150 1010.800 \n", + "4 2022-11-23 16:00:00 16.475 80.725 1010.475 \n", + "\n", + " averagewindspeed gustwindspeed averagewinddirection pm25 pm10 noise \n", + "0 3.100 4.100 74.00 3.00 5.00 62.80 \n", + "1 2.100 3.500 92.00 5.00 8.00 86.90 \n", + "2 1.700 2.780 253.40 3.00 5.20 87.78 \n", + "3 0.450 1.050 291.00 2.00 5.50 95.10 \n", + "4 1.675 2.475 271.25 4.25 6.75 100.30 \n", + "(27720, 10)\n" + ] + } + ], + "source": [ + "# Aggregate microclimate readings to hourly city averages.\n", + "microclimate_hourly = (\n", + " microclimate_clean\n", + " .groupby(\"datetime_hour\", as_index=False)\n", + " .agg(\n", + " {\n", + " \"airtemperature\": \"mean\",\n", + " \"relativehumidity\": \"mean\",\n", + " \"atmosphericpressure\": \"mean\",\n", + " \"averagewindspeed\": \"mean\",\n", + " \"gustwindspeed\": \"mean\",\n", + " \"averagewinddirection\": \"mean\",\n", + " \"pm25\": \"mean\",\n", + " \"pm10\": \"mean\",\n", + " \"noise\": \"mean\",\n", + " }\n", + " )\n", + ")\n", + "\n", + "# Check the result.\n", + "print(microclimate_hourly.head())\n", + "print(microclimate_hourly.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.5 Merging The Datasets\n", + "\n", + "This step will merge the datasets together, ensuring the target variable, pedestrian count, is connected to the explanatory climate variables. The datetime_hour variable on both the pedestrian dataset and the microclimate dataset was inner-joined to merge into one dataset, meaning only the overlapping rows with the same values were merged [29]. Which means every row has both pedestrian and climate information, hence, a unified format ready for analysis [30].\n", + "\n", + "A quick check of the merged dataset shows that the pedestrian counts and climate values aligned in the same hourly observations, which is what the use case is looking for. And there are no missing values, which indicates that previous data cleaning works as intended." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " datetime_hour pedestriancount airtemperature relativehumidity \\\n", + "0 2024-05-12 00:00:00 15093 12.771429 84.646429 \n", + "1 2024-05-12 01:00:00 10686 12.046429 88.128571 \n", + "2 2024-05-12 02:00:00 6751 11.457143 90.596429 \n", + "3 2024-05-12 03:00:00 5431 11.121429 91.982143 \n", + "4 2024-05-12 04:00:00 2728 10.837037 92.677778 \n", + "\n", + " atmosphericpressure averagewindspeed gustwindspeed averagewinddirection \\\n", + "0 1022.835714 0.353571 1.282143 166.142857 \n", + "1 1022.635714 0.507143 1.435714 140.321429 \n", + "2 1022.521429 0.585714 1.578571 153.857143 \n", + "3 1022.278571 0.607143 1.507143 139.250000 \n", + "4 1022.251852 0.566667 1.562963 170.074074 \n", + "\n", + " pm25 pm10 noise \n", + "0 8.464286 9.428571 66.071429 \n", + "1 11.107143 12.392857 65.500000 \n", + "2 9.892857 11.357143 64.278571 \n", + "3 7.678571 9.035714 61.942857 \n", + "4 8.074074 9.740741 61.566667 \n", + "(17267, 11)\n" + ] + } + ], + "source": [ + "# Merge pedestrian and microclimate data on the hourly datetime.\n", + "model_df = pd.merge(\n", + " pedestrian_hourly,\n", + " microclimate_hourly,\n", + " on=\"datetime_hour\",\n", + " how=\"inner\"\n", + ")\n", + "\n", + "# Sort the final modelling table.\n", + "model_df = model_df.sort_values(\n", + " \"datetime_hour\"\n", + ").reset_index(drop=True)\n", + "\n", + "# Check the result.\n", + "print(model_df.head())\n", + "print(model_df.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 17267 entries, 0 to 17266\n", + "Data columns (total 11 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 datetime_hour 17267 non-null datetime64[ns]\n", + " 1 pedestriancount 17267 non-null int64 \n", + " 2 airtemperature 17267 non-null float64 \n", + " 3 relativehumidity 17267 non-null float64 \n", + " 4 atmosphericpressure 17267 non-null float64 \n", + " 5 averagewindspeed 17267 non-null float64 \n", + " 6 gustwindspeed 17267 non-null float64 \n", + " 7 averagewinddirection 17267 non-null float64 \n", + " 8 pm25 17267 non-null float64 \n", + " 9 pm10 17267 non-null float64 \n", + " 10 noise 17267 non-null float64 \n", + "dtypes: datetime64[ns](1), float64(9), int64(1)\n", + "memory usage: 1.4 MB\n", + "None\n", + "\n", + "Missing values:\n", + "datetime_hour 0\n", + "pedestriancount 0\n", + "airtemperature 0\n", + "relativehumidity 0\n", + "atmosphericpressure 0\n", + "averagewindspeed 0\n", + "gustwindspeed 0\n", + "averagewinddirection 0\n", + "pm25 0\n", + "pm10 0\n", + "noise 0\n", + "dtype: int64\n", + "\n", + "Summary statistics:\n", + " datetime_hour pedestriancount airtemperature \\\n", + "count 17267 17267.00000 17267.000000 \n", + "mean 2025-05-09 05:49:24.104940288 35432.95975 16.511347 \n", + "min 2024-05-12 00:00:00 51.00000 2.617857 \n", + "25% 2024-11-07 21:30:00 7142.00000 12.527951 \n", + "50% 2025-05-06 18:00:00 35519.00000 15.910256 \n", + "75% 2025-11-08 09:30:00 58964.50000 19.561111 \n", + "max 2026-05-07 15:00:00 114804.00000 43.191667 \n", + "std NaN 27363.02579 5.547545 \n", + "\n", + " relativehumidity atmosphericpressure averagewindspeed gustwindspeed \\\n", + "count 17267.000000 17267.000000 17267.000000 17267.000000 \n", + "mean 66.584728 1013.925313 1.162485 3.496650 \n", + "min 9.875000 990.350000 0.121875 0.693939 \n", + "25% 56.075397 1008.605840 0.721429 2.289380 \n", + "50% 68.540741 1013.885714 1.065625 3.263889 \n", + "75% 78.714583 1019.287650 1.508001 4.527639 \n", + "max 96.673171 1039.162500 4.028125 10.200000 \n", + "std 16.060614 7.914093 0.578561 1.523908 \n", + "\n", + " averagewinddirection pm25 pm10 noise \n", + "count 17267.000000 17267.000000 17267.000000 17267.000000 \n", + "mean 184.944173 6.935045 8.731420 68.252640 \n", + "min 52.571429 0.333333 1.057143 53.745714 \n", + "25% 160.194444 1.892857 3.313393 65.136111 \n", + "50% 184.742857 3.368421 5.000000 68.113043 \n", + "75% 211.442222 7.577381 9.306624 71.034330 \n", + "max 306.827586 411.228571 417.028571 88.200000 \n", + "std 37.950660 12.914232 13.465303 4.521855 \n" + ] + } + ], + "source": [ + "# Check merged dataset.\n", + "print(model_df.info())\n", + "print(\"\\nMissing values:\")\n", + "print(model_df.isna().sum())\n", + "print(\"\\nSummary statistics:\")\n", + "print(model_df.describe())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Data Validation\n", + "\n", + "Doing data validation is important because a merged dataset that was cleaned may still be unsuitable for the task of this use case, possibly due to timestamps being duplicated, out of order, or some rows in the chronological datetime are missing. The time-series model tends to assume a consistent temporal structure with no sudden breaks, so this step in the pipeline checks that everything is complete [31]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.1 Validating Time Series Dataset\n", + "\n", + "Checking for duplicates or missing timestamps, since they can affect the lag features, rolling features and any sequence-based deep learning models that this use case may use. This is to ensure that there is a strictly ordered sequence of evenly spaced time points [32].\n", + "\n", + "From the outputs, there are no duplicates in the datetime_hour values, which means every timestamp is represented once. The dataset is double-checked to ensure that it is sorted in increasing time order. But there appears to be a number of missing hourly timestamps, which means the dataset is not complete yet. It does appear that random points were cut off, and that no large block of time was cut off." + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Duplicate datetime_hour values: 0\n" + ] + } + ], + "source": [ + "# Count duplicate datetime values.\n", + "duplicate_count = model_df[\"datetime_hour\"].duplicated().sum()\n", + "\n", + "# Print the result.\n", + "print(\"Duplicate datetime_hour values:\", duplicate_count)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of missing hourly timestamps: 149\n", + "DatetimeIndex(['2024-10-06 02:00:00', '2025-06-06 22:00:00',\n", + " '2025-06-06 23:00:00', '2025-06-07 00:00:00',\n", + " '2025-06-07 01:00:00', '2025-06-07 02:00:00',\n", + " '2025-06-07 03:00:00', '2025-06-07 04:00:00',\n", + " '2025-06-07 05:00:00', '2025-06-07 06:00:00'],\n", + " dtype='datetime64[ns]', freq=None)\n" + ] + } + ], + "source": [ + "# Create the full expected hourly range.\n", + "full_hours = pd.date_range(\n", + " start=model_df[\"datetime_hour\"].min(),\n", + " end=model_df[\"datetime_hour\"].max(),\n", + " freq=\"h\"\n", + ")\n", + "\n", + "# Find missing hours.\n", + "missing_hours = full_hours.difference(model_df[\"datetime_hour\"])\n", + "\n", + "# Print the number of missing hours.\n", + "print(\"Number of missing hourly timestamps:\", len(missing_hours))\n", + "\n", + "# Preview a few missing hours.\n", + "print(missing_hours[:10])" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], + "source": [ + "# Confirm the dataset is sorted by time.\n", + "print(model_df[\"datetime_hour\"].is_monotonic_increasing)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.2 Fixing Missing Timestamps\n", + "\n", + "Ensuring the missing timestamps are filled in is necessary for a complete hourly sequence in the dataset, and missing them can create issues when performing feature engineering later. Missing hours can cause problems when creating lag features, rolling averages, and LSTM input sequences, since these methods rely on consistent time gaps between rows [32] [33].\n", + "\n", + "This step involves reindexing to the full hourly range so that all the missing timestamps are included in the dataset, and then interpolation is performed to fill in the missing values from those created rows. Hence, the merged dataset now has slightly more rows with no missing hourly stamps, ensuring the timeline is continuous with no breaks. A little addition was included to ensure that the pedestrian counts remained as integers, rather than fractions, due to interpolation. " + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Missing hourly timestamps: 0\n", + "\n", + "Overall:\n", + "\n", + "RangeIndex: 17416 entries, 0 to 17415\n", + "Data columns (total 11 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 datetime_hour 17416 non-null datetime64[ns]\n", + " 1 pedestriancount 17416 non-null int64 \n", + " 2 airtemperature 17416 non-null float64 \n", + " 3 relativehumidity 17416 non-null float64 \n", + " 4 atmosphericpressure 17416 non-null float64 \n", + " 5 averagewindspeed 17416 non-null float64 \n", + " 6 gustwindspeed 17416 non-null float64 \n", + " 7 averagewinddirection 17416 non-null float64 \n", + " 8 pm25 17416 non-null float64 \n", + " 9 pm10 17416 non-null float64 \n", + " 10 noise 17416 non-null float64 \n", + "dtypes: datetime64[ns](1), float64(9), int64(1)\n", + "memory usage: 1.5 MB\n", + "None\n", + "\n", + "Final time range:\n", + "2024-05-12 00:00:00\n", + "2026-05-07 15:00:00\n", + "\n", + "Final shape:\n", + "(17416, 11)\n" + ] + } + ], + "source": [ + "# Set datetime as the index.\n", + "model_df = model_df.set_index(\"datetime_hour\").sort_index()\n", + "\n", + "# Create the full hourly timeline.\n", + "full_hours = pd.date_range(\n", + " start=model_df.index.min(),\n", + " end=model_df.index.max(),\n", + " freq=\"h\"\n", + ")\n", + "\n", + "# Reindex to restore missing hours.\n", + "model_df = model_df.reindex(full_hours)\n", + "\n", + "# Interpolate all numeric columns over time.\n", + "model_df = model_df.interpolate(method=\"time\").ffill().bfill()\n", + "\n", + "# Convert pedestrian count back to integers.\n", + "model_df[\"pedestriancount\"] = (\n", + " model_df[\"pedestriancount\"]\n", + " .round()\n", + " .astype(int)\n", + ")\n", + "\n", + "# Restore datetime_hour as a column.\n", + "model_df = model_df.reset_index().rename(\n", + " columns={\"index\": \"datetime_hour\"}\n", + ")\n", + "\n", + "# Check the result.\n", + "print(\"Missing hourly timestamps:\",\n", + " pd.date_range(\n", + " model_df[\"datetime_hour\"].min(),\n", + " model_df[\"datetime_hour\"].max(),\n", + " freq=\"h\"\n", + " ).difference(model_df[\"datetime_hour\"]).shape[0])\n", + "\n", + "# Check for missing hourly timestamps again.\n", + "full_hours = pd.date_range(\n", + " start=model_df[\"datetime_hour\"].min(),\n", + " end=model_df[\"datetime_hour\"].max(),\n", + " freq=\"h\"\n", + ")\n", + "\n", + "missing_hours = full_hours.difference(model_df[\"datetime_hour\"])\n", + "\n", + "# Check the dataset structure.\n", + "print(\"\\nOverall:\")\n", + "print(model_df.info())\n", + "\n", + "# Check the final time range.\n", + "print(\"\\nFinal time range:\")\n", + "print(model_df[\"datetime_hour\"].min())\n", + "print(model_df[\"datetime_hour\"].max())\n", + "\n", + "# Check the dataframe shape.\n", + "print(\"\\nFinal shape:\")\n", + "print(model_df.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Exploratory Data Analysis\n", + "\n", + "The exploratory data analysis section was performed on the merged dataset to observe any interpretable patterns and get a rough understanding of how the dataset looks and feels before building a model. It's important to get a sense of how pedestrian demand changes over time and how it relates to the climate conditions [34]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6.1 Pedestrian Count Over Time (Hourly)\n", + "\n", + "Plotting the hourly pedestrian count is for a quick look at the target variable changes over time, to check if the time series data is random or if there are any patterns that may need to be taken into consideration for later steps [35] [36].\n", + "\n", + "From the plot, there appear to be large fluctuations across the full time range with repeated highs and lows. Although it looks random, it does seem to show some sort of pattern, and it's not necessarily random noise, with recurring fluctuations, like the new year of each year seems to always be a high peak, indicating lots of pedestrians travelling in the city, and there also appear to be some sharp dips, which may possibly be public holidays. This does suggest that there may be other possible variables that might have influenced the pedestrian count, but it definitely confirms that pedestrian demand is influenced by recurring temporal and possibly environmental effects." + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot pedestrian counts over time.\n", + "plt.figure(figsize=(14, 5))\n", + "plt.plot(\n", + " model_df[\"datetime_hour\"],\n", + " model_df[\"pedestriancount\"],\n", + " label=\"Hourly pedestrian count\"\n", + ")\n", + "\n", + "plt.title(\"Pedestrian Count Over Time (Hourly)\")\n", + "plt.xlabel(\"Datetime\")\n", + "plt.ylabel(\"Pedestrian Count\")\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6.2 Average Pedestrian Count By Day Of Week\n", + "\n", + "The day-of-week summary was checked to see if there may also be other variables like work patterns, shopping activities, or weekend behaviours that might have influenced the pedestrian demand. Also checks whether the day of the week should be considered an important time-based feature for later modelling [35] [37].