admin2/template
1
0
Fork 0
mirror of https://github.com/TeachBooks/template.git synced 2026-11-12 22:25:52 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions
template/book/notebooks/traffic/Exercise_Traffic_Render.ipynb
Robert Lanzafame 3d24a01439 massive transfer of risk-reliability book
2024-02-23 16:41:43 +01:00

2250 lines
370 KiB
Text
Raw Blame History

This file contains ambiguous Unicode characters

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Traffic and Transport Reliability"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"````{note}\n",
"This page is generated from an `*.ipynb` file which requires several auxiliary files, all of which can be downloaded using the link below.\n",
"```{eval-rst}\n",
":download:`Download zip file for this exercise.<exercise_traffic.zip>`\n",
"```\n",
"````"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"Have you ever sat in a car during rush hour and been annoyed at the amount of traffic? Have you ever marvelled at the recklessness of fast drivers, watched rubber-neckers gawking at an accident or thought there must be a better way to design this highway? Today you will get a chance to give it a try on your own!\n",
"\n",
"One of the reasons traffic networks are difficult to design is that they must operate well under a wide range of difficult to predict conditions: think of severe weather events, a large number of vehicles on the road during a few key points per day and the unpredictable behavior of human drivers. This all suggests that a stochastic, or probabilistic approach can be well suited to traffic and transport evaluations.\n",
"\n",
"For this assignment you will consider a highway route between Dordrecht and The Hague (we will call this route the ‘trajectory’). You have data measuring travel time along this trajectory during two months, all measured between the hours of 5.00-11.00 (the morning commute), which we will use to estimate the probability that the travel time exceeds some critical threshold, and the threshold itself is determined based on travel time as a risk metric.\n",
"\n",
"## Estimating Traffic Patterns in Practice\n",
"\n",
"Before we begin, note that this exercise is a dramatic simplification of reality, as it uses simplified methods and a single dataset to evaluate one highway trajectory. In practice, this is done by setting up a complex network-based model and using stochastic processes and probability distributions to simulation things like vehicle flow rates, accidents, road closures, etc. Travel times are then described using probability distributions that are based on a large number of simulations.\n",
"\n",
"In other words: assume a distribution of flow rate or vehicles on the road, use stochastic processes to consider the range of highway conditions and the occurrence of accidents, then let the computer do the hard work. In the end you get a histogram of travel times for your trajectory of interest. You can then look at the database of simulations to try and make inference on what leads to undesirable conditions. For example, take the group of simulations with the longest travel times for a given trajectory and see if it is caused by insufficient flow capacity or emergency vehicle response rate (i.e., ability to clear accidents quickly).\n",
"\n",
"In this exercise we will not simulate an entire traffic network, but instead use two simplified methods for estimating travel time, each of which will give different insight into possible design decisions.\n",
"\n",
"## How does this link to MUDE Weeks 2.7-2.8 Risk and Reliability?\n",
"\n",
"This is in fact a relatively simple probabilistic design problem. Just as the simple example for computing dike height to withstand a river discharge with failure probability of 1% (a one random variable case), in this example we need to design the highway such that travel time on the highway only exceeds the threshold amount 5% of the time. In this case the only random variable is vehicle flow rate. As you continue reading, you will probably think the computational models and method for taking into account interventions is a bit complicated, but remember: you only need to design for one random variable!"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from ipywidgets import interact\n",
"import ipywidgets as widgets\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Traffic Variables\n",
"\n",
"We are interested in estimating tavel time, $t$ [minutes], measured along a single highway. Key variables that play a role in determining $t$ for the Dordrecht-Den Haag trajectory are:\n",
"\n",
"- Vehicle flow rate, $q$ [vehicles/hr]: typically measured per link, but for our purposes it will represent the entire highway trajectory. For the morning commute, the flow rate is typically 5500 veh/hr.\n",
"- Capacity, $C$ [veh/hr]: the maximum flow rate of a link. For our trajectory, the capacity is 5600 veh/hr.\n",
"- Delay, $t_{delay}$ [min]: additional time required to travel a trajectory for a single vehicle. Caused by an accident that lasts $t_{accident}$ minutes.\n",
"\n",
"A few other variables will be described below after we introduce two methods to calculate travel time, but first we will introduce you to the Python code that will be used to make it easy for you to solve this problem!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## `Trajectory` class\n",
"\n",
"As the objective of this exercise is to focus on the probabilistic design process (*not* to see how fast you can use Python), many key steps have been automated for you using a class, `Trajectory`, which is defined in `exercise_setup.py`. An instance of the class is initialized with default values already defined, and many of the methods can be used without the need to specify inputs (see examples below). Observations in the attached `csv` file are also initialized as a dataframe attribute of the class, so you don't even need to import the data. For most methods, making a user-specified input argument will allow you to quickly see how a design decision may modify the characteristics of our highway trajectory. \n",
"\n",
"An instance is created in the following cell, and the class is used repeatedly throughout this notebook to provide usage examples. We have tried to provide informative docstrings to help figure out what each method does, so you should not have to look in the `exercise_setup.py` file unless you are curious how the class is defined."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from exercise_setup import Trajectory\n",
"t=Trajectory()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The class is initialized with a number of default values (explained later), which you can check using the following method:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Print current values used in Method 1 and 2.\n",
"Current parameter values are:\n",
" alpha = 0.15\n",
" beta = 4\n",
" t_0 = 28.5\n",
" C = 5600\n",
" rCf = 0.2\n",
" q = 5500\n"
]
}
],
"source": [
"print(t.print_parameters.__doc__)\n",
"t.print_parameters()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It's easy to create a second instance of the class, which can be useful if you want to see what the affect of varying parameter values. For example, here we change $q$ from the default value of 5500 to 5000:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Current parameter values are:\n",
" alpha = 0.15\n",
" beta = 4\n",
" t_0 = 28.5\n",
" C = 5600\n",
" rCf = 0.2\n",
" q = 5000\n"
]
}
],
"source": [
"t2=Trajectory()\n",
"t2.q = 5000\n",
"t2.print_parameters()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Part 1. Two Methods for Estimating Travel-Time\n",
"\n",
"We will introduce two methods for computing the travel time, one that focuses on *flow rate* and *capacity*, the other on *accident duration.*\n",
"\n",
"### Method 1: Bureau of Public Roads (BPR) Curve\n",
"\n",
"Method 1 computes travel time as a function of vehicle flow rate, $q$:\n",
"\n",
"$$t\\left(q\\right)=t^0\\left(1+\\alpha\\left(\\frac{q}{C}\\right)^\\beta\\right)$$\n",
"\n",
"where $t^0$ is the free flow travel time, which is the time it would take to travel the link if there were no other vehicles on the road ($q=0$).\n",
"\n",
"$\\alpha=0.15$ and $\\beta=4.0$ are constants, especially for our simple case.\n",
"\n",
"Later in the exercise we will make changes to the highway (interventions) that will influence the parameters $q$ and $C$ and $t^0$. For example, adding a lane to the highway will increase $C$.\n",
"\n",
"Method 1 has been implemented in the `Trajectory` class as `travel_time_method_1()`."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Compute travel time with Method 1.\n",
" \n",
" Keyword arguments: q, alpha, beta, t_0, C.\n",
" Returns: travel time as float or array, depending on input.\n",
" \n"
]
}
],
"source": [
"print(t.travel_time_method_1.__doc__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use it to calculate the travel time for given variables."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Travel time computed with Method 1 = 32.5 minutes.\n"
]
}
],
"source": [
"print(f'Travel time computed with Method 1 = '\n",
" f'{t.travel_time_method_1():.1f} minutes.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can use *keyword arguments* to change the default values for capacity, free-flow travel time and flow rate to see how the estimated travel-time changes!"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Travel time computed with Method 1 = 31.5 minutes.\n"
]
}
],
"source": [
"print(f'Travel time computed with Method 1 = '\n",
" f'{t.travel_time_method_1(C=6000):.1f} minutes.')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Travel time computed with Method 1 = 28.9 minutes.\n"
]
}
],
"source": [
"print(f'Travel time computed with Method 1 = '\n",
" f'{t.travel_time_method_1(q=3000):.1f} minutes.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using sliders we can look at the effect of capacity and t_0 on the BPR curve."
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"```{note}\n",
"The widgets displayed on this page will not work in the website version, only when you use the `*.ipynb` file with a Python kernel (e.g., in Jupyter Notebook or Jupyter Lab).\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c0bdaad2190343f5b8c103bbd6bd9257",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(FloatSlider(value=28.5, description='t_0', max=32.0, min=26.0, step=0.5), FloatSlider(va…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from custom_widgets import method_1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Method 2: Delays Caused by Accidents\n",
"\n",
"This method takes into account accidents that may have occurred along our trajectory computing the delay experienced by a single vehicle, $t_\\mathrm{delay}$. The delay is related to the length of the accident itself, as well as the traffic flow and trajectory capacity:\n",
"\n",
"$$t_\\mathrm{delay}=t_{\\mathrm{accident}} \\cdot \\frac{(q - rC)}{2q}$$\n",
"\n",
"Where $rC$ is the remaining capacity of the link [veh/hr], approximated as a percentage of the link capacity:\n",
"\n",
"$$rC = rC_f \\cdot C$$\n",
"\n",
"In our case we assume $rC_f$=0.2.\n",
"\n",
"Total travel time along the trajectory can be estimated by adding $t_{delay}$ to the travel time without accidents. This is similar to the free flow travel time, $t^0$, except we must take into account the fact that accidents don't have a significant effect on travel time until traffic flow is already reaching the trajectory capacity. Thus, we introduce a cutoff time, $t_{cutoff}$, above which accidents affect $t$:\n",
"\n",
"$$t=t_{\\mathrm{cutoff}}+t_\\mathrm{delay}$$\n",
"\n",
"The value of $t_{cutoff}$ is estimated using Method 1 for a condition close to the capacity of the highway---100 veh/hr less---which returns 32.5 min, computed here:."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Travel time for q = 5500.0 and C = 5600.0 using Method 1: 32.50 min\n"
]
}
],
"source": [
"print(f'Travel time for q = {t.q} and C = {t.C} using Method 1: '\n",
" f'{t.travel_time_method_1(q=t.q, C=t.C):.2f} min')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Method 2 has been implemented in the `Trajectory` class as `travel_time_method_2()`, which computes travel time for a single vehicle and a single accident. The class also assumes $t_{accident}=10$ min as a default value, which is close to the median accident length during the morning commute. "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Compute travel time with Method 2 (delays).\n",
" \n",
" Keyword arguments: t_accident, q, C, rCf, t_cutoff.\n",
" Returns: travel time as float or array, depending on input.\n",
" \n"
]
}
],
"source": [
"print(t.travel_time_method_2.__doc__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use it to calculate the travel time for given variables, which is set up to assume that a single 10 min accident has occured for a vehicle traveling the trajectory."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"A single accident duration of 10 min is assumed by default. Include argument t_accident=? to override.\n",
"Travel time computed with Method 2 = 36.5 minutes.\n"
]
}
],
"source": [
"print(f'Travel time computed with Method 2 = '\n",
" f'{t.travel_time_method_2():.1f} minutes.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Investigating the effect of accident duration is easy because you can change the default argument values just like for Method 1 (see documentation for complete list of input arguments):"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Travel time computed with Method 2 = 44.4 minutes when accident time is 30 min.\n"
]
}
],
"source": [
"try_t_accident = 30\n",
"print(f'Travel time computed with Method 2 = '\n",
" f'{t.travel_time_method_2(t_accident=try_t_accident):.1f} '\n",
" f'minutes when accident time is {try_t_accident} min.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is also a method to compute delay:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"A single accident duration of 10 min is assumed by default. Include argument t_accident=? to override.\n",
"The delay for one vehicle is 3.98 min.\n",
"The delay for one vehicle due to a 30 min accident is 11.95 min.\n"
]
}
],
"source": [
"print(f'The delay for one vehicle is '\n",
" f'{t.estimate_t_delay():.2f} min.')\n",
"print(f'The delay for one vehicle due to a {try_t_accident} min '\n",
" f'accident is {t.estimate_t_delay(t_accident=try_t_accident):.2f} min.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Interesting, the previous calculation shows that if the accident time increases by 20 min, the delay is only increased by 8 min."
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"```{note}\n",