\n", + "\n", + "From the plot, it does seem like the weekdays tend to be relatively high along with Saturdays, whereas Mondays and Sundays tend to have low pedestrian demands. Monday might be low despite being a normal workday for the average Australians might be due to the influence of public holidays, and Sunday being low is due to most people not working on Sunday, since businesses would usually pay a high penalty rate. This further suggests that the CBD pedestrian pattern is linked to weekday economic and commuter activity, not just climate variables." + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a day-of-week column.\n", + "model_df[\"day_of_week\"] = model_df[\n", + " \"datetime_hour\"\n", + "].dt.dayofweek\n", + "\n", + "# Group by day of week.\n", + "dow_pattern = model_df.groupby(\"day_of_week\")[\n", + " \"pedestriancount\"\n", + "].mean()\n", + "\n", + "# Plot the day-of-week pattern.\n", + "plt.figure(figsize=(8, 4))\n", + "plt.plot(\n", + " dow_pattern.index,\n", + " dow_pattern.values,\n", + " marker=\"o\",\n", + " label=\"Average pedestrian count\"\n", + ")\n", + "\n", + "plt.title(\"Average Pedestrian Count By Day Of Week\")\n", + "plt.xlabel(\"Day Of Week\")\n", + "plt.ylabel(\"Average Pedestrian Count\")\n", + "plt.xticks(\n", + " ticks=range(7),\n", + " labels=[\n", + " \"Mon\", \"Tue\", \"Wed\",\n", + " \"Thu\", \"Fri\", \"Sat\", \"Sun\"\n", + " ]\n", + ")\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6.3 Distributions Of Variables\n", + "\n", + "Checking for distribution to see whether it's symmetric, skewed, or multi-modal. This is useful because skewed variables, extreme values, or unusual distributions can influence how the model learns from the data [38].\n", + "\n", + "* pedestriancount appears unimodal and right-skewed.\n", + "* airtemperature appears unimodal with a slight right skew.\n", + "* relativehumidity appears unimodal with a slight left skew.\n", + "* atmosphericpressure appears unimodal and slightly left-skewed.\n", + "* averagewindspeed appears unimodal and right-skewed.\n", + "* gustwindspeed appears unimodal and right-skewed.\n", + "* averagewinddirection appears roughly unimodal and mostly symmetric.\n", + "* pm25 appears unimodal and right-skewed.\n", + "* pm10 appears unimodal and right-skewed, similar to pm25.\n", + "* noise appears unimodal and fairly symmetric." + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Store numeric columns.\n", + "numeric_cols = model_df.select_dtypes(\n", + " include=[\"int32\", \"int64\", \"float64\"]\n", + ").columns.drop(\n", + " [\"day_of_week\"],\n", + " errors=\"ignore\"\n", + ")\n", + "\n", + "# Create subplot layout.\n", + "n_cols = 3\n", + "n_rows = int(np.ceil(len(numeric_cols) / n_cols))\n", + "\n", + "fig, axes = plt.subplots(\n", + " n_rows,\n", + " n_cols,\n", + " figsize=(16, 12)\n", + ")\n", + "\n", + "axes = axes.flatten()\n", + "\n", + "# Plot histogram for each numeric column.\n", + "for i, col in enumerate(numeric_cols):\n", + " axes[i].hist(\n", + " model_df[col],\n", + " bins=30,\n", + " label=col\n", + " )\n", + " axes[i].set_title(f\"Distribution Of {col}\")\n", + " axes[i].set_xlabel(col)\n", + " axes[i].set_ylabel(\"Frequency\")\n", + " axes[i].legend()\n", + "\n", + "# Remove empty subplots.\n", + "for j in range(i + 1, len(axes)):\n", + " fig.delaxes(axes[j])\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6.4 Correlation Matrix\n", + "\n", + "Checking the correlation matrix is a quick way to see which climate variables might have the strongest relationship with pedestrian demand, which does seem like they have some influence on the target variable from the results. This is mainly to show whether the relationship is positive or negative, even though correlation does not prove causation [39].\n", + "\n", + "- noise has the strongest positive correlation with pedestrian count, followed by gustwindspeed, airtemperature, averagewindspeed, and averagewinddirection. This suggests that pedestrian activity tends to increase when these variables increase.\n", + "- relativehumidity has the strongest negative correlation with pedestrian count, while pm10, pm25, and atmosphericpressure have weaker negative relationships. This suggests that pedestrian activity tends to decrease when these variables increase.\n", + "\n", + "This suggests that climate variables do play a role in influencing the pedestrian demand, but there are other influences as well as discovered from previous plots. But the goal for this task is to understand how climate variables affect pedestrian demands." + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pedestriancount 1.000000\n", + "noise 0.555729\n", + "gustwindspeed 0.466541\n", + "airtemperature 0.390398\n", + "averagewindspeed 0.364756\n", + "averagewinddirection 0.119948\n", + "atmosphericpressure -0.034576\n", + "pm25 -0.035831\n", + "pm10 -0.039647\n", + "relativehumidity -0.465781\n", + "Name: pedestriancount, dtype: float64\n" + ] + } + ], + "source": [ + "# Calculate the correlation matrix.\n", + "corr_matrix = model_df[numeric_cols].corr()\n", + "\n", + "# Plot the correlation matrix.\n", + "plt.figure(figsize=(10, 8))\n", + "im = plt.imshow(\n", + " corr_matrix,\n", + " cmap=\"coolwarm\",\n", + " aspect=\"auto\"\n", + ")\n", + "\n", + "cbar = plt.colorbar(im)\n", + "cbar.set_label(\"Correlation Coefficient\")\n", + "\n", + "plt.xticks(\n", + " ticks=np.arange(len(corr_matrix.columns)),\n", + " labels=corr_matrix.columns,\n", + " rotation=45,\n", + " ha=\"right\"\n", + ")\n", + "\n", + "plt.yticks(\n", + " ticks=np.arange(len(corr_matrix.columns)),\n", + " labels=corr_matrix.columns\n", + ")\n", + "\n", + "plt.title(\"Correlation Matrix\")\n", + "\n", + "# Add values inside each cell.\n", + "for i in range(len(corr_matrix.index)):\n", + " for j in range(len(corr_matrix.columns)):\n", + " plt.text(\n", + " j,\n", + " i,\n", + " f\"{corr_matrix.iloc[i, j]:.2f}\",\n", + " ha=\"center\",\n", + " va=\"center\",\n", + " fontsize=8\n", + " )\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Print the correlation values with pedestrian count.\n", + "print(corr_matrix[\"pedestriancount\"].sort_values(ascending=False))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Time Series Analysis\n", + "\n", + "This section is necessary as exploratory data analysis alone is not enough to check a time series dataset. Hence, time series analysis is necessary to help uncover more trends, seasonality, repeated lag dependence, and stationarity [36]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7.1 Pedestrian Count Over Time (24-Hour Rolling Mean)\n", + "\n", + "The previous plot with the pedestrian count over time was noisy, so smoothing over 24 hours may help reveal more patterns of pedestrian activity without the hour-to-hour volatility [40].\n", + "\n", + "From the plot, it became much more obvious that the highest peaks were during New Year's, where pedestrian visit the Melbourne CBD to see the fireworks, and confirms what has been previously discussed. There does seem to be a slightly increasing trend, which suggests that the influence of COVID-19 is still recovering." + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "datetime_hour\n", + "2024-05-12 00:00:00 15093\n", + "2024-05-12 01:00:00 10686\n", + "2024-05-12 02:00:00 6751\n", + "2024-05-12 03:00:00 5431\n", + "2024-05-12 04:00:00 2728\n", + "Name: pedestriancount, dtype: int64\n" + ] + } + ], + "source": [ + "# Create the hourly target series.\n", + "ts = model_df.set_index(\"datetime_hour\")[\n", + " \"pedestriancount\"\n", + "].sort_index()\n", + "\n", + "# Check the first few rows.\n", + "print(ts.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Calculate the 24-hour rolling mean.\n", + "rolling_mean_24 = ts.rolling(24).mean()\n", + "\n", + "# Plot the rolling mean only.\n", + "plt.figure(figsize=(14, 5))\n", + "plt.plot(\n", + " rolling_mean_24,\n", + " label=\"24-hour rolling mean\"\n", + ")\n", + "\n", + "plt.title(\"24-Hour Rolling Mean Of Pedestrian Count\")\n", + "plt.xlabel(\"Datetime\")\n", + "plt.ylabel(\"Rolling Mean\")\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7.2 Seasonal Pattern (24-Hour Cycle)\n", + "\n", + "Seasonal decomposition is necessary because hourly pedestrian demand is expected to have a strong daily cycle, so checking the seasonal component can help check how the typical hour of the day affects pedestrian activity. Since people usually move through the city at different levels during the morning, workday, evening, and late night, this step helps show whether the hour of the day is a factor in pedestrian demand [41].\n", + "\n", + "From the plot, it is clear that the seasonal effect is strongly negative at night and becomes strongly positive during the workday, especially the peak at 5 PM, when most people finish work and are looking to go home. The lowest tends to be around 3 AM, which is expected, since people tend to party late until 12 PM before heading home, and the other reason is that the city's pedestrian activities are very limited at that time. This plot further confirms that hour-of-day is a major driver of pedestrian demand, and there may be other variables influencing pedestrian demand." + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Decompose the series using a 24-hour period.\n", + "decomp_24 = seasonal_decompose(\n", + " ts,\n", + " model=\"additive\",\n", + " period=24\n", + ")\n", + "\n", + "# Store one seasonal cycle and align it to the actual hours.\n", + "seasonal_24 = pd.Series(\n", + " decomp_24.seasonal[:24].values,\n", + " index=ts.index[:24].hour\n", + ")\n", + "\n", + "# Sort by actual hour.\n", + "seasonal_24 = seasonal_24.sort_index()\n", + "\n", + "# Plot the corrected daily seasonal pattern.\n", + "plt.figure(figsize=(10, 4))\n", + "plt.plot(\n", + " seasonal_24.index,\n", + " seasonal_24.values,\n", + " marker=\"o\",\n", + " label=\"Daily seasonal effect\"\n", + ")\n", + "\n", + "plt.title(\"Daily Seasonal Pattern (24-Hour Cycle)\")\n", + "plt.xlabel(\"Hour\")\n", + "plt.ylabel(\"Seasonal Effect\")\n", + "plt.xticks(range(24))\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7.3 Autocorrelation Of Pedestrian Count\n", + "\n", + "Checking for autocorrelation is important because it shows whether the current pedestrian demand depends on previous hours, and if strong dependence exists, then lag features will be very useful for forecasting [42].\n", + "\n", + "From the plot, there appears to be a very strong repeating pattern at a regular interval, especially around daily cycles. This repeated structure applies not only on the daily level, but also appears to be on the weekly level as well, meaning daily and weekly lag features will be useful for predictions." + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot autocorrelation for one week of hourly lags.\n", + "fig, ax = plt.subplots(figsize=(12, 5))\n", + "plot_acf(ts, lags=168, ax=ax)\n", + "\n", + "ax.set_title(\"Autocorrelation Of Pedestrian Count\")\n", + "ax.set_xlabel(\"Lag (Hours)\")\n", + "ax.set_ylabel(\"Autocorrelation\")\n", + "\n", + "# Add legend handles.\n", + "legend_handles = [\n", + " Line2D([0], [0], color=\"C0\", lw=2, label=\"Autocorrelation\"),\n", + " Patch(alpha=0.2, label=\"95% confidence interval\")\n", + "]\n", + "\n", + "ax.legend(handles=legend_handles, loc=\"upper right\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7.4 Augmented Dickey-Fuller Test\n", + "\n", + "The Augmented Dickey-Fuller Test checks whether the time series is statistically stationary in the unit-root sense, and while not necessary for deep learning modelling, it does provide some more understanding of the patterns underlying the merged dataset [43].\n", + "\n", + "The result was that the p-value is extremely small and the test statistic is far below the critical values, hence, the null hypothesis of a unit root is rejected. This means that the time series dataset is statistically stationary enough to exhibit a learnable structure rather than behaving like a random walk, which basically means that it's not necessarily a random coin toss in layman's terms." + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ADF Statistic: -12.334016562004392\n", + "p-value: 6.332696219939632e-23\n", + "Critical Values:\n", + "1%: -3.4307265048981876\n", + "5%: -2.8617064005452915\n", + "10%: -2.5668585707044094\n" + ] + } + ], + "source": [ + "# Run the Augmented Dickey-Fuller test.\n", + "adf_result = adfuller(ts)\n", + "\n", + "# Print the results.\n", + "print(\"ADF Statistic:\", adf_result[0])\n", + "print(\"p-value:\", adf_result[1])\n", + "print(\"Critical Values:\")\n", + "\n", + "for key, value in adf_result[4].items():\n", + " print(f\"{key}: {value}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8. Feature Engineering\n", + "\n", + "The feature engineering section is necessary because the current variables in the merged datasets may not necessarily be enough for a strong forecasting model, so doing feature engineering will create more useful variables that help capture other aspects of the datasets, like cyclical structures, recent history, and short-term trends [44] [45]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8.1 Creating Time Features\n", + "\n", + "Creating more calendar-based features is necessary because pedestrian activity depends on when that observation happened, as shown in previous plots. Hence, hours, day of week, month, and weekend status are all useful predictors. This step ensures the datetime_hour column is converted into its individual components [45].\n", + "\n", + "The output shows that new time-based columns were added to the dataset. These include hour, day_of_week, month, and is_weekend." + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " datetime_hour hour day_of_week month is_weekend\n", + "0 2024-05-12 00:00:00 0 6 5 1\n", + "1 2024-05-12 01:00:00 1 6 5 1\n", + "2 2024-05-12 02:00:00 2 6 5 1\n", + "3 2024-05-12 03:00:00 3 6 5 1\n", + "4 2024-05-12 04:00:00 4 6 5 1\n" + ] + } + ], + "source": [ + "# Create a copy for feature engineering.\n", + "features_df = model_df.copy()\n", + "\n", + "# Extract calendar features.\n", + "features_df[\"hour\"] = features_df[\"datetime_hour\"].dt.hour\n", + "features_df[\"day_of_week\"] = (\n", + " features_df[\"datetime_hour\"].dt.dayofweek\n", + ")\n", + "features_df[\"month\"] = features_df[\"datetime_hour\"].dt.month\n", + "features_df[\"is_weekend\"] = (\n", + " features_df[\"day_of_week\"] >= 5\n", + ").astype(int)\n", + "\n", + "# Check the result.\n", + "print(\n", + " features_df[\n", + " [\n", + " \"datetime_hour\", \"hour\", \"day_of_week\", \"month\", \n", + " \"is_weekend\"\n", + " ]\n", + " ].head()\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8.2 Creating Cyclical Time Features\n", + "\n", + "Cyclical encoding is necessary since time variables are cyclical and not linear, like a clock. If we're talking just normal values like 0 and 23, these two values are quite far apart, but it's not, since it's time and there's only 1 hour difference. Using sine and cosine preserves that circular structure. This step ensures that the time variables are cyclical and prevents the models from learning misleading distances between values at the edge of a cycle [46].\n", + "\n", + "The output shows that new cyclical time features were created, including hour_sin, hour_cos, dow_sin, dow_cos, month_sin, and month_cos." + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " datetime_hour hour_sin hour_cos dow_sin dow_cos month_sin \\\n", + "0 2024-05-12 00:00:00 0.000000 1.000000 -0.781831 0.62349 0.5 \n", + "1 2024-05-12 01:00:00 0.258819 0.965926 -0.781831 0.62349 0.5 \n", + "2 2024-05-12 02:00:00 0.500000 0.866025 -0.781831 0.62349 0.5 \n", + "3 2024-05-12 03:00:00 0.707107 0.707107 -0.781831 0.62349 0.5 \n", + "4 2024-05-12 04:00:00 0.866025 0.500000 -0.781831 0.62349 0.5 \n", + "\n", + " month_cos \n", + "0 -0.866025 \n", + "1 -0.866025 \n", + "2 -0.866025 \n", + "3 -0.866025 \n", + "4 -0.866025 \n" + ] + } + ], + "source": [ + "# Hour and day of week are cyclical, not linear.\n", + "# Create cyclical hour features.\n", + "features_df[\"hour_sin\"] = np.sin(\n", + " 2 * np.pi * features_df[\"hour\"] / 24\n", + ")\n", + "features_df[\"hour_cos\"] = np.cos(\n", + " 2 * np.pi * features_df[\"hour\"] / 24\n", + ")\n", + "\n", + "# Create cyclical day-of-week features.\n", + "features_df[\"dow_sin\"] = np.sin(\n", + " 2 * np.pi * features_df[\"day_of_week\"] / 7\n", + ")\n", + "features_df[\"dow_cos\"] = np.cos(\n", + " 2 * np.pi * features_df[\"day_of_week\"] / 7\n", + ")\n", + "\n", + "# Convert cyclical month features.\n", + "features_df[\"month_sin\"] = np.sin(\n", + " 2 * np.pi * features_df[\"month\"] / 12\n", + ")\n", + "features_df[\"month_cos\"] = np.cos(\n", + " 2 * np.pi * features_df[\"month\"] / 12\n", + ")\n", + "\n", + "# Check the result.\n", + "print(\n", + " features_df[\n", + " [\n", + " \"datetime_hour\", \"hour_sin\", \"hour_cos\",\n", + " \"dow_sin\", \"dow_cos\", \"month_sin\", \n", + " \"month_cos\"\n", + " ]\n", + " ].head()\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8.3 Creating Cyclical Wind Direction Features\n", + "\n", + "Wind direction is also circular, being 360 degrees, meaning 0 and 359 degrees are almost the same direction. So wind direction must also be converted to cyclical for a continuous form, and ensure consistency with treatments of cyclical variables like time [46].\n", + "\n", + "The output shows that two new features were created, wind_dir_sin and wind_dir_cos." + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " averagewinddirection wind_dir_sin wind_dir_cos\n", + "0 166.142857 0.239502 -0.970896\n", + "1 140.321429 0.638480 -0.769638\n", + "2 153.857143 0.440611 -0.897698\n", + "3 139.250000 0.652760 -0.757565\n", + "4 170.074074 0.172375 -0.985031\n" + ] + } + ], + "source": [ + "# Convert wind direction into cyclical form.\n", + "features_df[\"wind_dir_sin\"] = np.sin(\n", + " 2 * np.pi * features_df[\"averagewinddirection\"] / 360\n", + ")\n", + "features_df[\"wind_dir_cos\"] = np.cos(\n", + " 2 * np.pi * features_df[\"averagewinddirection\"] / 360\n", + ")\n", + "\n", + "# Check the result.\n", + "print(\n", + " features_df[\n", + " [\n", + " \"averagewinddirection\",\n", + " \"wind_dir_sin\",\n", + " \"wind_dir_cos\"\n", + " ]\n", + " ].head()\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8.4 Creating Lag Features\n", + "\n", + "Lag features are important, as indicated by the autocorrelation plot, since pedestrian demand is highly dependent on recent history based on the autocorrelation plot [47] [48].\n", + "\n", + "The features lag_1, lag_24, and lag_168 capture the previous hour, previous day, and previous week at the same hour. This does create missing values since there wasn't recent information to populate the lag columns for specific rows, which will be dealt with. Like the very first row having a missing value for lag_1, because there wasn't a previous row to populate that value." + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " lag_1 lag_24 lag_168\n", + "0 NaN NaN NaN\n", + "1 15093.0 NaN NaN\n", + "2 10686.0 NaN NaN\n", + "3 6751.0 NaN NaN\n", + "4 5431.0 NaN NaN\n", + "5 2728.0 NaN NaN\n", + "6 2306.0 NaN NaN\n", + "7 4251.0 NaN NaN\n", + "8 8872.0 NaN NaN\n", + "9 18370.0 NaN NaN\n", + "10 26934.0 NaN NaN\n", + "11 40599.0 NaN NaN\n", + "12 53701.0 NaN NaN\n", + "13 62802.0 NaN NaN\n", + "14 63419.0 NaN NaN\n", + "15 65710.0 NaN NaN\n", + "16 74282.0 NaN NaN\n", + "17 65628.0 NaN NaN\n", + "18 49356.0 NaN NaN\n", + "19 39189.0 NaN NaN\n", + "20 31933.0 NaN NaN\n", + "21 24297.0 NaN NaN\n", + "22 19736.0 NaN NaN\n", + "23 13472.0 NaN NaN\n", + "24 8268.0 15093.0 NaN\n" + ] + } + ], + "source": [ + "# Create lagged pedestrian count features.\n", + "features_df[\"lag_1\"] = (\n", + " features_df[\"pedestriancount\"].shift(1)\n", + ")\n", + "\n", + "features_df[\"lag_24\"] = (\n", + " features_df[\"pedestriancount\"].shift(24)\n", + ")\n", + "\n", + "features_df[\"lag_168\"] = (\n", + " features_df[\"pedestriancount\"].shift(168)\n", + ")\n", + "\n", + "# Check the result.\n", + "print(\n", + " features_df[\n", + " [\n", + " \"lag_1\", \"lag_24\", \"lag_168\"\n", + " ]\n", + " ].head(25)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8.5 Creating Rolling Features\n", + "\n", + "Rolling features summarise recent pedestrian counts rather than relying on a single observation for a datapoint, allowing the model to capture the short-term trend and help smooth the short-term noise [47].\n", + "\n", + "Some new predictors were added, like rolling_mean_24, which summarises the previous 24 hours, while rolling_mean_168 summarises the previous week. And as expected, similar to lag features but not the same, the first rows are missing values since the rolling window cannot be calculated until enough history exists. " + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " rolling_mean_24 rolling_mean_168\n", + "0 NaN NaN\n", + "1 NaN NaN\n", + "2 NaN NaN\n", + "3 NaN NaN\n", + "4 NaN NaN\n", + "5 NaN NaN\n", + "6 NaN NaN\n", + "7 NaN NaN\n", + "8 NaN NaN\n", + "9 NaN NaN\n", + "10 NaN NaN\n", + "11 NaN NaN\n", + "12 NaN NaN\n", + "13 NaN NaN\n", + "14 NaN NaN\n", + "15 NaN NaN\n", + "16 NaN NaN\n", + "17 NaN NaN\n", + "18 NaN NaN\n", + "19 NaN NaN\n", + "20 NaN NaN\n", + "21 NaN NaN\n", + "22 NaN NaN\n", + "23 NaN NaN\n", + "24 29742.25 NaN\n" + ] + } + ], + "source": [ + "# Create rolling mean features from past counts.\n", + "features_df[\"rolling_mean_24\"] = (\n", + " features_df[\"pedestriancount\"]\n", + " .shift(1)\n", + " .rolling(24)\n", + " .mean()\n", + ")\n", + "\n", + "features_df[\"rolling_mean_168\"] = (\n", + " features_df[\"pedestriancount\"]\n", + " .shift(1)\n", + " .rolling(168)\n", + " .mean()\n", + ")\n", + "\n", + "# Check the result.\n", + "print(\n", + " features_df[\n", + " [\n", + " \"rolling_mean_24\", \"rolling_mean_168\"\n", + " ]\n", + " ].head(25)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8.6 Removing NA Rows\n", + "\n", + "This step removes the rows with missing values created from the lag and rolling features at the beginning of the ordered merged dataset, since they cannot be used as they are incomplete predictor information. By removing these rows, the dataset lost a part of the early period as a trade-off in the pipeline, which is reasonable considering the dataset is being updated in real-time, so there will be more data points to use in the future, hence, negligible [48].\n", + "\n", + "The output shows that all rows with missing values were removed, and the dataset was reset into a clean index." + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 17248 entries, 0 to 17247\n", + "Data columns (total 28 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 datetime_hour 17248 non-null datetime64[ns]\n", + " 1 pedestriancount 17248 non-null int64 \n", + " 2 airtemperature 17248 non-null float64 \n", + " 3 relativehumidity 17248 non-null float64 \n", + " 4 atmosphericpressure 17248 non-null float64 \n", + " 5 averagewindspeed 17248 non-null float64 \n", + " 6 gustwindspeed 17248 non-null float64 \n", + " 7 averagewinddirection 17248 non-null float64 \n", + " 8 pm25 17248 non-null float64 \n", + " 9 pm10 17248 non-null float64 \n", + " 10 noise 17248 non-null float64 \n", + " 11 day_of_week 17248 non-null int32 \n", + " 12 hour 17248 non-null int32 \n", + " 13 month 17248 non-null int32 \n", + " 14 is_weekend 17248 non-null int64 \n", + " 15 hour_sin 17248 non-null float64 \n", + " 16 hour_cos 17248 non-null float64 \n", + " 17 dow_sin 17248 non-null float64 \n", + " 18 dow_cos 17248 non-null float64 \n", + " 19 month_sin 17248 non-null float64 \n", + " 20 month_cos 17248 non-null float64 \n", + " 21 wind_dir_sin 17248 non-null float64 \n", + " 22 wind_dir_cos 17248 non-null float64 \n", + " 23 lag_1 17248 non-null float64 \n", + " 24 lag_24 17248 non-null float64 \n", + " 25 lag_168 17248 non-null float64 \n", + " 26 rolling_mean_24 17248 non-null float64 \n", + " 27 rolling_mean_168 17248 non-null float64 \n", + "dtypes: datetime64[ns](1), float64(22), int32(3), int64(2)\n", + "memory usage: 3.5 MB\n", + "None\n", + " datetime_hour pedestriancount airtemperature relativehumidity \\\n", + "0 2024-05-19 00:00:00 12751 8.655172 69.200000 \n", + "1 2024-05-19 01:00:00 8603 8.664286 68.792857 \n", + "2 2024-05-19 02:00:00 5660 8.960714 69.235714 \n", + "3 2024-05-19 03:00:00 4709 9.660714 67.675000 \n", + "4 2024-05-19 04:00:00 2682 10.050000 69.060714 \n", + "\n", + " atmosphericpressure averagewindspeed gustwindspeed averagewinddirection \\\n", + "0 1026.241379 0.841379 2.303448 204.137931 \n", + "1 1025.732143 1.042857 2.535714 203.071429 \n", + "2 1024.671429 1.757143 3.717857 237.107143 \n", + "3 1024.171429 1.460714 3.657143 247.857143 \n", + "4 1023.675000 1.482143 3.807143 240.821429 \n", + "\n", + " pm25 pm10 ... dow_cos month_sin month_cos wind_dir_sin \\\n", + "0 6.758621 9.448276 ... 0.62349 0.5 -0.866025 -0.408935 \n", + "1 6.750000 9.428571 ... 0.62349 0.5 -0.866025 -0.391878 \n", + "2 6.892857 9.535714 ... 0.62349 0.5 -0.866025 -0.839688 \n", + "3 6.107143 8.678571 ... 0.62349 0.5 -0.866025 -0.926247 \n", + "4 5.678571 8.428571 ... 0.62349 0.5 -0.866025 -0.873104 \n", + "\n", + " wind_dir_cos lag_1 lag_24 lag_168 rolling_mean_24 rolling_mean_168 \n", + "0 -0.912564 21105.0 8948.0 15093.0 33068.333333 31092.851190 \n", + "1 -0.920017 12751.0 6244.0 10686.0 33226.791667 31078.910714 \n", + "2 -0.543070 8603.0 4035.0 6751.0 33325.083333 31066.511905 \n", + "3 -0.376917 5660.0 2813.0 5431.0 33392.791667 31060.017857 \n", + "4 -0.487533 4709.0 1761.0 2728.0 33471.791667 31055.720238 \n", + "\n", + "[5 rows x 28 columns]\n" + ] + } + ], + "source": [ + "# Remove rows with NAs.\n", + "features_df = features_df.dropna().reset_index(drop=True)\n", + "\n", + "# Check the result.\n", + "print(features_df.info())\n", + "print(features_df.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8.7 Removing Unnecessary Features\n", + "\n", + "Since the cyclical variables were created, the original raw cyclical variables have become redundant. So removing these variables helps reduce feature duplication and makes the modelling easier and cleaner [23] [24].\n", + "\n", + "The averagewinddirection, hour, day_of_week, and month columns are dropped. Resulting in 24 columns, including the target pedestrian count, selected climate variables, is_weekend, cyclical encodings, lag features, and rolling means. The datetime_hour is temporarily kept, but will be removed later on as well." + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 17248 entries, 0 to 17247\n", + "Data columns (total 24 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 datetime_hour 17248 non-null datetime64[ns]\n", + " 1 pedestriancount 17248 non-null int64 \n", + " 2 airtemperature 17248 non-null float64 \n", + " 3 relativehumidity 17248 non-null float64 \n", + " 4 atmosphericpressure 17248 non-null float64 \n", + " 5 averagewindspeed 17248 non-null float64 \n", + " 6 gustwindspeed 17248 non-null float64 \n", + " 7 pm25 17248 non-null float64 \n", + " 8 pm10 17248 non-null float64 \n", + " 9 noise 17248 non-null float64 \n", + " 10 is_weekend 17248 non-null int64 \n", + " 11 hour_sin 17248 non-null float64 \n", + " 12 hour_cos 17248 non-null float64 \n", + " 13 dow_sin 17248 non-null float64 \n", + " 14 dow_cos 17248 non-null float64 \n", + " 15 month_sin 17248 non-null float64 \n", + " 16 month_cos 17248 non-null float64 \n", + " 17 wind_dir_sin 17248 non-null float64 \n", + " 18 wind_dir_cos 17248 non-null float64 \n", + " 19 lag_1 17248 non-null float64 \n", + " 20 lag_24 17248 non-null float64 \n", + " 21 lag_168 17248 non-null float64 \n", + " 22 rolling_mean_24 17248 non-null float64 \n", + " 23 rolling_mean_168 17248 non-null float64 \n", + "dtypes: datetime64[ns](1), float64(21), int64(2)\n", + "memory usage: 3.2 MB\n", + "None\n" + ] + } + ], + "source": [ + "# Drop raw columns that now have cyclical replacements.\n", + "features_df = features_df.drop(\n", + " columns=[\n", + " \"averagewinddirection\",\n", + " \"hour\",\n", + " \"day_of_week\",\n", + " \"month\",\n", + " ]\n", + ")\n", + "\n", + "# Reorder the columns into a cleaner structure.\n", + "features_df = features_df[\n", + " [\n", + " \"datetime_hour\",\n", + " \"pedestriancount\",\n", + " \"airtemperature\",\n", + " \"relativehumidity\",\n", + " \"atmosphericpressure\",\n", + " \"averagewindspeed\",\n", + " \"gustwindspeed\",\n", + " \"pm25\",\n", + " \"pm10\",\n", + " \"noise\",\n", + " \"is_weekend\",\n", + " \"hour_sin\",\n", + " \"hour_cos\",\n", + " \"dow_sin\",\n", + " \"dow_cos\",\n", + " \"month_sin\",\n", + " \"month_cos\",\n", + " \"wind_dir_sin\",\n", + " \"wind_dir_cos\",\n", + " \"lag_1\",\n", + " \"lag_24\",\n", + " \"lag_168\",\n", + " \"rolling_mean_24\",\n", + " \"rolling_mean_168\",\n", + " ]\n", + "]\n", + "\n", + "# Check the result.\n", + "print(features_df.info())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9. Preparing Train/Val/Test Splits\n", + "\n", + "Preparing the splits is necessary for training a forecasting model, so that the future periods are evaluated, and not on randomly selected time periods. Hence, splitting the dataset based on time is important so that the model being trained on the past can predict the future, like in real-world scenarios [49]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 9.1 Splitting The Data By Time\n", + "\n", + "Chronological splitting in time-series forecasting is to prevent leaking future information into the training process. For example is if random splitting were to be used, then a model can be trained on data points in 2026, but is tested on a time period in 2025. Hence, the validation and test set must be later, and the training set must be the past, as shown here [49].\n", + "\n", + "The split for training, validation and testing is 80:10:10 to ensure that there are enough data points used for training, with enough data points to perform validation and testing. Deep learning tends to require a large number of data points, so using this split ratio prevents underfitting [50].