"The widgets displayed on this page will not work in the website version, only when you use the `*.ipynb` file with a Python kernel (e.g., in Jupyter Notebook or Jupyter Lab).\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d00800ac309946df9291756aa1ffcbc4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(FloatSlider(value=10.0, description='t_accident', max=60.0, min=5.0, step=5.0), FloatSli…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from custom_widgets import method_2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Part 2. Importing the Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we do 'the usual': import data using pandas, clean it up and explore it with a few summary statistics and visualizations. Luckily we've already done this for you in the `process_observations` method in the `Trajectory` class, and class is initialized with the datafram as an attribute."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Create dataframe with observations.\n",
" \n",
" Steps:\n",
" - Import file defined by string attribute observations_filename.\n",
" - Extract all columns with observations.\n",
" - Set up and return new dataframe:\n",
" - travel_time_df\n",
" - Set up and return new arrays:\n",
" - travel_time\n",
" - flow_rate\n",
" - t_accident\n",
" - dataframe and arrays are added as attributes in init\n",
" \n"
]
}
],
"source": [
"print(t.process_observations.__doc__)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Travel Time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>30.144795</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>30.064739</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>29.912339</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>29.972393</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>30.084815</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Travel Time\n",
"0 30.144795\n",
"1 30.064739\n",
"2 29.912339\n",
"3 29.972393\n",
"4 30.084815"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t.travel_time_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Travel Time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>22021.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>34.344555</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>9.524935</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>28.524847</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>30.146187</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>31.284827</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>34.625918</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>129.301866</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Travel Time\n",
"count 22021.000000\n",
"mean 34.344555\n",
"std 9.524935\n",
"min 28.524847\n",
"25% 30.146187\n",
"50% 31.284827\n",
"75% 34.625918\n",
"max 129.301866"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t.travel_time_df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"hist = t.travel_time_df.hist(density=True,\n",
" bins = 25,\n",
" edgecolor='k',\n",
" linewidth=1.1)\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"bin_count, bin_position = np.histogram(t.travel_time_df, bins=25)\n",
"travel_time_freq = bin_count / sum(bin_count)\n",
"travel_time_cdf = np.cumsum(travel_time_freq)\n",
"plt.plot(bin_position[1:], travel_time_freq, color=\"red\",\n",
" linewidth=3, label=\"Relative frequency\")\n",
"plt.plot(bin_position[1:], travel_time_cdf,\n",
" label=\"Cumulative empirical distribution\", linewidth=3)\n",
"plt.xlabel('Travel Times [min]')\n",
"plt.ylabel('Relative Frequency and Empirical CDF [$-$]')\n",
"plt.legend();"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Empirical Exceedance CDF')"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(bin_position[1:], 1-travel_time_cdf, label=\"CDF\", linewidth=3)\n",
"plt.yscale('log')\n",
"plt.ylim([1e-4, 1]);\n",
"plt.xlabel('Travel Times [min]')\n",
"plt.ylabel('Empirical Exceedance CDF')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Part 3. Fitting a Burr-Distribution to the Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It turns out the Burr distribution often is a good fit for traffic flow rate data, and we can obtain the parameters using MLE incorporated in the `scipy.stats` method [`Burr12`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.burr12.html)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The standardized form of the Burr12 distribution (with location, `loc` = 0 and `scale` = 1) is:\n",
"\n",
"$$\n",
"f(x, c, d)=c d \\frac{x^{c-1}}{\\left(1+x^{c}\\right)^{d+1}}\n",
"$$\n",
"\n",
"The distribution has to be shifted and scaled to fit our data, which is done with the location and scale parameters that are returnd by the method in `scipy`. If the Burr distribution as shown in the introductory video is a two-parameter formulation, and if it is used with the location and scale parameters as follows, it is equivalent to the `scipy` formulation (four parameters):\n",
"\n",
"$$\n",
"f_{scipy}(x, c, d, \\mathrm{loc}, \\mathrm{scale}) = f_{video}(\\frac{x-\\mathrm{loc}}{\\mathrm{scale}}, c, d) \\cdot \\mathrm{scale}\n",
"$$\n",
"\n",
"The CDF of the Burr distribution is very easy to evaluate:\n",
"$$\n",
"F(x,c, d, \\mathrm{loc}, \\mathrm{scale})=1−(1+x^c)^{-k}\n",
"$$\n",
"\n",
"*NB:*\n",
"- *In the video the parameters loc and scale are a and b, respectively. c and k are used equivalently.*\n",
"- *You are asked to make a calculation with the CDF below, one of the few things you are required to code on your own in this notebook.*\n",
"- *There are actually twelve Burr distributions (yes, 12!). They were 'invented' in the 1940's to avoid tedious hand calculations easier. The scipy distribution is equation 12 from [Burr's 1942 paper \"Cumulative Frequency Functions,\"](https://doi.org/10.1093%2Fbiomet%2F64.1.129) hence the name*\n",
"\n",
"In `Trajectory`, the distribution is fit during initialization of the class with `fit_burr`, and the parameter values can be printed using `print_parameters_burr`. The example below illustrates the use of `scipy.stats.Burr12` without requiring to dig into the py file."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Parameters are:\n",
" c = 84.137\n",
" k = 6.421e-02\n",
" loc = 12.00\n",
" scale = 17.49\n"
]
}
],
"source": [
"t.print_parameters_burr()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we will plot the distribution with the histogram to check the fit visually. The PDF is calculated two ways: one using the 4-parameter version provided by `scipy.stats`, the other using the shifted and scaled 2 parameter version. It is clear that the restuls are identical."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot the distribution and the histogram\n",
"from scipy import stats\n",
"from scipy.stats import burr12\n",
"c, k, loc, scale = t.burr_parameters\n",
"x = np.linspace(20,130, 100)\n",
"plt.plot(x, burr12.pdf(x, c, k, loc, scale),\n",
" 'b-', lw=3, label='PDF using 4 parameters')\n",
"plt.plot(x, burr12.pdf((x-loc)/scale, c, k, 0, 1)/scale,\n",
" 'r:', lw=3, label='PDF using 2 parameters')\n",
"plt.hist(t.travel_time_df,density=True, bins=25,\n",
" edgecolor='k', linewidth=1.1)\n",
"plt.legend(loc='best', frameon=False)\n",
"plt.xlabel('Travel time, $t$ [min]')\n",
"plt.ylabel('Probability density and histogram [$-$]')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Burr fit on the data with 4 parameters (red) and with 2 parameters (blue)\n",
"fig, ax = plt.subplots(1, 1)\n",
"ax.plot(x, 1 - burr12.cdf(x, c, k, loc, scale),\n",
" 'b-', lw=3, label='Burr-fit 4 params')\n",
"ax.plot(x, 1 - burr12.cdf((x-loc)/scale, c, k, 0, 1),\n",
" 'r:', lw=3, label='Burr-fit 2 params')\n",
"ax.plot(bin_position[1:], 1 - travel_time_cdf,\n",
" 'k--', lw=3, label=\"CDF of data\")\n",
"ax.legend(loc='best', frameon=False)\n",
"plt.yscale('log')\n",
"plt.xlim([25, 60])\n",
"plt.ylim([1e-4, 1])\n",
"plt.xlabel('Travel time, $t$ [min]')\n",
"plt.ylabel('Exceedance probability, $1-F_t(t)\\:$ [$-$]')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What do you think of the fit?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Part 4. Evaluating the Trajectory"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The requirement for the trajectory is that the 95 percentile value is no more than 10 minutes longer than the free-flow travel time:\n",
"\n",
"$$\n",
"t_{95} \\leq t_0 + 10\\: \\mathrm{min}\n",
"$$\n",
"\n",
"**Using the information provided above, compute the 95th percentile of the Burr distribution in the cell below.**"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"burr_95th_percentile = None"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Use this cell to check whether the trajectory meets the requirement, after you compute the 95th percentile above.**"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Replace the variable in the cell above with your calculation!\n"
]
}
],
"source": [
"if burr_95th_percentile is None:\n",
" print('Replace the variable in the cell above with your calculation!')\n",
"elif burr_95th_percentile > t.t_0 + 10:\n",
" print('The requirement is not met, interventions are needed.')\n",
"elif burr_95th_percentile < 0:\n",
" print('The trajectory meets the requirement.')\n",
"elif burr_95th_percentile < t.t_0 + 10:\n",
" print('The trajectory meets the requirement.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Part 5. Effect of Interventions on Travel Time\n",
"\n",
"Now it's time for the fun part: let's look at a variety of interventions and see if we can improve the travel time on this highway trajectory! The following sections describe the interventions and illustrate the Python tools that are available for you to re-design the highway.\n",
"\n",
"## Description of Intervention Effects\n",
"\n",
"The following interventions can be considered for our trajectory:\n",
"\n",
"1. **Extra Lane:** widens existing road by one or two lanes \n",
"2. **Hard Shoulder:** adds a hard shoulder to the existing trajectory, providing extra capacity, especially during peak flow moments \n",
"3. **Max Speed:** sets the maximum allowed speed on trajectory (current value, 130 km/h; maximum value: 170km/h)\n",
"4. **Alternate Trajectory:** constructs one or two alternative trajectory options (e.g. a second highway or large road), decreasing peak flow rate on the original trajectory \n",
"5. **Incident Management:** speeds up towing and accident clean-up services by X% which reduces time duration of an accident \n",
"6. **Accident Risk Reduction:** employs cooperative intelligent transport systems, C-ITS (vehicle to vehicle communication), to reduce the number of incidents by X%. For our case it can also include a number of measures such as smart signs, lights and lane control. \n",
"7. **Modal Shift, Trucks:** decreases the number of trucks during the morning commute by X% by promoting alternative modes and commute times for truck transport \n",
"8. **Modal Shift, Cars:** equivalent to the above intervention, but for cars \n",
"\n",
"## Implementation of Intervention Effects\n",
"\n",
"The following table indicates which Method can take into account each intervention and how, as well as key information for using the Python tools:\n",
"\n",
"| No. | Intervention | Variable | Method 1 | Method 2 | Keyword Argument | Values |\n",
"| :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n",
"| 1 | Extra Lane | $C$ | X | X | `extra_lane` | `None`, 1, 2 |\n",
"| 2 | Hard Shoulder | $C$ | | X | `hard_shoulder` | `True`/`False` |,\n",
"| 3 | Max. Speed | $t_0$ | X | X | `max_speed` | >0, 170 max |\n",
"| 4 | Alternate trajectory | $q$ | X | X | `alt_route` | `None`, 1, 2 |\n",
"| 5 | Incident Management | $t_{accident}$ | | X | `incident_management` | 1%$-$100% |\n",
"| 6 | Accident Risk Reduction | $t_{accident}$ | | X | `accident_risk_reduction` |1%$-$100% |\n",
"| 7 | Modal Shift, Trucks | $q$ | X | X | `modal_shift_trucks` | 0%$-$20% |\n",
"| 8 | Modal Shift, Cars | $q$ | X | X | `modal_shift_cars` | 0%$-$20% |\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Method 1: BPR"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The expected travel time changes when the free-flow travel time ($t_0$), flow ($q$) and capacity $C$ are modified. The interventions listed above have been implemented as keyword arguments in the method `intervention_method_1`, illustrated with an example below the docstring:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Compute travel time with Method 1.\n",
" \n",
" Keyword arguments: q, alpha, beta, t_0, C.\n",
" Returns: travel time as float or array, depending on input.\n",
" \n"
]
}
],
"source": [
"print(t.travel_time_method_1.__doc__)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The baseline travel time is 32.50 min\n",
"The travel time with an intervention is 28.17 min\n",
" (the difference is -4.33 min)\n"
]
}
],
"source": [
"travel_time_baseline = t.travel_time_method_1()\n",
"travel_time_with_intervention = t.intervention_method_1(max_speed=150)\n",
"print(f'The baseline travel time is '\n",
" f'{travel_time_baseline:0.2f} min')\n",
"print(f'The travel time with an intervention is '\n",
" f'{travel_time_with_intervention:0.2f} min'\n",
" f'\\n (the difference is '\n",
" f'{travel_time_with_intervention - travel_time_baseline:.2f} min)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Burr distribution can also be estimated for the same intervention using `plot_distribution_method_1`, which relies on `intervention_method_1`. Here it is set up with widgets to let you play with the effect of each intervention:"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"```{note}\n",
"The widgets displayed on this page will not work in the website version, only when you use the `*.ipynb` file with a Python kernel (e.g., in Jupyter Notebook or Jupyter Lab).\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7974564f725f4bb1ba9a4b1d8a221aca",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(Dropdown(description='1: Extra Lanes', options=(('None', None), ('1', 1), ('2', 2)), val…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from custom_widgets import method_1_interv_dist"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This function will compute the 95th percentile for the baseline parameter values:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"42.5\n"
]
}
],
"source": [
"print(f'{t.burr_ppf_with_int_1(0.95):.3}')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The same function allows you to check out how the 95th percentile changes by applying interventions.\n",
"\n",
"*What interventions could you apply in order to meet the requirement for the trajectory?*\n"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"33.4\n"
]
}
],
"source": [
"print(f'{t.burr_ppf_with_int_1(0.95,alt_route=1):.3}')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Method 2: Delays\n",