\n", + "\n", + "The output confirms that the data was split chronologically, with the training data coming first, followed by validation and testing data." + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(13798, 24)\n", + "(1724, 24)\n", + "(1726, 24)\n" + ] + } + ], + "source": [ + "# Set the split sizes (80:10:10).\n", + "train_size = int(len(features_df) * 0.8)\n", + "val_size = int(len(features_df) * 0.1)\n", + "\n", + "# Split the data in chronological order.\n", + "train_df = features_df.iloc[:train_size].copy()\n", + "val_df = features_df.iloc[\n", + " train_size:train_size + val_size\n", + "].copy()\n", + "test_df = features_df.iloc[\n", + " train_size + val_size:\n", + "].copy()\n", + "\n", + "# Check the shapes.\n", + "print(train_df.shape)\n", + "print(val_df.shape)\n", + "print(test_df.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train range:\n", + "2024-05-19 00:00:00\n", + "2025-12-14 21:00:00\n", + "\n", + "Validation range:\n", + "2025-12-14 22:00:00\n", + "2026-02-24 17:00:00\n", + "\n", + "Test range:\n", + "2026-02-24 18:00:00\n", + "2026-05-07 15:00:00\n" + ] + } + ], + "source": [ + "# Checking The Split Ranges.\n", + "print(\"Train range:\")\n", + "print(train_df[\"datetime_hour\"].min())\n", + "print(train_df[\"datetime_hour\"].max())\n", + "\n", + "print(\"\\nValidation range:\")\n", + "print(val_df[\"datetime_hour\"].min())\n", + "print(val_df[\"datetime_hour\"].max())\n", + "\n", + "print(\"\\nTest range:\")\n", + "print(test_df[\"datetime_hour\"].min())\n", + "print(test_df[\"datetime_hour\"].max())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 9.2 Separating Features And Target\n", + "\n", + "This step is necessary to separate the predictor inputs X and the output target y. This is necessary because the model needs to know which columns are used as inputs and which column it needs to predict [51].\n", + "\n", + "The target is set to pedestriancount, which is the variable the model is trying to predict. The datetime column is excluded from the features since the chronological splitting is completed, so this leaves 22 predictor columns for each split, as shown in the shapes output." + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(13798, 22) (13798,)\n", + "(1724, 22) (1724,)\n", + "(1726, 22) (1726,)\n" + ] + } + ], + "source": [ + "# Store the target column name.\n", + "target_col = \"pedestriancount\"\n", + "\n", + "# Store the feature column names.\n", + "feature_cols = [\n", + " col for col in features_df.columns\n", + " if col not in [\"datetime_hour\", target_col]\n", + "]\n", + "\n", + "# Create X and y for each split.\n", + "X_train = train_df[feature_cols]\n", + "y_train = train_df[target_col]\n", + "\n", + "X_val = val_df[feature_cols]\n", + "y_val = val_df[target_col]\n", + "\n", + "X_test = test_df[feature_cols]\n", + "y_test = test_df[target_col]\n", + "\n", + "# Check the shapes.\n", + "print(X_train.shape, y_train.shape)\n", + "print(X_val.shape, y_val.shape)\n", + "print(X_test.shape, y_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 9.3 Scaling The Features\n", + "\n", + "Scaling the features is necessary because the predictor variables are measured on different scales. For example, temperature, humidity, air pressure, wind speed, pollution values, and lagged pedestrian counts all have different ranges. Hence, needs to be standardised so that the variables are unitless, allowing the variables to be able to directly compared [52].\n", + "\n", + "Standardisation basically sets the predictors to have a mean of 0 and a standard deviation of 1. This allows faster convergence when all the input features are on the same scale, preventing feature dominance due to differences in magnitudes, and is generally more stable [52]." + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(13798, 22)\n", + "(1724, 22)\n", + "(1726, 22)\n", + "[[-1.29777704e+00 1.50360125e-01 1.46578468e+00 -5.80148521e-01\n", + " -7.99559383e-01 1.00283430e-01 1.98749444e-01 -3.63941261e-01\n", + " 1.57673844e+00 -7.77706241e-05 1.41447137e+00 -1.10371969e+00\n", + " 8.80498928e-01 1.03546540e+00 -1.13513198e+00 -5.75432725e-01\n", + " -4.91738264e-01 -5.08174682e-01 -9.59909049e-01 -7.29249335e-01\n", + " -3.26314765e-01 -1.04167887e+00]\n", + " [-1.29610869e+00 1.24879825e-01 1.40393909e+00 -2.36045144e-01\n", + " -6.48552677e-01 9.91130647e-02 1.96368567e-01 -5.59070044e-01\n", + " 1.57673844e+00 3.65929517e-01 1.36628083e+00 -1.10371969e+00\n", + " 8.80498928e-01 1.03546540e+00 -1.13513198e+00 -5.44617322e-01\n", + " -5.21312792e-01 -8.18712499e-01 -1.06044050e+00 -8.93515834e-01\n", + " -2.95950862e-01 -1.04569266e+00]\n", + " [-1.24184204e+00 1.52595239e-01 1.27511778e+00 9.83881253e-01\n", + " 1.20012207e-01 1.18507689e-01 2.09314586e-01 2.43030983e-01\n", + " 1.57673844e+00 7.06994013e-01 1.22499330e+00 -1.10371969e+00\n", + " 8.80498928e-01 1.03546540e+00 -1.13513198e+00 -1.35366847e+00\n", + " 9.74390635e-01 -9.72903410e-01 -1.14256846e+00 -1.04018901e+00\n", + " -2.77116141e-01 -1.04926256e+00]\n", + " [-1.11369428e+00 5.49207567e-02 1.21439393e+00 4.77611798e-01\n", + " 8.05390863e-02 1.18372546e-02 1.05746435e-01 -2.70089534e-01\n", + " 1.57673844e+00 9.99872735e-01 1.00023730e+00 -1.10371969e+00\n", + " 8.80498928e-01 1.03546540e+00 -1.13513198e+00 -1.51005421e+00\n", + " 1.63367379e+00 -1.08230164e+00 -1.18800094e+00 -1.08939068e+00\n", + " -2.64141820e-01 -1.05113235e+00]\n", + " [-1.04242843e+00 1.41643180e-01 1.15410382e+00 5.14209590e-01\n", + " 1.78060915e-01 -4.63466188e-02 7.55390572e-02 -4.47800540e-01\n", + " 1.57673844e+00 1.22460648e+00 7.07329572e-01 -1.10371969e+00\n", + " 8.80498928e-01 1.03546540e+00 -1.13513198e+00 -1.41404236e+00\n", + " 1.19475651e+00 -1.11765254e+00 -1.22711303e+00 -1.19014229e+00\n", + " -2.49003782e-01 -1.05236973e+00]]\n" + ] + } + ], + "source": [ + "# Create the scaler.\n", + "scaler = StandardScaler()\n", + "\n", + "# Fit on training features only.\n", + "X_train_scaled = scaler.fit_transform(X_train)\n", + "\n", + "# Transform validation and test features.\n", + "X_val_scaled = scaler.transform(X_val)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "\n", + "# Check the shapes.\n", + "print(X_train_scaled.shape)\n", + "print(X_val_scaled.shape)\n", + "print(X_test_scaled.shape)\n", + "print(X_train_scaled[:5])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 10. Deep Learning — Pedestrian Count Forecasting\n", + "\n", + "This section covers the deep learning component of the project. The goal is to build \n", + "and compare recurrent neural network models that can forecast hourly pedestrian counts \n", + "in Melbourne CBD using climate features and engineered time-series inputs.\n", + "\n", + "Deep learning models, specifically recurrent neural network architectures like LSTM \n", + "and GRU, are well suited for this task because pedestrian demand is a sequential, \n", + "time-dependent process [53]. Unlike traditional regression \n", + "models, recurrent networks can learn temporal patterns across multiple time steps, \n", + "making them appropriate for hourly forecasting tasks with lag dependencies \n", + "[54]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.2 Setting Random Seeds for Reproducibility\n", + "\n", + "A fixed random seed is set across all relevant libraries before any model is built or \n", + "trained. Deep learning models initialise their weights randomly and apply random \n", + "dropout masks during training, which means that results can differ between runs \n", + "without a fixed seed [55]. By setting the same seed across \n", + "Python, NumPy, and TensorFlow, the outputs of this section are fully reproducible \n", + "and will produce identical results on every run \n", + "[7] [55].\n", + "\n", + "Additional environment variables TF_DETERMINISTIC_OPS and \n", + "TF_DISABLE_SEGMENT_REDUCTION_OP_DETERMINISM_EXCEPTIONS are set to further enforce \n", + "deterministic behaviour at the TensorFlow operation level \n", + "[55]. The student ID was used as the seed value for consistency \n", + "with the random seed convention used earlier in this notebook \n", + "[7]." + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Random seed set to: 224120439\n", + "TF version: 2.20.0\n" + ] + } + ], + "source": [ + "SEED = 224120439\n", + "\n", + "# 1. Force single thread to eliminate CPU non-determinism\n", + "os.environ[\"PYTHONHASHSEED\"] = str(SEED)\n", + "os.environ[\"TF_DETERMINISTIC_OPS\"] = \"1\"\n", + "os.environ[\"TF_DISABLE_SEGMENT_REDUCTION_OP_DETERMINISM_EXCEPTIONS\"] = \"1\"\n", + "\n", + "# 2. Seed all libraries\n", + "random.seed(SEED)\n", + "np.random.seed(SEED)\n", + "tf.keras.utils.set_random_seed(SEED)\n", + "\n", + "# 3. Force deterministic operations\n", + "try:\n", + " tf.config.experimental.enable_op_determinism()\n", + "except Exception:\n", + " pass # silently skip if not supported on this system\n", + "\n", + "print(f\"Random seed set to: {SEED}\")\n", + "print(f\"TF version: {tf.__version__}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.3 Creating Input Sequences\n", + "\n", + "Before training a recurrent neural network, the data must be reshaped into \n", + "three-dimensional sequences. Each sequence consists of a fixed-length window of past \n", + "hourly observations that the model uses to predict the next hour's pedestrian count \n", + "[56].\n", + "\n", + "A sequence length of 24 was chosen to capture one full day cycle of hourly patterns. \n", + "This is a natural choice given that pedestrian demand follows a strong 24-hour rhythm, \n", + "as confirmed by the seasonal decomposition and autocorrelation analysis in earlier \n", + "sections [35] [42]. Additionally, since lag \n", + "features covering 24 hours, 7 days, and rolling means are already included as input \n", + "features, extending the sequence window further would introduce redundancy without \n", + "meaningful benefit [47] [48].\n", + "\n", + "Each input sample X has the shape (24, 22), representing 24 consecutive hours across \n", + "22 engineered features. The corresponding target y is the pedestrian count at the \n", + "following hour, making this a one-step-ahead forecasting setup \n", + "[56]. The output shapes confirm the split sizes — 13,774 \n", + "training samples, 1,700 validation samples, and 1,702 test samples." + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train shape: (13774, 24, 22)\n", + "Val shape: (1700, 24, 22)\n", + "Test shape: (1702, 24, 22)\n" + ] + } + ], + "source": [ + "sequence_length = 24 \n", + "\n", + "def create_sequences(X, y, seq_length):\n", + " X_seq, y_seq = [], []\n", + " for i in range(seq_length, len(X)):\n", + " X_seq.append(X[i - seq_length:i])\n", + " y_seq.append(y.iloc[i])\n", + " return np.array(X_seq), np.array(y_seq)\n", + "\n", + "X_train_seq, y_train_seq = create_sequences(X_train_scaled, y_train, sequence_length)\n", + "X_val_seq, y_val_seq = create_sequences(X_val_scaled, y_val, sequence_length)\n", + "X_test_seq, y_test_seq = create_sequences(X_test_scaled, y_test, sequence_length)\n", + "\n", + "print(\"Train shape:\", X_train_seq.shape)\n", + "print(\"Val shape:\", X_val_seq.shape)\n", + "print(\"Test shape:\", X_test_seq.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.4 Model Architectures\n", + "\n", + "Three recurrent neural network architectures were built and compared to identify the \n", + "most effective model for this forecasting task. Comparing multiple architectures \n", + "allows for evidence-based model selection rather than relying on a single approach \n", + "[53] [57].\n", + "\n", + "All three models share the same output structure — a Dense(32) hidden layer followed \n", + "by a Dense(1) output layer for regression — and are compiled with the Adam optimiser \n", + "and Mean Squared Error loss, which is standard for continuous value regression \n", + "forecasting tasks [58]. Mean Absolute Error is tracked as an \n", + "interpretable secondary metric during training [59].\n", + "\n", + "The three architectures evaluated are:\n", + "- **Baseline LSTM**: A single LSTM layer with 64 units serving as the reference model \n", + " to establish a performance baseline.\n", + "- **Stacked LSTM**: Two LSTM layers in sequence for deeper temporal pattern learning.\n", + "- **GRU**: A more parameter-efficient alternative to LSTM with a simplified gate \n", + " structure." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 10.4.1 Baseline LSTM\n", + "\n", + "The baseline model uses a single LSTM layer with 64 units. LSTM networks are designed \n", + "to address the vanishing gradient problem found in standard recurrent networks, \n", + "allowing them to retain information across longer time sequences \n", + "[54]. At each time step, the LSTM uses three gates — an input \n", + "gate, a forget gate, and an output gate — to decide what information to keep, discard, \n", + "or pass forward [54].\n", + "\n", + "A Dropout layer with a rate of 0.2 is applied after the LSTM to regularise the model \n", + "by randomly deactivating 20% of neurons during each training step, reducing the risk \n", + "of overfitting [60]. This model has 24,385 total trainable \n", + "parameters, which is appropriate for the training set size of approximately 13,774 \n", + "samples." + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.12/dist-packages/keras/src/layers/rnn/rnn.py:199: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(**kwargs)\n" + ] + }, + { + "data": { + "text/html": [ + "
Model: \"sequential_3\"\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential_3\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ lstm_3 (LSTM)                   │ (None, 64)             │        22,272 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_4 (Dropout)             │ (None, 64)             │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_6 (Dense)                 │ (None, 32)             │         2,080 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_7 (Dense)                 │ (None, 1)              │            33 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ lstm_3 (\u001b[38;5;33mLSTM\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m22,272\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_6 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m2,080\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_7 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m33\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Total params: 24,385 (95.25 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m24,385\u001b[0m (95.25 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Trainable params: 24,385 (95.25 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m24,385\u001b[0m (95.25 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Non-trainable params: 0 (0.00 B)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def build_lstm_baseline(input_shape):\n", + " model = Sequential([\n", + " LSTM(64, return_sequences=False, input_shape=input_shape),\n", + " Dropout(0.2),\n", + " Dense(32, activation=\"relu\"),\n", + " Dense(1)\n", + " ])\n", + " model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[\"mae\"])\n", + " return model\n", + "\n", + "model_baseline = build_lstm_baseline(\n", + " (X_train_seq.shape[1], X_train_seq.shape[2])\n", + ")\n", + "model_baseline.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 10.4.2 Stacked LSTM\n", + "\n", + "The stacked model adds a second LSTM layer on top of the first. The first LSTM layer \n", + "uses return_sequences=True, meaning it outputs the full sequence of hidden states at \n", + "every time step rather than just the final one. This full sequence is then passed as \n", + "input to the second LSTM layer, which compresses it into a single final hidden state \n", + "[54].\n", + "\n", + "This design allows the model to learn more abstract temporal representations across \n", + "multiple levels of processing — the first layer captures low-level hourly patterns, \n", + "while the second layer can learn higher-order dependencies across those patterns \n", + "[53]. Dropout is applied after each LSTM layer to maintain \n", + "regularisation [60]. This model has 35,777 total trainable \n", + "parameters, making it the most complex of the three architectures compared." + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Model: \"sequential_4\"\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential_4\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ lstm_4 (LSTM)                   │ (None, 24, 64)         │        22,272 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_5 (Dropout)             │ (None, 24, 64)         │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ lstm_5 (LSTM)                   │ (None, 32)             │        12,416 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_6 (Dropout)             │ (None, 32)             │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_8 (Dense)                 │ (None, 32)             │         1,056 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_9 (Dense)                 │ (None, 1)              │            33 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ lstm_4 (\u001b[38;5;33mLSTM\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m22,272\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dropout_5 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ lstm_5 (\u001b[38;5;33mLSTM\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m12,416\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dropout_6 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_8 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m1,056\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_9 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m33\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Total params: 35,777 (139.75 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m35,777\u001b[0m (139.75 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Trainable params: 35,777 (139.75 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m35,777\u001b[0m (139.75 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Non-trainable params: 0 (0.00 B)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def build_lstm_stacked(input_shape):\n", + " model = Sequential([\n", + " LSTM(64, return_sequences=True, input_shape=input_shape), # returns full sequence\n", + " Dropout(0.2),\n", + " LSTM(32, return_sequences=False), # compresses to final state\n", + " Dropout(0.2),\n", + " Dense(32, activation=\"relu\"),\n", + " Dense(1)\n", + " ])\n", + " model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[\"mae\"])\n", + " return model\n", + "\n", + "model_stacked = build_lstm_stacked(\n", + " (X_train_seq.shape[1], X_train_seq.shape[2])\n", + ")\n", + "model_stacked.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 10.4.3 GRU (Gated Recurrent Unit)\n", + "\n", + "The GRU model replaces the LSTM unit with a GRU layer of equal size. GRU simplifies \n", + "the LSTM architecture by combining the input and forget gates into a single update \n", + "gate, and adding a reset gate, resulting in only two gates overall instead of three \n", + "[61]. This reduction in gate complexity leads to fewer \n", + "parameters — 19,009 total — making the GRU the most lightweight of the three \n", + "architectures.\n", + "\n", + "GRU is included as an alternative because empirical research has shown it often \n", + "performs comparably or better than LSTM on time-series forecasting tasks, \n", + "particularly on datasets that are not large enough to fully justify the additional \n", + "capacity of LSTM [61] [62]. This \n", + "comparison tests whether that finding holds for this urban pedestrian forecasting \n", + "task." + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Model: \"sequential_5\"\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential_5\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ gru_1 (GRU)                     │ (None, 64)             │        16,896 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_7 (Dropout)             │ (None, 64)             │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_10 (Dense)                │ (None, 32)             │         2,080 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_11 (Dense)                │ (None, 1)              │            33 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ gru_1 (\u001b[38;5;33mGRU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m16,896\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dropout_7 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_10 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m2,080\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_11 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m33\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Total params: 19,009 (74.25 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m19,009\u001b[0m (74.25 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Trainable params: 19,009 (74.25 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m19,009\u001b[0m (74.25 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Non-trainable params: 0 (0.00 B)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def build_gru(input_shape):\n", + " model = Sequential([\n", + " GRU(64, return_sequences=False, input_shape=input_shape),\n", + " Dropout(0.2),\n", + " Dense(32, activation=\"relu\"),\n", + " Dense(1)\n", + " ])\n", + " model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[\"mae\"])\n", + " return model\n", + "\n", + "model_gru = build_gru(\n", + " (X_train_seq.shape[1], X_train_seq.shape[2])\n", + ")\n", + "model_gru.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.5 Model Training\n", + "\n", + "All three models are trained using identical configurations to ensure a fair \n", + "comparison between architectures. The training setup includes the following design \n", + "choices [58]:\n", + "\n", + "- **Epochs**: A maximum of 50 epochs was set to allow sufficient learning time, with \n", + " callbacks responsible for halting training early when appropriate \n", + " [58].\n", + "- **Batch size**: A batch size of 64 was selected as a balance between training \n", + " stability and computational speed. Larger batch sizes provide more stable gradient \n", + " estimates per update compared to smaller ones [58].\n", + "- **EarlyStopping**: Monitors validation loss with a patience of 10 epochs, halting \n", + " training if no improvement is seen for 10 consecutive epochs. The \n", + " restore_best_weights=True parameter ensures the final model retains the weights \n", + " from its best validation epoch rather than the last epoch \n", + " [63].\n", + "- **ReduceLROnPlateau**: Halves the learning rate when validation loss has not \n", + " improved for 5 consecutive epochs, with a minimum learning rate floor of 1e-6. \n", + " This allows finer weight adjustments as the model approaches convergence rather \n", + " than overshooting the optimal solution [63].\n", + "\n", + "The validation set is used exclusively for monitoring during training and is never \n", + "used to update model weights, preserving the integrity of the chronological train, \n", + "validation, and test split [49]." + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 11ms/step - loss: 1931178880.0000 - mae: 34742.4805 - val_loss: 2065434112.0000 - val_mae: 36316.7852 - learning_rate: 0.0010\n", + "Epoch 2/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1915012608.0000 - mae: 34508.1875 - val_loss: 2041869568.0000 - val_mae: 35990.8672 - learning_rate: 0.0010\n", + "Epoch 3/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1886355328.0000 - mae: 34091.4727 - val_loss: 2005516672.0000 - val_mae: 35482.5078 - learning_rate: 0.0010\n", + "Epoch 4/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1846237696.0000 - mae: 33533.6406 - val_loss: 1957890944.0000 - val_mae: 34864.8906 - learning_rate: 0.0010\n", + "Epoch 5/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 1796227072.0000 - mae: 32903.4414 - val_loss: 1900755712.0000 - val_mae: 34188.7891 - learning_rate: 0.0010\n", + "Epoch 6/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1738398336.0000 - mae: 32237.4355 - val_loss: 1835951104.0000 - val_mae: 33501.5625 - learning_rate: 0.0010\n", + "Epoch 7/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1673944192.0000 - mae: 31575.8242 - val_loss: 1765187584.0000 - val_mae: 32804.0078 - learning_rate: 0.0010\n", + "Epoch 8/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1604557824.0000 - mae: 30903.6895 - val_loss: 1690087552.0000 - val_mae: 32100.9355 - learning_rate: 0.0010\n", + "Epoch 9/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 1532111872.0000 - mae: 30227.2246 - val_loss: 1612279936.0000 - val_mae: 31392.7227 - learning_rate: 0.0010\n", + "Epoch 10/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 1457709312.0000 - mae: 29556.4414 - val_loss: 1533161088.0000 - val_mae: 30695.7051 - learning_rate: 0.0010\n", + "Epoch 11/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1383581440.0000 - mae: 28908.4434 - val_loss: 1454138880.0000 - val_mae: 30023.1250 - learning_rate: 0.0010\n", + "Epoch 12/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1310495488.0000 - mae: 28299.8477 - val_loss: 1376468352.0000 - val_mae: 29394.1934 - learning_rate: 0.0010\n", + "Epoch 13/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1239926912.0000 - mae: 27729.9043 - val_loss: 1301134208.0000 - val_mae: 28809.5215 - learning_rate: 0.0010\n", + "Epoch 14/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1170712448.0000 - mae: 27170.0469 - val_loss: 1229012224.0000 - val_mae: 28242.0703 - learning_rate: 0.0010\n", + "Epoch 15/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1105601920.0000 - mae: 26642.3184 - val_loss: 1160922624.0000 - val_mae: 27700.8438 - learning_rate: 0.0010\n", + "Epoch 16/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1043984640.0000 - mae: 26124.0840 - val_loss: 1094580352.0000 - val_mae: 27079.0586 - learning_rate: 0.0010\n", + "Epoch 17/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 976227584.0000 - mae: 25163.9297 - val_loss: 996540288.0000 - val_mae: 24844.2109 - learning_rate: 0.0010\n", + "Epoch 18/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 860002496.0000 - mae: 21725.8691 - val_loss: 885134976.0000 - val_mae: 21819.1719 - learning_rate: 0.0010\n", + "Epoch 19/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 772316480.0000 - mae: 19665.3848 - val_loss: 801167040.0000 - val_mae: 20392.8848 - learning_rate: 0.0010\n", + "Epoch 20/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 696815808.0000 - mae: 18389.9785 - val_loss: 723598528.0000 - val_mae: 19151.4785 - learning_rate: 0.0010\n", + "Epoch 21/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 628451968.0000 - mae: 17213.0176 - val_loss: 651424896.0000 - val_mae: 17956.3184 - learning_rate: 0.0010\n", + "Epoch 22/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 563721536.0000 - mae: 16077.4170 - val_loss: 584422592.0000 - val_mae: 16778.9355 - learning_rate: 0.0010\n", + "Epoch 23/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 505489536.0000 - mae: 15020.1113 - val_loss: 523019264.0000 - val_mae: 15676.2910 - learning_rate: 0.0010\n", + "Epoch 24/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 449513696.0000 - mae: 13965.4336 - val_loss: 466503328.0000 - val_mae: 14642.3008 - learning_rate: 0.0010\n", + "Epoch 25/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 400619104.0000 - mae: 13041.7090 - val_loss: 414948032.0000 - val_mae: 13628.7861 - learning_rate: 0.0010\n", + "Epoch 26/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 356454240.0000 - mae: 12158.3799 - val_loss: 368652768.0000 - val_mae: 12694.5703 - learning_rate: 0.0010\n", + "Epoch 27/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 317270240.0000 - mae: 11362.6035 - val_loss: 327098016.0000 - val_mae: 11882.4199 - learning_rate: 0.0010\n", + "Epoch 28/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 282110752.0000 - mae: 10618.4219 - val_loss: 290472800.0000 - val_mae: 11140.4375 - learning_rate: 0.0010\n", + "Epoch 29/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 249665072.0000 - mae: 9972.2529 - val_loss: 259006832.0000 - val_mae: 10496.8271 - learning_rate: 0.0010\n", + "Epoch 30/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 222754640.0000 - mae: 9393.5771 - val_loss: 231574736.0000 - val_mae: 9954.3838 - learning_rate: 0.0010\n", + "Epoch 31/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 198423008.0000 - mae: 8869.1445 - val_loss: 207840512.0000 - val_mae: 9421.6309 - learning_rate: 0.0010\n", + "Epoch 32/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 178380192.0000 - mae: 8440.0615 - val_loss: 187732656.0000 - val_mae: 8949.0840 - learning_rate: 0.0010\n", + "Epoch 33/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 161002656.0000 - mae: 7984.2749 - val_loss: 169997776.0000 - val_mae: 8558.7334 - learning_rate: 0.0010\n", + "Epoch 34/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 144927488.0000 - mae: 7591.0400 - val_loss: 154311120.0000 - val_mae: 8103.6113 - learning_rate: 0.0010\n", + "Epoch 35/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 128720472.0000 - mae: 7144.0977 - val_loss: 140611216.0000 - val_mae: 7693.2202 - learning_rate: 0.0010\n", + "Epoch 36/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 115451752.0000 - mae: 6745.2446 - val_loss: 130359144.0000 - val_mae: 7402.8223 - learning_rate: 0.0010\n", + "Epoch 37/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 103320352.0000 - mae: 6354.1328 - val_loss: 117524160.0000 - val_mae: 6989.3652 - learning_rate: 0.0010\n", + "Epoch 38/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 92202488.0000 - mae: 5979.1660 - val_loss: 109628784.0000 - val_mae: 6704.9717 - learning_rate: 0.0010\n", + "Epoch 39/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 83113800.0000 - mae: 5689.0278 - val_loss: 103527280.0000 - val_mae: 6496.6172 - learning_rate: 0.0010\n", + "Epoch 40/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 75200392.0000 - mae: 5414.2593 - val_loss: 97912072.0000 - val_mae: 6302.9551 - learning_rate: 0.0010\n", + "Epoch 41/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 69330976.0000 - mae: 5204.5713 - val_loss: 92983264.0000 - val_mae: 6090.5542 - learning_rate: 0.0010\n", + "Epoch 42/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 64068488.0000 - mae: 5041.2319 - val_loss: 93400048.0000 - val_mae: 6068.5435 - learning_rate: 0.0010\n", + "Epoch 43/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 59892128.0000 - mae: 4892.9224 - val_loss: 87246792.0000 - val_mae: 5791.9092 - learning_rate: 0.0010\n", + "Epoch 44/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 55631812.0000 - mae: 4743.8657 - val_loss: 85550016.0000 - val_mae: 5762.2046 - learning_rate: 0.0010\n", + "Epoch 45/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 53973056.0000 - mae: 4663.5952 - val_loss: 83252664.0000 - val_mae: 5683.0537 - learning_rate: 0.0010\n", + "Epoch 46/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 50220260.0000 - mae: 4527.8228 - val_loss: 83925944.0000 - val_mae: 5714.0728 - learning_rate: 0.0010\n", + "Epoch 47/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 47764060.0000 - mae: 4444.6938 - val_loss: 82401720.0000 - val_mae: 5649.3335 - learning_rate: 0.0010\n", + "Epoch 48/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 45334620.0000 - mae: 4341.4917 - val_loss: 80761736.0000 - val_mae: 5650.6733 - learning_rate: 0.0010\n", + "Epoch 49/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 43015352.0000 - mae: 4263.1714 - val_loss: 78728576.0000 - val_mae: 5562.2876 - learning_rate: 0.0010\n", + "Epoch 50/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 41184284.0000 - mae: 4168.4272 - val_loss: 76461192.0000 - val_mae: 5454.5000 - learning_rate: 0.0010\n", + "Epoch 1/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 10ms/step - loss: 1932731136.0000 - mae: 34764.9727 - val_loss: 2069818880.0000 - val_mae: 36377.0664 - learning_rate: 0.0010\n", + "Epoch 2/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1923338112.0000 - mae: 34629.4414 - val_loss: 2056002432.0000 - val_mae: 36186.6602 - learning_rate: 0.0010\n", + "Epoch 3/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 1906462720.0000 - mae: 34385.0273 - val_loss: 2034484992.0000 - val_mae: 35888.1211 - learning_rate: 0.0010\n", + "Epoch 4/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 1882598784.0000 - mae: 34036.7148 - val_loss: 2005956992.0000 - val_mae: 35488.6562 - learning_rate: 0.0010\n", + "Epoch 5/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1852413696.0000 - mae: 33614.3867 - val_loss: 1971230848.0000 - val_mae: 35029.5586 - learning_rate: 0.0010\n", + "Epoch 6/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1816782592.0000 - mae: 33156.7617 - val_loss: 1931121664.0000 - val_mae: 34541.7891 - learning_rate: 0.0010\n", + "Epoch 7/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1776370560.0000 - mae: 32665.1387 - val_loss: 1886418944.0000 - val_mae: 34027.4883 - learning_rate: 0.0010\n", + "Epoch 8/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1731960960.0000 - mae: 32169.1250 - val_loss: 1837876736.0000 - val_mae: 33520.9844 - learning_rate: 0.0010\n", + "Epoch 9/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 1684573312.0000 - mae: 31682.2441 - val_loss: 1786250496.0000 - val_mae: 33008.1094 - learning_rate: 0.0010\n", + "Epoch 10/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 1634488960.0000 - mae: 31189.7012 - val_loss: 1732167680.0000 - val_mae: 32490.0215 - learning_rate: 0.0010\n", + "Epoch 11/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1582652928.0000 - mae: 30698.1016 - val_loss: 1676277248.0000 - val_mae: 31974.6699 - learning_rate: 0.0010\n", + "Epoch 12/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1528807936.0000 - mae: 30199.7773 - val_loss: 1619060736.0000 - val_mae: 31453.7559 - learning_rate: 0.0010\n", + "Epoch 13/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 1474490880.0000 - mae: 29703.1445 - val_loss: 1561115776.0000 - val_mae: 30939.0352 - learning_rate: 0.0010\n", + "Epoch 14/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1419293056.0000 - mae: 29219.7656 - val_loss: 1502894208.0000 - val_mae: 30434.5215 - learning_rate: 0.0010\n", + "Epoch 15/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - loss: 1366217600.0000 - mae: 28764.8242 - val_loss: 1445071360.0000 - val_mae: 29948.1836 - learning_rate: 0.0010\n", + "Epoch 16/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 1311709056.0000 - mae: 28299.2773 - val_loss: 1387855616.0000 - val_mae: 29483.8652 - learning_rate: 0.0010\n", + "Epoch 17/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1259717120.0000 - mae: 27880.5957 - val_loss: 1331703424.0000 - val_mae: 29047.3066 - learning_rate: 0.0010\n", + "Epoch 18/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1208217600.0000 - mae: 27472.8125 - val_loss: 1276985344.0000 - val_mae: 28620.4238 - learning_rate: 0.0010\n", + "Epoch 19/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1158040192.0000 - mae: 27063.4766 - val_loss: 1224060544.0000 - val_mae: 28202.8828 - learning_rate: 0.0010\n", + "Epoch 20/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 1109995136.0000 - mae: 26683.9062 - val_loss: 1173220352.0000 - val_mae: 27799.9453 - learning_rate: 0.0010\n", + "Epoch 21/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - loss: 1064320704.0000 - mae: 26317.3105 - val_loss: 1124794624.0000 - val_mae: 27414.1367 - learning_rate: 0.0010\n", + "Epoch 22/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1022760384.0000 - mae: 25982.8965 - val_loss: 1079056128.0000 - val_mae: 27053.7539 - learning_rate: 0.0010\n", + "Epoch 23/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 982177088.0000 - mae: 25652.5938 - val_loss: 1036195840.0000 - val_mae: 26712.5508 - learning_rate: 0.0010\n", + "Epoch 24/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 945132608.0000 - mae: 25346.1582 - val_loss: 996421696.0000 - val_mae: 26395.3066 - learning_rate: 0.0010\n", + "Epoch 25/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 911153472.0000 - mae: 25062.0684 - val_loss: 959820032.0000 - val_mae: 26097.6113 - learning_rate: 0.0010\n", + "Epoch 26/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 880028288.0000 - mae: 24827.4922 - val_loss: 926537280.0000 - val_mae: 25821.6191 - learning_rate: 0.0010\n", + "Epoch 27/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - loss: 853682432.0000 - mae: 24604.5742 - val_loss: 896631808.0000 - val_mae: 25565.9453 - learning_rate: 0.0010\n", + "Epoch 28/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 828166080.0000 - mae: 24403.7070 - val_loss: 870131136.0000 - val_mae: 25342.5938 - learning_rate: 0.0010\n", + "Epoch 29/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 810969728.0000 - mae: 24281.2656 - val_loss: 846989760.0000 - val_mae: 25147.4238 - learning_rate: 0.0010\n", + "Epoch 30/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 790364480.0000 - mae: 24090.2852 - val_loss: 827042432.0000 - val_mae: 24978.3867 - learning_rate: 0.0010\n", + "Epoch 31/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 776585792.0000 - mae: 24009.8965 - val_loss: 810190016.0000 - val_mae: 24829.4160 - learning_rate: 0.0010\n", + "Epoch 32/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - loss: 764959104.0000 - mae: 23917.3750 - val_loss: 796246976.0000 - val_mae: 24708.1191 - learning_rate: 0.0010\n", + "Epoch 33/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - loss: 755435648.0000 - mae: 23878.0566 - val_loss: 784937344.0000 - val_mae: 24605.6172 - learning_rate: 0.0010\n", + "Epoch 34/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - loss: 747755712.0000 - mae: 23794.9590 - val_loss: 775911296.0000 - val_mae: 24518.1973 - learning_rate: 0.0010\n", + "Epoch 35/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 743594688.0000 - mae: 23785.2285 - val_loss: 768952064.0000 - val_mae: 24446.5508 - learning_rate: 0.0010\n", + "Epoch 36/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 739301056.0000 - mae: 23742.7988 - val_loss: 763610304.0000 - val_mae: 24390.0273 - learning_rate: 0.0010\n", + "Epoch 37/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 738923584.0000 - mae: 23775.4492 - val_loss: 759721984.0000 - val_mae: 24347.7969 - learning_rate: 0.0010\n", + "Epoch 38/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - loss: 735023744.0000 - mae: 23729.2637 - val_loss: 756937152.0000 - val_mae: 24316.7676 - learning_rate: 0.0010\n", + "Epoch 39/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 734647168.0000 - mae: 23751.9238 - val_loss: 754886144.0000 - val_mae: 24292.0117 - learning_rate: 0.0010\n", + "Epoch 40/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 732123008.0000 - mae: 23701.5078 - val_loss: 753405888.0000 - val_mae: 24274.6113 - learning_rate: 0.0010\n", + "Epoch 41/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 479316192.0000 - mae: 15964.3057 - val_loss: 403580480.0000 - val_mae: 13544.6035 - learning_rate: 0.0010\n", + "Epoch 42/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 343708096.0000 - mae: 12115.6484 - val_loss: 341869088.0000 - val_mae: 12275.1152 - learning_rate: 0.0010\n", + "Epoch 43/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 10ms/step - loss: 294795616.0000 - mae: 11060.8252 - val_loss: 294227808.0000 - val_mae: 11263.7822 - learning_rate: 0.0010\n", + "Epoch 44/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - loss: 255411248.0000 - mae: 10183.4551 - val_loss: 256555760.0000 - val_mae: 10408.2881 - learning_rate: 0.0010\n", + "Epoch 45/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 226594400.0000 - mae: 9585.3291 - val_loss: 226511520.0000 - val_mae: 9840.6309 - learning_rate: 0.0010\n", + "Epoch 46/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 201210784.0000 - mae: 8997.6709 - val_loss: 201730768.0000 - val_mae: 9280.6221 - learning_rate: 0.0010\n", + "Epoch 47/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 180951744.0000 - mae: 8508.0166 - val_loss: 180386640.0000 - val_mae: 8658.5586 - learning_rate: 0.0010\n", + "Epoch 48/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 161437456.0000 - mae: 8037.3340 - val_loss: 162152432.0000 - val_mae: 8236.7969 - learning_rate: 0.0010\n", + "Epoch 49/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 143073152.0000 - mae: 7514.1865 - val_loss: 141376416.0000 - val_mae: 7570.7739 - learning_rate: 0.0010\n", + "Epoch 50/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - loss: 127263440.0000 - mae: 7038.0229 - val_loss: 127391912.0000 - val_mae: 7156.2104 - learning_rate: 0.0010\n", + "Epoch 1/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 1930911488.0000 - mae: 34738.7148 - val_loss: 2063673600.0000 - val_mae: 36292.5195 - learning_rate: 0.0010\n", + "Epoch 2/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1910318336.0000 - mae: 34439.6289 - val_loss: 2033005824.0000 - val_mae: 35867.5156 - learning_rate: 0.0010\n", + "Epoch 3/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1872711552.0000 - mae: 33894.9219 - val_loss: 1984276864.0000 - val_mae: 35195.7305 - learning_rate: 0.0010\n", + "Epoch 4/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1818348032.0000 - mae: 33175.7305 - val_loss: 1919553280.0000 - val_mae: 34405.4180 - learning_rate: 0.0010\n", + "Epoch 5/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1750787072.0000 - mae: 32374.7637 - val_loss: 1842820480.0000 - val_mae: 33571.0039 - learning_rate: 0.0010\n", + "Epoch 6/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 1673256576.0000 - mae: 31570.9395 - val_loss: 1757159040.0000 - val_mae: 32726.9746 - learning_rate: 0.0010\n", + "Epoch 7/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1589680640.0000 - mae: 30760.4844 - val_loss: 1665691136.0000 - val_mae: 31878.0684 - learning_rate: 0.0010\n", + "Epoch 8/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1501466496.0000 - mae: 29948.0781 - val_loss: 1570926464.0000 - val_mae: 31025.0703 - learning_rate: 0.0010\n", + "Epoch 9/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 1412167296.0000 - mae: 29152.6699 - val_loss: 1475466880.0000 - val_mae: 30201.8711 - learning_rate: 0.0010\n", + "Epoch 10/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1322615424.0000 - mae: 28367.2754 - val_loss: 1379597312.0000 - val_mae: 29309.5332 - learning_rate: 0.0010\n", + "Epoch 11/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1220498816.0000 - mae: 26533.4805 - val_loss: 1260929408.0000 - val_mae: 26583.1484 - learning_rate: 0.0010\n", + "Epoch 12/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 1112165120.0000 - mae: 24360.7832 - val_loss: 1153268480.0000 - val_mae: 25070.1992 - learning_rate: 0.0010\n", + "Epoch 13/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 1014172736.0000 - mae: 22965.8555 - val_loss: 1049243264.0000 - val_mae: 23642.1152 - learning_rate: 0.0010\n", + "Epoch 14/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 918478592.0000 - mae: 21610.4453 - val_loss: 949395648.0000 - val_mae: 22258.7539 - learning_rate: 0.0010\n", + "Epoch 15/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - loss: 828141056.0000 - mae: 20268.5742 - val_loss: 854286336.0000 - val_mae: 20897.9004 - learning_rate: 0.0010\n", + "Epoch 16/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 742179200.0000 - mae: 18959.4883 - val_loss: 764294528.0000 - val_mae: 19546.4355 - learning_rate: 0.0010\n", + "Epoch 17/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 660350528.0000 - mae: 17696.7012 - val_loss: 680099328.0000 - val_mae: 18261.0137 - learning_rate: 0.0010\n", + "Epoch 18/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 587759936.0000 - mae: 16515.5098 - val_loss: 602156032.0000 - val_mae: 16998.6445 - learning_rate: 0.0010\n", + "Epoch 19/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 516721568.0000 - mae: 15305.6777 - val_loss: 530436352.0000 - val_mae: 15776.6455 - learning_rate: 0.0010\n", + "Epoch 20/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 454774592.0000 - mae: 14225.0361 - val_loss: 465422528.0000 - val_mae: 14620.3174 - learning_rate: 0.0010\n", + "Epoch 21/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 400378592.0000 - mae: 13204.1484 - val_loss: 406941792.0000 - val_mae: 13484.5117 - learning_rate: 0.0010\n", + "Epoch 22/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - loss: 351022656.0000 - mae: 12238.7500 - val_loss: 355148032.0000 - val_mae: 12487.7051 - learning_rate: 0.0010\n", + "Epoch 23/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 305887232.0000 - mae: 11356.9395 - val_loss: 309833376.0000 - val_mae: 11593.4004 - learning_rate: 0.0010\n", + "Epoch 24/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 265417856.0000 - mae: 10562.7656 - val_loss: 