"\n",
"Interventions for Method two have been implemented as keyword arguments in the method `intervention_method_2`, and can be used the same as with those for Methods 1, although there are 8 possible interventions instead of 5:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Compute travel time with Method 2 (delays).\n",
" \n",
" Keyword arguments: t_accident, q, C, rCf, t_cutoff.\n",
" Returns: travel time as float or array, depending on input.\n",
" \n"
]
}
],
"source": [
"print(t.travel_time_method_2.__doc__)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"```{note}\n",
"The widgets displayed on this page will not work in the website version, only when you use the `*.ipynb` file with a Python kernel (e.g., in Jupyter Notebook or Jupyter Lab).\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "892aced71d0140a48243e07efbf54f4f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(Dropdown(description='1: Extra Lanes', options=(('None', None), ('1', 1), ('2', 2)), val…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from custom_widgets import method_2_interv_dist"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Part 6. Cost-Benefit Analysis\n",
"\n",
"Now it's time for the fun part: coming up with an optimal design! It's easy to use the intervention functions introduced above to find a combination that gives a 95th percentile value that meets the requirement. However, in real life interventions cost money, and we will take that into account here using the costs specified in the table, which have also been implemented for you in the methods below, `benefit_cost_method_x`. These functions work with keyword arguments, just like the intervention functions above. With that, the assignment is simply:\n",
"\n",
"**Maximize the ratio of benefits to cost such that: 1) the maximum cost of 250 FCoins is not exceeded, and 2) the 95th percentile is less than $t_0+10$.** Your 'answer' should be in the form of a specific set of interventions (using keyword argument values), costs, benefit-cost ratio, and a short justification for how you came to the result.\n",
"\n",
"Although the assignment is simple, there are a few things to note, in particular: Method 1 and 2 are different, and *both* are simplified versions of reality. They will give you different results because they are different methods, not only because Method 2 includes more interventions. In addition, the costs are defined in terms of FCoins, which are not 'real' monetary units, and have not been scaled to reality; as such, the benefit-cost ratio will does not need to be greatere than 1 to be a viable project.\n",
"\n",
"| Intervention | Keyword Argument | Costs (FCoins) |\n",
"| :--- | :---: | :---: |\n",
"| New Alternative Route | `alt_trajectory` | --- |\n",
"| 1 lane | --- | 150 |\n",
"| 2 lanes | --- | 180 |\n",
"| New Lane | `extra_lane` | --- |\n",
"| 1 additional lane | --- | 70 |\n",
"| 2 additional lanes | --- | 100 |\n",
"| Create Hard Shoulder | `hard_shoulder` | 60 |\n",
"| Increase Max Speed | `max_speed` | 7/percent |\n",
"| Accident Risk Reduction | `accident_risk_reduction` | 5/percent |\n",
"| Incident Management | `incident_management` | 2/percent |\n",
"| Modal Shift Trucks | `modal_shift_trucks` | 5/percent |\n",
"| Modal Shift Cars | `modal_shift_cars` | 8/percent |\n",
"\n",
"NB: *the \"/percent\" cost is determined per percent change in the intervention basedo on the pre-intervention value. For example, changing the maximum speed from 130 to 140 km/hr is a +8% change, thus it would cost 56 FCoins*"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"from benefit_cost import benefit_cost_method_1\n",
"from benefit_cost import benefit_cost_method_2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These functions compute the benefit to cost ratio, which you need to maximize without exceeding the total cost."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The benefit-cost ratio using Method 1 is: 0.095\n",
"Total cost for all interventions applied: 100.000 FCoins\n"
]
}
],
"source": [
"BC1 = benefit_cost_method_1(modal_shift_trucks=20)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The benefit-cost ratio using Method 2 is: -0.058\n",
"Total cost for all interventions applied: 100.000 FCoins\n"
]
}
],
"source": [
"BC2 = benefit_cost_method_2(modal_shift_trucks=20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Good luck!"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13 (main, Aug 25 2022, 23:51:50) [MSC v.1916 64 bit (AMD64)]"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
"autoclose": false,
"autocomplete": true,
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 1,
"hotkeys": {
"equation": "Ctrl-E",
"itemize": "Ctrl-I"
},
"labels_anchors": false,
"latex_user_defs": false,
"report_style_numbering": false,
"user_envs_cfg": false
},
"vscode": {
"interpreter": {
"hash": "85471ad55176ca86192ed6be6d105faaef73891404cad6bd910e89e8513744d9"
}
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
"0187e7214d8a445dbb5c831f13f34dd3": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"08f445fa1f7a42dab8956fa6ca209e16": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"0953d24691094e3d9b652d497901a09a": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": "initial"
}
},
"0bb275f5ec1542b5ae0ef636577f1b72": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"description_width": ""
}
},
"100c0581cfc747c8af541fc9d5145845": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DropdownModel",
"state": {
"_options_labels": [
"No alternate routes",
"1 alternate route",
"2 alternate routes"
],
"description": "4: Alt. Routes ",
"index": null,
"layout": "IPY_MODEL_7f4e913978bd4d6f9657243f779f9850",
"style": "IPY_MODEL_58db643065bc4aa09ff04267189c6b52"
}
},
"1087ad9b423d40ff9b9d0d0a4b24df42": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": "initial"
}
},
"22f1b77ee9c1494d92078b1300a98833": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatSliderModel",
"state": {
"description": "t_cutoff",
"layout": "IPY_MODEL_9b82df908c8b40248e3f065ede04b43b",
"max": 45,
"min": 20,
"step": 2.5,
"style": "IPY_MODEL_3573b71722c74a3c87daec00c719e7b7",
"value": 32.5
}
},
"23505dba595b426a86b5df0c759e9d3a": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": "initial"
}
},
"23783a41b3db4b2095ac7bcab87a74ae": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"2551df80a02445859a0ccd6fc548693a": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"26045de01f29405180b7e1b1434ce181": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": "initial"
}
},
"26c9d8ccf54242c0b94fb533b091b19d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"29b615e502b74b62a274b74c7ca390c5": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"2cce8708af1d4a05a0524a12ed2132ad": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatSliderModel",
"state": {
"description": "7: Modal Shift, Trucks",
"layout": "IPY_MODEL_b0a6b79bf7bc49489632e3dc04c373fb",
"max": 20,
"step": 4,
"style": "IPY_MODEL_869499b6fbea4715b4fb5a8a63384f5c"
}
},
"2d4a20417d044afcaf61f059716e5ebc": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": ""
}
},
"3385de2b8e114324919ed816877addf9": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_8ec2f013205f4c4797cb03608de966e9",
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": "<Figure size 640x480 with 1 Axes>"
},
"metadata": {},
"output_type": "display_data"
}
]
}
},
"3573b71722c74a3c87daec00c719e7b7": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": ""
}
},
"38cfb9ece79a4347a7bf1842d6ea3932": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"4542024c6c2b478eaef6d5ff81808e34": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DropdownModel",
"state": {
"_options_labels": [
"No alternate routes",
"1 alternate route",
"2 alternate routes"
],
"description": "4: Alt. Routes ",
"index": null,
"layout": "IPY_MODEL_eee3ad0fd45c47d5ab47454238d366a2",
"style": "IPY_MODEL_7f14219ee396472a8558de07bd7404e3"
}
},
"49e2865e48e44c7d88ea961604260cbf": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "ToggleButtonsStyleModel",
"state": {
"button_width": "",
"description_width": ""
}
},
"4a4cb1653cd14b8ba63f5ce8b294857f": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"4c76dbfaddda4c84888c273562e48074": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"4cfc18f404d74f96a0f52738322a7e45": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatSliderModel",
"state": {
"description": "3: Speed",
"layout": "IPY_MODEL_503da8f079f941bc88d25fc2dc55a03c",
"max": 170,
"min": 1,
"step": 10,
"style": "IPY_MODEL_0953d24691094e3d9b652d497901a09a",
"value": 130
}
},
"4fefe5bdbf3f4be8bf2f032fddb4b8f7": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_853ac92d93bf4f1386363ef9d907c589",
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAjMAAAG2CAYAAACKxwc0AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAABRVElEQVR4nO3deVxU5f4H8M+IMLK7AoMgooIruGGI9hNc0cxQyxVTtGuZmltdDU3FVEi7mnYtMyvUysCuVqam0C1w31CvBIaoqOgFSUUQgUHg+f1xLiMj2wyCZwY/79frec2cZc75zomaT895zjkKIYQAERERkZGqJ3cBRERERE+CYYaIiIiMGsMMERERGTWGGSIiIjJqDDNERERk1BhmiIiIyKgxzBAREZFRY5ghIiIio8YwQ0REREaNYYaIiIiMmuxh5ubNm5gwYQKaNGkCCwsLdOnSBXFxcZrlQgiEhITA0dER5ubm8PPzQ0JCgowVExERkSGRNcxkZmaid+/eMDU1xS+//ILExESsWbMGDRs21KyzevVqrF27Fhs2bMCpU6fg4OCAgQMH4v79+/IVTkRERAZDIeeDJt99910cOXIEhw4dKne5EAKOjo6YM2cOFixYAABQq9Wwt7fHqlWr8MYbbzzNcomIiMgAyRpmOnToAH9/f9y4cQOxsbFo3rw5pk+fjqlTpwIArly5gtatW+PMmTPo2rWr5nMBAQFo2LAhtm7dWmabarUaarVaM11cXIy7d++iSZMmUCgUtf+liIiI6IkJIXD//n04OjqiXr0qTiQJGSmVSqFUKkVwcLA4c+aM+Oyzz0SDBg3E1q1bhRBCHDlyRAAQN2/e1Prc1KlTxaBBg8rd5tKlSwUANjY2NjY2tjrQUlNTq8wT9SGj4uJieHl5ITQ0FADQtWtXJCQkYOPGjZg4caJmvcd7VIQQFfayBAcHY968eZrprKwstGjRAqmpqbCxsamFb0FEREQ1LTs7G87OzrC2tq5yXVnDjEqlQocOHbTmtW/fHjt37gQAODg4AADS09OhUqk062RkZMDe3r7cbSqVSiiVyjLzbWxsGGaIiIiMjC5DRGS9mql3795ISkrSmnfx4kW4uLgAAFxdXeHg4IDo6GjN8oKCAsTGxqJXr15PtVYiIiIyTLL2zMydOxe9evVCaGgoRo8ejZMnT+Lzzz/H559/DkBKY3PmzEFoaCjc3Nzg5uaG0NBQWFhYYPz48XKWTkRERAZC1jDTo0cP/PDDDwgODsb7778PV1dXrFu3DoGBgZp15s+fj7y8PEyfPh2ZmZnw9vZGVFSUTufQiIiIqO6T9dLspyE7Oxu2trbIysrimBkiqtOKiorw8OFDucsg0ompqSlMTEwqXK7P77esPTNERPTkhBBIT0/HvXv35C6FSC8NGzaEg4PDE98HjmGGiMjIlQQZOzs7WFhY8AahZPCEEMjNzUVGRgYAaF2xXB0MM0RERqyoqEgTZJo0aSJ3OUQ6Mzc3ByDdbsXOzq7SU05Vkf2p2UREVH0lY2QsLCxkroRIfyV/t0861othhoioDuCpJTJGNfV3yzBDRERERo1hhoiI6jQ/Pz/MmTOnxrcbEhKCLl261Ph2AeC3335Du3btUFxcXGP7v3r1KhQKBc6dO/fkBeogPj4eTk5OePDgQa3vi2GGiIhkERQUBIVCgWnTppVZNn36dCgUCgQFBem8vZiYGCgUCoO5RP0///kPxo0bB2dnZ5ibm6N9+/ZYv369Tp+dP38+Fi1ahHr1jPdn2sPDA8899xw++uijWt+X8R4lIiIyes7OzoiIiEBeXp5mXn5+Pr777ju0aNFCxsqeXFxcHJo1a4ZvvvkGCQkJWLRoEYKDg7Fhw4ZKP3f06FEkJydj1KhRT6nS2jN58mRs3LgRRUVFtbofhhkiIpJNt27d0KJFC+zatUszb9euXXB2dkbXrl211hVCYPXq1WjVqhXMzc3RuXNn/Otf/wIgnULp27cvAKBRo0ZlenWKi4sxf/58NG7cGA4ODggJCdHa9vXr1xEQEAArKyvY2Nhg9OjRuHXrltY6H3zwAezt7WFtbY3XXnsN+fn5lX63KVOm4OOPP4avry9atWqFCRMmYPLkyVrftTwREREYNGgQGjRooPf+w8PD0b59ezRo0ADt2rXDp59+WuF+ioqK8Nprr8HV1RXm5uZo27atVs/RwYMHYWpqivT0dK3Pvf322+jTpw8A4Nq1axg2bBgaNWoES0tLdOzYEfv27dOs6+/vjzt37iA2NrbS7/ykGGaIiOoaIYAHD+Rp1XhCzuTJkxEeHq6Z/uqrrzBlypQy67333nsIDw/Hxo0bkZCQgLlz52LChAmIjY2Fs7Mzdu7cCQBISkpCWlqa1g/z1q1bYWlpiRMnTmD16tV4//33ER0d/b/DJTB8+HDcvXsXsbGxiI6OxuXLlzFmzBjN53fs2IGlS5di5cqVOH36NFQqVaVBoSJZWVlo3LhxpescPHgQXl5eWvN02f/mzZuxaNEirFy5EhcuXEBoaCgWL16MrVu3lruf4uJiODk5YceOHUhMTMSSJUuwcOFC7NixAwDQp08ftGrVCl9//bXmM4WFhfjmm28wefJkAMCMGTOgVqtx8OBBxMfHY9WqVbCystKsb2Zmhs6dO+PQoUO6H6TqEHVcVlaWACCysrLkLoWIqMbl5eWJxMREkZeX92hmTo4QUqx4+i0nR+faJ02aJAICAsRff/0llEqlSElJEVevXhUNGjQQf/31lwgICBCTJk3631fKEQ0aNBBHjx7V2sZrr70mxo0bJ4QQ4vfffxcARGZmptY6vr6+4vnnn9ea16NHD7FgwQIhhBBRUVHCxMREXL9+XbM8ISFBABAnT54UQgjh4+Mjpk2bprUNb29v0blzZ52/79GjR4WpqamIioqqdD1bW1uxbds2rXm67N/Z2Vls375da53ly5cLHx8fIYQQKSkpAoA4e/ZshfuePn26ePnllzXTq1atEu3bt9dM//jjj8LKykrk/O+fs4eHhwgJCan0+4wYMUIEBQWVu6zcv9//0ef3mz0zREQkq6ZNm2Lo0KHYunUrwsPDMXToUDRt2lRrncTEROTn52PgwIGwsrLStG3btuHy5ctV7sPT01NrWqVSaW6lf+HCBTg7O8PZ2VmzvEOHDmjYsCEuXLigWcfHx0drG49PVyYhIQEBAQFYsmQJBg4cWOm6eXl5ZU4xVbX/v/76C6mpqXjttde0js+KFSsqPT6fffYZvLy80KxZM1hZWWHz5s24fv26ZnlQUBAuXbqE48ePA5B6zUaPHg1LS0sAwKxZs7BixQr07t0bS5cuxfnz58vsw9zcHLm5uZV+5yfFxxkQEdU1FhZATo58+66GKVOmYObMmQCATz75pMzykkuU9+7di+bNm2stUyqVVW7f1NRUa1qhUGi2KYQo9+ZtFc3XV2JiIvr164epU6fivffeq3L9pk2bIjMzU699lHyXzZs3w9vbW2tZRY8J2LFjB+bOnYs1a9bAx8cH1tbW+PDDD3HixAnNOnZ2dhg2bBjCw8PRqlUr7Nu3DzExMZrlf/vb3+Dv74+9e/ciKioKYWFhWLNmDd566y3NOnfv3kXr1q31+j76YpghIqprFArgf//nbCwGDx6MgoICANKg0cd16NABSqUS169fh6+vb7nbMDMzAwC9r5zp0KEDrl+/jtTUVE3vTGJiIrKystC+fXsAQPv27XH8+HFMnDhR87mS3orKJCQkoF+/fpg0aRJWrlypUz1du3ZFYmKi1ryq9m9vb4/mzZvjypUrCAwM1Gk/hw4dQq9evTB9+nTNvPJ6cf72t79h7NixcHJyQuvWrdG7d2+t5c7Ozpg2bRqmTZuG4OBgbN68WSvM/PHHH3jllVd0qqm6GGaIiEh2JiYmmlM65fUkWFtb45133sHcuXNRXFyM559/HtnZ2Th69CisrKwwadIkuLi4QKFQYM+ePXjhhRdgbm6uNRi1IgMGDICnpycCAwOxbt06FBYWYvr06fD19dUMxJ09ezYmTZoELy8vPP/88/j222+RkJCAVq1aVbjdhIQE9O3bF4MGDcK8efM0VwWZmJigWbNmFX7O39+/zKBdXfYfEhKCWbNmwcbGBkOGDIFarcbp06eRmZmJefPmldlPmzZtsG3bNhw4cACurq74+uuvcerUKbi6upapx9bWFitWrMD777+vtWzOnDkYMmQI3N3dkZmZid9++00TAAHpKrObN29iwIABFX7fmsAxM0REZBBsbGxgY2NT4fLly5djyZIlCAsLQ/v27eHv74+ff/5Z8+PbvHlzLFu2DO+++y7s7e01p62qolAo8OOPP6JRo0bo06cPBgwYgFatWiEyMlKzzpgxY7BkyRIsWLAA3bt3x7Vr1/Dmm29Wut3vv/8ef/31F7799luoVCpN69GjR6WfmzBhAhITE5GUlKTX/v/2t7/hiy++wJYtW+Dh4QFfX19s2bKlTDgpMW3aNIwcORJjxoyBt7c37ty5o9VLU6JevXoICgpCUVGRVs8QIPWCzZgxA+3bt8fgwYPRtm1brausvvvuOwwaNAguLi6VfucnpRCiGtfRGZHs7GzY2toiKyur0n9JiIiMUX5+PlJSUuDq6lpm0CgZr/nz5yMrKwubNm2SuxQAwNSpU3Hr1i3s3r1b58+o1Wq4ubnhu+++K3NqqkRlf7/6/H6zZ4aIiMjALFq0CC4uLrV+59yqZGVl4ddff8W3336rNQ5GF9euXcOiRYsqDDI1iWNmiIiIDIytrS0WLlwodxkICAjAyZMn8cYbb1R5Sfnj3N3d4e7uXkuVaWOYISIionKVvgzbkPE0ExERERk1hhkiojqgjl/LQXVUTf3dMswQERmxkjvb1vbt4olqQ8nf7eN3aNYXx8wQERkxExMTNGzYUPOcIQsLixq5BT9RbRJCIDc3FxkZGWjYsGGFj1zQFcMMEZGRc3BwAABNoCEyFg0bNtT8/T4JhhkiIiOnUCigUqlgZ2eHhw8fyl0OkU5MTU2fuEemBMMMEVEdYWJiUmM/DkTGhAOAiYiIyKgxzBAREZFRY5ghIiIio8YwQ0REREaNYYaIiIiMGsMMERERGTWGGSIiIjJqsoaZkJAQKBQKrVb6ToBBQUFllvfs2VPGiomIiMjQyH7TvI4dO+LXX3/VTD9+w6fBgwcjPDxcM21mZvbUaiMiIiLDJ3uYqV+/fqXPZVAqlTXy3AYiIiKqm2QfM5OcnAxHR0e4urpi7NixuHLlitbymJgY2NnZwd3dHVOnTq3yQWpqtRrZ2dlajYiIiOouhRBCyLXzX375Bbm5uXB3d8etW7ewYsUK/Pnnn0hISECTJk0QGRkJKysruLi4ICUlBYsXL0ZhYSHi4uKgVCrL3WZISAiWLVtWZn5WVhZsbGxq+ysRERFRDcjOzoatra1Ov9+yhpnHPXjwAK1bt8b8+fMxb968MsvT0tLg4uKCiIgIjBw5stxtqNVqqNVqzXR2djacnZ0ZZoiIiIyIPmFG9jEzpVlaWsLDwwPJycnlLlepVHBxcalwOSCNsamo14aIiIjqHtnHzJSmVqtx4cIFqFSqcpffuXMHqampFS4nIiKiZ4+sYeadd95BbGwsUlJScOLECbzyyivIzs7GpEmTkJOTg3feeQfHjh3D1atXERMTg2HDhqFp06YYMWKEnGUTERGRAZH1NNONGzcwbtw43L59G82aNUPPnj1x/PhxuLi4IC8vD/Hx8di2bRvu3bsHlUqFvn37IjIyEtbW1nKWTURERAbEoAYA1wZ9BhARERGRYdDn99ugxswQERER6YthhoiIiIwawwwREREZNYYZIiIiMmoMM0RERGTUGGaIiIjIqDHMEBERkVFjmCEiIiKjxjBDRERERo1hhoiIiIwawwwREREZNYYZIiIiMmoMM0RERGTUGGaIiIjIqDHMEBERkVFjmCEiIiKjxjBDRERERo1hhoiIiIwawwwREREZNYYZIiIiMmoMM0RERGTUGGaIiIjIqDHMEBERkVFjmCEiIiKjxjBDRERERo1hhoiIiIwawwwREREZNYYZIiIiMmoMM0RERGTUGGaIiIjIqDHMEBERkVFjmCEiIiKjxjBDRERERo1hhoiIiIwawwwREREZNVnDTEhICBQKhVZzcHDQLBdCICQkBI6OjjA3N4efnx8SEhJkrJiIiIgMjew9Mx07dkRaWpqmxcfHa5atXr0aa9euxYYNG3Dq1Ck4ODhg4MCBuH//vowVExERkSGRPczUr18fDg4OmtasWTMAUq/MunXrsGjRIowcORKdOnXC1q1bkZubi+3bt8tcNRERERkK2cNMcnIyHB0d4erqirFjx+LKlSsAgJSUFKSnp2PQoEGadZVKJXx9fXH06FG5yiUiIiIDU1/OnXt7e2Pbtm1wd3fHrVu3sGLFCvTq1QsJCQlIT08HANjb22t9xt7eHteuXatwm2q1Gmq1WjOdnZ1dO8UTERGRQZA1zAwZMkTz3sPDAz4+PmjdujW2bt2Knj17AgAUCoXWZ4QQZeaVFhYWhmXLltVOwURERGRwZD/NVJqlpSU8PDyQnJysuaqppIemREZGRpnemtKCg4ORlZWlaampqbVaMxEREcnLoMKMWq3GhQsXoFKp4OrqCgcHB0RHR2uWFxQUIDY2Fr169apwG0qlEjY2NlqNiIiI6i5ZTzO98847GDZsGFq0aIGMjAysWLEC2dnZmDRpEhQKBebMmYPQ0FC4ubnBzc0NoaGhsLCwwPjx4+Usm4iIiAyIrGHmxo0bGDduHG7fvo1mzZqhZ8+eOH78OFxcXAAA8+fPR15eHqZPn47MzEx4e3sjKioK1tbWcpZNREREBkQhhBByF1GbsrOzYWtri6ysLJ5yIiIiMhL6/H4b1JgZIiIiIn0xzBAREZFRY5ghIiIio8YwQ0REREaNYYaIiIiMGsMMERERGTWGGSIiIjJqDDNERERk1BhmiIiIyKgxzBAREZFRY5ghIiIio8YwQ0REREaNYYaIiIiMGsMMERERGTWGGSIiIjJqDDNERERk1BhmiIiIyKgxzBAREZFRY5ghIiIio8YwQ0REREaNYYaIiIiMGsMMERERGTWGGSIiIjJqDDNERERk1BhmiIiIyKgxzBAREZFRY5ghIiIio1Zfl5U+/vhjvTc8efJkWFtb6/05IiIiIn0ohBCiqpXq1asHJycnmJiY6LTR1NRUXLx4Ea1atXriAp9UdnY2bG1tkZWVBRsbG7nLISIiIh3o8/utU88MAJw+fRp2dnY6rcseGSIiInpadBozs3TpUlhZWem80YULF6Jx48bVLoqIiIhIVzqdZjJmPM1ERERkfPT5/ebVTERERGTU9A4zt27dwquvvgpHR0fUr18fJiYmWo2IiIjoadJ5AHCJoKAgXL9+HYsXL4ZKpYJCoaiNuoiIiIh0oneYOXz4MA4dOoQuXbrUQjlERERE+tH7NJOzszNqY8xwWFgYFAoF5syZo5kXFBQEhUKh1Xr27Fnj+yYiIiLjpXeYWbduHd59911cvXq1xoo4deoUPv/8c3h6epZZNnjwYKSlpWnavn37amy/REREZPz0Ps00ZswY5ObmonXr1rCwsICpqanW8rt37+q1vZycHAQGBmLz5s1YsWJFmeVKpRIODg76lklERETPCL3DzLp162q0gBkzZmDo0KEYMGBAuWEmJiYGdnZ2aNiwIXx9fbFy5cpK70SsVquhVqs109nZ2TVaLxERERkWvcPMpEmTamznERERiIuLw+nTp8tdPmTIEIwaNQouLi5ISUnB4sWL0a9fP8TFxUGpVJb7mbCwMCxbtqzGaiQiIiLDptMdgLOzszV336uqp0PXu+ympqbCy8sLUVFR6Ny5MwDAz88PXbp0qbD3Jy0tDS4uLoiIiMDIkSPLXae8nhlnZ2feAZiIiMiI1PiDJhs1aoS0tDTN6Z7y7i0jhIBCoUBRUZFORcbFxSEjIwPdu3fXzCsqKsLBgwexYcMGqNXqMjfhU6lUcHFxQXJycoXbVSqVFfbaEBERUd2jU5j57bffNA+O/P3332tkx/3790d8fLzWvMmTJ6Ndu3ZYsGBBuXcTvnPnDlJTU6FSqWqkBiIiIjJ+OoUZX1/fct8/CWtra3Tq1ElrnqWlJZo0aYJOnTohJycHISEhePnll6FSqXD16lUsXLgQTZs2xYgRI2qkBiIiIjJ+eg8ABoD8/HycP38eGRkZKC4u1lr20ksv1UhhJiYmiI+Px7Zt23Dv3j2oVCr07dsXkZGRsLa2rpF9EBERkfHTaQBwafv378fEiRNx+/btshvTY8zM06LPACIiIiIyDPr8fut9B+CZM2di1KhRSEtLQ3FxsVYztCBDREREdZ/eYSYjIwPz5s2Dvb19bdRDREREpBe9w8wrr7yCmJiYWiiFiIiISH96j5nJzc3FqFGj0KxZM3h4eJR5NtOsWbNqtMAnxTEzRERExqfGb5pX2vbt23HgwAGYm5sjJiZG6wZ6CoXC4MIMERER1W16h5n33nsP77//Pt59913Uq6f3WSoiIiKiGqV3GikoKMCYMWMYZIiIiMgg6J1IJk2ahMjIyNqohYiIiEhvep9mKioqwurVq3HgwAF4enqWGQC8du3aGiuOiIiIqCp6h5n4+Hh07doVAPDHH39oLSvvadpEREREtUnvMFNTT80mIiIiqgkcxUtERERGTacwM3LkSGRnZ+u80cDAQGRkZFS7KCIiIiJd6XQHYBMTE1y8eBHNmjWrcoNCCDg7O+PcuXNo1apVjRT5JHgHYCIiIuNT43cAFkLA3d29RoojIiIiqkk6hZnqDPpt3ry53p8hIiIi0pdOYcbX17e26yAiIiKqFl7NREREREaNYYaIiIiMGsMMERERGTWGGSIiIjJq1QozhYWF+PXXX7Fp0ybcv38fAPDf//4XOTk5NVocERERUVX0fjbTtWvXMHjwYFy/fh1qtRoDBw6EtbU1Vq9ejfz8fHz22We1UScRERFRufTumZk9eza8vLyQmZkJc3NzzfwRI0bg3//+d40WR0RERFQVvXtmDh8+jCNHjsDMzExrvouLC27evFljhRERERHpQu+emeLiYhQVFZWZf+PGDVhbW9dIUURERES60jvMDBw4EOvWrdNMKxQK5OTkYOnSpXjhhRdqsjYiIiKiKun01OzS/vvf/6Jv374wMTFBcnIyvLy8kJycjKZNm+LgwYOws7OrrVqrhU/NJiIiMj41/tTs0hwdHXHu3Dl89913OHPmDIqLi/Haa68hMDBQa0AwERER0dOgd8+MsWHPDBERkfGp1Z4ZALh58yaOHDmCjIwMFBcXay2bNWtWdTZJREREVC16h5nw8HBMmzYNZmZmaNKkCRQKhWaZQqFgmCEiIqKnSu/TTM7Ozpg2bRqCg4NRr57hP9qJp5mIiIiMjz6/33qnkdzcXIwdO9YoggwRERHVfXonktdeew3ff/99bdRCREREpDe9TzMVFRXhxRdfRF5eHjw8PGBqaqq1fO3atdUqJCwsDAsXLsTs2bM1N+UTQmDZsmX4/PPPkZmZCW9vb3zyySfo2LGjztvlaSYiIiLjU6tXM4WGhuLAgQNo27YtAJQZAFwdp06dwueffw5PT0+t+atXr8batWuxZcsWuLu7Y8WKFRg4cCCSkpL46AQiIiICUI0ws3btWnz11VcICgqqkQJycnIQGBiIzZs3Y8WKFZr5QgisW7cOixYtwsiRIwEAW7duhb29PbZv34433nijRvZPRERExk3vMTNKpRK9e/eusQJmzJiBoUOHYsCAAVrzU1JSkJ6ejkGDBmnt29fXF0ePHq1we2q1GtnZ2VqNiIiI6i69w8zs2bPxz3/+s0Z2HhERgbi4OISFhZVZlp6eDgCwt7fXmm9vb69ZVp6wsDDY2tpqmrOzc43USkRERIZJ79NMJ0+exG+//YY9e/agY8eOZQYA79q1S6ftpKamYvbs2YiKikKDBg0qXO/xcThCiErH5gQHB2PevHma6ezsbAYaIiKiOkzvMNOwYUPNGJYnERcXh4yMDHTv3l0zr6ioCAcPHsSGDRuQlJQEQOqhUalUmnUyMjLK9NaUplQqoVQqn7g+IiIiMg7VepxBTejfvz/i4+O15k2ePBnt2rXDggUL0KpVKzg4OCA6Ohpdu3YFABQUFCA2NharVq2qkRqIiIjI+FXrQZM1wdraGp06ddKaZ2lpiSZNmmjmz5kzB6GhoXBzc4ObmxtCQ0NhYWGB8ePHy1EyERERGSCdwky3bt3w73//G40aNULXrl0rHbNy5syZGitu/vz5yMvLw/Tp0zU3zYuKiuI9ZoiIiEhDpzATEBCgGYcyfPjwWismJiZGa1qhUCAkJAQhISG1tk8iIiIybjo/zmDKlClYv3690fWK8HEGRERExqdWnpq9detW5OXlPXFxRERERDVJ5zCj5/MoiYiIiJ4Kve4AXN0HSRIRERHVFr0uzXZ3d68y0Ny9e/eJCiIiIiLSh15hZtmyZbC1ta2tWoiIiIj0pleYGTt2LOzs7GqrFiIiIiK96TxmhuNliIiIyBDxaiYiIiIyajqfZiouLq7NOoiIiIiqRa9Ls4mIiIgMDcMMERERGTWGGSIiIjJqDDNERERk1HQaALx7926dN/jSSy9VuxgiIiIifekUZoYPH67TxhQKBYqKip6kHiIiIiK96BRmeFk2ERERGaonGjOTn59fU3UQERERVYveYaaoqAjLly9H8+bNYWVlhStXrgAAFi9ejC+//LLGCyQiIiKqjN5hZuXKldiyZQtWr14NMzMzzXwPDw988cUXNVocERERUVX0DjPbtm3D559/jsDAQJiYmGjme3p64s8//6zR4oiIiIiqoneYuXnzJtq0aVNmfnFxMR4+fFgjRRERERHpSu8w07FjRxw6dKjM/O+//x5du3atkaKIiIiIdKXzU7NLLF26FK+++ipu3ryJ4uJi7Nq1C0lJSdi2bRv27NlTGzUSERFRTRECePjwUSsoqHy6slZYKL126QI895xsX0nvMDNs2DBERkYiNDQUCoUCS5YsQbdu3fDzzz9j4MCBtVEjERGR4RICKCqSQkBBAaBWl//6eKtofkmY0GVZVe8fDycFBVKtNW3hQuMKMwDg7+8Pf3//mq6FiIhIN8XFUhjIz5daee8rmvf4+4pafv6j0FHSSk+XDiTGfnPZ+vUBU9Pqt3bt5C1f3w9MnjwZEyZMQL9+/aBQKGqjJiIiMhZCSD/meXlAbq70+vj7kpafX/F0yfuS8FHedOlWUCD3N6+cmRmgVEqvJa1kWqmUAsDjy8trpdcrCQ6lp0u/ll6n9PTj88sLI0b+e653mLlz5w6GDh2KJk2aYOzYsZgwYQIH/hIRGSIhpEDw4MGjlptb9n1uru6tJKiUfi+EvN9ToQDMzaUf+QYNHr2Wfq9UPmqPT1enPR5WSs+vA+HA2CiE0P+v8N69e9ixYwe2b9+OQ4cOoW3btpgwYQLGjx+Pli1b1kKZ1ZednQ1bW1tkZWXBxsZG7nKIiMpXUADk5Ejt/v3y31fUHjx49FrScnKeftCoVw+wsJCCRVWtQYOy7x9/VSofTZcOJ6XnlfRyMDzUOfr8flcrzJR248YNfPfdd/jqq6+QnJyMwsLCJ9lcjWOYIaJaU1QkhYasLCA7W2ol7+/ffzSvpJXMK/1a0mr7tEmDBoClpdQsLMq+L/1a0szNtadLzyvvlaGCapA+v9/VGgBc4uHDhzh9+jROnDiBq1evwt7e/kk2R0T0dBUUAJmZwL17j14fb1lZZV9LQsv9+zVfk1IJWFsDVlZSK/2+ZNrS8tF0yfuScFLSSs+zsABK3bGdqK6pVpj5/fffsX37duzcuRNFRUUYOXIkfv75Z/Tr16+m6yMiqpwQUri4exe4c0dqmZnSdOmWmVm25ebWTA1KJWBj86jZ2mpPW1tLrfT7ipqpac3URPQM0TvMODk54c6dO/D398emTZswbNgwNGjQoDZqI6JnjRDSaZu//pLa7dva7a+/HgWW27el17t3pRt3PQlbW6BRI6BhQ+1ma1v29fFmYyOdwiEi2egdZpYsWYJRo0ahUaNGtVEPEdU1hYVSCLl1S7tlZJRtf/0l3bejOszNgSZNgMaNpddGjaT3jRs/et+oUdlmY8NTMERGTu8w8/rrr9dGHURkbHJzgf/+V2ppaY9aerp2++sv/a+oadAAaNbsUWvaVGpNmmi/L93MzWvnexKRwavWmJlTp07h+++/x/Xr11Hw2Aj8Xbt26bydjRs3YuPGjbh69SoA6SGWS5YswZAhQwAAQUFB2Lp1q9ZnvL29cfz48eqUTUS6EEI6fXPjRtl28+ajAHPvnu7brFdPCiX29oCdnfRa8r6klQ4vlpa8KoaIdKZ3mImIiMDEiRMxaNAgREdHY9CgQUhOTkZ6ejpGjBih17acnJzwwQcfoE2bNgCArVu3IiAgAGfPnkXHjh0BAIMHD0Z4eLjmM2ZmZvqWTESl5ecD169L7dq1R6+pqVK7cUO6GZouzM0BR0dApSrbHBykplJJPSc8lUNEtUTvMBMaGoqPPvoIM2bMgLW1NdavXw9XV1e88cYbUKlUem1r2LBhWtMrV67Exo0bcfz4cU2YUSqVcHBw0LdMomdXYaEUSK5ckVpKitSuXpVaWppu27GzA5ydgebNAScn6bWkOTpKrzY27EEhItnpHWYuX76MoUOHApCCxoMHD6BQKDB37lz069cPy5Ytq1YhRUVF+P777/HgwQP4+Pho5sfExMDOzg4NGzaEr68vVq5cCTs7uwq3o1aroS41gDA7O7ta9RAZtIICKaBcvAhcugRcvvzo9erVqq/usbAAXFyk1qKF9OrsLL13cpKaUvlUvgoR0ZPSO8w0btwY9/93o6jmzZvjjz/+gIeHB+7du4fcatyzIT4+Hj4+PsjPz4eVlRV++OEHdOjQAQAwZMgQjBo1Ci4uLkhJScHixYvRr18/xMXFQVnBf2jDwsKqHaiIDIoQ0lU/f/4JJCU9er14UQosRUUVf9bMDHB1BVq1kl5dXYGWLR+1Jk3Yo0JEdYbejzMYP348vLy8MG/ePKxcuRLr169HQEAAoqOj0a1bN70GAANAQUEBrl+/jnv37mHnzp344osvEBsbqwk0paWlpcHFxQUREREYOXJkudsrr2fG2dmZjzMgwyWEdFooMRFISJBeS1pWVsWfs7QE3Nyk1ro10KaN1Fq3lk4D1av39L4DEVENq9XHGWzYsAH5+fkAgODgYJiamuLw4cMYOXIkFi9erHexZmZmmgHAXl5eOHXqFNavX49NmzaVWVelUsHFxQXJyckVbk+pVFbYa0Mku5wc4Pz5Ry0+Hvjjj4qvDKpXT+pVaddOau7uQNu2UoBRqdi7QkQEPcNMYWEhfv75Z/j7+wMA6tWrh/nz52P+/Pk1VpAQQqtnpbQ7d+4gNTVV74HGRLL46y8gLk5q584B//mPNK6lvM5QExMpqHTsCHToIL22by+FFt5dloioUnqFmfr16+PNN9/EhQsXamTnCxcuxJAhQ+Ds7Iz79+8jIiICMTEx2L9/P3JychASEoKXX34ZKpUKV69excKFC9G0aVO9LwEnqnX37gGnTwMnTwKnTkkBJjW1/HWbNwc8PaXm4SG1tm054JaIqJr0Ps3k7e2Ns2fPwsXF5Yl3fuvWLbz66qtIS0uDra0tPD09sX//fgwcOBB5eXmIj4/Htm3bcO/ePahUKvTt2xeRkZGwtrZ+4n0TVVtxsTS25ehR4Ngx4MQJaXDu4xQKqbelWzepdekCdO4s3RSOiIhqjN5hZvr06Xj77bdx48YNdO/eHZaWllrLPT09dd7Wl19+WeEyc3NzHDhwQN/yiGpefr7U43LwIHD4MHD8ePkDc11dAW9voEcPwMtLCi8cdE5EVOt0vpppypQpWLduHRo2bFh2IwoFhBBQKBQoquxyURnoMxqaCID0oMOjR4HffwdiY6Wel8fHcVlaSsGlVy+gZ0/guefY40JEVIP0+f3WOcyYmJggLS0NeVXc5rwmTj/VJIYZqlJxsTQ499dfpXboUNnb+dvbA76+wPPPA717S+Nd6lfr0WZERKSDWrk0uyTzGFpYIaqWu3eB6Ghg3z7gwAHp5nSl2dsD/ftLAcbXVxr7wsugiYgMkl7/a6ngf8zJmF28CPz0E7B7t3Qaqbj40TIrK8DPDxgwQGodOjC8EBEZCb3CjLu7e5WB5u7du09UEFGNEUK6RHrnTuDHH8tecdSxIzBkiNSef156BAARERkdvcLMsmXLYGtrW1u1ED05IYCzZ4EdO6SWkvJoWf36QN++QEAAMGyY9FBFIiIyenqFmbFjx1b6xGoi2Vy5AnzzDfD119JddktYWAAvvgiMGCH1wDCMExHVOTqHGY6XIYOTlQV8/z2wbZt0BVIJc3Ng6FBg9Gjp1cJCvhqJiKjW6X01E5GshJAeF7BpExARAeTmSvMVCmng7sSJwPDh0oBeIiJ6JugcZopLX/lB9LTl5EinkTZtkh7aWKJ9eyAoCAgMlJ55REREzxze9YsMW2oq8M9/Aps3Sw9zBKQHMo4eDbzxhnQHXp4CJSJ6pjHMkGE6dQpYswb417+AkkdkuLkB06dLp5IaN5a3PiIiMhgMM2RYDh8Gli8HoqIezevbF5g3D3jhBaBePflqIyIig8QwQ/ITQnqo4/LlQEyMNM/EBBg/XgoxXbrIWR0RERk4hhmS16lTwPz5j0KMqSkweTLw7ruAq6uspRERkXFgmCF5XLoELFwo3ScGkAb1Tp0qBRtnZ3lrIyIio8IwQ0/X3bvA0qXAZ58BhYXSlUgTJwLvv8/HCxARUbUwzNDTUVwMbN0q9bzcvi3NGzwYWLUK8PSUtzYiIjJqDDNU+86fly6pPnJEmu7QAfj4Y6B/f3nrIiKiOoHXuVLtyc8H/v53oFs3KchYWgKrV0t38GWQISKiGsKeGaodp09LY2EuXJCmX3kF+OgjwMlJ3rqIiKjOYc8M1ayHD6UBvj17SkHGwQH4+WfpqiUGGSIiqgXsmaGak5QEjBsHnD0rTY8eDXz6KdCkibx1ERFRncaeGaoZO3cCPXpIQaZxYyAiAoiMZJAhIqJaxzBDT+bhQ+Cdd6QxMffvA336APHxwJgxcldGRETPCJ5moupLS5NCy6FD0vTf/w6EhgL1+WdFRERPD391qHrOngWGDpUCjY0NsGULMGKE3FUREdEziGGG9BcVBbz8MpCTA3TsCPzwA+DmJndVRET0jOKYGdLPli1Sj0xODtCvn3QzPAYZIiKSEcMM6UYIYPlyYPJk6QGRgYHAL78AtrZyV0ZERM84hhmqmhDA7NnAkiXS9LvvAtu2AWZm8tZFREQEjpmhqggBzJsH/POfgEIBbNggPTSSiIjIQDDMUMWEABYsANatk6Y3bwZee03WkoiIiB7H00xUPiGA994DPvxQmt64kUGGiIgMEsMMlW/ZMukGeIB0imnaNHnrISIiqoCsYWbjxo3w9PSEjY0NbGxs4OPjg19++UWzXAiBkJAQODo6wtzcHH5+fkhISJCx4mfEp59KYQYA1q4FZs6Utx4iIqJKyBpmnJyc8MEHH+D06dM4ffo0+vXrh4CAAE1gWb16NdauXYsNGzbg1KlTcHBwwMCBA3H//n05y67boqOBWbOk98uXA3PnylsPERFRFRRCCCF3EaU1btwYH374IaZMmQJHR0fMmTMHCxYsAACo1WrY29tj1apVeOONN3TaXnZ2NmxtbZGVlQUbG5vaLN34/fkn0LMnkJUFTJwo3SBPoZC7KiIiegbp8/ttMGNmioqKEBERgQcPHsDHxwcpKSlIT0/HoEGDNOsolUr4+vri6NGjFW5HrVYjOztbq5EObt8GXnxRCjK9ewOff84gQ0RERkH2MBMfHw8rKysolUpMmzYNP/zwAzp06ID09HQAgL29vdb69vb2mmXlCQsLg62traY5OzvXav11QkGB9Kyly5eBli2lZy0plXJXRUREpBPZw0zbtm1x7tw5HD9+HG+++SYmTZqExMREzXLFY70DQogy80oLDg5GVlaWpqWmptZa7XXGzJnAwYOAtTWwZw/QrJncFREREelM9pvmmZmZoU2bNgAALy8vnDp1CuvXr9eMk0lPT4dKpdKsn5GRUaa3pjSlUgklexV0t2OHdDM8hQKIjJSegk1ERGREZO+ZeZwQAmq1Gq6urnBwcEB0dLRmWUFBAWJjY9GrVy8ZK6xDUlOBkoHUwcHAkCHy1kNERFQNsvbMLFy4EEOGDIGzszPu37+PiIgIxMTEYP/+/VAoFJgzZw5CQ0Ph5uYGNzc3hIaGwsLCAuPHj5ez7LqhqAh49VXg3j2gRw8gJETuioiIiKpF1jBz69YtvPrqq0hLS4OtrS08PT2xf/9+DBw4EAAwf/585OXlYfr06cjMzIS3tzeioqJgbW0tZ9l1wz/+AcTGApaWwLffAqamcldERERULQZ3n5maxvvMlCMuTrqfTGEh8OWXwJQpcldERESkxSjvM0NPyYMHwPjxUpB5+WVg8mS5KyIiInoiDDPPmqVLgYsXgebNeWM8IiKqExhmniUXLgDr10vvN28GGjeWtx4iIqIawDDzrBBCeoBkYSHw0ku8DJuIiOoMhplnxa5dwK+/So8p+OgjuashIiKqMQwzz4LcXGDuXOn9ggVAq1by1kNERFSDGGaeBWFh0t1+W7SQwgwREVEdwjBT112+DHz4ofT+o48ACwt56yEiIqphDDN13dy5gFoNDBwIjBghdzVEREQ1jmGmLjt6FPj5Z6B+feDjj3lPGSIiqpMYZuqyFSuk16AgoF07WUshIiKqLQwzdVVcHPDLL0C9esC778pdDRERUa1hmKmrVq6UXsePB1q3lrcWIiKiWsQwUxf98Qfwww/SGJngYLmrISIiqlUMM3VRSa/Myy8DHTrIWwsREVEtY5ipa5KSgMhI6f2iRfLWQkRE9BQwzNQ1H3wgPVRy2DCgSxe5qyEiIqp1DDN1ydWrwNdfS+/ZK0NERM8Ihpm65B//AIqKpLv9envLXQ0REdFTwTBTV+TmPuqV+fvf5a2FiIjoKWKYqSv+9S8gOxtwdQX695e7GiIioqeGYaau+PJL6XXKFOmuv0RERM8I/urVBcnJwMGDUogJCpK7GiIioqeKYaYu+Oor6XXwYMDJSd5aiIiInjKGGWNXWAhs2SK9f+01WUshIiKSA8OMsdu3D0hPB5o1A158Ue5qiIiInjqGGWNXMvB30iTAzEzeWoiIiGTAMGPM0tKAvXul9zzFREREzyiGGWO2dat0x9/evYF27eSuhoiISBYMM8ZKiEdXMbFXhoiInmEMM8bq2DHp/jJWVsCoUXJXQ0REJBuGGWP100/Sa0CAFGiIiIieUQwzxmr3bun1pZfkrYOIiEhmDDPG6NIl4M8/gfr1AX9/uashIiKSFcOMMfr5Z+nV1xewtZW3FiIiIpnJGmbCwsLQo0cPWFtbw87ODsOHD0dSUpLWOkFBQVAoFFqtZ8+eMlVsIEpOMQ0bJm8dREREBkDWMBMbG4sZM2bg+PHjiI6ORmFhIQYNGoQHDx5orTd48GCkpaVp2r59+2Sq2ABkZgKHDknvGWaIiIhQX86d79+/X2s6PDwcdnZ2iIuLQ58+fTTzlUolHBwcnnZ5hmn/fulGeR07Aq1ayV0NERGR7AxqzExWVhYAoHHjxlrzY2JiYGdnB3d3d0ydOhUZGRlylGcYeIqJiIhIi0IIIeQuAgCEEAgICEBmZiYOlZxGARAZGQkrKyu4uLggJSUFixcvRmFhIeLi4qBUKstsR61WQ61Wa6azs7Ph7OyMrKws2NjYPJXvUmsePpSejp2VBRw5AvTqJXdFREREtSI7Oxu2trY6/X7LepqptJkzZ+L8+fM4fPiw1vwxY8Zo3nfq1AleXl5wcXHB3r17MXLkyDLbCQsLw7Jly2q9XlkcPiwFmaZNAW9vuashIiIyCAZxmumtt97C7t278fvvv8PJyanSdVUqFVxcXJCcnFzu8uDgYGRlZWlaampqbZQsj5JLsl98ETAxkbcWIiIiAyFrz4wQAm+99RZ++OEHxMTEwNXVtcrP3LlzB6mpqVCpVOUuVyqV5Z5+MnpCcLwMERFROWTtmZkxYwa++eYbbN++HdbW1khPT0d6ejry8vIAADk5OXjnnXdw7NgxXL16FTExMRg2bBiaNm2KESNGyFn60/fnn8Dly4CZGTBokNzVEBERGQxZe2Y2btwIAPDz89OaHx4ejqCgIJiYmCA+Ph7btm3DvXv3oFKp0LdvX0RGRsLa2lqGimVUcoqpb18+WJKIiKgU2U8zVcbc3BwHDhx4StUYuJIwwwdLEhERaTGIAcBUhbw84MQJ6T0fLElERKSFYcYYxMVJ95ixt+ddf4mIiB7DMGMMjh2TXnv1AhQKeWshIiIyMAwzxuDoUemVd/wlIiIqg2HG0AnBMENERFQJhhlDd+UKkJEh3V+mWze5qyEiIjI4DDOGrqRXpnt3oEEDeWshIiIyQAwzho6nmIiIiCrFMGPoGGaIiIgqxTBjyLKzgfh46b2Pj7y1EBERGSiGGUN24oR0NZOrK1DBU8KJiIiedQwzhoynmIiIiKrEMGPIGGaIiIiqxDBjqIqKgOPHpfcMM0RERBVimDFUiYnSAGArK6BTJ7mrISIiMlgMM4aq5BSTtzdQv768tRARERkwhhlDxfEyREREOmGYMVQMM0RERDphmDFEGRnApUvS+5495a2FiIjIwDHMGKJjx6TXjh2Bhg1lLYWIiMjQMcwYorg46dXbW946iIiIjADDjCFKTJReeUk2ERFRlRhmDNGFC9Jr+/by1kFERGQEGGYMzcOHwMWL0vsOHeSthYiIyAgwzBiay5eBwkLA0hJwdpa7GiIiIoPHMGNoSk4xtWsHKBTy1kJERGQEGGYMTcngX55iIiIi0gnDjKHh4F8iIiK9MMwYmpKeGYYZIiIinTDMGJLiYuDPP6X3PM1ERESkE4YZQ3L9OpCXB5iZAa1ayV0NERGRUWCYMSQlp5jc3YH69eWthYiIyEgwzBgSDv4lIiLSG8OMIeHgXyIiIr0xzBiSkp4ZDv4lIiLSGcOMoRCCp5mIiIiqQdYwExYWhh49esDa2hp2dnYYPnw4kpKStNYRQiAkJASOjo4wNzeHn58fEhISZKq4FqWnA/fuAfXqSQOAiYiISCeyhpnY2FjMmDEDx48fR3R0NAoLCzFo0CA8ePBAs87q1auxdu1abNiwAadOnYKDgwMGDhyI+/fvy1h5LSjplWnVCmjQQN5aiIiIjIis1//u379fazo8PBx2dnaIi4tDnz59IITAunXrsGjRIowcORIAsHXrVtjb22P79u1444035Ci7dnDwLxERUbUY1M1MsrKyAACNGzcGAKSkpCA9PR2DBg3SrKNUKuHr64ujR4+WG2bUajXUanWZbWZnZ9dm6U/uP/+RXlu1Agy9ViIiolpW8rsthKhyXYMJM0IIzJs3D88//zw6deoEAEhPTwcA2Nvba61rb2+Pa9eulbudsLAwLFu2rMx8Z2fnGq64lqxfLzUiIiLC/fv3YWtrW+k6BhNmZs6cifPnz+Pw4cNllikUCq1pIUSZeSWCg4Mxb948zXRxcTHu3r2LJk2aVPgZXWRnZ8PZ2RmpqamwsbGp9naeFTxe+uHx0g+Pl354vPTD46W72jxWQgjcv38fjo6OVa5rEGHmrbfewu7du3Hw4EE4OTlp5js4OACQemhUKpVmfkZGRpnemhJKpRJKpVJrXsOGDWusVhsbG/5x64HHSz88Xvrh8dIPj5d+eLx0V1vHqqoemRKyXs0khMDMmTOxa9cu/Pbbb3B1ddVa7urqCgcHB0RHR2vmFRQUIDY2Fr169Xra5RIREZEBkrVnZsaMGdi+fTt++uknWFtba8bI2NrawtzcHAqFAnPmzEFoaCjc3Nzg5uaG0NBQWFhYYPz48XKWTkRERAZC1jCzceNGAICfn5/W/PDwcAQFBQEA5s+fj7y8PEyfPh2ZmZnw9vZGVFQUrK2tn2qtSqUSS5cuLXMKi8rH46UfHi/98Hjph8dLPzxeujOUY6UQulzzRERERGSg+GwmIiIiMmoMM0RERGTUGGaIiIjIqDHMEBERkVFjmNHBp59+CldXVzRo0ADdu3fHoUOH5C7pqTh48CCGDRsGR0dHKBQK/Pjjj1rLhRAICQmBo6MjzM3N4efnh4SEBK111Go13nrrLTRt2hSWlpZ46aWXcOPGDa11MjMz8eqrr8LW1ha2trZ49dVXce/evVr+djUrLCwMPXr0gLW1Nezs7DB8+HAkJSVprcPj9cjGjRvh6empudGWj48PfvnlF81yHqvKhYWFaW5dUYLH7JGQkBAoFAqtVnITVoDHqjw3b97EhAkT0KRJE1hYWKBLly6Ii4vTLDf4YyaoUhEREcLU1FRs3rxZJCYmitmzZwtLS0tx7do1uUurdfv27ROLFi0SO3fuFADEDz/8oLX8gw8+ENbW1mLnzp0iPj5ejBkzRqhUKpGdna1ZZ9q0aaJ58+YiOjpanDlzRvTt21d07txZFBYWatYZPHiw6NSpkzh69Kg4evSo6NSpk3jxxRef1tesEf7+/iI8PFz88ccf4ty5c2Lo0KGiRYsWIicnR7MOj9cju3fvFnv37hVJSUkiKSlJLFy4UJiamoo//vhDCMFjVZmTJ0+Kli1bCk9PTzF79mzNfB6zR5YuXSo6duwo0tLSNC0jI0OznMdK2927d4WLi4sICgoSJ06cECkpKeLXX38Vly5d0qxj6MeMYaYKzz33nJg2bZrWvHbt2ol3331Xpork8XiYKS4uFg4ODuKDDz7QzMvPzxe2trbis88+E0IIce/ePWFqaioiIiI069y8eVPUq1dP7N+/XwghRGJiogAgjh8/rlnn2LFjAoD4888/a/lb1Z6MjAwBQMTGxgoheLx00ahRI/HFF1/wWFXi/v37ws3NTURHRwtfX19NmOEx07Z06VLRuXPncpfxWJW1YMEC8fzzz1e43BiOGU8zVaKgoABxcXEYNGiQ1vxBgwbh6NGjMlVlGFJSUpCenq51bJRKJXx9fTXHJi4uDg8fPtRax9HREZ06ddKsc+zYMdja2sLb21uzTs+ePWFra2vUxzgrKwsA0LhxYwA8XpUpKipCREQEHjx4AB8fHx6rSsyYMQNDhw7FgAEDtObzmJWVnJwMR0dHuLq6YuzYsbhy5QoAHqvy7N69G15eXhg1ahTs7OzQtWtXbN68WbPcGI4Zw0wlbt++jaKiojIPtbS3t9c8euFZVfL9Kzs26enpMDMzQ6NGjSpdx87Orsz27ezsjPYYCyEwb948PP/88+jUqRMAHq/yxMfHw8rKCkqlEtOmTcMPP/yADh068FhVICIiAnFxcQgLCyuzjMdMm7e3N7Zt24YDBw5g8+bNSE9PR69evXDnzh0eq3JcuXIFGzduhJubGw4cOIBp06Zh1qxZ2LZtGwDj+PsyiKdmGzqFQqE1LYQoM+9ZVZ1j8/g65a1vzMd45syZOH/+PA4fPlxmGY/XI23btsW5c+dw79497Ny5E5MmTUJsbKxmOY/VI6mpqZg9ezaioqLQoEGDCtfjMZMMGTJE897DwwM+Pj5o3bo1tm7dip49ewLgsSqtuLgYXl5eCA0NBQB07doVCQkJ2LhxIyZOnKhZz5CPGXtmKtG0aVOYmJiUSYwZGRllEuqzpuTKgMqOjYODAwoKCpCZmVnpOrdu3Sqz/b/++ssoj/Fbb72F3bt34/fff4eTk5NmPo9XWWZmZmjTpg28vLwQFhaGzp07Y/369TxW5YiLi0NGRga6d++O+vXro379+oiNjcXHH3+M+vXra74Pj1n5LC0t4eHhgeTkZP59lUOlUqFDhw5a89q3b4/r168DMI7/fjHMVMLMzAzdu3dHdHS01vzo6Gj06tVLpqoMg6urKxwcHLSOTUFBAWJjYzXHpnv37jA1NdVaJy0tDX/88YdmHR8fH2RlZeHkyZOadU6cOIGsrCyjOsZCCMycORO7du3Cb7/9BldXV63lPF5VE0JArVbzWJWjf//+iI+Px7lz5zTNy8sLgYGBOHfuHFq1asVjVgm1Wo0LFy5ApVLx76scvXv3LnMriYsXL8LFxQWAkfz364mGDz8DSi7N/vLLL0ViYqKYM2eOsLS0FFevXpW7tFp3//59cfbsWXH27FkBQKxdu1acPXtWc1n6Bx98IGxtbcWuXbtEfHy8GDduXLmX6jk5OYlff/1VnDlzRvTr16/cS/U8PT3FsWPHxLFjx4SHh4fRXd745ptvCltbWxETE6N1OWhubq5mHR6vR4KDg8XBgwdFSkqKOH/+vFi4cKGoV6+eiIqKEkLwWOmi9NVMQvCYlfb222+LmJgYceXKFXH8+HHx4osvCmtra81/t3mstJ08eVLUr19frFy5UiQnJ4tvv/1WWFhYiG+++UazjqEfM4YZHXzyySfCxcVFmJmZiW7dumkut63rfv/9dwGgTJs0aZIQQrpcb+nSpcLBwUEolUrRp08fER8fr7WNvLw8MXPmTNG4cWNhbm4uXnzxRXH9+nWtde7cuSMCAwOFtbW1sLa2FoGBgSIzM/MpfcuaUd5xAiDCw8M16/B4PTJlyhTNv1PNmjUT/fv31wQZIXisdPF4mOExe6TkHiimpqbC0dFRjBw5UiQkJGiW81iV9fPPP4tOnToJpVIp2rVrJz7//HOt5YZ+zBRCCPFkfTtERERE8uGYGSIiIjJqDDNERERk1BhmiIiIyKgxzBAREZFRY5ghIiIio8YwQ0REREaNYYaIiIiMGsMMkQ6EEHj99dfRuHFjKBQKnDt3rtx5fn5+mDNnTq3V0bJlS6xbt67Wtm/oYmJioFAooFAoMHz48BrdtkKhwI8//ljlvu/du6fT9mr7b+Fp2rJlCxo2bCjb/kNCQjT/3J/lv3+qGMMM1Skl/8GrqAUFBVVru/v378eWLVuwZ88epKWloVOnTuXO27VrF5YvX17t+v38/Mqtu7CwsNrbfBJBQUGaGurXr48WLVrgzTffLPMwOV22U5PhIykpCVu2bKmx7emiV69eSEtLg62t7VPdLwHvvPMO0tLStB7eSlRafbkLIKpJaWlpmveRkZFYsmSJ1gPUzM3NtdZ/+PAhTE1Nq9zu5cuXoVKptB6GVt68xo0bP0n5AICpU6fi/fff15pXv758/6oOHjwY4eHhKCwsRGJiIqZMmYJ79+7hu+++k60mOzu7p95TYGZmpnl6cF2l678PT0tRUREUCgWsrKxgZWUFExMTuUsiA8WeGapTHBwcNM3W1hYKhUIznZ+fj4YNG2LHjh3w8/NDgwYN8M033+DOnTsYN24cnJycYGFhAQ8PD60f6qCgILz11lu4fv06FAoFWrZsWe48oOypBbVajfnz58PZ2RlKpRJubm748ssvK/0OFhYWWt+jsh/Q69evIyAgAFZWVrCxscHo0aNx69YtAEBWVhZMTEwQFxcHQDpV1rhxY/To0UPz+e+++w4qlarSepRKJRwcHODk5IRBgwZhzJgxiIqK0iwvKirCa6+9BldXV5ibm6Nt27ZYv369ZnlISAi2bt2Kn376SdPLExMTAwC4efMmxowZg0aNGqFJkyYICAjA1atXK63ncZs2bULz5s1RXFysNf+ll17CpEmTNNM///wzunfvjgYNGqBVq1ZYtmxZmR6v27dvY8SIEbCwsICbmxt2796tWVbeaaYjR47A19cXFhYWaNSoEfz9/SvstSooKMD8+fPRvHlzWFpawtvbW3McAODatWsYNmwYGjVqBEtLS3Ts2BH79u2r8Hu3bNkSy5cvx/jx42FlZQVHR0f885//1FonKysLr7/+Ouzs7GBjY4N+/frhP//5j2Z5SEgIunTpgq+++gqtWrWCUqlEZU+4OXDgANq3bw8rKysMHjxY638eiouL8f7778PJyQlKpRJdunTB/v37Kz1+586dg0Kh0PwzLzmdtWfPHnTo0AFKpRLXrl2rsB6iEgwz9MxZsGABZs2ahQsXLsDf3x/5+fno3r079uzZgz/++AOvv/46Xn31VZw4cQIAsH79es1/pNPS0nDq1Kly55Vn4sSJiIiIwMcff4wLFy7gs88+g5WVVY18DyEEhg8fjrt37yI2NhbR0dG4fPkyxowZAwCwtbVFly5dND+Y58+f17xmZ2cDkH5gfH19dd7nlStXsH//fq3/ey8uLoaTkxN27NiBxMRELFmyBAsXLsSOHTsASKcIRo8erfnxS0tLQ69evZCbm4u+ffvCysoKBw8exOHDhzU/kgUFBTrXNGrUKNy+fRu///67Zl5mZiYOHDiAwMBAANKP8IQJEzBr1iwkJiZi06ZN2LJlC1auXKm1rWXLlmH06NE4f/48XnjhBQQGBuLu3bvl7vfcuXPo378/OnbsiGPHjuHw4cMYNmwYioqKyl1/8uTJOHLkCCIiInD+/HmMGjUKgwcPRnJyMgBgxowZUKvVOHjwIOLj47Fq1aoq/1Y+/PBDeHp64syZMwgODsbcuXMRHR0NQPr7GDp0KNLT07Fv3z7ExcWhW7du6N+/v9Z3unTpEnbs2IGdO3fi3LlzFe4rNzcX//jHP/D111/j4MGDuH79Ot555x3N8vXr12PNmjX4xz/+gfPnz8Pf3x8vvfSS5vvpKjc3F2FhYfjiiy+QkJAAOzs7vT5Pz6gnflQlkYEKDw8Xtra2mumUlBQBQKxbt67Kz77wwgvi7bff1kx/9NFHwsXFRWud8uaVfpJxUlKSACCio6N1rtnX11eYmpoKS0tLTZs3b55muYuLi/joo4+EEEJERUUJExMTrafSJiQkCADi5MmTQggh5s2bJ1588UUhhBDr1q0Tr7zyiujWrZvYu3evEEIId3d3sXHjxgrrmTRpkjAxMRGWlpaiQYMGmqeBr127ttLvMX36dPHyyy9rbScgIEBrnS+//FK0bdtWFBcXa+ap1Wphbm4uDhw4UO52S57k/vhTdl966SUxZcoUzfSmTZuEg4ODKCwsFEII8X//938iNDRU6zNff/21UKlUmmkA4r333tNM5+TkCIVCIX755Zdy9z1u3DjRu3fvCo9B6b+FS5cuCYVCIW7evKm1Tv/+/UVwcLAQQggPDw8REhJS4fYe5+LiIgYPHqw1b8yYMWLIkCFCCCH+/e9/CxsbG5Gfn6+1TuvWrcWmTZuEEEIsXbpUmJqaioyMjEr3FR4eLgCIS5cuaeZ98sknwt7eXjPt6OgoVq5cqfW5Hj16iOnTpwshyv9nd/bsWQFApKSkaO3n3LlzFX7nkr9/otI4ZoaeOV5eXlrTRUVF+OCDDxAZGYmbN29CrVZDrVbD0tLyifZz7tw5mJiY6NXzAQCBgYFYtGiRZrqisSEXLlyAs7MznJ2dNfM6dOiAhg0b4sKFC+jRowf8/Pzw5Zdfori4GLGxsejfvz9atGiB2NhYdOvWDRcvXqyyvr59+2Ljxo3Izc3FF198gYsXL+Ktt97SWuezzz7DF198gWvXriEvLw8FBQXo0qVLpduNi4vDpUuXYG1trTU/Pz8fly9frvSzjwsMDMTrr7+OTz/9FEqlEt9++y3Gjh2rGWMRFxeHU6dOafXEFBUVIT8/H7m5ubCwsAAAeHp6apZbWlrC2toaGRkZ5e7z3LlzGDVqlE71nTlzBkIIuLu7a81Xq9Vo0qQJAGDWrFl48803ERUVhQEDBuDll1/Wqqc8Pj4+ZaZLrvaJi4tDTk6OZvsl8vLytI6vi4sLmjVrVuV3sLCwQOvWrTXTKpVKc2yys7Px3//+F71799b6TO/evbVOa+nCzMysyu9N9DiGGXrmPB5S1qxZg48++gjr1q2Dh4cHLC0tMWfOHL1OdZTn8cHGurK1tUWbNm2qXE8IAYVCUen8Pn364P79+zhz5gwOHTqE5cuXw9nZGaGhoejSpQvs7OzQvn37SvdjaWmpqefjjz9G3759sWzZMs1VWzt27MDcuXOxZs0a+Pj4wNraGh9++KHmNF1FiouL0b17d3z77bdlluny41rasGHDUFxcjL1796JHjx44dOgQ1q5dq7WvZcuWYeTIkWU+26BBA837xwe/KhSKMmNxSujzz7e4uFgzfunxQawlp5L+9re/wd/fH3v37kVUVBTCwsKwZs2aMsGxKiX/7IuLi6FSqbTG5ZQoHZB1De3lHRvx2Piax/8eS/8t1qtXTzOvxMOHD8vsx9zcvNy/a6LKMMzQM+/QoUMICAjAhAkTAEg/AsnJyVX+yFfFw8ND0yMyYMCAmihVS4cOHXD9+nWkpqZqemcSExORlZWlqb1k3MyGDRugUCjQoUMHODo64uzZs9izZ4/evUYAsHTpUgwZMgRvvvkmHB0dcejQIfTq1QvTp0/XrPN4z4qZmVmZsSTdunVDZGSkZnDqkzA3N8fIkSPx7bff4tKlS3B3d0f37t219pWUlKRTSNSVp6cn/v3vf2PZsmVVrtu1a1cUFRUhIyMD//d//1fhes7Ozpg2bRqmTZuG4OBgbN68udIwc/z48TLT7dq1AyB95/T0dNSvX18zQL222NjYwNHREYcPH0afPn00848ePYrnnnsOwKOAmpaWhkaNGgFApWN0iPTBAcD0zGvTpg2io6Nx9OhRXLhwAW+88QbS09OfeLstW7bEpEmTMGXKFPz4449ISUlBTEyMZmDskxowYAA8PT0RGBiIM2fO4OTJk5g4cSJ8fX21TqX5+fnhm2++ga+vLxQKBRo1aoQOHTogMjISfn5+eu/Xz88PHTt2RGhoKADp+J0+fRoHDhzAxYsXsXjx4jIDolu2bInz588jKSkJt2/fxsOHDxEYGIimTZsiICAAhw4dQkpKCmJjYzF79mzcuHFD77oCAwOxd+9efPXVV5pgWmLJkiXYtm0bQkJCkJCQgAsXLiAyMhLvvfee3vspERwcjFOnTmH69Ok4f/48/vzzT2zcuBG3b98us667uzsCAwMxceJE7Nq1CykpKTh16hRWrVqluWJpzpw5OHDgAFJSUnDmzBn89ttvVQbqI0eOYPXq1bh48SI++eQTfP/995g9ezYA6e/Dx8cHw4cPx4EDB3D16lUcPXoU7733Hk6fPl3t712Rv//971i1ahUiIyORlJSEd999F+fOndPU06ZNGzg7OyMkJAQXL17E3r17sWbNmhqvg55NDDP0zFu8eDG6desGf39/+Pn5wcHBocZu8LZx40a88sormD59Otq1a4epU6fiwYMHNbLtkjvWNmrUCH369MGAAQPQqlUrREZGaq3Xt29fFBUVaQUXX19fFBUVVatnBgDmzZuHzZs3IzU1FdOmTcPIkSMxZswYeHt7486dO1q9NIB075y2bdvCy8sLzZo1w5EjR2BhYYGDBw+iRYsWGDlyJNq3b48pU6YgLy+vWj01/fr1Q+PGjZGUlITx48drLfP398eePXsQHR2NHj16oGfPnli7di1cXFyq9f0BKaBERUXhP//5D5577jn4+Pjgp59+qvCeQOHh4Zg4cSLefvtttG3bFi+99BJOnDih6VUrKirCjBkz0L59ewwePBht27bFp59+WmkNb7/9NuLi4tC1a1csX74ca9asgb+/PwDp72Pfvn3o06cPpkyZAnd3d4wdOxZXr16Fvb19tb93RWbNmoW3334bb7/9Njw8PLB//37s3r0bbm5uAKTTVN999x3+/PNPdO7cGatWrcKKFStqvA56NinE4yc9iYgMVExMDPr27YvMzExZb69vCFq2bIk5c+bUmUcm6OJZ/M6kG/bMEJHRcXJywrhx4+Qug56S0NBQWFlZ4fr163KXQgaKA4CJyGh4e3trbsJWUzcfJMM3bdo0jB49GoD+V7rRs4GnmYiIiMio8TQTERERGTWGGSIiIjJqDDNERERk1BhmiIiIyKgxzBAREZFRY5ghIiIio8YwQ0REREaNYYaIiIiMGsMMERERGbX/B2tFPaG1hCqxAAAAAElFTkSuQmCC\n",
"text/plain": "<Figure size 640x480 with 1 Axes>"
},
"metadata": {},
"output_type": "display_data"
}
]
}
},
"503da8f079f941bc88d25fc2dc55a03c": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"51bf0d7f0a8c441db6161ae646a407b7": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"52ddf983296042168eadf3ef742c3de1": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"description_width": ""
}
},
"5587e0efb67f4453a7598ec815e422bb": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"57e3c907cb71406a8ccbb9b398698b8d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"5806dda8eabb4e7d97f2e379167ce5d9": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_bb7a481065664052a1c2dd14393b571d",
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": "<Figure size 640x480 with 1 Axes>"
},
"metadata": {},
"output_type": "display_data"
}
]
}
},
"58db643065bc4aa09ff04267189c6b52": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"description_width": ""
}
},
"58e9c96e989c453e80cc319cd1d6c542": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "ToggleButtonsModel",
"state": {
"_options_labels": [
"PDF",
"CDF",
"1-CDF"
],
"button_style": "",
"description": "Plot Type:",
"icons": [],
"index": 0,
"layout": "IPY_MODEL_6d4615dd9d7b496ab3cad20962f2c95a",
"style": "IPY_MODEL_49e2865e48e44c7d88ea961604260cbf",
"tooltips": []
}
},
"5f1a83ad2b454d0f802985af20993727": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "CheckboxModel",
"state": {
"description": "2: Hard Shoulder",
"disabled": false,
"indent": false,
"layout": "IPY_MODEL_c02af5dbfda345779d1ed59ab7e606d5",
"style": "IPY_MODEL_a16439206a34479da22d97225c4220cb",
"value": false
}
},
"6580aaf036a6475abc0c0832c04510fd": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatSliderModel",
"state": {
"description": "C",
"layout": "IPY_MODEL_2551df80a02445859a0ccd6fc548693a",
"max": 7000,
"min": 4000,
"step": 100,
"style": "IPY_MODEL_6b9a6dc442134d669865be4c3b5ff1dd",
"value": 5600
}
},
"69e3ec50096842f38c6f74081b2f5542": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatSliderModel",
"state": {
"description": "8: Modal Shift, Cars",
"layout": "IPY_MODEL_a9a413eef0ff44d3ade17141373257de",
"max": 20,
"step": 4,
"style": "IPY_MODEL_b9d80e0f8cc644829e3b5ea6cedcdd66"
}
},
"6b9a6dc442134d669865be4c3b5ff1dd": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": ""
}
},
"6cd0a2b1a14342bd9651d2db5a05be10": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": ""
}
},
"6d4615dd9d7b496ab3cad20962f2c95a": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"75c1f539256c4115a4eb71ca610b6baf": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"description_width": ""
}
},
"7974564f725f4bb1ba9a4b1d8a221aca": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "VBoxModel",
"state": {
"_dom_classes": [
"widget-interact"
],
"children": [
"IPY_MODEL_a6366e489ea64c94b53a44cd90d3509b",
"IPY_MODEL_ac52d024453d4073a77795b4082f8fe5",
"IPY_MODEL_4542024c6c2b478eaef6d5ff81808e34",
"IPY_MODEL_2cce8708af1d4a05a0524a12ed2132ad",
"IPY_MODEL_69e3ec50096842f38c6f74081b2f5542",
"IPY_MODEL_9daea46dd3004ad2a39098dc8f0c4d83",
"IPY_MODEL_58e9c96e989c453e80cc319cd1d6c542",
"IPY_MODEL_3385de2b8e114324919ed816877addf9"
],
"layout": "IPY_MODEL_29b615e502b74b62a274b74c7ca390c5"
}
},
"7b28a775fe594b7d851b4a210e0eb11e": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": ""
}
},
"7dadfb09825746b39cfca7a47bcc2740": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatSliderModel",
"state": {
"description": "7: Modal Shift, Trucks",
"layout": "IPY_MODEL_aeeb304402b0462cb994598fa86e6796",
"max": 20,
"step": 4,
"style": "IPY_MODEL_23505dba595b426a86b5df0c759e9d3a"
}
},
"7f14219ee396472a8558de07bd7404e3": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"description_width": ""
}
},
"7f4e913978bd4d6f9657243f779f9850": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"80b5142c6fce447a96f3bdafaff94e14": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": "initial"
}
},
"83fd64cc893c429bae04c91ebe52209b": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": ""
}
},
"84b87cab5bd3437d955a7851dafdaf7f": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_b150edabeff74e6bb2994d402dca96cd",
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAlIAAAG2CAYAAAC04mh6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB1J0lEQVR4nO3deVxU9f7H8dcw7KsLCJiIS7kvKZppmZqG2aatZq7t3jJT61ZmXc17y+q2WN20PbNF7Ze2XLWSurmlbSZlamWJQgYiLqCyz5zfH8NMjKACDhyYeT8fj/OQ+c6Zcz6HAefN93zP91gMwzAQERERkWrzM7sAERERkYZKQUpERESkhhSkRERERGpIQUpERESkhhSkRERERGpIQUpERESkhhSkRERERGpIQUpERESkhhSkRERERGpIQUpERESkhkwPUvPmzaN169YEBweTlJTEunXrjrvusmXLuOCCC4iJiSEyMpK+ffvy6aefVlhv6dKldOrUiaCgIDp16sT7779/SvsVERERqYypQWrJkiVMmTKFGTNmsHnzZvr378+wYcNIT0+vdP21a9dywQUXsHLlSjZt2sSgQYO49NJL2bx5s2udjRs3MnLkSMaOHcsPP/zA2LFjueaaa/j6669rvF8RERGRyljMvGlxnz596NmzJ/Pnz3e1dezYkREjRjBnzpwqbaNz586MHDmSf/zjHwCMHDmSvLw8Pv74Y9c6F154IY0bN2bRokUe26+IiIiIv1k7Li4uZtOmTdx3331u7cnJyWzYsKFK27Db7Rw+fJgmTZq42jZu3MjUqVPd1hs6dChz5849pf0WFRVRVFTktu8DBw7QtGlTLBZLleoVERERcxmGweHDh2nevDl+fqd+Ys60IJWTk4PNZiM2NtatPTY2lqysrCpt48knn+To0aNcc801rrasrKwTbrOm+50zZw4PPfRQleoSERGR+i0jI4MWLVqc8nZMC1JOx/bmGIZRpR6eRYsWMWvWLD788EOaNWtW7W1Wd7/Tp09n2rRprse5ubm0bNmSjIwMIiMjT1qvNCA9esDOnQwFHv78c3r16nXi9b/7DgYP5g+gM/DNN9/Qvn37OihURESqKy8vj4SEBCIiIjyyPdOCVHR0NFartUIvUHZ2doXeomMtWbKEG2+8kf/7v/9jyJAhbs/FxcWdcJs13W9QUBBBQUEV2iMjIxWkvFjjxo1P/v6WhaZ2OK7eyMvL08+EiEg956lhOaZdtRcYGEhSUhIpKSlu7SkpKfTr1++4r1u0aBETJkzgnXfe4eKLL67wfN++fStsc9WqVa5t1nS/4kNKSwGw4fh5Oam4OLBa8QdigT179tRmdSIiUo+Yempv2rRpjB07ll69etG3b19eeukl0tPTmThxIuA4nbZnzx4WLlwIOELUuHHjeOaZZzj77LNdvUohISFERUUBcOedd3Leeefx2GOPMXz4cD788EM+++wz1q9fX+X9io+z2QAopYpBymqF5s0hI4MWwJ9//lmr5YmISP1hapAaOXIk+/fvZ/bs2WRmZtKlSxdWrlxJYmIiAJmZmW5zO7344ouUlpZy++23c/vtt7vax48fz4IFCwDo168fixcv5oEHHuDBBx+kbdu2LFmyhD59+lR5v+LjyvVIBQQEVO01LVpARgYJqEdKRMSXmDqPVEOWl5dHVFQUubm5Gg/jbWJiICeHzkDKnj00b9785K8ZORLefZc7gYzLL2fZsmW1XaVIrbPZbJSUlJhdhki1BAQEYLVaj/u8pz+/Tb9qT6S+MUpLsVCNMVLg6JECEoCvdWpPGjjDMMjKyuLQoUNmlyJSI40aNSIuLq5O5nlUkBI5VnXHSIErSLVAp/ak4XOGqGbNmhEaGqpJh6XBMAyD/Px8srOzAYiPj6/1fSpIiRyrulftASQkAI4glZmZid1u98iMuSJ1zWazuUJU06ZNzS5HpNpCQkIAx7RGzZo1O+FpPk/Q//QixyrXI1WtweY4Tu3ZbDbXX0MiDY1zTFRoaKjJlYjUnPPnty7G+ClIiRyrrEfKsFiq/pdMWZBqjuOXSlMgSEOn03nSkNXlz6+ClEh5hoHFbgfAWtXTegDx8WC1EoAm5RQR8SUKUiLllYUoAEtVT+uBY1LOskGNGnAu4rtatWrF3LlzXY8tFgsffPCBafVI7VOQEimv7LQeVLNHCtwGnOvUnkjdmzBhAhaLxbU0bdqUCy+8kB9//NG0mjIzMxk2bJhp+5fapyAlUl7ZQHOoZo8UuA04V4+UiDkuvPBCMjMzyczM5PPPP8ff359LLrnEtHri4uIqveG9eA8FKZHyTqVHqtxcUuqREjFHUFAQcXFxxMXFceaZZ3LvvfeSkZHBvn37ALj33ntp164doaGhtGnThgcffNDtyq4ffviBQYMGERERQWRkJElJSXz33Xeu5zds2MB5551HSEgICQkJTJ48maNHjx63nvKn9nbt2oXFYmHZsmUMGjSI0NBQunfvzsaNG91eU919iLkUpETKK9cjZa3uX5Flp/bUIyXexjAMjh49aspyKncxO3LkCG+//Tann366a06siIgIFixYwLZt23jmmWd4+eWXefrpp12vGT16NC1atODbb79l06ZN3Hfffa5pULZs2cLQoUO54oor+PHHH1myZAnr169n0qRJ1aprxowZ3H333aSmptKuXTtGjRpFadkfcZ7ah9QhQ2okNzfXAIzc3FyzSxFPys42DDAMMLp06lS91777rmGAsQ6MJk2a1E59IrWsoKDA2LZtm1FQUOBqO3LkiAGYshw5cqTKtY8fP96wWq1GWFiYERYWZgBGfHy8sWnTpuO+5vHHHzeSkpJcjyMiIowFCxZUuu7YsWONW265xa1t3bp1hp+fn+v7lZiYaDz99NOu5wHj/fffNwzDMNLS0gzAeOWVV1zPb9261QCM7du3V3kfcnKV/Rw7efrzWz1SIuWV9UjZgYDq9kiVO7V34MABCgsLPVubiJzUoEGDSE1NJTU1la+//prk5GSGDRvG7t27AXjvvfc499xziYuLIzw8nAcffJD09HTX66dNm8ZNN93EkCFDePTRR/n9999dz23atIkFCxYQHh7uWoYOHYrdbictLa3KNXbr1s31tfMWJs5JfD21D6k7ukWMSHnlbg9T5VnNncpO7Z3GX5NytmnTxqPliZghNDSUI0eOmLbv6ggLC+P00093PU5KSiIqKoqXX36ZSy65hGuvvZaHHnqIoUOHEhUVxeLFi3nyySdd68+aNYvrrruOFStW8PHHHzNz5kwWL17M5Zdfjt1u59Zbb2Xy5MkV9tuyZcsq11j+/xbnxJH2sqlXPLUPqTsKUiLl1eSGxU5xceDnR4DdTjMUpMR7WCwWwsLCzC6jRiwWC35+fhQUFPDll1+SmJjIjBkzXM87e6rKa9euHe3atWPq1KmMGjWK119/ncsvv5yePXuydetWt6DmaXWxD/EsndoTKa8mNyx28vd3TcqpAeci5igqKiIrK4usrCy2b9/OHXfcwZEjR7j00ks5/fTTSU9PZ/Hixfz+++88++yzvP/++67XFhQUMGnSJFavXs3u3bv58ssv+fbbb+nYsSPguOJv48aN3H777aSmprJjxw4++ugj7rjjDo/VXxf7EM9Sj5RIeafSIwWO03t79mh2cxGTfPLJJ65xRxEREXTo0IH/+7//Y+DAgQBMnTqVSZMmUVRUxMUXX8yDDz7IrFmzALBarezfv59x48axd+9eoqOjueKKK3jooYcAx9imNWvWMGPGDPr3749hGLRt25aRI0d6rP662Id4lsUwTuHaUh+Wl5dHVFQUubm5REZGml2OeMr27dCpE/uBGy67jA8//LB6r7/6anjvPSYD/lOn8tRTT9VGlSK1prCwkLS0NFq3bk1wcLDZ5YjUyIl+jj39+a1TeyLlncpgc3CbS6r8lUAiIuKdFKREyjvVU3vlpkDIyMjwXF0iIlIvKUiJlHcqg83BLUipR0pExPspSImU54nB5jhO7WVlZVFUVOS52kREpN5RkBIpz0M9UqcBFnTlnoiIt1OQEimvXI9UjQabx8c7JuUEYtHpPRERb6cgJVLeqfZIlZuUUwPORUS8n4KUSHmnOkYKNOBcRMSHKEiJlHeqPVLgClIJqEdKRMTbKUiJlOeJHqnERMc/qEdKxBsNHDiQKVOmeHy7s2bN4swzz/T4dgH+97//0aFDB+x2u8e3vXr1aiwWC4cOHfL4tiuzfPlyevToUSvHUhMKUiLlnerM5gCtWgHQGvVIidSlCRMmYLFYmDhxYoXnbrvtNiwWCxMmTKjy9uo6IJzMDz/8wKhRo0hISCAkJISOHTvyzDPPVOm199xzDzNmzMDPz/Gxv2DBAiwWCxaLBavVSuPGjenTpw+zZ88mNze3Ng/jlF1yySVYLBbeeecds0sBFKRE3HmiR6p1a8c/qEdKpK4lJCSwePFiCgoKXG2FhYUsWrSIli1bmljZqdu0aRMxMTG89dZbbN26lRkzZjB9+nT+85//nPB1GzZsYMeOHVx99dVu7ZGRkWRmZvLHH3+wYcMGbrnlFhYuXMiZZ57Jn3/+WZuHcsquv/56nnvuObPLABSkRNyVBalTGiNVFqRa4bg5Zn3/607Em/Ts2ZOWLVuybNkyV9uyZctISEigR48ebusahsHjjz9OmzZtCAkJoXv37rz33nsA7Nq1i0GDBgHQuHHjCr1Zdrude+65hyZNmhAXF8esWbPctp2ens7w4cMJDw8nMjKSa665hr1797qt8+ijjxIbG0tERAQ33ngjhYWFJzy2G264gWeffZYBAwbQpk0bxowZw/XXX+92rJVZvHgxycnJFW7ea7FYiIuLIz4+no4dO3LjjTeyYcMGjhw5wj333FOl71Nl9u/fz6hRo2jRogWhoaF07dqVRYsWuZ5fuHAhTZs2rTBh8ZVXXsm4ceMAR+/boEGDiIiIIDIykqSkJL777jvXupdddhnffPMNO3fuPOGx1wUFKZHyPDHYvGyMVGMgCp3eEy9gGHD0qDmLYVS73Ouvv57XX3/d9fi1117jhhtuqLDeAw88wOuvv878+fPZunUrU6dOZcyYMaxZs4aEhASWLl0KwC+//EJmZqbbabQ33niDsLAwvv76ax5//HFmz55NSkpK2bfLYMSIERw4cIA1a9aQkpLC77//zsiRI12vf/fdd5k5cyYPP/ww3333HfHx8cybN6/ax5qbm0uTJk1OuM7atWvp1atXlbbXrFkzRo8ezUcffYSt7A/LE32fKlNYWEhSUhLLly/np59+4pZbbmHs2LF8/fXXAFx99dXYbDY++ugj12tycnJYvnw5119/PQCjR4+mRYsWfPvtt2zatIn77rvPbbhFYmIizZo1Y926dVU6rlplSI3k5uYagJGbm2t2KeJJCxcaBhifgPH222/XfDsxMYYBxplgrFy50nP1idSygoICY9u2bUZBQcFfjUeOGIYj0tT9cuRIlWsfP368MXz4cGPfvn1GUFCQkZaWZuzatcsIDg429u3bZwwfPtwYP3582SEdMYKDg40NGza4bePGG280Ro0aZRiGYXzxxRcGYBw8eNBtnQEDBhjnnnuuW1vv3r2Ne++91zAMw1i1apVhtVqN9PR01/Nbt241AOObb74xDMMw+vbta0ycONFtG3369DG6d+9e5ePdsGGDERAQYKxateqE60VFRRkLFy50a3v99deNqKioStefP3++ARh79+49pe9TeRdddJFx1113uR7/7W9/M4YNG+Z6PHfuXKNNmzaG3W43DMMwIiIijAULFpzwuHr06GHMmjWr0ucq/Tku4+nPb38TM5xI/eOJwebgGHC+bx+t0DgpkboWHR3NxRdfzBtvvIFhGFx88cVER0e7rbNt2zYKCwu54IIL3NqLi4srnAKsTLdu3dwex8fHk52dDcD27dtJSEggoezemwCdOnWiUaNGbN++nd69e7N9+/YKg+L79u3LF198UaVj3Lp1K8OHD+cf//hHhWM4VkFBQYXTeidilPUCWiyWGn2fbDYbjz76KEuWLGHPnj0UFRVRVFREWFiYa52bb76Z3r17s2fPHk477TRef/1118UCANOmTeOmm27izTffZMiQIVx99dW0bdvWbT8hISHk5+dX+bhqi4KUSHmeGGwOjnFS336rAefiHUJD4cgR8/ZdAzfccAOTJk0C4Pnnn6/wvPPS+RUrVnDaaae5PRcUFHTS7R/7h5bFYnFt0zAMVyAo73jt1bVt2zbOP/98br75Zh544IGTrh8dHc3BgwervP3t27cTGRlJ06ZNXWOQqvN9evLJJ3n66aeZO3cuXbt2JSwsjClTplBcXOxap0ePHnTv3p2FCxcydOhQtmzZwn//+1/X87NmzeK6665jxYoVfPzxx8ycOZPFixdz+eWXu9Y5cOAAMTExVT6u2mL6GKl58+bRunVrgoODSUpKOuH5zszMTK677jrat2+Pn59fpfN4DBw40HVJZ/nl4osvdq0za9asCs/HxcXVxuFJQ+OJMVLgNuBcY6SkwbNYICzMnKWGwePCCy+kuLiY4uJihg4dWuH5Tp06ERQURHp6Oqeffrrb4uxJcv4f4BwrVFWdOnUiPT3d7Xd/27Zt5Obm0rFjRwA6duzIV1995fa6Yx9XZuvWrQwaNIjx48fz8MMPV6meHj16sG3btiqtm52dzTvvvMOIESPw8/Or0vfpWOvWrWP48OGMGTOG7t2706ZNG3bs2FFhvZtuuonXX3+d1157jSFDhlTYXrt27Zg6dSqrVq3iiiuucBv3VlhYyO+//16l3sPaZmqP1JIlS5gyZQrz5s3jnHPO4cUXX2TYsGFs27at0stUi4qKiImJYcaMGTz99NOVbnPZsmVuqXf//v107969wmWfnTt35rPPPnM9tlqtHjoqadA81SNVbi6pZeqREqlzVquV7du3u74+VkREBHfffTdTp07Fbrdz7rnnkpeXx4YNGwgPD2f8+PEkJiZisVhYvnw5F110ESEhIYSHh59030OGDKFbt26MHj2auXPnUlpaym233caAAQNcg77vvPNOxo8fT69evTj33HN5++232bp1K23atDnudp0hKjk5mWnTppGVleU6vhP1zAwdOpQ33nijQrthGGRlZWEYBocOHWLjxo088sgjREVF8eijj1b5+3Ss008/naVLl7JhwwYaN27MU089RVZWlitEOo0ePZq7776bl19+mYULF7raCwoK+Pvf/85VV11F69at+eOPP/j222+58sorXet89dVXBAUF0bdv3+Med53xyEirGjrrrLMqDLbr0KGDcd999530tQMGDDDuvPPOk6739NNPGxEREcaRcgMWZ86cWa0BfZXRYHMvNXeuYYCxCIy1a9fWfDuffGIYYGwBo02bNp6rT6SWnWiQbn3nHGx+POUHmxuGYdjtduOZZ54x2rdvbwQEBBgxMTHG0KFDjTVr1rjWmT17thEXF2dYLBbXayv7/Dl227t37zYuu+wyIywszIiIiDCuvvpqIysry+01Dz/8sBEdHW2Eh4cb48ePN+65554TfjbNnDnTACosiYmJJ/y+HDhwwAgJCTF+/vlnV9vrr7/uer3FYjGioqKMs846y5g9e3aFz7WTfZ+OHWy+f/9+Y/jw4UZ4eLjRrFkz44EHHjDGjRtX6XszduxYo0mTJkZhYaGrraioyLj22muNhIQEIzAw0GjevLkxadIkt5/JW265xbj11luPe8x1OdjctCBVVFRkWK1WY9myZW7tkydPNs4777yTvr6qQapLly7GzTff7NY2c+ZMIzQ01IiPjzdatWpljBw50vj9999PuJ3CwkIjNzfXtWRkZChIeaMnnzQMMN4E46uvvqr5dn75xTDAOAxGgL+/YbPZPFejSC1qyEFKju/vf/+7ccstt5hdRgVDhgwx7rjjjmq9Jjs722jSpImxc+fO465Tl0HKtDFSOTk52Gw2YmNj3dpjY2Nd3ZWn6ptvvuGnn37ipptucmvv06cPCxcu5NNPP+Xll18mKyuLfv36sX///uNua86cOURFRbmW450blgbOU1ftlZ2aDgeiSktdV/OIiJhhxowZJCYmVnu8V205cOAAixcv5n//+x+33357tV6blpbmGl9dH5h+1d6xVzAYHrqqAeDVV1+lS5cunHXWWW7tw4YNc33dtWtX+vbtS9u2bXnjjTeYNm1apduaPn2623N5eXkKU97IU2OkgoOheXP480/XlXu6oEFEzBIVFcX9999vdhkuPXv25ODBgzz22GO0b9++Wq8966yzKnyum8m0IBUdHY3Vaq3Q+5SdnV2hl6om8vPzWbx4MbNnzz7pumFhYXTt2rXSqwqcgoKCqnRJrDRwnrhFjFPr1vDnn64r9+rTL76IiJl27dpldgkeY9qpvcDAQJKSklxT6julpKTQr1+/U97+u+++S1FREWPGjDnpukVFRWzfvp34+PhT3q80cJ6a/gDcrtzTXFIiIt7J1FN706ZNY+zYsfTq1Yu+ffvy0ksvkZ6e7prtdfr06ezZs8ftssjU1FQAjhw5wr59+0hNTSUwMJBOnTq5bfvVV19lxIgRNG3atMJ+7777bi699FJatmxJdnY2//rXv8jLy6v0Mk7xMZ46tQduc0nt0FxS0sAYNbjHnUh9UZc/v6YGqZEjR7J//35mz55NZmYmXbp0YeXKlSSW3fQ1MzOzwl/y5Sff2rRpE++88w6JiYlu3YS//vor69evZ9WqVZXu948//mDUqFHk5OQQExPD2WefzVdffeXar/guo6QECx4YbA5uPVKfq0dKGgjnz31+fj4hISEmVyNSM85bx5zy/+NVYPpg89tuu43bbrut0ucWLFhQoa0qKbNdu3YnXG/x4sVVrk98i62kBH882yPVGs1uLg2H1WqlUaNGritNQ0NDPXYBkEhtMwyD/Px8srOzadSoUZ1Mtm16kBKpT+xFRYAHB5sDiUDG7t2nti2ROuS8wlTTdkhD1ahRozq7UlpBSqQcW0kJ4KEeqRYtMPz8CLbbYe9eioqKdOWnNAgWi4X4+HiaNWtGSdnvhEhDERAQUKe3fVOQEinH2SNlxwP3XwwIgIQE2L2b1sCePXtOeB8tkfrGarXqPqQiJ2Ha9Aci9ZG9bPoDw0MfHpZyV+5pCgQREe+jICVSjr242PGFp/4KL3flngaci4h4HwUpkXKMsvEghr+HznqXu3JPPVIiIt5HQUqkHHtZkLJ4uEeqFeqREhHxRgpSIuU4e6RQj5SIiFSBgpRIOUbZYHOPjZEqC1IJwJ8KUiIiXkdBSqQc16k9T/VIxcdjBAQQAJRqUk4REa+jICVSjqtHylNBymrFaNkSgOgjR8jNzfXMdkVEpF5QkBIpz9kj5cEbXfqVTcLZCg04FxHxNgpSIuU4e6Q8dmoPNJeUiIgXU5ASKa82gpSu3BMR8VoKUiLlGDYb4NlTe+Xnktq1a5fntisiIqZTkBIpr6xHys+TQapcj9TOnTs9t10RETGdgpRIebXRI1UWpE4D0n/7zXPbFRER0ylIiZRjqY0eqWbNsAcH4weU/P6757YrIiKmU5ASKa82eqQsFkhMBKBxbi6HDh3y3LZFRMRUClIi5dntAFgDAz26Wb+2bQHHgPO0tDSPbltERMyjICVSjqWsR8rPw0HKOU6qLfC7Tu+JiHgNBSmR8pxBypOn9gDOOMPxD7pyT0TEmyhIiZTjVxakPH1qj3btHP+gICUi4k0UpETKsZSNkfL4qb2yHqnTgTSd2hMR8RoKUiLlWGppsDmtWmG3WgkB8n/91bPbFhER0yhIiZTjClJBQZ7dsL8/trIpEEL++IPSsvmqRESkYVOQEinHr7aCFODfsSMAbe12MjIyPL59ERGpewpSIuU4g5R/LQQpS9mAc125JyLiPRSkRMrxMwygFsZIga7cExHxQgpSIuW4glQt9EhpLikREe+jICVSjqUWT+05e6TaAGk7dnh++yIiUucUpETKsZb1SPkHB3t+46edhi0wkACg8OefPb99ERGpcwpSIuU4T+0F1EaQ8vOjpGwKhMDduz2/fRERqXMKUiJOhoF/2Ze1MkaKv6ZAiD9yhIMHD9bKPkREpO4oSIk4lY2PglrqkQL8O3UCHFfupaWl1co+RESk7pgepObNm0fr1q0JDg4mKSmJdevWHXfdzMxMrrvuOtq3b4+fnx9TpkypsM6CBQuwWCwVlsLCwhrvV3xEudnGa2WwObhdufe77rknItLgmRqklixZwpQpU5gxYwabN2+mf//+DBs2jPT09ErXLyoqIiYmhhkzZtC9e/fjbjcyMpLMzEy3JbhcD0N19ys+wmZzfRkQElI7+9BcUiIiXsXUIPXUU09x4403ctNNN9GxY0fmzp1LQkIC8+fPr3T9Vq1a8cwzzzBu3DiioqKOu12LxUJcXJzbcir7FR9Rrkeqtk7tOXukWgLpunmxiEiDZ1qQKi4uZtOmTSQnJ7u1Jycns2HDhlPa9pEjR0hMTKRFixZccsklbN68uU72Kw1cXfRINWtGcUgIfkDhtm21sw8REakzpgWpnJwcbDYbsbGxbu2xsbFkZWXVeLsdOnRgwYIFfPTRRyxatIjg4GDOOeccdpRNgFjT/RYVFZGXl+e2iJepix4pi4Wili0d+9BgcxGRBs/0weYWi8XtsWEYFdqq4+yzz2bMmDF0796d/v378+6779KuXTuee+65U9rvnDlziIqKci0JCQk1rlHqqbIeKTsQWFtBCrB26ABAVHY2JSUltbYfERGpfaYFqejoaKxWa4VeoOzs7Aq9RafCz8+P3r17u3qkarrf6dOnk5ub61oyMjI8VqPUE2VBygYE1sZNi8uEdOsGwOmGoZ8jEZEGzrQgFRgYSFJSEikpKW7tKSkp9OvXz2P7MQyD1NRU4uPjT2m/QUFBREZGui3iXYyy3qFSajdIWdq3B3TzYhERb+B/8lVqz7Rp0xg7diy9evWib9++vPTSS6SnpzNx4kTA0Qu0Z88eFi5c6HpNamoq4BhQvm/fPlJTUwkMDKRT2USHDz30EGeffTZnnHEGeXl5PPvss6SmpvL8889Xeb/im0oKCwmk9nuknFfutQOWK0iJiDRopgapkSNHsn//fmbPnk1mZiZdunRh5cqVJJbdjywzM7PC3E49evRwfb1p0ybeeecdEhMT2bVrFwCHDh3illtuISsri6ioKHr06MHatWs566yzqrxf8U0lBQUEUtYjFRBQezsqC1LNgT9082IRkQbNYhhld2mVasnLyyMqKorc3Fyd5vMShzZupFG/fuwHokpK8Pevvb8z8sPDCT16lL8PHsy/P/us1vYjIiLuPP35bfpVeyL1RWnZbYRsgNVqrdV9FbZoAYBVp/ZERBo0BSmRMiVlQaqUitNjeJqlbAqE8MzMWt2PiIjULgUpkTK2oiIA7LUcogDCzjwTgITCQg4ePFjr+xMRkdqhICVSxtkjZauDIBXYuTOgmxeLiDR0ClIiZZxjpOqiR8p55d4ZwO+//177+xMRkVqhICVSprTs1F5d9Ehx+ukARAN7tmyp/f2JiEitUJASKeMcI2X41cGvRXg4eRERABzZvLn29yciIrVCQUqkjK24GKijU3tAYcuWjv1qUk4RkQZLQUqkjPPUnr0ueqQAa8eOAIT/8QeaF1dEpGFSkBIpY6vjIBVRdtuiNkVFZGdn18k+RUTEsxSkRMo4T+3VyRgpILBsLqnOwC+//FIn+xQREc9SkBIpU9c9UpTNJXU6sENX7omINEgKUiJl7CUlji/qKkjFx5MfFIQVOPjVV3WzTxER8SgFKZEydX1qD4uF3LKbF9vVIyUi0iApSImUsTuDlNVaZ/s0OnUCIHz37jrbp4iIeI6ClEgZM4JUeJ8+ADQ/dIjCslvUiIhIw6EgJVLGeWqPOgxSEWVBqjOwY8eOOtuviIh4hoKUSBnDOdi8DoOUpUsXANoAv/74Y53tV0REPENBSqSM86q9ujy1R2wsR8qu3DuwYUPd7VdERDxCQUqkjGv6A3//utupxcLB+HgASlJT626/IiLiEQpSImXMOLUHUNquHQDBO3fW6X5FROTUKUiJlLGXlgJgqcseKSC0d28AYvft082LRUQaGAUpESczTu0BTfr3B6C9zcaff/5Zp/sWEZFToyAlUsYo65Gq6yAVUHbz4jbArxonJSLSoChIiZRxjpGq61N7NGtGXmAgfkD2unV1u28RETklClIiZZw9UpaAgLrdscVCTrNmABRt2lS3+xYRkVOiICXiVBak/Oq6RwooOv10AAJ/+63O9y0iIjWnICXiZLMBJvRIAUE9ewLQNCurzvctIiI1pyAl4mTS9AcATc87D4A2hYUcOXKkzvcvIiI1oyAlUsZw9kgFBtb5vqP69QOgNbDjhx/qfP8iIlIzClIiTiaOkSImhkP+/vgBWatX1/3+RUSkRhSkRMpYynqk/EzokQLYGx0NQMG335qyfxERqT4FKREnux0APxMGmwMUtGkDgPXnn03Zv4iIVJ+ClEgZs3uk/Lt3B6CxbhMjItJgKEiJlDE7SDU65xwAWh45gr2sd0xEROo3BSmRMs4gZTUpSMUNHgxAK8MgY/t2U2oQEZHqMT1IzZs3j9atWxMcHExSUhLrTnCvsczMTK677jrat2+Pn58fU6ZMqbDOyy+/TP/+/WncuDGNGzdmyJAhfPPNN27rzJo1C4vF4rbExcV5+tCkgbGU9QKZFaT84+LYb7UCsOezz0ypQUREqsfUILVkyRKmTJnCjBkz2Lx5M/3792fYsGGkp6dXun5RURExMTHMmDGD7mXjSY61evVqRo0axRdffMHGjRtp2bIlycnJ7Nmzx229zp07k5mZ6Vq2bNni8eOThsUVpIKCTKvhzyZNADj89dem1SAiIlVnapB66qmnuPHGG7npppvo2LEjc+fOJSEhgfnz51e6fqtWrXjmmWcYN24cUVFRla7z9ttvc9ttt3HmmWfSoUMHXn75Zex2O59//rnbev7+/sTFxbmWmJgYjx+fNCxm90gBHG3VylGLgr2ISINgWpAqLi5m06ZNJCcnu7UnJyezYcMGj+0nPz+fkpISmpT9pe+0Y8cOmjdvTuvWrbn22mvZuXPnCbdTVFREXl6e2yLexc85/YGJQcq/Vy8AmuzebVoNIiJSdaYFqZycHGw2G7GxsW7tsbGxZHnwxq333Xcfp512GkOGDHG19enTh4ULF/Lpp5/y8ssvk5WVRb9+/di/f/9xtzNnzhyioqJcS0JCgsdqFPMZhoGfYQDgb+KpvZiyPyzaHj6MrWymdRERqb9MH2xusVjcHhuGUaGtph5//HEWLVrEsmXLCA4OdrUPGzaMK6+8kq5duzJkyBBWrFgBwBtvvHHcbU2fPp3c3FzXkpGR4ZEapX4oKSnBeWMYM0/ttRg6lGKgMbB77VrT6hARkaoxLUhFR0djtVor9D5lZ2dX6KWqiSeeeIJHHnmEVatW0a1btxOuGxYWRteuXdmxY8dx1wkKCiIyMtJtEe9RXFyMtexr/3Khu65ZQ0JICw0FIOvjj02rQ0REqsa0IBUYGEhSUhIpKSlu7SkpKfTr1++Utv3vf/+bf/7zn3zyySf0KhtzciJFRUVs376d+Pj4U9qvNFwlJSV/BSkTT+0B7DvtNACKdeWeiEi9Z8Jt7v8ybdo0xo4dS69evejbty8vvfQS6enpTJw4EXCcTtuzZw8LFy50vSY1NRWAI0eOsG/fPlJTUwkMDKRTp06A43Tegw8+yDvvvEOrVq1cPV7h4eGEh4cDcPfdd3PppZfSsmVLsrOz+de//kVeXh7jx4+vw6OX+qS4uNj1y2DmYHOA0m7dYMcOwn791dQ6RETk5EwNUiNHjmT//v3Mnj2bzMxMunTpwsqVK0lMTAQcE3AeO6dUjx49XF9v2rSJd955h8TERHbt2gU4JvgsLi7mqquucnvdzJkzmTVrFgB//PEHo0aNIicnh5iYGM4++2y++uor137F95Q/tWfxN/XXgqgBA2DpUlrk5Jhah4iInJzFMMouVZJqycvLIyoqitzcXI2X8gK///47B08/nV4AK1bARReZVsuB3btp1KoVfkDejh1Enn66abWIiHgbT39+m37Vnkh9UL5HCqv1RKvWuiaJiaSV9YplfPSRqbWIiMiJKUiJ4D79ASaf2gP4o2ym/cOaAkFEpF5TkBKhfvVIAeS3bw+A/08/mVyJiIiciIKUCO5X7dWHIBV89tkANDvmZtsiIlK/KEiJcEyPVD04tRdfNti9ZWEh9txck6sREZHjqVGQWrBgAfn5+Z6uRcQ09a1Hqu3ZZ/NH2ddZn35qai0iInJ8NQpS06dPJy4ujhtvvJENGzZ4uiaROld+ZvP60CMVEBDA72WX5eYcM/u/iIjUHzUKUn/88QdvvfUWBw8eZNCgQXTo0IHHHnuswn3zRBqK+jbYHOBgq1YA2DdtMrcQERE5rhoFKavVymWXXcayZcvIyMjglltu4e2336Zly5ZcdtllfPjhh9jtdk/XKlJr3E7t1YMeKQBL2Sz+jXbuNLkSERE5nlMebN6sWTPOOecc+vbti5+fH1u2bGHChAm0bduW1atXe6BEkdpXH3ukmgweDMBpublQVGRyNSIiUpkaB6m9e/fyxBNP0LlzZwYOHEheXh7Lly8nLS2NP//8kyuuuEI3AZYGoz72SLW74AIOAAFAwXffmV2OiIhUokZB6tJLLyUhIYEFCxZw8803s2fPHhYtWsSQIUMACAkJ4a677iIjI8OjxYrUFrfB5vWkRyo2Lo6tAQEAZH78scnViIhIZWr0p3ezZs1Ys2YNffv2Pe468fHxpKWl1bgwkbpUH3ukALLi4yE9nQJdHSsiUi/VqEdqwIAB9OzZs0J7cXExCxcuBMBisZCYmHhq1YnUkfo4RgqguEsXAEJ+/tnkSkREpDI1ClLXX389uZXMtnz48GGuv/76Uy5KpK4VFxXVyx6p8HPPBSB+716w2UyuRkREjlWjIGUYBhaLpUL7H3/8QVRU1CkXJVLXSspfFVePeqQSk5PJB0LsdowdO8wuR0REjlGtP7179OiBxWLBYrEwePBg/Mv95W6z2UhLS+PCCy/0eJEitc1WT4NUxy5dSAX6AAc+/5ymHTqYXJGIiJRXrSA1YsQIAFJTUxk6dCjh4eGu5wIDA2nVqhVXXnmlRwsUqQul5YNUPTq1FxQURFrjxvQ5eJDczz+n6e23m12SiIiUU61PjJkzZwLQqlUrRo4cSXBwcK0UJVLXSutpjxTAwXbt4OuvCfj+e7NLERGRY9RojNT48eMVosSr2OppjxRA4HnnAdAsIwNKSkyuRkREyqtykGrSpAk5OTkANG7cmCZNmhx3EWlo6nOP1OnDhnEACLLb4ccfzS5HRETKqfKf3k8//TQRERGuryu7ak+kobIXFwNgWCxY/E75FpQe1bNXL9YDw4C8VauITEoyuyQRESlT5SBV/r55EyZMqI1aREzj7JGyWyzUr/4oiIiIYGdMDOzb5whS06ebXZKIiJSpcpDKy8ur8kYjIyNrVIyIWVw9UvXstJ5T4ZlnQkoKIT/8YHYpIiJSTpWDVKNGjU56Os85UadNMzBLA1PqDFL17LSeU6PkZEhJoenBg5CTA9HRZpckIiJUI0h98cUXtVmHiKnsZaf26muPVPeBA9kOdASMr77CcsklZpckIiJUI0gNGDCgNusQMZW9bFqB+toj1a1bNxb5+dHRbufgJ5/QREFKRKReqHKQ+vHHH+nSpQt+fn78eJJLsLt163bKhYnUpfo+RiowMJDMli1h1y6K16wxuxwRESlT5SB15plnkpWVRbNmzTjzzDOxWCwYhlFhPY2RkobI2SNV3+aQKs/o0wd27aLRr7+CzVavaxUR8RVVDlJpaWnExMS4vhbxJs4eKerpqT2A05KTObJkCeHFxfDzz9C5s9kliYj4vCoHqcTExEq/FvEGrjFS9ez2MOX17tuXb4FBgO3LL7EqSImImK7Gf37/8ssvTJo0icGDBzNkyBAmTZrEL7/84snaROqMvbQUAEs9Pl3Wvn17vg8MBCD3009NrkZERKCGQeq9996jS5cubNq0ie7du9OtWze+//57unTpwv/93/95ukaRWmc4T+3V4x4pPz8/DrZr53jw9dfmFiMiIkA1Tu2Vd8899zB9+nRmz57t1j5z5kzuvfderr76ao8UJ1JXjLIeqfo+gDvovPPgp59otGcP5OWB7iIgImKqGvVIZWVlMW7cuArtY8aMISsr65SLEqlrhvOqvYAAcws5iY6DBpFG2S/ut9+aXI2IiNQoSA0cOJB169ZVaF+/fj39+/ev1rbmzZtH69atCQ4OJikpqdLtOmVmZnLdddfRvn17/Pz8mDJlSqXrLV26lE6dOhEUFESnTp14//33T2m/4v2cg80t9fjUHsBZZ53FV2Vfl6xfb2otIiJSjSD10UcfuZbLLruMe++9l0mTJvHWW2/x1ltvMWnSJO677z4uv/zyKu98yZIlTJkyhRkzZrB582b69+/PsGHDSE9Pr3T9oqIiYmJimDFjBt27d690nY0bNzJy5EjGjh3LDz/8wNixY7nmmmv4utyYkuruV7yfpWzus/oepBISEtgaHg7AkVWrTK5GREQsRmWzalbCr4rz61RnQs4+ffrQs2dP5s+f72rr2LEjI0aMYM6cOSd87cCBAznzzDOZO3euW/vIkSPJy8vj448/drVdeOGFNG7cmEWLFp3yfp3y8vKIiooiNzeXSI1TadAMw2CEnx8fAiVJSQR8953ZJZ3QtHPO4akNGygICyPk8GE4yc3ERUTkL57+/K5yj5Tdbq/SUtUQVVxczKZNm0hOTnZrT05OZsOGDdU7inI2btxYYZtDhw51bbOm+y0qKiIvL89tEe9QWlr611UX9XyMFEDTwYMpAkKOHoXffze7HBERn2baNM45OTnYbDZiY2Pd2mNjY09pwHpWVtYJt1nT/c6ZM4eoqCjXkpCQUOMapX4pLi7Gea2eXz0/tQfQs29fvnc++OqrE60qIiK1rMafGkePHmXNmjWkp6dT7JyDp8zkyZOrvB3LMaclDMOo0FZdVdlmdfc7ffp0pk2b5nqcl5enMOUliouLXb8IlrIJL+uz3r178ybQFyj6/HOCxowxuyQREZ9VoyC1efNmLrroIvLz8zl69ChNmjQhJyeH0NBQmjVrVqUgFR0djdVqrdALlJ2dXaG3qDri4uJOuM2a7jcoKIigoKAa1yX1V0lJiatHqr4PNgfHz/DPzZpBdjal//sf+qkUETFPjU7tTZ06lUsvvZQDBw4QEhLCV199xe7du0lKSuKJJ56o0jYCAwNJSkoiJSXFrT0lJYV+/frVpCwA+vbtW2Gbq1atcm2ztvYrDVf5U3v1+RYx5ZX07YsdCEtPh717zS5HRMRn1ShIpaamctddd2G1WrFarRQVFZGQkMDjjz/O/fffX+XtTJs2jVdeeYXXXnuN7du3M3XqVNLT05k4cSLgOJ127MSfqamppKamcuTIEfbt20dqairbtm1zPX/nnXeyatUqHnvsMX7++Wcee+wxPvvsM7c5p062X/Et5U/t1edbxJTXbeBAfnQ+WLPGzFJERHxajT41AgICXOOJYmNjSU9Pp2PHjkRFRVVrLqaRI0eyf/9+Zs+eTWZmJl26dGHlypUkJiYCjgk4j91ejx49XF9v2rSJd955h8TERHbt2gVAv379WLx4MQ888AAPPvggbdu2ZcmSJfTp06fK+xXfUr5Hqr7fIsbpvPPOYzVwJmD/4gv8rrnG3IJERHxUleeRKi85OZkJEyZw3XXXMXHiRDZv3szkyZN58803OXjwoNvkl95K80h5jx9//JEXu3fneYCrroIGcONtm83GmPBwFhUWUti6NcE7d5pdkohIg2DaPFLlPfLII8THxwPwz3/+k6ZNm/K3v/2N7OxsXnrppVMuSqQulR9s3lB6pKxWK6X9+mEHgtPSNE5KRMQkNTq116tXL9fXMTExrFy50mMFidS1hjhGCqDH4MFs+d//6A6OcVI6vSciUudOaULO7Oxs1q1bx/r169m3b5+nahKpUw1xjBT8NU4KwFi9+gRriohIbalRkMrLy2Ps2LGcdtppDBgwgPPOO4/mzZszZswYcnNzPV2jSK1qqD1SvXv35suyeouPmc5DRETqRo2C1E033cTXX3/N8uXLOXToELm5uSxfvpzvvvuOm2++2dM1itSqhtojFRQUREHv3o6vf/sNsrNNrkhExPfUKEitWLGC1157jaFDhxIZGUlERARDhw7l5ZdfZsWKFZ6uUaRWlZSUNMgeKYDu55/PD84Hmk9KRKTO1ShINW3alKioqArtUVFRNG7c+JSLEqlLDbVHCtzHSaFxUiIida5GQeqBBx5g2rRpZGZmutqysrL4+9//zoMPPuix4kTqgtsYqQYWpPr27cvasslxSz77zORqRER8T5XPY/To0cM1mznAjh07SExMpGXLlgCkp6cTFBTEvn37uPXWWz1fqUgtceuRamCn9iIiIsjt3h1SUwn49VfHOKlmzcwuS0TEZ1T5U2PEiBG1WIaIeRryqT2AboMG8WNqKt0A1q51zM4uIiJ1ospBaubMmbVZh4hpGvJgcygbJ/X0044gtXq1gpSISB06pU+NTZs2sX37diwWC506dXK7obBIQ1FcXEyQ80ED7JE699xzuQWYDJR+/vmp/VKLiEi11Oj/3OzsbK699lpWr15No0aNMAyD3NxcBg0axOLFi4mJifF0nSK1pri4mDDngwbYIxUdHc3edu3g11/x//ln2LcP9DsoIlInanTV3h133EFeXh5bt27lwIEDHDx4kJ9++om8vDwmT57s6RpFalVDHyMF0HXQIH50Pli71sxSRER8So2C1CeffML8+fPp2LGjq61Tp048//zzfPzxxx4rTqQuNNRbxJTnNp+UpkEQEakzNQpSdrudgICACu0BAQHY7fZTLkqkLpWUlDT4Hqn+/fuzquxr+8cfg2GYWo+IiK+oUZA6//zzufPOO/nzzz9dbXv27GHq1KkMHjzYY8WJ1AVv6JFKSEhgZ8uWFAF+u3fDL7+YXZKIiE+oUZD6z3/+w+HDh2nVqhVt27bl9NNPp3Xr1hw+fJjnnnvO0zWK1CpvGCMF0GvAAFx32/vkEzNLERHxGTX68zshIYHvv/+elJQUfv75ZwzDoFOnTgwZMsTT9YnUOm/okQIYPHgwn7z5JskAH38MU6aYXJGIiPer9qdGaWkpwcHBpKamcsEFF3DBBRfURl0idcZbeqSSk5N5FHgKMNaswZKfD6GhZpclIuLVqn1qz9/fn8TERGw2W23UI1LnvCVIxcfHE9i1K7sBS1GRY5ZzERGpVTUaI/XAAw8wffp0Dhw44Ol6ROpcQUGBV5zaA0geOhTXBCSaikREpNbV6FPj2Wef5bfffqN58+YkJiYSFhbm9vz333/vkeJE6kJ+fr5X9EgBDB06lOeeeIKJgPHJJ1jMLkhExMvVKEiNGDECi8WCoblqxAvk5+d7TY/Uueeey3XBwRQXFhL422/w229w+ulmlyUi4rWq9amRn5/P3//+dz744ANKSkoYPHgwzz33HNHR0bVVn0it86YeqeDgYHoNGsT6jz/mfHCc3rvjDrPLEhHxWtUaIzVz5kwWLFjAxRdfzKhRo/jss8/429/+Vlu1idQJb+qRAsfVe67RUZpPSkSkVlXrU2PZsmW8+uqrXHvttQCMHj2ac845B5vNhrWB/yUvvsubeqTAMU7qGuDfgPHFF1gKCyE42OyyRES8UrV6pDIyMujfv7/r8VlnnYW/v7/brWJEGhpv65Hq0KEDuS1a8AdgKSiAtWvNLklExGtVK0jZbDYCAwPd2vz9/SktLfVoUSJ1ydt6pCwWC0MvvBDXST1NgyAiUmuq9ee3YRhMmDCBoKAgV1thYSETJ050mwJh2bJlnqtQpBbZ7XYKCwu9qkcKHKf3Fr3yCjeBI0g9/bTZJYmIeKVqfWqMHz++QtuYMWM8VoxIXSsoKADwqh4pcNx372aLhVLDwP+XXyAtDVq3NrssERGvU60g9frrr9dWHSKmyM/PB8oFKS/pkWrcuDEd+vRhw1dfcR44rt7TFbYiIh5Xo1vEiHgLZ5AKsJTNAe4lPVLgOL23wvngww/NLEVExGspSIlPcwYpfy8NUu+XfW18/jkcPGhqPSIi3khBSnxahR4pLzm1B9C7d2/2NWrEFsBSWgrLl5tdkoiI1zE9SM2bN4/WrVsTHBxMUlIS69atO+H6a9asISkpieDgYNq0acMLL7zg9vzAgQOxWCwVlosvvti1zqxZsyo8HxcXVyvHJ/VbhTFSXtQj5e/vz+DBg3FdQ7t0qZnliIh4JVOD1JIlS5gyZQozZsxg8+bN9O/fn2HDhpGenl7p+mlpaVx00UX079+fzZs3c//99zN58mSWlvuAWLZsGZmZma7lp59+wmq1cvXVV7ttq3Pnzm7rbdmypVaPVeonb+6RArj44otx/XZ8+ikcOWJmOSIiXsfUIPXUU09x4403ctNNN9GxY0fmzp1LQkIC8+fPr3T9F154gZYtWzJ37lw6duzITTfdxA033MATTzzhWqdJkybExcW5lpSUFEJDQysEKX9/f7f1YmJiavVYpX7y5h4pgEsvvZRtfn78BlBYqMk5RUQ8zLQgVVxczKZNm0hOTnZrT05OZsOGDZW+ZuPGjRXWHzp0KN999x0lJSWVvsZ5b8DyE4YC7Nixg+bNm9O6dWuuvfZadu7cecJ6i4qKyMvLc1uk4Tt69CgAVsNwNHhZj1R0dDTnDRjwV6+UJssVEfEo04JUTk4ONpuN2NhYt/bY2FiysrIqfU1WVlal65eWlpKTk1Nh/W+++YaffvqJm266ya29T58+LFy4kE8//ZSXX36ZrKws+vXrx/79+49b75w5c4iKinItCQkJVT1Uqce8vUcK4PLLL/9rnNTy5Y6eKRER8QjTB5tbnGNTyhiGUaHtZOtX1g6O3qguXbpw1llnubUPGzaMK6+8kq5duzJkyBBWrHDMtvPGG28cd7/Tp08nNzfXtWRkZJz4wKRBcAUpL+2RAhgxYgTfAn+AY4zUZ5+ZXJGIiPcwLUhFR0djtVor9D5lZ2dX6HVyiouLq3R9f39/mjZt6taen5/P4sWLK/RGVSYsLIyuXbuyY8eO464TFBREZGSk2yINnzNI+TmDlBf2SCUkJNCrd29dvSciUgtMC1KBgYEkJSWRkpLi1p6SkkK/fv0qfU3fvn0rrL9q1Sp69epFQECAW/u7775LUVFRle4FWFRUxPbt24mPj6/mUUhD5ws9UnDM6b2PPoLjjCkUEZHqMfXU3rRp03jllVd47bXX2L59O1OnTiU9PZ2JEycCjtNp48aNc60/ceJEdu/ezbRp09i+fTuvvfYar776KnfffXeFbb/66quMGDGiQk8VwN13382aNWtIS0vj66+/5qqrriIvL6/SmzKLd8vPz3f/JfDCHilwBKl1QDbAgQOwZo3JFYmIeAdT//weOXIk+/fvZ/bs2WRmZtKlSxdWrlxJYmIiAJmZmW5zSrVu3ZqVK1cydepUnn/+eZo3b86zzz7LlVde6bbdX3/9lfXr17Nq1apK9/vHH38watQocnJyiImJ4eyzz+arr75y7Vd8R35+Pm7RyUt7pDp06ED7jh35cPt2bgbH1XtDhphdlohIg2cxnKO1pVry8vKIiooiNzdX46UasHHjxvHem2+S72w4fBjCw80sqdbMmDGDTY88wicAcXGwZw/4mX69iYhInfL057f+FxWfVqFHyktP7YHj9N7/gFyArCzYuNHkikREGj4FKfFp+fn57ue3vfTUHkBSUhJxCQn819nw7rtmliMi4hUUpMSn+VKPlMVi4fLLL2exs2HxYl29JyJyihSkxKe59UhZLF4/Zujyyy/nU2CfxQLZ2XCcCzJERKRqvPtTQ+Qk3HqkvLg3yuncc88lqmlT3nZeY/Lmm+YWJCLSwClIiU9z65Hy4vFRTv7+/gwfPhxXfPrgA8jNNbEiEZGGTUFKfJqv9UgBXHvttXwP/Gy1QlERvPee2SWJiDRYClLi03ytRwrg/PPPp3nz5iyw2RwNCxeaW5CISAOmICU+yzAMn+yRslqtXHfddbwN2AHWroVdu8wtSkSkgVKQEp9VXFyMzWb7K0j5SI8UwNixY/kDWGOxOBreesvUekREGioFKfFZ+fmOG8O44pOP9EgBdOvWjW7durGg/NV7uluUiEi1KUiJz3IGqSBngPKhIAWOXqllQKGfH/z6K3zzjdkliYg0OApS4rOcQSosKMjR4EOn9gCuu+468v38eM9udzRoTikRkWpTkBKf5QpSwcGOBh/rkWrevDmDBw/+a06pRYuguNjMkkREGhwFKfFZvt4jBY7Te58De61WOHAAVq40uyQRkQZFQUp8ljNIhTqDlI/1SIHj3ntBoaEsdM4p9eqr5hYkItLAKEiJz1KPFISHh3PFFVfwirNhxQrNKSUiUg0KUuKznEEqJDDQ0eCDPVLgOL33K7Da398xBcJLL5ldkohIg6EgJT7LdWrPGaR8sEcKYPDgwcTHx/NMaamj4ZVXHPfgExGRk1KQEp/l6pHy4TFS4LhlzOjRo/kvkBMcDPv2wdKlZpclItIgKEiJz3IFqYAAR4OP9kgB3HTTTdiA5woLHQ3z5plaj4hIQ6EgJT6rwqk9H+2RAmjfvj1DhgzhZcDm5wdffgk//mh2WSIi9Z6ClPgsZ5AKVo8UALfddhuZwHLn92H+fFPrERFpCBSkxGdVOLXnwz1SAJdeeiktWrTgaefs5m++CXl55hYlIlLPKUiJz6rQI+XjQcrf359bb72VNUBaSAgcPar774mInISClPgsV5Bynsry8VN74Bh07u/vz1MFBY6GefMcc0uJiEilFKTEZzmDVJAzQPl4jxRAXFwcV155JQuBIn9/2LYN1q0zuywRkXpLQUp81tGjRwEIcgYo9UgBjkHnecBbzoannzaxGhGR+k1BSnyWeqQq179/fzp37swTzpnOP/wQfv7Z3KJEROopBSnxWa4gpR4pNxaLhdtvv52fgc/Cwx1jpP79b7PLEhGplxSkxGc5g1SgM0ipR8plzJgxhIeH88CRI46GN9+EP/4wtygRkXpIQUp8litI+ZX9GqhHyiUiIoJx48bxNbClSRMoKYG5c80uS0Sk3lGQEp+lHqkTmzJlCn5+ftxz4ICj4cUX4eBBc4sSEalnFKTEZzmDVIDGSFXqjDPO4KqrruITYHejRnDkiG5mLCJyDAUp8Uk2m42ioiIAAi0WR6N6pCq47777AHggN9fR8Mwz4JysU0REzA9S8+bNo3Xr1gQHB5OUlMS6k0z+t2bNGpKSkggODqZNmza88MILbs8vWLAAi8VSYSksLDyl/Yp3KSgXBgI0Ruq4evTowYUXXsgiwyAnIgL27YPXXze7LBGResPUILVkyRKmTJnCjBkz2Lx5M/3792fYsGGkp6dXun5aWhoXXXQR/fv3Z/Pmzdx///1MnjyZpUuXuq0XGRlJZmam2xIcHFzj/Yr3cZ7WA3DFJ/VIVWr69OnYgH85v2dPPAHOOaZERHycqUHqqaee4sYbb+Smm26iY8eOzJ07l4SEBObPn1/p+i+88AItW7Zk7ty5dOzYkZtuuokbbriBJ554wm09i8VCXFyc23Iq+xXv4wxSISEhWOx2R6OCVKX69+9Pv379eMlm40hICKSlwbvvml2WiEi9YFqQKi4uZtOmTSQnJ7u1Jycns2HDhkpfs3HjxgrrDx06lO+++46SkhJX25EjR0hMTKRFixZccsklbN68+ZT2C1BUVEReXp7bIg2XM0iFhob+1buiU3uVslgsTJ8+nQLgKZvN0Th7tnqlREQwMUjl5ORgs9mIjY11a4+NjSUrK6vS12RlZVW6fmlpKTk5OQB06NCBBQsW8NFHH7Fo0SKCg4M555xz2LFjR433CzBnzhyioqJcS0JCQrWPWeoPZ5AKCwsDZzhQj9RxXXzxxXTt2pUniovJDw2FX36BN94wuywREdOZPtjc4rxiqoxhGBXaTrZ++fazzz6bMWPG0L17d/r378+7775Lu3bteO65505pv9OnTyc3N9e1ZGRknPzgpN5Sj1T1WCwW7rvvPg4Dc5y/J7NmwTEXcYiI+BrTglR0dDRWq7VCL1B2dnaF3iKnuLi4Stf39/enadOmlb7Gz8+P3r17u3qkarJfgKCgICIjI90WabjcgpR6pKrkmmuuoU2bNvz76FEON2rkuGWMxhWKiI8zLUgFBgaSlJRESkqKW3tKSgr9+vWr9DV9+/atsP6qVavo1asXAQEBlb7GMAxSU1OJj4+v8X7F+6hHqvr8/f259957KQL+4fyePfIIHD5sal0iImYy9dTetGnTeOWVV3jttdfYvn07U6dOJT09nYkTJwKO02njxo1zrT9x4kR2797NtGnT2L59O6+99hqvvvoqd999t2udhx56iE8//ZSdO3eSmprKjTfeSGpqqmubVdmveD/1SNXM9ddfzxlnnMFzR46wv0kTyMmBp54yuywREdOY+if4yJEj2b9/P7NnzyYzM5MuXbqwcuVKEhMTAcjMzHSb26l169asXLmSqVOn8vzzz9O8eXOeffZZrrzyStc6hw4d4pZbbiErK4uoqCh69OjB2rVrOeuss6q8X/F+R48eBY4JUuqROqmAgAAefvhhrrnmGqYdPcobAE8+CbffDtHRZpcnIlLnLIZztLZUS15eHlFRUeTm5mq8VAP09NNPM23aNK677jreLi6G996D556DSZPMLq3eMwyDs846i03ffUd6dDQtcnJg2jRHoBIRqec8/flt+lV7ImZwO7XnnBNMgbhKLBYLjz32GAbwt4MHHY3PP+8YfC4i4mMUpMQnuQUp5w15o6JMrKhhOf/880lOTma5zcbPMTFQVAQPPGB2WSIidU5BSnySgtSpe/TRRwEYv2+fo+GNN2DjRhMrEhGpewpS4pMUpE5djx49GDVqFN8An552mqPxjjv+GrwvIuIDFKTEJylIeca//vUvAgICGLdnDyVhYbBpE7z6qtlliYjUGQUp8UnOIBUeFARlXytIVV+bNm249dZbyQaecA7Wnz4d9u83tS4RkbqiICU+yRmkosrfX1FX7dXIrFmzaNq0KQ9mZpITFwcHDsCDD5pdlohInVCQEp/kDFKRdrujISwMjnObITmxpk2b8vjjj2MDxjhPk77wAmzebGpdIiJ1QUFKfJIzSEU4g5RO652SCRMm0K9fPz4tKGB9QgIYhmNyU833KyJeTkFKfJJrjJTzCjMFqVPi5+fH/PnzsVqtjMzIoDQ4GDZsgIULzS5NRKRWKUiJT3IGqTAFKY/p1q0bkydP5k/gydBQR+PUqZCZaWpdIiK1SUFKfJJr+oPiYkeDgpRHzJo1i+bNmzPjwAH+jI+Hgwdh4kSd4hMRr6UgJT7JGaSCi4ocDQpSHhEZGcnTTz+NDbg0JwcjIAA++gjeftvs0kREaoWClPgcwzBcQSqosNDRqCDlMVdffTUXXHAB35eU8GqLFo7GyZN1ik9EvJKClPic4uJi7GVX6wUWFDgaGzUyryAvY7FYmD9/PmFhYUxMS2NvixaOU3y33qpTfCLidRSkxOccPXrU9XWA82v1SHlU27ZtefLJJ7EBw/buxR4QAP/9L7z1ltmliYh4lIKU+Bznab2AgAD8Dh92NCpIedwtt9zCsGHD2FxSwryYGEfj5Mnw55/mFiYi4kEKUuJzdMPiumGxWHj11Vdp0qQJU/78kz3x8XDoEEyYAM6JUEVEGjgFKfE5ClJ1Jz4+nhdeeAEbcOHevdiCgiAlBebMMbs0ERGPUJASn6MgVbeuvvpqRo8ezU92O/c7B/X/4x+wZo2pdYmIeIKClPgcBam699xzz3Haaafx+N69bDjjDMepvVGjIDvb7NJERE6JgpT4HAWpute4cWPeeOMNLBYLF+zYwcH4eMe8UmPGaLyUiDRoClLic5xBKjI4GDSPVJ0ZPHgws2fPJh84PycHu8ZLiYgXUJASn+MMUtEBAX81RkaaVI1vuf/++7nkkktILSnhnvBwR+M//gGrV5tal4hITSlIic9xBqmmziAVHg5Wq4kV+Q4/Pz/efPNN2rRpw5P795Ny2mmOU3tXXQW//252eSIi1aYgJT7HGaSaOMOTxkfVqUaNGrFs2TKCg4MZvmcPe5o3h/374dJL/xqzJiLSQChIic9xBqnGFoujQUGqznXv3p0XX3yRAuCsP/+koGlT2L4drrkGSkvNLk9EpMoUpMTnOINUIwUpU40bN46JEyfyJ3BBfj624GBYtQqmTDG7NBGRKlOQEp/jDFKu+KQgZZpnnnmGwYMH82VBATcHBzsan3/esYiINAAKUuJznEEqwjl/kYKUaQIDA1m6dCldu3bl9UOHeMp5c+M774RPPjG3OBGRKlCQEp/jClI2m6NBc0iZKioqipUrV3Laaadx1759fBIbCzYbXHEFfPml2eWJiJyQgpT4HGeQCnMOalaPlOlatGjBypUriYiI4LK9e0mNj3dMlnrRRbB5s9nliYgcl4KU+BxnkAopKXE0KEjVC926dWPp0qUY/v70y8zk9+bNIS8PkpPh55/NLk9EpFIKUuJzjh49CkBIUZGjQUGq3rjgggt49dVXKQB6/vknGbGxkJMDQ4bArl1mlyciUoGClPgcZ49UUGGho0FBql4ZN24c8+fPJw/osXcve5s2hT17HGEqM9Ps8kRE3JgepObNm0fr1q0JDg4mKSmJdevWnXD9NWvWkJSURHBwMG3atOGFF15we/7ll1+mf//+NG7cmMaNGzNkyBC++eYbt3VmzZqFxWJxW+Li4jx+bFI/OYNUoIJUvTVx4kT+85//sB/osX8/Bxo1ctxCZsAASE83uzwRERdTg9SSJUuYMmUKM2bMYPPmzfTv359hw4aRfpz/KNPS0rjooovo378/mzdv5v7772fy5MksXbrUtc7q1asZNWoUX3zxBRs3bqRly5YkJyezZ88et2117tyZzMxM17Jly5ZaPVapP5xBKqDsXwWp+un222/n2WefJRNIOnSIg1FRsGMH9O8Pv/1mdnkiIgBYDMMwzNp5nz596NmzJ/Pnz3e1dezYkREjRjBnzpwK699777189NFHbN++3dU2ceJEfvjhBzZu3FjpPmw2G40bN+Y///kP48aNAxw9Uh988AGpqak1rj0vL4+oqChyc3OJjIys8Xak7jVu3JhDhw5REh2Nf04O/PADdOtmdllyHHPnzmXq1Km0AL5v0oSYAwcgLg5SUqBLF7PLE5EGxtOf36b1SBUXF7Np0yaSk5Pd2pOTk9mwYUOlr9m4cWOF9YcOHcp3331HifMKrGPk5+dTUlJCkyZN3Np37NhB8+bNad26Nddeey07d+48haORhsTZI2U9fNjRoB6pem3KlCk88cQT/AF0OXCAjCZNICvLcZrvu+/MLk9EfJxpQSonJwebzUZsbKxbe2xsLFlZWZW+Jisrq9L1S0tLycnJqfQ19913H6eddhpDhgxxtfXp04eFCxfy6aef8vLLL5OVlUW/fv3Yv3//cestKioiLy/PbZGGp7S0lOLiYgIBi67aazDuuusuXnrpJXL8/Oh+4AC/NGoEBw7A+efDmjVmlyciPsz0weYW541jyxiGUaHtZOtX1g7w+OOPs2jRIpYtW0aw8z5ewLBhw7jyyivp2rUrQ4YMYcWKFQC88cYbx93vnDlziIqKci0JCQknPzipdwoKCoBy99kDiIgwpRapnptvvpn333+fguBgeh06xPeRkXD4MFxwASxcaHZ5IuKjTAtS0dHRWK3WCr1P2dnZFXqdnOLi4ipd39/fn6ZNm7q1P/HEEzzyyCOsWrWKbicZ/xIWFkbXrl3ZsWPHcdeZPn06ubm5riUjI+OE25T6qcINiyMiwGo1rR6pnssuu4zPPvuMgMaNOScvj08jIqCkBMaPhwceAOf9E0VE6ohpQSowMJCkpCRSUlLc2lNSUujXr1+lr+nbt2+F9VetWkWvXr0ICAhwtf373//mn//8J5988gm9evU6aS1FRUVs376d+Pj4464TFBREZGSk2yINjzNIxQYFORp0Wq/BOeecc1i/fj3RLVow7PBh5oaEOJ54+GEYNcpxaxkRkTpi6qm9adOm8corr/Daa6+xfft2pk6dSnp6OhMnTgQcvUDOK+3AcYXe7t27mTZtGtu3b+e1117j1Vdf5e6773at8/jjj/PAAw/w2muv0apVK7KyssjKyuLIkSOude6++27WrFlDWloaX3/9NVdddRV5eXmMHz++7g5eTOEMUs0UpBq0Tp06sXHjRs7s0YOpBQXc6OeHzWqFd9+FQYMcg9FFROqCYbLnn3/eSExMNAIDA42ePXsaa9ascT03fvx4Y8CAAW7rr1692ujRo4cRGBhotGrVypg/f77b84mJiQZQYZk5c6ZrnZEjRxrx8fFGQECA0bx5c+OKK64wtm7dWq26c3NzDcDIzc2t9jGLeb755hsDMG6JjjYMMIx+/cwuSU7B0aNHjeuuu84AjPPAOBwU5Hhf4+MNo9z/JSIiTp7+/DZ1HqmGTPNINUxr1qxh4MCB3B8fz8OZmXDRRVB2sYE0TIZh8NRTT3HPPffQxm7nk5AQ2hYUOMa+Pfww/P3v4Gf6dTUiUk94zTxSImZwntpr6hxgrlN7DZ7FYuGuu+7ik08+4UCTJnQrKGBJYCDYbHDffTB8uGOqBBGRWqAgJT7FGaQaO3soFKS8xgUXXMC3335Lx6Qkri0u5magxGqF5cuhZ0/46iuzSxQRL6QgJT7FNf2Bc94xBSmv0qZNGzZs2MA999zDK0Bvm43dAQGwezeccw7MmAHFxWaXKSJeREFKfIorSDmHBipIeZ3AwEAee+wxUlJSyI6Pp1tJCe/4+TnmmHrkETjrLPjxR7PLFBEvoSAlPuXo0aMARChIeb0hQ4bw448/MvCyyxhtt3MlcNDf33GT6l694NFHHeOoREROgYKU+BRnj1SE8wNUQcqrRUdH88EHH/D666/zRePGdCgt5UNwzIY+fTqcfbZufCwip0RBSnyKM0iFlZY6GhSkvJ7FYmHChAls27aNAVdfzQhgPJDn5+cIUWedBZMmwaFD5hYqIg2SgpT4FGeQCi0pcTQ0amReMVKn4uLiePfdd/nggw/4rHlz2tntvAVgGPD889ChA7zzjuOxiEgVKUiJT3EGqRDnlVvqkfI5w4cPZ9u2bYycPJkJVivnA78A7N0Lo0fDgAHwzTcmVykiDYWClPgUZ5AKKix0NChI+aSoqCieeeYZfvzxRwKHDqUbMAMoAFi3Dvr0cdwAOS3N3EJFpN5TkBKfkpWVRRDgr8HmguPmxx9//DHLli/nvXbtOANYANgBFi/G6NAB7r5bM6OLyHEpSInPMAyD77//Hld0slggIsLMkqQesFgsXHzxxWzZsoV7n32W+2Jj6QmkAJbiYnjySYxWrRyTee7fb3K1IlLfKEiJz9i1axcHDx4k2t/f0RARoZvZiktgYCB33HEHO3fu5LrHHuPaxo25EEgFLIcPwyOPOALV/fdDTo65xYpIvaFPEfEZmzZtAqBn27aOBp3Wk0qEhoZyzz33sDMtjb6zZjEwIoIRwGbAcuQIzJnjCFR33QXp6eYWKyKmU5ASn/H9998D0KNNG0eDgpScQFRUFDNnzmR3RgZ9H32UYc2acRnwPWA5ehSeegqjTRvHoHRN6inisxSkxGc4e6S6tGjhaNAcUlIFUVFR3HvvvezavZtLXnyRkW3bchHwOWCx2WDxYujdG+O88+C99xyzpouIz1CQEp9gGIYrSJ3RrJmjUT1SUg3BwcHccsst/PzLL9y8bBlzBg/mTGAhUAJY1q2Dq6/G1qIFPPAA7N5tbsEiUicUpMQnZGRksH//fvz9/WnhvFJPQUpqwGq1cvnll/PZZ5+xaNs2vp00iS5hYTwMZAHW7Gx4+GGM1q2xX3QRLFsGRUVmly0itURBSnyCszeqc+fOBJRNyqkgJaeqY8eOPPfcc3yXmUn0Cy9wVe/eXAV8BlgMA7+PP4Yrr6QkJgbjb3+DjRt1CxoRL6MgJT7BGaSSkpIgN9fRqCAlHhIREcGtt97K+m++YfbWraz6+9/pFx3NY8AeIODwYSwvvAD9+lGYmIjx4IOwZYtClYgXUJASn+C8Yq9nz54KUlKrOnXqxOOPP87azEy6rljB/aNHMzwkhIXAUSA4IwPLv/4F3bpxNDER2/33ww8/KFSJNFAKUuL1yg80V4+U1BV/f38uuugi3njrLZYcOEDEsmXcdsUV3BgYyIdAIRCWkYF1zhw480yOxMZScMst8Nln4LyptojUewpS4vX27NlDdnY2VquV7t27/xWkNP2B1JHg4GAuv/xy3li6lOcOHsTy4YfcNWYMEyMiWIbjZsnh+/YR8vLLcMEFFEZEkDNoELaXX4aMDLPLF5ET8De7AJHa5jyt17FjR0JCQuDQIccT6pESE4SGhnLZZZdx2WWXYbPZ+Oqrr3j4vfc4/P77dN29m0uAuOJiglevhtWrAdjfrBmlgwYRfe21WM8/HyIjzTwEESlHQUq8nttpPdCpPak3rFYr55xzDueccw48/TR//vknn3zyCb8uWkSj9evpX1jIWUDT7GxYsgSWLMEG7D3tNGznnkuzq64iaMgQ9a6KmEhBSryegpQ0FM2bN2fCDTfADTdgt9v58ccfeXHFCnI/+IBmP/zAwJIS2gLN9+xxBSs7kNW0KUe7dyciOZnYESOwtGsHFovZhyPiEyyGoUtFaiIvL4+oqChyc3OJVDd7vda8eXMyMzP58ssv6de3LwQFOW7jkZ4OCQlmlydSJTabjdTUVFKXL+foypU0+ekneufn076SdXMDAshKSMB25pk0uuAC4i65BD/nrZFEfJynP78VpGpIQaphyMzMpHnz5vj5+ZGXl0eYnx+EhjqezM3VWBNpsAzDYPfu3WxasYJDH39M0ObNtMnMpKdhEFzJ+jkBAWTFx1PcqROhffvS/KKLiOzRA6zWOq9dxEwKUvWEglTDsHz5ci699FI6derE1q1bISsL4uMdpz1KS8FPF66K9ygpKeGn778n7f33KVy/nvBffqH1/v10Mgwqi0v5FgsZEREcatECe4cOhPfpQ/MLLqBJ9+5Y9LshXsrTn98aIyVezXnFXoXxUZGRClHidQICAujRpw89+vRxtZWWlvLz99+T8d//UvD11wT//DNxe/fSvriYUMOgfV4ebNvmWJYtg3vv5QiQERrKwZgYilq1IqBrV6J69SK+f3+atm6NReOvRFwUpMSrOQea9+zZ09GgOaTEx/j7+9P5rLPofNZZbu2H9u9n+yefcHD9emw//EBoWhqx+/fTuqSEcKBjfj7s3u1Y1qxxvW6fxcKekBAONm1KUfPm+LVpQ3CnTjTu0YPmvXvTJCZGQUt8ioKUeLUKV+xpDikRABo1bUrS6NEwerRbe0FuLmlr1rB/wwaKfvgB/99/p9HevcQdOUK03U6MYRCTnw/5+Y7JQr/+2vXaYmCXxUJ2cDB5UVEUxMRgJCTg37o1Ye3aEdW5M7EdOtAsNhZ/f338iHfQT7J4rb1797Jnzx4sFgtnnnmmo1FTH4icUEhUFO0uuwwuu6zCc4XZ2WR9+SUHv/2Wwp9+wrJ7N6FZWTTOzSW2qIhAoLVh0LqgAAoKHGMSt2xx20YBsAvH4PfcsDAKoqIoiY7GiIvDv0ULghMTCW/blkZnnEGz+HiaNm1KQEBAXRy6SI0oSInXco6PateuHREREY5GBSmRGgtu1oxWl19Oq8svr/ikzUbhzp3kfP89eT/9ROGvv2Ls3k1gVhbhBw/SKD+fxqWlhACnA6eXlDh6iA8dcpw+PIYdyAF+AQ76+3M4OJj8sDCKo6KwN2kCTZtijY0lIC6OkIQEwhISiIqPp0nTpjRu3JiIiAidYpQ6oSAlXqvCaT1QkBKpLVYrwWecQYszzoCRIytfp7AQW0YGB7du5fAvv1C4cyelGRmQlUVgTg4heXlE5OcTVVKCH9CsbKG0FI4ccSx79x63hCLgAJABHAKOBASQHxREUUgIJWFh2CIiMCIjoVEj/Bo3xr9pUwJjYghq1ozgZs0Ij44mIjKSyMhIIiIiiIiIUG+YnJSClHilRYsW8e9//xuA3r17//WEgpSIeYKDsZ5xBtFnnEH0idYrLYWcHGx//snh33/n6K5dFGZkUPLnn9izs/E7cICA3FyCjhwhtKCAiOJiAgyDICC+bAEcE++WlDgC2L59Jy2vFMgrW/YAh4GjFguFAQEUBQRQHBREaVAQtpAQ7CEhGGFhEBaGJTwcS3g41shI/KOi8I+KIiAqisBGjQhq3JiQyEhCQkLcltDQUEJCQvD391fPWQNnepCaN28e//73v8nMzKRz587MnTuX/v37H3f9NWvWMG3aNLZu3Urz5s255557mDhxots6S5cu5cEHH+T333+nbdu2PPzww1x+TFd0dfcrDcPhw4eZNGkSCxcuBKBv377ccMMNf62gICVS//n7Q1wc1rg4GvXsSaOTrW8YcPQoHDgABw5QlJXFkd27Kdq7l+LsbEr37cN+4AAcOoTf4cP4HzlCQEEBQYWFhBQXE1Jaih+OD8QmZYvbtouLHcvRozU6nBIcY8Ocy0EcQa0AKASKrVZKrFZK/P0p9ffH5u+PLSAAe2AgRmCg61+Cgx13ZggKwhIc7Fr8QkLwCw3FPyQEa2gofsHB+IeGVlgCg4MJDAwkMDCQgIAAt6+dj51fWzVRa5WZGqSWLFnClClTmDdvHueccw4vvvgiw4YNY9u2bbRs2bLC+mlpaVx00UXcfPPNvPXWW3z55ZfcdtttxMTEcOWVVwKwceNGRo4cyT//+U8uv/xy3n//fa655hrWr19Pn7K5Vaq7X2kYvv76a6677jp27tyJn58fDzzwAA8++KDj6qCCAscl3F9+6VhZQUrEe1gsEB7uWFq2JAgIqs7r7XbHVYh5ea6l9OBBCvftozAnh+L9+yk5eBB7bi62vDw4fBjj6FH8jhzBr7AQa2Eh1qIiAoqLCSwpIbC0lKDSUtckqAFly3GnfrTZHEtxcc2/B1Vgw3FlZTGOcFdS9vWRco/LL6UWCzY/P+xl/5b6+WEvtxhWK3ar1fV1hcXf3zFzvtUK/v4YVisWf39HUC772lKu3VK+PSDA1ea2BATg5++PxWrFr2wdP+f6Vit+/v74HbOOn78/1nLr5hcVefT7aurM5n369KFnz57Mnz/f1daxY0dGjBjBnDlzKqx/77338tFHH7F9+3ZX28SJE/nhhx/YuHEjACNHjiQvL4+PP/7Ytc6FF15I48aNWbRoUY32WxnnzKifTZ5MWFC1fmXlBJw/joZ7I3a73W0pLi4mNzeXQ4cOuZbffvsNw24nJjqaO++4gw7t2zvGU3zyCaxe7QhTTu++C1dfXZeHJiK+xDAcpxXz8x09Wc4rGfPzMfLzKcnLozg3l5LDhyk9fJjSI0ewHTmC7ehR7Pn5GAUF2AsLHa8vKoKiIixFRVhKSvArKsKvpAS/0lL8Skuxlpbib7Nhtdnwt9v/WnTjkkrlAVHQ8Gc2Ly4uZtOmTdx3331u7cnJyWzYsKHS12zcuJHk5GS3tqFDh/Lqq69SUlJCQEAAGzduZOrUqRXWmTt3bo33C1BUVERRuRSbW3aKqP2zzx7/rwwxR04OzJxJ3rHt8fFwwQVw8cWQnOz4y1NEpDb5+UFEhGOphLVs8dSf43b+6nXCZvtrnFhJyV+nKI9tKy2ltLAQW9ni/NpeVIS9qAhb2b9GSYmjraTE8XVxMUZpKTj/LS11/UtJiWP/paV/9biVlmIp+9pitzva7HYsZY/dFsPAr+xfi92OX/l/yxa/cv86v7YaBhbAr9xyLOf//J7qRzItSOXk5GCz2YiNjXVrj42NJSsrq9LXZGVlVbp+aWkpOTk5xMfHH3cd5zZrsl+AOXPm8NBDD1VoTzj+IUp9k5kJCxc6FhER8Wn79+8nygPDPEwfbH7s1QqGYZzwCobK1j+2vSrbrO5+p0+fzrRp01yPDx06RGJiIunp6R55IxqKvLw8EhISyMjI8KmbNeu4ddy+QMet4/YFubm5tGzZkiZNmpx85SowLUhFR0djtVor9AJlZ2dX6C1yiouLq3R9f39/mjZtesJ1nNusyX4BgoKCCKpkLFRUVJRP/QA6RZbNteJrdNy+RcftW3TcvsXPQzeu98xWaiAwMJCkpCRSUlLc2lNSUujXr1+lr+nbt2+F9VetWkWvXr1ck6Ydbx3nNmuyXxEREZHKmHpqb9q0aYwdO5ZevXrRt29fXnrpJdLT013zQk2fPp09e/a45gSaOHEi//nPf5g2bRo333wzGzdu5NVXX3VdjQdw5513ct555/HYY48xfPhwPvzwQz777DPWr19f5f2KiIiIVIlhsueff95ITEw0AgMDjZ49expr1qxxPTd+/HhjwIABbuuvXr3a6NGjhxEYGGi0atXKmD9/foVt/t///Z/Rvn17IyAgwOjQoYOxdOnSau23KgoLC42ZM2cahYWF1XpdQ6fj1nH7Ah23jtsX6Lg9c9ymziMlIiIi0pCZNkZKREREpKFTkBIRERGpIQUpERERkRpSkBIRERGpIQWpE5gzZw69e/cmIiKCZs2aMWLECH755Re3dQzDYNasWTRv3pyQkBAGDhzI1q1bTarYM6py3BMmTMBisbgtZ599tkkVe8b8+fPp1q2ba3K6vn37ut382hvfazj5cXvje12ZOXPmYLFYmDJliqvNW9/z8io7bm98z2fNmlXhmOLi4lzPe+t7fbLj9sb32mnPnj2MGTOGpk2bEhoayplnnsmmTZtcz3vqPVeQOoE1a9Zw++2389VXX5GSkkJpaSnJyckcPXrUtc7jjz/OU089xX/+8x++/fZb4uLiuOCCCzh8+LCJlZ+aqhw3wIUXXkhmZqZrWblypUkVe0aLFi149NFH+e677/juu+84//zzGT58uOsXyxvfazj5cYP3vdfH+vbbb3nppZfo1q2bW7u3vudOxztu8M73vHPnzm7HtGXLFtdz3vxen+i4wTvf64MHD3LOOecQEBDAxx9/zLZt23jyySdp1KiRax2PvecemUTBR2RnZxuAa84pu91uxMXFGY8++qhrncLCQiMqKsp44YUXzCrT4449bsNwzPE1fPhw84qqI40bNzZeeeUVn3mvnZzHbRje/14fPnzYOOOMM4yUlBRjwIABxp133mkYhvf/fh/vuA3DO9/zmTNnGt27d6/0OW9+r0903Ibhne+1YRjGvffea5x77rnHfd6T77l6pKohNzcXwHWjw7S0NLKyskhOTnatExQUxIABA9iwYYMpNdaGY4/bafXq1TRr1ox27dpx8803k52dbUZ5tcJms7F48WKOHj1K3759fea9Pva4nbz5vb799tu5+OKLGTJkiFu7t7/nxztuJ298z3fs2EHz5s1p3bo11157LTt37gS8/70+3nE7eeN7/dFHH9GrVy+uvvpqmjVrRo8ePXj55Zddz3vyPTf1FjENiWEYTJs2jXPPPZcuXboAuG58fOzNjmNjY9m9e3ed11gbKjtugGHDhnH11VeTmJhIWloaDz74IOeffz6bNm2q9ObODcWWLVvo27cvhYWFhIeH8/7779OpUyfXL5a3vtfHO27w3vcaYPHixWzatInvvvuuwnPe/Pt9ouMG73zP+/Tpw8KFC2nXrh179+7lX//6F/369WPr1q1e/V6f6LibNm3qle81wM6dO5k/fz7Tpk3j/vvv55tvvmHy5MkEBQUxbtw4j77nClJVNGnSJH788Ue3e/Y5WSwWt8eGYVRoa6iOd9wjR450fd2lSxd69epFYmIiK1as4IorrqjrMj2mffv2pKamcujQIZYuXcr48eNZs2aN63lvfa+Pd9ydOnXy2vc6IyODO++8k1WrVhEcHHzc9bztPa/KcXvjez5s2DDX1127dqVv3760bduWN954wzW42tveazjxcU+bNs0r32sAu91Or169eOSRRwDo0aMHW7duZf78+YwbN861nifec53aq4I77riDjz76iC+++IIWLVq42p1XPjiTrVN2dnaFlNsQHe+4KxMfH09iYiI7duyoo+pqR2BgIKeffjq9evVizpw5dO/enWeeecbr3+vjHXdlvOW93rRpE9nZ2SQlJeHv74+/vz9r1qzh2Wefxd/f3/W+ett7frLjttlsFV7jLe95eWFhYXTt2pUdO3Z4/e93eeWPuzLe8l7Hx8e7etWdOnbsSHp6OuDZz28FqRMwDINJkyaxbNky/ve//9G6dWu351u3bk1cXBwpKSmutuLiYtasWUO/fv3qulyPOdlxV2b//v1kZGQQHx9fBxXWHcMwKCoq8tr3+nicx10Zb3mvBw8ezJYtW0hNTXUtvXr1YvTo0aSmptKmTRuvfM9PdtxWq7XCa7zlPS+vqKiI7du3Ex8f71O/3+WPuzLe8l6fc845Fabt+fXXX0lMTAQ8/PldvXHwvuVvf/ubERUVZaxevdrIzMx0Lfn5+a51Hn30USMqKspYtmyZsWXLFmPUqFFGfHy8kZeXZ2Llp+Zkx3348GHjrrvuMjZs2GCkpaUZX3zxhdG3b1/jtNNOa9DHPX36dGPt2rVGWlqa8eOPPxr333+/4efnZ6xatcowDO98rw3jxMftre/18Rx79Zq3vufHKn/c3vqe33XXXcbq1auNnTt3Gl999ZVxySWXGBEREcauXbsMw/De9/pEx+2t77VhGMY333xj+Pv7Gw8//LCxY8cO4+233zZCQ0ONt956y7WOp95zBakTACpdXn/9ddc6drvdmDlzphEXF2cEBQUZ5513nrFlyxbzivaAkx13fn6+kZycbMTExBgBAQFGy5YtjfHjxxvp6enmFn6KbrjhBiMxMdEIDAw0YmJijMGDB7tClGF453ttGCc+bm99r4/n2CDlre/5scoft7e+5yNHjjTi4+ONgIAAo3nz5sYVV1xhbN261fW8t77XJzpub32vnf773/8aXbp0MYKCgowOHToYL730ktvznnrPLYZhGDXqNxMRERHxcRojJSIiIlJDClIiIiIiNaQgJSIiIlJDClIiIiIiNaQgJSIiIlJDClIiIiIiNaQgJSIiIlJDClIiIiIiNaQgJSKms1gsfPDBBzV+/axZszjzzDM9Vk9VrV69GovFgsViYcSIER7Z1qFDhzxSW22aMGGC67hP5X0T8QYKUiI+wvnBd7xlwoQJZpdYqarUfffdd/P555+bVuMvv/zCggULTmkb/fr1IzMzk6ioKM8UVaZVq1bMnTvXo9t85plnyMzM9Og2RRoqf7MLEJG6Uf6Db8mSJfzjH/9wuzt6SEiI2/olJSUEBATUWX3HU5W6w8PDCQ8PN6M8AJo1a0ajRo1OaRuBgYHExcV5pqBaFhUV5fHAJ9JQqUdKxEfExcW5lqioKCwWi+txYWEhjRo14t1332XgwIEEBwfz1ltvsX//fkaNGkWLFi0IDQ2la9euLFq0yLXNF198kdNOOw273e62r8suu4zx48e7Hv/3v/8lKSmJ4OBg2rRpw0MPPURpaekp1+1sO/bU3oQJExgxYgSPPPIIsbGxNGrUyLXPv//97zRp0oQWLVrw2muvue1rz549jBw5ksaNG9O0aVOGDx/Orl27qv29HjhwIHfccQdTpkyhcePGxMbG8tJLL3H06FGuv/56IiIiaNu2LR9//LHrNcee2luwYAGNGjXi008/pWPHjoSHh3PhhRe6BcuBAwcyZcoUt32PGDHC1bs4cOBAdu/ezdSpU109eE4bNmzgvPPOIyQkhISEBCZPnszRo0ddz8+bN48zzjiD4OBgYmNjueqqq6r9fRDxBQpSIuJy7733MnnyZLZv387QoUMpLCwkKSmJ5cuX89NPP3HLLbcwduxYvv76awCuvvpqcnJy+OKLL1zbOHjwIJ9++imjR48G4NNPP2XMmDFMnjyZbdu28eKLL7JgwQIefvjhWj2W//3vf/z555+sXbuWp556ilmzZnHJJZfQuHFjvv76ayZOnMjEiRPJyMgAID8/n0GDBhEeHs7atWtZv369K7wUFxdXe/9vvPEG0dHRfPPNN9xxxx387W9/4+qrr6Zfv358//33DB06lLFjx5Kfn3/cbeTn5/PEE0/w5ptvsnbtWtLT07n77rurXMOyZcto0aIFs2fPJjMz0xXCtmzZwtChQ7niiiv48ccfWbJkCevXr2fSpEkAfPfdd0yePJnZs2fzyy+/8Mknn3DeeedV+3sg4hMMEfE5r7/+uhEVFeV6nJaWZgDG3LlzT/raiy66yLjrrrtcjy+77DLjhhtucD1+8cUXjbi4OKO0tNQwDMPo37+/8cgjj7ht48033zTi4+NdjwHj/fffr3bdTjNnzjS6d+/uejx+/HgjMTHRsNlsrrb27dsb/fv3dz0uLS01wsLCjEWLFhmGYRivvvqq0b59e8Nut7vWKSoqMkJCQoxPP/200nq++OILAzAOHjzo1j5gwADj3HPPrbCvsWPHutoyMzMNwNi4cWOl23r99dcNwPjtt99cr3n++eeN2NhYt/3ceeedbvsePny4MX78eNfjxMRE4+mnn3ZbZ+zYscYtt9zi1rZu3TrDz8/PKCgoMJYuXWpERkYaeXl5lR63U1XfNxFvpjFSIuLSq1cvt8c2m41HH32UJUuWsGfPHoqKiigqKiIsLMy1zujRo7nllluYN28eQUFBvP3221x77bVYrVYANm3axLfffuvWA2Wz2SgsLCQ/P5/Q0NBaOZbOnTvj5/dXp3tsbCxdunRxPbZarTRt2pTs7GxXnb/99hsRERFu2yksLOT333+v9v67detWYV9du3Z1qwdw7b8yoaGhtG3b1vU4Pj7+hOtXlfNY3377bVebYRjY7XbS0tK44IILSExMpE2bNlx44YVceOGFXH755bX2Xok0ZApSIuJSPiABPPnkkzz99NPMnTuXrl27EhYWxpQpU9xOdV166aXY7XZWrFhB7969WbduHU899ZTrebvdzkMPPcQVV1xRYX/BwcG1dizHDpS3WCyVtjnHd9ntdpKSktzChVNMTIzH9+8cr3Ts+LKTbcMwDNdjPz8/t8fguEjgZOx2O7feeiuTJ0+u8FzLli0JDAzk+++/Z/Xq1axatYp//OMfzJo1i2+//faUB9WLeBsFKRE5rnXr1jF8+HDGjBkDOD6Ad+zYQceOHV3rhISEcMUVV/D222/z22+/0a5dO5KSklzP9+zZk19++YXTTz+9zuuvjp49e7JkyRKaNWtGZGSk2eVUSUxMjNvgc5vNxk8//cSgQYNcbYGBgdhsNrfX9ezZk61bt57wPfH392fIkCEMGTKEmTNn0qhRI/73v/9VGohFfJkGm4vIcZ1++umkpKSwYcMGtm/fzq233kpWVlaF9UaPHs2KFSt47bXXXKHL6R//+AcLFy5k1qxZbN26le3bt7NkyRIeeOCBujqMKhk9ejTR0dEMHz6cdevWkZaWxpo1a7jzzjv5448/zC6vUueffz4rVqxgxYoV/Pzzz9x2220VJvRs1aoVa9euZc+ePeTk5ACOiwo2btzI7bffTmpqKjt27OCjjz7ijjvuAGD58uU8++yzpKamsnv3bhYuXIjdbqd9+/Z1fYgi9Z6ClIgc14MPPkjPnj0ZOnQoAwcOJC4urtIZvM8//3yaNGnCL7/8wnXXXef23NChQ1m+fDkpKSn07t2bs88+m6eeeorExMQ6OoqqCQ0NZe3atbRs2ZIrrriCjh07csMNN1BQUFBve6huuOEGxo8fz7hx4xgwYACtW7d2640CmD17Nrt27aJt27auU5TdunVjzZo17Nixg/79+9OjRw8efPBB4uPjAWjUqBHLli3j/PPPp2PHjrzwwgssWrSIzp071/kxitR3FuPYE+wiIlIlq1evZtCgQRw8eNAnxw5ZLBbef//9U749jkhDph4pEZFT1KJFC0aNGmV2GXVm4sSJps4kL1KfqEdKRKSGCgoK2LNnDwDh4eEN5hYvpyo7O5u8vDzAMSXDsVd7ivgSBSkRERGRGtKpPREREZEaUpASERERqSEFKREREZEaUpASERERqSEFKREREZEaUpASERERqSEFKREREZEaUpASERERqaH/B4AJAlZ26gkVAAAAAElFTkSuQmCC\n",
"text/plain": "<Figure size 640x480 with 1 Axes>"
},
"metadata": {},
"output_type": "display_data"
}
]
}
},
"853ac92d93bf4f1386363ef9d907c589": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"869499b6fbea4715b4fb5a8a63384f5c": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": "initial"
}
},
"86e1e23be66a4c5e9419b9f3686f3519": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"88d3886f1827468b947c3fc5b8970ecb": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatSliderModel",
"state": {
"description": "t_accident",
"layout": "IPY_MODEL_f104eea6bc2a4c9b93bdc17347744de6",
"max": 60,
"min": 5,
"step": 5,
"style": "IPY_MODEL_2d4a20417d044afcaf61f059716e5ebc",
"value": 10
}
},
"892aced71d0140a48243e07efbf54f4f": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "VBoxModel",
"state": {
"_dom_classes": [
"widget-interact"
],
"children": [
"IPY_MODEL_b9b532f5518f4a159e8a416143eba53d",
"IPY_MODEL_5f1a83ad2b454d0f802985af20993727",
"IPY_MODEL_4cfc18f404d74f96a0f52738322a7e45",
"IPY_MODEL_100c0581cfc747c8af541fc9d5145845",
"IPY_MODEL_b4ae300d9cfa4267a8f157cf7eeedea5",
"IPY_MODEL_e5174c32d6de44218f37e6442c81063b",
"IPY_MODEL_7dadfb09825746b39cfca7a47bcc2740",
"IPY_MODEL_f0c87e070a5646479350445fd78c2e0f",
"IPY_MODEL_8ac63bf062834a3bbf7b4c0c3b3fc50e",
"IPY_MODEL_d34b1a0a4a4f4545a129ef56988bebf4",
"IPY_MODEL_84b87cab5bd3437d955a7851dafdaf7f"
],
"layout": "IPY_MODEL_08f445fa1f7a42dab8956fa6ca209e16"
}
},
"8ac63bf062834a3bbf7b4c0c3b3fc50e": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "CheckboxModel",
"state": {
"description": "Distribution, no interventions",
"disabled": false,
"indent": false,
"layout": "IPY_MODEL_0187e7214d8a445dbb5c831f13f34dd3",
"style": "IPY_MODEL_a552a0d7442c41ad9fdcf735b583dff0",
"value": true
}
},
"8de8ac7ccefe46f78fc8d7d9de845013": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"8ec2f013205f4c4797cb03608de966e9": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"96151c6995104b5ab6cfa85d2114d4b8": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatSliderModel",
"state": {
"description": "t_0",
"layout": "IPY_MODEL_4a4cb1653cd14b8ba63f5ce8b294857f",
"max": 32,
"min": 26,
"step": 0.5,
"style": "IPY_MODEL_6cd0a2b1a14342bd9651d2db5a05be10",
"value": 28.5
}
},
"9b82df908c8b40248e3f065ede04b43b": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"9daea46dd3004ad2a39098dc8f0c4d83": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "CheckboxModel",
"state": {
"description": "Distribution, no interventions",
"disabled": false,
"indent": false,
"layout": "IPY_MODEL_8de8ac7ccefe46f78fc8d7d9de845013",
"style": "IPY_MODEL_0bb275f5ec1542b5ae0ef636577f1b72",
"value": true
}
},
"9f01a383259449be8eee6cce670c7641": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatSliderModel",
"state": {
"description": "C",
"layout": "IPY_MODEL_ba029c7af3054cc7a46cd3bc491ac4a6",
"max": 7000,
"min": 4000,
"step": 100,
"style": "IPY_MODEL_83fd64cc893c429bae04c91ebe52209b",
"value": 5600
}
},
"a16439206a34479da22d97225c4220cb": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"description_width": ""
}
},
"a552a0d7442c41ad9fdcf735b583dff0": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"description_width": ""
}
},
"a6366e489ea64c94b53a44cd90d3509b": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DropdownModel",
"state": {
"_options_labels": [
"None",
"1",
"2"
],
"description": "1: Extra Lanes",
"index": null,
"layout": "IPY_MODEL_51bf0d7f0a8c441db6161ae646a407b7",
"style": "IPY_MODEL_52ddf983296042168eadf3ef742c3de1"
}
},
"a9a413eef0ff44d3ade17141373257de": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"ac52d024453d4073a77795b4082f8fe5": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatSliderModel",
"state": {
"description": "3: Speed",
"layout": "IPY_MODEL_e1af691108804baaaaa56eebff580f06",
"max": 170,
"min": 1,
"step": 10,
"style": "IPY_MODEL_80b5142c6fce447a96f3bdafaff94e14",
"value": 130
}
},
"aeeb304402b0462cb994598fa86e6796": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"b0a6b79bf7bc49489632e3dc04c373fb": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"b150edabeff74e6bb2994d402dca96cd": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"b4ae300d9cfa4267a8f157cf7eeedea5": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatSliderModel",
"state": {
"description": "5: Incident Management",
"layout": "IPY_MODEL_86e1e23be66a4c5e9419b9f3686f3519",
"min": 1,
"step": 10,
"style": "IPY_MODEL_1087ad9b423d40ff9b9d0d0a4b24df42",
"value": 1
}
},
"b9b532f5518f4a159e8a416143eba53d": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DropdownModel",
"state": {
"_options_labels": [
"None",
"1",
"2"
],
"description": "1: Extra Lanes",
"index": null,
"layout": "IPY_MODEL_4c76dbfaddda4c84888c273562e48074",
"style": "IPY_MODEL_75c1f539256c4115a4eb71ca610b6baf"
}
},
"b9d80e0f8cc644829e3b5ea6cedcdd66": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": "initial"
}
},
"ba029c7af3054cc7a46cd3bc491ac4a6": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"bb7a481065664052a1c2dd14393b571d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"c02af5dbfda345779d1ed59ab7e606d5": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"c0bdaad2190343f5b8c103bbd6bd9257": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "VBoxModel",
"state": {
"_dom_classes": [
"widget-interact"
],
"children": [
"IPY_MODEL_96151c6995104b5ab6cfa85d2114d4b8",
"IPY_MODEL_6580aaf036a6475abc0c0832c04510fd",
"IPY_MODEL_5806dda8eabb4e7d97f2e379167ce5d9"
],
"layout": "IPY_MODEL_26c9d8ccf54242c0b94fb533b091b19d"
}
},
"d00800ac309946df9291756aa1ffcbc4": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "VBoxModel",
"state": {
"_dom_classes": [
"widget-interact"
],
"children": [
"IPY_MODEL_88d3886f1827468b947c3fc5b8970ecb",
"IPY_MODEL_9f01a383259449be8eee6cce670c7641",
"IPY_MODEL_d457391a891f412db94c5c719c19a9db",
"IPY_MODEL_22f1b77ee9c1494d92078b1300a98833",
"IPY_MODEL_4fefe5bdbf3f4be8bf2f032fddb4b8f7"
],
"layout": "IPY_MODEL_5587e0efb67f4453a7598ec815e422bb"
}
},
"d34b1a0a4a4f4545a129ef56988bebf4": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "ToggleButtonsModel",
"state": {
"_options_labels": [
"PDF",
"CDF",
"1-CDF"
],
"button_style": "",
"description": "Plot Type:",
"icons": [],
"index": 0,
"layout": "IPY_MODEL_ff68c66b64af4e308edaf79d636caf78",
"style": "IPY_MODEL_f6751048669c4ee3931c28b61d183f18",
"tooltips": []
}
},
"d457391a891f412db94c5c719c19a9db": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatSliderModel",
"state": {
"description": "rCf",
"layout": "IPY_MODEL_38cfb9ece79a4347a7bf1842d6ea3932",
"max": 0.6,
"min": 0.1,
"step": 0.1,
"style": "IPY_MODEL_7b28a775fe594b7d851b4a210e0eb11e",
"value": 0.2
}
},
"e1af691108804baaaaa56eebff580f06": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"e5174c32d6de44218f37e6442c81063b": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatSliderModel",
"state": {
"description": "6: Accident Risk Reduction",
"layout": "IPY_MODEL_57e3c907cb71406a8ccbb9b398698b8d",
"min": 1,
"step": 10,
"style": "IPY_MODEL_ee79a8ff52934cffb12227cd7c049065",
"value": 1
}
},
"ee79a8ff52934cffb12227cd7c049065": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "SliderStyleModel",
"state": {
"description_width": "initial"
}
},
"eee3ad0fd45c47d5ab47454238d366a2": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"f0c87e070a5646479350445fd78c2e0f": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatSliderModel",
"state": {
"description": "8: Modal Shift, Cars",
"layout": "IPY_MODEL_23783a41b3db4b2095ac7bcab87a74ae",
"max": 20,
"step": 4,
"style": "IPY_MODEL_26045de01f29405180b7e1b1434ce181"
}
},
"f104eea6bc2a4c9b93bdc17347744de6": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"f6751048669c4ee3931c28b61d183f18": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "ToggleButtonsStyleModel",
"state": {
"button_width": "",
"description_width": ""
}
},
"ff68c66b64af4e308edaf79d636caf78": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
}
},
"version_major": 2,
"version_minor": 0
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}
Reference in a new issue View git blame Copy permalink