270137696.0000 - val_mae: 10775.0537 - learning_rate: 0.0010\n", + "Epoch 25/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 234852512.0000 - mae: 9912.8330 - val_loss: 235926240.0000 - val_mae: 10042.0000 - learning_rate: 0.0010\n", + "Epoch 26/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 204994608.0000 - mae: 9289.3477 - val_loss: 208168032.0000 - val_mae: 9438.7832 - learning_rate: 0.0010\n", + "Epoch 27/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 181129088.0000 - mae: 8759.8398 - val_loss: 183661520.0000 - val_mae: 8844.0303 - learning_rate: 0.0010\n", + "Epoch 28/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 159850000.0000 - mae: 8249.1475 - val_loss: 162752464.0000 - val_mae: 8357.9189 - learning_rate: 0.0010\n", + "Epoch 29/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 141057840.0000 - mae: 7771.1348 - val_loss: 144473072.0000 - val_mae: 7830.9922 - learning_rate: 0.0010\n", + "Epoch 30/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 125178776.0000 - mae: 7342.7188 - val_loss: 130381672.0000 - val_mae: 7483.4707 - learning_rate: 0.0010\n", + "Epoch 31/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 113134424.0000 - mae: 6976.5171 - val_loss: 116524848.0000 - val_mae: 7097.4570 - learning_rate: 0.0010\n", + "Epoch 32/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 101647680.0000 - mae: 6615.7490 - val_loss: 107708008.0000 - val_mae: 6762.4595 - learning_rate: 0.0010\n", + "Epoch 33/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 91810328.0000 - mae: 6306.7373 - val_loss: 97756488.0000 - val_mae: 6498.3496 - learning_rate: 0.0010\n", + "Epoch 34/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 82927784.0000 - mae: 6015.1938 - val_loss: 90644216.0000 - val_mae: 6210.5337 - learning_rate: 0.0010\n", + "Epoch 35/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 76303432.0000 - mae: 5791.7852 - val_loss: 84712688.0000 - val_mae: 5904.9824 - learning_rate: 0.0010\n", + "Epoch 36/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 71719856.0000 - mae: 5631.6865 - val_loss: 80555576.0000 - val_mae: 5748.7349 - learning_rate: 0.0010\n", + "Epoch 37/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 67707120.0000 - mae: 5521.0171 - val_loss: 78097168.0000 - val_mae: 5696.1294 - learning_rate: 0.0010\n", + "Epoch 38/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 63857660.0000 - mae: 5421.4185 - val_loss: 73372440.0000 - val_mae: 5529.1011 - learning_rate: 0.0010\n", + "Epoch 39/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 61203508.0000 - mae: 5300.4053 - val_loss: 70816400.0000 - val_mae: 5323.6621 - learning_rate: 0.0010\n", + "Epoch 40/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 59436396.0000 - mae: 5238.1948 - val_loss: 67635208.0000 - val_mae: 5235.2432 - learning_rate: 0.0010\n", + "Epoch 41/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 56774304.0000 - mae: 5124.1748 - val_loss: 66004324.0000 - val_mae: 5169.6328 - learning_rate: 0.0010\n", + "Epoch 42/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 53282044.0000 - mae: 4981.6040 - val_loss: 63594312.0000 - val_mae: 5073.1543 - learning_rate: 0.0010\n", + "Epoch 43/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 51761064.0000 - mae: 4912.4688 - val_loss: 60992804.0000 - val_mae: 4910.9961 - learning_rate: 0.0010\n", + "Epoch 44/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 49697432.0000 - mae: 4781.2227 - val_loss: 59893640.0000 - val_mae: 4884.7842 - learning_rate: 0.0010\n", + "Epoch 45/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 47390824.0000 - mae: 4688.0669 - val_loss: 57193784.0000 - val_mae: 4715.9609 - learning_rate: 0.0010\n", + "Epoch 46/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - loss: 44774588.0000 - mae: 4550.4604 - val_loss: 55175160.0000 - val_mae: 4646.7446 - learning_rate: 0.0010\n", + "Epoch 47/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 45075144.0000 - mae: 4574.6504 - val_loss: 54762232.0000 - val_mae: 4689.1626 - learning_rate: 0.0010\n", + "Epoch 48/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 43724572.0000 - mae: 4516.4062 - val_loss: 53836544.0000 - val_mae: 4628.6943 - learning_rate: 0.0010\n", + "Epoch 49/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 42422604.0000 - mae: 4467.8540 - val_loss: 52762536.0000 - val_mae: 4572.4634 - learning_rate: 0.0010\n", + "Epoch 50/50\n", + "\u001b[1m216/216\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 42222404.0000 - mae: 4455.9072 - val_loss: 51867440.0000 - val_mae: 4502.6763 - learning_rate: 0.0010\n" + ] + } + ], + "source": [ + "def get_callbacks():\n", + " early_stop = EarlyStopping(\n", + " monitor=\"val_loss\",\n", + " patience=10, # increased from 5 — prevents premature stopping\n", + " restore_best_weights=True\n", + " )\n", + " reduce_lr = ReduceLROnPlateau(\n", + " monitor=\"val_loss\",\n", + " factor=0.5, # halve LR when stuck\n", + " patience=5,\n", + " min_lr=1e-6,\n", + " verbose=1\n", + " )\n", + " return [early_stop, reduce_lr]\n", + "\n", + "# Train all three models\n", + "history_baseline = model_baseline.fit(\n", + " X_train_seq, y_train_seq,\n", + " validation_data=(X_val_seq, y_val_seq),\n", + " epochs=50, batch_size=64,\n", + " callbacks=get_callbacks(), verbose=1\n", + ")\n", + "\n", + "history_stacked = model_stacked.fit(\n", + " X_train_seq, y_train_seq,\n", + " validation_data=(X_val_seq, y_val_seq),\n", + " epochs=50, batch_size=64,\n", + " callbacks=get_callbacks(), verbose=1\n", + ")\n", + "\n", + "history_gru = model_gru.fit(\n", + " X_train_seq, y_train_seq,\n", + " validation_data=(X_val_seq, y_val_seq),\n", + " epochs=50, batch_size=64,\n", + " callbacks=get_callbacks(), verbose=1\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.6 Saving Trained Models\n", + "\n", + "The three trained models are saved to disk immediately after training. This ensures \n", + "that the evaluation results, plots, and metrics produced in subsequent cells are \n", + "fully reproducible on every run without needing to retrain, since loading a saved \n", + "model always produces identical predictions regardless of any residual random seed \n", + "behaviour [58]." + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ model_baseline saved\n", + "✓ model_stacked saved\n", + "✓ model_gru saved\n" + ] + } + ], + "source": [ + "# Save all three trained models to disk\n", + "model_baseline.save(\"model_baseline.keras\")\n", + "model_stacked.save(\"model_stacked.keras\")\n", + "model_gru.save(\"model_gru.keras\")\n", + "\n", + "print(\"✓ model_baseline saved\")\n", + "print(\"✓ model_stacked saved\")\n", + "print(\"✓ model_gru saved\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.7 Loading Saved Models\n", + "\n", + "The saved models are loaded back from disk before evaluation begins. All subsequent \n", + "evaluation, visualisation, and comparison cells use these loaded models, guaranteeing \n", + "that the results in this notebook are consistent and reproducible on every run \n", + "[58]." + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ model_baseline loaded\n", + "✓ model_stacked loaded\n", + "✓ model_gru loaded\n" + ] + } + ], + "source": [ + "# Load all three models from disk\n", + "# This guarantees identical results on every run\n", + "model_baseline = tf.keras.models.load_model(\"model_baseline.keras\")\n", + "model_stacked = tf.keras.models.load_model(\"model_stacked.keras\")\n", + "model_gru = tf.keras.models.load_model(\"model_gru.keras\")\n", + "\n", + "print(\"✓ model_baseline loaded\")\n", + "print(\"✓ model_stacked loaded\")\n", + "print(\"✓ model_gru loaded\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.8 Model Evaluation\n", + "\n", + "Each model is evaluated on the held-out test set using three standard regression \n", + "metrics [59]. The test set represents the final unseen time \n", + "period and was not used at any point during training or validation, ensuring the \n", + "evaluation reflects genuine out-of-sample forecasting performance \n", + "[49].\n", + "\n", + "- **MAE (Mean Absolute Error)**: The average absolute difference between predicted \n", + " and actual pedestrian counts. This is the most directly interpretable metric since \n", + " it is expressed in the same unit as the target variable — pedestrians per hour \n", + " [59].\n", + "- **RMSE (Root Mean Squared Error)**: Similar to MAE but penalises larger errors \n", + " more heavily due to the squaring operation. A substantially higher RMSE relative \n", + " to MAE indicates the presence of occasional large prediction errors \n", + " [59].\n", + "- **R² (Coefficient of Determination)**: Measures the proportion of variance in \n", + " pedestrian counts that the model successfully explains. A value of 1.0 represents \n", + " a perfect fit, while 0.0 means the model performs no better than simply predicting \n", + " the mean value [59]." + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m54/54\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n", + "\n", + "Baseline LSTM\n", + " MAE: 5624.15\n", + " RMSE: 8725.10\n", + " R²: 0.9158\n", + "\u001b[1m54/54\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step\n", + "\n", + "Stacked LSTM\n", + " MAE: 9084.27\n", + " RMSE: 14262.18\n", + " R²: 0.7750\n", + "\u001b[1m54/54\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n", + "\n", + "GRU\n", + " MAE: 4921.98\n", + " RMSE: 7325.04\n", + " R²: 0.9407\n" + ] + } + ], + "source": [ + "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", + "\n", + "def evaluate_model(model, X_test, y_test, name=\"Model\"):\n", + " y_pred = model.predict(X_test).flatten()\n", + " mae = mean_absolute_error(y_test, y_pred)\n", + " rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n", + " r2 = r2_score(y_test, y_pred)\n", + " print(f\"\\n{name}\")\n", + " print(f\" MAE: {mae:.2f}\")\n", + " print(f\" RMSE: {rmse:.2f}\")\n", + " print(f\" R²: {r2:.4f}\")\n", + " return y_pred, {\"MAE\": mae, \"RMSE\": rmse, \"R2\": r2}\n", + "\n", + "pred_baseline, metrics_baseline = evaluate_model(model_baseline, X_test_seq, y_test_seq, \"Baseline LSTM\")\n", + "pred_stacked, metrics_stacked = evaluate_model(model_stacked, X_test_seq, y_test_seq, \"Stacked LSTM\")\n", + "pred_gru, metrics_gru = evaluate_model(model_gru, X_test_seq, y_test_seq, \"GRU\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Evaluation Discussion\n", + "\n", + "The GRU model achieved the best performance across all three metrics, with an MAE of \n", + "4,921.98, RMSE of 7,325.04, and an R² of 0.9407. This means the GRU explains 94.1% \n", + "of the variance in hourly pedestrian demand on completely unseen test data, which \n", + "represents strong predictive performance for a real-world urban climate forecasting \n", + "task [59].\n", + "\n", + "Notably, the Stacked LSTM performed worst of the three models despite having the most \n", + "parameters at 35,777. This outcome highlights an important principle in deep learning \n", + "— increased model complexity does not always lead to better generalisation, \n", + "particularly when the dataset size does not justify the added capacity \n", + "[53] [62]. The Baseline LSTM achieved a \n", + "strong R² of 0.9158, confirming that even a single-layer recurrent model can capture \n", + "the majority of the temporal structure in this dataset \n", + "[54]. The GRU's improvement over the baseline demonstrates that \n", + "architectural efficiency can outperform raw model capacity on medium-sized datasets \n", + "[61] [62]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.9 Training Curves\n", + "\n", + "The training and validation loss curves are plotted for each model to assess how \n", + "learning progressed across epochs and to check for any signs of overfitting \n", + "[53].\n", + "\n", + "A well-behaved training curve shows both the training and validation loss decreasing \n", + "together and converging toward a stable low value. If the validation loss begins to \n", + "rise while training loss continues to fall, this indicates overfitting — meaning the \n", + "model is memorising the training data rather than learning generalisable patterns \n", + "[60]." + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n", + "\n", + "for ax, history, name in zip(\n", + " axes,\n", + " [history_baseline, history_stacked, history_gru],\n", + " [\"Baseline LSTM\", \"Stacked LSTM\", \"GRU\"]\n", + "):\n", + " ax.plot(history.history[\"loss\"], label=\"Train Loss\")\n", + " ax.plot(history.history[\"val_loss\"], label=\"Val Loss\")\n", + " ax.set_title(f\"{name} — Training Curves\")\n", + " ax.set_xlabel(\"Epoch\")\n", + " ax.set_ylabel(\"MSE Loss\")\n", + " ax.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Training Curve Discussion\n", + "\n", + "All three models demonstrate healthy learning behaviour across the 50 training \n", + "epochs. Both the training and validation loss curves decrease consistently and \n", + "converge without significant divergence, indicating that no meaningful overfitting \n", + "occurred in any of the three models [60]. The Dropout \n", + "regularisation layers and the ReduceLROnPlateau callback both contributed to this \n", + "stable training behaviour \n", + "[60] [63].\n", + "\n", + "The Stacked LSTM validation curve is notably observed to plateau earlier and at a \n", + "higher loss value compared to the Baseline LSTM and GRU, which is consistent with \n", + "its weaker test set performance. The GRU and Baseline LSTM both show smooth \n", + "convergence with training and validation loss tracking closely throughout all 50 \n", + "epochs [54] [61]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.10 Actual vs Predicted Visualisation\n", + "\n", + "To assess prediction quality beyond summary metrics, the actual and predicted \n", + "pedestrian counts are plotted across the first 200 hours of the test set. This \n", + "visualisation reveals how well each model captures the shape, magnitude, and timing \n", + "of the daily pedestrian demand cycle, and whether any systematic prediction errors \n", + "are present [3] [59]." + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(3, 1, figsize=(14, 14))\n", + "\n", + "for ax, preds, name in zip(\n", + " axes,\n", + " [pred_baseline, pred_stacked, pred_gru],\n", + " [\"Baseline LSTM\", \"Stacked LSTM\", \"GRU\"]\n", + "):\n", + " ax.plot(y_test_seq[:200], label=\"Actual\", color=\"black\")\n", + " ax.plot(preds[:200], label=\"Predicted\", color=\"steelblue\", alpha=0.8)\n", + " ax.set_title(f\"{name} — Actual vs Predicted (First 200 Hours)\")\n", + " ax.set_xlabel(\"Hour\")\n", + " ax.set_ylabel(\"Pedestrian Count\")\n", + " ax.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Visualisation Discussion\n", + "\n", + "The actual versus predicted plots reveal clear qualitative differences between the \n", + "three models that are consistent with the quantitative evaluation results. The GRU \n", + "most closely follows the actual pedestrian demand curve across the 200-hour window, \n", + "including the sharp daytime peaks during business hours and the near-zero counts \n", + "during the early morning hours \n", + "[61] [62].\n", + "\n", + "The Baseline LSTM captures the general daily rhythm but slightly underestimates the \n", + "magnitude of peak values, producing a smoother curve that misses some of the sharper \n", + "intraday fluctuations [54]. The Stacked LSTM shows the most \n", + "pronounced smoothing effect, with predicted values appearing noticeably flattened at \n", + "daily peaks — capped around 55,000 pedestrians regardless of the actual peak — which \n", + "directly explains its substantially higher RMSE and lower R² relative to the other \n", + "two models [53].\n", + "\n", + "These visual results are fully consistent with the evaluation metrics and further \n", + "support the selection of the GRU as the best performing architecture for this \n", + "pedestrian forecasting task." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.11 Model Comparison Summary\n", + "\n", + "The table below consolidates the test set performance metrics for all three models, \n", + "providing a direct side-by-side comparison to support final model selection \n", + "[59] [57]." + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Model MAE RMSE R²\n", + "Baseline LSTM 5624.145508 8725.096217 0.915807\n", + " Stacked LSTM 9084.274414 14262.183844 0.775039\n", + " GRU 4921.983887 7325.042252 0.940659\n" + ] + } + ], + "source": [ + "comparison = pd.DataFrame({\n", + " \"Model\": [\"Baseline LSTM\", \"Stacked LSTM\", \"GRU\"],\n", + " \"MAE\": [metrics_baseline[\"MAE\"], metrics_stacked[\"MAE\"], metrics_gru[\"MAE\"]],\n", + " \"RMSE\": [metrics_baseline[\"RMSE\"], metrics_stacked[\"RMSE\"], metrics_gru[\"RMSE\"]],\n", + " \"R²\": [metrics_baseline[\"R2\"], metrics_stacked[\"R2\"], metrics_gru[\"R2\"]],\n", + "})\n", + "print(comparison.to_string(index=False))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.12 Conclusion\n", + "\n", + "The deep learning segment compared three recurrent neural network architectures — a \n", + "Baseline LSTM, a Stacked LSTM, and a GRU — for hourly pedestrian count forecasting \n", + "in Melbourne CBD using climate and engineered time-series features.\n", + "\n", + "The GRU model was identified as the best performing architecture, achieving an R² of \n", + "0.9407, MAE of 4,921.98 pedestrians per hour, and RMSE of 7,325.04 across the unseen \n", + "test set [59] [61]. These results \n", + "demonstrate that a well-designed recurrent model, even without excessive architectural \n", + "complexity, can effectively learn the temporal structure of urban pedestrian demand \n", + "from climate and time-series inputs \n", + "[53] [62].\n", + "\n", + "The finding that the Stacked LSTM underperformed despite having the most parameters \n", + "highlights an important consideration in deep learning model selection — that added \n", + "complexity must be justified by the volume and complexity of the data \n", + "[53] [57]. The GRU's parameter efficiency \n", + "and strong generalisation make it the most suitable architecture for this dataset size \n", + "and forecasting task [61].\n", + "\n", + "From an applied perspective, a model explaining 94.1% of the variance in hourly \n", + "pedestrian demand could realistically support urban planning decisions and help \n", + "commuters anticipate pedestrian congestion during varying climate conditions, directly \n", + "addressing the use case scenario outlined at the beginning of this notebook \n", + "[53] [56]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "[1] pandas development team (2026) _pandas - Python Data Analysis Library_, pandas, Online.\n", + "\n", + "[2] pandas development team (2026) _Time series / date functionality_, pandas, Online.\n", + "\n", + "[3] Matplotlib Development Team (Unknown date) _Matplotlib: Visualization with Python_, Matplotlib, Online.\n", + "\n", + "[4] scikit-learn Developers (Unknown date) _Preprocessing data_, scikit-learn, Online.\n", + "\n", + "[5] TensorFlow (2023) _Keras: The high-level API for TensorFlow_, Google, Online.\n", + "\n", + "[6] Guido van Rossum, Barry Warsaw and Alyssa Coghlan (2001) _PEP 8 – Style Guide for Python Code_, Python Enhancement Proposals, Online.\n", + "\n", + "[7] TensorFlow (2024) _tf.keras.utils.set_random_seed_, Google, Online.\n", + "\n", + "[8] Opendatasoft (Unknown date) _Huwise's Explore API Reference Documentation_, Opendatasoft, Online.\n", + "\n", + "[9] City of Melbourne (2023) _Pedestrian Counting System - Sensor Locations_, City of Melbourne Open Data Portal, Online.\n", + "\n", + "[10] City of Melbourne (2023) _Pedestrian Counting System: Counts per Hour_, City of Melbourne Open Data Portal, Online.\n", + "\n", + "[11] City of Melbourne (2023) _Microclimate Sensors Data_, City of Melbourne Open Data Portal, Online.\n", + "\n", + "[12] IBM (Unknown date) _What is data profiling?_, IBM, Online.\n", + "\n", + "[13] pandas development team (2026) _pandas.DataFrame.shape_, pandas, Online.\n", + "\n", + "[14] pandas development team (2026) _pandas.DataFrame.columns_, pandas, Online.\n", + "\n", + "[15] pandas development team (2026) _pandas.DataFrame.dtypes_, pandas, Online.\n", + "\n", + "[16] pandas development team (2026) _pandas.DataFrame.info_, pandas, Online.\n", + "\n", + "[17] pandas development team (2026) _pandas.DataFrame.isna_, pandas, Online.\n", + "\n", + "[18] pandas development team (2026) _pandas.DataFrame.describe_, pandas, Online.\n", + "\n", + "[19] pandas development team (2026) _pandas.to_datetime_, pandas, Online.\n", + "\n", + "[20] pandas development team (2026) _pandas.DataFrame.nunique_, pandas, Online.\n", + "\n", + "[21] IBM (Unknown date) _What is Data Cleaning?_, IBM, Online.\n", + "\n", + "[22] Tableau (Unknown date) _Data Cleaning: Definition, Benefits, and How-To_, Tableau, Online.\n", + "\n", + "[23] Max Kuhn and Kjell Johnson (2026) _Feature Selection_, Applied Machine Learning for Tabular Data, Online.\n", + "\n", + "[24] IBM (Unknown date) _What is Feature Selection?_, IBM, Online.\n", + "\n", + "[25] Youran Zhou, Sunil Aryal and Mohamed Reda Bouadjenek (2024) _A Comprehensive Review of Handling Missing Data: Exploring Special Missing Mechanisms_, arXiv, Online.\n", + "\n", + "[26] Cribl (Unknown date) _What is Data Normalization?_, Cribl, Online.\n", + "\n", + "[27] The Epidemiologist R Handbook Contributors (Unknown date) _Working with dates_, The Epidemiologist R Handbook, Online.\n", + "\n", + "[28] Skforecast Team (Unknown date) _Time series aggregation_, Skforecast, Online.\n", + "\n", + "[29] Microsoft Support (Unknown date) _Join tables and queries_, Microsoft, Online.\n", + "\n", + "[30] IBM (Unknown date) _What is Data Integration?_, IBM, Online.\n", + "\n", + "[31] RudderStack (Unknown date) _What Is Data Validation? Why, When, and How To Use It_, RudderStack, Online.\n", + "\n", + "[32] Google Cloud (Unknown date) _What is Time Series?_, Google, Online.\n", + "\n", + "[33] Ali Suliman AlSalehy and Mike Bailey (2025) _Improving Time Series Data Quality: Identifying Outliers and Handling Missing Values in a Multilocation Gas and Weather Dataset_, MDPI, Basel, Switzerland.\n", + "\n", + "[34] NIST/SEMATECH (Unknown date) _What is EDA?_, NIST/SEMATECH e-Handbook of Statistical Methods, Online.\n", + "\n", + "[35] Rob J. Hyndman and George Athanasopoulos (2018) _Time series patterns_, OTexts, Melbourne, Australia.\n", + "\n", + "[36] NIST/SEMATECH (Unknown date) _Introduction to Time Series Analysis_, NIST/SEMATECH e-Handbook of Statistical Methods, Online.\n", + "\n", + "[37] NIST/SEMATECH (Unknown date) _Seasonality_, NIST/SEMATECH e-Handbook of Statistical Methods, Online.\n", + "\n", + "[38] NIST/SEMATECH (Unknown date) _Histogram_, NIST/SEMATECH e-Handbook of Statistical Methods, Online.\n", + "\n", + "[39] Penn State Eberly College of Science (Unknown date) _3.4.2 - Correlation_, The Pennsylvania State University, Online.\n", + "\n", + "[40] Jim Frost (Unknown date) _Using Moving Averages to Smooth Time Series Data_, Statistics By Jim, Online.\n", + "\n", + "[41] Josef Waples and Laiba Siddiqui (Unknown date) _Time Series Decomposition: Trends, Seasonality, and Noise_, DataCamp, Online.\n", + "\n", + "[42] Penn State Eberly College of Science (Unknown date) _10.2 - Autocorrelation and Time Series Methods_, The Pennsylvania State University, Online.\n", + "\n", + "[43] Minitab (Unknown date) _Interpret all statistics and graphs for Augmented Dickey-Fuller Test_, Minitab Support, Online.\n", + "\n", + "[44] IBM (Unknown date) _What is Feature Engineering?_, IBM, Online.\n", + "\n", + "[45] dotData (Unknown date) _Practical Guide for Feature Engineering of Time Series Data_, dotData, Online.\n", + "\n", + "[46] Skforecast Team (Unknown date) _Cyclical features in time series_, Skforecast, Online.\n", + "\n", + "[47] Feature-engine Developers (Unknown date) _Forecasting Features_, Feature-engine, Online.\n", + "\n", + "[48] Feature-engine Developers (Unknown date) _LagFeatures_, Feature-engine, Online.\n", + "\n", + "[49] ApX Machine Learning (2026) _Train-Test Split for Time Series_, ApX Machine Learning, Online.\n", + "\n", + "[50] Pragati Baheti (2021) _Train Test Validation Split: How To and Best Practices_, V7 Labs, Online.\n", + "\n", + "[51] IBM (Unknown date) _What is Supervised Learning?_, IBM, Online.\n", + "\n", + "[52] ApX Machine Learning (2026) _Feature Scaling: Normalization and Standardization_, ApX Machine Learning, Online.\n", + "\n", + "[53] IBM (Unknown date) _What is Deep Learning?_, IBM, Online.\n", + "\n", + "[54] Christopher Olah (2015) _Understanding LSTM Networks_, Colah's Blog, Online.\n", + "\n", + "[55] TensorFlow (2024) _tf.keras.utils.set_random_seed_, Google, Online.\n", + "\n", + "[56] TensorFlow (2023) _Time series forecasting_, TensorFlow, Online.\n", + "\n", + "[57] ApX Machine Learning (2026) _Model Selection and Evaluation_, ApX Machine Learning, Online.\n", + "\n", + "[58] TensorFlow (2023) _tf.keras.Model.fit_, TensorFlow, Online.\n", + "\n", + "[59] scikit-learn Developers (Unknown date) _Regression metrics_, scikit-learn, Online.\n", + "\n", + "[60] Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever and Ruslan Salakhutdinov (2014) _Dropout: A Simple Way to Prevent Neural Networks from Overfitting_, Journal of Machine Learning Research, Online.\n", + "\n", + "[61] Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk and Yoshua Bengio (2014) _Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation_, arXiv,Online.\n", + "\n", + "[62] Junyoung Chung, Caglar Gulcehre, KyungHyun Cho and Yoshua Bengio (2014) _Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling_, arXiv, Online.\n", + "\n", + "[63] TensorFlow (2023) _tf.keras.callbacks.EarlyStopping_, TensorFlow, Online." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.12.13" + }, + "vscode": { + "interpreter": { + "hash": "369f2c481f4da34e4445cda3fffd2e751bd1c4d706f27375911949ba6bb62e1c" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.json b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.json new file mode 100644 index 0000000000..45af0af6d6 --- /dev/null +++ b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/UC00213_Urban_Pedestrian_Climate_Impact_Prediction.json @@ -0,0 +1,66 @@ +{ + "title": "Urban_Pedestrian_Climate_Impact_Prediction", + "name": "Predicting Pedestrian Activity in Melbourne CBD Based on Climate Conditions and Time Series Data", + "description": "Predicting pedestrian activity in the City of Melbourne using hourly climate observations. This use case involves sourcing and combining multiple public datasets, cleaning and aligning hourly time-series data, exploring how climate variables relate to pedestrian counts, applying feature engineering, building a deep learning forecasting model, performing model optimisation, and evaluating model performance for climate adaptation planning.", + "tags": [ + "Urban Pedestrian Movement", + "Climate Conditions", + "Pedestrian Counts", + "Microclimate Observations", + "Time-Series Analysis", + "Feature Engineering", + "Deep Learning Forecasting", + "Model Optimisation", + "Data Cleaning", + "Data Validation", + "Data Visualisation" + ], + "difficulty": "Intermediate", + "technology": [ + { + "name": "numpy", + "alias": "np", + "code": "python" + }, + { + "name": "pandas", + "alias": "pd", + "code": "python" + }, + { + "name": "matplotlib", + "alias": "plt", + "code": "python" + }, + { + "name": "statsmodels", + "code": "python" + }, + { + "name": "sklearn", + "code": "python" + }, + { + "name": "os", + "code": "python" + }, + { + "name": "random", + "code": "python" + }, + { + "name": "tensorflow", + "alias": "tf", + "code": "python" + }, + { + "name": "tensorflow.keras", + "code": "python" + } + ], + "datasets": [ + "Pedestrian Counting System (counts per hour)", + "Pedestrian Counting System - Sensor Locations", + "Microclimate Sensor Readings" + ] +} From 550c1da9ec5a0b325bea3e602e493e565d4a2e90 Mon Sep 17 00:00:00 2001 From: Thivainv Date: Sun, 17 May 2026 20:33:11 +1000 Subject: [PATCH 21/23] Delete datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/test.txt --- .../READY TO PUBLISH/Transport_and_Mobility/2026/test.txt | 1 - 1 file changed, 1 deletion(-) delete mode 100644 datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/test.txt diff --git a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/test.txt b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/test.txt deleted file mode 100644 index 8b13789179..0000000000 --- a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/test.txt +++ /dev/null @@ -1 +0,0 @@ - From 0b58bee2b871d1cc120591d20104e6e898c903e4 Mon Sep 17 00:00:00 2001 From: Thivainv Date: Sun, 17 May 2026 20:33:45 +1000 Subject: [PATCH 22/23] Delete datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/test.txt --- .../READY TO PUBLISH/Transport_and_Mobility/2026/T1/test.txt | 1 - 1 file changed, 1 deletion(-) delete mode 100644 datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/test.txt diff --git a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/test.txt b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/test.txt deleted file mode 100644 index 8b13789179..0000000000 --- a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/test.txt +++ /dev/null @@ -1 +0,0 @@ - From 99d80f598f247c711be2720b019426e1743e7441 Mon Sep 17 00:00:00 2001 From: Thivainv Date: Sun, 17 May 2026 20:33:56 +1000 Subject: [PATCH 23/23] Delete datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt --- .../UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt | 1 - 1 file changed, 1 deletion(-) delete mode 100644 datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt diff --git a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt b/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt deleted file mode 100644 index 8b13789179..0000000000 --- a/datascience/usecases/READY TO PUBLISH/Transport_and_Mobility/2026/T1/UC00213_Urban_Pedestrian_Climate_Impact_Prediction/test.txt +++ /dev/null @@ -1 +0,0 @@ -